diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile
index 955a2962ff4..da164dcfa5b 100644
--- a/.devops/intel.Dockerfile
+++ b/.devops/intel.Dockerfile
@@ -1,12 +1,19 @@
-ARG ONEAPI_VERSION=2025.3.2-0-devel-ubuntu24.04
+ARG ONEAPI_VERSION=2025.3.3-0-devel-ubuntu24.04
 
 ## Build Image
 
 FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS build
 
 ARG GGML_SYCL_F16=OFF
+ARG LEVEL_ZERO_VERSION=1.28.2
+ARG LEVEL_ZERO_UBUNTU_VERSION=u24.04
 RUN apt-get update && \
-    apt-get install -y git libssl-dev
+    apt-get install -y git libssl-dev wget ca-certificates && \
+    cd /tmp && \
+    wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero.deb && \
+    wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb && \
+    apt-get -o Dpkg::Options::="--force-overwrite" install -y ./level-zero.deb ./level-zero-devel.deb && \
+    rm -f /tmp/level-zero.deb /tmp/level-zero-devel.deb
 
 WORKDIR /app
 
@@ -33,11 +40,11 @@ RUN mkdir -p /app/full \
 
 FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS base
 
-ARG IGC_VERSION=v2.30.1
-ARG IGC_VERSION_FULL=2_2.30.1+20950
-ARG COMPUTE_RUNTIME_VERSION=26.09.37435.1
-ARG COMPUTE_RUNTIME_VERSION_FULL=26.09.37435.1-0
-ARG IGDGMM_VERSION=22.9.0
+ARG IGC_VERSION=v2.20.5
+ARG IGC_VERSION_FULL=2_2.20.5+19972
+ARG COMPUTE_RUNTIME_VERSION=25.40.35563.10
+ARG COMPUTE_RUNTIME_VERSION_FULL=25.40.35563.10-0
+ARG IGDGMM_VERSION=22.8.2
 RUN mkdir /tmp/neo/ && cd /tmp/neo/ \
   && wget https://github.com/intel/intel-graphics-compiler/releases/download/$IGC_VERSION/intel-igc-core-${IGC_VERSION_FULL}_amd64.deb \
   && wget https://github.com/intel/intel-graphics-compiler/releases/download/$IGC_VERSION/intel-igc-opencl-${IGC_VERSION_FULL}_amd64.deb \
@@ -109,4 +116,3 @@ WORKDIR /app
 HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
 
 ENTRYPOINT [ "/app/llama-server" ]
-
diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix
index 61b60d0718a..0ead80f1c3e 100644
--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -147,7 +147,6 @@ effectiveStdenv.mkDerivation (finalAttrs: {
       ninja
       pkg-config
       git
-      spirv-headers
     ]
     ++ optionals useCuda [
       cudaPackages.cuda_nvcc
diff --git a/.devops/openvino.Dockerfile b/.devops/openvino.Dockerfile
index 3ee4dd20180..31b58736d7e 100644
--- a/.devops/openvino.Dockerfile
+++ b/.devops/openvino.Dockerfile
@@ -2,7 +2,19 @@ ARG OPENVINO_VERSION_MAJOR=2026.0
 ARG OPENVINO_VERSION_FULL=2026.0.0.20965.c6d6a13a886
 ARG UBUNTU_VERSION=24.04
 
-# Optional proxy build arguments - empty by default
+# Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
+ARG IGC_VERSION=v2.30.1
+ARG IGC_VERSION_FULL=2_2.30.1+20950
+ARG COMPUTE_RUNTIME_VERSION=26.09.37435.1
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.09.37435.1-0
+ARG IGDGMM_VERSION=22.9.0
+
+# Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
+ARG NPU_DRIVER_VERSION=v1.32.0
+ARG NPU_DRIVER_FULL=v1.32.0.20260402-23905121947
+ARG LIBZE1_VERSION=1.27.0-1~24.04~ppa2
+
+# Optional proxy build arguments
 ARG http_proxy=
 ARG https_proxy=
 
@@ -78,13 +90,47 @@ ARG http_proxy
 ARG https_proxy
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 libtbb12 curl \
+    && apt-get install -y libgomp1 libtbb12 curl wget ocl-icd-libopencl1 \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \
     && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
     && find /var/cache -type f -delete
 
+# Install GPU drivers
+ARG IGC_VERSION
+ARG IGC_VERSION_FULL
+ARG COMPUTE_RUNTIME_VERSION
+ARG COMPUTE_RUNTIME_VERSION_FULL
+ARG IGDGMM_VERSION
+RUN mkdir /tmp/neo/ && cd /tmp/neo/ \
+    && wget https://github.com/intel/intel-graphics-compiler/releases/download/${IGC_VERSION}/intel-igc-core-${IGC_VERSION_FULL}_amd64.deb \
+    && wget https://github.com/intel/intel-graphics-compiler/releases/download/${IGC_VERSION}/intel-igc-opencl-${IGC_VERSION_FULL}_amd64.deb \
+    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-ocloc-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-ocloc_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-opencl-icd-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-opencl-icd_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/libigdgmm12_${IGDGMM_VERSION}_amd64.deb \
+    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/libze-intel-gpu1-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/libze-intel-gpu1_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+    && dpkg --install *.deb \
+    && rm -rf /tmp/neo/
+
+# Install NPU drivers
+ARG NPU_DRIVER_VERSION
+ARG NPU_DRIVER_FULL
+ARG LIBZE1_VERSION
+RUN mkdir /tmp/npu/ && cd /tmp/npu/ \
+    && wget https://github.com/intel/linux-npu-driver/releases/download/${NPU_DRIVER_VERSION}/linux-npu-driver-${NPU_DRIVER_FULL}-ubuntu2404.tar.gz \
+    && tar -xf linux-npu-driver-${NPU_DRIVER_FULL}-ubuntu2404.tar.gz \
+    && dpkg --install *.deb \
+    && rm -rf /tmp/npu/
+
+RUN cd /tmp \
+    && wget https://snapshot.ppa.launchpadcontent.net/kobuk-team/intel-graphics/ubuntu/20260324T100000Z/pool/main/l/level-zero-loader/libze1_${LIBZE1_VERSION}_amd64.deb \
+    && dpkg --install libze1_${LIBZE1_VERSION}_amd64.deb \
+    && rm libze1_${LIBZE1_VERSION}_amd64.deb
+
 COPY --from=build /app/lib/ /app/
 
 ### Full (all binaries)
diff --git a/.editorconfig b/.editorconfig
index b995dbff7dd..5663b8fdbfc 100644
--- a/.editorconfig
+++ b/.editorconfig
@@ -45,15 +45,7 @@ insert_final_newline = unset
 trim_trailing_whitespace = unset
 insert_final_newline = unset
 
-[tools/server/webui/**]
-indent_style = unset
-indent_size = unset
-end_of_line = unset
-charset = unset
-trim_trailing_whitespace = unset
-insert_final_newline = unset
-
-[tools/server/public/**]
+[tools/ui/**]
 indent_style = unset
 indent_size = unset
 end_of_line = unset
diff --git a/.gitattributes b/.gitattributes
deleted file mode 100644
index 06c85ad56e8..00000000000
--- a/.gitattributes
+++ /dev/null
@@ -1,4 +0,0 @@
-# Treat the generated single-file WebUI build as binary for diff purposes.
-# Git's pack-file delta compression still works (byte-level), but this prevents
-# git diff from printing the entire minified file on every change.
-tools/server/public/index.html -diff
diff --git a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
index b55bc1a3f80..51ef87a5906 100644
--- a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
+++ b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml
@@ -12,6 +12,8 @@ body:
         after recreating the CMake build directory and with `-DGGML_CCACHE=OFF`.
         If the compilation succeeds with ccache disabled you should be able to permanently fix the issue
         by clearing `~/.cache/ccache` (on Linux).
+
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
   - type: textarea
     id: commit
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/011-bug-results.yml b/.github/ISSUE_TEMPLATE/011-bug-results.yml
index 653d75e1687..c7001edf050 100644
--- a/.github/ISSUE_TEMPLATE/011-bug-results.yml
+++ b/.github/ISSUE_TEMPLATE/011-bug-results.yml
@@ -1,5 +1,5 @@
 name: Bug (model use)
-description: Something goes wrong when using a model (in general, not specific to a single llama.cpp module).
+description: Something goes wrong when running a model (crashes, garbled outputs, etc.).
 title: "Eval bug: "
 labels: ["bug-unconfirmed", "model evaluation"]
 body:
@@ -12,6 +12,8 @@ body:
         If you encountered the issue while using an external UI (e.g. ollama),
         please reproduce your issue using one of the examples/binaries in this repository.
         The `llama-completion` binary can be used for simple and reproducible model inference.
+
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
   - type: textarea
     id: version
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/019-bug-misc.yml b/.github/ISSUE_TEMPLATE/019-bug-misc.yml
index 8e867e7f60f..831c98eb637 100644
--- a/.github/ISSUE_TEMPLATE/019-bug-misc.yml
+++ b/.github/ISSUE_TEMPLATE/019-bug-misc.yml
@@ -10,6 +10,8 @@ body:
         This issue template is intended for miscellaneous bugs that don't fit into any other category.
         If you encountered the issue while using an external UI (e.g. ollama),
         please reproduce your issue using one of the examples/binaries in this repository.
+
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
   - type: textarea
     id: version
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/020-enhancement.yml b/.github/ISSUE_TEMPLATE/020-enhancement.yml
index cee1446f5a0..fdca622ae9d 100644
--- a/.github/ISSUE_TEMPLATE/020-enhancement.yml
+++ b/.github/ISSUE_TEMPLATE/020-enhancement.yml
@@ -8,6 +8,8 @@ body:
       value: |
         [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggml-org/llama.cpp/discussions/categories/ideas)
 
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
+
   - type: checkboxes
     id: prerequisites
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/030-research.yml b/.github/ISSUE_TEMPLATE/030-research.yml
index e774550d590..172edc1326a 100644
--- a/.github/ISSUE_TEMPLATE/030-research.yml
+++ b/.github/ISSUE_TEMPLATE/030-research.yml
@@ -8,6 +8,8 @@ body:
       value: |
         Don't forget to check for any [duplicate research issue tickets](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22)
 
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
+
   - type: checkboxes
     id: research-stage
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/040-refactor.yml b/.github/ISSUE_TEMPLATE/040-refactor.yml
index 2fe94e26c69..efd10d79d99 100644
--- a/.github/ISSUE_TEMPLATE/040-refactor.yml
+++ b/.github/ISSUE_TEMPLATE/040-refactor.yml
@@ -9,6 +9,8 @@ body:
         Don't forget to [check for existing refactor issue tickets](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered.
         Also you may want to check [Pull request refactor label as well](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too.
 
+        Please fill out this template yourself, copypasting language model outputs is [strictly prohibited](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md#ai-usage-policy).
+
   - type: textarea
     id: background-description
     attributes:
diff --git a/.github/labeler.yml b/.github/labeler.yml
index 70384994dcb..60aa51d2cc2 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -73,11 +73,10 @@ android:
     - changed-files:
         - any-glob-to-any-file:
             - examples/llama.android/**
-server/webui:
+server/ui:
     - changed-files:
         - any-glob-to-any-file:
-            - tools/server/webui/**
-            - tools/server/public/**
+            - tools/ui/**
 server:
     - changed-files:
         - any-glob-to-any-file:
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index eca2248a00d..d9844103a95 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -6,7 +6,7 @@
 
 <!-- You can provide more details and link related discussions here. Delete this section if not applicable -->
 
-# Requirements
+## Requirements
 
 <!-- IMPORTANT: Please do NOT delete this section, otherwise your PR may be rejected -->
 
diff --git a/.github/workflows/build-and-test-snapdragon.yml b/.github/workflows/build-and-test-snapdragon.yml
new file mode 100644
index 00000000000..ef3fe502fa7
--- /dev/null
+++ b/.github/workflows/build-and-test-snapdragon.yml
@@ -0,0 +1,148 @@
+name: CI (snapdragon)
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - master
+    paths:
+      - '.github/workflows/build-and-test-snapdragon.yml'
+      - 'ggml/include/ggml-hexagon.h'
+      - 'ggml/src/ggml-hexagon/**'
+      - 'docs/backend/snapdragon/**'
+      - 'scripts/snapdragon/**'
+      - 'CMakePresets.json'
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths:
+      - '.github/workflows/build-and-test-snapdragon.yml'
+      - 'ggml/include/ggml-hexagon.h'
+      - 'ggml/src/ggml-hexagon/**'
+      - 'docs/backend/snapdragon/**'
+      - 'scripts/snapdragon/**'
+      - 'CMakePresets.json'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  android-ndk-snapdragon:
+    runs-on: ubuntu-latest
+    container:
+      image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.3'
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          lfs: false
+
+      - name: Build Llama.CPP for Snapdragon Android
+        id: build_llama_cpp_snapdragon_android
+        run: |
+          cp docs/backend/snapdragon/CMakeUserPresets.json .
+          cmake --preset arm64-android-snapdragon-release -B build
+          cmake --build build
+          cmake --install build --prefix pkg-snapdragon/llama.cpp
+
+      - name: Upload Llama.CPP Snapdragon Android Build Artifact
+        if: ${{ always() && steps.build_llama_cpp_snapdragon_android.outcome == 'success' }}
+        uses: actions/upload-artifact@v6
+        with:
+          name: llama-cpp-android-arm64-snapdragon
+          path: pkg-snapdragon/llama.cpp
+
+  linux-iot-snapdragon:
+    runs-on: ubuntu-latest
+    container:
+      image: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.1'
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          lfs: false
+
+      - name: Build Llama.CPP for Snapdragon Linux IoT
+        id: build_llama_cpp_snapdragon_linux
+        run: |
+          cp docs/backend/snapdragon/CMakeUserPresets.json .
+          cmake --preset arm64-linux-snapdragon-release -B build-snapdragon -DGGML_OPENCL=ON
+          cmake --build build-snapdragon -j $(nproc)
+          cmake --install build-snapdragon --prefix pkg-snapdragon/llama.cpp
+
+      - name: Upload Llama.CPP Snapdragon Linux IoT Build Artifact
+        if: ${{ always() && steps.build_llama_cpp_snapdragon_linux.outcome == 'success' }}
+        uses: actions/upload-artifact@v6
+        with:
+          name: llama-cpp-linux-arm64-snapdragon
+          path: pkg-snapdragon/llama.cpp
+
+  test-snapdragon-qdc:
+    name: Test on QDC Device (${{ matrix.device }})
+    needs: [android-ndk-snapdragon, linux-iot-snapdragon]
+    runs-on: ubuntu-24.04-arm
+    timeout-minutes: 90
+    strategy:
+      fail-fast: false
+      matrix:
+        device: [SM8750, SM8850, QCS9075M]
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+
+      - name: Download build artifact
+        uses: actions/download-artifact@v7
+        with:
+          name: ${{ startsWith(matrix.device, 'QCS') && 'llama-cpp-linux-arm64-snapdragon' || 'llama-cpp-android-arm64-snapdragon' }}
+          path: pkg-snapdragon/llama.cpp
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: '3.x'
+          cache: pip
+
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y curl unzip
+
+      - name: Install QDC SDK wheel
+        run: |
+          curl -fSL -o qdc_sdk.zip https://softwarecenter.qualcomm.com/api/download/software/tools/Qualcomm_Device_Cloud_SDK/All/0.2.3/qualcomm_device_cloud_sdk-0.2.3.zip
+          unzip qdc_sdk.zip -d qdc_sdk
+          pip install qdc_sdk/qualcomm_device_cloud_sdk-0.2.3-py3-none-any.whl
+
+      - name: Check QDC API key
+        id: check_secret
+        env:
+          QDC_API_KEY: ${{ secrets.QDC_API_KEY }}
+        run: echo "has-qdc-key=${{ env.QDC_API_KEY != '' }}" >> "$GITHUB_OUTPUT"
+
+      - name: Run QDC tests (${{ matrix.device }})
+        if: steps.check_secret.outputs.has-qdc-key == 'true'
+        run: |
+          python scripts/snapdragon/qdc/run_qdc_jobs.py \
+              --test       all \
+              --pkg-dir    pkg-snapdragon/llama.cpp \
+              --model-url  "https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf" \
+              --device     ${{ matrix.device }} \
+              ${{ startsWith(matrix.device, 'QCS') && '--retries 2 --retry-delay 300' || '' }}
+        env:
+          QDC_API_KEY: ${{ secrets.QDC_API_KEY }}
+
+      - name: Cleanup
+        if: always()
+        run: rm -rf pkg-snapdragon qdc_sdk qdc_sdk.zip
diff --git a/.github/workflows/build-android.yml b/.github/workflows/build-android.yml
index b38a793f186..5d88305a4f0 100644
--- a/.github/workflows/build-android.yml
+++ b/.github/workflows/build-android.yml
@@ -1,26 +1,24 @@
 name: CI (android)
 
 on:
-  workflow_dispatch: # allows manual triggering
+  workflow_dispatch:
   push:
     branches:
       - master
-    paths: [
-      '.github/workflows/build-android.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp'
-    ]
+    paths:
+      - '.github/workflows/build-android.yml'
+      - '**/CMakeLists.txt'
+      - '**/.cmake'
+      - '**/*.h'
+      - '**/*.hpp'
+      - '**/*.c'
+      - '**/*.cpp'
 
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/build-android.yml',
-      'examples/llama.android/**'
-    ]
+    paths:
+      - '.github/workflows/build-android.yml'
+      - 'examples/llama.android/**'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@@ -67,35 +65,24 @@ jobs:
     defaults:
       run:
         shell: bash
-    strategy:
-      matrix:
-        include:
-          - build: 'arm64-cpu'
-            defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_OPENSSL=OFF -D GGML_OPENMP=OFF'
-          - build: 'arm64-snapdragon'
-            defines: '--preset arm64-android-snapdragon-release'
 
     steps:
       - name: Clone
-        id: checkout
         uses: actions/checkout@v6
         with:
           fetch-depth: 0
           lfs: false
 
-      - name: Build Llama.CPP for Hexagon Android
-        id: build_llama_cpp_hexagon_android
+      - name: Build
+        id: ndk_build
         run: |
-          if [[ "${{ matrix.build }}" == "arm64-snapdragon" ]]; then
-            cp docs/backend/snapdragon/CMakeUserPresets.json .
-          fi
-          cmake ${{ matrix.defines }} -B build
+          cmake -D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_OPENSSL=OFF -D GGML_OPENMP=OFF -B build
           cmake --build build
           cmake --install build --prefix pkg-adb/llama.cpp
 
-      - name: Upload Llama.CPP Hexagon Android Build Artifact
-        if: ${{ always() && steps.build_llama_cpp_hexagon_android.outcome == 'success' }}
+      - name: Upload Android Build Artifact
+        if: ${{ always() && steps.ndk_build.outcome == 'success' }}
         uses: actions/upload-artifact@v6
         with:
-          name: llama-cpp-android-${{ matrix.build }}
+          name: llama-cpp-android-arm64-cpu
           path: pkg-adb/llama.cpp
diff --git a/.github/workflows/build-cross.yml b/.github/workflows/build-cross.yml
index aef45afdeac..97c7368387e 100644
--- a/.github/workflows/build-cross.yml
+++ b/.github/workflows/build-cross.yml
@@ -301,16 +301,17 @@ jobs:
           export RISCV_ROOT_PATH=${PWD}/spacemit_toolchain
           cmake -B build -DLLAMA_OPENSSL=OFF \
                          -DCMAKE_BUILD_TYPE=Release \
-                         -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DGGML_CPU_REPACK=OFF \
                          -DLLAMA_BUILD_TOOLS=ON \
                          -DLLAMA_BUILD_TESTS=OFF \
                          -DGGML_CPU_RISCV64_SPACEMIT=ON \
                          -DGGML_RVV=ON \
+                         -DGGML_RV_ZVFH=ON \
                          -DGGML_RV_ZFH=ON \
                          -DGGML_RV_ZICBOP=ON \
                          -DGGML_RV_ZIHINTPAUSE=ON \
-                         -DRISCV64_SPACEMIT_IME_SPEC=RISCV64_SPACEMIT_IME1 \
+                         -DGGML_RV_ZBA=ON \
                          -DCMAKE_TOOLCHAIN_FILE=${PWD}/cmake/riscv64-spacemit-linux-gnu-gcc.cmake
 
           cmake --build build --config Release -j $(nproc)
diff --git a/.github/workflows/build-openvino.yml b/.github/workflows/build-openvino.yml
new file mode 100644
index 00000000000..f7177f6be37
--- /dev/null
+++ b/.github/workflows/build-openvino.yml
@@ -0,0 +1,120 @@
+name: CI (openvino)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/build-openvino.yml',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp',
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/build-openvino.yml',
+      'ggml/src/ggml-openvino/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_LOG_COLORS: 1
+  LLAMA_LOG_PREFIX: 1
+  LLAMA_LOG_TIMESTAMPS: 1
+
+jobs:
+  ubuntu-24-openvino:
+    name: ubuntu-24-openvino-${{ matrix.openvino_device }}
+
+    concurrency:
+      group: openvino-${{ matrix.variant }}-${{ github.head_ref || github.ref }}
+      cancel-in-progress: false
+
+    strategy:
+      matrix:
+        include:
+          - variant: cpu
+            runner: '"ubuntu-24.04"'
+            openvino_device: "CPU"
+          - variant: gpu
+            runner: '["self-hosted","Linux","Intel","OpenVINO"]'
+            openvino_device: "GPU"
+
+    runs-on: ${{ fromJSON(matrix.runner) }}
+
+    env:
+      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
+      OPENVINO_VERSION_MAJOR: "2026.0"
+      OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: ccache
+        if: runner.environment == 'github-hosted'
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: ubuntu-24-openvino-${{ matrix.variant }}-no-preset-v1
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential libssl-dev libtbb12 cmake ninja-build python3-pip
+          sudo apt-get install -y ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd
+
+      - name: Use OpenVINO Toolkit Cache
+        if: runner.environment == 'github-hosted'
+        uses: actions/cache@v5
+        id: cache-openvino
+        with:
+          path: ./openvino_toolkit
+          key: openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }}
+
+      - name: Setup OpenVINO Toolkit
+        if: steps.cache-openvino.outputs.cache-hit != 'true'
+        uses: ./.github/actions/linux-setup-openvino
+        with:
+          path: ./openvino_toolkit
+          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
+          version_full: ${{ env.OPENVINO_VERSION_FULL }}
+
+      - name: Install OpenVINO dependencies
+        run: |
+          cd ./openvino_toolkit
+          chmod +x ./install_dependencies/install_openvino_dependencies.sh
+          echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh
+
+      - name: Build
+        id: cmake_build
+        run: |
+          source ./openvino_toolkit/setupvars.sh
+          cmake -B build/ReleaseOV -G Ninja \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_OPENVINO=ON
+          time cmake --build build/ReleaseOV --config Release -j $(nproc)
+
+      - name: Test
+        id: cmake_test
+        # TODO: fix and re-enable the `test-llama-archs` test below
+        run: |
+          cd ${{ github.workspace }}
+          if [ "${{ matrix.openvino_device }}" = "GPU" ]; then
+            export GGML_OPENVINO_DEVICE=GPU
+          fi
+          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 2000
diff --git a/.github/workflows/build-self-hosted.yml b/.github/workflows/build-self-hosted.yml
index 52624a46d77..2851c45601f 100644
--- a/.github/workflows/build-self-hosted.yml
+++ b/.github/workflows/build-self-hosted.yml
@@ -55,7 +55,24 @@ env:
   LLAMA_LOG_TIMESTAMPS: 1
 
 jobs:
+  determine-tag:
+    name: Determine tag name
+    runs-on: ubuntu-slim
+    outputs:
+      tag_name: ${{ steps.tag.outputs.name }}
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+        env:
+          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+
   ggml-ci-nvidia-cuda:
+    needs: determine-tag
     runs-on: [self-hosted, Linux, NVIDIA]
 
     steps:
@@ -65,11 +82,14 @@ jobs:
 
       - name: Test
         id: ggml-ci
+        env:
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           nvidia-smi
           GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
 
   ggml-ci-nvidia-vulkan-cm:
+    needs: determine-tag
     runs-on: [self-hosted, Linux, NVIDIA]
 
     steps:
@@ -79,11 +99,14 @@ jobs:
 
       - name: Test
         id: ggml-ci
+        env:
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
 
   ggml-ci-nvidia-vulkan-cm2:
+    needs: determine-tag
     runs-on: [self-hosted, Linux, NVIDIA, COOPMAT2]
 
     steps:
@@ -93,39 +116,40 @@ jobs:
 
       - name: Test
         id: ggml-ci
+        env:
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
 
-  # TODO: investigate slight precision issues in some operations for test-backend-ops on the WebGPU backend.
-  #ggml-ci-nvidia-webgpu:
-  #  runs-on: [self-hosted, Linux, NVIDIA]
+  ggml-ci-nvidia-webgpu:
+    runs-on: [self-hosted, Linux, NVIDIA]
 
-  #  steps:
-  #    - name: Clone
-  #      id: checkout
-  #      uses: actions/checkout@v6
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
 
-  #    - name: Dawn Dependency
-  #      id: dawn-depends
-  #      run: |
-  #        DAWN_VERSION="v20260317.182325"
-  #        DAWN_OWNER="google"
-  #        DAWN_REPO="dawn"
-  #        DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-ubuntu-latest-Release"
-  #        echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
-  #        curl -L -o artifact.tar.gz \
-  #          "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
-  #        mkdir dawn
-  #        tar -xvf artifact.tar.gz -C dawn --strip-components=1
+      - name: Dawn Dependency
+        id: dawn-depends
+        run: |
+          DAWN_VERSION="v20260317.182325"
+          DAWN_OWNER="google"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-ubuntu-latest-Release"
+          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          mkdir dawn
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1
 
-  #    - name: Test
-  #      id: ggml-ci
-  #      run: |
-  #        GG_BUILD_WEBGPU=1 \
-  #        GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
-  #        GG_BUILD_WEBGPU_DAWN_DIR="$GITHUB_WORKSPACE/dawn/lib64/cmake/Dawn" \
-  #          bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
+      - name: Test
+        id: ggml-ci
+        run: |
+          GG_BUILD_WEBGPU=1 \
+          GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
+          GG_BUILD_WEBGPU_DAWN_DIR="$GITHUB_WORKSPACE/dawn/lib64/cmake/Dawn" \
+            bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
 
   # TODO: provision AMX-compatible machine
   #ggml-ci-cpu-amx:
@@ -172,6 +196,7 @@ jobs:
   #         GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
 
   ggml-ci-mac-metal:
+    needs: determine-tag
     runs-on: [self-hosted, macOS, ARM64]
 
     steps:
@@ -181,10 +206,13 @@ jobs:
 
       - name: Test
         id: ggml-ci
+        env:
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
   ggml-ci-mac-webgpu:
+    needs: determine-tag
     runs-on: [self-hosted, macOS, ARM64]
 
     steps:
@@ -207,11 +235,14 @@ jobs:
 
       - name: Test
         id: ggml-ci
+        env:
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
             bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
   ggml-ci-mac-vulkan:
+    needs: determine-tag
     runs-on: [self-hosted, macOS, ARM64]
 
     steps:
@@ -221,11 +252,14 @@ jobs:
 
       - name: Test
         id: ggml-ci
+        env:
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
   ggml-ci-linux-intel-vulkan:
+    needs: determine-tag
     runs-on: [self-hosted, Linux, Intel]
 
     steps:
@@ -237,11 +271,14 @@ jobs:
 
       - name: Test
         id: ggml-ci
+        env:
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
   ggml-ci-win-intel-vulkan:
+    needs: determine-tag
     runs-on: [self-hosted, Windows, X64, Intel]
 
     steps:
@@ -256,6 +293,7 @@ jobs:
           MSYSTEM: UCRT64
           CHERE_INVOKING: 1
           PATH: C:\msys64\ucrt64\bin;C:\msys64\usr\bin;C:\Windows\System32;${{ env.PATH }}
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           vulkaninfo --summary
           # Skip python related tests with GG_BUILD_LOW_PERF=1 since Windows MSYS2 UCRT64 currently fails to create
@@ -263,8 +301,13 @@ jobs:
           LLAMA_FATAL_WARNINGS=OFF GG_BUILD_NINJA=1 GG_BUILD_VULKAN=1 GG_BUILD_LOW_PERF=1 ./ci/run.sh ./results/llama.cpp ./mnt/llama.cpp
 
   ggml-ci-intel-openvino-gpu-low-perf:
+    needs: determine-tag
     runs-on: [self-hosted, Linux, Intel, OpenVINO]
 
+    concurrency:
+      group: openvino-gpu-${{ github.head_ref || github.ref }}
+      cancel-in-progress: false
+
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
       OPENVINO_VERSION_MAJOR: "2026.0"
@@ -290,6 +333,8 @@ jobs:
 
       - name: Test
         id: ggml-ci
+        env:
+          HF_UI_VERSION: ${{ needs.determine-tag.outputs.tag_name }}
         run: |
           source ./openvino_toolkit/setupvars.sh
           GG_BUILD_OPENVINO=1 GGML_OPENVINO_DEVICE=GPU GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
diff --git a/.github/workflows/build-sycl.yml b/.github/workflows/build-sycl.yml
new file mode 100644
index 00000000000..09635f64edb
--- /dev/null
+++ b/.github/workflows/build-sycl.yml
@@ -0,0 +1,160 @@
+name: CI (sycl)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/build-sycl.yml',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/build-sycl.yml',
+      'ggml/src/ggml-sycl/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_LOG_COLORS: 1
+  LLAMA_LOG_PREFIX: 1
+  LLAMA_LOG_TIMESTAMPS: 1
+
+jobs:
+
+  ubuntu-24-sycl:
+    strategy:
+      matrix:
+        build: [fp32, fp16]
+        include:
+          - build: fp32
+            fp16: OFF
+          - build: fp16
+            fp16: ON
+
+    runs-on: ubuntu-24.04
+
+    env:
+      ONEAPI_ROOT: /opt/intel/oneapi/
+      ONEAPI_INSTALLER_VERSION: "2025.3.3"
+      LEVEL_ZERO_VERSION: "1.28.2"
+      LEVEL_ZERO_UBUNTU_VERSION: "u24.04"
+
+    continue-on-error: true
+
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Use oneAPI Installation Cache
+        uses: actions/cache@v5
+        id: cache-sycl
+        with:
+          path: ${{ env.ONEAPI_ROOT }}
+          key: oneAPI-${{ env.ONEAPI_INSTALLER_VERSION }}-${{ runner.os }}
+
+      - name: Download & Install oneAPI
+        shell: bash
+        if: steps.cache-sycl.outputs.cache-hit != 'true'
+        run: |
+          cd /tmp
+          wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/56f7923a-adb8-43f3-8b02-2b60fcac8cab/intel-deep-learning-essentials-2025.3.3.16_offline.sh -O intel-deep-learning-essentials_offline.sh
+          sudo bash intel-deep-learning-essentials_offline.sh -s -a --silent --eula accept
+
+      - name: Install Level Zero SDK
+        shell: bash
+        run: |
+          cd /tmp
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero.deb
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
+          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: ubuntu-24-sycl-${{ matrix.build }}
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      - name: Build
+        id: cmake_build
+        run: |
+          source /opt/intel/oneapi/setvars.sh
+          cmake -B build \
+            -G "Ninja" \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_SYCL=ON \
+            -DCMAKE_C_COMPILER=icx \
+            -DCMAKE_CXX_COMPILER=icpx \
+            -DLLAMA_OPENSSL=OFF \
+            -DGGML_NATIVE=OFF \
+            -DGGML_SYCL_F16=${{ matrix.fp16 }}
+          time cmake --build build --config Release -j $(nproc)
+
+  windows-latest-sycl:
+    runs-on: windows-2022
+
+    defaults:
+      run:
+        shell: bash
+
+    env:
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b60765d1-2b85-4e85-86b6-cb0e9563a699/intel-deep-learning-essentials-2025.3.3.18_offline.exe
+      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
+      LEVEL_ZERO_SDK_URL: https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero-win-sdk-1.28.2.zip
+      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
+      ONEAPI_INSTALLER_VERSION: "2025.3.3"
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Use oneAPI Installation Cache
+        uses: actions/cache@v5
+        id: cache-sycl
+        with:
+          path: ${{ env.ONEAPI_ROOT }}
+          key: oneAPI-${{ env.ONEAPI_INSTALLER_VERSION }}-${{ runner.os }}
+
+      - name: Download & Install oneAPI
+        shell: bash
+        if: steps.cache-sycl.outputs.cache-hit != 'true'
+        run: |
+          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
+
+      - name: Install Level Zero SDK
+        shell: pwsh
+        run: |
+          Invoke-WebRequest -Uri "${{ env.LEVEL_ZERO_SDK_URL }}" -OutFile "level-zero-win-sdk.zip"
+          Expand-Archive -Path "level-zero-win-sdk.zip" -DestinationPath "C:/level-zero-sdk" -Force
+          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: windows-latest-sycl
+          variant: ccache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      # TODO: add ssl support ; we will also need to modify win-build-sycl.bat to accept user-specified args
+
+      - name: Build
+        id: cmake_build
+        run:  examples/sycl/win-build-sycl.bat
diff --git a/.github/workflows/build-virtgpu.yml b/.github/workflows/build-virtgpu.yml
new file mode 100644
index 00000000000..5b740590d6b
--- /dev/null
+++ b/.github/workflows/build-virtgpu.yml
@@ -0,0 +1,50 @@
+name: CI (virtgpu)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/build-virtgpu.yml',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/build-virtgpu.yml',
+      'ggml/src/ggml-virtgpu/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  ubuntu-24-virtgpu:
+    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential libdrm-dev pkg-config libssl-dev
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build \
+            -DGGML_VIRTGPU=ON \
+            -DGGML_VIRTGPU_BACKEND=ON
+          cmake --build build --config Release -j $(nproc)
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 28c8665bd8b..65fa24f4468 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -456,7 +456,8 @@ jobs:
         run: |
           cd build
           # This is using llvmpipe and runs slower than other backends
-          ctest -L main --verbose --timeout 900
+          # test-backend-ops is too slow on llvmpipe, skip it
+          ctest -L main -E test-backend-ops --verbose --timeout 900
 
   ubuntu-24-webgpu-wasm:
     runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
@@ -555,186 +556,6 @@ jobs:
             -DGGML_MUSA=ON
           time cmake --build build --config Release -j $(nproc)
 
-  ubuntu-22-sycl:
-    runs-on: ubuntu-22.04
-
-    continue-on-error: true
-
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: add oneAPI to apt
-        shell: bash
-        run: |
-          cd /tmp
-          wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-          sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-          rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-          sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
-
-      - name: install oneAPI dpcpp compiler
-        shell: bash
-        run: |
-          sudo apt update
-          sudo apt install intel-oneapi-compiler-dpcpp-cpp libssl-dev
-
-      - name: install oneAPI MKL library
-        shell: bash
-        run: |
-          sudo apt install intel-oneapi-mkl-devel
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-22-sycl
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          source /opt/intel/oneapi/setvars.sh
-          cmake -B build \
-            -DGGML_SYCL=ON \
-            -DCMAKE_C_COMPILER=icx \
-            -DCMAKE_CXX_COMPILER=icpx
-          time cmake --build build --config Release -j $(nproc)
-
-  ubuntu-22-sycl-fp16:
-    runs-on: ubuntu-22.04
-
-    continue-on-error: true
-
-    steps:
-      - uses: actions/checkout@v6
-
-      - name: add oneAPI to apt
-        shell: bash
-        run: |
-          cd /tmp
-          wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-          sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-          rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-          sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
-
-      - name: install oneAPI dpcpp compiler
-        shell: bash
-        run: |
-          sudo apt update
-          sudo apt install intel-oneapi-compiler-dpcpp-cpp libssl-dev ninja-build
-
-      - name: install oneAPI MKL library
-        shell: bash
-        run: |
-          sudo apt install intel-oneapi-mkl-devel
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-22-sycl-fp16
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          source /opt/intel/oneapi/setvars.sh
-          cmake -B build \
-            -G "Ninja" \
-            -DCMAKE_BUILD_TYPE=Release \
-            -DGGML_SYCL=ON \
-            -DCMAKE_C_COMPILER=icx \
-            -DCMAKE_CXX_COMPILER=icpx \
-            -DGGML_SYCL_F16=ON
-          time cmake --build build --config Release -j $(nproc)
-
-  ubuntu-24-openvino:
-      name: ubuntu-24-openvino-${{ matrix.openvino_device }}
-      strategy:
-        matrix:
-          include:
-            - variant: cpu
-              runner: '"ubuntu-24.04"'
-              openvino_device: "CPU"
-            - variant: gpu
-              runner: '["self-hosted","Linux","X64","Intel"]'
-              openvino_device: "GPU"
-
-      runs-on: ${{ fromJSON(matrix.runner) }}
-
-      env:
-        # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-        OPENVINO_VERSION_MAJOR: "2026.0"
-        OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
-
-      steps:
-        - name: Clone
-          id: checkout
-          uses: actions/checkout@v6
-
-        - name: ccache
-          if: runner.environment == 'github-hosted'
-          uses: ggml-org/ccache-action@v1.2.21
-          with:
-            key: ubuntu-24-openvino-${{ matrix.variant }}-no-preset-v1
-            evict-old-files: 1d
-            save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-        - name: Dependencies
-          id: depends
-          run: |
-            sudo apt-get update
-            sudo apt-get install -y build-essential libssl-dev libtbb12 cmake ninja-build python3-pip
-            sudo apt-get install -y ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd
-
-        - name: Use OpenVINO Toolkit Cache
-          if: runner.environment == 'github-hosted'
-          uses: actions/cache@v5
-          id: cache-openvino
-          with:
-            path: ./openvino_toolkit
-            key: openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }}
-
-        - name: Setup OpenVINO Toolkit
-          if: steps.cache-openvino.outputs.cache-hit != 'true'
-          uses: ./.github/actions/linux-setup-openvino
-          with:
-            path: ./openvino_toolkit
-            version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
-            version_full: ${{ env.OPENVINO_VERSION_FULL }}
-
-        - name: Install OpenVINO dependencies
-          run: |
-            cd ./openvino_toolkit
-            chmod +x ./install_dependencies/install_openvino_dependencies.sh
-            echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh
-
-        - name: Build
-          id: cmake_build
-          run: |
-            source ./openvino_toolkit/setupvars.sh
-            cmake -B build/ReleaseOV -G Ninja \
-              -DCMAKE_BUILD_TYPE=Release \
-              -DGGML_OPENVINO=ON
-            time cmake --build build/ReleaseOV --config Release -j $(nproc)
-
-        - name: Test
-          id: cmake_test
-          # TODO: fix and re-enable the `test-llama-archs` test below
-          run: |
-            cd ${{ github.workspace }}
-            if [ "${{ matrix.openvino_device }}" = "GPU" ]; then
-              export GGML_OPENVINO_DEVICE=GPU
-            fi
-            ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 2000
 
   windows-latest:
     runs-on: windows-2025
@@ -943,39 +764,6 @@ jobs:
           cmake --build build --config Release -j %NINJA_JOBS% -t ggml
           cmake --build build --config Release
 
-  windows-latest-sycl:
-    runs-on: windows-2022
-
-    defaults:
-      run:
-        shell: bash
-
-    env:
-      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/24751ead-ddc5-4479-b9e6-f9fe2ff8b9f2/intel-deep-learning-essentials-2025.2.1.25_offline.exe
-      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
-      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: windows-latest-sycl
-          variant: ccache
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Install
-        run:  |
-          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
-
-      # TODO: add ssl support ; we will also need to modify win-build-sycl.bat to accept user-specified args
-
-      - name: Build
-        id: cmake_build
-        run:  examples/sycl/win-build-sycl.bat
 
   windows-latest-hip:
     runs-on: windows-2022
diff --git a/.github/workflows/code-style.yml b/.github/workflows/code-style.yml
new file mode 100644
index 00000000000..c88396c0a7d
--- /dev/null
+++ b/.github/workflows/code-style.yml
@@ -0,0 +1,51 @@
+name: Code Style Checker
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  model-naming:
+    runs-on: ubuntu-slim
+    steps:
+      - uses: actions/checkout@v6
+      - name: Check model naming conventions
+        run: |
+          python3 - << 'EOF'
+          import re, os, sys
+
+          pairs = re.findall(
+              r'case\s+(LLM_ARCH_\w+)\s*:\s*\n\s+return new (llama_model_\w+)\s*\(',
+              open("src/llama-model.cpp").read())
+
+          errors = []
+          for arch, cls in pairs:
+              suffix  = arch[len("LLM_ARCH_"):]
+              csuffix = cls[len("llama_model_"):]
+              fname   = csuffix.replace("_", "-") + ".cpp"
+
+              if not re.fullmatch(r'[A-Z][A-Z0-9_]*',   suffix):
+                  errors.append(f"{arch}: suffix not upper snake case, example: LLM_ARCH_MY_MODEL")
+
+              if not re.fullmatch(r'[a-z][a-z0-9_]*', csuffix):
+                  errors.append(f"{arch}: class suffix not lower snake case, example: llama_model_my_model")
+
+              elif suffix.lower() != csuffix:
+                  errors.append(f"{arch}: arch/class name mismatch, expected class 'llama_model_{suffix.lower()}' but got '{cls}'")
+
+              elif not os.path.isfile(f"src/models/{fname}"):
+                  errors.append(f"{arch}: expects model file name to be src/models/{fname}, but not found")
+
+          if errors:
+              print('\n'.join(f"  - {e}" for e in errors)); sys.exit(1)
+          print(f"OK: {len(pairs)} mappings validated.")
+          EOF
diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml
index a2d4d0a3a78..53f6a0ccfda 100644
--- a/.github/workflows/editorconfig.yml
+++ b/.github/workflows/editorconfig.yml
@@ -2,11 +2,6 @@ name: EditorConfig Checker
 
 on:
   workflow_dispatch: # allows manual triggering
-    inputs:
-      create_release:
-        description: 'Create new release'
-        required: true
-        type: boolean
   push:
     branches:
       - master
diff --git a/.github/workflows/gguf-publish.yml b/.github/workflows/gguf-publish.yml
index a1fba046a93..fb8eab3cdb3 100644
--- a/.github/workflows/gguf-publish.yml
+++ b/.github/workflows/gguf-publish.yml
@@ -29,10 +29,10 @@ jobs:
       uses: actions/setup-python@v6
       with:
         python-version: '3.11'
+        pip-install: poetry==2.4.0
     - name: Install dependencies
       run: |
         cd gguf-py
-        python -m pip install poetry==2.3.2
         poetry install
 
     - name: Build package
diff --git a/.github/workflows/python-type-check.yml b/.github/workflows/python-type-check.yml
index dc7aebe24ca..cbeeb39d05b 100644
--- a/.github/workflows/python-type-check.yml
+++ b/.github/workflows/python-type-check.yml
@@ -31,7 +31,7 @@ jobs:
         uses: actions/setup-python@v6
         with:
           python-version: "3.11"
-          pip-install: -r requirements/requirements-all.txt ty==0.0.26
+          pip-install: -r requirements/requirements-all.txt ty==0.0.35
       # - name: Type-check with Pyright
       #   uses: jakebailey/pyright-action@v2
       #   with:
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index f1cc12cd452..1880c155c85 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -36,7 +36,9 @@ env:
   CMAKE_ARGS: "-DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=ON -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON"
 
 jobs:
+
   macOS-cpu:
+
     strategy:
       matrix:
         include:
@@ -64,6 +66,13 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
@@ -91,7 +100,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-${{ matrix.build }}.tar.gz -s ",./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-${{ matrix.build }}.tar.gz -s ",^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -100,6 +109,7 @@ jobs:
           name: llama-bin-macos-${{ matrix.build }}.tar.gz
 
   ubuntu-cpu:
+
     strategy:
       matrix:
         include:
@@ -119,6 +129,13 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: ccache
         if: ${{ matrix.build != 's390x' }}
         uses: ggml-org/ccache-action@v1.2.21
@@ -160,7 +177,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -169,6 +186,7 @@ jobs:
           name: llama-bin-ubuntu-${{ matrix.build }}.tar.gz
 
   ubuntu-vulkan:
+
     strategy:
       matrix:
         include:
@@ -186,6 +204,13 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
@@ -228,7 +253,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -237,6 +262,7 @@ jobs:
           name: llama-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz
 
   android-arm64:
+
     runs-on: ubuntu-latest
 
     env:
@@ -249,6 +275,13 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
@@ -297,7 +330,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-android-arm64.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-android-arm64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -306,6 +339,7 @@ jobs:
           name: llama-bin-android-arm64.tar.gz
 
   ubuntu-24-openvino:
+
     runs-on: ubuntu-24.04
 
     outputs:
@@ -327,6 +361,13 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
@@ -377,7 +418,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/ReleaseOV/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/ReleaseOV/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/ReleaseOV/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -386,6 +427,7 @@ jobs:
           name: llama-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz
 
   windows-cpu:
+
     runs-on: windows-2025
 
     strategy:
@@ -400,6 +442,13 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
@@ -438,6 +487,7 @@ jobs:
           name: llama-bin-win-cpu-${{ matrix.arch }}.zip
 
   windows:
+
     runs-on: windows-2025
 
     env:
@@ -461,6 +511,13 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
@@ -520,6 +577,7 @@ jobs:
           name: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip
 
   windows-cuda:
+
     runs-on: windows-2022
 
     strategy:
@@ -531,6 +589,13 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: Install ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
@@ -591,6 +656,7 @@ jobs:
           name: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip
 
   windows-sycl:
+
     runs-on: windows-2022
 
     defaults:
@@ -598,15 +664,44 @@ jobs:
         shell: bash
 
     env:
-      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/24751ead-ddc5-4479-b9e6-f9fe2ff8b9f2/intel-deep-learning-essentials-2025.2.1.25_offline.exe
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b60765d1-2b85-4e85-86b6-cb0e9563a699/intel-deep-learning-essentials-2025.3.3.18_offline.exe
       WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
+      LEVEL_ZERO_SDK_URL: https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero-win-sdk-1.28.2.zip
       ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
+      ONEAPI_INSTALLER_VERSION: "2025.3.3"
 
     steps:
       - name: Clone
         id: checkout
         uses: actions/checkout@v6
 
+      - name: Use oneAPI Installation Cache
+        uses: actions/cache@v5
+        id: cache-sycl
+        with:
+          path: ${{ env.ONEAPI_ROOT }}
+          key: oneAPI-${{ env.ONEAPI_INSTALLER_VERSION }}-${{ runner.os }}
+
+      - name: Download & Install oneAPI
+        shell: bash
+        if: steps.cache-sycl.outputs.cache-hit != 'true'
+        run: |
+          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
+
+      - name: Install Level Zero SDK
+        shell: pwsh
+        run: |
+          Invoke-WebRequest -Uri "${{ env.LEVEL_ZERO_SDK_URL }}" -OutFile "level-zero-win-sdk.zip"
+          Expand-Archive -Path "level-zero-win-sdk.zip" -DestinationPath "C:/level-zero-sdk" -Force
+          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
@@ -614,10 +709,6 @@ jobs:
           variant: ccache
           evict-old-files: 1d
 
-      - name: Install
-        run:  |
-          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
-
       - name: Build
         id: cmake_build
         shell: cmd
@@ -645,6 +736,13 @@ jobs:
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_opencl.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin
+          ZE_LOADER_DLL=$(find "${{ env.ONEAPI_ROOT }}" "$LEVEL_ZERO_V1_SDK_PATH" -iname ze_loader.dll -print -quit 2>/dev/null || true)
+          if [ -n "$ZE_LOADER_DLL" ]; then
+            echo "Using Level Zero loader: $ZE_LOADER_DLL"
+            cp "$ZE_LOADER_DLL" ./build/bin
+          else
+            echo "Level Zero loader DLL not found in oneAPI or SDK; relying on system driver/runtime"
+          fi
 
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl8.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin
@@ -670,7 +768,102 @@ jobs:
           path: llama-bin-win-sycl-x64.zip
           name: llama-bin-win-sycl-x64.zip
 
+  ubuntu-24-sycl:
+
+    strategy:
+      matrix:
+        build: [fp32, fp16]
+        include:
+          - build: fp32
+            fp16: OFF
+          - build: fp16
+            fp16: ON
+
+    runs-on: ubuntu-24.04
+
+    env:
+      ONEAPI_ROOT: /opt/intel/oneapi/
+      ONEAPI_INSTALLER_VERSION: "2025.3.3"
+      LEVEL_ZERO_VERSION: "1.28.2"
+      LEVEL_ZERO_UBUNTU_VERSION: "u24.04"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: Use oneAPI Installation Cache
+        uses: actions/cache@v5
+        id: cache-sycl
+        with:
+          path: ${{ env.ONEAPI_ROOT }}
+          key: oneAPI-${{ env.ONEAPI_INSTALLER_VERSION }}-${{ runner.os }}
+
+      - name: Download & Install oneAPI
+        shell: bash
+        if: steps.cache-sycl.outputs.cache-hit != 'true'
+        run: |
+          cd /tmp
+          wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/56f7923a-adb8-43f3-8b02-2b60fcac8cab/intel-deep-learning-essentials-2025.3.3.16_offline.sh -O intel-deep-learning-essentials_offline.sh
+          sudo bash intel-deep-learning-essentials_offline.sh -s -a --silent --eula accept
+
+      - name: Install Level Zero SDK
+        shell: bash
+        run: |
+          cd /tmp
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero.deb
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
+          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: ubuntu-24-sycl-${{ matrix.build }}
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      - name: Build
+        id: cmake_build
+        run: |
+          source /opt/intel/oneapi/setvars.sh
+          cmake -B build \
+            -G "Ninja" \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_SYCL=ON \
+            -DCMAKE_C_COMPILER=icx \
+            -DCMAKE_CXX_COMPILER=icpx \
+            -DLLAMA_OPENSSL=OFF \
+            -DGGML_NATIVE=OFF \
+            -DGGML_SYCL_F16=${{ matrix.fp16 }}
+          time cmake --build build --config Release -j $(nproc)
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v6
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
+          name: llama-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
+
   ubuntu-22-rocm:
+
     runs-on: ubuntu-22.04
 
     strategy:
@@ -687,6 +880,13 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: Free up disk space
         uses: ggml-org/free-disk-space@v1.3.1
         with:
@@ -765,7 +965,7 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -774,6 +974,7 @@ jobs:
           name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
 
   windows-hip:
+
     runs-on: windows-2022
 
     env:
@@ -790,6 +991,13 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: Grab rocWMMA package
         id: grab_rocwmma
         run: |
@@ -1017,7 +1225,7 @@ jobs:
       - name: Pack artifacts
         run: |
           cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -1045,11 +1253,15 @@ jobs:
       - ubuntu-cpu
       - ubuntu-vulkan
       - ubuntu-24-openvino
+      - ubuntu-24-sycl
       - android-arm64
       - macOS-cpu
       - ios-xcode-build
       - openEuler-cann
 
+    outputs:
+      tag_name: ${{ steps.tag.outputs.name }}
+
     steps:
       - name: Clone
         id: checkout
@@ -1133,6 +1345,8 @@ jobs:
             - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
             - [Ubuntu x64 (ROCm 7.2)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.2-x64.tar.gz)
             - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ needs.ubuntu-24-openvino.outputs.openvino_version }}-x64.tar.gz)
+            - [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp32-x64.tar.gz)
+            - [Ubuntu x64 (SYCL FP16)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp16-x64.tar.gz)
 
             **Android:**
             - [Android arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-android-arm64.tar.gz)
@@ -1173,3 +1387,15 @@ jobs:
                 });
               }
             }
+
+  ui-publish:
+    if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+
+    needs:
+      - release
+
+    uses: ./.github/workflows/ui-publish.yml
+    with:
+      version_tag: ${{ needs.release.outputs.tag_name }}
+    secrets:
+      hf_token: ${{ secrets.HF_TOKEN_UI_STATIC_OUTPUT }}
diff --git a/.github/workflows/server-sanitize.yml b/.github/workflows/server-sanitize.yml
index 4c9f447cf8b..53c9968ee96 100644
--- a/.github/workflows/server-sanitize.yml
+++ b/.github/workflows/server-sanitize.yml
@@ -67,6 +67,13 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: Build
         id: cmake_build
         run: |
diff --git a/.github/workflows/server-self-hosted.yml b/.github/workflows/server-self-hosted.yml
index 4b9f4b631a2..d3b3e4cc7e8 100644
--- a/.github/workflows/server-self-hosted.yml
+++ b/.github/workflows/server-self-hosted.yml
@@ -67,6 +67,13 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: Build
         id: cmake_build
         run: |
diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index 750c29f08e5..7b9c5a3a3d8 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -93,6 +93,13 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
       - name: Build
         id: cmake_build
         run: |
@@ -135,6 +142,11 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+
       - name: Build
         id: cmake_build
         run: |
diff --git a/.github/workflows/ui-build.yml b/.github/workflows/ui-build.yml
new file mode 100644
index 00000000000..511c96fb6fc
--- /dev/null
+++ b/.github/workflows/ui-build.yml
@@ -0,0 +1,44 @@
+name: UI Build
+
+on:
+  workflow_call:
+
+jobs:
+  build:
+    name: Build static output
+    runs-on: ubuntu-slim
+    env:
+      BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v6
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
+      - name: Install dependencies
+        run: npm ci
+        working-directory: tools/ui
+
+      - name: Build application
+        run: npm run build
+        working-directory: tools/ui
+
+      - name: Generate checksums
+        run: |
+          cd build/tools/ui/dist
+          for f in *; do
+            sha256sum "$f" | awk '{print $1, $2}' >> checksums.txt
+          done
+
+      - name: Upload built UI
+        uses: actions/upload-artifact@v6
+        with:
+          name: ui-build
+          path: build/tools/ui/dist/
+          retention-days: 1
diff --git a/.github/workflows/server-webui.yml b/.github/workflows/ui-ci.yml
similarity index 56%
rename from .github/workflows/server-webui.yml
rename to .github/workflows/ui-ci.yml
index 492107ffd85..43d6e125687 100644
--- a/.github/workflows/server-webui.yml
+++ b/.github/workflows/ui-ci.yml
@@ -1,7 +1,7 @@
-name: Server WebUI
+name: CI (UI)
 
 on:
-  workflow_dispatch: # allows manual triggering
+  workflow_dispatch:
     inputs:
       sha:
         description: 'Commit SHA1 to build'
@@ -11,18 +11,16 @@ on:
     branches:
       - master
     paths: [
-      '.github/workflows/server-webui.yml',
-      'tools/server/webui/**.*',
-      'tools/server/tests/**.*',
-      'tools/server/public/**'
+      '.github/workflows/ui-ci.yml',
+      'tools/ui/**.*',
+      'tools/server/tests/**.*'
     ]
   pull_request:
     types: [opened, synchronize, reopened]
     paths: [
-      '.github/workflows/server-webui.yml',
-      'tools/server/webui/**.*',
-      'tools/server/tests/**.*',
-      'tools/server/public/**'
+      '.github/workflows/ui-ci.yml',
+      'tools/ui/**.*',
+      'tools/server/tests/**.*'
     ]
 
 env:
@@ -36,9 +34,14 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  webui-check:
-    name: WebUI Checks
-    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+  ui-build:
+    name: Build static output
+    uses: ./.github/workflows/ui-build.yml
+
+  ui-checks:
+    name: UI Checks
+    needs: ui-build
+    runs-on: ubuntu-24.04-arm
     continue-on-error: true
     steps:
       - name: Checkout code
@@ -51,58 +54,89 @@ jobs:
         id: node
         uses: actions/setup-node@v6
         with:
-          node-version: "22"
+          node-version: "24"
           cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
+          cache-dependency-path: "tools/ui/package-lock.json"
 
       - name: Install dependencies
         id: setup
         if: ${{ steps.node.conclusion == 'success' }}
         run: npm ci
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run type checking
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run check
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run linting
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npm run lint
-        working-directory: tools/server/webui
-
-      - name: Build application
-        if: ${{ always() && steps.setup.conclusion == 'success' }}
-        run: npm run build
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Install Playwright browsers
         id: playwright
         if: ${{ always() && steps.setup.conclusion == 'success' }}
         run: npx playwright install --with-deps
-        working-directory: tools/server/webui
-
-      - name: Build Storybook
-        if: ${{ always() && steps.playwright.conclusion == 'success' }}
-        run: npm run build-storybook
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run Client tests
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:client
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run Unit tests
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:unit
-        working-directory: tools/server/webui
+        working-directory: tools/ui
+
+  e2e-tests:
+    name: E2E Tests
+    needs: ui-build
+    runs-on: ubuntu-24.04-arm
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Setup Node.js
+        id: node
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
+      - name: Install dependencies
+        id: setup
+        if: ${{ steps.node.conclusion == 'success' }}
+        run: npm ci
+        working-directory: tools/ui
+
+      - name: Build application
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
+        run: npm run build
+        working-directory: tools/ui
+
+      - name: Install Playwright browsers
+        id: playwright
+        if: ${{ always() && steps.setup.conclusion == 'success' }}
+        run: npx playwright install --with-deps
+        working-directory: tools/ui
+
+      - name: Build Storybook
+        if: ${{ always() && steps.playwright.conclusion == 'success' }}
+        run: npm run build-storybook
+        working-directory: tools/ui
 
       - name: Run UI tests
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:ui -- --testTimeout=60000
-        working-directory: tools/server/webui
+        working-directory: tools/ui
 
       - name: Run E2E tests
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:e2e
-        working-directory: tools/server/webui
+        working-directory: tools/ui
diff --git a/.github/workflows/ui-publish.yml b/.github/workflows/ui-publish.yml
new file mode 100644
index 00000000000..0df358557ef
--- /dev/null
+++ b/.github/workflows/ui-publish.yml
@@ -0,0 +1,70 @@
+name: UI Publish
+
+on:
+  workflow_call:
+    inputs:
+      version_tag:
+        description: 'Version tag to publish under (e.g., b1234)'
+        required: true
+        type: string
+    secrets:
+      hf_token:
+        description: 'Hugging Face token with write access'
+        required: true
+
+jobs:
+  build:
+    name: Build static output
+    uses: ./.github/workflows/ui-build.yml
+
+  publish:
+    name: Publish UI Static Output
+    needs: build
+    runs-on: ubuntu-24.04-arm
+
+    permissions:
+      contents: read
+
+    env:
+      HF_BUCKET_NAME: ${{ vars.HF_BUCKET_UI_STATIC_OUTPUT }}
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 1
+
+      - name: Download UI build artifact
+        uses: actions/download-artifact@v7
+        with:
+          name: ui-build
+          path: build/tools/ui/dist/
+
+      - name: Install Hugging Face Hub CLI
+        run: pip install -U huggingface_hub
+
+      - name: Authenticate with Hugging Face
+        run: hf auth login --token ${{ secrets.hf_token }}
+
+      - name: Sync built files to Hugging Face bucket (version tag)
+        run: |
+          # Upload the built files to the Hugging Face bucket under the release version
+          hf buckets sync build/tools/ui/dist hf://buckets/ggml-org/${{ env.HF_BUCKET_NAME }}/${{ inputs.version_tag }} --delete --quiet
+
+      - name: Sync built files to Hugging Face bucket (latest)
+        run: |
+          # Also upload to the 'latest' directory for fallback downloads
+          hf buckets sync build/tools/ui/dist hf://buckets/ggml-org/${{ env.HF_BUCKET_NAME }}/latest --delete --quiet
+
+      - name: Verify upload
+        run: |
+          # List the files in the bucket to verify the upload
+          hf buckets list hf://buckets/ggml-org/${{ env.HF_BUCKET_NAME }}/${{ inputs.version_tag }} -R -h
+
+      - name: Clean up root-level files
+        run: |
+          # Clean up any old root-level files from previous non-versioned deployments
+          hf buckets rm ggml-org/${{ env.HF_BUCKET_NAME }}/index.html --yes 2>/dev/null || true
+          hf buckets rm ggml-org/${{ env.HF_BUCKET_NAME }}/bundle.js --yes 2>/dev/null || true
+          hf buckets rm ggml-org/${{ env.HF_BUCKET_NAME }}/bundle.css --yes 2>/dev/null || true
+          hf buckets rm ggml-org/${{ env.HF_BUCKET_NAME }}/loading.html --yes 2>/dev/null || true
diff --git a/.gitignore b/.gitignore
index 15dc4014f43..8dc9d7d0b8d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -34,7 +34,6 @@
 /.vscode/
 /nppBackup
 
-
 # Coverage
 
 /gcovr-report/
@@ -74,6 +73,7 @@
 !/models/templates
 
 # Zig
+
 /zig-out/
 /zig-cache/
 
@@ -92,11 +92,12 @@
 !/examples/sycl/*.bat
 !/examples/sycl/*.sh
 
-# Server Web UI temporary files
+# Server Web UI temporary files (+ legacy directory)
+
 /tools/server/webui/node_modules
 /tools/server/webui/dist
-# we no longer use gz for index.html
-/tools/server/public/index.html.gz
+/tools/ui/node_modules
+/tools/ui/dist
 
 # Python
 
@@ -104,11 +105,16 @@
 __pycache__/
 */poetry.lock
 poetry.toml
+poetry.lock
+uv.lock
 
 # Nix
+
+flake.lock
 /result
 
 # Test binaries
+
 /tests/test-backend-ops
 /tests/test-double-float
 /tests/test-grad0
@@ -124,6 +130,7 @@ poetry.toml
 /tests/test-tokenizer-1-spm
 
 # Scripts
+
 !/scripts/install-oneapi.bat
 
 # Generated by scripts
@@ -132,16 +139,24 @@ poetry.toml
 /wikitext-2-raw/
 
 # Test models for lora adapters
+
 /lora-tests
 
 # Local scripts
+
 /run-vim.sh
 /run-chat.sh
 /run-spec.sh
 /.ccache/
 
 # IDE
+
 /*.code-workspace
 /.windsurf/
 # emscripten
 a.out.*
+
+# AGENTS
+
+AGENTS.local.md
+.pi/SYSTEM.md
diff --git a/.pi/gg/SYSTEM.md b/.pi/gg/SYSTEM.md
new file mode 100644
index 00000000000..727a850b183
--- /dev/null
+++ b/.pi/gg/SYSTEM.md
@@ -0,0 +1,34 @@
+You are a coding agent. Here are some very important rules that you must follow:
+
+General:
+- By very precise and concise when writing code, comments, explanations, etc.
+- PR and commit titles format: `<module> : <title>`. Lookup recents for examples
+- Don't try to build or run the code unless you are explicitly asked to do so
+- Use the `gh` CLI tool when querying PRs, issues, or other GitHub resources
+
+Coding:
+- When in doubt, always refer to the CONTRIBUTING.md file of the project
+- When referencing issues or PRs in comments, use the format:
+  - C/C++ code: `// ref: <url>`
+  - Other (CMake, etc.): `# ref: <url>`
+
+Pull requests (PRs):
+- New branch names are prefixed with "gg/"
+- Before opening a pull request, ask the user to confirm the description
+- When creating a pull request, look for the repository's PR template and follow it
+- For the AI usage disclosure section, write "YES. llama.cpp + pi"
+- Always create the pull requests in draft mode
+
+Commits:
+- On every commit that you make, include a "Assisted-by: llama.cpp:local pi" tag
+- Do not explicitly set the git author in commits - rely on the default git config
+
+Resources (read on demand):
+- [CONTRIBUTING.md](CONTRIBUTING.md)
+- [Build documentation](docs/build.md)
+- [Server usage documentation](tools/server/README.md)
+- [Server development documentation](tools/server/README-dev.md)
+- [PEG parser](docs/development/parsing.md)
+- [Auto parser](docs/autoparser.md)
+- [Jinja engine](common/jinja/README.md)
+- [PR template](.github/pull_request_template.md)
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 310a3dcfd24..537ef1239de 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -104,13 +104,24 @@ option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
 option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE})
 
 # extra artifacts
-option(LLAMA_BUILD_TESTS    "llama: build tests"          ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_WEBUI    "llama: build the embedded Web UI for server"  ON)
-option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
-option(LLAMA_TESTS_INSTALL  "llama: install tests"        ON)
+option(LLAMA_BUILD_TESTS            "llama: build tests"                                                                            ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_TOOLS            "llama: build tools"                                                                            ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_EXAMPLES         "llama: build examples"                                                                         ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_SERVER           "llama: build server example"                                                                   ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_UI                "llama: build the embedded Web UI for server"                                                   ON)
+option(LLAMA_USE_PREBUILT_UI         "llama: use prebuilt UI from HF Bucket when available (requires LLAMA_BUILD_UI=ON)"             ON)
+
+# Backward compat: when old var is set but new one isn't, forward the value
+if(DEFINED LLAMA_BUILD_WEBUI)
+    set(LLAMA_BUILD_UI ${LLAMA_BUILD_WEBUI})
+    message(DEPRECATION "LLAMA_BUILD_WEBUI is deprecated, use LLAMA_BUILD_UI instead")
+endif()
+if(DEFINED LLAMA_USE_PREBUILT_WEBUI)
+    set(LLAMA_USE_PREBUILT_UI ${LLAMA_USE_PREBUILT_WEBUI})
+    message(DEPRECATION "LLAMA_USE_PREBUILT_WEBUI is deprecated, use LLAMA_USE_PREBUILT_UI instead")
+endif()
+option(LLAMA_TOOLS_INSTALL          "llama: install tools"                                                                          ${LLAMA_TOOLS_INSTALL_DEFAULT})
+option(LLAMA_TESTS_INSTALL          "llama: install tests"                                                                          ON)
 
 # 3rd party libs
 option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" ON)
diff --git a/CODEOWNERS b/CODEOWNERS
index 612fcdda1c0..f58f0f830fa 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -15,7 +15,7 @@
 # ggml-org/llama-common     : ggerganov, aldehir, angt, danbev, ngxson, pwilkin
 # ggml-org/llama-mtmd       : ngxson
 # ggml-org/llama-server     : ggerganov, ngxson, allozaur, angt, ServeurpersoCom
-# ggml-org/llama-webui      : allozaur
+# ggml-org/llama-ui           : allozaur
 
 /.devops/*.Dockerfile                   @ngxson
 /.github/actions/                       @ggml-org/ci
@@ -53,28 +53,30 @@
 /examples/speculative/                  @ggerganov
 /ggml/cmake/                            @ggerganov
 /ggml/include/                          @ggerganov
+/ggml/src/ggml-backend-meta.cpp         @JohannesGaessler
 /ggml/src/ggml-cann/                    @ggml-org/ggml-cann
 /ggml/src/ggml-common.h                 @ggerganov
 /ggml/src/ggml-cpu/                     @ggerganov
 /ggml/src/ggml-cpu/spacemit/            @alex-spacemit
 /ggml/src/ggml-cuda/                    @ggml-org/ggml-cuda
+/ggml/src/ggml-cuda/vendors/hip.h       @IMbackK
 /ggml/src/ggml-cuda/fattn-wmma*         @IMbackK
+/ggml/src/ggml-hexagon/                 @ggml-org/ggml-hexagon
 /ggml/src/ggml-hip/                     @IMbackK
-/ggml/src/ggml-cuda/vendors/hip.h       @IMbackK
 /ggml/src/ggml-impl.h                   @ggerganov
 /ggml/src/ggml-metal/                   @ggml-org/ggml-metal
 /ggml/src/ggml-opencl/                  @ggml-org/ggml-opencl
-/ggml/src/ggml-hexagon/                 @ggml-org/ggml-hexagon
+/ggml/src/ggml-openvino/                @cavusmustafa @wine99
 /ggml/src/ggml-opt.cpp                  @JohannesGaessler
 /ggml/src/ggml-quants.*                 @ggerganov
 /ggml/src/ggml-rpc/                     @ggml-org/ggml-rpc
 /ggml/src/ggml-sycl/                    @ggml-org/ggml-sycl
 /ggml/src/ggml-threading.*              @ggerganov
-/ggml/src/ggml-vulkan/                  @ggml-org/ggml-vulkan
 /ggml/src/ggml-virtgpu/                 @kpouget
+/ggml/src/ggml-vulkan/                  @ggml-org/ggml-vulkan
 /ggml/src/ggml-webgpu/                  @ggml-org/ggml-webgpu
 /ggml/src/ggml-zdnn/                    @ggml-org/ggml-zdnn @Andreas-Krebbel @AlekseiNikiforovIBM
-/ggml/src/ggml-openvino/                @cavusmustafa @wine99
+/ggml/src/ggml-zendnn/                  @avinashcpandey @Jiten1parmar @z-vishal
 /ggml/src/ggml.c                        @ggerganov
 /ggml/src/ggml.cpp                      @ggerganov
 /ggml/src/gguf.cpp                      @JohannesGaessler @Green-Sky
@@ -105,7 +107,7 @@
 /tools/rpc/                             @ggml-org/ggml-rpc
 /tools/server/*                         @ggml-org/llama-server # no subdir
 /tools/server/tests/                    @ggml-org/llama-server
-/tools/server/webui/                    @ggml-org/llama-webui
+/tools/ui/                              @ggml-org/llama-ui
 /tools/tokenize/                        @ggerganov
 /tools/tts/                             @ggerganov
 /vendor/                                @ggerganov
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 8000b471867..99504f14f31 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -46,7 +46,9 @@ Before submitting your PR:
     - provide KL divergence data calculated vs. the FP16/BF16 (whichever is the native precision) version for both the new type as well as types of similar size
     - provide [performance data](https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench) for the new type in comparison to types of similar size on pure CPU
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
-- If you are a new contributor, limit your open PRs to 1.
+- If you are a new contributor
+    - Limit your open PRs to 1
+    - Do not submit trivial fixes (e.g. typos, formatting changes)
 
 After submitting your PR:
 - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability
diff --git a/README.md b/README.md
index be23abcea67..a0c14b9d7f0 100644
--- a/README.md
+++ b/README.md
@@ -172,6 +172,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
 - Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
+- Ruby: [docusealco/rllama](https://github.com/docusealco/rllama)
 - Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
 - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
@@ -529,6 +530,7 @@ To learn more about model quantization, [read this documentation](tools/quantize
 - [How to build](docs/build.md)
 - [Running on Docker](docs/docker.md)
 - [Build on Android](docs/android.md)
+- [Multi-GPU usage](docs/multi-gpu.md)
 - [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
 - [GGML tips & tricks](https://github.com/ggml-org/llama.cpp/wiki/GGML-Tips-&-Tricks)
 
diff --git a/cmake/riscv64-spacemit-linux-gnu-gcc.cmake b/cmake/riscv64-spacemit-linux-gnu-gcc.cmake
index 08fdbf50630..faed7fb7831 100644
--- a/cmake/riscv64-spacemit-linux-gnu-gcc.cmake
+++ b/cmake/riscv64-spacemit-linux-gnu-gcc.cmake
@@ -24,6 +24,6 @@ set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
 set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
 set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
 set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)
-set(CMAKE_C_FLAGS "-march=rv64gcv_zfh_zba_zicbop -mabi=lp64d ${CMAKE_C_FLAGS}")
-set(CMAKE_CXX_FLAGS "-march=rv64gcv_zfh_zba_zicbop -mabi=lp64d ${CXX_FLAGS}")
+set(CMAKE_C_FLAGS "-march=rv64gcv_zfh_zvfh_zba_zicbop -mabi=lp64d -fno-tree-vectorize -fno-tree-loop-vectorize ${CMAKE_C_FLAGS}")
+set(CMAKE_CXX_FLAGS "-march=rv64gcv_zfh_zvfh_zba_zicbop -mabi=lp64d -fno-tree-vectorize -fno-tree-loop-vectorize ${CMAKE_CXX_FLAGS}")
 set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -latomic")
diff --git a/common/arg.cpp b/common/arg.cpp
index 1ad83fa9e42..7e2b48aec44 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -97,8 +97,13 @@ common_arg & common_arg::set_env(const char * env) {
     return *this;
 }
 
-common_arg & common_arg::set_sparam() {
-    is_sparam = true;
+common_arg & common_arg::set_sampling() {
+    is_sampling = true;
+    return *this;
+}
+
+common_arg & common_arg::set_spec() {
+    is_spec = true;
     return *this;
 }
 
@@ -243,6 +248,8 @@ std::vector<std::string> common_arg::get_env() const {
 
 // Helper function to parse tensor buffer override strings
 static void parse_tensor_buffer_overrides(const std::string & value, std::vector<llama_model_tensor_buft_override> & overrides) {
+    ggml_backend_load_all();
+
     std::map<std::string, ggml_backend_buffer_type_t> buft_list;
     for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
         auto * dev = ggml_backend_dev_get(i);
@@ -301,12 +308,14 @@ static bool common_params_handle_remote_preset(common_params & params, llama_exa
     common_download_opts opts;
     opts.bearer_token = params.hf_token;
     opts.offline = params.offline;
+
+    LOG_TRC("%s: looking for remote preset at %s\n", __func__, preset_url.c_str());
     const int status = common_download_file_single(preset_url, preset_path, opts);
     const bool has_preset = status >= 200 && status < 400;
 
     // remote preset is optional, so we don't error out if not found
     if (has_preset) {
-        LOG_INF("applying remote preset from %s\n", preset_url.c_str());
+        LOG_TRC("%s: applying remote preset from %s\n", __func__, preset_url.c_str());
         common_preset_context ctx(ex, /* only_remote_allowed */ true);
         common_preset global;
         auto remote_presets = ctx.load_from_ini(preset_path, global);
@@ -319,7 +328,7 @@ static bool common_params_handle_remote_preset(common_params & params, llama_exa
             throw std::runtime_error("Remote preset.ini does not contain [" + std::string(hf_tag) + "] section");
         }
     } else {
-        LOG_INF("%s", "no remote preset found, skipping\n");
+        LOG_TRC("%s: no remote preset found, skipping\n", __func__);
     }
 
     return has_preset;
@@ -328,11 +337,15 @@ static bool common_params_handle_remote_preset(common_params & params, llama_exa
 struct handle_model_result {
     bool found_mmproj = false;
     common_params_model mmproj;
+
+    bool found_mtp = false;
+    common_params_model mtp;
 };
 
 static handle_model_result common_params_handle_model(struct common_params_model & model,
                                                       const std::string          & bearer_token,
-                                                      bool                         offline) {
+                                                      bool                         offline,
+                                                      bool                         search_mtp = false) {
     handle_model_result result;
 
     if (!model.docker_repo.empty()) {
@@ -347,11 +360,10 @@ static handle_model_result common_params_handle_model(struct common_params_model
         common_download_opts opts;
         opts.bearer_token = bearer_token;
         opts.offline = offline;
-        auto download_result = common_download_model(model, opts, true);
+        auto download_result = common_download_model(model, opts, true, search_mtp);
 
         if (download_result.model_path.empty()) {
-            LOG_ERR("error: failed to download model from Hugging Face\n");
-            exit(1);
+            throw std::runtime_error("failed to download model from Hugging Face");
         }
 
         model.name = model.hf_repo;
@@ -361,6 +373,11 @@ static handle_model_result common_params_handle_model(struct common_params_model
             result.found_mmproj = true;
             result.mmproj.path  = download_result.mmproj_path;
         }
+
+        if (!download_result.mtp_path.empty()) {
+            result.found_mtp = true;
+            result.mtp.path  = download_result.mtp_path;
+        }
     } else if (!model.url.empty()) {
         if (model.path.empty()) {
             auto f = string_split<std::string>(model.url, '#').front();
@@ -373,8 +390,7 @@ static handle_model_result common_params_handle_model(struct common_params_model
         opts.offline = offline;
         auto download_result = common_download_model(model, opts);
         if (download_result.model_path.empty()) {
-            LOG_ERR("error: failed to download model from %s\n", model.url.c_str());
-            exit(1);
+            throw std::runtime_error("failed to download model from " + model.url);
         }
     }
 
@@ -423,10 +439,45 @@ static bool parse_bool_value(const std::string & value) {
     }
 }
 
+[[noreturn]] static void arg_removed(const std::string & msg) {
+    throw std::invalid_argument("the argument has been removed. " + msg);
+}
+
 //
 // CLI argument parsing functions
 //
 
+void common_params_handle_models(common_params & params, llama_example curr_ex) {
+    const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
+                                         params.speculative.types.end(),
+                                         COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
+
+    auto res = common_params_handle_model(params.model, params.hf_token, params.offline, spec_type_draft_mtp);
+    if (params.no_mmproj) {
+        params.mmproj = {};
+    } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
+        // optionally, handle mmproj model when -hf is specified
+        params.mmproj = res.mmproj;
+    }
+    // only download mmproj if the current example is using it
+    for (const auto & ex : mmproj_examples) {
+        if (curr_ex == ex) {
+            common_params_handle_model(params.mmproj,    params.hf_token, params.offline);
+            break;
+        }
+    }
+    // when --spec-type mtp is set and no draft model was provided explicitly,
+    // fall back to the MTP head discovered alongside the -hf model
+    if (spec_type_draft_mtp && res.found_mtp &&
+        params.speculative.draft.mparams.path.empty() &&
+        params.speculative.draft.mparams.hf_repo.empty() &&
+        params.speculative.draft.mparams.url.empty()) {
+        params.speculative.draft.mparams.path = res.mtp.path;
+    }
+    common_params_handle_model(params.speculative.draft.mparams, params.hf_token, params.offline);
+    common_params_handle_model(params.vocoder.model,             params.hf_token, params.offline);
+}
+
 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
     common_params & params = ctx_arg.params;
 
@@ -571,8 +622,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     postprocess_cpu_params(params.cpuparams,       nullptr);
     postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
 
-    postprocess_cpu_params(params.speculative.cpuparams,       &params.cpuparams);
-    postprocess_cpu_params(params.speculative.cpuparams_batch, &params.cpuparams_batch);
+    postprocess_cpu_params(params.speculative.draft.cpuparams,       &params.cpuparams);
+    postprocess_cpu_params(params.speculative.draft.cpuparams_batch, &params.cpuparams_batch);
 
     if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
@@ -580,22 +631,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
 
     // handle model and download
     if (!skip_model_download) {
-        auto res = common_params_handle_model(params.model, params.hf_token, params.offline);
-        if (params.no_mmproj) {
-            params.mmproj = {};
-        } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
-            // optionally, handle mmproj model when -hf is specified
-            params.mmproj = res.mmproj;
-        }
-        // only download mmproj if the current example is using it
-        for (const auto & ex : mmproj_examples) {
-            if (ctx_arg.ex == ex) {
-                common_params_handle_model(params.mmproj,    params.hf_token, params.offline);
-                break;
-            }
-        }
-        common_params_handle_model(params.speculative.mparams_dft, params.hf_token, params.offline);
-        common_params_handle_model(params.vocoder.model,           params.hf_token, params.offline);
+        common_params_handle_models(params, ctx_arg.ex);
     }
 
     // model is required (except for server)
@@ -614,10 +650,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         for (auto & seq_breaker : params.sampling.dry_sequence_breakers) {
             string_process_escapes(seq_breaker);
         }
-        for (auto & pair : params.speculative.replacements) {
-            string_process_escapes(pair.first);
-            string_process_escapes(pair.second);
-        }
     }
 
     if (!params.kv_overrides.empty()) {
@@ -631,8 +663,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         params.tensor_buft_overrides.push_back({nullptr, nullptr});
     }
 
-    if (!params.speculative.tensor_buft_overrides.empty()) {
-        params.speculative.tensor_buft_overrides.push_back({nullptr, nullptr});
+    if (!params.speculative.draft.tensor_buft_overrides.empty()) {
+        params.speculative.draft.tensor_buft_overrides.push_back({nullptr, nullptr});
     }
 
     if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
@@ -654,12 +686,15 @@ static void common_params_print_usage(common_params_context & ctx_arg) {
     };
 
     std::vector<common_arg *> common_options;
-    std::vector<common_arg *> sparam_options;
+    std::vector<common_arg *> sampling_options;
+    std::vector<common_arg *> spec_options;
     std::vector<common_arg *> specific_options;
     for (auto & opt : ctx_arg.options) {
         // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
-        if (opt.is_sparam) {
-            sparam_options.push_back(&opt);
+        if (opt.is_sampling) {
+            sampling_options.push_back(&opt);
+        } else if (opt.is_spec) {
+            spec_options.push_back(&opt);
         } else if (opt.in_example(ctx_arg.ex)) {
             specific_options.push_back(&opt);
         } else {
@@ -669,7 +704,9 @@ static void common_params_print_usage(common_params_context & ctx_arg) {
     printf("----- common params -----\n\n");
     print_options(common_options);
     printf("\n\n----- sampling params -----\n\n");
-    print_options(sparam_options);
+    print_options(sampling_options);
+    printf("\n\n----- speculative params -----\n\n");
+    print_options(spec_options);
     // TODO: maybe convert enum llama_example to string
     printf("\n\n----- example-specific params -----\n\n");
     print_options(specific_options);
@@ -677,12 +714,15 @@ static void common_params_print_usage(common_params_context & ctx_arg) {
 
 static void common_params_print_completion(common_params_context & ctx_arg) {
     std::vector<common_arg *> common_options;
-    std::vector<common_arg *> sparam_options;
+    std::vector<common_arg *> sampling_options;
+    std::vector<common_arg *> spec_options;
     std::vector<common_arg *> specific_options;
 
     for (auto & opt : ctx_arg.options) {
-        if (opt.is_sparam) {
-            sparam_options.push_back(&opt);
+        if (opt.is_sampling) {
+            sampling_options.push_back(&opt);
+        } else if (opt.is_spec) {
+            spec_options.push_back(&opt);
         } else if (opt.in_example(ctx_arg.ex)) {
             specific_options.push_back(&opt);
         } else {
@@ -706,7 +746,8 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
     };
 
     print_options(common_options);
-    print_options(sparam_options);
+    print_options(sampling_options);
+    print_options(spec_options);
     print_options(specific_options);
     printf("\"\n\n");
 
@@ -792,6 +833,7 @@ static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & val
     if (dev_names.size() == 1 && dev_names[0] == "none") {
         devices.push_back(nullptr);
     } else {
+        ggml_backend_load_all();
         for (const auto & device : dev_names) {
             auto * dev = ggml_backend_dev_by_name(device.c_str());
             if (!dev || ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
@@ -809,6 +851,7 @@ static void add_rpc_devices(const std::string & servers) {
     if (rpc_servers.empty()) {
         throw std::invalid_argument("no RPC servers specified");
     }
+    ggml_backend_load_all();
     ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
     if (!rpc_reg) {
         throw std::invalid_argument("failed to find RPC backend");
@@ -1005,9 +1048,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
 
     params.use_color = tty_can_use_colors();
 
-    // load dynamic backends
-    ggml_backend_load_all();
-
     common_params_context ctx_arg(params);
     ctx_arg.print_usage = print_usage;
     ctx_arg.ex          = ex;
@@ -1226,14 +1266,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"-lcs", "--lookup-cache-static"}, "FNAME",
         "path to static lookup cache to use for lookup decoding (not updated by generation)",
         [](common_params & params, const std::string & value) {
-            params.speculative.lookup_cache_static = value;
+            params.speculative.ngram_cache.lookup_cache_static = value;
         }
     ).set_examples({LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-lcd", "--lookup-cache-dynamic"}, "FNAME",
         "path to dynamic lookup cache to use for lookup decoding (updated by generation)",
         [](common_params & params, const std::string & value) {
-            params.speculative.lookup_cache_dynamic = value;
+            params.speculative.ngram_cache.lookup_cache_dynamic = value;
         }
     ).set_examples({LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
@@ -1579,28 +1619,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.samplers = common_sampler_types_from_names(sampler_names, true);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"-s", "--seed"}, "SEED",
         string_format("RNG seed (default: %d, use random seed for %d)", params.sampling.seed, LLAMA_DEFAULT_SEED),
         [](common_params & params, const std::string & value) {
             params.sampling.seed = std::stoul(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--sampler-seq", "--sampling-seq"}, "SEQUENCE",
         string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
         [](common_params & params, const std::string & value) {
             params.sampling.samplers = common_sampler_types_from_chars(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--ignore-eos"},
         "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)",
         [](common_params & params) {
             params.sampling.ignore_eos = true;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--temp", "--temperature"}, "N",
         string_format("temperature (default: %.2f)", (double)params.sampling.temp),
@@ -1609,7 +1649,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.temp = std::max(params.sampling.temp, 0.0f);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TEMP;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--top-k"}, "N",
         string_format("top-k sampling (default: %d, 0 = disabled)", params.sampling.top_k),
@@ -1617,7 +1657,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.top_k = value;
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K;
         }
-    ).set_sparam().set_env("LLAMA_ARG_TOP_K"));
+    ).set_sampling().set_env("LLAMA_ARG_TOP_K"));
     add_opt(common_arg(
         {"--top-p"}, "N",
         string_format("top-p sampling (default: %.2f, 1.0 = disabled)", (double)params.sampling.top_p),
@@ -1625,7 +1665,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.top_p = std::stof(value);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--min-p"}, "N",
         string_format("min-p sampling (default: %.2f, 0.0 = disabled)", (double)params.sampling.min_p),
@@ -1633,14 +1673,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.min_p = std::stof(value);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--top-nsigma", "--top-n-sigma"}, "N",
         string_format("top-n-sigma sampling (default: %.2f, -1.0 = disabled)", params.sampling.top_n_sigma),
         [](common_params & params, const std::string & value) {
             params.sampling.top_n_sigma = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--xtc-probability"}, "N",
         string_format("xtc probability (default: %.2f, 0.0 = disabled)", (double)params.sampling.xtc_probability),
@@ -1648,7 +1688,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.xtc_probability = std::stof(value);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--xtc-threshold"}, "N",
         string_format("xtc threshold (default: %.2f, 1.0 = disabled)", (double)params.sampling.xtc_threshold),
@@ -1656,14 +1696,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.xtc_threshold = std::stof(value);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--typical", "--typical-p"}, "N",
         string_format("locally typical sampling, parameter p (default: %.2f, 1.0 = disabled)", (double)params.sampling.typ_p),
         [](common_params & params, const std::string & value) {
             params.sampling.typ_p = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--repeat-last-n"}, "N",
         string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
@@ -1675,7 +1715,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--repeat-penalty"}, "N",
         string_format("penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)", (double)params.sampling.penalty_repeat),
@@ -1683,28 +1723,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.penalty_repeat = std::stof(value);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--presence-penalty"}, "N",
         string_format("repeat alpha presence penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_present),
         [](common_params & params, const std::string & value) {
             params.sampling.penalty_present = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--frequency-penalty"}, "N",
         string_format("repeat alpha frequency penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_freq),
         [](common_params & params, const std::string & value) {
             params.sampling.penalty_freq = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--dry-multiplier"}, "N",
         string_format("set DRY sampling multiplier (default: %.2f, 0.0 = disabled)", (double)params.sampling.dry_multiplier),
         [](common_params & params, const std::string & value) {
             params.sampling.dry_multiplier = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--dry-base"}, "N",
         string_format("set DRY sampling base value (default: %.2f)", (double)params.sampling.dry_base),
@@ -1715,14 +1755,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.sampling.dry_base = potential_base;
             }
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--dry-allowed-length"}, "N",
         string_format("set allowed length for DRY sampling (default: %d)", params.sampling.dry_allowed_length),
         [](common_params & params, int value) {
             params.sampling.dry_allowed_length = value;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--dry-penalty-last-n"}, "N",
         string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
@@ -1732,7 +1772,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
             params.sampling.dry_penalty_last_n = value;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--dry-sequence-breaker"}, "STRING",
         string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n",
@@ -1758,7 +1798,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.sampling.dry_sequence_breakers.emplace_back(value);
             }
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--adaptive-target"}, "N",
         string_format("adaptive-p: select tokens near this probability (valid range 0.0 "
@@ -1768,7 +1808,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.sampling.adaptive_target = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--adaptive-decay"}, "N",
         string_format("adaptive-p: decay rate for target adaptation over time. lower values "
@@ -1778,21 +1818,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.sampling.adaptive_decay = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--dynatemp-range"}, "N",
         string_format("dynamic temperature range (default: %.2f, 0.0 = disabled)", (double)params.sampling.dynatemp_range),
         [](common_params & params, const std::string & value) {
             params.sampling.dynatemp_range = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--dynatemp-exp"}, "N",
         string_format("dynamic temperature exponent (default: %.2f)", (double)params.sampling.dynatemp_exponent),
         [](common_params & params, const std::string & value) {
             params.sampling.dynatemp_exponent = std::stof(value);
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--mirostat"}, "N",
         string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n"
@@ -1801,7 +1841,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.mirostat = value;
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--mirostat-lr"}, "N",
         string_format("Mirostat learning rate, parameter eta (default: %.2f)", (double)params.sampling.mirostat_eta),
@@ -1809,7 +1849,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.mirostat_eta = std::stof(value);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--mirostat-ent"}, "N",
         string_format("Mirostat target entropy, parameter tau (default: %.2f)", (double)params.sampling.mirostat_tau),
@@ -1817,7 +1857,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.mirostat_tau = std::stof(value);
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU;
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"-l", "--logit-bias"}, "TOKEN_ID(+/-)BIAS",
         "modifies the likelihood of token appearing in the completion,\n"
@@ -1839,28 +1879,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 throw std::invalid_argument("invalid input format");
             }
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--grammar"}, "GRAMMAR",
         "BNF-like grammar to constrain generations (see samples in grammars/ dir)",
         [](common_params & params, const std::string & value) {
             params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, value};
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"--grammar-file"}, "FNAME",
         "file to read grammar from",
         [](common_params & params, const std::string & value) {
             params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, read_file(value)};
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"-j", "--json-schema"}, "SCHEMA",
         "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
         [](common_params & params, const std::string & value) {
             params.sampling.grammar = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, json_schema_to_grammar(json::parse(value))};
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"-jf", "--json-schema-file"}, "FILE",
         "File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
@@ -1877,14 +1917,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             );
             params.sampling.grammar = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, json_schema_to_grammar(json::parse(schema))};
         }
-    ).set_sparam());
+    ).set_sampling());
     add_opt(common_arg(
         {"-bs", "--backend-sampling"},
         "enable backend sampling (experimental) (default: disabled)",
         [](common_params & params) {
             params.sampling.backend_sampling = true;
         }
-    ).set_sparam().set_env("LLAMA_ARG_BACKEND_SAMPLING"));
+    ).set_sampling().set_env("LLAMA_ARG_BACKEND_SAMPLING"));
     add_opt(common_arg(
         {"--pooling"}, "{none,mean,cls,last,rank}",
         "pooling type for embeddings, use model default if unspecified",
@@ -2207,7 +2247,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     if (llama_supports_rpc()) {
         add_opt(common_arg(
             {"--rpc"}, "SERVERS",
-            "comma separated list of RPC servers (host:port)",
+            "comma-separated list of RPC servers (host:port)",
             [](common_params & params, const std::string & value) {
                 add_rpc_devices(value);
                 GGML_UNUSED(params);
@@ -2264,6 +2304,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--list-devices"},
         "print list of available devices and exit",
         [](common_params &) {
+            ggml_backend_load_all();
             std::vector<ggml_backend_dev_t> devices;
             for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
                 auto * dev = ggml_backend_dev_get(i);
@@ -2286,12 +2327,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             parse_tensor_buffer_overrides(value, params.tensor_buft_overrides);
         }
     ).set_env("LLAMA_ARG_OVERRIDE_TENSOR"));
-    add_opt(common_arg(
-        {"-otd", "--override-tensor-draft"}, "<tensor name pattern>=<buffer type>,...",
-        "override tensor buffer type for draft model", [](common_params & params, const std::string & value) {
-            parse_tensor_buffer_overrides(value, params.speculative.tensor_buft_overrides);
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"-cmoe", "--cpu-moe"},
         "keep all Mixture of Experts (MoE) weights in the CPU",
@@ -2314,27 +2349,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_env("LLAMA_ARG_N_CPU_MOE"));
-    add_opt(common_arg(
-        {"-cmoed", "--cpu-moe-draft"},
-        "keep all Mixture of Experts (MoE) weights in the CPU for the draft model",
-        [](common_params & params) {
-            params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
-    add_opt(common_arg(
-        {"-ncmoed", "--n-cpu-moe-draft"}, "N",
-        "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model",
-        [](common_params & params, int value) {
-            if (value < 0) {
-                throw std::invalid_argument("invalid value");
-            }
-            for (int i = 0; i < value; ++i) {
-                static std::list<std::string> buft_overrides_draft;
-                buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i));
-                params.speculative.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
     GGML_ASSERT(params.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
@@ -2617,13 +2631,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = value;
         }
     ).set_env("LLAMA_ARG_HF_REPO"));
-    add_opt(common_arg(
-        {"-hfd", "-hfrd", "--hf-repo-draft"}, "<user>/<model>[:quant]",
-        "Same as --hf-repo, but for the draft model (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.speculative.mparams_dft.hf_repo = value;
-        }
-    ).set_env("LLAMA_ARG_HFD_REPO"));
     add_opt(common_arg(
         {"-hff", "--hf-file"}, "FILE",
         "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)",
@@ -2861,45 +2868,93 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.api_prefix = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX"));
+    // Deprecated: use --ui-config instead (kept for backward compat)
     add_opt(common_arg(
         {"--webui-config"}, "JSON",
-        "JSON that provides default WebUI settings (overrides WebUI defaults)",
+        "[DEPRECATED: use --ui-config] JSON that provides default WebUI settings (overrides WebUI defaults)",
         [](common_params & params, const std::string & value) {
+            params.ui_config_json = value;
             params.webui_config_json = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG"));
+
+    add_opt(common_arg(
+        {"--ui-config"}, "JSON",
+        "JSON that provides default UI settings (overrides UI defaults)",
+        [](common_params & params, const std::string & value) {
+            params.ui_config_json = value;
+            params.webui_config_json = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_CONFIG"));
+
+    // Deprecated: use --ui-config-file instead (kept for backward compat)
     add_opt(common_arg(
         {"--webui-config-file"}, "PATH",
-        "JSON file that provides default WebUI settings (overrides WebUI defaults)",
+        "[DEPRECATED: use --ui-config-file] JSON file that provides default WebUI settings (overrides WebUI defaults)",
         [](common_params & params, const std::string & value) {
-            params.webui_config_json = read_file(value);
+            params.ui_config_json = read_file(value);
+            params.webui_config_json = params.ui_config_json;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG_FILE"));
+
+    add_opt(common_arg(
+        {"--ui-config-file"}, "PATH",
+        "JSON file that provides default UI settings (overrides UI defaults)",
+        [](common_params & params, const std::string & value) {
+            params.ui_config_json = read_file(value);
+            params.webui_config_json = params.ui_config_json;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_CONFIG_FILE"));
+
+    // Deprecated: use --ui-mcp-proxy instead (kept for backward compat)
     add_opt(common_arg(
         {"--webui-mcp-proxy"},
         {"--no-webui-mcp-proxy"},
-        string_format("experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: %s)", params.webui_mcp_proxy ? "enabled" : "disabled"),
+        "[DEPRECATED: use --ui-mcp-proxy/--no-ui-mcp-proxy] experimental: whether to enable MCP CORS proxy",
         [](common_params & params, bool value) {
+            params.ui_mcp_proxy = value;
             params.webui_mcp_proxy = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_MCP_PROXY"));
+
+    add_opt(common_arg(
+        {"--ui-mcp-proxy"},
+        {"--no-ui-mcp-proxy"},
+        "experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)",
+        [](common_params & params, bool value) {
+            params.ui_mcp_proxy = value;
+            params.webui_mcp_proxy = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_MCP_PROXY"));
     add_opt(common_arg(
         {"--tools"}, "TOOL1,TOOL2,...",
         "experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)\n"
         "specify \"all\" to enable all tools\n"
-        "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff",
+        "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff, get_datetime",
         [](common_params & params, const std::string & value) {
             params.server_tools = parse_csv_row(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
+    // Deprecated: use --ui/--no-ui instead (kept for backward compat)
     add_opt(common_arg(
         {"--webui"},
         {"--no-webui"},
-        string_format("whether to enable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
+        "[DEPRECATED: use --ui/--no-ui] whether to enable the Web UI",
         [](common_params & params, bool value) {
+            params.ui = value;
             params.webui = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI"));
+
+    add_opt(common_arg(
+        {"--ui"},
+        {"--no-ui"},
+        string_format("whether to enable the Web UI (default: %s)", params.ui ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.ui = value;
+            params.webui = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI"));
     add_opt(common_arg(
         {"--embedding", "--embeddings"},
         string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
@@ -3125,14 +3180,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)",
         [](common_params & params, int value) {
             if (value < -1) { throw std::invalid_argument("invalid value"); }
-            params.reasoning_budget = value;
+            params.sampling.reasoning_budget_tokens = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET"));
     add_opt(common_arg(
         {"--reasoning-budget-message"}, "MESSAGE",
         "message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)",
         [](common_params & params, const std::string & value) {
-            params.reasoning_budget_message = value;
+            params.sampling.reasoning_budget_message = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET_MESSAGE"));
     add_opt(common_arg(
@@ -3320,183 +3375,243 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_LOG_VERBOSITY"));
     add_opt(common_arg(
         {"--log-prefix"},
+        {"--no-log-prefix"},
         "Enable prefix in log messages",
-        [](common_params &) {
-            common_log_set_prefix(common_log_main(), true);
+        [](common_params &, bool value) {
+            common_log_set_prefix(common_log_main(), value);
         }
-    ).set_env("LLAMA_LOG_PREFIX"));
+    ).set_env("LLAMA_ARG_LOG_PREFIX"));
     add_opt(common_arg(
         {"--log-timestamps"},
+        {"--no-log-timestamps"},
         "Enable timestamps in log messages",
-        [](common_params &) {
-            common_log_set_timestamps(common_log_main(), true);
+        [](common_params &, bool value) {
+            common_log_set_timestamps(common_log_main(), value);
         }
-    ).set_env("LLAMA_LOG_TIMESTAMPS"));
+    ).set_env("LLAMA_ARG_LOG_TIMESTAMPS"));
 
+    //
     // speculative parameters
+    //
+
+    add_opt(common_arg(
+        {"--spec-draft-hf", "-hfd", "-hfrd", "--hf-repo-draft"}, "<user>/<model>[:quant]",
+        "Same as --hf-repo, but for the draft model (default: unused)",
+        [](common_params & params, const std::string & value) {
+            params.speculative.draft.mparams.hf_repo = value;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_HF_REPO"));
     add_opt(common_arg(
-        {"-td", "--threads-draft"}, "N",
+        {"--spec-draft-threads", "-td", "--threads-draft"}, "N",
         "number of threads to use during generation (default: same as --threads)",
         [](common_params & params, int value) {
-            params.speculative.cpuparams.n_threads = value;
-            if (params.speculative.cpuparams.n_threads <= 0) {
-                params.speculative.cpuparams.n_threads = std::thread::hardware_concurrency();
+            params.speculative.draft.cpuparams.n_threads = value;
+            if (params.speculative.draft.cpuparams.n_threads <= 0) {
+                params.speculative.draft.cpuparams.n_threads = std::thread::hardware_concurrency();
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-tbd", "--threads-batch-draft"}, "N",
+        {"--spec-draft-threads-batch", "-tbd", "--threads-batch-draft"}, "N",
         "number of threads to use during batch and prompt processing (default: same as --threads-draft)",
         [](common_params & params, int value) {
-            params.speculative.cpuparams_batch.n_threads = value;
-            if (params.speculative.cpuparams_batch.n_threads <= 0) {
-                params.speculative.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
+            params.speculative.draft.cpuparams_batch.n_threads = value;
+            if (params.speculative.draft.cpuparams_batch.n_threads <= 0) {
+                params.speculative.draft.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-Cd", "--cpu-mask-draft"}, "M",
+        {"--spec-draft-cpu-mask", "-Cd", "--cpu-mask-draft"}, "M",
         "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
         [](common_params & params, const std::string & mask) {
-            params.speculative.cpuparams.mask_valid = true;
-            if (!parse_cpu_mask(mask, params.speculative.cpuparams.cpumask)) {
+            params.speculative.draft.cpuparams.mask_valid = true;
+            if (!parse_cpu_mask(mask, params.speculative.draft.cpuparams.cpumask)) {
                 throw std::invalid_argument("invalid cpumask");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-Crd", "--cpu-range-draft"}, "lo-hi",
+        {"--spec-draft-cpu-range", "-Crd", "--cpu-range-draft"}, "lo-hi",
         "Ranges of CPUs for affinity. Complements --cpu-mask-draft",
         [](common_params & params, const std::string & range) {
-            params.speculative.cpuparams.mask_valid = true;
-            if (!parse_cpu_range(range, params.speculative.cpuparams.cpumask)) {
+            params.speculative.draft.cpuparams.mask_valid = true;
+            if (!parse_cpu_range(range, params.speculative.draft.cpuparams.cpumask)) {
                 throw std::invalid_argument("invalid range");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--cpu-strict-draft"}, "<0|1>",
+        {"--spec-draft-cpu-strict", "--cpu-strict-draft"}, "<0|1>",
         "Use strict CPU placement for draft model (default: same as --cpu-strict)",
         [](common_params & params, int value) {
-            params.speculative.cpuparams.strict_cpu = value;
+            params.speculative.draft.cpuparams.strict_cpu = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--prio-draft"}, "N",
-        string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams.priority),
+        {"--spec-draft-prio", "--prio-draft"}, "N",
+        string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.draft.cpuparams.priority),
         [](common_params & params, int prio) {
             if (prio < 0 || prio > 3) {
                 throw std::invalid_argument("invalid value");
             }
-            params.speculative.cpuparams.priority = (enum ggml_sched_priority) prio;
+            params.speculative.draft.cpuparams.priority = (enum ggml_sched_priority) prio;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--poll-draft"}, "<0|1>",
-        "Use polling to wait for draft model work (default: same as --poll])",
+        {"--spec-draft-poll", "--poll-draft"}, "<0|1>",
+        "Use polling to wait for draft model work (default: same as --poll)",
         [](common_params & params, int value) {
-            params.speculative.cpuparams.poll = value;
+            params.speculative.draft.cpuparams.poll = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-Cbd", "--cpu-mask-batch-draft"}, "M",
+        {"--spec-draft-cpu-mask-batch", "-Cbd", "--cpu-mask-batch-draft"}, "M",
         "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
         [](common_params & params, const std::string & mask) {
-            params.speculative.cpuparams_batch.mask_valid = true;
-            if (!parse_cpu_mask(mask, params.speculative.cpuparams_batch.cpumask)) {
+            params.speculative.draft.cpuparams_batch.mask_valid = true;
+            if (!parse_cpu_mask(mask, params.speculative.draft.cpuparams_batch.cpumask)) {
                 throw std::invalid_argument("invalid cpumask");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
+        {"--spec-draft-cpu-range-batch", "-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
         "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
         [](common_params & params, const std::string & range) {
-            params.speculative.cpuparams_batch.mask_valid = true;
-            if (!parse_cpu_range(range, params.speculative.cpuparams_batch.cpumask)) {
+            params.speculative.draft.cpuparams_batch.mask_valid = true;
+            if (!parse_cpu_range(range, params.speculative.draft.cpuparams_batch.cpumask)) {
                 throw std::invalid_argument("invalid cpumask");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
     add_opt(common_arg(
-        {"--cpu-strict-batch-draft"}, "<0|1>",
+        {"--spec-draft-cpu-strict-batch", "--cpu-strict-batch-draft"}, "<0|1>",
         "Use strict CPU placement for draft model (default: --cpu-strict-draft)",
         [](common_params & params, int value) {
-            params.speculative.cpuparams_batch.strict_cpu = value;
+            params.speculative.draft.cpuparams_batch.strict_cpu = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--prio-batch-draft"}, "N",
-        string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams_batch.priority),
+        {"--spec-draft-prio-batch", "--prio-batch-draft"}, "N",
+        string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.draft.cpuparams_batch.priority),
         [](common_params & params, int prio) {
             if (prio < 0 || prio > 3) {
                 throw std::invalid_argument("invalid value");
             }
-            params.speculative.cpuparams_batch.priority = (enum ggml_sched_priority) prio;
+            params.speculative.draft.cpuparams_batch.priority = (enum ggml_sched_priority) prio;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--poll-batch-draft"}, "<0|1>",
+        {"--spec-draft-poll-batch", "--poll-batch-draft"}, "<0|1>",
         "Use polling to wait for draft model work (default: --poll-draft)",
         [](common_params & params, int value) {
-            params.speculative.cpuparams_batch.poll = value;
+            params.speculative.draft.cpuparams_batch.poll = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--draft", "--draft-n", "--draft-max"}, "N",
-        string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max),
+        {"--spec-draft-type-k", "-ctkd", "--cache-type-k-draft"}, "TYPE",
+        string_format(
+            "KV cache data type for K for the draft model\n"
+            "allowed values: %s\n"
+            "(default: %s)",
+            get_all_kv_cache_types().c_str(),
+            ggml_type_name(params.speculative.draft.cache_type_k)
+        ),
+        [](common_params & params, const std::string & value) {
+            params.speculative.draft.cache_type_k = kv_cache_type_from_str(value);
+        }
+    ).set_env("LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K"));
+    add_opt(common_arg(
+        {"--spec-draft-type-v", "-ctvd", "--cache-type-v-draft"}, "TYPE",
+        string_format(
+            "KV cache data type for V for the draft model\n"
+            "allowed values: %s\n"
+            "(default: %s)",
+            get_all_kv_cache_types().c_str(),
+            ggml_type_name(params.speculative.draft.cache_type_v)
+        ),
+        [](common_params & params, const std::string & value) {
+            params.speculative.draft.cache_type_v = kv_cache_type_from_str(value);
+        }
+    ).set_env("LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V"));
+    add_opt(common_arg(
+        {"--spec-draft-override-tensor", "-otd", "--override-tensor-draft"}, "<tensor name pattern>=<buffer type>,...",
+        "override tensor buffer type for draft model", [](common_params & params, const std::string & value) {
+            parse_tensor_buffer_overrides(value, params.speculative.draft.tensor_buft_overrides);
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    add_opt(common_arg(
+        {"--spec-draft-cpu-moe", "-cmoed", "--cpu-moe-draft"},
+        "keep all Mixture of Experts (MoE) weights in the CPU for the draft model",
+        [](common_params & params) {
+            params.speculative.draft.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_CPU_MOE"));
+    add_opt(common_arg(
+        {"--spec-draft-n-cpu-moe", "--spec-draft-ncmoe", "-ncmoed", "--n-cpu-moe-draft"}, "N",
+        "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model",
         [](common_params & params, int value) {
-            params.speculative.n_max = value;
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
+            for (int i = 0; i < value; ++i) {
+                static std::list<std::string> buft_overrides_draft;
+                buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i));
+                params.speculative.draft.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
+            }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MAX"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE"));
+
     add_opt(common_arg(
-        {"--draft-min", "--draft-n-min"}, "N",
-        string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min),
+        {"--spec-draft-n-max"}, "N",
+        string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.draft.n_max),
         [](common_params & params, int value) {
-            params.speculative.n_min = value;
+            params.speculative.draft.n_max = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MIN"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_MAX"));
     add_opt(common_arg(
-        {"--draft-p-split"}, "P",
-        string_format("speculative decoding split probability (default: %.2f)", (double)params.speculative.p_split),
-        [](common_params & params, const std::string & value) {
-            params.speculative.p_split = std::stof(value);
+        {"--spec-draft-n-min"}, "N",
+        string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.draft.n_min),
+        [](common_params & params, int value) {
+            params.speculative.draft.n_min = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_MIN"));
+
     add_opt(common_arg(
-        {"--draft-p-min"}, "P",
-        string_format("minimum speculative decoding probability (greedy) (default: %.2f)", (double)params.speculative.p_min),
+        {"--spec-draft-p-split", "--draft-p-split"}, "P",
+        string_format("speculative decoding split probability (default: %.2f)", (double)params.speculative.draft.p_split),
         [](common_params & params, const std::string & value) {
-            params.speculative.p_min = std::stof(value);
+            params.speculative.draft.p_split = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_P_MIN"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_P_SPLIT"));
     add_opt(common_arg(
-        {"-cd", "--ctx-size-draft"}, "N",
-        string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),
-        [](common_params & params, int value) {
-            params.speculative.n_ctx = value;
+        {"--spec-draft-p-min", "--draft-p-min"}, "P",
+        string_format("minimum speculative decoding probability (greedy) (default: %.2f)", (double)params.speculative.draft.p_min),
+        [](common_params & params, const std::string & value) {
+            params.speculative.draft.p_min = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_P_MIN"));
     add_opt(common_arg(
-        {"-devd", "--device-draft"}, "<dev1,dev2,..>",
+        {"--spec-draft-device", "-devd", "--device-draft"}, "<dev1,dev2,..>",
         "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
         "use --list-devices to see a list of available devices",
         [](common_params & params, const std::string & value) {
-            params.speculative.devices = parse_device_list(value);
+            params.speculative.draft.devices = parse_device_list(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
-    GGML_ASSERT(params.speculative.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    GGML_ASSERT(params.speculative.draft.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
     add_opt(common_arg(
-        {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
+        {"--spec-draft-ngl", "-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
         string_format("max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)",
-            params.speculative.n_gpu_layers == -1 ? "auto" : "all"),
+            params.speculative.draft.n_gpu_layers == -1 ? "auto" : "all"),
         [](common_params & params, const std::string & value) {
             if (value == "auto") {
-                params.speculative.n_gpu_layers = -1;
+                params.speculative.draft.n_gpu_layers = -1;
             } else if (value == "all") {
-                params.speculative.n_gpu_layers = -2;
+                params.speculative.draft.n_gpu_layers = -2;
             } else {
-                params.speculative.n_gpu_layers = std::stoi(value);
+                params.speculative.draft.n_gpu_layers = std::stoi(value);
             }
             if (!llama_supports_gpu_offload()) {
                 fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n");
@@ -3504,99 +3619,191 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT"));
     add_opt(common_arg(
-        {"-md", "--model-draft"}, "FNAME",
+        {"--spec-draft-model", "-md", "--model-draft"}, "FNAME",
         "draft model for speculative decoding (default: unused)",
         [](common_params & params, const std::string & value) {
-            params.speculative.mparams_dft.path = value;
+            params.speculative.draft.mparams.path = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_MODEL_DRAFT"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_MODEL"));
     add_opt(common_arg(
-        {"--spec-replace"}, "TARGET", "DRAFT",
-        "translate the string in TARGET into DRAFT if the draft model and main model are not compatible",
-        [](common_params & params, const std::string & tgt, const std::string & dft) {
-            params.speculative.replacements.push_back({ tgt, dft });
+        {"--spec-type"}, common_speculative_all_types_str(),
+        string_format("comma-separated list of types of speculative decoding to use (default: %s)\n",
+            common_speculative_type_name_str(params.speculative.types).c_str()),
+        [](common_params & params, const std::string & value) {
+            const auto types_str = string_split<std::string>(value, ',');
+            auto types = common_speculative_types_from_names(types_str);
+            params.speculative.types.insert(params.speculative.types.end(), types.begin(), types.end());
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_TYPE"));
     add_opt(common_arg(
-        {"--spec-type"}, "[none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]",
-        string_format("type of speculative decoding to use when no draft model is provided (default: %s)\n",
-            common_speculative_type_to_str(params.speculative.type).c_str()),
-        [](common_params & params, const std::string & value) {
-            if (value == "none") {
-                params.speculative.type = COMMON_SPECULATIVE_TYPE_NONE;
-            } else if (value == "ngram-cache") {
-                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_CACHE;
-            } else if (value == "ngram-simple") {
-                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE;
-            } else if (value == "ngram-map-k") {
-                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K;
-            } else if (value == "ngram-map-k4v") {
-                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V;
-            } else if (value == "ngram-mod") {
-                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MOD;
-            } else {
-                throw std::invalid_argument("unknown speculative decoding type without draft model");
+        {"--spec-ngram-mod-n-min"}, "N",
+        string_format("minimum number of ngram tokens to use for ngram-based speculative decoding (default: %d)", params.speculative.ngram_mod.n_min),
+        [](common_params & params, int value) {
+            if (value < 0 || value > 1024) {
+                throw std::invalid_argument("ngram n-min must be between 0 and 1024 inclusive");
             }
+            params.speculative.ngram_mod.n_min = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SPEC_TYPE"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--spec-ngram-size-n"}, "N",
-        string_format("ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: %d)", params.speculative.ngram_size_n),
+        {"--spec-ngram-mod-n-max"}, "N",
+        string_format("maximum number of ngram tokens to use for ngram-based speculative decoding (default: %d)", params.speculative.ngram_mod.n_max),
+        [](common_params & params, int value) {
+            if (value < 0 || value > 1024) {
+                throw std::invalid_argument("ngram n-max must be between 0 and 1024 inclusive");
+            }
+            params.speculative.ngram_mod.n_max = value;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    add_opt(common_arg(
+        {"--spec-ngram-mod-n-match"}, "N",
+        string_format("ngram-mod lookup length (default: %d)", params.speculative.ngram_mod.n_match),
         [](common_params & params, int value) {
             if (value < 1 || value > 1024) {
                 throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive");
             }
-            params.speculative.ngram_size_n = value;
+            params.speculative.ngram_mod.n_match = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+
     add_opt(common_arg(
-        {"--spec-ngram-size-m"}, "N",
-        string_format("ngram size M for ngram-simple/ngram-map speculative decoding, length of draft m-gram (default: %d)", params.speculative.ngram_size_m),
+        {"--spec-ngram-simple-size-n"}, "N",
+        string_format("ngram size N for ngram-simple speculative decoding, length of lookup n-gram (default: %d)", params.speculative.ngram_simple.size_n),
+        [](common_params & params, int value) {
+            if (value < 1 || value > 1024) {
+                throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive");
+            }
+            params.speculative.ngram_simple.size_n = value;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    add_opt(common_arg(
+        {"--spec-ngram-simple-size-m"}, "N",
+        string_format("ngram size M for ngram-simple speculative decoding, length of draft m-gram (default: %d)", params.speculative.ngram_simple.size_m),
         [](common_params & params, int value) {
             if (value < 1 || value > 1024) {
                 throw std::invalid_argument("ngram size M must be between 1 and 1024 inclusive");
             }
-            params.speculative.ngram_size_m = value;
+            params.speculative.ngram_simple.size_m = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"--spec-ngram-min-hits"}, "N",
-        string_format("minimum hits for ngram-map speculative decoding (default: %d)", params.speculative.ngram_min_hits),
+        {"--spec-ngram-simple-min-hits"}, "N",
+        string_format("minimum hits for ngram-simple speculative decoding (default: %d)", params.speculative.ngram_simple.min_hits),
         [](common_params & params, int value) {
             if (value < 1) {
                 throw std::invalid_argument("ngram min hits must be at least 1");
             }
-            params.speculative.ngram_min_hits = value;
+            params.speculative.ngram_simple.min_hits = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+
     add_opt(common_arg(
-        {"-ctkd", "--cache-type-k-draft"}, "TYPE",
-        string_format(
-            "KV cache data type for K for the draft model\n"
-            "allowed values: %s\n"
-            "(default: %s)",
-            get_all_kv_cache_types().c_str(),
-            ggml_type_name(params.speculative.cache_type_k)
-        ),
-        [](common_params & params, const std::string & value) {
-            params.speculative.cache_type_k = kv_cache_type_from_str(value);
+        {"--spec-ngram-map-k-size-n"}, "N",
+        string_format("ngram size N for ngram-map-k speculative decoding, length of lookup n-gram (default: %d)", params.speculative.ngram_map_k.size_n),
+        [](common_params & params, int value) {
+            if (value < 1 || value > 1024) {
+                throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive");
+            }
+            params.speculative.ngram_map_k.size_n = value;
         }
-    ).set_env("LLAMA_ARG_CACHE_TYPE_K_DRAFT"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
-        {"-ctvd", "--cache-type-v-draft"}, "TYPE",
-        string_format(
-            "KV cache data type for V for the draft model\n"
-            "allowed values: %s\n"
-            "(default: %s)",
-            get_all_kv_cache_types().c_str(),
-            ggml_type_name(params.speculative.cache_type_v)
-        ),
-        [](common_params & params, const std::string & value) {
-            params.speculative.cache_type_v = kv_cache_type_from_str(value);
+        {"--spec-ngram-map-k-size-m"}, "N",
+        string_format("ngram size M for ngram-map-k speculative decoding, length of draft m-gram (default: %d)", params.speculative.ngram_map_k.size_m),
+        [](common_params & params, int value) {
+            if (value < 1 || value > 1024) {
+                throw std::invalid_argument("ngram size M must be between 1 and 1024 inclusive");
+            }
+            params.speculative.ngram_map_k.size_m = value;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    add_opt(common_arg(
+        {"--spec-ngram-map-k-min-hits"}, "N",
+        string_format("minimum hits for ngram-map-k speculative decoding (default: %d)", params.speculative.ngram_map_k.min_hits),
+        [](common_params & params, int value) {
+            if (value < 1) {
+                throw std::invalid_argument("ngram min hits must be at least 1");
+            }
+            params.speculative.ngram_map_k.min_hits = value;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+
+    add_opt(common_arg(
+        {"--spec-ngram-map-k4v-size-n"}, "N",
+        string_format("ngram size N for ngram-map-k4v speculative decoding, length of lookup n-gram (default: %d)", params.speculative.ngram_map_k4v.size_n),
+        [](common_params & params, int value) {
+            if (value < 1 || value > 1024) {
+                throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive");
+            }
+            params.speculative.ngram_map_k4v.size_n = value;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    add_opt(common_arg(
+        {"--spec-ngram-map-k4v-size-m"}, "N",
+        string_format("ngram size M for ngram-map-k4v speculative decoding, length of draft m-gram (default: %d)", params.speculative.ngram_map_k4v.size_m),
+        [](common_params & params, int value) {
+            if (value < 1 || value > 1024) {
+                throw std::invalid_argument("ngram size M must be between 1 and 1024 inclusive");
+            }
+            params.speculative.ngram_map_k4v.size_m = value;
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    add_opt(common_arg(
+        {"--spec-ngram-map-k4v-min-hits"}, "N",
+        string_format("minimum hits for ngram-map-k4v speculative decoding (default: %d)", params.speculative.ngram_map_k4v.min_hits),
+        [](common_params & params, int value) {
+            if (value < 1) {
+                throw std::invalid_argument("ngram min hits must be at least 1");
+            }
+            params.speculative.ngram_map_k4v.min_hits = value;
         }
-    ).set_env("LLAMA_ARG_CACHE_TYPE_V_DRAFT"));
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+
+    //
+    // removed params
+    //
+
+    add_opt(common_arg(
+        {"--draft", "--draft-n", "--draft-max"}, "N",
+        "the argument has been removed. use --spec-draft-n-max or --spec-ngram-mod-n-max",
+        [](common_params & /*params*/, int /*value*/) {
+            arg_removed("use --spec-draft-n-max or --spec-ngram-mod-n-max");
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MAX"));
+    add_opt(common_arg(
+        {"--draft-min", "--draft-n-min"}, "N",
+        "the argument has been removed. use --spec-draft-n-min or --spec-ngram-mod-n-min",
+        [](common_params & /*params*/, int /*value*/) {
+            arg_removed("use --spec-draft-n-min or --spec-ngram-mod-n-min");
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MIN"));
+    add_opt(common_arg(
+        {"--spec-ngram-size-n"}, "N",
+        "the argument has been removed. use the respective --spec-ngram-*-size-n or --spec-ngram-mod-n-match",
+        [](common_params & /*params*/, int /*value*/) {
+            arg_removed("use the respective --spec-ngram-*-size-n");
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"--spec-ngram-size-m"}, "N",
+        "the argument has been removed. use the respective --spec-ngram-*-size-m",
+        [](common_params & /*params*/, int /*value*/) {
+            arg_removed("use the respective --spec-ngram-*-size-m");
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"--spec-ngram-min-hits"}, "N",
+        "the argument has been removed. use the respective --spec-ngram-*-min-hits",
+        [](common_params & /*params*/, int /*value*/) {
+            arg_removed("use the respective --spec-ngram-*-min-hits");
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SERVER}));
+
+    //
+    // TTS params
+    //
 
     add_opt(common_arg(
         {"-mv", "--model-vocoder"}, "FNAME",
@@ -3620,6 +3827,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_TTS}));
 
+    //
+    // diffusion params
+    //
+
     add_opt(common_arg(
         {"--diffusion-steps"}, "N",
         string_format("number of diffusion steps (default: %d)", params.diffusion.steps),
@@ -3637,7 +3848,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
     add_opt(common_arg(
         {"--diffusion-algorithm"}, "N",
-        string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", params.diffusion.algorithm),
+        string_format(
+            "diffusion algorithm: 0=DIFFUSION_ALGORITHM_ORIGIN, 1=DIFFUSION_ALGORITHM_ENTROPY_BASED, "
+            "2=DIFFUSION_ALGORITHM_MARGIN_BASED, 3=DIFFUSION_ALGORITHM_RANDOM, "
+            "4=DIFFUSION_ALGORITHM_CONFIDENCE_BASED (default: %d)", params.diffusion.algorithm),
         [](common_params & params, int value) { params.diffusion.algorithm = value; }
     ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
     add_opt(common_arg(
@@ -3804,8 +4018,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf";
-            params.speculative.mparams_dft.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
-            params.speculative.mparams_dft.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
+            params.speculative.draft.mparams.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
+            params.speculative.draft.mparams.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
             params.port = 8012;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
@@ -3820,8 +4034,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-14B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-14b-q8_0.gguf";
-            params.speculative.mparams_dft.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
-            params.speculative.mparams_dft.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
+            params.speculative.draft.mparams.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
+            params.speculative.draft.mparams.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
             params.port = 8012;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
@@ -3909,10 +4123,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--spec-default"},
         string_format("enable default speculative decoding config"),
         [](common_params & params) {
-            params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MOD;
-            params.speculative.ngram_size_n = 24;
-            params.speculative.n_min = 48;
-            params.speculative.n_max = 64;
+            params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_NGRAM_MOD);
+            params.speculative.ngram_mod.n_match = 24;
+            params.speculative.ngram_mod.n_min = 48;
+            params.speculative.ngram_mod.n_max = 64;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
 
diff --git a/common/arg.h b/common/arg.h
index 55782a158d7..2a85f09f3eb 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -25,7 +25,8 @@ struct common_arg {
     const char * value_hint_2 = nullptr; // for second arg value
     const char * env          = nullptr;
     std::string help;
-    bool is_sparam = false; // is current arg a sampling param?
+    bool is_sampling = false; // is current arg a sampling param?
+    bool is_spec = false; // is current arg a speculative decoding param?
     bool is_preset_only = false; // is current arg preset-only (not treated as CLI arg)
     void (*handler_void)   (common_params & params) = nullptr;
     void (*handler_string) (common_params & params, const std::string &) = nullptr;
@@ -74,7 +75,8 @@ struct common_arg {
     common_arg & set_examples(std::initializer_list<enum llama_example> examples);
     common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
     common_arg & set_env(const char * env);
-    common_arg & set_sparam();
+    common_arg & set_sampling();
+    common_arg & set_spec();
     common_arg & set_preset_only();
     bool in_example(enum llama_example ex);
     bool is_exclude(enum llama_example ex);
@@ -127,5 +129,8 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
 // see: https://github.com/ggml-org/llama.cpp/issues/18163
 void common_params_add_preset_options(std::vector<common_arg> & args);
 
+// Populate model paths (main model, mmproj, etc) from -hf if necessary
+void common_params_handle_models(common_params & params, llama_example curr_ex);
+
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
diff --git a/common/chat-auto-parser-generator.cpp b/common/chat-auto-parser-generator.cpp
index 453559a4b04..6021fc4ede5 100644
--- a/common/chat-auto-parser-generator.cpp
+++ b/common/chat-auto-parser-generator.cpp
@@ -136,10 +136,10 @@ common_peg_parser analyze_reasoning::build_parser(parser_build_context & ctx) co
         if (!end.empty()) {
             if (!start.empty()) {
                 // Standard tag-based: optional(<think>reasoning</think>)
-                return p.optional(start + p.reasoning(p.until(end)) + end + p.space());
+                return p.optional(p.optspace(start) + p.reasoning(p.until(trim_whitespace(end))) + p.optspace(end));
             }
             // Delimiter-style (empty start)
-            return p.optional(p.reasoning(p.until(end)) + end + p.space());
+            return p.optional(p.reasoning(p.until(trim_whitespace(end))) + p.optspace(end));
         }
     }
 
@@ -186,7 +186,6 @@ common_peg_parser analyze_tools::build_parser(parser_build_context & ctx) const
 common_peg_parser analyze_tools::build_tool_parser_json_native(parser_build_context & ctx) const {
     auto &       p           = ctx.p;
     const auto & inputs      = ctx.inputs;
-    bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
 
     // Build effective field names with dot notation if function_field is set
     std::string name_field = format.name_field;
@@ -225,8 +224,7 @@ common_peg_parser analyze_tools::build_tool_parser_json_native(parser_build_cont
         tool_start = format.per_call_start;
     }
 
-    return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(p.until(tool_start)))) + tools_parser +
-           p.end();
+    return ctx.reasoning_parser + p.optional(p.content(p.until(tool_start))) + tools_parser + p.end();
 }
 
 common_peg_parser analyze_tools::build_func_parser(common_chat_peg_builder & p, const std::string & name,
@@ -270,7 +268,6 @@ common_peg_parser analyze_tools::build_func_parser(common_chat_peg_builder & p,
 common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context & ctx) const {
     auto &       p           = ctx.p;
     const auto & inputs      = ctx.inputs;
-    bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
 
     common_peg_parser tool_choice = p.choice();
 
@@ -336,14 +333,12 @@ common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context
 
     std::string trigger_marker       = !format.section_start.empty() ? format.section_start : format.per_call_start;
     auto        content_before_tools = trigger_marker.empty() ? p.eps() : p.until(trigger_marker);
-    return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(content_before_tools))) + tool_calls +
-           p.end();
+    return ctx.reasoning_parser + p.optional(p.content(content_before_tools)) + tool_calls + p.end();
 }
 
 common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_context & ctx) const {
     auto &       p           = ctx.p;
     const auto & inputs      = ctx.inputs;
-    bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
 
     auto until_suffix = p.rule("until-suffix", p.until(arguments.value_suffix));
 
@@ -374,9 +369,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
                                            arguments.name_suffix) +
                            arguments.value_prefix +
                            (schema_info.resolves_to_string(param_schema) ?
-                                p.tool_arg_string_value(p.schema(until_suffix,
-                                                                 "tool-" + name + "-arg-" + param_name + "-schema",
-                                                                 param_schema, true)) :
+                                p.tool_arg_string_value(until_suffix) :
                                 p.tool_arg_json_value(p.schema(
                                     p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false)) +
                                     p.space()) +
@@ -471,8 +464,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
 
     std::string trigger_marker       = !format.section_start.empty() ? format.section_start : format.per_call_start;
     auto        content_before_tools = trigger_marker.empty() ? p.eps() : p.until(trigger_marker);
-    return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(content_before_tools))) + tool_calls +
-           p.end();
+    return ctx.reasoning_parser + p.optional(p.content(content_before_tools)) + tool_calls + p.end();
 }
 
 }  // namespace autoparser
diff --git a/common/chat-diff-analyzer.cpp b/common/chat-diff-analyzer.cpp
index 2f0bd14afa3..9c7c9678acd 100644
--- a/common/chat-diff-analyzer.cpp
+++ b/common/chat-diff-analyzer.cpp
@@ -296,7 +296,7 @@ void analyze_reasoning::compare_reasoning_presence() {
             return p.literal(reasoning_content) + p.space() + p.optional(p.tag("post", (p.marker() + p.space())) + p.rest());
         });
         auto parser_wrapped = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
-            return p.tag("pre", p.marker() + p.space()) + p.literal(reasoning_content) + p.space() + p.tag("post", (p.marker() + p.space())) + p.rest();
+            return p.tag("pre", p.marker() + p.space()) + p.literal(reasoning_content) + p.tag("post", (p.space() + p.marker() + p.space())) + p.rest();
         });
         // try the more aggressive parse first, if it fails, fall back to the delimiter one
         auto result = parser_wrapped.parse_anywhere_and_extract(comparison->output_B);
@@ -306,11 +306,11 @@ void analyze_reasoning::compare_reasoning_presence() {
         if (result.result.success()) {
             if (!result.tags["pre"].empty() && !result.tags["post"].empty()) {
                 mode = reasoning_mode::TAG_BASED;
-                start = trim_leading_whitespace(result.tags["pre"]);
-                end   = trim_trailing_whitespace(result.tags["post"]);
+                start = result.tags["pre"];
+                end   = result.tags["post"];
             } else if (!result.tags["post"].empty()) {
                 mode = reasoning_mode::TAG_BASED;
-                end = trim_trailing_whitespace(result.tags["post"]);
+                end = result.tags["post"];
             }
         }
     }
@@ -342,7 +342,7 @@ void analyze_reasoning::compare_thinking_enabled() {
     if (left_trimmed.empty() && !diff.right.empty()) {
         if (!right_trimmed.empty() && string_ends_with(comparison->output_B, right_trimmed)) {
             if (start.empty()) {
-                start = trim_leading_whitespace(diff.right);
+                start = diff.right;
                 mode  = reasoning_mode::TAG_BASED;
             }
         }
@@ -353,7 +353,7 @@ void analyze_reasoning::compare_thinking_enabled() {
                 if (seg.size() >= 2 && seg[seg.size() - 1].value == left_trimmed && seg[seg.size() - 2].type == segment_type::MARKER) {
                     start = seg[seg.size() - 2].value;
                 }
-                end = trim_trailing_whitespace(diff.left);
+                end = diff.left;
                 mode = reasoning_mode::TAG_BASED;
             }
         }
@@ -445,14 +445,14 @@ void analyze_reasoning::compare_reasoning_scope() {
         auto result = parser_wrapped.parse_anywhere_and_extract(comparison->output_B);
         if (result.result.success()) {
             start = result.tags["pre"];
-            end = trim_trailing_whitespace(result.tags["post"]);
+            end = result.tags["post"];
         } else {
             auto parser_delimiter = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
                 return p.literal(reasoning_content) + p.space() + p.optional(p.tag("post", (p.marker() + p.space())));
             });
             result = parser_delimiter.parse_anywhere_and_extract(comparison->output_B);
             if (result.result.success()) {
-                end = trim_trailing_whitespace(result.tags["post"]);
+                end = result.tags["post"];
             } else {
                 LOG_DBG(ANSI_ORANGE "%s: Unable to extract reasoning markers, falling back to reasoning = NONE\n" ANSI_RESET, __func__);
                 mode = reasoning_mode::NONE;
diff --git a/common/chat-peg-parser.cpp b/common/chat-peg-parser.cpp
index 56eb567df0a..a4818859a6d 100644
--- a/common/chat-peg-parser.cpp
+++ b/common/chat-peg-parser.cpp
@@ -816,6 +816,32 @@ common_peg_parser common_chat_peg_builder::prefix(const std::string & s, const s
     return literal(s.substr(0, s.rfind(delimiter)));
 }
 
+common_peg_parser common_chat_peg_builder::optspace(const std::string & tag) {
+    auto parser = eps();
+    size_t end_of_prefix_space = tag.size();
+    size_t start_of_suffix_space = tag.size();
+    for (size_t i = 0; i < tag.size(); i++) {
+        if (!std::isspace(tag[i])) {
+            end_of_prefix_space = i;
+            break;
+        }
+    }
+    for (size_t i = tag.size(); i > 0; i--) {
+        if (!std::isspace(tag[i - 1])) {
+            start_of_suffix_space = i;
+            break;
+        }
+    }
+    for (size_t i = 0; i < end_of_prefix_space; i++) {
+        parser += optional(literal(std::string(1, tag[i])));
+    }
+    parser += literal(tag.substr(end_of_prefix_space, start_of_suffix_space - end_of_prefix_space));
+    for (size_t i = start_of_suffix_space; i < tag.size(); i++) {
+        parser += optional(literal(std::string(1, tag[i])));
+    }
+    return parser;
+}
+
 common_peg_parser common_chat_peg_builder::standard_json_tools(
                                                        const std::string &              section_start,
                                                        const std::string &              section_end,
diff --git a/common/chat-peg-parser.h b/common/chat-peg-parser.h
index 1ea3eb7eb86..c684d773564 100644
--- a/common/chat-peg-parser.h
+++ b/common/chat-peg-parser.h
@@ -96,6 +96,9 @@ class common_chat_peg_builder : public common_peg_parser_builder {
     // Return a parser that parses the prefix of a string, up to a given delimiter.
     common_peg_parser prefix(const std::string & s, const std::string & delimiter = {});
 
+    // Return a parser that parses all elements of tag, but leading and trailing spaces are optional
+    common_peg_parser optspace(const std::string & tag);
+
     // Legacy-compatible helper for building standard JSON tool calls
     // Used by tests and manual parsers
     // name_key/args_key: JSON key names for function name and arguments
diff --git a/common/chat.cpp b/common/chat.cpp
index e424206af86..70b9f5dc2c5 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -80,7 +80,7 @@ json common_chat_msg::to_json_oaicompat(bool concat_typed_text) const {
     if (!content.empty()) {
         jmsg["content"] = content;
     } else if (!content_parts.empty()) {
-        if (concat_typed_text) {
+        if (concat_typed_text || contains_media()) {
             std::string text;
             bool last_was_media_marker = false;
             // join parts with newline, do not add newline before or after media markers
@@ -397,6 +397,25 @@ json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msg
     return render_message_to_json(msgs, c);
 }
 
+json common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools) {
+    if (tools.empty()) {
+        return json();
+    }
+
+    auto result = json::array();
+    for (const auto & tool : tools) {
+        result.push_back({
+            { "type",     "function" },
+            { "function", {
+                { "name", tool.name },
+                { "description", tool.description },
+                { "parameters", json::parse(tool.parameters) },
+            }},
+        });
+    }
+    return result;
+}
+
 std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & tools) {
     std::vector<common_chat_tool> result;
 
@@ -432,56 +451,6 @@ std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & too
     return result;
 }
 
-json common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools) {
-    if (tools.empty()) {
-        return json();
-    }
-
-    auto result = json::array();
-    for (const auto & tool : tools) {
-        result.push_back({
-            { "type",     "function" },
-            { "function",
-             {
-                  { "name", tool.name },
-                  { "description", tool.description },
-                  { "parameters", json::parse(tool.parameters) },
-              }                      },
-        });
-    }
-    return result;
-}
-
-json common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff) {
-    json delta = json::object();
-    if (!diff.reasoning_content_delta.empty()) {
-        delta["reasoning_content"] = diff.reasoning_content_delta;
-    }
-    if (!diff.content_delta.empty()) {
-        delta["content"] = diff.content_delta;
-    }
-    if (diff.tool_call_index != std::string::npos) {
-        json tool_call;
-        tool_call["index"] = diff.tool_call_index;
-        if (!diff.tool_call_delta.id.empty()) {
-            tool_call["id"]   = diff.tool_call_delta.id;
-            tool_call["type"] = "function";
-        }
-        if (!diff.tool_call_delta.name.empty() || !diff.tool_call_delta.arguments.empty()) {
-            json function = json::object();
-            if (!diff.tool_call_delta.name.empty()) {
-                function["name"] = diff.tool_call_delta.name;
-            }
-            if (!diff.tool_call_delta.arguments.empty()) {
-                function["arguments"] = diff.tool_call_delta.arguments;
-            }
-            tool_call["function"] = function;
-        }
-        delta["tool_calls"] = json::array({ tool_call });
-    }
-    return delta;
-}
-
 bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) {
     if (use_jinja) {
         try {
@@ -575,6 +544,26 @@ bool common_chat_templates_was_explicit(const struct common_chat_templates * tmp
     return tmpls->has_explicit_template;
 }
 
+// LFM2 format detection: template uses <|tool_list_start|>[...]<|tool_list_end|> around the tool list
+// and <|tool_call_start|>[...]<|tool_call_end|> around each tool call
+static bool is_lfm2_template(const std::string & src) {
+    return src.find("<|tool_list_start|>") != std::string::npos &&
+           src.find("<|tool_list_end|>")   != std::string::npos;
+}
+
+common_chat_prompt_preset common_chat_get_asr_prompt(const common_chat_templates * chat_templates) {
+    common_chat_prompt_preset asr_preset;
+    asr_preset.system = "";
+    asr_preset.user   = "Transcribe audio to text";
+
+    if (chat_templates && chat_templates->template_default && is_lfm2_template(chat_templates->template_default->source())) {
+        asr_preset.system = "Perform ASR.";
+        asr_preset.user   = "";
+    }
+
+    return asr_preset;
+}
+
 std::string common_chat_templates_source(const struct common_chat_templates * tmpls, const std::string & variant) {
     if (!variant.empty()) {
         if (variant == "tool_use") {
@@ -2084,10 +2073,7 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         return common_chat_params_init_kimi_k2(tmpl, params);
     }
 
-    // LFM2 format detection: template uses <|tool_list_start|>[...]<|tool_list_end|> around the tool list
-    // and <|tool_call_start|>[...]<|tool_call_end|> around each tool call
-    if (src.find("<|tool_list_start|>") != std::string::npos &&
-        src.find("<|tool_list_end|>") != std::string::npos) {
+    if (is_lfm2_template(src)) {
         LOG_DBG("Using specialized template: LFM2\n");
         return common_chat_params_init_lfm2(tmpl, params);
     }
@@ -2130,22 +2116,38 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
     return std::nullopt;
 }
 
+static std::string common_chat_templates_generation_prompt(const common_chat_template & tmpl, const autoparser::generation_params & inputs) {
+    autoparser::generation_params params = inputs;
+    params.add_generation_prompt = false;
+    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params);
+    params.add_generation_prompt = true;
+    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params);
+
+    size_t prefix_len = 0;
+    size_t min_size = std::min(no_gen_prompt.size(), gen_prompt.size());
+    while (prefix_len < min_size && no_gen_prompt[prefix_len] == gen_prompt[prefix_len]) {
+        prefix_len++;
+    }
+    return gen_prompt.substr(prefix_len);
+}
+
 static common_chat_params common_chat_templates_apply_jinja(const struct common_chat_templates *        tmpls,
                                                             const struct common_chat_templates_inputs & inputs) {
     autoparser::generation_params params;
     params.tools = common_chat_tools_to_json_oaicompat(inputs.tools);
     const auto & tmpl =
         params.tools.is_array() && tmpls->template_tool_use ? *tmpls->template_tool_use : *tmpls->template_default;
-    const auto & src        = tmpl.source();
-    const auto & caps       = tmpl.original_caps();
-    params.messages         = render_message_to_json(inputs.messages, tmpl.original_caps());
-    params.tool_choice      = inputs.tool_choice;
-    params.reasoning_format = inputs.reasoning_format;
-    params.enable_thinking  = inputs.enable_thinking;
-    params.grammar          = inputs.grammar;
-    params.now              = inputs.now;
-    params.add_bos          = tmpls->add_bos;
-    params.add_eos          = tmpls->add_eos;
+    const auto & src             = tmpl.source();
+    const auto & caps            = tmpl.original_caps();
+    params.messages              = render_message_to_json(inputs.messages, tmpl.original_caps());
+    params.tool_choice           = inputs.tool_choice;
+    params.reasoning_format      = inputs.reasoning_format;
+    params.enable_thinking       = inputs.enable_thinking;
+    params.grammar               = inputs.grammar;
+    params.now                   = inputs.now;
+    params.add_generation_prompt = inputs.add_generation_prompt;
+    params.add_bos               = tmpls->add_bos;
+    params.add_eos               = tmpls->add_eos;
 
     if (src.find("<|channel|>") == std::string::npos) {
         // map developer to system for all models except for GPT-OSS
@@ -2167,14 +2169,7 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         workaround::func_args_not_string(params.messages);
     }
 
-    params.add_generation_prompt = false;
-    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params);
-    params.add_generation_prompt = true;
-    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params);
-    auto        diff             = calculate_diff_split(no_gen_prompt, gen_prompt);
-    params.generation_prompt     = diff.right + diff.suffix;
-
-    params.add_generation_prompt = inputs.add_generation_prompt;
+    params.generation_prompt = common_chat_templates_generation_prompt(tmpl, params);
 
     params.extra_context = common_chat_extra_context();
     for (auto el : inputs.chat_template_kwargs) {
@@ -2226,8 +2221,8 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser);
         auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE;
         if (auto_params.supports_thinking) {
-            auto_params.thinking_start_tag = autoparser.reasoning.start;
-            auto_params.thinking_end_tag   = autoparser.reasoning.end;
+            auto_params.thinking_start_tag = trim_whitespace(autoparser.reasoning.start);
+            auto_params.thinking_end_tag   = trim_whitespace(autoparser.reasoning.end);
         }
         auto_params.generation_prompt = params.generation_prompt;
         common_peg_arena arena;
@@ -2396,4 +2391,3 @@ std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_tem
     GGML_ASSERT(chat_templates->template_default != nullptr);
     return chat_templates->template_default->caps.to_map();
 }
-
diff --git a/common/chat.h b/common/chat.h
index b06ca37fd74..054f5ffe777 100644
--- a/common/chat.h
+++ b/common/chat.h
@@ -94,6 +94,15 @@ struct common_chat_msg {
                tool_name.empty() && tool_call_id.empty();
     }
 
+    bool contains_media() const {
+        for (const auto & part : content_parts) {
+            if (part.type == "media_marker") {
+                return true;
+            }
+        }
+        return false;
+    }
+
     void set_tool_call_ids(std::vector<std::string> &           ids_cache,
                            const std::function<std::string()> & gen_tool_call_id) {
         for (auto i = 0u; i < tool_calls.size(); i++) {
@@ -256,14 +265,13 @@ bool common_chat_templates_support_enable_thinking(const common_chat_templates *
 // Parses a JSON array of messages in OpenAI's chat completion API format.
 std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const nlohmann::ordered_json & messages);
 
+std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);
+
 // DEPRECATED: only used in tests
 nlohmann::ordered_json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);
 
-std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);
 nlohmann::ordered_json common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools);
 
-nlohmann::ordered_json common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff);
-
 // get template caps, useful for reporting to server /props endpoint
 std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_templates * chat_templates);
 
@@ -275,3 +283,11 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         const common_chat_template &          tmpl,
         const std::string &                   src,
         autoparser::generation_params & params);
+
+// specialized per-task preset
+struct common_chat_prompt_preset {
+    std::string system;
+    std::string user;
+};
+
+common_chat_prompt_preset common_chat_get_asr_prompt(const common_chat_templates * chat_templates);
diff --git a/common/common.cpp b/common/common.cpp
index 24e91f2d6c0..506db3ef64d 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -7,6 +7,7 @@
 #include "log.h"
 #include "llama.h"
 #include "sampling.h"
+#include "speculative.h"
 #include "unicode.h"
 
 #include <algorithm>
@@ -76,7 +77,7 @@ common_time_meas::~common_time_meas() {
 // CPU utils
 //
 
-int32_t cpu_get_num_physical_cores() {
+int32_t common_cpu_get_num_physical_cores() {
 #ifdef __linux__
     // enumerate the set of thread siblings, num entries is num cores
     std::unordered_set<std::string> siblings;
@@ -191,11 +192,11 @@ static int cpu_count_math_cpus(int n_cpu) {
 /**
  * Returns number of CPUs on system that are useful for math.
  */
-int32_t cpu_get_num_math() {
+int32_t common_cpu_get_num_math() {
 #if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
     int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
     if (n_cpu < 1) {
-        return cpu_get_num_physical_cores();
+        return common_cpu_get_num_physical_cores();
     }
     if (is_hybrid_cpu()) {
         cpu_set_t affinity;
@@ -208,7 +209,7 @@ int32_t cpu_get_num_math() {
         }
     }
 #endif
-    return cpu_get_num_physical_cores();
+    return common_cpu_get_num_physical_cores();
 }
 
 // Helper for setting process priority
@@ -269,7 +270,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {
 //
 
 
-void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
+void postprocess_cpu_params(common_cpu_params & cpuparams, const common_cpu_params * role_model) {
     int32_t n_set = 0;
 
     if (cpuparams.n_threads < 0) {
@@ -277,7 +278,7 @@ void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model)
         if (role_model != nullptr) {
             cpuparams = *role_model;
         } else {
-            cpuparams.n_threads = cpu_get_num_math();
+            cpuparams.n_threads = common_cpu_get_num_math();
         }
     }
 
@@ -372,15 +373,33 @@ void common_init() {
     SetConsoleCP(CP_UTF8);
 #endif
 
+    common_log_set_prefix(common_log_main(), true);
+    common_log_set_timestamps(common_log_main(), true);
+
     llama_log_set(common_log_default_callback, NULL);
+}
 
+void common_params_print_info(const common_params & params, bool print_devices) {
 #ifdef NDEBUG
     const char * build_type = "";
 #else
     const char * build_type = " (debug)";
 #endif
+    LOG_TRC("%s: build %d (%s) with %s for %s%s\n", __func__, llama_build_number(), llama_commit(), llama_compiler(), llama_build_target(), build_type);
+
+    LOG_INF("log_info: verbosity = %d (adjust with the `-lv N` CLI arg)\n", common_log_get_verbosity_thold());
 
-    LOG_DBG("build: %d (%s) with %s for %s%s\n", llama_build_number(), llama_commit(), llama_compiler(), llama_build_target(), build_type);
+    // device enumeration creates a primary context on CUDA backends, skip it when the caller does not own any device
+    if (print_devices) {
+        LOG_INF("device_info:\n");
+        for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+            auto * dev = ggml_backend_dev_get(i);
+            size_t free, total;
+            ggml_backend_dev_memory(dev, &free, &total);
+            LOG_INF("  - %-8s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
+        }
+    }
+    LOG_INF("%s\n", common_params_get_system_info(params).c_str());
 }
 
 std::string common_params_get_system_info(const common_params & params) {
@@ -1153,7 +1172,8 @@ common_init_result::common_init_result(common_params & params) :
     auto cparams = common_context_params_to_llama(params);
 
     if (params.fit_params) {
-        LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
+        LOG_INF("%s: fitting params to device memory ...\n", __func__);
+        LOG_INF("%s: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)\n", __func__);
         common_fit_params(params.model.path.c_str(), &mparams, &cparams,
             params.tensor_split,
             params.tensor_buft_overrides.data(),
@@ -1202,7 +1222,7 @@ common_init_result::common_init_result(common_params & params) :
     // initialize once
     for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
         if (llama_vocab_is_eog(vocab, i)) {
-            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
+            LOG_TRC("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
             params.sampling.logit_bias_eog.push_back({i, -INFINITY});
         }
     }
@@ -1215,12 +1235,12 @@ common_init_result::common_init_result(common_params & params) :
     }
 
     //if (params.sampling.penalty_last_n == -1) {
-    //    LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    LOG_TRC("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
     //    params.sampling.penalty_last_n = llama_n_ctx(lctx);
     //}
 
     //if (params.sampling.dry_penalty_last_n == -1) {
-    //    LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    LOG_TRC("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
     //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
     //}
 
@@ -1238,6 +1258,29 @@ common_init_result::common_init_result(common_params & params) :
         cparams.n_samplers = pimpl->samplers_seq_config.size();
     }
 
+    // [TAG_RS_STATE_ROLLBACK_SUPPORT]
+    // TODO: ngram speculative methods require checkpointing in addition to partial RS rollback
+    //       currently this is not supported. so we disable the partial rollback
+    if (cparams.n_rs_seq > 0 && (llama_model_is_recurrent(model) || llama_model_is_hybrid(model))) {
+        auto & types = params.speculative.types;
+
+        for (int i = 0; i < (int) types.size(); i++) {
+            if (types[i] == COMMON_SPECULATIVE_TYPE_NONE) {
+                continue;
+            }
+            if (types[i] == COMMON_SPECULATIVE_TYPE_DRAFT_MTP) {
+                continue;
+            }
+
+            cparams.n_rs_seq = 0;
+
+            LOG_WRN("%s: recurrent state rollback is not compatible with '%s' - disabling rollback support\n", __func__,
+                    common_speculative_type_to_str(types[i]).c_str());
+
+            break;
+        }
+    }
+
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
@@ -1426,9 +1469,15 @@ common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
         goto done;
     }
 
+    if (llama_n_rs_seq(ctx) > 0) {
+        LOG_INF("%s: the context supports bounded partial sequence removal\n", __func__);
+        res = COMMON_CONTEXT_SEQ_RM_TYPE_RS;
+        goto done;
+    }
+
     // try to remove the last tokens
     if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
-        LOG_WRN("%s: the target context does not support partial sequence removal\n", __func__);
+        LOG_TRC("%s: the context does not support partial sequence removal\n", __func__);
         res = COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
         goto done;
     }
@@ -1440,6 +1489,23 @@ common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
     return res;
 }
 
+void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    auto * mem = llama_get_memory(ctx);
+    if (!llama_memory_seq_rm(mem, seq_id, p0, p1)) {
+        GGML_ABORT("%s", string_format("failed to remove sequence %d with p0=%d, p1=%d\n", seq_id, p0, p1).c_str());
+    }
+}
+
+void common_context_seq_cp(llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+    auto * mem = llama_get_memory(ctx);
+    llama_memory_seq_cp(mem, seq_id_src, seq_id_dst, p0, p1);
+}
+
+void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
+    auto * mem = llama_get_memory(ctx);
+    llama_memory_seq_add(mem, seq_id, p0, p1, delta);
+}
+
 void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
     std::vector<llama_adapter_lora *> loras;
     std::vector<float> scales;
@@ -1496,6 +1562,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
 
     cparams.n_ctx             = params.n_ctx;
     cparams.n_seq_max         = params.n_parallel;
+    cparams.n_rs_seq          = params.speculative.need_n_rs_seq();
     cparams.n_batch           = params.n_batch;
     cparams.n_ubatch          = params.n_ubatch;
     cparams.n_threads         = params.cpuparams.n_threads;
@@ -1527,7 +1594,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     return cparams;
 }
 
-struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
+struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const common_cpu_params & params) {
     struct ggml_threadpool_params tpp;
 
     ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults
@@ -1966,3 +2033,110 @@ bool common_prompt_batch_decode(
 
     return true;
 }
+
+size_t common_prompt_checkpoint::size() const {
+    return data_tgt.size() + data_dft.size();
+}
+
+bool common_prompt_checkpoint::empty() const {
+    return data_tgt.empty();
+}
+
+void common_prompt_checkpoint::clear() {
+    n_tokens = 0;
+
+    pos_min = 0;
+    pos_max = 0;
+
+    data_tgt.clear();
+    data_dft.clear();
+}
+
+void common_prompt_checkpoint::update_pos(
+        int64_t n_tokens,
+        llama_pos pos_min,
+        llama_pos pos_max) {
+    this->n_tokens = n_tokens;
+    this->pos_min  = pos_min;
+    this->pos_max  = pos_max;
+}
+
+void common_prompt_checkpoint::update_tgt(
+        llama_context * ctx,
+        llama_seq_id seq_id,
+        llama_state_seq_flags flags) {
+    if (ctx == nullptr) {
+        return;
+    }
+
+    const size_t ckpt_size = llama_state_seq_get_size_ext(ctx, seq_id, flags);
+
+    data_tgt.resize(ckpt_size);
+
+    const size_t n = llama_state_seq_get_data_ext(ctx, data_tgt.data(), ckpt_size, seq_id, flags);
+    if (n != ckpt_size) {
+        GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", ckpt_size, n);
+    }
+}
+
+void common_prompt_checkpoint::update_dft(
+        llama_context * ctx,
+        llama_seq_id seq_id,
+        llama_state_seq_flags flags) {
+    if (ctx == nullptr) {
+        return;
+    }
+
+    const size_t ckpt_size = llama_state_seq_get_size_ext(ctx, seq_id, flags);
+
+    data_dft.resize(ckpt_size);
+
+    const size_t n = llama_state_seq_get_data_ext(ctx, data_dft.data(), ckpt_size, seq_id, flags);
+    if (n != ckpt_size) {
+        GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", ckpt_size, n);
+    }
+}
+
+void common_prompt_checkpoint::load_tgt(
+        llama_context * ctx,
+        llama_seq_id seq_id,
+        llama_state_seq_flags flags) const {
+    if (ctx == nullptr) {
+        return;
+    }
+
+    if (data_tgt.empty()) {
+        return;
+    }
+
+    const size_t n = llama_state_seq_set_data_ext(ctx, data_tgt.data(), data_tgt.size(), seq_id, flags);
+    if (n != data_tgt.size()) {
+        GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", data_tgt.size(), n);
+    }
+}
+
+void common_prompt_checkpoint::load_dft(
+        llama_context * ctx,
+        llama_seq_id seq_id,
+        llama_state_seq_flags flags) const {
+    if (ctx == nullptr) {
+        return;
+    }
+
+    if (data_dft.empty()) {
+        return;
+    }
+
+    const size_t n = llama_state_seq_set_data_ext(ctx, data_dft.data(), data_dft.size(), seq_id, flags);
+    if (n != data_dft.size()) {
+        GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", data_dft.size(), n);
+    }
+}
+
+void common_prompt_checkpoint::clear_tgt() {
+    data_tgt.clear();
+}
+
+void common_prompt_checkpoint::clear_dft() {
+    data_dft.clear();
+}
diff --git a/common/common.h b/common/common.h
index 4137a87f1d2..1d3d788b2de 100644
--- a/common/common.h
+++ b/common/common.h
@@ -13,6 +13,7 @@
 #include <string_view>
 #include <vector>
 #include <map>
+#include <algorithm>
 
 #if defined(_WIN32) && !defined(_WIN32_WINNT)
 #define _WIN32_WINNT 0x0A00
@@ -54,7 +55,7 @@ struct common_control_vector_load_info;
 // CPU utils
 //
 
-struct cpu_params {
+struct common_cpu_params {
     int      n_threads                   = -1;
     bool     cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
     bool     mask_valid                  = false;   // Default: any CPU
@@ -63,8 +64,8 @@ struct cpu_params {
     uint32_t poll                        = 50;      // Polling (busywait) level (0 - no polling, 100 - mostly polling)
 };
 
-int32_t cpu_get_num_physical_cores();
-int32_t cpu_get_num_math();
+int32_t common_cpu_get_num_physical_cores();
+int32_t common_cpu_get_num_math();
 
 //
 // Common params
@@ -157,9 +158,10 @@ enum common_params_sampling_config : uint64_t {
 
 enum common_speculative_type {
     COMMON_SPECULATIVE_TYPE_NONE,          // no speculative decoding
-    COMMON_SPECULATIVE_TYPE_DRAFT,         // draft model
-    COMMON_SPECULATIVE_TYPE_EAGLE3,        // eagle draft model
-    COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,  // simple self-speculative decoding
+    COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE,  // standalone draft model speculative decoding
+    COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3,  // Eagle3 speculative decoding
+    COMMON_SPECULATIVE_TYPE_DRAFT_MTP,     // Multi-token prediction
+    COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,  // simple self-speculative decoding based on n-grams
     COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K,   // self-speculative decoding with n-gram keys only
     COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values
     COMMON_SPECULATIVE_TYPE_NGRAM_MOD,
@@ -274,6 +276,7 @@ struct common_params_sampling {
     std::vector<llama_token> reasoning_budget_start;           // start tag token sequence
     std::vector<llama_token> reasoning_budget_end;             // end tag token sequence
     std::vector<llama_token> reasoning_budget_forced;          // forced sequence (message + end tag)
+    std::string              reasoning_budget_message;         // message injected before end tag when budget exhausted
 
     bool backend_sampling = false;
 
@@ -294,62 +297,82 @@ struct common_params_model {
     std::string name        = ""; // in format <user>/<model>[:<tag>] (tag is optional)     // NOLINT
 };
 
-struct common_ngram_mod;
+// draft-model-based speculative decoding parameters
+struct common_params_speculative_draft {
+    int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding
+    int32_t n_min = 0;  // minimum number of draft tokens to use for speculative decoding
 
-struct common_params_speculative {
-    common_speculative_type type = COMMON_SPECULATIVE_TYPE_NONE; // type of speculative decoding
+    float p_split = 0.1f;  // speculative decoding split probability
+    float p_min   = 0.75f; // minimum speculative decoding probability (greedy) // TODO: change default to 0.0f
 
-    // general-purpose speculative decoding parameters
+    common_params_model mparams;
 
-    int32_t n_max   = 16; // maximum number of tokens to draft during speculative decoding
-    int32_t n_min   = 0;  // minimum number of draft tokens to use for speculative decoding
-    float   p_split = 0.1f; // speculative decoding split probability
-    float   p_min   = 0.75f; // minimum speculative decoding probability (greedy)
+    llama_context * ctx_tgt = nullptr;
+    llama_context * ctx_dft = nullptr;
 
-    // ngram-based speculative decoding
+    int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
 
-    uint16_t ngram_size_n   = 12; // ngram size for lookup
-    uint16_t ngram_size_m   = 48; // mgram size for speculative tokens
-    uint16_t ngram_min_hits = 1; // minimum hits at ngram/mgram lookup for mgram to be proposed
+    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
+    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
 
-    std::shared_ptr<common_ngram_mod> ngram_mod;
+    common_cpu_params cpuparams;
+    common_cpu_params cpuparams_batch;
 
-    std::string lookup_cache_static;  // path of static ngram cache file for lookup decoding           // NOLINT
-    std::string lookup_cache_dynamic; // path of dynamic ngram cache file for lookup decoding          // NOLINT
+    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
 
-    // draft-model speculative decoding
+    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
+};
 
-    struct common_params_model mparams_dft;
+struct common_params_speculative_ngram_mod {
+    int32_t n_match = 24;
 
-    llama_model * model_dft = nullptr; // a llama_model that can be shared by multiple speculative contexts
+    int32_t n_max = 64;
+    int32_t n_min = 48;
+};
 
-    llama_context_params cparams_dft; // these are the parameters for the draft llama_context
+struct common_params_speculative_ngram_map {
+    uint16_t size_n   = 12; // ngram size for lookup
+    uint16_t size_m   = 48; // mgram size for speculative tokens
+    uint16_t min_hits = 1;  // minimum hits at ngram/mgram lookup for mgram to be proposed
+};
 
-    int32_t n_ctx        = 0;  // draft context size
-    int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
+struct common_params_speculative_ngram_cache {
+    std::string lookup_cache_static;  // path of static ngram cache file for lookup decoding
+    std::string lookup_cache_dynamic; // path of dynamic ngram cache file for lookup decoding
+};
 
-    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
-    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
+struct common_params_speculative {
+    std::vector<enum common_speculative_type> types = { COMMON_SPECULATIVE_TYPE_NONE };
 
-    struct cpu_params cpuparams;
-    struct cpu_params cpuparams_batch;
+    // used by Simple, MTP, Eagle3, etc. - all methods that require some kind of draft model
+    common_params_speculative_draft draft;
 
-    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+    common_params_speculative_ngram_mod ngram_mod;
+    common_params_speculative_ngram_map ngram_simple;
+    common_params_speculative_ngram_map ngram_map_k;
+    common_params_speculative_ngram_map ngram_map_k4v;
 
-    std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
-    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
+    common_params_speculative_ngram_cache ngram_cache;
 
     bool has_dft() const {
-        return !mparams_dft.path.empty() || !mparams_dft.hf_repo.empty();
+        return !draft.mparams.path.empty() || !draft.mparams.hf_repo.empty();
+    }
+
+    uint32_t need_n_rs_seq() const {
+        bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
+            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP;
+        });
+
+        return needs_rs_seq ? draft.n_max : 0u;
     }
 };
 
 struct common_params_vocoder {
     struct common_params_model model;
 
-    std::string speaker_file = ""; // speaker file path                                      // NOLINT
+    std::string speaker_file; // speaker file path
 
-    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy            // NOLINT
+    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy
 };
 
 struct common_params_diffusion {
@@ -432,8 +455,8 @@ struct common_params {
 
     enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
-    struct cpu_params cpuparams;
-    struct cpu_params cpuparams_batch;
+    common_cpu_params cpuparams;
+    common_cpu_params cpuparams_batch;
 
     ggml_backend_sched_eval_callback cb_eval = nullptr;
     void * cb_eval_user_data                 = nullptr;
@@ -581,8 +604,6 @@ struct common_params {
     bool force_pure_content_parser = false;
     common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
     int enable_reasoning = -1; // -1 = auto, 0 = disable, 1 = enable
-    int reasoning_budget = -1;
-    std::string reasoning_budget_message; // message injected before end tag when budget exhausted
     bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response
     int sleep_idle_seconds = -1;   // if >0, server will sleep after this many seconds of idle time
 
@@ -593,11 +614,21 @@ struct common_params {
 
     std::map<std::string, std::string> default_template_kwargs;
 
-    // webui configs
-    bool webui = true;
+    // UI configs
+#ifdef LLAMA_UI_DEFAULT_ENABLED
+    bool ui = LLAMA_UI_DEFAULT_ENABLED != 0;
+#else
+    bool ui = true; // default to enabled when not set
+#endif
+
+    // Deprecated: use ui, ui_mcp_proxy, ui_config_json instead
+    bool webui = ui;
     bool webui_mcp_proxy = false;
     std::string webui_config_json;
 
+    bool ui_mcp_proxy = false;
+    std::string ui_config_json;
+
     // "advanced" endpoints are disabled by default for better security
     bool endpoint_slots   = true;
     bool endpoint_props   = false; // only control POST requests, not GET
@@ -675,11 +706,12 @@ struct common_params {
 // initializes the logging system and prints info about the build
 void common_init();
 
+void common_params_print_info(const common_params & params, bool print_devices = true);
 std::string common_params_get_system_info(const common_params & params);
 
 bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]);
 bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
-void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr);
+void postprocess_cpu_params(common_cpu_params & cpuparams, const common_cpu_params * role_model = nullptr);
 bool set_process_priority(enum ggml_sched_priority prio);
 
 //
@@ -747,6 +779,11 @@ inline bool string_starts_with(std::string_view str, std::string_view prefix) {
            str.compare(0, prefix.size(), prefix) == 0;
 }
 
+// remove when moving to c++20
+inline bool string_starts_with(std::string_view str, char prefix) {
+    return !str.empty() && str.front() == prefix;
+}
+
 // remove when moving to c++20
 inline bool string_ends_with(std::string_view str, std::string_view suffix) {
     return str.size() >= suffix.size() &&
@@ -842,7 +879,7 @@ common_init_result_ptr common_init_from_params(common_params & params);
 
 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
-struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
+struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const common_cpu_params & params);
 
 // clear LoRA adapters from context, then apply new list of adapters
 void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);
@@ -855,15 +892,20 @@ std::string common_get_model_endpoint();
 //
 
 enum common_context_seq_rm_type {
-    COMMON_CONTEXT_SEQ_RM_TYPE_NO   = 0, // seq_rm not supported (e.g. no memory module)
-    COMMON_CONTEXT_SEQ_RM_TYPE_PART = 1, // can seq_rm partial sequences
-    COMMON_CONTEXT_SEQ_RM_TYPE_FULL = 2, // can seq_rm full sequences only
+    COMMON_CONTEXT_SEQ_RM_TYPE_NO           = 0, // seq_rm not supported (e.g. no memory module)
+    COMMON_CONTEXT_SEQ_RM_TYPE_PART         = 1, // can seq_rm partial sequences
+    COMMON_CONTEXT_SEQ_RM_TYPE_FULL         = 2, // can seq_rm full sequences only
+    COMMON_CONTEXT_SEQ_RM_TYPE_RS = 3, // can seq_rm partial sequences, bounded by n_rs_seq
 };
 
 // check if the llama_context can remove sequences
 // note: clears the memory of the context
 common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx);
 
+// aborts execution on failure
+void common_context_seq_rm (llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1);
+void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta);
+void common_context_seq_cp (llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1);
 
 //
 // Batch utils
@@ -1002,3 +1044,50 @@ ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std
 
 // "adamw" or "sgd" (case insensitive)
 enum ggml_opt_optimizer_type common_opt_get_optimizer(const char *);
+
+//
+// prompt utils
+//
+
+struct common_prompt_checkpoint {
+    int64_t n_tokens;
+
+    llama_pos pos_min;
+    llama_pos pos_max;
+
+    std::vector<uint8_t> data_tgt;
+    std::vector<uint8_t> data_dft;
+
+    size_t size() const;
+
+    bool empty() const;
+    void clear();
+
+    void update_pos(
+            int64_t n_tokens,
+            llama_pos pos_min,
+            llama_pos pos_max);
+
+    void update_tgt(
+            llama_context * ctx,
+            llama_seq_id seq_id,
+            llama_state_seq_flags flags);
+
+    void update_dft(
+            llama_context * ctx,
+            llama_seq_id seq_id,
+            llama_state_seq_flags flags);
+
+    void load_tgt(
+            llama_context * ctx,
+            llama_seq_id seq_id,
+            llama_state_seq_flags flags) const;
+
+    void load_dft(
+            llama_context * ctx,
+            llama_seq_id seq_id,
+            llama_state_seq_flags flags) const;
+
+    void clear_tgt();
+    void clear_dft();
+};
diff --git a/common/debug.cpp b/common/debug.cpp
index 0df409a79db..102c6924dc9 100644
--- a/common/debug.cpp
+++ b/common/debug.cpp
@@ -1,9 +1,38 @@
 #include "debug.h"
 
+#include "common.h"
 #include "log.h"
 
 #include <cmath>
+#include <regex>
 #include <string>
+#include <vector>
+
+struct common_debug_cb_user_data::impl {
+    std::vector<uint8_t>    data;
+    std::vector<std::regex> tensor_filters;
+    bool                    abort_on_nan{false};
+};
+
+common_debug_cb_user_data::common_debug_cb_user_data() : pimpl(std::make_unique<impl>()) {}
+common_debug_cb_user_data::~common_debug_cb_user_data() = default;
+
+common_debug_cb_user_data::common_debug_cb_user_data(common_params & params, const std::vector<std::string> & filter_patterns, bool abort_on_nan)
+    : pimpl(std::make_unique<impl>())
+{
+    for (const auto & pattern : filter_patterns) {
+        try {
+            std::string anchored_pattern = "^" + pattern;
+            pimpl->tensor_filters.emplace_back(anchored_pattern, std::regex::optimize);
+        } catch (const std::regex_error & e) {
+            throw std::runtime_error("Invalid regex pattern '" + pattern + "': " + e.what());
+        }
+    }
+    pimpl->abort_on_nan = abort_on_nan;
+
+    params.cb_eval           = common_debug_cb_eval;
+    params.cb_eval_user_data = this;
+}
 
 static std::string common_ggml_ne_string(const ggml_tensor * t) {
     std::string str;
@@ -47,8 +76,7 @@ static float common_ggml_get_float_value(const uint8_t * data,
 
 #define INDENT "    "
 
-template <bool abort>
-void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
+static void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n, bool abort_on_nan) {
     GGML_ASSERT(n > 0);
     float sum = 0;
     for (int64_t i3 = 0; i3 < ne[3]; i3++) {
@@ -94,7 +122,7 @@ void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * n
         LOG(INDENT "sum = %f\n", sum);
     }
 
-    if constexpr (abort) {
+    if (abort_on_nan) {
         if (std::isnan(sum)) {
             LOG("encountered NaN - aborting\n");
             exit(0);
@@ -112,8 +140,9 @@ void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * n
  * @param user_data user data to pass at each call back
  * @return true to receive data or continue the graph, false otherwise
  */
-template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
-    auto * cb_data = (base_callback_data *) user_data;
+bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
+    auto * cb_data = (common_debug_cb_user_data *) user_data;
+    auto * pimpl = cb_data->pimpl.get();
 
     const struct ggml_tensor * src0 = t->src[0];
     const struct ggml_tensor * src1 = t->src[1];
@@ -122,10 +151,10 @@ template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, b
         return true;  // Always retrieve data
     }
 
-    bool matches_filter = cb_data->tensor_filters.empty();
+    bool matches_filter = pimpl->tensor_filters.empty();
 
     if (!matches_filter) {
-        for (const auto & filter : cb_data->tensor_filters) {
+        for (const auto & filter : pimpl->tensor_filters) {
             if (std::regex_search(t->name, filter)) {
                 matches_filter = true;
                 break;
@@ -148,20 +177,14 @@ template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, b
 
     if (!is_host) {
         auto n_bytes = ggml_nbytes(t);
-        cb_data->data.resize(n_bytes);
-        ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
+        pimpl->data.resize(n_bytes);
+        ggml_backend_tensor_get(t, pimpl->data.data(), 0, n_bytes);
     }
 
     if (!ggml_is_quantized(t->type) && matches_filter) {
-        uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
-        common_debug_print_tensor<abort_on_nan>(data, t->type, t->ne, t->nb, 3);
+        uint8_t * data = is_host ? (uint8_t *) t->data : pimpl->data.data();
+        common_debug_print_tensor(data, t->type, t->ne, t->nb, 3, pimpl->abort_on_nan);
     }
 
     return true;
 }
-
-// Explicit template instantiations
-template bool common_debug_cb_eval<false>(ggml_tensor *, bool, void *);
-template bool common_debug_cb_eval<true>(ggml_tensor *, bool, void *);
-template void common_debug_print_tensor<false>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);
-template void common_debug_print_tensor<true>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);
diff --git a/common/debug.h b/common/debug.h
index e563b40d68f..8b8f8c7aa9f 100644
--- a/common/debug.h
+++ b/common/debug.h
@@ -1,43 +1,31 @@
 #pragma once
-#include "common.h"
+
+#include <memory>
 #include <string>
 #include <vector>
-#include <regex>
 
 // common debug functions and structs
 
-// Print a tensor's detailed data
-// data - the tensor's data in byte format
-// type - the tensor's quantization type
-// ne   - the tensor dimensions array
-// nb   - the tensor strides array
-// n    - the number of rows/columns to fully print
-template <bool abort_on_nan> void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n);
+struct common_params;
 
 // Intended to use as callback for ggml_backend_sched_eval_callback
 // prints tensors that are processed in the computation graph
-// by default prints all tensors, but can be configured by creating a `base_callback_data` instance with
-// non-empty filter_patterns. See examples/debug.ccp for possible usage patterns
-// The template parameter determines whether an error should be thrown whenever a NaN is encountered
+// by default prints all tensors, but can be configured by creating a `common_debug_cb_user_data` instance with
+// non-empty filter_patterns. See examples/debug.cpp for possible usage patterns
+// `common_debug_cb_user_data` contains `abort_on_nan` flag that determines whether an error should be thrown whenever a NaN is encountered
 // in a tensor (useful for stopping debug sessions on first erroneous tensor)
 // The callback data will be passed as the third parameter (user_data)
-template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data);
-struct base_callback_data {
-    std::vector<uint8_t>    data;
-    std::vector<std::regex> tensor_filters;
-
-    base_callback_data() = default;
-
-    base_callback_data(common_params & params, const std::vector<std::string> & filter_patterns) {
-        for (const auto & pattern : filter_patterns) {
-            try {
-                std::string anchored_pattern = "^" + pattern;
-                tensor_filters.emplace_back(anchored_pattern, std::regex::optimize);
-            } catch (const std::regex_error & e) {
-                throw std::runtime_error("Invalid regex pattern '" + pattern + "': " + e.what());
-            }
-        }
-        params.cb_eval           = common_debug_cb_eval<false>;
-        params.cb_eval_user_data = this;
-    }
+bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data);
+
+struct common_debug_cb_user_data {
+    struct impl;
+    std::unique_ptr<impl> pimpl;
+
+    common_debug_cb_user_data();
+    ~common_debug_cb_user_data();
+
+    common_debug_cb_user_data(const common_debug_cb_user_data &) = delete;
+    common_debug_cb_user_data & operator=(const common_debug_cb_user_data &) = delete;
+
+    common_debug_cb_user_data(common_params & params, const std::vector<std::string> & filter_patterns, bool abort_on_nan = false);
 };
diff --git a/common/download.cpp b/common/download.cpp
index c4bb02d90c2..103bc408faf 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -320,9 +320,9 @@ static int common_download_file_single_online(const std::string & url,
 
     auto head = cli.Head(parts.path);
     if (!head || head->status < 200 || head->status >= 300) {
-        LOG_WRN("%s: HEAD failed, status: %d\n", __func__, head ? head->status : -1);
+        LOG_TRC("%s: HEAD failed, status: %d\n", __func__, head ? head->status : -1);
         if (file_exists) {
-            LOG_INF("%s: using cached file (HEAD failed): %s\n", __func__, path.c_str());
+            LOG_TRC("%s: using cached file (HEAD failed): %s\n", __func__, path.c_str());
             return 304; // 304 Not Modified - fake cached response
         }
         return head ? head->status : -1;
@@ -566,8 +566,11 @@ static hf_cache::hf_files get_split_files(const hf_cache::hf_files & files,
     return result;
 }
 
-static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
-                                          const std::string        & model) {
+// pick the best sibling GGUF whose filename contains `keyword` (e.g. "mmproj" / "mtp"),
+// preferring deeper shared directory prefix with the model, then closest quantization
+static hf_cache::hf_file find_best_sibling(const hf_cache::hf_files & files,
+                                           const std::string        & model,
+                                           const std::string        & keyword) {
     hf_cache::hf_file best;
     size_t best_depth = 0;
     int best_diff = 0;
@@ -579,20 +582,20 @@ static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
 
     for (const auto & f : files) {
         if (!string_ends_with(f.path, ".gguf") ||
-            f.path.find("mmproj") == std::string::npos) {
+            f.path.find(keyword) == std::string::npos) {
             continue;
         }
 
-        auto mmproj_parts = string_split<std::string>(f.path, '/');
-        auto mmproj_dir = mmproj_parts.end() - 1;
+        auto sib_parts = string_split<std::string>(f.path, '/');
+        auto sib_dir = sib_parts.end() - 1;
 
         auto [_, dir] = std::mismatch(model_parts.begin(), model_dir,
-                                      mmproj_parts.begin(), mmproj_dir);
-        if (dir != mmproj_dir) {
+                                      sib_parts.begin(), sib_dir);
+        if (dir != sib_dir) {
             continue;
         }
 
-        size_t depth = dir - mmproj_parts.begin();
+        size_t depth = dir - sib_parts.begin();
         auto bits = extract_quant_bits(f.path);
         auto diff = std::abs(bits - model_bits);
 
@@ -606,6 +609,16 @@ static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
     return best;
 }
 
+static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
+                                          const std::string        & model) {
+    return find_best_sibling(files, model, "mmproj");
+}
+
+static hf_cache::hf_file find_best_mtp(const hf_cache::hf_files & files,
+                                       const std::string        & model) {
+    return find_best_sibling(files, model, "mtp-");
+}
+
 static bool gguf_filename_is_model(const std::string & filepath) {
     if (!string_ends_with(filepath, ".gguf")) {
         return false;
@@ -617,7 +630,8 @@ static bool gguf_filename_is_model(const std::string & filepath) {
     }
 
     return filename.find("mmproj")  == std::string::npos &&
-           filename.find("imatrix") == std::string::npos;
+           filename.find("imatrix") == std::string::npos &&
+           filename.find("mtp-")    == std::string::npos;
 }
 
 static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
@@ -627,7 +641,7 @@ static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
     if (!tag.empty()) {
         tags.push_back(tag);
     } else {
-        tags = {"Q4_K_M", "Q4_0"};
+        tags = {"Q4_K_M", "Q8_0"};
     }
 
     for (const auto & t : tags) {
@@ -673,11 +687,13 @@ struct hf_plan {
     hf_cache::hf_file primary;
     hf_cache::hf_files model_files;
     hf_cache::hf_file mmproj;
+    hf_cache::hf_file mtp;
 };
 
 static hf_plan get_hf_plan(const common_params_model  & model,
                            const common_download_opts & opts,
-                           bool download_mmproj) {
+                           bool download_mmproj,
+                           bool download_mtp) {
     hf_plan plan;
     hf_cache::hf_files all;
 
@@ -723,6 +739,10 @@ static hf_plan get_hf_plan(const common_params_model  & model,
         plan.mmproj = find_best_mmproj(all, primary.path);
     }
 
+    if (download_mtp) {
+        plan.mtp = find_best_mtp(all, primary.path);
+    }
+
     return plan;
 }
 
@@ -756,7 +776,8 @@ static std::vector<download_task> get_url_tasks(const common_params_model & mode
 
 common_download_model_result common_download_model(const common_params_model  & model,
                                                    const common_download_opts & opts,
-                                                   bool download_mmproj) {
+                                                   bool download_mmproj,
+                                                   bool download_mtp) {
     common_download_model_result result;
     std::vector<download_task> tasks;
     hf_plan hf;
@@ -764,13 +785,16 @@ common_download_model_result common_download_model(const common_params_model  &
     bool is_hf = !model.hf_repo.empty();
 
     if (is_hf) {
-        hf = get_hf_plan(model, opts, download_mmproj);
+        hf = get_hf_plan(model, opts, download_mmproj, download_mtp);
         for (const auto & f : hf.model_files) {
             tasks.push_back({f.url, f.local_path});
         }
         if (!hf.mmproj.path.empty()) {
             tasks.push_back({hf.mmproj.url, hf.mmproj.local_path});
         }
+        if (!hf.mtp.path.empty()) {
+            tasks.push_back({hf.mtp.url, hf.mtp.local_path});
+        }
     } else if (!model.url.empty()) {
         tasks = get_url_tasks(model);
     } else {
@@ -807,6 +831,10 @@ common_download_model_result common_download_model(const common_params_model  &
         if (!hf.mmproj.path.empty()) {
             result.mmproj_path = hf_cache::finalize_file(hf.mmproj);
         }
+
+        if (!hf.mtp.path.empty()) {
+            result.mtp_path = hf_cache::finalize_file(hf.mtp);
+        }
     } else {
         result.model_path = model.path;
     }
@@ -946,7 +974,8 @@ std::vector<common_cached_model_info> common_list_cached_models() {
     for (const auto & f : files) {
         auto split = get_gguf_split_info(f.path);
         if (split.index != 1 || split.tag.empty() ||
-            split.prefix.find("mmproj") != std::string::npos) {
+            split.prefix.find("mmproj") != std::string::npos ||
+            split.prefix.find("mtp-")   != std::string::npos) {
             continue;
         }
         if (seen.insert(f.repo_id + ":" + split.tag).second) {
diff --git a/common/download.h b/common/download.h
index edc3e9f1a71..4a169ef7796 100644
--- a/common/download.h
+++ b/common/download.h
@@ -59,6 +59,7 @@ struct common_download_opts {
 struct common_download_model_result {
     std::string model_path;
     std::string mmproj_path;
+    std::string mtp_path;
 };
 
 // Download model from HuggingFace repo or URL
@@ -83,12 +84,14 @@ struct common_download_model_result {
 // when opts.offline=true, no network requests are made
 // when download_mmproj=true, searches for mmproj in same directory as model or any parent directory
 // then with the closest quantization bits
+// when download_mtp=true, applies the same sibling search for an MTP-head GGUF
 //
-// returns result with model_path and mmproj_path (empty on failure)
+// returns result with model_path, mmproj_path and mtp_path (empty when not found / on failure)
 common_download_model_result common_download_model(
     const common_params_model & model,
     const common_download_opts & opts = {},
-    bool download_mmproj = false
+    bool download_mmproj = false,
+    bool download_mtp    = false
 );
 
 // returns list of cached models
diff --git a/common/fit.cpp b/common/fit.cpp
index 4b952889070..c10cb7f08b4 100644
--- a/common/fit.cpp
+++ b/common/fit.cpp
@@ -109,16 +109,24 @@ static std::vector<llama_device_memory_data> common_get_device_memory_data(
         ret.back().total = total;
     }
     for (size_t i = 0; i < nd; i++) {
+        ggml_backend_dev_t dev = llama_model_get_device(model, i);
+
         size_t free;
         size_t total;
-        ggml_backend_dev_memory(llama_model_get_device(model, i), &free, &total);
+        ggml_backend_dev_memory(dev, &free, &total);
 
-        // devices can return 0 bytes for free and total memory if they do not
-        // have any to report. in this case, we will use the host memory as a fallback
-        // fixes: https://github.com/ggml-org/llama.cpp/issues/18577
+        // Some non-GPU accelerator backends, such as BLAS, report 0/0 and rely on
+        // the host-memory fallback. For GPU-like backends, keep 0/0 so --fit does
+        // not assign anything to a device with an unknown memory budget.
         if (free == 0 && total == 0) {
-            free  = ret.back().free;
-            total = ret.back().total;
+            const enum ggml_backend_dev_type type = ggml_backend_dev_type(dev);
+            if (type == GGML_BACKEND_DEVICE_TYPE_GPU || type == GGML_BACKEND_DEVICE_TYPE_IGPU) {
+                LOG_WRN("%s: device %s did not report memory; --fit will not use it\n",
+                        __func__, ggml_backend_dev_name(dev));
+            } else {
+                free  = ret.back().free;
+                total = ret.back().total;
+            }
         }
         ret[i].free  = free;
         ret[i].total = total;
@@ -160,7 +168,7 @@ static void common_params_fit_impl(
 
     // step 1: get data for default parameters and check whether any changes are necessary in the first place
 
-    LOG_INF("%s: getting device memory data for initial parameters:\n", __func__);
+    LOG_TRC("%s: getting device memory data for initial parameters:\n", __func__);
     const dmds_t dmds_full = common_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
     const size_t nd = devs.size(); // number of devices
 
@@ -205,13 +213,13 @@ static void common_params_fit_impl(
         LOG_INF("%s: projected to use %" PRId64 " MiB of host memory vs. %" PRId64 " MiB of total host memory\n",
             __func__, sum_projected_used/MiB, sum_free/MiB);
         if (sum_projected_free >= margins[0]) {
-            LOG_INF("%s: will leave %" PRId64 " >= %" PRId64 " MiB of system memory, no changes needed\n",
+            LOG_TRC("%s: will leave %" PRId64 " >= %" PRId64 " MiB of system memory, no changes needed\n",
                 __func__, sum_projected_free/MiB, margins[0]/MiB);
             return;
         }
     } else {
         if (nd > 1) {
-            LOG_INF("%s: projected memory use with initial parameters [MiB]:\n", __func__);
+            LOG_TRC("%s: projected memory use with initial parameters [MiB]:\n", __func__);
         }
         for (size_t id = 0; id < nd; id++) {
             const llama_device_memory_data & dmd = dmds_full[id];
@@ -226,16 +234,16 @@ static void common_params_fit_impl(
             sum_projected_model += dmd.mb.model;
 
             if (nd > 1) {
-                LOG_INF("%s:   - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " free vs. target of %6" PRId64 "\n",
+                LOG_TRC("%s:   - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " free vs. target of %6" PRId64 "\n",
                     __func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, projected_free/MiB, margins[id]/MiB);
             }
         }
         assert(sum_free >= 0 && sum_projected_used >= 0);
-        LOG_INF("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n",
+        LOG_TRC("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n",
             __func__, sum_projected_used/MiB, sum_free/MiB);
         if (nd == 1) {
             if (projected_free_per_device[0] >= margins[0]) {
-                LOG_INF("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n",
+                LOG_TRC("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n",
                     __func__, projected_free_per_device[0]/MiB, margins[0]/MiB);
                 return;
             }
@@ -248,7 +256,7 @@ static void common_params_fit_impl(
                 }
             }
             if (!changes_needed) {
-                LOG_INF("%s: targets for free memory can be met on all devices, no changes needed\n", __func__);
+                LOG_TRC("%s: targets for free memory can be met on all devices, no changes needed\n", __func__);
                 return;
             }
         }
@@ -267,10 +275,10 @@ static void common_params_fit_impl(
         }
         if (global_surplus < 0) {
             if (nd <= 1) {
-                LOG_INF("%s: cannot meet free memory target of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n",
+                LOG_TRC("%s: cannot meet free memory target of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n",
                     __func__, margins[0]/MiB, -global_surplus/MiB);
             } else {
-                LOG_INF(
+                LOG_TRC(
                     "%s: cannot meet free memory targets on all devices, need to use %" PRId64 " MiB less in total\n",
                     __func__, -global_surplus/MiB);
             }
@@ -312,28 +320,28 @@ static void common_params_fit_impl(
 
                         const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (hp_nct - n_ctx_min);
                         const int64_t memory_reduction = (hp_nct - cparams->n_ctx) * bytes_per_ctx;
-                        LOG_INF("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
+                        LOG_TRC("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
                             __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
                         if (nd <= 1) {
-                            LOG_INF("%s: entire model can be fit by reducing context\n", __func__);
+                            LOG_TRC("%s: entire model can be fit by reducing context\n", __func__);
                             return;
                         }
-                        LOG_INF("%s: entire model should be fit across devices by reducing context\n", __func__);
+                        LOG_TRC("%s: entire model should be fit across devices by reducing context\n", __func__);
                     } else {
                         const int64_t memory_reduction = sum_projected_used - sum_projected_used_min_ctx;
-                        LOG_INF("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
+                        LOG_TRC("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
                             __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
                     }
                 } else {
                     if (n_ctx_min == UINT32_MAX) {
-                        LOG_INF("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct);
+                        LOG_TRC("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct);
                     } else {
-                        LOG_INF("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
+                        LOG_TRC("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
                             __func__, hp_nct, n_ctx_min);
                     }
                 }
             } else {
-                LOG_INF("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
+                LOG_TRC("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
             }
         }
     }
@@ -477,10 +485,10 @@ static void common_params_fit_impl(
         const dmds_t dmd_nl = common_get_device_memory_data(
             path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
 
-        LOG_INF("%s: memory for test allocation by device:\n", func_name);
+        LOG_TRC("%s: memory for test allocation by device:\n", func_name);
         for (size_t id = 0; id < nd; id++) {
             const ngl_t & n = ngl_per_device[id];
-            LOG_INF(
+            LOG_TRC(
                 "%s: id=%zu, n_layer=%2" PRIu32 ", n_part=%2" PRIu32 ", overflow_type=%d, mem=%6" PRId64 " MiB\n",
                 func_name, id, n.n_layer, n.n_part, int(n.overflow_type), dmd_nl[id].mb.total()/MiB);
         }
@@ -501,7 +509,7 @@ static void common_params_fit_impl(
         tensor_buft_overrides[1] = {nullptr, nullptr};
         mparams->tensor_buft_overrides = tensor_buft_overrides;
 
-        LOG_INF("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
+        LOG_TRC("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
         const dmds_t dmds_cpu_moe = common_get_device_memory_data(
             path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
 
@@ -511,10 +519,10 @@ static void common_params_fit_impl(
         }
 
         if (global_surplus_cpu_moe > 0) {
-            LOG_INF("%s: with only dense weights in device memory there is a total surplus of %" PRId64 " MiB\n",
+            LOG_TRC("%s: with only dense weights in device memory there is a total surplus of %" PRId64 " MiB\n",
                 __func__, global_surplus_cpu_moe/MiB);
         } else {
-            LOG_INF("%s: with only dense weights in device memory there is still a total deficit of %" PRId64 " MiB\n",
+            LOG_TRC("%s: with only dense weights in device memory there is still a total deficit of %" PRId64 " MiB\n",
                 __func__, -global_surplus_cpu_moe/MiB);
         }
 
@@ -527,7 +535,7 @@ static void common_params_fit_impl(
     targets.reserve(nd);
     for (size_t id = 0; id < nd; id++) {
         targets.push_back(dmds_full[id].free - margins[id]);
-        LOG_INF("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB);
+        LOG_TRC("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB);
     }
 
     std::vector<ggml_backend_buffer_type_t> overflow_bufts; // which bufts the first partial layer of a device overflows to:
@@ -547,9 +555,9 @@ static void common_params_fit_impl(
     //   - once we only have a difference of a single layer, stop and return the lower bound that just barely still fits
     //   - the last device has the output layer, which cannot be a partial layer
     if (hp_nex == 0) {
-        LOG_INF("%s: filling dense layers back-to-front:\n", __func__);
+        LOG_TRC("%s: filling dense layers back-to-front:\n", __func__);
     } else {
-        LOG_INF("%s: filling dense-only layers back-to-front:\n", __func__);
+        LOG_TRC("%s: filling dense-only layers back-to-front:\n", __func__);
     }
     for (int id = nd - 1; id >= 0; id--) {
         uint32_t n_unassigned = hp_ngl + 1;
@@ -568,7 +576,7 @@ static void common_params_fit_impl(
             if (mem_high[id] > targets[id]) {
                 assert(ngl_per_device_high[id].n_layer > ngl_per_device[id].n_layer);
                 uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
-                LOG_INF("%s: start filling device %" PRIu32 ", delta=%" PRIu32 "\n", __func__, id, delta);
+                LOG_TRC("%s: start filling device %" PRIu32 ", delta=%" PRIu32 "\n", __func__, id, delta);
                 while (delta > 1) {
                     uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
                     step_size = std::max(step_size, uint32_t(1));
@@ -585,11 +593,11 @@ static void common_params_fit_impl(
                     if (mem_test[id] <= targets[id]) {
                         ngl_per_device = ngl_per_device_test;
                         mem            = mem_test;
-                        LOG_INF("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
+                        LOG_TRC("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
                     } else {
                         ngl_per_device_high = ngl_per_device_test;
                         mem_high            = mem_test;
-                        LOG_INF("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device_high[id].n_layer);
+                        LOG_TRC("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device_high[id].n_layer);
                     }
                     delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
                 }
@@ -597,12 +605,12 @@ static void common_params_fit_impl(
                 assert(ngl_per_device_high[id].n_layer == n_unassigned);
                 ngl_per_device = ngl_per_device_high;
                 mem            = mem_high;
-                LOG_INF("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
+                LOG_TRC("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
             }
         }
 
         const int64_t projected_margin = dmds_full[id].free - mem[id];
-        LOG_INF(
+        LOG_TRC(
             "%s:   - %s: %2" PRIu32 " layers, %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
             __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, mem[id]/MiB, projected_margin/MiB);
     }
@@ -626,7 +634,7 @@ static void common_params_fit_impl(
     }
     assert(id_dense_start < nd);
 
-    LOG_INF("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__);
+    LOG_TRC("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__);
     for (size_t id = 0; id <= id_dense_start && id_dense_start < nd; id++) {
         std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
         for (size_t jd = id_dense_start; jd < nd; jd++) {
@@ -666,13 +674,13 @@ static void common_params_fit_impl(
                     ngl_per_device = ngl_per_device_test;
                     mem            = mem_test;
                     id_dense_start = id_dense_start_test;
-                    LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
+                    LOG_TRC("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
                         __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
                 } else {
                     ngl_per_device_high = ngl_per_device_test;
                     mem_high            = mem_test;
                     id_dense_start_high = id_dense_start_test;
-                    LOG_INF("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n",
+                    LOG_TRC("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n",
                         __func__, id, ngl_per_device_high[id].n_layer, ngl_per_device_high[id].n_part, id_dense_start_high);
                 }
                 assert(ngl_per_device_high[id].n_full() >= ngl_per_device[id].n_full());
@@ -682,7 +690,7 @@ static void common_params_fit_impl(
             ngl_per_device = ngl_per_device_high;
             mem            = mem_high;
             id_dense_start = id_dense_start_high;
-            LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
+            LOG_TRC("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
                 __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
         }
 
@@ -702,44 +710,44 @@ static void common_params_fit_impl(
             if (id < nd - 1) {
                 overflow_bufts_test[id] = ggml_backend_dev_buffer_type(devs[id + 1]);
             }
-            LOG_INF("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__);
+            LOG_TRC("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__);
             std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
             if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
                 ngl_per_device = ngl_per_device_test;
                 overflow_bufts = overflow_bufts_test;
                 mem            = mem_test;
                 id_dense_start = id_dense_start_test;
-                LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n",
+                LOG_TRC("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n",
                     __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
 
                 ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE;
-                LOG_INF("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__);
+                LOG_TRC("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__);
                 mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
                 if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
                     ngl_per_device = ngl_per_device_test;
                     overflow_bufts = overflow_bufts_test;
                     mem            = mem_test;
                     id_dense_start = id_dense_start_test;
-                    LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n",
+                    LOG_TRC("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n",
                         __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
                 }
             } else {
                 ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN;
-                LOG_INF("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__);
+                LOG_TRC("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__);
                 mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
                 if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
                     ngl_per_device = ngl_per_device_test;
                     overflow_bufts = overflow_bufts_test;
                     mem            = mem_test;
                     id_dense_start = id_dense_start_test;
-                    LOG_INF("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n",
+                    LOG_TRC("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n",
                         __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
                 }
             }
         }
 
         const int64_t projected_margin = dmds_full[id].free - mem[id];
-        LOG_INF(
+        LOG_TRC(
             "%s:   - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
             __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
     }
@@ -747,7 +755,7 @@ static void common_params_fit_impl(
     // print info for devices that were not changed during the conversion from dense only to full layers:
     for (size_t id = id_dense_start + 1; id < nd; id++) {
         const int64_t projected_margin = dmds_full[id].free - mem[id];
-        LOG_INF(
+        LOG_TRC(
             "%s:   - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
             __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
     }
@@ -768,7 +776,7 @@ enum common_params_fit_status common_fit_params(
     common_params_fit_status status = COMMON_PARAMS_FIT_STATUS_SUCCESS;
     try {
         common_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margins, n_ctx_min, log_level);
-        LOG_INF("%s: successfully fit params to free device memory\n", __func__);
+        LOG_TRC("%s: successfully fit params to free device memory\n", __func__);
     } catch (const common_params_fit_exception & e) {
         LOG_WRN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
         status = COMMON_PARAMS_FIT_STATUS_FAILURE;
@@ -777,7 +785,7 @@ enum common_params_fit_status common_fit_params(
         status = COMMON_PARAMS_FIT_STATUS_ERROR;
     }
     const int64_t t1_us = llama_time_us();
-    LOG_INF("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6);
+    LOG_TRC("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6);
     return status;
 }
 
@@ -856,7 +864,7 @@ void common_memory_breakdown_print(const struct llama_context * ctx) {
         ggml_backend_dev_memory(dev, &free, &total);
 
         const size_t self = mb.model + mb.context + mb.compute;
-        const size_t unaccounted = total - self - free;
+        const int64_t unaccounted = static_cast<int64_t>(total) - static_cast<int64_t>(free) - static_cast<int64_t>(self);
 
         table_data.push_back({
             template_gpu,
@@ -867,7 +875,7 @@ void common_memory_breakdown_print(const struct llama_context * ctx) {
             std::to_string(mb.model / MiB),
             std::to_string(mb.context / MiB),
             std::to_string(mb.compute / MiB),
-            std::to_string(unaccounted / MiB)});
+            std::to_string(unaccounted / static_cast<int64_t>(MiB))});
     }
 
     // print memory breakdown for host:
@@ -917,7 +925,7 @@ void common_memory_breakdown_print(const struct llama_context * ctx) {
         }
     }
     for (const auto & td : table_data) {
-        LOG_INF(td[0].c_str(),
+        LOG_TRC(td[0].c_str(),
             __func__, td[1].c_str(), td[2].c_str(), td[3].c_str(), td[4].c_str(), td[5].c_str(),
             td[6].c_str(), td[7].c_str(), td[8].c_str());
     }
diff --git a/common/hf-cache.cpp b/common/hf-cache.cpp
index ea5b2150de4..20f33e4c7f4 100644
--- a/common/hf-cache.cpp
+++ b/common/hf-cache.cpp
@@ -57,7 +57,7 @@ static fs::path get_cache_directory() {
 #ifndef _WIN32
         const struct passwd * pw = getpwuid(getuid());
 
-        if (pw->pw_dir && *pw->pw_dir) {
+        if (pw && pw->pw_dir && *pw->pw_dir) {
             return fs::path(pw->pw_dir) / ".cache" / "huggingface" / "hub";
         }
 #endif
diff --git a/common/jinja/caps.cpp b/common/jinja/caps.cpp
index ec207a53e85..ead864763e1 100644
--- a/common/jinja/caps.cpp
+++ b/common/jinja/caps.cpp
@@ -1,4 +1,3 @@
-#include "log.h"
 #include "value.h"
 #include "runtime.h"
 #include "caps.h"
diff --git a/common/jinja/runtime.h b/common/jinja/runtime.h
index 3ca5f1754fa..b6f4a6ab48e 100644
--- a/common/jinja/runtime.h
+++ b/common/jinja/runtime.h
@@ -106,10 +106,16 @@ struct statement {
     size_t pos; // position in source, for debugging
     virtual ~statement() = default;
     virtual std::string type() const { return "Statement"; }
+
     // execute_impl must be overridden by derived classes
-    virtual value execute_impl(context &) { throw std::runtime_error("cannot exec " + type()); }
+    virtual value execute_impl(context &) { throw_exec_error(); }
     // execute is the public method to execute a statement with error handling
     value execute(context &);
+
+private:
+    [[noreturn]] void throw_exec_error() const {
+        throw std::runtime_error("cannot exec " + type());
+    }
 };
 
 // Type Checking Utilities
@@ -143,7 +149,7 @@ struct program : public statement {
     program() = default;
     explicit program(statements && body) : body(std::move(body)) {}
     std::string type() const override { return "Program"; }
-    value execute_impl(context &) override {
+    [[noreturn]] value execute_impl(context &) override {
         throw std::runtime_error("Cannot execute program directly, use jinja::runtime instead");
     }
 };
@@ -195,7 +201,7 @@ struct break_statement : public statement {
         }
     };
 
-    value execute_impl(context &) override {
+    [[noreturn]] value execute_impl(context &) override {
         throw break_statement::signal();
     }
 };
@@ -209,7 +215,7 @@ struct continue_statement : public statement {
         }
     };
 
-    value execute_impl(context &) override {
+    [[noreturn]] value execute_impl(context &) override {
         throw continue_statement::signal();
     }
 };
@@ -509,7 +515,7 @@ struct slice_expression : public expression {
         chk_type<expression>(this->step_expr);
     }
     std::string type() const override { return "SliceExpression"; }
-    value execute_impl(context &) override {
+    [[noreturn]] value execute_impl(context &) override {
         throw std::runtime_error("must be handled by MemberExpression");
     }
 };
diff --git a/common/jinja/value.cpp b/common/jinja/value.cpp
index 8e86a715f5f..0b79098cd1e 100644
--- a/common/jinja/value.cpp
+++ b/common/jinja/value.cpp
@@ -590,6 +590,10 @@ static bool string_endswith(const std::string & str, const std::string & suffix)
     return str.compare(str.length() - suffix.length(), suffix.length(), suffix) == 0;
 }
 
+[[noreturn]] static value string_join_not_implemented(const func_args &) {
+    throw not_implemented_exception("String join builtin not implemented");
+}
+
 const func_builtins & value_string_t::get_builtins() const {
     static const func_builtins builtins = {
         {"default", default_value},
@@ -851,9 +855,7 @@ const func_builtins & value_string_t::get_builtins() const {
             res->val_str.mark_input_based_on(val_input->as_string());
             return res;
         }},
-        {"join", [](const func_args &) -> value {
-            throw not_implemented_exception("String join builtin not implemented");
-        }},
+        {"join", string_join_not_implemented},
     };
     return builtins;
 }
@@ -884,6 +886,9 @@ const func_builtins & value_bool_t::get_builtins() const {
     return builtins;
 }
 
+[[noreturn]] static value array_unique_not_implemented(const func_args &) {
+    throw not_implemented_exception("Array unique builtin not implemented");
+}
 
 const func_builtins & value_array_t::get_builtins() const {
     static const func_builtins builtins = {
@@ -1084,13 +1089,14 @@ const func_builtins & value_array_t::get_builtins() const {
             std::reverse(arr.begin(), arr.end());
             return is_val<value_tuple>(val) ? mk_val<value_tuple>(std::move(arr)) : mk_val<value_array>(std::move(arr));
         }},
-        {"unique", [](const func_args &) -> value {
-            throw not_implemented_exception("Array unique builtin not implemented");
-        }},
+        {"unique", array_unique_not_implemented},
     };
     return builtins;
 }
 
+[[noreturn]] static value object_join_not_implemented(const func_args &) {
+    throw not_implemented_exception("object join not implemented");
+}
 
 const func_builtins & value_object_t::get_builtins() const {
     if (!has_builtins) {
@@ -1183,9 +1189,7 @@ const func_builtins & value_object_t::get_builtins() const {
             });
             return result;
         }},
-        {"join", [](const func_args &) -> value {
-            throw not_implemented_exception("object join not implemented");
-        }},
+        {"join", object_join_not_implemented},
     };
     return builtins;
 }
diff --git a/common/jinja/value.h b/common/jinja/value.h
index 7d164588ad9..5cf85e4f544 100644
--- a/common/jinja/value.h
+++ b/common/jinja/value.h
@@ -129,27 +129,25 @@ struct value_t {
     // Note: only for debugging and error reporting purposes
     virtual std::string type() const { return ""; }
 
-    virtual int64_t as_int() const { throw std::runtime_error(type() + " is not an int value"); }
-    virtual double as_float() const { throw std::runtime_error(type() + " is not a float value"); }
-    virtual string as_string() const { throw std::runtime_error(type() + " is not a string value"); }
-    virtual bool as_bool() const { throw std::runtime_error(type() + " is not a bool value"); }
-    virtual const std::vector<value> & as_array() const { throw std::runtime_error(type() + " is not an array value"); }
-    virtual const std::vector<std::pair<value, value>> & as_ordered_object() const { throw std::runtime_error(type() + " is not an object value"); }
-    virtual value invoke(const func_args &) const { throw std::runtime_error(type() + " is not a function value"); }
+    virtual int64_t as_int() const { throw_type_error("is not an int value"); }
+    virtual double as_float() const { throw_type_error("is not a float value"); }
+    virtual string as_string() const { throw_type_error("is not a string value"); }
+    virtual bool as_bool() const { throw_type_error("is not a bool value"); }
+    virtual const std::vector<value> & as_array() const { throw_type_error("is not an array value"); }
+    virtual const std::vector<std::pair<value, value>> & as_ordered_object() const { throw_type_error("is not an object value"); }
+    virtual value invoke(const func_args &) const { throw_type_error("is not a function value"); }
     virtual bool is_none() const { return false; }
     virtual bool is_undefined() const { return false; }
-    virtual const func_builtins & get_builtins() const {
-        throw std::runtime_error("No builtins available for type " + type());
-    }
+    virtual const func_builtins & get_builtins() const { throw_type_error("has no builtins"); }
 
-    virtual bool has_key(const value &) { throw std::runtime_error(type() + " is not an object value"); }
-    virtual void insert(const value & /* key */, const value & /* val */) { throw std::runtime_error(type() + " is not an object value"); }
-    virtual value & at(const value & /* key */, value & /* default_val */) { throw std::runtime_error(type() + " is not an object value"); }
-    virtual value & at(const value & /* key */) { throw std::runtime_error(type() + " is not an object value"); }
-    virtual value & at(const std::string & /* key */, value & /* default_val */) { throw std::runtime_error(type() + " is not an object value"); }
-    virtual value & at(const std::string & /* key */) { throw std::runtime_error(type() + " is not an object value"); }
-    virtual value & at(int64_t /* idx */, value & /* default_val */) { throw std::runtime_error(type() + " is not an array value"); }
-    virtual value & at(int64_t /* idx */) { throw std::runtime_error(type() + " is not an array value"); }
+    virtual bool has_key(const value &) { throw_type_error("is not an object value"); }
+    virtual void insert(const value & /* key */, const value & /* val */) { throw_type_error("is not an object value"); }
+    virtual value & at(const value & /* key */, value & /* default_val */) { throw_type_error("is not an object value"); }
+    virtual value & at(const value & /* key */) { throw_type_error("is not an object value"); }
+    virtual value & at(const std::string & /* key */, value & /* default_val */) { throw_type_error("is not an object value"); }
+    virtual value & at(const std::string & /* key */) { throw_type_error("is not an object value"); }
+    virtual value & at(int64_t /* idx */, value & /* default_val */) { throw_type_error("is not an array value"); }
+    virtual value & at(int64_t /* idx */) { throw_type_error("is not an array value"); }
 
     virtual bool is_numeric() const { return false; }
     virtual bool is_hashable() const { return false; }
@@ -163,6 +161,11 @@ struct value_t {
     // Note: only for debugging purposes
     virtual std::string as_repr() const { return as_string().str(); }
 
+private:
+    [[noreturn]] void throw_type_error(const char* expected) const {
+        throw std::runtime_error(type() + " " + expected);
+    }
+
 protected:
     virtual bool equivalent(const value_t &) const = 0;
     virtual bool nonequal(const value_t & other) const { return !equivalent(other); }
diff --git a/common/log.cpp b/common/log.cpp
index dec4ef5fc70..bd62616d8a1 100644
--- a/common/log.cpp
+++ b/common/log.cpp
@@ -49,7 +49,7 @@ enum common_log_col : int {
 };
 
 // disable colors by default
-static std::vector<const char *> g_col = {
+static const char* g_col[] = {
     "",
     "",
     "",
@@ -247,7 +247,6 @@ struct common_log {
 
             entries = std::move(new_entries);
         }
-
         cv.notify_one();
     }
 
@@ -265,7 +264,6 @@ struct common_log {
                 {
                     std::unique_lock<std::mutex> lock(mtx);
                     cv.wait(lock, [this]() { return head != tail; });
-
                     cur = entries[head];
 
                     head = (head + 1) % entries.size();
@@ -301,7 +299,6 @@ struct common_log {
 
                 tail = (tail + 1) % entries.size();
             }
-
             cv.notify_one();
         }
 
@@ -338,7 +335,7 @@ struct common_log {
             g_col[COMMON_LOG_COL_CYAN]    = LOG_COL_CYAN;
             g_col[COMMON_LOG_COL_WHITE]   = LOG_COL_WHITE;
         } else {
-            for (size_t i = 0; i < g_col.size(); i++) {
+            for (size_t i = 0; i < std::size(g_col); i++) {
                 g_col[i] = "";
             }
         }
@@ -368,14 +365,20 @@ struct common_log * common_log_init() {
 }
 
 struct common_log * common_log_main() {
-    static struct common_log log;
+    // We intentionally leak (i.e. do not delete) the logger singleton because
+    // common_log destructor called at DLL teardown phase will cause hanging on Windows.
+    // OS will release resources anyway so it should not be a significant issue,
+    // though this design may cause logs to be lost if not flushed before the program exits.
+    // Refer to https://github.com/ggml-org/llama.cpp/issues/22142 for details.
+    static struct common_log * log;
     static std::once_flag    init_flag;
     std::call_once(init_flag, [&]() {
+        log = new common_log;
         // Set default to auto-detect colors
-        log.set_colors(tty_can_use_colors());
+        log->set_colors(tty_can_use_colors());
     });
 
-    return &log;
+    return log;
 }
 
 void common_log_pause(struct common_log * log) {
@@ -432,10 +435,10 @@ void common_log_flush(struct common_log * log) {
 static int common_get_verbosity(enum ggml_log_level level) {
     switch (level) {
         case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG;
-        case GGML_LOG_LEVEL_INFO:  return LOG_LEVEL_INFO;
+        case GGML_LOG_LEVEL_INFO:  return LOG_LEVEL_TRACE;
         case GGML_LOG_LEVEL_WARN:  return LOG_LEVEL_WARN;
         case GGML_LOG_LEVEL_ERROR: return LOG_LEVEL_ERROR;
-        case GGML_LOG_LEVEL_CONT:  return LOG_LEVEL_INFO; // same as INFO
+        case GGML_LOG_LEVEL_CONT:  return LOG_LEVEL_TRACE;
         case GGML_LOG_LEVEL_NONE:
         default:
             return LOG_LEVEL_OUTPUT;
diff --git a/common/log.h b/common/log.h
index cf32ca185ca..45d82f4dde1 100644
--- a/common/log.h
+++ b/common/log.h
@@ -21,7 +21,8 @@
 #    define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
 #endif
 
-#define LOG_LEVEL_DEBUG  4
+#define LOG_LEVEL_DEBUG  5
+#define LOG_LEVEL_TRACE  4
 #define LOG_LEVEL_INFO   3
 #define LOG_LEVEL_WARN   2
 #define LOG_LEVEL_ERROR  1
@@ -49,7 +50,11 @@ void common_log_default_callback(enum ggml_log_level level, const char * text, v
 struct common_log;
 
 struct common_log * common_log_init();
-struct common_log * common_log_main(); // singleton, automatically destroys itself on exit
+
+// Singleton, intentionally leaked to avoid Windows teardown hangs.
+// Call common_log_flush() before exit if you want to ensure all logs are flushed.
+struct common_log * common_log_main();
+
 void                common_log_pause (struct common_log * log); // pause  the worker thread, not thread-safe
 void                common_log_resume(struct common_log * log); // resume the worker thread, not thread-safe
 void                common_log_free  (struct common_log * log);
@@ -107,13 +112,15 @@ void common_log_flush         (struct common_log * log);                    // f
 #define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity,        __VA_ARGS__)
 
 #define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_LEVEL_DEBUG,  __VA_ARGS__)
+#define LOG_TRC(...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  LOG_LEVEL_TRACE,  __VA_ARGS__)
 #define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  LOG_LEVEL_INFO,   __VA_ARGS__)
 #define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  LOG_LEVEL_WARN,   __VA_ARGS__)
 #define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, LOG_LEVEL_ERROR,  __VA_ARGS__)
 #define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  LOG_LEVEL_INFO,   __VA_ARGS__) // same as INFO
 
+#define LOG_DBGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, verbosity, __VA_ARGS__)
+#define LOG_TRCV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_TRACE, verbosity, __VA_ARGS__)
 #define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  verbosity, __VA_ARGS__)
 #define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  verbosity, __VA_ARGS__)
 #define LOG_ERRV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, verbosity, __VA_ARGS__)
-#define LOG_DBGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, verbosity, __VA_ARGS__)
 #define LOG_CNTV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  verbosity, __VA_ARGS__)
diff --git a/common/preset.cpp b/common/preset.cpp
index 57ccd000b5c..51ea984d8cd 100644
--- a/common/preset.cpp
+++ b/common/preset.cpp
@@ -43,7 +43,7 @@ static std::set<std::string> get_remote_preset_whitelist(const std::map<std::str
     for (const auto & it : key_to_opt) {
         const std::string & key = it.first;
         const common_arg & opt = it.second;
-        if (allowed_options.find(key) != allowed_options.end() || opt.is_sparam) {
+        if (allowed_options.find(key) != allowed_options.end() || opt.is_sampling) {
             allowed_keys.insert(key);
             // also add variant keys (args without leading dashes and env vars)
             for (const auto & arg : opt.get_args()) {
@@ -163,8 +163,13 @@ void common_preset::merge(const common_preset & other) {
     }
 }
 
-void common_preset::apply_to_params(common_params & params) const {
+void common_preset::apply_to_params(common_params & params, const std::set<std::string> & handled_keys) const {
     for (const auto & [opt, val] : options) {
+        if (!handled_keys.empty()) {
+            if (!opt.env || handled_keys.find(opt.env) == handled_keys.end()) {
+                continue;
+            }
+        }
         // apply each option to params
         if (opt.handler_string) {
             opt.handler_string(params, val);
diff --git a/common/preset.h b/common/preset.h
index 11ba6ef8124..06f829c3e5e 100644
--- a/common/preset.h
+++ b/common/preset.h
@@ -43,7 +43,8 @@ struct common_preset {
     void merge(const common_preset & other);
 
     // apply preset options to common_params
-    void apply_to_params(common_params & params) const;
+    // optionally specify handled_keys to only apply a subset of options (identified by their env), if empty, apply all options
+    void apply_to_params(common_params & params, const std::set<std::string> & handled_keys = std::set<std::string>()) const;
 };
 
 // interface for multiple presets in one file
diff --git a/common/reasoning-budget.cpp b/common/reasoning-budget.cpp
index cc408a68699..958c9cacf51 100644
--- a/common/reasoning-budget.cpp
+++ b/common/reasoning-budget.cpp
@@ -122,6 +122,20 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
             }
             break;
         case REASONING_BUDGET_DONE:
+            // Re-arm on a new start tag: some models emit multiple <think> blocks
+            // per response, and each should get a fresh budget window.
+            if (ctx->start_matcher.advance(token)) {
+                ctx->state = REASONING_BUDGET_COUNTING;
+                ctx->remaining = ctx->budget;
+                ctx->end_matcher.reset();
+                LOG_INF("reasoning-budget: re-activated on new start tag, budget=%d tokens\n", ctx->budget);
+
+                if (ctx->remaining <= 0) {
+                    ctx->state = REASONING_BUDGET_FORCING;
+                    ctx->force_pos = 0;
+                    LOG_INF("reasoning-budget: budget=0, forcing immediately\n");
+                }
+            }
             break;
     }
 }
@@ -157,22 +171,12 @@ static void common_reasoning_budget_reset(struct llama_sampler * smpl) {
     ctx->force_pos = 0;
 }
 
-// forward declaration for use in clone
 static struct llama_sampler * common_reasoning_budget_init_state(
         const struct llama_vocab * vocab, const std::vector<llama_token> & start_tokens,
         const std::vector<llama_token> & end_tokens, const std::vector<llama_token> & forced_tokens,
         int32_t budget, common_reasoning_budget_state initial_state);
 
-static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl) {
-    const auto * ctx = (const common_reasoning_budget_ctx *) smpl->ctx;
-    return common_reasoning_budget_init_state(
-        ctx->vocab,
-        ctx->start_matcher.tokens,
-        ctx->end_matcher.tokens,
-        ctx->forced_tokens,
-        ctx->budget,
-        ctx->state);
-}
+static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl);
 
 static void common_reasoning_budget_free(struct llama_sampler * smpl) {
     delete (common_reasoning_budget_ctx *) smpl->ctx;
@@ -191,6 +195,15 @@ static struct llama_sampler_i common_reasoning_budget_i = {
     /* .backend_set_input = */ nullptr,
 };
 
+static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl) {
+    const auto * ctx = (const common_reasoning_budget_ctx *) smpl->ctx;
+
+    return llama_sampler_init(
+        /* .iface = */ &common_reasoning_budget_i,
+        /* .ctx   = */ new common_reasoning_budget_ctx(*ctx)
+    );
+}
+
 static struct llama_sampler * common_reasoning_budget_init_state(
         const struct llama_vocab             * vocab,
         const std::vector<llama_token>       & start_tokens,
@@ -218,34 +231,6 @@ static struct llama_sampler * common_reasoning_budget_init_state(
     );
 }
 
-struct llama_sampler * common_reasoning_budget_init(
-        const struct llama_vocab       * vocab,
-        const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens,
-        const std::vector<llama_token> & forced_tokens,
-        int32_t                          budget,
-        const std::vector<llama_token> & prefill_tokens) {
-    // Determine initial state from prefill: COUNTING if the prefill begins with
-    // the start sequence but does not also contain the end sequence after it.
-    common_reasoning_budget_state initial_state = REASONING_BUDGET_IDLE;
-    if (!prefill_tokens.empty() && !start_tokens.empty() &&
-            prefill_tokens.size() >= start_tokens.size() &&
-            std::equal(start_tokens.begin(), start_tokens.end(), prefill_tokens.begin())) {
-        initial_state = REASONING_BUDGET_COUNTING;
-        // If the end sequence also follows the start in the prefill, reasoning
-        // was opened and immediately closed — stay IDLE.
-        if (!end_tokens.empty() &&
-                prefill_tokens.size() >= start_tokens.size() + end_tokens.size()) {
-            auto end_start = prefill_tokens.end() - (ptrdiff_t) end_tokens.size();
-            if (end_start >= prefill_tokens.begin() + (ptrdiff_t) start_tokens.size() &&
-                    std::equal(end_tokens.begin(), end_tokens.end(), end_start)) {
-                initial_state = REASONING_BUDGET_IDLE;
-            }
-        }
-    }
-    return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state);
-}
-
 struct llama_sampler * common_reasoning_budget_init(
         const struct llama_vocab       * vocab,
         const std::vector<llama_token> & start_tokens,
diff --git a/common/reasoning-budget.h b/common/reasoning-budget.h
index ee1a30ed3c1..ef37f46ee4d 100644
--- a/common/reasoning-budget.h
+++ b/common/reasoning-budget.h
@@ -29,10 +29,7 @@ enum common_reasoning_budget_state {
 //   end_tokens     - token sequence for natural deactivation
 //   forced_tokens  - token sequence forced when budget expires
 //   budget         - max tokens allowed in the reasoning block
-//   prefill_tokens - tokens already present in the prompt (generation prompt);
-//                    used to determine the initial state: COUNTING if they begin
-//                    with start_tokens (but don't also end with end_tokens),
-//                    IDLE otherwise. COUNTING with budget <= 0 is promoted to FORCING.
+//   initial_state  - initial state
 //
 struct llama_sampler * common_reasoning_budget_init(
         const struct llama_vocab       * vocab,
@@ -40,16 +37,6 @@ struct llama_sampler * common_reasoning_budget_init(
         const std::vector<llama_token> & end_tokens,
         const std::vector<llama_token> & forced_tokens,
         int32_t                          budget,
-        const std::vector<llama_token> & prefill_tokens = {});
-
-// Variant that takes an explicit initial state (used by tests and clone).
-// COUNTING with budget <= 0 is promoted to FORCING.
-struct llama_sampler * common_reasoning_budget_init(
-        const struct llama_vocab       * vocab,
-        const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens,
-        const std::vector<llama_token> & forced_tokens,
-        int32_t                          budget,
-        common_reasoning_budget_state    initial_state);
+        common_reasoning_budget_state    initial_state = REASONING_BUDGET_IDLE);
 
 common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl);
diff --git a/common/sampling.cpp b/common/sampling.cpp
index b2e6d8e8d89..5665d0a706c 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -260,32 +260,35 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
         }
     }
 
-    // Feed generation prompt tokens to the grammar sampler so it advances past
-    // tokens the template already placed in the prompt.
-    // Only applies to output-format and tool-call grammars; user-supplied grammars must not be prefilled.
+    // Compute prefill tokens from the generation prompt
     std::vector<llama_token> prefill_tokens;
-    if (!params.generation_prompt.empty() && common_grammar_needs_prefill(params.grammar)) {
+    if (!params.generation_prompt.empty()) {
         GGML_ASSERT(vocab != nullptr);
-        prefill_tokens = common_tokenize(vocab, params.generation_prompt, false, true);
-        if (!prefill_tokens.empty()) {
-            std::string first_token = common_token_to_piece(vocab, prefill_tokens[0], true);
-            if (std::isspace(first_token[0]) && !std::isspace(params.generation_prompt[0])) {
-                // Some tokenizers will add a space before the first special token, need to remove
-                prefill_tokens = std::vector<llama_token>(prefill_tokens.begin() + 1, prefill_tokens.end());
+        auto tokens = common_tokenize(vocab, params.generation_prompt, false, true);
+        for (size_t i = 0; i < tokens.size(); i++) {
+            std::string piece = common_token_to_piece(vocab, tokens[i], true);
+            if (i == 0 && std::isspace(piece[0]) && !std::isspace(params.generation_prompt[0])) {
+                // Some tokenizers will add a space before the first special token, need to exclude
+                continue;
             }
+            LOG_DBG("%s: prefill token: %d = %s\n", __func__, tokens[i], piece.c_str());
+            prefill_tokens.push_back(tokens[i]);
         }
+    }
 
-        if (grmr && !params.grammar_lazy) {
-            try {
-                for (const auto & token : prefill_tokens) {
-                    llama_sampler_accept(grmr, token);
-                    LOG_DBG("%s: accepted prefill token (%d)\n", __func__, token);
-                }
-            } catch (std::exception &e) {
-                LOG_ERR("%s: error initializing grammar sampler for grammar:\n%s\n\nGeneration prompt:\n'%s'\n", __func__,
-                    common_grammar_value(params.grammar).c_str(), params.generation_prompt.c_str());
-                throw e;
+    // Feed generation prompt tokens to the grammar sampler so it advances past
+    // tokens the template already placed in the prompt.
+    // Only applies to output-format and tool-call grammars; user-supplied grammars must not be prefilled.
+    if (grmr && !params.grammar_lazy && common_grammar_needs_prefill(params.grammar)) {
+        try {
+            for (const auto & token : prefill_tokens) {
+                llama_sampler_accept(grmr, token);
+                LOG_DBG("%s: grammar accepted prefill token (%d)\n", __func__, token);
             }
+        } catch (std::exception &e) {
+            LOG_ERR("%s: error initializing grammar sampler for grammar:\n%s\n\nGeneration prompt:\n'%s'\n", __func__,
+                common_grammar_value(params.grammar).c_str(), params.generation_prompt.c_str());
+            throw e;
         }
     }
 
@@ -296,8 +299,12 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
             params.reasoning_budget_start,
             params.reasoning_budget_end,
             params.reasoning_budget_forced,
-            params.reasoning_budget_tokens < 0 ? INT_MAX : params.reasoning_budget_tokens,
-            prefill_tokens);
+            params.reasoning_budget_tokens < 0 ? INT_MAX : params.reasoning_budget_tokens);
+
+        for (const auto & token : prefill_tokens) {
+            llama_sampler_accept(rbudget, token);
+            LOG_DBG("%s: reasoning-budget accepted prefill token (%d)\n", __func__, token);
+        }
     }
 
     if (params.has_logit_bias()) {
@@ -431,7 +438,7 @@ static bool grammar_should_apply(struct common_sampler * gsmpl) {
     return true;
 }
 
-void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
+void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool is_generated) {
     if (!gsmpl) {
         return;
     }
@@ -439,9 +446,11 @@ void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, boo
     const auto tm = gsmpl->tm();
 
     // grammar_should_apply() checks the reasoning budget state, so calculate this before we accept
-    accept_grammar = accept_grammar && grammar_should_apply(gsmpl);
+    const auto accept_grammar = is_generated && grammar_should_apply(gsmpl);
 
-    llama_sampler_accept(gsmpl->rbudget, token);
+    if (gsmpl->rbudget && is_generated) {
+        llama_sampler_accept(gsmpl->rbudget, token);
+    }
 
     if (gsmpl->grmr && accept_grammar) {
         llama_sampler_accept(gsmpl->grmr, token);
@@ -538,6 +547,8 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     auto & chain = gsmpl->chain;
     auto & cur_p = gsmpl->cur_p; // initialized by set_logits
 
+    gsmpl->set_logits(ctx, idx);
+
     // Check if a backend sampler has already sampled a token in which case we
     // return that token id directly.
     {
@@ -549,17 +560,17 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
             GGML_ASSERT(!gsmpl->grmr    && "using grammar in combination with backend sampling is not supported");
             GGML_ASSERT(!gsmpl->rbudget && "using reasoning budget in combination with backend sampling is not supported");
 
-            // TODO: simplify
-            gsmpl->cur.resize(1);
-            gsmpl->cur[0] = { id, 0.0f, 1.0f };
-            cur_p = { gsmpl->cur.data(), gsmpl->cur.size(), 0, true };
+            for (size_t i = 0; i < cur_p.size; ++i) {
+                if (cur_p.data[i].id == id) {
+                    cur_p.selected = i;
+                    break;
+                }
+            }
 
             return id;
         }
     }
 
-    gsmpl->set_logits(ctx, idx);
-
     // apply reasoning budget first
     llama_sampler_apply(rbudget, &cur_p);
 
diff --git a/common/sampling.h b/common/sampling.h
index 5b57ad65811..49506a00cd8 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -41,8 +41,8 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
 
 void common_sampler_free(struct common_sampler * gsmpl);
 
-// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar
-void                    common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar);
+// if is_generated is true, the token is accepted by the sampling chain, the reasoning budget sampler, and the grammar sampler
+void                    common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool is_generated);
 void                    common_sampler_reset (struct common_sampler * gsmpl);
 struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
 
diff --git a/common/speculative.cpp b/common/speculative.cpp
index 20e38bec46c..3488b9393c5 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -3,6 +3,7 @@
 #include "common.h"
 #include "ggml.h"
 #include "llama.h"
+#include "../src/llama-ext.h" // staging API: llama_set_embeddings_pre_norm / llama_get_embeddings_pre_norm_ith (used by MTP)
 #include "log.h"
 #include "ngram-cache.h"
 #include "ngram-map.h"
@@ -10,6 +11,7 @@
 #include "sampling.h"
 
 #include <algorithm>
+#include <cassert>
 #include <cstring>
 #include <iomanip>
 #include <map>
@@ -18,26 +20,16 @@
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  128
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
 
-const std::vector<enum common_speculative_type> common_speculative_types = {
-    COMMON_SPECULATIVE_TYPE_NONE,
-    COMMON_SPECULATIVE_TYPE_DRAFT,
-    COMMON_SPECULATIVE_TYPE_EAGLE3,
-    COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,
-    COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K,
-    COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V,
-    COMMON_SPECULATIVE_TYPE_NGRAM_MOD,
-    COMMON_SPECULATIVE_TYPE_NGRAM_CACHE
-};
-
-const std::map<std::string, enum common_speculative_type> common_speculative_type_from_name_map = {
+const std::map<std::string, common_speculative_type> common_speculative_type_from_name_map = {
     {"none",          COMMON_SPECULATIVE_TYPE_NONE},
-    {"draft",         COMMON_SPECULATIVE_TYPE_DRAFT},
-    {"eagle3",        COMMON_SPECULATIVE_TYPE_EAGLE3},
-    {"ngram_simple",  COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE},
-    {"ngram_map_k",   COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K},
-    {"ngram_map_k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V},
-    {"ngram_mod",     COMMON_SPECULATIVE_TYPE_NGRAM_MOD},
-    {"ngram_cache",   COMMON_SPECULATIVE_TYPE_NGRAM_CACHE}
+    {"draft-simple",  COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE},
+    {"draft-eagle3",  COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3},
+    {"draft-mtp",     COMMON_SPECULATIVE_TYPE_DRAFT_MTP},
+    {"ngram-simple",  COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE},
+    {"ngram-map-k",   COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K},
+    {"ngram-map-k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V},
+    {"ngram-mod",     COMMON_SPECULATIVE_TYPE_NGRAM_MOD},
+    {"ngram-cache",   COMMON_SPECULATIVE_TYPE_NGRAM_CACHE}
 };
 
 struct common_speculative_config {
@@ -61,18 +53,26 @@ static bool common_speculative_are_compatible(
     LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
 
     if (vocab_type_tgt != vocab_type_dft) {
-        LOG_DBG("%s: draft model vocab type must match target model to use speculation but ", __func__);
-        LOG_DBG("vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
+        LOG_WRN("%s: draft model vocab type must match target model to use speculation but "
+                "vocab_type_dft = %d while vocab_type_tgt = %d\n", __func__, vocab_type_dft, vocab_type_tgt);
+        return false;
+    }
+
+    if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
+        (llama_vocab_get_add_bos(vocab_tgt) && llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft))) {
+        LOG_WRN("%s: draft model bos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
+                __func__,
+                llama_vocab_get_add_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_dft),
+                llama_vocab_bos(vocab_tgt), llama_vocab_bos(vocab_dft));
         return false;
     }
 
-    if (
-        llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
-        llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
-        llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
-        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)
-    ) {
-        LOG_DBG("%s: draft model special tokens must match target model to use speculation\n", __func__);
+    if (llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
+        (llama_vocab_get_add_eos(vocab_tgt) && llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft))) {
+        LOG_WRN("%s: draft model eos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
+                __func__,
+                llama_vocab_get_add_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_dft),
+                llama_vocab_eos(vocab_tgt), llama_vocab_eos(vocab_dft));
         return false;
     }
 
@@ -107,12 +107,16 @@ static bool common_speculative_are_compatible(
     return true;
 }
 
+using common_speculative_draft_params_vec = std::vector<common_speculative_draft_params>;
+
 // state of an implementation of speculative decoding
 //
 // each implementation has a unique type and a state that is implementation-specific
-// in a subclass of common_speculative_state
-struct common_speculative_state {
-    const enum common_speculative_type type;
+// in a subclass of common_speculative_impl
+struct common_speculative_impl {
+    const common_speculative_type type;
+
+    uint32_t n_seq;
 
     size_t n_call_begin  = 0; // number of times this implementation was called for refresh.
     size_t n_call_draft  = 0; // number of times this implementation was called for generation.
@@ -130,64 +134,37 @@ struct common_speculative_state {
     int64_t t_draft_us  = 0; // total time spent in generating drafts in this implementation in microseconds.
     int64_t t_accept_us = 0; // total time spent in accumulation of this implementation in microseconds.
 
-    common_speculative_state(enum common_speculative_type type) : type(type) {}
-
-    virtual ~common_speculative_state() = default;
-
-    virtual void begin(const llama_tokens & prompt) = 0;
-
-    virtual void draft(
-            const common_params_speculative & params,
-            const llama_tokens & prompt_tgt,
-            llama_token id_last,
-            llama_tokens & result) = 0;
+    common_speculative_impl(common_speculative_type type, uint32_t n_seq) : type(type), n_seq(n_seq) {}
 
-    virtual void accept(uint16_t n_accepted) = 0;
-};
+    virtual ~common_speculative_impl() = default;
 
-struct common_speculative_checkpoint {
-    llama_pos pos_min  = 0;
-    llama_pos pos_max  = 0;
+    virtual void begin(llama_seq_id seq_id, const llama_tokens & prompt) = 0;
 
-    int64_t   n_tokens = 0;
+    virtual bool process(const llama_batch & batch) = 0;
 
-    std::vector<uint8_t> data;
+    virtual void draft(common_speculative_draft_params_vec & dparams) = 0;
 
-    size_t size() const {
-        return data.size();
-    }
+    virtual void accept(llama_seq_id seq_id, uint16_t n_accepted) = 0;
 
-    size_t ckpt_size   = 0;
+    // true if this implementation requires the target context to extract embeddings
+    virtual bool need_embd() const = 0;
 };
 
-struct common_speculative_state_draft : public common_speculative_state {
-    llama_context * ctx_tgt; // only used for retokenizing from ctx_dft
-    llama_context * ctx_dft;
-
-    bool use_ckpt = false;
-    struct common_speculative_checkpoint ckpt;
+struct common_speculative_impl_draft_simple : public common_speculative_impl {
+    common_params_speculative_draft params;
 
-    common_sampler * smpl;
+    llama_batch batch;
 
-    llama_batch  batch;
-    llama_tokens prompt_dft;
+    std::vector<common_sampler_ptr> smpls;
 
-    bool vocab_cmpt = true; // whether retokenization is needed
-    std::unordered_map<std::string, std::string> vocab_map;
-
-    common_speculative_state_draft(
-            enum common_speculative_type type,
-            llama_context * ctx_tgt,
-            llama_context * ctx_dft,
-            const std::vector<std::pair<std::string, std::string>> & replacements,
-            bool use_ckpt)
-        : common_speculative_state(type)
-        , ctx_tgt(ctx_tgt)
-        , ctx_dft(ctx_dft)
-        , use_ckpt(use_ckpt)
+    common_speculative_impl_draft_simple(const common_params_speculative & params, uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, n_seq)
+        , params(params.draft)
     {
+        auto * ctx_dft = this->params.ctx_dft;
+        auto * ctx_tgt = this->params.ctx_tgt;
+
         batch = llama_batch_init(llama_n_batch(ctx_dft), 0, 1);
-        smpl = nullptr;
 
         // TODO: optimize or pass from outside?
         // {
@@ -205,7 +182,9 @@ struct common_speculative_state_draft : public common_speculative_state {
         //
         //     result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
         // }
-        {
+
+        smpls.resize(n_seq);
+        for (auto & smpl : smpls) {
             common_params_sampling params;
             params.no_perf = false;
             params.top_k = 10;
@@ -213,451 +192,655 @@ struct common_speculative_state_draft : public common_speculative_state {
                 COMMON_SAMPLER_TYPE_TOP_K,
             };
 
-            smpl = common_sampler_init(llama_get_model(ctx_dft), params);
+            smpl.reset(common_sampler_init(llama_get_model(ctx_dft), params));
         }
 
-        vocab_cmpt = common_speculative_are_compatible(llama_get_model(ctx_tgt), llama_get_model(ctx_dft));
-        LOG_DBG("vocab_cmpt = %d\n", vocab_cmpt);
+        const bool vocab_cmpt = common_speculative_are_compatible(llama_get_model(ctx_tgt), llama_get_model(ctx_dft));
+        LOG_DBG("%s: vocab_cmpt = %d\n", __func__, vocab_cmpt);
 
         if (!vocab_cmpt) {
-            LOG_WRN("the target and draft vocabs are not compatible - tokens will be translated between the two\n");
+            LOG_ERR("%s: the target and draft vocabs are not compatible\n", __func__);
 
-            for (const auto & pair : replacements) {
-                vocab_map[pair.first] = pair.second;
-            }
+            throw std::runtime_error("draft model vocab type must match target model to use speculation");
         }
-    }
 
-    ~common_speculative_state_draft() override {
-        llama_perf_context_print(ctx_dft);
+        if (n_seq != llama_n_seq_max(ctx_dft)) {
+            LOG_ERR("%s: n_seq mismatch: %d != %d\n", __func__, n_seq, llama_n_seq_max(ctx_dft));
 
-        llama_free(ctx_dft);
-
-        common_sampler_free(smpl);
+            throw std::runtime_error("the draft model number of sequences is incompatible with the speculative n_seq");
+        }
+    }
 
+    ~common_speculative_impl_draft_simple() override {
         llama_batch_free(batch);
     }
 
-    void begin(const llama_tokens & prompt) override {
-        if (use_ckpt && ckpt.size() > 0) {
-            // delete checkpoint
-            LOG_DBG("%s: delete checkpoint, prompt.size=%zu, pos_min=%d, pos_max=%d, n_tokens=%" PRId64 ", size=%.3f MiB\n",
-                    __func__, prompt.size(), ckpt.pos_min, ckpt.pos_max, ckpt.n_tokens, (float) ckpt.data.size() / 1024 / 1024);
-            ckpt.pos_min   = 0;
-            ckpt.pos_max   = 0;
-            ckpt.n_tokens  = 0;
-            ckpt.ckpt_size = 0;
-            ckpt.data.clear();
-        }
+    void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
+        // noop
     }
 
-    size_t draft_create_checkpoint(int n_tokens_prompt, int n_tokens_batch) {
-        int slot_id = 0;
-        const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx_dft, slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+    bool process(const llama_batch & batch) override {
+        auto * ctx_dft = params.ctx_dft;
 
-        ckpt.pos_min  = llama_memory_seq_pos_min(llama_get_memory(ctx_dft), slot_id);
-        ckpt.pos_max  = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), slot_id);
-        ckpt.n_tokens = n_tokens_prompt - n_tokens_batch;
-        ckpt.data.resize(checkpoint_size);
+        const int ret = llama_decode(ctx_dft, batch);
 
-        const size_t n = llama_state_seq_get_data_ext(ctx_dft, ckpt.data.data(), checkpoint_size, slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-        if (n != checkpoint_size) {
-            GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", checkpoint_size, n);
+        if (ret != 0) {
+            LOG_ERR("%s: failed to decode draft batch, ret = %d\n", __func__, ret);
+
+            return false;
         }
 
-        LOG_DBG("%s: pos_min = %d, pos_max = %d, size = %.3f MiB\n", __func__,
-                ckpt.pos_min, ckpt.pos_max, (float) ckpt.data.size() / 1024 / 1024);
-        return n;
+        return true;
     }
 
-    size_t draft_restore_checkpoint(size_t ckpt_size_part_expected) {
-        int slot_id = 0;
-        LOG_DBG("%s: pos_min = %d, pos_max = %d\n", __func__, ckpt.pos_min, ckpt.pos_max);
-        const size_t n = llama_state_seq_set_data_ext(ctx_dft, ckpt.data.data(), ckpt.size(), slot_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-        if (n != ckpt_size_part_expected) {
-            GGML_ABORT("%s: failed to restore context checkpoint (pos_min=%d, pos_max=%d, size=%zu, get_data_ext->%zu, set_data_ext->%zu",
-                        __func__, ckpt.pos_min, ckpt.pos_max, ckpt.size(), ckpt_size_part_expected, n);
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        auto & ctx_dft = params.ctx_dft;
+
+        common_batch_clear(batch);
+
+        // keep track of which sequences are still drafting
+        int n_drafting = 0;
+        std::vector<bool> drafting(n_seq);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+
+            if (!dp.drafting) {
+                continue;
+            }
+
+            n_drafting++;
+            drafting[seq_id] = true;
+            common_sampler_reset(smpls[seq_id].get());
+
+            common_batch_add(batch, dp.id_last, dp.n_past, { seq_id }, true);
         }
-        llama_memory_seq_rm(llama_get_memory(ctx_dft), slot_id, ckpt.pos_max + 1, -1);
 
-        return n;
-    }
+        int ret = llama_decode(ctx_dft, batch);
+        if (ret != 0) {
+            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+            return;
+        }
 
-    void draft(
-            const common_params_speculative & params,
-            const llama_tokens & prompt_tgt,
-            llama_token id_last,
-            llama_tokens & result) override {
-        auto * spec = this;
+        int i = 0;
 
-        auto & batch      = spec->batch;
-        auto & ctx_tgt    = spec->ctx_tgt;
-        auto & ctx_dft    = spec->ctx_dft;
-        auto & smpl       = spec->smpl;
-        auto & prompt_dft = spec->prompt_dft;
+        while (n_drafting > 0) {
+            int i_batch = 0;
 
-        auto * mem_dft = llama_get_memory(ctx_dft);
+            common_batch_clear(batch);
+
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                if (!drafting[seq_id]) {
+                    continue;
+                }
 
-        int reuse_i = 0; // index of part to be reused in prompt_dft
-        int reuse_n = 0; // length of part to be reused in prompt_dft
+                auto * smpl = smpls[seq_id].get();
 
-        const int n_ctx = llama_n_ctx(ctx_dft) - params.n_max;
+                common_sampler_sample(smpl, ctx_dft, i_batch, true);
+                ++i_batch;
 
-        llama_tokens prompt_cnv;
-        if (!spec->vocab_cmpt) {
-            std::string text;
+                const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
-            text = common_detokenize(ctx_tgt, prompt_tgt, true);
-            text = replace_to_dft(text);
+                for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                            seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
+                            common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                }
 
-            LOG_DBG("%s: main->draft detokenized string: '%s'\n", __func__, text.c_str());
+                // add drafted token for each sequence
+                const llama_token id = cur_p->data[0].id;
 
-            prompt_cnv = common_tokenize(ctx_dft, text, false, true);
+                // only collect very high-confidence draft tokens
+                if (cur_p->data[0].p < params.p_min) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
 
-            // convert id_last to draft vocab. llama_detokenize is called directly to avoid an allocation
-            const auto * model_tgt = llama_get_model(ctx_tgt);
-            const auto * vocab_tgt = llama_model_get_vocab(model_tgt);
+                    continue;
+                }
 
-            int32_t n_chars = llama_detokenize(vocab_tgt, &id_last, 1, nullptr, 0, false, false);
-            GGML_ASSERT(n_chars < 0 && "failed to detokenize id_last");
+                common_sampler_accept(smpl, id, true);
 
-            text.resize(-n_chars);
-            llama_detokenize(vocab_tgt, &id_last, 1, text.data(), text.size(), false, false);
-            text = replace_to_dft(text);
+                auto & dp = dparams.at(seq_id);
+                auto & result = *dp.result;
 
-            LOG_DBG("main->draft detokenized id_last(%d): '%s'\n", id_last, text.c_str());
-            id_last = common_tokenize(ctx_dft, text, false, true)[0];
-        }
+                result.push_back(id);
 
-        const llama_tokens & prompt_cur = spec->vocab_cmpt ? prompt_tgt : prompt_cnv;
+                if ((params.n_max <= (int) result.size()) ||
+                    (dp.n_max > 0 && dp.n_max <= (int) result.size())) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+                    continue;
+                }
 
-        const int i_start = std::max<int>(0, (int) prompt_cur.size() - n_ctx);
+                common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
+            }
 
-        // reuse as much as possible from the old draft context
-        // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
-        for (int i = 0; i < (int) prompt_dft.size(); ++i) {
-            int cur = 0;
-            while (i_start + cur < (int) prompt_cur.size() &&
-                    i       + cur < (int) prompt_dft.size() &&
-                    prompt_cur[i_start + cur] == prompt_dft[i + cur]) {
-                cur++;
+            if (batch.n_tokens == 0) {
+                break;
             }
 
-            if ((cur >= 256 || n_ctx >= (int) prompt_cur.size()) && cur > reuse_n) {
-                reuse_i = i;
-                reuse_n = cur;
+            // evaluate the drafted tokens on the draft model
+            ret = llama_decode(ctx_dft, batch);
+            if (ret != 0) {
+                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                break;
             }
+
+            ++i;
         }
 
-        LOG_DBG("%s: reuse_i = %d, reuse_n = %d, #prompt_dft = %zu, #prompt_cur = %zu\n",
-                __func__, reuse_i, reuse_n, prompt_dft.size(), prompt_cur.size());
-        if (use_ckpt && ckpt.ckpt_size == 0 && reuse_n > 0) {
-            LOG_DBG("%s: no checkpoint available, no reuse, (reuse_i=%d, reuse_n=%d) -> (0, 0)\n",
-                    __func__, reuse_i, reuse_n);
-            reuse_i = 0;
-            reuse_n = 0;
+        for (auto & dp : dparams) {
+            if (!dp.drafting) {
+                continue;
+            }
+
+            if (dp.result->size() < (size_t) params.n_min) {
+                dp.result->clear();
+            }
         }
+    }
 
-        result.clear();
-        result.reserve(params.n_max);
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+        // noop
+    }
 
-        bool needs_ckpt = use_ckpt && prompt_dft.size() > 0;
-        if (reuse_n == 0 || (use_ckpt && reuse_i > 0)) {
-            llama_memory_clear(mem_dft, false);
-            prompt_dft.clear();
-        } else {
-            // this happens when a previous draft has been discarded (for example, due to being too small), but the
-            // target model agreed with it. in this case, we simply pass back the previous results to save compute
-            if (reuse_i + reuse_n < (int64_t) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) {
-                for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) {
-                    result.push_back(prompt_dft[i]);
-
-                    if (params.n_max <= (int) result.size()) {
-                        break;
-                    }
-                }
+    bool need_embd() const override {
+        return false;
+    }
+};
 
-                return;
-            }
+struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
+    //common_params_speculative_eagle3 params;
 
-            bool do_restore = false;
-            if (prompt_dft.size() > prompt_cur.size() && reuse_i + reuse_n < (int64_t) prompt_dft.size()) {
-                // This can happen after a partial acceptance (speculative decoding with checkpoints)
-                LOG_DBG("%s: #prompt_dft=%zu, #prompt_cur=%zu, shorten draft\n",
-                        __func__, prompt_dft.size(), prompt_cur.size());
-                prompt_dft.resize(prompt_cur.size());
-                do_restore = true;
-            }
+    common_speculative_impl_draft_eagle3(const common_params_speculative & /*params*/, uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq) {}
 
-            if (reuse_i > 0) {
-                bool is_removed = llama_memory_seq_rm (mem_dft, 0, 0, reuse_i);
-                if (!is_removed) {
-                    LOG_ERR("%s: llama_memory_seq_rm failed, reuse_i=%d\n", __func__, reuse_i);
-                }
-                llama_memory_seq_add(mem_dft, 0, reuse_i, -1, -reuse_i);
+    void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
+        // noop
+    }
 
-                prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i);
-            }
+    bool process(const llama_batch & /*batch*/) override {
+        // TODO: implement
+        return true;
+    }
 
-            if (reuse_n < (int) prompt_dft.size() || do_restore) {
-                if (use_ckpt) {
-                    if (ckpt.n_tokens > (int64_t) prompt_dft.size()) {
-                        LOG_INF("%s: checkpoint is too large, prompt_tgt.size=%zu, ckpt.n_tokens=%" PRId64 ", reuse_n=%d, prompt_dft.size=%zu\n",
-                                __func__, prompt_tgt.size(), ckpt.n_tokens, reuse_n, prompt_dft.size());
-                    }
-                    draft_restore_checkpoint(ckpt.ckpt_size);
-                    reuse_n = ckpt.n_tokens;
-                    prompt_dft.resize(reuse_n);
-                    needs_ckpt = false;
-                } else {
-                    bool is_removed = llama_memory_seq_rm (mem_dft, 0, reuse_n, -1);
-                    if (!is_removed) {
-                        LOG_ERR("%s: llama_memory_seq_rm failed, reuse_n=%d, prompt_dft.size=%zu\n",
-                                __func__, reuse_n, prompt_dft.size());
+    void draft(common_speculative_draft_params_vec & /*dparams*/) override {
+        // TODO: implement
+    }
+
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+        // noop
+    }
+
+    bool need_embd() const override {
+        return false;
+    }
+};
+
+struct common_speculative_state_draft_mtp : public common_speculative_impl {
+    common_params_speculative_draft params; // reuses the draft-model params slot (ctx_tgt/ctx_dft)
+
+    llama_batch batch;
+
+    std::vector<common_sampler_ptr> smpls;
+
+    int32_t n_embd = 0;
+
+    // Per-sequence cross-batch carryover: pair (h_p, x_{p+1}) at MTP pos p+1.
+    // The last h-row of one process() call needs the first token of the NEXT
+    // call to pair with, so it's stashed here until that next call fires.
+    std::vector<std::vector<float>> pending_h;   // [n_seq][n_embd]
+
+    std::vector<int32_t> i_batch_beg;
+    std::vector<int32_t> i_batch_end;
+
+    // Hidden rows from the most recent target verification batch, grouped by seq.
+    // Row 0 corresponds to the sampled token, row N to the Nth accepted draft token.
+    std::vector<std::vector<float>> verify_h;
+    std::vector<int32_t> verify_h_rows;
+
+    // Per-seq draft length from the last draft() call, used in accept() to
+    // roll back ctx_dft's recurrent state past the AR draft's redundant
+    // pre-advancement before process() mirrored the verify batch.
+    std::vector<uint16_t> last_n_drafted;
+
+    common_speculative_state_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
+        , params(params.draft)
+    {
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+        GGML_ASSERT(ctx_tgt && ctx_dft && "MTP requires ctx_tgt and ctx_dft to be set");
+
+        n_embd = llama_model_n_embd(llama_get_model(ctx_dft));
+
+        const int32_t n_b = (int32_t) llama_n_batch(ctx_dft);
+        batch = llama_batch_init(/*n_tokens=*/ n_b, /*embd=*/ n_embd, /*n_seq_max=*/ 1);
+        // llama_batch_init allocates only one of token/embd; MTP needs both.
+        // TODO: fix, how to call without malloc
+        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_b);
+
+        smpls.resize(n_seq);
+        for (auto & s : smpls) {
+            common_params_sampling sparams;
+            sparams.no_perf  = false;
+            sparams.top_k    = 1; // TODO: re-enable top_k == 10 and utilize `p_min` spec param
+            sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K };
+            s.reset(common_sampler_init(llama_get_model(ctx_dft), sparams));
+        }
+
+        llama_set_embeddings_pre_norm(ctx_tgt, true);
+        llama_set_embeddings_pre_norm(ctx_dft, true);
+
+        pending_h.assign(n_seq, std::vector<float>(n_embd, 0.0f));
+
+        i_batch_beg.assign(n_seq, -1);
+        i_batch_end.assign(n_seq, -1);
+
+        verify_h.assign(n_seq, {});
+        verify_h_rows.assign(n_seq, 0);
+
+        last_n_drafted.assign(n_seq, 0);
+    }
+
+    ~common_speculative_state_draft_mtp() override {
+        if (batch.token != nullptr) {
+            free(batch.token);
+            batch.token = nullptr;
+        }
+        llama_batch_free(batch);
+    }
+
+    void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
+        const int32_t N = (int32_t) prompt.size();
+        if (N <= 0) {
+            return;
+        }
+        auto * ctx_dft = this->params.ctx_dft;
+        const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
+        if (pos_max < N - 1) {
+            LOG_WRN("%s: ctx_dft pos_max=%d < N-1=%d — "
+                    "process() hook may not have run on every prefill ubatch "
+                    "(need_embd / logits=1 on every prompt position?). "
+                    "Drafts may degrade.\n",
+                    __func__, (int) pos_max, N - 1);
+        }
+    }
+
+    bool process(const llama_batch & batch_in) override {
+        if (batch_in.n_tokens <= 0) {
+            return true;
+        }
+
+        // TODO: how to make it work with vision tokens?
+        if (batch_in.token == nullptr || batch_in.embd != nullptr) {
+            return true;
+        }
+
+        const int32_t n_tokens = batch_in.n_tokens;
+
+        // remember the frist and last batch index for each sequence
+        std::fill(i_batch_beg.begin(), i_batch_beg.end(), -1);
+        std::fill(i_batch_end.begin(), i_batch_end.end(), -1);
+
+        for (int k = 0; k < n_tokens; ++k) {
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                GGML_ASSERT(batch_in.n_seq_id[k] == 1);
+
+                if (batch_in.seq_id[k][0] == seq_id) {
+                    i_batch_end[seq_id] = k;
+                    if (i_batch_beg[seq_id] < 0) {
+                        i_batch_beg[seq_id] = k;
                     }
-                    prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end());
                 }
             }
         }
 
-        if (needs_ckpt) {
-            ckpt.ckpt_size = draft_create_checkpoint(prompt_dft.size(), batch.n_tokens);
-        }
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+
+        const size_t row_bytes = (size_t) n_embd * sizeof(float);
 
-        // prepare a batch to evaluate any new tokens in the prompt
         common_batch_clear(batch);
 
-        for (size_t i = i_start + reuse_n; i < prompt_cur.size(); ++i) {
-            //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_cur[i]);
-            common_batch_add(batch, prompt_cur[i], i - i_start, { 0 }, false);
+        for (int k = 0; k < n_tokens; ++k) {
+            common_batch_add(batch, batch_in.token[k], batch_in.pos[k], { batch_in.seq_id[k][0] }, 0);
+        }
+
+        // shift the tgt embeddings to the right by one position
+        // assumes that the tokens in the batch are sequential for each sequence
+        // i.e. we cannot have seq_id like this: [0, 0, 0, 1, 1, 0, 1, 1]
+        //                                                       ^--- this is a problem
+        // TODO:this is generally true, but would be nice to assert it
+        {
+            const float * h_tgt = llama_get_embeddings_pre_norm(ctx_tgt);
+            std::memcpy(batch.embd + (size_t) 1 * n_embd, h_tgt, row_bytes * (n_tokens-1));
+
+            //{
+            //    // string with seq_ids in the batch
+            //    std::stringstream ss;
+            //    for (int i = 0; i < n_tokens; ++i) {
+            //        ss << batch_in.seq_id[i][0] << ",";
+            //    }
+            //    LOG_WRN("%s: batch_in.seq_id = %s\n", __func__, ss.str().c_str());
+            //}
+        }
+
+        // fill the pending embeddings from a previous run
+        auto set_h = [&](int idx, const float * h_row) {
+            std::memcpy(batch.embd + (size_t) idx * n_embd, h_row, row_bytes);
+        };
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            if (i_batch_beg[seq_id] < 0) {
+                continue;
+            }
+
+            set_h(i_batch_beg[seq_id], pending_h[seq_id].data());
+        }
 
-            prompt_dft.push_back(prompt_cur[i]);
+        const int32_t rc = llama_decode(ctx_dft, batch);
+        if (rc != 0) {
+            LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (pos=%d)\n", __func__, (int) rc, (int) batch_in.pos[0]);
+            return false;
         }
 
-        // we should rarely end-up here during normal decoding
-        if (batch.n_tokens > 0) {
-            //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            if (i_batch_end[seq_id] < 0) {
+                continue;
+            }
+
+            const int32_t n_rows = i_batch_end[seq_id] - i_batch_beg[seq_id] + 1;
+            verify_h_rows[seq_id] = n_rows;
+            verify_h[seq_id].resize((size_t) n_rows * n_embd);
 
-            int ret = llama_decode(ctx_dft, batch);
-            if (ret != 0 && ret != 1) {
-                LOG_WRN("%s: llama_decode returned %d, prompt_cur.size=%zu\n",
-                        __func__, ret, prompt_cur.size());
+            for (int32_t i = 0; i < n_rows; ++i) {
+                const float * h = llama_get_embeddings_pre_norm_ith(ctx_tgt, i_batch_beg[seq_id] + i);
+                std::memcpy(verify_h[seq_id].data() + (size_t) i * n_embd, h, row_bytes);
             }
+
+            std::memcpy(pending_h[seq_id].data(),
+                    verify_h[seq_id].data() + (size_t) (n_rows - 1) * n_embd, row_bytes);
         }
 
-        const llama_pos n_past = prompt_dft.size();
+        return true;
+    }
 
-        LOG_DBG("%s: n_past = %d\n", __func__, n_past);
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        auto & ctx_dft = params.ctx_dft;
 
         common_batch_clear(batch);
-        common_batch_add  (batch, id_last, n_past, { 0 }, true);
 
-        prompt_dft.push_back(id_last);
+        // keep track of which sequences are still drafting
+        int n_drafting = 0;
+        std::vector<bool> drafting(n_seq);
+
+        const float * h_row = nullptr;
+        const size_t row_bytes = (size_t) n_embd * sizeof(float);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+
+            if (!dp.drafting) {
+                continue;
+            }
+
+            n_drafting++;
+            drafting[seq_id] = true;
+            common_sampler_reset(smpls[seq_id].get());
+
+            common_batch_add(batch, dp.id_last, dp.n_past, { seq_id }, true);
 
-        LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str());
+            h_row = pending_h[seq_id].data();
+            std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
+        }
 
         int ret = llama_decode(ctx_dft, batch);
-        if (ret != 0 && ret != 1) {
-            LOG_WRN("%s: llama_decode returned %d, prompt_cur.size=%zu, prompt_dft.size=%zu\n",
-                    __func__, ret, prompt_cur.size(), prompt_dft.size());
+        if (ret != 0) {
+            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+            return;
         }
 
-        common_sampler_reset(smpl);
+        int i = 0;
+
+        while (n_drafting > 0) {
+            int i_batch = 0;
 
-        // sample n_draft tokens from the draft model
-        for (int i = 0; i < params.n_max; ++i) {
             common_batch_clear(batch);
 
-            common_sampler_sample(smpl, ctx_dft, 0, true);
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                if (!drafting[seq_id]) {
+                    continue;
+                }
 
-            const auto * cur_p = common_sampler_get_candidates(smpl, true);
+                auto * smpl = smpls[seq_id].get();
 
-            for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
-                LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
-                        k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
-            }
+                common_sampler_sample(smpl, ctx_dft, i_batch, true);
+                h_row = llama_get_embeddings_pre_norm_ith(ctx_dft, i_batch);
+                ++i_batch;
 
-            // add drafted token for each sequence
-            const llama_token id = cur_p->data[0].id;
+                const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
-            common_sampler_accept(smpl, id, true);
+                for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                            seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
+                            common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                }
 
-            result.push_back(id);
+                // add drafted token for each sequence
+                const llama_token id = cur_p->data[0].id;
 
-            if (params.n_max <= (int) result.size()) {
-                break;
+                common_sampler_accept(smpl, id, true);
+
+                auto & dp = dparams.at(seq_id);
+                auto & result = *dp.result;
+
+                result.push_back(id);
+
+                if (params.n_max <= (int) result.size()) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+                    continue;
+                }
+
+                common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
+                std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
             }
 
-            // only collect very high-confidence draft tokens
-            if (cur_p->data[0].p < params.p_min) {
+            if (batch.n_tokens == 0) {
                 break;
             }
 
-            common_batch_add(batch, id, n_past + i + 1, { 0 }, true);
-
             // evaluate the drafted tokens on the draft model
             ret = llama_decode(ctx_dft, batch);
             if (ret != 0) {
-                LOG_WRN("%s: llama_decode[%d] returned %d, prompt_cur.size=%zu, prompt_dft.size=%zu\n",
-                        __func__, i, ret, prompt_cur.size(), prompt_dft.size());
+                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                break;
             }
 
-            prompt_dft.push_back(id);
+            ++i;
         }
 
-        if (!spec->vocab_cmpt) {
-            std::string detokenized = common_detokenize(ctx_dft, result, true);
-            detokenized = replace_to_tgt(detokenized);
-            LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str());
-            result = common_tokenize(ctx_tgt, detokenized, false, true);
-            if (result.size() > (size_t)params.n_max) {
-                result.resize(params.n_max);
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
             }
-        }
-    }
 
-    void accept(uint16_t n_accepted) override {
-        // noop
-        GGML_UNUSED(n_accepted);
-    }
-
-    std::string replace_to_dft(const std::string & input) const {
-        std::string result = input;
-
-        for (const auto & pair : this->vocab_map) {
-            size_t pos = result.find(pair.first);
-            while (pos != std::string::npos) {
-                result.replace(pos, pair.first.length(), pair.second);
-                pos = result.find(pair.first, pos + pair.second.length());
+            if (dp.result->size() < (size_t) params.n_min) {
+                dp.result->clear();
             }
-        }
 
-        return result;
+            last_n_drafted[seq_id] = (uint16_t) dp.result->size();
+        }
     }
 
-    std::string replace_to_tgt(const std::string & input) const {
-        std::string result = input;
+    void accept(llama_seq_id seq_id, uint16_t n_accepted) override {
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return;
+        }
 
-        for (const auto & pair : this->vocab_map) {
-            size_t pos = result.find(pair.second);
-            while (pos != std::string::npos) {
-                result.replace(pos, pair.second.length(), pair.first);
-                pos = result.find(pair.second, pos + pair.first.length());
-            }
+        const int32_t n_rows = verify_h_rows[seq_id];
+        if (n_rows <= 0) {
+            return;
         }
 
-        return result;
+        const int32_t i_h = std::min<int32_t>(n_accepted, n_rows - 1);
+        const size_t row_bytes = (size_t) n_embd * sizeof(float);
+        std::memcpy(pending_h[seq_id].data(), verify_h[seq_id].data() + (size_t) i_h * n_embd, row_bytes);
+    }
+
+    bool need_embd() const override {
+        return true;
     }
 };
 
-struct common_speculative_state_eagle3 : public common_speculative_state {
-    common_speculative_state_eagle3(enum common_speculative_type type) : common_speculative_state(type) {}
+// state of self-speculation (simple implementation, not ngram-map)
+struct common_speculative_impl_ngram_simple : public common_speculative_impl {
+    common_params_speculative_ngram_map params;
+
+    // shared across all sequences
+    common_ngram_simple_config config;
+
+    common_speculative_impl_ngram_simple(
+            const common_params_speculative & params, uint32_t n_seq,
+            common_ngram_simple_config config)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, n_seq)
+        , params(params.ngram_simple)
+        , config(config) {}
 
-    void begin(const llama_tokens & prompt) override {
-        GGML_UNUSED(prompt);
+    void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
+        // noop
     }
 
-    void draft(
-            const common_params_speculative & params,
-            const llama_tokens & prompt_tgt,
-            llama_token id_last,
-            llama_tokens & draft_tokens) override {
+    bool process(const llama_batch & /*batch*/) override {
         // TODO: implement
-        GGML_UNUSED(params);
-        GGML_UNUSED(prompt_tgt);
-        GGML_UNUSED(id_last);
-        GGML_UNUSED(draft_tokens);
+        return true;
+    }
+
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        assert(dparams.size() == n_seq);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
+            }
+
+            *dp.result = common_ngram_simple_draft(config, *dp.prompt, dp.id_last);
+        }
     }
 
-    void accept(uint16_t n_accepted) override {
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
         // noop
-        GGML_UNUSED(n_accepted);
+    }
+
+    bool need_embd() const override {
+        return false;
     }
 };
 
-// state of self-speculation (simple implementation, not ngram-map)
-struct common_speculative_state_ngram_simple : public common_speculative_state {
-    common_ngram_simple_config config;
+struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
+    common_params_speculative_ngram_map params;
 
-    common_speculative_state_ngram_simple(
-            enum common_speculative_type type,
-            common_ngram_simple_config config)
-        : common_speculative_state(type), config(config) {}
+    // n_seq configs
+    std::vector<common_ngram_map> config;
 
-    void begin(const llama_tokens & prompt) override {
-        GGML_UNUSED(prompt);
+    common_speculative_impl_ngram_map_k(
+            const common_params_speculative & params,
+            const common_ngram_map & config,
+            uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, n_seq)
+        , params(params.ngram_map_k) {
+        for (uint32_t i = 0; i < n_seq; i++) {
+            this->config.push_back(config);
+        }
     }
 
-    void draft(
-            const common_params_speculative & params,
-            const llama_tokens & prompt_tgt,
-            llama_token id_last,
-            llama_tokens & result) override {
+    void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
+        GGML_ASSERT(seq_id < (llama_seq_id) n_seq);
 
-        result = common_ngram_simple_draft(config, prompt_tgt, id_last);
-        GGML_UNUSED(params);
+        common_ngram_map_begin(config[seq_id], prompt);
     }
 
-    void accept(uint16_t n_accepted) override {
-        // noop
-        GGML_UNUSED(n_accepted);
+    bool process(const llama_batch & /*batch*/) override {
+        // TODO: implement
+        return true;
     }
-};
 
-struct common_speculative_state_ngram_map_k : public common_speculative_state {
-    // draft ngram map for speculative decoding without draft model
-    common_ngram_map map;
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        assert(dparams.size() == n_seq);
 
-    common_speculative_state_ngram_map_k(
-            enum common_speculative_type type,
-            common_ngram_map map)
-        : common_speculative_state(type), map(std::move(map)) {}
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
+            }
 
-    void begin(const llama_tokens & prompt) override {
-        common_ngram_map_begin(map, prompt);
+            common_ngram_map_draft(config[seq_id], *dp.prompt, dp.id_last, *dp.result);
+        }
     }
 
-    void draft(
-            const common_params_speculative & params,
-            const llama_tokens & prompt_tgt,
-            llama_token id_last,
-            llama_tokens & result) override {
-        common_ngram_map_draft(map, prompt_tgt, id_last, result);
-        GGML_UNUSED(params);
+    void accept(llama_seq_id seq_id, uint16_t n_accepted) override {
+        GGML_ASSERT((seq_id < (llama_seq_id) config.size()));
+
+        common_ngram_map_accept(config[seq_id], n_accepted);
     }
 
-    void accept(uint16_t n_accepted) override {
-        common_ngram_map_accept(map, n_accepted);
+    bool need_embd() const override {
+        return false;
     }
 };
 
-struct common_speculative_state_ngram_mod : public common_speculative_state {
-    common_ngram_mod & mod;
-
-    // the last position in the prompt that was added to the ngram container
-    size_t i_last = 0;
-
-    // length of the last drafted n‑gram (number of tokens returned by draft)
-    size_t n_draft_last = 0;
+struct common_speculative_impl_ngram_mod : public common_speculative_impl {
+    common_params_speculative_ngram_mod params;
 
-    // consecutive accept rounds with low acceptance fraction (< 0.5)
-    int n_low = 0;
+    // shared across all sequences
+    common_ngram_mod mod;
 
     // enable trace logging if LLAMA_TRACE is set
     const bool verbose;
 
-    common_speculative_state_ngram_mod(enum common_speculative_type type, common_ngram_mod & mod)
-        : common_speculative_state(type), mod(mod), verbose(std::getenv("LLAMA_TRACE") != nullptr) {
+    struct seq_info {
+        // the last position in the prompt that was added to the ngram container
+        size_t i_last = 0;
+
+        // length of the last drafted n‑gram (number of tokens returned by draft)
+        size_t n_draft_last = 0;
+
+        // consecutive accept rounds with low acceptance fraction (< 0.5)
+        int n_low = 0;
+    };
+
+    std::vector<seq_info> sinfos;
+
+    common_speculative_impl_ngram_mod(
+            const common_params_speculative & params,
+            uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, n_seq)
+        , params(params.ngram_mod)
+        , mod(params.ngram_mod.n_match, 4*1024*1024)
+        , verbose(std::getenv("LLAMA_TRACE") != nullptr) {
         static_assert(sizeof(llama_token) == sizeof(common_ngram_mod::entry_t));
+
+        LOG_INF("%s: initialized ngram_mod with n_match=%d, size=%zu (%.3f MB)\n", __func__,
+                this->params.n_match, mod.size(), (float)(mod.size_bytes())/1024/1024);
+
+        if (this->params.n_match < 16) {
+            LOG_WRN("%s: ngram_mod n_match=%d is too small - poor quality is possible, "
+                    "see: https://github.com/ggml-org/llama.cpp/pull/19164\n", __func__, this->params.n_match);
+        }
+
+        sinfos.resize(n_seq);
     }
 
-    void begin(const llama_tokens & prompt) override {
-        i_last = 0;
+    void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
+        auto & sinfo = sinfos[seq_id];
 
-        n_draft_last = 0;
+        sinfo.i_last = 0;
+        sinfo.n_draft_last = 0;
 
         const size_t n = mod.get_n();
-
         if (prompt.size() < n) {
             return;
         }
@@ -666,7 +849,7 @@ struct common_speculative_state_ngram_mod : public common_speculative_state {
             mod.add(prompt.data() + i);
         }
 
-        i_last = prompt.size() - n;
+        sinfo.i_last = prompt.size() - n;
 
         const double f = (double)mod.get_used() / (double)mod.size();
         LOG_INF("%s: ngram_mod occupancy = %zu/%zu (%.2f)\n", __func__, mod.get_used(), mod.size(), f);
@@ -679,16 +862,17 @@ struct common_speculative_state_ngram_mod : public common_speculative_state {
         }
     }
 
-    void draft(
-            const common_params_speculative & params,
-            const llama_tokens & prompt_tgt,
-            llama_token id_last,
-            llama_tokens & result) override {
-        GGML_UNUSED(params);
+    void draft_one(
+            llama_seq_id seq_id,
+            common_speculative_draft_params & dparams) {
+        auto & sinfo = sinfos[seq_id];
+        auto & result = *dparams.result;
 
-        n_draft_last = 0;
+        const auto & prompt = *dparams.prompt;
 
-        const size_t cur_len = prompt_tgt.size();
+        sinfo.n_draft_last = 0;
+
+        const size_t cur_len = prompt.size();
         if (cur_len < mod.get_n()) {
             return;
         }
@@ -696,19 +880,19 @@ struct common_speculative_state_ngram_mod : public common_speculative_state {
         const size_t n = mod.get_n();
 
         // add new ngrams in chunks
-        if (i_last + 32 < cur_len) {
-            for (size_t i = i_last; i < cur_len - n; ++i) {
-                mod.add(prompt_tgt.data() + i);
+        if (sinfo.i_last + 32 < cur_len) {
+            for (size_t i = sinfo.i_last; i < cur_len - n; ++i) {
+                mod.add(prompt.data() + i);
             }
 
-            i_last = cur_len - n;
+            sinfo.i_last = cur_len - n;
         }
 
         result.resize(n + params.n_max);
         for (size_t i = 0; i < n - 1; ++i) {
-            result[i] = prompt_tgt[cur_len - n + 1 + i];
+            result[i] = prompt.at(cur_len - n + 1 + i);
         }
-        result[n - 1] = id_last;
+        result[n - 1] = dparams.id_last;
 
         for (int i = 0; i < params.n_max; ++i) {
             const llama_token token = mod.get(result.data() + i);
@@ -731,59 +915,96 @@ struct common_speculative_state_ngram_mod : public common_speculative_state {
         result.resize(result.size() - n);
 
         // store length of drafted n‑gram for later acceptance analysis
-        n_draft_last = result.size();
+        sinfo.n_draft_last = result.size();
+    }
+
+    bool process(const llama_batch & /*batch*/) override {
+        // TODO: implement
+        return true;
     }
 
-    void accept(uint16_t n_accepted) override {
-        if (verbose) {
-            LOG_INF("%s: accepted %d tokens from %zu drafted tokens\n", __func__, n_accepted, n_draft_last);
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        assert(dparams.size() == n_seq);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
+            }
+
+            draft_one(seq_id, dp);
         }
+    }
+
+    void accept(llama_seq_id seq_id, uint16_t n_accepted) override {
+        auto & sinfo = sinfos[seq_id];
 
         // compute acceptance fraction if we have a recorded draft length
-        if (n_draft_last > 0) {
-            const double f_acc = (double)n_accepted / (double)n_draft_last;
+        if (sinfo.n_draft_last > 0) {
+            const double f_acc = (double)n_accepted / (double)sinfo.n_draft_last;
             if (f_acc < 0.5) {
-                n_low++;
-                if (n_low >= 3) {
-                    LOG_WRN("%s: low acceptance streak (%d) – resetting ngram_mod\n", __func__, n_low);
+                sinfo.n_low++;
+                if (sinfo.n_low >= 3) {
+                    if (verbose) {
+                        LOG_WRN("%s: low acceptance streak (%d) – resetting ngram_mod\n", __func__, sinfo.n_low);
+                    }
 
                     mod.reset();
-                    n_low = 0;
-                    i_last = 0;
+                    sinfo.n_low = 0;
+                    sinfo.i_last = 0;
                 }
             } else {
-                n_low = 0;
+                sinfo.n_low = 0;
             }
         }
     }
+
+    bool need_embd() const override {
+        return false;
+    }
 };
 
-struct common_speculative_state_ngram_cache : public common_speculative_state {
+struct common_speculative_impl_ngram_cache : public common_speculative_impl {
+    common_params_speculative_ngram_cache params;
+
     uint16_t n_draft;
+
     bool save_dynamic;
     bool save_static;
 
-    common_ngram_cache ngram_cache_context;
-    common_ngram_cache ngram_cache_dynamic;
-    common_ngram_cache ngram_cache_static;
+    struct seq_info {
+        size_t cache_size = 0; // number of tokens in n-gram cache
+
+        common_ngram_cache ngram_cache_context;
+        common_ngram_cache ngram_cache_dynamic;
+        common_ngram_cache ngram_cache_static;
+    };
 
-    size_t cache_size = 0; // number of tokens in n-gram cache
+    std::vector<seq_info> sinfos;
 
-    common_speculative_state_ngram_cache(
-            const enum common_speculative_type type,
+    common_speculative_impl_ngram_cache(
+            const common_params_speculative & params,
+            uint32_t n_seq,
+            uint16_t n_draft,
             const std::string & path_static,
             const std::string & path_dynamic,
-            uint16_t            n_draft,
-            bool                save_dynamic,
-            bool                save_static)
-        : common_speculative_state(type)
+            bool save_dynamic,
+            bool save_static)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, n_seq)
+        , params(params.ngram_cache)
         , n_draft(n_draft)
         , save_dynamic(save_dynamic)
         , save_static(save_static)
     {
+        sinfos.resize(n_seq);
+
         if (!path_static.empty()) {
             try {
-                ngram_cache_static = common_ngram_cache_load(path_static);
+                auto ngram_cache_static = common_ngram_cache_load(path_static);
+
+                for (auto & sinfo : sinfos) {
+                    sinfo.ngram_cache_static = ngram_cache_static;
+                }
             } catch (...) {
                 LOG_ERR("failed to open static lookup cache: %s", path_static.c_str());
                 GGML_ABORT("Couldn't read static lookup cache");
@@ -792,7 +1013,11 @@ struct common_speculative_state_ngram_cache : public common_speculative_state {
 
         if (!path_dynamic.empty()) {
             try {
-                ngram_cache_dynamic = common_ngram_cache_load(path_dynamic);
+                auto ngram_cache_dynamic = common_ngram_cache_load(path_dynamic);
+
+                for (auto & sinfo : sinfos) {
+                    sinfo.ngram_cache_dynamic = ngram_cache_dynamic;
+                }
             } catch (...) {
                 LOG_ERR("failed to open dynamic lookup cache: %s", path_dynamic.c_str());
                 GGML_ABORT("Couldn't read dynamic lookup cache");
@@ -800,44 +1025,48 @@ struct common_speculative_state_ngram_cache : public common_speculative_state {
         }
     }
 
-    void begin(const llama_tokens & prompt) override {
-        GGML_UNUSED(prompt);
+    void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
+        // noop
     }
 
-    void draft(
-            const common_params_speculative & params,
-            const llama_tokens & prompt_tgt,
-            llama_token id_last,
-            llama_tokens & result) override {
-        GGML_UNUSED(params);
+    void draft_one(
+            llama_seq_id seq_id,
+            common_speculative_draft_params & dparams) {
+        auto & sinfo = sinfos[seq_id];
+        auto & result = *dparams.result;
 
-        if (cache_size < prompt_tgt.size() + 1) {
+        const auto & prompt = *dparams.prompt;
+
+        if (sinfo.cache_size < prompt.size() + 1) {
             llama_tokens tokens_new;
-            tokens_new.reserve(prompt_tgt.size() + 1 - cache_size);
-            for (size_t j = cache_size; j < prompt_tgt.size(); ++j) {
-                tokens_new.push_back(prompt_tgt[j]);
+            tokens_new.reserve(prompt.size() + 1 - sinfo.cache_size);
+            for (size_t j = sinfo.cache_size; j < prompt.size(); ++j) {
+                tokens_new.push_back(prompt[j]);
             }
-            tokens_new.push_back(id_last); // add the last token
+            tokens_new.push_back(dparams.id_last); // add the last token
 
-            // Update context ngram cache with new prompt_tgt:
-            common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX,
+            // Update context ngram cache with new dparams.prompt:
+            common_ngram_cache_update(
+                    sinfo.ngram_cache_context,
+                    LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX,
                     tokens_new, tokens_new.size(), false);
-            cache_size = prompt_tgt.size() + 1;
+            sinfo.cache_size = prompt.size() + 1;
         }
 
         llama_tokens inp;
-        inp.reserve(prompt_tgt.size() + 1);
-        for (size_t j = 0; j < prompt_tgt.size(); ++j) {
-            inp.push_back(prompt_tgt[j]);
+        inp.reserve(prompt.size() + 1);
+        for (size_t j = 0; j < prompt.size(); ++j) {
+            inp.push_back(prompt[j]);
         }
-        inp.push_back(id_last);
+        inp.push_back(dparams.id_last);
 
-        result.push_back(id_last);
+        result.push_back(dparams.id_last);
 
-        common_ngram_cache_draft(inp, result, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX,
-                ngram_cache_context,
-                ngram_cache_dynamic,
-                ngram_cache_static);
+        common_ngram_cache_draft(
+                inp, result, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX,
+                sinfo.ngram_cache_context,
+                sinfo.ngram_cache_dynamic,
+                sinfo.ngram_cache_static);
 
         if (result.size() > 0) {
             // delete first token in result (which is the id_last token)
@@ -845,67 +1074,129 @@ struct common_speculative_state_ngram_cache : public common_speculative_state {
         }
     }
 
-    void accept(uint16_t n_accepted) override {
-        // TODO: noop
-        GGML_UNUSED(n_accepted);
+    bool process(const llama_batch & /*batch*/) override {
+        // TODO: implement
+        return true;
+    }
+
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        assert(dparams.size() == n_seq);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
+            }
+
+            draft_one(seq_id, dp);
+        }
+    }
+
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/) override {
+        // noop
+    }
+
+    bool need_embd() const override {
+        return false;
     }
 };
 
 struct common_speculative {
-    std::vector<std::unique_ptr<common_speculative_state>> impls; // list of implementations to use and their states
+    common_speculative_draft_params_vec dparams;
+
+    // list of implementations to use and their states
+    std::vector<std::unique_ptr<common_speculative_impl>> impls;
 
-    common_speculative_state * curr_impl = nullptr; // current implementation in use (for stats)
+    // which implementaion was used for a given seq_id
+    std::vector<common_speculative_impl *> impl_last;
 };
 
-static common_ngram_map get_common_ngram_map(const common_speculative_config & config) {
-    uint16_t size_key   = config.params.ngram_size_n;
-    uint16_t size_value = config.params.ngram_size_m;
-    bool     key_only   = (config.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
-    uint16_t min_hits   = config.params.ngram_min_hits;
+static common_ngram_map get_common_ngram_map(
+        common_speculative_type type,
+        const common_params_speculative_ngram_map & config) {
+    uint16_t size_key   = config.size_n;
+    uint16_t size_value = config.size_m;
+    bool     key_only   = type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K;
+    uint16_t min_hits   = config.min_hits;
 
     return common_ngram_map(size_key, size_value, key_only, min_hits);
 }
 
-static common_speculative_state_ngram_cache create_state_ngram_cache(
-        const std::string & path_static, const std::string & path_dynamic,
-        const common_speculative_config & config) {
+static common_speculative_impl_ngram_cache create_state_ngram_cache(
+        const common_speculative_config & config,
+        uint32_t n_seq,
+        const std::string & path_static,
+        const std::string & path_dynamic) {
     uint16_t n_draft = 8; // TODO get from config?
 
     // TODO bool param in common/common.h to set save_static/save_dynamic?
     bool save_static = false;
     bool save_dynamic = false;
 
-    common_speculative_state_ngram_cache state(config.type, path_static, path_dynamic, n_draft, save_static, save_dynamic);
+    common_speculative_impl_ngram_cache state(config.params, n_seq, n_draft, path_static, path_dynamic, save_static, save_dynamic);
 
     return state;
 }
 
-std::string common_speculative_type_name_str() {
+std::string common_speculative_type_name_str(const std::vector<common_speculative_type> & types) {
     std::string result;
-    for (size_t i = 0; i < common_speculative_types.size(); i++) {
+
+    for (size_t i = 0; i < types.size(); i++) {
         if (i > 0) {
-            result += ", ";
+            result += ",";
         }
-        result += common_speculative_type_to_str(common_speculative_types[i]);
+        result += common_speculative_type_to_str(types[i]);
     }
     return result;
 }
 
-std::string common_speculative_type_to_str(enum common_speculative_type type) {
+const char * common_speculative_all_types_str() {
+    static std::string all_types_str = []() {
+        std::vector<common_speculative_type> types;
+        types.reserve(COMMON_SPECULATIVE_TYPE_COUNT);
+        for (int i = 0; i < COMMON_SPECULATIVE_TYPE_COUNT; i++) {
+            types.push_back((common_speculative_type) i);
+        }
+        return common_speculative_type_name_str(types);
+    }();
+    return all_types_str.c_str();
+}
+
+std::string common_speculative_type_to_str(common_speculative_type type) {
     switch (type) {
         case COMMON_SPECULATIVE_TYPE_NONE:          return "none";
-        case COMMON_SPECULATIVE_TYPE_DRAFT:         return "draft";
-        case COMMON_SPECULATIVE_TYPE_EAGLE3:        return "eagle3";
-        case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:  return "ngram_simple";
-        case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:   return "ngram_map_k";
-        case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram_map_k4v";
-        case COMMON_SPECULATIVE_TYPE_NGRAM_MOD:     return "ngram_mod";
-        case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE:   return "ngram_cache";
+        case COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE:  return "draft-simple";
+        case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3:  return "draft-eagle3";
+        case COMMON_SPECULATIVE_TYPE_DRAFT_MTP:     return "draft-mtp";
+        case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:  return "ngram-simple";
+        case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:   return "ngram-map-k";
+        case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram-map-k4v";
+        case COMMON_SPECULATIVE_TYPE_NGRAM_MOD:     return "ngram-mod";
+        case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE:   return "ngram-cache";
         default:                                    return "unknown";
     }
 }
 
-enum common_speculative_type common_speculative_type_from_name(const std::string & name) {
+std::vector<common_speculative_type> common_speculative_types_from_names(const std::vector<std::string> & names) {
+    std::vector<common_speculative_type> types;
+    types.reserve(names.size());
+
+    for (const auto & name : names) {
+        auto type = common_speculative_type_from_name_map.find(name);
+        if (type != common_speculative_type_from_name_map.end()) {
+            if (type->second == COMMON_SPECULATIVE_TYPE_NONE) {
+                return std::vector<common_speculative_type> { COMMON_SPECULATIVE_TYPE_NONE };
+            }
+            types.push_back(type->second);
+            continue;
+        }
+        throw std::invalid_argument("unknown speculative type: " + name);
+    }
+
+    return types;
+}
+
+common_speculative_type common_speculative_type_from_name(const std::string & name) {
     const auto it = common_speculative_type_from_name_map.find(name);
     if (it == common_speculative_type_from_name_map.end()) {
         return COMMON_SPECULATIVE_TYPE_COUNT;
@@ -913,34 +1204,39 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
     return it->second;
 }
 
-// initialization of the speculative decoding system
-//
-common_speculative * common_speculative_init(
-        common_params_speculative & params,
-        llama_context             * ctx_tgt) {
-    llama_context * ctx_dft = nullptr;
-    if (params.model_dft) {
-        ctx_dft = llama_init_from_model(params.model_dft, params.cparams_dft);
-        if (ctx_dft == nullptr) {
-            LOG_ERR("%s", "failed to create draft context\n");
-            return nullptr;
-        }
+static uint32_t common_get_enabled_speculative_configs(const std::vector<common_speculative_type> & configs) {
+    uint32_t result = 0;
+    for (size_t i = 0; i < configs.size(); i++) {
+        result |= (1u << configs[i]);
     }
+    return result;
+}
 
+// initialization of the speculative decoding system
+//
+common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq) {
     // Compute the implementations to use based on the config and their order of preference
     std::vector<common_speculative_config> configs = {}; // list of speculative configs to try
     {
-        bool has_draft = !params.mparams_dft.path.empty();
+        uint32_t enabled_configs = common_get_enabled_speculative_configs(params.types);
+
+        bool has_draft_model_path = !params.draft.mparams.path.empty();
+
+        bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
         bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3
+        bool has_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
 
-        bool has_ngram_cache   = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_CACHE);
-        bool has_ngram_simple  = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE);
-        bool has_ngram_map_k   = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
-        bool has_ngram_map_k4v = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V);
-        bool has_ngram_mod     = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MOD);
+        bool has_ngram_cache   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_CACHE));
+        bool has_ngram_simple  = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE));
+        bool has_ngram_map_k   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K));
+        bool has_ngram_map_k4v = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V));
+        bool has_ngram_mod     = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MOD));
 
-        // In a more complex implementation we could use the same implementation but with different parameters.
-        // This was initially used in PR-18471 but removed to simplify the code.
+        // when adding a new type - update here the logic above
+        static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 9);
+
+        // this list here defines the priority of the speculators
+        // the one with highest priority are listed first
         if (has_ngram_simple) {
             // This implementation can guess a lot of tokens without any draft model.
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, params));
@@ -953,88 +1249,87 @@ common_speculative * common_speculative_init(
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, params));
         }
         if (has_ngram_mod) {
-            // shared instance for all speculative decoding contexts
-            if (!params.ngram_mod) {
-                params.ngram_mod = std::make_shared<common_ngram_mod>(params.ngram_size_n, 4*1024*1024);
-
-                LOG_INF("%s: initialized ngram_mod with n=%d, size=%zu (%.3f MB)\n", __func__,
-                        params.ngram_size_n, params.ngram_mod->size(),
-                        (float)(params.ngram_mod->size_bytes())/1024/1024);
-
-                if (params.ngram_size_n < 16) {
-                    LOG_WRN("%s: ngram_mod n=%d is too small - poor quality is possible, see: https://github.com/ggml-org/llama.cpp/pull/19164\n", __func__, params.ngram_size_n);
-                }
-            }
-
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, params));
         }
         if (has_ngram_cache) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, params));
         }
-        if (has_draft) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT, params));
+        if (has_draft_simple) {
+            if (!has_draft_model_path) {
+                LOG_WRN("%s: draft model is not specified - cannot use 'draft' type\n", __func__);
+                has_draft_simple = false;
+            }
+        } else if (has_draft_model_path && !has_mtp && !has_draft_eagle3) {
+            LOG_WRN("%s: draft model is specified but 'draft' speculative type is not explicitly enabled - enabling it\n", __func__);
+            has_draft_simple = true;
+        }
+
+        if (has_draft_simple) {
+            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, params));
         }
         if (has_draft_eagle3) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_EAGLE3, params));
+            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params));
+        }
+        if (has_mtp) {
+            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params));
         }
     }
 
-    std::vector<std::unique_ptr<common_speculative_state>> impls = {};
+    std::vector<std::unique_ptr<common_speculative_impl>> impls = {};
 
     for (const common_speculative_config & config : configs) {
-        LOG_DBG("%s: adding implementation %s\n", __func__, common_speculative_type_to_str(config.type).c_str());
+        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(config.type).c_str());
         switch (config.type) {
             case COMMON_SPECULATIVE_TYPE_NONE:
                 break;
-            case COMMON_SPECULATIVE_TYPE_DRAFT: {
-                const bool use_ckpt = common_context_can_seq_rm(ctx_dft) == COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
-
-                impls.push_back(std::make_unique<common_speculative_state_draft>(config.type,
-                    /* .ctx_tgt      = */ ctx_tgt,
-                    /* .ctx_dft      = */ ctx_dft,
-                    /* .replacements = */ params.replacements,
-                    /* .use_ckpt     = */ use_ckpt
-                ));
+            case COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE: {
+                impls.push_back(std::make_unique<common_speculative_impl_draft_simple>(config.params, n_seq));
                 break;
             }
-            case COMMON_SPECULATIVE_TYPE_EAGLE3: {
-                impls.push_back(std::make_unique<common_speculative_state_eagle3>(config.type));
+            case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3: {
+                impls.push_back(std::make_unique<common_speculative_impl_draft_eagle3>(config.params, n_seq));
+                break;
+            }
+            case COMMON_SPECULATIVE_TYPE_DRAFT_MTP: {
+                impls.push_back(std::make_unique<common_speculative_state_draft_mtp>(config.params, n_seq));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: {
-                common_ngram_map ngram_map = get_common_ngram_map(config);
+                common_ngram_map ngram_map = get_common_ngram_map(config.type, config.params.ngram_simple);
 
                 uint16_t ngram_size_key   = ngram_map.size_key;
                 uint16_t mgram_size_value = ngram_map.size_value;
 
                 auto config_simple = common_ngram_simple_config {
-                    /* .size_ngram      = */ ngram_size_key,
-                    /* .size_mgram      = */ mgram_size_value
+                    /* .size_ngram = */ ngram_size_key,
+                    /* .size_mgram = */ mgram_size_value
                 };
-                auto state = std::make_unique<common_speculative_state_ngram_simple>(
-                    /* .type            = */ config.type,
-                    /* .state           = */ config_simple
+                auto state = std::make_unique<common_speculative_impl_ngram_simple>(
+                    /* .params = */ config.params,
+                    /* .n_seq  = */ n_seq,
+                    /* .state  = */ config_simple
                 );
                 impls.push_back(std::move(state));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:
             case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: {
-                impls.push_back(std::make_unique<common_speculative_state_ngram_map_k>(
-                    (config.type),
-                    get_common_ngram_map(config)
-                ));
+                impls.push_back(
+                        std::make_unique<common_speculative_impl_ngram_map_k>(
+                            config.params, get_common_ngram_map(config.type, config.params.ngram_map_k), n_seq));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_MOD: {
-                GGML_ASSERT(config.params.ngram_mod);
-                impls.push_back(std::make_unique<common_speculative_state_ngram_mod>(config.type, *config.params.ngram_mod));
+                impls.push_back(
+                        std::make_unique<common_speculative_impl_ngram_mod>(config.params, n_seq));
                 break;
             }
             case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: {
                 auto state = create_state_ngram_cache(
-                        params.lookup_cache_static, params.lookup_cache_dynamic, config);
-                impls.push_back(std::make_unique<common_speculative_state_ngram_cache>(state));
+                        config, n_seq,
+                        params.ngram_cache.lookup_cache_static,
+                        params.ngram_cache.lookup_cache_dynamic);
+                impls.push_back(std::make_unique<common_speculative_impl_ngram_cache>(state));
                 break;
             }
             default:
@@ -1043,13 +1338,14 @@ common_speculative * common_speculative_init(
     }
 
     if (impls.empty()) {
-        LOG_WRN("%s", "no implementations specified for speculative decoding\n");
+        LOG_WRN("%s: no implementations specified for speculative decoding\n", __func__);
         return nullptr;
     }
 
     auto * result = new common_speculative {
+        /* .dparams   = */ common_speculative_draft_params_vec(n_seq),
         /* .impls     = */ std::move(impls),
-        /* .curr_impl = */ nullptr,
+        /* .impl_last = */ std::vector<common_speculative_impl *>(n_seq, nullptr)
     };
 
     return result;
@@ -1063,59 +1359,146 @@ void common_speculative_free(common_speculative * spec) {
     delete spec;
 }
 
-void common_speculative_begin(common_speculative * spec, const llama_tokens & prompt) {
+common_speculative_draft_params & common_speculative_get_draft_params(
+        common_speculative * spec,
+        llama_seq_id seq_id) {
+    GGML_ASSERT(spec);
+    GGML_ASSERT(seq_id < (llama_seq_id) spec->dparams.size());
+
+    return spec->dparams[seq_id];
+}
+
+void common_speculative_begin(common_speculative * spec, llama_seq_id seq_id, const llama_tokens & prompt) {
     if (spec == nullptr) {
         return;
     }
 
     for (auto & impl : spec->impls) {
         common_time_meas tm(impl->t_begin_us, !impl->gen_perf);
-        impl->begin(prompt);
+        impl->begin(seq_id, prompt);
         impl->n_call_begin++;
     }
 }
 
-llama_tokens common_speculative_draft(
-        common_speculative * spec,
-        const common_params_speculative & params,
-        const llama_tokens & prompt_tgt, // specified in target model vocab
-        llama_token id_last) {
-    llama_tokens result;
+bool common_speculative_process(common_speculative * spec, const llama_batch & batch) {
+    bool result = true;
+
+    if (spec == nullptr) {
+        return result;
+    }
+
+    for (auto & impl : spec->impls) {
+        result = result && impl->process(batch);
+    }
+
+    return result;
+}
 
-    spec->curr_impl = nullptr; // reset current implementation
+bool common_speculative_need_embd(common_speculative * spec) {
+    if (spec == nullptr) {
+        return false;
+    }
+
+    for (auto & impl : spec->impls) {
+        if (impl->need_embd()) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+void common_speculative_draft(common_speculative * spec) {
+    if (spec == nullptr) {
+        return;
+    }
+
+    auto & dparams = spec->dparams;
+
+    {
+        int n_drafting = 0;
+
+        for (auto & dp : dparams) {
+            GGML_ASSERT(!dp.drafting || dp.result->empty());
+
+            if (dp.drafting) {
+                n_drafting++;
+            }
+        }
+
+        if (n_drafting == 0) {
+            return;
+        }
+    }
 
     for (auto & impl : spec->impls) {
         {
             common_time_meas tm(impl->t_draft_us, !impl->gen_perf);
-            impl->draft(params, prompt_tgt, id_last, result);
+            impl->draft(dparams);
             impl->n_call_draft++;
         }
 
-        if (!result.empty()) {
-            LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__,
-                    common_speculative_type_to_str(impl.get()->type).c_str(), prompt_tgt.size(),
-                    impl.get()->n_call_draft, result.size());
+        int n_drafting = 0;
 
-            spec->curr_impl = impl.get(); // set current implementation for stats
-            impl->n_gen_drafts++;
-            impl->n_gen_tokens += result.size();
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) dparams.size(); ++seq_id) {
+            auto & dp = dparams[seq_id];
 
-            break; // We have a draft, so break out of the loop and return it.
+            auto & result = *dp.result;
+
+            // a new draft has been sampled
+            if (dp.drafting && !result.empty()) {
+                dp.drafting = false;
+
+                if (dp.n_max > 0) {
+                    if (!result.empty() && (int) result.size() > dp.n_max) {
+                        LOG_DBG("%s: truncating draft to %d tokens\n", __func__, dp.n_max);
+                        result.resize(dp.n_max);
+                    }
+                }
+
+                if (!result.empty()) {
+                    LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__,
+                            common_speculative_type_to_str(impl.get()->type).c_str(), dp.prompt->size(),
+                            impl.get()->n_call_draft, result.size());
+
+                    // remember which implementation was used
+                    spec->impl_last[seq_id] = impl.get();
+
+                    impl->n_gen_drafts++;
+                    impl->n_gen_tokens += result.size();
+                }
+            }
+
+            if (dp.drafting) {
+                n_drafting++;
+            }
+        }
+
+        if (n_drafting == 0) {
+            break;
         }
     }
 
-    return result;
-}
+    // these sequences failed to generate a draft
+    for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) dparams.size(); ++seq_id) {
+        auto & dp = dparams[seq_id];
 
-void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) {
-    if (n_accepted == 0) {
-        return;
+        if (dp.drafting) {
+            dp.drafting = false;
+        }
     }
+}
 
-    common_speculative_state * impl = spec->curr_impl;
+void common_speculative_accept(common_speculative * spec, llama_seq_id seq_id, uint16_t n_accepted) {
+    common_speculative_impl * impl = spec->impl_last[seq_id];
 
     GGML_ASSERT(impl);
 
+    // TODO: currently only the implementation that generated the draft is used to accept it
+    //       however, some implementations (such as MTP) need to also "see" the accepted tokens
+    //       extend `common_speculative_impl::accept()` with an extra argument `bool is_other` to
+    //       inform the implementation if the accepted tokens are from another implementation and
+    //       pass the accepted tokens to all remaining implementations using `is_other == true`
     {
         common_time_meas tm(impl->t_accept_us, !impl->gen_perf);
         if (n_accepted > 0) {
@@ -1123,7 +1506,7 @@ void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) {
             impl->n_acc_tokens += n_accepted;
         }
 
-        impl->accept(n_accepted);
+        impl->accept(seq_id, n_accepted);
         impl->n_call_accept++;
     }
 }
diff --git a/common/speculative.h b/common/speculative.h
index bca78d32b5b..614db9b1b50 100644
--- a/common/speculative.h
+++ b/common/speculative.h
@@ -5,8 +5,14 @@
 
 struct common_speculative;
 
+// comma separated list the provided types
+std::string common_speculative_type_name_str(const std::vector<enum common_speculative_type> & types);
+
 // comma separated list of all types
-std::string common_speculative_type_name_str();
+const char * common_speculative_all_types_str();
+
+// parse user provided types
+std::vector<enum common_speculative_type> common_speculative_types_from_names(const std::vector<std::string> & names);
 
 // convert string to type
 enum common_speculative_type common_speculative_type_from_name(const std::string & name);
@@ -14,24 +20,47 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
 // convert type to string
 std::string common_speculative_type_to_str(enum common_speculative_type type);
 
-common_speculative * common_speculative_init(
-        common_params_speculative & params,
-        llama_context             * ctx_tgt);
+common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq);
 
 void common_speculative_free(common_speculative * spec);
 
+struct common_speculative_draft_params {
+    // this flag is used to chain the drafts through all the available implementations
+    // after the first successful draft from an implementation, we set it
+    //   to false to prevent further drafts for that sequence
+    // at the end of the draft() call, all drafting flags will be reset to false
+    bool drafting = false;
+
+    // overrides individual configurations (-1 disabled)
+    // can be used to constraint the max draft based on the remaining context size
+    int32_t n_max = -1;
+
+    llama_pos   n_past;
+    llama_token id_last;
+
+    // TODO: remove in the future by keeping track of the prompt from the _begin() call and the consecutive accept calls
+    const llama_tokens * prompt;
+
+    // the generated draft from the last _draft() call
+    llama_tokens * result;
+};
+
+common_speculative_draft_params & common_speculative_get_draft_params(common_speculative * spec, llama_seq_id seq_id);
+
 // optionally call once at the beginning of a new generation
-void common_speculative_begin(common_speculative * spec, const llama_tokens & prompt);
+void common_speculative_begin(common_speculative * spec, llama_seq_id seq_id, const llama_tokens & prompt);
+
+// process the batch and update the internal state of the speculative context
+bool common_speculative_process(common_speculative * spec, const llama_batch & batch);
+
+// true if any implementation requires target embeddings to be extracted
+bool common_speculative_need_embd(common_speculative * spec);
 
-// sample up to n_draft tokens and add them to the batch using the draft model
-llama_tokens common_speculative_draft(
-                     common_speculative * spec,
-        const common_params_speculative & params,
-                     const llama_tokens & prompt,
-                            llama_token   id_last);
+// generate drafts for the sequences specified with `common_speculative_get_draft_params`
+void common_speculative_draft(common_speculative * spec);
 
-// informs the speculative decoder that n_accepted tokens were accepted by the target model
-void common_speculative_accept(common_speculative * spec, uint16_t n_accepted);
+// informs the speculative context that n_accepted tokens were accepted by the target model
+void common_speculative_accept(common_speculative * spec, llama_seq_id, uint16_t n_accepted);
 
 // print statistics about the speculative decoding
 void common_speculative_print_stats(const common_speculative * spec);
diff --git a/conversion/__init__.py b/conversion/__init__.py
new file mode 100644
index 00000000000..2c38123dff8
--- /dev/null
+++ b/conversion/__init__.py
@@ -0,0 +1,333 @@
+from __future__ import annotations
+
+from .base import (
+    ModelBase, TextModel, MmprojModel, ModelType, SentencePieceTokenTypes,
+    logger, _mistral_common_installed, _mistral_import_error_msg,
+    get_model_architecture, LazyTorchTensor,
+)
+from typing import Type
+
+
+__all__ = [
+    "ModelBase", "TextModel", "MmprojModel", "ModelType", "SentencePieceTokenTypes",
+    "get_model_architecture", "LazyTorchTensor", "logger",
+    "_mistral_common_installed", "_mistral_import_error_msg",
+    "get_model_class", "print_registered_models", "load_all_models",
+]
+
+
+TEXT_MODEL_MAP: dict[str, str] = {
+    "AfmoeForCausalLM": "afmoe",
+    "ApertusForCausalLM": "llama",
+    "ArceeForCausalLM": "llama",
+    "ArcticForCausalLM": "arctic",
+    "AudioFlamingo3ForConditionalGeneration": "qwen",
+    "BaiChuanForCausalLM": "baichuan",
+    "BaichuanForCausalLM": "baichuan",
+    "BailingMoeForCausalLM": "bailingmoe",
+    "BailingMoeV2ForCausalLM": "bailingmoe",
+    "BambaForCausalLM": "granite",
+    "BertForMaskedLM": "bert",
+    "BertForSequenceClassification": "bert",
+    "BertModel": "bert",
+    "BitnetForCausalLM": "bitnet",
+    "BloomForCausalLM": "bloom",
+    "BloomModel": "bloom",
+    "CamembertModel": "bert",
+    "ChameleonForCausalLM": "chameleon",
+    "ChameleonForConditionalGeneration": "chameleon",
+    "ChatGLMForConditionalGeneration": "chatglm",
+    "ChatGLMModel": "chatglm",
+    "CodeShellForCausalLM": "codeshell",
+    "CogVLMForCausalLM": "cogvlm",
+    "Cohere2ForCausalLM": "command_r",
+    "CohereForCausalLM": "command_r",
+    "DbrxForCausalLM": "dbrx",
+    "DeciLMForCausalLM": "deci",
+    "DeepseekForCausalLM": "deepseek",
+    "DeepseekV2ForCausalLM": "deepseek",
+    "DeepseekV3ForCausalLM": "deepseek",
+    "DistilBertForMaskedLM": "bert",
+    "DistilBertForSequenceClassification": "bert",
+    "DistilBertModel": "bert",
+    "Dots1ForCausalLM": "dots1",
+    "DotsOCRForCausalLM": "qwen",
+    "DreamModel": "dream",
+    "Ernie4_5ForCausalLM": "ernie",
+    "Ernie4_5_ForCausalLM": "ernie",
+    "Ernie4_5_MoeForCausalLM": "ernie",
+    "EuroBertModel": "bert",
+    "Exaone4ForCausalLM": "exaone",
+    "ExaoneForCausalLM": "exaone",
+    "ExaoneMoEForCausalLM": "exaone",
+    "FalconForCausalLM": "falcon",
+    "FalconH1ForCausalLM": "falcon_h1",
+    "FalconMambaForCausalLM": "mamba",
+    "GPT2LMHeadModel": "gpt2",
+    "GPTBigCodeForCausalLM": "starcoder",
+    "GPTNeoXForCausalLM": "gptneox",
+    "GPTRefactForCausalLM": "refact",
+    "Gemma2ForCausalLM": "gemma",
+    "Gemma3ForCausalLM": "gemma",
+    "Gemma3ForConditionalGeneration": "gemma",
+    "Gemma3TextModel": "gemma",
+    "Gemma3nForCausalLM": "gemma",
+    "Gemma3nForConditionalGeneration": "gemma",
+    "Gemma4ForConditionalGeneration": "gemma",
+    "GemmaForCausalLM": "gemma",
+    "Glm4ForCausalLM": "glm",
+    "Glm4MoeForCausalLM": "glm",
+    "Glm4MoeLiteForCausalLM": "glm",
+    "Glm4vForConditionalGeneration": "glm",
+    "Glm4vMoeForConditionalGeneration": "glm",
+    "GlmForCausalLM": "chatglm",
+    "GlmMoeDsaForCausalLM": "glm",
+    "GlmOcrForConditionalGeneration": "glm",
+    "GptOssForCausalLM": "gpt_oss",
+    "GraniteForCausalLM": "granite",
+    "GraniteMoeForCausalLM": "granite",
+    "GraniteMoeHybridForCausalLM": "granite",
+    "GraniteMoeSharedForCausalLM": "granite",
+    "GraniteSpeechForConditionalGeneration": "granite",
+    "Grok1ForCausalLM": "grok",
+    "GrokForCausalLM": "grok",
+    "GroveMoeForCausalLM": "grovemoe",
+    "HunYuanDenseV1ForCausalLM": "hunyuan",
+    "HunYuanMoEV1ForCausalLM": "hunyuan",
+    "HunYuanVLForConditionalGeneration": "hunyuan",
+    "IQuestCoderForCausalLM": "llama",
+    "InternLM2ForCausalLM": "internlm",
+    "InternLM3ForCausalLM": "internlm",
+    "JAISLMHeadModel": "jais",
+    "Jais2ForCausalLM": "jais",
+    "JambaForCausalLM": "jamba",
+    "JanusForConditionalGeneration": "januspro",
+    "JinaBertForMaskedLM": "bert",
+    "JinaBertModel": "bert",
+    "JinaEmbeddingsV5Model": "bert",
+    "KORMoForCausalLM": "qwen",
+    "KimiK25ForConditionalGeneration": "deepseek",
+    "KimiLinearForCausalLM": "kimi_linear",
+    "KimiLinearModel": "kimi_linear",
+    "KimiVLForConditionalGeneration": "deepseek",
+    "LFM2ForCausalLM": "lfm2",
+    "LLaDAMoEModel": "llada",
+    "LLaDAMoEModelLM": "llada",
+    "LLaDAModelLM": "llada",
+    "LLaMAForCausalLM": "llama",
+    "Lfm25AudioTokenizer": "lfm2",
+    "Lfm2ForCausalLM": "lfm2",
+    "Lfm2Model": "lfm2",
+    "Lfm2MoeForCausalLM": "lfm2",
+    "Llama4ForCausalLM": "llama",
+    "Llama4ForConditionalGeneration": "llama",
+    "LlamaBidirectionalModel": "llama",
+    "LlamaForCausalLM": "llama",
+    "LlamaModel": "llama",
+    "LlavaForConditionalGeneration": "llama",
+    "LlavaStableLMEpochForCausalLM": "stablelm",
+    "MPTForCausalLM": "mpt",
+    "MT5ForConditionalGeneration": "t5",
+    "MaincoderForCausalLM": "maincoder",
+    "Mamba2ForCausalLM": "mamba",
+    "MambaForCausalLM": "mamba",
+    "MambaLMHeadModel": "mamba",
+    "MiMoV2FlashForCausalLM": "mimo",
+    "MiMoV2ForCausalLM": "mimo",
+    "MiniCPM3ForCausalLM": "minicpm",
+    "MiniCPMForCausalLM": "minicpm",
+    "MiniCPMV4_6ForConditionalGeneration": "minicpm",
+    "MiniMaxM2ForCausalLM": "minimax",
+    "Ministral3ForCausalLM": "mistral3",
+    "Mistral3ForConditionalGeneration": "mistral3",
+    "MistralForCausalLM": "llama",
+    "MixtralForCausalLM": "llama",
+    "ModernBertForMaskedLM": "bert",
+    "ModernBertForSequenceClassification": "bert",
+    "ModernBertModel": "bert",
+    "NemotronForCausalLM": "nemotron",
+    "NemotronHForCausalLM": "nemotron",
+    "NeoBERT": "bert",
+    "NeoBERTForSequenceClassification": "bert",
+    "NeoBERTLMHead": "bert",
+    "NomicBertModel": "bert",
+    "OLMoForCausalLM": "olmo",
+    "Olmo2ForCausalLM": "olmo",
+    "Olmo3ForCausalLM": "olmo",
+    "OlmoForCausalLM": "olmo",
+    "OlmoeForCausalLM": "olmo",
+    "OpenELMForCausalLM": "openelm",
+    "OrionForCausalLM": "orion",
+    "PLMForCausalLM": "plm",
+    "PLaMo2ForCausalLM": "plamo",
+    "PLaMo3ForCausalLM": "plamo",
+    "PaddleOCRVLForConditionalGeneration": "ernie",
+    "PanguEmbeddedForCausalLM": "pangu",
+    "Phi3ForCausalLM": "phi",
+    "Phi4ForCausalLMV": "phi",
+    "PhiForCausalLM": "phi",
+    "PhiMoEForCausalLM": "phi",
+    "Plamo2ForCausalLM": "plamo",
+    "Plamo3ForCausalLM": "plamo",
+    "PlamoForCausalLM": "plamo",
+    "QWenLMHeadModel": "qwen",
+    "Qwen2AudioForConditionalGeneration": "qwen",
+    "Qwen2ForCausalLM": "qwen",
+    "Qwen2Model": "qwen",
+    "Qwen2MoeForCausalLM": "qwen",
+    "Qwen2VLForConditionalGeneration": "qwenvl",
+    "Qwen2VLModel": "qwenvl",
+    "Qwen2_5OmniModel": "qwenvl",
+    "Qwen2_5_VLForConditionalGeneration": "qwenvl",
+    "Qwen3ASRForConditionalGeneration": "qwen3vl",
+    "Qwen3ForCausalLM": "qwen",
+    "Qwen3Model": "qwen",
+    "Qwen3MoeForCausalLM": "qwen",
+    "Qwen3NextForCausalLM": "qwen",
+    "Qwen3OmniMoeForConditionalGeneration": "qwen3vl",
+    "Qwen3VLForConditionalGeneration": "qwen3vl",
+    "Qwen3VLMoeForConditionalGeneration": "qwen3vl",
+    "Qwen3_5ForCausalLM": "qwen",
+    "Qwen3_5ForConditionalGeneration": "qwen",
+    "Qwen3_5MoeForCausalLM": "qwen",
+    "Qwen3_5MoeForConditionalGeneration": "qwen",
+    "RND1": "qwen",
+    "RWForCausalLM": "falcon",
+    "RWKV6Qwen2ForCausalLM": "rwkv",
+    "RWKV7ForCausalLM": "rwkv",
+    "RobertaForSequenceClassification": "bert",
+    "RobertaModel": "bert",
+    "RuGPT3XLForCausalLM": "gpt2",
+    "Rwkv6ForCausalLM": "rwkv",
+    "Rwkv7ForCausalLM": "rwkv",
+    "RwkvHybridForCausalLM": "rwkv",
+    "Sarashina2VisionForCausalLM": "sarashina2",
+    "SarvamMoEForCausalLM": "bailingmoe",
+    "SeedOssForCausalLM": "olmo",
+    "SmallThinkerForCausalLM": "smallthinker",
+    "SmolLM3ForCausalLM": "llama",
+    "SolarOpenForCausalLM": "glm",
+    "StableLMEpochForCausalLM": "stablelm",
+    "StableLmForCausalLM": "stablelm",
+    "Starcoder2ForCausalLM": "starcoder",
+    "Step3p5ForCausalLM": "step3",
+    "StepVLForConditionalGeneration": "step3",
+    "T5EncoderModel": "t5",
+    "T5ForConditionalGeneration": "t5",
+    "T5WithLMHeadModel": "t5",
+    "UMT5ForConditionalGeneration": "t5",
+    "UMT5Model": "t5",
+    "UltravoxModel": "ultravox",
+    "VLlama3ForCausalLM": "llama",
+    "VoxtralForConditionalGeneration": "llama",
+    "WavTokenizerDec": "wavtokenizer",
+    "XLMRobertaForSequenceClassification": "bert",
+    "XLMRobertaModel": "bert",
+    "XverseForCausalLM": "xverse",
+    "YoutuForCausalLM": "deepseek",
+    "YoutuVLForConditionalGeneration": "deepseek",
+    "modeling_grove_moe.GroveMoeForCausalLM": "grovemoe",
+    "modeling_sarvam_moe.SarvamMoEForCausalLM": "bailingmoe",
+}
+
+
+MMPROJ_MODEL_MAP: dict[str, str] = {
+    "AudioFlamingo3ForConditionalGeneration": "ultravox",
+    "CogVLMForCausalLM": "cogvlm",
+    "DeepseekOCRForCausalLM": "deepseek",
+    "DotsOCRForCausalLM": "dotsocr",
+    "Gemma3ForConditionalGeneration": "gemma",
+    "Gemma3nForConditionalGeneration": "gemma",
+    "Gemma4ForConditionalGeneration": "gemma",
+    "Glm4vForConditionalGeneration": "qwen3vl",
+    "Glm4vMoeForConditionalGeneration": "qwen3vl",
+    "GlmOcrForConditionalGeneration": "qwen3vl",
+    "GlmasrModel": "ultravox",
+    "GraniteSpeechForConditionalGeneration": "granite",
+    "HunYuanVLForConditionalGeneration": "hunyuan",
+    "Idefics3ForConditionalGeneration": "smolvlm",
+    "InternVisionModel": "internvl",
+    "JanusForConditionalGeneration": "januspro",
+    "KimiK25ForConditionalGeneration": "kimivl",
+    "KimiVLForConditionalGeneration": "kimivl",
+    "Lfm2AudioForConditionalGeneration": "lfm2",
+    "Lfm2VlForConditionalGeneration": "lfm2",
+    "LightOnOCRForConditionalGeneration": "lighton_ocr",
+    "Llama4ForConditionalGeneration": "llama4",
+    "LlavaForConditionalGeneration": "llava",
+    "MERaLiON2ForConditionalGeneration": "ultravox",
+    "MiMoV2ForCausalLM": "mimo",
+    "MiniCPMV4_6ForConditionalGeneration": "minicpm",
+    "Mistral3ForConditionalGeneration": "llava",
+    "NemotronH_Nano_VL_V2": "nemotron",
+    "PaddleOCRVisionModel": "ernie",
+    "Phi4ForCausalLMV": "phi",
+    "Qwen2AudioForConditionalGeneration": "ultravox",
+    "Qwen2VLForConditionalGeneration": "qwenvl",
+    "Qwen2VLModel": "qwenvl",
+    "Qwen2_5OmniModel": "qwenvl",
+    "Qwen2_5_VLForConditionalGeneration": "qwenvl",
+    "Qwen3ASRForConditionalGeneration": "qwen3vl",
+    "Qwen3OmniMoeForConditionalGeneration": "qwen3vl",
+    "Qwen3VLForConditionalGeneration": "qwen3vl",
+    "Qwen3VLMoeForConditionalGeneration": "qwen3vl",
+    "Qwen3_5ForConditionalGeneration": "qwen3vl",
+    "Qwen3_5MoeForConditionalGeneration": "qwen3vl",
+    "RADIOModel": "nemotron",
+    "Sarashina2VisionForCausalLM": "sarashina2",
+    "SmolVLMForConditionalGeneration": "smolvlm",
+    "StepVLForConditionalGeneration": "step3",
+    "UltravoxModel": "ultravox",
+    "VoxtralForConditionalGeneration": "ultravox",
+    "YoutuVLForConditionalGeneration": "youtuvl",
+}
+
+
+_TEXT_MODEL_MODULES = sorted(set(TEXT_MODEL_MAP.values()))
+_MMPROJ_MODEL_MODULES = sorted(set(MMPROJ_MODEL_MAP.values()))
+
+
+_loaded_text_modules: set[str] = set()
+_loaded_mmproj_modules: set[str] = set()
+
+
+def load_all_models() -> None:
+    """Import all model modules to trigger @ModelBase.register() decorators."""
+    if len(_loaded_text_modules) != len(_TEXT_MODEL_MODULES):
+        for module_name in _TEXT_MODEL_MODULES:
+            if module_name not in _loaded_text_modules:
+                try:
+                    __import__(f"conversion.{module_name}")
+                    _loaded_text_modules.add(module_name)
+                except Exception as e:
+                    logger.warning(f"Failed to load model module {module_name}: {e}")
+
+    if len(_loaded_mmproj_modules) != len(_MMPROJ_MODEL_MODULES):
+        for module_name in _MMPROJ_MODEL_MODULES:
+            if module_name not in _loaded_mmproj_modules:
+                try:
+                    __import__(f"conversion.{module_name}")
+                    _loaded_mmproj_modules.add(module_name)
+                except Exception as e:
+                    logger.warning(f"Failed to load model module {module_name}: {e}")
+
+
+def get_model_class(name: str, mmproj: bool = False) -> Type[ModelBase]:
+    """Dynamically import and return a model class by its HuggingFace architecture name."""
+    relevant_map = MMPROJ_MODEL_MAP if mmproj else TEXT_MODEL_MAP
+    if name not in relevant_map:
+        raise NotImplementedError(f"Architecture {name!r} not supported!")
+    module_name = relevant_map[name]
+    __import__(f"conversion.{module_name}")
+    model_type = ModelType.MMPROJ if mmproj else ModelType.TEXT
+    return ModelBase._model_classes[model_type][name]
+
+
+def print_registered_models() -> None:
+    load_all_models()
+    logger.error("TEXT models:")
+    for name in sorted(TEXT_MODEL_MAP.keys()):
+        logger.error(f"  - {name}")
+    logger.error("MMPROJ models:")
+    for name in sorted(MMPROJ_MODEL_MAP.keys()):
+        logger.error(f"  - {name}")
diff --git a/conversion/afmoe.py b/conversion/afmoe.py
new file mode 100644
index 00000000000..5e66a51da61
--- /dev/null
+++ b/conversion/afmoe.py
@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, gguf
+
+from .llama import LlamaModel
+
+
+@ModelBase.register("AfmoeForCausalLM")
+class AfmoeModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.AFMOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # MoE parameters
+        if (n_shared_experts := self.hparams.get("num_shared_experts")) is not None:
+            self.gguf_writer.add_expert_shared_count(n_shared_experts)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (n_dense_layers := self.hparams.get("num_dense_layers")) is not None:
+            self.gguf_writer.add_leading_dense_block_count(n_dense_layers)
+
+        # Route normalization and scaling
+        if (route_norm := self.hparams.get("route_norm")) is not None:
+            self.gguf_writer.add_expert_weights_norm(route_norm)
+        if (route_scale := self.hparams.get("route_scale")) is not None:
+            self.gguf_writer.add_expert_weights_scale(route_scale)
+
+        # Sliding window attention
+        if (sliding_window := self.hparams.get("sliding_window")) is not None:
+            self.gguf_writer.add_sliding_window(sliding_window)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.endswith(".expert_bias"):
+            name = name.replace(".expert_bias", ".expert_bias.bias")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Handle expert weights - they're already merged in the HF format
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["gate_proj", "up_proj", "down_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename_to_retrieve])
+                        del self._experts[bid][ename_to_retrieve]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+                    yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid)
+
+                return
+            else:
+                return
+
+        yield from ModelBase.modify_tensors(self, data_torch, name, bid)
diff --git a/conversion/arctic.py b/conversion/arctic.py
new file mode 100644
index 00000000000..775cacaab9f
--- /dev/null
+++ b/conversion/arctic.py
@@ -0,0 +1,162 @@
+from __future__ import annotations
+
+import json
+import sys
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, SentencePieceTokenTypes, TextModel, gguf, logger
+
+from .llama import LlamaModel
+
+
+@ModelBase.register("ArcticForCausalLM")
+class ArcticModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.ARCTIC
+
+    def set_vocab(self):
+        # The reason for using a custom implementation here is that the
+        # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from
+        # tokenizer.model and used them as BOS and EOS instead of adding new tokens.
+        from sentencepiece import SentencePieceProcessor
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        if not tokenizer_path.is_file():
+            logger.error(f'Error: Missing {tokenizer_path}')
+            sys.exit(1)
+
+        # Read the whole vocabulary from the tokenizer.model file
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        # Use the added_tokens_decoder field from tokeniser_config.json as the source
+        # of information about added/redefined tokens and modify them accordingly.
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+
+                if "added_tokens_decoder" in tokenizer_config_json:
+                    added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"]
+                    for token_id, token_json in added_tokens_decoder.items():
+                        token_id = int(token_id)
+                        if token_id >= vocab_size:
+                            logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                            continue
+
+                        token_content = token_json["content"]
+                        token_type = SentencePieceTokenTypes.USER_DEFINED
+                        token_score = -10000.0
+
+                        # Map unk_token to UNKNOWN, other special tokens to CONTROL
+                        # Set the score to 0.0 as in the original tokenizer.model
+                        if ("special" in token_json) and token_json["special"]:
+                            if token_content == tokenizer_config_json["unk_token"]:
+                                token_type = SentencePieceTokenTypes.UNKNOWN
+                            else:
+                                token_type = SentencePieceTokenTypes.CONTROL
+                            token_score = 0.0
+
+                        logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})")
+                        tokens[token_id] = token_content.encode("utf-8")
+                        toktypes[token_id] = token_type
+                        scores[token_id] = token_score
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith("q_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith("k_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams["num_local_experts"]
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for wid in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/baichuan.py b/conversion/baichuan.py
new file mode 100644
index 00000000000..4cf34057cd9
--- /dev/null
+++ b/conversion/baichuan.py
@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
+class BaichuanModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.BAICHUAN
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
+        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        head_count = self.hparams["num_attention_heads"]
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+
+        if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight":
+            logger.info(f"Unpacking and permuting layer {bid}")
+            yield from [
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid),
+                    self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)),
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid),
+                    self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)),
+                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid),
+                    self._reverse_hf_part(data_torch, 2)),
+            ]
+        else:
+            yield from self.modify_tensors(data_torch, self.map_tensor_name(name), bid)
+
+    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
+        if n_kv_head is not None and n_head != n_kv_head:
+            n_head //= n_kv_head
+
+        return (
+            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+            .swapaxes(1, 2)
+            .reshape(weights.shape)
+        )
+
+    def _reverse_hf_permute_part(
+        self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
+    ) -> Tensor:
+        r = weights.shape[0] // 3
+        return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)
+
+    def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
+        r = weights.shape[0] // 3
+        return weights[r * n_part:r * n_part + r, ...]
diff --git a/conversion/bailingmoe.py b/conversion/bailingmoe.py
new file mode 100644
index 00000000000..319ff6dabee
--- /dev/null
+++ b/conversion/bailingmoe.py
@@ -0,0 +1,216 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("BailingMoeForCausalLM")
+class BailingMoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.BAILINGMOE
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_weights_scale(1.0)
+        self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"])
+        self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+        n_embd = self.hparams["hidden_size"]
+        if (head_dim := self.hparams.get("head_dim")) is None:
+            head_dim = n_embd // n_head
+
+        output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
+
+        if name.endswith("attention.dense.weight"):
+            yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, bid), bid)
+            return
+        elif name.endswith("query_key_value.weight"):
+            q, k, v = data_torch.split([n_head * head_dim, n_kv_head * head_dim, n_kv_head * head_dim], dim=-2)
+
+            yield from super().modify_tensors(BailingMoeModel.permute(q, n_head, n_head), self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
+            yield from super().modify_tensors(BailingMoeModel.permute(k, n_head, n_kv_head), self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
+            yield from super().modify_tensors(v,self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
+            return
+        elif name.find("mlp.experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    yield from super().modify_tensors(data_torch, new_name, bid)
+
+            return
+
+        new_name = self.map_tensor_name(name)
+
+        if new_name == output_name and self.hparams.get("norm_head"):
+            data_torch = data_torch.float()
+            data_torch /= torch.norm(data_torch, p=2, dim=0, keepdim=True) + 1e-7
+
+        yield from super().modify_tensors(data_torch, new_name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("BailingMoeV2ForCausalLM")
+class BailingMoeV2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.BAILINGMOE2
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if nextn_layers := self.hparams.get("num_nextn_predict_layers", 0):
+            self.block_count = self.hparams["num_hidden_layers"] + nextn_layers
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(hparams.get("moe_shared_expert_intermediate_size", hparams["moe_intermediate_size"] * hparams["num_shared_experts"]))
+        self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
+        self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"])
+        self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
+
+        if (nextn_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+            self.gguf_writer.add_nextn_predict_layers(nextn_layers)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.endswith(".expert_bias"):
+            name = name.replace(".expert_bias", ".expert_bias.bias")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "mlp.experts" in name:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("SarvamMoEForCausalLM", "modeling_sarvam_moe.SarvamMoEForCausalLM")
+class SarvamMoEModel(BailingMoeV2Model):
+    model_arch = gguf.MODEL_ARCH.BAILINGMOE2
+    # Sarvam-MoE shares the BailingMoeV2 architecture; only differences:
+    #  - full rotary (no partial_rotary_factor)
+    #  - expert bias is zero-mean normalized at load time
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        # Override the partial-rotary value written by BailingMoeV2 with the full rotary dim
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.endswith(".expert_bias"):
+            # Sarvam normalizes expert bias to zero mean
+            inner = gen
+
+            def gen():
+                t = inner()
+                return t - t.mean()
+        return super().filter_tensors((name, gen))
diff --git a/conversion/base.py b/conversion/base.py
new file mode 100644
index 00000000000..30c2124c2b9
--- /dev/null
+++ b/conversion/base.py
@@ -0,0 +1,2474 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from __future__ import annotations
+
+import ast
+import logging
+import contextlib
+import json
+import os
+import re
+import sys
+from enum import IntEnum
+from pathlib import Path
+from hashlib import sha256
+from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
+from itertools import chain
+from transformers import AutoConfig
+
+import numpy as np
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py'))
+import gguf
+from gguf.vocab import MistralTokenizerType, MistralVocab
+
+try:
+    from mistral_common.tokens.tokenizers.base import TokenizerVersion # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found, ty:unresolved-import]
+        SentencePieceTokenizer,
+    )
+
+    _mistral_common_installed = True
+    _mistral_import_error_msg = ""
+except ImportError:
+    _MISTRAL_COMMON_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
+    _MISTRAL_COMMON_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
+
+    _mistral_common_installed = False
+    TokenizerVersion: Any = None
+    Tekkenizer: Any = None
+    SentencePieceTokenizer: Any = None
+    _mistral_import_error_msg = (
+        "Mistral format requires `mistral-common` to be installed. Please run "
+        "`pip install mistral-common[image,audio]` to install it."
+    )
+
+
+logger = logging.getLogger("hf-to-gguf")
+
+
+AnyModel = TypeVar("AnyModel", bound="type[ModelBase]")
+
+
+class SentencePieceTokenTypes(IntEnum):
+    NORMAL = 1
+    UNKNOWN = 2
+    CONTROL = 3
+    USER_DEFINED = 4
+    UNUSED = 5
+    BYTE = 6
+
+
+class ModelType(IntEnum):
+    TEXT = 1
+    MMPROJ = 2
+
+
+class ModelBase:
+    _model_classes: dict[ModelType, dict[str, type[ModelBase]]] = {
+        ModelType.TEXT: {},
+        ModelType.MMPROJ: {},
+    }
+
+    dir_model: Path
+    ftype: gguf.LlamaFileType
+    fname_out: Path
+    is_big_endian: bool
+    endianess: gguf.GGUFEndian
+    use_temp_file: bool
+    lazy: bool
+    dry_run: bool
+    hparams: dict[str, Any]
+    model_tensors: dict[str, Callable[[], Tensor]]
+    gguf_writer: gguf.GGUFWriter
+    model_name: str | None
+    metadata_override: Path | None
+    metadata: gguf.Metadata
+    dir_model_card: Path
+    remote_hf_model_id: str | None
+
+    # subclasses should define this!
+    model_arch: gguf.MODEL_ARCH
+
+    # subclasses should initialize this!
+    block_count: int
+    tensor_map: gguf.TensorNameMap
+
+    # Mistral format specifics
+    is_mistral_format: bool = False
+    disable_mistral_community_chat_template: bool = False
+    sentence_transformers_dense_modules: bool = False
+
+    # MTP (multi-token prediction) export modes; set by main() before instantiation.
+    # Architectures opt in by overriding the handling (see _Qwen35MtpMixin).
+    mtp_only: bool = False
+    no_mtp: bool = False
+
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False,
+                 use_temp_file: bool = False, eager: bool = False,
+                 metadata_override: Path | None = None, model_name: str | None = None,
+                 split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
+                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
+                 disable_mistral_community_chat_template: bool = False,
+                 sentence_transformers_dense_modules: bool = False,
+                 fuse_gate_up_exps: bool = False):
+        if type(self) is ModelBase or \
+                type(self) is TextModel or \
+                type(self) is MmprojModel:
+            raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
+
+        if self.is_mistral_format and not _mistral_common_installed:
+            raise ImportError(_mistral_import_error_msg)
+
+        self.dir_model = dir_model
+        self.ftype = ftype
+        self.fname_out = fname_out
+        self.is_big_endian = is_big_endian
+        self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
+        self.use_temp_file = use_temp_file
+        self.lazy = not eager or (remote_hf_model_id is not None)
+        self.dry_run = dry_run
+        self.remote_hf_model_id = remote_hf_model_id
+        self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
+        self.fuse_gate_up_exps = fuse_gate_up_exps
+        self._gate_exp_buffer: dict[int, Tensor] = {}
+        self._up_exp_buffer: dict[int, Tensor] = {}
+        self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
+        self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
+        self.metadata_override = metadata_override
+        self.model_name = model_name
+        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
+        self._is_nvfp4 = False
+        self._is_mxfp4 = False
+
+        # Apply heuristics to figure out typical tensor encoding based on first tensor's dtype
+        # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
+        if self.ftype == gguf.LlamaFileType.GUESSED:
+            for _, tensor in self.get_tensors():
+                if tensor.dim() < 2:
+                    continue
+
+                if tensor.dtype == torch.bfloat16:
+                    self.ftype = gguf.LlamaFileType.MOSTLY_BF16
+                    logger.info("heuristics detected bfloat16 tensor dtype, setting --outtype bf16")
+                    break
+                elif tensor.dtype == torch.float16:
+                    self.ftype = gguf.LlamaFileType.MOSTLY_F16
+                    logger.info("heuristics detected float16 tensor dtype, setting --outtype f16")
+                    break
+            else:
+                self.ftype = gguf.LlamaFileType.MOSTLY_F16
+                logger.info("heuristics unable to detect tensor dtype, defaulting to --outtype f16")
+
+        # Configure GGUF Writer
+        self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
+                                           split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
+
+        # Mistral specific
+        self.disable_mistral_community_chat_template = disable_mistral_community_chat_template
+
+    @classmethod
+    def add_prefix_to_filename(cls, path: Path, prefix: str) -> Path:
+        stem, suffix = path.stem, path.suffix
+        new_name = f"{prefix}{stem}{suffix}"
+        return path.with_name(new_name)
+
+    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        key = next((k for k in keys if k in self.hparams), None)
+        if key is not None:
+            return self.hparams[key]
+        if optional:
+            return None
+        raise KeyError(f"could not find any of: {keys}")
+
+    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+        tensors: dict[str, Callable[[], Tensor]] = {}
+
+        if remote_hf_model_id is not None:
+            is_safetensors = True
+
+            logger.info(f"Using remote model with HuggingFace id: {remote_hf_model_id}")
+            remote_tensors = gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id)
+            for name, remote_tensor in remote_tensors.items():
+                data_gen = lambda r=remote_tensor: LazyTorchTensor.from_remote_tensor(r)  # noqa: E731
+                if titem := self.filter_tensors((name, data_gen)):
+                    tname, tgen = titem
+                    tensors[tname] = tgen
+
+            return tensors
+
+        prefix = "model" if not self.is_mistral_format else "consolidated"
+        part_names: list[str] = ModelBase.get_model_part_names(self.dir_model, prefix, ".safetensors")
+        is_safetensors: bool = len(part_names) > 0
+        if not is_safetensors:
+            part_names = ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
+
+        tensor_names_from_index: set[str] = set()
+        tensor_names_from_parts: set[str] = set()
+
+        if not self.is_mistral_format:
+            index_name = "model.safetensors" if is_safetensors else "pytorch_model.bin"
+            index_name += ".index.json"
+            index_file = self.dir_model / index_name
+
+            if index_file.is_file():
+                logger.info(f"gguf: loading model weight map from '{index_name}'")
+                with open(index_file, "r", encoding="utf-8") as f:
+                    index: dict[str, Any] = json.load(f)
+                    weight_map = index.get("weight_map")
+                    if weight_map is None or not isinstance(weight_map, dict):
+                        raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
+                    tensor_names_from_index.update(weight_map.keys())
+                    part_dict: dict[str, None] = dict.fromkeys(weight_map.values(), None) # ty: ignore[invalid-assignment]
+                    part_names = sorted(part_dict.keys())
+            else:
+                weight_map = {}
+        else:
+            weight_map = {}
+
+        for part_name in part_names:
+            logger.info(f"gguf: indexing model part '{part_name}'")
+            ctx: ContextManager[Any]
+            if is_safetensors:
+                ctx = cast(ContextManager[Any], gguf.utility.SafetensorsLocal(self.dir_model / part_name))
+            else:
+                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
+
+            with ctx as model_part:
+                assert model_part is not None
+
+                for name in model_part.keys():
+                    tensor_names_from_parts.add(name)
+                    if is_safetensors:
+                        data: gguf.utility.LocalTensor = model_part[name]
+                        if self.lazy:
+                            data_gen = lambda data=data: LazyTorchTensor.from_local_tensor(data)  # noqa: E731
+                        else:
+                            dtype = LazyTorchTensor._dtype_str_map[data.dtype]
+                            data_gen = lambda data=data, dtype=dtype: torch.from_numpy(data.mmap_bytes()).view(dtype).reshape(data.shape)  # noqa: E731
+                    else:
+                        data_torch: Tensor = model_part[name]
+                        if self.lazy:
+                            data_gen = lambda data=data_torch: LazyTorchTensor.from_eager(data)  # noqa: E731
+                        else:
+                            data_gen = lambda data=data_torch: data  # noqa: E731
+                    if titem := self.filter_tensors((name, data_gen)):
+                        tname, tgen = titem
+                        tensors[tname] = tgen
+
+        # verify tensor name presence and identify potentially missing files
+        if len(tensor_names_from_index) > 0:
+            if len(tensor_names_from_parts.symmetric_difference(tensor_names_from_index)) > 0:
+                missing = sorted(tensor_names_from_index.difference(tensor_names_from_parts))
+                extra = sorted(tensor_names_from_parts.difference(tensor_names_from_index))
+                missing_files = sorted(set(weight_map[n] for n in missing if n in weight_map))
+                if len(extra) == 0 and len(missing_files) > 0:
+                    raise ValueError(f"Missing or incomplete model files: {missing_files}\n"
+                                     f"Missing tensors: {missing}")
+                else:
+                    raise ValueError("Mismatch between weight map and model parts for tensor names:\n"
+                                     f"Missing tensors: {missing}\n"
+                                     f"Extra tensors: {extra}")
+
+        return tensors
+
+    @staticmethod
+    def _scale_is_trivial(scale: Tensor) -> bool:
+        return scale.numel() <= 1 and abs(float(scale.float().sum()) - 1.0) < 1e-6
+
+    def _write_scale_tensor(self, scale_name: str, scale: Tensor):
+        if not self._scale_is_trivial(scale):
+            scale_f32 = scale.float().numpy().flatten()
+            logger.info(f"  + {scale_name} (per-tensor scale, shape [{scale_f32.size}])")
+            self.gguf_writer.add_tensor(scale_name, scale_f32)
+
+    def _write_scales_tensor(self, scale_name: str, scales: list[float]):
+        if not np.allclose(scales, 1.0, atol=1e-6):
+            scale_vals = np.array(scales, dtype=np.float32)
+            logger.info(f"  + {scale_name} (per-expert scale, shape [{len(scales)}])")
+            self.gguf_writer.add_tensor(scale_name, scale_vals)
+
+    def dequant_model(self):
+        # If all quantized tensors were already handled (e.g. pure NVFP4), skip
+        if self._is_nvfp4 and not any(k.endswith((".weight_scale", ".weight_scale_inv")) for k in self.model_tensors):
+            return
+
+        tensors_to_remove: list[str] = []
+        new_tensors: dict[str, Callable[[], Tensor]] = {}
+
+        if (quant_config := self.hparams.get("quantization_config")) and isinstance(quant_config, dict):
+            quant_method = quant_config.get("quant_method")
+
+            def dequant_bitnet(weight: Tensor, scale: Tensor) -> Tensor:
+                weight = weight.view(torch.uint8)
+                orig_shape = weight.shape
+
+                shift = torch.tensor([0, 2, 4, 6], dtype=torch.uint8).reshape((4, *(1 for _ in range(len(orig_shape)))))
+                data = weight.unsqueeze(0).expand((4, *orig_shape)) >> shift
+                data = data & 3
+                data = (data.float() - 1).reshape((orig_shape[0] * 4, *orig_shape[1:]))
+
+                # The scale is inverted
+                return data / scale.float()
+
+            def dequant_simple(weight: Tensor, scale: Tensor, block_size: Sequence[int] | None = None) -> Tensor:
+                scale = scale.float()
+
+                if block_size is not None:
+                    dim_offset = scale.ndim - len(block_size)
+                    for i, size in enumerate(block_size):
+                        scale = scale.repeat_interleave(size, dim_offset + i)
+                    # unpad the scale (e.g. when the tensor size isn't a multiple of the block size)
+                    scale = scale[tuple(slice(0, size) for size in weight.shape)]
+
+                # align scale dims to weight for correct broadcasting (e.g. [128] -> [128, 1, 1])
+                while scale.ndim < weight.ndim:
+                    scale = scale.unsqueeze(-1)
+
+                return weight.float() * scale
+
+            # ref: https://github.com/ModelCloud/GPTQModel/blob/037c5c0f6c9e33c500d975b038d02e7ca437546d/gptqmodel/nn_modules/qlinear/__init__.py#L437-L476
+            def dequant_gptq(g_idx: Tensor, qweight: Tensor, qzeros: Tensor, scales: Tensor) -> Tensor:
+                bits = quant_config["bits"]
+                assert bits in (2, 3, 4, 8)
+                assert qweight.dtype == qzeros.dtype
+                maxq = (2 ** bits) - 1
+                weight = None
+                zeros = None
+                pack_dtype_bits = qweight.dtype.itemsize * 8
+
+                if bits in [2, 4, 8]:
+                    pack_factor = pack_dtype_bits // bits
+                    wf = torch.tensor(list(range(0, pack_dtype_bits, bits)), dtype=torch.int32).unsqueeze(0)
+                    if self.lazy:
+                        wf = LazyTorchTensor.from_eager(wf)
+
+                    zeros = torch.bitwise_right_shift(
+                        qzeros.unsqueeze(2).expand(-1, -1, pack_factor),
+                        wf.unsqueeze(0)
+                    ).to(torch.int16 if bits == 8 else torch.int8)
+                    zeros = torch.bitwise_and(zeros, maxq).reshape(scales.shape)
+
+                    weight = torch.bitwise_and(
+                        torch.bitwise_right_shift(
+                            qweight.unsqueeze(1).expand(-1, pack_factor, -1),
+                            wf.unsqueeze(-1)
+                        ).to(torch.int16 if bits == 8 else torch.int8),
+                        maxq
+                    )
+                elif bits == 3:
+                    raise NotImplementedError("3-bit gptq dequantization is not yet implemented")
+
+                assert weight is not None
+                assert zeros is not None
+
+                weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
+
+                # gptq_v2 doesn't need to offset zeros
+                if quant_config.get("checkpoint_format", "gptq") == "gptq":
+                    zeros += 1
+
+                return (scales[g_idx].float() * (weight - zeros[g_idx]).float()).T
+
+            def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: Tensor | None, num_bits: int, group_size: int):
+                assert w.dtype == torch.int32
+                shape = tuple(shape_tensor.tolist())
+                assert len(shape) == 2
+                mask = (1 << num_bits) - 1
+
+                shifts = torch.arange(0, 32 - (num_bits - 1), num_bits, dtype=torch.int32)
+                if self.lazy:
+                    shifts = LazyTorchTensor.from_eager(shifts)
+
+                if zero_point is None:
+                    offset = 1 << (num_bits - 1)
+                else:
+                    assert len(zero_point.shape) == 2
+                    offset = (zero_point.unsqueeze(1) >> shifts.reshape(1, -1, 1)) & mask
+                    offset = offset.reshape(-1, zero_point.shape[1])
+                    # trim padding, and prepare for broadcast
+                    # NOTE: the zero-point is packed along dim 0
+                    offset = offset[:shape[0], :].unsqueeze(-1)
+
+                # extract values
+                # NOTE: the weights are packed along dim 1
+                unpacked = (w.unsqueeze(-1) >> shifts.reshape(1, 1, -1)) & mask
+                unpacked = unpacked.reshape(shape[0], -1)
+
+                # trim padding
+                unpacked = unpacked[:, :shape[1]]
+
+                # prepare for broadcast of the scale
+                unpacked = unpacked.reshape(shape[0], (unpacked.shape[-1] + group_size - 1) // group_size, group_size)
+                unpacked = unpacked - offset
+
+                return (unpacked * scale.unsqueeze(-1).float()).reshape(shape)
+
+            if quant_method == "bitnet":
+                for name in self.model_tensors.keys():
+                    if name.endswith(".weight_scale"):
+                        weight_name = name.removesuffix("_scale")
+                        w = self.model_tensors[weight_name]
+                        s = self.model_tensors[name]
+                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_bitnet(w(), s())
+                        tensors_to_remove.append(name)
+            elif quant_method == "fp8":
+                block_size = quant_config.get("weight_block_size")
+                for name in self.model_tensors.keys():
+                    if name.endswith("_scale_inv"):
+                        weight_name = name.removesuffix("_scale_inv")
+                        w = self.model_tensors[weight_name]
+                        s = self.model_tensors[name]
+                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
+                        tensors_to_remove.append(name)
+                    if name.endswith(".activation_scale"):  # unused
+                        tensors_to_remove.append(name)
+                    if name.endswith("_activation_scale"):  # Mistral-Small-4-119B-2602, unused
+                        tensors_to_remove.append(name)
+                    # mistral format
+                    if name.endswith(".qscale_weight"):
+                        weight_name = name.removesuffix("qscale_weight") + "weight"
+                        w = self.model_tensors[weight_name]
+                        s = self.model_tensors[name]
+                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
+                        tensors_to_remove.append(name)
+                    if name.endswith(".qscale_act"):
+                        tensors_to_remove.append(name)
+            elif quant_method == "gptq":
+                for name in self.model_tensors.keys():
+                    if name.endswith(".qweight"):
+                        base_name = name.removesuffix(".qweight")
+                        g_idx = self.model_tensors[base_name + ".g_idx"]
+                        qweight = self.model_tensors[base_name + ".qweight"]
+                        qzeros = self.model_tensors[base_name + ".qzeros"]
+                        scales = self.model_tensors[base_name + ".scales"]
+                        new_tensors[base_name + ".weight"] = (
+                            lambda g=g_idx, z=qzeros, w=qweight, s=scales: dequant_gptq(
+                                g(), w(), z(), s()
+                            )
+                        )
+                        tensors_to_remove += [
+                            base_name + n
+                            for n in (
+                                ".g_idx",
+                                ".qzeros",
+                                ".qweight",
+                                ".scales",
+                            )
+                        ]
+            elif quant_method == "compressed-tensors":
+                quant_format = quant_config["format"]
+                groups = quant_config["config_groups"]
+                if len(groups) > 1:
+                    raise NotImplementedError("Can't handle multiple config groups for compressed-tensors yet")
+                weight_config = tuple(groups.values())[0]["weights"]
+
+                if quant_format == "float-quantized" or quant_format == "int-quantized" or quant_format == "naive-quantized":
+                    block_size = weight_config.get("block_structure", None)
+                    strategy = weight_config.get("strategy")
+                    assert strategy == "channel" or strategy == "block"
+                    assert weight_config.get("group_size") is None  # didn't find a model using this yet
+                    for name in self.model_tensors.keys():
+                        if name.endswith(".weight_scale"):
+                            weight_name = name.removesuffix("_scale")
+                            w = self.model_tensors[weight_name]
+                            s = self.model_tensors[name]
+                            self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), block_size)
+                            tensors_to_remove.append(name)
+                elif quant_format == "pack-quantized":
+                    assert weight_config.get("strategy") == "group"
+                    assert weight_config.get("type", "int") == "int"
+                    num_bits = weight_config.get("num_bits")
+                    group_size = weight_config.get("group_size")
+                    assert isinstance(num_bits, int)
+                    assert isinstance(group_size, int)
+                    for name in self.model_tensors.keys():
+                        if name.endswith(".weight_packed"):
+                            base_name = name.removesuffix("_packed")
+                            w = self.model_tensors[name]
+                            scale = self.model_tensors[base_name + "_scale"]
+                            shape = self.model_tensors[base_name + "_shape"]
+                            zero_point = self.model_tensors.get(base_name + "_zero_point", lambda: None)
+                            new_tensors[base_name] = (
+                                lambda w=w, scale=scale, shape=shape, zero_point=zero_point: dequant_packed(
+                                    w(), scale(), shape(), zero_point(), num_bits, group_size,
+                                )
+                            )
+                            tensors_to_remove += [base_name + n for n in ("_packed", "_shape", "_scale")]
+                            if (base_name + "_zero_point") in self.model_tensors:
+                                tensors_to_remove.append(base_name + "_zero_point")
+                else:
+                    raise NotImplementedError(f"Quant format {quant_format!r} for method {quant_method!r} is not yet supported")
+            elif quant_method == "modelopt":
+                # Mixed-precision ModelOpt models: NVFP4 tensors are handled by
+                # _generate_nvfp4_tensors; FP8 tensors have 1D weight_scale and
+                # are dequantized here. k/v scale tensors are unused.
+                for name in self.model_tensors.keys():
+                    if name.endswith(".weight_scale"):
+                        weight_name = name.removesuffix("_scale")
+                        w = self.model_tensors[weight_name]
+                        s = self.model_tensors[name]
+                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None)
+                        tensors_to_remove.append(name)
+                    if name.endswith((".input_scale", ".k_scale", ".v_scale")):
+                        tensors_to_remove.append(name)
+            elif quant_method is not None:
+                raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}")
+
+        for name in tensors_to_remove:
+            if name in self.model_tensors:
+                del self.model_tensors[name]
+
+        for name, value in new_tensors.items():
+            self.model_tensors[name] = value
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        if "language_model." in name:
+            name = name.replace("language_model.", "")
+
+        return name, gen
+
+    def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
+        for name, gen in self.model_tensors.items():
+            yield name, gen()
+
+    def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
+        if key not in gguf.MODEL_TENSORS[self.model_arch]:
+            raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
+        name: str = gguf.TENSOR_NAMES[key]
+        if "{bid}" in name:
+            assert bid is not None
+            name = name.format(bid=bid)
+        return name + suffix
+
+    def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool:
+        if key not in gguf.MODEL_TENSORS[self.model_arch]:
+            return False
+        key_name: str = gguf.TENSOR_NAMES[key]
+        if "{bid}" in key_name:
+            if bid is None:
+                return False
+            key_name = key_name.format(bid=bid)
+        else:
+            if bid is not None:
+                return False
+        return name == (key_name + suffix)
+
+    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
+        new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
+        if new_name is None:
+            raise ValueError(f"Can not map tensor {name!r}")
+        return new_name
+
+    def set_gguf_parameters(self):
+        raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        new_name = self.map_tensor_name(name)
+
+        # Handle gate/up expert tensor fusion if enabled
+        if self.fuse_gate_up_exps and bid is not None:
+            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid):
+                self._gate_exp_buffer[bid] = data_torch
+            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
+                self._up_exp_buffer[bid] = data_torch
+
+            # Check if both gate and up are buffered for this layer
+            if bid in self._gate_exp_buffer and bid in self._up_exp_buffer:
+                gate_data = self._gate_exp_buffer.pop(bid)
+                up_data = self._up_exp_buffer.pop(bid)
+                # gate/up shape: (n_expert, n_ff, n_embd), concatenate to (n_expert, n_ff*2, n_embd)
+                fused_data = torch.cat([gate_data, up_data], dim=1)
+                fused_name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_UP_EXP, bid)
+                logger.info(f"Fused gate_exps and up_exps for layer {bid}")
+                return [(fused_name, fused_data)]
+
+            # If we buffered a gate/up tensor, wait for the other
+            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid) or \
+               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
+                return []
+
+        return [(new_name, data_torch)]
+
+    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
+        del name, new_name, bid, n_dims  # unused
+
+        return False
+
+    # some models need extra generated tensors (like rope_freqs)
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        return ()
+
+    @staticmethod
+    def _nvfp4_pack(weight: Tensor, scale: Tensor) -> tuple[np.ndarray, list[int]]:
+        """Repack NVFP4 ModelOpt tensors into ggml super-block layout.
+        Preserves original E4M3 scale bits as UE4M3 (strip sign bit).
+        The per-tensor scale2 factor is stored as a separate tensor and applied at inference time via ggml_mul().
+        Returns (raw_data, logical_shape)."""
+
+        out_features = weight.shape[0]
+        n_blocks = scale.shape[1]
+
+        # Unpack ModelOpt nibble-packed weights
+        w = weight.reshape(out_features, n_blocks, 8)
+        vals = torch.stack([w & 0x0F, w >> 4], dim=-1).reshape(out_features, n_blocks, 16)
+
+        # Preserve original E4M3 scale bits as UE4M3 (strip sign bit)
+        d_ue = scale.view(torch.uint8).numpy().reshape(out_features, n_blocks) & 0x7F
+        qs = (vals[:, :, :8] | (vals[:, :, 8:] << 4)).to(torch.uint8).numpy()
+
+        # Pack into super-blocks: [4 UE4M3 scales, 32 qs bytes] = 36 bytes per 64 elements
+        n_super = n_blocks // 4
+        d_grouped = d_ue.reshape(out_features, n_super, 4)
+        qs_grouped = qs.reshape(out_features, n_super, 4, 8).reshape(out_features, n_super, 32)
+        raw = np.concatenate([d_grouped, qs_grouped], axis=-1).reshape(out_features, n_super * 36)
+        return raw, [out_features, n_super * 64]
+
+    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
+        new_name = self.map_tensor_name(name)
+
+        raw, shape = self._nvfp4_pack(weight, scale)
+        logger.info(f"Repacked {new_name} with shape {shape} and quantization NVFP4")
+        self.gguf_writer.add_tensor(new_name, raw, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
+
+        self._write_scale_tensor(new_name.replace(".weight", ".scale"), scale2)
+        self._write_scale_tensor(new_name.replace(".weight", ".input_scale"), input_scale)
+
+    def _generate_nvfp4_tensors(self):
+        # Per-layer expert merging to avoid holding all experts in memory
+        expert_blocks: dict[tuple[int, str], list[tuple[int, np.ndarray]]] = {}
+        expert_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
+        expert_input_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
+        expert_shapes: dict[tuple[int, str], list[int]] = {}
+        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
+        consumed: list[str] = []
+
+        for name in self.model_tensors.keys():
+            if not name.endswith(".weight"):
+                continue
+            scale_name = name.replace(".weight", ".weight_scale")
+            scale2_name = name.replace(".weight", ".weight_scale_2")
+            input_scale_name = name.replace(".weight", ".input_scale")
+            if scale_name not in self.model_tensors:
+                continue
+            # Force eager materialization of lazy tensors
+            weight = LazyTorchTensor.to_eager(self.model_tensors[name]())
+            scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]())
+
+            # Skip non-NVFP4 tensors (e.g. FP8 with per-channel 1D scales)
+            if scale.ndim < 2:
+                continue
+
+            scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))())
+            input_scale = LazyTorchTensor.to_eager(self.model_tensors.get(input_scale_name, lambda: torch.tensor(1.0))())
+
+            # Mark tensors for removal from model_tensors (already written to gguf)
+            consumed.extend([name, scale_name])
+            if scale2_name in self.model_tensors:
+                consumed.append(scale2_name)
+            if input_scale_name in self.model_tensors:
+                consumed.append(input_scale_name)
+
+            # Check if this is a per-expert tensor
+            m = re.search(r'\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight$', name)
+            if m:
+                expert_id = int(m.group(1))
+                proj_type = m.group(2)
+                bid_m = re.search(r'\.layers\.(\d+)\.', name)
+                bid = int(bid_m.group(1)) if bid_m else 0
+                key = (bid, proj_type)
+
+                raw, shape = self._nvfp4_pack(weight, scale)
+
+                if key not in expert_blocks:
+                    expert_blocks[key] = []
+                    expert_scales[key] = []
+                    expert_input_scales[key] = []
+                    expert_shapes[key] = shape
+                expert_blocks[key].append((expert_id, raw.copy()))
+                # Collect per-expert scale2 (scalar per expert)
+                expert_scales[key].append((expert_id, float(scale2.float().sum())))
+                # Collect per-expert input_scale (scalar per expert)
+                expert_input_scales[key].append((expert_id, float(input_scale.float().sum())))
+
+                # Flush when all experts for this (layer, proj) are collected
+                if n_experts > 0 and len(expert_blocks[key]) >= n_experts:
+                    self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
+            else:
+                self._repack_nvfp4(name, weight, scale, scale2, input_scale)
+
+        # Flush any remaining experts (fallback if n_experts was unknown)
+        for bid, proj_type in list(expert_blocks.keys()):
+            self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
+
+        # Remove consumed tensors so get_tensors/modify_tensors won't see them
+        for name in consumed:
+            self.model_tensors.pop(name, None)
+
+        # Remove any remaining unused auxiliary tensors
+        for name in list(self.model_tensors.keys()):
+            if name.endswith((".k_scale", ".v_scale")):
+                del self.model_tensors[name]
+
+    def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type):
+        experts = expert_blocks.pop(key)
+        scales = expert_scales.pop(key)
+        input_scales = expert_input_scales.pop(key)
+        shape = expert_shapes.pop(key)
+
+        experts.sort(key=lambda x: x[0])
+        merged = np.stack([e[1] for e in experts], axis=0)
+        merged_name = f"model.layers.{bid}.mlp.experts.{proj_type}.weight"
+        new_name = self.map_tensor_name(merged_name)
+        logger.info(f"Repacked {new_name} with shape [{len(experts)}, {shape[0]}, {shape[1]}] and quantization NVFP4")
+        self.gguf_writer.add_tensor(new_name, merged, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
+
+        scales.sort(key=lambda x: x[0])
+        self._write_scales_tensor(new_name.replace(".weight", ".scale"), [s[1] for s in scales])
+
+        input_scales.sort(key=lambda x: x[0])
+        self._write_scales_tensor(new_name.replace(".weight", ".input_scale"), [s[1] for s in input_scales])
+
+        del experts, merged
+
+    def prepare_tensors(self):
+        # detect NVFP4 quantization (ModelOpt format)
+        quant_algo = (self.hparams.get("quantization_config") or {}).get("quant_algo")
+        quant_method = (self.hparams.get("quantization_config") or {}).get("quant_method")
+        quant_layers = (self.hparams.get("quantization_config") or {}).get("quantized_layers") or {}
+        quant_config_file = self.dir_model / "hf_quant_config.json"
+
+        if (not quant_algo or not quant_layers) and quant_config_file.is_file():
+            with open(quant_config_file, "r", encoding="utf-8") as f:
+                hf_quant_config = json.load(f)
+                quant_config = hf_quant_config.get("quantization") or {}
+                producer = hf_quant_config.get("producer") or {}
+                producer_name = (producer.get("name") or "").lower()
+                if quant_method is None:
+                    self.hparams.setdefault("quantization_config", {})["quant_method"] = producer_name
+                quant_algo = quant_config.get("quant_algo", quant_algo)
+                quant_layers = quant_config.get("quantized_layers", quant_layers) or {}
+
+        # Some models use per-tensor quant_algo (e.g. "MIXED_PRECISION" with
+        # per-layer NVFP4/FP8) instead of a single global "NVFP4" value.
+        if quant_algo != "NVFP4":
+            if any(v.get("quant_algo") == "NVFP4" for v in quant_layers.values() if isinstance(v, dict)):
+                quant_algo = "NVFP4"
+
+        self._is_nvfp4 = quant_algo == "NVFP4"
+        self._is_mxfp4 = quant_method == "mxfp4"
+
+        # NVFP4 weights are repacked and written directly to gguf_writer.
+        # This must run before dequant_model so NVFP4 tensors are removed
+        # from model_tensors, leaving only non-NVFP4 (e.g. FP8) for dequant.
+        if self._is_nvfp4:
+            self._generate_nvfp4_tensors()
+
+        self.dequant_model()
+
+        # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
+        if self.tensor_map.mapping:
+            max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
+        else:
+            max_name_len = len("vision_encoder.weight,")  # Default reasonable length
+
+        for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()):
+            # we don't need these
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            # use the first number-like part of the tensor name as the block id
+            bid = None
+            for part in name.split("."):
+                if part.isdecimal():
+                    bid = int(part)
+                    break
+
+            for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
+                # TODO: why do we squeeze here?
+                # data = data_torch.squeeze().numpy()
+                data = data_torch.numpy()
+
+                n_dims = len(data.shape)
+                data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
+
+                # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
+                if n_dims <= 1 or new_name.endswith("_norm.weight"):
+                    data_qtype = gguf.GGMLQuantizationType.F32
+
+                # Conditions should closely match those in llama_model_quantize_internal in llama.cpp
+                # Some tensor types are always in float32
+                if data_qtype is False and (
+                    any(
+                        self.match_model_tensor_name(new_name, key, bid)
+                        for key in (
+                            gguf.MODEL_TENSOR.FFN_GATE_INP,
+                            gguf.MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+                            gguf.MODEL_TENSOR.POS_EMBD,
+                            gguf.MODEL_TENSOR.TOKEN_TYPES,
+                            gguf.MODEL_TENSOR.SSM_CONV1D,
+                            gguf.MODEL_TENSOR.SHORTCONV_CONV,
+                            gguf.MODEL_TENSOR.TIME_MIX_FIRST,
+                            gguf.MODEL_TENSOR.TIME_MIX_W1,
+                            gguf.MODEL_TENSOR.TIME_MIX_W2,
+                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
+                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
+                            gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
+                            gguf.MODEL_TENSOR.POSNET_NORM1,
+                            gguf.MODEL_TENSOR.POSNET_NORM2,
+                            gguf.MODEL_TENSOR.V_ENC_EMBD_POS,
+                            gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
+                            gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
+                            gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
+                            # Kimi KDA conv weights should be F32
+                            gguf.MODEL_TENSOR.SSM_CONV1D_Q,
+                            gguf.MODEL_TENSOR.SSM_CONV1D_K,
+                            gguf.MODEL_TENSOR.SSM_CONV1D_V,
+                        )
+                    )
+                    or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
+                ):
+                    data_qtype = gguf.GGMLQuantizationType.F32
+
+                if data_qtype is False and any(
+                    self.match_model_tensor_name(new_name, key, bid)
+                    for key in (
+                        gguf.MODEL_TENSOR.TOKEN_EMBD,
+                        gguf.MODEL_TENSOR.PER_LAYER_TOKEN_EMBD,
+                        gguf.MODEL_TENSOR.OUTPUT,
+                        gguf.MODEL_TENSOR.ALTUP_ROUTER,
+                        gguf.MODEL_TENSOR.LAUREL_L,
+                        gguf.MODEL_TENSOR.LAUREL_R,
+                    )
+                ):
+                    if self.ftype in (
+                        gguf.LlamaFileType.MOSTLY_TQ1_0,
+                        gguf.LlamaFileType.MOSTLY_TQ2_0,
+                    ):
+                        # TODO: use Q4_K and Q6_K
+                        data_qtype = gguf.GGMLQuantizationType.F16
+
+                # No override (data_qtype is False), or wants to be quantized (data_qtype is True)
+                if isinstance(data_qtype, bool):
+                    if self.ftype == gguf.LlamaFileType.ALL_F32:
+                        data_qtype = gguf.GGMLQuantizationType.F32
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_F16:
+                        data_qtype = gguf.GGMLQuantizationType.F16
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
+                        data_qtype = gguf.GGMLQuantizationType.BF16
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
+                        data_qtype = gguf.GGMLQuantizationType.Q8_0
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ1_0:
+                        data_qtype = gguf.GGMLQuantizationType.TQ1_0
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ2_0:
+                        data_qtype = gguf.GGMLQuantizationType.TQ2_0
+                    else:
+                        raise ValueError(f"Unknown file type: {self.ftype.name}")
+
+                try:
+                    data = gguf.quants.quantize(data, data_qtype)
+                except gguf.QuantError as e:
+                    logger.warning("%s, %s", e, "falling back to F16")
+                    data_qtype = gguf.GGMLQuantizationType.F16
+                    data = gguf.quants.quantize(data, data_qtype)
+
+                shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape
+
+                # reverse shape to make it similar to the internal ggml dimension order
+                shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}"
+
+                # n_dims is implicit in the shape
+                logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+
+                self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
+
+    def set_type(self):
+        self.gguf_writer.add_type(gguf.GGUFType.MODEL)
+
+    def prepare_metadata(self, vocab_only: bool):
+
+        total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count()
+
+        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params)
+
+        # If we are using HF model id, set the metadata name to the model id
+        if self.remote_hf_model_id:
+            self.metadata.name = self.remote_hf_model_id
+
+        # Fallback to model directory name if metadata name is still missing
+        if self.metadata.name is None:
+            self.metadata.name = self.dir_model.name
+
+        if self.ftype in (gguf.LlamaFileType.ALL_F32, gguf.LlamaFileType.MOSTLY_F16, gguf.LlamaFileType.MOSTLY_BF16):
+            if self._is_nvfp4:
+                self.ftype = gguf.LlamaFileType.MOSTLY_NVFP4
+            elif self._is_mxfp4:
+                self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE
+
+        # Generate parameter weight class (useful for leader boards) if not yet determined
+        if self.metadata.size_label is None and total_params > 0:
+            self.metadata.size_label = gguf.size_label(total_params, shared_params, expert_params, expert_count)
+
+        self.set_type()
+
+        logger.info("Set meta model")
+        self.metadata.set_gguf_meta_model(self.gguf_writer)
+
+        logger.info("Set model parameters")
+        self.set_gguf_parameters()
+
+        logger.info("Set model quantization version")
+        self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+
+    def write_vocab(self):
+        raise NotImplementedError("write_vocab() must be implemented in subclasses")
+
+    def write(self):
+        self.prepare_tensors()
+        self.prepare_metadata(vocab_only=False)
+        self.gguf_writer.write_header_to_file(path=self.fname_out)
+        self.gguf_writer.write_kv_data_to_file()
+        self.gguf_writer.write_tensors_to_file(progress=True)
+        self.gguf_writer.close()
+
+    @staticmethod
+    def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]:
+        part_names: list[str] = []
+        for filename in os.listdir(dir_model):
+            if filename.startswith(prefix) and filename.endswith(suffix):
+                part_names.append(filename)
+
+        part_names.sort()
+
+        return part_names
+
+    @staticmethod
+    def load_hparams(dir_model: Path, is_mistral_format: bool):
+        if is_mistral_format:
+            with open(dir_model / "params.json", "r", encoding="utf-8") as f:
+                config = json.load(f)
+            return config
+
+        try:
+            # for security reason, we don't allow loading remote code by default
+            # if a model need remote code, we will fallback to config.json
+            config = AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict()
+        except Exception as e:
+            logger.warning(f"Failed to load model config from {dir_model}: {e}")
+            logger.warning("Trying to load config.json instead")
+            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+                config = json.load(f)
+        if "llm_config" in config:
+            # rename for InternVL
+            config["text_config"] = config["llm_config"]
+        if "lm_config" in config:
+            # rename for GlmASR
+            config["text_config"] = config["lm_config"]
+        if "thinker_config" in config:
+            # rename for Qwen2.5-Omni
+            config["text_config"] = config["thinker_config"]["text_config"]
+        if "language_config" in config:
+            # rename for DeepSeekOCR
+            config["text_config"] = config["language_config"]
+        if "lfm" in config:
+            # rename for LFM2-Audio
+            config["text_config"] = config["lfm"]
+        return config
+
+    @classmethod
+    def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
+        assert names
+
+        def func(modelcls: AnyModel) -> AnyModel:
+            model_type = ModelType.MMPROJ if modelcls.model_arch == gguf.MODEL_ARCH.MMPROJ else ModelType.TEXT
+            for name in names:
+                cls._model_classes[model_type][name] = modelcls
+            return modelcls
+        return func
+
+    @classmethod
+    def print_registered_models(cls):
+        for model_type, model_classes in cls._model_classes.items():
+            logger.error(f"{model_type.name} models:")
+            for name in sorted(model_classes.keys()):
+                logger.error(f"  - {name}")
+
+    @classmethod
+    def from_model_architecture(cls, arch: str, model_type = ModelType.TEXT) -> type[ModelBase]:
+        try:
+            return cls._model_classes[model_type][arch]
+        except KeyError:
+            raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
+
+
+class TextModel(ModelBase):
+    model_type = ModelType.TEXT
+    hf_arch: str
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if not self.is_mistral_format:
+            self.hf_arch = get_model_architecture(self.hparams, self.model_type)
+        else:
+            self.hf_arch = ""
+
+        if "text_config" in self.hparams:
+            # move the text_config to the root level
+            self.hparams = {**self.hparams, **self.hparams["text_config"]}
+
+        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+        self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {}
+
+        rope_theta = self.find_hparam(["global_rope_theta", "rope_global_theta", "rope_theta_global", "rope_theta", "rotary_emb_base"], optional=True)
+        local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "rope_theta_local", "swa_rope_theta", "rope_local_base_freq"], optional=True)
+
+        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
+        if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
+            if local_rope_theta is not None:
+                self.rope_parameters["sliding_attention"] = {"rope_theta": local_rope_theta}
+            if "rope_theta" not in self.rope_parameters and rope_theta is not None:
+                self.rope_parameters["rope_theta"] = rope_theta
+            if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
+                self.rope_parameters["rope_type"] = rope_type
+
+    @classmethod
+    def __init_subclass__(cls):
+        # can't use an abstract property, because overriding it without type errors
+        # would require using decorated functions instead of simply defining the property
+        if "model_arch" not in cls.__dict__:
+            raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}")
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Skip multimodal tensors
+        if name.startswith(("mlp", "vit.", "vpm.", "siglip2.", "conformer.", "merger.", "resampler.", "sound_encoder.", "sound_projection.", "speech_embeddings.")) \
+                or "visual." in name or "vision." in name or "audio." in name or "talker." in name \
+                or "vision_" in name or "audio_" in name or "sam_model" in name \
+                or "token2wav." in name or "code2wav." in name \
+                or "projector." in name or "pre_mm_projector_norm" in name \
+                or "image_newline" in name or "view_seperator" in name \
+                or "patch_embed" in name or "patch_embedding" in name \
+                or "patch_merger." in name or "model.connector." in name:
+            return None
+
+        return super().filter_tensors(item)
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def prepare_metadata(self, vocab_only: bool):
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        total_params = self.gguf_writer.get_total_parameter_count()[0]
+        # Extract the encoding scheme from the file type name. e.g. 'gguf.LlamaFileType.MOSTLY_Q8_0' --> 'Q8_0'
+        output_type: str = self.ftype.name.partition("_")[2]
+
+        # Filename Output
+        if self.fname_out.is_dir():
+            # Generate default filename based on model specification and available metadata
+            if not vocab_only:
+                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None)
+            else:
+                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab")
+
+            # Use the default filename
+            self.fname_out = self.fname_out / f"{fname_default}.gguf"
+        else:
+            # Output path is a custom defined templated filename
+            # Note: `not is_dir()` is used because `.is_file()` will not detect
+            #       file template strings as it doesn't actually exist as a file
+
+            # Process templated file name with the output ftype, useful with the "auto" ftype
+            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
+
+        logger.info("Set model tokenizer")
+        self.set_vocab()
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_block_count(self.block_count)
+
+        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length", "max_sequence_length", "model_max_length"], optional=True)) is not None:
+            self.gguf_writer.add_context_length(n_ctx)
+            logger.info(f"gguf: context length = {n_ctx}")
+
+        if (n_embd := self.find_hparam(["hidden_size", "n_embd", "dim"], optional=True)) is not None:
+            self.gguf_writer.add_embedding_length(n_embd)
+            logger.info(f"gguf: embedding length = {n_embd}")
+
+        if (n_ff := self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
+            self.gguf_writer.add_feed_forward_length(n_ff)
+            logger.info(f"gguf: feed forward length = {n_ff}")
+
+        if (n_head := self.find_hparam(["num_attention_heads", "n_head", "n_heads"], optional=True)) is not None:
+            self.gguf_writer.add_head_count(n_head)
+            logger.info(f"gguf: head count = {n_head}")
+
+        if (n_head_kv := self.find_hparam(["num_key_value_heads", "n_kv_heads"], optional=True)) is not None:
+            self.gguf_writer.add_head_count_kv(n_head_kv)
+            logger.info(f"gguf: key-value head count = {n_head_kv}")
+
+        if self.hparams.get("is_causal") is False:
+            self.gguf_writer.add_causal_attention(False)
+            logger.info("gguf: causal attention = False")
+
+        # TODO: Handle "sliding_attention" similarly when models start implementing it
+        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
+        if (rope_type := rope_params.get("rope_type")) is not None:
+            rope_factor = rope_params.get("factor")
+            rope_gguf_type = gguf.RopeScalingType.NONE
+            if rope_type == "linear" and rope_factor is not None:
+                rope_gguf_type = gguf.RopeScalingType.LINEAR
+                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
+                self.gguf_writer.add_rope_scaling_factor(rope_factor)
+            elif rope_type == "yarn" and rope_factor is not None:
+                rope_gguf_type = gguf.RopeScalingType.YARN
+                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
+                self.gguf_writer.add_rope_scaling_factor(rope_factor)
+                self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"])
+                if (yarn_ext_factor := rope_params.get("extrapolation_factor")) is not None:
+                    self.gguf_writer.add_rope_scaling_yarn_ext_factor(yarn_ext_factor)
+                if (yarn_attn_factor := rope_params.get("attention_factor", rope_params.get("attn_factor"))) is not None:
+                    self.gguf_writer.add_rope_scaling_yarn_attn_factor(yarn_attn_factor)
+                if (yarn_beta_fast := rope_params.get("beta_fast")) is not None:
+                    self.gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_beta_fast)
+                if (yarn_beta_slow := rope_params.get("beta_slow")) is not None:
+                    self.gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_beta_slow)
+                # self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
+            elif rope_type == "su" or rope_type == "longrope":
+                rope_gguf_type = gguf.RopeScalingType.LONGROPE
+                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
+            elif rope_type == "dynamic":
+                # HunYuan, handled in model class
+                pass
+            elif rope_type.lower() == "llama3":
+                # Handled in generate_extra_tensors
+                pass
+            else:
+                logger.warning(f"Unknown RoPE type: {rope_type}")
+            logger.info(f"gguf: rope scaling type = {rope_gguf_type.name}")
+
+        if "mrope_section" in self.rope_parameters:
+            mrope_section = self.rope_parameters["mrope_section"]
+            # Pad to 4 dimensions [time, height, width, extra]
+            while len(mrope_section) < 4:
+                mrope_section.append(0)
+            self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
+            logger.info(f"gguf: mrope sections: {mrope_section[:4]}")
+
+        if (rope_theta := rope_params.get("rope_theta")) is not None:
+            self.gguf_writer.add_rope_freq_base(rope_theta)
+            logger.info(f"gguf: rope theta = {rope_theta}")
+        if (local_rope_theta := self.rope_parameters.get("sliding_attention", {}).get("rope_theta")) is not None:
+            self.gguf_writer.add_rope_freq_base_swa(local_rope_theta)
+            logger.info(f"gguf: rope theta swa = {local_rope_theta}")
+        if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"], optional=True)) is not None:
+            self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
+            logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
+        if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
+            self.gguf_writer.add_layer_norm_eps(f_norm_eps)
+            logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
+        if (n_experts := self.find_hparam(["num_local_experts", "num_experts"], optional=True)) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+            logger.info(f"gguf: expert count = {n_experts}")
+        if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token", "top_k_experts"], optional=True)) is not None:
+            self.gguf_writer.add_expert_used_count(n_experts_used)
+            logger.info(f"gguf: experts used count = {n_experts_used}")
+        if (n_expert_groups := self.hparams.get("n_group")) is not None:
+            self.gguf_writer.add_expert_group_count(n_expert_groups)
+            logger.info(f"gguf: expert groups count = {n_expert_groups}")
+        if (n_group_used := self.hparams.get("topk_group")) is not None:
+            self.gguf_writer.add_expert_group_used_count(n_group_used)
+            logger.info(f"gguf: expert groups used count = {n_group_used}")
+
+        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None:
+            if score_func == "sigmoid":
+                self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+            elif score_func == "softmax":
+                self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+            else:
+                raise ValueError(f"Unsupported expert score gating function value: {score_func}")
+            logger.info(f"gguf: expert score gating function = {score_func}")
+
+        if (head_dim := self.hparams.get("head_dim")) is not None:
+            self.gguf_writer.add_key_length(head_dim)
+            self.gguf_writer.add_value_length(head_dim)
+
+        self.gguf_writer.add_file_type(self.ftype)
+        logger.info(f"gguf: file type = {self.ftype}")
+
+    def write_vocab(self):
+        if len(self.gguf_writer.tensors) != 1:
+            raise ValueError('Splitting the vocabulary is not supported')
+
+        self.prepare_metadata(vocab_only=True)
+        self.gguf_writer.write_header_to_file(path=self.fname_out)
+        self.gguf_writer.write_kv_data_to_file()
+        self.gguf_writer.close()
+
+    def does_token_look_special(self, token: str | bytes) -> bool:
+        if isinstance(token, (bytes, bytearray)):
+            token_text = token.decode(encoding="utf-8")
+        elif isinstance(token, memoryview):
+            token_text = token.tobytes().decode(encoding="utf-8")
+        else:
+            token_text = token
+
+        # Some models mark some added tokens which ought to be control tokens as not special.
+        # (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
+        seems_special = token_text in (
+            "<pad>",  # deepseek-coder
+            "<mask>", "<2mass>", "[@BOS@]",  # gemma{,-2}
+        )
+
+        seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
+        seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>"))  # deepseek-coder
+
+        # TODO: should these be marked as UNUSED instead? (maybe not)
+        seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">"))  # gemma{,-2}
+
+        return seems_special
+
+    # used for GPT-2 BPE and WordPiece vocabs
+    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
+        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
+
+        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
+                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
+                    if not added_tokens_decoder[i].normalized:
+                        previous_token = token
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
+                        if previous_token != token:
+                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
+
+                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        # NOTE: this was added for Gemma.
+                        # Encoding and decoding the tokens above isn't sufficient for this case.
+                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
+
+        return tokens, toktypes, tokpre
+
+    # NOTE: this function is generated by convert_hf_to_gguf_update.py
+    #       do not modify it manually!
+    # ref:  https://github.com/ggml-org/llama.cpp/pull/6920
+    # Marker: Start get_vocab_base_pre
+    def get_vocab_base_pre(self, tokenizer) -> str:
+        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
+        # is specific for the BPE pre-tokenizer used by the model
+        # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
+        # use in llama.cpp to implement the same pre-tokenizer
+
+        chktxt = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
+
+        chktok = tokenizer.encode(chktxt)
+        chkhsh = sha256(str(chktok).encode()).hexdigest()
+
+        logger.debug(f"chktok: {chktok}")
+        logger.debug(f"chkhsh: {chkhsh}")
+
+        res = None
+
+        # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
+        #       or pull the latest version of the model from Huggingface
+        #       don't edit the hashes manually!
+        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
+            res = "chatglm-bpe"
+        if chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
+            res = "chatglm-bpe"
+        if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-hf
+            res = "glm4"
+        if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
+            # ref: https://huggingface.co/zai-org/GLM-4.5-Air
+            res = "glm4"
+        if chkhsh == "cdf5f35325780597efd76153d4d1c16778f766173908894c04afc20108536267":
+            # ref: https://huggingface.co/zai-org/GLM-4.7-Flash
+            res = "glm4"
+        if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
+            # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
+            res = "minerva-7b"
+        if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
+            # ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
+            res = "hunyuan"
+        if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
+            # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
+            res = "hunyuan-dense"
+        if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
+            # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
+            res = "falcon-h1"
+        if chkhsh == "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86":
+            # ref: https://huggingface.co/tiiuae/Falcon-H1-1B-Base
+            res = "falcon-h1"
+        if chkhsh == "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896":
+            # ref: https://huggingface.co/tiiuae/Falcon-H1-7B-Base
+            res = "falcon-h1"
+        if chkhsh == "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b":
+            # ref: https://huggingface.co/tiiuae/Falcon-H1-34B-Base
+            res = "falcon-h1"
+        if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890":
+            # ref: https://huggingface.co/moonshotai/Kimi-K2-Base
+            res = "kimi-k2"
+        if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
+            # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
+            res = "qwen2"
+        if chkhsh == "1444df51289cfa8063b96f0e62b1125440111bc79a52003ea14b6eac7016fd5f":
+            # ref: https://huggingface.co/openbmb/MiniCPM-V-4_6
+            res = "qwen35"
+        if chkhsh == "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273":
+            # ref: https://huggingface.co/alvarobartt/grok-2-tokenizer
+            res = "grok-2"
+        if chkhsh == "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df":
+            # ref: https://huggingface.co/aari1995/German_Semantic_V3
+            res = "jina-v2-de"
+        if chkhsh == "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4":
+            # ref: https://huggingface.co/evilfreelancer/ruGPT3XL
+            res = "gpt-2"
+        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
+            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+            res = "llama-bpe"
+        if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754":
+            # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base
+            res = "deepseek-llm"
+        if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821":
+            # ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
+            res = "deepseek-coder"
+        if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
+            # ref: https://huggingface.co/tiiuae/falcon-7b
+            res = "falcon"
+        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
+            # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
+            res = "bert-bge"
+        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
+            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
+            res = "falcon3"
+        if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7":
+            # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5
+            res = "bert-bge-large"
+        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
+            # ref: https://huggingface.co/mosaicml/mpt-7b
+            res = "mpt"
+        if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34":
+            # ref: https://huggingface.co/bigcode/starcoder2-3b
+            res = "starcoder"
+        if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
+            # ref: https://huggingface.co/openai-community/gpt2
+            res = "gpt-2"
+        if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3":
+            # ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b
+            res = "stablelm2"
+        if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
+            # ref: https://huggingface.co/smallcloudai/Refact-1_6-base
+            res = "refact"
+        if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
+            # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
+            res = "command-r"
+        if chkhsh == "d772b220ace2baec124bed8cfafce0ead7d6c38a4b65ef11261cf9d5d62246d1":
+            # ref: https://huggingface.co/CohereLabs/tiny-aya-base
+            res = "tiny_aya"
+        if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
+            # ref: https://huggingface.co/Qwen/Qwen1.5-7B
+            res = "qwen2"
+        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
+            # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf
+            res = "olmo"
+        if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
+            # ref: https://huggingface.co/databricks/dbrx-base
+            res = "dbrx"
+        if chkhsh == "c7699093ba4255a91e702aa38a596aa81669f3525dae06c2953267dde580f448":
+            # ref: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
+            res = "jina-v1-en"
+        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
+            res = "jina-v2-en"
+        if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es
+            res = "jina-v2-es"
+        if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
+            res = "jina-v2-de"
+        if chkhsh == "a023e9fdc5a11f034d3ef515b92350e56fb2af1f66c6b6811a4444ea9bf8763d":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v5-text-nano
+            res = "jina-v5-nano"
+        if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d":
+            # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
+            res = "smaug-bpe"
+        if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360":
+            # ref: https://huggingface.co/LumiOpen/Poro-34B-chat
+            res = "poro-chat"
+        if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
+            res = "jina-v2-code"
+        if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
+            # ref: https://huggingface.co/LumiOpen/Viking-7B
+            res = "viking"
+        if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
+            # ref: https://huggingface.co/core42/jais-13b
+            res = "jais"
+        if chkhsh == "bc5108ee1eb6a3d600cadd065f63190fbd0554dbc9e4bbd6a0d977970afc8d2a":
+            # ref: https://huggingface.co/inceptionai/Jais-2-8B-Chat
+            res = "jais-2"
+        if chkhsh == "7b3e7548e4308f52a76e8229e4e6cc831195d0d1df43aed21ac6c93da05fec5f":
+            # ref: https://huggingface.co/WisdomShell/CodeShell-7B
+            res = "codeshell"
+        if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e":
+            # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
+            res = "tekken"
+        if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249":
+            # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M
+            res = "smollm"
+        if chkhsh == "3c30d3ad1d6b64202cd222813e7736c2db6e1bd6d67197090fc1211fbc612ae7":
+            # ref: https://huggingface.co/bigscience/bloom
+            res = "bloom"
+        if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21":
+            # ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small
+            res = "gpt3-finnish"
+        if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae":
+            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
+            res = "exaone"
+        if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085":
+            # ref: https://huggingface.co/microsoft/phi-2
+            res = "phi-2"
+        if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450":
+            # ref: https://huggingface.co/facebook/chameleon-7b
+            res = "chameleon"
+        if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65":
+            # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
+            res = "roberta-bpe"
+        if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
+            # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
+            res = "gigachat"
+        if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
+            # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
+            res = "megrez"
+        if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
+            # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
+            res = "deepseek-v3"
+        if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5":
+            # ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+            res = "deepseek-r1-qwen"
+        if chkhsh == "ccc2ef013c104be7bae2965776d611e1d7a8a2a9c547dd93a682c9a9fc80352e":
+            # ref: https://huggingface.co/Xenova/gpt-4o
+            res = "gpt-4o"
+        if chkhsh == "7dec86086fcc38b66b7bc1575a160ae21cf705be7718b9d5598190d7c12db76f":
+            # ref: https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k
+            res = "superbpe"
+        if chkhsh == "1994ffd01900cfb37395608534236ecd63f2bd5995d6cb1004dda1af50240f15":
+            # ref: https://huggingface.co/trillionlabs/Trillion-7B-preview
+            res = "trillion"
+        if chkhsh == "96a5f08be6259352137b512d4157e333e21df7edd3fcd152990608735a65b224":
+            # ref: https://huggingface.co/inclusionAI/Ling-lite
+            res = "bailingmoe"
+        if chkhsh == "d353350c764d8c3b39c763113960e4fb4919bea5fbf208a0e3b22e8469dc7406":
+            # ref: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
+            res = "llama4"
+        if chkhsh == "0e9433cbbb161f89e264eb32e8e64bfe69e834973ffca5d41d3948a604a3e2a3":
+            # ref: https://huggingface.co/mistral-community/pixtral-12b
+            res = "pixtral"
+        if chkhsh == "d5f1dd6f980fec569fb218a81a7658ac45fc56b38c5a0adeb1c232fbe04ef5ec":
+            # ref: https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base
+            res = "seed-coder"
+        if chkhsh == "b0a6b1c0bd5998ebd9df08611efde34a4ff03faed45ae09c43e6b31ebd4b94cf":
+            # ref: https://huggingface.co/skt/A.X-4.0
+            res = "a.x-4.0"
+        if chkhsh == "f6791d196f87ce6b56a7d234be618e0d58f8cda3549416635b2bebcd22cd95c4":
+            # ref: https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct
+            res = "midm-2.0"
+        if chkhsh == "169bf0296a13c4d9b7672313f749eb36501d931022de052aad6e36f2bf34dd51":
+            # ref: https://huggingface.co/LiquidAI/LFM2-Tokenizer
+            res = "lfm2"
+        if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb":
+            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
+            res = "exaone4"
+        if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
+            # ref: https://huggingface.co/JetBrains/Mellum-4b-base
+            res = "mellum"
+        if chkhsh == "a0b64b4385f123663873756336c085744376d015ff328bb1d901598f63c44152":
+            # ref: https://huggingface.co/answerdotai/ModernBERT-base
+            res = "modern-bert"
+        if chkhsh == "49fc0303c9e0d2c2c565c510f64b2d9b271276acdcdadff733249eda9f7d59df":
+            # ref: https://huggingface.co/arcee-ai/Trinity-Tokenizer
+            res = "afmoe"
+        if chkhsh == "9b1be57e70d20d9501b2b3186e792d81181ae36ada3903c26f9fea418cf87206":
+            # ref: https://huggingface.co/inclusionAI/Ling-mini-base-2.0
+            res = "bailingmoe2"
+        if chkhsh == "53e325976a6e142379c19b09afcae354f2f496f147afa8f9e189a33fe4e3024e":
+            # ref: https://huggingface.co/ibm-granite/granite-docling-258M
+            res = "granite-docling"
+        if chkhsh == "f4f37b6c8eb9ea29b3eac6bb8c8487c5ab7885f8d8022e67edc1c68ce8403e95":
+            # ref: https://huggingface.co/MiniMaxAI/MiniMax-M2
+            res = "minimax-m2"
+        if chkhsh == "4a2e2abae11ca2b86d570fc5b44be4d5eb5e72cc8f22dd136a94b37da83ab665":
+            # ref: https://huggingface.co/KORMo-Team/KORMo-tokenizer
+            res = "kormo"
+        if chkhsh == "9d70134b369a70e5735009b6de918f7581b5211f7c074d1f89f753aea8248af1":
+            # ref: https://huggingface.co/tencent/Youtu-LLM-2B
+            res = "youtu"
+        if chkhsh == "16389f0a1f51ee53e562ffd51c371dc508639ab0e4261502071836e50e223e91":
+            # ref: https://huggingface.co/upstage/Solar-Open-100B
+            res = "solar-open"
+        if chkhsh == "6c81ce329e0802883b22eabab0d3fa48357337ef1ecb45443828bf1f6254833f":
+            # ref: https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B
+            res = "exaone-moe"
+        if chkhsh == "d30d75d9059f1aa2c19359de71047b3ae408c70875e8a3ccf8c5fba56c9d8af4":
+            # ref: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct
+            res = "qwen35"
+        if chkhsh == "b4b8ca1f9769494fbd956ebc4c249de6131fb277a4a3345a7a92c7dd7a55808d":
+            # ref: https://huggingface.co/jdopensource/JoyAI-LLM-Flash
+            res = "joyai-llm"
+        if chkhsh == "e4d54df1ebc1f2b91acd986c5b51aa50837d5faf7c7398e73c1f9e9ee5d19869":
+            # ref: https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601
+            res = "kanana2"
+        if chkhsh == "862f827721df956049dff5ca81a57f29e575280bc622e290d3bf4e35eca29015":
+            # ref: https://huggingface.co/codefuse-ai/F2LLM-v2-4B
+            res = "f2llmv2"
+        if chkhsh == "62f6fb0a6fd5098caeabb19b07a5c1099cafc8b9c40eab6ea89ece4ec02fbc57":
+            # ref: https://huggingface.co/sarvamai/sarvam-30b
+            res = "sarvam-moe"
+
+        if res is None:
+            logger.warning("\n")
+            logger.warning("**************************************************************************************")
+            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
+            logger.warning("**          There are 2 possible reasons for this:")
+            logger.warning("**          - the model has not been added to convert_hf_to_gguf_update.py yet")
+            logger.warning("**          - the pre-tokenization config has changed upstream")
+            logger.warning("**          Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
+            logger.warning("** ref:     https://github.com/ggml-org/llama.cpp/pull/6920")
+            logger.warning("**")
+            logger.warning(f"** chkhsh:  {chkhsh}")
+            logger.warning("**************************************************************************************")
+            logger.warning("\n")
+            raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
+
+        logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
+        logger.debug(f"chkhsh: {chkhsh}")
+
+        return res
+        # Marker: End get_vocab_base_pre
+
+    def _set_vocab_none(self) -> None:
+        self.gguf_writer.add_tokenizer_model("none")
+
+    def _set_vocab_gpt2(self) -> None:
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _set_vocab_qwen(self):
+        from .qwen import QwenModel
+
+        dir_model = self.dir_model
+        hparams = self.hparams
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        vocab_size = hparams["vocab_size"]
+        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        merges = []
+        vocab = {}
+        mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
+        for token, rank in mergeable_ranks.items():
+            vocab[QwenModel.token_bytes_to_string(token)] = rank
+            if len(token) == 1:
+                continue
+            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+            assert len(merged) == 2
+            merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
+        added_vocab = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
+        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            elif reverse_vocab[i] in added_vocab:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.CONTROL)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
+        special_vocab.merges = merges
+        # only add special tokens when they were not already loaded from config.json
+        if len(special_vocab.special_token_ids) == 0:
+            special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+            special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        # this one is usually not in config.json anyway
+        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _set_vocab_sentencepiece(self, add_to_gguf=True):
+        tokens, scores, toktypes = self._create_vocab_sentencepiece()
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _create_vocab_sentencepiece(self):
+        from sentencepiece import SentencePieceProcessor
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        if not tokenizer_path.is_file():
+            raise FileNotFoundError(f"File not found: {tokenizer_path}")
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.find_hparam([
+            "vocab_size_per_layer_input", # gemma3n
+            "vocab_size",
+        ], optional=True) or tokenizer.vocab_size()
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+            if token_id >= vocab_size:
+                logger.warning(f'ignore tokens from {token_id}: id is out of range, max={vocab_size - 1}')
+                break
+
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if token_id >= vocab_size:
+                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
+                for token_id, token_data in added_tokens_decoder.items():
+                    token_id = int(token_id)
+                    token: str = token_data["content"]
+                    if token_id >= vocab_size:
+                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token.encode("utf-8"):
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}')
+                    if token_data.get("special") or self.does_token_look_special(token):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+                    else:
+                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
+                        toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+                    scores[token_id] = -1000.0
+                    tokens[token_id] = token.encode("utf-8")
+
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
+        return tokens, scores, toktypes
+
+    def _set_vocab_llama_hf(self):
+        vocab = gguf.LlamaHfVocab(self.dir_model)
+        tokens = []
+        scores = []
+        toktypes = []
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _set_vocab_rwkv_world(self):
+        assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file()
+        vocab_size = self.hparams.get("vocab_size", 65536)
+
+        tokens: list[bytes] = ['<s>'.encode("utf-8")]
+        toktypes: list[int] = [gguf.TokenType.CONTROL]
+
+        with open(self.dir_model / "rwkv_vocab_v20230424.txt", "r", encoding="utf-8") as f:
+            lines = f.readlines()
+            for line in lines:
+                parts = line.split(' ')
+                assert len(parts) >= 3
+                token, token_len = ast.literal_eval(' '.join(parts[1:-1])), int(parts[-1])
+                token = token.encode("utf-8") if isinstance(token, str) else token
+                assert isinstance(token, bytes)
+                assert len(token) == token_len
+                token_text: str = repr(token)[2:-1]  # "b'\xff'" -> "\xff"
+                tokens.append(token_text.encode("utf-8"))
+                toktypes.append(gguf.TokenType.NORMAL)
+        remainder = vocab_size - len(tokens)
+        assert remainder >= 0
+        for i in range(len(tokens), vocab_size):
+            tokens.append(f"[PAD{i}]".encode("utf-8"))
+            toktypes.append(gguf.TokenType.UNUSED)
+
+        self.gguf_writer.add_tokenizer_model("rwkv")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+        if special_vocab.chat_template is None:
+            template_path = Path(__file__).parent.parent / "models" / "templates" / "llama-cpp-rwkv-world.jinja"
+            if template_path.is_file():
+                with open(template_path, "r", encoding="utf-8") as f:
+                    template = f.read()
+            else:
+                template = "rwkv-world"
+            special_vocab.chat_template = template
+        # hack: Add '\n\n' as the EOT token to make it chat normally
+        special_vocab._set_special_token("eot", 261)
+        # hack: Override these as they have already been set (incorrectly)
+        special_vocab.special_token_ids["bos"] = 0
+        special_vocab.special_token_ids["eos"] = 0
+
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int):
+        tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf"
+        logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
+        vocab_reader = gguf.GGUFReader(tokenizer_path, "r")
+
+        default_pre = "mpt" if model_name == "gpt-neox" else "default"
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL)
+        assert field  # tokenizer model
+        self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8"))
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE)
+        self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre)
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST)
+        assert field  # token list
+        self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
+
+        if model_name == "llama-spm":
+            field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES)
+            assert field  # token scores
+            self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
+        assert field  # token types
+        self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
+        if model_name != "llama-spm":
+            field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES)
+            assert field  # token merges
+            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
+
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None:
+            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None:
+            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None:
+            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None:
+            self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None:
+            self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None:
+            self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0])
+
+    def _try_set_pooling_type(self) -> None:
+        # get pooling path
+        pooling_path = None
+        module_path = self.dir_model / "modules.json"
+        if module_path.is_file():
+            with open(module_path, encoding="utf-8") as f:
+                modules = json.load(f)
+            for mod in modules:
+                if mod["type"].endswith("Pooling"):
+                    pooling_path = mod["path"]
+                    break
+
+        mode_mapping = {
+            "mean": gguf.PoolingType.MEAN,
+            "cls": gguf.PoolingType.CLS,
+            "lasttoken": gguf.PoolingType.LAST,
+        }
+
+        # get pooling type
+        if pooling_path is not None:
+            with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
+                pooling = json.load(f)
+            if pooling.get("pooling_mode_mean_tokens"):
+                pooling_type = gguf.PoolingType.MEAN
+            elif pooling.get("pooling_mode_cls_token"):
+                pooling_type = gguf.PoolingType.CLS
+            elif pooling.get("pooling_mode_lasttoken"):
+                pooling_type = gguf.PoolingType.LAST
+            elif (pooling_mode := pooling.get("pooling_mode")) in mode_mapping:
+                pooling_type = mode_mapping[pooling_mode]
+            else:
+                raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported")
+            self.gguf_writer.add_pooling_type(pooling_type)
+
+    def _set_vocab_glmedge(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _set_vocab_glm(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        # Special tokens
+        # Note: Using <|endoftext|> (151329) for eot causes endless generation
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # ty: ignore[unresolved-attribute]  # 151331
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]  # 151336
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]  # 151329
+        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # ty: ignore[unresolved-attribute]  # 151338
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _set_vocab_interns1(self):
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())  # ty: ignore[unresolved-attribute]
+        vocab_size = self.hparams.get("vocab_size", len(vocab))
+        assert max(vocab.values()) < vocab_size
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
+
+        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
+                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
+                    if not added_tokens_decoder[i].normalized:
+                        previous_token = token
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
+                        if previous_token != token:
+                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
+
+                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
+
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab._set_special_token("bos", 151643)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _set_vocab_mistral(self):
+        from .mistral import MistralModel
+
+        if not _mistral_common_installed:
+            raise ImportError(_mistral_import_error_msg)
+
+        vocab = MistralVocab(self.dir_model)
+        logger.info(
+            f"Converting tokenizer {vocab.tokenizer_type} of size {vocab.vocab_size}."
+        )
+
+        self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model)
+
+        tokens = []
+        scores = []
+        toktypes = []
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size, (
+            f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})"
+        )
+
+        if vocab.tokenizer_type == MistralTokenizerType.tekken:
+            self.gguf_writer.add_tokenizer_pre("tekken")
+            self.gguf_writer.add_token_merges(
+                vocab.extract_vocab_merges_from_model()
+            )
+
+        logger.info(
+            f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}."
+        )
+
+        self.gguf_writer.add_bos_token_id(vocab.bos_id)
+        self.gguf_writer.add_eos_token_id(vocab.eos_id)
+        self.gguf_writer.add_unk_token_id(vocab.unk_id)
+        self.gguf_writer.add_pad_token_id(vocab.pad_id)
+
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_vocab_size(vocab.vocab_size)
+
+        self.gguf_writer.add_add_bos_token(True)
+        self.gguf_writer.add_add_eos_token(False)
+
+        local_template_file_path = self.dir_model / "chat_template.jinja"
+
+        if self.is_mistral_format and local_template_file_path.is_file():
+            # Ministral-3 and other new Mistral models come with chat templates.
+            # ref: https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/tree/main
+            logger.info("Using an existing Mistral local chat template.")
+
+            with open(local_template_file_path, "r", encoding="utf-8") as f:
+                template = f.read()
+        elif not self.is_mistral_format or not self.disable_mistral_community_chat_template:
+            template_dir = Path(__file__).parent.parent / "models/templates/"
+
+            # Log only for Mistral format that the official tokenization and detokenization is via `mistral-common`.
+            if self.is_mistral_format:
+                logger.info(
+                    "Using a Mistral community chat template. These templates can be subject to errors in early days or weeks after a release. "
+                    "Mistral recommends to use `mistral-common` to perform tokenization and detokenization."
+                )
+            template = MistralModel.get_community_chat_template(vocab, template_dir, self.is_mistral_format)
+        else:
+            logger.info("Not using a Mistral local or community chat template. Ensure to perform the tokenization and detokenization via `mistral-common`.")
+            template = None
+
+        if template is not None:
+            self.gguf_writer.add_chat_template(template)
+
+    def _set_vocab_plamo(self):
+        # PLaMo models use a custom tokenizer with a .jsonl file
+        tokenizer_jsonl_path = self.dir_model / "tokenizer.jsonl"
+        tokenizer_config_path = self.dir_model / "tokenizer_config.json"
+
+        if not tokenizer_jsonl_path.is_file():
+            raise FileNotFoundError(f"PLaMo tokenizer file not found: {tokenizer_jsonl_path}")
+
+        # Load tokenizer config
+        with open(tokenizer_config_path, "r", encoding="utf-8") as f:
+            tokenizer_config = json.load(f)
+
+        # Load tokens from JSONL file (actually a list format)
+        tokens = []
+        scores = []
+        toktypes = []
+
+        with open(tokenizer_jsonl_path, "r", encoding="utf-8") as f:
+            for line_num, line in enumerate(f):
+                if line.strip():
+                    token_data = json.loads(line)
+                    # Format: [token, score, type, ?, ?, ?, ?]
+                    token = token_data[0].encode("utf-8")
+                    score = float(token_data[1])
+                    token_type_str = token_data[2] if len(token_data) > 2 else "NORMAL"
+
+                    tokens.append(token)
+                    scores.append(score)
+
+                    if token_type_str == "UNKNOWN":
+                        toktypes.append(gguf.TokenType.UNKNOWN)
+                    elif token_type_str == "CONTROL":
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    elif token_type_str == "BYTE":
+                        toktypes.append(gguf.TokenType.BYTE)
+                    else:
+                        token_str = token_data[0]
+                        if token_str.startswith("<|plamo:") and token_str.endswith("|>"):
+                            toktypes.append(gguf.TokenType.CONTROL)
+                        else:
+                            toktypes.append(gguf.TokenType.NORMAL)
+
+        vocab_size = self.hparams["vocab_size"]
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(gguf.TokenType.UNUSED)
+
+        self.gguf_writer.add_tokenizer_model("plamo2")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] is not None:
+            token_id = tokens.index(tokenizer_config["bos_token"].encode("utf-8"))
+            self.gguf_writer.add_bos_token_id(token_id)
+        if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] is not None:
+            token_id = tokens.index(tokenizer_config["eos_token"].encode("utf-8"))
+            self.gguf_writer.add_eos_token_id(token_id)
+        if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] is not None:
+            token_id = tokens.index(tokenizer_config["pad_token"].encode("utf-8"))
+            self.gguf_writer.add_pad_token_id(token_id)
+        if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] is not None:
+            token_id = tokens.index(tokenizer_config["sep_token"].encode("utf-8"))
+            self.gguf_writer.add_sep_token_id(token_id)
+        if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] is not None:
+            token_id = tokens.index(tokenizer_config["unk_token"].encode("utf-8"))
+            self.gguf_writer.add_unk_token_id(token_id)
+
+        # Add <|plamo:op|> as EOT to ensure appropriate end of generation
+        self.gguf_writer.add_eot_token_id(4)
+
+        self.gguf_writer.add_add_space_prefix(False)
+
+
+class MmprojModel(ModelBase):
+    model_type = ModelType.MMPROJ
+    model_arch = gguf.MODEL_ARCH.MMPROJ
+    preprocessor_config: dict[str, Any]
+    global_config: dict[str, Any]
+
+    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "layers", "encoder_layers", "vt_num_hidden_layers"]
+
+    has_vision_encoder: bool = True # by default
+    has_audio_encoder: bool = False
+
+    # for models having multiple encoders, we need to separate their hparams
+    hparams_vision: dict[str, Any] | None = None
+    hparams_audio: dict[str, Any] | None = None
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        if self.model_arch != gguf.MODEL_ARCH.MMPROJ:
+            raise TypeError("MmprojModel must be subclassed with model_arch = gguf.MODEL_ARCH.MMPROJ")
+
+        # get n_embd of the text model
+        if not self.is_mistral_format:
+            if "text_config" not in self.hparams:
+                self.hparams["text_config"] = {}
+            if "audio_config" not in self.hparams:
+                self.hparams["audio_config"] = {}
+            text_config = {**self.hparams, **self.hparams["text_config"]}
+            self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0))
+        else:
+            text_config = {
+                k: v for k, v in self.hparams.items() if k not in ["vision_encoder", "audio_encoder"]
+            }
+            # mistral native params.json: "dim" is the text hidden size ("hidden_dim" is the FFN intermediate size)
+            self.n_embd_text = text_config.get("dim", 0)
+
+        assert self.n_embd_text > 0, "n_embd not found in hparams"
+
+        # move vision config to the top level, while preserving the original hparams in global_config
+        import copy
+        self.global_config = copy.deepcopy(self.hparams)
+        self.hparams_vision = self.get_vision_config()
+        self.hparams_audio = self.get_audio_config()
+
+        if self.hparams_vision is None and self.hparams_audio is None:
+            raise ValueError("vision_config / audio_config not found in hparams")
+
+        # for compat with vision-only models
+        self.hparams = self.hparams_vision or self.hparams_audio or self.hparams
+
+        # TODO @ngxson : this is a hack to support both vision and audio encoders
+        have_multiple_encoders = self.has_audio_encoder and self.has_vision_encoder
+        self.block_count = 128 if have_multiple_encoders else self.find_hparam(self.n_block_keys, True)
+        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)
+
+        # load preprocessor config
+        self.preprocessor_config = {}
+
+        # prefer preprocessor_config.json if possible
+        preprocessor_config_path = self.dir_model / "preprocessor_config.json"
+        if preprocessor_config_path.is_file():
+            with open(preprocessor_config_path, "r", encoding="utf-8") as f:
+                cfg = json.load(f)
+                # move media_proc_cfg to root level for compat
+                if "media_proc_cfg" in cfg:
+                    cfg = {
+                        **cfg,
+                        **cfg["media_proc_cfg"],
+                    }
+                # merge configs
+                self.preprocessor_config = {**self.preprocessor_config, **cfg}
+
+        # prefer processor_config.json if possible
+        processor_config_path = self.dir_model / "processor_config.json"
+        if processor_config_path.is_file():
+            with open(processor_config_path, "r", encoding="utf-8") as f:
+                cfg = json.load(f)
+                # move image_processor to root level for compat
+                if "image_processor" in cfg:
+                    cfg = {
+                        **cfg,
+                        **cfg["image_processor"],
+                    }
+                # merge configs
+                self.preprocessor_config = {**self.preprocessor_config, **cfg}
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Skip non-multimodal tensors
+        if "language_model." in name:
+            return None
+
+        return super().filter_tensors(item)
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        config_name = "vision_config" if not self.is_mistral_format else "vision_encoder"
+        return self.global_config.get(config_name)
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        mm_config_key = "whisper_config" if "whisper_config" in self.hparams else "audio_config"
+        return self.global_config.get(mm_config_key)
+
+    def set_type(self):
+        self.gguf_writer.add_type(gguf.GGUFType.MMPROJ)
+
+    def prepare_metadata(self, vocab_only: bool):
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        output_type: str = self.ftype.name.partition("_")[2]
+
+        if self.fname_out.is_dir():
+            fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+            self.fname_out = self.fname_out / f"mmproj-{fname_default}.gguf"
+        else:
+            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_file_type(self.ftype)
+
+        if self.has_vision_encoder:
+            self.gguf_writer.add_clip_has_vision_encoder(True)
+            self.gguf_writer.add_vision_projection_dim(self.n_embd_text)
+
+            # vision config
+            self.image_size = self.find_vparam(["image_size"])
+            self.gguf_writer.add_vision_image_size(self.image_size)
+            self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
+            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "width", "vt_hidden_size"]))
+            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"]))
+            self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
+            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "heads", "vt_num_attention_heads"]))
+
+            # preprocessor config
+            image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
+            image_std = _MISTRAL_COMMON_DATASET_STD if self.is_mistral_format else self.preprocessor_config["image_std"]
+
+            self.gguf_writer.add_vision_image_mean(image_mean)
+            self.gguf_writer.add_vision_image_std(image_std)
+
+        if self.has_audio_encoder:
+            self.gguf_writer.add_clip_has_audio_encoder(True)
+            self.gguf_writer.add_audio_projection_dim(self.n_embd_text)
+
+            # audio config
+            self.gguf_writer.add_audio_embedding_length(self.find_aparam(["hidden_size"]))
+            self.gguf_writer.add_audio_feed_forward_length(self.find_aparam(["intermediate_size"]))
+            self.gguf_writer.add_audio_block_count(self.find_aparam(self.n_block_keys))
+            self.gguf_writer.add_audio_head_count(self.find_aparam(["num_attention_heads"]))
+
+        if not self.has_vision_encoder and not self.has_audio_encoder:
+            raise ValueError("MmprojModel must have either vision or audio encoder")
+
+    def write_vocab(self):
+        raise ValueError("MmprojModel does not support vocab writing")
+
+    def find_vparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        assert self.hparams_vision is not None
+        return self._find_param(self.hparams_vision, keys, optional)
+
+    def find_aparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        assert self.hparams_audio is not None
+        return self._find_param(self.hparams_audio, keys, optional)
+
+    def _find_param(self, obj: dict[str, Any], keys: Iterable[str], optional: bool = False) -> Any:
+        key = next((k for k in keys if k in obj), None)
+        if key is not None:
+            return obj[key]
+        if optional:
+            return None
+        raise KeyError(f"could not find any of: {keys}")
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del bid, name, n_dims  # unused
+        if ".patch_embd.weight" in new_name or ".patch_merger.weight" in new_name:
+            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
+        return False
+
+
+class LazyTorchTensor(gguf.LazyBase):
+    _tensor_type = torch.Tensor
+    # to keep the type-checker happy
+    dtype: torch.dtype
+    shape: torch.Size
+
+    # only used when converting a torch.Tensor to a np.ndarray
+    _dtype_map: dict[torch.dtype, type] = {
+        torch.float16: np.float16,
+        torch.float32: np.float32,
+        torch.uint8: np.uint8,
+    }
+
+    # only used when byteswapping data. Only correct size is needed
+    # TODO: uncomment uint64, uint32, and uint16, ref: https://github.com/pytorch/pytorch/issues/58734
+    _dtype_byteswap_map: dict[torch.dtype, type] = {
+        torch.float64: np.float64,
+        torch.float32: np.float32,
+        torch.bfloat16: np.float16,
+        torch.float16: np.float16,
+        torch.int64: np.int64,
+        # torch.uint64: np.uint64,
+        torch.int32: np.int32,
+        # torch.uint32: np.uint32,
+        torch.int16: np.int16,
+        # torch.uint16: np.uint16,
+        torch.int8: np.int8,
+        torch.uint8: np.uint8,
+        torch.bool: np.uint8,
+        torch.float8_e4m3fn: np.uint8,
+        torch.float8_e5m2: np.uint8,
+    }
+
+    # used for safetensors slices
+    # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
+    # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
+    _dtype_str_map: dict[str, torch.dtype] = {
+        "F64": torch.float64,
+        "F32": torch.float32,
+        "BF16": torch.bfloat16,
+        "F16": torch.float16,
+        # "U64": torch.uint64,
+        "I64": torch.int64,
+        # "U32": torch.uint32,
+        "I32": torch.int32,
+        # "U16": torch.uint16,
+        "I16": torch.int16,
+        "U8": torch.uint8,
+        "I8": torch.int8,
+        "BOOL": torch.bool,
+        "F8_E4M3": torch.float8_e4m3fn,
+        "F8_E5M2": torch.float8_e5m2,
+    }
+
+    def numpy(self) -> gguf.LazyNumpyTensor:
+        dtype = self._dtype_map[self.dtype]
+        return gguf.LazyNumpyTensor(
+            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
+            args=(self,),
+            func=(lambda s: s.numpy())
+        )
+
+    @classmethod
+    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor:
+        return torch.empty(size=shape, dtype=dtype, device="meta")
+
+    @classmethod
+    def from_safetensors_slice(cls, st_slice: Any) -> Tensor:
+        dtype = cls._dtype_str_map[st_slice.get_dtype()]
+        shape: tuple[int, ...] = tuple(st_slice.get_shape())
+        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[...] if len(s.get_shape()) == 0 else s[:])
+        return cast(torch.Tensor, lazy)
+
+    @classmethod
+    def from_local_tensor(cls, t: gguf.utility.LocalTensor) -> Tensor:
+        def load_tensor(tensor: gguf.utility.LocalTensor) -> Tensor:
+            def byteswap_tensor(tensor: np.ndarray, dtype: type) -> np.ndarray:
+                if sys.byteorder == 'big':
+                    # switch data back to big endian
+                    tensor = tensor.view(dtype).byteswap(inplace=False)
+                return tensor
+            dtype = cls._dtype_str_map[tensor.dtype]
+            numpy_dtype = cls._dtype_byteswap_map[dtype]
+            return torch.from_numpy(byteswap_tensor(tensor.mmap_bytes(), numpy_dtype)).view(dtype).reshape(tensor.shape)
+        dtype = cls._dtype_str_map[t.dtype]
+        shape = t.shape
+        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(t,), func=lambda r: load_tensor(r))
+        return cast(torch.Tensor, lazy)
+
+    @classmethod
+    def from_remote_tensor(cls, remote_tensor: gguf.utility.RemoteTensor):
+        def byteswap_tensor(tensor: np.ndarray, dtype: type) -> np.ndarray:
+            if sys.byteorder == 'big':
+                # switch data back to big endian
+                tensor = tensor.view(dtype).byteswap(inplace=False)
+            return tensor
+        dtype = cls._dtype_str_map[remote_tensor.dtype]
+        numpy_dtype = cls._dtype_byteswap_map[dtype]
+        shape = remote_tensor.shape
+        meta = cls.meta_with_dtype_and_shape(dtype, shape)
+        lazy = cls(meta=meta, args=(remote_tensor,), func=lambda r: torch.from_numpy(byteswap_tensor(np.frombuffer(r.data(), dtype=numpy_dtype), numpy_dtype)).view(dtype).reshape(shape))
+        return cast(torch.Tensor, lazy)
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        del types  # unused
+
+        if kwargs is None:
+            kwargs = {}
+
+        if func is torch.Tensor.numpy:
+            assert len(args)
+            return args[0].numpy()
+
+        return cls._wrap_fn(func)(*args, **kwargs)
+
+
+def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> str:
+    # TODO @ngxson : this won't work correctly if the model has both audio & vision encoders
+    # maybe we should fallback to text model's arch in that case, since not many models have both
+    text_config = hparams.get("text_config", {})
+    vision_config = hparams.get("vision_config", {})
+    arch = None
+    if (arches := hparams.get("architectures")) is not None and len(arches) > 0:
+        arch = arches[0]
+    elif "ssm_cfg" in hparams:
+        # For non-hf Mamba and Mamba2 models
+        arch = hparams["ssm_cfg"].get("layer", "Mamba") + "ForCausalLM"
+
+    # Step3-VL keeps text config under text_config but uses a custom top-level architecture.
+    # For text conversion we route to a dedicated text-only class.
+    # TODO: refactor this later to avoid adding exception here
+    if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM"):
+        return arch
+
+    # if "architectures" is found in the sub-config, use that instead
+    if model_type == ModelType.TEXT and text_config.get("architectures") is not None:
+        arch = text_config["architectures"][0]
+    elif model_type == ModelType.MMPROJ and vision_config.get("architectures") is not None:
+        arch = vision_config["architectures"][0]
+    if arch is None:
+        raise ValueError("Failed to detect model architecture")
+    return arch
diff --git a/conversion/bert.py b/conversion/bert.py
new file mode 100644
index 00000000000..8af6c534d07
--- /dev/null
+++ b/conversion/bert.py
@@ -0,0 +1,616 @@
+from __future__ import annotations
+
+import json
+import os
+
+from pathlib import Path
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, SentencePieceTokenTypes, TextModel, gguf, logger
+
+
+@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel", "BertForSequenceClassification")
+class BertModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.BERT
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.vocab_size = None
+
+        if cls_out_labels := self.hparams.get("id2label"):
+            if len(cls_out_labels) == 2 and cls_out_labels[0] == "LABEL_0":
+                # Remove dummy labels added by AutoConfig
+                cls_out_labels = None
+        self.cls_out_labels = cls_out_labels
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_causal_attention(False)
+        self._try_set_pooling_type()
+
+        if self.cls_out_labels:
+            self.gguf_writer.add_classifier_output_labels([v for k, v in sorted(self.cls_out_labels.items())])
+
+    def set_vocab(self):
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.vocab_size = len(tokens)
+
+        # we need this to validate the size of the token_type embeddings
+        # though currently we are passing all zeros to the token_type embeddings
+        # "Sequence A" or "Sequence B"
+        self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
+
+        # convert to phantom space vocab
+        def phantom(tok, toktype):
+            if toktype == gguf.TokenType.CONTROL:
+                return tok
+            if tok.startswith("##"):
+                return tok[2:]
+            return "\u2581" + tok
+        assert len(tokens) == len(toktypes)
+        tokens = list(map(phantom, tokens, toktypes))
+
+        # add vocab to gguf
+        self.gguf_writer.add_tokenizer_model("bert")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        # handle special tokens
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("bert."):
+            name = name[5:]
+
+        if name.endswith(".gamma"):
+            name = name[:-6] + ".weight"
+
+        if name.endswith(".beta"):
+            name = name[:-5] + ".bias"
+
+        # we are only using BERT for embeddings so we don't need the pooling layer
+        if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
+            return None
+
+        if name.startswith("cls.predictions"):
+            return None
+
+        if name.startswith("cls.seq_relationship"):
+            return None
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if self.cls_out_labels:
+            # For BertForSequenceClassification (direct projection layer)
+            if name == "classifier.weight":
+                name = "classifier.out_proj.weight"
+
+            if name == "classifier.bias":
+                name = "classifier.out_proj.bias"
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def _xlmroberta_tokenizer_init(self) -> None:
+        # we need the pad_token_id to know how to chop down position_embd matrix
+        if (pad_token_id := self.hparams.get("pad_token_id")) is not None:
+            self._position_offset = 1 + pad_token_id
+            if "max_position_embeddings" in self.hparams:
+                self.hparams["max_position_embeddings"] -= self._position_offset
+        else:
+            self._position_offset = None
+
+    def _xlmroberta_set_vocab(self) -> None:
+        # to avoid TypeError: Descriptors cannot be created directly
+        # exception when importing sentencepiece_model_pb2
+        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        tokenizer_path = self.dir_model / 'sentencepiece.bpe.model'
+
+        tokenizer_json = {}
+        tokenizer_config_json = {}
+        if not tokenizer_path.is_file():
+            tokenizer_path = self.dir_model / 'tokenizer.json'
+            tokenizer_config_path = self.dir_model / 'tokenizer_config.json'
+
+            if not tokenizer_path.is_file():
+                raise FileNotFoundError(f"File not found: {tokenizer_path}")
+
+            from base64 import b64decode
+            from transformers import AutoTokenizer
+            tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+
+            with open(tokenizer_path, "r", encoding="utf-8") as fp:
+                tokenizer_json = json.load(fp)
+
+            if tokenizer_config_path.is_file():
+                with open(tokenizer_config_path, "r", encoding="utf-8") as fp:
+                    tokenizer_config_json = json.load(fp)
+
+            add_prefix = tokenizer.add_prefix_space  # ty: ignore[unresolved-attribute]
+            remove_whitespaces = tokenizer.clean_up_tokenization_spaces  # ty: ignore[unresolved-attribute]
+            precompiled_charsmap = b64decode(tokenizer_json["normalizer"]["precompiled_charsmap"])
+
+            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size)  # ty: ignore[unresolved-attribute]
+        else:
+            sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+            sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
+
+            add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+            remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
+            precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
+
+            tokenizer = SentencePieceProcessor()
+            tokenizer.LoadFromFile(str(tokenizer_path))
+
+            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        if isinstance(tokenizer, SentencePieceProcessor):
+            for token_id in range(tokenizer.vocab_size()):
+                piece = tokenizer.IdToPiece(token_id)
+                text = piece.encode("utf-8")
+                score = tokenizer.GetScore(token_id)
+
+                toktype = SentencePieceTokenTypes.NORMAL
+                if tokenizer.IsUnknown(token_id):
+                    toktype = SentencePieceTokenTypes.UNKNOWN
+                elif tokenizer.IsControl(token_id):
+                    toktype = SentencePieceTokenTypes.CONTROL
+                elif tokenizer.IsUnused(token_id):
+                    toktype = SentencePieceTokenTypes.UNUSED
+                elif tokenizer.IsByte(token_id):
+                    toktype = SentencePieceTokenTypes.BYTE
+
+                tokens[token_id] = text
+                scores[token_id] = score
+                toktypes[token_id] = toktype
+        else:
+            added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
+            unk_token = tokenizer_config_json.get("unk_token")
+            unk_token_id = added_vocab.get(unk_token, tokenizer_json["model"].get("unk_id", 3))  # ty: ignore[no-matching-overload]
+
+            for token_id in range(tokenizer.vocab_size):  # ty: ignore[unresolved-attribute]
+                piece = tokenizer._convert_id_to_token(token_id)  # ty: ignore[unresolved-attribute]
+                if (piece := tokenizer._convert_id_to_token(token_id)) is not None:  # ty: ignore[unresolved-attribute]
+                    text = piece.encode("utf-8")
+                    score = tokenizer_json["model"]["vocab"][token_id][1]
+
+                    toktype = SentencePieceTokenTypes.NORMAL
+                    if token_id == unk_token_id:
+                        toktype = SentencePieceTokenTypes.UNKNOWN
+                    elif token_id in tokenizer.all_special_ids:  # ty: ignore[unresolved-attribute]
+                        toktype = SentencePieceTokenTypes.CONTROL
+                    elif token_id in added_vocab.values():
+                        toktype = SentencePieceTokenTypes.USER_DEFINED
+                    # No reliable way to detect this, but jina doesn't have any
+                    # elif tokenizer.IsByte(token_id):
+                    #     toktype = SentencePieceTokenTypes.BYTE
+
+                    tokens[token_id] = text
+                    scores[token_id] = score
+                    toktypes[token_id] = toktype
+
+        if isinstance(tokenizer, SentencePieceProcessor):
+            # realign tokens (see HF tokenizer code)
+            tokens = [b'<s>', b'<pad>', b'</s>', b'<unk>'] + tokens[3:-1]
+            scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1]
+            toktypes = [
+                SentencePieceTokenTypes.CONTROL,
+                SentencePieceTokenTypes.CONTROL,
+                SentencePieceTokenTypes.CONTROL,
+                SentencePieceTokenTypes.UNKNOWN,
+            ] + toktypes[3:-1]
+
+            if self.model_arch == gguf.MODEL_ARCH.NOMIC_BERT_MOE:
+                # Add mask token missing from sentencepiece.bpe.model
+                tokens[250001] = b'<mask>'
+                scores[250001] = 0.0
+                toktypes[250001] = SentencePieceTokenTypes.CONTROL
+
+        self.gguf_writer.add_tokenizer_model("t5")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+        self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
+        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
+        if precompiled_charsmap:
+            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+
+@ModelBase.register("DistilBertModel", "DistilBertForMaskedLM", "DistilBertForSequenceClassification")
+class DistilBertModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.BERT
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_layer_norm_eps(1e-12)
+        logger.info("gguf: layer norm epsilon = 1e-12")
+        super().set_gguf_parameters()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("distilbert."):
+            name = name[11:]
+
+        # These layers act as MLM head, so we don't need them
+        if name.startswith("vocab_"):
+            return None
+
+        return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
+class RobertaModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.BERT
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # we need the pad_token_id to know how to chop down position_embd matrix
+        if (pad_token_id := self.hparams.get("pad_token_id")) is not None:
+            self._position_offset = 1 + pad_token_id
+            if "max_position_embeddings" in self.hparams:
+                self.hparams["max_position_embeddings"] -= self._position_offset
+        else:
+            self._position_offset = None
+
+    def set_vocab(self):
+        """Support BPE tokenizers for roberta models"""
+        bpe_tok_path = self.dir_model / "tokenizer.json"
+        if bpe_tok_path.exists():
+            self._set_vocab_gpt2()
+
+            # we need this to validate the size of the token_type embeddings
+            # though currently we are passing all zeros to the token_type embeddings
+            # "Sequence A" or "Sequence B"
+            self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
+
+        else:
+            return super().set_vocab()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # if name starts with "roberta.", remove the prefix
+        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
+        if name.startswith("roberta."):
+            name = name[8:]
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
+        if name == "embeddings.position_embeddings.weight":
+            if self._position_offset is not None:
+                data_torch = data_torch[self._position_offset:,:]
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("NomicBertModel")
+class NomicBertModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.BERT
+
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            hparams = ModelBase.load_hparams(dir_model, False)
+
+        self.is_moe = bool(hparams.get("moe_every_n_layers"))
+        self.model_arch = gguf.MODEL_ARCH.NOMIC_BERT_MOE if self.is_moe else gguf.MODEL_ARCH.NOMIC_BERT
+
+        super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs)
+
+        self._tokenizer_is_xlmroberta = self._is_tokenizer_xlmroberta()
+        if self._tokenizer_is_xlmroberta:
+            self._xlmroberta_tokenizer_init()
+
+        npos, mtp = self.hparams["n_positions"], self.hparams.get("max_trained_positions", 2048)
+        if npos == 8192 and mtp == 2048:
+            self.hparams["n_positions"] = 2048  # nomic-embed-text v1 and v1.5 are trained for 2048 tokens.
+        elif npos == 2048 and mtp == 2048:
+            self.hparams["n_positions"] = 512   # nomic-embed-text-v2-moe is trained for 512 tokens.
+        else:
+            raise ValueError(f"unrecognized parameters: n_positions={npos}, max_trained_positions={mtp}")
+
+        assert self.hparams["activation_function"] == "gelu" if self.is_moe else "swiglu"
+
+        # this doesn't do anything in the HF version
+        assert self.hparams["causal"] is False
+        # no bias tensors unless MoE
+        assert self.hparams["qkv_proj_bias"] == self.is_moe
+        assert self.hparams["mlp_fc1_bias"]  == self.is_moe
+        assert self.hparams["mlp_fc2_bias"]  == self.is_moe
+
+        # norm at end of layer
+        assert self.hparams["prenorm"] is False
+        # standard RoPE
+        assert self.hparams["rotary_emb_fraction"] == 1.0
+        assert self.hparams["rotary_emb_interleaved"] is False
+        assert self.hparams["rotary_emb_scale_base"] is None
+
+    def set_vocab(self) -> None:
+        if self._tokenizer_is_xlmroberta:
+            return self._xlmroberta_set_vocab()
+        return super().set_vocab()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # If the tensor is an experts bias tensor, skip it.
+        if "mlp.experts.bias" in name:
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
+        n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+        if "mlp.experts.mlp.w1" in name:
+            data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
+            name += ".weight"
+
+        if "mlp.experts.mlp.w2" in name:
+            data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
+            data_torch = data_torch.transpose(1, 2)
+            name += ".weight"
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.is_moe:
+            self.gguf_writer.add_moe_every_n_layers(self.hparams["moe_every_n_layers"])
+            self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
+
+    def _is_tokenizer_xlmroberta(self) -> bool:
+        with open(self.dir_model / "tokenizer.json") as f:
+            tokenizer_json = json.load(f)
+        toktyp = tokenizer_json["model"]["type"]
+        if toktyp == "Unigram":
+            return True
+        if toktyp == "WordPiece":
+            return False
+        raise ValueError(f"unknown tokenizer: {toktyp}")
+
+
+@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification")
+class NeoBert(BertModel):
+    model_arch = gguf.MODEL_ARCH.NEO_BERT
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # NeoBERT uses 2/3 of the intermediate size as feed forward length
+        self.gguf_writer.add_feed_forward_length(int(2 * self.hparams["intermediate_size"] / 3))
+        self.gguf_writer.add_rope_freq_base(10000.0)  # default value for NeoBERT
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+        f_rms_eps = self.hparams.get("norm_eps", 1e-6)  # default value for NeoBERT
+        self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
+        logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
+
+        self.gguf_writer.add_pooling_type(gguf.PoolingType.CLS) # https://huggingface.co/chandar-lab/NeoBERT#how-to-use
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("decoder."):
+            return None
+
+        if name.startswith("model."):
+            name = name[6:]
+
+        return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("EuroBertModel", "JinaEmbeddingsV5Model")
+class EuroBertModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.EUROBERT
+
+    def set_vocab(self):
+        self.gguf_writer.add_add_bos_token(False)
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # EuroBert is bidirectional (encoder)
+        self.gguf_writer.add_causal_attention(False)
+
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+        self._try_set_pooling_type()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("model."):
+            name = name[6:]
+
+        return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
+class XLMRobertaModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.BERT
+    _lora_files = {}
+    _lora_names = []
+
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            hparams = ModelBase.load_hparams(dir_model, False)
+
+        if lora_names := hparams.get("lora_adaptations"):
+            self._lora_names = lora_names
+            self.model_arch = gguf.MODEL_ARCH.JINA_BERT_V3
+
+        super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs)
+        self._xlmroberta_tokenizer_init()
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if self._lora_names:
+            for name in self._lora_names:
+                fname = self.add_prefix_to_filename(self.fname_out, f"lora-{name}-")
+                self._lora_files[name] = gguf.GGUFWriter(fname, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, dry_run=self.dry_run)
+
+        return super().generate_extra_tensors()
+
+    def set_type(self):
+        for lora_writer in self._lora_files.values():
+            lora_writer.add_type(gguf.GGUFType.ADAPTER)
+            lora_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
+        super().set_type()
+
+    def set_vocab(self):
+        self._xlmroberta_set_vocab()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # if name starts with "roberta.", remove the prefix
+        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
+        if name.startswith("roberta."):
+            name = name[8:]
+
+        # jina-embeddings-v3
+        if ".parametrizations." in name:
+            name = name.replace(".parametrizations.", ".")
+            if name.endswith(".original"):
+                name = name[:-9]
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
+        if name == "embeddings.position_embeddings.weight":
+            if self._position_offset is not None:
+                data_torch = data_torch[self._position_offset:,:]
+
+        if name.endswith(".0.lora_A") or name.endswith(".0.lora_B"):
+            if name.startswith("pooler.dense"):
+                return
+
+            num_loras = data_torch.size(0)
+            assert num_loras == len(self._lora_names)
+
+            # Split out each LoRA in their own GGUF
+            for i, lora_writer in enumerate(self._lora_files.values()):
+                new_name = self.map_tensor_name(name[:-9]) + name[-7:].lower()
+                data = data_torch[i, :, :]
+                # Transpose/flip token_embd/types into correct shape
+                if new_name == "token_embd.weight.lora_b":
+                    data = data.T
+                elif new_name.startswith("token_types.weight."):
+                    new_name = new_name[:-1] + ("a" if new_name[-1:] == "b" else "b")
+                lora_writer.add_tensor(new_name, data.float().numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
+
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # jina-embeddings-v3
+        lora_alpha = self.hparams.get("lora_alpha")
+        if lora_prompt_prefixes := self.hparams.get("task_instructions"):
+            assert self._lora_files and all(lora_name in lora_prompt_prefixes for lora_name in self._lora_files.keys())
+        for lora_name, lora_writer in self._lora_files.items():
+            lora_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, lora_alpha if lora_alpha is not None else 1.0)
+            lora_writer.add_string(gguf.Keys.Adapter.LORA_TASK_NAME, lora_name)
+            if lora_prompt_prefixes:
+                lora_writer.add_string(gguf.Keys.Adapter.LORA_PROMPT_PREFIX, lora_prompt_prefixes[lora_name])
+
+    def write(self):
+        super().write()
+        for lora_writer in self._lora_files.values():
+            lora_writer.write_header_to_file()
+            lora_writer.write_kv_data_to_file()
+            lora_writer.write_tensors_to_file(progress=True)
+            lora_writer.close()
+
+
+@ModelBase.register("JinaBertModel", "JinaBertForMaskedLM")
+class JinaBertV2Model(BertModel):
+    model_arch = gguf.MODEL_ARCH.JINA_BERT_V2
+
+    def set_vocab(self):
+        tokenizer_class = 'BertTokenizer'
+        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+            tokenizer_class = json.load(f)['tokenizer_class']
+
+        if tokenizer_class == 'BertTokenizer':
+            super().set_vocab()
+        elif tokenizer_class == 'RobertaTokenizer':
+            self._set_vocab_gpt2()
+            self.gguf_writer.add_token_type_count(2)
+        else:
+            raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel')
+
+
+@ModelBase.register("ModernBertModel", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
+class ModernBertModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.MODERN_BERT
+
+    def set_vocab(self):
+        self.gguf_writer.add_add_bos_token(True)
+        self.gguf_writer.add_add_eos_token(True)
+        self.gguf_writer.add_add_sep_token(True)
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_sliding_window(self.hparams["local_attention"])
+        if (sliding_window_pattern := self.hparams.get("global_attn_every_n_layers")) is not None:
+            self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("model."):
+            name = name[6:]
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if self.cls_out_labels:
+            # For BertForSequenceClassification (direct projection layer)
+            if name == "classifier.weight":
+                name = "classifier.out_proj.weight"
+
+            if name == "classifier.bias":
+                name = "classifier.out_proj.bias"
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/bitnet.py b/conversion/bitnet.py
new file mode 100644
index 00000000000..a66446abee2
--- /dev/null
+++ b/conversion/bitnet.py
@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("BitnetForCausalLM")
+class BitnetModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.BITNET
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+        self.gguf_writer.add_rope_scaling_factor(1.0)
+
+    def weight_quant(self, weight: Tensor) -> Tensor:
+        dtype = weight.dtype
+        weight = weight.float()
+        scale = weight.abs().mean().clamp(min=1e-5)
+        iscale = 1 / scale
+        # TODO: multiply by the scale directly instead of inverting it twice
+        # (this is also unnecessarily doubly inverted upstream)
+        # ref: https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/af89e318d78a70802061246bf037199d2fb97020/utils_quant.py#L10
+        result = (weight * iscale).round().clamp(-1, 1) / iscale
+        return result.type(dtype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        new_name = self.map_tensor_name(name)
+
+        if any(self.match_model_tensor_name(new_name, key, bid) for key in [
+            gguf.MODEL_TENSOR.ATTN_Q,
+            gguf.MODEL_TENSOR.ATTN_K,
+            gguf.MODEL_TENSOR.ATTN_V,
+            gguf.MODEL_TENSOR.ATTN_OUT,
+            gguf.MODEL_TENSOR.FFN_UP,
+            gguf.MODEL_TENSOR.FFN_DOWN,
+            gguf.MODEL_TENSOR.FFN_GATE,
+        ]):
+            # transform weight into 1/0/-1 (in fp32)
+            data_torch = self.weight_quant(data_torch)
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/bloom.py b/conversion/bloom.py
new file mode 100644
index 00000000000..d98edf6d500
--- /dev/null
+++ b/conversion/bloom.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+import re
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("BloomForCausalLM", "BloomModel")
+class BloomModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.BLOOM
+
+    def set_gguf_parameters(self):
+        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
+        assert n_head is not None
+        assert n_embed is not None
+        self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
+        self.gguf_writer.add_embedding_length(n_embed)
+        self.gguf_writer.add_feed_forward_length(4 * n_embed)
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head)
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
+        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+        assert n_head is not None
+        assert n_embed is not None
+
+        name = re.sub(r'transformer\.', '', name)
+
+        if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
+            # Map bloom-style qkv_linear to gpt-style qkv_linear
+            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
+            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
+            qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
+            data_torch = torch.cat(
+                (
+                    qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
+                    qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
+                    qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
+                ),
+                dim=0,
+            )
+            logger.info("re-format attention.linear_qkv.weight")
+        elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
+            qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
+            data_torch = torch.cat(
+                (
+                    qkv_bias[:, 0, :].reshape((n_embed,)),
+                    qkv_bias[:, 1, :].reshape((n_embed,)),
+                    qkv_bias[:, 2, :].reshape((n_embed,)),
+                ),
+                dim=0,
+            )
+            logger.info("re-format attention.linear_qkv.bias")
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/chameleon.py b/conversion/chameleon.py
new file mode 100644
index 00000000000..a996bfa53cf
--- /dev/null
+++ b/conversion/chameleon.py
@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+from .llama import LlamaModel
+
+
+@ModelBase.register("ChameleonForConditionalGeneration")
+@ModelBase.register("ChameleonForCausalLM")  # obsolete
+class ChameleonModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.CHAMELEON
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False))
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # ignore image tokenizer for now
+        # TODO: image support for Chameleon
+        if name.startswith("model.vqmodel"):
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+        hidden_dim = self.hparams.get("hidden_size")
+
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+        if name.endswith(("q_norm.weight", "q_norm.bias")):
+            data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim)
+        if name.endswith(("k_norm.weight", "k_norm.bias")):
+            data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    # see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203
+    @staticmethod
+    def _reverse_hf_permute(data_torch, n_heads, hidden_dim):
+        head_dim = hidden_dim // n_heads
+        data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1)
+        data_torch = data_torch.repeat_interleave(n_heads, 0)
+        return data_torch
diff --git a/conversion/chatglm.py b/conversion/chatglm.py
new file mode 100644
index 00000000000..7e323b89004
--- /dev/null
+++ b/conversion/chatglm.py
@@ -0,0 +1,167 @@
+from __future__ import annotations
+
+from typing import Callable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, SentencePieceTokenTypes, TextModel, gguf
+
+
+@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
+class ChatGLMModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.CHATGLM
+
+    def set_vocab_chatglm3(self):
+        dir_model = self.dir_model
+        hparams = self.hparams
+        tokens: list[bytes] = []
+        toktypes: list[int] = []
+        scores: list[float] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))  # ty: ignore[unresolved-attribute]
+        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
+        role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
+        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
+        for token_id in range(vocab_size):
+            piece = tokenizer._convert_id_to_token(token_id)  # ty: ignore[unresolved-attribute]
+            if token_id == 0:
+                piece = "<unk>"
+            elif token_id == 1:
+                piece = "<bos>"
+            elif token_id == 2:
+                piece = "<eos>"
+
+            text = piece.encode("utf-8")  # ty: ignore[unresolved-attribute]
+            score = 0.0
+            # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py),
+            # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size()
+            if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():  # ty: ignore[unresolved-attribute, invalid-argument-type]
+                score = tokenizer.tokenizer.sp_model.get_score(token_id)  # ty: ignore[unresolved-attribute]
+
+            if token_id >= tokenizer.tokenizer.sp_model.vocab_size():  # ty: ignore[unresolved-attribute]
+                if piece in special_tokens:
+                    toktype = SentencePieceTokenTypes.CONTROL
+                elif len(piece) == 0:  # ty: ignore[invalid-argument-type]
+                    text = f"[PAD{token_id}]".encode("utf-8")
+                    toktype = SentencePieceTokenTypes.UNUSED
+                else:
+                    toktype = SentencePieceTokenTypes.USER_DEFINED
+                tokens.append(text)
+                scores.append(score)
+                toktypes.append(toktype)
+                continue
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.tokenizer.sp_model.is_unknown(token_id):  # ty: ignore[unresolved-attribute]
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.tokenizer.sp_model.is_control(token_id):  # ty: ignore[unresolved-attribute]
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.tokenizer.sp_model.is_unused(token_id):  # ty: ignore[unresolved-attribute]
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.tokenizer.sp_model.is_byte(token_id):  # ty: ignore[unresolved-attribute]
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        # glm3 needs prefix and suffix formatted as:
+        # prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>"
+        self.gguf_writer.add_tokenizer_pre("chatglm-spm")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    @staticmethod
+    def token_bytes_to_string(b):
+        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
+        byte_encoder = bytes_to_unicode()
+        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
+
+    @staticmethod
+    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
+        parts = [bytes([b]) for b in token]
+        while True:
+            min_idx = None
+            min_rank = None
+            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
+                rank = mergeable_ranks.get(pair[0] + pair[1])
+                if rank is not None and (min_rank is None or rank < min_rank):
+                    min_idx = i
+                    min_rank = rank
+            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
+                break
+            assert min_idx is not None
+            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
+        return parts
+
+    def set_vocab(self):
+        if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""):
+            self.set_vocab_chatglm3()
+            return
+
+        dir_model = self.dir_model
+        hparams = self.hparams
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
+        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
+
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        # only add special tokens when they were not already loaded from config.json
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
+        # this one is usually not in config.json anyway
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+        assert n_embed is not None
+        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
+        assert n_head is not None
+        n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
+        self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
+        self.gguf_writer.add_embedding_length(n_embed)
+        self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head_kv)
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))
+        self.gguf_writer.add_file_type(self.ftype)
+        if "attention_dim" in self.hparams:
+            rope_dim = self.hparams["attention_dim"]
+        else:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_add_bos_token(False)
+        rope_freq = 10000
+        if "rope_ratio" in self.hparams:
+            rope_freq = rope_freq * self.hparams["rope_ratio"]
+        self.gguf_writer.add_rope_freq_base(rope_freq)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.endswith(".rotary_pos_emb.inv_freq"):
+            return None
+
+        name = name.removeprefix("transformer.")
+
+        return super().filter_tensors((name, gen))
diff --git a/conversion/codeshell.py b/conversion/codeshell.py
new file mode 100644
index 00000000000..8bfc3178d46
--- /dev/null
+++ b/conversion/codeshell.py
@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("CodeShellForCausalLM")
+class CodeShellModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.CODESHELL
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_rope_freq_base(10000.0)
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+        self.gguf_writer.add_rope_scaling_factor(1.0)
diff --git a/conversion/cogvlm.py b/conversion/cogvlm.py
new file mode 100644
index 00000000000..d92df55d46b
--- /dev/null
+++ b/conversion/cogvlm.py
@@ -0,0 +1,33 @@
+from __future__ import annotations
+
+from typing import Callable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf
+
+from .llama import LlamaModel
+
+
+@ModelBase.register("CogVLMForCausalLM")
+class CogVLMVisionModel(MmprojModel):
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.COGVLM)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not name.startswith("model.vision."):
+            return None
+
+        return super().filter_tensors(item)
+
+
+@ModelBase.register("CogVLMForCausalLM")
+class CogVLMModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.COGVLM
diff --git a/conversion/command_r.py b/conversion/command_r.py
new file mode 100644
index 00000000000..603288d165c
--- /dev/null
+++ b/conversion/command_r.py
@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("CohereForCausalLM")
+class CommandR2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.COMMAND_R
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # max_position_embeddings = 8192 in config.json but model was actually
+        # trained on 128k context length
+        # aya-23 models don't have model_max_length specified
+        self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"])
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+
+@ModelBase.register("Cohere2ForCausalLM")
+class Cohere2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.COHERE2
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+        rotary_pct = self.hparams["rotary_pct"]
+        hidden_size = self.hparams["hidden_size"]
+        num_attention_heads = self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Cohere2 runtime in llama.cpp expects no bias tensors;
+        # the actual weight only contains 0-value tensors as bias, we can skip them
+        if name.endswith(".bias"):
+            if torch.any(data_torch != 0):
+                raise ValueError(f"Bias tensor {name!r} is not zero.")
+            logger.debug(f"Skipping bias tensor {name!r} for Cohere2 conversion.")
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/dbrx.py b/conversion/dbrx.py
new file mode 100644
index 00000000000..207ebcb8931
--- /dev/null
+++ b/conversion/dbrx.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("DbrxForCausalLM")
+class DbrxModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.DBRX
+
+    def set_gguf_parameters(self):
+        ffn_config = self.hparams["ffn_config"]
+        attn_config = self.hparams["attn_config"]
+        self.gguf_writer.add_block_count(self.block_count)
+
+        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
+        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
+        self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
+
+        self.gguf_writer.add_head_count(self.hparams["n_heads"])
+        self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
+
+        self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
+
+        self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
+
+        self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
+
+        self.gguf_writer.add_layer_norm_eps(1e-5)
+
+        self.gguf_writer.add_file_type(self.ftype)
+        logger.info(f"gguf: file type = {self.ftype}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_expert = self.hparams["ffn_config"]["moe_num_experts"]
+        n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
+        n_embd = self.hparams["d_model"]
+
+        # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
+        # original implementation expects (n_expert, n_ff, n_embd) for all experts weights
+        # But llama.cpp moe graph works differently
+        # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
+        # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
+        exp_tensor_names = {"ffn.experts.mlp.w1": None,       # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff,   n_expert}
+                            "ffn.experts.mlp.w2": (0, 2, 1),  # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff,   n_embd, n_expert}
+                            "ffn.experts.mlp.v1": None}       # LLM_TENSOR_FFN_UP_EXPS   ggml_tensor->ne{n_embd, n_ff,   n_expert}
+        experts = False
+
+        for exp_tensor_name in exp_tensor_names.keys():
+            if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
+                experts = True
+                data_torch = data_torch.view(n_expert, n_ff, n_embd)
+                if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
+                    data_torch = data_torch.permute(*permute_tensor)
+                break
+
+        # map tensor names
+        # In MoE models the ffn tensors are typically most of the model weights,
+        # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
+        # Every other model has the weight names ending in .weight,
+        # let's assume that is the convention which is not the case for dbrx:
+        # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
+        new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
+
+        yield from super().modify_tensors(data_torch, new_name, bid)
+
+    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
+        del name, new_name, bid  # unused
+
+        return n_dims > 1
diff --git a/conversion/deci.py b/conversion/deci.py
new file mode 100644
index 00000000000..46d8568c5a4
--- /dev/null
+++ b/conversion/deci.py
@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+import math
+
+from typing import Any, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("DeciLMForCausalLM")
+class DeciModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.DECI
+
+    @staticmethod
+    def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
+        # DeciLM-specific code
+        intermediate_size = int(2 * ffn_mult * n_embd / 3)
+        return DeciModel._find_multiple(intermediate_size, 256)
+
+    @staticmethod
+    def _find_multiple(n: int, k: int) -> int:
+        # DeciLM-specific code
+        if n % k == 0:
+            return n
+        return n + k - (n % k)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
+            _block_configs: list[dict[str,Any]] = self.hparams["block_configs"]
+            assert self.block_count == len(_block_configs)
+            self._num_kv_heads = list()
+            self._num_heads = list()
+            _ffn_multipliers = list()
+            # ***linear attention layer***
+            # if n_heads_in_group is None and replace_with_linear is True
+            # then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads
+            # ***attention-free layer***
+            # if n_heads_in_group is None and replace_with_linear is False
+            # then _num_kv_heads[il] is 0 and _num_heads[il] is 0
+            # ***normal attention-layer***
+            # if n_heads_in_group is not None, then
+            # _num_kv_heads[il] is num_attention_head // n_heads_in_group and
+            # _num_heads[il] is num_attention_head
+            # ***dummy layer*** for nemotron 253B
+            # if n_heads_in_group is None and ffn_mult is None
+            # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 and _ffn_dims is 0
+            for il in range(len(_block_configs)):
+                if _block_configs[il]["attention"]["n_heads_in_group"] is None:
+                    if _block_configs[il]["attention"]["replace_with_linear"] is True:
+                        self._num_kv_heads.append(0)
+                        self._num_heads.append(self.hparams["num_attention_heads"])
+                    else:
+                        self._num_kv_heads.append(0)
+                        self._num_heads.append(0)
+                else:
+                    self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"])
+                    self._num_heads.append(self.hparams["num_attention_heads"])
+                if _block_configs[il]["ffn"]["ffn_mult"] is None: # dummy layer
+                    _ffn_multipliers.append(0.0)
+                else:
+                    _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
+            assert self.block_count == len(self._num_kv_heads)
+            assert self.block_count == len(self._num_heads)
+            assert self.block_count == len(_ffn_multipliers)
+            assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
+            assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int)
+            assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float)
+            self._ffn_dims: list[int] = [
+                DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"])
+                for multiplier in _ffn_multipliers
+            ]
+
+    def set_vocab(self):
+        # Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's
+        # eos_token from '|eot_id|' to '|end_of_text|'
+        if self.hparams.get("vocab_size", 128256) == 128256:
+            tokens, toktypes, tokpre = self.get_vocab_base()
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+            special_vocab.add_to_gguf(self.gguf_writer)
+        else:
+            # DeciLM-7B
+            self._set_vocab_llama_hf()
+
+    def set_gguf_parameters(self):
+        if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
+            assert self.block_count == len(self._num_kv_heads)
+            assert self.block_count == len(self._num_heads)
+            assert self.block_count == len(self._ffn_dims)
+            if (rope_theta := self.rope_parameters.get("rope_theta")) is not None:
+                self.gguf_writer.add_rope_freq_base(rope_theta)
+            self.gguf_writer.add_head_count_kv(self._num_kv_heads)
+            self.gguf_writer.add_head_count(self._num_heads)
+            self.gguf_writer.add_feed_forward_length(self._ffn_dims)
+            self.gguf_writer.add_block_count(self.block_count)
+            self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+            self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+            self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+            self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+            self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+            self.gguf_writer.add_file_type(self.ftype)
+        else: # DeciLM-7B
+            super().set_gguf_parameters()
+            if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B
+                self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"]
+                assert self.block_count == len(self._num_kv_heads)
+                self.gguf_writer.add_head_count_kv(self._num_kv_heads)
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        if bid is not None:
+            if "num_key_value_heads_per_layer" in self.hparams:
+                n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid]
+            elif "block_configs" in self.hparams:
+                n_kv_head = self._num_kv_heads[bid]
+                n_head = self._num_heads[bid]
+            else:
+                n_kv_head = self.hparams.get("num_key_value_heads")
+        else:
+            n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = DeciModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = DeciModel.permute(data_torch, n_head, n_kv_head)
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
+            if rope_params.get("rope_type", '').lower() == "llama3":
+                base = rope_params.get("rope_theta", 10000.0)
+                if (dim := self.hparams.get("head_dim")) is None:
+                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+                factor = rope_params.get("factor", 8.0)
+                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
+                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+
+                low_freq_wavelen = old_context_len / low_freq_factor
+                high_freq_wavelen = old_context_len / high_freq_factor
+                assert low_freq_wavelen != high_freq_wavelen
+
+                rope_factors = []
+                for freq in freqs:
+                    wavelen = 2 * math.pi / freq
+                    if wavelen < high_freq_wavelen:
+                        rope_factors.append(1)
+                    elif wavelen > low_freq_wavelen:
+                        rope_factors.append(factor)
+                    else:
+                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
diff --git a/conversion/deepseek.py b/conversion/deepseek.py
new file mode 100644
index 00000000000..e149fcbf752
--- /dev/null
+++ b/conversion/deepseek.py
@@ -0,0 +1,388 @@
+from __future__ import annotations
+
+import re
+
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf, logger
+
+from .qwen import QwenModel
+
+
+@ModelBase.register("DeepseekOCRForCausalLM")
+class DeepseekOCRVisionModel(MmprojModel):
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DEEPSEEKOCR)
+        # default values below are taken from HF tranformers code
+        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
+        self.gguf_writer.add_vision_use_gelu(True)
+        # calculate proj_scale_factor (used by tinygemma3 test model)
+        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
+        n_per_side = int(image_seq_length ** 0.5)
+        image_size = self.hparams["image_size"]
+        patch_size = self.hparams["patch_size"]
+        proj_scale_factor = (image_size // patch_size) // n_per_side
+        if proj_scale_factor > 0 and proj_scale_factor != 4:
+            # we only need to write this if it's not the default value
+            # in this case, we are converting a test model
+            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
+        # @bluebread: there's no window_size in config but just add it here anyway
+        self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14))
+
+        # SAM configuration
+        sam_hparams = hparams['sam']
+        self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers'])
+        self.gguf_writer.add_vision_sam_embedding_length(sam_hparams['width'])
+        self.gguf_writer.add_vision_sam_head_count(sam_hparams['heads'])
+
+    def get_vision_config(self) -> dict[str, Any]:
+        vision_config: dict[str, Any] | None = self.global_config.get("vision_config")
+
+        if not vision_config:
+            raise ValueError("DeepseekOCR model requires 'vision_config' in the model configuration, but it was not found")
+
+        vision_config['sam'] = vision_config['width']['sam_vit_b']
+        vision_config.update(vision_config['width']['clip-l-14-224'])
+        vision_config['hidden_size'] = vision_config['width']
+        vision_config['num_heads'] = vision_config['heads']
+        vision_config['intermediate_size'] = vision_config['heads'] * 4
+
+        return vision_config
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".embeddings." in name or 'pos_embed' in name:
+            return gguf.GGMLQuantizationType.F32
+        if ".rel_pos_h" in name or '.rel_pos_w' in name:
+            return gguf.GGMLQuantizationType.F32
+        if ".neck." in name or ".net_" in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Only process vision-related tensors, skip language model tensors
+        # Vision components: sam_model, vision_model, projector, image_newline, view_seperator
+        # Language model components to skip: lm_head, embed_tokens, layers, norm
+        if name.startswith(("lm_head.", "model.embed_tokens.", "model.layers.", "model.norm.")):
+            return None
+
+        if name.endswith("pos_embed") or name.endswith("rel_pos_h") or name.endswith("rel_pos_w"):
+            name += ".weight"
+
+        return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("DeepseekForCausalLM")
+class DeepseekModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_weights_scale(1.0)
+        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
+        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = DeepseekModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head)
+
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register(
+    "DeepseekV2ForCausalLM",
+    "DeepseekV3ForCausalLM",
+    "KimiVLForConditionalGeneration",
+    "KimiK25ForConditionalGeneration",
+    "YoutuForCausalLM",
+    "YoutuVLForConditionalGeneration",
+)
+class DeepseekV2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
+
+    # TODO @ngxson : remove this when we support MTP for deepseek models
+    skip_mtp = True
+
+    merge_expert = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        hparams: dict = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
+        self.origin_hf_arch = hparams.get('architectures', [None])[0]
+
+        # special handling for Deepseek OCR
+        if self.origin_hf_arch == "DeepseekOCRForCausalLM":
+            self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
+            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
+            self.gguf_writer.add_architecture()
+            # default jinja template
+            self.gguf_writer.add_chat_template("{% for m in messages %}{{m['content']}}{% endfor %}")
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_gpt2()
+            return
+        except Exception:
+            pass
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        if tokpre == "kimi-k2":
+            # Build merges list using the approach similar to HunYuanMoE
+            merges = []
+            vocab = {}
+            mergeable_ranks = tokenizer.model._mergeable_ranks  # ty: ignore[unresolved-attribute]
+            for token, rank in mergeable_ranks.items():
+                vocab[QwenModel.token_bytes_to_string(token)] = rank
+                if len(token) == 1:
+                    continue
+                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+                if len(merged) == 2:
+                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+            # Build token list
+            vocab_size = self.hparams["vocab_size"]
+            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
+            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+            tokens: list[str] = []
+            toktypes: list[int] = []
+
+            for i in range(vocab_size):
+                if i not in reverse_vocab:
+                    tokens.append(f"[PAD{i}]")
+                    toktypes.append(gguf.TokenType.UNUSED)
+                else:
+                    token = reverse_vocab[i]
+                    tokens.append(token)
+                    if i in special_tokens.values():
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.NORMAL)
+
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+            self.gguf_writer.add_token_merges(merges)
+
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+            special_vocab.add_to_gguf(self.gguf_writer)
+        else:
+            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
+
+    def set_gguf_parameters(self):
+        is_ocr = (self.model_arch == gguf.MODEL_ARCH.DEEPSEEK2OCR)
+
+        if is_ocr:
+            self.hparams['rope_theta'] = self.hparams.get('rope_theta', 10000.0)
+        else:
+            # note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
+            self.hparams["num_key_value_heads"] = 1
+
+        self.hparams['rms_norm_eps'] = self.hparams.get('rms_norm_eps', 1e-6)
+
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        # first_k_dense_replace: number of leading layers using dense FFN instead of MoE
+        # For non-MoE models (like Youtu), set to n_layer to use dense FFN for all layers
+        # For MoE models (like DeepSeek-V2), this is the number of leading non-MoE layers
+        has_moe = hparams.get("n_routed_experts") is not None
+        first_k_dense_replace = hparams.get("first_k_dense_replace")
+        if first_k_dense_replace is None:
+            # Default: if no MoE, all layers are dense; if MoE, none are dense
+            first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
+        self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
+        kv_lora_rank = hparams.get("kv_lora_rank", 512)
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
+            self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
+
+        # note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
+        if not is_ocr:
+            self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
+            self.gguf_writer.add_key_length(kv_lora_rank + hparams["qk_rope_head_dim"])
+            self.gguf_writer.add_value_length(kv_lora_rank)
+            self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
+            self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
+
+        # MoE parameters (required by C++ code for DEEPSEEK2 arch)
+        # For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
+        moe_intermediate_size = self.find_hparam(["moe_intermediate_size", "intermediate_size"], optional=False)
+        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+
+        if (n_routed_experts := hparams.get("n_routed_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_routed_experts)
+
+        # expert_shared_count is required by C++ code, default to 0 for non-MoE models
+        n_shared_experts = hparams.get("n_shared_experts", 0)
+        self.gguf_writer.add_expert_shared_count(n_shared_experts)
+
+        # When not set, C++ code will use scale_w = false to skip the no-op scaling
+        if (routed_scaling_factor := hparams.get("routed_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
+
+        if (norm_topk_prob := hparams.get("norm_topk_prob")) is not None and norm_topk_prob:
+            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
+
+        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
+
+        if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None:
+            # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+            # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
+            # ref https://github.com/ggml-org/llama.cpp/pull/17945
+            self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # skip lm_head.weight if tie_word_embeddings is True
+        if self.hparams.get("tie_word_embeddings", False):
+            if name == "lm_head.weight" or name == "model.lm_head.weight":
+                logger.info("Skipping tied output layer 'lm_head.weight' (will use token_embd.weight)")
+                return
+
+        # skip Multi-Token Prediction (MTP) layers
+        if self.skip_mtp:
+            block_count = self.hparams["num_hidden_layers"]
+            match = re.match(r"model.layers.(\d+)", name)
+            if match and int(match.group(1)) >= block_count:
+                return
+
+        # process the experts separately
+        if self.merge_expert and name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
+        if name.endswith("kv_b_proj.weight"):
+            name_kb = name.replace("kv_b_proj", "k_b_proj")
+            name_vb = name.replace("kv_b_proj", "v_b_proj")
+
+            n_head_kv = self.hparams["num_key_value_heads"]
+            v_head_dim = self.hparams["v_head_dim"]
+            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+
+            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
+
+            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
+            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
+            k_b = k_b.transpose(1, 2)
+
+            yield from super().modify_tensors(k_b, name_kb, bid)
+            yield from super().modify_tensors(v_b, name_vb, bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/dots1.py b/conversion/dots1.py
new file mode 100644
index 00000000000..7ac299a6e65
--- /dev/null
+++ b/conversion/dots1.py
@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, gguf
+
+from .qwen import Qwen2MoeModel
+
+
+@ModelBase.register("Dots1ForCausalLM")
+class Dots1Model(Qwen2MoeModel):
+    model_arch = gguf.MODEL_ARCH.DOTS1
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.hparams["num_experts"] = self.hparams["n_routed_experts"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
+        self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
+        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        if "shared_experts" in name:
+            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/dotsocr.py b/conversion/dotsocr.py
new file mode 100644
index 00000000000..f87f62abde9
--- /dev/null
+++ b/conversion/dotsocr.py
@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf
+
+
+@ModelBase.register("DotsOCRForCausalLM")
+class DotsOCRVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = 0 # dynamic resolution
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DOTSOCR)
+        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
+        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["rms_norm_eps"]))
+        self.gguf_writer.add_vision_projector_scale_factor(self.find_vparam(["spatial_merge_size"]))
+        self.gguf_writer.add_vision_use_silu(True)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not name.startswith("vision_tower."):
+            return None
+
+        if "vision_tower.blocks." in name and ".mlp." in name:
+            # note: to avoid naming conflicts in tensor_mapping.py, we need to handle FFN renaming here
+            # x = F.silu(self.fc1(x)) * self.fc3(x)
+            # x = self.fc2(x)
+            # fc1 -> gate, fc2 -> down, fc3 -> up
+            # mapping original names to Qwen2.5 naming scheme
+            name = name.replace("vision_tower.blocks.", "visual.blocks.")
+            name = name.replace(".fc1", ".gate_proj")
+            name = name.replace(".fc2", ".down_proj")
+            name = name.replace(".fc3", ".up_proj")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/dream.py b/conversion/dream.py
new file mode 100644
index 00000000000..459e8d46afb
--- /dev/null
+++ b/conversion/dream.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("DreamModel")
+class DreamModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.DREAM
+
+    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+        vocab_dict = tokenizer.get_vocab()  # ty: ignore[unresolved-attribute]
+        vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
+        assert max(vocab_dict.values()) < vocab_size
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            elif reverse_vocab[i] in added_vocab:
+                tokens.append(reverse_vocab[i])
+                # Check if it's a special token - treat special tokens as CONTROL tokens
+                if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
+                    if tokenizer.added_tokens_decoder[i].special:
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
+                    toktypes.append(gguf.TokenType.CONTROL)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+
+        return tokens, toktypes, tokpre
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
+
+        # Dream models use non-causal attention for diffusion
+        self.gguf_writer.add_causal_attention(False)
+
+        # Add Dream-specific parameters
+        mask_token_id = self.hparams.get("mask_token_id")
+        if mask_token_id is not None:
+            self.gguf_writer.add_mask_token_id(mask_token_id)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Dream model tensors should be mapped directly since it's the base model
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/ernie.py b/conversion/ernie.py
new file mode 100644
index 00000000000..aa8a3bc8ee5
--- /dev/null
+++ b/conversion/ernie.py
@@ -0,0 +1,200 @@
+from __future__ import annotations
+
+import json
+import math
+import re
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf
+
+
+@ModelBase.register("Ernie4_5_ForCausalLM", "Ernie4_5ForCausalLM")
+class Ernie4_5Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.ERNIE4_5
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                if "add_prefix_space" in tokenizer_config_json:
+                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if "ernie." in name:
+            name = name.replace("ernie.", "model.")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        num_heads = self.hparams["num_attention_heads"]
+        num_kv_heads = self.hparams["num_key_value_heads"]
+        if (head_dim := self.hparams.get("head_dim")) is None:
+            head_dim = self.hparams["hidden_size"] // num_heads
+
+        # split the qkv weights
+        # qkv_proj shape: [(num_heads + 2 * num_kv_heads) * head_dim, hidden_size]
+        if "qkv_proj" in name:
+            name_q = name.replace("qkv_proj.weight", "q_proj.weight")
+            name_k = name.replace("qkv_proj.weight", "k_proj.weight")
+            name_v = name.replace("qkv_proj.weight", "v_proj.weight")
+            total_q_dim = num_heads * head_dim
+            total_k_dim = num_kv_heads * head_dim
+            total_v_dim = num_kv_heads * head_dim
+            q_proj_weight, k_proj_weight, v_proj_weight = data_torch.split([total_q_dim, total_k_dim, total_v_dim], dim=0)
+            yield from super().modify_tensors(q_proj_weight, name_q, bid)
+            yield from super().modify_tensors(k_proj_weight, name_k, bid)
+            yield from super().modify_tensors(v_proj_weight, name_v, bid)
+        # split the up_gate_proj into gate and up
+        # up_gate_proj shape: [2 * intermediate_size, hidden_size]
+        elif "up_gate_proj" in name:
+            name_up = name.replace("up_gate_proj.weight", "up_proj.weight")
+            name_gate = name.replace("up_gate_proj.weight", "gate_proj.weight")
+            dim_half = data_torch.shape[0] // 2
+            gate_proj_weight, up_proj_weight = data_torch.split(dim_half, dim=0)
+            yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
+            yield from super().modify_tensors(up_proj_weight, name_up, bid)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Ernie4_5_MoeForCausalLM")
+class Ernie4_5MoeModel(Ernie4_5Model):
+    model_arch = gguf.MODEL_ARCH.ERNIE4_5_MOE
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._experts = [{} for _ in range(self.block_count)]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(self.hparams["moe_k"])
+        self.gguf_writer.add_interleave_moe_layer_step(self.hparams["moe_layer_interval"])
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["moe_layer_start_index"])
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (shared_expert_count := self.hparams.get('moe_num_shared_experts')) is not None:
+            self.gguf_writer.add_expert_shared_count(shared_expert_count)
+            if shared_expert_count > 0 and (shared_expert_intermediate_size := self.hparams.get('intermediate_size')) is not None and (num_key_value_heads := self.hparams.get('num_key_value_heads')) is not None:
+                self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size // num_key_value_heads)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # skip Multi-Token Prediction (MTP) layers (again, same as DeepseekV2)
+        match = re.match(r"model.mtp_block.(\d+)", name)
+        if match:
+            return None
+
+        # skip all other MTP tensors for now
+        match = re.match(r"model.mtp_emb_norm.(\d+)", name)
+        if match:
+            return None
+
+        match = re.match(r"model.mtp_hidden_norm.(\d+)", name)
+        if match:
+            return None
+
+        match = re.match(r"model.mtp_linear_proj.(\d+)", name)
+        if match:
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["moe_num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["gate_proj", "up_proj", "down_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename_to_retrieve])
+                        del self._experts[bid][ename_to_retrieve]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+        else:
+            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("PaddleOCRVLForConditionalGeneration")
+class PaddleOCRModel(Ernie4_5Model):
+    model_arch = gguf.MODEL_ARCH.PADDLEOCR
+
+
+@ModelBase.register("PaddleOCRVisionModel")
+class PaddleOCRVisionModel(MmprojModel):
+    # PaddleOCR-VL uses a modified version of Siglip
+    min_pixels: int = 0
+    max_pixels: int = 0
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.min_pixels = self.preprocessor_config["min_pixels"]
+        self.max_pixels = self.preprocessor_config["max_pixels"]
+        self.hparams_vision["image_size"] = int(math.sqrt(self.max_pixels))
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+        hparams = self.hparams_vision
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PADDLEOCR)
+        self.gguf_writer.add_vision_max_pixels(self.max_pixels)
+        self.gguf_writer.add_vision_min_pixels(self.min_pixels)
+        self.gguf_writer.add_vision_use_gelu(True)
+        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("rms_norm_eps", 1e-6))
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if "vision_model" not in name and "mlp_AR" not in name:
+            return None
+        name = name.replace("visual.", "model.")
+        if "packing_position_embedding" in name:
+            # unused
+            return None
+        if "vision_model.head" in name:
+            # we don't yet support image embeddings for this model
+            return None
+
+        return super().filter_tensors((name, gen))
diff --git a/conversion/exaone.py b/conversion/exaone.py
new file mode 100644
index 00000000000..aa1313e2f80
--- /dev/null
+++ b/conversion/exaone.py
@@ -0,0 +1,210 @@
+from __future__ import annotations
+
+import math
+
+from pathlib import Path
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("ExaoneForCausalLM")
+class ExaoneModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.EXAONE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        assert (hparams["activation_function"] == "silu")
+
+        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True)
+        rotary_factor = rotary_factor if rotary_factor is not None else 1.0
+        self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
+            if rope_params.get("rope_type", '').lower() == "llama3":
+                base = self.rope_parameters.get("rope_theta", 10000.0)
+                if (dim := self.hparams.get("head_dim")) is None:
+                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+                factor = rope_params.get("factor", 8.0)
+                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
+                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+
+                low_freq_wavelen = old_context_len / low_freq_factor
+                high_freq_wavelen = old_context_len / high_freq_factor
+                assert low_freq_wavelen != high_freq_wavelen
+
+                rope_factors = []
+                for freq in freqs:
+                    wavelen = 2 * math.pi / freq
+                    if wavelen < high_freq_wavelen:
+                        rope_factors.append(1)
+                    elif wavelen > low_freq_wavelen:
+                        rope_factors.append(factor)
+                    else:
+                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+
+@ModelBase.register("Exaone4ForCausalLM")
+class Exaone4Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.EXAONE4
+
+    def set_vocab(self):
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if hparams.get("sliding_window") is not None:
+            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+            if "layer_types" in hparams:
+                self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
+            elif "sliding_window_pattern" in hparams:
+                sliding_window_pattern = []
+                if isinstance(hparams["sliding_window_pattern"], str):  # e.g. LLLG
+                    for i in range(hparams["num_hidden_layers"]):
+                        sliding_window_pattern.append(hparams["sliding_window_pattern"][i % len(hparams["sliding_window_pattern"])] == "L")
+                if isinstance(hparams["sliding_window_pattern"], int):  # e.g. 4
+                    for i in range(hparams["num_hidden_layers"]):
+                        sliding_window_pattern.append((i + 1) % hparams["sliding_window_pattern"] != 0)
+                if len(sliding_window_pattern) == hparams["num_hidden_layers"]:
+                    self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
+            if rope_params.get("rope_type", '').lower() == "llama3":
+                base = rope_params.get("rope_theta", 10_000.0)
+                if (dim := self.hparams.get("head_dim")) is None:
+                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+                factor = rope_params.get("factor", 16.0)
+                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
+                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+
+                low_freq_wavelen = old_context_len / low_freq_factor
+                high_freq_wavelen = old_context_len / high_freq_factor
+
+                rope_factors = []
+                for freq in freqs:
+                    wavelen = 2 * math.pi / freq
+                    if wavelen < high_freq_wavelen:
+                        rope_factors.append(1)
+                    elif wavelen > low_freq_wavelen:
+                        rope_factors.append(factor)
+                    else:
+                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+
+@ModelBase.register("ExaoneMoEForCausalLM")
+class ExaoneMoEModel(Exaone4Model):
+    model_arch = gguf.MODEL_ARCH.EXAONE_MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        moe_intermediate_size = self.hparams["moe_intermediate_size"]
+        num_shared_experts = self.hparams["num_shared_experts"]
+        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        self.gguf_writer.add_expert_shared_count(num_shared_experts)
+        self.gguf_writer.add_expert_shared_feed_forward_length(moe_intermediate_size * num_shared_experts)
+        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
+        n_dense_layer = self.hparams.get("first_k_dense_replace", self.hparams.get("first_last_k_dense_replace", 0))
+        self.gguf_writer.add_leading_dense_block_count(n_dense_layer)
+        self.gguf_writer.add_nextn_predict_layers(self.hparams.get("num_nextn_predict_layers", 0))
+
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("mtp."):
+            if name.find("layers.") != -1:
+                # `mtp.layers.0.[module_name]` format
+                name = name.replace(f"mtp.layers.{bid}", f"model.layers.{bid + self.hparams['num_hidden_layers']}")
+            else:
+                # mtp fc/norm weights
+                remapper = {
+                    "mtp.fc": "model.layers.{bid}.eh_proj",
+                    "mtp.pre_fc_norm_embedding": "model.layers.{bid}.enorm",
+                    "mtp.pre_fc_norm_hidden": "model.layers.{bid}.hnorm",
+                    "mtp.norm": "model.layers.{bid}.shared_head.norm",
+                }
+                _n = Path(name)
+                new_name = remapper[_n.stem] + _n.suffix
+
+                # set shared weights for all NextN/MTP layers
+                for bid in range(self.hparams['num_hidden_layers'], self.block_count):
+                    yield from super().modify_tensors(data_torch, new_name.format(bid=bid), bid)
+                return
+
+        if name.find("mlp.experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    yield from super().modify_tensors(data_torch, new_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/falcon.py b/conversion/falcon.py
new file mode 100644
index 00000000000..085fd4cd33f
--- /dev/null
+++ b/conversion/falcon.py
@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("FalconForCausalLM", "RWForCausalLM")
+class FalconModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.FALCON
+
+    def set_gguf_parameters(self):
+        n_head = self.hparams.get("num_attention_heads")
+        if n_head is None:
+            n_head = self.hparams["n_head"]  # old name
+
+        n_head_kv = self.hparams.get("num_kv_heads")
+        if n_head_kv is None:
+            n_head_kv = self.hparams.get("n_head_kv", 1)  # old name
+
+        self.gguf_writer.add_context_length(2048)  # not in config.json
+        self.gguf_writer.add_tensor_data_layout("jploski")  # qkv tensor transform
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head_kv)
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # QKV tensor transform
+        # The original query_key_value tensor contains n_head_kv "kv groups",
+        # each consisting of n_head/n_head_kv query weights followed by one key
+        # and one value weight (shared by all query heads in the kv group).
+        # This layout makes it a big pain to work with in GGML.
+        # So we rearrange them here,, so that we have n_head query weights
+        # followed by n_head_kv key weights followed by n_head_kv value weights,
+        # in contiguous fashion.
+        # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
+
+        if "query_key_value" in name:
+            n_head = self.find_hparam(["num_attention_heads", "n_head"])
+            n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1
+            head_dim = self.hparams["hidden_size"] // n_head
+
+            qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
+            q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
+            k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
+            v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
+            data_torch = torch.cat((q, k, v)).reshape_as(data_torch)
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/falcon_h1.py b/conversion/falcon_h1.py
new file mode 100644
index 00000000000..a8bc880b2c4
--- /dev/null
+++ b/conversion/falcon_h1.py
@@ -0,0 +1,118 @@
+from __future__ import annotations
+
+from typing import Any, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, gguf
+
+from .llama import LlamaModel
+from .mamba import Mamba2Model
+
+
+@ModelBase.register("FalconH1ForCausalLM")
+class FalconH1Model(Mamba2Model):
+    model_arch = gguf.MODEL_ARCH.FALCON_H1
+
+    def __init__(self, *args, **kwargs):
+        # Set the hparam prefixes for Falcon Mamba2
+        self.hparam_prefixes = ["mamba"]
+
+        # Initialize the base Mamba2Model
+        super().__init__(*args, **kwargs)
+
+        # Use Llama conversion for attention
+        self._transformer_model_class = LlamaModel
+
+        # n_group and d_inner are used during reshape_tensors for mamba2
+        self.n_group = self.find_hparam(["n_groups"])
+        self.d_inner = self.find_hparam(["mamba_d_ssm"])
+        self.d_head = self.find_hparam(["d_head"])
+
+        # Initialize any Falcon Mamba2 specific attributes
+        self.has_attention = True  # Falcon Mamba2 has attention components
+
+        # Load Falcon-H1 multipliers from hyperparameters
+        self.attention_in_multiplier = self.find_hparam(["attention_in_multiplier"], optional=True)
+        self.attention_out_multiplier = self.find_hparam(["attention_out_multiplier"], optional=True)
+        self.ssm_in_multiplier = self.find_hparam(["ssm_in_multiplier"], optional=True)
+        self.ssm_out_multiplier = self.find_hparam(["ssm_out_multiplier"], optional=True)
+        self.mlp_multipliers = self.find_hparam(["mlp_multipliers"], optional=True)
+        self.ssm_multipliers = self.find_hparam(["ssm_multipliers"], optional=True)
+        self.intermediate_size = self.find_hparam(["intermediate_size"])
+        self.key_multiplier = self.find_hparam(["key_multiplier"], optional=True)
+
+    def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any:
+        prefixed = []
+        for pfx in self.hparam_prefixes:
+            prefixed.extend(
+                "_".join([pfx, k])
+                for k in keys
+            )
+        keys = list(keys) + prefixed
+        return super().find_hparam(keys, *args, **kwargs)
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        tensors = list(super().modify_tensors(data_torch, name, bid))
+        tensor = tensors[0][1]
+
+        if "down_proj" in name:
+            tensor = tensor  * self.mlp_multipliers[1]
+        elif "gate_proj" in name:
+            tensor = tensor * self.mlp_multipliers[0]
+        elif "k_proj" in name:
+            tensor = tensor * self.key_multiplier * self.attention_in_multiplier
+        elif "q_proj" in name:
+            tensor = tensor * self.attention_in_multiplier
+        elif "v_proj" in name:
+            tensor = tensor * self.attention_in_multiplier
+        elif "o_proj" in name:
+            tensor = tensor * self.attention_out_multiplier
+        elif "out_proj" in name:
+            tensor = tensor * self.ssm_out_multiplier
+        elif "in_proj" in name:
+            tensor = tensor * self.ssm_in_multiplier
+            zxbcdt_multipliers = self.hparams["ssm_multipliers"]
+            intermediate_size = self.hparams["mamba_d_ssm"]
+            groups_time_state_size = self.hparams["mamba_n_groups"] * self.hparams["mamba_d_state"]
+            tensor[:intermediate_size, :] *= zxbcdt_multipliers[0]
+            tensor[intermediate_size:2 * intermediate_size, :] *= zxbcdt_multipliers[1]
+            tensor[2 * intermediate_size:2 * intermediate_size + groups_time_state_size, :] *= zxbcdt_multipliers[2]
+            tensor[2 * intermediate_size + groups_time_state_size:2 * intermediate_size + 2 * groups_time_state_size, :] *= zxbcdt_multipliers[3]
+            tensor[2 * intermediate_size + 2 * groups_time_state_size:, :] *= zxbcdt_multipliers[4]
+        elif "lm_head" in name:
+            tensor = tensor * self.hparams["lm_head_multiplier"]
+        elif "embed_tokens" in name:
+            tensor = tensor * self.hparams["embedding_multiplier"]
+        elif "mamba.norm" in name:
+            tensor = tensor.reshape(self.n_group, self.d_inner // self.n_group)
+
+        tensors = [(tensors[0][0], tensor)]
+        return tensors
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        ## General Params ##
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+        # Override some Mamba2 defaults
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_context_length(self.hparams.get("max_position_embeddings", 0))
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+
+        ## Attention params ##
+        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) # Override value 0 from Mamba2
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+        self.gguf_writer.add_key_length(self.hparams["head_dim"])
+        self.gguf_writer.add_value_length(self.hparams["head_dim"])
+
+        ## Validation ##
+        assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported"
+        assert self.d_inner % self.d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {self.d_head}"
+
+        # Add any other Falcon Mamba2 specific configuration
+        self.gguf_writer.add_rope_freq_base(self.rope_parameters["rope_theta"])
diff --git a/conversion/gemma.py b/conversion/gemma.py
new file mode 100644
index 00000000000..a6e14fbcb98
--- /dev/null
+++ b/conversion/gemma.py
@@ -0,0 +1,840 @@
+from __future__ import annotations
+
+import json
+import re
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("GemmaForCausalLM")
+class GemmaModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GEMMA
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+        # TODO: these special tokens should be exported only for the CodeGemma family
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
+        special_vocab._set_special_token("prefix", 67)
+        special_vocab._set_special_token("suffix", 69)
+        special_vocab._set_special_token("middle", 68)
+        special_vocab._set_special_token("fsep",   70)
+        special_vocab._set_special_token("eot",    107)
+        special_vocab.chat_template = None  # do not add it twice
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        self.gguf_writer.add_add_space_prefix(False)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_key_length(hparams["head_dim"])
+        self.gguf_writer.add_value_length(hparams["head_dim"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
+        # To prevent errors, skip loading lm_head.weight.
+        if name == "lm_head.weight":
+            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
+        if name.endswith("norm.weight"):
+            data_torch = data_torch + 1
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Gemma2ForCausalLM")
+class Gemma2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.GEMMA2
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+        self.gguf_writer.add_add_space_prefix(False)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_key_length(hparams["head_dim"])
+        self.gguf_writer.add_value_length(hparams["head_dim"])
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_attn_logit_softcapping(
+            self.hparams["attn_logit_softcapping"]
+        )
+        self.gguf_writer.add_final_logit_softcapping(
+            self.hparams["final_logit_softcapping"]
+        )
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
+        # To prevent errors, skip loading lm_head.weight.
+        if name == "lm_head.weight":
+            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
+        if name.endswith("norm.weight"):
+            data_torch = data_torch + 1
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Gemma3ForCausalLM", "Gemma3ForConditionalGeneration")
+class Gemma3Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.GEMMA3
+
+    def norm_shift(self, name: str) -> float:
+        return 1.0 if name.endswith("norm.weight") else 0.0  # Gemma3RMSNorm adds 1.0 to the norm value
+
+    def set_vocab(self):
+        if (self.dir_model / "tokenizer.model").is_file():
+            self._set_vocab_sentencepiece()
+            self.gguf_writer.add_add_space_prefix(False)
+        else:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        # some default values are not specified in the hparams
+        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072))
+        self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8))
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6))
+        self.gguf_writer.add_key_length(hparams.get("head_dim", 256))
+        self.gguf_writer.add_value_length(hparams.get("head_dim", 256))
+        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_theta", 1_000_000.0)) # for global layers
+        # attn_logit_softcapping is removed in Gemma3
+        assert hparams.get("attn_logit_softcapping") is None
+        if (final_logit_softcap := hparams.get("final_logit_softcapping")):
+            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
+        if hparams.get("sliding_window_pattern") != 1:
+            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+        self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # remove OOV (out-of-vocabulary) rows in token_embd
+        if "embed_tokens.weight" in name:
+            n_vocab_real = -1
+            if (self.dir_model / "tokenizer.model").is_file():
+                tokens = self._create_vocab_sentencepiece()[0]
+                n_vocab_real = len(tokens)
+            else:
+                with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
+                    tokenizer_json = json.load(f)
+                    n_vocab_real = len(tokenizer_json["model"]["vocab"]) + len(tokenizer_json["added_tokens"])
+            data_torch = data_torch[:n_vocab_real]
+
+        # ref code in Gemma3RMSNorm
+        # output = output * (1.0 + self.weight.float())
+        # note: this is not the case on gemma3n
+        f_shift = self.norm_shift(name)
+        if f_shift != 0.0:
+            data_torch = data_torch + f_shift
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Gemma3TextModel")
+class EmbeddingGemma(Gemma3Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
+    module_paths = []
+    dense_features_dims = {}
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.sentence_transformers_dense_modules:
+            # read modules.json to determine if model has Dense layers
+            modules_file = self.dir_model / "modules.json"
+            if modules_file.is_file():
+                with open(modules_file, encoding="utf-8") as modules_json_file:
+                    mods = json.load(modules_json_file)
+                for mod in mods:
+                    if mod["type"].endswith("Dense"):
+                        mod_path = mod["path"]
+                        # check if model.safetensors file for Dense layer exists
+                        model_tensors_file = self.dir_model / mod_path / "model.safetensors"
+                        if model_tensors_file.is_file():
+                            self.module_paths.append(mod_path)
+                            # read config.json of the Dense layer to get in/out features
+                            mod_conf_file = self.dir_model / mod_path / "config.json"
+                            if mod_conf_file.is_file():
+                                with open(mod_conf_file, encoding="utf-8") as mod_conf_json_file:
+                                    mod_conf = json.load(mod_conf_json_file)
+                                    # hparams dense_2_feat_out and dense_3_feat_in are required when loading model's dense weights
+                                    prefix = self._get_dense_prefix(mod_path)
+                                    if mod_conf["in_features"] is not None and mod_conf["out_features"] is not None:
+                                        self.dense_features_dims[prefix] = (mod_conf["in_features"], mod_conf["out_features"])
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        from safetensors.torch import load_file
+        module_paths = list(self.module_paths)
+        for i, module_path in enumerate(module_paths):
+            tensors_file = self.dir_model / module_path / "model.safetensors"
+            local_tensors = load_file(tensors_file)
+            tensor_name = self._get_dense_prefix(module_path)
+            for name, local_tensor in local_tensors.items():
+                if not name.endswith(".weight"):
+                    continue
+                orig_name = name.replace("linear", tensor_name)
+                name = self.map_tensor_name(orig_name)
+                yield name, local_tensor.clone()
+
+    @staticmethod
+    def _get_dense_prefix(module_path) -> str:
+        """Get the tensor name prefix for the Dense layer from module path."""
+        tensor_name = "dense_2" if module_path == "2_Dense" else "dense_3"
+        return tensor_name
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # Override the sliding window size as it gets adjusted by the Gemma3TextConfig
+        # constructor. We want to use the value from the original model's config.json.
+        # ref: https://github.com/huggingface/transformers/pull/40700
+        with open(self.dir_model / "config.json", "r", encoding="utf-8") as f:
+            config = json.load(f)
+            orig_sliding_window = config.get("sliding_window")
+            if orig_sliding_window is None:
+                raise ValueError("sliding_window not found in model config - this is required for the model")
+
+            logger.info(f"Using original sliding_window from config: {orig_sliding_window} "
+                        f"instead of {self.hparams['sliding_window']}")
+            self.gguf_writer.add_sliding_window(orig_sliding_window)
+        if self.sentence_transformers_dense_modules:
+            for dense, dims in self.dense_features_dims.items():
+                logger.info(f"Setting dense layer {dense} in/out features to {dims}")
+                self.gguf_writer.add_dense_features_dims(dense, dims[0], dims[1])
+
+        self._try_set_pooling_type()
+
+
+@ModelBase.register("Gemma3ForConditionalGeneration")
+class Gemma3VisionModel(MmprojModel):
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GEMMA3)
+        # default values below are taken from HF transformers code
+        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
+        self.gguf_writer.add_vision_use_gelu(True)
+        # calculate proj_scale_factor (used by tinygemma3 test model)
+        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
+        n_per_side = int(image_seq_length ** 0.5)
+        image_size = self.hparams["image_size"]
+        patch_size = self.hparams["patch_size"]
+        proj_scale_factor = (image_size // patch_size) // n_per_side
+        if proj_scale_factor > 0 and proj_scale_factor != 4:
+            # we only need to write this if it's not the default value
+            # in this case, we are converting a test model
+            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # related to https://github.com/ggml-org/llama.cpp/issues/13025
+        if "input_projection" in name:
+            return gguf.GGMLQuantizationType.F16
+        if ".embeddings." in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if "vision_model.head." in name:
+            # skip redundant tensors for tinygemma3
+            return None
+
+        if not name.startswith(("multi_modal_projector.", "vision_tower.", "multimodal_projector.", "vision_model.")):
+            return None
+
+        name = name.replace("_weight", ".weight")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # correct norm value ; only this "soft_emb_norm" need to be corrected as it's part of Gemma projector
+        # the other norm values are part of SigLIP model, and they are already correct
+        # ref code: Gemma3RMSNorm
+        if "soft_emb_norm.weight" in name:
+            logger.info(f"Correcting norm value for '{name}'")
+            data_torch = data_torch + 1
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+class ConformerAudioModel(MmprojModel):
+    _batch_norm_tensors: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def is_audio_tensor(name: str):
+        return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ConformerAudioModel.is_audio_tensor(name):
+            if ".conv" in name or "_conv" in name and ".weight" in name:
+                return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # fold running_mean, running_var and eps into weight and bias for batch_norm
+        if "batch_norm" in name:
+            if self._batch_norm_tensors is None:
+                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
+            assert bid is not None
+            self._batch_norm_tensors[bid][name] = data_torch
+
+            if len(self._batch_norm_tensors[bid]) < 5:
+                return
+
+            weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
+            bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
+            running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
+            running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
+            eps = 1e-5 # default value
+
+            a = weight / torch.sqrt(running_var + eps)
+            b = bias - running_mean * a
+            yield from super().modify_tensors(a, f"conformer.layers.{bid}.conv.batch_norm.weight", bid)
+            yield from super().modify_tensors(b, f"conformer.layers.{bid}.conv.batch_norm.bias", bid)
+            return
+
+        # reshape conv weights
+        if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
+            data_torch = data_torch[:, None, None]
+        if "conv.depthwise_conv" in name and name.endswith(".weight"):
+            assert data_torch.shape[1] == 1
+            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
+        if "conv.pointwise_conv" in name and name.endswith(".weight"):
+            assert data_torch.shape[2] == 1
+            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
+
+        mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
+        yield (mapped_name, data_torch)
+
+
+@ModelBase.register("Gemma3nForConditionalGeneration")
+class Gemma3nVisionAudioModel(ConformerAudioModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    # Double indexed mapping for MobileNetV5 blocks (not supported by tensor_mapping.py)
+    # This is the only known model having this, so we prefer implementing it outside of tensor_mapping.py
+    block_tensor_mapping = {
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_exp.weight":             "v.blk.{bid}.{sid}.conv_exp.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn1.weight":                  "v.blk.{bid}.{sid}.bn1.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_pwl.weight":             "v.blk.{bid}.{sid}.conv_pwl.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn2.weight":                  "v.blk.{bid}.{sid}.bn2.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.conv.weight":        "v.blk.{bid}.{sid}.dw_start.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.bn.weight":          "v.blk.{bid}.{sid}.dw_start.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.conv.weight":          "v.blk.{bid}.{sid}.dw_mid.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.bn.weight":            "v.blk.{bid}.{sid}.dw_mid.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.conv.weight":          "v.blk.{bid}.{sid}.pw_exp.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.bn.weight":            "v.blk.{bid}.{sid}.pw_exp.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.conv.weight":         "v.blk.{bid}.{sid}.pw_proj.conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.bn.weight":           "v.blk.{bid}.{sid}.pw_proj.bn.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.layer_scale.gamma":           "v.blk.{bid}.{sid}.layer_scale.gamma",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.query.proj.weight":      "v.blk.{bid}.{sid}.attn.query.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.proj.weight":        "v.blk.{bid}.{sid}.attn.key.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.proj.weight":      "v.blk.{bid}.{sid}.attn.value.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.output.proj.weight":     "v.blk.{bid}.{sid}.attn.output.proj.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.down_conv.weight":   "v.blk.{bid}.{sid}.attn.key.down_conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.norm.weight":        "v.blk.{bid}.{sid}.attn.key.norm.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.down_conv.weight": "v.blk.{bid}.{sid}.attn.value.down_conv.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.norm.weight":      "v.blk.{bid}.{sid}.attn.value.norm.weight",
+        "model.vision_tower.timm_model.blocks.{bid}.{sid}.norm.weight":                 "v.blk.{bid}.{sid}.norm.weight",
+    }
+
+    def __init__(self, *args, **kwargs):
+        # Parent init will call find_hparam which now returns 0 for empty keys
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["n_layers"] = 128 # fake value for audio encoder, vision encoder doesn't use it
+        self.hparams_vision["intermediate_size"] = self.hparams_vision.get("intermediate_size", 2048) * 4
+        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_attention_heads", 8)
+
+        # MobileNetV5 does not use image_mean/std
+        self.preprocessor_config["image_mean"] = [0.0 ,0.0 , 0.0]
+        self.preprocessor_config["image_std"] = [1.0 ,1.0 ,1.0]
+        self.hparams_vision["image_size"] = self.preprocessor_config.get(
+            "size", {"height": 768, "width": 768}
+        )["height"]
+
+        # Image sequence length (256 tokens = 16x16 for Gemma3n)
+        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
+        image_size = self.hparams_vision["image_size"]
+        self.hparams_vision["patch_size"] = image_size // image_seq_length
+
+        # remap audio hparams
+        assert self.hparams_audio is not None
+        self.hparams_audio["n_layers"] = self.hparams_audio["conf_num_hidden_layers"]
+        self.hparams_audio["num_attention_heads"] = self.hparams_audio["conf_num_attention_heads"]
+        self.hparams_audio["feat_in"] = self.hparams_audio["input_feat_size"]
+        self.hparams_audio["intermediate_size"] = self.hparams_audio.get("intermediate_size", 6144)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # vision params
+        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA3NV)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
+
+        # audio params
+        assert self.hparams_audio is not None
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA3NA)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # Force quantization settings for specific tensor types
+        if "input_projection" in name or "input_proj" in name:
+            return gguf.GGMLQuantizationType.F16
+        if ".embeddings." in name or "stem" in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def custom_map(self, name: str) -> str:
+        """Parses names like model.vision_tower.timm_model.blocks.1.2.suffix and applies template mapping."""
+        parts = name.split(".")
+        # MobileNet blocks have at least 7 parts: model, vision_tower, timm_model, blocks, bid, sid, and suffix
+        if len(parts) >= 7:
+            bid, sid = parts[4], parts[5]
+            suffix = ".".join(parts[6:])
+            template = f"model.vision_tower.timm_model.blocks.{{bid}}.{{sid}}.{suffix}"
+            if template in self.block_tensor_mapping:
+                return self.block_tensor_mapping[template].format(bid=bid, sid=sid)
+
+        raise ValueError(f"Unknown name: {name}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if (ConformerAudioModel.is_audio_tensor(name)):
+            name = name.replace("model.audio_tower.conformer.", "conformer.layers.")
+            yield from super().modify_tensors(data_torch, name, bid)
+
+        # Gemma3n uses
+        # - model.embed_vision.* for projection layers
+        # - model.vision_tower.* for vision encoder
+        # Skip non-vision tensors
+        if not (name.startswith("model.embed_vision.") or name.startswith("model.vision_tower.")):
+            return
+
+        if name.startswith("model.vision_tower.timm_model.blocks."):
+            # Double-indexed block tensors through custom logic
+            yield (self.custom_map(name), data_torch)
+            return
+        else:
+            # Route non-repeating (conv_stem, msfa, embedding, etc.) and un-catched through tensor_mapping.py
+            new_name = self.map_tensor_name(name)
+
+        if new_name.endswith("conv_stem.conv.bias") or new_name.endswith("layer_scale.gamma"):
+            data_torch = data_torch.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) # [1, C, 1, 1]
+
+        yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
+
+
+@ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
+class Gemma3NModel(Gemma3Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA3N
+
+    _altup_proj: list[Tensor] = []
+    _altup_unembd: list[Tensor] = []
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams["altup_num_inputs"] == 4, "Current conversion only supports 4 altup inputs"
+        self._altup_proj = [
+            torch.Tensor(), # to be replaced
+            torch.Tensor(), # to be replaced
+            torch.Tensor(), # to be replaced
+        ]
+        self._altup_unembd = [
+            torch.Tensor(), # to be replaced
+            torch.Tensor(), # to be replaced
+            torch.Tensor(), # to be replaced
+        ]
+
+    def norm_shift(self, name: str) -> float:
+        del name
+        return 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code
+
+    def set_vocab(self):
+        # For Gemma3n multimodal models, we need the FULL vocab_size (262400)
+        # which includes special tokens from 262144-262399 for vision/audio.
+        # The vocab_size_per_layer_input (262144) is only the embedding size per layer.
+        # Temporarily override the hparams lookup order to prioritize vocab_size.
+
+        # Store original vocab_size_per_layer_input if it exists
+        vocab_size_per_layer_input = self.hparams.get("vocab_size_per_layer_input")
+
+        # Temporarily remove vocab_size_per_layer_input to force using vocab_size
+        if vocab_size_per_layer_input is not None:
+            del self.hparams["vocab_size_per_layer_input"]
+
+        # Call parent set_vocab which will now use vocab_size (262400)
+        super().set_vocab()
+
+        # Restore vocab_size_per_layer_input for later use
+        if vocab_size_per_layer_input is not None:
+            self.hparams["vocab_size_per_layer_input"] = vocab_size_per_layer_input
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_altup_active_idx(self.hparams["altup_active_idx"])
+        self.gguf_writer.add_altup_num_inputs(self.hparams["altup_num_inputs"])
+        self.gguf_writer.add_embedding_length_per_layer_input(self.hparams["hidden_size_per_layer_input"])
+        self.gguf_writer.add_shared_kv_layers(self.hparams["num_kv_shared_layers"])
+
+        activation_sparsity_scale = []
+        for s in self.hparams["activation_sparsity_pattern"]:
+            normal_dist = torch.distributions.normal.Normal(0, 1)
+            std_multiplier = normal_dist.icdf(torch.tensor(s, dtype=torch.float32))
+            activation_sparsity_scale.append(std_multiplier.item())
+        self.gguf_writer.add_activation_sparsity_scale(activation_sparsity_scale)
+
+        sliding_window_pattern = []
+        for t in self.hparams["layer_types"]:
+            sliding_window_pattern.append(t == "sliding_attention")
+        self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
+
+    def _stack_matrices(self, matrices: list[Tensor]) -> Tensor | None:
+        has_all = all(m.numel() > 0 for m in matrices)
+        if not has_all:
+            return None
+        else:
+            return torch.stack(matrices, dim=0)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.endswith("_scale"):
+            name = name + ".weight"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # TODO: implement self.prediction_coefs.weight.clamp_(...)
+
+        # Pad token embeddings for vision/audio special tokens (262144-262399)
+        if "embed_tokens.weight" in name or "embed_tokens_per_layer" in name:
+            # Move to CPU to avoid meta device issues during padding
+            data_torch = data_torch.to(device="cpu")
+
+            vocab_size = self.hparams.get("vocab_size", 262400)
+            current_size = data_torch.shape[0]  # First dimension is vocab_size
+
+            if current_size < vocab_size:
+                # Pad with zeros for vision/audio tokens (they get embeddings from vision tower)
+                padding_size = vocab_size - current_size
+                tensor_type = "per-layer embeddings" if "per_layer" in name else "token embeddings"
+                logger.info(f"Padding {tensor_type} shape {list(data_torch.shape)} from {current_size} to {vocab_size} (adding {padding_size} vision/audio token slots)")
+
+                # Create padding with zeros (vision tokens won't use these embeddings)
+                padding = torch.zeros((padding_size, data_torch.shape[1]), dtype=data_torch.dtype, device=data_torch.device)
+                data_torch = torch.cat([data_torch, padding], dim=0)
+
+            # Continue with normal processing
+            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+            return
+
+        if "altup_unembed_projections" in name:
+            data_torch = data_torch.to(device="cpu")
+            # altup_unembed matrices are [hidden_size, hidden_size], NOT vocab-based
+            # They should NOT be padded
+            if ".0." in name:
+                self._altup_unembd[0] = data_torch
+            elif ".1." in name:
+                self._altup_unembd[1] = data_torch
+            elif ".2." in name:
+                self._altup_unembd[2] = data_torch
+            else:
+                raise ValueError(f"Unknown name: {name}")
+            out = self._stack_matrices(self._altup_unembd)
+            if out is not None:
+                yield from ModelBase.modify_tensors(self, out, "model.altup_unembed_projections.weight", bid)
+                return
+            else:
+                return
+
+        if "altup_projections" in name:
+            data_torch = data_torch.to(device="cpu")
+            if ".0." in name:
+                self._altup_proj[0] = data_torch
+            elif ".1." in name:
+                self._altup_proj[1] = data_torch
+            elif ".2." in name:
+                self._altup_proj[2] = data_torch
+            else:
+                raise ValueError(f"Unknown name: {name}")
+            out = self._stack_matrices(self._altup_proj)
+            if out is not None:
+                yield from ModelBase.modify_tensors(self, out, "model.altup_projections.weight", bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Gemma4ForConditionalGeneration")
+class Gemma4Model(Gemma3Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA4
+
+    def norm_shift(self, name: str) -> float:
+        del name # unused
+        return 0.0
+
+    def set_vocab(self):
+        vocab = gguf.LlamaHfVocab(self.dir_model)
+        tokens = []
+        scores = []
+        toktypes = []
+        visible_tokens = {"<|channel>", "<channel|>", "<|tool_call>", "<tool_call|>", "<|tool_response>", "<tool_response|>", "<|\"|>"}
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            text_str = text.decode()
+            if text_str in visible_tokens:
+                # always render these tokens, so that the chat parser can read them
+                toktypes.append(gguf.TokenType.USER_DEFINED)
+                logger.info(f"Token '{text_str}' is set to USER_DEFINED")
+            else:
+                toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size
+
+        self.gguf_writer.add_tokenizer_model("gemma4")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+        self.gguf_writer.add_add_space_prefix(False)
+        self.gguf_writer.add_add_bos_token(True)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        num_kv_shared_layers = self.hparams["num_kv_shared_layers"]
+        self.gguf_writer.add_shared_kv_layers(num_kv_shared_layers)
+
+        # per-layer embedding is optional
+        n_pl_embd = self.hparams.get("hidden_size_per_layer_input") or 0
+        self.gguf_writer.add_embedding_length_per_layer_input(n_pl_embd)
+
+        swa_layers = [t == "sliding_attention" for t in self.hparams["layer_types"]]
+        self.gguf_writer.add_sliding_window_pattern(swa_layers)
+
+        head_dim_full = self.hparams["global_head_dim"]
+        head_dim_swa = self.hparams["head_dim"]
+        # correct the head dim for global/swa layers
+        self.gguf_writer.add_key_length(head_dim_full)
+        self.gguf_writer.add_value_length(head_dim_full)
+        self.gguf_writer.add_key_length_swa(head_dim_swa)
+        self.gguf_writer.add_value_length_swa(head_dim_swa)
+
+        expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"])
+        if expert_intermediate_size is not None:
+            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
+
+        # if use_double_wide_mlp is set, we need to adjust the value for kv shared layers
+        use_double_wide_mlp = self.hparams.get("use_double_wide_mlp", False)
+        first_kv_shared_layer_idx = self.block_count - num_kv_shared_layers
+        if use_double_wide_mlp:
+            n_ff = self.hparams["intermediate_size"]
+            n_ff_arr = [n_ff if il < first_kv_shared_layer_idx else n_ff * 2 for il in range(self.block_count)]
+            self.gguf_writer.add_feed_forward_length(n_ff_arr)
+
+        # handle num_global_key_value_heads
+        num_key_value_heads_full = self.hparams.get("num_global_key_value_heads")
+        num_key_value_heads_swa = self.hparams.get("num_key_value_heads")
+        if num_key_value_heads_full is not None and num_key_value_heads_swa is not None:
+            value_arr = [num_key_value_heads_swa if is_swa else num_key_value_heads_full for is_swa in swa_layers]
+            self.gguf_writer.add_head_count_kv(value_arr)
+
+        # handle n_rot differently for global vs swa layers
+        partial_rotary_factor_swa = self.hparams.get("partial_rotary_factor", 1.0)
+        n_rot_full = int(head_dim_full) # "proportional" is used, see generate_extra_tensors
+        n_rot_swa = int(head_dim_swa * partial_rotary_factor_swa)
+        self.gguf_writer.add_rope_dimension_count(n_rot_full)
+        self.gguf_writer.add_rope_dimension_count_swa(n_rot_swa)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # full layer uses "proportional" rope with partial_rotary_factor=0.25
+        # the expected ordering is cc000000ss000000 (c = cos, s = sin, 0 = unrotated),
+        # but ggml neox only supports ccss000000000000, and we cannot rearrange the head because that will break use_alternative_attention
+        # solution is to set specific freq_factors for the unrotated dims
+
+        # IMPORTANT: this ROPE_FREQS tensor is ONLY used by the full_attention layers
+        rope_params_full = self.hparams["rope_parameters"]["full_attention"]
+        assert rope_params_full["rope_type"] == "proportional"
+        head_dim_full = (self.hparams["global_head_dim"])
+        partial_rotary_factor_full = rope_params_full["partial_rotary_factor"]
+        n_rot_full = int(head_dim_full * partial_rotary_factor_full / 2)
+        n_unrot_full = int(head_dim_full / 2) - n_rot_full
+        values = [1.0] * n_rot_full + [1e30] * n_unrot_full
+        rope_freqs_full = torch.tensor(values, dtype=torch.float32)
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), rope_freqs_full)
+
+    def _generate_nvfp4_tensors(self):
+        # Gemma-4 stores a per-layer router.per_expert_scale ([n_expert]) that scales
+        # each expert's contribution. It's mathematically equivalent to a per-expert
+        # scalar on the down_proj output, which is exactly where ffn_down_exps_s is
+        # applied at inference. Fold it into each expert's NVFP4 weight_scale_2 so the
+        # existing NVFP4 path produces the right scales.
+        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
+        for name in [n for n in self.model_tensors if n.endswith(".router.per_expert_scale")]:
+            bid_match = re.search(r"\.layers\.(\d+)\.", name)
+            if bid_match is None:
+                continue
+            bid = bid_match.group(1)
+            prefix = name[: name.index(f".layers.{bid}.") + len(f".layers.{bid}.")]
+            w2_targets = [f"{prefix}experts.{e}.down_proj.weight_scale_2" for e in range(n_experts)]
+            present = [w2 in self.model_tensors for w2 in w2_targets]
+            if not any(present):
+                continue
+            assert all(present), f"layer {bid}: partial NVFP4 quantization across experts"
+            r = self.model_tensors.pop(name)
+            for e, w2 in enumerate(w2_targets):
+                s = self.model_tensors[w2]
+                self.model_tensors[w2] = lambda s=s, r=r, i=e: s() * r()[i]
+        super()._generate_nvfp4_tensors()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.endswith("per_dim_scale") or name.endswith("layer_scalar"):
+            name = name + ".weight"
+        if ".experts." in name and not name.endswith((".weight", ".weight_scale", ".weight_scale_2", ".input_scale")):
+            name += ".weight"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith("router.scale"):
+            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_INP, bid, ".scale")
+            yield (name, data_torch)
+            return
+        if ".per_expert_scale" in name:
+            # convert per-expert scale to FFN down scale
+            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN_EXP, bid, ".scale")
+            yield (name, data_torch)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Gemma4ForConditionalGeneration")
+class Gemma4VisionAudioModel(MmprojModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = 224 # unused, but set to avoid error
+
+        # remap audio hparams
+        if self.hparams_audio:
+            self.hparams_audio["feat_in"] = self.hparams_audio.get("input_feat_size", 128)
+            self.hparams_audio["intermediate_size"] = self.hparams_audio["hidden_size"] * 4
+        else:
+            self.has_audio_encoder = False
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # vision params
+        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA4V)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
+
+        # audio params
+        if self.hparams_audio:
+            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA4A)
+            self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
+            self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+    def is_audio_tensor(self, name: str) -> bool:
+        return "audio_tower" in name or "embed_audio" in name
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if self.is_audio_tensor(name):
+            if ".conv" in name or "_conv" in name and ".weight" in name:
+                return gguf.GGMLQuantizationType.F32
+        if "position_embedding_table" in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid # unused
+
+        if len(data_torch.shape) == 0:
+            # convert scalar tensors (input/output_mix/max) to 1D tensors
+            data_torch = data_torch.unsqueeze(0)
+
+        if self.is_audio_tensor(name):
+            assert self.hparams_audio is not None
+            name = name.replace("model.audio_tower.", "conformer.")
+            name = name.replace(".linear.", ".")
+            if name.endswith("per_dim_key_scale") or name.endswith("per_dim_scale"):
+                name = name + ".weight"
+                data_torch = torch.nn.functional.softplus(data_torch)
+            if "lconv1d.depthwise_conv1d" in name and name.endswith(".weight"):
+                assert data_torch.shape[1] == 1
+                data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
+            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
+            yield (mapped_name, data_torch)
+
+        else:
+            name = name.replace("model.vision_tower.encoder.", "vision_model.model.")
+            name = name.replace(".linear.weight", ".weight")
+            if name.endswith("layer_scalar") or name.endswith("position_embedding_table"):
+                name = name + ".weight"
+            if name.endswith("patch_embedder.input_proj.weight"):
+                n_embd, ksize_sq_c = data_torch.shape
+                patch_size = int((ksize_sq_c // 3) ** 0.5)
+                data_torch = data_torch.reshape(n_embd, patch_size, patch_size, 3)
+                data_torch = data_torch.permute(0, 3, 1, 2).contiguous()
+            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
+            yield (mapped_name, data_torch)
diff --git a/conversion/glm.py b/conversion/glm.py
new file mode 100644
index 00000000000..641937720d6
--- /dev/null
+++ b/conversion/glm.py
@@ -0,0 +1,259 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+from .deepseek import DeepseekV2Model
+
+
+@ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration")
+class Glm4Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.GLM4
+    use_mrope = False
+    partial_rotary_factor = 0.5
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 0.5)
+        if "mrope_section" in self.rope_parameters:
+            self.use_mrope = True
+            logger.info("Q/K weight will need to be permuted for M-RoPE")
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.partial_rotary_factor))
+
+    @staticmethod
+    def normal_to_neox(weights: Tensor, n_head: int, n_head_kv: int, head_dim: int, partial_rotary_factor: float) -> Tensor:
+        orig_shape = weights.shape
+        if len(orig_shape) == 1:
+            weights = weights.unsqueeze(1)  # [out_dim, 1]
+        if len(weights.shape) != 2:
+            raise ValueError("Only 1D and 2D tensors are supported.")
+        n_effective_heads = weights.shape[0] // head_dim
+        if n_head_kv is not None and n_effective_heads != n_head:
+            if n_effective_heads != n_head_kv:
+                raise AssertionError(f"Mismatch in effective heads: computed {n_effective_heads}, expected {n_head} or {n_head_kv}")
+        rotary_dim = int(head_dim * partial_rotary_factor)
+        if rotary_dim % 2 != 0:
+            raise ValueError("rotary_dim must be even.")
+        reshaped = weights.reshape(n_effective_heads, head_dim, -1)
+        rot_part = reshaped[:, :rotary_dim, :]
+        non_rot_part = reshaped[:, rotary_dim:, :]
+        permuted_rot = torch.cat((rot_part[:, ::2, :], rot_part[:, 1::2, :]), dim=1)
+        combined = torch.cat((permuted_rot, non_rot_part), dim=1)
+        result = combined.reshape(weights.shape)
+        return result if len(orig_shape) != 1 else result.squeeze(1)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if self.use_mrope:
+            n_head = self.hparams["num_attention_heads"]
+            n_kv_head = self.hparams["num_key_value_heads"]
+            n_embd = self.hparams["hidden_size"]
+            head_dim = self.hparams.get("head_dim", n_embd // n_head)
+            # because llama.cpp M-RoPE kernel only supports Neox ordering, we have to permute the weights here
+            if name.endswith(("q_proj.weight", "q_proj.bias")):
+                data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_head, head_dim, self.partial_rotary_factor)
+            if name.endswith(("k_proj.weight", "k_proj.bias")):
+                data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_kv_head, head_dim, self.partial_rotary_factor)
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("GlmOcrForConditionalGeneration")
+class GlmOCRModel(Glm4Model):
+    model_arch = gguf.MODEL_ARCH.GLM4
+    use_mrope = False
+    partial_rotary_factor = 0.5
+
+    # Note: GLM-OCR is the same as GLM4, but with an extra NextN/MTP prediction layer
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # GLM-OCR has num_hidden_layers + 1 actual layers (including NextN layer)
+        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        # NextN/MTP prediction layers
+        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
+
+
+@ModelBase.register("Glm4MoeForCausalLM", "Glm4vMoeForConditionalGeneration")
+class Glm4MoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GLM4_MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
+        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_vocab(self):
+        return self._set_vocab_glm()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = (
+                self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+            )
+        self.gguf_writer.add_rope_dimension_count(
+            int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
+        )
+
+        # MoE parameters - Use only routed expert count (shared experts handled separately)
+        if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_routed_experts)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
+            self.gguf_writer.add_expert_shared_count(n_shared_experts)
+        if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
+            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
+
+        # Expert gating function (sigmoid for GLM4_MOE)
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        # Routed scaling factor
+        if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
+
+        # Normalise topk probabilities
+        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
+
+        # NextN/MTP prediction layers
+        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    # note: unlike GLM4V non-MoE, we don't need to permute Q/K here since GLM4V_MOE uses Neox ordering already
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Handle main token embedding (but not layer-specific NextN embeddings)
+        if name == "model.embed_tokens.weight" and ".layers." not in name:
+            yield from super().modify_tensors(data_torch, "token_embd.weight", bid)
+            return
+
+        # Handle routed experts
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("Glm4MoeLiteForCausalLM")
+class Glm4MoeLiteModel(DeepseekV2Model):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
+
+    def set_vocab(self):
+        return self._set_vocab_glm()
+
+
+@ModelBase.register("GlmMoeDsaForCausalLM")
+class GlmMoeDsaModel(DeepseekV2Model):
+    model_arch = gguf.MODEL_ARCH.GLM_DSA
+    skip_mtp = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_vocab(self):
+        return self._set_vocab_glm()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        rope_dim = self.hparams["qk_rope_head_dim"]
+        partial_rotary_factor = self.hparams.get("partial_rotary_factor", 1.0)
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))
+
+        # NextN/MTP prediction layers
+        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
+
+        # DSA indexer parameters
+        self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"])
+        self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"])
+        self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])
+
+
+@ModelBase.register("SolarOpenForCausalLM")
+class SolarOpenModel(Glm4MoeModel):
+    model_arch = gguf.MODEL_ARCH.GLM4_MOE
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<unk>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|startoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab.add_to_gguf(self.gguf_writer)
diff --git a/conversion/gpt2.py b/conversion/gpt2.py
new file mode 100644
index 00000000000..1cf06ae8b50
--- /dev/null
+++ b/conversion/gpt2.py
@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("GPT2LMHeadModel")
+class GPT2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.GPT2
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_context_length(self.hparams["n_ctx"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # we don't need these
+        if name.endswith((".attn.bias", ".attn.masked_bias")):
+            yield from super().modify_tensors(data_torch, name, bid)
+            return
+
+        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
+            data_torch = data_torch.transpose(1, 0)
+
+        new_name = self.map_tensor_name(name)
+
+        yield from super().modify_tensors(data_torch, new_name, bid)
+
+
+@ModelBase.register("RuGPT3XLForCausalLM")
+class RuGPT3XLModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GPT2
+
+    _qkv_parts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Fuse separate Q, K, V projections into a single QKV tensor
+        if ".self_attn.q_proj." in name or ".self_attn.k_proj." in name or ".self_attn.v_proj." in name:
+            suffix = "weight" if name.endswith(".weight") else "bias"
+            part = "q" if ".q_proj." in name else ("k" if ".k_proj." in name else "v")
+            key = f"{part}.{suffix}"
+
+            assert bid is not None
+            if self._qkv_parts is None:
+                self._qkv_parts = [{} for _ in range(self.block_count)]
+            self._qkv_parts[bid][key] = data_torch
+
+            q_key, k_key, v_key = f"q.{suffix}", f"k.{suffix}", f"v.{suffix}"
+            if all(k in self._qkv_parts[bid] for k in [q_key, k_key, v_key]):
+                q = self._qkv_parts[bid].pop(q_key)
+                k = self._qkv_parts[bid].pop(k_key)
+                v = self._qkv_parts[bid].pop(v_key)
+                data_torch = torch.cat([q, k, v], dim=0)
+                name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid, f".{suffix}")
+                logger.debug(f"Fused Q/K/V {suffix} for layer {bid} -> {name}")
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._qkv_parts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            parts = [f"({i}){k}" for i, d in enumerate(self._qkv_parts) for k in d.keys()]
+            if len(parts) > 0:
+                raise ValueError(f"Unprocessed Q/K/V parts: {parts}")
diff --git a/conversion/gpt_oss.py b/conversion/gpt_oss.py
new file mode 100644
index 00000000000..d2c70c0bba5
--- /dev/null
+++ b/conversion/gpt_oss.py
@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("GptOssForCausalLM")
+class GptOssModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GPT_OSS
+
+    # TODO: remove once MXFP4 is supported more generally
+    def dequant_model(self):
+        if self._is_mxfp4:
+            return
+        return super().dequant_model()
+
+    def transform_nibble_layout(self, tensor):
+        assert tensor.dtype == torch.uint8
+        assert tensor.shape[-1] == 16
+        # swap nibbles
+        t_lo = tensor & 0x0F
+        t_hi = tensor & 0xF0
+        t_swapped = (t_lo << 4) | (t_hi >> 4)
+        tensor = t_swapped
+        # transform aaaa...bbbb... to abababab...
+        blk_a, blk_b = tensor.chunk(2, dim=-1)
+        # get a_
+        blk_a0 = (blk_a & 0xF0).view(-1, 1)
+        blk_a1 = (blk_a << 4).view(-1, 1)
+        blk_a = torch.stack((blk_a0, blk_a1), dim=2).view(tensor.shape)
+        # get _b
+        blk_b0 = (blk_b >> 4).view(-1, 1)
+        blk_b1 = (blk_b & 0x0F).view(-1, 1)
+        blk_b = torch.stack((blk_b0, blk_b1), dim=2).view(tensor.shape)
+        # swap once more
+        out = blk_a | blk_b
+        out_h = out & 0xF0
+        out_l = out & 0x0F
+        out = (out_h >> 4) | (out_l << 4)
+        return out
+
+    def repack_mxfp4(self, new_name: str, blocks: Tensor, scales: Tensor):
+        assert blocks.dtype == torch.uint8
+        assert scales.dtype == torch.uint8
+        scales = scales.unsqueeze(-1)
+        assert len(blocks.shape) == 4
+        assert len(scales.shape) == 4
+        blocks = self.transform_nibble_layout(blocks)
+        new_data = torch.concat((scales, blocks), dim=-1)
+        new_shape = [new_data.shape[0], new_data.shape[1], new_data.shape[2] * 32]
+        logger.info(f"Repacked {new_name} with shape {new_shape} and quantization MXFP4")
+        # flatten last dim
+        new_data = new_data.view(new_data.shape[0], new_data.shape[1], new_data.shape[2] * new_data.shape[3])
+        new_data = new_data.numpy()
+        self.gguf_writer.add_tensor(new_name, new_data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        blocks0: Tensor = torch.zeros(1)
+        blocks1: Tensor = torch.zeros(1)
+        # we assume that tensors are loaded in the correct order
+        for name, data_torch in self.get_tensors():
+            if "mlp.experts.down_proj_blocks" in name:
+                blocks0 = data_torch
+            elif "mlp.experts.down_proj_scales" in name:
+                new_name = self.map_tensor_name(name.replace("_scales", ".weight"))
+                self.repack_mxfp4(new_name, blocks0, data_torch)
+            elif "mlp.experts.gate_up_proj_blocks" in name:
+                blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :]
+            elif "mlp.experts.gate_up_proj_scales" in name:
+                scales0, scales1 = data_torch[:, ::2, :], data_torch[:, 1::2, :]
+                new_name_gate = self.map_tensor_name(name.replace("gate_up_proj_scales", "gate_proj.weight"))
+                new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight"))
+                self.repack_mxfp4(new_name_gate, blocks0, scales0)
+                self.repack_mxfp4(new_name_up, blocks1, scales1)
+        return []
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if "sinks" in name:
+            name += ".weight"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # correct naming for down_proj
+        if "down_proj" in name:
+            if name.endswith("_bias"):
+                name = name.replace("down_proj_bias", "down_proj.bias")
+            elif "_blocks" not in name and "_scales" not in name:
+                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
+                name = name.replace("down_proj", "down_proj.weight")
+                data_torch = data_torch.transpose(-1, -2)
+            else:
+                # otherwise, it should already be repacked to ggml MXFP4 format
+                return
+
+        # split the gate_up into gate and up
+        if "gate_up_proj" in name:
+            if name.endswith("_bias"):
+                name_up = name.replace("gate_up_proj_bias", "up_proj.bias")
+                name_gate = name.replace("gate_up_proj_bias", "gate_proj.bias")
+                gate_proj_bias, up_proj_bias = data_torch[..., ::2], data_torch[..., 1::2]
+                yield from super().modify_tensors(gate_proj_bias, name_gate, bid)
+                yield from super().modify_tensors(up_proj_bias, name_up, bid)
+            elif "_blocks" not in name and "_scales" not in name:
+                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
+                name_up = name.replace("gate_up_proj", "up_proj.weight")
+                name_gate = name.replace("gate_up_proj", "gate_proj.weight")
+                data_torch = data_torch.transpose(-1, -2)
+                gate_proj_weight, up_proj_weight = data_torch[:, ::2, :], data_torch[:, 1::2, :]
+                yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
+                yield from super().modify_tensors(up_proj_weight, name_up, bid)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"])
diff --git a/conversion/gptneox.py b/conversion/gptneox.py
new file mode 100644
index 00000000000..6a42b12b15a
--- /dev/null
+++ b/conversion/gptneox.py
@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+import re
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("GPTNeoXForCausalLM")
+class GPTNeoXModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GPTNEOX
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_rope_dimension_count(
+            int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
+        )
+        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+        self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
+        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+        assert n_head is not None
+        assert n_embed is not None
+
+        if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
+            # Map bloom-style qkv_linear to gpt-style qkv_linear
+            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
+            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
+            qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
+            data_torch = torch.cat(
+                (
+                    qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
+                    qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
+                    qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
+                ),
+                dim=0,
+            )
+            logger.info("re-format attention.linear_qkv.weight")
+        elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name):
+            qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
+            data_torch = torch.cat(
+                (
+                    qkv_bias[:, 0, :].reshape((n_embed,)),
+                    qkv_bias[:, 1, :].reshape((n_embed,)),
+                    qkv_bias[:, 2, :].reshape((n_embed,)),
+                ),
+                dim=0,
+            )
+            logger.info("re-format attention.linear_qkv.bias")
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/granite.py b/conversion/granite.py
new file mode 100644
index 00000000000..647269ba740
--- /dev/null
+++ b/conversion/granite.py
@@ -0,0 +1,328 @@
+from __future__ import annotations
+
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf, logger
+
+from .llama import LlamaModel
+from .mamba import Mamba2Model
+
+
+@ModelBase.register("GraniteForCausalLM", "GraniteSpeechForConditionalGeneration")
+class GraniteModel(LlamaModel):
+    """Conversion for IBM's GraniteForCausalLM"""
+    model_arch = gguf.MODEL_ARCH.GRANITE
+
+    def set_gguf_parameters(self):
+        """Granite uses standard llama parameters with the following differences:
+
+        - No head_dim support
+        - New multiplier params:
+            - attention_scale
+            - embedding_scale
+            - residual_scale
+        - logits_scaling
+        """
+        if head_dim := self.hparams.pop("head_dim", None):
+            logger.warning("Ignoring head_dim (%s) from config for Granite", head_dim)
+        super().set_gguf_parameters()
+        # NOTE: Convert _multiplier params to _scale params for naming
+        #   consistency
+        if attention_scale := self.hparams.get("attention_multiplier"):
+            self.gguf_writer.add_attention_scale(attention_scale)
+            logger.info("gguf: (granite) attention_scale = %s", attention_scale)
+        if embedding_scale := self.hparams.get("embedding_multiplier"):
+            self.gguf_writer.add_embedding_scale(embedding_scale)
+            logger.info("gguf: (granite) embedding_scale = %s", embedding_scale)
+        if residual_scale := self.hparams.get("residual_multiplier"):
+            self.gguf_writer.add_residual_scale(residual_scale)
+            logger.info("gguf: (granite) residual_scale = %s", residual_scale)
+        if logits_scale := self.hparams.get("logits_scaling"):
+            self.gguf_writer.add_logit_scale(logits_scale)
+            logger.info("gguf: (granite) logits_scale = %s", logits_scale)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.startswith("encoder."):
+            return None
+        return super().filter_tensors(item)
+
+
+@ModelBase.register("GraniteMoeForCausalLM", "GraniteMoeSharedForCausalLM")
+class GraniteMoeModel(GraniteModel):
+    """Conversion for IBM's GraniteMoeForCausalLM"""
+    model_arch = gguf.MODEL_ARCH.GRANITE_MOE
+
+    def set_gguf_parameters(self):
+        """GraniteMoeShared uses GraniteMoe parameters plus the following:
+        - shared_intermediate_size
+        """
+        super().set_gguf_parameters()
+        if shared_feed_forward_length := self.hparams.get("shared_intermediate_size"):
+            self.gguf_writer.add_expert_shared_feed_forward_length(shared_feed_forward_length)
+            logger.info("gguf: (granitemoeshared) shared_feed_forward_length = %s", shared_feed_forward_length)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        """In modeling_granitemoe, the JetMoe implementation of parallel experts
+        is used. This essentially merges w1 and w3 into a single tensor with 2x
+        the hidden size that is then split during forward. To keep compatibility
+        with existing mixtral support, we pull them apart here.
+        """
+
+        if name.endswith("block_sparse_moe.input_linear.weight"):
+            ffn_dim = self.hparams["intermediate_size"]
+            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
+            gate, up = data_torch.split(ffn_dim, dim=-2)
+            yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid)
+            yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid)
+            return
+
+        has_experts = bool(self.hparams.get('num_local_experts'))
+
+        if name.endswith("shared_mlp.input_linear.weight"):
+            ffn_dim = self.hparams["shared_intermediate_size"]
+            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size"
+            gate, up = data_torch.split(ffn_dim, dim=-2)
+            if has_experts:
+                yield from ModelBase.modify_tensors(self, gate,self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), bid)
+                yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), bid)
+                return
+            yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)
+            yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)
+            return
+
+        if not has_experts and name.endswith("shared_mlp.output_linear.weight"):
+            yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("GraniteMoeHybridForCausalLM", "BambaForCausalLM")
+class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
+    """GraniteHybrid is a hybrid SSM + Attention model that uses Mamba2 SSM
+    layers and optionally uses MoE w/ a shared expert"""
+    model_arch = gguf.MODEL_ARCH.GRANITE_HYBRID
+    undo_permute = True
+
+    def __init__(self, *args, **kwargs):
+
+        # Hybrid mamba models use a prefix for the mamba-specific params.
+        # TODO: Extend this if the prefix(es) need to be configurable
+        self.hparam_prefixes = ["mamba"]
+
+        super().__init__(*args, **kwargs)
+
+        # Lists of which layers use ssm vs attention
+        self._attn_layers = self.get_attn_layers()
+        self._ssm_layers = [
+            i for i in range(self.block_count)
+            if i not in self._attn_layers
+        ]
+
+        # There are some models in this family that are non-hybrid, but keep the
+        # same parent class by setting all layers to "attention." If this is the
+        # case, the model architecture needs to be updated to a standard
+        # "granite" or "granitemoe" model
+        if not self._ssm_layers:
+            has_experts = self.find_hparam(["num_experts_per_tok", "num_experts_per_token"], optional=True)
+            new_arch = (
+                gguf.MODEL_ARCH.GRANITE_MOE
+                if has_experts else
+                gguf.MODEL_ARCH.GRANITE
+            )
+            self.model_arch = new_arch
+            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[new_arch]
+            self.gguf_writer.add_architecture()
+
+        # n_group and d_inner are used during reshape_tensors for mamba2
+        # NOTE: Explicitly include hparam prefix prefix for d_model to
+        #   disambiguate with top-level head_dim
+        # NOTE 2: If needed for future models, this can be isolated in a method
+        #   to separate the prefix setting and the keys used
+        self.d_model = self.find_hparam([f"{self.hparam_prefixes[0]}_head_dim", "hidden_size", "d_model"])
+        self.n_group = self.find_hparam(["n_groups", "num_groups"])
+        self.d_inner = self.find_hparam(["expand", "num_heads"]) * self.d_model
+
+    def get_attn_layers(self):
+        # Explicit list of layer type names
+        if layer_types := self.hparams.get("layer_types"):
+            return [
+                i for i, typ in enumerate(layer_types)
+                if typ == "attention"
+            ]
+
+        # Layer types indicated by index or period
+        attn_layers = self.hparams.get("attn_layer_indices", [])
+        if not attn_layers:
+            attn_period = self.hparams.get("attn_layer_period")
+            assert attn_period, "Didn't find attn_layer_indices or attn_layer_period"
+            attn_offset = self.hparams.get("attn_layer_offset")
+            assert attn_offset is not None, "No attention layer offset set with attn_layer_period"
+            attn_layers = [
+                i for i in range(self.block_count)
+                if i % attn_period == attn_offset
+            ]
+        return attn_layers
+
+    def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any:
+        prefixed = []
+        for pfx in self.hparam_prefixes:
+            prefixed.extend(
+                "_".join([pfx, k])
+                for k in keys
+            )
+        keys = list(keys) + prefixed
+        return Mamba2Model.find_hparam(self, keys, *args, **kwargs)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if (
+            name.endswith("block_sparse_moe.input_linear.weight")
+            or "shared_mlp" in name
+        ):
+            yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
+            return
+
+        # Determine whether this is a mamba layer or an attention layer
+        if bid in self._ssm_layers:
+            yield from Mamba2Model.modify_tensors(self, data_torch, name, bid)
+            return
+        elif bid in self._attn_layers:
+            yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
+            return
+        yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+
+    def set_gguf_parameters(self):
+        """This method merges params from both parents and some that are
+        specific to this model. The result is some duplication of how the params
+        get set. The following warnings are expected during conversion:
+
+        WARNING:Duplicated key name 'granitehybrid.attention.head_count_kv'
+        WARNING:Duplicated key name 'granitehybrid.context_length'
+        """
+        GraniteMoeModel.set_gguf_parameters(self)
+
+        ## Mamba mixer params ##
+        self.gguf_writer.add_ssm_conv_kernel(self.find_hparam(["conv_kernel", "d_conv"]))
+        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state", "state_dim", "ssm_state_size"]))
+        self.gguf_writer.add_ssm_group_count(self.n_group)
+        self.gguf_writer.add_ssm_inner_size(self.d_inner)
+        # NOTE: The mamba_dt_rank is _not_ the right field for how this is used
+        #   in llama.cpp
+        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads", "num_heads"]))
+
+        ## Attention params ##
+        head_count_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
+        head_count_kv_vec = [
+            head_count_kv if i in self._attn_layers else 0 for i in range(self.block_count)
+        ]
+        if rope_dim := self.hparams.get("attn_rotary_emb"):
+            self.gguf_writer.add_rope_dimension_count(rope_dim)
+        self.gguf_writer.add_head_count_kv(head_count_kv_vec)
+
+        ## If Bamba or non-hybrid, use rope, otherwise don't
+        use_rope = (
+            "BambaForCausalLM" in self.hparams["architectures"]
+            or not self._ssm_layers
+        )
+        self.gguf_writer.add_rope_scaling_finetuned(use_rope)
+        if not use_rope:
+            self.gguf_writer.add_context_length(2**20)
+
+        ## Validation ##
+        d_head = self.find_hparam(["d_head"], optional=True) or 64
+        assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported"
+        assert self.d_inner % d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {d_head}"
+
+    def set_vocab(self):
+        self.hparams["pad_vocab_size_multiple"] = 8
+        Mamba2Model.set_vocab(self)
+
+
+@ModelBase.register("GraniteSpeechForConditionalGeneration")
+class GraniteSpeechMmprojModel(MmprojModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    _batch_norm_tensors: list[dict[str, Tensor]] | None = None
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("encoder_config")
+
+    def set_gguf_parameters(self):
+        assert self.hparams_audio is not None
+        a = self.hparams_audio
+        a["hidden_size"] = a["hidden_dim"]
+        a["intermediate_size"] = a["hidden_dim"] * a["feedforward_mult"]
+        a["num_attention_heads"] = a["num_heads"]
+        a["num_hidden_layers"] = a["num_layers"]
+
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GRANITE_SPEECH)
+        self.gguf_writer.add_audio_num_mel_bins(a["input_dim"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+        self.gguf_writer.add_audio_chunk_size(a["context_size"])
+        self.gguf_writer.add_audio_conv_kernel_size(a["conv_kernel_size"])
+        self.gguf_writer.add_audio_max_pos_emb(a["max_pos_emb"])
+
+        p = self.global_config
+        self.gguf_writer.add_audio_projector_window_size(p["window_size"])
+        self.gguf_writer.add_audio_projector_downsample_rate(p["downsample_rate"])
+        self.gguf_writer.add_audio_projector_head_count(p["projector_config"]["num_attention_heads"])
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if "encoder" in name or "projector" in name:
+            if ".conv" in name and ".weight" in name:
+                return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if "attention_dists" in name or "num_batches_tracked" in name:
+            return None
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # fold running_mean, running_var and eps into weight and bias for batch_norm
+        if "batch_norm" in name and "encoder.layers." in name:
+            if self._batch_norm_tensors is None:
+                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
+            assert bid is not None
+            self._batch_norm_tensors[bid][name] = data_torch
+            if len(self._batch_norm_tensors[bid]) < 4:
+                return
+            prefix = f"encoder.layers.{bid}.conv.batch_norm"
+            weight = self._batch_norm_tensors[bid][f"{prefix}.weight"]
+            bias = self._batch_norm_tensors[bid][f"{prefix}.bias"]
+            running_mean = self._batch_norm_tensors[bid][f"{prefix}.running_mean"]
+            running_var = self._batch_norm_tensors[bid][f"{prefix}.running_var"]
+            eps = 1e-5
+            a = weight / torch.sqrt(running_var + eps)
+            b = bias - running_mean * a
+            yield from super().modify_tensors(a, f"encoder.layers.{bid}.conv.batch_norm.weight", bid)
+            yield from super().modify_tensors(b, f"encoder.layers.{bid}.conv.batch_norm.bias", bid)
+            return
+
+        if ".attn.to_kv.weight" in name:
+            k_weight, v_weight = data_torch.chunk(2, dim=0)
+            yield from super().modify_tensors(k_weight, name.replace("to_kv", "to_k"), bid)
+            yield from super().modify_tensors(v_weight, name.replace("to_kv", "to_v"), bid)
+            return
+
+        if ("up_conv" in name or "down_conv" in name) and name.endswith(".weight"):
+            if data_torch.ndim == 3 and data_torch.shape[2] == 1:
+                data_torch = data_torch.squeeze(2)
+
+        if "depth_conv" in name and name.endswith(".weight"):
+            if data_torch.ndim == 3 and data_torch.shape[1] == 1:
+                data_torch = data_torch.squeeze(1)
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/grok.py b/conversion/grok.py
new file mode 100644
index 00000000000..9098e514a3a
--- /dev/null
+++ b/conversion/grok.py
@@ -0,0 +1,116 @@
+from __future__ import annotations
+
+import sys
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
+class GrokModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GROK
+
+    def set_vocab(self):
+        if (self.dir_model / 'tokenizer.model').is_file():
+            self._set_vocab_sentencepiece()
+            return
+
+        if not (self.dir_model / 'tokenizer.json').is_file() or not (self.dir_model / 'chat_template.jinja').is_file():
+            logger.error('Error: Missing vocab and chat template, download files from https://huggingface.co/alvarobartt/grok-2-tokenizer')
+            sys.exit(1)
+
+        self._set_vocab_gpt2()
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_attn_logit_softcapping(self.hparams.get("attn_logit_softcapping", 30.0))
+        self.gguf_writer.add_router_logit_softcapping(self.hparams.get("router_logit_softcapping", 30.0))
+        if (final_logit_softcap := self.hparams.get("final_logit_softcapping")):
+            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
+
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+
+        # Treat "original" as "yarn", seems to have been a mistake
+        if self.hparams.get("rope_type") in ("yarn", "original"):
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(self.hparams["scaling_factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["original_max_position_embeddings"])
+            self.gguf_writer.add_rope_scaling_yarn_ext_factor(self.hparams["extrapolation_factor"])
+            self.gguf_writer.add_rope_scaling_yarn_attn_factor(self.hparams["attn_factor"])
+            self.gguf_writer.add_rope_scaling_yarn_beta_fast(self.hparams["beta_fast"])
+            self.gguf_writer.add_rope_scaling_yarn_beta_slow(self.hparams["beta_slow"])
+
+        if temp_len := self.hparams.get("attn_temperature_len"):
+            self.gguf_writer.add_attn_temperature_length(temp_len)
+
+        self.gguf_writer.add_attn_output_scale(self.hparams.get("attn_output_multiplier", rope_dim**-0.5))
+        self.gguf_writer.add_embedding_scale(self.hparams["embedding_multiplier_scale"])
+        self.gguf_writer.add_logit_scale(self.hparams["output_multiplier_scale"])
+
+    _experts: list[dict[str, list[Tensor]]] | None = None
+    _cur_expert = ""
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        deferred: list[tuple[Tensor, str, int | None]] = []
+        is_expert = ".moe." in name or ".block_sparse_moe.experts." in name
+
+        if not is_expert:
+            deferred.append((data_torch, name, bid))
+
+        # process the experts separately
+        if is_expert or self._cur_expert:
+            n_experts = self.hparams["num_local_experts"]
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            # concatenate split tensors
+            if name in self._experts[bid]:
+                self._cur_expert = name
+                self._experts[bid][name].append(data_torch)
+                return
+            elif is_expert:
+                self._cur_expert = name
+                self._experts[bid][name] = [data_torch]
+                return
+            else:
+                self._cur_expert = ""
+
+            for bid in range(self.block_count):
+                if len(self._experts[bid]) >= n_experts * 3:
+                    # merge the experts into a single 3d tensor
+                    for wid in [("linear", "w1", 0), ("linear_1", "w2", 1), ("linear_v", "w3", 0)]:
+                        datas: list[Tensor] = []
+
+                        for xid in range(n_experts):
+                            ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid[0]}.weight"
+                            if ename not in self._experts[bid]:
+                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid[1]}.weight"
+                            tensor_list = self._experts[bid][ename]
+                            datas.append(torch.cat(tensor_list, dim=wid[2]) if len(tensor_list) > 1 else tensor_list[0])
+                            del self._experts[bid][ename]
+
+                        data_torch = torch.stack(datas, dim=0)
+
+                        merged_name = f"transformer.decoder_layer.{bid}.moe.{wid[0]}.weight"
+
+                        yield from super().modify_tensors(data_torch, merged_name, bid)
+
+        for t in deferred:
+            yield from super().modify_tensors(*t)
diff --git a/conversion/grovemoe.py b/conversion/grovemoe.py
new file mode 100644
index 00000000000..a8be931cb90
--- /dev/null
+++ b/conversion/grovemoe.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
+class GroveMoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GROVEMOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L299
+        self.gguf_writer.add_expert_chunk_feed_forward_length(self.hparams.get("head_dim") or 128)
+        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L298
+        self.gguf_writer.add_experts_per_group(2)
+        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376
+        self.gguf_writer.add_expert_group_scale(0.05)
+
+    _experts: list[dict[str, Tensor]] | None = None
+    _chunk_experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith(".expert_bias"):
+            # FIXME?: Unused https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L303
+            return
+
+        # process the experts separately
+        if name.find("chunk_experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"]) // 2 # see add_experts_per_group
+            assert bid is not None
+
+            if self._chunk_experts is None:
+                self._chunk_experts = [{} for _ in range(self.block_count)]
+
+            self._chunk_experts[bid][name] = data_torch
+
+            if len(self._chunk_experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.chunk_experts.{xid}.{w_name}.weight"
+                        datas.append(self._chunk_experts[bid][ename])
+                        del self._chunk_experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.chunk_experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+        elif name.find("experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._chunk_experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            chunk_experts = [k for d in self._chunk_experts for k in d.keys()]
+            if len(chunk_experts) > 0:
+                raise ValueError(f"Unprocessed adjugate experts: {chunk_experts}")
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/hunyuan.py b/conversion/hunyuan.py
new file mode 100644
index 00000000000..be54f5810b0
--- /dev/null
+++ b/conversion/hunyuan.py
@@ -0,0 +1,407 @@
+from __future__ import annotations
+
+import json
+
+from pathlib import Path
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf, logger
+
+from .qwen import QwenModel
+
+
+@ModelBase.register("HunYuanMoEV1ForCausalLM")
+class HunYuanMoEModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+        # 1. Get the pre-tokenizer identifier hash
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        # 2. Reverse-engineer the merges list from mergeable_ranks
+        merges = []
+        vocab = {}
+        mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
+        for token, rank in mergeable_ranks.items():
+            vocab[QwenModel.token_bytes_to_string(token)] = rank
+            if len(token) == 1:
+                continue
+            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+            if len(merged) == 2: # todo this is an assert in Qwen, why?
+                merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+        # 3. Generate the tokens and toktypes lists
+        vocab_size = self.hparams["vocab_size"]
+        assert tokenizer.vocab_size == vocab_size  # ty: ignore[unresolved-attribute]
+        special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
+        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+        tokens: list[str] = []
+        toktypes: list[int] = []
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token = reverse_vocab[i]
+                tokens.append(token)
+                if i in special_tokens.values():
+                    toktypes.append(gguf.TokenType.CONTROL)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+
+        # 4. Write all vocab-related fields to the GGUF writer
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_token_merges(merges)
+
+        # 5. Add special tokens and chat templates
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+        special_vocab.add_to_gguf(self.gguf_writer)
+        # FIX for BOS token: Overwrite incorrect id read from config.json
+        self.gguf_writer.add_bos_token_id(127959) # <|bos|>
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        self.gguf_writer.add_expert_shared_feed_forward_length(hparams["intermediate_size"])
+
+        moe_intermediate_size = hparams["moe_intermediate_size"]
+        assert all(n == moe_intermediate_size[0] for n in moe_intermediate_size)
+        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size[0])
+
+        moe_topk = hparams["moe_topk"]
+        assert all(topk == moe_topk[0] for topk in moe_topk)
+        self.gguf_writer.add_expert_used_count(moe_topk[0])
+
+        moe_shared_expert = hparams["num_shared_expert"]
+        assert all(n == moe_shared_expert[0] for n in moe_shared_expert)
+        self.gguf_writer.add_expert_shared_count(moe_shared_expert[0])
+
+        # Rope
+        if self.rope_parameters.get("rope_type") == "dynamic":
+            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
+            alpha = self.rope_parameters.get("alpha", 1000)
+            base = self.rope_parameters.get("rope_theta", 10000.0)
+            dim = (hparams["hidden_size"] // hparams["num_attention_heads"]) # 128
+            scaled_base = base * (alpha ** (dim / (dim - 2))) # 10000 * (1000 ** (128 / 126)) = 11158839.9251
+            self.gguf_writer.add_rope_freq_base(scaled_base)
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+            self.gguf_writer.add_rope_scaling_factor(1)
+            # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
+            self.gguf_writer.add_context_length(256 * 1024) # 256k context length
+
+            # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
+            assert alpha == 1000 and base == 10000.0 and dim == 128 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
+                "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "lm_head.weight":
+            if self.hparams.get("tie_word_embeddings", False):
+                logger.info("Skipping tied output layer 'lm_head.weight'")
+                return
+
+        if name.find("mlp.experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("HunYuanDenseV1ForCausalLM")
+class HunYuanModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
+
+    def _get_eod_token_id(self) -> int | None:
+        """Get the actual end-of-generation token from config (eod_token_id)."""
+        return self.hparams.get("eod_token_id")
+
+    def _get_eot_token_id(self) -> int | None:
+        """Get the end-of-turn token from generation_config.json.
+        This is the first entry in eos_token_id when it's a list."""
+        gen_cfg_path = self.dir_model / "generation_config.json"
+        if gen_cfg_path.is_file():
+            with open(gen_cfg_path, encoding="utf-8") as f:
+                gen_cfg = json.load(f)
+            eos = gen_cfg.get("eos_token_id")
+            if isinstance(eos, list) and len(eos) >= 2:
+                return eos[0]
+        return None
+
+    def _fix_special_tokens(self):
+        """Fix EOS/EOT tokens that are incorrect in upstream configs."""
+        eod_id = self._get_eod_token_id()
+        if eod_id is not None:
+            self.gguf_writer.add_eos_token_id(eod_id)
+        eot_id = self._get_eot_token_id()
+        if eot_id is not None:
+            self.gguf_writer.add_eot_token_id(eot_id)
+
+    def set_vocab(self):
+        if (self.dir_model / "tokenizer.json").is_file():
+            tokens, toktypes, tokpre = self.get_vocab_base()
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+
+            # HunyuanOCR has pad_token_id=-1 in config.json; exclude pad from SpecialVocab
+            token_types = None
+            if (self.hparams.get("pad_token_id") or 0) < 0:
+                token_types = ('bos', 'eos', 'unk', 'sep', 'cls', 'mask')
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True, special_token_types=token_types)
+            special_vocab.add_to_gguf(self.gguf_writer)
+            self._fix_special_tokens()
+        else:
+            from transformers import AutoTokenizer
+            tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+            # 1. Get the pre-tokenizer identifier hash
+            tokpre = self.get_vocab_base_pre(tokenizer)
+
+            # 2. Reverse-engineer the merges list from mergeable_ranks
+            merges = []
+            vocab = {}
+            mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
+            for token, rank in mergeable_ranks.items():
+                vocab[QwenModel.token_bytes_to_string(token)] = rank
+                if len(token) == 1:
+                    continue
+                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+                if len(merged) == 2:
+                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+            # 3. Generate the tokens and toktypes lists
+            vocab_size = self.hparams["vocab_size"]
+            assert tokenizer.vocab_size == vocab_size  # ty: ignore[unresolved-attribute]
+            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
+            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+            tokens: list[str] = []
+            toktypes: list[int] = []
+            for i in range(vocab_size):
+                if i not in reverse_vocab:
+                    tokens.append(f"[PAD{i}]")
+                    toktypes.append(gguf.TokenType.UNUSED)
+                else:
+                    token = reverse_vocab[i]
+                    tokens.append(token)
+                    if i in special_tokens.values():
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.NORMAL)
+
+            # 4. Write all vocab-related fields to the GGUF writer
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+            self.gguf_writer.add_token_merges(merges)
+
+            # 5. Add special tokens and chat templates
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+            special_vocab.add_to_gguf(self.gguf_writer)
+            # FIX for BOS token: Overwrite incorrect id read from config.json
+            if self.hparams['hidden_size'] == 4096:
+                self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
+            self._fix_special_tokens()
+
+    def set_gguf_parameters(self):
+        # HunyuanOCR has num_experts=1 which is not MoE, prevent parent from writing it
+        saved_num_experts = self.hparams.pop("num_experts", None)
+        super().set_gguf_parameters()
+        if saved_num_experts is not None and saved_num_experts > 1:
+            self.hparams["num_experts"] = saved_num_experts
+        hparams = self.hparams
+
+        # Rope
+        if self.rope_parameters.get("rope_type") in ("dynamic", "xdrope"):
+            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
+            alpha = self.rope_parameters.get("alpha", 50)
+            base = self.rope_parameters.get("rope_theta", 10000.0)
+            dim = hparams["head_dim"]
+            scaled_base = base * (alpha ** (dim / (dim - 2)))
+            self.gguf_writer.add_rope_freq_base(scaled_base)
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+            self.gguf_writer.add_rope_scaling_factor(1)
+            if self.rope_parameters.get("rope_type") == "dynamic":
+                # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
+                self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
+                self.gguf_writer.add_context_length(256 * 1024) # 256k context length
+
+                # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
+                assert base == 10000.0 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
+                    "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "lm_head.weight":
+            if self.hparams.get("tie_word_embeddings", False):
+                logger.info("Skipping tied output layer 'lm_head.weight'")
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("HunYuanVLForConditionalGeneration")
+class HunyuanVLVisionModel(MmprojModel):
+    # Handles both HunyuanOCR and HunyuanVL, which share the HF architecture name
+    # "HunYuanVLForConditionalGeneration" and the `vit.perceive.*` vision layout.
+    # Each variant maps to a different projector type in clip.cpp so image
+    # preprocessing follows the correct code path.
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        # HunyuanOCR / HunyuanVL uses max_image_size instead of image_size
+        if "image_size" not in self.hparams_vision:
+            self.hparams_vision["image_size"] = self.hparams_vision.get("max_image_size", 2048)
+
+    @staticmethod
+    def is_ocr_variant(hparams: dict) -> bool:
+        """Return True for HunyuanOCR, False for HunyuanVL.
+
+        The projector's output dim must equal the text model's hidden_size by
+        construction (that's what "projector" means). HunyuanOCR pairs a 1B text
+        backbone (hidden=1024); HunyuanVL pairs a 4B one (hidden=3072). So the
+        ViT -> LLM projection dim is a hard architectural signature, not a
+        magic number.
+        """
+        vision_out = int((hparams.get("vision_config") or {}).get("out_hidden_size", 0))
+        return vision_out == 1024
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+        vcfg = self.hparams_vision
+
+        if self.is_ocr_variant(self.global_config):
+            # --- HunyuanOCR ---
+            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.HUNYUANOCR)
+            self.gguf_writer.add_vision_use_gelu(True)
+            self.gguf_writer.add_vision_attention_layernorm_eps(vcfg.get("rms_norm_eps", 1e-5))
+            self.gguf_writer.add_vision_spatial_merge_size(vcfg.get("spatial_merge_size", 2))
+            self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
+            self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
+            return
+
+        # --- HunyuanVL ---
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.HUNYUANVL)
+        self.gguf_writer.add_vision_use_gelu(str(vcfg["hidden_act"]).lower() == "gelu")
+        self.gguf_writer.add_vision_attention_layernorm_eps(float(vcfg["rms_norm_eps"]))
+        self.gguf_writer.add_vision_spatial_merge_size(int(vcfg["spatial_merge_size"]))
+        self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"]))
+        self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"]))
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not name.startswith("vit."):
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # strip CLS token (row 0) from position embeddings so resize_position_embeddings works
+        if "position_embedding" in name:
+            data_torch = data_torch[1:]  # [n_patches+1, n_embd] -> [n_patches, n_embd]
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # force conv weights to F32 or F16 to avoid BF16 IM2COL issues on Metal
+        # Both HunyuanOCR and HunyuanVL emit the ViT -> LLM projection as mm.0/mm.2.
+        if ("mm.0." in new_name or "mm.2." in new_name) and new_name.endswith(".weight"):
+            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+
+@ModelBase.register("HunYuanVLForConditionalGeneration")
+class HunyuanVLTextModel(HunYuanModel):
+    # The "HunYuanVLForConditionalGeneration" HF architecture covers both HunyuanOCR
+    # and HunyuanVL. HunyuanOCR reuses the HunYuan-Dense text backbone (standard RoPE),
+    # while HunyuanVL introduces a new LLM arch with XD-RoPE. Detect the variant from
+    # the config and pick the matching GGUF architecture.
+    model_arch = gguf.MODEL_ARCH.HUNYUAN_VL
+
+    @staticmethod
+    def _is_ocr_config(hparams: dict) -> bool:
+        # OCR pairs a 1B text backbone (hidden=1024) with a ViT projector that
+        # outputs 1024-d; HunyuanVL uses 3072-d. Keep in sync with
+        # HunyuanVLVisionModel.is_ocr_variant.
+        return int((hparams.get("vision_config") or {}).get("out_hidden_size", 0)) == 1024
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        raw_hparams = kwargs.get("hparams") or ModelBase.load_hparams(dir_model, is_mistral_format=False)
+        if self._is_ocr_config(raw_hparams):
+            self.model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
+        else:
+            self.model_arch = gguf.MODEL_ARCH.HUNYUAN_VL
+        super().__init__(dir_model, *args, **kwargs)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # Only emit XD-RoPE metadata for the HunyuanVL backbone; HunyuanOCR uses
+        # the HunYuan-Dense arch which already handles standard rope in super().
+        if self.model_arch != gguf.MODEL_ARCH.HUNYUAN_VL:
+            return
+
+        if self.rope_parameters.get("rope_type") != "xdrope":
+            return
+
+        # defaults for HunyuanVL. The C++ side later computes:
+        #   freq_base = rope_theta * alpha ** (head_dim / (head_dim - 2))
+        self.gguf_writer.add_rope_freq_base(float(self.rope_parameters["rope_theta"]))
+        self.gguf_writer.add_rope_scaling_alpha(float(self.rope_parameters["alpha"]))
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        self.gguf_writer.add_rope_scaling_factor(float(self.rope_parameters.get("factor", 1)))
+
+        ctx_len = int(self.hparams["max_position_embeddings"])
+        self.gguf_writer.add_rope_scaling_orig_ctx_len(ctx_len)
+        self.gguf_writer.add_context_length(ctx_len)
+
+        self.gguf_writer.add_rope_dimension_sections(list(self.rope_parameters["xdrope_section"]))
diff --git a/conversion/internlm.py b/conversion/internlm.py
new file mode 100644
index 00000000000..7e11aca3ce0
--- /dev/null
+++ b/conversion/internlm.py
@@ -0,0 +1,232 @@
+from __future__ import annotations
+
+import json
+import sys
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, SentencePieceTokenTypes, TextModel, gguf, logger
+
+from .llama import LlamaModel
+
+
+@ModelBase.register("InternLM2ForCausalLM")
+class InternLM2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.INTERNLM2
+
+    def set_vocab(self):
+        # (TODO): Is there a better way?
+        # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
+        # \x00 specially and convert it into an emoji character to prevent it from being mistakenly
+        # recognized as an empty string in C++.
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        tokens: list[bytes] = []
+        scores: list[float] = []
+        toktypes: list[int] = []
+
+        if not tokenizer_path.is_file():
+            logger.error(f'Error: Missing {tokenizer_path}')
+            sys.exit(1)
+
+        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        for token_id in range(vocab_size):
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+            if text == b"\x00":
+                # (TODO): fixme
+                # Hack here and replace the \x00 characters.
+                logger.warning(f"InternLM2 convert token '{text}' to '🐉'!")
+                text = "🐉".encode("utf-8")
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+            # take care of ununsed raw token
+            if piece.startswith('[UNUSED'):
+                toktype = SentencePieceTokenTypes.UNUSED
+
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+
+                for key in added_tokens_json:
+                    tokens.append(key.encode("utf-8"))
+                    scores.append(-1000.0)
+                    toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
+
+        chat_eos_token = '<|im_end|>'
+        chat_eos_token_id = None
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
+                for token_id, foken_data in added_tokens_decoder.items():
+                    token_id = int(token_id)
+                    token = foken_data["content"]
+                    if token == chat_eos_token:
+                        chat_eos_token_id = token_id
+                    token = token.encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
+        tokenizer_file = self.dir_model / 'tokenizer.json'
+        if tokenizer_file.is_file():
+            with open(tokenizer_file, "r", encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+                added_tokens = tokenizer_json.get("added_tokens", [])
+                for foken_data in added_tokens:
+                    token_id = int(foken_data["id"])
+                    token = foken_data["content"]
+                    if token == chat_eos_token:
+                        chat_eos_token_id = token_id
+                    token = token.encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        old_eos = special_vocab.special_token_ids["eos"]
+        if chat_eos_token_id is not None:
+            # For the chat model, we replace the eos with '<|im_end|>'.
+            # TODO: this is a hack, should be fixed
+            #       https://github.com/ggml-org/llama.cpp/pull/6745#issuecomment-2067687048
+            special_vocab.special_token_ids["eos"] = chat_eos_token_id
+            logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}"
+                           " in chat mode so that the conversation can end normally.")
+
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        num_heads = self.hparams["num_attention_heads"]
+        num_kv_heads = self.hparams["num_key_value_heads"]
+        n_embd = self.hparams["hidden_size"]
+        q_per_kv = num_heads // num_kv_heads
+        head_dim = n_embd // num_heads
+        num_groups = num_heads // q_per_kv
+
+        if bid is not None and f"model.layers.{bid}.attention.wqkv" in name:
+            qkv = data_torch
+
+            qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd))
+            q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1]
+
+            # The model weights of q and k equire additional reshape.
+            q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads)
+            k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads)
+            v = v.reshape((-1, v.shape[-1]))
+
+            yield from super().modify_tensors(q, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
+            yield from super().modify_tensors(k, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
+            yield from super().modify_tensors(v, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("InternLM3ForCausalLM")
+class InternLM3Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+
+    def set_vocab(self):
+        tokens, scores, toktypes = self._create_vocab_sentencepiece()
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                if "add_prefix_space" in tokenizer_config_json:
+                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
+
+                if "added_tokens_decoder" in tokenizer_config_json:
+                    for token_id, token_data in tokenizer_config_json["added_tokens_decoder"].items():
+                        if token_data.get("special"):
+                            token_id = int(token_id)
+                            token = token_data["content"]
+                            special_vocab._set_special_token(token, token_id)
+                            # update eos token
+                            if token == '<|im_end|>' and "eos" in special_vocab.special_token_ids:
+                                special_vocab.special_token_ids["eos"] = token_id
+
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith(("mlp", "vision_model")):
+            # skip visual tensors
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/internvl.py b/conversion/internvl.py
new file mode 100644
index 00000000000..9a2a1e43df7
--- /dev/null
+++ b/conversion/internvl.py
@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf
+
+
+@ModelBase.register("InternVisionModel")
+class InternVisionModel(MmprojModel):
+
+    min_dynamic_tiles: int = 0
+    max_dynamic_tiles: int = 0
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.min_dynamic_tiles = self.global_config.get("min_dynamic_patch", 0)
+        self.max_dynamic_tiles = self.global_config.get("max_dynamic_patch", 0)
+
+    def set_gguf_parameters(self):
+        assert self.hparams_vision is not None
+        if isinstance(self.hparams_vision['image_size'], list):
+            self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
+        if isinstance(self.hparams_vision['patch_size'], list):
+            self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
+        super().set_gguf_parameters()
+
+        hparams = self.hparams
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
+        self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
+        # hidden_act
+        if hparams["hidden_act"] == "silu":
+            self.gguf_writer.add_vision_use_silu(True)
+        elif hparams["hidden_act"] == "gelu":
+            self.gguf_writer.add_vision_use_gelu(True)
+        else:
+            raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
+        # downsample_ratio
+        downsample_ratio = self.global_config.get("downsample_ratio")
+        assert downsample_ratio is not None
+        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
+        # older models may not have min/max_dynamic_patch in config
+        if self.min_dynamic_tiles > 0:
+            self.gguf_writer.add_vision_preproc_min_tiles(self.min_dynamic_tiles)
+        if self.max_dynamic_tiles > 0:
+            self.gguf_writer.add_vision_preproc_max_tiles(self.max_dynamic_tiles)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".position_embd." in new_name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
+        if not any([name.startswith(prefix) for prefix in vision_prefix]):
+            return None
+        # deal with intern-s1 special case
+        names_map = {
+            "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
+            "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
+            "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
+            "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
+            "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
+            "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
+        }
+        if name in names_map:
+            name = names_map[name]
+        # correct name
+        if name.startswith("vision_model"):
+            name = "vision_tower." + name
+        if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
+            name += ".weight"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # split QKV tensors if needed
+        if ".qkv." in name:
+            if data_torch.ndim == 2: # weight
+                c3, _ = data_torch.shape
+            else: # bias
+                c3 = data_torch.shape[0]
+            assert c3 % 3 == 0
+            c = c3 // 3
+            wq = data_torch[:c]
+            wk = data_torch[c: c * 2]
+            wv = data_torch[c * 2:]
+            yield from super().modify_tensors(wq, name.replace("attn.qkv", "self_attn.q_proj"), bid)
+            yield from super().modify_tensors(wk, name.replace("attn.qkv", "self_attn.k_proj"), bid)
+            yield from super().modify_tensors(wv, name.replace("attn.qkv", "self_attn.v_proj"), bid)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/jais.py b/conversion/jais.py
new file mode 100644
index 00000000000..00add4c77fc
--- /dev/null
+++ b/conversion/jais.py
@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+import math
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("Jais2ForCausalLM")
+class Jais2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.JAIS2
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        head_dim = hparams.get("head_dim", hparams["hidden_size"] // hparams["num_attention_heads"])
+        self.gguf_writer.add_rope_dimension_count(head_dim)
+
+
+@ModelBase.register("JAISLMHeadModel")
+class JaisModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.JAIS
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # SwigLU activation
+        assert self.hparams["activation_function"] == "swiglu"
+        # ALiBi position embedding
+        assert self.hparams["position_embedding_type"] == "alibi"
+
+        # Embeddings scale
+        self.embeddings_scale = 1.0
+        if 'mup_embeddings_scale' in self.hparams:
+            self.embeddings_scale = self.hparams['mup_embeddings_scale']
+        elif 'embeddings_scale' in self.hparams:
+            self.embeddings_scale = self.hparams['embeddings_scale']
+        else:
+            assert False
+
+        self.width_scale = 1.0
+        if 'mup_output_alpha' in self.hparams:
+            assert 'mup_width_scale' in self.hparams
+            self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
+        elif 'width_scale' in self.hparams:
+            self.width_scale = self.hparams['width_scale']
+        else:
+            assert False
+
+        self.max_alibi_bias = 8.0
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # we don't need these
+        if name.endswith((".attn.bias")):
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith(("relative_pe.slopes")):
+            # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)
+            # Some other models has max_alibi_bias spelled out explicitly in the hyperparams,
+            # but Jais's PyTorch model simply precalculates the slope values and places them
+            # in relative_pes.slopes
+            n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
+            first_val = float(data_torch[0].item())
+            self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
+
+            return
+
+        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
+            data_torch = data_torch.transpose(1, 0)
+
+        new_name = self.map_tensor_name(name)
+
+        if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
+            yield from super().modify_tensors(data_torch * self.embeddings_scale, new_name, bid)
+        elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
+            yield from super().modify_tensors(data_torch * self.width_scale, new_name, bid)
+        else:
+            yield from super().modify_tensors(data_torch, new_name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
diff --git a/conversion/jamba.py b/conversion/jamba.py
new file mode 100644
index 00000000000..da712ba5014
--- /dev/null
+++ b/conversion/jamba.py
@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("JambaForCausalLM")
+class JambaModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.JAMBA
+
+    def set_vocab(self):
+        if (self.dir_model / "tokenizer.model").is_file():
+            self._set_vocab_sentencepiece()
+        else:
+            self._set_vocab_llama_hf()
+            self.gguf_writer.add_add_space_prefix(False)
+
+    def set_gguf_parameters(self):
+        d_model = self.find_hparam(["hidden_size", "mamba_d_model"])
+        d_conv  = self.find_hparam(["mamba_d_conv"],  optional=True) or 4
+        d_inner = self.hparams["mamba_expand"] * d_model
+        d_state = self.find_hparam(["mamba_d_state"], optional=True) or 16
+        # ceiling division
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
+        dt_rank      = self.find_hparam(["mamba_dt_rank"], optional=True) or -(d_model // -16)
+        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-6
+        n_kv_head = self.hparams["num_key_value_heads"]
+        attn_offset = self.hparams["attn_layer_offset"]
+        attn_period = self.hparams["attn_layer_period"]
+        n_kv_vec = [0 for _ in range(attn_offset)] + [
+            n_kv_head if (i - attn_offset) % attn_period == 0 else 0 for i in range(attn_offset, self.block_count)
+        ]
+
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_context_length(self.find_hparam(["max_position_embeddings", "n_ctx"]))
+        self.gguf_writer.add_embedding_length(d_model)
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(n_kv_vec)
+        self.gguf_writer.add_ssm_conv_kernel(d_conv)
+        self.gguf_writer.add_ssm_inner_size(d_inner)
+        self.gguf_writer.add_ssm_state_size(d_state)
+        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
+        self.gguf_writer.add_expert_count(self.find_hparam(["num_local_experts", "num_experts"]))
+        self.gguf_writer.add_expert_used_count(self.find_hparam(["num_experts_per_tok", "num_experts_per_token"]))
+        self.gguf_writer.add_file_type(self.ftype)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+
+        # Mini-Jamba
+        name = name.replace(".moe.", ".feed_forward.")
+        if bid is not None:
+            moe_offset = self.hparams["expert_layer_offset"]
+            moe_period = self.hparams["expert_layer_period"]
+
+            if not (bid >= moe_offset and (bid - moe_offset) % moe_period == 0):
+                name = name.replace(".experts.0.", ".")
+
+        # process the experts separately
+        if ".feed_forward.experts." in name:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+
+                # merge the experts into a single 3d tensor
+                for wid in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    # using the same merged name as qwen2moe
+                    merged_name = f"model.layers.{bid}.mlp.experts.{wid}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    yield new_name, data_torch
+            return
+
+        new_name = self.map_tensor_name(name)
+
+        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
+            data_torch = data_torch.squeeze()
+
+        if name.endswith(".A_log"):
+            logger.debug("A_log --> A ==> " + new_name)
+            data_torch = -torch.exp(data_torch)
+
+        yield (new_name, data_torch)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/januspro.py b/conversion/januspro.py
new file mode 100644
index 00000000000..b49691205cc
--- /dev/null
+++ b/conversion/januspro.py
@@ -0,0 +1,116 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf
+
+from .llama import LlamaModel
+
+
+@ModelBase.register("JanusForConditionalGeneration")
+class JanusProModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA  # reuse Llama arch
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Skip vision, aligner, and generation tensors
+        skip_prefixes = (
+            'model.vision_model.',
+            'model.aligner.',
+            'model.vqmodel.',
+            'model.generation_embeddings.',
+            'model.generation_aligner.',
+            'model.generation_head.',
+        )
+        if name.startswith(skip_prefixes):
+            return None
+
+        return super().filter_tensors(item)
+
+
+@ModelBase.register("JanusForConditionalGeneration")
+class JanusProVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        if "intermediate_size" not in self.hparams_vision:
+            mlp_ratio = self.hparams_vision.get("mlp_ratio")
+            hidden_size = self.hparams_vision.get("hidden_size")
+            if mlp_ratio is not None and hidden_size is not None:
+                self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.JANUS_PRO)
+
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))
+
+        hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
+        if hidden_act == "gelu":
+            self.gguf_writer.add_vision_use_gelu(True)
+        elif hidden_act == "silu":
+            self.gguf_writer.add_vision_use_silu(True)
+
+    def _map_aligner_tensor(self, data_torch: Tensor, name: str) -> Iterable[tuple[str, Tensor]]:
+        """Map aligner tensors to projector format"""
+        suffix = ".bias" if name.endswith(".bias") else ".weight"
+
+        if name.startswith("model.aligner."):
+            local_name = name[len("model.aligner."):]
+        elif name.startswith("aligner."):
+            local_name = name[len("aligner."):]
+        else:
+            raise ValueError(f"Unsupported Janus aligner prefix: {name}")
+
+        if local_name.startswith("fc1."):
+            mm_index = 0
+        elif local_name.startswith("hidden_layers."):
+            parts = local_name.split(".", 2)
+            if len(parts) < 3:
+                raise ValueError(f"Unexpected Janus aligner tensor name: {name}")
+            mm_index = int(parts[1]) + 1
+        else:
+            raise ValueError(f"Unsupported Janus aligner tensor: {name}")
+
+        tensor_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_index, suffix=suffix)
+        return [(tensor_name, data_torch)]
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Skip generation-related components
+        skip_generation_prefixes = (
+            'model.vqmodel.',
+            'vqmodel.',
+            'model.generation_embeddings.',
+            'generation_embeddings.',
+            'model.generation_aligner.',
+            'generation_aligner.',
+            'model.generation_head.',
+            'generation_head.',
+        )
+        if name.startswith(skip_generation_prefixes):
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Handle aligner tensors
+        if name.startswith(('model.aligner.', 'aligner.')):
+            yield from self._map_aligner_tensor(data_torch, name)
+            return
+
+        # Handle vision tensors
+        if name.startswith(('model.vision_model.', 'vision_model.')):
+            yield from super().modify_tensors(data_torch, name, bid)
+            return
+
+        return
diff --git a/conversion/kimi_linear.py b/conversion/kimi_linear.py
new file mode 100644
index 00000000000..f2e6cda83c1
--- /dev/null
+++ b/conversion/kimi_linear.py
@@ -0,0 +1,223 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+from .qwen import QwenModel
+
+
+@ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM")
+class KimiLinearModel(TextModel):
+    """Kimi-Linear model with hybrid MLA+KDA architecture"""
+    model_arch = gguf.MODEL_ARCH.KIMI_LINEAR
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_gpt2()
+            return
+        except Exception:
+            pass
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        if tokpre == "kimi-k2":
+            # Build merges list using the approach similar to HunYuanMoE
+            merges = []
+            vocab = {}
+            mergeable_ranks = tokenizer.model._mergeable_ranks  # ty: ignore[unresolved-attribute]
+            for token, rank in mergeable_ranks.items():
+                vocab[QwenModel.token_bytes_to_string(token)] = rank
+                if len(token) == 1:
+                    continue
+                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+                if len(merged) == 2:
+                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+            # Build token list
+            vocab_size = self.hparams["vocab_size"]
+            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
+            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+            tokens: list[str] = []
+            toktypes: list[int] = []
+
+            for i in range(vocab_size):
+                if i not in reverse_vocab:
+                    tokens.append(f"[PAD{i}]")
+                    toktypes.append(gguf.TokenType.UNUSED)
+                else:
+                    token = reverse_vocab[i]
+                    tokens.append(token)
+                    if i in special_tokens.values():
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.NORMAL)
+
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+            self.gguf_writer.add_token_merges(merges)
+
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+            special_vocab.add_to_gguf(self.gguf_writer)
+            # override eos id in config.json with tiktoken eos id
+            self.gguf_writer.add_eos_token_id(tokenizer.eos_id)  # ty: ignore[unresolved-attribute]
+        else:
+            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
+
+    def set_gguf_parameters(self):
+        # note: To enable MLA KV cache, attention needs to be converted into MQA (ie: GQA with 1 group)
+        self.hparams["num_key_value_heads"] = 1
+
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+        # KDA & MLA params
+        # Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv
+        linear_attn_config = self.hparams["linear_attn_config"]
+        # n_head == 0 for KDA layers, n_head > 0 for MLA layers
+        # full_attention_layers list will be used to distinguish layer type
+        _num_kv_heads = list()
+        _full_attn_layers = linear_attn_config["full_attn_layers"]
+        for il in range(self.hparams["num_hidden_layers"]):
+            if il + 1 in _full_attn_layers:
+                _num_kv_heads.append(self.hparams["num_key_value_heads"])
+            else:
+                _num_kv_heads.append(0)
+        assert len(_num_kv_heads) == self.hparams["num_hidden_layers"]
+        self.gguf_writer.add_head_count_kv(_num_kv_heads)
+
+        if (ssm_d_conv := linear_attn_config.get("short_conv_kernel_size")) is not None:
+            self.gguf_writer.add_ssm_conv_kernel(ssm_d_conv)
+        if (kda_head_dim := linear_attn_config.get("head_dim")) is not None:
+            self.gguf_writer.add_kda_head_dim(kda_head_dim)
+
+        # MLA params - use add_* methods that handle arch substitution
+        # Support both HuggingFace naming (q_lora_rank, kv_lora_rank) and internal naming (n_lora_q, n_lora_kv)
+        if (q_lora_rank := self.find_hparam(["q_lora_rank", "n_lora_q"], optional=True)) is not None:
+            self.gguf_writer.add_q_lora_rank(q_lora_rank)
+        # To enable MLA KV cache, MLA needs to be converted into MQA with larger heads, then decompresses to MHA
+        kv_lora_rank = self.find_hparam(["kv_lora_rank", "n_lora_kv"], optional=False)
+        self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
+
+        # MLA head dimensions
+        # Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
+        qk_nope_head_dim = self.hparams.get("qk_nope_head_dim")
+        # Rotation - use qk_rope_head_dim for Kimi
+        qk_rope_head_dim = self.find_hparam(["qk_rope_head_dim", "n_rot"], optional=False)
+        self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
+        self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
+        v_head_dim = self.hparams.get("v_head_dim")
+
+        # Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
+        if (n_embd_head_k_mla := self.find_hparam(["n_embd_head_k_mla"], optional=True)) is not None:
+            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
+        elif qk_nope_head_dim is not None:
+            n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
+            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
+
+        # n_embd_head_v_mla = v_head_dim
+        if (n_embd_head_v_mla := self.hparams.get("n_embd_head_v_mla")) is not None:
+            self.gguf_writer.add_value_length_mla(n_embd_head_v_mla)
+        elif v_head_dim is not None:
+            self.gguf_writer.add_value_length_mla(v_head_dim)
+
+        # moe_intermediate_size (1024 for Kimi)
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        # num_shared_experts (1 for Kimi)
+        self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
+        # first_k_dense_replace (1 for Kimi - first layer uses dense MLP)
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
+        # Routed scaling factor (expert_weights_scale = 2.446 for Kimi)
+        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        logger.info(f"Processing {name}: shape before = {tuple(data_torch.shape)}")
+
+        # Handle KDA conv1d weights
+        # HuggingFace/vLLM stores as [d_inner, d_conv] (2D), memory layout: conv_step changes fastest
+        # llama.cpp expects ggml ne = [d_conv, 1, d_inner, 1], memory layout: ne[0]=d_conv changes fastest
+        # GGUF reverses numpy shape when writing, so numpy (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
+        # Memory layouts match: both have conv_step (d_conv) changing fastest
+        if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
+            # HF shape: [d_inner, d_conv] e.g. [4096, 4]
+            # Target numpy shape: (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
+            if data_torch.ndim == 2:
+                d_inner, d_conv = data_torch.shape
+                # Reshape to (1, d_inner, 1, d_conv) - memory layout preserved (d_conv fastest)
+                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
+            elif data_torch.ndim == 3:
+                # Already 3D [d_inner, 1, d_conv] from unsqueeze
+                d_inner, _, d_conv = data_torch.shape
+                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, 1, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
+
+        # Handle A_log: iHF stores as [1, 1, num_heads, 1]
+        # llama.cpp expects ggml ne = [1, num_heads, 1, 1]
+        # GGUF reverses numpy shape: numpy (1, 1, num_heads, 1) -> ggml ne = [1, num_heads, 1, 1]
+        if name.endswith(".A_log"):
+            data_torch = -torch.exp(data_torch)
+        if name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+            logger.info("Changed dt_bias to dt_proj.bias")
+
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                # w1: gate, w2: down, w3: up
+                for wid, tname in [("w1", gguf.MODEL_TENSOR.FFN_GATE_EXP),
+                                   ("w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP),
+                                   ("w3", gguf.MODEL_TENSOR.FFN_UP_EXP)]:
+                    datas: list[Tensor] = []
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+                    data_torch = torch.stack(datas, dim=0)
+                    new_name = self.format_tensor_name(tname, bid)
+                    yield from super().modify_tensors(data_torch, new_name, bid)
+            return
+
+        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
+        if name.endswith("kv_b_proj.weight"):
+            name_kb = name.replace("kv_b_proj", "k_b_proj")
+            name_vb = name.replace("kv_b_proj", "v_b_proj")
+            n_head_kv = self.hparams["num_key_value_heads"]
+            v_head_dim = self.find_hparam(["n_embd_head_v_mla", "v_head_dim"], optional=False)
+            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+            logger.info("Split kv_b n_head_kv %d\n" % n_head_kv)
+            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
+            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
+            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
+            k_b = k_b.transpose(1, 2)
+            yield from super().modify_tensors(k_b, name_kb, bid)
+            yield from super().modify_tensors(v_b, name_vb, bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/kimivl.py b/conversion/kimivl.py
new file mode 100644
index 00000000000..63b8a079b72
--- /dev/null
+++ b/conversion/kimivl.py
@@ -0,0 +1,154 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf
+
+
+@ModelBase.register("KimiVLForConditionalGeneration")
+class KimiVLModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = 64 * 14 # for compatibility
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIVL)
+        self.gguf_writer.add_vision_use_gelu(True)
+        self.gguf_writer.add_vision_projector_scale_factor(2)
+        # eps is the same as pytorch's default value
+        assert self.hparams_vision is not None
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-5))
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
+
+        if not is_vision_tensor:
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "pos_emb.weight" in name:
+            data_torch = data_torch.view(data_torch.shape[0] * data_torch.shape[1], data_torch.shape[2])
+
+        if "wqkv" in name:
+            split_dim = 0 if "weight" in name else -1
+            wq, wk, wv = data_torch.chunk(3, dim=split_dim)
+            yield from super().modify_tensors(wq, name.replace("wqkv", "wq"), bid)
+            yield from super().modify_tensors(wk, name.replace("wqkv", "wk"), bid)
+            yield from super().modify_tensors(wv, name.replace("wqkv", "wv"), bid)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("KimiK25ForConditionalGeneration")
+class KimiK25Model(MmprojModel):
+    """Kimi-K2.5 with MoonViT3d vision encoder"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        assert self.hparams_vision is not None, "Kimi-K2.5 requires vision_config in model config"
+
+        self.merge_kernel_size = tuple(self.hparams_vision.get("merge_kernel_size", [2, 2]))
+        self.patch_size = self.hparams_vision.get("patch_size", 14)
+
+        # Set image_size for compatibility with base class
+        # Use position embedding dimensions as image_size reference
+        pos_emb_h = self.hparams_vision.get("init_pos_emb_height", 64)
+        self.hparams_vision["image_size"] = pos_emb_h * self.patch_size
+
+    def set_gguf_parameters(self):
+        # Base class MmprojModel.set_gguf_parameters() already writes:
+        # - vision_block_count, vision_head_count, vision_embedding_length
+        # - vision_feed_forward_length, vision_patch_size, image_mean, image_std
+        # via find_vparam() which handles the vt_* prefixed keys in Kimi-K2.5's config
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIK25)
+
+        # Position embedding parameters (for interpolation)
+        self.gguf_writer.add_uint32("vision.pos_emb_height", self.hparams_vision.get("init_pos_emb_height", 64))
+        self.gguf_writer.add_uint32("vision.pos_emb_width", self.hparams_vision.get("init_pos_emb_width", 64))
+        self.gguf_writer.add_uint32("vision.pos_emb_time", self.hparams_vision.get("init_pos_emb_time", 4))
+
+        # Projector parameters
+        self.gguf_writer.add_vision_use_gelu(self.hparams_vision.get("projector_hidden_act", "gelu") == "gelu")
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("projector_ln_eps", 1e-5))
+        self.gguf_writer.add_vision_projector_scale_factor(self.merge_kernel_size[0])
+
+        # Image size limits
+        # Note: in_patch_limit is for images, in_patch_limit_each_frame is for video (not supported yet)
+        in_patch_limit = self.preprocessor_config.get("in_patch_limit", 16384)
+        min_patches = 8  # reasonable minimum
+        pixels_per_patch = self.patch_size ** 2
+        self.gguf_writer.add_vision_min_pixels(min_patches * pixels_per_patch)
+        self.gguf_writer.add_vision_max_pixels(in_patch_limit * pixels_per_patch)
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int) -> Tensor:
+        out_dim, in_dim = weights.shape
+        head_dim = out_dim // n_head
+        w = weights.reshape(n_head, head_dim // 4, 2, 2, in_dim)
+        w = w.permute(0, 2, 1, 3, 4)
+        return w.reshape(out_dim, in_dim)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Only process vision and projector tensors
+        is_vision = any(x in name for x in ["vision_tower", "mm_projector"])
+
+        if not is_vision:
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        assert self.hparams_vision is not None
+        n_head = self.hparams_vision.get("num_attention_heads", 16)
+
+        # Permute Q/K weights/biases from interleaved to split RoPE format
+        # This allows using build_rope_2d at runtime without post-permutation.
+        if "wqkv" in name:
+            out_dim = data_torch.shape[0]
+            qkv_dim = out_dim // 3
+            head_dim = qkv_dim // n_head
+
+            if "weight" in name:
+                wq, wk, wv = data_torch[:qkv_dim, :], data_torch[qkv_dim:2 * qkv_dim, :], data_torch[2 * qkv_dim:, :]
+                wq = self.permute(wq, n_head)
+                wk = self.permute(wk, n_head)
+                data_torch = torch.cat([wq, wk, wv], dim=0)
+            elif "bias" in name:
+                bq, bk, bv = data_torch[:qkv_dim], data_torch[qkv_dim:2 * qkv_dim], data_torch[2 * qkv_dim:]
+                bq = bq.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
+                bk = bk.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
+                data_torch = torch.cat([bq, bk, bv], dim=0)
+
+        # Temporal embeddings: (T, 1, C) → (T, C)
+        if "pos_emb.time_weight" in name:
+            T, _, C = data_torch.shape
+            data_torch = data_torch.reshape(T, C)
+
+        # PatchMergerMLP tensor name mapping
+        # proj.0.weight → proj.linear_1.weight
+        # proj.2.weight → proj.linear_2.weight
+        if "mm_projector.proj.0." in name:
+            name = name.replace(".proj.0.", ".proj.linear_1.")
+        elif "mm_projector.proj.2." in name:
+            name = name.replace(".proj.2.", ".proj.linear_2.")
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/lfm2.py b/conversion/lfm2.py
new file mode 100644
index 00000000000..f28fccf10f2
--- /dev/null
+++ b/conversion/lfm2.py
@@ -0,0 +1,256 @@
+from __future__ import annotations
+
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf
+
+from .gemma import ConformerAudioModel
+
+
+@ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM")
+class LFM2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.LFM2
+
+    def _add_feed_forward_length(self):
+        ff_dim = self.find_hparam(["block_ff_dim", "intermediate_size"])
+        auto_adjust_ff_dim = self.hparams["block_auto_adjust_ff_dim"]
+        ffn_dim_multiplier = self.hparams["block_ffn_dim_multiplier"]
+        multiple_of = self.hparams["block_multiple_of"]
+
+        if auto_adjust_ff_dim:
+            ff_dim = int(2 * ff_dim / 3)
+            # custom dim factor multiplier
+            if ffn_dim_multiplier is not None:
+                ff_dim = int(ffn_dim_multiplier * ff_dim)
+            ff_dim = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)
+
+        self.gguf_writer.add_feed_forward_length(ff_dim)
+
+    def set_gguf_parameters(self):
+        # set num_key_value_heads only for attention layers
+        self.hparams["num_key_value_heads"] = [
+            self.hparams["num_key_value_heads"] if layer_type != "conv" else 0
+            for layer_type in self.hparams["layer_types"]
+        ]
+
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+        self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["norm_eps"])
+        self._add_feed_forward_length()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if ConformerAudioModel.is_audio_tensor(name):
+            # skip multimodal tensors
+            return None
+
+        name = name.replace("lfm.", "model.")      # audio
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # conv op requires 2d tensor
+        if 'conv.conv' in name:
+            data_torch = data_torch.squeeze(1)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Lfm2Model")
+class LFM2ColBertModel(LFM2Model):
+    model_arch = gguf.MODEL_ARCH.LFM2
+    dense_tensor_name = "dense_2"
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if not name.startswith(self.dense_tensor_name):
+            name = "model." + name
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # dense tensor is stored in a separate safetensors file
+        from safetensors.torch import load_file
+        tensors_file = self.dir_model / "1_Dense" / "model.safetensors"
+        assert tensors_file.is_file()
+        tensor = load_file(tensors_file)["linear.weight"]
+        self.gguf_writer.add_embedding_length_out(tensor.shape[0])
+        yield f"{self.dense_tensor_name}.weight", tensor.clone()
+
+
+@ModelBase.register("Lfm2MoeForCausalLM")
+class LFM2MoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LFM2MOE
+
+    def set_gguf_parameters(self):
+        # set num_key_value_heads only for attention layers
+        self.hparams["num_key_value_heads"] = [
+            self.hparams["num_key_value_heads"] if layer_type == "full_attention" else 0
+            for layer_type in self.hparams["layer_types"]
+        ]
+
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["num_dense_layers"])
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+        self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
+
+    # cache for experts weights for merging
+    _experts_cache: dict[int, dict[str, Tensor]] = {}
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.endswith(".expert_bias"):
+            name = name.replace(".expert_bias", ".expert_bias.bias")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # conv op requires 2d tensor
+        if 'conv.conv' in name:
+            data_torch = data_torch.squeeze(1)
+
+        # merge expert weights
+        if 'experts' in name:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            expert_cache = self._experts_cache.setdefault(bid, {})
+            expert_cache[name] = data_torch
+            expert_weights = ["w1", "w2", "w3"]
+
+            # not enough expert weights to merge
+            if len(expert_cache) < n_experts * len(expert_weights):
+                return
+
+            for w_name in expert_weights:
+                datas: list[Tensor] = []
+
+                for xid in range(n_experts):
+                    ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{w_name}.weight"
+                    datas.append(expert_cache[ename])
+                    del expert_cache[ename]
+
+                data_torch = torch.stack(datas, dim=0)
+                merged_name = f"layers.{bid}.feed_forward.experts.{w_name}.weight"
+
+                yield from super().modify_tensors(data_torch, merged_name, bid)
+
+            del self._experts_cache[bid]
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        assert not self._experts_cache
+
+
+@ModelBase.register("Lfm2VlForConditionalGeneration")
+class LFM2VLModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        # TODO(tarek): for dynamic resolution image_size is not specified, setting here for compatibility
+        self.hparams_vision["image_size"] = 256
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["layer_norm_eps"]))
+        self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("downsample_factor", 2))
+        self.gguf_writer.add_vision_use_gelu(True)
+        # python notation, e.g. for vision_feature_layer == -1, we pick last layer -> vision_feature_layers_to_drop = 0
+        vision_feature_layers_to_drop = -(self.global_config.get("vision_feature_layer", -1) + 1)
+        self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys) - vision_feature_layers_to_drop)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        name = name.replace("model.vision_tower.", "vision_tower.")
+        name = name.replace("model.multi_modal_projector.", "multi_modal_projector.")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "patch_embedding.weight" in name:
+            data_torch = data_torch.view(data_torch.shape[0], 16, 16, 3).permute(0, 3, 1, 2)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Lfm2AudioForConditionalGeneration")
+class LFM2AudioModel(ConformerAudioModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+    model_name = "Lfm2AudioEncoder"
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("encoder")
+
+    def set_gguf_parameters(self):
+        assert self.hparams_audio is not None
+        self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
+        self.hparams_audio["intermediate_size"] = self.hparams_audio["d_model"]
+        self.hparams_audio["num_attention_heads"] = self.hparams_audio["n_heads"]
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2A)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # skip language model tensors
+        if name.startswith("lfm."):
+            return None
+
+        # for training only
+        if any(p in name for p in ["audio_loss_weight"]):
+            return None
+
+        # for audio output
+        if any(p in name for p in ["codebook_offsets", "depth_embeddings", "depth_linear", "depthformer"]):
+            return None
+
+        return super().filter_tensors(item)
+
+
+@ModelBase.register("Lfm25AudioTokenizer")
+class LFM25AudioTokenizer(LFM2Model):
+    model_arch = gguf.MODEL_ARCH.LFM2
+
+    def set_vocab(self):
+        self._set_vocab_none()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_embedding_length_out(self.hparams["output_size"])
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # skip language model tensors
+        if name == "istft.window" or name.startswith("emb.emb"):
+            return None
+
+        if name.startswith("lin"):
+            name = name.replace("lin", "dense_2_out")
+
+        return super().filter_tensors((name, gen))
diff --git a/conversion/lighton_ocr.py b/conversion/lighton_ocr.py
new file mode 100644
index 00000000000..ead3200ac18
--- /dev/null
+++ b/conversion/lighton_ocr.py
@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+from typing import Callable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, gguf
+
+from .llava import LlavaVisionModel
+
+
+@ModelBase.register("LightOnOCRForConditionalGeneration")
+class LightOnOCRVisionModel(LlavaVisionModel):
+    is_mistral_format = False
+    use_break_tok = False
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LIGHTONOCR)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        name = name.replace("model.vision_encoder.", "vision_tower.")
+        name = name.replace("model.vision_projection.", "multi_modal_projector.")
+
+        return super().filter_tensors((name, gen))
diff --git a/conversion/llada.py b/conversion/llada.py
new file mode 100644
index 00000000000..98dc9de95b3
--- /dev/null
+++ b/conversion/llada.py
@@ -0,0 +1,172 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("LLaDAModelLM")
+class LLaDAModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLADA
+    undo_permute = True
+
+    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+        vocab_dict = tokenizer.get_vocab()  # ty: ignore[unresolved-attribute]
+        vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
+        assert max(vocab_dict.values()) < vocab_size
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            elif reverse_vocab[i] in added_vocab:
+                tokens.append(reverse_vocab[i])
+                # Check if it's a special token - treat special tokens as CONTROL tokens
+                if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
+                    if tokenizer.added_tokens_decoder[i].special:
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
+                    toktypes.append(gguf.TokenType.CONTROL)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+
+        return tokens, toktypes, tokpre
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+        # LLaDA specific parameters
+        self.gguf_writer.add_add_bos_token(True)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
+
+        # Add parameters similar to LlamaModel
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if (rope_dim := hparams.get("head_dim")) is None:
+            n_heads = hparams.get("num_attention_heads", hparams.get("n_heads"))
+            assert n_heads is not None
+            rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+        # Set context length for LLaDA
+        context_length = self.hparams.get("max_sequence_length", 4096)
+        self.gguf_writer.add_context_length(context_length)
+
+        # Set embedding length (dimension size)
+        embedding_length = self.hparams.get("d_model", 4096)
+        self.gguf_writer.add_embedding_length(embedding_length)
+
+        # Set feed forward length (MLP hidden size)
+        feed_forward_length = self.hparams.get("mlp_hidden_size", 12288)
+        self.gguf_writer.add_feed_forward_length(feed_forward_length)
+
+        # LLaDA models use non-causal attention for diffusion, similar to Dream
+        self.gguf_writer.add_causal_attention(False)
+
+        # LLaDA models don't shift their logits
+        self.gguf_writer.add_diffusion_shift_logits(False)
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads"))
+        assert n_head is not None
+        n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads"))
+
+        if self.undo_permute:
+            if name.endswith(("q_proj.weight", "q_proj.bias")):
+                data_torch = LLaDAModel.permute(data_torch, n_head, n_head)
+            if name.endswith(("k_proj.weight", "k_proj.bias")):
+                data_torch = LLaDAModel.permute(data_torch, n_head, n_kv_head)
+
+        # LLaDA model tensors should be mapped directly since it's the base model
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("LLaDAMoEModel", "LLaDAMoEModelLM")
+class LLaDAMoEModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLADA_MOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (expert_intermediate_size := self.hparams.get("expert_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
+
+        self.gguf_writer.add_mask_token_id(156895)
+        self.gguf_writer.add_causal_attention(False)
+        self.gguf_writer.add_diffusion_shift_logits(False)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    # Copied from: Qwen2MoeModel
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    # Copied from: Qwen2MoeModel
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/llama.py b/conversion/llama.py
new file mode 100644
index 00000000000..41fde5143f8
--- /dev/null
+++ b/conversion/llama.py
@@ -0,0 +1,312 @@
+from __future__ import annotations
+
+import json
+import math
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register(
+    "LLaMAForCausalLM",
+    "LlamaForCausalLM",
+    "MistralForCausalLM",
+    "MixtralForCausalLM",
+    "VLlama3ForCausalLM",
+    "LlavaForConditionalGeneration",
+    "VoxtralForConditionalGeneration",
+    "IQuestCoderForCausalLM",
+    "LlamaModel")
+class LlamaModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+    undo_permute = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # fix for SmolVLM2, missing `num_attention_heads` in config.json
+        if self.hf_arch == "VLlama3ForCausalLM":
+            self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
+        # Mistral consolidated format has no config.json; origin_hf_arch is HF-only.
+        if self.is_mistral_format:
+            self.origin_hf_arch = None
+        else:
+            hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
+            self.origin_hf_arch = hparams.get('architectures', [None])[0]
+
+    def set_vocab(self):
+        if self.origin_hf_arch == "GlmasrModel":
+            return self._set_vocab_glmedge()
+
+        if self.is_mistral_format:
+            return self._set_vocab_mistral()
+
+        path_tekken_json = self.dir_model / "tekken.json"
+        path_tokenizer_json = self.dir_model / "tokenizer.json"
+        if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
+            self._set_vocab_mistral()
+
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            try:
+                self._set_vocab_llama_hf()
+            except (FileNotFoundError, TypeError):
+                # Llama 3
+                self._set_vocab_gpt2()
+
+        # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
+        if self.hparams.get("vocab_size", 32000) == 32016:
+            special_vocab = gguf.SpecialVocab(
+                self.dir_model, load_merges=False,
+                special_token_types = ['prefix', 'suffix', 'middle', 'eot']
+            )
+            special_vocab._set_special_token("prefix", 32007)
+            special_vocab._set_special_token("suffix", 32008)
+            special_vocab._set_special_token("middle", 32009)
+            special_vocab._set_special_token("eot",    32010)
+            special_vocab.add_to_gguf(self.gguf_writer)
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                if "add_prefix_space" in tokenizer_config_json:
+                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
+
+        # Apply to granite small models only
+        if self.hparams.get("vocab_size", 32000) == 49152:
+            self.gguf_writer.add_add_bos_token(False)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        if not self.is_mistral_format:
+            self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
+        # Mirror the BF16 Q/K RoPE permutation site in modify_tensors; the NVFP4 path bypasses it.
+        if self.undo_permute:
+            n_head = self.find_hparam(["n_heads", "num_attention_heads"], optional=True)
+            n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"], optional=True)
+            if n_head is not None:
+                if name.endswith("q_proj.weight"):
+                    weight = LlamaModel.permute(weight, n_head, n_head)
+                    scale  = LlamaModel.permute(scale, n_head, n_head)
+                elif name.endswith("k_proj.weight"):
+                    weight = LlamaModel.permute(weight, n_head, n_kv_head)
+                    scale  = LlamaModel.permute(scale, n_head, n_kv_head)
+        super()._repack_nvfp4(name, weight, scale, scale2, input_scale)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if "text_model." in name:
+            name = name.replace("text_model.", "") # for SmolVLM
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.find_hparam(["n_heads", "num_attention_heads"])
+        n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"])
+
+        if self.hf_arch == "LlamaModel":
+            name = "model." + name
+
+        if self.undo_permute:
+            if name.endswith(("q_proj.weight", "q_proj.bias")):
+                data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+            if name.endswith(("k_proj.weight", "k_proj.bias")):
+                data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams["num_local_experts"]
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for wid in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
+            if rope_params.get("rope_type", '').lower() == "llama3":
+                base = rope_params.get("rope_theta", 10000.0)
+                if (dim := self.hparams.get("head_dim")) is None:
+                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+                factor = rope_params.get("factor", 8.0)
+                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
+                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+
+                low_freq_wavelen = old_context_len / low_freq_factor
+                high_freq_wavelen = old_context_len / high_freq_factor
+                # assert low_freq_wavelen != high_freq_wavelen # Errors for Llama4
+
+                rope_factors = []
+                for freq in freqs:
+                    wavelen = 2 * math.pi / freq
+                    if wavelen < high_freq_wavelen:
+                        rope_factors.append(1)
+                    elif wavelen > low_freq_wavelen:
+                        rope_factors.append(factor)
+                    else:
+                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("ArceeForCausalLM")
+class ArceeModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.ARCEE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
+
+
+@ModelBase.register(
+    "Llama4ForConditionalGeneration",
+    "Llama4ForCausalLM",
+)
+class Llama4Model(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA4
+    undo_permute = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # IMPORTANT: the normal "intermediate_size" is renamed to "intermediate_size_mlp", we need to undo this
+        self.hparams["intermediate_size_moe"] = self.hparams["intermediate_size"]
+        self.hparams["intermediate_size"] = self.hparams["intermediate_size_mlp"]
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_interleave_moe_layer_step(self.hparams["interleave_moe_layer_step"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size_moe"])
+        if "layer_types" in self.hparams:
+            if all(lt == "full_attention" for lt in self.hparams["layer_types"]):
+                # all layers are full attention (for MobileLLM), disable swa
+                self.gguf_writer.add_sliding_window(0)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        # split the gate_up into gate and up
+        if "gate_up_proj" in name:
+            name_up = name.replace("gate_up_proj", "up_proj.weight")
+            name_gate = name.replace("gate_up_proj", "gate_proj.weight")
+            dim_half = data_torch.shape[-1] // 2
+            gate_proj_weight, up_proj_weight = data_torch.transpose(-1, -2).split(dim_half, dim=-2)
+            yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
+            yield from super().modify_tensors(up_proj_weight, name_up, bid)
+            return
+
+        if name.endswith("down_proj"):
+            name += ".weight"
+            data_torch = data_torch.transpose(-1, -2)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("LlamaBidirectionalModel")
+class LlamaEmbedNemotronModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA_EMBED
+
+
+@ModelBase.register("SmolLM3ForCausalLM")
+class SmolLM3Model(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.SMOLLM3
+
+
+@ModelBase.register("ApertusForCausalLM")
+class ApertusModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.APERTUS
+    undo_permute = False
+
+    _alpha_n = {}
+    _alpha_p = {}
+    _beta = {}
+    _eps = {}
+
+    def modify_tensors(self, data_torch, name, bid):
+        # Handle xIELU activation parameters
+        n_layers = self.hparams["num_hidden_layers"]
+        if name.endswith(".act_fn.alpha_n"):
+            self._alpha_n[bid] = data_torch.to("cpu").float().item()
+            if (len(self._alpha_n) == n_layers):
+                self.gguf_writer.add_xielu_alpha_n([self._alpha_n[k] for k in sorted(self._alpha_n)])
+            return
+        if name.endswith(".act_fn.alpha_p"):
+            self._alpha_p[bid] = data_torch.to("cpu").float().item()
+            if (len(self._alpha_p) == n_layers):
+                self.gguf_writer.add_xielu_alpha_p([self._alpha_p[k] for k in sorted(self._alpha_p)])
+            return
+        if name.endswith(".act_fn.beta"):
+            self._beta[bid] = data_torch.to("cpu").float().item()
+            if (len(self._beta) == n_layers):
+                self.gguf_writer.add_xielu_beta([self._beta[k] for k in sorted(self._beta)])
+            return
+        if name.endswith(".act_fn.eps"):
+            self._eps[bid] = data_torch.to("cpu").float().item()
+            if (len(self._eps) == n_layers):
+                self.gguf_writer.add_xielu_eps([self._eps[k] for k in sorted(self._eps)])
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/llama4.py b/conversion/llama4.py
new file mode 100644
index 00000000000..f84c7629619
--- /dev/null
+++ b/conversion/llama4.py
@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf
+
+
+@ModelBase.register("Llama4ForConditionalGeneration")
+class Llama4VisionModel(MmprojModel):
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LLAMA4)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams["norm_eps"])
+        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / self.hparams["pixel_shuffle_ratio"]))
+        assert self.hparams["hidden_act"] == "gelu"
+        self.gguf_writer.add_vision_use_gelu(True)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if "multi_modal_projector" not in name and "vision_model" not in name:
+            return None
+
+        if "positional_embedding_vlm" in name and ".weight" not in name:
+            name += ".weight"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "multi_modal_projector.linear_1" in name:
+            # despite the name with number postfix, this is a single fully connected layer
+            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC] + '.weight', data_torch)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/llava.py b/conversion/llava.py
new file mode 100644
index 00000000000..31d6e2ad80e
--- /dev/null
+++ b/conversion/llava.py
@@ -0,0 +1,129 @@
+from __future__ import annotations
+
+import json
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf, logger
+
+from .llama import LlamaModel
+
+
+@ModelBase.register(
+    "LlavaForConditionalGeneration", # pixtral
+    "Mistral3ForConditionalGeneration", # mistral small 3.1
+)
+class LlavaVisionModel(MmprojModel):
+    img_break_tok_id = -1
+    use_break_tok = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.hparams.get("model_type") == "pixtral":
+            # layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py
+            self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5)
+            if self.use_break_tok:
+                self.img_break_tok_id = self.get_token_id("[IMG_BREAK]")
+        elif self.is_mistral_format:
+            # hparams is already vision config here so norm_eps is only defined in global_config.
+            self.hparams["norm_eps"] = self.global_config.get("norm_eps", None)
+            assert self.hparams["norm_eps"] is not None, "norm_eps not found in params.json"
+            if self.use_break_tok:
+                self.img_break_tok_id = self.find_vparam(["image_break_token_id"])
+
+                # params.json may ship -1 placeholders (Mistral Medium 3.5)
+                # resolve the real id from the bundled tokenizer in that case
+                if self.img_break_tok_id < 0:
+                    self.img_break_tok_id = self.get_mistral_token_id("[IMG_BREAK]")
+        else:
+            raise ValueError(f"Unsupported model type: {self.hparams['model_type']}")
+        logger.info(f"Image break token id: {self.img_break_tok_id}")
+
+    def get_token_id(self, token: str) -> int:
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+            added_tokens_decoder = json.load(f).get('added_tokens_decoder') or {}
+            for id_, token_data in added_tokens_decoder.items():
+                if token_data.get("content") == token:
+                    return int(id_)
+            # fallthrough to tokenizer.json
+        with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
+            tokenizer_json = json.load(f)
+            for token_data in tokenizer_json["added_tokens"]:
+                if token_data["content"] == token:
+                    return int(token_data["id"])
+        raise ValueError(f"Token '{token}' not found in tokenizer config.")
+
+    def get_mistral_token_id(self, token: str) -> int:
+        # mistral native format ships tekken.json or a versioned spm tokenizer
+        tekken_file = self.dir_model / "tekken.json"
+        if tekken_file.is_file():
+            with open(tekken_file, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            for entry in data.get("special_tokens", []):
+                if entry.get("token_str") == token:
+                    return int(entry["rank"])
+        tokenizer_json_file = self.dir_model / "tokenizer.json"
+        if tokenizer_json_file.is_file():
+            with open(tokenizer_json_file, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            for entry in data.get("added_tokens", []):
+                if entry.get("content") == token:
+                    return int(entry["id"])
+        raise ValueError(f"Token '{token}' not found in mistral tokenizer files.")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if hparams.get("model_type") == "pixtral":
+            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
+            self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
+
+            # hidden_act
+            if hparams["hidden_act"] == "silu":
+                self.gguf_writer.add_vision_use_silu(True)
+            elif hparams["hidden_act"] == "gelu":
+                self.gguf_writer.add_vision_use_gelu(True)
+            else:
+                raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
+
+            # spatial_merge_size
+            if "spatial_merge_size" in self.global_config:
+                self.gguf_writer.add_vision_spatial_merge_size(self.global_config["spatial_merge_size"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = (
+            self.hparams["num_attention_heads"] if not self.is_mistral_format else self.find_vparam(["num_attention_heads"])
+        )
+        n_kv_head = n_head
+
+        valid_prefixes = (
+            "multi_modal_projector.",
+            "vision_tower.",
+            "vision_encoder.",
+            "vision_language_adapter.",
+            "patch_merger.",
+            "pre_mm_projector_norm",
+        )
+
+        if any(name.startswith(prefix) for prefix in valid_prefixes):
+            # process vision tensors
+            if name.endswith(("q_proj.weight", "q_proj.bias")) and not self.is_mistral_format:
+                data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+            if name.endswith(("k_proj.weight", "k_proj.bias")) and not self.is_mistral_format:
+                data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+            yield from super().modify_tensors(data_torch, name, bid)
+            return
+
+        embed_key = "embed_tokens.weight" if not self.is_mistral_format else "tok_embeddings.weight"
+        if self.img_break_tok_id > 0 and embed_key in name:
+            logger.info(f"Extracting [IMG_BREAK] token embedding from {name}")
+            # for pixtral model, we need to extract the [IMG_BREAK] token embedding
+            img_break_embd = data_torch[self.img_break_tok_id]
+            name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK]
+            yield from super().modify_tensors(img_break_embd, name, bid)
+
+        return # skip other tensors
diff --git a/conversion/maincoder.py b/conversion/maincoder.py
new file mode 100644
index 00000000000..18b625b08fb
--- /dev/null
+++ b/conversion/maincoder.py
@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("MaincoderForCausalLM")
+class MaincoderModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.MAINCODER
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        if (head_dim := self.hparams.get("head_dim")) is not None:
+            self.gguf_writer.add_rope_dimension_count(head_dim)
diff --git a/conversion/mamba.py b/conversion/mamba.py
new file mode 100644
index 00000000000..be0e36a29ba
--- /dev/null
+++ b/conversion/mamba.py
@@ -0,0 +1,199 @@
+from __future__ import annotations
+
+import json
+
+from pathlib import Path
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
+class MambaModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.MAMBA
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        # Avoid using AutoConfig for hparams
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+                hparams = json.load(f)
+        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
+
+    def set_vocab(self):
+        vocab_size = self.hparams["vocab_size"]
+        # Round vocab size to next multiple of 8
+        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
+        # pad using ceiling division
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
+        self.hparams["vocab_size"] = vocab_size
+
+        if (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        elif (self.dir_model / "tokenizer.model").is_file():
+            self._set_vocab_sentencepiece()
+        else:
+            # Use the GPT-NeoX tokenizer when no tokenizer files are present
+            self._set_vocab_builtin("gpt-neox", vocab_size)
+
+    def set_gguf_parameters(self):
+        d_model = self.find_hparam(["hidden_size",       "d_model"])
+        d_conv  = self.find_hparam(["conv_kernel",       "d_conv"],  optional=True) or 4
+        d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
+        d_state = self.find_hparam(["state_size",        "d_state"], optional=True) or 16
+        # ceiling division
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
+        dt_rank      = self.find_hparam(["time_step_rank",     "dt_rank"],      optional=True) or -(d_model // -16)
+        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
+        use_dt_b_c_norm = False
+        # For falconmamba we do apply RMS norm on B / DT and C layers
+        if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",):
+            use_dt_b_c_norm = True
+        # Fail early for models which don't have a block expansion factor of 2
+        assert d_inner == 2 * d_model
+
+        self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
+        self.gguf_writer.add_embedding_length(d_model)
+        self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
+        self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_ssm_conv_kernel(d_conv)
+        self.gguf_writer.add_ssm_inner_size(d_inner)
+        self.gguf_writer.add_ssm_state_size(d_state)
+        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
+        self.gguf_writer.add_ssm_dt_b_c_rms(use_dt_b_c_norm) # For classic Mamba we don't apply rms norm on B / DT layers
+        self.gguf_writer.add_file_type(self.ftype)
+
+    _tok_embd = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
+        tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD)
+
+        new_name = self.map_tensor_name(name)
+
+        if name.endswith(".A_log"):
+            logger.debug("A_log --> A ==> " + new_name)
+            data_torch = -torch.exp(data_torch)
+
+        # [4 1 8192 1] -> [4 8192 1 1]
+        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
+            data_torch = data_torch.squeeze()
+
+        # assuming token_embd.weight is seen before output.weight
+        if self._tok_embd is not None and new_name == output_name:
+            if torch.equal(self._tok_embd, data_torch):
+                logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting")
+                return
+        elif new_name == tok_embd_name:
+            self._tok_embd = data_torch
+
+        yield from super().modify_tensors(data_torch, new_name, bid)
+
+
+@ModelBase.register("Mamba2ForCausalLM")
+class Mamba2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.MAMBA2
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        # Avoid using AutoConfig for hparams
+        # It wrongly assumes all Mamba2 models are Mamba-Codestral-7B-v0.1
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+                hparams = json.load(f)
+        if "llm_config" in hparams:
+            hparams["text_config"] = hparams["llm_config"]
+        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
+        self.d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
+        self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or 2 * self.d_model
+        self.n_group = self.find_hparam(["n_groups"], optional=True) or 1
+
+    def set_vocab(self):
+        vocab_size = self.hparams["vocab_size"]
+        # Round vocab size to next multiple of 16
+        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16)
+        # pad using ceiling division
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
+        self.hparams["vocab_size"] = vocab_size
+
+        if (self.dir_model / "tokenizer.model").is_file():
+            self._set_vocab_sentencepiece()
+        elif (self.dir_model / "tokenizer.model.v3").is_file():
+            # mamba-codestral
+            raise NotImplementedError(f"Please rename {self.dir_model / 'tokenizer.model.v3'} to {self.dir_model / 'tokenizer.model'}")
+        elif (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        else:
+            # Use the GPT-NeoX tokenizer when no tokenizer files are present
+            self._set_vocab_builtin("gpt-neox", vocab_size)
+
+    def set_gguf_parameters(self):
+        d_conv  = self.find_hparam(["conv_kernel", "d_conv"],     optional=True) or 4
+        d_state = self.find_hparam(["state_size",  "d_state"],    optional=True) or 128
+        head_dim = self.find_hparam(["mamba_d_head", "head_dim"], optional=True) or 64
+
+        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
+
+        # Fail early for models which don't have a block expansion factor of 2
+        # TODO: does this really matter?
+        # skip the assertion for FalconH1 Model
+        if self.model_arch != gguf.MODEL_ARCH.FALCON_H1:
+            assert self.d_inner == 2 * self.d_model
+            assert self.d_inner % head_dim == 0
+
+        self.gguf_writer.add_context_length(2**20)  # arbitrary value; for those who use the default
+        self.gguf_writer.add_embedding_length(self.d_model)
+        self.gguf_writer.add_feed_forward_length(0)  # unused, but seemingly required when loading
+        self.gguf_writer.add_head_count(0)  # unused, but seemingly required when loading
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_ssm_conv_kernel(d_conv)
+        self.gguf_writer.add_ssm_inner_size(self.d_inner)
+        self.gguf_writer.add_ssm_state_size(d_state)
+        self.gguf_writer.add_ssm_time_step_rank(self.d_inner // head_dim)
+        self.gguf_writer.add_ssm_group_count(self.n_group)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
+        self.gguf_writer.add_file_type(self.ftype)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith(("model.backbone", "model.lm_head")):
+            # map Mamba-Codestral-7B-v0.1 tensor names to the names used by Mamba-2
+            name = name.removeprefix("model.")
+
+        if name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        new_name = self.map_tensor_name(name)
+
+        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
+            data_torch = data_torch.squeeze()
+        elif any(self.match_model_tensor_name(new_name, t, bid, suffix="") for t in [
+            gguf.MODEL_TENSOR.SSM_A,
+            gguf.MODEL_TENSOR.SSM_D,
+        ]):
+            # unsqueeze A to use similar shape semantics as Mamba-1
+            # (D is also unsqueezed, but for more straightforward broadcast internally)
+            data_torch = data_torch.reshape((*data_torch.shape, 1))
+        elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_NORM, bid):
+            data_torch = data_torch.reshape((self.n_group, self.d_inner // self.n_group))
+
+        if name.endswith(".A_log"):
+            logger.debug("A_log --> A ==> " + new_name)
+            data_torch = -torch.exp(data_torch)
+
+        yield (new_name, data_torch)
diff --git a/conversion/mimo.py b/conversion/mimo.py
new file mode 100644
index 00000000000..d4067aab4b6
--- /dev/null
+++ b/conversion/mimo.py
@@ -0,0 +1,295 @@
+from __future__ import annotations
+
+import re
+
+from typing import Callable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf
+
+
+@ModelBase.register("MiMoV2FlashForCausalLM", "MiMoV2ForCausalLM")
+class MimoV2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.MIMO2
+
+    # MiMo V2-Flash, V2.5 and V2.5-Pro all ship 3 trained MTP layers under model.mtp.layers.{0,1,2}.
+    # The HF config does not expose the count, so it's hardcoded to match the count found in the safetensors.
+    _n_nextn = 3
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.block_count = self.hparams["num_hidden_layers"] + self._n_nextn
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    @staticmethod
+    def _tp_aware_qkv_dequant(weight: Tensor, scale_inv: Tensor,
+                              n_q: int, n_kv: int, hd: int, vhd: int,
+                              bs: int = 128) -> Tensor:
+        # MiMo-V2.5 (TP=4) and V2.5-Pro (TP=8) ship qkv_proj sharded across TP
+        # ranks; per rank, rows are stacked as [Q_per | K_per | V_per].
+        # weight_scale_inv has ceil(rows_per_rank/bs) block-rows per rank (last
+        # may extend past rows_per_rank with phantom rows not in the weight).
+        # Naive repeat_interleave aligns rank 0 only and mis-applies scales to
+        # later ranks once rows_per_rank isn't a multiple of bs.
+        # Re-group the per-rank [Q_per|K_per|V_per] rows into a single fused
+        # [Q | K | V] tensor matching the un-sharded original layout.
+        q_size = n_q * hd
+        k_size = n_kv * hd
+        v_size = n_kv * vhd
+        total_rows = q_size + k_size + v_size
+        if weight.shape[0] != total_rows:
+            raise ValueError(f"qkv_proj weight rows {weight.shape[0]} != q+k+v {total_rows}")
+
+        # detect TP from scale_inv block count, descending order so larger matches first
+        tp = None
+        for cand in (8, 4):
+            if total_rows % cand != 0:
+                continue
+            rpr = total_rows // cand
+            bpr = (rpr + bs - 1) // bs
+            if scale_inv.shape[0] == cand * bpr:
+                tp = cand
+                break
+        if tp is None:
+            raise ValueError(
+                f"qkv_proj: cannot detect TP - scale_inv rows {scale_inv.shape[0]}, "
+                f"q+k+v {total_rows}")
+
+        q_per = q_size // tp
+        k_per = k_size // tp
+        v_per = v_size // tp
+        rows_per_rank = q_per + k_per + v_per
+        blocks_per_rank = (rows_per_rank + bs - 1) // bs
+
+        scale_inv = scale_inv.float()
+        # per-row scale-row index: rank * blocks_per_rank + (rr_in_rank // bs)
+        row_idx = torch.arange(total_rows)
+        rr = row_idx % rows_per_rank
+        rank = row_idx // rows_per_rank
+        scale_row_idx = rank * blocks_per_rank + (rr // bs)
+        # gather: (total_rows, n_col_blocks)
+        scale_per_row_block = scale_inv[scale_row_idx]
+        # expand col-blocks -> cols: each block-col covers `bs` weight cols
+        scale_full = scale_per_row_block.repeat_interleave(bs, dim=1)
+        # crop to weight col count (in case last col-block isn't full)
+        scale_full = scale_full[:, : weight.shape[1]]
+        dequant = weight.float() * scale_full
+
+        if tp == 1:
+            return dequant
+
+        # Re-group per-rank [Q_per|K_per|V_per] rows into unified [Q | K | V]
+        qs, ks, vs = [], [], []
+        for r in range(tp):
+            base = r * rows_per_rank
+            qs.append(dequant[base : base + q_per])
+            ks.append(dequant[base + q_per : base + q_per + k_per])
+            vs.append(dequant[base + q_per + k_per : base + rows_per_rank])
+        return torch.cat(qs + ks + vs, dim=0)
+
+    def dequant_model(self):
+        # Capture raw FP8 (weight, scale_inv) lambdas for qkv_proj BEFORE super
+        # rewrites them with the existing dequant. Replace super's lambda after
+        # it runs so scale_inv removal still happens via the standard path.
+        qkv_overrides: dict[str, tuple[Callable, Callable, int]] = {}
+        qc = self.hparams.get("quantization_config")
+        if isinstance(qc, dict) and qc.get("quant_method") == "fp8":
+            pat = re.compile(r"^model\.layers\.(\d+)\.self_attn\.qkv_proj\.weight_scale_inv$")
+            for name in list(self.model_tensors.keys()):
+                m = pat.match(name)
+                if not m:
+                    continue
+                weight_name = name.removesuffix("_scale_inv")
+                if weight_name not in self.model_tensors:
+                    continue
+                qkv_overrides[weight_name] = (
+                    self.model_tensors[weight_name],
+                    self.model_tensors[name],
+                    int(m.group(1)),
+                )
+
+        super().dequant_model()
+
+        if not qkv_overrides:
+            return
+
+        n_q = self.hparams["num_attention_heads"]
+        hd = self.hparams["head_dim"]
+        vhd = self.hparams["v_head_dim"]
+        hybrid = self.hparams["hybrid_layer_pattern"]
+        n_layer_text = self.hparams["num_hidden_layers"]
+        for weight_name, (w_fn, s_fn, bid) in qkv_overrides.items():
+            # MTP layers (bid >= n_layer_text) use SWA-style attention dims
+            is_swa = True if bid >= n_layer_text else hybrid[bid] == 1
+            n_kv = self.hparams["swa_num_key_value_heads" if is_swa else "num_key_value_heads"]
+            self.model_tensors[weight_name] = (
+                lambda w_fn=w_fn, s_fn=s_fn, n_q=n_q, n_kv=n_kv, hd=hd, vhd=vhd:
+                    MimoV2Model._tp_aware_qkv_dequant(w_fn(), s_fn(), n_q, n_kv, hd, vhd)
+            )
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        assert self.hparams["swa_head_dim"] == self.hparams["head_dim"]
+        assert self.hparams["swa_num_attention_heads"] == self.hparams["num_attention_heads"]
+        assert self.hparams["swa_v_head_dim"] == self.hparams["v_head_dim"]
+        assert self.hparams["topk_method"] == "noaux_tc"
+
+        n_head_kv = self.hparams["num_key_value_heads"]
+        n_head_kv_swa = self.hparams["swa_num_key_value_heads"]
+        # Extend the per-layer pattern with SWA entries for the MTP blocks so the
+        # runtime arrays (sized to extended block_count) are fully populated.
+        hybrid = list(self.hparams["hybrid_layer_pattern"]) + [1] * self._n_nextn
+        n_head_kv_arr = [n_head_kv_swa if use_swa == 1 else n_head_kv for use_swa in hybrid]
+        self.gguf_writer.add_head_count_kv(n_head_kv_arr)
+
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern(hybrid)
+        self.gguf_writer.add_value_length(self.hparams["v_head_dim"])
+        self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+
+        rope_dim = int(self.hparams["head_dim"] * self.hparams["partial_rotary_factor"])
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon", 1e-5))
+
+        v_scale = self.hparams.get("attention_value_scale")
+        if v_scale is not None:
+            self.gguf_writer.add_attn_value_scale(float(v_scale))
+
+        self.gguf_writer.add_nextn_predict_layers(self._n_nextn)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if "attention_sink" in name and not name.endswith(".weight"):
+            name += ".weight"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch, name, bid):
+        # Remap MTP/NextN tensors to additional layer slots so the standard tensor map handles them.
+        # HF: model.mtp.layers.{i}.foo  ->  model.layers.{n_layer_text + i}.foo
+        m = re.match(r"^model\.mtp\.layers\.(\d+)\.(.*)$", name)
+        if m is not None:
+            mtp_idx = int(m.group(1))
+            assert mtp_idx < self._n_nextn, f"MTP layer index {mtp_idx} >= _n_nextn ({self._n_nextn})"
+            rest = m.group(2)
+            n_layer_text = self.hparams["num_hidden_layers"]
+            new_bid = n_layer_text + mtp_idx
+            name = f"model.layers.{new_bid}.{rest}"
+            bid = new_bid
+
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["gate_proj", "up_proj", "down_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename_to_retrieve])
+                        del self._experts[bid][ename_to_retrieve]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("MiMoV2ForCausalLM")
+class MiMoV2VisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        hp = self.hparams_vision
+
+        hp["image_size"] = hp.get("image_size", 560)
+        hp["num_attention_heads"] = hp.get("num_heads", 32)
+        hp["num_hidden_layers"] = hp.get("depth", 28)
+
+        self.n_q_heads = int(hp["num_heads"])
+        self.num_kv_heads = int(hp.get("num_key_value_heads", 8))
+        self.head_dim = int(hp.get("qk_channels", 64))
+        self.spatial_merge_size = int(hp["spatial_merge_size"])
+        # MiMoV2 vision RMSNorm: HF uses getattr(config, "rms_norm_eps", 1e-6) and the
+        # field is absent from MiMo-V2.5's vision_config
+        self.rms_norm_eps = float(hp.get("rms_norm_eps", 1e-6))
+
+        # fullatt_block_indexes are also reflected in vit_window_attn_types as -1
+        self.fullatt_block_indexes = list(hp.get("fullatt_block_indexes") or [])
+        self.vit_window_attn_types = list(hp.get("vit_window_attn_types") or [])
+        self.visual_token_window_size = int(hp.get("visual_token_window_size", -1))
+        self.use_sink = bool(hp.get("use_sink", False))
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MIMOVL)
+        self.gguf_writer.add_vision_use_silu(True)
+        self.gguf_writer.add_vision_head_count_kv(self.num_kv_heads)
+        self.gguf_writer.add_vision_spatial_merge_size(self.spatial_merge_size)
+        self.gguf_writer.add_uint32(gguf.Keys.ClipVision.WINDOW_SIZE, self.visual_token_window_size)
+        self.gguf_writer.add_vision_wa_pattern_mode(self.vit_window_attn_types)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.rms_norm_eps)
+        self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"]))
+        self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"]))
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # Sinks must be F32: any sink-style softmax/mask add in ggml requires
+        # F32, and we fold sinks into a host-built F32 mask at encode time.
+        if new_name.endswith(".attn_sinks"):
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, _ = item
+        if not name.startswith("visual."):
+            return None
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch, name, bid):
+        # Conv3D patch embed: split along the temporal axis (kt=2) into two Conv2D
+        # weights that the existing qwen2vl-style two-Conv2D path consumes.
+        if name == "visual.patch_embed.proj.weight":
+            _, _, kt, _, _ = data_torch.shape
+            if kt != 2:
+                raise ValueError(f"unexpected temporal_patch_size: {kt}")
+            embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH]
+            yield (embd_name + ".weight",   data_torch[:, :, 0, ...])
+            yield (embd_name + ".weight.1", data_torch[:, :, 1, ...])
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/minicpm.py b/conversion/minicpm.py
new file mode 100644
index 00000000000..e9a4c4a74dc
--- /dev/null
+++ b/conversion/minicpm.py
@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf, logger
+
+from .llama import LlamaModel
+from .qwen import Qwen3_5TextModel
+
+
+@ModelBase.register("MiniCPMForCausalLM")
+class MiniCPMModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.MINICPM
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        embedding_scale = float(self.hparams["scale_emb"])
+        self.gguf_writer.add_embedding_scale(embedding_scale)
+        logger.info(f"gguf: (minicpm) embedding_scale = {embedding_scale}")
+        residual_scale = self.hparams["scale_depth"] / self.hparams["num_hidden_layers"] ** 0.5
+        self.gguf_writer.add_residual_scale(residual_scale)
+        logger.info(f"gguf: (minicpm) residual_scale = {residual_scale}")
+        logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"]
+        self.gguf_writer.add_logit_scale(logit_scale)
+        logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}")
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+
+        rope_scaling = self.find_hparam(['rope_scaling'], True)
+        if rope_scaling is not None:
+            long_factors = rope_scaling.get('long_factor', None)
+            short_factors = rope_scaling.get('short_factor', None)
+
+            if long_factors is None or short_factors is None:
+                raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
+
+            if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
+                raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
+
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        # HF models permute some of the tensors, so we need to undo that
+        if name.endswith(("q_proj.weight")):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight")):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("MiniCPM3ForCausalLM")
+class MiniCPM3Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.MINICPM3
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
+            self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
+        self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
+        self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
+        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        rope_scaling = self.find_hparam(['rope_scaling'], True)
+        if rope_scaling is not None:
+            rope_dims = self.hparams["qk_rope_head_dim"]
+
+            long_factors = rope_scaling.get('long_factor', None)
+            short_factors = rope_scaling.get('short_factor', None)
+
+            if long_factors is None or short_factors is None:
+                raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
+
+            if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
+                raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
+
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
+        if n_kv_head is not None and n_head != n_kv_head:
+            n_head //= n_kv_head
+
+        return (
+            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+            .swapaxes(1, 2)
+            .reshape(weights.shape)
+        )
+
+
+# MiniCPM-V 4.6: text tower is Qwen3.5 (linear+full hybrid attention) wrapped under
+# `model.language_model.*`; vision tower is SigLIP + a window-attention ViT merger
+# + a final DownsampleMLP merger. The same HF arch is registered twice below: once as
+# the LM (text mode) and once as the mmproj (vision mode), mirroring the Qwen3-VL setup.
+
+@ModelBase.register("MiniCPMV4_6ForConditionalGeneration")
+class MiniCPMV4_6TextModel(Qwen3_5TextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN35
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("model.merger."):
+            return None
+        # MTP tensors are not used at inference yet; align with Qwen3Next behaviour
+        if name.startswith("mtp"):
+            return None
+
+        return super().filter_tensors(item)
+
+
+@ModelBase.register("MiniCPMV4_6ForConditionalGeneration")
+class MiniCPMV4_6VisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.hparams_vision is not None:
+            # In MiniCPM-V 4.6 `vision_config.image_size` (980) describes the SigLIP
+            # positional embedding bucket grid (70 x 70), while the per-slice processing
+            # resolution is the preprocessor's `scale_resolution` (typically 448).
+            # The CLIP loader in tools/mtmd/clip.cpp consumes `clip.vision.image_size`
+            # as the slice size and warmup resolution, so report `scale_resolution` there
+            # to match the upstream MiniCPMV4_6ImageProcessorPil slicing rules.
+            scale_resolution = self.preprocessor_config.get("scale_resolution")
+            if scale_resolution is not None:
+                self.hparams_vision["image_size"] = int(scale_resolution)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+
+        # projector type string is consumed by clip_projector_type_from_string() in clip.cpp
+        # (mapped to PROJECTOR_TYPE_MINICPMV4_6).
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINICPMV4_6)
+
+        # ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension; used for slice alignment
+        self.gguf_writer.add_vision_projector_scale_factor(4)
+
+        # borrow wa_layer_indexes for vit_merger insertion point
+        insert_layer_id = int(self.global_config.get(
+            "insert_layer_id", self.hparams_vision.get("insert_layer_id", 6)))
+        self.gguf_writer.add_vision_wa_layer_indexes([insert_layer_id])
+
+        # SigLIP vision body uses gelu_pytorch_tanh, which matches ggml_gelu (tanh approx).
+        self.gguf_writer.add_vision_use_gelu(True)
+        self.gguf_writer.add_vision_attention_layernorm_eps(
+            self.hparams_vision.get("layer_norm_eps", 1e-6))
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # lm_head / MTP -> belong to the LM file
+        if name.startswith(("lm_head.", "mtp")):
+            return None
+
+        return super().filter_tensors(item)
diff --git a/conversion/minimax.py b/conversion/minimax.py
new file mode 100644
index 00000000000..4857775cbfb
--- /dev/null
+++ b/conversion/minimax.py
@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("MiniMaxM2ForCausalLM")
+class MiniMaxM2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.MINIMAXM2
+    _experts_cache: dict[int, dict[str, Tensor]] = {}
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
+        self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        # merge expert weights
+        if 'experts' in name:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            expert_cache = self._experts_cache.setdefault(bid, {})
+            expert_cache[name] = data_torch
+            expert_weights = ["w1", "w2", "w3"]
+
+            # not enough expert weights to merge
+            if len(expert_cache) < n_experts * len(expert_weights):
+                return
+
+            for w_name in expert_weights:
+                datas: list[Tensor] = []
+
+                for xid in range(n_experts):
+                    ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
+                    datas.append(expert_cache[ename])
+                    del expert_cache[ename]
+
+                data_torch = torch.stack(datas, dim=0)
+                merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
+                new_name = self.map_tensor_name(merged_name)
+                yield from super().modify_tensors(data_torch, new_name, bid)
+
+            del self._experts_cache[bid]
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/mistral.py b/conversion/mistral.py
new file mode 100644
index 00000000000..7a7d6e03933
--- /dev/null
+++ b/conversion/mistral.py
@@ -0,0 +1,201 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Callable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MistralTokenizerType, MistralVocab, _mistral_common_installed, _mistral_import_error_msg, gguf, logger
+
+from .deepseek import DeepseekV2Model
+from .llama import LlamaModel
+
+if _mistral_common_installed:
+    from mistral_common.tokens.tokenizers.base import TokenizerVersion  # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.tekken import Tekkenizer  # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.sentencepiece import SentencePieceTokenizer  # type: ignore[import-not-found, ty:unresolved-import]
+else:
+    TokenizerVersion = None  # type: ignore[assignment]
+    Tekkenizer = None  # type: ignore[assignment]
+    SentencePieceTokenizer = None  # type: ignore[assignment]
+
+
+class MistralModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.MISTRAL3
+    model_name = "Mistral"
+    hf_arch = ""
+    is_mistral_format = True
+    undo_permute = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # for compatibility, we use LLAMA arch for older models
+        # TODO: remove this once everyone migrates to newer version of llama.cpp
+        if "llama_4_scaling" not in self.hparams:
+            self.model_arch = gguf.MODEL_ARCH.LLAMA
+            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
+            self.gguf_writer.add_architecture()
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def dequant_model(self):
+        # transform quantization config into HF format
+        quant_config = self.hparams.get("quantization")
+        if quant_config is not None:
+            assert quant_config["qformat_weight"] == "fp8_e4m3"
+            self.hparams["quantization_config"] = {
+                "activation_scheme": "static",
+                "quant_method": "fp8",
+                "weight_block_size": None,
+            }
+        return super().dequant_model()
+
+    @staticmethod
+    def get_community_chat_template(vocab: MistralVocab, templates_dir: Path, is_mistral_format: bool):
+        assert TokenizerVersion is not None and Tekkenizer is not None and SentencePieceTokenizer is not None, _mistral_import_error_msg
+        assert isinstance(vocab.tokenizer, (Tekkenizer, SentencePieceTokenizer)), (
+            f"Expected Tekkenizer or SentencePieceTokenizer, got {type(vocab.tokenizer)}"
+        )
+
+        if vocab.tokenizer.version == TokenizerVersion.v1:
+            return "mistral-v1"
+        elif vocab.tokenizer.version == TokenizerVersion.v3 and vocab.tokenizer_type == MistralTokenizerType.spm:
+            return "mistral-v3"
+        elif vocab.tokenizer.version == TokenizerVersion.v3 and vocab.tokenizer_type == MistralTokenizerType.tekken:
+            return "mistral-v3-tekken"
+        elif vocab.tokenizer.version == TokenizerVersion.v7 and vocab.tokenizer_type == MistralTokenizerType.spm:
+            return "mistral-v7"
+        elif vocab.tokenizer.version == TokenizerVersion.v7 and vocab.tokenizer_type == MistralTokenizerType.tekken:
+            return "mistral-v7-tekken"
+        elif vocab.tokenizer.version == TokenizerVersion.v11:
+            template_file = "Mistral-Small-3.2-24B-Instruct-2506.jinja"
+        elif vocab.tokenizer.version == TokenizerVersion.v13:
+            template_file = "unsloth-mistral-Devstral-Small-2507.jinja"
+        else:
+            err_message = f"Unknown tokenizer type: {vocab.tokenizer_type} and version {vocab.tokenizer.version}"
+            if is_mistral_format:
+                err_message += (
+                    " . Please pass --disable-mistral-community-chat-template argument to the CLI "
+                    "if you want to skip this error and use the Mistral official `mistral-common` pre-processing library."
+                )
+            raise ValueError(err_message)
+
+        template_path = templates_dir / template_file
+        if not template_path.exists():
+            raise FileNotFoundError(f"Template file not found: {template_path}")
+
+        with open(template_path, "r", encoding="utf-8") as f:
+            template = f.read()
+
+        return template
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        MistralModel.set_mistral_config(self.gguf_writer, self.hparams)
+
+    @staticmethod
+    def set_mistral_config(gguf_writer: gguf.GGUFWriter, hparams: dict):
+        if "yarn" in hparams:
+            yarn_params = hparams["yarn"]
+            mscale_all_dim = 1.0 if not yarn_params["apply_scale"] else 0.0
+            gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            gguf_writer.add_rope_scaling_factor(yarn_params["factor"])
+            gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_params["beta"])
+            gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_params["alpha"])
+            gguf_writer.add_rope_scaling_yarn_log_mul(mscale_all_dim)
+            gguf_writer.add_rope_scaling_orig_ctx_len(yarn_params["original_max_position_embeddings"])
+
+        if "llama_4_scaling" in hparams:
+            gguf_writer.add_attn_temperature_scale(hparams["llama_4_scaling"]["beta"])
+
+
+class MistralMoeModel(DeepseekV2Model):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
+    model_name = "Mistral"
+    hf_arch = ""
+    is_mistral_format = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        logger.info("Using MistralMoeModel")
+        # remap hparams from Mistral MoE format to DeepseekV2 format
+        # we do this way to be able to reuse DeepseekV2Model set_gguf_parameters logic
+        # ref: https://github.com/vllm-project/vllm/blob/b294e28db2c5dee61bc25157664edcada8b90b31/vllm/transformers_utils/configs/mistral.py
+        config = self.hparams
+        # Mistral key -> HF key
+        config_mapping = {
+            "dim": "hidden_size",
+            "norm_eps": "rms_norm_eps",
+            "n_kv_heads": "num_key_value_heads",
+            "n_layers": "num_hidden_layers",
+            "n_heads": "num_attention_heads",
+            "hidden_dim": "intermediate_size",
+        }
+        # HF key -> (Mistral key, default value)
+        top_level_mapping_with_default = {
+            "model_type": ("model_type", "transformer"),
+            "hidden_act": ("activation", "silu"),
+            "tie_word_embeddings": ("tied_embeddings", False),
+            "max_seq_len": ("max_seq_len", config.get("max_position_embeddings", 128_000)),
+            "max_position_embeddings": ("max_position_embeddings", 128_000),
+        }
+        # mapping top-level keys
+        for key, new_key in config_mapping.items():
+            if key in config:
+                config[new_key] = config[key]
+        for new_key, (key, default_value) in top_level_mapping_with_default.items():
+            config[new_key] = config.get(key, default_value)
+        # mapping MoE-specific keys
+        moe_config_map = {
+            "route_every_n": "moe_layer_freq",
+            "first_k_dense_replace": "first_k_dense_replace",
+            "num_experts_per_tok": "num_experts_per_tok",
+            "num_experts": "n_routed_experts",
+            "expert_hidden_dim": "moe_intermediate_size",
+            "routed_scale": "routed_scaling_factor",
+            "num_shared_experts": "n_shared_experts",
+            "num_expert_groups": "n_group",
+            "num_expert_groups_per_tok": "topk_group",
+        }
+        moe = config["moe"]
+        for key, new_key in moe_config_map.items():
+            if key in moe:
+                config[new_key] = moe[key]
+        # provide missing values
+        config["topk_method"] = None
+        config["norm_topk_prob"] = True
+        config["scoring_func"] = "softmax"
+
+    def set_vocab(self):
+        self._set_vocab_mistral()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        MistralModel.set_mistral_config(self.gguf_writer, self.hparams)
+        yarn_params = self.hparams["yarn"]
+        self.gguf_writer.add_attn_temperature_length(yarn_params["original_max_position_embeddings"])
+
+        # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+        # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
+        # ref https://github.com/ggml-org/llama.cpp/pull/17945
+        self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # rename certain tensors so that we can reuse DeepseekV2Model modify_tensors logic
+        if name.endswith(".qscale_act"):
+            name = name.replace(".qscale_act", ".input_scale")
+        if name.endswith(".qscale_weight"):
+            name = name.replace(".qscale_weight", ".weight_scale")
+        if ".wkv_b." in name:
+            name = name.replace(".wkv_b.", ".kv_b_proj.")
+        if ".experts." in name:
+            name = name.replace(".experts.", ".mlp.experts.")
+            name = name.replace(".w1.", ".gate_proj.")
+            name = name.replace(".w2.", ".down_proj.")
+            name = name.replace(".w3.", ".up_proj.")
+            name = "model." + name
+
+        return super().filter_tensors((name, gen))
diff --git a/conversion/mistral3.py b/conversion/mistral3.py
new file mode 100644
index 00000000000..af9438ae705
--- /dev/null
+++ b/conversion/mistral3.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+from .deepseek import DeepseekV2Model
+from .llama import LlamaModel
+
+
+@ModelBase.register(
+    "Mistral3ForConditionalGeneration",
+    "Ministral3ForCausalLM",
+)
+class Mistral3Model(TextModel):
+    class Ministral3Model(LlamaModel):
+        model_arch = gguf.MODEL_ARCH.MISTRAL3
+
+        def set_gguf_parameters(self):
+            super().set_gguf_parameters()
+            rope_params = self.rope_parameters
+            if self.hparams.get("model_type") == "ministral3":
+                assert rope_params, "ministral3 must have 'rope_parameters' config"
+                assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
+                self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
+                self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
+
+    class Mistral4Model(DeepseekV2Model):
+        model_arch = gguf.MODEL_ARCH.MISTRAL4
+        skip_mtp = False # model contains no MTP layers, so no need to skip
+        merge_expert = False # experts are already stacked as 3D
+
+        def modify_tensors(self, data_torch, name, bid):
+            if name.endswith(".down_proj") or name.endswith(".gate_up_proj"):
+                name = name + ".weight"
+            yield from super().modify_tensors(data_torch, name, bid)
+
+    model_arch = gguf.MODEL_ARCH.MISTRAL3 # unused
+    impl: TextModel
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.hparams.get("model_type") == "mistral4":
+            self.impl = Mistral3Model.Mistral4Model(*args, **kwargs)
+        else:
+            self.impl = Mistral3Model.Ministral3Model(*args, **kwargs)
+
+    def set_vocab(self):
+        self.impl.set_vocab()
+
+    def set_gguf_parameters(self):
+        self.impl.set_gguf_parameters()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        yield from self.impl.modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        self.impl.prepare_tensors()
+
+    def write_vocab(self):
+        self.impl.write_vocab()
+
+    def write(self):
+        self.impl.write()
diff --git a/conversion/mpt.py b/conversion/mpt.py
new file mode 100644
index 00000000000..9557ab7fa64
--- /dev/null
+++ b/conversion/mpt.py
@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("MPTForCausalLM")
+class MPTModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.MPT
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_gpt2()
+        except Exception:
+            # Fallback for SEA-LION model
+            self._set_vocab_sentencepiece()
+            self.gguf_writer.add_add_bos_token(False)
+            self.gguf_writer.add_pad_token_id(3)
+            self.gguf_writer.add_eos_token_id(1)
+            self.gguf_writer.add_unk_token_id(0)
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
+        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
+        self.gguf_writer.add_head_count(self.hparams["n_heads"])
+        if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
+            self.gguf_writer.add_head_count_kv(kv_n_heads)
+        self.gguf_writer.add_layer_norm_eps(1e-5)
+        if self.hparams["attn_config"]["clip_qkv"] is not None:
+            self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
+        if self.hparams["attn_config"]["alibi"]:
+            self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
+        else:
+            self.gguf_writer.add_max_alibi_bias(0.0)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "scales" in name:
+            new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales"))
+            new_name = new_name.replace("scales", "act.scales")
+        else:
+            new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias"))
+
+        yield from super().modify_tensors(data_torch, new_name, bid)
diff --git a/conversion/nemotron.py b/conversion/nemotron.py
new file mode 100644
index 00000000000..dfeeb978582
--- /dev/null
+++ b/conversion/nemotron.py
@@ -0,0 +1,384 @@
+from __future__ import annotations
+
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf, logger
+
+from .granite import GraniteHybridModel
+
+
+@ModelBase.register(
+    "NemotronH_Nano_VL_V2",
+    "RADIOModel",
+)
+class NemotronNanoV2VLModel(MmprojModel):
+    # ViT-Huge architecture parameters for RADIO v2.5-h
+    _vit_hidden_size = 1280
+    _vit_intermediate_size = 5120
+    _vit_num_layers = 32
+    _vit_num_heads = 16
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        # RADIO config doesn't have standard ViT parameters, so they need to be constructed manually
+        vision_config = self.global_config.get("vision_config")
+        if vision_config is None:
+            return None
+        # Add ViT-H parameters
+        vision_config = {
+            **vision_config,
+            "hidden_size": self._vit_hidden_size,
+            "intermediate_size": self._vit_intermediate_size,
+            "num_hidden_layers": self._vit_num_layers,
+            "num_attention_heads": self._vit_num_heads,
+            "image_size": self.global_config.get("force_image_size", 512),
+        }
+        return vision_config
+
+    def set_gguf_parameters(self):
+        if "image_mean" not in self.preprocessor_config:
+            self.preprocessor_config["image_mean"] = [0.485, 0.456, 0.406]
+        if "image_std" not in self.preprocessor_config:
+            self.preprocessor_config["image_std"] = [0.229, 0.224, 0.225]
+
+        super().set_gguf_parameters()
+        hparams = self.global_config
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
+        self.gguf_writer.add_vision_attention_layernorm_eps(1e-6)
+        self.gguf_writer.add_vision_use_gelu(True)
+        downsample_ratio = hparams.get("downsample_ratio", 0.5)
+        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".position_embd." in new_name or "pos_embed" in new_name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if "input_conditioner" in name:
+            return None
+
+        # mtmd does not support video yet so skip tensors related to video.
+        if "radio_model.model.patch_generator.video_embedder" in name:
+            return None
+
+        if not name.startswith("vision_model.radio_model.model.") and not name.startswith("mlp1."):
+            return None
+
+        if "patch_generator.pos_embed" in name:
+            if not name.endswith(".weight"):
+                name += ".weight"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # RADIO's pos_embed doesn't have .weight suffix, but clip.cpp expects it
+        if "patch_generator.pos_embed" in name:
+            # Downsample position embeddings for fixed 512x512 image size
+            import torch.nn.functional as F
+            n_embd = self.hparams["hidden_size"]
+            image_size = self.global_config.get("force_image_size", 512)
+            patch_size = self.hparams["patch_size"]
+            target_patches_per_side = image_size // patch_size  # 32
+            max_patches_per_side = int((data_torch.shape[1]) ** 0.5)  # 128
+            if target_patches_per_side != max_patches_per_side:
+                # Reshape to grid, interpolate, flatten back
+                data_torch = data_torch.reshape(1, max_patches_per_side, max_patches_per_side, n_embd)
+                data_torch = data_torch.permute(0, 3, 1, 2).float()  # [1, n_embd, 128, 128]
+                data_torch = F.interpolate(data_torch, size=(target_patches_per_side, target_patches_per_side),
+                                           mode='bilinear', align_corners=True)
+                data_torch = data_torch.permute(0, 2, 3, 1)  # [1, 32, 32, n_embd]
+                data_torch = data_torch.reshape(1, target_patches_per_side * target_patches_per_side, n_embd)
+
+        # Reshape linear patch embedding to conv2d format for ggml_conv_2d
+        # From [n_embd, patch_size*patch_size*3] to [n_embd, 3, patch_size, patch_size]
+        if "patch_generator.embedder" in name:
+            patch_size = self.hparams["patch_size"]
+            n_embd = self.hparams["hidden_size"]
+            data_torch = data_torch.reshape(n_embd, 3, patch_size, patch_size)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("NemotronForCausalLM")
+class NemotronModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.NEMOTRON
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+        self.gguf_writer.add_pad_token_id(0)
+        self.gguf_writer.add_unk_token_id(1)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"])
+        self.gguf_writer.add_layer_norm_eps(f_norm_eps)
+
+        # * Partial RoPE
+        rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
+
+        # * RopeScaling for Nemotron
+        if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        else:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+            self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side
+        #   model.layers.{l}.input_layernorm.weight
+        #   model.layers.{l}.post_attention_layernorm.weight
+        #   model.norm.weight
+        if name.endswith("norm.weight"):
+            data_torch = data_torch + 1
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("NemotronHForCausalLM")
+class NemotronHModel(GraniteHybridModel):
+    """Hybrid mamba2/attention model from NVIDIA"""
+    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
+    is_moe: bool = False
+
+    def __init__(self, *args, **kwargs):
+        # We have to determine the correct model architecture (MoE vs non-MoE) before
+        # calling the parent __init__. This is because the parent constructor
+        # uses self.model_arch to build the tensor name map, and all MoE-specific
+        # mappings would be missed if it were called with the default non-MoE arch.
+        hparams = ModelBase.load_hparams(args[0], self.is_mistral_format)
+        has_moe_params = (
+            "num_experts_per_tok" in hparams
+            or (isinstance(hparams.get("llm_config"), dict) and "num_experts_per_tok" in hparams["llm_config"])
+        )
+        if has_moe_params:
+            self.model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
+            self.is_moe = True
+
+        super().__init__(*args, **kwargs)
+
+        # Save the top-level head_dim for later
+        self.head_dim = self.hparams.get("head_dim", self.hparams.get("attention_head_dim"))
+        assert self.head_dim is not None, "Could not find the attention head dim in config"
+
+        # Don't use expand to calculate d_inner
+        self.d_inner = self.find_hparam(["num_heads"]) * self.d_model
+
+        # Update the ssm / attn / mlp layers
+        # M: Mamba2, *: Attention, -: MLP
+        # MoE:
+        # M: Mamba2, *: Attention, E: Expert
+        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
+        if pattern is None:
+            self._ssm_layers = []
+            self._mlp_layers = []
+        elif isinstance(pattern, str):
+            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "M"]
+            self._mlp_layers = [i for i, val in enumerate(pattern) if val == ("E" if self.is_moe else "-")]
+        else:
+            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"]
+            self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"]
+
+    def get_attn_layers(self):
+        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
+        if pattern is None:
+            return []
+        assert len(pattern) == self.block_count, f"Mismatch between pattern ({len(pattern)}) and block_count ({self.block_count})!"
+        if isinstance(pattern, str):
+            return [i for i, val in enumerate(pattern) if val == "*"]
+
+        return [i for i, val in enumerate(pattern) if val == "attention"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        head_dim = self.head_dim
+        if head_dim is None:
+            raise ValueError("Could not find the attention head dim in config")
+        self.gguf_writer.add_key_length(head_dim)
+        self.gguf_writer.add_value_length(head_dim)
+
+        # Set feed_forward_length
+        # NOTE: This will trigger an override warning. This is preferable to
+        #   duplicating all the parent logic
+        if not self.is_moe:
+            n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
+            self.gguf_writer.add_feed_forward_length([
+                n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
+            ])
+        else:
+            moe_intermediate_size = self.hparams["moe_intermediate_size"]
+            self.gguf_writer.add_feed_forward_length([
+                moe_intermediate_size if i in self._mlp_layers else 0 for i in range(self.block_count)
+            ])
+            self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"])
+            self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+            self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["moe_shared_expert_intermediate_size"])
+            self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
+            self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
+            self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
+            self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+            self.gguf_writer.add_expert_group_count(self.hparams["n_group"])
+
+            # number of experts used per token (top-k)
+            if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
+                self.gguf_writer.add_expert_used_count(n_experts_used)
+
+            if (latent_size := self.hparams.get("moe_latent_size")) is not None:
+                self.gguf_writer.add_moe_latent_size(latent_size)
+
+    def set_vocab(self):
+        # The NemotronH config uses pattern characters (e.g. '-') that may not
+        # be supported by the installed transformers version. AutoTokenizer
+        # internally calls AutoConfig which triggers this parsing failure.
+        # Using trust_remote_code=True to load the model's own config class.
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+        # Pad vocab size (from Mamba2Model/GraniteHybridModel)
+        self.hparams["pad_vocab_size_multiple"] = 8 # Setting this here since GraniteHybridModel.set_vocab() isn't being invoked now.
+        # From Mamba2Model.set_vocab():
+        vocab_size = self.hparams["vocab_size"]
+        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16)
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
+        self.hparams["vocab_size"] = vocab_size
+
+        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
+
+        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    if not added_tokens_decoder[i].normalized:
+                        previous_token = token
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
+                        if previous_token != token:
+                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
+
+                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
+
+        # From TextModel.set_vocab_gpt2():
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        # The tokenizer _does_ add a BOS token (via post_processor type
+        # TemplateProcessing) but does not set add_bos_token to true in the
+        # config, so we need to explicitly override it here.
+        if not self.is_moe:
+            self.gguf_writer.add_add_bos_token(True)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if self.is_moe and bid is not None:
+            # Skip Multi-Token Prediction (MTP) tensors. These are used for
+            # for speculative decoding but we don't include them in this model
+            # conversion. See https://github.com/ggml-org/llama.cpp/pull/18886
+            if name.startswith("mtp."):
+                logger.info(f"gguf: Skipping MTP (Speculative) layer: {name}")
+                return
+
+            if name.endswith("mixer.gate.e_score_correction.bias"):
+                yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+                return
+
+            if name.endswith("mixer.dt_bias"):
+                new_name = name.replace("dt_bias", "dt.bias")
+                yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
+                return
+
+            if name.endswith("mixer.conv1d.weight"):
+                squeezed_data = data_torch.squeeze()
+                yield from ModelBase.modify_tensors(self, squeezed_data, name, bid)
+                return
+
+            if name.endswith("mixer.A_log"):
+                transformed_data = -torch.exp(data_torch)
+                reshaped_data = transformed_data.squeeze().reshape(-1, 1)
+                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
+                return
+
+            if name.endswith("mixer.D"):
+                reshaped_data = data_torch.squeeze().reshape(-1, 1)
+                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
+                return
+
+            if name.endswith("mixer.norm.weight"):
+                reshaped_data = data_torch.reshape(self.n_group, -1)
+                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
+                return
+
+            if name.find("mixer.experts") != -1:
+                n_experts = self.hparams["n_routed_experts"]
+                assert bid is not None
+
+                if self._experts is None:
+                    self._experts = [{} for _ in range(self.block_count)]
+
+                self._experts[bid][name] = data_torch
+
+                if len(self._experts[bid]) >= n_experts * 2:
+                    # merge the experts into a single tensor
+                    for w_name in ["down_proj", "up_proj"]:
+                        datas: list[Tensor] = []
+
+                        for xid in range(n_experts):
+                            ename = f"backbone.layers.{bid}.mixer.experts.{xid}.{w_name}.weight"
+                            datas.append(self._experts[bid][ename])
+                            del self._experts[bid][ename]
+
+                        data_torch = torch.stack(datas, dim=0)
+                        merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                        yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid)
+                    return
+                else:
+                    return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/olmo.py b/conversion/olmo.py
new file mode 100644
index 00000000000..1664c30e402
--- /dev/null
+++ b/conversion/olmo.py
@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+from .llama import LlamaModel
+
+
+@ModelBase.register("OlmoForCausalLM")
+@ModelBase.register("OLMoForCausalLM")
+class OlmoModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.OLMO
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_layer_norm_eps(1e-5)
+        clip_qkv = self.hparams.get("clip_qkv")
+        if clip_qkv is not None:
+            self.gguf_writer.add_clamp_kqv(clip_qkv)
+
+    # Same as super class, but permuting q_proj, k_proj
+    # Copied from: LlamaModel
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith("q_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith("k_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("SeedOssForCausalLM")
+class SeedOssModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.SEED_OSS
+
+
+@ModelBase.register("Olmo2ForCausalLM")
+@ModelBase.register("Olmo3ForCausalLM")
+class Olmo2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.OLMO2
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        if "sliding_window" in self.hparams:
+            self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+
+            sliding_window_pattern = []
+            if "layer_types" in self.hparams:
+                sliding_window_pattern = [t == "sliding_attention" for t in self.hparams["layer_types"]]
+            else:
+                # Olmo2 does not use sliding window attention.
+                # Olmo3 defaults to using sliding window for all layers except every 4th.
+                for i in range(self.hparams["num_hidden_layers"]):
+                    sliding_window_pattern.append((i + 1) % 4 != 0)
+
+            self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
+
+
+@ModelBase.register("OlmoeForCausalLM")
+class OlmoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.OLMOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_layer_norm_rms_eps(1e-5)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    # Copied from: Qwen2MoeModel
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    # Copied from: Qwen2MoeModel
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/openelm.py b/conversion/openelm.py
new file mode 100644
index 00000000000..ecc746dc348
--- /dev/null
+++ b/conversion/openelm.py
@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from typing import Any, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("OpenELMForCausalLM")
+class OpenELMModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.OPENELM
+
+    @staticmethod
+    def _make_divisible(v: float | int, divisor: int) -> int:
+        # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38
+        new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
+        # Make sure that round down does not go down by more than 10%.
+        if new_v < 0.9 * v:
+            new_v += divisor
+        return new_v
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        ffn_multipliers: list[float] = self.hparams["ffn_multipliers"]
+        ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"]
+        self._n_embd: int = self.hparams["model_dim"]
+        self._num_kv_heads: list[int] = self.hparams["num_kv_heads"]
+        self._num_query_heads: list[int] = self.hparams["num_query_heads"]
+        self._ffn_dims: list[int] = [
+            OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor)
+            for multiplier in ffn_multipliers
+        ]
+        assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
+        assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int)
+
+    # Uses the tokenizer from meta-llama/Llama-2-7b-hf
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"])
+
+    def set_gguf_parameters(self):
+        n_embd = self._n_embd
+        head_dim = self.hparams["head_dim"]
+        rot_pct = 1.0
+        assert self.block_count == len(self._num_kv_heads)
+        assert self.block_count == len(self._num_query_heads)
+        assert self.block_count == len(self._ffn_dims)
+
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_context_length(self.hparams["max_context_length"])
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(self._ffn_dims)
+        self.gguf_writer.add_head_count(self._num_query_heads)
+        self.gguf_writer.add_head_count_kv(self._num_kv_heads)
+        self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"])
+        # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30
+        self.gguf_writer.add_layer_norm_rms_eps(1e-6)
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim))
+        self.gguf_writer.add_key_length(head_dim)
+        self.gguf_writer.add_value_length(head_dim)
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        if "n_layers" in keys:
+            return self.hparams["num_transformer_layers"]
+
+        return super().find_hparam(keys, optional)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+
+        # split ff
+        if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight":
+            ff_dim = self._ffn_dims[bid]
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])
+            return
+
+        yield (self.map_tensor_name(name), data_torch)
diff --git a/conversion/orion.py b/conversion/orion.py
new file mode 100644
index 00000000000..8dfceeed1f7
--- /dev/null
+++ b/conversion/orion.py
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("OrionForCausalLM")
+class OrionModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.ORION
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        head_count = self.hparams["num_attention_heads"]
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+
+        ctx_length = 0
+        if "max_sequence_length" in self.hparams:
+            ctx_length = self.hparams["max_sequence_length"]
+        elif "max_position_embeddings" in self.hparams:
+            ctx_length = self.hparams["max_position_embeddings"]
+        elif "model_max_length" in self.hparams:
+            ctx_length = self.hparams["model_max_length"]
+        else:
+            raise ValueError("gguf: can not find ctx length parameter.")
+
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
+        self.gguf_writer.add_context_length(ctx_length)
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(head_count)
+        self.gguf_writer.add_head_count_kv(head_count_kv)
+        # note: config provides rms norm but it is actually layer norm
+        # ref:  https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
+        self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
diff --git a/conversion/pangu.py b/conversion/pangu.py
new file mode 100644
index 00000000000..42016ba0286
--- /dev/null
+++ b/conversion/pangu.py
@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import json
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("PanguEmbeddedForCausalLM")
+class PanguEmbeddedModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.PANGU_EMBED
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                if "add_prefix_space" in tokenizer_config_json:
+                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        # PanguEmbedded's hparam loaded from config.json without head_dim
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+        if hparams.get("head_dim") is None:
+            self.gguf_writer.add_key_length(rope_dim)
+            self.gguf_writer.add_value_length(rope_dim)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "lm_head.weight":
+            if self.hparams.get("tie_word_embeddings", False):
+                logger.info("Skipping tied output layer 'lm_head.weight'")
+                return
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/phi.py b/conversion/phi.py
new file mode 100644
index 00000000000..5e0d72847aa
--- /dev/null
+++ b/conversion/phi.py
@@ -0,0 +1,390 @@
+from __future__ import annotations
+
+import json
+import math
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, SentencePieceTokenTypes, TextModel, gguf, logger
+
+
+@ModelBase.register("PhiForCausalLM")
+class Phi2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.PHI2
+
+    def set_gguf_parameters(self):
+        rot_pct = self.find_hparam(["partial_rotary_factor"])
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
+
+        self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
+
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(4 * n_embd)
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head)
+        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_add_bos_token(False)
+
+
+@ModelBase.register("Phi3ForCausalLM", "Phi4ForCausalLMV")
+class Phi3MiniModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.PHI3
+
+    def set_vocab(self):
+        # Phi-4 model uses GPT2Tokenizer
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                tokenizer_class = tokenizer_config_json['tokenizer_class']
+                if tokenizer_class == 'GPT2Tokenizer':
+                    return self._set_vocab_gpt2()
+
+        from sentencepiece import SentencePieceProcessor
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        if not tokenizer_path.is_file():
+            raise ValueError(f'Error: Missing {tokenizer_path}')
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if token_id >= vocab_size:
+                        logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
+                for token_id, foken_data in added_tokens_decoder.items():
+                    token_id = int(token_id)
+                    token = foken_data["content"].encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
+        tokenizer_file = self.dir_model / 'tokenizer.json'
+        if tokenizer_file.is_file():
+            with open(tokenizer_file, "r", encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+                added_tokens = tokenizer_json.get("added_tokens", [])
+                for foken_data in added_tokens:
+                    token_id = int(foken_data["id"])
+                    token = foken_data["content"].encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
+        n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
+        rms_eps = self.find_hparam(["rms_norm_eps"])
+        max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
+        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
+        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
+        rope_dims = int(rot_pct * n_embd) // n_head
+
+        self.gguf_writer.add_context_length(max_pos_embds)
+        self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"]))
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head_kv)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
+        self.gguf_writer.add_rope_dimension_count(rope_dims)
+        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters)["rope_theta"])
+        self.gguf_writer.add_file_type(self.ftype)
+        sliding_window = self.hparams.get("sliding_window")
+        # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models
+        if sliding_window is None:
+            sliding_window = 0
+        self.gguf_writer.add_sliding_window(sliding_window)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
+        max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
+        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
+        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
+        rope_dims = int(rot_pct * n_embd) // n_head
+
+        # write rope scaling for long context (128k) model
+        rope_scaling = self.find_hparam(['rope_scaling'], True)
+        if rope_scaling is None:
+            return
+
+        scale = max_pos_embds / orig_max_pos_embds
+
+        rope_scaling_type = rope_scaling.get('rope_type', rope_scaling.get('type', '')).lower()
+        if len(rope_scaling_type) == 0:
+            raise KeyError('Missing the required key rope_scaling.type')
+
+        if rope_scaling_type == 'su' or rope_scaling_type == 'longrope':
+            attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0
+        elif rope_scaling_type == 'yarn':
+            attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0
+        else:
+            raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet')
+
+        self.gguf_writer.add_rope_scaling_attn_factors(attn_factor)
+
+        long_factors = rope_scaling.get('long_factor', None)
+        short_factors = rope_scaling.get('short_factor', None)
+
+        if long_factors is None or short_factors is None:
+            raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
+
+        if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
+            raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}. long_factors = {len(long_factors)}, short_factors = {len(short_factors)}.')
+
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
+
+
+@ModelBase.register("Phi4ForCausalLMV")
+class Phi4VisionMmprojModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+
+        self.vision_total_layers = int(self.find_vparam(self.n_block_keys))
+        if self.vision_total_layers < 2:
+            raise ValueError(
+                f"Phi-4 vision mmproj conversion requires at least 2 vision layers, got {self.vision_total_layers}"
+            )
+
+        # Phi-4 uses SigLIP2 hidden_states[-2], so export one fewer encoder block and
+        # drop post-layernorm/head weights. This makes the GGUF runtime output match
+        # the feature map consumed by the patched siglip.cpp Phi-4 projector path.
+        self.vision_export_layers = self.vision_total_layers - 1
+        self.vision_last_layer_idx = self.vision_total_layers - 1
+
+        for key in self.n_block_keys:
+            if key in self.hparams_vision:
+                self.hparams_vision[key] = self.vision_export_layers
+                break
+
+        self.block_count = self.vision_export_layers
+        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)
+
+        patch_size = self.preprocessor_config.get("patch_size")
+        if patch_size is None:
+            raise KeyError("Phi-4 vision mmproj conversion requires patch_size in preprocessor_config.json")
+
+        self.hparams_vision["patch_size"] = patch_size
+
+        pos_emb_name = next(
+            (
+                name for name in self.model_tensors
+                if name.endswith("vision_model.embeddings.position_embedding.weight")
+            ),
+            None,
+        )
+        if pos_emb_name is None:
+            raise KeyError("Phi-4 vision mmproj conversion could not find position_embedding.weight")
+
+        pos_emb_shape = self.model_tensors[pos_emb_name]().shape
+        base_grid_tokens = int(pos_emb_shape[0])
+        grid_side = math.isqrt(base_grid_tokens)
+        if grid_side * grid_side != base_grid_tokens:
+            raise ValueError(f"Unexpected Phi-4 position embedding shape: {tuple(pos_emb_shape)}")
+
+        self.hparams_vision["image_size"] = grid_side * patch_size
+
+        min_num_patches = self.preprocessor_config.get("min_num_patches", self.global_config.get("min_num_patches"))
+        max_num_patches = self.preprocessor_config.get("max_num_patches", self.global_config.get("max_num_patches"))
+        if min_num_patches is None or max_num_patches is None:
+            raise KeyError("Phi-4 vision mmproj conversion requires min_num_patches and max_num_patches")
+
+        self.min_pixels = int(min_num_patches) * patch_size * patch_size
+        self.max_pixels = int(max_num_patches) * patch_size * patch_size
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PHI4)
+        self.gguf_writer.add_vision_min_pixels(self.min_pixels)
+        self.gguf_writer.add_vision_max_pixels(self.max_pixels)
+        self.gguf_writer.add_vision_use_gelu(True)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        name = name.replace("model.vision_tower.vision_tower.", "vision_tower.")
+
+        if not name.startswith(("vision_tower.", "model.mm_projector.", "mm_projector.")):
+            return None
+
+        if ".vision_model.head." in name:
+            return None
+
+        if ".vision_model.post_layernorm." in name:
+            return None
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("vision_tower."):
+            if bid is not None and bid == self.vision_last_layer_idx:
+                return
+
+            if name.endswith("vision_model.embeddings.patch_embedding.weight"):
+                assert self.hparams_vision is not None
+                if data_torch.ndim != 2:
+                    raise ValueError(f"Unexpected Phi-4 patch embedding shape: {tuple(data_torch.shape)}")
+
+                patch_area = self.hparams_vision["patch_size"] ** 2
+                in_features = data_torch.shape[1]
+                if in_features % patch_area != 0:
+                    raise ValueError(
+                        f"Phi-4 patch embedding input dim {in_features} is not divisible by patch area {patch_area}"
+                    )
+
+                num_channels = in_features // patch_area
+                patch_size = self.hparams_vision["patch_size"]
+                data_torch = data_torch.view(data_torch.shape[0], patch_size, patch_size, num_channels)
+                data_torch = data_torch.permute(0, 3, 1, 2)
+
+            yield from super().modify_tensors(data_torch, name, bid)
+            return
+
+        if name.startswith(("model.mm_projector.", "mm_projector.")):
+            local_name = name
+            local_name = local_name.replace("model.mm_projector.", "")
+            local_name = local_name.replace("mm_projector.", "")
+
+            if not (local_name.startswith("0.") or local_name.startswith("2.")):
+                return
+
+            suffix = ".bias" if local_name.endswith(".bias") else ".weight"
+            mm_idx = int(local_name.split(".", maxsplit=1)[0])
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_idx, suffix=suffix), data_torch)
+            return
+
+        return
+
+
+@ModelBase.register("PhiMoEForCausalLM")
+class PhiMoeModel(Phi3MiniModel):
+    model_arch = gguf.MODEL_ARCH.PHIMOE
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_expert_used_count(self.find_hparam(["num_experts_per_tok", "num_experts_per_token"]))
+        self.gguf_writer.add_expert_count(self.find_hparam(["num_local_experts", "num_experts"]))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/pixtral.py b/conversion/pixtral.py
new file mode 100644
index 00000000000..acd9ce1cf70
--- /dev/null
+++ b/conversion/pixtral.py
@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from typing import Sequence
+
+from .base import gguf
+
+from .llava import LlavaVisionModel
+
+
+class PixtralModel(LlavaVisionModel):
+    model_name = "Pixtral"
+    hf_arch = ""
+    is_mistral_format = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
+
+        self.gguf_writer.add_vision_attention_layernorm_eps(
+            self.find_hparam(["norm_eps"])
+        )
+        self.gguf_writer.add_rope_freq_base(self.find_vparam(["rope_theta"]))
+
+        self.gguf_writer.add_vision_use_silu(True)
+
+        # spatial_merge_size
+        if self.find_vparam(["mm_projector_id"], optional=True) == "patch_merge":
+            self.gguf_writer.add_vision_spatial_merge_size(
+                self.find_vparam(["spatial_merge_size"])
+            )
+
+    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
+        if name == "vision_language_adapter.w_in.weight":
+            return "mm.1.weight"
+        elif name == "vision_language_adapter.w_in.bias":
+            return "mm.1.bias"
+        elif name == "vision_language_adapter.w_out.weight":
+            return "mm.2.weight"
+        elif name == "vision_language_adapter.w_out.bias":
+            return "mm.2.bias"
+        return super().map_tensor_name(name, try_suffixes)
diff --git a/conversion/plamo.py b/conversion/plamo.py
new file mode 100644
index 00000000000..c4bcbdf06bc
--- /dev/null
+++ b/conversion/plamo.py
@@ -0,0 +1,195 @@
+from __future__ import annotations
+
+import json
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("PlamoForCausalLM")
+class PlamoModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.PLAMO
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+
+        self.gguf_writer.add_context_length(4096)  # not in config.json
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(5)  # hparams["num_key_value_heads"]) is wrong
+        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def shuffle_attn_q_weight(self, data_torch):
+        assert data_torch.size() == (5120, 5120)
+        data_torch = data_torch.reshape(8, 5, 128, 5120)
+        data_torch = torch.permute(data_torch, (1, 0, 2, 3))
+        data_torch = torch.reshape(data_torch, (5120, 5120))
+        return data_torch
+
+    def shuffle_attn_output_weight(self, data_torch):
+        assert data_torch.size() == (5120, 5120)
+        data_torch = data_torch.reshape(5120, 8, 5, 128)
+        data_torch = torch.permute(data_torch, (0, 2, 1, 3))
+        data_torch = torch.reshape(data_torch, (5120, 5120))
+        return data_torch
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        new_name = self.map_tensor_name(name)
+
+        # shuffle for broadcasting of gqa in ggml_mul_mat
+        if new_name.endswith("attn_q.weight"):
+            data_torch = self.shuffle_attn_q_weight(data_torch)
+        elif new_name.endswith("attn_output.weight"):
+            data_torch = self.shuffle_attn_output_weight(data_torch)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Plamo2ForCausalLM", "PLaMo2ForCausalLM")
+class Plamo2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.PLAMO2
+
+    def set_vocab(self):
+        self._set_vocab_plamo()
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+        # Which layers are Mamba layers
+        # PLaMo 2 uses mamba_step to indicate the pattern (e.g., 2 means every other layer)
+        # This logic matches modeling_plamo.py's is_mamba function
+        mamba_step = hparams.get("mamba_step", 2)
+        mamba_enabled = hparams.get("mamba_enabled", True)
+        num_key_value_heads = []
+        num_attention_heads = []
+
+        if mamba_enabled:
+            for i in range(self.block_count):
+                if self.block_count <= (mamba_step // 2):
+                    # use attention in last layer
+                    is_mamba = (i != self.block_count - 1)
+                else:
+                    is_mamba = (i % mamba_step) != (mamba_step // 2)
+                if is_mamba:
+                    num_key_value_heads.append(0)
+                    num_attention_heads.append(0)
+                else:
+                    num_key_value_heads.append(hparams.get("num_key_value_heads", 4))
+                    num_attention_heads.append(hparams.get("num_attention_heads", 32))
+
+        if num_key_value_heads and num_attention_heads:
+            self.gguf_writer.add_head_count_kv(num_key_value_heads)
+            self.gguf_writer.add_head_count(num_attention_heads)
+
+        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 2048))
+        self.gguf_writer.add_embedding_length(hparams.get("hidden_size", 4096))
+        self.gguf_writer.add_key_length(hparams.get("hidden_size_per_head", 128))
+        self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128))
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
+        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("rope_theta", 10000))
+
+        # Mamba parameters
+        self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))
+        self.gguf_writer.add_ssm_conv_kernel(hparams.get("mamba_d_conv", 4))
+        self.gguf_writer.add_ssm_time_step_rank(hparams.get("mamba_num_heads", 64))
+        intermediate_size = hparams.get("mamba_num_heads", 64) * hparams.get("hidden_size_per_head", 128)
+        self.gguf_writer.add_ssm_inner_size(intermediate_size)
+        self.gguf_writer.add_ssm_group_count(0)
+
+        # MLP feed forward parameters (for attention layers)
+        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312))
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith(".A_log"):
+            data_torch = -torch.exp(data_torch)
+        elif name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+        elif name.endswith(".dt_norm_weight"):
+            name = name.rpartition(".dt_norm_weight")[0] + ".dt_norm.weight"
+        elif name.endswith(".B_norm_weight"):
+            name = name.rpartition(".B_norm_weight")[0] + ".B_norm.weight"
+        elif name.endswith(".C_norm_weight"):
+            name = name.rpartition(".C_norm_weight")[0] + ".C_norm.weight"
+        elif name.endswith(".k_weight"):
+            name = name.rpartition(".k_weight")[0] + ".k.weight"
+        elif name.endswith(".q_weight"):
+            name = name.rpartition(".q_weight")[0] + ".q.weight"
+        elif name.endswith(".conv1d.weight"):
+            data_torch = torch.squeeze(data_torch)  # remove (, 1, )
+            assert data_torch.ndim == 2
+        elif name.endswith(".pre_mixer_norm.weight"):
+            data_torch += 1.0
+        elif name.endswith(".post_mixer_norm.weight"):
+            data_torch += 1.0 / 5
+        elif name.endswith(".pre_mlp_norm.weight"):
+            data_torch += 1.0
+        elif name.endswith(".post_mlp_norm.weight"):
+            data_torch += 1.0 / (5**1.5)
+        elif name.endswith(".norm.weight"):
+            data_torch += 1.0
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Plamo3ForCausalLM", "PLaMo3ForCausalLM")
+class Plamo3Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.PLAMO3
+
+    def set_vocab(self):
+        self._set_vocab_plamo()
+
+        tokenizer_config_path = self.dir_model / "tokenizer_config.json"
+        tokenizer_config = {}
+
+        if tokenizer_config_path.is_file():
+            with open(tokenizer_config_path, encoding="utf-8") as f:
+                tokenizer_config = json.load(f)
+
+        chat_template = tokenizer_config.get("chat_template")
+        chat_template_jinja = self.dir_model / "chat_template.jinja"
+
+        if chat_template_jinja.is_file():
+            with open(chat_template_jinja, encoding="utf-8") as f:
+                chat_template = f.read()
+
+        if chat_template:
+            self.gguf_writer.add_chat_template(chat_template)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+        if (sliding_window := self.find_hparam(["window_size", "sliding_window"], optional=True)) is not None:
+            self.gguf_writer.add_sliding_window(sliding_window)
+            self.gguf_writer.add_sliding_window_pattern(self.hparams["sliding_window_pattern"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+
+        if name.endswith(".pre_mixer_norm.weight"):
+            data_torch = data_torch + 1.0
+        elif name.endswith(".post_mixer_norm.weight"):
+            data_torch = data_torch + 1.0 / 5
+        elif name.endswith(".pre_mlp_norm.weight"):
+            data_torch = data_torch + 1.0
+        elif name.endswith(".post_mlp_norm.weight"):
+            data_torch = data_torch + 1.0 / (5**1.5)
+        elif name.endswith((".mixer.q_norm.weight", ".mixer.k_norm.weight")):
+            data_torch = data_torch + 1.0
+        elif name.endswith(".norm.weight"):
+            data_torch = data_torch + 1.0
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/plm.py b/conversion/plm.py
new file mode 100644
index 00000000000..3fde487085b
--- /dev/null
+++ b/conversion/plm.py
@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("PLMForCausalLM")
+class PLMModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.PLM
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
+        self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
+        self.gguf_writer.add_value_length(hparams["v_head_dim"])
+        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
diff --git a/conversion/qwen.py b/conversion/qwen.py
new file mode 100644
index 00000000000..4b86404262a
--- /dev/null
+++ b/conversion/qwen.py
@@ -0,0 +1,627 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("QWenLMHeadModel")
+class QwenModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN
+
+    @staticmethod
+    def token_bytes_to_string(b):
+        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
+        byte_encoder = bytes_to_unicode()
+        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
+
+    @staticmethod
+    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
+        parts = [bytes([b]) for b in token]
+        while True:
+            min_idx = None
+            min_rank = None
+            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
+                rank = mergeable_ranks.get(pair[0] + pair[1])
+                if rank is not None and (min_rank is None or rank < min_rank):
+                    min_idx = i
+                    min_rank = rank
+            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
+                break
+            assert min_idx is not None
+            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
+        return parts
+
+    def set_vocab(self):
+        self._set_vocab_qwen()
+
+
+@ModelBase.register(
+    "Qwen2Model",
+    "Qwen2ForCausalLM",
+    "Qwen2AudioForConditionalGeneration",
+    "KORMoForCausalLM",
+    "AudioFlamingo3ForConditionalGeneration",
+    "DotsOCRForCausalLM",
+)
+class Qwen2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN2
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if self.hf_arch == "Qwen2Model":
+            name = f"model.{name}"  # map to Qwen2ForCausalLM tensors
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen2MoeForCausalLM")
+class Qwen2MoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN2MOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+        if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None:
+            self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size)
+            logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}")
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # handle aggregated expert tensors
+        # GGUF stores dimensions reversed from PyTorch, so:
+        # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
+        # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
+        # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
+        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
+            mapped = f"{name}.weight" if not name.endswith(".weight") else name
+            # HF: [n_expert, n_embd, n_ff] -> GGML: {n_ff, n_embd, n_expert}
+            yield from super().modify_tensors(data_torch, mapped, bid)
+            return
+
+        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
+            if data_torch.ndim < 3 or data_torch.shape[-2] % 2 != 0:
+                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
+            # HF: [n_expert, 2*n_ff, n_embd] -> split on dim=-2
+            n_ff = data_torch.shape[-2] // 2
+            gate = data_torch[..., :n_ff, :].contiguous()
+            up = data_torch[..., n_ff:, :].contiguous()
+            # gate/up: [n_expert, n_ff, n_embd] -> GGML: {n_embd, n_ff, n_expert}
+            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
+            mapped_gate = f"{base_name}.gate_proj.weight"
+            mapped_up = f"{base_name}.up_proj.weight"
+            yield from super().modify_tensors(gate, mapped_gate, bid)
+            yield from super().modify_tensors(up, mapped_up, bid)
+            return
+
+        if name.find("experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("Qwen3ForCausalLM", "Qwen3Model")
+class Qwen3Model(Qwen2Model):
+    model_arch = gguf.MODEL_ARCH.QWEN3
+
+    # extra logic for rerank models
+    is_rerank: bool = False
+    is_tied_embeddings: bool = False
+    token_false_id: int | None = None
+    token_true_id: int | None = None
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # track for intern-s1-mini
+        hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
+        self.origin_hf_arch = hparams.get('architectures', [None])[0]
+
+        if self._is_qwen3_reranker():
+            self._find_rerank_config()
+
+    def _is_qwen3_reranker(self) -> bool:
+        readme_path = self.dir_model / "README.md"
+        readme_text = ""
+        if readme_path.exists():
+            with readme_path.open("r", encoding="utf-8") as f:
+                readme_text = f.read()
+
+        name_hints = [
+            str(self.dir_model.name),
+            str(self.hparams.get("_name_or_path", "")),
+            str(self.hparams.get("model_type", "")),
+            str(self.origin_hf_arch or ""),
+        ]
+        name_hints = [hint.lower() for hint in name_hints if hint]
+
+        if "# qwen3-reranker" in readme_text.lower() or "# qwen3-vl-reranker" in readme_text.lower():
+            return True
+
+        if any("qwen3-reranker" in hint or "qwen3-vl-reranker" in hint for hint in name_hints):
+            return True
+
+        return "sequenceclassification" in (self.origin_hf_arch or "").lower()
+
+    def set_vocab(self):
+        # deal with intern-s1-mini
+        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
+            self._set_vocab_interns1()
+            return
+
+        super().set_vocab()
+
+    def _find_rerank_config(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+
+        self.is_rerank = True
+        self.is_tied_embeddings = self.hparams.get("tie_word_embeddings", False)
+        self.token_false_id = tokenizer.convert_tokens_to_ids("no")  # ty: ignore[unresolved-attribute, invalid-assignment]
+        self.token_true_id = tokenizer.convert_tokens_to_ids("yes")  # ty: ignore[unresolved-attribute, invalid-assignment]
+        self.sep_token_id = tokenizer.convert_tokens_to_ids("|")  # ty: ignore[unresolved-attribute]
+
+        assert self.token_false_id is not None and self.token_true_id is not None
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.is_rerank:
+            self.gguf_writer.add_pooling_type(gguf.PoolingType.RANK)
+            self.gguf_writer.add_classifier_output_labels(["yes", "no"])
+            self.gguf_writer.add_chat_template([{
+                "name": "rerank",
+                "template": "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n"
+                            "<|im_start|>user\n<Instruct>: Given a web search query, retrieve relevant passages that answer the query\n<Query>: {query}\n<Document>: {document}<|im_end|>\n"
+                            "<|im_start|>assistant\n<think>\n\n</think>\n\n"
+            }])
+
+    def _get_cls_out_tensor(self, data_torch: Tensor) -> Tensor:
+        # extract "yes" and "no" tokens from the output lm_head tensor
+        false_row = data_torch[self.token_false_id]
+        true_row = data_torch[self.token_true_id]
+        return torch.stack([true_row, false_row], dim=0)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if self.is_rerank:
+            is_tied_head = self.is_tied_embeddings and "embed_tokens" in name
+            is_real_head = not self.is_tied_embeddings and "lm_head" in name
+            if is_tied_head or is_real_head:
+                cls_out_head = (
+                    gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.CLS_OUT] + ".weight",
+                    self._get_cls_out_tensor(data_torch),
+                )
+                yield cls_out_head
+                if is_tied_head:
+                    yield from super().modify_tensors(data_torch, name, bid)
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3MoeForCausalLM")
+class Qwen3MoeModel(Qwen2MoeModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        hparams = ModelBase.load_hparams(self.dir_model, False)
+        self.origin_hf_arch = hparams.get('architectures', [None])[0]
+
+    def set_vocab(self):
+        # deal with intern-s1
+        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
+            self._set_vocab_interns1()
+            return
+
+        super().set_vocab()
+
+
+@ModelBase.register("Qwen3NextForCausalLM")
+class Qwen3NextModel(Qwen2MoeModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3NEXT
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_ssm_conv_kernel(self.hparams["linear_conv_kernel_dim"])
+        self.gguf_writer.add_ssm_state_size(self.hparams["linear_key_head_dim"])
+        self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
+        self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
+        self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
+        self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("mtp"):
+            # ignore MTP layers for now
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith(".A_log"):
+            data_torch = -torch.exp(data_torch)
+        elif name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+        elif "conv1d" in name:
+            data_torch = data_torch.squeeze()
+        elif name.endswith("norm.weight") and not name.endswith("linear_attn.norm.weight"):
+            data_torch = data_torch + 1
+
+        if "in_proj_qkvz.weight" in name:
+            # original order:  [q, k, v, z] * head_count
+            # corrected order: [q * head_count, k * head_count, v * head_count, z * head_count]
+            head_k_dim = self.hparams["linear_key_head_dim"]
+            head_v_dim = self.hparams["linear_value_head_dim"]
+            num_v_heads = self.hparams["linear_num_value_heads"]
+            num_k_heads = self.hparams["linear_num_key_heads"]
+            hidden_size = self.hparams["hidden_size"]
+            split_arg_list_qkvz = [
+                head_k_dim, # q partition
+                head_k_dim, # k partition
+                (num_v_heads // num_k_heads * head_v_dim), # v partition
+                (num_v_heads // num_k_heads * head_v_dim), # z partition
+            ]
+            # view as (n_embd, head_count, [q+k+v+z])
+            data_torch = data_torch.permute(1, 0).contiguous()
+            data_torch = data_torch.view(-1, num_k_heads, sum(split_arg_list_qkvz))
+            # split into q, k, v, z
+            q, k, v, z = torch.split(data_torch, split_arg_list_qkvz, dim=-1)
+            # flatten dim + head_count
+            q = q.contiguous().view(hidden_size, -1)
+            k = k.contiguous().view(hidden_size, -1)
+            v = v.contiguous().view(hidden_size, -1)
+            z = z.contiguous().view(hidden_size, -1)
+            # stack back
+            qkv = torch.cat([q, k, v], dim=-1).permute(1, 0).contiguous()
+            z = z.permute(1, 0).contiguous()
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV,  bid, ".weight"), qkv)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, bid, ".weight"), z)
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("RND1")
+class RND1Model(Qwen2MoeModel):
+    model_arch = gguf.MODEL_ARCH.RND1
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # RND1 specific parameters
+        # RND1 uses bidirectional attention
+        self.gguf_writer.add_causal_attention(False)
+
+        if (mask_token_id := self.hparams.get("mask_token_id")) is not None:
+            self.gguf_writer.add_mask_token_id(mask_token_id)
+
+
+class _LinearAttentionVReorderBase(Qwen3NextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3NEXT  # overridden by subclasses
+    """reorders V heads from grouped to tiled order for ggml broadcast
+
+    see https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
+
+    Linear attention may has num_k_heads < num_v_heads. The HF weights store
+    V heads grouped by K head: [G0_v0..v{r-1}, G1_v0..v{r-1}, ...].
+    ggml binary ops use tiled broadcast: [K0, K1, ..., K0, K1, ...].
+    We reorder V heads to tiled order so ggml_repeat can replace the expensive
+    interleaved repeat: [G0_v0, G1_v0, ..., G0_v1, G1_v1, ...].
+    """
+
+    @staticmethod
+    def _reorder_v_heads(tensor: Tensor, dim: int, num_k_heads: int, num_v_per_k: int, head_dim: int) -> Tensor:
+        """Reorder V heads from grouped (by K head) to tiled order along the given dimension."""
+        shape = list(tensor.shape)
+        if dim < 0:
+            dim += len(shape)
+        new_shape = shape[:dim] + [num_k_heads, num_v_per_k, head_dim] + shape[dim + 1:]
+        tensor = tensor.reshape(*new_shape)
+        perm = list(range(len(new_shape)))
+        perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
+        return tensor.permute(*perm).contiguous().reshape(*shape)
+
+    def _transform_nvfp4_weight(self, name: str, weight: Tensor, scale: Tensor) -> tuple[Tensor, Tensor]:
+        if not name.endswith((
+            ".linear_attn.in_proj_qkv.weight",
+            ".linear_attn.in_proj_z.weight",
+            ".linear_attn.in_proj_a.weight",
+            ".linear_attn.in_proj_b.weight",
+            ".linear_attn.out_proj.weight",
+        )):
+            return weight, scale
+
+        num_k_heads = self.hparams["linear_num_key_heads"]
+        num_v_heads = self.hparams["linear_num_value_heads"]
+        head_k_dim = self.hparams["linear_key_head_dim"]
+        head_v_dim = self.hparams["linear_value_head_dim"]
+        num_v_per_k = num_v_heads // num_k_heads
+
+        def unpack_nibbles(qs: Tensor) -> Tensor:
+            lo = torch.bitwise_and(qs, 0x0F)
+            hi = torch.bitwise_right_shift(qs, 4)
+            return torch.stack((lo, hi), dim=-1).reshape(*qs.shape[:-1], qs.shape[-1] * 2)
+
+        def pack_nibbles(codes: Tensor) -> Tensor:
+            codes = codes.reshape(*codes.shape[:-1], codes.shape[-1] // 2, 2)
+            lo = torch.bitwise_and(codes[..., 0], 0x0F)
+            hi = torch.bitwise_left_shift(torch.bitwise_and(codes[..., 1], 0x0F), 4)
+            return torch.bitwise_or(lo, hi).contiguous()
+
+        def apply_col_perm(qs: Tensor, scales: Tensor, col_perm: Tensor) -> tuple[Tensor, Tensor]:
+            assert qs.ndim >= 2
+            assert scales.ndim >= 2
+
+            k = qs.shape[-1] * 2
+            assert col_perm.numel() == k
+            assert k % 16 == 0
+
+            group_cols = col_perm.reshape(-1, 16)
+            group_starts = group_cols[:, 0]
+            expected = group_starts.unsqueeze(1) + torch.arange(16, dtype=col_perm.dtype)
+            assert torch.equal(group_cols, expected)
+            assert torch.all(group_starts % 16 == 0)
+
+            group_perm = (group_starts // 16).to(dtype=torch.long)
+            expected_groups = torch.arange(scales.shape[-1], dtype=torch.long)
+            assert group_perm.numel() == scales.shape[-1]
+            assert torch.equal(torch.sort(group_perm).values, expected_groups)
+
+            codes = unpack_nibbles(qs)
+            codes = codes.index_select(-1, col_perm.to(device=qs.device, dtype=torch.long))
+            qs = pack_nibbles(codes)
+            scales = scales.index_select(-1, group_perm.to(device=scales.device))
+            return qs, scales
+
+        def reorder_rows(qs: Tensor, scales: Tensor, head_dim: int) -> tuple[Tensor, Tensor]:
+            row_perm = self._reorder_v_heads(
+                torch.arange(num_v_heads * head_dim, dtype=torch.long).unsqueeze(-1),
+                0, num_k_heads, num_v_per_k, head_dim,
+            ).squeeze(-1)
+            return (
+                qs.index_select(0, row_perm.to(device=qs.device)),
+                scales.index_select(0, row_perm.to(device=scales.device)),
+            )
+
+        if name.endswith(".linear_attn.in_proj_qkv.weight"):
+            q_dim = head_k_dim * num_k_heads
+            k_dim = head_k_dim * num_k_heads
+            q = weight[:q_dim]
+            k = weight[q_dim:q_dim + k_dim]
+            v = weight[q_dim + k_dim:]
+            q_scale = scale[:q_dim]
+            k_scale = scale[q_dim:q_dim + k_dim]
+            v_scale = scale[q_dim + k_dim:]
+            v, v_scale = reorder_rows(v, v_scale, head_v_dim)
+            return torch.cat([q, k, v], dim=0), torch.cat([q_scale, k_scale, v_scale], dim=0)
+
+        if name.endswith(".linear_attn.in_proj_z.weight"):
+            weight, scale = reorder_rows(weight, scale, head_v_dim)
+        elif name.endswith((".linear_attn.in_proj_a.weight", ".linear_attn.in_proj_b.weight")):
+            weight, scale = reorder_rows(weight, scale, 1)
+        elif name.endswith(".linear_attn.out_proj.weight"):
+            col_perm = self._reorder_v_heads(
+                torch.arange(num_v_heads * head_v_dim, dtype=torch.long).unsqueeze(0),
+                1, num_k_heads, num_v_per_k, head_v_dim,
+            ).squeeze(0)
+            weight, scale = apply_col_perm(weight, scale, col_perm)
+
+        return weight, scale
+
+    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
+        weight, scale = self._transform_nvfp4_weight(name, weight, scale)
+        super()._repack_nvfp4(name, weight, scale, scale2, input_scale)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        num_k_heads = self.hparams.get("linear_num_key_heads", 0)
+        num_v_heads = self.hparams.get("linear_num_value_heads", 0)
+
+        if num_k_heads > 0 and num_v_heads > 0 and num_k_heads != num_v_heads and "linear_attn." in name:
+            head_k_dim = self.hparams["linear_key_head_dim"]
+            head_v_dim = self.hparams["linear_value_head_dim"]
+            num_v_per_k = num_v_heads // num_k_heads
+
+            if ".in_proj_qkv." in name:
+                # QKV weight: reorder only the V rows
+                q_dim = head_k_dim * num_k_heads
+                k_dim = head_k_dim * num_k_heads
+                q = data_torch[:q_dim]
+                k = data_torch[q_dim:q_dim + k_dim]
+                v = data_torch[q_dim + k_dim:]
+                v = self._reorder_v_heads(v, 0, num_k_heads, num_v_per_k, head_v_dim)
+                data_torch = torch.cat([q, k, v], dim=0)
+
+            elif ".in_proj_z." in name:
+                # Z gate weight: reorder rows (num_v_heads * head_v_dim)
+                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, head_v_dim)
+
+            elif ".in_proj_b." in name or ".in_proj_a." in name:
+                # Beta/Alpha weight: reorder rows (num_v_heads, head_dim=1)
+                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, 1)
+
+            elif ".A_log" in name or ".dt_bias" in name or ".dt_proj" in name:
+                # A_log / dt_bias: 1D parameters with num_v_heads elements
+                if data_torch.ndim == 1:
+                    data_torch = self._reorder_v_heads(
+                        data_torch.unsqueeze(-1), 0, num_k_heads, num_v_per_k, 1
+                    ).squeeze(-1)
+                else:
+                    data_torch = self._reorder_v_heads(data_torch, -1, num_k_heads, num_v_per_k, 1)
+
+            elif ".conv1d" in name:
+                # Conv1d kernel: reorder only the V channel portion
+                data = data_torch.squeeze()
+                qk_channels = head_k_dim * num_k_heads * 2
+                qk_part = data[:qk_channels]
+                v_part = data[qk_channels:]
+                v_part = self._reorder_v_heads(v_part, 0, num_k_heads, num_v_per_k, head_v_dim)
+                data_torch = torch.cat([qk_part, v_part], dim=0)
+
+            elif ".out_proj." in name:
+                # Out projection weight: reorder columns (input dimension)
+                data_torch = self._reorder_v_heads(data_torch, 1, num_k_heads, num_v_per_k, head_v_dim)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+class _Qwen35MRopeMixin:
+    # Qwen3.5 always applies interleaved MRoPE (see Qwen3_5RotaryEmbedding in transformers);
+    # the upstream default mrope_section is [11, 11, 10] and llama.cpp's QWEN35 / QWEN35MOE
+    # loaders treat qwen35.rope.dimension_sections as required, so make sure it is always
+    # written even when a particular checkpoint omits the field in `rope_parameters`.
+    _QWEN35_DEFAULT_MROPE_SECTION = [11, 11, 10, 0]
+
+    gguf_writer: gguf.GGUFWriter
+    rope_parameters: dict
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()  # ty: ignore[unresolved-attribute]
+        if "mrope_section" not in self.rope_parameters:
+            self.gguf_writer.add_rope_dimension_sections(self._QWEN35_DEFAULT_MROPE_SECTION)
+
+
+class _Qwen35MtpMixin:
+    """Shared MTP wiring for Qwen3.5/3.6 text variants. The HF config carries
+    the MTP block under `mtp_num_hidden_layers` and the tensors under
+    `mtp.*`; we extend block_count, emit the nextn metadata key, and remap
+    `mtp.*` to the standard layer-indexed nextn naming so the existing
+    tensor_map handles them."""
+
+    hparams: dict[str, Any]
+    model_arch: gguf.MODEL_ARCH
+    gguf_writer: gguf.GGUFWriter
+    block_count: int
+    tensor_map: gguf.TensorNameMap
+    no_mtp: bool
+    mtp_only: bool
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.block_count = self.hparams["num_hidden_layers"]
+        if not self.no_mtp:
+            self.block_count += self.hparams.get("mtp_num_hidden_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    @classmethod
+    def filter_tensors(cls, item):
+        name, _ = item
+        if name.startswith("mtp."):
+            if cls.no_mtp:
+                return None
+            return item
+        if cls.mtp_only:
+            canonical = name.replace("language_model.", "")
+            keep = canonical in (
+                "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+                "embed_tokens.weight", "norm.weight",
+            )
+            if not keep:
+                return None
+        return super().filter_tensors(item)  # ty: ignore[unresolved-attribute]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()  # ty: ignore[unresolved-attribute]
+        if self.no_mtp:
+            return
+        if (n := self.hparams.get("mtp_num_hidden_layers", 0)) > 0:
+            self.gguf_writer.add_nextn_predict_layers(n)
+
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)  # ty: ignore[unresolved-attribute]
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,                  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)    # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("mtp."):
+            n_layer = self.hparams["num_hidden_layers"]
+            if name.find("layers.") != -1:
+                assert bid is not None
+                name = name.replace(f"mtp.layers.{bid}", f"model.layers.{bid + n_layer}")
+            else:
+                remapper = {
+                    "mtp.fc":                    "model.layers.{bid}.eh_proj",
+                    "mtp.pre_fc_norm_embedding": "model.layers.{bid}.enorm",
+                    "mtp.pre_fc_norm_hidden":    "model.layers.{bid}.hnorm",
+                    "mtp.norm":                  "model.layers.{bid}.shared_head.norm",
+                }
+                stem   = Path(name).stem
+                suffix = Path(name).suffix
+                tmpl   = remapper[stem] + suffix
+                for b in range(n_layer, self.block_count):
+                    yield from super().modify_tensors(data_torch, tmpl.format(bid=b), b)  # ty: ignore[unresolved-attribute]
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)  # ty: ignore[unresolved-attribute]
+
+
+@ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM")
+class Qwen3_5TextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.QWEN35
+
+
+@ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM")
+class Qwen3_5MoeTextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.QWEN35MOE
diff --git a/conversion/qwen3vl.py b/conversion/qwen3vl.py
new file mode 100644
index 00000000000..9f11757697f
--- /dev/null
+++ b/conversion/qwen3vl.py
@@ -0,0 +1,360 @@
+from __future__ import annotations
+
+import json
+
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf, logger
+
+from .qwen import Qwen3Model, Qwen3MoeModel
+from .qwenvl import Qwen25AudioModel
+
+
+@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration", "Qwen3_5ForConditionalGeneration", "Qwen3_5MoeForConditionalGeneration")
+class Qwen3VLVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.hparams_vision is None:
+            logger.info("No vision config found, skipping vision tensor processing")
+            return
+
+        # Compute image_size if not present
+        if "image_size" not in self.hparams_vision:
+            # For Qwen3VL/Qwen3VLMoe, compute from num_position_embeddings
+            num_pos = self.hparams_vision.get("num_position_embeddings", 2304)
+            patch_size = self.hparams_vision.get("patch_size", 16)
+            # num_position_embeddings = (image_size / patch_size) ** 2
+            # So image_size = sqrt(num_position_embeddings) * patch_size
+            image_size = int(num_pos**0.5 * patch_size)
+            self.hparams_vision["image_size"] = image_size
+
+        # Rename config values for compatibility
+        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
+        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")
+
+        self.is_deepstack_layers = [False] * int(self.hparams_vision["num_hidden_layers"] or 0)
+        for idx in self.hparams_vision.get("deepstack_visual_indexes", []):
+            self.is_deepstack_layers[idx] = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        # in case mixed modalities, the arch will be handled by subclass
+        if not self.has_audio_encoder:
+            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN3VL)
+        self.gguf_writer.add_vision_use_gelu(True)
+
+        if self.hparams_vision is not None:
+            merge_size = self.hparams_vision.get("spatial_merge_size")
+            if merge_size is not None:
+                self.gguf_writer.add_vision_spatial_merge_size(int(merge_size))
+
+        # Use text config's rms_norm_eps for vision attention layernorm eps
+        rms_norm_eps = self.global_config.get("text_config", {}).get("rms_norm_eps", 1e-6)
+        self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)
+
+        if self.is_deepstack_layers:
+            self.gguf_writer.add_vision_is_deepstack_layers(self.is_deepstack_layers)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Skip text model tensors
+        if name.startswith("lm_head."):
+            return None
+
+        # Skip MTP tensors
+        if name.startswith("mtp."):
+            return None
+
+        if name.startswith("model.visual."):
+            name = name.replace("model.visual.", "visual.", 1)
+
+        if not name.startswith("visual."):
+            return None
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        assert self.hparams_vision is not None
+
+        if name.startswith("visual.deepstack_merger_list."):
+            prefix, rest = name.split(".", maxsplit=3)[2:]
+            # prefix is the layer index, convert to absolute clip layer index!
+            idx = self.hparams_vision.get("deepstack_visual_indexes", [])[int(prefix)]
+            target = rest
+
+            tensor_type: gguf.MODEL_TENSOR
+            if target.startswith("norm."):
+                tensor_type = gguf.MODEL_TENSOR.V_DS_NORM
+                suffix = target.split(".", 1)[1]
+            elif target.startswith("linear_fc1."):
+                tensor_type = gguf.MODEL_TENSOR.V_DS_FC1
+                suffix = target.split(".", 1)[1]
+            elif target.startswith("linear_fc2."):
+                tensor_type = gguf.MODEL_TENSOR.V_DS_FC2
+                suffix = target.split(".", 1)[1]
+            else:
+                raise ValueError(f"Unexpected deepstack tensor: {name}")
+
+            new_name = self.format_tensor_name(tensor_type, idx, suffix=f".{suffix}")
+            yield from super().modify_tensors(data_torch, new_name, bid)
+            return
+
+        if name.startswith("visual.merger."):
+            suffix = name.split(".", 2)[2]
+            if suffix.startswith("linear_fc"):
+                fc_idx_str, tail = suffix.split(".", 1)
+                fc_num = int(fc_idx_str.replace("linear_fc", ""))
+                # Qwen3VL has linear_fc1 and linear_fc2
+                # Map to indices 0 and 2 (matching Qwen2VL which uses indices 0 and 2)
+                if fc_num == 1:
+                    fc_idx = 0
+                elif fc_num == 2:
+                    fc_idx = 2
+                else:
+                    raise ValueError(f"unexpected fc index {fc_num} in {name}")
+                new_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, fc_idx, suffix=f".{tail}")
+            elif suffix.startswith("norm."):
+                new_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_POST_NORM, suffix=f".{suffix.split('.', 1)[1]}")
+            else:
+                raise ValueError(f"Unexpected merger tensor: {name}")
+            yield (new_name, data_torch)
+            return
+
+        if name == "visual.patch_embed.proj.weight":
+            # split Conv3D into Conv2Ds along temporal dimension
+            c1, c2, kt, _, _ = data_torch.shape
+            del c1, c2
+            if kt != 2:
+                raise ValueError("Current implementation only supports temporal_patch_size of 2")
+            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight", data_torch[:, :, 0, ...])
+            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...])
+            return
+
+        if name == "visual.patch_embed.proj.bias":
+            # Include the bias - it's used by the C++ code
+            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".bias", data_torch)
+            return
+
+        yield from MmprojModel.modify_tensors(self, data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
+class Qwen3OmniMmprojModel(Qwen3VLVisionModel, Qwen25AudioModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        if self.has_vision_encoder:
+            return self.global_config["thinker_config"].get("vision_config")
+        else:
+            return None
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        if self.has_audio_encoder:
+            return self.global_config["thinker_config"].get("audio_config")
+        else:
+            return None
+
+    def set_gguf_parameters(self):
+        if self.has_vision_encoder:
+            Qwen3VLVisionModel.set_gguf_parameters(self)
+            self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.QWEN3VL)
+        if self.has_audio_encoder:
+            Qwen25AudioModel.set_gguf_parameters(self)
+            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3A)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Skip text model tensors
+        if name.startswith("lm_head."):
+            return None
+
+        # Skip MTP tensors
+        if name.startswith("mtp."):
+            return None
+
+        if name.startswith("model.visual."):
+            name = name.replace("model.visual.", "visual.", 1)
+
+        if name.startswith("thinker.audio_tower."):
+            name = name.replace("thinker.audio_tower.", "audio_tower.", 1)
+
+        if "visual." not in name and "audio_tower." not in name:
+            return None
+
+        return MmprojModel.filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "visual." in name:
+            if not self.has_vision_encoder:
+                raise ValueError(f"Model does not have vision encoder, but found tensor {name}")
+            # need to transform vision tensor naming, so that modify_tensors() logic can be used correctly
+            name = name.replace("thinker.visual.", "model.visual.")
+            if ".merger_list." in name:
+                name = name.replace(".merger_list.", ".deepstack_merger_list.")
+                name = name.replace(".ln_q", ".norm")
+                name = name.replace(".mlp.0", ".linear_fc1")
+                name = name.replace(".mlp.2", ".linear_fc2")
+            elif ".merger." in name:
+                name = name.replace(".ln_q", ".norm")
+                name = name.replace(".mlp.0", ".linear_fc1")
+                name = name.replace(".mlp.2", ".linear_fc2")
+            yield from Qwen3VLVisionModel.modify_tensors(self, data_torch, name, bid)
+        elif "audio_tower." in name:
+            if not self.has_audio_encoder:
+                raise ValueError(f"Model does not have audio encoder, but found tensor {name}")
+            if "conv2d" in name and name.endswith(".bias"):
+                # transform conv2d bias [n_embd] --> [1, 1, n_embd]
+                data_torch = data_torch.unsqueeze(-1).unsqueeze(-1)
+            yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3ASRForConditionalGeneration")
+class Qwen3ASRMmprojModel(Qwen3OmniMmprojModel):
+    has_audio_encoder = True
+    has_vision_encoder = False
+
+
+@ModelBase.register("Glm4vForConditionalGeneration", "Glm4vMoeForConditionalGeneration", "GlmOcrForConditionalGeneration")
+class Glm4VVisionModel(Qwen3VLVisionModel):
+    def set_gguf_parameters(self):
+        MmprojModel.set_gguf_parameters(self) # skip Qwen3VLVisionModel parameters
+        assert self.hparams_vision is not None
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLM4V)
+
+        hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
+        if hidden_act == "gelu":
+            self.gguf_writer.add_vision_use_gelu(True)
+        elif hidden_act == "silu":
+            self.gguf_writer.add_vision_use_silu(True)
+
+        rms_norm_eps = self.hparams_vision.get("rms_norm_eps", 1e-5)
+        self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("visual.merger."):
+            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+            return
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3VLForConditionalGeneration")
+class Qwen3VLTextModel(Qwen3Model):
+    model_arch = gguf.MODEL_ARCH.QWEN3VL
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if "thinker_config" in self.hparams:
+            vision_config = self.hparams["thinker_config"].get("vision_config", {})
+        else:
+            vision_config = self.hparams.get("vision_config", {})
+        deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
+        self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        name = name.replace("thinker.", "")
+
+        return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("Qwen3VLMoeForConditionalGeneration")
+class Qwen3VLMoeTextModel(Qwen3MoeModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        vision_config = self.hparams.get("vision_config", {})
+        deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
+        self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        name = name.replace("thinker.", "")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Qwen3VL has transposed packed tensors, so we treat it differently from general Qwen2MoE packed tensors
+        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
+            mapped = f"{name}.weight" if not name.endswith(".weight") else name
+            permuted = data_torch.permute(0, 2, 1).contiguous()
+            yield from ModelBase.modify_tensors(self, permuted, mapped, bid)
+            return
+
+        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
+            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
+                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
+            split_dim = data_torch.shape[-1] // 2
+            gate = data_torch[..., :split_dim].contiguous()
+            up = data_torch[..., split_dim:].contiguous()
+            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
+            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
+            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
+            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
+            base_name = name.removesuffix(".weight")
+            base = base_name.rsplit('.', 1)[0]
+            mapped_gate = f"{base}.gate_proj.weight"
+            mapped_up = f"{base}.up_proj.weight"
+            perm_gate = gate.permute(0, 2, 1).contiguous()
+            perm_up = up.permute(0, 2, 1).contiguous()
+            yield from ModelBase.modify_tensors(self, perm_gate, mapped_gate, bid)
+            yield from ModelBase.modify_tensors(self, perm_up, mapped_up, bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
+class Qwen3OmniMoeTextModel(Qwen3VLMoeTextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
+
+    def set_vocab(self):
+        super().set_vocab()
+        # correct BOS/EOS tokens
+        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+            tokenizer_config = json.load(f)
+            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
+            for token_id, data in added_tokens.items():
+                if data.get("content") == "<|im_end|>":
+                    self.gguf_writer.add_bos_token_id(int(token_id))
+                    self.gguf_writer.add_eos_token_id(int(token_id))
+                    break
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_num_deepstack_layers(0)
+
+
+@ModelBase.register("Qwen3ASRForConditionalGeneration")
+class Qwen3ASRTextModel(Qwen3VLTextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3VL
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_num_deepstack_layers(0)
+
+    def set_vocab(self):
+        super().set_vocab()
+        # fix chat template, use correct chatml format
+        self.gguf_writer.add_chat_template("{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}")
+        # correct BOS/EOS tokens
+        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+            tokenizer_config = json.load(f)
+            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
+            for token_id, data in added_tokens.items():
+                if data.get("content") == "<|im_end|>":
+                    self.gguf_writer.add_bos_token_id(int(token_id))
+                    self.gguf_writer.add_eos_token_id(int(token_id))
+                    break
diff --git a/conversion/qwenvl.py b/conversion/qwenvl.py
new file mode 100644
index 00000000000..7befd0c8d81
--- /dev/null
+++ b/conversion/qwenvl.py
@@ -0,0 +1,200 @@
+from __future__ import annotations
+
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import numpy as np
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf
+
+
+@ModelBase.register(
+    "Qwen2VLModel",
+    "Qwen2VLForConditionalGeneration",
+    "Qwen2_5_VLForConditionalGeneration",
+    "Qwen2_5OmniModel",
+)
+class Qwen2VLModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN2VL
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("thinker."):
+            name = name.replace("thinker.", "")
+
+        return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("Qwen2VLModel", "Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration")
+class Qwen2VLVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
+        # rename config.json values
+        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
+        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")
+        if "embed_dim" in self.hparams_vision: # qwen2vl
+            self.hparams_vision["intermediate_size"] = self.hparams_vision.get("hidden_size")
+            self.hparams_vision["hidden_size"] = self.hparams_vision.get("embed_dim")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+        hparams = self.hparams_vision
+        model_type = self.global_config['model_type']
+        if model_type == 'qwen2_vl':
+            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2VL)
+        elif model_type == 'qwen2_5_vl' or model_type == 'qwen2_5_omni':
+            if model_type == 'qwen2_5_omni':
+                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
+            else:
+                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25VL)
+            self.gguf_writer.add_vision_use_silu(True)
+            # find n_wa_pattern (window attention pattern)
+            fullatt_block_indexes = hparams.get("fullatt_block_indexes")
+            assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for qwen2_5_vl"
+            n_wa_pattern = fullatt_block_indexes[0] + 1
+            # validate n_wa_pattern
+            for i in range(1, len(fullatt_block_indexes)):
+                if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern:
+                    raise ValueError(f"Invalid fullatt_block_indexes: {fullatt_block_indexes}")
+            self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern)
+        else:
+            raise ValueError(f"Unknown QwenVL model type: {self.global_config['model_type']}")
+        # default values below are taken from HF tranformers code
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6))
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".position_embd." in new_name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not name.startswith("visual."):
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # split QKV tensors if needed
+        if ".qkv." in name:
+            if data_torch.ndim == 2: # weight
+                c3, _ = data_torch.shape
+            else: # bias
+                c3 = data_torch.shape[0]
+            assert c3 % 3 == 0
+            c = c3 // 3
+            wq = data_torch[:c]
+            wk = data_torch[c: c * 2]
+            wv = data_torch[c * 2:]
+            yield from super().modify_tensors(wq, name.replace("qkv", "q"), bid)
+            yield from super().modify_tensors(wk, name.replace("qkv", "k"), bid)
+            yield from super().modify_tensors(wv, name.replace("qkv", "v"), bid)
+        elif 'patch_embed.proj.weight' in name:
+            # split Conv3D into Conv2Ds
+            c1, c2, kt, kh, kw = data_torch.shape
+            del c1, c2, kh, kw  # unused
+            assert kt == 2, "Current implementation only support temporal_patch_size of 2"
+            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight"  , data_torch[:, :, 0, ...])
+            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...])
+        else:
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+class Qwen25AudioModel(MmprojModel):
+    has_audio_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_audio is not None
+        self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
+        self.hparams_audio["intermediate_size"] = self.hparams_audio["encoder_ffn_dim"]
+        self.hparams_audio["num_attention_heads"] = self.hparams_audio["encoder_attention_heads"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_audio is not None
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # SinusoidsPositionEmbedding
+        assert self.hparams_audio is not None
+        max_timescale = 10000
+        length = 1500
+        channels = self.hparams_audio["hidden_size"]
+        log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
+        scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+        pos_embd = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1).to(dtype=torch.float32)
+        yield ("audio_tower.embed_positions.weight", pos_embd)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".conv" in name and ".weight" in name:
+            return gguf.GGMLQuantizationType.F16
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "conv1.bias" in name or "conv2.bias" in name:
+            # transpose conv1 and conv2 bias
+            data_torch = data_torch.unsqueeze(-1)
+
+        yield from MmprojModel.modify_tensors(self, data_torch, name, bid)
+
+
+@ModelBase.register("Qwen2_5OmniModel")
+class Qwen25OmniModel(Qwen2VLVisionModel, Qwen25AudioModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        return self.global_config["thinker_config"].get("vision_config")
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config["thinker_config"].get("audio_config")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not name.startswith("visual.") and not name.startswith("audio_tower."):
+            return None
+
+        if name.startswith("thinker."):
+            name = name.replace("thinker.", "")
+
+        if "audio_bos_eos_token" in name:
+            # this tensor is left unused in transformers code
+            # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
+            return None
+
+        return MmprojModel.filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "visual." in name:
+            yield from Qwen2VLVisionModel.modify_tensors(self, data_torch, name, bid)
+        elif "audio_tower." in name:
+            yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
+        return  # skip other tensors
diff --git a/conversion/refact.py b/conversion/refact.py
new file mode 100644
index 00000000000..1170cddeb2c
--- /dev/null
+++ b/conversion/refact.py
@@ -0,0 +1,68 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("GPTRefactForCausalLM")
+class RefactModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.REFACT
+
+    def set_vocab(self):
+        super().set_vocab()
+
+        # TODO: how to determine special FIM tokens automatically?
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
+        special_vocab._set_special_token("prefix", 1)
+        special_vocab._set_special_token("suffix", 3)
+        special_vocab._set_special_token("middle", 2)
+        special_vocab.chat_template = None  # do not add it twice
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        hidden_dim = self.hparams["n_embd"]
+        inner_dim = 4 * hidden_dim
+        hidden_dim = int(2 * inner_dim / 3)
+        multiple_of = 256
+        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        # refact uses Alibi. So this is from config.json which might be used by training.
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+
+        self.gguf_writer.add_feed_forward_length(ff_dim)
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_head_count_kv(1)
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        hidden_dim = self.hparams["n_embd"]
+        inner_dim = 4 * hidden_dim
+        hidden_dim = int(2 * inner_dim / 3)
+        multiple_of = 256
+        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        n_head = self.hparams["n_head"]
+        n_head_kv = 1
+        head_dim = self.hparams["n_embd"] // n_head
+
+        if bid is not None:
+            if name == f"transformer.h.{bid}.attn.kv.weight":
+                yield from super().modify_tensors(data_torch[:n_head_kv * head_dim], self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
+                yield from super().modify_tensors(data_torch[n_head_kv * head_dim:], self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
+                return
+            if name == f"transformer.h.{bid}.attn.q.weight":
+                yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
+                return
+            if name == f"transformer.h.{bid}.mlp.gate_up_proj.weight":
+                yield from super().modify_tensors(data_torch[:ff_dim], self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)
+                yield from super().modify_tensors(data_torch[ff_dim:], self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/rwkv.py b/conversion/rwkv.py
new file mode 100644
index 00000000000..2de0aa5346e
--- /dev/null
+++ b/conversion/rwkv.py
@@ -0,0 +1,302 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("Rwkv6ForCausalLM")
+class Rwkv6Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.RWKV6
+
+    def set_vocab(self):
+        self._set_vocab_rwkv_world()
+
+    def set_gguf_parameters(self):
+        head_size = self.hparams["head_size"]
+        hidden_size = self.hparams["hidden_size"]
+        layer_norm_eps = self.hparams["layer_norm_epsilon"]
+        rescale_every_n_layers = self.hparams["rescale_every"]
+        intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else int((hidden_size * 3.5) // 32 * 32)
+        time_mix_extra_dim = 64 if hidden_size == 4096 else 32
+        time_decay_extra_dim = 128 if hidden_size == 4096 else 64
+
+        # RWKV isn't context limited
+        self.gguf_writer.add_context_length(1048576)
+        self.gguf_writer.add_embedding_length(hidden_size)
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
+        self.gguf_writer.add_rescale_every_n_layers(rescale_every_n_layers)
+        self.gguf_writer.add_wkv_head_size(head_size)
+        self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
+        self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
+        self.gguf_writer.add_feed_forward_length(intermediate_size)
+        self.gguf_writer.add_file_type(self.ftype)
+
+        # required by llama.cpp, unused
+        self.gguf_writer.add_head_count(0)
+
+    lerp_weights: dict[int, dict[str, Tensor]] = {}
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        new_name = self.map_tensor_name(name)
+
+        if not (new_name.endswith(".weight") or new_name.endswith(".bias")):
+            new_name += ".weight"
+
+        if new_name.endswith("time_mix_w1.weight") or new_name.endswith("time_mix_decay_w1.weight") or new_name.endswith("time_mix_decay_w2.weight"):
+            data_torch = data_torch.transpose(0, 1)
+
+        if new_name.endswith("time_mix_w2.weight"):
+            data_torch = data_torch.permute(0, 2, 1)
+
+        if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name:
+            data_torch = data_torch.squeeze()
+
+        try:
+            rescale_every_n_layers = self.hparams["rescale_every"]
+            if rescale_every_n_layers > 0:
+                if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
+                    data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
+        except KeyError:
+            pass
+
+        # concat time_mix_lerp weights to reduce some cpu overhead
+        # also reduces the number of tensors in the model
+        if bid is not None and "time_mix_lerp" in new_name and "time_mix_lerp_x" not in new_name:
+            try:
+                self.lerp_weights[bid][new_name] = data_torch
+            except KeyError:
+                self.lerp_weights[bid] = {new_name: data_torch}
+            if all(f"blk.{bid}.time_mix_lerp_{i}.weight" in self.lerp_weights[bid].keys() for i in ["w", "k", "v", "r", "g"]):
+                new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
+                data = torch.stack([self.lerp_weights[bid][f"blk.{bid}.time_mix_lerp_{i}.weight"].unsqueeze(0) for i in ["w", "k", "v", "r", "g"]], dim=0).unsqueeze(1)
+                yield (new_name, data)
+            return
+
+        yield (new_name, data_torch)
+
+
+@ModelBase.register("RWKV6Qwen2ForCausalLM")
+class RWKV6Qwen2Model(Rwkv6Model):
+    model_arch = gguf.MODEL_ARCH.RWKV6QWEN2
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        num_attention_heads = self.hparams["num_attention_heads"]
+        num_key_value_heads = self.hparams["num_key_value_heads"]
+        hidden_size = self.hparams["hidden_size"]
+        head_size = hidden_size // num_attention_heads
+        rms_norm_eps = self.hparams["rms_norm_eps"]
+        intermediate_size = self.hparams["intermediate_size"]
+        time_mix_extra_dim = self.hparams.get("lora_rank_tokenshift", 64 if hidden_size >= 4096 else 32)
+        time_decay_extra_dim = self.hparams.get("lora_rank_decay", 128 if hidden_size >= 4096 else 64)
+
+        # RWKV isn't context limited
+        self.gguf_writer.add_context_length(1048576)
+        self.gguf_writer.add_embedding_length(hidden_size)
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_wkv_head_size(head_size)
+        self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
+        self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
+        self.gguf_writer.add_feed_forward_length(intermediate_size)
+        self.gguf_writer.add_file_type(self.ftype)
+
+        # special parameters for time_mixing in RWKV6QWEN2
+        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
+        self.gguf_writer.add_token_shift_count(1)
+        # RWKV6QWEN2 use grouped key/value like GQA
+        self.gguf_writer.add_head_count_kv(num_key_value_heads)
+
+        # required by llama.cpp, unused
+        self.gguf_writer.add_head_count(0)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        for new_name, data in super().modify_tensors(data_torch, name, bid):
+            if "time_mix_w1" in new_name or "time_mix_w2" in new_name:
+                data = data.view(5, -1, data.shape[-1])
+                # rwkv6qwen2 has a different order of rkvwg instead of the original wkvrg
+                # permute them here to avoid code changes
+                data = torch.stack([data[3], data[1], data[2], data[0], data[4]], dim=0).view(-1, data.shape[-1])
+                if "w2" in new_name:
+                    data = data.view(5, -1, data.shape[-1])
+                yield (new_name, data)
+                continue
+            yield (new_name, data)
+
+
+@ModelBase.register("Rwkv7ForCausalLM", "RWKV7ForCausalLM")
+class Rwkv7Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.RWKV7
+
+    def set_vocab(self):
+        self._set_vocab_rwkv_world()
+
+    def calc_lora_rank(self, hidden_size, exponent, multiplier):
+        return max(1, round(hidden_size ** exponent * multiplier / 32)) * 32
+
+    def set_gguf_parameters(self):
+        try:
+            head_size = self.hparams["head_size"]
+            layer_norm_eps = self.hparams["layer_norm_epsilon"]
+        except KeyError:
+            head_size = self.hparams["head_dim"]
+            layer_norm_eps = self.hparams["norm_eps"]
+        hidden_size = self.hparams["hidden_size"]
+        intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else (hidden_size * 4)
+
+        # ICLR: In-Context-Learning-Rate
+        try:
+            lora_rank_decay = self.hparams["lora_rank_decay"] if self.hparams["lora_rank_decay"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
+            lora_rank_iclr = self.hparams["lora_rank_iclr"] if self.hparams["lora_rank_iclr"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
+            lora_rank_value_residual_mix = self.hparams["lora_rank_value_residual_mix"] if self.hparams["lora_rank_value_residual_mix"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
+            lora_rank_gate = self.hparams["lora_rank_gate"] if self.hparams["lora_rank_gate"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)
+        except KeyError:
+            lora_rank_decay = self.hparams["decay_low_rank_dim"] if self.hparams["decay_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
+            lora_rank_iclr = self.hparams["a_low_rank_dim"] if self.hparams["a_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
+            lora_rank_value_residual_mix = self.hparams["v_low_rank_dim"] if self.hparams["v_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
+            lora_rank_gate = self.hparams["gate_low_rank_dim"] if self.hparams["gate_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)
+
+        # RWKV isn't context limited
+        self.gguf_writer.add_context_length(1048576)
+        self.gguf_writer.add_embedding_length(hidden_size)
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
+        self.gguf_writer.add_wkv_head_size(head_size)
+        self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
+        self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr)
+        self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix)
+        self.gguf_writer.add_gate_lora_rank(lora_rank_gate)
+        self.gguf_writer.add_feed_forward_length(intermediate_size)
+        self.gguf_writer.add_file_type(self.ftype)
+
+        # required by llama.cpp, unused
+        self.gguf_writer.add_head_count(0)
+
+    lerp_weights: dict[int, dict[str, Tensor]] = {}
+    lora_needs_transpose: bool = True
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # unify tensor names here to make life easier
+        name = name.replace("blocks", "layers").replace("ffn", "feed_forward")
+        name = name.replace("self_attn", "attention").replace("attn", "attention")
+        name = name.replace("time_mixer.", "")
+
+        name = name.replace("feed_forward_norm", "ln2")
+        name = name.replace("g_norm", "ln_x")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # lora layer names in fla-hub's impl
+        if "_lora.lora" in name:
+            self.lora_needs_transpose = False
+        name = name.replace("_lora.lora.0.weight", "1.weight")
+        name = name.replace("_lora.lora.2.weight", "2.weight")
+        name = name.replace("_lora.lora.2.bias", "0.weight")
+
+        if "attention.v" in name and "value" not in self.map_tensor_name(name) and bid == 0:
+            # some models have dummy v0/v1/v2 on first layer while others don't
+            # ignore them all since they are not used
+            return
+
+        wkv_has_gate = self.hparams.get("wkv_has_gate", True)
+        lerp_list = ["r", "w", "k", "v", "a", "g"] if wkv_has_gate else ["r", "w", "k", "v", "a"]
+
+        if bid is not None and "attention.x_" in name:
+            if "attention.x_x" in name:
+                # already concatenated
+                new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
+                data = data_torch.reshape(len(lerp_list), 1, 1, -1)
+                yield (new_name, data)
+            else:
+                try:
+                    self.lerp_weights[bid][name] = data_torch
+                except KeyError:
+                    self.lerp_weights[bid] = {name: data_torch}
+                if all(f"model.layers.{bid}.attention.x_{i}" in self.lerp_weights[bid].keys() for i in lerp_list):
+                    new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
+                    data = torch.stack([self.lerp_weights[bid][f"model.layers.{bid}.attention.x_{i}"] for i in lerp_list], dim=0)
+                    yield (new_name, data)
+            return
+        else:
+            data_torch = data_torch.squeeze()
+            new_name = self.map_tensor_name(name)
+
+            if not (new_name.endswith(".weight") or new_name.endswith(".bias")):
+                new_name += ".weight"
+
+            if self.lora_needs_transpose and any(
+                new_name.endswith(t) for t in [
+                    "time_mix_w1.weight", "time_mix_w2.weight",
+                    "time_mix_a1.weight", "time_mix_a2.weight",
+                    "time_mix_v1.weight", "time_mix_v2.weight",
+                    "time_mix_g1.weight", "time_mix_g2.weight",
+                ]
+            ):
+                data_torch = data_torch.transpose(0, 1)
+
+            if 'r_k' in new_name:
+                data_torch = data_torch.flatten()
+
+            if bid == 0 and "time_mix_a" in new_name:
+                # dummy v0/v1/v2 on first layer
+                # easiest way to make llama happy
+                yield (new_name.replace("time_mix_a", "time_mix_v"), data_torch)
+
+            yield (new_name, data_torch)
+
+
+@ModelBase.register("RwkvHybridForCausalLM")
+class ARwkv7Model(Rwkv7Model):
+    model_arch = gguf.MODEL_ARCH.ARWKV7
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        hidden_size = self.hparams["hidden_size"]
+        head_size = self.hparams["head_size"]
+        rms_norm_eps = self.hparams["rms_norm_eps"]
+        intermediate_size = self.hparams["intermediate_size"]
+        wkv_has_gate = self.hparams["wkv_has_gate"]
+        assert self.hparams["wkv_version"] == 7
+
+        # ICLR: In-Context-Learning-Rate
+        lora_rank_decay = 64
+        lora_rank_iclr = 64
+        lora_rank_value_residual_mix = 32
+        lora_rank_gate = 128 if wkv_has_gate else 0
+
+        # RWKV isn't context limited
+        self.gguf_writer.add_context_length(1048576)
+        self.gguf_writer.add_embedding_length(hidden_size)
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
+        self.gguf_writer.add_wkv_head_size(head_size)
+        self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
+        self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr)
+        self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix)
+        self.gguf_writer.add_gate_lora_rank(lora_rank_gate)
+        self.gguf_writer.add_feed_forward_length(intermediate_size)
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_token_shift_count(1)
+
+        # required by llama.cpp, unused
+        self.gguf_writer.add_head_count(0)
diff --git a/conversion/sarashina2.py b/conversion/sarashina2.py
new file mode 100644
index 00000000000..05448db812e
--- /dev/null
+++ b/conversion/sarashina2.py
@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from typing import Callable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, gguf
+
+from .llama import LlamaModel
+from .qwenvl import Qwen2VLVisionModel
+
+
+@ModelBase.register("Sarashina2VisionForCausalLM")
+class Sarashina2VLTextModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.startswith("llm."):
+            name = name.replace("llm.", "", 1)
+        elif name.startswith("norm."):
+            return None
+        return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("Sarashina2VisionForCausalLM")
+class Sarashina2VLVisionModel(Qwen2VLVisionModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.global_config['model_type'] = "qwen2_vl"
diff --git a/conversion/smallthinker.py b/conversion/smallthinker.py
new file mode 100644
index 00000000000..1b0f79aa3ea
--- /dev/null
+++ b/conversion/smallthinker.py
@@ -0,0 +1,82 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("SmallThinkerForCausalLM")
+class SmallThinkerModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.SMALLTHINKER
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (n_experts := self.hparams.get("moe_num_primary_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+        if (n_experts_used := self.hparams.get("moe_num_active_primary_experts")) is not None:
+            self.gguf_writer.add_expert_used_count(n_experts_used)
+        if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+            self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
+            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+        if (self.hparams.get('moe_primary_router_apply_softmax')):
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+        else:
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        sliding_window_layout = self.hparams.get("sliding_window_layout")
+        if sliding_window_layout:
+            for i in sliding_window_layout:
+                if i != 0:
+                    sliding_window = self.hparams.get("sliding_window_size")
+                    if sliding_window:
+                        self.gguf_writer.add_sliding_window(sliding_window)
+                    break
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("experts") != -1:
+            n_experts = self.hparams.get("moe_num_primary_experts") or self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for w_name in ["down", "gate", "up"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
+
+                    yield from super().modify_tensors(data_torch, merged_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/conversion/smolvlm.py b/conversion/smolvlm.py
new file mode 100644
index 00000000000..30e9dca329b
--- /dev/null
+++ b/conversion/smolvlm.py
@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+from typing import Callable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf
+
+
+@ModelBase.register("Idefics3ForConditionalGeneration", "SmolVLMForConditionalGeneration")
+class SmolVLMModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.hparams["model_type"] == "smolvlm_vision":
+            # fix for SmolVLM2, missing some keys in config.json
+            # default values are taken from transformers code
+            self.hparams["hidden_size"] = self.hparams.get("hidden_size", 1152)
+            self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 16)
+            self.hparams["intermediate_size"] = self.hparams.get("intermediate_size", 3072)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.IDEFICS3)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
+        self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("scale_factor", 2))
+        self.gguf_writer.add_vision_use_gelu(True)
+
+        # Add the preprocessor longest edge size
+        preproc_image_size = self.preprocessor_config.get("size", {}).get("longest_edge", self.image_size)
+        self.gguf_writer.add_vision_preproc_image_size(preproc_image_size)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".embeddings." in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        is_vision_tensor = "vision_tower" in name or "vision_model" in name or "model.connector" in name
+
+        if not is_vision_tensor:
+            return None
+
+        return super().filter_tensors(item)
diff --git a/conversion/stablelm.py b/conversion/stablelm.py
new file mode 100644
index 00000000000..ba5e9aa6ca9
--- /dev/null
+++ b/conversion/stablelm.py
@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
+class StableLMModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.STABLELM
+
+    def set_vocab(self):
+        if (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        else:
+            # StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
+            self._set_vocab_qwen()
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
+        self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
+        self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
+        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
+        self.gguf_writer.add_file_type(self.ftype)
+
+    _q_norms: list[dict[str, Tensor]] | None = None
+    _k_norms: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams["num_key_value_heads"]
+
+        if name.find("q_layernorm.norms") != -1:
+            assert bid is not None
+
+            if self._q_norms is None:
+                self._q_norms = [{} for _ in range(self.block_count)]
+
+            self._q_norms[bid][name] = data_torch
+
+            if len(self._q_norms[bid]) >= n_head:
+                return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm")
+            else:
+                return
+
+        if name.find("k_layernorm.norms") != -1:
+            assert bid is not None
+
+            if self._k_norms is None:
+                self._k_norms = [{} for _ in range(self.block_count)]
+
+            self._k_norms[bid][name] = data_torch
+
+            if len(self._k_norms[bid]) >= n_kv_head:
+                return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm")
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"):
+        datas: list[Tensor] = []
+        # extract the norms in order
+        for xid in range(n_head):
+            ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight"
+            datas.append(norms[ename])
+            del norms[ename]
+        data_torch = torch.stack(datas, dim=0)
+
+        merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"
+
+        yield from super().modify_tensors(data_torch, merged_name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._q_norms is not None or self._k_norms is not None:
+            # flatten two `list[dict[str, Tensor]]` into a single `list[str]`
+            norms = (
+                [k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else []
+            ) + (
+                [k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else []
+            )
+            if len(norms) > 0:
+                raise ValueError(f"Unprocessed norms: {norms}")
diff --git a/conversion/starcoder.py b/conversion/starcoder.py
new file mode 100644
index 00000000000..0b4ffd84702
--- /dev/null
+++ b/conversion/starcoder.py
@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("GPTBigCodeForCausalLM")
+class StarCoderModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.STARCODER
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_head_count_kv(1)
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+
+@ModelBase.register("Starcoder2ForCausalLM")
+class StarCoder2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.STARCODER2
diff --git a/conversion/step3.py b/conversion/step3.py
new file mode 100644
index 00000000000..ba867fb831b
--- /dev/null
+++ b/conversion/step3.py
@@ -0,0 +1,231 @@
+from __future__ import annotations
+
+import math
+import re
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, _MISTRAL_COMMON_DATASET_MEAN, _MISTRAL_COMMON_DATASET_STD, gguf
+
+from .qwen import Qwen3Model
+
+
+@ModelBase.register("StepVLForConditionalGeneration")
+class Step3VLVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+
+        if not self.hparams_vision.get("intermediate_size"):
+            hidden_size = self.hparams_vision.get("hidden_size") or self.hparams_vision.get("width") or 0
+            assert hidden_size > 0
+            mlp_ratio = float(self.hparams_vision.get("mlp_ratio", 8960 / 1536))
+            self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
+
+        self.preprocessor_config.setdefault("image_mean", list(_MISTRAL_COMMON_DATASET_MEAN))
+        self.preprocessor_config.setdefault("image_std", list(_MISTRAL_COMMON_DATASET_STD))
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+
+        projector_stride = int(self.global_config.get("understand_projector_stride", -1))
+        hidden_size = int(self.hparams_vision.get("hidden_size", self.hparams_vision.get("width", -1)))
+        num_layers = int(self.hparams_vision.get("num_hidden_layers", self.hparams_vision.get("layers", -1)))
+        assert (projector_stride, int(self.hparams_vision.get("image_size", -1)), hidden_size, num_layers) == (2, 728, 1536, 47), (
+            "current Step3-VL conversion path is only validated for Step3-VL-10B"
+        )
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.STEP3VL)
+        self.gguf_writer.add_vision_attention_layernorm_eps(float(self.hparams_vision.get("layer_norm_eps", 1e-5)))
+        self.gguf_writer.add_vision_projector_scale_factor(projector_stride ** 2)
+        # 3024 max resize comes from step3-vl-10b processing_step3.py.
+        self.gguf_writer.add_vision_preproc_image_size(3024)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".position_embd." in new_name:
+            return gguf.GGMLQuantizationType.F32
+        if ("mm.0." in new_name or "mm.1." in new_name) and new_name.endswith(".weight"):
+            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith(("model.", "lm_head.")):
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("vision_model.vit_downsampler"):
+            match = re.match(r"vision_model\.vit_downsampler(\d+)\.(weight|bias)", name)
+            if match is None:
+                raise ValueError(f"Unexpected Step3-VL projector tensor {name!r}")
+
+            proj_id = int(match.group(1)) - 1
+            suffix = f".{match.group(2)}"
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, proj_id, suffix=suffix), data_torch)
+            return
+
+        if name == "vit_large_projector.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ_FC), data_torch)
+            return
+
+        if name.startswith("vision_model."):
+            if name == "vision_model.positional_embedding":
+                name += ".weight"
+            elif name.endswith(".gamma") and ".ls_" in name:
+                name = name.removesuffix(".gamma") + ".weight"
+
+            name = name.replace("attn.in_proj_weight", "attn.in_proj.weight")
+            name = name.replace("attn.in_proj_bias", "attn.in_proj.bias")
+
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("StepVLForConditionalGeneration")
+class Step3VLTextModel(Qwen3Model):
+    model_arch = gguf.MODEL_ARCH.QWEN3
+
+
+@ModelBase.register("Step3p5ForCausalLM")
+class Step35Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.STEP35
+
+    def set_gguf_parameters(self):
+        rope_theta = self.hparams.get("rope_theta")
+        if isinstance(rope_theta, list):
+            self.hparams["rope_theta"] = float(rope_theta[0])
+            self.hparams["local_rope_theta"] = float(rope_theta[1])
+            self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
+            self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}
+
+        super().set_gguf_parameters()
+
+        layer_types = self.hparams.get("layer_types") or []
+        partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
+        attn_other = self.hparams.get("attention_other_setting") or {}
+
+        n_head_base = self.hparams["num_attention_heads"]
+        n_kv_base = self.hparams["num_attention_groups"]
+
+        n_head_swa = attn_other.get("num_attention_heads", n_head_base)
+        n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
+
+        layer_types = layer_types[: self.block_count]
+        partial_rotary_factors = partial_rotary_factors[: self.block_count]
+        assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
+        head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
+        kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
+        swa_pat = [lt == "sliding_attention" for lt in layer_types]
+
+        self.gguf_writer.add_head_count(head_arr)
+        self.gguf_writer.add_head_count_kv(kv_arr)
+
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern(swa_pat)
+
+        self.gguf_writer.add_value_length(self.hparams["head_dim"])
+
+        # MoE params
+        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])
+
+        if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
+        if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_expert_weight)
+
+        # leading dense blocks
+        leading_dense = 0
+        moe_layers_enum = self.hparams.get("moe_layers_enum")
+        if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
+            moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
+            if moe_layers:
+                leading_dense = max(0, moe_layers[0])
+        self.gguf_writer.add_leading_dense_block_count(leading_dense)
+        self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))
+
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
+
+        # Optional per-layer SwiGLU clamps.
+        if (limits := self.hparams.get("swiglu_limits")) is not None:
+            limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_exp(limits_f)
+        if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
+            limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Map router bias (expert selection bias) to a GGUF bias tensor
+        if name.endswith(".moe.router_bias"):
+            name += ".bias"
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        # remove mtp layers
+        if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
+            il = int(m.group(1))
+            n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
+            if il >= n_main:
+                return
+        if name.endswith("norm.weight"):
+            data_torch += 1.0
+
+        if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
+            data_torch = data_torch.squeeze().contiguous()
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
+        # llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
+        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
+        rope_type = rope_params.get("rope_type") or ""
+        if rope_type.lower() != "llama3":
+            return
+
+        # Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
+        rope_theta = self.hparams.get("rope_theta", 10000.0)
+        if isinstance(rope_theta, list):
+            rope_theta = rope_theta[0]
+        base = float(rope_theta)
+        if (dim := self.hparams.get("head_dim")) is None:
+            dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        dim = int(dim)
+
+        freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+        factor = float(rope_params.get("factor", 8.0))
+        low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
+        high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
+        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
+
+        low_freq_wavelen = old_context_len / low_freq_factor
+        high_freq_wavelen = old_context_len / high_freq_factor
+
+        rope_factors: list[float] = []
+        for freq in freqs:
+            wavelen = 2 * math.pi / float(freq)
+            if wavelen < high_freq_wavelen:
+                rope_factors.append(1.0)
+            elif wavelen > low_freq_wavelen:
+                rope_factors.append(factor)
+            else:
+                smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))
+
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
diff --git a/conversion/t5.py b/conversion/t5.py
new file mode 100644
index 00000000000..73dcfd1a2ce
--- /dev/null
+++ b/conversion/t5.py
@@ -0,0 +1,286 @@
+from __future__ import annotations
+
+import json
+import os
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, SentencePieceTokenTypes, TextModel, gguf, logger
+
+
+@ModelBase.register("T5WithLMHeadModel")
+@ModelBase.register("T5ForConditionalGeneration")
+@ModelBase.register("MT5ForConditionalGeneration")
+@ModelBase.register("UMT5ForConditionalGeneration")
+@ModelBase.register("UMT5Model")
+class T5Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.T5
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.shared_token_embeddings_found = False
+
+    def set_vocab(self):
+        # to avoid TypeError: Descriptors cannot be created directly
+        # exception when importing sentencepiece_model_pb2
+        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        # many older models use spiece.model tokenizer model filename
+        if not tokenizer_path.is_file():
+            tokenizer_path = self.dir_model / 'spiece.model'
+
+        if not tokenizer_path.is_file():
+            raise FileNotFoundError(f"File not found: {tokenizer_path}")
+
+        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+
+        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
+        if sentencepiece_model.trainer_spec.model_type == 2:  # BPE
+            # assure the tokenizer model file name is correct
+            assert tokenizer_path.name == 'tokenizer.model'
+            return self._set_vocab_sentencepiece()
+        else:
+            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
+
+        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
+        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if token_id >= vocab_size:
+                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
+        self.gguf_writer.add_tokenizer_model("t5")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
+        if precompiled_charsmap:
+            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
+            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
+            n_ctx = 512
+        self.gguf_writer.add_context_length(n_ctx)
+        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
+        self.gguf_writer.add_block_count(self.block_count)
+        if (dec_n_layer := self.hparams.get("num_decoder_layers")) is not None:
+            self.gguf_writer.add_decoder_block_count(dec_n_layer)
+        self.gguf_writer.add_head_count(self.hparams["num_heads"])
+        self.gguf_writer.add_key_length(self.hparams["d_kv"])
+        self.gguf_writer.add_value_length(self.hparams["d_kv"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
+        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
+        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
+        # and decoder and ignore the remaining ones.
+        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
+            if not self.shared_token_embeddings_found:
+                name = "shared.weight"
+                self.shared_token_embeddings_found = True
+            else:
+                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("T5EncoderModel")
+class T5EncoderModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.T5ENCODER
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.shared_token_embeddings_found = False
+
+    def set_vocab(self):
+        # to avoid TypeError: Descriptors cannot be created directly
+        # exception when importing sentencepiece_model_pb2
+        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        # many older models use spiece.model tokenizer model filename
+        if not tokenizer_path.is_file():
+            tokenizer_path = self.dir_model / 'spiece.model'
+
+        if not tokenizer_path.is_file():
+            raise FileNotFoundError(f"File not found: {tokenizer_path}")
+
+        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+
+        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
+        if sentencepiece_model.trainer_spec.model_type == 2:  # BPE
+            # assure the tokenizer model file name is correct
+            assert tokenizer_path.name == 'tokenizer.model'
+            return self._set_vocab_sentencepiece()
+        else:
+            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
+
+        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
+        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if token_id >= vocab_size:
+                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
+        self.gguf_writer.add_tokenizer_model("t5")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
+        if precompiled_charsmap:
+            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
+            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
+            n_ctx = 512
+        self.gguf_writer.add_context_length(n_ctx)
+        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(self.hparams["num_heads"])
+        self.gguf_writer.add_key_length(self.hparams["d_kv"])
+        self.gguf_writer.add_value_length(self.hparams["d_kv"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
+        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
+        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
+        # and decoder and ignore the remaining ones.
+        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
+            if not self.shared_token_embeddings_found:
+                name = "shared.weight"
+                self.shared_token_embeddings_found = True
+            else:
+                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/conversion/ultravox.py b/conversion/ultravox.py
new file mode 100644
index 00000000000..347188733a5
--- /dev/null
+++ b/conversion/ultravox.py
@@ -0,0 +1,203 @@
+from __future__ import annotations
+
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf
+
+
+@ModelBase.register("UltravoxModel")
+class UltravoxModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA # dummy
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument")
+
+
+@ModelBase.register("GlmasrModel")
+class GlmASRWhisperEncoderModel(MmprojModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
+            self.hparams["hidden_size"] = self.hparams["d_model"]
+            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
+            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLMA)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
+        self.gguf_writer.add_audio_stack_factor(self.global_config["merge_factor"])
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".conv" in name and ".weight" in name:
+            return gguf.GGMLQuantizationType.F16
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith(("model.", "lm_head.")):
+            # skip language model tensors
+            return None
+
+        if name.startswith("audio_encoder.whisper."):
+            name = name.replace("audio_encoder.whisper.","audio_tower.")
+        if "audio_encoder.layer_norm." in name or "audio_encoder.proj." in name:
+            name = name.replace("audio_encoder.", "audio_encoder.adapting.")
+        if name.startswith("audio_encoder.adapting."):
+            name = name.replace("audio_encoder.adapting.","audio.multi_modal_projector.")
+            if ".layer_norm." in name:
+                name = name.replace(".layer_norm.", ".ln_pre.")
+            if ".0." in name:
+                name = name.replace(".0.", ".linear_1.")
+            if ".2." in name:
+                name = name.replace(".2.", ".linear_2.")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("audio_encoder.audio_bos_eos_token."):
+            yield from super().modify_tensors(data_torch[0], "model.vision.boi", bid)
+            yield from super().modify_tensors(data_torch[1], "model.vision.eoi", bid)
+            return
+
+        if name.startswith("audio_encoder.adapting."):
+            if ".proj." in name:
+                return
+
+        if "conv1.bias" in name or "conv2.bias" in name:
+            # transpose conv1 and conv2 bias
+            data_torch = data_torch.unsqueeze(-1)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen2AudioForConditionalGeneration")
+class WhisperEncoderModel(MmprojModel):
+    has_vision_encoder = False # no vision encoder
+    has_audio_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
+            self.hparams["hidden_size"] = self.hparams["d_model"]
+            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
+            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2A)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".conv" in name and ".weight" in name:
+            return gguf.GGMLQuantizationType.F16
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # prevent clash naming with vision tensors
+        if name.startswith("multi_modal_projector"):
+            name = "audio." + name
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "conv1.bias" in name or "conv2.bias" in name:
+            # transpose conv1 and conv2 bias
+            data_torch = data_torch.unsqueeze(-1)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("UltravoxModel")
+class UltravoxWhisperEncoderModel(WhisperEncoderModel):
+    has_vision_encoder = False # no vision encoder
+    has_audio_encoder = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.ULTRAVOX)
+        self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
+
+
+@ModelBase.register("MERaLiON2ForConditionalGeneration")
+class MERaLiONWhisperEncoderModel(WhisperEncoderModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("speech_config")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MERALION)
+        self.gguf_writer.add_audio_stack_factor(self.global_config.get("speech_mlp_scale_factor", 15))
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("text_decoder."):
+            return None
+
+        if name.startswith("speech_encoder."):
+            name = name.replace("speech_encoder.", "audio_tower.")
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        suffix = "." + name.rsplit(".", 1)[-1]
+
+        if name.startswith("ln_speech."):
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MM_NORM_PRE, suffix=suffix), data_torch)
+            return
+
+        if name.startswith("speech_audio_adapter."):
+            if ".mlp_adapter.0." in name:
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 0, suffix=suffix), data_torch)
+            elif ".gate_proj." in name:
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 1, suffix=suffix), data_torch)
+            elif ".pool_proj." in name:
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 2, suffix=suffix), data_torch)
+            elif ".out_proj." in name:
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 3, suffix=suffix), data_torch)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("VoxtralForConditionalGeneration")
+class VoxtralWhisperEncoderModel(WhisperEncoderModel):
+    has_vision_encoder = False # no vision encoder
+    has_audio_encoder = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.VOXTRAL)
+        self.gguf_writer.add_audio_stack_factor(4) # == intermediate_size // hidden_size
+
+
+@ModelBase.register("AudioFlamingo3ForConditionalGeneration")
+class AudioFlamingo3WhisperEncoderModel(WhisperEncoderModel):
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MUSIC_FLAMINGO)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".conv" in name and ".weight" in name:
+            # Was trained in BF16, being safe, avoiding quantizing to FP16
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
diff --git a/conversion/wavtokenizer.py b/conversion/wavtokenizer.py
new file mode 100644
index 00000000000..7d25447be88
--- /dev/null
+++ b/conversion/wavtokenizer.py
@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+from typing import Callable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("WavTokenizerDec")
+class WavTokenizerDecModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if \
+                name.endswith("codebook.cluster_size") or \
+                name.endswith("codebook.embed_avg") or \
+                name.endswith("codebook.inited"):
+            logger.debug(f"Skipping {name!r}")
+            return None
+
+        return super().filter_tensors(item)
+
+    def set_vocab(self):
+        self._set_vocab_none()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size         (self.hparams["vocab_size"])
+        self.gguf_writer.add_features_length    (self.hparams["n_embd_features"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"])
+        self.gguf_writer.add_group_norm_eps     (self.hparams["group_norm_epsilon"])
+        self.gguf_writer.add_group_norm_groups  (self.hparams["group_norm_groups"])
+
+        self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"])
+        self.gguf_writer.add_posnet_block_count     (self.hparams["posnet"]["n_layer"])
+
+        self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"])
+        self.gguf_writer.add_convnext_block_count     (self.hparams["convnext"]["n_layer"])
+
+        self.gguf_writer.add_causal_attention(False)
diff --git a/conversion/xverse.py b/conversion/xverse.py
new file mode 100644
index 00000000000..fa8a31a133f
--- /dev/null
+++ b/conversion/xverse.py
@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import re
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("XverseForCausalLM")
+class XverseModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.XVERSE
+
+    def set_vocab(self):
+        assert (self.dir_model / "tokenizer.json").is_file()
+        dir_model = self.dir_model
+        hparams = self.hparams
+
+        tokens: list[bytes] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model)
+        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
+        # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size,
+        # because vocab_size is the count of items, and indexes start at 0.
+        max_vocab_index = max(tokenizer.get_vocab().values())  # ty: ignore[unresolved-attribute]
+        if max_vocab_index >= vocab_size:
+            raise ValueError("Vocabulary size exceeds expected maximum size.")
+
+        reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
+
+        for token_id in range(vocab_size):
+            token_text = reverse_vocab[token_id].encode('utf-8')
+            # replace "\x00" to string with length > 0
+            if token_text == b"\x00":
+                toktype = gguf.TokenType.BYTE  # special
+                token_text = f"<{token_text}>".encode('utf-8')
+            elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
+                toktype = gguf.TokenType.BYTE  # special
+            elif reverse_vocab[token_id] in added_vocab:
+                if tokenizer.added_tokens_decoder[token_id].special:  # ty: ignore[unresolved-attribute]
+                    toktype = gguf.TokenType.CONTROL
+                else:
+                    toktype = gguf.TokenType.USER_DEFINED
+            else:
+                toktype = gguf.TokenType.NORMAL
+
+            tokens.append(token_text)
+            toktypes.append(toktype)
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
+        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        head_count = self.hparams["num_attention_heads"]
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+
+        # HF models permute some of the tensors, so we need to undo that
+        if name.endswith("q_proj.weight"):
+            data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
+        if name.endswith("k_proj.weight"):
+            data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
+        if n_kv_head is not None and n_head != n_kv_head:
+            n_head //= n_kv_head
+
+        return (
+            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+            .swapaxes(1, 2)
+            .reshape(weights.shape)
+        )
diff --git a/conversion/youtuvl.py b/conversion/youtuvl.py
new file mode 100644
index 00000000000..cabc44445f3
--- /dev/null
+++ b/conversion/youtuvl.py
@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, gguf, logger
+
+
+@ModelBase.register("YoutuVLForConditionalGeneration")
+class YoutuVLVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.YOUTUVL)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
+
+        # Handle activation function
+        hidden_act = str(self.hparams.get("hidden_act", "gelu_pytorch_tanh")).lower()
+        if hidden_act in ("gelu", "gelu_pytorch_tanh", "gelu_fast", "gelu_new", "gelu_accurate"):
+            self.gguf_writer.add_vision_use_gelu(True)
+        elif hidden_act == "silu":
+            self.gguf_writer.add_vision_use_silu(True)
+        else:
+            raise ValueError(f"Unsupported activation function for YOUTUVL: {hidden_act}")
+
+        self.gguf_writer.add_vision_spatial_merge_size(self.hparams.get("spatial_merge_size", 2))
+
+        window_size = self.hparams.get("window_size")
+        if window_size is not None:
+            self.gguf_writer.add_vision_window_size(window_size)
+        # fullatt_block_indexes contains explicit layer indices that use full attention
+        # e.g., [2, 5, 8, 11] means layers 2, 5, 8, 11 use full attention
+        # All other layers use window attention
+        fullatt_block_indexes = self.hparams.get("fullatt_block_indexes")
+        assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for youtuvl"
+        # Store the explicit layer indices for YoutuVL (irregular pattern approach)
+        self.gguf_writer.add_vision_wa_layer_indexes(layers=fullatt_block_indexes)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Skip language model tensors
+        skip_prefixes = ('lm_head.', 'model.layers.', 'model.embed_tokens.', 'model.norm.')
+        if name.startswith(skip_prefixes):
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Try to map the tensor using TensorNameMap (handles vision encoder and projector)
+        try:
+            yield from super().modify_tensors(data_torch, name, bid)
+        except ValueError:
+            # If mapping fails, log warning and skip
+            logger.warning(f"Cannot map tensor: {name}")
+            return
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 5b4fb79fc1b..ff840050861 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3,13228 +3,46 @@
 
 from __future__ import annotations
 
-import ast
-import logging
-import argparse
-import contextlib
-import json
-import os
-import re
-import sys
-from enum import IntEnum
-from pathlib import Path
-from hashlib import sha256
-from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
-from itertools import chain
-from transformers import AutoConfig
-
-import math
-import numpy as np
-import torch
-
-if TYPE_CHECKING:
-    from torch import Tensor
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
-import gguf
-from gguf.vocab import MistralTokenizerType, MistralVocab
-
-try:
-    from mistral_common.tokens.tokenizers.base import TokenizerVersion # type: ignore[import-not-found, ty:unresolved-import]
-    from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # type: ignore[import-not-found, ty:unresolved-import]
-    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found, ty:unresolved-import]
-    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found, ty:unresolved-import]
-        SentencePieceTokenizer,
-    )
-
-    _mistral_common_installed = True
-    _mistral_import_error_msg = ""
-except ImportError:
-    _MISTRAL_COMMON_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
-    _MISTRAL_COMMON_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
-
-    _mistral_common_installed = False
-    TokenizerVersion: Any = None
-    Tekkenizer: Any = None
-    SentencePieceTokenizer: Any = None
-    _mistral_import_error_msg = (
-        "Mistral format requires `mistral-common` to be installed. Please run "
-        "`pip install mistral-common[image,audio]` to install it."
-    )
-
-
-logger = logging.getLogger("hf-to-gguf")
-
-
-###### MODEL DEFINITIONS ######
-
-class SentencePieceTokenTypes(IntEnum):
-    NORMAL = 1
-    UNKNOWN = 2
-    CONTROL = 3
-    USER_DEFINED = 4
-    UNUSED = 5
-    BYTE = 6
-
-
-class ModelType(IntEnum):
-    TEXT = 1
-    MMPROJ = 2
-
-
-AnyModel = TypeVar("AnyModel", bound="type[ModelBase]")
-
-
-class ModelBase:
-    _model_classes: dict[ModelType, dict[str, type[ModelBase]]] = {
-        ModelType.TEXT: {},
-        ModelType.MMPROJ: {},
-    }
-
-    dir_model: Path
-    ftype: gguf.LlamaFileType
-    fname_out: Path
-    is_big_endian: bool
-    endianess: gguf.GGUFEndian
-    use_temp_file: bool
-    lazy: bool
-    dry_run: bool
-    hparams: dict[str, Any]
-    model_tensors: dict[str, Callable[[], Tensor]]
-    gguf_writer: gguf.GGUFWriter
-    model_name: str | None
-    metadata_override: Path | None
-    dir_model_card: Path
-    remote_hf_model_id: str | None
-
-    # subclasses should define this!
-    model_arch: gguf.MODEL_ARCH
-
-    # subclasses should initialize this!
-    block_count: int
-    tensor_map: gguf.TensorNameMap
-
-    # Mistral format specifics
-    is_mistral_format: bool = False
-    disable_mistral_community_chat_template: bool = False
-    sentence_transformers_dense_modules: bool = False
-
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False,
-                 use_temp_file: bool = False, eager: bool = False,
-                 metadata_override: Path | None = None, model_name: str | None = None,
-                 split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
-                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
-                 disable_mistral_community_chat_template: bool = False,
-                 sentence_transformers_dense_modules: bool = False,
-                 fuse_gate_up_exps: bool = False):
-        if type(self) is ModelBase or \
-                type(self) is TextModel or \
-                type(self) is MmprojModel:
-            raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
-
-        if self.is_mistral_format and not _mistral_common_installed:
-            raise ImportError(_mistral_import_error_msg)
-
-        self.dir_model = dir_model
-        self.ftype = ftype
-        self.fname_out = fname_out
-        self.is_big_endian = is_big_endian
-        self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
-        self.use_temp_file = use_temp_file
-        self.lazy = not eager or (remote_hf_model_id is not None)
-        self.dry_run = dry_run
-        self.remote_hf_model_id = remote_hf_model_id
-        self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
-        self.fuse_gate_up_exps = fuse_gate_up_exps
-        self._gate_exp_buffer: dict[int, Tensor] = {}
-        self._up_exp_buffer: dict[int, Tensor] = {}
-        self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
-        self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
-        self.metadata_override = metadata_override
-        self.model_name = model_name
-        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
-        self._is_nvfp4 = False
-        self._is_mxfp4 = False
-
-        # Apply heuristics to figure out typical tensor encoding based on first tensor's dtype
-        # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
-        if self.ftype == gguf.LlamaFileType.GUESSED:
-            for _, tensor in self.get_tensors():
-                if tensor.dim() < 2:
-                    continue
-
-                if tensor.dtype == torch.bfloat16:
-                    self.ftype = gguf.LlamaFileType.MOSTLY_BF16
-                    logger.info("heuristics detected bfloat16 tensor dtype, setting --outtype bf16")
-                    break
-                elif tensor.dtype == torch.float16:
-                    self.ftype = gguf.LlamaFileType.MOSTLY_F16
-                    logger.info("heuristics detected float16 tensor dtype, setting --outtype f16")
-                    break
-            else:
-                self.ftype = gguf.LlamaFileType.MOSTLY_F16
-                logger.info("heuristics unable to detect tensor dtype, defaulting to --outtype f16")
-
-        # Configure GGUF Writer
-        self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
-                                           split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
-
-        # Mistral specific
-        self.disable_mistral_community_chat_template = disable_mistral_community_chat_template
-
-    @classmethod
-    def add_prefix_to_filename(cls, path: Path, prefix: str) -> Path:
-        stem, suffix = path.stem, path.suffix
-        new_name = f"{prefix}{stem}{suffix}"
-        return path.with_name(new_name)
-
-    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
-        key = next((k for k in keys if k in self.hparams), None)
-        if key is not None:
-            return self.hparams[key]
-        if optional:
-            return None
-        raise KeyError(f"could not find any of: {keys}")
-
-    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
-        tensors: dict[str, Callable[[], Tensor]] = {}
-
-        if remote_hf_model_id is not None:
-            is_safetensors = True
-
-            logger.info(f"Using remote model with HuggingFace id: {remote_hf_model_id}")
-            remote_tensors = gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id)
-            for name, remote_tensor in remote_tensors.items():
-                tensors[name] = lambda r=remote_tensor: LazyTorchTensor.from_remote_tensor(r)
-
-            return tensors
-
-        prefix = "model" if not self.is_mistral_format else "consolidated"
-        part_names: list[str] = ModelBase.get_model_part_names(self.dir_model, prefix, ".safetensors")
-        is_safetensors: bool = len(part_names) > 0
-        if not is_safetensors:
-            part_names = ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
-
-        tensor_names_from_index: set[str] = set()
-
-        if not self.is_mistral_format:
-            index_name = "model.safetensors" if is_safetensors else "pytorch_model.bin"
-            index_name += ".index.json"
-            index_file = self.dir_model / index_name
-
-            if index_file.is_file():
-                logger.info(f"gguf: loading model weight map from '{index_name}'")
-                with open(index_file, "r", encoding="utf-8") as f:
-                    index: dict[str, Any] = json.load(f)
-                    weight_map = index.get("weight_map")
-                    if weight_map is None or not isinstance(weight_map, dict):
-                        raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
-                    tensor_names_from_index.update(weight_map.keys())
-                    part_dict: dict[str, None] = dict.fromkeys(weight_map.values(), None) # ty: ignore[invalid-assignment]
-                    part_names = sorted(part_dict.keys())
-            else:
-                weight_map = {}
-        else:
-            weight_map = {}
-
-        for part_name in part_names:
-            logger.info(f"gguf: indexing model part '{part_name}'")
-            ctx: ContextManager[Any]
-            if is_safetensors:
-                ctx = cast(ContextManager[Any], gguf.utility.SafetensorsLocal(self.dir_model / part_name))
-            else:
-                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
-
-            with ctx as model_part:
-                assert model_part is not None
-
-                for name in model_part.keys():
-                    if is_safetensors:
-                        data: gguf.utility.LocalTensor = model_part[name]
-                        if self.lazy:
-                            data_gen = lambda data=data: LazyTorchTensor.from_local_tensor(data)  # noqa: E731
-                        else:
-                            dtype = LazyTorchTensor._dtype_str_map[data.dtype]
-                            data_gen = lambda data=data, dtype=dtype: torch.from_numpy(data.mmap_bytes()).view(dtype).reshape(data.shape)  # noqa: E731
-                    else:
-                        data_torch: Tensor = model_part[name]
-                        if self.lazy:
-                            data_gen = lambda data=data_torch: LazyTorchTensor.from_eager(data)  # noqa: E731
-                        else:
-                            data_gen = lambda data=data_torch: data  # noqa: E731
-                    tensors[name] = data_gen
-
-        # verify tensor name presence and identify potentially missing files
-        if len(tensor_names_from_index) > 0:
-            tensor_names_from_parts = set(tensors.keys())
-            if len(tensor_names_from_parts.symmetric_difference(tensor_names_from_index)) > 0:
-                missing = sorted(tensor_names_from_index.difference(tensor_names_from_parts))
-                extra = sorted(tensor_names_from_parts.difference(tensor_names_from_index))
-                missing_files = sorted(set(weight_map[n] for n in missing if n in weight_map))
-                if len(extra) == 0 and len(missing_files) > 0:
-                    raise ValueError(f"Missing or incomplete model files: {missing_files}\n"
-                                     f"Missing tensors: {missing}")
-                else:
-                    raise ValueError("Mismatch between weight map and model parts for tensor names:\n"
-                                     f"Missing tensors: {missing}\n"
-                                     f"Extra tensors: {extra}")
-
-        return tensors
-
-    def dequant_model(self):
-        # If all quantized tensors were already handled (e.g. pure NVFP4), skip
-        if self._is_nvfp4 and not any(k.endswith((".weight_scale", ".weight_scale_inv")) for k in self.model_tensors):
-            return
-
-        tensors_to_remove: list[str] = []
-        new_tensors: dict[str, Callable[[], Tensor]] = {}
-
-        if (quant_config := self.hparams.get("quantization_config")) and isinstance(quant_config, dict):
-            quant_method = quant_config.get("quant_method")
-
-            def dequant_bitnet(weight: Tensor, scale: Tensor) -> Tensor:
-                weight = weight.view(torch.uint8)
-                orig_shape = weight.shape
-
-                shift = torch.tensor([0, 2, 4, 6], dtype=torch.uint8).reshape((4, *(1 for _ in range(len(orig_shape)))))
-                data = weight.unsqueeze(0).expand((4, *orig_shape)) >> shift
-                data = data & 3
-                data = (data.float() - 1).reshape((orig_shape[0] * 4, *orig_shape[1:]))
-
-                # The scale is inverted
-                return data / scale.float()
-
-            def dequant_simple(weight: Tensor, scale: Tensor, block_size: Sequence[int] | None = None) -> Tensor:
-                scale = scale.float()
-
-                if block_size is not None:
-                    dim_offset = scale.ndim - len(block_size)
-                    for i, size in enumerate(block_size):
-                        scale = scale.repeat_interleave(size, dim_offset + i)
-                    # unpad the scale (e.g. when the tensor size isn't a multiple of the block size)
-                    scale = scale[tuple(slice(0, size) for size in weight.shape)]
-
-                # align scale dims to weight for correct broadcasting (e.g. [128] -> [128, 1, 1])
-                while scale.ndim < weight.ndim:
-                    scale = scale.unsqueeze(-1)
-
-                return weight.float() * scale
-
-            # ref: https://github.com/ModelCloud/GPTQModel/blob/037c5c0f6c9e33c500d975b038d02e7ca437546d/gptqmodel/nn_modules/qlinear/__init__.py#L437-L476
-            def dequant_gptq(g_idx: Tensor, qweight: Tensor, qzeros: Tensor, scales: Tensor) -> Tensor:
-                bits = quant_config["bits"]
-                assert bits in (2, 3, 4, 8)
-                assert qweight.dtype == qzeros.dtype
-                maxq = (2 ** bits) - 1
-                weight = None
-                zeros = None
-                pack_dtype_bits = qweight.dtype.itemsize * 8
-
-                if bits in [2, 4, 8]:
-                    pack_factor = pack_dtype_bits // bits
-                    wf = torch.tensor(list(range(0, pack_dtype_bits, bits)), dtype=torch.int32).unsqueeze(0)
-                    if self.lazy:
-                        wf = LazyTorchTensor.from_eager(wf)
-
-                    zeros = torch.bitwise_right_shift(
-                        qzeros.unsqueeze(2).expand(-1, -1, pack_factor),
-                        wf.unsqueeze(0)
-                    ).to(torch.int16 if bits == 8 else torch.int8)
-                    zeros = torch.bitwise_and(zeros, maxq).reshape(scales.shape)
-
-                    weight = torch.bitwise_and(
-                        torch.bitwise_right_shift(
-                            qweight.unsqueeze(1).expand(-1, pack_factor, -1),
-                            wf.unsqueeze(-1)
-                        ).to(torch.int16 if bits == 8 else torch.int8),
-                        maxq
-                    )
-                elif bits == 3:
-                    raise NotImplementedError("3-bit gptq dequantization is not yet implemented")
-
-                assert weight is not None
-                assert zeros is not None
-
-                weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
-
-                # gptq_v2 doesn't need to offset zeros
-                if quant_config.get("checkpoint_format", "gptq") == "gptq":
-                    zeros += 1
-
-                return (scales[g_idx].float() * (weight - zeros[g_idx]).float()).T
-
-            def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: Tensor | None, num_bits: int, group_size: int):
-                assert w.dtype == torch.int32
-                shape = tuple(shape_tensor.tolist())
-                assert len(shape) == 2
-                mask = (1 << num_bits) - 1
-
-                shifts = torch.arange(0, 32 - (num_bits - 1), num_bits, dtype=torch.int32)
-                if self.lazy:
-                    shifts = LazyTorchTensor.from_eager(shifts)
-
-                if zero_point is None:
-                    offset = 1 << (num_bits - 1)
-                else:
-                    assert len(zero_point.shape) == 2
-                    offset = (zero_point.unsqueeze(1) >> shifts.reshape(1, -1, 1)) & mask
-                    offset = offset.reshape(-1, zero_point.shape[1])
-                    # trim padding, and prepare for broadcast
-                    # NOTE: the zero-point is packed along dim 0
-                    offset = offset[:shape[0], :].unsqueeze(-1)
-
-                # extract values
-                # NOTE: the weights are packed along dim 1
-                unpacked = (w.unsqueeze(-1) >> shifts.reshape(1, 1, -1)) & mask
-                unpacked = unpacked.reshape(shape[0], -1)
-
-                # trim padding
-                unpacked = unpacked[:, :shape[1]]
-
-                # prepare for broadcast of the scale
-                unpacked = unpacked.reshape(shape[0], (unpacked.shape[-1] + group_size - 1) // group_size, group_size)
-                unpacked = unpacked - offset
-
-                return (unpacked * scale.unsqueeze(-1).float()).reshape(shape)
-
-            if quant_method == "bitnet":
-                for name in self.model_tensors.keys():
-                    if name.endswith(".weight_scale"):
-                        weight_name = name.removesuffix("_scale")
-                        w = self.model_tensors[weight_name]
-                        s = self.model_tensors[name]
-                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_bitnet(w(), s())
-                        tensors_to_remove.append(name)
-            elif quant_method == "fp8":
-                block_size = quant_config.get("weight_block_size")
-                for name in self.model_tensors.keys():
-                    if name.endswith("_scale_inv"):
-                        weight_name = name.removesuffix("_scale_inv")
-                        w = self.model_tensors[weight_name]
-                        s = self.model_tensors[name]
-                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
-                        tensors_to_remove.append(name)
-                    if name.endswith(".activation_scale"):  # unused
-                        tensors_to_remove.append(name)
-                    if name.endswith("_activation_scale"):  # Mistral-Small-4-119B-2602, unused
-                        tensors_to_remove.append(name)
-                    # mistral format
-                    if name.endswith(".qscale_weight"):
-                        weight_name = name.removesuffix("qscale_weight") + "weight"
-                        w = self.model_tensors[weight_name]
-                        s = self.model_tensors[name]
-                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
-                        tensors_to_remove.append(name)
-                    if name.endswith(".qscale_act"):
-                        tensors_to_remove.append(name)
-            elif quant_method == "gptq":
-                for name in self.model_tensors.keys():
-                    if name.endswith(".qweight"):
-                        base_name = name.removesuffix(".qweight")
-                        g_idx = self.model_tensors[base_name + ".g_idx"]
-                        qweight = self.model_tensors[base_name + ".qweight"]
-                        qzeros = self.model_tensors[base_name + ".qzeros"]
-                        scales = self.model_tensors[base_name + ".scales"]
-                        new_tensors[base_name + ".weight"] = (
-                            lambda g=g_idx, z=qzeros, w=qweight, s=scales: dequant_gptq(
-                                g(), w(), z(), s()
-                            )
-                        )
-                        tensors_to_remove += [
-                            base_name + n
-                            for n in (
-                                ".g_idx",
-                                ".qzeros",
-                                ".qweight",
-                                ".scales",
-                            )
-                        ]
-            elif quant_method == "compressed-tensors":
-                quant_format = quant_config["format"]
-                groups = quant_config["config_groups"]
-                if len(groups) > 1:
-                    raise NotImplementedError("Can't handle multiple config groups for compressed-tensors yet")
-                weight_config = tuple(groups.values())[0]["weights"]
-
-                if quant_format == "float-quantized" or quant_format == "int-quantized" or quant_format == "naive-quantized":
-                    block_size = weight_config.get("block_structure", None)
-                    strategy = weight_config.get("strategy")
-                    assert strategy == "channel" or strategy == "block"
-                    assert weight_config.get("group_size") is None  # didn't find a model using this yet
-                    for name in self.model_tensors.keys():
-                        if name.endswith(".weight_scale"):
-                            weight_name = name.removesuffix("_scale")
-                            w = self.model_tensors[weight_name]
-                            s = self.model_tensors[name]
-                            self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), block_size)
-                            tensors_to_remove.append(name)
-                elif quant_format == "pack-quantized":
-                    assert weight_config.get("strategy") == "group"
-                    assert weight_config.get("type", "int") == "int"
-                    num_bits = weight_config.get("num_bits")
-                    group_size = weight_config.get("group_size")
-                    assert isinstance(num_bits, int)
-                    assert isinstance(group_size, int)
-                    for name in self.model_tensors.keys():
-                        if name.endswith(".weight_packed"):
-                            base_name = name.removesuffix("_packed")
-                            w = self.model_tensors[name]
-                            scale = self.model_tensors[base_name + "_scale"]
-                            shape = self.model_tensors[base_name + "_shape"]
-                            zero_point = self.model_tensors.get(base_name + "_zero_point", lambda: None)
-                            new_tensors[base_name] = (
-                                lambda w=w, scale=scale, shape=shape, zero_point=zero_point: dequant_packed(
-                                    w(), scale(), shape(), zero_point(), num_bits, group_size,
-                                )
-                            )
-                            tensors_to_remove += [base_name + n for n in ("_packed", "_shape", "_scale")]
-                            if (base_name + "_zero_point") in self.model_tensors:
-                                tensors_to_remove.append(base_name + "_zero_point")
-                else:
-                    raise NotImplementedError(f"Quant format {quant_format!r} for method {quant_method!r} is not yet supported")
-            elif quant_method == "modelopt":
-                # Mixed-precision ModelOpt models: NVFP4 tensors are handled by
-                # _generate_nvfp4_tensors; FP8 tensors have 1D weight_scale and
-                # are dequantized here. k/v scale tensors are unused.
-                for name in self.model_tensors.keys():
-                    if name.endswith(".weight_scale"):
-                        weight_name = name.removesuffix("_scale")
-                        w = self.model_tensors[weight_name]
-                        s = self.model_tensors[name]
-                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None)
-                        tensors_to_remove.append(name)
-                    if name.endswith((".k_scale", ".v_scale")):
-                        tensors_to_remove.append(name)
-            elif quant_method is not None:
-                raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}")
-
-        for name in tensors_to_remove:
-            if name in self.model_tensors:
-                del self.model_tensors[name]
-
-        for name, value in new_tensors.items():
-            self.model_tensors[name] = value
-
-    def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
-        for name, gen in self.model_tensors.items():
-            yield name, gen()
-
-    def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
-        if key not in gguf.MODEL_TENSORS[self.model_arch]:
-            raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
-        name: str = gguf.TENSOR_NAMES[key]
-        if "{bid}" in name:
-            assert bid is not None
-            name = name.format(bid=bid)
-        return name + suffix
-
-    def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool:
-        if key not in gguf.MODEL_TENSORS[self.model_arch]:
-            return False
-        key_name: str = gguf.TENSOR_NAMES[key]
-        if "{bid}" in key_name:
-            if bid is None:
-                return False
-            key_name = key_name.format(bid=bid)
-        else:
-            if bid is not None:
-                return False
-        return name == (key_name + suffix)
-
-    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
-        new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
-        if new_name is None:
-            raise ValueError(f"Can not map tensor {name!r}")
-        return new_name
-
-    def set_gguf_parameters(self):
-        raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        new_name = self.map_tensor_name(name)
-
-        # Handle gate/up expert tensor fusion if enabled
-        if self.fuse_gate_up_exps and bid is not None:
-            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid):
-                self._gate_exp_buffer[bid] = data_torch
-            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
-                self._up_exp_buffer[bid] = data_torch
-
-            # Check if both gate and up are buffered for this layer
-            if bid in self._gate_exp_buffer and bid in self._up_exp_buffer:
-                gate_data = self._gate_exp_buffer.pop(bid)
-                up_data = self._up_exp_buffer.pop(bid)
-                # gate/up shape: (n_expert, n_ff, n_embd), concatenate to (n_expert, n_ff*2, n_embd)
-                fused_data = torch.cat([gate_data, up_data], dim=1)
-                fused_name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_UP_EXP, bid)
-                logger.info(f"Fused gate_exps and up_exps for layer {bid}")
-                return [(fused_name, fused_data)]
-
-            # If we buffered a gate/up tensor, wait for the other
-            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid) or \
-               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
-                return []
-
-        return [(new_name, data_torch)]
-
-    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
-        del name, new_name, bid, n_dims  # unused
-
-        return False
-
-    # some models need extra generated tensors (like rope_freqs)
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        return ()
-
-    @staticmethod
-    def _nvfp4_pack(weight: Tensor, scale: Tensor) -> tuple[np.ndarray, list[int]]:
-        """Repack NVFP4 ModelOpt tensors into ggml super-block layout.
-        Preserves original E4M3 scale bits as UE4M3 (strip sign bit).
-        The per-tensor scale2 factor is stored as a separate tensor and applied at inference time via ggml_mul().
-        Returns (raw_data, logical_shape)."""
-
-        out_features = weight.shape[0]
-        n_blocks = scale.shape[1]
-
-        # Unpack ModelOpt nibble-packed weights
-        w = weight.reshape(out_features, n_blocks, 8)
-        vals = torch.stack([w & 0x0F, w >> 4], dim=-1).reshape(out_features, n_blocks, 16)
-
-        # Preserve original E4M3 scale bits as UE4M3 (strip sign bit)
-        d_ue = scale.view(torch.uint8).numpy().reshape(out_features, n_blocks) & 0x7F
-        qs = (vals[:, :, :8] | (vals[:, :, 8:] << 4)).to(torch.uint8).numpy()
-
-        # Pack into super-blocks: [4 UE4M3 scales, 32 qs bytes] = 36 bytes per 64 elements
-        n_super = n_blocks // 4
-        d_grouped = d_ue.reshape(out_features, n_super, 4)
-        qs_grouped = qs.reshape(out_features, n_super, 4, 8).reshape(out_features, n_super, 32)
-        raw = np.concatenate([d_grouped, qs_grouped], axis=-1).reshape(out_features, n_super * 36)
-        return raw, [out_features, n_super * 64]
-
-    @staticmethod
-    def _nvfp4_scale2_is_trivial(scale2: Tensor) -> bool:
-        return scale2.numel() <= 1 and abs(float(scale2.float().sum()) - 1.0) < 1e-6
-
-    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
-        if "language_model." in name:
-            name = name.replace("language_model.", "")
-
-        new_name = self.map_tensor_name(name)
-
-        raw, shape = self._nvfp4_pack(weight, scale)
-        logger.info(f"Repacked {new_name} with shape {shape} and quantization NVFP4")
-        self.gguf_writer.add_tensor(new_name, raw, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
-
-        # Emit per-tensor scale2 as a separate F32 tensor when non-trivial
-        if not self._nvfp4_scale2_is_trivial(scale2):
-            scale2_f32 = scale2.float().numpy().flatten()
-            scale_name = new_name.replace(".weight", ".scale")
-            logger.info(f"  + {scale_name} (per-tensor NVFP4 scale2, shape [{scale2_f32.size}])")
-            self.gguf_writer.add_tensor(scale_name, scale2_f32)
-
-        # Emit per-tensor input_scale as a separate F32 tensor when non-trivial
-        if not self._nvfp4_scale2_is_trivial(input_scale):
-            input_scale_f32 = input_scale.float().numpy().flatten()
-            input_scale_name = new_name.replace(".weight", ".input_scale")
-            logger.info(f"  + {input_scale_name} (per-tensor NVFP4 input_scale, shape [{input_scale_f32.size}])")
-            self.gguf_writer.add_tensor(input_scale_name, input_scale_f32)
-
-    def _generate_nvfp4_tensors(self):
-        # Per-layer expert merging to avoid holding all experts in memory
-        expert_blocks: dict[tuple[int, str], list[tuple[int, np.ndarray]]] = {}
-        expert_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
-        expert_input_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
-        expert_shapes: dict[tuple[int, str], list[int]] = {}
-        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
-        consumed: list[str] = []
-
-        for name in list(self.model_tensors.keys()):
-            if not name.endswith(".weight"):
-                continue
-            scale_name = name.replace(".weight", ".weight_scale")
-            scale2_name = name.replace(".weight", ".weight_scale_2")
-            input_scale_name = name.replace(".weight", ".input_scale")
-            if scale_name not in self.model_tensors:
-                continue
-            # Force eager materialization of lazy tensors
-            weight = LazyTorchTensor.to_eager(self.model_tensors[name]())
-            scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]())
-
-            # Skip non-NVFP4 tensors (e.g. FP8 with per-channel 1D scales)
-            if scale.ndim < 2:
-                continue
-
-            scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))())
-            input_scale = LazyTorchTensor.to_eager(self.model_tensors.get(input_scale_name, lambda: torch.tensor(1.0))())
-
-            # Mark tensors for removal from model_tensors (already written to gguf)
-            consumed.extend([name, scale_name])
-            if scale2_name in self.model_tensors:
-                consumed.append(scale2_name)
-            if input_scale_name in self.model_tensors:
-                consumed.append(input_scale_name)
-
-            # Check if this is a per-expert tensor
-            m = re.search(r'\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight$', name)
-            if m:
-                expert_id = int(m.group(1))
-                proj_type = m.group(2)
-                bid_m = re.search(r'\.layers\.(\d+)\.', name)
-                bid = int(bid_m.group(1)) if bid_m else 0
-                key = (bid, proj_type)
-
-                raw, shape = self._nvfp4_pack(weight, scale)
-
-                if key not in expert_blocks:
-                    expert_blocks[key] = []
-                    expert_scales[key] = []
-                    expert_input_scales[key] = []
-                    expert_shapes[key] = shape
-                expert_blocks[key].append((expert_id, raw.copy()))
-                # Collect per-expert scale2 (scalar per expert)
-                expert_scales[key].append((expert_id, float(scale2.float().sum())))
-                # Collect per-expert input_scale (scalar per expert)
-                expert_input_scales[key].append((expert_id, float(input_scale.float().sum())))
-
-                # Flush when all experts for this (layer, proj) are collected
-                if n_experts > 0 and len(expert_blocks[key]) >= n_experts:
-                    self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
-            else:
-                self._repack_nvfp4(name, weight, scale, scale2, input_scale)
-
-        # Flush any remaining experts (fallback if n_experts was unknown)
-        for (bid, proj_type) in list(expert_blocks.keys()):
-            self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
-
-        # Remove consumed tensors so get_tensors/modify_tensors won't see them
-        for name in consumed:
-            self.model_tensors.pop(name, None)
-
-        # Remove any remaining unused auxiliary tensors
-        for name in list(self.model_tensors.keys()):
-            if name.endswith((".k_scale", ".v_scale")):
-                del self.model_tensors[name]
-
-    def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type):
-        experts = expert_blocks.pop(key)
-        scales = expert_scales.pop(key)
-        input_scales = expert_input_scales.pop(key)
-        shape = expert_shapes.pop(key)
-
-        experts.sort(key=lambda x: x[0])
-        merged = np.stack([e[1] for e in experts], axis=0)
-        merged_name = f"model.layers.{bid}.mlp.experts.{proj_type}.weight"
-        new_name = self.map_tensor_name(merged_name)
-        logger.info(f"Repacked {new_name} with shape [{len(experts)}, {shape[0]}, {shape[1]}] and quantization NVFP4")
-        self.gguf_writer.add_tensor(new_name, merged, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
-
-        # Emit per-expert scale2 tensor if any expert has non-trivial scale2
-        scales.sort(key=lambda x: x[0])
-        scale_vals = np.array([s[1] for s in scales], dtype=np.float32)
-        if not np.allclose(scale_vals, 1.0, atol=1e-6):
-            scale_name = new_name.replace(".weight", ".scale")
-            logger.info(f"  + {scale_name} (per-expert NVFP4 scale2, shape [{len(scales)}])")
-            self.gguf_writer.add_tensor(scale_name, scale_vals)
-
-        # Emit per-expert input_scale tensor if any expert has non-trivial input_scale
-        input_scales.sort(key=lambda x: x[0])
-        input_scale_vals = np.array([s[1] for s in input_scales], dtype=np.float32)
-        if not np.allclose(input_scale_vals, 1.0, atol=1e-6):
-            input_scale_name = new_name.replace(".weight", ".input_scale")
-            logger.info(f"  + {input_scale_name} (per-expert NVFP4 input_scale, shape [{len(input_scales)}])")
-            self.gguf_writer.add_tensor(input_scale_name, input_scale_vals)
-
-        del experts, merged
-
-    def prepare_tensors(self):
-        # detect NVFP4 quantization (ModelOpt format)
-        quant_algo = (self.hparams.get("quantization_config") or {}).get("quant_algo")
-        quant_method = (self.hparams.get("quantization_config") or {}).get("quant_method")
-        quant_layers = (self.hparams.get("quantization_config") or {}).get("quantized_layers") or {}
-        quant_config_file = self.dir_model / "hf_quant_config.json"
-
-        if (not quant_algo or not quant_layers) and quant_config_file.is_file():
-            with open(quant_config_file, "r", encoding="utf-8") as f:
-                quant_config = json.load(f).get("quantization") or {}
-                quant_algo = quant_config.get("quant_algo", quant_algo)
-                quant_layers = quant_config.get("quantized_layers", quant_layers) or {}
-
-        # Some models use per-tensor quant_algo (e.g. "MIXED_PRECISION" with
-        # per-layer NVFP4/FP8) instead of a single global "NVFP4" value.
-        if quant_algo != "NVFP4":
-            if any(v.get("quant_algo") == "NVFP4" for v in quant_layers.values() if isinstance(v, dict)):
-                quant_algo = "NVFP4"
-
-        self._is_nvfp4 = quant_algo == "NVFP4"
-        self._is_mxfp4 = quant_method == "mxfp4"
-
-        # NVFP4 weights are repacked and written directly to gguf_writer.
-        # This must run before dequant_model so NVFP4 tensors are removed
-        # from model_tensors, leaving only non-NVFP4 (e.g. FP8) for dequant.
-        if self._is_nvfp4:
-            self._generate_nvfp4_tensors()
-
-        self.dequant_model()
-
-        # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
-        if self.tensor_map.mapping:
-            max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
-        else:
-            max_name_len = len("vision_encoder.weight,")  # Default reasonable length
-
-        for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()):
-            # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
-                continue
-
-            old_dtype = data_torch.dtype
-
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            # use the first number-like part of the tensor name as the block id
-            bid = None
-            for part in name.split("."):
-                if part.isdecimal():
-                    bid = int(part)
-                    break
-
-            for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
-                # TODO: why do we squeeze here?
-                # data = data_torch.squeeze().numpy()
-                data = data_torch.numpy()
-
-                n_dims = len(data.shape)
-                data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
-
-                # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
-                if n_dims <= 1 or new_name.endswith("_norm.weight"):
-                    data_qtype = gguf.GGMLQuantizationType.F32
-
-                # Conditions should closely match those in llama_model_quantize_internal in llama.cpp
-                # Some tensor types are always in float32
-                if data_qtype is False and (
-                    any(
-                        self.match_model_tensor_name(new_name, key, bid)
-                        for key in (
-                            gguf.MODEL_TENSOR.FFN_GATE_INP,
-                            gguf.MODEL_TENSOR.FFN_GATE_INP_SHEXP,
-                            gguf.MODEL_TENSOR.POS_EMBD,
-                            gguf.MODEL_TENSOR.TOKEN_TYPES,
-                            gguf.MODEL_TENSOR.SSM_CONV1D,
-                            gguf.MODEL_TENSOR.SHORTCONV_CONV,
-                            gguf.MODEL_TENSOR.TIME_MIX_FIRST,
-                            gguf.MODEL_TENSOR.TIME_MIX_W1,
-                            gguf.MODEL_TENSOR.TIME_MIX_W2,
-                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
-                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
-                            gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
-                            gguf.MODEL_TENSOR.POSNET_NORM1,
-                            gguf.MODEL_TENSOR.POSNET_NORM2,
-                            gguf.MODEL_TENSOR.V_ENC_EMBD_POS,
-                            gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
-                            gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
-                            gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
-                            # Kimi KDA conv weights should be F32
-                            gguf.MODEL_TENSOR.SSM_CONV1D_Q,
-                            gguf.MODEL_TENSOR.SSM_CONV1D_K,
-                            gguf.MODEL_TENSOR.SSM_CONV1D_V,
-                        )
-                    )
-                    or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
-                ):
-                    data_qtype = gguf.GGMLQuantizationType.F32
-
-                if data_qtype is False and any(
-                    self.match_model_tensor_name(new_name, key, bid)
-                    for key in (
-                        gguf.MODEL_TENSOR.TOKEN_EMBD,
-                        gguf.MODEL_TENSOR.PER_LAYER_TOKEN_EMBD,
-                        gguf.MODEL_TENSOR.OUTPUT,
-                        gguf.MODEL_TENSOR.ALTUP_ROUTER,
-                        gguf.MODEL_TENSOR.LAUREL_L,
-                        gguf.MODEL_TENSOR.LAUREL_R,
-                    )
-                ):
-                    if self.ftype in (
-                        gguf.LlamaFileType.MOSTLY_TQ1_0,
-                        gguf.LlamaFileType.MOSTLY_TQ2_0,
-                    ):
-                        # TODO: use Q4_K and Q6_K
-                        data_qtype = gguf.GGMLQuantizationType.F16
-
-                # No override (data_qtype is False), or wants to be quantized (data_qtype is True)
-                if isinstance(data_qtype, bool):
-                    if self.ftype == gguf.LlamaFileType.ALL_F32:
-                        data_qtype = gguf.GGMLQuantizationType.F32
-                    elif self.ftype == gguf.LlamaFileType.MOSTLY_F16:
-                        data_qtype = gguf.GGMLQuantizationType.F16
-                    elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
-                        data_qtype = gguf.GGMLQuantizationType.BF16
-                    elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
-                        data_qtype = gguf.GGMLQuantizationType.Q8_0
-                    elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ1_0:
-                        data_qtype = gguf.GGMLQuantizationType.TQ1_0
-                    elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ2_0:
-                        data_qtype = gguf.GGMLQuantizationType.TQ2_0
-                    else:
-                        raise ValueError(f"Unknown file type: {self.ftype.name}")
-
-                try:
-                    data = gguf.quants.quantize(data, data_qtype)
-                except gguf.QuantError as e:
-                    logger.warning("%s, %s", e, "falling back to F16")
-                    data_qtype = gguf.GGMLQuantizationType.F16
-                    data = gguf.quants.quantize(data, data_qtype)
-
-                shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape
-
-                # reverse shape to make it similar to the internal ggml dimension order
-                shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}"
-
-                # n_dims is implicit in the shape
-                logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
-
-                self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
-
-    def set_type(self):
-        self.gguf_writer.add_type(gguf.GGUFType.MODEL)
-
-    def prepare_metadata(self, vocab_only: bool):
-
-        total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count()
-
-        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params)
-
-        # If we are using HF model id, set the metadata name to the model id
-        if self.remote_hf_model_id:
-            self.metadata.name = self.remote_hf_model_id
-
-        # Fallback to model directory name if metadata name is still missing
-        if self.metadata.name is None:
-            self.metadata.name = self.dir_model.name
-
-        if self.ftype in (gguf.LlamaFileType.ALL_F32, gguf.LlamaFileType.MOSTLY_F16, gguf.LlamaFileType.MOSTLY_BF16):
-            if self._is_nvfp4:
-                self.ftype = gguf.LlamaFileType.MOSTLY_NVFP4
-            elif self._is_mxfp4:
-                self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE
-
-        # Generate parameter weight class (useful for leader boards) if not yet determined
-        if self.metadata.size_label is None and total_params > 0:
-            self.metadata.size_label = gguf.size_label(total_params, shared_params, expert_params, expert_count)
-
-        self.set_type()
-
-        logger.info("Set meta model")
-        self.metadata.set_gguf_meta_model(self.gguf_writer)
-
-        logger.info("Set model parameters")
-        self.set_gguf_parameters()
-
-        logger.info("Set model quantization version")
-        self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
-
-    def write_vocab(self):
-        raise NotImplementedError("write_vocab() must be implemented in subclasses")
-
-    def write(self):
-        self.prepare_tensors()
-        self.prepare_metadata(vocab_only=False)
-        self.gguf_writer.write_header_to_file(path=self.fname_out)
-        self.gguf_writer.write_kv_data_to_file()
-        self.gguf_writer.write_tensors_to_file(progress=True)
-        self.gguf_writer.close()
-
-    @staticmethod
-    def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]:
-        part_names: list[str] = []
-        for filename in os.listdir(dir_model):
-            if filename.startswith(prefix) and filename.endswith(suffix):
-                part_names.append(filename)
-
-        part_names.sort()
-
-        return part_names
-
-    @staticmethod
-    def load_hparams(dir_model: Path, is_mistral_format: bool):
-        if is_mistral_format:
-            with open(dir_model / "params.json", "r", encoding="utf-8") as f:
-                config = json.load(f)
-            return config
-
-        try:
-            # for security reason, we don't allow loading remote code by default
-            # if a model need remote code, we will fallback to config.json
-            config = AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict()
-        except Exception as e:
-            logger.warning(f"Failed to load model config from {dir_model}: {e}")
-            logger.warning("Trying to load config.json instead")
-            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
-                config = json.load(f)
-        if "llm_config" in config:
-            # rename for InternVL
-            config["text_config"] = config["llm_config"]
-        if "lm_config" in config:
-            # rename for GlmASR
-            config["text_config"] = config["lm_config"]
-        if "thinker_config" in config:
-            # rename for Qwen2.5-Omni
-            config["text_config"] = config["thinker_config"]["text_config"]
-        if "language_config" in config:
-            # rename for DeepSeekOCR
-            config["text_config"] = config["language_config"]
-        if "lfm" in config:
-            # rename for LFM2-Audio
-            config["text_config"] = config["lfm"]
-        return config
-
-    @classmethod
-    def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
-        assert names
-
-        def func(modelcls: AnyModel) -> AnyModel:
-            model_type = ModelType.MMPROJ if modelcls.model_arch == gguf.MODEL_ARCH.MMPROJ else ModelType.TEXT
-            for name in names:
-                cls._model_classes[model_type][name] = modelcls
-            return modelcls
-        return func
-
-    @classmethod
-    def print_registered_models(cls):
-        for model_type, model_classes in cls._model_classes.items():
-            logger.error(f"{model_type.name} models:")
-            for name in sorted(model_classes.keys()):
-                logger.error(f"  - {name}")
-
-    @classmethod
-    def from_model_architecture(cls, arch: str, model_type = ModelType.TEXT) -> type[ModelBase]:
-        try:
-            return cls._model_classes[model_type][arch]
-        except KeyError:
-            raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
-
-
-class TextModel(ModelBase):
-    model_type = ModelType.TEXT
-    hf_arch: str
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if not self.is_mistral_format:
-            self.hf_arch = get_model_architecture(self.hparams, self.model_type)
-        else:
-            self.hf_arch = ""
-
-        if "text_config" in self.hparams:
-            # move the text_config to the root level
-            self.hparams = {**self.hparams, **self.hparams["text_config"]}
-
-        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-        self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {}
-
-        rope_theta = self.find_hparam(["global_rope_theta", "rope_global_theta", "rope_theta_global", "rope_theta", "rotary_emb_base"], optional=True)
-        local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "rope_theta_local", "swa_rope_theta", "rope_local_base_freq"], optional=True)
-
-        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
-        if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
-            if local_rope_theta is not None:
-                self.rope_parameters["sliding_attention"] = {"rope_theta": local_rope_theta}
-            if "rope_theta" not in self.rope_parameters and rope_theta is not None:
-                self.rope_parameters["rope_theta"] = rope_theta
-            if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
-                self.rope_parameters["rope_type"] = rope_type
-
-    @classmethod
-    def __init_subclass__(cls):
-        # can't use an abstract property, because overriding it without type errors
-        # would require using decorated functions instead of simply defining the property
-        if "model_arch" not in cls.__dict__:
-            raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}")
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def prepare_metadata(self, vocab_only: bool):
-        super().prepare_metadata(vocab_only=vocab_only)
-
-        total_params = self.gguf_writer.get_total_parameter_count()[0]
-        # Extract the encoding scheme from the file type name. e.g. 'gguf.LlamaFileType.MOSTLY_Q8_0' --> 'Q8_0'
-        output_type: str = self.ftype.name.partition("_")[2]
-
-        # Filename Output
-        if self.fname_out.is_dir():
-            # Generate default filename based on model specification and available metadata
-            if not vocab_only:
-                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None)
-            else:
-                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab")
-
-            # Use the default filename
-            self.fname_out = self.fname_out / f"{fname_default}.gguf"
-        else:
-            # Output path is a custom defined templated filename
-            # Note: `not is_dir()` is used because `.is_file()` will not detect
-            #       file template strings as it doesn't actually exist as a file
-
-            # Process templated file name with the output ftype, useful with the "auto" ftype
-            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
-
-        logger.info("Set model tokenizer")
-        self.set_vocab()
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_block_count(self.block_count)
-
-        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length", "max_sequence_length", "model_max_length"], optional=True)) is not None:
-            self.gguf_writer.add_context_length(n_ctx)
-            logger.info(f"gguf: context length = {n_ctx}")
-
-        if (n_embd := self.find_hparam(["hidden_size", "n_embd", "dim"], optional=True)) is not None:
-            self.gguf_writer.add_embedding_length(n_embd)
-            logger.info(f"gguf: embedding length = {n_embd}")
-
-        if (n_ff := self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
-            self.gguf_writer.add_feed_forward_length(n_ff)
-            logger.info(f"gguf: feed forward length = {n_ff}")
-
-        if (n_head := self.find_hparam(["num_attention_heads", "n_head", "n_heads"], optional=True)) is not None:
-            self.gguf_writer.add_head_count(n_head)
-            logger.info(f"gguf: head count = {n_head}")
-
-        if (n_head_kv := self.find_hparam(["num_key_value_heads", "n_kv_heads"], optional=True)) is not None:
-            self.gguf_writer.add_head_count_kv(n_head_kv)
-            logger.info(f"gguf: key-value head count = {n_head_kv}")
-
-        if self.hparams.get("is_causal") is False:
-            self.gguf_writer.add_causal_attention(False)
-            logger.info("gguf: causal attention = False")
-
-        # TODO: Handle "sliding_attention" similarly when models start implementing it
-        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
-        if (rope_type := rope_params.get("rope_type")) is not None:
-            rope_factor = rope_params.get("factor")
-            rope_gguf_type = gguf.RopeScalingType.NONE
-            if rope_type == "linear" and rope_factor is not None:
-                rope_gguf_type = gguf.RopeScalingType.LINEAR
-                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
-                self.gguf_writer.add_rope_scaling_factor(rope_factor)
-            elif rope_type == "yarn" and rope_factor is not None:
-                rope_gguf_type = gguf.RopeScalingType.YARN
-                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
-                self.gguf_writer.add_rope_scaling_factor(rope_factor)
-                self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"])
-                if (yarn_ext_factor := rope_params.get("extrapolation_factor")) is not None:
-                    self.gguf_writer.add_rope_scaling_yarn_ext_factor(yarn_ext_factor)
-                if (yarn_attn_factor := rope_params.get("attention_factor", rope_params.get("attn_factor"))) is not None:
-                    self.gguf_writer.add_rope_scaling_yarn_attn_factor(yarn_attn_factor)
-                if (yarn_beta_fast := rope_params.get("beta_fast")) is not None:
-                    self.gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_beta_fast)
-                if (yarn_beta_slow := rope_params.get("beta_slow")) is not None:
-                    self.gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_beta_slow)
-                # self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
-            elif rope_type == "su" or rope_type == "longrope":
-                rope_gguf_type = gguf.RopeScalingType.LONGROPE
-                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
-            elif rope_type == "dynamic":
-                # HunYuan, handled in model class
-                pass
-            elif rope_type.lower() == "llama3":
-                # Handled in generate_extra_tensors
-                pass
-            else:
-                logger.warning(f"Unknown RoPE type: {rope_type}")
-            logger.info(f"gguf: rope scaling type = {rope_gguf_type.name}")
-
-        if "mrope_section" in self.rope_parameters:
-            mrope_section = self.rope_parameters["mrope_section"]
-            # Pad to 4 dimensions [time, height, width, extra]
-            while len(mrope_section) < 4:
-                mrope_section.append(0)
-            self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
-            logger.info(f"gguf: mrope sections: {mrope_section[:4]}")
-
-        if (rope_theta := rope_params.get("rope_theta")) is not None:
-            self.gguf_writer.add_rope_freq_base(rope_theta)
-            logger.info(f"gguf: rope theta = {rope_theta}")
-        if (local_rope_theta := self.rope_parameters.get("sliding_attention", {}).get("rope_theta")) is not None:
-            self.gguf_writer.add_rope_freq_base_swa(local_rope_theta)
-            logger.info(f"gguf: rope theta swa = {local_rope_theta}")
-        if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"], optional=True)) is not None:
-            self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
-            logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
-        if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
-            self.gguf_writer.add_layer_norm_eps(f_norm_eps)
-            logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
-        if (n_experts := self.find_hparam(["num_local_experts", "num_experts"], optional=True)) is not None:
-            self.gguf_writer.add_expert_count(n_experts)
-            logger.info(f"gguf: expert count = {n_experts}")
-        if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token", "top_k_experts"], optional=True)) is not None:
-            self.gguf_writer.add_expert_used_count(n_experts_used)
-            logger.info(f"gguf: experts used count = {n_experts_used}")
-        if (n_expert_groups := self.hparams.get("n_group")) is not None:
-            self.gguf_writer.add_expert_group_count(n_expert_groups)
-            logger.info(f"gguf: expert groups count = {n_expert_groups}")
-        if (n_group_used := self.hparams.get("topk_group")) is not None:
-            self.gguf_writer.add_expert_group_used_count(n_group_used)
-            logger.info(f"gguf: expert groups used count = {n_group_used}")
-
-        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None:
-            if score_func == "sigmoid":
-                self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
-            elif score_func == "softmax":
-                self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
-            else:
-                raise ValueError(f"Unsupported expert score gating function value: {score_func}")
-            logger.info(f"gguf: expert score gating function = {score_func}")
-
-        if (head_dim := self.hparams.get("head_dim")) is not None:
-            self.gguf_writer.add_key_length(head_dim)
-            self.gguf_writer.add_value_length(head_dim)
-
-        self.gguf_writer.add_file_type(self.ftype)
-        logger.info(f"gguf: file type = {self.ftype}")
-
-    def write_vocab(self):
-        if len(self.gguf_writer.tensors) != 1:
-            raise ValueError('Splitting the vocabulary is not supported')
-
-        self.prepare_metadata(vocab_only=True)
-        self.gguf_writer.write_header_to_file(path=self.fname_out)
-        self.gguf_writer.write_kv_data_to_file()
-        self.gguf_writer.close()
-
-    def does_token_look_special(self, token: str | bytes) -> bool:
-        if isinstance(token, (bytes, bytearray)):
-            token_text = token.decode(encoding="utf-8")
-        elif isinstance(token, memoryview):
-            token_text = token.tobytes().decode(encoding="utf-8")
-        else:
-            token_text = token
-
-        # Some models mark some added tokens which ought to be control tokens as not special.
-        # (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
-        seems_special = token_text in (
-            "<pad>",  # deepseek-coder
-            "<mask>", "<2mass>", "[@BOS@]",  # gemma{,-2}
-        )
-
-        seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
-        seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>"))  # deepseek-coder
-
-        # TODO: should these be marked as UNUSED instead? (maybe not)
-        seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">"))  # gemma{,-2}
-
-        return seems_special
-
-    # used for GPT-2 BPE and WordPiece vocabs
-    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-        vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
-        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
-        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
-
-        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            else:
-                token: str = reverse_vocab[i]
-                if token in added_vocab:
-                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
-                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
-                    if not added_tokens_decoder[i].normalized:
-                        previous_token = token
-                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
-                        if previous_token != token:
-                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
-
-                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        # NOTE: this was added for Gemma.
-                        # Encoding and decoding the tokens above isn't sufficient for this case.
-                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    toktypes.append(gguf.TokenType.NORMAL)
-                tokens.append(token)
-
-        return tokens, toktypes, tokpre
-
-    # NOTE: this function is generated by convert_hf_to_gguf_update.py
-    #       do not modify it manually!
-    # ref:  https://github.com/ggml-org/llama.cpp/pull/6920
-    # Marker: Start get_vocab_base_pre
-    def get_vocab_base_pre(self, tokenizer) -> str:
-        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
-        # is specific for the BPE pre-tokenizer used by the model
-        # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
-        # use in llama.cpp to implement the same pre-tokenizer
-
-        chktxt = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
-
-        chktok = tokenizer.encode(chktxt)
-        chkhsh = sha256(str(chktok).encode()).hexdigest()
-
-        logger.debug(f"chktok: {chktok}")
-        logger.debug(f"chkhsh: {chkhsh}")
-
-        res = None
-
-        # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
-        #       or pull the latest version of the model from Huggingface
-        #       don't edit the hashes manually!
-        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
-            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
-            res = "chatglm-bpe"
-        if chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
-            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
-            res = "chatglm-bpe"
-        if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
-            # ref: https://huggingface.co/THUDM/glm-4-9b-hf
-            res = "glm4"
-        if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
-            # ref: https://huggingface.co/zai-org/GLM-4.5-Air
-            res = "glm4"
-        if chkhsh == "cdf5f35325780597efd76153d4d1c16778f766173908894c04afc20108536267":
-            # ref: https://huggingface.co/zai-org/GLM-4.7-Flash
-            res = "glm4"
-        if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
-            # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
-            res = "minerva-7b"
-        if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
-            # ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
-            res = "hunyuan"
-        if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
-            # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
-            res = "hunyuan-dense"
-        if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
-            # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
-            res = "falcon-h1"
-        if chkhsh == "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86":
-            # ref: https://huggingface.co/tiiuae/Falcon-H1-1B-Base
-            res = "falcon-h1"
-        if chkhsh == "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896":
-            # ref: https://huggingface.co/tiiuae/Falcon-H1-7B-Base
-            res = "falcon-h1"
-        if chkhsh == "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b":
-            # ref: https://huggingface.co/tiiuae/Falcon-H1-34B-Base
-            res = "falcon-h1"
-        if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890":
-            # ref: https://huggingface.co/moonshotai/Kimi-K2-Base
-            res = "kimi-k2"
-        if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
-            # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
-            res = "qwen2"
-        if chkhsh == "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273":
-            # ref: https://huggingface.co/alvarobartt/grok-2-tokenizer
-            res = "grok-2"
-        if chkhsh == "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df":
-            # ref: https://huggingface.co/aari1995/German_Semantic_V3
-            res = "jina-v2-de"
-        if chkhsh == "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4":
-            # ref: https://huggingface.co/evilfreelancer/ruGPT3XL
-            res = "gpt-2"
-        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
-            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
-            res = "llama-bpe"
-        if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754":
-            # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base
-            res = "deepseek-llm"
-        if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821":
-            # ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
-            res = "deepseek-coder"
-        if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
-            # ref: https://huggingface.co/tiiuae/falcon-7b
-            res = "falcon"
-        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
-            # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
-            res = "bert-bge"
-        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
-            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
-            res = "falcon3"
-        if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7":
-            # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5
-            res = "bert-bge-large"
-        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
-            # ref: https://huggingface.co/mosaicml/mpt-7b
-            res = "mpt"
-        if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34":
-            # ref: https://huggingface.co/bigcode/starcoder2-3b
-            res = "starcoder"
-        if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
-            # ref: https://huggingface.co/openai-community/gpt2
-            res = "gpt-2"
-        if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3":
-            # ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b
-            res = "stablelm2"
-        if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
-            # ref: https://huggingface.co/smallcloudai/Refact-1_6-base
-            res = "refact"
-        if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
-            # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
-            res = "command-r"
-        if chkhsh == "d772b220ace2baec124bed8cfafce0ead7d6c38a4b65ef11261cf9d5d62246d1":
-            # ref: https://huggingface.co/CohereLabs/tiny-aya-base
-            res = "tiny_aya"
-        if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
-            # ref: https://huggingface.co/Qwen/Qwen1.5-7B
-            res = "qwen2"
-        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
-            # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf
-            res = "olmo"
-        if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
-            # ref: https://huggingface.co/databricks/dbrx-base
-            res = "dbrx"
-        if chkhsh == "c7699093ba4255a91e702aa38a596aa81669f3525dae06c2953267dde580f448":
-            # ref: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
-            res = "jina-v1-en"
-        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
-            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
-            res = "jina-v2-en"
-        if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643":
-            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es
-            res = "jina-v2-es"
-        if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
-            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
-            res = "jina-v2-de"
-        if chkhsh == "a023e9fdc5a11f034d3ef515b92350e56fb2af1f66c6b6811a4444ea9bf8763d":
-            # ref: https://huggingface.co/jinaai/jina-embeddings-v5-text-nano
-            res = "jina-v5-nano"
-        if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d":
-            # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
-            res = "smaug-bpe"
-        if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360":
-            # ref: https://huggingface.co/LumiOpen/Poro-34B-chat
-            res = "poro-chat"
-        if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
-            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
-            res = "jina-v2-code"
-        if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
-            # ref: https://huggingface.co/LumiOpen/Viking-7B
-            res = "viking"
-        if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
-            # ref: https://huggingface.co/core42/jais-13b
-            res = "jais"
-        if chkhsh == "bc5108ee1eb6a3d600cadd065f63190fbd0554dbc9e4bbd6a0d977970afc8d2a":
-            # ref: https://huggingface.co/inceptionai/Jais-2-8B-Chat
-            res = "jais-2"
-        if chkhsh == "7b3e7548e4308f52a76e8229e4e6cc831195d0d1df43aed21ac6c93da05fec5f":
-            # ref: https://huggingface.co/WisdomShell/CodeShell-7B
-            res = "codeshell"
-        if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e":
-            # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
-            res = "tekken"
-        if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249":
-            # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M
-            res = "smollm"
-        if chkhsh == "3c30d3ad1d6b64202cd222813e7736c2db6e1bd6d67197090fc1211fbc612ae7":
-            # ref: https://huggingface.co/bigscience/bloom
-            res = "bloom"
-        if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21":
-            # ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small
-            res = "gpt3-finnish"
-        if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae":
-            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
-            res = "exaone"
-        if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085":
-            # ref: https://huggingface.co/microsoft/phi-2
-            res = "phi-2"
-        if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450":
-            # ref: https://huggingface.co/facebook/chameleon-7b
-            res = "chameleon"
-        if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65":
-            # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
-            res = "roberta-bpe"
-        if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
-            # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
-            res = "gigachat"
-        if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
-            # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
-            res = "megrez"
-        if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
-            # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
-            res = "deepseek-v3"
-        if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5":
-            # ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
-            res = "deepseek-r1-qwen"
-        if chkhsh == "ccc2ef013c104be7bae2965776d611e1d7a8a2a9c547dd93a682c9a9fc80352e":
-            # ref: https://huggingface.co/Xenova/gpt-4o
-            res = "gpt-4o"
-        if chkhsh == "7dec86086fcc38b66b7bc1575a160ae21cf705be7718b9d5598190d7c12db76f":
-            # ref: https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k
-            res = "superbpe"
-        if chkhsh == "1994ffd01900cfb37395608534236ecd63f2bd5995d6cb1004dda1af50240f15":
-            # ref: https://huggingface.co/trillionlabs/Trillion-7B-preview
-            res = "trillion"
-        if chkhsh == "96a5f08be6259352137b512d4157e333e21df7edd3fcd152990608735a65b224":
-            # ref: https://huggingface.co/inclusionAI/Ling-lite
-            res = "bailingmoe"
-        if chkhsh == "d353350c764d8c3b39c763113960e4fb4919bea5fbf208a0e3b22e8469dc7406":
-            # ref: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
-            res = "llama4"
-        if chkhsh == "0e9433cbbb161f89e264eb32e8e64bfe69e834973ffca5d41d3948a604a3e2a3":
-            # ref: https://huggingface.co/mistral-community/pixtral-12b
-            res = "pixtral"
-        if chkhsh == "d5f1dd6f980fec569fb218a81a7658ac45fc56b38c5a0adeb1c232fbe04ef5ec":
-            # ref: https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base
-            res = "seed-coder"
-        if chkhsh == "b0a6b1c0bd5998ebd9df08611efde34a4ff03faed45ae09c43e6b31ebd4b94cf":
-            # ref: https://huggingface.co/skt/A.X-4.0
-            res = "a.x-4.0"
-        if chkhsh == "f6791d196f87ce6b56a7d234be618e0d58f8cda3549416635b2bebcd22cd95c4":
-            # ref: https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct
-            res = "midm-2.0"
-        if chkhsh == "169bf0296a13c4d9b7672313f749eb36501d931022de052aad6e36f2bf34dd51":
-            # ref: https://huggingface.co/LiquidAI/LFM2-Tokenizer
-            res = "lfm2"
-        if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb":
-            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
-            res = "exaone4"
-        if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
-            # ref: https://huggingface.co/JetBrains/Mellum-4b-base
-            res = "mellum"
-        if chkhsh == "a0b64b4385f123663873756336c085744376d015ff328bb1d901598f63c44152":
-            # ref: https://huggingface.co/answerdotai/ModernBERT-base
-            res = "modern-bert"
-        if chkhsh == "49fc0303c9e0d2c2c565c510f64b2d9b271276acdcdadff733249eda9f7d59df":
-            # ref: https://huggingface.co/arcee-ai/Trinity-Tokenizer
-            res = "afmoe"
-        if chkhsh == "9b1be57e70d20d9501b2b3186e792d81181ae36ada3903c26f9fea418cf87206":
-            # ref: https://huggingface.co/inclusionAI/Ling-mini-base-2.0
-            res = "bailingmoe2"
-        if chkhsh == "53e325976a6e142379c19b09afcae354f2f496f147afa8f9e189a33fe4e3024e":
-            # ref: https://huggingface.co/ibm-granite/granite-docling-258M
-            res = "granite-docling"
-        if chkhsh == "f4f37b6c8eb9ea29b3eac6bb8c8487c5ab7885f8d8022e67edc1c68ce8403e95":
-            # ref: https://huggingface.co/MiniMaxAI/MiniMax-M2
-            res = "minimax-m2"
-        if chkhsh == "4a2e2abae11ca2b86d570fc5b44be4d5eb5e72cc8f22dd136a94b37da83ab665":
-            # ref: https://huggingface.co/KORMo-Team/KORMo-tokenizer
-            res = "kormo"
-        if chkhsh == "9d70134b369a70e5735009b6de918f7581b5211f7c074d1f89f753aea8248af1":
-            # ref: https://huggingface.co/tencent/Youtu-LLM-2B
-            res = "youtu"
-        if chkhsh == "16389f0a1f51ee53e562ffd51c371dc508639ab0e4261502071836e50e223e91":
-            # ref: https://huggingface.co/upstage/Solar-Open-100B
-            res = "solar-open"
-        if chkhsh == "6c81ce329e0802883b22eabab0d3fa48357337ef1ecb45443828bf1f6254833f":
-            # ref: https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B
-            res = "exaone-moe"
-        if chkhsh == "d30d75d9059f1aa2c19359de71047b3ae408c70875e8a3ccf8c5fba56c9d8af4":
-            # ref: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct
-            res = "qwen35"
-        if chkhsh == "b4b8ca1f9769494fbd956ebc4c249de6131fb277a4a3345a7a92c7dd7a55808d":
-            # ref: https://huggingface.co/jdopensource/JoyAI-LLM-Flash
-            res = "joyai-llm"
-        if chkhsh == "e4d54df1ebc1f2b91acd986c5b51aa50837d5faf7c7398e73c1f9e9ee5d19869":
-            # ref: https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601
-            res = "kanana2"
-        if chkhsh == "862f827721df956049dff5ca81a57f29e575280bc622e290d3bf4e35eca29015":
-            # ref: https://huggingface.co/codefuse-ai/F2LLM-v2-4B
-            res = "f2llmv2"
-
-        if res is None:
-            logger.warning("\n")
-            logger.warning("**************************************************************************************")
-            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
-            logger.warning("**          There are 2 possible reasons for this:")
-            logger.warning("**          - the model has not been added to convert_hf_to_gguf_update.py yet")
-            logger.warning("**          - the pre-tokenization config has changed upstream")
-            logger.warning("**          Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
-            logger.warning("** ref:     https://github.com/ggml-org/llama.cpp/pull/6920")
-            logger.warning("**")
-            logger.warning(f"** chkhsh:  {chkhsh}")
-            logger.warning("**************************************************************************************")
-            logger.warning("\n")
-            raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
-
-        logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
-        logger.debug(f"chkhsh: {chkhsh}")
-
-        return res
-        # Marker: End get_vocab_base_pre
-
-    def _set_vocab_none(self) -> None:
-        self.gguf_writer.add_tokenizer_model("none")
-
-    def _set_vocab_gpt2(self) -> None:
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _set_vocab_qwen(self):
-        dir_model = self.dir_model
-        hparams = self.hparams
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams["vocab_size"]
-        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        merges = []
-        vocab = {}
-        mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
-        for token, rank in mergeable_ranks.items():
-            vocab[QwenModel.token_bytes_to_string(token)] = rank
-            if len(token) == 1:
-                continue
-            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-            assert len(merged) == 2
-            merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
-
-        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
-        added_vocab = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
-        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.CONTROL)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
-        special_vocab.merges = merges
-        # only add special tokens when they were not already loaded from config.json
-        if len(special_vocab.special_token_ids) == 0:
-            special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-            special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        # this one is usually not in config.json anyway
-        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _set_vocab_sentencepiece(self, add_to_gguf=True):
-        tokens, scores, toktypes = self._create_vocab_sentencepiece()
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _create_vocab_sentencepiece(self):
-        from sentencepiece import SentencePieceProcessor
-
-        tokenizer_path = self.dir_model / 'tokenizer.model'
-
-        if not tokenizer_path.is_file():
-            raise FileNotFoundError(f"File not found: {tokenizer_path}")
-
-        tokenizer = SentencePieceProcessor()
-        tokenizer.LoadFromFile(str(tokenizer_path))
-
-        vocab_size = self.find_hparam([
-            "vocab_size_per_layer_input", # gemma3n
-            "vocab_size",
-        ], optional=True) or tokenizer.vocab_size()
-
-        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
-        scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
-
-        for token_id in range(tokenizer.vocab_size()):
-            if token_id >= vocab_size:
-                logger.warning(f'ignore tokens from {token_id}: id is out of range, max={vocab_size - 1}')
-                break
-
-            piece = tokenizer.IdToPiece(token_id)
-            text = piece.encode("utf-8")
-            score = tokenizer.GetScore(token_id)
-
-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.IsUnknown(token_id):
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.IsControl(token_id):
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.IsUnused(token_id):
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.IsByte(token_id):
-                toktype = SentencePieceTokenTypes.BYTE
-
-            tokens[token_id] = text
-            scores[token_id] = score
-            toktypes[token_id] = toktype
-
-        added_tokens_file = self.dir_model / 'added_tokens.json'
-        if added_tokens_file.is_file():
-            with open(added_tokens_file, "r", encoding="utf-8") as f:
-                added_tokens_json = json.load(f)
-                for key in added_tokens_json:
-                    token_id = added_tokens_json[key]
-                    if token_id >= vocab_size:
-                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
-                        continue
-
-                    tokens[token_id] = key.encode("utf-8")
-                    scores[token_id] = -1000.0
-                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
-                for token_id, token_data in added_tokens_decoder.items():
-                    token_id = int(token_id)
-                    token: str = token_data["content"]
-                    if token_id >= vocab_size:
-                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
-                        continue
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        if tokens[token_id] != token.encode("utf-8"):
-                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}')
-                    if token_data.get("special") or self.does_token_look_special(token):
-                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
-                    else:
-                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
-                        toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-
-                    scores[token_id] = -1000.0
-                    tokens[token_id] = token.encode("utf-8")
-
-        if vocab_size > len(tokens):
-            pad_count = vocab_size - len(tokens)
-            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
-            for i in range(1, pad_count + 1):
-                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
-                scores.append(-1000.0)
-                toktypes.append(SentencePieceTokenTypes.UNUSED)
-
-        return tokens, scores, toktypes
-
-    def _set_vocab_llama_hf(self):
-        vocab = gguf.LlamaHfVocab(self.dir_model)
-        tokens = []
-        scores = []
-        toktypes = []
-
-        for text, score, toktype in vocab.all_tokens():
-            tokens.append(text)
-            scores.append(score)
-            toktypes.append(toktype)
-
-        assert len(tokens) == vocab.vocab_size
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _set_vocab_rwkv_world(self):
-        assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file()
-        vocab_size = self.hparams.get("vocab_size", 65536)
-
-        tokens: list[bytes] = ['<s>'.encode("utf-8")]
-        toktypes: list[int] = [gguf.TokenType.CONTROL]
-
-        with open(self.dir_model / "rwkv_vocab_v20230424.txt", "r", encoding="utf-8") as f:
-            lines = f.readlines()
-            for line in lines:
-                parts = line.split(' ')
-                assert len(parts) >= 3
-                token, token_len = ast.literal_eval(' '.join(parts[1:-1])), int(parts[-1])
-                token = token.encode("utf-8") if isinstance(token, str) else token
-                assert isinstance(token, bytes)
-                assert len(token) == token_len
-                token_text: str = repr(token)[2:-1]  # "b'\xff'" -> "\xff"
-                tokens.append(token_text.encode("utf-8"))
-                toktypes.append(gguf.TokenType.NORMAL)
-        remainder = vocab_size - len(tokens)
-        assert remainder >= 0
-        for i in range(len(tokens), vocab_size):
-            tokens.append(f"[PAD{i}]".encode("utf-8"))
-            toktypes.append(gguf.TokenType.UNUSED)
-
-        self.gguf_writer.add_tokenizer_model("rwkv")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
-        if special_vocab.chat_template is None:
-            template_path = Path(__file__).parent / "models" / "templates" / "llama-cpp-rwkv-world.jinja"
-            if template_path.is_file():
-                with open(template_path, "r", encoding="utf-8") as f:
-                    template = f.read()
-            else:
-                template = "rwkv-world"
-            special_vocab.chat_template = template
-        # hack: Add '\n\n' as the EOT token to make it chat normally
-        special_vocab._set_special_token("eot", 261)
-        # hack: Override these as they have already been set (incorrectly)
-        special_vocab.special_token_ids["bos"] = 0
-        special_vocab.special_token_ids["eos"] = 0
-
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int):
-        tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf"
-        logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
-        vocab_reader = gguf.GGUFReader(tokenizer_path, "r")
-
-        default_pre = "mpt" if model_name == "gpt-neox" else "default"
-
-        field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL)
-        assert field  # tokenizer model
-        self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8"))
-
-        field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE)
-        self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre)
-
-        field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST)
-        assert field  # token list
-        self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
-
-        if model_name == "llama-spm":
-            field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES)
-            assert field  # token scores
-            self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
-
-        field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
-        assert field  # token types
-        self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
-
-        if model_name != "llama-spm":
-            field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES)
-            assert field  # token merges
-            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
-
-        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None:
-            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
-        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None:
-            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
-        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None:
-            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
-        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None:
-            self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0])
-        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None:
-            self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0])
-        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None:
-            self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0])
-
-    def _try_set_pooling_type(self) -> None:
-        # get pooling path
-        pooling_path = None
-        module_path = self.dir_model / "modules.json"
-        if module_path.is_file():
-            with open(module_path, encoding="utf-8") as f:
-                modules = json.load(f)
-            for mod in modules:
-                if mod["type"].endswith("Pooling"):
-                    pooling_path = mod["path"]
-                    break
-
-        mode_mapping = {
-            "mean": gguf.PoolingType.MEAN,
-            "cls": gguf.PoolingType.CLS,
-            "lasttoken": gguf.PoolingType.LAST,
-        }
-
-        # get pooling type
-        if pooling_path is not None:
-            with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
-                pooling = json.load(f)
-            if pooling.get("pooling_mode_mean_tokens"):
-                pooling_type = gguf.PoolingType.MEAN
-            elif pooling.get("pooling_mode_cls_token"):
-                pooling_type = gguf.PoolingType.CLS
-            elif pooling.get("pooling_mode_lasttoken"):
-                pooling_type = gguf.PoolingType.LAST
-            elif (pooling_mode := pooling.get("pooling_mode")) in mode_mapping:
-                pooling_type = mode_mapping[pooling_mode]
-            else:
-                raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported")
-            self.gguf_writer.add_pooling_type(pooling_type)
-
-    def _set_vocab_glmedge(self):
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _set_vocab_glm(self):
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-        # Special tokens
-        # Note: Using <|endoftext|> (151329) for eot causes endless generation
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # ty: ignore[unresolved-attribute]  # 151331
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]  # 151336
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]  # 151329
-        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # ty: ignore[unresolved-attribute]  # 151338
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _set_vocab_interns1(self):
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())  # ty: ignore[unresolved-attribute]
-        vocab_size = self.hparams.get("vocab_size", len(vocab))
-        assert max(vocab.values()) < vocab_size
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
-        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
-
-        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            else:
-                token: str = reverse_vocab[i]
-                if token in added_vocab:
-                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
-                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
-                    if not added_tokens_decoder[i].normalized:
-                        previous_token = token
-                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
-                        if previous_token != token:
-                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
-
-                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    toktypes.append(gguf.TokenType.NORMAL)
-                tokens.append(token)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_vocab._set_special_token("bos", 151643)
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _set_vocab_mistral(self):
-        if not _mistral_common_installed:
-            raise ImportError(_mistral_import_error_msg)
-
-        vocab = MistralVocab(self.dir_model)
-        logger.info(
-            f"Converting tokenizer {vocab.tokenizer_type} of size {vocab.vocab_size}."
-        )
-
-        self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model)
-
-        tokens = []
-        scores = []
-        toktypes = []
-
-        for text, score, toktype in vocab.all_tokens():
-            tokens.append(text)
-            scores.append(score)
-            toktypes.append(toktype)
-
-        assert len(tokens) == vocab.vocab_size, (
-            f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})"
-        )
-
-        if vocab.tokenizer_type == MistralTokenizerType.tekken:
-            self.gguf_writer.add_tokenizer_pre("tekken")
-            self.gguf_writer.add_token_merges(
-                vocab.extract_vocab_merges_from_model()
-            )
-
-        logger.info(
-            f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}."
-        )
-
-        self.gguf_writer.add_bos_token_id(vocab.bos_id)
-        self.gguf_writer.add_eos_token_id(vocab.eos_id)
-        self.gguf_writer.add_unk_token_id(vocab.unk_id)
-        self.gguf_writer.add_pad_token_id(vocab.pad_id)
-
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-        self.gguf_writer.add_vocab_size(vocab.vocab_size)
-
-        self.gguf_writer.add_add_bos_token(True)
-        self.gguf_writer.add_add_eos_token(False)
-
-        local_template_file_path = self.dir_model / "chat_template.jinja"
-
-        if self.is_mistral_format and local_template_file_path.is_file():
-            # Ministral-3 and other new Mistral models come with chat templates.
-            # ref: https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/tree/main
-            logger.info("Using an existing Mistral local chat template.")
-
-            with open(local_template_file_path, "r", encoding="utf-8") as f:
-                template = f.read()
-        elif not self.is_mistral_format or not self.disable_mistral_community_chat_template:
-            template_dir = Path(__file__).parent / "models/templates/"
-
-            # Log only for Mistral format that the official tokenization and detokenization is via `mistral-common`.
-            if self.is_mistral_format:
-                logger.info(
-                    "Using a Mistral community chat template. These templates can be subject to errors in early days or weeks after a release. "
-                    "Mistral recommends to use `mistral-common` to perform tokenization and detokenization."
-                )
-            template = MistralModel.get_community_chat_template(vocab, template_dir, self.is_mistral_format)
-        else:
-            logger.info("Not using a Mistral local or community chat template. Ensure to perform the tokenization and detokenization via `mistral-common`.")
-            template = None
-
-        if template is not None:
-            self.gguf_writer.add_chat_template(template)
-
-    def _set_vocab_plamo(self):
-        # PLaMo models use a custom tokenizer with a .jsonl file
-        tokenizer_jsonl_path = self.dir_model / "tokenizer.jsonl"
-        tokenizer_config_path = self.dir_model / "tokenizer_config.json"
-
-        if not tokenizer_jsonl_path.is_file():
-            raise FileNotFoundError(f"PLaMo tokenizer file not found: {tokenizer_jsonl_path}")
-
-        # Load tokenizer config
-        with open(tokenizer_config_path, "r", encoding="utf-8") as f:
-            tokenizer_config = json.load(f)
-
-        # Load tokens from JSONL file (actually a list format)
-        tokens = []
-        scores = []
-        toktypes = []
-
-        with open(tokenizer_jsonl_path, "r", encoding="utf-8") as f:
-            for line_num, line in enumerate(f):
-                if line.strip():
-                    token_data = json.loads(line)
-                    # Format: [token, score, type, ?, ?, ?, ?]
-                    token = token_data[0].encode("utf-8")
-                    score = float(token_data[1])
-                    token_type_str = token_data[2] if len(token_data) > 2 else "NORMAL"
-
-                    tokens.append(token)
-                    scores.append(score)
-
-                    if token_type_str == "UNKNOWN":
-                        toktypes.append(gguf.TokenType.UNKNOWN)
-                    elif token_type_str == "CONTROL":
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    elif token_type_str == "BYTE":
-                        toktypes.append(gguf.TokenType.BYTE)
-                    else:
-                        token_str = token_data[0]
-                        if token_str.startswith("<|plamo:") and token_str.endswith("|>"):
-                            toktypes.append(gguf.TokenType.CONTROL)
-                        else:
-                            toktypes.append(gguf.TokenType.NORMAL)
-
-        vocab_size = self.hparams["vocab_size"]
-        if vocab_size > len(tokens):
-            pad_count = vocab_size - len(tokens)
-            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
-            for i in range(1, pad_count + 1):
-                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
-                scores.append(-1000.0)
-                toktypes.append(gguf.TokenType.UNUSED)
-
-        self.gguf_writer.add_tokenizer_model("plamo2")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] is not None:
-            token_id = tokens.index(tokenizer_config["bos_token"].encode("utf-8"))
-            self.gguf_writer.add_bos_token_id(token_id)
-        if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] is not None:
-            token_id = tokens.index(tokenizer_config["eos_token"].encode("utf-8"))
-            self.gguf_writer.add_eos_token_id(token_id)
-        if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] is not None:
-            token_id = tokens.index(tokenizer_config["pad_token"].encode("utf-8"))
-            self.gguf_writer.add_pad_token_id(token_id)
-        if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] is not None:
-            token_id = tokens.index(tokenizer_config["sep_token"].encode("utf-8"))
-            self.gguf_writer.add_sep_token_id(token_id)
-        if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] is not None:
-            token_id = tokens.index(tokenizer_config["unk_token"].encode("utf-8"))
-            self.gguf_writer.add_unk_token_id(token_id)
-
-        # Add <|plamo:op|> as EOT to ensure appropriate end of generation
-        self.gguf_writer.add_eot_token_id(4)
-
-        self.gguf_writer.add_add_space_prefix(False)
-
-
-class MmprojModel(ModelBase):
-    model_type = ModelType.MMPROJ
-    model_arch = gguf.MODEL_ARCH.MMPROJ
-    preprocessor_config: dict[str, Any]
-    global_config: dict[str, Any]
-
-    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "layers", "encoder_layers", "vt_num_hidden_layers"]
-
-    has_vision_encoder: bool = True # by default
-    has_audio_encoder: bool = False
-
-    # for models having multiple encoders, we need to separate their hparams
-    hparams_vision: dict[str, Any] | None = None
-    hparams_audio: dict[str, Any] | None = None
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        if self.model_arch != gguf.MODEL_ARCH.MMPROJ:
-            raise TypeError("MmprojModel must be subclassed with model_arch = gguf.MODEL_ARCH.MMPROJ")
-
-        # get n_embd of the text model
-        if not self.is_mistral_format:
-            if "text_config" not in self.hparams:
-                self.hparams["text_config"] = {}
-            if "audio_config" not in self.hparams:
-                self.hparams["audio_config"] = {}
-            text_config = {**self.hparams, **self.hparams["text_config"]}
-            self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0))
-        else:
-            text_config = {
-                k: v for k, v in self.hparams.items() if k not in ["vision_encoder", "audio_encoder"]
-            }
-            self.n_embd_text = text_config.get("hidden_dim", 0)
-
-        assert self.n_embd_text > 0, "n_embd not found in hparams"
-
-        # move vision config to the top level, while preserving the original hparams in global_config
-        import copy
-        self.global_config = copy.deepcopy(self.hparams)
-        self.hparams_vision = self.get_vision_config()
-        self.hparams_audio = self.get_audio_config()
-
-        if self.hparams_vision is None and self.hparams_audio is None:
-            raise ValueError("vision_config / audio_config not found in hparams")
-
-        # for compat with vision-only models
-        self.hparams = self.hparams_vision or self.hparams_audio or self.hparams
-
-        # TODO @ngxson : this is a hack to support both vision and audio encoders
-        have_multiple_encoders = self.has_audio_encoder and self.has_vision_encoder
-        self.block_count = 128 if have_multiple_encoders else self.find_hparam(self.n_block_keys, True)
-        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)
-
-        # load preprocessor config
-        self.preprocessor_config = {}
-
-        # prefer preprocessor_config.json if possible
-        preprocessor_config_path = self.dir_model / "preprocessor_config.json"
-        if preprocessor_config_path.is_file():
-            with open(preprocessor_config_path, "r", encoding="utf-8") as f:
-                cfg = json.load(f)
-                # move media_proc_cfg to root level for compat
-                if "media_proc_cfg" in cfg:
-                    cfg = {
-                        **cfg,
-                        **cfg["media_proc_cfg"],
-                    }
-                # merge configs
-                self.preprocessor_config = {**self.preprocessor_config, **cfg}
-
-        # prefer processor_config.json if possible
-        processor_config_path = self.dir_model / "processor_config.json"
-        if processor_config_path.is_file():
-            with open(processor_config_path, "r", encoding="utf-8") as f:
-                cfg = json.load(f)
-                # move image_processor to root level for compat
-                if "image_processor" in cfg:
-                    cfg = {
-                        **cfg,
-                        **cfg["image_processor"],
-                    }
-                # merge configs
-                self.preprocessor_config = {**self.preprocessor_config, **cfg}
-
-    def get_vision_config(self) -> dict[str, Any] | None:
-        config_name = "vision_config" if not self.is_mistral_format else "vision_encoder"
-        return self.global_config.get(config_name)
-
-    def get_audio_config(self) -> dict[str, Any] | None:
-        mm_config_key = "whisper_config" if "whisper_config" in self.hparams else "audio_config"
-        return self.global_config.get(mm_config_key)
-
-    def set_type(self):
-        self.gguf_writer.add_type(gguf.GGUFType.MMPROJ)
-
-    def prepare_metadata(self, vocab_only: bool):
-        super().prepare_metadata(vocab_only=vocab_only)
-
-        output_type: str = self.ftype.name.partition("_")[2]
-
-        if self.fname_out.is_dir():
-            fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=output_type, model_type=None)
-            self.fname_out = self.fname_out / f"mmproj-{fname_default}.gguf"
-        else:
-            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_file_type(self.ftype)
-
-        if self.has_vision_encoder:
-            self.gguf_writer.add_clip_has_vision_encoder(True)
-            self.gguf_writer.add_vision_projection_dim(self.n_embd_text)
-
-            # vision config
-            self.image_size = self.find_vparam(["image_size"])
-            self.gguf_writer.add_vision_image_size(self.image_size)
-            self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
-            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "width", "vt_hidden_size"]))
-            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"]))
-            self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
-            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "heads", "vt_num_attention_heads"]))
-
-            # preprocessor config
-            image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
-            image_std = _MISTRAL_COMMON_DATASET_STD if self.is_mistral_format else self.preprocessor_config["image_std"]
-
-            self.gguf_writer.add_vision_image_mean(image_mean)
-            self.gguf_writer.add_vision_image_std(image_std)
-
-        if self.has_audio_encoder:
-            self.gguf_writer.add_clip_has_audio_encoder(True)
-            self.gguf_writer.add_audio_projection_dim(self.n_embd_text)
-
-            # audio config
-            self.gguf_writer.add_audio_embedding_length(self.find_aparam(["hidden_size"]))
-            self.gguf_writer.add_audio_feed_forward_length(self.find_aparam(["intermediate_size"]))
-            self.gguf_writer.add_audio_block_count(self.find_aparam(self.n_block_keys))
-            self.gguf_writer.add_audio_head_count(self.find_aparam(["num_attention_heads"]))
-
-        if not self.has_vision_encoder and not self.has_audio_encoder:
-            raise ValueError("MmprojModel must have either vision or audio encoder")
-
-    def write_vocab(self):
-        raise ValueError("MmprojModel does not support vocab writing")
-
-    def find_vparam(self, keys: Iterable[str], optional: bool = False) -> Any:
-        assert self.hparams_vision is not None
-        return self._find_param(self.hparams_vision, keys, optional)
-
-    def find_aparam(self, keys: Iterable[str], optional: bool = False) -> Any:
-        assert self.hparams_audio is not None
-        return self._find_param(self.hparams_audio, keys, optional)
-
-    def _find_param(self, obj: dict[str, Any], keys: Iterable[str], optional: bool = False) -> Any:
-        key = next((k for k in keys if k in obj), None)
-        if key is not None:
-            return obj[key]
-        if optional:
-            return None
-        raise KeyError(f"could not find any of: {keys}")
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, name, n_dims  # unused
-        if ".patch_embd.weight" in new_name or ".patch_merger.weight" in new_name:
-            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
-        return False
-
-
-@ModelBase.register("GPTNeoXForCausalLM")
-class GPTNeoXModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GPTNEOX
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(
-            int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
-        )
-        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
-        self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-        assert n_head is not None
-        assert n_embed is not None
-
-        if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
-            # Map bloom-style qkv_linear to gpt-style qkv_linear
-            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
-            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
-            qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
-            data_torch = torch.cat(
-                (
-                    qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
-                    qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
-                    qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
-                ),
-                dim=0,
-            )
-            logger.info("re-format attention.linear_qkv.weight")
-        elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name):
-            qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
-            data_torch = torch.cat(
-                (
-                    qkv_bias[:, 0, :].reshape((n_embed,)),
-                    qkv_bias[:, 1, :].reshape((n_embed,)),
-                    qkv_bias[:, 2, :].reshape((n_embed,)),
-                ),
-                dim=0,
-            )
-            logger.info("re-format attention.linear_qkv.bias")
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("BloomForCausalLM", "BloomModel")
-class BloomModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.BLOOM
-
-    def set_gguf_parameters(self):
-        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        assert n_head is not None
-        assert n_embed is not None
-        self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
-        self.gguf_writer.add_embedding_length(n_embed)
-        self.gguf_writer.add_feed_forward_length(4 * n_embed)
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_head_count_kv(n_head)
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-        assert n_head is not None
-        assert n_embed is not None
-
-        name = re.sub(r'transformer\.', '', name)
-
-        if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
-            # Map bloom-style qkv_linear to gpt-style qkv_linear
-            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
-            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
-            qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
-            data_torch = torch.cat(
-                (
-                    qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
-                    qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
-                    qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
-                ),
-                dim=0,
-            )
-            logger.info("re-format attention.linear_qkv.weight")
-        elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
-            qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
-            data_torch = torch.cat(
-                (
-                    qkv_bias[:, 0, :].reshape((n_embed,)),
-                    qkv_bias[:, 1, :].reshape((n_embed,)),
-                    qkv_bias[:, 2, :].reshape((n_embed,)),
-                ),
-                dim=0,
-            )
-            logger.info("re-format attention.linear_qkv.bias")
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("MPTForCausalLM")
-class MPTModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.MPT
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_gpt2()
-        except Exception:
-            # Fallback for SEA-LION model
-            self._set_vocab_sentencepiece()
-            self.gguf_writer.add_add_bos_token(False)
-            self.gguf_writer.add_pad_token_id(3)
-            self.gguf_writer.add_eos_token_id(1)
-            self.gguf_writer.add_unk_token_id(0)
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
-        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
-        self.gguf_writer.add_head_count(self.hparams["n_heads"])
-        if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
-            self.gguf_writer.add_head_count_kv(kv_n_heads)
-        self.gguf_writer.add_layer_norm_eps(1e-5)
-        if self.hparams["attn_config"]["clip_qkv"] is not None:
-            self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
-        if self.hparams["attn_config"]["alibi"]:
-            self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
-        else:
-            self.gguf_writer.add_max_alibi_bias(0.0)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "scales" in name:
-            new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales"))
-            new_name = new_name.replace("scales", "act.scales")
-        else:
-            new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias"))
-
-        yield from super().modify_tensors(data_torch, new_name, bid)
-
-
-@ModelBase.register("OrionForCausalLM")
-class OrionModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.ORION
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-    def set_gguf_parameters(self):
-        head_count = self.hparams["num_attention_heads"]
-        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-
-        ctx_length = 0
-        if "max_sequence_length" in self.hparams:
-            ctx_length = self.hparams["max_sequence_length"]
-        elif "max_position_embeddings" in self.hparams:
-            ctx_length = self.hparams["max_position_embeddings"]
-        elif "model_max_length" in self.hparams:
-            ctx_length = self.hparams["model_max_length"]
-        else:
-            raise ValueError("gguf: can not find ctx length parameter.")
-
-        self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
-        self.gguf_writer.add_context_length(ctx_length)
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_head_count(head_count)
-        self.gguf_writer.add_head_count_kv(head_count_kv)
-        # note: config provides rms norm but it is actually layer norm
-        # ref:  https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
-        self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
-
-
-@ModelBase.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
-class BaichuanModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.BAICHUAN
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
-        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        head_count = self.hparams["num_attention_heads"]
-        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-
-        if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight":
-            logger.info(f"Unpacking and permuting layer {bid}")
-            yield from [
-                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid),
-                    self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)),
-                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid),
-                    self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)),
-                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid),
-                    self._reverse_hf_part(data_torch, 2)),
-            ]
-        else:
-            yield from self.modify_tensors(data_torch, self.map_tensor_name(name), bid)
-
-    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
-        if n_kv_head is not None and n_head != n_kv_head:
-            n_head //= n_kv_head
-
-        return (
-            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-            .swapaxes(1, 2)
-            .reshape(weights.shape)
-        )
-
-    def _reverse_hf_permute_part(
-        self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
-    ) -> Tensor:
-        r = weights.shape[0] // 3
-        return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)
-
-    def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
-        r = weights.shape[0] // 3
-        return weights[r * n_part:r * n_part + r, ...]
-
-
-@ModelBase.register("XverseForCausalLM")
-class XverseModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.XVERSE
-
-    def set_vocab(self):
-        assert (self.dir_model / "tokenizer.json").is_file()
-        dir_model = self.dir_model
-        hparams = self.hparams
-
-        tokens: list[bytes] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model)
-        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
-        # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size,
-        # because vocab_size is the count of items, and indexes start at 0.
-        max_vocab_index = max(tokenizer.get_vocab().values())  # ty: ignore[unresolved-attribute]
-        if max_vocab_index >= vocab_size:
-            raise ValueError("Vocabulary size exceeds expected maximum size.")
-
-        reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
-        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
-
-        for token_id in range(vocab_size):
-            token_text = reverse_vocab[token_id].encode('utf-8')
-            # replace "\x00" to string with length > 0
-            if token_text == b"\x00":
-                toktype = gguf.TokenType.BYTE  # special
-                token_text = f"<{token_text}>".encode('utf-8')
-            elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
-                toktype = gguf.TokenType.BYTE  # special
-            elif reverse_vocab[token_id] in added_vocab:
-                if tokenizer.added_tokens_decoder[token_id].special:  # ty: ignore[unresolved-attribute]
-                    toktype = gguf.TokenType.CONTROL
-                else:
-                    toktype = gguf.TokenType.USER_DEFINED
-            else:
-                toktype = gguf.TokenType.NORMAL
-
-            tokens.append(token_text)
-            toktypes.append(toktype)
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
-        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        head_count = self.hparams["num_attention_heads"]
-        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-
-        # HF models permute some of the tensors, so we need to undo that
-        if name.endswith("q_proj.weight"):
-            data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
-        if name.endswith("k_proj.weight"):
-            data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
-        if n_kv_head is not None and n_head != n_kv_head:
-            n_head //= n_kv_head
-
-        return (
-            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-            .swapaxes(1, 2)
-            .reshape(weights.shape)
-        )
-
-
-@ModelBase.register("FalconForCausalLM", "RWForCausalLM")
-class FalconModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.FALCON
-
-    def set_gguf_parameters(self):
-        n_head = self.hparams.get("num_attention_heads")
-        if n_head is None:
-            n_head = self.hparams["n_head"]  # old name
-
-        n_head_kv = self.hparams.get("num_kv_heads")
-        if n_head_kv is None:
-            n_head_kv = self.hparams.get("n_head_kv", 1)  # old name
-
-        self.gguf_writer.add_context_length(2048)  # not in config.json
-        self.gguf_writer.add_tensor_data_layout("jploski")  # qkv tensor transform
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_head_count_kv(n_head_kv)
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # QKV tensor transform
-        # The original query_key_value tensor contains n_head_kv "kv groups",
-        # each consisting of n_head/n_head_kv query weights followed by one key
-        # and one value weight (shared by all query heads in the kv group).
-        # This layout makes it a big pain to work with in GGML.
-        # So we rearrange them here,, so that we have n_head query weights
-        # followed by n_head_kv key weights followed by n_head_kv value weights,
-        # in contiguous fashion.
-        # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
-
-        if "query_key_value" in name:
-            n_head = self.find_hparam(["num_attention_heads", "n_head"])
-            n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1
-            head_dim = self.hparams["hidden_size"] // n_head
-
-            qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
-            q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
-            k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
-            v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
-            data_torch = torch.cat((q, k, v)).reshape_as(data_torch)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("GPTBigCodeForCausalLM")
-class StarCoderModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.STARCODER
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_context_length(self.hparams["n_positions"])
-        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(self.hparams["n_head"])
-        self.gguf_writer.add_head_count_kv(1)
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-
-@ModelBase.register("GPTRefactForCausalLM")
-class RefactModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.REFACT
-
-    def set_vocab(self):
-        super().set_vocab()
-
-        # TODO: how to determine special FIM tokens automatically?
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
-        special_vocab._set_special_token("prefix", 1)
-        special_vocab._set_special_token("suffix", 3)
-        special_vocab._set_special_token("middle", 2)
-        special_vocab.chat_template = None  # do not add it twice
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        hidden_dim = self.hparams["n_embd"]
-        inner_dim = 4 * hidden_dim
-        hidden_dim = int(2 * inner_dim / 3)
-        multiple_of = 256
-        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-
-        # refact uses Alibi. So this is from config.json which might be used by training.
-        self.gguf_writer.add_context_length(self.hparams["n_positions"])
-        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
-
-        self.gguf_writer.add_feed_forward_length(ff_dim)
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(self.hparams["n_head"])
-        self.gguf_writer.add_head_count_kv(1)
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        hidden_dim = self.hparams["n_embd"]
-        inner_dim = 4 * hidden_dim
-        hidden_dim = int(2 * inner_dim / 3)
-        multiple_of = 256
-        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-        n_head = self.hparams["n_head"]
-        n_head_kv = 1
-        head_dim = self.hparams["n_embd"] // n_head
-
-        if bid is not None:
-            if name == f"transformer.h.{bid}.attn.kv.weight":
-                yield from super().modify_tensors(data_torch[:n_head_kv * head_dim], self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
-                yield from super().modify_tensors(data_torch[n_head_kv * head_dim:], self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
-                return
-            if name == f"transformer.h.{bid}.attn.q.weight":
-                yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
-                return
-            if name == f"transformer.h.{bid}.mlp.gate_up_proj.weight":
-                yield from super().modify_tensors(data_torch[:ff_dim], self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)
-                yield from super().modify_tensors(data_torch[ff_dim:], self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
-class StableLMModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.STABLELM
-
-    def set_vocab(self):
-        if (self.dir_model / "tokenizer.json").is_file():
-            self._set_vocab_gpt2()
-        else:
-            # StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
-            self._set_vocab_qwen()
-
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-
-        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
-        self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
-        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
-        self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
-        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
-        self.gguf_writer.add_file_type(self.ftype)
-
-    _q_norms: list[dict[str, Tensor]] | None = None
-    _k_norms: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams["num_key_value_heads"]
-
-        if name.find("q_layernorm.norms") != -1:
-            assert bid is not None
-
-            if self._q_norms is None:
-                self._q_norms = [{} for _ in range(self.block_count)]
-
-            self._q_norms[bid][name] = data_torch
-
-            if len(self._q_norms[bid]) >= n_head:
-                return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm")
-            else:
-                return
-
-        if name.find("k_layernorm.norms") != -1:
-            assert bid is not None
-
-            if self._k_norms is None:
-                self._k_norms = [{} for _ in range(self.block_count)]
-
-            self._k_norms[bid][name] = data_torch
-
-            if len(self._k_norms[bid]) >= n_kv_head:
-                return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm")
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"):
-        datas: list[Tensor] = []
-        # extract the norms in order
-        for xid in range(n_head):
-            ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight"
-            datas.append(norms[ename])
-            del norms[ename]
-        data_torch = torch.stack(datas, dim=0)
-
-        merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"
-
-        yield from super().modify_tensors(data_torch, merged_name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._q_norms is not None or self._k_norms is not None:
-            # flatten two `list[dict[str, Tensor]]` into a single `list[str]`
-            norms = (
-                [k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else []
-            ) + (
-                [k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else []
-            )
-            if len(norms) > 0:
-                raise ValueError(f"Unprocessed norms: {norms}")
-
-
-@ModelBase.register(
-    "LLaMAForCausalLM",
-    "LlamaForCausalLM",
-    "MistralForCausalLM",
-    "MixtralForCausalLM",
-    "VLlama3ForCausalLM",
-    "LlavaForConditionalGeneration",
-    "VoxtralForConditionalGeneration",
-    "IQuestCoderForCausalLM",
-    "LlamaModel")
-class LlamaModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.LLAMA
-    undo_permute = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # fix for SmolVLM2, missing `num_attention_heads` in config.json
-        if self.hf_arch == "VLlama3ForCausalLM":
-            self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
-        hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
-        self.origin_hf_arch = hparams.get('architectures', [None])[0]
-
-    def set_vocab(self):
-        if self.origin_hf_arch == "GlmasrModel":
-            return self._set_vocab_glmedge()
-
-        if self.is_mistral_format:
-            return self._set_vocab_mistral()
-
-        path_tekken_json = self.dir_model / "tekken.json"
-        path_tokenizer_json = self.dir_model / "tokenizer.json"
-        if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
-            self._set_vocab_mistral()
-
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            try:
-                self._set_vocab_llama_hf()
-            except (FileNotFoundError, TypeError):
-                # Llama 3
-                self._set_vocab_gpt2()
-
-        # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
-        if self.hparams.get("vocab_size", 32000) == 32016:
-            special_vocab = gguf.SpecialVocab(
-                self.dir_model, load_merges=False,
-                special_token_types = ['prefix', 'suffix', 'middle', 'eot']
-            )
-            special_vocab._set_special_token("prefix", 32007)
-            special_vocab._set_special_token("suffix", 32008)
-            special_vocab._set_special_token("middle", 32009)
-            special_vocab._set_special_token("eot",    32010)
-            special_vocab.add_to_gguf(self.gguf_writer)
-
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                if "add_prefix_space" in tokenizer_config_json:
-                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
-
-        # Apply to granite small models only
-        if self.hparams.get("vocab_size", 32000) == 49152:
-            self.gguf_writer.add_add_bos_token(False)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-
-        if not self.is_mistral_format:
-            self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-
-        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-
-    @staticmethod
-    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.find_hparam(["n_heads", "num_attention_heads"])
-        n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"])
-
-        vision_prefixes = [
-            "vision_encoder.",
-            "vision_language_adapter.",
-            "patch_merger.",
-            "pre_mm_projector_norm",
-            "audio_encoder.",
-        ]
-
-        is_multimodal_tensor = "vision_tower" in name \
-            or "vision_model" in name \
-            or "audio_tower" in name \
-            or "model.connector" in name \
-            or "multi_modal_projector" in name \
-            or any(
-                name.startswith(prefix)
-                for prefix in vision_prefixes
-            )
-
-        if is_multimodal_tensor:
-            return  # skip vision tensors
-        elif self.hf_arch == "LlamaModel":
-            name = "model." + name
-        elif name.startswith("model.text_model"):
-            name = name.replace("text_model.", "") # for SmolVLM
-        elif name.startswith("language_model."):
-            name = name.replace("language_model.", "") # for the rest
-
-        if self.undo_permute:
-            if name.endswith(("q_proj.weight", "q_proj.bias")):
-                data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-            if name.endswith(("k_proj.weight", "k_proj.bias")):
-                data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
-
-        # process the experts separately
-        if name.find("block_sparse_moe.experts") != -1:
-            n_experts = self.hparams["num_local_experts"]
-
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for wid in ["w1", "w2", "w3"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
-            if rope_params.get("rope_type", '').lower() == "llama3":
-                base = rope_params.get("rope_theta", 10000.0)
-                if (dim := self.hparams.get("head_dim")) is None:
-                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
-
-                factor = rope_params.get("factor", 8.0)
-                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
-                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
-
-                low_freq_wavelen = old_context_len / low_freq_factor
-                high_freq_wavelen = old_context_len / high_freq_factor
-                # assert low_freq_wavelen != high_freq_wavelen # Errors for Llama4
-
-                rope_factors = []
-                for freq in freqs:
-                    wavelen = 2 * math.pi / freq
-                    if wavelen < high_freq_wavelen:
-                        rope_factors.append(1)
-                    elif wavelen > low_freq_wavelen:
-                        rope_factors.append(factor)
-                    else:
-                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
-                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
-
-                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("ArceeForCausalLM")
-class ArceeModel(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.ARCEE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self._try_set_pooling_type()
-
-
-@ModelBase.register("AfmoeForCausalLM")
-class AfmoeModel(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.AFMOE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # MoE parameters
-        if (n_shared_experts := self.hparams.get("num_shared_experts")) is not None:
-            self.gguf_writer.add_expert_shared_count(n_shared_experts)
-        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-        if (n_dense_layers := self.hparams.get("num_dense_layers")) is not None:
-            self.gguf_writer.add_leading_dense_block_count(n_dense_layers)
-
-        # Route normalization and scaling
-        if (route_norm := self.hparams.get("route_norm")) is not None:
-            self.gguf_writer.add_expert_weights_norm(route_norm)
-        if (route_scale := self.hparams.get("route_scale")) is not None:
-            self.gguf_writer.add_expert_weights_scale(route_scale)
-
-        # Sliding window attention
-        if (sliding_window := self.hparams.get("sliding_window")) is not None:
-            self.gguf_writer.add_sliding_window(sliding_window)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Handle expert weights - they're already merged in the HF format
-        # process the experts separately
-        if name.find("mlp.experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["gate_proj", "up_proj", "down_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename_to_retrieve])
-                        del self._experts[bid][ename_to_retrieve]
-
-                    data_torch = torch.stack(datas, dim=0)
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-                    yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid)
-
-                return
-            else:
-                return
-
-        if name.endswith(".expert_bias"):
-            name = name.replace(".expert_bias", ".expert_bias.bias")
-
-        yield from ModelBase.modify_tensors(self, data_torch, name, bid)
-
-
-@ModelBase.register(
-    "LlavaForConditionalGeneration", # pixtral
-    "Mistral3ForConditionalGeneration", # mistral small 3.1
-)
-class LlavaVisionModel(MmprojModel):
-    img_break_tok_id = -1
-    use_break_tok = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.hparams.get("model_type") == "pixtral":
-            # layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py
-            self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5)
-            if self.use_break_tok:
-                self.img_break_tok_id = self.get_token_id("[IMG_BREAK]")
-        elif self.is_mistral_format:
-            # hparams is already vision config here so norm_eps is only defined in global_config.
-            self.hparams["norm_eps"] = self.global_config.get("norm_eps", None)
-            assert self.hparams["norm_eps"] is not None, "norm_eps not found in params.json"
-            if self.use_break_tok:
-                self.img_break_tok_id = self.find_vparam(["image_break_token_id"])
-        else:
-            raise ValueError(f"Unsupported model type: {self.hparams['model_type']}")
-        logger.info(f"Image break token id: {self.img_break_tok_id}")
-
-    def get_token_id(self, token: str) -> int:
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-            added_tokens_decoder = json.load(f).get('added_tokens_decoder') or {}
-            for id_, token_data in added_tokens_decoder.items():
-                if token_data.get("content") == token:
-                    return int(id_)
-            # fallthrough to tokenizer.json
-        with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
-            tokenizer_json = json.load(f)
-            for token_data in tokenizer_json["added_tokens"]:
-                if token_data["content"] == token:
-                    return int(token_data["id"])
-        raise ValueError(f"Token '{token}' not found in tokenizer config.")
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        if hparams.get("model_type") == "pixtral":
-            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
-            self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
-
-            # hidden_act
-            if hparams["hidden_act"] == "silu":
-                self.gguf_writer.add_vision_use_silu(True)
-            elif hparams["hidden_act"] == "gelu":
-                self.gguf_writer.add_vision_use_gelu(True)
-            else:
-                raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
-
-            # spatial_merge_size
-            if "spatial_merge_size" in self.global_config:
-                self.gguf_writer.add_vision_spatial_merge_size(self.global_config["spatial_merge_size"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = (
-            self.hparams["num_attention_heads"] if not self.is_mistral_format else self.find_vparam(["num_attention_heads"])
-        )
-        n_kv_head = n_head
-
-        valid_prefixes = (
-            "multi_modal_projector.",
-            "vision_tower.",
-            "vision_encoder.",
-            "vision_language_adapter.",
-            "patch_merger.",
-            "pre_mm_projector_norm",
-        )
-
-        if any(name.startswith(prefix) for prefix in valid_prefixes):
-            # process vision tensors
-            if name.endswith(("q_proj.weight", "q_proj.bias")) and not self.is_mistral_format:
-                data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-            if name.endswith(("k_proj.weight", "k_proj.bias")) and not self.is_mistral_format:
-                data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
-            yield from super().modify_tensors(data_torch, name, bid)
-            return
-
-        embed_key = "embed_tokens.weight" if not self.is_mistral_format else "tok_embeddings.weight"
-        if self.img_break_tok_id > 0 and embed_key in name:
-            logger.info(f"Extracting [IMG_BREAK] token embedding from {name}")
-            # for pixtral model, we need to extract the [IMG_BREAK] token embedding
-            img_break_embd = data_torch[self.img_break_tok_id]
-            name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK]
-            yield from super().modify_tensors(img_break_embd, name, bid)
-
-        return # skip other tensors
-
-
-@ModelBase.register("Idefics3ForConditionalGeneration", "SmolVLMForConditionalGeneration")
-class SmolVLMModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.hparams["model_type"] == "smolvlm_vision":
-            # fix for SmolVLM2, missing some keys in config.json
-            # default values are taken from transformers code
-            self.hparams["hidden_size"] = self.hparams.get("hidden_size", 1152)
-            self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 16)
-            self.hparams["intermediate_size"] = self.hparams.get("intermediate_size", 3072)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.IDEFICS3)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
-        self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("scale_factor", 2))
-        self.gguf_writer.add_vision_use_gelu(True)
-
-        # Add the preprocessor longest edge size
-        preproc_image_size = self.preprocessor_config.get("size", {}).get("longest_edge", self.image_size)
-        self.gguf_writer.add_vision_preproc_image_size(preproc_image_size)
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".embeddings." in name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        is_vision_tensor = "vision_tower" in name or "vision_model" in name or "model.connector" in name
-
-        if is_vision_tensor:
-            yield from super().modify_tensors(data_torch, name, bid)
-
-        return # skip other tensors
-
-
-@ModelBase.register(
-    "Llama4ForConditionalGeneration",
-    "Llama4ForCausalLM",
-)
-class Llama4Model(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.LLAMA4
-    undo_permute = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # IMPORTANT: the normal "intermediate_size" is renamed to "intermediate_size_mlp", we need to undo this
-        self.hparams["intermediate_size_moe"] = self.hparams["intermediate_size"]
-        self.hparams["intermediate_size"] = self.hparams["intermediate_size_mlp"]
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_interleave_moe_layer_step(self.hparams["interleave_moe_layer_step"])
-        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size_moe"])
-        if "layer_types" in self.hparams:
-            if all(lt == "full_attention" for lt in self.hparams["layer_types"]):
-                # all layers are full attention (for MobileLLM), disable swa
-                self.gguf_writer.add_sliding_window(0)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        if name.startswith("language_model."):
-            name = name.replace("language_model.", "")
-
-        # split the gate_up into gate and up
-        if "gate_up_proj" in name:
-            name_up = name.replace("gate_up_proj", "up_proj.weight")
-            name_gate = name.replace("gate_up_proj", "gate_proj.weight")
-            dim_half = data_torch.shape[-1] // 2
-            gate_proj_weight, up_proj_weight = data_torch.transpose(-1, -2).split(dim_half, dim=-2)
-            yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
-            yield from super().modify_tensors(up_proj_weight, name_up, bid)
-            return
-
-        if name.endswith("down_proj"):
-            name += ".weight"
-            data_torch = data_torch.transpose(-1, -2)
-
-        if "multi_modal_projector" in name or "vision_model" in name:
-            return
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Llama4ForConditionalGeneration")
-class Llama4VisionModel(MmprojModel):
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LLAMA4)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams["norm_eps"])
-        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / self.hparams["pixel_shuffle_ratio"]))
-        assert self.hparams["hidden_act"] == "gelu"
-        self.gguf_writer.add_vision_use_gelu(True)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "multi_modal_projector" in name or "vision_model" in name:
-            # process vision tensors
-            if "positional_embedding_vlm" in name and ".weight" not in name:
-                name += ".weight"
-            if "multi_modal_projector.linear_1" in name:
-                # despite the name with number postfix, this is a single fully connected layer
-                yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC] + '.weight', data_torch)
-            else:
-                yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("DeciLMForCausalLM")
-class DeciModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.DECI
-
-    @staticmethod
-    def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
-        # DeciLM-specific code
-        intermediate_size = int(2 * ffn_mult * n_embd / 3)
-        return DeciModel._find_multiple(intermediate_size, 256)
-
-    @staticmethod
-    def _find_multiple(n: int, k: int) -> int:
-        # DeciLM-specific code
-        if n % k == 0:
-            return n
-        return n + k - (n % k)
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
-            _block_configs: list[dict[str,Any]] = self.hparams["block_configs"]
-            assert self.block_count == len(_block_configs)
-            self._num_kv_heads = list()
-            self._num_heads = list()
-            _ffn_multipliers = list()
-            # ***linear attention layer***
-            # if n_heads_in_group is None and replace_with_linear is True
-            # then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads
-            # ***attention-free layer***
-            # if n_heads_in_group is None and replace_with_linear is False
-            # then _num_kv_heads[il] is 0 and _num_heads[il] is 0
-            # ***normal attention-layer***
-            # if n_heads_in_group is not None, then
-            # _num_kv_heads[il] is num_attention_head // n_heads_in_group and
-            # _num_heads[il] is num_attention_head
-            # ***dummy layer*** for nemotron 253B
-            # if n_heads_in_group is None and ffn_mult is None
-            # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 and _ffn_dims is 0
-            for il in range(len(_block_configs)):
-                if _block_configs[il]["attention"]["n_heads_in_group"] is None:
-                    if _block_configs[il]["attention"]["replace_with_linear"] is True:
-                        self._num_kv_heads.append(0)
-                        self._num_heads.append(self.hparams["num_attention_heads"])
-                    else:
-                        self._num_kv_heads.append(0)
-                        self._num_heads.append(0)
-                else:
-                    self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"])
-                    self._num_heads.append(self.hparams["num_attention_heads"])
-                if _block_configs[il]["ffn"]["ffn_mult"] is None: # dummy layer
-                    _ffn_multipliers.append(0.0)
-                else:
-                    _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
-            assert self.block_count == len(self._num_kv_heads)
-            assert self.block_count == len(self._num_heads)
-            assert self.block_count == len(_ffn_multipliers)
-            assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
-            assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int)
-            assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float)
-            self._ffn_dims: list[int] = [
-                DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"])
-                for multiplier in _ffn_multipliers
-            ]
-
-    def set_vocab(self):
-        # Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's
-        # eos_token from '|eot_id|' to '|end_of_text|'
-        if self.hparams.get("vocab_size", 128256) == 128256:
-            tokens, toktypes, tokpre = self.get_vocab_base()
-            self.gguf_writer.add_tokenizer_model("gpt2")
-            self.gguf_writer.add_tokenizer_pre(tokpre)
-            self.gguf_writer.add_token_list(tokens)
-            self.gguf_writer.add_token_types(toktypes)
-
-            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-            special_vocab.add_to_gguf(self.gguf_writer)
-        else:
-            # DeciLM-7B
-            self._set_vocab_llama_hf()
-
-    def set_gguf_parameters(self):
-        if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
-            assert self.block_count == len(self._num_kv_heads)
-            assert self.block_count == len(self._num_heads)
-            assert self.block_count == len(self._ffn_dims)
-            if (rope_theta := self.rope_parameters.get("rope_theta")) is not None:
-                self.gguf_writer.add_rope_freq_base(rope_theta)
-            self.gguf_writer.add_head_count_kv(self._num_kv_heads)
-            self.gguf_writer.add_head_count(self._num_heads)
-            self.gguf_writer.add_feed_forward_length(self._ffn_dims)
-            self.gguf_writer.add_block_count(self.block_count)
-            self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
-            self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-            self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-            self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-            self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-            self.gguf_writer.add_file_type(self.ftype)
-        else: # DeciLM-7B
-            super().set_gguf_parameters()
-            if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B
-                self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"]
-                assert self.block_count == len(self._num_kv_heads)
-                self.gguf_writer.add_head_count_kv(self._num_kv_heads)
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-
-        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-
-    @staticmethod
-    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        if bid is not None:
-            if "num_key_value_heads_per_layer" in self.hparams:
-                n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid]
-            elif "block_configs" in self.hparams:
-                n_kv_head = self._num_kv_heads[bid]
-                n_head = self._num_heads[bid]
-            else:
-                n_kv_head = self.hparams.get("num_key_value_heads")
-        else:
-            n_kv_head = self.hparams.get("num_key_value_heads")
-
-        if name.endswith(("q_proj.weight", "q_proj.bias")):
-            data_torch = DeciModel.permute(data_torch, n_head, n_head)
-        if name.endswith(("k_proj.weight", "k_proj.bias")):
-            data_torch = DeciModel.permute(data_torch, n_head, n_kv_head)
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
-            if rope_params.get("rope_type", '').lower() == "llama3":
-                base = rope_params.get("rope_theta", 10000.0)
-                if (dim := self.hparams.get("head_dim")) is None:
-                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
-
-                factor = rope_params.get("factor", 8.0)
-                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
-                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
-
-                low_freq_wavelen = old_context_len / low_freq_factor
-                high_freq_wavelen = old_context_len / high_freq_factor
-                assert low_freq_wavelen != high_freq_wavelen
-
-                rope_factors = []
-                for freq in freqs:
-                    wavelen = 2 * math.pi / freq
-                    if wavelen < high_freq_wavelen:
-                        rope_factors.append(1)
-                    elif wavelen > low_freq_wavelen:
-                        rope_factors.append(factor)
-                    else:
-                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
-                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
-
-                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-
-@ModelBase.register("BitnetForCausalLM")
-class BitnetModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.BITNET
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-        self.gguf_writer.add_rope_scaling_factor(1.0)
-
-    def weight_quant(self, weight: Tensor) -> Tensor:
-        dtype = weight.dtype
-        weight = weight.float()
-        scale = weight.abs().mean().clamp(min=1e-5)
-        iscale = 1 / scale
-        # TODO: multiply by the scale directly instead of inverting it twice
-        # (this is also unnecessarily doubly inverted upstream)
-        # ref: https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/af89e318d78a70802061246bf037199d2fb97020/utils_quant.py#L10
-        result = (weight * iscale).round().clamp(-1, 1) / iscale
-        return result.type(dtype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        new_name = self.map_tensor_name(name)
-
-        if any(self.match_model_tensor_name(new_name, key, bid) for key in [
-            gguf.MODEL_TENSOR.ATTN_Q,
-            gguf.MODEL_TENSOR.ATTN_K,
-            gguf.MODEL_TENSOR.ATTN_V,
-            gguf.MODEL_TENSOR.ATTN_OUT,
-            gguf.MODEL_TENSOR.FFN_UP,
-            gguf.MODEL_TENSOR.FFN_DOWN,
-            gguf.MODEL_TENSOR.FFN_GATE,
-        ]):
-            # transform weight into 1/0/-1 (in fp32)
-            data_torch = self.weight_quant(data_torch)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
-class GrokModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GROK
-
-    def set_vocab(self):
-        if (self.dir_model / 'tokenizer.model').is_file():
-            self._set_vocab_sentencepiece()
-            return
-
-        if not (self.dir_model / 'tokenizer.json').is_file() or not (self.dir_model / 'chat_template.jinja').is_file():
-            logger.error('Error: Missing vocab and chat template, download files from https://huggingface.co/alvarobartt/grok-2-tokenizer')
-            sys.exit(1)
-
-        self._set_vocab_gpt2()
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_attn_logit_softcapping(self.hparams.get("attn_logit_softcapping", 30.0))
-        self.gguf_writer.add_router_logit_softcapping(self.hparams.get("router_logit_softcapping", 30.0))
-        if (final_logit_softcap := self.hparams.get("final_logit_softcapping")):
-            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
-
-        if (rope_dim := self.hparams.get("head_dim")) is None:
-            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-
-        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-
-        # Treat "original" as "yarn", seems to have been a mistake
-        if self.hparams.get("rope_type") in ("yarn", "original"):
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(self.hparams["scaling_factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["original_max_position_embeddings"])
-            self.gguf_writer.add_rope_scaling_yarn_ext_factor(self.hparams["extrapolation_factor"])
-            self.gguf_writer.add_rope_scaling_yarn_attn_factor(self.hparams["attn_factor"])
-            self.gguf_writer.add_rope_scaling_yarn_beta_fast(self.hparams["beta_fast"])
-            self.gguf_writer.add_rope_scaling_yarn_beta_slow(self.hparams["beta_slow"])
-
-        if temp_len := self.hparams.get("attn_temperature_len"):
-            self.gguf_writer.add_attn_temperature_length(temp_len)
-
-        self.gguf_writer.add_attn_output_scale(self.hparams.get("attn_output_multiplier", rope_dim**-0.5))
-        self.gguf_writer.add_embedding_scale(self.hparams["embedding_multiplier_scale"])
-        self.gguf_writer.add_logit_scale(self.hparams["output_multiplier_scale"])
-
-    _experts: list[dict[str, list[Tensor]]] | None = None
-    _cur_expert = ""
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        deferred: list[tuple[Tensor, str, int | None]] = []
-        is_expert = ".moe." in name or ".block_sparse_moe.experts." in name
-
-        if not is_expert:
-            deferred.append((data_torch, name, bid))
-
-        # process the experts separately
-        if is_expert or self._cur_expert:
-            n_experts = self.hparams["num_local_experts"]
-
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            # concatenate split tensors
-            if name in self._experts[bid]:
-                self._cur_expert = name
-                self._experts[bid][name].append(data_torch)
-                return
-            elif is_expert:
-                self._cur_expert = name
-                self._experts[bid][name] = [data_torch]
-                return
-            else:
-                self._cur_expert = ""
-
-            for bid in range(self.block_count):
-                if len(self._experts[bid]) >= n_experts * 3:
-                    # merge the experts into a single 3d tensor
-                    for wid in [("linear", "w1", 0), ("linear_1", "w2", 1), ("linear_v", "w3", 0)]:
-                        datas: list[Tensor] = []
-
-                        for xid in range(n_experts):
-                            ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid[0]}.weight"
-                            if ename not in self._experts[bid]:
-                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid[1]}.weight"
-                            tensor_list = self._experts[bid][ename]
-                            datas.append(torch.cat(tensor_list, dim=wid[2]) if len(tensor_list) > 1 else tensor_list[0])
-                            del self._experts[bid][ename]
-
-                        data_torch = torch.stack(datas, dim=0)
-
-                        merged_name = f"transformer.decoder_layer.{bid}.moe.{wid[0]}.weight"
-
-                        yield from super().modify_tensors(data_torch, merged_name, bid)
-
-        for t in deferred:
-            yield from super().modify_tensors(*t)
-
-
-@ModelBase.register("DbrxForCausalLM")
-class DbrxModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.DBRX
-
-    def set_gguf_parameters(self):
-        ffn_config = self.hparams["ffn_config"]
-        attn_config = self.hparams["attn_config"]
-        self.gguf_writer.add_block_count(self.block_count)
-
-        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
-        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
-        self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
-
-        self.gguf_writer.add_head_count(self.hparams["n_heads"])
-        self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
-
-        self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
-
-        self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
-
-        self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
-        self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
-
-        self.gguf_writer.add_layer_norm_eps(1e-5)
-
-        self.gguf_writer.add_file_type(self.ftype)
-        logger.info(f"gguf: file type = {self.ftype}")
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_expert = self.hparams["ffn_config"]["moe_num_experts"]
-        n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
-        n_embd = self.hparams["d_model"]
-
-        # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
-        # original implementation expects (n_expert, n_ff, n_embd) for all experts weights
-        # But llama.cpp moe graph works differently
-        # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
-        # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
-        exp_tensor_names = {"ffn.experts.mlp.w1": None,       # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff,   n_expert}
-                            "ffn.experts.mlp.w2": (0, 2, 1),  # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff,   n_embd, n_expert}
-                            "ffn.experts.mlp.v1": None}       # LLM_TENSOR_FFN_UP_EXPS   ggml_tensor->ne{n_embd, n_ff,   n_expert}
-        experts = False
-
-        for exp_tensor_name in exp_tensor_names.keys():
-            if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
-                experts = True
-                data_torch = data_torch.view(n_expert, n_ff, n_embd)
-                if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
-                    data_torch = data_torch.permute(*permute_tensor)
-                break
-
-        # map tensor names
-        # In MoE models the ffn tensors are typically most of the model weights,
-        # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
-        # Every other model has the weight names ending in .weight,
-        # let's assume that is the convention which is not the case for dbrx:
-        # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
-        new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
-
-        yield from super().modify_tensors(data_torch, new_name, bid)
-
-    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
-        del name, new_name, bid  # unused
-
-        return n_dims > 1
-
-
-@ModelBase.register("MiniCPMForCausalLM")
-class MiniCPMModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.MINICPM
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        embedding_scale = float(self.hparams["scale_emb"])
-        self.gguf_writer.add_embedding_scale(embedding_scale)
-        logger.info(f"gguf: (minicpm) embedding_scale = {embedding_scale}")
-        residual_scale = self.hparams["scale_depth"] / self.hparams["num_hidden_layers"] ** 0.5
-        self.gguf_writer.add_residual_scale(residual_scale)
-        logger.info(f"gguf: (minicpm) residual_scale = {residual_scale}")
-        logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"]
-        self.gguf_writer.add_logit_scale(logit_scale)
-        logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}")
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is not None:
-            long_factors = rope_scaling.get('long_factor', None)
-            short_factors = rope_scaling.get('short_factor', None)
-
-            if long_factors is None or short_factors is None:
-                raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
-
-            if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
-                raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
-
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-
-        # HF models permute some of the tensors, so we need to undo that
-        if name.endswith(("q_proj.weight")):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-        if name.endswith(("k_proj.weight")):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("MiniCPM3ForCausalLM")
-class MiniCPM3Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.MINICPM3
-
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-
-        self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
-        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
-            self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
-        self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
-        self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
-        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is not None:
-            rope_dims = self.hparams["qk_rope_head_dim"]
-
-            long_factors = rope_scaling.get('long_factor', None)
-            short_factors = rope_scaling.get('short_factor', None)
-
-            if long_factors is None or short_factors is None:
-                raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
-
-            if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
-                raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
-
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
-        if n_kv_head is not None and n_head != n_kv_head:
-            n_head //= n_kv_head
-
-        return (
-            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-            .swapaxes(1, 2)
-            .reshape(weights.shape)
-        )
-
-
-@ModelBase.register("QWenLMHeadModel")
-class QwenModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN
-
-    @staticmethod
-    def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
-        byte_encoder = bytes_to_unicode()
-        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
-
-    @staticmethod
-    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
-        parts = [bytes([b]) for b in token]
-        while True:
-            min_idx = None
-            min_rank = None
-            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
-                rank = mergeable_ranks.get(pair[0] + pair[1])
-                if rank is not None and (min_rank is None or rank < min_rank):
-                    min_idx = i
-                    min_rank = rank
-            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
-                break
-            assert min_idx is not None
-            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
-        return parts
-
-    def set_vocab(self):
-        self._set_vocab_qwen()
-
-
-@ModelBase.register(
-    "Qwen2Model",
-    "Qwen2ForCausalLM",
-    "Qwen2AudioForConditionalGeneration",
-    "KORMoForCausalLM",
-    "AudioFlamingo3ForConditionalGeneration",
-    "DotsOCRForCausalLM",
-)
-class Qwen2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN2
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self._try_set_pooling_type()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if self.hf_arch == "Qwen2Model":
-            name = f"model.{name}"  # map to Qwen2ForCausalLM tensors
-        if "language_model." in name:
-            name = name.replace("language_model.", "") # for InternVL
-        if name.startswith("mlp") or name.startswith("multi_modal_projector") \
-                or name.startswith("vision_model") or name.startswith("audio_tower") \
-                or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") \
-                or name.startswith("vision_tower."):
-            # skip vision and audio tensors
-            return
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("DreamModel")
-class DreamModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.DREAM
-
-    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-
-        vocab_dict = tokenizer.get_vocab()  # ty: ignore[unresolved-attribute]
-        vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
-        assert max(vocab_dict.values()) < vocab_size
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
-        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                # Check if it's a special token - treat special tokens as CONTROL tokens
-                if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
-                    if tokenizer.added_tokens_decoder[i].special:
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
-                    toktypes.append(gguf.TokenType.CONTROL)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
-        return tokens, toktypes, tokpre
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self._try_set_pooling_type()
-
-        # Dream models use non-causal attention for diffusion
-        self.gguf_writer.add_causal_attention(False)
-
-        # Add Dream-specific parameters
-        mask_token_id = self.hparams.get("mask_token_id")
-        if mask_token_id is not None:
-            self.gguf_writer.add_mask_token_id(mask_token_id)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Dream model tensors should be mapped directly since it's the base model
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("LLaDAModelLM")
-class LLaDAModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.LLADA
-    undo_permute = True
-
-    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-
-        vocab_dict = tokenizer.get_vocab()  # ty: ignore[unresolved-attribute]
-        vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
-        assert max(vocab_dict.values()) < vocab_size
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
-        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                # Check if it's a special token - treat special tokens as CONTROL tokens
-                if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
-                    if tokenizer.added_tokens_decoder[i].special:
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
-                    toktypes.append(gguf.TokenType.CONTROL)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
-        return tokens, toktypes, tokpre
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-        # LLaDA specific parameters
-        self.gguf_writer.add_add_bos_token(True)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self._try_set_pooling_type()
-
-        # Add parameters similar to LlamaModel
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-
-        if (rope_dim := hparams.get("head_dim")) is None:
-            n_heads = hparams.get("num_attention_heads", hparams.get("n_heads"))
-            assert n_heads is not None
-            rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-
-        # Set context length for LLaDA
-        context_length = self.hparams.get("max_sequence_length", 4096)
-        self.gguf_writer.add_context_length(context_length)
-
-        # Set embedding length (dimension size)
-        embedding_length = self.hparams.get("d_model", 4096)
-        self.gguf_writer.add_embedding_length(embedding_length)
-
-        # Set feed forward length (MLP hidden size)
-        feed_forward_length = self.hparams.get("mlp_hidden_size", 12288)
-        self.gguf_writer.add_feed_forward_length(feed_forward_length)
-
-        # LLaDA models use non-causal attention for diffusion, similar to Dream
-        self.gguf_writer.add_causal_attention(False)
-
-        # LLaDA models don't shift their logits
-        self.gguf_writer.add_diffusion_shift_logits(False)
-
-    @staticmethod
-    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads"))
-        assert n_head is not None
-        n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads"))
-
-        if self.undo_permute:
-            if name.endswith(("q_proj.weight", "q_proj.bias")):
-                data_torch = LLaDAModel.permute(data_torch, n_head, n_head)
-            if name.endswith(("k_proj.weight", "k_proj.bias")):
-                data_torch = LLaDAModel.permute(data_torch, n_head, n_kv_head)
-
-        # LLaDA model tensors should be mapped directly since it's the base model
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Ernie4_5_ForCausalLM", "Ernie4_5ForCausalLM")
-class Ernie4_5Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.ERNIE4_5
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                if "add_prefix_space" in tokenizer_config_json:
-                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        num_heads = self.hparams["num_attention_heads"]
-        num_kv_heads = self.hparams["num_key_value_heads"]
-        if (head_dim := self.hparams.get("head_dim")) is None:
-            head_dim = self.hparams["hidden_size"] // num_heads
-
-        if "mlp_AR" in name or "vision_model" in name:
-            # skip vision model and projector tensors
-            return
-
-        if "ernie." in name:
-            name = name.replace("ernie.", "model.")
-        # split the qkv weights
-        # qkv_proj shape: [(num_heads + 2 * num_kv_heads) * head_dim, hidden_size]
-        if "qkv_proj" in name:
-            name_q = name.replace("qkv_proj.weight", "q_proj.weight")
-            name_k = name.replace("qkv_proj.weight", "k_proj.weight")
-            name_v = name.replace("qkv_proj.weight", "v_proj.weight")
-            total_q_dim = num_heads * head_dim
-            total_k_dim = num_kv_heads * head_dim
-            total_v_dim = num_kv_heads * head_dim
-            q_proj_weight, k_proj_weight, v_proj_weight = data_torch.split([total_q_dim, total_k_dim, total_v_dim], dim=0)
-            yield from super().modify_tensors(q_proj_weight, name_q, bid)
-            yield from super().modify_tensors(k_proj_weight, name_k, bid)
-            yield from super().modify_tensors(v_proj_weight, name_v, bid)
-        # split the up_gate_proj into gate and up
-        # up_gate_proj shape: [2 * intermediate_size, hidden_size]
-        elif "up_gate_proj" in name:
-            name_up = name.replace("up_gate_proj.weight", "up_proj.weight")
-            name_gate = name.replace("up_gate_proj.weight", "gate_proj.weight")
-            dim_half = data_torch.shape[0] // 2
-            gate_proj_weight, up_proj_weight = data_torch.split(dim_half, dim=0)
-            yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
-            yield from super().modify_tensors(up_proj_weight, name_up, bid)
-        else:
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Ernie4_5_MoeForCausalLM")
-class Ernie4_5MoeModel(Ernie4_5Model):
-    model_arch = gguf.MODEL_ARCH.ERNIE4_5_MOE
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._experts = [{} for _ in range(self.block_count)]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
-        self.gguf_writer.add_expert_used_count(self.hparams["moe_k"])
-        self.gguf_writer.add_interleave_moe_layer_step(self.hparams["moe_layer_interval"])
-        self.gguf_writer.add_leading_dense_block_count(self.hparams["moe_layer_start_index"])
-        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-        if (shared_expert_count := self.hparams.get('moe_num_shared_experts')) is not None:
-            self.gguf_writer.add_expert_shared_count(shared_expert_count)
-            if shared_expert_count > 0 and (shared_expert_intermediate_size := self.hparams.get('intermediate_size')) is not None and (num_key_value_heads := self.hparams.get('num_key_value_heads')) is not None:
-                self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size // num_key_value_heads)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Modify correction bias name as in DeepseekV2
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-
-        # skip Multi-Token Prediction (MTP) layers (again, same as DeepseekV2)
-        match = re.match(r"model.mtp_block.(\d+)", name)
-        if match:
-            return
-
-        # skip all other MTP tensors for now
-        match = re.match(r"model.mtp_emb_norm.(\d+)", name)
-        if match:
-            return
-
-        match = re.match(r"model.mtp_hidden_norm.(\d+)", name)
-        if match:
-            return
-
-        match = re.match(r"model.mtp_linear_proj.(\d+)", name)
-        if match:
-            return
-
-        # process the experts separately
-        if name.find("mlp.experts") != -1:
-            n_experts = self.hparams["moe_num_experts"]
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["gate_proj", "up_proj", "down_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename_to_retrieve])
-                        del self._experts[bid][ename_to_retrieve]
-
-                    data_torch = torch.stack(datas, dim=0)
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-        else:
-            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("PaddleOCRVLForConditionalGeneration")
-class PaddleOCRModel(Ernie4_5Model):
-    model_arch = gguf.MODEL_ARCH.PADDLEOCR
-
-
-@ModelBase.register("PaddleOCRVisionModel")
-class PaddleOCRVisionModel(MmprojModel):
-    # PaddleOCR-VL uses a modified version of Siglip
-    min_pixels: int = 0
-    max_pixels: int = 0
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        self.min_pixels = self.preprocessor_config["min_pixels"]
-        self.max_pixels = self.preprocessor_config["max_pixels"]
-        self.hparams_vision["image_size"] = int(math.sqrt(self.max_pixels))
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-        hparams = self.hparams_vision
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PADDLEOCR)
-        self.gguf_writer.add_vision_max_pixels(self.max_pixels)
-        self.gguf_writer.add_vision_min_pixels(self.min_pixels)
-        self.gguf_writer.add_vision_use_gelu(True)
-        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("rms_norm_eps", 1e-6))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        name = name.replace("visual.", "model.")
-
-        if "vision_model" in name or "mlp_AR" in name:
-            if "packing_position_embedding" in name:
-                return # unused
-            elif "vision_model.head" in name:
-                # we don't yet support image embeddings for this model
-                return
-            else:
-                yield from super().modify_tensors(data_torch, name, bid)
-        return # skip other tensors
-
-
-@ModelBase.register(
-    "Qwen2VLModel",
-    "Qwen2VLForConditionalGeneration",
-    "Qwen2_5_VLForConditionalGeneration",
-    "Qwen2_5OmniModel",
-)
-class Qwen2VLModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN2VL
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("thinker."):
-            name = name.replace("thinker.", "")
-        if name.startswith("visual") or name.startswith("audio") or \
-                name.startswith("talker") or name.startswith("token2wav"):
-            # skip multimodal tensors
-            return
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen2VLModel", "Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration")
-class Qwen2VLVisionModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
-        # rename config.json values
-        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
-        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")
-        if "embed_dim" in self.hparams_vision: # qwen2vl
-            self.hparams_vision["intermediate_size"] = self.hparams_vision.get("hidden_size")
-            self.hparams_vision["hidden_size"] = self.hparams_vision.get("embed_dim")
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-        hparams = self.hparams_vision
-        model_type = self.global_config['model_type']
-        if model_type == 'qwen2_vl':
-            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2VL)
-        elif model_type == 'qwen2_5_vl' or model_type == 'qwen2_5_omni':
-            if model_type == 'qwen2_5_omni':
-                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
-            else:
-                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25VL)
-            self.gguf_writer.add_vision_use_silu(True)
-            # find n_wa_pattern (window attention pattern)
-            fullatt_block_indexes = hparams.get("fullatt_block_indexes")
-            assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for qwen2_5_vl"
-            n_wa_pattern = fullatt_block_indexes[0] + 1
-            # validate n_wa_pattern
-            for i in range(1, len(fullatt_block_indexes)):
-                if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern:
-                    raise ValueError(f"Invalid fullatt_block_indexes: {fullatt_block_indexes}")
-            self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern)
-        else:
-            raise ValueError(f"Unknown QwenVL model type: {self.global_config['model_type']}")
-        # default values below are taken from HF tranformers code
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6))
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".position_embd." in new_name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("visual."):
-            # process visual tensors
-            # split QKV tensors if needed
-            if ".qkv." in name:
-                if data_torch.ndim == 2: # weight
-                    c3, _ = data_torch.shape
-                else: # bias
-                    c3 = data_torch.shape[0]
-                assert c3 % 3 == 0
-                c = c3 // 3
-                wq = data_torch[:c]
-                wk = data_torch[c: c * 2]
-                wv = data_torch[c * 2:]
-                yield from super().modify_tensors(wq, name.replace("qkv", "q"), bid)
-                yield from super().modify_tensors(wk, name.replace("qkv", "k"), bid)
-                yield from super().modify_tensors(wv, name.replace("qkv", "v"), bid)
-            elif 'patch_embed.proj.weight' in name:
-                # split Conv3D into Conv2Ds
-                c1, c2, kt, kh, kw = data_torch.shape
-                del c1, c2, kh, kw  # unused
-                assert kt == 2, "Current implementation only support temporal_patch_size of 2"
-                yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight"  , data_torch[:, :, 0, ...])
-                yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...])
-            else:
-                yield from super().modify_tensors(data_torch, name, bid)
-
-
-class Qwen25AudioModel(MmprojModel):
-    has_audio_encoder = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_audio is not None
-        self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
-        self.hparams_audio["intermediate_size"] = self.hparams_audio["encoder_ffn_dim"]
-        self.hparams_audio["num_attention_heads"] = self.hparams_audio["encoder_attention_heads"]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_audio is not None
-        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
-        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        # SinusoidsPositionEmbedding
-        assert self.hparams_audio is not None
-        max_timescale = 10000
-        length = 1500
-        channels = self.hparams_audio["hidden_size"]
-        log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
-        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
-        scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
-        pos_embd = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1).to(dtype=torch.float32)
-        yield ("audio_tower.embed_positions.weight", pos_embd)
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".conv" in name and ".weight" in name:
-            return gguf.GGMLQuantizationType.F16
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("thinker."):
-            name = name.replace("thinker.", "")
-
-        if name.startswith("audio_tower"):
-            # process audio tensors
-            if "conv1.bias" in name or "conv2.bias" in name:
-                # transpose conv1 and conv2 bias
-                data_torch = data_torch.unsqueeze(-1)
-            if "audio_bos_eos_token" in name:
-                # this tensor is left unused in transformers code
-                # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
-                return
-            yield from MmprojModel.modify_tensors(self, data_torch, name, bid)
-
-        return  # skip other tensors
-
-
-@ModelBase.register("Qwen2_5OmniModel")
-class Qwen25OmniModel(Qwen2VLVisionModel, Qwen25AudioModel):
-    has_audio_encoder = True
-    has_vision_encoder = True
-
-    def get_vision_config(self) -> dict[str, Any] | None:
-        return self.global_config["thinker_config"].get("vision_config")
-
-    def get_audio_config(self) -> dict[str, Any] | None:
-        return self.global_config["thinker_config"].get("audio_config")
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "visual." in name:
-            yield from Qwen2VLVisionModel.modify_tensors(self, data_torch, name, bid)
-        elif "audio_tower." in name:
-            yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
-        return  # skip other tensors
-
-
-@ModelBase.register("InternVisionModel")
-class InternVisionModel(MmprojModel):
-
-    min_dynamic_tiles: int = 0
-    max_dynamic_tiles: int = 0
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        self.min_dynamic_tiles = self.global_config.get("min_dynamic_patch", 0)
-        self.max_dynamic_tiles = self.global_config.get("max_dynamic_patch", 0)
-
-    def set_gguf_parameters(self):
-        assert self.hparams_vision is not None
-        if isinstance(self.hparams_vision['image_size'], list):
-            self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
-        if isinstance(self.hparams_vision['patch_size'], list):
-            self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
-        super().set_gguf_parameters()
-
-        hparams = self.hparams
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
-        self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
-        # hidden_act
-        if hparams["hidden_act"] == "silu":
-            self.gguf_writer.add_vision_use_silu(True)
-        elif hparams["hidden_act"] == "gelu":
-            self.gguf_writer.add_vision_use_gelu(True)
-        else:
-            raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
-        # downsample_ratio
-        downsample_ratio = self.global_config.get("downsample_ratio")
-        assert downsample_ratio is not None
-        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
-        # older models may not have min/max_dynamic_patch in config
-        if self.min_dynamic_tiles > 0:
-            self.gguf_writer.add_vision_preproc_min_tiles(self.min_dynamic_tiles)
-        if self.max_dynamic_tiles > 0:
-            self.gguf_writer.add_vision_preproc_max_tiles(self.max_dynamic_tiles)
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".position_embd." in new_name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def _mapping_interns1_name(self, name):
-        names_map = {
-            "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
-            "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
-            "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
-            "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
-            "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
-            "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
-        }
-        if name in names_map:
-            name = names_map[name]
-        return name
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
-        # deal with intern-s1 special case
-        name = self._mapping_interns1_name(name)
-        if any([name.startswith(prefix) for prefix in vision_prefix]):
-            # process visual tensors
-            # correct name
-            if name.startswith("vision_model"):
-                name = "vision_tower." + name
-            if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
-                name += ".weight"
-            # split QKV tensors if needed
-            if ".qkv." in name:
-                if data_torch.ndim == 2: # weight
-                    c3, _ = data_torch.shape
-                else: # bias
-                    c3 = data_torch.shape[0]
-                assert c3 % 3 == 0
-                c = c3 // 3
-                wq = data_torch[:c]
-                wk = data_torch[c: c * 2]
-                wv = data_torch[c * 2:]
-                yield from super().modify_tensors(wq, name.replace("attn.qkv", "self_attn.q_proj"), bid)
-                yield from super().modify_tensors(wk, name.replace("attn.qkv", "self_attn.k_proj"), bid)
-                yield from super().modify_tensors(wv, name.replace("attn.qkv", "self_attn.v_proj"), bid)
-            else:
-                yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register(
-    "NemotronH_Nano_VL_V2",
-    "RADIOModel",
-)
-class NemotronNanoV2VLModel(MmprojModel):
-    # ViT-Huge architecture parameters for RADIO v2.5-h
-    _vit_hidden_size = 1280
-    _vit_intermediate_size = 5120
-    _vit_num_layers = 32
-    _vit_num_heads = 16
-
-    def get_vision_config(self) -> dict[str, Any] | None:
-        # RADIO config doesn't have standard ViT parameters, so they need to be constructed manually
-        vision_config = self.global_config.get("vision_config")
-        if vision_config is None:
-            return None
-        # Add ViT-H parameters
-        vision_config = {
-            **vision_config,
-            "hidden_size": self._vit_hidden_size,
-            "intermediate_size": self._vit_intermediate_size,
-            "num_hidden_layers": self._vit_num_layers,
-            "num_attention_heads": self._vit_num_heads,
-            "image_size": self.global_config.get("force_image_size", 512),
-        }
-        return vision_config
-
-    def set_gguf_parameters(self):
-        if "image_mean" not in self.preprocessor_config:
-            self.preprocessor_config["image_mean"] = [0.485, 0.456, 0.406]
-        if "image_std" not in self.preprocessor_config:
-            self.preprocessor_config["image_std"] = [0.229, 0.224, 0.225]
-
-        super().set_gguf_parameters()
-        hparams = self.global_config
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
-        self.gguf_writer.add_vision_attention_layernorm_eps(1e-6)
-        self.gguf_writer.add_vision_use_gelu(True)
-        downsample_ratio = hparams.get("downsample_ratio", 0.5)
-        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".position_embd." in new_name or "pos_embed" in new_name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "input_conditioner" in name:
-            return
-
-        # RADIO's pos_embed doesn't have .weight suffix, but clip.cpp expects it
-        if "patch_generator.pos_embed" in name:
-            if not name.endswith(".weight"):
-                name += ".weight"
-            # Downsample position embeddings for fixed 512x512 image size
-            import torch.nn.functional as F
-            n_embd = self.hparams["hidden_size"]
-            image_size = self.global_config.get("force_image_size", 512)
-            patch_size = self.hparams["patch_size"]
-            target_patches_per_side = image_size // patch_size  # 32
-            max_patches_per_side = int((data_torch.shape[1]) ** 0.5)  # 128
-            if target_patches_per_side != max_patches_per_side:
-                # Reshape to grid, interpolate, flatten back
-                data_torch = data_torch.reshape(1, max_patches_per_side, max_patches_per_side, n_embd)
-                data_torch = data_torch.permute(0, 3, 1, 2).float()  # [1, n_embd, 128, 128]
-                data_torch = F.interpolate(data_torch, size=(target_patches_per_side, target_patches_per_side),
-                                           mode='bilinear', align_corners=True)
-                data_torch = data_torch.permute(0, 2, 3, 1)  # [1, 32, 32, n_embd]
-                data_torch = data_torch.reshape(1, target_patches_per_side * target_patches_per_side, n_embd)
-
-        # Reshape linear patch embedding to conv2d format for ggml_conv_2d
-        # From [n_embd, patch_size*patch_size*3] to [n_embd, 3, patch_size, patch_size]
-        if "patch_generator.embedder" in name:
-            patch_size = self.hparams["patch_size"]
-            n_embd = self.hparams["hidden_size"]
-            data_torch = data_torch.reshape(n_embd, 3, patch_size, patch_size)
-
-        if name.startswith("vision_model.radio_model.model.") or name.startswith("mlp1."):
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("WavTokenizerDec")
-class WavTokenizerDecModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if \
-                name.endswith("codebook.cluster_size") or \
-                name.endswith("codebook.embed_avg") or \
-                name.endswith("codebook.inited"):
-            logger.debug(f"Skipping {name!r}")
-            return
-
-        logger.info(f"{self.map_tensor_name(name)} -> {data_torch.shape}")
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def set_vocab(self):
-        self._set_vocab_none()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_vocab_size         (self.hparams["vocab_size"])
-        self.gguf_writer.add_features_length    (self.hparams["n_embd_features"])
-        self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"])
-        self.gguf_writer.add_group_norm_eps     (self.hparams["group_norm_epsilon"])
-        self.gguf_writer.add_group_norm_groups  (self.hparams["group_norm_groups"])
-
-        self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"])
-        self.gguf_writer.add_posnet_block_count     (self.hparams["posnet"]["n_layer"])
-
-        self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"])
-        self.gguf_writer.add_convnext_block_count     (self.hparams["convnext"]["n_layer"])
-
-        self.gguf_writer.add_causal_attention(False)
-
-
-@ModelBase.register("Qwen2MoeForCausalLM")
-class Qwen2MoeModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN2MOE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
-        if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None:
-            self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size)
-            logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}")
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # process the experts separately
-        name = name.replace("language_model.", "") # InternVL
-
-        # NVFP4 expert weights are handled in _generate_nvfp4_tensors
-        if self._is_nvfp4 and "experts" in name:
-            if name.endswith((".weight", ".weight_scale", ".weight_scale_2", ".input_scale")):
-                if name.endswith(".weight") and name.replace(".weight", ".weight_scale") in self.model_tensors:
-                    return
-                if not name.endswith(".weight"):
-                    return
-
-        # handle aggregated expert tensors
-        # GGUF stores dimensions reversed from PyTorch, so:
-        # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
-        # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
-        # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
-        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
-            mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            # HF: [n_expert, n_embd, n_ff] -> GGML: {n_ff, n_embd, n_expert}
-            yield from super().modify_tensors(data_torch, mapped, bid)
-            return
-
-        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            if data_torch.ndim < 3 or data_torch.shape[-2] % 2 != 0:
-                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
-            # HF: [n_expert, 2*n_ff, n_embd] -> split on dim=-2
-            n_ff = data_torch.shape[-2] // 2
-            gate = data_torch[..., :n_ff, :].contiguous()
-            up = data_torch[..., n_ff:, :].contiguous()
-            # gate/up: [n_expert, n_ff, n_embd] -> GGML: {n_embd, n_ff, n_expert}
-            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
-            mapped_gate = f"{base_name}.gate_proj.weight"
-            mapped_up = f"{base_name}.up_proj.weight"
-            yield from super().modify_tensors(gate, mapped_gate, bid)
-            yield from super().modify_tensors(up, mapped_up, bid)
-            return
-
-        if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
-            # skip visual tensors
-            return
-
-        if name.find("experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("Qwen3ForCausalLM", "Qwen3Model")
-class Qwen3Model(Qwen2Model):
-    model_arch = gguf.MODEL_ARCH.QWEN3
-
-    # extra logic for rerank models
-    is_rerank: bool = False
-    is_tied_embeddings: bool = False
-    token_false_id: int | None = None
-    token_true_id: int | None = None
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # track for intern-s1-mini
-        hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
-        self.origin_hf_arch = hparams.get('architectures', [None])[0]
-
-        if self._is_qwen3_reranker():
-            self._find_rerank_config()
-
-    def _is_qwen3_reranker(self) -> bool:
-        readme_path = self.dir_model / "README.md"
-        readme_text = ""
-        if readme_path.exists():
-            with readme_path.open("r", encoding="utf-8") as f:
-                readme_text = f.read()
-
-        name_hints = [
-            str(self.dir_model.name),
-            str(self.hparams.get("_name_or_path", "")),
-            str(self.hparams.get("model_type", "")),
-            str(self.origin_hf_arch or ""),
-        ]
-        name_hints = [hint.lower() for hint in name_hints if hint]
-
-        if "# qwen3-reranker" in readme_text.lower() or "# qwen3-vl-reranker" in readme_text.lower():
-            return True
-
-        if any("qwen3-reranker" in hint or "qwen3-vl-reranker" in hint for hint in name_hints):
-            return True
-
-        return "sequenceclassification" in (self.origin_hf_arch or "").lower()
-
-    def set_vocab(self):
-        # deal with intern-s1-mini
-        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
-            self._set_vocab_interns1()
-            return
-
-        super().set_vocab()
-
-    def _find_rerank_config(self):
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-
-        self.is_rerank = True
-        self.is_tied_embeddings = self.hparams.get("tie_word_embeddings", False)
-        self.token_false_id = tokenizer.convert_tokens_to_ids("no")  # ty: ignore[unresolved-attribute, invalid-assignment]
-        self.token_true_id = tokenizer.convert_tokens_to_ids("yes")  # ty: ignore[unresolved-attribute, invalid-assignment]
-        self.sep_token_id = tokenizer.convert_tokens_to_ids("|")  # ty: ignore[unresolved-attribute]
-
-        assert self.token_false_id is not None and self.token_true_id is not None
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if self.is_rerank:
-            self.gguf_writer.add_pooling_type(gguf.PoolingType.RANK)
-            self.gguf_writer.add_classifier_output_labels(["yes", "no"])
-            self.gguf_writer.add_chat_template([{
-                "name": "rerank",
-                "template": "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n"
-                            "<|im_start|>user\n<Instruct>: Given a web search query, retrieve relevant passages that answer the query\n<Query>: {query}\n<Document>: {document}<|im_end|>\n"
-                            "<|im_start|>assistant\n<think>\n\n</think>\n\n"
-            }])
-
-    def _get_cls_out_tensor(self, data_torch: Tensor) -> Tensor:
-        # extract "yes" and "no" tokens from the output lm_head tensor
-        false_row = data_torch[self.token_false_id]
-        true_row = data_torch[self.token_true_id]
-        return torch.stack([true_row, false_row], dim=0)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "model.vision_" in name:
-            # skip multimodal tensors
-            return
-
-        if self.is_rerank:
-            is_tied_head = self.is_tied_embeddings and "embed_tokens" in name
-            is_real_head = not self.is_tied_embeddings and "lm_head" in name
-            if is_tied_head or is_real_head:
-                cls_out_head = (
-                    gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.CLS_OUT] + ".weight",
-                    self._get_cls_out_tensor(data_torch),
-                )
-                yield cls_out_head
-                if is_tied_head:
-                    yield from super().modify_tensors(data_torch, name, bid)
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3MoeForCausalLM")
-class Qwen3MoeModel(Qwen2MoeModel):
-    model_arch = gguf.MODEL_ARCH.QWEN3MOE
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        hparams = ModelBase.load_hparams(self.dir_model, False)
-        self.origin_hf_arch = hparams.get('architectures', [None])[0]
-
-    def set_vocab(self):
-        # deal with intern-s1
-        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
-            self._set_vocab_interns1()
-            return
-
-        super().set_vocab()
-
-
-@ModelBase.register("Qwen3NextForCausalLM")
-class Qwen3NextModel(Qwen2MoeModel):
-    model_arch = gguf.MODEL_ARCH.QWEN3NEXT
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_ssm_conv_kernel(self.hparams["linear_conv_kernel_dim"])
-        self.gguf_writer.add_ssm_state_size(self.hparams["linear_key_head_dim"])
-        self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
-        self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
-        self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
-        self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
-        if (rope_dim := self.hparams.get("head_dim")) is None:
-            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("mtp"):
-            return  # ignore MTP layers for now
-        if name.endswith(".A_log"):
-            data_torch = -torch.exp(data_torch)
-        elif name.endswith(".dt_bias"):
-            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
-        elif "conv1d" in name:
-            data_torch = data_torch.squeeze()
-        elif name.endswith("norm.weight") and not name.endswith("linear_attn.norm.weight"):
-            data_torch = data_torch + 1
-
-        if "in_proj_qkvz.weight" in name:
-            # original order:  [q, k, v, z] * head_count
-            # corrected order: [q * head_count, k * head_count, v * head_count, z * head_count]
-            head_k_dim = self.hparams["linear_key_head_dim"]
-            head_v_dim = self.hparams["linear_value_head_dim"]
-            num_v_heads = self.hparams["linear_num_value_heads"]
-            num_k_heads = self.hparams["linear_num_key_heads"]
-            hidden_size = self.hparams["hidden_size"]
-            split_arg_list_qkvz = [
-                head_k_dim, # q partition
-                head_k_dim, # k partition
-                (num_v_heads // num_k_heads * head_v_dim), # v partition
-                (num_v_heads // num_k_heads * head_v_dim), # z partition
-            ]
-            # view as (n_embd, head_count, [q+k+v+z])
-            data_torch = data_torch.permute(1, 0).contiguous()
-            data_torch = data_torch.view(-1, num_k_heads, sum(split_arg_list_qkvz))
-            # split into q, k, v, z
-            q, k, v, z = torch.split(data_torch, split_arg_list_qkvz, dim=-1)
-            # flatten dim + head_count
-            q = q.contiguous().view(hidden_size, -1)
-            k = k.contiguous().view(hidden_size, -1)
-            v = v.contiguous().view(hidden_size, -1)
-            z = z.contiguous().view(hidden_size, -1)
-            # stack back
-            qkv = torch.cat([q, k, v], dim=-1).permute(1, 0).contiguous()
-            z = z.permute(1, 0).contiguous()
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV,  bid, ".weight"), qkv)
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, bid, ".weight"), z)
-        else:
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("RND1")
-class RND1Model(Qwen2MoeModel):
-    model_arch = gguf.MODEL_ARCH.RND1
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # RND1 specific parameters
-        # RND1 uses bidirectional attention
-        self.gguf_writer.add_causal_attention(False)
-
-        if (mask_token_id := self.hparams.get("mask_token_id")) is not None:
-            self.gguf_writer.add_mask_token_id(mask_token_id)
-
-
-@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration", "Qwen3_5ForConditionalGeneration", "Qwen3_5MoeForConditionalGeneration")
-class Qwen3VLVisionModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.hparams_vision is None:
-            logger.info("No vision config found, skipping vision tensor processing")
-            return
-
-        # Compute image_size if not present
-        if "image_size" not in self.hparams_vision:
-            # For Qwen3VL/Qwen3VLMoe, compute from num_position_embeddings
-            num_pos = self.hparams_vision.get("num_position_embeddings", 2304)
-            patch_size = self.hparams_vision.get("patch_size", 16)
-            # num_position_embeddings = (image_size / patch_size) ** 2
-            # So image_size = sqrt(num_position_embeddings) * patch_size
-            image_size = int(num_pos**0.5 * patch_size)
-            self.hparams_vision["image_size"] = image_size
-
-        # Rename config values for compatibility
-        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
-        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")
-
-        self.is_deepstack_layers = [False] * int(self.hparams_vision["num_hidden_layers"] or 0)
-        for idx in self.hparams_vision.get("deepstack_visual_indexes", []):
-            self.is_deepstack_layers[idx] = True
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        # in case mixed modalities, the arch will be handled by subclass
-        if not self.has_audio_encoder:
-            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN3VL)
-        self.gguf_writer.add_vision_use_gelu(True)
-
-        if self.hparams_vision is not None:
-            merge_size = self.hparams_vision.get("spatial_merge_size")
-            if merge_size is not None:
-                self.gguf_writer.add_vision_spatial_merge_size(int(merge_size))
-
-        # Use text config's rms_norm_eps for vision attention layernorm eps
-        rms_norm_eps = self.global_config.get("text_config", {}).get("rms_norm_eps", 1e-6)
-        self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)
-
-        if self.is_deepstack_layers:
-            self.gguf_writer.add_vision_is_deepstack_layers(self.is_deepstack_layers)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        assert self.hparams_vision is not None
-        # Skip text model tensors - they go in the text model file
-        if name.startswith("model.language_model.") or name.startswith("lm_head."):
-            return
-
-        # Skip MTP tensors
-        if name.startswith("mtp."):
-            return
-
-        if name.startswith("model.visual."):
-            name = name.replace("model.visual.", "visual.", 1)
-
-        if name.startswith("visual.deepstack_merger_list."):
-            prefix, rest = name.split(".", maxsplit=3)[2:]
-            # prefix is the layer index, convert to absolute clip layer index!
-            idx = self.hparams_vision.get("deepstack_visual_indexes", [])[int(prefix)]
-            target = rest
-
-            tensor_type: gguf.MODEL_TENSOR
-            if target.startswith("norm."):
-                tensor_type = gguf.MODEL_TENSOR.V_DS_NORM
-                suffix = target.split(".", 1)[1]
-            elif target.startswith("linear_fc1."):
-                tensor_type = gguf.MODEL_TENSOR.V_DS_FC1
-                suffix = target.split(".", 1)[1]
-            elif target.startswith("linear_fc2."):
-                tensor_type = gguf.MODEL_TENSOR.V_DS_FC2
-                suffix = target.split(".", 1)[1]
-            else:
-                raise ValueError(f"Unexpected deepstack tensor: {name}")
-
-            new_name = self.format_tensor_name(tensor_type, idx, suffix=f".{suffix}")
-            yield from super().modify_tensors(data_torch, new_name, bid)
-            return
-
-        if name.startswith("visual.merger."):
-            suffix = name.split(".", 2)[2]
-            if suffix.startswith("linear_fc"):
-                fc_idx_str, tail = suffix.split(".", 1)
-                fc_num = int(fc_idx_str.replace("linear_fc", ""))
-                # Qwen3VL has linear_fc1 and linear_fc2
-                # Map to indices 0 and 2 (matching Qwen2VL which uses indices 0 and 2)
-                if fc_num == 1:
-                    fc_idx = 0
-                elif fc_num == 2:
-                    fc_idx = 2
-                else:
-                    raise ValueError(f"unexpected fc index {fc_num} in {name}")
-                new_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, fc_idx, suffix=f".{tail}")
-            elif suffix.startswith("norm."):
-                new_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_POST_NORM, suffix=f".{suffix.split('.', 1)[1]}")
-            else:
-                raise ValueError(f"Unexpected merger tensor: {name}")
-            yield (new_name, data_torch)
-            return
-
-        if name == "visual.patch_embed.proj.weight":
-            # split Conv3D into Conv2Ds along temporal dimension
-            c1, c2, kt, _, _ = data_torch.shape
-            del c1, c2
-            if kt != 2:
-                raise ValueError("Current implementation only supports temporal_patch_size of 2")
-            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight", data_torch[:, :, 0, ...])
-            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...])
-            return
-
-        if name == "visual.patch_embed.proj.bias":
-            # Include the bias - it's used by the C++ code
-            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".bias", data_torch)
-            return
-
-        if name.startswith("visual."):
-            yield from MmprojModel.modify_tensors(self, data_torch, name, bid)
-        return  # skip other tensors
-
-
-@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
-class Qwen3OmniMmprojModel(Qwen3VLVisionModel, Qwen25AudioModel):
-    has_audio_encoder = True
-    has_vision_encoder = True
-
-    def get_vision_config(self) -> dict[str, Any] | None:
-        if self.has_vision_encoder:
-            return self.global_config["thinker_config"].get("vision_config")
-        else:
-            return None
-
-    def get_audio_config(self) -> dict[str, Any] | None:
-        if self.has_audio_encoder:
-            return self.global_config["thinker_config"].get("audio_config")
-        else:
-            return None
-
-    def set_gguf_parameters(self):
-        if self.has_vision_encoder:
-            Qwen3VLVisionModel.set_gguf_parameters(self)
-            self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.QWEN3VL)
-        if self.has_audio_encoder:
-            Qwen25AudioModel.set_gguf_parameters(self)
-            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3A)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "visual." in name:
-            if not self.has_vision_encoder:
-                raise ValueError(f"Model does not have vision encoder, but found tensor {name}")
-            # need to transform vision tensor naming, so that modify_tensors() logic can be used correctly
-            name = name.replace("thinker.visual.", "model.visual.")
-            if ".merger_list." in name:
-                name = name.replace(".merger_list.", ".deepstack_merger_list.")
-                name = name.replace(".ln_q", ".norm")
-                name = name.replace(".mlp.0", ".linear_fc1")
-                name = name.replace(".mlp.2", ".linear_fc2")
-            elif ".merger." in name:
-                name = name.replace(".ln_q", ".norm")
-                name = name.replace(".mlp.0", ".linear_fc1")
-                name = name.replace(".mlp.2", ".linear_fc2")
-            yield from Qwen3VLVisionModel.modify_tensors(self, data_torch, name, bid)
-        elif "audio_tower." in name:
-            if not self.has_audio_encoder:
-                raise ValueError(f"Model does not have audio encoder, but found tensor {name}")
-            if "conv2d" in name and name.endswith(".bias"):
-                # transform conv2d bias [n_embd] --> [1, 1, n_embd]
-                data_torch = data_torch.unsqueeze(-1).unsqueeze(-1)
-            yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3ASRForConditionalGeneration")
-class Qwen3ASRMmprojModel(Qwen3OmniMmprojModel):
-    has_audio_encoder = True
-    has_vision_encoder = False
-
-
-@ModelBase.register("Glm4vForConditionalGeneration", "Glm4vMoeForConditionalGeneration", "GlmOcrForConditionalGeneration")
-class Glm4VVisionModel(Qwen3VLVisionModel):
-    def set_gguf_parameters(self):
-        MmprojModel.set_gguf_parameters(self) # skip Qwen3VLVisionModel parameters
-        assert self.hparams_vision is not None
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLM4V)
-
-        hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
-        if hidden_act == "gelu":
-            self.gguf_writer.add_vision_use_gelu(True)
-        elif hidden_act == "silu":
-            self.gguf_writer.add_vision_use_silu(True)
-
-        rms_norm_eps = self.hparams_vision.get("rms_norm_eps", 1e-5)
-        self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("model.visual."):
-            name = name.replace("model.visual.", "visual.")
-        if name.startswith("visual.merger."):
-            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
-            return
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("StepVLForConditionalGeneration")
-class Step3VLVisionModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-
-        if not self.hparams_vision.get("intermediate_size"):
-            hidden_size = self.hparams_vision.get("hidden_size") or self.hparams_vision.get("width") or 0
-            assert hidden_size > 0
-            mlp_ratio = float(self.hparams_vision.get("mlp_ratio", 8960 / 1536))
-            self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
-
-        self.preprocessor_config.setdefault("image_mean", list(_MISTRAL_COMMON_DATASET_MEAN))
-        self.preprocessor_config.setdefault("image_std", list(_MISTRAL_COMMON_DATASET_STD))
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-
-        projector_stride = int(self.global_config.get("understand_projector_stride", -1))
-        hidden_size = int(self.hparams_vision.get("hidden_size", self.hparams_vision.get("width", -1)))
-        num_layers = int(self.hparams_vision.get("num_hidden_layers", self.hparams_vision.get("layers", -1)))
-        assert (projector_stride, int(self.hparams_vision.get("image_size", -1)), hidden_size, num_layers) == (2, 728, 1536, 47), (
-            "current Step3-VL conversion path is only validated for Step3-VL-10B"
-        )
-
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.STEP3VL)
-        self.gguf_writer.add_vision_attention_layernorm_eps(float(self.hparams_vision.get("layer_norm_eps", 1e-5)))
-        self.gguf_writer.add_vision_projector_scale_factor(projector_stride ** 2)
-        # 3024 max resize comes from step3-vl-10b processing_step3.py.
-        self.gguf_writer.add_vision_preproc_image_size(3024)
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".position_embd." in new_name:
-            return gguf.GGMLQuantizationType.F32
-        if ("mm.0." in new_name or "mm.1." in new_name) and new_name.endswith(".weight"):
-            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("model.") or name.startswith("lm_head."):
-            return
-
-        if name.startswith("vision_model.vit_downsampler"):
-            match = re.match(r"vision_model\.vit_downsampler(\d+)\.(weight|bias)", name)
-            if match is None:
-                raise ValueError(f"Unexpected Step3-VL projector tensor {name!r}")
-
-            proj_id = int(match.group(1)) - 1
-            suffix = f".{match.group(2)}"
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, proj_id, suffix=suffix), data_torch)
-            return
-
-        if name == "vit_large_projector.weight":
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ_FC), data_torch)
-            return
-
-        if name.startswith("vision_model."):
-            if name == "vision_model.positional_embedding":
-                name += ".weight"
-            elif name.endswith(".gamma") and ".ls_" in name:
-                name = name.removesuffix(".gamma") + ".weight"
-
-            name = name.replace("attn.in_proj_weight", "attn.in_proj.weight")
-            name = name.replace("attn.in_proj_bias", "attn.in_proj.bias")
-
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3VLForConditionalGeneration")
-class Qwen3VLTextModel(Qwen3Model):
-    model_arch = gguf.MODEL_ARCH.QWEN3VL
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if "thinker_config" in self.hparams:
-            vision_config = self.hparams["thinker_config"].get("vision_config", {})
-        else:
-            vision_config = self.hparams.get("vision_config", {})
-        deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
-        self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Skip vision tensors - they go in the mmproj file
-        if name.startswith("model.visual."):
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("StepVLForConditionalGeneration")
-class Step3VLTextModel(Qwen3Model):
-    model_arch = gguf.MODEL_ARCH.QWEN3
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("vision_model.") or name.startswith("model.vision_model.") or name.startswith("vit_large_projector."):
-            return
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3VLMoeForConditionalGeneration")
-class Qwen3VLMoeTextModel(Qwen3MoeModel):
-    model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        vision_config = self.hparams.get("vision_config", {})
-        deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
-        self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Skip vision tensors - they go in the mmproj file
-        if name.startswith("model.visual."):
-            return
-
-        # Qwen3VL has transposed packed tensors, so we treat it differently from general Qwen2MoE packed tensors
-        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
-            name = name.replace("language_model.", "")
-            mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            permuted = data_torch.permute(0, 2, 1).contiguous()
-            yield from ModelBase.modify_tensors(self, permuted, mapped, bid)
-            return
-
-        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            name = name.replace("language_model.", "")
-            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
-                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
-            split_dim = data_torch.shape[-1] // 2
-            gate = data_torch[..., :split_dim].contiguous()
-            up = data_torch[..., split_dim:].contiguous()
-            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
-            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
-            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
-            base_name = name.removesuffix(".weight")
-            base = base_name.rsplit('.', 1)[0]
-            mapped_gate = f"{base}.gate_proj.weight"
-            mapped_up = f"{base}.up_proj.weight"
-            perm_gate = gate.permute(0, 2, 1).contiguous()
-            perm_up = up.permute(0, 2, 1).contiguous()
-            yield from ModelBase.modify_tensors(self, perm_gate, mapped_gate, bid)
-            yield from ModelBase.modify_tensors(self, perm_up, mapped_up, bid)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
-class Qwen3OmniMoeTextModel(Qwen3VLMoeTextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
-
-    def set_vocab(self):
-        super().set_vocab()
-        # correct BOS/EOS tokens
-        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
-            tokenizer_config = json.load(f)
-            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
-            for token_id, data in added_tokens.items():
-                if data.get("content") == "<|im_end|>":
-                    self.gguf_writer.add_bos_token_id(int(token_id))
-                    self.gguf_writer.add_eos_token_id(int(token_id))
-                    break
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_num_deepstack_layers(0)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Skip vision and audio tensors - they go in the mmproj file
-        if "visual." in name or "audio_tower." in name \
-                or "talker." in name or "code2wav." in name:
-            return
-
-        name = name.replace("thinker.", "")
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3ASRForConditionalGeneration")
-class Qwen3ASRTextModel(Qwen3VLTextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN3VL
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_num_deepstack_layers(0)
-
-    def set_vocab(self):
-        super().set_vocab()
-        # fix chat template, use correct chatml format
-        self.gguf_writer.add_chat_template("{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}")
-        # correct BOS/EOS tokens
-        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
-            tokenizer_config = json.load(f)
-            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
-            for token_id, data in added_tokens.items():
-                if data.get("content") == "<|im_end|>":
-                    self.gguf_writer.add_bos_token_id(int(token_id))
-                    self.gguf_writer.add_eos_token_id(int(token_id))
-                    break
-
-    def modify_tensors(self, data_torch, name, bid):
-        # qwen3-omni
-        name = name.replace("thinker.", "")
-
-        # Skip vision and audio tensors - they go in the mmproj file
-        if "visual." in name or "audio_tower." in name \
-                or "talker." in name or "code2wav." in name:
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-class _LinearAttentionVReorderBase(Qwen3NextModel):
-    model_arch = gguf.MODEL_ARCH.QWEN3NEXT  # overridden by subclasses
-    """reorders V heads from grouped to tiled order for ggml broadcast
-
-    see https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
-
-    Linear attention may has num_k_heads < num_v_heads. The HF weights store
-    V heads grouped by K head: [G0_v0..v{r-1}, G1_v0..v{r-1}, ...].
-    ggml binary ops use tiled broadcast: [K0, K1, ..., K0, K1, ...].
-    We reorder V heads to tiled order so ggml_repeat can replace the expensive
-    interleaved repeat: [G0_v0, G1_v0, ..., G0_v1, G1_v1, ...].
-    """
-
-    @staticmethod
-    def _reorder_v_heads(tensor: Tensor, dim: int, num_k_heads: int, num_v_per_k: int, head_dim: int) -> Tensor:
-        """Reorder V heads from grouped (by K head) to tiled order along the given dimension."""
-        shape = list(tensor.shape)
-        if dim < 0:
-            dim += len(shape)
-        new_shape = shape[:dim] + [num_k_heads, num_v_per_k, head_dim] + shape[dim + 1:]
-        tensor = tensor.reshape(*new_shape)
-        perm = list(range(len(new_shape)))
-        perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
-        return tensor.permute(*perm).contiguous().reshape(*shape)
-
-    def _transform_nvfp4_weight(self, name: str, weight: Tensor, scale: Tensor) -> tuple[Tensor, Tensor]:
-        if not name.endswith((
-            ".linear_attn.in_proj_qkv.weight",
-            ".linear_attn.in_proj_z.weight",
-            ".linear_attn.in_proj_a.weight",
-            ".linear_attn.in_proj_b.weight",
-            ".linear_attn.out_proj.weight",
-        )):
-            return weight, scale
-
-        num_k_heads = self.hparams["linear_num_key_heads"]
-        num_v_heads = self.hparams["linear_num_value_heads"]
-        head_k_dim = self.hparams["linear_key_head_dim"]
-        head_v_dim = self.hparams["linear_value_head_dim"]
-        num_v_per_k = num_v_heads // num_k_heads
-
-        def unpack_nibbles(qs: Tensor) -> Tensor:
-            lo = torch.bitwise_and(qs, 0x0F)
-            hi = torch.bitwise_right_shift(qs, 4)
-            return torch.stack((lo, hi), dim=-1).reshape(*qs.shape[:-1], qs.shape[-1] * 2)
-
-        def pack_nibbles(codes: Tensor) -> Tensor:
-            codes = codes.reshape(*codes.shape[:-1], codes.shape[-1] // 2, 2)
-            lo = torch.bitwise_and(codes[..., 0], 0x0F)
-            hi = torch.bitwise_left_shift(torch.bitwise_and(codes[..., 1], 0x0F), 4)
-            return torch.bitwise_or(lo, hi).contiguous()
-
-        def apply_col_perm(qs: Tensor, scales: Tensor, col_perm: Tensor) -> tuple[Tensor, Tensor]:
-            assert qs.ndim >= 2
-            assert scales.ndim >= 2
-
-            k = qs.shape[-1] * 2
-            assert col_perm.numel() == k
-            assert k % 16 == 0
-
-            group_cols = col_perm.reshape(-1, 16)
-            group_starts = group_cols[:, 0]
-            expected = group_starts.unsqueeze(1) + torch.arange(16, dtype=col_perm.dtype)
-            assert torch.equal(group_cols, expected)
-            assert torch.all(group_starts % 16 == 0)
-
-            group_perm = (group_starts // 16).to(dtype=torch.long)
-            expected_groups = torch.arange(scales.shape[-1], dtype=torch.long)
-            assert group_perm.numel() == scales.shape[-1]
-            assert torch.equal(torch.sort(group_perm).values, expected_groups)
-
-            codes = unpack_nibbles(qs)
-            codes = codes.index_select(-1, col_perm.to(device=qs.device, dtype=torch.long))
-            qs = pack_nibbles(codes)
-            scales = scales.index_select(-1, group_perm.to(device=scales.device))
-            return qs, scales
-
-        def reorder_rows(qs: Tensor, scales: Tensor, head_dim: int) -> tuple[Tensor, Tensor]:
-            row_perm = self._reorder_v_heads(
-                torch.arange(num_v_heads * head_dim, dtype=torch.long).unsqueeze(-1),
-                0, num_k_heads, num_v_per_k, head_dim,
-            ).squeeze(-1)
-            return (
-                qs.index_select(0, row_perm.to(device=qs.device)),
-                scales.index_select(0, row_perm.to(device=scales.device)),
-            )
-
-        if name.endswith(".linear_attn.in_proj_qkv.weight"):
-            q_dim = head_k_dim * num_k_heads
-            k_dim = head_k_dim * num_k_heads
-            q = weight[:q_dim]
-            k = weight[q_dim:q_dim + k_dim]
-            v = weight[q_dim + k_dim:]
-            q_scale = scale[:q_dim]
-            k_scale = scale[q_dim:q_dim + k_dim]
-            v_scale = scale[q_dim + k_dim:]
-            v, v_scale = reorder_rows(v, v_scale, head_v_dim)
-            return torch.cat([q, k, v], dim=0), torch.cat([q_scale, k_scale, v_scale], dim=0)
-
-        if name.endswith(".linear_attn.in_proj_z.weight"):
-            weight, scale = reorder_rows(weight, scale, head_v_dim)
-        elif name.endswith((".linear_attn.in_proj_a.weight", ".linear_attn.in_proj_b.weight")):
-            weight, scale = reorder_rows(weight, scale, 1)
-        elif name.endswith(".linear_attn.out_proj.weight"):
-            col_perm = self._reorder_v_heads(
-                torch.arange(num_v_heads * head_v_dim, dtype=torch.long).unsqueeze(0),
-                1, num_k_heads, num_v_per_k, head_v_dim,
-            ).squeeze(0)
-            weight, scale = apply_col_perm(weight, scale, col_perm)
-
-        return weight, scale
-
-    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
-        weight, scale = self._transform_nvfp4_weight(name, weight, scale)
-        super()._repack_nvfp4(name, weight, scale, scale2, input_scale)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        num_k_heads = self.hparams.get("linear_num_key_heads", 0)
-        num_v_heads = self.hparams.get("linear_num_value_heads", 0)
-
-        if num_k_heads > 0 and num_v_heads > 0 and num_k_heads != num_v_heads and "linear_attn." in name:
-            head_k_dim = self.hparams["linear_key_head_dim"]
-            head_v_dim = self.hparams["linear_value_head_dim"]
-            num_v_per_k = num_v_heads // num_k_heads
-
-            if ".in_proj_qkv." in name:
-                # QKV weight: reorder only the V rows
-                q_dim = head_k_dim * num_k_heads
-                k_dim = head_k_dim * num_k_heads
-                q = data_torch[:q_dim]
-                k = data_torch[q_dim:q_dim + k_dim]
-                v = data_torch[q_dim + k_dim:]
-                v = self._reorder_v_heads(v, 0, num_k_heads, num_v_per_k, head_v_dim)
-                data_torch = torch.cat([q, k, v], dim=0)
-
-            elif ".in_proj_z." in name:
-                # Z gate weight: reorder rows (num_v_heads * head_v_dim)
-                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, head_v_dim)
-
-            elif ".in_proj_b." in name or ".in_proj_a." in name:
-                # Beta/Alpha weight: reorder rows (num_v_heads, head_dim=1)
-                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, 1)
-
-            elif ".A_log" in name or ".dt_bias" in name or ".dt_proj" in name:
-                # A_log / dt_bias: 1D parameters with num_v_heads elements
-                if data_torch.ndim == 1:
-                    data_torch = self._reorder_v_heads(
-                        data_torch.unsqueeze(-1), 0, num_k_heads, num_v_per_k, 1
-                    ).squeeze(-1)
-                else:
-                    data_torch = self._reorder_v_heads(data_torch, -1, num_k_heads, num_v_per_k, 1)
-
-            elif ".conv1d" in name:
-                # Conv1d kernel: reorder only the V channel portion
-                data = data_torch.squeeze()
-                qk_channels = head_k_dim * num_k_heads * 2
-                qk_part = data[:qk_channels]
-                v_part = data[qk_channels:]
-                v_part = self._reorder_v_heads(v_part, 0, num_k_heads, num_v_per_k, head_v_dim)
-                data_torch = torch.cat([qk_part, v_part], dim=0)
-
-            elif ".out_proj." in name:
-                # Out projection weight: reorder columns (input dimension)
-                data_torch = self._reorder_v_heads(data_torch, 1, num_k_heads, num_v_per_k, head_v_dim)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM")
-class Qwen3_5TextModel(_LinearAttentionVReorderBase):
-    model_arch = gguf.MODEL_ARCH.QWEN35
-
-
-@ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM")
-class Qwen3_5MoeTextModel(_LinearAttentionVReorderBase):
-    model_arch = gguf.MODEL_ARCH.QWEN35MOE
-
-
-@ModelBase.register("GPT2LMHeadModel")
-class GPT2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.GPT2
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_context_length(self.hparams["n_ctx"])
-        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
-        self.gguf_writer.add_head_count(self.hparams["n_head"])
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # we don't need these
-        if name.endswith((".attn.bias", ".attn.masked_bias")):
-            yield from super().modify_tensors(data_torch, name, bid)
-            return
-
-        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
-            data_torch = data_torch.transpose(1, 0)
-
-        new_name = self.map_tensor_name(name)
-
-        yield from super().modify_tensors(data_torch, new_name, bid)
-
-
-@ModelBase.register("RuGPT3XLForCausalLM")
-class RuGPT3XLModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GPT2
-
-    _qkv_parts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Fuse separate Q, K, V projections into a single QKV tensor
-        if ".self_attn.q_proj." in name or ".self_attn.k_proj." in name or ".self_attn.v_proj." in name:
-            suffix = "weight" if name.endswith(".weight") else "bias"
-            part = "q" if ".q_proj." in name else ("k" if ".k_proj." in name else "v")
-            key = f"{part}.{suffix}"
-
-            assert bid is not None
-            if self._qkv_parts is None:
-                self._qkv_parts = [{} for _ in range(self.block_count)]
-            self._qkv_parts[bid][key] = data_torch
-
-            q_key, k_key, v_key = f"q.{suffix}", f"k.{suffix}", f"v.{suffix}"
-            if all(k in self._qkv_parts[bid] for k in [q_key, k_key, v_key]):
-                q = self._qkv_parts[bid].pop(q_key)
-                k = self._qkv_parts[bid].pop(k_key)
-                v = self._qkv_parts[bid].pop(v_key)
-                data_torch = torch.cat([q, k, v], dim=0)
-                name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid, f".{suffix}")
-                logger.debug(f"Fused Q/K/V {suffix} for layer {bid} -> {name}")
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._qkv_parts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            parts = [f"({i}){k}" for i, d in enumerate(self._qkv_parts) for k in d.keys()]
-            if len(parts) > 0:
-                raise ValueError(f"Unprocessed Q/K/V parts: {parts}")
-
-
-@ModelBase.register("PhiForCausalLM")
-class Phi2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.PHI2
-
-    def set_gguf_parameters(self):
-        rot_pct = self.find_hparam(["partial_rotary_factor"])
-        n_embd = self.find_hparam(["hidden_size", "n_embd"])
-        n_head = self.find_hparam(["num_attention_heads", "n_head"])
-
-        self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
-
-        self.gguf_writer.add_embedding_length(n_embd)
-        self.gguf_writer.add_feed_forward_length(4 * n_embd)
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_head_count_kv(n_head)
-        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
-        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
-        self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_add_bos_token(False)
-
-
-@ModelBase.register("Phi3ForCausalLM", "Phi4ForCausalLMV")
-class Phi3MiniModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.PHI3
-
-    def set_vocab(self):
-        # Phi-4 model uses GPT2Tokenizer
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                tokenizer_class = tokenizer_config_json['tokenizer_class']
-                if tokenizer_class == 'GPT2Tokenizer':
-                    return self._set_vocab_gpt2()
-
-        from sentencepiece import SentencePieceProcessor
-
-        tokenizer_path = self.dir_model / 'tokenizer.model'
-
-        if not tokenizer_path.is_file():
-            raise ValueError(f'Error: Missing {tokenizer_path}')
-
-        tokenizer = SentencePieceProcessor()
-        tokenizer.LoadFromFile(str(tokenizer_path))
-
-        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
-
-        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
-        scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
-
-        for token_id in range(tokenizer.vocab_size()):
-
-            piece = tokenizer.IdToPiece(token_id)
-            text = piece.encode("utf-8")
-            score = tokenizer.GetScore(token_id)
-
-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.IsUnknown(token_id):
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.IsControl(token_id):
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.IsUnused(token_id):
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.IsByte(token_id):
-                toktype = SentencePieceTokenTypes.BYTE
-
-            tokens[token_id] = text
-            scores[token_id] = score
-            toktypes[token_id] = toktype
-
-        added_tokens_file = self.dir_model / 'added_tokens.json'
-        if added_tokens_file.is_file():
-            with open(added_tokens_file, "r", encoding="utf-8") as f:
-                added_tokens_json = json.load(f)
-
-                for key in added_tokens_json:
-                    token_id = added_tokens_json[key]
-                    if token_id >= vocab_size:
-                        logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
-                        continue
-
-                    tokens[token_id] = key.encode("utf-8")
-                    scores[token_id] = -1000.0
-                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
-                for token_id, foken_data in added_tokens_decoder.items():
-                    token_id = int(token_id)
-                    token = foken_data["content"].encode("utf-8")
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        if tokens[token_id] != token:
-                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
-                    tokens[token_id] = token
-                    scores[token_id] = -1000.0
-                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-                    if foken_data.get("special"):
-                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
-
-        tokenizer_file = self.dir_model / 'tokenizer.json'
-        if tokenizer_file.is_file():
-            with open(tokenizer_file, "r", encoding="utf-8") as f:
-                tokenizer_json = json.load(f)
-                added_tokens = tokenizer_json.get("added_tokens", [])
-                for foken_data in added_tokens:
-                    token_id = int(foken_data["id"])
-                    token = foken_data["content"].encode("utf-8")
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        if tokens[token_id] != token:
-                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
-                    tokens[token_id] = token
-                    scores[token_id] = -1000.0
-                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-                    if foken_data.get("special"):
-                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        n_embd = self.find_hparam(["hidden_size", "n_embd"])
-        n_head = self.find_hparam(["num_attention_heads", "n_head"])
-        n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
-        rms_eps = self.find_hparam(["rms_norm_eps"])
-        max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
-        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
-        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
-        rope_dims = int(rot_pct * n_embd) // n_head
-
-        self.gguf_writer.add_context_length(max_pos_embds)
-        self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
-        self.gguf_writer.add_embedding_length(n_embd)
-        self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"]))
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_head_count_kv(n_head_kv)
-        self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
-        self.gguf_writer.add_rope_dimension_count(rope_dims)
-        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters)["rope_theta"])
-        self.gguf_writer.add_file_type(self.ftype)
-        sliding_window = self.hparams.get("sliding_window")
-        # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models
-        if sliding_window is None:
-            sliding_window = 0
-        self.gguf_writer.add_sliding_window(sliding_window)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        n_embd = self.find_hparam(["hidden_size", "n_embd"])
-        n_head = self.find_hparam(["num_attention_heads", "n_head"])
-        max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
-        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
-        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
-        rope_dims = int(rot_pct * n_embd) // n_head
-
-        # write rope scaling for long context (128k) model
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is None:
-            return
-
-        scale = max_pos_embds / orig_max_pos_embds
-
-        rope_scaling_type = rope_scaling.get('rope_type', rope_scaling.get('type', '')).lower()
-        if len(rope_scaling_type) == 0:
-            raise KeyError('Missing the required key rope_scaling.type')
-
-        if rope_scaling_type == 'su' or rope_scaling_type == 'longrope':
-            attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0
-        elif rope_scaling_type == 'yarn':
-            attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0
-        else:
-            raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet')
-
-        self.gguf_writer.add_rope_scaling_attn_factors(attn_factor)
-
-        long_factors = rope_scaling.get('long_factor', None)
-        short_factors = rope_scaling.get('short_factor', None)
-
-        if long_factors is None or short_factors is None:
-            raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
-
-        if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
-            raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}. long_factors = {len(long_factors)}, short_factors = {len(short_factors)}.')
-
-        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
-        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith(("model.vision_tower.", "vision_tower.", "model.mm_projector.", "mm_projector.")):
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Phi4ForCausalLMV")
-class Phi4VisionMmprojModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-
-        self.vision_total_layers = int(self.find_vparam(self.n_block_keys))
-        if self.vision_total_layers < 2:
-            raise ValueError(
-                f"Phi-4 vision mmproj conversion requires at least 2 vision layers, got {self.vision_total_layers}"
-            )
-
-        # Phi-4 uses SigLIP2 hidden_states[-2], so export one fewer encoder block and
-        # drop post-layernorm/head weights. This makes the GGUF runtime output match
-        # the feature map consumed by the patched siglip.cpp Phi-4 projector path.
-        self.vision_export_layers = self.vision_total_layers - 1
-        self.vision_last_layer_idx = self.vision_total_layers - 1
-
-        for key in self.n_block_keys:
-            if key in self.hparams_vision:
-                self.hparams_vision[key] = self.vision_export_layers
-                break
-
-        self.block_count = self.vision_export_layers
-        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)
-
-        patch_size = self.preprocessor_config.get("patch_size")
-        if patch_size is None:
-            raise KeyError("Phi-4 vision mmproj conversion requires patch_size in preprocessor_config.json")
-
-        self.hparams_vision["patch_size"] = patch_size
-
-        pos_emb_name = next(
-            (
-                name for name in self.model_tensors
-                if name.endswith("vision_model.embeddings.position_embedding.weight")
-            ),
-            None,
-        )
-        if pos_emb_name is None:
-            raise KeyError("Phi-4 vision mmproj conversion could not find position_embedding.weight")
-
-        pos_emb_shape = self.model_tensors[pos_emb_name]().shape
-        base_grid_tokens = int(pos_emb_shape[0])
-        grid_side = math.isqrt(base_grid_tokens)
-        if grid_side * grid_side != base_grid_tokens:
-            raise ValueError(f"Unexpected Phi-4 position embedding shape: {tuple(pos_emb_shape)}")
-
-        self.hparams_vision["image_size"] = grid_side * patch_size
-
-        min_num_patches = self.preprocessor_config.get("min_num_patches", self.global_config.get("min_num_patches"))
-        max_num_patches = self.preprocessor_config.get("max_num_patches", self.global_config.get("max_num_patches"))
-        if min_num_patches is None or max_num_patches is None:
-            raise KeyError("Phi-4 vision mmproj conversion requires min_num_patches and max_num_patches")
-
-        self.min_pixels = int(min_num_patches) * patch_size * patch_size
-        self.max_pixels = int(max_num_patches) * patch_size * patch_size
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PHI4)
-        self.gguf_writer.add_vision_min_pixels(self.min_pixels)
-        self.gguf_writer.add_vision_max_pixels(self.max_pixels)
-        self.gguf_writer.add_vision_use_gelu(True)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith(("model.vision_tower.vision_tower.", "vision_tower.")):
-            if ".vision_model.head." in name:
-                return
-
-            new_name = name.replace("model.vision_tower.vision_tower.", "vision_tower.")
-
-            if ".vision_model.post_layernorm." in new_name:
-                return
-
-            if bid is not None and bid == self.vision_last_layer_idx:
-                return
-
-            if new_name.endswith("vision_model.embeddings.patch_embedding.weight"):
-                assert self.hparams_vision is not None
-                if data_torch.ndim != 2:
-                    raise ValueError(f"Unexpected Phi-4 patch embedding shape: {tuple(data_torch.shape)}")
-
-                patch_area = self.hparams_vision["patch_size"] ** 2
-                in_features = data_torch.shape[1]
-                if in_features % patch_area != 0:
-                    raise ValueError(
-                        f"Phi-4 patch embedding input dim {in_features} is not divisible by patch area {patch_area}"
-                    )
-
-                num_channels = in_features // patch_area
-                patch_size = self.hparams_vision["patch_size"]
-                data_torch = data_torch.view(data_torch.shape[0], patch_size, patch_size, num_channels)
-                data_torch = data_torch.permute(0, 3, 1, 2)
-
-            yield from super().modify_tensors(data_torch, new_name, bid)
-            return
-
-        if name.startswith(("model.mm_projector.", "mm_projector.")):
-            local_name = name
-            local_name = local_name.replace("model.mm_projector.", "")
-            local_name = local_name.replace("mm_projector.", "")
-
-            if not (local_name.startswith("0.") or local_name.startswith("2.")):
-                return
-
-            suffix = ".bias" if local_name.endswith(".bias") else ".weight"
-            mm_idx = int(local_name.split(".", maxsplit=1)[0])
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_idx, suffix=suffix), data_torch)
-            return
-
-        return
-
-
-@ModelBase.register("PhiMoEForCausalLM")
-class PhiMoeModel(Phi3MiniModel):
-    model_arch = gguf.MODEL_ARCH.PHIMOE
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_expert_used_count(self.find_hparam(["num_experts_per_tok", "num_experts_per_token"]))
-        self.gguf_writer.add_expert_count(self.find_hparam(["num_local_experts", "num_experts"]))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # process the experts separately
-        if name.find("block_sparse_moe.experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["w1", "w2", "w3"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("PlamoForCausalLM")
-class PlamoModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.PLAMO
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-
-        self.gguf_writer.add_context_length(4096)  # not in config.json
-        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
-        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count_kv(5)  # hparams["num_key_value_heads"]) is wrong
-        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def shuffle_attn_q_weight(self, data_torch):
-        assert data_torch.size() == (5120, 5120)
-        data_torch = data_torch.reshape(8, 5, 128, 5120)
-        data_torch = torch.permute(data_torch, (1, 0, 2, 3))
-        data_torch = torch.reshape(data_torch, (5120, 5120))
-        return data_torch
-
-    def shuffle_attn_output_weight(self, data_torch):
-        assert data_torch.size() == (5120, 5120)
-        data_torch = data_torch.reshape(5120, 8, 5, 128)
-        data_torch = torch.permute(data_torch, (0, 2, 1, 3))
-        data_torch = torch.reshape(data_torch, (5120, 5120))
-        return data_torch
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        new_name = self.map_tensor_name(name)
-
-        # shuffle for broadcasting of gqa in ggml_mul_mat
-        if new_name.endswith("attn_q.weight"):
-            data_torch = self.shuffle_attn_q_weight(data_torch)
-        elif new_name.endswith("attn_output.weight"):
-            data_torch = self.shuffle_attn_output_weight(data_torch)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Plamo2ForCausalLM", "PLaMo2ForCausalLM")
-class Plamo2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.PLAMO2
-
-    def set_vocab(self):
-        self._set_vocab_plamo()
-
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-
-        # Which layers are Mamba layers
-        # PLaMo 2 uses mamba_step to indicate the pattern (e.g., 2 means every other layer)
-        # This logic matches modeling_plamo.py's is_mamba function
-        mamba_step = hparams.get("mamba_step", 2)
-        mamba_enabled = hparams.get("mamba_enabled", True)
-        num_key_value_heads = []
-        num_attention_heads = []
-
-        if mamba_enabled:
-            for i in range(self.block_count):
-                if self.block_count <= (mamba_step // 2):
-                    # use attention in last layer
-                    is_mamba = (i != self.block_count - 1)
-                else:
-                    is_mamba = (i % mamba_step) != (mamba_step // 2)
-                if is_mamba:
-                    num_key_value_heads.append(0)
-                    num_attention_heads.append(0)
-                else:
-                    num_key_value_heads.append(hparams.get("num_key_value_heads", 4))
-                    num_attention_heads.append(hparams.get("num_attention_heads", 32))
-
-        if num_key_value_heads and num_attention_heads:
-            self.gguf_writer.add_head_count_kv(num_key_value_heads)
-            self.gguf_writer.add_head_count(num_attention_heads)
-
-        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 2048))
-        self.gguf_writer.add_embedding_length(hparams.get("hidden_size", 4096))
-        self.gguf_writer.add_key_length(hparams.get("hidden_size_per_head", 128))
-        self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128))
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
-        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("rope_theta", 10000))
-
-        # Mamba parameters
-        self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))
-        self.gguf_writer.add_ssm_conv_kernel(hparams.get("mamba_d_conv", 4))
-        self.gguf_writer.add_ssm_time_step_rank(hparams.get("mamba_num_heads", 64))
-        intermediate_size = hparams.get("mamba_num_heads", 64) * hparams.get("hidden_size_per_head", 128)
-        self.gguf_writer.add_ssm_inner_size(intermediate_size)
-        self.gguf_writer.add_ssm_group_count(0)
-
-        # MLP feed forward parameters (for attention layers)
-        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312))
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.endswith(".A_log"):
-            data_torch = -torch.exp(data_torch)
-        elif name.endswith(".dt_bias"):
-            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
-        elif name.endswith(".dt_norm_weight"):
-            name = name.rpartition(".dt_norm_weight")[0] + ".dt_norm.weight"
-        elif name.endswith(".B_norm_weight"):
-            name = name.rpartition(".B_norm_weight")[0] + ".B_norm.weight"
-        elif name.endswith(".C_norm_weight"):
-            name = name.rpartition(".C_norm_weight")[0] + ".C_norm.weight"
-        elif name.endswith(".k_weight"):
-            name = name.rpartition(".k_weight")[0] + ".k.weight"
-        elif name.endswith(".q_weight"):
-            name = name.rpartition(".q_weight")[0] + ".q.weight"
-        elif name.endswith(".conv1d.weight"):
-            data_torch = torch.squeeze(data_torch)  # remove (, 1, )
-            assert data_torch.ndim == 2
-        elif name.endswith(".pre_mixer_norm.weight"):
-            data_torch += 1.0
-        elif name.endswith(".post_mixer_norm.weight"):
-            data_torch += 1.0 / 5
-        elif name.endswith(".pre_mlp_norm.weight"):
-            data_torch += 1.0
-        elif name.endswith(".post_mlp_norm.weight"):
-            data_torch += 1.0 / (5**1.5)
-        elif name.endswith(".norm.weight"):
-            data_torch += 1.0
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Plamo3ForCausalLM", "PLaMo3ForCausalLM")
-class Plamo3Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.PLAMO3
-
-    def set_vocab(self):
-        self._set_vocab_plamo()
-
-        tokenizer_config_path = self.dir_model / "tokenizer_config.json"
-        tokenizer_config = {}
-
-        if tokenizer_config_path.is_file():
-            with open(tokenizer_config_path, encoding="utf-8") as f:
-                tokenizer_config = json.load(f)
-
-        chat_template = tokenizer_config.get("chat_template")
-        chat_template_jinja = self.dir_model / "chat_template.jinja"
-
-        if chat_template_jinja.is_file():
-            with open(chat_template_jinja, encoding="utf-8") as f:
-                chat_template = f.read()
-
-        if chat_template:
-            self.gguf_writer.add_chat_template(chat_template)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-        if (sliding_window := self.find_hparam(["window_size", "sliding_window"], optional=True)) is not None:
-            self.gguf_writer.add_sliding_window(sliding_window)
-            self.gguf_writer.add_sliding_window_pattern(self.hparams["sliding_window_pattern"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-
-        if name.endswith(".pre_mixer_norm.weight"):
-            data_torch = data_torch + 1.0
-        elif name.endswith(".post_mixer_norm.weight"):
-            data_torch = data_torch + 1.0 / 5
-        elif name.endswith(".pre_mlp_norm.weight"):
-            data_torch = data_torch + 1.0
-        elif name.endswith(".post_mlp_norm.weight"):
-            data_torch = data_torch + 1.0 / (5**1.5)
-        elif name.endswith((".mixer.q_norm.weight", ".mixer.k_norm.weight")):
-            data_torch = data_torch + 1.0
-        elif name.endswith(".norm.weight"):
-            data_torch = data_torch + 1.0
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("CodeShellForCausalLM")
-class CodeShellModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.CODESHELL
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_context_length(self.hparams["n_positions"])
-        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(self.hparams["n_head"])
-        self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_rope_freq_base(10000.0)
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-        self.gguf_writer.add_rope_scaling_factor(1.0)
-
-
-@ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM")
-class KimiLinearModel(TextModel):
-    """Kimi-Linear model with hybrid MLA+KDA architecture"""
-    model_arch = gguf.MODEL_ARCH.KIMI_LINEAR
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_gpt2()
-            return
-        except Exception:
-            pass
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        if tokpre == "kimi-k2":
-            # Build merges list using the approach similar to HunYuanMoE
-            merges = []
-            vocab = {}
-            mergeable_ranks = tokenizer.model._mergeable_ranks  # ty: ignore[unresolved-attribute]
-            for token, rank in mergeable_ranks.items():
-                vocab[QwenModel.token_bytes_to_string(token)] = rank
-                if len(token) == 1:
-                    continue
-                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-                if len(merged) == 2:
-                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
-            # Build token list
-            vocab_size = self.hparams["vocab_size"]
-            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
-            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
-            tokens: list[str] = []
-            toktypes: list[int] = []
-
-            for i in range(vocab_size):
-                if i not in reverse_vocab:
-                    tokens.append(f"[PAD{i}]")
-                    toktypes.append(gguf.TokenType.UNUSED)
-                else:
-                    token = reverse_vocab[i]
-                    tokens.append(token)
-                    if i in special_tokens.values():
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.NORMAL)
-
-            self.gguf_writer.add_tokenizer_model("gpt2")
-            self.gguf_writer.add_tokenizer_pre(tokpre)
-            self.gguf_writer.add_token_list(tokens)
-            self.gguf_writer.add_token_types(toktypes)
-            self.gguf_writer.add_token_merges(merges)
-
-            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
-            special_vocab.add_to_gguf(self.gguf_writer)
-            # override eos id in config.json with tiktoken eos id
-            self.gguf_writer.add_eos_token_id(tokenizer.eos_id)  # ty: ignore[unresolved-attribute]
-        else:
-            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
-
-    def set_gguf_parameters(self):
-        # note: To enable MLA KV cache, attention needs to be converted into MQA (ie: GQA with 1 group)
-        self.hparams["num_key_value_heads"] = 1
-
-        super().set_gguf_parameters()
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-
-        # KDA & MLA params
-        # Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv
-        linear_attn_config = self.hparams["linear_attn_config"]
-        # n_head == 0 for KDA layers, n_head > 0 for MLA layers
-        # full_attention_layers list will be used to distinguish layer type
-        _num_kv_heads = list()
-        _full_attn_layers = linear_attn_config["full_attn_layers"]
-        for il in range(self.hparams["num_hidden_layers"]):
-            if il + 1 in _full_attn_layers:
-                _num_kv_heads.append(self.hparams["num_key_value_heads"])
-            else:
-                _num_kv_heads.append(0)
-        assert len(_num_kv_heads) == self.hparams["num_hidden_layers"]
-        self.gguf_writer.add_head_count_kv(_num_kv_heads)
-
-        if (ssm_d_conv := linear_attn_config.get("short_conv_kernel_size")) is not None:
-            self.gguf_writer.add_ssm_conv_kernel(ssm_d_conv)
-        if (kda_head_dim := linear_attn_config.get("head_dim")) is not None:
-            self.gguf_writer.add_kda_head_dim(kda_head_dim)
-
-        # MLA params - use add_* methods that handle arch substitution
-        # Support both HuggingFace naming (q_lora_rank, kv_lora_rank) and internal naming (n_lora_q, n_lora_kv)
-        if (q_lora_rank := self.find_hparam(["q_lora_rank", "n_lora_q"], optional=True)) is not None:
-            self.gguf_writer.add_q_lora_rank(q_lora_rank)
-        # To enable MLA KV cache, MLA needs to be converted into MQA with larger heads, then decompresses to MHA
-        kv_lora_rank = self.find_hparam(["kv_lora_rank", "n_lora_kv"], optional=False)
-        self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
-
-        # MLA head dimensions
-        # Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
-        qk_nope_head_dim = self.hparams.get("qk_nope_head_dim")
-        # Rotation - use qk_rope_head_dim for Kimi
-        qk_rope_head_dim = self.find_hparam(["qk_rope_head_dim", "n_rot"], optional=False)
-        self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
-        self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
-        v_head_dim = self.hparams.get("v_head_dim")
-
-        # Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
-        if (n_embd_head_k_mla := self.find_hparam(["n_embd_head_k_mla"], optional=True)) is not None:
-            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
-        elif qk_nope_head_dim is not None:
-            n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
-            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
-
-        # n_embd_head_v_mla = v_head_dim
-        if (n_embd_head_v_mla := self.hparams.get("n_embd_head_v_mla")) is not None:
-            self.gguf_writer.add_value_length_mla(n_embd_head_v_mla)
-        elif v_head_dim is not None:
-            self.gguf_writer.add_value_length_mla(v_head_dim)
-
-        # moe_intermediate_size (1024 for Kimi)
-        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
-        # num_shared_experts (1 for Kimi)
-        self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
-        # first_k_dense_replace (1 for Kimi - first layer uses dense MLP)
-        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
-        # Routed scaling factor (expert_weights_scale = 2.446 for Kimi)
-        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        if self._experts is not None:
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        logger.info(f"Processing {name}: shape before = {tuple(data_torch.shape)}")
-
-        # Handle KDA conv1d weights
-        # HuggingFace/vLLM stores as [d_inner, d_conv] (2D), memory layout: conv_step changes fastest
-        # llama.cpp expects ggml ne = [d_conv, 1, d_inner, 1], memory layout: ne[0]=d_conv changes fastest
-        # GGUF reverses numpy shape when writing, so numpy (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
-        # Memory layouts match: both have conv_step (d_conv) changing fastest
-        if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
-            # HF shape: [d_inner, d_conv] e.g. [4096, 4]
-            # Target numpy shape: (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
-            if data_torch.ndim == 2:
-                d_inner, d_conv = data_torch.shape
-                # Reshape to (1, d_inner, 1, d_conv) - memory layout preserved (d_conv fastest)
-                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
-                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
-            elif data_torch.ndim == 3:
-                # Already 3D [d_inner, 1, d_conv] from unsqueeze
-                d_inner, _, d_conv = data_torch.shape
-                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
-                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, 1, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
-
-        # Kimi specific bias
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-
-        # Handle A_log: iHF stores as [1, 1, num_heads, 1]
-        # llama.cpp expects ggml ne = [1, num_heads, 1, 1]
-        # GGUF reverses numpy shape: numpy (1, 1, num_heads, 1) -> ggml ne = [1, num_heads, 1, 1]
-        if name.endswith(".A_log"):
-            data_torch = -torch.exp(data_torch)
-        if name.endswith(".dt_bias"):
-            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
-            logger.info("Changed dt_bias to dt_proj.bias")
-
-        # process the experts separately
-        if name.find("block_sparse_moe.experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                # w1: gate, w2: down, w3: up
-                for wid, tname in [("w1", gguf.MODEL_TENSOR.FFN_GATE_EXP),
-                                   ("w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP),
-                                   ("w3", gguf.MODEL_TENSOR.FFN_UP_EXP)]:
-                    datas: list[Tensor] = []
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-                    data_torch = torch.stack(datas, dim=0)
-                    new_name = self.format_tensor_name(tname, bid)
-                    yield from super().modify_tensors(data_torch, new_name, bid)
-            return
-
-        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
-        if name.endswith("kv_b_proj.weight"):
-            name_kb = name.replace("kv_b_proj", "k_b_proj")
-            name_vb = name.replace("kv_b_proj", "v_b_proj")
-            n_head_kv = self.hparams["num_key_value_heads"]
-            v_head_dim = self.find_hparam(["n_embd_head_v_mla", "v_head_dim"], optional=False)
-            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
-            logger.info("Split kv_b n_head_kv %d\n" % n_head_kv)
-            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
-            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
-            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
-            k_b = k_b.transpose(1, 2)
-            yield from super().modify_tensors(k_b, name_kb, bid)
-            yield from super().modify_tensors(v_b, name_vb, bid)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("InternLM2ForCausalLM")
-class InternLM2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.INTERNLM2
-
-    def set_vocab(self):
-        # (TODO): Is there a better way?
-        # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
-        # \x00 specially and convert it into an emoji character to prevent it from being mistakenly
-        # recognized as an empty string in C++.
-        from sentencepiece import SentencePieceProcessor
-        from sentencepiece import sentencepiece_model_pb2 as model
-
-        tokenizer_path = self.dir_model / 'tokenizer.model'
-
-        tokens: list[bytes] = []
-        scores: list[float] = []
-        toktypes: list[int] = []
-
-        if not tokenizer_path.is_file():
-            logger.error(f'Error: Missing {tokenizer_path}')
-            sys.exit(1)
-
-        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
-        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
-        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
-
-        tokenizer = SentencePieceProcessor()
-        tokenizer.LoadFromFile(str(tokenizer_path))
-
-        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
-
-        for token_id in range(vocab_size):
-            piece = tokenizer.IdToPiece(token_id)
-            text = piece.encode("utf-8")
-            score = tokenizer.GetScore(token_id)
-            if text == b"\x00":
-                # (TODO): fixme
-                # Hack here and replace the \x00 characters.
-                logger.warning(f"InternLM2 convert token '{text}' to '🐉'!")
-                text = "🐉".encode("utf-8")
-
-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.IsUnknown(token_id):
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.IsControl(token_id):
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.IsUnused(token_id):
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.IsByte(token_id):
-                toktype = SentencePieceTokenTypes.BYTE
-            # take care of ununsed raw token
-            if piece.startswith('[UNUSED'):
-                toktype = SentencePieceTokenTypes.UNUSED
-
-            tokens.append(text)
-            scores.append(score)
-            toktypes.append(toktype)
-
-        added_tokens_file = self.dir_model / 'added_tokens.json'
-        if added_tokens_file.is_file():
-            with open(added_tokens_file, "r", encoding="utf-8") as f:
-                added_tokens_json = json.load(f)
-
-                for key in added_tokens_json:
-                    tokens.append(key.encode("utf-8"))
-                    scores.append(-1000.0)
-                    toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
-
-        chat_eos_token = '<|im_end|>'
-        chat_eos_token_id = None
-
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
-                for token_id, foken_data in added_tokens_decoder.items():
-                    token_id = int(token_id)
-                    token = foken_data["content"]
-                    if token == chat_eos_token:
-                        chat_eos_token_id = token_id
-                    token = token.encode("utf-8")
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        if tokens[token_id] != token:
-                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
-                    tokens[token_id] = token
-                    scores[token_id] = -1000.0
-                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-                    if foken_data.get("special"):
-                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
-
-        tokenizer_file = self.dir_model / 'tokenizer.json'
-        if tokenizer_file.is_file():
-            with open(tokenizer_file, "r", encoding="utf-8") as f:
-                tokenizer_json = json.load(f)
-                added_tokens = tokenizer_json.get("added_tokens", [])
-                for foken_data in added_tokens:
-                    token_id = int(foken_data["id"])
-                    token = foken_data["content"]
-                    if token == chat_eos_token:
-                        chat_eos_token_id = token_id
-                    token = token.encode("utf-8")
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        if tokens[token_id] != token:
-                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
-                    tokens[token_id] = token
-                    scores[token_id] = -1000.0
-                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-                    if foken_data.get("special"):
-                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-        self.gguf_writer.add_add_space_prefix(add_prefix)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        old_eos = special_vocab.special_token_ids["eos"]
-        if chat_eos_token_id is not None:
-            # For the chat model, we replace the eos with '<|im_end|>'.
-            # TODO: this is a hack, should be fixed
-            #       https://github.com/ggml-org/llama.cpp/pull/6745#issuecomment-2067687048
-            special_vocab.special_token_ids["eos"] = chat_eos_token_id
-            logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}"
-                           " in chat mode so that the conversation can end normally.")
-
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        num_heads = self.hparams["num_attention_heads"]
-        num_kv_heads = self.hparams["num_key_value_heads"]
-        n_embd = self.hparams["hidden_size"]
-        q_per_kv = num_heads // num_kv_heads
-        head_dim = n_embd // num_heads
-        num_groups = num_heads // q_per_kv
-
-        name = name.replace("language_model.", "") # InternVL
-        if name.startswith("mlp") or name.startswith("vision_model"):
-            # skip visual tensors
-            return
-
-        if bid is not None and f"model.layers.{bid}.attention.wqkv" in name:
-            qkv = data_torch
-
-            qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd))
-            q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1]
-
-            # The model weights of q and k equire additional reshape.
-            q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads)
-            k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads)
-            v = v.reshape((-1, v.shape[-1]))
-
-            yield from super().modify_tensors(q, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
-            yield from super().modify_tensors(k, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
-            yield from super().modify_tensors(v, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
-        else:
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("InternLM3ForCausalLM")
-class InternLM3Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.LLAMA
-
-    def set_vocab(self):
-        tokens, scores, toktypes = self._create_vocab_sentencepiece()
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                if "add_prefix_space" in tokenizer_config_json:
-                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
-
-                if "added_tokens_decoder" in tokenizer_config_json:
-                    for token_id, token_data in tokenizer_config_json["added_tokens_decoder"].items():
-                        if token_data.get("special"):
-                            token_id = int(token_id)
-                            token = token_data["content"]
-                            special_vocab._set_special_token(token, token_id)
-                            # update eos token
-                            if token == '<|im_end|>' and "eos" in special_vocab.special_token_ids:
-                                special_vocab.special_token_ids["eos"] = token_id
-
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-
-        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-        name = name.replace("language_model.", "") # InternVL
-        if name.startswith("mlp") or name.startswith("vision_model"):
-            # skip visual tensors
-            return
-        if name.endswith(("q_proj.weight", "q_proj.bias")):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-        if name.endswith(("k_proj.weight", "k_proj.bias")):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel", "BertForSequenceClassification")
-class BertModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.BERT
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.vocab_size = None
-
-        if cls_out_labels := self.hparams.get("id2label"):
-            if len(cls_out_labels) == 2 and cls_out_labels[0] == "LABEL_0":
-                # Remove dummy labels added by AutoConfig
-                cls_out_labels = None
-        self.cls_out_labels = cls_out_labels
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_causal_attention(False)
-        self._try_set_pooling_type()
-
-        if self.cls_out_labels:
-            self.gguf_writer.add_classifier_output_labels([v for k, v in sorted(self.cls_out_labels.items())])
-
-    def set_vocab(self):
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.vocab_size = len(tokens)
-
-        # we need this to validate the size of the token_type embeddings
-        # though currently we are passing all zeros to the token_type embeddings
-        # "Sequence A" or "Sequence B"
-        self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
-
-        # convert to phantom space vocab
-        def phantom(tok, toktype):
-            if toktype == gguf.TokenType.CONTROL:
-                return tok
-            if tok.startswith("##"):
-                return tok[2:]
-            return "\u2581" + tok
-        assert len(tokens) == len(toktypes)
-        tokens = list(map(phantom, tokens, toktypes))
-
-        # add vocab to gguf
-        self.gguf_writer.add_tokenizer_model("bert")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        # handle special tokens
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("bert."):
-            name = name[5:]
-
-        if name.endswith(".gamma"):
-            name = name[:-6] + ".weight"
-
-        if name.endswith(".beta"):
-            name = name[:-5] + ".bias"
-
-        # we are only using BERT for embeddings so we don't need the pooling layer
-        if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
-            return # we don't need these
-
-        if name.startswith("cls.predictions"):
-            return
-
-        if name.startswith("cls.seq_relationship"):
-            return
-
-        if self.cls_out_labels:
-            # For BertForSequenceClassification (direct projection layer)
-            if name == "classifier.weight":
-                name = "classifier.out_proj.weight"
-
-            if name == "classifier.bias":
-                name = "classifier.out_proj.bias"
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def _xlmroberta_tokenizer_init(self) -> None:
-        # we need the pad_token_id to know how to chop down position_embd matrix
-        if (pad_token_id := self.hparams.get("pad_token_id")) is not None:
-            self._position_offset = 1 + pad_token_id
-            if "max_position_embeddings" in self.hparams:
-                self.hparams["max_position_embeddings"] -= self._position_offset
-        else:
-            self._position_offset = None
-
-    def _xlmroberta_set_vocab(self) -> None:
-        # to avoid TypeError: Descriptors cannot be created directly
-        # exception when importing sentencepiece_model_pb2
-        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
-        from sentencepiece import SentencePieceProcessor
-        from sentencepiece import sentencepiece_model_pb2 as model
-
-        tokenizer_path = self.dir_model / 'sentencepiece.bpe.model'
-
-        tokenizer_json = {}
-        tokenizer_config_json = {}
-        if not tokenizer_path.is_file():
-            tokenizer_path = self.dir_model / 'tokenizer.json'
-            tokenizer_config_path = self.dir_model / 'tokenizer_config.json'
-
-            if not tokenizer_path.is_file():
-                raise FileNotFoundError(f"File not found: {tokenizer_path}")
-
-            from base64 import b64decode
-            from transformers import AutoTokenizer
-            tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-
-            with open(tokenizer_path, "r", encoding="utf-8") as fp:
-                tokenizer_json = json.load(fp)
-
-            if tokenizer_config_path.is_file():
-                with open(tokenizer_config_path, "r", encoding="utf-8") as fp:
-                    tokenizer_config_json = json.load(fp)
-
-            add_prefix = tokenizer.add_prefix_space  # ty: ignore[unresolved-attribute]
-            remove_whitespaces = tokenizer.clean_up_tokenization_spaces  # ty: ignore[unresolved-attribute]
-            precompiled_charsmap = b64decode(tokenizer_json["normalizer"]["precompiled_charsmap"])
-
-            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size)  # ty: ignore[unresolved-attribute]
-        else:
-            sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
-            sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
-            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
-
-            add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
-            remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
-            precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
-
-            tokenizer = SentencePieceProcessor()
-            tokenizer.LoadFromFile(str(tokenizer_path))
-
-            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size())
-
-        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
-        scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size  # ty: ignore[invalid-assignment]
-
-        if isinstance(tokenizer, SentencePieceProcessor):
-            for token_id in range(tokenizer.vocab_size()):
-                piece = tokenizer.IdToPiece(token_id)
-                text = piece.encode("utf-8")
-                score = tokenizer.GetScore(token_id)
-
-                toktype = SentencePieceTokenTypes.NORMAL
-                if tokenizer.IsUnknown(token_id):
-                    toktype = SentencePieceTokenTypes.UNKNOWN
-                elif tokenizer.IsControl(token_id):
-                    toktype = SentencePieceTokenTypes.CONTROL
-                elif tokenizer.IsUnused(token_id):
-                    toktype = SentencePieceTokenTypes.UNUSED
-                elif tokenizer.IsByte(token_id):
-                    toktype = SentencePieceTokenTypes.BYTE
-
-                tokens[token_id] = text
-                scores[token_id] = score
-                toktypes[token_id] = toktype
-        else:
-            added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
-            unk_token = tokenizer_config_json.get("unk_token")
-            unk_token_id = added_vocab.get(unk_token, tokenizer_json["model"].get("unk_id", 3))  # ty: ignore[no-matching-overload]
-
-            for token_id in range(tokenizer.vocab_size):  # ty: ignore[unresolved-attribute]
-                piece = tokenizer._convert_id_to_token(token_id)  # ty: ignore[unresolved-attribute]
-                if (piece := tokenizer._convert_id_to_token(token_id)) is not None:  # ty: ignore[unresolved-attribute]
-                    text = piece.encode("utf-8")
-                    score = tokenizer_json["model"]["vocab"][token_id][1]
-
-                    toktype = SentencePieceTokenTypes.NORMAL
-                    if token_id == unk_token_id:
-                        toktype = SentencePieceTokenTypes.UNKNOWN
-                    elif token_id in tokenizer.all_special_ids:  # ty: ignore[unresolved-attribute]
-                        toktype = SentencePieceTokenTypes.CONTROL
-                    elif token_id in added_vocab.values():
-                        toktype = SentencePieceTokenTypes.USER_DEFINED
-                    # No reliable way to detect this, but jina doesn't have any
-                    # elif tokenizer.IsByte(token_id):
-                    #     toktype = SentencePieceTokenTypes.BYTE
-
-                    tokens[token_id] = text
-                    scores[token_id] = score
-                    toktypes[token_id] = toktype
-
-        if isinstance(tokenizer, SentencePieceProcessor):
-            # realign tokens (see HF tokenizer code)
-            tokens = [b'<s>', b'<pad>', b'</s>', b'<unk>'] + tokens[3:-1]
-            scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1]
-            toktypes = [
-                SentencePieceTokenTypes.CONTROL,
-                SentencePieceTokenTypes.CONTROL,
-                SentencePieceTokenTypes.CONTROL,
-                SentencePieceTokenTypes.UNKNOWN,
-            ] + toktypes[3:-1]
-
-            if self.model_arch == gguf.MODEL_ARCH.NOMIC_BERT_MOE:
-                # Add mask token missing from sentencepiece.bpe.model
-                tokens[250001] = b'<mask>'
-                scores[250001] = 0.0
-                toktypes[250001] = SentencePieceTokenTypes.CONTROL
-
-        self.gguf_writer.add_tokenizer_model("t5")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-        self.gguf_writer.add_add_space_prefix(add_prefix)
-        self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
-        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
-        if precompiled_charsmap:
-            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-
-@ModelBase.register("DistilBertModel", "DistilBertForMaskedLM", "DistilBertForSequenceClassification")
-class DistilBertModel(BertModel):
-    model_arch = gguf.MODEL_ARCH.BERT
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_layer_norm_eps(1e-12)
-        logger.info("gguf: layer norm epsilon = 1e-12")
-        super().set_gguf_parameters()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("distilbert."):
-            name = name[11:]
-
-        # These layers act as MLM head, so we don't need them
-        if name.startswith("vocab_"):
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
-class RobertaModel(BertModel):
-    model_arch = gguf.MODEL_ARCH.BERT
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # we need the pad_token_id to know how to chop down position_embd matrix
-        if (pad_token_id := self.hparams.get("pad_token_id")) is not None:
-            self._position_offset = 1 + pad_token_id
-            if "max_position_embeddings" in self.hparams:
-                self.hparams["max_position_embeddings"] -= self._position_offset
-        else:
-            self._position_offset = None
-
-    def set_vocab(self):
-        """Support BPE tokenizers for roberta models"""
-        bpe_tok_path = self.dir_model / "tokenizer.json"
-        if bpe_tok_path.exists():
-            self._set_vocab_gpt2()
-
-            # we need this to validate the size of the token_type embeddings
-            # though currently we are passing all zeros to the token_type embeddings
-            # "Sequence A" or "Sequence B"
-            self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
-
-        else:
-            return super().set_vocab()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # if name starts with "roberta.", remove the prefix
-        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
-        if name.startswith("roberta."):
-            name = name[8:]
-
-        # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
-        if name == "embeddings.position_embeddings.weight":
-            if self._position_offset is not None:
-                data_torch = data_torch[self._position_offset:,:]
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("NomicBertModel")
-class NomicBertModel(BertModel):
-    model_arch = gguf.MODEL_ARCH.BERT
-
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
-        hparams = kwargs.pop("hparams", None)
-        if hparams is None:
-            hparams = ModelBase.load_hparams(dir_model, False)
-
-        self.is_moe = bool(hparams.get("moe_every_n_layers"))
-        self.model_arch = gguf.MODEL_ARCH.NOMIC_BERT_MOE if self.is_moe else gguf.MODEL_ARCH.NOMIC_BERT
-
-        super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs)
-
-        self._tokenizer_is_xlmroberta = self._is_tokenizer_xlmroberta()
-        if self._tokenizer_is_xlmroberta:
-            self._xlmroberta_tokenizer_init()
-
-        npos, mtp = self.hparams["n_positions"], self.hparams.get("max_trained_positions", 2048)
-        if npos == 8192 and mtp == 2048:
-            self.hparams["n_positions"] = 2048  # nomic-embed-text v1 and v1.5 are trained for 2048 tokens.
-        elif npos == 2048 and mtp == 2048:
-            self.hparams["n_positions"] = 512   # nomic-embed-text-v2-moe is trained for 512 tokens.
-        else:
-            raise ValueError(f"unrecognized parameters: n_positions={npos}, max_trained_positions={mtp}")
-
-        assert self.hparams["activation_function"] == "gelu" if self.is_moe else "swiglu"
-
-        # this doesn't do anything in the HF version
-        assert self.hparams["causal"] is False
-        # no bias tensors unless MoE
-        assert self.hparams["qkv_proj_bias"] == self.is_moe
-        assert self.hparams["mlp_fc1_bias"]  == self.is_moe
-        assert self.hparams["mlp_fc2_bias"]  == self.is_moe
-
-        # norm at end of layer
-        assert self.hparams["prenorm"] is False
-        # standard RoPE
-        assert self.hparams["rotary_emb_fraction"] == 1.0
-        assert self.hparams["rotary_emb_interleaved"] is False
-        assert self.hparams["rotary_emb_scale_base"] is None
-
-    def set_vocab(self) -> None:
-        if self._tokenizer_is_xlmroberta:
-            return self._xlmroberta_set_vocab()
-        return super().set_vocab()
-
-    def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
-        # If the tensor is an experts bias tensor, skip it by returning an empty list.
-        if "mlp.experts.bias" in name:
-            return # Explicitly return.
-
-        n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-        if "mlp.experts.mlp.w1" in name:
-            data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
-            name += ".weight"
-
-        if "mlp.experts.mlp.w2" in name:
-            data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
-            data_torch = data_torch.transpose(1, 2)
-            name += ".weight"
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if self.is_moe:
-            self.gguf_writer.add_moe_every_n_layers(self.hparams["moe_every_n_layers"])
-            self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
-
-    def _is_tokenizer_xlmroberta(self) -> bool:
-        with open(self.dir_model / "tokenizer.json") as f:
-            tokenizer_json = json.load(f)
-        toktyp = tokenizer_json["model"]["type"]
-        if toktyp == "Unigram":
-            return True
-        if toktyp == "WordPiece":
-            return False
-        raise ValueError(f"unknown tokenizer: {toktyp}")
-
-
-@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification")
-class NeoBert(BertModel):
-    model_arch = gguf.MODEL_ARCH.NEO_BERT
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # NeoBERT uses 2/3 of the intermediate size as feed forward length
-        self.gguf_writer.add_feed_forward_length(int(2 * self.hparams["intermediate_size"] / 3))
-        self.gguf_writer.add_rope_freq_base(10000.0)  # default value for NeoBERT
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-
-        f_rms_eps = self.hparams.get("norm_eps", 1e-6)  # default value for NeoBERT
-        self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
-        logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
-
-        self.gguf_writer.add_pooling_type(gguf.PoolingType.CLS) # https://huggingface.co/chandar-lab/NeoBERT#how-to-use
-
-    def modify_tensors(self, data_torch, name, bid):
-        if name.startswith("decoder."):
-            return
-
-        if name.startswith("model."):
-            name = name[6:]
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("EuroBertModel", "JinaEmbeddingsV5Model")
-class EuroBertModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.EUROBERT
-
-    def set_vocab(self):
-        self.gguf_writer.add_add_bos_token(False)
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # EuroBert is bidirectional (encoder)
-        self.gguf_writer.add_causal_attention(False)
-
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-
-        self._try_set_pooling_type()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Strip "model." prefix from tensor names
-        if name.startswith("model."):
-            name = name[6:]
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
-class XLMRobertaModel(BertModel):
-    model_arch = gguf.MODEL_ARCH.BERT
-    _lora_files = {}
-    _lora_names = []
-
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
-        hparams = kwargs.pop("hparams", None)
-        if hparams is None:
-            hparams = ModelBase.load_hparams(dir_model, False)
-
-        if lora_names := hparams.get("lora_adaptations"):
-            self._lora_names = lora_names
-            self.model_arch = gguf.MODEL_ARCH.JINA_BERT_V3
-
-        super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs)
-        self._xlmroberta_tokenizer_init()
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if self._lora_names:
-            for name in self._lora_names:
-                fname = self.add_prefix_to_filename(self.fname_out, f"lora-{name}-")
-                self._lora_files[name] = gguf.GGUFWriter(fname, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, dry_run=self.dry_run)
-
-        return super().generate_extra_tensors()
-
-    def set_type(self):
-        for lora_writer in self._lora_files.values():
-            lora_writer.add_type(gguf.GGUFType.ADAPTER)
-            lora_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
-        super().set_type()
-
-    def set_vocab(self):
-        self._xlmroberta_set_vocab()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # if name starts with "roberta.", remove the prefix
-        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
-        if name.startswith("roberta."):
-            name = name[8:]
-
-        # jina-embeddings-v3
-        if ".parametrizations." in name:
-            name = name.replace(".parametrizations.", ".")
-            if name.endswith(".original"):
-                name = name[:-9]
-
-        # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
-        if name == "embeddings.position_embeddings.weight":
-            if self._position_offset is not None:
-                data_torch = data_torch[self._position_offset:,:]
-
-        if name.endswith(".0.lora_A") or name.endswith(".0.lora_B"):
-            if name.startswith("pooler.dense"):
-                return
-
-            num_loras = data_torch.size(0)
-            assert num_loras == len(self._lora_names)
-
-            # Split out each LoRA in their own GGUF
-            for i, lora_writer in enumerate(self._lora_files.values()):
-                new_name = self.map_tensor_name(name[:-9]) + name[-7:].lower()
-                data = data_torch[i, :, :]
-                # Transpose/flip token_embd/types into correct shape
-                if new_name == "token_embd.weight.lora_b":
-                    data = data.T
-                elif new_name.startswith("token_types.weight."):
-                    new_name = new_name[:-1] + ("a" if new_name[-1:] == "b" else "b")
-                lora_writer.add_tensor(new_name, data.float().numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
-
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # jina-embeddings-v3
-        lora_alpha = self.hparams.get("lora_alpha")
-        if lora_prompt_prefixes := self.hparams.get("task_instructions"):
-            assert self._lora_files and all(lora_name in lora_prompt_prefixes for lora_name in self._lora_files.keys())
-        for lora_name, lora_writer in self._lora_files.items():
-            lora_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, lora_alpha if lora_alpha is not None else 1.0)
-            lora_writer.add_string(gguf.Keys.Adapter.LORA_TASK_NAME, lora_name)
-            if lora_prompt_prefixes:
-                lora_writer.add_string(gguf.Keys.Adapter.LORA_PROMPT_PREFIX, lora_prompt_prefixes[lora_name])
-
-    def write(self):
-        super().write()
-        for lora_writer in self._lora_files.values():
-            lora_writer.write_header_to_file()
-            lora_writer.write_kv_data_to_file()
-            lora_writer.write_tensors_to_file(progress=True)
-            lora_writer.close()
-
-
-@ModelBase.register("GemmaForCausalLM")
-class GemmaModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GEMMA
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-        # TODO: these special tokens should be exported only for the CodeGemma family
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
-        special_vocab._set_special_token("prefix", 67)
-        special_vocab._set_special_token("suffix", 69)
-        special_vocab._set_special_token("middle", 68)
-        special_vocab._set_special_token("fsep",   70)
-        special_vocab._set_special_token("eot",    107)
-        special_vocab.chat_template = None  # do not add it twice
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-        self.gguf_writer.add_add_space_prefix(False)
-
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-
-        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-        self.gguf_writer.add_key_length(hparams["head_dim"])
-        self.gguf_writer.add_value_length(hparams["head_dim"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
-        # To prevent errors, skip loading lm_head.weight.
-        if name == "lm_head.weight":
-            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
-            return
-
-        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
-        if name.endswith("norm.weight"):
-            data_torch = data_torch + 1
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Gemma2ForCausalLM")
-class Gemma2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.GEMMA2
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-        self.gguf_writer.add_add_space_prefix(False)
-
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-
-        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-        self.gguf_writer.add_key_length(hparams["head_dim"])
-        self.gguf_writer.add_value_length(hparams["head_dim"])
-        self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_attn_logit_softcapping(
-            self.hparams["attn_logit_softcapping"]
-        )
-        self.gguf_writer.add_final_logit_softcapping(
-            self.hparams["final_logit_softcapping"]
-        )
-        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
-        # To prevent errors, skip loading lm_head.weight.
-        if name == "lm_head.weight":
-            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
-            return
-
-        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
-        if name.endswith("norm.weight"):
-            data_torch = data_torch + 1
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Gemma3ForCausalLM", "Gemma3ForConditionalGeneration")
-class Gemma3Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.GEMMA3
-
-    def norm_shift(self, name: str) -> float:
-        return 1.0 if name.endswith("norm.weight") else 0.0  # Gemma3RMSNorm adds 1.0 to the norm value
-
-    def set_vocab(self):
-        if (self.dir_model / "tokenizer.model").is_file():
-            self._set_vocab_sentencepiece()
-            self.gguf_writer.add_add_space_prefix(False)
-        else:
-            self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-
-        # some default values are not specified in the hparams
-        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072))
-        self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8))
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6))
-        self.gguf_writer.add_key_length(hparams.get("head_dim", 256))
-        self.gguf_writer.add_value_length(hparams.get("head_dim", 256))
-        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_theta", 1_000_000.0)) # for global layers
-        # attn_logit_softcapping is removed in Gemma3
-        assert hparams.get("attn_logit_softcapping") is None
-        if (final_logit_softcap := hparams.get("final_logit_softcapping")):
-            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
-        if hparams.get("sliding_window_pattern") != 1:
-            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
-        self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "language_model." in name:
-            name = name.replace("language_model.", "")
-
-        elif name.startswith("multi_modal_projector.") or name.startswith("vision_tower.") \
-                or name.startswith("multimodal_projector.") or name.startswith("vision_model."):
-            return # skip vision tensors
-
-        # remove OOV (out-of-vocabulary) rows in token_embd
-        if "embed_tokens.weight" in name:
-            n_vocab_real = -1
-            if (self.dir_model / "tokenizer.model").is_file():
-                tokens = self._create_vocab_sentencepiece()[0]
-                n_vocab_real = len(tokens)
-            else:
-                with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
-                    tokenizer_json = json.load(f)
-                    n_vocab_real = len(tokenizer_json["model"]["vocab"]) + len(tokenizer_json["added_tokens"])
-            data_torch = data_torch[:n_vocab_real]
-
-        # ref code in Gemma3RMSNorm
-        # output = output * (1.0 + self.weight.float())
-        # note: this is not the case on gemma3n
-        f_shift = self.norm_shift(name)
-        if f_shift != 0.0:
-            data_torch = data_torch + f_shift
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Gemma3TextModel")
-class EmbeddingGemma(Gemma3Model):
-    model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
-    module_paths = []
-    dense_features_dims = {}
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.sentence_transformers_dense_modules:
-            # read modules.json to determine if model has Dense layers
-            modules_file = self.dir_model / "modules.json"
-            if modules_file.is_file():
-                with open(modules_file, encoding="utf-8") as modules_json_file:
-                    mods = json.load(modules_json_file)
-                for mod in mods:
-                    if mod["type"].endswith("Dense"):
-                        mod_path = mod["path"]
-                        # check if model.safetensors file for Dense layer exists
-                        model_tensors_file = self.dir_model / mod_path / "model.safetensors"
-                        if model_tensors_file.is_file():
-                            self.module_paths.append(mod_path)
-                            # read config.json of the Dense layer to get in/out features
-                            mod_conf_file = self.dir_model / mod_path / "config.json"
-                            if mod_conf_file.is_file():
-                                with open(mod_conf_file, encoding="utf-8") as mod_conf_json_file:
-                                    mod_conf = json.load(mod_conf_json_file)
-                                    # hparams dense_2_feat_out and dense_3_feat_in are required when loading model's dense weights
-                                    prefix = self._get_dense_prefix(mod_path)
-                                    if mod_conf["in_features"] is not None and mod_conf["out_features"] is not None:
-                                        self.dense_features_dims[prefix] = (mod_conf["in_features"], mod_conf["out_features"])
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        from safetensors.torch import load_file
-        module_paths = list(self.module_paths)
-        for i, module_path in enumerate(module_paths):
-            tensors_file = self.dir_model / module_path / "model.safetensors"
-            local_tensors = load_file(tensors_file)
-            tensor_name = self._get_dense_prefix(module_path)
-            for name, local_tensor in local_tensors.items():
-                if not name.endswith(".weight"):
-                    continue
-                orig_name = name.replace("linear", tensor_name)
-                name = self.map_tensor_name(orig_name)
-                yield name, local_tensor.clone()
-
-    @staticmethod
-    def _get_dense_prefix(module_path) -> str:
-        """Get the tensor name prefix for the Dense layer from module path."""
-        tensor_name = "dense_2" if module_path == "2_Dense" else "dense_3"
-        return tensor_name
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # Override the sliding window size as it gets adjusted by the Gemma3TextConfig
-        # constructor. We want to use the value from the original model's config.json.
-        # ref: https://github.com/huggingface/transformers/pull/40700
-        with open(self.dir_model / "config.json", "r", encoding="utf-8") as f:
-            config = json.load(f)
-            orig_sliding_window = config.get("sliding_window")
-            if orig_sliding_window is None:
-                raise ValueError("sliding_window not found in model config - this is required for the model")
-
-            logger.info(f"Using original sliding_window from config: {orig_sliding_window} "
-                        f"instead of {self.hparams['sliding_window']}")
-            self.gguf_writer.add_sliding_window(orig_sliding_window)
-        if self.sentence_transformers_dense_modules:
-            for dense, dims in self.dense_features_dims.items():
-                logger.info(f"Setting dense layer {dense} in/out features to {dims}")
-                self.gguf_writer.add_dense_features_dims(dense, dims[0], dims[1])
-
-        self._try_set_pooling_type()
-
-
-@ModelBase.register("Gemma3ForConditionalGeneration")
-class Gemma3VisionModel(MmprojModel):
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GEMMA3)
-        # default values below are taken from HF transformers code
-        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
-        self.gguf_writer.add_vision_use_gelu(True)
-        # calculate proj_scale_factor (used by tinygemma3 test model)
-        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
-        n_per_side = int(image_seq_length ** 0.5)
-        image_size = self.hparams["image_size"]
-        patch_size = self.hparams["patch_size"]
-        proj_scale_factor = (image_size // patch_size) // n_per_side
-        if proj_scale_factor > 0 and proj_scale_factor != 4:
-            # we only need to write this if it's not the default value
-            # in this case, we are converting a test model
-            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        # related to https://github.com/ggml-org/llama.cpp/issues/13025
-        if "input_projection" in name:
-            return gguf.GGMLQuantizationType.F16
-        if ".embeddings." in name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "vision_model.head." in name:
-            return # skip redundant tensors for tinygemma3
-
-        if name.startswith("multi_modal_projector.") or name.startswith("vision_tower.") \
-                or name.startswith("multimodal_projector.") or name.startswith("vision_model."):
-            # process vision tensors
-            name = name.replace("_weight", ".weight")
-
-            # correct norm value ; only this "soft_emb_norm" need to be corrected as it's part of Gemma projector
-            # the other norm values are part of SigLIP model, and they are already correct
-            # ref code: Gemma3RMSNorm
-            if "soft_emb_norm.weight" in name:
-                logger.info(f"Correcting norm value for '{name}'")
-                data_torch = data_torch + 1
-
-            yield from super().modify_tensors(data_torch, name, bid)
-
-        return # skip other tensors
-
-
-class ConformerAudioModel(MmprojModel):
-    _batch_norm_tensors: list[dict[str, Tensor]] | None = None
-
-    @staticmethod
-    def is_audio_tensor(name: str):
-        return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ConformerAudioModel.is_audio_tensor(name):
-            if ".conv" in name or "_conv" in name and ".weight" in name:
-                return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # fold running_mean, running_var and eps into weight and bias for batch_norm
-        if "batch_norm" in name:
-            if self._batch_norm_tensors is None:
-                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
-            assert bid is not None
-            self._batch_norm_tensors[bid][name] = data_torch
-
-            if len(self._batch_norm_tensors[bid]) < 5:
-                return
-
-            weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
-            bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
-            running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
-            running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
-            eps = 1e-5 # default value
-
-            a = weight / torch.sqrt(running_var + eps)
-            b = bias - running_mean * a
-            yield from super().modify_tensors(a, f"conformer.layers.{bid}.conv.batch_norm.weight", bid)
-            yield from super().modify_tensors(b, f"conformer.layers.{bid}.conv.batch_norm.bias", bid)
-            return
-
-        # reshape conv weights
-        if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
-            data_torch = data_torch[:, None, None]
-        if "conv.depthwise_conv" in name and name.endswith(".weight"):
-            assert data_torch.shape[1] == 1
-            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
-        if "conv.pointwise_conv" in name and name.endswith(".weight"):
-            assert data_torch.shape[2] == 1
-            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
-
-        mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
-        yield (mapped_name, data_torch)
-
-
-@ModelBase.register("DeepseekOCRForCausalLM")
-class DeepseekOCRVisionModel(MmprojModel):
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DEEPSEEKOCR)
-        # default values below are taken from HF tranformers code
-        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
-        self.gguf_writer.add_vision_use_gelu(True)
-        # calculate proj_scale_factor (used by tinygemma3 test model)
-        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
-        n_per_side = int(image_seq_length ** 0.5)
-        image_size = self.hparams["image_size"]
-        patch_size = self.hparams["patch_size"]
-        proj_scale_factor = (image_size // patch_size) // n_per_side
-        if proj_scale_factor > 0 and proj_scale_factor != 4:
-            # we only need to write this if it's not the default value
-            # in this case, we are converting a test model
-            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
-        # @bluebread: there's no window_size in config but just add it here anyway
-        self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14))
-
-        # SAM configuration
-        sam_hparams = hparams['sam']
-        self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers'])
-        self.gguf_writer.add_vision_sam_embedding_length(sam_hparams['width'])
-        self.gguf_writer.add_vision_sam_head_count(sam_hparams['heads'])
-
-    def get_vision_config(self) -> dict[str, Any]:
-        vision_config: dict[str, Any] | None = self.global_config.get("vision_config")
-
-        if not vision_config:
-            raise ValueError("DeepseekOCR model requires 'vision_config' in the model configuration, but it was not found")
-
-        vision_config['sam'] = vision_config['width']['sam_vit_b']
-        vision_config.update(vision_config['width']['clip-l-14-224'])
-        vision_config['hidden_size'] = vision_config['width']
-        vision_config['num_heads'] = vision_config['heads']
-        vision_config['intermediate_size'] = vision_config['heads'] * 4
-
-        return vision_config
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".embeddings." in name or 'pos_embed' in name:
-            return gguf.GGMLQuantizationType.F32
-        if ".rel_pos_h" in name or '.rel_pos_w' in name:
-            return gguf.GGMLQuantizationType.F32
-        if ".neck." in name or ".net_" in name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Only process vision-related tensors, skip language model tensors
-        # Vision components: sam_model, vision_model, projector, image_newline, view_seperator
-        # Language model components to skip: lm_head, embed_tokens, layers, norm
-        if name.startswith(("lm_head.", "model.embed_tokens.", "model.layers.", "model.norm.")):
-            return
-
-        if name.endswith("pos_embed") or name.endswith("rel_pos_h") or name.endswith("rel_pos_w"):
-            name += ".weight"
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Gemma3nForConditionalGeneration")
-class Gemma3nVisionAudioModel(ConformerAudioModel):
-    has_audio_encoder = True
-    has_vision_encoder = True
-
-    # Double indexed mapping for MobileNetV5 blocks (not supported by tensor_mapping.py)
-    # This is the only known model having this, so we prefer implementing it outside of tensor_mapping.py
-    block_tensor_mapping = {
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_exp.weight":             "v.blk.{bid}.{sid}.conv_exp.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn1.weight":                  "v.blk.{bid}.{sid}.bn1.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_pwl.weight":             "v.blk.{bid}.{sid}.conv_pwl.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn2.weight":                  "v.blk.{bid}.{sid}.bn2.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.conv.weight":        "v.blk.{bid}.{sid}.dw_start.conv.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.bn.weight":          "v.blk.{bid}.{sid}.dw_start.bn.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.conv.weight":          "v.blk.{bid}.{sid}.dw_mid.conv.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.bn.weight":            "v.blk.{bid}.{sid}.dw_mid.bn.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.conv.weight":          "v.blk.{bid}.{sid}.pw_exp.conv.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.bn.weight":            "v.blk.{bid}.{sid}.pw_exp.bn.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.conv.weight":         "v.blk.{bid}.{sid}.pw_proj.conv.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.bn.weight":           "v.blk.{bid}.{sid}.pw_proj.bn.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.layer_scale.gamma":           "v.blk.{bid}.{sid}.layer_scale.gamma",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.query.proj.weight":      "v.blk.{bid}.{sid}.attn.query.proj.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.proj.weight":        "v.blk.{bid}.{sid}.attn.key.proj.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.proj.weight":      "v.blk.{bid}.{sid}.attn.value.proj.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.output.proj.weight":     "v.blk.{bid}.{sid}.attn.output.proj.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.down_conv.weight":   "v.blk.{bid}.{sid}.attn.key.down_conv.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.norm.weight":        "v.blk.{bid}.{sid}.attn.key.norm.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.down_conv.weight": "v.blk.{bid}.{sid}.attn.value.down_conv.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.norm.weight":      "v.blk.{bid}.{sid}.attn.value.norm.weight",
-        "model.vision_tower.timm_model.blocks.{bid}.{sid}.norm.weight":                 "v.blk.{bid}.{sid}.norm.weight",
-    }
-
-    def __init__(self, *args, **kwargs):
-        # Parent init will call find_hparam which now returns 0 for empty keys
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        self.hparams_vision["n_layers"] = 128 # fake value for audio encoder, vision encoder doesn't use it
-        self.hparams_vision["intermediate_size"] = self.hparams_vision.get("intermediate_size", 2048) * 4
-        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_attention_heads", 8)
-
-        # MobileNetV5 does not use image_mean/std
-        self.preprocessor_config["image_mean"] = [0.0 ,0.0 , 0.0]
-        self.preprocessor_config["image_std"] = [1.0 ,1.0 ,1.0]
-        self.hparams_vision["image_size"] = self.preprocessor_config.get(
-            "size", {"height": 768, "width": 768}
-        )["height"]
-
-        # Image sequence length (256 tokens = 16x16 for Gemma3n)
-        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
-        image_size = self.hparams_vision["image_size"]
-        self.hparams_vision["patch_size"] = image_size // image_seq_length
-
-        # remap audio hparams
-        assert self.hparams_audio is not None
-        self.hparams_audio["n_layers"] = self.hparams_audio["conf_num_hidden_layers"]
-        self.hparams_audio["num_attention_heads"] = self.hparams_audio["conf_num_attention_heads"]
-        self.hparams_audio["feat_in"] = self.hparams_audio["input_feat_size"]
-        self.hparams_audio["intermediate_size"] = self.hparams_audio.get("intermediate_size", 6144)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # vision params
-        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA3NV)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
-
-        # audio params
-        assert self.hparams_audio is not None
-        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA3NA)
-        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
-        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        # Force quantization settings for specific tensor types
-        if "input_projection" in name or "input_proj" in name:
-            return gguf.GGMLQuantizationType.F16
-        if ".embeddings." in name or "stem" in name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def custom_map(self, name: str) -> str:
-        """Parses names like model.vision_tower.timm_model.blocks.1.2.suffix and applies template mapping."""
-        parts = name.split(".")
-        # MobileNet blocks have at least 7 parts: model, vision_tower, timm_model, blocks, bid, sid, and suffix
-        if len(parts) >= 7:
-            bid, sid = parts[4], parts[5]
-            suffix = ".".join(parts[6:])
-            template = f"model.vision_tower.timm_model.blocks.{{bid}}.{{sid}}.{suffix}"
-            if template in self.block_tensor_mapping:
-                return self.block_tensor_mapping[template].format(bid=bid, sid=sid)
-
-        raise ValueError(f"Unknown name: {name}")
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if (ConformerAudioModel.is_audio_tensor(name)):
-            name = name.replace("model.audio_tower.conformer.", "conformer.layers.")
-            yield from super().modify_tensors(data_torch, name, bid)
-
-        # Gemma3n uses
-        # - model.embed_vision.* for projection layers
-        # - model.vision_tower.* for vision encoder
-        # Skip non-vision tensors
-        if not (name.startswith("model.embed_vision.") or name.startswith("model.vision_tower.")):
-            return
-
-        if name.startswith("model.vision_tower.timm_model.blocks."):
-            # Double-indexed block tensors through custom logic
-            yield (self.custom_map(name), data_torch)
-            return
-        else:
-            # Route non-repeating (conv_stem, msfa, embedding, etc.) and un-catched through tensor_mapping.py
-            new_name = self.map_tensor_name(name)
-
-        if new_name.endswith("conv_stem.conv.bias") or new_name.endswith("layer_scale.gamma"):
-            data_torch = data_torch.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) # [1, C, 1, 1]
-
-        yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
-
-
-@ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
-class Gemma3NModel(Gemma3Model):
-    model_arch = gguf.MODEL_ARCH.GEMMA3N
-
-    _altup_proj: list[Tensor] = []
-    _altup_unembd: list[Tensor] = []
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams["altup_num_inputs"] == 4, "Current conversion only supports 4 altup inputs"
-        self._altup_proj = [
-            torch.Tensor(), # to be replaced
-            torch.Tensor(), # to be replaced
-            torch.Tensor(), # to be replaced
-        ]
-        self._altup_unembd = [
-            torch.Tensor(), # to be replaced
-            torch.Tensor(), # to be replaced
-            torch.Tensor(), # to be replaced
-        ]
-
-    def norm_shift(self, name: str) -> float:
-        del name
-        return 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code
-
-    def set_vocab(self):
-        # For Gemma3n multimodal models, we need the FULL vocab_size (262400)
-        # which includes special tokens from 262144-262399 for vision/audio.
-        # The vocab_size_per_layer_input (262144) is only the embedding size per layer.
-        # Temporarily override the hparams lookup order to prioritize vocab_size.
-
-        # Store original vocab_size_per_layer_input if it exists
-        vocab_size_per_layer_input = self.hparams.get("vocab_size_per_layer_input")
-
-        # Temporarily remove vocab_size_per_layer_input to force using vocab_size
-        if vocab_size_per_layer_input is not None:
-            del self.hparams["vocab_size_per_layer_input"]
-
-        # Call parent set_vocab which will now use vocab_size (262400)
-        super().set_vocab()
-
-        # Restore vocab_size_per_layer_input for later use
-        if vocab_size_per_layer_input is not None:
-            self.hparams["vocab_size_per_layer_input"] = vocab_size_per_layer_input
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_altup_active_idx(self.hparams["altup_active_idx"])
-        self.gguf_writer.add_altup_num_inputs(self.hparams["altup_num_inputs"])
-        self.gguf_writer.add_embedding_length_per_layer_input(self.hparams["hidden_size_per_layer_input"])
-        self.gguf_writer.add_shared_kv_layers(self.hparams["num_kv_shared_layers"])
-
-        activation_sparsity_scale = []
-        for s in self.hparams["activation_sparsity_pattern"]:
-            normal_dist = torch.distributions.normal.Normal(0, 1)
-            std_multiplier = normal_dist.icdf(torch.tensor(s, dtype=torch.float32))
-            activation_sparsity_scale.append(std_multiplier.item())
-        self.gguf_writer.add_activation_sparsity_scale(activation_sparsity_scale)
-
-        sliding_window_pattern = []
-        for t in self.hparams["layer_types"]:
-            sliding_window_pattern.append(t == "sliding_attention")
-        self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
-
-    def _stack_matrices(self, matrices: list[Tensor]) -> Tensor | None:
-        has_all = all(m.numel() > 0 for m in matrices)
-        if not has_all:
-            return None
-        else:
-            return torch.stack(matrices, dim=0)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.endswith("_scale"):
-            name = name + ".weight"
-
-        # TODO: implement self.prediction_coefs.weight.clamp_(...)
-
-        if "language_model." not in name:
-            return # skip non-language model tensors
-
-        # Pad token embeddings for vision/audio special tokens (262144-262399)
-        if "embed_tokens.weight" in name or "embed_tokens_per_layer" in name:
-            # Move to CPU to avoid meta device issues during padding
-            data_torch = data_torch.to(device="cpu")
-
-            vocab_size = self.hparams.get("vocab_size", 262400)
-            current_size = data_torch.shape[0]  # First dimension is vocab_size
-
-            if current_size < vocab_size:
-                # Pad with zeros for vision/audio tokens (they get embeddings from vision tower)
-                padding_size = vocab_size - current_size
-                tensor_type = "per-layer embeddings" if "per_layer" in name else "token embeddings"
-                logger.info(f"Padding {tensor_type} shape {list(data_torch.shape)} from {current_size} to {vocab_size} (adding {padding_size} vision/audio token slots)")
-
-                # Create padding with zeros (vision tokens won't use these embeddings)
-                padding = torch.zeros((padding_size, data_torch.shape[1]), dtype=data_torch.dtype, device=data_torch.device)
-                data_torch = torch.cat([data_torch, padding], dim=0)
-
-            # Continue with normal processing
-            name = name.replace("language_model.", "")
-            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
-            return
-
-        if "altup_unembed_projections" in name:
-            data_torch = data_torch.to(device="cpu")
-            # altup_unembed matrices are [hidden_size, hidden_size], NOT vocab-based
-            # They should NOT be padded
-            if ".0." in name:
-                self._altup_unembd[0] = data_torch
-            elif ".1." in name:
-                self._altup_unembd[1] = data_torch
-            elif ".2." in name:
-                self._altup_unembd[2] = data_torch
-            else:
-                raise ValueError(f"Unknown name: {name}")
-            out = self._stack_matrices(self._altup_unembd)
-            if out is not None:
-                yield from ModelBase.modify_tensors(self, out, "model.altup_unembed_projections.weight", bid)
-                return
-            else:
-                return
-
-        if "altup_projections" in name:
-            data_torch = data_torch.to(device="cpu")
-            if ".0." in name:
-                self._altup_proj[0] = data_torch
-            elif ".1." in name:
-                self._altup_proj[1] = data_torch
-            elif ".2." in name:
-                self._altup_proj[2] = data_torch
-            else:
-                raise ValueError(f"Unknown name: {name}")
-            out = self._stack_matrices(self._altup_proj)
-            if out is not None:
-                yield from ModelBase.modify_tensors(self, out, "model.altup_projections.weight", bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Gemma4ForConditionalGeneration")
-class Gemma4Model(Gemma3Model):
-    model_arch = gguf.MODEL_ARCH.GEMMA4
-
-    def norm_shift(self, name: str) -> float:
-        del name # unused
-        return 0.0
-
-    def set_vocab(self):
-        vocab = gguf.LlamaHfVocab(self.dir_model)
-        tokens = []
-        scores = []
-        toktypes = []
-        visible_tokens = {"<|channel>", "<channel|>", "<|tool_call>", "<tool_call|>", "<|tool_response>", "<tool_response|>", "<|\"|>"}
-
-        for text, score, toktype in vocab.all_tokens():
-            tokens.append(text)
-            scores.append(score)
-            text_str = text.decode()
-            if text_str in visible_tokens:
-                # always render these tokens, so that the chat parser can read them
-                toktypes.append(gguf.TokenType.USER_DEFINED)
-                logger.info(f"Token '{text_str}' is set to USER_DEFINED")
-            else:
-                toktypes.append(toktype)
-
-        assert len(tokens) == vocab.vocab_size
-
-        self.gguf_writer.add_tokenizer_model("gemma4")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_vocab.add_to_gguf(self.gguf_writer)
-        self.gguf_writer.add_add_space_prefix(False)
-        self.gguf_writer.add_add_bos_token(True)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        num_kv_shared_layers = self.hparams["num_kv_shared_layers"]
-        self.gguf_writer.add_shared_kv_layers(num_kv_shared_layers)
-
-        # per-layer embedding is optional
-        n_pl_embd = self.hparams.get("hidden_size_per_layer_input") or 0
-        self.gguf_writer.add_embedding_length_per_layer_input(n_pl_embd)
-
-        swa_layers = [t == "sliding_attention" for t in self.hparams["layer_types"]]
-        self.gguf_writer.add_sliding_window_pattern(swa_layers)
-
-        head_dim_full = self.hparams["global_head_dim"]
-        head_dim_swa = self.hparams["head_dim"]
-        # correct the head dim for global/swa layers
-        self.gguf_writer.add_key_length(head_dim_full)
-        self.gguf_writer.add_value_length(head_dim_full)
-        self.gguf_writer.add_key_length_swa(head_dim_swa)
-        self.gguf_writer.add_value_length_swa(head_dim_swa)
-
-        expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"])
-        if expert_intermediate_size is not None:
-            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
-
-        # if use_double_wide_mlp is set, we need to adjust the value for kv shared layers
-        use_double_wide_mlp = self.hparams.get("use_double_wide_mlp", False)
-        first_kv_shared_layer_idx = self.block_count - num_kv_shared_layers
-        if use_double_wide_mlp:
-            n_ff = self.hparams["intermediate_size"]
-            n_ff_arr = [n_ff if il < first_kv_shared_layer_idx else n_ff * 2 for il in range(self.block_count)]
-            self.gguf_writer.add_feed_forward_length(n_ff_arr)
-
-        # handle num_global_key_value_heads
-        num_key_value_heads_full = self.hparams.get("num_global_key_value_heads")
-        num_key_value_heads_swa = self.hparams.get("num_key_value_heads")
-        if num_key_value_heads_full is not None and num_key_value_heads_swa is not None:
-            value_arr = [num_key_value_heads_swa if is_swa else num_key_value_heads_full for is_swa in swa_layers]
-            self.gguf_writer.add_head_count_kv(value_arr)
-
-        # handle n_rot differently for global vs swa layers
-        partial_rotary_factor_swa = self.hparams.get("partial_rotary_factor", 1.0)
-        n_rot_full = int(head_dim_full) # "proportional" is used, see generate_extra_tensors
-        n_rot_swa = int(head_dim_swa * partial_rotary_factor_swa)
-        self.gguf_writer.add_rope_dimension_count(n_rot_full)
-        self.gguf_writer.add_rope_dimension_count_swa(n_rot_swa)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        # full layer uses "proportional" rope with partial_rotary_factor=0.25
-        # the expected ordering is cc000000ss000000 (c = cos, s = sin, 0 = unrotated),
-        # but ggml neox only supports ccss000000000000, and we cannot rearrange the head because that will break use_alternative_attention
-        # solution is to set specific freq_factors for the unrotated dims
-
-        # IMPORTANT: this ROPE_FREQS tensor is ONLY used by the full_attention layers
-        rope_params_full = self.hparams["rope_parameters"]["full_attention"]
-        assert rope_params_full["rope_type"] == "proportional"
-        head_dim_full = (self.hparams["global_head_dim"])
-        partial_rotary_factor_full = rope_params_full["partial_rotary_factor"]
-        n_rot_full = int(head_dim_full * partial_rotary_factor_full / 2)
-        n_unrot_full = int(head_dim_full / 2) - n_rot_full
-        values = [1.0] * n_rot_full + [1e30] * n_unrot_full
-        rope_freqs_full = torch.tensor(values, dtype=torch.float32)
-        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), rope_freqs_full)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.endswith("per_dim_scale") or name.endswith("layer_scalar"):
-            name = name + ".weight"
-
-        if "language_model." not in name and "rope_freqs" not in name:
-            return # skip non-language model tensors
-
-        name = name.replace("language_model.", "")
-        if name.endswith("router.scale"):
-            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_INP, bid, ".scale")
-            yield (name, data_torch)
-            return
-        if ".per_expert_scale" in name:
-            # convert per-expert scale to FFN down scale
-            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN_EXP, bid, ".scale")
-            yield (name, data_torch)
-            return
-        if ".experts." in name and not name.endswith(".weight"):
-            name += ".weight"
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Gemma4ForConditionalGeneration")
-class Gemma4VisionAudioModel(MmprojModel):
-    has_audio_encoder = True
-    has_vision_encoder = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        self.hparams_vision["image_size"] = 224 # unused, but set to avoid error
-
-        # remap audio hparams
-        if self.hparams_audio:
-            self.hparams_audio["feat_in"] = self.hparams_audio.get("input_feat_size", 128)
-            self.hparams_audio["intermediate_size"] = self.hparams_audio["hidden_size"] * 4
-        else:
-            self.has_audio_encoder = False
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # vision params
-        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA4V)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
-
-        # audio params
-        if self.hparams_audio:
-            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA4A)
-            self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
-            self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
-
-    def is_audio_tensor(self, name: str) -> bool:
-        return "audio_tower" in name or "embed_audio" in name
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if self.is_audio_tensor(name):
-            if ".conv" in name or "_conv" in name and ".weight" in name:
-                return gguf.GGMLQuantizationType.F32
-        if "position_embedding_table" in name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        del bid # unused
-
-        if name.startswith("model.language_model."):
-            return # skip
-
-        if len(data_torch.shape) == 0:
-            # convert scalar tensors (input/output_mix/max) to 1D tensors
-            data_torch = data_torch.unsqueeze(0)
-
-        if self.is_audio_tensor(name):
-            assert self.hparams_audio is not None
-            name = name.replace("model.audio_tower.", "conformer.")
-            name = name.replace(".linear.", ".")
-            if name.endswith("per_dim_key_scale") or name.endswith("per_dim_scale"):
-                name = name + ".weight"
-                data_torch = torch.nn.functional.softplus(data_torch)
-            if "lconv1d.depthwise_conv1d" in name and name.endswith(".weight"):
-                assert data_torch.shape[1] == 1
-                data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
-            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
-            yield (mapped_name, data_torch)
-
-        else:
-            name = name.replace("model.vision_tower.encoder.", "vision_model.model.")
-            name = name.replace(".linear.weight", ".weight")
-            if name.endswith("layer_scalar") or name.endswith("position_embedding_table"):
-                name = name + ".weight"
-            if name.endswith("patch_embedder.input_proj.weight"):
-                n_embd, ksize_sq_c = data_torch.shape
-                patch_size = int((ksize_sq_c // 3) ** 0.5)
-                data_torch = data_torch.reshape(n_embd, patch_size, patch_size, 3)
-                data_torch = data_torch.permute(0, 3, 1, 2).contiguous()
-            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
-            yield (mapped_name, data_torch)
-
-
-@ModelBase.register("Starcoder2ForCausalLM")
-class StarCoder2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.STARCODER2
-
-
-@ModelBase.register("Rwkv6ForCausalLM")
-class Rwkv6Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.RWKV6
-
-    def set_vocab(self):
-        self._set_vocab_rwkv_world()
-
-    def set_gguf_parameters(self):
-        head_size = self.hparams["head_size"]
-        hidden_size = self.hparams["hidden_size"]
-        layer_norm_eps = self.hparams["layer_norm_epsilon"]
-        rescale_every_n_layers = self.hparams["rescale_every"]
-        intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else int((hidden_size * 3.5) // 32 * 32)
-        time_mix_extra_dim = 64 if hidden_size == 4096 else 32
-        time_decay_extra_dim = 128 if hidden_size == 4096 else 64
-
-        # RWKV isn't context limited
-        self.gguf_writer.add_context_length(1048576)
-        self.gguf_writer.add_embedding_length(hidden_size)
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
-        self.gguf_writer.add_rescale_every_n_layers(rescale_every_n_layers)
-        self.gguf_writer.add_wkv_head_size(head_size)
-        self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
-        self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
-        self.gguf_writer.add_feed_forward_length(intermediate_size)
-        self.gguf_writer.add_file_type(self.ftype)
-
-        # required by llama.cpp, unused
-        self.gguf_writer.add_head_count(0)
-
-    lerp_weights: dict[int, dict[str, Tensor]] = {}
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        new_name = self.map_tensor_name(name)
-
-        if not (new_name.endswith(".weight") or new_name.endswith(".bias")):
-            new_name += ".weight"
-
-        if new_name.endswith("time_mix_w1.weight") or new_name.endswith("time_mix_decay_w1.weight") or new_name.endswith("time_mix_decay_w2.weight"):
-            data_torch = data_torch.transpose(0, 1)
-
-        if new_name.endswith("time_mix_w2.weight"):
-            data_torch = data_torch.permute(0, 2, 1)
-
-        if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name:
-            data_torch = data_torch.squeeze()
-
-        try:
-            rescale_every_n_layers = self.hparams["rescale_every"]
-            if rescale_every_n_layers > 0:
-                if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
-                    data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
-        except KeyError:
-            pass
-
-        # concat time_mix_lerp weights to reduce some cpu overhead
-        # also reduces the number of tensors in the model
-        if bid is not None and "time_mix_lerp" in new_name and "time_mix_lerp_x" not in new_name:
-            try:
-                self.lerp_weights[bid][new_name] = data_torch
-            except KeyError:
-                self.lerp_weights[bid] = {new_name: data_torch}
-            if all(f"blk.{bid}.time_mix_lerp_{i}.weight" in self.lerp_weights[bid].keys() for i in ["w", "k", "v", "r", "g"]):
-                new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
-                data = torch.stack([self.lerp_weights[bid][f"blk.{bid}.time_mix_lerp_{i}.weight"].unsqueeze(0) for i in ["w", "k", "v", "r", "g"]], dim=0).unsqueeze(1)
-                yield (new_name, data)
-            return
-
-        yield (new_name, data_torch)
-
-
-@ModelBase.register("RWKV6Qwen2ForCausalLM")
-class RWKV6Qwen2Model(Rwkv6Model):
-    model_arch = gguf.MODEL_ARCH.RWKV6QWEN2
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        num_attention_heads = self.hparams["num_attention_heads"]
-        num_key_value_heads = self.hparams["num_key_value_heads"]
-        hidden_size = self.hparams["hidden_size"]
-        head_size = hidden_size // num_attention_heads
-        rms_norm_eps = self.hparams["rms_norm_eps"]
-        intermediate_size = self.hparams["intermediate_size"]
-        time_mix_extra_dim = self.hparams.get("lora_rank_tokenshift", 64 if hidden_size >= 4096 else 32)
-        time_decay_extra_dim = self.hparams.get("lora_rank_decay", 128 if hidden_size >= 4096 else 64)
-
-        # RWKV isn't context limited
-        self.gguf_writer.add_context_length(1048576)
-        self.gguf_writer.add_embedding_length(hidden_size)
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_wkv_head_size(head_size)
-        self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
-        self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
-        self.gguf_writer.add_feed_forward_length(intermediate_size)
-        self.gguf_writer.add_file_type(self.ftype)
-
-        # special parameters for time_mixing in RWKV6QWEN2
-        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
-        self.gguf_writer.add_token_shift_count(1)
-        # RWKV6QWEN2 use grouped key/value like GQA
-        self.gguf_writer.add_head_count_kv(num_key_value_heads)
-
-        # required by llama.cpp, unused
-        self.gguf_writer.add_head_count(0)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        for new_name, data in super().modify_tensors(data_torch, name, bid):
-            if "time_mix_w1" in new_name or "time_mix_w2" in new_name:
-                data = data.view(5, -1, data.shape[-1])
-                # rwkv6qwen2 has a different order of rkvwg instead of the original wkvrg
-                # permute them here to avoid code changes
-                data = torch.stack([data[3], data[1], data[2], data[0], data[4]], dim=0).view(-1, data.shape[-1])
-                if "w2" in new_name:
-                    data = data.view(5, -1, data.shape[-1])
-                yield (new_name, data)
-                continue
-            yield (new_name, data)
-
-
-@ModelBase.register("Rwkv7ForCausalLM", "RWKV7ForCausalLM")
-class Rwkv7Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.RWKV7
-
-    def set_vocab(self):
-        self._set_vocab_rwkv_world()
-
-    def calc_lora_rank(self, hidden_size, exponent, multiplier):
-        return max(1, round(hidden_size ** exponent * multiplier / 32)) * 32
-
-    def set_gguf_parameters(self):
-        try:
-            head_size = self.hparams["head_size"]
-            layer_norm_eps = self.hparams["layer_norm_epsilon"]
-        except KeyError:
-            head_size = self.hparams["head_dim"]
-            layer_norm_eps = self.hparams["norm_eps"]
-        hidden_size = self.hparams["hidden_size"]
-        intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else (hidden_size * 4)
-
-        # ICLR: In-Context-Learning-Rate
-        try:
-            lora_rank_decay = self.hparams["lora_rank_decay"] if self.hparams["lora_rank_decay"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
-            lora_rank_iclr = self.hparams["lora_rank_iclr"] if self.hparams["lora_rank_iclr"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
-            lora_rank_value_residual_mix = self.hparams["lora_rank_value_residual_mix"] if self.hparams["lora_rank_value_residual_mix"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
-            lora_rank_gate = self.hparams["lora_rank_gate"] if self.hparams["lora_rank_gate"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)
-        except KeyError:
-            lora_rank_decay = self.hparams["decay_low_rank_dim"] if self.hparams["decay_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
-            lora_rank_iclr = self.hparams["a_low_rank_dim"] if self.hparams["a_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
-            lora_rank_value_residual_mix = self.hparams["v_low_rank_dim"] if self.hparams["v_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
-            lora_rank_gate = self.hparams["gate_low_rank_dim"] if self.hparams["gate_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)
-
-        # RWKV isn't context limited
-        self.gguf_writer.add_context_length(1048576)
-        self.gguf_writer.add_embedding_length(hidden_size)
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
-        self.gguf_writer.add_wkv_head_size(head_size)
-        self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
-        self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr)
-        self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix)
-        self.gguf_writer.add_gate_lora_rank(lora_rank_gate)
-        self.gguf_writer.add_feed_forward_length(intermediate_size)
-        self.gguf_writer.add_file_type(self.ftype)
-
-        # required by llama.cpp, unused
-        self.gguf_writer.add_head_count(0)
-
-    lerp_weights: dict[int, dict[str, Tensor]] = {}
-    lora_needs_transpose: bool = True
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # unify tensor names here to make life easier
-        name = name.replace("blocks", "layers").replace("ffn", "feed_forward")
-        name = name.replace("self_attn", "attention").replace("attn", "attention")
-        name = name.replace("time_mixer.", "")
-        # lora layer names in fla-hub's impl
-        if "_lora.lora" in name:
-            self.lora_needs_transpose = False
-        name = name.replace("_lora.lora.0.weight", "1.weight")
-        name = name.replace("_lora.lora.2.weight", "2.weight")
-        name = name.replace("_lora.lora.2.bias", "0.weight")
-
-        name = name.replace("feed_forward_norm", "ln2")
-        name = name.replace("g_norm", "ln_x")
-
-        if "attention.v" in name and "value" not in self.map_tensor_name(name) and bid == 0:
-            # some models have dummy v0/v1/v2 on first layer while others don't
-            # ignore them all since they are not used
-            return
-
-        wkv_has_gate = self.hparams.get("wkv_has_gate", True)
-        lerp_list = ["r", "w", "k", "v", "a", "g"] if wkv_has_gate else ["r", "w", "k", "v", "a"]
-
-        if bid is not None and "attention.x_" in name:
-            if "attention.x_x" in name:
-                # already concatenated
-                new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
-                data = data_torch.reshape(len(lerp_list), 1, 1, -1)
-                yield (new_name, data)
-            else:
-                try:
-                    self.lerp_weights[bid][name] = data_torch
-                except KeyError:
-                    self.lerp_weights[bid] = {name: data_torch}
-                if all(f"model.layers.{bid}.attention.x_{i}" in self.lerp_weights[bid].keys() for i in lerp_list):
-                    new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
-                    data = torch.stack([self.lerp_weights[bid][f"model.layers.{bid}.attention.x_{i}"] for i in lerp_list], dim=0)
-                    yield (new_name, data)
-            return
-        else:
-            data_torch = data_torch.squeeze()
-            new_name = self.map_tensor_name(name)
-
-            if not (new_name.endswith(".weight") or new_name.endswith(".bias")):
-                new_name += ".weight"
-
-            if self.lora_needs_transpose and any(
-                new_name.endswith(t) for t in [
-                    "time_mix_w1.weight", "time_mix_w2.weight",
-                    "time_mix_a1.weight", "time_mix_a2.weight",
-                    "time_mix_v1.weight", "time_mix_v2.weight",
-                    "time_mix_g1.weight", "time_mix_g2.weight",
-                ]
-            ):
-                data_torch = data_torch.transpose(0, 1)
-
-            if 'r_k' in new_name:
-                data_torch = data_torch.flatten()
-
-            if bid == 0 and "time_mix_a" in new_name:
-                # dummy v0/v1/v2 on first layer
-                # easiest way to make llama happy
-                yield (new_name.replace("time_mix_a", "time_mix_v"), data_torch)
-
-            yield (new_name, data_torch)
-
-
-@ModelBase.register("RwkvHybridForCausalLM")
-class ARwkv7Model(Rwkv7Model):
-    model_arch = gguf.MODEL_ARCH.ARWKV7
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        hidden_size = self.hparams["hidden_size"]
-        head_size = self.hparams["head_size"]
-        rms_norm_eps = self.hparams["rms_norm_eps"]
-        intermediate_size = self.hparams["intermediate_size"]
-        wkv_has_gate = self.hparams["wkv_has_gate"]
-        assert self.hparams["wkv_version"] == 7
-
-        # ICLR: In-Context-Learning-Rate
-        lora_rank_decay = 64
-        lora_rank_iclr = 64
-        lora_rank_value_residual_mix = 32
-        lora_rank_gate = 128 if wkv_has_gate else 0
-
-        # RWKV isn't context limited
-        self.gguf_writer.add_context_length(1048576)
-        self.gguf_writer.add_embedding_length(hidden_size)
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
-        self.gguf_writer.add_wkv_head_size(head_size)
-        self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
-        self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr)
-        self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix)
-        self.gguf_writer.add_gate_lora_rank(lora_rank_gate)
-        self.gguf_writer.add_feed_forward_length(intermediate_size)
-        self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_token_shift_count(1)
-
-        # required by llama.cpp, unused
-        self.gguf_writer.add_head_count(0)
-
-
-@ModelBase.register("MaincoderForCausalLM")
-class MaincoderModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.MAINCODER
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        if (head_dim := self.hparams.get("head_dim")) is not None:
-            self.gguf_writer.add_rope_dimension_count(head_dim)
-
-
-@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
-class MambaModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.MAMBA
-
-    def __init__(self, dir_model: Path, *args, **kwargs):
-        # Avoid using AutoConfig for hparams
-        hparams = kwargs.pop("hparams", None)
-        if hparams is None:
-            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
-                hparams = json.load(f)
-        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
-
-    def set_vocab(self):
-        vocab_size = self.hparams["vocab_size"]
-        # Round vocab size to next multiple of 8
-        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
-        # pad using ceiling division
-        # ref: https://stackoverflow.com/a/17511341/22827863
-        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
-        self.hparams["vocab_size"] = vocab_size
-
-        if (self.dir_model / "tokenizer.json").is_file():
-            self._set_vocab_gpt2()
-        elif (self.dir_model / "tokenizer.model").is_file():
-            self._set_vocab_sentencepiece()
-        else:
-            # Use the GPT-NeoX tokenizer when no tokenizer files are present
-            self._set_vocab_builtin("gpt-neox", vocab_size)
-
-    def set_gguf_parameters(self):
-        d_model = self.find_hparam(["hidden_size",       "d_model"])
-        d_conv  = self.find_hparam(["conv_kernel",       "d_conv"],  optional=True) or 4
-        d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
-        d_state = self.find_hparam(["state_size",        "d_state"], optional=True) or 16
-        # ceiling division
-        # ref: https://stackoverflow.com/a/17511341/22827863
-        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
-        dt_rank      = self.find_hparam(["time_step_rank",     "dt_rank"],      optional=True) or -(d_model // -16)
-        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
-        use_dt_b_c_norm = False
-        # For falconmamba we do apply RMS norm on B / DT and C layers
-        if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",):
-            use_dt_b_c_norm = True
-        # Fail early for models which don't have a block expansion factor of 2
-        assert d_inner == 2 * d_model
-
-        self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
-        self.gguf_writer.add_embedding_length(d_model)
-        self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
-        self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_ssm_conv_kernel(d_conv)
-        self.gguf_writer.add_ssm_inner_size(d_inner)
-        self.gguf_writer.add_ssm_state_size(d_state)
-        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
-        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
-        self.gguf_writer.add_ssm_dt_b_c_rms(use_dt_b_c_norm) # For classic Mamba we don't apply rms norm on B / DT layers
-        self.gguf_writer.add_file_type(self.ftype)
-
-    _tok_embd = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
-        tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD)
-
-        new_name = self.map_tensor_name(name)
-
-        if name.endswith(".A_log"):
-            logger.debug("A_log --> A ==> " + new_name)
-            data_torch = -torch.exp(data_torch)
-
-        # [4 1 8192 1] -> [4 8192 1 1]
-        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
-            data_torch = data_torch.squeeze()
-
-        # assuming token_embd.weight is seen before output.weight
-        if self._tok_embd is not None and new_name == output_name:
-            if torch.equal(self._tok_embd, data_torch):
-                logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting")
-                return
-        elif new_name == tok_embd_name:
-            self._tok_embd = data_torch
-
-        yield from super().modify_tensors(data_torch, new_name, bid)
-
-
-@ModelBase.register("Mamba2ForCausalLM")
-class Mamba2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.MAMBA2
-
-    def __init__(self, dir_model: Path, *args, **kwargs):
-        # Avoid using AutoConfig for hparams
-        # It wrongly assumes all Mamba2 models are Mamba-Codestral-7B-v0.1
-        hparams = kwargs.pop("hparams", None)
-        if hparams is None:
-            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
-                hparams = json.load(f)
-        if "llm_config" in hparams:
-            hparams["text_config"] = hparams["llm_config"]
-        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
-        self.d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
-        self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or 2 * self.d_model
-        self.n_group = self.find_hparam(["n_groups"], optional=True) or 1
-
-    def set_vocab(self):
-        vocab_size = self.hparams["vocab_size"]
-        # Round vocab size to next multiple of 16
-        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16)
-        # pad using ceiling division
-        # ref: https://stackoverflow.com/a/17511341/22827863
-        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
-        self.hparams["vocab_size"] = vocab_size
-
-        if (self.dir_model / "tokenizer.model").is_file():
-            self._set_vocab_sentencepiece()
-        elif (self.dir_model / "tokenizer.model.v3").is_file():
-            # mamba-codestral
-            raise NotImplementedError(f"Please rename {self.dir_model / 'tokenizer.model.v3'} to {self.dir_model / 'tokenizer.model'}")
-        elif (self.dir_model / "tokenizer.json").is_file():
-            self._set_vocab_gpt2()
-        else:
-            # Use the GPT-NeoX tokenizer when no tokenizer files are present
-            self._set_vocab_builtin("gpt-neox", vocab_size)
-
-    def set_gguf_parameters(self):
-        d_conv  = self.find_hparam(["conv_kernel", "d_conv"],     optional=True) or 4
-        d_state = self.find_hparam(["state_size",  "d_state"],    optional=True) or 128
-        head_dim = self.find_hparam(["mamba_d_head", "head_dim"], optional=True) or 64
-
-        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
-
-        # Fail early for models which don't have a block expansion factor of 2
-        # TODO: does this really matter?
-        # skip the assertion for FalconH1 Model
-        if self.model_arch != gguf.MODEL_ARCH.FALCON_H1:
-            assert self.d_inner == 2 * self.d_model
-            assert self.d_inner % head_dim == 0
-
-        self.gguf_writer.add_context_length(2**20)  # arbitrary value; for those who use the default
-        self.gguf_writer.add_embedding_length(self.d_model)
-        self.gguf_writer.add_feed_forward_length(0)  # unused, but seemingly required when loading
-        self.gguf_writer.add_head_count(0)  # unused, but seemingly required when loading
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_ssm_conv_kernel(d_conv)
-        self.gguf_writer.add_ssm_inner_size(self.d_inner)
-        self.gguf_writer.add_ssm_state_size(d_state)
-        self.gguf_writer.add_ssm_time_step_rank(self.d_inner // head_dim)
-        self.gguf_writer.add_ssm_group_count(self.n_group)
-        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-
-        if name.startswith("model.backbone") or name.startswith("model.lm_head"):
-            # map Mamba-Codestral-7B-v0.1 tensor names to the names used by Mamba-2
-            name = name.removeprefix("model.")
-
-        if name.endswith(".dt_bias"):
-            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
-
-        new_name = self.map_tensor_name(name)
-
-        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
-            data_torch = data_torch.squeeze()
-        elif any(self.match_model_tensor_name(new_name, t, bid, suffix="") for t in [
-            gguf.MODEL_TENSOR.SSM_A,
-            gguf.MODEL_TENSOR.SSM_D,
-        ]):
-            # unsqueeze A to use similar shape semantics as Mamba-1
-            # (D is also unsqueezed, but for more straightforward broadcast internally)
-            data_torch = data_torch.reshape((*data_torch.shape, 1))
-        elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_NORM, bid):
-            data_torch = data_torch.reshape((self.n_group, self.d_inner // self.n_group))
-
-        if name.endswith(".A_log"):
-            logger.debug("A_log --> A ==> " + new_name)
-            data_torch = -torch.exp(data_torch)
-
-        yield (new_name, data_torch)
-
-
-@ModelBase.register("JambaForCausalLM")
-class JambaModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.JAMBA
-
-    def set_vocab(self):
-        if (self.dir_model / "tokenizer.model").is_file():
-            self._set_vocab_sentencepiece()
-        else:
-            self._set_vocab_llama_hf()
-            self.gguf_writer.add_add_space_prefix(False)
-
-    def set_gguf_parameters(self):
-        d_model = self.find_hparam(["hidden_size", "mamba_d_model"])
-        d_conv  = self.find_hparam(["mamba_d_conv"],  optional=True) or 4
-        d_inner = self.hparams["mamba_expand"] * d_model
-        d_state = self.find_hparam(["mamba_d_state"], optional=True) or 16
-        # ceiling division
-        # ref: https://stackoverflow.com/a/17511341/22827863
-        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
-        dt_rank      = self.find_hparam(["mamba_dt_rank"], optional=True) or -(d_model // -16)
-        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-6
-        n_kv_head = self.hparams["num_key_value_heads"]
-        attn_offset = self.hparams["attn_layer_offset"]
-        attn_period = self.hparams["attn_layer_period"]
-        n_kv_vec = [0 for _ in range(attn_offset)] + [
-            n_kv_head if (i - attn_offset) % attn_period == 0 else 0 for i in range(attn_offset, self.block_count)
-        ]
-
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_context_length(self.find_hparam(["max_position_embeddings", "n_ctx"]))
-        self.gguf_writer.add_embedding_length(d_model)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count_kv(n_kv_vec)
-        self.gguf_writer.add_ssm_conv_kernel(d_conv)
-        self.gguf_writer.add_ssm_inner_size(d_inner)
-        self.gguf_writer.add_ssm_state_size(d_state)
-        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
-        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
-        self.gguf_writer.add_expert_count(self.find_hparam(["num_local_experts", "num_experts"]))
-        self.gguf_writer.add_expert_used_count(self.find_hparam(["num_experts_per_tok", "num_experts_per_token"]))
-        self.gguf_writer.add_file_type(self.ftype)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-
-        # Mini-Jamba
-        name = name.replace(".moe.", ".feed_forward.")
-        if bid is not None:
-            moe_offset = self.hparams["expert_layer_offset"]
-            moe_period = self.hparams["expert_layer_period"]
-
-            if not (bid >= moe_offset and (bid - moe_offset) % moe_period == 0):
-                name = name.replace(".experts.0.", ".")
-
-        # process the experts separately
-        if ".feed_forward.experts." in name:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-
-                # merge the experts into a single 3d tensor
-                for wid in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{wid}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    # using the same merged name as qwen2moe
-                    merged_name = f"model.layers.{bid}.mlp.experts.{wid}.weight"
-
-                    new_name = self.map_tensor_name(merged_name)
-
-                    yield new_name, data_torch
-            return
-
-        new_name = self.map_tensor_name(name)
-
-        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
-            data_torch = data_torch.squeeze()
-
-        if name.endswith(".A_log"):
-            logger.debug("A_log --> A ==> " + new_name)
-            data_torch = -torch.exp(data_torch)
-
-        yield (new_name, data_torch)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("CohereForCausalLM")
-class CommandR2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.COMMAND_R
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # max_position_embeddings = 8192 in config.json but model was actually
-        # trained on 128k context length
-        # aya-23 models don't have model_max_length specified
-        self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"])
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-
-
-@ModelBase.register("Cohere2ForCausalLM")
-class Cohere2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.COHERE2
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
-        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-
-        rotary_pct = self.hparams["rotary_pct"]
-        hidden_size = self.hparams["hidden_size"]
-        num_attention_heads = self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Cohere2 runtime in llama.cpp expects no bias tensors;
-        # the actual weight only contains 0-value tensors as bias, we can skip them
-        if name.endswith(".bias"):
-            if torch.any(data_torch != 0):
-                raise ValueError(f"Bias tensor {name!r} is not zero.")
-            logger.debug(f"Skipping bias tensor {name!r} for Cohere2 conversion.")
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("OlmoForCausalLM")
-@ModelBase.register("OLMoForCausalLM")
-class OlmoModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.OLMO
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_layer_norm_eps(1e-5)
-        clip_qkv = self.hparams.get("clip_qkv")
-        if clip_qkv is not None:
-            self.gguf_writer.add_clamp_kqv(clip_qkv)
-
-    # Same as super class, but permuting q_proj, k_proj
-    # Copied from: LlamaModel
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-
-        if name.endswith("q_proj.weight"):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-        if name.endswith("k_proj.weight"):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("SeedOssForCausalLM")
-class SeedOssModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.SEED_OSS
-
-
-@ModelBase.register("Olmo2ForCausalLM")
-@ModelBase.register("Olmo3ForCausalLM")
-class Olmo2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.OLMO2
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        if "sliding_window" in self.hparams:
-            self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-
-            sliding_window_pattern = []
-            if "layer_types" in self.hparams:
-                sliding_window_pattern = [t == "sliding_attention" for t in self.hparams["layer_types"]]
-            else:
-                # Olmo2 does not use sliding window attention.
-                # Olmo3 defaults to using sliding window for all layers except every 4th.
-                for i in range(self.hparams["num_hidden_layers"]):
-                    sliding_window_pattern.append((i + 1) % 4 != 0)
-
-            self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
-
-
-@ModelBase.register("OlmoeForCausalLM")
-class OlmoeModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.OLMOE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_layer_norm_rms_eps(1e-5)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    # Copied from: Qwen2MoeModel
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # process the experts separately
-        if name.find("experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    # Copied from: Qwen2MoeModel
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("JinaBertModel", "JinaBertForMaskedLM")
-class JinaBertV2Model(BertModel):
-    model_arch = gguf.MODEL_ARCH.JINA_BERT_V2
-
-    def set_vocab(self):
-        tokenizer_class = 'BertTokenizer'
-        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
-            tokenizer_class = json.load(f)['tokenizer_class']
-
-        if tokenizer_class == 'BertTokenizer':
-            super().set_vocab()
-        elif tokenizer_class == 'RobertaTokenizer':
-            self._set_vocab_gpt2()
-            self.gguf_writer.add_token_type_count(2)
-        else:
-            raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel')
-
-
-@ModelBase.register("OpenELMForCausalLM")
-class OpenELMModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.OPENELM
-
-    @staticmethod
-    def _make_divisible(v: float | int, divisor: int) -> int:
-        # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38
-        new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
-        # Make sure that round down does not go down by more than 10%.
-        if new_v < 0.9 * v:
-            new_v += divisor
-        return new_v
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        ffn_multipliers: list[float] = self.hparams["ffn_multipliers"]
-        ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"]
-        self._n_embd: int = self.hparams["model_dim"]
-        self._num_kv_heads: list[int] = self.hparams["num_kv_heads"]
-        self._num_query_heads: list[int] = self.hparams["num_query_heads"]
-        self._ffn_dims: list[int] = [
-            OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor)
-            for multiplier in ffn_multipliers
-        ]
-        assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
-        assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int)
-
-    # Uses the tokenizer from meta-llama/Llama-2-7b-hf
-    def set_vocab(self):
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"])
-
-    def set_gguf_parameters(self):
-        n_embd = self._n_embd
-        head_dim = self.hparams["head_dim"]
-        rot_pct = 1.0
-        assert self.block_count == len(self._num_kv_heads)
-        assert self.block_count == len(self._num_query_heads)
-        assert self.block_count == len(self._ffn_dims)
-
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_context_length(self.hparams["max_context_length"])
-        self.gguf_writer.add_embedding_length(n_embd)
-        self.gguf_writer.add_feed_forward_length(self._ffn_dims)
-        self.gguf_writer.add_head_count(self._num_query_heads)
-        self.gguf_writer.add_head_count_kv(self._num_kv_heads)
-        self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"])
-        # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30
-        self.gguf_writer.add_layer_norm_rms_eps(1e-6)
-        self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim))
-        self.gguf_writer.add_key_length(head_dim)
-        self.gguf_writer.add_value_length(head_dim)
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
-        if "n_layers" in keys:
-            return self.hparams["num_transformer_layers"]
-
-        return super().find_hparam(keys, optional)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-
-        # split ff
-        if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight":
-            ff_dim = self._ffn_dims[bid]
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])
-            return
-
-        yield (self.map_tensor_name(name), data_torch)
-
-
-@ModelBase.register("ArcticForCausalLM")
-class ArcticModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.ARCTIC
-
-    def set_vocab(self):
-        # The reason for using a custom implementation here is that the
-        # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from
-        # tokenizer.model and used them as BOS and EOS instead of adding new tokens.
-        from sentencepiece import SentencePieceProcessor
-
-        tokenizer_path = self.dir_model / 'tokenizer.model'
-
-        if not tokenizer_path.is_file():
-            logger.error(f'Error: Missing {tokenizer_path}')
-            sys.exit(1)
-
-        # Read the whole vocabulary from the tokenizer.model file
-        tokenizer = SentencePieceProcessor()
-        tokenizer.LoadFromFile(str(tokenizer_path))
-
-        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
-
-        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
-        scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
-
-        for token_id in range(tokenizer.vocab_size()):
-
-            piece = tokenizer.IdToPiece(token_id)
-            text = piece.encode("utf-8")
-            score = tokenizer.GetScore(token_id)
-
-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.IsUnknown(token_id):
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.IsControl(token_id):
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.IsUnused(token_id):
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.IsByte(token_id):
-                toktype = SentencePieceTokenTypes.BYTE
-
-            tokens[token_id] = text
-            scores[token_id] = score
-            toktypes[token_id] = toktype
-
-        # Use the added_tokens_decoder field from tokeniser_config.json as the source
-        # of information about added/redefined tokens and modify them accordingly.
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-
-                if "added_tokens_decoder" in tokenizer_config_json:
-                    added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"]
-                    for token_id, token_json in added_tokens_decoder.items():
-                        token_id = int(token_id)
-                        if token_id >= vocab_size:
-                            logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
-                            continue
-
-                        token_content = token_json["content"]
-                        token_type = SentencePieceTokenTypes.USER_DEFINED
-                        token_score = -10000.0
-
-                        # Map unk_token to UNKNOWN, other special tokens to CONTROL
-                        # Set the score to 0.0 as in the original tokenizer.model
-                        if ("special" in token_json) and token_json["special"]:
-                            if token_content == tokenizer_config_json["unk_token"]:
-                                token_type = SentencePieceTokenTypes.UNKNOWN
-                            else:
-                                token_type = SentencePieceTokenTypes.CONTROL
-                            token_score = 0.0
-
-                        logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})")
-                        tokens[token_id] = token_content.encode("utf-8")
-                        toktypes[token_id] = token_type
-                        scores[token_id] = token_score
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-
-        if name.endswith("q_proj.weight"):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-        if name.endswith("k_proj.weight"):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
-
-        # process the experts separately
-        if name.find("block_sparse_moe.experts") != -1:
-            n_experts = self.hparams["num_local_experts"]
-
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for wid in ["w1", "w2", "w3"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("DeepseekForCausalLM")
-class DeepseekModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.DEEPSEEK
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
-
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
-        self.gguf_writer.add_expert_weights_scale(1.0)
-        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
-        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    @staticmethod
-    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-
-        if name.endswith(("q_proj.weight", "q_proj.bias")):
-            data_torch = DeepseekModel.permute(data_torch, n_head, n_head)
-        if name.endswith(("k_proj.weight", "k_proj.bias")):
-            data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head)
-
-        # process the experts separately
-        if name.find("mlp.experts") != -1:
-            n_experts = self.hparams["n_routed_experts"]
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register(
-    "DeepseekV2ForCausalLM",
-    "DeepseekV3ForCausalLM",
-    "KimiVLForConditionalGeneration",
-    "KimiK25ForConditionalGeneration",
-    "YoutuForCausalLM",
-    "YoutuVLForConditionalGeneration",
-)
-class DeepseekV2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
-
-    # TODO @ngxson : remove this when we support MTP for deepseek models
-    skip_mtp = True
-
-    merge_expert = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        hparams: dict = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
-        self.origin_hf_arch = hparams.get('architectures', [None])[0]
-
-        # special handling for Deepseek OCR
-        if self.origin_hf_arch == "DeepseekOCRForCausalLM":
-            self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
-            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
-            self.gguf_writer.add_architecture()
-            # default jinja template
-            self.gguf_writer.add_chat_template("{% for m in messages %}{{m['content']}}{% endfor %}")
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_gpt2()
-            return
-        except Exception:
-            pass
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        if tokpre == "kimi-k2":
-            # Build merges list using the approach similar to HunYuanMoE
-            merges = []
-            vocab = {}
-            mergeable_ranks = tokenizer.model._mergeable_ranks  # ty: ignore[unresolved-attribute]
-            for token, rank in mergeable_ranks.items():
-                vocab[QwenModel.token_bytes_to_string(token)] = rank
-                if len(token) == 1:
-                    continue
-                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-                if len(merged) == 2:
-                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
-
-            # Build token list
-            vocab_size = self.hparams["vocab_size"]
-            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
-            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
-            tokens: list[str] = []
-            toktypes: list[int] = []
-
-            for i in range(vocab_size):
-                if i not in reverse_vocab:
-                    tokens.append(f"[PAD{i}]")
-                    toktypes.append(gguf.TokenType.UNUSED)
-                else:
-                    token = reverse_vocab[i]
-                    tokens.append(token)
-                    if i in special_tokens.values():
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.NORMAL)
-
-            self.gguf_writer.add_tokenizer_model("gpt2")
-            self.gguf_writer.add_tokenizer_pre(tokpre)
-            self.gguf_writer.add_token_list(tokens)
-            self.gguf_writer.add_token_types(toktypes)
-            self.gguf_writer.add_token_merges(merges)
-
-            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
-            special_vocab.add_to_gguf(self.gguf_writer)
-        else:
-            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
-
-    def set_gguf_parameters(self):
-        is_ocr = (self.model_arch == gguf.MODEL_ARCH.DEEPSEEK2OCR)
-
-        if is_ocr:
-            self.hparams['rope_theta'] = self.hparams.get('rope_theta', 10000.0)
-        else:
-            # note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
-            self.hparams["num_key_value_heads"] = 1
-
-        self.hparams['rms_norm_eps'] = self.hparams.get('rms_norm_eps', 1e-6)
-
-        super().set_gguf_parameters()
-        hparams = self.hparams
-
-        # first_k_dense_replace: number of leading layers using dense FFN instead of MoE
-        # For non-MoE models (like Youtu), set to n_layer to use dense FFN for all layers
-        # For MoE models (like DeepSeek-V2), this is the number of leading non-MoE layers
-        has_moe = hparams.get("n_routed_experts") is not None
-        first_k_dense_replace = hparams.get("first_k_dense_replace")
-        if first_k_dense_replace is None:
-            # Default: if no MoE, all layers are dense; if MoE, none are dense
-            first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
-        self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
-        kv_lora_rank = hparams.get("kv_lora_rank", 512)
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
-            self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
-
-        # note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
-        if not is_ocr:
-            self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
-            self.gguf_writer.add_key_length(kv_lora_rank + hparams["qk_rope_head_dim"])
-            self.gguf_writer.add_value_length(kv_lora_rank)
-            self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
-            self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
-
-        # MoE parameters (required by C++ code for DEEPSEEK2 arch)
-        # For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
-        moe_intermediate_size = self.find_hparam(["moe_intermediate_size", "intermediate_size"], optional=False)
-        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-
-        if (n_routed_experts := hparams.get("n_routed_experts")) is not None:
-            self.gguf_writer.add_expert_count(n_routed_experts)
-
-        # expert_shared_count is required by C++ code, default to 0 for non-MoE models
-        n_shared_experts = hparams.get("n_shared_experts", 0)
-        self.gguf_writer.add_expert_shared_count(n_shared_experts)
-
-        # When not set, C++ code will use scale_w = false to skip the no-op scaling
-        if (routed_scaling_factor := hparams.get("routed_scaling_factor")) is not None:
-            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
-
-        if (norm_topk_prob := hparams.get("norm_topk_prob")) is not None and norm_topk_prob:
-            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
-
-        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
-
-        if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None:
-            # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
-            # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
-            # ref https://github.com/ggml-org/llama.cpp/pull/17945
-            self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5, and DeepSeek-OCR
-        if ("vision_tower" in name
-                or "multi_modal_projector" in name
-                or "mm_projector" in name
-                or "vision_model" in name
-                or "image_newline" in name
-                or "model.projector" in name
-                or "sam_model" in name
-                or "view_seperator" in name):
-            return
-        if name.startswith("siglip2.") or name.startswith("merger."):
-            return
-        if name.startswith("language_model."):
-            name = name.replace("language_model.", "")
-
-        # skip lm_head.weight if tie_word_embeddings is True
-        if self.hparams.get("tie_word_embeddings", False):
-            if name == "lm_head.weight" or name == "model.lm_head.weight":
-                logger.info("Skipping tied output layer 'lm_head.weight' (will use token_embd.weight)")
-                return
-
-        # rename e_score_correction_bias tensors
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-
-        # skip Multi-Token Prediction (MTP) layers
-        if self.skip_mtp:
-            block_count = self.hparams["num_hidden_layers"]
-            match = re.match(r"model.layers.(\d+)", name)
-            if match and int(match.group(1)) >= block_count:
-                return
-
-        # process the experts separately
-        if self.merge_expert and name.find("mlp.experts") != -1:
-            n_experts = self.hparams["n_routed_experts"]
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
-        if name.endswith("kv_b_proj.weight"):
-            name_kb = name.replace("kv_b_proj", "k_b_proj")
-            name_vb = name.replace("kv_b_proj", "v_b_proj")
-
-            n_head_kv = self.hparams["num_key_value_heads"]
-            v_head_dim = self.hparams["v_head_dim"]
-            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
-
-            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
-
-            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
-            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
-            k_b = k_b.transpose(1, 2)
-
-            yield from super().modify_tensors(k_b, name_kb, bid)
-            yield from super().modify_tensors(v_b, name_vb, bid)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register(
-    "Mistral3ForConditionalGeneration",
-    "Ministral3ForCausalLM",
-)
-class Mistral3Model(TextModel):
-    class Ministral3Model(LlamaModel):
-        model_arch = gguf.MODEL_ARCH.MISTRAL3
-
-        def set_gguf_parameters(self):
-            super().set_gguf_parameters()
-            rope_params = self.rope_parameters
-            if self.hparams.get("model_type") == "ministral3":
-                assert rope_params, "ministral3 must have 'rope_parameters' config"
-                assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
-                self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
-                self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
-
-        def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-            name = name.replace("language_model.", "")
-            if "multi_modal_projector" in name or "vision_tower" in name:
-                return
-
-            yield from super().modify_tensors(data_torch, name, bid)
-
-    class Mistral4Model(DeepseekV2Model):
-        model_arch = gguf.MODEL_ARCH.MISTRAL4
-        skip_mtp = False # model contains no MTP layers, so no need to skip
-        merge_expert = False # experts are already stacked as 3D
-
-        def modify_tensors(self, data_torch, name, bid):
-            if name.endswith(".down_proj") or name.endswith(".gate_up_proj"):
-                name = name + ".weight"
-            yield from super().modify_tensors(data_torch, name, bid)
-
-    model_arch = gguf.MODEL_ARCH.MISTRAL3 # unused
-    impl: TextModel
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.hparams.get("model_type") == "mistral4":
-            self.impl = Mistral3Model.Mistral4Model(*args, **kwargs)
-        else:
-            self.impl = Mistral3Model.Ministral3Model(*args, **kwargs)
-
-    def set_vocab(self):
-        self.impl.set_vocab()
-
-    def set_gguf_parameters(self):
-        self.impl.set_gguf_parameters()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        yield from self.impl.modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        self.impl.prepare_tensors()
-
-    def write_vocab(self):
-        self.impl.write_vocab()
-
-    def write(self):
-        self.impl.write()
-
-
-@ModelBase.register("MiniMaxM2ForCausalLM")
-class MiniMaxM2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.MINIMAXM2
-    _experts_cache: dict[int, dict[str, Tensor]] = {}
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
-        self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-
-        # merge expert weights
-        if 'experts' in name:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            expert_cache = self._experts_cache.setdefault(bid, {})
-            expert_cache[name] = data_torch
-            expert_weights = ["w1", "w2", "w3"]
-
-            # not enough expert weights to merge
-            if len(expert_cache) < n_experts * len(expert_weights):
-                return
-
-            for w_name in expert_weights:
-                datas: list[Tensor] = []
-
-                for xid in range(n_experts):
-                    ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
-                    datas.append(expert_cache[ename])
-                    del expert_cache[ename]
-
-                data_torch = torch.stack(datas, dim=0)
-                merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
-                new_name = self.map_tensor_name(merged_name)
-                yield from super().modify_tensors(data_torch, new_name, bid)
-
-            del self._experts_cache[bid]
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("MiMoV2FlashForCausalLM")
-class MimoV2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.MIMO2
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        assert self.hparams["swa_head_dim"] == self.hparams["head_dim"]
-        assert self.hparams["swa_num_attention_heads"] == self.hparams["num_attention_heads"]
-        assert self.hparams["swa_v_head_dim"] == self.hparams["v_head_dim"]
-        assert self.hparams["topk_method"] == "noaux_tc"
-
-        n_head_kv = self.hparams["num_key_value_heads"]
-        n_head_kv_swa = self.hparams["swa_num_key_value_heads"]
-        n_head_kv_arr = [n_head_kv_swa if use_swa == 1 else n_head_kv for use_swa in self.hparams["hybrid_layer_pattern"]]
-        self.gguf_writer.add_head_count_kv(n_head_kv_arr)
-
-        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-        self.gguf_writer.add_sliding_window_pattern(self.hparams["hybrid_layer_pattern"])
-        self.gguf_writer.add_value_length(self.hparams["v_head_dim"])
-        self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
-        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
-
-        rope_dim = int(self.hparams["head_dim"] * self.hparams["partial_rotary_factor"])
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon", 1e-5))
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch, name, bid):
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-
-        if "attention_sink" in name and not name.endswith(".weight"):
-            name += ".weight"
-
-        # TODO: mimo v2 does not indicate the number of next-token-prediction layers, therefore we cannot do the same way as GLM4_MOE
-        if "model.mtp." in name:
-            return
-
-        # process the experts separately
-        if name.find("mlp.experts") != -1:
-            n_experts = self.hparams["n_routed_experts"]
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["gate_proj", "up_proj", "down_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename_to_retrieve])
-                        del self._experts[bid][ename_to_retrieve]
-
-                    data_torch = torch.stack(datas, dim=0)
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("Step3p5ForCausalLM")
-class Step35Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.STEP35
-
-    def set_gguf_parameters(self):
-        rope_theta = self.hparams.get("rope_theta")
-        if isinstance(rope_theta, list):
-            self.hparams["rope_theta"] = float(rope_theta[0])
-            self.hparams["local_rope_theta"] = float(rope_theta[1])
-            self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
-            self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}
-
-        super().set_gguf_parameters()
-
-        layer_types = self.hparams.get("layer_types") or []
-        partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
-        attn_other = self.hparams.get("attention_other_setting") or {}
-
-        n_head_base = self.hparams["num_attention_heads"]
-        n_kv_base = self.hparams["num_attention_groups"]
-
-        n_head_swa = attn_other.get("num_attention_heads", n_head_base)
-        n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
-
-        layer_types = layer_types[: self.block_count]
-        partial_rotary_factors = partial_rotary_factors[: self.block_count]
-        assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
-        head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
-        kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
-        swa_pat = [lt == "sliding_attention" for lt in layer_types]
-
-        self.gguf_writer.add_head_count(head_arr)
-        self.gguf_writer.add_head_count_kv(kv_arr)
-
-        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-        self.gguf_writer.add_sliding_window_pattern(swa_pat)
-
-        self.gguf_writer.add_value_length(self.hparams["head_dim"])
-
-        # MoE params
-        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
-        self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
-        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
-        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])
-
-        if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
-            self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
-        if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
-            self.gguf_writer.add_expert_weights_norm(norm_expert_weight)
-
-        # leading dense blocks
-        leading_dense = 0
-        moe_layers_enum = self.hparams.get("moe_layers_enum")
-        if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
-            moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
-            if moe_layers:
-                leading_dense = max(0, moe_layers[0])
-        self.gguf_writer.add_leading_dense_block_count(leading_dense)
-        self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))
-
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
-
-        # Optional per-layer SwiGLU clamps.
-        if (limits := self.hparams.get("swiglu_limits")) is not None:
-            limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
-            self.gguf_writer.add_swiglu_clamp_exp(limits_f)
-        if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
-            limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
-            self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        # remove mtp layers
-        if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
-            il = int(m.group(1))
-            n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
-            if il >= n_main:
-                return
-        if name.endswith("norm.weight"):
-            data_torch += 1.0
-        # Map router bias (expert selection bias) to a GGUF bias tensor
-        if name.endswith(".moe.router_bias"):
-            name += ".bias"
-
-        if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
-            data_torch = data_torch.squeeze().contiguous()
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        # Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
-        # llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
-        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
-        rope_type = rope_params.get("rope_type") or ""
-        if rope_type.lower() != "llama3":
-            return
-
-        # Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
-        rope_theta = self.hparams.get("rope_theta", 10000.0)
-        if isinstance(rope_theta, list):
-            rope_theta = rope_theta[0]
-        base = float(rope_theta)
-        if (dim := self.hparams.get("head_dim")) is None:
-            dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        dim = int(dim)
-
-        freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
-
-        factor = float(rope_params.get("factor", 8.0))
-        low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
-        high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
-        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
-
-        low_freq_wavelen = old_context_len / low_freq_factor
-        high_freq_wavelen = old_context_len / high_freq_factor
-
-        rope_factors: list[float] = []
-        for freq in freqs:
-            wavelen = 2 * math.pi / float(freq)
-            if wavelen < high_freq_wavelen:
-                rope_factors.append(1.0)
-            elif wavelen > low_freq_wavelen:
-                rope_factors.append(factor)
-            else:
-                smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
-                rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))
-
-        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
-
-
-@ModelBase.register("PanguEmbeddedForCausalLM")
-class PanguEmbeddedModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.PANGU_EMBED
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
-        if tokenizer_config_file.is_file():
-            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-                tokenizer_config_json = json.load(f)
-                if "add_prefix_space" in tokenizer_config_json:
-                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-
-        # PanguEmbedded's hparam loaded from config.json without head_dim
-        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-
-        if hparams.get("head_dim") is None:
-            self.gguf_writer.add_key_length(rope_dim)
-            self.gguf_writer.add_value_length(rope_dim)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "lm_head.weight":
-            if self.hparams.get("tie_word_embeddings", False):
-                logger.info("Skipping tied output layer 'lm_head.weight'")
-                return
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Dots1ForCausalLM")
-class Dots1Model(Qwen2MoeModel):
-    model_arch = gguf.MODEL_ARCH.DOTS1
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.hparams["num_experts"] = self.hparams["n_routed_experts"]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
-        self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
-        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
-        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-        if "shared_experts" in name:
-            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
-        else:
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("PLMForCausalLM")
-class PLMModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.PLM
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
-        self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
-        self.gguf_writer.add_value_length(hparams["v_head_dim"])
-        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-
-@ModelBase.register("T5WithLMHeadModel")
-@ModelBase.register("T5ForConditionalGeneration")
-@ModelBase.register("MT5ForConditionalGeneration")
-@ModelBase.register("UMT5ForConditionalGeneration")
-@ModelBase.register("UMT5Model")
-class T5Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.T5
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.shared_token_embeddings_found = False
-
-    def set_vocab(self):
-        # to avoid TypeError: Descriptors cannot be created directly
-        # exception when importing sentencepiece_model_pb2
-        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
-        from sentencepiece import SentencePieceProcessor
-        from sentencepiece import sentencepiece_model_pb2 as model
-
-        tokenizer_path = self.dir_model / 'tokenizer.model'
-
-        # many older models use spiece.model tokenizer model filename
-        if not tokenizer_path.is_file():
-            tokenizer_path = self.dir_model / 'spiece.model'
-
-        if not tokenizer_path.is_file():
-            raise FileNotFoundError(f"File not found: {tokenizer_path}")
-
-        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
-        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
-
-        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
-        if sentencepiece_model.trainer_spec.model_type == 2:  # BPE
-            # assure the tokenizer model file name is correct
-            assert tokenizer_path.name == 'tokenizer.model'
-            return self._set_vocab_sentencepiece()
-        else:
-            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
-
-        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
-        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
-        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
-
-        tokenizer = SentencePieceProcessor()
-        tokenizer.LoadFromFile(str(tokenizer_path))
-
-        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
-
-        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
-        scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
-
-        for token_id in range(tokenizer.vocab_size()):
-            piece = tokenizer.IdToPiece(token_id)
-            text = piece.encode("utf-8")
-            score = tokenizer.GetScore(token_id)
-
-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.IsUnknown(token_id):
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.IsControl(token_id):
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.IsUnused(token_id):
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.IsByte(token_id):
-                toktype = SentencePieceTokenTypes.BYTE
-
-            tokens[token_id] = text
-            scores[token_id] = score
-            toktypes[token_id] = toktype
-
-        added_tokens_file = self.dir_model / 'added_tokens.json'
-        if added_tokens_file.is_file():
-            with open(added_tokens_file, "r", encoding="utf-8") as f:
-                added_tokens_json = json.load(f)
-                for key in added_tokens_json:
-                    token_id = added_tokens_json[key]
-                    if token_id >= vocab_size:
-                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
-                        continue
-
-                    tokens[token_id] = key.encode("utf-8")
-                    scores[token_id] = -1000.0
-                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-
-        if vocab_size > len(tokens):
-            pad_count = vocab_size - len(tokens)
-            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
-            for i in range(1, pad_count + 1):
-                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
-                scores.append(-1000.0)
-                toktypes.append(SentencePieceTokenTypes.UNUSED)
-
-        self.gguf_writer.add_tokenizer_model("t5")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-        self.gguf_writer.add_add_space_prefix(add_prefix)
-        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
-        if precompiled_charsmap:
-            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
-            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
-            n_ctx = 512
-        self.gguf_writer.add_context_length(n_ctx)
-        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
-        self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
-        self.gguf_writer.add_block_count(self.block_count)
-        if (dec_n_layer := self.hparams.get("num_decoder_layers")) is not None:
-            self.gguf_writer.add_decoder_block_count(dec_n_layer)
-        self.gguf_writer.add_head_count(self.hparams["num_heads"])
-        self.gguf_writer.add_key_length(self.hparams["d_kv"])
-        self.gguf_writer.add_value_length(self.hparams["d_kv"])
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
-        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
-        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
-        # and decoder and ignore the remaining ones.
-        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
-            if not self.shared_token_embeddings_found:
-                name = "shared.weight"
-                self.shared_token_embeddings_found = True
-            else:
-                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("T5EncoderModel")
-class T5EncoderModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.T5ENCODER
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.shared_token_embeddings_found = False
-
-    def set_vocab(self):
-        # to avoid TypeError: Descriptors cannot be created directly
-        # exception when importing sentencepiece_model_pb2
-        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
-        from sentencepiece import SentencePieceProcessor
-        from sentencepiece import sentencepiece_model_pb2 as model
-
-        tokenizer_path = self.dir_model / 'tokenizer.model'
-
-        # many older models use spiece.model tokenizer model filename
-        if not tokenizer_path.is_file():
-            tokenizer_path = self.dir_model / 'spiece.model'
-
-        if not tokenizer_path.is_file():
-            raise FileNotFoundError(f"File not found: {tokenizer_path}")
-
-        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
-        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
-
-        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
-        if sentencepiece_model.trainer_spec.model_type == 2:  # BPE
-            # assure the tokenizer model file name is correct
-            assert tokenizer_path.name == 'tokenizer.model'
-            return self._set_vocab_sentencepiece()
-        else:
-            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
-
-        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
-        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
-        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
-
-        tokenizer = SentencePieceProcessor()
-        tokenizer.LoadFromFile(str(tokenizer_path))
-
-        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
-
-        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
-        scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
-
-        for token_id in range(tokenizer.vocab_size()):
-            piece = tokenizer.IdToPiece(token_id)
-            text = piece.encode("utf-8")
-            score = tokenizer.GetScore(token_id)
-
-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.IsUnknown(token_id):
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.IsControl(token_id):
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.IsUnused(token_id):
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.IsByte(token_id):
-                toktype = SentencePieceTokenTypes.BYTE
-
-            tokens[token_id] = text
-            scores[token_id] = score
-            toktypes[token_id] = toktype
-
-        added_tokens_file = self.dir_model / 'added_tokens.json'
-        if added_tokens_file.is_file():
-            with open(added_tokens_file, "r", encoding="utf-8") as f:
-                added_tokens_json = json.load(f)
-                for key in added_tokens_json:
-                    token_id = added_tokens_json[key]
-                    if token_id >= vocab_size:
-                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
-                        continue
-
-                    tokens[token_id] = key.encode("utf-8")
-                    scores[token_id] = -1000.0
-                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
-
-        if vocab_size > len(tokens):
-            pad_count = vocab_size - len(tokens)
-            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
-            for i in range(1, pad_count + 1):
-                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
-                scores.append(-1000.0)
-                toktypes.append(SentencePieceTokenTypes.UNUSED)
-
-        self.gguf_writer.add_tokenizer_model("t5")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-        self.gguf_writer.add_add_space_prefix(add_prefix)
-        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
-        if precompiled_charsmap:
-            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
-            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
-            n_ctx = 512
-        self.gguf_writer.add_context_length(n_ctx)
-        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
-        self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(self.hparams["num_heads"])
-        self.gguf_writer.add_key_length(self.hparams["d_kv"])
-        self.gguf_writer.add_value_length(self.hparams["d_kv"])
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
-        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
-        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
-        # and decoder and ignore the remaining ones.
-        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
-            if not self.shared_token_embeddings_found:
-                name = "shared.weight"
-                self.shared_token_embeddings_found = True
-            else:
-                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Jais2ForCausalLM")
-class Jais2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.JAIS2
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        head_dim = hparams.get("head_dim", hparams["hidden_size"] // hparams["num_attention_heads"])
-        self.gguf_writer.add_rope_dimension_count(head_dim)
-
-
-@ModelBase.register("JAISLMHeadModel")
-class JaisModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.JAIS
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # SwigLU activation
-        assert self.hparams["activation_function"] == "swiglu"
-        # ALiBi position embedding
-        assert self.hparams["position_embedding_type"] == "alibi"
-
-        # Embeddings scale
-        self.embeddings_scale = 1.0
-        if 'mup_embeddings_scale' in self.hparams:
-            self.embeddings_scale = self.hparams['mup_embeddings_scale']
-        elif 'embeddings_scale' in self.hparams:
-            self.embeddings_scale = self.hparams['embeddings_scale']
-        else:
-            assert False
-
-        self.width_scale = 1.0
-        if 'mup_output_alpha' in self.hparams:
-            assert 'mup_width_scale' in self.hparams
-            self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
-        elif 'width_scale' in self.hparams:
-            self.width_scale = self.hparams['width_scale']
-        else:
-            assert False
-
-        self.max_alibi_bias = 8.0
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_context_length(self.hparams["n_positions"])
-        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
-        self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
-        self.gguf_writer.add_head_count(self.hparams["n_head"])
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # we don't need these
-        if name.endswith((".attn.bias")):
-            return
-
-        if name.endswith(("relative_pe.slopes")):
-            # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)
-            # Some other models has max_alibi_bias spelled out explicitly in the hyperparams,
-            # but Jais's PyTorch model simply precalculates the slope values and places them
-            # in relative_pes.slopes
-            n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
-            first_val = float(data_torch[0].item())
-            self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
-
-            return
-
-        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
-            data_torch = data_torch.transpose(1, 0)
-
-        new_name = self.map_tensor_name(name)
-
-        if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
-            yield from super().modify_tensors(data_torch * self.embeddings_scale, new_name, bid)
-        elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
-            yield from super().modify_tensors(data_torch * self.width_scale, new_name, bid)
-        else:
-            yield from super().modify_tensors(data_torch, new_name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
-
-
-@ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration")
-class Glm4Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.GLM4
-    use_mrope = False
-    partial_rotary_factor = 0.5
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 0.5)
-        if "mrope_section" in self.rope_parameters:
-            self.use_mrope = True
-            logger.info("Q/K weight will need to be permuted for M-RoPE")
-
-    def set_vocab(self):
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (rope_dim := self.hparams.get("head_dim")) is None:
-            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.partial_rotary_factor))
-
-    @staticmethod
-    def normal_to_neox(weights: Tensor, n_head: int, n_head_kv: int, head_dim: int, partial_rotary_factor: float) -> Tensor:
-        orig_shape = weights.shape
-        if len(orig_shape) == 1:
-            weights = weights.unsqueeze(1)  # [out_dim, 1]
-        if len(weights.shape) != 2:
-            raise ValueError("Only 1D and 2D tensors are supported.")
-        n_effective_heads = weights.shape[0] // head_dim
-        if n_head_kv is not None and n_effective_heads != n_head:
-            if n_effective_heads != n_head_kv:
-                raise AssertionError(f"Mismatch in effective heads: computed {n_effective_heads}, expected {n_head} or {n_head_kv}")
-        rotary_dim = int(head_dim * partial_rotary_factor)
-        if rotary_dim % 2 != 0:
-            raise ValueError("rotary_dim must be even.")
-        reshaped = weights.reshape(n_effective_heads, head_dim, -1)
-        rot_part = reshaped[:, :rotary_dim, :]
-        non_rot_part = reshaped[:, rotary_dim:, :]
-        permuted_rot = torch.cat((rot_part[:, ::2, :], rot_part[:, 1::2, :]), dim=1)
-        combined = torch.cat((permuted_rot, non_rot_part), dim=1)
-        result = combined.reshape(weights.shape)
-        return result if len(orig_shape) != 1 else result.squeeze(1)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("model.visual."): # ignore visual part of Glm4v
-            return
-        elif name.startswith("model.language_model."):
-            name = name.replace("language_model.", "") # for Glm4v
-        if self.use_mrope:
-            n_head = self.hparams["num_attention_heads"]
-            n_kv_head = self.hparams["num_key_value_heads"]
-            n_embd = self.hparams["hidden_size"]
-            head_dim = self.hparams.get("head_dim", n_embd // n_head)
-            # because llama.cpp M-RoPE kernel only supports Neox ordering, we have to permute the weights here
-            if name.endswith(("q_proj.weight", "q_proj.bias")):
-                data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_head, head_dim, self.partial_rotary_factor)
-            if name.endswith(("k_proj.weight", "k_proj.bias")):
-                data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_kv_head, head_dim, self.partial_rotary_factor)
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("GlmOcrForConditionalGeneration")
-class GlmOCRModel(Glm4Model):
-    model_arch = gguf.MODEL_ARCH.GLM4
-    use_mrope = False
-    partial_rotary_factor = 0.5
-
-    # Note: GLM-OCR is the same as GLM4, but with an extra NextN/MTP prediction layer
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # GLM-OCR has num_hidden_layers + 1 actual layers (including NextN layer)
-        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        # NextN/MTP prediction layers
-        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
-            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
-
-
-@ModelBase.register("Glm4MoeForCausalLM", "Glm4vMoeForConditionalGeneration")
-class Glm4MoeModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GLM4_MOE
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
-        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def set_vocab(self):
-        return self._set_vocab_glm()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (rope_dim := self.hparams.get("head_dim")) is None:
-            rope_dim = (
-                self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-            )
-        self.gguf_writer.add_rope_dimension_count(
-            int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
-        )
-
-        # MoE parameters - Use only routed expert count (shared experts handled separately)
-        if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None:
-            self.gguf_writer.add_expert_count(n_routed_experts)
-        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-        if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
-            self.gguf_writer.add_expert_shared_count(n_shared_experts)
-        if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
-            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
-
-        # Expert gating function (sigmoid for GLM4_MOE)
-        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
-
-        # Routed scaling factor
-        if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
-            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
-
-        # Normalise topk probabilities
-        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
-            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
-
-        # NextN/MTP prediction layers
-        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
-            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    # note: unlike GLM4V non-MoE, we don't need to permute Q/K here since GLM4V_MOE uses Neox ordering already
-    def modify_tensors(
-        self, data_torch: Tensor, name: str, bid: int | None
-    ) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("model.visual."):  # ignore visual part
-            return
-        elif name.startswith("model.language_model."):
-            name = name.replace("language_model.", "")  # for multimodal variants
-
-        # Handle main token embedding (but not layer-specific NextN embeddings)
-        if name == "model.embed_tokens.weight" and ".layers." not in name:
-            yield from super().modify_tensors(data_torch, "token_embd.weight", bid)
-            return
-
-        # Handle routed experts
-        if name.find("mlp.experts") != -1:
-            n_experts = self.hparams["n_routed_experts"]
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("Glm4MoeLiteForCausalLM")
-class Glm4MoeLiteModel(DeepseekV2Model):
-    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
-
-    def set_vocab(self):
-        return self._set_vocab_glm()
-
-
-@ModelBase.register("GlmMoeDsaForCausalLM")
-class GlmMoeDsaModel(DeepseekV2Model):
-    model_arch = gguf.MODEL_ARCH.GLM_DSA
-    skip_mtp = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def set_vocab(self):
-        return self._set_vocab_glm()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        rope_dim = self.hparams["qk_rope_head_dim"]
-        partial_rotary_factor = self.hparams.get("partial_rotary_factor", 1.0)
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))
-
-        # NextN/MTP prediction layers
-        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
-            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
-
-        # DSA indexer parameters
-        self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"])
-        self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"])
-        self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])
-
-
-@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
-class ChatGLMModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.CHATGLM
-
-    def set_vocab_chatglm3(self):
-        dir_model = self.dir_model
-        hparams = self.hparams
-        tokens: list[bytes] = []
-        toktypes: list[int] = []
-        scores: list[float] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))  # ty: ignore[unresolved-attribute]
-        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
-        role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
-        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
-        for token_id in range(vocab_size):
-            piece = tokenizer._convert_id_to_token(token_id)  # ty: ignore[unresolved-attribute]
-            if token_id == 0:
-                piece = "<unk>"
-            elif token_id == 1:
-                piece = "<bos>"
-            elif token_id == 2:
-                piece = "<eos>"
-
-            text = piece.encode("utf-8")  # ty: ignore[unresolved-attribute]
-            score = 0.0
-            # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py),
-            # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size()
-            if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():  # ty: ignore[unresolved-attribute, invalid-argument-type]
-                score = tokenizer.tokenizer.sp_model.get_score(token_id)  # ty: ignore[unresolved-attribute]
-
-            if token_id >= tokenizer.tokenizer.sp_model.vocab_size():  # ty: ignore[unresolved-attribute]
-                if piece in special_tokens:
-                    toktype = SentencePieceTokenTypes.CONTROL
-                elif len(piece) == 0:  # ty: ignore[invalid-argument-type]
-                    text = f"[PAD{token_id}]".encode("utf-8")
-                    toktype = SentencePieceTokenTypes.UNUSED
-                else:
-                    toktype = SentencePieceTokenTypes.USER_DEFINED
-                tokens.append(text)
-                scores.append(score)
-                toktypes.append(toktype)
-                continue
-
-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.tokenizer.sp_model.is_unknown(token_id):  # ty: ignore[unresolved-attribute]
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.tokenizer.sp_model.is_control(token_id):  # ty: ignore[unresolved-attribute]
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.tokenizer.sp_model.is_unused(token_id):  # ty: ignore[unresolved-attribute]
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.tokenizer.sp_model.is_byte(token_id):  # ty: ignore[unresolved-attribute]
-                toktype = SentencePieceTokenTypes.BYTE
-
-            tokens.append(text)
-            scores.append(score)
-            toktypes.append(toktype)
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        # glm3 needs prefix and suffix formatted as:
-        # prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>"
-        self.gguf_writer.add_tokenizer_pre("chatglm-spm")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    @staticmethod
-    def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
-        byte_encoder = bytes_to_unicode()
-        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
-
-    @staticmethod
-    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
-        parts = [bytes([b]) for b in token]
-        while True:
-            min_idx = None
-            min_rank = None
-            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
-                rank = mergeable_ranks.get(pair[0] + pair[1])
-                if rank is not None and (min_rank is None or rank < min_rank):
-                    min_idx = i
-                    min_rank = rank
-            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
-                break
-            assert min_idx is not None
-            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
-        return parts
-
-    def set_vocab(self):
-        if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""):
-            self.set_vocab_chatglm3()
-            return
-
-        dir_model = self.dir_model
-        hparams = self.hparams
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
-        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
-
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        # only add special tokens when they were not already loaded from config.json
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
-        # this one is usually not in config.json anyway
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-        assert n_embed is not None
-        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        assert n_head is not None
-        n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
-        self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
-        self.gguf_writer.add_embedding_length(n_embed)
-        self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_head_count_kv(n_head_kv)
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))
-        self.gguf_writer.add_file_type(self.ftype)
-        if "attention_dim" in self.hparams:
-            rope_dim = self.hparams["attention_dim"]
-        else:
-            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
-        self.gguf_writer.add_add_bos_token(False)
-        rope_freq = 10000
-        if "rope_ratio" in self.hparams:
-            rope_freq = rope_freq * self.hparams["rope_ratio"]
-        self.gguf_writer.add_rope_freq_base(rope_freq)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."):
-            return
-
-        name = name.removeprefix("transformer.")
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("NemotronForCausalLM")
-class NemotronModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.NEMOTRON
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-        self.gguf_writer.add_pad_token_id(0)
-        self.gguf_writer.add_unk_token_id(1)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-
-        f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"])
-        self.gguf_writer.add_layer_norm_eps(f_norm_eps)
-
-        # * Partial RoPE
-        rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
-        n_embd = self.find_hparam(["hidden_size", "n_embd"])
-        n_head = self.find_hparam(["num_attention_heads", "n_head"])
-        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
-
-        # * RopeScaling for Nemotron
-        if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-        else:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side
-        #   model.layers.{l}.input_layernorm.weight
-        #   model.layers.{l}.post_attention_layernorm.weight
-        #   model.norm.weight
-        if name.endswith("norm.weight"):
-            data_torch = data_torch + 1
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("ExaoneForCausalLM")
-class ExaoneModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.EXAONE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-
-        assert (hparams["activation_function"] == "silu")
-
-        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True)
-        rotary_factor = rotary_factor if rotary_factor is not None else 1.0
-        self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
-            if rope_params.get("rope_type", '').lower() == "llama3":
-                base = self.rope_parameters.get("rope_theta", 10000.0)
-                if (dim := self.hparams.get("head_dim")) is None:
-                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
-
-                factor = rope_params.get("factor", 8.0)
-                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
-                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
-
-                low_freq_wavelen = old_context_len / low_freq_factor
-                high_freq_wavelen = old_context_len / high_freq_factor
-                assert low_freq_wavelen != high_freq_wavelen
-
-                rope_factors = []
-                for freq in freqs:
-                    wavelen = 2 * math.pi / freq
-                    if wavelen < high_freq_wavelen:
-                        rope_factors.append(1)
-                    elif wavelen > low_freq_wavelen:
-                        rope_factors.append(factor)
-                    else:
-                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
-                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
-
-                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
-
-
-@ModelBase.register("Exaone4ForCausalLM")
-class Exaone4Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.EXAONE4
-
-    def set_vocab(self):
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-
-        if hparams.get("sliding_window") is not None:
-            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
-            if "layer_types" in hparams:
-                self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
-            elif "sliding_window_pattern" in hparams:
-                sliding_window_pattern = []
-                if isinstance(hparams["sliding_window_pattern"], str):  # e.g. LLLG
-                    for i in range(hparams["num_hidden_layers"]):
-                        sliding_window_pattern.append(hparams["sliding_window_pattern"][i % len(hparams["sliding_window_pattern"])] == "L")
-                if isinstance(hparams["sliding_window_pattern"], int):  # e.g. 4
-                    for i in range(hparams["num_hidden_layers"]):
-                        sliding_window_pattern.append((i + 1) % hparams["sliding_window_pattern"] != 0)
-                if len(sliding_window_pattern) == hparams["num_hidden_layers"]:
-                    self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
-            if rope_params.get("rope_type", '').lower() == "llama3":
-                base = rope_params.get("rope_theta", 10_000.0)
-                if (dim := self.hparams.get("head_dim")) is None:
-                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
-
-                factor = rope_params.get("factor", 16.0)
-                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
-                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
-
-                low_freq_wavelen = old_context_len / low_freq_factor
-                high_freq_wavelen = old_context_len / high_freq_factor
-
-                rope_factors = []
-                for freq in freqs:
-                    wavelen = 2 * math.pi / freq
-                    if wavelen < high_freq_wavelen:
-                        rope_factors.append(1)
-                    elif wavelen > low_freq_wavelen:
-                        rope_factors.append(factor)
-                    else:
-                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
-                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
-
-                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
-
-
-@ModelBase.register("ExaoneMoEForCausalLM")
-class ExaoneMoEModel(Exaone4Model):
-    model_arch = gguf.MODEL_ARCH.EXAONE_MOE
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        moe_intermediate_size = self.hparams["moe_intermediate_size"]
-        num_shared_experts = self.hparams["num_shared_experts"]
-        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-        self.gguf_writer.add_expert_shared_count(num_shared_experts)
-        self.gguf_writer.add_expert_shared_feed_forward_length(moe_intermediate_size * num_shared_experts)
-        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
-        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
-        n_dense_layer = self.hparams.get("first_k_dense_replace", self.hparams.get("first_last_k_dense_replace", 0))
-        self.gguf_writer.add_leading_dense_block_count(n_dense_layer)
-        self.gguf_writer.add_nextn_predict_layers(self.hparams.get("num_nextn_predict_layers", 0))
-
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("mtp."):
-            if name.find("layers.") != -1:
-                # `mtp.layers.0.[module_name]` format
-                name = name.replace(f"mtp.layers.{bid}", f"model.layers.{bid + self.hparams['num_hidden_layers']}")
-            else:
-                # mtp fc/norm weights
-                remapper = {
-                    "mtp.fc": "model.layers.{bid}.eh_proj",
-                    "mtp.pre_fc_norm_embedding": "model.layers.{bid}.enorm",
-                    "mtp.pre_fc_norm_hidden": "model.layers.{bid}.hnorm",
-                    "mtp.norm": "model.layers.{bid}.shared_head.norm",
-                }
-                _n = Path(name)
-                new_name = remapper[_n.stem] + _n.suffix
-
-                # set shared weights for all NextN/MTP layers
-                for bid in range(self.hparams['num_hidden_layers'], self.block_count):
-                    yield from super().modify_tensors(data_torch, new_name.format(bid=bid), bid)
-                return
-
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-
-        if name.find("mlp.experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    new_name = self.map_tensor_name(merged_name)
-
-                    yield from super().modify_tensors(data_torch, new_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("GraniteForCausalLM")
-class GraniteModel(LlamaModel):
-    """Conversion for IBM's GraniteForCausalLM"""
-    model_arch = gguf.MODEL_ARCH.GRANITE
-
-    def set_gguf_parameters(self):
-        """Granite uses standard llama parameters with the following differences:
-
-        - No head_dim support
-        - New multiplier params:
-            - attention_scale
-            - embedding_scale
-            - residual_scale
-        - logits_scaling
-        """
-        if head_dim := self.hparams.pop("head_dim", None):
-            logger.warning("Ignoring head_dim (%s) from config for Granite", head_dim)
-        super().set_gguf_parameters()
-        # NOTE: Convert _multiplier params to _scale params for naming
-        #   consistency
-        if attention_scale := self.hparams.get("attention_multiplier"):
-            self.gguf_writer.add_attention_scale(attention_scale)
-            logger.info("gguf: (granite) attention_scale = %s", attention_scale)
-        if embedding_scale := self.hparams.get("embedding_multiplier"):
-            self.gguf_writer.add_embedding_scale(embedding_scale)
-            logger.info("gguf: (granite) embedding_scale = %s", embedding_scale)
-        if residual_scale := self.hparams.get("residual_multiplier"):
-            self.gguf_writer.add_residual_scale(residual_scale)
-            logger.info("gguf: (granite) residual_scale = %s", residual_scale)
-        if logits_scale := self.hparams.get("logits_scaling"):
-            self.gguf_writer.add_logit_scale(logits_scale)
-            logger.info("gguf: (granite) logits_scale = %s", logits_scale)
-
-
-@ModelBase.register("GraniteMoeForCausalLM", "GraniteMoeSharedForCausalLM")
-class GraniteMoeModel(GraniteModel):
-    """Conversion for IBM's GraniteMoeForCausalLM"""
-    model_arch = gguf.MODEL_ARCH.GRANITE_MOE
-
-    def set_gguf_parameters(self):
-        """GraniteMoeShared uses GraniteMoe parameters plus the following:
-        - shared_intermediate_size
-        """
-        super().set_gguf_parameters()
-        if shared_feed_forward_length := self.hparams.get("shared_intermediate_size"):
-            self.gguf_writer.add_expert_shared_feed_forward_length(shared_feed_forward_length)
-            logger.info("gguf: (granitemoeshared) shared_feed_forward_length = %s", shared_feed_forward_length)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        """In modeling_granitemoe, the JetMoe implementation of parallel experts
-        is used. This essentially merges w1 and w3 into a single tensor with 2x
-        the hidden size that is then split during forward. To keep compatibility
-        with existing mixtral support, we pull them apart here.
-        """
-
-        if name.endswith("block_sparse_moe.input_linear.weight"):
-            ffn_dim = self.hparams["intermediate_size"]
-            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
-            gate, up = data_torch.split(ffn_dim, dim=-2)
-            yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid)
-            yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid)
-            return
-
-        has_experts = bool(self.hparams.get('num_local_experts'))
-
-        if name.endswith("shared_mlp.input_linear.weight"):
-            ffn_dim = self.hparams["shared_intermediate_size"]
-            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size"
-            gate, up = data_torch.split(ffn_dim, dim=-2)
-            if has_experts:
-                yield from ModelBase.modify_tensors(self, gate,self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), bid)
-                yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), bid)
-                return
-            yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)
-            yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)
-            return
-
-        if not has_experts and name.endswith("shared_mlp.output_linear.weight"):
-            yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), bid)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("GraniteMoeHybridForCausalLM", "BambaForCausalLM")
-class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
-    """GraniteHybrid is a hybrid SSM + Attention model that uses Mamba2 SSM
-    layers and optionally uses MoE w/ a shared expert"""
-    model_arch = gguf.MODEL_ARCH.GRANITE_HYBRID
-    undo_permute = True
-
-    def __init__(self, *args, **kwargs):
-
-        # Hybrid mamba models use a prefix for the mamba-specific params.
-        # TODO: Extend this if the prefix(es) need to be configurable
-        self.hparam_prefixes = ["mamba"]
-
-        super().__init__(*args, **kwargs)
-
-        # Lists of which layers use ssm vs attention
-        self._attn_layers = self.get_attn_layers()
-        self._ssm_layers = [
-            i for i in range(self.block_count)
-            if i not in self._attn_layers
-        ]
-
-        # There are some models in this family that are non-hybrid, but keep the
-        # same parent class by setting all layers to "attention." If this is the
-        # case, the model architecture needs to be updated to a standard
-        # "granite" or "granitemoe" model
-        if not self._ssm_layers:
-            has_experts = self.find_hparam(["num_experts_per_tok", "num_experts_per_token"], optional=True)
-            new_arch = (
-                gguf.MODEL_ARCH.GRANITE_MOE
-                if has_experts else
-                gguf.MODEL_ARCH.GRANITE
-            )
-            self.model_arch = new_arch
-            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[new_arch]
-            self.gguf_writer.add_architecture()
-
-        # n_group and d_inner are used during reshape_tensors for mamba2
-        # NOTE: Explicitly include hparam prefix prefix for d_model to
-        #   disambiguate with top-level head_dim
-        # NOTE 2: If needed for future models, this can be isolated in a method
-        #   to separate the prefix setting and the keys used
-        self.d_model = self.find_hparam([f"{self.hparam_prefixes[0]}_head_dim", "hidden_size", "d_model"])
-        self.n_group = self.find_hparam(["n_groups", "num_groups"])
-        self.d_inner = self.find_hparam(["expand", "num_heads"]) * self.d_model
-
-    def get_attn_layers(self):
-        # Explicit list of layer type names
-        if layer_types := self.hparams.get("layer_types"):
-            return [
-                i for i, typ in enumerate(layer_types)
-                if typ == "attention"
-            ]
-
-        # Layer types indicated by index or period
-        attn_layers = self.hparams.get("attn_layer_indices", [])
-        if not attn_layers:
-            attn_period = self.hparams.get("attn_layer_period")
-            assert attn_period, "Didn't find attn_layer_indices or attn_layer_period"
-            attn_offset = self.hparams.get("attn_layer_offset")
-            assert attn_offset is not None, "No attention layer offset set with attn_layer_period"
-            attn_layers = [
-                i for i in range(self.block_count)
-                if i % attn_period == attn_offset
-            ]
-        return attn_layers
-
-    def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any:
-        prefixed = []
-        for pfx in self.hparam_prefixes:
-            prefixed.extend(
-                "_".join([pfx, k])
-                for k in keys
-            )
-        keys = list(keys) + prefixed
-        return Mamba2Model.find_hparam(self, keys, *args, **kwargs)
-
-    def modify_tensors(
-        self, data_torch: Tensor, name: str, bid: int | None
-    ) -> Iterable[tuple[str, Tensor]]:
-        if (
-            name.endswith("block_sparse_moe.input_linear.weight")
-            or "shared_mlp" in name
-        ):
-            yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
-            return
-
-        # Determine whether this is a mamba layer or an attention layer
-        if bid in self._ssm_layers:
-            yield from Mamba2Model.modify_tensors(self, data_torch, name, bid)
-            return
-        elif bid in self._attn_layers:
-            yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
-            return
-        yield from ModelBase.modify_tensors(self, data_torch, name, bid)
-
-    def set_gguf_parameters(self):
-        """This method merges params from both parents and some that are
-        specific to this model. The result is some duplication of how the params
-        get set. The following warnings are expected during conversion:
-
-        WARNING:Duplicated key name 'granitehybrid.attention.head_count_kv'
-        WARNING:Duplicated key name 'granitehybrid.context_length'
-        """
-        GraniteMoeModel.set_gguf_parameters(self)
-
-        ## Mamba mixer params ##
-        self.gguf_writer.add_ssm_conv_kernel(self.find_hparam(["conv_kernel", "d_conv"]))
-        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state", "state_dim", "ssm_state_size"]))
-        self.gguf_writer.add_ssm_group_count(self.n_group)
-        self.gguf_writer.add_ssm_inner_size(self.d_inner)
-        # NOTE: The mamba_dt_rank is _not_ the right field for how this is used
-        #   in llama.cpp
-        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads", "num_heads"]))
-
-        ## Attention params ##
-        head_count_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
-        head_count_kv_vec = [
-            head_count_kv if i in self._attn_layers else 0 for i in range(self.block_count)
-        ]
-        if rope_dim := self.hparams.get("attn_rotary_emb"):
-            self.gguf_writer.add_rope_dimension_count(rope_dim)
-        self.gguf_writer.add_head_count_kv(head_count_kv_vec)
-
-        ## If Bamba or non-hybrid, use rope, otherwise don't
-        use_rope = (
-            "BambaForCausalLM" in self.hparams["architectures"]
-            or not self._ssm_layers
-        )
-        self.gguf_writer.add_rope_scaling_finetuned(use_rope)
-        if not use_rope:
-            self.gguf_writer.add_context_length(2**20)
-
-        ## Validation ##
-        d_head = self.find_hparam(["d_head"], optional=True) or 64
-        assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported"
-        assert self.d_inner % d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {d_head}"
-
-    def set_vocab(self):
-        self.hparams["pad_vocab_size_multiple"] = 8
-        Mamba2Model.set_vocab(self)
-
-
-@ModelBase.register("NemotronHForCausalLM")
-class NemotronHModel(GraniteHybridModel):
-    """Hybrid mamba2/attention model from NVIDIA"""
-    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
-    is_moe: bool = False
-
-    def __init__(self, *args, **kwargs):
-        # We have to determine the correct model architecture (MoE vs non-MoE) before
-        # calling the parent __init__. This is because the parent constructor
-        # uses self.model_arch to build the tensor name map, and all MoE-specific
-        # mappings would be missed if it were called with the default non-MoE arch.
-        hparams = ModelBase.load_hparams(args[0], self.is_mistral_format)
-        if "num_experts_per_tok" in hparams:
-            self.model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
-            self.is_moe = True
-
-        super().__init__(*args, **kwargs)
-
-        # Save the top-level head_dim for later
-        self.head_dim = self.hparams.get("head_dim", self.hparams.get("attention_head_dim"))
-        assert self.head_dim is not None, "Could not find the attention head dim in config"
-
-        # Don't use expand to calculate d_inner
-        self.d_inner = self.find_hparam(["num_heads"]) * self.d_model
-
-        # Update the ssm / attn / mlp layers
-        # M: Mamba2, *: Attention, -: MLP
-        # MoE:
-        # M: Mamba2, *: Attention, E: Expert
-        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
-        if pattern is None:
-            self._ssm_layers = []
-            self._mlp_layers = []
-        elif isinstance(pattern, str):
-            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "M"]
-            self._mlp_layers = [i for i, val in enumerate(pattern) if val == ("E" if self.is_moe else "-")]
-        else:
-            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"]
-            self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"]
-
-    def get_attn_layers(self):
-        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
-        if pattern is None:
-            return []
-        assert len(pattern) == self.block_count, f"Mismatch between pattern ({len(pattern)}) and block_count ({self.block_count})!"
-        if isinstance(pattern, str):
-            return [i for i, val in enumerate(pattern) if val == "*"]
-
-        return [i for i, val in enumerate(pattern) if val == "attention"]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        head_dim = self.head_dim
-        if head_dim is None:
-            raise ValueError("Could not find the attention head dim in config")
-        self.gguf_writer.add_key_length(head_dim)
-        self.gguf_writer.add_value_length(head_dim)
-
-        # Set feed_forward_length
-        # NOTE: This will trigger an override warning. This is preferable to
-        #   duplicating all the parent logic
-        if not self.is_moe:
-            n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
-            self.gguf_writer.add_feed_forward_length([
-                n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
-            ])
-        else:
-            moe_intermediate_size = self.hparams["moe_intermediate_size"]
-            self.gguf_writer.add_feed_forward_length([
-                moe_intermediate_size if i in self._mlp_layers else 0 for i in range(self.block_count)
-            ])
-            self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"])
-            self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
-            self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["moe_shared_expert_intermediate_size"])
-            self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
-            self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
-            self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
-            self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
-            self.gguf_writer.add_expert_group_count(self.hparams["n_group"])
-
-            # number of experts used per token (top-k)
-            if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
-                self.gguf_writer.add_expert_used_count(n_experts_used)
-
-            if (latent_size := self.hparams.get("moe_latent_size")) is not None:
-                self.gguf_writer.add_moe_latent_size(latent_size)
-
-    def set_vocab(self):
-        # The NemotronH config uses pattern characters (e.g. '-') that may not
-        # be supported by the installed transformers version. AutoTokenizer
-        # internally calls AutoConfig which triggers this parsing failure.
-        # Using trust_remote_code=True to load the model's own config class.
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-
-        # Pad vocab size (from Mamba2Model/GraniteHybridModel)
-        self.hparams["pad_vocab_size_multiple"] = 8 # Setting this here since GraniteHybridModel.set_vocab() isn't being invoked now.
-        # From Mamba2Model.set_vocab():
-        vocab_size = self.hparams["vocab_size"]
-        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16)
-        # ref: https://stackoverflow.com/a/17511341/22827863
-        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
-        self.hparams["vocab_size"] = vocab_size
-
-        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
-        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
-
-        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            else:
-                token: str = reverse_vocab[i]
-                if token in added_vocab:
-                    if not added_tokens_decoder[i].normalized:
-                        previous_token = token
-                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
-                        if previous_token != token:
-                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
-
-                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    toktypes.append(gguf.TokenType.NORMAL)
-                tokens.append(token)
-
-        # From TextModel.set_vocab_gpt2():
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-        # The tokenizer _does_ add a BOS token (via post_processor type
-        # TemplateProcessing) but does not set add_bos_token to true in the
-        # config, so we need to explicitly override it here.
-        if not self.is_moe:
-            self.gguf_writer.add_add_bos_token(True)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Skip vision model and projector tensors for VLM models (handled by mmproj) (e.g., Nemotron Nano 12B v2 VL)
-        if name.startswith(("vision_model.", "mlp1.")):
-            return
-
-        # Strip language_model. prefix for VLM models (e.g., Nemotron Nano 12B v2 VL)
-        if name.startswith("language_model."):
-            name = name[len("language_model."):]
-
-        if self.is_moe and bid is not None:
-            # Skip Multi-Token Prediction (MTP) tensors. These are used for
-            # for speculative decoding but we don't include them in this model
-            # conversion. See https://github.com/ggml-org/llama.cpp/pull/18886
-            if name.startswith("mtp."):
-                logger.info(f"gguf: Skipping MTP (Speculative) layer: {name}")
-                return
-
-            if name.endswith("mixer.gate.e_score_correction_bias"):
-                new_name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-                yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
-                return
-
-            if name.endswith("mixer.dt_bias"):
-                new_name = name.replace("dt_bias", "dt.bias")
-                yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
-                return
-
-            if name.endswith("mixer.conv1d.weight"):
-                squeezed_data = data_torch.squeeze()
-                yield from ModelBase.modify_tensors(self, squeezed_data, name, bid)
-                return
-
-            if name.endswith("mixer.A_log"):
-                transformed_data = -torch.exp(data_torch)
-                reshaped_data = transformed_data.squeeze().reshape(-1, 1)
-                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
-                return
-
-            if name.endswith("mixer.D"):
-                reshaped_data = data_torch.squeeze().reshape(-1, 1)
-                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
-                return
-
-            if name.endswith("mixer.norm.weight"):
-                reshaped_data = data_torch.reshape(self.n_group, -1)
-                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
-                return
-
-            if name.find("mixer.experts") != -1:
-                n_experts = self.hparams["n_routed_experts"]
-                assert bid is not None
-
-                if self._experts is None:
-                    self._experts = [{} for _ in range(self.block_count)]
-
-                self._experts[bid][name] = data_torch
-
-                if len(self._experts[bid]) >= n_experts * 2:
-                    # merge the experts into a single tensor
-                    for w_name in ["down_proj", "up_proj"]:
-                        datas: list[Tensor] = []
-
-                        for xid in range(n_experts):
-                            ename = f"backbone.layers.{bid}.mixer.experts.{xid}.{w_name}.weight"
-                            datas.append(self._experts[bid][ename])
-                            del self._experts[bid][ename]
-
-                        data_torch = torch.stack(datas, dim=0)
-                        merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                        yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid)
-                    return
-                else:
-                    return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("LlamaBidirectionalModel")
-class LlamaEmbedNemotronModel(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.LLAMA_EMBED
-
-
-@ModelBase.register("BailingMoeForCausalLM")
-class BailingMoeModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.BAILINGMOE
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
-
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
-        self.gguf_writer.add_expert_weights_scale(1.0)
-        self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"])
-        self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    @staticmethod
-    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-        n_embd = self.hparams["hidden_size"]
-        if (head_dim := self.hparams.get("head_dim")) is None:
-            head_dim = n_embd // n_head
-
-        output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
-
-        if name.endswith("attention.dense.weight"):
-            yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, bid), bid)
-            return
-        elif name.endswith("query_key_value.weight"):
-            q, k, v = data_torch.split([n_head * head_dim, n_kv_head * head_dim, n_kv_head * head_dim], dim=-2)
-
-            yield from super().modify_tensors(BailingMoeModel.permute(q, n_head, n_head), self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
-            yield from super().modify_tensors(BailingMoeModel.permute(k, n_head, n_kv_head), self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
-            yield from super().modify_tensors(v,self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
-            return
-        elif name.find("mlp.experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    new_name = self.map_tensor_name(merged_name)
-
-                    yield from super().modify_tensors(data_torch, new_name, bid)
-
-            return
-
-        new_name = self.map_tensor_name(name)
-
-        if new_name == output_name and self.hparams.get("norm_head"):
-            data_torch = data_torch.float()
-            data_torch /= torch.norm(data_torch, p=2, dim=0, keepdim=True) + 1e-7
-
-        yield from super().modify_tensors(data_torch, new_name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("BailingMoeV2ForCausalLM")
-class BailingMoeV2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.BAILINGMOE2
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if nextn_layers := self.hparams.get("num_nextn_predict_layers", 0):
-            self.block_count = self.hparams["num_hidden_layers"] + nextn_layers
-            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        if (rope_dim := hparams.get("head_dim")) is None:
-            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
-
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
-        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
-        self.gguf_writer.add_expert_shared_feed_forward_length(hparams.get("moe_shared_expert_intermediate_size", hparams["moe_intermediate_size"] * hparams["num_shared_experts"]))
-        self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
-        self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"])
-        self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
-
-        if (nextn_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
-            self.gguf_writer.add_nextn_predict_layers(nextn_layers)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "mlp.experts" in name:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-            return
-
-        if name.endswith(".expert_bias"):
-            name = name.replace(".expert_bias", ".expert_bias.bias")
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
-class GroveMoeModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GROVEMOE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
-        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L299
-        self.gguf_writer.add_expert_chunk_feed_forward_length(self.hparams.get("head_dim") or 128)
-        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L298
-        self.gguf_writer.add_experts_per_group(2)
-        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376
-        self.gguf_writer.add_expert_group_scale(0.05)
-
-    _experts: list[dict[str, Tensor]] | None = None
-    _chunk_experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.endswith(".expert_bias"):
-            # FIXME?: Unused https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L303
-            return
-
-        # process the experts separately
-        if name.find("chunk_experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"]) // 2 # see add_experts_per_group
-            assert bid is not None
-
-            if self._chunk_experts is None:
-                self._chunk_experts = [{} for _ in range(self.block_count)]
-
-            self._chunk_experts[bid][name] = data_torch
-
-            if len(self._chunk_experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.chunk_experts.{xid}.{w_name}.weight"
-                        datas.append(self._chunk_experts[bid][ename])
-                        del self._chunk_experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.chunk_experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-        elif name.find("experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._chunk_experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            chunk_experts = [k for d in self._chunk_experts for k in d.keys()]
-            if len(chunk_experts) > 0:
-                raise ValueError(f"Unprocessed adjugate experts: {chunk_experts}")
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("ChameleonForConditionalGeneration")
-@ModelBase.register("ChameleonForCausalLM")  # obsolete
-class ChameleonModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.CHAMELEON
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False))
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # ignore image tokenizer for now
-        # TODO: remove this once image support is implemented for Chameleon
-        if name.startswith("model.vqmodel"):
-            return
-
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-        hidden_dim = self.hparams.get("hidden_size")
-
-        if name.endswith(("q_proj.weight", "q_proj.bias")):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-        if name.endswith(("k_proj.weight", "k_proj.bias")):
-            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
-        if name.endswith(("q_norm.weight", "q_norm.bias")):
-            data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim)
-        if name.endswith(("k_norm.weight", "k_norm.bias")):
-            data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    # see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203
-    @staticmethod
-    def _reverse_hf_permute(data_torch, n_heads, hidden_dim):
-        head_dim = hidden_dim // n_heads
-        data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1)
-        data_torch = data_torch.repeat_interleave(n_heads, 0)
-        return data_torch
-
-
-@ModelBase.register("UltravoxModel")
-class UltravoxModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.LLAMA # dummy
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument")
-
-
-@ModelBase.register("GlmasrModel")
-class GlmASRWhisperEncoderModel(MmprojModel):
-    has_vision_encoder = False
-    has_audio_encoder = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
-            self.hparams["hidden_size"] = self.hparams["d_model"]
-            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
-            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLMA)
-        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
-        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
-        self.gguf_writer.add_audio_stack_factor(self.global_config["merge_factor"])
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".conv" in name and ".weight" in name:
-            return gguf.GGMLQuantizationType.F16
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("model.") or name.startswith("lm_head."):
-            # skip language model tensors
-            return
-
-        if name.startswith("audio_encoder.whisper."):
-            name = name.replace("audio_encoder.whisper.","audio_tower.")
-        if "audio_encoder.layer_norm." in name or "audio_encoder.proj." in name:
-            name = name.replace("audio_encoder.", "audio_encoder.adapting.")
-
-        if name.startswith("audio_encoder.audio_bos_eos_token."):
-            yield from super().modify_tensors(data_torch[0], "model.vision.boi", bid)
-            yield from super().modify_tensors(data_torch[1], "model.vision.eoi", bid)
-            return
-
-        if name.startswith("audio_encoder.adapting."):
-            name = name.replace("audio_encoder.adapting.","audio.multi_modal_projector.")
-            if ".layer_norm." in name:
-                name = name.replace(".layer_norm.", ".ln_pre.")
-            if ".0." in name:
-                name = name.replace(".0.", ".linear_1.")
-            if ".2." in name:
-                name = name.replace(".2.", ".linear_2.")
-            if ".proj." in name:
-                return
-
-        if "conv1.bias" in name or "conv2.bias" in name:
-            # transpose conv1 and conv2 bias
-            data_torch = data_torch.unsqueeze(-1)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Qwen2AudioForConditionalGeneration")
-class WhisperEncoderModel(MmprojModel):
-    has_vision_encoder = False # no vision encoder
-    has_audio_encoder = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
-            self.hparams["hidden_size"] = self.hparams["d_model"]
-            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
-            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2A)
-        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
-        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".conv" in name and ".weight" in name:
-            return gguf.GGMLQuantizationType.F16
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("language_model."):
-            # skip language model tensors
-            return
-
-        # prevent clash naming with vision tensors
-        if name.startswith("multi_modal_projector"):
-            name = "audio." + name
-
-        if "conv1.bias" in name or "conv2.bias" in name:
-            # transpose conv1 and conv2 bias
-            data_torch = data_torch.unsqueeze(-1)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("UltravoxModel")
-class UltravoxWhisperEncoderModel(WhisperEncoderModel):
-    has_vision_encoder = False # no vision encoder
-    has_audio_encoder = True
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.ULTRAVOX)
-        self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
-
-
-@ModelBase.register("MERaLiON2ForConditionalGeneration")
-class MERaLiONWhisperEncoderModel(WhisperEncoderModel):
-    has_vision_encoder = False
-    has_audio_encoder = True
-
-    def get_audio_config(self) -> dict[str, Any] | None:
-        return self.global_config.get("speech_config")
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MERALION)
-        self.gguf_writer.add_audio_stack_factor(self.global_config.get("speech_mlp_scale_factor", 15))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("text_decoder."):
-            return
-
-        if name.startswith("speech_encoder."):
-            name = name.replace("speech_encoder.", "audio_tower.")
-            yield from super().modify_tensors(data_torch, name, bid)
-            return
-
-        suffix = "." + name.rsplit(".", 1)[-1]
-
-        if name.startswith("ln_speech."):
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MM_NORM_PRE, suffix=suffix), data_torch)
-            return
-
-        if name.startswith("speech_audio_adapter."):
-            if ".mlp_adapter.0." in name:
-                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 0, suffix=suffix), data_torch)
-            elif ".gate_proj." in name:
-                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 1, suffix=suffix), data_torch)
-            elif ".pool_proj." in name:
-                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 2, suffix=suffix), data_torch)
-            elif ".out_proj." in name:
-                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 3, suffix=suffix), data_torch)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("VoxtralForConditionalGeneration")
-class VoxtralWhisperEncoderModel(WhisperEncoderModel):
-    has_vision_encoder = False # no vision encoder
-    has_audio_encoder = True
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.VOXTRAL)
-        self.gguf_writer.add_audio_stack_factor(4) # == intermediate_size // hidden_size
-
-
-@ModelBase.register("AudioFlamingo3ForConditionalGeneration")
-class AudioFlamingo3WhisperEncoderModel(WhisperEncoderModel):
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MUSIC_FLAMINGO)
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".conv" in name and ".weight" in name:
-            # Was trained in BF16, being safe, avoiding quantizing to FP16
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-
-@ModelBase.register("FalconH1ForCausalLM")
-class FalconH1Model(Mamba2Model):
-    model_arch = gguf.MODEL_ARCH.FALCON_H1
-
-    def __init__(self, *args, **kwargs):
-        # Set the hparam prefixes for Falcon Mamba2
-        self.hparam_prefixes = ["mamba"]
-
-        # Initialize the base Mamba2Model
-        super().__init__(*args, **kwargs)
-
-        # Use Llama conversion for attention
-        self._transformer_model_class = LlamaModel
-
-        # n_group and d_inner are used during reshape_tensors for mamba2
-        self.n_group = self.find_hparam(["n_groups"])
-        self.d_inner = self.find_hparam(["mamba_d_ssm"])
-        self.d_head = self.find_hparam(["d_head"])
-
-        # Initialize any Falcon Mamba2 specific attributes
-        self.has_attention = True  # Falcon Mamba2 has attention components
-
-        # Load Falcon-H1 multipliers from hyperparameters
-        self.attention_in_multiplier = self.find_hparam(["attention_in_multiplier"], optional=True)
-        self.attention_out_multiplier = self.find_hparam(["attention_out_multiplier"], optional=True)
-        self.ssm_in_multiplier = self.find_hparam(["ssm_in_multiplier"], optional=True)
-        self.ssm_out_multiplier = self.find_hparam(["ssm_out_multiplier"], optional=True)
-        self.mlp_multipliers = self.find_hparam(["mlp_multipliers"], optional=True)
-        self.ssm_multipliers = self.find_hparam(["ssm_multipliers"], optional=True)
-        self.intermediate_size = self.find_hparam(["intermediate_size"])
-        self.key_multiplier = self.find_hparam(["key_multiplier"], optional=True)
-
-    def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any:
-        prefixed = []
-        for pfx in self.hparam_prefixes:
-            prefixed.extend(
-                "_".join([pfx, k])
-                for k in keys
-            )
-        keys = list(keys) + prefixed
-        return super().find_hparam(keys, *args, **kwargs)
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        tensors = list(super().modify_tensors(data_torch, name, bid))
-        tensor = tensors[0][1]
-
-        if "down_proj" in name:
-            tensor = tensor  * self.mlp_multipliers[1]
-        elif "gate_proj" in name:
-            tensor = tensor * self.mlp_multipliers[0]
-        elif "k_proj" in name:
-            tensor = tensor * self.key_multiplier * self.attention_in_multiplier
-        elif "q_proj" in name:
-            tensor = tensor * self.attention_in_multiplier
-        elif "v_proj" in name:
-            tensor = tensor * self.attention_in_multiplier
-        elif "o_proj" in name:
-            tensor = tensor * self.attention_out_multiplier
-        elif "out_proj" in name:
-            tensor = tensor * self.ssm_out_multiplier
-        elif "in_proj" in name:
-            tensor = tensor * self.ssm_in_multiplier
-            zxbcdt_multipliers = self.hparams["ssm_multipliers"]
-            intermediate_size = self.hparams["mamba_d_ssm"]
-            groups_time_state_size = self.hparams["mamba_n_groups"] * self.hparams["mamba_d_state"]
-            tensor[:intermediate_size, :] *= zxbcdt_multipliers[0]
-            tensor[intermediate_size:2 * intermediate_size, :] *= zxbcdt_multipliers[1]
-            tensor[2 * intermediate_size:2 * intermediate_size + groups_time_state_size, :] *= zxbcdt_multipliers[2]
-            tensor[2 * intermediate_size + groups_time_state_size:2 * intermediate_size + 2 * groups_time_state_size, :] *= zxbcdt_multipliers[3]
-            tensor[2 * intermediate_size + 2 * groups_time_state_size:, :] *= zxbcdt_multipliers[4]
-        elif "lm_head" in name:
-            tensor = tensor * self.hparams["lm_head_multiplier"]
-        elif "embed_tokens" in name:
-            tensor = tensor * self.hparams["embedding_multiplier"]
-        elif "mamba.norm" in name:
-            tensor = tensor.reshape(self.n_group, self.d_inner // self.n_group)
-
-        tensors = [(tensors[0][0], tensor)]
-        return tensors
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        ## General Params ##
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-        # Override some Mamba2 defaults
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_context_length(self.hparams.get("max_position_embeddings", 0))
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-
-        ## Attention params ##
-        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) # Override value 0 from Mamba2
-        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
-        self.gguf_writer.add_key_length(self.hparams["head_dim"])
-        self.gguf_writer.add_value_length(self.hparams["head_dim"])
-
-        ## Validation ##
-        assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported"
-        assert self.d_inner % self.d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {self.d_head}"
-
-        # Add any other Falcon Mamba2 specific configuration
-        self.gguf_writer.add_rope_freq_base(self.rope_parameters["rope_theta"])
-
-
-@ModelBase.register("HunYuanMoEV1ForCausalLM")
-class HunYuanMoEModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE
-
-    def set_vocab(self):
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-
-        # 1. Get the pre-tokenizer identifier hash
-        tokpre = self.get_vocab_base_pre(tokenizer)
-
-        # 2. Reverse-engineer the merges list from mergeable_ranks
-        merges = []
-        vocab = {}
-        mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
-        for token, rank in mergeable_ranks.items():
-            vocab[QwenModel.token_bytes_to_string(token)] = rank
-            if len(token) == 1:
-                continue
-            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-            if len(merged) == 2: # todo this is an assert in Qwen, why?
-                merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
-
-        # 3. Generate the tokens and toktypes lists
-        vocab_size = self.hparams["vocab_size"]
-        assert tokenizer.vocab_size == vocab_size  # ty: ignore[unresolved-attribute]
-        special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
-        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
-        tokens: list[str] = []
-        toktypes: list[int] = []
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            else:
-                token = reverse_vocab[i]
-                tokens.append(token)
-                if i in special_tokens.values():
-                    toktypes.append(gguf.TokenType.CONTROL)
-                else:
-                    toktypes.append(gguf.TokenType.NORMAL)
-
-        # 4. Write all vocab-related fields to the GGUF writer
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-        self.gguf_writer.add_token_merges(merges)
-
-        # 5. Add special tokens and chat templates
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
-        special_vocab.add_to_gguf(self.gguf_writer)
-        # FIX for BOS token: Overwrite incorrect id read from config.json
-        self.gguf_writer.add_bos_token_id(127959) # <|bos|>
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-
-        self.gguf_writer.add_expert_shared_feed_forward_length(hparams["intermediate_size"])
-
-        moe_intermediate_size = hparams["moe_intermediate_size"]
-        assert all(n == moe_intermediate_size[0] for n in moe_intermediate_size)
-        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size[0])
-
-        moe_topk = hparams["moe_topk"]
-        assert all(topk == moe_topk[0] for topk in moe_topk)
-        self.gguf_writer.add_expert_used_count(moe_topk[0])
-
-        moe_shared_expert = hparams["num_shared_expert"]
-        assert all(n == moe_shared_expert[0] for n in moe_shared_expert)
-        self.gguf_writer.add_expert_shared_count(moe_shared_expert[0])
-
-        # Rope
-        if self.rope_parameters.get("rope_type") == "dynamic":
-            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
-            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
-            alpha = self.rope_parameters.get("alpha", 1000)
-            base = self.rope_parameters.get("rope_theta", 10000.0)
-            dim = (hparams["hidden_size"] // hparams["num_attention_heads"]) # 128
-            scaled_base = base * (alpha ** (dim / (dim - 2))) # 10000 * (1000 ** (128 / 126)) = 11158839.9251
-            self.gguf_writer.add_rope_freq_base(scaled_base)
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-            self.gguf_writer.add_rope_scaling_factor(1)
-            # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
-            self.gguf_writer.add_context_length(256 * 1024) # 256k context length
-
-            # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
-            assert alpha == 1000 and base == 10000.0 and dim == 128 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
-                "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "lm_head.weight":
-            if self.hparams.get("tie_word_embeddings", False):
-                logger.info("Skipping tied output layer 'lm_head.weight'")
-                return
-
-        if name.find("mlp.experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        if self._experts is not None:
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("LLaDAMoEModel", "LLaDAMoEModelLM")
-class LLaDAMoEModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.LLADA_MOE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (expert_intermediate_size := self.hparams.get("expert_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
-
-        self.gguf_writer.add_mask_token_id(156895)
-        self.gguf_writer.add_causal_attention(False)
-        self.gguf_writer.add_diffusion_shift_logits(False)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    # Copied from: Qwen2MoeModel
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # process the experts separately
-        if name.find("experts") != -1:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    # Copied from: Qwen2MoeModel
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("HunYuanDenseV1ForCausalLM", "HunYuanVLForConditionalGeneration")
-class HunYuanModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
-
-    def _get_eod_token_id(self) -> int | None:
-        """Get the actual end-of-generation token from config (eod_token_id)."""
-        return self.hparams.get("eod_token_id")
-
-    def _get_eot_token_id(self) -> int | None:
-        """Get the end-of-turn token from generation_config.json.
-        This is the first entry in eos_token_id when it's a list."""
-        gen_cfg_path = self.dir_model / "generation_config.json"
-        if gen_cfg_path.is_file():
-            with open(gen_cfg_path, encoding="utf-8") as f:
-                gen_cfg = json.load(f)
-            eos = gen_cfg.get("eos_token_id")
-            if isinstance(eos, list) and len(eos) >= 2:
-                return eos[0]
-        return None
-
-    def _fix_special_tokens(self):
-        """Fix EOS/EOT tokens that are incorrect in upstream configs."""
-        eod_id = self._get_eod_token_id()
-        if eod_id is not None:
-            self.gguf_writer.add_eos_token_id(eod_id)
-        eot_id = self._get_eot_token_id()
-        if eot_id is not None:
-            self.gguf_writer.add_eot_token_id(eot_id)
-
-    def set_vocab(self):
-        if (self.dir_model / "tokenizer.json").is_file():
-            tokens, toktypes, tokpre = self.get_vocab_base()
-            self.gguf_writer.add_tokenizer_model("gpt2")
-            self.gguf_writer.add_tokenizer_pre(tokpre)
-            self.gguf_writer.add_token_list(tokens)
-            self.gguf_writer.add_token_types(toktypes)
-
-            # HunyuanOCR has pad_token_id=-1 in config.json; exclude pad from SpecialVocab
-            token_types = None
-            if (self.hparams.get("pad_token_id") or 0) < 0:
-                token_types = ('bos', 'eos', 'unk', 'sep', 'cls', 'mask')
-            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True, special_token_types=token_types)
-            special_vocab.add_to_gguf(self.gguf_writer)
-            self._fix_special_tokens()
-        else:
-            from transformers import AutoTokenizer
-            tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-
-            # 1. Get the pre-tokenizer identifier hash
-            tokpre = self.get_vocab_base_pre(tokenizer)
-
-            # 2. Reverse-engineer the merges list from mergeable_ranks
-            merges = []
-            vocab = {}
-            mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
-            for token, rank in mergeable_ranks.items():
-                vocab[QwenModel.token_bytes_to_string(token)] = rank
-                if len(token) == 1:
-                    continue
-                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-                if len(merged) == 2:
-                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
-
-            # 3. Generate the tokens and toktypes lists
-            vocab_size = self.hparams["vocab_size"]
-            assert tokenizer.vocab_size == vocab_size  # ty: ignore[unresolved-attribute]
-            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
-            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
-            tokens: list[str] = []
-            toktypes: list[int] = []
-            for i in range(vocab_size):
-                if i not in reverse_vocab:
-                    tokens.append(f"[PAD{i}]")
-                    toktypes.append(gguf.TokenType.UNUSED)
-                else:
-                    token = reverse_vocab[i]
-                    tokens.append(token)
-                    if i in special_tokens.values():
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.NORMAL)
-
-            # 4. Write all vocab-related fields to the GGUF writer
-            self.gguf_writer.add_tokenizer_model("gpt2")
-            self.gguf_writer.add_tokenizer_pre(tokpre)
-            self.gguf_writer.add_token_list(tokens)
-            self.gguf_writer.add_token_types(toktypes)
-            self.gguf_writer.add_token_merges(merges)
-
-            # 5. Add special tokens and chat templates
-            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
-            special_vocab.add_to_gguf(self.gguf_writer)
-            # FIX for BOS token: Overwrite incorrect id read from config.json
-            if self.hparams['hidden_size'] == 4096:
-                self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
-            self._fix_special_tokens()
-
-    def set_gguf_parameters(self):
-        # HunyuanOCR has num_experts=1 which is not MoE, prevent parent from writing it
-        saved_num_experts = self.hparams.pop("num_experts", None)
-        super().set_gguf_parameters()
-        if saved_num_experts is not None and saved_num_experts > 1:
-            self.hparams["num_experts"] = saved_num_experts
-        hparams = self.hparams
-
-        # Rope
-        if self.rope_parameters.get("rope_type") in ("dynamic", "xdrope"):
-            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
-            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
-            alpha = self.rope_parameters.get("alpha", 50)
-            base = self.rope_parameters.get("rope_theta", 10000.0)
-            dim = hparams["head_dim"]
-            scaled_base = base * (alpha ** (dim / (dim - 2)))
-            self.gguf_writer.add_rope_freq_base(scaled_base)
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-            self.gguf_writer.add_rope_scaling_factor(1)
-            if self.rope_parameters.get("rope_type") == "dynamic":
-                # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
-                self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
-                self.gguf_writer.add_context_length(256 * 1024) # 256k context length
-
-                # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
-                assert base == 10000.0 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
-                    "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "lm_head.weight":
-            if self.hparams.get("tie_word_embeddings", False):
-                logger.info("Skipping tied output layer 'lm_head.weight'")
-                return
-
-        # skip vision tensors for HunyuanVL models
-        if name.startswith("vit."):
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("HunYuanVLForConditionalGeneration")
-class HunyuanOCRVisionModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        # HunyuanOCR uses max_image_size instead of image_size
-        if "image_size" not in self.hparams_vision:
-            self.hparams_vision["image_size"] = self.hparams_vision.get("max_image_size", 2048)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-        hparams = self.hparams_vision
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.HUNYUANOCR)
-        self.gguf_writer.add_vision_use_gelu(True)
-        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("rms_norm_eps", 1e-5))
-        self.gguf_writer.add_vision_spatial_merge_size(hparams.get("spatial_merge_size", 2))
-        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
-        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if not name.startswith("vit."):
-            return  # skip text tensors
-        # strip CLS token (row 0) from position embeddings so resize_position_embeddings works
-        if "position_embedding" in name:
-            data_torch = data_torch[1:]  # [n_patches+1, n_embd] -> [n_patches, n_embd]
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        # force conv weights to F32 or F16 to avoid BF16 IM2COL issues on Metal
-        if ("mm.0." in new_name or "mm.2." in new_name) and new_name.endswith(".weight"):
-            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-
-@ModelBase.register("SmolLM3ForCausalLM")
-class SmolLM3Model(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.SMOLLM3
-
-
-@ModelBase.register("GptOssForCausalLM")
-class GptOssModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GPT_OSS
-
-    # TODO: remove once MXFP4 is supported more generally
-    def dequant_model(self):
-        if self._is_mxfp4:
-            return
-        return super().dequant_model()
-
-    def transform_nibble_layout(self, tensor):
-        assert tensor.dtype == torch.uint8
-        assert tensor.shape[-1] == 16
-        # swap nibbles
-        t_lo = tensor & 0x0F
-        t_hi = tensor & 0xF0
-        t_swapped = (t_lo << 4) | (t_hi >> 4)
-        tensor = t_swapped
-        # transform aaaa...bbbb... to abababab...
-        blk_a, blk_b = tensor.chunk(2, dim=-1)
-        # get a_
-        blk_a0 = (blk_a & 0xF0).view(-1, 1)
-        blk_a1 = (blk_a << 4).view(-1, 1)
-        blk_a = torch.stack((blk_a0, blk_a1), dim=2).view(tensor.shape)
-        # get _b
-        blk_b0 = (blk_b >> 4).view(-1, 1)
-        blk_b1 = (blk_b & 0x0F).view(-1, 1)
-        blk_b = torch.stack((blk_b0, blk_b1), dim=2).view(tensor.shape)
-        # swap once more
-        out = blk_a | blk_b
-        out_h = out & 0xF0
-        out_l = out & 0x0F
-        out = (out_h >> 4) | (out_l << 4)
-        return out
-
-    def repack_mxfp4(self, new_name: str, blocks: Tensor, scales: Tensor):
-        assert blocks.dtype == torch.uint8
-        assert scales.dtype == torch.uint8
-        scales = scales.unsqueeze(-1)
-        assert len(blocks.shape) == 4
-        assert len(scales.shape) == 4
-        blocks = self.transform_nibble_layout(blocks)
-        new_data = torch.concat((scales, blocks), dim=-1)
-        new_shape = [new_data.shape[0], new_data.shape[1], new_data.shape[2] * 32]
-        logger.info(f"Repacked {new_name} with shape {new_shape} and quantization MXFP4")
-        # flatten last dim
-        new_data = new_data.view(new_data.shape[0], new_data.shape[1], new_data.shape[2] * new_data.shape[3])
-        new_data = new_data.numpy()
-        self.gguf_writer.add_tensor(new_name, new_data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        blocks0: Tensor = torch.zeros(1)
-        blocks1: Tensor = torch.zeros(1)
-        # we assume that tensors are loaded in the correct order
-        for name, data_torch in self.get_tensors():
-            if "mlp.experts.down_proj_blocks" in name:
-                blocks0 = data_torch
-            elif "mlp.experts.down_proj_scales" in name:
-                new_name = self.map_tensor_name(name.replace("_scales", ".weight"))
-                self.repack_mxfp4(new_name, blocks0, data_torch)
-            elif "mlp.experts.gate_up_proj_blocks" in name:
-                blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :]
-            elif "mlp.experts.gate_up_proj_scales" in name:
-                scales0, scales1 = data_torch[:, ::2, :], data_torch[:, 1::2, :]
-                new_name_gate = self.map_tensor_name(name.replace("gate_up_proj_scales", "gate_proj.weight"))
-                new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight"))
-                self.repack_mxfp4(new_name_gate, blocks0, scales0)
-                self.repack_mxfp4(new_name_up, blocks1, scales1)
-        return []
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "sinks" in name:
-            name += ".weight"
-
-        # correct naming for down_proj
-        if "down_proj" in name:
-            if name.endswith("_bias"):
-                name = name.replace("down_proj_bias", "down_proj.bias")
-            elif "_blocks" not in name and "_scales" not in name:
-                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
-                name = name.replace("down_proj", "down_proj.weight")
-                data_torch = data_torch.transpose(-1, -2)
-            else:
-                # otherwise, it should already be repacked to ggml MXFP4 format
-                return
-
-        # split the gate_up into gate and up
-        if "gate_up_proj" in name:
-            if name.endswith("_bias"):
-                name_up = name.replace("gate_up_proj_bias", "up_proj.bias")
-                name_gate = name.replace("gate_up_proj_bias", "gate_proj.bias")
-                gate_proj_bias, up_proj_bias = data_torch[..., ::2], data_torch[..., 1::2]
-                yield from super().modify_tensors(gate_proj_bias, name_gate, bid)
-                yield from super().modify_tensors(up_proj_bias, name_up, bid)
-            elif "_blocks" not in name and "_scales" not in name:
-                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
-                name_up = name.replace("gate_up_proj", "up_proj.weight")
-                name_gate = name.replace("gate_up_proj", "gate_proj.weight")
-                data_torch = data_torch.transpose(-1, -2)
-                gate_proj_weight, up_proj_weight = data_torch[:, ::2, :], data_torch[:, 1::2, :]
-                yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
-                yield from super().modify_tensors(up_proj_weight, name_up, bid)
-        else:
-            yield from super().modify_tensors(data_torch, name, bid)
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"])
-
-
-@ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM")
-class LFM2Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.LFM2
-
-    def _add_feed_forward_length(self):
-        ff_dim = self.find_hparam(["block_ff_dim", "intermediate_size"])
-        auto_adjust_ff_dim = self.hparams["block_auto_adjust_ff_dim"]
-        ffn_dim_multiplier = self.hparams["block_ffn_dim_multiplier"]
-        multiple_of = self.hparams["block_multiple_of"]
-
-        if auto_adjust_ff_dim:
-            ff_dim = int(2 * ff_dim / 3)
-            # custom dim factor multiplier
-            if ffn_dim_multiplier is not None:
-                ff_dim = int(ffn_dim_multiplier * ff_dim)
-            ff_dim = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)
-
-        self.gguf_writer.add_feed_forward_length(ff_dim)
-
-    def set_gguf_parameters(self):
-        # set num_key_value_heads only for attention layers
-        self.hparams["num_key_value_heads"] = [
-            self.hparams["num_key_value_heads"] if layer_type != "conv" else 0
-            for layer_type in self.hparams["layer_types"]
-        ]
-
-        super().set_gguf_parameters()
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-        self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["norm_eps"])
-        self._add_feed_forward_length()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if self._is_vision_tensor(name) or ConformerAudioModel.is_audio_tensor(name):
-            # skip multimodal tensors
-            return
-
-        name = name.replace("language_model.", "") # vision
-        name = name.replace("lfm.", "model.")      # audio
-
-        # conv op requires 2d tensor
-        if 'conv.conv' in name:
-            data_torch = data_torch.squeeze(1)
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def _is_vision_tensor(self, name: str) -> bool:
-        return "vision_tower" in name or "multi_modal_projector" in name
-
-
-@ModelBase.register("Lfm2Model")
-class LFM2ColBertModel(LFM2Model):
-    model_arch = gguf.MODEL_ARCH.LFM2
-    dense_tensor_name = "dense_2"
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if not name.startswith(self.dense_tensor_name):
-            name = "model." + name
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        # dense tensor is stored in a separate safetensors file
-        from safetensors.torch import load_file
-        tensors_file = self.dir_model / "1_Dense" / "model.safetensors"
-        assert tensors_file.is_file()
-        tensor = load_file(tensors_file)["linear.weight"]
-        self.gguf_writer.add_embedding_length_out(tensor.shape[0])
-        yield f"{self.dense_tensor_name}.weight", tensor.clone()
-
-
-@ModelBase.register("Lfm2MoeForCausalLM")
-class LFM2MoeModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.LFM2MOE
-
-    def set_gguf_parameters(self):
-        # set num_key_value_heads only for attention layers
-        self.hparams["num_key_value_heads"] = [
-            self.hparams["num_key_value_heads"] if layer_type == "full_attention" else 0
-            for layer_type in self.hparams["layer_types"]
-        ]
-
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
-        self.gguf_writer.add_leading_dense_block_count(self.hparams["num_dense_layers"])
-        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
-
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-        self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
-
-    # cache for experts weights for merging
-    _experts_cache: dict[int, dict[str, Tensor]] = {}
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # conv op requires 2d tensor
-        if 'conv.conv' in name:
-            data_torch = data_torch.squeeze(1)
-
-        if name.endswith(".expert_bias"):
-            name = name.replace(".expert_bias", ".expert_bias.bias")
-
-        # merge expert weights
-        if 'experts' in name:
-            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            expert_cache = self._experts_cache.setdefault(bid, {})
-            expert_cache[name] = data_torch
-            expert_weights = ["w1", "w2", "w3"]
-
-            # not enough expert weights to merge
-            if len(expert_cache) < n_experts * len(expert_weights):
-                return
-
-            for w_name in expert_weights:
-                datas: list[Tensor] = []
-
-                for xid in range(n_experts):
-                    ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{w_name}.weight"
-                    datas.append(expert_cache[ename])
-                    del expert_cache[ename]
-
-                data_torch = torch.stack(datas, dim=0)
-                merged_name = f"layers.{bid}.feed_forward.experts.{w_name}.weight"
-
-                yield from super().modify_tensors(data_torch, merged_name, bid)
-
-            del self._experts_cache[bid]
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        assert not self._experts_cache
-
-
-@ModelBase.register("Lfm2VlForConditionalGeneration")
-class LFM2VLModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        # TODO(tarek): for dynamic resolution image_size is not specified, setting here for compatibility
-        self.hparams_vision["image_size"] = 256
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["layer_norm_eps"]))
-        self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("downsample_factor", 2))
-        self.gguf_writer.add_vision_use_gelu(True)
-        # python notation, e.g. for vision_feature_layer == -1, we pick last layer -> vision_feature_layers_to_drop = 0
-        vision_feature_layers_to_drop = -(self.global_config.get("vision_feature_layer", -1) + 1)
-        self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys) - vision_feature_layers_to_drop)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
-
-        if is_vision_tensor:
-            # remove "model." prefix
-            name = name.replace("model.vision_tower.", "vision_tower.")
-            name = name.replace("model.multi_modal_projector.", "multi_modal_projector.")
-
-            if "patch_embedding.weight" in name:
-                data_torch = data_torch.view(data_torch.shape[0], 16, 16, 3).permute(0, 3, 1, 2)
-
-            yield from super().modify_tensors(data_torch, name, bid)
-            return
-
-        return # skip other tensors
-
-
-@ModelBase.register("Lfm2AudioForConditionalGeneration")
-class LFM2AudioModel(ConformerAudioModel):
-    has_vision_encoder = False
-    has_audio_encoder = True
-    model_name = "Lfm2AudioEncoder"
-
-    def get_audio_config(self) -> dict[str, Any] | None:
-        return self.global_config.get("encoder")
-
-    def set_gguf_parameters(self):
-        assert self.hparams_audio is not None
-        self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
-        self.hparams_audio["intermediate_size"] = self.hparams_audio["d_model"]
-        self.hparams_audio["num_attention_heads"] = self.hparams_audio["n_heads"]
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2A)
-        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
-        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
-
-    def modify_tensors(self, data_torch, name, bid):
-        # skip language model tensors
-        if name.startswith("lfm."):
-            return
-
-        # for training only
-        if any(p in name for p in ["audio_loss_weight"]):
-            return
-
-        # for audio output
-        if any(p in name for p in ["codebook_offsets", "depth_embeddings", "depth_linear", "depthformer"]):
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Lfm25AudioTokenizer")
-class LFM25AudioTokenizer(LFM2Model):
-    model_arch = gguf.MODEL_ARCH.LFM2
-
-    def set_vocab(self):
-        self._set_vocab_none()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-        self.gguf_writer.add_embedding_length_out(self.hparams["output_size"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "istft.window" or name.startswith("emb.emb"):
-            return
-
-        if name.startswith("lin"):
-            name = name.replace("lin", "dense_2_out")
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("SmallThinkerForCausalLM")
-class SmallThinkerModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.SMALLTHINKER
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (n_experts := self.hparams.get("moe_num_primary_experts")) is not None:
-            self.gguf_writer.add_expert_count(n_experts)
-        if (n_experts_used := self.hparams.get("moe_num_active_primary_experts")) is not None:
-            self.gguf_writer.add_expert_used_count(n_experts_used)
-        if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-            self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
-            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
-        if (self.hparams.get('moe_primary_router_apply_softmax')):
-            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
-        else:
-            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
-
-        sliding_window_layout = self.hparams.get("sliding_window_layout")
-        if sliding_window_layout:
-            for i in sliding_window_layout:
-                if i != 0:
-                    sliding_window = self.hparams.get("sliding_window_size")
-                    if sliding_window:
-                        self.gguf_writer.add_sliding_window(sliding_window)
-                    break
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # process the experts separately
-        if name.find("experts") != -1:
-            n_experts = self.hparams.get("moe_num_primary_experts") or self.find_hparam(["num_local_experts", "num_experts"])
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
-                for w_name in ["down", "gate", "up"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
-
-                    yield from super().modify_tensors(data_torch, merged_name, bid)
-                return
-            else:
-                return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("ModernBertModel", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
-class ModernBertModel(BertModel):
-    model_arch = gguf.MODEL_ARCH.MODERN_BERT
-
-    def set_vocab(self):
-        self.gguf_writer.add_add_bos_token(True)
-        self.gguf_writer.add_add_eos_token(True)
-        self.gguf_writer.add_add_sep_token(True)
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_sliding_window(self.hparams["local_attention"])
-        if (sliding_window_pattern := self.hparams.get("global_attn_every_n_layers")) is not None:
-            self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("model."):
-            name = name[6:]
-
-        if self.cls_out_labels:
-            # For BertForSequenceClassification (direct projection layer)
-            if name == "classifier.weight":
-                name = "classifier.out_proj.weight"
-
-            if name == "classifier.bias":
-                name = "classifier.out_proj.bias"
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("ApertusForCausalLM")
-class ApertusModel(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.APERTUS
-    undo_permute = False
-
-    _alpha_n = {}
-    _alpha_p = {}
-    _beta = {}
-    _eps = {}
-
-    def modify_tensors(self, data_torch, name, bid):
-        # Handle xIELU activation parameters
-        n_layers = self.hparams["num_hidden_layers"]
-        if name.endswith(".act_fn.alpha_n"):
-            self._alpha_n[bid] = data_torch.to("cpu").float().item()
-            if (len(self._alpha_n) == n_layers):
-                self.gguf_writer.add_xielu_alpha_n([self._alpha_n[k] for k in sorted(self._alpha_n)])
-            return
-        if name.endswith(".act_fn.alpha_p"):
-            self._alpha_p[bid] = data_torch.to("cpu").float().item()
-            if (len(self._alpha_p) == n_layers):
-                self.gguf_writer.add_xielu_alpha_p([self._alpha_p[k] for k in sorted(self._alpha_p)])
-            return
-        if name.endswith(".act_fn.beta"):
-            self._beta[bid] = data_torch.to("cpu").float().item()
-            if (len(self._beta) == n_layers):
-                self.gguf_writer.add_xielu_beta([self._beta[k] for k in sorted(self._beta)])
-            return
-        if name.endswith(".act_fn.eps"):
-            self._eps[bid] = data_torch.to("cpu").float().item()
-            if (len(self._eps) == n_layers):
-                self.gguf_writer.add_xielu_eps([self._eps[k] for k in sorted(self._eps)])
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-class MistralModel(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.MISTRAL3
-    model_name = "Mistral"
-    hf_arch = ""
-    is_mistral_format = True
-    undo_permute = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # for compatibility, we use LLAMA arch for older models
-        # TODO: remove this once everyone migrates to newer version of llama.cpp
-        if "llama_4_scaling" not in self.hparams:
-            self.model_arch = gguf.MODEL_ARCH.LLAMA
-            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
-            self.gguf_writer.add_architecture()
-            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def dequant_model(self):
-        # transform quantization config into HF format
-        quant_config = self.hparams.get("quantization")
-        if quant_config is not None:
-            assert quant_config["qformat_weight"] == "fp8_e4m3"
-            self.hparams["quantization_config"] = {
-                "activation_scheme": "static",
-                "quant_method": "fp8",
-                "weight_block_size": None,
-            }
-        return super().dequant_model()
-
-    @staticmethod
-    def get_community_chat_template(vocab: MistralVocab, templates_dir: Path, is_mistral_format: bool):
-        assert TokenizerVersion is not None and Tekkenizer is not None and SentencePieceTokenizer is not None, _mistral_import_error_msg
-        assert isinstance(vocab.tokenizer, (Tekkenizer, SentencePieceTokenizer)), (
-            f"Expected Tekkenizer or SentencePieceTokenizer, got {type(vocab.tokenizer)}"
-        )
-
-        if vocab.tokenizer.version == TokenizerVersion.v1:
-            return "mistral-v1"
-        elif vocab.tokenizer.version == TokenizerVersion.v3 and vocab.tokenizer_type == MistralTokenizerType.spm:
-            return "mistral-v3"
-        elif vocab.tokenizer.version == TokenizerVersion.v3 and vocab.tokenizer_type == MistralTokenizerType.tekken:
-            return "mistral-v3-tekken"
-        elif vocab.tokenizer.version == TokenizerVersion.v7 and vocab.tokenizer_type == MistralTokenizerType.spm:
-            return "mistral-v7"
-        elif vocab.tokenizer.version == TokenizerVersion.v7 and vocab.tokenizer_type == MistralTokenizerType.tekken:
-            return "mistral-v7-tekken"
-        elif vocab.tokenizer.version == TokenizerVersion.v11:
-            template_file = "Mistral-Small-3.2-24B-Instruct-2506.jinja"
-        elif vocab.tokenizer.version == TokenizerVersion.v13:
-            template_file = "unsloth-mistral-Devstral-Small-2507.jinja"
-        else:
-            err_message = f"Unknown tokenizer type: {vocab.tokenizer_type} and version {vocab.tokenizer.version}"
-            if is_mistral_format:
-                err_message += (
-                    " . Please pass --disable-mistral-community-chat-template argument to the CLI "
-                    "if you want to skip this error and use the Mistral official `mistral-common` pre-processing library."
-                )
-            raise ValueError(err_message)
-
-        template_path = templates_dir / template_file
-        if not template_path.exists():
-            raise FileNotFoundError(f"Template file not found: {template_path}")
-
-        with open(template_path, "r", encoding="utf-8") as f:
-            template = f.read()
-
-        return template
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        MistralModel.set_mistral_config(self.gguf_writer, self.hparams)
-
-    @staticmethod
-    def set_mistral_config(gguf_writer: gguf.GGUFWriter, hparams: dict):
-        if "yarn" in hparams:
-            yarn_params = hparams["yarn"]
-            gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            gguf_writer.add_rope_scaling_factor(yarn_params["factor"])
-            gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_params["beta"])
-            gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_params["alpha"])
-            gguf_writer.add_rope_scaling_yarn_log_mul(1.0) # mscale_all_dim
-            gguf_writer.add_rope_scaling_orig_ctx_len(yarn_params["original_max_position_embeddings"])
-
-        if "llama_4_scaling" in hparams:
-            gguf_writer.add_attn_temperature_scale(hparams["llama_4_scaling"]["beta"])
-
-
-class MistralMoeModel(DeepseekV2Model):
-    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
-    model_name = "Mistral"
-    hf_arch = ""
-    is_mistral_format = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        logger.info("Using MistralMoeModel")
-        # remap hparams from Mistral MoE format to DeepseekV2 format
-        # we do this way to be able to reuse DeepseekV2Model set_gguf_parameters logic
-        # ref: https://github.com/vllm-project/vllm/blob/b294e28db2c5dee61bc25157664edcada8b90b31/vllm/transformers_utils/configs/mistral.py
-        config = self.hparams
-        # Mistral key -> HF key
-        config_mapping = {
-            "dim": "hidden_size",
-            "norm_eps": "rms_norm_eps",
-            "n_kv_heads": "num_key_value_heads",
-            "n_layers": "num_hidden_layers",
-            "n_heads": "num_attention_heads",
-            "hidden_dim": "intermediate_size",
-        }
-        # HF key -> (Mistral key, default value)
-        top_level_mapping_with_default = {
-            "model_type": ("model_type", "transformer"),
-            "hidden_act": ("activation", "silu"),
-            "tie_word_embeddings": ("tied_embeddings", False),
-            "max_seq_len": ("max_seq_len", config.get("max_position_embeddings", 128_000)),
-            "max_position_embeddings": ("max_position_embeddings", 128_000),
-        }
-        # mapping top-level keys
-        for key, new_key in config_mapping.items():
-            if key in config:
-                config[new_key] = config[key]
-        for new_key, (key, default_value) in top_level_mapping_with_default.items():
-            config[new_key] = config.get(key, default_value)
-        # mapping MoE-specific keys
-        moe_config_map = {
-            "route_every_n": "moe_layer_freq",
-            "first_k_dense_replace": "first_k_dense_replace",
-            "num_experts_per_tok": "num_experts_per_tok",
-            "num_experts": "n_routed_experts",
-            "expert_hidden_dim": "moe_intermediate_size",
-            "routed_scale": "routed_scaling_factor",
-            "num_shared_experts": "n_shared_experts",
-            "num_expert_groups": "n_group",
-            "num_expert_groups_per_tok": "topk_group",
-        }
-        moe = config["moe"]
-        for key, new_key in moe_config_map.items():
-            if key in moe:
-                config[new_key] = moe[key]
-        # provide missing values
-        config["topk_method"] = None
-        config["norm_topk_prob"] = True
-        config["scoring_func"] = "softmax"
-
-    def set_vocab(self):
-        self._set_vocab_mistral()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        MistralModel.set_mistral_config(self.gguf_writer, self.hparams)
-        yarn_params = self.hparams["yarn"]
-        self.gguf_writer.add_attn_temperature_length(yarn_params["original_max_position_embeddings"])
-
-        # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
-        # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
-        # ref https://github.com/ggml-org/llama.cpp/pull/17945
-        self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        if name.startswith("vision_") or name.startswith("patch_merger.") or "mm_projector" in name:
-            return
-
-        # rename certain tensors so that we can reuse DeepseekV2Model modify_tensors logic
-        if name.endswith(".qscale_act"):
-            name = name.replace(".qscale_act", ".input_scale")
-        if name.endswith(".qscale_weight"):
-            name = name.replace(".qscale_weight", ".weight_scale")
-        if ".wkv_b." in name:
-            name = name.replace(".wkv_b.", ".kv_b_proj.")
-        if ".experts." in name:
-            name = name.replace(".experts.", ".mlp.experts.")
-            name = name.replace(".w1.", ".gate_proj.")
-            name = name.replace(".w2.", ".down_proj.")
-            name = name.replace(".w3.", ".up_proj.")
-            name = "model." + name
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-class PixtralModel(LlavaVisionModel):
-    model_name = "Pixtral"
-    hf_arch = ""
-    is_mistral_format = True
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
-
-        self.gguf_writer.add_vision_attention_layernorm_eps(
-            self.find_hparam(["norm_eps"])
-        )
-        self.gguf_writer.add_rope_freq_base(self.find_vparam(["rope_theta"]))
-
-        self.gguf_writer.add_vision_use_silu(True)
-
-        # spatial_merge_size
-        if self.find_vparam(["mm_projector_id"]) == "patch_merge":
-            self.gguf_writer.add_vision_spatial_merge_size(
-                self.find_vparam(["spatial_merge_size"])
-            )
-
-    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
-        if name == "vision_language_adapter.w_in.weight":
-            return "mm.1.weight"
-        elif name == "vision_language_adapter.w_out.weight":
-            return "mm.2.weight"
-        return super().map_tensor_name(name, try_suffixes)
-
-
-@ModelBase.register("LightOnOCRForConditionalGeneration")
-class LightOnOCRVisionModel(LlavaVisionModel):
-    is_mistral_format = False
-    use_break_tok = False
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LIGHTONOCR)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        name = name.replace("model.vision_encoder.", "vision_tower.")
-        name = name.replace("model.vision_projection.", "multi_modal_projector.")
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("KimiVLForConditionalGeneration")
-class KimiVLModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        self.hparams_vision["image_size"] = 64 * 14 # for compatibility
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIVL)
-        self.gguf_writer.add_vision_use_gelu(True)
-        self.gguf_writer.add_vision_projector_scale_factor(2)
-        # eps is the same as pytorch's default value
-        assert self.hparams_vision is not None
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-5))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
-
-        if is_vision_tensor:
-            if "pos_emb.weight" in name:
-                data_torch = data_torch.view(data_torch.shape[0] * data_torch.shape[1], data_torch.shape[2])
-
-            if "wqkv" in name:
-                split_dim = 0 if "weight" in name else -1
-                wq, wk, wv = data_torch.chunk(3, dim=split_dim)
-                yield from super().modify_tensors(wq, name.replace("wqkv", "wq"), bid)
-                yield from super().modify_tensors(wk, name.replace("wqkv", "wk"), bid)
-                yield from super().modify_tensors(wv, name.replace("wqkv", "wv"), bid)
-            else:
-                yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("KimiK25ForConditionalGeneration")
-class KimiK25Model(MmprojModel):
-    """Kimi-K2.5 with MoonViT3d vision encoder"""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        assert self.hparams_vision is not None, "Kimi-K2.5 requires vision_config in model config"
-
-        self.merge_kernel_size = tuple(self.hparams_vision.get("merge_kernel_size", [2, 2]))
-        self.patch_size = self.hparams_vision.get("patch_size", 14)
-
-        # Set image_size for compatibility with base class
-        # Use position embedding dimensions as image_size reference
-        pos_emb_h = self.hparams_vision.get("init_pos_emb_height", 64)
-        self.hparams_vision["image_size"] = pos_emb_h * self.patch_size
-
-    def set_gguf_parameters(self):
-        # Base class MmprojModel.set_gguf_parameters() already writes:
-        # - vision_block_count, vision_head_count, vision_embedding_length
-        # - vision_feed_forward_length, vision_patch_size, image_mean, image_std
-        # via find_vparam() which handles the vt_* prefixed keys in Kimi-K2.5's config
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIK25)
-
-        # Position embedding parameters (for interpolation)
-        self.gguf_writer.add_uint32("vision.pos_emb_height", self.hparams_vision.get("init_pos_emb_height", 64))
-        self.gguf_writer.add_uint32("vision.pos_emb_width", self.hparams_vision.get("init_pos_emb_width", 64))
-        self.gguf_writer.add_uint32("vision.pos_emb_time", self.hparams_vision.get("init_pos_emb_time", 4))
-
-        # Projector parameters
-        self.gguf_writer.add_vision_use_gelu(self.hparams_vision.get("projector_hidden_act", "gelu") == "gelu")
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("projector_ln_eps", 1e-5))
-        self.gguf_writer.add_vision_projector_scale_factor(self.merge_kernel_size[0])
-
-        # Image size limits
-        # Note: in_patch_limit is for images, in_patch_limit_each_frame is for video (not supported yet)
-        in_patch_limit = self.preprocessor_config.get("in_patch_limit", 16384)
-        min_patches = 8  # reasonable minimum
-        pixels_per_patch = self.patch_size ** 2
-        self.gguf_writer.add_vision_min_pixels(min_patches * pixels_per_patch)
-        self.gguf_writer.add_vision_max_pixels(in_patch_limit * pixels_per_patch)
-
-    @staticmethod
-    def permute(weights: Tensor, n_head: int) -> Tensor:
-        out_dim, in_dim = weights.shape
-        head_dim = out_dim // n_head
-        w = weights.reshape(n_head, head_dim // 4, 2, 2, in_dim)
-        w = w.permute(0, 2, 1, 3, 4)
-        return w.reshape(out_dim, in_dim)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Only process vision and projector tensors
-        is_vision = any(x in name for x in ["vision_tower", "mm_projector"])
-
-        if not is_vision:
-            return
-
-        assert self.hparams_vision is not None
-        n_head = self.hparams_vision.get("num_attention_heads", 16)
-
-        # Permute Q/K weights/biases from interleaved to split RoPE format
-        # This allows using build_rope_2d at runtime without post-permutation.
-        if "wqkv" in name:
-            out_dim = data_torch.shape[0]
-            qkv_dim = out_dim // 3
-            head_dim = qkv_dim // n_head
-
-            if "weight" in name:
-                wq, wk, wv = data_torch[:qkv_dim, :], data_torch[qkv_dim:2 * qkv_dim, :], data_torch[2 * qkv_dim:, :]
-                wq = self.permute(wq, n_head)
-                wk = self.permute(wk, n_head)
-                data_torch = torch.cat([wq, wk, wv], dim=0)
-            elif "bias" in name:
-                bq, bk, bv = data_torch[:qkv_dim], data_torch[qkv_dim:2 * qkv_dim], data_torch[2 * qkv_dim:]
-                bq = bq.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
-                bk = bk.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
-                data_torch = torch.cat([bq, bk, bv], dim=0)
-
-        # Temporal embeddings: (T, 1, C) → (T, C)
-        if "pos_emb.time_weight" in name:
-            T, _, C = data_torch.shape
-            data_torch = data_torch.reshape(T, C)
-
-        # PatchMergerMLP tensor name mapping
-        # proj.0.weight → proj.linear_1.weight
-        # proj.2.weight → proj.linear_2.weight
-        if "mm_projector.proj.0." in name:
-            name = name.replace(".proj.0.", ".proj.linear_1.")
-        elif "mm_projector.proj.2." in name:
-            name = name.replace(".proj.2.", ".proj.linear_2.")
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("CogVLMForCausalLM")
-class CogVLMVisionModel(MmprojModel):
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.COGVLM)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if not name.startswith("model.vision."):
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("CogVLMForCausalLM")
-class CogVLMModel(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.COGVLM
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # block vision tensors
-        if name.startswith("model.vision."):
-            return
-
-        yield from ModelBase.modify_tensors(self, data_torch, name, bid)
-
-
-@ModelBase.register("JanusForConditionalGeneration")
-class JanusProModel(LlamaModel):
-    model_arch = gguf.MODEL_ARCH.LLAMA  # reuse Llama arch
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Skip vision, aligner, and generation tensors
-        skip_prefixes = (
-            'model.vision_model.',
-            'model.aligner.',
-            'model.vqmodel.',
-            'model.generation_embeddings.',
-            'model.generation_aligner.',
-            'model.generation_head.',
-        )
-        if name.startswith(skip_prefixes):
-            return
-
-        if name.startswith('model.language_model.'):
-            name = name.replace('model.language_model.', 'model.')
-        elif name.startswith('language_model.'):
-            name = name.replace('language_model.', '')
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("JanusForConditionalGeneration")
-class JanusProVisionModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        if "intermediate_size" not in self.hparams_vision:
-            mlp_ratio = self.hparams_vision.get("mlp_ratio")
-            hidden_size = self.hparams_vision.get("hidden_size")
-            if mlp_ratio is not None and hidden_size is not None:
-                self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.JANUS_PRO)
-
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))
-
-        hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
-        if hidden_act == "gelu":
-            self.gguf_writer.add_vision_use_gelu(True)
-        elif hidden_act == "silu":
-            self.gguf_writer.add_vision_use_silu(True)
-
-    def _map_aligner_tensor(self, data_torch: Tensor, name: str) -> Iterable[tuple[str, Tensor]]:
-        """Map aligner tensors to projector format"""
-        suffix = ".bias" if name.endswith(".bias") else ".weight"
-
-        if name.startswith("model.aligner."):
-            local_name = name[len("model.aligner."):]
-        elif name.startswith("aligner."):
-            local_name = name[len("aligner."):]
-        else:
-            raise ValueError(f"Unsupported Janus aligner prefix: {name}")
-
-        if local_name.startswith("fc1."):
-            mm_index = 0
-        elif local_name.startswith("hidden_layers."):
-            parts = local_name.split(".", 2)
-            if len(parts) < 3:
-                raise ValueError(f"Unexpected Janus aligner tensor name: {name}")
-            mm_index = int(parts[1]) + 1
-        else:
-            raise ValueError(f"Unsupported Janus aligner tensor: {name}")
-
-        tensor_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_index, suffix=suffix)
-        return [(tensor_name, data_torch)]
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Skip language model tensors as they will be handled by `JanusProModel`
-        if name.startswith(('model.language_model.', 'language_model.')):
-            return
-
-        # Skip generation-related components
-        skip_generation_prefixes = (
-            'model.vqmodel.',
-            'vqmodel.',
-            'model.generation_embeddings.',
-            'generation_embeddings.',
-            'model.generation_aligner.',
-            'generation_aligner.',
-            'model.generation_head.',
-            'generation_head.',
-        )
-        if name.startswith(skip_generation_prefixes):
-            return
-
-        # Handle aligner tensors
-        if name.startswith(('model.aligner.', 'aligner.')):
-            yield from self._map_aligner_tensor(data_torch, name)
-            return
-
-        # Handle vision tensors
-        if name.startswith(('model.vision_model.', 'vision_model.')):
-            yield from super().modify_tensors(data_torch, name, bid)
-            return
-
-        return
-
-
-@ModelBase.register("YoutuVLForConditionalGeneration")
-class YoutuVLVisionModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.YOUTUVL)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
-
-        # Handle activation function
-        hidden_act = str(self.hparams.get("hidden_act", "gelu_pytorch_tanh")).lower()
-        if hidden_act in ("gelu", "gelu_pytorch_tanh", "gelu_fast", "gelu_new", "gelu_accurate"):
-            self.gguf_writer.add_vision_use_gelu(True)
-        elif hidden_act == "silu":
-            self.gguf_writer.add_vision_use_silu(True)
-        else:
-            raise ValueError(f"Unsupported activation function for YOUTUVL: {hidden_act}")
-
-        self.gguf_writer.add_vision_spatial_merge_size(self.hparams.get("spatial_merge_size", 2))
-
-        window_size = self.hparams.get("window_size")
-        if window_size is not None:
-            self.gguf_writer.add_vision_window_size(window_size)
-        # fullatt_block_indexes contains explicit layer indices that use full attention
-        # e.g., [2, 5, 8, 11] means layers 2, 5, 8, 11 use full attention
-        # All other layers use window attention
-        fullatt_block_indexes = self.hparams.get("fullatt_block_indexes")
-        assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for youtuvl"
-        # Store the explicit layer indices for YoutuVL (irregular pattern approach)
-        self.gguf_writer.add_vision_wa_layer_indexes(layers=fullatt_block_indexes)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # Skip language model tensors
-        skip_prefixes = ('lm_head.', 'model.layers.', 'model.embed_tokens.', 'model.norm.')
-        if name.startswith(skip_prefixes):
-            return
-
-        # Try to map the tensor using TensorNameMap (handles vision encoder and projector)
-        try:
-            yield from super().modify_tensors(data_torch, name, bid)
-        except ValueError:
-            # If mapping fails, log warning and skip
-            logger.warning(f"Cannot map tensor: {name}")
-            return
-
-
-@ModelBase.register("SolarOpenForCausalLM")
-class SolarOpenModel(Glm4MoeModel):
-    model_arch = gguf.MODEL_ARCH.GLM4_MOE
-
-    def set_vocab(self):
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<unk>"])  # ty: ignore[unresolved-attribute]
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|startoftext|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-
-@ModelBase.register("DotsOCRForCausalLM")
-class DotsOCRVisionModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        self.hparams_vision["image_size"] = 0 # dynamic resolution
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DOTSOCR)
-        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
-        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["rms_norm_eps"]))
-        self.gguf_writer.add_vision_projector_scale_factor(self.find_vparam(["spatial_merge_size"]))
-        self.gguf_writer.add_vision_use_silu(True)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("vision_tower."):
-            if "vision_tower.blocks." in name and ".mlp." in name:
-                # note: to avoid naming conflicts in tensor_mapping.py, we need to handle FFN renaming here
-                # x = F.silu(self.fc1(x)) * self.fc3(x)
-                # x = self.fc2(x)
-                # fc1 -> gate, fc2 -> down, fc3 -> up
-                # mapping original names to Qwen2.5 naming scheme
-                name = name.replace("vision_tower.blocks.", "visual.blocks.")
-                name = name.replace(".fc1", ".gate_proj")
-                name = name.replace(".fc2", ".down_proj")
-                name = name.replace(".fc3", ".up_proj")
-            yield from super().modify_tensors(data_torch, name, bid)
-
-
-###### CONVERSION LOGIC ######
-
-
-# tree of lazy tensors
-class LazyTorchTensor(gguf.LazyBase):
-    _tensor_type = torch.Tensor
-    # to keep the type-checker happy
-    dtype: torch.dtype
-    shape: torch.Size
-
-    # only used when converting a torch.Tensor to a np.ndarray
-    _dtype_map: dict[torch.dtype, type] = {
-        torch.float16: np.float16,
-        torch.float32: np.float32,
-        torch.uint8: np.uint8,
-    }
-
-    # only used when byteswapping data. Only correct size is needed
-    _dtype_byteswap_map: dict[torch.dtype, type] = {
-        torch.float64: np.float64,
-        torch.float32: np.float32,
-        torch.bfloat16: np.float16,
-        torch.float16: np.float16,
-        torch.int64: np.int64,
-        torch.uint64: np.uint64,
-        torch.int32: np.int32,
-        torch.uint32: np.uint32,
-        torch.int16: np.int16,
-        torch.uint16: np.uint16,
-        torch.int8: np.int8,
-        torch.uint8: np.uint8,
-        torch.bool: np.uint8,
-        torch.float8_e4m3fn: np.uint8,
-        torch.float8_e5m2: np.uint8,
-    }
-
-    # used for safetensors slices
-    # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
-    # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
-    _dtype_str_map: dict[str, torch.dtype] = {
-        "F64": torch.float64,
-        "F32": torch.float32,
-        "BF16": torch.bfloat16,
-        "F16": torch.float16,
-        # "U64": torch.uint64,
-        "I64": torch.int64,
-        # "U32": torch.uint32,
-        "I32": torch.int32,
-        # "U16": torch.uint16,
-        "I16": torch.int16,
-        "U8": torch.uint8,
-        "I8": torch.int8,
-        "BOOL": torch.bool,
-        "F8_E4M3": torch.float8_e4m3fn,
-        "F8_E5M2": torch.float8_e5m2,
-    }
-
-    def numpy(self) -> gguf.LazyNumpyTensor:
-        dtype = self._dtype_map[self.dtype]
-        return gguf.LazyNumpyTensor(
-            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
-            args=(self,),
-            func=(lambda s: s.numpy())
-        )
-
-    @classmethod
-    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor:
-        return torch.empty(size=shape, dtype=dtype, device="meta")
+import argparse
+import logging
+import os
+import sys
+from pathlib import Path
 
-    @classmethod
-    def from_safetensors_slice(cls, st_slice: Any) -> Tensor:
-        dtype = cls._dtype_str_map[st_slice.get_dtype()]
-        shape: tuple[int, ...] = tuple(st_slice.get_shape())
-        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[...] if len(s.get_shape()) == 0 else s[:])
-        return cast(torch.Tensor, lazy)
+import torch
 
-    @classmethod
-    def from_local_tensor(cls, t: gguf.utility.LocalTensor) -> Tensor:
-        def load_tensor(tensor: gguf.utility.LocalTensor) -> Tensor:
-            def byteswap_tensor(tensor: np.ndarray, dtype: type) -> np.ndarray:
-                if sys.byteorder == 'big':
-                    # switch data back to big endian
-                    tensor = tensor.view(dtype).byteswap(inplace=False)
-                return tensor
-            dtype = cls._dtype_str_map[tensor.dtype]
-            numpy_dtype = cls._dtype_byteswap_map[dtype]
-            return torch.from_numpy(byteswap_tensor(tensor.mmap_bytes(), numpy_dtype)).view(dtype).reshape(tensor.shape)
-        dtype = cls._dtype_str_map[t.dtype]
-        shape = t.shape
-        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(t,), func=lambda r: load_tensor(r))
-        return cast(torch.Tensor, lazy)
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+import gguf
 
-    @classmethod
-    def from_remote_tensor(cls, remote_tensor: gguf.utility.RemoteTensor):
-        def byteswap_tensor(tensor: np.ndarray, dtype: type) -> np.ndarray:
-            if sys.byteorder == 'big':
-                # switch data back to big endian
-                tensor = tensor.view(dtype).byteswap(inplace=False)
-            return tensor
-        dtype = cls._dtype_str_map[remote_tensor.dtype]
-        numpy_dtype = cls._dtype_byteswap_map[dtype]
-        shape = remote_tensor.shape
-        meta = cls.meta_with_dtype_and_shape(dtype, shape)
-        lazy = cls(meta=meta, args=(remote_tensor,), func=lambda r: torch.from_numpy(byteswap_tensor(np.frombuffer(r.data(), dtype=numpy_dtype), numpy_dtype)).view(dtype).reshape(shape))
-        return cast(torch.Tensor, lazy)
+from conversion import (
+    ModelBase,
+    ModelType,
+    get_model_architecture,
+    get_model_class,
+    logger,
+    print_registered_models,
+    _mistral_common_installed,
+    _mistral_import_error_msg,
+)
 
-    @classmethod
-    def __torch_function__(cls, func, types, args=(), kwargs=None):
-        del types  # unused
 
-        if kwargs is None:
-            kwargs = {}
+def split_str_to_n_bytes(split_str: str) -> int:
+    if split_str.endswith("K"):
+        n = int(split_str[:-1]) * 1000
+    elif split_str.endswith("M"):
+        n = int(split_str[:-1]) * 1000 * 1000
+    elif split_str.endswith("G"):
+        n = int(split_str[:-1]) * 1000 * 1000 * 1000
+    elif split_str.isnumeric():
+        n = int(split_str)
+    else:
+        raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G")
 
-        if func is torch.Tensor.numpy:
-            assert len(args)
-            return args[0].numpy()
+    if n < 0:
+        raise ValueError(f"Invalid split size: {split_str}, must be positive")
 
-        return cls._wrap_fn(func)(*args, **kwargs)
+    return n
 
 
 def parse_args() -> argparse.Namespace:
@@ -13299,6 +117,14 @@ def parse_args() -> argparse.Namespace:
         "--mmproj", action="store_true",
         help="(Experimental) Export multimodal projector (mmproj) for vision models. This will only work on some vision models. A prefix 'mmproj-' will be added to the output file name.",
     )
+    parser.add_argument(
+        "--mtp", action="store_true",
+        help="(Experimental) Export only the multi-token prediction (MTP) head as a separate GGUF, suitable for use as a speculative draft. Output file name will get a '-MTP' suffix.",
+    )
+    parser.add_argument(
+        "--no-mtp", action="store_true",
+        help="(Experimental) Exclude the multi-token prediction (MTP) head from the converted GGUF. Pair with --mtp on a second run to publish trunk and MTP as two files. Note: the split form duplicates embeddings, so the bundled default is more space-efficient overall.",
+    )
     parser.add_argument(
         "--mistral-format", action="store_true",
         help="Whether the model is stored following the Mistral format.",
@@ -13329,58 +155,12 @@ def parse_args() -> argparse.Namespace:
     return args
 
 
-def split_str_to_n_bytes(split_str: str) -> int:
-    if split_str.endswith("K"):
-        n = int(split_str[:-1]) * 1000
-    elif split_str.endswith("M"):
-        n = int(split_str[:-1]) * 1000 * 1000
-    elif split_str.endswith("G"):
-        n = int(split_str[:-1]) * 1000 * 1000 * 1000
-    elif split_str.isnumeric():
-        n = int(split_str)
-    else:
-        raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G")
-
-    if n < 0:
-        raise ValueError(f"Invalid split size: {split_str}, must be positive")
-
-    return n
-
-
-def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> str:
-    # TODO @ngxson : this won't work correctly if the model has both audio & vision encoders
-    # maybe we should fallback to text model's arch in that case, since not many models have both
-    text_config = hparams.get("text_config", {})
-    vision_config = hparams.get("vision_config", {})
-    arch = None
-    if (arches := hparams.get("architectures")) is not None and len(arches) > 0:
-        arch = arches[0]
-    elif "ssm_cfg" in hparams:
-        # For non-hf Mamba and Mamba2 models
-        arch = hparams["ssm_cfg"].get("layer", "Mamba") + "ForCausalLM"
-
-    # Step3-VL keeps text config under text_config but uses a custom top-level architecture.
-    # For text conversion we route to a dedicated text-only class.
-    # TODO: refactor this later to avoid adding exception here
-    if model_type == ModelType.TEXT and arch == "StepVLForConditionalGeneration":
-        return arch
-
-    # if "architectures" is found in the sub-config, use that instead
-    if model_type == ModelType.TEXT and text_config.get("architectures") is not None:
-        arch = text_config["architectures"][0]
-    elif model_type == ModelType.MMPROJ and vision_config.get("architectures") is not None:
-        arch = vision_config["architectures"][0]
-    if arch is None:
-        raise ValueError("Failed to detect model architecture")
-    return arch
-
-
 def main() -> None:
     args = parse_args()
 
     if args.print_supported_models:
         logger.error("Supported models:")
-        ModelBase.print_registered_models()
+        print_registered_models()
         sys.exit(0)
 
     if args.verbose:
@@ -13446,18 +226,35 @@ def main() -> None:
             model_architecture = get_model_architecture(hparams, model_type)
             logger.info(f"Model architecture: {model_architecture}")
             try:
-                model_class = ModelBase.from_model_architecture(model_architecture, model_type=model_type)
+                model_class = get_model_class(model_architecture, mmproj=(model_type == ModelType.MMPROJ))
             except NotImplementedError:
                 logger.error(f"Model {model_architecture} is not supported")
                 sys.exit(1)
         elif args.mmproj:
             assert hparams.get("vision_encoder") is not None, "This model does not support multimodal"
+            from conversion.pixtral import PixtralModel
             model_class = PixtralModel
         elif "moe" in hparams:
+            from conversion.mistral import MistralMoeModel
             model_class = MistralMoeModel
         else:
+            from conversion.mistral import MistralModel
             model_class = MistralModel
 
+        if args.mtp and args.no_mtp:
+            logger.error("--mtp and --no-mtp are mutually exclusive")
+            sys.exit(1)
+
+        if args.mtp or args.no_mtp:
+            from conversion.qwen import _Qwen35MtpMixin
+            if not issubclass(model_class, _Qwen35MtpMixin):
+                logger.error("--mtp / --no-mtp are only supported for Qwen3.5/3.6 text variants today")
+                sys.exit(1)
+            if args.no_mtp:
+                model_class.no_mtp = True
+            if args.mtp:
+                model_class.mtp_only = True
+
         model_instance = model_class(dir_model, output_type, fname_out,
                                      is_big_endian=args.bigendian, use_temp_file=args.use_temp_file,
                                      eager=args.no_lazy,
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index d8d10a10128..8b2a9454f98 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -19,7 +19,7 @@
 logger = logging.getLogger("convert_hf_to_gguf_update")
 sess = requests.Session()
 
-convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
+convert_py_pth = pathlib.Path("conversion/base.py")
 convert_py = convert_py_pth.read_text(encoding="utf-8")
 hf_token_pth = pathlib.Path.home() / ".cache" / "huggingface" / "token"
 hf_token = hf_token_pth.read_text(encoding="utf-8").strip() if hf_token_pth.exists() else None
@@ -155,6 +155,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "joyai-llm",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jdopensource/JoyAI-LLM-Flash", },
     {"name": "kanana2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601", },
     {"name": "f2llmv2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/codefuse-ai/F2LLM-v2-4B", },
+    {"name": "sarvam-moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sarvamai/sarvam-30b", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -175,6 +176,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
     {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
     {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
+    {"name": "qwen35",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openbmb/MiniCPM-V-4_6", "chkhsh": "1444df51289cfa8063b96f0e62b1125440111bc79a52003ea14b6eac7016fd5f"},
     {"name": "grok-2",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
     # jina-v2-de variants
     {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
@@ -372,7 +374,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 
 convert_py_pth.write_text(convert_py, encoding="utf-8")
 
-logger.info("+++ convert_hf_to_gguf.py was updated")
+logger.info(f"+++ {convert_py_pth} was updated")
 
 # generate tests for each tokenizer model
 
diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
index d5833420560..1b7334617d1 100755
--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -22,12 +22,11 @@
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
-
-# reuse model definitions from convert_hf_to_gguf.py
-from convert_hf_to_gguf import LazyTorchTensor, ModelBase
-
 from gguf.constants import GGUFValueType
 
+# reuse model definitions from the conversion/ package
+from conversion import LazyTorchTensor, ModelBase, get_model_class
+
 logger = logging.getLogger("lora-to-gguf")
 
 
@@ -188,6 +187,24 @@ def transpose(self, dim0: int, dim1: int) -> LoraTorchTensor:
     def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor:
         return self.transpose(axis0, axis1)
 
+    def split(self, split_size: int | Sequence[int], dim: int = 0) -> tuple[LoraTorchTensor, ...]:
+        shape = self.shape
+        ndim = len(shape)
+        if dim < 0:
+            dim += ndim
+        if dim == ndim - 1:
+            A_chunks = self._lora_A.split(split_size, dim=-1)
+            return tuple(LoraTorchTensor(a, self._lora_B) for a in A_chunks)
+        elif dim == ndim - 2:
+            B_chunks = self._lora_B.split(split_size, dim=-2)
+            return tuple(LoraTorchTensor(self._lora_A, b) for b in B_chunks)
+        else:
+            B_chunks = self._lora_B.split(split_size, dim=dim)
+            if self._lora_A.shape[dim] == 1:
+                return tuple(LoraTorchTensor(self._lora_A, b) for b in B_chunks)
+            A_chunks = self._lora_A.split(split_size, dim=dim)
+            return tuple(LoraTorchTensor(a, b) for a, b in zip(A_chunks, B_chunks))
+
     def to(self, *args, **kwargs):
         return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs))
 
@@ -230,6 +247,11 @@ def __torch_function__(cls, func: Callable, types, args=(), kwargs=None):
                 )
             else:
                 raise NotImplementedError
+        elif func is torch.split:
+            assert len(args) and len(args) >= 2
+            tensor, split_size = args[0], args[1]
+            dim = args[2] if len(args) > 2 else kwargs.get("dim", 0)
+            return tensor.split(split_size, dim=dim)
         else:
             raise NotImplementedError
 
@@ -361,7 +383,7 @@ def load_hparams_from_hf(hf_model_id: str) -> tuple[dict[str, Any], Path | None]
 
     with torch.inference_mode():
         try:
-            model_class = ModelBase.from_model_architecture(hparams["architectures"][0])
+            model_class = get_model_class(hparams["architectures"][0])
         except NotImplementedError:
             logger.error(f"Model {hparams['architectures'][0]} is not supported")
             sys.exit(1)
diff --git a/docs/backend/OPENVINO.md b/docs/backend/OPENVINO.md
index 96d0f672e30..b0e19abb090 100644
--- a/docs/backend/OPENVINO.md
+++ b/docs/backend/OPENVINO.md
@@ -57,17 +57,22 @@ Although OpenVINO supports a wide range of [Intel hardware](https://docs.openvin
 
 ## Validated Models
 
-The following models have been validated for functionality on Intel® Core™ Ultra Series 1 and Series 2:
-
-- [Llama-3.2-1B-Instruct-GGUF](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/)
-- [Llama-3.1-8B-Instruct](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF)
-- [microsoft/Phi-3-mini-4k-instruct-gguf](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf)
-- [Qwen/Qwen2.5-1.5B-Instruct-GGUF](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF)
-- [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B-GGUF)
-- [openbmb/MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-S-1B-sft-gguf)
-- [tencent/Hunyuan-7B-Instruct](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF)
-- [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF)
-- [bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF)
+The following models were validated on Intel® Core™ Ultra Series 2. While our testing was limited, the OpenVINO backend is expected to work across a broad range of [Intel hardware](https://docs.openvino.ai/2026/about-openvino/release-notes-openvino/system-requirements.html).
+- Use `GGML_OPENVINO_STATEFUL_EXECUTION=1` when using GPU device.
+- `-fa 1` is required when running llama-bench with the OpenVINO backend.
+- Additional model support, quantization formats and validations are work in progress.
+
+| Model  | Validated   | Known Issues  |
+| :------| :---------- | :-------------|
+| [Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/) | `FP16`, `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M` on CPU/GPU/NPU | — |
+| [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF) | `Q8_0`, `Q4_K_M` on CPU/GPU/NPU | `Q4_0_8_8`, `Q4_0_4_8`, `Q4_0_4_4` fail |
+| [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | `FP16`, `Q4` on CPU/NPU | GPU unsupported for `FP16` and `Q4` (`llama-cli`, `llama-bench`) |
+| [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) | `FP16`, `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M` on CPU/GPU/NPU | — |
+| [Qwen3-8B-Instruct](https://huggingface.co/Qwen/Qwen3-8B-GGUF) | `FP16`, `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M` on CPU/NPU; GPU works via `llama-bench` | GPU `llama-cli` unsupported for all quantizations |
+| [MiniCPM-V-2_6-GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `Q4_0` on CPU/GPU/NPU | — |
+| [DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF) | `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M` on CPU/GPU/NPU | — |
+| [Hunyuan-7B-Instruct](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF) | CPU: `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M`; GPU: `Q8_0`, `Q4_0`, `Q4_1`; NPU (`llama-bench` only): `Q4_0`, `Q4_1`, `Q4_K_M` | GPU `Q4_K_M` unsupported; NPU `llama-cli` unsupported |
+| [Mistral-7B-Instruct-v0.3](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF/) | CPU/GPU: `Q8_0`, `Q4_K_M`; NPU: `Q8_0`, `Q4_K_M` (via `llama-bench`) | NPU `llama-cli` unsupported for `Q8_0`, `Q4_K_M` |
 
 ## Build Instructions
 
@@ -244,7 +249,6 @@ build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf"
 - `-fa 1` is required when running llama-bench with the OpenVINO backend.
   - `GGML_OPENVINO_STATEFUL_EXECUTION=1 GGML_OPENVINO_DEVICE=GPU ./llama-bench -fa 1`
 - `llama-server` with OpenVINO backend supports only one chat session/thread, when `GGML_OPENVINO_STATEFUL_EXECUTION=1` is enabled.
-- For Intel GPU, NPU detection in containers, GPU, NPU user-space drivers/libraries must be present inside the image. We will include in a future PR. Until then, you can use this reference Dockerfile: [openvino.Dockerfile](https://github.com/ravi9/llama.cpp/blob/ov-docker-update/.devops/openvino.Dockerfile)
 
 > [!NOTE]
 > The OpenVINO backend is actively under development. Fixes are underway, and this document will continue to be updated as issues are resolved.
@@ -274,8 +278,6 @@ docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_p
 Run llama.cpp with OpenVINO backend Docker container.
 Save sample models in `~/models` as [shown above](#3-download-sample-model). It will be mounted to the container in the examples below.
 
-> [!NOTE]
-> Intel GPU, NPU detection in containers will be included in a future PR. Until then, you can use this reference Dockerfile: [openvino.Dockerfile](https://github.com/ravi9/llama.cpp/blob/ov-docker-update/.devops/openvino.Dockerfile).
 
 ```bash
 #  Run Docker container
diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index d52c61acb66..155f933b805 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -31,6 +31,8 @@ SYCL cross-platform capabilities enable support for other vendor GPUs as well.
 
 ## Recommended Release
 
+### Windows
+
 The following releases are verified and recommended:
 
 |Commit ID|Tag|Release|Verified  Platform| Update date|
@@ -39,9 +41,22 @@ The following releases are verified and recommended:
 |3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc A770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19|
 |fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc A770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1||
 
+### Ubuntu 24.04
+
+The release packages for Ubuntu 24.04 x64 (FP32/FP16) only include the binary files of the llama.cpp SYCL backend. They require the target machine to have pre-installed Intel GPU drivers and oneAPI packages that are the same version as the build package. To get the version and installation info, refer to release.yml: ubuntu-24-sycl -> Download & Install oneAPI.
+
+It is recommended to use them with Intel Docker.
+
+The packages for FP32 and FP16 would have different accuracy and performance on LLMs. Please choose it acording to the test result.
 
 ## News
 
+- 2026.04
+
+  - Optimize mul_mat by reorder feature for data type: Q4_K, Q5_K, Q_K, Q8_0.
+  - Fused MoE.
+  - Upgrate CI and built package for oneAPI 2025.3.3, support Ubuntu 24.04 built package.
+
 - 2026.03
   - Support Flash-Attention: less memory usage, performance impact depends on LLM.
 
@@ -229,6 +244,7 @@ Upon a successful installation, SYCL is enabled for the available intel devices,
 
 |Verified release|
 |-|
+|2025.3.3 |
 |2025.2.1|
 |2025.1|
 |2024.1|
@@ -339,6 +355,12 @@ Choose one of following methods to run.
 ./examples/sycl/test.sh
 ```
 
+- Run llama-server:
+
+```sh
+./examples/sycl/start-svr.sh -m PATH/MODEL_FILE
+```
+
 2. Command line
 Launch inference
 
@@ -627,10 +649,18 @@ Choose one of following methods to run.
 
 1. Script
 
+- Run test:
+
 ```
 examples\sycl\win-test.bat
 ```
 
+- Run llama-server:
+
+```
+examples\sycl\win-start-svr.bat -m PATH\MODEL_FILE
+```
+
 2. Command line
 
 Launch inference
@@ -690,6 +720,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_GRAPH    | OFF *(default)* \|ON *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
 | GGML_SYCL_DNN      | ON *(default)* \|OFF *(Optional)*     | Enable build with oneDNN.                   |
 | GGML_SYCL_HOST_MEM_FALLBACK | ON *(default)* \|OFF *(Optional)* | Allow host memory fallback when device memory is full during quantized weight reorder. Enables inference to continue at reduced speed (reading over PCIe) instead of failing. Requires Linux kernel 6.8+. |
+| GGML_SYCL_SUPPORT_LEVEL_ZERO | ON *(default)* \|OFF *(Optional)* | Enable Level Zero API for device memory allocation. Requires Level Zero headers/library at build time and Intel GPU driver (Level Zero runtime) at run time. Reduces system RAM usage during multi-GPU inference. |
 | CMAKE_C_COMPILER   | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path.      |
 | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
 
@@ -703,9 +734,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
 | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10) |
 | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
+| GGML_SYCL_ENABLE_LEVEL_ZERO | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO=ON at build time. |
 | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
-| UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.|
+| UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Allow SYCL/Unified Runtime Level Zero device allocations larger than 4 GiB. llama.cpp's direct Level Zero allocation path requests the relaxed maximum-size limit itself when GGML_SYCL_ENABLE_LEVEL_ZERO=1. |
+
+## Compile-time Flags
+
+Pass these via `CXXFLAGS` or add a one-off `#define` to enable a flag on the spot.
+
+| Name            | Function                                                                         |
+|-----------------|----------------------------------------------------------------------------------|
+| DEBUG_SYCL_POOL | Enable device memory pool logging on teardown. Useful for profiling allocations. |
 
 ## Design Rule
 
@@ -781,7 +821,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 - `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 5000000000 Bytes of memory on device`
 
-  You need to enable to support 4GB memory malloc by:
+  With the default `GGML_SYCL_ENABLE_LEVEL_ZERO=1`, llama.cpp requests Level Zero's relaxed maximum-size allocation limit directly. If Level Zero support is disabled at build time or runtime and the allocation goes through SYCL/Unified Runtime instead, enable support for allocations larger than 4 GiB by:
   ```
     export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
     set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
diff --git a/docs/backend/snapdragon/README.md b/docs/backend/snapdragon/README.md
index e13fdfd05e7..2414eeaf6a4 100644
--- a/docs/backend/snapdragon/README.md
+++ b/docs/backend/snapdragon/README.md
@@ -249,18 +249,27 @@ build: 6a8cf8914 (6733)
   ```
 
 - `GGML_HEXAGON_PROFILE=1`
-  Generates a host-side profile for the ggml-hexagon Ops.
+  Enables Op profiling:
 
-- `GGML_HEXAGON_OPMASK=0x0`
-  Allows enabling specific stages of the processing pipeline:
+  - `1` Basic profile with per-op `usecs` and `cycles` counters
+  - `2` Extended profile with per-op `usecs`, `cycles` and default PMU counter data
+  - `0x1,...,0x8` Extended profile with per-op `usecs`, `cycles` and custom PMU counter data
+
+  The logging output can be either saved into a file for post-processing or it can be piped directly into the post-processing tool to generate the report.
+  Examples:
+
+      `GGML_HEXAGON_PROFILE=1 llama-completion ... |& ./scripts/snapdragon/ggml-hexagon-profile.py -`
+
+- `GGML_HEXAGON_OPSTAGE=0x0`
+  Allows enabling specific stages of the Op processing pipeline:
 
   - `0x1` Enable Op Queue (i.e., queuing Ops into NPU)
   - `0x2` Enable Op Compute (MUL_MAT, etc.)
 
   Examples:
 
-      `GGML_HEXAGON_OPMASK=0x1 llama-completion ...` - Ops are enqueued but NPU-side processing is stubbed out
-      `GGML_HEXAGON_OPMASK=0x3 llama-completion ...` - Full queuing and processing of Ops (default)
+      `GGML_HEXAGON_OPSTAGE=0x1 llama-completion ...` - Ops are enqueued to the NPU but dma & compute are disabled
+      `GGML_HEXAGON_OPSTAGE=0x3 llama-completion ...` - Full queuing and processing of Ops (default)
 
 - `GGML_HEXAGON_OPFILTER=regex`
   Allows filtering (disabling) Ops that match the regex pattern:
diff --git a/docs/build-riscv64-spacemit.md b/docs/build-riscv64-spacemit.md
index cd6bbe199d3..08301d51cae 100644
--- a/docs/build-riscv64-spacemit.md
+++ b/docs/build-riscv64-spacemit.md
@@ -9,18 +9,20 @@ wget https://archive.spacemit.com/toolchain/spacemit-toolchain-linux-glibc-x86_6
 ~~~
 
 2. Build
-Below is the build script: it requires utilizing RISC-V vector instructions for acceleration. Ensure the `GGML_CPU_RISCV64_SPACEMIT` compilation option is enabled. The currently supported optimization version is `RISCV64_SPACEMIT_IME1`, corresponding to the `RISCV64_SPACEMIT_IME_SPEC` compilation option. Compiler configurations are defined in the `riscv64-spacemit-linux-gnu-gcc.cmake` file. Please ensure you have installed the RISC-V compiler and set the environment variable via `export RISCV_ROOT_PATH={your_compiler_path}`.
+Below is the build script: it requires utilizing RISC-V vector instructions for acceleration. Ensure the `GGML_CPU_RISCV64_SPACEMIT` compilation option is enabled. The currently supported optimization version is `RISCV64_SPACEMIT_IME1` and `RISCV64_SPACEMIT_IME2`, corresponding to the `RISCV64_SPACEMIT_IME_SPEC` compilation option. Compiler configurations are defined in the `riscv64-spacemit-linux-gnu-gcc.cmake` file. Please ensure you have installed the RISC-V compiler and set the environment variable via `export RISCV_ROOT_PATH={your_compiler_path}`.
 ```bash
 
 cmake -B build \
     -DCMAKE_BUILD_TYPE=Release \
     -DGGML_CPU_RISCV64_SPACEMIT=ON \
+    -DGGML_CPU_REPACK=OFF \
     -DLLAMA_OPENSSL=OFF \
     -DGGML_RVV=ON \
+    -DGGML_RV_ZVFH=ON \
     -DGGML_RV_ZFH=ON \
     -DGGML_RV_ZICBOP=ON \
     -DGGML_RV_ZIHINTPAUSE=ON \
-    -DRISCV64_SPACEMIT_IME_SPEC=RISCV64_SPACEMIT_IME1 \
+    -DGGML_RV_ZBA=ON \
     -DCMAKE_TOOLCHAIN_FILE=${PWD}/cmake/riscv64-spacemit-linux-gnu-gcc.cmake \
     -DCMAKE_INSTALL_PREFIX=build/installed
 
@@ -47,8 +49,25 @@ export RISCV_ROOT_PATH_IME1={your RISC-V compiler path}
 
 ${QEMU_ROOT_PATH}/bin/qemu-riscv64 -L ${RISCV_ROOT_PATH_IME1}/sysroot -cpu max,vlen=256,elen=64,vext_spec=v1.0 ${PWD}/build/bin/llama-cli -m ${PWD}/models/Qwen2.5-0.5B-Instruct-Q4_0.gguf -t 1
 ~~~
+
+## Quantization Support For Matrix
+
+| Quantization Type | X60 | A100 |
+| ---: | ---: | ---: |
+| Q2_K |  | :heavy_check_mark: |
+| Q3_K |  | :heavy_check_mark: |
+| Q4_0 | :heavy_check_mark: | :heavy_check_mark: |
+| Q4_1 | :heavy_check_mark: | :heavy_check_mark: |
+| Q4_K | :heavy_check_mark: | :heavy_check_mark: |
+| Q5_0 |  | :heavy_check_mark: |
+| Q5_1 |  | :heavy_check_mark: |
+| Q5_K |  | :heavy_check_mark: |
+| Q6_K |  | :heavy_check_mark: |
+| Q8_0 |  | :heavy_check_mark: |
+
+
 ## Performance
-#### Quantization Support For Matrix
+* Spacemit(R) X60
 ~~~
 model name      : Spacemit(R) X60
 isa             : rv64imafdcv_zicbom_zicboz_zicntr_zicond_zicsr_zifencei_zihintpause_zihpm_zfh_zfhmin_zca_zcd_zba_zbb_zbc_zbs_zkt_zve32f_zve32x_zve64d_zve64f_zve64x_zvfh_zvfhmin_zvkt_sscofpmf_sstc_svinval_svnapot_svpbmt
@@ -58,33 +77,34 @@ mvendorid       : 0x710
 marchid         : 0x8000000058000001
 ~~~
 
-Q4_0
-|   Model    |   Size   | Params | backend | threads | test | t/s |
-| -----------| -------- | ------ | ------- | ------- | ---- |------|
-Qwen2.5 0.5B |403.20 MiB|630.17 M|   cpu   |    4    | pp512|64.12 ± 0.26|
-Qwen2.5 0.5B |403.20 MiB|630.17 M|   cpu   |    4    | tg128|10.03 ± 0.01|
-Qwen2.5 1.5B |1011.16 MiB| 1.78 B |   cpu   |    4    | pp512|24.16 ± 0.02|
-Qwen2.5 1.5B |1011.16 MiB| 1.78 B |   cpu   |    4    | tg128|3.83 ± 0.06|
-Qwen2.5 3B   | 1.86 GiB  | 3.40 B |   cpu   |    4    | pp512|12.08 ± 0.02|
-Qwen2.5 3B   | 1.86 GiB  | 3.40 B |   cpu   |    4    | tg128|2.23 ± 0.02|
-
-Q4_1
-|   Model    |   Size   | Params | backend | threads | test | t/s |
-| -----------| -------- | ------ | ------- | ------- | ---- |------|
-Qwen2.5 0.5B |351.50 MiB|494.03 M|   cpu   |    4    | pp512|62.07 ± 0.12|
-Qwen2.5 0.5B |351.50 MiB|494.03 M|   cpu   |    4    | tg128|9.91 ± 0.01|
-Qwen2.5 1.5B |964.06 MiB| 1.54 B |   cpu   |    4    | pp512|22.95 ± 0.25|
-Qwen2.5 1.5B |964.06 MiB| 1.54 B |   cpu   |    4    | tg128|4.01 ± 0.15|
-Qwen2.5 3B   | 1.85 GiB | 3.09 B |   cpu   |    4    | pp512|11.55 ± 0.16|
-Qwen2.5 3B   | 1.85 GiB | 3.09 B |   cpu   |    4    | tg128|2.25 ± 0.04|
-
-
-Q4_K
-|   Model    |   Size   | Params | backend | threads | test | t/s |
-| -----------| -------- | ------ | ------- | ------- | ---- |------|
-Qwen2.5 0.5B |462.96 MiB|630.17 M|   cpu   |    4    | pp512|9.29 ± 0.05|
-Qwen2.5 0.5B |462.96 MiB|630.17 M|   cpu   |    4    | tg128|5.67 ± 0.04|
-Qwen2.5 1.5B | 1.04 GiB | 1.78 B |   cpu   |    4    | pp512|10.38 ± 0.10|
-Qwen2.5 1.5B | 1.04 GiB | 1.78 B |   cpu   |    4    | tg128|3.17 ± 0.08|
-Qwen2.5 3B   | 1.95 GiB | 3.40 B |   cpu   |    4    | pp512|4.23 ± 0.04|
-Qwen2.5 3B   | 1.95 GiB | 3.40 B |   cpu   |    4    | tg128|1.73 ± 0.00|
+| model                          |       size |     params | backend    | threads | n_ubatch | fa | mmap |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | ---: | --------------: | -------------------: |
+| qwen35 2B Q4_1                 |   1.19 GiB |     1.88 B | CPU        |       4 |      128 |  1 |    0 |           pp128 |         10.32 ± 0.02 |
+| qwen35 2B Q4_1                 |   1.19 GiB |     1.88 B | CPU        |       4 |      128 |  1 |    0 |           tg128 |          3.07 ± 0.01 |
+| qwen3 0.6B Q4_0                | 358.78 MiB |   596.05 M | CPU        |       4 |      128 |  1 |    0 |           pp128 |         49.15 ± 0.25 |
+| qwen3 0.6B Q4_0                | 358.78 MiB |   596.05 M | CPU        |       4 |      128 |  1 |    0 |           tg128 |         11.73 ± 0.02 |
+
+
+* Spacemit(R) A100
+~~~
+model name      : Spacemit(R) A100
+isa             : rv64imafdcvh_zicbom_zicbop_zicboz_zicntr_zicond_zicsr_zifencei_zihintntl_zihintpause_zihpm_zimop_zaamo_zalrsc_zawrs_zfa_zfh_zfhmin_zca_zcb_zcd_zcmop_zba_zbb_zbc_zbs_zkt_zvbb_zvbc_zve32f_zve32x_zve64d_zve64f_zve64x_zvfh_zvfhmin_zvkb_zvkg_zvkned_zvknha_zvknhb_zvksed_zvksh_zvkt_smaia_smstateen_ssaia_sscofpmf_sstc_svinval_svnapot_svpbmt_sdtrig
+mmu             : sv39
+mvendorid       : 0x710
+marchid         : 0x8000000041000002
+mimpid          : 0x10000000d5686200
+hart isa        : rv64imafdcv_zicbom_zicbop_zicboz_zicntr_zicond_zicsr_zifencei_zihintntl_zihintpause_zihpm_zimop_zaamo_zalrsc_zawrs_zfa_zfh_zfhmin_zca_zcb_zcd_zcmop_zba_zbb_zbc_zbs_zkt_zvbb_zvbc_zve32f_zve32x_zve64d_zve64f_zve64x_zvfh_zvfhmin_zvkb_zvkg_zvkned_zvknha_zvknhb_zvksed_zvksh_zvkt_smaia_smstateen_ssaia_sscofpmf_sstc_svinval_svnapot_svpbmt_sdtrig
+~~~
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa | mmap |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | ---: | --------------: | -------------------: |
+| qwen3 0.6B Q4_0                | 358.78 MiB |   596.05 M | CPU        |       8 |      128 |  1 |    0 |           pp128 |        565.83 ± 0.31 |
+| qwen3 0.6B Q4_0                | 358.78 MiB |   596.05 M | CPU        |       8 |      128 |  1 |    0 |           tg128 |         55.77 ± 0.02 |
+| qwen3 4B Q4_0                  |   2.21 GiB |     4.02 B | CPU        |       8 |      128 |  1 |    0 |           pp128 |         79.74 ± 0.04 |
+| qwen3 4B Q4_0                  |   2.21 GiB |     4.02 B | CPU        |       8 |      128 |  1 |    0 |           tg128 |         11.29 ± 0.00 |
+| qwen3moe 30B.A3B Q4_0          |  16.18 GiB |    30.53 B | CPU        |       8 |      128 |  1 |    0 |           pp128 |         57.88 ± 0.31 |
+| qwen3moe 30B.A3B Q4_0          |  16.18 GiB |    30.53 B | CPU        |       8 |      128 |  1 |    0 |           tg128 |         12.79 ± 0.00 |
+| qwen35 2B Q4_1                 |   1.19 GiB |     1.88 B | CPU        |       8 |      128 |  1 |    0 |           pp128 |        115.23 ± 0.04 |
+| qwen35 2B Q4_1                 |   1.19 GiB |     1.88 B | CPU        |       8 |      128 |  1 |    0 |           tg128 |         16.49 ± 0.01 |
+| gemma4 E4B Q4_K - Medium       |   4.76 GiB |     7.52 B | CPU        |       8 |      128 |  1 |    0 |           pp128 |         21.13 ± 0.01 |
+| gemma4 E4B Q4_K - Medium       |   4.76 GiB |     7.52 B | CPU        |       8 |      128 |  1 |    0 |           tg128 |          5.66 ± 0.00 |
diff --git a/docs/multi-gpu.md b/docs/multi-gpu.md
new file mode 100644
index 00000000000..0d9eea7c2fb
--- /dev/null
+++ b/docs/multi-gpu.md
@@ -0,0 +1,127 @@
+# Using multiple GPUs with llama.cpp
+
+This guide explains how to run [llama.cpp](https://github.com/ggml-org/llama.cpp) across more than one GPU. It covers the split modes, the command-line flags that control them, the limitations you need to know about, and ready-to-use recipes for `llama-cli` and `llama-server`.
+
+The CLI arguments listed here are the same for both tools - or most llama.cpp binaries for that matter.
+
+---
+
+## When you need multi-GPU
+
+Reach for multi-GPU when one of these is true:
+
+- **The model doesn't fit in a single GPU's VRAM.** By spreading the weights across two or more GPUs the whole model can stay on accelerators. Otherwise part of the model will need to be run off of the comparatively slower system RAM.
+- **You want more throughput.** By distributing the computation across multiple GPUs, each individual GPU has to do less work. This can result in better prefill and/or token generation performance, depending on the split mode and interconnect speed vs. the speed of an individual GPU.
+
+---
+
+## The split modes
+
+Set with `--split-mode` / `-sm`.
+
+| Mode | What it does | When to use |
+|---|---|---|
+| `none` | Use a single GPU only. Pick which one with `--main-gpu`. | You explicitly want to confine the model to one GPU even though more are visible. |
+| `layer` (**default**) | Pipeline parallelism. Each GPU holds a contiguous slice of layers. The KV cache for layer *l* lives on the GPU that owns layer *l*. | Default and most compatible multi-GPU choice. You want more memory than a single GPU provides and your priority is a fast prefill. Can tolerate slow interconnect speeds between GPUs. |
+| `row` | **Deprecated.** Older row-split tensor-parallel path with comparatively poor performance. Splits only dense weights across GPUs. Superseded by `tensor` which should be universally superior if it can be used. | Avoid in new deployments. |
+| `tensor` | **EXPERIMENTAL.** Tensor parallelism that splits both weights *and* KV across the participating GPUs via a "meta device" abstraction. | You want more memory than a single GPU provides and your priority is fast token generation. Prefill speeds approach pipeline parallel speeds for large, dense models and fast GPU interconnect speeds. Treat as experimental as the code is less mature than pipeline parallelism. Performance should be good for multiple NVIDIA GPUs using the CUDA backend, no guarantees otherwise. |
+
+> Pipeline parallel (`layer`) vs. tensor parallel (`tensor`): pipeline-parallel runs different layers on different GPUs and processes tokens sequentially through the pipeline. This minimizes data transfers between GPUs but requires many tokens to scale well. Tensor-parallel splits each layer across GPUs and does multiple cross-GPU reductions per layer. This enables parallelizing any workload but is much more bottlenecked by the GPU interconnect speed. Pipeline-parallel maximizes batch throughput; tensor-parallel minimizes latency.
+
+---
+
+## Command-line arguments reference
+
+| Short | Long | Value | Default | Notes |
+|---|---|---|---|---|
+| `-sm` | `--split-mode` | `none` \| `layer` \| `tensor` | `layer` | See modes above. |
+| `-ts` | `--tensor-split` | comma-separated proportions, e.g. `3,1` | mode-dependent | How much of the model goes to each GPU. If omitted, `layer`/`row` use automatic splitting proportional to memory, while `tensor` splits tensor segments evenly. With `3,1` on two GPUs, GPU 0 gets 75 %, GPU 1 gets 25 %. The values follow the order in `--device`. |
+| `-mg` | `--main-gpu` | integer device index | `0` | The single GPU used in `--split-mode none`. |
+| `-ngl` | `--n-gpu-layers` / `--gpu-layers` | integer \| `auto` \| `all` | `auto` | Maximum number of layers to keep in VRAM. Use `999` or `all` to push everything possible to the GPUs. |
+| `-dev` | `--device` | comma-separated device names, or `none` | auto | Restrict which devices llama.cpp may use. See `--list-devices` for names. |
+| | `--list-devices` | - | - | Print the available devices and their memory. Run this first to learn the names you'd pass to `--device`. |
+| `-fa` | `--flash-attn` | `on` \| `off` \| `auto` | `auto` | Required when using `--split-mode tensor` and/or quantized V cache. Supported (and therefore enabled by default) for most combinations of models and backends. |
+| `-ctk` | `--cache-type-k` | `f32` \| `f16` \| `bf16` \| `q8_0` \| `q4_0` \| ... | `f16` | KV cache type for K. |
+| `-ctv` | `--cache-type-v` | same as `-ctk` | `f16` | KV cache type for V. |
+| `-fit` | `--fit` | `on` \| `off` | `on` | Auto-fit unset args to device memory. **Not supported with `tensor`. You may need to manually set the `--ctx-size` to make the model fit.**  |
+
+As for any CUDA program, the environment variable `CUDA_VISIBLE_DEVICES` can be used to control which GPUs to use for the CUDA backend: if you set it, llama.cpp only sees the specified GPUs. Use `--device` for selecting GPUs from among those visible to llama.cpp, this works for any backend.
+
+---
+
+## Recipes
+
+### 1. Default - pipeline parallel across all visible GPUs
+
+```bash
+llama-cli -m model.gguf
+llama-server -m model.gguf
+```
+
+Easiest configuration. KV cache spreads across the GPUs along with the layers. `--fit` (on by default) sizes things automatically.
+
+### 2. Pipeline parallel with a custom split ratio
+
+```bash
+llama-cli -m model.gguf -ts 3,1
+```
+
+Useful when GPUs have different memory: GPU 0 (3 parts) and GPU 1 (1 part). Proportions are normalized so `-ts 3,1` is the same as e.g. `-ts 75,25`.
+
+### 3. Single-GPU mode, picking a specific GPU
+
+```bash
+llama-cli --list-devices
+llama-cli -m model.gguf -dev CUDA1
+```
+
+Use only the device listed as `CUDA1` when calling with `--list-devices`.
+
+### 4. Tensor parallelism (experimental)
+
+```bash
+llama-cli -m model.gguf -sm tensor -ctk f16 -ctv f16
+```
+
+- `--flash-attn off` or (`--flash-attn auto` resolving to `off` when it isn't supported) is a hard error.
+- KV cache types must be non-quantized: `f32`, `f16`, or `bf16`. Support for quantized KV cache is not implemented and trying to use it will result in an error.
+- Mark this configuration as experimental in your tooling: validate output quality before deploying.
+- `--split-mode tensor`is not implemented for all architectures. The following will fail with *"LLAMA_SPLIT_MODE_TENSOR not implemented for architecture '...'"*:
+
+  - **MoE / hybrid:** Grok, MPT, OLMoE, DeepSeek2, GLM-DSA, Nemotron-H, Nemotron-H-MoE, Granite-Hybrid, LFM2-MoE, Minimax-M2, Mistral4, Kimi-Linear, Jamba, Falcon-H1
+  - **State-space / RWKV-style:** Mamba, Mamba2 (and the hybrid Mamba-attention models above)
+  - **Other:** PLAMO2, MiniCPM3, Gemma-3n, OLMo2, BitNet, T5
+
+### 5. With NCCL
+
+There's no runtime flag for NCCL - it's selected at build time (`-DGGML_CUDA_NCCL=ON`, this is the default). Note that NCCL is **not** automatically distributed with CUDA and you may need to install it manually - when in doubt check the CMake log to see whether or not it can find the package. When llama.cpp is compiled with NCCL support it uses it automatically for cross-GPU reductions in `tensor` mode. When NCCL is missing on a multi-GPU build, you'll see this one-time warning and performance will be lower:
+
+```
+NVIDIA Collective Communications Library (NCCL) is unavailable, multi GPU performance will be suboptimal
+```
+
+When using the "ROCm" backend (which is the ggml CUDA code translated for AMD via HIP), the AMD equivalent RCCL can be used by compiling with `-DGGML_HIP_RCCL=ON`. Note that RCCL is by default *disabled* because (unlike NCCL) it was not universally beneficial during testing.
+### 6. With CUDA peer-to-peer access (`GGML_CUDA_P2P`)
+
+CUDA peer-to-peer (P2P) lets GPUs transfer data directly between each other instead of going through system memory, which generally improves multi-GPU performance. It is **opt-in** at runtime - set the environment variable `GGML_CUDA_P2P` to any value to enable it:
+
+```bash
+GGML_CUDA_P2P=1 llama-cli -m model.gguf -sm tensor
+```
+
+P2P requires driver support (usually restricted to workstation/datacenter GPUs) and **may cause crashes or corrupted outputs on some motherboards or BIOS configurations** (e.g. when IOMMU is enabled). If you see instability after enabling it, unset the variable.
+
+---
+
+## Troubleshooting
+
+| Symptom | How to fix |
+|---|---|
+| Startup error *"SPLIT_MODE_TENSOR requires flash_attn to be enabled"* | Add `-fa on` or remove `-fa off`. |
+| Startup error *"simultaneous use of SPLIT_MODE_TENSOR and KV cache quantization not implemented"* | Use `-ctk f16 -ctv f16` (or `bf16`/`f32`) with `--split-mode tensor`. |
+| Startup error *"LLAMA_SPLIT_MODE_TENSOR not implemented for architecture 'X'"* | Architecture not on the TENSOR allow-list. Use `--split-mode layer`. |
+| Warning *"NCCL is unavailable, multi GPU performance will be suboptimal"* | llama.cpp wasn't built with NCCL. Either accept the lower performance or install NCCL and rebuild. |
+| CUDA OOM at startup or during prefill in `--split-mode tensor` | Auto-fit is disabled in this mode, so reduce memory pressure yourself. In order from least to most disruptive: lower `--ctx-size` (`-c`) (KV cache is roughly proportional to `n_ctx`); for `llama-server`, lower `--parallel` (`-np`) (a slot KV cache is allocated per concurrent sequence); as a last resort, reduce `--n-gpu-layers` (`-ngl`) (the remaining layers run on CPU and inference will be much slower). |
+| Performance is worse with multi-GPU than single-GPU | The performance is bottlenecked by GPU interconnect speed. For `--split-mode tensor`, verify that NCCL is being used. Try `--split-mode layer` (less communication than `tensor`). Increase GPU interconnect speed via more PCIe lanes or e.g. NVLink (if available). |
+| GPU not used at all | `--n-gpu-layers` is `0` or too low - try explicitly setting `-ngl all`. Or you are accidentally hiding the GPUs via an environment variable like `CUDA_VISIBLE_DEVICES=-1`. Or your build doesn't include support for the relevant backend. |
+| Crashes or corrupted outputs after setting `GGML_CUDA_P2P=1` | Some motherboards and BIOS settings (e.g. with IOMMU enabled) don't support CUDA peer-to-peer reliably. Unset `GGML_CUDA_P2P`. |
diff --git a/docs/multimodal/minicpmv4.6.md b/docs/multimodal/minicpmv4.6.md
new file mode 100644
index 00000000000..4aa13f8eb0d
--- /dev/null
+++ b/docs/multimodal/minicpmv4.6.md
@@ -0,0 +1,49 @@
+## MiniCPM-V 4.6
+
+### Prepare models and code
+
+Download [MiniCPM-V-4_6](https://huggingface.co/openbmb/MiniCPM-V-4_6) PyTorch model from huggingface to "MiniCPM-V-4_6" folder.
+
+The model must be the standard `transformers` v5.7.0+ checkpoint (no `trust_remote_code`); the architecture in `config.json` is `MiniCPMV4_6ForConditionalGeneration` with a `qwen3_5_text` text model and a SigLIP-based vision tower plus a window-attention `vit_merger`.
+
+### Build llama.cpp
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
+
+Clone llama.cpp:
+```bash
+git clone https://github.com/ggml-org/llama.cpp
+cd llama.cpp
+```
+
+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
+### Usage of MiniCPM-V 4.6
+
+Unlike older MiniCPM-V variants, MiniCPM-V 4.6 is converted directly through `convert_hf_to_gguf.py`. The same script is invoked twice on the original Hugging Face directory: once to produce the language-model GGUF and once with `--mmproj` to produce the multimodal projector GGUF.
+
+```bash
+# language model
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4_6 --outfile ../MiniCPM-V-4_6/ggml-model-f16.gguf
+
+# multimodal projector (vision tower + window-attention vit_merger + DownsampleMLP merger)
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4_6 --mmproj --outfile ../MiniCPM-V-4_6/mmproj-model-f16.gguf
+
+# optional: quantize to Q4_K_M
+./build/bin/llama-quantize ../MiniCPM-V-4_6/ggml-model-f16.gguf ../MiniCPM-V-4_6/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+
+Inference on Linux or Mac
+```bash
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_6/ggml-model-f16.gguf --mmproj ../MiniCPM-V-4_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_6/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-4_6/mmproj-model-f16.gguf
+```
diff --git a/docs/ops.md b/docs/ops.md
index 12b67151713..dace2d3d537 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -17,8 +17,8 @@ Legend:
 |                              ABS | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
@@ -26,7 +26,7 @@ Legend:
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
-|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -36,15 +36,15 @@ Legend:
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                             DIAG | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
+|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             DIAG | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
+|                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
@@ -60,17 +60,18 @@ Legend:
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                              LOG | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 |
@@ -101,11 +102,11 @@ Legend:
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                              SQR | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                             SQRT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                             STEP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUM | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
@@ -117,5 +118,5 @@ Legend:
 |                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            XIELU | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv
index afcb7e4b8e3..345129b5a6c 100644
--- a/docs/ops/SYCL.csv
+++ b/docs/ops/SYCL.csv
@@ -310,10 +310,10 @@
 "SYCL0","GET_ROWS","type=f16,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=f16,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=f16,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","SYCL"
-"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","SYCL"
-"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","SYCL"
-"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","SYCL"
-"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","SYCL"
+"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","SYCL"
+"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","SYCL"
+"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","SYCL"
+"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=q4_0,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=q4_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=q4_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","SYCL"
@@ -334,10 +334,18 @@
 "SYCL0","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","SYCL"
+"SYCL0","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","SYCL"
+"SYCL0","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","SYCL"
+"SYCL0","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","SYCL"
+"SYCL0","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","SYCL"
 "SYCL0","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","SYCL"
 "SYCL0","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","SYCL"
 "SYCL0","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","SYCL"
 "SYCL0","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","SYCL"
+"SYCL0","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","SYCL"
+"SYCL0","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","SYCL"
+"SYCL0","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","SYCL"
+"SYCL0","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","SYCL"
 "SYCL0","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","SYCL"
 "SYCL0","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","SYCL"
 "SYCL0","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","SYCL"
@@ -415,8 +423,12 @@
 "SYCL0","GET_ROWS_BACK","type=q5_1,n=256,m=5,r=4,b=1,v=1","support","0","no","SYCL"
 "SYCL0","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=0","support","0","no","SYCL"
 "SYCL0","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=1","support","0","no","SYCL"
+"SYCL0","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=0","support","0","no","SYCL"
+"SYCL0","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=1","support","0","no","SYCL"
 "SYCL0","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","SYCL"
 "SYCL0","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=1","support","0","no","SYCL"
+"SYCL0","GET_ROWS_BACK","type=nvfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","SYCL"
+"SYCL0","GET_ROWS_BACK","type=nvfp4,n=256,m=5,r=4,b=1,v=1","support","0","no","SYCL"
 "SYCL0","GET_ROWS_BACK","type=q2_K,n=256,m=5,r=4,b=1,v=0","support","0","no","SYCL"
 "SYCL0","GET_ROWS_BACK","type=q2_K,n=256,m=5,r=4,b=1,v=1","support","0","no","SYCL"
 "SYCL0","GET_ROWS_BACK","type=q3_K,n=256,m=5,r=4,b=1,v=0","support","0","no","SYCL"
@@ -570,6 +582,18 @@
 "SYCL0","SET_ROWS","type=q8_0,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","1","yes","SYCL"
 "SYCL0","SET_ROWS","type=q8_0,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","1","yes","SYCL"
 "SYCL0","SET_ROWS","type=q8_0,type_idx=i64,ne=[96,3,7,1],nr23=[2,3],r=2,v=1","support","1","yes","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[384,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[384,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[384,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=q1_0,type_idx=i64,ne=[384,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","SYCL"
 "SYCL0","SET_ROWS","type=mxfp4,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","SYCL"
 "SYCL0","SET_ROWS","type=mxfp4,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","SYCL"
 "SYCL0","SET_ROWS","type=mxfp4,type_idx=i64,ne=[96,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","SYCL"
@@ -582,6 +606,18 @@
 "SYCL0","SET_ROWS","type=mxfp4,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","SYCL"
 "SYCL0","SET_ROWS","type=mxfp4,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","SYCL"
 "SYCL0","SET_ROWS","type=mxfp4,type_idx=i64,ne=[96,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[192,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[192,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[192,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","SYCL"
+"SYCL0","SET_ROWS","type=nvfp4,type_idx=i64,ne=[192,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","SYCL"
 "SYCL0","SET_ROWS","type=q2_K,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","SYCL"
 "SYCL0","SET_ROWS","type=q2_K,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","SYCL"
 "SYCL0","SET_ROWS","type=q2_K,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","SYCL"
@@ -1014,2057 +1050,2057 @@
 "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","SYCL"
 "SYCL0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL"
 "SYCL0","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL"
-"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=1,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=1,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=1,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=0,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=0,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=0,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=1,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=1,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
 "SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
 "SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
 "SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
@@ -5011,9 +5047,12 @@
 "SYCL0","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,2,2,1],s0=1,p0=0,d0=1","support","1","yes","SYCL"
 "SYCL0","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,1,2,1],s0=1,p0=0,d0=1","support","1","yes","SYCL"
 "SYCL0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","SYCL"
 "SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","SYCL"
 "SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","SYCL"
 "SYCL0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","SYCL"
@@ -5141,6 +5180,15 @@
 "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[128,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[128,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[128,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[384,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[384,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=q1_0,ne=[384,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
@@ -5150,6 +5198,15 @@
 "SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[128,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[128,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[128,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[192,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[192,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[192,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","SYCL"
@@ -5292,8 +5349,12 @@
 "SYCL0","CPY","type_src=f16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=f16,type_dst=q1_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=f16,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f16,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f16,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=f16,type_dst=nvfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=f16,type_dst=nvfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
@@ -5338,8 +5399,12 @@
 "SYCL0","CPY","type_src=bf16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=bf16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=bf16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=bf16,type_dst=q1_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=bf16,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=bf16,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=bf16,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=bf16,type_dst=nvfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=bf16,type_dst=nvfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=bf16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=bf16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=bf16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
@@ -5384,8 +5449,12 @@
 "SYCL0","CPY","type_src=f32,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=f32,type_dst=q1_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=f32,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=f32,type_dst=nvfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=f32,type_dst=nvfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
@@ -5430,8 +5499,12 @@
 "SYCL0","CPY","type_src=q5_1,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=q8_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=q8_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=q1_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=mxfp4,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=mxfp4,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
+"SYCL0","CPY","type_src=nvfp4,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=q2_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=q2_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=q3_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
@@ -5514,368 +5587,366 @@
 "SYCL0","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","0","no","SYCL"
 "SYCL0","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","0","no","SYCL"
 "SYCL0","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","0","no","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","SUB","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL"
-"SYCL0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL"
-"SYCL0","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=1","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","ADD","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","SUB","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
+"SYCL0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0,src_overlap=0","support","1","yes","SYCL"
 "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","SYCL"
 "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL"
 "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","SYCL"
@@ -5997,9 +6068,10 @@
 "SYCL0","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL"
 "SYCL0","SSM_CONV","type=f32,ne_a=[72,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL"
 "SYCL0","SSM_CONV","type=f32,ne_a=[72,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL"
-"SYCL0","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL"
-"SYCL0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL"
-"SYCL0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL"
+"SYCL0","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4,xbc_overlap=0","support","0","no","SYCL"
+"SYCL0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4,xbc_overlap=0","support","1","yes","SYCL"
+"SYCL0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4,xbc_overlap=0","support","1","yes","SYCL"
+"SYCL0","SSM_SCAN","type=f32,d_state=128,head_dim=128,n_head=4,n_group=4,n_seq_tokens=16,n_seqs=2,xbc_overlap=1","support","1","yes","SYCL"
 "SYCL0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=1,n_seqs=1","support","1","yes","SYCL"
 "SYCL0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","1","yes","SYCL"
 "SYCL0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","SYCL"
@@ -6012,6 +6084,10 @@
 "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","1","yes","SYCL"
 "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","SYCL"
 "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=64,n=1,k=64,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=256,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=32,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6030,15 +6106,15 @@
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6084,6 +6160,15 @@
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6093,6 +6178,15 @@
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6513,6 +6607,68 @@
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
@@ -6761,13 +6917,75 @@
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6779,26 +6997,26 @@
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6810,16 +7028,16 @@
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6831,6 +7049,8 @@
 "SYCL0","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6844,8 +7064,8 @@
 "SYCL0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=1,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=1,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=128,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6859,13 +7079,15 @@
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=2048,k=8192,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q5_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q1_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=nvfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q3_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6883,386 +7105,386 @@
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=6,n=4096,k=5120,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=0,m=32,n=1024,k=16","support","1","yes","SYCL"
@@ -7489,6 +7711,78 @@
 "SYCL0","MUL_MAT_ID","type_a=q8_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q8_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q8_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=4,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=5,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","0","no","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q4_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q4_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q4_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
@@ -7777,18 +8071,90 @@
 "SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
@@ -7796,11 +8162,11 @@
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
@@ -7808,23 +8174,23 @@
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
@@ -7832,11 +8198,11 @@
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
@@ -7844,11 +8210,11 @@
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q4_1,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q4_1,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q5_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
@@ -7857,6 +8223,8 @@
 "SYCL0","MUL_MAT_ID","type_a=q5_1,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q8_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q8_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","0","no","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q2_K,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q2_K,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q3_K,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
@@ -7881,8 +8249,8 @@
 "SYCL0","MUL_MAT_ID","type_a=iq3_s,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq4_xs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq4_xs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","1","yes","SYCL"
 "SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
@@ -8267,6 +8635,134 @@
 "SYCL0","OUT_PROD","type_a=q8_0,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=q8_0,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=q8_0,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f32,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=1,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=q1_0,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=q4_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=q4_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=q4_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
@@ -8779,6 +9275,134 @@
 "SYCL0","OUT_PROD","type_a=mxfp4,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=mxfp4,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=mxfp4,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f32,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=1,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=nvfp4,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","SYCL"
@@ -8907,6 +9531,10 @@
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","1","yes","SYCL"
+"SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[8,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[16,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
+"SYCL0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[32,1],nr=[1,1],trans_b=0","support","0","no","SYCL"
 "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","SYCL"
 "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","SYCL"
 "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","SYCL"
@@ -9863,10 +10491,12 @@
 "SYCL0","ARGSORT","type=f32,ne=[1023,2,1,3],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[1024,2,1,3],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[1025,2,1,3],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[1025,256,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[2047,2,1,3],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[2048,2,1,3],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[2049,2,1,3],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[2,8,8192,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[2048,512,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","SYCL"
@@ -9910,10 +10540,12 @@
 "SYCL0","ARGSORT","type=f32,ne=[1023,2,1,3],order=1","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[1024,2,1,3],order=1","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[1025,2,1,3],order=1","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[1025,256,1,1],order=1","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[2047,2,1,3],order=1","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[2048,2,1,3],order=1","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[2049,2,1,3],order=1","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[2,8,8192,1],order=1","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[2048,512,1,1],order=1","support","1","yes","SYCL"
 "SYCL0","TOP_K","type=f32,ne=[1,1,1,1],k=1,ties=0","support","1","yes","SYCL"
 "SYCL0","TOP_K","type=f32,ne=[12,1,2,1],k=1,ties=0","support","1","yes","SYCL"
 "SYCL0","TOP_K","type=f32,ne=[2,1,1,1],k=1,ties=0","support","1","yes","SYCL"
@@ -10284,40 +10916,43 @@
 "SYCL0","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","SYCL"
 "SYCL0","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","SYCL"
 "SYCL0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[10,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[127,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[128,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[128,128,4,4]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[255,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[256,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[511,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[512,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[1023,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[1024,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[2047,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","SYCL"
-"SYCL0","CUMSUM","type=f32,ne=[20481,4,1,1]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[127,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[128,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[128,128,4,4]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[255,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[256,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[511,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[512,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[1023,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[1024,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[2047,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","1","yes","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[20481,4,1,1]","support","1","yes","SYCL"
 "SYCL0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","SYCL"
+"SYCL0","XIELU","type=f16,ne=[10,5,4,3]","support","0","no","SYCL"
+"SYCL0","XIELU","type=f32,ne=[512,16,1,1]","support","0","no","SYCL"
+"SYCL0","XIELU","type=f16,ne=[512,16,1,1]","support","0","no","SYCL"
 "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","SYCL"
 "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","SYCL"
 "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","1","yes","SYCL"
 "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=0","support","1","yes","SYCL"
-"SYCL0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","0","no","SYCL"
-"SYCL0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","SYCL"
-"SYCL0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","SYCL"
-"SYCL0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","SYCL"
-"SYCL0","DIAG","type=f32,ne=[10,1,4,3]","support","0","no","SYCL"
-"SYCL0","DIAG","type=f32,ne=[79,1,19,13]","support","0","no","SYCL"
-"SYCL0","DIAG","type=f32,ne=[256,1,8,16]","support","0","no","SYCL"
-"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","SYCL"
-"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","SYCL"
-"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","SYCL"
-"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","SYCL"
-"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","SYCL"
-"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","SYCL"
-"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","0","no","SYCL"
+"SYCL0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","1","yes","SYCL"
+"SYCL0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","SYCL"
+"SYCL0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","SYCL"
+"SYCL0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","SYCL"
+"SYCL0","DIAG","type=f32,ne=[10,1,4,3]","support","1","yes","SYCL"
+"SYCL0","DIAG","type=f32,ne=[79,1,19,13]","support","1","yes","SYCL"
+"SYCL0","DIAG","type=f32,ne=[256,1,8,16]","support","1","yes","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","1","yes","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","1","yes","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","1","yes","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","1","yes","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","1","yes","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","1","yes","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","1","yes","SYCL"
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[79,79,5,3],ne_rhs=[417,79,5,3]","support","0","no","SYCL"
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,2],ne_rhs=[32,128,4,2]","support","0","no","SYCL"
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[80,80,2,8]","support","0","no","SYCL"
@@ -10339,3518 +10974,5110 @@
 "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=0,circular=0","support","1","yes","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=0,circular=1","support","0","no","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=0,circular=1","support","0","no","SYCL"
-"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=1,circular=0","support","0","no","SYCL"
-"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=1,circular=0","support","0","no","SYCL"
+"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=1,circular=0","support","1","yes","SYCL"
+"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=1,circular=0","support","1","yes","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=1,circular=1","support","0","no","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=1,circular=1","support","0","no","SYCL"
-"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=2,circular=0","support","0","no","SYCL"
-"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=0","support","0","no","SYCL"
+"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=2,circular=0","support","1","yes","SYCL"
+"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=0","support","1","yes","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=2,circular=1","support","0","no","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=1","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=1,nr23=[32,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=320,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=96,hsv=96,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[12,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=512,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q8_0,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f32,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=128,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","SYCL"
+"SYCL0","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=64,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=f16,permute=[0,1,2,3]","support","0","no","SYCL"
 "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","SYCL"
 "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","SYCL"
 "SYCL0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","SYCL"
@@ -13875,3 +16102,13 @@
 "SYCL0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1","support","1","yes","SYCL"
 "SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","1","yes","SYCL"
 "SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=16,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=64,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=127,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=256,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=65,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=100,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=200,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=127,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=64,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=33,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL"
+"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=100,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL"
diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv
index f11a3fa3726..95042e72d9c 100644
--- a/docs/ops/WebGPU.csv
+++ b/docs/ops/WebGPU.csv
@@ -334,10 +334,18 @@
 "WebGPU: WebGPU","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU"
@@ -415,8 +423,12 @@
 "WebGPU: WebGPU","GET_ROWS_BACK","type=q5_1,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","GET_ROWS_BACK","type=nvfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","GET_ROWS_BACK","type=nvfp4,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","GET_ROWS_BACK","type=q2_K,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","GET_ROWS_BACK","type=q2_K,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","GET_ROWS_BACK","type=q3_K,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU"
@@ -570,6 +582,18 @@
 "WebGPU: WebGPU","SET_ROWS","type=q8_0,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=q8_0,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=q8_0,type_idx=i64,ne=[96,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[384,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[384,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[384,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=q1_0,type_idx=i64,ne=[384,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=mxfp4,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=mxfp4,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=mxfp4,type_idx=i64,ne=[96,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU"
@@ -582,6 +606,18 @@
 "WebGPU: WebGPU","SET_ROWS","type=mxfp4,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=mxfp4,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=mxfp4,type_idx=i64,ne=[96,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[192,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[192,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[192,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","SET_ROWS","type=nvfp4,type_idx=i64,ne=[192,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=q2_K,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=q2_K,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","SET_ROWS","type=q2_K,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU"
@@ -926,94 +962,94 @@
 "WebGPU: WebGPU","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=0,p1=0,d0=1,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=0,p1=0,d0=3,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=3,p1=0,d0=1,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=3,p1=0,d0=3,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=3,s1=0,p0=0,p1=0,d0=1,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=3,s1=0,p0=0,p1=0,d0=3,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=3,s1=0,p0=3,p1=0,d0=1,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=3,s1=0,p0=3,p1=0,d0=3,d1=0,is_2D=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=0,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=0,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=0,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=3,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=3,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=3,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=3,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=3,p1=0,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=3,p1=0,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=3,p1=0,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=3,p1=0,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=3,p1=3,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=3,p1=3,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=3,p1=3,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=3,p1=3,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=0,p1=0,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=0,p1=0,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=0,p1=0,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=0,p1=3,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=0,p1=3,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=0,p1=3,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=0,p1=3,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=3,p1=0,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=3,p1=0,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=3,p1=0,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=3,p1=0,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=3,p1=3,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=3,p1=3,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=3,p1=3,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=3,p0=3,p1=3,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=0,p1=0,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=0,p1=0,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=0,p1=0,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=0,p1=3,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=0,p1=3,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=0,p1=3,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=0,p1=3,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=3,p1=0,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=3,p1=0,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=3,p1=0,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=3,p1=0,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=3,p1=3,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=3,p1=3,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=1,p0=3,p1=3,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
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-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
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-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=0,p1=0,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
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-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=0,p1=3,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=0,p1=3,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=0,p1=3,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=0,p1=3,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=3,p1=0,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=3,p1=0,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=3,p1=0,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=3,p1=0,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
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-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=3,p1=3,d0=1,d1=3,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=3,p1=3,d0=3,d1=1,is_2D=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=3,s1=3,p0=3,p1=3,d0=3,d1=3,is_2D=1","support","0","no","WebGPU"
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-"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,32],ne_kernel=[3,3,2,32],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","0","no","WebGPU"
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@@ -3065,1574 +3101,1574 @@
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 "WebGPU: WebGPU","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","WebGPU"
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+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","0","no","WebGPU"
@@ -5011,9 +5047,12 @@
 "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,2,2,1],s0=1,p0=0,d0=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,1,2,1],s0=1,p0=0,d0=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","WebGPU"
-"WebGPU: WebGPU","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","WebGPU"
+"WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","WebGPU"
+"WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","WebGPU"
 "WebGPU: WebGPU","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","WebGPU"
 "WebGPU: WebGPU","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","WebGPU"
@@ -5141,6 +5180,15 @@
 "WebGPU: WebGPU","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[128,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[128,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[128,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[384,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[384,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=q1_0,ne=[384,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
@@ -5150,6 +5198,15 @@
 "WebGPU: WebGPU","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[128,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[128,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[128,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[192,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[192,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=nvfp4,ne=[192,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU"
@@ -5292,8 +5349,12 @@
 "WebGPU: WebGPU","CPY","type_src=f16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=f16,type_dst=q1_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=f16,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f16,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f16,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=f16,type_dst=nvfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=f16,type_dst=nvfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
@@ -5338,8 +5399,12 @@
 "WebGPU: WebGPU","CPY","type_src=bf16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=bf16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=bf16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=q1_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=bf16,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=bf16,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=nvfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=nvfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=bf16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=bf16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=bf16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
@@ -5384,8 +5449,12 @@
 "WebGPU: WebGPU","CPY","type_src=f32,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f32,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f32,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=f32,type_dst=q1_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=f32,type_dst=q1_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f32,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f32,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=f32,type_dst=nvfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=f32,type_dst=nvfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f32,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f32,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=f32,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
@@ -5430,8 +5499,12 @@
 "WebGPU: WebGPU","CPY","type_src=q5_1,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q8_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q8_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=q1_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=mxfp4,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=mxfp4,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
+"WebGPU: WebGPU","CPY","type_src=nvfp4,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q2_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q2_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CPY","type_src=q3_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU"
@@ -5514,439 +5587,437 @@
 "WebGPU: WebGPU","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","0","no","WebGPU"
 "WebGPU: WebGPU","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=1","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=1","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1,perm1=1","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=1","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,6],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[10,5,4,5],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,120,120],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","ADD","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","SUB","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","SUB","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","DIV","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","ADD1","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD1","type=f32,ne=[1024,1024,1,1]","support","0","no","WebGPU"
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 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","0","no","WebGPU"
+"WebGPU: WebGPU","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","0","no","WebGPU"
+"WebGPU: WebGPU","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.100000","support","0","no","WebGPU"
 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000","support","0","no","WebGPU"
+"WebGPU: WebGPU","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000","support","0","no","WebGPU"
+"WebGPU: WebGPU","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.100000","support","0","no","WebGPU"
 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","NORM","type=f32,ne=[64,5,4,3],v=0,eps=10.000000","support","0","no","WebGPU"
+"WebGPU: WebGPU","NORM","type=f32,ne=[64,5,4,3],v=0,eps=10.000000","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=10.000000,inplace=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","NORM","type=f32,ne=[64,5,4,3],v=1,eps=10.000000","support","0","no","WebGPU"
+"WebGPU: WebGPU","NORM","type=f32,ne=[64,5,4,3],v=1,eps=10.000000","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=10.000000,inplace=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=10.000000","support","0","no","WebGPU"
 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[64,5,4,3],eps=10.000000,v=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[64,5,4,3],eps=10.000000,v=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=10.000000","support","0","no","WebGPU"
+"WebGPU: WebGPU","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=10.000000","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=10.000000,inplace=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=10.000000","support","0","no","WebGPU"
+"WebGPU: WebGPU","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=10.000000","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=10.000000,inplace=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=10.000000","support","0","no","WebGPU"
 "WebGPU: WebGPU","L2_NORM","type=f32,ne=[1025,5,4,3],eps=10.000000,v=0","support","1","yes","WebGPU"
@@ -5997,9 +6068,10 @@
 "WebGPU: WebGPU","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","SSM_CONV","type=f32,ne_a=[72,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","SSM_CONV","type=f32,ne_a=[72,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","WebGPU"
-"WebGPU: WebGPU","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","WebGPU"
-"WebGPU: WebGPU","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","WebGPU"
-"WebGPU: WebGPU","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","WebGPU"
+"WebGPU: WebGPU","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4,xbc_overlap=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4,xbc_overlap=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4,xbc_overlap=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","SSM_SCAN","type=f32,d_state=128,head_dim=128,n_head=4,n_group=4,n_seq_tokens=16,n_seqs=2,xbc_overlap=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=1,n_seqs=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","0","no","WebGPU"
@@ -6084,15 +6156,33 @@
 "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
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+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
@@ -6513,6 +6603,68 @@
 "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
 "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
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+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
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@@ -6699,37 +6851,37 @@
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@@ -6761,6 +6913,68 @@
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@@ -6831,6 +7045,8 @@
 "WebGPU: WebGPU","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
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@@ -6865,7 +7081,9 @@
 "WebGPU: WebGPU","MUL_MAT","type_a=q5_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
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+"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU"
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@@ -7272,6 +7490,7 @@
 "WebGPU: WebGPU","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=2,n_used=2,b=1,m=32,n=8192,k=64","support","1","yes","WebGPU"
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@@ -7488,6 +7707,78 @@
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@@ -7704,6 +7995,78 @@
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@@ -7784,6 +8219,8 @@
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@@ -7792,6 +8229,24 @@
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@@ -8816,42 +9527,42 @@
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-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=32","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=129","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=32","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=129","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=32","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=129","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","0","no","WebGPU"
-"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","0","no","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","WebGPU"
 "WebGPU: WebGPU","SQR","type=f16,ne=[10,5,4,3]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","SQRT","type=f16,ne=[10,3,3,2]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","LOG","type=f16,ne=[10,5,4,3]","support","1","yes","WebGPU"
@@ -9772,10 +10483,12 @@
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[1023,2,1,3],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[1024,2,1,3],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[1025,2,1,3],order=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARGSORT","type=f32,ne=[1025,256,1,1],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[2047,2,1,3],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[2048,2,1,3],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[2049,2,1,3],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[2,8,8192,1],order=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARGSORT","type=f32,ne=[2048,512,1,1],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","WebGPU"
@@ -9819,10 +10532,12 @@
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[1023,2,1,3],order=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[1024,2,1,3],order=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[1025,2,1,3],order=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARGSORT","type=f32,ne=[1025,256,1,1],order=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[2047,2,1,3],order=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[2048,2,1,3],order=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[2049,2,1,3],order=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","ARGSORT","type=f32,ne=[2,8,8192,1],order=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARGSORT","type=f32,ne=[2048,512,1,1],order=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TOP_K","type=f32,ne=[1,1,1,1],k=1,ties=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TOP_K","type=f32,ne=[12,1,2,1],k=1,ties=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TOP_K","type=f32,ne=[2,1,1,1],k=1,ties=0","support","1","yes","WebGPU"
@@ -10128,28 +10843,28 @@
 "WebGPU: WebGPU","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","1","yes","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=nearest","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear,transpose=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear,transpose=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=0","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=1","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|antialias","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear|antialias","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|align_corners","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear|align_corners","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear|align_corners","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic|align_corners","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic|align_corners","support","0","no","WebGPU"
-"WebGPU: WebGPU","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic|align_corners","support","0","no","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=nearest","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear,transpose=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear,transpose=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=0","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=1","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|antialias","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear|antialias","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|align_corners","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear|align_corners","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear|align_corners","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic|align_corners","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic|align_corners","support","1","yes","WebGPU"
+"WebGPU: WebGPU","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic|align_corners","support","1","yes","WebGPU"
 "WebGPU: WebGPU","SUM","type=f32,ne=[10,5,4,3]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","SUM","type=f32,ne=[11,5,6,3],permute=[0,2,1,3]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","SUM","type=f32,ne=[11,5,6,3],permute=[0,3,2,1]","support","1","yes","WebGPU"
@@ -10209,6 +10924,9 @@
 "WebGPU: WebGPU","CUMSUM","type=f32,ne=[375960,1,1,1]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","CUMSUM","type=f32,ne=[20481,4,1,1]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","XIELU","type=f32,ne=[10,5,4,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","XIELU","type=f16,ne=[10,5,4,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","XIELU","type=f32,ne=[512,16,1,1]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","XIELU","type=f16,ne=[512,16,1,1]","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","1","yes","WebGPU"
@@ -10256,3510 +10974,4974 @@
 "WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=0","support","1","yes","WebGPU"
 "WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=2,circular=1","support","1","yes","WebGPU"
 "WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=1","support","1","yes","WebGPU"
-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","WebGPU"
-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU"
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-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","WebGPU"
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-"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU"
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+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=1,nr23=[20,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
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+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
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+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
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+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU"
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+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=f16,permute=[0,1,2,3]","support","0","no","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f32,permute=[0,1,2,3]","support","0","no","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=128,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","WebGPU"
+"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=64,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=f16,permute=[0,1,2,3]","support","0","no","WebGPU"
 "WebGPU: WebGPU","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU"
 "WebGPU: WebGPU","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","WebGPU"
 "WebGPU: WebGPU","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU"
diff --git a/docs/speculative.md b/docs/speculative.md
index 29da332875f..fb6ef03067d 100644
--- a/docs/speculative.md
+++ b/docs/speculative.md
@@ -33,18 +33,18 @@ An example to use this approach can be the rewriting of source code by a LLM.
 This implementation looks for the last n-gram in history that matches the current n-gram and creates a draft using the m tokens following the matched n-gram. It is the simplest self-speculative approach with minimal overhead.
 
 ```
-llama-server [...] --spec-type ngram-simple --draft-max 64
+llama-server [...] --spec-type ngram-simple --spec-draft-n-max 64
 ```
 
 #### n-gram Map Key (`ngram-map-k`)
 
-This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-min-hits`, default is 1) before generating drafts.
+This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-map-k-min-hits`, default is 1) before generating drafts.
 
 The number of accepted tokens is stored for each used n-gram.
 
 **Example:**
 ```
-llama-server [...] --spec-type ngram-map-k --draft-max 64
+llama-server [...] --spec-type ngram-map-k --spec-draft-n-max 64
 ```
 
 #### n-gram Map Key-4-Values (`ngram-map-k4v`)
@@ -55,7 +55,7 @@ The number of accepted tokens is stored for each used n-gram.
 
 **Example:** Server options to be used if there are a lot of longer repetitions.
 ```
-llama-server [...] --spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 --draft-max 64
+llama-server [...] --spec-type ngram-map-k4v --spec-ngram-map-k4v-size-n 8 --spec-ngram-map-k4v-size-m 8 --spec-ngram-map-k4v-min-hits 2 --spec-draft-n-max 64
 ```
 
 ### n-gram Mod (`ngram-mod`)
@@ -80,9 +80,9 @@ Currently, a single hash pool is shared across all server slots, so different re
 # notes:
 # - small `n` are not recommended
 # - MoEs require long drafts
-# - dense models: can reduce `--draft-min` and `--draft-max`
+# - dense models: can reduce `--spec-ngram-mod-n-min` and `--spec-ngram-mod-n-max`
 
-llama-server ... --spec-type ngram-mod --spec-ngram-size-n 24 --draft-min 48 --draft-max 64
+llama-server ... --spec-type ngram-mod --spec-ngram-mod-n-match 24 --spec-ngram-mod-n-min 48 --spec-ngram-mod-n-max 64
 ```
 
 Applications:
@@ -105,21 +105,90 @@ Example Video:
 
 If a draft model is combined with a draftless decoding the draftless decoding has higher precedence.
 
+### General Speculative Parameters
+
 ```
---draft, --draft-n, --draft-max N       number of tokens to draft for speculative decoding (default: 16)
-                                        (env: LLAMA_ARG_DRAFT_MAX)
---draft-min, --draft-n-min N            minimum number of draft tokens to use for speculative decoding
-                                        (default: 0)
-                                        (env: LLAMA_ARG_DRAFT_MIN)
-[...]
 --spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
                                         type of speculative decoding to use when no draft model is provided
                                         (default: none)
---spec-ngram-size-n N                   ngram size N for ngram-simple/ngram-map speculative decoding, length
-                                        of lookup n-gram (default: 12)
---spec-ngram-size-m N                   ngram size M for ngram-simple/ngram-map speculative decoding, length
-                                        of draft m-gram (default: 48)
---spec-ngram-min-hits N                 minimum hits for ngram-map speculative decoding (default: 1)
+                                        (env: LLAMA_ARG_SPEC_TYPE)
+--spec-default                          use default speculative decoding
+```
+
+### Draft Model Parameters
+
+```
+--spec-draft-model, -md, --model-draft  FNAME
+                                        draft model for speculative decoding (default: unused)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_MODEL)
+--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft  <user>/<model>[:quant]
+                                        HuggingFace repository for the draft model
+--spec-draft-n-max                      N
+                                        number of tokens to draft for speculative decoding (default: 16)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_N_MAX)
+--spec-draft-n-min                      N
+                                        minimum number of draft tokens to use for speculative decoding (default: 0)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_N_MIN)
+--spec-draft-p-split, --draft-p-split   P
+                                        speculative decoding split probability (default: 0.10)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT)
+--spec-draft-p-min, --draft-p-min       P
+                                        minimum speculative decoding probability (greedy) (default: 0.75)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_P_MIN)
+--spec-draft-ctx-size, -cd, --ctx-size-draft  N
+                                        size of the prompt context for the draft model (default: 0, 0 = loaded from model)
+                                        (env: LLAMA_ARG_SPEC_DRAFT_CTX_SIZE)
+--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft  N
+                                        max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
+                                        (env: LLAMA_ARG_N_GPU_LAYERS_DRAFT)
+--spec-draft-device, -devd, --device-draft  <dev1,dev2,..>
+                                        comma-separated list of devices to use for offloading the draft model
+--spec-draft-replace, --spec-replace    TARGET  DRAFT
+                                        translate the string in TARGET into DRAFT if the draft model and main model are not compatible
+```
+
+### n-gram Mod Parameters
+
+```
+--spec-ngram-mod-n-match                N
+                                        ngram-mod lookup length (default: 24)
+--spec-ngram-mod-n-min                  N
+                                        minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48)
+--spec-ngram-mod-n-max                  N
+                                        maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64)
+```
+
+### n-gram Simple Parameters
+
+```
+--spec-ngram-simple-size-n              N
+                                        ngram size N for ngram-simple speculative decoding, length of lookup n-gram (default: 12)
+--spec-ngram-simple-size-m              N
+                                        ngram size M for ngram-simple speculative decoding, length of draft m-gram (default: 48)
+--spec-ngram-simple-min-hits            N
+                                        minimum hits for ngram-simple speculative decoding (default: 1)
+```
+
+### n-gram Map Key Parameters
+
+```
+--spec-ngram-map-k-size-n               N
+                                        ngram size N for ngram-map-k speculative decoding, length of lookup n-gram (default: 12)
+--spec-ngram-map-k-size-m               N
+                                        ngram size M for ngram-map-k speculative decoding, length of draft m-gram (default: 48)
+--spec-ngram-map-k-min-hits             N
+                                        minimum hits for ngram-map-k speculative decoding (default: 1)
+```
+
+### n-gram Map Key-4-Values Parameters
+
+```
+--spec-ngram-map-k4v-size-n             N
+                                        ngram size N for ngram-map-k4v speculative decoding, length of lookup n-gram (default: 12)
+--spec-ngram-map-k4v-size-m             N
+                                        ngram size M for ngram-map-k4v speculative decoding, length of draft m-gram (default: 48)
+--spec-ngram-map-k4v-min-hits           N
+                                        minimum hits for ngram-map-k4v speculative decoding (default: 1)
 ```
 
 ### `--spec-type TYPE`
@@ -140,21 +209,40 @@ Specifies a type of speculative decoding without draft model.
 ./llama-server [...] --spec-type ngram-simple
 ```
 
-### `--spec-ngram-size-n N`
+### `--spec-ngram-*-size-n N`
 
 Sets the size N of the lookup n-gram for n-gram map based speculative decoding.
 The n-gram size N determines how many tokens in a row to look back when searching for matching patterns.
 
-### `--spec-ngram-size-m M`
+Each n-gram implementation has its own parameter:
+
+- `--spec-ngram-simple-size-n` for `ngram-simple`
+- `--spec-ngram-map-k-size-n` for `ngram-map-k`
+- `--spec-ngram-map-k4v-size-n` for `ngram-map-k4v`
+- `--spec-ngram-mod-n-match` for `ngram-mod`
+
+### `--spec-ngram-*-size-m M`
 
 Sets the size M of the draft m-gram for n-gram map based speculative decoding.
 The m-gram size determines how many tokens to draft when a match is found.
 Larger values can provide more speedup but may reduce acceptance rate.
 
-### `--spec-ngram-min-hits H`
+Each n-gram implementation has its own parameter:
+
+- `--spec-ngram-simple-size-m` for `ngram-simple`
+- `--spec-ngram-map-k-size-m` for `ngram-map-k`
+- `--spec-ngram-map-k4v-size-m` for `ngram-map-k4v`
+
+### `--spec-ngram-*-min-hits H`
 
 This option defines how often a key has to appear in the token history to be used as a draft (default is 1).
 
+Each n-gram implementation has its own parameter:
+
+- `--spec-ngram-simple-min-hits` for `ngram-simple`
+- `--spec-ngram-map-k-min-hits` for `ngram-map-k`
+- `--spec-ngram-map-k4v-min-hits` for `ngram-map-k4v`
+
 ## Statistics
 Each speculative decoding implementation prints statistics.
 
@@ -180,4 +268,3 @@ statistics ngram_map_k: #calls(b,g,a) = 6 1690 26, #gen drafts = 26, #acc drafts
 - `#gen tokens`: number of tokens generated by this implementation (including rejected tokens)
 - `#acc tokens`: number of tokens accepted by the main model
 - `dur(b,g,a): durations of begin (new prompt), generation and accumulation (process acceptance).
-
diff --git a/examples/debug/debug.cpp b/examples/debug/debug.cpp
index 7ba63b4ff60..761e7a2db54 100644
--- a/examples/debug/debug.cpp
+++ b/examples/debug/debug.cpp
@@ -202,10 +202,14 @@ static bool run(llama_context * ctx, const common_params & params) {
     print_tokenized_prompt(ctx, tokens, params.prompt);
 
     if (params.save_logits) {
-        output_data output {ctx, model, params};
-        std::filesystem::path model_path{params.model.path};
-        std::string model_name{model_path.stem().string()};
-        save_output_data(output, model_name, params.logits_output_dir);
+        try {
+            output_data output {ctx, model, params};
+            std::filesystem::path model_path{params.model.path};
+            std::string model_name{model_path.stem().string()};
+            save_output_data(output, model_name, params.logits_output_dir);
+        } catch (const std::exception & e) {
+            LOG_ERR("%s : error saving logits: %s\n", __func__, e.what());
+        }
     }
 
     return true;
@@ -223,7 +227,7 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    std::optional<base_callback_data> cb_data;
+    std::optional<common_debug_cb_user_data> cb_data;
     if (!params.save_logits) {
         cb_data.emplace(params, params.tensor_filter);
     }
diff --git a/examples/diffusion/CMakeLists.txt b/examples/diffusion/CMakeLists.txt
index 70228d4079b..42a84b2dfe5 100644
--- a/examples/diffusion/CMakeLists.txt
+++ b/examples/diffusion/CMakeLists.txt
@@ -1,5 +1,10 @@
+set(TARGET llama-diffusion)
+add_library(${TARGET} STATIC diffusion.cpp diffusion.h)
+target_link_libraries(${TARGET} PUBLIC llama llama-common ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PUBLIC cxx_std_17)
+
 set(TARGET llama-diffusion-cli)
 add_executable(${TARGET} diffusion-cli.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama llama-common ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama-diffusion llama llama-common ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/diffusion/README.md b/examples/diffusion/README.md
index b3942002147..6d2fffd64e3 100644
--- a/examples/diffusion/README.md
+++ b/examples/diffusion/README.md
@@ -12,11 +12,11 @@ The diffusion CLI supports various parameters to control the generation process:
 ### Core Diffusion Parameters
 - `--diffusion-steps`: Number of diffusion steps (default: 256)
 - `--diffusion-algorithm`: Algorithm for token selection
-  - `0`: ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006.
-  - `1`: ENTROPY_BASED - Entropy-based selection
-  - `2`: MARGIN_BASED - Margin-based selection
-  - `3`: RANDOM - Random selection
-  - `4`: CONFIDENCE_BASED - Confidence-based selection (default)
+  - `0`: DIFFUSION_ALGORITHM_ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006.
+  - `1`: DIFFUSION_ALGORITHM_ENTROPY_BASED - Entropy-based selection
+  - `2`: DIFFUSION_ALGORITHM_MARGIN_BASED - Margin-based selection
+  - `3`: DIFFUSION_ALGORITHM_RANDOM - Random selection
+  - `4`: DIFFUSION_ALGORITHM_CONFIDENCE_BASED - Confidence-based selection (default)
   - More documentation here https://github.com/DreamLM/Dream
 - `--diffusion-visual`: Enable live visualization during generation
 
diff --git a/examples/diffusion/diffusion-cli.cpp b/examples/diffusion/diffusion-cli.cpp
index 403b9b47445..86ebbf88c98 100644
--- a/examples/diffusion/diffusion-cli.cpp
+++ b/examples/diffusion/diffusion-cli.cpp
@@ -1,127 +1,23 @@
 #include "arg.h"
 #include "chat.h"
 #include "common.h"
+#include "diffusion.h"
 #include "llama.h"
 #include "log.h"
 
 #include <limits.h>
 
-#include <algorithm>
 #include <clocale>
-#include <cmath>
 #include <cstring>
-#include <limits>
-#include <random>
 #include <string>
 #include <vector>
 
-enum diffusion_algorithm { ORIGIN = 0, ENTROPY_BASED = 1, MARGIN_BASED = 2, RANDOM = 3, CONFIDENCE_BASED = 4 };
-
-// Unified transfer scheduling methods
-enum transfer_schedule {
-    TIMESTEP_BASED = 0,  // Dream-style: (1.0 - s/t) * remaining
-    BLOCK_BASED    = 1,  // LLaDA-style: process in blocks with get_num_transfer_tokens
-};
-
-typedef bool (*diffusion_step_callback_t)(int32_t             step,
-                                          int32_t             total_steps,
-                                          const llama_token * tokens,
-                                          int32_t             n_tokens,
-                                          void *              user_data);
-
-struct diffusion_params {
-    int32_t                   steps                   = 0;
-    float                     temperature             = 0;
-    llama_token               mask_token_id           = LLAMA_TOKEN_NULL;
-    diffusion_step_callback_t step_callback           = nullptr;
-    void *                    step_callback_user_data = nullptr;
-    int32_t                   seed                    = 0;
-    bool                      visual_mode             = false;
-    bool                      shift_logits            = false;  // Shift logits by -1 after decode
-
-    float   top_p = 0.;
-    int32_t top_k = 0.;
-
-    diffusion_algorithm algorithm = CONFIDENCE_BASED;
-    transfer_schedule   schedule  = TIMESTEP_BASED;
-
-    float   cfg_scale        = 0.;     // Config scale for classifier-free guidance
-    float   eps              = 0.;     // Timestep scheduling
-    int32_t block_length     = 0;      // Block size (for block scheduling)
-    float   alg_temp         = 0;      // algorithm temperature (0.0 = deterministic)
-    bool    add_gumbel_noise = false;  // Add gumbel noise to the logits if temp > 0.0
-
-    int32_t max_length = 0;            // Maximum sequence length
-};
-
 struct callback_data {
     diffusion_params *  diff_params;
     const llama_vocab * vocab;
     int32_t             n_input;
 };
 
-static float calculate_confidence(const llama_token_data_array & cur_p,
-                                  diffusion_algorithm            algorithm,
-                                  std::mt19937 &                 rng) {
-    switch (algorithm) {
-        case CONFIDENCE_BASED:
-            return cur_p.data[cur_p.selected].p;  // Selected token probability
-
-        case ENTROPY_BASED:
-            {
-                float       entropy = 0.0f;
-                const float epsilon = 1e-10f;
-                for (size_t i = 0; i < cur_p.size; i++) {
-                    float prob = cur_p.data[i].p;
-                    entropy += prob * logf(prob + epsilon);
-                }
-                return -entropy;  // Higher entropy = lower confidence
-            }
-
-        case MARGIN_BASED:
-            return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p;
-
-        case RANDOM:
-            {
-                std::uniform_real_distribution<float> uniform(0.0f, 1.0f);
-                return uniform(rng);  // Random confidence
-            }
-
-        case ORIGIN:
-            return cur_p.data[cur_p.selected].p;
-
-        default:
-            return 0.0f;
-    }
-}
-
-// Unified transfer count calculation function
-static int32_t calculate_transfer_count(int32_t                      step,
-                                        int32_t                      total_steps,
-                                        int32_t                      remaining_masked,
-                                        transfer_schedule            schedule,
-                                        float                        eps,
-                                        const std::vector<int32_t> & num_transfer_tokens = {}) {
-    switch (schedule) {
-        case TIMESTEP_BASED:
-            {
-                float t          = 1.0f - (float) step / total_steps * (1.0f - eps);
-                float s          = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps);
-                float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f;
-                return (int32_t) (remaining_masked * p_transfer);
-            }
-
-        case BLOCK_BASED:
-            if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) {
-                return num_transfer_tokens[step];
-            }
-            return remaining_masked / (total_steps - step);  // Fallback
-
-        default:
-            return remaining_masked / (total_steps - step);
-    }
-}
-
 static bool diffusion_step_callback(int32_t             step,
                                     int32_t             total_steps,
                                     const llama_token * tokens,
@@ -176,341 +72,6 @@ static bool diffusion_step_callback(int32_t             step,
     return true;
 }
 
-static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) {
-    if (temperature == 0.0f) {
-        return;
-    }
-
-    std::uniform_real_distribution<double> uniform(0.0, 1.0);
-    for (int32_t i = 0; i < n_vocab; i++) {
-        double noise        = uniform(rng);
-        // Prevent log(0)
-        noise               = std::max(noise, 1e-20);
-        double gumbel_noise = std::pow(-std::log(noise), temperature);
-        logits[i]           = std::exp(logits[i]) / gumbel_noise;
-    }
-}
-
-static std::vector<int32_t> get_num_transfer_tokens(int32_t mask_count, int32_t steps) {
-    std::vector<int32_t> num_transfer_tokens(steps);
-
-    int32_t base      = mask_count / steps;
-    int32_t remainder = mask_count % steps;
-
-    for (int32_t i = 0; i < steps; i++) {
-        num_transfer_tokens[i] = base + (i < remainder ? 1 : 0);
-    }
-
-    return num_transfer_tokens;
-}
-
-static void diffusion_generate(llama_context *          ctx,
-                               const llama_token *      input_tokens,
-                               llama_token *            output_tokens,
-                               int32_t                  n_input,
-                               const diffusion_params & params,
-                               int32_t &                n_generated) {
-    n_generated = 0;
-    if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) {
-        return;
-    }
-
-    const llama_model * model = llama_get_model(ctx);
-
-    // Initialize with input and pad with mask tokens
-    std::copy(input_tokens, input_tokens + n_input, output_tokens);
-    std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id);
-
-    std::mt19937 rng(params.seed);
-
-    llama_set_causal_attn(ctx, false);
-
-    int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
-
-    std::vector<llama_token_data> candidates(n_vocab);
-    std::vector<llama_token_data> conf_candidates;
-    conf_candidates.reserve(params.max_length);
-    std::vector<int32_t> mask_positions;
-    mask_positions.reserve(params.max_length);
-
-    // Setup sampler chain
-    struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
-    if (params.top_k > 0) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
-    }
-    if (params.top_p < 1.0f) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
-    }
-    if (params.temperature > 0.0f) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
-    }
-    llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
-
-    struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
-
-    llama_batch batch = llama_batch_init(params.max_length, 0, 1);
-    batch.n_tokens    = params.max_length;
-
-    // Pre-allocate buffers for CFG if needed
-    int32_t                  logits_size = n_vocab * params.max_length;
-    std::vector<float>       cond_logits_buffer;
-    std::vector<llama_token> un_x_buffer;
-    if (params.cfg_scale > 0.0f) {
-        cond_logits_buffer.resize(logits_size);
-        un_x_buffer.resize(params.max_length);
-    }
-
-    // For block-based processing
-    std::vector<int32_t> num_transfer_tokens;
-    int32_t              num_blocks      = 1;
-    int32_t              steps_per_block = params.steps;
-
-    if (params.schedule == BLOCK_BASED) {
-        GGML_ASSERT(params.max_length % params.block_length == 0);
-        num_blocks = params.max_length / params.block_length;
-        GGML_ASSERT(params.steps % num_blocks == 0);
-        steps_per_block = params.steps / num_blocks;
-    }
-
-    std::vector<float> confidence(params.max_length);
-
-    int64_t total_sampling_time = 0;
-    int64_t total_time          = 0;
-    int64_t time_start          = ggml_time_us();
-
-    for (int block_num = 0; block_num < num_blocks; block_num++) {
-        int32_t block_start = (params.schedule == BLOCK_BASED) ? n_input + block_num * params.block_length : 0;
-        int32_t block_end   = (params.schedule == BLOCK_BASED) ?
-                                  std::min(n_input + (block_num + 1) * params.block_length, params.max_length) :
-                                  params.max_length;
-
-        // Count masked tokens in current block for block-based processing
-        if (params.schedule == BLOCK_BASED) {
-            int32_t block_mask_count = 0;
-            for (int i = block_start; i < block_end; i++) {
-                if (output_tokens[i] == params.mask_token_id) {
-                    block_mask_count++;
-                }
-            }
-            num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block);
-        }
-
-        for (int32_t step = 0; step < steps_per_block; step++) {
-            int32_t global_step = block_num * steps_per_block + step;
-
-            if (params.step_callback) {
-                if (!params.step_callback(
-                        global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) {
-                    break;
-                }
-            }
-
-            // Setup batch
-            for (int32_t i = 0; i < params.max_length; i++) {
-                batch.token[i]     = output_tokens[i];
-                batch.pos[i]       = i;
-                batch.n_seq_id[i]  = 1;
-                batch.seq_id[i][0] = 0;
-                batch.logits[i]    = 1;
-            }
-
-            float * logits = nullptr;
-
-            if (params.cfg_scale > 0.0f) {
-                int ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("Failed to generate conditional");
-                    break;
-                }
-                float * cond_logits_ptr = llama_get_logits(ctx);
-                std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float));
-
-                // Unconditional generation (mask input)
-                std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin());
-                for (int32_t i = 0; i < n_input; i++) {
-                    un_x_buffer[i] = params.mask_token_id;
-                }
-
-                for (int32_t i = 0; i < params.max_length; i++) {
-                    batch.token[i] = un_x_buffer[i];
-                }
-                ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("Failed to generate unconditional");
-                    break;
-                }
-                float * uncond_logits = llama_get_logits(ctx);
-
-                // Apply CFG
-                for (int32_t i = 0; i < logits_size; i++) {
-                    cond_logits_buffer[i] =
-                        uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]);
-                }
-                logits = cond_logits_buffer.data();
-            } else {
-                int ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret);
-                    break;
-                }
-                logits = llama_get_logits(ctx);
-            }
-
-            if (!logits) {
-                LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step);
-                break;
-            }
-
-            auto get_logits_for_pos = [&](int32_t pos) -> const float * {
-                if (params.shift_logits) {
-                    return pos == 0 ? logits : logits + (pos - 1) * n_vocab;
-                }
-                return logits + (pos) *n_vocab;
-            };
-
-            int64_t time_start_sampling = ggml_time_us();
-
-            mask_positions.clear();
-            for (int32_t i = 0; i < params.max_length; i++) {
-                if (output_tokens[i] == params.mask_token_id) {
-                    // For block-based, only consider current block
-                    if (params.schedule != BLOCK_BASED || (i >= block_start && i < block_end)) {
-                        mask_positions.push_back(i);
-                    }
-                }
-            }
-
-            if (mask_positions.empty()) {
-                break;
-            }
-
-            if (params.add_gumbel_noise && params.temperature > 0.0f) {
-                add_gumbel_noise(logits, n_vocab, params.temperature, rng);
-            }
-
-            if (params.algorithm == ORIGIN) {
-                int32_t transfer_count = calculate_transfer_count(
-                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
-                float p_transfer = (float) transfer_count / mask_positions.size();
-
-                for (int32_t pos : mask_positions) {
-                    if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
-                        const float * pos_logits = get_logits_for_pos(pos);
-                        for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
-                            candidates[token_id].id    = token_id;
-                            candidates[token_id].logit = pos_logits[token_id];
-                            candidates[token_id].p     = 0.0f;
-                        }
-
-                        llama_token_data_array cur_p = {
-                            candidates.data(),
-                            (size_t) n_vocab,
-                            -1,
-                            false,
-                        };
-
-                        llama_sampler_apply(sampler, &cur_p);
-                        output_tokens[pos] = cur_p.data[cur_p.selected].id;
-                    }
-                }
-            } else {
-                std::vector<std::pair<float, int32_t>> confidences;
-                std::vector<llama_token>               sampled_tokens(mask_positions.size());
-
-                for (size_t i = 0; i < mask_positions.size(); i++) {
-                    int32_t       pos        = mask_positions[i];
-                    const float * pos_logits = get_logits_for_pos(pos);
-
-                    for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
-                        candidates[token_id].logit = pos_logits[token_id];
-                        candidates[token_id].p     = 0.0f;
-                        candidates[token_id].id    = token_id;
-                    }
-
-                    llama_token_data_array cur_p = {
-                        candidates.data(),
-                        candidates.size(),
-                        -1,
-                        false,
-                    };
-
-                    llama_sampler_apply(sampler, &cur_p);
-                    llama_token sampled_token = cur_p.data[cur_p.selected].id;
-
-                    float conf = calculate_confidence(cur_p, params.algorithm, rng);
-
-                    sampled_tokens[i] = sampled_token;
-                    confidences.emplace_back(conf, i);
-                }
-
-                int32_t transfer_count = calculate_transfer_count(
-                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
-
-                if (transfer_count > 0) {
-                    if (params.alg_temp == 0.0f) {
-                        std::partial_sort(confidences.begin(),
-                                          confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()),
-                                          confidences.end(),
-                                          [](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
-                                              if (a.first != b.first) {
-                                                  return a.first > b.first;
-                                              }
-                                              return a.second < b.second;
-                                          });
-
-                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
-                            int32_t mask_idx   = confidences[i].second;
-                            int32_t pos        = mask_positions[mask_idx];
-                            output_tokens[pos] = sampled_tokens[mask_idx];
-                        }
-                    } else {
-                        conf_candidates.clear();
-                        for (size_t i = 0; i < confidences.size(); i++) {
-                            float conf_logit = confidences[i].first / params.alg_temp;
-                            conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f });
-                        }
-
-                        llama_token_data_array conf_array = {
-                            conf_candidates.data(),
-                            conf_candidates.size(),
-                            -1,
-                            false,
-                        };
-
-                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
-                            llama_sampler_apply(dist_sampler, &conf_array);
-                            int32_t selected_idx = conf_array.selected;
-                            int32_t mask_idx     = selected_idx;
-                            int32_t pos          = mask_positions[mask_idx];
-                            output_tokens[pos]   = sampled_tokens[mask_idx];
-
-                            conf_candidates[selected_idx].p = 0.0f;
-                            conf_array.selected             = -1;
-                        }
-                    }
-                }
-            }
-
-            int64_t time_end_sampling = ggml_time_us();
-            total_sampling_time += time_end_sampling - time_start_sampling;
-        }
-    }
-
-    int64_t time_end = ggml_time_us();
-    total_time += time_end - time_start;
-
-    LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
-            total_time / 1000.0,
-            total_time / 1000.0 / params.steps,
-            total_sampling_time / 1000.0 / params.steps);
-
-    llama_batch_free(batch);
-    llama_sampler_free(sampler);
-    llama_sampler_free(dist_sampler);
-
-    n_generated = params.max_length;
-}
-
 static std::string format_input_text(const std::string & prompt, const std::string & system_prompt, bool use_chat_template, llama_model * model) {
     if (!use_chat_template) {
         return prompt;
@@ -631,10 +192,10 @@ int main(int argc, char ** argv) {
     GGML_ASSERT((params.diffusion.eps == 0) ^ (params.diffusion.block_length == 0));
 
     if (params.diffusion.eps) {
-        diff_params.schedule = TIMESTEP_BASED;
+        diff_params.schedule = DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED;
         diff_params.eps      = params.diffusion.eps;
     } else if (params.diffusion.block_length) {
-        diff_params.schedule     = BLOCK_BASED;
+        diff_params.schedule     = DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED;
         diff_params.block_length = params.diffusion.block_length;
     }
 
@@ -653,8 +214,17 @@ int main(int argc, char ** argv) {
     callback_data cb_data               = { &diff_params, vocab, n_input };
     diff_params.step_callback_user_data = &cb_data;
 
-    const char * alg_names[]   = { "ORIGIN", "ENTROPY_BASED", "MARGIN_BASED", "RANDOM", "CONFIDENCE_BASED" };
-    const char * sched_names[] = { "TIMESTEP_BASED", "BLOCK_BASED" };
+    const char * alg_names[]   = {
+        "DIFFUSION_ALGORITHM_ORIGIN",
+        "DIFFUSION_ALGORITHM_ENTROPY_BASED",
+        "DIFFUSION_ALGORITHM_MARGIN_BASED",
+        "DIFFUSION_ALGORITHM_RANDOM",
+        "DIFFUSION_ALGORITHM_CONFIDENCE_BASED",
+    };
+    const char * sched_names[] = {
+        "DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED",
+        "DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED",
+    };
     const char * alg_name =
         (diff_params.algorithm >= 0 && diff_params.algorithm <= 4) ? alg_names[diff_params.algorithm] : "UNKNOWN";
     const char * sched_name =
@@ -666,11 +236,11 @@ int main(int argc, char ** argv) {
     LOG_INF("diffusion_params: - %-25s enum             = %d (%s)\n", "algorithm", diff_params.algorithm, alg_name);
     LOG_INF("diffusion_params: - %-25s enum             = %d (%s)\n", "schedule", diff_params.schedule, sched_name);
     LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "temperature", diff_params.temperature);
-    if (diff_params.schedule == TIMESTEP_BASED) {
+    if (diff_params.schedule == DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED) {
         LOG_INF("diffusion_params: - %-25s f32              = %.6f\n", "eps", diff_params.eps);
         LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "alg_temp", diff_params.alg_temp);
     }
-    if (diff_params.schedule == BLOCK_BASED) {
+    if (diff_params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) {
         LOG_INF("diffusion_params: - %-25s u32              = %d\n", "block_length", diff_params.block_length);
         LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "cfg_scale", diff_params.cfg_scale);
     }
diff --git a/examples/diffusion/diffusion.cpp b/examples/diffusion/diffusion.cpp
new file mode 100644
index 00000000000..97d6b69449e
--- /dev/null
+++ b/examples/diffusion/diffusion.cpp
@@ -0,0 +1,408 @@
+#include "diffusion.h"
+
+#include "log.h"
+
+#include <algorithm>
+#include <cstddef>
+#include <cmath>
+#include <cstring>
+#include <random>
+#include <utility>
+#include <vector>
+
+static float calculate_confidence(const llama_token_data_array & cur_p,
+                                  diffusion_algorithm            algorithm,
+                                  std::mt19937 &                 rng) {
+    switch (algorithm) {
+        case DIFFUSION_ALGORITHM_CONFIDENCE_BASED:
+            return cur_p.data[cur_p.selected].p;  // Selected token probability
+
+        case DIFFUSION_ALGORITHM_ENTROPY_BASED:
+            {
+                float       entropy = 0.0f;
+                const float epsilon = 1e-10f;
+                for (size_t i = 0; i < cur_p.size; i++) {
+                    float prob = cur_p.data[i].p;
+                    entropy += prob * logf(prob + epsilon);
+                }
+                return -entropy;  // Higher entropy = lower confidence
+            }
+
+        case DIFFUSION_ALGORITHM_MARGIN_BASED:
+            return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p;
+
+        case DIFFUSION_ALGORITHM_RANDOM:
+            {
+                std::uniform_real_distribution<float> uniform(0.0f, 1.0f);
+                return uniform(rng);  // Random confidence
+            }
+
+        case DIFFUSION_ALGORITHM_ORIGIN:
+            return cur_p.data[cur_p.selected].p;
+
+        default:
+            return 0.0f;
+    }
+}
+
+// Unified transfer count calculation function
+static int32_t calculate_transfer_count(int32_t                      step,
+                                        int32_t                      total_steps,
+                                        int32_t                      remaining_masked,
+                                        diffusion_transfer_schedule  schedule,
+                                        float                        eps,
+                                        const std::vector<int32_t> & num_transfer_tokens = {}) {
+    switch (schedule) {
+        case DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED:
+            {
+                float t          = 1.0f - (float) step / total_steps * (1.0f - eps);
+                float s          = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps);
+                float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f;
+                return (int32_t) (remaining_masked * p_transfer);
+            }
+
+        case DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED:
+            if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) {
+                return num_transfer_tokens[step];
+            }
+            return remaining_masked / (total_steps - step);  // Fallback
+
+        default:
+            return remaining_masked / (total_steps - step);
+    }
+}
+
+static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) {
+    if (temperature == 0.0f) {
+        return;
+    }
+
+    std::uniform_real_distribution<double> uniform(0.0, 1.0);
+    for (int32_t i = 0; i < n_vocab; i++) {
+        double noise        = uniform(rng);
+        // Prevent log(0)
+        noise               = std::max(noise, 1e-20);
+        double gumbel_noise = std::pow(-std::log(noise), temperature);
+        logits[i]           = std::exp(logits[i]) / gumbel_noise;
+    }
+}
+
+static std::vector<int32_t> get_num_transfer_tokens(int32_t mask_count, int32_t steps) {
+    std::vector<int32_t> num_transfer_tokens(steps);
+
+    int32_t base      = mask_count / steps;
+    int32_t remainder = mask_count % steps;
+
+    for (int32_t i = 0; i < steps; i++) {
+        num_transfer_tokens[i] = base + (i < remainder ? 1 : 0);
+    }
+
+    return num_transfer_tokens;
+}
+
+void diffusion_generate(llama_context *          ctx,
+                        const llama_token *      input_tokens,
+                        llama_token *            output_tokens,
+                        int32_t                  n_input,
+                        const diffusion_params & params,
+                        int32_t &                n_generated) {
+    n_generated = 0;
+    if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) {
+        return;
+    }
+
+    const llama_model * model = llama_get_model(ctx);
+
+    // Initialize with input and pad with mask tokens
+    std::copy(input_tokens, input_tokens + n_input, output_tokens);
+    std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id);
+
+    std::mt19937 rng(params.seed);
+
+    llama_set_causal_attn(ctx, false);
+
+    int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
+
+    std::vector<llama_token_data> candidates(n_vocab);
+    std::vector<llama_token_data> conf_candidates;
+    conf_candidates.reserve(params.max_length);
+    std::vector<int32_t> mask_positions;
+    mask_positions.reserve(params.max_length);
+
+    // Setup sampler chain
+    struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
+    if (params.top_k > 0) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
+    }
+    if (params.top_p < 1.0f) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
+    }
+    if (params.temperature > 0.0f) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
+    }
+    llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
+
+    struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
+
+    llama_batch batch = llama_batch_init(params.max_length, 0, 1);
+    batch.n_tokens    = params.max_length;
+
+    // Pre-allocate buffers for CFG if needed
+    int32_t                  logits_size = n_vocab * params.max_length;
+    std::vector<float>       cond_logits_buffer;
+    std::vector<llama_token> un_x_buffer;
+    if (params.cfg_scale > 0.0f) {
+        cond_logits_buffer.resize(logits_size);
+        un_x_buffer.resize(params.max_length);
+    }
+
+    // For block-based processing
+    std::vector<int32_t> num_transfer_tokens;
+    int32_t              num_blocks      = 1;
+    int32_t              steps_per_block = params.steps;
+
+    if (params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) {
+        GGML_ASSERT(params.max_length % params.block_length == 0);
+        num_blocks = params.max_length / params.block_length;
+        GGML_ASSERT(params.steps % num_blocks == 0);
+        steps_per_block = params.steps / num_blocks;
+    }
+
+    std::vector<float> confidence(params.max_length);
+
+    int64_t total_sampling_time = 0;
+    int64_t total_time          = 0;
+    int64_t time_start          = ggml_time_us();
+
+    for (int block_num = 0; block_num < num_blocks; block_num++) {
+        int32_t block_start = (params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) ? n_input + block_num * params.block_length : 0;
+        int32_t block_end   = (params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) ?
+                                  std::min(n_input + (block_num + 1) * params.block_length, params.max_length) :
+                                  params.max_length;
+
+        // Count masked tokens in current block for block-based processing
+        if (params.schedule == DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED) {
+            int32_t block_mask_count = 0;
+            for (int i = block_start; i < block_end; i++) {
+                if (output_tokens[i] == params.mask_token_id) {
+                    block_mask_count++;
+                }
+            }
+            num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block);
+        }
+
+        for (int32_t step = 0; step < steps_per_block; step++) {
+            int32_t global_step = block_num * steps_per_block + step;
+
+            if (params.step_callback) {
+                if (!params.step_callback(
+                        global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) {
+                    break;
+                }
+            }
+
+            // Setup batch
+            for (int32_t i = 0; i < params.max_length; i++) {
+                batch.token[i]     = output_tokens[i];
+                batch.pos[i]       = i;
+                batch.n_seq_id[i]  = 1;
+                batch.seq_id[i][0] = 0;
+                batch.logits[i]    = 1;
+            }
+
+            float * logits = nullptr;
+
+            if (params.cfg_scale > 0.0f) {
+                int ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("Failed to generate conditional");
+                    break;
+                }
+                float * cond_logits_ptr = llama_get_logits(ctx);
+                std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float));
+
+                // Unconditional generation (mask input)
+                std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin());
+                for (int32_t i = 0; i < n_input; i++) {
+                    un_x_buffer[i] = params.mask_token_id;
+                }
+
+                for (int32_t i = 0; i < params.max_length; i++) {
+                    batch.token[i] = un_x_buffer[i];
+                }
+                ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("Failed to generate unconditional");
+                    break;
+                }
+                float * uncond_logits = llama_get_logits(ctx);
+
+                // Apply CFG
+                for (int32_t i = 0; i < logits_size; i++) {
+                    cond_logits_buffer[i] =
+                        uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]);
+                }
+                logits = cond_logits_buffer.data();
+            } else {
+                int ret = llama_decode(ctx, batch);
+                if (ret != 0) {
+                    LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret);
+                    break;
+                }
+                logits = llama_get_logits(ctx);
+            }
+
+            if (!logits) {
+                LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step);
+                break;
+            }
+
+            auto get_logits_for_pos = [&](int32_t pos) -> const float * {
+                if (params.shift_logits) {
+                    return pos == 0 ? logits : logits + (pos - 1) * n_vocab;
+                }
+                return logits + pos * n_vocab;
+            };
+
+            int64_t time_start_sampling = ggml_time_us();
+
+            mask_positions.clear();
+            for (int32_t i = 0; i < params.max_length; i++) {
+                if (output_tokens[i] == params.mask_token_id) {
+                    // For block-based, only consider current block
+                    if (params.schedule != DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED || (i >= block_start && i < block_end)) {
+                        mask_positions.push_back(i);
+                    }
+                }
+            }
+
+            if (mask_positions.empty()) {
+                break;
+            }
+
+            if (params.add_gumbel_noise && params.temperature > 0.0f) {
+                add_gumbel_noise(logits, n_vocab, params.temperature, rng);
+            }
+
+            if (params.algorithm == DIFFUSION_ALGORITHM_ORIGIN) {
+                int32_t transfer_count = calculate_transfer_count(
+                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
+                float p_transfer = (float) transfer_count / mask_positions.size();
+
+                for (int32_t pos : mask_positions) {
+                    if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
+                        const float * pos_logits = get_logits_for_pos(pos);
+                        for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
+                            candidates[token_id].id    = token_id;
+                            candidates[token_id].logit = pos_logits[token_id];
+                            candidates[token_id].p     = 0.0f;
+                        }
+
+                        llama_token_data_array cur_p = {
+                            candidates.data(),
+                            (size_t) n_vocab,
+                            -1,
+                            false,
+                        };
+
+                        llama_sampler_apply(sampler, &cur_p);
+                        output_tokens[pos] = cur_p.data[cur_p.selected].id;
+                    }
+                }
+            } else {
+                std::vector<std::pair<float, int32_t>> confidences;
+                std::vector<llama_token>               sampled_tokens(mask_positions.size());
+
+                for (size_t i = 0; i < mask_positions.size(); i++) {
+                    int32_t       pos        = mask_positions[i];
+                    const float * pos_logits = get_logits_for_pos(pos);
+
+                    for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
+                        candidates[token_id].logit = pos_logits[token_id];
+                        candidates[token_id].p     = 0.0f;
+                        candidates[token_id].id    = token_id;
+                    }
+
+                    llama_token_data_array cur_p = {
+                        candidates.data(),
+                        candidates.size(),
+                        -1,
+                        false,
+                    };
+
+                    llama_sampler_apply(sampler, &cur_p);
+                    llama_token sampled_token = cur_p.data[cur_p.selected].id;
+
+                    float conf = calculate_confidence(cur_p, params.algorithm, rng);
+
+                    sampled_tokens[i] = sampled_token;
+                    confidences.emplace_back(conf, i);
+                }
+
+                int32_t transfer_count = calculate_transfer_count(
+                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
+
+                if (transfer_count > 0) {
+                    if (params.alg_temp == 0.0f) {
+                        std::partial_sort(confidences.begin(),
+                                          confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()),
+                                          confidences.end(),
+                                          [](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
+                                              if (a.first != b.first) {
+                                                  return a.first > b.first;
+                                              }
+                                              return a.second < b.second;
+                                          });
+
+                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
+                            int32_t mask_idx   = confidences[i].second;
+                            int32_t pos        = mask_positions[mask_idx];
+                            output_tokens[pos] = sampled_tokens[mask_idx];
+                        }
+                    } else {
+                        conf_candidates.clear();
+                        for (size_t i = 0; i < confidences.size(); i++) {
+                            float conf_logit = confidences[i].first / params.alg_temp;
+                            conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f });
+                        }
+
+                        llama_token_data_array conf_array = {
+                            conf_candidates.data(),
+                            conf_candidates.size(),
+                            -1,
+                            false,
+                        };
+
+                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
+                            llama_sampler_apply(dist_sampler, &conf_array);
+                            int32_t selected_idx = conf_array.selected;
+                            int32_t mask_idx     = selected_idx;
+                            int32_t pos          = mask_positions[mask_idx];
+                            output_tokens[pos]   = sampled_tokens[mask_idx];
+
+                            conf_candidates[selected_idx].p = 0.0f;
+                            conf_array.selected             = -1;
+                        }
+                    }
+                }
+            }
+
+            int64_t time_end_sampling = ggml_time_us();
+            total_sampling_time += time_end_sampling - time_start_sampling;
+        }
+    }
+
+    int64_t time_end = ggml_time_us();
+    total_time += time_end - time_start;
+
+    LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
+            total_time / 1000.0,
+            total_time / 1000.0 / params.steps,
+            total_sampling_time / 1000.0 / params.steps);
+
+    llama_batch_free(batch);
+    llama_sampler_free(sampler);
+    llama_sampler_free(dist_sampler);
+
+    n_generated = params.max_length;
+}
diff --git a/examples/diffusion/diffusion.h b/examples/diffusion/diffusion.h
new file mode 100644
index 00000000000..7831445224c
--- /dev/null
+++ b/examples/diffusion/diffusion.h
@@ -0,0 +1,57 @@
+#pragma once
+
+#include "llama.h"
+
+#include <cstdint>
+
+enum diffusion_algorithm {
+    DIFFUSION_ALGORITHM_ORIGIN           = 0,
+    DIFFUSION_ALGORITHM_ENTROPY_BASED    = 1,
+    DIFFUSION_ALGORITHM_MARGIN_BASED     = 2,
+    DIFFUSION_ALGORITHM_RANDOM           = 3,
+    DIFFUSION_ALGORITHM_CONFIDENCE_BASED = 4,
+};
+
+// Unified transfer scheduling methods
+enum diffusion_transfer_schedule {
+    DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED = 0,  // Dream-style: (1.0 - s/t) * remaining
+    DIFFUSION_TRANSFER_SCHEDULE_BLOCK_BASED    = 1,  // LLaDA-style: process in blocks with get_num_transfer_tokens
+};
+
+typedef bool (*diffusion_step_callback_t)(int32_t             step,
+                                          int32_t             total_steps,
+                                          const llama_token * tokens,
+                                          int32_t             n_tokens,
+                                          void *              user_data);
+
+struct diffusion_params {
+    int32_t                   steps                   = 0;
+    float                     temperature             = 0;
+    llama_token               mask_token_id           = LLAMA_TOKEN_NULL;
+    diffusion_step_callback_t step_callback           = nullptr;
+    void *                    step_callback_user_data = nullptr;
+    int32_t                   seed                    = 0;
+    bool                      visual_mode             = false;
+    bool                      shift_logits            = false;  // Shift logits by -1 after decode
+
+    float   top_p = 0.;
+    int32_t top_k = 0.;
+
+    diffusion_algorithm         algorithm = DIFFUSION_ALGORITHM_CONFIDENCE_BASED;
+    diffusion_transfer_schedule schedule  = DIFFUSION_TRANSFER_SCHEDULE_TIMESTEP_BASED;
+
+    float   cfg_scale        = 0.;     // Config scale for classifier-free guidance
+    float   eps              = 0.;     // Timestep scheduling
+    int32_t block_length     = 0;      // Block size (for block scheduling)
+    float   alg_temp         = 0;      // algorithm temperature (0.0 = deterministic)
+    bool    add_gumbel_noise = false;  // Add gumbel noise to the logits if temp > 0.0
+
+    int32_t max_length = 0;            // Maximum sequence length
+};
+
+void diffusion_generate(llama_context *          ctx,
+                        const llama_token *      input_tokens,
+                        llama_token *            output_tokens,
+                        int32_t                  n_input,
+                        const diffusion_params & params,
+                        int32_t &                n_generated);
diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index 8832468451c..4ce8d600b1a 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -3,7 +3,6 @@
 #include "debug.h"
 #include "log.h"
 #include "llama.h"
-#include "llama-cpp.h"
 
 #include <clocale>
 #include <string>
@@ -38,7 +37,7 @@ static bool run(llama_context * ctx, const common_params & params) {
 int main(int argc, char ** argv) {
     std::setlocale(LC_NUMERIC, "C");
 
-    base_callback_data cb_data;
+    common_debug_cb_user_data cb_data;
 
     common_params params;
 
@@ -53,7 +52,7 @@ int main(int argc, char ** argv) {
 
     // pass the callback to the backend scheduler
     // it will be executed for each node during the graph computation
-    params.cb_eval = common_debug_cb_eval<false>;
+    params.cb_eval = common_debug_cb_eval;
     params.cb_eval_user_data = &cb_data;
     params.warmup = false;
 
diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp
index 7ba7d79f721..baf61bf27b5 100644
--- a/examples/gen-docs/gen-docs.cpp
+++ b/examples/gen-docs/gen-docs.cpp
@@ -73,12 +73,12 @@ static void write_help(std::ostringstream & ss, const md_file & md) {
     auto ctx_arg = common_params_parser_init(params, md.ex);
 
     std::vector<common_arg *> common_options;
-    std::vector<common_arg *> sparam_options;
+    std::vector<common_arg *> sampling_options;
     std::vector<common_arg *> specific_options;
     for (auto & opt : ctx_arg.options) {
         // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
-        if (opt.is_sparam) {
-            sparam_options.push_back(&opt);
+        if (opt.is_sampling) {
+            sampling_options.push_back(&opt);
         } else if (opt.in_example(ctx_arg.ex)) {
             specific_options.push_back(&opt);
         } else {
@@ -93,7 +93,7 @@ static void write_help(std::ostringstream & ss, const md_file & md) {
     ss << "### Common params\n\n";
     write_table(ss, common_options);
     ss << "\n\n### Sampling params\n\n";
-    write_table(ss, sparam_options);
+    write_table(ss, sampling_options);
     ss << "\n\n### " << md.specific_section_header << "\n\n";
     write_table(ss, specific_options);
 
diff --git a/examples/llama-eval/README.md b/examples/llama-eval/README.md
new file mode 100644
index 00000000000..3c5c35f78f6
--- /dev/null
+++ b/examples/llama-eval/README.md
@@ -0,0 +1,26 @@
+# llama-eval
+
+Simple evaluation tool for llama.cpp with support for multiple datasets.
+
+For a full description, usage examples, and sample results, see:
+
+- [PR 21152](https://github.com/ggml-org/llama.cpp/pull/21152)
+
+## Quick start
+
+```bash
+# Single server
+python3 llama-eval.py \
+  --server http://localhost:8033 \
+  --model my-model \
+  --dataset gsm8k --n_cases 100 \
+  --grader-type regex --threads 32
+
+# Multiple servers (comma-separated URLs and thread counts)
+python3 llama-eval.py \
+  --server http://server1:8033,http://server2:8033 \
+  --server-name server1,server2 \
+  --threads 16,16 \
+  --dataset aime2025 --n_cases 240 \
+  --grader-type regex
+```
diff --git a/examples/llama-eval/llama-eval.py b/examples/llama-eval/llama-eval.py
new file mode 100755
index 00000000000..e833070eee9
--- /dev/null
+++ b/examples/llama-eval/llama-eval.py
@@ -0,0 +1,1472 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+import os
+import re
+import subprocess
+import sys
+import threading
+import time
+from abc import ABC, abstractmethod
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from dataclasses import dataclass, asdict, field
+from pathlib import Path
+from queue import Queue
+from typing import Dict, List, Optional, Any, Tuple
+import requests
+from tqdm import tqdm
+import random
+from math import sqrt
+
+
+@dataclass
+class ServerConfig:
+    url: str
+    threads: int
+    name: str = ""
+
+def wilson_interval(correct: int, total: int, z: float = 1.96) -> Tuple[float, float]:
+    """Wilson score confidence interval for a proportion."""
+    if total == 0:
+        return (0.0, 1.0)
+    p = correct / total
+    z2 = z * z / total
+    center = (p + z2 / 2) / (1 + z2)
+    margin = z * sqrt((p * (1 - p) + z2 / 4) / total) / (1 + z2)
+    return (center - margin, center + margin)
+
+cache_dir = Path.home() / ".cache" / "huggingface" / "datasets"
+cache_dir.mkdir(parents=True, exist_ok=True)
+os.environ["HF_DATASETS_CACHE"] = str(cache_dir)
+os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1"
+
+GRADER_PATTERNS = {
+    "aime": r'\boxed{(\d+)}|\b(\d+)\b',
+    "aime2025": r'\boxed{(\d+)}|\b(\d+)\b',
+    "aime2026": r'\boxed{(\d+)}|\b(\d+)\b',
+    "gsm8k": r'\b(\d+)\b',
+}
+
+SAMPLE_ANSWERS = {
+    "aime": [
+        "42",
+        "-123",
+        "999"
+    ],
+    "aime2025": [
+        "42",
+        "-123",
+        "999"
+    ],
+    "aime2026": [
+        "42",
+        "-123",
+        "999"
+    ],
+    "gsm8k": [
+        "42",
+        "-123",
+        "999"
+    ],
+    "gpqa": [
+        "A",
+        "D",
+        "C"
+    ],
+}
+
+TEMPLATE_REGISTRY = {
+    "aime": """Solve the following math problem step by step. Put your answer inside \\boxed{{}}.
+
+{question}
+
+Remember to put your answer inside \\boxed{{}}.
+""",
+    "aime2025": """Solve the following math problem step by step. Put your answer inside \\boxed{{}}.
+
+{question}
+
+Remember to put your answer inside \\boxed{{}}.
+""",
+    "aime2026": """Solve the following math problem step by step. Put your answer inside \\boxed{{}}.
+
+{question}
+
+Remember to put your answer inside \\boxed{{}}.
+""",
+    "gsm8k": """{question}
+Please reason step by step, and put your final numeric answer within \\boxed{{}} without any extra characters.
+""",
+    "gpqa": """Answer the following multiple choice question. The last line of your response should be in the following format: 'Answer: A/B/C/D' (e.g. 'Answer: A').
+
+{Question}
+
+A) {A}
+B) {B}
+C) {C}
+D) {D}
+""",
+}
+
+
+class BaseDataset(ABC):
+    questions: List[Dict]
+
+    @abstractmethod
+    def get_question(self, index: int) -> Dict:
+        pass
+
+    @abstractmethod
+    def get_question_text(self, question: Dict) -> str:
+        pass
+
+    @abstractmethod
+    def get_answer(self, question: Dict) -> str:
+        pass
+
+    @abstractmethod
+    def get_prompt(self, question: Dict) -> str:
+        pass
+
+    def __len__(self) -> int:
+        return len(self.questions)
+
+
+@dataclass
+class TaskState:
+    task_id: str
+    prompt: str
+    expected: str
+    question_text: str = ""
+    response: Optional[str] = None
+    answer: Optional[str] = None
+    grader_log: Dict[str, Any] = field(default_factory=dict)
+    correct: bool = False
+    status: str = "pending"
+    tokens: Optional[int] = None
+    tps_gen: Optional[float] = None
+    t_gen_ms: Optional[float] = None
+    reasoning_content: Optional[str] = None
+    server_name: Optional[str] = None
+
+
+class EvalState:
+    def __init__(
+        self,
+        dataset_type: str,
+        sampling_config: Dict[str, Any],
+        output_file: Path = Path("llama-eval-state.json"),
+        model_name: Optional[str] = None
+    ):
+        self.dataset_type = dataset_type
+        self.sampling_config = sampling_config
+        self.output_file = output_file
+        self.model_name = model_name
+        self.dataset: Optional[BaseDataset] = None
+        self.tasks: List[Tuple[int, str]] = []
+        self.all_tasks: List[Tuple[int, str]] = []
+        self.task_states: Dict[str, Any] = {}
+        self.total = 0
+        self.correct = 0
+        self.processed = 0
+        self.total_time: float = 0.0
+        self._lock = threading.Lock()
+
+    def load_dataset(self, seed: int = 1234):
+        if self.dataset_type == "aime":
+            self.dataset = AimeDataset()
+        elif self.dataset_type == "aime2025":
+            self.dataset = Aime2025Dataset()
+        elif self.dataset_type == "aime2026":
+            self.dataset = Aime2026Dataset()
+        elif self.dataset_type == "gsm8k":
+            self.dataset = Gsm8kDataset()
+        elif self.dataset_type == "gpqa":
+            self.dataset = GpqaDataset(variant="diamond", seed=seed)
+        else:
+            raise ValueError(f"Unknown dataset type: {self.dataset_type}")
+
+    def setup_tasks(self, n_cases: Optional[int] = None, seed: int = 1234):
+        if self.dataset is None:
+            raise ValueError("Dataset not loaded. Call load_dataset() first.")
+
+        if n_cases is None:
+            n_cases = len(self.dataset)
+
+        dataset_size = len(self.dataset)
+        rng = random.Random(seed)
+
+        self.tasks = []
+        for chunk_idx in range((n_cases + dataset_size - 1) // dataset_size):
+            chunk_size = min(dataset_size, n_cases - chunk_idx * dataset_size)
+            indices = list(range(dataset_size))
+            rng.shuffle(indices)
+            chunk_indices = indices[:chunk_size]
+
+            for i in chunk_indices:
+                task_id = f"{self.dataset_type}_{chunk_idx:03d}_{i:03d}"
+                self.tasks.append((i, task_id))
+
+        self.all_tasks = list(self.tasks)
+
+    def get_case(self, index: int) -> Tuple[str, str, str]:
+        if self.dataset is None:
+            raise ValueError("Dataset not loaded.")
+        question = self.dataset.get_question(index)
+        question_text = self.dataset.get_question_text(question)
+        prompt = self.dataset.get_prompt(question)
+        expected = self.dataset.get_answer(question)
+        return question_text, prompt, expected
+
+    def add_result(
+        self,
+        task_id: str,
+        prompt: str,
+        expected: str,
+        response: Optional[str],
+        answer: Optional[str],
+        grader_log: Dict[str, Any],
+        correct: bool,
+        status: str,
+        tokens: Optional[int] = None,
+        tps_gen: Optional[float] = None,
+        t_gen_ms: Optional[float] = None,
+        reasoning_content: Optional[str] = None,
+        server_name: Optional[str] = None
+    ):
+        with self._lock:
+            if "cases" not in self.task_states:
+                self.task_states["cases"] = {}
+
+            self.task_states["cases"][task_id] = {
+                "task_id": task_id,
+                "prompt": prompt,
+                "expected": expected,
+                "response": response,
+                "answer": answer,
+                "grader_log": grader_log,
+                "correct": correct,
+                "status": status,
+                "tokens": tokens,
+                "tps_gen": tps_gen,
+                "t_gen_ms": t_gen_ms,
+                "reasoning_content": reasoning_content,
+                "server_name": server_name
+            }
+
+            self.correct = sum(1 for c in self.task_states.get("cases", {}).values() if c.get("correct", False))
+
+    def print_progress(self, task_state: TaskState, total_tasks: int, n_correct: int = 0):
+        display_answer = task_state.answer if task_state.answer else "N/A"
+        display_tokens = str(task_state.tokens) if task_state.tokens is not None else "N/A"
+        display_tps = f"{task_state.tps_gen:.1f}" if task_state.tps_gen is not None else "N/A"
+        display_t_gen = f"{task_state.t_gen_ms/1000:.1f}" if task_state.t_gen_ms is not None else "N/A"
+        display_server = task_state.server_name if task_state.server_name else "N/A"
+        success_ratio = n_correct / self.processed if self.processed > 0 else 0.0
+        first_line = task_state.question_text.split('\n')[0]
+        truncated_question = first_line[:43]
+        if len(first_line) > 43:
+            truncated_question += "..."
+        else:
+            truncated_question = truncated_question.ljust(43) + "..."
+        print(f"{self.processed:3}/{total_tasks:3}  {task_state.task_id:<20} {self.dataset_type.upper()}   {truncated_question:<40}    {task_state.expected:<10} {display_answer:<10} {display_tokens:<6} {display_tps:<6} {display_t_gen:<8} {'✓' if task_state.correct else '✗'}  [{n_correct:3}/{self.processed:3}, {success_ratio:.3f}]  {display_server}")
+
+    def print_summary(self):
+        if self.total == 0:
+            print(f"\n{'='*60}")
+            print(f"Results: 0/0 correct (0.0%)")
+            print(f"{'='*60}")
+        else:
+            ci_lower, ci_upper = self.accuracy_ci()
+            print(f"\n{'='*60}")
+            print(f"Results: {self.correct}/{self.total} correct ({self.correct/self.total*100:.1f}%) [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]")
+            print(f"{'='*60}")
+
+    def dump(self):
+        with self._lock:
+            tasks_to_save = self.all_tasks if self.all_tasks else self.tasks
+            all_cases = {}
+            for i, task_id in tasks_to_save:
+                question_text, prompt, expected = self.get_case(i)
+                if task_id in self.task_states.get("cases", {}):
+                    all_cases[task_id] = self.task_states["cases"][task_id]
+                else:
+                    all_cases[task_id] = {
+                        "task_id": task_id,
+                        "prompt": prompt,
+                        "expected": expected,
+                        "question_text": question_text,
+                        "response": None,
+                        "answer": None,
+                        "grader_log": {},
+                        "correct": False,
+                        "status": "pending",
+                        "tokens": None,
+                        "tps_gen": None,
+                        "t_gen_ms": None,
+                        "reasoning_content": None,
+                        "server_name": None
+                    }
+
+            ci_lower, ci_upper = self.accuracy_ci()
+            data = {
+                "id": self.dataset_type,
+                "model_name": self.model_name,
+                "tasks": [tid for _, tid in tasks_to_save],
+                "task_states": {
+                    "total": self.total,
+                    "correct": self.correct,
+                    "total_time": self.total_time,
+                    "ci_lower": ci_lower,
+                    "ci_upper": ci_upper,
+                    "cases": all_cases,
+                },
+                "sampling_config": self.sampling_config
+            }
+            with open(self.output_file, "w") as f:
+                json.dump(data, f, indent=2)
+
+            self.dump_html(tasks_to_save, all_cases)
+
+    def dump_html(self, tasks_to_save: List[Tuple[int, str]], all_cases: Dict[str, Any]):
+        html_file = Path(str(self.output_file) + ".html")
+
+        cases = all_cases
+        completed = {tid: c for tid, c in cases.items() if c.get("status") == "ok"}
+        n_correct = sum(1 for c in completed.values() if c.get("correct", False))
+        n_incorrect = len(completed) - n_correct
+        n_pending = len(tasks_to_save) - len(completed)
+        accuracy = n_correct / len(completed) * 100 if completed else 0.0
+        ci_lower, ci_upper = wilson_interval(n_correct, len(completed)) if completed else (0.0, 1.0)
+
+        sampling_parts = []
+        for k, v in self.sampling_config.items():
+            if v is not None:
+                sampling_parts.append(f"{k}={v}")
+        sampling_str = ", ".join(sampling_parts) if sampling_parts else "default"
+
+        rows = []
+        for i, task_id in tasks_to_save:
+            case = cases.get(task_id, {})
+            status = case.get("status", "pending")
+            expected = case.get("expected", "")
+            answer = case.get("answer", "") if status == "ok" else ""
+            is_correct = case.get("correct", False) if status == "ok" else False
+            response = case.get("response", "") or ""
+            prompt = case.get("prompt", "") or ""
+            grader_log = case.get("grader_log", {})
+
+            if status == "ok":
+                status_class = "correct" if is_correct else "incorrect"
+                status_text = "✓" if is_correct else "✗"
+            elif status == "pending":
+                status_class = "pending"
+                status_text = "–"
+            else:
+                status_class = "error"
+                status_text = "!"
+
+            tokens = case.get("tokens")
+            tokens_str = str(tokens) if tokens is not None else ""
+            tps_gen = case.get("tps_gen")
+            tps_str = f"{tps_gen:.1f}" if tps_gen is not None else ""
+            t_gen_ms = case.get("t_gen_ms")
+            t_gen_str = f"{t_gen_ms/1000:.1f}" if t_gen_ms is not None else ""
+            reasoning_content = case.get("reasoning_content", "") or ""
+            server_name = case.get("server_name", "") or ""
+
+            escaped_response = self._escape_html(response)
+            escaped_prompt = self._escape_html(prompt)
+            escaped_reasoning = self._escape_html(reasoning_content)
+            grader_log_str = self._escape_html(json.dumps(grader_log, indent=2))
+            escaped_server = self._escape_html(server_name)
+
+            rows.append(f"""<tr class="task-row" onclick="toggleDetails('{task_id}')">
+                <td>{task_id}</td>
+                <td class="{status_class}">{status_text}</td>
+                <td>{self._escape_html(expected)}</td>
+                <td>{self._escape_html(answer)}</td>
+                <td>{tokens_str}</td>
+                <td>{tps_str}</td>
+                <td>{t_gen_str}</td>
+                <td>{escaped_server}</td>
+            </tr>
+            <tr id="details-{task_id}" class="details-row">
+                <td colspan="8">
+                    <div class="details-content">
+                        <b>Prompt</b><pre>{escaped_prompt}</pre>
+                        <b>Response</b><pre>{escaped_response}</pre>
+                        {f'<b>Reasoning</b><pre>{escaped_reasoning}</pre>' if escaped_reasoning else ''}
+                        <b>Grader</b><pre>{grader_log_str}</pre>
+                    </div>
+                </td>
+            </tr>""")
+
+        rows_html = "\n".join(rows)
+
+        html_content = f"""<!DOCTYPE html>
+<html>
+<head>
+<meta charset="UTF-8">
+<title>{self.dataset_type.upper()} Eval</title>
+<style>
+        body {{ font-family: system-ui, sans-serif; margin: 0; padding: 16px; background: #fff; color: #222; }}
+        .bar {{ padding: 8px 0; font-size: 14px; color: #555; }}
+        .bar span {{ margin-right: 20px; }}
+        .bar b {{ color: #222; }}
+        table {{ width: 100%; border-collapse: collapse; font-size: 13px; }}
+        th {{ text-align: left; padding: 6px 8px; border-bottom: 2px solid #ccc; font-weight: 600; }}
+        td {{ padding: 4px 8px; border-bottom: 1px solid #eee; vertical-align: top; }}
+        .task-row {{ cursor: pointer; }}
+        .task-row:hover {{ background: #f5f5f5; }}
+        .correct {{ color: #1a7f37; }}
+        .incorrect {{ color: #cf222e; }}
+        .pending {{ color: #888; }}
+        .error {{ color: #9a6700; }}
+        .details-row {{ display: none; }}
+        .details-row.open {{ display: table-row; }}
+        .details-content {{ padding: 8px 16px; background: #f6f8fa; font-size: 12px; }}
+        .details-content b {{ color: #555; }}
+        .details-content pre {{ background: #fff; border: 1px solid #e1e4e8; padding: 8px; overflow-x: auto; white-space: pre-wrap; word-wrap: break-word; margin: 4px 0 8px; }}
+</style>
+</head>
+<body>
+    <div class="bar">
+        <span><b>{self.dataset_type.upper()}</b></span>
+        <span>Model: {self.model_name or 'N/A'}</span>
+        <span>Accuracy: <b>{accuracy:.1f}%</b> [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]</span>
+        <span>Correct: <span class="correct">{n_correct}</span> / {len(completed)}</span>
+        <span>Pending: {n_pending}</span>
+        <span>Time: {self.total_time:.1f}s</span>
+        <span>Sampling: {sampling_str}</span>
+    </div>
+    <table>
+        <thead>
+            <tr>
+                <th>ID</th>
+                <th></th>
+                <th>Gold</th>
+                <th>Answer</th>
+                <th>Tokens</th>
+                <th>T/s</th>
+                <th>Gen s</th>
+                <th>Server</th>
+            </tr>
+        </thead>
+        <tbody>
+            {rows_html}
+        </tbody>
+    </table>
+    <script>
+        function toggleDetails(id) {{ document.getElementById('details-'+id).classList.toggle('open'); }}
+    </script>
+</body>
+</html>"""
+
+        with open(html_file, "w") as f:
+            f.write(html_content)
+
+    def _escape_html(self, s: str) -> str:
+        return (s.replace("&", "&amp;")
+                   .replace("<", "&lt;")
+                   .replace(">", "&gt;")
+                   .replace('"', "&quot;")
+                   .replace("'", "&#39;"))
+
+    @classmethod
+    def load(cls, path: Path) -> "EvalState":
+        with open(path, "r") as f:
+            data = json.load(f)
+
+        eval_state = cls(
+            dataset_type=data["id"],
+            sampling_config=data["sampling_config"],
+            output_file=path,
+            model_name=data.get("model_name")
+        )
+        eval_state.load_dataset()
+
+        eval_state.tasks = []
+        eval_state.all_tasks = []
+        for task_id in data.get("tasks", []):
+            parts = task_id.rsplit("_", 2)
+            if len(parts) >= 3:
+                idx = int(parts[-1])
+            else:
+                idx = 0
+            eval_state.tasks.append((idx, task_id))
+            eval_state.all_tasks.append((idx, task_id))
+
+        eval_state.task_states = data.get("task_states", {})
+
+        cases = eval_state.task_states.get("cases", {})
+        eval_state.total = eval_state.task_states.get("total", 0)
+        eval_state.correct = eval_state.task_states.get("correct", 0)
+        eval_state.total_time = eval_state.task_states.get("total_time", 0.0)
+
+        if eval_state.total == 0:
+            eval_state.total = len(cases)
+            eval_state.correct = sum(1 for c in cases.values() if c.get("correct", False))
+
+        return eval_state
+
+    def is_complete(self) -> bool:
+        if not self.all_tasks:
+            return False
+        cases = self.task_states.get("cases", {})
+        completed = {tid for tid in self.task_states.get("cases", {}).keys() if cases.get(tid, {}).get("status") == "ok"}
+        return len(completed) == len(self.all_tasks)
+
+    def get_pending_tasks(self) -> List[Tuple[int, str]]:
+        cases = self.task_states.get("cases", {})
+        pending = []
+        for i, task_id in self.all_tasks:
+            status = cases.get(task_id, {}).get("status", "pending")
+            if status != "ok":
+                pending.append((i, task_id))
+        return pending
+
+    def print_all_tasks(self):
+        cases = self.task_states.get("cases", {})
+        tasks_to_show = self.all_tasks if self.all_tasks else self.tasks
+        print()
+        print("Tasks:")
+        print("  Task ID             Dataset  Prompt (first 40 chars)                        Expected    Answer       Tokens  T/s     Gen s  Status")
+        for i, task_id in tasks_to_show:
+            question, prompt, expected = self.get_case(i)
+            case = cases.get(task_id, {})
+            status = case.get("status", "pending")
+            answer = case.get("answer", "N/A") if status == "ok" else "N/A"
+            tokens = case.get("tokens")
+            tokens_str = str(tokens) if tokens is not None else "N/A"
+            tps_gen = case.get("tps_gen")
+            tps_str = f"{tps_gen:.1f}" if tps_gen is not None else "N/A"
+            t_gen_ms = case.get("t_gen_ms")
+            t_gen_str = f"{t_gen_ms/1000:.1f}" if t_gen_ms is not None else "N/A"
+            server_name = case.get("server_name", "") or ""
+            is_correct = case.get("correct", False) if status == "ok" else False
+            symbol = "✓ " if is_correct else ("✗ " if status == "ok" else "")
+            first_line = question.split('\n')[0]
+            question_trunc = first_line[:43]
+            if len(first_line) > 43:
+                question_trunc += "..."
+            else:
+                question_trunc = question_trunc.ljust(43) + "..."
+            print(f"  {task_id:<20} {self.dataset_type.upper()}   {question_trunc:<40}    {expected:<10} {answer:<10} {tokens_str:<6} {tps_str:<6} {t_gen_str:<8} {symbol}{status}  {server_name}")
+        print()
+
+    def print_existing_summary(self):
+        cases = self.task_states.get("cases", {})
+        completed_cases = {tid: c for tid, c in cases.items() if c.get("status") == "ok"}
+        correct = sum(1 for c in completed_cases.values() if c.get("correct", False))
+        total = len(completed_cases)
+        if total == 0:
+            print(f"{'='*60}")
+            print(f"Results: 0/0 correct (0.0%)")
+            print(f"{'='*60}")
+        else:
+            ci_lower, ci_upper = self.accuracy_ci()
+            print(f"{'='*60}")
+            print(f"Results: {correct}/{total} correct ({correct/total*100:.1f}%) [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]")
+            print(f"{'='*60}")
+
+    def accuracy_ci(self) -> Tuple[float, float]:
+        """Compute Wilson score confidence interval from completed cases."""
+        cases = self.task_states.get("cases", {})
+        completed = {tid: c for tid, c in cases.items() if c.get("status") == "ok"}
+        correct = sum(1 for c in completed.values() if c.get("correct", False))
+        total = len(completed)
+        return wilson_interval(correct, total)
+
+def normalize_number(s: str) -> Optional[int]:
+    match = re.match(r"\d+", s)  # match digits from the start
+    if not match:
+        return None
+    return int(match.group(0))
+
+class AimeDataset(BaseDataset):
+    def __init__(self, split: str = "train"):
+        self.split = split
+        self.questions = []
+        self._load_dataset()
+
+    def _load_dataset(self):
+        print(f"Loading AIME dataset (split: {self.split})...")
+        from datasets import load_dataset
+
+        cache_path = cache_dir / "AI-MO___aimo-validation-aime" / "default" / "0.0.0"
+        if cache_path.exists():
+            print(f"Using cached dataset from {cache_path}")
+            ds = load_dataset("AI-MO/aimo-validation-aime", split=self.split, cache_dir=str(cache_path))
+        else:
+            ds = load_dataset("AI-MO/aimo-validation-aime", split=self.split)
+
+        self.questions = []
+        for row in ds:
+            question = dict(row)
+            question["dataset_type"] = "aime"
+            self.questions.append(question)
+
+        print(f"AIME dataset loaded: {len(self.questions)} questions")
+
+    def get_question(self, index: int) -> Dict:
+        """Get question by index"""
+        return self.questions[index]
+
+    def get_question_text(self, question: Dict) -> str:
+        """Get question string"""
+        return question["problem"] if "problem" in question else question["question"]
+
+    def get_answer(self, question: Dict) -> str:
+        answer = question["answer"]
+        if isinstance(answer, str):
+            normalized = normalize_number(answer)
+            return str(normalized) if normalized is not None else answer
+        return str(answer)
+
+    def get_prompt(self, question: Dict) -> str:
+        """Get formatted prompt for the question"""
+        return TEMPLATE_REGISTRY[question["dataset_type"]].format(
+            question=self.get_question_text(question),
+        )
+
+class Aime2025Dataset(BaseDataset):
+    def __init__(self):
+        self.questions = []
+        self._load_dataset()
+
+    def _load_dataset(self):
+        print(f"Loading AIME2025 dataset...")
+        from datasets import load_dataset
+
+        config_name = "AIME2025-I"
+        cache_path = cache_dir / "opencompass___AIME2025" / "default" / "0.0.0"
+        if cache_path.exists():
+            print(f"Using cached dataset from {cache_path}")
+            ds = load_dataset("opencompass/AIME2025", config_name, split="test", cache_dir=str(cache_path))
+        else:
+            ds = load_dataset("opencompass/AIME2025", config_name, split="test")
+
+        self.questions = []
+        for row in ds:
+            question = dict(row)
+            question["dataset_type"] = "aime2025"
+            self.questions.append(question)
+
+        print(f"AIME2025 dataset loaded: {len(self.questions)} questions")
+
+        print(f"Loading AIME2025 dataset (part 2)...")
+        config_name_2 = "AIME2025-II"
+        cache_path_2 = cache_dir / "opencompass___AIME2025" / "default" / "0.0.0"
+        if cache_path_2.exists():
+            print(f"Using cached dataset from {cache_path_2}")
+            ds_2 = load_dataset("opencompass/AIME2025", config_name_2, split="test", cache_dir=str(cache_path_2))
+        else:
+            ds_2 = load_dataset("opencompass/AIME2025", config_name_2, split="test")
+
+        for row in ds_2:
+            question = dict(row)
+            question["dataset_type"] = "aime2025"
+            self.questions.append(question)
+
+        print(f"AIME2025 dataset loaded: {len(self.questions)} questions (total)")
+
+    def get_question(self, index: int) -> Dict:
+        """Get question by index"""
+        return self.questions[index]
+
+    def get_question_text(self, question: Dict) -> str:
+        """Get question string"""
+        return question["question"]
+
+    def get_answer(self, question: Dict) -> str:
+        answer = question["answer"]
+        if isinstance(answer, str):
+            normalized = normalize_number(answer)
+            return str(normalized) if normalized is not None else answer
+        return str(answer)
+
+    def get_prompt(self, question: Dict) -> str:
+        """Get formatted prompt for the question"""
+        return TEMPLATE_REGISTRY["aime2025"].format(
+            question=self.get_question_text(question),
+        )
+
+class Aime2026Dataset(BaseDataset):
+    def __init__(self):
+        self.questions = []
+        self._load_dataset()
+
+    def _load_dataset(self):
+        print(f"Loading AIME2026 dataset...")
+        from datasets import load_dataset
+
+        cache_path = cache_dir / "MathArena___aime_2026" / "default" / "0.0.0"
+        if cache_path.exists():
+            print(f"Using cached dataset from {cache_path}")
+            ds = load_dataset("MathArena/aime_2026", "default", split="train", cache_dir=str(cache_path))
+        else:
+            ds = load_dataset("MathArena/aime_2026", "default", split="train")
+
+        self.questions = []
+        for row in ds:
+            question = dict(row)
+            question["dataset_type"] = "aime2026"
+            self.questions.append(question)
+
+        print(f"AIME2026 dataset loaded: {len(self.questions)} questions")
+
+    def get_question(self, index: int) -> Dict:
+        """Get question by index"""
+        return self.questions[index]
+
+    def get_question_text(self, question: Dict) -> str:
+        """Get question string"""
+        return question["problem"]
+
+    def get_answer(self, question: Dict) -> str:
+        return str(question["answer"])
+
+    def get_prompt(self, question: Dict) -> str:
+        """Get formatted prompt for the question"""
+        return TEMPLATE_REGISTRY["aime2026"].format(
+            question=self.get_question_text(question),
+        )
+
+class Gsm8kDataset(BaseDataset):
+    def __init__(self, split: str = "test"):
+        self.split = split
+        self.questions = []
+        self._load_dataset()
+
+    def _load_dataset(self):
+        print(f"Loading GSM8K dataset (split: {self.split})...")
+        from datasets import load_dataset
+
+        cache_path = cache_dir / "openai___gsm8k" / "default" / "0.0.0"
+        if cache_path.exists():
+            print(f"Using cached dataset from {cache_path}")
+            ds = load_dataset("openai/gsm8k", "main", split=self.split, cache_dir=str(cache_path))
+        else:
+            ds = load_dataset("openai/gsm8k", "main", split=self.split)
+
+        self.questions = []
+        for row in ds:
+            question = dict(row)
+            question["dataset_type"] = "gsm8k"
+
+            # Extract numeric answer from the answer field (already has #### prefix)
+            gold = question["answer"]
+            # Split by #### and take the last part
+            parts = gold.split("####")
+            if len(parts) > 1:
+                gold = parts[-1].strip()
+            # Extract the first number from the remaining text
+            normalized = normalize_number(gold)
+            question["gold"] = str(normalized) if normalized is not None else gold
+
+            self.questions.append(question)
+
+        print(f"GSM8K dataset loaded: {len(self.questions)} questions")
+
+    def get_question(self, index: int) -> Dict:
+        """Get question by index"""
+        return self.questions[index]
+
+    def get_question_text(self, question: Dict) -> str:
+        """Get question string"""
+        return question["problem"] if "problem" in question else question["question"]
+
+    def get_answer(self, question: Dict) -> str:
+        # GSM8K has pre-extracted gold field, AIME uses answer field
+        if "gold" in question:
+            return question["gold"]
+        answer = question["answer"]
+        if isinstance(answer, str):
+            normalized = normalize_number(answer)
+            return str(normalized) if normalized is not None else answer
+        return str(answer)
+
+    def get_prompt(self, question: Dict) -> str:
+        """Get formatted prompt for the question"""
+        return TEMPLATE_REGISTRY[question["dataset_type"]].format(
+            question=self.get_question_text(question),
+        )
+
+class GpqaDataset(BaseDataset):
+    def __init__(self, variant: str = "diamond", seed: int = 1234):
+        self.variant = variant
+        self.seed = seed
+        self.questions = []
+        self._load_dataset()
+
+    def _load_dataset(self):
+        print(f"Loading GPQA dataset (variant: {self.variant})...")
+        import pandas as pd
+
+        url = f"https://openaipublic.blob.core.windows.net/simple-evals/gpqa_{self.variant}.csv"
+        df = pd.read_csv(url)
+
+        rng = random.Random(self.seed)
+
+        self.questions = []
+        for _, row in df.iterrows():
+            question = row.to_dict()
+            question["dataset_type"] = "gpqa"
+
+            # Shuffle the answer options
+            correct_answer = question["Correct Answer"]
+            incorrect_answers = [
+                question["Incorrect Answer 1"],
+                question["Incorrect Answer 2"],
+                question["Incorrect Answer 3"]
+            ]
+
+            # Create list of (answer, is_correct) tuples
+            options = [(ans, ans == correct_answer) for ans in incorrect_answers]
+            options.append((correct_answer, True))
+
+            # Shuffle the options
+            rng.shuffle(options)
+
+            # Extract shuffled answers and determine correct letter
+            shuffled_answers = [ans for ans, _ in options]
+            correct_letter = chr(ord('A') + options.index((correct_answer, True)))
+
+            # Store shuffled answers and correct letter
+            question["shuffled_answers"] = shuffled_answers
+            question["correct_letter"] = correct_letter
+
+            self.questions.append(question)
+
+        print(f"GPQA dataset loaded: {len(self.questions)} questions")
+
+    def get_question(self, index: int) -> Dict:
+        """Get question by index"""
+        return self.questions[index]
+
+    def get_question_text(self, question: Dict) -> str:
+        """Get question string"""
+        return question["Question"]
+
+    def get_answer(self, question: Dict) -> str:
+        # GPQA returns the correct letter (A, B, C, or D)
+        return question["correct_letter"]
+
+    def get_prompt(self, question: Dict) -> str:
+        """Get formatted prompt for the question"""
+        return TEMPLATE_REGISTRY["gpqa"].format(
+            Question=self.get_question_text(question),
+            A=question["shuffled_answers"][0],
+            B=question["shuffled_answers"][1],
+            C=question["shuffled_answers"][2],
+            D=question["shuffled_answers"][3]
+        )
+
+class Grader:
+    def __init__(
+        self,
+        grader_type: str = "llm",
+        grader_script: Optional[str] = None,
+        grader_model_name: Optional[str] = None,
+        grader_server_url: str = "",
+        dataset_type: str = "aime"
+    ):
+        self.grader_type = grader_type
+        self.grader_script = grader_script
+        self.grader_model_name = grader_model_name
+        self.grader_server_url = grader_server_url
+        self.dataset_type = dataset_type
+        self.pattern = self._get_pattern()
+
+    def _get_pattern(self) -> Optional[str]:
+        if self.grader_type == "regex":
+            return GRADER_PATTERNS.get(self.dataset_type)  # Use dataset_type as key
+        return None
+
+    def _extract_answer_regex(self, pred: str) -> Optional[str]:
+        """Extract answer using regex pattern"""
+        if not self.pattern:
+            return None
+
+        # For AIME datasets, prioritize boxed answers
+        if self.dataset_type in ["aime", "aime2025"]:
+            boxed_pattern = r'\\boxed{([^}]+)}'
+            boxed_matches = re.findall(boxed_pattern, pred, re.IGNORECASE)
+            if boxed_matches:
+                # Return the last boxed answer found (most likely the final answer)
+                return boxed_matches[-1].strip()
+
+        # For other datasets, search for numbers from the end of the text
+        # This prioritizes numbers that appear later in the response
+        matches = re.findall(self.pattern, pred, re.IGNORECASE)
+        if not matches:
+            return None
+
+        # Process matches from end to start
+        for match in reversed(matches):
+            if isinstance(match, tuple):
+                match = match[0] if match[0] else match[1]
+            answer = match.strip()
+            if answer:
+                return answer
+        return None
+
+    def _grade_regex(self, gold: str, pred: str) -> Tuple[bool, Optional[str]]:
+        """Grade using regex pattern matching"""
+        answer = self._extract_answer_regex(pred)
+        if answer is None:
+            return False, None
+        is_correct = answer.strip() == gold.strip()
+        return is_correct, answer
+
+    def _grade_cli(self, gold: str, pred: str) -> Tuple[bool, Optional[str]]:
+        """Grade using external CLI script"""
+        if not self.grader_script:
+            raise ValueError("CLI grader requires --grader-script")
+
+        script_path = Path(self.grader_script)
+        if not script_path.exists():
+            raise FileNotFoundError(f"Grader script not found: {self.grader_script}")
+
+        try:
+            result = subprocess.run(
+                [str(script_path), "--answer", pred, "--expected", gold],
+                capture_output=True,
+                text=True,
+                timeout=30
+            )
+            is_correct = result.returncode == 0
+            answer = pred if is_correct else None
+            return is_correct, answer
+        except subprocess.TimeoutExpired:
+            return False, None
+        except Exception as e:
+            return False, None
+
+    def _grade_llm(self, gold: str, pred: str, problem: str) -> Tuple[bool, Optional[str]]:
+        """Grade using LLM-based extraction with few-shot examples"""
+        sample_answers = SAMPLE_ANSWERS.get(self.dataset_type, [])
+        sample_examples = "\n".join([
+            f"Example {i+1}: {ans}" for i, ans in enumerate(sample_answers)
+        ])
+
+        system_prompt = f"""You are an answer extraction system. Your task is to extract the answer from the model's response.
+
+Here are some examples of extracted answers to demonstrate what you are supposed to output:
+
+{sample_examples}
+
+When extracting the answer, provide only the extracted answer itself, nothing else. If there is no clear answer that can be extracted from the response, reply with 'no answer'."""
+
+        user_prompt = f"""Extract the answer from the following response:
+
+"{pred}"
+
+Please provide only the extracted answer, nothing else. If there is no clear answer that can be extracted from the response, reply with 'no answer'."""
+
+        url = f"{self.grader_server_url}/v1/chat/completions"
+        headers = {"Content-Type": "application/json"}
+        data = {
+            "model": self.grader_model_name,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ],
+            "temperature": 0,
+        }
+        #print(json.dumps(data, indent=2))
+
+        try:
+            response = requests.post(url, headers=headers, json=data)
+            response.raise_for_status()
+            answer = response.json()["choices"][0]["message"]["content"].strip()
+            is_correct = answer.strip().lower() == gold.strip().lower()
+            return is_correct, answer
+        except Exception as e:
+            return False, None
+
+    def _truncate_response(self, response: str, max_lines: int = 6) -> str:
+        """Keep only last N lines of response"""
+        lines = response.split('\n')
+        return '\n'.join(lines[-max_lines:]) if len(lines) > max_lines else response
+
+    def grade(self, gold: str, pred: str, problem: str = "") -> Tuple[bool, Optional[str]]:
+        """Grade the response"""
+        if self.grader_type == "regex":
+            return self._grade_regex(gold, pred)
+        elif self.grader_type == "cli":
+            return self._grade_cli(gold, pred)
+        elif self.grader_type == "llm":
+            return self._grade_llm(gold, pred, problem)
+        else:
+            raise ValueError(f"Unknown grader type: {self.grader_type}")
+
+class Processor:
+    def __init__(
+        self,
+        server_configs: List[ServerConfig],
+        grader: Grader,
+        model_name: Optional[str] = None,
+        n_predict: int = -1
+    ):
+        self.server_configs = server_configs
+        self.grader = grader
+        self.model_name = model_name
+        self.n_predict = n_predict
+
+    @staticmethod
+    def _check_server(server_config: ServerConfig) -> List[str]:
+        url = f"{server_config.url}/v1/models"
+        try:
+            response = requests.get(url)
+            response.raise_for_status()
+            models = [m["id"] for m in response.json().get("data", [])]
+            return models
+        except Exception as e:
+            print(f"Error: Cannot reach server {server_config.name} ({server_config.url}): {e}", file=sys.stderr)
+            sys.exit(1)
+
+    def _make_request(
+        self, server_config: ServerConfig, eval_state: EvalState, prompt: str
+    ) -> Tuple[Dict[str, Any], int, Optional[float], Optional[float], str]:
+        url = f"{server_config.url}/v1/chat/completions"
+        headers = {"Content-Type": "application/json"}
+        data = {
+            "model": self.model_name if self.model_name else "llama",
+            "messages": [{"role": "user", "content": prompt}],
+            "n_predict": self.n_predict
+        }
+        if eval_state.sampling_config.get("temperature") is not None:
+            data["temperature"] = eval_state.sampling_config["temperature"]
+        if eval_state.sampling_config.get("top_k") is not None:
+            data["top_k"] = eval_state.sampling_config["top_k"]
+        if eval_state.sampling_config.get("top_p") is not None:
+            data["top_p"] = eval_state.sampling_config["top_p"]
+        if eval_state.sampling_config.get("min_p") is not None:
+            data["min_p"] = eval_state.sampling_config["min_p"]
+
+        response = requests.post(url, headers=headers, json=data)
+        response.raise_for_status()
+        result = response.json()
+        tokens = result.get("usage", {}).get("completion_tokens", 0)
+        timings = result.get("timings", {})
+        tps_gen = timings.get("predicted_per_second") if timings else None
+        t_gen_ms = timings.get("predicted_ms") if timings else None
+        finish_reason = result.get("choices", [{}])[0].get("finish_reason", "stop")
+        return result, tokens, tps_gen, t_gen_ms, finish_reason
+
+    def _process_single_case(
+        self, server_config: ServerConfig, eval_state: EvalState, i: int, task_id: str
+    ) -> TaskState:
+        question_text, prompt, expected = eval_state.get_case(i)
+
+        task_state = TaskState(
+            task_id=task_id,
+            prompt=prompt,
+            expected=expected,
+            question_text=question_text,
+            server_name=server_config.name
+        )
+
+        try:
+            response, tokens, tps_gen, t_gen_ms, finish_reason = self._make_request(server_config, eval_state, prompt)
+            result = response["choices"][0]["message"]["content"]
+            reasoning_content = response["choices"][0].get("message", {}).get("reasoning_content")
+            task_state.response = result
+            task_state.tokens = tokens
+            task_state.tps_gen = tps_gen
+            task_state.t_gen_ms = t_gen_ms
+            task_state.reasoning_content = reasoning_content
+
+            if finish_reason != "stop":
+                task_state.status = f"error: finish_reason={finish_reason}"
+                eval_state.add_result(
+                    task_id, prompt, expected, result, None,
+                    {"finish_reason": finish_reason}, False, task_state.status,
+                    tokens, tps_gen, t_gen_ms, reasoning_content, server_config.name
+                )
+                eval_state.dump()
+                return task_state
+
+            result_truncated = self.grader._truncate_response(result, max_lines=10)
+            is_correct, answer = self.grader.grade(expected, result_truncated, prompt)
+
+            grader_log = {
+                "pred": result_truncated,
+                "grader_type": self.grader.grader_type
+            }
+            if self.grader.grader_type == "regex" and self.grader.pattern:
+                grader_log["pattern"] = self.grader.pattern
+
+            task_state.correct = is_correct
+            task_state.answer = answer
+            task_state.grader_log = grader_log
+            task_state.status = "ok"
+
+            eval_state.add_result(
+                task_id, prompt, expected, result, answer,
+                grader_log, is_correct, "ok",
+                tokens, tps_gen, t_gen_ms, reasoning_content, server_config.name
+            )
+
+            eval_state.dump()
+
+        except Exception as e:
+            task_state.status = f"error: {str(e)}"
+
+        return task_state
+
+    @staticmethod
+    def _worker(
+        server_config: ServerConfig,
+        processor: "Processor",
+        eval_state: EvalState,
+        task_queue: Queue,
+        results_queue: Queue,
+    ):
+        """Worker that pulls tasks from a shared queue and sends them to its server."""
+        while True:
+            task = task_queue.get()
+            if task is None:  # sentinel
+                task_queue.task_done()
+                break
+            try:
+                i, task_id = task
+                result = processor._process_single_case(server_config, eval_state, i, task_id)
+                results_queue.put(result)
+            finally:
+                task_queue.task_done()
+
+    def evaluate(self, eval_state: EvalState, verbose: bool = False, resume: bool = False):
+        total_tasks = len(eval_state.tasks)
+        eval_state.total = len(eval_state.all_tasks) if eval_state.all_tasks else total_tasks
+        eval_state.processed = 0
+        start_time = time.time()
+
+        # Check servers and list models
+        server_models = [self._check_server(sc) for sc in self.server_configs]
+
+        # Print server info
+        print(f"\nProcessing {len(eval_state.tasks)} {eval_state.dataset_type.upper()} tasks ...")
+        print(f"Servers ({len(self.server_configs)}):")
+        for i, sc in enumerate(self.server_configs):
+            models_str = ", ".join(server_models[i]) if server_models[i] else "(none)"
+            print(f"  {i+1}. {sc.name} — {sc.url} ({sc.threads} threads) [{models_str}]")
+        print(f"Model: {self.model_name}")
+        print(f"Grader: {self.grader.grader_type}")
+        print(f"Sampling: temp={eval_state.sampling_config.get('temperature', 'skip')}, top-k={eval_state.sampling_config.get('top_k', 'skip')}, top-p={eval_state.sampling_config.get('top_p', 'skip')}, min-p={eval_state.sampling_config.get('min_p', 'skip')}")
+        print()
+
+        # Shared task queue: all workers compete for tasks
+        task_queue: Queue = Queue()
+        for i, task_id in eval_state.tasks:
+            task_queue.put((i, task_id))
+
+        # Results queue: workers push completed TaskStates here
+        results_queue: Queue = Queue()
+
+        # Total worker threads across all servers
+        total_threads = sum(sc.threads for sc in self.server_configs)
+
+        # Add one sentinel per worker so every worker exits cleanly
+        for _ in range(total_threads):
+            task_queue.put(None)
+
+        # Launch workers: one ThreadPoolExecutor per server
+        executors: List[ThreadPoolExecutor] = []
+        worker_futures: List[Any] = []
+        for server_config in self.server_configs:
+            executor = ThreadPoolExecutor(max_workers=server_config.threads)
+            executors.append(executor)
+            for _ in range(server_config.threads):
+                future = executor.submit(
+                    self._worker, server_config, self, eval_state,
+                    task_queue, results_queue
+                )
+                worker_futures.append(future)
+
+        # Drain results as they complete
+        n_correct = 0
+        session_time = 0.0
+        completed_count = 0
+
+        while completed_count < total_tasks:
+            task_state = results_queue.get()
+            eval_state.processed += 1
+            completed_count += 1
+            if task_state.correct:
+                n_correct += 1
+            elapsed = time.time() - start_time
+            eval_state.total_time += elapsed
+            session_time += elapsed
+            start_time = time.time()
+            eval_state.print_progress(task_state, total_tasks, n_correct)
+
+            if verbose:
+                print(f"\nCase {eval_state.processed}: {task_state.correct}")
+                print(f"  Expected: {task_state.expected}")
+                if task_state.response:
+                    print(f"  Response: {task_state.response}")
+                if task_state.answer:
+                    print(f"  Answer: {task_state.answer}")
+                print(f"  Status: {task_state.status}")
+
+        # Wait for all workers to finish and shut down executors
+        for future in worker_futures:
+            future.result()
+        for executor in executors:
+            executor.shutdown(wait=True)
+
+        print(f"\nSession time: {session_time:.1f}s | Total accumulated time: {eval_state.total_time:.1f}s")
+        eval_state.print_summary()
+        eval_state.dump()
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Simplified evaluation tool for llama.cpp"
+    )
+    parser.add_argument(
+        "--server",
+        type=str,
+        default="http://localhost:8033",
+        help="Comma-separated llama-server URLs (default: http://localhost:8033)"
+    )
+    parser.add_argument(
+        "--server-name",
+        type=str,
+        default="",
+        help="Comma-separated display names for servers (default: use URLs)"
+    )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="aime",
+        choices=["aime", "aime2025", "aime2026", "gsm8k", "gpqa"],
+        help="Dataset type (default: aime)"
+    )
+    parser.add_argument(
+        "--n_cases",
+        type=int,
+        default=None,
+        help="Number of cases to evaluate (default: all)"
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=1234,
+        help="Random seed for shuffling (default: 1234)"
+    )
+    parser.add_argument(
+        "--n_predict",
+        type=int,
+        default=-1,
+        help="Max tokens to predict per prompt (default: -1, infinite)"
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=None,
+        help="Sampling temperature (default: not passed)"
+    )
+    parser.add_argument(
+        "--top-k",
+        type=int,
+        default=None,
+        help="Top K sampling (default: not passed)"
+    )
+    parser.add_argument(
+        "--top-p",
+        type=float,
+        default=None,
+        help="Top P sampling (default: not passed)"
+    )
+    parser.add_argument(
+        "--min-p",
+        type=float,
+        default=None,
+        help="Min P sampling (default: not passed)"
+    )
+    parser.add_argument(
+        "--threads",
+        type=str,
+        default="32",
+        help="Comma-separated thread counts per server (default: 32)"
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help="Model name to append as query parameter (e.g., gpt-oss-20b-hf)"
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="Show detailed output for each case"
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("llama-eval-state.json"),
+        help="Output file for eval state (default: llama-eval-state.json)"
+    )
+    parser.add_argument(
+        "--grader-type",
+        type=str,
+        default="llm",
+        choices=["regex", "cli", "llm"],
+        help="Grader type: regex, cli, or llm (default: llm)"
+    )
+    parser.add_argument(
+        "--grader-script",
+        type=str,
+        default=None,
+        help="CLI grader script path (required for --grader-type cli)"
+    )
+    parser.add_argument(
+        "--grader-server",
+        type=str,
+        default="",
+        help="Server URL for LLM grader (default: same as main server)"
+    )
+    parser.add_argument(
+        "--grader-model",
+        type=str,
+        default="",
+        help="Model name for LLM grader (default: same as main model)"
+    )
+    parser.add_argument(
+        "--resume",
+        action="store_true",
+        help="Resume from existing eval state"
+    )
+
+    args = parser.parse_args()
+
+    # Parse server URLs and thread counts
+    server_urls = [u.strip() for u in args.server.split(",") if u.strip()]
+    thread_counts = [int(t.strip()) for t in args.threads.split(",") if t.strip()]
+
+    if len(server_urls) != len(thread_counts):
+        print(f"Error: --server ({len(server_urls)} URLs) and --threads ({len(thread_counts)} values) must have the same count")
+        sys.exit(1)
+
+    # Parse server names (optional, defaults to URLs)
+    if args.server_name:
+        server_names = [n.strip() for n in args.server_name.split(",") if n.strip()]
+        if len(server_names) != len(server_urls):
+            print(f"Error: --server-name ({len(server_names)} names) and --server ({len(server_urls)} URLs) must have the same count")
+            sys.exit(1)
+    else:
+        server_names = server_urls  # fallback to URLs
+
+    server_configs = [
+        ServerConfig(url=url, threads=threads, name=name)
+        for url, threads, name in zip(server_urls, thread_counts, server_names)
+    ]
+
+    if args.dataset == "gpqa" and args.grader_type != "llm":
+        print("Error: GPQA dataset requires --grader-type llm")
+        parser.print_help()
+        sys.exit(1)
+
+    if args.output.exists():
+        print(f"Loading existing eval state from {args.output}")
+        eval_state = EvalState.load(args.output)
+
+        # Verify model matches
+        if eval_state.model_name is not None and args.model != eval_state.model_name:
+            print(f"Error: Model mismatch. State has '{eval_state.model_name}', but --model is '{args.model}'")
+            sys.exit(1)
+
+        eval_state.print_all_tasks()
+        eval_state.print_existing_summary()
+
+        if eval_state.is_complete():
+            return
+
+        print()
+
+        if not args.resume:
+            print(f"Evaluation incomplete. Run with --resume to continue.")
+            return
+
+        pending_tasks = eval_state.get_pending_tasks()
+        print(f"Resuming from {len(pending_tasks)} pending tasks")
+
+        existing_cases = eval_state.task_states.get("cases", {})
+
+        eval_state.tasks = pending_tasks
+        eval_state.task_states["cases"] = existing_cases
+
+        grader_server_url = args.grader_server if args.grader_server else server_configs[0].url
+        grader_model_name = args.grader_model if args.grader_model else args.model
+        if args.grader_type == "llm" and not grader_model_name:
+            print("Error: --grader-type llm requires --grader-model or --model")
+            sys.exit(1)
+        grader = Grader(
+            grader_type=args.grader_type,
+            grader_script=args.grader_script,
+            grader_model_name=grader_model_name,
+            grader_server_url=grader_server_url,
+            dataset_type=eval_state.dataset_type
+        )
+        resume = True
+    else:
+        if args.resume:
+            print("Error: No existing eval state found to resume")
+            sys.exit(1)
+
+        grader_server_url = args.grader_server if args.grader_server else server_configs[0].url
+        grader_model_name = args.grader_model if args.grader_model else args.model
+        if args.grader_type == "llm" and not grader_model_name:
+            print("Error: --grader-type llm requires --grader-model or --model")
+            sys.exit(1)
+
+        grader = Grader(
+            grader_type=args.grader_type,
+            grader_script=args.grader_script,
+            grader_model_name=grader_model_name,
+            grader_server_url=grader_server_url,
+            dataset_type=args.dataset
+        )
+
+        if args.grader_type == "llm" and not args.grader_server:
+            print("Warning: Using same server for LLM grader (no --grader-server specified)")
+
+        sampling_config = {}
+        if args.temperature is not None:
+            sampling_config["temperature"] = args.temperature
+        if args.top_k is not None:
+            sampling_config["top_k"] = args.top_k
+        if args.top_p is not None:
+            sampling_config["top_p"] = args.top_p
+        if args.min_p is not None:
+            sampling_config["min_p"] = args.min_p
+
+        eval_state = EvalState(
+            dataset_type=args.dataset,
+            sampling_config=sampling_config,
+            output_file=args.output,
+            model_name=args.model
+        )
+        eval_state.load_dataset(seed=args.seed)
+        eval_state.setup_tasks(n_cases=args.n_cases, seed=args.seed)
+        eval_state.dump()
+        resume = False
+
+        eval_state.print_all_tasks()
+
+    processor = Processor(
+        server_configs=server_configs,
+        grader=grader,
+        model_name=args.model,
+        n_predict=args.n_predict
+    )
+
+    processor.evaluate(eval_state, verbose=args.verbose, resume=resume)
+    print(f"\nEval state dumped to {args.output}")
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/llama-eval/llama-server-simulator.py b/examples/llama-eval/llama-server-simulator.py
new file mode 100755
index 00000000000..2f9cdc5450d
--- /dev/null
+++ b/examples/llama-eval/llama-server-simulator.py
@@ -0,0 +1,317 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+import random
+import re
+import time
+import sys
+import os
+import threading
+from http.server import HTTPServer, BaseHTTPRequestHandler
+from typing import Dict, List, Optional
+from dataclasses import dataclass
+from pathlib import Path
+
+import datasets
+
+# Set cache directory for HuggingFace datasets
+cache_dir = Path.home() / ".cache" / "huggingface" / "datasets"
+cache_dir.mkdir(parents=True, exist_ok=True)
+os.environ["HF_DATASETS_CACHE"] = str(cache_dir)
+
+def dice(s1: str, s2: str) -> float:
+    """Calculate Dice coefficient between two strings based on bigram overlap."""
+    if not s1 and not s2:
+        return 1.0
+
+    def _bigrams(s: str):
+        return [s[i : i + 2] for i in range(len(s) - 1)]
+
+    bigrams1 = _bigrams(s1)
+    bigrams2 = _bigrams(s2)
+
+    if not bigrams1 and not bigrams2:
+        return 1.0
+
+    from collections import Counter
+
+    freq1 = Counter(bigrams1)
+    freq2 = Counter(bigrams2)
+
+    intersection = sum(min(freq1[bg], freq2[bg]) for bg in freq1)
+    dice_coeff = 2 * intersection / (len(bigrams1) + len(bigrams2))
+    return dice_coeff
+
+def debug_log(message: str):
+    """Log debug messages to both stdout and a file"""
+    print(message, file=sys.stderr)
+    with open("/tmp/simulator-debug.log", "a") as f:
+        f.write(message + "\n")
+
+simulator: Optional["Simulator"] = None
+
+@dataclass
+class EvalState:
+    id: str
+    tasks: List[str]
+    task_states: Dict[str, Dict]
+    sampling_config: Dict
+
+def normalize_number(s: str) -> Optional[int]:
+    match = re.match(r"\d+", s)  # match digits from the start
+    if not match:
+        return None
+    return int(match.group(0))
+
+class AimeDataset:
+    def __init__(self, split: str = "train"):
+        self.split = split
+        self.questions: List[Dict] = []
+        self._load_dataset()
+
+    def _load_dataset(self):
+        print(f"Loading AIME dataset (split: {self.split})...")
+
+        cache_path = Path.home() / ".cache" / "huggingface" / "datasets" / "AI-MO___aimo-validation-aime" / "default" / "0.0.0"
+        if cache_path.exists():
+            print(f"Using cached dataset from {cache_path}")
+            ds = datasets.load_dataset("AI-MO/aimo-validation-aime", split=self.split, cache_dir=str(cache_path))
+        else:
+            ds = datasets.load_dataset("AI-MO/aimo-validation-aime", split=self.split)
+
+        self.questions = list(ds)
+        print(f"AIME dataset loaded: {len(self.questions)} questions")
+
+    def find_question(self, request_text: str) -> Optional[Dict]:
+        best_match = None
+        best_distance = -1
+        best_index = -1
+
+        for i, question in enumerate(self.questions):
+            question_text = question["problem"]
+            request_lower = request_text.lower()
+            question_lower = question_text.lower()
+
+            # Exact match
+            if question_lower == request_lower:
+                debug_log(f"DEBUG: Found exact match at index {i}")
+                return question
+
+            # Remove LaTeX formatting for more flexible matching
+            question_no_latex = re.sub(r'\$[^$]+\$', '', question_text)
+            if question_no_latex.lower() == request_lower:
+                debug_log(f"DEBUG: Found match (no LaTeX) at index {i}")
+                return question
+
+            # Calculate Dice coefficient for partial matches
+            # Only consider if request is at least 50% of question length
+            if len(request_lower) >= len(question_lower) * 0.5:
+                distance = dice(question_lower, request_lower)
+
+                if distance > best_distance:
+                    best_distance = distance
+                    best_match = question
+                    best_index = i
+
+        if best_match and best_distance > 0.3:  # Threshold for partial match
+            debug_log(f"DEBUG: Found best partial match at index {best_index} with distance {best_distance:.3f}")
+            return best_match
+
+        debug_log(f"DEBUG: No matching question found for: {request_text[:100]}...")
+        return None
+
+    def get_answer(self, question: Dict) -> str:
+        answer = question["answer"]
+        if isinstance(answer, str):
+            normalized = normalize_number(answer)
+            return str(normalized) if normalized is not None else answer
+        return str(answer)
+
+class Simulator:
+    def __init__(
+        self,
+        port: int = 8033,
+        host: str = "localhost",
+        success_rate: float = 0.8,
+        dataset_split: str = "train"
+    ):
+        self.port = port
+        self.host = host
+        self.success_rate = success_rate
+        self.dataset = AimeDataset(dataset_split)
+        self.eval_state = EvalState(
+            id="aime-2025",
+            tasks=["aime"],
+            task_states={},
+            sampling_config={"temperature": 0, "max_tokens": 2048}
+        )
+
+    def _generate_response(
+        self,
+        question: Dict,
+        should_be_correct: bool
+    ) -> Dict:
+        expected_answer = self.dataset.get_answer(question)
+
+        if should_be_correct:
+            response_text = expected_answer
+        else:
+            response_text = self._generate_wrong_answer(question)
+
+        return {
+            "id": f"chatcmpl-{int(time.time())}",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": "llama",
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": response_text
+                    },
+                    "finish_reason": "stop"
+                }
+            ],
+            "usage": {
+                "prompt_tokens": 100,
+                "completion_tokens": 50,
+                "total_tokens": 150
+            }
+        }
+
+    def _generate_wrong_answer(self, question: Dict) -> str:
+        expected_answer = self.dataset.get_answer(question)
+
+        if expected_answer.isdigit():
+            wrong_answer = str(int(expected_answer) + 1)
+        else:
+            wrong_answer = expected_answer + " (wrong)"
+
+        return wrong_answer
+
+    def _process_request(self, request_data: Dict) -> Dict:
+        messages = request_data.get("messages", [])
+        if not messages:
+            return {"error": "No messages in request"}
+
+        request_text = messages[0].get("content", "")
+        debug_log(f"DEBUG: Received request with content: {request_text[:150]}...")
+
+        question = self.dataset.find_question(request_text)
+        if not question:
+            debug_log(f"DEBUG: find_question returned None")
+            return {"error": "No matching question found"}
+
+        should_be_correct = random.random() < self.success_rate
+
+        response = self._generate_response(question, should_be_correct)
+
+        task_id = "aime"
+        self.eval_state.task_states[task_id] = {
+            "correct": should_be_correct,
+            "expected": self.dataset.get_answer(question),
+            "predicted": response["choices"][0]["message"]["content"]
+        }
+
+        return response
+
+class RequestHandler(BaseHTTPRequestHandler):
+    def do_POST(self):
+        if self.path != "/v1/chat/completions":
+            self._send_json({"error": "Not found"}, 404)
+            return
+
+        try:
+            content_length = int(self.headers.get("Content-Length", 0))
+            body = self.rfile.read(content_length)
+            request_data = json.loads(body) if body else None
+
+            if not request_data:
+                self._send_json({"error": "Invalid JSON"}, 400)
+                return
+
+            if simulator is None:
+                self._send_json({"error": "Simulator not initialized"}, 500)
+                return
+
+            response = simulator._process_request(request_data)
+            self._send_json(response, 200)
+
+        except json.JSONDecodeError:
+            self._send_json({"error": "Invalid JSON"}, 400)
+        except Exception as e:
+            print(f"Error processing request: {e}")
+            self._send_json({"error": str(e)}, 500)
+
+    def _send_json(self, data: dict, status: int = 200):
+        body = json.dumps(data).encode("utf-8")
+        self.send_response(status)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(body)))
+        self.end_headers()
+        self.wfile.write(body)
+
+    def log_message(self, format, *args):
+        # Suppress default request logging
+        pass
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="llama-server simulator for testing eval scripts"
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=8033,
+        help="Server port (default: 8033)"
+    )
+    parser.add_argument(
+        "--host",
+        type=str,
+        default="localhost",
+        help="Server host (default: localhost)"
+    )
+    parser.add_argument(
+        "--success-rate",
+        type=float,
+        default=0.8,
+        help="Success rate 0-1 (default: 0.8)"
+    )
+    parser.add_argument(
+        "--dataset-split",
+        type=str,
+        default="train",
+        help="AIME dataset split to use (default: train)"
+    )
+
+    args = parser.parse_args()
+
+    global simulator
+    simulator = Simulator(
+        port=args.port,
+        host=args.host,
+        success_rate=args.success_rate,
+        dataset_split=args.dataset_split
+    )
+
+    server = HTTPServer((args.host, args.port), RequestHandler)
+    server_thread = threading.Thread(target=server.serve_forever, daemon=True)
+    server_thread.start()
+
+    print("\n=== llama-server-simulator ===")
+    print(f"Server running on http://{args.host}:{args.port}")
+    print(f"Success rate: {args.success_rate}")
+    print(f"AIME dataset loaded: {len(simulator.dataset.questions)} questions")
+    print("\nPress Ctrl+C to stop\n")
+
+    try:
+        server_thread.join()
+    except KeyboardInterrupt:
+        print("\nShutting down...")
+        server.shutdown()
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/llama-eval/test-simulator.sh b/examples/llama-eval/test-simulator.sh
new file mode 100755
index 00000000000..f3ddf3e95d6
--- /dev/null
+++ b/examples/llama-eval/test-simulator.sh
@@ -0,0 +1,86 @@
+#!/bin/bash
+
+set -e
+
+# Get the directory where this script is located
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+echo "=== llama-server-simulator Test Script ==="
+echo ""
+
+PORT=8033
+SUCCESS_RATE=0.8
+TEST_PORT=8034
+
+echo "Starting simulator on port $PORT with success rate $SUCCESS_RATE..."
+source "$SCRIPT_DIR/venv/bin/activate"
+python3 "$SCRIPT_DIR/llama-server-simulator.py" --port $PORT --success-rate $SUCCESS_RATE > /tmp/simulator-test.log 2>&1 &
+SIMULATOR_PID=$!
+
+echo "Waiting for simulator to start..."
+sleep 5
+
+# Helper function to make a request and extract the answer
+make_request() {
+  local question="$1"
+  curl -s -X POST http://localhost:$PORT/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d "{
+      \"model\": \"llama\",
+      \"messages\": [
+        {\"role\": \"user\", \"content\": \"$question\"}
+      ],
+      \"temperature\": 0,
+      \"max_tokens\": 2048
+    }" | python3 -c "import sys, json; data = json.load(sys.stdin); print(data.get('choices', [{}])[0].get('message', {}).get('content', data.get('error', 'No response')))"
+}
+
+# Test question (repeated in multiple tests)
+TEST_QUESTION="Quadratic polynomials P(x) and Q(x) have leading coefficients 2 and -2, respectively. The graphs of both polynomials pass through the two points (16,54) and (20,53). Find P(0) + Q(0)."
+
+echo ""
+echo "=== Test 1: Correct Answer ==="
+echo "Sending request with known question..."
+answer=$(make_request "$TEST_QUESTION")
+echo "Answer: $answer"
+echo "Expected: 116"
+echo "Correct: $([ "$answer" == "116" ] && echo "Yes" || echo "No")"
+
+echo ""
+echo "=== Test 2: Wrong Answer ==="
+echo "Sending request with known question (success rate 0.0)..."
+answer=$(make_request "$TEST_QUESTION")
+echo "Answer: $answer"
+echo "Expected: 116"
+echo "Correct: $([ "$answer" == "116" ] && echo "Yes" || echo "No")"
+
+echo ""
+echo "=== Test 3: No Matching Question ==="
+echo "Sending request with non-matching text..."
+response=$(make_request "What is the capital of France?")
+echo "Response: $response"
+echo "Expected: No matching question found"
+echo "Correct: $([ "$response" == "No matching question found" ] && echo "Yes" || echo "No")"
+
+echo ""
+echo "=== Test 4: Success Rate Verification ==="
+echo "Sending 10 requests to test success rate..."
+correct_count=0
+for i in {1..10}; do
+  answer=$(make_request "$TEST_QUESTION")
+  if [ "$answer" == "116" ]; then
+    correct_count=$((correct_count + 1))
+  fi
+  echo "  Request $i: Answer = $answer"
+done
+echo "Correct answers: $correct_count/10"
+echo "Expected: ~8/10 (80% success rate)"
+echo "Success rate: $(echo "scale=1; $correct_count * 10" | bc)%"
+
+echo ""
+echo "=== Test Complete ==="
+echo "Stopping simulator..."
+kill $SIMULATOR_PID 2>/dev/null
+wait $SIMULATOR_PID 2>/dev/null || true
+
+echo "Simulator stopped."
diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp
index 6b8f258a4a5..c0f6c8fc202 100644
--- a/examples/lookup/lookup-create.cpp
+++ b/examples/lookup/lookup-create.cpp
@@ -37,9 +37,9 @@ int main(int argc, char ** argv){
 
     common_ngram_cache ngram_cache;
     common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
-    fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.speculative.lookup_cache_static.c_str());
+    fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.speculative.ngram_cache.lookup_cache_static.c_str());
 
-    common_ngram_cache_save(ngram_cache, params.speculative.lookup_cache_static);
+    common_ngram_cache_save(ngram_cache, params.speculative.ngram_cache.lookup_cache_static);
 
     return 0;
 }
diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp
index 847976ddc7d..84642e05d1d 100644
--- a/examples/lookup/lookup-stats.cpp
+++ b/examples/lookup/lookup-stats.cpp
@@ -24,7 +24,7 @@ int main(int argc, char ** argv){
         return 1;
     }
 
-    const int n_draft = params.speculative.n_max;
+    const int n_draft = params.speculative.draft.n_max;
 
     // init llama.cpp
     llama_backend_init();
@@ -49,18 +49,18 @@ int main(int argc, char ** argv){
     {
         const int64_t t_start_draft_us = ggml_time_us();
 
-        if (!params.speculative.lookup_cache_static.empty()) {
+        if (!params.speculative.ngram_cache.lookup_cache_static.empty()) {
             try {
-                ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static);
+                ngram_cache_static = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_static);
             } catch (std::ifstream::failure const &) {
-                LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str());
+                LOG_ERR("failed to open static lookup cache: %s", params.speculative.ngram_cache.lookup_cache_static.c_str());
                 exit(1);
             }
         }
 
-        if (!params.speculative.lookup_cache_dynamic.empty()) {
+        if (!params.speculative.ngram_cache.lookup_cache_dynamic.empty()) {
             try {
-                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic);
+                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_dynamic);
             } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
         }
 
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index 74272f17e71..2d4c0e528d3 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -25,7 +25,7 @@ int main(int argc, char ** argv){
     }
 
     // max. number of additional tokens to draft if match is found
-    const int n_draft = params.speculative.n_max;
+    const int n_draft = params.speculative.draft.n_max;
 
     // init llama.cpp
     llama_backend_init();
@@ -54,18 +54,18 @@ int main(int argc, char ** argv){
         const int64_t t_start_draft_us = ggml_time_us();
         common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
 
-        if (!params.speculative.lookup_cache_static.empty()) {
+        if (!params.speculative.ngram_cache.lookup_cache_static.empty()) {
             try {
-                ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static);
+                ngram_cache_static = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_static);
             } catch (std::ifstream::failure const &) {
-                LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str());
+                LOG_ERR("failed to open static lookup cache: %s", params.speculative.ngram_cache.lookup_cache_static.c_str());
                 exit(1);
             }
         }
 
-        if (!params.speculative.lookup_cache_dynamic.empty()) {
+        if (!params.speculative.ngram_cache.lookup_cache_dynamic.empty()) {
             try {
-                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic);
+                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.ngram_cache.lookup_cache_dynamic);
             } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
         }
 
@@ -213,7 +213,7 @@ int main(int argc, char ** argv){
 
     // Update dynamic ngram cache with context ngram cache and save it to disk:
     common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
-    common_ngram_cache_save(ngram_cache_dynamic, params.speculative.lookup_cache_dynamic);
+    common_ngram_cache_save(ngram_cache_dynamic, params.speculative.ngram_cache.lookup_cache_dynamic);
 
     LOG("\n\n");
 
diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile
index 9356aaf8546..0130b0493bb 100644
--- a/examples/model-conversion/Makefile
+++ b/examples/model-conversion/Makefile
@@ -52,6 +52,10 @@ causal-convert-mm-model:
 	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
 	./scripts/causal/convert-model.sh
 
+	$(MAKE) causal-convert-mmproj MM_OUTTYPE="$(MM_OUTTYPE)"
+
+causal-convert-mmproj:
+	$(call validate_model_path,causal-convert-mmproj)
 	@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(MM_OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
 	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
 	./scripts/causal/convert-model.sh --mmproj
diff --git a/examples/model-conversion/scripts/causal/convert-model.sh b/examples/model-conversion/scripts/causal/convert-model.sh
index a5865f6acd3..4aa72206288 100755
--- a/examples/model-conversion/scripts/causal/convert-model.sh
+++ b/examples/model-conversion/scripts/causal/convert-model.sh
@@ -25,7 +25,11 @@ MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"
 OUTPUT_DIR="${OUTPUT_DIR:-../../models}"
 TYPE="${OUTTYPE:-f16}"
 METADATA_OVERRIDE="${METADATA_OVERRIDE:-}"
-CONVERTED_MODEL="${OUTPUT_DIR}/${MODEL_NAME}.gguf"
+if [[ -n "$MMPROJ" ]]; then
+    CONVERTED_MODEL="${OUTPUT_DIR}/mmproj-${MODEL_NAME}.gguf"
+else
+    CONVERTED_MODEL="${OUTPUT_DIR}/${MODEL_NAME}.gguf"
+fi
 
 echo "Model path: ${MODEL_PATH}"
 echo "Model name: ${MODEL_NAME}"
@@ -38,6 +42,7 @@ if [[ -n "$DEBUG" ]]; then
 else
     CMD_ARGS=("python")
 fi
+
 CMD_ARGS+=("../../convert_hf_to_gguf.py" "--verbose")
 CMD_ARGS+=("${MODEL_PATH}")
 CMD_ARGS+=("--outfile" "${CONVERTED_MODEL}")
@@ -50,7 +55,3 @@ CMD_ARGS+=("--outtype" "${TYPE}")
 echo ""
 echo "The environment variable CONVERTED_MODEL can be set to this path using:"
 echo "export CONVERTED_MODEL=$(realpath ${CONVERTED_MODEL})"
-if [[ -n "$MMPROJ" ]]; then
-    mmproj_file="${OUTPUT_DIR}/mmproj-$(basename "${CONVERTED_MODEL}")"
-    echo "The mmproj model was created in $(realpath "$mmproj_file")"
-fi
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index a26fd73cb3c..e6f5e9802ab 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -38,8 +38,12 @@ int main(int argc, char ** argv) {
     std::string result0;
     std::string result1;
     std::string result2;
+    std::string result3;
 
     // init
+
+    ggml_backend_load_all();
+
     auto llama_init = common_init_from_params(params);
 
     auto * model = llama_init->model();
@@ -213,11 +217,83 @@ int main(int argc, char ** argv) {
         n_past += 1;
     }
 
+    // test on-device state save/load
+    auto params_ctx4 = common_context_params_to_llama(params);
+    params_ctx4.n_seq_max = 2;
+    llama_context * ctx4 = llama_init_from_model(model, params_ctx4);
+
+    llama_sampler * smpl4 = llama_sampler_chain_init(sparams);
+
+    llama_sampler_chain_add(smpl4, llama_sampler_init_dist(params.sampling.seed));
+
+    printf("\nsingle seq run: %s", params.prompt.c_str());
+
+    // load state (rng, logits, embedding and kv_cache) from file
+    n_token_count_out = 0;
+
+    if (!llama_state_load_file(ctx4, state_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
+        fprintf(stderr, "\n%s : failed to load state\n", __func__);
+        return 1;
+    }
+
+    fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
+
+    // restore state (last tokens)
+    n_past = n_token_count_out;
+    if (!common_replay_last_token(ctx4, tokens.back(), n_past)) {
+        return 1;
+    }
+    ++n_past;
+
+    // save seq 0 and load into seq 1
+    {
+        // save kv of seq 0
+        std::vector<uint8_t> seq_store(llama_state_seq_get_size_ext(ctx4, 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE));
+        const size_t ncopy = llama_state_seq_get_data_ext(ctx4, seq_store.data(), seq_store.size(), 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+        if (ncopy != seq_store.size()) {
+            fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
+            return 1;
+        }
+        fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
+
+        // erase whole kv
+        llama_memory_clear(llama_get_memory(ctx4), true);
+        fprintf(stderr, "%s : kv cache cleared\n", __func__);
+
+        // restore kv into seq 0
+        const size_t nset = llama_state_seq_set_data_ext(ctx4, seq_store.data(), seq_store.size(), 1, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+        if (nset != seq_store.size()) {
+            fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
+            return 1;
+        }
+        fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
+    }
+
+    // forth run
+    for (auto i = 0; i < params.n_predict; i++) {
+        auto next_token     = llama_sampler_sample(smpl4, ctx4, -1);
+        auto next_token_str = common_token_to_piece(ctx4, next_token);
+
+        printf("%s", next_token_str.c_str());
+        result3 += next_token_str;
+
+        common_batch_clear(batch);
+        common_batch_add(batch, next_token, n_past, {1}, true);
+
+        if (llama_decode(ctx4, batch)) {
+            fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
+            llama_batch_free(batch);
+            return 1;
+        }
+        n_past += 1;
+    }
+
     printf("\n");
 
     llama_sampler_free(smpl);
     llama_sampler_free(smpl2);
     llama_sampler_free(smpl3);
+    llama_sampler_free(smpl4);
 
     llama_batch_free(batch);
 
@@ -226,12 +302,18 @@ int main(int argc, char ** argv) {
 
     llama_free(ctx2);
     llama_free(ctx3);
+    llama_free(ctx4);
 
     if (result0 != result2) {
         fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
         return 1;
     }
 
+    if (result0 != result3) {
+        fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
+        return 1;
+    }
+
     fprintf(stderr, "\n%s : success\n", __func__);
 
     return 0;
diff --git a/examples/speculative-simple/README.md b/examples/speculative-simple/README.md
index e3a6c6b4aa0..f72129b3f92 100644
--- a/examples/speculative-simple/README.md
+++ b/examples/speculative-simple/README.md
@@ -6,7 +6,7 @@ Demonstration of basic greedy speculative decoding
 ./bin/llama-speculative-simple \
     -m  ../models/qwen2.5-32b-coder-instruct/ggml-model-q8_0.gguf \
     -md ../models/qwen2.5-1.5b-coder-instruct/ggml-model-q4_0.gguf \
-    -f test.txt -c 0 -ngl 99 --color \
-    --sampling-seq k --top-k 1 -fa --temp 0.0 \
-    -ngld 99 --draft-max 16 --draft-min 5 --draft-p-min 0.9
+    -f test.txt -c 0 -ngl 99 --color on \
+    --sampling-seq k --top-k 1 -fa on --temp 0.0 \
+    -ngld 99 --spec-draft-n-max 16 --spec-draft-n-draft-min 5 --draft-p-min 0.9
 ```
diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp
index a03dbce887f..5325bcc9e3f 100644
--- a/examples/speculative-simple/speculative-simple.cpp
+++ b/examples/speculative-simple/speculative-simple.cpp
@@ -8,8 +8,10 @@
 #include <clocale>
 #include <cstdio>
 #include <cstring>
+#include <cinttypes>
 #include <string>
 #include <vector>
+#include <utility>
 
 int main(int argc, char ** argv) {
     std::setlocale(LC_NUMERIC, "C");
@@ -27,11 +29,6 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (params.speculative.mparams_dft.path.empty()) {
-        LOG_ERR("%s: --model-draft is required\n", __func__);
-        return 1;
-    }
-
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -50,26 +47,24 @@ int main(int argc, char ** argv) {
 
     // load the draft model
     llama_model_ptr model_dft;
+    llama_context_ptr ctx_dft;
 
     // TODO: simplify this logic
     {
-        const auto & params_spec = params.speculative;
+        const auto & params_spec = params.speculative.draft;
 
         auto params_dft = params;
 
-        params_dft.n_parallel   = 1;
-        params_dft.n_ctx        = params_spec.n_ctx;
-        params_dft.n_batch      = llama_n_ctx_seq(ctx_tgt);
         params_dft.devices      = params_spec.devices;
-        params_dft.model        = params_spec.mparams_dft;
+        params_dft.model        = params_spec.mparams;
         params_dft.n_gpu_layers = params_spec.n_gpu_layers;
 
         if (params_spec.cpuparams.n_threads > 0) {
-            params_dft.cpuparams.n_threads       = params.speculative.cpuparams.n_threads;
-            params_dft.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
+            params_dft.cpuparams.n_threads       = params.speculative.draft.cpuparams.n_threads;
+            params_dft.cpuparams_batch.n_threads = params.speculative.draft.cpuparams_batch.n_threads;
         }
 
-        params_dft.tensor_buft_overrides = params.speculative.tensor_buft_overrides;
+        params_dft.tensor_buft_overrides = params.speculative.draft.tensor_buft_overrides;
 
         auto mparams_dft = common_model_params_to_llama(params_dft);
 
@@ -79,8 +74,19 @@ int main(int argc, char ** argv) {
             return 1;
         }
 
-        params.speculative.model_dft = model_dft.get();
-        params.speculative.cparams_dft = common_context_params_to_llama(params_dft);
+        auto cparams = common_context_params_to_llama(params_dft);
+        ctx_dft.reset(llama_init_from_model(model_dft.get(), cparams));
+
+        params.speculative.draft.ctx_tgt = ctx_tgt;
+        params.speculative.draft.ctx_dft = ctx_dft.get();
+    }
+
+    // check if the context supports partial sequence removal
+    const bool use_ckpt_tgt = (common_context_can_seq_rm(ctx_tgt)       == COMMON_CONTEXT_SEQ_RM_TYPE_FULL);
+    const bool use_ckpt_dft = (common_context_can_seq_rm(ctx_dft.get()) == COMMON_CONTEXT_SEQ_RM_TYPE_FULL);
+
+    if (use_ckpt_tgt) {
+        LOG_INF("speculative decoding will use checkpoints (context does not support partial sequence removal)\n");
     }
 
     // Tokenize the prompt
@@ -112,6 +118,8 @@ int main(int argc, char ** argv) {
     // used to determine end of generation
     bool has_eos = false;
 
+    llama_seq_id seq_id = 0;
+
     // ================================================
     // everything until here is standard initialization
     // the relevant stuff for speculative decoding starts here
@@ -119,10 +127,11 @@ int main(int argc, char ** argv) {
     const auto t_enc_start = ggml_time_us();
 
     // target model sampling context
-    struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling);
+    common_sampler_ptr smpl(common_sampler_init(model_tgt, params.sampling));
 
     // eval the prompt
-    llama_decode(ctx_tgt, llama_batch_get_one(inp.data(), inp.size() - 1));
+    llama_decode(ctx_tgt,       llama_batch_get_one(inp.data(), inp.size() - 1));
+    llama_decode(ctx_dft.get(), llama_batch_get_one(inp.data(), inp.size() - 1));
 
     // note: keep the last token separate!
     llama_token id_last = inp.back();
@@ -136,41 +145,81 @@ int main(int argc, char ** argv) {
     // init the speculator
     const auto & params_spec = params.speculative;
 
-    struct common_speculative * spec = common_speculative_init(params.speculative, ctx_tgt);
+    struct common_speculative * spec = common_speculative_init(params.speculative, 1);
 
-    common_speculative_begin(spec, prompt_tgt);
+    common_speculative_begin(spec, seq_id, prompt_tgt);
 
     llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
 
+    size_t n_draft = 0;
+
+    llama_tokens draft;
+    common_prompt_checkpoint ckpt;
+
     const auto t_enc_end = ggml_time_us();
 
     const auto t_dec_start = ggml_time_us();
 
     while (true) {
-        // optionally, generate draft tokens that can be appended to the target batch
+        // generate or reuse draft tokens
         //
         // this is the most important part of the speculation. the more probable tokens that are provided here
         // the better the performance will be. in theory, this computation can be performed asynchronously and even
         // offloaded to a remote device. it doesn't even have to be based on an LLM. instead, it can provide tokens
         // from a cache or lookup tables.
         //
-        llama_tokens draft = common_speculative_draft(spec, params_spec, prompt_tgt, id_last);
+        if (draft.empty()) {
+            ckpt.update_pos(
+                    prompt_tgt.size(),
+                    llama_memory_seq_pos_min(llama_get_memory(ctx_tgt), seq_id),
+                    llama_memory_seq_pos_max(llama_get_memory(ctx_tgt), seq_id));
+
+            if (use_ckpt_dft) {
+                ckpt.update_dft(ctx_dft.get(), seq_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+            }
+
+            // generate a new draft
+            common_speculative_get_draft_params(spec, seq_id) = {
+                /* .drafting   = */ true,
+                /* .n_max      = */ -1,
+                /* .n_past     = */ n_past,
+                /* .id_last    = */ id_last,
+                /* .prompt     = */ &prompt_tgt,
+                /* .result     = */ &draft, // output
+            };
+            common_speculative_draft(spec);
+
+            // save the original draft size
+            n_draft = draft.size();
+
+            // save a checkpoint of the target context before evaluating the draft
+            // this allows us to restore the state if partial draft acceptance occurs
+            if (!draft.empty()) {
+                if (use_ckpt_tgt) {
+                    ckpt.update_tgt(ctx_tgt, seq_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+                }
+            }
 
-        //LOG_DBG("draft: %s\n", string_from(ctx_dft, draft).c_str());
+            {
+                ckpt.load_dft(ctx_dft.get(), seq_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+
+                llama_memory_seq_rm(llama_get_memory(ctx_dft.get()), seq_id, ckpt.pos_max + 1, -1);
+            }
+        } else {
+            // we have a previous (partial) draft to reuse from checkpoint restoration
+            if (use_ckpt_tgt) {
+                GGML_ASSERT(!ckpt.empty());
+            }
+        }
 
         // always have a token to evaluate from before - id_last
         common_batch_clear(batch_tgt);
-        common_batch_add  (batch_tgt, id_last, n_past++, { 0 }, true);
+        common_batch_add  (batch_tgt, id_last, n_past++, { seq_id }, true);
 
         // evaluate the target model on [id_last, draft0, draft1, ..., draftN-1]
         {
-            // do not waste time on small drafts
-            if (draft.size() < (size_t) params_spec.n_min) {
-                draft.clear();
-            }
-
             for (size_t i = 0; i < draft.size(); ++i) {
-                common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
+                common_batch_add(batch_tgt, draft[i], n_past + i, { seq_id }, true);
             }
 
             //LOG_DBG("target batch: %s\n", string_from(ctx_tgt, batch_tgt).c_str());
@@ -178,6 +227,18 @@ int main(int argc, char ** argv) {
             llama_decode(ctx_tgt, batch_tgt);
         }
 
+        // evaluate the same batch with the draft model
+        {
+            // TODO: extend to support MTP, Eagle, etc. See server code for reference
+            llama_decode(ctx_dft.get(), batch_tgt);
+        }
+
+        // only save the sampler sampler state if we use checkpoints
+        common_sampler_ptr smpl_save;
+        if (use_ckpt_tgt) {
+            smpl_save.reset(common_sampler_clone(smpl.get()));
+        }
+
         // sample from the full target batch and return the accepted tokens based on the target sampler
         //
         // for each token to be accepted, the sampler would have to sample that same token
@@ -185,14 +246,45 @@ int main(int argc, char ** argv) {
         // available logits from the batch and sample the next token until we run out of logits or the sampler
         // disagrees with the draft
         //
-        const auto ids = common_sampler_sample_and_accept_n(smpl, ctx_tgt, draft);
+        auto ids = common_sampler_sample_and_accept_n(smpl.get(), ctx_tgt, draft);
 
         //LOG_DBG("ids: %s\n", string_from(ctx_tgt, ids).c_str());
 
         GGML_ASSERT(ids.size() > 0); // there will always be at least one accepted token
 
+        // check for partial draft acceptance:
+        // if the context doesn't support partial sequence removal, restore the checkpoint
+        // and make the accepted tokens the new partial draft for the next iteration
+        if (use_ckpt_tgt && ids.size() - 1 < draft.size()) {
+            LOG_DBG("partial acceptance: %zu < %zu, restoring checkpoint\n", ids.size() - 1, draft.size());
+
+            draft = std::move(ids);
+
+            {
+                ckpt.load_tgt(ctx_tgt, seq_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+
+                llama_memory_seq_rm(llama_get_memory(ctx_tgt), seq_id, ckpt.pos_max + 1, -1);
+            }
+
+            {
+                ckpt.load_dft(ctx_dft.get(), seq_id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+
+                llama_memory_seq_rm(llama_get_memory(ctx_dft.get()), seq_id, ckpt.pos_max + 1, -1);
+            }
+
+            prompt_tgt.resize(ckpt.n_tokens);
+            smpl = std::move(smpl_save);
+
+            n_past = (int) prompt_tgt.size();
+
+            continue;
+        }
+
+        common_speculative_accept(spec, seq_id, ids.size() - 1);
+
+        // full acceptance: consume the draft and commit accepted tokens
         n_past    += ids.size() - 1;
-        n_drafted += draft.size(); // note: we ignore the discarded small drafts
+        n_drafted += n_draft; // note: we ignore the discarded small drafts
         n_accept  += ids.size() - 1;
         n_predict += ids.size();
 
@@ -222,10 +314,14 @@ int main(int argc, char ** argv) {
 
         LOG_DBG("accepted %d/%d draft tokens, the last target token is: (%d)\n", (int) ids.size() - 1, (int) draft.size(), id_last);
 
+        // clear the draft since it has been consumed
+        draft.clear();
+
         {
             LOG_DBG("clear kv cache from any extra tokens, n_past = %d\n", n_past);
 
-            llama_memory_seq_rm(llama_get_memory(ctx_tgt), 0, n_past, -1);
+            llama_memory_seq_rm(llama_get_memory(ctx_tgt),       seq_id, n_past, -1);
+            llama_memory_seq_rm(llama_get_memory(ctx_dft.get()), seq_id, n_past, -1);
         }
 
         if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
@@ -243,7 +339,7 @@ int main(int argc, char ** argv) {
     LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
 
     LOG_INF("\n");
-    LOG_INF("n_draft   = %d\n", params_spec.n_max);
+    LOG_INF("n_draft   = %d\n", params_spec.draft.n_max);
     LOG_INF("n_predict = %d\n", n_predict);
     LOG_INF("n_drafted = %d\n", n_drafted);
     LOG_INF("n_accept  = %d\n", n_accept);
@@ -254,11 +350,10 @@ int main(int argc, char ** argv) {
 
     LOG_INF("\n");
     LOG_INF("target:\n\n");
-    common_perf_print(ctx_tgt, smpl);
+    common_perf_print(ctx_tgt, smpl.get());
 
     llama_batch_free(batch_tgt);
 
-    common_sampler_free(smpl);
     common_speculative_free(spec);
 
     llama_backend_free();
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 8f56a659b3e..f7fa5e30602 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -49,7 +49,7 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (params.speculative.mparams_dft.path.empty()) {
+    if (params.speculative.draft.mparams.path.empty()) {
         LOG_ERR("%s: --model-draft is required\n", __func__);
         return 1;
     }
@@ -58,7 +58,7 @@ int main(int argc, char ** argv) {
     const int n_seq_dft = params.n_parallel;
 
     // probability threshold for splitting a draft branch (only for n_seq_dft > 1)
-    const float p_draft_split = params.speculative.p_split;
+    const float p_draft_split = params.speculative.draft.p_split;
 
     std::default_random_engine rng(params.sampling.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sampling.seed);
     std::uniform_real_distribution<> u_dist;
@@ -80,15 +80,15 @@ int main(int argc, char ** argv) {
     ctx_tgt   = llama_init_tgt->context();
 
     // load the draft model
-    params.devices = params.speculative.devices;
-    params.model = params.speculative.mparams_dft;
-    params.n_gpu_layers = params.speculative.n_gpu_layers;
-    if (params.speculative.cpuparams.n_threads > 0) {
-        params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
+    params.devices = params.speculative.draft.devices;
+    params.model = params.speculative.draft.mparams;
+    params.n_gpu_layers = params.speculative.draft.n_gpu_layers;
+    if (params.speculative.draft.cpuparams.n_threads > 0) {
+        params.cpuparams.n_threads = params.speculative.draft.cpuparams.n_threads;
     }
 
-    params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
-    params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
+    params.cpuparams_batch.n_threads = params.speculative.draft.cpuparams_batch.n_threads;
+    params.tensor_buft_overrides     = params.speculative.draft.tensor_buft_overrides;
 
     auto llama_init_dft = common_init_from_params(params);
 
@@ -110,13 +110,21 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (
-        llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
-        llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
-        llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
-        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)
-    ) {
-        LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
+    if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
+        (llama_vocab_get_add_bos(vocab_tgt) && llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft))) {
+        LOG_ERR("%s: draft model bos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
+                __func__,
+                llama_vocab_get_add_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_dft),
+                llama_vocab_bos(vocab_tgt), llama_vocab_bos(vocab_dft));
+        return 1;
+    }
+
+    if (llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
+        (llama_vocab_get_add_eos(vocab_tgt) && llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft))) {
+        LOG_ERR("%s: draft model eos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
+                __func__,
+                llama_vocab_get_add_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_dft),
+                llama_vocab_eos(vocab_tgt), llama_vocab_eos(vocab_dft));
         return 1;
     }
 
@@ -137,11 +145,12 @@ int main(int argc, char ** argv) {
         for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
             const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
             const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
+
             if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
                 LOG_ERR("%s: draft model vocab must match target model to use speculation but ", __func__);
                 LOG_ERR("token %d content differs - target '%s', draft '%s'\n", i,
-                        common_token_to_piece(ctx_tgt, i).c_str(),
-                        common_token_to_piece(ctx_dft, i).c_str());
+                        common_token_to_piece(vocab_tgt, i).c_str(),
+                        common_token_to_piece(vocab_dft, i).c_str());
                 return 1;
             }
         }
@@ -183,7 +192,7 @@ int main(int argc, char ** argv) {
     //GGML_ASSERT(n_vocab == llama_vocab_n_tokens(model_dft));
 
     // how many tokens to draft each time
-    int n_draft = params.speculative.n_max;
+    int n_draft = params.speculative.draft.n_max;
 
     int n_predict = 0;
     int n_drafted = 0;
diff --git a/examples/sycl/start-svr.sh b/examples/sycl/start-svr.sh
new file mode 100755
index 00000000000..b7ea3096486
--- /dev/null
+++ b/examples/sycl/start-svr.sh
@@ -0,0 +1,124 @@
+#!/bin/bash
+
+#  MIT license
+#  Copyright (C) 2024 Intel Corporation
+#  SPDX-License-Identifier: MIT
+
+Help() {
+  cat << EOF
+Usage: $(basename "$0") [OPTIONS]
+
+This script processes files with specified options.
+
+Options:
+  -h, --help    Display this help message and exit.
+  -c, --context <value>    Set context length. Bigger need more memory.
+  -p, --promote <value>    Prompt to start generation with.
+  -m, --model   <value>    Full model file path.
+  -mg,--main-gpu <value>   Set main GPU ID (0 - n) for single GPU mode.
+  -sm,--split-mode <value> How to split the model across multiple GPUs, one of:
+                            - none: use one GPU only
+                            - layer (default): split layers and KV across GPUs
+                            - row: split rows across GPUs
+  -ngl,--n-gpu-layers <value>  Max. number of layers to store in VRAM (default: -1)
+  -lv,--log-verbosity <value>  Set the verbosity threshold. Messages with a higher verbosity will be
+                               ignored. Values:
+                                - 0: generic output
+                                - 1: error
+                                - 2: warning
+                                - 3: info
+                                - 4: debug
+
+
+EOF
+}
+
+BIN_FILE=./build/bin/llama-server
+SEED=0
+GPUS_SETTING=""
+
+MODEL_FILE=../models/Qwen3.5-4B-Q4_0.gguf
+NGL=99
+CONTEXT=4096
+GGML_SYCL_DEVICE=-1
+SPLIT_MODE=layer
+LOG_VERBOSE=3
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        -c|--context)
+            CONTEXT=$2
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -m|--model)
+            MODEL_FILE="$2"
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -mg|--main-gpu)
+            GGML_SYCL_DEVICE=$2
+            SPLIT_MODE=none
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -sm|--split-mode)
+            SPLIT_MODE=$2
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -ngl|--n-gpu-layers)
+            NGL=$2
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -lv|--log-verbosity)
+            LOG_VERBOSE=$2
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -h|--help)
+            Help
+            exit 0
+            ;;
+        *)
+            # Handle unknown options or stop processing options
+            echo "Invalid option: $1"
+            # Optional: exit script or shift to treat remaining as positional args
+            exit 1
+            ;;
+    esac
+done
+
+
+
+source /opt/intel/oneapi/setvars.sh
+
+#export GGML_SYCL_DEBUG=1
+
+#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
+
+#support malloc device memory more than 4GB.
+export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
+echo "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=${UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS}"
+
+if [ $GGML_SYCL_DEVICE -ne -1 ]; then
+    echo "Use $GGML_SYCL_DEVICE as main GPU"
+    #use signle GPU only
+    GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
+    export ONEAPI_DEVICE_SELECTOR="level_zero:${GGML_SYCL_DEVICE}"
+    echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
+else
+    echo "Use all Intel GPUs, including iGPU & dGPU"
+    GPUS_SETTING="-sm ${SPLIT_MODE}"
+ fi
+
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE}  --mmap --host 0.0.0.0 --port 8000"
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap --host 0.0.0.0 --port 8000
+
+
diff --git a/examples/sycl/test.sh b/examples/sycl/test.sh
index 140c191466e..38d2e926896 100755
--- a/examples/sycl/test.sh
+++ b/examples/sycl/test.sh
@@ -38,7 +38,7 @@ SEED=0
 GPUS_SETTING=""
 
 INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:"
-MODEL_FILE=models/llama-2-7b.Q4_0.gguf
+MODEL_FILE=../models/llama-2-7b.Q4_0.gguf
 NGL=99
 CONTEXT=4096
 GGML_SYCL_DEVICE=-1
@@ -119,12 +119,13 @@ if [ $GGML_SYCL_DEVICE -ne -1 ]; then
     echo "Use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
     GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
-    export ONEAPI_DEVICE_SELECTOR="level_zero:${$GGML_SYCL_DEVICE}"
+    export ONEAPI_DEVICE_SELECTOR="level_zero:${GGML_SYCL_DEVICE}"
     echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
 else
-   echo "Use all Intel GPUs, including iGPU & dGPU"
+    echo "Use all Intel GPUs, including iGPU & dGPU"
+    GPUS_SETTING="-sm ${SPLIT_MODE}"
  fi
 
-echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE}  --mmap "
-ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE}  --mmap "
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap
 
diff --git a/examples/sycl/win-start-svr.bat b/examples/sycl/win-start-svr.bat
new file mode 100644
index 00000000000..4d850cbaa6f
--- /dev/null
+++ b/examples/sycl/win-start-svr.bat
@@ -0,0 +1,179 @@
+::  MIT license
+::  Copyright (C) 2024 Intel Corporation
+::  SPDX-License-Identifier: MIT
+
+@echo off
+setlocal EnableExtensions EnableDelayedExpansion
+
+set "BIN_FILE=.\build\bin\llama-server.exe"
+set "SEED=0"
+set "GPUS_SETTING="
+
+set "MODEL_FILE=..\models\Qwen3.5-4B-Q4_0.gguf"
+set "NGL=99"
+set "CONTEXT=4096"
+set "GGML_SYCL_DEVICE=-1"
+set "SPLIT_MODE=layer"
+set "LOG_VERBOSE=3"
+
+if "%~1"=="" goto after_args
+
+:parse_args
+if "%~1"=="" goto after_args
+
+if /I "%~1"=="-c" (
+  if "%~2"=="" goto missing_value
+  set "CONTEXT=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--context" (
+  if "%~2"=="" goto missing_value
+  set "CONTEXT=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-m" (
+  if "%~2"=="" goto missing_value
+  set "MODEL_FILE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--model" (
+  if "%~2"=="" goto missing_value
+  set "MODEL_FILE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-mg" (
+  if "%~2"=="" goto missing_value
+  set "GGML_SYCL_DEVICE=%~2"
+  set "SPLIT_MODE=none"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--main-gpu" (
+  if "%~2"=="" goto missing_value
+  set "GGML_SYCL_DEVICE=%~2"
+  set "SPLIT_MODE=none"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-sm" (
+  if "%~2"=="" goto missing_value
+  set "SPLIT_MODE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--split-mode" (
+  if "%~2"=="" goto missing_value
+  set "SPLIT_MODE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-ngl" (
+  if "%~2"=="" goto missing_value
+  set "NGL=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--n-gpu-layers" (
+  if "%~2"=="" goto missing_value
+  set "NGL=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-lv" (
+  if "%~2"=="" goto missing_value
+  set "LOG_VERBOSE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--log-verbosity" (
+  if "%~2"=="" goto missing_value
+  set "LOG_VERBOSE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-h" goto help
+if /I "%~1"=="--help" goto help
+
+echo Invalid option: %~1
+exit /b 1
+
+:missing_value
+echo Missing value for option: %~1
+exit /b 1
+
+:help
+echo Usage: %~n0 [OPTIONS]
+echo.
+echo This script processes files with specified options.
+echo.
+echo Options:
+echo   -h, --help    Display this help message and exit.
+echo   -c, --context ^<value^>    Set context length. Bigger need more memory.
+echo   -m, --model   ^<value^>    Full model file path.
+echo   -mg,--main-gpu ^<value^>   Set main GPU ID (0 - n) for single GPU mode.
+echo   -sm,--split-mode ^<value^> How to split the model across multiple GPUs, one of:
+echo                             - none: use one GPU only
+echo                             - layer (default): split layers and KV across GPUs
+echo                             - row: split rows across GPUs
+echo   -ngl,--n-gpu-layers ^<value^>  Max. number of layers to store in VRAM (default: -1)
+echo   -lv,--log-verbosity ^<value^>  Set the verbosity threshold. Messages with a higher verbosity will be
+echo                                ignored. Values:
+echo                                 - 0: generic output
+echo                                 - 1: error
+echo                                 - 2: warning
+echo                                 - 3: info
+echo                                 - 4: debug
+exit /b 0
+
+:after_args
+
+REM In Windows CMD, source is not available; call oneAPI setvars if present.
+if exist "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" (
+  call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" >nul
+) else (
+  echo Warning: oneAPI setvars.bat not found. Continuing without environment setup.
+)
+
+REM Support malloc device memory more than 4GB.
+set "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1"
+echo UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=%UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS%
+
+if not "%GGML_SYCL_DEVICE%"=="-1" (
+  echo Use %GGML_SYCL_DEVICE% as main GPU
+  REM Use single GPU only.
+  set "GPUS_SETTING=-mg %GGML_SYCL_DEVICE% -sm %SPLIT_MODE%"
+  set "ONEAPI_DEVICE_SELECTOR=level_zero:%GGML_SYCL_DEVICE%"
+  echo ONEAPI_DEVICE_SELECTOR=%ONEAPI_DEVICE_SELECTOR%
+) else (
+  echo Use all Intel GPUs, including iGPU ^& dGPU
+  set "GPUS_SETTING=-sm %SPLIT_MODE%"
+)
+
+echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --mmap --host 0.0.0.0 --port 8000
+set "ZES_ENABLE_SYSMAN=1"
+%BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --mmap --host 0.0.0.0 --port 8000
+
+endlocal
+
diff --git a/examples/sycl/win-test.bat b/examples/sycl/win-test.bat
index 1f2dab8d0a8..781d17705db 100644
--- a/examples/sycl/win-test.bat
+++ b/examples/sycl/win-test.bat
@@ -2,10 +2,200 @@
 ::  Copyright (C) 2024 Intel Corporation
 ::  SPDX-License-Identifier: MIT
 
-set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
-@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
-:: support malloc device memory more than 4GB.
-set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
-set LOAD_MODE="--mmap"
-.\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 %LOAD_MODE%
+@echo off
+setlocal EnableExtensions EnableDelayedExpansion
+
+REM MIT license
+REM Copyright (C) 2024 Intel Corporation
+REM SPDX-License-Identifier: MIT
+
+set "BIN_FILE=.\build\bin\llama-completion.exe"
+set "SEED=0"
+set "GPUS_SETTING="
+
+set "INPUT_PROMPT=Building a website can be done in 10 simple steps:^nStep 1:"
+set "MODEL_FILE=..\models\llama-2-7b.Q4_0.gguf"
+set "NGL=99"
+set "CONTEXT=4096"
+set "GGML_SYCL_DEVICE=-1"
+set "SPLIT_MODE=layer"
+set "LOG_VERBOSE=3"
+
+if "%~1"=="" goto after_args
+
+:parse_args
+if "%~1"=="" goto after_args
+
+if /I "%~1"=="-c" (
+  if "%~2"=="" goto missing_value
+  set "CONTEXT=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--context" (
+  if "%~2"=="" goto missing_value
+  set "CONTEXT=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-p" (
+  if "%~2"=="" goto missing_value
+  set "INPUT_PROMPT=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--promote" (
+  if "%~2"=="" goto missing_value
+  set "INPUT_PROMPT=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-m" (
+  if "%~2"=="" goto missing_value
+  set "MODEL_FILE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--model" (
+  if "%~2"=="" goto missing_value
+  set "MODEL_FILE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-mg" (
+  if "%~2"=="" goto missing_value
+  set "GGML_SYCL_DEVICE=%~2"
+  set "SPLIT_MODE=none"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--main-gpu" (
+  if "%~2"=="" goto missing_value
+  set "GGML_SYCL_DEVICE=%~2"
+  set "SPLIT_MODE=none"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-sm" (
+  if "%~2"=="" goto missing_value
+  set "SPLIT_MODE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--split-mode" (
+  if "%~2"=="" goto missing_value
+  set "SPLIT_MODE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-ngl" (
+  if "%~2"=="" goto missing_value
+  set "NGL=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--n-gpu-layers" (
+  if "%~2"=="" goto missing_value
+  set "NGL=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-lv" (
+  if "%~2"=="" goto missing_value
+  set "LOG_VERBOSE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--log-verbosity" (
+  if "%~2"=="" goto missing_value
+  set "LOG_VERBOSE=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
+if /I "%~1"=="-h" goto help
+if /I "%~1"=="--help" goto help
+
+echo Invalid option: %~1
+exit /b 1
+
+:missing_value
+echo Missing value for option: %~1
+exit /b 1
+
+:help
+echo Usage: %~n0 [OPTIONS]
+echo.
+echo This script processes files with specified options.
+echo.
+echo Options:
+echo   -h, --help    Display this help message and exit.
+echo   -c, --context ^<value^>    Set context length. Bigger need more memory.
+echo   -p, --promote ^<value^>    Prompt to start generation with.
+echo   -m, --model   ^<value^>    Full model file path.
+echo   -mg,--main-gpu ^<value^>   Set main GPU ID (0 - n) for single GPU mode.
+echo   -sm,--split-mode ^<value^> How to split the model across multiple GPUs, one of:
+echo                             - none: use one GPU only
+echo                             - layer (default): split layers and KV across GPUs
+echo                             - row: split rows across GPUs
+echo   -ngl,--n-gpu-layers ^<value^>  Max. number of layers to store in VRAM (default: -1)
+echo   -lv,--log-verbosity ^<value^>  Set the verbosity threshold. Messages with a higher verbosity will be
+echo                                ignored. Values:
+echo                                 - 0: generic output
+echo                                 - 1: error
+echo                                 - 2: warning
+echo                                 - 3: info
+echo                                 - 4: debug
+exit /b 0
+
+:after_args
+
+REM In Windows CMD, source is not available; call oneAPI setvars if present.
+if exist "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" (
+  call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" >nul
+) else (
+  echo Warning: oneAPI setvars.bat not found. Continuing without environment setup.
+)
+
+REM Support malloc device memory more than 4GB.
+set "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1"
+echo UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=%UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS%
+
+if not "%GGML_SYCL_DEVICE%"=="-1" (
+  echo Use %GGML_SYCL_DEVICE% as main GPU
+  REM Use single GPU only.
+  set "GPUS_SETTING=-mg %GGML_SYCL_DEVICE% -sm %SPLIT_MODE%"
+  set "ONEAPI_DEVICE_SELECTOR=level_zero:%GGML_SYCL_DEVICE%"
+  echo ONEAPI_DEVICE_SELECTOR=%ONEAPI_DEVICE_SELECTOR%
+) else (
+  echo Use all Intel GPUs, including iGPU ^& dGPU
+  set "GPUS_SETTING=-sm %SPLIT_MODE%"
+)
+
+echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m %MODEL_FILE% -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --mmap
+set "ZES_ENABLE_SYSMAN=1"
+%BIN_FILE% -m "%MODEL_FILE%" -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --mmap
+
+endlocal
+
diff --git a/flake.lock b/flake.lock
deleted file mode 100644
index d114f4422a3..00000000000
--- a/flake.lock
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-  "nodes": {
-    "flake-parts": {
-      "inputs": {
-        "nixpkgs-lib": "nixpkgs-lib"
-      },
-      "locked": {
-        "lastModified": 1730504689,
-        "narHash": "sha256-hgmguH29K2fvs9szpq2r3pz2/8cJd2LPS+b4tfNFCwE=",
-        "owner": "hercules-ci",
-        "repo": "flake-parts",
-        "rev": "506278e768c2a08bec68eb62932193e341f55c90",
-        "type": "github"
-      },
-      "original": {
-        "owner": "hercules-ci",
-        "repo": "flake-parts",
-        "type": "github"
-      }
-    },
-    "nixpkgs": {
-      "locked": {
-        "lastModified": 1732014248,
-        "narHash": "sha256-y/MEyuJ5oBWrWAic/14LaIr/u5E0wRVzyYsouYY3W6w=",
-        "owner": "NixOS",
-        "repo": "nixpkgs",
-        "rev": "23e89b7da85c3640bbc2173fe04f4bd114342367",
-        "type": "github"
-      },
-      "original": {
-        "owner": "NixOS",
-        "ref": "nixos-unstable",
-        "repo": "nixpkgs",
-        "type": "github"
-      }
-    },
-    "nixpkgs-lib": {
-      "locked": {
-        "lastModified": 1730504152,
-        "narHash": "sha256-lXvH/vOfb4aGYyvFmZK/HlsNsr/0CVWlwYvo2rxJk3s=",
-        "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/cc2f28000298e1269cea6612cd06ec9979dd5d7f.tar.gz"
-      },
-      "original": {
-        "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/cc2f28000298e1269cea6612cd06ec9979dd5d7f.tar.gz"
-      }
-    },
-    "root": {
-      "inputs": {
-        "flake-parts": "flake-parts",
-        "nixpkgs": "nixpkgs"
-      }
-    }
-  },
-  "root": "root",
-  "version": 7
-}
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 2effd587b41..4aac5094d1c 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)
 
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 10)
+set(GGML_VERSION_MINOR 12)
 set(GGML_VERSION_PATCH 0)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
@@ -213,7 +213,7 @@ set   (GGML_CUDA_COMPRESSION_MODE "size" CACHE STRING
 set_property(CACHE GGML_CUDA_COMPRESSION_MODE PROPERTY STRINGS "none;speed;balance;size")
 
 option(GGML_HIP                             "ggml: use HIP"                                   OFF)
-option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental, slow"         OFF)
+option(GGML_HIP_GRAPHS                      "ggml: use HIP graph"                              ON)
 option(GGML_HIP_RCCL                        "ggml: use ROCm Collective Comm. Library"         OFF)
 option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"                 ON)
 option(GGML_HIP_ROCWMMA_FATTN               "ggml: enable rocWMMA for FlashAttention"         OFF)
@@ -249,6 +249,7 @@ option(GGML_SYCL                            "ggml: use SYCL"
 option(GGML_SYCL_F16                        "ggml: use 16 bit floats for sycl calculations"   OFF)
 option(GGML_SYCL_GRAPH                      "ggml: enable graphs in the SYCL backend"         ON)
 option(GGML_SYCL_HOST_MEM_FALLBACK          "ggml: allow host memory fallback in SYCL reorder (requires kernel 6.8+)" ON)
+option(GGML_SYCL_SUPPORT_LEVEL_ZERO         "ggml: use Level Zero API in SYCL backend"  ON)
 option(GGML_SYCL_DNN                        "ggml: enable oneDNN in the SYCL backend"         ON)
 set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
                                             "ggml: sycl target device")
@@ -352,7 +353,7 @@ if (GGML_STANDALONE)
         @ONLY)
 
     install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
-        DESTINATION share/pkgconfig)
+        DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
 endif()
 
 #
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index d0c7e5a1be0..b6f73739809 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -169,7 +169,7 @@ extern "C" {
         // device type
         enum ggml_backend_dev_type type;
         // device id
-        //   for PCI devices, this should be the PCI bus id formatted as "domain:bus:device.function" (e.g. "0000:01:00.0")
+        //   for PCI devices, this should be the lower-case PCI bus id formatted as "domain:bus:device.function" (e.g. "0000:c1:00.0")
         //   if the id is unknown, this should be NULL
         const char * device_id;
         // device capabilities
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index c3cd1c7eafe..86b73ff7086 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -443,6 +443,12 @@ extern "C" {
         GGML_PREC_F32     = 10,
     };
 
+    // op hint
+    enum ggml_op_hint {
+        GGML_HINT_NONE             = 0,
+        GGML_HINT_SRC0_IS_HADAMARD = 1,
+    };
+
     // model file types
     enum ggml_ftype {
         GGML_FTYPE_UNKNOWN        = -1,
@@ -1425,6 +1431,11 @@ extern "C" {
             struct ggml_tensor * a,
             enum ggml_prec       prec);
 
+    // change the hint of a matrix multiplication
+    GGML_API void ggml_mul_mat_set_hint(
+            struct ggml_tensor * a,
+            enum ggml_op_hint    hint);
+
     // indirect matrix multiplication
     GGML_API struct ggml_tensor * ggml_mul_mat_id(
             struct ggml_context * ctx,
@@ -2536,6 +2547,11 @@ extern "C" {
 
     // TODO: add ggml_gated_delta_net_set_bcast() to be able to configure Q, K broadcast type: tiled vs interleaved [TAG_GGML_GDN_BCAST]
     // ref: https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
+    //
+    // state is a 3D tensor of shape (S_v*S_v*H, K, n_seqs):
+    //   K == 1: output carries the final state only.
+    //   K  > 1: output carries K snapshot slots; the kernel writes the last min(n_tokens, K)
+    //   per-token snapshots into the trailing slots
     GGML_API struct ggml_tensor * ggml_gated_delta_net(
             struct ggml_context * ctx,
             struct ggml_tensor  * q,
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index af53c4c892b..4f5852d582c 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -471,11 +471,10 @@ endforeach()
 
 target_link_libraries(ggml-base PRIVATE Threads::Threads)
 
-find_library(MATH_LIBRARY m)
-if (MATH_LIBRARY)
-    if (NOT WIN32 OR NOT DEFINED ENV{ONEAPI_ROOT})
-        target_link_libraries(ggml-base PRIVATE m)
-    endif()
+if (DEFINED MATH_LIBRARY)
+    target_link_libraries(ggml-base PRIVATE ${MATH_LIBRARY})
+elseif (NOT WIN32 AND NOT DEFINED ENV{ONEAPI_ROOT})
+    target_link_libraries(ggml-base PRIVATE m)
 endif()
 
 if (CMAKE_SYSTEM_NAME MATCHES "Android")
diff --git a/ggml/src/ggml-backend-meta.cpp b/ggml/src/ggml-backend-meta.cpp
index 6d22f3421b1..df0f405ed9f 100644
--- a/ggml/src/ggml-backend-meta.cpp
+++ b/ggml/src/ggml-backend-meta.cpp
@@ -753,7 +753,9 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(co
         GGML_ASSERT(src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_1);
         GGML_ASSERT(src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_1);
         GGML_ASSERT(src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_1);
-        GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2);
+        // state shape is (S_v*S_v*H, K, n_seqs); the heads dim is nested inside axis 0,
+        // so a head-aligned split on the input cache reshapes to axis 0 here (not axis 2).
+        GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_1 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_0);
         return {GGML_BACKEND_SPLIT_AXIS_0, {0}, 1};
     };
 
@@ -1205,40 +1207,57 @@ static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, gg
 
     if (split_state.n_segments != 1) {
         GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
-        GGML_ASSERT(offset == 0);
-        GGML_ASSERT(size == ggml_nbytes(tensor));
         GGML_ASSERT(tensor->ne[3] == 1);
+
         size_t offset_data = 0;
         std::vector<size_t> simple_offsets(n_bufs, 0);
         if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_0) {
             GGML_ASSERT(tensor->ne[2] == 1);
+
+            const size_t row_stride = tensor->nb[1];
+            GGML_ASSERT(offset % row_stride == 0);
+            GGML_ASSERT(size   % row_stride == 0);
+            const int64_t r_start = offset / row_stride;
+            const int64_t r_count = size   / row_stride;
+            GGML_ASSERT(r_start + r_count <= tensor->ne[1]);
+
             const int64_t blck_size = ggml_blck_size(tensor->type);
             for (size_t s = 0; s < split_state.n_segments; s++) {
                 for (size_t j = 0; j < n_bufs; j++) {
                     ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
                     GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
                     const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
-                    ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data, simple_offsets[j], nbytes,
-                        tensor->ne[1], simple_tensor->nb[1], tensor->nb[1]);
+                    ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data,
+                        simple_offsets[j] + r_start * simple_tensor->nb[1], nbytes,
+                        r_count, simple_tensor->nb[1], tensor->nb[1]);
                     offset_data       += nbytes;
                     simple_offsets[j] += nbytes;
                 }
             }
-            GGML_ASSERT(offset_data*tensor->ne[1] == size);
+            GGML_ASSERT(offset_data*r_count == size);
             return;
         }
         GGML_ASSERT(split_state.axis == GGML_BACKEND_SPLIT_AXIS_1);
+
+        const size_t row_stride = tensor->nb[2];
+        GGML_ASSERT(offset % row_stride == 0);
+        GGML_ASSERT(size   % row_stride == 0);
+        const int64_t r_start = offset / row_stride;
+        const int64_t r_count = size   / row_stride;
+        GGML_ASSERT(r_start + r_count <= tensor->ne[2]);
+
         for (size_t s = 0; s < split_state.n_segments; s++) {
             for (size_t j = 0; j < n_bufs; j++) {
                 ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
                 const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
-                ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data, simple_offsets[j], nbytes,
-                    tensor->ne[2], simple_tensor->nb[2], tensor->nb[2]);
+                ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data,
+                    simple_offsets[j] + r_start * simple_tensor->nb[2], nbytes,
+                    r_count, simple_tensor->nb[2], tensor->nb[2]);
                 offset_data       += nbytes;
                 simple_offsets[j] += nbytes;
             }
         }
-        GGML_ASSERT(offset_data*tensor->ne[2] == size);
+        GGML_ASSERT(offset_data*r_count == size);
         return;
     }
 
@@ -1295,40 +1314,57 @@ static void ggml_backend_meta_buffer_get_tensor(ggml_backend_buffer_t buffer, co
 
     if (split_state.n_segments != 1) {
         GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
-        GGML_ASSERT(offset == 0);
-        GGML_ASSERT(size == ggml_nbytes(tensor));
         GGML_ASSERT(tensor->ne[3] == 1);
+
         size_t offset_data = 0;
         std::vector<size_t> simple_offsets(n_bufs, 0);
         if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_0) {
             GGML_ASSERT(tensor->ne[2] == 1);
+
+            const size_t row_stride = tensor->nb[1];
+            GGML_ASSERT(offset % row_stride == 0);
+            GGML_ASSERT(size   % row_stride == 0);
+            const int64_t r_start = offset / row_stride;
+            const int64_t r_count = size   / row_stride;
+            GGML_ASSERT(r_start + r_count <= tensor->ne[1]);
+
             const int64_t blck_size = ggml_blck_size(tensor->type);
             for (size_t s = 0; s < split_state.n_segments; s++) {
                 for (size_t j = 0; j < n_bufs; j++) {
                     const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
                     GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
                     const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
-                    ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data, simple_offsets[j], nbytes,
-                        tensor->ne[1], simple_tensor->nb[1], tensor->nb[1]);
+                    ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data,
+                        simple_offsets[j] + r_start * simple_tensor->nb[1], nbytes,
+                        r_count, simple_tensor->nb[1], tensor->nb[1]);
                     offset_data       += nbytes;
                     simple_offsets[j] += nbytes;
                 }
             }
-            GGML_ASSERT(offset_data*tensor->ne[1] == size);
+            GGML_ASSERT(offset_data*r_count == size);
             return;
         }
         GGML_ASSERT(split_state.axis == GGML_BACKEND_SPLIT_AXIS_1);
+
+        const size_t row_stride = tensor->nb[2];
+        GGML_ASSERT(offset % row_stride == 0);
+        GGML_ASSERT(size   % row_stride == 0);
+        const int64_t r_start = offset / row_stride;
+        const int64_t r_count = size   / row_stride;
+        GGML_ASSERT(r_start + r_count <= tensor->ne[2]);
+
         for (size_t s = 0; s < split_state.n_segments; s++) {
             for (size_t j = 0; j < n_bufs; j++) {
                 const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
                 const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
-                ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data, simple_offsets[j], nbytes,
-                    tensor->ne[2], simple_tensor->nb[2], tensor->nb[2]);
+                ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data,
+                    simple_offsets[j] + r_start * simple_tensor->nb[2], nbytes,
+                    r_count, simple_tensor->nb[2], tensor->nb[2]);
                 offset_data       += nbytes;
                 simple_offsets[j] += nbytes;
             }
         }
-        GGML_ASSERT(offset_data*tensor->ne[2] == size);
+        GGML_ASSERT(offset_data*r_count == size);
         return;
     }
 
@@ -1792,7 +1828,24 @@ static enum ggml_status ggml_backend_meta_graph_compute(ggml_backend_t backend,
                     continue;
                 }
 
-                i = get_i_delayed(i);
+                const int i_delayed = get_i_delayed(i);
+
+                // If we can delay the AllReduce we need to consider the interaction with zero-sized tensor slices.
+                // A backend with such a slice would normally have valid data after participating in the AllReduce with a node that has
+                //     its compute flag disabled and thus gets its data zeroed out.
+                // If the AllReduce is delayed then the nodes until that point also need to have their compute flag disabled.
+                if (i_delayed > i) {
+                    for (size_t j = 0; j < n_backends; j++) {
+                        auto & bcj = backend_ctx->backend_configs[j];
+                        if ((bcj.nodes[i]->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
+                            for (int ii = i + 1; ii <= i_delayed; ii++) {
+                                bcj.nodes[ii]->flags &= ~GGML_TENSOR_FLAG_COMPUTE;
+                            }
+                        }
+                    }
+                }
+
+                i = i_delayed;
 
                 for (size_t j = 0; j < n_backends; j++) {
                     auto & bcj = backend_ctx->backend_configs[j];
@@ -2049,8 +2102,8 @@ static const ggml_backend_i ggml_backend_meta_i = {
     /* .free                    = */ ggml_backend_meta_free,
     /* .set_tensor_async        = */ ggml_backend_meta_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_meta_get_tensor_async,
-    /* .get_tensor_2d_async     = */ nullptr,
     /* .set_tensor_2d_async     = */ nullptr,
+    /* .get_tensor_2d_async     = */ nullptr,
     /* .cpy_tensor_async        = */ nullptr,
     /* .synchronize             = */ ggml_backend_meta_synchronize,
     /* .graph_plan_create       = */ nullptr,
@@ -2089,4 +2142,3 @@ ggml_backend_t ggml_backend_meta_simple_backend(ggml_backend_t meta_backend, siz
     const ggml_backend_meta_context * backend_ctx = (const ggml_backend_meta_context *) meta_backend->context;
     return backend_ctx->backend_configs[index].backend;
 }
-
diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 0587109212e..8165ae2c8bb 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -181,6 +181,12 @@ struct ggml_backend_registry {
             return;
         }
 
+        for (auto & entry : backends) {
+            if (entry.reg == reg) {
+                return;
+            }
+        }
+
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n",
             __func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg));
@@ -192,6 +198,12 @@ struct ggml_backend_registry {
     }
 
     void register_device(ggml_backend_dev_t device) {
+        for (auto & dev : devices) {
+            if (dev == device) {
+                return;
+            }
+        }
+
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
 #endif
diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index d9f8aaec52f..4e36909f45e 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -965,7 +965,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
         }
         if (sched->debug > 1) {
             ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
-            GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s] use=%d,c=%d:", i, ggml_op_name(node->op), node->name,
+            GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s] use=%d,c=%d:", i, ggml_op_desc(node), node->name,
                 fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node),
                 graph->use_counts[ggml_hash_find(&graph->visited_hash_set, node)], node->flags & GGML_TENSOR_FLAG_COMPUTE ? 1 : 0);
             for (int j = 0; j < GGML_MAX_SRC; j++) {
diff --git a/ggml/src/ggml-blas/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp
index 05245b69807..b4c735267e0 100644
--- a/ggml/src/ggml-blas/ggml-blas.cpp
+++ b/ggml/src/ggml-blas/ggml-blas.cpp
@@ -262,9 +262,9 @@ static struct ggml_backend_i blas_backend_i = {
     /* .get_name                = */ ggml_backend_blas_get_name,
     /* .free                    = */ ggml_backend_blas_free,
     /* .set_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
-    /* .set_tensor_2d_async     = */ NULL,
     /* .get_tensor_async        = */ NULL,
+    /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ NULL,
diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index a950475fc3b..2dc0f40917d 100644
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -25,6 +25,7 @@
 #include "ggml-impl.h"
 #include "ggml.h"
 
+
 #include <aclnnop/aclnn_add.h>
 #include <aclnnop/aclnn_add_rms_norm.h>
 #include <aclnnop/aclnn_addcdiv.h>
@@ -45,7 +46,9 @@
 #include <aclnnop/aclnn_fused_infer_attention_score_v2.h>
 #include <aclnnop/aclnn_ger.h>
 #include <aclnnop/aclnn_group_norm.h>
+#include <aclnnop/aclnn_gather_v2.h>
 #include <aclnnop/aclnn_grouped_matmul_v3.h>
+#include <aclnnop/aclnn_scatter.h>
 #include <aclnnop/aclnn_gt_scalar.h>
 #include <aclnnop/aclnn_im2col.h>
 #include <aclnnop/aclnn_index_copy.h>
@@ -62,6 +65,7 @@
 #include <aclnnop/aclnn_permute.h>
 #include <aclnnop/aclnn_pow.h>
 #include <aclnnop/aclnn_pow_tensor_tensor.h>
+#include <aclnnop/aclnn_recurrent_gated_delta_rule.h>
 #include <aclnnop/aclnn_reduce_sum.h>
 #include <aclnnop/aclnn_reflection_pad1d.h>
 #include <aclnnop/aclnn_repeat.h>
@@ -69,11 +73,15 @@
 #include <aclnnop/aclnn_rms_norm.h>
 #include <aclnnop/aclnn_roll.h>
 #include <aclnnop/aclnn_softmax.h>
+#include <aclnnop/aclnn_softmax_cross_entropy_with_logits.h>
 #include <aclnnop/aclnn_sub.h>
 #include <aclnnop/aclnn_sum.h>
 #include <aclnnop/aclnn_threshold.h>
 #include <aclnnop/aclnn_tril.h>
+#include <aclnnop/aclnn_triangular_solve.h>
 #include <aclnnop/aclnn_triu.h>
+#include <aclnnop/aclnn_logical_not.h>
+#include <aclnnop/aclnn_masked_fill_scalar.h>
 #include <aclnnop/aclnn_upsample_nearest_2d.h>
 #include <aclnnop/aclnn_weight_quant_batch_matmul_v2.h>
 #include <aclnnop/aclnn_zero.h>
@@ -151,6 +159,107 @@ void ggml_cann_op_unary_gated(std::function<void(ggml_backend_cann_context &, ac
     GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, acl_dst.get(), acl_src1.get());
 }
 
+// Fused SwiGLU using aclnnSwiGlu: splits input along innermost dim, applies
+// SiLU to left half, multiplies by right half.
+//
+// Falls back to the generic two-kernel path when src[1] != nullptr (two
+// independent halves) or swapped != 0 (reversed activation order), as
+// aclnnSwiGlu only handles the single interleaved tensor in standard order.
+//
+// CANN tiling for SwiGlu requires (storageShapeDim + viewDims) to be even.
+// aclCreateTensor always uses storageShapeDim=1, so viewDims must be odd.
+// We use a 3D view (1+3=4, even) to satisfy this constraint while preserving
+// correct split semantics along the innermost (ne[0]) dimension.
+void ggml_cann_swiglu(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    auto silu_fn = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Silu, acl_src, acl_dst);
+    };
+
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    if (dst->src[1] != nullptr || swapped != 0) {
+        ggml_cann_op_unary_gated(silu_fn, ctx, dst);
+        return;
+    }
+
+    // aclnnSwiGlu requires the split dim (src->ne[0]) to be even; fall back otherwise.
+    if (dst->src[0]->ne[0] % 2 != 0) {
+        ggml_cann_op_unary_gated(silu_fn, ctx, dst);
+        return;
+    }
+
+    ggml_tensor * src0 = dst->src[0];
+    size_t elem_size = ggml_element_size(src0);
+
+    // src0 GGML: [2*ne0, ne1, ne2, ne3] → 3D view [2*ne0, ne1, ne2*ne3]
+    // CANN reversed: [ne2*ne3, ne1, 2*ne0], split along CANN dim 2 (last).
+    int64_t ne0_x2   = src0->ne[0];
+    int64_t ne1      = src0->ne[1];
+    int64_t ne23     = src0->ne[2] * src0->ne[3];
+    int64_t src3d_ne[] = { ne0_x2, ne1, ne23 };
+    size_t  src3d_nb[] = { (size_t)src0->nb[0], (size_t)src0->nb[1], (size_t)src0->nb[2] };
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0->data, ggml_cann_type_mapping(src0->type),
+                                                     elem_size, src3d_ne, src3d_nb, 3);
+
+    // dst GGML: [ne0, ne1, ne2, ne3] → 3D view [ne0, ne1, ne2*ne3]
+    int64_t ne0      = dst->ne[0];
+    int64_t dst3d_ne[] = { ne0, ne1, ne23 };
+    size_t  dst3d_nb[] = { (size_t)dst->nb[0], (size_t)dst->nb[1], (size_t)dst->nb[2] };
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst->data, ggml_cann_type_mapping(dst->type),
+                                                     elem_size, dst3d_ne, dst3d_nb, 3);
+
+    // CANN tensor [ne23, ne1, 2*ne0]: split along CANN dim 2 (last) = 2*ne0.
+    GGML_CANN_CALL_ACLNN_OP(ctx, SwiGlu, acl_src.get(), (int64_t)2, acl_dst.get());
+}
+
+// Fused GeGLU using aclnnGeGluV3: splits input along ne[0] (CANN last dim),
+// activates the LEFT half with GELU, multiplies by right half.
+// approximate: 0=tanh, 1=none(erf). activateLeft=true matches GGML convention.
+// outGelu is a required-but-discard output buffer.
+//
+// Falls back to the generic two-kernel path when src[1] != nullptr (two
+// independent halves) or swapped != 0 (reversed activation order), as
+// aclnnGeGluV3 only handles the single interleaved tensor in standard order.
+void ggml_cann_geglu(ggml_backend_cann_context & ctx, ggml_tensor * dst, int64_t approximate) {
+    auto gelu_fn = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Gelu, acl_src, acl_dst);
+    };
+
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    if (dst->src[1] != nullptr || swapped != 0) {
+        ggml_cann_op_unary_gated(gelu_fn, ctx, dst);
+        return;
+    }
+
+    // aclnnGeGluV3 requires the split dim (src->ne[0]) to be even; fall back otherwise.
+    if (dst->src[0]->ne[0] % 2 != 0) {
+        ggml_cann_op_unary_gated(gelu_fn, ctx, dst);
+        return;
+    }
+
+    ggml_tensor * src0 = dst->src[0];
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    // Allocate a temporary buffer for the required outGelu output (same shape as dst).
+    // Build contiguous strides since the pool allocation is a fresh buffer.
+    size_t  elem_size    = ggml_element_size(dst);
+    int64_t ne[GGML_MAX_DIMS] = { dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3] };
+    size_t  nb[GGML_MAX_DIMS];
+    nb[0] = elem_size;
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        nb[i] = nb[i - 1] * ne[i - 1];
+    }
+    size_t gelu_out_size = nb[GGML_MAX_DIMS - 1] * ne[GGML_MAX_DIMS - 1];
+    ggml_cann_pool_alloc gelu_out_alloc(ctx.pool(), gelu_out_size);
+
+    acl_tensor_ptr acl_gelu_out = ggml_cann_create_tensor(
+        gelu_out_alloc.get(), ggml_cann_type_mapping(dst->type), elem_size, ne, nb, GGML_MAX_DIMS);
+    // V3 adds activateLeft param; true → Gelu(left)*right, matching GGML convention.
+    // GGML dim 0 → CANN last dim (index GGML_MAX_DIMS-1 = 3 for 4D tensor).
+    GGML_CANN_CALL_ACLNN_OP(ctx, GeGluV3, acl_src.get(), (int64_t)(GGML_MAX_DIMS - 1), approximate, true,
+                             acl_dst.get(), acl_gelu_out.get());
+}
+
 /**
  * @brief Repeats elements of a tensor along each dimension according to the
  * specified repeat array.
@@ -445,28 +554,33 @@ void ggml_cann_l2_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes);
     void *               buffer = temp_buffer_allocator.get();
 
-    int64_t div_ne[] = { 1, src->ne[1], src->ne[2], src->ne[3] };
-    size_t  div_nb[GGML_MAX_DIMS];
-    div_nb[0] = sizeof(float);
+    int64_t norm_ne[] = { 1, src->ne[1], src->ne[2], src->ne[3] };
+    size_t  norm_nb[GGML_MAX_DIMS];
+    norm_nb[0] = sizeof(float);
     for (int i = 1; i < GGML_MAX_DIMS; ++i) {
-        div_nb[i] = div_nb[i - 1] * div_ne[i - 1];
+        norm_nb[i] = norm_nb[i - 1] * norm_ne[i - 1];
     }
-    acl_tensor_ptr acl_div = ggml_cann_create_tensor(buffer, ACL_FLOAT, type_size, div_ne, div_nb, GGML_MAX_DIMS);
+    acl_tensor_ptr acl_norm = ggml_cann_create_tensor(buffer, ACL_FLOAT, sizeof(float), norm_ne, norm_nb, GGML_MAX_DIMS);
 
     std::vector<int64_t> norm_dims  = { 3 };
     acl_int_array_ptr    dims_array = ggml_cann_create_int_array(norm_dims.data(), norm_dims.size());
 
     float          p_value  = 2.0f;
     acl_scalar_ptr p_scalar = ggml_cann_create_scalar(&p_value, aclDataType::ACL_FLOAT);
-    GGML_CANN_CALL_ACLNN_OP(ctx, Norm, acl_src.get(), p_scalar.get(), dims_array.get(), true, acl_div.get());
+    GGML_CANN_CALL_ACLNN_OP(ctx, Norm, acl_src.get(), p_scalar.get(), dims_array.get(), true, acl_norm.get());
+
+    ggml_cann_pool_alloc clamp_buffer_allocator(ctx.pool());
+    acl_tensor_ptr       acl_clamped;
 
-    // Clamp norm to at least eps: scale = 1/fmaxf(norm, eps)
-    acl_scalar_ptr acl_min = ggml_cann_create_scalar(&eps, aclDataType::ACL_FLOAT);
-    float          flt_max = FLT_MAX;
-    acl_scalar_ptr acl_max = ggml_cann_create_scalar(&flt_max, aclDataType::ACL_FLOAT);
-    GGML_CANN_CALL_ACLNN_OP(ctx, Clamp, acl_div.get(), acl_min.get(), acl_max.get(), acl_div.get());
+    if (eps > 0.0f) {
+        void *         clamp_buf  = clamp_buffer_allocator.alloc(n_bytes);
+        acl_clamped               = ggml_cann_create_tensor(clamp_buf, ACL_FLOAT, sizeof(float), norm_ne, norm_nb, GGML_MAX_DIMS);
+        acl_scalar_ptr eps_scalar = ggml_cann_create_scalar(&eps, aclDataType::ACL_FLOAT);
+        GGML_CANN_CALL_ACLNN_OP(ctx, ClampMin, acl_norm.get(), eps_scalar.get(), acl_clamped.get());
+    }
 
-    GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src.get(), acl_div.get(), acl_dst.get());
+    aclTensor * acl_div_input = acl_clamped ? acl_clamped.get() : acl_norm.get();
+    GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src.get(), acl_div_input, acl_dst.get());
 }
 
 void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
@@ -482,56 +596,30 @@ void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor *
     logits_nb[1]              = logits_nb[0] * logits_ne[0];
     acl_tensor_ptr acl_logits = ggml_cann_create_tensor(src0->data, ACL_FLOAT, sizeof(float), logits_ne, logits_nb, 2);
 
-    size_t               log_softmax_type_size = sizeof(float);
-    int64_t              log_softmax_n_bytes   = nr * nc * log_softmax_type_size;
-    ggml_cann_pool_alloc log_softmax_allocator(ctx.pool(), log_softmax_n_bytes);
-    void *               log_softmax_buffer = log_softmax_allocator.get();
-
-    int64_t log_softmax_ne[] = { nc, nr };
-    size_t  log_softmax_nb[2];
-    log_softmax_nb[0]              = log_softmax_type_size;
-    log_softmax_nb[1]              = log_softmax_nb[0] * log_softmax_ne[0];
-    acl_tensor_ptr acl_log_softmax = ggml_cann_create_tensor(log_softmax_buffer, ACL_FLOAT, log_softmax_type_size,
-                                                             log_softmax_ne, log_softmax_nb, 2);
-
-    GGML_CANN_CALL_ACLNN_OP(ctx, LogSoftmax, acl_logits.get(), 1, acl_log_softmax.get());
-
     int64_t labels_ne[] = { nc, nr };
     size_t  labels_nb[2];
     labels_nb[0]              = ggml_type_size(src1->type);
     labels_nb[1]              = labels_nb[0] * labels_ne[0];
     acl_tensor_ptr acl_labels = ggml_cann_create_tensor(src1->data, ACL_FLOAT, sizeof(float), labels_ne, labels_nb, 2);
 
-    size_t               mul_type_size = sizeof(float);
-    int64_t              mul_n_bytes   = nr * nc * mul_type_size;
-    ggml_cann_pool_alloc mul_allocator(ctx.pool(), mul_n_bytes);
-    void *               mul_buffer = mul_allocator.get();
-
-    int64_t mul_ne[] = { nc, nr };
-    size_t  mul_nb[2];
-    mul_nb[0]                     = mul_type_size;
-    mul_nb[1]                     = mul_nb[0] * mul_ne[0];
-    acl_tensor_ptr acl_mul_result = ggml_cann_create_tensor(mul_buffer, ACL_FLOAT, mul_type_size, mul_ne, mul_nb, 2);
-
-    GGML_CANN_CALL_ACLNN_OP(ctx, Mul, acl_log_softmax.get(), acl_labels.get(), acl_mul_result.get());
+    size_t               loss_per_sample_type_size = sizeof(float);
+    int64_t              loss_per_sample_n_bytes   = nr * loss_per_sample_type_size;
+    ggml_cann_pool_alloc loss_per_sample_allocator(ctx.pool(), loss_per_sample_n_bytes);
+    void *               loss_per_sample_buffer = loss_per_sample_allocator.get();
 
-    size_t               sum_per_sample_type_size = sizeof(float);
-    int64_t              sum_per_sample_n_bytes   = nr * sum_per_sample_type_size;
-    ggml_cann_pool_alloc sum_per_sample_allocator(ctx.pool(), sum_per_sample_n_bytes);
-    void *               sum_per_sample_buffer = sum_per_sample_allocator.get();
+    int64_t loss_per_sample_ne[] = { nr };
+    size_t  loss_per_sample_nb[1];
+    loss_per_sample_nb[0] = loss_per_sample_type_size;
+    acl_tensor_ptr acl_loss_per_sample = ggml_cann_create_tensor(
+        loss_per_sample_buffer, ACL_FLOAT, loss_per_sample_type_size, loss_per_sample_ne, loss_per_sample_nb, 1);
 
-    int64_t sum_per_sample_ne[] = { nr };
-    size_t  sum_per_sample_nb[1];
-    sum_per_sample_nb[0]              = sum_per_sample_type_size;
-    acl_tensor_ptr acl_sum_per_sample = ggml_cann_create_tensor(
-        sum_per_sample_buffer, ACL_FLOAT, sum_per_sample_type_size, sum_per_sample_ne, sum_per_sample_nb, 1);
+    size_t               backprop_n_bytes = nr * nc * sizeof(float);
+    ggml_cann_pool_alloc backprop_allocator(ctx.pool(), backprop_n_bytes);
+    void *               backprop_buffer = backprop_allocator.get();
+    acl_tensor_ptr acl_backprop = ggml_cann_create_tensor(backprop_buffer, ACL_FLOAT, sizeof(float), logits_ne, logits_nb, 2);
 
-    std::vector<int64_t> sum_dims   = { 1 };
-    acl_int_array_ptr    dims_array = ggml_cann_create_int_array(sum_dims.data(), sum_dims.size());
-    bool                 keep_dims  = false;
-
-    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_mul_result.get(), dims_array.get(), keep_dims, ACL_FLOAT,
-                            acl_sum_per_sample.get());
+    GGML_CANN_CALL_ACLNN_OP(ctx, SoftmaxCrossEntropyWithLogits, acl_logits.get(), acl_labels.get(),
+                            acl_loss_per_sample.get(), acl_backprop.get());
 
     size_t               total_sum_type_size = sizeof(float);
     int64_t              total_sum_n_bytes   = 1 * total_sum_type_size;
@@ -547,11 +635,12 @@ void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor *
 
     std::vector<int64_t> total_sum_dims    = { 0 };
     acl_int_array_ptr total_sum_dims_array = ggml_cann_create_int_array(total_sum_dims.data(), total_sum_dims.size());
+    bool              keep_dims            = false;
 
-    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_sum_per_sample.get(), total_sum_dims_array.get(), keep_dims, ACL_FLOAT,
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_loss_per_sample.get(), total_sum_dims_array.get(), keep_dims, ACL_FLOAT,
                             acl_total_sum.get());
 
-    float          value        = -1.0f / static_cast<float>(nr);
+    float          value        = 1.0f / static_cast<float>(nr);
     acl_scalar_ptr scale_factor = ggml_cann_create_scalar(&value, aclDataType::ACL_FLOAT);
     acl_tensor_ptr acl_dst =
         ggml_cann_create_tensor(dst->data, ACL_FLOAT, sizeof(float), total_sum_ne, total_sum_nb, 1);
@@ -589,6 +678,33 @@ void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
                             acl_mean_out.get(), acl_rstd_out.get());
 }
 
+void ggml_cann_set(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    size_t nb1     = ((int32_t *) dst->op_params)[0];
+    size_t nb2     = ((int32_t *) dst->op_params)[1];
+    size_t nb3     = ((int32_t *) dst->op_params)[2];
+    size_t offset  = ((int32_t *) dst->op_params)[3];
+    bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
+
+    size_t param_nb[] = { ggml_element_size(src0), nb1, nb2, nb3 };
+
+    // Create a view of dst at the target offset with src1's dimensions
+    acl_tensor_ptr acl_dst  = ggml_cann_create_tensor(dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
+    acl_tensor_ptr acl_src1 = ggml_cann_create_tensor(src1);
+
+    if (!inplace) {
+        // First copy src0 to dst entirely
+        size_t cpy_size = ggml_nbytes(dst);
+        ACL_CHECK(
+            aclrtMemcpyAsync(dst->data, cpy_size, src0->data, cpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+    }
+
+    // Copy src1 into the target region of dst
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst.get(), acl_src1.get());
+}
+
 void ggml_cann_acc(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor * src0 = dst->src[0];
     ggml_tensor * src1 = dst->src[1];
@@ -652,6 +768,113 @@ void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     aclnn_reduce_sum(ctx, dst, reduce_dims, 4);
 }
 
+void ggml_cann_cumsum(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+    // GGML cumsum operates along dim 0 (innermost / ne[0]).
+    // ggml_cann_create_tensor reverses dimensions to [ne3,ne2,ne1,ne0],
+    // so GGML dim 0 maps to CANN dim 3 (the last dim of the 4-D tensor).
+    GGML_CANN_CALL_ACLNN_OP(ctx, Cumsum, acl_src.get(), (int64_t)3,
+                            ggml_cann_type_mapping(dst->type), acl_dst.get());
+}
+
+void ggml_cann_solve_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // A: [N, N, B2, B3] lower triangular
+    ggml_tensor * src1 = dst->src[1];  // B: [K, N, B2, B3]
+
+    acl_tensor_ptr acl_a = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_b = ggml_cann_create_tensor(src1);
+    acl_tensor_ptr acl_x = ggml_cann_create_tensor(dst);
+
+    // mOut: triangular copy of A (required output), same shape as A.
+    const size_t a_bytes = ggml_nbytes(src0);
+    ggml_cann_pool_alloc m_alloc(ctx.pool(), a_bytes);
+    acl_tensor_ptr acl_m = ggml_cann_create_tensor(
+        m_alloc.get(), ggml_cann_type_mapping(src0->type),
+        ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS);
+
+    // Solve AX = B: upper=false (lower tri), transpose=false, unitriangular=false.
+    GGML_CANN_CALL_ACLNN_OP(ctx, TriangularSolve,
+        acl_b.get(), acl_a.get(), false, false, false,
+        acl_x.get(), acl_m.get());
+}
+
+void ggml_cann_diag(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    GGML_ASSERT(src->ne[1] == 1);
+
+    const int64_t N       = src->ne[0];
+    const int64_t n_batch = src->ne[2] * src->ne[3];
+    const size_t  nb_f32  = sizeof(float);
+
+    // Fill dst with zeros.
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+    {
+        float          zero = 0.0f;
+        acl_scalar_ptr acl_zero = ggml_cann_create_scalar(&zero, ACL_FLOAT);
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst.get(), acl_zero.get());
+    }
+
+    // Copy src vector onto the diagonal of dst via strided views.
+    // src viewed as [N, n_batch], contiguous strides.
+    int64_t ne_vec[2]      = { N, n_batch };
+    size_t  nb_src_vec[2]  = { nb_f32, N * nb_f32 };
+    // dst diagonal view: stride (N+1)*4 steps along the diagonal.
+    size_t  nb_dst_diag[2] = { (N + 1) * nb_f32, N * N * nb_f32 };
+
+    acl_tensor_ptr acl_src_vec  = ggml_cann_create_tensor(src->data, ACL_FLOAT, nb_f32, ne_vec, nb_src_vec, 2);
+    acl_tensor_ptr acl_dst_diag = ggml_cann_create_tensor(dst->data, ACL_FLOAT, nb_f32, ne_vec, nb_dst_diag, 2);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst_diag.get(), acl_src_vec.get());
+}
+
+void ggml_cann_fill(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    float c = ggml_get_op_params_f32(dst, 0);
+
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+    acl_scalar_ptr acl_c   = ggml_cann_create_scalar(&c, ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst.get(), acl_c.get());
+}
+
+void ggml_cann_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    const int64_t S       = src->ne[0];
+    const int64_t n_batch = src->ne[2] * src->ne[3];
+    const size_t  nb_f32  = sizeof(float);
+
+    int64_t ne3d[3] = { S, S, n_batch };
+    size_t  nb3d[3] = { nb_f32, S * nb_f32, S * S * nb_f32 };
+
+    const ggml_tri_type ttype = (ggml_tri_type) ggml_get_op_params_i32(dst, 0);
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src->data, ACL_FLOAT, nb_f32, ne3d, nb3d, 3);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst->data, ACL_FLOAT, nb_f32, ne3d, nb3d, 3);
+
+    switch (ttype) {
+        case GGML_TRI_TYPE_LOWER:
+            // Tril(-1): preserve row > col (strict lower), zero upper + diagonal.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_src.get(), (int64_t)-1, acl_dst.get());
+            break;
+        case GGML_TRI_TYPE_UPPER_DIAG:
+            // Triu(0): preserve row <= col (upper + diagonal), zero strict lower.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Triu, acl_src.get(), (int64_t)0, acl_dst.get());
+            break;
+        case GGML_TRI_TYPE_UPPER:
+            // Triu(1): preserve row < col (strict upper), zero lower + diagonal.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Triu, acl_src.get(), (int64_t)1, acl_dst.get());
+            break;
+        case GGML_TRI_TYPE_LOWER_DIAG:
+            // Tril(0): preserve row >= col (lower + diagonal), zero strict upper.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_src.get(), (int64_t)0, acl_dst.get());
+            break;
+        default:
+            GGML_ABORT("unsupported tri type");
+    }
+}
+
 void ggml_cann_upsample_nearest2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor *  src     = dst->src[0];
     acl_tensor_ptr acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
@@ -1695,152 +1918,90 @@ void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     aclnn_softmax(ctx, softmax_tensor.get(), 3, acl_dst.get());
 }
 
-/**
- * @brief Performs index select operation on a 4D tensor using the CANN backend.
- *
- * This function applies the `IndexSelect` operation along a specific dimension
- * of the source tensor (`src_buffer`) using the indices from the index tensor (`index`).
- * It iterates over the last two dimensions of the source tensor, creates the corresponding
- * CANN tensors for the source, index, and output slices, and executes the `IndexSelect`
- * operation for each slice.
- *
- * @param ctx The context for CANN backend operations.
- * @param src_buffer The source buffer containing the 4D input tensor data.
- * @param src_ne The dimensions of the source tensor.
- * @param src_nb The strides (byte offsets) of the source tensor.
- * @param dst_buffer The destination buffer where the output tensor data will be written.
- * @param dst_ne The dimensions of the destination tensor.
- * @param dst_nb The strides (byte offsets) of the destination tensor.
- * @param index The index tensor specifying the indices to select from the source tensor.
- * @param type The data type of the source and destination tensors.
- */
-static void aclnn_index_select_4d(ggml_backend_cann_context & ctx,
-                                  void *                      src_buffer,
-                                  int64_t *                   src_ne,
-                                  size_t *                    src_nb,
-                                  void *                      dst_buffer,
-                                  int64_t *                   dst_ne,
-                                  size_t *                    dst_nb,
-                                  ggml_tensor *               index,
-                                  ggml_type                   type) {
-    for (int64_t i = 0; i < src_ne[3]; i++) {
-        for (int64_t j = 0; j < src_ne[2]; j++) {
-            // src
-            acl_tensor_ptr acl_src_tensor =
-                ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2],
-                                        ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2);
-
-            // index
-            acl_tensor_ptr acl_index = ggml_cann_create_tensor(
-                (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
-                ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1);
-
-            // out
-            acl_tensor_ptr acl_out =
-                ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2],
-                                        ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2);
-            GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, acl_src_tensor.get(), 0, acl_index.get(), acl_out.get());
-        }
-    }
-}
-
-/**
- * @brief Performs inplace index copy operation on a 4D tensor using the CANN backend.
- *
- * This function applies the `IndexCopy` operation along a specific dimension of the
- * destination tensor (`dst_buffer`) by copying elements from the source tensor (`src_buffer`)
- * to positions specified by the index tensor (`index`).
- * It iterates over the last two dimensions of the tensors, creates the corresponding
- * CANN tensors for source, index, and destination slices, and performs the index copy
- * operation for each slice.
- *
- * @param ctx The context for CANN backend operations.
- * @param src_buffer The source buffer containing the 4D input tensor data to be copied.
- * @param src_ne The dimensions of the source tensor.
- * @param src_nb The strides (byte offsets) of the source tensor.
- * @param dst_buffer The destination buffer where values will be copied to.
- * @param dst_ne The dimensions of the destination tensor.
- * @param dst_nb The strides (byte offsets) of the destination tensor.
- * @param index The index tensor specifying target positions in the destination tensor.
- * @param type The data type of the source and destination tensors.
- */
-static void aclnn_index_copy_4d(ggml_backend_cann_context & ctx,
-                                void *                      src_buffer,
-                                int64_t *                   src_ne,
-                                size_t *                    src_nb,
-                                void *                      dst_buffer,
-                                int64_t *                   dst_ne,
-                                size_t *                    dst_nb,
-                                ggml_tensor *               index,
-                                ggml_type                   type) {
-    for (int64_t i = 0; i < src_ne[3]; i++) {
-        for (int64_t j = 0; j < src_ne[2]; j++) {
-            // src
-            acl_tensor_ptr acl_src_tensor =
-                ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2],
-                                        ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2);
-
-            // index
-            acl_tensor_ptr acl_index = ggml_cann_create_tensor(
-                (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
-                ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1);
-
-            // out
-            acl_tensor_ptr acl_out =
-                ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2],
-                                        ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_out.get(), 0, acl_index.get(), acl_src_tensor.get());
-        }
-    }
-}
 
 void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * src0 = dst->src[0];  // src
+    ggml_tensor * src0 = dst->src[0];  // weight
     ggml_tensor * src1 = dst->src[1];  // index
 
     GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16
                 || dst->type == GGML_TYPE_BF16);
 
+    // n_idx: number of row indices per (i2, i3) batch slice.
+    // ggml guarantees: src0->ne[2] == src1->ne[1], src0->ne[3] == src1->ne[2], src1->ne[3] == 1.
+    const int64_t n_idx = src1->ne[0];
+
+    // Gather all (i2, i3) batch slices from src into dst.
+    // ggml_cann_create_tensor reverses dims, so ACL sees [ne1, ne0].
+    // GatherV2 with dim=0 gathers along ACL dim-0 == ggml ne[1] (the vocabulary / row axis).
+    // nb: the 4 strides of the source buffer (nb[0..1] for the 2D slice shape,
+    //     nb[2..3] for computing per-batch-slice base pointer offsets).
+    auto gather_batched = [&](void * src_base, aclDataType acl_type, size_t type_size,
+                              const size_t * nb) {
+        int64_t src_ne[2]  = { src0->ne[0], src0->ne[1] };
+        size_t  src_nb_2d[2] = { nb[0], nb[1] };
+        int64_t dst_ne[2]  = { src0->ne[0], n_idx };
+        size_t  dst_nb_2d[2] = { dst->nb[0], dst->nb[1] };
+        int64_t idx_ne[1]  = { n_idx };
+        size_t  idx_nb[1]  = { (size_t)ggml_element_size(src1) };
+
+        for (int64_t i3 = 0; i3 < src0->ne[3]; i3++) {
+            for (int64_t i2 = 0; i2 < src0->ne[2]; i2++) {
+                acl_tensor_ptr acl_src = ggml_cann_create_tensor(
+                    (char *)src_base + i3 * nb[3] + i2 * nb[2],
+                    acl_type, type_size, src_ne, src_nb_2d, 2);
+                acl_tensor_ptr acl_idx = ggml_cann_create_tensor(
+                    (char *)src1->data + i3 * src1->nb[2] + i2 * src1->nb[1],
+                    ggml_cann_type_mapping(src1->type), (size_t)ggml_element_size(src1),
+                    idx_ne, idx_nb, 1);
+                acl_tensor_ptr acl_dst = ggml_cann_create_tensor(
+                    (char *)dst->data + i3 * dst->nb[3] + i2 * dst->nb[2],
+                    acl_type, type_size, dst_ne, dst_nb_2d, 2);
+                GGML_CANN_CALL_ACLNN_OP(ctx, GatherV2, acl_src.get(), 0, acl_idx.get(), acl_dst.get());
+            }
+        }
+    };
+
     switch (src0->type) {
         case GGML_TYPE_BF16:
         case GGML_TYPE_F16:
         case GGML_TYPE_F32:
             if (src0->type == dst->type) {
-                aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1,
-                                      dst->type);
+                gather_batched(src0->data,
+                               ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type),
+                               src0->nb);
             } else {
-                acl_tensor_ptr       acl_src0 = ggml_cann_create_tensor(src0);
-                ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * ggml_element_size(dst));
-                void *               src_trans_buffer = src_buffer_allocator.get();
-                size_t               src_trans_nb[GGML_MAX_DIMS];
-                src_trans_nb[0] = dst->nb[0];
+                // Cast src0 to dst type, then gather.
+                ggml_cann_pool_alloc src_cast_allocator(ctx.pool(),
+                                                        ggml_nelements(src0) * ggml_element_size(dst));
+                size_t src_cast_nb[GGML_MAX_DIMS];
+                src_cast_nb[0] = ggml_type_size(dst->type);
                 for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+                    src_cast_nb[i] = src_cast_nb[i - 1] * src0->ne[i - 1];
                 }
-                acl_tensor_ptr src_trans_tensor =
-                    ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(dst->type),
-                                            ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
-                aclnn_cast(ctx, acl_src0.get(), src_trans_tensor.get(), ggml_cann_type_mapping(dst->type));
-                aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1,
-                                      dst->type);
+                acl_tensor_ptr acl_src0     = ggml_cann_create_tensor(src0);
+                acl_tensor_ptr acl_src_cast = ggml_cann_create_tensor(
+                    src_cast_allocator.get(), ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                    src0->ne, src_cast_nb, GGML_MAX_DIMS);
+                aclnn_cast(ctx, acl_src0.get(), acl_src_cast.get(), ggml_cann_type_mapping(dst->type));
+
+                gather_batched(src_cast_allocator.get(),
+                               ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                               src_cast_nb);
             }
             break;
         case GGML_TYPE_Q8_0:
             {
-                // add 1 dim for bcast mul.
+                // Dequantize Q8_0 to dst type, then gather.
                 size_t  weight_nb[GGML_MAX_DIMS + 1], scale_nb[GGML_MAX_DIMS + 1], dequant_nb[GGML_MAX_DIMS + 1];
                 int64_t weight_ne[GGML_MAX_DIMS + 1], scale_ne[GGML_MAX_DIMS + 1], *dequant_ne;
-                int64_t scale_offset = 0;
-                // [3,4,5,64] -> [3,4,5,2,32]
-                weight_ne[0]         = QK8_0;
-                weight_ne[1]         = src0->ne[0] / QK8_0;
-                weight_nb[0]         = sizeof(int8_t);
-                weight_nb[1]         = weight_nb[0] * weight_ne[0];
+                weight_ne[0] = QK8_0;
+                weight_ne[1] = src0->ne[0] / QK8_0;
+                weight_nb[0] = sizeof(int8_t);
+                weight_nb[1] = weight_nb[0] * weight_ne[0];
                 for (int i = 2; i < GGML_MAX_DIMS + 1; i++) {
                     weight_ne[i] = src0->ne[i - 1];
                     weight_nb[i] = weight_nb[i - 1] * weight_ne[i - 1];
                 }
-                // [3,4,5,64] -> [3,4,5,2,1]
                 scale_ne[0] = 1;
                 scale_ne[1] = src0->ne[0] / QK8_0;
                 scale_nb[0] = sizeof(uint16_t);
@@ -1849,31 +2010,33 @@ void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
                     scale_ne[i] = src0->ne[i - 1];
                     scale_nb[i] = scale_nb[i - 1] * scale_ne[i - 1];
                 }
-                // [3,4,5,64] -> [3,4,5,2,32]
                 dequant_ne    = weight_ne;
                 dequant_nb[0] = ggml_type_size(dst->type);
                 for (int i = 1; i < GGML_MAX_DIMS + 1; i++) {
                     dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1];
                 }
-                scale_offset = ggml_nelements(src0) * sizeof(int8_t);
-                ggml_cann_pool_alloc dequant_buffer_allocator(ctx.pool(),
-                                                              ggml_nelements(src0) * ggml_type_size(dst->type));
-                acl_tensor_ptr       acl_weight_tensor = ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t),
-                                                                                 weight_ne, weight_nb, GGML_MAX_DIMS + 1);
-                acl_tensor_ptr       acl_scale_tensor =
-                    ggml_cann_create_tensor(src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb,
-                                            GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset);
-                acl_tensor_ptr dequant_tensor =
-                    ggml_cann_create_tensor(dequant_buffer_allocator.get(), ggml_cann_type_mapping(dst->type),
-                                            ggml_type_size(dst->type), dequant_ne, dequant_nb, GGML_MAX_DIMS + 1);
-                aclnn_mul(ctx, acl_weight_tensor.get(), acl_scale_tensor.get(), dequant_tensor.get());
-                dequant_nb[0] = ggml_type_size(dst->type);
+                const int64_t scale_offset = ggml_nelements(src0) * sizeof(int8_t);
+                ggml_cann_pool_alloc dequant_allocator(ctx.pool(),
+                                                       ggml_nelements(src0) * ggml_type_size(dst->type));
+                acl_tensor_ptr acl_weight = ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t),
+                                                                     weight_ne, weight_nb, GGML_MAX_DIMS + 1);
+                acl_tensor_ptr acl_scale  = ggml_cann_create_tensor(
+                    src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb,
+                    GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset);
+                acl_tensor_ptr acl_dequant = ggml_cann_create_tensor(
+                    dequant_allocator.get(), ggml_cann_type_mapping(dst->type),
+                    ggml_type_size(dst->type), dequant_ne, dequant_nb, GGML_MAX_DIMS + 1);
+                aclnn_mul(ctx, acl_weight.get(), acl_scale.get(), acl_dequant.get());
+
+                // Reinterpret dequant buffer as 4D [src0->ne] with contiguous strides.
                 dequant_ne    = src0->ne;
+                dequant_nb[0] = ggml_type_size(dst->type);
                 for (int i = 1; i < GGML_MAX_DIMS; i++) {
                     dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1];
                 }
-                aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(), dequant_ne, dequant_nb, dst->data, dst->ne,
-                                      dst->nb, src1, dst->type);
+                gather_batched(dequant_allocator.get(),
+                               ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                               dequant_nb);
                 break;
             }
         default:
@@ -1883,31 +2046,70 @@ void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
 }
 
 void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * src0 = dst->src[0];  // src
-    ggml_tensor * src1 = dst->src[1];  // index
+    ggml_tensor * src0 = dst->src[0];  // source values
+    ggml_tensor * src1 = dst->src[1];  // row indices
+
+    // n_idx: number of source rows to scatter per batch slice.
+    // ggml guarantees: src0->ne[1] == src1->ne[0].
+    const int64_t n_idx = src1->ne[0];
+
+    // Copy n_idx rows of src [ne0, n_idx] into dst [ne0, ne1] at positions given by a 1D index.
+    // ggml_cann_create_tensor reverses dims, so ACL sees [ne1, ne0] for dst.
+    // InplaceIndexCopy with dim=0 copies along ACL dim-0 == ggml ne[1] (the row axis).
+    // src_nb: the 4 strides of the source buffer (nb[0..1] for the 2D slice shape,
+    //         nb[2..3] for computing per-batch-slice base pointer offsets).
+    auto scatter_batched = [&](void * src_base, aclDataType acl_type, size_t type_size,
+                               const size_t * src_nb) {
+        int64_t d_ne[2]    = { dst->ne[0], dst->ne[1] };
+        size_t  d_nb[2]    = { dst->nb[0], dst->nb[1] };
+        int64_t s_ne[2]    = { dst->ne[0], n_idx };
+        size_t  s_nb_2d[2] = { src_nb[0], src_nb[1] };
+        int64_t i_ne[1]    = { n_idx };
+        size_t  i_nb[1]    = { (size_t)ggml_element_size(src1) };
+
+        for (int64_t i3 = 0; i3 < dst->ne[3]; i3++) {
+            for (int64_t i2 = 0; i2 < dst->ne[2]; i2++) {
+                acl_tensor_ptr acl_dst = ggml_cann_create_tensor(
+                    (char *)dst->data + i3 * dst->nb[3] + i2 * dst->nb[2],
+                    acl_type, type_size, d_ne, d_nb, 2);
+                acl_tensor_ptr acl_idx = ggml_cann_create_tensor(
+                    (char *)src1->data + (i3 % src1->ne[2]) * src1->nb[2] + (i2 % src1->ne[1]) * src1->nb[1],
+                    ggml_cann_type_mapping(src1->type), (size_t)ggml_element_size(src1),
+                    i_ne, i_nb, 1);
+                acl_tensor_ptr acl_src = ggml_cann_create_tensor(
+                    (char *)src_base + i3 * src_nb[3] + i2 * src_nb[2],
+                    acl_type, type_size, s_ne, s_nb_2d, 2);
+                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_dst.get(), 0, acl_idx.get(), acl_src.get());
+            }
+        }
+    };
 
     switch (dst->type) {
         case GGML_TYPE_F32:
-            {
-                aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1, dst->type);
-                break;
-            }
+            scatter_batched(src0->data,
+                            ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                            src0->nb);
+            break;
         case GGML_TYPE_F16:
         case GGML_TYPE_BF16:
             {
-                acl_tensor_ptr       acl_src0 = ggml_cann_create_tensor(src0);
-                ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t));
-                void *               src_trans_buffer = src_buffer_allocator.get();
-                size_t               src_trans_nb[GGML_MAX_DIMS];
-                src_trans_nb[0] = sizeof(uint16_t);
+                // Cast src0 (F32) to dst type first.
+                ggml_cann_pool_alloc src_cast_allocator(ctx.pool(),
+                                                        ggml_nelements(src0) * ggml_type_size(dst->type));
+                size_t src_cast_nb[GGML_MAX_DIMS];
+                src_cast_nb[0] = ggml_type_size(dst->type);
                 for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+                    src_cast_nb[i] = src_cast_nb[i - 1] * src0->ne[i - 1];
                 }
-                acl_tensor_ptr src_trans_tensor = ggml_cann_create_tensor(
-                    src_trans_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
-                aclnn_cast(ctx, acl_src0.get(), src_trans_tensor.get(), ggml_cann_type_mapping(dst->type));
-                aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1,
-                                    dst->type);
+                acl_tensor_ptr acl_src0     = ggml_cann_create_tensor(src0);
+                acl_tensor_ptr acl_src_cast = ggml_cann_create_tensor(
+                    src_cast_allocator.get(), ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                    src0->ne, src_cast_nb, GGML_MAX_DIMS);
+                aclnn_cast(ctx, acl_src0.get(), acl_src_cast.get(), ggml_cann_type_mapping(dst->type));
+
+                scatter_batched(src_cast_allocator.get(),
+                                ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                                src_cast_nb);
                 break;
             }
         default:
@@ -3268,29 +3470,50 @@ void ggml_cann_pad_reflect_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst
     int64_t           paddingsArray[2] = { opts[0], opts[1] };
     acl_int_array_ptr paddings         = ggml_cann_create_int_array(paddingsArray, 2);
 
-    for (int64_t i = 0; i < src0->ne[3]; i++) {
-        acl_tensor_ptr acl_src =
-            ggml_cann_create_tensor((char *) src0->data + i * src0->ne[3], ggml_cann_type_mapping(src0->type),
-                                    ggml_element_size(src0), src0->ne, src0->nb, 3);
+    // Collapsing ne[2]*ne[3] into a single batch dimension requires that dim3
+    // is contiguous with respect to dim2 in both src and dst.
+    GGML_ASSERT(src0->nb[3] == src0->nb[2] * src0->ne[2]);
+    GGML_ASSERT(dst->nb[3]  == dst->nb[2]  * dst->ne[2]);
 
-        acl_tensor_ptr acl_dst =
-            ggml_cann_create_tensor((char *) dst->data + i * src0->ne[3], ggml_cann_type_mapping(dst->type),
-                                    ggml_element_size(dst), dst->ne, dst->nb, 3);
+    int64_t src_ne_3d[3] = { src0->ne[0], src0->ne[1], src0->ne[2] * src0->ne[3] };
+    int64_t dst_ne_3d[3] = { dst->ne[0],  dst->ne[1],  dst->ne[2]  * dst->ne[3]  };
 
-        GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src.get(), paddings.get(), acl_dst.get());
-    }
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0->data, ggml_cann_type_mapping(src0->type),
+                                                     ggml_element_size(src0), src_ne_3d, src0->nb, 3);
+
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst->data, ggml_cann_type_mapping(dst->type),
+                                                     ggml_element_size(dst), dst_ne_3d, dst->nb, 3);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src.get(), paddings.get(), acl_dst.get());
 }
 
 void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor * src0 = dst->src[0];
     ggml_tensor * src1 = dst->src[1];
 
+    // Write element-wise equality (0 or 1) into a temporary buffer to avoid
+    // modifying src0 in-place.  Use the same type as src0 so ReduceSum can
+    // consume it directly without a type cast.
+    ggml_cann_pool_alloc eq_alloc(ctx.pool(), ggml_nelements(src0) * ggml_element_size(src0));
+    size_t eq_nb[GGML_MAX_DIMS];
+    eq_nb[0] = ggml_element_size(src0);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        eq_nb[i] = eq_nb[i - 1] * src0->ne[i - 1];
+    }
+    acl_tensor_ptr acl_eq = ggml_cann_create_tensor(
+        eq_alloc.get(), ggml_cann_type_mapping(src0->type), ggml_element_size(src0),
+        src0->ne, eq_nb, GGML_MAX_DIMS);
+
     acl_tensor_ptr acl_self  = ggml_cann_create_tensor(src0);
     acl_tensor_ptr acl_other = ggml_cann_create_tensor(src1);
-
-    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceEqTensor, acl_self.get(), acl_other.get());
-
-    ggml_cann_sum(ctx, dst);
+    GGML_CANN_CALL_ACLNN_OP(ctx, EqTensor, acl_self.get(), acl_other.get(), acl_eq.get());
+
+    // Sum the 0/1 values into dst.
+    acl_tensor_ptr    acl_dst    = ggml_cann_create_tensor(dst);
+    int64_t           dims[4]    = { 0, 1, 2, 3 };
+    acl_int_array_ptr dims_arr   = ggml_cann_create_int_array(dims, 4);
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_eq.get(), dims_arr.get(), true,
+                            ggml_cann_type_mapping(dst->type), acl_dst.get());
 }
 
 void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
@@ -3306,6 +3529,27 @@ void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     GGML_CANN_CALL_ACLNN_OP(ctx, GtScalar, acl_src.get(), alpha.get(), acl_dst.get());
 }
 
+void ggml_cann_softplus(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    float          beta_val      = 1.0f;
+    float          threshold_val = 20.0f;
+    acl_scalar_ptr beta          = ggml_cann_create_scalar(&beta_val,      ACL_FLOAT);
+    acl_scalar_ptr threshold     = ggml_cann_create_scalar(&threshold_val, ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Softplus, acl_src.get(), beta.get(), threshold.get(), acl_dst.get());
+}
+
+void ggml_cann_geglu_quick(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    auto gelu_quick_fn = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst);
+    };
+    ggml_cann_op_unary_gated(gelu_quick_fn, ctx, dst);
+}
+
 /**
  * @brief Performs expert-specific matrix multiplication (MoE) with
  * floating-point precision using the CANN backend.
@@ -3892,46 +4136,65 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst
 }
 
 static void ggml_cann_out_prod_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * src0 = dst->src[0];  // weight
-    ggml_tensor * src1 = dst->src[1];  // input
+    ggml_tensor * src0 = dst->src[0];  // weight  [ne00=m, ne01=K, ne02, ne03]
+    ggml_tensor * src1 = dst->src[1];  // input   [ne10=n, ne11=K, ne12, ne13]
     GGML_TENSOR_BINARY_OP_LOCALS
 
-    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
-    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, acl_dst.get());
+    // dst[i,j] = sum_k src0[i,k] * src1[j,k]  i.e. dst = src0 @ src1^T.
+    //
+    // ggml_cann_create_tensor reverses dimension order, so ACL sees:
+    //   acl_src0 slice:   ggml[m,K]  ->  ACL[K,m]
+    //   acl_src1 slice:   ggml[n,K]  ->  ACL[K,n]
+    //   acl_dst  slice:   ggml[m,n]  ->  ACL[n,m]
+    //
+    // Build a transposed view of src1 by swapping ne[0]/ne[1]:
+    //   src1_t:  ggml[K,n] (swapped strides)  ->  ACL[n,K]
+    //
+    // Matmul(src1_t [n,K], src0 [K,m]) = [n,m] = acl_dst  ✓
+    //
+    // The outer batch loop is kept because src0 may have fewer batch slices than
+    // dst (ne02 <= ne2, ne03 <= ne3): this is a strided-broadcast not supported
+    // by standard CANN Matmul broadcasting.
+
+    const aclDataType src0_acl_type = ggml_cann_type_mapping(src0->type);
+    const aclDataType src1_acl_type = ggml_cann_type_mapping(src1->type);
+    const aclDataType dst_acl_type  = ggml_cann_type_mapping(dst->type);
+    const size_t      src0_type_sz  = ggml_type_size(src0->type);
+    const size_t      src1_type_sz  = ggml_type_size(src1->type);
+    const size_t      dst_type_sz   = ggml_type_size(dst->type);
 
     const int64_t dps2 = ne2 / ne02;
     const int64_t dps3 = ne3 / ne03;
+
     for (int64_t i3 = 0; i3 < ne3; i3++) {
         for (int64_t i2 = 0; i2 < ne2; i2++) {
             const int64_t i02 = i2 / dps2;
             const int64_t i03 = i3 / dps3;
 
-            const int64_t  i12 = i2;
-            const int64_t  i13 = i3;
-            acl_tensor_ptr accumulator =
-                ggml_cann_create_tensor((char *) dst->data + i2 * nb2 + i3 * nb3, ggml_cann_type_mapping(dst->type),
-                                        ggml_type_size(dst->type), dst->ne, dst->nb, 2);
-
-            // The outer product needs to be accumulated in this dimension.
-            for (int64_t i1 = 0; i1 < ne11; i1++) {
-                acl_tensor_ptr acl_input = ggml_cann_create_tensor(
-                    (char *) src1->data + i1 * nb11 + i12 * nb12 + i13 * nb13, ggml_cann_type_mapping(src0->type),
-                    ggml_type_size(src0->type), src1->ne, src1->nb, 1);
-
-                acl_tensor_ptr acl_weight = ggml_cann_create_tensor(
-                    (char *) src0->data + i1 * nb01 + i02 * nb02 + i03 * nb03, ggml_cann_type_mapping(src0->type),
-                    ggml_type_size(src0->type), src0->ne, src0->nb, 1);
-
-                ggml_cann_pool_alloc output_allocator(ctx.pool());
-                void *               output_buffer = output_allocator.alloc(ggml_nbytes(dst));
-                acl_tensor_ptr       acl_out = ggml_cann_create_tensor(output_buffer, ggml_cann_type_mapping(dst->type),
-                                                                       ggml_type_size(dst->type), dst->ne, dst->nb, 2);
-
-                GGML_CANN_CALL_ACLNN_OP(ctx, Ger, acl_input.get(), acl_weight.get(), acl_out.get());
-                float       alpha_value = 1.0f;
-                aclScalar * alpha       = aclCreateScalar(&alpha_value, ACL_FLOAT);
-                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, accumulator.get(), acl_out.get(), alpha);
-            }
+            // src0 2D slice at [i02, i03]: ggml [m, K] -> ACL [K, m]
+            int64_t src0_ne[2] = { ne00, ne01 };
+            size_t  src0_nb[2] = { nb00, nb01 };
+            acl_tensor_ptr acl_src0_s = ggml_cann_create_tensor(
+                (char *) src0->data + i02 * nb02 + i03 * nb03,
+                src0_acl_type, src0_type_sz, src0_ne, src0_nb, 2);
+
+            // src1 transposed 2D slice at [i2, i3]: swap ne/nb -> ggml[K,n] -> ACL[n,K]
+            int64_t src1_t_ne[2] = { ne11, ne10 };
+            size_t  src1_t_nb[2] = { nb11, nb10 };
+            acl_tensor_ptr acl_src1_t = ggml_cann_create_tensor(
+                (char *) src1->data + i2 * nb12 + i3 * nb13,
+                src1_acl_type, src1_type_sz, src1_t_ne, src1_t_nb, 2);
+
+            // dst 2D slice at [i2, i3]: ggml [m, n] -> ACL [n, m]
+            int64_t dst_ne[2] = { ne0, ne1 };
+            size_t  dst_nb[2] = { nb0, nb1 };
+            acl_tensor_ptr acl_dst_s = ggml_cann_create_tensor(
+                (char *) dst->data + i2 * nb2 + i3 * nb3,
+                dst_acl_type, dst_type_sz, dst_ne, dst_nb, 2);
+
+            // Matmul(src1_t [n,K], src0 [K,m]) = [n,m] = acl_dst_s  ✓
+            GGML_CANN_CALL_ACLNN_OP(ctx, Matmul,
+                acl_src1_t.get(), acl_src0_s.get(), acl_dst_s.get(), (int8_t) 1);
         }
     }
 }
@@ -4170,3 +4433,4 @@ void ggml_cann_gated_linear_attn(ggml_backend_cann_context & ctx, ggml_tensor *
         }
     }
 }
+
diff --git a/ggml/src/ggml-cann/aclnn_ops.h b/ggml/src/ggml-cann/aclnn_ops.h
index 7f5ba4d3302..cdbf9260f85 100644
--- a/ggml/src/ggml-cann/aclnn_ops.h
+++ b/ggml/src/ggml-cann/aclnn_ops.h
@@ -32,6 +32,9 @@
 #include <aclnnop/aclnn_cat.h>
 #include <aclnnop/aclnn_clamp.h>
 #include <aclnnop/aclnn_cos.h>
+#include <aclnnop/aclnn_cumsum.h>
+#include <aclnnop/aclnn_tril.h>
+#include <aclnnop/aclnn_triu.h>
 #include <aclnnop/aclnn_exp.h>
 #include <aclnnop/aclnn_gelu.h>
 #include <aclnnop/aclnn_gelu_v2.h>
@@ -47,6 +50,9 @@
 #include <aclnnop/aclnn_sign.h>
 #include <aclnnop/aclnn_silu.h>
 #include <aclnnop/aclnn_sin.h>
+#include <aclnnop/aclnn_softplus.h>
+#include <aclnnop/aclnn_swi_glu.h>
+#include <aclnnop/aclnn_geglu.h>
 #include <aclnnop/aclnn_slice.h>
 #include <aclnnop/aclnn_sqrt.h>
 #include <aclnnop/aclnn_tanh.h>
@@ -69,6 +75,9 @@
  */
 void ggml_cann_repeat(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
+void ggml_cann_swiglu(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+void ggml_cann_geglu(ggml_backend_cann_context & ctx, ggml_tensor * dst, int64_t approximate);
+
 /**
  * @brief   Applies the Leaky ReLU activation function to a tensor using the CANN
  *          backend.
@@ -325,6 +334,48 @@ void ggml_cann_sum_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
 void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
+/**
+ * @brief   Computes the cumulative sum of a ggml tensor along dim 0 using the
+ *          CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_CUMSUM`.
+ */
+void ggml_cann_cumsum(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Computes a triangular mask (tril/triu) of a square ggml tensor
+ *          using the CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_TRI`.
+ */
+void ggml_cann_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Solves a triangular linear system AX=B using the CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_SOLVE_TRI`.
+ */
+void ggml_cann_solve_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Creates a diagonal matrix from a vector using the CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_DIAG`.
+ */
+void ggml_cann_diag(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Fills a tensor with a constant scalar value using the CANN backend.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor. dst->op is `GGML_OP_FILL`.
+ */
+void ggml_cann_fill(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
 /**
  * @brief   Upsamples a ggml tensor using nearest neighbor interpolation using
  *          the CANN backend.
@@ -461,6 +512,9 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context & ctx, ggml_tensor *
 // @see ggml_cann_dup.
 void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
+// @see ggml_cann_acc, but copies src1 into dst instead of adding.
+void ggml_cann_set(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
 /**
  * @brief   Computes the softmax activation with optional masking.
  *
@@ -813,6 +867,8 @@ void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst);
  *            dst->op is expected to be `GGML_OP_STEP`.
  */
 void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+void ggml_cann_softplus(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+void ggml_cann_geglu_quick(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
 /**
  * @brief   Performs the Flash Attention extended operator using the CANN backend.
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 5fc484b342b..5f51ea3bb3c 100644
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -1428,6 +1428,22 @@ static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
     return false;
 }
 
+/**
+ * @brief Set a region of a tensor's device memory to a specified value.
+ *
+ * @param buffer The CANN buffer containing the tensor.
+ * @param tensor Pointer to the tensor whose memory will be set.
+ * @param value The value to which each byte in the region will be set.
+ * @param offset Byte offset within the tensor's data to start setting.
+ * @param size Number of bytes to set.
+ */
+static void ggml_backend_cann_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context;
+
+    ggml_cann_set_device(ctx->device);
+    ACL_CHECK(aclrtMemset((char *) tensor->data + offset, size, value, size));
+}
+
 /**
  * @brief Clear a CANN buffer by setting all its memory to a specified value.
  *
@@ -1454,7 +1470,7 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
     /* .free_buffer     = */ ggml_backend_cann_buffer_free_buffer,
     /* .get_base        = */ ggml_backend_cann_buffer_get_base,
     /* .init_tensor     = */ ggml_backend_cann_buffer_init_tensor,
-    /* .memset_tensor   = */ NULL,
+    /* .memset_tensor   = */ ggml_backend_cann_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_cann_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cann_buffer_get_tensor,
     /* .set_tensor_2d   = */ NULL,
@@ -1835,6 +1851,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
                 case GGML_UNARY_OP_STEP:
                     ggml_cann_step(ctx, dst);
                     break;
+                case GGML_UNARY_OP_SOFTPLUS:
+                    ggml_cann_softplus(ctx, dst);
+                    break;
                 default:
                     return false;
             }
@@ -1845,20 +1864,16 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
                     GGML_CANN_CALL_OP_UNARY_GATED(Relu);
                     break;
                 case GGML_GLU_OP_GEGLU:
+                    ggml_cann_geglu(ctx, dst, 0);  // approximate=0 → tanh
+                    break;
                 case GGML_GLU_OP_GEGLU_ERF:
-                    // aclnnGelu internally uses the erf-based approximation.
-                    GGML_CANN_CALL_OP_UNARY_GATED(Gelu);
+                    ggml_cann_geglu(ctx, dst, 1);  // approximate=1 → erf
                     break;
                 case GGML_GLU_OP_SWIGLU:
-                    GGML_CANN_CALL_OP_UNARY_GATED(Silu);
+                    ggml_cann_swiglu(ctx, dst);
                     break;
                 case GGML_GLU_OP_GEGLU_QUICK:
-                    {
-                        auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
-                            GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst);
-                        };
-                        ggml_cann_op_unary_gated(lambda, ctx, dst);
-                    }
+                    ggml_cann_geglu_quick(ctx, dst);
                     break;
                 default:
                     return false;
@@ -1920,6 +1935,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
         case GGML_OP_CPY:
             ggml_cann_cpy(ctx, dst);
             break;
+        case GGML_OP_SET:
+            ggml_cann_set(ctx, dst);
+            break;
         case GGML_OP_CONT:
             ggml_cann_dup(ctx, dst);
             break;
@@ -1989,6 +2007,21 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
         case GGML_OP_SSM_CONV:
             ggml_cann_ssm_conv(ctx, dst);
             break;
+        case GGML_OP_CUMSUM:
+            ggml_cann_cumsum(ctx, dst);
+            break;
+        case GGML_OP_TRI:
+            ggml_cann_tri(ctx, dst);
+            break;
+        case GGML_OP_FILL:
+            ggml_cann_fill(ctx, dst);
+            break;
+        case GGML_OP_DIAG:
+            ggml_cann_diag(ctx, dst);
+            break;
+        case GGML_OP_SOLVE_TRI:
+            ggml_cann_solve_tri(ctx, dst);
+            break;
         default:
             return false;
     }
@@ -2324,6 +2357,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend,
     if (use_cann_graph) {
         // If no matching graph is found, the graph needs to be recaptured.
         graph_capture_required = !cann_ctx->graph_lru_cache.find_and_move_to_front(cgraph);
+
         if (graph_capture_required) {
             // If no matching graph is found, add a new ACL graph.
             ggml_cann_graph * new_graph = ggml_cann_graph::create_from_cgraph(cgraph);
@@ -2382,6 +2416,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
                 case GGML_UNARY_OP_SGN:
                 case GGML_UNARY_OP_STEP:
                 case GGML_UNARY_OP_GELU_ERF:
+                case GGML_UNARY_OP_SOFTPLUS:
                     return true;
                 default:
                     return false;
@@ -2572,6 +2607,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
+        case GGML_OP_SET:
         case GGML_OP_GROUP_NORM:
             return true;
         case GGML_OP_PAD:
@@ -2649,6 +2685,16 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
             }
         case GGML_OP_SSM_CONV:
             return true;
+        case GGML_OP_CUMSUM:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_TRI:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_FILL:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_DIAG:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_SOLVE_TRI:
+            return op->src[0]->type == GGML_TYPE_F32;
         default:
             return false;
     }
@@ -2700,8 +2746,8 @@ static const ggml_backend_i ggml_backend_cann_interface = {
     /* .free                    = */ ggml_backend_cann_free,
     /* .set_tensor_async        = */ ggml_backend_cann_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_cann_get_tensor_async,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ ggml_backend_cann_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_cann_synchronize,
     /* .graph_plan_create       = */ NULL,
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index beebc4760d2..f3eccff7d72 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -450,12 +450,22 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             ggml-cpu/arch/riscv/repack.cpp
             )
         if (GGML_CPU_RISCV64_SPACEMIT)
+            include(ggml-cpu/cmake/FindSMTIME.cmake)
             target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_CPU_RISCV64_SPACEMIT ${RISCV64_SPACEMIT_IME_SPEC})
             list(APPEND GGML_CPU_SOURCES
                 ggml-cpu/spacemit/ime.cpp
                 ggml-cpu/spacemit/ime.h
+                ggml-cpu/spacemit/spine_mem_pool.cpp
+                ggml-cpu/spacemit/spine_mem_pool.h
+                ggml-cpu/spacemit/repack.cpp
+                ggml-cpu/spacemit/repack.h
+                ggml-cpu/spacemit/ime_env.cpp
+                ggml-cpu/spacemit/ime_env.h
                 ggml-cpu/spacemit/ime1_kernels.cpp
+                ggml-cpu/spacemit/ime2_kernels.cpp
                 ggml-cpu/spacemit/ime_kernels.h
+                ggml-cpu/spacemit/rvv_kernels.cpp
+                ggml-cpu/spacemit/rvv_kernels.h
             )
         endif()
         if(NOT GGML_CPU_ALL_VARIANTS)
@@ -485,6 +495,16 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             if (GGML_RV_ZIHINTPAUSE)
                 string(APPEND MARCH_STR "_zihintpause")
             endif()
+            if (GGML_RV_ZBA)
+                string(APPEND MARCH_STR "_zba")
+            endif()
+            if (GGML_CPU_RISCV64_SPACEMIT)
+                # `xsmtvdotii' is only required for GCC >= 15.
+                if (CMAKE_C_COMPILER_ID STREQUAL "GNU" AND
+                    CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 15)
+                    string(APPEND MARCH_STR "_xsmtvdotii")
+                endif()
+            endif()
 
             list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
         else()
@@ -571,13 +591,13 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # Fetch KleidiAI sources:
         include(FetchContent)
-        set(KLEIDIAI_COMMIT_TAG "v1.22.0")
-        set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
-        set(KLEIDIAI_ARCHIVE_MD5  "54049037570ab0ee0a0d126b2ba5ece1")
+        set(KLEIDIAI_COMMIT_TAG "v1.24.0")
+        set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/releases/download/${KLEIDIAI_COMMIT_TAG}/kleidiai-${KLEIDIAI_COMMIT_TAG}-src.tar.gz")
+        set(KLEIDIAI_RELEASE_ARCHIVE_MD5  "2f02ebe29573d45813e671eb304f2a00")
 
         set(KLEIDIAI_FETCH_ARGS
             URL ${KLEIDIAI_DOWNLOAD_URL}
-            URL_HASH MD5=${KLEIDIAI_ARCHIVE_MD5}
+            URL_HASH MD5=${KLEIDIAI_RELEASE_ARCHIVE_MD5}
         )
         if (CMAKE_VERSION VERSION_GREATER_EQUAL "3.24")
             list(APPEND KLEIDIAI_FETCH_ARGS DOWNLOAD_EXTRACT_TIMESTAMP NEW)
diff --git a/ggml/src/ggml-cpu/amx/mmq.cpp b/ggml/src/ggml-cpu/amx/mmq.cpp
index 93a6d397f79..d9383a04be8 100644
--- a/ggml/src/ggml-cpu/amx/mmq.cpp
+++ b/ggml/src/ggml-cpu/amx/mmq.cpp
@@ -2005,12 +2005,12 @@ void tinygemm_kernel_amx(int M, int N, int KB, const void * RESTRICT _A, const v
     const int lda = KB * sizeof(TA);
     //const int ldb = KB * sizeof(TB);
 
-    static thread_local packed_B_t Tile0[TILE_N * TILE_K];
-    static thread_local packed_B_t Tile1[TILE_N * TILE_K];
-    static thread_local int8_t Tile23[TILE_M * TILE_K];
+    alignas(64) static thread_local packed_B_t Tile0[TILE_N * TILE_K];
+    alignas(64) static thread_local packed_B_t Tile1[TILE_N * TILE_K];
+    alignas(64) static thread_local int8_t Tile23[TILE_M * TILE_K];
 
-    static thread_local int32_t TileC0[TILE_M * TILE_N * 4];
-    static thread_local int32_t TileC1[TILE_M * TILE_N * 4];
+    alignas(64) static thread_local int32_t TileC0[TILE_M * TILE_N * 4];
+    alignas(64) static thread_local int32_t TileC1[TILE_M * TILE_N * 4];
 
     // double buffering C to interleave avx512 and amx
     int32_t * C_cur = TileC0;
@@ -2187,21 +2187,21 @@ void tinygemm_kernel_amx(int M, int N, int KB, const void * RESTRICT _A, const v
     const int m1 = std::max(M - TILE_M, 0);
     //const int lda = KB * sizeof(TA);
 
-    static thread_local int8_t Tile0[TILE_N * TILE_K];
-    static thread_local int8_t Tile1[TILE_N * TILE_K];
-    static thread_local int8_t Tile23[TILE_M * TILE_K];
+    alignas(64) static thread_local int8_t Tile0[TILE_N * TILE_K];
+    alignas(64) static thread_local int8_t Tile1[TILE_N * TILE_K];
+    alignas(64) static thread_local int8_t Tile23[TILE_M * TILE_K];
 
     // mat mul result for each group
-    static thread_local int32_t Tile4[TILE_M * TILE_N];
-    static thread_local int32_t Tile5[TILE_M * TILE_N];
-    static thread_local int32_t Tile6[TILE_M * TILE_N];
-    static thread_local int32_t Tile7[TILE_M * TILE_N];
+    alignas(64) static thread_local int32_t Tile4[TILE_M * TILE_N];
+    alignas(64) static thread_local int32_t Tile5[TILE_M * TILE_N];
+    alignas(64) static thread_local int32_t Tile6[TILE_M * TILE_N];
+    alignas(64) static thread_local int32_t Tile7[TILE_M * TILE_N];
 
     // sum of each QK_K block, contains 8 groups, int32
-    static thread_local int32_t Sumi4[TILE_M * TILE_N];
-    static thread_local int32_t Sumi5[TILE_M * TILE_N];
-    static thread_local int32_t Sumi6[TILE_M * TILE_N];
-    static thread_local int32_t Sumi7[TILE_M * TILE_N];
+    alignas(64) static thread_local int32_t Sumi4[TILE_M * TILE_N];
+    alignas(64) static thread_local int32_t Sumi5[TILE_M * TILE_N];
+    alignas(64) static thread_local int32_t Sumi6[TILE_M * TILE_N];
+    alignas(64) static thread_local int32_t Sumi7[TILE_M * TILE_N];
 
     const int k_group_size = std::is_same<TB, block_q6_K>::value ? 16 : 32;
     for (int i = 0; i < KB; ++i) {
diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h
index 595ded09f03..b0391a67c88 100644
--- a/ggml/src/ggml-cpu/arch-fallback.h
+++ b/ggml/src/ggml-cpu/arch-fallback.h
@@ -203,7 +203,6 @@
 #elif defined(__riscv)
 // quants.c
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
-#define ggml_vec_dot_q1_0_q8_0_generic ggml_vec_dot_q1_0_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x1_generic ggml_quantize_mat_q8_0_4x1
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp
index 80ff5ce549b..a7534443091 100644
--- a/ggml/src/ggml-cpu/arch/arm/repack.cpp
+++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp
@@ -5023,6 +5023,71 @@ void ggml_gemm_q8_0_4x8_q8_0(int                        n,
     UNUSED(ncols_interleaved);
     UNUSED(blocklen);
 
+#if defined(__aarch64__) && defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
+    if (svcntb() * 8 == 256) {
+        const block_q8_0x4 * b_ptr_base = (const block_q8_0x4 *) vx;
+
+        static const uint32_t idx_arr[8] = {0, 1, 4, 5, 2, 3, 6, 7};
+        svuint32_t idx = svld1(svptrue_b32(), idx_arr);
+        static const uint32_t idx_arr1[8] = {0, 1, 2, 3, 1, 2, 3, 0};
+        svuint32_t idx_sc1 = svld1(svptrue_b32(), idx_arr1);
+        static const uint32_t idx_arr2[8] = {0, 1, 2, 3, 0, 1, 2, 3};
+        svuint32_t idx_sc2 = svld1(svptrue_b32(), idx_arr2);
+
+        for (int y = 0; y < nr; y += 4) {
+            const block_q8_0x4 * a_ptr_base = (const block_q8_0x4 *) vy + (y / 4) * nb;
+
+            for (int x = 0; x < nc; x += ncols_interleaved) {
+                const block_q8_0x4 * b_ptr = b_ptr_base + (x / 4) * nb;
+                const block_q8_0x4 * a_ptr = a_ptr_base;
+
+                svfloat32_t acc_f32_01 = svdup_f32(0);
+                svfloat32_t acc_f32_23 = svdup_f32(0);
+
+                for (int b = 0; b < nb; b++) {
+
+                    svint32_t acc_01 = svdup_s32(0);
+                    svint32_t acc_23 = svdup_s32(0);
+
+                    // Process 4 chunks of 8 positions each
+                    for (int chunk = 0; chunk < 4; chunk++) {
+                        svint8_t s_a01 = svld1rq_s8(svptrue_b8(), a_ptr->qs + chunk * 32);
+                        svint8_t s_a23 = svld1rq_s8(svptrue_b8(), a_ptr->qs + chunk * 32 + 16);
+                        svint8_t s_b0123 = svld1_s8(svptrue_b8(), b_ptr->qs + chunk * 32);
+
+                        acc_01 = svmmla_s32(acc_01, s_a01, s_b0123);
+                        acc_23 = svmmla_s32(acc_23, s_a23, s_b0123);
+                    }
+
+                    // Reorder outputs from 2×2 tiles to row-major
+                    // acc[01] = [r0c0, r0c1, r1c0, r1c1, r0c2, r0c3, r1c2, r1c3]
+                    // acc[23] = [r2c0, r2c1, r3c0, r3c1, r2c2, r2c3, r3c2, r3c3]
+
+                    svint32_t row01 = svtbl_s32(acc_01, idx);
+                    svint32_t row23 = svtbl_s32(acc_23, idx);
+
+                    svfloat16_t temp1 = svld1_f16(svptrue_pat_b16(SV_VL4), (const __fp16 *) a_ptr->d);
+                    svfloat16_t temp2 = svld1_f16(svptrue_pat_b16(SV_VL4), (const __fp16 *) b_ptr->d);
+                    svfloat32_t sv_a_d = svtbl_f32(svcvt_f32_f16_x(svptrue_b32(), svzip1_f16(temp1, temp1)), idx_sc1);
+                    svfloat32_t sv_b_d = svtbl_f32(svcvt_f32_f16_x(svptrue_b32(), svzip1_f16(temp2, temp2)), idx_sc2);
+
+                    acc_f32_01 = svmla_f32_x(svptrue_b32(), acc_f32_01, svcvt_f32_s32_x(svptrue_b32(), row01), svmul_lane_f32(sv_b_d, sv_a_d, 0));
+                    acc_f32_23 = svmla_f32_x(svptrue_b32(), acc_f32_23, svcvt_f32_s32_x(svptrue_b32(), row23), svmul_lane_f32(sv_b_d, sv_a_d, 2));
+                    a_ptr++;
+                    b_ptr++;
+                }
+
+                svbool_t pg4 = svptrue_pat_b32(SV_VL4);
+                svst1_f32(pg4, s + (y+0) * bs + x, acc_f32_01);
+                svst1_f32(pg4, s + (y+1) * bs + x, svext_f32(acc_f32_01, acc_f32_01, 4));
+                svst1_f32(pg4, s + (y+2) * bs + x, acc_f32_23);
+                svst1_f32(pg4, s + (y+3) * bs + x, svext_f32(acc_f32_23, acc_f32_23, 4));
+            }
+        }
+        return;
+    }
+#endif  // SVE compile-time end
+
 #if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
     const block_q8_0x4 * b_ptr_base = (const block_q8_0x4 *) vx;
 
diff --git a/ggml/src/ggml-cpu/arch/riscv/quants.c b/ggml/src/ggml-cpu/arch/riscv/quants.c
index d3278d6489f..ee69e5ab5e5 100644
--- a/ggml/src/ggml-cpu/arch/riscv/quants.c
+++ b/ggml/src/ggml-cpu/arch/riscv/quants.c
@@ -480,6 +480,104 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 #endif
 }
 
+#if defined(__riscv_v)
+static NOINLINE void ggml_vec_dot_q1_0_q8_0_vl256(const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy) {
+    const int qk = QK1_0;
+    const int nb = n / qk;
+    assert(n % qk == 0);
+
+    const block_q1_0 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    //LMUL = 1, VLMAX = 32
+    const size_t vl32 = __riscv_vsetvl_e8m1(32);
+    assert(vl32 == 32);
+
+    const vint16m1_t zero = __riscv_vmv_v_x_i16m1(0, 1);
+
+    float sumf = 0;
+
+    for (int ib = 0; ib < nb; ++ib) {
+        const float d0 = GGML_CPU_FP16_TO_FP32(x[ib].d);
+
+        float acc = 0;
+
+        for (int k = 0; k < 4; ++k) {
+            const block_q8_0 * GGML_RESTRICT yb = &y[ib * 4 + k];
+            const vbool8_t is_not_zero = __riscv_vlm_v_b8(x[ib].qs + 4 * k, vl32);
+
+            const vint8m1_t qy = __riscv_vle8_v_i8m1(yb->qs, vl32);
+            const vint8m1_t neg_qy = __riscv_vneg_v_i8m1(qy, vl32);
+            const vint8m1_t sy = __riscv_vmerge_vvm_i8m1(neg_qy, qy, is_not_zero, vl32);
+
+            const vint16m1_t red = __riscv_vwredsum_vs_i8m1_i16m1(sy, zero, vl32);
+            acc += GGML_CPU_FP16_TO_FP32(yb->d) * (float)__riscv_vmv_x_s_i16m1_i16(red);
+        }
+
+        sumf += d0 * acc;
+    }
+
+    *s = sumf;
+}
+
+static NOINLINE void ggml_vec_dot_q1_0_q8_0_vl128(const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy) {
+    const int qk = QK1_0;
+    const int nb = n / qk;
+    assert(n % qk == 0);
+
+    const block_q1_0 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    //LMUL = 2, VLMAX = 32
+    const size_t vl32 = __riscv_vsetvl_e8m2(32);
+    assert(vl32 == 32);
+
+    const vint16m1_t zero = __riscv_vmv_v_x_i16m1(0, 1);
+
+    float sumf = 0;
+
+    for (int ib = 0; ib < nb; ++ib) {
+        const float d0 = GGML_CPU_FP16_TO_FP32(x[ib].d);
+
+        float acc = 0;
+
+        for (int k = 0; k < 4; ++k) {
+            const block_q8_0 * GGML_RESTRICT yb = &y[ib * 4 + k];
+            const vbool4_t is_not_zero = __riscv_vlm_v_b4(x[ib].qs + 4 * k, vl32);
+
+            const vint8m2_t qy = __riscv_vle8_v_i8m2(yb->qs, vl32);
+            const vint8m2_t neg_qy =__riscv_vneg_v_i8m2(qy, vl32);
+            const vint8m2_t sy = __riscv_vmerge_vvm_i8m2(neg_qy, qy, is_not_zero, vl32);
+
+            const vint16m1_t red = __riscv_vwredsum_vs_i8m2_i16m1(sy, zero, vl32);
+            acc += GGML_CPU_FP16_TO_FP32(yb->d) * (float)__riscv_vmv_x_s_i16m1_i16(red);
+        }
+
+        sumf += d0 * acc;
+    }
+
+    *s = sumf;
+}
+#endif
+
+void ggml_vec_dot_q1_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+#if defined(__riscv_v)
+    assert(nrc == 1);
+
+    const size_t vlen_bits = __riscv_vlenb() * 8;
+
+    if (vlen_bits >= 256) {
+        ggml_vec_dot_q1_0_q8_0_vl256(n, s, vx, vy);
+    } else if (vlen_bits >= 128) {
+        ggml_vec_dot_q1_0_q8_0_vl128(n, s, vx, vy);
+    } else {
+        ggml_vec_dot_q1_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+    }
+#else
+    ggml_vec_dot_q1_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
+#endif
+}
+
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     assert(nrc == 1);
     UNUSED(nrc);
diff --git a/ggml/src/ggml-cpu/arch/x86/quants.c b/ggml/src/ggml-cpu/arch/x86/quants.c
index 0a3e071e57c..94b19b82bbc 100644
--- a/ggml/src/ggml-cpu/arch/x86/quants.c
+++ b/ggml/src/ggml-cpu/arch/x86/quants.c
@@ -2300,9 +2300,8 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #if defined __AVX2__
 
-    const __m256i m4 = _mm256_set1_epi8(0xF);
-    const __m256i m2 = _mm256_set1_epi8(3);
-    const __m256i m32s = _mm256_set1_epi8(32);
+    const __m256i m3 = _mm256_set1_epi8(3);
+    const __m256i m15 = _mm256_set1_epi8(15);
 
     __m256 acc = _mm256_setzero_ps();
 
@@ -2314,53 +2313,45 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
         const uint8_t * GGML_RESTRICT qh = x[i].qh;
         const int8_t  * GGML_RESTRICT q8 = y[i].qs;
 
+        const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums);
         const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales);
+        const __m256i scales_16 = _mm256_cvtepi8_epi16(scales);
+        const __m256i q8sclsub = _mm256_slli_epi32(_mm256_madd_epi16(q8sums, scales_16), 5);
 
         __m256i sumi = _mm256_setzero_si256();
 
         int is = 0;
 
         for (int j = 0; j < QK_K/128; ++j) {
-
-            const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0));
-            const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1));
-            const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2));
-            const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3));
-            is += 4;
-
             const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32;
             const __m256i q4bits2 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32;
             const __m256i q4bitsH = _mm256_loadu_si256((const __m256i*)qh); qh += 32;
 
-            const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(q4bitsH, m2), 4);
-            const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 2), m2), 4);
-            const __m256i q4h_2 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 4), m2), 4);
-            const __m256i q4h_3 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 6), m2), 4);
+            const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(q4bitsH, m3), 4);
+            const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(q4bitsH, _mm256_set1_epi8(12)), 2);
+            const __m256i q4h_2 = _mm256_and_si256(q4bitsH, _mm256_set1_epi8(48));
+            const __m256i q4h_3 = _mm256_srli_epi16(_mm256_and_si256(q4bitsH, _mm256_set1_epi8(-64)), 2);
 
-            const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
-            const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(q4bits2, m4), q4h_1);
-            const __m256i q4_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_2);
-            const __m256i q4_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits2, 4), m4), q4h_3);
+            const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m15), q4h_0);
+            const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(q4bits2, m15), q4h_1);
+            const __m256i q4_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m15), q4h_2);
+            const __m256i q4_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits2, 4), m15), q4h_3);
 
             const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
             const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
             const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
             const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
 
-            __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0);
-            __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1);
-            __m256i q8s_2 = _mm256_maddubs_epi16(m32s, q8_2);
-            __m256i q8s_3 = _mm256_maddubs_epi16(m32s, q8_3);
-
             __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0);
             __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1);
             __m256i p16_2 = _mm256_maddubs_epi16(q4_2, q8_2);
             __m256i p16_3 = _mm256_maddubs_epi16(q4_3, q8_3);
 
-            p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
-            p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
-            p16_2 = _mm256_sub_epi16(p16_2, q8s_2);
-            p16_3 = _mm256_sub_epi16(p16_3, q8s_3);
+            const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0));
+            const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1));
+            const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2));
+            const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3));
+            is += 4;
 
             p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0);
             p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1);
@@ -2372,6 +2363,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
         }
 
+        sumi = _mm256_sub_epi32(sumi, q8sclsub);
         acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
     }
 
diff --git a/ggml/src/ggml-cpu/cmake/FindSMTIME.cmake b/ggml/src/ggml-cpu/cmake/FindSMTIME.cmake
new file mode 100644
index 00000000000..c8a4d4b4ec9
--- /dev/null
+++ b/ggml/src/ggml-cpu/cmake/FindSMTIME.cmake
@@ -0,0 +1,32 @@
+include(CheckCSourceRuns)
+
+if (CMAKE_SYSTEM_PROCESSOR MATCHES "^(riscv)" AND GGML_CPU_RISCV64_SPACEMIT)
+    set(SMT_MARCH_STR "-march=rv64gcv_zfh_zvfh_zba_zicbop")
+    if (CMAKE_C_COMPILER_ID STREQUAL "GNU" AND
+        CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 15)
+        string(APPEND SMT_MARCH_STR "_xsmtvdotii")
+    endif()
+    set(CMAKE_REQUIRED_FLAGS "${SMT_MARCH_STR}")
+
+    check_c_source_compiles("int main() {__asm__ volatile(\"vmadot v2, v0, v1\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_IME1)
+    check_c_source_compiles("int main() {__asm__ volatile(\"vmadot v2, v0, v1, i4\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_VMADOT_S4)
+    check_c_source_compiles("int main() {__asm__ volatile(\"vmadot v2, v0, v1, i8\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_VMADOT_S8)
+    check_c_source_compiles("int main() {__asm__ volatile(\"vfwmadot v2, v0, v1, fp16\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_VFWMADOT_FP16)
+    check_c_source_compiles("int main() {__asm__ volatile(\"vmadot.hp v2, v0, v1, v0, 0, i4\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_VFMADOT_S4)
+    check_c_source_compiles("int main() {__asm__ volatile(\"vmadot.hp v2, v0, v1, v0, 0, i8\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_VFMADOT_S8)
+    check_c_source_compiles("int main() {__asm__ volatile(\"vmadot1 v2, v0, v1\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_VMADOTN)
+    check_c_source_compiles("int main() {__asm__ volatile(\"vpack.vv v2, v0, v1, 2\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_VPACK)
+    check_c_source_compiles("int main() {__asm__ volatile(\"vnspack.vv v2, v0, v1, 2\");}" SPACEMIT_RISCV_COMPILER_SUPPORT_VNPACK)
+    unset(CMAKE_REQUIRED_FLAGS)
+
+    list(APPEND RISCV64_SPACEMIT_IME_SPEC "")
+    if (SPACEMIT_RISCV_COMPILER_SUPPORT_IME1)
+        set(RISCV64_SPACEMIT_IME_SPEC "RISCV64_SPACEMIT_IME1")
+    endif()
+
+    if (SPACEMIT_RISCV_COMPILER_SUPPORT_VMADOT_S4 AND SPACEMIT_RISCV_COMPILER_SUPPORT_VPACK AND SPACEMIT_RISCV_COMPILER_SUPPORT_VNPACK)
+        list(APPEND RISCV64_SPACEMIT_IME_SPEC "RISCV64_SPACEMIT_IME2")
+    endif()
+
+    message("RISCV64_SPACEMIT_IME_SPEC: ${RISCV64_SPACEMIT_IME_SPEC}")
+endif()
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 0b4680dca29..c36e7b1875d 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -51,6 +51,10 @@
 #include "llamafile/sgemm.h"
 #endif
 
+#ifdef GGML_USE_CPU_RISCV64_SPACEMIT
+#    include "spacemit/ime.h"
+#endif
+
 // Note: once we move threading into a separate C++ file
 // will use std::hardware_destructive_interference_size instead of hardcoding it here
 // and we'll use C++ attribute syntax.
@@ -1293,6 +1297,12 @@ void ggml_compute_forward_mul_mat(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
+    const int32_t hint = ggml_get_op_params_i32(dst, 1);
+    if (hint == GGML_HINT_SRC0_IS_HADAMARD && !params->use_ref) {
+        ggml_compute_forward_fwht(params, dst);
+        return;
+    }
+
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int ith = params->ith;
@@ -2986,7 +2996,9 @@ struct ggml_cplan ggml_graph_plan(
                 case GGML_OP_GATED_DELTA_NET:
                     {
                         const int64_t S_v = node->src[2]->ne[0];
-                        cur = S_v * sizeof(float) * n_tasks;
+                        const int64_t K   = node->src[5]->ne[1];  // state is (D, K, n_seqs)
+                        const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
+                        cur = per_thread * sizeof(float) * n_tasks;
                     } break;
                 case GGML_OP_TURBO_WHT:
                     {
@@ -3016,6 +3028,45 @@ struct ggml_cplan ggml_graph_plan(
     return cplan;
 }
 
+
+// Try to fuse the current node with subsequent nodes for better performance.
+// Returns the number of nodes skipped by fusion (>=1), or 0 if no fusion was applied.
+static bool ggml_cpu_disable_fusion = false;  // initialized once in ggml_cpu_init(), read-only afterwards
+
+static int ggml_cpu_try_fuse_ops(
+        const struct ggml_cgraph * cgraph,
+        const int node_n,
+        const struct ggml_compute_params * params,
+        const struct ggml_cplan * cplan) {
+
+    if (ggml_cpu_disable_fusion || cplan->use_ref) {
+        return 0;
+    }
+
+    struct ggml_tensor * node = cgraph->nodes[node_n];
+
+    if (node->op == GGML_OP_RMS_NORM) {
+        // RMS_NORM + MUL fusion
+        const enum ggml_op fuse_ops[] = { GGML_OP_RMS_NORM, GGML_OP_MUL };
+        if (ggml_can_fuse(cgraph, node_n, fuse_ops, 2)) {
+            struct ggml_tensor * mul_node = cgraph->nodes[node_n + 1];
+            const struct ggml_tensor * mul_w = (mul_node->src[0] == node)
+                ? mul_node->src[1] : mul_node->src[0];
+            if (node->src[0]->type  == GGML_TYPE_F32 &&
+                mul_node->type      == GGML_TYPE_F32 &&
+                mul_w->type         == GGML_TYPE_F32 &&
+                mul_w->ne[0]        == node->ne[0]   &&
+                mul_w->nb[0]        == sizeof(float)) {
+
+                ggml_compute_forward_rms_norm_mul_fused(params, node, mul_node);
+                return 1;
+            }
+        }
+    }
+
+    return 0;
+}
+
 static thread_ret_t ggml_graph_compute_thread(void * data) {
     struct ggml_compute_state * state = (struct ggml_compute_state *) data;
     struct ggml_threadpool    * tp    = state->threadpool;
@@ -3023,7 +3074,11 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
     const struct ggml_cgraph * cgraph = tp->cgraph;
     const struct ggml_cplan  * cplan  = tp->cplan;
 
+#ifdef GGML_USE_CPU_RISCV64_SPACEMIT
+    ggml_backend_cpu_riscv64_spacemit_set_numa_thread_affinity(state->ith);
+#else
     set_numa_thread_affinity(state->ith);
+#endif
 
     struct ggml_compute_params params = {
         /*.ith        =*/ state->ith,
@@ -3052,7 +3107,14 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             continue;
         }
 
-        ggml_compute_forward(&params, node);
+        // TODO: move fused-op detection into ggml_graph_plan so fusion decisions are made once at planning time
+        // Try fused ops, fall back to normal compute
+        const int n_fused = ggml_cpu_try_fuse_ops(cgraph, node_n, &params, cplan);
+        if (n_fused > 0) {
+            node_n += n_fused;
+        } else {
+            ggml_compute_forward(&params, node);
+        }
 
         if (state->ith == 0 && cplan->abort_callback &&
                 cplan->abort_callback(cplan->abort_callback_data)) {
@@ -3073,6 +3135,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
     ggml_barrier(state->threadpool);
 
+#ifdef GGML_USE_CPU_RISCV64_SPACEMIT
+    ggml_backend_cpu_riscv64_spacemit_clear_numa_thread_affinity_threaded(state->ith);
+#endif
+
     return 0;
 }
 
@@ -3927,6 +3993,11 @@ void ggml_cpu_init(void) {
         ggml_init_riscv_arch_features();
 #endif
 
+        {
+            const char * env = getenv("GGML_CPU_DISABLE_FUSION");
+            ggml_cpu_disable_fusion = (env != NULL && atoi(env) == 1);
+        }
+
         is_first_call = false;
     }
 
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index 49f840be207..128883b41ce 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -195,8 +195,8 @@ static const struct ggml_backend_i ggml_backend_cpu_i = {
     /* .free                    = */ ggml_backend_cpu_free,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ ggml_backend_cpu_graph_plan_create,
diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
index 34e320e2f50..e13828e3be6 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
@@ -2321,6 +2321,9 @@ class tinyBLAS_Q0_PPC {
     }
 
     void matmul(int64_t m, int64_t n) {
+    #if defined(_AIX) || defined(__BIG_ENDIAN__)
+        mnpack(0, m, 0, n);
+    #else
         const int64_t mc = 64;
         const int64_t kc = 64;
         int64_t nc = 64;
@@ -2334,7 +2337,6 @@ class tinyBLAS_Q0_PPC {
         } else {
             n_aligned = (n / 64) * 64;
         }
-
         if (n_aligned > 0) {
             if (n_aligned % 64 == 0)      nc = 64;
             else if (n_aligned == n)      nc = n;
@@ -2352,6 +2354,7 @@ class tinyBLAS_Q0_PPC {
         } else {
             mnpack(0, m, 0, n);
         }
+    #endif
     }
 
   private:
@@ -3191,12 +3194,16 @@ class tinyBLAS_PPC {
     }
 
     void matmul(int64_t m, int64_t n) {
+    #if defined(_AIX) || defined(__BIG_ENDIAN__)
+        mnpack(0, m, 0, n);
+    #else
         int64_t mc = 256; int64_t nc = 256; int64_t kc = 256;
         if (m % mc == 0 && n % nc == 0 && k % kc == 0) {
             matmul_tiled(m, n, mc, nc, kc);
         } else {
             mnpack(0, m, 0, n);
         }
+    #endif
     }
 
   private:
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index ca065370bd6..b655d3f9613 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -3723,11 +3723,27 @@ void ggml_compute_forward_norm(
 
 // ggml_compute_forward_group_rms_norm
 
+// fusion kinds that can be combined with the rms_norm computation in a single pass.
+// extend this enum when adding new fused variants (e.g. FUSE_ADD, FUSE_MUL_ADD, ...).
+enum ggml_rms_norm_fuse_op {
+    GGML_RMS_NORM_FUSE_OP_NONE,
+    GGML_RMS_NORM_FUSE_OP_MUL,
+};
+
+template <ggml_rms_norm_fuse_op FUSE_OP>
 static void ggml_compute_forward_rms_norm_f32(
         const ggml_compute_params * params,
-        ggml_tensor * dst) {
+        ggml_tensor * dst_rms_norm,
+        ggml_tensor * dst_fused = nullptr) {
 
-    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src0 = dst_rms_norm->src[0];
+    const ggml_tensor * src1 = nullptr;
+    ggml_tensor       * dst  = dst_rms_norm;
+
+    if constexpr (FUSE_OP == GGML_RMS_NORM_FUSE_OP_MUL) {
+        src1 = (dst_fused->src[0] == dst_rms_norm) ? dst_fused->src[1] : dst_fused->src[0];
+        dst  = dst_fused;
+    }
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
@@ -3736,11 +3752,10 @@ static void ggml_compute_forward_rms_norm_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    GGML_TENSOR_UNARY_OP_LOCALS
+    GGML_TENSOR_BINARY_OP_LOCALS
 
     float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
+    memcpy(&eps, dst_rms_norm->op_params, sizeof(float));
     GGML_ASSERT(eps >= 0.0f);
 
     // TODO: optimize
@@ -3750,25 +3765,32 @@ static void ggml_compute_forward_rms_norm_f32(
                 const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
 
                 ggml_float sum = 0.0;
+                // worth switching to explicit SIMD?
                 for (int64_t i00 = 0; i00 < ne00; i00++) {
                     sum += (ggml_float)(x[i00] * x[i00]);
                 }
 
-                const float mean = sum/ne00;
-
-                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
-
-                memcpy(y, x, ne00 * sizeof(float));
-                // for (int i00 = 0; i00 < ne00; i00++) {
-                //     y[i00] = x[i00];
-                // }
-
+                const float mean  = sum/ne00;
                 const float scale = 1.0f/sqrtf(mean + eps);
 
                 // if you hit this, likely you got an inf somewhere earlier
                 assert(scale > 0.0f);
 
-                ggml_vec_scale_f32(ne00, y, scale);
+                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
+
+                if constexpr (FUSE_OP == GGML_RMS_NORM_FUSE_OP_MUL) {
+                    const int64_t i11 = i01 % ne11;
+                    const int64_t i12 = i02 % ne12;
+                    const int64_t i13 = i03 % ne13;
+                    const float * w = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13);
+
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        y[i00] = x[i00] * scale * w[i00];
+                    }
+                } else {
+                    memcpy(y, x, ne00 * sizeof(float));
+                    ggml_vec_scale_f32(ne00, y, scale);
+                }
             }
         }
     }
@@ -3783,7 +3805,31 @@ void ggml_compute_forward_rms_norm(
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rms_norm_f32(params, dst);
+                ggml_compute_forward_rms_norm_f32<GGML_RMS_NORM_FUSE_OP_NONE>(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
+// Fused RMS_NORM + MUL: computes dst = rms_norm(src0) * src1 in a single pass.
+// This avoids materializing the intermediate rms_norm result in memory.
+void ggml_compute_forward_rms_norm_mul_fused(
+        const ggml_compute_params * params,
+        ggml_tensor * dst_rms_norm,
+        ggml_tensor * dst_mul) {
+
+    GGML_ASSERT(dst_mul != nullptr);
+    GGML_ASSERT(dst_mul->src[0] == dst_rms_norm || dst_mul->src[1] == dst_rms_norm);
+
+    const ggml_tensor * src0 = dst_rms_norm->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_rms_norm_f32<GGML_RMS_NORM_FUSE_OP_MUL>(params, dst_rms_norm, dst_mul);
             } break;
         default:
             {
@@ -10495,19 +10541,30 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
 
     const bool kda = (neg0 == S_v);
 
-    // scratch layout per thread: [delta(S_v)]
-    const int64_t scratch_per_thread = S_v;
+    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
+    const int64_t K = src_state->ne[1];
+    GGML_ASSERT(K >= 1);
+    // per-seq stride in floats (slot 0 of seq s lives at state + s * seq_stride)
+    const int64_t state_seq_stride = src_state->nb[2] / sizeof(float);
+
+    const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
     const int ith = params->ith;
 
-    float * delta = (float *)params->wdata + ith * scratch_per_thread + CACHE_LINE_SIZE_F32;
+    float * delta       = (float *)params->wdata + ith * per_thread + CACHE_LINE_SIZE_F32;
+    float * state_work  = K > 1 ? (delta + S_v) : nullptr;
 
     // output layout: [attn_scores | new_states]
-    // attn_scores: S_v * H * n_tokens * n_seqs floats
-    // new_states:  S_v * S_v * H * n_seqs floats
-    const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs;
+    // attn_scores: S_v * H * n_tokens * n_seqs    floats
+    // new_states:  S_v * S_v * H * n_seqs * K     floats  (K snapshot slots; last min(n_tokens, K))
+    const int64_t attn_score_elems    = S_v * H * n_tokens * n_seqs;
+    const int64_t state_size_per_snap = S_v * S_v * H * n_seqs;
     float * attn_out_base  = (float *)dst->data;
     float * state_out_base = (float *)dst->data + attn_score_elems;
 
+    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K only the last
+    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
+    const int64_t shift = n_tokens - K;
+
     const float * state_in_base = (const float *)src_state->data;
 
   //const int64_t rq1 = nev1 / neq1;
@@ -10527,10 +10584,15 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
         const int64_t iq3 = iv3 / rq3;
         const int64_t ik3 = iv3 / rk3;
 
-        float * s_out = state_out_base + (iv3 * H + iv1) * S_v * S_v;
+        // For K=1, write directly to the single output slot to avoid an extra memcpy at the end.
+        // For K>1, work in scratch and copy out per-token when the slot is in range.
+        float * s_out = (K > 1)
+            ? state_work
+            : state_out_base + (iv3 * H + iv1) * S_v * S_v;
 
-        // copy input state into output buffer and operate in-place
-        const float * s_in = state_in_base + (iv3 * H + iv1) * S_v * S_v;
+        // copy input state into the working buffer and operate in-place
+        // state layout (D, K, n_seqs): slot 0 of seq iv3 starts at iv3 * state_seq_stride.
+        const float * s_in = state_in_base + iv3 * state_seq_stride + iv1 * S_v * S_v;
         memcpy(s_out, s_in, S_v * S_v * sizeof(float));
 
         // attn output pointer for first token of this (head, seq)
@@ -10580,6 +10642,15 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
             }
 
             attn_data += S_v * H; // advance to next token
+
+            if (K > 1) {
+                const int64_t target_slot = t - shift;
+                if (target_slot >= 0 && target_slot < K) {
+                    float * curr_state_o = state_out_base + target_slot * state_size_per_snap +
+                                     (iv3 * H + iv1) * S_v * S_v;
+                    memcpy(curr_state_o, s_out, S_v * S_v * sizeof(float));
+                }
+            }
         }
     }
 }
@@ -11338,3 +11409,91 @@ void ggml_compute_forward_opt_step_sgd(const ggml_compute_params * params, ggml_
             }
     }
 }
+
+static void ggml_compute_forward_fwht_f32(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t n = ne10;
+    GGML_ASSERT((n & (n - 1)) == 0); // must be power of 2
+
+    const int64_t nr = ne11 * ne12 * ne13;
+    const int64_t rows_per_thread = (nr + nth - 1) / nth;
+    const int64_t start_row = ith * rows_per_thread;
+    const int64_t end_row = MIN(start_row + rows_per_thread, nr);
+
+    const float scale = 1.0f / sqrtf((float)n);
+
+#if defined(GGML_SIMD)
+    const GGML_F32_VEC v_minus_one = GGML_F32_VEC_SET1(-1.0f);
+#endif
+
+    for (int64_t r = start_row; r < end_row; r++) {
+        const int64_t i13 = r / (ne11 * ne12);
+        const int64_t i12 = (r - i13 * ne11 * ne12) / ne11;
+        const int64_t i11 = r - i13 * ne11 * ne12 - i12 * ne11;
+
+        const float * src_row = (const float *) ((const char *) src1->data + i11 * nb11 + i12 * nb12 + i13 * nb13);
+        float * dst_row = (float *) ((char *) dst->data + i11 * nb1 + i12 * nb2 + i13 * nb3);
+
+        for (int64_t j = 0; j < n; j++) {
+            dst_row[j] = src_row[j] * scale;
+        }
+
+        // Scalar passes
+#if defined(GGML_SIMD)
+        const int step = GGML_F32_EPR;
+#else
+        const int step = n;
+#endif
+        for (int64_t len = 1; len < step && len < n; len <<= 1) {
+            for (int64_t i = 0; i < n; i += 2 * len) {
+                for (int64_t j = 0; j < len; j++) {
+                    float u = dst_row[i + j];
+                    float v = dst_row[i + len + j];
+                    dst_row[i + j] = u + v;
+                    dst_row[i + len + j] = u - v;
+                }
+            }
+        }
+
+        // SIMD passes using GGML_F32_VEC_* macros for multi-architecture support
+#if defined(GGML_SIMD)
+        for (int64_t len = step; len < n; len <<= 1) {
+            for (int64_t i = 0; i < n; i += 2 * len) {
+                for (int64_t j = 0; j < len; j += step) {
+                    GGML_F32_VEC u = GGML_F32_VEC_LOAD(dst_row + i + j);
+                    GGML_F32_VEC v = GGML_F32_VEC_LOAD(dst_row + i + len + j);
+
+                    GGML_F32_VEC_STORE(dst_row + i + j,       GGML_F32_VEC_ADD(u, v));
+                    GGML_F32_VEC_STORE(dst_row + i + len + j, GGML_F32_VEC_FMA(u, v, v_minus_one));
+                }
+            }
+        }
+#endif
+    }
+}
+
+void ggml_compute_forward_fwht(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_tensor * src1 = dst->src[1];
+
+    switch (src1->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_fwht_f32(params, dst);
+            }
+            break;
+        default:
+            {
+                GGML_ABORT("fatal error - fwht is F32 only");
+            }
+    }
+}
diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h
index da8761a341a..8d4d904604d 100644
--- a/ggml/src/ggml-cpu/ops.h
+++ b/ggml/src/ggml-cpu/ops.h
@@ -44,6 +44,7 @@ void ggml_compute_forward_concat(const struct ggml_compute_params * params, stru
 void ggml_compute_forward_silu_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_rms_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_rms_norm_mul_fused(const struct ggml_compute_params * params, struct ggml_tensor * dst_rms_norm, struct ggml_tensor * dst_mul);
 void ggml_compute_forward_rms_norm_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_group_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_l2_norm(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@@ -112,6 +113,7 @@ void ggml_compute_forward_cross_entropy_loss(const struct ggml_compute_params *
 void ggml_compute_forward_cross_entropy_loss_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_opt_step_adamw(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_fwht(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_opt_step_sgd(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 #ifdef __cplusplus
 }
diff --git a/ggml/src/ggml-cpu/spacemit/ime.cpp b/ggml/src/ggml-cpu/spacemit/ime.cpp
index 91fe1925eaa..9563ea3e4bd 100644
--- a/ggml/src/ggml-cpu/spacemit/ime.cpp
+++ b/ggml/src/ggml-cpu/spacemit/ime.cpp
@@ -3,19 +3,32 @@
 
 #include "ime.h"
 
+#include "binary-ops.h"
+#include "common.h"
 #include "ggml-backend-impl.h"
 #include "ggml-common.h"
 #include "ggml-cpu.h"
+#include "ime_env.h"
 #include "ime_kernels.h"
+#include "ops.h"
+#include "repack.h"
+#include "rvv_kernels.h"
+#include "spine_mem_pool.h"
 #include "traits.h"
+#include "vec.h"
+
+#include <fcntl.h>
+#include <sys/mman.h>
+#include <unistd.h>
 
 #include <algorithm>
+#include <atomic>
 #include <cassert>
+#include <cerrno>
 #include <cmath>
 #include <cstdio>  // for GGML_ASSERT
 #include <stdexcept>
 #include <thread>
-
 // clang-format off
 #if defined(__riscv)
 
@@ -25,13 +38,17 @@
 #include <riscv_vector.h>
 #endif
 
-#if !defined(__riscv_zfh)
-#error "riscv zfh extension not enabled"
+#if !defined(__riscv_zfh) || !defined(__riscv_zvfh)
+#error "riscv zfh extension not enabled, GGML_RV_ZFH and GGML_RV_ZVFH must be defined to 1"
 #endif
 
-#if defined(RISCV64_SPACEMIT_IME1)
+#if !defined(__riscv_zba)
+#error "riscv zba extension not enabled, GGML_RV_ZBA must be defined to 1"
+#endif
+
+#if defined(RISCV64_SPACEMIT_IME1) || defined(RISCV64_SPACEMIT_IME2)
 #else
-#error "RISCV64_SPACEMIT_IME1 not defined"
+#error "RISCV64_SPACEMIT_IME1 or RISCV64_SPACEMIT_IME2 not defined"
 #endif
 
 #else
@@ -46,382 +63,490 @@
 #pragma GCC diagnostic ignored "-Wunused-parameter"
 #endif
 
-#if defined(RISCV64_SPACEMIT_IME1)
-#define QGEMM_STRIDEN_THREAD_ALIGN 16
-#else
-#define QGEMM_STRIDEN_THREAD_ALIGN 32
-#endif
-
 // clang-format on
 
-struct qnbitgemm_spacemit_ime_args {
-    const float *     a_ptr               = nullptr;
-    size_t            lda                 = 0;
-    const std::byte * packed_quant_b_data = nullptr;
-    const float *     quant_b_scale       = nullptr;
-    const void *      quant_b_zp          = nullptr;
-    const float *     quant_b_blksum      = nullptr;
-    const float *     bias                = nullptr;
-    float *           c_ptr               = nullptr;
-    size_t            ldc                 = 0;
-};
-
-constexpr size_t div_round_up(size_t up, size_t down) {
-    return (up + down - 1) / down;
-}
-
-constexpr size_t q8_blk_size(size_t blk_len) {
-    const size_t blk_size = sizeof(float) + blk_len * sizeof(int8_t);
-    // Currently, the strictest alignment requirement of a block is for a float.
-    // Ensure contiguous blocks are suitably aligned.
-    assert(blk_size % alignof(float) == 0);
-    return blk_size;
+extern "C" {
+extern void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value);
+extern int  ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value);
 }
 
 namespace ggml::cpu::riscv64_spacemit {
 
-const int num_ai_cores = std::thread::hardware_concurrency() / 2;
-
-}  // namespace ggml::cpu::riscv64_spacemit
+struct TLSContext {
+    int       cpu_id{ -1 };
+    cpu_set_t cpuset;
+    void *    tcm_buffer{ nullptr };
+    size_t    tcm_buffer_size{ 0 };
+};
 
-static void sqnbitgemm_spacemit_ime_i8i4(const size_t                        blk_len,
-                                         const size_t                        gemm_k,
-                                         const qnbitgemm_spacemit_ime_args * gemm_args,
-                                         void * const                        per_gemm_ws,
-                                         const size_t                        m_start,
-                                         const size_t                        m_count,
-                                         const size_t                        n_start,
-                                         const size_t                        n_count) {
-    constexpr size_t scale_stride = sizeof(uint16_t);
-    constexpr size_t blk_bitwidth = 4;
+thread_local TLSContext tls_context;
+
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> constexpr size_t get_repacked_block_type_size() {
+    if constexpr (std::is_same_v<BLOC_TYPE, block_q6_K> || std::is_same_v<BLOC_TYPE, block_q8_0>) {
+        return sizeof(block_q8_0);
+    } else if constexpr (std::is_same_v<BLOC_TYPE, block_q4_0>) {
+        return sizeof(block_q4_0) * INTER_SIZE / QK4_0;
+    } else if constexpr (std::is_same_v<BLOC_TYPE, block_q4_1> || std::is_same_v<BLOC_TYPE, block_q4_K>) {
+        return (sizeof(block_q4_0) + sizeof(uint8_t)) * INTER_SIZE / QK4_1;
+    } else if constexpr (std::is_same_v<BLOC_TYPE, block_q2_K>) {
+        return sizeof(spacemit_kernels::nrow_block_q2_k<1>);
+    } else if constexpr (std::is_same_v<BLOC_TYPE, block_q3_K>) {
+        return sizeof(spacemit_kernels::nrow_block_q3_k<1>);
+    } else if constexpr (std::is_same_v<BLOC_TYPE, block_mxfp4>) {
+        return sizeof(spacemit_kernels::nrow_block_mxfp4<1>);
+    } else if constexpr (std::is_same_v<BLOC_TYPE, block_q5_1> || std::is_same_v<BLOC_TYPE, block_q5_K>) {
+        return sizeof(spacemit_kernels::nrow_block_q5_1<1>);
+    } else if constexpr (std::is_same_v<BLOC_TYPE, block_q5_0>) {
+        return sizeof(spacemit_kernels::nrow_block_q5_0<1>);
+    } else {
+        assert(false);
+        return 0;
+    }
+}
 
-    const size_t k_blks = div_round_up(gemm_k, blk_len);
+template <typename BLOC_TYPE> constexpr bool block_type_has_zp() {
+    if constexpr (std::is_same_v<BLOC_TYPE, block_q6_K> || std::is_same_v<BLOC_TYPE, block_q8_0> ||
+                  std::is_same_v<BLOC_TYPE, block_q3_K> || std::is_same_v<BLOC_TYPE, block_q4_0> ||
+                  std::is_same_v<BLOC_TYPE, block_mxfp4> || std::is_same_v<BLOC_TYPE, block_q5_0>) {
+        return false;
+    } else if constexpr (std::is_same_v<BLOC_TYPE, block_q4_1> || std::is_same_v<BLOC_TYPE, block_q4_K> ||
+                         std::is_same_v<BLOC_TYPE, block_q2_K> || std::is_same_v<BLOC_TYPE, block_q5_1> ||
+                         std::is_same_v<BLOC_TYPE, block_q5_K>) {
+        return true;
+    } else {
+        assert(false);
+        return false;
+    }
+}
 
-    const size_t      lda         = k_blks * q8_blk_size(blk_len);
-    const size_t      ldc         = gemm_args->ldc;
-    const size_t      ldb         = k_blks * (blk_len * blk_bitwidth / 8);
-    const std::byte * quant_a_ptr = static_cast<const std::byte *>(per_gemm_ws) + m_start * lda;
+class tensor_traits_base : public ggml::cpu::tensor_traits {
+  public:
+    virtual int repack(ggml_tensor * t, const void * data, size_t data_size) = 0;
+};
 
-    const size_t      zero_point_stride   = gemm_args->quant_b_zp != nullptr ? sizeof(uint8_t) : 0;
-    const size_t      packed_b_stride     = ldb + k_blks * (scale_stride + zero_point_stride);
-    const std::byte * packed_quant_b_data = gemm_args->packed_quant_b_data + n_start * packed_b_stride;
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> class tensor_traits : public tensor_traits_base {
+    bool work_size(int /* n_threads */, const ggml_tensor * op, size_t & size) override {
+        switch (op->op) {
+            case GGML_OP_MUL_MAT:
+                {
+                    int64_t src1_nelements = ggml_nelements(op->src[1]);
+
+                    if constexpr (std::is_same_v<BLOC_TYPE, block_q2_K> || std::is_same_v<BLOC_TYPE, block_q3_K>) {
+                        size =
+                            spacemit_kernels::div_round_up(src1_nelements, QK_K) * spacemit_kernels::q8k_blk_size(QK_K);
+                    } else if constexpr (INTER_SIZE == QK4_0) {
+                        size = spacemit_kernels::div_round_up(src1_nelements, QK4_0) *
+                               spacemit_kernels::q8_blk_size(QK4_0, true);
+                    } else if constexpr (INTER_SIZE == 256) {
+                        size = spacemit_kernels::div_round_up(src1_nelements, 256) *
+                               spacemit_kernels::q8_hp_blk_size(256, true, true);
+                    } else {
+                        GGML_ABORT("unsupported block type");
+                    }
 
-    float * c_ptr = gemm_args->c_ptr + m_start * ldc + n_start;
+                    size = GGML_PAD(size, sizeof(int64_t));
 
-    size_t       count_n               = 0;
-    const size_t compute_block_count_n = m_count == 1 ? n_count : 16;
-    for (size_t n = 0; n < n_count; n += count_n) {
-        count_n = std::min(n_count - n, compute_block_count_n);
+                    return true;
+                }
+            case GGML_OP_MUL_MAT_ID:
+                {
+                    int64_t src1_nelements = ggml_nelements(op->src[1]);
+
+                    if constexpr (std::is_same_v<BLOC_TYPE, block_q2_K> || std::is_same_v<BLOC_TYPE, block_q3_K>) {
+                        size =
+                            spacemit_kernels::div_round_up(src1_nelements, QK_K) * spacemit_kernels::q8k_blk_size(QK_K);
+                    } else if constexpr (INTER_SIZE == QK4_0) {
+                        size = spacemit_kernels::div_round_up(src1_nelements, QK4_0) *
+                               spacemit_kernels::q8_blk_size(QK4_0, true);
+                    } else if constexpr (INTER_SIZE == 256) {
+                        size = spacemit_kernels::div_round_up(src1_nelements, 256) *
+                               spacemit_kernels::q8_hp_blk_size(256, true, true);
+                    } else {
+                        GGML_ABORT("unsupported block type");
+                    }
 
-        const std::byte * a_row    = quant_a_ptr;
-        const std::byte * b_col    = packed_quant_b_data + n * packed_b_stride;
-        const std::byte * b_col_zp = (zero_point_stride != 0) ? b_col : nullptr;
-        float *           c_blk    = c_ptr + n;
+                    size = GGML_PAD(size, sizeof(int64_t));
 
-        int32_t rows_remaining = m_count;
+                    const int64_t ne02 = op->src[0]->ne[2];  // n_as, n_expert
+                    const int64_t ne12 = op->src[1]->ne[2];  // n_tokens
 
-        while (rows_remaining > 0) {
-            const auto rows_handled = sqnbitgemm_spacemit_ime::ime1::gemm_kernel_i8i4(
-                blk_len, a_row, b_col, nullptr, b_col_zp, c_blk, rows_remaining, count_n, gemm_k, k_blks, ldc, nullptr,
-                scale_stride);
+                    const size_t sizeof_mmid_row_mapping = sizeof(int64_t);
+                    size += sizeof_mmid_row_mapping * ne02 * (ne12 + 1) + (ne02 + 1) * sizeof(int64_t);
 
-            c_blk += rows_handled * ldc;
-            a_row += rows_handled * lda;
+                    size = GGML_PAD(size, sizeof(int64_t));
 
-            rows_remaining -= rows_handled;
+                    return true;
+                }
+            default:
+                // GGML_ABORT("fatal error");
+                break;
         }
+        return false;
     }
-}
 
-template <int K> constexpr int QK_0() {
-    if constexpr (K == 4) {
-        return QK4_0;
-    }
-    if constexpr (K == 8) {
-        return QK8_0;
+    bool compute_forward(ggml_compute_params * params, ggml_tensor * op) override {
+        switch (op->op) {
+            case GGML_OP_MUL_MAT:
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_Q2_K:
+                    case GGML_TYPE_Q3_K:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q4_K:
+                    case GGML_TYPE_Q6_K:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q5_K:
+                        //case GGML_TYPE_MXFP4:
+                        forward_mul_mat(params, op);
+                        return true;
+                    default:
+                        // GGML_ABORT("fatal error: unsupported type for src0 in MUL_MAT");
+                        return false;
+                }
+                break;
+            case GGML_OP_MUL_MAT_ID:
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_Q2_K:
+                    case GGML_TYPE_Q3_K:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q4_K:
+                    case GGML_TYPE_Q6_K:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q5_K:
+                        //case GGML_TYPE_MXFP4:
+                        forward_mul_mat_id(params, op);
+                        return true;
+                    default:
+                        // GGML_ABORT("fatal error: unsupported type for src0 in MUL_MAT_ID");
+                        return false;
+                }
+                break;
+            default:
+                // GGML_ABORT("fatal error");
+                break;
+        }
+        return false;
     }
-    return -1;
-}
 
-template <int K, int N> struct block {
-    ggml_half d[N];                         // deltas for N qK_0 blocks
-    uint8_t   qs[(QK_0<K>() * N * K) / 8];  // quants for N qK_0 blocks
-};
+    void forward_mul_mat(ggml_compute_params * params, ggml_tensor * op) {
+        constexpr size_t a_blk_len = INTER_SIZE;
+        constexpr size_t b_blk_len = INTER_SIZE;
 
-template <int K, int N> struct block_with_zp {
-    ggml_half d[N];                         // deltas for N qK_1 blocks
-    uint8_t   zp[N];                        // zero points for N qK_1 blocks
-    uint8_t   qs[(QK_0<K>() * N * K) / 8];  // quants for N qK_1 blocks
-};
+        const ggml_tensor * src0 = op->src[0];
+        const ggml_tensor * src1 = op->src[1];
+        ggml_tensor *       dst  = op;
 
-// control size
-static_assert(sizeof(block<4, 16>) == 16 * sizeof(ggml_half) + QK4_0 * 8, "wrong block<4,16> size/padding");
-static_assert(sizeof(block_with_zp<4, 16>) == 16 * sizeof(ggml_half) + QK4_0 * 8 + 16 * sizeof(uint8_t),
-              "wrong block_with_zp<4,16> size/padding");
-static_assert(sizeof(block<8, 16>) == 16 * sizeof(ggml_half) + QK4_0 * 16, "wrong block<8,16> size/padding");
+        GGML_TENSOR_BINARY_OP_LOCALS
 
-using block_q4_0x16 = block<4, 16>;
-using block_q4_1x16 = block_with_zp<4, 16>;
-using block_q8_0x16 = block<8, 16>;
+        int ith = params->ith;
+        int nth = params->nth;
 
-static block_q4_0x16 make_block_q4_0x16(block_q4_0 * in, unsigned int blck_size_interleave) {
-    block_q4_0x16 out;
-    GGML_ASSERT(QK4_0 / blck_size_interleave == 2);
+        [[maybe_unused]] const enum ggml_type type = src0->type;
 
-    for (int i = 0; i < 16; i++) {
-        out.d[i] = in[i].d;
-    }
+        void *        w_data  = (void *) src0->data;
+        const float * feature = (const float *) src1->data;
+        float *       output  = (float *) dst->data;
 
-    for (int i = 0; i < 16; i++) {
-        // [0, 15], in.d & 0x0F
-        for (int j = 0; j < QK4_0 / 4; j++) {
-            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
-            //dst [b0 b8] ......... [b7 b15]
-            out.qs[i * QK4_0 / 4 + j] = (in[i].qs[j] & 0x0F) | ((in[i].qs[j + QK4_0 / 4] & 0x0F) << 4);
+        const int64_t gemm_m = ne11 * ne12 * ne13;
+        const int64_t gemm_k = ne10;
+        const int64_t gemm_n = ne01;
+
+        spacemit_kernels::quantize_a_row_def       quantize_a_row_i8;
+        spacemit_kernels::quantize_a_row_def       quantize_a_4row_i8;
+        spacemit_kernels::gemm_kernel_quantize_def gemm_kernel;
+        bool                                       set_kernel_impl = false;
+
+        int64_t block_stride_a = spacemit_kernels::q8_blk_size(a_blk_len);
+
+#if defined(RISCV64_SPACEMIT_IME2)
+        if (!set_kernel_impl && (global_spine_env_info.use_ime2)) {
+            quantize_a_row_i8  = spacemit_kernels::rvv::quantize_a_row_i8;
+            quantize_a_4row_i8 = spacemit_kernels::rvv::quantize_a_4row_i8;
+            block_stride_a     = spacemit_kernels::q8_blk_size(a_blk_len, true);
+
+            if constexpr (std::is_same_v<BLOC_TYPE, block_q6_K> || std::is_same_v<BLOC_TYPE, block_q8_0>) {
+                gemm_kernel     = spacemit_kernels::ime2::gemm_kernel_i8i8;
+                set_kernel_impl = true;
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_q4_0> || std::is_same_v<BLOC_TYPE, block_q4_1> ||
+                                 std::is_same_v<BLOC_TYPE, block_q4_K>) {
+                if constexpr (INTER_SIZE == 256) {
+                    gemm_kernel        = spacemit_kernels::ime2::gemm_kernel_i8i4_hp;
+                    quantize_a_row_i8  = spacemit_kernels::rvv::quantize_a_row_i8_hp;
+                    quantize_a_4row_i8 = spacemit_kernels::rvv::quantize_a_4row_i8_hp;
+                    block_stride_a     = spacemit_kernels::q8_hp_blk_size(a_blk_len, true, true);
+                    set_kernel_impl    = true;
+                } else {
+                    gemm_kernel        = spacemit_kernels::ime2::gemm_kernel_i8i4;
+                    quantize_a_row_i8  = spacemit_kernels::rvv::quantize_a_row_i8;
+                    quantize_a_4row_i8 = spacemit_kernels::rvv::quantize_a_4row_i8;
+                    block_stride_a     = spacemit_kernels::q8_blk_size(a_blk_len, true);
+                    set_kernel_impl    = true;
+                }
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_q2_K>) {
+                quantize_a_row_i8  = spacemit_kernels::rvv::quantize_a_row_i8k;
+                quantize_a_4row_i8 = spacemit_kernels::rvv::quantize_a_4row_i8k;
+                block_stride_a     = spacemit_kernels::q8k_blk_size(a_blk_len);
+
+                gemm_kernel     = spacemit_kernels::ime2::gemm_kernel_i8i2k;
+                set_kernel_impl = true;
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_q3_K>) {
+                quantize_a_row_i8  = spacemit_kernels::rvv::quantize_a_row_i8k;
+                quantize_a_4row_i8 = spacemit_kernels::rvv::quantize_a_4row_i8k;
+                block_stride_a     = spacemit_kernels::q8k_blk_size(a_blk_len);
+
+                gemm_kernel     = spacemit_kernels::ime2::gemm_kernel_i8i3k;
+                set_kernel_impl = true;
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_mxfp4>) {
+                gemm_kernel     = spacemit_kernels::ime2::gemm_kernel_i8mxfp4;
+                set_kernel_impl = true;
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_q5_1> || std::is_same_v<BLOC_TYPE, block_q5_K> ||
+                                 std::is_same_v<BLOC_TYPE, block_q5_0>) {
+                gemm_kernel     = spacemit_kernels::ime2::gemm_kernel_i8i5;
+                set_kernel_impl = true;
+            }
         }
-    }
+#endif
 
-    for (int i = 0; i < 16; i++) {
-        // [16, 31], in.d & 0xF0
-        for (int j = 0; j < QK4_0 / 4; j++) {
-            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
-            //dst [b16 b24] ......... [b23 b31]
-            out.qs[4 * QK4_0 + i * QK4_0 / 4 + j] = ((in[i].qs[j] & 0xF0) >> 4) | (in[i].qs[j + QK4_0 / 4] & 0xF0);
+#if defined(RISCV64_SPACEMIT_IME1)
+        if (!set_kernel_impl && (global_spine_env_info.use_ime1)) {
+            quantize_a_row_i8  = spacemit_kernels::ime1::quantize_a_row_i8;
+            quantize_a_4row_i8 = spacemit_kernels::ime1::quantize_a_4row_i8;
+
+            if constexpr (std::is_same_v<BLOC_TYPE, block_q4_0> || std::is_same_v<BLOC_TYPE, block_q4_1> ||
+                          std::is_same_v<BLOC_TYPE, block_q4_K>) {
+                gemm_kernel     = spacemit_kernels::ime1::gemm_kernel_i8i4;
+                set_kernel_impl = true;
+            }
+        }
+#endif
+        if (!set_kernel_impl) {
+            GGML_ABORT("no kernel implementation found for the block type");
         }
-    }
 
-    return out;
-}
+        const int64_t a_k_blks = spacemit_kernels::div_round_up(gemm_k, a_blk_len);
+        const int64_t b_k_blks = spacemit_kernels::div_round_up(gemm_k, b_blk_len);
 
-static block_q4_1x16 make_block_q4_1x16(block_q4_1 * in, unsigned int blck_size_interleave) {
-    block_q4_1x16 out;
-    GGML_ASSERT(QK4_1 / blck_size_interleave == 2);
-
-    for (int i = 0; i < 16; i++) {
-        float d   = GGML_FP16_TO_FP32(in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
-        float m   = GGML_FP16_TO_FP32(in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.m);
-        float mid = -std::nearbyintf(m / d);
-        mid       = std::min(15.0f, std::max(0.0f, mid));
-        out.d[i]  = GGML_FP32_TO_FP16(d);
-        out.zp[i] = static_cast<uint8_t>(mid);
-    }
+        const int64_t row_stride_a        = a_k_blks * block_stride_a;
+        const int64_t gemm_workspace_size = GGML_PAD(gemm_m * row_stride_a, alignof(int64_t));
 
-    for (int i = 0; i < 16; i++) {
-        // [0, 15], in.d & 0x0F
-        for (int j = 0; j < QK4_1 / 4; j++) {
-            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
-            //dst [b0 b8] ......... [b7 b15]
-            out.qs[i * QK4_1 / 4 + j] = (in[i].qs[j] & 0x0F) | ((in[i].qs[j + QK4_1 / 4] & 0x0F) << 4);
+        if (ith == 0 && params->wsize < gemm_workspace_size) {
+            GGML_ABORT("wsize less than gemm_workspace_size");
         }
-    }
 
-    for (int i = 0; i < 16; i++) {
-        // [16, 31], in.d & 0xF0
-        for (int j = 0; j < QK4_1 / 4; j++) {
-            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
-            //dst [b16 b24] ......... [b23 b31]
-            out.qs[4 * QK4_1 + i * QK4_1 / 4 + j] = ((in[i].qs[j] & 0xF0) >> 4) | (in[i].qs[j + QK4_1 / 4] & 0xF0);
-        }
-    }
+        uintptr_t ws_ptr = reinterpret_cast<uintptr_t>(params->wdata);
 
-    return out;
-}
+        void *        tcm_buffer      = ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer;
+        const int64_t tcm_buffer_size = ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer_size;
 
-static int repack_q4_0_to_q4_0_16_bl(struct ggml_tensor *       t,
-                                     int                        interleave_block,
-                                     const void * GGML_RESTRICT data,
-                                     size_t                     data_size) {
-    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
-    GGML_ASSERT(interleave_block == 16);
+        auto * quant_a_buffer = reinterpret_cast<uint8_t *>(ws_ptr);
 
-    constexpr int nrows_interleaved = 16;
+        constexpr int64_t row_align = 4;
+        const int64_t     row_blks  = spacemit_kernels::div_round_up(gemm_m, row_align);
 
-    block_q4_0x16 *    dst = (block_q4_0x16 *) t->data;
-    const block_q4_0 * src = (const block_q4_0 *) data;
-    block_q4_0         dst_tmp[16];
-    int                nrow    = ggml_nrows(t);
-    int                nblocks = t->ne[0] / QK4_0;
+        const int64_t row_stride_b      = b_k_blks * get_repacked_block_type_size<BLOC_TYPE, INTER_SIZE, NB_COLS>();
+        const int64_t per_mb_rows_wsize = row_align * row_stride_a;
+        const int64_t per_nb_cols_wsize = NB_COLS * row_stride_b;
 
-    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));
+        const int64_t barrier_idx = static_cast<int64_t>(ith / 2);
 
-    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_0 != 0) {
-        return -1;
-    }
+        GGML_ASSERT(global_spine_env_info.init_barrier != nullptr);
+        GGML_ASSERT(barrier_idx < spine_init_barrier_count);
+        spine_barrier_t * cur_barrier = &global_spine_env_info.init_barrier[barrier_idx];
 
-    for (int b = 0; b < nrow; b += nrows_interleaved) {
-        for (int64_t x = 0; x < nblocks; x++) {
-            for (int i = 0; i < nrows_interleaved; i++) {
-                dst_tmp[i] = src[x + i * nblocks];
+        if (gemm_m == 1) {
+            int task_per_thread = spacemit_kernels::div_round_up(a_k_blks, nth);
+            int a_blk_start     = ith * task_per_thread;
+            int a_blk_end       = std::min(a_blk_start + task_per_thread, (int) a_k_blks);
+            if (a_blk_start < a_blk_end) {
+                quantize_a_row_i8(a_blk_len, feature + a_blk_start * a_blk_len, (a_blk_end - a_blk_start) * a_blk_len,
+                                  quant_a_buffer + a_blk_start * block_stride_a);
+            }
+        } else {
+            int task_per_thread = spacemit_kernels::div_round_up(row_blks, nth);
+            int m_row_blk_start = ith * task_per_thread;
+            int m_row_blk_end   = std::min(m_row_blk_start + task_per_thread, (int) row_blks);
+            for (int m_row_blk = m_row_blk_start; m_row_blk < m_row_blk_end; m_row_blk++) {
+                int m_idx             = m_row_blk * row_align;
+                int rows_tobe_handled = (gemm_m - m_idx) > row_align ? row_align : (gemm_m - m_idx);
+
+                if (rows_tobe_handled == row_align && quantize_a_4row_i8 != nullptr) {
+                    const float * a_row_ptr       = feature + m_idx * gemm_k;
+                    auto *        quant_a_row_ptr = quant_a_buffer + m_idx * row_stride_a;
+                    quantize_a_4row_i8(a_blk_len, a_row_ptr, gemm_k, quant_a_row_ptr);
+                } else {
+                    while (rows_tobe_handled) {
+                        const float * a_row_ptr       = feature + m_idx * gemm_k;
+                        auto *        quant_a_row_ptr = quant_a_buffer + m_idx * row_stride_a;
+                        quantize_a_row_i8(a_blk_len, a_row_ptr, gemm_k, quant_a_row_ptr);
+                        rows_tobe_handled -= 1;
+                        m_idx += 1;
+                    }
+                }
             }
-            *dst++ = make_block_q4_0x16(dst_tmp, interleave_block);
         }
-        src += nrows_interleaved * nblocks;
-    }
-    return 0;
 
-    GGML_UNUSED(data_size);
-}
+        ggml_barrier(params->threadpool);
 
-static int repack_q4_1_to_q4_1_16_bl(struct ggml_tensor *       t,
-                                     int                        interleave_block,
-                                     const void * GGML_RESTRICT data,
-                                     size_t                     data_size) {
-    GGML_ASSERT(t->type == GGML_TYPE_Q4_1);
-    GGML_ASSERT(interleave_block == 16);
+        const int64_t gemm_m_stride     = gemm_n / gemm_m > 64 ? gemm_m : 16;
+        const int64_t gemm_m_blocked    = spacemit_kernels::div_round_up(gemm_m, gemm_m_stride);
+        const int64_t max_gemm_n_stride = spacemit_kernels::div_round_up(gemm_n * gemm_m_blocked, nth);
 
-    constexpr int nrows_interleaved = 16;
+        int64_t gemm_n_stride = gemm_n;
+        if (max_gemm_n_stride < gemm_n) {
+            gemm_n_stride =
+                std::min(gemm_n_stride, spacemit_kernels::div_round_up(max_gemm_n_stride, NB_COLS) * NB_COLS);
+        }
 
-    block_q4_1x16 *    dst = (block_q4_1x16 *) t->data;
-    const block_q4_1 * src = (const block_q4_1 *) data;
-    block_q4_1         dst_tmp[16];
-    int                nrow    = ggml_nrows(t);
-    int                nblocks = t->ne[0] / QK4_1;
+        if (gemm_n_stride == gemm_n && tcm_buffer != nullptr && per_mb_rows_wsize <= tcm_buffer_size) {
+            for (int64_t m_start = ith * row_align; m_start < gemm_m; m_start += row_align * nth) {
+                uint8_t * b_col    = reinterpret_cast<uint8_t *>(w_data);
+                uint8_t * b_col_zp = block_type_has_zp<BLOC_TYPE>() ? b_col : nullptr;
 
-    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_1));
+                int64_t m_row_real = std::min(gemm_m - m_start, row_align);
 
-    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_1 != 0) {
-        return -1;
-    }
+                spacemit_kernels::rvv::memcpy1d(tcm_buffer, quant_a_buffer + m_start * row_stride_a,
+                                                m_row_real * row_stride_a);
 
-    for (int b = 0; b < nrow; b += nrows_interleaved) {
-        for (int64_t x = 0; x < nblocks; x++) {
-            for (int i = 0; i < nrows_interleaved; i++) {
-                dst_tmp[i] = src[x + i * nblocks];
+                int64_t n_blk_real = 0;
+                for (int64_t ni = 0; ni < gemm_n; ni += n_blk_real, b_col += n_blk_real * row_stride_b) {
+                    n_blk_real = std::min(gemm_n - ni, (int64_t) NB_COLS);
+
+                    uint8_t * a_row_ptr = (uint8_t *) tcm_buffer;
+                    float *   c_blk     = output + m_start * gemm_n + ni;
+
+                    int32_t rows_remaining = m_row_real;
+
+                    while (rows_remaining > 0) {
+                        auto rows_handled = gemm_kernel(b_blk_len, a_row_ptr, b_col, b_col_zp, c_blk, rows_remaining,
+                                                        n_blk_real, b_k_blks, gemm_n);
+
+                        c_blk += rows_handled * gemm_n;
+                        a_row_ptr += rows_handled * row_stride_a;
+
+                        rows_remaining -= rows_handled;
+                    }
+                }
             }
-            *dst++ = make_block_q4_1x16(dst_tmp, interleave_block);
-        }
-        src += nrows_interleaved * nblocks;
-    }
-    return 0;
+        } else if (tcm_buffer != nullptr && per_nb_cols_wsize <= tcm_buffer_size) {
+            uint8_t * a_row = quant_a_buffer;
+            uint8_t * b_col = reinterpret_cast<uint8_t *>(tcm_buffer);
+            if ((gemm_workspace_size + per_nb_cols_wsize) <= tcm_buffer_size) {
+                a_row = (uint8_t *) tcm_buffer;
+                b_col = reinterpret_cast<uint8_t *>(tcm_buffer) + gemm_workspace_size;
+            }
+            uint8_t * b_col_zp = block_type_has_zp<BLOC_TYPE>() ? b_col : nullptr;
 
-    GGML_UNUSED(data_size);
-}
+            int64_t ni      = ith * NB_COLS;
+            int64_t nb_real = std::min(gemm_n - ni, NB_COLS);
 
-static inline void get_scale_min_k4(int                           j,
-                                    const uint8_t * GGML_RESTRICT q,
-                                    uint8_t * GGML_RESTRICT       d,
-                                    uint8_t * GGML_RESTRICT       m) {
-    if (j < 4) {
-        *d = q[j] & 63;
-        *m = q[j + 4] & 63;
-    } else {
-        *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
-        *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
-    }
-}
+            if (ith % 2 == 0 && nb_real > 0) {
+                spacemit_kernels::rvv::memcpy1d(b_col, reinterpret_cast<uint8_t *>(w_data) + ni * row_stride_b,
+                                                nb_real * row_stride_b);
+                if (a_row != quant_a_buffer) {
+                    spacemit_kernels::rvv::memcpy1d(a_row, quant_a_buffer, gemm_workspace_size);
+                }
+            }
 
-static int repack_q4_k_to_q4_1_16_bl(struct ggml_tensor *       t,
-                                     int                        interleave_block,
-                                     const void * GGML_RESTRICT data,
-                                     size_t                     data_size) {
-    GGML_ASSERT(t->type == GGML_TYPE_Q4_K);
-    GGML_ASSERT(interleave_block == 16);
-    GGML_ASSERT(QK_K / QK4_1 == 8);
+            spine_barrier_wait(cur_barrier);
 
-    constexpr int nrows_interleaved = 16;
+            if (ith % 2 != 0 && nb_real > 0) {
+                if (a_row != quant_a_buffer) {
+                    spacemit_kernels::rvv::memcpy1d(a_row, quant_a_buffer, gemm_workspace_size);
+                }
+                spacemit_kernels::rvv::memcpy1d(b_col, reinterpret_cast<uint8_t *>(w_data) + ni * row_stride_b,
+                                                nb_real * row_stride_b);
+            }
 
-    block_q4_1x16 *    dst = (block_q4_1x16 *) t->data;
-    const block_q4_K * src = (const block_q4_K *) data;
-    block_q4_1         dst_tmp[16];
-    int                nrow    = ggml_nrows(t);
-    int                nblocks = t->ne[0] / QK_K;
+            for (; ni < gemm_n; ni += NB_COLS * nth) {
+                int64_t rows_remaining = gemm_m;
+                float * c_blk          = output + ni;
+                auto *  a_row_cur      = a_row;
 
-    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK_K != 0) {
-        return -1;
-    }
+                if (ith % 2 != 0) {
+                    spine_barrier_wait(cur_barrier);
+                }
 
-    for (int b = 0; b < nrow; b += nrows_interleaved) {
-        for (int64_t x = 0; x < nblocks; x++) {
-            for (int j = 0; j < 8; j++) {
-                for (int i = 0; i < nrows_interleaved; i++) {
-                    uint8_t     sc, m;
-                    const float d = GGML_FP16_TO_FP32(src[x + i * nblocks].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
-                    const float min =
-                        GGML_FP16_TO_FP32(src[x + i * nblocks].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin);
-                    get_scale_min_k4(j, src[x + i * nblocks].scales, &sc, &m);
-                    const float d1 = d * sc;
-                    const float m1 = min * m;
-
-                    dst_tmp[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d = GGML_FP32_TO_FP16(d1);
-                    dst_tmp[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.m = GGML_FP32_TO_FP16(-m1);
-                    // src -> [b0, b32] [b1, b33] ... [b31, b63]
-                    // dst -> [b0, b16] [b1, b17] ... [b15, b31] [b32, b48] [b33, b49] ... [b47, b63]
-                    const uint8_t * q                                  = src[x + i * nblocks].qs + (j / 2) * QK4_1;
-                    if (j % 2 == 0) {
-                        for (int ii = 0; ii < 16; ii++) {
-                            dst_tmp[i].qs[ii] = (q[ii] & 0x0F) | ((q[ii + 16] & 0x0F) << 4);
-                        }
-                    } else {
-                        for (int ii = 0; ii < 16; ii++) {
-                            dst_tmp[i].qs[ii] = ((q[ii] & 0xF0) >> 4) | (q[ii + 16] & 0xF0);
-                        }
-                    }
+                while (rows_remaining > 0) {
+                    auto rows_handled = gemm_kernel(b_blk_len, a_row_cur, b_col, b_col_zp, c_blk, rows_remaining,
+                                                    nb_real, b_k_blks, gemm_n);
+
+                    c_blk += rows_handled * gemm_n;
+                    a_row_cur += rows_handled * row_stride_a;
+
+                    rows_remaining -= rows_handled;
+                }
+
+                if (ith % 2 == 0) {
+                    spine_barrier_wait(cur_barrier);
+                }
+
+                const int64_t next_ni = ni + NB_COLS * nth;
+                if (next_ni < gemm_n) {
+                    nb_real = std::min(gemm_n - next_ni, NB_COLS);
+                    spacemit_kernels::rvv::memcpy1d(b_col, reinterpret_cast<uint8_t *>(w_data) + next_ni * row_stride_b,
+                                                    nb_real * row_stride_b);
                 }
-                *dst++ = make_block_q4_1x16(dst_tmp, interleave_block);
             }
-        }
-        src += nrows_interleaved * nblocks;
-    }
-    return 0;
+        } else {
+            const int64_t task_count_m = spacemit_kernels::div_round_up(gemm_m, gemm_m_stride);
+            const int64_t task_count_n = spacemit_kernels::div_round_up(gemm_n, gemm_n_stride);
 
-    GGML_UNUSED(data_size);
-}
+            int64_t task_count      = task_count_m * task_count_n;
+            int64_t task_per_thread = (task_count + nth - 1) / nth;
+            int64_t start           = ith * task_per_thread;
+            int64_t end             = std::min((ith + 1) * task_per_thread, task_count);
+            for (int64_t compute_idx = start; compute_idx < end; compute_idx++) {
+                const auto tid_n = compute_idx / task_count_m;
+                const auto tid_m = compute_idx % task_count_m;
 
-namespace ggml::cpu::riscv64_spacemit {
+                const int64_t m_start = tid_m * gemm_m_stride;
+                const int64_t m_count = std::min(gemm_m - m_start, (int64_t) gemm_m_stride);
 
-template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
-int repack(struct ggml_tensor *, const void *, size_t);
+                const int64_t n_start = tid_n * gemm_n_stride;
+                const int64_t n_count = std::min(gemm_n - n_start, (int64_t) gemm_n_stride);
 
-template <> int repack<block_q4_0, 8, 16>(struct ggml_tensor * t, const void * data, size_t data_size) {
-    return repack_q4_0_to_q4_0_16_bl(t, 16, data, data_size);
-}
+                const int64_t n_blk = m_count == 1 ? n_count : NB_COLS;
 
-template <> int repack<block_q4_1, 8, 16>(struct ggml_tensor * t, const void * data, size_t data_size) {
-    return repack_q4_1_to_q4_1_16_bl(t, 16, data, data_size);
-}
+                uint8_t * b_col    = reinterpret_cast<uint8_t *>(w_data) + n_start * row_stride_b;
+                uint8_t * b_col_zp = block_type_has_zp<BLOC_TYPE>() ? b_col : nullptr;
 
-template <> int repack<block_q4_K, 8, 16>(struct ggml_tensor * t, const void * data, size_t data_size) {
-    return repack_q4_k_to_q4_1_16_bl(t, 16, data, data_size);
-}
+                int64_t n_blk_real = 0;
+                for (int64_t ni = 0; ni < n_count; ni += n_blk_real, b_col += n_blk_real * row_stride_b) {
+                    n_blk_real = std::min(n_count - ni, n_blk);
 
-class tensor_traits_base : public ggml::cpu::tensor_traits {
-  public:
-    virtual int repack(struct ggml_tensor * t, const void * data, size_t data_size) = 0;
-};
+                    uint8_t * a_row = quant_a_buffer + m_start * row_stride_a;
 
-template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> class tensor_traits : public tensor_traits_base {
-    bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
-        switch (op->op) {
-            case GGML_OP_MUL_MAT:
-                size = ggml_row_size(GGML_TYPE_Q8_0, ggml_nelements(op->src[1])) * 4;
-                size = ((size + QK4_0 - 1) / QK4_0) * (QK4_0 * sizeof(float) + sizeof(float));
-                return true;
-            default:
-                // GGML_ABORT("fatal error");
-                break;
-        }
-        return false;
-    }
+                    float * c_blk = output + m_start * gemm_n + n_start + ni;
 
-    bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) override {
-        switch (op->op) {
-            case GGML_OP_MUL_MAT:
-                if (op->src[0]->type == GGML_TYPE_Q4_0 ||  //
-                    op->src[0]->type == GGML_TYPE_Q4_1 ||  //
-                    op->src[0]->type == GGML_TYPE_Q4_K) {
-                    forward_mul_mat_q4(params, op);
-                    return true;
+                    int64_t rows_remaining = m_count;
+
+                    uint8_t * b_col_cur    = b_col;
+                    uint8_t * b_col_zp_cur = b_col_zp;
+
+                    while (rows_remaining > 0) {
+                        auto rows_handled = gemm_kernel(b_blk_len, a_row, b_col_cur, b_col_zp_cur, c_blk,
+                                                        rows_remaining, n_blk_real, b_k_blks, gemm_n);
+
+                        c_blk += rows_handled * gemm_n;
+                        a_row += rows_handled * row_stride_a;
+
+                        rows_remaining -= rows_handled;
+                    }
                 }
-            default:
-                // GGML_ABORT("fatal error");
-                break;
+            }
         }
-        return false;
     }
 
-    void forward_mul_mat_q4(ggml_compute_params * params, ggml_tensor * op) {
+    void forward_mul_mat_id(ggml_compute_params * params, ggml_tensor * op) {
+        constexpr size_t a_blk_len = INTER_SIZE;
+        constexpr size_t b_blk_len = INTER_SIZE;
+
         const ggml_tensor * src0 = op->src[0];
         const ggml_tensor * src1 = op->src[1];
+        const ggml_tensor * ids  = op->src[2];
         ggml_tensor *       dst  = op;
 
         GGML_TENSOR_BINARY_OP_LOCALS
@@ -429,133 +554,381 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> class tensor_
         int ith = params->ith;
         int nth = params->nth;
 
-        [[maybe_unused]] const enum ggml_type type = src0->type;
+        // row groups
+        const int n_ids = ids->ne[0];  // n_expert_used
+        const int n_as  = ne02;        // n_expert
+
+        struct mmid_row_mapping {
+            int32_t i1;
+            int32_t i2;
+        };
+
+        spacemit_kernels::quantize_a_row_def           quantize_a_row_i8;
+        spacemit_kernels::gemm_kernel_quantize_def     gemm_kernel;
+        spacemit_kernels::moe_gemm_kernel_quantize_def moe_gemm_kernel_m2;
+        bool                                           set_kernel_impl = false;
+        size_t                                         block_stride_a  = spacemit_kernels::q8_blk_size(QK4_0);
+
+#if defined(RISCV64_SPACEMIT_IME2)
+        if (!set_kernel_impl && (global_spine_env_info.use_ime2)) {
+            quantize_a_row_i8 = spacemit_kernels::rvv::quantize_a_row_i8;
+            block_stride_a    = spacemit_kernels::q8_blk_size(QK4_0, true);
+
+            if constexpr (std::is_same_v<BLOC_TYPE, block_q6_K> || std::is_same_v<BLOC_TYPE, block_q8_0>) {
+                gemm_kernel     = spacemit_kernels::ime2::gemm_kernel_i8i8;
+                set_kernel_impl = true;
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_q4_0> || std::is_same_v<BLOC_TYPE, block_q4_1> ||
+                                 std::is_same_v<BLOC_TYPE, block_q4_K>) {
+                if constexpr (INTER_SIZE == 256) {
+                    gemm_kernel       = spacemit_kernels::ime2::gemm_kernel_i8i4_hp;
+                    quantize_a_row_i8 = spacemit_kernels::rvv::quantize_a_row_i8_hp;
+                    block_stride_a    = spacemit_kernels::q8_hp_blk_size(a_blk_len, true, true);
+                    set_kernel_impl   = true;
+                } else {
+                    gemm_kernel        = spacemit_kernels::ime2::gemm_kernel_i8i4;
+                    moe_gemm_kernel_m2 = spacemit_kernels::ime2::moe_m2_gemm_kernel_i8i4;
+                    quantize_a_row_i8  = spacemit_kernels::rvv::quantize_a_row_i8;
+                    block_stride_a     = spacemit_kernels::q8_blk_size(a_blk_len, true);
+                    set_kernel_impl    = true;
+                }
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_q2_K>) {
+                quantize_a_row_i8 = spacemit_kernels::rvv::quantize_a_row_i8k;
+                block_stride_a    = spacemit_kernels::q8k_blk_size(a_blk_len);
+                gemm_kernel       = spacemit_kernels::ime2::gemm_kernel_i8i2k;
+                set_kernel_impl   = true;
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_q3_K>) {
+                quantize_a_row_i8 = spacemit_kernels::rvv::quantize_a_row_i8k;
+                block_stride_a    = spacemit_kernels::q8k_blk_size(a_blk_len);
+                gemm_kernel       = spacemit_kernels::ime2::gemm_kernel_i8i3k;
+                set_kernel_impl   = true;
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_mxfp4>) {
+                gemm_kernel        = spacemit_kernels::ime2::gemm_kernel_i8mxfp4;
+                moe_gemm_kernel_m2 = spacemit_kernels::ime2::moe_m2_gemm_kernel_i8mxfp4;
+                set_kernel_impl    = true;
+            } else if constexpr (std::is_same_v<BLOC_TYPE, block_q5_1> || std::is_same_v<BLOC_TYPE, block_q5_K> ||
+                                 std::is_same_v<BLOC_TYPE, block_q5_0>) {
+                gemm_kernel        = spacemit_kernels::ime2::gemm_kernel_i8i5;
+                moe_gemm_kernel_m2 = spacemit_kernels::ime2::moe_m2_gemm_kernel_i8i5;
+                set_kernel_impl    = true;
+            }
+        }
+#endif
 
-        void *        w_data  = (void *) src0->data;
-        const float * feature = (const float *) src1->data;
-        float *       output  = (float *) dst->data;
+#if defined(RISCV64_SPACEMIT_IME1)
+        if (!set_kernel_impl && (global_spine_env_info.use_ime1)) {
+            quantize_a_row_i8 = spacemit_kernels::ime1::quantize_a_row_i8;
+
+            if constexpr (std::is_same_v<BLOC_TYPE, block_q4_0> || std::is_same_v<BLOC_TYPE, block_q4_1> ||
+                          std::is_same_v<BLOC_TYPE, block_q4_K>) {
+                gemm_kernel     = spacemit_kernels::ime1::gemm_kernel_i8i4;
+                set_kernel_impl = true;
+            }
+        }
+#endif
+        if (!set_kernel_impl) {
+            GGML_ABORT("no kernel implementation found for the block type");
+        }
 
-        const size_t                  batch_feature = ne12 * ne13;
-        [[maybe_unused]] const size_t batch_weight  = ne02 * ne03;
-        const size_t                  gemm_m        = ne11;
-        const size_t                  gemm_k        = ne10;
-        const size_t                  gemm_n        = ne01;
+        const size_t a_k_blks = spacemit_kernels::div_round_up(ne10, a_blk_len);
+        const size_t b_k_blks = spacemit_kernels::div_round_up(ne10, b_blk_len);
 
-        GGML_ASSERT(batch_weight == 1);
+        const size_t nbw1                = a_k_blks * block_stride_a;
+        const size_t nbw2                = ne11 * nbw1;
+        const size_t nbw3                = nbw2 * ne12;
+        const size_t gemm_workspace_size = GGML_PAD(nbw3, alignof(int64_t));
 
-        const size_t block_count_k           = div_round_up(gemm_k, QK4_0);
-        const size_t per_gemm_workspace_size = gemm_m * block_count_k * q8_blk_size(QK4_0);
-        const size_t per_gemm_workspace_stride =
-            div_round_up(per_gemm_workspace_size, alignof(uint64_t)) * alignof(uint64_t);
-        const size_t gemm_workspace_size = batch_feature * per_gemm_workspace_stride;
-        const size_t desired_wsize       = gemm_workspace_size + alignof(uint64_t) - 1;
+        const uintptr_t ws_ptr         = reinterpret_cast<uintptr_t>(params->wdata);
+        auto *          quant_a_buffer = reinterpret_cast<uint8_t *>(ws_ptr);
 
-        if (ith == 0 && params->wsize < desired_wsize) {
-            throw std::runtime_error("wsize less than desired_wsize");
+        if (ne11 == 1) {
+            for (int64_t ii = ith; ii < ne12 * a_k_blks; ii += nth) {
+                int64_t i12       = ii / a_k_blks;
+                int64_t ak_blk_id = ii % a_k_blks;
+                quantize_a_row_i8(a_blk_len, (float *) ((char *) src1->data + i12 * nb12) + ak_blk_id * a_blk_len,
+                                  a_blk_len, quant_a_buffer + i12 * nbw2 + ak_blk_id * block_stride_a);
+            }
+        } else {
+            for (int64_t ii = ith; ii < ne12 * ne11; ii += nth) {
+                int64_t i12 = ii / ne11;
+                int64_t i11 = ii % ne11;
+                quantize_a_row_i8(a_blk_len, (float *) ((char *) src1->data + i12 * nb12 + i11 * nb11), ne10,
+                                  quant_a_buffer + i12 * nbw2 + i11 * nbw1);
+            }
         }
 
-        std::vector<qnbitgemm_spacemit_ime_args> qnbitgemm_args(batch_feature);
+#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) *ne12 + (i1)]
 
-        for (size_t i = 0; i < batch_feature; i++) {
-            qnbitgemm_args[i].a_ptr               = feature + gemm_m * gemm_k * i;
-            qnbitgemm_args[i].lda                 = gemm_k;
-            qnbitgemm_args[i].packed_quant_b_data = (const std::byte *) w_data;
-            qnbitgemm_args[i].quant_b_scale       = nullptr;
+        int64_t *          matrix_row_counts       = (int64_t *) (ws_ptr + gemm_workspace_size);
+        int32_t *          valid_ep_count          = (int32_t *) (matrix_row_counts + n_as);
+        int32_t *          valid_act_count         = (int32_t *) (valid_ep_count + 1);
+        int64_t *          valid_matrix_row_counts = (int64_t *) (valid_act_count + 1);
+        mmid_row_mapping * matrix_rows             = (mmid_row_mapping *) (valid_matrix_row_counts + n_as);
 
-            if constexpr (std::is_same_v<BLOC_TYPE, block_q4_0>) {
-                qnbitgemm_args[i].quant_b_zp = nullptr;
-            } else {
-                qnbitgemm_args[i].quant_b_zp = w_data;
+        if (ith == 0) {
+            // initialize matrix_row_counts
+            memset(matrix_row_counts, 0, n_as * sizeof(int64_t));
+
+            // group rows by src0 matrix
+            for (int32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
+                for (int32_t id = 0; id < n_ids; ++id) {
+                    const int32_t i02 =
+                        *(const int32_t *) ((const char *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]);
+
+                    GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+                    MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = { id, iid1 };
+                    matrix_row_counts[i02] += 1;
+                }
             }
 
-            qnbitgemm_args[i].bias  = nullptr;
-            qnbitgemm_args[i].c_ptr = output + gemm_m * gemm_n * i;
-            qnbitgemm_args[i].ldc   = gemm_n;
+            int32_t valid_ep_count_t  = 0;
+            int32_t valid_act_count_t = 0;
+            for (int cur_a = 0; cur_a < n_as; ++cur_a) {
+                const int64_t cne1 = matrix_row_counts[cur_a];
+                if (cne1 == 0) {
+                    continue;
+                }
+                valid_matrix_row_counts[valid_ep_count_t] = cur_a;
+                valid_act_count_t += cne1;
+                valid_ep_count_t += 1;
+            }
+            valid_ep_count[0]  = valid_ep_count_t;
+            valid_act_count[0] = valid_act_count_t;
         }
 
-        const uintptr_t ws_ptr = reinterpret_cast<uintptr_t>(params->wdata);
-        void *          ws = reinterpret_cast<void *>((ws_ptr + alignof(uint64_t) - 1) & (~(alignof(uint64_t) - 1)));
-        const size_t    quant_a_stride = block_count_k * q8_blk_size(QK4_0);
+        const int64_t barrier_idx = static_cast<int64_t>(ith / 2);
 
-        {
-            constexpr size_t block_size_m           = 4;
-            size_t           per_gemm_block_count_m = div_round_up(gemm_m, block_size_m);
-            int32_t          task_count             = batch_feature * per_gemm_block_count_m;
-            int32_t          task_per_thread        = (task_count + nth - 1) / nth;
-            int32_t          start                  = ith * task_per_thread;
-            int32_t          end                    = std::min((ith + 1) * task_per_thread, task_count);
-            for (int32_t compute_idx = start; compute_idx < end; compute_idx++) {
-                int32_t                             gemm_idx = compute_idx / per_gemm_block_count_m;
-                int32_t                             block_idx_in_gemm = compute_idx % per_gemm_block_count_m;
-                int32_t                             m_idx    = block_idx_in_gemm * block_size_m;
-                const qnbitgemm_spacemit_ime_args & data     = qnbitgemm_args[gemm_idx];
-                int32_t rows_tobe_handled = (gemm_m - m_idx) > block_size_m ? block_size_m : (gemm_m - m_idx);
-
-                if (rows_tobe_handled == block_size_m) {
-                    const float * a_row_ptr = data.a_ptr + m_idx * data.lda;
-                    std::byte *   quant_a_row_ptr =
-                        static_cast<std::byte *>(ws) + gemm_idx * per_gemm_workspace_stride + m_idx * quant_a_stride;
-                    sqnbitgemm_spacemit_ime::ime1::quantize_a_4row_i8(QK4_0, a_row_ptr, gemm_k, quant_a_row_ptr);
-                } else {
-                    while (rows_tobe_handled) {
-                        const float * a_row_ptr       = data.a_ptr + m_idx * data.lda;
-                        std::byte *   quant_a_row_ptr = static_cast<std::byte *>(ws) +
-                                                      gemm_idx * per_gemm_workspace_stride + m_idx * quant_a_stride;
-                        sqnbitgemm_spacemit_ime::ime1::quantize_a_row_i8(QK4_0, a_row_ptr, gemm_k, quant_a_row_ptr);
-                        rows_tobe_handled -= 1;
-                        m_idx += 1;
+        GGML_ASSERT(global_spine_env_info.init_barrier != nullptr);
+        GGML_ASSERT(barrier_idx < spine_init_barrier_count);
+        spine_barrier_t * cur_barrier = &global_spine_env_info.init_barrier[barrier_idx];
+
+        ggml_barrier(params->threadpool);
+
+        const size_t row_stride_b      = b_k_blks * get_repacked_block_type_size<BLOC_TYPE, INTER_SIZE, NB_COLS>();
+        const size_t expert_b_stride   = ne01 * row_stride_b;
+        const size_t per_nb_cols_wsize = NB_COLS * row_stride_b;
+
+        std::array<const uint8_t *, 2> src_workspaces;
+        std::array<float *, 2>         dst_workspaces;
+
+        auto *     tcm_buffer      = ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer;
+        const auto tcm_buffer_size = ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer_size;
+
+        const auto valid_ep_count_t  = valid_ep_count[0];
+        const auto valid_act_count_t = valid_act_count[0];
+
+        int nth_es = 1;
+        int nth_n  = nth;
+
+        int ith_es = ith % nth_es;
+        int ith_n  = (ith / nth_es) % nth_n;
+
+        if (valid_ep_count_t % nth == 0 && tcm_buffer != nullptr && valid_ep_count_t == n_as &&
+            valid_act_count_t == n_as && per_nb_cols_wsize <= tcm_buffer_size) {
+            for (int64_t valid_id = ith; valid_id < valid_ep_count_t; valid_id += nth) {
+                const int64_t cur_a = valid_matrix_row_counts[valid_id];
+
+                auto * src0_cur = (uint8_t *) src0->data + cur_a * expert_b_stride;
+
+                mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, 0);
+                const int        id          = row_mapping.i1;
+                const int64_t    i11         = id % ne11;
+                const int64_t    i12         = row_mapping.i2;
+                const int64_t    i1          = id;
+                const int64_t    i2          = i12;
+
+                auto *  src1_col = quant_a_buffer + (i11 * nbw1 + i12 * nbw2);
+                float * c_blk    = (float *) ((char *) dst->data + (i1 * nb1 + i2 * nb2));
+
+                uint8_t * a_row = src1_col;
+                uint8_t * b_col = reinterpret_cast<uint8_t *>(tcm_buffer);
+                if ((nbw1 + per_nb_cols_wsize) <= tcm_buffer_size) {
+                    a_row = (uint8_t *) tcm_buffer;
+                    b_col = reinterpret_cast<uint8_t *>(tcm_buffer) + nbw1;
+                }
+                uint8_t * b_col_zp = block_type_has_zp<BLOC_TYPE>() ? b_col : nullptr;
+
+                if (ith % 2 == 0) {
+                    spacemit_kernels::rvv::memcpy1d(b_col, reinterpret_cast<uint8_t *>(src0_cur), per_nb_cols_wsize);
+
+                    if (a_row != src1_col) {
+                        spacemit_kernels::rvv::memcpy1d(a_row, src1_col, nbw1);
+                    }
+                }
+
+                spine_barrier_wait(cur_barrier);
+
+                if (ith % 2 != 0) {
+                    if (a_row != src1_col) {
+                        spacemit_kernels::rvv::memcpy1d(a_row, src1_col, nbw1);
+                    }
+
+                    spacemit_kernels::rvv::memcpy1d(b_col, reinterpret_cast<uint8_t *>(src0_cur), per_nb_cols_wsize);
+                }
+
+                int64_t nb_real = std::min(ne01, NB_COLS);
+                for (int64_t ni = 0; ni < ne01; ni += NB_COLS) {
+                    if (ith % 2 != 0) {
+                        spine_barrier_wait(cur_barrier);
+                    }
+
+                    gemm_kernel(b_blk_len, a_row, b_col, b_col_zp, c_blk + ni, 1, nb_real, b_k_blks, ne01);
+
+                    if (ith % 2 == 0) {
+                        spine_barrier_wait(cur_barrier);
+                    }
+
+                    const int64_t next_ni = ni + NB_COLS;
+                    if (next_ni < ne01) {
+                        nb_real = std::min(ne01 - next_ni, NB_COLS);
+                        spacemit_kernels::rvv::memcpy1d(
+                            b_col, reinterpret_cast<uint8_t *>(src0_cur) + next_ni * row_stride_b, per_nb_cols_wsize);
                     }
                 }
             }
-        }
+        } else {
+            for (int64_t valid_id = ith_es; valid_id < valid_ep_count_t; valid_id += nth_es) {
+                const int64_t cur_a = valid_matrix_row_counts[valid_id];
+                const int64_t cne1  = matrix_row_counts[cur_a];
 
-        ggml_barrier(params->threadpool);
+                int64_t src1_cur_start = 0;
+                int64_t src1_cur_end   = cne1;
 
-        if (ith >= ggml::cpu::riscv64_spacemit::num_ai_cores) {
-            return;
-        }
-        nth = std::min(nth, int{ ggml::cpu::riscv64_spacemit::num_ai_cores });
-
-        size_t           threads_per_gemm = nth / batch_feature;
-        constexpr size_t gemm_m_stride    = 128;
-        size_t           nc               = gemm_n;
-        const size_t     gemm_m_blocked   = div_round_up(gemm_m, gemm_m_stride);
-        const size_t     max_nc           = div_round_up(gemm_n * gemm_m_blocked, threads_per_gemm);
-        if (max_nc < nc) {
-            nc = std::min(nc, div_round_up(max_nc, QGEMM_STRIDEN_THREAD_ALIGN) * QGEMM_STRIDEN_THREAD_ALIGN);
-        }
-        const size_t gemm_n_stride  = nc;
-        const size_t thread_count_m = div_round_up(gemm_m, gemm_m_stride);
-        const size_t thread_count_n = div_round_up(gemm_n, gemm_n_stride);
-        threads_per_gemm            = thread_count_m * thread_count_n;
+                int64_t src0_cur_start = (ith_n * ne01) / nth_n;
+                int64_t src0_cur_end   = MIN(((ith_n + 1) * ne01) / nth_n, ne01);
 
-        {
-            int task_count      = batch_feature * threads_per_gemm;
-            int task_per_thread = (task_count + nth - 1) / nth;
-            int start           = ith * task_per_thread;
-            int end             = std::min((ith + 1) * task_per_thread, task_count);
-            for (int compute_idx = start; compute_idx < end; compute_idx++) {
-                const auto   gemm_i = compute_idx / threads_per_gemm;
-                const auto   blk_i  = compute_idx % threads_per_gemm;
-                const auto * data   = &qnbitgemm_args[gemm_i];
+                if (src1_cur_start >= src1_cur_end || src0_cur_start >= src0_cur_end) {
+                    continue;
+                }
+
+                src0_cur_start =
+                    (src0_cur_start % NB_COLS) ? src0_cur_start + NB_COLS - (src0_cur_start % NB_COLS) : src0_cur_start;
+                src0_cur_end =
+                    (src0_cur_end % NB_COLS) ? src0_cur_end + NB_COLS - (src0_cur_end % NB_COLS) : src0_cur_end;
+
+                auto *    src0_cur = (uint8_t *) src0->data + cur_a * expert_b_stride + src0_cur_start * row_stride_b;
+                uint8_t * b_col_zp = block_type_has_zp<BLOC_TYPE>() ? src0_cur : nullptr;
+
+                size_t extra_tcm_buffer_size = tcm_buffer_size;
+                void * extra_tcm_buffer      = tcm_buffer;
+                if (tcm_buffer != nullptr && (src1_cur_end - src1_cur_start) >= 4 &&
+                    (src0_cur_end - src0_cur_start) * row_stride_b <= tcm_buffer_size) {
+                    spacemit_kernels::rvv::memcpy1d(tcm_buffer, src0_cur,
+                                                    (src0_cur_end - src0_cur_start) * row_stride_b);
+                    src0_cur = reinterpret_cast<uint8_t *>(tcm_buffer);
+                    b_col_zp = block_type_has_zp<BLOC_TYPE>() ? src0_cur : nullptr;
+                    extra_tcm_buffer_size -= (src0_cur_end - src0_cur_start) * row_stride_b;
+                    extra_tcm_buffer = reinterpret_cast<void *>(reinterpret_cast<uintptr_t>(tcm_buffer) +
+                                                                (src0_cur_end - src0_cur_start) * row_stride_b);
+                }
 
-                const auto tid_n = blk_i / thread_count_m;
-                const auto tid_m = blk_i % thread_count_m;
+                int ir1 = src1_cur_start;
 
-                const size_t m_start = tid_m * gemm_m_stride;
-                const size_t m_count = std::min(gemm_m - m_start, (size_t) gemm_m_stride);
+                if (extra_tcm_buffer_size >= nbw1 && extra_tcm_buffer != nullptr) {
+                    int64_t quant_a_tile_size = extra_tcm_buffer_size / nbw1;
+                    do {
+                        quant_a_tile_size = MIN(quant_a_tile_size, src1_cur_end - ir1);
 
-                const size_t n_start = tid_n * gemm_n_stride;
-                const size_t n_count = std::min(gemm_n - n_start, (size_t) gemm_n_stride);
+                        uint8_t * quant_a_tile_buffer = reinterpret_cast<uint8_t *>(extra_tcm_buffer);
 
-                void * per_gemm_ws = reinterpret_cast<std::byte *>(ws) + gemm_i * per_gemm_workspace_stride;
+                        int iir1 = ir1;
+                        for (; iir1 < (ir1 + quant_a_tile_size); ++iir1) {
+                            mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, iir1);
 
-                sqnbitgemm_spacemit_ime_i8i4(QK4_0, gemm_k, data, per_gemm_ws, m_start, m_count, n_start, n_count);
+                            const int id = row_mapping.i1;  // selected expert index
+
+                            const int64_t i11 = id % ne11;
+                            const int64_t i12 = row_mapping.i2;  // row index in src1
+
+                            auto * src1_col = quant_a_buffer + (i11 * nbw1 + i12 * nbw2);
+                            spacemit_kernels::rvv::memcpy1d(quant_a_tile_buffer, src1_col, nbw1);
+                            quant_a_tile_buffer = quant_a_tile_buffer + nbw1;
+                        }
+
+                        quant_a_tile_buffer = reinterpret_cast<uint8_t *>(extra_tcm_buffer);
+                        iir1                = ir1;
+
+                        if (moe_gemm_kernel_m2 != nullptr) {
+                            for (; iir1 < (ir1 + quant_a_tile_size - 1); iir1 += 2, quant_a_tile_buffer += 2 * nbw1) {
+                                mmid_row_mapping row_mapping_0 = MMID_MATRIX_ROW(cur_a, iir1);
+                                mmid_row_mapping row_mapping_1 = MMID_MATRIX_ROW(cur_a, iir1 + 1);
+
+                                src_workspaces[0] = quant_a_tile_buffer;
+                                src_workspaces[1] = quant_a_tile_buffer + nbw1;
+
+                                dst_workspaces[0] =
+                                    (float *) ((char *) dst->data + (row_mapping_0.i1 * nb1 + row_mapping_0.i2 * nb2)) +
+                                    src0_cur_start;
+                                dst_workspaces[1] = (float *) ((char *) dst->data +
+                                                               ((row_mapping_1.i1) * nb1 + (row_mapping_1.i2) * nb2)) +
+                                                    src0_cur_start;
+                                moe_gemm_kernel_m2(b_blk_len, src_workspaces.data(), src0_cur, b_col_zp,
+                                                   dst_workspaces.data(), 1, src0_cur_end - src0_cur_start, b_k_blks,
+                                                   ne01);
+                            }
+                        }
+
+                        for (; iir1 < (ir1 + quant_a_tile_size); iir1++, quant_a_tile_buffer += nbw1) {
+                            mmid_row_mapping row_mapping_0 = MMID_MATRIX_ROW(cur_a, iir1);
+
+                            gemm_kernel(
+                                b_blk_len, quant_a_tile_buffer, src0_cur, b_col_zp,
+                                (float *) ((char *) dst->data + (row_mapping_0.i1 * nb1 + row_mapping_0.i2 * nb2)) +
+                                    src0_cur_start,
+                                1, src0_cur_end - src0_cur_start, b_k_blks, ne01);
+                        }
+
+                        ir1 += quant_a_tile_size;
+                    } while (ir1 < src1_cur_end);
+                } else {
+                    if (moe_gemm_kernel_m2 != nullptr) {
+                        for (; ir1 < src1_cur_end - 1; ir1 += 2) {
+                            for (int iir1 = 0; iir1 < 2; ++iir1) {
+                                mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1 + iir1);
+
+                                const int id = row_mapping.i1;  // selected expert index
+
+                                const int64_t i11 = id % ne11;
+                                const int64_t i12 = row_mapping.i2;  // row index in src1
+
+                                const int64_t i1 = id;               // selected expert index
+                                const int64_t i2 = i12;              // row
+
+                                src_workspaces[iir1] = quant_a_buffer + (i11 * nbw1 + i12 * nbw2);
+
+                                dst_workspaces[iir1] =
+                                    (float *) ((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start;
+                            }
+
+                            moe_gemm_kernel_m2(b_blk_len, src_workspaces.data(), src0_cur, b_col_zp,
+                                               dst_workspaces.data(), 1, src0_cur_end - src0_cur_start, b_k_blks, ne01);
+                        }
+                    }
+
+                    for (; ir1 < src1_cur_end; ir1++) {
+                        mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1);
+
+                        const int id = row_mapping.i1;  // selected expert index
+
+                        const int64_t i11 = id % ne11;
+                        const int64_t i12 = row_mapping.i2;  // row index in src1
+
+                        const int64_t i1 = id;               // selected expert index
+                        const int64_t i2 = i12;              // row
+
+                        auto * src1_col = quant_a_buffer + (i11 * nbw1 + i12 * nbw2);
+
+                        gemm_kernel(b_blk_len, src1_col, src0_cur, b_col_zp,
+                                    (float *) ((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, 1,
+                                    src0_cur_end - src0_cur_start, b_k_blks, ne01);
+                    }
+                }
             }
         }
+#undef MMID_MATRIX_ROW
     }
 
-    int repack(struct ggml_tensor * t, const void * data, size_t data_size) override {
+    int repack(ggml_tensor * t, const void * data, size_t data_size) override {
         GGML_LOG_DEBUG("%s: repack tensor %s with %s_%dx%d\n", __func__, t->name, ggml_type_name(t->type),
                        (int) NB_COLS, (int) INTER_SIZE);
         return ggml::cpu::riscv64_spacemit::repack<BLOC_TYPE, INTER_SIZE, NB_COLS>(t, data, data_size);
@@ -563,309 +936,464 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> class tensor_
 };
 
 class tensor_traits_common : public tensor_traits_base {
-    bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
+    bool work_size(int n_threads, const ggml_tensor * op, size_t & size) override {
         switch (op->op) {
-            case GGML_OP_NORM:
-            case GGML_OP_RMS_NORM:
-                size = 0;
+            case GGML_OP_FLASH_ATTN_EXT:
+                {
+                    const int     n_tasks = n_threads;
+                    const int64_t neq2    = op->src[0]->ne[2];  // number of query heads
+                    const int64_t DK      = op->src[1]->ne[0];
+                    const int64_t DV      = op->src[2]->ne[0];  // DV
+
+                    // Tiled flash attention scratch (tile sizes defined in common.h)
+                    // Per-thread: Q_q + KQ + mask + VKQ32 + V32 + K_f32 + padding
+                    size_t prefill = sizeof(float) *
+                                     (GGML_FA_TILE_Q * DK + 2 * GGML_FA_TILE_Q * GGML_FA_TILE_KV + GGML_FA_TILE_Q * DV +
+                                      GGML_FA_TILE_KV * DV + GGML_FA_TILE_KV * DK) *
+                                     n_tasks;
+
+                    // Decode path: n_kv_chunks = n_tasks (one chunk per thread)
+                    // Per-thread: VKQ accmulator (DV), partial M, partial S + intra-thread scratch for V, Q and VKQ
+                    size_t n_chunks = n_tasks;
+                    size_t decode   = sizeof(float) * (neq2 * n_chunks * (2 + DV) + n_tasks * (DK + 2 * DV));
+
+                    size = MAX(prefill, decode);
+                }
                 return true;
             default:
-                // GGML_ABORT("fatal error");
                 break;
         }
         return false;
     }
 
-    bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) override {
+    bool compute_forward(ggml_compute_params * params, ggml_tensor * op) override {
         switch (op->op) {
             case GGML_OP_NORM:
-                forward_norm_f32(params, op);
-                return true;
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                        spacemit_kernels::rvv::forward_norm_f32(params, op);
+                        return true;
+                    default:
+                        GGML_ABORT("fatal error");
+                }
             case GGML_OP_RMS_NORM:
-                forward_rms_norm_f32(params, op);
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                        spacemit_kernels::rvv::forward_rms_norm_f32(params, op);
+                        return true;
+                    default:
+                        GGML_ABORT("fatal error");
+                }
+            case GGML_OP_ADD:
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                        spacemit_kernels::rvv::forward_binary<GGML_OP_ADD, float>(params, op);
+                        return true;
+                    case GGML_TYPE_F16:
+                        spacemit_kernels::rvv::forward_binary<GGML_OP_ADD, _Float16>(params, op);
+                        return true;
+                    default:
+                        ggml_compute_forward_add(params, op);
+                        return true;
+                }
+            case GGML_OP_SUB:
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                        spacemit_kernels::rvv::forward_binary<GGML_OP_SUB, float>(params, op);
+                        return true;
+                    case GGML_TYPE_F16:
+                        spacemit_kernels::rvv::forward_binary<GGML_OP_SUB, _Float16>(params, op);
+                        return true;
+                    default:
+                        ggml_compute_forward_sub(params, op);
+                        return true;
+                }
+            case GGML_OP_MUL:
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                        spacemit_kernels::rvv::forward_binary<GGML_OP_MUL, float>(params, op);
+                        return true;
+                    case GGML_TYPE_F16:
+                        spacemit_kernels::rvv::forward_binary<GGML_OP_MUL, _Float16>(params, op);
+                        return true;
+                    default:
+                        ggml_compute_forward_mul(params, op);
+                        return true;
+                }
+            case GGML_OP_DIV:
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                        spacemit_kernels::rvv::forward_binary<GGML_OP_DIV, float>(params, op);
+                        return true;
+                    case GGML_TYPE_F16:
+                        spacemit_kernels::rvv::forward_binary<GGML_OP_DIV, _Float16>(params, op);
+                        return true;
+                    default:
+                        ggml_compute_forward_div(params, op);
+                        return true;
+                }
+            case GGML_OP_FLASH_ATTN_EXT:
+                forward_flash_attn_ext_f16(params, op);
+                return true;
+            case GGML_OP_CONT:
+                {
+                    const ggml_tensor * src0 = op->src[0];
+                    if (op->type == src0->type && op->nb[0] != src0->nb[0] && op->nb[0] == src0->nb[1] &&
+                        op->ne[3] * op->ne[2] * op->nb[2] == src0->ne[3] * src0->ne[2] * src0->nb[2]) {
+                        spacemit_kernels::rvv::forward_cont_with_permute(params, op);
+                    } else {
+                        ggml_compute_forward_cont(params, op);
+                    }
+                    return true;
+                }
+            case GGML_OP_CPY:
+                {
+                    const ggml_tensor * src0 = op->src[0];
+                    if (op->type == src0->type && op->nb[0] == src0->nb[1] && src0->nb[0] != src0->nb[1] &&
+                        ggml_nelements(src0) == ggml_nelements(op)) {
+                        spacemit_kernels::rvv::forward_cpy_with_permute(params, op);
+                    } else {
+                        ggml_compute_forward_cpy(params, op);
+                    }
+                    return true;
+                }
+            case GGML_OP_REPEAT:
+                {
+                    const bool rows_equal         = ggml_nrows(op->src[0]) == ggml_nrows(op);
+                    const bool broadcast_or_equal = op->src[0]->ne[0] == 1 || op->src[0]->ne[0] == op->ne[0];
+
+                    if (rows_equal && broadcast_or_equal) {
+                        switch (op->src[0]->type) {
+                            case GGML_TYPE_F32:
+                                spacemit_kernels::rvv::forward_repeat_nrows<int32_t>(params, op);
+                                return true;
+                            case GGML_TYPE_F16:
+                                spacemit_kernels::rvv::forward_repeat_nrows<int16_t>(params, op);
+                                return true;
+                            default:
+                                break;
+                        }
+                    }
+
+                    if (op->src[0]->ne[1] == 1 && op->src[0]->ne[0] == op->ne[0]) {
+                        switch (op->src[0]->type) {
+                            case GGML_TYPE_F32:
+                                spacemit_kernels::rvv::forward_repeat_dim1<int32_t>(params, op);
+                                return true;
+                            case GGML_TYPE_F16:
+                                spacemit_kernels::rvv::forward_repeat_dim1<int16_t>(params, op);
+                                return true;
+                            default:
+                                break;
+                        }
+                    }
+
+                    ggml_compute_forward_repeat(params, op);
+                }
+                return true;
+            case GGML_OP_SUM_ROWS:
+                {
+                    if (op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) {
+                        spacemit_kernels::rvv::forward_sum_rows<float>(params, op);
+                    } else {
+                        ggml_compute_forward_sum_rows(params, op);
+                    }
+                }
+                return true;
+            case GGML_OP_GET_ROWS:
+                {
+                    if (op->src[0]->type == op->type) {
+                        switch (op->src[0]->type) {
+                            case GGML_TYPE_F32:
+                                spacemit_kernels::rvv::forward_get_rows<int32_t>(params, op);
+                                return true;
+                            case GGML_TYPE_F16:
+                                spacemit_kernels::rvv::forward_get_rows<int16_t>(params, op);
+                                return true;
+                            default:
+                                break;
+                        }
+                    }
+
+                    ggml_compute_forward_get_rows(params, op);
+                }
                 return true;
+            case GGML_OP_CONCAT:
+                {
+                    const int32_t dim = ggml_get_op_params_i32(op, 0);
+                    if (dim == 0 && op->type == op->src[0]->type) {
+                        switch (op->src[0]->type) {
+                            case GGML_TYPE_F32:
+                                spacemit_kernels::rvv::forward_concat<int32_t>(params, op);
+                                return true;
+                            case GGML_TYPE_F16:
+                                spacemit_kernels::rvv::forward_concat<int16_t>(params, op);
+                                return true;
+                            default:
+                                break;
+                        }
+                    }
+
+                    ggml_compute_forward_concat(params, op);
+                }
+                return true;
+            // TODO For GGML_OP_GATED_DELTA_NET
+            // case GGML_OP_GATED_DELTA_NET:
+            //     return true;
             default:
-                // GGML_ABORT("fatal error");
                 break;
         }
         return false;
     }
 
-    void forward_norm_f32(ggml_compute_params * params, ggml_tensor * op) {
-        const ggml_tensor * src0 = op->src[0];
-        ggml_tensor *       dst  = op;
-        GGML_ASSERT(ggml_are_same_shape(src0, dst));
-        GGML_ASSERT(src0->nb[0] == sizeof(float));
+    void forward_flash_attn_ext_f16(const ggml_compute_params * params, ggml_tensor * dst) {
+        const ggml_tensor * q = dst->src[0];
+        const ggml_tensor * k = dst->src[1];
+        const ggml_tensor * v = dst->src[2];
+
+        GGML_TENSOR_LOCALS(int64_t, neq, q, ne)
+        GGML_TENSOR_LOCALS(size_t, nbq, q, nb)
+        GGML_TENSOR_LOCALS(int64_t, nek, k, ne)
+        GGML_TENSOR_LOCALS(size_t, nbk, k, nb)
+        GGML_TENSOR_LOCALS(int64_t, nev, v, ne)
+        GGML_TENSOR_LOCALS(size_t, nbv, v, nb)
+        GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
+        GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
+
+        const int64_t DK = nek0;
+        const int64_t DV = nev0;
+
+        const bool supported_prec  = (dst->op_params[3] == GGML_PREC_F32 || dst->op_params[3] == GGML_PREC_DEFAULT);
+        const bool supported_types = (q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16 && v->type == GGML_TYPE_F16);
+        const bool supported_shape = (DK > 0 && DK <= 128 && DV > 0 && DV <= 128);
+        const bool supported_vlen  = (__riscv_vlenb() == 128);
+
+        if (!(supported_prec && supported_types && supported_shape && supported_vlen)) {
+            ggml_compute_forward_flash_attn_ext(params, dst);
+            return;
+        }
+
+        // total rows in q
+        const int64_t nr = neq1 * neq2 * neq3;
 
+        // rows per thread
         const int ith = params->ith;
         const int nth = params->nth;
 
-        GGML_TENSOR_UNARY_OP_LOCALS
+        static constexpr int64_t Q_TILE_SZ = ggml_fa_tile_config::Q;
+        const bool               use_tiled = !params->use_ref && (neq1 >= Q_TILE_SZ);
 
-        float epsilon;
-        memcpy(&epsilon, dst->op_params, sizeof(float));
+        // 4x chunks per thread
+        // int     nth_scaled = nth * 4;
+        // int64_t chunk_size = (nr + nth_scaled - 1) / nth_scaled;
+        // int64_t nchunk     = (nr + chunk_size - 1) / chunk_size;
 
-        GGML_ASSERT(epsilon > 0.0f);
+        // if (nth == 1 || nchunk < nth) {
+        //     nchunk = nth;
+        // }
 
-        auto * input  = (float *) src0->data;
-        auto * output = (float *) dst->data;
+        int64_t nchunk = nth;
 
-        const auto hidden_size     = ne00;
-        const auto task_count      = ne01 * ne02 * ne03;
-        const auto task_per_thread = (task_count + nth - 1) / nth;
-
-        const auto task_begin = ith * task_per_thread;
-        const auto task_end   = std::min((ith + 1) * task_per_thread, task_count);
+        if (ith == 0) {
+            // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
+            ggml_threadpool_chunk_set(params->threadpool, nth);
+        }
 
-        for (auto task_idx = task_begin; task_idx < task_end; task_idx++) {
-            auto   offset  = task_idx * hidden_size;
-            auto * p_input = const_cast<float *>(input + offset);
+        ggml_barrier(params->threadpool);
 
-            auto *       p_output      = output + offset;
-            auto *       p_temp_output = p_output;
-            auto *       p_gamma_data  = (const float *) nullptr;
-            auto *       p_beta_data   = (const float *) nullptr;
-            size_t       gvl           = __riscv_vsetvlmax_e32m4();
-            vfloat32m4_t sum           = __riscv_vfmv_v_f_f32m4(0.f, gvl);
-            vfloat32m4_t sum_sq        = __riscv_vfmv_v_f_f32m4(0.f, gvl);
-            int64_t      length        = hidden_size;
-            while (length > 0) {
-                gvl                   = __riscv_vsetvl_e32m4(length);
-                // load data
-                vfloat32m4_t src_data = __riscv_vle32_v_f32m4(p_input, gvl);
+        // The number of elements in each chunk
+        const int64_t dr = (nr + nchunk - 1) / nchunk;
 
-                sum    = __riscv_vfadd_vv_f32m4(sum, src_data, gvl);
-                sum_sq = __riscv_vfmacc_vv_f32m4(sum_sq, src_data, src_data, gvl);
+        // The first chunk comes from our thread_id, the rest will get auto-assigned.
+        int current_chunk = ith;
 
-                __riscv_vse32_v_f32m4(p_temp_output, src_data, gvl);
+        while (current_chunk < nchunk) {
+            const int64_t ir0 = dr * current_chunk;
+            const int64_t ir1 = MIN(ir0 + dr, nr);
 
-                p_input += gvl;
-                p_temp_output += gvl;
-                length -= gvl;
+            if (use_tiled) {
+                spacemit_kernels::rvv::forward_flash_attn_ext_f16_tiled_vlen1024_vf16(
+                    params, dst, ir0, ir1, ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer,
+                    ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer_size);
+            } else {
+                spacemit_kernels::rvv::forward_flash_attn_ext_f16_one_chunk_vlen1024_vf16(
+                    params, dst, ir0, ir1, ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer,
+                    ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer_size);
             }
 
-            gvl = __riscv_vsetvlmax_e32m1();
-
-            float        mean   = 0.f;
-            vfloat32m1_t zero_v = __riscv_vfmv_v_f_f32m1(0.f, gvl);
-            vfloat32m1_t mean_v =
-                __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m4_f32m1(sum, 0), __riscv_vget_v_f32m4_f32m1(sum, 1), gvl);
-            mean_v = __riscv_vfadd_vv_f32m1(mean_v, __riscv_vget_v_f32m4_f32m1(sum, 2), gvl);
-            mean_v = __riscv_vfadd_vv_f32m1(mean_v, __riscv_vget_v_f32m4_f32m1(sum, 3), gvl);
-            mean_v = __riscv_vfredusum_vs_f32m1_f32m1(mean_v, zero_v, gvl);
-            mean   = __riscv_vfmv_f_s_f32m1_f32(mean_v);
-            mean /= hidden_size;
-
-            vfloat32m1_t mean_square_v = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m4_f32m1(sum_sq, 0),
-                                                                __riscv_vget_v_f32m4_f32m1(sum_sq, 1), gvl);
-            mean_square_v = __riscv_vfadd_vv_f32m1(mean_square_v, __riscv_vget_v_f32m4_f32m1(sum_sq, 2), gvl);
-            mean_square_v = __riscv_vfadd_vv_f32m1(mean_square_v, __riscv_vget_v_f32m4_f32m1(sum_sq, 3), gvl);
-            mean_square_v = __riscv_vfredusum_vs_f32m1_f32m1(mean_square_v, zero_v, gvl);
-
-            float mean_square = __riscv_vfmv_f_s_f32m1_f32(mean_square_v);
-            mean_square /= hidden_size;
-            mean_square = sqrt(mean_square - mean * mean + epsilon);
-
-            mean_square   = 1.0f / mean_square;
-            length        = hidden_size;
-            p_temp_output = p_output;
-
-            if (p_gamma_data == nullptr && p_beta_data == nullptr) {
-                while (length > 0) {
-                    gvl                   = __riscv_vsetvl_e32m4(length);
-                    vfloat32m4_t src_data = __riscv_vle32_v_f32m4(p_temp_output, gvl);
-                    src_data              = __riscv_vfsub_vf_f32m4(src_data, mean, gvl);
-                    src_data              = __riscv_vfmul_vf_f32m4(src_data, mean_square, gvl);
-                    __riscv_vse32_v_f32m4(p_output, src_data, gvl);
-                    p_temp_output += gvl;
-                    p_output += gvl;
-                    length -= gvl;
-                }
-            } else if (p_beta_data == nullptr) {
-                while (length > 0) {
-                    gvl                       = __riscv_vsetvl_e32m4(length);
-                    vfloat32m4_t src_data     = __riscv_vle32_v_f32m4(p_temp_output, gvl);
-                    vfloat32m4_t gamma_data_v = __riscv_vle32_v_f32m4(p_gamma_data, gvl);
-                    src_data                  = __riscv_vfsub_vf_f32m4(src_data, mean, gvl);
-                    src_data                  = __riscv_vfmul_vf_f32m4(src_data, mean_square, gvl);
-                    src_data                  = __riscv_vfmul_vv_f32m4(src_data, gamma_data_v, gvl);
-                    __riscv_vse32_v_f32m4(p_output, src_data, gvl);
-                    p_temp_output += gvl;
-                    p_output += gvl;
-                    p_gamma_data += gvl;
-                    length -= gvl;
-                }
-            } else if (p_gamma_data != nullptr) {
-                while (length > 0) {
-                    gvl                       = __riscv_vsetvl_e32m4(length);
-                    vfloat32m4_t src_data     = __riscv_vle32_v_f32m4(p_temp_output, gvl);
-                    vfloat32m4_t gamma_data_v = __riscv_vle32_v_f32m4(p_gamma_data, gvl);
-                    src_data                  = __riscv_vfsub_vf_f32m4(src_data, mean, gvl);
-                    src_data                  = __riscv_vfmul_vf_f32m4(src_data, mean_square, gvl);
-                    src_data                  = __riscv_vfmul_vv_f32m4(src_data, gamma_data_v, gvl);
-                    vfloat32m4_t beta_data_v  = __riscv_vle32_v_f32m4(p_beta_data, gvl);
-                    src_data                  = __riscv_vfadd_vv_f32m4(src_data, beta_data_v, gvl);
-                    p_beta_data += gvl;
-                    __riscv_vse32_v_f32m4(p_output, src_data, gvl);
-                    p_temp_output += gvl;
-                    p_output += gvl;
-                    p_gamma_data += gvl;
-                    length -= gvl;
-                }
-            }
+            current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
         }
     }
 
-    void forward_rms_norm_f32(ggml_compute_params * params, ggml_tensor * op) {
-        const ggml_tensor * src0 = op->src[0];
-        ggml_tensor *       dst  = op;
-        GGML_ASSERT(ggml_are_same_shape(src0, dst));
-        GGML_ASSERT(src0->nb[0] == sizeof(float));
-
-        const int ith = params->ith;
-        const int nth = params->nth;
-
-        GGML_TENSOR_UNARY_OP_LOCALS
-
-        float epsilon;
-        memcpy(&epsilon, dst->op_params, sizeof(float));
-
-        GGML_ASSERT(epsilon > 0.0f);
-
-        auto * input  = (float *) src0->data;
-        auto * output = (float *) dst->data;
-
-        const auto hidden_size     = ne00;
-        const auto task_count      = ne01 * ne02 * ne03;
-        const auto task_per_thread = (task_count + nth - 1) / nth;
-
-        const auto task_begin = ith * task_per_thread;
-        const auto task_end   = std::min((ith + 1) * task_per_thread, task_count);
-
-        for (auto task_idx = task_begin; task_idx < task_end; task_idx++) {
-            auto   offset        = task_idx * hidden_size;
-            auto * p_input       = const_cast<float *>(input + offset);
-            auto * p_output      = output + offset;
-            auto * p_temp_output = p_output;
-            auto * p_gamma_data  = (const float *) nullptr;
-            auto * p_beta_data   = (const float *) nullptr;
-
-            size_t       gvl    = __riscv_vsetvlmax_e32m4();
-            // vfloat32m4_t sum = __riscv_vfmv_v_f_f32m4(0.f, gvl);
-            vfloat32m4_t sum_sq = __riscv_vfmv_v_f_f32m4(0.f, gvl);
-            int64_t      length = hidden_size;
-            while (length > 0) {
-                gvl                   = __riscv_vsetvl_e32m4(length);
-                // load data
-                vfloat32m4_t src_data = __riscv_vle32_v_f32m4(p_input, gvl);
+    int repack(ggml_tensor * t, const void * data, size_t data_size) override {
+        memcpy(t->data, data, data_size);
+        return 0;
+    }
+};
 
-                sum_sq = __riscv_vfmacc_vv_f32m4(sum_sq, src_data, src_data, gvl);
+// Impl By IME1
+static const tensor_traits<block_q4_0, 32, 16>  q4_0_16x32_q8_0;
+static const tensor_traits<block_q4_1, 32, 16>  q4_1_16x32_q8_0;
+static const tensor_traits<block_q4_K, 32, 16>  q4_k_16x32_q8_0;
+// Impl By IME2
+static const tensor_traits<block_q2_K, 256, 32> q2_k_32x256_q8_0;
+static const tensor_traits<block_q3_K, 256, 32> q3_k_32x256_q8_0;
+static const tensor_traits<block_q4_0, 32, 32>  q4_0_32x32_q8_0;
+static const tensor_traits<block_q4_1, 32, 32>  q4_1_32x32_q8_0;
+static const tensor_traits<block_q4_0, 256, 32> q4_0_32x256_q8_0;
+static const tensor_traits<block_q4_1, 256, 32> q4_1_32x256_q8_0;
+static const tensor_traits<block_q4_K, 32, 32>  q4_k_32x32_q8_0;
+static const tensor_traits<block_q6_K, 32, 32>  q6_k_32x32_q8_0;
+static const tensor_traits<block_q8_0, 32, 32>  q8_0_32x32_q8_0;
+static const tensor_traits<block_mxfp4, 32, 32> mxfp4_32x32_q8_0;
+static const tensor_traits<block_q5_K, 32, 32>  q5_k_32x32_q8_0;
+static const tensor_traits<block_q5_1, 32, 32>  q5_1_32x32_q8_0;
+static const tensor_traits<block_q5_0, 32, 32>  q5_0_32x32_q8_0;
+// Impl By RVV
+static const tensor_traits_common               rvv_impl;
 
-                __riscv_vse32_v_f32m4(p_temp_output, src_data, gvl);
+}  // namespace ggml::cpu::riscv64_spacemit
 
-                p_input += gvl;
-                p_temp_output += gvl;
-                length -= gvl;
+static const ggml::cpu::tensor_traits * ggml_riscv64_spacemit_get_optimal_repack_type(const ggml_tensor * cur) {
+    switch (cur->type) {
+        case GGML_TYPE_Q2_K:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q2_k_32x256_q8_0;
+                }
+#endif
             }
+            break;
+        case GGML_TYPE_Q3_K:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q3_k_32x256_q8_0;
+                }
+#endif
+            }
+            break;
+        case GGML_TYPE_Q4_0:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if (cur->ne[1] % 32 == 0 && cur->ne[0] % 256 == 0 &&
+                    (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q4_0_32x256_q8_0;
+                }
 
-            gvl = __riscv_vsetvlmax_e32m1();
-
-            // float mean = 0.f;
-            vfloat32m1_t zero_v = __riscv_vfmv_v_f_f32m1(0.f, gvl);
-
-            vfloat32m1_t mean_square_v = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m4_f32m1(sum_sq, 0),
-                                                                __riscv_vget_v_f32m4_f32m1(sum_sq, 1), gvl);
-            mean_square_v = __riscv_vfadd_vv_f32m1(mean_square_v, __riscv_vget_v_f32m4_f32m1(sum_sq, 2), gvl);
-            mean_square_v = __riscv_vfadd_vv_f32m1(mean_square_v, __riscv_vget_v_f32m4_f32m1(sum_sq, 3), gvl);
-            mean_square_v = __riscv_vfredusum_vs_f32m1_f32m1(mean_square_v, zero_v, gvl);
-
-            float mean_square = __riscv_vfmv_f_s_f32m1_f32(mean_square_v);
-            mean_square /= hidden_size;
+                if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q4_0_32x32_q8_0;
+                }
+#endif
 
-            mean_square = sqrt(mean_square + epsilon);
+#if defined(RISCV64_SPACEMIT_IME1)
+                if (cur->ne[1] % 16 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime1)) {
+                    return &ggml::cpu::riscv64_spacemit::q4_0_16x32_q8_0;
+                }
+#endif
+            }
+            break;
+        case GGML_TYPE_Q4_1:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                // TODO
+                // if (cur->ne[1] % 32 == 0 && cur->ne[0] % 256 == 0 &&
+                //     (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                //     return &ggml::cpu::riscv64_spacemit::q4_1_32x256_q8_0;
+                // }
+
+                if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q4_1_32x32_q8_0;
+                }
+#endif
 
-            mean_square   = 1.0f / mean_square;
-            length        = hidden_size;
-            p_temp_output = p_output;
+#if defined(RISCV64_SPACEMIT_IME1)
+                if (cur->ne[1] % 16 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime1)) {
+                    return &ggml::cpu::riscv64_spacemit::q4_1_16x32_q8_0;
+                }
+#endif
+            }
+            break;
+        case GGML_TYPE_Q4_K:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q4_k_32x32_q8_0;
+                }
+#endif
 
-            if (p_gamma_data == nullptr && p_beta_data == nullptr) {
-                while (length > 0) {
-                    gvl                   = __riscv_vsetvl_e32m4(length);
-                    vfloat32m4_t src_data = __riscv_vle32_v_f32m4(p_temp_output, gvl);
-                    src_data              = __riscv_vfmul_vf_f32m4(src_data, mean_square, gvl);
-                    __riscv_vse32_v_f32m4(p_output, src_data, gvl);
-                    p_temp_output += gvl;
-                    p_output += gvl;
-                    length -= gvl;
+#if defined(RISCV64_SPACEMIT_IME1)
+                if (cur->ne[1] % 16 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime1)) {
+                    return &ggml::cpu::riscv64_spacemit::q4_k_16x32_q8_0;
                 }
-            } else if (p_beta_data == nullptr) {
-                while (length > 0) {
-                    gvl                       = __riscv_vsetvl_e32m4(length);
-                    vfloat32m4_t src_data     = __riscv_vle32_v_f32m4(p_temp_output, gvl);
-                    vfloat32m4_t gamma_data_v = __riscv_vle32_v_f32m4(p_gamma_data, gvl);
-                    src_data                  = __riscv_vfmul_vf_f32m4(src_data, mean_square, gvl);
-                    src_data                  = __riscv_vfmul_vv_f32m4(src_data, gamma_data_v, gvl);
-                    __riscv_vse32_v_f32m4(p_output, src_data, gvl);
-                    p_temp_output += gvl;
-                    p_output += gvl;
-                    p_gamma_data += gvl;
-                    length -= gvl;
+#endif
+            }
+            break;
+        case GGML_TYPE_Q6_K:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if ((ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q6_k_32x32_q8_0;
                 }
-            } else if (p_gamma_data != nullptr) {
-                while (length > 0) {
-                    gvl                       = __riscv_vsetvl_e32m4(length);
-                    vfloat32m4_t src_data     = __riscv_vle32_v_f32m4(p_temp_output, gvl);
-                    vfloat32m4_t gamma_data_v = __riscv_vle32_v_f32m4(p_gamma_data, gvl);
-                    src_data                  = __riscv_vfmul_vf_f32m4(src_data, mean_square, gvl);
-                    src_data                  = __riscv_vfmul_vv_f32m4(src_data, gamma_data_v, gvl);
-                    vfloat32m4_t beta_data_v  = __riscv_vle32_v_f32m4(p_beta_data, gvl);
-                    src_data                  = __riscv_vfadd_vv_f32m4(src_data, beta_data_v, gvl);
-                    p_beta_data += gvl;
-                    __riscv_vse32_v_f32m4(p_output, src_data, gvl);
-                    p_temp_output += gvl;
-                    p_output += gvl;
-                    p_gamma_data += gvl;
-                    length -= gvl;
+#endif
+            }
+            break;
+        case GGML_TYPE_Q8_0:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if ((ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q8_0_32x32_q8_0;
                 }
+#endif
             }
-        }
-    }
-
-    int repack(struct ggml_tensor * t, const void * data, size_t data_size) override {
-        memcpy(t->data, data, data_size);
-        return 0;
-    }
-};
-
-static const tensor_traits<block_q4_0, 8, 16> q4_0_16x8_q8_0;
-static const tensor_traits<block_q4_1, 8, 16> q4_1_16x8_q8_0;
-static const tensor_traits<block_q4_K, 8, 16> q4_k_16x8_q8_0;
-static const tensor_traits_common             rvv_impl;
-
-}  // namespace ggml::cpu::riscv64_spacemit
-
-static const ggml::cpu::tensor_traits * ggml_riscv64_spacemit_get_optimal_repack_type(const struct ggml_tensor * cur) {
-    if (cur->type == GGML_TYPE_Q4_0) {
-        if (cur->ne[1] % 16 == 0) {
-            return &ggml::cpu::riscv64_spacemit::q4_0_16x8_q8_0;
-        }
-    } else if (cur->type == GGML_TYPE_Q4_1) {
-        if (cur->ne[1] % 16 == 0) {
-            return &ggml::cpu::riscv64_spacemit::q4_1_16x8_q8_0;
-        }
-    } else if (cur->type == GGML_TYPE_Q4_K) {
-        if (cur->ne[1] % 16 == 0) {
-            return &ggml::cpu::riscv64_spacemit::q4_k_16x8_q8_0;
-        }
-    } else if (cur->type == GGML_TYPE_F32) {
-        return &ggml::cpu::riscv64_spacemit::rvv_impl;
+            break;
+        case GGML_TYPE_MXFP4:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                // TODO
+                // if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                //     return &ggml::cpu::riscv64_spacemit::mxfp4_32x32_q8_0;
+                // }
+#endif
+            }
+            break;
+        case GGML_TYPE_Q5_K:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q5_k_32x32_q8_0;
+                }
+#endif
+            }
+            break;
+        case GGML_TYPE_Q5_1:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q5_1_32x32_q8_0;
+                }
+#endif
+            }
+            break;
+        case GGML_TYPE_Q5_0:
+            {
+#if defined(RISCV64_SPACEMIT_IME2)
+                if (cur->ne[1] % 32 == 0 && (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2)) {
+                    return &ggml::cpu::riscv64_spacemit::q5_0_32x32_q8_0;
+                }
+#endif
+            }
+            break;
+        default:
+            break;
     }
 
     return nullptr;
 }
 
 static enum ggml_status ggml_backend_riscv64_spacemit_buffer_init_tensor(ggml_backend_buffer_t buffer,
-                                                                         struct ggml_tensor *  tensor) {
+                                                                         ggml_tensor *         tensor) {
     tensor->extra =
         (void *) const_cast<ggml::cpu::tensor_traits *>(ggml_riscv64_spacemit_get_optimal_repack_type(tensor));
 
@@ -874,8 +1402,46 @@ static enum ggml_status ggml_backend_riscv64_spacemit_buffer_init_tensor(ggml_ba
     return GGML_STATUS_SUCCESS;
 }
 
+static void ggml_backend_riscv64_spacemit_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
+
+    void * base = buffer->context;
+    if (base == nullptr) {
+        return;
+    }
+
+    ggml::cpu::riscv64_spacemit::spine_mem_pool_free(base);
+}
+
+static void * ggml_backend_riscv64_spacemit_buffer_get_base(ggml_backend_buffer_t buffer) {
+    GGML_ASSERT(buffer);
+
+    void * base = buffer->context;
+    GGML_ASSERT(base != nullptr);
+    return base;
+}
+
+static void ggml_backend_riscv64_spacemit_buffer_memset_tensor(ggml_backend_buffer_t buffer,
+                                                               ggml_tensor *         tensor,
+                                                               uint8_t               value,
+                                                               size_t                offset,
+                                                               size_t                size) {
+    GGML_ASSERT(tensor);
+    memset((char *) tensor->data + offset, value, size);
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_riscv64_spacemit_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_ASSERT(buffer);
+
+    void * base = buffer->context;
+    GGML_ASSERT(base != nullptr);
+    memset(base, value, buffer->size);
+}
+
 static void ggml_backend_riscv64_spacemit_buffer_set_tensor(ggml_backend_buffer_t buffer,
-                                                            struct ggml_tensor *  tensor,
+                                                            ggml_tensor *         tensor,
                                                             const void *          data,
                                                             size_t                offset,
                                                             size_t                size) {
@@ -891,6 +1457,20 @@ static void ggml_backend_riscv64_spacemit_buffer_set_tensor(ggml_backend_buffer_
     GGML_UNUSED(buffer);
 }
 
+static const ggml_backend_buffer_i ggml_backend_riscv64_spacemit_buffer_i = {
+    /* .free_buffer     = */ ggml_backend_riscv64_spacemit_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_riscv64_spacemit_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_riscv64_spacemit_buffer_init_tensor,
+    /* .memset_tensor   = */ ggml_backend_riscv64_spacemit_buffer_memset_tensor,
+    /* .set_tensor      = */ ggml_backend_riscv64_spacemit_buffer_set_tensor,
+    /* .get_tensor      = */ nullptr,
+    /* .set_tensor_2d   = */ nullptr,
+    /* .get_tensor_2d   = */ nullptr,
+    /* .cpy_tensor      = */ nullptr,
+    /* .clear           = */ ggml_backend_riscv64_spacemit_buffer_clear,
+    /* .reset           = */ nullptr,
+};
+
 static const char * ggml_backend_cpu_riscv64_spacemit_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
     return "CPU_RISCV64_SPACEMIT";
 
@@ -899,18 +1479,12 @@ static const char * ggml_backend_cpu_riscv64_spacemit_buffer_type_get_name(ggml_
 
 static ggml_backend_buffer_t ggml_backend_cpu_riscv64_spacemit_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
                                                                                         size_t size) {
-    ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
-
-    if (buffer == nullptr) {
+    void * base = ggml::cpu::riscv64_spacemit::spine_mem_pool_alloc(size, 64);
+    if (base == nullptr) {
         return nullptr;
     }
 
-    buffer->buft              = buft;
-    buffer->iface.init_tensor = ggml_backend_riscv64_spacemit_buffer_init_tensor;
-    buffer->iface.set_tensor  = ggml_backend_riscv64_spacemit_buffer_set_tensor;
-    buffer->iface.get_tensor  = nullptr;
-    buffer->iface.cpy_tensor  = nullptr;
-    return buffer;
+    return ggml_backend_buffer_init(buft, ggml_backend_riscv64_spacemit_buffer_i, base, size);
 }
 
 static size_t ggml_backend_cpu_riscv64_spacemit_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
@@ -919,44 +1493,91 @@ static size_t ggml_backend_cpu_riscv64_spacemit_buffer_type_get_alignment(ggml_b
     GGML_UNUSED(buft);
 }
 
-static size_t ggml_backend_cpu_riscv64_spacemit_nbytes(ggml_backend_buffer_type_t buft,
-                                                       const struct ggml_tensor * tensor) {
+static size_t ggml_backend_cpu_riscv64_spacemit_nbytes(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
     for (int i = 0; i < GGML_MAX_DIMS; ++i) {
         if (tensor->ne[i] <= 0) {
             return 0;
         }
     }
 
-    size_t       nbytes;
+    GGML_UNUSED(buft);
+
+    const auto plain_nbytes = [&]() {
+        size_t total = ggml_type_size(tensor->type);
+        for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+            total += (tensor->ne[i] - 1) * tensor->nb[i];
+        }
+        return total;
+    };
+
     const size_t blck_size = ggml_blck_size(tensor->type);
     if (blck_size == 1) {
-        nbytes = ggml_type_size(tensor->type);
-        for (int i = 0; i < GGML_MAX_DIMS; ++i) {
-            nbytes += (tensor->ne[i] - 1) * tensor->nb[i];
+        return plain_nbytes();
+    }
+
+    const size_t row_nbytes = tensor->ne[0] * tensor->nb[0] / blck_size;
+
+    const auto add_strided_nbytes = [&](size_t total, size_t src_block_size, size_t dst_block_size) {
+        for (int i = 1; i < GGML_MAX_DIMS; ++i) {
+            total += (tensor->ne[i] - 1) * (tensor->nb[i] / src_block_size) * dst_block_size;
         }
-    } else {
-        nbytes = tensor->ne[0] * tensor->nb[0] / blck_size;
-        if (tensor->type == GGML_TYPE_Q4_K) {
-            GGML_ASSERT(nbytes % sizeof(block_q4_K) == 0);
-            nbytes = (nbytes / sizeof(block_q4_K)) * sizeof(block_q4_1) * 8;
-            for (int i = 1; i < GGML_MAX_DIMS; ++i) {
-                nbytes += (tensor->ne[i] - 1) * (tensor->nb[i] / sizeof(block_q4_K)) * sizeof(block_q4_1) * 8;
-            }
-        } else {
-            for (int i = 1; i < GGML_MAX_DIMS; ++i) {
-                nbytes += (tensor->ne[i] - 1) * tensor->nb[i];
-            }
+        return total;
+    };
+
+    const auto remap_block_nbytes = [&](size_t src_block_size, size_t dst_block_size, int64_t padded_rows = 0) {
+        GGML_ASSERT(row_nbytes % src_block_size == 0);
+
+        size_t total =
+            add_strided_nbytes((row_nbytes / src_block_size) * dst_block_size, src_block_size, dst_block_size);
+
+        if (padded_rows > 0 && tensor->ne[1] % padded_rows != 0) {
+            total += (padded_rows - tensor->ne[1] % padded_rows) * (tensor->nb[1] / src_block_size) * dst_block_size;
         }
+
+        return total;
+    };
+
+    size_t nbytes = row_nbytes;
+    switch (tensor->type) {
+        case GGML_TYPE_Q4_K:
+            nbytes = remap_block_nbytes(sizeof(block_q4_K), sizeof(block_q4_1) * 8);
+            break;
+        case GGML_TYPE_Q6_K:
+            nbytes = remap_block_nbytes(sizeof(block_q6_K), sizeof(block_q8_0) * 8, 32);
+            break;
+        case GGML_TYPE_Q8_0:
+            nbytes = remap_block_nbytes(sizeof(block_q8_0), sizeof(block_q8_0), 32);
+            break;
+        case GGML_TYPE_Q2_K:
+            nbytes = remap_block_nbytes(sizeof(block_q2_K), sizeof(spacemit_kernels::nrow_block_q2_k<1>));
+            break;
+        case GGML_TYPE_Q3_K:
+            nbytes = remap_block_nbytes(sizeof(block_q3_K), sizeof(spacemit_kernels::nrow_block_q3_k<1>));
+            break;
+        case GGML_TYPE_MXFP4:
+            nbytes = remap_block_nbytes(sizeof(block_mxfp4), sizeof(spacemit_kernels::nrow_block_mxfp4<1>));
+            break;
+        case GGML_TYPE_Q5_K:
+            nbytes = remap_block_nbytes(sizeof(block_q5_K), sizeof(spacemit_kernels::nrow_block_q5_1<1>) * 8);
+            break;
+        case GGML_TYPE_Q5_1:
+            nbytes = remap_block_nbytes(sizeof(block_q5_1), sizeof(spacemit_kernels::nrow_block_q5_1<1>));
+            break;
+        case GGML_TYPE_Q5_0:
+            nbytes = remap_block_nbytes(sizeof(block_q5_0), sizeof(spacemit_kernels::nrow_block_q5_0<1>));
+            break;
+        default:
+            nbytes = add_strided_nbytes(row_nbytes, 1, 1);
+            break;
     }
 
-    GGML_UNUSED(buft);
     return nbytes;
 }
 
 namespace ggml::cpu::riscv64_spacemit {
 
 class extra_buffer_type : ggml::cpu::extra_buffer_type {
-    bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
+    bool supports_op(ggml_backend_dev_t, const ggml_tensor * op) override {
         switch (op->op) {
             case GGML_OP_MUL_MAT:
                 if (op->src[0]->buffer && (ggml_n_dims(op->src[0]) == 2) &&
@@ -970,10 +1591,16 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
                     }
                 }
                 break;
-            case GGML_OP_NORM:
-            case GGML_OP_RMS_NORM:
-                if (op->src[0]->type == GGML_TYPE_F32) {
-                    return true;
+            case GGML_OP_MUL_MAT_ID:
+                if (op->src[0]->buffer && (ggml_n_dims(op->src[0]) == 3) &&
+                    op->src[0]->buffer->buft == ggml_backend_cpu_riscv64_spacemit_buffer_type() &&
+                    ggml_riscv64_spacemit_get_optimal_repack_type(op->src[0])) {
+                    if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
+                        return false;
+                    }
+                    if (op->src[1]->type == GGML_TYPE_F32) {
+                        return true;
+                    }
                 }
                 break;
             default:
@@ -983,15 +1610,28 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
         return false;
     }
 
-    ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override {
+    ggml::cpu::tensor_traits * get_tensor_traits(const ggml_tensor * op) override {
         switch (op->op) {
             case GGML_OP_MUL_MAT:
+            case GGML_OP_MUL_MAT_ID:
                 if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_riscv64_spacemit_buffer_type()) {
                     return (ggml::cpu::tensor_traits *) op->src[0]->extra;
                 }
                 break;
             case GGML_OP_NORM:
             case GGML_OP_RMS_NORM:
+            case GGML_OP_ADD:
+            case GGML_OP_SUB:
+            case GGML_OP_MUL:
+            case GGML_OP_DIV:
+            case GGML_OP_FLASH_ATTN_EXT:
+            case GGML_OP_CONT:
+            case GGML_OP_CPY:
+            case GGML_OP_REPEAT:
+            case GGML_OP_SUM_ROWS:
+            case GGML_OP_GET_ROWS:
+            case GGML_OP_CONCAT:
+                // case GGML_OP_GATED_DELTA_NET:
                 return (ggml::cpu::tensor_traits *) (&ggml::cpu::riscv64_spacemit::rvv_impl);
             default:
                 // GGML_ABORT("fatal error");
@@ -1005,7 +1645,7 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
 }  // namespace ggml::cpu::riscv64_spacemit
 
 ggml_backend_buffer_type_t ggml_backend_cpu_riscv64_spacemit_buffer_type(void) {
-    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_riscv64_spacemit = {
+    static ggml_backend_buffer_type ggml_backend_cpu_buffer_type_riscv64_spacemit = {
   /* .iface    = */
         {
          /* .get_name         = */ ggml_backend_cpu_riscv64_spacemit_buffer_type_get_name,
@@ -1023,3 +1663,78 @@ ggml_backend_buffer_type_t ggml_backend_cpu_riscv64_spacemit_buffer_type(void) {
 
     return &ggml_backend_cpu_buffer_type_riscv64_spacemit;
 }
+
+extern "C" {
+static int bind_ai_thread() {
+    int  fd, bytes;
+    char str[32];
+
+    fd = open("/proc/set_ai_thread", O_WRONLY);
+    if (fd < 0) {
+        GGML_LOG_ERROR("try open /proc/set_ai_thread failed\n");
+        return -1;
+    }
+
+    snprintf(str, 16, "%d", 0);
+    bytes = write(fd, str, strlen(str));
+    if (bytes < 0) {
+        GGML_LOG_ERROR("try write /proc/set_ai_thread failed\n");
+        close(fd);
+        return -1;
+    }
+
+    close(fd);
+    return 0;
+}
+
+void ggml_backend_cpu_riscv64_spacemit_set_numa_thread_affinity(int thread_n) {
+    int cpu_id = sched_getcpu();
+    if (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_ime2 &&
+        !((1 << cpu_id) & ggml::cpu::riscv64_spacemit::global_spine_env_info.cpu_mask)) {
+        GGML_PRINT_DEBUG("bind_ai_thread for thread %d, pid %d\n", thread_n, getpid());
+        bind_ai_thread();
+    }
+
+    if (ggml::cpu::riscv64_spacemit::global_spine_env_info.use_tcm &&
+        ggml::cpu::riscv64_spacemit::tls_context.cpu_id == -1) {
+        CPU_ZERO(&(ggml::cpu::riscv64_spacemit::tls_context.cpuset));
+        pthread_t    main_thread     = pthread_self();
+        const auto & perfer_core_ids = ggml::cpu::riscv64_spacemit::global_spine_env_info.perfer_core_ids;
+        if (thread_n < 0 || static_cast<size_t>(thread_n) >= perfer_core_ids.size()) {
+            GGML_ABORT("thread_n %d exceeds perfer_core_ids size %zu\n", thread_n, perfer_core_ids.size());
+        }
+        auto perfer_cpu_id = perfer_core_ids[static_cast<size_t>(thread_n)];
+        CPU_SET(perfer_cpu_id, &(ggml::cpu::riscv64_spacemit::tls_context.cpuset));
+        int s =
+            pthread_setaffinity_np(main_thread, sizeof(cpu_set_t), &(ggml::cpu::riscv64_spacemit::tls_context.cpuset));
+        if (s != 0) {
+            GGML_ABORT("set thread affinity error for thread_n %d, cpu_id %d\n", thread_n, perfer_cpu_id);
+        }
+
+        int ai_cpu_id = perfer_cpu_id - ggml::cpu::riscv64_spacemit::global_spine_env_info.aicpu_id_offset;
+        ggml::cpu::riscv64_spacemit::tls_context.cpu_id = ai_cpu_id;
+        ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer =
+            ggml::cpu::riscv64_spacemit::spine_mem_pool_tcm_mem_get(ai_cpu_id);
+        ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer_size =
+            ggml::cpu::riscv64_spacemit::global_spine_env_info.tcm_blk_size;
+    }
+
+    if (ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer != nullptr) {
+        void * rt =
+            ggml::cpu::riscv64_spacemit::spine_mem_pool_tcm_mem_wait(ggml::cpu::riscv64_spacemit::tls_context.cpu_id);
+        if (rt == nullptr) {
+            GGML_ABORT("wait tcm buffer failed for cpu_id: %d", ggml::cpu::riscv64_spacemit::tls_context.cpu_id);
+        }
+    }
+}
+
+void ggml_backend_cpu_riscv64_spacemit_clear_numa_thread_affinity_threaded(int thread_n) {
+    if (ggml::cpu::riscv64_spacemit::tls_context.tcm_buffer != nullptr) {
+        auto rt = ggml::cpu::riscv64_spacemit::spine_mem_pool_tcm_mem_release(
+            ggml::cpu::riscv64_spacemit::tls_context.cpu_id);
+        if (rt != 0) {
+            GGML_ABORT("release tcm buffer failed for cpu_id: %d", ggml::cpu::riscv64_spacemit::tls_context.cpu_id);
+        }
+    }
+}
+}
diff --git a/ggml/src/ggml-cpu/spacemit/ime.h b/ggml/src/ggml-cpu/spacemit/ime.h
index 800d91acdae..6849dd95e05 100644
--- a/ggml/src/ggml-cpu/spacemit/ime.h
+++ b/ggml/src/ggml-cpu/spacemit/ime.h
@@ -8,6 +8,14 @@ extern "C" {
 
 ggml_backend_buffer_type_t ggml_backend_cpu_riscv64_spacemit_buffer_type(void);
 
+void ggml_backend_cpu_riscv64_spacemit_set_numa_thread_affinity(int thread_n);
+
+void ggml_backend_cpu_riscv64_spacemit_clear_numa_thread_affinity_threaded(int thread_n);
+
+void * ggml_backend_cpu_riscv64_spacemit_alloc_shared(size_t size, size_t alignment);
+
+void ggml_backend_cpu_riscv64_spacemit_free_shared(void * ptr);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/ggml/src/ggml-cpu/spacemit/ime1_kernels.cpp b/ggml/src/ggml-cpu/spacemit/ime1_kernels.cpp
index cbbb6cd9160..6acc6819dfb 100644
--- a/ggml/src/ggml-cpu/spacemit/ime1_kernels.cpp
+++ b/ggml/src/ggml-cpu/spacemit/ime1_kernels.cpp
@@ -1,8 +1,26 @@
+#include "ggml-impl.h"
 #include "ggml.h"
 #include "ime_kernels.h"
+#include "rvv_kernels.h"
 
 #include <algorithm>
 #include <cmath>
+#include <stdexcept>
+
+#if !defined(__riscv_v) || !defined(__riscv_v_intrinsic)
+#    error "riscv v extension or v_intrinsic not enabled"
+#else
+#    include <riscv_vector.h>
+#endif
+
+#if !defined(__riscv_zfh)
+#    error "riscv zfh extension not enabled"
+#endif
+
+#if defined(RISCV64_SPACEMIT_IME1)
+#else
+#    error "RISCV64_SPACEMIT_IME1 not defined"
+#endif
 
 // clang-format off
 #if defined(__GNUC__)
@@ -11,7 +29,7 @@
 #pragma GCC diagnostic ignored "-Wunused-parameter"
 #endif
 // clang-format on
-namespace sqnbitgemm_spacemit_ime {
+namespace spacemit_kernels {
 
 #define QUANTIZEM4ROW_KERNEL                           \
     "vmv.s.x            v16, zero                \n\t" \
@@ -76,1093 +94,208 @@ namespace sqnbitgemm_spacemit_ime {
     "vse8.v             v31, (s1)                \n\t"
 
 namespace ime1 {
-void quantize_a_4row_i8(size_t BlkLen, const float * A, size_t CountK, std::byte * QuantA) {
+void quantize_a_4row_i8(size_t BlkLen, const float * A, size_t CountK, uint8_t * QuantA) {
     constexpr float range_max_reciprocal = 1.0f / ((1 << 7) - 1);
     const float     fone                 = 1.0f;
 
-    if (BlkLen == 16 || BlkLen == 32 || BlkLen == 64) {
-        for (size_t row_index = 0; row_index < 4; ++row_index) {
-            const float * SRC = A + row_index * CountK;
-            std::byte *   DST = QuantA + row_index * sizeof(float);
+    for (size_t row_index = 0; row_index < 4; ++row_index) {
+        const float * SRC = A + row_index * CountK;
+        uint8_t *     DST = QuantA + row_index * sizeof(float);
 
-            const size_t offset = (4 - row_index) * 4 + row_index * 8;
-            const size_t stride = 4 * (sizeof(float) + BlkLen);
-            __asm__ volatile(
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "addi               t2, %[CountK], 0         \n\t"
-                "addi               a1, %[DST], 0            \n\t"
-                "blt                t2, %[BlkLen], TAIL%=    \n\t"
-
-                "LOOP%=:                                     \n\t"
-                "vsetvli            t0, %[BlkLen], e32, m8   \n\t"
-                "vle32.v            v0, (%[SRC])             \n\t"
-                "sub                t2, t2, t0               \n\t"
-                "slli               t1, t0, 2                \n\t"
-                "add                %[SRC], %[SRC], t1       \n\t"
-                "add                s1, a1, %[OFFSET]        \n\t"
-
-                QUANTIZEM4ROW_KERNEL QUANTIZEM4ROW_STORE
-
-                "add                a1, a1, %[STRIDE]        \n\t"
-                "bge                t2, %[BlkLen], LOOP%=    \n\t"
-
-                "TAIL%=:                                     \n\t"
-                "blez               t2, QUIT%=               \n\t"
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "vxor.vv            v16, v16, v16            \n\t"
-                "vxor.vv            v24, v24, v24            \n\t"
-                "vsetvli            t0, t2, e32, m8          \n\t"
-                "vle32.v            v0, (%[SRC])             \n\t"
-                "add                s1, a1, %[OFFSET]        \n\t"
-
-                QUANTIZEM4ROW_KERNEL
-
-                "addi               t3, %[BlkLen], 0         \n\t"
-                "addi               s2, s1, 0                \n\t"
-                "vsetvli            t0, zero, e8, mf4        \n\t"
-                "vxor.vv            v8, v8, v8               \n\t"
-                "SET_ZERO%=:                                 \n\t"
-                "vse8.v             v8, (s2)                 \n\t"
-                "addi               s2, s2, 32               \n\t"
-                "addi               t3, t3, -8               \n\t"
-                "bnez               t3, SET_ZERO%=           \n\t"
-
-                QUANTIZEM4ROW_STORE
-
-                "QUIT%=:                                     \n\t"
-                : [SRC] "+r"(SRC)
-                : [DST] "r"(DST), [BlkLen] "r"(BlkLen), [OFFSET] "r"(offset), [STRIDE] "r"(stride),
-                  [CountK] "r"(CountK), [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal)
-                : "cc", "t0", "t1", "t2", "t3", "a1", "s1", "s2", "f10", "f11");
-        }
-    } else if (BlkLen == 128) {
-        for (size_t row_index = 0; row_index < 4; ++row_index) {
-            const float * SRC = A + row_index * CountK;
-            std::byte *   DST = QuantA + row_index * sizeof(float);
-
-            const size_t offset = (4 - row_index) * 4 + row_index * 8;
-            const size_t stride = 4 * (sizeof(float) + BlkLen);
-            __asm__ volatile(
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "li                 t6, 32                   \n\t"
-                "addi               t2, %[CountK], 0         \n\t"
-                "addi               a1, %[DST], 0            \n\t"
-                "add                s1, a1, %[OFFSET]        \n\t"
-                "blt                t2, %[BlkLen], TAIL%=    \n\t"
-
-                "LOOP%=:                                     \n\t"
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "vle32.v            v0, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vle32.v            v8, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "addi               t2, t2, -128             \n\t"
-
-                "QUANTIZE%=:                                 \n\t"
-                "add                s1, a1, %[OFFSET]        \n\t"
-                "vfabs.v            v16, v0                  \n\t"
-                "vfabs.v            v24, v8                  \n\t"
-                "vfmax.vv           v16, v24, v16            \n\t"
-                "vfredmax.vs        v24, v16, v24            \n\t"
-                "vfmv.f.s           f10, v24                 \n\t"
-                "fmul.s             f10, f10, %[RMAXREC]     \n\t"
-                "fsw                f10, (a1)                \n\t"
-                "fdiv.s             f11, %[FONE], f10        \n\t"
-                "vfmul.vf           v16, v0, f11             \n\t"
-                "vfmul.vf           v24, v8, f11             \n\t"
-                "vfcvt.x.f.v        v16, v16                 \n\t"
-                "vfcvt.x.f.v        v24, v24                 \n\t"
-                "vsetvli            t0, zero, e16, m4        \n\t"
-                "vnclip.wx          v16, v16, zero           \n\t"
-                "vnclip.wx          v20, v24, zero           \n\t"
-                "vsetvli            t0, zero, e8, m4         \n\t"
-                "vnclip.wx          v16, v16, zero           \n\t"
-                "vsetvli            t0, zero, e64, m4        \n\t"
-                "vsse64.v           v16, (s1), t6            \n\t"
-                "add                a1, a1, %[STRIDE]        \n\t"
-                "bge                t2, %[BlkLen], LOOP%=    \n\t"
-
-                "TAIL%=:                                     \n\t"
-                "blez               t2, QUIT%=               \n\t"
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "vxor.vv             v0, v0, v0              \n\t"
-                "vxor.vv             v8, v8, v8              \n\t"
-                "vxor.vv             v16, v16, v16           \n\t"
-                "vxor.vv             v24, v24, v24           \n\t"
-                "vsetvli            t0, t2, e32, m8          \n\t"
-                "sub                t2, t2, t0               \n\t"
-                "vle32.v            v0, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vsetvli            t0, t2, e32, m8          \n\t"
-                "vle32.v            v8, (%[SRC])             \n\t"
-                "sub                t2, t2, t2               \n\t"
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "jal                x0, QUANTIZE%=           \n\t"
-
-                "QUIT%=:                                     \n\t"
-                : [SRC] "+r"(SRC)
-                : [DST] "r"(DST), [BlkLen] "r"(BlkLen), [OFFSET] "r"(offset), [STRIDE] "r"(stride),
-                  [CountK] "r"(CountK), [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal)
-                : "cc", "t0", "t1", "t2", "t6", "a1", "s1", "s2", "f10", "f11");
-        }
-    } else if (BlkLen == 256) {
-        for (size_t row_index = 0; row_index < 4; ++row_index) {
-            const float * SRC    = A + row_index * CountK;
-            std::byte *   DST    = QuantA + row_index * sizeof(float);
-            const size_t  offset = (4 - row_index) * 4 + row_index * 8;
-            const size_t  stride = 4 * (sizeof(float) + BlkLen);
-            __asm__ volatile(
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "li                 t6, 32                   \n\t"
-                "addi               t2, %[CountK], 0         \n\t"
-                "addi               a1, %[DST], 0            \n\t"
-                "add                s1, a1, %[OFFSET]        \n\t"
-                "blt                t2, %[BlkLen], TAIL%=    \n\t"
-
-                "LOOP%=:                                     \n\t"
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "vle32.v            v0, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vle32.v            v8, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vle32.v            v16, (%[SRC])            \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vle32.v            v24, (%[SRC])            \n\t"
-                "addi               %[SRC], %[SRC], -768     \n\t"
-                "addi               t2, t2, -256             \n\t"
-                "vfabs.v            v0, v0                   \n\t"
-                "vfabs.v            v8, v8                   \n\t"
-                "vfabs.v            v16, v16                 \n\t"
-                "vfabs.v            v24, v24                 \n\t"
-                "vfmax.vv           v8, v0, v8               \n\t"
-                "vfmax.vv           v24, v24, v16            \n\t"
-                "vfmax.vv           v8, v8, v24              \n\t"
-                "vfredmax.vs        v24, v8, v24             \n\t"
-                "vfmv.f.s           f10, v24                 \n\t"
-                "vle32.v            v0, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vle32.v            v8, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vle32.v            v16, (%[SRC])            \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vle32.v            v24, (%[SRC])            \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-
-                "QUANTIZE%=:                                 \n\t"
-                "add                s1, a1, %[OFFSET]        \n\t"
-                "fmul.s             f10, f10, %[RMAXREC]     \n\t"
-                "fsw                f10, (a1)                \n\t"
-                "fdiv.s             f11, %[FONE], f10        \n\t"
-                "vfmul.vf           v0, v0, f11              \n\t"
-                "vfmul.vf           v8, v8, f11              \n\t"
-                "vfmul.vf           v16, v16, f11            \n\t"
-                "vfmul.vf           v24, v24, f11            \n\t"
-                "vfcvt.x.f.v        v0, v0                   \n\t"
-                "vfcvt.x.f.v        v8, v8                   \n\t"
-                "vfcvt.x.f.v        v16, v16                 \n\t"
-                "vfcvt.x.f.v        v24, v24                 \n\t"
-                "vsetvli            t0, zero, e16, m4        \n\t"
-                "vnclip.wx          v0, v0, zero             \n\t"
-                "vnclip.wx          v4, v8, zero             \n\t"
-                "vnclip.wx          v8, v16, zero            \n\t"
-                "vnclip.wx          v12, v24, zero           \n\t"
-                "vsetvli            t0, zero, e8, m4         \n\t"
-                "vnclip.wx          v0, v0, zero             \n\t"
-                "vnclip.wx          v4, v8, zero             \n\t"
-                "vsetvli            t0, zero, e64, m8        \n\t"
-                "vsse64.v           v0, (s1), t6             \n\t"
-                "add                a1, a1, %[STRIDE]        \n\t"
-                "bge                t2, %[BlkLen], LOOP%=    \n\t"
-
-                "TAIL%=:                                     \n\t"
-                "blez               t2, QUIT%=               \n\t"
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "vxor.vv            v0, v0, v0               \n\t"
-                "vxor.vv            v8, v8, v8               \n\t"
-                "vxor.vv            v16, v16, v16            \n\t"
-                "vxor.vv            v24, v24, v24            \n\t"
-                "addi               t1, t2, 0                \n\t"
-                "vsetvli            t0, t1, e32, m8          \n\t"
-                "sub                t1, t1, t0               \n\t"
-                "vle32.v            v0, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vsetvli            t0, t1, e32, m8          \n\t"
-                "sub                t1, t1, t0               \n\t"
-                "vle32.v            v8, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vsetvli            t0, t1, e32, m8          \n\t"
-                "sub                t1, t1, t0               \n\t"
-                "vle32.v            v16, (%[SRC])            \n\t"
-                "addi               %[SRC], %[SRC], 256      \n\t"
-                "vsetvli            t0, t1, e32, m8          \n\t"
-                "vle32.v            v24, (%[SRC])            \n\t"
-                "addi               %[SRC], %[SRC], -768     \n\t"
-                "vsetvli            t0, zero, e32, m8        \n\t"
-                "vfabs.v            v0, v0                   \n\t"
-                "vfabs.v            v8, v8                   \n\t"
-                "vfabs.v            v16, v16                 \n\t"
-                "vfabs.v            v24, v24                 \n\t"
-                "vfmax.vv           v8, v0, v8               \n\t"
-                "vfmax.vv           v24, v16, v24            \n\t"
-                "vfmax.vv           v8, v8, v24              \n\t"
-                "vfredmax.vs        v24, v8, v24             \n\t"
-                "vfmv.f.s           f10, v24                 \n\t"
-                "add                s1, a1, %[OFFSET]        \n\t"
-                "fmul.s             f10, f10, %[RMAXREC]     \n\t"
-                "fsw                f10, (a1)                \n\t"
-                "fdiv.s             f11, %[FONE], f10        \n\t"
-                "vsetvli            t0, zero, e64, m8        \n\t"
-                "vxor.vv            v0, v0, v0               \n\t"
-                "vsse64.v           v0, (s1), t6             \n\t"
-
-                "TAIL_LOOP%=:                                \n\t"
-                "vsetvli            t0, zero, e32, m4        \n\t"
-                "vxor.vv            v0, v0, v0               \n\t"
-                "vsetvli            t0, t2, e32, m1          \n\t"
-                "sub                t2, t2, t0               \n\t"
-                "vle32.v            v0, (%[SRC])             \n\t"
-                "addi               %[SRC], %[SRC], 32       \n\t"
-                "vfmul.vf           v1, v0, f11              \n\t"
-                "vfcvt.x.f.v        v2, v1                   \n\t"
-                "vsetvli            t0, zero, e16, mf2       \n\t"
-                "vnclip.wx          v3, v2, zero             \n\t"
-                "vsetvli            t0, zero, e8, mf4        \n\t"
-                "vnclip.wx          v3, v3, zero             \n\t"
-                "vse8.v             v3, (s1)                 \n\t"
-                "addi               s1, s1, 32               \n\t"
-                "bnez               t2, TAIL_LOOP%=          \n\t"
-
-                "QUIT%=:                                     \n\t"
-                : [SRC] "+r"(SRC)
-                : [DST] "r"(DST), [BlkLen] "r"(BlkLen), [OFFSET] "r"(offset), [STRIDE] "r"(stride),
-                  [CountK] "r"(CountK), [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal)
-                : "cc", "t0", "t1", "t2", "t6", "a1", "s1", "s2", "f10", "f11");
-        }
+        const size_t offset = (4 - row_index) * 4 + row_index * 8;
+        const size_t stride = 4 * (sizeof(float) + BlkLen);
+        __asm__ volatile(
+            "vsetvli            t0, zero, e32, m8        \n\t"
+            "addi               t2, %[CountK], 0         \n\t"
+            "addi               a1, %[DST], 0            \n\t"
+            "blt                t2, %[BlkLen], TAIL%=    \n\t"
+
+            "LOOP%=:                                     \n\t"
+            "vsetvli            t0, %[BlkLen], e32, m8   \n\t"
+            "vle32.v            v0, (%[SRC])             \n\t"
+            "sub                t2, t2, t0               \n\t"
+            "slli               t1, t0, 2                \n\t"
+            "add                %[SRC], %[SRC], t1       \n\t"
+            "add                s1, a1, %[OFFSET]        \n\t"
+
+            QUANTIZEM4ROW_KERNEL QUANTIZEM4ROW_STORE
+
+            "add                a1, a1, %[STRIDE]        \n\t"
+            "bge                t2, %[BlkLen], LOOP%=    \n\t"
+
+            "TAIL%=:                                     \n\t"
+            "blez               t2, QUIT%=               \n\t"
+            "vsetvli            t0, zero, e32, m8        \n\t"
+            "vxor.vv            v16, v16, v16            \n\t"
+            "vxor.vv            v24, v24, v24            \n\t"
+            "vsetvli            t0, t2, e32, m8          \n\t"
+            "vle32.v            v0, (%[SRC])             \n\t"
+            "add                s1, a1, %[OFFSET]        \n\t"
+
+            QUANTIZEM4ROW_KERNEL
+
+            "addi               t3, %[BlkLen], 0         \n\t"
+            "addi               s2, s1, 0                \n\t"
+            "vsetvli            t0, zero, e8, mf4        \n\t"
+            "vxor.vv            v8, v8, v8               \n\t"
+            "SET_ZERO%=:                                 \n\t"
+            "vse8.v             v8, (s2)                 \n\t"
+            "addi               s2, s2, 32               \n\t"
+            "addi               t3, t3, -8               \n\t"
+            "bnez               t3, SET_ZERO%=           \n\t"
+
+            QUANTIZEM4ROW_STORE
+
+            "QUIT%=:                                     \n\t"
+            : [SRC] "+r"(SRC)
+            : [DST] "r"(DST), [BlkLen] "r"(BlkLen), [OFFSET] "r"(offset), [STRIDE] "r"(stride), [CountK] "r"(CountK),
+              [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal)
+            : "cc", "t0", "t1", "t2", "t3", "a1", "s1", "s2", "f10", "f11");
     }
 }
 
-void quantize_a_row_i8(size_t BlkLen, const float * A, size_t CountK, std::byte * QuantA) {
+void quantize_a_row_i8(size_t BlkLen, const float * A, size_t CountK, uint8_t * QuantA) {
     const float *   SRC                  = A;
-    std::byte *     DST                  = QuantA;
+    uint8_t *       DST                  = QuantA;
     constexpr float range_max_reciprocal = 1.0f / ((1 << 7) - 1);
     const float     fone                 = 1.0f;
-    std::byte *     QuantA_offset        = QuantA + CountK + 4 * ((CountK + BlkLen - 1) / BlkLen);
+    uint8_t *       QuantA_offset        = QuantA + CountK + 4 * ((CountK + BlkLen - 1) / BlkLen);
     size_t          offset               = (CountK + BlkLen - 1) / BlkLen * BlkLen - CountK;
 
-    if (CountK <= BlkLen) {
-        float max_abs_A = 0.0f;
-        for (size_t k = 0; k < CountK; k++) {
-            max_abs_A = std::max(max_abs_A, fabsf(A[k]));
-        }
-        float scale_A = max_abs_A * range_max_reciprocal;
-
-        ((float *) QuantA)[0] = scale_A;
-
-        auto * QuantAData_offset = (int8_t *) (QuantA + sizeof(float));
-
-        for (size_t k = 0; k < CountK; k++) {
-            QuantAData_offset[k] =
-                (int8_t) std::clamp(roundf(A[k] / scale_A), (float) std::numeric_limits<int8_t>::lowest(),
-                                    (float) std::numeric_limits<int8_t>::max());
-        }
-        for (size_t k = CountK; k < BlkLen; k++) {
-            QuantAData_offset[k] = 0;
-        }
-
-        return;
-    }
-
-    if (BlkLen != 32 || BlkLen != 64 || BlkLen != 128) {
-        __asm__ volatile(
-            "vsetvli      t0, zero, e8, m8        \n\t"
-            "vxor.vv      v24, v24, v24           \n\t"
-            "LOOP%=:                              \n\t"
-            "vsetvli      t0, %[CNT], e8, m8      \n\t"
-            "vse8.v       v24, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 128     \n\t"
-            "sub          %[CNT], %[CNT], t0      \n\t"
-            "bnez         %[CNT], LOOP%=          \n\t"
-            : [DST] "+r"(QuantA_offset), [CNT] "+r"(offset)
-            :
-            : "cc", "t0");
-    }
-    if (BlkLen == 16) {
-        float buffer[64] = { 0.0f };
-        __asm__ volatile(
-            "addi         t3, zero, 16*8          \n\t"
-            "addi         t2, zero, 16            \n\t"
-            "blt          %[K], t3, LOOP_K%=      \n\t"
-            "blt          %[K], t2, TAIL%=        \n\t"
-            "LOOP_MAIN%=:                         \n\t"
-            "vsetvli      t1, zero, e32, m2       \n\t"
-            "addi         %[K], %[K], -128        \n\t"
-            "vle32.v      v0, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "vle32.v      v2, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "vle32.v      v4, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "vle32.v      v6, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "vle32.v      v8, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "vle32.v      v10, (%[SRC])           \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "vle32.v      v12, (%[SRC])           \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "vle32.v      v14, (%[SRC])           \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "addi         a1, %[BUFFER], 0        \n\t"
-            "vfabs.v      v16, v0                 \n\t"
-            "vfabs.v      v18, v2                 \n\t"
-            "vfabs.v      v20, v4                 \n\t"
-            "vfabs.v      v22, v6                 \n\t"
-            "vfabs.v      v24, v8                 \n\t"
-            "vfabs.v      v26, v10                \n\t"
-            "vfabs.v      v28, v12                \n\t"
-            "vfabs.v      v30, v14                \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v16, v16, v17           \n\t"
-            "vfmax.vv     v18, v18, v19           \n\t"
-            "vfmax.vv     v20, v20, v21           \n\t"
-            "vfmax.vv     v22, v22, v23           \n\t"
-            "vfmax.vv     v24, v24, v25           \n\t"
-            "vfmax.vv     v26, v26, v27           \n\t"
-            "vfmax.vv     v28, v28, v29           \n\t"
-            "vfmax.vv     v30, v30, v31           \n\t"
-            "vse32.v      v16, (a1)               \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "vse32.v      v18, (a1)               \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "vse32.v      v20, (a1)               \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "vse32.v      v22, (a1)               \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "vse32.v      v24, (a1)               \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "vse32.v      v26, (a1)               \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "vse32.v      v28, (a1)               \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "vse32.v      v30, (a1)               \n\t"
-            "addi         a1, %[BUFFER], 0        \n\t"
-            "flw          f0, (a1)                \n\t"
-            "flw          f1, 4(a1)               \n\t"
-            "flw          f2, 8(a1)               \n\t"
-            "flw          f3, 12(a1)              \n\t"
-            "flw          f4, 16(a1)              \n\t"
-            "flw          f5, 20(a1)              \n\t"
-            "flw          f6, 24(a1)              \n\t"
-            "flw          f7, 28(a1)              \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f10, f3, f7             \n\t"
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fsw          f10, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "fdiv.s       f10, %[FONE], f10       \n\t"
-            "flw          f0, (a1)                \n\t"
-            "flw          f1, 4(a1)               \n\t"
-            "flw          f2, 8(a1)               \n\t"
-            "flw          f3, 12(a1)              \n\t"
-            "flw          f4, 16(a1)              \n\t"
-            "flw          f5, 20(a1)              \n\t"
-            "flw          f6, 24(a1)              \n\t"
-            "flw          f7, 28(a1)              \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f11, f3, f7             \n\t"
-            "fmul.s       f11, f11, %[RMAXREC]    \n\t"
-            "fsw          f11, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "fdiv.s       f11, %[FONE], f11       \n\t"
-            "flw          f0, (a1)                \n\t"
-            "flw          f1, 4(a1)               \n\t"
-            "flw          f2, 8(a1)               \n\t"
-            "flw          f3, 12(a1)              \n\t"
-            "flw          f4, 16(a1)              \n\t"
-            "flw          f5, 20(a1)              \n\t"
-            "flw          f6, 24(a1)              \n\t"
-            "flw          f7, 28(a1)              \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f12, f3, f7             \n\t"
-            "fmul.s       f12, f12, %[RMAXREC]    \n\t"
-            "fsw          f12, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "fdiv.s       f12, %[FONE], f12       \n\t"
-            "flw          f0, (a1)                \n\t"
-            "flw          f1, 4(a1)               \n\t"
-            "flw          f2, 8(a1)               \n\t"
-            "flw          f3, 12(a1)              \n\t"
-            "flw          f4, 16(a1)              \n\t"
-            "flw          f5, 20(a1)              \n\t"
-            "flw          f6, 24(a1)              \n\t"
-            "flw          f7, 28(a1)              \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f13, f3, f7             \n\t"
-            "fmul.s       f13, f13, %[RMAXREC]    \n\t"
-            "fsw          f13, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "fdiv.s       f13, %[FONE], f13       \n\t"
-            "flw          f0, (a1)                \n\t"
-            "flw          f1, 4(a1)               \n\t"
-            "flw          f2, 8(a1)               \n\t"
-            "flw          f3, 12(a1)              \n\t"
-            "flw          f4, 16(a1)              \n\t"
-            "flw          f5, 20(a1)              \n\t"
-            "flw          f6, 24(a1)              \n\t"
-            "flw          f7, 28(a1)              \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f14, f3, f7             \n\t"
-            "fmul.s       f14, f14, %[RMAXREC]    \n\t"
-            "fsw          f14, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "fdiv.s       f14, %[FONE], f14       \n\t"
-            "flw          f0, (a1)                \n\t"
-            "flw          f1, 4(a1)               \n\t"
-            "flw          f2, 8(a1)               \n\t"
-            "flw          f3, 12(a1)              \n\t"
-            "flw          f4, 16(a1)              \n\t"
-            "flw          f5, 20(a1)              \n\t"
-            "flw          f6, 24(a1)              \n\t"
-            "flw          f7, 28(a1)              \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f15, f3, f7             \n\t"
-            "fmul.s       f15, f15, %[RMAXREC]    \n\t"
-            "fsw          f15, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "fdiv.s       f15, %[FONE], f15       \n\t"
-            "flw          f0, (a1)                \n\t"
-            "flw          f1, 4(a1)               \n\t"
-            "flw          f2, 8(a1)               \n\t"
-            "flw          f3, 12(a1)              \n\t"
-            "flw          f4, 16(a1)              \n\t"
-            "flw          f5, 20(a1)              \n\t"
-            "flw          f6, 24(a1)              \n\t"
-            "flw          f7, 28(a1)              \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f16, f3, f7             \n\t"
-            "fmul.s       f16, f16, %[RMAXREC]    \n\t"
-            "fsw          f16, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "fdiv.s       f16, %[FONE], f16       \n\t"
-            "flw          f0, (a1)                \n\t"
-            "flw          f1, 4(a1)               \n\t"
-            "flw          f2, 8(a1)               \n\t"
-            "flw          f3, 12(a1)              \n\t"
-            "flw          f4, 16(a1)              \n\t"
-            "flw          f5, 20(a1)              \n\t"
-            "flw          f6, 24(a1)              \n\t"
-            "flw          f7, 28(a1)              \n\t"
-            "addi         a1, a1, 32              \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f17, f3, f7             \n\t"
-            "fmul.s       f17, f17, %[RMAXREC]    \n\t"
-            "fsw          f17, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], -136    \n\t"
-            "fdiv.s       f17, %[FONE], f17       \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmul.vf     v16, v0, f10            \n\t"
-            "vfmul.vf     v18, v2, f11            \n\t"
-            "vfmul.vf     v20, v4, f12            \n\t"
-            "vfmul.vf     v22, v6, f13            \n\t"
-            "vfmul.vf     v24, v8, f14            \n\t"
-            "vfmul.vf     v26, v10, f15           \n\t"
-            "vfmul.vf     v28, v12, f16           \n\t"
-            "vfmul.vf     v30, v14, f17           \n\t"
-            "vfcvt.x.f.v  v16, v16                \n\t"
-            "vfcvt.x.f.v  v18, v18                \n\t"
-            "vfcvt.x.f.v  v20, v20                \n\t"
-            "vfcvt.x.f.v  v22, v22                \n\t"
-            "vfcvt.x.f.v  v24, v24                \n\t"
-            "vfcvt.x.f.v  v26, v26                \n\t"
-            "vfcvt.x.f.v  v28, v28                \n\t"
-            "vfcvt.x.f.v  v30, v30                \n\t"
-            "vsetvli      t0, zero, e16, m1       \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vnclip.wx    v18, v18, zero          \n\t"
-            "vnclip.wx    v20, v20, zero          \n\t"
-            "vnclip.wx    v22, v22, zero          \n\t"
-            "vnclip.wx    v24, v24, zero          \n\t"
-            "vnclip.wx    v26, v26, zero          \n\t"
-            "vnclip.wx    v28, v28, zero          \n\t"
-            "vnclip.wx    v30, v30, zero          \n\t"
-            "vsetvli      t0, t1, e8, mf2         \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vnclip.wx    v18, v18, zero          \n\t"
-            "vnclip.wx    v20, v20, zero          \n\t"
-            "vnclip.wx    v22, v22, zero          \n\t"
-            "vnclip.wx    v24, v24, zero          \n\t"
-            "vnclip.wx    v26, v26, zero          \n\t"
-            "vnclip.wx    v28, v28, zero          \n\t"
-            "vnclip.wx    v30, v30, zero          \n\t"
-            "vse8.v       v16, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "vse8.v       v18, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "vse8.v       v20, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "vse8.v       v22, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "vse8.v       v24, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "vse8.v       v26, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "vse8.v       v28, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 20      \n\t"
-            "vse8.v       v30, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 16      \n\t"
-            "bge          %[K], t3, LOOP_MAIN%=   \n\t"
-            "blt          %[K], t2, TAIL%=        \n\t"
-            "LOOP_K%=:                            \n\t"
-            "vsetvli      t1, %[K], e32, m2       \n\t"
-            "vle32.v      v0, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 64      \n\t"
-            "sub          %[K], %[K], t1          \n\t"
-            "vfabs.v      v16, v0                 \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v16, v16, v17           \n\t"
-            "vse32.v      v16, (%[BUFFER])        \n\t"
-            "flw          f0, (%[BUFFER])         \n\t"
-            "flw          f1, 4(%[BUFFER])        \n\t"
-            "flw          f2, 8(%[BUFFER])        \n\t"
-            "flw          f3, 12(%[BUFFER])       \n\t"
-            "flw          f4, 16(%[BUFFER])       \n\t"
-            "flw          f5, 20(%[BUFFER])       \n\t"
-            "flw          f6, 24(%[BUFFER])       \n\t"
-            "flw          f7, 28(%[BUFFER])       \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f10, f3, f7             \n\t"
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fsw          f10, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 4       \n\t"
-            "fdiv.s       f11, %[FONE], f10       \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmul.vf     v16, v0, f11            \n\t"
-            "vfcvt.x.f.v  v16, v16                \n\t"
-            "vsetvli      t0, zero, e16, m1       \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vsetvli      t0, t1, e8, mf2         \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vse8.v       v16, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 16      \n\t"
-            "bge          %[K], t2, LOOP_K%=      \n\t"
-            "TAIL%=:                              \n\t"
-            "blez         %[K], END%=             \n\t"
-            "vsetvli      t0, t3, e32, m2         \n\t"
-            "vxor.vv      v16, v16, v16           \n\t"
-            "jal          x0, LOOP_K%=            \n\t"
-            "END%=:                               \n\t"
-            : [SRC] "+r"(SRC), [DST] "+r"(DST), [K] "+r"(CountK)
-            : [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal), [BUFFER] "r"(buffer)
-            : "cc", "t3", "t2", "t1", "t0", "a1", "f0", "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f10", "f11", "f12",
-              "f13", "f14", "f15", "f16", "f17");
-    } else if (BlkLen == 32) {
-        __asm__ volatile(
-            "addi         t3, zero, 32*4          \n\t"
-            "addi         t2, zero, 32            \n\t"
-
-            "addi         a1, %[SRC], 0           \n\t"
-            "addi         a2, %[SRC], 128         \n\t"
-            "addi         a3, %[SRC], 256         \n\t"
-            "addi         a4, %[SRC], 384         \n\t"
-
-            "addi         s1, %[DST], 0           \n\t"
-            "addi         s2, %[DST], 36          \n\t"
-            "addi         s3, %[DST], 72          \n\t"
-            "addi         s4, %[DST], 108         \n\t"
-            "blt          %[K], t3, LOOP_K%=      \n\t"
-            "blt          %[K], t2, TAIL%=        \n\t"
-
-            "LOOP_MAIN%=:                         \n\t"
-            "vsetvli      t1, zero, e32, m4       \n\t"
-            "addi         %[K], %[K], -128        \n\t"
-            "vle32.v      v0, (a1)                \n\t"
-            "addi         a1, a1, 512             \n\t"
-            "vle32.v      v4, (a2)                \n\t"
-            "addi         a2, a2, 512             \n\t"
-            "vle32.v      v8, (a3)                \n\t"
-            "addi         a3, a3, 512             \n\t"
-            "vle32.v      v12, (a4)               \n\t"
-            "addi         a4, a4, 512             \n\t"
-            "vfabs.v      v16, v0                 \n\t"
-            "vfabs.v      v20, v4                 \n\t"
-            "vfabs.v      v24, v8                 \n\t"
-            "vfabs.v      v28, v12                \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmax.vv     v16, v16, v18           \n\t"
-            "vfmax.vv     v20, v20, v22           \n\t"
-            "vfmax.vv     v24, v24, v26           \n\t"
-            "vfmax.vv     v28, v28, v30           \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v16, v16, v17           \n\t"
-            "vfmax.vv     v20, v20, v21           \n\t"
-            "vfmax.vv     v24, v24, v25           \n\t"
-            "vfmax.vv     v28, v28, v29           \n\t"
-
-            "vfredmax.vs  v17, v16, v17           \n\t"
-            "vfredmax.vs  v21, v20, v21           \n\t"
-            "vfredmax.vs  v25, v24, v25           \n\t"
-            "vfredmax.vs  v29, v28, v29           \n\t"
-            "vfmv.f.s     f10,  v17               \n\t"
-            "vfmv.f.s     f11,  v21               \n\t"
-            "vfmv.f.s     f12,  v25               \n\t"
-            "vfmv.f.s     f13,  v29               \n\t"
-
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fmul.s       f11, f11, %[RMAXREC]    \n\t"
-            "fmul.s       f12, f12, %[RMAXREC]    \n\t"
-            "fmul.s       f13, f13, %[RMAXREC]    \n\t"
-            "fsw          f10, (s1)               \n\t"
-            "addi         s1, s1, 4               \n\t"
-
-            "fsw          f11, (s2)               \n\t"
-            "addi         s2, s2, 4               \n\t"
-            "fsw          f12, (s3)               \n\t"
-            "addi         s3, s3, 4               \n\t"
-            "fsw          f13, (s4)               \n\t"
-            "addi         s4, s4, 4               \n\t"
-            "fdiv.s       f10, %[FONE], f10       \n\t"
-            "fdiv.s       f11, %[FONE], f11       \n\t"
-            "fdiv.s       f12, %[FONE], f12       \n\t"
-            "fdiv.s       f13, %[FONE], f13       \n\t"
-            "vsetvli      t0, zero, e32, m4       \n\t"
-            "vfmul.vf     v16, v0, f10            \n\t"
-            "vfmul.vf     v20, v4, f11            \n\t"
-            "vfmul.vf     v24, v8, f12            \n\t"
-            "vfmul.vf     v28, v12, f13           \n\t"
-            "vfcvt.x.f.v  v16, v16                \n\t"
-            "vfcvt.x.f.v  v20, v20                \n\t"
-            "vfcvt.x.f.v  v24, v24                \n\t"
-            "vfcvt.x.f.v  v28, v28                \n\t"
-            "vsetvli      t0, zero, e16, m2       \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vnclip.wx    v20, v20, zero          \n\t"
-            "vnclip.wx    v24, v24, zero          \n\t"
-            "vnclip.wx    v28, v28, zero          \n\t"
-            "vsetvli      t0, t1, e8, m1          \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vnclip.wx    v20, v20, zero          \n\t"
-            "vnclip.wx    v24, v24, zero          \n\t"
-            "vnclip.wx    v28, v28, zero          \n\t"
-            "vse8.v       v16, (s1)               \n\t"
-            "addi         s1, s1, 140             \n\t"
-            "vse8.v       v20, (s2)               \n\t"
-            "addi         s2, s2, 140             \n\t"
-            "vse8.v       v24, (s3)               \n\t"
-            "addi         s3, s3, 140             \n\t"
-            "vse8.v       v28, (s4)               \n\t"
-            "addi         s4, s4, 140             \n\t"
-            "bge          %[K], t3, LOOP_MAIN%=   \n\t"
-            "blt          %[K], t2, TAIL%=        \n\t"
-            "LOOP_K%=:                            \n\t"
-            "vsetvli      t1, %[K], e32, m4       \n\t"
-            "vle32.v      v0, (a1)                \n\t"
-            "addi         a1, a1, 128             \n\t"
-            "sub          %[K], %[K], t1          \n\t"
-            "vfabs.v      v16, v0                 \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmax.vv     v16, v16, v18           \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v16, v16, v17           \n\t"
-            "vfredmax.vs  v17, v16, v17           \n\t"
-            "vfmv.f.s     f10,  v17               \n\t"
-
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fsw          f10, (s1)               \n\t"
-            "addi         s1, s1, 4               \n\t"
-            "fdiv.s       f11, %[FONE], f10       \n\t"
-            "vsetvli      t0, zero, e32, m4       \n\t"
-            "vfmul.vf     v16, v0, f11            \n\t"
-            "vfcvt.x.f.v  v16, v16                \n\t"
-            "vsetvli      t0, zero, e16, m2       \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vsetvli      t0, zero, e8, m1        \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vse8.v       v16, (s1)               \n\t"
-            "addi         s1, s1, 32              \n\t"
-            "bge          %[K], t2, LOOP_K%=      \n\t"
-            "TAIL%=:                              \n\t"
-            "blez         %[K], END%=             \n\t"
-            "vsetvli      t0, t3, e32, m4         \n\t"
-            "vxor.vv      v0, v0, v0              \n\t"
-            "vxor.vv      v16, v16, v16           \n\t"
-            "jal          x0, LOOP_K%=            \n\t"
-            "END%=:                               \n\t"
-            : [K] "+r"(CountK)
-            : [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal), [SRC] "r"(SRC), [DST] "r"(DST)
-            : "cc", "t3", "t2", "t1", "t0", "a1", "a2", "a3", "a4", "s1", "s2", "s3", "s4", "f10", "f11", "f12", "f13");
-    } else if (BlkLen == 64) {
-        __asm__ volatile(
-            "addi         t3, zero, 64*2          \n\t"
-            "addi         t2, zero, 64            \n\t"
-            "addi         a1, %[SRC], 0           \n\t"
-            "addi         a2, %[SRC], 256         \n\t"
-            "addi         s1, %[DST], 0           \n\t"
-            "addi         s2, %[DST], 68          \n\t"
-            "blt          %[K], t3, LOOP_K%=      \n\t"
-            "blt          %[K], t2, TAIL%=        \n\t"
-            "LOOP_MAIN%=:                         \n\t"
-            "vsetvli      t1, zero, e32, m8       \n\t"
-            "addi         %[K], %[K], -128        \n\t"
-            "vle32.v      v0, (a1)                \n\t"
-            "addi         a1, a1, 512             \n\t"
-            "vle32.v      v8, (a2)                \n\t"
-            "addi         a2, a2, 512             \n\t"
-            "vfabs.v      v16, v0                 \n\t"
-            "vfabs.v      v24, v8                 \n\t"
-            "vsetvli      t0, zero, e32, m4       \n\t"
-            "vfmax.vv     v16, v16, v20           \n\t"
-            "vfmax.vv     v24, v24, v28           \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmax.vv     v16, v16, v18           \n\t"
-            "vfmax.vv     v24, v24, v26           \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v16, v16, v17           \n\t"
-            "vfmax.vv     v24, v24, v25           \n\t"
-            "vfredmax.vs  v17, v16, v17           \n\t"
-            "vfredmax.vs  v25, v24, v25           \n\t"
-            "vfmv.f.s     f10,  v17               \n\t"
-            "vfmv.f.s     f11,  v25               \n\t"
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fmul.s       f11, f11, %[RMAXREC]    \n\t"
-            "fsw          f10, (s1)               \n\t"
-            "addi         s1, s1, 4               \n\t"
-            "fsw          f11, (s2)               \n\t"
-            "addi         s2, s2, 4               \n\t"
-            "fdiv.s       f10, %[FONE], f10       \n\t"
-            "fdiv.s       f11, %[FONE], f11       \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vfmul.vf     v16, v0, f10            \n\t"
-            "vfmul.vf     v24, v8, f11            \n\t"
-            "vfcvt.x.f.v  v16, v16                \n\t"
-            "vfcvt.x.f.v  v24, v24                \n\t"
-            "vsetvli      t0, zero, e16, m4       \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vnclip.wx    v24, v24, zero          \n\t"
-            "vsetvli      t0, t1, e8, m2          \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vnclip.wx    v24, v24, zero          \n\t"
-            "vse8.v       v16, (s1)               \n\t"
-            "addi         s1, s1, 132             \n\t"
-            "vse8.v       v24, (s2)               \n\t"
-            "addi         s2, s2, 132             \n\t"
-            "bge          %[K], t3, LOOP_MAIN%=   \n\t"
-            "blt          %[K], t2, TAIL%=        \n\t"
-            "LOOP_K%=:                            \n\t"
-            "vsetvli      t1, %[K], e32, m8       \n\t"
-            "vle32.v      v0, (a1)                \n\t"
-            "addi         a1, a1, 256             \n\t"
-            "sub          %[K], %[K], t1          \n\t"
-            "vfabs.v      v16, v0                 \n\t"
-            "vsetvli      t0, zero, e32, m4       \n\t"
-            "vfmax.vv     v16, v16, v20           \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmax.vv     v16, v16, v18           \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v16, v16, v17           \n\t"
-            "vfredmax.vs  v17, v16, v17           \n\t"
-            "vfmv.f.s     f10,  v17               \n\t"
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fsw          f10, (s1)               \n\t"
-            "addi         s1, s1, 4               \n\t"
-            "fdiv.s       f11, %[FONE], f10       \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vfmul.vf     v16, v0, f11            \n\t"
-            "vfcvt.x.f.v  v16, v16                \n\t"
-            "vsetvli      t0, zero, e16, m4       \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vsetvli      t0, zero, e8, m2        \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vse8.v       v16, (s1)               \n\t"
-            "addi         s1, s1, 64              \n\t"
-            "bge          %[K], t2, LOOP_K%=      \n\t"
-            "TAIL%=:                              \n\t"
-            "blez         %[K], END%=             \n\t"
-            "vsetvli      t0, t3, e32, m8         \n\t"
-            "vxor.vv      v0, v0, v0              \n\t"
-            "vxor.vv      v16, v16, v16           \n\t"
-            "jal          x0, LOOP_K%=            \n\t"
-            "END%=:                               \n\t"
-            : [K] "+r"(CountK)
-            : [SRC] "r"(SRC), [DST] "r"(DST), [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal)
-            : "cc", "t3", "t2", "t1", "t0", "a1", "a2", "s1", "s2", "f10", "f11");
-    } else if (BlkLen == 128) {
-        __asm__ volatile(
-            "addi         t2, zero, 128           \n\t"
-            "addi         a1, %[SRC], 0           \n\t"
-            "addi         a2, %[SRC], 256         \n\t"
-            "blt          %[K], t2, TAIL%=        \n\t"
-            "LOOP_K%=:                            \n\t"
-            "vsetvli      t1, zero, e32, m8       \n\t"
-            "vle32.v      v0, (a1)                \n\t"
-            "addi         a1, a1, 512             \n\t"
-            "vle32.v      v8, (a2)                \n\t"
-            "addi         a2, a2, 512             \n\t"
-            "sub          %[K], %[K], t2          \n\t"
-            "QUANT%=:                             \n\t"
-            "vfabs.v      v16, v0                 \n\t"
-            "vfabs.v      v24, v8                 \n\t"
-            "vfmax.vv     v24, v16, v24           \n\t"
-            "vsetvli      t1, zero, e32, m4       \n\t"
-            "vfmax.vv     v28, v24, v28           \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmax.vv     v30, v28, v30           \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v30, v30, v31           \n\t"
-            "vfredmax.vs  v31, v30, v31           \n\t"
-            "vfmv.f.s     f10, v31                \n\t"
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fsw          f10, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 4       \n\t"
-            "fdiv.s       f11, %[FONE], f10       \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vfmul.vf     v16, v0, f11            \n\t"
-            "vfmul.vf     v24, v8, f11            \n\t"
-            "vfcvt.x.f.v  v16, v16                \n\t"
-            "vfcvt.x.f.v  v24, v24                \n\t"
-            "vsetvli      t0, zero, e16, m4       \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vnclip.wx    v20, v24, zero          \n\t"
-            "vsetvli      t0, zero, e8, m4        \n\t"
-            "vnclip.wx    v16, v16, zero          \n\t"
-            "vse8.v       v16, (%[DST])           \n\t"
-            "addi         %[DST], %[DST], 128     \n\t"
-            "bge          %[K], t2, LOOP_K%=      \n\t"
-            "TAIL%=:                              \n\t"
-            "blez         %[K], END%=             \n\t"
-            "vsetvli      t1, zero, e32, m8       \n\t"
-            "vxor.vv      v0, v0, v0              \n\t"
-            "vxor.vv      v8, v8, v8              \n\t"
-            "vsetvli      t0, %[K], e32, m8       \n\t"
-            "vle32.v      v0, (a1)                \n\t"
-            "sub          %[K], %[K], t0          \n\t"
-            "vsetvli      t0, %[K], e32, m8       \n\t"
-            "vle32.v      v8, (a2)                \n\t"
-            "sub          %[K], %[K], t0          \n\t"
-            "vsetvli      t1, zero, e32, m8       \n\t"
-            "jal          x0, QUANT%=             \n\t"
-            "END%=:                               \n\t"
-
-            : [DST] "+r"(DST), [K] "+r"(CountK)
-            : [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal), [SRC] "r"(SRC)
-            : "cc", "t2", "t1", "t0", "a1", "a2", "f10", "f11");
-    } else {
-        float  buffer[8] = { 0.0f };
-        size_t cnt       = BlkLen / 256;
-
-        __asm__ volatile(
-            "slli         t3, %[BLK], 2           \n\t"
-            "blt       %[K], %[BLK], LOOP_TAIL%=  \n\t"
-            "LOOP_MAIN%=:                         \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vxor.vv      v31, v31, v31           \n\t"
-            "vse32.v      v31, (%[BUFFER])        \n\t"
-            "addi         t6, %[CNT], 0           \n\t"
-            "LOOP_CMP%=:                          \n\t"
-            "addi         t6, t6, -1              \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vle32.v      v0, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "vle32.v      v8, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "vle32.v      v16, (%[SRC])           \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "vle32.v      v24, (%[SRC])           \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "vfabs.v      v0, v0                  \n\t"
-            "vfabs.v      v8, v8                  \n\t"
-            "vfabs.v      v16, v16                \n\t"
-            "vfabs.v      v24, v24                \n\t"
-            "vfmax.vv     v8, v0, v8              \n\t"
-            "vfmax.vv     v16, v16, v24           \n\t"
-            "vfmax.vv     v0, v0, v16             \n\t"
-            "vsetvli      t0, zero, e32, m4       \n\t"
-            "vfmax.vv     v0, v0, v4              \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmax.vv     v0, v0, v2              \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v0, v0, v1              \n\t"
-            "vle32.v      v30, (%[BUFFER])        \n\t"
-            "vfmax.vv     v31, v30,  v0           \n\t"
-            "vse32.v      v31, (%[BUFFER])        \n\t"
-            "bnez         t6, LOOP_CMP%=          \n\t"
-            "sub          %[SRC], %[SRC], t3      \n\t"
-            "addi         t6, %[CNT], 0           \n\t"
-            "flw          f0, (%[BUFFER])         \n\t"
-            "flw          f1, 4(%[BUFFER])        \n\t"
-            "flw          f2, 8(%[BUFFER])        \n\t"
-            "flw          f3, 12(%[BUFFER])       \n\t"
-            "flw          f4, 16(%[BUFFER])       \n\t"
-            "flw          f5, 20(%[BUFFER])       \n\t"
-            "flw          f6, 24(%[BUFFER])       \n\t"
-            "flw          f7, 28(%[BUFFER])       \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f10, f3, f7             \n\t"
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fsw          f10,  (%[DST])          \n\t"
-            "addi         %[DST], %[DST], 4       \n\t"
-            "fdiv.s       f11, %[FONE], f10       \n\t"
-            "addi         t6,  %[CNT], 0          \n\t"
-            "LOOP_QUANT%=:                        \n\t"
-            "addi         t6, t6, -1              \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vle32.v      v0, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "vle32.v      v8, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "vle32.v      v16, (%[SRC])           \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "vle32.v      v24, (%[SRC])           \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vfmul.vf     v0, v0, f11             \n\t"
-            "vfmul.vf     v8, v8, f11             \n\t"
-            "vfmul.vf     v16, v16, f11           \n\t"
-            "vfmul.vf     v24, v24, f11           \n\t"
-            "vfcvt.x.f.v  v0, v0                  \n\t"
-            "vfcvt.x.f.v  v8, v8                  \n\t"
-            "vfcvt.x.f.v  v16, v16                \n\t"
-            "vfcvt.x.f.v  v24, v24                \n\t"
-            "vsetvli      t0, zero, e16, m4       \n\t"
-            "vnclip.wx    v0, v0, zero            \n\t"
-            "vnclip.wx    v4, v8, zero            \n\t"
-            "vnclip.wx    v8, v16, zero           \n\t"
-            "vnclip.wx    v12, v24, zero          \n\t"
-            "vsetvli      t0, zero, e8, m4        \n\t"
-            "vnclip.wx    v0, v0, zero            \n\t"
-            "vnclip.wx    v4, v8, zero            \n\t"
-            "vse8.v       v0, (%[DST])            \n\t"
-            "addi         %[DST], %[DST], 128     \n\t"
-            "vse8.v       v4, (%[DST])            \n\t"
-            "addi         %[DST], %[DST], 128     \n\t"
-            "bnez         t6, LOOP_QUANT%=        \n\t"
-            "sub           %[K], %[K], %[BLK]     \n\t"
-            "bge        %[K], %[BLK], LOOP_MAIN%= \n\t"
-            "blez         %[K], END%=             \n\t"
-            "LOOP_TAIL%=:                         \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vxor.vv      v31, v31, v31           \n\t"
-            "vse32.v      v31, (%[BUFFER])        \n\t"
-            "addi         t6, %[K], 0             \n\t"
-            "addi         s1, %[SRC], 0           \n\t"
-            "TAIL_CMP%=:                          \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vxor.vv       v0, v0, v0             \n\t"
-            "vsetvli      t0, t6, e32, m8         \n\t"
-            "vle32.v      v0, (%[SRC])            \n\t"
-            "addi         %[SRC], %[SRC], 256     \n\t"
-            "sub          t6, t6, t0              \n\t"
-            "vfabs.v      v0, v0                  \n\t"
-            "vsetvli      t0, zero, e32, m4       \n\t"
-            "vfmax.vv     v0, v0, v4              \n\t"
-            "vsetvli      t0, zero, e32, m2       \n\t"
-            "vfmax.vv     v0, v0, v2              \n\t"
-            "vsetvli      t0, zero, e32, m1       \n\t"
-            "vfmax.vv     v0, v0, v1              \n\t"
-            "vle32.v      v30, (%[BUFFER])        \n\t"
-            "vfmax.vv     v31, v30,  v0           \n\t"
-            "vse32.v      v31, (%[BUFFER])        \n\t"
-            "bnez         t6, TAIL_CMP%=          \n\t"
-            "addi         t6, %[K], 0             \n\t"
-            "flw          f0, (%[BUFFER])         \n\t"
-            "flw          f1, 4(%[BUFFER])        \n\t"
-            "flw          f2, 8(%[BUFFER])        \n\t"
-            "flw          f3, 12(%[BUFFER])       \n\t"
-            "flw          f4, 16(%[BUFFER])       \n\t"
-            "flw          f5, 20(%[BUFFER])       \n\t"
-            "flw          f6, 24(%[BUFFER])       \n\t"
-            "flw          f7, 28(%[BUFFER])       \n\t"
-            "fmax.s       f1, f0, f1              \n\t"
-            "fmax.s       f3, f2, f3              \n\t"
-            "fmax.s       f5, f4, f5              \n\t"
-            "fmax.s       f7, f6, f7              \n\t"
-            "fmax.s       f3, f1, f3              \n\t"
-            "fmax.s       f7, f5, f7              \n\t"
-            "fmax.s       f10, f3, f7             \n\t"
-            "fmul.s       f10, f10, %[RMAXREC]    \n\t"
-            "fsw          f10,  (%[DST])          \n\t"
-            "addi         %[DST], %[DST], 4       \n\t"
-            "fdiv.s       f11, %[FONE], f10       \n\t"
-            "addi         t6,  %[K], 0            \n\t"
-            "TAIL_QUANT%=:                        \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vxor.vv       v0, v0, v0             \n\t"
-            "vsetvli      t1, t6, e32, m8         \n\t"
-            "vle32.v      v0, (s1)                \n\t"
-            "addi         s1, s1, 256             \n\t"
-            "sub          t6, t6, t1              \n\t"
-            "vsetvli      t0, zero, e32, m8       \n\t"
-            "vfmul.vf     v0, v0, f11             \n\t"
-            "vfcvt.x.f.v  v0, v0                  \n\t"
-            "vsetvli      t0, zero, e16, m4       \n\t"
-            "vnclip.wx    v0, v0, zero            \n\t"
-            "vsetvli      t0, t1, e8, m2          \n\t"
-            "vnclip.wx    v0, v0, zero            \n\t"
-            "vse8.v       v0, (%[DST])            \n\t"
-            "addi         %[DST], %[DST], 64      \n\t"
-            "bnez         t6, TAIL_QUANT%=        \n\t"
-            "END%=:                               \n\t"
-            : [SRC] "+r"(SRC), [DST] "+r"(DST), [K] "+r"(CountK)
-            : [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal), [BLK] "r"(BlkLen), [BUFFER] "r"(buffer),
-              [CNT] "r"(cnt)
-            : "cc", "t1", "t0", "t6", "s1", "f0", "f1", "f2", "f3", "f4", "f5", "f6");
-    }
+    __asm__ volatile(
+        "addi         t3, zero, 32*4          \n\t"
+        "addi         t2, zero, 32            \n\t"
+
+        "addi         a1, %[SRC], 0           \n\t"
+        "addi         a2, %[SRC], 128         \n\t"
+        "addi         a3, %[SRC], 256         \n\t"
+        "addi         a4, %[SRC], 384         \n\t"
+
+        "addi         s1, %[DST], 0           \n\t"
+        "addi         s2, %[DST], 36          \n\t"
+        "addi         s3, %[DST], 72          \n\t"
+        "addi         s4, %[DST], 108         \n\t"
+        "blt          %[K], t3, LOOP_K%=      \n\t"
+        "blt          %[K], t2, TAIL%=        \n\t"
+
+        "LOOP_MAIN%=:                         \n\t"
+        "vsetvli      t1, zero, e32, m4       \n\t"
+        "addi         %[K], %[K], -128        \n\t"
+        "vle32.v      v0, (a1)                \n\t"
+        "addi         a1, a1, 512             \n\t"
+        "vle32.v      v4, (a2)                \n\t"
+        "addi         a2, a2, 512             \n\t"
+        "vle32.v      v8, (a3)                \n\t"
+        "addi         a3, a3, 512             \n\t"
+        "vle32.v      v12, (a4)               \n\t"
+        "addi         a4, a4, 512             \n\t"
+        "vfabs.v      v16, v0                 \n\t"
+        "vfabs.v      v20, v4                 \n\t"
+        "vfabs.v      v24, v8                 \n\t"
+        "vfabs.v      v28, v12                \n\t"
+        "vsetvli      t0, zero, e32, m2       \n\t"
+        "vfmax.vv     v16, v16, v18           \n\t"
+        "vfmax.vv     v20, v20, v22           \n\t"
+        "vfmax.vv     v24, v24, v26           \n\t"
+        "vfmax.vv     v28, v28, v30           \n\t"
+        "vsetvli      t0, zero, e32, m1       \n\t"
+        "vfmax.vv     v16, v16, v17           \n\t"
+        "vfmax.vv     v20, v20, v21           \n\t"
+        "vfmax.vv     v24, v24, v25           \n\t"
+        "vfmax.vv     v28, v28, v29           \n\t"
+
+        "vfredmax.vs  v17, v16, v17           \n\t"
+        "vfredmax.vs  v21, v20, v21           \n\t"
+        "vfredmax.vs  v25, v24, v25           \n\t"
+        "vfredmax.vs  v29, v28, v29           \n\t"
+        "vfmv.f.s     f10,  v17               \n\t"
+        "vfmv.f.s     f11,  v21               \n\t"
+        "vfmv.f.s     f12,  v25               \n\t"
+        "vfmv.f.s     f13,  v29               \n\t"
+
+        "fmul.s       f10, f10, %[RMAXREC]    \n\t"
+        "fmul.s       f11, f11, %[RMAXREC]    \n\t"
+        "fmul.s       f12, f12, %[RMAXREC]    \n\t"
+        "fmul.s       f13, f13, %[RMAXREC]    \n\t"
+        "fsw          f10, (s1)               \n\t"
+        "addi         s1, s1, 4               \n\t"
+
+        "fsw          f11, (s2)               \n\t"
+        "addi         s2, s2, 4               \n\t"
+        "fsw          f12, (s3)               \n\t"
+        "addi         s3, s3, 4               \n\t"
+        "fsw          f13, (s4)               \n\t"
+        "addi         s4, s4, 4               \n\t"
+        "fdiv.s       f10, %[FONE], f10       \n\t"
+        "fdiv.s       f11, %[FONE], f11       \n\t"
+        "fdiv.s       f12, %[FONE], f12       \n\t"
+        "fdiv.s       f13, %[FONE], f13       \n\t"
+        "vsetvli      t0, zero, e32, m4       \n\t"
+        "vfmul.vf     v16, v0, f10            \n\t"
+        "vfmul.vf     v20, v4, f11            \n\t"
+        "vfmul.vf     v24, v8, f12            \n\t"
+        "vfmul.vf     v28, v12, f13           \n\t"
+        "vfcvt.x.f.v  v16, v16                \n\t"
+        "vfcvt.x.f.v  v20, v20                \n\t"
+        "vfcvt.x.f.v  v24, v24                \n\t"
+        "vfcvt.x.f.v  v28, v28                \n\t"
+        "vsetvli      t0, zero, e16, m2       \n\t"
+        "vnclip.wx    v16, v16, zero          \n\t"
+        "vnclip.wx    v20, v20, zero          \n\t"
+        "vnclip.wx    v24, v24, zero          \n\t"
+        "vnclip.wx    v28, v28, zero          \n\t"
+        "vsetvli      t0, t1, e8, m1          \n\t"
+        "vnclip.wx    v16, v16, zero          \n\t"
+        "vnclip.wx    v20, v20, zero          \n\t"
+        "vnclip.wx    v24, v24, zero          \n\t"
+        "vnclip.wx    v28, v28, zero          \n\t"
+        "vse8.v       v16, (s1)               \n\t"
+        "addi         s1, s1, 140             \n\t"
+        "vse8.v       v20, (s2)               \n\t"
+        "addi         s2, s2, 140             \n\t"
+        "vse8.v       v24, (s3)               \n\t"
+        "addi         s3, s3, 140             \n\t"
+        "vse8.v       v28, (s4)               \n\t"
+        "addi         s4, s4, 140             \n\t"
+        "bge          %[K], t3, LOOP_MAIN%=   \n\t"
+        "blt          %[K], t2, TAIL%=        \n\t"
+        "LOOP_K%=:                            \n\t"
+        "vsetvli      t1, %[K], e32, m4       \n\t"
+        "vle32.v      v0, (a1)                \n\t"
+        "addi         a1, a1, 128             \n\t"
+        "sub          %[K], %[K], t1          \n\t"
+        "vfabs.v      v16, v0                 \n\t"
+        "vsetvli      t0, zero, e32, m2       \n\t"
+        "vfmax.vv     v16, v16, v18           \n\t"
+        "vsetvli      t0, zero, e32, m1       \n\t"
+        "vfmax.vv     v16, v16, v17           \n\t"
+        "vfredmax.vs  v17, v16, v17           \n\t"
+        "vfmv.f.s     f10,  v17               \n\t"
+
+        "fmul.s       f10, f10, %[RMAXREC]    \n\t"
+        "fsw          f10, (s1)               \n\t"
+        "addi         s1, s1, 4               \n\t"
+        "fdiv.s       f11, %[FONE], f10       \n\t"
+        "vsetvli      t0, zero, e32, m4       \n\t"
+        "vfmul.vf     v16, v0, f11            \n\t"
+        "vfcvt.x.f.v  v16, v16                \n\t"
+        "vsetvli      t0, zero, e16, m2       \n\t"
+        "vnclip.wx    v16, v16, zero          \n\t"
+        "vsetvli      t0, zero, e8, m1        \n\t"
+        "vnclip.wx    v16, v16, zero          \n\t"
+        "vse8.v       v16, (s1)               \n\t"
+        "addi         s1, s1, 32              \n\t"
+        "bge          %[K], t2, LOOP_K%=      \n\t"
+        "TAIL%=:                              \n\t"
+        "blez         %[K], END%=             \n\t"
+        "vsetvli      t0, t3, e32, m4         \n\t"
+        "vxor.vv      v0, v0, v0              \n\t"
+        "vxor.vv      v16, v16, v16           \n\t"
+        "jal          x0, LOOP_K%=            \n\t"
+        "END%=:                               \n\t"
+        : [K] "+r"(CountK)
+        : [FONE] "f"(fone), [RMAXREC] "f"(range_max_reciprocal), [SRC] "r"(SRC), [DST] "r"(DST)
+        : "cc", "t3", "t2", "t1", "t0", "a1", "a2", "a3", "a4", "s1", "s2", "s3", "s4", "f10", "f11", "f12", "f13");
 }
 
 }  // namespace ime1
@@ -1451,1746 +584,444 @@ namespace {
     "vadd.vi      v1, v1, -12             \n\t"
 
 template <bool HasZeroPoint>
-void SQ4BitGemmM4Kernel_CompInt8_ScaleFp16_Impl(size_t            BlkLen,
-                                                const std::byte * QuantA,
-                                                const std::byte * QuantBData,
-                                                const float *     QuantBScale,
-                                                const std::byte * QuantBZeroPoint,
-                                                float *           C,
-                                                size_t            CountN,
-                                                size_t            BlockCountK,
-                                                const float *     Bias,
-                                                const size_t      ldc) {
-    GGML_UNUSED(QuantBScale);
-    GGML_UNUSED(QuantBZeroPoint);
+void SQ4BitGemmM4Kernel_CompInt8_ScaleFp16_Impl(size_t          BlkLen,
+                                                const uint8_t * QuantA,
+                                                const uint8_t * QuantBData,
+                                                float *         C,
+                                                size_t          CountN,
+                                                size_t          BlockCountK,
+                                                const size_t    ldc) {
     size_t       LDC   = ldc * sizeof(float);
     const size_t INNER = BlkLen / 16;
     float        tmp[4 * 16];
 
     if constexpr (HasZeroPoint) {
         for (size_t n = 0; n < CountN; n += 16) {
-            size_t      NBLKS         = (CountN - n) > 16 ? 16 : CountN - n;
-            std::byte * QuantBDataPtr = (std::byte *) QuantBData +           //
-                                        n * BlockCountK * BlkLen / 2 +       // b data
-                                        n * BlockCountK * sizeof(uint8_t) +  // zp
-                                        n * BlockCountK * sizeof(_Float16);    // scale
+            size_t    NBLKS         = (CountN - n) > 16 ? 16 : CountN - n;
+            uint8_t * QuantBDataPtr = (uint8_t *) QuantBData +             //
+                                      n * BlockCountK * BlkLen / 2 +       // b data
+                                      n * BlockCountK * sizeof(uint8_t) +  // zp
+                                      n * BlockCountK * sizeof(_Float16);  // scale
             float * CPtr = C + n;
             if (NBLKS < 16) {
                 CPtr = tmp;
                 LDC  = 16 * sizeof(float);
             }
-            if (Bias != nullptr) {
-                const float * bias = Bias + n;
-                if (NBLKS < 16) {
-                    __asm__ volatile(
-                        "vsetvli        t0, %[N], e32, m2     \n\t"
-                        "vle32.v        v0, (%[SRC])          \n\t"
-                        "vse32.v        v0, (%[DST])          \n\t"
-                        :
-                        : [SRC] "r"(bias), [DST] "r"(tmp), [N] "r"(NBLKS)
-                        : "cc", "t0");
-                    bias = tmp;
-                }
-                __asm__ volatile(LOAD_BIAS
-
-                                 "addi               t3, %[BlockCountK], 0       \n\t"
-
-                                 "vsetvli            t0, zero, e8, m1            \n\t"
-                                 "li                 s1, 24                      \n\t"
-                                 "vmv.v.i            v1, 3                       \n\t"
-                                 "vsetvli            t0, s1, e8, m1              \n\t"
-                                 "vmv.v.i            v1, 2                       \n\t"
-                                 "vsetvli            t0, zero, e8, mf2           \n\t"
-                                 "vmv.v.i            v1, 1                       \n\t"
-                                 "vsetvli            t0, zero, e8, mf4           \n\t"
-                                 "vmv.v.i            v1, 0                       \n\t"
-
-                                 "addi               a1, %[A], 0                 \n\t"
-                                 "addi               s1, %[B], 0                 \n\t"
-
-                                 "BLOCK_COUNTK_LOOP%=:                           \n\t"
-                                 // scale offset
-                                 "addi               s5, s1, 0                   \n\t"
-                                 // zp offset
-                                 "addi               s6, s1, 32                  \n\t"
-                                 "addi               s1, s6, 16                  \n\t"
-                                 "addi               s2, s1, 32                  \n\t"
-                                 "addi               s3, s1, 32*2                \n\t"
-                                 "addi               s4, s1, 32*3                \n\t"
-
-                                 "vsetvli            t0, zero, e32, m8           \n\t"
-                                 "vxor.vv            v16, v16, v16               \n\t"
-                                 // load a scale
-                                 "flw                f1, (a1)                    \n\t"
-                                 "flw                f2, 4(a1)                   \n\t"
-                                 "flw                f3, 8(a1)                   \n\t"
-                                 "flw                f4, 12(a1)                  \n\t"
-                                 "addi               a1, a1, 16                  \n\t"
-                                 "addi               t2, %[INNER], 0             \n\t"
-
-                                 SQ4BIT_KERNEL_LOAD_ZP_16X1_v2
-
-                                 "BLOCK_INNER_LOOP%=:                            \n\t"
-
-                                 LOAD_B_16x8x2
-
-                                 "vle8.v             v10, (a1)                   \n\t"
-                                 "addi               a1, a1, 32                  \n\t"
-                                 "vle8.v             v11, (a1)                   \n\t"
-                                 "addi               a1, a1, 32                  \n\t"
-                                 "vsub.vv            v2, v2, v12                 \n\t"
-                                 "vsub.vv            v6, v6, v12                 \n\t"
-                                 "vsub.vv            v3, v3, v13                 \n\t"
-                                 "vsub.vv            v7, v7, v13                 \n\t"
-                                 "vsub.vv            v4, v4, v14                 \n\t"
-                                 "vsub.vv            v8, v8, v14                 \n\t"
-                                 "vsub.vv            v5, v5, v15                 \n\t"
-                                 "vsub.vv            v9, v9, v15                 \n\t"
-
-                                 SQ4BIT_KERNEL_COMP_4x16x16
-
-                                 "addi               t2, t2, -1                  \n\t"
-                                 "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
-
-                                 LOAD_SCALE_4x16_FP16
-
-                                 "vsetvli            t0, zero, e32, m8           \n\t"
-                                 "vfcvt.f.x.v        v16, v16                    \n\t"
-                                 "vfmacc.vv          v24, v16, v8                \n\t"
-                                 "addi               t3, t3, -1                  \n\t"
-                                 "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
-
-                                 "RESULT_SAVE%=:                                 \n\t"
-
-                                 SAVE_RESULT_4x16
-
-                                 :
-                                 : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
-                                   [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr), [BIAS] "r"(bias)
-                                 : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1",
-                                   "s2", "s3", "s4", "s5", "s6");
-
-            } else {
-                __asm__ volatile(
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vxor.vv            v24, v24, v24               \n\t"
-                    "addi               t3, %[BlockCountK], 0       \n\t"
-                    "vsetvli            t0, zero, e8, m1            \n\t"
-                    "li                 s1, 24                      \n\t"
-                    "vmv.v.i            v1, 3                       \n\t"
-                    "vsetvli            t0, s1, e8, m1              \n\t"
-                    "vmv.v.i            v1, 2                       \n\t"
-                    "vsetvli            t0, zero, e8, mf2           \n\t"
-                    "vmv.v.i            v1, 1                       \n\t"
-                    "vsetvli            t0, zero, e8, mf4           \n\t"
-                    "vmv.v.i            v1, 0                       \n\t"
-                    "addi               a1, %[A], 0                 \n\t"
-                    "addi               s1, %[B], 0                 \n\t"
-                    "BLOCK_COUNTK_LOOP%=:                           \n\t"
-                    // scale offset
-                    "addi               s5, s1, 0                   \n\t"
-                    // zp offset
-                    "addi               s6, s1, 32                  \n\t"
-                    "addi               s1, s6, 16                  \n\t"
-                    "addi               s2, s1, 32                  \n\t"
-                    "addi               s3, s1, 32*2                \n\t"
-                    "addi               s4, s1, 32*3                \n\t"
-
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vxor.vv            v16, v16, v16               \n\t"
-                    // load a scale
-                    "flw                f1, (a1)                    \n\t"
-                    "flw                f2, 4(a1)                   \n\t"
-                    "flw                f3, 8(a1)                   \n\t"
-                    "flw                f4, 12(a1)                  \n\t"
-                    "addi               a1, a1, 16                  \n\t"
-                    "addi               t2, %[INNER], 0             \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_ZP_16X1_v2
-
-                    "BLOCK_INNER_LOOP%=:                            \n\t"
-
-                    LOAD_B_16x8x2
-
-                    "vle8.v             v10, (a1)                   \n\t"
-                    "addi               a1, a1, 32                  \n\t"
-                    "vle8.v             v11, (a1)                   \n\t"
-                    "addi               a1, a1, 32                  \n\t"
-                    "vsub.vv            v2, v2, v12                 \n\t"
-                    "vsub.vv            v6, v6, v12                 \n\t"
-                    "vsub.vv            v3, v3, v13                 \n\t"
-                    "vsub.vv            v7, v7, v13                 \n\t"
-                    "vsub.vv            v4, v4, v14                 \n\t"
-                    "vsub.vv            v8, v8, v14                 \n\t"
-                    "vsub.vv            v5, v5, v15                 \n\t"
-                    "vsub.vv            v9, v9, v15                 \n\t"
-
-                    SQ4BIT_KERNEL_COMP_4x16x16
-
-                    "addi               t2, t2, -1                  \n\t"
-                    "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
-
-                    LOAD_SCALE_4x16_FP16
-
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vfcvt.f.x.v        v16, v16                    \n\t"
-                    "vfmacc.vv          v24, v16, v8                \n\t"
-                    "addi               t3, t3, -1                  \n\t"
-                    "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
-
-                    "RESULT_SAVE%=:                                 \n\t"
-
-                    SAVE_RESULT_4x16
-
-                    :
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
-                      [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr)
-                    : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1", "s2", "s3",
-                      "s4", "s5", "s6");
-            }
-        }
-    } else {
-        for (size_t n = 0; n < CountN; n += 16) {
-            size_t      NBLKS         = (CountN - n) > 16 ? 16 : CountN - n;
-            std::byte * QuantBDataPtr = (std::byte *) QuantBData +         //
-                                        n * BlockCountK * BlkLen / 2 +     // b data
-                                        n * BlockCountK * sizeof(_Float16);  // scale
-            float * CPtr = C + n;
-            if (NBLKS < 16) {
-                CPtr = tmp;
-                LDC  = 16 * sizeof(float);
-            }
-            if (Bias != nullptr) {
-                const float * bias = Bias + n;
-                if (NBLKS < 16) {
-                    __asm__ volatile(
-                        "vsetvli        t0, %[N], e32, m2     \n\t"
-                        "vle32.v        v0, (%[SRC])          \n\t"
-                        "vse32.v        v0, (%[DST])          \n\t"
-                        :
-                        : [SRC] "r"(bias), [DST] "r"(tmp), [N] "r"(NBLKS)
-                        : "cc", "t0");
-                    bias = tmp;
-                }
-                __asm__ volatile(LOAD_BIAS
-
-                                 "addi               t3, %[BlockCountK], 0       \n\t"
-                                 "addi               a1, %[A], 0                 \n\t"
-                                 "addi               s1, %[B], 0                 \n\t"
-                                 "BLOCK_COUNTK_LOOP%=:                           \n\t"
-                                 "addi               s5, s1, 0                   \n\t"
-                                 "addi               s1, s5, 32                  \n\t"
-                                 "addi               s2, s1, 32                  \n\t"
-                                 "addi               s3, s1, 32*2                \n\t"
-                                 "addi               s4, s1, 32*3                \n\t"
-                                 "vsetvli            t0, zero, e32, m8           \n\t"
-                                 "vxor.vv            v16, v16, v16               \n\t"
-                                 // load a scale
-                                 "flw                f1, (a1)                    \n\t"
-                                 "flw                f2, 4(a1)                   \n\t"
-                                 "flw                f3, 8(a1)                   \n\t"
-                                 "flw                f4, 12(a1)                  \n\t"
-                                 "addi               a1, a1, 16                  \n\t"
-                                 "addi               t2, %[INNER], 0             \n\t"
-                                 "BLOCK_INNER_LOOP%=:                            \n\t"
-
-                                 LOAD_B_16x8x2
-
-                                 "vsetvli            t0, zero, e8, m1            \n\t"
-                                 "vle8.v             v10, (a1)                   \n\t"
-                                 "addi               a1, a1, 32                  \n\t"
-                                 "vle8.v             v11, (a1)                   \n\t"
-                                 "addi               a1, a1, 32                  \n\t"
-                                 "vadd.vi            v2, v2, -8                  \n\t"
-                                 "vadd.vi            v3, v3, -8                  \n\t"
-                                 "vadd.vi            v4, v4, -8                  \n\t"
-                                 "vadd.vi            v5, v5, -8                  \n\t"
-                                 "vadd.vi            v6, v6, -8                  \n\t"
-                                 "vadd.vi            v7, v7, -8                  \n\t"
-                                 "vadd.vi            v8, v8, -8                  \n\t"
-                                 "vadd.vi            v9, v9, -8                  \n\t"
-
-                                 SQ4BIT_KERNEL_COMP_4x16x16
-
-                                 "addi               t2, t2, -1                  \n\t"
-                                 "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
-
-                                 LOAD_SCALE_4x16_FP16
-
-                                 "vsetvli            t0, zero, e32, m8           \n\t"
-                                 "vfcvt.f.x.v        v16, v16                    \n\t"
-                                 "vfmacc.vv          v24, v16, v8                \n\t"
-                                 "addi               t3, t3, -1                  \n\t"
-                                 "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
-                                 "RESULT_SAVE%=:                                 \n\t"
-
-                                 SAVE_RESULT_4x16
-
-                                 :
-                                 : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
-                                   [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr), [BIAS] "r"(bias)
-                                 : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1",
-                                   "s2", "s3", "s4", "s5", "s6");
-
-            } else {
-                __asm__ volatile(
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vxor.vv            v24, v24, v24               \n\t"
-                    "addi               t3, %[BlockCountK], 0       \n\t"
-                    "addi               a1, %[A], 0                 \n\t"
-                    "addi               s1, %[B], 0                 \n\t"
-                    "BLOCK_COUNTK_LOOP%=:                           \n\t"
-                    "addi               s5, s1, 0                   \n\t"
-                    "addi               s1, s5, 32                  \n\t"
-                    "addi               s2, s1, 32                  \n\t"
-                    "addi               s3, s1, 32*2                \n\t"
-                    "addi               s4, s1, 32*3                \n\t"
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vxor.vv            v16, v16, v16               \n\t"
-                    // load a scale
-                    "flw                f1, (a1)                    \n\t"
-                    "flw                f2, 4(a1)                   \n\t"
-                    "flw                f3, 8(a1)                   \n\t"
-                    "flw                f4, 12(a1)                  \n\t"
-                    "addi               a1, a1, 16                  \n\t"
-                    "addi               t2, %[INNER], 0             \n\t"
-                    "BLOCK_INNER_LOOP%=:                            \n\t"
-
-                    LOAD_B_16x8x2
-
-                    "vsetvli            t0, zero, e8, m1            \n\t"
-                    "vle8.v             v10, (a1)                   \n\t"
-                    "addi               a1, a1, 32                  \n\t"
-                    "vle8.v             v11, (a1)                   \n\t"
-                    "addi               a1, a1, 32                  \n\t"
-                    "vadd.vi            v2, v2, -8                  \n\t"
-                    "vadd.vi            v3, v3, -8                  \n\t"
-                    "vadd.vi            v4, v4, -8                  \n\t"
-                    "vadd.vi            v5, v5, -8                  \n\t"
-                    "vadd.vi            v6, v6, -8                  \n\t"
-                    "vadd.vi            v7, v7, -8                  \n\t"
-                    "vadd.vi            v8, v8, -8                  \n\t"
-                    "vadd.vi            v9, v9, -8                  \n\t"
-
-                    SQ4BIT_KERNEL_COMP_4x16x16
-
-                    "addi               t2, t2, -1                  \n\t"
-                    "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
-
-                    LOAD_SCALE_4x16_FP16
-
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vfcvt.f.x.v        v16, v16                    \n\t"
-                    "vfmacc.vv          v24, v16, v8                \n\t"
-                    "addi               t3, t3, -1                  \n\t"
-                    "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
-                    "RESULT_SAVE%=:                                 \n\t"
-
-                    SAVE_RESULT_4x16
-
-                    :
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
-                      [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr)
-                    : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1", "s2", "s3",
-                      "s4", "s5", "s6");
-            }
-        }
-    }
-    if (CountN % 16 != 0) {
-        // stroe output from tmp to C when NBLKS less than 16.
-        float *      CPtr = C + CountN / 16 * 16;
-        const size_t N    = CountN % 16;
-        LDC               = ldc * sizeof(float);
-        __asm__ volatile(
-            "vsetvli            t0, %[N], e32, m2       \n\t"
-            "vle32.v            v0, (%[SRC])            \n\t"
-            "addi               s2, %[SRC], 64          \n\t"
-            "addi               s3, %[SRC], 64*2        \n\t"
-            "addi               s4, %[SRC], 64*3        \n\t"
-            "vle32.v            v2, (s2)                \n\t"
-            "vle32.v            v4, (s3)                \n\t"
-            "vle32.v            v6, (s4)                \n\t"
-            "add                t2, %[DST], %[LDC]      \n\t"
-            "add                t3, t2, %[LDC]          \n\t"
-            "add                t4, t3, %[LDC]          \n\t"
-            "vse32.v            v0, (%[DST])            \n\t"
-            "vse32.v            v2, (t2)                \n\t"
-            "vse32.v            v4, (t3)                \n\t"
-            "vse32.v            v6, (t4)                \n\t"
-            :
-            : [N] "r"(N), [SRC] "r"(tmp), [DST] "r"(CPtr), [LDC] "r"(LDC)
-            : "cc", "t0", "t2", "t3", "t4", "s2", "s3", "s4");
-    }
-}
 
-template <bool HasZeroPoint>
-void SQ4BitGemmM4Kernel_CompInt8_Impl(size_t            BlkLen,
-                                      const std::byte * QuantA,
-                                      const std::byte * QuantBData,
-                                      const float *     QuantBScale,
-                                      const std::byte * QuantBZeroPoint,
-                                      float *           C,
-                                      size_t            CountN,
-                                      size_t            BlockCountK,
-                                      const float *     Bias,
-                                      const size_t      ldc) {
-    GGML_UNUSED(QuantBScale);
-    GGML_UNUSED(QuantBZeroPoint);
-    size_t       LDC   = ldc * sizeof(float);
-    const size_t INNER = BlkLen / 16;
-    float        tmp[4 * 16];
-
-    if constexpr (HasZeroPoint) {
-        for (size_t n = 0; n < CountN; n += 16) {
-            size_t      NBLKS         = (CountN - n) > 16 ? 16 : CountN - n;
-            std::byte * QuantBDataPtr = (std::byte *) QuantBData +           //
-                                        n * BlockCountK * BlkLen / 2 +       // b data
-                                        n * BlockCountK * sizeof(uint8_t) +  // zp
-                                        n * BlockCountK * sizeof(float);     // scale
-            float * CPtr = C + n;
-            if (NBLKS < 16) {
-                CPtr = tmp;
-                LDC  = 16 * sizeof(float);
-            }
-            if (Bias != nullptr) {
-                const float * bias = Bias + n;
-                if (NBLKS < 16) {
-                    __asm__ volatile(
-                        "vsetvli        t0, %[N], e32, m2     \n\t"
-                        "vle32.v        v0, (%[SRC])          \n\t"
-                        "vse32.v        v0, (%[DST])          \n\t"
-                        :
-                        : [SRC] "r"(bias), [DST] "r"(tmp), [N] "r"(NBLKS)
-                        : "cc", "t0");
-                    bias = tmp;
-                }
-
-                __asm__ volatile(LOAD_BIAS
-                                 "addi               t3, %[BlockCountK], 0       \n\t"
-                                 "vsetvli            t0, zero, e8, m1            \n\t"
-                                 "li                 s1, 24                      \n\t"
-                                 "vmv.v.i            v1, 3                       \n\t"
-                                 "vsetvli            t0, s1, e8, m1              \n\t"
-                                 "vmv.v.i            v1, 2                       \n\t"
-                                 "vsetvli            t0, zero, e8, mf2           \n\t"
-                                 "vmv.v.i            v1, 1                       \n\t"
-                                 "vsetvli            t0, zero, e8, mf4           \n\t"
-                                 "vmv.v.i            v1, 0                       \n\t"
-                                 "addi               a1, %[A], 0                 \n\t"
-                                 "addi               s1, %[B], 0                 \n\t"
-                                 "BLOCK_COUNTK_LOOP%=:                           \n\t"
-                                 // scale offset
-                                 "addi               s5, s1, 0                   \n\t"
-                                 // zp offset
-                                 "addi               s6, s1, 64                  \n\t"
-                                 "addi               s1, s6, 16                  \n\t"
-                                 "addi               s2, s1, 32                  \n\t"
-                                 "addi               s3, s1, 32*2                \n\t"
-                                 "addi               s4, s1, 32*3                \n\t"
-                                 "vsetvli            t0, zero, e32, m8           \n\t"
-                                 "vxor.vv            v16, v16, v16               \n\t"
-                                 // load a scale
-                                 "flw                f1, (a1)                    \n\t"
-                                 "flw                f2, 4(a1)                   \n\t"
-                                 "flw                f3, 8(a1)                   \n\t"
-                                 "flw                f4, 12(a1)                  \n\t"
-                                 "addi               a1, a1, 16                  \n\t"
-                                 "addi               t2, %[INNER], 0             \n\t"
-
-                                 SQ4BIT_KERNEL_LOAD_ZP_16X1_v2
-
-                                 "BLOCK_INNER_LOOP%=:                            \n\t"
-
-                                 LOAD_B_16x8x2
-
-                                 "vle8.v             v10, (a1)                   \n\t"
-                                 "addi               a1, a1, 32                  \n\t"
-                                 "vle8.v             v11, (a1)                   \n\t"
-                                 "addi               a1, a1, 32                  \n\t"
-                                 "vsub.vv            v2, v2, v12                 \n\t"
-                                 "vsub.vv            v6, v6, v12                 \n\t"
-                                 "vsub.vv            v3, v3, v13                 \n\t"
-                                 "vsub.vv            v7, v7, v13                 \n\t"
-                                 "vsub.vv            v4, v4, v14                 \n\t"
-                                 "vsub.vv            v8, v8, v14                 \n\t"
-                                 "vsub.vv            v5, v5, v15                 \n\t"
-                                 "vsub.vv            v9, v9, v15                 \n\t"
-
-                                 SQ4BIT_KERNEL_COMP_4x16x16
-
-                                 "addi               t2, t2, -1                  \n\t"
-                                 "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
-
-                                 LOAD_SCALE_4x16
-
-                                 "vsetvli            t0, zero, e32, m8           \n\t"
-                                 "vfcvt.f.x.v        v16, v16                    \n\t"
-                                 "vfmacc.vv          v24, v16, v8                \n\t"
-                                 "addi               t3, t3, -1                  \n\t"
-                                 "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
-
-                                 "RESULT_SAVE%=:                                 \n\t"
-
-                                 SAVE_RESULT_4x16
-
-                                 :
-                                 : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
-                                   [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr), [BIAS] "r"(bias)
-                                 : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1",
-                                   "s2", "s3", "s4", "s5", "s6");
-
-            } else {
-                __asm__ volatile(
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vxor.vv            v24, v24, v24               \n\t"
-                    "addi               t3, %[BlockCountK], 0       \n\t"
-                    "vsetvli            t0, zero, e8, m1            \n\t"
-                    "li                 s1, 24                      \n\t"
-                    "vmv.v.i            v1, 3                       \n\t"
-                    "vsetvli            t0, s1, e8, m1              \n\t"
-                    "vmv.v.i            v1, 2                       \n\t"
-                    "vsetvli            t0, zero, e8, mf2           \n\t"
-                    "vmv.v.i            v1, 1                       \n\t"
-                    "vsetvli            t0, zero, e8, mf4           \n\t"
-                    "vmv.v.i            v1, 0                       \n\t"
-                    "addi               a1, %[A], 0                 \n\t"
-                    "addi               s1, %[B], 0                 \n\t"
-                    "BLOCK_COUNTK_LOOP%=:                           \n\t"
-                    // scale offset
-                    "addi               s5, s1, 0                   \n\t"
-                    // zp offset
-                    "addi               s6, s1, 64                  \n\t"
-                    "addi               s1, s6, 16                  \n\t"
-                    "addi               s2, s1, 32                  \n\t"
-                    "addi               s3, s1, 32*2                \n\t"
-                    "addi               s4, s1, 32*3                \n\t"
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vxor.vv            v16, v16, v16               \n\t"
-                    // load a scale
-                    // load a scale
-                    "flw                f1, (a1)                    \n\t"
-                    "flw                f2, 4(a1)                   \n\t"
-                    "flw                f3, 8(a1)                   \n\t"
-                    "flw                f4, 12(a1)                  \n\t"
-                    "addi               a1, a1, 16                  \n\t"
-                    "addi               t2, %[INNER], 0             \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_ZP_16X1_v2
-
-                    "BLOCK_INNER_LOOP%=:                            \n\t"
-
-                    LOAD_B_16x8x2
-
-                    "vle8.v             v10, (a1)                   \n\t"
-                    "addi               a1, a1, 32                  \n\t"
-                    "vle8.v             v11, (a1)                   \n\t"
-                    "addi               a1, a1, 32                  \n\t"
-                    "vsub.vv            v2, v2, v12                 \n\t"
-                    "vsub.vv            v6, v6, v12                 \n\t"
-                    "vsub.vv            v3, v3, v13                 \n\t"
-                    "vsub.vv            v7, v7, v13                 \n\t"
-                    "vsub.vv            v4, v4, v14                 \n\t"
-                    "vsub.vv            v8, v8, v14                 \n\t"
-                    "vsub.vv            v5, v5, v15                 \n\t"
-                    "vsub.vv            v9, v9, v15                 \n\t"
-
-                    SQ4BIT_KERNEL_COMP_4x16x16
-
-                    "addi               t2, t2, -1                  \n\t"
-                    "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
-
-                    LOAD_SCALE_4x16
-
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vfcvt.f.x.v        v16, v16                    \n\t"
-                    "vfmacc.vv          v24, v16, v8                \n\t"
-                    "addi               t3, t3, -1                  \n\t"
-                    "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
-
-                    "RESULT_SAVE%=:                                 \n\t"
-
-                    SAVE_RESULT_4x16
-
-                    :
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
-                      [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr)
-                    : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1", "s2", "s3",
-                      "s4", "s5", "s6");
-            }
+            __asm__ volatile(
+                "vsetvli            t0, zero, e32, m8           \n\t"
+                "vxor.vv            v24, v24, v24               \n\t"
+                "addi               t3, %[BlockCountK], 0       \n\t"
+                "vsetvli            t0, zero, e8, m1            \n\t"
+                "li                 s1, 24                      \n\t"
+                "vmv.v.i            v1, 3                       \n\t"
+                "vsetvli            t0, s1, e8, m1              \n\t"
+                "vmv.v.i            v1, 2                       \n\t"
+                "vsetvli            t0, zero, e8, mf2           \n\t"
+                "vmv.v.i            v1, 1                       \n\t"
+                "vsetvli            t0, zero, e8, mf4           \n\t"
+                "vmv.v.i            v1, 0                       \n\t"
+                "addi               a1, %[A], 0                 \n\t"
+                "addi               s1, %[B], 0                 \n\t"
+                "BLOCK_COUNTK_LOOP%=:                           \n\t"
+                // scale offset
+                "addi               s5, s1, 0                   \n\t"
+                // zp offset
+                "addi               s6, s1, 32                  \n\t"
+                "addi               s1, s6, 16                  \n\t"
+                "addi               s2, s1, 32                  \n\t"
+                "addi               s3, s1, 32*2                \n\t"
+                "addi               s4, s1, 32*3                \n\t"
+
+                "vsetvli            t0, zero, e32, m8           \n\t"
+                "vxor.vv            v16, v16, v16               \n\t"
+                // load a scale
+                "flw                f1, (a1)                    \n\t"
+                "flw                f2, 4(a1)                   \n\t"
+                "flw                f3, 8(a1)                   \n\t"
+                "flw                f4, 12(a1)                  \n\t"
+                "addi               a1, a1, 16                  \n\t"
+                "addi               t2, %[INNER], 0             \n\t"
+
+                SQ4BIT_KERNEL_LOAD_ZP_16X1_v2
+
+                "BLOCK_INNER_LOOP%=:                            \n\t"
+
+                LOAD_B_16x8x2
+
+                "vle8.v             v10, (a1)                   \n\t"
+                "addi               a1, a1, 32                  \n\t"
+                "vle8.v             v11, (a1)                   \n\t"
+                "addi               a1, a1, 32                  \n\t"
+                "vsub.vv            v2, v2, v12                 \n\t"
+                "vsub.vv            v6, v6, v12                 \n\t"
+                "vsub.vv            v3, v3, v13                 \n\t"
+                "vsub.vv            v7, v7, v13                 \n\t"
+                "vsub.vv            v4, v4, v14                 \n\t"
+                "vsub.vv            v8, v8, v14                 \n\t"
+                "vsub.vv            v5, v5, v15                 \n\t"
+                "vsub.vv            v9, v9, v15                 \n\t"
+
+                SQ4BIT_KERNEL_COMP_4x16x16
+
+                "addi               t2, t2, -1                  \n\t"
+                "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
+
+                LOAD_SCALE_4x16_FP16
+
+                "vsetvli            t0, zero, e32, m8           \n\t"
+                "vfcvt.f.x.v        v16, v16                    \n\t"
+                "vfmacc.vv          v24, v16, v8                \n\t"
+                "addi               t3, t3, -1                  \n\t"
+                "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
+
+                "RESULT_SAVE%=:                                 \n\t"
+
+                SAVE_RESULT_4x16
+
+                :
+                : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
+                  [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr)
+                : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1", "s2", "s3", "s4",
+                  "s5", "s6");
         }
     } else {
         for (size_t n = 0; n < CountN; n += 16) {
-            size_t      NBLKS         = (CountN - n) > 16 ? 16 : CountN - n;
-            std::byte * QuantBDataPtr = (std::byte *) QuantBData +        //
-                                        n * BlockCountK * BlkLen / 2 +    // b data
-                                        n * BlockCountK * sizeof(float);  // scale
+            size_t    NBLKS         = (CountN - n) > 16 ? 16 : CountN - n;
+            uint8_t * QuantBDataPtr = (uint8_t *) QuantBData +             //
+                                      n * BlockCountK * BlkLen / 2 +       // b data
+                                      n * BlockCountK * sizeof(_Float16);  // scale
             float * CPtr = C + n;
             if (NBLKS < 16) {
                 CPtr = tmp;
                 LDC  = 16 * sizeof(float);
             }
-            if (Bias != nullptr) {
-                const float * bias = Bias + n;
-                if (NBLKS < 16) {
-                    __asm__ volatile(
-                        "vsetvli        t0, %[N], e32, m2     \n\t"
-                        "vle32.v        v0, (%[SRC])          \n\t"
-                        "vse32.v        v0, (%[DST])          \n\t"
-                        :
-                        : [SRC] "r"(bias), [DST] "r"(tmp), [N] "r"(NBLKS)
-                        : "cc", "t0");
-                    bias = tmp;
-                }
-                __asm__ volatile(LOAD_BIAS
-                                 "addi               t3, %[BlockCountK], 0       \n\t"
-                                 "addi               a1, %[A], 0                 \n\t"
-                                 "addi               s1, %[B], 0                 \n\t"
-                                 "BLOCK_COUNTK_LOOP%=:                           \n\t"
-                                 "addi               s5, s1, 0                   \n\t"
-                                 "addi               s1, s5, 64                  \n\t"
-                                 "addi               s2, s1, 32                  \n\t"
-                                 "addi               s3, s1, 32*2                \n\t"
-                                 "addi               s4, s1, 32*3                \n\t"
-                                 "vsetvli            t0, zero, e32, m8           \n\t"
-                                 "vxor.vv            v16, v16, v16               \n\t"
-                                 // load a scale
-                                 "flw                f1, (a1)                    \n\t"
-                                 "flw                f2, 4(a1)                   \n\t"
-                                 "flw                f3, 8(a1)                   \n\t"
-                                 "flw                f4, 12(a1)                  \n\t"
-                                 "addi               a1, a1, 16                  \n\t"
-                                 "addi               t2, %[INNER], 0             \n\t"
-                                 "BLOCK_INNER_LOOP%=:                            \n\t"
-
-                                 LOAD_B_16x8x2
-
-                                 "vsetvli            t0, zero, e8, m1            \n\t"
-                                 "vle8.v             v10, (a1)                   \n\t"
-                                 "addi               a1, a1, 32                  \n\t"
-                                 "vle8.v             v11, (a1)                   \n\t"
-                                 "addi               a1, a1, 32                  \n\t"
-                                 "vadd.vi            v2, v2, -8                  \n\t"
-                                 "vadd.vi            v3, v3, -8                  \n\t"
-                                 "vadd.vi            v4, v4, -8                  \n\t"
-                                 "vadd.vi            v5, v5, -8                  \n\t"
-                                 "vadd.vi            v6, v6, -8                  \n\t"
-                                 "vadd.vi            v7, v7, -8                  \n\t"
-                                 "vadd.vi            v8, v8, -8                  \n\t"
-                                 "vadd.vi            v9, v9, -8                  \n\t"
-
-                                 SQ4BIT_KERNEL_COMP_4x16x16
-
-                                 "addi               t2, t2, -1                  \n\t"
-                                 "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
-
-                                 LOAD_SCALE_4x16
-
-                                 "vsetvli            t0, zero, e32, m8           \n\t"
-                                 "vfcvt.f.x.v        v16, v16                    \n\t"
-                                 "vfmacc.vv          v24, v16, v8                \n\t"
-                                 "addi               t3, t3, -1                  \n\t"
-                                 "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
-
-                                 "RESULT_SAVE%=:                                 \n\t"
-
-                                 SAVE_RESULT_4x16
-
-                                 :
-                                 : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
-                                   [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr), [BIAS] "r"(bias)
-                                 : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1",
-                                   "s2", "s3", "s4", "s5", "s6");
-
-            } else {
-                __asm__ volatile(
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vxor.vv            v24, v24, v24               \n\t"
-                    "addi               t3, %[BlockCountK], 0       \n\t"
-                    "addi               a1, %[A], 0                 \n\t"
-                    "addi               s1, %[B], 0                 \n\t"
-                    "BLOCK_COUNTK_LOOP%=:                           \n\t"
-                    "addi               s5, s1, 0                   \n\t"
-                    "addi               s1, s5, 64                  \n\t"
-                    "addi               s2, s1, 32                  \n\t"
-                    "addi               s3, s1, 32*2                \n\t"
-                    "addi               s4, s1, 32*3                \n\t"
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vxor.vv            v16, v16, v16               \n\t"
-                    // load a scale
-                    "flw                f1, (a1)                    \n\t"
-                    "flw                f2, 4(a1)                   \n\t"
-                    "flw                f3, 8(a1)                   \n\t"
-                    "flw                f4, 12(a1)                  \n\t"
-                    "addi               a1, a1, 16                  \n\t"
-                    "addi               t2, %[INNER], 0             \n\t"
-                    "BLOCK_INNER_LOOP%=:                            \n\t"
-
-                    LOAD_B_16x8x2
-
-                    "vsetvli            t0, zero, e8, m1            \n\t"
-                    "vle8.v             v10, (a1)                   \n\t"
-
-                    "addi               a1, a1, 32                  \n\t"
-                    "vle8.v             v11, (a1)                   \n\t"
-                    "addi               a1, a1, 32                  \n\t"
-                    "vadd.vi            v2, v2, -8                  \n\t"
-                    "vadd.vi            v3, v3, -8                  \n\t"
-                    "vadd.vi            v4, v4, -8                  \n\t"
-                    "vadd.vi            v5, v5, -8                  \n\t"
-                    "vadd.vi            v6, v6, -8                  \n\t"
-                    "vadd.vi            v7, v7, -8                  \n\t"
-                    "vadd.vi            v8, v8, -8                  \n\t"
-                    "vadd.vi            v9, v9, -8                  \n\t"
-
-                    SQ4BIT_KERNEL_COMP_4x16x16
-
-                    "addi               t2, t2, -1                  \n\t"
-                    "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
-
-                    LOAD_SCALE_4x16
-
-                    "vsetvli            t0, zero, e32, m8           \n\t"
-                    "vfcvt.f.x.v        v16, v16                    \n\t"
-                    "vfmacc.vv          v24, v16, v8                \n\t"
-                    "addi               t3, t3, -1                  \n\t"
-                    "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
-
-                    "RESULT_SAVE%=:                                 \n\t"
-
-                    SAVE_RESULT_4x16
-
-                    :
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
-                      [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr)
-                    : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1", "s2", "s3",
-                      "s4", "s5", "s6");
-            }
+
+            __asm__ volatile(
+                "vsetvli            t0, zero, e32, m8           \n\t"
+                "vxor.vv            v24, v24, v24               \n\t"
+                "addi               t3, %[BlockCountK], 0       \n\t"
+                "addi               a1, %[A], 0                 \n\t"
+                "addi               s1, %[B], 0                 \n\t"
+                "BLOCK_COUNTK_LOOP%=:                           \n\t"
+                "addi               s5, s1, 0                   \n\t"
+                "addi               s1, s5, 32                  \n\t"
+                "addi               s2, s1, 32                  \n\t"
+                "addi               s3, s1, 32*2                \n\t"
+                "addi               s4, s1, 32*3                \n\t"
+                "vsetvli            t0, zero, e32, m8           \n\t"
+                "vxor.vv            v16, v16, v16               \n\t"
+                // load a scale
+                "flw                f1, (a1)                    \n\t"
+                "flw                f2, 4(a1)                   \n\t"
+                "flw                f3, 8(a1)                   \n\t"
+                "flw                f4, 12(a1)                  \n\t"
+                "addi               a1, a1, 16                  \n\t"
+                "addi               t2, %[INNER], 0             \n\t"
+                "BLOCK_INNER_LOOP%=:                            \n\t"
+
+                LOAD_B_16x8x2
+
+                "vsetvli            t0, zero, e8, m1            \n\t"
+                "vle8.v             v10, (a1)                   \n\t"
+                "addi               a1, a1, 32                  \n\t"
+                "vle8.v             v11, (a1)                   \n\t"
+                "addi               a1, a1, 32                  \n\t"
+                "vadd.vi            v2, v2, -8                  \n\t"
+                "vadd.vi            v3, v3, -8                  \n\t"
+                "vadd.vi            v4, v4, -8                  \n\t"
+                "vadd.vi            v5, v5, -8                  \n\t"
+                "vadd.vi            v6, v6, -8                  \n\t"
+                "vadd.vi            v7, v7, -8                  \n\t"
+                "vadd.vi            v8, v8, -8                  \n\t"
+                "vadd.vi            v9, v9, -8                  \n\t"
+
+                SQ4BIT_KERNEL_COMP_4x16x16
+
+                "addi               t2, t2, -1                  \n\t"
+                "bnez               t2, BLOCK_INNER_LOOP%=      \n\t"
+
+                LOAD_SCALE_4x16_FP16
+
+                "vsetvli            t0, zero, e32, m8           \n\t"
+                "vfcvt.f.x.v        v16, v16                    \n\t"
+                "vfmacc.vv          v24, v16, v8                \n\t"
+                "addi               t3, t3, -1                  \n\t"
+                "bnez               t3, BLOCK_COUNTK_LOOP%=     \n\t"
+                "RESULT_SAVE%=:                                 \n\t"
+
+                SAVE_RESULT_4x16
+
+                :
+                : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [LDC] "r"(LDC),
+                  [BlockCountK] "r"(BlockCountK), [C] "r"(CPtr)
+                : "cc", "t0", "t1", "t2", "t3", "a1", "a2", "a3", "a4", "f1", "f2", "f3", "f4", "s1", "s2", "s3", "s4",
+                  "s5", "s6");
         }
     }
-    if (CountN % 16 != 0) {
-        // stroe output from tmp to C when NBLKS less than 16.
-        float *      CPtr = C + CountN / 16 * 16;
-        const size_t N    = CountN % 16;
-        LDC               = ldc * sizeof(float);
-        __asm__ volatile(
-            "vsetvli            t0, %[N], e32, m2       \n\t"
-            "vle32.v            v0, (%[SRC])            \n\t"
-            "addi               s2, %[SRC], 64          \n\t"
-            "addi               s3, %[SRC], 64*2        \n\t"
-            "addi               s4, %[SRC], 64*3        \n\t"
-            "vle32.v            v2, (s2)                \n\t"
-            "vle32.v            v4, (s3)                \n\t"
-            "vle32.v            v6, (s4)                \n\t"
-            "add                t2, %[DST], %[LDC]      \n\t"
-            "add                t3, t2, %[LDC]          \n\t"
-            "add                t4, t3, %[LDC]          \n\t"
-            "vse32.v            v0, (%[DST])            \n\t"
-            "vse32.v            v2, (t2)                \n\t"
-            "vse32.v            v4, (t3)                \n\t"
-            "vse32.v            v6, (t4)                \n\t"
-            :
-            : [N] "r"(N), [SRC] "r"(tmp), [DST] "r"(CPtr), [LDC] "r"(LDC)
-            : "cc", "t0", "t2", "t3", "t4", "s2", "s3", "s4");
-    }
 }
 
 template <bool HasZeroPoint>
-void SQ4BitGemmM1Kernel_CompInt8_ScaleFp16_Impl(size_t            BlkLen,
-                                                const std::byte * QuantA,
-                                                const std::byte * QuantBData,
-                                                const float *     QuantBScale,
-                                                const std::byte * QuantBZeroPoint,
-                                                float *           C,
-                                                size_t            CountN,
-                                                size_t            BlockCountK,
-                                                const float *     Bias) {
-    GGML_UNUSED(QuantBScale);
-    GGML_UNUSED(QuantBZeroPoint);
+void SQ4BitGemmM1Kernel_CompInt8_ScaleFp16_Impl(size_t          BlkLen,
+                                                const uint8_t * QuantA,
+                                                const uint8_t * QuantBData,
+                                                float *         C,
+                                                size_t          CountN,
+                                                size_t          BlockCountK,
+                                                const size_t    ldc) {
+    GGML_UNUSED(ldc);
     size_t INNER = BlkLen / 16;
 
     if constexpr (HasZeroPoint) {
         for (size_t n = 0; n < CountN; n += 16) {
-            size_t      nblks         = (CountN - n) > 16 ? 16 : CountN - n;
-            std::byte * QuantBDataPtr = (std::byte *) QuantBData +           //
-                                        n * BlockCountK * BlkLen / 2 +       // b data
-                                        n * BlockCountK * sizeof(uint8_t) +  // zp
-                                        n * BlockCountK * sizeof(_Float16);    // scale
-            float * CPtr = C + n;
-            size_t  cnt  = BlockCountK;
-            if (Bias != nullptr) {
-                const float * bias = Bias + n;
-                __asm__ volatile(
-                    "addi         t3, %[NBLKS], 0         \n\t"
-                    "vsetvli      t0, zero, e8, m1        \n\t"
-
-                    "vmv.v.i      v13, 3                  \n\t"
-                    "li           s1, 24                  \n\t"
-                    "vsetvli      t0, s1, e8, m1          \n\t"
-                    "vmv.v.i      v13, 2                  \n\t"
-                    "vsetvli      t0, zero, e8, mf2       \n\t"
-                    "vmv.v.i      v13, 1                  \n\t"
-                    "vsetvli      t0, zero, e8, mf4       \n\t"
-                    "vmv.v.i      v13, 0                  \n\t"
-                    "addi         s1, %[B], 0             \n\t"
-                    "addi         s2, %[B], 8             \n\t"
-                    "addi         s3, %[B], 16            \n\t"
-                    "addi         s4, %[B], 24            \n\t"
-                    // zp offset
-                    "addi         s7, %[B], 32            \n\t"
-                    // a offset
-                    "addi         s5, %[A], 0             \n\t"
-                    "addi         s6, %[A], 12            \n\t"
-
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v28, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v29, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v30, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v31, (%[BIAS])          \n\t"
-
-                    "LOOP_K%=:                            \n\t"
-                    "vsetvli      t0, zero, e16, mf4      \n\t"
-
-                    "vle16.v      v4, (s1)                \n\t"
-                    "addi         s1, s1, 48              \n\t"
-                    "vle16.v      v5, (s2)                \n\t"
-                    "addi         s2, s2, 72              \n\t"
-                    "vle16.v      v6, (s3)                \n\t"
-                    "addi         s3, s3, 96              \n\t"
-                    "vle16.v      v7, (s4)                \n\t"
-                    "addi         s4, s4, 120             \n\t"
-                    "flw          f1, (s5)                \n\t"
-                    "addi         s5, s5, 4               \n\t"
-                    "vfwcvt.f.f.v v8, v4                  \n\t"
-                    "vfwcvt.f.f.v v9, v5                  \n\t"
-                    "vfwcvt.f.f.v v10, v6                 \n\t"
-                    "vfwcvt.f.f.v v11, v7                 \n\t"
-
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-                    "addi         t5, %[INNER], 0         \n\t"
-                    "vxor.vv      v16, v16, v16           \n\t"
-                    "vxor.vv      v18, v18, v18           \n\t"
-                    "vxor.vv      v20, v20, v20           \n\t"
-                    "vxor.vv      v22, v22, v22           \n\t"
-                    "vfmul.vf     v24, v8, f1             \n\t"
-                    "vfmul.vf     v25, v9, f1             \n\t"
-                    "vfmul.vf     v26, v10, f1            \n\t"
-                    "vfmul.vf     v27, v11, f1            \n\t"
-                    "addi         %[CNT], %[CNT], -1      \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_ZP_16X1
-
-                    "LOOP_INNER%=:                        \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
-
-                    "vsub.vv      v0, v0, v8              \n\t"
-                    "vsub.vv      v4, v4, v8              \n\t"
-                    "vsub.vv      v1, v1, v9              \n\t"
-                    "vsub.vv      v5, v5, v9              \n\t"
-                    "vsub.vv      v2, v2, v10             \n\t"
-                    "vsub.vv      v6, v6, v10             \n\t"
-                    "vsub.vv      v3, v3, v11             \n\t"
-                    "vsub.vv      v7, v7, v11             \n\t"
-
-                    SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
-
-                    "bnez         t5, LOOP_INNER%=        \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    SQ4BIT_KERNEL_ACC_F16_1X4X4
-                    "addi         s7, s1, 32              \n\t"
-
-                    "bnez         %[CNT], LOOP_K%=        \n\t"
-                    "addi         t3, zero, 16            \n\t"
-                    "addi         s1, %[C], 16            \n\t"
-                    "addi         s2, %[C], 32            \n\t"
-                    "addi         s3, %[C], 48            \n\t"
-                    "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "jal          x0, END%=               \n\t"
-
-                    "ST_TAIL%=:                           \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "END%=:                               \n\t"
-
-                    : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks), [BIAS] "+r"(bias)
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
-                    : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6", "s7");
-            } else {
-                __asm__ volatile(
-                    "vsetvli      t0, zero, e32, m4       \n\t"
-                    "vxor.vv      v28, v28, v28           \n\t"
-
-                    "vsetvli      t0, zero, e8, m1        \n\t"
-                    "vmv.v.i      v13, 3                  \n\t"
-                    "li           s1, 24                  \n\t"
-                    "vsetvli      t0, s1, e8, m1          \n\t"
-                    "vmv.v.i      v13, 2                  \n\t"
-                    "vsetvli      t0, zero, e8, mf2       \n\t"
-                    "vmv.v.i      v13, 1                  \n\t"
-                    "vsetvli      t0, zero, e8, mf4       \n\t"
-                    "vmv.v.i      v13, 0                  \n\t"
-
-                    "addi         s1, %[B], 0             \n\t"
-                    "addi         s2, %[B], 8             \n\t"
-                    "addi         s3, %[B], 16            \n\t"
-                    "addi         s4, %[B], 24            \n\t"
-
-                    "addi         s7, %[B], 32            \n\t"
-
-                    "addi         s5, %[A], 0             \n\t"
-                    "addi         s6, %[A], 12            \n\t"
-                    "LOOP_K%=:                            \n\t"
-                    "vsetvli      t0, zero, e16, mf4      \n\t"
-                    "vle16.v      v4, (s1)                \n\t"
-                    "addi         s1, s1, 48              \n\t"
-                    "vle16.v      v5, (s2)                \n\t"
-                    "addi         s2, s2, 72              \n\t"
-                    "vle16.v      v6, (s3)                \n\t"
-                    "addi         s3, s3, 96              \n\t"
-                    "vle16.v      v7, (s4)                \n\t"
-                    "addi         s4, s4, 120             \n\t"
-                    "flw          f1, (s5)                \n\t"
-                    "addi         s5, s5, 4               \n\t"
-
-                    "vfwcvt.f.f.v v8, v4                  \n\t"
-                    "vfwcvt.f.f.v v9, v5                  \n\t"
-                    "vfwcvt.f.f.v v10, v6                 \n\t"
-                    "vfwcvt.f.f.v v11, v7                 \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    "addi         t5, %[INNER], 0         \n\t"
-                    "vxor.vv      v16, v16, v16           \n\t"
-                    "vxor.vv      v18, v18, v18           \n\t"
-                    "vxor.vv      v20, v20, v20           \n\t"
-                    "vxor.vv      v22, v22, v22           \n\t"
-                    "vfmul.vf     v24, v8, f1             \n\t"
-                    "vfmul.vf     v25, v9, f1             \n\t"
-                    "vfmul.vf     v26, v10, f1            \n\t"
-                    "vfmul.vf     v27, v11, f1            \n\t"
-                    "addi         %[CNT], %[CNT], -1      \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_ZP_16X1
-
-                    "LOOP_INNER%=:                        \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
-
-                    "vsub.vv      v0, v0, v8              \n\t"
-                    "vsub.vv      v4, v4, v8              \n\t"
-                    "vsub.vv      v1, v1, v9              \n\t"
-                    "vsub.vv      v5, v5, v9              \n\t"
-                    "vsub.vv      v2, v2, v10             \n\t"
-                    "vsub.vv      v6, v6, v10             \n\t"
-                    "vsub.vv      v3, v3, v11             \n\t"
-                    "vsub.vv      v7, v7, v11             \n\t"
-
-                    SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
-
-                    "bnez         t5, LOOP_INNER%=        \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    SQ4BIT_KERNEL_ACC_F16_1X4X4
-                    "addi         s7, s1, 32              \n\t"
-
-                    "bnez         %[CNT], LOOP_K%=        \n\t"
-                    "addi         t3, zero, 16            \n\t"
-                    "addi         s1, %[C], 16            \n\t"
-                    "addi         s2, %[C], 32            \n\t"
-                    "addi         s3, %[C], 48            \n\t"
-                    "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "jal          x0, END%=               \n\t"
-
-                    "ST_TAIL%=:                           \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "END%=:                               \n\t"
-
-                    : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks)
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
-                    : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6", "s7");
-            }
-        }
-    } else {
-        for (size_t n = 0; n < CountN; n += 16) {
-            size_t      nblks         = (CountN - n) > 16 ? 16 : CountN - n;
-            std::byte * QuantBDataPtr = (std::byte *) QuantBData +         //
-                                        n * BlockCountK * BlkLen / 2 +     // b data
-                                        n * BlockCountK * sizeof(_Float16);  // scale
+            size_t    nblks         = (CountN - n) > 16 ? 16 : CountN - n;
+            uint8_t * QuantBDataPtr = (uint8_t *) QuantBData +             //
+                                      n * BlockCountK * BlkLen / 2 +       // b data
+                                      n * BlockCountK * sizeof(uint8_t) +  // zp
+                                      n * BlockCountK * sizeof(_Float16);  // scale
             float * CPtr = C + n;
             size_t  cnt  = BlockCountK;
-            if (Bias != nullptr) {
-                const float * bias = Bias + n;
-                __asm__ volatile(
-                    "addi         t3, %[NBLKS], 0         \n\t"
-                    "addi         s1, %[B], 0             \n\t"
-                    "addi         s2, %[B], 8             \n\t"
-                    "addi         s3, %[B], 16            \n\t"
-                    "addi         s4, %[B], 24            \n\t"
-                    "addi         s5, %[A], 0             \n\t"
-                    "addi         s6, %[A], 12            \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v28, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v29, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v30, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v31, (%[BIAS])          \n\t"
-
-                    "LOOP_K%=:                            \n\t"
-                    "vsetvli      t0, zero, e16, mf4      \n\t"
-
-                    "vle16.v      v4, (s1)                \n\t"
-                    "addi         s1, s1, 32              \n\t"
-                    "vle16.v      v5, (s2)                \n\t"
-                    "addi         s2, s2, 56              \n\t"
-                    "vle16.v      v6, (s3)                \n\t"
-                    "addi         s3, s3, 80              \n\t"
-                    "vle16.v      v7, (s4)                \n\t"
-                    "addi         s4, s4, 104             \n\t"
-                    "flw          f1, (s5)                \n\t"
-                    "addi         s5, s5, 4               \n\t"
-                    "vfwcvt.f.f.v v8, v4                  \n\t"
-                    "vfwcvt.f.f.v v9, v5                  \n\t"
-                    "vfwcvt.f.f.v v10, v6                 \n\t"
-                    "vfwcvt.f.f.v v11, v7                 \n\t"
-
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-                    "addi         t5, %[INNER], 0         \n\t"
-                    "vxor.vv      v16, v16, v16           \n\t"
-                    "vxor.vv      v18, v18, v18           \n\t"
-                    "vxor.vv      v20, v20, v20           \n\t"
-                    "vxor.vv      v22, v22, v22           \n\t"
-                    "vfmul.vf     v24, v8, f1             \n\t"
-                    "vfmul.vf     v25, v9, f1             \n\t"
-                    "vfmul.vf     v26, v10, f1            \n\t"
-                    "vfmul.vf     v27, v11, f1            \n\t"
-                    "addi         %[CNT], %[CNT], -1      \n\t"
-                    "vsetvli      t0, zero, e8, m1        \n\t"
-                    "LOOP_INNER%=:                        \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
-
-                    "vadd.vi      v0, v0, -8              \n\t"
-                    "vadd.vi      v1, v1, -8              \n\t"
-                    "vadd.vi      v2, v2, -8              \n\t"
-                    "vadd.vi      v3, v3, -8              \n\t"
-                    "vadd.vi      v4, v4, -8              \n\t"
-                    "vadd.vi      v5, v5, -8              \n\t"
-                    "vadd.vi      v6, v6, -8              \n\t"
-                    "vadd.vi      v7, v7, -8              \n\t"
-
-                    SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
-
-                    "bnez         t5, LOOP_INNER%=        \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    SQ4BIT_KERNEL_ACC_F16_1X4X4
-
-                    "bnez         %[CNT], LOOP_K%=        \n\t"
-                    "addi         t3, zero, 16            \n\t"
-                    "addi         s1, %[C], 16            \n\t"
-                    "addi         s2, %[C], 32            \n\t"
-                    "addi         s3, %[C], 48            \n\t"
-                    "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "jal          x0, END%=               \n\t"
-
-                    "ST_TAIL%=:                           \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "END%=:                               \n\t"
-
-                    : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks), [BIAS] "+r"(bias)
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
-                    : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6");
-            } else {
-                __asm__ volatile(
-                    "vsetvli      t0, zero, e32, m4       \n\t"
-                    "vxor.vv      v28, v28, v28           \n\t"
-                    "addi         s1, %[B], 0             \n\t"
-                    "addi         s2, %[B], 8             \n\t"
-                    "addi         s3, %[B], 16            \n\t"
-                    "addi         s4, %[B], 24            \n\t"
-
-                    "addi         s5, %[A], 0             \n\t"
-                    "addi         s6, %[A], 12            \n\t"
-                    "LOOP_K%=:                            \n\t"
-                    "vsetvli      t0, zero, e16, mf4      \n\t"
-                    "vle16.v      v4, (s1)                \n\t"
-                    "addi         s1, s1, 32              \n\t"
-                    "vle16.v      v5, (s2)                \n\t"
-                    "addi         s2, s2, 56              \n\t"
-                    "vle16.v      v6, (s3)                \n\t"
-                    "addi         s3, s3, 80              \n\t"
-                    "vle16.v      v7, (s4)                \n\t"
-                    "addi         s4, s4, 104             \n\t"
-                    "flw          f1, (s5)                \n\t"
-                    "addi         s5, s5, 4               \n\t"
-
-                    "vfwcvt.f.f.v v8, v4                  \n\t"
-                    "vfwcvt.f.f.v v9, v5                  \n\t"
-                    "vfwcvt.f.f.v v10, v6                 \n\t"
-                    "vfwcvt.f.f.v v11, v7                 \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    "addi         t5, %[INNER], 0         \n\t"
-                    "vxor.vv      v16, v16, v16           \n\t"
-                    "vxor.vv      v18, v18, v18           \n\t"
-                    "vxor.vv      v20, v20, v20           \n\t"
-                    "vxor.vv      v22, v22, v22           \n\t"
-                    "vfmul.vf     v24, v8, f1             \n\t"
-                    "vfmul.vf     v25, v9, f1             \n\t"
-                    "vfmul.vf     v26, v10, f1            \n\t"
-                    "vfmul.vf     v27, v11, f1            \n\t"
-                    "addi         %[CNT], %[CNT], -1      \n\t"
-                    "vsetvli      t0, zero, e8, m1        \n\t"
-                    "LOOP_INNER%=:                        \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
-
-                    "vadd.vi      v0, v0, -8              \n\t"
-                    "vadd.vi      v1, v1, -8              \n\t"
-                    "vadd.vi      v2, v2, -8              \n\t"
-                    "vadd.vi      v3, v3, -8              \n\t"
-                    "vadd.vi      v4, v4, -8              \n\t"
-                    "vadd.vi      v5, v5, -8              \n\t"
-                    "vadd.vi      v6, v6, -8              \n\t"
-                    "vadd.vi      v7, v7, -8              \n\t"
-
-                    SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
-
-                    "bnez         t5, LOOP_INNER%=        \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    SQ4BIT_KERNEL_ACC_F16_1X4X4
-
-                    "bnez         %[CNT], LOOP_K%=        \n\t"
-                    "addi         t3, zero, 16            \n\t"
-                    "addi         s1, %[C], 16            \n\t"
-                    "addi         s2, %[C], 32            \n\t"
-                    "addi         s3, %[C], 48            \n\t"
-                    "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "jal          x0, END%=               \n\t"
-
-                    "ST_TAIL%=:                           \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "END%=:                               \n\t"
-
-                    : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks)
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
-                    : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6");
-            }
-        }
-    }
-}
 
-template <bool HasZeroPoint>
-void SQ4BitGemmM1Kernel_CompInt8_Impl(size_t            BlkLen,
-                                      const std::byte * QuantA,
-                                      const std::byte * QuantBData,
-                                      const float *     QuantBScale,
-                                      const std::byte * QuantBZeroPoint,
-                                      float *           C,
-                                      size_t            CountN,
-                                      size_t            BlockCountK,
-                                      const float *     Bias) {
-    GGML_UNUSED(QuantBScale);
-    GGML_UNUSED(QuantBZeroPoint);
-    const size_t INNER = BlkLen / 16;
-    if constexpr (HasZeroPoint) {
-        for (size_t n = 0; n < CountN; n += 16) {
-            size_t      nblks         = (CountN - n) > 16 ? 16 : CountN - n;
-            std::byte * QuantBDataPtr = (std::byte *) QuantBData +           //
-                                        n * BlockCountK * BlkLen / 2 +       // b data
-                                        n * BlockCountK * sizeof(uint8_t) +  // zp
-                                        n * BlockCountK * sizeof(float);     // scale
-            float * CPtr = C + n;
-            size_t  cnt  = BlockCountK;
-            if (Bias != nullptr) {
-                const float * bias = Bias + n;
-                __asm__ volatile(
-                    "addi         t3, %[NBLKS], 0         \n\t"
-                    "vsetvli      t0, zero, e8, m1        \n\t"
-                    "vmv.v.i      v13, 3                  \n\t"
-                    "li           s1, 24                  \n\t"
-                    "vsetvli      t0, s1, e8, m1          \n\t"
-                    "vmv.v.i      v13, 2                  \n\t"
-                    "vsetvli      t0, zero, e8, mf2       \n\t"
-                    "vmv.v.i      v13, 1                  \n\t"
-                    "vsetvli      t0, zero, e8, mf4       \n\t"
-                    "vmv.v.i      v13, 0                  \n\t"
-                    "vsetvli      t0, zero, e32, m4       \n\t"
-                    "vxor.vv      v28, v28, v28           \n\t"
-
-                    // scale offset, scale0.0, scale1.0, scale2.0, scale3.0....scale15.0
-                    "addi         s1, %[B], 0             \n\t"
-                    "addi         s2, %[B], 16            \n\t"
-                    "addi         s3, %[B], 32            \n\t"
-                    "addi         s4, %[B], 48            \n\t"
-                    // zp offset
-                    "addi         s7, %[B], 64            \n\t"
-                    // a offset
-                    "addi         s5, %[A], 0             \n\t"
-                    "addi         s6, %[A], 12            \n\t"
-
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v28, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v29, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v30, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v31, (%[BIAS])          \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-                    "LOOP_K%=:                            \n\t"
-
-                    // load scale
-                    "vle32.v      v8, (s1)                \n\t"
-                    "addi         s1, s1, 80              \n\t"
-                    "vle32.v      v9, (s2)                \n\t"
-                    "addi         s2, s2, 96              \n\t"
-                    "vle32.v      v10, (s3)               \n\t"
-                    "addi         s3, s3, 112             \n\t"
-                    "vle32.v      v11, (s4)               \n\t"
-                    "addi         s4, s4, 128             \n\t"
-
-                    // load a scale
-                    "flw          f1, (s5)                \n\t"
-                    "addi         s5, s5, 4               \n\t"
-
-                    "addi         t5, %[INNER], 0         \n\t"
-                    "vxor.vv      v16, v16, v16           \n\t"
-                    "vxor.vv      v18, v18, v18           \n\t"
-                    "vxor.vv      v20, v20, v20           \n\t"
-                    "vxor.vv      v22, v22, v22           \n\t"
-
-                    // a scale * b scale
-                    "vfmul.vf     v24, v8, f1             \n\t"
-                    "vfmul.vf     v25, v9, f1             \n\t"
-                    "vfmul.vf     v26, v10, f1            \n\t"
-                    "vfmul.vf     v27, v11, f1            \n\t"
-                    "addi         %[CNT], %[CNT], -1      \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_ZP_16X1
-
-                    "LOOP_INNER%=:                        \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
-
-                    "vsub.vv      v0, v0, v8              \n\t"
-                    "vsub.vv      v4, v4, v8              \n\t"
-                    "vsub.vv      v1, v1, v9              \n\t"
-                    "vsub.vv      v5, v5, v9              \n\t"
-                    "vsub.vv      v2, v2, v10             \n\t"
-                    "vsub.vv      v6, v6, v10             \n\t"
-                    "vsub.vv      v3, v3, v11             \n\t"
-                    "vsub.vv      v7, v7, v11             \n\t"
-
-                    SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
-
-                    "bnez         t5, LOOP_INNER%=        \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    SQ4BIT_KERNEL_ACC_1X4X4
-                    "addi         s7, s1, 64              \n\t"
-
-                    "bnez         %[CNT], LOOP_K%=        \n\t"
-
-                    "addi         t3, zero, 16            \n\t"
-                    "addi         s1, %[C], 16            \n\t"
-                    "addi         s2, %[C], 32            \n\t"
-                    "addi         s3, %[C], 48            \n\t"
-                    "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "jal          x0, END%=               \n\t"
-
-                    "ST_TAIL%=:                           \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "END%=:                               \n\t"
-
-                    : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks), [BIAS] "+r"(bias)
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
-                    : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6", "s7");
-            } else {
-                __asm__ volatile(
-                    "vsetvli      t0, zero, e32, m4       \n\t"
-                    "vxor.vv      v28, v28, v28           \n\t"
-
-                    "vsetvli      t0, zero, e8, m1        \n\t"
-                    "vmv.v.i      v13, 3                  \n\t"
-                    "li           s1, 24                  \n\t"
-                    "vsetvli      t0, s1, e8, m1          \n\t"
-                    "vmv.v.i      v13, 2                  \n\t"
-                    "vsetvli      t0, zero, e8, mf2       \n\t"
-                    "vmv.v.i      v13, 1                  \n\t"
-                    "vsetvli      t0, zero, e8, mf4       \n\t"
-                    "vmv.v.i      v13, 0                  \n\t"
-                    "addi         s1, %[B], 0             \n\t"
-                    "addi         s2, %[B], 16            \n\t"
-                    "addi         s3, %[B], 32            \n\t"
-                    "addi         s4, %[B], 48            \n\t"
-
-                    "addi         s7, %[B], 64            \n\t"
-
-                    "addi         s5, %[A], 0             \n\t"
-                    "addi         s6, %[A], 12            \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    "LOOP_K%=:                            \n\t"
-                    "vle32.v      v8, (s1)                \n\t"
-                    "addi         s1, s1, 80              \n\t"
-                    "vle32.v      v9, (s2)                \n\t"
-                    "addi         s2, s2, 96              \n\t"
-                    "vle32.v      v10, (s3)               \n\t"
-                    "addi         s3, s3, 112             \n\t"
-                    "vle32.v      v11, (s4)               \n\t"
-                    "addi         s4, s4, 128             \n\t"
-
-                    "flw          f1, (s5)                \n\t"
-                    "addi         s5, s5, 4               \n\t"
-
-                    "addi         t5, %[INNER], 0         \n\t"
-                    "vxor.vv      v16, v16, v16           \n\t"
-                    "vxor.vv      v18, v18, v18           \n\t"
-                    "vxor.vv      v20, v20, v20           \n\t"
-                    "vxor.vv      v22, v22, v22           \n\t"
-
-                    "vfmul.vf     v24, v8, f1             \n\t"
-                    "vfmul.vf     v25, v9, f1             \n\t"
-                    "vfmul.vf     v26, v10, f1            \n\t"
-                    "vfmul.vf     v27, v11, f1            \n\t"
-                    "addi         %[CNT], %[CNT], -1      \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_ZP_16X1
-
-                    "LOOP_INNER%=:                        \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
-
-                    "vsub.vv      v0, v0, v8              \n\t"
-                    "vsub.vv      v4, v4, v8              \n\t"
-                    "vsub.vv      v1, v1, v9              \n\t"
-                    "vsub.vv      v5, v5, v9              \n\t"
-                    "vsub.vv      v2, v2, v10             \n\t"
-                    "vsub.vv      v6, v6, v10             \n\t"
-                    "vsub.vv      v3, v3, v11             \n\t"
-                    "vsub.vv      v7, v7, v11             \n\t"
-
-                    SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
-
-                    "bnez         t5, LOOP_INNER%=        \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    SQ4BIT_KERNEL_ACC_1X4X4
-                    "addi         s7, s1, 64              \n\t"
-
-                    "bnez         %[CNT], LOOP_K%=        \n\t"
-
-                    "addi         t3, zero, 16            \n\t"
-                    "addi         s1, %[C], 16            \n\t"
-                    "addi         s2, %[C], 32            \n\t"
-                    "addi         s3, %[C], 48            \n\t"
-                    "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "jal          x0, END%=               \n\t"
-
-                    "ST_TAIL%=:                           \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "END%=:                               \n\t"
-
-                    : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks)
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
-                    : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6", "s7");
-            }
+            __asm__ volatile(
+                "vsetvli      t0, zero, e32, m4       \n\t"
+                "vxor.vv      v28, v28, v28           \n\t"
+
+                "vsetvli      t0, zero, e8, m1        \n\t"
+                "vmv.v.i      v13, 3                  \n\t"
+                "li           s1, 24                  \n\t"
+                "vsetvli      t0, s1, e8, m1          \n\t"
+                "vmv.v.i      v13, 2                  \n\t"
+                "vsetvli      t0, zero, e8, mf2       \n\t"
+                "vmv.v.i      v13, 1                  \n\t"
+                "vsetvli      t0, zero, e8, mf4       \n\t"
+                "vmv.v.i      v13, 0                  \n\t"
+
+                "addi         s1, %[B], 0             \n\t"
+                "addi         s2, %[B], 8             \n\t"
+                "addi         s3, %[B], 16            \n\t"
+                "addi         s4, %[B], 24            \n\t"
+
+                "addi         s7, %[B], 32            \n\t"
+
+                "addi         s5, %[A], 0             \n\t"
+                "addi         s6, %[A], 12            \n\t"
+                "LOOP_K%=:                            \n\t"
+                "vsetvli      t0, zero, e16, mf4      \n\t"
+                "vle16.v      v4, (s1)                \n\t"
+                "addi         s1, s1, 48              \n\t"
+                "vle16.v      v5, (s2)                \n\t"
+                "addi         s2, s2, 72              \n\t"
+                "vle16.v      v6, (s3)                \n\t"
+                "addi         s3, s3, 96              \n\t"
+                "vle16.v      v7, (s4)                \n\t"
+                "addi         s4, s4, 120             \n\t"
+                "flw          f1, (s5)                \n\t"
+                "addi         s5, s5, 4               \n\t"
+
+                "vfwcvt.f.f.v v8, v4                  \n\t"
+                "vfwcvt.f.f.v v9, v5                  \n\t"
+                "vfwcvt.f.f.v v10, v6                 \n\t"
+                "vfwcvt.f.f.v v11, v7                 \n\t"
+                "vsetvli      t0, zero, e32, mf2      \n\t"
+
+                "addi         t5, %[INNER], 0         \n\t"
+                "vxor.vv      v16, v16, v16           \n\t"
+                "vxor.vv      v18, v18, v18           \n\t"
+                "vxor.vv      v20, v20, v20           \n\t"
+                "vxor.vv      v22, v22, v22           \n\t"
+                "vfmul.vf     v24, v8, f1             \n\t"
+                "vfmul.vf     v25, v9, f1             \n\t"
+                "vfmul.vf     v26, v10, f1            \n\t"
+                "vfmul.vf     v27, v11, f1            \n\t"
+                "addi         %[CNT], %[CNT], -1      \n\t"
+
+                SQ4BIT_KERNEL_LOAD_ZP_16X1
+
+                "LOOP_INNER%=:                        \n\t"
+
+                SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
+
+                "vsub.vv      v0, v0, v8              \n\t"
+                "vsub.vv      v4, v4, v8              \n\t"
+                "vsub.vv      v1, v1, v9              \n\t"
+                "vsub.vv      v5, v5, v9              \n\t"
+                "vsub.vv      v2, v2, v10             \n\t"
+                "vsub.vv      v6, v6, v10             \n\t"
+                "vsub.vv      v3, v3, v11             \n\t"
+                "vsub.vv      v7, v7, v11             \n\t"
+
+                SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
+
+                "bnez         t5, LOOP_INNER%=        \n\t"
+                "vsetvli      t0, zero, e32, mf2      \n\t"
+
+                SQ4BIT_KERNEL_ACC_F16_1X4X4
+                "addi         s7, s1, 32              \n\t"
+
+                "bnez         %[CNT], LOOP_K%=        \n\t"
+                "addi         t3, zero, 16            \n\t"
+                "addi         s1, %[C], 16            \n\t"
+                "addi         s2, %[C], 32            \n\t"
+                "addi         s3, %[C], 48            \n\t"
+                "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
+                "vse32.v      v28, (%[C])             \n\t"
+                "vse32.v      v29, (s1)               \n\t"
+                "vse32.v      v30, (s2)               \n\t"
+                "vse32.v      v31, (s3)               \n\t"
+                "jal          x0, END%=               \n\t"
+
+                "ST_TAIL%=:                           \n\t"
+                "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
+                "sub          %[NBLKS], %[NBLKS], t0  \n\t"
+                "vse32.v      v28, (%[C])             \n\t"
+                "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
+                "sub          %[NBLKS], %[NBLKS], t0  \n\t"
+                "vse32.v      v29, (s1)               \n\t"
+                "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
+                "sub          %[NBLKS], %[NBLKS], t0  \n\t"
+                "vse32.v      v30, (s2)               \n\t"
+                "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
+                "sub          %[NBLKS], %[NBLKS], t0  \n\t"
+                "vse32.v      v31, (s3)               \n\t"
+                "END%=:                               \n\t"
+
+                : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks)
+                : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
+                : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6", "s7");
         }
     } else {
         for (size_t n = 0; n < CountN; n += 16) {
-            size_t      nblks         = (CountN - n) > 16 ? 16 : CountN - n;
-            std::byte * QuantBDataPtr = (std::byte *) QuantBData +        //
-                                        n * BlockCountK * BlkLen / 2 +    // b data
-                                        n * BlockCountK * sizeof(float);  // scale
+            size_t    nblks         = (CountN - n) > 16 ? 16 : CountN - n;
+            uint8_t * QuantBDataPtr = (uint8_t *) QuantBData +             //
+                                      n * BlockCountK * BlkLen / 2 +       // b data
+                                      n * BlockCountK * sizeof(_Float16);  // scale
             float * CPtr = C + n;
             size_t  cnt  = BlockCountK;
-            if (Bias != nullptr) {
-                const float * bias = Bias + n;
-                __asm__ volatile(
-                    "addi         t3, %[NBLKS], 0         \n\t"
-                    "addi         s1, %[B], 0             \n\t"
-                    "addi         s2, %[B], 16            \n\t"
-                    "addi         s3, %[B], 32            \n\t"
-                    "addi         s4, %[B], 48            \n\t"
-                    "addi         s5, %[A], 0             \n\t"
-                    "addi         s6, %[A], 12            \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v28, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v29, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v30, (%[BIAS])          \n\t"
-                    "sub          t3, t3, t0              \n\t"
-                    "addi         %[BIAS], %[BIAS], 16    \n\t"
-                    "vsetvli      t0, t3, e32, mf2        \n\t"
-                    "vle32.v      v31, (%[BIAS])          \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-                    "LOOP_K%=:                            \n\t"
-                    "vle32.v      v8, (s1)                \n\t"
-                    "addi         s1, s1, 64              \n\t"
-                    "vle32.v      v9, (s2)                \n\t"
-                    "addi         s2, s2, 80              \n\t"
-                    "vle32.v      v10, (s3)               \n\t"
-                    "addi         s3, s3, 96              \n\t"
-                    "vle32.v      v11, (s4)               \n\t"
-                    "addi         s4, s4, 112             \n\t"
-                    "flw          f1, (s5)                \n\t"
-                    "addi         s5, s5, 4               \n\t"
-
-                    "addi         t5, %[INNER], 0         \n\t"
-                    "vxor.vv      v16, v16, v16           \n\t"
-                    "vxor.vv      v18, v18, v18           \n\t"
-                    "vxor.vv      v20, v20, v20           \n\t"
-                    "vxor.vv      v22, v22, v22           \n\t"
-                    "vfmul.vf     v24, v8, f1             \n\t"
-                    "vfmul.vf     v25, v9, f1             \n\t"
-                    "vfmul.vf     v26, v10, f1            \n\t"
-                    "vfmul.vf     v27, v11, f1            \n\t"
-                    "addi         %[CNT], %[CNT], -1      \n\t"
-                    "vsetvli      t0, zero, e8, m1        \n\t"
-                    "LOOP_INNER%=:                        \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
-
-                    "vadd.vi      v0, v0, -8              \n\t"
-                    "vadd.vi      v1, v1, -8              \n\t"
-                    "vadd.vi      v2, v2, -8              \n\t"
-                    "vadd.vi      v3, v3, -8              \n\t"
-                    "vadd.vi      v4, v4, -8              \n\t"
-                    "vadd.vi      v5, v5, -8              \n\t"
-                    "vadd.vi      v6, v6, -8              \n\t"
-                    "vadd.vi      v7, v7, -8              \n\t"
-
-                    SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
-
-                    "bnez         t5, LOOP_INNER%=        \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    SQ4BIT_KERNEL_ACC_1X4X4
-
-                    "bnez         %[CNT], LOOP_K%=        \n\t"
-                    "addi         t3, zero, 16            \n\t"
-                    "addi         s1, %[C], 16            \n\t"
-                    "addi         s2, %[C], 32            \n\t"
-                    "addi         s3, %[C], 48            \n\t"
-                    "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "jal          x0, END%=               \n\t"
-
-                    "ST_TAIL%=:                           \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "END%=:                               \n\t"
-
-                    : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks), [BIAS] "+r"(bias)
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
-                    : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6");
-            } else {
-                __asm__ volatile(
-                    "vsetvli      t0, zero, e32, m4       \n\t"
-                    "vxor.vv      v28, v28, v28           \n\t"
-                    "addi         s1, %[B], 0             \n\t"
-                    "addi         s2, %[B], 16            \n\t"
-                    "addi         s3, %[B], 32            \n\t"
-                    "addi         s4, %[B], 48            \n\t"
-
-                    "addi         s5, %[A], 0             \n\t"
-                    "addi         s6, %[A], 12            \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-                    "LOOP_K%=:                            \n\t"
-                    "vle32.v      v8, (s1)                \n\t"
-                    "addi         s1, s1, 64              \n\t"
-                    "vle32.v      v9, (s2)                \n\t"
-                    "addi         s2, s2, 80              \n\t"
-                    "vle32.v      v10, (s3)               \n\t"
-                    "addi         s3, s3, 96              \n\t"
-                    "vle32.v      v11, (s4)               \n\t"
-                    "addi         s4, s4, 112             \n\t"
-                    "flw          f1, (s5)                \n\t"
-                    "addi         s5, s5, 4               \n\t"
-
-                    "addi         t5, %[INNER], 0         \n\t"
-                    "vxor.vv      v16, v16, v16           \n\t"
-                    "vxor.vv      v18, v18, v18           \n\t"
-                    "vxor.vv      v20, v20, v20           \n\t"
-                    "vxor.vv      v22, v22, v22           \n\t"
-                    "vfmul.vf     v24, v8, f1             \n\t"
-                    "vfmul.vf     v25, v9, f1             \n\t"
-                    "vfmul.vf     v26, v10, f1            \n\t"
-                    "vfmul.vf     v27, v11, f1            \n\t"
-                    "addi         %[CNT], %[CNT], -1      \n\t"
-                    "vsetvli      t0, zero, e8, m1        \n\t"
-                    "LOOP_INNER%=:                        \n\t"
-
-                    SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
-
-                    "vadd.vi      v0, v0, -8              \n\t"
-                    "vadd.vi      v1, v1, -8              \n\t"
-                    "vadd.vi      v2, v2, -8              \n\t"
-                    "vadd.vi      v3, v3, -8              \n\t"
-                    "vadd.vi      v4, v4, -8              \n\t"
-                    "vadd.vi      v5, v5, -8              \n\t"
-                    "vadd.vi      v6, v6, -8              \n\t"
-                    "vadd.vi      v7, v7, -8              \n\t"
-
-                    SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
-
-                    "bnez         t5, LOOP_INNER%=        \n\t"
-                    "vsetvli      t0, zero, e32, mf2      \n\t"
-
-                    SQ4BIT_KERNEL_ACC_1X4X4
-
-                    "bnez         %[CNT], LOOP_K%=        \n\t"
-                    "addi         t3, zero, 16            \n\t"
-                    "addi         s1, %[C], 16            \n\t"
-                    "addi         s2, %[C], 32            \n\t"
-                    "addi         s3, %[C], 48            \n\t"
-                    "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "jal          x0, END%=               \n\t"
-
-                    "ST_TAIL%=:                           \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v28, (%[C])             \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v29, (s1)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v30, (s2)               \n\t"
-                    "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
-                    "sub          %[NBLKS], %[NBLKS], t0  \n\t"
-                    "vse32.v      v31, (s3)               \n\t"
-                    "END%=:                               \n\t"
-
-                    : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks)
-                    : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
-                    : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6");
-            }
-        }
-    }
-}
-
-template <bool HasZeroPoint>
-inline void SQ4BitGemmM4Kernel_CompInt8_DispatchOnBlkLen(size_t            BlkLen,
-                                                         const std::byte * QuantA,
-                                                         const std::byte * QuantBData,
-                                                         const float *     QuantBScale,
-                                                         const std::byte * QuantBZeroPoint,
-                                                         float *           C,
-                                                         size_t            CountM,
-                                                         size_t            CountN,
-                                                         size_t            BlockStrideQuantB,
-                                                         const float *     Bias,
-                                                         const size_t      ldc,
-                                                         const size_t      scalestride) {
-    if (scalestride == 4) {
-        SQ4BitGemmM4Kernel_CompInt8_Impl<HasZeroPoint>(BlkLen, QuantA, QuantBData, QuantBScale, QuantBZeroPoint, C,
-                                                       CountN, BlockStrideQuantB, Bias, ldc);
-
-    } else if (scalestride == 2) {
-        SQ4BitGemmM4Kernel_CompInt8_ScaleFp16_Impl<HasZeroPoint>(
-            BlkLen, QuantA, QuantBData, QuantBScale, QuantBZeroPoint, C, CountN, BlockStrideQuantB, Bias, ldc);
-    }
-}
 
-template <bool HasZeroPoint>
-inline void SQ4BitGemmM1Kernel_CompInt8_DispatchOnBlkLen(size_t            BlkLen,
-                                                         const std::byte * QuantA,
-                                                         const std::byte * QuantBData,
-                                                         const float *     QuantBScale,
-                                                         const std::byte * QuantBZeroPoint,
-                                                         float *           C,
-                                                         size_t            CountM,
-                                                         size_t            CountN,
-                                                         size_t            BlockStrideQuantB,
-                                                         const float *     Bias,
-                                                         const size_t      ldc,
-                                                         const size_t      scalestride) {
-    if (scalestride == 4) {
-        SQ4BitGemmM1Kernel_CompInt8_Impl<HasZeroPoint>(BlkLen, QuantA, QuantBData, QuantBScale, QuantBZeroPoint, C,
-                                                       CountN, BlockStrideQuantB, Bias);
-    } else if (scalestride == 2) {
-        SQ4BitGemmM1Kernel_CompInt8_ScaleFp16_Impl<HasZeroPoint>(BlkLen, QuantA, QuantBData, QuantBScale,
-                                                                 QuantBZeroPoint, C, CountN, BlockStrideQuantB, Bias);
+            __asm__ volatile(
+                "vsetvli      t0, zero, e32, m4       \n\t"
+                "vxor.vv      v28, v28, v28           \n\t"
+                "addi         s1, %[B], 0             \n\t"
+                "addi         s2, %[B], 8             \n\t"
+                "addi         s3, %[B], 16            \n\t"
+                "addi         s4, %[B], 24            \n\t"
+
+                "addi         s5, %[A], 0             \n\t"
+                "addi         s6, %[A], 12            \n\t"
+                "LOOP_K%=:                            \n\t"
+                "vsetvli      t0, zero, e16, mf4      \n\t"
+                "vle16.v      v4, (s1)                \n\t"
+                "addi         s1, s1, 32              \n\t"
+                "vle16.v      v5, (s2)                \n\t"
+                "addi         s2, s2, 56              \n\t"
+                "vle16.v      v6, (s3)                \n\t"
+                "addi         s3, s3, 80              \n\t"
+                "vle16.v      v7, (s4)                \n\t"
+                "addi         s4, s4, 104             \n\t"
+                "flw          f1, (s5)                \n\t"
+                "addi         s5, s5, 4               \n\t"
+
+                "vfwcvt.f.f.v v8, v4                  \n\t"
+                "vfwcvt.f.f.v v9, v5                  \n\t"
+                "vfwcvt.f.f.v v10, v6                 \n\t"
+                "vfwcvt.f.f.v v11, v7                 \n\t"
+                "vsetvli      t0, zero, e32, mf2      \n\t"
+
+                "addi         t5, %[INNER], 0         \n\t"
+                "vxor.vv      v16, v16, v16           \n\t"
+                "vxor.vv      v18, v18, v18           \n\t"
+                "vxor.vv      v20, v20, v20           \n\t"
+                "vxor.vv      v22, v22, v22           \n\t"
+                "vfmul.vf     v24, v8, f1             \n\t"
+                "vfmul.vf     v25, v9, f1             \n\t"
+                "vfmul.vf     v26, v10, f1            \n\t"
+                "vfmul.vf     v27, v11, f1            \n\t"
+                "addi         %[CNT], %[CNT], -1      \n\t"
+                "vsetvli      t0, zero, e8, m1        \n\t"
+                "LOOP_INNER%=:                        \n\t"
+
+                SQ4BIT_KERNEL_LOAD_1x8x2_4X8X4
+
+                "vadd.vi      v0, v0, -8              \n\t"
+                "vadd.vi      v1, v1, -8              \n\t"
+                "vadd.vi      v2, v2, -8              \n\t"
+                "vadd.vi      v3, v3, -8              \n\t"
+                "vadd.vi      v4, v4, -8              \n\t"
+                "vadd.vi      v5, v5, -8              \n\t"
+                "vadd.vi      v6, v6, -8              \n\t"
+                "vadd.vi      v7, v7, -8              \n\t"
+
+                SQ4BIT_KERNEL_COMP_1x8x2_4X8X4
+
+                "bnez         t5, LOOP_INNER%=        \n\t"
+                "vsetvli      t0, zero, e32, mf2      \n\t"
+
+                SQ4BIT_KERNEL_ACC_F16_1X4X4
+
+                "bnez         %[CNT], LOOP_K%=        \n\t"
+                "addi         t3, zero, 16            \n\t"
+                "addi         s1, %[C], 16            \n\t"
+                "addi         s2, %[C], 32            \n\t"
+                "addi         s3, %[C], 48            \n\t"
+                "blt          %[NBLKS], t3, ST_TAIL%= \n\t"
+                "vse32.v      v28, (%[C])             \n\t"
+                "vse32.v      v29, (s1)               \n\t"
+                "vse32.v      v30, (s2)               \n\t"
+                "vse32.v      v31, (s3)               \n\t"
+                "jal          x0, END%=               \n\t"
+
+                "ST_TAIL%=:                           \n\t"
+                "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
+                "sub          %[NBLKS], %[NBLKS], t0  \n\t"
+                "vse32.v      v28, (%[C])             \n\t"
+                "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
+                "sub          %[NBLKS], %[NBLKS], t0  \n\t"
+                "vse32.v      v29, (s1)               \n\t"
+                "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
+                "sub          %[NBLKS], %[NBLKS], t0  \n\t"
+                "vse32.v      v30, (s2)               \n\t"
+                "vsetvli      t0, %[NBLKS], e32, mf2  \n\t"
+                "sub          %[NBLKS], %[NBLKS], t0  \n\t"
+                "vse32.v      v31, (s3)               \n\t"
+                "END%=:                               \n\t"
+
+                : [CNT] "+r"(cnt), [NBLKS] "+r"(nblks)
+                : [INNER] "r"(INNER), [A] "r"(QuantA), [B] "r"(QuantBDataPtr), [C] "r"(CPtr)
+                : "cc", "t0", "t5", "t3", "f1", "s1", "s2", "s3", "s4", "s5", "s6");
+        }
     }
 }
-
 }  // namespace
 
 namespace ime1 {
-size_t gemm_kernel_i8i4(size_t            BlkLen,
-                        const std::byte * QuantA,
-                        const std::byte * QuantBData,
-                        const float *     QuantBScale,
-                        const std::byte * QuantBZeroPoint,
-                        float *           C,
-                        size_t            CountM,
-                        size_t            CountN,
-                        size_t            CountK,
-                        size_t            BlockCountK,
-                        size_t            ldc,
-                        const float *     Bias,
-                        const size_t      ScaleStride) {
-    GGML_UNUSED(CountM);
-    GGML_UNUSED(CountK);
-    GGML_UNUSED(ldc);
-    if (CountM >= 4) {
-        if (QuantBZeroPoint != nullptr) {
-            SQ4BitGemmM4Kernel_CompInt8_DispatchOnBlkLen<true>(BlkLen, QuantA, QuantBData, QuantBScale, QuantBZeroPoint,
-                                                               C, CountM, CountN, BlockCountK, Bias, ldc, ScaleStride);
+size_t gemm_kernel_i8i4(size_t          blk_len,
+                        const uint8_t * quant_a_ptr,
+                        const uint8_t * quant_b_data,
+                        const uint8_t * quant_b_zp,
+                        float *         c_ptr,
+                        size_t          count_m,
+                        size_t          count_n,
+                        size_t          k_blks,
+                        size_t          ldc) {
+    if (count_m >= 4) {
+        if (quant_b_zp != nullptr) {
+            SQ4BitGemmM4Kernel_CompInt8_ScaleFp16_Impl<true>(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_n, k_blks,
+                                                             ldc);
         } else {
-            SQ4BitGemmM4Kernel_CompInt8_DispatchOnBlkLen<false>(BlkLen, QuantA, QuantBData, QuantBScale,
-                                                                QuantBZeroPoint, C, CountM, CountN, BlockCountK, Bias,
-                                                                ldc, ScaleStride);
+            SQ4BitGemmM4Kernel_CompInt8_ScaleFp16_Impl<false>(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_n,
+                                                              k_blks, ldc);
         }
         return 4;
     } else {
-        if (QuantBZeroPoint != nullptr) {
-            SQ4BitGemmM1Kernel_CompInt8_DispatchOnBlkLen<true>(BlkLen, QuantA, QuantBData, QuantBScale, QuantBZeroPoint,
-                                                               C, CountM, CountN, BlockCountK, Bias, ldc, ScaleStride);
+        if (quant_b_zp != nullptr) {
+            SQ4BitGemmM1Kernel_CompInt8_ScaleFp16_Impl<true>(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_n, k_blks,
+                                                             ldc);
         } else {
-            SQ4BitGemmM1Kernel_CompInt8_DispatchOnBlkLen<false>(BlkLen, QuantA, QuantBData, QuantBScale,
-                                                                QuantBZeroPoint, C, CountM, CountN, BlockCountK, Bias,
-                                                                ldc, ScaleStride);
+            SQ4BitGemmM1Kernel_CompInt8_ScaleFp16_Impl<false>(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_n,
+                                                              k_blks, ldc);
         }
         return 1;
     }
 }
 }  // namespace ime1
-}  // namespace sqnbitgemm_spacemit_ime
+}  // namespace spacemit_kernels
diff --git a/ggml/src/ggml-cpu/spacemit/ime2_kernels.cpp b/ggml/src/ggml-cpu/spacemit/ime2_kernels.cpp
new file mode 100644
index 00000000000..0c7a036a92a
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/ime2_kernels.cpp
@@ -0,0 +1,5768 @@
+#include "ggml-impl.h"
+#include "ggml.h"
+#include "ime_kernels.h"
+#include "rvv_kernels.h"
+#include "string.h"
+
+#include <algorithm>
+#include <cmath>
+#include <stdexcept>
+
+#if !defined(__riscv_v) || !defined(__riscv_v_intrinsic)
+#    error "riscv v extension or v_intrinsic not enabled"
+#else
+#    include <riscv_vector.h>
+#endif
+
+#if !defined(__riscv_zfh)
+#    error "riscv zfh extension not enabled"
+#endif
+
+#if defined(RISCV64_SPACEMIT_IME2)
+#else
+#    error "RISCV64_SPACEMIT_IME2 not defined"
+#endif
+
+#if defined(__GNUC__)
+#    pragma GCC diagnostic ignored "-Woverlength-strings"
+#    pragma GCC diagnostic ignored "-Wcast-qual"
+#    pragma GCC diagnostic ignored "-Wunused-parameter"
+#endif
+
+namespace spacemit_kernels {
+namespace ime2 {
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void gemm_kernel_i8i2k_mrow_ref(size_t          blk_len,
+                                const uint8_t * quant_a_ptr,
+                                const uint8_t * quant_b_data,
+                                float *         c_ptr,
+                                size_t          count_m,
+                                size_t          count_n,
+                                size_t          k_blks,
+                                size_t          ldc) {
+    using blk_type                 = nrow_block_q2_k<NB_COLS>;
+    constexpr float refactor_scale = 16.0f;
+    constexpr float factor_scale   = 1.0f / refactor_scale;
+
+    int64_t a_blk_stride        = q8k_blk_size(256);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+    int64_t b_ncol_block_stride = sizeof(blk_type);
+
+    float      output[MB_ROWS * NB_COLS]     = { 0 };
+    _Float16   output_f16[MB_ROWS * NB_COLS] = { 0 };
+    blk_type * quant_b_blk_data              = (blk_type *) (quant_b_data);
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+        size_t nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+
+        int8_t * a_data = (int8_t *) quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * MB_ROWS * 16;
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < NB_COLS; ci++) {
+                output[ci + mi * NB_COLS] = 0;
+            }
+        }
+
+        for (size_t ki = 0; ki < k_blks; ki++, quant_b_blk_data++, a_data += a_nrow_block_stride) {
+            uint8_t * b_data   = quant_b_blk_data->qs;
+            uint8_t * scales   = quant_b_blk_data->scales;
+            uint8_t * scales16 = (uint8_t *) (quant_b_blk_data->scales16);
+            uint8_t * zeros16  = (uint8_t *) (quant_b_blk_data->zeros16);
+
+            _Float16 * scales_fp16 = (_Float16 *) scales16;
+            _Float16 * zeros_fp16  = (_Float16 *) zeros16;
+
+            float *   a_scale_row = (float *) (a_data - sizeof(float) * MB_ROWS - sizeof(int16_t) * MB_ROWS * 16);
+            int16_t * a_sum_row   = (int16_t *) (a_data - sizeof(int16_t) * MB_ROWS * 16);
+
+            memset(output_f16, 0, sizeof(output_f16));
+
+            uint8_t * scales_temp = scales;
+            uint8_t * zps_temp    = scales;
+            for (size_t kii = 0; kii < 16; kii++, scales_temp += NB_COLS, zps_temp++) {
+                size_t b_shift = (kii % 4) * 2;
+
+                uint8_t * b_data_col = b_data + (kii / 4) * NB_COLS * 16;
+
+                for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                    int16_t a_sum = a_sum_row[mi * 16 + kii];
+                    for (size_t ci = 0; ci < NB_COLS; ci++) {
+                        _Float16 acc_0 = 0.0;
+
+                        uint8_t b_zp    = zps_temp[ci * 16] >> 4;
+                        uint8_t b_scale = scales_temp[ci] & 0x0F;
+                        for (size_t bi = 0; bi < 16; bi++) {
+                            int8_t  a0 = a_data[mi * 256 + bi + kii * 16];
+                            uint8_t b0 = b_data_col[ci * 16 + bi];
+                            acc_0 += static_cast<int16_t>(a0) * static_cast<int16_t>((b0 >> b_shift) & 0x03);
+                        }
+
+                        _Float16 scale_item =
+                            static_cast<_Float16>(b_scale) * static_cast<_Float16>(factor_scale) * scales_fp16[ci];
+
+                        output_f16[ci + mi * NB_COLS] += acc_0 * scale_item;
+                        output[ci + mi * NB_COLS] += b_zp * a_sum * a_scale_row[mi] * zeros_fp16[ci];
+                    }
+                }
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                auto a_scale = a_scale_row[mi] * refactor_scale;
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    output[ci + mi * NB_COLS] += output_f16[ci + mi * NB_COLS] * a_scale;
+                }
+            }
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < nb_real; ci++) {
+                c_ptr[mi * ldc + ci] = output[mi * NB_COLS + ci];
+            }
+        }
+    }
+}
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void gemm_kernel_i8i3k_mrow_ref(size_t          blk_len,
+                                const uint8_t * quant_a_ptr,
+                                const uint8_t * quant_b_data,
+                                float *         c_ptr,
+                                size_t          count_m,
+                                size_t          count_n,
+                                size_t          k_blks,
+                                size_t          ldc) {
+    using blk_type                 = nrow_block_q2_k<NB_COLS>;
+    constexpr float refactor_scale = 16.0f;
+    constexpr float factor_scale   = 1.0f / refactor_scale;
+
+    int64_t a_blk_stride        = q8k_blk_size(256);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+    int64_t b_ncol_block_stride = sizeof(blk_type);
+
+    float    output[MB_ROWS * NB_COLS]     = { 0 };
+    _Float16 output_f16[MB_ROWS * NB_COLS] = { 0 };
+
+    blk_type * quant_b_blk_data = (blk_type *) (quant_b_data);
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+        size_t nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+
+        int8_t * a_data = (int8_t *) quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * MB_ROWS * 16;
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < NB_COLS; ci++) {
+                output[ci + mi * NB_COLS] = 0;
+            }
+        }
+
+        for (size_t ki = 0; ki < k_blks; ki++, quant_b_blk_data++, a_data += a_nrow_block_stride) {
+            uint8_t * b_data   = quant_b_blk_data->qs;
+            uint8_t * b_hmask  = quant_b_blk_data->hmask;
+            int8_t *  scales   = quant_b_blk_data->scales;
+            uint8_t * scales16 = (uint8_t *) (quant_b_blk_data->scales16);
+
+            _Float16 * scales_fp16 = (_Float16 *) scales16;
+
+            float *   a_scale_row = (float *) (a_data - sizeof(float) * MB_ROWS - sizeof(int16_t) * MB_ROWS * 16);
+            int16_t * a_sum_row   = (int16_t *) (a_data - sizeof(int16_t) * MB_ROWS * 16);
+
+            memset(output_f16, 0, sizeof(output_f16));
+
+            int8_t *   scales_temp = scales;
+            uint16_t * b_mask_col  = (uint16_t *) b_hmask;
+
+            float acc_0_max = 0.0f;
+            for (size_t kii = 0; kii < 16; kii++, scales_temp += NB_COLS, b_mask_col += NB_COLS) {
+                size_t b_shift = (kii % 4) * 2;
+
+                uint8_t * b_data_col = b_data + (kii / 4) * NB_COLS * 16;
+
+                for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                    for (size_t ci = 0; ci < NB_COLS; ci++) {
+                        _Float16 acc_0        = 0;
+                        // blk 2 * kii + 0
+                        uint16_t b_shift_mask = 1;
+                        for (size_t bi = 0; bi < 16; bi++, b_shift_mask <<= 1) {
+                            int8_t a0 = a_data[mi * 256 + bi + kii * 16];
+                            int8_t b0 = static_cast<int8_t>((b_data_col[ci * 16 + bi] >> b_shift) & 0x03);
+                            b0 -= b_mask_col[ci] & b_shift_mask ? 0 : 4;
+                            acc_0 += static_cast<int16_t>(a0) * static_cast<int16_t>(b0);
+                        }
+
+                        _Float16 scale_item = static_cast<_Float16>(scales_temp[ci]) * scales_fp16[ci] *
+                                              static_cast<_Float16>(factor_scale);
+
+                        output_f16[ci + mi * NB_COLS] += acc_0 * scale_item;
+                    }
+                }
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                auto a_scale = a_scale_row[mi] * refactor_scale;
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    output[ci + mi * NB_COLS] += output_f16[ci + mi * NB_COLS] * a_scale;
+                }
+            }
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < nb_real; ci++) {
+                c_ptr[mi * ldc + ci] = output[mi * NB_COLS + ci];
+            }
+        }
+    }
+}
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void gemm_kernel_i8i4_mrow_ref(size_t          blk_len,
+                               const uint8_t * quant_a_ptr,
+                               const uint8_t * quant_b_data,
+                               const uint8_t * quant_b_zp,
+                               float *         c_ptr,
+                               size_t          count_m,
+                               size_t          count_n,
+                               size_t          k_blks,
+                               size_t          ldc) {
+    constexpr size_t kblks_per_blk = 16;
+    GGML_ASSERT(k_blks % kblks_per_blk == 0);
+
+    int64_t b_blk_stride        = (sizeof(_Float16) + (blk_len / 2) + (quant_b_zp ? sizeof(uint8_t) : 0));
+    int64_t b_stride            = k_blks * b_blk_stride;
+    int64_t a_blk_stride        = q8_blk_size(blk_len, true);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+    int64_t b_ncol_block_stride = b_blk_stride * NB_COLS;
+
+    float    output[MB_ROWS * NB_COLS]     = { 0 };
+    _Float16 output_f16[MB_ROWS * NB_COLS] = { 0 };
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+        size_t    nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+        uint8_t * b_data  = (uint8_t *) quant_b_data + ni * b_stride + NB_COLS * sizeof(_Float16);
+        if (quant_b_zp) {
+            b_data += NB_COLS * sizeof(uint8_t);
+        }
+
+        int8_t * a_data = (int8_t *) quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * MB_ROWS;
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < NB_COLS; ci++) {
+                output[ci + mi * NB_COLS]     = 0.0f;
+                output_f16[ci + mi * NB_COLS] = static_cast<_Float16>(0.0f);
+            }
+        }
+
+        size_t kii = 0;
+        for (size_t ki = 0; ki < k_blks; ki++, a_data += a_nrow_block_stride, b_data += b_ncol_block_stride) {
+            _Float16 * b_scale_fp16 = (_Float16 *) (b_data - NB_COLS * sizeof(_Float16));
+            uint8_t *  b_zp         = nullptr;
+            if (quant_b_zp) {
+                b_scale_fp16 = (_Float16 *) (b_data - NB_COLS * sizeof(_Float16) - NB_COLS * sizeof(uint8_t));
+                b_zp         = (uint8_t *) (b_data - NB_COLS * sizeof(uint8_t));
+            }
+
+            float *   a_scale_row = (float *) (a_data - sizeof(float) * MB_ROWS - sizeof(int16_t) * MB_ROWS);
+            int16_t * a_sum_row   = (int16_t *) (a_data - sizeof(int16_t) * MB_ROWS);
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                _Float16 a_scale = a_scale_row[mi];
+                int16_t  a_sum   = a_sum_row[mi];
+
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    _Float16 b_scale = b_scale_fp16[ci];
+                    int32_t  acc     = 0;
+                    if (b_zp) {
+                        acc += a_sum * b_zp[ci];
+                    } else {
+                        acc += a_sum * 8;
+                    }
+                    for (size_t bi = 0; bi < blk_len / 2; bi++) {
+                        int8_t  a0 = a_data[mi * blk_len + 2 * bi];
+                        int8_t  a1 = a_data[mi * blk_len + 2 * bi + 1];
+                        uint8_t b  = b_data[ci * blk_len / 2 + bi];
+                        int8_t  b0 = static_cast<int8_t>(b & 0x0F);
+                        int8_t  b1 = static_cast<int8_t>((b & 0xF0) >> 4);
+                        acc += static_cast<int32_t>(a0) * static_cast<int32_t>(b0) +
+                               static_cast<int32_t>(a1) * static_cast<int32_t>(b1);
+                    }
+                    output_f16[ci + mi * NB_COLS] +=
+                        static_cast<float>(acc) * static_cast<float>(a_scale) * static_cast<float>(b_scale);
+                }
+            }
+
+            if (kii == kblks_per_blk - 1) {
+                for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                    for (size_t ci = 0; ci < NB_COLS; ci++) {
+                        output[ci + mi * NB_COLS] += static_cast<float>(output_f16[ci + mi * NB_COLS]);
+                        output_f16[ci + mi * NB_COLS] = 0.0f;
+                    }
+                }
+                kii = 0;
+            } else {
+                kii++;
+            }
+        }
+
+        if (kii == kblks_per_blk - 1) {
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    output[ci + mi * NB_COLS] += static_cast<float>(output_f16[ci + mi * NB_COLS]);
+                    output_f16[ci + mi * NB_COLS] = 0.0f;
+                }
+            }
+            kii = 0;
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < nb_real; ci++) {
+                c_ptr[mi * ldc + ci] = output[mi * NB_COLS + ci];
+            }
+        }
+    }
+}
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void gemm_kernel_i8i4_hp_mrow_ref(size_t          blk_len,
+                                  const uint8_t * quant_a_ptr,
+                                  const uint8_t * quant_b_data,
+                                  const uint8_t * quant_b_zp,
+                                  float *         c_ptr,
+                                  size_t          count_m,
+                                  size_t          count_n,
+                                  size_t          k_blks,
+                                  size_t          ldc) {
+    constexpr size_t k_subblks_per_superblk = 8;
+
+    struct block_q4_0x32_layout {
+        _Float16 d[NB_COLS];
+        uint8_t  qs[16 * NB_COLS];
+    };
+
+    GGML_ASSERT(blk_len == 256);
+
+    const size_t b_superblk_stride = sizeof(block_q4_0x32_layout) * k_subblks_per_superblk +
+                                     (quant_b_zp ? NB_COLS * k_subblks_per_superblk * sizeof(uint8_t) : 0);
+    const size_t b_tile_stride = k_blks * b_superblk_stride;
+
+    const size_t a_nrow_block_stride = q8_hp_blk_size(blk_len, true, true) * MB_ROWS;
+    const size_t a_subblk_stride     = q8_hp_blk_size(32, false, false) * MB_ROWS;
+
+    float output[MB_ROWS * NB_COLS] = { 0 };
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+        size_t          nb_real     = std::min<size_t>(NB_COLS, count_n - ni);
+        const uint8_t * b_tile_base = quant_b_data + (ni / NB_COLS) * b_tile_stride;
+        int8_t *        a_data      = (int8_t *) quant_a_ptr;
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < NB_COLS; ci++) {
+                output[ci + mi * NB_COLS] = 0.0f;
+            }
+        }
+
+        for (size_t ki = 0; ki < k_blks; ki++, a_data += a_nrow_block_stride) {
+            _Float16 output_f16[MB_ROWS * NB_COLS] = { 0 };
+
+            const uint8_t *              b_superblk_ptr = b_tile_base + ki * b_superblk_stride;
+            const block_q4_0x32_layout * b_blocks = reinterpret_cast<const block_q4_0x32_layout *>(b_superblk_ptr);
+            const uint8_t *              b_zps =
+                quant_b_zp ? b_superblk_ptr + sizeof(block_q4_0x32_layout) * k_subblks_per_superblk : nullptr;
+
+            _Float16 * a_sum_row       = (_Float16 *) (a_data + a_subblk_stride * k_subblks_per_superblk);
+            _Float16 * a_scale_avg_row = (_Float16 *) (a_data + a_nrow_block_stride - sizeof(_Float16) * MB_ROWS);
+            _Float16   scale_factor    = a_scale_avg_row[0];
+
+            for (size_t ksi = 0; ksi < k_subblks_per_superblk; ++ksi) {
+                const _Float16 * a_scale_row = reinterpret_cast<const _Float16 *>(a_data + a_subblk_stride * ksi);
+                int8_t *         a_subblk    = a_data + a_subblk_stride * ksi + MB_ROWS * sizeof(_Float16);
+                const _Float16   a_scale     = a_scale_row[0];
+                const block_q4_0x32_layout & b_block = b_blocks[ksi];
+
+                for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                    for (size_t ci = 0; ci < NB_COLS; ci++) {
+                        const uint8_t * b_qs    = b_block.qs + ci * 16;
+                        _Float16        b_scale = b_block.d[ci] * a_scale;
+
+                        int16_t acc = 0;
+                        for (size_t bi = 0; bi < 16; bi++) {
+                            uint8_t b  = b_qs[bi];
+                            int8_t  b0 = static_cast<int8_t>(b & 0x0F);
+                            int8_t  b1 = static_cast<int8_t>((b & 0xF0) >> 4);
+
+                            acc += static_cast<int16_t>(a_subblk[mi * 32 + 2 * bi]) * static_cast<int16_t>(b0) +
+                                   static_cast<int16_t>(a_subblk[mi * 32 + 2 * bi + 1]) * static_cast<int16_t>(b1);
+                        }
+
+                        const _Float16 scaled_acc = static_cast<_Float16>(acc) * b_scale;
+                        output_f16[ci + mi * NB_COLS] += scaled_acc;
+                    }
+                }
+            }
+
+            for (size_t ksi = 0; ksi < k_subblks_per_superblk; ++ksi) {
+                const _Float16 * a_scale_row = reinterpret_cast<const _Float16 *>(a_data + a_subblk_stride * ksi);
+                const block_q4_0x32_layout & b_block  = b_blocks[ksi];
+                const uint8_t *              b_zp_row = b_zps ? b_zps + ksi * NB_COLS : nullptr;
+                const _Float16               a_scale  = a_scale_row[0];
+
+                for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                    const _Float16 a_sum = a_sum_row[mi * k_subblks_per_superblk + ksi];
+                    for (size_t ci = 0; ci < NB_COLS; ci++) {
+                        _Float16 b_scale   = b_block.d[ci] * a_scale;
+                        _Float16 a_sum_bzp = a_sum;
+                        if (b_zp_row) {
+                            a_sum_bzp = a_sum * static_cast<_Float16>(0.125f) * static_cast<_Float16>(b_zp_row[ci]);
+                        }
+
+                        const _Float16 scaled_acc = a_sum_bzp * b_scale;
+                        output[ci + mi * NB_COLS] += scaled_acc * scale_factor;
+                    }
+                }
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    auto val = static_cast<float>(output_f16[ci + mi * NB_COLS]) * static_cast<float>(scale_factor);
+                    output[ci + mi * NB_COLS] += val;
+                }
+            }
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < nb_real; ci++) {
+                c_ptr[mi * ldc + ci] = output[mi * NB_COLS + ci];
+            }
+        }
+    }
+}
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void moe_gemm_kernel_i8i4_mrow_ref(size_t           blk_len,
+                                   const uint8_t ** quant_a_ptr,
+                                   const uint8_t *  quant_b_data,
+                                   const uint8_t *  quant_b_zp,
+                                   float **         c_ptr,
+                                   size_t           count_m,
+                                   size_t           count_n,
+                                   size_t           k_blks,
+                                   size_t           ldc) {
+    int64_t b_blk_stride        = (sizeof(ggml_fp16_t) + (blk_len / 2) + (quant_b_zp ? sizeof(uint8_t) : 0));
+    int64_t b_stride            = k_blks * b_blk_stride;
+    int64_t a_blk_stride        = q8_blk_size(blk_len, true);
+    int64_t b_ncol_block_stride = b_blk_stride * NB_COLS;
+
+    float                         output[MB_ROWS * NB_COLS] = { 0 };
+    std::array<int8_t *, MB_ROWS> a_data;
+    std::array<float *, MB_ROWS>  c_data;
+
+    for (size_t mi = 0; mi < MB_ROWS; mi++) {
+        c_data[mi] = c_ptr[mi];
+    }
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+        size_t    nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+        uint8_t * b_data  = (uint8_t *) quant_b_data + ni * b_stride + NB_COLS * sizeof(ggml_fp16_t);
+        if (quant_b_zp) {
+            b_data += NB_COLS * sizeof(uint8_t);
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            a_data[mi] = (int8_t *) quant_a_ptr[mi] + sizeof(float) + sizeof(int16_t);
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < NB_COLS; ci++) {
+                output[ci + mi * NB_COLS] = 0;
+            }
+        }
+
+        for (size_t ki = 0; ki < k_blks; ki++, b_data += b_ncol_block_stride) {
+            ggml_fp16_t * b_scale_fp16 = (ggml_fp16_t *) (b_data - NB_COLS * sizeof(ggml_fp16_t));
+            uint8_t *     b_zp         = nullptr;
+            if (quant_b_zp) {
+                b_scale_fp16 = (ggml_fp16_t *) (b_data - NB_COLS * sizeof(ggml_fp16_t) - NB_COLS * sizeof(uint8_t));
+                b_zp         = (uint8_t *) (b_data - NB_COLS * sizeof(uint8_t));
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                float *   a_scale_row = (float *) (a_data[mi] - sizeof(float) - sizeof(int16_t));
+                int16_t * a_sum_row   = (int16_t *) (a_data[mi] - sizeof(int16_t));
+
+                float   a_scale = *a_scale_row;
+                int16_t a_sum   = *a_sum_row;
+
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    float   b_scale = ggml_fp16_to_fp32(b_scale_fp16[ci]);
+                    int32_t acc     = 0;
+                    if (b_zp) {
+                        acc += a_sum * b_zp[ci];
+                    } else {
+                        acc += a_sum * 8;
+                    }
+                    for (size_t bi = 0; bi < blk_len / 2; bi++) {
+                        int8_t  a0 = (a_data[mi])[2 * bi];
+                        int8_t  a1 = (a_data[mi])[2 * bi + 1];
+                        uint8_t b  = b_data[ci * blk_len / 2 + bi];
+                        int8_t  b0 = static_cast<int8_t>(b & 0x0F);
+                        int8_t  b1 = static_cast<int8_t>((b & 0xF0) >> 4);
+                        acc += static_cast<int32_t>(a0) * static_cast<int32_t>(b0) +
+                               static_cast<int32_t>(a1) * static_cast<int32_t>(b1);
+                    }
+                    output[ci + mi * NB_COLS] += static_cast<float>(acc) * a_scale * b_scale;
+                }
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                a_data[mi] += a_blk_stride;
+            }
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < nb_real; ci++) {
+                (c_data[mi])[ci] = output[mi * NB_COLS + ci];
+            }
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            c_data[mi] += NB_COLS;
+        }
+    }
+}
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void moe_gemm_kernel_i8i5_mrow_ref(size_t           blk_len,
+                                   const uint8_t ** quant_a_ptr,
+                                   const uint8_t *  quant_b_data,
+                                   const uint8_t *  quant_b_zp,
+                                   float **         c_ptr,
+                                   size_t           count_m,
+                                   size_t           count_n,
+                                   size_t           k_blks,
+                                   size_t           ldc) {
+    GGML_UNUSED(count_m);
+    GGML_UNUSED(ldc);
+
+    // blk_len is expected to be 32 for Q5 types.
+    int64_t a_blk_stride = q8_blk_size(blk_len, true);
+
+    float                         output[MB_ROWS * NB_COLS] = { 0 };
+    std::array<int8_t *, MB_ROWS> a_data;
+    std::array<float *, MB_ROWS>  c_data;
+
+    for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+        c_data[mi] = c_ptr[mi];
+    }
+
+    if (quant_b_zp) {
+        using blk_type = nrow_block_q5_1<NB_COLS>;
+
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            size_t     nb_real          = std::min<size_t>(NB_COLS, count_n - ni);
+            blk_type * quant_b_blk_data = (blk_type *) quant_b_data + (ni / NB_COLS) * k_blks;
+
+            for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+                a_data[mi] = (int8_t *) quant_a_ptr[mi] + sizeof(float) + sizeof(int16_t);
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+                for (size_t ci = 0; ci < NB_COLS; ++ci) {
+                    output[ci + mi * NB_COLS] = 0;
+                }
+            }
+
+            for (size_t ki = 0; ki < k_blks; ++ki, ++quant_b_blk_data) {
+                for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+                    float *   a_scale_row = (float *) (a_data[mi] - sizeof(float) - sizeof(int16_t));
+                    int16_t * a_sum_row   = (int16_t *) (a_data[mi] - sizeof(int16_t));
+                    float     a_scale     = *a_scale_row;
+                    int16_t   a_sum       = *a_sum_row;
+
+                    for (size_t ci = 0; ci < NB_COLS; ++ci) {
+                        float   b_scale  = ggml_fp16_to_fp32(quant_b_blk_data->scales16[ci]);
+                        uint8_t b_zp_val = quant_b_blk_data->zp[ci];
+                        int32_t acc      = a_sum * static_cast<int32_t>(b_zp_val);
+
+                        for (size_t bi = 0; bi < blk_len / 2; ++bi) {
+                            int8_t  a0       = a_data[mi][2 * bi];
+                            int8_t  a1       = a_data[mi][2 * bi + 1];
+                            uint8_t qs_byte  = quant_b_blk_data->qs[ci * (blk_len / 2) + bi];
+                            int8_t  b0       = static_cast<int8_t>(qs_byte & 0x0F);
+                            int8_t  b1       = static_cast<int8_t>((qs_byte >> 4) & 0x0F);
+                            uint8_t qh_byte0 = quant_b_blk_data->qh[ci * 4 + (2 * bi) / 8];
+                            uint8_t qh_byte1 = quant_b_blk_data->qh[ci * 4 + (2 * bi + 1) / 8];
+                            uint8_t h0       = (qh_byte0 >> ((2 * bi) % 8)) & 1;
+                            uint8_t h1       = (qh_byte1 >> ((2 * bi + 1) % 8)) & 1;
+
+                            b0 |= (h0 << 4);
+                            b1 |= (h1 << 4);
+
+                            acc += static_cast<int32_t>(a0) * static_cast<int32_t>(b0) +
+                                   static_cast<int32_t>(a1) * static_cast<int32_t>(b1);
+                        }
+
+                        output[ci + mi * NB_COLS] += static_cast<float>(acc) * a_scale * b_scale;
+                    }
+
+                    a_data[mi] += a_blk_stride;
+                }
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+                for (size_t ci = 0; ci < nb_real; ++ci) {
+                    c_data[mi][ci] = output[mi * NB_COLS + ci];
+                }
+                c_data[mi] += NB_COLS;
+            }
+        }
+    } else {
+        using blk_type = nrow_block_q5_0<NB_COLS>;
+
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            size_t     nb_real          = std::min<size_t>(NB_COLS, count_n - ni);
+            blk_type * quant_b_blk_data = (blk_type *) quant_b_data + (ni / NB_COLS) * k_blks;
+
+            for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+                a_data[mi] = (int8_t *) quant_a_ptr[mi] + sizeof(float) + sizeof(int16_t);
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+                for (size_t ci = 0; ci < NB_COLS; ++ci) {
+                    output[ci + mi * NB_COLS] = 0;
+                }
+            }
+
+            for (size_t ki = 0; ki < k_blks; ++ki, ++quant_b_blk_data) {
+                for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+                    float *   a_scale_row = (float *) (a_data[mi] - sizeof(float) - sizeof(int16_t));
+                    int16_t * a_sum_row   = (int16_t *) (a_data[mi] - sizeof(int16_t));
+                    float     a_scale     = *a_scale_row;
+                    int16_t   a_sum       = *a_sum_row;
+
+                    for (size_t ci = 0; ci < NB_COLS; ++ci) {
+                        float   b_scale = ggml_fp16_to_fp32(quant_b_blk_data->scales16[ci]);
+                        int32_t acc     = a_sum * 16;
+
+                        for (size_t bi = 0; bi < blk_len / 2; ++bi) {
+                            int8_t  a0       = a_data[mi][2 * bi];
+                            int8_t  a1       = a_data[mi][2 * bi + 1];
+                            uint8_t qs_byte  = quant_b_blk_data->qs[ci * (blk_len / 2) + bi];
+                            int8_t  b0       = static_cast<int8_t>(qs_byte & 0x0F);
+                            int8_t  b1       = static_cast<int8_t>((qs_byte >> 4) & 0x0F);
+                            uint8_t qh_byte0 = quant_b_blk_data->qh[ci * 4 + (2 * bi) / 8];
+                            uint8_t qh_byte1 = quant_b_blk_data->qh[ci * 4 + (2 * bi + 1) / 8];
+                            uint8_t h0       = (qh_byte0 >> ((2 * bi) % 8)) & 1;
+                            uint8_t h1       = (qh_byte1 >> ((2 * bi + 1) % 8)) & 1;
+
+                            b0 |= (h0 << 4);
+                            b1 |= (h1 << 4);
+
+                            acc += static_cast<int32_t>(a0) * static_cast<int32_t>(b0) +
+                                   static_cast<int32_t>(a1) * static_cast<int32_t>(b1);
+                        }
+
+                        output[ci + mi * NB_COLS] += static_cast<float>(acc) * a_scale * b_scale;
+                    }
+
+                    a_data[mi] += a_blk_stride;
+                }
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; ++mi) {
+                for (size_t ci = 0; ci < nb_real; ++ci) {
+                    c_data[mi][ci] = output[mi * NB_COLS + ci];
+                }
+                c_data[mi] += NB_COLS;
+            }
+        }
+    }
+}
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void gemm_kernel_i8i8_mrow_ref(size_t          blk_len,
+                               const uint8_t * quant_a_ptr,
+                               const uint8_t * quant_b_data,
+                               const uint8_t * quant_b_zp,
+                               float *         c_ptr,
+                               size_t          count_m,
+                               size_t          count_n,
+                               size_t          k_blks,
+                               size_t          ldc) {
+    int64_t b_blk_stride        = (sizeof(ggml_fp16_t) + blk_len);
+    int64_t b_stride            = k_blks * b_blk_stride;
+    int64_t a_blk_stride        = q8_blk_size(blk_len, true);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+    int64_t b_ncol_block_stride = b_blk_stride * NB_COLS;
+
+    float output[MB_ROWS * NB_COLS] = { 0 };
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+        size_t   nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+        int8_t * b_data  = (int8_t *) quant_b_data + ni * b_stride + NB_COLS * sizeof(ggml_fp16_t);
+
+        int8_t * a_data = (int8_t *) quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * MB_ROWS;
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < NB_COLS; ci++) {
+                output[ci + mi * NB_COLS] = 0;
+            }
+        }
+
+        for (size_t ki = 0; ki < k_blks; ki++, a_data += a_nrow_block_stride, b_data += b_ncol_block_stride) {
+            ggml_fp16_t * b_scale_fp16 = (ggml_fp16_t *) (b_data - NB_COLS * sizeof(ggml_fp16_t));
+
+            float * a_scale_row = (float *) (a_data - sizeof(float) * MB_ROWS - sizeof(int16_t) * MB_ROWS);
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                float a_scale = a_scale_row[mi];
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    float   b_scale = ggml_fp16_to_fp32(b_scale_fp16[ci]);
+                    int32_t acc     = 0;
+                    for (size_t bi = 0; bi < blk_len; bi++) {
+                        int8_t a0 = a_data[mi * blk_len + bi];
+                        int8_t b0 = b_data[ci * blk_len + bi];
+                        acc += static_cast<int32_t>(a0) * static_cast<int32_t>(b0);
+                    }
+                    output[ci + mi * NB_COLS] += static_cast<float>(acc) * a_scale * b_scale;
+                }
+            }
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < nb_real; ci++) {
+                c_ptr[mi * ldc + ci] = output[mi * NB_COLS + ci];
+            }
+        }
+    }
+}
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void gemm_kernel_i8i5_mrow_ref(size_t          blk_len,
+                               const uint8_t * quant_a_ptr,
+                               const uint8_t * quant_b_data,
+                               const uint8_t * quant_b_zp,
+                               float *         c_ptr,
+                               size_t          count_m,
+                               size_t          count_n,
+                               size_t          k_blks,
+                               size_t          ldc) {
+    // blk_len is expected to be 32 for Q5 types
+    // quant_b_zp != nullptr => nrow_block_q5_1<NB_COLS> (has zp)
+    // quant_b_zp == nullptr => nrow_block_q5_0<NB_COLS> (no zp)
+
+    int64_t a_blk_stride        = q8_blk_size(blk_len, true);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+
+    float output[MB_ROWS * NB_COLS] = { 0 };
+
+    if (quant_b_zp) {
+        // nrow_block_q5_1<NB_COLS>: scales16[NB_COLS] + zp[NB_COLS] + qh[4*NB_COLS] + qs[16*NB_COLS]
+        using blk_type                 = nrow_block_q5_1<NB_COLS>;
+        int64_t    b_ncol_block_stride = sizeof(blk_type);
+        blk_type * quant_b_blk_data    = (blk_type *) quant_b_data;
+
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+            size_t nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+
+            int8_t * a_data = (int8_t *) quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * MB_ROWS;
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    output[ci + mi * NB_COLS] = 0;
+                }
+            }
+
+            for (size_t ki = 0; ki < k_blks; ki++, quant_b_blk_data++, a_data += a_nrow_block_stride) {
+                float *   a_scale_row = (float *) (a_data - sizeof(float) * MB_ROWS - sizeof(int16_t) * MB_ROWS);
+                int16_t * a_sum_row   = (int16_t *) (a_data - sizeof(int16_t) * MB_ROWS);
+
+                for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                    float   a_scale = a_scale_row[mi];
+                    int16_t a_sum   = a_sum_row[mi];
+
+                    for (size_t ci = 0; ci < NB_COLS; ci++) {
+                        float   b_scale  = ggml_fp16_to_fp32(quant_b_blk_data->scales16[ci]);
+                        uint8_t b_zp_val = quant_b_blk_data->zp[ci];
+                        int32_t acc      = a_sum * static_cast<int32_t>(b_zp_val);
+
+                        for (size_t bi = 0; bi < blk_len / 2; bi++) {
+                            int8_t  a0      = a_data[mi * blk_len + 2 * bi];
+                            int8_t  a1      = a_data[mi * blk_len + 2 * bi + 1];
+                            uint8_t qs_byte = quant_b_blk_data->qs[ci * (blk_len / 2) + bi];
+                            int8_t  b0      = static_cast<int8_t>(qs_byte & 0x0F);
+                            int8_t  b1      = static_cast<int8_t>((qs_byte >> 4) & 0x0F);
+
+                            // Extract high bits from qh
+                            // qh is packed as 4 bytes per column (32 bits for 32 elements)
+                            uint8_t qh_byte0 = quant_b_blk_data->qh[ci * 4 + (2 * bi) / 8];
+                            uint8_t qh_byte1 = quant_b_blk_data->qh[ci * 4 + (2 * bi + 1) / 8];
+                            uint8_t h0       = (qh_byte0 >> ((2 * bi) % 8)) & 1;
+                            uint8_t h1       = (qh_byte1 >> ((2 * bi + 1) % 8)) & 1;
+
+                            b0 |= (h0 << 4);
+                            b1 |= (h1 << 4);
+
+                            acc += static_cast<int32_t>(a0) * static_cast<int32_t>(b0) +
+                                   static_cast<int32_t>(a1) * static_cast<int32_t>(b1);
+                        }
+                        output[ci + mi * NB_COLS] += static_cast<float>(acc) * a_scale * b_scale;
+                    }
+                }
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                for (size_t ci = 0; ci < nb_real; ci++) {
+                    c_ptr[mi * ldc + ci] = output[mi * NB_COLS + ci];
+                }
+            }
+        }
+    } else {
+        // nrow_block_q5_0<NB_COLS>: scales16[NB_COLS] + qh[4*NB_COLS] + qs[16*NB_COLS]
+        using blk_type                 = nrow_block_q5_0<NB_COLS>;
+        int64_t    b_ncol_block_stride = sizeof(blk_type);
+        blk_type * quant_b_blk_data    = (blk_type *) quant_b_data;
+
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+            size_t nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+
+            int8_t * a_data = (int8_t *) quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * MB_ROWS;
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    output[ci + mi * NB_COLS] = 0;
+                }
+            }
+
+            for (size_t ki = 0; ki < k_blks; ki++, quant_b_blk_data++, a_data += a_nrow_block_stride) {
+                float *   a_scale_row = (float *) (a_data - sizeof(float) * MB_ROWS - sizeof(int16_t) * MB_ROWS);
+                int16_t * a_sum_row   = (int16_t *) (a_data - sizeof(int16_t) * MB_ROWS);
+
+                for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                    float   a_scale = a_scale_row[mi];
+                    int16_t a_sum   = a_sum_row[mi];
+
+                    for (size_t ci = 0; ci < NB_COLS; ci++) {
+                        float   b_scale = ggml_fp16_to_fp32(quant_b_blk_data->scales16[ci]);
+                        // Q5_0 has no zp, use default offset 16 (midpoint of 5-bit unsigned range)
+                        int32_t acc     = a_sum * 16;
+
+                        for (size_t bi = 0; bi < blk_len / 2; bi++) {
+                            int8_t  a0      = a_data[mi * blk_len + 2 * bi];
+                            int8_t  a1      = a_data[mi * blk_len + 2 * bi + 1];
+                            uint8_t qs_byte = quant_b_blk_data->qs[ci * (blk_len / 2) + bi];
+                            int8_t  b0      = static_cast<int8_t>(qs_byte & 0x0F);
+                            int8_t  b1      = static_cast<int8_t>((qs_byte >> 4) & 0x0F);
+
+                            // Extract high bits from qh
+                            uint8_t qh_byte0 = quant_b_blk_data->qh[ci * 4 + (2 * bi) / 8];
+                            uint8_t qh_byte1 = quant_b_blk_data->qh[ci * 4 + (2 * bi + 1) / 8];
+                            uint8_t h0       = (qh_byte0 >> ((2 * bi) % 8)) & 1;
+                            uint8_t h1       = (qh_byte1 >> ((2 * bi + 1) % 8)) & 1;
+
+                            b0 |= (h0 << 4);
+                            b1 |= (h1 << 4);
+
+                            acc += static_cast<int32_t>(a0) * static_cast<int32_t>(b0) +
+                                   static_cast<int32_t>(a1) * static_cast<int32_t>(b1);
+                        }
+                        output[ci + mi * NB_COLS] += static_cast<float>(acc) * a_scale * b_scale;
+                    }
+                }
+            }
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                for (size_t ci = 0; ci < nb_real; ci++) {
+                    c_ptr[mi * ldc + ci] = output[mi * NB_COLS + ci];
+                }
+            }
+        }
+    }
+}
+
+template <size_t MB_ROWS, size_t NB_COLS>
+void gemm_kernel_i8mxfp4_mrow_ref(size_t          blk_len,
+                                  const uint8_t * quant_a_ptr,
+                                  const uint8_t * quant_b_data,
+                                  const uint8_t * quant_b_zp,
+                                  float *         c_ptr,
+                                  size_t          count_m,
+                                  size_t          count_n,
+                                  size_t          k_blks,
+                                  size_t          ldc) {
+    // blk_len is expected to be 32 (QK_MXFP4)
+    // quant_b_zp is unused for MXFP4 (symmetric quantization)
+    GGML_UNUSED(quant_b_zp);
+
+    int64_t a_blk_stride        = q8_blk_size(blk_len, true);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+
+    float output[MB_ROWS * NB_COLS] = { 0 };
+
+    using blk_type              = nrow_block_mxfp4<NB_COLS>;
+    blk_type * quant_b_blk_data = (blk_type *) quant_b_data;
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+        size_t nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+
+        int8_t * a_data = (int8_t *) quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * MB_ROWS;
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < NB_COLS; ci++) {
+                output[ci + mi * NB_COLS] = 0;
+            }
+        }
+
+        for (size_t ki = 0; ki < k_blks; ki++, quant_b_blk_data++, a_data += a_nrow_block_stride) {
+            float *   a_scale_row = (float *) (a_data - sizeof(float) * MB_ROWS - sizeof(int16_t) * MB_ROWS);
+            int16_t * a_sum_row   = (int16_t *) (a_data - sizeof(int16_t) * MB_ROWS);
+
+            for (size_t mi = 0; mi < MB_ROWS; mi++) {
+                float a_scale = a_scale_row[mi];
+
+                for (size_t ci = 0; ci < NB_COLS; ci++) {
+                    float b_scale = GGML_E8M0_TO_FP32_HALF(quant_b_blk_data->e[ci]);
+
+                    // Read 32 sign bits for this column
+                    uint32_t sign_bits;
+                    memcpy(&sign_bits, &quant_b_blk_data->qh[ci * 4], 4);
+
+                    int32_t acc = 0;
+                    for (size_t bi = 0; bi < blk_len / 2; bi++) {
+                        int8_t a0 = a_data[mi * blk_len + 2 * bi];
+                        int8_t a1 = a_data[mi * blk_len + 2 * bi + 1];
+
+                        // qs[ci*16 + bi] stores abs(vals[bi*2]) in low 4 bits
+                        // and abs(vals[bi*2+1]) in high 4 bits
+                        uint8_t qs_byte = quant_b_blk_data->qs[ci * 16 + bi];
+                        int8_t  b_abs0  = static_cast<int8_t>(qs_byte & 0x0F);
+                        int8_t  b_abs1  = static_cast<int8_t>((qs_byte >> 4) & 0x0F);
+
+                        // Extract sign bits: bit (2*bi) for vals[2*bi], bit (2*bi+1) for vals[2*bi+1]
+                        int8_t b0 = (sign_bits >> (2 * bi)) & 1 ? -b_abs0 : b_abs0;
+                        int8_t b1 = (sign_bits >> (2 * bi + 1)) & 1 ? -b_abs1 : b_abs1;
+
+                        acc += static_cast<int32_t>(a0) * static_cast<int32_t>(b0) +
+                               static_cast<int32_t>(a1) * static_cast<int32_t>(b1);
+                    }
+                    output[ci + mi * NB_COLS] += static_cast<float>(acc) * a_scale * b_scale;
+                }
+            }
+        }
+
+        for (size_t mi = 0; mi < MB_ROWS; mi++) {
+            for (size_t ci = 0; ci < nb_real; ci++) {
+                c_ptr[mi * ldc + ci] = output[mi * NB_COLS + ci];
+            }
+        }
+    }
+}
+
+void gemm_kernel_i8i2k_m1(size_t          blk_len,
+                          const uint8_t * quant_a_ptr,
+                          const uint8_t * quant_b_data,
+                          float *         c_ptr,
+                          size_t          count_m,
+                          size_t          count_n,
+                          size_t          k_blks,
+                          size_t          ldc) {
+    constexpr size_t NB_COLS = 32;
+    using blk_type           = nrow_block_q2_k<NB_COLS>;
+
+    int64_t b_ncol_block_stride = sizeof(blk_type) * k_blks;
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+        uint8_t * b_data = (uint8_t *) quant_b_data + (ni / NB_COLS) * b_ncol_block_stride;
+        int8_t *  a_data = (int8_t *) quant_a_ptr;
+        float *   dst_c  = (float *) c_ptr + ni;
+
+        asm volatile(
+            "vsetvli        t0, x0, e16, m1         \n\t"
+            "vxor.vv        v31, v31, v31           \n\t"
+            "mv             s1, %[BK]               \n\t"
+
+            ".align 4                               \n\t"
+            "BLK_LOOP%=:                            \n\t"
+            // load scale A
+            "flw            fa0, (%[A])             \n\t"
+            "addi           %[A], %[A], 4           \n\t"
+
+            "li             t1, 4                   \n\t"
+            "addi           t2, %[B], 512           \n\t"  // B data addr
+            "addi           t3, %[A], 32            \n\t"  // A data addr
+            "addi           s3, %[B], 0             \n\t"
+            "vxor.vv        v30, v29, v29           \n\t"  // tmp result
+
+            "INNER_K_LOOP%=:                        \n\t"
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vxor.vv        v2, v2, v2              \n\t"
+            "vxor.vv        v3, v3, v3              \n\t"
+            "vxor.vv        v4, v4, v4              \n\t"
+            "vxor.vv        v5, v5, v5              \n\t"
+            "vxor.vv        v6, v6, v6              \n\t"
+            "vxor.vv        v28, v28, v28           \n\t"
+            "vxor.vv        v29, v29, v29           \n\t"
+
+            // load scale  B
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vle8.v         v0, (%[B])              \n\t"
+            "addi           %[B], %[B], 128         \n\t"
+
+            // A data, 1x64@i8
+            "vsetivli       t0, 16, e8, mf4         \n\t"
+            "vle8.v         v2, (t3)                \n\t"
+            "addi           t3, t3, 16              \n\t"
+
+            "vsetivli       t0, 16, e8, mf4         \n\t"
+            "vle8.v         v4, (t3)                \n\t"
+            "addi           t3, t3, 16              \n\t"
+
+            "vsetivli       t0, 16, e8, mf4         \n\t"
+            "vle8.v         v5, (t3)                \n\t"
+            "addi           t3, t3, 16              \n\t"
+
+            "vsetivli       t0, 16, e8, mf4         \n\t"
+            "vle8.v         v6, (t3)                \n\t"
+            "addi           t3, t3, 16              \n\t"
+
+            "vsetvli        t0, x0, e64, mf2        \n\t"
+            "vslideup.vi    v3, v4, 2               \n\t"
+            "vslideup.vi    v28, v5, 4              \n\t"
+            "vslideup.vi    v29, v6, 6              \n\t"
+
+            // init the accumu to zero
+            "vsetvli        t0, x0, e16, m1         \n\t"
+            "vxor.vv        v20, v18, v18           \n\t"
+            "vxor.vv        v22, v18, v18           \n\t"
+            "vxor.vv        v24, v18, v18           \n\t"
+            "vxor.vv        v26, v18, v18           \n\t"
+
+            // B data, 32x64@i2
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vl4r.v         v4, (t2)                \n\t"
+            "addi           t2, t2, 512             \n\t"
+            "vand.vi        v8, v4, 0x3             \n\t"  // 0-15
+            "vsrl.vi        v9, v4, 2               \n\t"
+            "vsrl.vi        v10, v4, 4              \n\t"
+            "vsrl.vi        v11, v4, 6              \n\t"  // 48-63
+            "vand.vi        v9, v9, 0x3             \n\t"  // 16-31
+            "vand.vi        v10, v10, 0x3           \n\t"  // 32-47
+
+            "vand.vi        v12, v5, 0x3            \n\t"  // 0-15
+            "vsrl.vi        v13, v5, 2              \n\t"
+            "vsrl.vi        v14, v5, 4              \n\t"
+            "vsrl.vi        v15, v5, 6              \n\t"  // 48-63
+            "vand.vi        v13, v13, 0x3           \n\t"  // 16-31
+            "vand.vi        v14, v14, 0x3           \n\t"  // 32-47
+
+            "vand.vi        v16, v6, 0x3            \n\t"  // 0-15
+            "vsrl.vi        v17, v6, 2              \n\t"
+            "vsrl.vi        v18, v6, 4              \n\t"
+            "vsrl.vi        v19, v6, 6              \n\t"  // 48-63
+            "vand.vi        v17, v17, 0x3           \n\t"  // 16-31
+            "vand.vi        v18, v18, 0x3           \n\t"  // 32-47
+
+            "vand.vi        v4, v7, 0x3             \n\t"  // 0-15
+            "vsrl.vi        v5, v7, 2               \n\t"
+            "vsrl.vi        v6, v7, 4               \n\t"
+            "vsrl.vi        v7, v7, 6               \n\t"  // 48-63
+            "vand.vi        v5, v5, 0x3             \n\t"  // 16-31
+            "vand.vi        v6, v6, 0x3             \n\t"  // 32-47
+
+            // i2 * i8 vmadot
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vmadotsu       v20, v2, v8, i8         \n\t"
+            "vmadotsu       v22, v2, v12, i8        \n\t"
+            "vmadotsu       v24, v2, v16, i8        \n\t"
+            "vmadotsu       v26, v2, v4, i8         \n\t"
+
+            "vmadotsu       v20, v3, v9, i8         \n\t"
+            "vmadotsu       v22, v3, v13, i8        \n\t"
+            "vmadotsu       v24, v3, v17, i8        \n\t"
+            "vmadotsu       v26, v3, v5, i8         \n\t"
+
+            "vmadotsu       v20, v28, v10, i8       \n\t"
+            "vmadotsu       v22, v28, v14, i8       \n\t"
+            "vmadotsu       v24, v28, v18, i8       \n\t"
+            "vmadotsu       v26, v28, v6, i8        \n\t"
+
+            "vmadotsu       v20, v29, v11, i8       \n\t"
+            "vmadotsu       v22, v29, v15, i8       \n\t"
+            "vmadotsu       v24, v29, v19, i8       \n\t"
+            "vmadotsu       v26, v29, v7, i8        \n\t"
+
+            "vand.vi        v10, v0, 0xf            \n\t"  // scale
+            "vwadd.vx       v12, v10, x0            \n\t"
+            "vsetvli        t0, x0, e16, m2         \n\t"
+            "vwadd.vx       v16, v12, x0            \n\t"
+
+            "vsetvli        t0, x0, e32, m1         \n\t"
+            "vpack.vv       v2, v20, v22, 2         \n\t"
+            "vpack.vv       v4, v24, v26, 2         \n\t"
+            "vpack.vv       v6, v2, v4, 3           \n\t"  // 0,1
+            "vpack.vv       v8, v3, v5, 3           \n\t"  // 2,3
+
+            // mul scale
+            "vmacc.vv       v30, v6, v16            \n\t"
+            "vmacc.vv       v30, v7, v17            \n\t"
+            "vmacc.vv       v30, v8, v18            \n\t"
+            "vmacc.vv       v30, v9, v19            \n\t"
+
+            "addi           t1, t1, -1              \n\t"
+            "bgtz           t1, INNER_K_LOOP%=      \n\t"
+
+            // load zp B
+            "vsetvli        t0, x0, e8, m4          \n\t"
+            "vle8.v         v4, (s3)                \n\t"
+            "vsrl.vi        v8, v4, 4               \n\t"  // zp
+
+            // asum * zp
+            "vsetvli        t0, x0, e16, m1         \n\t"
+            "vxor.vv        v20, v20, v20           \n\t"
+            "vxor.vv        v22, v22, v22           \n\t"
+            "vxor.vv        v24, v24, v24           \n\t"
+            "vxor.vv        v26, v26, v26           \n\t"
+
+            "vsetvli        t0, x0, e16, mf4        \n\t"
+            "vle16.v        v2, (%[A])              \n\t"
+            "vsetvli        t0, x0, e8, mf4         \n\t"
+            "vnsrl.wi       v12, v2, 0              \n\t"  // low 8
+            "vnsra.wi       v13, v2, 8              \n\t"  // high 8
+
+            "vsetvli        t0, x0, e32, m1         \n\t"
+            "vmadotsu       v20, v13, v8, i8        \n\t"
+            "vmadotsu       v22, v13, v9, i8        \n\t"
+            "vmadotsu       v24, v13, v10, i8       \n\t"
+            "vmadotsu       v26, v13, v11, i8       \n\t"
+
+            "vsll.vi        v20, v20, 8             \n\t"
+            "vsll.vi        v22, v22, 8             \n\t"
+            "vsll.vi        v24, v24, 8             \n\t"
+            "vsll.vi        v26, v26, 8             \n\t"
+
+            "vmadotu        v20, v12, v8, i8        \n\t"
+            "vmadotu        v22, v12, v9, i8        \n\t"
+            "vmadotu        v24, v12, v10, i8       \n\t"
+            "vmadotu        v26, v12, v11, i8       \n\t"
+
+            "vpack.vv       v2, v20, v22, 2         \n\t"
+            "vpack.vv       v4, v24, v26, 2         \n\t"
+            "vpack.vv       v28, v2, v4, 3          \n\t"
+
+            "vsetvli        t0, x0, e16, mf2        \n\t"
+            "vle16.v        v0, (t2)                \n\t"  // scale16
+            "addi           t2, t2, 64              \n\t"
+            "vle16.v        v1, (t2)                \n\t"  // zero16
+            "vfwcvt.f.f.v   v2, v0                  \n\t"
+            "vfwcvt.f.f.v   v4, v1                  \n\t"
+            "vsetvli        t0, x0, e32, m1         \n\t"
+            "vfcvt.f.x.v    v30, v30                \n\t"
+            "vfcvt.f.x.v    v28, v28                \n\t"
+            "addi           %[B], t2, 64            \n\t"
+            "mv             %[A], t3                \n\t"
+
+            "vfmul.vv       v30, v30, v2            \n\t"  // mul scale16
+            "vfmacc.vv      v30, v28, v4            \n\t"  // + mul zero16
+            "vfmacc.vf      v31, fa0, v30           \n\t"
+            "addi           s1, s1, -1              \n\t"
+            "bgtz           s1, BLK_LOOP%=          \n\t"
+
+            // save
+            "vsetvli        t0, x0, e32, m1         \n\t"
+            "vse32.v        v31, (%[DST])           \n\t"
+            : [A] "+r"(a_data), [B] "+r"(b_data)
+            : [DST] "r"(dst_c), [BK] "r"(k_blks)
+            : "t0", "t1", "t2", "t3", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12",
+              "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27",
+              "v28", "v29", "v30", "v31", "fa0", "t4", "t5", "t6", "s1", "s2", "s3");
+    }
+}
+
+void gemm_kernel_i8i2k_m4(size_t          blk_len,
+                          const uint8_t * quant_a_ptr,
+                          const uint8_t * quant_b_data,
+                          float *         c_ptr,
+                          size_t          count_m,
+                          size_t          count_n,
+                          size_t          k_blks,
+                          size_t          ldc) {
+    constexpr size_t NB_COLS = 32;
+    using blk_type           = nrow_block_q2_k<NB_COLS>;
+
+    int64_t  b_ncol_block_stride = sizeof(blk_type) * k_blks;
+    _Float16 scale               = 0.0625f;
+    _Float16 scale_1             = 16.0f;
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+        uint8_t * b_data = (uint8_t *) quant_b_data + (ni / NB_COLS) * b_ncol_block_stride;
+        int8_t *  a_data = (int8_t *) quant_a_ptr;
+        float *   dst_c  = (float *) c_ptr + ni;
+
+        asm volatile(
+            "vsetvli        t0, x0, e16, m1         \n\t"
+            "vxor.vv        v28, v31, v31           \n\t"  // init result
+            "vxor.vv        v29, v31, v31           \n\t"
+            "vxor.vv        v30, v31, v31           \n\t"
+            "vxor.vv        v31, v31, v31           \n\t"
+            "mv             s1, %[BK]               \n\t"
+
+            ".align 4                               \n\t"
+            "BLK_LOOP%=:                            \n\t"
+            // load scale A
+            "flw            fa0, (%[A])             \n\t"
+            "flw            fa1, 4(%[A])            \n\t"
+            "flw            fa2, 8(%[A])            \n\t"
+            "flw            fa3, 12(%[A])           \n\t"
+            "addi           %[A], %[A], 16          \n\t"
+
+            "li             t1, 4                   \n\t"
+            "addi           t2, %[B], 512           \n\t"  // B data addr
+            "addi           t3, %[A], 128           \n\t"  // A data addr
+            "addi           s4, t2, 1024            \n\t"  // scale16 addr
+            "addi           s4, s4, 1024            \n\t"  // TODO
+            "addi           s3, %[B], 0             \n\t"
+
+            "vsetvli        t0, x0, e16, mf2        \n\t"
+            "vle16.v        v1, (s4)                \n\t"  // load scale16
+            "vsetvli        t0, x0, e16, m1         \n\t"
+            "vpack.vv       v22, v1, v1, 3          \n\t"
+
+            "addi           s4, t3, 256             \n\t"  // addr 1
+            "addi           s5, t3, 512             \n\t"  // addr 2
+            "addi           s6, t3, 768             \n\t"  // addr 3
+
+            // init the accu to 0
+            "vxor.vv        v24, v24, v24           \n\t"
+            "vxor.vv        v25, v25, v25           \n\t"
+            "vxor.vv        v26, v26, v26           \n\t"
+            "vxor.vv        v27, v27, v27           \n\t"
+
+            "INNER_K_LOOP%=:                        \n\t"
+            // load scale  B
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vle8.v         v1, (%[B])              \n\t"
+            "addi           %[B], %[B], 128         \n\t"
+            "vand.vi        v1, v1, 0xf             \n\t"
+
+            "vfwcvt.f.x.v   v20, v1                 \n\t"  // f16 scale B
+            "vsetvli        t0, x0, e16, m1         \n\t"
+            "vfmul.vv       v0, v20, v22            \n\t"  // mul scale16
+            "vfmul.vv       v1, v21, v22            \n\t"  // mul scale16
+            "vfmul.vf       v0, v0, %[SCALE]        \n\t"  // mul magic
+            "vfmul.vf       v1, v1, %[SCALE]        \n\t"  // mul magic
+
+            // A data, 4x64@i8
+            "vsetvli        t0, x0, e8, mf2         \n\t"
+            "vle8.v         v2, (t3)                \n\t"
+            "addi           t3, t3, 64              \n\t"
+            "vle8.v         v3, (s4)                \n\t"
+            "addi           s4, s4, 64              \n\t"
+            "vle8.v         v4, (s5)                \n\t"
+            "addi           s5, s5, 64              \n\t"
+            "vle8.v         v5, (s6)                \n\t"
+            "addi           s6, s6, 64              \n\t"
+
+            // 4x64 => 4x16x4
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vpack.vv       v6, v2, v3, 1           \n\t"
+            "vpack.vv       v8, v4, v5, 1           \n\t"
+            "vpack.vv       v2, v6, v8, 2           \n\t"  // 0, 2
+
+            "vpack.vv       v20, v2, v2, 3          \n\t"  // 1
+            "vor.vv         v23, v21, v21           \n\t"
+            "vpack.vv       v20, v3, v3, 3          \n\t"  // 3
+
+            // B data, 32x64@i2
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vl4r.v         v4, (t2)                \n\t"
+            "addi           t2, t2, 512             \n\t"
+            "vand.vi        v8, v4, 0x3             \n\t"  // 0-15
+            "vsrl.vi        v9, v4, 2               \n\t"
+            "vsrl.vi        v10, v4, 4              \n\t"
+            "vsrl.vi        v11, v4, 6              \n\t"  // 48-63
+            "vand.vi        v9, v9, 0x3             \n\t"  // 16-31
+            "vand.vi        v10, v10, 0x3           \n\t"  // 32-47
+
+            "vand.vi        v12, v5, 0x3            \n\t"  // 0-15
+            "vsrl.vi        v13, v5, 2              \n\t"
+            "vsrl.vi        v14, v5, 4              \n\t"
+            "vsrl.vi        v15, v5, 6              \n\t"  // 48-63
+            "vand.vi        v13, v13, 0x3           \n\t"  // 16-31
+            "vand.vi        v14, v14, 0x3           \n\t"  // 32-47
+
+            "vand.vi        v16, v6, 0x3            \n\t"  // 0-15
+            "vsrl.vi        v17, v6, 2              \n\t"
+            "vsrl.vi        v18, v6, 4              \n\t"
+            "vsrl.vi        v19, v6, 6              \n\t"  // 48-63
+            "vand.vi        v17, v17, 0x3           \n\t"  // 16-31
+            "vand.vi        v18, v18, 0x3           \n\t"  // 32-47
+
+            "vand.vi        v4, v7, 0x3             \n\t"  // 0-15
+            "vsrl.vi        v5, v7, 2               \n\t"
+            "vsrl.vi        v6, v7, 4               \n\t"
+            "vsrl.vi        v7, v7, 6               \n\t"  // 48-63
+            "vand.vi        v5, v5, 0x3             \n\t"  // 16-31
+            "vand.vi        v6, v6, 0x3             \n\t"  // 32-47
+
+            // i2 * i8 vmadot
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vmadotsu.hp    v24, v2, v8, v0, 0, i8  \n\t"
+            "vmadotsu.hp    v25, v2, v12, v0, 1, i8 \n\t"
+            "vmadotsu.hp    v26, v2, v16, v0, 2, i8 \n\t"
+            "vmadotsu.hp    v27, v2, v4, v0, 3, i8  \n\t"
+
+            "vmadotsu.hp    v24, v23, v9, v0, 4, i8 \n\t"
+            "vmadotsu.hp    v25, v23, v13, v0, 5, i8\n\t"
+            "vmadotsu.hp    v26, v23, v17, v0, 6, i8\n\t"
+            "vmadotsu.hp    v27, v23, v5, v0, 7, i8 \n\t"
+
+            "vmadotsu.hp    v24, v3, v10, v1, 0, i8 \n\t"
+            "vmadotsu.hp    v25, v3, v14, v1, 1, i8 \n\t"
+            "vmadotsu.hp    v26, v3, v18, v1, 2, i8 \n\t"
+            "vmadotsu.hp    v27, v3, v6, v1, 3, i8  \n\t"
+
+            "vmadotsu.hp    v24, v21, v11, v1, 4, i8\n\t"
+            "vmadotsu.hp    v25, v21, v15, v1, 5, i8\n\t"
+            "vmadotsu.hp    v26, v21, v19, v1, 6, i8\n\t"
+            "vmadotsu.hp    v27, v21, v7, v1, 7, i8 \n\t"
+
+            "addi           t1, t1, -1              \n\t"
+            "bgtz           t1, INNER_K_LOOP%=      \n\t"
+
+            "vsetvli        t0, x0, e16, m1         \n\t"
+            "vpack.vv       v2, v24, v25, 1         \n\t"
+            "vpack.vv       v4, v26, v27, 1         \n\t"
+            "vpack.vv       v6, v2, v4, 2           \n\t"  // 0,1,2,3
+
+            "vxor.vv        v18, v18, v18           \n\t"
+            "vxor.vv        v20, v20, v20           \n\t"
+            "vxor.vv        v22, v22, v22           \n\t"
+            "vxor.vv        v24, v24, v24           \n\t"
+            // load zp B, 16x8x4@int4
+            "vsetvli        t0, x0, e8, m4          \n\t"
+            "vle8.v         v0, (s3)                \n\t"
+            "vsrl.vi        v0, v0, 4               \n\t"  // zp
+
+            // 4x16@int16
+            "vsetvli        t0, x0, e16, m1         \n\t"  // a sum
+            "vle16.v         v12, (%[A])             \n\t"
+            "vsetvli        t0, x0, e8, m1          \n\t"
+            "vnsrl.wi       v10, v12, 0             \n\t"  // low 8
+            "vnsra.wi       v11, v12, 8             \n\t"  // high 8
+
+            // asum * zp
+            "vsetvli        t0, x0, e32, m1          \n\t"
+            "vmadotsu       v18, v11, v0, i8        \n\t"
+            "vmadotsu       v20, v11, v1, i8        \n\t"
+            "vmadotsu       v22, v11, v2, i8        \n\t"
+            "vmadotsu       v24, v11, v3, i8        \n\t"
+            "vsll.vi        v18, v18, 8             \n\t"
+            "vsll.vi        v20, v20, 8             \n\t"
+            "vsll.vi        v22, v22, 8             \n\t"
+            "vsll.vi        v24, v24, 8             \n\t"
+            "vmadotu        v18, v10, v0, i8        \n\t"
+            "vmadotu        v20, v10, v1, i8        \n\t"
+            "vmadotu        v22, v10, v2, i8        \n\t"
+            "vmadotu        v24, v10, v3, i8        \n\t"
+
+            "vpack.vv       v10, v18, v20, 2        \n\t"
+            "vpack.vv       v12, v22, v24, 2        \n\t"
+            "vpack.vv       v14, v10, v12, 3        \n\t"
+            "vpack.vv       v16, v11, v13, 3        \n\t"
+
+            "vsetvli        t0, x0, e16, mf2        \n\t"
+            "addi           t2, t2, 64              \n\t"
+            "vle16.v        v20, (t2)               \n\t"  // zero16
+            "vfwcvt.f.f.v   v22, v20                \n\t"
+
+            // mul 1/magic
+            "vsetvli        t0, x0, e16, m1         \n\t"
+            "vfwmul.vf      v0, v6, %[SCALE_1]      \n\t"
+            "vfwmul.vf      v2, v7, %[SCALE_1]      \n\t"
+
+            "vsetvli        t0, x0, e32, m1         \n\t"
+            "vfcvt.f.x.v    v14, v14                \n\t"
+            "vfcvt.f.x.v    v15, v15                \n\t"
+            "vfcvt.f.x.v    v16, v16                \n\t"
+            "vfcvt.f.x.v    v17, v17                \n\t"
+
+            "addi           %[B], t2, 64            \n\t"
+            "mv             %[A], s6                \n\t"
+
+            "vfmacc.vv      v0, v14, v22            \n\t"  // + mul zero16
+            "vfmacc.vv      v1, v15, v22            \n\t"
+            "vfmacc.vv      v2, v16, v22            \n\t"
+            "vfmacc.vv      v3, v17, v22            \n\t"
+
+            "vfmacc.vf      v28, fa0, v0            \n\t"  // mul a scale
+            "vfmacc.vf      v29, fa1, v1            \n\t"
+            "vfmacc.vf      v30, fa2, v2            \n\t"
+            "vfmacc.vf      v31, fa3, v3            \n\t"
+
+            "addi           s1, s1, -1              \n\t"
+            "bgtz           s1, BLK_LOOP%=          \n\t"
+
+            // save
+            "vsetvli        t0, x0, e32, m1         \n\t"
+            "add            t1, %[LDC], %[DST]      \n\t"
+            "vse32.v        v28, (%[DST])           \n\t"
+            "vse32.v        v29, (t1)               \n\t"
+            "add            t1, t1, %[LDC]          \n\t"
+            "vse32.v        v30, (t1)               \n\t"
+            "add            t1, t1, %[LDC]          \n\t"
+            "vse32.v        v31, (t1)               \n\t"
+            : [A] "+r"(a_data), [B] "+r"(b_data)
+            : [DST] "r"(dst_c), [BK] "r"(k_blks), [LDC] "r"(ldc * 4), [SCALE] "f"(scale), [SCALE_1] "f"(scale_1)
+            : "t0", "t1", "t2", "t3", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12",
+              "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27",
+              "v28", "v29", "v30", "v31", "fa0", "t4", "t5", "t6", "s1", "s2", "s3", "s4", "s5", "s6");
+    }
+}
+
+void gemm_kernel_i8i3k_m1(size_t          blk_len,
+                          const uint8_t * quant_a_ptr,
+                          const uint8_t * quant_b_data,
+                          float *         c_ptr,
+                          size_t          count_m,
+                          size_t          count_n,
+                          size_t          k_blks,
+                          size_t          ldc) {
+    constexpr size_t NB_COLS = 32;  //only support 32 in ASM
+    using blk_type           = nrow_block_q3_k<NB_COLS>;
+
+    const blk_type * b_base = reinterpret_cast<const blk_type *>(quant_b_data);
+
+    int64_t a_blk_stride        = q8k_blk_size(256);
+    int64_t a_nrow_block_stride = a_blk_stride;
+    int64_t b_ncol_block_stride = sizeof(blk_type);
+
+    // Constants used by q3_k scaling in HP branch:
+    // - k_q3k_scale_step: per-nibble scale factor (1/16).
+    // - k_a_scale_post_mul: A_scale needs an extra *16 at the end (pairs with 1/16 above).
+    const _Float16 k_q3k_scale_step   = (_Float16) 0.0625f;  // 1 / 16
+    const float    k_a_scale_post_mul = 16.0f;
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+        size_t           nb_real          = std::min<size_t>(NB_COLS, count_n - ni);
+        const blk_type * quant_b_blk_data = b_base + (ni / NB_COLS) * k_blks;
+#if 0
+        //------------------------------------------------------------------------------
+        // A format
+        // Ascale   fp32 * 1    32bit
+        // Asum     int16 * 16  256bit
+        // A M1K256 int8        2048bit
+        //------------------------------------------------------------------------------
+        // B format
+        // B_scl    uint8*N32*16    4096bit
+        // B_Hmask  N32K16*16 1bit  8192bit
+        // B_Qs     N32K16*16 2bit  16384bit
+        // B scl16  fp16 * N32      512bit;
+        //------------------------------------------------------------------------------
+        //bias always be nullptr
+        __asm__ volatile(
+            // t2 = k_blks (each is K256 superblock)
+            "mv           t2, %[KBLKS]            \n\t"
+            // t3 = 256/64 = 4 (K64 iterations per superblock)
+            "li           t3, 4                   \n\t"
+            "mv           s2, %[pA]               \n\t"  // s2 = pASCL
+            "addi         s3, %[pA], 4+32         \n\t"  // s3 = pAData, (pA+AScl+ASum)
+
+            // B block layout for nrow_block_q3_k<32>:
+            // scales: 512B, hmask: 1024B, qs: 2048B, scales16: 64B
+            "addi         s5, %[pB], 32*16        \n\t"  // s5 = pB_hmask
+            "mv           s4, %[pB]               \n\t"  // s4 = pB_scales
+            "addi         s6, s5, 1024            \n\t"  // s6 = pB_qs
+            "mv           s7, %[pB]               \n\t"  // s7 = pB_base
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v31, v0, v0             \n\t"  // clear acc
+            "vxor.vv      v30, v0, v0             \n\t"  // clear acc of K256
+
+            // ordinary vmadot: vle*10 vecIns*78 vmadot*16
+            ".align 4                             \n\t"
+            "BLK_LPST%=:                          \n\t"
+            "K64_LPST%=:                          \n\t"
+
+            // K0-15
+            // load B scales (32 bytes per K16, 16 times => 512B)
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vle8.v       v2, (s4)                \n\t"
+            "addi         s4, s4, 128             \n\t"
+
+            // load B qs chunk (128B per K16, 16 times => 2048B)
+            "vle8.v       v4, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v5, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v6, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v7, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+
+            // load B hmask chunk (64B per K16, 16 times => 1024B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v1, (s3)                \n\t"
+            "addi         s3, s3, 64              \n\t"
+
+            // unpack 2-bit qs + hmask -> signed values
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vnot.v       v0, v0                  \n\t"
+            "vand.vi      v12, v4, 0x3            \n\t"
+            "vand.vi      v13, v5, 0x3            \n\t"
+            "vand.vi      v14, v6, 0x3            \n\t"
+            "vand.vi      v15, v7, 0x3            \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v1, v12, i8        \n\t"
+            "vmadot       v18, v1, v13, i8        \n\t"
+            "vmadot       v20, v1, v14, i8        \n\t"
+            "vmadot       v22, v1, v15, i8        \n\t"
+
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v24, v16, v18, 2        \n\t"
+            "vpack.vv     v26, v20, v22, 2        \n\t"
+            "vpack.vv     v16, v24, v26, 3        \n\t"  // N0-N31 in v16
+
+            // apply B int8 scales (-32 bias has been applyed)
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vwadd.vx     v18, v2, x0             \n\t"  // int8 -> int16
+
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vwadd.vx     v19, v18, x0            \n\t"  // int8 -> int16
+
+            // static_cast<int32_t>(qsum) * b_scale;
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmacc.vv     v30, v16, v19           \n\t"
+
+            //K16-31
+            // load B scales (32 bytes per K16, 16 times => 512B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vslidedown.vi  v2, v2, 4             \n\t"
+
+            // load B hmask chunk (64B per K16, 16 times => 1024B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+            "vsetvli      t0, x0, e64, mf2        \n\t"
+            "vslidedown.vi  v1, v1, 2             \n\t"
+
+            // unpack 2-bit qs + hmask -> signed values
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vsll.vi      v8, v4, 4               \n\t"
+            "vsll.vi      v9, v5, 4               \n\t"
+            "vsll.vi      v10, v6, 4              \n\t"
+            "vsll.vi      v11, v7, 4              \n\t"
+            "vnot.v       v0, v0                  \n\t"
+
+            "vsrl.vi      v12, v8, 6              \n\t"
+            "vsrl.vi      v13, v9, 6              \n\t"
+            "vsrl.vi      v14, v10, 6             \n\t"
+            "vsrl.vi      v15, v11, 6             \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v1, v12, i8        \n\t"
+            "vmadot       v18, v1, v13, i8        \n\t"
+            "vmadot       v20, v1, v14, i8        \n\t"
+            "vmadot       v22, v1, v15, i8        \n\t"
+
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v24, v16, v18, 2        \n\t"
+            "vpack.vv     v26, v20, v22, 2        \n\t"
+            "vpack.vv     v16, v24, v26, 3        \n\t"  // N0-N31 in v16
+
+            // apply B int8 scales (-32 bias has been applyed)
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vwadd.vx     v18, v2, x0              \n\t"  // int8 -> int16
+
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vwadd.vx     v19, v18, x0              \n\t"  // int8 -> int16
+
+            // static_cast<int32_t>(qsum) * b_scale;
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmacc.vv     v30, v16, v19            \n\t"
+
+            //K32-47
+            // load B scales (32 bytes per K16, 16 times => 512B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vslidedown.vi  v2, v2, 4             \n\t"
+
+            // load B hmask chunk (64B per K16, 16 times => 1024B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+            "vsetvli      t0, x0, e64, mf2        \n\t"
+            "vslidedown.vi  v1, v1, 2             \n\t"
+
+            // unpack 2-bit qs + hmask -> signed values
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vsll.vi      v8, v4, 2               \n\t"
+            "vsll.vi      v9, v5, 2               \n\t"
+            "vsll.vi      v10, v6, 2              \n\t"
+            "vsll.vi      v11, v7, 2              \n\t"
+            "vnot.v       v0, v0                  \n\t"
+
+            "vsrl.vi      v12, v8, 6              \n\t"
+            "vsrl.vi      v13, v9, 6              \n\t"
+            "vsrl.vi      v14, v10, 6             \n\t"
+            "vsrl.vi      v15, v11, 6             \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v1, v12, i8        \n\t"
+            "vmadot       v18, v1, v13, i8        \n\t"
+            "vmadot       v20, v1, v14, i8        \n\t"
+            "vmadot       v22, v1, v15, i8        \n\t"
+
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v24, v16, v18, 2        \n\t"
+            "vpack.vv     v26, v20, v22, 2        \n\t"
+            "vpack.vv     v16, v24, v26, 3        \n\t"
+
+            // apply B int8 scales (-32 bias has been applyed)
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vwadd.vx     v18, v2, x0              \n\t"  // int8 -> int16
+
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vwadd.vx     v19, v18, x0              \n\t"  // int8 -> int16
+
+            // static_cast<int32_t>(qsum) * b_scale;
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmacc.vv     v30, v16, v19            \n\t"
+
+            // K48-63
+            // load B scales (32 bytes per K16, 16 times => 512B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vslidedown.vi  v2, v2, 4             \n\t"
+
+            // load B hmask chunk (64B per K16, 16 times => 1024B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+            "vsetvli      t0, x0, e64, mf2        \n\t"
+            "vslidedown.vi  v1, v1, 2             \n\t"
+
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vnot.v       v0, v0                  \n\t"
+            "vsrl.vi      v12, v4, 6              \n\t"
+            "vsrl.vi      v13, v5, 6              \n\t"
+            "vsrl.vi      v14, v6, 6              \n\t"
+            "vsrl.vi      v15, v7, 6              \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v1, v12, i8        \n\t"
+            "vmadot       v18, v1, v13, i8        \n\t"
+            "vmadot       v20, v1, v14, i8        \n\t"
+            "vmadot       v22, v1, v15, i8        \n\t"
+
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v24, v16, v18, 2        \n\t"
+            "vpack.vv     v26, v20, v22, 2        \n\t"
+            "vpack.vv     v16, v24, v26, 3        \n\t"
+
+            // apply B int8 scales (-32 bias has been applyed)
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vwadd.vx     v18, v2, x0             \n\t"  // int8 -> int16
+
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vwadd.vx     v19, v18, x0            \n\t"  // int8 -> int16
+
+            // static_cast<int32_t>(qsum) * b_scale;
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmacc.vv     v30, v16, v19           \n\t"
+
+            "addi         t3, t3, -1              \n\t"
+            "bgtz         t3, K64_LPST%=          \n\t"
+            "K64_LPND%=:                          \n\t"
+
+            // load A scale (fp32) and advance A to next superblock
+            "flw          f0, (s2)                \n\t"
+            "addi         s2, s2, 4+32+256        \n\t"
+            "add          t4, s7, %[B_STR]        \n\t"  // t4 = next B blk base
+            "addi         s3, s2, 4+32            \n\t"
+
+            // load B scales16[32] (fp16) at end of qs region
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vle16.v      v2, (s6)                \n\t"
+
+            // pointer modify
+            "addi         s5, t4, 32*16           \n\t"
+            "mv           s4, t4                  \n\t"
+            "addi         s6, s5, 32*32           \n\t"
+            "addi         s7, t4, 0               \n\t"
+
+            // b_scale fp16 -> fp32
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vfwcvt.f.f.v v24, v2                 \n\t"
+
+            // a_scale * b_scale;
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vfcvt.f.x.v v26, v30                 \n\t"
+            "vfmul.vf     v1, v24, f0             \n\t"
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            // static_cast<float>(qsum) * a_scale * b_scale;
+            "vfmacc.vv    v31, v1, v26            \n\t"
+
+            // next K-superblock
+            "addi         t2, t2, -1              \n\t"
+            "vxor.vv      v30, v0, v0             \n\t"  // clear acc of K256
+            "li           t3, 4                   \n\t"
+            "bgtz         t2, BLK_LPST%=          \n\t"
+
+            "BLK_LPND%=:                          \n\t"
+            "vsetvli      t0, %[NBLKS], e32, m1   \n\t"
+            "vse32.v      v31, (%[pC])            \n\t"
+            "FUNC_END%=:                          \n\t"
+
+            :
+            : [KBLKS] "r"(k_blks), [NBLKS] "r"(nb_real), [pA] "r"(quant_a_ptr), [pB] "r"(quant_b_blk_data),
+              [pC] "r"(c_ptr), [B_STR] "r"(b_ncol_block_stride)
+            : "cc", "memory", "t0", "t2", "t3", "t4", "t5", "f0", "s2", "s3", "s4", "s5", "s6", "s7");
+#else
+
+        __asm__ volatile(
+            // =========================
+            // Kernel overview (M1 x N32)
+            // =========================
+            // Process one output row (M=1) and 32 columns (N=32) per call.
+            //
+            // Loop structure:
+            //   - Outer loop: K superblocks of size K=256 (k_blks times)
+            //   - Each K256 superblock is broken into 4 x K64
+            //   - Each K64 is processed as 4 x K16 "sub-blocks" (via unpack+dot)
+            //
+            // Data layout (high level):
+            //   A (q8k K=256, per superblock):
+            //     [ fp32 a_scale ][ int16 a_sum[16] ][ int8 a_qs[256] ]
+            //   B (nrow_block_q3_k<32>, per superblock):
+            //     [ int8  scales[32*16] ][ hmask[1024] ][ qs[2048] ][ fp16 scales16[32] ]
+            //
+            // Registers/pointers:
+            //   s2: pA (points at A superblock header; used to load fp32 a_scale)
+            //   s3: pA_qs (points at A int8 data within the current superblock)
+            //   s4: pB_scales (points at B int8 per-K16 scales)
+            //   s5: pB_hmask (points at B sign mask area)
+            //   s6: pB_qs (points at B 2-bit packed qs area)
+            //   s8: pB_scales16 (points at B fp16 scales16[32] at the end of block)
+            //   s7: pB_base (base pointer to current B block; used for block-to-block stride)
+
+            // t2 = number of K256 superblocks
+            "mv           t2, %[KBLKS]            \n\t"
+            // t3 = number of K64 chunks per K256 superblock (256 / 64)
+            "li           t3, 4                   \n\t"
+
+            // A pointers
+            "mv           s2, %[pA]               \n\t"  // s2 = pA_superblock (a_scale at +0)
+            "addi         s3, %[pA], 4+32         \n\t"  // s3 = pA_qs (skip a_scale + a_sum[16])
+
+            // B pointers for nrow_block_q3_k<32>
+            "addi         s5, %[pB], 32*16        \n\t"  // s5 = pB_hmask  (skip scales[32*16])
+            "mv           s4, %[pB]               \n\t"  // s4 = pB_scales
+            "addi         s6, s5, 1024            \n\t"  // s6 = pB_qs     (skip hmask)
+            // scales16 is at the end of the block: qs(2048) after hmask
+            "addi         s8, s6, 1024            \n\t"
+            "addi         s8, s8, 1024            \n\t"  // s8 = pB_scales16 (fp16 scales16[32])
+            "mv           s7, %[pB]               \n\t"  // s7 = pB_base (for next-block address calc)
+
+            // v31: final FP32 accumulator for N=32
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v31, v0, v0             \n\t"
+
+            // ---- Preload B scales16[32] and build FP16 scale vector used by vmadot.hp ----
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vle16.v      v1, (s8)                \n\t"  // load fp16 scales16[32]
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v26, v1, v1, 3          \n\t"  // broadcast/pack to match lanes
+            "vmv.v.v      v17, v26                \n\t"
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vfmul.vf     v30, v17, %[q3_step]    \n\t"  // v30 = scales16 * (1/16)
+
+            // v24-v27: fp16 partial accumulators for a K64 chunk (vmadot.hp outputs)
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v24, v16, v16           \n\t"
+            "vxor.vv      v25, v16, v16           \n\t"
+            "vxor.vv      v26, v16, v16           \n\t"
+            "vxor.vv      v27, v16, v16           \n\t"
+
+            // HP vmadot: vle*10 vecIns*38 vmadot.hp*16
+            ".align 4                             \n\t"
+            "BLK_LPST%=:                          \n\t"  // loop over K256 superblocks
+            "K64_LPST%=:                          \n\t"  // loop over 4 x K64 chunks
+
+            // ------------------------------------------------------------
+            // K0-15: load B scales + {hmask, qs} + A data; unpack and dot
+            // ------------------------------------------------------------
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vle8.v       v2, (s4)                \n\t"  // B int8 scales for this K16
+            "addi         s4, s4, 128             \n\t"
+
+            "vle8.v       v4, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v5, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v6, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v7, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"  // B hmask for this K16
+            "addi         s5, s5, 64              \n\t"
+
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v3, (s3)                \n\t"  // A int8 data for this K16
+            "addi         s3, s3, 64              \n\t"
+
+            // Convert B int8 scales to FP16 and apply scales16*(1/16)
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vfwcvt.f.x.v v28, v2                 \n\t"  // int8 -> fp16
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vfmul.vv     v1, v28, v30            \n\t"  // v1: FP16 scale vector for vmadot.hp
+            "vfmul.vv     v29, v29, v30           \n\t"
+
+            // Unpack B 2-bit qs + hmask -> signed int8 in v12..v15
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vnot.v       v0, v0                  \n\t"
+            "vand.vi      v12, v4, 0x3            \n\t"
+            "vand.vi      v13, v5, 0x3            \n\t"
+            "vand.vi      v14, v6, 0x3            \n\t"
+            "vand.vi      v15, v7, 0x3            \n\t"
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            // (Next K16 unpack path uses a fresh hmask load)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // Prepare another group from packed qs (bit shifts) + apply sign from hmask
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vsll.vi      v8, v4, 4               \n\t"
+            "vsll.vi      v9, v5, 4               \n\t"
+            "vsll.vi      v10, v6, 4              \n\t"
+            "vsll.vi      v11, v7, 4              \n\t"
+            "vsrl.vi      v16, v8, 6              \n\t"
+            "vsrl.vi      v17, v9, 6              \n\t"
+            "vnot.v       v0, v0                  \n\t"
+            "vsrl.vi      v18, v10, 6             \n\t"
+            "vsrl.vi      v19, v11, 6             \n\t"
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v16, v16, -4, v0.t      \n\t"
+
+            // A shift for the second dot within this K64
+            "vsetvli      t0, x0, e64, mf2        \n\t"
+            "vslidedown.vi  v2, v3, 2             \n\t"
+
+            // Dot products with FP16 scaling (accumulate into v24..v27)
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmadot.hp    v24, v3, v12, v1, 0, i8 \n\t"
+            "vmadot.hp    v25, v3, v13, v1, 1, i8 \n\t"
+            "vmadot.hp    v26, v3, v14, v1, 2, i8 \n\t"
+            "vmadot.hp    v27, v3, v15, v1, 3, i8 \n\t"
+            "vmadot.hp    v24, v2, v16, v1, 4, i8 \n\t"
+            "vmadot.hp    v25, v2, v17, v1, 5, i8 \n\t"
+            "vmadot.hp    v26, v2, v18, v1, 6, i8 \n\t"
+            "vmadot.hp    v27, v2, v19, v1, 7, i8 \n\t"
+
+            // (K32-47 / K48-63 blocks continue unchanged...)
+            // load B scales (32 bytes per K16, 16 times => 512B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vmv.v.v      v1, v29                 \n\t"
+
+            // load B hmask chunk (64B per K16, 16 times => 1024B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+            "vsetvli      t0, x0, e64, mf2        \n\t"
+            "vslidedown.vi  v3, v3, 4             \n\t"
+
+            // unpack 2-bit qs + hmask -> signed values
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vsll.vi      v8, v4, 2               \n\t"
+            "vsll.vi      v9, v5, 2               \n\t"
+            "vsll.vi      v10, v6, 2              \n\t"
+            "vsll.vi      v11, v7, 2              \n\t"
+
+            "vsrl.vi      v20, v8, 6              \n\t"
+            "vsrl.vi      v21, v9, 6              \n\t"
+            "vnot.v       v0, v0                  \n\t"
+            "vsrl.vi      v22, v10, 6             \n\t"
+            "vsrl.vi      v23, v11, 6             \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v20, v20, -4, v0.t      \n\t"
+
+            // K48-63
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vsrl.vi      v8, v4, 6               \n\t"
+            "vsrl.vi      v9, v5, 6               \n\t"
+            "vnot.v       v0, v0                  \n\t"
+            "vsrl.vi      v10, v6, 6              \n\t"
+            "vsrl.vi      v11, v7, 6              \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v8, v8, -4, v0.t        \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+            "vsetvli      t0, x0, e64, mf2        \n\t"
+            "vslidedown.vi  v2, v3, 2             \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmadot.hp    v24, v3, v20, v1, 0, i8 \n\t"
+            "vmadot.hp    v25, v3, v21, v1, 1, i8 \n\t"
+            "vmadot.hp    v26, v3, v22, v1, 2, i8 \n\t"
+            "vmadot.hp    v27, v3, v23, v1, 3, i8 \n\t"
+            "vmadot.hp    v24, v2, v8, v1, 4, i8  \n\t"
+            "vmadot.hp    v25, v2, v9, v1, 5, i8  \n\t"
+            "vmadot.hp    v26, v2, v10, v1, 6, i8 \n\t"
+            "vmadot.hp    v27, v2, v11, v1, 7, i8 \n\t"
+
+            "addi         t3, t3, -1              \n\t"
+            "bgtz         t3, K64_LPST%=          \n\t"
+            "K64_LPND%=:                          \n\t"
+
+            // ---- End of K64 chunk: reduce fp16 accumulators -> fp32 and scale by A ----
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v12, v24, v25, 1        \n\t"
+            "vpack.vv     v14, v26, v27, 1        \n\t"
+            "vpack.vv     v16, v12, v14, 2        \n\t"
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vfwcvt.f.f.v v26, v16                \n\t"  // fp16 -> fp32 vector (qsum * b_scales)
+
+            // Load A scale and advance A pointer to next K256 superblock
+            "flw          f0, (s2)                \n\t"
+            "addi         s2, s2, 4+32+256        \n\t"
+            "add          t4, s7, %[B_STR]        \n\t"  // next B block base
+            "addi         s3, s2, 4+32            \n\t"  // reset A data pointer for next block
+
+            // Advance B pointers to next K256 superblock
+            "addi         s5, t4, 32*16           \n\t"
+            "mv           s4, t4                  \n\t"
+            "addi         s6, s5, 32*32           \n\t"
+            "addi         s8, s6, 1024            \n\t"
+            "addi         s8, s8, 1024            \n\t"
+            "addi         s7, t4, 0               \n\t"
+            "addi         t2, t2, -1              \n\t"
+
+            // Final per-block scaling: a_scale * 16.0f
+            "fmul.s       f0, f0, %[a_post_mul]   \n\t"
+            // acc += (qsum * b_scales) * (a_scale*16)
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vfmacc.vf    v31, f0, v26            \n\t"
+
+            "beqz         t2, BLK_LPND%=          \n\t"
+
+            // Preload next block's scales16 and rebuild v30 for vmadot.hp
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vle16.v      v1, (s8)                \n\t"
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v26, v1, v1, 3          \n\t"
+            "vmv.v.v      v17, v26                \n\t"
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vfmul.vf     v30, v17, %[q3_step]    \n\t"
+
+            // Reset fp16 partial accumulators for next K64 loop(s)
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v24, v16, v16           \n\t"
+            "vxor.vv      v25, v16, v16           \n\t"
+            "vxor.vv      v26, v16, v16           \n\t"
+            "vxor.vv      v27, v16, v16           \n\t"
+
+            "li           t3, 4                   \n\t"
+            "bgtz         t2, BLK_LPST%=          \n\t"
+
+            "BLK_LPND%=:                          \n\t"
+            "vsetvli      t0, %[NBLKS], e32, m1   \n\t"
+            "vse32.v      v31, (%[pC])            \n\t"
+
+            :
+            : [KBLKS] "r"(k_blks), [NBLKS] "r"(nb_real), [pA] "r"(quant_a_ptr), [pB] "r"(quant_b_blk_data),
+              [pC] "r"(c_ptr), [B_STR] "r"(b_ncol_block_stride), [q3_step] "f"(k_q3k_scale_step),
+              [a_post_mul] "f"(k_a_scale_post_mul)
+            : "cc", "memory", "t0", "t2", "t3", "t4", "t5", "f0", "f1", "s2", "s3", "s4", "s5", "s6", "s7", "s8");
+#endif
+    }
+}
+
+void gemm_kernel_i8i3k_m4(size_t          blk_len,
+                          const uint8_t * quant_a_ptr,
+                          const uint8_t * quant_b_data,
+                          float *         c_ptr,
+                          size_t          count_m,
+                          size_t          count_n,
+                          size_t          k_blks,
+                          size_t          ldc) {
+    using blk_type           = nrow_block_q3_k<32>;
+    constexpr size_t NB_COLS = 32;  //only support 32 in ASM
+
+    const blk_type * b_base = reinterpret_cast<const blk_type *>(quant_b_data);
+
+    int64_t a_blk_stride        = q8k_blk_size(256);
+    int64_t a_nrow_block_stride = a_blk_stride * 4;
+    int64_t b_ncol_block_stride = sizeof(blk_type);
+
+    for (size_t ni = 0; ni < count_n; ni += NB_COLS, c_ptr += NB_COLS) {
+        size_t           nb_real          = std::min<size_t>(NB_COLS, count_n - ni);
+        const blk_type * quant_b_blk_data = b_base + (ni / NB_COLS) * k_blks;
+
+        //------------------------------------------------------------------------------
+        // A format
+        // Ascale   fp32 * 1* 4row    128bit
+        // Asum     int16 * 16 4row  1024bit
+        // A M1K256 int8 4row        8192bit
+        //------------------------------------------------------------------------------
+        // B format
+        // B_scl    uint8*N32*16    4096bit
+        // B_Hmask  N32K16*16 1bit  8192bit
+        // B_Qs     N32K16*16 2bit  16384bit
+        // B scl16  fp16 * N32      512bit;
+        //------------------------------------------------------------------------------
+        //bias always be nullptr
+        __asm__ volatile(
+            // t2 = k_blks (each is K256 superblock)
+            "mv           t2, %[KBLKS]            \n\t"
+            // t3 = 256/64 = 4 (K64 iterations per superblock)
+            "li           t3, 4                   \n\t"
+            "mv           s2, %[pA]               \n\t"  // s2 = pASCL
+            "addi         s3, %[pA], 16+128       \n\t"  // s3 = pAData, (pA+AScl+ASum)
+
+            // B block layout for nrow_block_q3_k<32>:
+            // scales: 512B, hmask: 1024B, qs: 2048B, scales16: 64B
+            "addi         s5, %[pB], 32*16        \n\t"  // s5 = pB_hmask (skip scales)
+            "mv           s4, %[pB]               \n\t"  // s4 = pB_scales
+            "addi         s6, s5, 1024            \n\t"  // s6 = pB_qs (skip hmask)
+            "mv           s7, %[pB]               \n\t"  // s7 = pB_base
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v24, v0, v0             \n\t"  // v24-v27: K256 temp accumulator
+            "vxor.vv      v25, v0, v0             \n\t"
+            "vxor.vv      v26, v0, v0             \n\t"
+            "vxor.vv      v27, v0, v0             \n\t"
+            "vxor.vv      v28, v0, v0             \n\t"  // v28-v31: final accumulator
+            "vxor.vv      v29, v0, v0             \n\t"
+            "vxor.vv      v30, v0, v0             \n\t"
+            "vxor.vv      v31, v0, v0             \n\t"
+
+            // ordinary vmadot: vle*13 vecIns*96 vmadot*16
+            ".align 4                             \n\t"
+            "BLK_LPST%=:                          \n\t"
+            "K64_LPST%=:                          \n\t"
+
+            // ========== K0-15: First K16 sub-block ==========
+            // Load B INT8 scale factors (32 cols × 16 K16 blocks)
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vle8.v       v8, (s4)                \n\t"
+            "addi         s4, s4, 128             \n\t"
+
+            // Load B quantized data (32 cols × 16 elements × 2bit, stored in 4 groups)
+            "vle8.v       v4, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v5, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v6, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+            "vle8.v       v7, (s6)                \n\t"
+            "addi         s6, s6, 128             \n\t"
+
+            // Load B hmask (32 cols × 16bit sign mask)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // Load A data (4 rows × 16 elements × INT8)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v12, (s3)               \n\t"
+            "addi         s3, s3, 256             \n\t"  // Jump to next row
+            "vle8.v       v13, (s3)               \n\t"
+            "addi         s3, s3, 256             \n\t"
+            "vle8.v       v14, (s3)               \n\t"
+            "addi         s3, s3, 256             \n\t"
+            "vle8.v       v15, (s3)               \n\t"
+            "addi         s3, s3, -768+64         \n\t"  // Back to first row, advance 16 elements
+
+            // Pack A data: merge 4 rows into 2 vectors
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vpack.vv     v16, v12, v13, 1        \n\t"
+            "vpack.vv     v18, v14, v15, 1        \n\t"
+            "vpack.vv     v2, v16, v18, 2         \n\t"
+
+            // unpack 2-bit qs + hmask -> signed values
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vnot.v       v0, v0                  \n\t"
+            "vand.vi      v12, v4, 0x3            \n\t"
+            "vand.vi      v13, v5, 0x3            \n\t"
+            "vand.vi      v14, v6, 0x3            \n\t"
+            "vand.vi      v15, v7, 0x3            \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v2, v12, i8        \n\t"  // 4 rows × cols 0-7
+            "vmadot       v18, v2, v13, i8        \n\t"  // 4 rows × cols 8-15
+            "vmadot       v20, v2, v14, i8        \n\t"  // 4 rows × cols 16-23
+            "vmadot       v22, v2, v15, i8        \n\t"  // 4 rows × cols 24-31
+
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v12, v16, v18, 2        \n\t"  // Merge cols 0-15
+            "vpack.vv     v14, v20, v22, 2        \n\t"  // Merge cols 16-31
+            "vpack.vv     v16, v12, v14, 3        \n\t"  // Inter-row results (INT16)
+            "vpack.vv     v18, v13, v15, 3        \n\t"
+
+            // apply B int8 scales (-32 bias has been applyed)
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vwadd.vx     v21, v8, x0             \n\t"  // INT8 → INT16
+
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vwadd.vx     v23, v21, x0            \n\t"  // INT16 → INT32
+
+            // Accumulate to K256 accumulator: qsum * b_scale
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmacc.vv     v24, v16, v23           \n\t"  // Row 0
+            "vmacc.vv     v25, v17, v23           \n\t"  // Row 1
+            "vmacc.vv     v26, v18, v23           \n\t"  // Row 2
+            "vmacc.vv     v27, v19, v23           \n\t"
+
+            // ========== K16-31, K32-47, K48-63: Similar processing ==========
+            // load B scales (32 bytes per K16, 16 times => 512B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vslidedown.vi  v8, v8, 4             \n\t"
+
+            // load B hmask chunk (64B per K16, 16 times => 1024B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vslidedown.vi  v2, v2, 8             \n\t"
+
+            // unpack 2-bit qs + hmask -> signed values
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vsll.vi      v12, v4, 4              \n\t"
+            "vsll.vi      v13, v5, 4              \n\t"
+            "vsll.vi      v14, v6, 4              \n\t"
+            "vsll.vi      v15, v7, 4              \n\t"
+            "vnot.v       v0, v0                  \n\t"
+
+            "vsrl.vi      v12, v12, 6             \n\t"
+            "vsrl.vi      v13, v13, 6             \n\t"
+            "vsrl.vi      v14, v14, 6             \n\t"
+            "vsrl.vi      v15, v15, 6             \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v2, v12, i8        \n\t"
+            "vmadot       v18, v2, v13, i8        \n\t"
+            "vmadot       v20, v2, v14, i8        \n\t"
+            "vmadot       v22, v2, v15, i8        \n\t"
+
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v12, v16, v18, 2        \n\t"
+            "vpack.vv     v14, v20, v22, 2        \n\t"
+            "vpack.vv     v16, v12, v14, 3        \n\t"  // N0-N31 in v16
+            "vpack.vv     v18, v13, v15, 3        \n\t"
+
+            // apply B int8 scales (-32 bias has been applyed)
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vwadd.vx     v21, v8, x0             \n\t"  // int8 -> int16
+
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vwadd.vx     v23, v21, x0            \n\t"  // int8 -> int16
+
+            // static_cast<int32_t>(qsum) * b_scale;
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmacc.vv     v24, v16, v23           \n\t"
+            "vmacc.vv     v25, v17, v23           \n\t"
+            "vmacc.vv     v26, v18, v23           \n\t"
+            "vmacc.vv     v27, v19, v23           \n\t"
+
+            //K32-47
+            // load B scales (32 bytes per K16, 16 times => 512B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vslidedown.vi  v8, v8, 4             \n\t"
+
+            // load B hmask chunk (64B per K16, 16 times => 1024B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+
+            // unpack 2-bit qs + hmask -> signed values
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vsll.vi      v12, v4, 2              \n\t"
+            "vsll.vi      v13, v5, 2              \n\t"
+            "vsll.vi      v14, v6, 2              \n\t"
+            "vsll.vi      v15, v7, 2              \n\t"
+            "vnot.v       v0, v0                  \n\t"
+
+            "vsrl.vi      v12, v12, 6             \n\t"
+            "vsrl.vi      v13, v13, 6             \n\t"
+            "vsrl.vi      v14, v14, 6             \n\t"
+            "vsrl.vi      v15, v15, 6             \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v3, v12, i8        \n\t"
+            "vmadot       v18, v3, v13, i8        \n\t"
+            "vmadot       v20, v3, v14, i8        \n\t"
+            "vmadot       v22, v3, v15, i8        \n\t"
+
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v12, v16, v18, 2        \n\t"
+            "vpack.vv     v14, v20, v22, 2        \n\t"
+            "vpack.vv     v16, v12, v14, 3        \n\t"  // N0-N31 in v16
+            "vpack.vv     v18, v13, v15, 3        \n\t"
+
+            // apply B int8 scales (-32 bias has been applyed)
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vwadd.vx     v21, v8, x0             \n\t"  // int8 -> int16
+
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vwadd.vx     v23, v21, x0            \n\t"  // int8 -> int16
+
+            // static_cast<int32_t>(qsum) * b_scale;
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmacc.vv     v24, v16, v23           \n\t"
+            "vmacc.vv     v25, v17, v23           \n\t"
+            "vmacc.vv     v26, v18, v23           \n\t"
+            "vmacc.vv     v27, v19, v23           \n\t"
+
+            // K48-63
+            // load B scales (32 bytes per K16, 16 times => 512B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vslidedown.vi  v8, v8, 4             \n\t"
+
+            // load B hmask chunk (64B per K16, 16 times => 1024B)
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s5)                \n\t"
+            "addi         s5, s5, 64              \n\t"
+
+            // load A data (16 bytes per K16, 16 times => 256B)
+            "vsetvli      t0, x0, e64, m1         \n\t"
+            "vslidedown.vi  v3, v3, 8             \n\t"
+
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vnot.v       v0, v0                  \n\t"
+            "vsrl.vi      v12, v4, 6              \n\t"
+            "vsrl.vi      v13, v5, 6              \n\t"
+            "vsrl.vi      v14, v6, 6              \n\t"
+            "vsrl.vi      v15, v7, 6              \n\t"
+
+            "vsetvli      t0, x0, e8, m4          \n\t"
+            "vadd.vi      v12, v12, -4, v0.t      \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v3, v12, i8        \n\t"
+            "vmadot       v18, v3, v13, i8        \n\t"
+            "vmadot       v20, v3, v14, i8        \n\t"
+            "vmadot       v22, v3, v15, i8        \n\t"
+
+            "vsetvli      t0, x0, e16, m1         \n\t"
+            "vpack.vv     v12, v16, v18, 2        \n\t"
+            "vpack.vv     v14, v20, v22, 2        \n\t"
+            "vpack.vv     v16, v12, v14, 3        \n\t"  // N0-N31 in v16
+            "vpack.vv     v18, v13, v15, 3        \n\t"
+
+            // apply B int8 scales (-32 bias has been applyed)
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vwadd.vx     v21, v8, x0             \n\t"  // int8 -> int16
+
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vwadd.vx     v23, v21, x0            \n\t"  // int8 -> int16
+
+            // static_cast<int32_t>(qsum) * b_scale;
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vmacc.vv     v24, v16, v23           \n\t"
+            "vmacc.vv     v25, v17, v23           \n\t"
+            "vmacc.vv     v26, v18, v23           \n\t"
+            "vmacc.vv     v27, v19, v23           \n\t"
+
+            "addi         t3, t3, -1              \n\t"
+            "bgtz         t3, K64_LPST%=          \n\t"
+            "K64_LPND%=:                          \n\t"
+
+            // ========== K256 superblock complete, apply scale factors ==========
+            // Load A's 4 row scale factors (FP32)
+            "flw          f0, (s2)                \n\t"
+            "flw          f1, 4(s2)               \n\t"
+            "flw          f2, 8(s2)               \n\t"
+            "flw          f3, 12(s2)              \n\t"
+            "add          s2, s2, %[A_STR]        \n\t"  // Advance to next superblock
+            "add          t4, s7, %[B_STR]        \n\t"  // t4 = next B block address
+            "addi         s3, s2, (4+32)*4        \n\t"
+
+            // Load B FP16 global scale factors (32 cols)
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vle16.v      v8, (s6)                \n\t"
+
+            // Update B pointers to next block
+            "addi         s5, t4, 32*16           \n\t"
+            "mv           s4, t4                  \n\t"
+            "addi         s6, s5, 32*32           \n\t"
+            "addi         s7, t4, 0               \n\t"
+
+            // ========== Type conversion and final scaling ==========
+            // FP16 → FP32
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vfwcvt.f.f.v v9, v8                 \n\t"
+
+            // INT32 → FP32
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vfcvt.f.x.v  v24, v24                \n\t"
+            "vfcvt.f.x.v  v25, v25                \n\t"
+            "vfcvt.f.x.v  v26, v26                \n\t"
+            "vfcvt.f.x.v  v27, v27                \n\t"
+
+            // Compute a_scale * b_scale (4 rows)
+            "vfmul.vf     v12, v9, f0             \n\t"
+            "vfmul.vf     v13, v9, f1             \n\t"
+            "vfmul.vf     v14, v9, f2             \n\t"
+            "vfmul.vf     v15, v9, f3             \n\t"
+
+            // Final accumulation: result += qsum * a_scale * b_scale
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vfmacc.vv    v28, v12, v24           \n\t"
+            "vfmacc.vv    v29, v13, v25           \n\t"
+            "vfmacc.vv    v30, v14, v26           \n\t"
+            "vfmacc.vv    v31, v15, v27           \n\t"
+
+            // Prepare for next K superblock
+            "addi         t2, t2, -1              \n\t"
+            "vxor.vv      v24, v0, v0             \n\t"  // Clear K256 accumulator
+            "vxor.vv      v25, v0, v0             \n\t"
+            "vxor.vv      v26, v0, v0             \n\t"
+            "vxor.vv      v27, v0, v0             \n\t"
+            "li           t3, 4                   \n\t"
+            "bgtz         t2, BLK_LPST%=          \n\t"
+
+            "BLK_LPND%=:                          \n\t"
+
+            // ========== Store results (4 rows × 32 cols) ==========
+            "mv           t5, %[pC]               \n\t"
+            "vsetvli      t0, %[NBLKS], e32, m1   \n\t"
+            "vse32.v      v28, (%[pC])            \n\t"
+            "add          t5, t5, %[LDC]          \n\t"
+            "vse32.v      v29, (t5)               \n\t"
+            "add          t5, t5, %[LDC]          \n\t"
+            "vse32.v      v30, (t5)               \n\t"
+            "add          t5, t5, %[LDC]          \n\t"
+            "vse32.v      v31, (t5)               \n\t"
+            "add          t5, t5, %[LDC]          \n\t"
+            "FUNC_END%=:                          \n\t"
+
+            :
+            : [KBLKS] "r"(k_blks), [NBLKS] "r"(nb_real), [pA] "r"(quant_a_ptr), [pB] "r"(quant_b_blk_data),
+              [pC] "r"(c_ptr), [B_STR] "r"(b_ncol_block_stride), [A_STR] "r"(a_nrow_block_stride), [LDC] "r"(ldc * 4)
+            : "cc", "memory", "t0", "t2", "t3", "t4", "t5", "f0", "f1", "f2", "f3", "s2", "s3", "s4", "s5", "s6", "s7");
+    }
+}
+
+void gemm_kernel_i8i4_m1(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc) {
+    if (quant_b_zp == NULL) {
+        for (size_t n = 0; n < count_n; n += 32) {
+            size_t    nblks         = (count_n - n) > 32 ? 32 : count_n - n;
+            uint8_t * QuantBDataPtr = (uint8_t *) quant_b_data +      //
+                                      n * k_blks * blk_len / 2 +      // b data
+                                      n * k_blks * sizeof(_Float16);  // scale
+            float * CPtr = c_ptr + n;
+            size_t  cnt  = k_blks;
+
+            // A format Version_1 (FP32 SCALE FOR Normal VMADOTins of IME2)
+            // A M1K32 int8    256bit
+            // Ascale fp32 * 1  32bit
+            // || scl*1(fp32) | Asum(int16) | blk0 || scl*1(fp32) | Asum(int16) | blk0 || ...
+            // || Element                          || Element                          || ...
+            // B format
+            // B N8K32 int4    1024bit
+            //   4VRF, N32K32, 4096bit
+            // Bscale fp16 * N32 512bit;
+            // || scl*32..(fp16) | blk0 blk1 ... blk31 || scl*32..(fp16) | blk0 blk1 ... blk31 || ...
+            // || Element                              || Element                              || ...
+#if 0
+            //bias always be nullptr
+            __asm__ volatile(
+
+                // t3 = k/32
+                "mv           t3, %[BCK]              \n\t"
+                "mv           t4, %[NBLKS]            \n\t"
+                "mv           s2, %[pA]               \n\t"  // s2 = pASCL
+                "addi         s3, %[pA], 4+2          \n\t"  // s3 = pAData, (pA+AScl+ASum)
+                "mv           s4, %[pB]               \n\t"  // s4 = pBSCL
+                "addi         s5, %[pB], 32*2         \n\t"  // s5 = pBdata;
+                "mv           s6, %[pC]               \n\t"
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vxor.vv      v2, v0, v0              \n\t"  // clear acc
+
+                // ordinary vmadot: vle*6 flw*1 vecIns*21 vmadot*8
+                ".align 4                             \n\t"
+                "_K_LPST%=:                           \n\t"
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vl4r.v       v4, (s5)                \n\t"  // B Data 4VRF * 8Row * 32
+                "addi         s5, s5, 128*4+64        \n\t"  // 1024bit
+
+                "vsetvli      t0, x0, e8, mf2         \n\t"
+                "vle8.v       v0, (s4)                \n\t"  // B Scale 4VRF*8Row*FP16 = 512bit
+                "addi         s4, s4, 64+128*4        \n\t"
+
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vle8.v       v3, (s3)                \n\t"  // A Data M1*K32*int8 = 256bit
+                "addi         s3, s3, 32+6            \n\t"
+
+                "flw          f0, (s2)                \n\t"  // A Scale fp32
+                "lh           t2, 4(s2)               \n\t"  // A sum of int16
+                "addi         s2, s2, 6+32            \n\t"
+
+                "vsetvli      t0, zero, e8, m1        \n\t"
+                "vsrl.vi      v24, v3, 4              \n\t"
+
+                "vnpack4.vv   v8, v3, v3, 3           \n\t"  // lo4 of A
+                "vnpack4.vv   v10, v24, v24, 3        \n\t"  // hi4 of A
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vxor.vv      v16, v16, v16           \n\t"
+                "vxor.vv      v18, v16, v16           \n\t"
+                "vxor.vv      v20, v16, v16           \n\t"
+                "vxor.vv      v22, v16, v16           \n\t"
+
+                "vmadotsu     v16, v10, v4, i4        \n\t"  // M0 N0 - N7 INT32(256bit)
+                "vmadotsu     v18, v10, v5, i4        \n\t"  // M0 N8 - N15
+                "vmadotsu     v20, v10, v6, i4        \n\t"  // M0 N16 - N23
+                "vmadotsu     v22, v10, v7, i4        \n\t"  // M0 N24 - N31
+
+                "vsll.vi      v16, v16, 4             \n\t"
+                "vsll.vi      v18, v18, 4             \n\t"
+                "vsll.vi      v20, v20, 4             \n\t"
+                "vsll.vi      v22, v22, 4             \n\t"
+
+                "vmadotu      v16, v8, v4, i4         \n\t"
+                "vmadotu      v18, v8, v5, i4         \n\t"
+                "vmadotu      v20, v8, v6, i4         \n\t"
+                "vmadotu      v22, v8, v7, i4         \n\t"
+
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vmv.v.i      v28, 8                  \n\t"
+                "vpack.vv     v24, v16, v18, 2        \n\t"
+                "vpack.vv     v26, v20, v22, 2        \n\t"
+                "vpack.vv     v16, v24, v26, 3        \n\t"
+
+                "vwmul.vx     v24, v28, t2            \n\t"
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vadd.vv      v16, v16, v24           \n\t"
+
+                // b_scale fp16 -> fp32
+                "vsetvli      t0, x0, e16, mf2        \n\t"
+                "vfwcvt.f.f.v v24, v0                 \n\t"
+                // mac result i32 -> fp32
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vfcvt.f.x.v  v26, v16                \n\t"
+                // a_scale * b_scale;
+                "vfmul.vf     v1, v24, f0             \n\t"
+                // static_cast<float>(qsum) * a_scale * b_scale;
+                "vfmacc.vv    v2, v1, v26             \n\t"
+
+                "addi         t3, t3, -1              \n\t"
+                "bgtz         t3, _K_LPST%=           \n\t"
+                "_K_LPND%=:                           \n\t"
+
+                //-----------------------------------------
+                // STORE Equal 32N-------------------------
+                "_ST32%=:                             \n\t"
+                "vsetvli      t0, t4, e32, m1         \n\t"
+                "vse32.v      v2, (s6)                \n\t"  // M0 [N0 : N32]; FP32(1024bit)
+
+                "_FUNC_END%=:                         \n\t"
+
+                :
+                : [BCK] "r"(cnt), [NBLKS] "r"(nblks), [pA] "r"(quant_a_ptr), [pB] "r"(QuantBDataPtr), [pC] "r"(CPtr)
+                : "cc", "t0", "t2", "t3", "t4", "f0", "s2", "s3", "s4", "s5", "s6");
+#else
+            __asm__ volatile(
+
+                // t3 = k/32
+                "mv           t3, %[BCK]              \n\t"
+                "mv           t4, %[NBLKS]            \n\t"
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vmv.v.i      v0, 1                   \n\t"  // init the scale
+                "mv           s2, %[pA]               \n\t"  // s2 = pASCL
+                "addi         s3, %[pA], 4+2          \n\t"  // s3 = pAData, (pA+AScl+ASum)
+                "mv           s4, %[pB]               \n\t"  // s4 = pBSCL
+                "addi         s5, %[pB], 32*2         \n\t"  // s5 = pBdata;
+                "mv           s6, %[pC]               \n\t"
+
+                "vsll.vi      v1, v0, 4               \n\t"
+                "vxor.vv      v2, v0, v0              \n\t"  // clear acc
+                "vfcvt.f.x.v  v0, v0                  \n\t"
+                "vfcvt.f.x.v  v1, v1                  \n\t"
+
+                // vmadot hp: vle*7 flw*1 vecIns*14 vmadot*8
+                ".align 4                             \n\t"
+                "_K_LPST%=:                           \n\t"
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vl4r.v       v4, (s5)                \n\t"  // B Data 4VRF * 8Row * 32
+                "addi         s5, s5, 128*4+64        \n\t"  // 1024bit
+
+                "vsetvli      t0, x0, e8, mf2         \n\t"
+                "vle8.v       v30, (s4)               \n\t"  // B Scale 4VRF*8Row*FP16 = 512bit
+                "addi         s4, s4, 64+128*4        \n\t"
+
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vle8.v       v3, (s3)                \n\t"  // A Data M1*K32*int8 = 256bit
+                "addi         s3, s3, 32+6            \n\t"
+
+                "flw          f0, (s2)                \n\t"  // A Scale fp32
+                "lh           t2, 4(s2)               \n\t"  // A sum of int16
+                "addi         s2, s2, 6+32            \n\t"
+
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vmv.v.i      v28, 8                  \n\t"  // Bzp u8 -> u16
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vsrl.vi      v24, v3, 4              \n\t"
+
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vmul.vx      v26, v28, t2            \n\t"  // asum*zp i16*i16
+                "vnpack4.vv   v8, v3, v3, 3           \n\t"  // lo4 of A
+                "vnpack4.vv   v10, v24, v24, 3        \n\t"  // hi4 of A
+
+                "vfcvt.f.x.v  v16, v26                \n\t"  // zp i16 -> fp16
+                "vadd.vi      v18, v16, 0             \n\t"
+                "vadd.vi      v20, v16, 0             \n\t"
+                "vadd.vi      v22, v16, 0             \n\t"
+
+                "vmadotsu.hp  v16, v10, v4, v1, 0, i4 \n\t"  // high 4
+                "vmadotsu.hp  v18, v10, v5, v1, 0, i4 \n\t"
+                "vmadotsu.hp  v20, v10, v6, v1, 0, i4 \n\t"
+                "vmadotsu.hp  v22, v10, v7, v1, 0, i4 \n\t"
+                "vmadotu.hp   v16, v8, v4, v0, 0, i4  \n\t"  // low 4
+                "vmadotu.hp   v18, v8, v5, v0, 0, i4  \n\t"
+                "vmadotu.hp   v20, v8, v6, v0, 0, i4  \n\t"
+                "vmadotu.hp   v22, v8, v7, v0, 0, i4  \n\t"
+
+                "vpack.vv     v24, v16, v18, 1        \n\t"
+                "vpack.vv     v26, v20, v22, 1        \n\t"
+                "vpack.vv     v16, v24, v26, 2        \n\t"
+
+                "vsetvli      t0, x0, e16, mf2        \n\t"
+                // mac result * b_scale; f16*f16->f32
+                "vfwmul.vv     v31, v30, v16          \n\t"
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                // static_cast<float>(qsum * b_scale) * a_scale;
+                "vfmacc.vf    v2, f0, v31             \n\t"
+
+                "addi         t3, t3, -1              \n\t"
+                "bgtz         t3, _K_LPST%=           \n\t"
+                "_K_LPND%=:                           \n\t"
+
+                //-----------------------------------------
+                // STORE Equal 32N-------------------------
+                "_ST32%=:                             \n\t"
+                "vsetvli      t0, t4, e32, m1         \n\t"
+                "vse32.v      v2, (s6)                \n\t"  // M0 [N0 : N32]; FP32(1024bit)
+
+                "_FUNC_END%=:                         \n\t"
+
+                :
+                : [BCK] "r"(cnt), [NBLKS] "r"(nblks), [pA] "r"(quant_a_ptr), [pB] "r"(QuantBDataPtr), [pC] "r"(CPtr)
+                : "cc", "t0", "t2", "t3", "t4", "f0", "s2", "s3", "s4", "s5", "s6");
+
+#endif
+        }
+    } else {
+        for (size_t n = 0; n < count_n; n += 32) {
+            size_t    nblks         = (count_n - n) > 32 ? 32 : count_n - n;
+            uint8_t * QuantBDataPtr = (uint8_t *) quant_b_data +      //
+                                      n * k_blks * blk_len / 2 +      // b data
+                                      n * k_blks * sizeof(uint8_t) +  // b zp
+                                      n * k_blks * sizeof(_Float16);  // scale
+            float * CPtr = c_ptr + n;
+            size_t  cnt  = k_blks;
+
+            // A format Version_1 (FP32 SCALE FOR Normal VMADOTins of IME2)
+            // A M1K32 int8    256bit
+            // Ascale fp32 * 1  32bit
+            // || scl*1(fp32) | Asum(int16) | blk0 || scl*1(fp32) | Asum(int16) | blk0 || ...
+            // || Element                          || Element                          || ...
+            // B format
+            // B N8K32 int4    1024bit
+            //   4VRF, N32K32, 4096bit
+            // Bscale fp16 * N32 512bit;
+            // Bzp uint8_t * N32 256bit;
+            // || scl*32..(fp16) | zp*32(uint8) | blk0 blk1 ... blk31 || scl*32..(fp16)  ...
+            // || Element                                             || Element         ...
+
+            //bias always be nullptr
+#if 0
+            __asm__ volatile(
+
+                // t3 = k/32
+                "mv           t3, %[BCK]              \n\t"
+                "mv           t4, %[NBLKS]            \n\t"
+                "mv           s2, %[pA]               \n\t"  // s2 = pASCL
+                "addi         s3, %[pA], 4+2          \n\t"  // s3 = pAData, (pA+AScl+ASum)
+                "mv           s4, %[pB]               \n\t"  // s4 = pBSCL
+                "addi         s5, %[pB], 32*3         \n\t"  // s5 = pBdata, (pB+BScl+Bzp)
+                "mv           s6, %[pC]               \n\t"
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vxor.vv      v2, v0, v0              \n\t"  // clear acc
+
+                // ordinary vmadot: vle*6 flw*1 vecIns*21 vmadot*8
+                ".align 4                             \n\t"
+                "_K_LPST%=:                           \n\t"
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vl4r.v       v4, (s5)                \n\t"  // B Data 4VRF * 8Row * 32
+                "addi         s5, s5, 128*4+96        \n\t"  // 1024bit
+
+                "vsetvli      t0, x0, e8, mf2         \n\t"
+                "vle8.v       v0, (s4)                \n\t"  // B Scale 4VRF*8Row*FP16 = 512bit
+                "addi         s4, s4, 64              \n\t"
+
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vle8.v       v3, (s3)                \n\t"  // A Data M1*K32*int8 = 256bit
+                "addi         s3, s3, 32+6            \n\t"
+
+                "flw          f0, (s2)                \n\t"  // A Scale fp32
+                "lh           t2, 4(s2)               \n\t"  // A sum of int16
+                "addi         s2, s2, 6+32            \n\t"
+
+                "vsetvli      t0, zero, e8, m1        \n\t"
+                "vsrl.vi      v24, v3, 4              \n\t"
+
+                "vnpack4.vv   v8, v3, v3, 3           \n\t"  // lo4 of A
+                "vnpack4.vv   v10, v24, v24, 3        \n\t"  // hi4 of A
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vxor.vv      v16, v16, v16           \n\t"
+                "vxor.vv      v18, v16, v16           \n\t"
+                "vxor.vv      v20, v16, v16           \n\t"
+                "vxor.vv      v22, v16, v16           \n\t"
+
+                "vmadotsu     v16, v10, v4, i4        \n\t"  // M0 N0 - N7 INT32(256bit)
+                "vmadotsu     v18, v10, v5, i4        \n\t"  // M0 N8 - N15
+                "vmadotsu     v20, v10, v6, i4        \n\t"  // M0 N16 - N23
+                "vmadotsu     v22, v10, v7, i4        \n\t"  // M0 N24 - N31
+
+                "vsll.vi      v16, v16, 4             \n\t"
+                "vsll.vi      v18, v18, 4             \n\t"
+                "vsll.vi      v20, v20, 4             \n\t"
+                "vsll.vi      v22, v22, 4             \n\t"
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vle8.v       v1, (s4)                \n\t"  // Bzp
+                "addi         s4, s4, 32+128*4        \n\t"
+
+                "vmadotu      v16, v8, v4, i4         \n\t"
+                "vmadotu      v18, v8, v5, i4         \n\t"
+                "vmadotu      v20, v8, v6, i4         \n\t"
+                "vmadotu      v22, v8, v7, i4         \n\t"
+
+                "vwaddu.vx    v28, v1, x0             \n\t"  // uint8 -> uint16
+                "vpack.vv     v24, v16, v18, 2        \n\t"
+                "vpack.vv     v26, v20, v22, 2        \n\t"
+                "vpack.vv     v16, v24, v26, 3        \n\t"
+
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vwmul.vx     v24, v28, t2            \n\t"
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vadd.vv      v16, v16, v24           \n\t"
+
+                // b_scale fp16 -> fp32
+                "vsetvli      t0, x0, e16, mf2        \n\t"
+                "vfwcvt.f.f.v v24, v0                 \n\t"
+                // mac result i32 -> fp32
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vfcvt.f.x.v  v26, v16                \n\t"
+                // a_scale * b_scale;
+                "vfmul.vf     v1, v24, f0             \n\t"
+                // static_cast<float>(qsum) * a_scale * b_scale;
+                "vfmacc.vv    v2, v1, v26             \n\t"
+
+                "addi         t3, t3, -1              \n\t"
+                "bgtz         t3, _K_LPST%=           \n\t"
+                "_K_LPND%=:                           \n\t"
+
+                //-----------------------------------------
+                // STORE Equal 32N-------------------------
+                "_ST32%=:                             \n\t"
+                "vsetvli      t0, t4, e32, m1         \n\t"
+                "vse32.v      v2, (s6)                \n\t"  // M0 [N0 : N32]; FP32(1024bit)
+
+                "_FUNC_END%=:                         \n\t"
+
+                :
+                : [BCK] "r"(cnt), [NBLKS] "r"(nblks), [pA] "r"(quant_a_ptr), [pB] "r"(QuantBDataPtr), [pC] "r"(CPtr)
+                : "cc", "t0", "t2", "t3", "t4", "f0", "s2", "s3", "s4", "s5", "s6");
+#else
+            __asm__ volatile(
+
+                // t3 = k/32
+                "mv           t3, %[BCK]              \n\t"
+                "mv           t4, %[NBLKS]            \n\t"
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vmv.v.i      v0, 1                   \n\t"  // init the scale
+                "mv           s2, %[pA]               \n\t"  // s2 = pASCL
+                "addi         s3, %[pA], 4+2          \n\t"  // s3 = pAData, (pA+AScl+ASum)
+                "mv           s4, %[pB]               \n\t"  // s4 = pBSCL
+                "addi         s5, %[pB], 32*3         \n\t"  // s5 = pBdata, (pB+BScl+Bzp)
+                "mv           s6, %[pC]               \n\t"
+
+                "vsll.vi      v1, v0, 4               \n\t"
+                "vxor.vv      v2, v0, v0              \n\t"  // clear acc
+                "vfcvt.f.x.v  v0, v0                  \n\t"
+                "vfcvt.f.x.v  v1, v1                  \n\t"
+
+                // vmadot hp: vle*6 flw*1 vecIns*14 vmadot*8
+                ".align 4                             \n\t"
+                "_K_LPST%=:                           \n\t"
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vl4r.v       v4, (s5)                \n\t"  // B Data 4VRF * 8Row * 32
+                "addi         s5, s5, 128*4+96        \n\t"  // 1024bit
+
+                "vsetvli      t0, x0, e8, mf2         \n\t"
+                "vle8.v       v30, (s4)               \n\t"  // B Scale 4VRF*8Row*FP16 = 512bit
+                "addi         s4, s4, 64              \n\t"
+
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vle8.v       v31, (s4)               \n\t"  // B zp 32Row*uint8 = 256bit
+                "addi         s4, s4, 32+128*4        \n\t"
+
+                "vle8.v       v3, (s3)                \n\t"  // A Data M1*K32*int8 = 256bit
+                "addi         s3, s3, 32+6            \n\t"
+
+                "flw          f0, (s2)                \n\t"  // A Scale fp32
+                "lh           t2, 4(s2)               \n\t"  // A sum of int16
+                "addi         s2, s2, 6+32            \n\t"
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vsrl.vi      v24, v3, 4              \n\t"
+
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vnpack4.vv   v8, v3, v3, 3           \n\t"  // lo4 of A
+                "vnpack4.vv   v10, v24, v24, 3        \n\t"  // hi4 of A
+
+                "vxor.vv      v16, v16, v16           \n\t"
+                "vxor.vv      v18, v16, v16           \n\t"
+                "vxor.vv      v20, v16, v16           \n\t"
+                "vxor.vv      v22, v16, v16           \n\t"
+
+                "vmadotsu.hp  v16, v10, v4, v1, 0, i4 \n\t"  // high 4
+                "vmadotsu.hp  v18, v10, v5, v1, 0, i4 \n\t"
+                "vmadotsu.hp  v20, v10, v6, v1, 0, i4 \n\t"
+                "vmadotsu.hp  v22, v10, v7, v1, 0, i4 \n\t"
+                "vmadotu.hp   v16, v8, v4, v0, 0, i4  \n\t"  // low 4
+                "vmadotu.hp   v18, v8, v5, v0, 0, i4  \n\t"
+                "vmadotu.hp   v20, v8, v6, v0, 0, i4  \n\t"
+                "vmadotu.hp   v22, v8, v7, v0, 0, i4  \n\t"
+
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vwaddu.vx    v28, v31, x0            \n\t"  // Bzp u8 -> u16
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vpack.vv     v24, v16, v18, 1        \n\t"
+                "vpack.vv     v26, v20, v22, 1        \n\t"
+                "vpack.vv     v16, v24, v26, 2        \n\t"
+
+                "vsetvli      t0, x0, e16, mf2        \n\t"
+                "vmul.vx      v26, v28, t2            \n\t"  // asum*zp i16*i16
+                "vfwcvt.f.f.v v22, v30                \n\t"  // b_scale fp16 -> fp32
+                "vfcvt.f.x.v  v18, v26                \n\t"  // zp i16 -> fp16
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vfwadd.vv    v20, v18, v16           \n\t"
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                // mac result * b_scale; f32*f32->f32
+                "vfmul.vv     v31, v22, v20           \n\t"
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                // static_cast<float>(qsum * b_scale) * a_scale;
+                "vfmacc.vf    v2, f0, v31             \n\t"
+
+                "addi         t3, t3, -1              \n\t"
+                "bgtz         t3, _K_LPST%=           \n\t"
+                "_K_LPND%=:                           \n\t"
+
+                //-----------------------------------------
+                // STORE Equal 32N-------------------------
+                "_ST32%=:                             \n\t"
+                "vsetvli      t0, t4, e32, m1         \n\t"
+                "vse32.v      v2, (s6)                \n\t"  // M0 [N0 : N32]; FP32(1024bit)
+
+                "_FUNC_END%=:                         \n\t"
+
+                :
+                : [BCK] "r"(cnt), [NBLKS] "r"(nblks), [pA] "r"(quant_a_ptr), [pB] "r"(QuantBDataPtr), [pC] "r"(CPtr)
+                : "cc", "t0", "t2", "t3", "t4", "f0", "s2", "s3", "s4", "s5", "s6");
+#endif
+        }
+    }
+}
+
+void gemm_kernel_i8i4_hp_m1(size_t          blk_len,
+                            const uint8_t * quant_a_ptr,
+                            const uint8_t * quant_b_data,
+                            const uint8_t * quant_b_zp,
+                            float *         c_ptr,
+                            size_t          count_m,
+                            size_t          count_n,
+                            size_t          k_blks,
+                            size_t          ldc) {
+    constexpr size_t NB_COLS                = 32;
+    constexpr size_t k_subblks_per_superblk = 8;
+
+    struct block_q4_0x32_layout {
+        _Float16 d[NB_COLS];
+        uint8_t  qs[16 * NB_COLS];
+    };
+
+    GGML_ASSERT(blk_len == 256);
+
+    const size_t b_superblk_stride = sizeof(block_q4_0x32_layout) * k_subblks_per_superblk +
+                                     (quant_b_zp ? NB_COLS * k_subblks_per_superblk * sizeof(uint8_t) : 0);
+    const size_t b_tile_stride = k_blks * b_superblk_stride;
+
+    if (quant_b_zp == NULL) {
+        for (size_t ni = 0; ni < count_n; ni += 32) {
+            uint8_t * b_data = (uint8_t *) quant_b_data + (ni / NB_COLS) * b_tile_stride;
+            int8_t *  a_data = (int8_t *) quant_a_ptr;
+            float *   dst_c  = c_ptr + ni;
+
+            asm volatile(
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vxor.vv        v31, v31, v31           \n\t"  // init acc to zero
+                "mv             t4, %[BK]               \n\t"
+                "li             t0, 0x4c00              \n\t"  // 16 in fp16
+                "fmv.h.x        fa0, t0                 \n\t"
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                "li             t5, 8                   \n\t"
+                "addi           t6, %[A], 288           \n\t"  // point to blk scale
+                "flh            ft1, (t6)               \n\t"
+                "addi           t6, %[A], 272           \n\t"  // point to asum
+
+                // init the acc fp16
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vxor.vv        v16, v18, v18           \n\t"
+                "vxor.vv        v17, v18, v18           \n\t"
+                "vxor.vv        v18, v18, v18           \n\t"
+                "vxor.vv        v19, v18, v18           \n\t"
+
+                "INNER_BLK_LOOP%=:                      \n\t"
+                // load a sum and scale
+                "flh            fa1, (t6)               \n\t"
+                "addi           t6, t6, 2               \n\t"
+                "flh            ft0, (%[A])             \n\t"
+                "addi           %[A], %[A], 2           \n\t"
+                // load A
+                "vsetvli        t0, x0, e8, mf4         \n\t"
+                "vle8.v         v3, (%[A])              \n\t"  // 1x32@i8
+                "addi           %[A], %[A], 32          \n\t"
+
+                // load scale B and B
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vle16.v        v8, (%[B])              \n\t"  // b_scale fp16
+                "addi           %[B], %[B], 64          \n\t"
+                "vl4r.v         v4, (%[B])              \n\t"  // 32*32@i4
+                "addi           %[B], %[B], 512         \n\t"
+                "vfmul.vf       v8, v8, ft0             \n\t"  // scale b * scale a
+                "vfmul.vf       v9, v8, fa0             \n\t"
+                "vfmul.vf       v10, v8, fa1            \n\t"  // scale b * scale a * asm
+                "vfwmacc.vf     v31, ft1, v10           \n\t"  // asum * scale a * scale b * blk scale
+
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vpack.vv       v0, v8, v9, 3           \n\t"
+                "vsrl.vi        v28, v3, 4              \n\t"
+
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vnpack4.vv     v2, v3, v3, 3           \n\t"  // lo4 of A
+                "vnpack4.vv     v3, v28, v28, 3         \n\t"  // hi4 of A
+
+                // i4 * i4 vmadot
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vmadotsu.hp    v16, v3, v4, v0, 4, i4  \n\t"  // high 4
+                "vmadotsu.hp    v17, v3, v5, v0, 5, i4  \n\t"
+                "vmadotsu.hp    v18, v3, v6, v0, 6, i4  \n\t"
+                "vmadotsu.hp    v19, v3, v7, v0, 7, i4  \n\t"
+                "vmadotu.hp     v16, v2, v4, v0, 0, i4  \n\t"  // low 4
+                "vmadotu.hp     v17, v2, v5, v0, 1, i4  \n\t"
+                "vmadotu.hp     v18, v2, v6, v0, 2, i4  \n\t"
+                "vmadotu.hp     v19, v2, v7, v0, 3, i4  \n\t"
+
+                "addi           t5, t5, -1              \n\t"
+                "bgtz           t5, INNER_BLK_LOOP%=    \n\t"
+
+                "vpack.vv       v8, v16, v17, 1         \n\t"
+                "vpack.vv       v12, v18, v19, 1        \n\t"
+                "vpack.vv       v20, v8, v12, 2         \n\t"
+
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "addi           t4, t4, -1              \n\t"
+                "vfwmacc.vf     v31, ft1, v20           \n\t"
+                //"vsetvli        t0, x0, e32, m1         \n\t"
+                //"vfmul.vf       v31, v31, ft1           \n\t"  // blk scale
+
+                // update A ptr
+                "addi           %[A], t6, 2             \n\t"
+
+                "bgtz           t4, BLK_LOOP%=          \n\t"
+
+                // save
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vse32.v        v31, (%[DST])           \n\t"
+                : [A] "+r"(a_data), [B] "+r"(b_data)
+                : [DST] "r"(dst_c), [BK] "r"(k_blks)
+                : "t0", "t1", "t2", "t3", "t4", "t5", "t6", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9",
+                  "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23",
+                  "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "fa0", "fa1", "ft0", "ft1");
+        }
+    } else {
+        // TODO: support quant_b_zp for i8i4 hp kernel
+        GGML_ABORT("gemm_kernel_i8i4_hp_m1 with quant_b_zp is not supported yet");
+    }
+}
+
+void gemm_kernel_i8i4_m4(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc) {
+    int64_t b_data_stride =
+        k_blks * (sizeof(ggml_fp16_t) + 16 * sizeof(int8_t) + (quant_b_zp != NULL ? sizeof(int8_t) : 0));
+    if (quant_b_zp == NULL) {
+        for (size_t ni = 0; ni < count_n; ni += 32) {
+            uint8_t * b_data = (uint8_t *) quant_b_data + ni * b_data_stride;
+            int8_t *  a_data = (int8_t *) quant_a_ptr;
+            float *   dst_c  = c_ptr + ni;
+#if 0
+            asm volatile(
+                "li             t1,  8              \n\t"
+                "vsetvli        t0, x0, e32, m1     \n\t"
+                "vxor.vv        v28, v28, v28       \n\t"
+                "vxor.vv        v29, v29, v29       \n\t"
+                "vxor.vv        v30, v30, v30       \n\t"
+                "vxor.vv        v31, v31, v31       \n\t"
+                "mv             t4, %[BK]           \n\t"
+
+                ".align 4                           \n\t"
+                "BLK_LOOP%=:                        \n\t"
+                // load scale A
+                "flw            fa0, (%[A])         \n\t"
+                "flw            fa1, 4(%[A])        \n\t"
+                "flw            fa2, 8(%[A])        \n\t"
+                "flw            fa3, 12(%[A])       \n\t"
+                "addi           %[A], %[A], 16      \n\t"
+
+                // load scale B
+                "vsetvli        t0, x0, e16, mf2    \n\t"
+                "vle16.v        v12, (%[B])         \n\t"
+                "addi           %[B], %[B], 64      \n\t"
+                "vfwcvt.f.f.v   v14, v12            \n\t"
+
+                "vsetivli       t0, 4, e16, mf2     \n\t"
+                "vle16.v        v8, (%[A])          \n\t"  // asum
+                "addi           %[A], %[A], 8       \n\t"
+                "vwmul.vx       v10, v8, t1         \n\t"  // 8*asum
+
+                "vsetvli        t0, x0, e8, m1      \n\t"
+                "vl1r.v         v0, (%[A])          \n\t"
+                "addi           %[A], %[A], 128     \n\t"  // 4*32@i8
+                "vl4r.v         v4, (%[B])          \n\t"  // 32*32@i4
+                "addi           %[B], %[B], 512     \n\t"
+                "vsrl.vi        v1, v0, 4           \n\t"
+                "vnpack4.vv     v12, v0, v1, 3      \n\t"  // A low  u4
+                "vupack.vv      v2, v12, v12, 2     \n\t"
+
+                // init the accumu to asum * zp
+                "vsetvli        t0, x0, e32, m1     \n\t"
+                "vxor.vv        v16, v16, v16       \n\t"
+                "vxor.vv        v18, v16, v16       \n\t"
+                "vxor.vv        v20, v16, v16       \n\t"
+                "vxor.vv        v22, v16, v16       \n\t"
+
+                // i4 * i4 vmadot
+                "vsetvli        t0, x0, e32, m1     \n\t"
+                "vmadotsu       v16, v3, v4, i4     \n\t"   // high 4
+                "vmadotsu       v18, v3, v5, i4     \n\t"
+                "vmadotsu       v20, v3, v6, i4     \n\t"
+                "vmadotsu       v22, v3, v7, i4     \n\t"
+                "vsll.vi        v16, v16, 4         \n\t"
+                "vsll.vi        v18, v18, 4         \n\t"
+                "vsll.vi        v20, v20, 4         \n\t"
+                "vsll.vi        v22, v22, 4         \n\t"
+                "vmadotu        v16, v2, v4, i4     \n\t"   // low 4
+                "vmadotu        v18, v2, v5, i4     \n\t"
+                "vmadotu        v20, v2, v6, i4     \n\t"
+                "vmadotu        v22, v2, v7, i4     \n\t"
+
+                "vpack.vv       v0, v16, v18, 2     \n\t"
+                "vpack.vv       v2, v20, v22, 2     \n\t"
+                "vpack.vv       v16, v0, v2, 3      \n\t"
+                "vpack.vv       v18, v1, v3, 3      \n\t"
+
+                "vrgather.vi    v0, v10, 0          \n\t"
+                "vrgather.vi    v1, v10, 1          \n\t"
+                "vrgather.vi    v2, v10, 2          \n\t"
+                "vrgather.vi    v3, v10, 3          \n\t"
+
+                "vadd.vv        v16, v16, v0        \n\t"
+                "vadd.vv        v17, v17, v1        \n\t"
+                "vadd.vv        v18, v18, v2        \n\t"
+                "vadd.vv        v19, v19, v3        \n\t"
+
+                "vfcvt.f.x.v    v16, v16            \n\t"
+                "vfcvt.f.x.v    v17, v17            \n\t"
+                "vfcvt.f.x.v    v18, v18            \n\t"
+                "vfcvt.f.x.v    v19, v19            \n\t"
+
+                // mul scale
+                "vfmul.vv       v16, v16, v14       \n\t"
+                "vfmul.vv       v17, v17, v14       \n\t"
+                "vfmul.vv       v18, v18, v14       \n\t"
+                "vfmul.vv       v19, v19, v14       \n\t"
+
+                "addi           t4, t4, -1          \n\t"
+                "vfmacc.vf      v28, fa0, v16       \n\t"
+                "vfmacc.vf      v29, fa1, v17       \n\t"
+                "vfmacc.vf      v30, fa2, v18       \n\t"
+                "vfmacc.vf      v31, fa3, v19       \n\t"
+
+                "bgtz           t4, BLK_LOOP%=      \n\t"
+
+                // save
+                "vsetvli        t0, x0, e32, m1     \n\t"
+                "add            t2, %[LDC], %[DST]  \n\t"
+                "vse32.v        v28, (%[DST])       \n\t"
+                "add            t3, %[LDC], t2      \n\t"
+                "vse32.v        v29, (t2)           \n\t"
+                "add            t2, %[LDC], t3      \n\t"
+                "vse32.v        v30, (t3)           \n\t"
+                "vse32.v        v31, (t2)           \n\t"
+                : [A] "+r"(a_data), [B] "+r"(b_data)
+                : [DST] "r"(dst_c), [LDC] "r"(ldc*4), [BK] "r"(k_blks)
+                : "t0", "t1", "t2", "t3", "t4", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11",
+                  "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
+                  "v26", "v27", "v28", "v29", "v30", "v31", "fa0", "fa1", "fa2", "fa3");
+#else
+            asm volatile(
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vxor.vv        v28, v28, v28           \n\t"
+                "vxor.vv        v29, v29, v29           \n\t"
+                "vxor.vv        v30, v30, v30           \n\t"
+                "vxor.vv        v31, v31, v31           \n\t"
+                "vmv.v.i        v0, 1                   \n\t"  // init the scale
+                "vsll.vi        v1, v0, 4               \n\t"
+                "vfcvt.f.x.v    v0, v0                  \n\t"
+                "vfcvt.f.x.v    v1, v1                  \n\t"
+                "mv             t4, %[BK]               \n\t"
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                // load scale A
+                "flw            fa0, (%[A])             \n\t"
+                "flw            fa1, 4(%[A])            \n\t"
+                "flw            fa2, 8(%[A])            \n\t"
+                "flw            fa3, 12(%[A])           \n\t"
+                "addi           %[A], %[A], 16          \n\t"
+
+                // load scale B
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vle16.v        v12, (%[B])             \n\t"
+                "addi           %[B], %[B], 64          \n\t"
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vpack.vv       v14, v12, v12, 3        \n\t"
+
+                "vsetivli       t0, 4, e16, mf2         \n\t"
+                "vle16.v        v8, (%[A])              \n\t"  // asum
+                "addi           %[A], %[A], 8           \n\t"
+                "vsll.vi        v8, v8, 3               \n\t"  // asum * 8
+                "vfcvt.f.x.v    v9, v8                  \n\t"
+                "vsetvli        t0, x0, e64, m1         \n\t"
+                "vrgather.vi    v10, v9, 0              \n\t"
+
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vl1r.v         v16, (%[A])             \n\t"
+                "addi           %[A], %[A], 128         \n\t"  // 4*32@i8
+                "vl4r.v         v4, (%[B])              \n\t"  // 32*32@i4
+                "addi           %[B], %[B], 512         \n\t"
+                "vsrl.vi        v17, v16, 4             \n\t"
+                "vnpack4.vv     v12, v16, v17, 3        \n\t"  // A low  u4
+                "vupack.vv      v2, v12, v12, 2         \n\t"
+
+                // init the accumu to asum * zp
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vpack.vv       v16, v10, v10,0         \n\t"
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vpack.vv       v20, v16, v16,0         \n\t"
+                "vsetvli        t0, x0, e64, m1         \n\t"
+                "vpack.vv       v18, v20, v20, 0        \n\t"
+                "vor.vv         v20, v18, v18           \n\t"
+                "vor.vv         v21, v18, v18           \n\t"
+
+                // i4 * i4 vmadot
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vmadotsu.hp    v18, v3, v4, v1, 0, i4  \n\t"  // high 4
+                "vmadotsu.hp    v19, v3, v5, v1, 0, i4  \n\t"
+                "vmadotsu.hp    v20, v3, v6, v1, 0, i4  \n\t"
+                "vmadotsu.hp    v21, v3, v7, v1, 0, i4  \n\t"
+                "vmadotu.hp     v18, v2, v4, v0, 0, i4  \n\t"  // low 4
+                "vmadotu.hp     v19, v2, v5, v0, 0, i4  \n\t"
+                "vmadotu.hp     v20, v2, v6, v0, 0, i4  \n\t"
+                "vmadotu.hp     v21, v2, v7, v0, 0, i4  \n\t"
+
+                "vpack.vv       v8, v18, v19, 1         \n\t"
+                "vpack.vv       v12, v20, v21, 1        \n\t"
+                "vpack.vv       v20, v8, v12, 2         \n\t"
+
+                "vfwmul.vv      v16, v20, v14           \n\t"
+                "vfwmul.vv      v18, v21, v14           \n\t"
+
+                "vsetvli        t0, x0, e32, m1         \n\t"
+
+                "addi           t4, t4, -1              \n\t"
+                "vfmacc.vf      v28, fa0, v16           \n\t"
+                "vfmacc.vf      v29, fa1, v17           \n\t"
+                "vfmacc.vf      v30, fa2, v18           \n\t"
+                "vfmacc.vf      v31, fa3, v19           \n\t"
+
+                "bgtz           t4, BLK_LOOP%=          \n\t"
+
+                // save
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "add            t2, %[LDC], %[DST]      \n\t"
+                "vse32.v        v28, (%[DST])           \n\t"
+                "add            t3, %[LDC], t2          \n\t"
+                "vse32.v        v29, (t2)               \n\t"
+                "add            t2, %[LDC], t3          \n\t"
+                "vse32.v        v30, (t3)               \n\t"
+                "vse32.v        v31, (t2)               \n\t"
+                : [A] "+r"(a_data), [B] "+r"(b_data)
+                : [DST] "r"(dst_c), [LDC] "r"(ldc * 4), [BK] "r"(k_blks)
+                : "t0", "t1", "t2", "t3", "t4", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10",
+                  "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24",
+                  "v25", "v26", "v27", "v28", "v29", "v30", "v31", "fa0", "fa1", "fa2", "fa3");
+#endif
+        }
+    } else {
+        for (size_t ni = 0; ni < count_n; ni += 32) {
+            uint8_t * b_data = (uint8_t *) quant_b_data + ni * b_data_stride;
+            int8_t *  a_data = (int8_t *) quant_a_ptr;
+            float *   dst_c  = c_ptr + ni;
+
+            asm volatile(
+                "li             t1,  8          \n\t"
+                "vsetvli        t0, x0, e32, m1 \n\t"
+                "vxor.vv        v28, v28, v28   \n\t"
+                "vxor.vv        v29, v29, v29   \n\t"
+                "vxor.vv        v30, v30, v30   \n\t"
+                "vxor.vv        v31, v31, v31   \n\t"
+                "mv             t4, %[BK]       \n\t"
+
+                ".align 4                        \n\t"
+                "BLK_LOOP%=:                     \n\t"
+                // load scale A
+                "flw            fa0, (%[A])     \n\t"
+                "flw            fa1, 4(%[A])    \n\t"
+                "flw            fa2, 8(%[A])    \n\t"
+                "flw            fa3, 12(%[A])   \n\t"
+                "addi           %[A], %[A], 16  \n\t"
+
+                // load scale B
+                "vsetvli        t0, x0, e16, mf2\n\t"
+                "vle16.v        v12, (%[B])     \n\t"
+                "addi           %[B], %[B], 64  \n\t"
+                "vfwcvt.f.f.v   v14, v12        \n\t"
+
+                // load zp
+                "vsetvli        t0, x0, e8, mf4 \n\t"
+                "vle8.v         v8, (%[B])      \n\t"
+                "addi           %[B], %[B], 32  \n\t"
+                "vwaddu.vx      v10, v8, x0     \n\t"
+
+                // load a sum
+                "lh             s1, (%[A])      \n\t"
+                "lh             s2, 2(%[A])     \n\t"
+                "lh             s3, 4(%[A])     \n\t"
+                "lh             s4, 6(%[A])     \n\t"
+                "addi           %[A], %[A], 8   \n\t"
+
+                "vsetvli        t0, x0, e8, m1  \n\t"
+                "vl1r.v         v0, (%[A])      \n\t"
+                "addi           %[A], %[A], 128 \n\t"  // 4*32@i8
+                "vl4r.v         v4, (%[B])      \n\t"  // 32*32@i4
+                "addi           %[B], %[B], 512 \n\t"
+                "vsrl.vi        v1, v0, 4       \n\t"
+                "vnpack4.vv     v12, v0, v1, 3  \n\t"  // A low  u4
+                "vupack.vv      v2, v12, v12, 2 \n\t"
+
+                // init the accumu to asum * zp
+                "vsetvli        t0, x0, e32, m1 \n\t"
+                "vxor.vv        v16, v16, v16   \n\t"
+                "vxor.vv        v18, v16, v16   \n\t"
+                "vxor.vv        v20, v16, v16   \n\t"
+                "vxor.vv        v22, v16, v16   \n\t"
+
+                // i4 * i4 vmadot
+                "vsetvli        t0, x0, e32, m1 \n\t"
+                "vmadotsu       v16, v3, v4, i4 \n\t"  // high 4
+                "vmadotsu       v18, v3, v5, i4 \n\t"
+                "vmadotsu       v20, v3, v6, i4 \n\t"
+                "vmadotsu       v22, v3, v7, i4 \n\t"
+                "vsll.vi        v16, v16, 4     \n\t"
+                "vsll.vi        v18, v18, 4     \n\t"
+                "vsll.vi        v20, v20, 4     \n\t"
+                "vsll.vi        v22, v22, 4     \n\t"
+                "vmadotu        v16, v2, v4, i4 \n\t"  // low 4
+                "vmadotu        v18, v2, v5, i4 \n\t"
+                "vmadotu        v20, v2, v6, i4 \n\t"
+                "vmadotu        v22, v2, v7, i4 \n\t"
+
+                "vpack.vv       v0, v16, v18, 2 \n\t"
+                "vpack.vv       v2, v20, v22, 2 \n\t"
+                "vpack.vv       v16, v0, v2, 3  \n\t"
+                "vpack.vv       v18, v1, v3, 3  \n\t"
+
+                "vsetvli        t0, x0, e16, m1 \n\t"
+                "vwmul.vx       v0, v10, s1     \n\t"
+                "vwmul.vx       v2, v10, s2     \n\t"
+                "vwmul.vx       v4, v10, s3     \n\t"
+                "vwmul.vx       v6, v10, s4     \n\t"
+
+                "vsetvli        t0, x0, e32, m1 \n\t"
+                "vadd.vv        v16, v16, v0    \n\t"
+                "vadd.vv        v17, v17, v2    \n\t"
+                "vadd.vv        v18, v18, v4    \n\t"
+                "vadd.vv        v19, v19, v6    \n\t"
+
+                "vfcvt.f.x.v    v16, v16        \n\t"
+                "vfcvt.f.x.v    v17, v17        \n\t"
+                "vfcvt.f.x.v    v18, v18        \n\t"
+                "vfcvt.f.x.v    v19, v19        \n\t"
+
+                // mul scale
+                "vfmul.vv       v16, v16, v14   \n\t"
+                "vfmul.vv       v17, v17, v14   \n\t"
+                "vfmul.vv       v18, v18, v14   \n\t"
+                "vfmul.vv       v19, v19, v14   \n\t"
+
+                "addi           t4, t4, -1      \n\t"
+                "vfmacc.vf      v28, fa0, v16   \n\t"
+                "vfmacc.vf      v29, fa1, v17   \n\t"
+                "vfmacc.vf      v30, fa2, v18   \n\t"
+                "vfmacc.vf      v31, fa3, v19   \n\t"
+
+                "bgtz           t4, BLK_LOOP%=  \n\t"
+
+                // save
+                "vsetvli        t0, x0, e32, m1 \n\t"
+                "add            t2, %[LDC], %[DST]\n\t"
+                "vse32.v        v28, (%[DST])   \n\t"
+                "add            t3, %[LDC], t2  \n\t"
+                "vse32.v        v29, (t2)       \n\t"
+                "add            t2, %[LDC], t3  \n\t"
+                "vse32.v        v30, (t3)       \n\t"
+                "vse32.v        v31, (t2)       \n\t"
+                : [A] "+r"(a_data), [B] "+r"(b_data)
+                : [DST] "r"(dst_c), [LDC] "r"(ldc * 4), [BK] "r"(k_blks)
+                : "t0", "t1", "t2", "t3", "t4", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10",
+                  "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24",
+                  "v25", "v26", "v27", "v28", "v29", "v30", "v31", "fa0", "fa1", "fa2", "fa3", "s1", "s2", "s3", "s4");
+        }
+    }
+}
+
+void gemm_kernel_i8i4_hp_m4(size_t          blk_len,
+                            const uint8_t * quant_a_ptr,
+                            const uint8_t * quant_b_data,
+                            const uint8_t * quant_b_zp,
+                            float *         c_ptr,
+                            size_t          count_m,
+                            size_t          count_n,
+                            size_t          k_blks,
+                            size_t          ldc) {
+    constexpr size_t NB_COLS                = 32;
+    constexpr size_t K_SUBBLKS_PER_SUPERBLK = 8;
+    constexpr size_t K_SUBBLK_LEN           = 32;
+
+    struct block_q4_0x32_layout {
+        _Float16 d[NB_COLS];
+        uint8_t  qs[16 * NB_COLS];
+    };
+
+    GGML_ASSERT(blk_len == 256);
+    GGML_ASSERT(count_m >= 4);
+
+    // Contract:
+    // - computes a 4-row x 32-col tile per inner invocation
+    // - A is q8 HP packed in m4 layout, one logical K256 block at a time
+    // - B is q4 HP packed in N32 tiles, optionally with a separate zp area
+    // - tail-N is currently not handled here; the caller must provide full N32 tiles
+
+    const size_t b_superblk_stride = sizeof(block_q4_0x32_layout) * K_SUBBLKS_PER_SUPERBLK +
+                                     (quant_b_zp ? NB_COLS * K_SUBBLKS_PER_SUPERBLK * sizeof(uint8_t) : 0);
+    const size_t b_tile_stride       = k_blks * b_superblk_stride;
+    const size_t a_nrow_block_stride = q8_hp_blk_size(blk_len, true, true) * 4;
+    const size_t a_subblk_stride     = q8_hp_blk_size(K_SUBBLK_LEN, false, false) * 4;
+
+    if (quant_b_zp != nullptr) {
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            const size_t nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+            if (nb_real != NB_COLS) {
+                break;
+            }
+
+            uint8_t * b_tile_base = (uint8_t *) quant_b_data + (ni / NB_COLS) * b_tile_stride;
+            uint8_t * a_block     = (uint8_t *) quant_a_ptr;
+            float *   dst_c       = c_ptr + ni;
+
+            // Data layout summary for the with-zp path.
+            //
+            // A: M4 x K256 q8 HP block
+            //   - split into 8 x K32 subblocks
+            //   - each K32 subblock is 136B:
+            //       8B   = 4 x fp16 row scales
+            //       128B = 4 x int8[32] row payloads
+            //   - trailer after 8 subblocks is 72B:
+            //       4 rows x fp16[8] a_sum values, indexed as [row][ksi]
+            //       4 rows x fp16 scale_avg tail
+            //
+            // B: N32 x K256 q4 HP block with explicit zp area
+            //   - each K32 subblock is 576B:
+            //       64B  = fp16 scale[32]
+            //       512B = packed q4 payload for 32 columns x 32 k-elements
+            //   - zp is stored separately, not interleaved with the 576B payload block
+            //   - one K256 superblock is laid out as:
+            //       8 x (scale + qs) blocks = 4608B
+            //       8 x zp[32]              =  256B
+            //
+            // C: 4 rows x 32 fp32 outputs
+            //
+            // ASM pointer convention:
+            //   - t6: current A K32 subblock base
+            //   - t2: current A a_sum base for this ksi
+            //         row1/row2/row3 are at +16/+32/+48 bytes
+            //   - s5: current B (scale + qs) K32 subblock base
+            //   - s6: current B zp[32] base for this ksi
+            //
+            // Loop progression:
+            //   - per ksi: A += 136, a_sum += 2, B_data += 576, B_zp += 32
+            //   - per ki : skip the 72B A trailer and advance B to the next 4864B superblock
+
+            const _Float16 hp_scale_16   = (_Float16) 16.0f;
+            const _Float16 hp_scale_1    = (_Float16) 1.0f;
+            const _Float16 hp_scale_0125 = (_Float16) 0.125f;
+
+            // VPR grouping used below:
+            // - v4-v7   : B q4 payload for N32 split as 4 x N8 groups
+            // - v8/v10  : zp u8 / widened fp16
+            // - v12     : B fp16 scale[32]
+            // - v14-v15 : packed (Bscale * Ascale) for rows [0,1] / [2,3]
+            // - v16-v19 : temporary per-row scaled B scales
+            // - v28-v31 : final fp32 accumulators for rows 0..3
+
+            asm volatile(
+                "mv             t5, %[BK]                 \n\t"
+                "mv             t6, %[A]                  \n\t"
+                "mv             s5, %[B]                  \n\t"
+                "vsetvli        t0, x0, e32, m1           \n\t"
+                "vxor.vv        v28, v28, v28             \n\t"
+                "vxor.vv        v29, v29, v29             \n\t"
+                "vxor.vv        v30, v30, v30             \n\t"
+                "vxor.vv        v31, v31, v31             \n\t"
+                "li             t4, 8                     \n\t"
+                "li             t1, 4608                  \n\t"
+                "addi           t2, t6, 1088              \n\t"  // 8 * 136B A K32 subblocks, a_sum trailer starts here
+                "add            s6, s5, t1                \n\t"  // 8 * 576B B(scale+qs), zp area starts here
+
+                ".align 4                                 \n\t"
+                "_BLK_LPST%=:                             \n\t"
+                "flh            fa1, 64(t2)               \n\t"  // a_scale_avg_row[0]
+                "vsetvli        t0, x0, e32, m1           \n\t"
+                "vxor.vv        v18, v30, v30             \n\t"
+                "vxor.vv        v19, v31, v31             \n\t"
+                "vxor.vv        v20, v30, v30             \n\t"
+                "vxor.vv        v21, v31, v31             \n\t"
+                "_KsubBLK_LPST%=:                         \n\t"
+                // load first subblock scales for 4 rows
+                "flh            fa0,   0(t6)              \n\t"  // ascale_fp16
+
+                // load B fp16 scales[32]
+                "vsetvli        t0, x0, e16, mf2          \n\t"
+                "vle16.v        v12, (s5)                 \n\t"
+
+                // load Bzp[32] for the current ksi from the dedicated zp area
+                "vsetvli        t0, x0, e8, mf4           \n\t"
+                "vle8.v         v8, (s6)                  \n\t"
+
+                "fmul.h         fa2, fa0, %[HP16]         \n\t"
+                "vfwcvt.f.xu.v  v10, v8                   \n\t"  // uint8 -> fp16
+
+                "vsetvli        t0, x0, e16, mf2          \n\t"
+                "vfmul.vf       v16, v12, fa0             \n\t"  // row0: Bscale * Ascale
+                "vfmul.vf       v17, v12, fa2             \n\t"
+
+                // load a_sum[row][ksi] from the trailer; t2 points to row0[ksi]
+                "flh            ft1, 0(t2)                \n\t"
+                "flh            ft2, 16(t2)               \n\t"
+                "flh            ft3, 32(t2)               \n\t"
+                "flh            ft4, 48(t2)               \n\t"
+
+                "fmul.h         ft1, ft1, %[HP0125]       \n\t"
+                "fmul.h         ft2, ft2, %[HP0125]       \n\t"
+                "fmul.h         ft3, ft3, %[HP0125]       \n\t"
+                "fmul.h         ft4, ft4, %[HP0125]       \n\t"
+
+                // load A payload from current K32 subblock and B q4 payload from current 576B block
+                "addi           t3, t6, 8                 \n\t"
+                "vsetvli        t0, x0, e8, m1            \n\t"
+                "vl1r.v         v0, (t3)                  \n\t"  //A
+                "addi           t3, s5, 64                \n\t"
+                "vl4r.v         v4, (t3)                  \n\t"  //B
+
+                "vsetvli        t0, x0, e8, m1            \n\t"
+                "vsrl.vi        v1, v0, 4                 \n\t"
+                "vnpack4.vv     v12, v0, v1, 3            \n\t"
+                "vpack.vv       v0, v17, v16, 3           \n\t"
+                "vupack.vv      v2, v12, v12, 2           \n\t"
+
+                "vsetvli        t0, x0, e16, mf2          \n\t"  // mf2 -> mf2
+                "vfmul.vv       v10, v10, v16             \n\t"  // zp * ascale * bscale; fp16*fp16
+
+                "vsetvli        t0, x0, e16, mf2          \n\t"  // mf2 -> m1
+                "vfmul.vf       v12, v10, ft1             \n\t"  // zp(1:n)* abscale * asum_m0; fp16*fp16
+                "vfmul.vf       v13, v10, ft2             \n\t"  // zp(1:n)* abscale * asum_m1; fp16*fp16
+                "vfmul.vf       v24, v10, ft3             \n\t"  // zp(1:n)* abscale * asum_m2; fp16*fp16
+                "vfmul.vf       v25, v10, ft4             \n\t"  // zp(1:n)* abscale * asum_m3; fp16*fp16
+
+                "vsetvli        t0, x0, e16, mf2           \n\t"
+                "vfwmacc.vf     v28, fa1, v12             \n\t"  // row0/1 accum += dot * packed scale
+                "vfwmacc.vf     v29, fa1, v13             \n\t"
+                "vfwmacc.vf     v30, fa1, v24             \n\t"
+                "vfwmacc.vf     v31, fa1, v25             \n\t"
+
+                "vsetvli        t0, x0, e32, m1           \n\t"
+                "vmadotsu.hp    v18, v3, v4, v0, 0, i4    \n\t"  //lo4;n0n7
+                "vmadotsu.hp    v19, v3, v5, v0, 1, i4    \n\t"  //lo4;n8n15
+                "vmadotsu.hp    v20, v3, v6, v0, 2, i4    \n\t"  //lo4;n16n23
+                "vmadotsu.hp    v21, v3, v7, v0, 3, i4    \n\t"  //lo4;n24n31
+                "vmadotu.hp     v18, v2, v4, v0, 4, i4    \n\t"  //hi4;n0n7
+                "vmadotu.hp     v19, v2, v5, v0, 5, i4    \n\t"  //hi4;n8n15
+                "vmadotu.hp     v20, v2, v6, v0, 6, i4    \n\t"  //hi4;n16n23
+                "vmadotu.hp     v21, v2, v7, v0, 7, i4    \n\t"  //hi4;n24n31
+
+                "addi           t4, t4, -1                \n\t"
+                "addi           t6, t6, 8+128             \n\t"  // next A K32 subblock
+                "addi           t2, t2, 2                 \n\t"  // next ksi entry in each a_sum row
+                "addi           s5, s5, 64+512            \n\t"  // next B (scale + qs) K32 block
+                "addi           s6, s6, 32                \n\t"  // next zp[32]
+                "bgtz           t4, _KsubBLK_LPST%=       \n\t"
+
+                "vsetvli        t0, x0, e16, m1           \n\t"
+                "vpack.vv       v8, v18, v19, 1           \n\t"  // 128(16*8)->256(16*16)
+                "vpack.vv       v12, v20, v21, 1          \n\t"
+                "vpack.vv       v26, v8, v12, 2           \n\t"  // 256(16*16)->512(16*32)
+
+                "vsetvli        t0, x0, e16, m1           \n\t"
+                "vfwmacc.vf     v28, fa1, v26             \n\t"  // row0/1 accum += dot * packed scale
+                "vfwmacc.vf     v30, fa1, v27             \n\t"
+
+                "li             t4, 8                     \n\t"
+                "addi           t5, t5, -1                \n\t"
+                "addi           t6, t6, 72                \n\t"  // skip A trailer after 8 subblocks and scale_avg tail
+                "mv             s5, s6                    \n\t"  // s6 already points to next B superblock base
+                "addi           t2, t6, 1088              \n\t"  // 8 * 136B A K32 subblocks, a_sum trailer starts here
+                "add            s6, s5, t1                \n\t"  // 8 * 576B B(scale+qs), zp area starts here
+                "bgtz           t5, _BLK_LPST%=           \n\t"
+
+                "_BLK_LPND%=:                             \n\t"
+                "vsetvli        t0, x0, e32, m1           \n\t"
+                "add            t2, %[LDC], %[DST]        \n\t"
+                "vse32.v        v28, (%[DST])             \n\t"
+                "add            t3, %[LDC], t2            \n\t"
+                "vse32.v        v29, (t2)                 \n\t"
+                "add            t2, %[LDC], t3            \n\t"
+                "vse32.v        v30, (t3)                 \n\t"
+                "vse32.v        v31, (t2)                 \n\t"
+                : [A] "+r"(a_block), [B] "+r"(b_tile_base)
+                : [DST] "r"(dst_c), [LDC] "r"(ldc * 4), [BK] "r"(k_blks), [HP16] "f"(hp_scale_16),
+                  [HP1] "f"(hp_scale_1), [HP0125] "f"(hp_scale_0125)
+                : "t0", "t1", "t2", "t3", "t4", "t5", "t6", "s5", "s6", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7",
+                  "v8", "v10", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v24",
+                  "v25", "v26", "v27", "v28", "v29", "v30", "v31", "fa0", "fa1", "fa2", "ft1", "ft2", "ft3", "ft4",
+                  "memory");
+        }
+        return;
+    } else {
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            const size_t nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+            if (nb_real != NB_COLS) {
+                break;
+            }
+
+            uint8_t * b_tile_base = (uint8_t *) quant_b_data + (ni / NB_COLS) * b_tile_stride;
+            uint8_t * a_block     = (uint8_t *) quant_a_ptr;
+            float *   dst_c       = c_ptr + ni;
+
+            // Data layout summary for the no-zp path.
+            //
+            // A layout is identical to the with-zp branch.
+            //
+            // B: N32 x K256 q4 HP block without explicit zp storage
+            //   - each K32 subblock is still 576B:
+            //       64B  = fp16 scale[32]
+            //       512B = packed q4 payload
+            //   - zp is implicit and treated as a constant value 8 in the kernel
+            //   - one K256 superblock therefore contains only:
+            //       8 x (scale + qs) blocks = 4608B
+            //
+            // C: 4 rows x 32 fp32 outputs
+            //
+            // ASM pointer convention:
+            //   - t6: current A K32 subblock base
+            //   - t2: current A a_sum base for this ksi
+            //   - s5: current B (scale + qs) K32 subblock base
+            //
+            // Loop progression:
+            //   - per ksi: A += 136, a_sum += 2, B_data += 576
+            //   - per ki : skip the 72B A trailer and advance B to the next 4608B superblock
+
+            const _Float16 hp_scale_16 = (_Float16) 16.0f;
+            const _Float16 hp_scale_1  = (_Float16) 1.0f;
+
+            // VPR grouping used below matches the with-zp path:
+            // - v4-v7   : B q4 payload for N32 split as 4 x N8 groups
+            // - v8/v10  : implicit zp lane / widened fp16
+            // - v12     : B fp16 scale[32]
+            // - v14-v15 : packed (Bscale * Ascale) for rows [0,1] / [2,3]
+            // - v16-v19 : temporary per-row scaled B scales
+            // - v28-v31 : final fp32 accumulators for rows 0..3
+
+            asm volatile(
+                "mv             t5, %[BK]                 \n\t"
+                "mv             t6, %[A]                  \n\t"
+                "mv             s5, %[B]                  \n\t"
+                "vsetvli        t0, x0, e32, m1           \n\t"
+                "vxor.vv        v28, v28, v28             \n\t"
+                "vxor.vv        v29, v29, v29             \n\t"
+                "vxor.vv        v30, v30, v30             \n\t"
+                "vxor.vv        v31, v31, v31             \n\t"
+                "li             t4, 8                     \n\t"
+                "addi           t2, t6, 1088              \n\t"  // 8 * 136B A K32 subblocks, a_sum trailer starts here
+
+                ".align 4                                 \n\t"
+                "_BLK_LPST%=:                             \n\t"
+                "flh            fa1, 64(t2)               \n\t"  // a_scale_avg_row[0]
+                "vsetvli        t0, x0, e32, m1           \n\t"
+                "vxor.vv        v18, v30, v30             \n\t"
+                "vxor.vv        v19, v31, v31             \n\t"
+                "vxor.vv        v20, v30, v30             \n\t"
+                "vxor.vv        v21, v31, v31             \n\t"
+                "_KsubBLK_LPST%=:                         \n\t"
+                // load first subblock scales for 4 rows
+                "flh            fa0,   0(t6)              \n\t"  // ascale_fp16
+
+                // load B fp16 scales[32]
+                "vsetvli        t0, x0, e16, mf2          \n\t"
+                "vle16.v        v12, (s5)                 \n\t"
+
+                "fmul.h         fa2, fa0, %[HP16]         \n\t"
+
+                "vsetvli        t0, x0, e16, mf2          \n\t"
+                "vfmul.vf       v16, v12, fa0             \n\t"  // row0: Bscale * Ascale
+                "vfmul.vf       v17, v12, fa2             \n\t"
+
+                // load a_sum[row][ksi] from the trailer; t2 points to row0[ksi]
+                "flh            ft1, 0(t2)                \n\t"
+                "flh            ft2, 16(t2)               \n\t"
+                "flh            ft3, 32(t2)               \n\t"
+                "flh            ft4, 48(t2)               \n\t"
+
+                // load A payload from current K32 subblock and B q4 payload from current 576B block
+                "addi           t3, t6, 8                 \n\t"
+                "vsetvli        t0, x0, e8, m1            \n\t"
+                "vl1r.v         v0, (t3)                  \n\t"  //A
+                "addi           t3, s5, 64                \n\t"
+                "vl4r.v         v4, (t3)                  \n\t"  //B
+
+                "vsetvli        t0, x0, e8, m1            \n\t"
+                "vsrl.vi        v1, v0, 4                 \n\t"
+                "vnpack4.vv     v12, v0, v1, 3            \n\t"
+                "vpack.vv       v0, v17, v16, 3           \n\t"
+                "vupack.vv      v2, v12, v12, 2           \n\t"
+
+                "vsetvli        t0, x0, e16, mf2          \n\t"  // mf2 -> m1
+                "vfmul.vf       v12, v16, ft1             \n\t"  // zp(1:n)* abscale * asum_m0; fp16*fp16
+                "vfmul.vf       v13, v16, ft2             \n\t"  // zp(1:n)* abscale * asum_m1; fp16*fp16
+                "vfmul.vf       v24, v16, ft3             \n\t"  // zp(1:n)* abscale * asum_m2; fp16*fp16
+                "vfmul.vf       v25, v16, ft4             \n\t"  // zp(1:n)* abscale * asum_m3; fp16*fp16
+
+                "vsetvli        t0, x0, e16, mf2          \n\t"
+                "vfwmacc.vf     v28, fa1, v12             \n\t"
+                "vfwmacc.vf     v29, fa1, v13             \n\t"
+                "vfwmacc.vf     v30, fa1, v24             \n\t"
+                "vfwmacc.vf     v31, fa1, v25             \n\t"
+
+                "vsetvli        t0, x0, e32, m1           \n\t"
+                "vmadotsu.hp    v18, v3, v4, v0, 0, i4    \n\t"  //lo4;n0n7
+                "vmadotsu.hp    v19, v3, v5, v0, 1, i4    \n\t"  //lo4;n8n15
+                "vmadotsu.hp    v20, v3, v6, v0, 2, i4    \n\t"  //lo4;n16n23
+                "vmadotsu.hp    v21, v3, v7, v0, 3, i4    \n\t"  //lo4;n24n31
+                "vmadotu.hp     v18, v2, v4, v0, 4, i4    \n\t"  //hi4;n0n7
+                "vmadotu.hp     v19, v2, v5, v0, 5, i4    \n\t"  //hi4;n8n15
+                "vmadotu.hp     v20, v2, v6, v0, 6, i4    \n\t"  //hi4;n16n23
+                "vmadotu.hp     v21, v2, v7, v0, 7, i4    \n\t"  //hi4;n24n31
+
+                "addi           t4, t4, -1                \n\t"
+
+                "addi           t6, t6, 8+128             \n\t"  // next A K32 subblock
+                "addi           t2, t2, 2                 \n\t"  // next ksi entry in each a_sum row
+                "addi           s5, s5, 64+512            \n\t"  // next B (scale + qs) K32 block
+                "bgtz           t4, _KsubBLK_LPST%=       \n\t"
+
+                "vsetvli        t0, x0, e16, m1           \n\t"  //N32in1register
+                "vpack.vv       v8, v18, v19, 1           \n\t"  // 128(16*8)->256(16*16)
+                "vpack.vv       v12, v20, v21, 1          \n\t"
+                "vpack.vv       v26, v8, v12, 2           \n\t"  // 256(16*16)->512(16*32)
+
+                "vsetvli        t0, x0, e16, m1           \n\t"
+                "vfwmacc.vf     v28, fa1, v26             \n\t"  // row0/1 accum += dot * packed scale
+                "vfwmacc.vf     v30, fa1, v27             \n\t"
+
+                "li             t4, 8                     \n\t"
+                "addi           t5, t5, -1                \n\t"
+                "addi           t6, t6, 72                \n\t"  // skip A trailer after 8 subblocks and scale_avg tail
+                // s5 already points to next B superblock base
+                "addi           t2, t6, 1088              \n\t"  // 8 * 136B A K32 subblocks, a_sum trailer starts here
+                "bgtz           t5, _BLK_LPST%=           \n\t"
+
+                "_BLK_LPND%=:                             \n\t"
+                "vsetvli        t0, x0, e32, m1           \n\t"
+                "add            t2, %[LDC], %[DST]        \n\t"
+                "vse32.v        v28, (%[DST])             \n\t"
+                "add            t3, %[LDC], t2            \n\t"
+                "vse32.v        v29, (t2)                 \n\t"
+                "add            t2, %[LDC], t3            \n\t"
+                "vse32.v        v30, (t3)                 \n\t"
+                "vse32.v        v31, (t2)                 \n\t"
+                : [A] "+r"(a_block), [B] "+r"(b_tile_base)
+                : [DST] "r"(dst_c), [LDC] "r"(ldc * 4), [BK] "r"(k_blks), [HP16] "f"(hp_scale_16), [HP1] "f"(hp_scale_1)
+                : "t0", "t2", "t3", "t4", "t5", "t6", "s5", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v10",
+                  "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v24", "v25", "v26",
+                  "v27", "v28", "v29", "v30", "v31", "fa0", "fa1", "fa2", "ft1", "ft2", "ft3", "ft4", "memory");
+        }
+        return;
+    }
+}
+
+void gemm_kernel_i8mxfp4_m1(size_t          blk_len,
+                            const uint8_t * quant_a_ptr,
+                            const uint8_t * quant_b_data,
+                            const uint8_t * quant_b_zp,
+                            float *         c_ptr,
+                            size_t          count_m,
+                            size_t          count_n,
+                            size_t          k_blks,
+                            size_t          ldc) {
+    constexpr size_t NB_COLS = 32;
+    constexpr size_t K_TILE  = 32;
+    using blk_type           = nrow_block_mxfp4<NB_COLS>;
+
+    GGML_ASSERT(blk_len == K_TILE);
+    GGML_ASSERT(count_m == 1);
+    GGML_UNUSED(quant_b_zp);
+
+    const size_t a_blk_stride  = q8_blk_size(blk_len, true);
+    const size_t b_blk_stride  = sizeof(blk_type);
+    const size_t b_tile_stride = k_blks * b_blk_stride;
+
+    if (quant_b_zp == NULL) {
+        for (size_t n = 0; n < count_n; n += 32) {
+            size_t    nblks         = (count_n - n) > 32 ? 32 : count_n - n;
+            // MXFP4 no-zp: per column per k-block stride = scale_e8m0(1B) + qs(16B) + qh(4B) = 21B
+            uint8_t * QuantBDataPtr = (uint8_t *) quant_b_data +     //
+                                      n * k_blks * (blk_len / 8) +   // qh sign/high-bit mask: n×k_blks×4
+                                      n * k_blks * blk_len / 2 +     // qs packed 4-bit magnitudes: n×k_blks×16
+                                      n * k_blks * sizeof(uint8_t);  // scale: n×k_blks×1
+            float * CPtr = c_ptr + n;
+            size_t  cnt  = k_blks;
+
+            // A format (q8 block with per-block scale and stored sum field):
+            //   || scl(fp32,4B) | asum(int16,2B) | data(int8,32B) || × k_blks
+            //
+            // Register map:
+            //   t3 = k_blks loop counter   t4 = nblks (tail)
+            //   f0 = A scale (fp32)
+            //   s2 = pA (scale/asum)       s3 = pA data
+            //   s4 = pB scales (u8×32)
+            //   s5 = pB qh (sign/high-bit mask, 128B)
+            //   s6 = pB qs (packed 4-bit magnitudes, 512B)
+            //   s7 = pC
+            //   v3  = fp32 accumulator (N32)
+            //   v2  = B scales u8 (loaded as bytes; later widened)
+            //   v0  = qh mask bytes (also used as v0.t mask after load)
+            //   v1  = A int8 (K32)
+            //   v8..v15 / v16..v23 = qs unpack/pack temporaries (build signed vmadot lanes)
+            //   v24/v26/v28/v30    = int32 dot accumulators & packing temps
+
+            __asm__ volatile(
+                "mv           t3, %[BCK]              \n\t"  // t3 = k_blks
+                "mv           t4, %[NBLKS]            \n\t"  // t4 = nblks (tail guard)
+
+                // ---- pre-loop: init fp16 constants in e16 m1 context ----
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vmv.v.i      v0, 1                   \n\t"  // v0 = int16(1)
+                "vfcvt.f.x.v  v0, v0                  \n\t"  // v0  = 1.0_fp16
+                "vxor.vv      v3, v16, v16            \n\t"
+
+                // ---- pointer setup ----
+                "mv           s2, %[pA]               \n\t"  // s2 = pA (scale, fp32)
+                "addi         s3, %[pA], 4+2          \n\t"  // s3 = pA data (skip scale+asum)
+                "mv           s4, %[pB]               \n\t"  // s4 = pBSCL
+                "addi         s5, %[pB], 32           \n\t"  // s5 = pBh  (pB + 32B scale)
+                "addi         s6, %[pB], 32+128       \n\t"  // s6 = pBs  (pB + 32 + 128 = pB+192)
+                "mv           s7, %[pC]               \n\t"  // s7 = pC
+
+                // =====================================================================
+                // K-block loop: each iteration processes one N32×K32 block
+                // Stride per k-block = 672B = 32(scl) + 512(Bs) + 128(Bh)
+                // =====================================================================
+                ".align 4                             \n\t"
+                "BLK_LPST%=:                          \n\t"
+
+                // ---- load qs (512B = 4 VRF) from s6, advance s6 by 672 ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vl4r.v       v8, (s6)                \n\t"  // v8..v11 = qs N32K32 packed 4-bit magnitudes
+                "addi         s6, s6, 128*4+128+32    \n\t"  // s6 += 672 (512+128+32)
+
+                // ---- load B scale (32B = 32×u8) from s4, advance s4 by 672 ----
+                "vsetvli      t0, x0, e8, mf2         \n\t"
+                "vle8.v       v2, (s4)                \n\t"  // v2 = scale_u8 × 32
+                "addi         s4, s4, 32+128*4+128    \n\t"  // s4 += 672 (32+512+128)
+
+                // ---- load qh (128B = 1 VRF) from s5, advance s5 by 672 ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vle8.v       v0, (s5)                \n\t"  // v0 = qh N32K32 sign/high-bit packed
+                "addi         s5, s5, 128+32+128*4    \n\t"  // s5 += 672 (128+32+512)
+
+                // ---- load A data (32B = K32 int8) from s3 ----
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vle8.v       v1, (s3)                \n\t"  // v1 = A M1K32 int8
+                "addi         s3, s3, 32+6            \n\t"  // s3 += 38 (data + scl + asum)
+
+                // ---- load A scale (fp32) and asum (int16) from s2 ----
+                "flw          f0, (s2)                \n\t"  // f0 = A scale (fp32)
+                "addi         s2, s2, 6+32            \n\t"  // s2 += 38
+
+                // ---- Decode packed MXFP4 payload into a vmadot-friendly signed-lane layout ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vand.vi      v12, v8, 0xF            \n\t"  //8bit(lo4) //[8*32]
+                "vand.vi      v13, v9, 0xF            \n\t"
+                "vand.vi      v14, v10, 0xF           \n\t"
+                "vand.vi      v15, v11, 0xF           \n\t"
+                "vsrl.vi      v8, v8, 4               \n\t"  //8bit(hi4)
+                "vsrl.vi      v9, v9, 4               \n\t"
+                "vsrl.vi      v10, v10, 4             \n\t"
+                "vsrl.vi      v11, v11, 4             \n\t"
+
+                // [4*32]*2
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vpack.vv     v16, v12, v8, 0         \n\t"
+                "vpack.vv     v18, v13, v9, 0         \n\t"
+                "vpack.vv     v20, v14, v10, 0        \n\t"
+                "vpack.vv     v22, v15, v11, 0        \n\t"
+
+                "vsetvli      t0, x0, e8, m8          \n\t"
+                "vrsub.vi     v16, v16, 0, v0.t       \n\t"
+
+                // [4*32]*2 -> [8*16]
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vupack.vv    v8, v16, v17, 1         \n\t"
+                "vupack.vv    v10, v18, v19, 1        \n\t"
+                "vupack.vv    v12, v20, v21, 1        \n\t"
+                "vupack.vv    v14, v22, v23, 1        \n\t"
+
+                "vsetvli      t0, x0, e64, m1         \n\t"
+                "vslidedown.vi  v16, v1, 2            \n\t"
+
+                // init the accumu to 0
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vxor.vv      v24, v16, v16           \n\t"
+                "vxor.vv      v26, v16, v16           \n\t"
+                "vxor.vv      v28, v16, v16           \n\t"
+                "vxor.vv      v30, v16, v16           \n\t"
+
+                // ---- int8 dot products over the decoded MXFP4 lane groups ----
+                "vmadot       v24, v1, v8, i8         \n\t"  // N0..7
+                "vmadot       v26, v1, v10, i8        \n\t"  // N8..15
+                "vmadot       v28, v1, v12, i8        \n\t"  // N16..23
+                "vmadot       v30, v1, v14, i8        \n\t"  // N24..31
+                "vmadot       v24, v16, v9, i8        \n\t"  // N0..7
+                "vmadot       v26, v16, v11, i8       \n\t"  // N8..15
+                "vmadot       v28, v16, v13, i8       \n\t"  // N16..23
+                "vmadot       v30, v16, v15, i8       \n\t"  // N24..31
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vpack.vv     v16, v24, v26, 2        \n\t"  // v16 = N0..15
+                "vpack.vv     v18, v28, v30, 2        \n\t"  // v18 = N16..31
+                "vpack.vv     v24, v16, v18, 3        \n\t"  // v24 = N0..31
+
+                "lui          t1, 0x00200             \n\t"
+                "vmv.v.x      v30, t1                 \n\t"
+                // b_scale e8m0 -> fp32
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vwaddu.vx    v28, v2, x0             \n\t"
+                "vsetvli      t0, x0, e16, mf2        \n\t"
+                "vwadd.vx     v2, v28, x0             \n\t"
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vmsle.vi     v0, v2, 1               \n\t"
+                "vadd.vi      v28, v2, -1             \n\t"
+                "vsll.vi      v28, v28, 23            \n\t"
+                "vsll.vv      v28, v30, v2, v0.t      \n\t"
+
+                // a_scale * b_scale;
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vfcvt.f.x.v  v26, v24                \n\t"
+                "vfmul.vf     v30, v28, f0            \n\t"
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                // static_cast<float>(qsum) * a_scale * b_scale;
+                "vfmacc.vv    v3, v30, v26            \n\t"
+
+                "addi         t3, t3, -1              \n\t"
+                "bgtz         t3, BLK_LPST%=          \n\t"
+                "BLK_LPND%=:                          \n\t"
+                "vsetvli      t0, %[NBLKS], e32, m1   \n\t"
+                "vse32.v      v3, (%[pC])             \n\t"
+                "FUNC_END%=:                          \n\t"
+
+                :
+                : [BCK] "r"(cnt), [NBLKS] "r"(nblks), [pA] "r"(quant_a_ptr), [pB] "r"(QuantBDataPtr), [pC] "r"(CPtr)
+                : "cc", "memory", "t0", "t1", "t2", "t3", "t4", "f0", "s2", "s3", "s4", "s5", "s6", "s7", "v0", "v1",
+                  "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v16", "v17", "v18", "v19",
+                  "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31");
+        }
+    }
+}
+
+void gemm_kernel_i8mxfp4_m4(size_t          blk_len,
+                            const uint8_t * quant_a_ptr,
+                            const uint8_t * quant_b_data,
+                            const uint8_t * quant_b_zp,
+                            float *         c_ptr,
+                            size_t          count_m,
+                            size_t          count_n,
+                            size_t          k_blks,
+                            size_t          ldc) {
+    constexpr size_t NB_COLS = 32;
+    constexpr size_t K_TILE  = 32;
+    using blk_type           = nrow_block_mxfp4<NB_COLS>;
+
+    GGML_ASSERT(blk_len == K_TILE);
+    GGML_ASSERT(count_m == 4);
+    GGML_UNUSED(quant_b_zp);
+
+    const size_t a_blk_stride  = q8_blk_size(blk_len, true);
+    const size_t b_blk_stride  = sizeof(blk_type);
+    const size_t b_tile_stride = k_blks * b_blk_stride;
+
+    if (quant_b_zp == NULL) {
+        // MXFP4 block layout per K32/N32 tile:
+        //   [scale_e8m0 x 32][qh sign/high-bit mask x 128B][qs packed 4-bit magnitudes x 512B]
+        // There is no explicit zp stream; qh is combined with qs to reconstruct signed MXFP4 values.
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            size_t    nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+            uint8_t * b_data  = (uint8_t *) quant_b_data + (ni / NB_COLS) * b_tile_stride;
+            uint8_t * a_data  = (uint8_t *) quant_a_ptr;
+            float *   dst_c   = c_ptr + ni;
+            size_t    cnt     = k_blks;
+
+            asm volatile(
+                // v4-v7 are the fp32 accumulators for rows 0..3 of the current N32 tile.
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v4, v4, v4              \n\t"
+                "vxor.vv        v5, v5, v5              \n\t"
+                "vxor.vv        v6, v6, v6              \n\t"
+                "vxor.vv        v7, v7, v7              \n\t"
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                // Load the 4 A-row scales for this K32 block and build row data pointers.
+                "flw            fa0, 0(%[A])            \n\t"
+                "flw            fa1, 4(%[A])            \n\t"
+                "flw            fa2, 8(%[A])            \n\t"
+                "flw            fa3, 12(%[A])           \n\t"
+                "addi           t3, %[A], 24            \n\t"
+                "addi           t4, t3, 32              \n\t"
+                "addi           t5, t3, 64              \n\t"
+                "addi           t6, t3, 96              \n\t"
+                "addi           %[A], %[A], 152         \n\t"
+
+                // B-side pointers:
+                //   t1 -> qh bitmask stream, t2 -> qs low-nibble stream.
+                "addi           t1, %[B], 32            \n\t"
+                "addi           t2, %[B], 160           \n\t"
+                "vsetvli        t0, x0, e8, mf2         \n\t"
+                "vle8.v         v2, (%[B])              \n\t"
+                "addi           %[B], %[B], 672         \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vle8.v         v0, (t1)                \n\t"
+                "vl4r.v         v8, (t2)                \n\t"
+
+                // Decode the packed MXFP4 payload once for the whole tile and expand it
+                // into a vmadot-friendly layout.
+                "vand.vi        v12, v8, 0xF            \n\t"
+                "vand.vi        v13, v9, 0xF            \n\t"
+                "vand.vi        v14, v10, 0xF           \n\t"
+                "vand.vi        v15, v11, 0xF           \n\t"
+                "vsrl.vi        v8, v8, 4               \n\t"
+                "vsrl.vi        v9, v9, 4               \n\t"
+                "vsrl.vi        v10, v10, 4             \n\t"
+                "vsrl.vi        v11, v11, 4             \n\t"
+
+                "vpack.vv       v16, v12, v8, 0         \n\t"
+                "vpack.vv       v18, v13, v9, 0         \n\t"
+                "vpack.vv       v20, v14, v10, 0        \n\t"
+                "vpack.vv       v22, v15, v11, 0        \n\t"
+
+                "vsetvli        t0, x0, e8, m8          \n\t"
+                "vrsub.vi       v16, v16, 0, v0.t       \n\t"
+
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vupack.vv      v8, v16, v17, 1         \n\t"
+                "vupack.vv      v10, v18, v19, 1        \n\t"
+                "vupack.vv      v12, v20, v21, 1        \n\t"
+                "vupack.vv      v14, v22, v23, 1        \n\t"
+
+                "lui            t1, 0x00200             \n\t"
+                "vmv.v.x        v30, t1                 \n\t"
+                // b_scale e8m0 -> fp32
+                "vsetvli        t0, x0, e8, mf4         \n\t"
+                "vwaddu.vx      v28, v2, x0             \n\t"
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vwadd.vx       v26, v28, x0            \n\t"
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vmsle.vi       v0, v26, 1              \n\t"
+                "vadd.vi        v24, v26, -1            \n\t"
+                "vsll.vi        v18, v24, 23            \n\t"
+                "vsll.vv        v18, v30, v26, v0.t     \n\t"
+
+                // Row 0: dot(A0, decoded MXFP4 lane groups), accumulate in int32 and
+                // then apply A/B scaling.
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vle8.v         v1, (t3)                \n\t"
+                "vsetvli        t0, x0, e64, m1         \n\t"
+                "vupack.vv      v16, v1, v2, 1          \n\t"
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v24, v24, v24           \n\t"
+                "vxor.vv        v26, v26, v26           \n\t"
+                "vxor.vv        v28, v28, v28           \n\t"
+                "vxor.vv        v30, v30, v30           \n\t"
+                "vmadot         v24, v16, v8, i8        \n\t"
+                "vmadot         v26, v16, v10, i8       \n\t"
+                "vmadot         v28, v16, v12, i8       \n\t"
+                "vmadot         v30, v16, v14, i8       \n\t"
+                "vmadot         v24, v17, v9, i8        \n\t"
+                "vmadot         v26, v17, v11, i8       \n\t"
+                "vmadot         v28, v17, v13, i8       \n\t"
+                "vmadot         v30, v17, v15, i8       \n\t"
+                "vpack.vv       v16, v24, v26, 2        \n\t"
+                "vpack.vv       v20, v28, v30, 2        \n\t"
+                "vpack.vv       v24, v16, v20, 3        \n\t"
+                "vpack.vv       v26, v17, v21, 3        \n\t"
+                "vfcvt.f.x.v    v24, v24                \n\t"
+                "vfcvt.f.x.v    v25, v25                \n\t"
+                "vfcvt.f.x.v    v26, v26                \n\t"
+                "vfcvt.f.x.v    v27, v27                \n\t"
+                "vfmul.vv       v24, v24, v18           \n\t"
+                "vfmul.vv       v25, v25, v18           \n\t"
+                "vfmul.vv       v26, v26, v18           \n\t"
+                "vfmul.vv       v27, v27, v18           \n\t"
+                "vfmacc.vf      v4, fa0, v24            \n\t"
+                "vfmacc.vf      v5, fa1, v25            \n\t"
+                "vfmacc.vf      v6, fa2, v26            \n\t"
+                "vfmacc.vf      v7, fa3, v27            \n\t"
+
+                "addi           %[BK], %[BK], -1        \n\t"
+                "bgtz           %[BK], BLK_LOOP%=       \n\t"
+
+                // Tail-aware store for the final N tile (`nb_real` may be < 32).
+                "vsetvli        t0, %[NBLKS], e32, m1   \n\t"
+                "add            t1, %[LDC], %[DST]      \n\t"
+                "vse32.v        v4, (%[DST])            \n\t"
+                "vse32.v        v5, (t1)                \n\t"
+                "add            t2, t1, %[LDC]          \n\t"
+                "vse32.v        v6, (t2)                \n\t"
+                "add            t3, t2, %[LDC]          \n\t"
+                "vse32.v        v7, (t3)                \n\t"
+                : [A] "+r"(a_data), [B] "+r"(b_data), [BK] "+r"(cnt)
+                : [DST] "r"(dst_c), [LDC] "r"(ldc * 4), [NBLKS] "r"(nb_real)
+                : "cc", "memory", "t0", "t1", "t2", "t3", "t4", "t5", "t6", "s1", "s2", "s3", "s4", "v0", "v1", "v2",
+                  "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17",
+                  "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31",
+                  "fa0", "fa1", "fa2", "fa3");
+        }
+    }
+}
+
+void gemm_kernel_i8i5_m1(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc) {
+    // =========================================================================
+    // i8i5: 8-bit activation × 5-bit weight (4-bit low + 1-bit high mask)
+    //
+    // B layout per N32K32 k-block (no-zp):
+    //   [0  .. 63 ] : scale_fp16 × 32              (64B)
+    //   [64 .. 191] : Bh i1-high-bit  × 32N × 32K  (128B = 1 VRF)
+    //   [192.. 703] : Bs i4-low-nibble × 32N × 32K (512B = 4 VRF)
+    //   Total: 704B per k-block stride
+    //
+    // B layout per N32K32 k-block (with-zp):
+    //   [0  .. 63 ] : scale_fp16 × 32              (64B)
+    //   [64 .. 95 ] : zp_uint8 × 32                (32B)
+    //   [96 .. 223] : Bh i1-high-bit  × 32N × 32K  (128B = 1 VRF)
+    //   [224.. 735] : Bs i4-low-nibble × 32N × 32K (512B = 4 VRF)
+    //   Total: 736B per k-block stride
+    //
+    // Bh format per N8K32 sub-block (32B):
+    //   K rows × N cols × 1bit packed as bytes (8 cols per byte, K groups of 4B)
+    //   Byte k gives 8 mask bits for columns N7..N0 at k-th K-element.
+    //
+    // Computation:
+    //   B5bit_signed = (Bs | (Bh << 4)) - zp
+    //   dot(A, B5) = dot(A, Bs_u4) + 16*dot(A, Bh_u1) - zp*asum
+    //   No-zp: implicit zp = 16 (unsigned [0..31] centered at 16)
+    //   With-zp: explicit zp from data
+    //
+    // =========================================================================
+
+    if (quant_b_zp == NULL) {
+        for (size_t n = 0; n < count_n; n += 32) {
+            size_t    nblks         = (count_n - n) > 32 ? 32 : count_n - n;
+            // i8i5 no-zp: per column per k-block stride = fp16(2B) + i4(16B) + i1(4B) = 22B
+            uint8_t * QuantBDataPtr = (uint8_t *) quant_b_data +      //
+                                      n * k_blks * (blk_len / 8) +    // Bh i1 mask: n×k_blks×4
+                                      n * k_blks * blk_len / 2 +      // Bs i4 data: n×k_blks×16
+                                      n * k_blks * sizeof(_Float16);  // scale: n×k_blks×2
+            float * CPtr = c_ptr + n;
+            size_t  cnt  = k_blks;
+
+            // A format (same as i8i4):
+            //   || scl(fp32,4B) | asum(int16,2B) | data(int8,32B) || × k_blks
+            //
+            // Register map:
+            //   t3 = k_blks loop counter   t4 = nblks (tail)
+            //   t2 = A asum (int16) << 4   f0 = A scale (fp32)
+            //   s2 = pA (scale/asum)       s3 = pA data
+            //   s4 = pB scales (fp16×32)
+            //   s5 = pB Bh (i1 mask, 128B)
+            //   s6 = pB Bs (i4 packed, 512B)
+            //   s7 = pC
+            //   v3  = fp32 accumulator (N32)
+            //   v2  = B scales fp16 (loaded as bytes; later widened)
+            //   v0  = Bh mask bytes (also used as v0.t mask after load)
+            //   v1  = A int8 (K32)
+            //   v8..v15 / v16..v23 = Bs unpack/pack temporaries (build b5bit bytes)
+            //   v24/v26/v28/v30    = int32 dot accumulators & packing temps
+
+            __asm__ volatile(
+                "mv           t3, %[BCK]              \n\t"  // t3 = k_blks
+                "mv           t4, %[NBLKS]            \n\t"  // t4 = nblks (tail guard)
+
+                // ---- pre-loop: init fp16 constants in e16 m1 context ----
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vmv.v.i      v0, 1                   \n\t"  // v0 = int16(1)
+                "vfcvt.f.x.v  v0, v0                  \n\t"  // v0  = 1.0_fp16
+                "vxor.vv      v3, v16, v16            \n\t"
+
+                // ---- pointer setup ----
+                "mv           s2, %[pA]               \n\t"  // s2 = pA (scale, fp32)
+                "addi         s3, %[pA], 4+2          \n\t"  // s3 = pA data (skip scale+asum)
+                "mv           s4, %[pB]               \n\t"  // s4 = pBSCL
+                "addi         s5, %[pB], 32*2         \n\t"  // s5 = pBh  (pB + 64B scale)
+                "addi         s6, %[pB], 32*2+128     \n\t"  // s6 = pBs  (pB + 64 + 128 = pB+192)
+                "mv           s7, %[pC]               \n\t"  // s7 = pC
+
+                // =====================================================================
+                // K-block loop: each iteration processes one N32×K32 block
+                // Stride per k-block = 704B = 64(scl) + 512(Bs) + 128(Bh)
+                // =====================================================================
+                ".align 4                             \n\t"
+                "BLK_LPST%=:                          \n\t"
+
+                // ---- load Bs (512B = 4 VRF) from s6, advance s6 by 704 ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vl4r.v       v8, (s6)                \n\t"  // v8..v11 = Bs N32K32 i4
+                "addi         s6, s6, 128*4+128+64    \n\t"  // s6 += 704 (512+128+64)
+
+                // ---- load B scale (64B = 32×fp16) from s4, advance s4 by 704 ----
+                "vsetvli      t0, x0, e8, mf2         \n\t"
+                "vle8.v       v2, (s4)                \n\t"  // v2 = scale_fp16 × 32
+                "addi         s4, s4, 64+128*4+128    \n\t"  // s4 += 704 (64+512+128)
+
+                // ---- load Bh (128B = 1 VRF) from s5, advance s5 by 704 ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vle8.v       v0, (s5)                \n\t"  // v0 = Bh N32K32 1-bit packed
+                "addi         s5, s5, 128+64+128*4    \n\t"  // s5 += 704 (128+64+512)
+
+                // ---- load A data (32B = K32 int8) from s3 ----
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vle8.v       v1, (s3)                \n\t"  // v1 = A M1K32 int8
+                "addi         s3, s3, 32+6            \n\t"  // s3 += 38 (data + scl + asum)
+
+                // ---- load A scale (fp32) and asum (int16) from s2 ----
+                "flw          f0, (s2)                \n\t"  // f0 = A scale (fp32)
+                "lh           t2, 4(s2)               \n\t"  // t2 = A asum (int16)
+                "addi         s2, s2, 6+32            \n\t"  // s2 += 38
+
+                //// ---- A nibble unpacking ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vand.vi      v12, v8, 0xF            \n\t"  //8bit(lo4) //[8*32]
+                "vand.vi      v13, v9, 0xF            \n\t"
+                "vand.vi      v14, v10, 0xF           \n\t"
+                "vand.vi      v15, v11, 0xF           \n\t"
+                "vsrl.vi      v8, v8, 4               \n\t"  //8bit(hi4)
+                "vsrl.vi      v9, v9, 4               \n\t"
+                "vsrl.vi      v10, v10, 4             \n\t"
+                "vsrl.vi      v11, v11, 4             \n\t"
+
+                "slli         t2, t2, 4               \n\t"  // a_sum * 16;
+                // [4*32]*2
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vpack.vv     v16, v12, v8, 0         \n\t"
+                "vpack.vv     v18, v13, v9, 0         \n\t"
+                "vpack.vv     v20, v14, v10, 0        \n\t"
+                "vpack.vv     v22, v15, v11, 0        \n\t"
+
+                "li           t1, 16                  \n\t"
+                "vsetvli      t0, x0, e8, m8          \n\t"
+                "vadd.vx      v16, v16, t1, v0.t      \n\t"
+
+                // [4*32]*2 -> [8*16]
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vupack.vv    v8, v16, v17, 1         \n\t"
+                "vupack.vv    v10, v18, v19, 1        \n\t"
+                "vupack.vv    v12, v20, v21, 1        \n\t"
+                "vupack.vv    v14, v22, v23, 1        \n\t"
+
+                "vsetvli      t0, x0, e64, m1         \n\t"
+                "vslidedown.vi  v16, v1, 2            \n\t"
+
+                // init the accumu to asum * zp
+                "vsetvli        t0, x0, e32, m1 \n\t"
+                "vxor.vv        v24, v16, v16   \n\t"
+                "vxor.vv        v26, v16, v16   \n\t"
+                "vxor.vv        v28, v16, v16   \n\t"
+                "vxor.vv        v30, v16, v16   \n\t"
+
+                // ---- i8 main dot products ----
+                // vmadot: A × unsigned Bh × 16 → fp16 accumulate
+                "vmadot       v24, v1, v8, i8         \n\t"  // N0..7
+                "vmadot       v26, v1, v10, i8        \n\t"  // N8..15
+                "vmadot       v28, v1, v12, i8        \n\t"  // N16..23
+                "vmadot       v30, v1, v14, i8        \n\t"  // N24..31
+                //// vmadot: A × unsigned Bh × 1 → fp16 accumulate
+                "vmadot       v24, v16, v9, i8        \n\t"  // N0..7
+                "vmadot       v26, v16, v11, i8       \n\t"  // N8..15
+                "vmadot       v28, v16, v13, i8       \n\t"  // N16..23
+                "vmadot       v30, v16, v15, i8       \n\t"  // N24..31
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vpack.vv     v16, v24, v26, 2        \n\t"  // v16 = N0..15
+                "vpack.vv     v18, v28, v30, 2        \n\t"  // v18 = N16..31
+                "vpack.vv     v24, v16, v18, 3        \n\t"  // v24 = N0..31
+
+                "vadd.vx      v24, v24, t2            \n\t"
+                // b_scale fp16 -> fp32
+                "vsetvli      t0, x0, e16, mf2        \n\t"
+                "vfwcvt.f.f.v v28, v2                 \n\t"
+
+                // a_scale * b_scale;
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vfcvt.f.x.v  v26, v24                \n\t"
+                "vfmul.vf     v30, v28, f0            \n\t"
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                // static_cast<float>(qsum) * a_scale * b_scale;
+                "vfmacc.vv    v3, v30, v26            \n\t"
+
+                "addi         t3, t3, -1              \n\t"
+                "bgtz         t3, BLK_LPST%=          \n\t"
+                "BLK_LPND%=:                          \n\t"
+                "vsetvli      t0, %[NBLKS], e32, m1   \n\t"
+                "vse32.v      v3, (%[pC])             \n\t"
+                "FUNC_END%=:                          \n\t"
+
+                :
+                : [BCK] "r"(cnt), [NBLKS] "r"(nblks), [pA] "r"(quant_a_ptr), [pB] "r"(QuantBDataPtr), [pC] "r"(CPtr)
+                : "cc", "memory", "t0", "t1", "t2", "t3", "t4", "f0", "s2", "s3", "s4", "s5", "s6", "s7", "v0", "v1",
+                  "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v16", "v17", "v18", "v19",
+                  "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31");
+        }
+    } else {
+        for (size_t n = 0; n < count_n; n += 32) {
+            size_t    nblks         = (count_n - n) > 32 ? 32 : count_n - n;
+            // i8i5 with-zp: per column per k-block stride = fp16(2B)+zp(1B)+i4(16B)+i1(4B)=23B
+            uint8_t * QuantBDataPtr = (uint8_t *) quant_b_data +      //
+                                      n * k_blks * blk_len / 2 +      // Bs i4: n×k_blks×16
+                                      n * k_blks * (blk_len / 8) +    // Bh i1: n×k_blks×4
+                                      n * k_blks * sizeof(uint8_t) +  // zp: n×k_blks×1
+                                      n * k_blks * sizeof(_Float16);  // scale: n×k_blks×2
+            float * CPtr = c_ptr + n;
+            size_t  cnt  = k_blks;
+
+            // A format (same as i8i4):
+            //   || scl(fp32,4B) | asum(int16,2B) | data(int8,32B) || × k_blks
+            //
+            // Register map:
+            //   t3 = k_blks loop counter   t4 = nblks (tail)
+            //   t2 = A asum (int16) << 4   f0 = A scale (fp32)
+            //   s2 = pA (scale/asum)       s3 = pA data
+            //   s4 = pB scales (fp16×32); 每个 k-block 先 +64 指向 zp，再 +672 到下一个 block
+            //   s5 = pB Bh (i1 mask, 128B) (offset +96)
+            //   s6 = pB Bs (i4 packed, 512B) (offset +224)
+            //   s7 = pC
+            //   v3  = fp32 accumulator (N32)
+            //   v2  = B scales fp16 (loaded as bytes; later widened)
+            //   v0  = Bh mask bytes (also used as v0.t mask after load)
+            //   v1  = A int8 (K32) / later reused to hold Bzp bytes
+            //   v8..v15 / v16..v23 = Bs unpack/pack temporaries (build b5bit bytes)
+            //   v24/v26/v28/v30    = int32 dot accumulators & packing temps
+
+            __asm__ volatile(
+                "mv           t3, %[BCK]              \n\t"  // t3 = k_blks
+                "mv           t4, %[NBLKS]            \n\t"  // t4 = nblks (tail guard)
+
+                // ---- pre-loop: init fp16 constants in e16 m1 context ----
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vmv.v.i      v0, 1                   \n\t"  // v0 = int16(1)
+                "vfcvt.f.x.v  v0, v0                  \n\t"  // v0  = 1.0_fp16
+                "vxor.vv      v3, v16, v16            \n\t"
+
+                // ---- pointer setup ----
+                "mv           s2, %[pA]               \n\t"  // s2 = pA (scale, fp32)
+                "addi         s3, %[pA], 4+2          \n\t"  // s3 = pA data (skip scale+asum)
+                "mv           s4, %[pB]               \n\t"  // s4 = pBSCL
+                "addi         s5, %[pB], 32*3         \n\t"  // s5 = pBh  (pB + 64B scale + 32B zp = pB+96)
+                "addi         s6, %[pB], 32*3+128     \n\t"  // s6 = pBs  (pB + 96 + 128 = pB+224)
+                "mv           s7, %[pC]               \n\t"  // s7 = pC
+
+                // =====================================================================
+                // K-block loop: each iteration processes one N32×K32 block
+                // Stride per k-block = 736B = 64(scale) + 32(zp) + 128(Bh) + 512(Bs)
+                // =====================================================================
+                ".align 4                             \n\t"
+                "BLK_LPST%=:                          \n\t"
+
+                // ---- load Bs (512B = 4 VRF) from s6, advance s6 by 736 ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vl4r.v       v8, (s6)                \n\t"  // v8..v11 = Bs N32K32 i4
+                "addi         s6, s6, 128*4+128+96    \n\t"  // s6 += 736 (512+128+96)
+
+                // ---- load B scale (64B = 32×fp16) from s4; then s4 points to zp[32] ----
+                "vsetvli      t0, x0, e8, mf2         \n\t"
+                "vle8.v       v2, (s4)                \n\t"  // v2 = scale_fp16 × 32
+                "addi         s4, s4, 64              \n\t"  // s4 += 64 (now points to zp)
+
+                // ---- load Bh (128B = 1 VRF) from s5, advance s5 by 736 ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vle8.v       v0, (s5)                \n\t"  // v0 = Bh N32K32 1-bit packed
+                "addi         s5, s5, 128+96+128*4    \n\t"  // s5 += 736 (128+96+512)
+
+                // ---- load A data (32B = K32 int8) from s3 ----
+                "vsetvli      t0, x0, e8, mf4         \n\t"
+                "vle8.v       v1, (s3)                \n\t"  // v1 = A M1K32 int8
+                "addi         s3, s3, 32+6            \n\t"  // s3 += 38 (data + scl + asum)
+
+                // ---- load A scale (fp32) and asum (int16) from s2 ----
+                "flw          f0, (s2)                \n\t"  // f0 = A scale (fp32)
+                "lh           t2, 4(s2)               \n\t"  // t2 = A asum (int16)
+                "addi         s2, s2, 6+32            \n\t"  // s2 += 38
+
+                //// ---- A nibble unpacking ----
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vand.vi      v12, v8, 0xF            \n\t"  //8bit(lo4) //[8*32]
+                "vand.vi      v13, v9, 0xF            \n\t"
+                "vand.vi      v14, v10, 0xF           \n\t"
+                "vand.vi      v15, v11, 0xF           \n\t"
+                "vsrl.vi      v8, v8, 4               \n\t"  //8bit(hi4)
+                "vsrl.vi      v9, v9, 4               \n\t"
+                "vsrl.vi      v10, v10, 4             \n\t"
+                "vsrl.vi      v11, v11, 4             \n\t"
+
+                // [4*32]*2
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vpack.vv     v16, v12, v8, 0         \n\t"
+                "vpack.vv     v18, v13, v9, 0         \n\t"
+                "vpack.vv     v20, v14, v10, 0        \n\t"
+                "vpack.vv     v22, v15, v11, 0        \n\t"
+
+                "li           t1, 16                  \n\t"
+                "vsetvli      t0, x0, e8, m8          \n\t"
+                "vadd.vx      v16, v16, t1, v0.t      \n\t"
+
+                // [4*32]*2 -> [8*16]
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vupack.vv    v8, v16, v17, 1         \n\t"
+                "vupack.vv    v10, v18, v19, 1        \n\t"
+                "vupack.vv    v12, v20, v21, 1        \n\t"
+                "vupack.vv    v14, v22, v23, 1        \n\t"
+
+                "vsetvli      t0, x0, e64, m1         \n\t"
+                "vslidedown.vi  v16, v1, 2            \n\t"
+
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vxor.vv      v24, v16, v16           \n\t"
+                "vxor.vv      v26, v16, v16           \n\t"
+                "vxor.vv      v28, v16, v16           \n\t"
+                "vxor.vv      v30, v16, v16           \n\t"
+
+                // ---- i8 main dot products ----
+                // vmadot: A × unsigned Bh × 16 → fp16 accumulate
+                "vmadot       v24, v1, v8, i8         \n\t"  // N0..7
+                "vmadot       v26, v1, v10, i8        \n\t"  // N8..15
+                "vmadot       v28, v1, v12, i8        \n\t"  // N16..23
+                "vmadot       v30, v1, v14, i8        \n\t"  // N24..31
+                // vmadot: A × unsigned Bh × 1 → fp16 accumulate
+                "vmadot       v24, v16, v9, i8        \n\t"  // N0..7
+                "vmadot       v26, v16, v11, i8       \n\t"  // N8..15
+                "vmadot       v28, v16, v13, i8       \n\t"  // N16..23
+                "vmadot       v30, v16, v15, i8       \n\t"  // N24..31
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vle8.v       v1, (s4)                \n\t"  // Bzp
+                "addi         s4, s4, 32+128*4+128    \n\t"
+
+                "vsetvli      t0, x0, e8, m1          \n\t"
+                "vpack.vv     v16, v24, v26, 2        \n\t"  // v16 = N0..15
+                "vpack.vv     v18, v28, v30, 2        \n\t"  // v18 = N16..31
+                "vpack.vv     v24, v16, v18, 3        \n\t"  // v24 = N0..31
+
+                "vwaddu.vx    v28, v1, x0             \n\t"  // uint8 -> uint16
+
+                "vsetvli      t0, x0, e16, m1         \n\t"
+                "vwmul.vx     v30, v28, t2            \n\t"
+
+                // b_scale fp16 -> fp32
+                "vsetvli      t0, x0, e16, mf2        \n\t"
+                "vfwcvt.f.f.v v28, v2                 \n\t"
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vadd.vv      v24, v24, v30           \n\t"
+
+                // a_scale * b_scale;
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                "vfmul.vf     v30, v28, f0            \n\t"
+                "vfcvt.f.x.v  v26, v24                \n\t"
+                "vsetvli      t0, x0, e32, m1         \n\t"
+                // static_cast<float>(qsum) * a_scale * b_scale;
+                "vfmacc.vv    v3, v30, v26            \n\t"
+
+                "addi         t3, t3, -1              \n\t"
+                "bgtz         t3, BLK_LPST%=          \n\t"
+                "BLK_LPND%=:                          \n\t"
+                "vsetvli      t0, %[NBLKS], e32, m1   \n\t"
+                "vse32.v      v3, (%[pC])             \n\t"
+                "FUNC_END%=:                          \n\t"
+                :
+                : [BCK] "r"(cnt), [NBLKS] "r"(nblks), [pA] "r"(quant_a_ptr), [pB] "r"(QuantBDataPtr), [pC] "r"(CPtr)
+                : "cc", "memory", "t0", "t1", "t2", "t3", "t4", "f0", "s2", "s3", "s4", "s5", "s6", "s7", "v0", "v1",
+                  "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v16", "v17", "v18", "v19",
+                  "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31");
+        }
+    }
+}
+
+void gemm_kernel_i8i5_m4(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc) {
+    constexpr size_t NB_COLS = 32;
+
+    GGML_UNUSED(count_m);
+    GGML_UNUSED(blk_len);
+
+    // This kernel computes a 4x32 output tile. For each K32 block we decode the
+    // packed Q5 weights once and reuse the decoded vectors across the 4 A rows.
+    constexpr size_t B_Q50_BLK_STRIDE = sizeof(nrow_block_q5_0<NB_COLS>);
+    constexpr size_t B_Q51_BLK_STRIDE = sizeof(nrow_block_q5_1<NB_COLS>);
+
+    if (quant_b_zp) {
+        // Q5_1 block layout per K32/N32 tile:
+        //   [scale_fp16 x 32][zp_u8 x 32][qh high-bit mask x 128B][qs low nibbles x 512B]
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            size_t    nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+            uint8_t * b_data  = (uint8_t *) quant_b_data + (ni / NB_COLS) * k_blks * B_Q51_BLK_STRIDE;
+            uint8_t * a_data  = (uint8_t *) quant_a_ptr;
+            float *   dst_c   = c_ptr + ni;
+            size_t    cnt     = k_blks;
+
+            asm volatile(
+                // v4-v7 are the fp32 accumulators for rows 0..3 of the current N32 tile.
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v4, v4, v4              \n\t"
+                "vxor.vv        v5, v5, v5              \n\t"
+                "vxor.vv        v6, v6, v6              \n\t"
+                "vxor.vv        v7, v7, v7              \n\t"
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                // Load the 4 A-row scales/sums for this K32 block and build row data pointers.
+                "flw            fa0, 0(%[A])            \n\t"
+                "flw            fa1, 4(%[A])            \n\t"
+                "flw            fa2, 8(%[A])            \n\t"
+                "flw            fa3, 12(%[A])           \n\t"
+                "lh             s1, 16(%[A])            \n\t"
+                "lh             s2, 18(%[A])            \n\t"
+                "lh             s3, 20(%[A])            \n\t"
+                "lh             s4, 22(%[A])            \n\t"
+                "addi           t3, %[A], 24            \n\t"
+                "addi           t4, t3, 32              \n\t"
+                "addi           t5, t3, 64              \n\t"
+                "addi           t6, t3, 96              \n\t"
+                "addi           %[A], %[A], 152         \n\t"
+
+                // B-side pointers:
+                //   t1 -> zp stream, t2 -> qh bitmask stream, s5 -> qs low-nibble stream.
+                "addi           t1, %[B], 64            \n\t"
+                "addi           t2, %[B], 96            \n\t"
+                "addi           s5, %[B], 224           \n\t"
+                "vsetvli        t0, x0, e8, mf2         \n\t"
+                "vle8.v         v2, (%[B])              \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vle8.v         v0, (t2)                \n\t"
+                "vl4r.v         v8, (s5)                \n\t"
+                "addi           %[B], %[B], 736         \n\t"
+
+                // Decode Q5 payload once for the whole tile:
+                //   1) split `qs` low/high nibbles,
+                //   2) repack into bytes,
+                //   3) use the `qh` mask to inject bit4 (+16) where needed,
+                //   4) expand into the vmadot-friendly layout reused by all 4 rows.
+                "vand.vi        v12, v8, 0xF            \n\t"
+                "vand.vi        v13, v9, 0xF            \n\t"
+                "vand.vi        v14, v10, 0xF           \n\t"
+                "vand.vi        v15, v11, 0xF           \n\t"
+                "vsrl.vi        v8, v8, 4               \n\t"
+                "vsrl.vi        v9, v9, 4               \n\t"
+                "vsrl.vi        v10, v10, 4             \n\t"
+                "vsrl.vi        v11, v11, 4             \n\t"
+
+                "vpack.vv       v16, v12, v8, 0         \n\t"
+                "vpack.vv       v18, v13, v9, 0         \n\t"
+                "li             t2, 16                  \n\t"
+                "vpack.vv       v20, v14, v10, 0        \n\t"
+                "vpack.vv       v22, v15, v11, 0        \n\t"
+
+                "vsetvli        t0, x0, e8, m8          \n\t"
+                "vadd.vx        v16, v16, t2, v0.t      \n\t"
+
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vupack.vv      v8, v16, v17, 1         \n\t"
+                "vupack.vv      v10, v18, v19, 1        \n\t"
+                "vupack.vv      v12, v20, v21, 1        \n\t"
+                "vupack.vv      v14, v22, v23, 1        \n\t"
+
+                // Convert per-column fp16 scales once; the same scale vector is shared by all 4 rows.
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vfwcvt.f.f.v   v18, v2                 \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vle8.v         v3, (t1)                \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+
+                // Row 0: dot(A0, decoded_q5) + a_sum0 * zp, then scale by A/B scales.
+                // The widen/mul correction sequence intentionally matches the proven m1 Q5_1 path.
+                "vle8.v         v1, (t3)                \n\t"
+                "vsetvli        t0, x0, e64, m1         \n\t"
+                "vupack.vv      v16, v1, v2, 1          \n\t"
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v24, v24, v24           \n\t"
+                "vxor.vv        v26, v26, v26           \n\t"
+                "vxor.vv        v28, v28, v28           \n\t"
+                "vxor.vv        v30, v30, v30           \n\t"
+                "vmadot         v24, v16, v8, i8        \n\t"
+                "vmadot         v26, v16, v10, i8       \n\t"
+                "vmadot         v28, v16, v12, i8       \n\t"
+                "vmadot         v30, v16, v14, i8       \n\t"
+                "vmadot         v24, v17, v9, i8        \n\t"
+                "vmadot         v26, v17, v11, i8       \n\t"
+                "vmadot         v28, v17, v13, i8       \n\t"
+                "vmadot         v30, v17, v15, i8       \n\t"
+                "vpack.vv       v16, v24, v26, 2        \n\t"
+                "vpack.vv       v20, v28, v30, 2        \n\t"
+                "vpack.vv       v24, v16, v20, 3        \n\t"
+                "vpack.vv       v26, v17, v21, 3        \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vwaddu.vx      v28, v3, x0             \n\t"
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vwmul.vx       v12, v28, s1            \n\t"
+                "vwmul.vx       v14, v28, s2            \n\t"
+                "vwmul.vx       v20, v28, s3            \n\t"
+                "vwmul.vx       v22, v28, s4            \n\t"
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vadd.vv        v24, v24, v12           \n\t"
+                "vadd.vv        v25, v25, v14           \n\t"
+                "vadd.vv        v26, v26, v20           \n\t"
+                "vadd.vv        v27, v27, v22           \n\t"
+                "vfcvt.f.x.v    v12, v24                \n\t"
+                "vfcvt.f.x.v    v14, v25                \n\t"
+                "vfcvt.f.x.v    v20, v26                \n\t"
+                "vfcvt.f.x.v    v22, v27                \n\t"
+                "vfmul.vv       v12, v12, v18           \n\t"
+                "vfmul.vv       v14, v14, v18           \n\t"
+                "vfmul.vv       v20, v20, v18           \n\t"
+                "vfmul.vv       v22, v22, v18           \n\t"
+                "vfmacc.vf      v4, fa0, v12            \n\t"
+                "vfmacc.vf      v5, fa1, v14            \n\t"
+                "vfmacc.vf      v6, fa2, v20            \n\t"
+                "vfmacc.vf      v7, fa3, v22            \n\t"
+
+                "addi           %[BK], %[BK], -1        \n\t"
+                "bgtz           %[BK], BLK_LOOP%=       \n\t"
+
+                // Tail-aware store for the final N tile (`nb_real` may be < 32).
+                "vsetvli        t0, %[NBLKS], e32, m1   \n\t"
+                "add            t1, %[LDC], %[DST]      \n\t"
+                "vse32.v        v4, (%[DST])            \n\t"
+                "vse32.v        v5, (t1)                \n\t"
+                "add            t2, t1, %[LDC]          \n\t"
+                "vse32.v        v6, (t2)                \n\t"
+                "add            t3, t2, %[LDC]          \n\t"
+                "vse32.v        v7, (t3)                \n\t"
+                : [A] "+r"(a_data), [B] "+r"(b_data), [BK] "+r"(cnt)
+                : [DST] "r"(dst_c), [LDC] "r"(ldc * 4), [NBLKS] "r"(nb_real)
+                : "cc", "memory", "t0", "t1", "t2", "t3", "t4", "t5", "t6", "s1", "s2", "s3", "s4", "s5", "v0", "v1",
+                  "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16",
+                  "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30",
+                  "v31", "fa0", "fa1", "fa2", "fa3");
+        }
+    } else {
+        // Q5_0 block layout per K32/N32 tile:
+        //   [scale_fp16 x 32][qh high-bit mask x 128B][qs low nibbles x 512B]
+        // There is no explicit zp stream; the implicit midpoint correction is +16.
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            size_t    nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+            uint8_t * b_data  = (uint8_t *) quant_b_data + (ni / NB_COLS) * k_blks * B_Q50_BLK_STRIDE;
+            uint8_t * a_data  = (uint8_t *) quant_a_ptr;
+            float *   dst_c   = c_ptr + ni;
+            size_t    cnt     = k_blks;
+
+            asm volatile(
+                // v4-v7 are the fp32 accumulators for rows 0..3 of the current N32 tile.
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v4, v4, v4              \n\t"
+                "vxor.vv        v5, v5, v5              \n\t"
+                "vxor.vv        v6, v6, v6              \n\t"
+                "vxor.vv        v7, v7, v7              \n\t"
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                // Load the 4 A-row scales/sums for this K32 block and build row data pointers.
+                "flw            fa0, 0(%[A])            \n\t"
+                "flw            fa1, 4(%[A])            \n\t"
+                "flw            fa2, 8(%[A])            \n\t"
+                "flw            fa3, 12(%[A])           \n\t"
+                "lh             s1, 16(%[A])            \n\t"
+                "lh             s2, 18(%[A])            \n\t"
+                "lh             s3, 20(%[A])            \n\t"
+                "lh             s4, 22(%[A])            \n\t"
+                "addi           t3, %[A], 24            \n\t"
+                "addi           t4, t3, 32              \n\t"
+                "addi           t5, t3, 64              \n\t"
+                "addi           t6, t3, 96              \n\t"
+                "addi           %[A], %[A], 152         \n\t"
+
+                // B-side pointers:
+                //   t1 -> qh bitmask stream, t2 -> qs low-nibble stream.
+                "addi           t1, %[B], 64            \n\t"
+                "addi           t2, %[B], 192           \n\t"
+                "vsetvli        t0, x0, e8, mf2         \n\t"
+                "vle8.v         v2, (%[B])              \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vle8.v         v0, (t1)                \n\t"
+                "vl4r.v         v8, (t2)                \n\t"
+                "addi           %[B], %[B], 704         \n\t"
+
+                // Decode Q5 payload once for the whole tile and expand it into the vmadot layout.
+                "vand.vi        v12, v8, 0xF            \n\t"
+                "vand.vi        v13, v9, 0xF            \n\t"
+                "vand.vi        v14, v10, 0xF           \n\t"
+                "vand.vi        v15, v11, 0xF           \n\t"
+                "vsrl.vi        v8, v8, 4               \n\t"
+                "vsrl.vi        v9, v9, 4               \n\t"
+                "vsrl.vi        v10, v10, 4             \n\t"
+                "vsrl.vi        v11, v11, 4             \n\t"
+
+                "vpack.vv       v16, v12, v8, 0         \n\t"
+                "vpack.vv       v18, v13, v9, 0         \n\t"
+                "li             t2, 16                  \n\t"
+                "vpack.vv       v20, v14, v10, 0        \n\t"
+                "vpack.vv       v22, v15, v11, 0        \n\t"
+
+                "vsetvli        t0, x0, e8, m8          \n\t"
+                "vadd.vx        v16, v16, t2, v0.t      \n\t"
+
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vupack.vv      v8, v16, v17, 1         \n\t"
+                "vupack.vv      v10, v18, v19, 1        \n\t"
+                "vupack.vv      v12, v20, v21, 1        \n\t"
+                "vupack.vv      v14, v22, v23, 1        \n\t"
+
+                // Convert per-column fp16 scales once; the same scale vector is shared by all 4 rows.
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vfwcvt.f.f.v   v18, v2                 \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+
+                // Row 0: dot(A0, decoded_q5) + a_sum0 * 16 (implicit Q5_0 midpoint correction).
+                "vle8.v         v1, (t3)                \n\t"
+                "vsetvli        t0, x0, e64, m1         \n\t"
+                "vupack.vv      v16, v1, v2, 1          \n\t"
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v24, v24, v24           \n\t"
+                "vxor.vv        v26, v26, v26           \n\t"
+                "vxor.vv        v28, v28, v28           \n\t"
+                "vxor.vv        v30, v30, v30           \n\t"
+                "vmadot         v24, v16, v8, i8        \n\t"
+                "vmadot         v26, v16, v10, i8       \n\t"
+                "vmadot         v28, v16, v12, i8       \n\t"
+                "vmadot         v30, v16, v14, i8       \n\t"
+                "vmadot         v24, v17, v9, i8        \n\t"
+                "vmadot         v26, v17, v11, i8       \n\t"
+                "vmadot         v28, v17, v13, i8       \n\t"
+                "vmadot         v30, v17, v15, i8       \n\t"
+                "vpack.vv       v16, v24, v26, 2        \n\t"
+                "slli           s1, s1, 4               \n\t"
+                "vpack.vv       v20, v28, v30, 2        \n\t"
+                "slli           s2, s2, 4               \n\t"
+                "vpack.vv       v24, v16, v20, 3        \n\t"
+                "slli           s3, s3, 4               \n\t"
+                "vpack.vv       v26, v17, v21, 3        \n\t"
+                "slli           s4, s4, 4               \n\t"
+                "vadd.vx        v24, v24, s1            \n\t"
+                "vadd.vx        v25, v25, s2            \n\t"
+                "vadd.vx        v26, v26, s3            \n\t"
+                "vadd.vx        v27, v27, s4            \n\t"
+                "vfcvt.f.x.v    v24, v24                \n\t"
+                "vfcvt.f.x.v    v25, v25                \n\t"
+                "vfcvt.f.x.v    v26, v26                \n\t"
+                "vfcvt.f.x.v    v27, v27                \n\t"
+                "vfmul.vv       v24, v24, v18           \n\t"
+                "vfmul.vv       v25, v25, v18           \n\t"
+                "vfmul.vv       v26, v26, v18           \n\t"
+                "vfmul.vv       v27, v27, v18           \n\t"
+                "vfmacc.vf      v4, fa0, v24            \n\t"
+                "vfmacc.vf      v5, fa1, v25            \n\t"
+                "vfmacc.vf      v6, fa2, v26            \n\t"
+                "vfmacc.vf      v7, fa3, v27            \n\t"
+
+                "addi           %[BK], %[BK], -1        \n\t"
+                "bgtz           %[BK], BLK_LOOP%=       \n\t"
+
+                // Tail-aware store for the final N tile (`nb_real` may be < 32).
+                "vsetvli        t0, %[NBLKS], e32, m1   \n\t"
+                "add            t1, %[LDC], %[DST]      \n\t"
+                "vse32.v        v4, (%[DST])            \n\t"
+                "vse32.v        v5, (t1)                \n\t"
+                "add            t2, t1, %[LDC]          \n\t"
+                "vse32.v        v6, (t2)                \n\t"
+                "add            t3, t2, %[LDC]          \n\t"
+                "vse32.v        v7, (t3)                \n\t"
+                : [A] "+r"(a_data), [B] "+r"(b_data), [BK] "+r"(cnt)
+                : [DST] "r"(dst_c), [LDC] "r"(ldc * 4), [NBLKS] "r"(nb_real)
+                : "cc", "memory", "t0", "t1", "t2", "t3", "t4", "t5", "t6", "s1", "s2", "s3", "s4", "v0", "v1", "v2",
+                  "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17",
+                  "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31",
+                  "fa0", "fa1", "fa2", "fa3");
+        }
+    }
+}
+
+void gemm_kernel_i8i8_m1(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc) {
+    for (size_t n = 0; n < count_n; n += 32) {
+        size_t    nblks         = (count_n - n) > 32 ? 32 : count_n - n;
+        uint8_t * QuantBDataPtr = (uint8_t *) quant_b_data +      //
+                                  n * k_blks * blk_len +          // b data
+                                  n * k_blks * sizeof(_Float16);  // scale
+        float * CPtr = c_ptr + n;
+        size_t  cnt  = k_blks;
+
+        // A format Version_1 (FP32 SCALE FOR Normal VMADOTins of IME2)
+        // A M1K32 int8    256bit
+        // Ascale fp32 * 1  32bit
+        // || scl*1(fp32) | Asum(int16) | blk0 || scl*1(fp32) | Asum(int16) | blk0 || ...
+        // || Element                          || Element                          || ...
+        // B format
+        // B N8K32 int4    2048bit
+        //   4VRF, N32K32, 8192bit
+        // Bscale fp16 * N32 512bit;
+        // || scl*32..(fp16) | blk0 blk1 ... blk31 || scl*32..(fp16) | blk0 blk1 ... blk31 || ...
+        // || Element                              || Element                              || ...
+
+        //bias always be nullptr
+        __asm__ volatile(
+
+            // t3 = k/32
+            "mv           t3, %[BCK]              \n\t"
+            "mv           t4, %[NBLKS]            \n\t"
+            "mv           s2, %[pA]               \n\t"  // s2 = pASCL
+            "addi         s3, %[pA], 4+2          \n\t"  // s3 = pAData, (pA+AScl+ASum)
+            "mv           s4, %[pB]               \n\t"  // s4 = pBSCL
+            "addi         s5, %[pB], 32*2         \n\t"  // s5 = pBdata;
+            "mv           s6, %[pC]               \n\t"
+
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vxor.vv      v2, v0, v0              \n\t"  // clear acc
+
+            // ordinary vmadot: vle*6 flw*1 vecIns*64 vmadot*8
+            ".align 4                             \n\t"
+            "_K_LPST%=:                           \n\t"
+
+            "vsetvli      t0, x0, e8, m1          \n\t"
+            "vl4r.v       v4, (s5)                \n\t"  // B Data 4VRF * 8Row * 32
+            "addi         s5, s5, 128*4           \n\t"
+            "vl4r.v       v8, (s5)                \n\t"  // B Data 4VRF * 8Row * 32
+            "addi         s5, s5, 128*4+64        \n\t"
+
+            "vsetvli      t0, x0, e8, mf2         \n\t"
+            "vle8.v       v0, (s4)                \n\t"  // B Scale 4VRF*8Row*FP16 = 512bit
+            "addi         s4, s4, 64+128*8        \n\t"
+
+            "vsetvli      t0, x0, e8, mf4         \n\t"
+            "vle8.v       v3, (s3)                \n\t"  // A Data M1*K32*int8 = 256bit
+            "addi         s3, s3, 32+6            \n\t"
+
+            "flw          f0, (s2)                \n\t"  // A Scale fp32
+            "addi         s2, s2, 6+32            \n\t"  // AScale + Asum(FP32+i16)
+
+            "vsetvli      t0, zero, e32, m1       \n\t"
+            "vupack.vv    v24, v4, v5, 1          \n\t"
+            "vupack.vv    v26, v6, v7, 1          \n\t"
+            "vupack.vv    v28, v8, v9, 1          \n\t"
+            "vupack.vv    v30, v10, v11, 1        \n\t"
+
+            "vslidedown.vi  v4, v3, 4             \n\t"
+
+            "vxor.vv      v16, v16, v16           \n\t"
+            "vxor.vv      v18, v16, v16           \n\t"
+            "vxor.vv      v20, v16, v16           \n\t"
+            "vxor.vv      v22, v16, v16           \n\t"
+
+            "vmadot       v16, v3, v24, i8         \n\t"  // M0 N0 - N7 INT32(256bit)
+            "vmadot       v18, v3, v26, i8         \n\t"  // M0 N8 - N15
+            "vmadot       v20, v3, v28, i8         \n\t"  // M0 N16 - N23
+            "vmadot       v22, v3, v30, i8         \n\t"  // M0 N24 - N31
+
+            "vmadot       v16, v4, v25, i8         \n\t"
+            "vmadot       v18, v4, v27, i8         \n\t"
+            "vmadot       v20, v4, v29, i8         \n\t"
+            "vmadot       v22, v4, v31, i8         \n\t"
+
+            "vpack.vv     v24, v16, v18, 2        \n\t"
+            "vpack.vv     v26, v20, v22, 2        \n\t"
+            "vpack.vv     v16, v24, v26, 3        \n\t"
+
+            // b_scale fp16 -> fp32
+            "vsetvli      t0, x0, e16, mf2        \n\t"
+            "vfwcvt.f.f.v v24, v0                 \n\t"
+            // mac result i32 -> fp32
+            "vsetvli      t0, x0, e32, m1         \n\t"
+            "vfcvt.f.x.v  v26, v16                \n\t"
+            // a_scale * b_scale;
+            "vfmul.vf     v1, v24, f0             \n\t"
+            // static_cast<float>(qsum) * a_scale * b_scale;
+            "vfmacc.vv    v2, v1, v26             \n\t"
+
+            "addi         t3, t3, -1              \n\t"
+            "bgtz         t3, _K_LPST%=           \n\t"
+            "_K_LPND%=:                           \n\t"
+
+            //-----------------------------------------
+            // STORE Equal 32N-------------------------
+            "_ST32%=:                             \n\t"
+            "vsetvli      t0, t4, e32, m1         \n\t"
+            "vse32.v      v2, (s6)                \n\t"  // M0 [N0 : N32]; FP32(1024bit)
+
+            "_FUNC_END%=:                         \n\t"
+
+            :
+            : [BCK] "r"(cnt), [NBLKS] "r"(nblks), [pA] "r"(quant_a_ptr), [pB] "r"(QuantBDataPtr), [pC] "r"(CPtr)
+            : "cc", "t0", "t3", "t4", "f0", "s2", "s3", "s4", "s5", "s6");
+    }
+}
+
+void gemm_kernel_i8i8_m4(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc) {
+    int64_t b_data_stride = k_blks * sizeof(ggml_fp16_t) + k_blks * blk_len;
+    for (size_t ni = 0; ni < count_n; ni += 32) {
+        uint8_t * b_data = (uint8_t *) quant_b_data + ni * b_data_stride;
+        int8_t *  a_data = (int8_t *) quant_a_ptr;
+        float *   dst_c  = c_ptr + ni;
+
+        asm volatile(
+            "vsetvli        t0, x0, e32, m1       \n\t"
+            "vxor.vv        v28, v28, v28         \n\t"
+            "vxor.vv        v29, v29, v29         \n\t"
+            "vxor.vv        v30, v30, v30         \n\t"
+            "vxor.vv        v31, v31, v31         \n\t"
+
+            ".align 4                             \n\t"
+            "BLK_LOOP%=:                          \n\t"
+            // load scale A
+            "flw            fa0, (%[A])           \n\t"
+            "flw            fa1, 4(%[A])          \n\t"
+            "flw            fa2, 8(%[A])          \n\t"
+            "flw            fa3, 12(%[A])         \n\t"
+            "addi           %[A], %[A], 16+8      \n\t"  // Ascl+Asum; FP32*4+i16*4
+
+            // load scale B
+            "vsetvli        t0, x0, e16, mf2      \n\t"
+            "vle16.v        v12, (%[B])           \n\t"
+            "addi           %[B], %[B], 64        \n\t"
+            "vfwcvt.f.f.v   v14, v12              \n\t"
+
+            "vsetvli        t0, x0, e8, m1        \n\t"
+            "vl1r.v         v0, (%[A])            \n\t"
+            "addi           %[A], %[A], 128       \n\t"  // 4*32@i8
+            "vl4r.v         v4, (%[B])            \n\t"  // 32*32@i8
+            "addi           %[B], %[B], 512       \n\t"
+            "vl4r.v         v8, (%[B])            \n\t"  // 32*32@i8
+            "addi           %[B], %[B], 512       \n\t"
+
+            "vsetvli        t0, zero, e32, m1     \n\t"
+            "vupack.vv      v2, v0, v0, 1         \n\t"
+
+            "vupack.vv      v24, v4, v5, 1        \n\t"
+            "vupack.vv      v26, v6, v7, 1        \n\t"
+            "vupack.vv      v4, v8, v9, 1         \n\t"
+            "vupack.vv      v6, v10, v11, 1       \n\t"
+
+            // init the accumu to asum * zp
+            "vsetvli        t0, x0, e32, m1       \n\t"
+            "vxor.vv        v16, v16, v16         \n\t"
+            "vxor.vv        v18, v16, v16         \n\t"
+            "vxor.vv        v20, v16, v16         \n\t"
+            "vxor.vv        v22, v16, v16         \n\t"
+
+            // i4 * i4 vmadot
+            "vsetvli        t0, x0, e32, m1       \n\t"
+            "vmadot         v16, v2, v24, i8      \n\t"
+            "vmadot         v18, v2, v26, i8      \n\t"
+            "vmadot         v20, v2, v4, i8       \n\t"
+            "vmadot         v22, v2, v6, i8       \n\t"
+            "vmadot         v16, v3, v25, i8      \n\t"
+            "vmadot         v18, v3, v27, i8      \n\t"
+            "vmadot         v20, v3, v5, i8       \n\t"
+            "vmadot         v22, v3, v7, i8       \n\t"
+
+            "vpack.vv       v0, v16, v18, 2       \n\t"
+            "vpack.vv       v2, v20, v22, 2       \n\t"
+            "vpack.vv       v16, v0, v2, 3        \n\t"
+            "vpack.vv       v18, v1, v3, 3        \n\t"
+
+            "vfcvt.f.x.v    v16, v16              \n\t"
+            "vfcvt.f.x.v    v17, v17              \n\t"
+            "vfcvt.f.x.v    v18, v18              \n\t"
+            "vfcvt.f.x.v    v19, v19              \n\t"
+
+            // mul scale
+            "vfmul.vv       v16, v16, v14         \n\t"
+            "vfmul.vv       v17, v17, v14         \n\t"
+            "vfmul.vv       v18, v18, v14         \n\t"
+            "vfmul.vv       v19, v19, v14         \n\t"
+
+            "addi           %[BK], %[BK], -1      \n\t"
+            "vfmacc.vf      v28, fa0, v16         \n\t"
+            "vfmacc.vf      v29, fa1, v17         \n\t"
+            "vfmacc.vf      v30, fa2, v18         \n\t"
+            "vfmacc.vf      v31, fa3, v19         \n\t"
+
+            "bgtz           %[BK], BLK_LOOP%=     \n\t"
+
+            // save
+            "vsetvli        t0, x0, e32, m1       \n\t"
+            "add            t2, %[LDC], %[DST]    \n\t"
+            "vse32.v        v28, (%[DST])         \n\t"
+            "add            t3, %[LDC], t2        \n\t"
+            "vse32.v        v29, (t2)             \n\t"
+            "add            t2, %[LDC], t3        \n\t"
+            "vse32.v        v30, (t3)             \n\t"
+            "vse32.v        v31, (t2)             \n\t"
+            : [A] "+r"(a_data), [B] "+r"(b_data)
+            : [DST] "r"(dst_c), [LDC] "r"(ldc * 4), [BK] "r"(k_blks)
+            : "t0", "t1", "t2", "t3", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12",
+              "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27",
+              "v28", "v29", "v30", "v31", "fa0", "fa1", "fa2", "fa3");
+    }
+}
+
+void moe_m2_gemm_kernel_i8i4_impl(size_t           blk_len,
+                                  const uint8_t ** quant_a_ptr,
+                                  const uint8_t *  quant_b_data,
+                                  const uint8_t *  quant_b_zp,
+                                  float **         c_ptr,
+                                  size_t           count_m,
+                                  size_t           count_n,
+                                  size_t           k_blks,
+                                  size_t           ldc) {
+#if 0
+    moe_gemm_kernel_i8i4_mrow_ref<2, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks,
+                                     ldc);
+#else
+    int64_t b_data_stride =
+        k_blks * (sizeof(ggml_fp16_t) + 16 * sizeof(int8_t) + (quant_b_zp != NULL ? sizeof(int8_t) : 0));
+    if (quant_b_zp == NULL) {
+        for (size_t ni = 0; ni < count_n; ni += 32) {
+            uint8_t * b_data  = (uint8_t *) quant_b_data + ni * b_data_stride;
+            int8_t *  a_data0 = (int8_t *) quant_a_ptr[0];
+            int8_t *  a_data1 = (int8_t *) quant_a_ptr[1];
+            float *   dst_c0  = (float *) c_ptr[0] + ni;
+            float *   dst_c1  = (float *) c_ptr[1] + ni;
+
+            asm volatile(
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vxor.vv        v28, v28, v28           \n\t"
+                "vxor.vv        v29, v29, v29           \n\t"
+                "vmv.v.i        v0, 1                   \n\t"  // init the scale
+                "vsll.vi        v1, v0, 4               \n\t"
+                "vfcvt.f.x.v    v0, v0                  \n\t"
+                "vfcvt.f.x.v    v1, v1                  \n\t"
+                "mv             t3, %[BK]               \n\t"
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                // load scale A0
+                "flw            fa0, (%[A0])            \n\t"  // A0 scale
+                "lh             t1, 4(%[A0])            \n\t"  // A0 asum
+                "addi           %[A0], %[A0], 6         \n\t"
+
+                // load scale B
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vle16.v        v12, (%[B])             \n\t"
+                "addi           %[B], %[B], 64          \n\t"
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vpack.vv       v14, v12, v12, 3        \n\t"
+
+                // load scale A1
+                "flw            fa1, (%[A1])            \n\t"  // A1 scale
+                "lh             t2, 4(%[A1])            \n\t"  // A1 asum
+                "addi           %[A1], %[A1], 6         \n\t"
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vmv.v.x        v10, t1                 \n\t"
+                "vmv.v.x        v11, t2                 \n\t"
+
+                "vpack.vv       v18, v10, v11, 1        \n\t"
+                "vsll.vi        v18, v18, 3             \n\t"  // mul 8
+                "vfcvt.f.x.v    v18, v18                \n\t"
+
+                "vsetvli        t0, x0, e8, mf4         \n\t"  // A0 data
+                "vle8.v         v16, (%[A0])            \n\t"
+                "addi           %[A0], %[A0], 32        \n\t"  // 1*32@i8
+                "vle8.v         v20, (%[A1])            \n\t"
+                "addi           %[A1], %[A1], 32        \n\t"  // 1*32@i8
+
+                "vl4r.v         v4, (%[B])              \n\t"  // 32*32@i4
+                "addi           %[B], %[B], 512         \n\t"
+
+                "vsrl.vi        v17, v16, 4             \n\t"
+                "vsrl.vi        v21, v20, 4             \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vnpack4.vv     v2, v16, v20, 2         \n\t"  // low  u4
+                "vnpack4.vv     v3, v17, v21, 2         \n\t"  // high s4
+
+                // init the accumu to asum * zp
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vor.vv         v19, v18, v18           \n\t"
+                "vor.vv         v20, v18, v18           \n\t"
+                "vor.vv         v21, v18, v18           \n\t"
+
+                // i4 * i4 vmadot
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vmadotsu.hp    v18, v3, v4, v1, 0, i4  \n\t"  // high 4
+                "vmadotsu.hp    v19, v3, v5, v1, 0, i4  \n\t"
+                "vmadotsu.hp    v20, v3, v6, v1, 0, i4  \n\t"
+                "vmadotsu.hp    v21, v3, v7, v1, 0, i4  \n\t"
+                "vmadotu.hp     v18, v2, v4, v0, 0, i4  \n\t"  // low 4
+                "vmadotu.hp     v19, v2, v5, v0, 0, i4  \n\t"
+                "vmadotu.hp     v20, v2, v6, v0, 0, i4  \n\t"
+                "vmadotu.hp     v21, v2, v7, v0, 0, i4  \n\t"
+
+                "vpack.vv       v8, v18, v19, 1         \n\t"
+                "vpack.vv       v12, v20, v21, 1        \n\t"
+                "vpack.vv       v20, v8, v12, 2         \n\t"
+
+                "vfwmul.vv      v16, v20, v14           \n\t"
+
+                "vsetvli        t0, x0, e32, m1         \n\t"
+
+                "addi           t3, t3, -1              \n\t"
+                "vfmacc.vf      v28, fa0, v16           \n\t"
+                "vfmacc.vf      v29, fa1, v17           \n\t"
+
+                "bgtz           t3, BLK_LOOP%=          \n\t"
+
+                // save
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vse32.v        v28, (%[DST0])          \n\t"
+                "vse32.v        v29, (%[DST1])          \n\t"
+                : [A0] "+r"(a_data0), [A1] "+r"(a_data1), [B] "+r"(b_data)
+                : [DST0] "r"(dst_c0), [DST1] "r"(dst_c1), [BK] "r"(k_blks)
+                : "t0", "t1", "t2", "t3", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11",
+                  "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
+                  "v26", "v27", "v28", "v29", "v30", "v31", "fa0", "fa1", "fa2", "fa3");
+        }
+    } else {
+#    if 0
+        moe_gemm_kernel_i8i4_mrow_ref<2, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                         k_blks, ldc);
+#    else
+        for (size_t ni = 0; ni < count_n; ni += 32) {
+            uint8_t * b_data  = (uint8_t *) quant_b_data + ni * b_data_stride;
+            int8_t *  a_data0 = (int8_t *) quant_a_ptr[0];
+            int8_t *  a_data1 = (int8_t *) quant_a_ptr[1];
+            float *   dst_c0  = (float *) c_ptr[0] + ni;
+            float *   dst_c1  = (float *) c_ptr[1] + ni;
+
+            asm volatile(
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vxor.vv        v28, v28, v28           \n\t"
+                "vxor.vv        v29, v29, v29           \n\t"
+                "vmv.v.i        v0, 1                   \n\t"  // init the scale
+                "vsll.vi        v1, v0, 4               \n\t"
+                "vfcvt.f.x.v    v0, v0                  \n\t"
+                "vfcvt.f.x.v    v1, v1                  \n\t"
+                "mv             t3, %[BK]               \n\t"
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                // load scale A0
+                "flw            fa0, (%[A0])            \n\t"  // A0 scale
+                "lh             t1, 4(%[A0])            \n\t"  // A0 asum
+                "addi           %[A0], %[A0], 6         \n\t"
+
+                // load scale B
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vle16.v        v12, (%[B])             \n\t"
+                "addi           %[B], %[B], 64          \n\t"
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vpack.vv       v14, v12, v12, 3        \n\t"
+
+                // load scale A1
+                "flw            fa1, (%[A1])            \n\t"  // A1 scale
+                "lh             t2, 4(%[A1])            \n\t"  // A1 asum
+                "addi           %[A1], %[A1], 6         \n\t"
+
+                // load zp
+                "vsetvli        t0, x0, e8, mf4         \n\t"
+                "vle8.v         v8, (%[B])              \n\t"
+                "addi           %[B], %[B], 32          \n\t"
+                "vwaddu.vx      v10, v8, x0             \n\t"
+
+                "vsetvli        t0, x0, e8, mf4         \n\t"  // A0 data
+                "vle8.v         v16, (%[A0])            \n\t"
+                "addi           %[A0], %[A0], 32        \n\t"  // 1*32@i8
+                "vle8.v         v20, (%[A1])            \n\t"
+                "addi           %[A1], %[A1], 32        \n\t"  // 1*32@i8
+
+                "vl4r.v         v4, (%[B])              \n\t"  // 32*32@i4
+                "addi           %[B], %[B], 512         \n\t"
+
+                "vsrl.vi        v17, v16, 4             \n\t"
+                "vsrl.vi        v21, v20, 4             \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vnpack4.vv     v2, v16, v20, 2         \n\t"  // low  u4
+                "vnpack4.vv     v3, v17, v21, 2         \n\t"  // high s4
+
+                // init the accumu to asum * zp
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vxor.vv        v18, v18, v18           \n\t"
+                "vxor.vv        v19, v19, v19           \n\t"
+                "vxor.vv        v20, v20, v20           \n\t"
+                "vxor.vv        v21, v21, v21           \n\t"
+
+                // i4 * i4 vmadot
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vmadotsu.hp    v18, v3, v4, v1, 0, i4  \n\t"  // high 4
+                "vmadotsu.hp    v19, v3, v5, v1, 0, i4  \n\t"
+                "vmadotsu.hp    v20, v3, v6, v1, 0, i4  \n\t"
+                "vmadotsu.hp    v21, v3, v7, v1, 0, i4  \n\t"
+                "vmadotu.hp     v18, v2, v4, v0, 0, i4  \n\t"  // low 4
+                "vmadotu.hp     v19, v2, v5, v0, 0, i4  \n\t"
+                "vmadotu.hp     v20, v2, v6, v0, 0, i4  \n\t"
+                "vmadotu.hp     v21, v2, v7, v0, 0, i4  \n\t"
+
+                "vpack.vv       v8, v18, v19, 1         \n\t"
+                "vpack.vv       v12, v20, v21, 1        \n\t"
+                "vpack.vv       v20, v8, v12, 2         \n\t"
+                // asum*zp
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vwmul.vx       v2, v10, t1             \n\t"
+                "vwmul.vx       v4, v10, t2             \n\t"
+
+                "vsetvli        t0, x0, e32, m1         \n\t"
+
+                "vfcvt.f.x.v    v2, v2                  \n\t"
+                "vfcvt.f.x.v    v4, v4                  \n\t"
+
+                "vsetvli        t0, x0, e16, m1         \n\t"
+                "vfwcvt.f.f.v   v16, v20                \n\t"
+
+                "vfwcvt.f.f.v   v18, v14                \n\t"
+
+                // +asum*zp
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vfadd.vv       v16, v16, v2            \n\t"
+                "vfadd.vv       v17, v17, v4            \n\t"
+                "vfmul.vv       v16, v16, v18           \n\t"
+                "vfmul.vv       v17, v17, v18           \n\t"
+
+                "addi           t3, t3, -1              \n\t"
+                "vfmacc.vf      v28, fa0, v16           \n\t"
+                "vfmacc.vf      v29, fa1, v17           \n\t"
+
+                "bgtz           t3, BLK_LOOP%=          \n\t"
+
+                // save
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vse32.v        v28, (%[DST0])          \n\t"
+                "vse32.v        v29, (%[DST1])          \n\t"
+                : [A0] "+r"(a_data0), [A1] "+r"(a_data1), [B] "+r"(b_data)
+                : [DST0] "r"(dst_c0), [DST1] "r"(dst_c1), [BK] "r"(k_blks)
+                : "t0", "t1", "t2", "t3", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11",
+                  "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",
+                  "v26", "v27", "v28", "v29", "v30", "v31", "fa0", "fa1", "fa2", "fa3");
+        }
+#    endif
+    }
+#endif
+}
+
+void moe_m2_gemm_kernel_i8i5_impl(size_t           blk_len,
+                                  const uint8_t ** quant_a_ptr,
+                                  const uint8_t *  quant_b_data,
+                                  const uint8_t *  quant_b_zp,
+                                  float **         c_ptr,
+                                  size_t           count_m,
+                                  size_t           count_n,
+                                  size_t           k_blks,
+                                  size_t           ldc) {
+    constexpr size_t NB_COLS          = 32;
+    constexpr size_t B_Q50_BLK_STRIDE = sizeof(nrow_block_q5_0<NB_COLS>);
+    constexpr size_t B_Q51_BLK_STRIDE = sizeof(nrow_block_q5_1<NB_COLS>);
+
+    GGML_UNUSED(blk_len);
+    GGML_UNUSED(count_m);
+    GGML_UNUSED(ldc);
+
+    if (quant_b_zp == NULL) {
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            size_t    nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+            uint8_t * b_data  = (uint8_t *) quant_b_data + (ni / NB_COLS) * k_blks * B_Q50_BLK_STRIDE;
+            int8_t *  a_data0 = (int8_t *) quant_a_ptr[0];
+            int8_t *  a_data1 = (int8_t *) quant_a_ptr[1];
+            float *   dst_c0  = (float *) c_ptr[0] + ni;
+            float *   dst_c1  = (float *) c_ptr[1] + ni;
+
+            asm volatile(
+                "mv             t4, %[BK]               \n\t"
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v2, v0, v0              \n\t"
+                "vxor.vv        v3, v0, v0              \n\t"
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                // ---- load B scale/Bh/Bs and advance to the next q5_0 k-block ----
+                "vsetvli        t0, x0, e8, mf2         \n\t"
+                "vle8.v         v1, (%[B])              \n\t"  // v1 = scale_fp16 × 32
+                "addi           %[B], %[B], 64          \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vle8.v         v0, (%[B])              \n\t"  // v0 = Bh N32K32 1-bit packed
+                "addi           %[B], %[B], 128         \n\t"
+                "vl4r.v         v8, (%[B])              \n\t"  // v8..v11 = Bs N32K32 i4
+                "addi           %[B], %[B], 512         \n\t"
+
+                // ---- load A0/A1 header then payload, each block stride = 38B ----
+                "flw            f0, (%[A0])             \n\t"  // f0 = A0 scale (fp32)
+                "lh             t2, 4(%[A0])            \n\t"  // t2 = A0 asum (int16)
+                "addi           %[A0], %[A0], 6         \n\t"
+                "flw            f1, (%[A1])             \n\t"  // f1 = A1 scale (fp32)
+                "lh             t3, 4(%[A1])            \n\t"  // t3 = A1 asum (int16)
+                "addi           %[A1], %[A1], 6         \n\t"
+                "vsetvli        t0, x0, e8, mf4         \n\t"
+                "vle8.v         v4, (%[A0])             \n\t"  // v4 = A0 M1K32 int8
+                "addi           %[A0], %[A0], 32        \n\t"
+                "vle8.v         v5, (%[A1])             \n\t"  // v5 = A1 M1K32 int8
+                "addi           %[A1], %[A1], 32        \n\t"
+
+                //// ---- A nibble unpacking ----
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vand.vi        v12, v8, 0xF            \n\t"  //8bit(lo4) //[8*32]
+                "vand.vi        v13, v9, 0xF            \n\t"
+                "vand.vi        v14, v10, 0xF           \n\t"
+                "vand.vi        v15, v11, 0xF           \n\t"
+                "vsrl.vi        v8, v8, 4               \n\t"  //8bit(hi4)
+                "vsrl.vi        v9, v9, 4               \n\t"
+                "vsrl.vi        v10, v10, 4             \n\t"
+                "vsrl.vi        v11, v11, 4             \n\t"
+
+                "slli           t2, t2, 4               \n\t"  // a_sum * 16;
+                "slli           t3, t3, 4               \n\t"
+                // [4*32]*2
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vpack.vv       v16, v12, v8, 0         \n\t"
+                "vpack.vv       v18, v13, v9, 0         \n\t"
+                "vpack.vv       v20, v14, v10, 0        \n\t"
+                "vpack.vv       v22, v15, v11, 0        \n\t"
+
+                "li             t1, 16                  \n\t"
+                "vsetvli        t0, x0, e8, m8          \n\t"
+                "vadd.vx        v16, v16, t1, v0.t      \n\t"
+
+                // [4*32]*2 -> [8*16]
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vupack.vv      v8, v16, v17, 1         \n\t"
+                "vupack.vv      v10, v18, v19, 1        \n\t"
+                "vupack.vv      v12, v20, v21, 1        \n\t"
+                "vupack.vv      v14, v22, v23, 1        \n\t"
+
+                "vpack.vv       v6, v4, v5, 2           \n\t"
+
+                // init the accumu to asum * zp
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v24, v16, v16           \n\t"
+                "vxor.vv        v26, v16, v16           \n\t"
+                "vupack.vv      v4, v6, v7, 1           \n\t"
+                "vxor.vv        v28, v16, v16           \n\t"
+                "vxor.vv        v30, v16, v16           \n\t"
+
+                // ---- i8 main dot products ----
+                // vmadot: A × unsigned Bh × 16 → fp16 accumulate
+                "vmadot         v24, v4, v8, i8         \n\t"  // N0..7
+                "vmadot         v26, v4, v10, i8        \n\t"  // N8..15
+                "vmadot         v28, v4, v12, i8        \n\t"  // N16..23
+                "vmadot         v30, v4, v14, i8        \n\t"  // N24..31
+                // vmadot: A × unsigned Bh × 1 → fp16 accumulate
+                "vmadot         v24, v5, v9, i8         \n\t"  // N0..7
+                "vmadot         v26, v5, v11, i8        \n\t"  // N8..15
+                "vmadot         v28, v5, v13, i8        \n\t"  // N16..23
+                "vmadot         v30, v5, v15, i8        \n\t"  // N24..31
+
+                "vpack.vv       v16, v24, v26, 2        \n\t"  // v16 = N0..15
+                "vpack.vv       v18, v28, v30, 2        \n\t"  // v18 = N16..31
+                "vpack.vv       v24, v16, v18, 3        \n\t"  // v24 = N0..31
+
+                "vadd.vx        v24, v24, t2            \n\t"
+                "vadd.vx        v25, v25, t3            \n\t"
+                // b_scale fp16 -> fp32
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vfwcvt.f.f.v   v28, v1                 \n\t"
+
+                // a_scale * b_scale;
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vfcvt.f.x.v    v26, v24                \n\t"
+                "vfcvt.f.x.v    v27, v25                \n\t"
+                "vfmul.vf       v30, v28, f0            \n\t"
+                "vfmul.vf       v31, v28, f1            \n\t"
+                // static_cast<float>(qsum) * a_scale * b_scale;
+                "vfmacc.vv      v2, v30, v26            \n\t"
+                "vfmacc.vv      v3, v31, v27            \n\t"
+
+                "addi           t4, t4, -1              \n\t"
+                "bgtz           t4, BLK_LOOP%=          \n\t"
+
+                "vsetvli        t0, %[NR], e32, m1      \n\t"
+                "vse32.v        v2, (%[DST0])           \n\t"
+                "vse32.v        v3, (%[DST1])           \n\t"
+                : [A0] "+r"(a_data0), [A1] "+r"(a_data1), [B] "+r"(b_data)
+                : [DST0] "r"(dst_c0), [DST1] "r"(dst_c1), [BK] "r"(k_blks), [NR] "r"(nb_real)
+                : "cc", "memory", "t0", "t1", "t2", "t3", "t4", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8",
+                  "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22",
+                  "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "f0", "f1");
+        }
+    } else {
+        for (size_t ni = 0; ni < count_n; ni += NB_COLS) {
+            size_t    nb_real = std::min<size_t>(NB_COLS, count_n - ni);
+            uint8_t * b_data  = (uint8_t *) quant_b_data + (ni / NB_COLS) * k_blks * B_Q51_BLK_STRIDE;
+            int8_t *  a_data0 = (int8_t *) quant_a_ptr[0];
+            int8_t *  a_data1 = (int8_t *) quant_a_ptr[1];
+            float *   dst_c0  = (float *) c_ptr[0] + ni;
+            float *   dst_c1  = (float *) c_ptr[1] + ni;
+
+            asm volatile(
+                "mv             t4, %[BK]               \n\t"
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v2, v0, v0              \n\t"
+                "vxor.vv        v3, v0, v0              \n\t"
+                "addi           t5, %[B], 64            \n\t"  // t5 = zp   (32B)
+                "addi           t6, %[B], 96            \n\t"  // t6 = qh   (128B)
+                "addi           s1, %[B], 224           \n\t"  // s1 = qs   (512B)
+
+                ".align 4                               \n\t"
+                "BLK_LOOP%=:                            \n\t"
+                // ---- load B scale/zp/Bh/Bs and advance to the next q5_1 k-block ----
+                "vsetvli        t0, x0, e8, mf2         \n\t"
+                "vle8.v         v1, (%[B])              \n\t"  // v1 = scale_fp16 × 32
+                "addi           %[B], %[B], 736         \n\t"
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vle8.v         v0, (t6)                \n\t"  // v0 = Bh N32K32 1-bit packed
+                "addi           t6, t6, 736             \n\t"
+                "vl4r.v         v8, (s1)                \n\t"  // v8..v11 = Bs N32K32 i4
+                "addi           s1, s1, 736             \n\t"
+
+                // ---- load A0/A1 header then payload, each block stride = 38B ----
+                "flw            f0, (%[A0])             \n\t"  // f0 = A0 scale (fp32)
+                "lh             t2, 4(%[A0])            \n\t"  // t2 = A0 asum (int16)
+                "addi           %[A0], %[A0], 6         \n\t"
+                "flw            f1, (%[A1])             \n\t"  // f1 = A1 scale (fp32)
+                "lh             t3, 4(%[A1])            \n\t"  // t3 = A1 asum (int16)
+                "addi           %[A1], %[A1], 6         \n\t"
+                "vsetvli        t0, x0, e8, mf4         \n\t"
+                "vle8.v         v4, (%[A0])             \n\t"  // v4 = A0 M1K32 int8
+                "addi           %[A0], %[A0], 32        \n\t"
+                "vle8.v         v5, (%[A1])             \n\t"  // v5 = A1 M1K32 int8
+                "addi           %[A1], %[A1], 32        \n\t"
+
+                //// ---- A nibble unpacking ----
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vand.vi        v12, v8, 0xF            \n\t"  //8bit(lo4) //[8*32]
+                "vand.vi        v13, v9, 0xF            \n\t"
+                "vand.vi        v14, v10, 0xF           \n\t"
+                "vand.vi        v15, v11, 0xF           \n\t"
+                "vsrl.vi        v8, v8, 4               \n\t"  //8bit(hi4)
+                "vsrl.vi        v9, v9, 4               \n\t"
+                "vsrl.vi        v10, v10, 4             \n\t"
+                "vsrl.vi        v11, v11, 4             \n\t"
+
+                // q5_1 uses explicit zp, so keep a_sum unshifted here.
+                // [4*32]*2
+                "vpack.vv       v16, v12, v8, 0         \n\t"
+                "vpack.vv       v18, v13, v9, 0         \n\t"
+                "vpack.vv       v20, v14, v10, 0        \n\t"
+                "vpack.vv       v22, v15, v11, 0        \n\t"
+
+                "li             t1, 16                  \n\t"
+                "vsetvli        t0, x0, e8, m8          \n\t"
+                "vadd.vx        v16, v16, t1, v0.t      \n\t"
+
+                // [4*32]*2 -> [8*16]
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vupack.vv      v8, v16, v17, 1         \n\t"
+                "vupack.vv      v10, v18, v19, 1        \n\t"
+                "vupack.vv      v12, v20, v21, 1        \n\t"
+                "vupack.vv      v14, v22, v23, 1        \n\t"
+
+                "vpack.vv       v6, v4, v5, 2           \n\t"
+
+                // init the accumu to asum * zp
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vxor.vv        v24, v16, v16           \n\t"
+                "vxor.vv        v26, v16, v16           \n\t"
+                "vupack.vv      v4, v6, v7, 1           \n\t"
+                "vxor.vv        v28, v16, v16           \n\t"
+                "vxor.vv        v30, v16, v16           \n\t"
+
+                // ---- i8 main dot products ----
+                // vmadot: A × unsigned Bh × 16 → fp16 accumulate
+                "vmadot         v24, v4, v8, i8         \n\t"  // N0..7
+                "vmadot         v26, v4, v10, i8        \n\t"  // N8..15
+                "vmadot         v28, v4, v12, i8        \n\t"  // N16..23
+                "vmadot         v30, v4, v14, i8        \n\t"  // N24..31
+                // vmadot: A × unsigned Bh × 1 → fp16 accumulate
+                "vmadot         v24, v5, v9, i8         \n\t"  // N0..7
+                "vmadot         v26, v5, v11, i8        \n\t"  // N8..15
+                "vmadot         v28, v5, v13, i8        \n\t"  // N16..23
+                "vmadot         v30, v5, v15, i8        \n\t"  // N24..31
+
+                "vsetvli        t0, x0, e8, mf4         \n\t"
+                "vle8.v         v4, (t5)                \n\t"  // v4 = Bzp N32 uint8
+                "addi           t5, t5, 736             \n\t"
+
+                "vsetvli        t0, x0, e8, m1          \n\t"
+                "vpack.vv       v16, v24, v26, 2        \n\t"  // v16 = N0..15
+                "vpack.vv       v18, v28, v30, 2        \n\t"  // v18 = N16..31
+                "vpack.vv       v24, v16, v18, 3        \n\t"  // v24 = N0..31
+
+                "vsetvli        t0, x0, e8, mf4         \n\t"
+                "vwaddu.vx      v28, v4, x0             \n\t"
+
+                "vsetvli        t0, x0, e16, mf2        \n\t"
+                "vwmul.vx       v30, v28, t2            \n\t"
+                "vwmul.vx       v31, v28, t3            \n\t"
+
+                // b_scale fp16 -> fp32
+                "vfwcvt.f.f.v   v28, v1                 \n\t"
+
+                "vsetvli        t0, x0, e32, m1         \n\t"
+                "vadd.vv        v24, v24, v30           \n\t"
+                "vadd.vv        v25, v25, v31           \n\t"
+
+                // a_scale * b_scale;
+                "vfcvt.f.x.v    v26, v24                \n\t"
+                "vfcvt.f.x.v    v27, v25                \n\t"
+                "vfmul.vf       v30, v28, f0            \n\t"
+                "vfmul.vf       v31, v28, f1            \n\t"
+                // static_cast<float>(qsum) * a_scale * b_scale;
+                "vfmacc.vv      v2, v30, v26            \n\t"
+                "vfmacc.vv      v3, v31, v27            \n\t"
+
+                "addi           t4, t4, -1              \n\t"
+                "bgtz           t4, BLK_LOOP%=          \n\t"
+
+                "vsetvli        t0, %[NR], e32, m1      \n\t"
+                "vse32.v        v2, (%[DST0])           \n\t"
+                "vse32.v        v3, (%[DST1])           \n\t"
+                : [A0] "+r"(a_data0), [A1] "+r"(a_data1), [B] "+r"(b_data)
+                : [DST0] "r"(dst_c0), [DST1] "r"(dst_c1), [BK] "r"(k_blks), [NR] "r"(nb_real)
+                : "cc", "memory", "t0", "t1", "t2", "t3", "t4", "t5", "t6", "s1", "v0", "v1", "v2", "v3", "v4", "v5",
+                  "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20",
+                  "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "f0", "f1");
+        }
+    }
+}
+
+size_t gemm_kernel_i8i2k(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc) {
+    if (count_m >= 4) {
+#if 0
+        gemm_kernel_i8i2k_mrow_ref<4, 32>(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_m, count_n, k_blks, ldc);
+#else
+        gemm_kernel_i8i2k_m4(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 4;
+    } else {
+#if 0
+        gemm_kernel_i8i2k_mrow_ref<1, 32>(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_m, count_n, k_blks,
+                                      ldc);
+#else
+        gemm_kernel_i8i2k_m1(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 1;
+    }
+}
+
+size_t gemm_kernel_i8i3k(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc) {
+    if (count_m >= 4) {
+#if 0
+        gemm_kernel_i8i3k_mrow_ref<4, 32>(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_m, count_n, k_blks, ldc);
+#else
+        gemm_kernel_i8i3k_m4(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 4;
+    } else {
+#if 0
+        gemm_kernel_i8i3k_mrow_ref<1, 32>(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_m, count_n, k_blks, ldc);
+#else
+        gemm_kernel_i8i3k_m1(blk_len, quant_a_ptr, quant_b_data, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 1;
+    }
+}
+
+size_t gemm_kernel_i8i4(size_t          blk_len,
+                        const uint8_t * quant_a_ptr,
+                        const uint8_t * quant_b_data,
+                        const uint8_t * quant_b_zp,
+                        float *         c_ptr,
+                        size_t          count_m,
+                        size_t          count_n,
+                        size_t          k_blks,
+                        size_t          ldc) {
+    if (count_m >= 4) {
+#if 0
+        gemm_kernel_i8i4_mrow_ref<4, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                         k_blks, ldc);
+#else
+        gemm_kernel_i8i4_m4(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 4;
+    } else {
+#if 0
+        gemm_kernel_i8i4_mrow_ref<1, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                         k_blks, ldc);
+#else
+        gemm_kernel_i8i4_m1(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 1;
+    }
+}
+
+size_t gemm_kernel_i8i4_hp(size_t          blk_len,
+                           const uint8_t * quant_a_ptr,
+                           const uint8_t * quant_b_data,
+                           const uint8_t * quant_b_zp,
+                           float *         c_ptr,
+                           size_t          count_m,
+                           size_t          count_n,
+                           size_t          k_blks,
+                           size_t          ldc) {
+    if (count_m >= 4) {
+#if 0
+        gemm_kernel_i8i4_hp_mrow_ref<4, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                            k_blks, ldc);
+#else
+        gemm_kernel_i8i4_hp_m4(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 4;
+    } else {
+#if 0
+        gemm_kernel_i8i4_hp_mrow_ref<1, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                            k_blks, ldc);
+#else
+        gemm_kernel_i8i4_hp_m1(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 1;
+    }
+}
+
+size_t moe_m2_gemm_kernel_i8i4(size_t           blk_len,
+                               const uint8_t ** quant_a_ptr,
+                               const uint8_t *  quant_b_data,
+                               const uint8_t *  quant_b_zp,
+                               float **         c_ptr,
+                               size_t           count_m,
+                               size_t           count_n,
+                               size_t           k_blks,
+                               size_t           ldc) {
+    moe_m2_gemm_kernel_i8i4_impl(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+    return 2;
+}
+
+size_t gemm_kernel_i8i8(size_t          blk_len,
+                        const uint8_t * quant_a_ptr,
+                        const uint8_t * quant_b_data,
+                        const uint8_t * quant_b_zp,
+                        float *         c_ptr,
+                        size_t          count_m,
+                        size_t          count_n,
+                        size_t          k_blks,
+                        size_t          ldc) {
+    if (count_m >= 4) {
+#if 0
+        gemm_kernel_i8i8_mrow_ref<4, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                         k_blks, ldc);
+#else
+        gemm_kernel_i8i8_m4(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 4;
+    } else {
+#if 0
+        gemm_kernel_i8i8_mrow_ref<1, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                         k_blks, ldc);
+#else
+        gemm_kernel_i8i8_m1(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 1;
+    }
+}
+
+size_t gemm_kernel_i8mxfp4(size_t          blk_len,
+                           const uint8_t * quant_a_ptr,
+                           const uint8_t * quant_b_data,
+                           const uint8_t * quant_b_zp,
+                           float *         c_ptr,
+                           size_t          count_m,
+                           size_t          count_n,
+                           size_t          k_blks,
+                           size_t          ldc) {
+    if (count_m >= 4) {
+#if 1
+        gemm_kernel_i8mxfp4_mrow_ref<4, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                            k_blks, ldc);
+#else
+        gemm_kernel_i8mxfp4_m4(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 4;
+    } else {
+#if 1
+        gemm_kernel_i8mxfp4_mrow_ref<1, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                            k_blks, ldc);
+#else
+        gemm_kernel_i8mxfp4_m1(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 1;
+    }
+}
+
+size_t moe_m2_gemm_kernel_i8mxfp4(size_t           blk_len,
+                                  const uint8_t ** quant_a_ptr,
+                                  const uint8_t *  quant_b_data,
+                                  const uint8_t *  quant_b_zp,
+                                  float **         c_ptr,
+                                  size_t           count_m,
+                                  size_t           count_n,
+                                  size_t           k_blks,
+                                  size_t           ldc) {
+    //moe_m2_gemm_kernel_i8mxfp4_impl(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+    return 2;
+}
+
+size_t gemm_kernel_i8i5(size_t          blk_len,
+                        const uint8_t * quant_a_ptr,
+                        const uint8_t * quant_b_data,
+                        const uint8_t * quant_b_zp,
+                        float *         c_ptr,
+                        size_t          count_m,
+                        size_t          count_n,
+                        size_t          k_blks,
+                        size_t          ldc) {
+    if (count_m >= 4) {
+#if 0
+        gemm_kernel_i8i5_mrow_ref<4, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                         k_blks, ldc);
+#else
+        gemm_kernel_i8i5_m4(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 4;
+    } else {
+#if 0
+        gemm_kernel_i8i5_mrow_ref<1, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                         k_blks, ldc);
+#else
+        gemm_kernel_i8i5_m1(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+        return 1;
+    }
+}
+
+size_t moe_m2_gemm_kernel_i8i5(size_t           blk_len,
+                               const uint8_t ** quant_a_ptr,
+                               const uint8_t *  quant_b_data,
+                               const uint8_t *  quant_b_zp,
+                               float **         c_ptr,
+                               size_t           count_m,
+                               size_t           count_n,
+                               size_t           k_blks,
+                               size_t           ldc) {
+#if 0
+    moe_gemm_kernel_i8i5_mrow_ref<2, 32>(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n,
+                                         k_blks, ldc);
+#else
+    moe_m2_gemm_kernel_i8i5_impl(blk_len, quant_a_ptr, quant_b_data, quant_b_zp, c_ptr, count_m, count_n, k_blks, ldc);
+#endif
+    return 2;
+}
+
+}  // namespace ime2
+}  // namespace spacemit_kernels
diff --git a/ggml/src/ggml-cpu/spacemit/ime_env.cpp b/ggml/src/ggml-cpu/spacemit/ime_env.cpp
new file mode 100644
index 00000000000..a13ba391da2
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/ime_env.cpp
@@ -0,0 +1,320 @@
+#include "ime_env.h"
+
+#include "ggml-impl.h"
+#include "spine_mem_pool.h"
+
+#include <fcntl.h>
+#include <unistd.h>
+
+#include <algorithm>
+#include <array>
+#include <cctype>
+#include <fstream>
+#include <string>
+#include <thread>
+#include <unordered_map>
+
+namespace ggml::cpu::riscv64_spacemit {
+bool spine_core_info::get_spine_core_info(std::vector<spine_core_info> & result) {
+    static std::unordered_map<uint64_t, spine_core_arch_id> spine_march_mapping_ = {
+        {0x8000000058000001,  spine_core_arch_id::core_arch_x60 },
+        { 0x8000000041000001, spine_core_arch_id::core_arch_a60 },
+        { 0x8000000058000002, spine_core_arch_id::core_arch_x100},
+        { 0x8000000041000002, spine_core_arch_id::core_arch_a100},
+    };
+
+    result.clear();
+    std::ifstream file("/proc/cpuinfo");
+    std::string   line;
+
+    std::vector<std::array<uint64_t, 2>> cpu_info_list;
+
+    uint64_t current_processor = spine_invalid_core_id;
+    uint64_t current_marchid   = 0;
+    bool     has_processor     = false;
+    bool     has_marchid       = false;
+
+    if (!file.is_open()) {
+        return false;
+    }
+
+    while (std::getline(file, line)) {
+        if (line.substr(0, 9) == "processor") {
+            if (has_processor && has_marchid) {
+                cpu_info_list.push_back({ current_processor, current_marchid });
+            }
+
+            size_t colon_pos = line.find(':');
+            if (colon_pos != std::string::npos) {
+                current_processor = std::stoi(line.substr(colon_pos + 1));
+                has_processor     = true;
+            }
+
+            has_marchid = false;
+        } else if (line.substr(0, 7) == "marchid") {
+            size_t colon_pos = line.find(':');
+            if (colon_pos != std::string::npos) {
+                std::string marchid_str = line.substr(colon_pos + 1);
+                marchid_str.erase(std::remove_if(marchid_str.begin(), marchid_str.end(), isspace), marchid_str.end());
+                current_marchid = std::stoull(marchid_str, nullptr, 16);
+                has_marchid     = true;
+            }
+        }
+    }
+
+    if (has_processor && has_marchid) {
+        cpu_info_list.push_back({ current_processor, current_marchid });
+    }
+
+    if (has_processor && has_marchid) {
+        for (auto & cpu_info : cpu_info_list) {
+            if (cpu_info[0] != spine_invalid_core_id &&
+                spine_march_mapping_.find(cpu_info[1]) != spine_march_mapping_.end()) {
+                auto core_info    = spine_core_info();
+                core_info.core_id = cpu_info[0];
+                core_info.arch_id = spine_core_arch_id(spine_march_mapping_[cpu_info[1]]);
+
+                result.push_back(core_info);
+            }
+        }
+    }
+
+    return has_processor && has_marchid;
+}
+
+namespace {
+uint16_t hex_string_to_u16(const std::string & hex_str) {
+    try {
+        size_t pos = 0;
+        if (hex_str.substr(0, 2) == "0x" || hex_str.substr(0, 2) == "0X") {
+            pos = 2;
+        }
+        unsigned long result = std::stoul(hex_str.substr(pos), nullptr, 16);
+        if (result > std::numeric_limits<uint16_t>::max()) {
+            throw std::out_of_range("Converted value is out of range for uint16_t");
+        }
+        return static_cast<uint16_t>(result);
+    } catch (const std::invalid_argument & e) {
+        throw std::invalid_argument("Invalid hexadecimal string");
+    } catch (const std::out_of_range & e) {
+        throw;
+    }
+}
+
+const char * spine_mem_pool_backend_to_string(spine_mem_pool_backend backend) {
+    switch (backend) {
+        case spine_mem_pool_backend::none:
+            return "NONE";
+        case spine_mem_pool_backend::posix_memalign:
+            return "POSIX";
+        case spine_mem_pool_backend::transparent_hugepage:
+            return "HPAGE";
+        case spine_mem_pool_backend::hugetlb_1g:
+            return "HPAGE1GB";
+    }
+
+    return "unknown";
+}
+
+spine_mem_pool_backend parse_mem_backend(const char * mem_backend_str) {
+    if (mem_backend_str == nullptr || mem_backend_str[0] == '\0') {
+        return spine_mem_pool_backend::transparent_hugepage;
+    }
+
+    std::string value(mem_backend_str);
+    std::transform(value.begin(), value.end(), value.begin(),
+                   [](unsigned char ch) { return static_cast<char>(std::tolower(ch)); });
+
+    if (value == "none") {
+        return spine_mem_pool_backend::none;
+    }
+
+    if (value == "posix") {
+        return spine_mem_pool_backend::posix_memalign;
+    }
+
+    if (value == "hpage") {
+        return spine_mem_pool_backend::transparent_hugepage;
+    }
+
+    if (value == "hpage1gb") {
+        return spine_mem_pool_backend::hugetlb_1g;
+    }
+
+    throw std::runtime_error("invalid SPACEMIT_MEM_BACKEND: " + value + ", expected NONE, POSIX, HPAGE or HPAGE1GB");
+}
+}  // namespace
+
+spine_env_info::spine_env_info() {
+    num_cores = static_cast<int>(std::thread::hardware_concurrency());
+    spine_core_info::get_spine_core_info(core_info_list);
+
+    // special for x60 K1
+    if (core_info_list.size() == 8 && core_info_list[0].arch_id == spine_core_arch_id::core_arch_x60) {
+        for (int i = 0; i < 4; i++) {
+            core_info_list[i].arch_id = spine_core_arch_id::core_arch_a60;
+        }
+    }
+
+    // special for qemu
+    if (core_info_list.size() == 0) {
+        char * spine_core_arch_str = getenv("SPACEMIT_CORE_ARCH");
+        if (spine_core_arch_str != nullptr) {
+            auto arch_id = hex_string_to_u16(spine_core_arch_str);
+            for (int i = 0; i < num_cores; i++) {
+                auto core_info    = spine_core_info();
+                core_info.core_id = i;
+                core_info.arch_id = spine_core_arch_id{ arch_id };
+                core_info_list.push_back(core_info);
+            }
+        }
+    }
+
+    if (core_info_list.size() == 0) {
+        throw std::runtime_error(
+            "Failed to get SPACEMIT_CORE_ARCH from environment or failed to parse it from /proc/cpuinfo");
+    }
+
+    char * spine_perfer_core_arch_str = getenv("SPACEMIT_PERFER_CORE_ARCH");
+    if (spine_perfer_core_arch_str != nullptr && spine_perfer_core_arch_str != "") {
+        perfer_core_arch_id = spine_core_arch_id{ hex_string_to_u16(spine_perfer_core_arch_str) };
+    }
+
+    char *           spine_perfer_core_id_str = getenv("SPACEMIT_PERFER_CORE_ID");
+    std::vector<int> perfer_core_id_vec;
+    if (spine_perfer_core_id_str != nullptr && spine_perfer_core_id_str != "") {
+        std::string perfer_core_id_str(spine_perfer_core_id_str);
+        size_t      start = 0;
+        size_t      end   = 0;
+        while ((end = perfer_core_id_str.find(',', start)) != std::string::npos) {
+            std::string core_id_substr = perfer_core_id_str.substr(start, end - start);
+            perfer_core_id_vec.push_back(std::stoi(core_id_substr));
+            start = end + 1;
+        }
+        std::string core_id_substr = perfer_core_id_str.substr(start);
+        perfer_core_id_vec.push_back(std::stoi(core_id_substr));
+    }
+
+    perfer_core_ids.reserve(num_cores);
+    if (perfer_core_arch_id == spine_core_arch_id::core_arch_none) {
+        for (auto & core_info : core_info_list) {
+            auto core_arch_id   = core_info.arch_id;
+            auto core_arch_head = (uint16_t) (core_arch_id) >> 12;
+            if (core_arch_head == 0xA) {
+                num_perfer_cores++;
+                perfer_core_arch_id = core_arch_id;
+                cpu_mask |= (1ULL << core_info.core_id);
+                perfer_core_ids.push_back(core_info.core_id);
+            }
+        }
+    } else {
+        for (auto & core_info : core_info_list) {
+            auto core_arch_id = core_info.arch_id;
+            if (core_arch_id == perfer_core_arch_id) {
+                num_perfer_cores++;
+                cpu_mask |= (1ULL << core_info.core_id);
+
+                auto core_arch_head = (uint16_t) (core_arch_id) >> 12;
+                if (core_arch_head == 0xA) {
+                    perfer_core_ids.push_back(core_info.core_id);
+                }
+            }
+        }
+        if (num_perfer_cores == 0) {
+            GGML_ABORT("can not find core with arch id %x for SPACEMIT_PERFER_CORE_ARCH in core info list\n",
+                       (uint16_t) perfer_core_arch_id);
+        }
+    }
+
+    if (perfer_core_id_vec.size() > 0) {
+        perfer_core_ids.clear();
+        cpu_mask         = 0;
+        num_perfer_cores = 0;
+        for (int core_id : perfer_core_id_vec) {
+            if (core_id < 0 || core_id >= num_cores) {
+                GGML_ABORT("invalid core id in SPACEMIT_PERFER_CORE_ID: %d, should be between 0 and %d\n", core_id,
+                           num_cores - 1);
+            }
+            auto core_info    = core_info_list[core_id];
+            auto core_arch_id = core_info.arch_id;
+            if (core_arch_id == perfer_core_arch_id) {
+                cpu_mask |= (1ULL << core_id);
+                perfer_core_ids.push_back(core_id);
+            } else {
+                GGML_ABORT(
+                    "core id %d in SPACEMIT_PERFER_CORE_ID has arch id %x which does not match "
+                    "SPACEMIT_PERFER_CORE_ARCH %x\n",
+                    core_id, (uint16_t) core_arch_id, (uint16_t) perfer_core_arch_id);
+            }
+        }
+        std::string perfer_core_id_vec_str;
+        for (int core_id : perfer_core_id_vec) {
+            perfer_core_id_vec_str += std::to_string(core_id) + ",";
+        }
+        perfer_core_id_vec_str.pop_back();
+        GGML_LOG_DEBUG("SPACEMIT_PERFER_CORE_ID is set, perferred core ids: %s\n", perfer_core_id_vec_str.c_str());
+        num_perfer_cores = static_cast<int>(perfer_core_id_vec.size());
+    }
+
+    use_ime1 = perfer_core_arch_id == spine_core_arch_id::core_arch_a60 ||
+               perfer_core_arch_id == spine_core_arch_id::core_arch_x100;
+
+    use_ime2 = perfer_core_arch_id == spine_core_arch_id::core_arch_a100;
+
+    mem_backend                  = parse_mem_backend(getenv("SPACEMIT_MEM_BACKEND"));
+    char * spine_disable_tcm_str = getenv("SPACEMIT_DISABLE_TCM");
+    auto   user_disable_tcm      = spine_disable_tcm_str != nullptr && strcmp(spine_disable_tcm_str, "0") != 0;
+
+    if (!user_disable_tcm) {
+        spine_mem_pool_tcm_info tcm_info;
+        if (spine_mem_pool_tcm_init(&tcm_info)) {
+            use_tcm      = tcm_info.available;
+            tcm_blk_size = tcm_info.blk_size;
+            GGML_LOG_DEBUG("CPU_RISCV64_SPACEMIT: tcm is available, blk_size: %zu, blk_num: %zu, is_fake_tcm: %d\n",
+                           tcm_info.blk_size, tcm_info.blk_num, tcm_info.is_fake_tcm);
+
+            for (auto & core_info : core_info_list) {
+                auto core_arch_head = (uint16_t) (core_info.arch_id) >> 12;
+                if (core_arch_head != 0xA) {
+                    aicpu_id_offset++;
+                } else {
+                    break;
+                }
+            }
+        }
+    }
+
+    GGML_LOG_DEBUG(
+        "CPU_RISCV64_SPACEMIT: num_cores: %d, num_perfer_cores: %d, perfer_core_arch_id: %x, exclude_main_thread: %d, "
+        "use_ime1: %d, use_ime2: %d, mem_backend: %s, cpu_mask: %lx, aicpu_id_offset: %d\n",
+        num_cores, num_perfer_cores, (uint16_t) perfer_core_arch_id, exclude_main_thread, use_ime1, use_ime2,
+        spine_mem_pool_backend_to_string(mem_backend), cpu_mask, aicpu_id_offset);
+
+    const size_t init_barrier_size = sizeof(spine_barrier_t) * spine_init_barrier_count;
+    init_barrier =
+        static_cast<spine_barrier_t *>(spine_mem_pool_shared_mem_alloc(init_barrier_size, alignof(spine_barrier_t)));
+    if (init_barrier != nullptr) {
+        init_barrier_is_shared_mem = true;
+    } else {
+        GGML_LOG_WARN("CPU_RISCV64_SPACEMIT: failed to allocate init_barrier from shared mem, falling back to heap\n",
+                      __func__);
+        init_barrier = new spine_barrier_t[spine_init_barrier_count];
+    }
+
+    spine_barrier_init(init_barrier, spine_init_barrier_count, 2);
+}
+
+spine_env_info::~spine_env_info() {
+    if (init_barrier_is_shared_mem) {
+        spine_mem_pool_shared_mem_free(init_barrier);
+    } else {
+        delete[] init_barrier;
+    }
+
+    init_barrier               = nullptr;
+    init_barrier_is_shared_mem = false;
+}
+
+spine_env_info global_spine_env_info;
+
+}  // namespace ggml::cpu::riscv64_spacemit
diff --git a/ggml/src/ggml-cpu/spacemit/ime_env.h b/ggml/src/ggml-cpu/spacemit/ime_env.h
new file mode 100644
index 00000000000..a6ca06d26a4
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/ime_env.h
@@ -0,0 +1,55 @@
+#pragma once
+
+#include "spine_barrier.h"
+#include "spine_mem_pool.h"
+
+#include <cstddef>
+#include <cstdint>
+#include <vector>
+
+namespace ggml::cpu::riscv64_spacemit {
+
+constexpr uint64_t spine_invalid_core_id    = 0xFFFFFFFF;
+constexpr size_t   spine_init_barrier_count = 16;
+
+enum class spine_core_arch_id : uint16_t {
+    core_arch_none = 0,
+    core_arch_x60  = 0x503C,
+    core_arch_x100 = 0x5064,
+    core_arch_x200 = 0x50C8,
+    core_arch_a60  = 0xA03C,
+    core_arch_a100 = 0xA064,
+    core_arch_a200 = 0xA0C8,
+};
+
+struct spine_core_info {
+    uint64_t           core_id{ spine_invalid_core_id };
+    spine_core_arch_id arch_id{ spine_core_arch_id::core_arch_none };
+
+    static bool get_spine_core_info(std::vector<spine_core_info> & result);
+};
+
+struct spine_env_info {
+    std::vector<spine_core_info> core_info_list;
+    std::vector<int>             perfer_core_ids;
+    int                          aicpu_id_offset{ 0 };
+    int                          num_cores{ 0 };
+    int                          num_perfer_cores{ 0 };
+    spine_core_arch_id           perfer_core_arch_id{ spine_core_arch_id::core_arch_none };
+    bool                         exclude_main_thread{ false };
+    bool                         use_ime2{ false };
+    bool                         use_ime1{ false };
+    bool                         use_tcm{ false };
+    spine_mem_pool_backend       mem_backend{ spine_mem_pool_backend::transparent_hugepage };
+    uint64_t                     tcm_blk_size{ 0 };
+    uint64_t                     cpu_mask{ 0 };
+    spine_barrier_t *            init_barrier{ nullptr };
+    bool                         init_barrier_is_shared_mem{ false };
+
+    spine_env_info();
+    ~spine_env_info();
+};
+
+extern spine_env_info global_spine_env_info;
+
+}  // namespace ggml::cpu::riscv64_spacemit
diff --git a/ggml/src/ggml-cpu/spacemit/ime_kernels.h b/ggml/src/ggml-cpu/spacemit/ime_kernels.h
index 75706341505..0a1fafffb25 100644
--- a/ggml/src/ggml-cpu/spacemit/ime_kernels.h
+++ b/ggml/src/ggml-cpu/spacemit/ime_kernels.h
@@ -1,26 +1,189 @@
 #pragma once
 
+#include <cassert>
 #include <cstddef>
+#include <functional>
+
+namespace spacemit_kernels {
+
+#define BLOCK_QNK_LEN 256
+
+template <int N> struct nrow_block_q2_k {
+    // [4bit scale + 4bit zp] * N * 16
+    uint8_t  scales[N * BLOCK_QNK_LEN / 16];
+    // [b0, b16, b32, b48] [b1, b17, b33, b49] ... [b15, b31, b47, b63]
+    // [b64, b80, b96, b112] ...[b79, b95, b111, b127]
+    // [b128, b144, b160, b176] ...[b143, b159, b175, b191]
+    // [b192, b208, b224, b240] ...[b207, b223, b239, b255]
+    uint8_t  qs[N * BLOCK_QNK_LEN / 4];
+    uint16_t scales16[N];
+    uint16_t zeros16[N];
+};
+
+template <int N> struct nrow_block_q3_k {
+    // [8bit scale] * N * 16
+    int8_t   scales[N * 16];
+    // [b0, b1, b2, b3, b4, b5, b6, b7] ... [b248, b249, b250, b251, b252, b253, b254, b255]
+    uint8_t  hmask[N * BLOCK_QNK_LEN / 8];
+    // [b0, b16, b32, b48] [b1, b17, b33, b49] ... [b15, b31, b47, b63]
+    // [b64, b80, b96, b112] ...[b79, b95, b111, b127]
+    // [b128, b144, b160, b176] ...[b143, b159, b175, b191]
+    // [b192, b208, b224, b240] ...[b207, b223, b239, b255]
+    uint8_t  qs[N * BLOCK_QNK_LEN / 4];
+    uint16_t scales16[N];
+};
+
+template <int N> struct nrow_block_mxfp4 {
+    uint8_t e[N];
+    uint8_t qh[4 * N];
+    uint8_t qs[16 * N];
+};
+
+template <int N> struct __attribute__((packed)) nrow_block_q5_1 {
+    uint16_t scales16[N];
+    uint8_t  zp[N];
+    // n0 [bh0, bh1, bh2, bh3, bh4, bh5, bh6, bh7] ....
+    uint8_t  qh[4 * N];
+    // n0 [b0, b1], [b2, b3] ....  [b30, b31]
+    // n1 [b0, b1], [b2, b3] ....  [b30, b31]
+    uint8_t  qs[16 * N];
+};
+
+static_assert(sizeof(nrow_block_q5_1<1>) == sizeof(uint8_t) + 22, "wrong nrow_block_q5_1 block size/padding");
+
+template <int N> struct __attribute__((packed)) nrow_block_q5_0 {
+    uint16_t scales16[N];
+    // n0 [bh0, bh1, bh2, bh3, bh4, bh5, bh6, bh7] ....
+    uint8_t  qh[4 * N];
+    // n0 [b0, b1], [b2, b3] ....  [b30, b31]
+    // n1 [b0, b1], [b2, b3] ....  [b30, b31]
+    uint8_t  qs[16 * N];
+};
+
+static_assert(sizeof(nrow_block_q5_0<1>) == 22, "wrong nrow_block_q5_0 block size/padding");
+
+using gemm_kernel_quantize_def = std::function<
+    size_t(size_t, const uint8_t *, const uint8_t *, const uint8_t *, float *, size_t, size_t, size_t, size_t)>;
+
+using moe_gemm_kernel_quantize_def = std::function<
+    size_t(size_t, const uint8_t **, const uint8_t *, const uint8_t *, float **, size_t, size_t, size_t, size_t)>;
 
-namespace sqnbitgemm_spacemit_ime {
 namespace ime1 {
-size_t gemm_kernel_i8i4(size_t            blk_len,
-                        const std::byte * quant_a_ptr,
-                        const std::byte * quant_b_data,
-                        const float *     quant_b_scale,
-                        const std::byte * quant_b_zp,
-                        float *           c_ptr,
-                        size_t            count_m,
-                        size_t            count_n,
-                        size_t            count_k,
-                        size_t            block_count_k,
-                        size_t            ldc,
-                        const float *     bias,
-                        const size_t      scale_stride);
-
-void quantize_a_row_i8(size_t blk_len, const float * a_ptr, size_t count_k, std::byte * quant_a_ptr);
-
-void quantize_a_4row_i8(size_t blk_len, const float * a_ptr, size_t count_k, std::byte * quant_a_ptr);
+size_t gemm_kernel_i8i4(size_t          blk_len,
+                        const uint8_t * quant_a_ptr,
+                        const uint8_t * quant_b_data,
+                        const uint8_t * quant_b_zp,
+                        float *         c_ptr,
+                        size_t          count_m,
+                        size_t          count_n,
+                        size_t          k_blks,
+                        size_t          ldc);
+
+void quantize_a_row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
+
+void quantize_a_4row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
 
 }  // namespace ime1
-}  // namespace sqnbitgemm_spacemit_ime
+
+namespace ime2 {
+size_t gemm_kernel_i8i2k(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc);
+
+size_t gemm_kernel_i8i3k(size_t          blk_len,
+                         const uint8_t * quant_a_ptr,
+                         const uint8_t * quant_b_data,
+                         const uint8_t * quant_b_zp,
+                         float *         c_ptr,
+                         size_t          count_m,
+                         size_t          count_n,
+                         size_t          k_blks,
+                         size_t          ldc);
+
+size_t gemm_kernel_i8i4(size_t          blk_len,
+                        const uint8_t * quant_a_ptr,
+                        const uint8_t * quant_b_data,
+                        const uint8_t * quant_b_zp,
+                        float *         c_ptr,
+                        size_t          count_m,
+                        size_t          count_n,
+                        size_t          k_blks,
+                        size_t          ldc);
+
+size_t gemm_kernel_i8i4_hp(size_t          blk_len,
+                           const uint8_t * quant_a_ptr,
+                           const uint8_t * quant_b_data,
+                           const uint8_t * quant_b_zp,
+                           float *         c_ptr,
+                           size_t          count_m,
+                           size_t          count_n,
+                           size_t          k_blks,
+                           size_t          ldc);
+
+size_t moe_m2_gemm_kernel_i8i4(size_t           blk_len,
+                               const uint8_t ** quant_a_ptr,
+                               const uint8_t *  quant_b_data,
+                               const uint8_t *  quant_b_zp,
+                               float **         c_ptr,
+                               size_t           count_m,
+                               size_t           count_n,
+                               size_t           k_blks,
+                               size_t           ldc);
+
+size_t gemm_kernel_i8i8(size_t          blk_len,
+                        const uint8_t * quant_a_ptr,
+                        const uint8_t * quant_b_data,
+                        const uint8_t * quant_b_zp,
+                        float *         c_ptr,
+                        size_t          count_m,
+                        size_t          count_n,
+                        size_t          k_blks,
+                        size_t          ldc);
+
+size_t gemm_kernel_i8mxfp4(size_t          blk_len,
+                           const uint8_t * quant_a_ptr,
+                           const uint8_t * quant_b_data,
+                           const uint8_t * quant_b_zp,
+                           float *         c_ptr,
+                           size_t          count_m,
+                           size_t          count_n,
+                           size_t          k_blks,
+                           size_t          ldc);
+
+size_t moe_m2_gemm_kernel_i8mxfp4(size_t           blk_len,
+                                  const uint8_t ** quant_a_ptr,
+                                  const uint8_t *  quant_b_data,
+                                  const uint8_t *  quant_b_zp,
+                                  float **         c_ptr,
+                                  size_t           count_m,
+                                  size_t           count_n,
+                                  size_t           k_blks,
+                                  size_t           ldc);
+
+size_t gemm_kernel_i8i5(size_t          blk_len,
+                        const uint8_t * quant_a_ptr,
+                        const uint8_t * quant_b_data,
+                        const uint8_t * quant_b_zp,
+                        float *         c_ptr,
+                        size_t          count_m,
+                        size_t          count_n,
+                        size_t          k_blks,
+                        size_t          ldc);
+
+size_t moe_m2_gemm_kernel_i8i5(size_t           blk_len,
+                               const uint8_t ** quant_a_ptr,
+                               const uint8_t *  quant_b_data,
+                               const uint8_t *  quant_b_zp,
+                               float **         c_ptr,
+                               size_t           count_m,
+                               size_t           count_n,
+                               size_t           k_blks,
+                               size_t           ldc);
+}  // namespace ime2
+}  // namespace spacemit_kernels
diff --git a/ggml/src/ggml-cpu/spacemit/repack.cpp b/ggml/src/ggml-cpu/spacemit/repack.cpp
new file mode 100644
index 00000000000..3c879c4b7a0
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/repack.cpp
@@ -0,0 +1,1795 @@
+#define GGML_COMMON_IMPL_CPP
+#define GGML_COMMON_DECL_CPP
+
+#include "repack.h"
+
+#include "ggml-common.h"
+#include "ggml-cpu.h"
+#include "ggml-impl.h"
+#include "ime_kernels.h"
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <cstring>
+
+// clang-format off
+#if defined(__riscv)
+
+#if !defined(__riscv_v) || !defined(__riscv_v_intrinsic)
+#error "riscv v extension or v_intrinsic not enabled"
+#else
+#include <riscv_vector.h>
+#endif
+
+#if !defined(__riscv_zfh)
+#error "riscv zfh extension not enabled"
+#endif
+
+#else
+#error "riscv not enabled in this build"
+#endif
+
+#if defined(__GNUC__)
+#pragma GCC diagnostic ignored "-Wcast-qual"
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#endif
+
+// clang-format on
+
+template <int K> constexpr int QK_0() {
+    if constexpr (K == 4) {
+        return QK4_0;
+    }
+    if constexpr (K == 8) {
+        return QK8_0;
+    }
+    return -1;
+}
+
+template <int K, int N> struct block {
+    ggml_half d[N];                         // deltas for N qK_0 blocks
+    uint8_t   qs[(QK_0<K>() * N * K) / 8];  // quants for N qK_0 blocks
+};
+
+template <int K, int N> struct block_with_zp {
+    ggml_half d[N];                         // deltas for N qK_1 blocks
+    uint8_t   zp[N];                        // zero points for N qK_1 blocks
+    uint8_t   qs[(QK_0<K>() * N * K) / 8];  // quants for N qK_1 blocks
+};
+
+// control size
+static_assert(sizeof(block<4, 16>) == 16 * sizeof(ggml_half) + QK4_0 * 8, "wrong block<4,16> size/padding");
+static_assert(sizeof(block_with_zp<4, 16>) == 16 * sizeof(ggml_half) + QK4_0 * 8 + 16 * sizeof(uint8_t),
+              "wrong block_with_zp<4,16> size/padding");
+
+static_assert(sizeof(block<8, 16>) == 16 * sizeof(ggml_half) + QK4_0 * 16, "wrong block<8,16> size/padding");
+
+static_assert(sizeof(block<4, 32>) == 32 * sizeof(ggml_half) + QK4_0 * 16, "wrong block<4,32> size/padding");
+static_assert(sizeof(block_with_zp<4, 32>) == 32 * sizeof(ggml_half) + QK4_0 * 16 + 32 * sizeof(uint8_t),
+              "wrong block_with_zp<4,32> size/padding");
+
+using block_q4_0x16 = block<4, 16>;
+using block_q4_1x16 = block_with_zp<4, 16>;
+using block_q8_0x16 = block<8, 16>;
+
+using block_q4_0x32 = block<4, 32>;
+using block_q4_1x32 = block_with_zp<4, 32>;
+using block_q8_0x32 = block<8, 32>;
+
+struct block_q4_0x32x256 {
+    block_q4_0x32 blocks[8];  // [f16 * 32 | i4 * 32 * 32] * 8
+};
+
+struct block_q4_1x32x256 {
+    block_q4_0x32 blocks[8];
+    uint8_t       zps[32 * 8];
+};
+
+static block_q4_0x16 make_block_q4_0x16(block_q4_0 * in, unsigned int blck_size_interleave) {
+    block_q4_0x16 out;
+    GGML_ASSERT(QK4_0 / blck_size_interleave == 2);
+
+    for (int i = 0; i < 16; i++) {
+        out.d[i] = in[i].d;
+    }
+
+    for (int i = 0; i < 16; i++) {
+        // [0, 15], in.d & 0x0F
+        for (int j = 0; j < QK4_0 / 4; j++) {
+            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
+            //dst [b0 b8] ......... [b7 b15]
+            out.qs[i * QK4_0 / 4 + j] = (in[i].qs[j] & 0x0F) | ((in[i].qs[j + QK4_0 / 4] & 0x0F) << 4);
+        }
+    }
+
+    for (int i = 0; i < 16; i++) {
+        // [16, 31], in.d & 0xF0
+        for (int j = 0; j < QK4_0 / 4; j++) {
+            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
+            //dst [b16 b24] ......... [b23 b31]
+            out.qs[4 * QK4_0 + i * QK4_0 / 4 + j] = ((in[i].qs[j] & 0xF0) >> 4) | (in[i].qs[j + QK4_0 / 4] & 0xF0);
+        }
+    }
+
+    return out;
+}
+
+static block_q4_1x16 make_block_q4_1x16(block_q4_1 * in, unsigned int blck_size_interleave) {
+    block_q4_1x16 out;
+    GGML_ASSERT(QK4_1 / blck_size_interleave == 2);
+
+    for (int i = 0; i < 16; i++) {
+        float d   = GGML_FP16_TO_FP32(in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
+        float m   = GGML_FP16_TO_FP32(in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.m);
+        float mid = -std::nearbyintf(m / d);
+        mid       = std::min(15.0f, std::max(0.0f, mid));
+        out.d[i]  = GGML_FP32_TO_FP16(d);
+        out.zp[i] = static_cast<uint8_t>(mid);
+    }
+
+    for (int i = 0; i < 16; i++) {
+        // [0, 15], in.d & 0x0F
+        for (int j = 0; j < QK4_1 / 4; j++) {
+            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
+            //dst [b0 b8] ......... [b7 b15]
+            out.qs[i * QK4_1 / 4 + j] = (in[i].qs[j] & 0x0F) | ((in[i].qs[j + QK4_1 / 4] & 0x0F) << 4);
+        }
+    }
+
+    for (int i = 0; i < 16; i++) {
+        // [16, 31], in.d & 0xF0
+        for (int j = 0; j < QK4_1 / 4; j++) {
+            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
+            //dst [b16 b24] ......... [b23 b31]
+            out.qs[4 * QK4_1 + i * QK4_1 / 4 + j] = ((in[i].qs[j] & 0xF0) >> 4) | (in[i].qs[j + QK4_1 / 4] & 0xF0);
+        }
+    }
+
+    return out;
+}
+
+static int repack_q4_0_to_q4_0_16_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(interleave_block == 16);
+
+    constexpr int nrows_interleaved = 16;
+
+    block_q4_0x16 *    dst = (block_q4_0x16 *) t->data;
+    const block_q4_0 * src = (const block_q4_0 *) data;
+    block_q4_0         dst_tmp[16];
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK4_0;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_0 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            *dst++ = make_block_q4_0x16(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static int repack_q4_1_to_q4_1_16_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_1);
+    GGML_ASSERT(interleave_block == 16);
+
+    constexpr int nrows_interleaved = 16;
+
+    block_q4_1x16 *    dst = (block_q4_1x16 *) t->data;
+    const block_q4_1 * src = (const block_q4_1 *) data;
+    block_q4_1         dst_tmp[16];
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK4_1;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_1));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_1 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            *dst++ = make_block_q4_1x16(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static inline void get_scale_min_k4(int                           j,
+                                    const uint8_t * GGML_RESTRICT q,
+                                    uint8_t * GGML_RESTRICT       d,
+                                    uint8_t * GGML_RESTRICT       m) {
+    if (j < 4) {
+        *d = q[j] & 63;
+        *m = q[j + 4] & 63;
+    } else {
+        *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
+        *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
+    }
+}
+
+static int repack_q4_k_to_q4_1_16_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_K);
+    GGML_ASSERT(interleave_block == 16);
+    GGML_ASSERT(QK_K / QK4_1 == 8);
+
+    constexpr int nrows_interleaved = 16;
+
+    block_q4_1x16 *    dst = (block_q4_1x16 *) t->data;
+    const block_q4_K * src = (const block_q4_K *) data;
+    block_q4_1         dst_tmp[16];
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK_K;
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK_K != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int j = 0; j < 8; j++) {
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    uint8_t     sc, m;
+                    const float d = GGML_FP16_TO_FP32(src[x + i * nblocks].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
+                    const float min =
+                        GGML_FP16_TO_FP32(src[x + i * nblocks].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin);
+                    get_scale_min_k4(j, src[x + i * nblocks].scales, &sc, &m);
+                    const float d1 = d * sc;
+                    const float m1 = min * m;
+
+                    dst_tmp[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d = GGML_FP32_TO_FP16(d1);
+                    dst_tmp[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.m = GGML_FP32_TO_FP16(-m1);
+                    // src -> [b0, b32] [b1, b33] ... [b31, b63]
+                    // dst -> [b0, b16] [b1, b17] ... [b15, b31] [b32, b48] [b33, b49] ... [b47, b63]
+                    const uint8_t * q                                  = src[x + i * nblocks].qs + (j / 2) * QK4_1;
+                    if (j % 2 == 0) {
+                        for (int ii = 0; ii < 16; ii++) {
+                            dst_tmp[i].qs[ii] = (q[ii] & 0x0F) | ((q[ii + 16] & 0x0F) << 4);
+                        }
+                    } else {
+                        for (int ii = 0; ii < 16; ii++) {
+                            dst_tmp[i].qs[ii] = ((q[ii] & 0xF0) >> 4) | (q[ii + 16] & 0xF0);
+                        }
+                    }
+                }
+                *dst++ = make_block_q4_1x16(dst_tmp, interleave_block);
+            }
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static block_q4_0x32 make_block_q4_0x32(block_q4_0 * in, unsigned int blck_size_interleave) {
+    block_q4_0x32 out;
+    assert(QK4_0 / blck_size_interleave == 1);
+    GGML_UNUSED(blck_size_interleave);
+
+    for (int i = 0; i < 32; i++) {
+        out.d[i] = in[i].d;
+    }
+
+    for (int i = 0; i < 32; i++) {
+        // [0, 15], in.d & 0x0F
+        for (int j = 0; j < QK4_0 / 4; j++) {
+            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
+            //dst [b0 b1] .........  [b14 b15]
+            out.qs[i * QK4_0 / 2 + j] = (in[i].qs[j * 2] & 0x0F) | ((in[i].qs[j * 2 + 1] & 0x0F) << 4);
+        }
+    }
+
+    for (int i = 0; i < 32; i++) {
+        // [16, 31], in.d & 0xF0
+        for (int j = 0; j < QK4_0 / 4; j++) {
+            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
+            //dst [b16 b17] ......... [b30 b31]
+            out.qs[i * QK4_0 / 2 + QK4_0 / 4 + j] = ((in[i].qs[j * 2] & 0xF0) >> 4) | (in[i].qs[j * 2 + 1] & 0xF0);
+        }
+    }
+
+    return out;
+}
+
+static block_q4_1x32 make_block_q4_1x32(block_q4_1 * in, unsigned int blck_size_interleave) {
+    block_q4_1x32 out;
+    GGML_ASSERT(QK4_1 / blck_size_interleave == 1);
+    GGML_UNUSED(blck_size_interleave);
+
+    for (int i = 0; i < 32; i++) {
+        float d   = GGML_FP16_TO_FP32(in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
+        float m   = GGML_FP16_TO_FP32(in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.m);
+        float mid = -std::nearbyintf(m / d);
+        mid       = std::min(15.0f, std::max(0.0f, mid));
+        out.d[i]  = GGML_FP32_TO_FP16(d);
+        out.zp[i] = static_cast<uint8_t>(mid);
+    }
+
+    for (int i = 0; i < 32; i++) {
+        // [0, 15], in.d & 0x0F
+        for (int j = 0; j < QK4_1 / 4; j++) {
+            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
+            //dst [b0 b1] ......... [b14 b15]
+            out.qs[i * QK4_1 / 2 + j] = (in[i].qs[j * 2] & 0x0F) | ((in[i].qs[j * 2 + 1] & 0x0F) << 4);
+        }
+    }
+
+    for (int i = 0; i < 32; i++) {
+        // [16, 31], in.d & 0xF0
+        for (int j = 0; j < QK4_1 / 4; j++) {
+            //src [b0 b16] ......... [b8 b24] ......... [b15 b31]
+            //dst [b16 b24] ......... [b23 b31]
+            out.qs[i * QK4_1 / 2 + QK4_1 / 4 + j] = ((in[i].qs[j * 2] & 0xF0) >> 4) | (in[i].qs[j * 2 + 1] & 0xF0);
+        }
+    }
+
+    return out;
+}
+
+static block_q8_0x32 make_block_q8_0x32(block_q8_0 * in, unsigned int blck_size_interleave) {
+    block_q8_0x32 out;
+    GGML_ASSERT(QK8_0 / blck_size_interleave == 1);
+    GGML_UNUSED(blck_size_interleave);
+
+    for (int i = 0; i < 32; i++) {
+        out.d[i] = in[i].d;
+    }
+
+    for (int i = 0; i < 32; i++) {
+        memcpy(out.qs + i * QK8_0, in[i].qs, QK8_0);
+    }
+
+    return out;
+}
+
+static int repack_q2_k_to_q2_k_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q2_K);
+    GGML_ASSERT(interleave_block == 32);
+    GGML_ASSERT(QK_K == 256);
+
+    constexpr int nrows_interleaved = 32;
+
+    const block_q2_K * src = (const block_q2_K *) data;
+
+    auto * dst = (spacemit_kernels::nrow_block_q2_k<32> *) t->data;
+
+    int nrow    = ggml_nrows(t);
+    int nblocks = t->ne[0] / QK_K;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q2_K));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK_K != 0) {
+        return -1;
+    }
+
+    uint8_t qs_aux[256] = { 0 };
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                const block_q2_K * src_block = &src[(b + i) * nblocks + x];
+
+                // scale for [16, N]
+                for (int j = 0; j < 16; j++) {
+                    auto zp_aux = (dst->scales[j * nrows_interleaved + i]) & 0xF0;
+
+                    dst->scales[j * nrows_interleaved + i] = (src_block->scales[j] & 0x0F) | zp_aux;
+                }
+
+                // zp for [N, 16]
+                for (int j = 0; j < 16; j++) {
+                    auto scale_aux = (dst->scales[16 * i + j]) & 0x0F;
+
+                    dst->scales[16 * i + j] = (src_block->scales[j] & 0xF0) | scale_aux;
+                }
+
+                for (int k = 0; k < 4; k++) {
+                    for (int j = 0; j < 32; j++) {
+                        qs_aux[k * 32 + j] = (src_block->qs[j] >> (2 * k)) & 0x03;
+                    }
+                }
+
+                for (int k = 0; k < 4; k++) {
+                    for (int j = 0; j < 32; j++) {
+                        qs_aux[k * 32 + j + 128] = (src_block->qs[j + 32] >> (2 * k)) & 0x03;
+                    }
+                }
+
+                // from nrows_interleaved * [2 * 32byte]
+                // to 4 * [nrows_interleaved * 16byte]
+                for (int k = 0; k < 4; k++) {
+                    for (int j = 0; j < 16; j++) {
+                        uint8_t qs0  = qs_aux[j + k * 64];
+                        uint8_t qs16 = qs_aux[j + 16 + k * 64];
+                        uint8_t qs32 = qs_aux[j + 32 + k * 64];
+                        uint8_t qs48 = qs_aux[j + 48 + k * 64];
+
+                        dst->qs[(k * nrows_interleaved + i) * 16 + j] =
+                            (qs0 & 0x03) | ((qs16 & 0x03) << 2) | ((qs32 & 0x03) << 4) | ((qs48 & 0x03) << 6);
+                    }
+                }
+
+                dst->scales16[i] = src_block->GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d;
+                dst->zeros16[i]  = src_block->GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin;
+            }
+            dst++;
+        }
+    }
+
+    return 0;
+}
+
+static int repack_q3_k_to_q3_k_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q3_K);
+    GGML_ASSERT(interleave_block == 32);
+    GGML_ASSERT(QK_K == 256);
+
+    constexpr int nrows_interleaved = 32;
+
+    const uint32_t kmask1 = 0x03030303;
+    const uint32_t kmask2 = 0x0f0f0f0f;
+
+    const block_q3_K * src = (const block_q3_K *) data;
+
+    auto * dst = (spacemit_kernels::nrow_block_q3_k<32> *) t->data;
+
+    int nrow    = ggml_nrows(t);
+    int nblocks = t->ne[0] / QK_K;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q3_K));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK_K != 0) {
+        return -1;
+    }
+
+    uint32_t b_scale_aux[4] = { 0 };
+    uint8_t  qs_aux[256]    = { 0 };
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                const block_q3_K * src_block = &src[(b + i) * nblocks + x];
+
+                uint32_t * auxs  = b_scale_aux;
+                int8_t *   scale = (int8_t *) auxs;
+                memcpy(auxs, src_block->scales, 12);
+
+                uint32_t tmp = auxs[2];
+                auxs[2]      = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+                auxs[3]      = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+                auxs[0]      = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+                auxs[1]      = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+
+                for (int j = 0; j < 16; j++) {
+                    dst->scales[j * nrows_interleaved + i] = scale[j] - 32;
+                }
+
+                for (int k = 0; k < 4; k++) {
+                    for (int j = 0; j < 32; j++) {
+                        qs_aux[k * 32 + j] = (src_block->qs[j] >> (2 * k)) & 0x03;
+                    }
+                }
+
+                for (int k = 0; k < 4; k++) {
+                    for (int j = 0; j < 32; j++) {
+                        qs_aux[k * 32 + j + 128] = (src_block->qs[j + 32] >> (2 * k)) & 0x03;
+                    }
+                }
+
+                // from nrows_interleaved * [2 * 32byte]
+                // to 4 * [nrows_interleaved * 16byte]
+                for (int k = 0; k < 4; k++) {
+                    for (int j = 0; j < 16; j++) {
+                        uint8_t qs0  = qs_aux[j + k * 64];
+                        uint8_t qs16 = qs_aux[j + 16 + k * 64];
+                        uint8_t qs32 = qs_aux[j + 32 + k * 64];
+                        uint8_t qs48 = qs_aux[j + 48 + k * 64];
+
+                        dst->qs[(k * nrows_interleaved + i) * 16 + j] =
+                            (qs0 & 0x03) | ((qs16 & 0x03) << 2) | ((qs32 & 0x03) << 4) | ((qs48 & 0x03) << 6);
+                    }
+                }
+
+                //memcpy(dst->hmask + i * 32, src_block->hmask, 32);
+
+                // from nrows_interleaved * [32byte]
+                // to 16 * [nrows_interleaved * uint16_t]
+                uint16_t * dst_mask = ((uint16_t *) dst->hmask) + i;
+                for (int j = 0; j < 16; j++, dst_mask += nrows_interleaved) {
+                    uint8_t   b_shift    = j / 2;
+                    uint8_t * b_mask_col = (uint8_t *) (src_block->hmask + (j % 2) * 16);
+                    // b0 - b15
+                    uint16_t  msk_out_0  = 0;
+
+                    for (int k = 0; k < 8; k++) {
+                        msk_out_0 |= (uint16_t) ((b_mask_col[k] >> b_shift) & 0x01) << k;
+                    }
+                    for (int k = 8; k < 16; k++) {
+                        msk_out_0 |= (uint16_t) ((b_mask_col[k] >> b_shift) & 0x01) << k;
+                    }
+
+                    dst_mask[0] = msk_out_0;
+                }
+
+                dst->scales16[i] = src_block->d;
+            }
+
+            dst++;
+        }
+    }
+
+    return 0;
+}
+
+static int repack_q4_0_to_q4_0_32_bl_ref(ggml_tensor *              t,
+                                         int                        interleave_block,
+                                         const void * GGML_RESTRICT data,
+                                         size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(interleave_block == 32);  // unused
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q4_0x32 *    dst = (block_q4_0x32 *) t->data;
+    const block_q4_0 * src = (const block_q4_0 *) data;
+    block_q4_0         dst_tmp[32];
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK4_0;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_0 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            *dst++ = make_block_q4_0x32(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static int repack_q4_0_to_q4_0_256_32_bl_ref(ggml_tensor *              t,
+                                             int                        interleave_block,
+                                             const void * GGML_RESTRICT data,
+                                             size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(interleave_block == 32);  // unused
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q4_0x32x256 * dst = (block_q4_0x32x256 *) t->data;
+    const block_q4_0 *  src = (const block_q4_0 *) data;
+    block_q4_0          dst_tmp[32];
+    int                 nrow    = ggml_nrows(t);
+    int                 nblocks = t->ne[0] / QK4_0;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));
+    GGML_ASSERT(nblocks % 8 == 0);  // for 256-block interleaving
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_0 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x += 8) {
+            for (int j = 0; j < 8; j++) {
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    dst_tmp[i] = src[x + j + i * nblocks];
+                }
+                dst->blocks[j] = make_block_q4_0x32(dst_tmp, interleave_block);
+            }
+            dst++;
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static int repack_q4_0_to_q4_1_256_32_bl_ref(ggml_tensor *              t,
+                                             int                        interleave_block,
+                                             const void * GGML_RESTRICT data,
+                                             size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_1);
+    GGML_ASSERT(interleave_block == 32);  // unused
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q4_1x32x256 * dst = (block_q4_1x32x256 *) t->data;
+    const block_q4_1 *  src = (const block_q4_1 *) data;
+    block_q4_1          dst_tmp[32];
+    int                 nrow    = ggml_nrows(t);
+    int                 nblocks = t->ne[0] / QK4_0;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_1));
+    GGML_ASSERT(nblocks % 8 == 0);  // for 256-block interleaving
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_0 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x += 8) {
+            for (int j = 0; j < 8; j++) {
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    dst_tmp[i] = src[x + j + i * nblocks];
+                }
+
+                block_q4_0x32 * dst_block = &dst->blocks[j];
+                uint8_t *       dst_zp    = dst->zps + j * nrows_interleaved;
+
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    float d   = GGML_FP16_TO_FP32(dst_tmp[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
+                    float m   = GGML_FP16_TO_FP32(dst_tmp[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.m);
+                    float mid = -std::nearbyintf(m / d);
+                    mid       = std::min(15.0f, std::max(0.0f, mid));
+
+                    dst_block->d[i] = GGML_FP32_TO_FP16(d);
+                    dst_zp[i]       = static_cast<uint8_t>(mid);
+                }
+
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    for (int k = 0; k < QK4_1 / 4; k++) {
+                        dst_block->qs[i * QK4_1 / 2 + k] =
+                            (dst_tmp[i].qs[k * 2] & 0x0F) | ((dst_tmp[i].qs[k * 2 + 1] & 0x0F) << 4);
+                    }
+                }
+
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    for (int k = 0; k < QK4_1 / 4; k++) {
+                        dst_block->qs[i * QK4_1 / 2 + QK4_1 / 4 + k] =
+                            ((dst_tmp[i].qs[k * 2] & 0xF0) >> 4) | (dst_tmp[i].qs[k * 2 + 1] & 0xF0);
+                    }
+                }
+            }
+            dst++;
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+// RVV optimized version of repack_q4_0_to_q4_0_32_bl
+// Eliminates the intermediate dst_tmp buffer and vectorizes nibble repack.
+static int repack_q4_0_to_q4_0_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(interleave_block == 32);
+
+    constexpr int nrows_interleaved = 32;
+    constexpr int qs_bytes          = QK4_0 / 2;  // 16
+
+    block_q4_0x32 *    dst     = (block_q4_0x32 *) t->data;
+    const block_q4_0 * src     = (const block_q4_0 *) data;
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK4_0;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_0 != 0) {
+        return -1;
+    }
+
+    const ptrdiff_t row_stride = (ptrdiff_t) nblocks * sizeof(block_q4_0);
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            const block_q4_0 * col_src = src + x;
+
+            // --- 1) Gather 32 scale values (ggml_half d) with stride load ---
+            // d is at offset 0 of each block_q4_0, stride between rows = row_stride
+            {
+                const uint8_t * d_base    = (const uint8_t *) &col_src->d;
+                ggml_half *     d_dst     = dst->d;
+                size_t          remaining = 32;
+                size_t          offset    = 0;
+                while (remaining > 0) {
+                    size_t      vl = __riscv_vsetvl_e16m1(remaining);
+                    vuint16m1_t vd =
+                        __riscv_vlse16_v_u16m1((const uint16_t *) (d_base + offset * row_stride), row_stride, vl);
+                    __riscv_vse16_v_u16m1((uint16_t *) (d_dst + offset), vd, vl);
+                    offset += vl;
+                    remaining -= vl;
+                }
+            }
+
+            // --- 2) Nibble repack qs for each of the 32 rows ---
+            // For each row i:
+            //   src qs[16]: [b0|b16] [b1|b17] ... [b15|b31]  (lo nibble = b_j, hi nibble = b_{j+16})
+            //   dst qs low  8B: (qs[2j] & 0x0F) | ((qs[2j+1] & 0x0F) << 4)  for j=0..7
+            //   dst qs high 8B: ((qs[2j] >> 4))  | (qs[2j+1] & 0xF0)         for j=0..7
+            {
+                const size_t vl8 = __riscv_vsetvl_e8m1(8);
+                for (int i = 0; i < 32; i++) {
+                    const uint8_t * sq = col_src[i * nblocks].qs;
+                    uint8_t *       dq = dst->qs + i * qs_bytes;
+
+                    // stride-2 load to separate even/odd bytes
+                    vuint8m1_t v_even = __riscv_vlse8_v_u8m1(sq, 2, vl8);      // qs[0], qs[2], ..., qs[14]
+                    vuint8m1_t v_odd  = __riscv_vlse8_v_u8m1(sq + 1, 2, vl8);  // qs[1], qs[3], ..., qs[15]
+
+                    // low nibble part: (even & 0x0F) | ((odd & 0x0F) << 4)
+                    vuint8m1_t v_even_lo = __riscv_vand_vx_u8m1(v_even, 0x0F, vl8);
+                    vuint8m1_t v_odd_lo  = __riscv_vand_vx_u8m1(v_odd, 0x0F, vl8);
+                    vuint8m1_t v_lo      = __riscv_vor_vv_u8m1(v_even_lo, __riscv_vsll_vx_u8m1(v_odd_lo, 4, vl8), vl8);
+
+                    // high nibble part: (even >> 4) | (odd & 0xF0)
+                    vuint8m1_t v_even_hi = __riscv_vsrl_vx_u8m1(v_even, 4, vl8);
+                    vuint8m1_t v_odd_hi  = __riscv_vand_vx_u8m1(v_odd, 0xF0, vl8);
+                    vuint8m1_t v_hi      = __riscv_vor_vv_u8m1(v_even_hi, v_odd_hi, vl8);
+
+                    __riscv_vse8_v_u8m1(dq, v_lo, vl8);
+                    __riscv_vse8_v_u8m1(dq + 8, v_hi, vl8);
+                }
+            }
+
+            dst++;
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static int repack_q4_1_to_q4_1_32_bl_ref(ggml_tensor *              t,
+                                         int                        interleave_block,
+                                         const void * GGML_RESTRICT data,
+                                         size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_1);
+    GGML_ASSERT(interleave_block == 32);  // unused
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q4_1x32 *    dst = (block_q4_1x32 *) t->data;
+    const block_q4_1 * src = (const block_q4_1 *) data;
+    block_q4_1         dst_tmp[32];
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK4_1;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_1));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_1 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            *dst++ = make_block_q4_1x32(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+// RVV optimized version of repack_q4_1_to_q4_1_32_bl
+// Eliminates the intermediate dst_tmp buffer and vectorizes nibble repack + zp computation.
+static int repack_q4_1_to_q4_1_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_1);
+    GGML_ASSERT(interleave_block == 32);
+
+    constexpr int nrows_interleaved = 32;
+    constexpr int qs_bytes          = QK4_1 / 2;  // 16
+
+    block_q4_1x32 *    dst     = (block_q4_1x32 *) t->data;
+    const block_q4_1 * src     = (const block_q4_1 *) data;
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK4_1;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_1));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK4_1 != 0) {
+        return -1;
+    }
+
+    const ptrdiff_t row_stride = (ptrdiff_t) nblocks * sizeof(block_q4_1);
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            const block_q4_1 * col_src = src + x;
+
+            // --- 1) Gather d and m, compute zp = clamp(nearbyint(-m/d), 0, 15) ---
+            // block_q4_1 layout: [d(f16), m(f16), qs[16]]
+            // d is at byte offset 0, m is at byte offset 2 from each block start
+            {
+                const uint8_t * dm_base   = (const uint8_t *) &col_src->GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d;
+                ggml_half *     d_dst     = dst->d;
+                uint8_t *       zp_dst    = dst->zp;
+                size_t          remaining = 32;
+                size_t          offset    = 0;
+                while (remaining > 0) {
+                    size_t vl = __riscv_vsetvl_e16m1(remaining);
+
+                    // stride load d (f16) from each row
+                    vuint16m1_t vd_raw =
+                        __riscv_vlse16_v_u16m1((const uint16_t *) (dm_base + offset * row_stride), row_stride, vl);
+                    __riscv_vse16_v_u16m1((uint16_t *) (d_dst + offset), vd_raw, vl);
+
+                    // stride load m (f16) from each row (offset +2 bytes from d)
+                    vuint16m1_t vm_raw =
+                        __riscv_vlse16_v_u16m1((const uint16_t *) (dm_base + 2 + offset * row_stride), row_stride, vl);
+
+                    // convert to f32 for zp computation: zp = nearbyint(-m / d)
+                    vfloat16m1_t vd_f16 = __riscv_vreinterpret_v_u16m1_f16m1(vd_raw);
+                    vfloat16m1_t vm_f16 = __riscv_vreinterpret_v_u16m1_f16m1(vm_raw);
+
+                    // -m / d in f16 directly (SpaceMIT X60 supports f16 arithmetic)
+                    vfloat16m1_t v_neg_m = __riscv_vfneg_v_f16m1(vm_f16, vl);
+                    vfloat16m1_t v_ratio = __riscv_vfdiv_vv_f16m1(v_neg_m, vd_f16, vl);
+
+                    // Convert to f32 for nearbyint, then clamp
+                    vfloat32m2_t v_ratio_f32 = __riscv_vfwcvt_f_f_v_f32m2(v_ratio, vl);
+
+                    // Use integer rounding: convert f32 -> int (rounds to nearest)
+                    vint32m2_t v_zp_i32 = __riscv_vfcvt_x_f_v_i32m2(v_ratio_f32, vl);
+
+                    // clamp to [0, 15]
+                    v_zp_i32 = __riscv_vmax_vx_i32m2(v_zp_i32, 0, vl);
+                    v_zp_i32 = __riscv_vmin_vx_i32m2(v_zp_i32, 15, vl);
+
+                    // narrow i32 -> u8
+                    vint16m1_t  v_zp_i16 = __riscv_vncvt_x_x_w_i16m1(v_zp_i32, vl);
+                    vint8mf2_t  v_zp_i8  = __riscv_vncvt_x_x_w_i8mf2(v_zp_i16, vl);
+                    vuint8mf2_t v_zp_u8  = __riscv_vreinterpret_v_i8mf2_u8mf2(v_zp_i8);
+                    __riscv_vse8_v_u8mf2(zp_dst + offset, v_zp_u8, vl);
+
+                    offset += vl;
+                    remaining -= vl;
+                }
+            }
+
+            // --- 2) Nibble repack qs for each of the 32 rows ---
+            {
+                const size_t vl8 = __riscv_vsetvl_e8m1(8);
+                for (int i = 0; i < 32; i++) {
+                    const uint8_t * sq = col_src[i * nblocks].qs;
+                    uint8_t *       dq = dst->qs + i * qs_bytes;
+
+                    // stride-2 load to separate even/odd bytes
+                    vuint8m1_t v_even = __riscv_vlse8_v_u8m1(sq, 2, vl8);
+                    vuint8m1_t v_odd  = __riscv_vlse8_v_u8m1(sq + 1, 2, vl8);
+
+                    // low nibble part: (even & 0x0F) | ((odd & 0x0F) << 4)
+                    vuint8m1_t v_even_lo = __riscv_vand_vx_u8m1(v_even, 0x0F, vl8);
+                    vuint8m1_t v_odd_lo  = __riscv_vand_vx_u8m1(v_odd, 0x0F, vl8);
+                    vuint8m1_t v_lo      = __riscv_vor_vv_u8m1(v_even_lo, __riscv_vsll_vx_u8m1(v_odd_lo, 4, vl8), vl8);
+
+                    // high nibble part: (even >> 4) | (odd & 0xF0)
+                    vuint8m1_t v_even_hi = __riscv_vsrl_vx_u8m1(v_even, 4, vl8);
+                    vuint8m1_t v_odd_hi  = __riscv_vand_vx_u8m1(v_odd, 0xF0, vl8);
+                    vuint8m1_t v_hi      = __riscv_vor_vv_u8m1(v_even_hi, v_odd_hi, vl8);
+
+                    __riscv_vse8_v_u8m1(dq, v_lo, vl8);
+                    __riscv_vse8_v_u8m1(dq + 8, v_hi, vl8);
+                }
+            }
+
+            dst++;
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static int repack_q4_k_to_q4_1_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_K);
+    GGML_ASSERT(interleave_block == 32);
+    GGML_ASSERT(QK_K / QK4_1 == 8);
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q4_1x32 *    dst = (block_q4_1x32 *) t->data;
+    const block_q4_K * src = (const block_q4_K *) data;
+    block_q4_1         dst_tmp[32];
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK_K;
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK_K != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int j = 0; j < 8; j++) {
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    uint8_t     sc, m;
+                    const float d = GGML_FP16_TO_FP32(src[x + i * nblocks].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
+                    const float min =
+                        GGML_FP16_TO_FP32(src[x + i * nblocks].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin);
+                    get_scale_min_k4(j, src[x + i * nblocks].scales, &sc, &m);
+                    const float d1 = d * sc;
+                    const float m1 = min * m;
+
+                    dst_tmp[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d = GGML_FP32_TO_FP16(d1);
+                    dst_tmp[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.m = GGML_FP32_TO_FP16(-m1);
+                    // src -> [b0, b32] [b1, b33] ... [b31, b63]
+                    // dst -> [b0, b16] [b1, b17] ... [b15, b31] [b32, b48] [b33, b49] ... [b47, b63]
+                    const uint8_t * q                                  = src[x + i * nblocks].qs + (j / 2) * QK4_1;
+                    if (j % 2 == 0) {
+                        for (int ii = 0; ii < 16; ii++) {
+                            dst_tmp[i].qs[ii] = (q[ii] & 0x0F) | ((q[ii + 16] & 0x0F) << 4);
+                        }
+                    } else {
+                        for (int ii = 0; ii < 16; ii++) {
+                            dst_tmp[i].qs[ii] = ((q[ii] & 0xF0) >> 4) | (q[ii + 16] & 0xF0);
+                        }
+                    }
+                }
+                *dst++ = make_block_q4_1x32(dst_tmp, interleave_block);
+            }
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static int repack_q6_k_to_q8_0_32_bl_ref(ggml_tensor *              t,
+                                         int                        interleave_block,
+                                         const void * GGML_RESTRICT data,
+                                         size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q6_K);
+    GGML_ASSERT(interleave_block == 32);
+    GGML_ASSERT(QK_K / QK4_1 == 8);
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q8_0x32 *    dst = (block_q8_0x32 *) t->data;
+    const block_q6_K * src = (const block_q6_K *) data;
+    block_q8_0         dst_tmp[32];
+    int8_t             aux8[QK4_1];
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK_K;
+
+    if (t->ne[0] % QK_K != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        int64_t nrow_real = std::min((int64_t) nrow - b, (int64_t) nrows_interleaved);
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int bi = 0; bi < 8; bi++) {
+                int i = 0;
+                for (; i < nrow_real; i++) {
+                    const uint8_t * q4     = src[x + i * nblocks].ql;
+                    const uint8_t * qh     = src[x + i * nblocks].qh;
+                    const int8_t *  scales = src[x + i * nblocks].scales;
+                    float           d      = GGML_FP16_TO_FP32(src[x + i * nblocks].d);
+
+                    q4 += 64 * (bi / 4);
+                    qh += 32 * (bi / 4);
+                    int8_t * GGML_RESTRICT a = aux8;
+
+                    int8_t bi_idx = bi % 4;
+
+                    if (bi_idx == 0) {
+                        for (int l = 0; l < 32; ++l) {
+                            a[l] = (int8_t) ((q4[l] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                        }
+                    } else if (bi_idx == 1) {
+                        for (int l = 0; l < 32; ++l) {
+                            a[l] = (int8_t) ((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                        }
+                    } else if (bi_idx == 2) {
+                        for (int l = 0; l < 32; ++l) {
+                            a[l] = (int8_t) ((q4[l + 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                        }
+                    } else if (bi_idx == 3) {
+                        for (int l = 0; l < 32; ++l) {
+                            a[l] = (int8_t) ((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+                        }
+                    }
+                    a = aux8;
+
+                    float a_max_abs = 0.0f;
+                    float scale_0   = scales[bi * 2 + 0] * d;
+                    float scale_1   = scales[bi * 2 + 1] * d;
+                    for (int l = 0; l < 16; ++l) {
+                        a_max_abs = std::max(a_max_abs, std::abs(a[l] * scale_0));
+                    }
+
+                    for (int l = 16; l < 32; ++l) {
+                        a_max_abs = std::max(a_max_abs, std::abs(a[l] * scale_1));
+                    }
+
+                    float reflect_scale   = a_max_abs / ((1 << 7) - 1);
+                    float reflect_scale_0 = scale_0 / reflect_scale;
+                    float reflect_scale_1 = scale_1 / reflect_scale;
+
+                    for (int l = 0; l < 16; ++l) {
+                        float a_temp = std::clamp(std::nearbyintf(a[l] * reflect_scale_0), -128.0f, 127.0f);
+                        a[l]         = (int8_t) (a_temp);
+                    }
+
+                    for (int l = 16; l < 32; ++l) {
+                        float a_temp = std::clamp(std::nearbyintf(a[l] * reflect_scale_1), -128.0f, 127.0f);
+                        a[l]         = (int8_t) (a_temp);
+                    }
+
+                    dst_tmp[i].d = GGML_FP32_TO_FP16(reflect_scale);
+
+                    memcpy(dst_tmp[i].qs, a, 32 * sizeof(int8_t));
+                }
+
+                for (; i < nrows_interleaved; i++) {
+                    memset(&dst_tmp[i], 0, sizeof(block_q8_0));
+                }
+
+                *dst++ = make_block_q8_0x32(dst_tmp, interleave_block);
+            }
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+// RVV optimized version of repack_q6_k_to_q8_0_32_bl
+// Vectorizes the Q6_K dequant -> requant pipeline using RVV intrinsics.
+// For each sub-block (bi), dequant 32 Q6_K values to int6 -> apply two sub-block scales ->
+// find max abs -> compute reflect_scale -> requant to int8 -> gather d with stride load.
+static int repack_q6_k_to_q8_0_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q6_K);
+    GGML_ASSERT(interleave_block == 32);
+    GGML_ASSERT(QK_K / QK4_1 == 8);
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q8_0x32 *    dst     = (block_q8_0x32 *) t->data;
+    const block_q6_K * src     = (const block_q6_K *) data;
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK_K;
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK_K != 0) {
+        return -1;
+    }
+
+    const ptrdiff_t row_stride = (ptrdiff_t) nblocks * sizeof(block_q6_K);
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int bi = 0; bi < 8; bi++) {
+                // --- 1) Gather 32 d values with stride load ---
+                // We need to compute reflect_scale per row first, so gather d later.
+                // Process each row: dequant Q6_K sub-block -> requant to Q8_0
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    const block_q6_K * src_blk = &src[x + i * nblocks];
+                    const uint8_t *    q4      = src_blk->ql + 64 * (bi / 4);
+                    const uint8_t *    qh      = src_blk->qh + 32 * (bi / 4);
+                    const int8_t *     scales  = src_blk->scales;
+                    float              d       = GGML_FP16_TO_FP32(src_blk->d);
+
+                    int8_t bi_idx = bi % 4;
+
+                    // --- Dequant 32 Q6_K values to int6 (range [-32, 31]) using RVV ---
+                    // vl = 32 for e8m2 (VLEN=256) or loop for smaller VLEN
+                    const size_t vl16 = __riscv_vsetvl_e8m1(16);
+
+                    vint8m1_t va_lo, va_hi;  // 16 elements each
+
+                    if (bi_idx == 0) {
+                        // a[l] = (q4[l] & 0xF) | (((qh[l] >> 0) & 3) << 4) - 32
+                        vuint8m1_t vq4_lo = __riscv_vle8_v_u8m1(q4, vl16);
+                        vuint8m1_t vq4_hi = __riscv_vle8_v_u8m1(q4 + 16, vl16);
+                        vuint8m1_t vqh_lo = __riscv_vle8_v_u8m1(qh, vl16);
+                        vuint8m1_t vqh_hi = __riscv_vle8_v_u8m1(qh + 16, vl16);
+
+                        vuint8m1_t vlo4_lo = __riscv_vand_vx_u8m1(vq4_lo, 0x0F, vl16);
+                        vuint8m1_t vlo4_hi = __riscv_vand_vx_u8m1(vq4_hi, 0x0F, vl16);
+                        vuint8m1_t vh_lo   = __riscv_vsll_vx_u8m1(__riscv_vand_vx_u8m1(vqh_lo, 0x03, vl16), 4, vl16);
+                        vuint8m1_t vh_hi   = __riscv_vsll_vx_u8m1(__riscv_vand_vx_u8m1(vqh_hi, 0x03, vl16), 4, vl16);
+
+                        vuint8m1_t vcomb_lo = __riscv_vor_vv_u8m1(vlo4_lo, vh_lo, vl16);
+                        vuint8m1_t vcomb_hi = __riscv_vor_vv_u8m1(vlo4_hi, vh_hi, vl16);
+
+                        va_lo = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(vcomb_lo), 32, vl16);
+                        va_hi = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(vcomb_hi), 32, vl16);
+                    } else if (bi_idx == 1) {
+                        // a[l] = (q4[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4) - 32
+                        vuint8m1_t vq4_lo = __riscv_vle8_v_u8m1(q4 + 32, vl16);
+                        vuint8m1_t vq4_hi = __riscv_vle8_v_u8m1(q4 + 48, vl16);
+                        vuint8m1_t vqh_lo = __riscv_vle8_v_u8m1(qh, vl16);
+                        vuint8m1_t vqh_hi = __riscv_vle8_v_u8m1(qh + 16, vl16);
+
+                        vuint8m1_t vlo4_lo = __riscv_vand_vx_u8m1(vq4_lo, 0x0F, vl16);
+                        vuint8m1_t vlo4_hi = __riscv_vand_vx_u8m1(vq4_hi, 0x0F, vl16);
+                        vuint8m1_t vh_lo   = __riscv_vsll_vx_u8m1(
+                            __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vqh_lo, 2, vl16), 0x03, vl16), 4, vl16);
+                        vuint8m1_t vh_hi = __riscv_vsll_vx_u8m1(
+                            __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vqh_hi, 2, vl16), 0x03, vl16), 4, vl16);
+
+                        vuint8m1_t vcomb_lo = __riscv_vor_vv_u8m1(vlo4_lo, vh_lo, vl16);
+                        vuint8m1_t vcomb_hi = __riscv_vor_vv_u8m1(vlo4_hi, vh_hi, vl16);
+
+                        va_lo = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(vcomb_lo), 32, vl16);
+                        va_hi = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(vcomb_hi), 32, vl16);
+                    } else if (bi_idx == 2) {
+                        // a[l] = (q4[l] >> 4) | (((qh[l] >> 4) & 3) << 4) - 32
+                        vuint8m1_t vq4_lo = __riscv_vle8_v_u8m1(q4, vl16);
+                        vuint8m1_t vq4_hi = __riscv_vle8_v_u8m1(q4 + 16, vl16);
+                        vuint8m1_t vqh_lo = __riscv_vle8_v_u8m1(qh, vl16);
+                        vuint8m1_t vqh_hi = __riscv_vle8_v_u8m1(qh + 16, vl16);
+
+                        vuint8m1_t vhi4_lo = __riscv_vsrl_vx_u8m1(vq4_lo, 4, vl16);
+                        vuint8m1_t vhi4_hi = __riscv_vsrl_vx_u8m1(vq4_hi, 4, vl16);
+                        vuint8m1_t vh_lo   = __riscv_vsll_vx_u8m1(
+                            __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vqh_lo, 4, vl16), 0x03, vl16), 4, vl16);
+                        vuint8m1_t vh_hi = __riscv_vsll_vx_u8m1(
+                            __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vqh_hi, 4, vl16), 0x03, vl16), 4, vl16);
+
+                        vuint8m1_t vcomb_lo = __riscv_vor_vv_u8m1(vhi4_lo, vh_lo, vl16);
+                        vuint8m1_t vcomb_hi = __riscv_vor_vv_u8m1(vhi4_hi, vh_hi, vl16);
+
+                        va_lo = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(vcomb_lo), 32, vl16);
+                        va_hi = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(vcomb_hi), 32, vl16);
+                    } else {  // bi_idx == 3
+                        // a[l] = (q4[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4) - 32
+                        vuint8m1_t vq4_lo = __riscv_vle8_v_u8m1(q4 + 32, vl16);
+                        vuint8m1_t vq4_hi = __riscv_vle8_v_u8m1(q4 + 48, vl16);
+                        vuint8m1_t vqh_lo = __riscv_vle8_v_u8m1(qh, vl16);
+                        vuint8m1_t vqh_hi = __riscv_vle8_v_u8m1(qh + 16, vl16);
+
+                        vuint8m1_t vhi4_lo = __riscv_vsrl_vx_u8m1(vq4_lo, 4, vl16);
+                        vuint8m1_t vhi4_hi = __riscv_vsrl_vx_u8m1(vq4_hi, 4, vl16);
+                        vuint8m1_t vh_lo   = __riscv_vsll_vx_u8m1(
+                            __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vqh_lo, 6, vl16), 0x03, vl16), 4, vl16);
+                        vuint8m1_t vh_hi = __riscv_vsll_vx_u8m1(
+                            __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vqh_hi, 6, vl16), 0x03, vl16), 4, vl16);
+
+                        vuint8m1_t vcomb_lo = __riscv_vor_vv_u8m1(vhi4_lo, vh_lo, vl16);
+                        vuint8m1_t vcomb_hi = __riscv_vor_vv_u8m1(vhi4_hi, vh_hi, vl16);
+
+                        va_lo = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(vcomb_lo), 32, vl16);
+                        va_hi = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(vcomb_hi), 32, vl16);
+                    }
+
+                    // --- Widen to i16 for scaled abs computation ---
+                    float scale_0 = scales[bi * 2 + 0] * d;
+                    float scale_1 = scales[bi * 2 + 1] * d;
+
+                    // Widen i8 -> i16 -> f32 for abs*scale computation
+                    vint16m2_t va_lo_w = __riscv_vsext_vf2_i16m2(va_lo, vl16);
+                    vint16m2_t va_hi_w = __riscv_vsext_vf2_i16m2(va_hi, vl16);
+
+                    // Compute |a[l] * scale_0| for lo half, |a[l] * scale_1| for hi half
+                    vfloat32m4_t vf_lo = __riscv_vfcvt_f_x_v_f32m4(__riscv_vsext_vf2_i32m4(va_lo_w, vl16), vl16);
+                    vfloat32m4_t vf_hi = __riscv_vfcvt_f_x_v_f32m4(__riscv_vsext_vf2_i32m4(va_hi_w, vl16), vl16);
+
+                    vfloat32m4_t vabs_lo = __riscv_vfabs_v_f32m4(__riscv_vfmul_vf_f32m4(vf_lo, scale_0, vl16), vl16);
+                    vfloat32m4_t vabs_hi = __riscv_vfabs_v_f32m4(__riscv_vfmul_vf_f32m4(vf_hi, scale_1, vl16), vl16);
+
+                    // Find max abs across both halves
+                    vfloat32m4_t vabs_max = __riscv_vfmax_vv_f32m4(vabs_lo, vabs_hi, vl16);
+
+                    // Reduce to scalar max
+                    vfloat32m1_t vzero     = __riscv_vfmv_v_f_f32m1(0.0f, 1);
+                    vfloat32m1_t vmax_red  = __riscv_vfredmax_vs_f32m4_f32m1(vabs_max, vzero, vl16);
+                    float        a_max_abs = __riscv_vfmv_f_s_f32m1_f32(vmax_red);
+
+                    float reflect_scale   = a_max_abs / 127.0f;
+                    float reflect_scale_0 = scale_0 / reflect_scale;
+                    float reflect_scale_1 = scale_1 / reflect_scale;
+
+                    // --- Requant: a[l] = clamp(nearbyint(a[l] * reflect_scale_x), -128, 127) ---
+                    vfloat32m4_t vscaled_lo = __riscv_vfmul_vf_f32m4(vf_lo, reflect_scale_0, vl16);
+                    vfloat32m4_t vscaled_hi = __riscv_vfmul_vf_f32m4(vf_hi, reflect_scale_1, vl16);
+
+                    // fcvt.x rounds to nearest (using current rounding mode)
+                    vint32m4_t vi_lo = __riscv_vfcvt_x_f_v_i32m4(vscaled_lo, vl16);
+                    vint32m4_t vi_hi = __riscv_vfcvt_x_f_v_i32m4(vscaled_hi, vl16);
+
+                    // Clamp to [-128, 127]
+                    vi_lo = __riscv_vmax_vx_i32m4(vi_lo, -128, vl16);
+                    vi_lo = __riscv_vmin_vx_i32m4(vi_lo, 127, vl16);
+                    vi_hi = __riscv_vmax_vx_i32m4(vi_hi, -128, vl16);
+                    vi_hi = __riscv_vmin_vx_i32m4(vi_hi, 127, vl16);
+
+                    // Narrow i32 -> i16 -> i8
+                    vint16m2_t vi16_lo = __riscv_vncvt_x_x_w_i16m2(vi_lo, vl16);
+                    vint16m2_t vi16_hi = __riscv_vncvt_x_x_w_i16m2(vi_hi, vl16);
+                    vint8m1_t  vi8_lo  = __riscv_vncvt_x_x_w_i8m1(vi16_lo, vl16);
+                    vint8m1_t  vi8_hi  = __riscv_vncvt_x_x_w_i8m1(vi16_hi, vl16);
+
+                    // Store d and qs directly into dst block
+                    dst->d[i]   = GGML_FP32_TO_FP16(reflect_scale);
+                    int8_t * dq = (int8_t *) dst->qs + i * QK8_0;
+                    __riscv_vse8_v_i8m1(dq, vi8_lo, vl16);
+                    __riscv_vse8_v_i8m1(dq + 16, vi8_hi, vl16);
+                }
+                dst++;
+            }
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static int repack_q8_0_to_q8_0_32_bl_ref(ggml_tensor *              t,
+                                         int                        interleave_block,
+                                         const void * GGML_RESTRICT data,
+                                         size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q8_0);
+    GGML_ASSERT(interleave_block == 32);  // unused
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q8_0x32 *    dst = (block_q8_0x32 *) t->data;
+    const block_q8_0 * src = (const block_q8_0 *) data;
+    block_q8_0         dst_tmp[32];
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK8_0;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q8_0));
+
+    if (t->ne[0] % QK8_0 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        int64_t nrows_real = std::min((int64_t) nrow - b, (int64_t) nrows_interleaved);
+        for (int64_t x = 0; x < nblocks; x++) {
+            int i = 0;
+            for (; i < nrows_real; i++) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            for (; i < nrows_interleaved; i++) {
+                memset(&dst_tmp[i], 0, sizeof(block_q8_0));
+            }
+            *dst++ = make_block_q8_0x32(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+// RVV optimized version of repack_q8_0_to_q8_0_32_bl
+// Eliminates the intermediate dst_tmp buffer and vectorizes scale gather + qs copy.
+static int repack_q8_0_to_q8_0_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q8_0);
+    GGML_ASSERT(interleave_block == 32);
+
+    constexpr int nrows_interleaved = 32;
+
+    block_q8_0x32 *    dst     = (block_q8_0x32 *) t->data;
+    const block_q8_0 * src     = (const block_q8_0 *) data;
+    int                nrow    = ggml_nrows(t);
+    int                nblocks = t->ne[0] / QK8_0;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q8_0));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK8_0 != 0) {
+        return -1;
+    }
+
+    const ptrdiff_t row_stride = (ptrdiff_t) nblocks * sizeof(block_q8_0);
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            const block_q8_0 * col_src = src + x;
+
+            // --- 1) Gather 32 scale values (ggml_half d) with stride load ---
+            {
+                const uint8_t * d_base    = (const uint8_t *) &col_src->d;
+                ggml_half *     d_dst     = dst->d;
+                size_t          remaining = 32;
+                size_t          offset    = 0;
+                while (remaining > 0) {
+                    size_t      vl = __riscv_vsetvl_e16m1(remaining);
+                    vuint16m1_t vd =
+                        __riscv_vlse16_v_u16m1((const uint16_t *) (d_base + offset * row_stride), row_stride, vl);
+                    __riscv_vse16_v_u16m1((uint16_t *) (d_dst + offset), vd, vl);
+                    offset += vl;
+                    remaining -= vl;
+                }
+            }
+
+            // --- 2) Copy qs for each of the 32 rows (32 bytes per row) ---
+            {
+                for (int i = 0; i < 32; i++) {
+                    const int8_t * sq = col_src[i * nblocks].qs;
+                    int8_t *       dq = (int8_t *) dst->qs + i * QK8_0;
+
+                    size_t len = QK8_0;
+                    size_t idx = 0;
+                    while (len > 0) {
+                        size_t    vl = __riscv_vsetvl_e8m2(len);
+                        vint8m2_t vs = __riscv_vle8_v_i8m2(sq + idx, vl);
+                        __riscv_vse8_v_i8m2(dq + idx, vs, vl);
+                        idx += vl;
+                        len -= vl;
+                    }
+                }
+            }
+
+            dst++;
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static void convert_mxfp4_to_5bit(const block_mxfp4 & src, spacemit_kernels::nrow_block_mxfp4<1> & dst) {
+    dst.e[0] = src.e;
+
+    // Decode all 32 mxfp4 values to signed integers via kvalues_mxfp4
+    int8_t vals[32];
+    for (int j = 0; j < QK_MXFP4 / 2; j++) {
+        vals[j]                = kvalues_mxfp4[src.qs[j] & 0xF];
+        vals[j + QK_MXFP4 / 2] = kvalues_mxfp4[src.qs[j] >> 4];
+    }
+
+    // vals [b0, b1, b2, b3, ..., b30, b31]
+    // Pack abs into qs with reorder: [b0,b1]..[b14,b15]..[b30,b31]
+    for (int j = 0; j < QK_MXFP4 / 2; j++) {
+        uint8_t lo0 = static_cast<uint8_t>(std::abs(vals[j * 2]));
+        uint8_t lo1 = static_cast<uint8_t>(std::abs(vals[j * 2 + 1]));
+        dst.qs[j]   = (lo0 & 0x0F) | ((lo1 & 0x0F) << 4);
+    }
+
+    // Pack sign bits into qh[4] (32 bits total, 1 bit per weight)
+    // reorder: [0,1,2,...,15,16,17,...,31] after the qs reorder above
+    uint32_t sign_bits = 0;
+    for (int j = 0; j < 32; j++) {
+        if (vals[j] < 0) {
+            sign_bits |= (1u << j);
+        }
+    }
+    memcpy(dst.qh, &sign_bits, 4);
+}
+
+static spacemit_kernels::nrow_block_mxfp4<32> make_block_mxfp4x32(spacemit_kernels::nrow_block_mxfp4<1> * in,
+                                                                  unsigned int blck_size_interleave) {
+    spacemit_kernels::nrow_block_mxfp4<32> out;
+    GGML_ASSERT(QK_MXFP4 / blck_size_interleave == 1);
+    GGML_UNUSED(blck_size_interleave);
+
+    for (int i = 0; i < 32; i++) {
+        out.e[i] = in[i].e[0];
+    }
+
+    // qs: copy per-row 16 bytes
+    for (int i = 0; i < 32; i++) {
+        memcpy(out.qs + i * 16, in[i].qs, 16);
+    }
+
+    // qh: copy per-row 4 bytes
+    for (int i = 0; i < 32; i++) {
+        memcpy(out.qh + i * 4, in[i].qh, 4);
+    }
+
+    return out;
+}
+
+static int repack_mxfp4_to_mxfp4_32_bl(ggml_tensor *              t,
+                                       int                        interleave_block,
+                                       const void * GGML_RESTRICT data,
+                                       size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_MXFP4);
+    GGML_ASSERT(interleave_block == 32);
+
+    constexpr int nrows_interleaved = 32;
+
+    spacemit_kernels::nrow_block_mxfp4<32> * dst = (spacemit_kernels::nrow_block_mxfp4<32> *) t->data;
+    const block_mxfp4 *                      src = (const block_mxfp4 *) data;
+    spacemit_kernels::nrow_block_mxfp4<1>    dst_tmp[32];
+    int                                      nrow    = ggml_nrows(t);
+    int                                      nblocks = t->ne[0] / QK_MXFP4;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_mxfp4));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK_MXFP4 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                convert_mxfp4_to_5bit(src[x + i * nblocks], dst_tmp[i]);
+            }
+            *dst++ = make_block_mxfp4x32(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+}
+
+static spacemit_kernels::nrow_block_q5_1<32> make_block_q5_1x32(spacemit_kernels::nrow_block_q5_1<1> * in,
+                                                                unsigned int blck_size_interleave) {
+    spacemit_kernels::nrow_block_q5_1<32> out;
+    GGML_ASSERT(QK5_1 / blck_size_interleave == 1);
+    GGML_UNUSED(blck_size_interleave);
+
+    for (int i = 0; i < 32; i++) {
+        out.scales16[i] = in[i].scales16[0];
+        out.zp[i]       = in[i].zp[0];
+    }
+
+    // qs: low 4 bits, reorder from [b0,b16],[b1,b17]... to [b0,b1]...[b14,b15] and [b16,b17]...[b30,b31]
+    for (int i = 0; i < 32; i++) {
+        // low half [0..15]
+        for (int j = 0; j < QK5_1 / 4; j++) {
+            out.qs[i * QK5_1 / 2 + j] = (in[i].qs[j * 2] & 0x0F) | ((in[i].qs[j * 2 + 1] & 0x0F) << 4);
+        }
+        // high half [16..31]
+        for (int j = 0; j < QK5_1 / 4; j++) {
+            out.qs[i * QK5_1 / 2 + QK5_1 / 4 + j] = ((in[i].qs[j * 2] & 0xF0) >> 4) | (in[i].qs[j * 2 + 1] & 0xF0);
+        }
+    }
+
+    // qh: 5th bit, copy directly
+    for (int i = 0; i < 32; i++) {
+        for (int j = 0; j < 4; j++) {
+            out.qh[i * 4 + j] = in[i].qh[j];
+        }
+    }
+
+    return out;
+}
+
+static spacemit_kernels::nrow_block_q5_0<32> make_block_q5_0x32(spacemit_kernels::nrow_block_q5_0<1> * in,
+                                                                unsigned int blck_size_interleave) {
+    spacemit_kernels::nrow_block_q5_0<32> out;
+    GGML_ASSERT(QK5_0 / blck_size_interleave == 1);
+    GGML_UNUSED(blck_size_interleave);
+
+    for (int i = 0; i < 32; i++) {
+        out.scales16[i] = in[i].scales16[0];
+    }
+
+    // qs: low 4 bits, reorder from [b0,b16],[b1,b17]... to [b0,b1]...[b14,b15] and [b16,b17]...[b30,b31]
+    for (int i = 0; i < 32; i++) {
+        // low half [0..15]
+        for (int j = 0; j < QK5_0 / 4; j++) {
+            out.qs[i * QK5_0 / 2 + j] = (in[i].qs[j * 2] & 0x0F) | ((in[i].qs[j * 2 + 1] & 0x0F) << 4);
+        }
+        // high half [16..31]
+        for (int j = 0; j < QK5_0 / 4; j++) {
+            out.qs[i * QK5_0 / 2 + QK5_0 / 4 + j] = ((in[i].qs[j * 2] & 0xF0) >> 4) | (in[i].qs[j * 2 + 1] & 0xF0);
+        }
+    }
+
+    // qh: 5th bit, copy directly
+    for (int i = 0; i < 32; i++) {
+        for (int j = 0; j < 4; j++) {
+            out.qh[i * 4 + j] = in[i].qh[j];
+        }
+    }
+
+    return out;
+}
+
+static int repack_q5_0_to_q5_0_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q5_0);
+    GGML_ASSERT(interleave_block == 32);  // unused
+
+    constexpr int nrows_interleaved = 32;
+
+    spacemit_kernels::nrow_block_q5_0<32> * dst = (spacemit_kernels::nrow_block_q5_0<32> *) t->data;
+    const block_q5_0 *                      src = (const block_q5_0 *) data;
+    spacemit_kernels::nrow_block_q5_0<1>    dst_tmp[32];
+    int                                     nrow    = ggml_nrows(t);
+    int                                     nblocks = t->ne[0] / QK5_0;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q5_0));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK5_0 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                const block_q5_0 & s = src[x + i * nblocks];
+
+                dst_tmp[i].scales16[0] = s.d;
+                memcpy(dst_tmp[i].qs, s.qs, sizeof(dst_tmp[i].qs));
+                memcpy(dst_tmp[i].qh, s.qh, sizeof(dst_tmp[i].qh));
+            }
+            *dst++ = make_block_q5_0x32(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+}
+
+static int repack_q5_1_to_q5_1_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q5_1);
+    GGML_ASSERT(interleave_block == 32);  // unused
+
+    constexpr int nrows_interleaved = 32;
+
+    spacemit_kernels::nrow_block_q5_1<32> * dst = (spacemit_kernels::nrow_block_q5_1<32> *) t->data;
+    const block_q5_1 *                      src = (const block_q5_1 *) data;
+    spacemit_kernels::nrow_block_q5_1<1>    dst_tmp[32];
+    int                                     nrow    = ggml_nrows(t);
+    int                                     nblocks = t->ne[0] / QK5_1;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q5_1));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK5_1 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                const block_q5_1 & s = src[x + i * nblocks];
+
+                float d = GGML_FP16_TO_FP32(s.GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
+                float m = GGML_FP16_TO_FP32(s.GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.m);
+
+                if (d == 0.0f) {
+                    dst_tmp[i].scales16[0] = GGML_FP32_TO_FP16(std::fabs(m));
+                    dst_tmp[i].zp[0]       = m < 0.0f ? 1 : 0;
+                    memset(dst_tmp[i].qh, 0, sizeof(dst_tmp[i].qh));
+                    memset(dst_tmp[i].qs, m > 0.0f ? 0x11 : 0x00, sizeof(dst_tmp[i].qs));
+                    continue;
+                }
+
+                float mid = std::nearbyintf(-m / d);
+                mid       = std::min(31.0f, std::max(0.0f, mid));
+
+                dst_tmp[i].scales16[0] = GGML_FP32_TO_FP16(d);
+                dst_tmp[i].zp[0]       = static_cast<uint8_t>(mid);
+
+                // qs: copy low 4 bits directly (same nibble packing)
+                memcpy(dst_tmp[i].qs, s.qs, QK5_1 / 2);
+
+                // qh: copy 5th bit directly
+                memcpy(dst_tmp[i].qh, s.qh, 4);
+            }
+            *dst++ = make_block_q5_1x32(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+}
+
+static int repack_q5_k_to_q5_1_32_bl(ggml_tensor *              t,
+                                     int                        interleave_block,
+                                     const void * GGML_RESTRICT data,
+                                     size_t                     data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q5_K);
+    GGML_ASSERT(interleave_block == 32);
+    GGML_ASSERT(QK_K / QK5_1 == 8);
+
+    constexpr int nrows_interleaved = 32;
+
+    spacemit_kernels::nrow_block_q5_1<32> * dst = (spacemit_kernels::nrow_block_q5_1<32> *) t->data;
+    const block_q5_K *                      src = (const block_q5_K *) data;
+    spacemit_kernels::nrow_block_q5_1<1>    dst_tmp[32];
+    int                                     nrow    = ggml_nrows(t);
+    int                                     nblocks = t->ne[0] / QK_K;
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % QK_K != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int j = 0; j < 8; j++) {
+                for (int i = 0; i < nrows_interleaved; i++) {
+                    uint8_t     sc, m;
+                    const float d = GGML_FP16_TO_FP32(src[x + i * nblocks].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d);
+                    const float min =
+                        GGML_FP16_TO_FP32(src[x + i * nblocks].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin);
+                    get_scale_min_k4(j, src[x + i * nblocks].scales, &sc, &m);
+
+                    float d1 = d * sc;
+                    float m1 = min * m;
+
+                    float mid              = std::nearbyintf(m1 / d1);
+                    mid                    = std::min(31.0f, std::max(0.0f, mid));
+                    dst_tmp[i].scales16[0] = GGML_FP32_TO_FP16(d1);
+                    dst_tmp[i].zp[0]       = static_cast<uint8_t>(mid);
+
+                    // src -> [b0, b32] [b1, b33] ... [b31, b63]
+                    // dst -> [b0, b16] [b1, b17] ... [b15, b31] [b32, b48] [b33, b49] ... [b47, b63]
+                    const uint8_t * q = src[x + i * nblocks].qs + (j / 2) * QK5_1;
+                    if (j % 2 == 0) {
+                        for (int ii = 0; ii < 16; ii++) {
+                            dst_tmp[i].qs[ii] = (q[ii] & 0x0F) | ((q[ii + 16] & 0x0F) << 4);
+                        }
+                    } else {
+                        for (int ii = 0; ii < 16; ii++) {
+                            dst_tmp[i].qs[ii] = ((q[ii] & 0xF0) >> 4) | (q[ii + 16] & 0xF0);
+                        }
+                    }
+
+                    // Extract the 5th bit (qh) for this sub-block
+                    // block_q5_K.qh[32]: for sub-block j, the 5th bit is at bit position j in qh[l]
+                    // qs was reordered: dst_qs maps to src weights [0,16,1,17,...,15,31]
+                    // So qh must follow the same reorder to stay aligned with qs
+                    // dst qh[4] = 32 bits for 32 weights in the reordered layout:
+                    //   byte 0: weights 0..7   (from src_qh[0..7])
+                    //   byte 1: weights 8..15  (from src_qh[8..15])
+                    //   byte 2: weights 16..23 (from src_qh[16..23])
+                    //   byte 3: weights 24..31 (from src_qh[24..31])
+                    const uint8_t * src_qh = src[x + i * nblocks].qh;
+                    for (int bi = 0; bi < 4; bi++) {
+                        uint8_t qh_byte = 0;
+                        for (int k = 0; k < 8; k++) {
+                            int src_idx = bi * 8 + k;
+                            qh_byte |= ((src_qh[src_idx] >> j) & 1) << k;
+                        }
+                        dst_tmp[i].qh[bi] = qh_byte;
+                    }
+                }
+                *dst++ = make_block_q5_1x32(dst_tmp, interleave_block);
+            }
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+}
+
+namespace ggml::cpu::riscv64_spacemit {
+
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> int repack(ggml_tensor *, const void *, size_t);
+
+template <> int repack<block_q4_0, 32, 16>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q4_0_to_q4_0_16_bl(t, 16, data, data_size);
+}
+
+template <> int repack<block_q4_1, 32, 16>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q4_1_to_q4_1_16_bl(t, 16, data, data_size);
+}
+
+template <> int repack<block_q4_K, 32, 16>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q4_k_to_q4_1_16_bl(t, 16, data, data_size);
+}
+
+template <> int repack<block_q2_K, 256, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q2_k_to_q2_k_32_bl(t, 32, data, data_size);
+}
+
+template <> int repack<block_q3_K, 256, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q3_k_to_q3_k_32_bl(t, 32, data, data_size);
+}
+
+template <> int repack<block_q4_0, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+#if 0
+    return repack_q4_0_to_q4_0_32_bl_ref(t, 32, data, data_size);
+#else
+    return repack_q4_0_to_q4_0_32_bl(t, 32, data, data_size);
+#endif
+}
+
+template <> int repack<block_q4_0, 256, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+#if 1
+    return repack_q4_0_to_q4_0_256_32_bl_ref(t, 32, data, data_size);
+#else
+    //return repack_q4_0_to_q4_0_256_32_bl(t, 32, data, data_size);
+#endif
+}
+
+template <> int repack<block_q4_1, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+#if 0
+    return repack_q4_1_to_q4_1_32_bl_ref(t, 32, data, data_size);
+#else
+    return repack_q4_1_to_q4_1_32_bl(t, 32, data, data_size);
+#endif
+}
+
+template <> int repack<block_q4_1, 256, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+#if 1
+    return repack_q4_0_to_q4_1_256_32_bl_ref(t, 32, data, data_size);
+#else
+    return repack_q4_1_to_q4_1_256_32_bl(t, 32, data, data_size);
+#endif
+}
+
+template <> int repack<block_q4_K, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q4_k_to_q4_1_32_bl(t, 32, data, data_size);
+}
+
+template <> int repack<block_q6_K, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+#if 1
+    return repack_q6_k_to_q8_0_32_bl_ref(t, 32, data, data_size);
+#else
+    return repack_q6_k_to_q8_0_32_bl(t, 32, data, data_size);
+#endif
+}
+
+template <> int repack<block_q8_0, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+#if 1
+    return repack_q8_0_to_q8_0_32_bl_ref(t, 32, data, data_size);
+#else
+    return repack_q8_0_to_q8_0_32_bl(t, 32, data, data_size);
+#endif
+}
+
+template <> int repack<block_mxfp4, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_mxfp4_to_mxfp4_32_bl(t, 32, data, data_size);
+}
+
+template <> int repack<block_q5_0, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q5_0_to_q5_0_32_bl(t, 32, data, data_size);
+}
+
+template <> int repack<block_q5_1, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q5_1_to_q5_1_32_bl(t, 32, data, data_size);
+}
+
+template <> int repack<block_q5_K, 32, 32>(ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q5_k_to_q5_1_32_bl(t, 32, data, data_size);
+}
+
+}  // namespace ggml::cpu::riscv64_spacemit
diff --git a/ggml/src/ggml-cpu/spacemit/repack.h b/ggml/src/ggml-cpu/spacemit/repack.h
new file mode 100644
index 00000000000..950cbde7593
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/repack.h
@@ -0,0 +1,14 @@
+#pragma once
+
+#include "ggml-common.h"
+#include "ggml.h"
+
+#include <cstddef>
+#include <cstdint>
+
+namespace ggml::cpu::riscv64_spacemit {
+
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
+int repack(ggml_tensor * t, const void * data, size_t data_size);
+
+}  // namespace ggml::cpu::riscv64_spacemit
diff --git a/ggml/src/ggml-cpu/spacemit/rvv_kernels.cpp b/ggml/src/ggml-cpu/spacemit/rvv_kernels.cpp
new file mode 100644
index 00000000000..d2f89743622
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/rvv_kernels.cpp
@@ -0,0 +1,3178 @@
+#include "rvv_kernels.h"
+
+#include "common.h"
+#include "ggml.h"
+#include "ops.h"
+#include "string.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <stdexcept>
+
+#if !defined(__riscv_v) || !defined(__riscv_v_intrinsic)
+#    error "riscv v extension or v_intrinsic not enabled"
+#else
+#    include <riscv_vector.h>
+#endif
+
+#if !defined(__riscv_zfh)
+#    error "riscv zfh extension not enabled"
+#endif
+
+#if defined(__GNUC__)
+#    pragma GCC diagnostic ignored "-Woverlength-strings"
+#    pragma GCC diagnostic ignored "-Wcast-qual"
+#    pragma GCC diagnostic ignored "-Wunused-parameter"
+#endif
+
+namespace spacemit_kernels::rvv {
+
+namespace {
+
+auto align_up(size_t value, size_t alignment) {
+    return (value + alignment - 1) / alignment * alignment;
+}
+
+static inline bool flash_attn_ext_supported_d_vlen1024_vf16(int64_t d) {
+    return d > 0 && d <= 128;
+}
+
+static inline bool flash_attn_ext_supported_shape_vlen1024_vf16(int64_t DK, int64_t DV) {
+    return flash_attn_ext_supported_d_vlen1024_vf16(DK) && flash_attn_ext_supported_d_vlen1024_vf16(DV);
+}
+
+static inline float reduce_sum_f32m4_vlen1024(vfloat32m4_t v, size_t vl) {
+    vfloat32m1_t s_v = __riscv_vfmv_v_f_f32m1(0.0f, 1);
+    s_v              = __riscv_vfredusum_vs_f32m4_f32m1(v, s_v, vl);
+    return __riscv_vfmv_f_s_f32m1_f32(s_v);
+}
+
+static inline float reduce_sum_f32m2_vlen1024(vfloat32m2_t v, size_t vl) {
+    vfloat32m1_t s_v = __riscv_vfmv_v_f_f32m1(0.0f, 1);
+    s_v              = __riscv_vfredusum_vs_f32m2_f32m1(v, s_v, vl);
+    return __riscv_vfmv_f_s_f32m1_f32(s_v);
+}
+
+// Adapted from ggml_v_expf_m2 in vec.h. This is accurate enough for softmax.
+static inline vfloat32m2_t rvv_expf_approx_f32m2(vfloat32m2_t x, size_t vl) {
+    const vfloat32m2_t r = __riscv_vfmv_v_f_f32m2(0x1.8p23f, vl);
+    const vfloat32m2_t z = __riscv_vfmacc_vf_f32m2(r, 0x1.715476p+0f, x, vl);
+    const vfloat32m2_t n = __riscv_vfsub_vv_f32m2(z, r, vl);
+    const vfloat32m2_t b =
+        __riscv_vfnmsac_vf_f32m2(__riscv_vfnmsac_vf_f32m2(x, 0x1.62e4p-1f, n, vl), 0x1.7f7d1cp-20f, n, vl);
+    const vuint32m2_t  e = __riscv_vsll_vx_u32m2(__riscv_vreinterpret_v_f32m2_u32m2(z), 23, vl);
+    const vfloat32m2_t k = __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vadd_vx_u32m2(e, 0x3f800000, vl));
+    const vbool16_t    c = __riscv_vmfgt_vf_f32m2_b16(__riscv_vfabs_v_f32m2(n, vl), 126.0f, vl);
+    const vfloat32m2_t u = __riscv_vfmul_vv_f32m2(b, b, vl);
+    const vfloat32m2_t j = __riscv_vfmacc_vv_f32m2(
+        __riscv_vfmul_vf_f32m2(b, 0x1.ffffecp-1f, vl),
+        __riscv_vfmacc_vv_f32m2(
+            __riscv_vfmacc_vf_f32m2(__riscv_vfmv_v_f_f32m2(0x1.fffdb6p-2f, vl), 0x1.555e66p-3f, b, vl),
+            __riscv_vfmacc_vf_f32m2(__riscv_vfmv_v_f_f32m2(0x1.573e2ep-5f, vl), 0x1.0e4020p-7f, b, vl), u, vl),
+        u, vl);
+
+    if (!__riscv_vcpop_m_b16(c, vl)) {
+        return __riscv_vfmacc_vv_f32m2(k, j, k, vl);
+    }
+
+    const vbool16_t    dm = __riscv_vmfle_vf_f32m2_b16(n, 0.0f, vl);
+    const vuint32m2_t  d  = __riscv_vmerge_vxm_u32m2(__riscv_vmv_v_x_u32m2(0, vl), 0x82000000, dm, vl);
+    const vfloat32m2_t s1 = __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vadd_vx_u32m2(d, 0x7f000000, vl));
+    const vfloat32m2_t s2 = __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vsub_vv_u32m2(e, d, vl));
+    const vfloat32m2_t r1 =
+        __riscv_vmerge_vvm_f32m2(__riscv_vfmacc_vv_f32m2(k, k, j, vl),
+                                 __riscv_vfmul_vv_f32m2(__riscv_vfmacc_vv_f32m2(s2, s2, j, vl), s1, vl), c, vl);
+    return __riscv_vmerge_vvm_f32m2(r1, __riscv_vfmul_vv_f32m2(s1, s1, vl),
+                                    __riscv_vmfgt_vf_f32m2_b16(__riscv_vfabs_v_f32m2(n, vl), 192.0f, vl), vl);
+}
+
+static inline vfloat32m2_t rvv_tanh_approx_f32m2(vfloat32m2_t x, size_t vl) {
+    const vfloat32m2_t abs_x       = __riscv_vfabs_v_f32m2(x, vl);
+    const vfloat32m2_t neg_2_abs   = __riscv_vfmul_vf_f32m2(abs_x, -2.0f, vl);
+    const vfloat32m2_t exp_term    = rvv_expf_approx_f32m2(neg_2_abs, vl);
+    const vfloat32m2_t numerator   = __riscv_vfsub_vf_f32m2(exp_term, 1.0f, vl);
+    const vfloat32m2_t denominator = __riscv_vfadd_vf_f32m2(exp_term, 1.0f, vl);
+    const vfloat32m2_t tanh_abs    = __riscv_vfneg_v_f32m2(__riscv_vfdiv_vv_f32m2(numerator, denominator, vl), vl);
+    const vbool16_t    neg_mask    = __riscv_vmflt_vf_f32m2_b16(x, 0.0f, vl);
+    const vfloat32m2_t tanh_neg    = __riscv_vfneg_v_f32m2(tanh_abs, vl);
+    return __riscv_vmerge_vvm_f32m2(tanh_abs, tanh_neg, neg_mask, vl);
+}
+
+static void rvv_softcap_tanh_inplace_f32(float * dst, int64_t dst_stride, int64_t tile_rows, int64_t n, float softcap) {
+    for (int tq = 0; tq < tile_rows; ++tq, dst += dst_stride) {
+        float * dst_row   = dst;
+        int64_t remaining = n;
+        while (remaining > 0) {
+            const size_t vl = __riscv_vsetvl_e32m2(remaining);
+            vfloat32m2_t v  = __riscv_vle32_v_f32m2(dst_row, vl);
+            v               = rvv_tanh_approx_f32m2(v, vl);
+            v               = __riscv_vfmul_vf_f32m2(v, softcap, vl);
+            __riscv_vse32_v_f32m2(dst_row, v, vl);
+            dst_row += vl;
+            remaining -= vl;
+        }
+    }
+}
+
+static inline float rvv_softmax_exp_inplace_f32(float * dst, int64_t n, float max_value) {
+    float row_sum = 0.0f;
+    while (n > 0) {
+        const size_t vl = __riscv_vsetvl_e32m2(n);
+        vfloat32m2_t v  = __riscv_vle32_v_f32m2(dst, vl);
+        v               = __riscv_vfsub_vf_f32m2(v, max_value, vl);
+        v               = rvv_expf_approx_f32m2(v, vl);
+        __riscv_vse32_v_f32m2(dst, v, vl);
+        row_sum += reduce_sum_f32m2_vlen1024(v, vl);
+        dst += vl;
+        n -= vl;
+    }
+    return row_sum;
+}
+
+static inline float rvv_add_max_inplace_f32(float * dst, const float * src, int64_t n) {
+    float max_val = -INFINITY;
+    while (n > 0) {
+        const size_t vl   = __riscv_vsetvl_e32m4(n);
+        vfloat32m4_t vdst = __riscv_vle32_v_f32m4(dst, vl);
+        vfloat32m4_t vsrc = __riscv_vle32_v_f32m4(src, vl);
+        vdst              = __riscv_vfadd_vv_f32m4(vdst, vsrc, vl);
+        __riscv_vse32_v_f32m4(dst, vdst, vl);
+
+        vfloat32m1_t seed = __riscv_vfmv_v_f_f32m1(max_val, 1);
+        seed              = __riscv_vfredmax_vs_f32m4_f32m1(vdst, seed, vl);
+        max_val           = __riscv_vfmv_f_s_f32m1_f32(seed);
+
+        dst += vl;
+        src += vl;
+        n -= vl;
+    }
+    return max_val;
+}
+
+static inline float rvv_softcap_add_max_inplace_f32(float * dst, const float * src, int64_t n, float softcap) {
+    if (softcap == 0.0f) {
+        return rvv_add_max_inplace_f32(dst, src, n);
+    }
+
+    float max_val = -INFINITY;
+    while (n > 0) {
+        const size_t vl   = __riscv_vsetvl_e32m2(n);
+        vfloat32m2_t vdst = __riscv_vle32_v_f32m2(dst, vl);
+        vfloat32m2_t vsrc = __riscv_vle32_v_f32m2(src, vl);
+        vdst              = rvv_tanh_approx_f32m2(vdst, vl);
+        vdst              = __riscv_vfmul_vf_f32m2(vdst, softcap, vl);
+        vdst              = __riscv_vfadd_vv_f32m2(vdst, vsrc, vl);
+        __riscv_vse32_v_f32m2(dst, vdst, vl);
+
+        vfloat32m1_t seed = __riscv_vfmv_v_f_f32m1(max_val, 1);
+        seed              = __riscv_vfredmax_vs_f32m2_f32m1(vdst, seed, vl);
+        max_val           = __riscv_vfmv_f_s_f32m1_f32(seed);
+
+        dst += vl;
+        src += vl;
+        n -= vl;
+    }
+    return max_val;
+}
+
+static inline void rvv_zero_f32(float * dst, int64_t n) {
+    while (n > 0) {
+        const size_t       vl = __riscv_vsetvl_e32m4(n);
+        const vfloat32m4_t z  = __riscv_vfmv_v_f_f32m4(0.0f, vl);
+        __riscv_vse32_v_f32m4(dst, z, vl);
+        dst += vl;
+        n -= vl;
+    }
+}
+
+static inline void rvv_scale_f32(float * dst, float scale, int64_t n) {
+    while (n > 0) {
+        const size_t vl = __riscv_vsetvl_e32m4(n);
+        vfloat32m4_t v  = __riscv_vle32_v_f32m4(dst, vl);
+        v               = __riscv_vfmul_vf_f32m4(v, scale, vl);
+        __riscv_vse32_v_f32m4(dst, v, vl);
+        dst += vl;
+        n -= vl;
+    }
+}
+
+static inline void rvv_add_inplace_f32(float *       dst,
+                                       int64_t       dst_stride,
+                                       const float * src,
+                                       int64_t       src_stride,
+                                       int64_t       tile_rows,
+                                       int64_t       n) {
+    for (int tq = 0; tq < tile_rows; ++tq, dst += dst_stride, src += src_stride) {
+        int64_t       remaining = n;
+        float *       dst_row   = dst;
+        const float * src_row   = src;
+        while (remaining > 0) {
+            const size_t vl   = __riscv_vsetvl_e32m4(remaining);
+            vfloat32m4_t vdst = __riscv_vle32_v_f32m4(dst_row, vl);
+            vfloat32m4_t vsrc = __riscv_vle32_v_f32m4(src_row, vl);
+            vdst              = __riscv_vfadd_vv_f32m4(vdst, vsrc, vl);
+            __riscv_vse32_v_f32m4(dst_row, vdst, vl);
+            dst_row += vl;
+            src_row += vl;
+            remaining -= vl;
+        }
+    }
+}
+
+static inline float rvv_max_f32(const float * src, int64_t n) {
+    float max_val = -INFINITY;
+    while (n > 0) {
+        const size_t       vl   = __riscv_vsetvl_e32m4(n);
+        const vfloat32m4_t v    = __riscv_vle32_v_f32m4(src, vl);
+        vfloat32m1_t       seed = __riscv_vfmv_v_f_f32m1(max_val, 1);
+        seed                    = __riscv_vfredmax_vs_f32m4_f32m1(v, seed, vl);
+        max_val                 = __riscv_vfmv_f_s_f32m1_f32(seed);
+        src += vl;
+        n -= vl;
+    }
+    return max_val;
+}
+
+static void rvv_pack_f32_as_scaled_f16(void *       dst,
+                                       int64_t      dst_row_stride,
+                                       const void * src,
+                                       int64_t      src_row_stride,
+                                       int64_t      tile_rows,
+                                       int64_t      n,
+                                       float        scale) {
+    for (int tq = 0; tq < tile_rows; ++tq) {
+        const float * row_ptr     = (const float *) ((const char *) src + tq * src_row_stride);
+        _Float16 *    dst_row_ptr = (_Float16 *) ((char *) dst + tq * dst_row_stride);
+        int64_t       remaining   = n;
+        while (remaining > 0) {
+            const size_t vl        = __riscv_vsetvl_e32m4(remaining);
+            vfloat32m4_t v32       = __riscv_vle32_v_f32m4(row_ptr, vl);
+            v32                    = __riscv_vfmul_vf_f32m4(v32, scale, vl);
+            const vfloat16m2_t v16 = __riscv_vfncvt_f_f_w_f16m2(v32, vl);
+            __riscv_vse16_v_f16m2(dst_row_ptr, v16, vl);
+            dst_row_ptr += vl;
+            row_ptr += vl;
+            remaining -= vl;
+        }
+    }
+}
+
+static void rvv_pack_scaled_f16_as_f32(void *       dst,
+                                       int64_t      dst_row_stride,
+                                       const void * src,
+                                       int64_t      src_row_stride,
+                                       int64_t      tile_rows,
+                                       int64_t      n,
+                                       float        scale) {
+    for (int tq = 0; tq < tile_rows; ++tq) {
+        const _Float16 * row_ptr     = (const _Float16 *) ((const char *) src + tq * src_row_stride);
+        float *          dst_row_ptr = (float *) ((char *) dst + tq * dst_row_stride);
+        int64_t          remaining   = n;
+        while (remaining > 0) {
+            const size_t       vl  = __riscv_vsetvl_e16m2(remaining);
+            const vfloat16m2_t v16 = __riscv_vle16_v_f16m2(row_ptr, vl);
+            vfloat32m4_t       v32 = __riscv_vfwcvt_f_f_v_f32m4(v16, vl);
+            v32                    = __riscv_vfmul_vf_f32m4(v32, scale, vl);
+            __riscv_vse32_v_f32m4(dst_row_ptr, v32, vl);
+            dst_row_ptr += vl;
+            row_ptr += vl;
+            remaining -= vl;
+        }
+    }
+}
+
+static void rvv_pack_scaled_f32_as_f32(void *       dst,
+                                       int64_t      dst_row_stride,
+                                       const void * src,
+                                       int64_t      src_row_stride,
+                                       int64_t      tile_rows,
+                                       int64_t      n,
+                                       float *      scale) {
+    for (int tq = 0; tq < tile_rows; ++tq) {
+        const float * row_ptr     = (const float *) ((const char *) src + tq * src_row_stride);
+        float *       dst_row_ptr = (float *) ((char *) dst + tq * dst_row_stride);
+        int64_t       remaining   = n;
+        while (remaining > 0) {
+            const size_t vl  = __riscv_vsetvl_e32m4(remaining);
+            vfloat32m4_t v32 = __riscv_vle32_v_f32m4(row_ptr, vl);
+            v32              = __riscv_vfmul_vf_f32m4(v32, scale[tq], vl);
+            __riscv_vse32_v_f32m4(dst_row_ptr, v32, vl);
+            dst_row_ptr += vl;
+            row_ptr += vl;
+            remaining -= vl;
+        }
+    }
+}
+
+static inline void rvv_transposed_s32_mn_to_nm(int8_t * dst,
+                                               int64_t  n_dst_stride,
+                                               int8_t * src,
+                                               int64_t  m_src_stride,
+                                               int64_t  m,
+                                               int64_t  n) {
+    int8_t * in  = src;
+    int8_t * out = dst;
+
+    __asm__ volatile(
+        "vsetvli                t0, zero, e32, m1, tu, mu     \n\t"
+        "mul                    t3, t0, %[os0]                \n\t"
+        "srli                   t2, %[isz0], 3                \n\t"
+        "blez                   t2, M1%=                      \n\t"
+
+        "LOOP_M8%=:                                           \n\t"
+        "addi                   a1, %[dst], 0                 \n\t"
+        "addi                   s1, %[src], 0                 \n\t"
+        "add                    s2, %[src], %[is0]            \n\t"
+        "add                    s3, s2, %[is0]                \n\t"
+        "add                    s4, s3, %[is0]                \n\t"
+        "add                    s5, s4, %[is0]                \n\t"
+        "add                    s6, s5, %[is0]                \n\t"
+        "add                    s7, s6, %[is0]                \n\t"
+        "add                    s8, s7, %[is0]                \n\t"
+        "addi                   t1, %[isz1], 0                \n\t"
+
+        "LOOP_M8N%=:                                          \n\t"
+        "vsetvli                t0, t1, e32, m1, tu, mu       \n\t"
+        "sub                    t1, t1, t0                    \n\t"
+        "vle32.v                v0, (s1)                      \n\t"
+        "sh2add                 s1, t0, s1                    \n\t"
+        "vle32.v                v1, (s2)                      \n\t"
+        "sh2add                 s2, t0, s2                    \n\t"
+        "vle32.v                v2, (s3)                      \n\t"
+        "sh2add                 s3, t0, s3                    \n\t"
+        "vle32.v                v3, (s4)                      \n\t"
+        "sh2add                 s4, t0, s4                    \n\t"
+        "vle32.v                v4, (s5)                      \n\t"
+        "sh2add                 s5, t0, s5                    \n\t"
+        "vle32.v                v5, (s6)                      \n\t"
+        "sh2add                 s6, t0, s6                    \n\t"
+        "vle32.v                v6, (s7)                      \n\t"
+        "sh2add                 s7, t0, s7                    \n\t"
+        "vle32.v                v7, (s8)                      \n\t"
+        "sh2add                 s8, t0, s8                    \n\t"
+        "vssseg8e32.v           v0, (a1), %[os0]              \n\t"
+        "add                    a1, a1, t3                    \n\t"
+        "bnez                   t1, LOOP_M8N%=                \n\t"
+        "sh3add                 %[src], %[is0], %[src]        \n\t"
+        "addi                   %[dst], %[dst], 32            \n\t"
+        "addi                   t2, t2, -1                    \n\t"
+        "bnez                   t2, LOOP_M8%=                 \n\t"
+
+        "M1%=:                                                \n\t"
+        "andi                   t2, %[isz0], 7                \n\t"
+        "blez                   t2, END%=                     \n\t"
+
+        "LOOP_M1%=:                                           \n\t"
+        "addi                   a1, %[dst], 0                 \n\t"
+        "addi                   s1, %[src], 0                 \n\t"
+        "addi                   t1, %[isz1], 0                \n\t"
+
+        "LOOP_M1N%=:                                          \n\t"
+        "vsetvli                t0, t1, e32, m1, tu, mu       \n\t"
+        "sub                    t1, t1, t0                    \n\t"
+        "vle32.v                v0, (s1)                      \n\t"
+        "sh2add                 s1, t0, s1                    \n\t"
+        "vsse32.v               v0, (a1), %[os0]              \n\t"
+        "add                    a1, a1, t3                    \n\t"
+        "bnez                   t1, LOOP_M1N%=                \n\t"
+        "add                    %[src], %[is0], %[src]        \n\t"
+        "addi                   %[dst], %[dst], 4             \n\t"
+        "addi                   t2, t2, -1                    \n\t"
+        "bnez                   t2, LOOP_M1%=                 \n\t"
+        "END%=:                                               \n\t"
+
+        : [src] "+r"(in), [dst] "+r"(out), [isz0] "+r"(m)
+        : [isz1] "r"(n), [is0] "r"(m_src_stride), [os0] "r"(n_dst_stride)
+        : "cc", "t0", "t1", "t2", "t3", "s1", "s2", "s3", "s4", "s5", "s6", "s7", "s8", "a1");
+}
+
+static inline void rvv_transposed_s16_mn_to_nm(int8_t * dst,
+                                               int64_t  n_dst_stride,
+                                               int8_t * src,
+                                               int64_t  m_src_stride,
+                                               int64_t  m,
+                                               int64_t  n) {
+    int8_t * in  = src;
+    int8_t * out = dst;
+
+    __asm__ volatile(
+        "vsetvli                t0, zero, e16, m1, tu, mu     \n\t"
+        "mul                    t3, t0, %[os0]                \n\t"
+        "srli                   t2, %[isz0], 3                \n\t"
+        "blez                   t2, M1%=                      \n\t"
+
+        "LOOP_M8%=:                                           \n\t"
+        "addi                   a1, %[dst], 0                 \n\t"
+        "addi                   s1, %[src], 0                 \n\t"
+        "add                    s2, %[src], %[is0]            \n\t"
+        "add                    s3, s2, %[is0]                \n\t"
+        "add                    s4, s3, %[is0]                \n\t"
+        "add                    s5, s4, %[is0]                \n\t"
+        "add                    s6, s5, %[is0]                \n\t"
+        "add                    s7, s6, %[is0]                \n\t"
+        "add                    s8, s7, %[is0]                \n\t"
+        "addi                   t1, %[isz1], 0                \n\t"
+
+        "LOOP_M8N%=:                                          \n\t"
+        "vsetvli                t0, t1, e16, m1, tu, mu       \n\t"
+        "sub                    t1, t1, t0                    \n\t"
+        "vle16.v                v0, (s1)                      \n\t"
+        "sh1add                 s1, t0, s1                    \n\t"
+        "vle16.v                v1, (s2)                      \n\t"
+        "sh1add                 s2, t0, s2                    \n\t"
+        "vle16.v                v2, (s3)                      \n\t"
+        "sh1add                 s3, t0, s3                    \n\t"
+        "vle16.v                v3, (s4)                      \n\t"
+        "sh1add                 s4, t0, s4                    \n\t"
+        "vle16.v                v4, (s5)                      \n\t"
+        "sh1add                 s5, t0, s5                    \n\t"
+        "vle16.v                v5, (s6)                      \n\t"
+        "sh1add                 s6, t0, s6                    \n\t"
+        "vle16.v                v6, (s7)                      \n\t"
+        "sh1add                 s7, t0, s7                    \n\t"
+        "vle16.v                v7, (s8)                      \n\t"
+        "sh1add                 s8, t0, s8                    \n\t"
+        "vssseg8e16.v           v0, (a1), %[os0]              \n\t"
+        "add                    a1, a1, t3                    \n\t"
+        "bnez                   t1, LOOP_M8N%=                \n\t"
+        "sh3add                 %[src], %[is0], %[src]        \n\t"
+        "addi                   %[dst], %[dst], 16            \n\t"
+        "addi                   t2, t2, -1                    \n\t"
+        "bnez                   t2, LOOP_M8%=                 \n\t"
+
+        "M1%=:                                                \n\t"
+        "andi                   t2, %[isz0], 7                \n\t"
+        "blez                   t2, END%=                     \n\t"
+
+        "LOOP_M1%=:                                           \n\t"
+        "addi                   a1, %[dst], 0                 \n\t"
+        "addi                   s1, %[src], 0                 \n\t"
+        "addi                   t1, %[isz1], 0                \n\t"
+
+        "LOOP_M1N%=:                                          \n\t"
+        "vsetvli                t0, t1, e16, m1, tu, mu       \n\t"
+        "sub                    t1, t1, t0                    \n\t"
+        "vle16.v                v0, (s1)                      \n\t"
+        "sh1add                 s1, t0, s1                    \n\t"
+        "vsse16.v               v0, (a1), %[os0]              \n\t"
+        "add                    a1, a1, t3                    \n\t"
+        "bnez                   t1, LOOP_M1N%=                \n\t"
+        "add                    %[src], %[is0], %[src]        \n\t"
+        "addi                   %[dst], %[dst], 2             \n\t"
+        "addi                   t2, t2, -1                    \n\t"
+        "bnez                   t2, LOOP_M1%=                 \n\t"
+        "END%=:                                               \n\t"
+
+        : [src] "+r"(in), [dst] "+r"(out), [isz0] "+r"(m)
+        : [isz1] "r"(n), [is0] "r"(m_src_stride), [os0] "r"(n_dst_stride)
+        : "cc", "t0", "t1", "t2", "t3", "s1", "s2", "s3", "s4", "s5", "s6", "s7", "s8", "a1");
+}
+
+static inline void rvv_qk_dot_tile_f16_x1(float *          dst,
+                                          const _Float16 * q_row,
+                                          const _Float16 * k_pack,
+                                          int64_t          dk,
+                                          int64_t          kv_tile) {
+    const size_t vl  = __riscv_vsetvl_e16m1(kv_tile);
+    vfloat32m2_t acc = __riscv_vfmv_v_f_f32m2(0.0f, vl);
+
+    for (int64_t d = 0; d < dk; ++d) {
+        const vfloat16m1_t k_vec = __riscv_vle16_v_f16m1(k_pack + d * ggml_fa_tile_config::KV, vl);
+        acc                      = __riscv_vfwmacc_vf_f32m2(acc, q_row[d], k_vec, vl);
+    }
+
+    __riscv_vse32_v_f32m2(dst, acc, vl);
+}
+
+static inline void rvv_qk_dot_tile_f16_x4(float *          dst0,
+                                          float *          dst1,
+                                          float *          dst2,
+                                          float *          dst3,
+                                          const _Float16 * q0,
+                                          const _Float16 * q1,
+                                          const _Float16 * q2,
+                                          const _Float16 * q3,
+                                          const _Float16 * k_pack,
+                                          int64_t          dk,
+                                          int64_t          kv_tile) {
+    const size_t vl   = __riscv_vsetvl_e16m1(kv_tile);
+    vfloat32m2_t acc0 = __riscv_vfmv_v_f_f32m2(0.0f, vl);
+    vfloat32m2_t acc1 = __riscv_vfmv_v_f_f32m2(0.0f, vl);
+    vfloat32m2_t acc2 = __riscv_vfmv_v_f_f32m2(0.0f, vl);
+    vfloat32m2_t acc3 = __riscv_vfmv_v_f_f32m2(0.0f, vl);
+
+    for (int64_t d = 0; d < dk; ++d) {
+        const vfloat16m1_t k_vec = __riscv_vle16_v_f16m1(k_pack + d * ggml_fa_tile_config::KV, vl);
+        acc0                     = __riscv_vfwmacc_vf_f32m2(acc0, q0[d], k_vec, vl);
+        acc1                     = __riscv_vfwmacc_vf_f32m2(acc1, q1[d], k_vec, vl);
+        acc2                     = __riscv_vfwmacc_vf_f32m2(acc2, q2[d], k_vec, vl);
+        acc3                     = __riscv_vfwmacc_vf_f32m2(acc3, q3[d], k_vec, vl);
+    }
+
+    __riscv_vse32_v_f32m2(dst0, acc0, vl);
+    __riscv_vse32_v_f32m2(dst1, acc1, vl);
+    __riscv_vse32_v_f32m2(dst2, acc2, vl);
+    __riscv_vse32_v_f32m2(dst3, acc3, vl);
+}
+
+static inline void rvv_pv_accumulate_f16_x1(float *          dst,
+                                            const float *    prob,
+                                            const _Float16 * v_pack,
+                                            int64_t          kv_tile,
+                                            int64_t          dv) {
+    int64_t d_left = dv;
+    int64_t d_off  = 0;
+
+    while (d_left > 0) {
+        const size_t vl  = __riscv_vsetvl_e16m2(d_left);
+        vfloat32m4_t acc = __riscv_vle32_v_f32m4(dst + d_off, vl);
+
+        for (int64_t tk = 0; tk < kv_tile; ++tk) {
+            const vfloat16m2_t v16 = __riscv_vle16_v_f16m2(v_pack + tk * dv + d_off, vl);
+            const vfloat32m4_t v32 = __riscv_vfwcvt_f_f_v_f32m4(v16, vl);
+            acc                    = __riscv_vfmacc_vf_f32m4(acc, prob[tk], v32, vl);
+        }
+
+        __riscv_vse32_v_f32m4(dst + d_off, acc, vl);
+        d_left -= vl;
+        d_off += vl;
+    }
+}
+
+static inline void rvv_pv_accumulate_f16_x4(float *          dst0,
+                                            float *          dst1,
+                                            float *          dst2,
+                                            float *          dst3,
+                                            const float *    prob0,
+                                            const float *    prob1,
+                                            const float *    prob2,
+                                            const float *    prob3,
+                                            const _Float16 * v_pack,
+                                            int64_t          kv_tile,
+                                            int64_t          dv) {
+    int64_t d_left = dv;
+    int64_t d_off  = 0;
+
+    while (d_left > 0) {
+        const size_t vl   = __riscv_vsetvl_e16m2(d_left);
+        vfloat32m4_t acc0 = __riscv_vle32_v_f32m4(dst0 + d_off, vl);
+        vfloat32m4_t acc1 = __riscv_vle32_v_f32m4(dst1 + d_off, vl);
+        vfloat32m4_t acc2 = __riscv_vle32_v_f32m4(dst2 + d_off, vl);
+        vfloat32m4_t acc3 = __riscv_vle32_v_f32m4(dst3 + d_off, vl);
+
+        for (int64_t tk = 0; tk < kv_tile; ++tk) {
+            const vfloat16m2_t v16 = __riscv_vle16_v_f16m2(v_pack + tk * dv + d_off, vl);
+            const vfloat32m4_t v32 = __riscv_vfwcvt_f_f_v_f32m4(v16, vl);
+            acc0                   = __riscv_vfmacc_vf_f32m4(acc0, prob0[tk], v32, vl);
+            acc1                   = __riscv_vfmacc_vf_f32m4(acc1, prob1[tk], v32, vl);
+            acc2                   = __riscv_vfmacc_vf_f32m4(acc2, prob2[tk], v32, vl);
+            acc3                   = __riscv_vfmacc_vf_f32m4(acc3, prob3[tk], v32, vl);
+        }
+
+        __riscv_vse32_v_f32m4(dst0 + d_off, acc0, vl);
+        __riscv_vse32_v_f32m4(dst1 + d_off, acc1, vl);
+        __riscv_vse32_v_f32m4(dst2 + d_off, acc2, vl);
+        __riscv_vse32_v_f32m4(dst3 + d_off, acc3, vl);
+        d_left -= vl;
+        d_off += vl;
+    }
+}
+
+static inline void rvv_qk_dot_tile(float *       dst,
+                                   const float * q_row,
+                                   const float * k_pack,
+                                   int64_t       dk,
+                                   int64_t       kv_tile,
+                                   float         scale) {
+    const size_t vl  = __riscv_vsetvl_e32m4(kv_tile);
+    vfloat32m4_t acc = __riscv_vfmv_v_f_f32m4(0.0f, vl);
+
+    for (int64_t d = 0; d < dk; ++d) {
+        const vfloat32m4_t k_vec = __riscv_vle32_v_f32m4(k_pack + d * kv_tile, vl);
+        acc                      = __riscv_vfmacc_vf_f32m4(acc, q_row[d] * scale, k_vec, vl);
+    }
+
+    __riscv_vse32_v_f32m4(dst, acc, vl);
+}
+
+static inline void rvv_pv_accumulate(float *       dst,
+                                     const float * prob,
+                                     const float * v_pack,
+                                     int64_t       kv_tile,
+                                     int64_t       dv) {
+    int64_t d_left = dv;
+    int64_t d_off  = 0;
+
+    while (d_left > 0) {
+        const size_t vl  = __riscv_vsetvl_e32m4(d_left);
+        vfloat32m4_t acc = __riscv_vle32_v_f32m4(dst + d_off, vl);
+
+        for (int64_t tk = 0; tk < kv_tile; ++tk) {
+            const vfloat32m4_t v_vec = __riscv_vle32_v_f32m4(v_pack + tk * dv + d_off, vl);
+            acc                      = __riscv_vfmacc_vf_f32m4(acc, prob[tk], v_vec, vl);
+        }
+
+        __riscv_vse32_v_f32m4(dst + d_off, acc, vl);
+        d_left -= vl;
+        d_off += vl;
+    }
+}
+
+static void permute_transpose_impl(const ggml_tensor * src0,
+                                   ggml_tensor *       dst,
+                                   int64_t             batch,
+                                   int64_t             m,
+                                   int64_t             n,
+                                   int64_t             batch_stride,
+                                   int64_t             m_src_stride,
+                                   int64_t             n_src_stride,
+                                   int64_t             n_dst_stride,
+                                   int                 ith,
+                                   int                 nth) {
+    GGML_ASSERT(n_src_stride == sizeof(int32_t) || n_src_stride == sizeof(int16_t));
+
+    if (n_src_stride == sizeof(int32_t)) {
+        for (int64_t bi = ith; bi < batch; bi += nth) {
+            rvv_transposed_s32_mn_to_nm((int8_t *) ((char *) dst->data + bi * batch_stride), n_dst_stride,
+                                        (int8_t *) ((char *) src0->data + bi * batch_stride), m_src_stride, m, n);
+        }
+    } else if (n_src_stride == sizeof(int16_t)) {
+        for (int64_t bi = ith; bi < batch; bi += nth) {
+            rvv_transposed_s32_mn_to_nm((int8_t *) ((char *) dst->data + bi * batch_stride), n_dst_stride,
+                                        (int8_t *) ((char *) src0->data + bi * batch_stride), m_src_stride, m, n);
+        }
+    } else {
+        GGML_ABORT("not implemented");
+    }
+}
+
+template <size_t QLEN>
+static void flash_attn_ext_f16_one_chunk_inner_vlen1024_vf16_mrow(float **            pq,
+                                                                  const char *        k_data_row,
+                                                                  const char *        v_data_row,
+                                                                  const ggml_fp16_t * mp,
+                                                                  float **            sinks,
+                                                                  float **            dst,
+                                                                  float               scale,
+                                                                  float               logit_softcap,
+                                                                  float               slope,
+                                                                  int64_t             nek1,
+                                                                  int64_t             nbk1,
+                                                                  int64_t             nbv1,
+                                                                  int64_t             DV,
+                                                                  int64_t             DK,
+                                                                  void *              tcm_buffer,
+                                                                  size_t              tcm_buffer_size) {
+    GGML_ASSERT(flash_attn_ext_supported_shape_vlen1024_vf16(DK, DV));
+    float S[QLEN] = { 0.0f };       // sum
+    float M[QLEN] = { -INFINITY };  // maximum KQ value
+
+    _Float16 *   kq16_buffer          = (_Float16 *) tcm_buffer;
+    _Float16 *   qv_buffer            = kq16_buffer + QLEN * DV;
+    const size_t qkv_temp_buffer_size = (QLEN * DV + QLEN * DK) * sizeof(_Float16);
+    char *       kv_tile_buffer       = (char *) (qv_buffer + QLEN * DK);
+
+    {
+        vfloat16m2_t VKQ16_v = __riscv_vfmv_v_f_f16m2(0.0f, DV);
+        for (int64_t i = 0; i < QLEN; ++i) {
+            __riscv_vse16_v_f16m2(kq16_buffer + i * DV, VKQ16_v, DV);
+            vfloat16m2_t Q_q_v = __riscv_vfncvt_f_f_w_f16m2(__riscv_vle32_v_f32m4(pq[i], DK), DK);
+            __riscv_vse16_v_f16m2(qv_buffer + i * DK, Q_q_v, DK);
+        }
+    }
+
+    const uintptr_t scratch_addr = reinterpret_cast<uintptr_t>(kv_tile_buffer);
+    const size_t    scratch_size = tcm_buffer_size > qkv_temp_buffer_size ? tcm_buffer_size - qkv_temp_buffer_size : 0;
+    const uintptr_t kq_tile_addr = align_up(scratch_addr, alignof(float));
+    const size_t    scratch_prefix = kq_tile_addr - scratch_addr;
+    const size_t    packed_tile_size =
+        QLEN * sizeof(float) + DK * sizeof(_Float16) + DV * sizeof(_Float16) + sizeof(float);
+    const int64_t max_ic_tile_step = ((int64_t) __riscv_vsetvlmax_e16m1()) & ~((int64_t) 7);
+    const int64_t max_fit_by_tcm =
+        scratch_size > scratch_prefix ? (int64_t) ((scratch_size - scratch_prefix) / packed_tile_size) : 0;
+    const int64_t ic_tile_step = std::min(max_ic_tile_step, max_fit_by_tcm) & ~((int64_t) 7);
+
+    const uintptr_t k_tile_addr  = kq_tile_addr + QLEN * ic_tile_step * sizeof(float);
+    const uintptr_t v_tile_addr  = k_tile_addr + DK * ic_tile_step * sizeof(_Float16);
+    const uintptr_t mv_tile_addr = v_tile_addr + ic_tile_step * DV * sizeof(_Float16);
+
+    if (ic_tile_step >= 8) {
+        float *    kq_tile_buffer = reinterpret_cast<float *>(kq_tile_addr);
+        _Float16 * k_tile_pack    = reinterpret_cast<_Float16 *>(k_tile_addr);
+        _Float16 * v_tile_pack    = reinterpret_cast<_Float16 *>(v_tile_addr);
+        float *    mv_tile_pack   = reinterpret_cast<float *>(mv_tile_addr);
+
+        const int64_t k_tile_byte_stride = ic_tile_step * (int64_t) sizeof(_Float16);
+
+        int64_t ic_step = 0;
+        for (int64_t ic = 0; ic < nek1; ++ic) {
+            const float mv = mp ? slope * ((_Float16 *) mp)[ic] : 0.0f;
+
+            if (mv != -INFINITY) {
+                const _Float16 * k_data = (const _Float16 *) (k_data_row + ic * nbk1);
+                const _Float16 * v_data = (const _Float16 *) (v_data_row + ic * nbv1);
+
+                const vfloat16m2_t k_data_v = __riscv_vle16_v_f16m2(k_data, DK);
+                const vfloat16m2_t v_data_v = __riscv_vle16_v_f16m2(v_data, DV);
+                __riscv_vsse16_v_f16m2(k_tile_pack + ic_step, k_tile_byte_stride, k_data_v, DK);
+                __riscv_vse16_v_f16m2(v_tile_pack + ic_step * DV, v_data_v, DV);
+                mv_tile_pack[ic_step] = mv;
+                ic_step++;
+            }
+
+            if (ic_step > 0 && (ic_step == ic_tile_step || ic == (nek1 - 1))) {
+                if constexpr (QLEN == 4) {
+                    const size_t qk_vl   = __riscv_vsetvl_e16m1(ic_step);
+                    vfloat32m2_t qk_acc0 = __riscv_vfmv_v_f_f32m2(0.0f, qk_vl);
+                    vfloat32m2_t qk_acc1 = __riscv_vfmv_v_f_f32m2(0.0f, qk_vl);
+                    vfloat32m2_t qk_acc2 = __riscv_vfmv_v_f_f32m2(0.0f, qk_vl);
+                    vfloat32m2_t qk_acc3 = __riscv_vfmv_v_f_f32m2(0.0f, qk_vl);
+
+                    for (int64_t d = 0; d < DK; ++d) {
+                        const vfloat16m1_t k_vec = __riscv_vle16_v_f16m1(k_tile_pack + d * ic_tile_step, qk_vl);
+                        qk_acc0 = __riscv_vfwmacc_vf_f32m2(qk_acc0, qv_buffer[0 * DK + d], k_vec, qk_vl);
+                        qk_acc1 = __riscv_vfwmacc_vf_f32m2(qk_acc1, qv_buffer[1 * DK + d], k_vec, qk_vl);
+                        qk_acc2 = __riscv_vfwmacc_vf_f32m2(qk_acc2, qv_buffer[2 * DK + d], k_vec, qk_vl);
+                        qk_acc3 = __riscv_vfwmacc_vf_f32m2(qk_acc3, qv_buffer[3 * DK + d], k_vec, qk_vl);
+                    }
+
+                    qk_acc0 = __riscv_vfmul_vf_f32m2(qk_acc0, scale, qk_vl);
+                    qk_acc1 = __riscv_vfmul_vf_f32m2(qk_acc1, scale, qk_vl);
+                    qk_acc2 = __riscv_vfmul_vf_f32m2(qk_acc2, scale, qk_vl);
+                    qk_acc3 = __riscv_vfmul_vf_f32m2(qk_acc3, scale, qk_vl);
+
+                    __riscv_vse32_v_f32m2(kq_tile_buffer + 0 * ic_tile_step, qk_acc0, qk_vl);
+                    __riscv_vse32_v_f32m2(kq_tile_buffer + 1 * ic_tile_step, qk_acc1, qk_vl);
+                    __riscv_vse32_v_f32m2(kq_tile_buffer + 2 * ic_tile_step, qk_acc2, qk_vl);
+                    __riscv_vse32_v_f32m2(kq_tile_buffer + 3 * ic_tile_step, qk_acc3, qk_vl);
+                } else {
+                    static_assert(QLEN == 2, "unsupported QLEN");
+
+                    const size_t qk_vl   = __riscv_vsetvl_e16m1(ic_step);
+                    vfloat32m2_t qk_acc0 = __riscv_vfmv_v_f_f32m2(0.0f, qk_vl);
+                    vfloat32m2_t qk_acc1 = __riscv_vfmv_v_f_f32m2(0.0f, qk_vl);
+
+                    for (int64_t d = 0; d < DK; ++d) {
+                        const vfloat16m1_t k_vec = __riscv_vle16_v_f16m1(k_tile_pack + d * ic_tile_step, qk_vl);
+                        qk_acc0 = __riscv_vfwmacc_vf_f32m2(qk_acc0, qv_buffer[0 * DK + d], k_vec, qk_vl);
+                        qk_acc1 = __riscv_vfwmacc_vf_f32m2(qk_acc1, qv_buffer[1 * DK + d], k_vec, qk_vl);
+                    }
+
+                    qk_acc0 = __riscv_vfmul_vf_f32m2(qk_acc0, scale, qk_vl);
+                    qk_acc1 = __riscv_vfmul_vf_f32m2(qk_acc1, scale, qk_vl);
+
+                    __riscv_vse32_v_f32m2(kq_tile_buffer + 0 * ic_tile_step, qk_acc0, qk_vl);
+                    __riscv_vse32_v_f32m2(kq_tile_buffer + 1 * ic_tile_step, qk_acc1, qk_vl);
+                }
+
+                for (int i = 0; i < QLEN; ++i) {
+                    float *     row_ptr = kq_tile_buffer + i * ic_tile_step;
+                    const float tile_max =
+                        rvv_softcap_add_max_inplace_f32(row_ptr, mv_tile_pack, ic_step, logit_softcap);
+
+                    const float Mold = M[i];
+
+                    if (tile_max > Mold) {
+                        const float ms = expf(Mold - tile_max);
+                        M[i]           = tile_max;
+                        S[i] *= ms;
+
+                        vfloat16m2_t VKQ16_v = __riscv_vle16_v_f16m2(kq16_buffer + i * DV, DV);
+                        VKQ16_v              = __riscv_vfmul_vf_f16m2(VKQ16_v, (_Float16) ms, DV);
+                        __riscv_vse16_v_f16m2(kq16_buffer + i * DV, VKQ16_v, DV);
+                    }
+
+                    S[i] += rvv_softmax_exp_inplace_f32(row_ptr, ic_step, M[i]);
+                }
+
+                if constexpr (QLEN == 4) {
+                    vfloat16m2_t pv_acc0 = __riscv_vle16_v_f16m2(kq16_buffer + 0 * DV, DV);
+                    vfloat16m2_t pv_acc1 = __riscv_vle16_v_f16m2(kq16_buffer + 1 * DV, DV);
+                    vfloat16m2_t pv_acc2 = __riscv_vle16_v_f16m2(kq16_buffer + 2 * DV, DV);
+                    vfloat16m2_t pv_acc3 = __riscv_vle16_v_f16m2(kq16_buffer + 3 * DV, DV);
+
+                    for (int64_t tk = 0; tk < ic_step; ++tk) {
+                        const vfloat16m2_t v16 = __riscv_vle16_v_f16m2(v_tile_pack + tk * DV, DV);
+                        pv_acc0 =
+                            __riscv_vfmacc_vf_f16m2(pv_acc0, (_Float16) kq_tile_buffer[0 * ic_tile_step + tk], v16, DV);
+                        pv_acc1 =
+                            __riscv_vfmacc_vf_f16m2(pv_acc1, (_Float16) kq_tile_buffer[1 * ic_tile_step + tk], v16, DV);
+                        pv_acc2 =
+                            __riscv_vfmacc_vf_f16m2(pv_acc2, (_Float16) kq_tile_buffer[2 * ic_tile_step + tk], v16, DV);
+                        pv_acc3 =
+                            __riscv_vfmacc_vf_f16m2(pv_acc3, (_Float16) kq_tile_buffer[3 * ic_tile_step + tk], v16, DV);
+                    }
+
+                    __riscv_vse16_v_f16m2(kq16_buffer + 0 * DV, pv_acc0, DV);
+                    __riscv_vse16_v_f16m2(kq16_buffer + 1 * DV, pv_acc1, DV);
+                    __riscv_vse16_v_f16m2(kq16_buffer + 2 * DV, pv_acc2, DV);
+                    __riscv_vse16_v_f16m2(kq16_buffer + 3 * DV, pv_acc3, DV);
+                } else {
+                    static_assert(QLEN == 2, "unsupported QLEN");
+                    vfloat16m2_t pv_acc0 = __riscv_vle16_v_f16m2(kq16_buffer + 0 * DV, DV);
+                    vfloat16m2_t pv_acc1 = __riscv_vle16_v_f16m2(kq16_buffer + 1 * DV, DV);
+
+                    for (int64_t tk = 0; tk < ic_step; ++tk) {
+                        const vfloat16m2_t v16 = __riscv_vle16_v_f16m2(v_tile_pack + tk * DV, DV);
+                        pv_acc0 =
+                            __riscv_vfmacc_vf_f16m2(pv_acc0, (_Float16) kq_tile_buffer[0 * ic_tile_step + tk], v16, DV);
+                        pv_acc1 =
+                            __riscv_vfmacc_vf_f16m2(pv_acc1, (_Float16) kq_tile_buffer[1 * ic_tile_step + tk], v16, DV);
+                    }
+
+                    __riscv_vse16_v_f16m2(kq16_buffer + 0 * DV, pv_acc0, DV);
+                    __riscv_vse16_v_f16m2(kq16_buffer + 1 * DV, pv_acc1, DV);
+                }
+
+                ic_step = 0;
+            }
+        }
+    } else {
+        for (int64_t ic = 0; ic < nek1; ++ic) {
+            const float mv = mp ? slope * ((_Float16 *) mp)[ic] : 0.0f;
+
+            const char * k_data = k_data_row + ic * nbk1;
+            const char * v_data = v_data_row + ic * nbv1;
+
+            vfloat16m2_t k_data_v;
+            vfloat16m2_t v_data_v;
+
+            if (mv != -INFINITY) {
+                k_data_v = __riscv_vle16_v_f16m2((_Float16 *) k_data, DK);
+                v_data_v = __riscv_vle16_v_f16m2((_Float16 *) v_data, DV);
+            } else {
+                continue;
+            }
+
+            for (int i = 0; i < QLEN; ++i) {
+                vfloat16m2_t Q_q_v    = __riscv_vle16_v_f16m2(qv_buffer + i * DK, DK);
+                vfloat32m4_t qk_acc_v = __riscv_vfwmul_vv_f32m4(k_data_v, Q_q_v, DK);
+                float        s        = reduce_sum_f32m4_vlen1024(qk_acc_v, DK);
+                s                     = s * scale;
+                if (logit_softcap != 0.0f) {
+                    s = logit_softcap * tanhf(s);
+                }
+                s += mv;
+
+                const float Mold = M[i];
+
+                float ms = 1.0f;  // upon new higher max val, scale VKQ and KQ sum with this value
+                float vs = 1.0f;  // post-softmax KQ value, expf(s - M)
+
+                vfloat16m2_t VKQ16_v = __riscv_vle16_v_f16m2(kq16_buffer + i * DV, DV);
+                if (s > M[i]) {
+                    // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f
+                    M[i] = s;
+                    ms   = expf(Mold - M[i]);
+
+                    // V = V*expf(Mold - M)
+                    VKQ16_v = __riscv_vfmul_vf_f16m2(VKQ16_v, ms, DV);
+                } else {
+                    // no new maximum, ms == 1.0f, vs != 1.0f
+                    vs = expf(s - M[i]);
+                }
+                VKQ16_v = __riscv_vfmacc_vf_f16m2(VKQ16_v, vs, v_data_v, DV);
+                __riscv_vse16_v_f16m2(kq16_buffer + i * DV, VKQ16_v, DV);
+                S[i] = S[i] * ms + vs;  // scale and increment sum with partial sum
+            }
+        }
+    }
+
+    for (int i = 0; i < QLEN; ++i) {
+        vfloat16m2_t VKQ16_v = __riscv_vle16_v_f16m2(kq16_buffer + i * DV, DV);
+        vfloat32m4_t VKQ32_v = __riscv_vfwcvt_f_f_v_f32m4(VKQ16_v, DV);
+
+        // sinks
+        if (sinks[i]) {
+            const float s = *(sinks[i]);
+
+            float ms = 1.0f;
+            float vs = 1.0f;
+
+            if (s > M[i]) {
+                ms      = expf(M[i] - s);
+                M[i]    = s;
+                VKQ32_v = __riscv_vfmul_vf_f32m4(VKQ32_v, ms, DV);
+            } else {
+                vs = expf(s - M[i]);
+            }
+
+            S[i] = S[i] * ms + vs;
+        }
+
+        // V /= S
+        const float S_inv = S[i] == 0.0f ? 0.0f : 1.0f / S[i];
+
+        VKQ32_v = __riscv_vfmul_vf_f32m4(VKQ32_v, S_inv, DV);
+
+        __riscv_vse32_v_f32m4(dst[i], VKQ32_v, DV);
+    }
+}
+
+static void flash_attn_ext_f16_one_chunk_inner_vlen1024_vf16_m1(const float *       pq,
+                                                                const char *        k_data_row,
+                                                                const char *        v_data_row,
+                                                                const ggml_fp16_t * mp,
+                                                                const float *       sinks,
+                                                                float *             dst,
+                                                                float               scale,
+                                                                float               logit_softcap,
+                                                                float               slope,
+                                                                int64_t             nek1,
+                                                                int64_t             nbk1,
+                                                                int64_t             nbv1,
+                                                                int64_t             DV,
+                                                                int64_t             DK) {
+    GGML_ASSERT(flash_attn_ext_supported_shape_vlen1024_vf16(DK, DV));
+
+    float S = 0.0f;       // sum
+    float M = -INFINITY;  // maximum KQ value
+
+    vfloat16m2_t VKQ16_v = __riscv_vfmv_v_f_f16m2(0.0f, DV);
+
+    vfloat16m2_t Q_q_v = __riscv_vfncvt_f_f_w_f16m2(__riscv_vle32_v_f32m4(pq, DK), DK);
+
+    for (int64_t ic = 0; ic < nek1; ++ic) {
+        const float mv = mp ? slope * ((_Float16 *) mp)[ic] : 0.0f;
+        if (mv == -INFINITY) {
+            continue;
+        }
+
+        const char * k_data = k_data_row + ic * nbk1;
+
+        vfloat16m2_t k_data_v = __riscv_vle16_v_f16m2((_Float16 *) k_data, DK);
+
+        vfloat32m4_t qk_acc_v = __riscv_vfwmul_vv_f32m4(k_data_v, Q_q_v, DK);
+        float        s        = reduce_sum_f32m4_vlen1024(qk_acc_v, DK);
+
+        s = s * scale;  // scale KQ value
+
+        if (logit_softcap != 0.0f) {
+            s = logit_softcap * tanhf(s);
+        }
+
+        s += mv;  // apply mask
+
+        const float Mold = M;
+
+        float ms = 1.0f;  // upon new higher max val, scale VKQ and KQ sum with this value
+        float vs = 1.0f;  // post-softmax KQ value, expf(s - M)
+
+        const char * v_data = v_data_row + ic * nbv1;
+
+        vfloat16m2_t v_data_v = __riscv_vle16_v_f16m2((_Float16 *) v_data, DV);
+
+        if (s > M) {
+            // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f
+            M  = s;
+            ms = expf(Mold - M);
+
+            // V = V*expf(Mold - M)
+            VKQ16_v = __riscv_vfmul_vf_f16m2(VKQ16_v, ms, DV);
+        } else {
+            // no new maximum, ms == 1.0f, vs != 1.0f
+            vs = expf(s - M);
+        }
+
+        VKQ16_v = __riscv_vfmacc_vf_f16m2(VKQ16_v, vs, v_data_v, DV);
+
+        S = S * ms + vs;  // scale and increment sum with partial sum
+    }
+
+    vfloat32m4_t VKQ32_v = __riscv_vfwcvt_f_f_v_f32m4(VKQ16_v, DV);
+
+    // sinks
+    if (sinks) {
+        const float s = *sinks;
+
+        float ms = 1.0f;
+        float vs = 1.0f;
+
+        if (s > M) {
+            ms      = expf(M - s);
+            M       = s;
+            VKQ32_v = __riscv_vfmul_vf_f32m4(VKQ32_v, ms, DV);
+        } else {
+            vs = expf(s - M);
+        }
+
+        S = S * ms + vs;
+    }
+
+    // V /= S
+    const float S_inv = S == 0.0f ? 0.0f : 1.0f / S;
+
+    VKQ32_v = __riscv_vfmul_vf_f32m4(VKQ32_v, S_inv, DV);
+
+    __riscv_vse32_v_f32m4(dst, VKQ32_v, DV);
+}
+
+}  // namespace
+
+void memcpy1d(void * dst, const void * src, int64_t size) {
+    size_t byte_size_all = size;
+    size_t vlen          = __riscv_vlenb() * 8;
+    if (vlen == 256) {
+        // 1024 bytes
+        __asm__ volatile(
+            //
+            "srli           t0, %[size], 10             \n\t"
+            "blez           t0, memcpy_tail%=           \n\t"
+            "vsetvli        t1, x0, e8, m8, tu, mu      \n\t"
+            "memcpy_main_loop%=:                        \n\t"
+            "addi           t0, t0, -1                  \n\t"
+            "vle8.v         v0, (%[s])                  \n\t"
+            "addi           %[s], %[s], 256             \n\t"
+            "vle8.v         v8, (%[s])                  \n\t"
+            "addi           %[s], %[s], 256             \n\t"
+            "vle8.v         v16, (%[s])                 \n\t"
+            "addi           %[s], %[s], 256             \n\t"
+            "vle8.v         v24, (%[s])                 \n\t"
+            "addi           %[s], %[s], 256             \n\t"
+            //
+            "vse8.v         v0, (%[d])                  \n\t"
+            "addi           %[d], %[d], 256             \n\t"
+            "vse8.v         v8, (%[d])                  \n\t"
+            "addi           %[d], %[d], 256             \n\t"
+            "vse8.v         v16, (%[d])                 \n\t"
+            "addi           %[d], %[d], 256             \n\t"
+            "vse8.v         v24, (%[d])                 \n\t"
+            "addi           %[d], %[d], 256             \n\t"
+            //
+            "bnez           t0, memcpy_main_loop%=      \n\t"
+            "memcpy_tail%=:                             \n\t"
+            "andi           t1, %[size], 1023           \n\t"
+            "blez           t1, out%=                   \n\t"
+            "memcpy_tail_loop%=:                        \n\t"
+            "vsetvli        t0, t1, e8, m8, tu, mu      \n\t"
+            "sub            t1, t1, t0                  \n\t"
+            "vle8.v         v0, (%[s])                  \n\t"
+            "add            %[s], %[s], t0              \n\t"
+            "vse8.v         v0, (%[d])                  \n\t"
+            "add            %[d], %[d], t0              \n\t"
+            "bnez           t1, memcpy_tail_loop%=      \n\t"
+            "out%=:                                     \n\t"
+            : [s] "+r"(src), [d] "+r"(dst)
+            : [size] "r"(byte_size_all)
+            : "cc", "t0", "t1");
+    } else if (vlen == 1024) {
+        // 2048 bytes
+        __asm__ volatile(
+            //
+            "srli           t0, %[size], 11             \n\t"
+            "blez           t0, memcpy_tail%=           \n\t"
+            "vsetvli        t1, x0, e8, m8, tu, mu      \n\t"
+            "addi           t2, %[s], 1024              \n\t"
+            "addi           t3, %[d], 1024              \n\t"
+            "li             t5, 2048                    \n\t"
+            "memcpy_main_loop%=:                        \n\t"
+            "addi           t0, t0, -1                  \n\t"
+            "vle8.v         v0, (%[s])                  \n\t"
+            "add            %[s], %[s], t5              \n\t"
+            "vle8.v         v8, (t2)                    \n\t"
+            "add            t2, t2, t5                  \n\t"
+            //
+            "vse8.v         v0, (%[d])                  \n\t"
+            "add            %[d], %[d], t5              \n\t"
+            "vse8.v         v8, (t3)                    \n\t"
+            "add            t3, t3, t5                  \n\t"
+            //
+            "bnez           t0, memcpy_main_loop%=      \n\t"
+            "memcpy_tail%=:                             \n\t"
+            "andi           t1, %[size], 2047           \n\t"
+            "blez           t1, out%=                   \n\t"
+            "memcpy_tail_loop%=:                        \n\t"
+            "vsetvli        t0, t1, e8, m2, tu, mu      \n\t"
+            "sub            t1, t1, t0                  \n\t"
+            "vle8.v         v0, (%[s])                  \n\t"
+            "add            %[s], %[s], t0              \n\t"
+            "vse8.v         v0, (%[d])                  \n\t"
+            "add            %[d], %[d], t0              \n\t"
+            "bnez           t1, memcpy_tail_loop%=      \n\t"
+            "out%=:                                     \n\t"
+            : [s] "+r"(src), [d] "+r"(dst)
+            : [size] "r"(byte_size_all)
+            : "cc", "t0", "t1", "t2", "t3", "t5");
+    } else {
+        __asm__ volatile(
+            //
+            "add            t1, %[size], zero           \n\t"
+            "memcpy_tail_loop%=:                        \n\t"
+            "vsetvli        t0, t1, e8, m8, tu, mu      \n\t"
+            "sub            t1, t1, t0                  \n\t"
+            "vle8.v         v0, (%[s])                  \n\t"
+            "add            %[s], %[s], t0              \n\t"
+            "vse8.v         v0, (%[d])                  \n\t"
+            "add            %[d], %[d], t0              \n\t"
+            "bnez           t1, memcpy_tail_loop%=      \n\t"
+            : [s] "+r"(src), [d] "+r"(dst)
+            : [size] "r"(byte_size_all)
+            : "cc", "t0", "t1", "t2", "t4", "t3");
+    }
+}
+
+void memcpy2d(void * dst, int64_t dst_stride, const void * src, int64_t src_stride, int64_t tile_rows, int64_t size) {
+    for (int64_t i = 0; i < tile_rows; ++i) {
+        memcpy1d((char *) dst + i * dst_stride, (const char *) src + i * src_stride, size);
+    }
+}
+
+void forward_flash_attn_ext_f16_one_chunk_vlen1024_vf16(const ggml_compute_params * params,
+                                                        ggml_tensor *               dst,
+                                                        int                         ir0,
+                                                        int                         ir1,
+                                                        void *                      tcm_buffer,
+                                                        size_t                      tcm_buffer_size) {
+    const ggml_tensor * q     = dst->src[0];
+    const ggml_tensor * k     = dst->src[1];
+    const ggml_tensor * v     = dst->src[2];
+    const ggml_tensor * mask  = dst->src[3];
+    const ggml_tensor * sinks = dst->src[4];
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q, ne)
+    GGML_TENSOR_LOCALS(size_t, nbq, q, nb)
+    GGML_TENSOR_LOCALS(int64_t, nek, k, ne)
+    GGML_TENSOR_LOCALS(size_t, nbk, k, nb)
+    GGML_TENSOR_LOCALS(int64_t, nev, v, ne)
+    GGML_TENSOR_LOCALS(size_t, nbv, v, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
+    GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
+
+    const int64_t DK = nek0;
+    const int64_t DV = nev0;
+    const int64_t N  = neq1;
+
+    GGML_ASSERT(flash_attn_ext_supported_shape_vlen1024_vf16(DK, DV));
+
+    // broadcast factors
+    const int64_t rk2 = neq2 / nek2;
+    const int64_t rk3 = neq3 / nek3;
+
+    const int64_t rv2 = neq2 / nev2;
+    const int64_t rv3 = neq3 / nev3;
+
+    // parallelize by q rows using ggml_vec_dot_f32
+
+    float scale         = *((float *) dst->op_params + 0);
+    float max_bias      = *((float *) dst->op_params + 1);
+    float logit_softcap = *((float *) dst->op_params + 2);
+
+    if (logit_softcap != 0) {
+        scale /= logit_softcap;
+    }
+
+    const uint32_t n_head      = neq2;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    const int KV_row_size = DK * sizeof(_Float16) + DV * sizeof(_Float16);
+
+    int ith     = params->ith;
+    int ir_step = 1;
+    for (int ir = ir0; ir < ir1; ir += ir_step) {
+        // q indices
+        const int iq3 = ir / (neq2 * neq1);
+        const int iq2 = (ir - iq3 * neq2 * neq1) / neq1;
+        const int iq1 = (ir - iq3 * neq2 * neq1 - iq2 * neq1);
+
+        const int iq3_1 = (ir + 1) / (neq2 * neq1);
+        const int iq2_1 = (ir + 1 - iq3_1 * neq2 * neq1) / neq1;
+        const int iq1_1 = (ir + 1 - iq3_1 * neq2 * neq1 - iq2_1 * neq1);
+
+        const int iq3_2 = (ir + 2) / (neq2 * neq1);
+        const int iq2_2 = (ir + 2 - iq3_2 * neq2 * neq1) / neq1;
+        const int iq1_2 = (ir + 2 - iq3_2 * neq2 * neq1 - iq2_2 * neq1);
+
+        const int iq3_3 = (ir + 3) / (neq2 * neq1);
+        const int iq2_3 = (ir + 3 - iq3_3 * neq2 * neq1) / neq1;
+        const int iq1_3 = (ir + 3 - iq3_3 * neq2 * neq1 - iq2_3 * neq1);
+
+        const uint32_t h = iq2;  // head index
+        const float    slope =
+            (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2 * (h - n_head_log2) + 1) : 1.0f;
+
+        const ggml_fp16_t * mp =
+            mask ? (ggml_fp16_t *) ((char *) mask->data + iq1 * mask->nb[1] + (iq2 % mask->ne[2]) * mask->nb[2] +
+                                    (iq3 % mask->ne[3]) * mask->nb[3]) :
+                   NULL;
+
+        const bool mp_equal_2 = iq1_1 == iq1 && (iq2 % mask->ne[2]) == (iq2_1 % mask->ne[2]) &&
+                                (iq3 % mask->ne[3]) == (iq3_1 % mask->ne[3]);
+
+        const bool mp_equal_4 = mp_equal_2 && iq1_2 == iq1 && (iq2 % mask->ne[2]) == (iq2_2 % mask->ne[2]) &&
+                                (iq3 % mask->ne[3]) == (iq3_2 % mask->ne[3]) && iq1_3 == iq1 &&
+                                (iq2 % mask->ne[2]) == (iq2_3 % mask->ne[2]) &&
+                                (iq3 % mask->ne[3]) == (iq3_3 % mask->ne[3]);
+
+        // k indices
+        const int ik3 = iq3 / rk3;
+        const int ik2 = iq2 / rk2;
+
+        const int ik3_1 = iq3_1 / rk3;
+        const int ik2_1 = iq2_1 / rk2;
+
+        const int ik3_2 = iq3_2 / rk3;
+        const int ik2_2 = iq2_2 / rk2;
+
+        const int ik3_3 = iq3_3 / rk3;
+        const int ik2_3 = iq2_3 / rk2;
+
+        // v indices
+        const int iv3 = iq3 / rv3;
+        const int iv2 = iq2 / rv2;
+
+        const int iv3_1 = iq3_1 / rv3;
+        const int iv2_1 = iq2_1 / rv2;
+
+        const int iv3_2 = iq3_2 / rv3;
+        const int iv2_2 = iq2_2 / rv2;
+
+        const int iv3_3 = iq3_3 / rv3;
+        const int iv2_3 = iq2_3 / rv2;
+
+        const float * pq = (const float *) ((char *) q->data + (iq1 * nbq1 + iq2 * nbq2 + iq3 * nbq3));
+
+        std::array<float *, 4> pq_buffer;
+        std::array<float *, 4> sinks_buffer;
+        std::array<float *, 4> dst_buffer;
+
+        if (tcm_buffer != nullptr && 4 * KV_row_size < tcm_buffer_size && ir < (ir1 - 3) && mp_equal_4 &&
+            ik3_3 == ik3 && ik2_3 == ik2 && iv3_3 == iv3 && iv2_3 == iv2 && ik3_2 == ik3 && ik2_2 == ik2 &&
+            iv3_2 == iv3 && iv2_2 == iv2 && ik3_1 == ik3 && ik2_1 == ik2 && iv3_1 == iv3 && iv2_1 == iv2) {
+            ir_step = 4;
+
+            pq_buffer[0] = (float *) ((char *) q->data + (iq1 * nbq1 + iq2 * nbq2 + iq3 * nbq3));
+            pq_buffer[1] = (float *) ((char *) q->data + (iq1_1 * nbq1 + iq2_1 * nbq2 + iq3_1 * nbq3));
+            pq_buffer[2] = (float *) ((char *) q->data + (iq1_2 * nbq1 + iq2_2 * nbq2 + iq3_2 * nbq3));
+            pq_buffer[3] = (float *) ((char *) q->data + (iq1_3 * nbq1 + iq2_3 * nbq2 + iq3_3 * nbq3));
+
+            sinks_buffer[0] = sinks ? ((float *) ((char *) sinks->data)) + iq2 : nullptr;
+            sinks_buffer[1] = sinks ? ((float *) ((char *) sinks->data)) + iq2_1 : nullptr;
+            sinks_buffer[2] = sinks ? ((float *) ((char *) sinks->data)) + iq2_2 : nullptr;
+            sinks_buffer[3] = sinks ? ((float *) ((char *) sinks->data)) + iq2_3 : nullptr;
+
+            dst_buffer[0] = (float *) ((char *) dst->data + (iq3 * ne2 * ne1 + iq2 + iq1 * ne1) * nb1);
+            dst_buffer[1] = (float *) ((char *) dst->data + (iq3_1 * ne2 * ne1 + iq2_1 + iq1_1 * ne1) * nb1);
+            dst_buffer[2] = (float *) ((char *) dst->data + (iq3_2 * ne2 * ne1 + iq2_2 + iq1_2 * ne1) * nb1);
+            dst_buffer[3] = (float *) ((char *) dst->data + (iq3_3 * ne2 * ne1 + iq2_3 + iq1_3 * ne1) * nb1);
+
+            flash_attn_ext_f16_one_chunk_inner_vlen1024_vf16_mrow<4>(  //
+                pq_buffer.data(),                                      //
+                (const char *) k->data + (ik2 * nbk2 + ik3 * nbk3),    //
+                (const char *) v->data + (iv2 * nbv2 + iv3 * nbv3),    //
+                mp,                                                    //
+                sinks_buffer.data(),                                   //
+                dst_buffer.data(),                                     //
+                scale, logit_softcap, slope, nek1, nbk1, nbv1, DV, DK, tcm_buffer, tcm_buffer_size);
+        } else if (tcm_buffer != nullptr && 2 * KV_row_size < tcm_buffer_size && ir < (ir1 - 1) && mp_equal_2 &&
+                   ik3_1 == ik3 && ik2_1 == ik2 && iv3_1 == iv3 && iv2_1 == iv2) {
+            ir_step = 2;
+
+            pq_buffer[0] = (float *) ((char *) q->data + (iq1 * nbq1 + iq2 * nbq2 + iq3 * nbq3));
+            pq_buffer[1] = (float *) ((char *) q->data + (iq1_1 * nbq1 + iq2_1 * nbq2 + iq3_1 * nbq3));
+
+            sinks_buffer[0] = sinks ? ((float *) ((char *) sinks->data)) + iq2 : nullptr;
+            sinks_buffer[1] = sinks ? ((float *) ((char *) sinks->data)) + iq2_1 : nullptr;
+
+            dst_buffer[0] = (float *) ((char *) dst->data + (iq3 * ne2 * ne1 + iq2 + iq1 * ne1) * nb1);
+            dst_buffer[1] = (float *) ((char *) dst->data + (iq3_1 * ne2 * ne1 + iq2_1 + iq1_1 * ne1) * nb1);
+
+            flash_attn_ext_f16_one_chunk_inner_vlen1024_vf16_mrow<2>(  //
+                pq_buffer.data(),                                      //
+                (const char *) k->data + (ik2 * nbk2 + ik3 * nbk3),    //
+                (const char *) v->data + (iv2 * nbv2 + iv3 * nbv3),    //
+                mp,                                                    //
+                sinks_buffer.data(),                                   //
+                dst_buffer.data(),                                     //
+                scale, logit_softcap, slope, nek1, nbk1, nbv1, DV, DK, tcm_buffer, tcm_buffer_size);
+        } else {
+            ir_step = 1;
+            flash_attn_ext_f16_one_chunk_inner_vlen1024_vf16_m1(                             //
+                pq,                                                                          //
+                (const char *) k->data + (ik2 * nbk2 + ik3 * nbk3),                          //
+                (const char *) v->data + (iv2 * nbv2 + iv3 * nbv3),                          //
+                mp,                                                                          //
+                sinks ? ((float *) ((char *) sinks->data)) + h : nullptr,                    //
+                (float *) ((char *) dst->data + (iq3 * ne2 * ne1 + iq2 + iq1 * ne1) * nb1),  //
+                scale, logit_softcap, slope, nek1, nbk1, nbv1, DV, DK);
+        }
+    }
+}
+
+void forward_flash_attn_ext_f16_tiled_vlen1024_vf16(const ggml_compute_params * params,
+                                                    ggml_tensor *               dst,
+                                                    int                         ir0,
+                                                    int                         ir1,
+                                                    void *                      tcm_buffer,
+                                                    size_t                      tcm_buffer_size) {
+    const ggml_tensor * q     = dst->src[0];
+    const ggml_tensor * k     = dst->src[1];
+    const ggml_tensor * v     = dst->src[2];
+    const ggml_tensor * mask  = dst->src[3];
+    const ggml_tensor * sinks = dst->src[4];
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q, ne)
+    GGML_TENSOR_LOCALS(size_t, nbq, q, nb)
+    GGML_TENSOR_LOCALS(int64_t, nek, k, ne)
+    GGML_TENSOR_LOCALS(size_t, nbk, k, nb)
+    GGML_TENSOR_LOCALS(int64_t, nev, v, ne)
+    GGML_TENSOR_LOCALS(size_t, nbv, v, nb)
+    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
+    GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
+
+    const int64_t DK = nek0;
+    const int64_t DV = nev0;
+    const int64_t N  = neq1;
+
+    GGML_ASSERT(flash_attn_ext_supported_shape_vlen1024_vf16(DK, DV));
+
+    GGML_ASSERT(ne0 == DV);
+    GGML_ASSERT(ne2 == N);
+
+    // input tensor rows must be contiguous
+    GGML_ASSERT(nbq0 == ggml_type_size(q->type));
+    GGML_ASSERT(nbk0 == ggml_type_size(k->type));
+    GGML_ASSERT(nbv0 == ggml_type_size(v->type));
+
+    GGML_ASSERT(neq0 == DK);
+    GGML_ASSERT(nek0 == DK);
+    GGML_ASSERT(nev0 == DV);
+
+    GGML_ASSERT(neq1 == N);
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    GGML_ASSERT(k->type == v->type);
+    const ggml_type kv_type = k->type;
+
+    // broadcast factors
+    const int64_t rk2 = neq2 / nek2;
+    const int64_t rk3 = neq3 / nek3;
+
+    const int64_t rv2 = neq2 / nev2;
+    const int64_t rv3 = neq3 / nev3;
+
+    float * param_list    = (float *) dst->op_params;
+    float   scale         = param_list[0];
+    float   max_bias      = param_list[1];
+    float   logit_softcap = param_list[2];
+
+    if (logit_softcap != 0) {
+        scale /= logit_softcap;
+    }
+
+    const uint32_t n_head      = neq2;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    int ith = params->ith;
+
+    static constexpr int Q_TILE_SZ  = ggml_fa_tile_config::Q;
+    static constexpr int KV_TILE_SZ = ggml_fa_tile_config::KV;
+
+    // Per-thread scratch layout:
+    // Q_f32:   Q_TILE_SZ * DK
+    // KQ:      Q_TILE_SZ * KV_TILE_SZ
+    // mask32:  Q_TILE_SZ * KV_TILE_SZ
+    // VKQ32:   Q_TILE_SZ * DV
+    // V32:     KV_TILE_SZ * DV
+    // K_f32:   DK * KV_TILE_SZ (transposed K tile)
+    float *      base = (float *) params->wdata + ith * (Q_TILE_SZ * DK + 2 * Q_TILE_SZ * KV_TILE_SZ + Q_TILE_SZ * DV +
+                                                    KV_TILE_SZ * DV + KV_TILE_SZ * DK + CACHE_LINE_SIZE_F32);
+    const size_t base_size =
+        (Q_TILE_SZ * DK + 2 * Q_TILE_SZ * KV_TILE_SZ + Q_TILE_SZ * DV + KV_TILE_SZ * DV + KV_TILE_SZ * DK) *
+            sizeof(float) +
+        CACHE_LINE_SIZE_F32;
+
+    if (base_size <= tcm_buffer_size && tcm_buffer != nullptr) {
+        base = (float *) tcm_buffer;
+    }
+
+    float   S_M_Buf[Q_TILE_SZ * 2];  // buffer to hold S, M, bias for one tile to reduce register pressure in main loop
+    float * S = S_M_Buf;
+    float * M = S_M_Buf + Q_TILE_SZ;
+
+    int ir = ir0;
+    while (ir < ir1) {
+        // q indices for the start of this tile
+        const int iq3 = ir / (neq2 * neq1);
+        const int iq2 = (ir - iq3 * neq2 * neq1) / neq1;
+        const int iq1 = (ir - iq3 * neq2 * neq1 - iq2 * neq1);
+
+        // Number of valid rows in this tile:
+        // - limited by tile size (Q_TILE_SZ)
+        // - limited by chunk boundary (ir1 - ir)
+        // - limited by head boundary (neq1 - iq1) to avoid crossing into next head
+        const int tile_rows = MIN(Q_TILE_SZ, MIN((int) (ir1 - ir), (int) (neq1 - iq1)));
+        GGML_ASSERT(tile_rows > 0);
+
+        const uint32_t h = iq2;  // head index
+        const float    slope =
+            (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2 * (h - n_head_log2) + 1) : 1.0f;
+
+        for (int i = 0; i < Q_TILE_SZ; ++i) {
+            S[i] = 0.;
+            M[i] = -INFINITY;
+        }
+
+        float *    Q_f32  = base;
+        float *    KQ     = (float *) ((char *) base + Q_TILE_SZ * DK * sizeof(float));
+        float *    mask32 = KQ + Q_TILE_SZ * KV_TILE_SZ;
+        float *    VKQ32  = mask32 + Q_TILE_SZ * KV_TILE_SZ;
+        float *    V32    = VKQ32 + Q_TILE_SZ * DV;
+        float *    K_f32  = V32 + KV_TILE_SZ * DV;
+        _Float16 * Q_f16  = (_Float16 *) Q_f32;
+        _Float16 * V_f16  = (_Float16 *) V32;
+        _Float16 * K_f16  = (_Float16 *) K_f32;
+
+        rvv_zero_f32(VKQ32, Q_TILE_SZ * DV);
+
+        // k indices
+        const int ik3 = iq3 / rk3;
+        const int ik2 = iq2 / rk2;
+
+        // v indices
+        const int iv3 = iq3 / rv3;
+        const int iv2 = iq2 / rv2;
+
+        const float * pq = (const float *) ((char *) q->data + (iq1 * nbq1 + iq2 * nbq2 + iq3 * nbq3));
+        if (kv_type == GGML_TYPE_F16) {
+            rvv_pack_f32_as_scaled_f16((uint8_t *) Q_f16, DK * sizeof(_Float16), (uint8_t *) pq, nbq1, tile_rows, DK,
+                                       scale);
+        } else {
+            memcpy2d(Q_f32, DK * sizeof(float), pq, nbq1, tile_rows, DK * sizeof(float));
+        }
+
+        for (int64_t ic = 0; ic < nek1; ic += KV_TILE_SZ) {
+            const int kv_tile = (int) std::min((int64_t) KV_TILE_SZ, nek1 - ic);
+
+            rvv_zero_f32(K_f32, DK * KV_TILE_SZ);
+            rvv_zero_f32(V32, KV_TILE_SZ * DV);
+
+            // skip the tile entirely if all the masks are -inf
+            if (mask) {
+                bool                can_skip = true;
+                const ggml_fp16_t * mp_row =
+                    (const ggml_fp16_t *) ((const char *) mask->data + iq1 * mask->nb[1] +
+                                           (iq2 % mask->ne[2]) * mask->nb[2] + (iq3 % mask->ne[3]) * mask->nb[3]);
+                rvv_pack_scaled_f16_as_f32(mask32, KV_TILE_SZ * sizeof(float), mp_row + ic, mask->nb[1], tile_rows,
+                                           kv_tile, slope);
+
+                for (int tq = 0; tq < tile_rows; tq++) {
+                    for (int tk = 0; tk < kv_tile; tk++) {
+                        if (mask32[tq * KV_TILE_SZ + tk] != -INFINITY) {
+                            can_skip = false;
+                        }
+                    }
+                    // Pad remaining mask entries with -inf
+                    for (int tk = kv_tile; tk < KV_TILE_SZ; tk++) {
+                        mask32[tq * KV_TILE_SZ + tk] = -INFINITY;
+                    }
+                }
+
+                if (can_skip) {
+                    continue;
+                }
+            }
+
+            if (kv_type == GGML_TYPE_F16) {
+                rvv_transposed_s16_mn_to_nm((int8_t *) K_f16, KV_TILE_SZ * sizeof(_Float16),
+                                            (int8_t *) k->data + ic * nbk1 + ik2 * nbk2 + ik3 * nbk3, nbk1, kv_tile,
+                                            DK);
+
+                int tq = 0;
+                for (; tq + 3 < tile_rows; tq += 4) {
+                    rvv_qk_dot_tile_f16_x4(KQ + (tq + 0) * KV_TILE_SZ, KQ + (tq + 1) * KV_TILE_SZ,
+                                           KQ + (tq + 2) * KV_TILE_SZ, KQ + (tq + 3) * KV_TILE_SZ,
+                                           Q_f16 + (tq + 0) * DK, Q_f16 + (tq + 1) * DK, Q_f16 + (tq + 2) * DK,
+                                           Q_f16 + (tq + 3) * DK, K_f16, DK, kv_tile);
+                }
+                for (; tq < tile_rows; ++tq) {
+                    rvv_qk_dot_tile_f16_x1(KQ + tq * KV_TILE_SZ, Q_f16 + tq * DK, K_f16, DK, kv_tile);
+                }
+            } else {
+                for (int tk = 0; tk < kv_tile; tk++) {
+                    const char *  k_data = (const char *) k->data + (ic + tk) * nbk1 + ik2 * nbk2 + ik3 * nbk3;
+                    float *       k_col  = K_f32 + tk;
+                    const float * k_src  = (const float *) k_data;
+                    for (int64_t dk = 0; dk < DK; ++dk) {
+                        k_col[dk * KV_TILE_SZ] = k_src[dk];
+                    }
+                }
+
+                for (int tq = 0; tq < tile_rows; ++tq) {
+                    rvv_qk_dot_tile(KQ + tq * KV_TILE_SZ, Q_f32 + tq * DK, K_f32, DK, KV_TILE_SZ, scale);
+                }
+            }
+
+            // Set padded KQ entries to -inf so softmax gives them zero weight
+            if (kv_tile < KV_TILE_SZ) {
+                for (int tq = 0; tq < tile_rows; tq++) {
+                    for (int tk = kv_tile; tk < KV_TILE_SZ; tk++) {
+                        KQ[tq * KV_TILE_SZ + tk] = -INFINITY;
+                    }
+                }
+            }
+
+            if (logit_softcap != 0.0f) {
+                rvv_softcap_tanh_inplace_f32(KQ, KV_TILE_SZ, tile_rows, KV_TILE_SZ, logit_softcap);
+            }
+
+            if (mask) {
+                rvv_add_inplace_f32(KQ, KV_TILE_SZ, mask32, KV_TILE_SZ, tile_rows, KV_TILE_SZ);
+            }
+
+            bool skip[Q_TILE_SZ] = {};
+
+            for (int tq = 0; tq < tile_rows; tq++) {
+                float * kq_row = KQ + tq * KV_TILE_SZ;
+
+                const float tile_max = rvv_max_f32(kq_row, KV_TILE_SZ);
+
+                if (tile_max == -INFINITY) {
+                    skip[tq] = true;
+                    continue;
+                }
+
+                const float Mold = M[tq];
+                const float Mnew = fmaxf(Mold, tile_max);
+
+                if (Mnew > Mold) {
+                    const float ms = expf(Mold - Mnew);
+                    rvv_scale_f32(VKQ32 + tq * DV, ms, DV);
+                    S[tq] *= ms;
+                }
+                M[tq] = Mnew;
+
+                S[tq] += rvv_softmax_exp_inplace_f32(kq_row, KV_TILE_SZ, Mnew);
+            }
+
+            // Pack V as contiguous [KV_TILE_SZ][DV].
+            if (kv_type == GGML_TYPE_F16) {
+                const char * v_data = (const char *) v->data + ic * nbv1 + iv2 * nbv2 + iv3 * nbv3;
+                memcpy2d(V_f16, DV * sizeof(_Float16), v_data, nbv1, kv_tile, DV * sizeof(_Float16));
+
+                int tq = 0;
+                for (; tq + 3 < tile_rows; tq += 4) {
+                    if (skip[tq + 0] || skip[tq + 1] || skip[tq + 2] || skip[tq + 3]) {
+                        for (int i = 0; i < 4; ++i) {
+                            if (!skip[tq + i]) {
+                                rvv_pv_accumulate_f16_x1(VKQ32 + (tq + i) * DV, KQ + (tq + i) * KV_TILE_SZ, V_f16,
+                                                         KV_TILE_SZ, DV);
+                            }
+                        }
+                        continue;
+                    }
+
+                    rvv_pv_accumulate_f16_x4(VKQ32 + (tq + 0) * DV, VKQ32 + (tq + 1) * DV, VKQ32 + (tq + 2) * DV,
+                                             VKQ32 + (tq + 3) * DV, KQ + (tq + 0) * KV_TILE_SZ,
+                                             KQ + (tq + 1) * KV_TILE_SZ, KQ + (tq + 2) * KV_TILE_SZ,
+                                             KQ + (tq + 3) * KV_TILE_SZ, V_f16, KV_TILE_SZ, DV);
+                }
+                for (; tq < tile_rows; ++tq) {
+                    if (!skip[tq]) {
+                        rvv_pv_accumulate_f16_x1(VKQ32 + tq * DV, KQ + tq * KV_TILE_SZ, V_f16, KV_TILE_SZ, DV);
+                    }
+                }
+            } else {
+                const char * v_data = (const char *) v->data + ic * nbv1 + iv2 * nbv2 + iv3 * nbv3;
+                memcpy2d(V32, DV * sizeof(float), v_data, nbv1, kv_tile, DV * sizeof(float));
+
+                for (int tq = 0; tq < tile_rows; ++tq) {
+                    if (!skip[tq]) {
+                        rvv_pv_accumulate(VKQ32 + tq * DV, KQ + tq * KV_TILE_SZ, V32, KV_TILE_SZ, DV);
+                    }
+                }
+            }
+        }
+
+        // sinks (apply only to valid rows in the tile)
+        if (sinks) {
+            const float s = ((float *) ((char *) sinks->data))[h];
+
+            for (int tq = 0; tq < tile_rows; tq++) {
+                float ms = 1.0f;
+                float vs = 1.0f;
+
+                if (s > M[tq]) {
+                    ms = expf(M[tq] - s);
+                    rvv_scale_f32(VKQ32 + tq * DV, ms, DV);
+                } else {
+                    vs = expf(s - M[tq]);
+                }
+
+                float S_temp = S[tq] * ms + vs;
+                S[tq]        = S_temp == 0.0f ? 0.0f : 1.0f / S_temp;
+            }
+        } else {
+            for (int tq = 0; tq < tile_rows; tq++) {
+                const float S_inv = S[tq] == 0.0f ? 0.0f : 1.0f / S[tq];
+                S[tq]             = S_inv;
+            }
+        }
+
+        float * dst_ptr = (float *) ((char *) dst->data + (iq3 * ne2 * ne1 + iq2 + (iq1) *ne1) * nb1);
+        rvv_pack_scaled_f32_as_f32(dst_ptr, nb1 * ne1, VKQ32, DV * sizeof(float), tile_rows, DV, S);
+
+        ir += tile_rows;
+    }
+}
+
+void forward_rms_norm_f32(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    ggml_tensor *       dst  = op;
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    int ith = params->ith;
+    int nth = params->nth;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    float epsilon = *((float *) dst->op_params);
+
+    GGML_ASSERT(epsilon > 0.0f);
+
+    auto * input  = (char *) src0->data;
+    auto * output = (char *) dst->data;
+
+    const auto hidden_size     = ne00;
+    const auto task_count      = ne01 * ne02 * ne03;
+    const auto task_per_thread = (task_count + nth - 1) / nth;
+
+    const auto task_begin = ith * task_per_thread;
+    const auto task_end   = std::min((ith + 1) * task_per_thread, task_count);
+
+    for (auto task_idx = task_begin; task_idx < task_end; task_idx++) {
+        int64_t i03 = task_idx / (ne02 * ne01);
+        int64_t i02 = (task_idx - i03 * ne02 * ne01) / ne01;
+        int64_t i01 = (task_idx - i03 * ne02 * ne01 - i02 * ne01);
+
+        auto * p_input       = (float *) (input + i01 * nb01 + i02 * nb02 + i03 * nb03);
+        auto * p_output      = (float *) (output + i01 * nb1 + i02 * nb2 + i03 * nb3);
+        auto * p_temp_output = p_output;
+
+        size_t       gvl    = __riscv_vsetvlmax_e32m4();
+        vfloat32m4_t sum_sq = __riscv_vfmv_v_f_f32m4(0.f, gvl);
+        int64_t      length = hidden_size;
+        while (length > 0) {
+            gvl                   = __riscv_vsetvl_e32m4(length);
+            vfloat32m4_t src_data = __riscv_vle32_v_f32m4(p_input, gvl);
+            sum_sq                = __riscv_vfmacc_vv_f32m4(sum_sq, src_data, src_data, gvl);
+            __riscv_vse32_v_f32m4(p_temp_output, src_data, gvl);
+
+            p_input += gvl;
+            p_temp_output += gvl;
+            length -= gvl;
+        }
+
+        gvl                 = __riscv_vsetvlmax_e32m1();
+        vfloat32m1_t zero_v = __riscv_vfmv_v_f_f32m1(0.f, gvl);
+        vfloat32m1_t mean_square_v =
+            __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m4_f32m1(sum_sq, 0), __riscv_vget_v_f32m4_f32m1(sum_sq, 1), gvl);
+
+        mean_square_v = __riscv_vfadd_vv_f32m1(mean_square_v, __riscv_vget_v_f32m4_f32m1(sum_sq, 2), gvl);
+        mean_square_v = __riscv_vfadd_vv_f32m1(mean_square_v, __riscv_vget_v_f32m4_f32m1(sum_sq, 3), gvl);
+        mean_square_v = __riscv_vfredusum_vs_f32m1_f32m1(mean_square_v, zero_v, gvl);
+
+        float mean_square = __riscv_vfmv_f_s_f32m1_f32(mean_square_v);
+        mean_square /= hidden_size;
+
+        mean_square = sqrt(mean_square + epsilon);
+
+        mean_square   = 1.0f / mean_square;
+        length        = hidden_size;
+        p_temp_output = p_output;
+
+        while (length > 0) {
+            gvl                   = __riscv_vsetvl_e32m4(length);
+            vfloat32m4_t src_data = __riscv_vle32_v_f32m4(p_temp_output, gvl);
+            src_data              = __riscv_vfmul_vf_f32m4(src_data, mean_square, gvl);
+            __riscv_vse32_v_f32m4(p_output, src_data, gvl);
+            p_temp_output += gvl;
+            p_output += gvl;
+            length -= gvl;
+        }
+    }
+}
+
+template <size_t MB_ROWS>
+void quantize_a_nrow_i8_ref(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    int64_t a_blk_stride        = q8_blk_size(blk_len, true);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+    for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+        float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+        int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float) * MB_ROWS);
+        int8_t *  quant_a_blk =
+            reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * MB_ROWS);
+
+        for (size_t row = 0; row < MB_ROWS; row++) {
+            float max_abs_a = 0.0f;
+            for (size_t bk = 0; bk < blk_len; bk++) {
+                max_abs_a = std::max(max_abs_a, std::abs(a_ptr[row * count_k + k + bk]));
+            }
+
+            float rep_scale_a = ((1 << 7) - 1) / max_abs_a;
+            scale_a_ptr[row]  = 1 / rep_scale_a;
+
+            int16_t a_sum = 0;
+            for (size_t bk = 0; bk < blk_len; bk++) {
+                const int8_t quantized = static_cast<int8_t>(
+                    std::clamp(std::nearbyintf(a_ptr[row * count_k + k + bk] * rep_scale_a), -128.0f, 127.0f));
+                quant_a_blk[row * blk_len + bk] = quantized;
+                a_sum += quantized;
+            }
+            a_sum_ptr[row] = -a_sum;
+        }
+    }
+}
+
+template <size_t MB_ROWS>
+void quantize_a_nrow_i8_hp_ref(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    constexpr size_t k_subblk_len = 32;
+    const size_t     subblk_count = blk_len / k_subblk_len;
+
+    GGML_ASSERT(blk_len == 256);
+
+    float   scale_temp[8]       = { 0.0f };
+    int64_t a_blk_stride        = q8_hp_blk_size(blk_len, true, true);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+    int64_t a_subblk_stride     = q8_hp_blk_size(k_subblk_len, false, false) * MB_ROWS;
+
+    for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+        _Float16 * a_sum_ptr = reinterpret_cast<_Float16 *>(quant_a_ptr + a_subblk_stride * subblk_count);
+
+        float scale_avg = 0.0f;
+        for (size_t kk = 0; kk < subblk_count; kk++) {
+            float max_abs_a = 0.0f;
+            for (size_t row = 0; row < MB_ROWS; row++) {
+                for (size_t bk = 0; bk < k_subblk_len; bk++) {
+                    max_abs_a = std::max(max_abs_a, std::abs(a_ptr[row * count_k + k + bk + kk * k_subblk_len]));
+                }
+            }
+            scale_temp[kk] = max_abs_a / ((1 << 7) - 1);
+            scale_avg += scale_temp[kk];
+        }
+
+        scale_avg /= subblk_count;
+        float scale_factor = 1.0f / scale_avg;
+
+        _Float16 * scale_avg_ptr =
+            reinterpret_cast<_Float16 *>(quant_a_ptr + a_nrow_block_stride - sizeof(_Float16) * MB_ROWS);
+        scale_avg_ptr[0] = scale_avg;
+
+        for (size_t kk = 0; kk < subblk_count; kk++) {
+            uint8_t *  a_subblk_base = quant_a_ptr + kk * a_subblk_stride;
+            _Float16 * scale_a_ptr   = reinterpret_cast<_Float16 *>(a_subblk_base);
+            int8_t *   quant_a_blk   = reinterpret_cast<int8_t *>(a_subblk_base + sizeof(_Float16) * MB_ROWS);
+
+            scale_a_ptr[0] = static_cast<_Float16>(scale_temp[kk] * scale_factor);
+
+            const float rep_scale_a = 1.0f / scale_temp[kk];
+
+            for (size_t row = 0; row < MB_ROWS; row++) {
+                int16_t a_sum = 0;
+                for (size_t bk = 0; bk < k_subblk_len; bk++) {
+                    const int8_t quantized = static_cast<int8_t>(
+                        std::clamp(std::nearbyintf(a_ptr[row * count_k + k + bk + kk * k_subblk_len] * rep_scale_a),
+                                   -128.0f, 127.0f));
+                    quant_a_blk[row * k_subblk_len + bk] = quantized;
+                    a_sum += quantized;
+                }
+                a_sum_ptr[row * subblk_count + kk] = static_cast<_Float16>(-a_sum) * static_cast<_Float16>(8.0f);
+            }
+        }
+    }
+}
+
+template <size_t MB_ROWS>
+void quantize_a_nrow_i8k_ref(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    int64_t a_blk_stride        = q8k_blk_size(256);
+    int64_t a_nrow_block_stride = a_blk_stride * MB_ROWS;
+    int64_t a_sum_size          = 256 / 16;
+
+    for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+        float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+        int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float) * MB_ROWS);
+        int8_t *  quant_a_blk =
+            reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) * MB_ROWS + sizeof(int16_t) * a_sum_size * MB_ROWS);
+
+        for (size_t row = 0; row < MB_ROWS; row++) {
+            float max_a     = 0.0f;
+            float max_abs_a = 0.0f;
+            for (size_t bk = 0; bk < blk_len; bk++) {
+                float ax = std::abs(a_ptr[row * count_k + k + bk]);
+                if (ax > max_abs_a) {
+                    max_abs_a = ax;
+                    max_a     = a_ptr[row * count_k + k + bk];
+                }
+            }
+
+            if (!max_abs_a) {
+                scale_a_ptr[row] = 0;
+                for (size_t bki = 0; bki < a_sum_size; bki++) {
+                    for (size_t bk = bki * 16; bk < (bki + 1) * 16; bk++) {
+                        quant_a_blk[row * blk_len + bk] = 0;
+                    }
+                    a_sum_ptr[row * a_sum_size + bki] = 0;
+                }
+                continue;
+            }
+
+            float rep_scale_a = ((1 << 7) - 1) / max_abs_a;
+            scale_a_ptr[row]  = 1 / rep_scale_a;
+
+            for (size_t bki = 0; bki < a_sum_size; bki++) {
+                int16_t a_sum = 0;
+                for (size_t bk = bki * 16; bk < (bki + 1) * 16; bk++) {
+                    const int8_t quantized = static_cast<int8_t>(
+                        std::clamp(std::nearbyintf(a_ptr[row * count_k + k + bk] * rep_scale_a), -128.0f, 127.0f));
+                    quant_a_blk[row * blk_len + bk] = quantized;
+                    a_sum += quantized;
+                }
+                a_sum_ptr[row * a_sum_size + bki] = -a_sum;
+            }
+        }
+    }
+}
+
+void quantize_a_row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    GGML_ASSERT(blk_len == 32);
+    int64_t a_blk_stride = q8_blk_size(blk_len, true);
+    size_t  vlenb        = __riscv_vlenb();
+
+    if (vlenb == 128) {
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_blk_stride) {
+            float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+            int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float));
+            int8_t *  quant_a_blk = reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) + sizeof(int16_t));
+
+            size_t       vl      = __riscv_vsetvl_e32m1(blk_len);
+            vfloat32m1_t v_a     = __riscv_vle32_v_f32m1(a_ptr + k, vl);
+            vfloat32m1_t v_a_abs = __riscv_vfabs_v_f32m1(v_a, vl);
+
+            vfloat32m1_t tmp       = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+            vfloat32m1_t v_a_max   = __riscv_vfredmax_vs_f32m1_f32m1(v_a_abs, tmp, vl);
+            float        max_abs_a = __riscv_vfmv_f_s_f32m1_f32(v_a_max);
+
+            float scale_a     = max_abs_a / ((1 << 7) - 1);
+            float rep_scale_a = scale_a ? 1.0f / scale_a : 0.0f;
+            scale_a_ptr[0]    = scale_a;
+
+            vfloat32m1_t v_a_scale    = __riscv_vfmul_vf_f32m1(v_a, rep_scale_a, vl);
+            vint16mf2_t  v_a_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a_scale, vl);
+            vint8mf4_t   v_a_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a_quant, vl);
+
+            vint16m1_t tmp_sum = __riscv_vmv_v_x_i16m1(0, vl);
+            vint16m1_t v_a_sum = __riscv_vwredsum_vs_i8mf4_i16m1(v_a_quant_i8, tmp_sum, vl);
+            int16_t    a_sum   = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+            a_sum_ptr[0]       = -a_sum;
+
+            __riscv_vse8_v_i8mf4(quant_a_blk, v_a_quant_i8, vl);
+        }
+    } else if (vlenb == 32) {
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_blk_stride) {
+            float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+            int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float));
+            int8_t *  quant_a_blk = reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) + sizeof(int16_t));
+
+            size_t       vl      = __riscv_vsetvl_e32m4(blk_len);
+            vfloat32m4_t v_a     = __riscv_vle32_v_f32m4(a_ptr + k, vl);
+            vfloat32m4_t v_a_abs = __riscv_vfabs_v_f32m4(v_a, vl);
+
+            vfloat32m1_t tmp       = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+            vfloat32m1_t v_a_max   = __riscv_vfredmax_vs_f32m4_f32m1(v_a_abs, tmp, vl);
+            float        max_abs_a = __riscv_vfmv_f_s_f32m1_f32(v_a_max);
+
+            float scale_a     = max_abs_a / ((1 << 7) - 1);
+            float rep_scale_a = scale_a ? 1.0f / scale_a : 0.0f;
+            scale_a_ptr[0]    = scale_a;
+
+            vfloat32m4_t v_a_scale    = __riscv_vfmul_vf_f32m4(v_a, rep_scale_a, vl);
+            vint16m2_t   v_a_quant    = __riscv_vfncvt_x_f_w_i16m2(v_a_scale, vl);
+            vint8m1_t    v_a_quant_i8 = __riscv_vncvt_x_x_w_i8m1(v_a_quant, vl);
+
+            vint16m1_t tmp_sum = __riscv_vmv_v_x_i16m1(0, vl);
+            vint16m1_t v_a_sum = __riscv_vwredsum_vs_i8m1_i16m1(v_a_quant_i8, tmp_sum, vl);
+            int16_t    a_sum   = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+            a_sum_ptr[0]       = -a_sum;
+
+            __riscv_vse8_v_i8m1(quant_a_blk, v_a_quant_i8, vl);
+        }
+    } else {
+        quantize_a_nrow_i8_ref<1>(blk_len, a_ptr, count_k, quant_a_ptr);
+    }
+}
+
+void quantize_a_4row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    GGML_ASSERT(blk_len == 32);
+    int64_t a_blk_stride        = q8_blk_size(blk_len, true);
+    int64_t a_nrow_block_stride = a_blk_stride * 4;
+    size_t  vlenb               = __riscv_vlenb();
+
+    if (vlenb == 128) {
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+            float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+            int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float) * 4);
+            int8_t *  quant_a_blk = reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) * 4 + sizeof(int16_t) * 4);
+
+            for (size_t mi = 0; mi < 4; mi++) {
+                size_t       vl      = __riscv_vsetvl_e32m1(blk_len);
+                vfloat32m1_t v_a     = __riscv_vle32_v_f32m1(a_ptr + mi * count_k + k, vl);
+                vfloat32m1_t v_a_abs = __riscv_vfabs_v_f32m1(v_a, vl);
+
+                vfloat32m1_t tmp       = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+                vfloat32m1_t v_a_max   = __riscv_vfredmax_vs_f32m1_f32m1(v_a_abs, tmp, vl);
+                float        max_abs_a = __riscv_vfmv_f_s_f32m1_f32(v_a_max);
+
+                float scale_a     = max_abs_a / ((1 << 7) - 1);
+                float rep_scale_a = scale_a ? 1.0f / scale_a : 0.0f;
+                scale_a_ptr[mi]   = scale_a;
+
+                vfloat32m1_t v_a_scale    = __riscv_vfmul_vf_f32m1(v_a, rep_scale_a, vl);
+                vint16mf2_t  v_a_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a_scale, vl);
+                vint8mf4_t   v_a_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a_quant, vl);
+
+                vint16m1_t tmp_sum = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t v_a_sum = __riscv_vwredsum_vs_i8mf4_i16m1(v_a_quant_i8, tmp_sum, vl);
+                int16_t    a_sum   = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+                a_sum_ptr[mi]      = -a_sum;
+
+                __riscv_vse8_v_i8mf4(quant_a_blk + mi * blk_len, v_a_quant_i8, vl);
+            }
+        }
+    } else if (vlenb == 32) {
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+            float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+            int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float) * 4);
+            int8_t *  quant_a_blk = reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) * 4 + sizeof(int16_t) * 4);
+
+            for (size_t mi = 0; mi < 4; mi++) {
+                size_t       vl      = __riscv_vsetvl_e32m4(blk_len);
+                vfloat32m4_t v_a     = __riscv_vle32_v_f32m4(a_ptr + mi * count_k + k, vl);
+                vfloat32m4_t v_a_abs = __riscv_vfabs_v_f32m4(v_a, vl);
+
+                vfloat32m1_t tmp       = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+                vfloat32m1_t v_a_max   = __riscv_vfredmax_vs_f32m4_f32m1(v_a_abs, tmp, vl);
+                float        max_abs_a = __riscv_vfmv_f_s_f32m1_f32(v_a_max);
+
+                float scale_a     = max_abs_a / ((1 << 7) - 1);
+                float rep_scale_a = scale_a ? 1.0f / scale_a : 0.0f;
+                scale_a_ptr[mi]   = scale_a;
+
+                vfloat32m4_t v_a_scale    = __riscv_vfmul_vf_f32m4(v_a, rep_scale_a, vl);
+                vint16m2_t   v_a_quant    = __riscv_vfncvt_x_f_w_i16m2(v_a_scale, vl);
+                vint8m1_t    v_a_quant_i8 = __riscv_vncvt_x_x_w_i8m1(v_a_quant, vl);
+
+                vint16m1_t tmp_sum = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t v_a_sum = __riscv_vwredsum_vs_i8m1_i16m1(v_a_quant_i8, tmp_sum, vl);
+                int16_t    a_sum   = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+                a_sum_ptr[mi]      = -a_sum;
+
+                __riscv_vse8_v_i8m1(quant_a_blk + mi * blk_len, v_a_quant_i8, vl);
+            }
+        }
+    } else {
+        quantize_a_nrow_i8_ref<4>(blk_len, a_ptr, count_k, quant_a_ptr);
+    }
+}
+
+void quantize_a_row_i8_hp(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    constexpr size_t k_subblk_len = 32;
+    GGML_ASSERT(blk_len == 256);
+
+    constexpr size_t subblk_count             = 256 / k_subblk_len;
+    int64_t          a_blk_stride             = q8_hp_blk_size(blk_len, true, true);
+    int64_t          a_subblk_stride          = q8_hp_blk_size(k_subblk_len, false, false);
+    size_t           vlenb                    = __riscv_vlenb();
+    float            scale_temp[subblk_count] = { 0.0f };
+
+    if (vlenb == 128) {
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_blk_stride) {
+            _Float16 * a_sum_ptr     = reinterpret_cast<_Float16 *>(quant_a_ptr + a_subblk_stride * subblk_count);
+            _Float16 * scale_avg_ptr = reinterpret_cast<_Float16 *>(quant_a_ptr + a_blk_stride - sizeof(_Float16));
+            float      scale_avg     = 0.0f;
+
+            for (size_t kk = 0; kk < subblk_count; ++kk) {
+                const float * a_src_ptr = a_ptr + k + kk * k_subblk_len;
+
+                size_t       vl      = __riscv_vsetvl_e32m1(k_subblk_len);
+                vfloat32m1_t v_a     = __riscv_vle32_v_f32m1(a_src_ptr, vl);
+                vfloat32m1_t v_a_abs = __riscv_vfabs_v_f32m1(v_a, vl);
+
+                vfloat32m1_t tmp       = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+                vfloat32m1_t v_a_max   = __riscv_vfredmax_vs_f32m1_f32m1(v_a_abs, tmp, vl);
+                float        max_abs_a = __riscv_vfmv_f_s_f32m1_f32(v_a_max);
+
+                scale_temp[kk] = max_abs_a / ((1 << 7) - 1);
+                scale_avg += scale_temp[kk];
+            }
+
+            scale_avg /= subblk_count;
+            const float scale_factor = scale_avg ? 1.0f / scale_avg : 0.0f;
+            scale_avg_ptr[0]         = static_cast<_Float16>(scale_avg);
+
+            for (size_t kk = 0; kk < subblk_count; ++kk) {
+                uint8_t *     a_subblk_base = quant_a_ptr + kk * a_subblk_stride;
+                _Float16 *    scale_a_ptr   = reinterpret_cast<_Float16 *>(a_subblk_base);
+                int8_t *      quant_a_blk   = reinterpret_cast<int8_t *>(a_subblk_base + sizeof(_Float16));
+                const float * a_src_ptr     = a_ptr + k + kk * k_subblk_len;
+
+                size_t       vl          = __riscv_vsetvl_e32m1(k_subblk_len);
+                vfloat32m1_t v_a         = __riscv_vle32_v_f32m1(a_src_ptr, vl);
+                float        rep_scale_a = scale_temp[kk] ? 1.0f / scale_temp[kk] : 0.0f;
+                scale_a_ptr[0]           = static_cast<_Float16>(scale_temp[kk] * scale_factor);
+
+                vfloat32m1_t v_a_scale    = __riscv_vfmul_vf_f32m1(v_a, rep_scale_a, vl);
+                vint16mf2_t  v_a_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a_scale, vl);
+                vint8mf4_t   v_a_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a_quant, vl);
+
+                vint16m1_t tmp_sum = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t v_a_sum = __riscv_vwredsum_vs_i8mf4_i16m1(v_a_quant_i8, tmp_sum, vl);
+                int16_t    a_sum   = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+                a_sum_ptr[kk]      = static_cast<_Float16>(-a_sum) * static_cast<_Float16>(8.0f);
+
+                __riscv_vse8_v_i8mf4(quant_a_blk, v_a_quant_i8, vl);
+            }
+        }
+    } else if (vlenb == 32) {
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_blk_stride) {
+            _Float16 * a_sum_ptr     = reinterpret_cast<_Float16 *>(quant_a_ptr + a_subblk_stride * subblk_count);
+            _Float16 * scale_avg_ptr = reinterpret_cast<_Float16 *>(quant_a_ptr + a_blk_stride - sizeof(_Float16));
+            float      scale_avg     = 0.0f;
+
+            for (size_t kk = 0; kk < subblk_count; ++kk) {
+                const float * a_src_ptr = a_ptr + k + kk * k_subblk_len;
+
+                size_t       vl      = __riscv_vsetvl_e32m4(k_subblk_len);
+                vfloat32m4_t v_a     = __riscv_vle32_v_f32m4(a_src_ptr, vl);
+                vfloat32m4_t v_a_abs = __riscv_vfabs_v_f32m4(v_a, vl);
+
+                vfloat32m1_t tmp       = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+                vfloat32m1_t v_a_max   = __riscv_vfredmax_vs_f32m4_f32m1(v_a_abs, tmp, vl);
+                float        max_abs_a = __riscv_vfmv_f_s_f32m1_f32(v_a_max);
+
+                scale_temp[kk] = max_abs_a / ((1 << 7) - 1);
+                scale_avg += scale_temp[kk];
+            }
+
+            scale_avg /= subblk_count;
+            const float scale_factor = scale_avg ? 1.0f / scale_avg : 0.0f;
+            scale_avg_ptr[0]         = static_cast<_Float16>(scale_avg);
+
+            for (size_t kk = 0; kk < subblk_count; ++kk) {
+                uint8_t *     a_subblk_base = quant_a_ptr + kk * a_subblk_stride;
+                _Float16 *    scale_a_ptr   = reinterpret_cast<_Float16 *>(a_subblk_base);
+                int8_t *      quant_a_blk   = reinterpret_cast<int8_t *>(a_subblk_base + sizeof(_Float16));
+                const float * a_src_ptr     = a_ptr + k + kk * k_subblk_len;
+
+                size_t       vl          = __riscv_vsetvl_e32m4(k_subblk_len);
+                vfloat32m4_t v_a         = __riscv_vle32_v_f32m4(a_src_ptr, vl);
+                float        rep_scale_a = scale_temp[kk] ? 1.0f / scale_temp[kk] : 0.0f;
+                scale_a_ptr[0]           = static_cast<_Float16>(scale_temp[kk] * scale_factor);
+
+                vfloat32m4_t v_a_scale    = __riscv_vfmul_vf_f32m4(v_a, rep_scale_a, vl);
+                vint16m2_t   v_a_quant    = __riscv_vfncvt_x_f_w_i16m2(v_a_scale, vl);
+                vint8m1_t    v_a_quant_i8 = __riscv_vncvt_x_x_w_i8m1(v_a_quant, vl);
+
+                vint16m1_t tmp_sum = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t v_a_sum = __riscv_vwredsum_vs_i8m1_i16m1(v_a_quant_i8, tmp_sum, vl);
+                int16_t    a_sum   = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+                a_sum_ptr[kk]      = static_cast<_Float16>(-a_sum) * static_cast<_Float16>(8.0f);
+
+                __riscv_vse8_v_i8m1(quant_a_blk, v_a_quant_i8, vl);
+            }
+        }
+    } else {
+        quantize_a_nrow_i8_hp_ref<1>(blk_len, a_ptr, count_k, quant_a_ptr);
+    }
+}
+
+void quantize_a_4row_i8_hp(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    constexpr size_t k_subblk_len = 32;
+    GGML_ASSERT(blk_len == 256);
+
+    constexpr size_t subblk_count             = 256 / k_subblk_len;
+    int64_t          a_blk_stride             = q8_hp_blk_size(blk_len, true, true);
+    int64_t          a_nrow_block_stride      = a_blk_stride * 4;
+    int64_t          a_subblk_stride          = q8_hp_blk_size(k_subblk_len, false, false) * 4;
+    size_t           vlenb                    = __riscv_vlenb();
+    float            scale_temp[subblk_count] = { 0.0f };
+
+    if (vlenb == 128) {
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+            _Float16 * a_sum_ptr = reinterpret_cast<_Float16 *>(quant_a_ptr + a_subblk_stride * subblk_count);
+            _Float16 * scale_avg_ptr =
+                reinterpret_cast<_Float16 *>(quant_a_ptr + a_nrow_block_stride - sizeof(_Float16) * 4);
+            float scale_avg = 0.0f;
+
+            for (size_t kk = 0; kk < subblk_count; ++kk) {
+                const float * a_src_ptr0 = a_ptr + 0 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr1 = a_ptr + 1 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr2 = a_ptr + 2 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr3 = a_ptr + 3 * count_k + k + kk * k_subblk_len;
+
+                size_t       vl       = __riscv_vsetvl_e32m1(k_subblk_len);
+                vfloat32m1_t v_a0     = __riscv_vle32_v_f32m1(a_src_ptr0, vl);
+                vfloat32m1_t v_a1     = __riscv_vle32_v_f32m1(a_src_ptr1, vl);
+                vfloat32m1_t v_a2     = __riscv_vle32_v_f32m1(a_src_ptr2, vl);
+                vfloat32m1_t v_a3     = __riscv_vle32_v_f32m1(a_src_ptr3, vl);
+                vfloat32m1_t v_a0_abs = __riscv_vfabs_v_f32m1(v_a0, vl);
+                vfloat32m1_t v_a1_abs = __riscv_vfabs_v_f32m1(v_a1, vl);
+                vfloat32m1_t v_a2_abs = __riscv_vfabs_v_f32m1(v_a2, vl);
+                vfloat32m1_t v_a3_abs = __riscv_vfabs_v_f32m1(v_a3, vl);
+
+                vfloat32m1_t v_max_abs = __riscv_vfmax_vv_f32m1(v_a0_abs, v_a1_abs, vl);
+                v_max_abs              = __riscv_vfmax_vv_f32m1(v_max_abs, v_a2_abs, vl);
+                v_max_abs              = __riscv_vfmax_vv_f32m1(v_max_abs, v_a3_abs, vl);
+
+                vfloat32m1_t tmp       = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+                vfloat32m1_t v_a_max   = __riscv_vfredmax_vs_f32m1_f32m1(v_max_abs, tmp, vl);
+                float        max_abs_a = __riscv_vfmv_f_s_f32m1_f32(v_a_max);
+
+                scale_temp[kk] = max_abs_a / ((1 << 7) - 1);
+                scale_avg += scale_temp[kk];
+            }
+
+            scale_avg /= subblk_count;
+            const float scale_factor = scale_avg ? 1.0f / scale_avg : 0.0f;
+            scale_avg_ptr[0]         = static_cast<_Float16>(scale_avg);
+
+            for (size_t kk = 0; kk < subblk_count; ++kk) {
+                uint8_t *     a_subblk_base = quant_a_ptr + kk * a_subblk_stride;
+                _Float16 *    scale_a_ptr   = reinterpret_cast<_Float16 *>(a_subblk_base);
+                int8_t *      quant_a_blk   = reinterpret_cast<int8_t *>(a_subblk_base + sizeof(_Float16) * 4);
+                const float * a_src_ptr0    = a_ptr + 0 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr1    = a_ptr + 1 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr2    = a_ptr + 2 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr3    = a_ptr + 3 * count_k + k + kk * k_subblk_len;
+
+                size_t       vl   = __riscv_vsetvl_e32m1(k_subblk_len);
+                vfloat32m1_t v_a0 = __riscv_vle32_v_f32m1(a_src_ptr0, vl);
+                vfloat32m1_t v_a1 = __riscv_vle32_v_f32m1(a_src_ptr1, vl);
+                vfloat32m1_t v_a2 = __riscv_vle32_v_f32m1(a_src_ptr2, vl);
+                vfloat32m1_t v_a3 = __riscv_vle32_v_f32m1(a_src_ptr3, vl);
+
+                float rep_scale_a = scale_temp[kk] ? 1.0f / scale_temp[kk] : 0.0f;
+                scale_a_ptr[0]    = static_cast<_Float16>(scale_temp[kk] * scale_factor);
+
+                vfloat32m1_t v_a0_scale    = __riscv_vfmul_vf_f32m1(v_a0, rep_scale_a, vl);
+                vfloat32m1_t v_a1_scale    = __riscv_vfmul_vf_f32m1(v_a1, rep_scale_a, vl);
+                vfloat32m1_t v_a2_scale    = __riscv_vfmul_vf_f32m1(v_a2, rep_scale_a, vl);
+                vfloat32m1_t v_a3_scale    = __riscv_vfmul_vf_f32m1(v_a3, rep_scale_a, vl);
+                vint16mf2_t  v_a0_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a0_scale, vl);
+                vint16mf2_t  v_a1_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a1_scale, vl);
+                vint16mf2_t  v_a2_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a2_scale, vl);
+                vint16mf2_t  v_a3_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a3_scale, vl);
+                vint8mf4_t   v_a0_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a0_quant, vl);
+                vint8mf4_t   v_a1_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a1_quant, vl);
+                vint8mf4_t   v_a2_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a2_quant, vl);
+                vint8mf4_t   v_a3_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a3_quant, vl);
+
+                vint16m1_t tmp_sum0 = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t tmp_sum1 = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t tmp_sum2 = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t tmp_sum3 = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t v_a0_sum = __riscv_vwredsum_vs_i8mf4_i16m1(v_a0_quant_i8, tmp_sum0, vl);
+                vint16m1_t v_a1_sum = __riscv_vwredsum_vs_i8mf4_i16m1(v_a1_quant_i8, tmp_sum1, vl);
+                vint16m1_t v_a2_sum = __riscv_vwredsum_vs_i8mf4_i16m1(v_a2_quant_i8, tmp_sum2, vl);
+                vint16m1_t v_a3_sum = __riscv_vwredsum_vs_i8mf4_i16m1(v_a3_quant_i8, tmp_sum3, vl);
+
+                a_sum_ptr[0 * subblk_count + kk] =
+                    static_cast<_Float16>(-__riscv_vmv_x_s_i16m1_i16(v_a0_sum)) * static_cast<_Float16>(8.0f);
+                a_sum_ptr[1 * subblk_count + kk] =
+                    static_cast<_Float16>(-__riscv_vmv_x_s_i16m1_i16(v_a1_sum)) * static_cast<_Float16>(8.0f);
+                a_sum_ptr[2 * subblk_count + kk] =
+                    static_cast<_Float16>(-__riscv_vmv_x_s_i16m1_i16(v_a2_sum)) * static_cast<_Float16>(8.0f);
+                a_sum_ptr[3 * subblk_count + kk] =
+                    static_cast<_Float16>(-__riscv_vmv_x_s_i16m1_i16(v_a3_sum)) * static_cast<_Float16>(8.0f);
+
+                __riscv_vse8_v_i8mf4(quant_a_blk + 0 * k_subblk_len, v_a0_quant_i8, vl);
+                __riscv_vse8_v_i8mf4(quant_a_blk + 1 * k_subblk_len, v_a1_quant_i8, vl);
+                __riscv_vse8_v_i8mf4(quant_a_blk + 2 * k_subblk_len, v_a2_quant_i8, vl);
+                __riscv_vse8_v_i8mf4(quant_a_blk + 3 * k_subblk_len, v_a3_quant_i8, vl);
+            }
+        }
+    } else if (vlenb == 32) {
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+            _Float16 * a_sum_ptr = reinterpret_cast<_Float16 *>(quant_a_ptr + a_subblk_stride * subblk_count);
+            _Float16 * scale_avg_ptr =
+                reinterpret_cast<_Float16 *>(quant_a_ptr + a_nrow_block_stride - sizeof(_Float16) * 4);
+            float scale_avg = 0.0f;
+
+            for (size_t kk = 0; kk < subblk_count; ++kk) {
+                const float * a_src_ptr0 = a_ptr + 0 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr1 = a_ptr + 1 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr2 = a_ptr + 2 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr3 = a_ptr + 3 * count_k + k + kk * k_subblk_len;
+
+                size_t       vl   = __riscv_vsetvl_e32m4(k_subblk_len);
+                vfloat32m4_t v_a0 = __riscv_vle32_v_f32m4(a_src_ptr0, vl);
+                vfloat32m4_t v_a1 = __riscv_vle32_v_f32m4(a_src_ptr1, vl);
+                vfloat32m4_t v_a2 = __riscv_vle32_v_f32m4(a_src_ptr2, vl);
+                vfloat32m4_t v_a3 = __riscv_vle32_v_f32m4(a_src_ptr3, vl);
+
+                vfloat32m4_t v_a0_abs = __riscv_vfabs_v_f32m4(v_a0, vl);
+                vfloat32m4_t v_a1_abs = __riscv_vfabs_v_f32m4(v_a1, vl);
+                vfloat32m4_t v_a2_abs = __riscv_vfabs_v_f32m4(v_a2, vl);
+                vfloat32m4_t v_a3_abs = __riscv_vfabs_v_f32m4(v_a3, vl);
+
+                vfloat32m4_t v_max_abs = __riscv_vfmax_vv_f32m4(v_a0_abs, v_a1_abs, vl);
+                v_max_abs              = __riscv_vfmax_vv_f32m4(v_max_abs, v_a2_abs, vl);
+                v_max_abs              = __riscv_vfmax_vv_f32m4(v_max_abs, v_a3_abs, vl);
+
+                vfloat32m1_t tmp       = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+                vfloat32m1_t v_a_max   = __riscv_vfredmax_vs_f32m4_f32m1(v_max_abs, tmp, vl);
+                float        max_abs_a = __riscv_vfmv_f_s_f32m1_f32(v_a_max);
+
+                scale_temp[kk] = max_abs_a / ((1 << 7) - 1);
+                scale_avg += scale_temp[kk];
+            }
+
+            scale_avg /= subblk_count;
+            const float scale_factor = scale_avg ? 1.0f / scale_avg : 0.0f;
+            scale_avg_ptr[0]         = static_cast<_Float16>(scale_avg);
+
+            for (size_t kk = 0; kk < subblk_count; ++kk) {
+                uint8_t *     a_subblk_base = quant_a_ptr + kk * a_subblk_stride;
+                _Float16 *    scale_a_ptr   = reinterpret_cast<_Float16 *>(a_subblk_base);
+                int8_t *      quant_a_blk   = reinterpret_cast<int8_t *>(a_subblk_base + sizeof(_Float16) * 4);
+                const float * a_src_ptr0    = a_ptr + 0 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr1    = a_ptr + 1 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr2    = a_ptr + 2 * count_k + k + kk * k_subblk_len;
+                const float * a_src_ptr3    = a_ptr + 3 * count_k + k + kk * k_subblk_len;
+
+                size_t       vl   = __riscv_vsetvl_e32m4(k_subblk_len);
+                vfloat32m4_t v_a0 = __riscv_vle32_v_f32m4(a_src_ptr0, vl);
+                vfloat32m4_t v_a1 = __riscv_vle32_v_f32m4(a_src_ptr1, vl);
+                vfloat32m4_t v_a2 = __riscv_vle32_v_f32m4(a_src_ptr2, vl);
+                vfloat32m4_t v_a3 = __riscv_vle32_v_f32m4(a_src_ptr3, vl);
+
+                float rep_scale_a = scale_temp[kk] ? 1.0f / scale_temp[kk] : 0.0f;
+                scale_a_ptr[0]    = static_cast<_Float16>(scale_temp[kk] * scale_factor);
+
+                vfloat32m4_t v_a0_scale    = __riscv_vfmul_vf_f32m4(v_a0, rep_scale_a, vl);
+                vfloat32m4_t v_a1_scale    = __riscv_vfmul_vf_f32m4(v_a1, rep_scale_a, vl);
+                vfloat32m4_t v_a2_scale    = __riscv_vfmul_vf_f32m4(v_a2, rep_scale_a, vl);
+                vfloat32m4_t v_a3_scale    = __riscv_vfmul_vf_f32m4(v_a3, rep_scale_a, vl);
+                vint16m2_t   v_a0_quant    = __riscv_vfncvt_x_f_w_i16m2(v_a0_scale, vl);
+                vint16m2_t   v_a1_quant    = __riscv_vfncvt_x_f_w_i16m2(v_a1_scale, vl);
+                vint16m2_t   v_a2_quant    = __riscv_vfncvt_x_f_w_i16m2(v_a2_scale, vl);
+                vint16m2_t   v_a3_quant    = __riscv_vfncvt_x_f_w_i16m2(v_a3_scale, vl);
+                vint8m1_t    v_a0_quant_i8 = __riscv_vncvt_x_x_w_i8m1(v_a0_quant, vl);
+                vint8m1_t    v_a1_quant_i8 = __riscv_vncvt_x_x_w_i8m1(v_a1_quant, vl);
+                vint8m1_t    v_a2_quant_i8 = __riscv_vncvt_x_x_w_i8m1(v_a2_quant, vl);
+                vint8m1_t    v_a3_quant_i8 = __riscv_vncvt_x_x_w_i8m1(v_a3_quant, vl);
+
+                vint16m1_t tmp_sum0 = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t tmp_sum1 = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t tmp_sum2 = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t tmp_sum3 = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t v_a0_sum = __riscv_vwredsum_vs_i8m1_i16m1(v_a0_quant_i8, tmp_sum0, vl);
+                vint16m1_t v_a1_sum = __riscv_vwredsum_vs_i8m1_i16m1(v_a1_quant_i8, tmp_sum1, vl);
+                vint16m1_t v_a2_sum = __riscv_vwredsum_vs_i8m1_i16m1(v_a2_quant_i8, tmp_sum2, vl);
+                vint16m1_t v_a3_sum = __riscv_vwredsum_vs_i8m1_i16m1(v_a3_quant_i8, tmp_sum3, vl);
+
+                a_sum_ptr[0 * subblk_count + kk] =
+                    static_cast<_Float16>(-__riscv_vmv_x_s_i16m1_i16(v_a0_sum)) * static_cast<_Float16>(8.0f);
+                a_sum_ptr[1 * subblk_count + kk] =
+                    static_cast<_Float16>(-__riscv_vmv_x_s_i16m1_i16(v_a1_sum)) * static_cast<_Float16>(8.0f);
+                a_sum_ptr[2 * subblk_count + kk] =
+                    static_cast<_Float16>(-__riscv_vmv_x_s_i16m1_i16(v_a2_sum)) * static_cast<_Float16>(8.0f);
+                a_sum_ptr[3 * subblk_count + kk] =
+                    static_cast<_Float16>(-__riscv_vmv_x_s_i16m1_i16(v_a3_sum)) * static_cast<_Float16>(8.0f);
+
+                __riscv_vse8_v_i8m1(quant_a_blk + 0 * k_subblk_len, v_a0_quant_i8, vl);
+                __riscv_vse8_v_i8m1(quant_a_blk + 1 * k_subblk_len, v_a1_quant_i8, vl);
+                __riscv_vse8_v_i8m1(quant_a_blk + 2 * k_subblk_len, v_a2_quant_i8, vl);
+                __riscv_vse8_v_i8m1(quant_a_blk + 3 * k_subblk_len, v_a3_quant_i8, vl);
+            }
+        }
+    } else {
+        quantize_a_nrow_i8_hp_ref<4>(blk_len, a_ptr, count_k, quant_a_ptr);
+    }
+}
+
+void quantize_a_row_i8k(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    GGML_ASSERT(blk_len == 256);
+    constexpr int64_t a_blk_stride = q8k_blk_size(256);
+    constexpr int64_t a_sum_size   = 256 / 16;
+    size_t            vlenb        = __riscv_vlenb();
+
+    if (vlenb == 128) {
+        // vlen = 1024 bits, can process 32 float32 elements with m1
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_blk_stride) {
+            float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+            int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float));
+            int8_t *  quant_a_blk =
+                reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) + sizeof(int16_t) * a_sum_size);
+
+            // Find max absolute value across all 256 elements
+            size_t       vl        = __riscv_vsetvl_e32m1(16);
+            vfloat32m1_t v_max_abs = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+
+            for (size_t bki = 0; bki < a_sum_size; bki++) {
+                vfloat32m1_t v_a     = __riscv_vle32_v_f32m1(a_ptr + k + bki * 16, vl);
+                vfloat32m1_t v_a_abs = __riscv_vfabs_v_f32m1(v_a, vl);
+                v_max_abs            = __riscv_vfmax_vv_f32m1(v_a_abs, v_max_abs, vl);
+            }
+            vfloat32m1_t tmp         = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+            vfloat32m1_t v_local_max = __riscv_vfredmax_vs_f32m1_f32m1(v_max_abs, tmp, vl);
+            float        max_abs_a   = __riscv_vfmv_f_s_f32m1_f32(v_local_max);
+
+            float scale_a     = max_abs_a / ((1 << 7) - 1);
+            float rep_scale_a = scale_a ? 1.0f / scale_a : 0.0f;
+            scale_a_ptr[0]    = scale_a;
+
+            // Quantize and compute sums for each 16-element group
+            for (size_t bki = 0; bki < a_sum_size; bki++) {
+                vfloat32m1_t v_a          = __riscv_vle32_v_f32m1(a_ptr + k + bki * 16, vl);
+                vfloat32m1_t v_a_scale    = __riscv_vfmul_vf_f32m1(v_a, rep_scale_a, vl);
+                vint16mf2_t  v_a_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a_scale, vl);
+                vint8mf4_t   v_a_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a_quant, vl);
+
+                vint16m1_t tmp_sum = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t v_a_sum = __riscv_vwredsum_vs_i8mf4_i16m1(v_a_quant_i8, tmp_sum, vl);
+                int16_t    a_sum   = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+                a_sum_ptr[bki]     = -a_sum;
+
+                __riscv_vse8_v_i8mf4(quant_a_blk + bki * 16, v_a_quant_i8, vl);
+            }
+        }
+    } else if (vlenb == 32) {
+        // vlen = 256 bits, can process 8 float32 elements with m1
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_blk_stride) {
+            float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+            int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float));
+            int8_t *  quant_a_blk =
+                reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) + sizeof(int16_t) * a_sum_size);
+
+            // Find max absolute value across all 256 elements
+            size_t       vl        = __riscv_vsetvl_e32m2(16);
+            vfloat32m2_t v_max_abs = __riscv_vfmv_v_f_f32m2(0.0f, vl);
+
+            for (size_t bki = 0; bki < a_sum_size; bki++) {
+                vfloat32m2_t v_a     = __riscv_vle32_v_f32m2(a_ptr + k + bki * 16, vl);
+                vfloat32m2_t v_a_abs = __riscv_vfabs_v_f32m2(v_a, vl);
+                v_max_abs            = __riscv_vfmax_vv_f32m2(v_a_abs, v_max_abs, vl);
+            }
+            vfloat32m1_t tmp         = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+            vfloat32m1_t v_local_max = __riscv_vfredmax_vs_f32m2_f32m1(v_max_abs, tmp, vl);
+            float        max_abs_a   = __riscv_vfmv_f_s_f32m1_f32(v_local_max);
+
+            float scale_a     = max_abs_a / ((1 << 7) - 1);
+            float rep_scale_a = scale_a ? 1.0f / scale_a : 0.0f;
+            scale_a_ptr[0]    = scale_a;
+
+            // Quantize and compute sums for each 16-element group
+            for (size_t bki = 0; bki < a_sum_size; bki++) {
+                vfloat32m2_t v_a          = __riscv_vle32_v_f32m2(a_ptr + k + bki * 16, vl);
+                vfloat32m2_t v_a_scale    = __riscv_vfmul_vf_f32m2(v_a, rep_scale_a, vl);
+                vint16m1_t   v_a_quant    = __riscv_vfncvt_x_f_w_i16m1(v_a_scale, vl);
+                vint8mf2_t   v_a_quant_i8 = __riscv_vncvt_x_x_w_i8mf2(v_a_quant, vl);
+
+                vint16m1_t tmp_sum = __riscv_vmv_v_x_i16m1(0, vl);
+                vint16m1_t v_a_sum = __riscv_vwredsum_vs_i8mf2_i16m1(v_a_quant_i8, tmp_sum, vl);
+                int16_t    a_sum   = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+                a_sum_ptr[bki]     = -a_sum;
+
+                __riscv_vse8_v_i8mf2(quant_a_blk + bki * 16, v_a_quant_i8, vl);
+            }
+        }
+    } else {
+        quantize_a_nrow_i8k_ref<1>(blk_len, a_ptr, count_k, quant_a_ptr);
+    }
+}
+
+void quantize_a_4row_i8k(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr) {
+    GGML_ASSERT(blk_len == 256);
+    constexpr int64_t a_blk_stride        = q8k_blk_size(256);
+    constexpr int64_t a_nrow_block_stride = a_blk_stride * 4;
+    constexpr int64_t a_sum_size          = 256 / 16;
+    size_t            vlenb               = __riscv_vlenb();
+
+    if (vlenb == 128) {
+        // vlen = 1024 bits
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+            float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+            int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float) * 4);
+            int8_t *  quant_a_blk =
+                reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) * 4 + sizeof(int16_t) * a_sum_size * 4);
+
+            for (size_t mi = 0; mi < 4; mi++) {
+                // Find max absolute value across all 256 elements for this row
+                size_t       vl        = __riscv_vsetvl_e32m1(16);
+                vfloat32m1_t v_max_abs = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+
+                for (size_t bki = 0; bki < a_sum_size; bki++) {
+                    vfloat32m1_t v_a     = __riscv_vle32_v_f32m1(a_ptr + mi * count_k + k + bki * 16, vl);
+                    vfloat32m1_t v_a_abs = __riscv_vfabs_v_f32m1(v_a, vl);
+                    v_max_abs            = __riscv_vfmax_vv_f32m1(v_a_abs, v_max_abs, vl);
+                }
+                vfloat32m1_t tmp         = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+                vfloat32m1_t v_local_max = __riscv_vfredmax_vs_f32m1_f32m1(v_max_abs, tmp, vl);
+                float        max_abs_a   = __riscv_vfmv_f_s_f32m1_f32(v_local_max);
+
+                float scale_a     = max_abs_a / ((1 << 7) - 1);
+                float rep_scale_a = scale_a ? 1.0f / scale_a : 0.0f;
+                scale_a_ptr[mi]   = scale_a;
+
+                // Quantize and compute sums for each 16-element group
+                for (size_t bki = 0; bki < a_sum_size; bki++) {
+                    vfloat32m1_t v_a          = __riscv_vle32_v_f32m1(a_ptr + mi * count_k + k + bki * 16, vl);
+                    vfloat32m1_t v_a_scale    = __riscv_vfmul_vf_f32m1(v_a, rep_scale_a, vl);
+                    vint16mf2_t  v_a_quant    = __riscv_vfncvt_x_f_w_i16mf2(v_a_scale, vl);
+                    vint8mf4_t   v_a_quant_i8 = __riscv_vncvt_x_x_w_i8mf4(v_a_quant, vl);
+
+                    vint16m1_t tmp_sum               = __riscv_vmv_v_x_i16m1(0, vl);
+                    vint16m1_t v_a_sum               = __riscv_vwredsum_vs_i8mf4_i16m1(v_a_quant_i8, tmp_sum, vl);
+                    int16_t    a_sum                 = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+                    a_sum_ptr[mi * a_sum_size + bki] = -a_sum;
+
+                    __riscv_vse8_v_i8mf4(quant_a_blk + mi * blk_len + bki * 16, v_a_quant_i8, vl);
+                }
+            }
+        }
+    } else if (vlenb == 32) {
+        // vlen = 256 bits
+        for (size_t k = 0; k < count_k; k += blk_len, quant_a_ptr += a_nrow_block_stride) {
+            float *   scale_a_ptr = reinterpret_cast<float *>(quant_a_ptr);
+            int16_t * a_sum_ptr   = reinterpret_cast<int16_t *>(quant_a_ptr + sizeof(float) * 4);
+            int8_t *  quant_a_blk =
+                reinterpret_cast<int8_t *>(quant_a_ptr + sizeof(float) * 4 + sizeof(int16_t) * a_sum_size * 4);
+
+            for (size_t mi = 0; mi < 4; mi++) {
+                // Find max absolute value across all 256 elements for this row
+                size_t       vl        = __riscv_vsetvl_e32m2(16);
+                vfloat32m2_t v_max_abs = __riscv_vfmv_v_f_f32m2(0.0f, vl);
+
+                for (size_t bki = 0; bki < a_sum_size; bki++) {
+                    vfloat32m2_t v_a     = __riscv_vle32_v_f32m2(a_ptr + mi * count_k + k + bki * 16, vl);
+                    vfloat32m2_t v_a_abs = __riscv_vfabs_v_f32m2(v_a, vl);
+                    v_max_abs            = __riscv_vfmax_vv_f32m2(v_a_abs, v_max_abs, vl);
+                }
+                vfloat32m1_t tmp         = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+                vfloat32m1_t v_local_max = __riscv_vfredmax_vs_f32m2_f32m1(v_max_abs, tmp, vl);
+                float        max_abs_a   = __riscv_vfmv_f_s_f32m1_f32(v_local_max);
+
+                float scale_a     = max_abs_a / ((1 << 7) - 1);
+                float rep_scale_a = scale_a ? 1.0f / scale_a : 0.0f;
+                scale_a_ptr[mi]   = scale_a;
+
+                // Quantize and compute sums for each 16-element group
+                for (size_t bki = 0; bki < a_sum_size; bki++) {
+                    vfloat32m2_t v_a          = __riscv_vle32_v_f32m2(a_ptr + mi * count_k + k + bki * 16, vl);
+                    vfloat32m2_t v_a_scale    = __riscv_vfmul_vf_f32m2(v_a, rep_scale_a, vl);
+                    vint16m1_t   v_a_quant    = __riscv_vfncvt_x_f_w_i16m1(v_a_scale, vl);
+                    vint8mf2_t   v_a_quant_i8 = __riscv_vncvt_x_x_w_i8mf2(v_a_quant, vl);
+
+                    vint16m1_t tmp_sum               = __riscv_vmv_v_x_i16m1(0, vl);
+                    vint16m1_t v_a_sum               = __riscv_vwredsum_vs_i8mf2_i16m1(v_a_quant_i8, tmp_sum, vl);
+                    int16_t    a_sum                 = __riscv_vmv_x_s_i16m1_i16(v_a_sum);
+                    a_sum_ptr[mi * a_sum_size + bki] = -a_sum;
+
+                    __riscv_vse8_v_i8mf2(quant_a_blk + mi * blk_len + bki * 16, v_a_quant_i8, vl);
+                }
+            }
+        }
+    } else {
+        quantize_a_nrow_i8k_ref<4>(blk_len, a_ptr, count_k, quant_a_ptr);
+    }
+}
+
+void forward_cpy_with_permute(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    ggml_tensor *       dst  = op;
+    const int           ith  = params->ith;
+    const int           nth  = params->nth;
+
+    // [batch, m, n] -> [batch, n, m]
+    int64_t batch = src0->ne[2] * src0->ne[3];
+    int64_t m     = src0->ne[1];
+    int64_t n     = src0->ne[0];
+
+    int64_t batch_stride = src0->nb[2];
+    int64_t m_src_stride = src0->nb[0];
+    int64_t n_src_stride = src0->nb[1];
+    int64_t n_dst_stride = n_src_stride * m;
+
+    permute_transpose_impl(src0, dst, batch, m, n, batch_stride, m_src_stride, n_src_stride, n_dst_stride, ith, nth);
+}
+
+void forward_cont_with_permute(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    ggml_tensor *       dst  = op;
+    const int           ith  = params->ith;
+    const int           nth  = params->nth;
+
+    // [batch, m, n] -> [batch, n, m]
+    int64_t batch = dst->ne[2] * dst->ne[3];
+    int64_t n     = dst->ne[1];
+    int64_t m     = dst->ne[0];
+
+    int64_t batch_stride = dst->nb[2];
+    int64_t m_src_stride = src0->nb[0];
+    int64_t n_src_stride = src0->nb[1];
+    int64_t n_dst_stride = dst->nb[1];
+
+    permute_transpose_impl(src0, dst, batch, m, n, batch_stride, m_src_stride, n_src_stride, n_dst_stride, ith, nth);
+}
+
+void forward_norm_f32(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    ggml_tensor *       dst  = op;
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    int ith = params->ith;
+    int nth = params->nth;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    float epsilon = *((float *) dst->op_params);
+
+    GGML_ASSERT(epsilon > 0.0f);
+
+    auto * input  = (char *) src0->data;
+    auto * output = (char *) dst->data;
+
+    const auto hidden_size     = ne00;
+    const auto task_count      = ne01 * ne02 * ne03;
+    const auto task_per_thread = (task_count + nth - 1) / nth;
+
+    const auto task_begin = ith * task_per_thread;
+    const auto task_end   = std::min((ith + 1) * task_per_thread, task_count);
+
+    for (auto task_idx = task_begin; task_idx < task_end; task_idx++) {
+        int64_t i03 = task_idx / (ne02 * ne01);
+        int64_t i02 = (task_idx - i03 * ne02 * ne01) / ne01;
+        int64_t i01 = (task_idx - i03 * ne02 * ne01 - i02 * ne01);
+
+        auto * p_input       = (float *) (input + i01 * nb01 + i02 * nb02 + i03 * nb03);
+        auto * p_output      = (float *) (output + i01 * nb1 + i02 * nb2 + i03 * nb3);
+        auto * p_temp_output = p_output;
+
+        size_t       gvl    = __riscv_vsetvlmax_e32m4();
+        vfloat32m4_t sum    = __riscv_vfmv_v_f_f32m4(0.f, gvl);
+        vfloat32m4_t sum_sq = __riscv_vfmv_v_f_f32m4(0.f, gvl);
+        int64_t      length = hidden_size;
+        while (length > 0) {
+            gvl                   = __riscv_vsetvl_e32m4(length);
+            // load data
+            vfloat32m4_t src_data = __riscv_vle32_v_f32m4(p_input, gvl);
+
+            sum    = __riscv_vfadd_vv_f32m4(sum, src_data, gvl);
+            sum_sq = __riscv_vfmacc_vv_f32m4(sum_sq, src_data, src_data, gvl);
+
+            __riscv_vse32_v_f32m4(p_temp_output, src_data, gvl);
+
+            p_input += gvl;
+            p_temp_output += gvl;
+            length -= gvl;
+        }
+
+        gvl = __riscv_vsetvlmax_e32m1();
+
+        float        mean   = 0.f;
+        vfloat32m1_t zero_v = __riscv_vfmv_v_f_f32m1(0.f, gvl);
+        vfloat32m1_t mean_v =
+            __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m4_f32m1(sum, 0), __riscv_vget_v_f32m4_f32m1(sum, 1), gvl);
+        mean_v = __riscv_vfadd_vv_f32m1(mean_v, __riscv_vget_v_f32m4_f32m1(sum, 2), gvl);
+        mean_v = __riscv_vfadd_vv_f32m1(mean_v, __riscv_vget_v_f32m4_f32m1(sum, 3), gvl);
+        mean_v = __riscv_vfredusum_vs_f32m1_f32m1(mean_v, zero_v, gvl);
+        mean   = __riscv_vfmv_f_s_f32m1_f32(mean_v);
+        mean /= hidden_size;
+
+        vfloat32m1_t mean_square_v =
+            __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m4_f32m1(sum_sq, 0), __riscv_vget_v_f32m4_f32m1(sum_sq, 1), gvl);
+        mean_square_v = __riscv_vfadd_vv_f32m1(mean_square_v, __riscv_vget_v_f32m4_f32m1(sum_sq, 2), gvl);
+        mean_square_v = __riscv_vfadd_vv_f32m1(mean_square_v, __riscv_vget_v_f32m4_f32m1(sum_sq, 3), gvl);
+        mean_square_v = __riscv_vfredusum_vs_f32m1_f32m1(mean_square_v, zero_v, gvl);
+
+        float mean_square = __riscv_vfmv_f_s_f32m1_f32(mean_square_v);
+        mean_square /= hidden_size;
+        mean_square = sqrt(mean_square - mean * mean + epsilon);
+
+        mean_square   = 1.0f / mean_square;
+        length        = hidden_size;
+        p_temp_output = p_output;
+
+        while (length > 0) {
+            gvl                   = __riscv_vsetvl_e32m4(length);
+            vfloat32m4_t src_data = __riscv_vle32_v_f32m4(p_temp_output, gvl);
+            src_data              = __riscv_vfsub_vf_f32m4(src_data, mean, gvl);
+            src_data              = __riscv_vfmul_vf_f32m4(src_data, mean_square, gvl);
+            __riscv_vse32_v_f32m4(p_output, src_data, gvl);
+            p_temp_output += gvl;
+            p_output += gvl;
+            length -= gvl;
+        }
+    }
+}
+
+template <ggml_op op_type, typename T> void forward_binary(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    const ggml_tensor * src1 = op->src[1];
+    ggml_tensor *       dst  = op;
+    GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    auto src0_rows = ggml_nrows(src0);
+    auto src1_rows = ggml_nrows(src1);
+
+    int ith = params->ith;
+    int nth = params->nth;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(nb0 == sizeof(T));
+    GGML_ASSERT(nb00 == sizeof(T));
+
+    const auto [ir0, ir1] = get_thread_range(params, src0);
+
+    auto compute_func_vv = [&](int64_t blk_len, int64_t r, T * src0_ptr, T * src1_ptr, T * dst_ptr) {
+        int64_t idx = 0;
+        if constexpr (op_type == GGML_OP_ADD) {
+            if constexpr (std::is_same_v<T, float>) {
+                for (size_t vl; blk_len > 0; blk_len -= vl, idx += vl) {
+                    vl               = __riscv_vsetvl_e32m4(blk_len);
+                    vfloat32m4_t lhs = __riscv_vle32_v_f32m4(src0_ptr + idx + r, vl);
+                    vfloat32m4_t rhs = __riscv_vle32_v_f32m4(src1_ptr + idx, vl);
+                    vfloat32m4_t res = __riscv_vfadd_vv_f32m4(lhs, rhs, vl);
+                    __riscv_vse32_v_f32m4(dst_ptr + idx + r, res, vl);
+                }
+            } else if constexpr (std::is_same_v<T, _Float16>) {
+                for (size_t vl; blk_len > 0; blk_len -= vl, idx += vl) {
+                    vl               = __riscv_vsetvl_e16m4(blk_len);
+                    vfloat16m4_t lhs = __riscv_vle16_v_f16m4((src0_ptr + idx + r), vl);
+                    vfloat16m4_t rhs = __riscv_vle16_v_f16m4((src1_ptr + idx), vl);
+                    vfloat16m4_t res = __riscv_vfadd_vv_f16m4(lhs, rhs, vl);
+                    __riscv_vse16_v_f16m4((dst_ptr + idx + r), res, vl);
+                }
+            } else {
+                GGML_ABORT("fatal error");
+            }
+        } else if constexpr (op_type == GGML_OP_SUB) {
+            if constexpr (std::is_same_v<T, float>) {
+                for (size_t vl; blk_len > 0; blk_len -= vl, idx += vl) {
+                    vl               = __riscv_vsetvl_e32m4(blk_len);
+                    vfloat32m4_t lhs = __riscv_vle32_v_f32m4(src0_ptr + idx + r, vl);
+                    vfloat32m4_t rhs = __riscv_vle32_v_f32m4(src1_ptr + idx, vl);
+                    vfloat32m4_t res = __riscv_vfsub_vv_f32m4(lhs, rhs, vl);
+                    __riscv_vse32_v_f32m4(dst_ptr + idx + r, res, vl);
+                }
+            } else if constexpr (std::is_same_v<T, _Float16>) {
+                for (size_t vl; blk_len > 0; blk_len -= vl, idx += vl) {
+                    vl               = __riscv_vsetvl_e16m4(blk_len);
+                    vfloat16m4_t lhs = __riscv_vle16_v_f16m4((src0_ptr + idx + r), vl);
+                    vfloat16m4_t rhs = __riscv_vle16_v_f16m4((src1_ptr + idx), vl);
+                    vfloat16m4_t res = __riscv_vfsub_vv_f16m4(lhs, rhs, vl);
+                    __riscv_vse16_v_f16m4((dst_ptr + idx + r), res, vl);
+                }
+            } else {
+                GGML_ABORT("fatal error");
+            }
+        } else if constexpr (op_type == GGML_OP_MUL) {
+            if constexpr (std::is_same_v<T, float>) {
+                for (size_t vl; blk_len > 0; blk_len -= vl, idx += vl) {
+                    vl               = __riscv_vsetvl_e32m4(blk_len);
+                    vfloat32m4_t lhs = __riscv_vle32_v_f32m4(src0_ptr + idx + r, vl);
+                    vfloat32m4_t rhs = __riscv_vle32_v_f32m4(src1_ptr + idx, vl);
+                    vfloat32m4_t res = __riscv_vfmul_vv_f32m4(lhs, rhs, vl);
+                    __riscv_vse32_v_f32m4(dst_ptr + idx + r, res, vl);
+                }
+            } else if constexpr (std::is_same_v<T, _Float16>) {
+                for (size_t vl; blk_len > 0; blk_len -= vl, idx += vl) {
+                    vl               = __riscv_vsetvl_e16m4(blk_len);
+                    vfloat16m4_t lhs = __riscv_vle16_v_f16m4((src0_ptr + idx + r), vl);
+                    vfloat16m4_t rhs = __riscv_vle16_v_f16m4((src1_ptr + idx), vl);
+                    vfloat16m4_t res = __riscv_vfmul_vv_f16m4(lhs, rhs, vl);
+                    __riscv_vse16_v_f16m4((dst_ptr + idx + r), res, vl);
+                }
+            } else {
+                GGML_ABORT("fatal error");
+            }
+        } else if constexpr (op_type == GGML_OP_DIV) {
+            if constexpr (std::is_same_v<T, float>) {
+                for (size_t vl; blk_len > 0; blk_len -= vl, idx += vl) {
+                    vl               = __riscv_vsetvl_e32m4(blk_len);
+                    vfloat32m4_t lhs = __riscv_vle32_v_f32m4(src0_ptr + idx + r, vl);
+                    vfloat32m4_t rhs = __riscv_vle32_v_f32m4(src1_ptr + idx, vl);
+                    vfloat32m4_t res = __riscv_vfdiv_vv_f32m4(lhs, rhs, vl);
+                    __riscv_vse32_v_f32m4(dst_ptr + idx + r, res, vl);
+                }
+            } else if constexpr (std::is_same_v<T, _Float16>) {
+                for (size_t vl; blk_len > 0; blk_len -= vl, idx += vl) {
+                    vl               = __riscv_vsetvl_e16m4(blk_len);
+                    vfloat16m4_t lhs = __riscv_vle16_v_f16m4((src0_ptr + idx + r), vl);
+                    vfloat16m4_t rhs = __riscv_vle16_v_f16m4((src1_ptr + idx), vl);
+                    vfloat16m4_t res = __riscv_vfdiv_vv_f16m4(lhs, rhs, vl);
+                    __riscv_vse16_v_f16m4((dst_ptr + idx + r), res, vl);
+                }
+            } else {
+                GGML_ABORT("fatal error");
+            }
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    };
+
+    if (src0_rows == src1_rows && src0_rows == 1 && ne00 == ne10) {
+        int64_t task_per_thread = (ne00 + nth - 1) / nth;
+        int64_t task_begin      = ith * task_per_thread;
+        int64_t task_end        = std::min((ith + 1) * task_per_thread, ne00);
+
+        T * dst_ptr  = ((T *) dst->data) + task_begin;
+        T * src0_ptr = ((T *) src0->data) + task_begin;
+        T * src1_ptr = ((T *) src1->data) + task_begin;
+
+        compute_func_vv(task_end - task_begin, 0, src0_ptr, src1_ptr, dst_ptr);
+    } else if (ne10 > 1) {
+        for (int64_t ir = ir0; ir < ir1; ++ir) {
+            const int64_t i03 = ir / (ne02 * ne01);
+            const int64_t i02 = (ir - i03 * ne02 * ne01) / ne01;
+            const int64_t i01 = (ir - i03 * ne02 * ne01 - i02 * ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+
+            T * dst_ptr  = (T *) ((char *) dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1);
+            T * src0_ptr = (T *) ((char *) src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01);
+            T * src1_ptr = (T *) ((char *) src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11);
+
+            // src1 is broadcastable across src0 and dst in i1, i2, i3
+            for (int64_t r = 0; r < ne00; r += ne10) {
+                compute_func_vv(ne10, r, src0_ptr, src1_ptr, dst_ptr);
+            }
+        }
+    } else {
+        for (int64_t ir = ir0; ir < ir1; ++ir) {
+            const int64_t i03 = ir / (ne02 * ne01);
+            const int64_t i02 = (ir - i03 * ne02 * ne01) / ne01;
+            const int64_t i01 = (ir - i03 * ne02 * ne01 - i02 * ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+
+            T * dst_ptr  = (T *) ((char *) dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1);
+            T * src0_ptr = (T *) ((char *) src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01);
+            T * src1_ptr = (T *) ((char *) src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11);
+
+            T       rhs_scalar = src1_ptr[0];
+            int64_t blk_len    = ne00;
+            int64_t r          = 0;
+
+            for (size_t vl; blk_len > 0; blk_len -= vl, r += vl) {
+                if constexpr (op_type == GGML_OP_ADD) {
+                    if constexpr (std::is_same_v<T, float>) {
+                        vl               = __riscv_vsetvl_e32m4(blk_len);
+                        vfloat32m4_t lhs = __riscv_vle32_v_f32m4(src0_ptr + r, vl);
+                        vfloat32m4_t res = __riscv_vfadd_vf_f32m4(lhs, rhs_scalar, vl);
+                        __riscv_vse32_v_f32m4(dst_ptr + r, res, vl);
+                    } else if constexpr (std::is_same_v<T, _Float16>) {
+                        vl               = __riscv_vsetvl_e16m4(blk_len);
+                        vfloat16m4_t lhs = __riscv_vle16_v_f16m4((src0_ptr + r), vl);
+                        vfloat16m4_t res = __riscv_vfadd_vf_f16m4(lhs, rhs_scalar, vl);
+                        __riscv_vse16_v_f16m4((dst_ptr + r), res, vl);
+                    } else {
+                        GGML_ABORT("fatal error");
+                    }
+                } else if constexpr (op_type == GGML_OP_SUB) {
+                    if constexpr (std::is_same_v<T, float>) {
+                        vl               = __riscv_vsetvl_e32m4(blk_len);
+                        vfloat32m4_t lhs = __riscv_vle32_v_f32m4(src0_ptr + r, vl);
+                        vfloat32m4_t res = __riscv_vfsub_vf_f32m4(lhs, rhs_scalar, vl);
+                        __riscv_vse32_v_f32m4(dst_ptr + r, res, vl);
+                    } else if constexpr (std::is_same_v<T, _Float16>) {
+                        vl               = __riscv_vsetvl_e16m4(blk_len);
+                        vfloat16m4_t lhs = __riscv_vle16_v_f16m4((src0_ptr + r), vl);
+                        vfloat16m4_t res = __riscv_vfsub_vf_f16m4(lhs, rhs_scalar, vl);
+                        __riscv_vse16_v_f16m4((dst_ptr + r), res, vl);
+                    } else {
+                        GGML_ABORT("fatal error");
+                    }
+                } else if constexpr (op_type == GGML_OP_MUL) {
+                    if constexpr (std::is_same_v<T, float>) {
+                        vl               = __riscv_vsetvl_e32m4(blk_len);
+                        vfloat32m4_t lhs = __riscv_vle32_v_f32m4(src0_ptr + r, vl);
+                        vfloat32m4_t res = __riscv_vfmul_vf_f32m4(lhs, rhs_scalar, vl);
+                        __riscv_vse32_v_f32m4(dst_ptr + r, res, vl);
+                    } else if constexpr (std::is_same_v<T, _Float16>) {
+                        vl               = __riscv_vsetvl_e16m4(blk_len);
+                        vfloat16m4_t lhs = __riscv_vle16_v_f16m4((src0_ptr + r), vl);
+                        vfloat16m4_t res = __riscv_vfmul_vf_f16m4(lhs, rhs_scalar, vl);
+                        __riscv_vse16_v_f16m4((dst_ptr + r), res, vl);
+                    } else {
+                        GGML_ABORT("fatal error");
+                    }
+                } else if constexpr (op_type == GGML_OP_DIV) {
+                    if constexpr (std::is_same_v<T, float>) {
+                        vl               = __riscv_vsetvl_e32m4(blk_len);
+                        vfloat32m4_t lhs = __riscv_vle32_v_f32m4(src0_ptr + r, vl);
+                        vfloat32m4_t res = __riscv_vfdiv_vf_f32m4(lhs, rhs_scalar, vl);
+                        __riscv_vse32_v_f32m4(dst_ptr + r, res, vl);
+                    } else if constexpr (std::is_same_v<T, _Float16>) {
+                        vl               = __riscv_vsetvl_e16m4(blk_len);
+                        vfloat16m4_t lhs = __riscv_vle16_v_f16m4((src0_ptr + r), vl);
+                        vfloat16m4_t res = __riscv_vfdiv_vf_f16m4(lhs, rhs_scalar, vl);
+                        __riscv_vse16_v_f16m4((dst_ptr + r), res, vl);
+                    } else {
+                        GGML_ABORT("fatal error");
+                    }
+                } else {
+                    GGML_ABORT("fatal error");
+                }
+            }
+        }
+    }
+}
+
+template <typename T> void forward_sum_rows(const ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    ggml_tensor *       dst  = op;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT(ne0 == 1);
+    GGML_ASSERT(ne1 == ne01);
+    GGML_ASSERT(ne2 == ne02);
+    GGML_ASSERT(ne3 == ne03);
+
+    int64_t n_task          = ne01 * ne02 * ne03;
+    int64_t task_per_thread = (n_task + nth - 1) / nth;
+    int64_t ir_start        = ith * task_per_thread;
+    int64_t ir_end          = std::min(ir_start + task_per_thread, n_task);
+
+    for (int64_t ir = ir_start; ir < ir_end; ir++) {
+        const int64_t i3 = ir / (ne02 * ne01);
+        const int64_t i2 = (ir - i3 * ne02 * ne01) / ne01;
+        const int64_t i1 = (ir - i3 * ne02 * ne01 - i2 * ne01);
+
+        T * src_row = (T *) ((char *) src0->data + i1 * nb01 + i2 * nb02 + i3 * nb03);
+        T * dst_row = (T *) ((char *) op->data + i1 * nb1 + i2 * nb2 + i3 * nb3);
+
+        float row_sum = 0;
+
+        if constexpr (std::is_same_v<T, float>) {
+            size_t        gvl     = __riscv_vsetvlmax_e32m4();
+            vfloat32m4_t  acc_vec = __riscv_vfmv_v_f_f32m4(0.0f, gvl);
+            int64_t       length  = ne00;
+            const float * p_data  = src_row;
+
+            while (length > 0) {
+                size_t       vl  = __riscv_vsetvl_e32m4(length);
+                vfloat32m4_t vec = __riscv_vle32_v_f32m4(p_data, vl);
+                acc_vec          = __riscv_vfadd_vv_f32m4(acc_vec, vec, vl);
+                p_data += vl;
+                length -= vl;
+            }
+
+            gvl                 = __riscv_vsetvlmax_e32m1();
+            vfloat32m1_t zero_v = __riscv_vfmv_v_f_f32m1(0.0f, gvl);
+            vfloat32m1_t sum_v  = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m4_f32m1(acc_vec, 0),
+                                                         __riscv_vget_v_f32m4_f32m1(acc_vec, 1), gvl);
+            sum_v               = __riscv_vfadd_vv_f32m1(sum_v, __riscv_vget_v_f32m4_f32m1(acc_vec, 2), gvl);
+            sum_v               = __riscv_vfadd_vv_f32m1(sum_v, __riscv_vget_v_f32m4_f32m1(acc_vec, 3), gvl);
+            sum_v               = __riscv_vfredusum_vs_f32m1_f32m1(sum_v, zero_v, gvl);
+            row_sum             = __riscv_vfmv_f_s_f32m1_f32(sum_v);
+        } else if constexpr (std::is_same_v<T, _Float16>) {
+            size_t           gvl     = __riscv_vsetvlmax_e16m2();
+            vfloat32m4_t     acc_vec = __riscv_vfmv_v_f_f32m4(0.0f, gvl);
+            int64_t          length  = ne00;
+            const _Float16 * p_data  = src_row;
+
+            while (length > 0) {
+                size_t       vl      = __riscv_vsetvl_e16m2(length);
+                vfloat16m2_t vec_f16 = __riscv_vle16_v_f16m2(p_data, vl);
+                vfloat32m4_t vec_f32 = __riscv_vfwcvt_f_f_v_f32m4(vec_f16, vl);
+                acc_vec              = __riscv_vfadd_vv_f32m4(acc_vec, vec_f32, vl);
+                p_data += vl;
+                length -= vl;
+            }
+
+            gvl                 = __riscv_vsetvlmax_e32m1();
+            vfloat32m1_t zero_v = __riscv_vfmv_v_f_f32m1(0.0f, gvl);
+            vfloat32m1_t sum_v  = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m4_f32m1(acc_vec, 0),
+                                                         __riscv_vget_v_f32m4_f32m1(acc_vec, 1), gvl);
+            sum_v               = __riscv_vfadd_vv_f32m1(sum_v, __riscv_vget_v_f32m4_f32m1(acc_vec, 2), gvl);
+            sum_v               = __riscv_vfadd_vv_f32m1(sum_v, __riscv_vget_v_f32m4_f32m1(acc_vec, 3), gvl);
+            sum_v               = __riscv_vfredusum_vs_f32m1_f32m1(sum_v, zero_v, gvl);
+            row_sum             = __riscv_vfmv_f_s_f32m1_f32(sum_v);
+        } else {
+            GGML_ABORT("fatal error");
+        }
+
+        dst_row[0] = row_sum;
+    }
+}
+
+template <typename T> void forward_repeat_nrows(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    ggml_tensor *       dst  = op;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    int64_t nrows            = ggml_nrows(src0);
+    int64_t nrows_per_thread = (nrows + nth - 1) / nth;
+    int64_t ir_start         = ith * nrows_per_thread;
+    int64_t ir_end           = std::min(ir_start + nrows_per_thread, nrows);
+
+    if (src0->ne[0] == 1) {
+        for (int64_t ir = ir_start; ir < ir_end; ir++) {
+            T * src_row = (T *) ((char *) src0->data + ir * src0->nb[1]);
+            T * dst_row = (T *) ((char *) dst->data + ir * dst->nb[1]);
+
+            T src_scalar = src_row[0];
+
+            int64_t length = dst->ne[0];
+            int64_t idx    = 0;
+            size_t  vl     = 0;
+
+            while (length > 0) {
+                if constexpr (std::is_same_v<T, int32_t>) {
+                    vl             = __riscv_vsetvl_e32m4(length);
+                    vint32m4_t vec = __riscv_vmv_v_x_i32m4(src_scalar, vl);
+                    __riscv_vse32_v_i32m4(dst_row + idx, vec, vl);
+                } else if constexpr (std::is_same_v<T, int16_t>) {
+                    vl             = __riscv_vsetvl_e16m4(length);
+                    vint16m4_t vec = __riscv_vmv_v_x_i16m4(src_scalar, vl);
+                    __riscv_vse16_v_i16m4((dst_row + idx), vec, vl);
+                } else {
+                    GGML_ABORT("fatal error");
+                }
+                idx += vl;
+                length -= vl;
+            }
+        }
+    } else if (src0->ne[0] == dst->ne[0]) {
+        for (int64_t ir = ir_start; ir < ir_end; ir++) {
+            T * src_row = (T *) ((char *) src0->data + ir * src0->nb[1]);
+            T * dst_row = (T *) ((char *) dst->data + ir * dst->nb[1]);
+
+            int64_t length = dst->ne[0];
+            int64_t idx    = 0;
+            size_t  vl     = 0;
+
+            while (length > 0) {
+                if constexpr (std::is_same_v<T, int32_t>) {
+                    vl             = __riscv_vsetvl_e32m4(length);
+                    vint32m4_t vec = __riscv_vle32_v_i32m4(src_row + idx, vl);
+                    __riscv_vse32_v_i32m4(dst_row + idx, vec, vl);
+                } else if constexpr (std::is_same_v<T, int16_t>) {
+                    vl             = __riscv_vsetvl_e16m4(length);
+                    vint16m4_t vec = __riscv_vle16_v_i16m4((src_row + idx), vl);
+                    __riscv_vse16_v_i16m4((dst_row + idx), vec, vl);
+                } else {
+                    GGML_ABORT("fatal error");
+                }
+                idx += vl;
+                length -= vl;
+            }
+        }
+    } else {
+        GGML_ABORT("fatal error");
+    }
+}
+
+template <typename T> void forward_repeat_dim1(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    ggml_tensor *       dst  = op;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    const int64_t ne3 = dst->ne[3];
+
+    const int64_t total_batches      = ne2 * ne3;
+    const int64_t batches_per_thread = (total_batches + nth - 1) / nth;
+    const int64_t batch_start        = ith * batches_per_thread;
+    const int64_t batch_end          = std::min(batch_start + batches_per_thread, total_batches);
+
+    for (int64_t b = batch_start; b < batch_end; b++) {
+        const int64_t i3 = b / ne2;
+        const int64_t i2 = b % ne2;
+
+        T * src_base  = (T *) ((char *) src0->data + i2 * src0->nb[2] + i3 * src0->nb[3]);
+        T * dst_batch = (T *) ((char *) dst->data + i2 * dst->nb[2] + i3 * dst->nb[3]);
+
+        for (int64_t i1 = 0; i1 < ne1; i1++) {
+            T *     dst_ptr = (T *) ((char *) dst_batch + i1 * dst->nb[1]);
+            int64_t length  = ne0;
+            int64_t idx     = 0;
+
+            while (length > 0) {
+                if constexpr (std::is_same_v<T, int32_t>) {
+                    size_t     vl  = __riscv_vsetvl_e32m4(length);
+                    vint32m4_t vec = __riscv_vle32_v_i32m4(src_base + idx, vl);
+                    __riscv_vse32_v_i32m4(dst_ptr + idx, vec, vl);
+                    idx += vl;
+                    length -= vl;
+                } else if constexpr (std::is_same_v<T, int16_t>) {
+                    size_t     vl  = __riscv_vsetvl_e16m4(length);
+                    vint16m4_t vec = __riscv_vle16_v_i16m4((src_base + idx), vl);
+                    __riscv_vse16_v_i16m4((dst_ptr + idx), vec, vl);
+                    idx += vl;
+                    length -= vl;
+                } else {
+                    GGML_ABORT("fatal error");
+                }
+            }
+        }
+    }
+}
+
+template <typename T> void forward_get_rows(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    const ggml_tensor * src1 = op->src[1];
+    ggml_tensor *       dst  = op;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int64_t nc = ne00;
+    const int64_t nr = ggml_nelements(src1);
+
+    assert(ne0 == nc);
+    assert(ne02 == ne11);
+    assert(nb00 == sizeof(float));
+    assert(ggml_nrows(op) == nr);
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    int rows_nth = nth;
+    int cols_nth = 1;
+
+    if (nr == 1) {
+        rows_nth = 1;
+        cols_nth = nth;
+    }
+
+    // rows per thread
+    const int dr = (nr + rows_nth - 1) / rows_nth;
+    const int dc = (nc + cols_nth - 1) / cols_nth;
+
+    int rows_ith = ith % rows_nth;
+    int cols_ith = ith % cols_nth;
+
+    // row range for this thread
+    const int ir0 = dr * rows_ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    const int cr0 = dc * cols_ith;
+    const int cr1 = MIN(cr0 + dc, nc);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i / (ne11 * ne10);
+        const int64_t i11 = (i - i12 * ne11 * ne10) / ne10;
+        const int64_t i10 = (i - i12 * ne11 * ne10 - i11 * ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10 * nb10 + i11 * nb11 + i12 * nb12);
+
+        GGML_ASSERT(i01 >= 0 && i01 < ne01);
+
+        memcpy1d(((char *) dst->data + i10 * nb1 + i11 * nb2 + i12 * nb3) + cr0 * sizeof(T),
+                 ((char *) src0->data + i01 * nb01 + i11 * nb02 + i12 * nb03) + cr0 * sizeof(T),
+                 (cr1 - cr0) * sizeof(T));
+    }
+}
+
+template <typename T> void forward_concat(ggml_compute_params * params, ggml_tensor * op) {
+    const ggml_tensor * src0 = op->src[0];
+    const ggml_tensor * src1 = op->src[1];
+    ggml_tensor *       dst  = op;
+
+    GGML_ASSERT(ggml_type_size(src0->type) == sizeof(float));
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int32_t dim = ggml_get_op_params_i32(dst, 0);
+
+    GGML_ASSERT(dim == 0 && nb0 == sizeof(float) && nb1 == sizeof(float) * (ne00 + ne10));
+
+    const int64_t nr = ggml_nrows(dst);
+    const int64_t nc = ne0;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    int rows_nth = nth;
+    int cols_nth = 1;
+
+    if (nr == 1) {
+        rows_nth = 1;
+        cols_nth = nth;
+    }
+
+    const int dr = (nr + rows_nth - 1) / rows_nth;
+    const int dc = (nc + cols_nth - 1) / cols_nth;
+
+    int rows_ith = ith % rows_nth;
+    int cols_ith = ith % cols_nth;
+
+    // row range for this thread
+    const int ir0 = dr * rows_ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    const int cr0 = dc * cols_ith;
+    const int cr1 = MIN(cr0 + dc, nc);
+
+    int64_t o[4] = { 0, 0, 0, 0 };
+    o[dim]       = src0->ne[dim];
+    const float * x;
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i3 = i / (ne02 * ne01);
+        const int64_t i2 = (i - i3 * ne02 * ne01) / ne01;
+        const int64_t i1 = (i - i3 * ne02 * ne01 - i2 * ne01);
+
+        for (int i0 = cr0; i0 < cr1; i0++) {
+            if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+                x = (const float *) ((const char *) src0->data + (i0) *nb00 + (i1) *nb01 + (i2) *nb02 + (i3) *nb03);
+            } else {
+                x = (const float *) ((const char *) src1->data + (i0 - o[0]) * nb10 + (i1 - o[1]) * nb11 +
+                                     (i2 - o[2]) * nb12 + (i3 - o[3]) * nb13);
+            }
+
+            float * y = (float *) ((char *) dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3);
+
+            *y = *x;
+        }
+    }
+}
+
+template void forward_binary<GGML_OP_ADD, float>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_binary<GGML_OP_SUB, float>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_binary<GGML_OP_MUL, float>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_binary<GGML_OP_DIV, float>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_binary<GGML_OP_ADD, _Float16>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_binary<GGML_OP_SUB, _Float16>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_binary<GGML_OP_MUL, _Float16>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_binary<GGML_OP_DIV, _Float16>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_sum_rows<float>(const ggml_compute_params * params, ggml_tensor * op);
+template void forward_sum_rows<_Float16>(const ggml_compute_params * params, ggml_tensor * op);
+template void forward_repeat_nrows<int32_t>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_repeat_nrows<int16_t>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_repeat_dim1<int32_t>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_repeat_dim1<int16_t>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_get_rows<int32_t>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_get_rows<int16_t>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_concat<int32_t>(ggml_compute_params * params, ggml_tensor * op);
+template void forward_concat<int16_t>(ggml_compute_params * params, ggml_tensor * op);
+
+}  // namespace spacemit_kernels::rvv
diff --git a/ggml/src/ggml-cpu/spacemit/rvv_kernels.h b/ggml/src/ggml-cpu/spacemit/rvv_kernels.h
new file mode 100644
index 00000000000..edddf957c21
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/rvv_kernels.h
@@ -0,0 +1,95 @@
+#pragma once
+
+#include "ggml-cpu-impl.h"
+
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+
+namespace spacemit_kernels {
+
+constexpr auto div_round_up(auto up, auto down) {
+    return (up + down - 1) / down;
+}
+
+// Q8 Blk [f32] [s16] [int8 * blk_len]
+// Q8 Blk N [f32 * N] [s16 * N] [int8 * blk_len * N]
+constexpr size_t q8_blk_size(size_t blk_len, bool with_blk_sum = false) {
+    const size_t blk_size = sizeof(float) + blk_len * sizeof(int8_t) + (with_blk_sum ? sizeof(int16_t) : 0);
+    return blk_size;
+}
+
+// Q8 HP row block: K is split into K32 subblocks.
+// Each subblock stores [f32 scale] [int8 * 32], with an optional fp16 sum trailer per subblock.
+constexpr size_t q8_hp_blk_size(size_t blk_len, bool with_blk_sum = false, bool with_blk_scale = false) {
+    const size_t subblk_count = div_round_up(blk_len, size_t(32));
+    const size_t blk_size     = blk_len * sizeof(int8_t) + subblk_count * sizeof(_Float16) +
+                            (with_blk_sum ? subblk_count * sizeof(_Float16) : 0) +
+                            (with_blk_scale ? sizeof(_Float16) : 0);
+    return blk_size;
+}
+
+// Q8K Blk [f32] [s16 * (blk_len / 16)] [int8 * blk_len]
+// Q8K Blk N [f32 * N] [s16 * (blk_len / 16) * N] [int8 * blk_len * N]
+constexpr size_t q8k_blk_size(size_t blk_len) {
+    const size_t blk_size = sizeof(float) + blk_len * sizeof(int8_t) + sizeof(int16_t) * blk_len / 16;
+    return blk_size;
+}
+
+using quantize_a_row_def = std::function<void(size_t, const float *, size_t, uint8_t *)>;
+
+namespace rvv {
+void memcpy1d(void * dst, const void * src, int64_t size);
+
+void memcpy2d(void * dst, int64_t dst_stride, const void * src, int64_t src_stride, int64_t tile_rows, int64_t size);
+
+void forward_flash_attn_ext_f16_one_chunk_vlen1024_vf16(const ggml_compute_params * params,
+                                                        ggml_tensor *               dst,
+                                                        int                         ir0,
+                                                        int                         ir1,
+                                                        void *                      tcm_buffer,
+                                                        size_t                      tcm_buffer_size);
+
+void forward_flash_attn_ext_f16_tiled_vlen1024_vf16(const ggml_compute_params * params,
+                                                    ggml_tensor *               dst,
+                                                    int                         ir0,
+                                                    int                         ir1,
+                                                    void *                      tcm_buffer,
+                                                    size_t                      tcm_buffer_size);
+
+void forward_rms_norm_f32(ggml_compute_params * params, ggml_tensor * op);
+
+void forward_norm_f32(ggml_compute_params * params, ggml_tensor * op);
+
+void forward_cont_with_permute(ggml_compute_params * params, ggml_tensor * op);
+
+void forward_cpy_with_permute(ggml_compute_params * params, ggml_tensor * op);
+
+template <typename T> void forward_get_rows(ggml_compute_params * params, ggml_tensor * op);
+
+template <typename T> void forward_concat(ggml_compute_params * params, ggml_tensor * op);
+
+template <ggml_op op_type, typename T> void forward_binary(ggml_compute_params * params, ggml_tensor * op);
+
+template <typename T> void forward_sum_rows(const ggml_compute_params * params, ggml_tensor * op);
+
+template <typename T> void forward_repeat_nrows(ggml_compute_params * params, ggml_tensor * op);
+
+template <typename T> void forward_repeat_dim1(ggml_compute_params * params, ggml_tensor * op);
+
+void quantize_a_row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
+
+void quantize_a_4row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
+
+void quantize_a_row_i8_hp(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
+
+void quantize_a_4row_i8_hp(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
+
+void quantize_a_row_i8k(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
+
+void quantize_a_4row_i8k(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
+
+}  // namespace rvv
+
+}  // namespace spacemit_kernels
diff --git a/ggml/src/ggml-cpu/spacemit/spine_barrier.h b/ggml/src/ggml-cpu/spacemit/spine_barrier.h
new file mode 100644
index 00000000000..f897dad4b8a
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/spine_barrier.h
@@ -0,0 +1,34 @@
+#pragma once
+
+#include <atomic>
+#include <cstdint>
+
+#define SPINE_CACHE_LINE  64
+#define SPINE_CACHE_ALIGN __attribute__((aligned(SPINE_CACHE_LINE)))
+
+struct spine_barrier_t {
+    SPINE_CACHE_ALIGN std::atomic<int64_t> pending_;
+    SPINE_CACHE_ALIGN std::atomic<int64_t> rounds_;
+    SPINE_CACHE_ALIGN int64_t              total_;
+};
+
+inline void spine_barrier_wait(spine_barrier_t * b) {
+    auto cur_round = b->rounds_.load(std::memory_order_acquire);
+    auto cnt       = --b->pending_;
+    if (cnt == 0) {
+        b->pending_.store(b->total_);
+        b->rounds_.store(cur_round + 1);
+    } else {
+        while (cur_round == b->rounds_.load(std::memory_order_relaxed)) {
+            __asm__ volatile("pause " ::: "memory");
+        }
+    }
+}
+
+inline void spine_barrier_init(spine_barrier_t * b, int num_barriers, uint64_t thread_count) {
+    for (int i = 0; i < num_barriers; i++) {
+        b[i].total_ = thread_count;
+        b[i].pending_.store(thread_count);
+        b[i].rounds_.store(0);
+    }
+}
diff --git a/ggml/src/ggml-cpu/spacemit/spine_mem_pool.cpp b/ggml/src/ggml-cpu/spacemit/spine_mem_pool.cpp
new file mode 100644
index 00000000000..1409423b145
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/spine_mem_pool.cpp
@@ -0,0 +1,760 @@
+#include "spine_mem_pool.h"
+
+#include "common.h"
+#include "ime_env.h"
+#include "spine_tcm.h"
+
+#include <fcntl.h>
+#include <sys/ioctl.h>
+#include <sys/mman.h>
+#include <unistd.h>
+
+#include <algorithm>
+#include <cerrno>
+#include <cstdint>
+#include <cstdlib>
+#include <limits>
+#include <memory>
+#include <mutex>
+#include <unordered_map>
+#include <vector>
+
+namespace ggml::cpu::riscv64_spacemit {
+namespace {
+
+constexpr size_t   SPINE_MEM_POOL_CHUNK_SIZE         = 512ull * 1024ull * 1024ull;
+constexpr size_t   SPINE_SHARE_MEM_POOL_CHUNK_SIZE   = 512ull * 1024ull;
+constexpr size_t   SPINE_MEM_POOL_1G_REGION_SIZE     = 1ull << 30;
+constexpr uint64_t HUGETLB_1G_FLAG_REQUIRE_PUD       = 1ull << 0;
+constexpr char     SPINE_MEM_POOL_HUGETLB_1G_DEV[]   = "/dev/hugetlb_1g";
+constexpr char     SPINE_MEM_POOL_TCM_SYNC_MEM_DEV[] = "/dev/tcm_sync_mem";
+
+struct hugetlb_1g_region {
+    uint64_t size{ 0 };
+    uint64_t dma_addr{ 0 };
+    uint64_t flags{ 0 };
+    uint64_t reserved{ 0 };
+};
+
+#define HUGETLB_1G_IOC_MAGIC 'M'
+#define HUGETLB_1G_IOC_ALLOC _IOWR(HUGETLB_1G_IOC_MAGIC, 0x00, struct hugetlb_1g_region)
+#define HUGETLB_1G_IOC_FREE  _IO(HUGETLB_1G_IOC_MAGIC, 0x01)
+
+struct free_block {
+    size_t offset{ 0 };
+    size_t size{ 0 };
+};
+
+struct pool_chunk {
+    uint8_t *               base{ nullptr };
+    size_t                  size{ 0 };
+    int                     fd{ -1 };
+    std::vector<free_block> free_blocks;
+};
+
+struct pool_allocation {
+    void * chunk_base{ nullptr };
+    size_t chunk_size{ 0 };
+    void * base{ nullptr };
+    size_t size{ 0 };
+};
+
+bool is_power_of_two(size_t value) {
+    return value != 0 && (value & (value - 1)) == 0;
+}
+
+bool align_up(size_t value, size_t alignment, size_t * aligned_value) {
+    if (aligned_value == nullptr || alignment == 0) {
+        return false;
+    }
+
+    const size_t remainder = value % alignment;
+    if (remainder == 0) {
+        *aligned_value = value;
+        return true;
+    }
+
+    const size_t padding = alignment - remainder;
+    if (value > std::numeric_limits<size_t>::max() - padding) {
+        return false;
+    }
+
+    *aligned_value = value + padding;
+    return true;
+}
+
+bool align_up_uintptr(uintptr_t value, size_t alignment, uintptr_t * aligned_value) {
+    if (aligned_value == nullptr || alignment == 0) {
+        return false;
+    }
+
+    const uintptr_t remainder = value % alignment;
+    if (remainder == 0) {
+        *aligned_value = value;
+        return true;
+    }
+
+    const uintptr_t padding = alignment - remainder;
+    if (value > std::numeric_limits<uintptr_t>::max() - padding) {
+        return false;
+    }
+
+    *aligned_value = value + padding;
+    return true;
+}
+
+class spine_mem_pool_manager {
+  public:
+    explicit spine_mem_pool_manager(size_t default_chunk_size) : default_chunk_size_(default_chunk_size) {}
+
+    virtual ~spine_mem_pool_manager() = default;
+
+    void * alloc(size_t size, size_t alignment) {
+        if (size == 0 || !is_power_of_two(alignment)) {
+            return nullptr;
+        }
+
+        size_t aligned_size = 0;
+        if (!align_up(size, alignment, &aligned_size)) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: align_up failed for size %zu alignment %zu\n", __func__, size,
+                           alignment);
+            return nullptr;
+        }
+
+        pool_allocation allocation;
+
+        std::lock_guard<std::mutex> lock(mutex_);
+
+        if (!try_alloc_locked(aligned_size, alignment, &allocation)) {
+            if (!add_chunk_locked(aligned_size, alignment)) {
+                return nullptr;
+            }
+
+            if (!try_alloc_locked(aligned_size, alignment, &allocation)) {
+                GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: allocation retry failed for size %zu alignment %zu\n",
+                               __func__, aligned_size, alignment);
+                return nullptr;
+            }
+        }
+
+        try {
+            const auto [allocation_it, inserted] = allocations_.emplace(allocation.base, allocation);
+            if (!inserted) {
+                GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: duplicate allocation key %p\n", __func__, allocation.base);
+                rollback_allocation_locked(allocation);
+                return nullptr;
+            }
+        } catch (const std::bad_alloc &) {
+            rollback_allocation_locked(allocation);
+            throw;
+        }
+
+        return allocation.base;
+    }
+
+    void free(void * base) {
+        if (base == nullptr) {
+            return;
+        }
+
+        std::lock_guard<std::mutex> lock(mutex_);
+
+        auto allocation_it = allocations_.find(base);
+        if (allocation_it == allocations_.end()) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: unknown allocation %p\n", __func__, base);
+            return;
+        }
+
+        pool_allocation allocation = allocation_it->second;
+        allocations_.erase(allocation_it);
+
+        auto chunk_it = find_chunk_locked(allocation);
+        if (chunk_it == chunks_.end()) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: unknown chunk for allocation %p size %zu\n", __func__,
+                           allocation.base, allocation.size);
+            return;
+        }
+
+        auto * chunk_base = chunk_it->base;
+        auto * alloc_base = static_cast<uint8_t *>(allocation.base);
+        if (alloc_base < chunk_base || alloc_base >= chunk_base + chunk_it->size) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: allocation %p out of chunk range %p..%p\n", __func__,
+                           allocation.base, chunk_base, chunk_base + chunk_it->size);
+            return;
+        }
+
+        const size_t offset = static_cast<size_t>(alloc_base - chunk_base);
+        if (offset > chunk_it->size || allocation.size > chunk_it->size - offset) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: allocation %p size %zu exceeds chunk size %zu\n", __func__,
+                           allocation.base, allocation.size, chunk_it->size);
+            return;
+        }
+
+        insert_free_block_locked(*chunk_it, { offset, allocation.size });
+        maybe_release_empty_chunk_locked(chunk_it);
+    }
+
+  protected:
+    void release_chunks() {
+        std::lock_guard<std::mutex> lock(mutex_);
+
+        allocations_.clear();
+        for (auto & chunk : chunks_) {
+            dealloc_chunk(&chunk);
+        }
+        chunks_.clear();
+    }
+
+    size_t default_chunk_size() const { return default_chunk_size_; }
+
+    static void clear_chunk(pool_chunk * chunk) {
+        chunk->base = nullptr;
+        chunk->size = 0;
+        chunk->fd   = -1;
+        chunk->free_blocks.clear();
+    }
+
+    virtual bool alloc_chunk(size_t min_size, size_t alignment, void * hint_addr, pool_chunk * chunk) = 0;
+    virtual void dealloc_chunk(pool_chunk * chunk)                                                    = 0;
+
+  private:
+    struct alloc_candidate {
+        size_t    chunk_index{ 0 };
+        size_t    block_index{ 0 };
+        size_t    aligned_offset{ 0 };
+        uintptr_t address{ std::numeric_limits<uintptr_t>::max() };
+        bool      valid{ false };
+    };
+
+    std::vector<pool_chunk>::iterator find_chunk_locked(const pool_allocation & allocation) {
+        return std::find_if(chunks_.begin(), chunks_.end(), [&](const pool_chunk & chunk) {
+            return chunk.base == allocation.chunk_base && chunk.size == allocation.chunk_size;
+        });
+    }
+
+    bool add_chunk_locked(size_t min_size, size_t alignment) {
+        pool_chunk   chunk;
+        const size_t chunk_request = default_chunk_size_ == 0 ? min_size : std::max(min_size, default_chunk_size_);
+        void *       hint_addr     = nullptr;
+
+        for (const auto & existing_chunk : chunks_) {
+            auto * chunk_end = existing_chunk.base + existing_chunk.size;
+            if (hint_addr == nullptr || chunk_end > hint_addr) {
+                hint_addr = chunk_end;
+            }
+        }
+
+        if (!alloc_chunk(chunk_request, alignment, hint_addr, &chunk)) {
+            return false;
+        }
+
+        if (chunk.base == nullptr || chunk.size < min_size) {
+            GGML_LOG_ERROR(
+                "CPU_RISCV64_SPACEMIT: %s: invalid chunk returned for request size %zu, chunk_base=%p chunk_size=%zu\n",
+                __func__, min_size, chunk.base, chunk.size);
+            dealloc_chunk(&chunk);
+            return false;
+        }
+
+        try {
+            chunk.free_blocks.push_back({ 0, chunk.size });
+            chunks_.push_back(std::move(chunk));
+        } catch (const std::bad_alloc &) {
+            dealloc_chunk(&chunk);
+            throw;
+        }
+
+        return true;
+    }
+
+    void rollback_allocation_locked(const pool_allocation & allocation) {
+        auto chunk_it = find_chunk_locked(allocation);
+        if (chunk_it == chunks_.end()) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: failed to rollback allocation %p, owning chunk not found\n",
+                           __func__, allocation.base);
+            return;
+        }
+
+        auto * chunk_base = chunk_it->base;
+        auto * alloc_base = static_cast<uint8_t *>(allocation.base);
+        if (alloc_base < chunk_base || alloc_base >= chunk_base + chunk_it->size) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: failed to rollback allocation %p, chunk range is invalid\n",
+                           __func__, allocation.base);
+            return;
+        }
+
+        const size_t offset = static_cast<size_t>(alloc_base - chunk_base);
+        if (offset > chunk_it->size || allocation.size > chunk_it->size - offset) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: failed to rollback allocation %p size %zu\n", __func__,
+                           allocation.base, allocation.size);
+            return;
+        }
+
+        insert_free_block_locked(*chunk_it, { offset, allocation.size });
+        maybe_release_empty_chunk_locked(chunk_it);
+    }
+
+    bool try_alloc_locked(size_t size, size_t alignment, pool_allocation * allocation) {
+        alloc_candidate best;
+
+        for (size_t chunk_index = 0; chunk_index < chunks_.size(); ++chunk_index) {
+            const auto & chunk = chunks_[chunk_index];
+            for (size_t block_index = 0; block_index < chunk.free_blocks.size(); ++block_index) {
+                const auto & block = chunk.free_blocks[block_index];
+
+                uintptr_t  aligned_addr = 0;
+                const auto block_addr   = reinterpret_cast<uintptr_t>(chunk.base + block.offset);
+                if (!align_up_uintptr(block_addr, alignment, &aligned_addr)) {
+                    continue;
+                }
+
+                if (aligned_addr < block_addr) {
+                    continue;
+                }
+
+                const size_t aligned_offset = block.offset + static_cast<size_t>(aligned_addr - block_addr);
+                const size_t padding        = aligned_offset - block.offset;
+                if (padding > block.size || size > block.size - padding) {
+                    continue;
+                }
+
+                if (!best.valid || aligned_addr < best.address) {
+                    best.chunk_index    = chunk_index;
+                    best.block_index    = block_index;
+                    best.aligned_offset = aligned_offset;
+                    best.address        = aligned_addr;
+                    best.valid          = true;
+                }
+            }
+        }
+
+        if (!best.valid) {
+            return false;
+        }
+
+        auto &           chunk     = chunks_[best.chunk_index];
+        const free_block block     = chunk.free_blocks[best.block_index];
+        const size_t     padding   = best.aligned_offset - block.offset;
+        const size_t     alloc_end = best.aligned_offset + size;
+        const size_t     block_end = block.offset + block.size;
+
+        chunk.free_blocks.erase(chunk.free_blocks.begin() + best.block_index);
+        auto insert_it = chunk.free_blocks.begin() + best.block_index;
+        if (padding != 0) {
+            insert_it = chunk.free_blocks.insert(insert_it, { block.offset, padding });
+            ++insert_it;
+        }
+        if (alloc_end < block_end) {
+            chunk.free_blocks.insert(insert_it, { alloc_end, block_end - alloc_end });
+        }
+
+        allocation->chunk_base = chunk.base;
+        allocation->chunk_size = chunk.size;
+        allocation->base       = chunk.base + best.aligned_offset;
+        allocation->size       = size;
+        return true;
+    }
+
+    void maybe_release_empty_chunk_locked(std::vector<pool_chunk>::iterator chunk_it) {
+        if (chunk_it->free_blocks.size() != 1) {
+            return;
+        }
+
+        const auto & block = chunk_it->free_blocks.front();
+        if (block.offset != 0 || block.size != chunk_it->size) {
+            return;
+        }
+
+        dealloc_chunk(&*chunk_it);
+        chunks_.erase(chunk_it);
+    }
+
+    void insert_free_block_locked(pool_chunk & chunk, free_block block) {
+        auto it = chunk.free_blocks.begin();
+        while (it != chunk.free_blocks.end() && it->offset < block.offset) {
+            ++it;
+        }
+
+        if (it != chunk.free_blocks.begin()) {
+            const auto & prev = *(it - 1);
+            if (prev.offset + prev.size > block.offset) {
+                GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: overlapping free block at offset %zu size %zu\n", __func__,
+                               block.offset, block.size);
+                return;
+            }
+        }
+
+        if (it != chunk.free_blocks.end() && block.offset + block.size > it->offset) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: overlapping next free block at offset %zu size %zu\n", __func__,
+                           block.offset, block.size);
+            return;
+        }
+
+        it = chunk.free_blocks.insert(it, block);
+
+        if (it != chunk.free_blocks.begin()) {
+            auto prev = it - 1;
+            if (prev->offset + prev->size == it->offset) {
+                it->offset = prev->offset;
+                it->size += prev->size;
+                it = chunk.free_blocks.erase(prev);
+            }
+        }
+
+        if (it + 1 != chunk.free_blocks.end() && it->offset + it->size == (it + 1)->offset) {
+            it->size += (it + 1)->size;
+            chunk.free_blocks.erase(it + 1);
+        }
+    }
+
+    std::mutex                                  mutex_;
+    std::vector<pool_chunk>                     chunks_;
+    std::unordered_map<void *, pool_allocation> allocations_;
+    size_t                                      default_chunk_size_{ 0 };
+};
+
+class spine_mem_pool_posix final : public spine_mem_pool_manager {
+  public:
+    spine_mem_pool_posix() : spine_mem_pool_manager(0) {}
+
+    ~spine_mem_pool_posix() override { release_chunks(); }
+
+  private:
+    bool alloc_chunk(size_t min_size, size_t alignment, void * hint_addr, pool_chunk * chunk) override {
+        (void) hint_addr;
+
+        const size_t alloc_alignment = std::max(alignment, sizeof(void *));
+        void *       base            = nullptr;
+        const int    rc              = posix_memalign(&base, alloc_alignment, min_size);
+        if (rc != 0) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: posix_memalign failed for size %zu alignment %zu, rc=%d\n",
+                           __func__, min_size, alloc_alignment, rc);
+            return false;
+        }
+
+        chunk->base = static_cast<uint8_t *>(base);
+        chunk->size = min_size;
+        chunk->fd   = -1;
+        return true;
+    }
+
+    void dealloc_chunk(pool_chunk * chunk) override {
+        std::free(chunk->base);
+        clear_chunk(chunk);
+    }
+};
+
+class spine_mem_pool_transparent_hugepage final : public spine_mem_pool_manager {
+  public:
+    spine_mem_pool_transparent_hugepage() : spine_mem_pool_manager(SPINE_MEM_POOL_CHUNK_SIZE) {}
+
+    ~spine_mem_pool_transparent_hugepage() override { release_chunks(); }
+
+  private:
+    bool alloc_chunk(size_t min_size, size_t alignment, void * hint_addr, pool_chunk * chunk) override {
+        (void) alignment;
+
+        size_t chunk_size = 0;
+        if (!align_up(min_size, default_chunk_size(), &chunk_size)) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: failed to round chunk size for %zu\n", __func__, min_size);
+            return false;
+        }
+
+        void * map_addr = mmap(hint_addr, chunk_size, PROT_READ | PROT_WRITE, MAP_PRIVATE | MAP_ANONYMOUS, -1, 0);
+        if (map_addr == MAP_FAILED) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: mmap failed for chunk size %zu, errno=%d\n", __func__, chunk_size,
+                           errno);
+            return false;
+        }
+
+        if (madvise(map_addr, chunk_size, MADV_HUGEPAGE) != 0) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: madvise(MADV_HUGEPAGE) failed for chunk size %zu, errno=%d\n",
+                           __func__, chunk_size, errno);
+            munmap(map_addr, chunk_size);
+            return false;
+        }
+
+        chunk->base = static_cast<uint8_t *>(map_addr);
+        chunk->size = chunk_size;
+        chunk->fd   = -1;
+        return true;
+    }
+
+    void dealloc_chunk(pool_chunk * chunk) override {
+        if (chunk->base != nullptr && chunk->size != 0 && munmap(chunk->base, chunk->size) != 0) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: munmap failed for chunk %p size %zu, errno=%d\n", __func__,
+                           chunk->base, chunk->size, errno);
+        }
+
+        clear_chunk(chunk);
+    }
+};
+
+class spine_mem_pool_hugetlb_1g final : public spine_mem_pool_manager {
+  public:
+    spine_mem_pool_hugetlb_1g() : spine_mem_pool_manager(SPINE_MEM_POOL_1G_REGION_SIZE) {}
+
+    ~spine_mem_pool_hugetlb_1g() override { release_chunks(); }
+
+  private:
+    bool alloc_chunk(size_t min_size, size_t alignment, void * hint_addr, pool_chunk * chunk) override {
+        (void) alignment;
+        (void) hint_addr;
+
+        size_t region_size = 0;
+        if (!align_up(min_size, SPINE_MEM_POOL_1G_REGION_SIZE, &region_size)) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: failed to round hugetlb_1g size for %zu\n", __func__, min_size);
+            return false;
+        }
+
+        const int fd = open(SPINE_MEM_POOL_HUGETLB_1G_DEV, O_RDWR);
+        if (fd < 0) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: open(%s) failed, errno=%d\n", __func__,
+                           SPINE_MEM_POOL_HUGETLB_1G_DEV, errno);
+            return false;
+        }
+
+        hugetlb_1g_region region;
+        region.size  = region_size;
+        region.flags = HUGETLB_1G_FLAG_REQUIRE_PUD;
+        if (ioctl(fd, HUGETLB_1G_IOC_ALLOC, &region) < 0) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: HUGETLB_1G_IOC_ALLOC failed for size %zu, errno=%d\n", __func__,
+                           region_size, errno);
+            close(fd);
+            return false;
+        }
+
+        void * map_addr = mmap(nullptr, region.size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
+        if (map_addr == MAP_FAILED) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: mmap failed for hugetlb_1g size %llu, errno=%d\n", __func__,
+                           static_cast<unsigned long long>(region.size), errno);
+            ioctl(fd, HUGETLB_1G_IOC_FREE);
+            close(fd);
+            return false;
+        }
+
+        chunk->base = static_cast<uint8_t *>(map_addr);
+        chunk->size = region.size;
+        chunk->fd   = fd;
+        return true;
+    }
+
+    void dealloc_chunk(pool_chunk * chunk) override {
+        if (chunk->base != nullptr && chunk->size != 0 && munmap(chunk->base, chunk->size) != 0) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: munmap failed for hugetlb_1g chunk %p size %zu, errno=%d\n",
+                           __func__, chunk->base, chunk->size, errno);
+        }
+
+        if (chunk->fd >= 0) {
+            if (ioctl(chunk->fd, HUGETLB_1G_IOC_FREE) < 0) {
+                GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: HUGETLB_1G_IOC_FREE failed for chunk %p, errno=%d\n",
+                               __func__, chunk->base, errno);
+            }
+
+            close(chunk->fd);
+        }
+
+        clear_chunk(chunk);
+    }
+};
+
+class spine_mem_pool_shared_mem final : public spine_mem_pool_manager {
+  public:
+    spine_mem_pool_shared_mem() : spine_mem_pool_manager(SPINE_SHARE_MEM_POOL_CHUNK_SIZE) {}
+
+    ~spine_mem_pool_shared_mem() override { release_chunks(); }
+
+  private:
+    bool alloc_chunk(size_t min_size, size_t alignment, void * hint_addr, pool_chunk * chunk) override {
+        (void) alignment;
+
+        if (hint_addr != nullptr) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: shared_mem does not support multiple active chunks\n", __func__);
+            return false;
+        }
+
+        if (min_size > default_chunk_size()) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: shared_mem request %zu exceeds chunk size %zu\n", __func__,
+                           min_size, default_chunk_size());
+            return false;
+        }
+
+        const int fd = open(SPINE_MEM_POOL_TCM_SYNC_MEM_DEV, O_RDWR | O_SYNC);
+        if (fd < 0) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: open(%s) failed, errno=%d\n", __func__,
+                           SPINE_MEM_POOL_TCM_SYNC_MEM_DEV, errno);
+            return false;
+        }
+
+        void * map_addr = mmap(nullptr, default_chunk_size(), PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
+        if (map_addr == MAP_FAILED) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: mmap failed for %s size %zu, errno=%d\n", __func__,
+                           SPINE_MEM_POOL_TCM_SYNC_MEM_DEV, default_chunk_size(), errno);
+            close(fd);
+            return false;
+        }
+
+        chunk->base = static_cast<uint8_t *>(map_addr);
+        chunk->size = default_chunk_size();
+        chunk->fd   = fd;
+        return true;
+    }
+
+    void dealloc_chunk(pool_chunk * chunk) override {
+        if (chunk->base != nullptr && chunk->size != 0 && munmap(chunk->base, chunk->size) != 0) {
+            GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: munmap failed for shared_mem chunk %p size %zu, errno=%d\n",
+                           __func__, chunk->base, chunk->size, errno);
+        }
+
+        if (chunk->fd >= 0) {
+            close(chunk->fd);
+        }
+
+        clear_chunk(chunk);
+    }
+};
+
+spine_mem_pool_manager & get_spine_mem_pool_manager() {
+    static std::once_flag                          pool_once;
+    static std::unique_ptr<spine_mem_pool_manager> selected_pool;
+    static spine_mem_pool_backend                  selected_backend = spine_mem_pool_backend::none;
+
+    spine_mem_pool_backend backend = global_spine_env_info.mem_backend;
+    if (backend == spine_mem_pool_backend::none) {
+        backend = spine_mem_pool_backend::transparent_hugepage;
+    }
+
+    std::call_once(pool_once, [&]() {
+        selected_backend = backend;
+
+        switch (selected_backend) {
+            case spine_mem_pool_backend::posix_memalign:
+                selected_pool = std::make_unique<spine_mem_pool_posix>();
+                break;
+            case spine_mem_pool_backend::transparent_hugepage:
+                selected_pool = std::make_unique<spine_mem_pool_transparent_hugepage>();
+                break;
+            case spine_mem_pool_backend::hugetlb_1g:
+                selected_pool = std::make_unique<spine_mem_pool_hugetlb_1g>();
+                break;
+            case spine_mem_pool_backend::none:
+                selected_backend = spine_mem_pool_backend::transparent_hugepage;
+                selected_pool    = std::make_unique<spine_mem_pool_transparent_hugepage>();
+                break;
+        }
+    });
+
+    if (backend != selected_backend) {
+        GGML_LOG_ERROR(
+            "CPU_RISCV64_SPACEMIT: %s: mem pool backend is process-global and mutually exclusive, requested=%d but "
+            "selected=%d\n",
+            __func__, static_cast<int>(backend), static_cast<int>(selected_backend));
+    }
+
+    if (selected_pool) {
+        return *selected_pool;
+    }
+
+    throw std::bad_alloc();
+}
+
+spine_mem_pool_manager & get_spine_mem_pool_shared_mem_manager() {
+    static std::once_flag                             shared_mem_pool_once;
+    static std::unique_ptr<spine_mem_pool_shared_mem> shared_mem_pool;
+
+    std::call_once(shared_mem_pool_once, [&]() { shared_mem_pool = std::make_unique<spine_mem_pool_shared_mem>(); });
+
+    if (shared_mem_pool) {
+        return *shared_mem_pool;
+    }
+
+    throw std::bad_alloc();
+}
+
+}  // namespace
+
+bool spine_mem_pool_tcm_init(spine_mem_pool_tcm_info * info) noexcept {
+    if (info == nullptr) {
+        return false;
+    }
+
+    *info = {};
+
+    if (spine_tcm_open_handle(NULL) != 0 || !spine_tcm_is_available()) {
+        return false;
+    }
+
+    spine_tcm_mem_info_t mem_info;
+    if (spine_tcm_mem_info(&mem_info) != 0) {
+        return false;
+    }
+
+    info->available   = true;
+    info->blk_size    = mem_info.blk_size;
+    info->blk_num     = mem_info.blk_num;
+    info->is_fake_tcm = mem_info.is_fake_tcm != 0;
+    return true;
+}
+
+void * spine_mem_pool_tcm_mem_get(int cpu_id) noexcept {
+    return spine_tcm_mem_get(cpu_id);
+}
+
+void * spine_mem_pool_tcm_mem_wait(int cpu_id) noexcept {
+    return spine_tcm_mem_try_wait(cpu_id, 1000 * 1000);
+}
+
+int spine_mem_pool_tcm_mem_release(int cpu_id) noexcept {
+    return spine_tcm_mem_release(cpu_id);
+}
+
+void * spine_mem_pool_alloc(size_t size, size_t alignment) noexcept {
+    try {
+        return get_spine_mem_pool_manager().alloc(size, alignment);
+    } catch (const std::bad_alloc &) {
+        GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: bad_alloc while allocating size %zu\n", __func__, size);
+        return nullptr;
+    }
+}
+
+void * spine_mem_pool_shared_mem_alloc(size_t size, size_t alignment) noexcept {
+    try {
+        return get_spine_mem_pool_shared_mem_manager().alloc(size, alignment);
+    } catch (const std::bad_alloc &) {
+        GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: bad_alloc while allocating shared memory size %zu\n", __func__, size);
+        return nullptr;
+    }
+}
+
+void spine_mem_pool_free(void * base) noexcept {
+    try {
+        get_spine_mem_pool_manager().free(base);
+    } catch (const std::bad_alloc &) {
+        GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: bad_alloc while freeing allocation %p\n", __func__, base);
+    }
+}
+
+void spine_mem_pool_shared_mem_free(void * base) noexcept {
+    try {
+        get_spine_mem_pool_shared_mem_manager().free(base);
+    } catch (const std::bad_alloc &) {
+        GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: bad_alloc while freeing shared allocation %p\n", __func__, base);
+    }
+}
+
+}  // namespace ggml::cpu::riscv64_spacemit
+
+extern "C" {
+void * ggml_backend_cpu_riscv64_spacemit_alloc_shared(size_t size, size_t alignment) {
+    void * result = ggml::cpu::riscv64_spacemit::spine_mem_pool_shared_mem_alloc(size, alignment);
+    if (result == nullptr) {
+        GGML_LOG_ERROR("CPU_RISCV64_SPACEMIT: %s: failed to allocate shared memory size %zu alignment %zu\n", __func__,
+                       size, alignment);
+    }
+    return result;
+}
+
+void ggml_backend_cpu_riscv64_spacemit_free_shared(void * ptr) {
+    ggml::cpu::riscv64_spacemit::spine_mem_pool_shared_mem_free(ptr);
+}
+}
diff --git a/ggml/src/ggml-cpu/spacemit/spine_mem_pool.h b/ggml/src/ggml-cpu/spacemit/spine_mem_pool.h
new file mode 100644
index 00000000000..8740d2c99ef
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/spine_mem_pool.h
@@ -0,0 +1,32 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+
+namespace ggml::cpu::riscv64_spacemit {
+
+enum class spine_mem_pool_backend : uint8_t {
+    none,
+    posix_memalign,
+    transparent_hugepage,
+    hugetlb_1g,
+};
+
+struct spine_mem_pool_tcm_info {
+    bool   available{ false };
+    size_t blk_size{ 0 };
+    size_t blk_num{ 0 };
+    bool   is_fake_tcm{ false };
+};
+
+bool   spine_mem_pool_tcm_init(spine_mem_pool_tcm_info * info) noexcept;
+void * spine_mem_pool_tcm_mem_get(int cpu_id) noexcept;
+void * spine_mem_pool_tcm_mem_wait(int cpu_id) noexcept;
+int    spine_mem_pool_tcm_mem_release(int cpu_id) noexcept;
+
+void * spine_mem_pool_alloc(size_t size, size_t alignment) noexcept;
+void * spine_mem_pool_shared_mem_alloc(size_t size, size_t alignment) noexcept;
+void   spine_mem_pool_free(void * base) noexcept;
+void   spine_mem_pool_shared_mem_free(void * base) noexcept;
+
+}  // namespace ggml::cpu::riscv64_spacemit
diff --git a/ggml/src/ggml-cpu/spacemit/spine_tcm.h b/ggml/src/ggml-cpu/spacemit/spine_tcm.h
new file mode 100644
index 00000000000..f300d7d5c04
--- /dev/null
+++ b/ggml/src/ggml-cpu/spacemit/spine_tcm.h
@@ -0,0 +1,409 @@
+#ifndef SPINE_TCM_PUBLIC_H_
+#define SPINE_TCM_PUBLIC_H_
+
+/*
+ * spine_tcm public API
+ *
+ * Usage:
+ *   1. Direct link mode
+ *      Define SPINE_TCM_DIRECT_LINK and link against libspine_tcm.so.
+ *
+ *      if (spine_tcm_is_available()) {
+ *          void *buffer = spine_tcm_mem_get(0);
+ *          spine_tcm_mem_free(0);
+ *      }
+ *
+ *   2. Header-only loader mode
+ *      Include this header without linking libspine_tcm.so. The loader first
+ *      tries to reuse a process-global spine_tcm instance and falls back to
+ *      dlopen("libspine_tcm.so") when needed.
+ *
+ *      spine_tcm_open_handle(NULL);  // optional pre-bind
+ *      if (spine_tcm_is_available()) {
+ *          void *buffer = spine_tcm_mem_get(0);
+ *          spine_tcm_mem_free(0);
+ *      }
+ */
+
+#include <stddef.h>
+#include <stdint.h>
+#include <string.h>
+
+#if !defined(SPINE_TCM_BUILD_SHARED) && !defined(SPINE_TCM_DIRECT_LINK)
+#    include <dlfcn.h>
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(_WIN32)
+#    if defined(SPINE_TCM_BUILD_SHARED)
+#        define SPINE_TCM_API __declspec(dllexport)
+#    else
+#        define SPINE_TCM_API __declspec(dllimport)
+#    endif
+#else
+#    define SPINE_TCM_API __attribute__((visibility("default")))
+#endif
+
+typedef struct spine_tcm_mem_info {
+    size_t blk_size;
+    size_t blk_num;
+    int    is_fake_tcm;
+} spine_tcm_mem_info_t;
+
+typedef struct spine_tcm_block_info {
+    int      id;
+    void *   va;
+    size_t   size;
+    uint64_t phys_addr;
+    uint64_t cpu_affinity_mask;
+    int      owner_tid;
+    int      is_acquired;
+} spine_tcm_block_info_t;
+
+/* Shared-library runtime ABI exported by libspine_tcm.so. */
+SPINE_TCM_API const char * spine_tcm_runtime_version(void);
+SPINE_TCM_API int          spine_tcm_runtime_is_available(void);
+SPINE_TCM_API int          spine_tcm_runtime_layout_info(spine_tcm_mem_info_t * info);
+SPINE_TCM_API int          spine_tcm_runtime_mem_info(int id, spine_tcm_block_info_t * info);
+SPINE_TCM_API void *       spine_tcm_runtime_mem_get(int id);
+SPINE_TCM_API int          spine_tcm_runtime_mem_free(int id);
+SPINE_TCM_API void *       spine_tcm_runtime_mem_try_wait(int id, size_t timeout_us);
+SPINE_TCM_API int          spine_tcm_runtime_mem_release(int id);
+SPINE_TCM_API int          spine_tcm_runtime_mem_force_release(int id);
+SPINE_TCM_API int          spine_tcm_runtime_mem_query(int id);
+
+#if defined(SPINE_TCM_DIRECT_LINK)
+/* Optional no-op in direct-link mode. */
+static inline int spine_tcm_open_handle(const char * so_path) {
+    (void) so_path;
+    return 0;
+}
+
+static inline const char * spine_tcm_version(void) {
+    return spine_tcm_runtime_version();
+}
+
+/* Returns 1 when the runtime driver is available, otherwise 0. */
+static inline int spine_tcm_is_available(void) {
+    return spine_tcm_runtime_is_available();
+}
+
+/* Returns runtime memory geometry and whether the current backend is fake TCM. */
+static inline int spine_tcm_mem_info(spine_tcm_mem_info_t * info) {
+    return spine_tcm_runtime_layout_info(info);
+}
+
+/* Returns per-block runtime metadata for the given TCM id. */
+static inline int spine_tcm_block_info(int id, spine_tcm_block_info_t * info) {
+    return spine_tcm_runtime_mem_info(id, info);
+}
+
+/* Returns a cached buffer for the given TCM id, or NULL on failure. */
+static inline void * spine_tcm_mem_get(int id) {
+    return spine_tcm_runtime_mem_get(id);
+}
+
+/* Releases one reference acquired by spine_tcm_mem_get(id). */
+static inline int spine_tcm_mem_free(int id) {
+    return spine_tcm_runtime_mem_free(id);
+}
+
+/* Waits for a TCM block handoff and returns the driver-owned buffer when available. */
+static inline void * spine_tcm_mem_try_wait(int id, size_t over_time) {
+    return spine_tcm_runtime_mem_try_wait(id, over_time);
+}
+
+/* Releases a buffer acquired by spine_tcm_mem_try_wait(id, over_time). */
+static inline int spine_tcm_mem_release(int id) {
+    return spine_tcm_runtime_mem_release(id);
+}
+
+/* Forces a release for the given TCM id when the backend supports it. */
+static inline int spine_tcm_mem_force_release(int id) {
+    return spine_tcm_runtime_mem_force_release(id);
+}
+
+/* Returns whether the given TCM id is currently acquired. */
+static inline int spine_tcm_mem_query(int id) {
+    return spine_tcm_runtime_mem_query(id);
+}
+#elif !defined(SPINE_TCM_BUILD_SHARED)
+typedef struct spine_tcm_handle {
+    void * module_handle;
+    int    use_global_scope;
+    int    owns_module_handle;
+    const char * (*runtime_version)(void);
+    int (*runtime_is_available)(void);
+    int (*runtime_layout_info)(spine_tcm_mem_info_t * info);
+    int (*runtime_mem_info)(int id, spine_tcm_block_info_t * info);
+    void * (*runtime_mem_get)(int id);
+    int (*runtime_mem_free)(int id);
+    void * (*runtime_mem_try_wait)(int id, size_t over_time);
+    int (*runtime_mem_release)(int id);
+    int (*runtime_mem_force_release)(int id);
+    int (*runtime_mem_query)(int id);
+} spine_tcm_handle_t;
+
+static inline spine_tcm_handle_t * spine_tcm_default_handle(void) {
+    static spine_tcm_handle_t handle = { 0 };
+    return &handle;
+}
+
+static inline void spine_tcm_handle_reset(spine_tcm_handle_t * handle) {
+    if (handle != NULL) {
+        memset(handle, 0, sizeof(*handle));
+    }
+}
+
+static inline int spine_tcm_handle_bind(spine_tcm_handle_t * handle) {
+    void * symbol_scope = handle->use_global_scope ? RTLD_DEFAULT : handle->module_handle;
+
+    handle->runtime_version      = (const char * (*) (void) ) dlsym(symbol_scope, "spine_tcm_runtime_version");
+    handle->runtime_is_available = (int (*)(void)) dlsym(symbol_scope, "spine_tcm_runtime_is_available");
+    handle->runtime_layout_info =
+        (int (*)(spine_tcm_mem_info_t *)) dlsym(symbol_scope, "spine_tcm_runtime_layout_info");
+    handle->runtime_mem_info =
+        (int (*)(int, spine_tcm_block_info_t *)) dlsym(symbol_scope, "spine_tcm_runtime_mem_info");
+    handle->runtime_mem_get      = (void * (*) (int) ) dlsym(symbol_scope, "spine_tcm_runtime_mem_get");
+    handle->runtime_mem_free     = (int (*)(int)) dlsym(symbol_scope, "spine_tcm_runtime_mem_free");
+    handle->runtime_mem_try_wait = (void * (*) (int, size_t)) dlsym(symbol_scope, "spine_tcm_runtime_mem_try_wait");
+    handle->runtime_mem_release  = (int (*)(int)) dlsym(symbol_scope, "spine_tcm_runtime_mem_release");
+    handle->runtime_mem_force_release = (int (*)(int)) dlsym(symbol_scope, "spine_tcm_runtime_mem_force_release");
+    handle->runtime_mem_query         = (int (*)(int)) dlsym(symbol_scope, "spine_tcm_runtime_mem_query");
+
+    return handle->runtime_version != NULL && handle->runtime_is_available != NULL &&
+                   handle->runtime_layout_info != NULL && handle->runtime_mem_info != NULL &&
+                   handle->runtime_mem_get != NULL && handle->runtime_mem_free != NULL &&
+                   handle->runtime_mem_try_wait != NULL && handle->runtime_mem_release != NULL &&
+                   handle->runtime_mem_force_release != NULL && handle->runtime_mem_query != NULL ?
+               0 :
+               -1;
+}
+
+/*
+ * Try to bind against an already-loaded process-global spine_tcm instance.
+ * The shared library exports spine_tcm_runtime_marker only for this probe.
+ */
+static inline int spine_tcm_try_bind_global(spine_tcm_handle_t * handle) {
+    if (dlsym(RTLD_DEFAULT, "spine_tcm_runtime_marker") == NULL) {
+        return -1;
+    }
+
+    handle->use_global_scope = 1;
+    return spine_tcm_handle_bind(handle);
+}
+
+/*
+ * Optional pre-bind entry point.
+ *
+ * Behavior:
+ *   - Reuses an already-loaded global spine_tcm instance when available.
+ *   - Otherwise loads the shared library from so_path or the default soname.
+ *   - Repeated calls are safe and return 0 after the first successful bind.
+ */
+static inline int spine_tcm_open_handle(const char * so_path) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+    const char *         library  = (so_path != NULL && so_path[0] != '\0') ? so_path : "libspine_tcm.so";
+
+    if (resolved->module_handle != NULL || resolved->use_global_scope) {
+        return 0;
+    }
+
+    if (spine_tcm_try_bind_global(resolved) == 0) {
+        return 0;
+    }
+
+    spine_tcm_handle_reset(resolved);
+
+    resolved->module_handle      = dlopen(library, RTLD_LAZY | RTLD_GLOBAL);
+    resolved->owns_module_handle = resolved->module_handle != NULL ? 1 : 0;
+
+    if (resolved->module_handle == NULL) {
+        spine_tcm_handle_reset(resolved);
+        return -1;
+    }
+
+    if (spine_tcm_handle_bind(resolved) != 0) {
+        if (resolved->owns_module_handle) {
+            dlclose(resolved->module_handle);
+        }
+        spine_tcm_handle_reset(resolved);
+        return -1;
+    }
+
+    return 0;
+}
+
+/* Returns 1 when the runtime driver is available, otherwise 0. */
+static inline int spine_tcm_is_available(void) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if ((resolved->module_handle == NULL && !resolved->use_global_scope) || resolved->runtime_is_available == NULL) {
+        return 0;
+    }
+
+    return resolved->runtime_is_available();
+}
+
+/* Returns runtime memory geometry and whether the current backend is fake TCM. */
+static inline int spine_tcm_mem_info(spine_tcm_mem_info_t * info) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if ((resolved->module_handle == NULL && !resolved->use_global_scope) || resolved->runtime_layout_info == NULL) {
+        return -1;
+    }
+
+    return resolved->runtime_layout_info(info);
+}
+
+static inline const char * spine_tcm_version(void) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if ((resolved->module_handle == NULL && !resolved->use_global_scope) || resolved->runtime_version == NULL) {
+        return "unknown";
+    }
+
+    return resolved->runtime_version();
+}
+
+/* Returns per-block runtime metadata for the given TCM id. */
+static inline int spine_tcm_block_info(int id, spine_tcm_block_info_t * info) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if ((resolved->module_handle == NULL && !resolved->use_global_scope) || resolved->runtime_mem_info == NULL) {
+        return -1;
+    }
+
+    return resolved->runtime_mem_info(id, info);
+}
+
+/* Returns a cached buffer for the given TCM id, or NULL on failure. */
+static inline void * spine_tcm_mem_get(int id) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        return NULL;
+    }
+
+    if (resolved->runtime_mem_get == NULL) {
+        return NULL;
+    }
+
+    return resolved->runtime_mem_get(id);
+}
+
+/* Releases one reference acquired by spine_tcm_mem_get(id). */
+static inline int spine_tcm_mem_free(int id) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if ((resolved->module_handle == NULL && !resolved->use_global_scope) || resolved->runtime_mem_free == NULL) {
+        return -1;
+    }
+
+    return resolved->runtime_mem_free(id);
+}
+
+/* Waits for a TCM block handoff and returns the driver-owned buffer when available. */
+static inline void * spine_tcm_mem_try_wait(int id, size_t over_time) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        return NULL;
+    }
+
+    if (resolved->runtime_mem_try_wait == NULL) {
+        return NULL;
+    }
+
+    return resolved->runtime_mem_try_wait(id, over_time);
+}
+
+/* Releases a buffer acquired by spine_tcm_mem_try_wait(id, over_time). */
+static inline int spine_tcm_mem_release(int id) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if ((resolved->module_handle == NULL && !resolved->use_global_scope) || resolved->runtime_mem_release == NULL) {
+        return -1;
+    }
+
+    return resolved->runtime_mem_release(id);
+}
+
+/* Forces a release for the given TCM id when the backend supports it. */
+static inline int spine_tcm_mem_force_release(int id) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if ((resolved->module_handle == NULL && !resolved->use_global_scope) ||
+        resolved->runtime_mem_force_release == NULL) {
+        return -1;
+    }
+
+    return resolved->runtime_mem_force_release(id);
+}
+
+/* Returns whether the given TCM id is currently acquired. */
+static inline int spine_tcm_mem_query(int id) {
+    spine_tcm_handle_t * resolved = spine_tcm_default_handle();
+
+    if (resolved->module_handle == NULL && !resolved->use_global_scope) {
+        (void) spine_tcm_open_handle(NULL);
+    }
+
+    if ((resolved->module_handle == NULL && !resolved->use_global_scope) || resolved->runtime_mem_query == NULL) {
+        return -1;
+    }
+
+    return resolved->runtime_mem_query(id);
+}
+#else
+static inline const char * spine_tcm_version(void) {
+    return spine_tcm_runtime_version();
+}
+#endif
+
+#define SPINE_TCM_VERSION (spine_tcm_version())
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h
index a0375a28de0..bcd68da9aa9 100644
--- a/ggml/src/ggml-cpu/vec.h
+++ b/ggml/src/ggml-cpu/vec.h
@@ -1036,12 +1036,12 @@ inline static float ggml_gelu_quick_f32(float x) {
     return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x)));
 }
 
-//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
-//    const uint16_t * i16 = (const uint16_t *) x;
-//    for (int i = 0; i < n; ++i) {
-//        y[i] = ggml_table_gelu_quick_f16[i16[i]];
-//    }
-//}
+inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
+    const uint16_t * i16 = (const uint16_t *) x;
+    for (int i = 0; i < n; ++i) {
+        y[i] = ggml_table_gelu_quick_f16[i16[i]];
+    }
+}
 
 #ifdef GGML_GELU_QUICK_FP16
 inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) {
@@ -1060,13 +1060,6 @@ inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float *
 }
 #endif
 
-inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
-    for (int i = 0; i < n; ++i) {
-        float v = GGML_CPU_FP16_TO_FP32(x[i]);
-        y[i] = GGML_CPU_FP32_TO_FP16(v*(1.0f/(1.0f+expf(GELU_QUICK_COEF*v))));
-    }
-}
-
 // Sigmoid Linear Unit (SiLU) function
 inline static float ggml_silu_f32(float x) {
     return x/(1.0f + expf(-x));
diff --git a/ggml/src/ggml-cuda/allreduce.cu b/ggml/src/ggml-cuda/allreduce.cu
new file mode 100644
index 00000000000..d56129a227e
--- /dev/null
+++ b/ggml/src/ggml-cuda/allreduce.cu
@@ -0,0 +1,971 @@
+#include "allreduce.cuh"
+
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+
+#include "convert.cuh"
+#include "ggml-impl.h"
+
+#include <algorithm>
+#include <cstdlib>
+#include <cstring>
+#include <limits>
+
+// ---------------------------------------------------------------------------
+// CUDA AllReduce for tensor-parallel inference across two GPUs.
+//
+// Provides an in-place sum reduction over matching tensors on two CUDA
+// devices in the same process.  Used by the tensor-split path alongside
+// NCCL; targets setups without NVLink, where data is exchanged between the
+// GPUs by staging it through pinned host memory over PCIe.
+//
+// Two reduction strategies are selected per call by tensor size:
+//
+//   * Chunked kernel path (small reductions): a single CUDA kernel both
+//     stages data through pinned host memory and performs the local sum.
+//     Cross-GPU synchronization happens *inside the kernel* (busy-wait on
+//     a host-memory flag), which keeps launch overhead low for the
+//     latency-sensitive token-generation case.
+//
+//   * Copy-engine path (large reductions): the transfer is split into
+//     D2H + H2D cudaMemcpyAsync chunks driven by the GPU's copy engine,
+//     followed by a small device-side add kernel.  Cross-GPU
+//     synchronization happens *outside the kernel*, via CUDA events
+//     between streams.  This keeps the compute engine free while large
+//     transfers are in flight, which matters for prefill-sized tensors.
+//     Reductions larger than the per-call inner cap are processed by an
+//     outer chunker that issues sequential inner calls.
+// ---------------------------------------------------------------------------
+
+// ---------------------------------------------------------------------------
+// Cross-GPU signal mechanism
+//
+// One int per (slot, rank) pair in pinned host memory.  Each AR call writes a
+// strictly increasing token (= the AR call number) into its own arrival int.
+// The peer spins until its read of the other's arrival int equals the token
+// it expects for this call -- a mismatch means the peer hasn't arrived yet.
+// Tokens never repeat over realistic call rates (32-bit int wraps in tens of
+// days at thousands of ARs/sec), so arrival ints don't need to be reset
+// between calls; we initialize once at pipeline init and let the values
+// accumulate.
+//
+// There is exactly one writer (the owning GPU) and one reader (the peer), so
+// we don't need atomics.  A volatile store paired with __threadfence_system()
+// provides the release ordering that makes the D2H writes visible system-wide
+// before the arrival token is observed.
+//
+// atomicAdd_system() requires hostNativeAtomicSupported, which is unavailable
+// on PCIe-attached consumer GPUs without NVLink, so the volatile path is the
+// portable choice.
+// ---------------------------------------------------------------------------
+
+static __device__ __forceinline__ void ggml_cuda_ar_signal_set(int * p, int token) {
+    *(volatile int *)p = token;
+}
+static __device__ __forceinline__ int ggml_cuda_ar_signal_get(const int * p) {
+    return *(const volatile int *)p;
+}
+
+// Byte spacing between adjacent arrival ints.  64 bytes (one cache line)
+// ensures each GPU/block's arrival slot lives on its own line, preventing
+// false-sharing stalls on the polling GPU.
+static constexpr size_t GGML_CUDA_AR_ARRIVAL_STRIDE = 64;
+
+// Number of blocks the chunked kernel launches with.  Each block stripes a
+// disjoint slice of the data and synchronizes through its own arrival-token
+// slot so multiple SMs can pump PCIe stores in parallel.
+static constexpr int GGML_CUDA_AR_KERNEL_BLOCKS = 8;
+
+// ---------------------------------------------------------------------------
+// Chunked kernel AllReduce -- 2 GPUs, supports float, half, and bfloat16.
+//
+// Both GPUs run this kernel simultaneously on independent streams.  sendbuf
+// and recvbuf live in T_dst (the caller's tensor type); host_mine / host_other
+// carry data in T_wire (the on-wire type, possibly narrower than T_dst -- e.g.
+// T_dst=F32 with T_wire=BF16 halves the bytes pushed across PCIe).  When
+// T_dst == T_wire the casts below are no-ops.
+//
+// Each GPU runs three phases:
+//
+//   Phase 1 (all threads): cast sendbuf (T_dst) -> T_wire and store as
+//                          single-instruction-width vectors into host_mine.
+//                          __threadfence_system() commits these writes to host
+//                          memory.
+//   Phase 2 (thread 0):    write token to arrival_mine; spin until
+//                          arrival_other == token.
+//   Phase 3 (all threads): read T_wire vectors from host_other, cast
+//                          each element to T_dst, and sum with the local
+//                          sendbuf value (also rounded through T_wire so that
+//                          both GPUs truncate identically -- this guarantees
+//                          bit-equivalent results across the two devices).
+//
+// Multi-block: blocks stripe vectors across (gridDim.x * blockDim.x) global
+// threads to keep multiple SMs issuing PCIe stores in parallel.  Each block
+// has its own arrival-token slot (offset by blockIdx.x * ARRIVAL_STRIDE);
+// thread 0 of each block signals/spins on that slot independently of other
+// blocks.  Tail elements (the leftover < ELEMS_PER_VEC at the end) are
+// handled only by block 0 to avoid cross-block writes to the same slots.
+// ---------------------------------------------------------------------------
+template <typename T_dst, typename T_wire>
+static __global__ void ggml_cuda_ar_kernel(
+        const T_dst  *              sendbuf,
+        T_dst        *              recvbuf,
+        T_wire       * __restrict__ host_mine,
+        const T_wire * __restrict__ host_other,
+        int                         count,
+        int *                       arrival_mine,
+        int *                       arrival_other,
+        int                         token) {
+
+    // Vector unit for the wire type, sized to the arch's widest single-instruction
+    // copy (16 B on Volta+).  Each phase-1 iter writes one vector to host memory;
+    // each phase-3 iter reads one and produces ELEMS_PER_VEC sums.
+    constexpr int ELEMS_PER_VEC = ggml_cuda_get_max_cpy_bytes() / sizeof(T_wire);
+    constexpr int ARRIVAL_INTS  = (int)(GGML_CUDA_AR_ARRIVAL_STRIDE / sizeof(int));
+
+    const int tid       = threadIdx.x;
+    const int nt        = blockDim.x;
+    const int bid       = blockIdx.x;
+    const int gtid      = bid * nt + tid;
+    const int gnt       = gridDim.x * nt;
+    const int count_vec = count / ELEMS_PER_VEC;
+    const int tail      = count_vec * ELEMS_PER_VEC;
+
+    // Phase 1: cast sendbuf (T_dst) -> host_mine (T_wire) and store as vectors.
+    {
+        for (int i = gtid; i < count_vec; i += gnt) {
+            const int off = i * ELEMS_PER_VEC;
+            T_wire wire[ELEMS_PER_VEC];
+            #pragma unroll
+            for (int k = 0; k < ELEMS_PER_VEC; ++k) {
+                wire[k] = ggml_cuda_cast<T_wire>(sendbuf[off + k]);
+            }
+            ggml_cuda_memcpy_1<sizeof(wire)>(&host_mine[off], wire);
+        }
+        if (bid == 0 && tid < count - tail) {
+            host_mine[tail + tid] = ggml_cuda_cast<T_wire>(sendbuf[tail + tid]);
+        }
+    }
+
+    // Commit this block's host writes before signalling.
+    __threadfence_system();
+    __syncthreads();
+
+    // Phase 2: thread 0 of each block signals on its own arrival slot, then
+    // spins for the matching slot from peer.  Per-block tokens mean blocks
+    // proceed independently -- no inter-block barrier needed.
+    if (tid == 0) {
+        int       * my_slot    = arrival_mine  + bid * ARRIVAL_INTS;
+        const int * other_slot = arrival_other + bid * ARRIVAL_INTS;
+
+        ggml_cuda_ar_signal_set(my_slot, token);
+        __threadfence_system(); // make our signal visible system-wide
+
+        while (ggml_cuda_ar_signal_get(other_slot) != token) {
+#if __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
+            __nanosleep(100);
+#else
+            NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
+        }
+    }
+
+    __syncthreads();
+
+    // Acquire peer's host_other writes (this block's stripe of them).
+    __threadfence_system();
+
+    // Phase 3: read peer's T_wire vector, cast both sides through T_wire for
+    // bit-equivalence, sum in T_dst precision, and write back to recvbuf.
+    {
+        for (int i = gtid; i < count_vec; i += gnt) {
+            const int off = i * ELEMS_PER_VEC;
+            T_wire wire[ELEMS_PER_VEC];
+            ggml_cuda_memcpy_1<sizeof(wire)>(wire, &host_other[off]);
+            #pragma unroll
+            for (int k = 0; k < ELEMS_PER_VEC; ++k) {
+                const T_wire d_low = ggml_cuda_cast<T_wire>(sendbuf[off + k]);
+                recvbuf[off + k] = ggml_cuda_cast<T_dst>(
+                    ggml_cuda_cast<float>(d_low) + ggml_cuda_cast<float>(wire[k]));
+            }
+        }
+        if (bid == 0 && tid < count - tail) {
+            const T_wire d_low = ggml_cuda_cast<T_wire>(sendbuf[tail + tid]);
+            recvbuf[tail + tid] = ggml_cuda_cast<T_dst>(
+                ggml_cuda_cast<float>(d_low) +
+                ggml_cuda_cast<float>(host_other[tail + tid]));
+        }
+    }
+}
+
+// Combined load-convert-add kernel.  The peer's contribution arrives as T_src
+// (which may be a lower-precision type than T_dst when the BF16 round-trip is
+// active).  For bit-equivalence between the two GPUs, dst is first rounded
+// through T_src's precision via ggml_cuda_cast -- peer already truncated its
+// own value the same way before sending -- so both sides perform identical
+// arithmetic.  When T_dst == T_src the round-trip cast is a no-op.
+template <typename T_dst, typename T_src>
+static __global__ void ggml_cuda_ar_add_kernel(
+        T_dst       * __restrict__ dst,
+        const T_src * __restrict__ src,
+        int count) {
+    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
+    const int nt  = gridDim.x * blockDim.x;
+    for (int i = tid; i < count; i += nt) {
+        const T_src d_low = ggml_cuda_cast<T_src>(dst[i]);
+        dst[i] = ggml_cuda_cast<T_dst>(
+            ggml_cuda_cast<float>(d_low) + ggml_cuda_cast<float>(src[i]));
+    }
+}
+
+// ---------------------------------------------------------------------------
+// Pipeline structure
+// ---------------------------------------------------------------------------
+
+// Number of slots in the event / arrival ring.  Two slots is sufficient:
+// lockstep guarantees the two GPUs are at most one AR (or chunk) apart, so
+// slot[N%2] is always safe to reuse -- peer has already consumed slot[N%2]
+// from AR N-2 by the time we get to AR N.  acquire_slot's
+// cudaEventSynchronize on ev.ker for both devices makes that consumption
+// explicit before we overwrite host_buf[slot] for the new AR.
+static constexpr int GGML_CUDA_AR_POOL_SIZE = 2;
+
+// Maximum chunk size (bytes per GPU) handled by one chunked kernel launch.
+// Larger tensors are reduced by issuing multiple chunked launches.
+static constexpr size_t GGML_CUDA_AR_MAX_BYTES = 1024 * 1024; // 1 MB
+
+// Copy-engine path: largest tensor accepted on this path; sets host_large /
+// dev_tmp allocation size.
+static constexpr size_t GGML_CUDA_AR_COPY_MAX_BYTES = 32 * 1024 * 1024; // 32 MB
+
+// AR wire size at which the copy-engine path takes over from the chunked-
+// kernel path.  Override via GGML_CUDA_AR_COPY_THRESHOLD.
+static constexpr size_t GGML_CUDA_AR_COPY_THRESHOLD_DEFAULT = 1024 * 1024; // 1 MB
+// Per-call CE chunk-size heuristic: chunk_bytes = clamp(nbytes / 4, MIN, MAX).
+// The /4 keeps ~4 chunks in flight at any moment (good D2H/H2D overlap with
+// the peer); the clamps cover the cases where nbytes/4 is too small (per-
+// memcpy fixed cost dominates) or too large (chunk-level pipelining stalls).
+// Env var GGML_CUDA_AR_COPY_CHUNK_BYTES can override with a fixed value.
+static constexpr size_t GGML_CUDA_AR_COPY_CHUNK_BYTES_HEURISTIC_MIN = 512 * 1024;       // 512 KB
+static constexpr size_t GGML_CUDA_AR_COPY_CHUNK_BYTES_HEURISTIC_MAX = 2 * 1024 * 1024;  // 2 MB
+// Absolute floor that an env-var override is allowed to set; this caps the
+// per-slot copy-event array.  256 KB -> up to 128 chunks per 32 MB tensor.
+static constexpr size_t GGML_CUDA_AR_COPY_CHUNK_BYTES_MIN = 256 * 1024;
+static constexpr int GGML_CUDA_AR_COPY_MAX_CHUNKS =
+    static_cast<int>((GGML_CUDA_AR_COPY_MAX_BYTES + GGML_CUDA_AR_COPY_CHUNK_BYTES_MIN - 1) /
+                    GGML_CUDA_AR_COPY_CHUNK_BYTES_MIN);
+
+struct ggml_cuda_ar_event_slot {
+    cudaEvent_t app = nullptr;  // upstream computation complete
+    cudaEvent_t cpy[GGML_CUDA_AR_COPY_MAX_CHUNKS] = {};  // copy-engine D2H chunks complete
+    cudaEvent_t h2d = nullptr;  // copy-engine H2Ds complete (handoff AR stream -> compute stream)
+    cudaEvent_t ker = nullptr;  // AllReduce kernel complete
+};
+
+// Mapped pinned host allocation: cudaHostAlloc + cudaHostGetDevicePointer
+// in one place, with the host handle preserved for cudaFreeHost.  Used where
+// the CPU never touches the buffer -- only the device reads/writes via the
+// mapped device pointer.  Required on systems where cudaDevAttrCanUseHost-
+// PointerForRegisteredMem is 0 and the host pointer can't be used as a
+// device pointer.
+struct ggml_cuda_ar_host_mapping {
+    uint8_t * host = nullptr;   // cudaFreeHost handle; also the H-side ptr for cudaMemcpyAsync
+    uint8_t * dev  = nullptr;   // device-side pointer for kernels / cudaMemset
+
+    cudaError_t alloc(size_t bytes) {
+        cudaError_t rc = cudaHostAlloc(reinterpret_cast<void **>(&host), bytes,
+                                       cudaHostAllocPortable | cudaHostAllocMapped);
+        if (rc != cudaSuccess) {
+            host = nullptr;
+            return rc;
+        }
+        rc = cudaHostGetDevicePointer(reinterpret_cast<void **>(&dev), host, 0);
+        if (rc != cudaSuccess) {
+            cudaFreeHost(host);
+            host = nullptr;
+            dev  = nullptr;
+        }
+        return rc;
+    }
+
+    void free() {
+        if (host) {
+            cudaFreeHost(host);
+            host = nullptr;
+            dev  = nullptr;
+        }
+    }
+};
+
+struct ggml_cuda_ar_pipeline {
+    int      n_devices;
+    int      devices[GGML_CUDA_MAX_DEVICES];
+    size_t   buf_bytes;    // bytes per device in host_buf[]
+    size_t   copy_bytes;   // bytes per device in host_large[] / dev_tmp[]
+    size_t   copy_threshold;
+    size_t   copy_chunk_bytes;
+    size_t   bf16_threshold; // tensors >= this size (bytes) are reduced via FP32->BF16 round-trip; 0 disables
+    uint64_t call_count;
+
+    // Per-device resources.
+    ggml_cuda_ar_host_mapping host_buf[GGML_CUDA_MAX_DEVICES];   // pinned staging (chunked kernel)
+    ggml_cuda_ar_host_mapping host_large[GGML_CUDA_MAX_DEVICES]; // pinned staging (copy-engine)
+    char *                    dev_tmp[GGML_CUDA_MAX_DEVICES];    // device scratch for copy-engine path
+    cudaStream_t             streams[GGML_CUDA_MAX_DEVICES];   // non-blocking
+    ggml_cuda_ar_event_slot  ev_pool[GGML_CUDA_MAX_DEVICES][GGML_CUDA_AR_POOL_SIZE];
+
+    // Copy-engine: per-device "I finished reading my peer's host_large"
+    // event.  Indexed by RECORDER device.  Recorded same-device on streams[i]
+    // after stage 2's last H2D from host_large[peer].  Waited cross-device
+    // by peer's stage-1 stream before the next AR overwrites host_large[peer].
+    cudaEvent_t              host_large_read_done[GGML_CUDA_MAX_DEVICES];
+    bool                     host_large_read_done_valid;
+
+    // Copy-engine: per-device "my add_kernel is done with dev_tmp" event.
+    // Recorded on the compute stream after each add_kernel; the AR stream
+    // waits on it before the next copy_impl's H2D overwrites dev_tmp.  Lets us
+    // single-buffer dev_tmp despite add_kernel running on a separate stream.
+    cudaEvent_t              dev_tmp_kernel_done[GGML_CUDA_MAX_DEVICES];
+    bool                     dev_tmp_kernel_done_valid;
+
+    // Arrival ring: ARRIVAL_STRIDE bytes between adjacent ints.  Mapped pinned
+    // memory; CPU never reads/writes -- only the kernel and cudaMemset.
+    // Use ggml_cuda_ar_arrival_ptr() to index.
+    ggml_cuda_ar_host_mapping arrival;
+};
+
+// Base pointer for the (slot, rank) per-block token block.  The kernel adds
+// blockIdx.x * (ARRIVAL_STRIDE/sizeof(int)) internally to land on its own slot.
+static int * ggml_cuda_ar_arrival_ptr(const ggml_cuda_ar_pipeline * p, int slot, int rank) {
+    const size_t offset = ((size_t)slot * p->n_devices + rank) *
+                          GGML_CUDA_AR_KERNEL_BLOCKS * GGML_CUDA_AR_ARRIVAL_STRIDE;
+    return reinterpret_cast<int *>(p->arrival.dev + offset);
+}
+
+static uint64_t ggml_cuda_ar_env_u64(const char * name, uint64_t default_value) {
+    const char * value = getenv(name);
+    if (value == nullptr || value[0] == '\0') {
+        return default_value;
+    }
+
+    char * end = nullptr;
+    const unsigned long long parsed = strtoull(value, &end, 10);
+    return end != value ? (uint64_t) parsed : default_value;
+}
+
+struct ggml_cuda_ar_slot_info {
+    int slot;
+    int token;
+};
+
+static ggml_cuda_ar_slot_info ggml_cuda_ar_acquire_slot(ggml_cuda_ar_pipeline * p) {
+    const int  slot        = static_cast<int>(p->call_count % GGML_CUDA_AR_POOL_SIZE);
+    const bool pool_lapped = p->call_count >= GGML_CUDA_AR_POOL_SIZE;
+    p->call_count++;
+
+    if (pool_lapped) {
+        for (int i = 0; i < p->n_devices; ++i) {
+            ggml_cuda_set_device(p->devices[i]);
+            CUDA_CHECK(cudaEventSynchronize(p->ev_pool[i][slot].ker));
+        }
+    }
+
+    return { slot, (int) p->call_count };
+}
+
+// Per-AR copy-engine chunk size: env-var override if set, else heuristic
+// (clamp(nbytes/4, HEURISTIC_MIN, HEURISTIC_MAX)).
+static size_t ggml_cuda_ar_chunk_bytes(const ggml_cuda_ar_pipeline * p, size_t nbytes) {
+    if (p->copy_chunk_bytes > 0) {
+        return p->copy_chunk_bytes;
+    }
+    return std::min(GGML_CUDA_AR_COPY_CHUNK_BYTES_HEURISTIC_MAX,
+                    std::max(GGML_CUDA_AR_COPY_CHUNK_BYTES_HEURISTIC_MIN, nbytes / 4));
+}
+
+static void ggml_cuda_ar_wait_for_compute(
+        ggml_cuda_ar_pipeline * p, ggml_backend_cuda_context * cuda_ctx, int rank, int slot) {
+    ggml_cuda_ar_event_slot & ev = p->ev_pool[rank][slot];
+    CUDA_CHECK(cudaEventRecord(ev.app, cuda_ctx->stream()));
+    CUDA_CHECK(cudaStreamWaitEvent(p->streams[rank], ev.app));
+}
+
+// ---------------------------------------------------------------------------
+// Init / free
+// ---------------------------------------------------------------------------
+
+ggml_cuda_ar_pipeline * ggml_cuda_ar_pipeline_init(const int * devices, size_t n_devices) {
+
+    if (n_devices != 2) {
+        GGML_LOG_DEBUG("%s: internal AllReduce only supports n_devices=2 (got %zu); "
+                       "falling back\n", __func__, n_devices);
+        return nullptr;
+    }
+
+    // The chunked kernel uses __nanosleep, which is sm70+ (Volta+).
+    for (size_t i = 0; i < n_devices; ++i) {
+        const int cc = ggml_cuda_info().devices[devices[i]].cc;
+        if (cc < GGML_CUDA_CC_VOLTA) {
+            GGML_LOG_DEBUG("%s: internal AllReduce requires compute capability >= %d "
+                           "(device %d has cc=%d); falling back\n",
+                           __func__, GGML_CUDA_CC_VOLTA, devices[i], cc);
+            return nullptr;
+        }
+    }
+
+    auto * p = new ggml_cuda_ar_pipeline{};
+    p->n_devices        = n_devices;
+    p->copy_bytes       = GGML_CUDA_AR_COPY_MAX_BYTES;
+    p->copy_threshold   = ggml_cuda_ar_env_u64("GGML_CUDA_AR_COPY_THRESHOLD", GGML_CUDA_AR_COPY_THRESHOLD_DEFAULT);
+    // 0 = use the per-call heuristic (default).  Non-zero env value forces a
+    // fixed chunk size for diagnostics, with a floor at COPY_CHUNK_BYTES_MIN.
+    p->copy_chunk_bytes = ggml_cuda_ar_env_u64("GGML_CUDA_AR_COPY_CHUNK_BYTES", 0);
+    if (p->copy_chunk_bytes > 0 && p->copy_chunk_bytes < GGML_CUDA_AR_COPY_CHUNK_BYTES_MIN) {
+        GGML_LOG_WARN("%s: GGML_CUDA_AR_COPY_CHUNK_BYTES=%zu below minimum %zu; clamping\n",
+                      __func__, p->copy_chunk_bytes, GGML_CUDA_AR_COPY_CHUNK_BYTES_MIN);
+        p->copy_chunk_bytes = GGML_CUDA_AR_COPY_CHUNK_BYTES_MIN;
+    }
+    // Default 1: BF16 round-trip is always on for F32 inputs (any non-zero
+    // ne).  Set GGML_CUDA_AR_BF16_THRESHOLD=0 to disable, or to a larger
+    // byte threshold to opt out for small tensors.
+    p->bf16_threshold   = ggml_cuda_ar_env_u64("GGML_CUDA_AR_BF16_THRESHOLD", 1);
+    for (size_t i = 0; i < n_devices; ++i) {
+        p->devices[i] = devices[i];
+    }
+
+    // Per-device streams and event pools.
+    for (size_t i = 0; i < n_devices; ++i) {
+        ggml_cuda_set_device(p->devices[i]);
+
+        cudaStream_t stream = nullptr;
+        if (cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking) != cudaSuccess) {
+            GGML_LOG_ERROR("%s: cudaStreamCreateWithFlags failed for device %d\n",
+                           __func__, p->devices[i]);
+            ggml_cuda_ar_pipeline_free(p);
+            return nullptr;
+        }
+        p->streams[i] = stream;
+
+        for (int s = 0; s < GGML_CUDA_AR_POOL_SIZE; ++s) {
+            bool ok =
+                cudaEventCreateWithFlags(&p->ev_pool[i][s].app, cudaEventDisableTiming) == cudaSuccess &&
+                cudaEventCreateWithFlags(&p->ev_pool[i][s].h2d, cudaEventDisableTiming) == cudaSuccess &&
+                cudaEventCreateWithFlags(&p->ev_pool[i][s].ker, cudaEventDisableTiming) == cudaSuccess;
+            for (int c = 0; ok && c < GGML_CUDA_AR_COPY_MAX_CHUNKS; ++c) {
+                ok = cudaEventCreateWithFlags(&p->ev_pool[i][s].cpy[c], cudaEventDisableTiming) == cudaSuccess;
+            }
+            if (!ok) {
+                GGML_LOG_ERROR("%s: cudaEventCreate failed for device %d slot %d\n",
+                               __func__, p->devices[i], s);
+                ggml_cuda_ar_pipeline_free(p);
+                return nullptr;
+            }
+        }
+
+        if (cudaEventCreateWithFlags(&p->host_large_read_done[i], cudaEventDisableTiming) != cudaSuccess) {
+            GGML_LOG_ERROR("%s: cudaEventCreate for host_large_read_done failed for device %d\n",
+                           __func__, p->devices[i]);
+            ggml_cuda_ar_pipeline_free(p);
+            return nullptr;
+        }
+        if (cudaEventCreateWithFlags(&p->dev_tmp_kernel_done[i], cudaEventDisableTiming) != cudaSuccess) {
+            GGML_LOG_ERROR("%s: cudaEventCreate for dev_tmp_kernel_done failed for device %d\n",
+                           __func__, p->devices[i]);
+            ggml_cuda_ar_pipeline_free(p);
+            return nullptr;
+        }
+    }
+
+    // Arrival ring: cache-line padded so each GPU's int is on its own line.
+    const size_t arrival_bytes =
+        (size_t)GGML_CUDA_AR_POOL_SIZE * n_devices *
+        GGML_CUDA_AR_KERNEL_BLOCKS * GGML_CUDA_AR_ARRIVAL_STRIDE;
+    if (p->arrival.alloc(arrival_bytes) != cudaSuccess) {
+        GGML_LOG_ERROR("%s: alloc for arrival ring failed (%zu bytes)\n",
+                       __func__, arrival_bytes);
+        ggml_cuda_ar_pipeline_free(p);
+        return nullptr;
+    }
+    ggml_cuda_set_device(p->devices[0]);
+    if (cudaMemset(p->arrival.dev, 0, arrival_bytes) != cudaSuccess) {
+        GGML_LOG_ERROR("%s: cudaMemset for arrival ring failed (%zu bytes)\n",
+                       __func__, arrival_bytes);
+        ggml_cuda_ar_pipeline_free(p);
+        return nullptr;
+    }
+
+    // Per-device pinned staging buffers -- POOL_SIZE-deep ring so the chunked-
+    // kernel can write the next slot's data while the peer is still reading
+    // the previous slot's. Indexed by (slot * buf_bytes) at the call site.
+    p->buf_bytes = GGML_CUDA_AR_MAX_BYTES;
+    const size_t host_buf_total = (size_t) GGML_CUDA_AR_POOL_SIZE * p->buf_bytes;
+    for (size_t i = 0; i < n_devices; ++i) {
+        if (p->host_buf[i].alloc(host_buf_total) != cudaSuccess) {
+            GGML_LOG_ERROR("%s: alloc for staging failed (%zu bytes)\n",
+                           __func__, host_buf_total);
+            ggml_cuda_ar_pipeline_free(p);
+            return nullptr;
+        }
+    }
+
+    // Copy-engine path: pinned host staging + device scratch, sized for the
+    // largest tensor we accept on this path (GGML_CUDA_AR_COPY_MAX_BYTES).
+    // dev_tmp is single-buffered; cross-AR safety is enforced by an explicit
+    // cross-stream wait in copy_impl on the prior AR's add_kernel-done event.
+    for (size_t i = 0; i < n_devices; ++i) {
+        ggml_cuda_set_device(p->devices[i]);
+        if (p->host_large[i].alloc(p->copy_bytes) != cudaSuccess) {
+            GGML_LOG_ERROR("%s: alloc for large staging failed (%zu bytes)\n",
+                           __func__, p->copy_bytes);
+            ggml_cuda_ar_pipeline_free(p);
+            return nullptr;
+        }
+        if (cudaMalloc(reinterpret_cast<void **>(&p->dev_tmp[i]), p->copy_bytes) != cudaSuccess) {
+            GGML_LOG_ERROR("%s: cudaMalloc for copy scratch failed (%zu bytes) on device %d\n",
+                           __func__, p->copy_bytes, p->devices[i]);
+            ggml_cuda_ar_pipeline_free(p);
+            return nullptr;
+        }
+    }
+
+    GGML_LOG_INFO("%s: initialized AllReduce pipeline: %zu GPUs, "
+                  "%zu KB chunked kernel staging + %zu MB copy-engine staging per GPU\n",
+                  __func__, n_devices, p->buf_bytes >> 10, p->copy_bytes >> 20);
+
+    return p;
+}
+
+void ggml_cuda_ar_pipeline_free(ggml_cuda_ar_pipeline * p) {
+    if (!p) {
+        return;
+    }
+
+    // Drain all in-flight kernels before tearing down resources.
+    for (int i = 0; i < p->n_devices; ++i) {
+        if (p->streams[i]) {
+            ggml_cuda_set_device(p->devices[i]);
+            cudaStreamSynchronize(p->streams[i]);
+        }
+    }
+
+    for (int i = 0; i < p->n_devices; ++i) {
+        p->host_buf[i].free();
+        p->host_large[i].free();
+        if (p->dev_tmp[i]) {
+            ggml_cuda_set_device(p->devices[i]);
+            cudaFree(p->dev_tmp[i]);
+        }
+        ggml_cuda_set_device(p->devices[i]);
+        for (int s = 0; s < GGML_CUDA_AR_POOL_SIZE; ++s) {
+            if (p->ev_pool[i][s].app) { cudaEventDestroy(p->ev_pool[i][s].app); }
+            for (int c = 0; c < GGML_CUDA_AR_COPY_MAX_CHUNKS; ++c) {
+                if (p->ev_pool[i][s].cpy[c]) { cudaEventDestroy(p->ev_pool[i][s].cpy[c]); }
+            }
+            if (p->ev_pool[i][s].h2d) { cudaEventDestroy(p->ev_pool[i][s].h2d); }
+            if (p->ev_pool[i][s].ker) { cudaEventDestroy(p->ev_pool[i][s].ker); }
+        }
+        if (p->host_large_read_done[i]) {
+            ggml_cuda_set_device(p->devices[i]);
+            cudaEventDestroy(p->host_large_read_done[i]);
+        }
+        if (p->dev_tmp_kernel_done[i]) {
+            ggml_cuda_set_device(p->devices[i]);
+            cudaEventDestroy(p->dev_tmp_kernel_done[i]);
+        }
+        if (p->streams[i]) {
+            ggml_cuda_set_device(p->devices[i]);
+            cudaStreamDestroy(p->streams[i]);
+        }
+    }
+    p->arrival.free();
+    delete p;
+}
+
+// ---------------------------------------------------------------------------
+// Dispatch
+// ---------------------------------------------------------------------------
+
+// Asymmetric copy_impl: data sent over PCIe in T_src precision (one element of
+// nbytes per ne element); accumulated locally into a T_dst buffer.  When
+// T_src == T_dst this is the original homogeneous reduction.  When they differ
+// (e.g. BF16 wire / F32 accumulator) the add kernel rounds dst through T_src
+// for bit-equivalence between GPUs and we skip the otherwise-needed
+// post-conversion entirely.
+template <typename T_src, typename T_dst>
+static bool ggml_cuda_ar_allreduce_copy_impl(
+        ggml_cuda_ar_pipeline * p,
+        ggml_backend_t        * backends,
+        T_src * const           src_buf[GGML_CUDA_MAX_DEVICES],
+        T_dst * const           dst_buf[GGML_CUDA_MAX_DEVICES],
+        const bool              compute[GGML_CUDA_MAX_DEVICES],
+        int64_t                 ne,
+        size_t                  nbytes) {
+    GGML_ASSERT(p->n_devices == 2);
+    GGML_ASSERT(nbytes <= p->copy_bytes);
+    GGML_ASSERT(ne <= std::numeric_limits<int>::max());
+
+    const size_t chunk_bytes = ggml_cuda_ar_chunk_bytes(p, nbytes);
+    GGML_ASSERT(chunk_bytes > 0);
+
+    const int slot = ggml_cuda_ar_acquire_slot(p).slot;
+    const size_t copy_chunks = (nbytes + chunk_bytes - 1) / chunk_bytes;
+    GGML_ASSERT(copy_chunks <= GGML_CUDA_AR_COPY_MAX_CHUNKS);
+
+    ggml_backend_cuda_context * cuda_ctx[2] = {};
+
+    // Stage 1: both GPUs copy their local contribution to pinned host memory.
+    for (int i = 0; i < 2; ++i) {
+        ggml_cuda_set_device(p->devices[i]);
+        cuda_ctx[i] = static_cast<ggml_backend_cuda_context *>(backends[i]->context);
+        GGML_ASSERT(cuda_ctx[i]->device == p->devices[i]);
+
+        ggml_cuda_ar_wait_for_compute(p, cuda_ctx[i], i, slot);
+
+        // Wait for peer's H2D from our host_large[i] (recorded in the
+        // previous AR's stage 2) to complete before we overwrite host_large[i].
+        // host_large_read_done[peer] = peer finished reading host_large[i].
+        // No-op on the first AR -- no prior record exists.
+        if (p->host_large_read_done_valid) {
+            const int peer = 1 - i;
+            CUDA_CHECK(cudaStreamWaitEvent(p->streams[i], p->host_large_read_done[peer]));
+        }
+
+        if (!compute[i]) {
+            CUDA_CHECK(cudaMemsetAsync(src_buf[i], 0, nbytes, p->streams[i]));
+        }
+
+        for (size_t c = 0; c < copy_chunks; ++c) {
+            const size_t offset = c * chunk_bytes;
+            const size_t this_bytes = (nbytes - offset) < chunk_bytes ?
+                (nbytes - offset) : chunk_bytes;
+
+            CUDA_CHECK(cudaMemcpyAsync(
+                p->host_large[i].host + offset, reinterpret_cast<char *>(src_buf[i]) + offset, this_bytes,
+                cudaMemcpyDeviceToHost, p->streams[i]));
+            CUDA_CHECK(cudaEventRecord(p->ev_pool[i][slot].cpy[c], p->streams[i]));
+        }
+    }
+
+    // Stage 2: each GPU waits for each peer D2H chunk, pulls that chunk back to
+    // local device scratch (dev_tmp), then performs one device-local add over
+    // the assembled peer tensor.  The H2Ds run on the AR stream (copy engine)
+    // and the add_kernel runs on the caller's compute stream, so the AR stream
+    // stays pure-copy and avoids an in-stream copy->compute engine switch every
+    // AR.  dev_tmp is single-buffered: the AR stream waits cross-stream on the
+    // prior AR's add_kernel-done event before overwriting it.
+    for (int i = 0; i < 2; ++i) {
+        const int peer = 1 - i;
+        ggml_cuda_set_device(p->devices[i]);
+
+        // Wait for the previous AR's add_kernel (on the compute stream) to
+        // finish reading dev_tmp before our H2D overwrites it.  No-op on the
+        // first copy_impl call.
+        if (p->dev_tmp_kernel_done_valid) {
+            CUDA_CHECK(cudaStreamWaitEvent(p->streams[i], p->dev_tmp_kernel_done[i]));
+        }
+
+        for (size_t c = 0; c < copy_chunks; ++c) {
+            const size_t offset = c * chunk_bytes;
+            const size_t this_bytes = (nbytes - offset) < chunk_bytes ?
+                (nbytes - offset) : chunk_bytes;
+
+            CUDA_CHECK(cudaStreamWaitEvent(p->streams[i], p->ev_pool[peer][slot].cpy[c]));
+            CUDA_CHECK(cudaMemcpyAsync(
+                p->dev_tmp[i] + offset, p->host_large[peer].host + offset, this_bytes,
+                cudaMemcpyHostToDevice, p->streams[i]));
+        }
+
+        // Mark our reads of host_large[peer] complete so peer's next AR can
+        // safely overwrite it.
+        CUDA_CHECK(cudaEventRecord(p->host_large_read_done[i], p->streams[i]));
+
+        // Hand off from AR stream (copy engine) to compute stream: compute
+        // stream waits for all H2Ds to finish, then runs the add_kernel.
+        CUDA_CHECK(cudaEventRecord(p->ev_pool[i][slot].h2d, p->streams[i]));
+        CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx[i]->stream(), p->ev_pool[i][slot].h2d));
+
+        const int block_size = 256;
+        int n_blocks = (int) ((ne + block_size - 1) / block_size);
+        if (n_blocks > 1024) {
+            n_blocks = 1024;
+        }
+        ggml_cuda_ar_add_kernel<T_dst, T_src><<<n_blocks, block_size, 0, cuda_ctx[i]->stream()>>>(
+            dst_buf[i],
+            reinterpret_cast<const T_src *>(p->dev_tmp[i]),
+            (int) ne);
+        CUDA_CHECK(cudaGetLastError());
+
+        // Record dev_tmp-released on the compute stream so the next copy_impl
+        // can wait for the kernel to finish before overwriting dev_tmp.  Also
+        // record AR-done as ev.ker for acquire_slot's pool-wraparound sync.
+        CUDA_CHECK(cudaEventRecord(p->dev_tmp_kernel_done[i], cuda_ctx[i]->stream()));
+        CUDA_CHECK(cudaEventRecord(p->ev_pool[i][slot].ker, cuda_ctx[i]->stream()));
+    }
+    p->host_large_read_done_valid = true;
+    p->dev_tmp_kernel_done_valid = true;
+
+    return true;
+}
+
+// Outer-level chunker: copy_impl handles up to copy_bytes per call (limited by
+// the host_large / dev_tmp allocation size).  When the full AR exceeds that,
+// slice the tensor into copy_bytes-sized pieces and call copy_impl repeatedly.
+// Each slice goes through its own stage 1 -> stage 2 cycle and acquires its own
+// slot, so cross-AR fences and pool wraparound work the same way as for any
+// other sequence of small ARs.
+template <typename T_src, typename T_dst>
+static bool ggml_cuda_ar_allreduce_copy_outer(
+        ggml_cuda_ar_pipeline * p,
+        ggml_backend_t        * backends,
+        T_src * const           src_buf[GGML_CUDA_MAX_DEVICES],
+        T_dst * const           dst_buf[GGML_CUDA_MAX_DEVICES],
+        const bool              compute[GGML_CUDA_MAX_DEVICES],
+        int64_t                 ne) {
+    const int64_t outer_max_elems = (int64_t) (p->copy_bytes / sizeof(T_src));
+    GGML_ASSERT(outer_max_elems > 0);
+
+    bool ok = true;
+    for (int64_t outer_start = 0; outer_start < ne && ok; outer_start += outer_max_elems) {
+        const int64_t outer_ne     = std::min(outer_max_elems, ne - outer_start);
+        const size_t  outer_nbytes = (size_t) outer_ne * sizeof(T_src);
+
+        T_src * src[GGML_CUDA_MAX_DEVICES] = {};
+        T_dst * dst[GGML_CUDA_MAX_DEVICES] = {};
+        for (int i = 0; i < p->n_devices; ++i) {
+            src[i] = src_buf[i] + outer_start;
+            dst[i] = dst_buf[i] + outer_start;
+        }
+        ok = ggml_cuda_ar_allreduce_copy_impl<T_src, T_dst>(
+            p, backends, src, dst, compute, outer_ne, outer_nbytes);
+    }
+    return ok;
+}
+
+bool ggml_cuda_ar_allreduce(
+        ggml_cuda_ar_pipeline * p,
+        ggml_backend_t        * backends,
+        ggml_tensor           ** tensors) {
+    GGML_ASSERT(p != nullptr);
+
+    const int n = p->n_devices;
+    GGML_ASSERT(n == 2);
+
+    const ggml_type input_type = tensors[0]->type;
+    GGML_ASSERT(input_type == GGML_TYPE_F32 || input_type == GGML_TYPE_F16 || input_type == GGML_TYPE_BF16);
+
+    const int64_t ne = ggml_nelements(tensors[0]);
+    GGML_ASSERT(ne > 0);
+
+    const size_t   input_nbytes = ggml_nbytes(tensors[0]);
+
+    // BF16 round-trip: F32 inputs >= bf16_threshold are converted to BF16 for
+    // the reduction (chunked or copy-engine), halving on-wire bytes. Matches
+    // NCCL's behaviour. The pre-conversion zeroes inactive shards so the
+    // inner paths see them as already-prepared compute tensors.
+    const bool use_bf16 =
+        input_type == GGML_TYPE_F32 &&
+        p->bf16_threshold > 0 &&
+        input_nbytes >= p->bf16_threshold;
+
+    const ggml_type kernel_type = use_bf16 ? GGML_TYPE_BF16 : input_type;
+    const size_t    type_size   = ggml_type_size(kernel_type);
+    GGML_ASSERT(p->buf_bytes >= type_size);
+    const size_t    nbytes      = (size_t) ne * type_size;
+
+    bool compute_flag[GGML_CUDA_MAX_DEVICES] = {};
+    for (int i = 0; i < n; ++i) {
+        compute_flag[i] = (tensors[i]->flags & GGML_TENSOR_FLAG_COMPUTE) != 0;
+    }
+
+    // Decide between copy-engine and chunked kernel paths based on the working
+    // type's actual byte count.  No upper bound: copy_outer slices reductions
+    // larger than copy_bytes into copy_bytes-sized pieces.
+    const bool use_copy_engine =
+        p->copy_threshold > 0 &&
+        nbytes >= p->copy_threshold;
+
+    // BF16 inactive-shard zeroing: when use_bf16 is on, the combined kernel
+    // (chunked kernel path) and the combined add kernel (copy_engine path)
+    // both accumulate into the F32 tensor data directly, so an inactive
+    // shard's accumulator must start at zero.
+    if (use_bf16) {
+        for (int i = 0; i < n; ++i) {
+            if (!compute_flag[i]) {
+                auto * cuda_ctx = static_cast<ggml_backend_cuda_context *>(backends[i]->context);
+                GGML_ASSERT(cuda_ctx->device == p->devices[i]);
+                ggml_cuda_set_device(p->devices[i]);
+                CUDA_CHECK(cudaMemsetAsync(tensors[i]->data, 0, (size_t) ne * sizeof(float), cuda_ctx->stream()));
+            }
+        }
+    }
+
+    // Pre-convert F32 -> BF16 into bf16_tmp ONLY for the copy_engine + use_bf16
+    // path; the chunked kernel path's combined kernel does the conversion
+    // inline as it writes to host_buf.
+    ggml_cuda_pool_alloc<nv_bfloat16> bf16_tmp[GGML_CUDA_MAX_DEVICES];
+    void * copy_src_ptr[GGML_CUDA_MAX_DEVICES] = {};
+
+    if (use_copy_engine && use_bf16) {
+        to_bf16_cuda_t to_bf16 = ggml_get_to_bf16_cuda(GGML_TYPE_F32);
+        for (int i = 0; i < n; ++i) {
+            auto * cuda_ctx = static_cast<ggml_backend_cuda_context *>(backends[i]->context);
+            GGML_ASSERT(cuda_ctx->device == p->devices[i]);
+            bf16_tmp[i].pool = &cuda_ctx->pool();
+            bf16_tmp[i].alloc(ne);
+            ggml_cuda_set_device(p->devices[i]);
+            if (compute_flag[i]) {
+                to_bf16(tensors[i]->data, bf16_tmp[i].get(), ne, cuda_ctx->stream());
+                CUDA_CHECK(cudaGetLastError());
+            } else {
+                CUDA_CHECK(cudaMemsetAsync(bf16_tmp[i].get(), 0, nbytes, cuda_ctx->stream()));
+            }
+            copy_src_ptr[i] = bf16_tmp[i].get();
+        }
+    }
+
+    bool ok = true;
+    if (use_copy_engine) {
+        // After up-front BF16 conversion, the tmp buffers already hold the
+        // (possibly zeroed-for-inactive) data, so the inner path can treat
+        // every shard as compute.
+        bool inner_compute[GGML_CUDA_MAX_DEVICES];
+        for (int i = 0; i < n; ++i) {
+            inner_compute[i] = use_bf16 ? true : compute_flag[i];
+        }
+
+        // Dispatch into copy_impl with explicit src/dst types.  When use_bf16
+        // is on, the wire type is BF16 (src = bf16_tmp) and the accumulator
+        // is F32 (dst = tensors[i]->data); the combined add kernel rounds dst
+        // through BF16 for bit-equivalence and writes F32 directly, so no
+        // post-conversion is needed.  Otherwise src == dst (same native type).
+        if (use_bf16) {
+            GGML_ASSERT(kernel_type == GGML_TYPE_BF16);
+            nv_bfloat16 * src[GGML_CUDA_MAX_DEVICES] = {};
+            float       * dst[GGML_CUDA_MAX_DEVICES] = {};
+            for (int i = 0; i < n; ++i) {
+                src[i] = static_cast<nv_bfloat16 *>(copy_src_ptr[i]);
+                dst[i] = static_cast<float *>(tensors[i]->data);
+            }
+            ok = ggml_cuda_ar_allreduce_copy_outer<nv_bfloat16, float>(
+                p, backends, src, dst, inner_compute, ne);
+        } else {
+            switch (kernel_type) {
+                case GGML_TYPE_F32: {
+                    float * buf[GGML_CUDA_MAX_DEVICES] = {};
+                    for (int i = 0; i < n; ++i) {
+                        buf[i] = static_cast<float *>(tensors[i]->data);
+                    }
+                    ok = ggml_cuda_ar_allreduce_copy_outer<float, float>(
+                        p, backends, buf, buf, inner_compute, ne);
+                    break;
+                }
+                case GGML_TYPE_BF16: {
+                    nv_bfloat16 * buf[GGML_CUDA_MAX_DEVICES] = {};
+                    for (int i = 0; i < n; ++i) {
+                        buf[i] = static_cast<nv_bfloat16 *>(tensors[i]->data);
+                    }
+                    ok = ggml_cuda_ar_allreduce_copy_outer<nv_bfloat16, nv_bfloat16>(
+                        p, backends, buf, buf, inner_compute, ne);
+                    break;
+                }
+                case GGML_TYPE_F16: {
+                    half * buf[GGML_CUDA_MAX_DEVICES] = {};
+                    for (int i = 0; i < n; ++i) {
+                        buf[i] = static_cast<half *>(tensors[i]->data);
+                    }
+                    ok = ggml_cuda_ar_allreduce_copy_outer<half, half>(
+                        p, backends, buf, buf, inner_compute, ne);
+                    break;
+                }
+                default:
+                    GGML_ASSERT(false);
+            }
+        }
+    } else {
+        // host_buf carries T_wire-typed data; max_chunk_elems is the count that
+        // fits in one host_buf at the wire size.
+        const size_t max_chunk_elems = p->buf_bytes / type_size;
+        const size_t input_type_size = ggml_type_size(input_type);
+
+        // Chunked kernel path runs entirely on the caller's compute stream:
+        // since AR is a barrier here, same-stream ordering subsumes any
+        // cross-stream event handshake that the copy-engine path needs, and
+        // skips the cross-stream scheduling overhead that was hurting the
+        // small-tensor (tg) latency on the AR-stream variant.  Only ev.ker is
+        // still recorded at end-of-AR for acquire_slot's pool-wraparound check.
+        for (int64_t chunk_start = 0; chunk_start < ne; chunk_start += (int64_t) max_chunk_elems) {
+            const size_t remaining_elems = (size_t) (ne - chunk_start);
+            const size_t chunk_elems = remaining_elems < max_chunk_elems ? remaining_elems : max_chunk_elems;
+            const size_t chunk_dst_bytes  = chunk_elems * input_type_size;
+
+            const auto [slot, token] = ggml_cuda_ar_acquire_slot(p);
+            const bool last_chunk = chunk_start + (int64_t) chunk_elems == ne;
+
+            for (int i = 0; i < n; ++i) {
+                const int peer = 1 - i;  // valid for n == 2 only
+                ggml_cuda_set_device(p->devices[i]);
+                auto * cuda_ctx = static_cast<ggml_backend_cuda_context *>(backends[i]->context);
+                GGML_ASSERT(cuda_ctx->device == p->devices[i]);
+                cudaStream_t stream = cuda_ctx->stream();
+
+                char * data = static_cast<char *>(tensors[i]->data) + chunk_start * (int64_t) input_type_size;
+
+                // Match NCCL/meta-backend semantics: inactive shards contribute
+                // zeros.  On the BF16 path the F32 tensor data was already
+                // zeroed up-front (above), so per-chunk zeroing isn't needed.
+                if (!compute_flag[i] && !use_bf16) {
+                    CUDA_CHECK(cudaMemsetAsync(data, 0, chunk_dst_bytes, stream));
+                }
+
+#define LAUNCH_AR_KERNEL(T_dst, T_wire) \
+                ggml_cuda_ar_kernel<T_dst, T_wire><<<dim3(GGML_CUDA_AR_KERNEL_BLOCKS), dim3(256), 0, stream>>>( \
+                    reinterpret_cast<const T_dst *>(data), \
+                    reinterpret_cast<T_dst *>(data), \
+                    reinterpret_cast<T_wire *>(p->host_buf[i].dev + (size_t) slot * p->buf_bytes), \
+                    reinterpret_cast<const T_wire *>(p->host_buf[peer].dev + (size_t) slot * p->buf_bytes), \
+                    static_cast<int>(chunk_elems), \
+                    ggml_cuda_ar_arrival_ptr(p, slot, i), \
+                    ggml_cuda_ar_arrival_ptr(p, slot, peer), \
+                    token)
+
+                if (use_bf16) {
+                    GGML_ASSERT(input_type == GGML_TYPE_F32);
+                    LAUNCH_AR_KERNEL(float, nv_bfloat16);
+                } else {
+                    switch (input_type) {
+                        case GGML_TYPE_F32:  LAUNCH_AR_KERNEL(float,       float);       break;
+                        case GGML_TYPE_F16:  LAUNCH_AR_KERNEL(half,        half);        break;
+                        case GGML_TYPE_BF16: LAUNCH_AR_KERNEL(nv_bfloat16, nv_bfloat16); break;
+                        default: GGML_ASSERT(false);
+                    }
+                }
+
+#undef LAUNCH_AR_KERNEL
+                CUDA_CHECK(cudaGetLastError());
+
+                if (last_chunk) {
+                    CUDA_CHECK(cudaEventRecord(p->ev_pool[i][slot].ker, stream));
+                }
+            }
+        }
+    }
+
+    return ok;
+}
+
+#else // defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)
+
+// HIP and MUSA lack the host-mapped pinned-memory APIs (cudaHostAllocPortable
+// / cudaHostAllocMapped / cudaHostGetDevicePointer) and __nanosleep that this
+// implementation relies on, so the internal AllReduce is a CUDA-only feature.
+// The dispatcher in ggml-cuda.cu treats a nullptr pipeline as "init failed"
+// and silently falls back to the meta backend's generic AllReduce.
+ggml_cuda_ar_pipeline * ggml_cuda_ar_pipeline_init(const int *, size_t) {
+    return nullptr;
+}
+void ggml_cuda_ar_pipeline_free(ggml_cuda_ar_pipeline *) {
+}
+bool ggml_cuda_ar_allreduce(ggml_cuda_ar_pipeline *, ggml_backend_t *, ggml_tensor **) {
+    return false;
+}
+
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
diff --git a/ggml/src/ggml-cuda/allreduce.cuh b/ggml/src/ggml-cuda/allreduce.cuh
new file mode 100644
index 00000000000..0f2c9518d5d
--- /dev/null
+++ b/ggml/src/ggml-cuda/allreduce.cuh
@@ -0,0 +1,29 @@
+#pragma once
+
+#include "common.cuh"
+#include "ggml-backend-impl.h"
+
+#include <cstddef>
+
+// Opaque pipeline context -- owns all pinned buffers, streams, and events.
+struct ggml_cuda_ar_pipeline;
+
+// Allocate a pipeline for n_devices GPUs.
+// devices[] holds the CUDA device IDs in rank order.
+// Returns nullptr on allocation failure.
+ggml_cuda_ar_pipeline * ggml_cuda_ar_pipeline_init(
+    const int * devices, size_t n_devices);
+
+// Release all resources owned by the pipeline.
+void ggml_cuda_ar_pipeline_free(ggml_cuda_ar_pipeline * pipeline);
+
+// Execute an in-place AllReduce (sum) across tensors[0..n_devices-1].
+// tensors[i] must live on the device managed by backends[i] and be
+// contiguous F32, F16, or BF16.
+// Preconditions are checked by the CUDA comm dispatcher before calling this.
+// Returns true once the reduction work has been enqueued successfully.
+bool ggml_cuda_ar_allreduce(
+    ggml_cuda_ar_pipeline * pipeline,
+    ggml_backend_t        * backends,
+    ggml_tensor           ** tensors);
+
diff --git a/ggml/src/ggml-cuda/argsort.cu b/ggml/src/ggml-cuda/argsort.cu
index 0f3f017b534..c4f08091e79 100644
--- a/ggml/src/ggml-cuda/argsort.cu
+++ b/ggml/src/ggml-cuda/argsort.cu
@@ -4,6 +4,7 @@
 #    include <cub/cub.cuh>
 #    if (CCCL_MAJOR_VERSION >= 3 && CCCL_MINOR_VERSION >= 1)
 #        define STRIDED_ITERATOR_AVAILABLE
+#        include <cuda/iterator>
 #    endif
 using namespace cub;
 #endif  // GGML_CUDA_USE_CUB
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 85edd4ca565..62e710381b8 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -830,6 +830,18 @@ static __device__ __forceinline__ float ggml_cuda_ue4m3_to_fp32(uint8_t x) {
 #endif // defined(GGML_USE_HIP) && defined(CDNA3) && defined(FP8_AVAILABLE) && HIP_VERSION >= 60200000
 }
 
+static __device__ __forceinline__ uint8_t ggml_cuda_fp32_to_ue4m3(float x) {
+#if defined(BLACKWELL_MMA_AVAILABLE) // This is used for NVFP4 subblock scale quantizations only
+    if (!(x > 0.0f)) {
+        return 0;
+    }
+    const __nv_fp8_e4m3 xf(x);
+    return xf.__x;
+#else
+     NO_DEVICE_CODE; // Used only for NVFP4 Scales for Activations, only for Blackwell
+#endif // defined(BLACKWELL_MMA_AVAILABLE)
+}
+
 __device__ __forceinline__ uint8_t ggml_cuda_float_to_fp4_e2m1(float x, float e) {
     const uint8_t sign_bit = (x < 0.0f) << 3;
     float         ax       = fabsf(x) * e;
diff --git a/ggml/src/ggml-cuda/concat.cu b/ggml/src/ggml-cuda/concat.cu
index e9ffd274b99..102f944f924 100644
--- a/ggml/src/ggml-cuda/concat.cu
+++ b/ggml/src/ggml-cuda/concat.cu
@@ -1,96 +1,79 @@
 #include "concat.cuh"
 
 // contiguous kernels
-static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) {
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
-
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-
-    if (nidx < ne00) { // src0
-        int offset_src =
-            nidx +
-            blockIdx.y * ne00 +
-            blockIdx.z * ne00 * gridDim.y;
-        dst[offset_dst] = x[offset_src];
-    } else {
-        int offset_src =
-            (nidx - ne00) +
-            blockIdx.y * (ne0 - ne00) +
-            blockIdx.z * (ne0 - ne00) * gridDim.y;
-        dst[offset_dst] = y[offset_src];
-    }
-}
-
-static __global__ void concat_f32_dim1(const float * x, const float * y, float * dst, const int ne0, const int ne01) {
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
+template <int dim>
+static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) concat_f32_cont(const float * x,
+                                                                                 const float * y,
+                                                                                 float *       dst,
+                                                                                 int64_t       ne00,
+                                                                                 int64_t       ne01,
+                                                                                 int64_t       ne02,
+                                                                                 int64_t       ne0,
+                                                                                 int64_t       ne1,
+                                                                                 int64_t       ne2) {
+    static_assert(dim >= 0 && dim <= 2, "dim must be in [0, 2]");
+
+    const int64_t n = ne0 * ne1 * ne2;
+
+    for (int64_t i = (int64_t) blockIdx.x * blockDim.x + threadIdx.x; i < n; i += (int64_t) blockDim.x * gridDim.x) {
+        if constexpr (dim == 0) {
+            const int64_t row = i / ne0;
+            const int64_t i0  = i - row * ne0;
+
+            if (i0 < ne00) {
+                dst[i] = x[row * ne00 + i0];
+            } else {
+                dst[i] = y[row * (ne0 - ne00) + (i0 - ne00)];
+            }
+        } else if constexpr (dim == 1) {
+            const int64_t dst_plane  = ne0 * ne1;
+            const int64_t src0_plane = ne0 * ne01;
+            const int64_t src1_plane = dst_plane - src0_plane;
+            const int64_t i2         = i / dst_plane;
+            const int64_t i01        = i - i2 * dst_plane;
+
+            if (i01 < src0_plane) {
+                dst[i] = x[i2 * src0_plane + i01];
+            } else {
+                dst[i] = y[i2 * src1_plane + (i01 - src0_plane)];
+            }
+        } else {
+            const int64_t src0_size = ne0 * ne1 * ne02;
 
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-
-    if (blockIdx.y < (unsigned)ne01) { // src0
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            blockIdx.z * ne0 * ne01;
-        dst[offset_dst] = x[offset_src];
-    } else {
-        int offset_src =
-            nidx +
-            (blockIdx.y - ne01) * ne0 +
-            blockIdx.z * ne0 * (gridDim.y - ne01);
-        dst[offset_dst] = y[offset_src];
+            if (i < src0_size) {
+                dst[i] = x[i];
+            } else {
+                dst[i] = y[i - src0_size];
+            }
+        }
     }
 }
 
-static __global__ void concat_f32_dim2(const float * x, const float * y, float * dst, const int ne0, const int ne02) {
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
-
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-
-    if (blockIdx.z < (unsigned)ne02) { // src0
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            blockIdx.z * ne0 * gridDim.y;
-        dst[offset_dst] = x[offset_src];
-    } else {
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            (blockIdx.z - ne02) * ne0 *  gridDim.y;
-        dst[offset_dst] = y[offset_src];
-    }
-}
+static void concat_f32_cuda(const float * x,
+                            const float * y,
+                            float *       dst,
+                            int64_t       ne00,
+                            int64_t       ne01,
+                            int64_t       ne02,
+                            int64_t       ne0,
+                            int64_t       ne1,
+                            int64_t       ne2,
+                            int           dim,
+                            cudaStream_t  stream) {
+    const int64_t n          = ne0 * ne1 * ne2;
+    const int     num_blocks = (n + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
 
-static void concat_f32_cuda(const float * x, const float * y, float * dst, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, int dim, cudaStream_t stream) {
-    int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, ne1, ne2);
     if (dim == 0) {
-        concat_f32_dim0<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne00);
+        concat_f32_cont<0>
+            <<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
         return;
     }
     if (dim == 1) {
-        concat_f32_dim1<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne01);
+        concat_f32_cont<1>
+            <<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
         return;
     }
-    concat_f32_dim2<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
+    concat_f32_cont<2><<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
 }
 
 // non-contiguous kernel (slow)
diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index 861d8446709..d3e95f678a5 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -1309,8 +1309,10 @@ void launch_fattn(
         hip_f16_alloc(cudaStream_t s) : stream(s) {}
         ~hip_f16_alloc() {
             if (ptr) {
-                cudaStreamSynchronize(stream);
-                cudaFree(ptr);
+                // Cast to void: hipStreamSynchronize / hipFree are [[nodiscard]] under
+                // HIP's -Werror policy; we're in a destructor and can't propagate errors.
+                (void) cudaStreamSynchronize(stream);
+                (void) cudaFree(ptr);
             }
         }
         void alloc(size_t nelements) {
diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index 119883c61eb..d230c707968 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -61,11 +61,19 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 32, 128, 2,  64,  64,  64,  64, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 64, 128, 2,  64,  64,  64,  64, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128,  8,  64, 4,  64,  96,  64,  64, 2, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 16,  64, 4,  32,  96,  64,  64, 2, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 32, 128, 2,  32,  96,  64,  64, 2, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 64, 128, 2,  32,  96,  64,  64, 2, true);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256,  8,  64, 4,  64, 128, 128, 128, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16,  64, 4,  32, 128, 128, 128, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  32, 128, 128, 128, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  32, 128, 128, 128, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 256, 1,  32, 128, 128, 128, 1, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8,  64, 4,  32, 256, 256, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32, 256, 256, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
@@ -90,6 +98,9 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  64, 128, 128,  64, 2, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  64, 128, 128,  64, 2, true);
 
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 256, 1,  32, 128, 128, 128, 1, false);
+
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8,  64, 4,  32,  96,  64, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32,  96,  64, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
@@ -129,62 +140,116 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
 }
 
 static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_rdna(const int DKQ, const int DV, const int ncols) {
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16, 128, 2,  64, 128, 128, 128, 2, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  64, 128, 128,  64, 2, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  64, 128, 128,  64, 2, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64,  8, 128, 2,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 16, 128, 2,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 32, 128, 2,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 64, 128, 2,  64,  32,  32,  32, 1, true);
 
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16,  64, 4,  32, 128, 128, 128, 1, false);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, false);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1,  32, 128, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80,  8,  64, 2,  32,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 16,  64, 2,  32,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 32, 128, 2,  64,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 64, 128, 2,  64,  40,  40,  40, 1, true);
 
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16,  64, 4,  32,  96,  64, 128, 1, false);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2,  32, 160, 128, 128, 1, false);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 256, 1,  32, 160, 128, 128, 1, false);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96,  8,  64, 2,  32,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 16,  64, 2,  32,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 32, 128, 2,  64,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 64, 128, 2,  64,  48,  48,  48, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112,  8,  64, 2,  32,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 16,  64, 2,  32,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 32, 128, 2,  64,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 64, 128, 2,  64,  56,  56,  56, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128,  8,  64, 2,  32,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 16,  64, 2,  32,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 32, 128, 2,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 64, 128, 2,  64,  64,  64,  64, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128,  8,  64, 2,  32,  96,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 16,  64, 2,  32,  96,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 32, 128, 2,  64,  96,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 64, 128, 2,  64,  96,  64,  64, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256,  8,  64, 2,  32, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16,  64, 2,  32, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  64, 128, 128,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2,  64, 128, 128,  64, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2,  32, 160, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 128, 2,  32, 160, 128, 128, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8, 128, 3,  64,  96,  64, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16, 128, 3,  64,  96,  64, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2,  32, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 128, 2,  32, 128, 128, 128, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512,  8, 128, 3,  64,  96,  64, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16, 128, 3,  64,  96,  64, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2,  32, 160, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 128, 2,  32, 160, 128, 128, 1, true);
 
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(640, 512, 16,  64, 4,  32,  96,  64, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(640, 512, 32, 128, 2,  32, 160, 128, 128, 1, false);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(640, 512, 64, 256, 1,  32, 160, 128, 128, 1, false);
 
-    // TODO tune specifically for RDNA
+    // Fall back to ampere config rather than upstream's zero-sentinel —
+    // template instances (e.g. fattn-mma-f16-instance-ncols1_1-ncols2_16.cu)
+    // do constexpr arithmetic on the returned config (np = nwarps * cols_per_warp /
+    // ncols, etc); a sentinel with nwarps=0 triggers compile-time div/mod-by-zero
+    // under -Werror,-Wdivision-by-zero on the HIP quality build.
     return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
 }
 
 static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_cdna(const int DKQ, const int DV, const int ncols) {
-    // Conservative configs for CDNA (MI100+): 64KB LDS, wavefront64, nstages=1 (no cp.async).
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64,  8, 128, 2, 128,  32,  32,  32, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 16, 128, 2,  64,  32,  32,  32, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 32, 128, 2,  64,  32,  32,  32, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 64, 256, 2,  64,  32,  32,  32, 1, true);
-
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80,  8, 128, 2, 128,  40,  40,  40, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 16, 128, 2,  64,  40,  40,  40, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 32, 128, 2,  64,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64,  8, 128, 1,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 16, 256, 2,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 32, 256, 2,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 64, 256, 4,  64,  32,  32,  32, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80,  8, 256, 2,  64,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 16, 256, 2,  64,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 32, 256, 2,  64,  40,  40,  40, 1, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 64, 256, 2,  64,  40,  40,  40, 1, true);
 
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96,  8, 128, 2, 128,  48,  48,  48, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 16, 128, 2,  64,  48,  48,  48, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 32, 128, 2,  64,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96,  8, 256, 2,  64,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 16, 256, 2,  64,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 32, 256, 2,  64,  48,  48,  48, 1, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 64, 256, 2,  64,  48,  48,  48, 1, true);
 
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112,  8, 128, 2, 128,  56,  56,  56, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 16, 128, 2,  64,  56,  56,  56, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 32, 128, 2,  64,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112,  8, 256, 2,  64,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 16, 256, 2,  64,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 32, 256, 2,  64,  56,  56,  56, 1, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 64, 256, 2,  64,  56,  56,  56, 1, true);
 
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128,  8, 128, 2, 128,  64,  64,  64, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 16, 128, 2,  64,  64,  64,  64, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 32, 128, 2,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128,  8, 256, 2,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 16, 256, 2,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 32, 256, 2,  64,  64,  64,  64, 1, true);
     GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 64, 256, 2,  64,  64,  64,  64, 1, true);
 
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256,  8,  64, 4,  64, 128, 128, 128, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16,  64, 4,  32, 128, 128, 128, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  32, 128, 128, 128, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 256, 2,  32, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128,  8, 256, 1,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 16, 256, 1,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 32, 256, 1,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(192, 128, 64, 512, 1,  64,  64,  64,  64, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256,  8, 256, 1,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16, 256, 1,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 256, 1,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 512, 1,  64, 128, 128,  64, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 256, 1,  64, 160, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 256, 1,  64, 160, 128, 128, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512,  8, 256, 1,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16, 256, 1,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 256, 1,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1,  64, 128, 128, 128, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512,  8, 256, 1,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16, 256, 1,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 256, 1,  64, 160, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 256, 1,  64, 160, 128, 128, 1, true);
 
-    // Fallback for unsupported DKQ values (e.g. 576). Must return non-zero values to satisfy
-    // compile-time static_asserts even though the kernel guard prevents runtime execution.
-    // nthreads=256 gives nwarps=4 (warp_size=64) or 8 (warp_size=32), nbatch_fa=128 satisfies np*16 divisibility.
-    return fattn_mma_config(256, 1, 128, 4, 4, 4, 1, false);
+    return fattn_mma_config(32, 1, 0, 0, 0, 0, 0, false);
 }
 
 static __host__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, const int DV, const int ncols, const int cc) {
@@ -515,7 +580,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         const int jt,
         const int kb0,
         const int k_VKQ_sup) {
-#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
+#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
     constexpr int  warp_size       = ggml_cuda_get_physical_warp_size();
     constexpr int  ncols           = ncols1 * ncols2;
     constexpr int  cols_per_warp   = T_B_KQ::I;
@@ -717,6 +782,18 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
             for (int i00 = 0; i00 < nbatch_fa; i00 += np*T_C_KQ::J) {
                 const int i0 = i00 + (threadIdx.y % np)*T_C_KQ::J;
+
+                // The mask is stored as 16 bit half values, loading them as 32 bit half2 values is preferred in terms of speed.
+                // However, this is not possible for RDNA3 where 2 consecutive l indices are not consecutive in the mask memory layout.
+#ifdef RDNA3
+#pragma unroll
+                for (int l = 0; l < T_C_KQ::ne; ++l) {
+                    const int i = i0 + T_C_KQ::get_j(l);
+                    const int j = ((threadIdx.y / np)*cols_per_warp + T_C_KQ::get_i(l)) / ncols2;
+
+                    KQ_C[i00/(np*T_C_KQ::J)].x[l] += __half2float(tile_mask[j*(nbatch_fa + 8) + i]);
+                }
+#else
 #pragma unroll
                 for (int l0 = 0; l0 < T_C_KQ::ne; l0 += 2) {
                     const int i = (i0 + T_C_KQ::get_j(l0)) / 2;
@@ -726,6 +803,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                     KQ_C[i00/(np*T_C_KQ::J)].x[l0 + 0] += slope*tmp.x;
                     KQ_C[i00/(np*T_C_KQ::J)].x[l0 + 1] += slope*tmp.y;
                 }
+#endif // RDNA3
             }
         }
 
@@ -832,13 +910,23 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
             }
         }
 #elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
-        const half2 KQ_max_scale_h2 = make_half2(
-            KQ_max_scale[0], KQ_max_scale[0]);
+        if constexpr (std::is_same_v<decltype(T_C_VKQ::x), half2[T_C_VKQ::ne]>) {
+            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[0]);
 #pragma unroll
-        for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
+            for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
 #pragma unroll
-            for (int l = 0; l < T_C_VKQ::ne; ++l) {
-                VKQ_C[i].x[l] *= KQ_max_scale_h2;
+                for (int l = 0; l < T_C_VKQ::ne; ++l) {
+                    VKQ_C[i].x[l] *= KQ_max_scale_h2;
+                }
+            }
+        } else {
+            static_assert(std::is_same_v<decltype(T_C_VKQ::x), float[T_C_VKQ::ne]>, "bad VKQ type");
+#pragma unroll
+            for (int i = 0; i < DV/T_C_VKQ::J; ++i) {
+#pragma unroll
+                for (int l = 0; l < T_C_VKQ::ne; ++l) {
+                    VKQ_C[i].x[l] *= KQ_max_scale[0];
+                }
             }
         }
 #else // Volta
@@ -906,9 +994,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         const half2 * tile_V_i = !V_is_K_view || i0_stop > 2*nbatch_K2 ? tile_V : tile_V + i0_start/2;
 
 #if defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
-        constexpr int i0_stride = cols_per_warp == 8 ? T_C_VKQ::I : 2*T_C_VKQ::J;
 #pragma unroll
-        for (int i_VKQ_0 = i0_start; i_VKQ_0 < i0_stop; i_VKQ_0 += i0_stride) {
+        for (int i_VKQ_0 = i0_start; i_VKQ_0 < i0_stop; i_VKQ_0 += T_A_VKQ::I) {
             static_assert((nbatch_fa/2) % (np*T_A_VKQ::J) == 0, "bad loop size");
 #pragma unroll
             for (int k00 = 0; k00 < nbatch_fa/2; k00 += np*T_A_VKQ::J) {
@@ -917,15 +1004,15 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                 T_A_VKQ A; // Transposed in SRAM but not in registers, gets transposed on load.
                 load_ldmatrix_trans(A, tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V);
                 if constexpr (T_B_KQ::I == 8) {
-                    mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]);
+                    mma(VKQ_C[i_VKQ_0/T_A_VKQ::I], A, B[k00/(np*T_A_VKQ::J)]);
                 } else {
                     // Wide version of VKQ_C is column-major.
 #if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                     // AMD matrix C is column-major.
-                    mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]);
+                    mma(VKQ_C[i_VKQ_0/T_A_VKQ::I], A, B[k00/(np*T_A_VKQ::J)]);
 #else
                     // swap A and B for CUDA.
-                    mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::J)], A);
+                    mma(VKQ_C[i_VKQ_0/T_A_VKQ::I], B[k00/(np*T_A_VKQ::J)], A);
 #endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                 }
             }
@@ -958,11 +1045,11 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         tile_Q, tile_K, tile_V, tile_mask,
         Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
     NO_DEVICE_CODE;
-#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
+#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
 }
 
 #if defined(TURING_MMA_AVAILABLE)
-template<int ncols> struct mma_tile_sizes {
+template<int DV, int ncols> struct mma_tile_sizes {
     using T_A_KQ  = tile<16,  8, half2>; // row-major
     using T_B_KQ  = tile<16,  8, half2>; // column-major
     using T_C_KQ  = tile<16, 16, float>; // column-major
@@ -970,7 +1057,7 @@ template<int ncols> struct mma_tile_sizes {
     using T_B_VKQ = tile<16,  8, half2>; // column-major
     using T_C_VKQ = tile<16,  8, half2>; // column-major
 };
-template<> struct mma_tile_sizes<8> {
+template<int DV> struct mma_tile_sizes<DV, 8> {
     using T_A_KQ  = tile<16,  8, half2>; // row-major
     using T_B_KQ  = tile< 8,  8, half2>; // column-major
     using T_C_KQ  = tile<16,  8, float>; // row-major
@@ -978,8 +1065,60 @@ template<> struct mma_tile_sizes<8> {
     using T_B_VKQ = tile< 8,  8, half2>; // column-major
     using T_C_VKQ = tile<16,  4, half2>; // row-major
 };
-#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
-template<int ncols> struct mma_tile_sizes {
+#elif defined(AMD_WMMA_AVAILABLE)
+#ifdef RDNA3
+template<int DV, int ncols> struct mma_tile_sizes {
+    using T_A_KQ  = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
+    using T_B_KQ  = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // column-major
+    using T_C_KQ  = tile<16, 16, float, DATA_LAYOUT_I_MAJOR>;          // column-major
+    using T_A_VKQ = tile<32,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
+    using T_B_VKQ = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // column-major
+    using T_C_VKQ = tile<16, 16, half2, DATA_LAYOUT_I_MAJOR>;          // column-major
+};
+template<int ncols> struct mma_tile_sizes<80, ncols> {
+    using T_A_KQ  = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
+    using T_B_KQ  = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // column-major
+    using T_C_KQ  = tile<16, 16, float, DATA_LAYOUT_I_MAJOR>;          // column-major
+    using T_A_VKQ = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
+    using T_B_VKQ = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // column-major
+    using T_C_VKQ = tile<16, 16, float, DATA_LAYOUT_I_MAJOR>;          // column-major
+};
+template<int ncols> struct mma_tile_sizes<112, ncols> {
+    using T_A_KQ  = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
+    using T_B_KQ  = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // column-major
+    using T_C_KQ  = tile<16, 16, float, DATA_LAYOUT_I_MAJOR>;          // column-major
+    using T_A_VKQ = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
+    using T_B_VKQ = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // column-major
+    using T_C_VKQ = tile<16, 16, float, DATA_LAYOUT_I_MAJOR>;          // column-major
+};
+#else
+template<int DV, int ncols> struct mma_tile_sizes {
+    using T_A_KQ  = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR>;           // row-major
+    using T_B_KQ  = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR>;           // column-major
+    using T_C_KQ  = tile<16, 16, float, DATA_LAYOUT_I_MAJOR>;           // column-major
+    using T_A_VKQ = tile<32,  8, half2, DATA_LAYOUT_I_MAJOR>;           // row-major
+    using T_B_VKQ = tile<16,  8, half2, DATA_LAYOUT_I_MAJOR>;           // column-major
+    using T_C_VKQ = tile<16, 16, half2, DATA_LAYOUT_I_MAJOR_SCRAMBLED>; // column-major
+};
+template<int ncols> struct mma_tile_sizes<80, ncols> {
+    using T_A_KQ  = tile<16,  8, half2>; // row-major
+    using T_B_KQ  = tile<16,  8, half2>; // column-major
+    using T_C_KQ  = tile<16, 16, float>; // column-major
+    using T_A_VKQ = tile<16,  8, half2>; // row-major
+    using T_B_VKQ = tile<16,  8, half2>; // column-major
+    using T_C_VKQ = tile<16,  8, half2>; // column-major
+};
+template<int ncols> struct mma_tile_sizes<112, ncols> {
+    using T_A_KQ  = tile<16,  8, half2>; // row-major
+    using T_B_KQ  = tile<16,  8, half2>; // column-major
+    using T_C_KQ  = tile<16, 16, float>; // column-major
+    using T_A_VKQ = tile<16,  8, half2>; // row-major
+    using T_B_VKQ = tile<16,  8, half2>; // column-major
+    using T_C_VKQ = tile<16,  8, half2>; // column-major
+};
+#endif // RDNA3
+#elif defined(AMD_MFMA_AVAILABLE)
+template<int DV, int ncols> struct mma_tile_sizes {
     using T_A_KQ  = tile<16,  8, half2>; // row-major
     using T_B_KQ  = tile<16,  8, half2>; // column-major
     using T_C_KQ  = tile<16, 16, float>; // column-major
@@ -988,7 +1127,7 @@ template<int ncols> struct mma_tile_sizes {
     using T_C_VKQ = tile<16,  8, half2>; // column-major
 };
 #else // Volta
-template<int ncols> struct mma_tile_sizes {
+template<int DV, int ncols> struct mma_tile_sizes {
     using T_A_KQ  = tile< 8,  4, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
     using T_B_KQ  = tile<32,  4, half2, DATA_LAYOUT_I_MAJOR>;          // column-major
     using T_C_KQ  = tile<32,  8, float, DATA_LAYOUT_I_MAJOR>;          // column-major
@@ -1023,17 +1162,17 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const int zt_gqa,
         const int kb0_start,
         const int kb0_stop) {
-#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
+#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
     constexpr int ncols = ncols1 * ncols2;
-    using     T_A_KQ    = typename mma_tile_sizes<ncols>::T_A_KQ;
-    using     T_B_KQ    = typename mma_tile_sizes<ncols>::T_B_KQ;
-    using     T_C_KQ    = typename mma_tile_sizes<ncols>::T_C_KQ;
-    using     T_A_VKQ   = typename mma_tile_sizes<ncols>::T_A_VKQ;
-    using     T_B_VKQ   = typename mma_tile_sizes<ncols>::T_B_VKQ;
-    using     T_C_VKQ   = typename mma_tile_sizes<ncols>::T_C_VKQ;
+    using     T_A_KQ    = typename mma_tile_sizes<DV, ncols>::T_A_KQ;
+    using     T_B_KQ    = typename mma_tile_sizes<DV, ncols>::T_B_KQ;
+    using     T_C_KQ    = typename mma_tile_sizes<DV, ncols>::T_C_KQ;
+    using     T_A_VKQ   = typename mma_tile_sizes<DV, ncols>::T_A_VKQ;
+    using     T_B_VKQ   = typename mma_tile_sizes<DV, ncols>::T_B_VKQ;
+    using     T_C_VKQ   = typename mma_tile_sizes<DV, ncols>::T_C_VKQ;
 
     constexpr int  cols_per_warp   = T_B_KQ::I;
     constexpr int  cols_per_thread = get_cols_per_thread();
@@ -1066,6 +1205,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     T_B_KQ    Q_B[(Q_in_reg ? DKQ/(2*T_B_KQ::J) : 1)];
 #if defined(TURING_MMA_AVAILABLE)
     T_C_VKQ VKQ_C[cols_per_warp == 8 ? DV/T_C_VKQ::I : DV/(2*T_C_VKQ::J)];
+#elif defined(AMD_WMMA_AVAILABLE) && defined(RDNA3)
+    T_C_VKQ VKQ_C[DV % 32 != 0       ? DV/T_C_VKQ::J : DV/(2*T_C_VKQ::J)];
 #elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
     T_C_VKQ VKQ_C[                                     DV/(2*T_C_VKQ::J)];
 #else // Volta
@@ -1236,7 +1377,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         float KQ_max_scale[cols_per_thread];
 #pragma unroll
         for (int col = 0; col < cols_per_thread; ++col) {
-            const int jc = cols_per_warp == 8 ? T_C_KQ::get_j(col) : T_C_KQ::get_i(2*col);
+            const int jc = (threadIdx.y/np)*cols_per_warp + (cols_per_warp == 8 ? T_C_KQ::get_j(col) : T_C_KQ::get_i(2*col));
             const float sink = sinks_f[jc % ncols2];
 
             const float KQ_max_new = fmaxf(KQ_max[col], sink);
@@ -1274,12 +1415,23 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
             }
         }
 #elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
-        const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[0]);
+        if constexpr (std::is_same_v<decltype(T_C_VKQ::x), half2[T_C_VKQ::ne]>) {
+            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[0]);
 #pragma unroll
-        for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
+            for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
 #pragma unroll
-            for (int l = 0; l < T_C_VKQ::ne; ++l) {
-                VKQ_C[i].x[l] *= KQ_max_scale_h2;
+                for (int l = 0; l < T_C_VKQ::ne; ++l) {
+                    VKQ_C[i].x[l] *= KQ_max_scale_h2;
+                }
+            }
+        } else {
+            static_assert(std::is_same_v<decltype(T_C_VKQ::x), float[T_C_VKQ::ne]>, "bad VKQ type");
+#pragma unroll
+            for (int i = 0; i < DV/T_C_VKQ::J; ++i) {
+#pragma unroll
+                for (int l = 0; l < T_C_VKQ::ne; ++l) {
+                    VKQ_C[i].x[l] *= KQ_max_scale[0];
+                }
             }
         }
 #else // Volta
@@ -1438,6 +1590,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
 #pragma unroll
     for (int k00 = 0; k00 < DV/2; k00 += nbatch_combine) {
         if constexpr (cols_per_warp == 8) {
+            static_assert(std::is_same_v<decltype(T_C_VKQ::x), half2[T_C_VKQ::ne]>, "bad VKQ type");
             const int jc_cwd = threadIdx.y*T_B_KQ::I + T_B_KQ::get_i(-1); // jc combine write data
 #pragma unroll
             for (int k1 = 0; k1 < nbatch_combine; k1 += T_B_KQ::J) {
@@ -1452,14 +1605,45 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
             }
         } else {
             const int j0 = threadIdx.y*cols_per_warp;
+            if constexpr (std::is_same_v<decltype(T_C_VKQ::x), half2[T_C_VKQ::ne]>) {
+                if constexpr (T_C_VKQ::dl == DATA_LAYOUT_I_MAJOR) {
 #pragma unroll
-            for (int k1 = 0; k1 < nbatch_combine; k1 += T_C_VKQ::J) {
+                    for (int k1 = 0; k1 < nbatch_combine; k1 += T_C_VKQ::J) {
 #pragma unroll
-                for (int l = 0; l < T_C_VKQ::ne; ++l) {
-                    const int j = j0 + T_C_VKQ::get_i(l);
-                    const int k = k1 + T_C_VKQ::get_j(l);
+                        for (int l = 0; l < T_C_VKQ::ne; ++l) {
+                            const int j = j0 + T_C_VKQ::get_i(l);
+                            const int k = k1 + T_C_VKQ::get_j(l);
 
-                    tile_Q[j*tile_stride + k] = VKQ_C[(k00 + k1)/T_C_VKQ::J].x[l];
+                            tile_Q[j*tile_stride + k] = VKQ_C[(k00 + k1)/T_C_VKQ::J].x[l];
+                        }
+                    }
+                } else {
+                    static_assert(T_C_VKQ::dl == DATA_LAYOUT_I_MAJOR_SCRAMBLED, "bad T_C_VKQ data layout");
+                    using T_C_VKQ_us = tile<T_C_VKQ::I, T_C_VKQ::J, half2, DATA_LAYOUT_I_MAJOR>; // us == unscrambled
+#pragma unroll
+                    for (int k1 = 0; k1 < nbatch_combine; k1 += T_C_VKQ::J) {
+                        const T_C_VKQ_us VKQ_C_us = unscramble(VKQ_C[(k00 + k1)/T_C_VKQ::J]);
+#pragma unroll
+                        for (int l = 0; l < T_C_VKQ_us::ne; ++l) {
+                            const int j = j0 + T_C_VKQ_us::get_i(l);
+                            const int k = k1 + T_C_VKQ_us::get_j(l);
+
+                            tile_Q[j*tile_stride + k] = VKQ_C_us.x[l];
+                        }
+                    }
+                }
+            } else {
+                static_assert(std::is_same_v<decltype(T_C_VKQ::x), float[T_C_VKQ::ne]>, "bad VKQ type");
+                half * tile_Q_h = (half *) tile_Q;
+#pragma unroll
+                for (int k1 = 0; k1 < nbatch_combine; k1 += T_C_VKQ::J/2) {
+#pragma unroll
+                    for (int l = 0; l < T_C_VKQ::ne; ++l) {
+                        const int j = j0 + T_C_VKQ::get_i(l);
+                        const int k = 2*k1 + T_C_VKQ::get_j(l);
+
+                        tile_Q_h[j*(2*tile_stride) + k] = VKQ_C[(k00 + k1)/(T_C_VKQ::J/2)].x[l];
+                    }
                 }
             }
         }
@@ -1537,7 +1721,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         stride_Q1, stride_Q2, stride_K, stride_V, stride_mask,
         jt, kb0_start, kb0_stop);
     NO_DEVICE_CODE;
-#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
+#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, bool use_logit_softcap, bool V_is_K_view>
@@ -1564,13 +1748,17 @@ static __global__ void flash_attn_ext_f16(
                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
                             const int32_t nb31, const int32_t nb32, const int64_t nb33) {
-#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE))
+#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE))
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(DKQ == 128 || DKQ == 256 || DKQ == 512)) {
         NO_DEVICE_CODE;
         return;
     }
+    if (DKQ == 192 && ncols2 != 8 && ncols2 != 16) {
+        NO_DEVICE_CODE;
+        return;
+    }
 #ifdef VOLTA_MMA_AVAILABLE
     if (ncols1*ncols2 < 32) {
         NO_DEVICE_CODE;
@@ -1586,14 +1774,14 @@ static __global__ void flash_attn_ext_f16(
 #endif // __CUDA_ARCH__ == GGML_CUDA_CC_TURING
 
 #if defined(AMD_WMMA_AVAILABLE)
-    if (ncols1*ncols2 > 32 || ncols1*ncols2 < 16 || DKQ > 128 || ncols2 == 1) {
+    if (ncols1*ncols2 < 16 || ncols2 == 1 || DKQ > 128) {
         NO_DEVICE_CODE;
         return;
     }
 #endif // defined(AMD_WMMA_AVAILABLE)
 
 #if defined(AMD_MFMA_AVAILABLE)
-    if (DKQ != 64 && DKQ != 80 && DKQ != 96 && DKQ != 112 && DKQ != 128) {
+    if (ncols1*ncols2 < 16 || DKQ > 256) {
         NO_DEVICE_CODE;
         return;
     }
@@ -1716,7 +1904,7 @@ static __global__ void flash_attn_ext_f16(
               ne31, ne32, ne33,
               nb31, nb32, nb33);
     NO_DEVICE_CODE;
-#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE))
+#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE))
 }
 
 template <int DKQ, int DV, int ncols1, int ncols2>
@@ -1844,6 +2032,10 @@ extern DECL_FATTN_MMA_F16_CASE(576, 512, 1, 16);
 extern DECL_FATTN_MMA_F16_CASE(576, 512, 2, 16);
 extern DECL_FATTN_MMA_F16_CASE(576, 512, 4, 16);
 
+// Mistral Small 4 (DKQ=320, DV=256), GQA=32-only build:
+extern DECL_FATTN_MMA_F16_CASE(320, 256,  1, 32);
+extern DECL_FATTN_MMA_F16_CASE(320, 256,  2, 32);
+
 // For GLM 4.7 Flash
 extern DECL_FATTN_MMA_F16_CASE(576, 512,  4,  4);
 extern DECL_FATTN_MMA_F16_CASE(576, 512,  8,  4);
diff --git a/ggml/src/ggml-cuda/fattn-tile.cu b/ggml/src/ggml-cuda/fattn-tile.cu
index 5680f842da9..a4a0b212b9a 100644
--- a/ggml/src/ggml-cuda/fattn-tile.cu
+++ b/ggml/src/ggml-cuda/fattn-tile.cu
@@ -34,10 +34,18 @@ void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor
             GGML_ASSERT(V->ne[0] == K->ne[0]);
             ggml_cuda_flash_attn_ext_tile_case<128, 128>(ctx, dst);
         } break;
+        case 192: {
+            GGML_ASSERT(V->ne[0] == 128);
+            ggml_cuda_flash_attn_ext_tile_case<192, 128>(ctx, dst);
+        } break;
         case 256: {
             GGML_ASSERT(V->ne[0] == K->ne[0]);
             ggml_cuda_flash_attn_ext_tile_case<256, 256>(ctx, dst);
         } break;
+        case 320: {
+            GGML_ASSERT(V->ne[0] == 256);
+            ggml_cuda_flash_attn_ext_tile_case<320, 256>(ctx, dst);
+        } break;
         case 512: {
             GGML_ASSERT(V->ne[0] == K->ne[0]);
             ggml_cuda_flash_attn_ext_tile_case<512, 512>(ctx, dst);
diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh
index be4f3281a5b..81fd54a727c 100644
--- a/ggml/src/ggml-cuda/fattn-tile.cuh
+++ b/ggml/src/ggml-cuda/fattn-tile.cuh
@@ -62,12 +62,20 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nv
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2,  64,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  2,  64, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  4, 128, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  8, 256, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128, 16, 256, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128, 32, 256, 2,  64,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  2,  64, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  64,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 16, 256, 2,  64,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  64,  64)
@@ -126,12 +134,20 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nv
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 128, 3,  32, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2,  64,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  2, 128, 3,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  4, 128, 3,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  8, 256, 2,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128, 16, 256, 2,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128, 32, 256, 2,  32,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  2, 128, 3,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  4, 128, 3,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  8, 256, 2,  32, 256)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  32, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  32,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 16, 256, 2,  32,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  32,  64)
@@ -197,12 +213,20 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_am
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 64, 256, 2,  64,  32)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  2, 256, 2, 128,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  4, 256, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  8, 256, 2,  64,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128, 16, 256, 2,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128, 32, 256, 2,  32,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  2, 256, 2, 128,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  4, 256, 2,  64, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  8, 256, 2,  64, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2,  32, 128)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2,  32, 128)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 32, 512, 1, 128,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  64,  64)
@@ -271,12 +295,20 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_am
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 3, 128,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 64, 256, 3,  64,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  2,  64, 8,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  4, 128, 6,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128,  8, 128, 6,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128, 16, 256, 5,  32,  64)
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(192, 128, 32, 256, 3,  64,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  2,  64, 8,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  4, 128, 6,  32, 256)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256,  8, 128, 6,  32, 256)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 5,  32, 256)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 3,  64, 128)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 32, 256, 2, 128,  64)
+
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 4,  64,  64)
@@ -1134,7 +1166,7 @@ static void launch_fattn_tile_switch_ncols1(ggml_backend_cuda_context & ctx, ggm
     constexpr size_t nbytes_shared = 0;
 
 #ifdef GGML_USE_HIP
-    if constexpr (DV <= 128) {
+    if constexpr (DKQ <= 128) {
         if (Q->ne[1] > 32/ncols2) {
             constexpr int cols_per_block = 64;
             const int nwarps    = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
@@ -1148,7 +1180,7 @@ static void launch_fattn_tile_switch_ncols1(ggml_backend_cuda_context & ctx, ggm
 #endif // GGML_USE_HIP
 
 #ifndef GGML_USE_HIP
-    if constexpr (DV <= 256)
+    if constexpr (DKQ <= 256)
 #endif // GGML_USE_HIP
     {
         if (Q->ne[1] > 16/ncols2) {
@@ -1162,14 +1194,16 @@ static void launch_fattn_tile_switch_ncols1(ggml_backend_cuda_context & ctx, ggm
         }
     }
 
-    if (Q->ne[1] > 8/ncols2) {
-        constexpr int cols_per_block = 16;
-        const int nwarps    = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
-        const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
-        fattn_kernel_t fattn_kernel = flash_attn_tile<DKQ, DV, cols_per_block/ncols2, ncols2, use_logit_softcap>;
-        launch_fattn<DV, cols_per_block/ncols2, ncols2>
-            (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size);
-        return;
+    if constexpr (ncols2 <= 16) {
+        if (Q->ne[1] > 8/ncols2) {
+            constexpr int cols_per_block = 16;
+            const int nwarps    = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size;
+            const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc);
+            fattn_kernel_t fattn_kernel = flash_attn_tile<DKQ, DV, cols_per_block/ncols2, ncols2, use_logit_softcap>;
+            launch_fattn<DV, cols_per_block/ncols2, ncols2>
+                (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size);
+            return;
+        }
     }
 
     if constexpr (ncols2 <= 8) {
@@ -1228,6 +1262,25 @@ static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggm
     const int gqa_limit = nvidia && gqa_ratio <= 4 && DV <= 256 ? 16 : INT_MAX;
     const bool use_gqa_opt = mask && max_bias == 0.0f && Q->ne[1] <= gqa_limit && K->ne[1] % FATTN_KQ_STRIDE == 0;
 
+    if constexpr (DKQ == 320) {
+        // This branch is only used for Mistral Small 4 which has a GQA ratio of 32.
+        // On AMD, simply use that GQA ratio with 32 columns / block since we always have enough SRAM.
+        // On NVIDIA however, the tile kernel is only used for GPUs that can't use the mma kernel (Pascal and older).
+        // Therefore, use a GQA ratio of 16 with 16 columns / block to stay below 48 kiB of SRAM / block.
+#ifdef GGML_USE_HIP
+        if (use_gqa_opt && gqa_ratio % 32 == 0) {
+            launch_fattn_tile_switch_ncols1<DKQ, DV, 32, use_logit_softcap>(ctx, dst);
+            return;
+        }
+#else
+        if (use_gqa_opt && gqa_ratio % 16 == 0) {
+            launch_fattn_tile_switch_ncols1<DKQ, DV, 16, use_logit_softcap>(ctx, dst);
+            return;
+        }
+#endif // GGML_USE_HIP
+        GGML_ABORT("flash-attn tile (320/256): expected GQA ratio multiple of 32");
+    }
+
     if constexpr (DKQ == 576) {
         if (use_gqa_opt && gqa_ratio % 16 == 0) {
             launch_fattn_tile_switch_ncols1<DKQ, DV, 16, use_logit_softcap>(ctx, dst);
@@ -1239,7 +1292,20 @@ static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggm
         }
     }
 
-    if constexpr (DKQ <= 512) {
+    if constexpr (DKQ == 192) {
+        // MiMo-V2.5 / V2.5-Pro / V2-Flash: gqa_ratio is 8 (SWA) or 16 (full attn)
+        if (use_gqa_opt && gqa_ratio % 16 == 0) {
+            launch_fattn_tile_switch_ncols1<DKQ, DV, 16, use_logit_softcap>(ctx, dst);
+            return;
+        }
+        if (use_gqa_opt && gqa_ratio % 8 == 0) {
+            launch_fattn_tile_switch_ncols1<DKQ, DV,  8, use_logit_softcap>(ctx, dst);
+            return;
+        }
+        GGML_ABORT("flash-attn tile (192/128): expected GQA ratio multiple of 8");
+    }
+
+    if constexpr (DKQ <= 512 && DKQ != 320 && DKQ != 192) {
         if (use_gqa_opt && gqa_ratio % 8 == 0) {
             launch_fattn_tile_switch_ncols1<DKQ, DV, 8, use_logit_softcap>(ctx, dst);
             return;
@@ -1292,7 +1358,9 @@ extern DECL_FATTN_TILE_CASE( 80,  80);
 extern DECL_FATTN_TILE_CASE( 96,  96);
 extern DECL_FATTN_TILE_CASE(112, 112);
 extern DECL_FATTN_TILE_CASE(128, 128);
+extern DECL_FATTN_TILE_CASE(192, 128);
 extern DECL_FATTN_TILE_CASE(256, 256);
+extern DECL_FATTN_TILE_CASE(320, 256);
 extern DECL_FATTN_TILE_CASE(512, 512);
 extern DECL_FATTN_TILE_CASE(576, 512);
 extern DECL_FATTN_TILE_CASE(640, 512);
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index b58cd3fcbfd..ffa5a556453 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -19,13 +19,14 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ggml_backend_cuda_con
     }
 
     if constexpr (ncols2 <= 16) {
-        if ((turing_mma_available(cc) || amd_wmma_available(cc)) && Q->ne[1] <= 16/ncols2) {
+        if (Q->ne[1] <= 16/ncols2) {
             ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 16/ncols2, ncols2>(ctx, dst);
             return;
         }
     }
 
-    if (ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_TURING || amd_wmma_available(cc) || Q->ne[1] <= 32/ncols2) {
+    if (Q->ne[1] <= 32/ncols2 || (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_TURING) ||
+            (GGML_CUDA_CC_IS_AMD(cc) && DKQ > 256)) {
         ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 32/ncols2, ncols2>(ctx, dst);
         return;
     }
@@ -139,10 +140,42 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg
             GGML_ASSERT(V->ne[0] == 128);
             ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<128, 128>(ctx, dst);
             break;
+        case 192: {
+            // MiMo-V2.5 / V2.5-Pro / V2-Flash: gqa_ratio is 8 (SWA) or 16 (full attn)
+            GGML_ASSERT(V->ne[0] == 128);
+            float max_bias = 0.0f;
+            memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
+            const bool use_gqa_opt = mask && max_bias == 0.0f;
+            GGML_ASSERT(use_gqa_opt);
+            GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
+            const int gqa_ratio = Q->ne[2] / K->ne[2];
+            if (gqa_ratio % 16 == 0) {
+                ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<192, 128, 16>(ctx, dst);
+            } else {
+                GGML_ASSERT(gqa_ratio % 8 == 0);
+                ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<192, 128,  8>(ctx, dst);
+            }
+        } break;
         case 256:
             GGML_ASSERT(V->ne[0] == 256);
             ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<256, 256>(ctx, dst);
             break;
+        case 320:
+            // For Mistral Small 4, go straight to the ncols1 switch (ncols2=32-only build).
+            GGML_ASSERT(V->ne[0] == 256);
+            {
+                float max_bias = 0.0f;
+                memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
+
+                const bool use_gqa_opt = mask && max_bias == 0.0f;
+                GGML_ASSERT(use_gqa_opt);
+                GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
+                const int gqa_ratio = Q->ne[2] / K->ne[2];
+                GGML_ASSERT(gqa_ratio % 32 == 0);
+
+                ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<320, 256, 32>(ctx, dst);
+            }
+            break;
         case 512:
             GGML_ASSERT(V->ne[0] == 512);
             ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<512, 512>(ctx, dst);
@@ -408,6 +441,22 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
                 return BEST_FATTN_KERNEL_NONE;
             }
             break;
+        case 192:
+            if (V->ne[0] != 128 || !gqa_opt_applies) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            if (gqa_ratio % 8 != 0) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
+        case 320:
+            if (V->ne[0] != 256 || !gqa_opt_applies) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            if (gqa_ratio % 32 != 0) {
+                return BEST_FATTN_KERNEL_NONE;
+            }
+            break;
         case 512:
             if (V->ne[0] != K->ne[0]) {
                 return BEST_FATTN_KERNEL_NONE;
@@ -485,7 +534,8 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
     }
 
     // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes:
-    const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    // 192 satisfies % 64 == 0 but has no vec instance (DKQ != DV); force it onto the MMA path.
+    const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && Q->ne[0] != 192 && K->ne[1] % FATTN_KQ_STRIDE == 0;
 
 #ifdef GGML_USE_HIP
     // HIP/ROCm: the TILE/MMA/WMMA FA paths allocate unbounded f16 temp buffers
@@ -525,12 +575,13 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
         return BEST_FATTN_KERNEL_MMA_F16;
     }
 
+    const int ncols2_max = Q->ne[0] == 320 ? 32 : ((Q->ne[0] == 576 || Q->ne[0] == 640 || Q->ne[0] == 192) ? 16 : 8);
+    int gqa_ratio_eff = 1;
+    while (gqa_ratio % (2*gqa_ratio_eff) == 0 && gqa_ratio_eff < ncols2_max) {
+        gqa_ratio_eff *= 2;
+    }
+
     if (volta_mma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72) {
-        int gqa_ratio_eff = 1;
-        const int ncols2_max = (Q->ne[0] == 576 || Q->ne[0] == 640) ? 16 : 8;
-        while (gqa_ratio % (2*gqa_ratio_eff) == 0 && gqa_ratio_eff < ncols2_max) {
-            gqa_ratio_eff *= 2;
-        }
         if (can_use_vector_kernel && Q->ne[1] * gqa_ratio_eff <= 2) {
             return BEST_FATTN_KERNEL_VEC;
         }
@@ -541,13 +592,14 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
     }
 
     // Use the WMMA kernel if possible:
-    if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 512 && Q->ne[0] != 576 && Q->ne[0] != 640) {
+    if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 192 && Q->ne[0] != 512 && Q->ne[0] != 576 && Q->ne[0] != 640) {
         if (can_use_vector_kernel && Q->ne[1] <= 2) {
             return BEST_FATTN_KERNEL_VEC;
         }
         return BEST_FATTN_KERNEL_WMMA_F16;
     }
 
+    // TQ: RDNA4 fast path for TurboQuant cache types — prefer VEC for quantized K/V at small q-cols
     if (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc) && gqa_opt_applies && Q->ne[0] <= 128 && Q->ne[0] != 40 && Q->ne[0] != 72) {
         if (can_use_vector_kernel) {
             if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) {
@@ -562,27 +614,33 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
                 }
             }
         }
-        int gqa_ratio_eff = 1;
-        const int ncols2_max = (Q->ne[0] == 576 || Q->ne[0] == 640) ? 16 : 8;
-        while (gqa_ratio % (2*gqa_ratio_eff) == 0 && gqa_ratio_eff < ncols2_max) {
-            gqa_ratio_eff *= 2;
+        int gqa_ratio_eff_rdna4 = 1;
+        const int ncols2_max_rdna4 = (Q->ne[0] == 576 || Q->ne[0] == 640) ? 16 : 8;
+        while (gqa_ratio % (2*gqa_ratio_eff_rdna4) == 0 && gqa_ratio_eff_rdna4 < ncols2_max_rdna4) {
+            gqa_ratio_eff_rdna4 *= 2;
         }
-        if (Q->ne[1] * gqa_ratio_eff <= 8) {
-            return BEST_FATTN_KERNEL_TILE; // AMD WMMA is only faster if the full tile width of 16 can be utilized.
+        if (Q->ne[1] * gqa_ratio_eff_rdna4 <= 8) {
+            return BEST_FATTN_KERNEL_TILE;
         }
         return BEST_FATTN_KERNEL_MMA_F16;
     }
 
-    // Use MFMA flash attention for CDNA (MI100+):
-    if (amd_mfma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 256 && Q->ne[0] != 512 && Q->ne[0] != 576 && Q->ne[0] != 640) {
-        const int64_t eff_nq = Q->ne[1] * (gqa_opt_applies ? gqa_ratio : 1);
-        // MMA vs tile crossover benchmarked on MI300X @ d32768:
-        //   hsk=64  (gqa=4): MMA wins at eff >= 128 (+11%)
-        //   hsk=128 (gqa=4): MMA wins at eff >= 128 (+4%)
-        if (eff_nq >= (GGML_CUDA_CC_IS_CDNA1(cc) && Q->ne[0] == 64 ? 64 : 128)) {
+    // AMD MFMA needs a certain minimum batch size to outscale the tile kernel for large head sizes.
+    if ((amd_mfma_available(cc) && Q->ne[0] <= 256) && Q->ne[0] != 40 && Q->ne[0] != 72) {
+        if ((Q->ne[0] <= 64 && Q->ne[1] * gqa_ratio_eff > 8)) {
+            return BEST_FATTN_KERNEL_MMA_F16;
+        }
+        if ((Q->ne[0] <= 128 && Q->ne[1] * gqa_ratio_eff > 16)) {
+            return BEST_FATTN_KERNEL_MMA_F16;
+        }
+        if ((Q->ne[0] <= 256 && Q->ne[1] * gqa_ratio_eff > 64)) {
             return BEST_FATTN_KERNEL_MMA_F16;
         }
-        // Fall through to tile kernel for small effective batch sizes.
+    }
+
+    // AMD WMMA is always faster than the tile kernel if the full tile width of 16 can be utilized.
+    if ((amd_wmma_available(cc) && gqa_opt_applies && Q->ne[0] <= 128) && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[1] * gqa_ratio_eff > 8) {
+        return BEST_FATTN_KERNEL_MMA_F16;
     }
 
     // If there are no tensor cores available, use the generic tile kernel:
diff --git a/ggml/src/ggml-cuda/gated_delta_net.cu b/ggml/src/ggml-cuda/gated_delta_net.cu
index 6b44bec7317..b4c9845e7a7 100644
--- a/ggml/src/ggml-cuda/gated_delta_net.cu
+++ b/ggml/src/ggml-cuda/gated_delta_net.cu
@@ -1,6 +1,6 @@
 #include "gated_delta_net.cuh"
 
-template <int S_v, bool KDA>
+template <int S_v, bool KDA, bool keep_rs_t>
 __global__ void __launch_bounds__((ggml_cuda_get_physical_warp_size() < S_v ? ggml_cuda_get_physical_warp_size() : S_v) * 4, 2)
 gated_delta_net_cuda(const float * q,
                                      const float * k,
@@ -23,7 +23,8 @@ gated_delta_net_cuda(const float * q,
                                      int64_t       sb3,
                                      const uint3   neqk1_magic,
                                      const uint3   rq3_magic,
-                                     float         scale) {
+                                     float         scale,
+                                     int           K) {
     const uint32_t h_idx    = blockIdx.x;
     const uint32_t sequence = blockIdx.y;
     // each warp owns one column, using warp-level primitives to reduce across rows
@@ -37,9 +38,13 @@ gated_delta_net_cuda(const float * q,
     float *       attn_data        = dst;
     float *       state            = dst + attn_score_elems;
 
-    const int64_t state_offset = (sequence * H + h_idx) * S_v * S_v;
-    state += state_offset;
-    curr_state += state_offset + col * S_v;
+    // input state layout (D, K, n_seqs) — seq stride is K * D = K * H * S_v * S_v.
+    // output state layout (per-slot D * n_seqs) — same per-(seq,head) offset as before.
+    const int64_t state_in_offset      = sequence * K * H * S_v * S_v + h_idx * S_v * S_v;
+    const int64_t state_out_offset     = (sequence * H + h_idx) * S_v * S_v;
+    const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output
+    state += state_out_offset;
+    curr_state += state_in_offset + col * S_v;
     attn_data += (sequence * n_tokens * H + h_idx) * S_v;
 
     constexpr int warp_size = ggml_cuda_get_physical_warp_size() < S_v ? ggml_cuda_get_physical_warp_size() : S_v;
@@ -54,6 +59,10 @@ gated_delta_net_cuda(const float * q,
         s_shard[r]  = curr_state[i];
     }
 
+    // slot mapping: target_slot = t - shift. When n_tokens < K only the last n_tokens slots
+    // are written; earlier slots are left untouched (caller-owned).
+    const int shift = (int) n_tokens - K;
+
     for (int t = 0; t < n_tokens; t++) {
         const float * q_t = q + iq3 * sq3 + t * sq2 + iq1 * sq1;
         const float * k_t = k + iq3 * sq3 + t * sq2 + iq1 * sq1;
@@ -135,17 +144,30 @@ gated_delta_net_cuda(const float * q,
         }
 
         attn_data += S_v * H;
+
+        if constexpr (keep_rs_t) {
+            const int target_slot = t - shift;
+            if (target_slot >= 0 && target_slot < K) {
+                float * curr_state = (dst + attn_score_elems) + target_slot * state_size_per_token + state_out_offset;
+#pragma unroll
+                for (int r = 0; r < rows_per_lane; r++) {
+                    const int i = r * warp_size + lane;
+                    curr_state[col * S_v + i] = s_shard[r];
+                }
+            }
+        }
     }
 
-    // Write state back to global memory (transposed layout)
+    if constexpr (!keep_rs_t) {
 #pragma unroll
-    for (int r = 0; r < rows_per_lane; r++) {
-        const int i          = r * warp_size + lane;
-        state[col * S_v + i] = s_shard[r];
+        for (int r = 0; r < rows_per_lane; r++) {
+            const int i          = r * warp_size + lane;
+            state[col * S_v + i] = s_shard[r];
+        }
     }
 }
 
-template <bool KDA>
+template <bool KDA, bool keep_rs_t>
 static void launch_gated_delta_net(
         const float * q_d, const float * k_d, const float * v_d,
         const float * g_d, const float * b_d, const float * s_d,
@@ -155,7 +177,7 @@ static void launch_gated_delta_net(
         int64_t sv1,   int64_t sv2, int64_t sv3,
         int64_t sb1,   int64_t sb2, int64_t sb3,
         int64_t neqk1, int64_t rq3,
-        float scale, cudaStream_t stream) {
+        float scale, int K, cudaStream_t stream) {
     //TODO: Add chunked kernel for even faster pre-fill
     const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
     const int num_warps = 4;
@@ -169,29 +191,29 @@ static void launch_gated_delta_net(
 
     switch (S_v) {
         case 16:
-            gated_delta_net_cuda<16, KDA><<<grid_dims, block_dims, 0, stream>>>(
+            gated_delta_net_cuda<16, KDA, keep_rs_t><<<grid_dims, block_dims, 0, stream>>>(
                 q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
             break;
         case 32:
-            gated_delta_net_cuda<32, KDA><<<grid_dims, block_dims, 0, stream>>>(
+            gated_delta_net_cuda<32, KDA, keep_rs_t><<<grid_dims, block_dims, 0, stream>>>(
                 q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
             break;
         case 64: {
-            gated_delta_net_cuda<64, KDA><<<grid_dims, block_dims, 0, stream>>>(
+            gated_delta_net_cuda<64, KDA, keep_rs_t><<<grid_dims, block_dims, 0, stream>>>(
                 q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
             break;
         }
         case 128: {
-            gated_delta_net_cuda<128, KDA><<<grid_dims, block_dims, 0, stream>>>(
+            gated_delta_net_cuda<128, KDA, keep_rs_t><<<grid_dims, block_dims, 0, stream>>>(
                 q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
             break;
         }
         default:
@@ -261,13 +283,29 @@ void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor *
 
     cudaStream_t stream = ctx.stream();
 
+    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
+    const int K = (int) src_state->ne[1];
+    const bool keep_rs = K > 1;
+
     if (kda) {
-        launch_gated_delta_net<true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
-            S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-            sb1, sb2, sb3, neqk1, rq3, scale, stream);
+        if (keep_rs) {
+            launch_gated_delta_net<true, true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+                S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+        } else {
+            launch_gated_delta_net<true, false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+                S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+        }
     } else {
-        launch_gated_delta_net<false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
-            S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-            sb1, sb2, sb3, neqk1, rq3, scale, stream);
+        if (keep_rs) {
+            launch_gated_delta_net<false, true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+                S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+        } else {
+            launch_gated_delta_net<false, false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+                S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
+                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+        }
     }
 }
diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu
index e99cba63d34..36b840e8148 100644
--- a/ggml/src/ggml-cuda/getrows.cu
+++ b/ggml/src/ggml-cuda/getrows.cu
@@ -6,17 +6,18 @@ template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static __global__ void k_get_rows(
         const void * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst,
         const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/
-        /*const int64_t ne10,*/ const int64_t ne11, const int64_t ne12, /*const int64_t ne13,*/
+        /*const int64_t ne10,*/ const int64_t ne11, const uint3 ne12_fdv, /*const int64_t ne13,*/
         /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3,
         /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
         const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
-    for (int64_t z = blockIdx.z; z < ne11*ne12; z += gridDim.z) {
+    for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) {
         for (int64_t i00 = 2*(blockIdx.y*blockDim.x + threadIdx.x); i00 < ne00; i00 += gridDim.y*blockDim.x) {
             // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
             const int i10 =  blockIdx.x;
-            const int i11 =  z / ne12; // TODO fastdiv
-            const int i12 =  z % ne12;
+            const uint2 dm  = fast_div_modulo((uint32_t)z, ne12_fdv);
+            const int i11 =  dm.x;
+            const int i12 =  dm.y;
 
             const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
 
@@ -42,17 +43,18 @@ template<typename src0_t, typename dst_t>
 static __global__ void k_get_rows_float(
         const src0_t * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst,
         const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/
-        /*const int64_t ne10,*/ const int64_t ne11, const int64_t ne12, /*const int64_t ne13,*/
+        /*const int64_t ne10,*/ const int64_t ne11, const uint3 ne12_fdv, /*const int64_t ne13,*/
         /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3,
         /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
         const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
 
-    for (int64_t z = blockIdx.z; z < ne11*ne12; z += gridDim.z) {
+    for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) {
         for (int64_t i00 = blockIdx.y*blockDim.x + threadIdx.x; i00 < ne00; i00 += gridDim.y*blockDim.x) {
             // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
             const int i10 = blockIdx.x;
-            const int i11 = z / ne12; // TODO fastdiv
-            const int i12 = z % ne12;
+            const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv);
+            const int i11 = dm.x;
+            const int i12 = dm.y;
 
             if (i00 >= ne00) {
                 return;
@@ -115,10 +117,14 @@ static void get_rows_cuda_q(
 
     GGML_ASSERT(ne00 % 2 == 0);
 
+    GGML_ASSERT(ne12 > 0);
+    GGML_ASSERT(ne11 <= std::numeric_limits<uint32_t>::max() / ne12);
+    const uint3 ne12_fdv = init_fastdiv_values(ne12);
+
     k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
         src0_d, src1_d, dst_d,
         ne00, /*ne01, ne02, ne03,*/
-        /*ne10,*/ ne11, ne12, /*ne13,*/
+        /*ne10,*/ ne11, ne12_fdv, /*ne13,*/
         /* s0,*/ s1, s2, s3,
         /* nb00,*/ nb01, nb02, nb03,
         s10, s11, s12/*, s13*/);
@@ -146,10 +152,14 @@ static void get_rows_cuda_float(
     const size_t s12 = nb12 / sizeof(int32_t);
     // const size_t s13 = nb13 / sizeof(int32_t);
 
+    GGML_ASSERT(ne12 > 0);
+    GGML_ASSERT(ne11 <= std::numeric_limits<uint32_t>::max() / ne12);
+    const uint3 ne12_fdv = init_fastdiv_values(ne12);
+
     k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
         src0_d, src1_d, dst_d,
         ne00, /*ne01, ne02, ne03,*/
-        /*ne10,*/ ne11, ne12, /*ne13,*/
+        /*ne10,*/ ne11, ne12_fdv, /*ne13,*/
         /* s0,*/ s1, s2, s3,
         /* nb00,*/ nb01, nb02, nb03,
         s10, s11, s12/*, s13*/);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index b925afae510..9ec6ef95bef 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2,6 +2,7 @@
 #include "ggml-impl.h"
 #include "ggml-backend-impl.h"
 
+#include "ggml-cuda/allreduce.cuh"
 #include "ggml-cuda/common.cuh"
 #include "ggml-cuda/acc.cuh"
 #include "ggml-cuda/add-id.cuh"
@@ -39,6 +40,7 @@
 #include "ggml-cuda/rope.cuh"
 #include "ggml-cuda/roll.cuh"
 #include "ggml-cuda/scale.cuh"
+#include "ggml-cuda/snake.cuh"
 #include "ggml-cuda/softcap.cuh"
 #include "ggml-cuda/softmax.cuh"
 #include "ggml-cuda/ssm-conv.cuh"
@@ -87,6 +89,9 @@
 
 static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
 
+#define GGML_LOG_WARN_ONCE(str) \
+    { static std::once_flag warn_flag; std::call_once(warn_flag, []() { GGML_LOG_WARN(str); }); }
+
 [[noreturn]]
 void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
     int id = -1; // in case cudaGetDevice fails
@@ -1186,70 +1191,46 @@ static const ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_inte
     /* .is_host          = */ ggml_backend_cuda_split_buffer_type_is_host,
 };
 
-#ifdef GGML_USE_NCCL
+// Communication context for multi-GPU AllReduce during tensor parallelism.
+//
+// Created once per meta backend instance.  Resources for the selected mode
+// (NCCL communicators or the internal AllReduce pipeline) are initialised
+// eagerly during comm_init so any init failure surfaces at startup rather
+// than mid-run.
 struct ggml_backend_cuda_comm_context {
+    using try_allreduce_fn = bool(*)(ggml_backend_cuda_comm_context *, struct ggml_tensor **);
+
     std::vector<ggml_backend_t> backends;
-    std::vector<ncclComm_t> comms;
+    std::vector<int>            dev_ids;
 
-    ~ggml_backend_cuda_comm_context() {
-        for (ncclComm_t comm : comms) {
-            NCCL_CHECK(ncclCommDestroy(comm));
-        }
-    }
-};
-#endif // GGML_USE_NCCL
+    // Set by the init chain (comm_init_{nccl, internal, none}) to one of
+    // try_allreduce_{nccl, internal, butterfly}.  nccl needs `comms`,
+    // internal needs `ar_pipeline`, butterfly needs nothing.  Per-call
+    // failures return false; the meta backend's generic implementation then
+    // handles that call.
+    try_allreduce_fn            try_allreduce = nullptr;
+
+    ggml_cuda_ar_pipeline *     ar_pipeline = nullptr;
 
-static void ggml_backend_cuda_comm_free(void * comm_ctx_v) {
 #ifdef GGML_USE_NCCL
-    if (comm_ctx_v == nullptr) {
-        return;
-    }
-    ggml_backend_cuda_comm_context * comm_ctx = (ggml_backend_cuda_comm_context *) comm_ctx_v;
-    delete comm_ctx;
-#else
-    GGML_UNUSED(comm_ctx_v);
+    std::vector<ncclComm_t>     comms;
 #endif // GGML_USE_NCCL
-}
 
-static void * ggml_backend_cuda_comm_init(ggml_backend_t * backends, size_t n_backends) {
+    ~ggml_backend_cuda_comm_context() {
 #ifdef GGML_USE_NCCL
-    for (size_t i = 0; i < n_backends; i++) {
-        if (!ggml_backend_is_cuda(backends[i])) {
-            return nullptr;
+        for (ncclComm_t comm : comms) {
+            NCCL_CHECK(ncclCommDestroy(comm));
         }
-    }
-    ggml_backend_cuda_comm_context * ret = new ggml_backend_cuda_comm_context;
-    std::vector<int> dev_ids;
-    ret->backends.reserve(n_backends);
-    dev_ids.reserve(n_backends);
-    for (size_t i = 0; i < n_backends; i++) {
-        ret->backends.push_back(backends[i]);
-        ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backends[i]->context;
-        dev_ids.push_back(cuda_ctx->device);
-    }
-
-    ret->comms.resize(n_backends);
-    NCCL_CHECK(ncclCommInitAll(ret->comms.data(), n_backends, dev_ids.data()));
-    return ret;
-#else
-    // If NCCL is installed it is used by default for optimal performance.
-    // However, NVIDIA does not distribute NCCL with CUDA so users may be unwittingly missing this package.
-    // RCCL is disabled by default, users are explicitly opting in.
-    // Therefore print no warning for RCCL.
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    static bool warning_printed = false;
-    if (!warning_printed) {
-        GGML_LOG_WARN("%s: NVIDIA Collective Communications Library (NCCL) is unavailable, multi GPU performance will be suboptimal\n", __func__);
-        warning_printed = true;
-    }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    GGML_UNUSED_VARS(backends, n_backends);
-    return nullptr;
 #endif // GGML_USE_NCCL
-}
+        ggml_cuda_ar_pipeline_free(ar_pipeline);
+    }
+};
 
-static bool ggml_backend_cuda_comm_allreduce_tensor(void * comm_ctx_v, struct ggml_tensor ** tensors) {
 #ifdef GGML_USE_NCCL
+// AllReduce via NCCL. Reduces as FP32 for small tensors and BF16 for large
+// tensors (bandwidth-bound), then converts back to FP32.
+static bool ggml_backend_cuda_comm_allreduce_nccl(
+        ggml_backend_cuda_comm_context * comm_ctx, struct ggml_tensor ** tensors) {
     const int64_t ne = ggml_nelements(tensors[0]);
     // FIXME the input of llm_graph_context::build_in_out_ids can produce a tensor with 0 elements if n_outputs == 0
     // This then causes a crash in this function
@@ -1257,8 +1238,6 @@ static bool ggml_backend_cuda_comm_allreduce_tensor(void * comm_ctx_v, struct gg
         return true;
     }
 
-    GGML_ASSERT(comm_ctx_v != nullptr);
-    ggml_backend_cuda_comm_context * comm_ctx = (ggml_backend_cuda_comm_context *) comm_ctx_v;
     const size_t n_backends = comm_ctx->backends.size();
 
     for (size_t i = 0; i < n_backends; ++i) {
@@ -1283,7 +1262,6 @@ static bool ggml_backend_cuda_comm_allreduce_tensor(void * comm_ctx_v, struct gg
             NCCL_CHECK(ncclAllReduce(tensors[i]->data, tensors[i]->data, ne, ncclFloat, ncclSum, comm_ctx->comms[i], cuda_ctx->stream()));
         }
         NCCL_CHECK(ncclGroupEnd());
-
         return true;
     }
 
@@ -1322,10 +1300,184 @@ static bool ggml_backend_cuda_comm_allreduce_tensor(void * comm_ctx_v, struct gg
     }
 
     return true;
-#else
-    GGML_UNUSED_VARS(comm_ctx_v, tensors);
+}
+#endif // GGML_USE_NCCL
+
+// Run the internal AR pipeline.  Returns false on unsupported / failed input
+// -- the caller decides whether to abort (env-forced) or fall back silently.
+static bool ggml_backend_cuda_comm_allreduce_internal(
+        ggml_backend_cuda_comm_context * comm_ctx, struct ggml_tensor ** tensors) {
+    GGML_ASSERT(comm_ctx->ar_pipeline != nullptr);
+
+    const size_t n_backends = comm_ctx->backends.size();
+    GGML_ASSERT(n_backends == 2);
+    GGML_ASSERT(tensors[0] != nullptr);
+
+    const int64_t   ne   = ggml_nelements(tensors[0]);
+    const ggml_type type = tensors[0]->type;
+
+    if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16 && type != GGML_TYPE_BF16) {
+        GGML_LOG_DEBUG("%s: internal unsupported: type=%d\n", __func__, (int) type);
+        return false;
+    }
+
+    if (ne == 0) {
+        return true;
+    }
+
+    for (size_t i = 0; i < n_backends; ++i) {
+        if (tensors[i] == nullptr) {
+            GGML_LOG_ERROR("%s: internal failed: tensor[%zu] is null\n", __func__, i);
+            return false;
+        }
+        if (ggml_nelements(tensors[i]) != ne || tensors[i]->type != type) {
+            GGML_LOG_ERROR("%s: internal failed: tensor[%zu] ne=%" PRId64 " type=%d expected ne=%" PRId64 " type=%d\n",
+                           __func__, i, ggml_nelements(tensors[i]), (int) tensors[i]->type, ne, (int) type);
+            return false;
+        }
+        if (!ggml_is_contiguously_allocated(tensors[i])) {
+            GGML_LOG_DEBUG("%s: internal unsupported: tensor[%zu] is not contiguously allocated: ne=%" PRId64 " nbytes=%zu packed=%zu type=%d\n",
+                           __func__, i, ne, ggml_nbytes(tensors[i]),
+                           (size_t) ne * ggml_type_size(type) / ggml_blck_size(type), (int) type);
+            return false;
+        }
+        if (((uintptr_t) tensors[i]->data & 0xF) != 0) {
+            GGML_LOG_DEBUG("%s: internal unsupported: tensor[%zu] data pointer is not 16-byte aligned: %p type=%d ne=%" PRId64 "\n",
+                           __func__, i, tensors[i]->data, (int) type, ne);
+            return false;
+        }
+        GGML_ASSERT((ggml_nbytes(tensors[i]) & 0xF) == 0);
+    }
+
+    return ggml_cuda_ar_allreduce(comm_ctx->ar_pipeline, comm_ctx->backends.data(), tensors);
+}
+
+// ---------------------------------------------------------------------------
+// Per-call dispatch -- three variants, one per backend.  Each is set as
+// comm_ctx->try_allreduce by the matching init step.  Per-call failure
+// returns false; the meta backend's generic implementation handles that call.
+// ---------------------------------------------------------------------------
+
+#ifdef GGML_USE_NCCL
+static bool ggml_backend_cuda_comm_try_allreduce_nccl(
+        ggml_backend_cuda_comm_context * comm_ctx, struct ggml_tensor ** tensors) {
+    return ggml_backend_cuda_comm_allreduce_nccl(comm_ctx, tensors);
+}
+#endif // GGML_USE_NCCL
+
+static bool ggml_backend_cuda_comm_try_allreduce_internal(
+        ggml_backend_cuda_comm_context * comm_ctx, struct ggml_tensor ** tensors) {
+    return ggml_backend_cuda_comm_allreduce_internal(comm_ctx, tensors);
+}
+
+static bool ggml_backend_cuda_comm_try_allreduce_butterfly(
+        ggml_backend_cuda_comm_context *, struct ggml_tensor **) {
     return false;
+}
+
+static void ggml_backend_cuda_comm_free(void * comm_ctx_v) {
+    if (comm_ctx_v == nullptr) {
+        return;
+    }
+    delete static_cast<ggml_backend_cuda_comm_context *>(comm_ctx_v);
+}
+
+// ---------------------------------------------------------------------------
+// Init -- chained nccl -> internal -> none.  Each step tries to bring up its
+// resource; on failure it warns and recurses into the next step.
+// ---------------------------------------------------------------------------
+static void ggml_backend_cuda_comm_init_none(ggml_backend_cuda_comm_context * ret) {
+    ret->try_allreduce = ggml_backend_cuda_comm_try_allreduce_butterfly;
+}
+
+static void ggml_backend_cuda_comm_init_internal(ggml_backend_cuda_comm_context * ret) {
+    ret->ar_pipeline = ggml_cuda_ar_pipeline_init(ret->dev_ids.data(), ret->dev_ids.size());
+    if (ret->ar_pipeline) {
+        ret->try_allreduce = ggml_backend_cuda_comm_try_allreduce_internal;
+        return;
+    }
+
+    // Clear sticky CUDA error from the failed init.
+    (void) cudaGetLastError();
+    GGML_LOG_WARN("internal AllReduce init failed (n_devices != 2?); "
+                  "falling back to meta-backend butterfly\n");
+    ggml_backend_cuda_comm_init_none(ret);
+}
+
+static void ggml_backend_cuda_comm_init_nccl(ggml_backend_cuda_comm_context * ret) {
+#ifdef GGML_USE_NCCL
+    const size_t n = ret->dev_ids.size();
+    ret->comms.resize(n);
+    ncclResult_t rc = ncclCommInitAll(ret->comms.data(), (int) n, ret->dev_ids.data());
+    if (rc == ncclSuccess) {
+        ret->try_allreduce = ggml_backend_cuda_comm_try_allreduce_nccl;
+        return;
+    }
+
+    ret->comms.clear();
+    GGML_LOG_WARN("NCCL init failed (%s); falling back to internal AllReduce\n",
+                  ncclGetErrorString(rc));
+#else // GGML_USE_NCCL
+#ifndef GGML_USE_HIP
+    GGML_LOG_WARN("NCCL not compiled in; falling back to internal AllReduce.  "
+                  "Recompile with -DGGML_CUDA_NCCL=ON for best multi-GPU performance.\n");
+#endif // !GGML_USE_HIP
 #endif // GGML_USE_NCCL
+
+    ggml_backend_cuda_comm_init_internal(ret);
+}
+
+// Top-level init.  Picks one of the three init paths based on
+// GGML_CUDA_ALLREDUCE (or the platform default) and lets the chain handle
+// any fallback.  Unrecognised env values warn and fall through to the
+// platform default.
+static void * ggml_backend_cuda_comm_init(ggml_backend_t * backends, size_t n_backends) {
+    for (size_t i = 0; i < n_backends; i++) {
+        if (!ggml_backend_is_cuda(backends[i])) {
+            return nullptr;
+        }
+    }
+
+    auto * ret = new ggml_backend_cuda_comm_context;
+    ret->backends.assign(backends, backends + n_backends);
+    ret->dev_ids.reserve(n_backends);
+    for (size_t i = 0; i < n_backends; i++) {
+        ret->dev_ids.push_back(static_cast<ggml_backend_cuda_context *>(backends[i]->context)->device);
+    }
+
+    const char * env = getenv("GGML_CUDA_ALLREDUCE");
+    if (!env) {
+        // Platform default: Linux uses NCCL, otherwise (generally Windows) internal
+#if defined(__linux__)
+        ggml_backend_cuda_comm_init_nccl(ret);
+#else
+        ggml_backend_cuda_comm_init_internal(ret);
+#endif // defined(__linux__)
+    } else {
+        std::string env_str(env);
+        if (env_str == "nccl") {
+            ggml_backend_cuda_comm_init_nccl(ret);
+        } else if (env_str == "internal") {
+            ggml_backend_cuda_comm_init_internal(ret);
+        } else if (env_str == "none") {
+            ggml_backend_cuda_comm_init_none(ret);
+        } else {
+            GGML_LOG_WARN("unknown GGML_CUDA_ALLREDUCE value: %s\n", env);
+            ggml_backend_cuda_comm_init_none(ret);
+        }
+    }
+
+    return ret;
+}
+
+// Top-level dispatch -- calls the function pointer chosen by comm_init.
+// Returns false to let the meta-backend's butterfly run.
+static bool ggml_backend_cuda_comm_allreduce_tensor(void * comm_ctx_v, struct ggml_tensor ** tensors) {
+    if (comm_ctx_v == nullptr) {
+        return false;
+    }
+    auto * comm_ctx = static_cast<ggml_backend_cuda_comm_context *>(comm_ctx_v);
+    return comm_ctx->try_allreduce(comm_ctx, tensors);
 }
 
 ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split) {
@@ -3622,6 +3774,9 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
      && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_SILU) {
         const ggml_tensor * ssm_conv = cgraph->nodes[node_idx];
         const ggml_tensor * silu     = cgraph->nodes[node_idx+1];
+        if (ggml_get_unary_op(silu) != unary_ops.begin()[0]) {
+            return false;
+        }
 
         if (ssm_conv->type != GGML_TYPE_F32 || silu->type != GGML_TYPE_F32) {
             return false;
@@ -3630,6 +3785,31 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
         return true;
     }
 
+    if (ops.size() == 3 && ops.begin()[0] == GGML_OP_SSM_CONV && ops.begin()[1] == GGML_OP_ADD
+     && ops.begin()[2] == GGML_OP_UNARY && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_SILU) {
+        const ggml_tensor * ssm_conv = cgraph->nodes[node_idx];
+        const ggml_tensor * add      = cgraph->nodes[node_idx+1];
+        const ggml_tensor * silu     = cgraph->nodes[node_idx+2];
+        if (ggml_get_unary_op(silu) != unary_ops.begin()[0]) {
+            return false;
+        }
+
+        if (ssm_conv->type != GGML_TYPE_F32 || add->type != GGML_TYPE_F32 || silu->type != GGML_TYPE_F32) {
+            return false;
+        }
+
+        // ADD must consume ssm_conv's output and broadcast a 1-D channel-wise bias.
+        const ggml_tensor * bias = (add->src[0] == ssm_conv) ? add->src[1] : add->src[0];
+        if (bias->type != GGML_TYPE_F32 || !ggml_is_contiguous(bias)) {
+            return false;
+        }
+        if (ggml_nelements(bias) != ssm_conv->ne[0] || bias->ne[0] != ssm_conv->ne[0]) {
+            return false;
+        }
+
+        return true;
+    }
+
     if (ops.size() == 2 && ops.begin()[0] == GGML_OP_UNARY && ops.begin()[1] == GGML_OP_MUL
      && unary_ops.size() == 1 && (unary_ops.begin()[0] == GGML_UNARY_OP_SILU || unary_ops.begin()[0] == GGML_UNARY_OP_SIGMOID || unary_ops.begin()[0] == GGML_UNARY_OP_SOFTPLUS)) {
         const ggml_tensor * unary = cgraph->nodes[node_idx];
@@ -3658,6 +3838,30 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
         return true;
     }
 
+    if (ops.size() == 2 && ops.begin()[0] == GGML_OP_UNARY && ops.begin()[1] == GGML_OP_SQR
+     && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_RELU) {
+        const ggml_tensor * unary = cgraph->nodes[node_idx];
+        const ggml_tensor * sqr   = cgraph->nodes[node_idx+1];
+
+        if (ggml_get_unary_op(unary) != GGML_UNARY_OP_RELU) {
+            return false;
+        }
+
+        if (unary->type != GGML_TYPE_F32 && unary->type != GGML_TYPE_F16) {
+            return false;
+        }
+
+        if (unary->type != sqr->type) {
+            return false;
+        }
+
+        if (!ggml_is_contiguous(unary->src[0])) {
+            return false;
+        }
+
+        return true;
+    }
+
     if (ops.size() == 3 && ops.begin()[0] == GGML_OP_SCALE && ops.begin()[1] == GGML_OP_UNARY && ops.begin()[2] == GGML_OP_SCALE
      && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_TANH) {
         const ggml_tensor *scale  = cgraph->nodes[node_idx];
@@ -3682,6 +3886,406 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
     return false;
 }
 
+// try and fuse nodes and return the number of nodes to skip
+static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, int i) {
+
+    static bool disable_fusion = getenv("GGML_CUDA_DISABLE_FUSION") != nullptr && std::atoi(getenv("GGML_CUDA_DISABLE_FUSION"));
+    if (disable_fusion) {
+        return 0;
+    }
+
+    ggml_tensor * node = cgraph->nodes[i];
+
+    //topk-moe
+    if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX ||
+            cgraph->nodes[i]->op == GGML_OP_ARGSORT) {
+        ggml_cuda_topk_moe_args args;
+        const bool              can_fuse = ggml_cuda_topk_moe_fusion(cgraph, i, args);
+        std::vector<ggml_op>    ops;
+
+        if (can_fuse) {
+            const ggml_tensor * logits  = node->src[0];
+            ggml_tensor *       weights = nullptr;
+            ggml_tensor *       ids     = nullptr;
+            const ggml_tensor * bias    = nullptr;
+            const ggml_tensor * clamp   = nullptr;
+            const ggml_tensor * scale   = nullptr;
+
+            if (!args.delayed_softmax) {
+                ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX;
+                int     out_nodes[2];  // nodes which can't be elided
+
+                if (args.prob_bias) {
+                    bias = cgraph->nodes[i + 2]->src[1];
+                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW,
+                                            GGML_OP_GET_ROWS });
+                    out_nodes[0] = i + 4;
+                    ids          = cgraph->nodes[i + 4];
+                } else {
+                    ops.insert(ops.end(),
+                               { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS });
+                    out_nodes[0] = i + 3;
+                    ids          = cgraph->nodes[i + 3];
+                }
+
+                if (args.norm) {
+                    ops.insert(ops.end(),
+                               { GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP, GGML_OP_DIV, GGML_OP_RESHAPE });
+                    clamp = cgraph->nodes[i + ops.size() - 3];
+                }
+                if (args.scale) {
+                    ops.insert(ops.end(), { GGML_OP_SCALE });
+                    scale = cgraph->nodes[i + ops.size() - 1];
+                }
+
+                weights      = cgraph->nodes[i + ops.size() - 1];
+                out_nodes[1] = i + ops.size() - 1;
+
+                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
+                        ggml_cuda_should_use_topk_moe(node, logits, weights, ids) &&
+                        ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/true)) {
+                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
+                    return ops.size() - 1;
+                }
+            } else if (!args.norm && !args.prob_bias) {
+                //special case gpt-oss, no norm, no bias.
+                ops.insert(ops.end(), { GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
+                                        GGML_OP_SOFT_MAX, GGML_OP_RESHAPE });
+                weights                     = cgraph->nodes[i + 5];
+                ids                         = cgraph->nodes[i + 1];
+                const ggml_tensor * softmax = cgraph->nodes[i + 4];
+
+                int out_nodes[2] = { i + 1, i + 5 };
+                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
+                        ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids) &&
+                        ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/true)) {
+                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
+                    return ops.size() - 1;
+                }
+            }
+        }
+    }
+
+    //RoPE + view + set-rows
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) {
+        ggml_tensor * rope     = cgraph->nodes[i];
+        ggml_tensor * set_rows = cgraph->nodes[i + 2];
+
+        ggml_cuda_op_rope_fused(*cuda_ctx, rope, set_rows);
+        return 2;
+    }
+
+    // Snake activation: y = x + sin(a*x)^2 * inv_b
+    // Naive 5-op decomposition emitted by frontends: mul -> sin -> sqr -> mul -> add
+    if (ggml_can_fuse_subgraph(cgraph, i,
+            { GGML_OP_MUL, GGML_OP_SIN, GGML_OP_SQR, GGML_OP_MUL, GGML_OP_ADD },
+            { i + 4 })) {
+        const ggml_tensor * mul0 = cgraph->nodes[i];
+        const ggml_tensor * sqr  = cgraph->nodes[i + 2];
+        const ggml_tensor * mul1 = cgraph->nodes[i + 3];
+        ggml_tensor *       add  = cgraph->nodes[i + 4];
+
+        // x carries the full activation shape, a is the broadcast operand
+        const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1];
+        const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0];
+
+        // mul1 reads sqr and inv_b in either operand order
+        const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0];
+
+        // closure check: the trailing add must read the same x as the leading mul
+        const ggml_tensor * x_in_add = (add->src[0] == mul1) ? add->src[1] : add->src[0];
+
+        // Kernel iterates over total = T * C, so x and add must be 2D and
+        // a / inv_b must collapse to [1, C, 1, 1]. Higher dims are not handled.
+        const bool dim_ok   = (x->ne[2]   == 1 && x->ne[3]   == 1) &&
+                              (add->ne[2] == 1 && add->ne[3] == 1) &&
+                              (a->ne[2]   == 1 && a->ne[3]   == 1);
+        const bool shape_ok = ggml_are_same_shape(a, inv_b) && a->ne[0] == 1 && a->ne[1] == x->ne[1];
+
+        // x must be in the supported whitelist and every operand / intermediate
+        // result must share x's type, since launch_snake casts a / inv_b as
+        // float and templates the kernel on a single T. Mixed precision chains
+        // fall back to the naive path.
+        const ggml_tensor * sin1 = cgraph->nodes[i + 1];
+        const bool types_ok = (x->type == GGML_TYPE_F32 || x->type == GGML_TYPE_F16 || x->type == GGML_TYPE_BF16) &&
+                              (a->type    == x->type) && (inv_b->type == x->type) &&
+                              (mul0->type == x->type) && (sin1->type  == x->type) &&
+                              (sqr->type  == x->type) && (mul1->type  == x->type) &&
+                              (add->type  == x->type);
+
+        if (types_ok && shape_ok && dim_ok && x_in_add == x) {
+            ggml_cuda_op_snake_fused(*cuda_ctx, x, a, inv_b, add);
+            return 4;
+        }
+    }
+
+    // multi-(add or mul)
+    if (node->op == GGML_OP_ADD || node->op == GGML_OP_MUL) {
+        int     n_fuse = 0;
+        ggml_op ops[8];
+        std::fill(ops, ops + 8, node->op);
+
+        for (; n_fuse <= 6; ++n_fuse) {
+            if (!ggml_can_fuse(cgraph, i + n_fuse, ops + n_fuse, 2)) {
+                break;
+            }
+            if (cgraph->nodes[i + n_fuse] != cgraph->nodes[i + n_fuse + 1]->src[0]) {
+                break;
+            }
+            if (!ggml_are_same_layout(cgraph->nodes[i + n_fuse]->src[1], cgraph->nodes[i + n_fuse + 1]->src[1])) {
+                break;
+            }
+        }
+
+        n_fuse++;
+
+        if (n_fuse > 1) {
+            ggml_tensor fused_node;
+            memcpy(&fused_node, node, sizeof(ggml_tensor));
+            for (int j = 0; j < n_fuse - 1; ++j) {
+                fused_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
+            }
+            fused_node.data = cgraph->nodes[i + n_fuse - 1]->data;
+            if (node->op == GGML_OP_ADD) {
+                ggml_cuda_op_fused_add(*cuda_ctx, &fused_node, n_fuse);
+            } else {
+                ggml_cuda_op_fused_mul(*cuda_ctx, &fused_node, n_fuse);
+            }
+            return n_fuse - 1;
+        }
+    }
+
+    bool fused_mul_mat_vec = false;
+    int  fused_node_count  = 0;
+
+    // gate + glu + up
+    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+        if (ggml_cuda_can_fuse(cgraph, i, { op, bias_op, op, bias_op, GGML_OP_GLU }, {})) {
+            ggml_tensor * glu         = cgraph->nodes[i + 4];
+            ggml_tensor * gate_bias_n = glu->src[0];
+            ggml_tensor * up_bias_n   = glu->src[1];
+
+            //we don't assume the order for {gate, up}. Instead infer it from the bias tensor
+            ggml_tensor * gate_n = nullptr;
+            ggml_tensor * up_n   = nullptr;
+
+            if (gate_bias_n->src[0] == cgraph->nodes[i] || gate_bias_n->src[1] == cgraph->nodes[i]) {
+                gate_n = cgraph->nodes[i];
+                up_n   = cgraph->nodes[i + 2];
+            } else if (gate_bias_n->src[0] == cgraph->nodes[i + 2] || gate_bias_n->src[1] == cgraph->nodes[i + 2]) {
+                gate_n = cgraph->nodes[i + 2];
+                up_n   = cgraph->nodes[i];
+            } else {
+                continue;
+            }
+
+            auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) {
+                if (op_bias == GGML_OP_ADD) {
+                    if (bias_node->src[0] == mul_node) {
+                        return bias_node->src[1];
+                    }
+                    if (bias_node->src[1] == mul_node) {
+                        return bias_node->src[0];
+                    }
+                    return (ggml_tensor *) nullptr;
+                }
+                GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
+                GGML_ASSERT(bias_node->src[0] == mul_node);
+                return bias_node->src[1];
+            };
+
+            ggml_tensor * up_bias_tensor   = get_bias_tensor(up_bias_n, up_n, bias_op);
+            ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
+
+            if (!up_bias_tensor || !gate_bias_tensor) {
+                continue;
+            }
+
+            // we don't support repeating adds
+            if (bias_op == GGML_OP_ADD && (!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
+                                           !ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
+                continue;
+            }
+
+            const ggml_tensor * src0 = up_n->src[0];
+            const ggml_tensor * src1 = up_n->src[1];
+            const ggml_tensor * ids  = up_n->src[2];
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_f(up_n)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate      = gate_n->src[0];
+                fusion_data.x_bias    = up_bias_tensor;
+                fusion_data.gate_bias = gate_bias_tensor;
+                fusion_data.glu_op    = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 5;
+                break;
+            }
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate      = gate_n->src[0];
+                fusion_data.x_bias    = up_bias_tensor;
+                fusion_data.gate_bias = gate_bias_tensor;
+                fusion_data.glu_op    = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 5;
+                break;
+            }
+        } else if (ggml_cuda_can_fuse(cgraph, i, { op, op, GGML_OP_GLU }, {})) {
+            ggml_tensor * glu  = cgraph->nodes[i + 2];
+            ggml_tensor * gate = glu->src[0];
+            ggml_tensor * up   = glu->src[1];
+
+            bool ok = (gate == cgraph->nodes[i] && up == cgraph->nodes[i + 1]) ||
+                      (gate == cgraph->nodes[i + 1] && up == cgraph->nodes[i]);
+
+            if (!ok) {
+                continue;
+            }
+
+            const ggml_tensor * src0 = up->src[0];
+            const ggml_tensor * src1 = up->src[1];
+            const ggml_tensor * ids  = up->src[2];
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_f(up)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate   = gate->src[0];
+                fusion_data.glu_op = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 3;
+                break;
+            }
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_q(up)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate   = gate->src[0];
+                fusion_data.glu_op = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 3;
+                break;
+            }
+        }
+    }
+
+    if (fused_mul_mat_vec) {
+        return fused_node_count - 1;
+    }
+
+    fused_mul_mat_vec = false;
+    fused_node_count  = 0;
+
+    // gate + add + glu + up + add
+    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+        if (!ggml_can_fuse(cgraph, i, { op, bias_op })) {
+            continue;
+        }
+
+        ggml_tensor * mm_node   = cgraph->nodes[i];
+        ggml_tensor * bias_node = cgraph->nodes[i + 1];
+
+        ggml_tensor * bias_tensor = nullptr;
+        if (bias_op == GGML_OP_ADD) {
+            if (bias_node->src[0] == mm_node) {
+                bias_tensor = bias_node->src[1];
+            } else if (bias_node->src[1] == mm_node) {
+                bias_tensor = bias_node->src[0];
+            } else {
+                continue;
+            }
+        } else {
+            if (bias_node->src[0] != mm_node) {
+                continue;
+            }
+            bias_tensor = bias_node->src[1];
+        }
+
+        const ggml_tensor * src0 = mm_node->src[0];
+        const ggml_tensor * src1 = mm_node->src[1];
+        const ggml_tensor * ids  = mm_node->src[2];
+
+        if (bias_op == GGML_OP_ADD_ID && bias_node->src[2] != ids) {
+            continue;
+        }
+
+        if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
+            continue;
+        }
+
+        ggml_cuda_mm_fusion_args_host fusion_data{};
+        fusion_data.x_bias = bias_tensor;
+
+        if (ggml_cuda_should_fuse_mul_mat_vec_f(mm_node)) {
+            ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
+            fused_mul_mat_vec = true;
+            fused_node_count  = 2;
+            break;
+        }
+
+        if (ggml_cuda_should_fuse_mul_mat_vec_q(mm_node)) {
+            ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
+            fused_mul_mat_vec = true;
+            fused_node_count  = 2;
+            break;
+        }
+    }
+
+    if (fused_mul_mat_vec) {
+        return fused_node_count - 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD }, {})) {
+        ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i + 1], cgraph->nodes[i + 2]);
+        return 2;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL }, {})) {
+        ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_ADD, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) {
+        ggml_cuda_op_ssm_conv(*cuda_ctx, node, cgraph->nodes[i + 1], cgraph->nodes[i + 2]);
+        return 2;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) {
+        ggml_cuda_op_ssm_conv(*cuda_ctx, node, /*bias_add_node=*/ nullptr, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SILU }) ||
+        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SIGMOID }) ||
+        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SOFTPLUS })) {
+        ggml_cuda_op_unary_mul(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_SQR }, { GGML_UNARY_OP_RELU })) {
+        ggml_cuda_op_relu_sqr(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SCALE, GGML_OP_UNARY, GGML_OP_SCALE }, { GGML_UNARY_OP_TANH })) {
+        ggml_cuda_op_softcap(*cuda_ctx, cgraph->nodes[i + 2], node);
+        return 2;
+    }
+
+    return 0;
+}
+
 static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, const bool use_cuda_graph, const bool cuda_graph_update_required, const void * graph_key) {
     bool graph_evaluated_or_captured = false;
 
@@ -3828,349 +4432,11 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                     continue;
                 }
 
-                // start of fusion operations
-                static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
-                if (!disable_fusion) {
-                    ggml_cuda_topk_moe_args args;
-
-                    if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX ||
-                        cgraph->nodes[i]->op == GGML_OP_ARGSORT) {
-                        const bool can_fuse = ggml_cuda_topk_moe_fusion(cgraph, i, args);
-
-                        std::vector<ggml_op> ops;
-
-                        if (can_fuse) {
-                            const ggml_tensor * logits  = node->src[0];
-                            ggml_tensor *       weights = nullptr;
-                            ggml_tensor *       ids     = nullptr;
-                            const ggml_tensor * bias    = nullptr;
-                            const ggml_tensor * clamp   = nullptr;
-                            const ggml_tensor * scale   = nullptr;
-
-                            if (!args.delayed_softmax) {
-                                ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX;
-                                int     out_nodes[2];  // nodes which can't be elided
-
-                                if (args.prob_bias) {
-                                    bias = cgraph->nodes[i + 2]->src[1];
-                                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT,
-                                                            GGML_OP_VIEW, GGML_OP_GET_ROWS });
-                                    out_nodes[0] = i + 4;
-                                    ids          = cgraph->nodes[i + 4];
-                                } else {
-                                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW,
-                                                            GGML_OP_GET_ROWS });
-                                    out_nodes[0] = i + 3;
-                                    ids          = cgraph->nodes[i + 3];
-                                }
-
-                                if (args.norm) {
-                                    ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP,
-                                                            GGML_OP_DIV, GGML_OP_RESHAPE });
-                                    clamp = cgraph->nodes[i + ops.size() - 3];
-                                }
-                                if (args.scale) {
-                                    ops.insert(ops.end(), { GGML_OP_SCALE });
-                                    scale = cgraph->nodes[i + ops.size() - 1];
-                                }
-
-                                weights      = cgraph->nodes[i + ops.size() - 1];
-                                out_nodes[1] = i + ops.size() - 1;
-
-                                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
-                                    ggml_cuda_should_use_topk_moe(node, logits, weights, ids) &&
-                                    ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/ true)) {
-                                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
-                                    i += ops.size() - 1;
-                                    continue;
-                                }
-                            } else if (!args.norm && !args.prob_bias) {
-                                //special case gpt-oss, no norm, no bias.
-                                ops.insert(ops.end(), { GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS,
-                                                        GGML_OP_RESHAPE, GGML_OP_SOFT_MAX, GGML_OP_RESHAPE });
-                                weights                     = cgraph->nodes[i + 5];
-                                ids                         = cgraph->nodes[i + 1];
-                                const ggml_tensor * softmax = cgraph->nodes[i + 4];
-
-                                int out_nodes[2] = { i + 1, i + 5 };
-                                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
-                                    ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids) &&
-                                    ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/ true)) {
-                                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
-                                    i += ops.size() - 1;
-                                    continue;
-                                }
-                            }
-                        }
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) {
-                        ggml_tensor * rope = cgraph->nodes[i];
-                        ggml_tensor * set_rows = cgraph->nodes[i + 2];
+                int nodes_to_skip = ggml_cuda_try_fuse(cuda_ctx, cgraph, i);
 
-                        ggml_cuda_op_rope_fused(*cuda_ctx, rope, set_rows);
-                        i += 2;
-                        continue;
-                    }
-
-                    if (node->op == GGML_OP_ADD || node->op == GGML_OP_MUL) {
-                        int n_fuse = 0;
-                        ggml_op ops[8];
-                        std::fill(ops, ops + 8, node->op);
-
-                        for (; n_fuse <= 6; ++n_fuse){
-                            if (!ggml_can_fuse(cgraph, i + n_fuse, ops + n_fuse, 2)) {
-                                break;
-                            }
-                            if (cgraph->nodes[i + n_fuse] != cgraph->nodes[i + n_fuse + 1]->src[0]) {
-                                break;
-                            }
-                            if (!ggml_are_same_layout(cgraph->nodes[i + n_fuse]->src[1], cgraph->nodes[i + n_fuse + 1]->src[1])) {
-                                break;
-                            }
-                        }
-
-                        n_fuse++;
-
-                        if (n_fuse > 1) {
-                            ggml_tensor fused_node;
-                            memcpy(&fused_node, node, sizeof(ggml_tensor));
-                            for (int j = 0; j < n_fuse - 1; ++j) {
-                                fused_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
-                            }
-                            fused_node.data = cgraph->nodes[i + n_fuse - 1]->data;
-                            if (node->op == GGML_OP_ADD) {
-                                ggml_cuda_op_fused_add(*cuda_ctx, &fused_node, n_fuse);
-                            } else {
-                                ggml_cuda_op_fused_mul(*cuda_ctx, &fused_node, n_fuse);
-                            }
-                            i += n_fuse - 1;
-
-                            continue;
-                        }
-                    }
-
-                    bool fused_mul_mat_vec = false;
-                    int fused_node_count = 0;
-
-                    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
-                        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
-
-                        if (ggml_cuda_can_fuse(cgraph, i, { op, bias_op, op, bias_op, GGML_OP_GLU }, {})) {
-                            ggml_tensor * glu         = cgraph->nodes[i + 4];
-                            ggml_tensor * gate_bias_n = glu->src[0];
-                            ggml_tensor * up_bias_n   = glu->src[1];
-
-                            //we don't assume the order for {gate, up}. Instead infer it from the bias tensor
-                            ggml_tensor * gate_n      = nullptr;
-                            ggml_tensor * up_n        = nullptr;
-
-                            if (gate_bias_n->src[0] == cgraph->nodes[i] || gate_bias_n->src[1] == cgraph->nodes[i]) {
-                                gate_n = cgraph->nodes[i];
-                                up_n   = cgraph->nodes[i + 2];
-                            } else if (gate_bias_n->src[0] == cgraph->nodes[i + 2] || gate_bias_n->src[1] == cgraph->nodes[i + 2]) {
-                                gate_n = cgraph->nodes[i + 2];
-                                up_n   = cgraph->nodes[i];
-                            } else {
-                                continue;
-                            }
-
-                            auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) {
-                                if (op_bias == GGML_OP_ADD) {
-                                    if (bias_node->src[0] == mul_node) {
-                                        return bias_node->src[1];
-                                    }
-                                    if (bias_node->src[1] == mul_node) {
-                                        return bias_node->src[0];
-                                    }
-                                    return (ggml_tensor *) nullptr;
-                                }
-                                GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
-                                GGML_ASSERT(bias_node->src[0] == mul_node);
-                                return bias_node->src[1];
-                            };
-
-                            ggml_tensor * up_bias_tensor   = get_bias_tensor(up_bias_n, up_n, bias_op);
-                            ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
-
-                            if (!up_bias_tensor || !gate_bias_tensor) {
-                                continue;
-                            }
-
-                            // we don't support repeating adds
-                            if (bias_op == GGML_OP_ADD &&
-                                (!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
-                                 !ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
-                                continue;
-                            }
-
-                            const ggml_tensor * src0 = up_n->src[0];
-                            const ggml_tensor * src1 = up_n->src[1];
-                            const ggml_tensor * ids  = up_n->src[2];
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_f(up_n)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate      = gate_n->src[0];
-                                fusion_data.x_bias    = up_bias_tensor;
-                                fusion_data.gate_bias = gate_bias_tensor;
-                                fusion_data.glu_op    = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 5;
-                                break;
-                            }
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate      = gate_n->src[0];
-                                fusion_data.x_bias    = up_bias_tensor;
-                                fusion_data.gate_bias = gate_bias_tensor;
-                                fusion_data.glu_op    = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 5;
-                                break;
-                            }
-                        } else if (ggml_cuda_can_fuse(cgraph, i, { op, op, GGML_OP_GLU }, {})) {
-                            ggml_tensor * glu  = cgraph->nodes[i + 2];
-                            ggml_tensor * gate = glu->src[0];
-                            ggml_tensor * up   = glu->src[1];
-
-                            bool ok = (gate == cgraph->nodes[i] && up == cgraph->nodes[i + 1])
-                                || (gate == cgraph->nodes[i + 1] && up == cgraph->nodes[i]);
-
-                            if (!ok) continue;
-
-                            const ggml_tensor * src0 = up->src[0];
-                            const ggml_tensor * src1 = up->src[1];
-                            const ggml_tensor * ids  = up->src[2];
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_f(up)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate   = gate->src[0];
-                                fusion_data.glu_op = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 3;
-                                break;
-                            }
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_q(up)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate   = gate->src[0];
-                                fusion_data.glu_op = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 3;
-                                break;
-                            }
-                        }
-                    }
-
-                    if (fused_mul_mat_vec) {
-                        i += fused_node_count - 1;
-                        continue;
-                    }
-
-                    fused_mul_mat_vec = false;
-                    fused_node_count = 0;
-
-                    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
-                        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
-
-                        if (!ggml_can_fuse(cgraph, i, { op, bias_op })) {
-                            continue;
-                        }
-
-                        ggml_tensor * mm_node   = cgraph->nodes[i];
-                        ggml_tensor * bias_node = cgraph->nodes[i + 1];
-
-                        ggml_tensor * bias_tensor = nullptr;
-                        if (bias_op == GGML_OP_ADD) {
-                            if (bias_node->src[0] == mm_node) {
-                                bias_tensor = bias_node->src[1];
-                            } else if (bias_node->src[1] == mm_node) {
-                                bias_tensor = bias_node->src[0];
-                            } else {
-                                continue;
-                            }
-                        } else {
-                            if (bias_node->src[0] != mm_node) {
-                                continue;
-                            }
-                            bias_tensor = bias_node->src[1];
-                        }
-
-                        const ggml_tensor * src0 = mm_node->src[0];
-                        const ggml_tensor * src1 = mm_node->src[1];
-                        const ggml_tensor * ids  = mm_node->src[2];
-
-                        if (bias_op == GGML_OP_ADD_ID && bias_node->src[2] != ids) {
-                            continue;
-                        }
-
-                        if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
-                            continue;
-                        }
-
-                        ggml_cuda_mm_fusion_args_host fusion_data{};
-                        fusion_data.x_bias = bias_tensor;
-
-                        if (ggml_cuda_should_fuse_mul_mat_vec_f(mm_node)) {
-                            ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
-                            fused_mul_mat_vec = true;
-                            fused_node_count = 2;
-                            break;
-                        }
-
-                        if (ggml_cuda_should_fuse_mul_mat_vec_q(mm_node)) {
-                            ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
-                            fused_mul_mat_vec = true;
-                            fused_node_count = 2;
-                            break;
-                        }
-                    }
-
-                    if (fused_mul_mat_vec) {
-                        i += fused_node_count - 1;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD}, {})) {
-                        ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i+1], cgraph->nodes[i+2]);
-                        i += 2;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL}, {})) {
-                        ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) {
-                        ggml_cuda_op_ssm_conv(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SILU }) ||
-                        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SIGMOID }) ||
-                        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SOFTPLUS })) {
-                        ggml_cuda_op_unary_mul(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SCALE, GGML_OP_UNARY, GGML_OP_SCALE }, { GGML_UNARY_OP_TANH })) {
-                        i += 2;
-                        ggml_cuda_op_softcap(*cuda_ctx, cgraph->nodes[i], node);
-                        continue;
-                    }
+                if (nodes_to_skip != 0) {
+                    i += nodes_to_skip;
+                    continue;
                 }
 #ifndef NDEBUG
                 assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
@@ -4584,8 +4850,8 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
     /* .free                    = */ ggml_backend_cuda_free,
     /* .set_tensor_async        = */ ggml_backend_cuda_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_cuda_get_tensor_async,
-    /* .get_tensor_2d_async     = */ ggml_backend_cuda_set_tensor_2d_async,
-    /* .set_tensor_2d_async     = */ ggml_backend_cuda_get_tensor_2d_async,
+    /* .set_tensor_2d_async     = */ ggml_backend_cuda_set_tensor_2d_async,
+    /* .get_tensor_2d_async     = */ ggml_backend_cuda_get_tensor_2d_async,
     /* .cpy_tensor_async        = */ ggml_backend_cuda_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_cuda_synchronize,
     /* .graph_plan_create       = */ NULL,
@@ -5119,12 +5385,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-        case GGML_OP_ADD:
         case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
-        case GGML_OP_SUB:
-        case GGML_OP_MUL:
-        case GGML_OP_DIV:
         case GGML_OP_SCALE:
         case GGML_OP_SQR:
         case GGML_OP_SQRT:
@@ -5136,6 +5398,13 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_TURBO_WHT:
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 &&
                    op->src[0]->ne[0] % 32 == 0;  // supports 32, 64, and 128 WHT groups
+        case GGML_OP_ADD:
+        case GGML_OP_SUB:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                   (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16) &&
+                   (op->type         == GGML_TYPE_F32 || op->type         == GGML_TYPE_F16);
         case GGML_OP_SSM_SCAN: {
             if (op->src[3]->ne[0] == 1) {
                 // Mamba2
@@ -5443,9 +5712,12 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
                 CUDA_CHECK(cudaGetDeviceProperties(&prop, i));
                 dev_ctx->description = prop.name;
 
-                char pci_bus_id[16] = {};
-                snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.0", prop.pciDomainID, prop.pciBusID, prop.pciDeviceID);
+                char pci_bus_id[32] = {};
+                CUDA_CHECK(cudaDeviceGetPCIBusId(pci_bus_id, sizeof(pci_bus_id), i));
                 dev_ctx->pci_bus_id = pci_bus_id;
+                for (char & c : dev_ctx->pci_bus_id) {
+                    c = std::tolower(c);
+                }
                 dev_ctx->op_offload_min_batch_size = min_batch_size;
 
                 ggml_backend_dev_t dev = new ggml_backend_device {
diff --git a/ggml/src/ggml-cuda/im2col.cu b/ggml/src/ggml-cuda/im2col.cu
index 56dc0545742..28c79ab462e 100644
--- a/ggml/src/ggml-cuda/im2col.cu
+++ b/ggml/src/ggml-cuda/im2col.cu
@@ -1,5 +1,6 @@
 #include "im2col.cuh"
 
+#define MAX_GRIDDIM_Y 65535
 #define MAX_GRIDDIM_Z 65535
 
 template <typename T>
@@ -18,22 +19,23 @@ static  __global__ void im2col_kernel(
     const int64_t ikh = rem / KW;
     const int64_t ikw = rem - ikh * KW;
 
-    const int64_t  iow = blockIdx.y;
-    for (int64_t iz = blockIdx.z; iz < N_OH; iz+=MAX_GRIDDIM_Z) {
-        const int64_t  in = iz / OH;
-        const int64_t  ioh = iz - in * OH;
+    for (int64_t iow = blockIdx.y; iow < OW; iow += MAX_GRIDDIM_Y) {
+        for (int64_t iz = blockIdx.z; iz < N_OH; iz += MAX_GRIDDIM_Z) {
+            const int64_t  in = iz / OH;
+            const int64_t  ioh = iz - in * OH;
 
-        const int64_t iiw = iow * s0 + ikw * d0 - p0;
-        const int64_t iih = ioh * s1 + ikh * d1 - p1;
+            const int64_t iiw = iow * s0 + ikw * d0 - p0;
+            const int64_t iih = ioh * s1 + ikh * d1 - p1;
 
-        const int64_t offset_dst =
-            ((in * OH + ioh) * OW + iow) * IC_KH_KW + iic * KH_KW + ikh * KW + ikw;
+            const int64_t offset_dst =
+                ((in * OH + ioh) * OW + iow) * IC_KH_KW + iic * KH_KW + ikh * KW + ikw;
 
-        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-            dst[offset_dst] = 0.0f;
-        } else {
-            const int64_t offset_src = iic * IC_IH_IW + in * IH_IW;
-            dst[offset_dst] = x[offset_src + iih * IW + iiw];
+            if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+                dst[offset_dst] = 0.0f;
+            } else {
+                const int64_t offset_src = iic * IC_IH_IW + in * IH_IW;
+                dst[offset_dst] = x[offset_src + iih * IW + iiw];
+            }
         }
     }
 
@@ -51,7 +53,7 @@ static void im2col_cuda(const float * x, T* dst,
     const int64_t num_blocks = (IC_KH_KW + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
     const int64_t N_OH = N * OH;
     const int64_t KH_KW = KW*KH;
-    dim3 block_nums(num_blocks, OW, MIN(N_OH, MAX_GRIDDIM_Z));
+    dim3 block_nums(num_blocks, MIN(OW, MAX_GRIDDIM_Y), MIN(N_OH, MAX_GRIDDIM_Z));
     im2col_kernel<<<block_nums, MIN(IC_KH_KW, CUDA_IM2COL_BLOCK_SIZE) , 0, stream>>>(x, dst, IC, IW, IH, OH, OW, KW, KH,
                                                                                      IC_IH_IW, IH_IW, N_OH, KH_KW, IC_KH_KW,
                                                                                      s0, s1, p0, p1, d0, d1);
@@ -136,23 +138,24 @@ static  __global__ void im2col_3d_kernel(
     const int64_t ikh = (i - iic * KD_KH_KW - ikd * KH_KW) / KW;
     const int64_t ikw = i % KW;
 
-    const int64_t  iow = blockIdx.y;
-    for (int64_t iz = blockIdx.z; iz < N_OD_OH; iz+=MAX_GRIDDIM_Z) {
-        const int64_t in  = iz / OD_OH;
-        const int64_t iod = (iz - in*OD_OH) / OH;
-        const int64_t ioh = iz % OH;
+    for (int64_t iow = blockIdx.y; iow < OW; iow += MAX_GRIDDIM_Y) {
+        for (int64_t iz = blockIdx.z; iz < N_OD_OH; iz += MAX_GRIDDIM_Z) {
+            const int64_t in  = iz / OD_OH;
+            const int64_t iod = (iz - in*OD_OH) / OH;
+            const int64_t ioh = iz % OH;
 
-        const int64_t iiw = iow * s0 + ikw * d0 - p0;
-        const int64_t iih = ioh * s1 + ikh * d1 - p1;
-        const int64_t iid = iod * s2 + ikd * d2 - p2;
+            const int64_t iiw = iow * s0 + ikw * d0 - p0;
+            const int64_t iih = ioh * s1 + ikh * d1 - p1;
+            const int64_t iid = iod * s2 + ikd * d2 - p2;
 
-        const int64_t offset_dst = in*OD_OH_OW_IC_KD_KH_KW + iod*OH_OW_IC_KD_KH_KW + ioh*OW_IC_KD_KH_KW + iow*IC_KD_KH_KW + iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw;
+            const int64_t offset_dst = in*OD_OH_OW_IC_KD_KH_KW + iod*OH_OW_IC_KD_KH_KW + ioh*OW_IC_KD_KH_KW + iow*IC_KD_KH_KW + iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw;
 
-        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) {
-            dst[offset_dst] = 0.0f;
-        } else {
-            const int64_t offset_src = ((in * IC + iic) * stride_q) + (iid * stride_z) + (iih * stride_y) + (iiw * stride_x);
-            dst[offset_dst] = src[offset_src];
+            if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) {
+                dst[offset_dst] = 0.0f;
+            } else {
+                const int64_t offset_src = ((in * IC + iic) * stride_q) + (iid * stride_z) + (iih * stride_y) + (iiw * stride_x);
+                dst[offset_dst] = src[offset_src];
+            }
         }
     }
 }
@@ -178,7 +181,7 @@ static void im2col_3d_cuda(const float * src, T* dst,
     const int64_t OH_OW_IC_KD_KH_KW = OH*OW*IC*KD*KH*KW;
     const int64_t OW_IC_KD_KH_KW = OW*IC*KD*KH*KW;
     const int64_t num_blocks = (IC_KD_KH_KW + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
-    dim3 block_nums(num_blocks, OW, MIN(N_OD_OH, MAX_GRIDDIM_Z));
+    dim3 block_nums(num_blocks, MIN(OW, MAX_GRIDDIM_Y), MIN(N_OD_OH, MAX_GRIDDIM_Z));
     im2col_3d_kernel<<<block_nums, MIN(IC_KD_KH_KW, CUDA_IM2COL_BLOCK_SIZE) , 0, stream>>>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
                                                                                            OH_OW, KD_KH_KW, ID_IH_IW, KH_KW, IH_IW, IC_ID_IH_IW,
                                                                                            IC_KD_KH_KW, OW_KD_KH_KW, OD_OH_OW_IC_KD_KH_KW,
diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
index b0f674635f1..8d7c69dc3e8 100644
--- a/ggml/src/ggml-cuda/mma.cuh
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -80,6 +80,7 @@ namespace ggml_cuda_mma {
         DATA_LAYOUT_J_MAJOR           = 10, // Matrix C for CDNA and RDNA4, int and float matrix C for RDNA3.
         DATA_LAYOUT_I_MAJOR_MIRRORED  = 20, // Volta, matrix A&B for RDNA3.
         DATA_LAYOUT_J_MAJOR_MIRRORED  = 30,
+        DATA_LAYOUT_I_MAJOR_SCRAMBLED = 40, // Scrambled matrix C for faster transposition (RDNA4/CDNA), convert to float to unscramble.
     };
     // Implemented mma combinations are:
     //   - (I_MAJOR, I_MAJOR)          -> I_MAJOR
@@ -312,13 +313,19 @@ namespace ggml_cuda_mma {
         half2 x[ne] = {{0.0f, 0.0f}};
 
         static constexpr __device__ bool supported() {
-            if (I == 16 && J == 8) return true;
+            if (I == 16 && J ==  8) return true;
+            if (I == 16 && J == 16) return true;
+            if (I == 32 && J ==  8) return true;
             return false;
         }
 
         static __device__ __forceinline__ int get_i(const int l) {
             if constexpr (I == 16 && J == 8) {
                 return threadIdx.x % 16;
+            } else if constexpr (I == 16 && J == 16) {
+                return threadIdx.x % 16;
+            } else if constexpr (I == 32 && J == 8) {
+                return (threadIdx.x % 16) * 2 + l / (ne/2);
             } else {
                 NO_DEVICE_CODE;
                 return -1;
@@ -327,7 +334,15 @@ namespace ggml_cuda_mma {
 
         static __device__ __forceinline__ int get_j(const int l) {
             if constexpr (I == 16 && J == 8) {
-                return ne * (threadIdx.x / 16) + l;
+                return (threadIdx.x / 16) * ne + l;
+            } else if constexpr (I == 16 && J == 16) {
+#ifdef RDNA3
+                return l*2 + (threadIdx.x / 16);
+#else
+                return (threadIdx.x / 16) * ne + l;
+#endif // RDNA3
+            } else if constexpr (I == 32 && J == 8) {
+                return (threadIdx.x / 16) * (ne/2) + l % (ne/2);
             } else {
                 NO_DEVICE_CODE;
                 return -1;
@@ -338,13 +353,19 @@ namespace ggml_cuda_mma {
         half2 x[ne] = {{0.0f, 0.0f}};
 
         static constexpr __device__ bool supported() {
-            if (I == 16 && J == 8) return true;
+            if (I == 16 && J ==  8) return true;
+            if (I == 16 && J == 16) return true;
+            if (I == 32 && J ==  8) return true;
             return false;
         }
 
         static __device__ __forceinline__ int get_i(const int l) {
             if constexpr (I == 16 && J == 8) {
                 return threadIdx.x % 16;
+            } else if constexpr (I == 16 && J == 16) {
+                return threadIdx.x % 16;
+            } else if constexpr (I == 32 && J == 8) {
+                return (threadIdx.x % 16) * 2 + l / (ne/2);
             } else {
                 NO_DEVICE_CODE;
                 return -1;
@@ -353,7 +374,11 @@ namespace ggml_cuda_mma {
 
         static __device__ __forceinline__ int get_j(const int l) {
             if constexpr (I == 16 && J == 8) {
-                return ne * (threadIdx.x / 16) + l;
+                return (threadIdx.x / 16) * ne + l;
+            } else if constexpr (I == 16 && J == 16) {
+                return (threadIdx.x / 16) * ne + l;
+            } else if constexpr (I == 32 && J == 8) {
+                return (threadIdx.x / 16) * (ne/2) + l % (ne/2);
             } else {
                 NO_DEVICE_CODE;
                 return -1;
@@ -516,12 +541,15 @@ namespace ggml_cuda_mma {
             if (I == 16 && J == 16) return true;
             if (I == 16 && J == 8)  return true;
             if (I == 16 && J == 4)  return true;
+            if (I == 32 && J == 8)  return true;
             return false;
         }
 
-        static __device__ __forceinline__ int get_i(const int /*l*/) {
-            if constexpr (supported()) {
+        static __device__ __forceinline__ int get_i(const int l) {
+            if constexpr (I == 16) {
                 return threadIdx.x % 16;
+            } else if constexpr (I == 32) {
+                return (threadIdx.x % 16) * 2 + l / (ne/2);
             } else {
                 NO_DEVICE_CODE;
                 return -1;
@@ -529,8 +557,10 @@ namespace ggml_cuda_mma {
         }
 
         static __device__ __forceinline__ int get_j(const int l) {
-            if constexpr (supported()) {
+            if constexpr (I == 16) {
                 return l;
+            } else if constexpr (I == 32) {
+                return l % (ne/2);
             } else {
                 NO_DEVICE_CODE;
                 return -1;
@@ -644,6 +674,40 @@ namespace ggml_cuda_mma {
         }
     };
 
+    template <int I_, int J_>
+    struct tile<I_, J_, half2, DATA_LAYOUT_I_MAJOR_SCRAMBLED> {
+        static constexpr int         I  = I_;
+        static constexpr int         J  = J_;
+        static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_SCRAMBLED;
+
+        static constexpr int ne = I * J / ggml_cuda_get_physical_warp_size();
+        half2 x[ne] = {{0.0f, 0.0f}};
+
+        static constexpr __device__ bool supported() {
+            if (I == 16 && J == 16) return true;
+            return false;
+        }
+
+        static __device__ __forceinline__ int get_i(const int l) {
+            return tile<I_, J_, half2, DATA_LAYOUT_I_MAJOR>::get_i(l);
+        }
+    };
+
+    static __device__ __forceinline__ tile<16, 16, half2, DATA_LAYOUT_I_MAJOR> unscramble(const tile<16, 16, half2, DATA_LAYOUT_I_MAJOR_SCRAMBLED> & t) {
+#if defined(AMD_MFMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+        tile<16, 16, half2, DATA_LAYOUT_I_MAJOR> ret;
+#pragma unroll
+        for (int l0 = 0; l0 < t.ne/2; ++l0) {
+            ret.x[2*l0 + 0] =  __lows2half2(t.x[l0], t.x[l0 + t.ne/2]);
+            ret.x[2*l0 + 1] = __highs2half2(t.x[l0], t.x[l0 + t.ne/2]);
+        }
+        return ret;
+#else
+        NO_DEVICE_CODE;
+        GGML_UNUSED(t);
+#endif // defined(AMD_MFMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+    }
+
 #if defined(TURING_MMA_AVAILABLE)
     template <int I, int J>
     static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {
@@ -660,6 +724,21 @@ namespace ggml_cuda_mma {
         ret.x[0] = ggml_cuda_movmatrix(t.x[0]);
         ret.x[1] = ggml_cuda_movmatrix(t.x[1]);
 
+        return ret;
+    }
+#elif defined(AMD_WMMA_AVAILABLE) && defined(RDNA3)
+    static __device__ __forceinline__ tile<16, 8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> get_half2(
+            const tile<16, 16, float, DATA_LAYOUT_I_MAJOR> & tile_float) {
+        tile<16, 8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> ret;
+#pragma unroll
+        for (int l = 0; l < tile_float.ne; ++l) {
+            float tmp[2];
+            int i = threadIdx.x / 16;
+            tmp[i] = tile_float.x[l];
+            i ^= 1;
+            tmp[i] = __shfl_xor_sync(0xFFFFFFFF, tile_float.x[l], 16, WARP_SIZE);
+            ret.x[l] = make_half2(tmp[0], tmp[1]);
+        }
         return ret;
     }
 #elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
@@ -802,21 +881,35 @@ namespace ggml_cuda_mma {
 #endif // defined(VOLTA_MMA_AVAILABLE)
     }
 
-    template <typename T>
+    template <int I, typename T, data_layout dl>
     static __device__ __forceinline__ void load_ldmatrix_trans(
-            tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
+            tile<I, 8, T, dl> & t, const T * __restrict__ xs0, const int stride) {
 #ifdef TURING_MMA_AVAILABLE
+        static_assert(I == 16, "bad tile width");
+        static_assert(dl == DATA_LAYOUT_I_MAJOR, "bad data layout");
         int * xi = (int *) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
         asm volatile("ldmatrix.sync.aligned.m8n8.x4.trans.b16 {%0, %1, %2, %3}, [%4];"
             : "=r"(xi[0]), "=r"(xi[2]), "=r"(xi[1]), "=r"(xi[3])
             : "l"(xs));
 #elif defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
-        half * xh = (half *) t.x;
+        static_assert(dl == DATA_LAYOUT_I_MAJOR || dl == DATA_LAYOUT_I_MAJOR_MIRRORED, "bad data layout");
+        if constexpr (I == 32) {
 #pragma unroll
-        for (int l = 0; l < t.ne; ++l) {
-            xh[2*l + 0] = ((const half *) xs0)[(2*t.get_j(l) + 0)*(2*stride) + t.get_i(l)];
-            xh[2*l + 1] = ((const half *) xs0)[(2*t.get_j(l) + 1)*(2*stride) + t.get_i(l)];
+            for (int l0 = 0; l0 < t.ne/2; ++l0) {
+                const half2 tmp0 = xs0[(2*t.get_j(l0) + 0)*stride + t.get_i(l0)/2];
+                const half2 tmp1 = xs0[(2*t.get_j(l0) + 1)*stride + t.get_i(l0)/2];
+
+                t.x[l0]          =  __lows2half2(tmp0, tmp1);
+                t.x[l0 + t.ne/2] = __highs2half2(tmp0, tmp1);
+            }
+        } else {
+            half * xh = (half *) t.x;
+#pragma unroll
+            for (int l = 0; l < t.ne; ++l) {
+                xh[2*l + 0] = ((const half *) xs0)[(2*t.get_j(l) + 0)*(2*stride) + t.get_i(l)];
+                xh[2*l + 1] = ((const half *) xs0)[(2*t.get_j(l) + 1)*(2*stride) + t.get_i(l)];
+            }
         }
 #else
         GGML_UNUSED_VARS(t, xs0, stride);
@@ -972,6 +1065,20 @@ namespace ggml_cuda_mma {
 #endif // TURING_MMA_AVAILABLE
     }
 
+    static __device__ __forceinline__ void mma(
+            tile<16, 16, half2, DATA_LAYOUT_I_MAJOR_SCRAMBLED> & D, const tile<32, 8, half2, DATA_LAYOUT_I_MAJOR> & A,
+            const tile<16, 8, half2, DATA_LAYOUT_I_MAJOR> & B) {
+#if defined(AMD_MFMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+        tile<16, 8, half2>       * D16 = (tile<16, 8, half2>       *) &D;
+        const tile<16, 8, half2> * A16 = (const tile<16, 8, half2> *) &A;
+        mma(D16[0], A16[0], B);
+        mma(D16[1], A16[1], B);
+#else
+        GGML_UNUSED_VARS(D, A, B);
+        NO_DEVICE_CODE;
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)
+    }
+
     template <data_layout dl_ab, data_layout dl_d>
     static __device__ __forceinline__ void mma(
             tile<16, 8, float, dl_d> & D, const tile<16, 8, float, dl_ab> & A, const tile<8, 8, float, dl_ab> & B) {
@@ -1015,25 +1122,35 @@ namespace ggml_cuda_mma {
 #endif // AMD_MFMA_AVAILABLE
     }
 
-    static __device__ __forceinline__ void mma_block_scaled(tile<16, 8, float> &     D,
-                                                            const tile<16, 8, int> & A,
-                                                            const tile<8, 8, int> &  B,
-                                                            uint32_t                 a_scale,
-                                                            uint32_t                 b_scale) {
+    template <ggml_type type>
+    static __device__ __forceinline__ void mma_block_scaled_fp4(tile<16, 8, float> &     D,
+                                                                const tile<16, 8, int> & A,
+                                                                const tile<8, 8, int> &  B,
+                                                                uint32_t                 a_scale,
+                                                                uint32_t                 b_scale) {
 #ifdef BLACKWELL_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         float *     Dxi = (float *) D.x;
 
-        asm volatile(
-            "mma.sync.aligned.kind::mxf4.block_scale.scale_vec::2X.m16n8k64.row.col.f32.e2m1.e2m1.f32.ue8m0 "
-            "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3}, "
-            "%10, {0, 0}, %11, {0, 0};"
-            : "+f"(Dxi[0]), "+f"(Dxi[1]), "+f"(Dxi[2]), "+f"(Dxi[3])
-            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]), "r"(a_scale), "r"(b_scale));
+        if constexpr (type == GGML_TYPE_MXFP4) {
+            asm volatile(
+                "mma.sync.aligned.kind::mxf4.block_scale.scale_vec::2X.m16n8k64.row.col.f32.e2m1.e2m1.f32.ue8m0 "
+                "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3}, "
+                "%10, {0, 0}, %11, {0, 0};"
+                : "+f"(Dxi[0]), "+f"(Dxi[1]), "+f"(Dxi[2]), "+f"(Dxi[3])
+                : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]), "r"(a_scale), "r"(b_scale));
+        } else {
+            asm volatile(
+                "mma.sync.aligned.kind::mxf4nvf4.block_scale.scale_vec::4X.m16n8k64.row.col.f32.e2m1.e2m1.f32.ue4m3 "
+                "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3}, "
+                "%10, {0, 0}, %11, {0, 0};"
+                : "+f"(Dxi[0]), "+f"(Dxi[1]), "+f"(Dxi[2]), "+f"(Dxi[3])
+                : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]), "r"(a_scale), "r"(b_scale));
+        }
 #else
         GGML_UNUSED_VARS(D, A, B, a_scale, b_scale);
-#endif  // BLACKWELL_MMA_AVAILABLE
+#endif // BLACKWELL_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
@@ -1286,6 +1403,22 @@ namespace ggml_cuda_mma {
 #endif // defined(VOLTA_MMA_AVAILABLE)
     }
 
+    static __device__ __forceinline__ void mma(
+            tile<16, 16, half2, DATA_LAYOUT_I_MAJOR> & D, const tile<32,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> & A,
+            const tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> & B) {
+#if defined(AMD_WMMA_AVAILABLE) && defined(RDNA3)
+        using halfx16_t = __attribute__((ext_vector_type(16))) _Float16;
+        halfx16_t       * xD = (halfx16_t       *) D.x;
+        const halfx16_t * xA = (const halfx16_t *) A.x;
+        const halfx16_t * xB = (const halfx16_t *) B.x;
+        xD[0] = __builtin_amdgcn_wmma_f16_16x16x16_f16_w32(xA[0], xB[0], xD[0], /*opsel =*/ 0);
+        xD[0] = __builtin_amdgcn_wmma_f16_16x16x16_f16_w32(xA[1], xB[0], xD[0], /*opsel =*/ 1);
+#else
+        GGML_UNUSED_VARS(D, A, B);
+        NO_DEVICE_CODE;
+#endif // TURING_MMA_AVAILABLE
+    }
+
     template <data_layout dl_d, data_layout dl_ab>
     static __device__ __forceinline__ void mma(
             tile<16, 16, int, dl_d> & D, const tile<16, 4, int, dl_ab> & A, const tile<16, 4, int, dl_ab> & B) {
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index 3f01ff5bfb0..e1add5e0331 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -122,7 +122,7 @@ void ggml_cuda_mul_mat_q(
                             || GGML_CUDA_CC_IS_CDNA(cc);
 
     // TODO: tighter pool buffer size vs q8 path
-    const bool use_native_mxfp4 = blackwell_mma_available(cc) && src0->type == GGML_TYPE_MXFP4;
+    const bool use_native_fp4 = blackwell_mma_available(cc) && (src0->type == GGML_TYPE_MXFP4 || src0->type == GGML_TYPE_NVFP4);
 
     if (!ids) {
         const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 +
@@ -133,9 +133,9 @@ void ggml_cuda_mul_mat_q(
             const int64_t s11 = src1->nb[1] / ts_src1;
             const int64_t s12 = src1->nb[2] / ts_src1;
             const int64_t s13 = src1->nb[3] / ts_src1;
-            if (use_native_mxfp4) {
+            if (use_native_fp4) {
                 static_assert(sizeof(block_fp4_mmq) == 4 * sizeof(block_q8_1));
-                quantize_mmq_mxfp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
+                quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
                                         ne11, ne12, ne13, stream);
 
             } else {
@@ -146,10 +146,8 @@ void ggml_cuda_mul_mat_q(
         }
 
         // Stride depends on quantization format
-        const int64_t s12 = use_native_mxfp4 ?
-                                ne11 * ne10_padded * sizeof(block_fp4_mmq) /
-                                    (8 * QK_MXFP4 * sizeof(int))  // block_fp4_mmq holds 256 values (8 blocks of 32)
-                                :
+        const int64_t s12 = use_native_fp4 ?
+                                ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) :  // block_fp4_mmq holds 256 values
                                 ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
         const int64_t s13 = ne12*s12;
 
@@ -198,8 +196,8 @@ void ggml_cuda_mul_mat_q(
         const int64_t s12 = src1->nb[2] / ts_src1;
         const int64_t s13 = src1->nb[3] / ts_src1;
 
-        if (use_native_mxfp4) {
-            quantize_mmq_mxfp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
+        if (use_native_fp4) {
+            quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
                                     ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
         } else {
             quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
@@ -208,8 +206,9 @@ void ggml_cuda_mul_mat_q(
         CUDA_CHECK(cudaGetLastError());
     }
 
-    const int64_t s12 = use_native_mxfp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (8 * QK_MXFP4 * sizeof(int)) :
-                                           ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
+    static_assert(QK_K == 8 * QK_MXFP4, "QK_K needs to be 8 * QK_MXFP4");
+    const int64_t s12 = use_native_fp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) :
+                                         ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
     const int64_t s13 = ne12*s12;
 
     // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid.
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index b1a319de9be..edf546d8f1e 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -10,9 +10,9 @@
 using namespace ggml_cuda_mma;
 
 #define MMQ_DP4A_MAX_BATCH_SIZE 64 // Max. batch size to use for dp4a MMQ kernels when FP16 tensor cores are available.
-#define MMQ_ITER_K 256
-#define MMQ_ITER_K_MXFP4_FP4    512
-#define MMQ_NWARPS 8
+#define MMQ_ITER_K             256
+#define MMQ_ITER_K_FP4         512
+#define MMQ_NWARPS               8
 
 typedef void (*load_tiles_mmq_t)(const char * __restrict__ x, int * x_tile, const int kbx0, const int i_max, const int stride);
 typedef void (*vec_dot_mmq_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00);
@@ -46,9 +46,12 @@ struct block_q8_1_mmq {
     int8_t qs[4*QK8_1]; // 128 values quantized to 8 bit each
 };
 
+// this struct is used for fp4 data types (currently only used for Blackwell)
+// mxfp4 has block size 32, each int32 of d4 contains 2 e8m0 scales in the lower 16 bits
+// nvfp4 has block size 16, each int32 of d4 contains 4 ue4m3 scales
 struct block_fp4_mmq {
-    uint32_t d4[4];       // 8 E8M0 scales (1 per 32 values), 2 packed per uint32: d4[0]={s0,s1}, d4[1]={s2,s3}, etc.
-    int8_t   qs[4 * 32];  // 256 FP4 values packed as 4-bit pairs (2 per byte), 8 blocks of 32 values
+    uint32_t d4[4];
+    int8_t   qs[4 * 32];  // 256 FP4 values packed as 4-bit pairs (2 per byte)
 };
 
 static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size");
@@ -143,10 +146,11 @@ static int get_mmq_y_host(const int cc) {
 
 static constexpr __device__ int get_iter_k([[maybe_unused]] const ggml_type type) {
 #if defined(BLACKWELL_MMA_AVAILABLE)
-    return type == GGML_TYPE_MXFP4 ? MMQ_ITER_K_MXFP4_FP4 : MMQ_ITER_K;
-#else
-    return MMQ_ITER_K;
+if (type == GGML_TYPE_NVFP4 || type == GGML_TYPE_MXFP4) {
+    return MMQ_ITER_K_FP4;
+}
 #endif // defined(BLACKWELL_MMA_AVAILABLE)
+    return MMQ_ITER_K;
 }
 
 static constexpr __device__ int get_mmq_y_device() {
@@ -213,8 +217,8 @@ static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml
 }
 
 #define MMQ_MMA_TILE_X_K_Q8_0  (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0                   + 4)
-#define MMQ_MMA_TILE_X_K_FP4   (2*MMQ_TILE_NE_K + 8                                       + 4) // MXFP4
-#define MMQ_MMA_TILE_X_K_NVFP4 (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4) // NVFP4
+#define MMQ_MMA_TILE_X_K_FP4   (2*MMQ_TILE_NE_K + 8                                       + 4) // MXFP4 and NVFP4 Blackwell
+#define MMQ_MMA_TILE_X_K_NVFP4 (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4) // NVFP4 Generic
 #define MMQ_MMA_TILE_X_K_Q8_1  (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0                   + 4)
 #define MMQ_MMA_TILE_X_K_Q2_K  (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K                           + 4)
 #define MMQ_MMA_TILE_X_K_Q3_K  (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2                         + 4)
@@ -240,7 +244,11 @@ static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
         case GGML_TYPE_Q8_0:    return MMQ_MMA_TILE_X_K_Q8_0;
         // tile sizes are the same for Q8_1 and FP4 for blackwell
         case GGML_TYPE_MXFP4:   return MMQ_MMA_TILE_X_K_Q8_1;
+#if defined(BLACKWELL_MMA_AVAILABLE)
+        case GGML_TYPE_NVFP4:   return MMQ_MMA_TILE_X_K_FP4;
+#else
         case GGML_TYPE_NVFP4:   return MMQ_MMA_TILE_X_K_NVFP4;
+#endif // defined(BLACKWELL_MMA_AVAILABLE)
         case GGML_TYPE_Q2_K:    return MMQ_MMA_TILE_X_K_Q2_K;
         case GGML_TYPE_Q3_K:    return MMQ_MMA_TILE_X_K_Q3_K;
         case GGML_TYPE_Q4_K:    return MMQ_MMA_TILE_X_K_Q8_1;
@@ -934,6 +942,128 @@ static __device__ __forceinline__ void load_tiles_mxfp4_fp4(const char * __restr
     }
 }
 
+#ifdef BLACKWELL_MMA_AVAILABLE
+template <int mmq_y, bool need_check>
+static __device__ __forceinline__ void load_tiles_nvfp4_nvfp4(const char * __restrict__ x,
+                                                            int * __restrict__ x_tile,
+                                                            const int kbx0,
+                                                            const int i_max,
+                                                            const int stride) {
+    constexpr int nwarps = mmq_get_nwarps_device();
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+    constexpr int iter_k = get_iter_k(GGML_TYPE_NVFP4);
+    constexpr int threads_per_row = iter_k / QK_NVFP4; // each thread processes 1 block
+    constexpr int rows_per_warp = warp_size / threads_per_row;
+
+    uint32_t * x_u32 = (uint32_t *) x_tile;
+
+    const int txi = threadIdx.x;
+    const int kbx = txi % threads_per_row;
+    const int row_in_warp = txi / threads_per_row;
+
+    const block_nvfp4 * bxi_base = (const block_nvfp4 *) x + kbx0 + kbx;
+    uint32_t * x_u32_scale = x_u32 + 64 + kbx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += rows_per_warp * nwarps) {
+        int i = i0 + threadIdx.y * rows_per_warp + row_in_warp;
+
+        if constexpr (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_nvfp4 * bxi = bxi_base + i * stride;
+        const int row_base = i * MMQ_MMA_TILE_X_K_FP4;
+        const int q_base = row_base + 8 * kbx;
+
+        const uint32_t * src_qs = reinterpret_cast<const uint32_t *>(bxi->qs);
+
+#pragma unroll
+        for (int sub = 0; sub < QK_NVFP4 / QK_NVFP4_SUB; ++sub) {
+            x_u32[q_base + 2 * sub + 0] = src_qs[2 * sub + 0];
+            x_u32[q_base + 2 * sub + 1] = src_qs[2 * sub + 1];
+        }
+
+        x_u32_scale[row_base] = get_int_b4(bxi->d, 0);
+    }
+}
+
+// Shared MMA kernel for MXFP4 and NVFP4 on Blackwell.
+// Both quantizations encode values as e2m1 (FP4) and produce one uint32 scale per
+// m16n8k64 MMA call; only the PTX kind (scale_vec::2X ue8m0 vs scale_vec::4X ue4m3)
+// and the per-type stride constant differ.
+template <int mmq_x, int mmq_y, ggml_type type>
+static __device__ __forceinline__ void vec_dot_fp4_fp4_mma(const int * __restrict__ x,
+                                                           const int * __restrict__ y,
+                                                           float * __restrict__ sum,
+                                                           const int k00) {
+    static_assert(type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4,
+                  "vec_dot_fp4_fp4_mma: type must be MXFP4 or NVFP4");
+
+    typedef tile<16, 8, int>   tile_A;
+    typedef tile<8, 8, int>    tile_B;
+    typedef tile<16, 8, float> tile_C;
+
+    constexpr int stride        = MMQ_MMA_TILE_X_K_FP4;
+    constexpr int granularity   = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx           = rows_per_warp / tile_C::I;
+    constexpr int nfrags        = MMQ_TILE_NE_K / tile_A::J;
+
+    y += (threadIdx.y % ntx) * (tile_C::J * MMQ_TILE_Y_K);
+
+    const int *      x_qs = (const int *) x;
+    const uint32_t * x_sc = (const uint32_t *) (x_qs + 2 * MMQ_TILE_NE_K);
+    const int *      y_qs = (const int *) y + 4;
+    const uint32_t * y_sc = (const uint32_t *) y;
+
+    // 2 threads per quad supply the packed scale register to the block_scale MMA,
+    // see https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-block-scaling
+    const int tidx_A = threadIdx.x / 4 + (threadIdx.x % 2) * 8;
+    const int tidx_B = threadIdx.x / 4;
+    const int i0     = (threadIdx.y / ntx) * rows_per_warp;
+
+    tile_A   A[ntx][nfrags];
+    uint32_t scaleA[ntx][nfrags];
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int frag = 0; frag < nfrags; ++frag) {
+            const int k0 = k00 + frag * tile_A::J;
+            load_ldmatrix(A[n][frag], x_qs + (i0 + n * tile_A::I) * stride + k0, stride);
+            scaleA[n][frag] = x_sc[(i0 + n * tile_A::I + tidx_A) * stride + k0 / tile_A::J];
+        }
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx * tile_C::J) {
+        tile_B   B[nfrags];
+        uint32_t scaleB[nfrags];
+
+#pragma unroll
+        for (int frag = 0; frag < nfrags; ++frag) {
+            const int k0 = frag * tile_B::J;
+            load_generic(B[frag], y_qs + j0 * MMQ_TILE_Y_K + k0, MMQ_TILE_Y_K);
+            scaleB[frag] = y_sc[(j0 + tidx_B) * MMQ_TILE_Y_K + frag];
+        }
+
+#pragma unroll
+        for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+            for (int frag = 0; frag < nfrags; ++frag) {
+                tile_C C = {};
+                mma_block_scaled_fp4<type>(C, A[n][frag], B[frag], scaleA[n][frag], scaleB[frag]);
+#pragma unroll
+                for (int l = 0; l < tile_C::ne; ++l) {
+                    sum[(j0 / tile_C::J + n) * tile_C::ne + l] += C.x[l];
+                }
+            }
+        }
+    }
+}
+#endif // BLACKWELL_MMA_AVAILABLE
+
 
 template <int mmq_y, bool need_check>
 static __device__ __forceinline__ void load_tiles_nvfp4(const char * __restrict__ x,
@@ -1163,77 +1293,6 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
 #endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
 }
 
-template <int mmq_x, int mmq_y>
-static __device__ __forceinline__ void vec_dot_mxfp4_mxfp4_mma(const int * __restrict__ x,
-                                                               const int * __restrict__ y,
-                                                               float * __restrict__ sum,
-                                                               const int k00) {
-    typedef tile<16, 8, int>   tile_A;
-    typedef tile<8, 8, int>    tile_B;
-    typedef tile<16, 8, float> tile_C;  // Output is float for native scaled MMA
-
-    constexpr int granularity   = mmq_get_granularity_device(mmq_x);
-    constexpr int rows_per_warp = 2 * granularity;
-    constexpr int ntx           = rows_per_warp / tile_C::I;  // Number of x minitiles per warp.
-
-    y += (threadIdx.y % ntx) * (tile_C::J * MMQ_TILE_Y_FP4_K);
-
-    // Match layout from load_tiles_mxfp4_fp4
-    const int *      x_qs = (const int *) x;
-    const uint32_t * x_sc = (const uint32_t *) (x_qs + 2 * MMQ_TILE_NE_K);
-    const int *      y_qs = (const int *) y + 4;
-    const uint32_t * y_sc = (const uint32_t *) y;
-
-    // tile_A has a length of 64 logical values vs. 32 values in block_mxfp4
-    tile_A   A[ntx][MMQ_TILE_NE_K / (2 * QI_MXFP4)];
-    uint32_t scaleA[ntx][MMQ_TILE_NE_K / (2 * QI_MXFP4)];
-
-    // Block scale
-    // Each thread has to point to a 4 byte scale value
-    // https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-block-scaling
-
-    const int i0 = (threadIdx.y / ntx) * rows_per_warp;
-
-#pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-        for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 2 * QI_MXFP4) {
-            const int k0 = k00 + k01;
-
-            load_ldmatrix(A[n][k01 / (2 * QI_MXFP4)], x_qs + (i0 + n * tile_A::I) * MMQ_MMA_TILE_X_K_FP4 + k0,
-                          MMQ_MMA_TILE_X_K_FP4);
-
-            // based on block-scaling document, 2 threads in each quad need to supply to the scale value
-            const int tidx         = threadIdx.x / 4 + (threadIdx.x % 2) * 8;
-            scaleA[n][k01 / (2 * QI_MXFP4)] =
-                *(x_sc + (i0 + n * tile_A::I + tidx) * MMQ_MMA_TILE_X_K_FP4 + k0 / (2 * QI_MXFP4));
-        }
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx * tile_C::J) {
-#pragma unroll
-        for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 2 * QI_MXFP4) {
-            tile_B   B;
-            uint32_t scaleB;  // 2xN scales
-
-            load_generic(B, y_qs + j0 * MMQ_TILE_Y_FP4_K + k01, MMQ_TILE_Y_FP4_K);
-
-            scaleB = y_sc[(j0 + threadIdx.x / 4) * MMQ_TILE_Y_FP4_K + k01 / (2 * QI_MXFP4)];
-
-#pragma unroll
-            for (int n = 0; n < ntx; ++n) {
-                tile_C C;
-
-                mma_block_scaled(C, A[n][k01 / (2 * QI_MXFP4)], B, scaleA[n][k01 / (2 * QI_MXFP4)], scaleB);
-#pragma unroll
-                for (int l = 0; l < tile_C::ne; ++l) {
-                    sum[(j0 / tile_C::J + n) * tile_C::ne + l] += C.x[l];
-                }
-            }
-        }
-    }
-}
 
 template <int mmq_x, int mmq_y>
 static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a(
@@ -3259,7 +3318,7 @@ struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_MXFP4> {
     static constexpr int              vdr          = VDR_MXFP4_Q8_1_MMQ;
 #ifdef BLACKWELL_MMA_AVAILABLE
     static constexpr load_tiles_mmq_t load_tiles  = load_tiles_mxfp4_fp4<mmq_y, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma = vec_dot_mxfp4_mxfp4_mma<mmq_x, mmq_y>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma = vec_dot_fp4_fp4_mma<mmq_x, mmq_y, GGML_TYPE_MXFP4>;
 #else
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_mxfp4<mmq_y, need_check>;
     static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, MMQ_Q8_1_DS_LAYOUT_D4>;
@@ -3270,8 +3329,13 @@ struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_MXFP4> {
 template <int mmq_x, int mmq_y, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_NVFP4> {
     static constexpr int              vdr          = VDR_NVFP4_Q8_1_MMQ;
+#ifdef BLACKWELL_MMA_AVAILABLE
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_nvfp4_nvfp4<mmq_y, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_fp4_fp4_mma<mmq_x, mmq_y, GGML_TYPE_NVFP4>;
+#else
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_nvfp4<mmq_y, need_check>;
     static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y>;
+#endif // BLACKWELL_MMA_AVAILABLE
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_16_q8_1_dp4a<mmq_x, mmq_y>;
 };
 
@@ -3406,7 +3470,7 @@ static __device__ __forceinline__ void mul_mat_q_process_tile(
 
 #if defined(BLACKWELL_MMA_AVAILABLE)
     // FP4 tile stores 8 blocks
-    constexpr int ne_block = (type == GGML_TYPE_MXFP4) ? 8 * QK_MXFP4 : 4 * QK8_1;
+    constexpr int ne_block = (type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4) ? QK_K : 4 * QK8_1;
 #else
     constexpr int ne_block = 4 * QK8_1;
 #endif  // defined(BLACKWELL_MMA_AVAILABLE)
@@ -3478,10 +3542,10 @@ template <ggml_type type, int mmq_x, bool need_check>
 static __global__ void mul_mat_q(
         const char * __restrict__ x, const int * __restrict__ y, const int32_t * __restrict__ ids_dst,
         const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, float * __restrict__ tmp_fixup,
-        const int ncols_x, const int nrows_x, const int ncols_dst, const int stride_row_x, const int ncols_y, const int stride_col_dst,
-        const int channel_ratio, const int nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
-        const int sample_ratio, const int nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
-        const int ncols_max) {
+        const uint3 blocks_per_ne00, const int nrows_x, const int ncols_dst, const int stride_row_x, const int ncols_y, const int stride_col_dst,
+        const uint3 channel_ratio, const uint3 nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
+        const uint3 sample_ratio, const uint3 nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
+        const uint3 ntx) {
 
     // Skip unused template specializations for faster compilation:
     if (mmq_x > get_mmq_x_max_device() || mmq_x % mmq_get_granularity_device(mmq_x) != 0) {
@@ -3495,8 +3559,7 @@ static __global__ void mul_mat_q(
     constexpr int qk    = ggml_cuda_type_traits<type>::qk;
     constexpr int mmq_y = get_mmq_y_device();
 
-    const int ntx = (ncols_max + mmq_x - 1) / mmq_x; // Number of tiles x
-    const int nty = (nrows_x   + mmq_y - 1) / mmq_y; // Number of tiles y
+    const uint32_t nty = (nrows_x + mmq_y - 1) / mmq_y; // Number of tiles y
 
     // Initialize the ids for writing back data with just the index.
     // For regular matrix multiplications this is never changed.
@@ -3517,8 +3580,9 @@ static __global__ void mul_mat_q(
     // On non-CDNA AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead:
 #if (defined(GGML_USE_HIP) && !defined(CDNA)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA
     {
-        const int wt = blockIdx.z / nchannels_y;
-        const int zt = blockIdx.z - wt*nchannels_y;
+        const uint2 tmp2 = fast_div_modulo(blockIdx.z, nchannels_y);
+        const int wt = tmp2.x;
+        const int zt = tmp2.y;
         const int jt = blockIdx.y;
         const int it = blockIdx.x;
 
@@ -3561,40 +3625,40 @@ static __global__ void mul_mat_q(
         const int tile_x_max_i = nrows_x  - it*mmq_y - 1;
         const int tile_y_max_j = col_diff - jt*mmq_x - 1;
 
-        const int offset_x = (wt/sample_ratio)*stride_sample_x + (zt/channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x;
+        const int offset_x = fastdiv(wt, sample_ratio)*stride_sample_x + fastdiv(zt, channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x;
 
         constexpr bool fixup = false;
         mul_mat_q_process_tile<type, mmq_x, need_check, fixup>
             (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst,
-             tile_x_max_i, tile_y_max_j, 0, ncols_x/qk);
+             tile_x_max_i, tile_y_max_j, 0, blocks_per_ne00.z);
         return;
     }
 #endif // (defined(GGML_USE_HIP) && !defined(CDNA4) && !defined(CDNA3)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA
 
-    constexpr int ITER_K = get_iter_k(type);
-
-    const     int64_t blocks_per_ne00 = ncols_x / qk;
-    constexpr int     blocks_per_iter = ITER_K / qk;
+    constexpr int ITER_K          = get_iter_k(type);
+    constexpr int blocks_per_iter = ITER_K / qk;
 
     // kbc == k block continuous, current index in continuous ijk space.
-    int64_t kbc      = (int64_t) blockIdx.x     *nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x;
-    int64_t kbc_stop = (int64_t)(blockIdx.x + 1)*nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x;
+    int kbc      = int64_t(blockIdx.x)    *(nsamples_y.z*nchannels_y.z*ntx.z*nty*blocks_per_ne00.z) / gridDim.x;
+    int kbc_stop = int64_t(blockIdx.x + 1)*(nsamples_y.z*nchannels_y.z*ntx.z*nty*blocks_per_ne00.z) / gridDim.x;
 
-    kbc      -= (kbc      % blocks_per_ne00) % blocks_per_iter;
-    kbc_stop -= (kbc_stop % blocks_per_ne00) % blocks_per_iter;
+    kbc      -= fastmodulo(kbc,      blocks_per_ne00) % blocks_per_iter;
+    kbc_stop -= fastmodulo(kbc_stop, blocks_per_ne00) % blocks_per_iter;
 
     // kb0 == k index when doing the matrix multiplication for an output tile.
-    int kb0_start = kbc % blocks_per_ne00;
-    int kb0_stop  = min(blocks_per_ne00, kb0_start + kbc_stop - kbc);
-    while (kbc < kbc_stop && kb0_stop == blocks_per_ne00) {
-        int tmp = kbc;
-        const int it = tmp / (nsamples_y*nchannels_y*ntx*blocks_per_ne00);
-        tmp -= it * (nsamples_y*nchannels_y*ntx*blocks_per_ne00);
-        const int wt = tmp / (nchannels_y*ntx*blocks_per_ne00);
-        tmp -= wt * (nchannels_y*ntx*blocks_per_ne00);
-        const int zt = tmp / (ntx*blocks_per_ne00);
-        tmp -= zt * (ntx*blocks_per_ne00);
-        const int jt = tmp / blocks_per_ne00;
+    int kb0_start = fastmodulo(kbc, blocks_per_ne00);
+    int kb0_stop  = min(blocks_per_ne00.z, uint32_t(kb0_start + kbc_stop - kbc));
+    while (kbc < kbc_stop && kb0_stop == int(blocks_per_ne00.z)) {
+        int tmp = fastdiv(kbc, blocks_per_ne00);
+        uint2 tmp2 = fast_div_modulo(tmp, ntx);
+        const int jt = tmp2.y;
+        tmp = tmp2.x;
+        tmp2 = fast_div_modulo(tmp, nchannels_y);
+        const int zt = tmp2.y;
+        tmp = tmp2.x;
+        tmp2 = fast_div_modulo(tmp, nsamples_y);
+        const int wt = tmp2.y;
+        const int it = tmp2.x;
 
         // Defaults for regular matrix multiplication:
         int col_low    = 0;
@@ -3612,11 +3676,11 @@ static __global__ void mul_mat_q(
             offset_dst = 0;
 
             if (jt*mmq_x >= col_diff) {
-                kbc += blocks_per_ne00;
-                kbc -= kbc % blocks_per_ne00;
+                kbc += blocks_per_ne00.z;
+                kbc -= fastmodulo(kbc, blocks_per_ne00);
 
                 kb0_start = 0;
-                kb0_stop  = min(blocks_per_ne00, kbc_stop - kbc);
+                kb0_stop  = min(blocks_per_ne00.z, uint32_t(kbc_stop - kbc));
 
                 continue;
             }
@@ -3641,32 +3705,34 @@ static __global__ void mul_mat_q(
         const int tile_x_max_i = nrows_x  - it*mmq_y - 1;
         const int tile_y_max_j = col_diff - jt*mmq_x - 1;
 
-        const int offset_x = (wt/sample_ratio)*stride_sample_x + (zt/channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x;
+        const int offset_x = fastdiv(wt, sample_ratio)*stride_sample_x + fastdiv(zt, channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x;
 
         constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
         mul_mat_q_process_tile<type, mmq_x, need_check, fixup>
             (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst,
              tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop);
 
-        kbc += blocks_per_ne00;
-        kbc -= kbc % blocks_per_ne00;
+        kbc += blocks_per_ne00.z;
+        kbc -= fastmodulo(kbc, blocks_per_ne00);
 
         kb0_start = 0;
-        kb0_stop  = min(blocks_per_ne00, kbc_stop - kbc);
+        kb0_stop  = min(blocks_per_ne00.z, uint32_t(kbc_stop - kbc));
     }
 
     if (kbc >= kbc_stop) {
         return;
     }
 
-    int tmp = kbc;
-    const int it = tmp / (nsamples_y*nchannels_y*ntx*blocks_per_ne00);
-    tmp -= it * (nsamples_y*nchannels_y*ntx*blocks_per_ne00);
-    const int wt = tmp / (nchannels_y*ntx*blocks_per_ne00);
-    tmp -= wt * (nchannels_y*ntx*blocks_per_ne00);
-    const int zt = tmp / (ntx*blocks_per_ne00);
-    tmp -= zt * (ntx*blocks_per_ne00);
-    const int jt = tmp / blocks_per_ne00;
+    int tmp = fastdiv(kbc, blocks_per_ne00);
+    uint2 tmp2 = fast_div_modulo(tmp, ntx);
+    const int jt = tmp2.y;
+    tmp = tmp2.x;
+    tmp2 = fast_div_modulo(tmp, nchannels_y);
+    const int zt = tmp2.y;
+    tmp = tmp2.x;
+    tmp2 = fast_div_modulo(tmp, nsamples_y);
+    const int wt = tmp2.y;
+    const int it = tmp2.x;
 
     // Defaults for regular matrix multiplication:
     int col_low    = 0;
@@ -3708,7 +3774,7 @@ static __global__ void mul_mat_q(
     const int tile_x_max_i = nrows_x  - it*mmq_y - 1;
     const int tile_y_max_j = col_diff - jt*mmq_x - 1;
 
-    const int offset_x = (wt/sample_ratio)*stride_sample_x + (zt/channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x;
+    const int offset_x = fastdiv(wt, sample_ratio)*stride_sample_x + fastdiv(zt, channel_ratio)*stride_channel_x + it*mmq_y*stride_row_x;
 
     constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
     mul_mat_q_process_tile<type, mmq_x, need_check, fixup>
@@ -3717,46 +3783,37 @@ static __global__ void mul_mat_q(
 }
 
 template <ggml_type type, int mmq_x, bool need_check>
-static __global__ void mul_mat_q_stream_k_fixup(const int32_t * ids_dst,
-                                                const int32_t * expert_bounds,
-                                                float * __restrict__ dst,
-                                                const float * __restrict__ tmp_last_tile,
-                                                const int    ncols_x,
-                                                const int    nrows_x,
-                                                const int    ncols_dst,
-                                                const size_t stride_col_dst,
-                                                const int    nchannels_y,
-                                                const size_t stride_channel_dst,
-                                                const int    nsamples_y,
-                                                const size_t stride_sample_dst,
-                                                const int    ncols_max) {
-    constexpr int     mmq_y           = get_mmq_y_device();
-    constexpr int     qk              = ggml_cuda_type_traits<type>::qk;
-    constexpr int     ITER_K          = get_iter_k(type);
-
-    constexpr int     blocks_per_iter = ITER_K / qk;
-    const     int64_t blocks_per_ne00 = ncols_x / qk;
+__launch_bounds__(ggml_cuda_get_physical_warp_size()*mmq_get_nwarps_device()/2, 1)
+static __global__ void mul_mat_q_stream_k_fixup(
+        const int32_t * __restrict__ ids_dst, const int32_t * __restrict__ expert_bounds, float * __restrict__ dst,
+        float * __restrict__ tmp_last_tile, const uint3 blocks_per_ne00, const int nrows_x, const int ncols_dst,
+        const int stride_col_dst, const uint3 nchannels_y, const int stride_channel_dst, const uint3 nsamples_y,
+        const int stride_sample_dst, const uint3 ntx) {
+    constexpr int mmq_y           = get_mmq_y_device();
+    constexpr int qk              = ggml_cuda_type_traits<type>::qk;
+    constexpr int ITER_K          = get_iter_k(type);
+    constexpr int blocks_per_iter = ITER_K / qk;
 
-    constexpr int nwarps = mmq_get_nwarps_device();
+    constexpr int nwarps = mmq_get_nwarps_device()/2;
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
-    float sum[mmq_x*mmq_y / (nwarps*warp_size)] = {0.0f};
+    float sum[mmq_x / nwarps] = {0.0f};
+    const int i = blockIdx.y*warp_size + threadIdx.x;
 
-    const int ntx  = (ncols_max + mmq_x - 1) / mmq_x;
-    const int nty  = (nrows_x   + mmq_y - 1) / mmq_y;
+    const int nty = (nrows_x + mmq_y - 1) / mmq_y;
 
     const int bidx0 = blockIdx.x;
 
     // kbc == k block continuous, current index in continuous ijk space.
-    int64_t kbc0      = (int64_t) bidx0     *nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x;
-    int64_t kbc0_stop = (int64_t)(bidx0 + 1)*nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x;
+    int kbc0      = int64_t(blockIdx.x)    *(nsamples_y.z*nchannels_y.z*ntx.z*nty*blocks_per_ne00.z) / gridDim.x;
+    int kbc0_stop = int64_t(blockIdx.x + 1)*(nsamples_y.z*nchannels_y.z*ntx.z*nty*blocks_per_ne00.z) / gridDim.x;
 
-    kbc0      -= (kbc0      % blocks_per_ne00) % blocks_per_iter;
-    kbc0_stop -= (kbc0_stop % blocks_per_ne00) % blocks_per_iter;
+    kbc0      -= fastmodulo(kbc0,      blocks_per_ne00) % blocks_per_iter;
+    kbc0_stop -= fastmodulo(kbc0_stop, blocks_per_ne00) % blocks_per_iter;
 
     const bool did_not_have_any_data   = kbc0 == kbc0_stop;
-    const bool wrote_beginning_of_tile = kbc0 % blocks_per_ne00 == 0;
-    const bool did_not_write_last      = kbc0/blocks_per_ne00 == kbc0_stop/blocks_per_ne00 && kbc0_stop % blocks_per_ne00 != 0;
+    const bool wrote_beginning_of_tile = fastmodulo(kbc0, blocks_per_ne00) == 0;
+    const bool did_not_write_last      = fastdiv(kbc0, blocks_per_ne00) == fastdiv(kbc0_stop, blocks_per_ne00) && fastmodulo(kbc0_stop, blocks_per_ne00) != 0;
     if (did_not_have_any_data || wrote_beginning_of_tile || did_not_write_last) {
         return;
     }
@@ -3765,11 +3822,11 @@ static __global__ void mul_mat_q_stream_k_fixup(const int32_t * ids_dst,
 
     // Iterate over previous blocks and sum up partial sums written to fixup buffer.
     // All CUDA blocks that get here must have a previous block that needs a fixup.
-    int64_t bidx = bidx0 - 1;
-    int64_t kbc_stop = kbc0;
+    int bidx = bidx0 - 1;
+    int kbc_stop = kbc0;
     while(true) {
-        int64_t kbc = bidx*nsamples_y*nchannels_y*ntx*nty*blocks_per_ne00 / gridDim.x;
-        kbc -= (kbc % blocks_per_ne00) % blocks_per_iter;
+        int kbc = int64_t(bidx)*(nsamples_y.z*nchannels_y.z*ntx.z*nty*blocks_per_ne00.z) / gridDim.x;
+        kbc -= fastmodulo(kbc, blocks_per_ne00) % blocks_per_iter;
 
         if (kbc == kbc_stop) { // Did not have any data.
             bidx--;
@@ -3779,20 +3836,16 @@ static __global__ void mul_mat_q_stream_k_fixup(const int32_t * ids_dst,
 
         any_fixup = true;
 
+
 #pragma unroll
         for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
             const int j = j0 + threadIdx.y;
 
-#pragma unroll
-            for (int i0 = 0; i0 < mmq_y; i0 += warp_size) {
-                const int i = i0 + threadIdx.x;
-
-                sum[(j0/nwarps) * (mmq_y/warp_size) + i0/warp_size] += tmp_last_tile[bidx*(mmq_x*mmq_y) + j*mmq_y + i];
-            }
+            sum[j0/nwarps] += tmp_last_tile[bidx*(mmq_x*mmq_y) + j*mmq_y + i];
         }
 
         // If this block started in a previous tile we are done and don't need to combine additional partial results.
-        if (kbc % blocks_per_ne00 == 0 || kbc/blocks_per_ne00 < kbc0/blocks_per_ne00) {
+        if (fastmodulo(kbc, blocks_per_ne00) == 0 || fastdiv(kbc, blocks_per_ne00) < fastdiv(kbc0, blocks_per_ne00)) {
             break;
         }
         bidx--;
@@ -3803,14 +3856,16 @@ static __global__ void mul_mat_q_stream_k_fixup(const int32_t * ids_dst,
         return;
     }
 
-    int tmp = kbc0;
-    const int it = tmp / (nsamples_y*nchannels_y*ntx*blocks_per_ne00);
-    tmp -= it * (nsamples_y*nchannels_y*ntx*blocks_per_ne00);
-    const int wt = tmp / (nchannels_y*ntx*blocks_per_ne00);
-    tmp -= wt * (nchannels_y*ntx*blocks_per_ne00);
-    const int zt = tmp / (ntx*blocks_per_ne00);
-    tmp -= zt * (ntx*blocks_per_ne00);
-    const int jt = tmp / blocks_per_ne00;
+    int tmp = fastdiv(kbc0, blocks_per_ne00);
+    uint2 tmp2 = fast_div_modulo(tmp, ntx);
+    const int jt = tmp2.y;
+    tmp = tmp2.x;
+    tmp2 = fast_div_modulo(tmp, nchannels_y);
+    const int zt = tmp2.y;
+    tmp = tmp2.x;
+    tmp2 = fast_div_modulo(tmp, nsamples_y);
+    const int wt = tmp2.y;
+    const int it = tmp2.x;
 
     if (!ids_dst) {
         const int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*mmq_x*stride_col_dst + it*mmq_y;
@@ -3818,6 +3873,9 @@ static __global__ void mul_mat_q_stream_k_fixup(const int32_t * ids_dst,
 
         const int i_max = nrows_x   - it*mmq_y - 1;
         const int j_max = ncols_dst - jt*mmq_x - 1;
+        if (need_check && i > i_max) {
+            return;
+        }
 
 #pragma unroll
         for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
@@ -3827,16 +3885,7 @@ static __global__ void mul_mat_q_stream_k_fixup(const int32_t * ids_dst,
                 return;
             }
 
-#pragma unroll
-            for (int i0 = 0; i0 < mmq_y; i0 += warp_size) {
-                const int i = i0 + threadIdx.x;
-
-                if (need_check && i > i_max) {
-                    continue;
-                }
-
-                dst[j*stride_col_dst + i] += sum[(j0/nwarps) * (mmq_y/warp_size) + i0/warp_size];
-            }
+            dst[j*stride_col_dst + i] += sum[j0/nwarps];
         }
         return;
     }
@@ -3856,6 +3905,9 @@ static __global__ void mul_mat_q_stream_k_fixup(const int32_t * ids_dst,
 
     const int i_max = nrows_x  - it*mmq_y - 1;
     const int j_max = col_diff - jt*mmq_x - 1;
+    if (need_check && i > i_max) {
+        return;
+    }
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
@@ -3865,16 +3917,7 @@ static __global__ void mul_mat_q_stream_k_fixup(const int32_t * ids_dst,
             return;
         }
 
-#pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += warp_size) {
-            const int i = i0 + threadIdx.x;
-
-            if (need_check && i > i_max) {
-                continue;
-            }
-
-            dst[ids_dst_shared[j]*stride_col_dst + i] += sum[(j0/nwarps) * (mmq_y/warp_size) + i0/warp_size];
-        }
+        dst[ids_dst_shared[j]*stride_col_dst + i] += sum[j0/nwarps];
     }
 }
 
@@ -3922,29 +3965,44 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
     const int channel_ratio = args.nchannels_y / args.nchannels_x;
     const int sample_ratio  = args.nsamples_y  / args.nsamples_x;
 
+    const uint3 blocks_per_ne00_fd = init_fastdiv_values(args.ncols_x / ggml_cuda_type_traits<type>::qk);
+    const uint3 ntx_fd             = init_fastdiv_values(ntx);
+    const uint3 nchannels_y_fd     = init_fastdiv_values(args.nchannels_y);
+    const uint3 nsamples_y_fd      = init_fastdiv_values(args.nsamples_y);
+    const uint3 channel_ratio_fd   = init_fastdiv_values(channel_ratio);
+    const uint3 sample_ratio_fd    = init_fastdiv_values(sample_ratio);
+
     if (!args.use_stream_k) {
         if (args.nrows_x % mmq_y == 0) {
             constexpr bool need_check = false;
             mul_mat_q<type, mmq_x, need_check><<<block_nums_xy_tiling, block_dims, nbytes_shared, stream>>>
                 (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr,
-                 args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
-                 channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
-                 sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
-                 args.ncols_max);
+                 blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
+                 channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
+                 sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
+                 ntx_fd);
         } else {
             constexpr bool need_check = true;
             mul_mat_q<type, mmq_x, need_check><<<block_nums_xy_tiling, block_dims, nbytes_shared, stream>>>
                 (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr,
-                 args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
-                 channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
-                 sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
-                 args.ncols_max);
+                 blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
+                 channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
+                 sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
+                 ntx_fd);
         }
         return;
     }
 
-    const dim3 block_nums_stream_k(nsm, 1, 1);
-    const bool fixup_needed = ntx*nty*ntzw % nsm != 0;
+    // For the stream-k kernel it is possible to run it with tiling by setting the number of CUDA blocks equal to the number of tiles.
+    // This is worthwhile if the efficiency of tiling is high and skipping the fixup kernel is more important.
+    const int ntiles_dst = ntx * nty * ntzw;
+    const int tiles_nwaves = (ntiles_dst + nsm - 1) / nsm;
+    const int tiles_efficiency_percent = 100 * ntiles_dst / (nsm*tiles_nwaves);
+    const dim3 block_nums_stream_k(GGML_CUDA_CC_IS_NVIDIA(cc) && tiles_efficiency_percent >= 90 ? ntiles_dst : nsm, 1, 1);
+
+    GGML_ASSERT(ntiles_dst * blocks_per_ne00_fd.z < (1 << 30)); // Assert that variable kbc will not overflow.
+
+    const bool fixup_needed = ntiles_dst % block_nums_stream_k.x != 0;
 
     ggml_cuda_pool & pool = ctx.pool(id);
     ggml_cuda_pool_alloc<float> tmp_fixup(pool);
@@ -3952,40 +4010,45 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
         tmp_fixup.alloc(block_nums_stream_k.x * mmq_x*mmq_y);
     }
 
+    const dim3 block_nums_fixup(block_nums_stream_k.x, mmq_y/warp_size, 1);
+    const dim3 block_dims_fixup(block_dims.x, block_dims.y/2, block_dims.z);
+
     if (args.nrows_x % mmq_y == 0) {
         constexpr bool need_check = false;
         mul_mat_q<type, mmq_x, need_check><<<block_nums_stream_k, block_dims, nbytes_shared, stream>>>
             (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr,
-             args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
-             channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
-             sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
-             args.ncols_max);
+             blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
+             channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
+             sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
+             ntx_fd);
 
         if (!fixup_needed) {
             return;
         }
 
-        mul_mat_q_stream_k_fixup<type, mmq_x, need_check><<<block_nums_stream_k, block_dims, 0, stream>>>
-            (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_dst,
-             args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst,
-             args.ncols_max);
+        CUDA_CHECK(cudaGetLastError());
+        mul_mat_q_stream_k_fixup<type, mmq_x, need_check><<<block_nums_fixup, block_dims_fixup, 0, stream>>>
+            (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, blocks_per_ne00_fd, args.nrows_x, args.ncols_dst,
+             args.nrows_dst, nchannels_y_fd, args.stride_channel_dst, nsamples_y_fd, args.stride_sample_dst,
+             ntx_fd);
     } else {
         constexpr bool need_check = true;
         mul_mat_q<type, mmq_x, need_check><<<block_nums_stream_k, block_dims, nbytes_shared, stream>>>
             (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr,
-             args.ncols_x, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
-             channel_ratio, args.nchannels_y, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
-             sample_ratio, args.nsamples_y, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
-             args.ncols_max);
+             blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
+             channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
+             sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
+             ntx_fd);
 
         if (!fixup_needed) {
             return;
         }
 
-        mul_mat_q_stream_k_fixup<type, mmq_x, need_check><<<block_nums_stream_k, block_dims, 0, stream>>>
-            (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.ncols_x, args.nrows_x, args.ncols_dst,
-             args.nrows_dst, args.nchannels_y, args.stride_channel_dst, args.nsamples_y, args.stride_sample_dst,
-             args.ncols_max);
+        CUDA_CHECK(cudaGetLastError());
+        mul_mat_q_stream_k_fixup<type, mmq_x, need_check><<<block_nums_fixup, block_dims_fixup, 0, stream>>>
+            (args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, blocks_per_ne00_fd, args.nrows_x, args.ncols_dst,
+             args.nrows_dst, nchannels_y_fd, args.stride_channel_dst, nsamples_y_fd, args.stride_sample_dst,
+             ntx_fd);
     }
 }
 
diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu
index 8f55cace1a1..da48f313a38 100644
--- a/ggml/src/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -115,6 +115,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_pascal_older(gg
         case GGML_TYPE_IQ4_NL:  return 6;
         case GGML_TYPE_IQ4_XS:  return 5;
         case GGML_TYPE_MXFP4:   return 4;
+        case GGML_TYPE_NVFP4:   return 4;
         case GGML_TYPE_Q2_K:    return 4;
         case GGML_TYPE_Q3_K:    return 4;
         case GGML_TYPE_Q4_0:    return 6;
@@ -135,6 +136,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_turing_plus(ggm
         case GGML_TYPE_IQ3_S:   return 6;
         case GGML_TYPE_IQ3_XXS: return 7;
         case GGML_TYPE_MXFP4:   return 7;
+        case GGML_TYPE_NVFP4:   return 8;
         case GGML_TYPE_Q2_K:    return 7;
         case GGML_TYPE_Q3_K:    return 5;
         default:                return MMVQ_MAX_BATCH_SIZE;
@@ -221,6 +223,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna4(ggml_type
         case GGML_TYPE_IQ4_NL:  return 7;
         case GGML_TYPE_IQ4_XS:  return 5;
         case GGML_TYPE_MXFP4:   return 5;
+        case GGML_TYPE_NVFP4:   return 5;
         case GGML_TYPE_Q3_K:    return 4;
         case GGML_TYPE_Q4_0:    return 7;
         case GGML_TYPE_Q4_1:    return 7;
diff --git a/ggml/src/ggml-cuda/out-prod.cu b/ggml/src/ggml-cuda/out-prod.cu
index c9b2b699c6a..499903d09b1 100644
--- a/ggml/src/ggml-cuda/out-prod.cu
+++ b/ggml/src/ggml-cuda/out-prod.cu
@@ -54,15 +54,31 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int64_t dps2 = ne2 / ne02;
     const int64_t dps3 = ne3 / ne03;
 
-    // TODO batched matrix multiplication
-    for (int64_t i3 = 0; i3 < ne3; ++i3) {
-        for (int64_t i2 = 0; i2 < ne2; ++i2) {
+    if (dps2 == 1 && ne2 > 1) {
+        // src0 has uniform stride s02 along dim 2; batch the inner loop with a strided GEMM
+        GGML_ASSERT(ne2 <= std::numeric_limits<int>::max());
+        const int batch_count = (int) ne2;
+        for (int64_t i3 = 0; i3 < ne3; ++i3) {
             CUBLAS_CHECK(
-                cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
+                cublasSgemmStridedBatched(handle, CUBLAS_OP_N, src1_cublas_op,
                         ne0, ne1, ne01,
-                        &alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, lda,
-                                src1_d +  i3      *s13 +  i2      *s12, ldb,
-                        &beta,  dst_d  +  i3      *s3  +  i2      *s2,  ldc));
+                        &alpha, src0_d + (i3/dps3)*s03, lda, s02,
+                                src1_d +  i3     *s13, ldb, s12,
+                        &beta,  dst_d  +  i3     *s3,  ldc, s2,
+                        batch_count));
+        }
+    } else {
+        // Fallback: ne2 == 1 (no batching benefit) or dps2 > 1 (src0 broadcast along dim 2
+        // with non-uniform stride; would need cublasSgemmBatched with pointer arrays).
+        for (int64_t i3 = 0; i3 < ne3; ++i3) {
+            for (int64_t i2 = 0; i2 < ne2; ++i2) {
+                CUBLAS_CHECK(
+                    cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
+                            ne0, ne1, ne01,
+                            &alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, lda,
+                                    src1_d +  i3      *s13 +  i2      *s12, ldb,
+                            &beta,  dst_d  +  i3      *s3  +  i2      *s2,  ldc));
+            }
         }
     }
 }
diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu
index 4300ffc148c..52f664719ae 100644
--- a/ggml/src/ggml-cuda/quantize.cu
+++ b/ggml/src/ggml-cuda/quantize.cu
@@ -70,6 +70,102 @@ __device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) {
     return static_cast<uint8_t>(biased);
 }
 
+
+static __global__ void quantize_mmq_nvfp4(
+        const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy,
+        const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
+        const int64_t ne0, const int64_t ne1, const int64_t ne2) {
+#if defined(BLACKWELL_MMA_AVAILABLE)
+
+    const int64_t i0_base = ((int64_t) blockDim.x * blockIdx.y + threadIdx.x) * QK_NVFP4_SUB;
+    if (i0_base >= ne0) {
+        return;
+    }
+
+    const int64_t i1 = blockIdx.x;
+    const int64_t i2 = blockIdx.z % ne2;
+    const int64_t i3 = blockIdx.z / ne2;
+    const int64_t i01 = ids ? ids[i1] : i1;
+    const int64_t k_block = i0_base / QK_K;
+    const int64_t blocks_per_col = (ne0 + QK_K - 1) / QK_K;
+    if (k_block >= blocks_per_col) {
+        return;
+    }
+
+    const int64_t ib = blockIdx.z * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x;
+    block_fp4_mmq * y = (block_fp4_mmq *) vy;
+    block_fp4_mmq * yb = y + ib;
+
+    const int sub = (i0_base % QK_K) / QK_NVFP4_SUB;
+
+    float vals_raw[QK_NVFP4_SUB];
+    float amax_raw = 0.0f;
+    const int64_t base_idx = i3 * s03 + i2 * s02 + i01 * s01;
+#pragma unroll
+    for (int k = 0; k < QK_NVFP4_SUB; k++) {
+        const int64_t i00 = i0_base + k;
+        if (i00 < ne00) {
+            const float v = x[base_idx + i00];
+            vals_raw[k] = v;
+            amax_raw = fmaxf(amax_raw, fabsf(v));
+        } else {
+            vals_raw[k] = 0.0f;
+        }
+    }
+
+    static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2};
+    const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_raw / 6.0f);
+
+    float best_err = FLT_MAX;
+    uint8_t fp8_code = 0;
+    float subblock_scale = 0.0f;
+
+#pragma unroll // Check +/- 2 to find best code to reduce NVFP4 activation loss. Negligible overhead on Blackwell.
+    for (int i = 0; i < 5; i++) {
+        const int test_code = first_fp8_code + test_offsets[i];
+        if (test_code < 0 || test_code > 0x7e) {
+            continue;
+        }
+        const uint8_t code = (uint8_t) test_code;
+        const float test_scale = ggml_cuda_ue4m3_to_fp32(code);
+        const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f;
+        float cur_err = 0.0f;
+#pragma unroll
+        for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+            const float v = vals_raw[k];
+            const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale);
+            const float err_diff = fabsf(v) - fabsf(kvalues_mxfp4[q & 0x7]) * test_scale;
+            cur_err = fmaf(err_diff, err_diff, cur_err);
+        }
+
+        if (cur_err < best_err) {
+            best_err = cur_err;
+            fp8_code = test_code;
+            subblock_scale = test_scale;
+        }
+    }
+
+    const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
+    uint32_t q0 = 0;
+    uint32_t q1 = 0;
+#pragma unroll // this is faster than the previous __nv_fp4x4_e2m1
+    for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) {
+        q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  0], inv_scale) << (8 * k);
+        q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  8], inv_scale) << (8 * k + 4);
+        q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  4], inv_scale) << (8 * k);
+        q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 12], inv_scale) << (8 * k + 4);
+    }
+
+    uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
+    yqs[2 * sub + 0] = q0;
+    yqs[2 * sub + 1] = q1;
+    reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
+#else
+    NO_DEVICE_CODE; // This is for Blackwell NVFP4 activations only.
+#endif // defined(BLACKWELL_MMA_AVAILABLE)
+
+}
+
 // quantize values in the format mxfp4 is stored which is interleaved nibbles
 // i.e. a block a0-a31 is represented as a0a16,a1a17 ...a15a31
 static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x,
@@ -316,28 +412,32 @@ void quantize_mmq_q8_1_cuda(
     }
 }
 
-void quantize_mmq_mxfp4_cuda(const float *                    x,
-                             const int32_t *                  ids,
-                             void *                           vy,
-                             [[maybe_unused]] const ggml_type type_src0,
-                             const int64_t                    ne00,
-                             const int64_t                    s01,
-                             const int64_t                    s02,
-                             const int64_t                    s03,
-                             const int64_t                    ne0,
-                             const int64_t                    ne1,
-                             const int64_t                    ne2,
-                             const int64_t                    ne3,
-                             cudaStream_t                     stream) {
-    GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0);
-
-    constexpr int nwarps = 8;
-    constexpr int vals_per_warp  = 2 * QK_MXFP4;
-    constexpr int vals_per_block = nwarps * vals_per_warp;
-
-    const int64_t block_num_y = (ne0 + vals_per_block - 1) / vals_per_block;
-    const dim3    num_blocks(ne1, block_num_y, ne2 * ne3);
-    const dim3    block_size(WARP_SIZE, nwarps, 1);
-
-    quantize_mmq_mxfp4<<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2);
+void quantize_mmq_fp4_cuda(
+        const float * x, const int32_t * ids, void * vy, const ggml_type type_src0,
+        const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
+        const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) {
+    GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4 || type_src0 == GGML_TYPE_NVFP4);
+    GGML_ASSERT(ne0 > 0);
+
+    if (type_src0 == GGML_TYPE_NVFP4) {
+        GGML_ASSERT(ne00 % QK_NVFP4 == 0);
+        constexpr int nvfp4_block_size = 128;
+        const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size);
+        const dim3 block_size(nvfp4_block_size, 1, 1);
+        const dim3 num_blocks(ne1, block_num_y, ne2 * ne3);
+        quantize_mmq_nvfp4<<<num_blocks, block_size, 0, stream>>>(
+            x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2);
+    } else {
+        GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0);
+
+        constexpr int nwarps = 8;
+        constexpr int vals_per_warp  = 2 * QK_MXFP4;
+        constexpr int vals_per_block = nwarps * vals_per_warp;
+
+        const int64_t block_num_y = (ne0 + vals_per_block - 1) / vals_per_block;
+        const dim3    num_blocks(ne1, block_num_y, ne2 * ne3);
+        const dim3    block_size(WARP_SIZE, nwarps, 1);
+
+        quantize_mmq_mxfp4<<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2);
+    }
 }
diff --git a/ggml/src/ggml-cuda/quantize.cuh b/ggml/src/ggml-cuda/quantize.cuh
index 6a91df63578..768a3ae6de6 100644
--- a/ggml/src/ggml-cuda/quantize.cuh
+++ b/ggml/src/ggml-cuda/quantize.cuh
@@ -26,7 +26,7 @@ void quantize_mmq_q8_1_cuda(
         ggml_type type_src0, int64_t ne00, int64_t s01, int64_t s02, int64_t s03,
         int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, cudaStream_t stream);
 
-void quantize_mmq_mxfp4_cuda(const float *   x,
+void quantize_mmq_fp4_cuda(const float *   x,
                              const int32_t * ids,
                              void *          vy,
                              ggml_type       type_src0,
diff --git a/ggml/src/ggml-cuda/snake.cu b/ggml/src/ggml-cuda/snake.cu
new file mode 100644
index 00000000000..384638c1f47
--- /dev/null
+++ b/ggml/src/ggml-cuda/snake.cu
@@ -0,0 +1,72 @@
+#include "snake.cuh"
+#include "convert.cuh"
+
+// Fused Snake activation: y = x + sin^2(a * x) * inv_b
+// x: [T, C] (T contiguous), a: [1, C], inv_b: [1, C]
+// Supports F32, F16, BF16 data with F32 compute.
+
+template <typename T>
+static __global__ void snake_kernel(
+        const T     * __restrict__ x,
+        const float * __restrict__ a,
+        const float * __restrict__ inv_b,
+        T           * __restrict__ dst,
+        const int    total,
+        const uint3  T_len_fastdiv) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx >= total) return;
+
+    const int c = (int) fastdiv((uint32_t) idx, T_len_fastdiv);
+
+    const float xi = ggml_cuda_cast<float>(x[idx]);
+    const float s  = sinf(a[c] * xi);
+    dst[idx] = ggml_cuda_cast<T>(xi + s * s * inv_b[c]);
+}
+
+// Internal launcher with explicit x/a/inv_b/dst tensors.
+// Shared by the public op (reads dst->src) and the fusion path (explicit args).
+static void launch_snake(ggml_backend_cuda_context & ctx,
+                         const ggml_tensor * x,
+                         const ggml_tensor * a,
+                         const ggml_tensor * inv_b,
+                         ggml_tensor *       dst) {
+    const float * a_d     = (const float *)a->data;
+    const float * inv_b_d = (const float *)inv_b->data;
+
+    const int   T = (int)x->ne[0];
+    const int   C = (int)x->ne[1];
+    const int   total = T * C;
+    const uint3 T_len_fastdiv = init_fastdiv_values((uint64_t) T);
+
+    const int block_size = 256;
+    const int grid_size  = (total + block_size - 1) / block_size;
+
+    cudaStream_t stream = ctx.stream();
+
+    switch (x->type) {
+        case GGML_TYPE_F32: {
+            snake_kernel<<<grid_size, block_size, 0, stream>>>(
+                (const float *)x->data, a_d, inv_b_d, (float *)dst->data, total, T_len_fastdiv);
+        } break;
+        case GGML_TYPE_F16: {
+            snake_kernel<<<grid_size, block_size, 0, stream>>>(
+                (const half *)x->data, a_d, inv_b_d, (half *)dst->data, total, T_len_fastdiv);
+        } break;
+        case GGML_TYPE_BF16: {
+            snake_kernel<<<grid_size, block_size, 0, stream>>>(
+                (const nv_bfloat16 *)x->data, a_d, inv_b_d, (nv_bfloat16 *)dst->data, total, T_len_fastdiv);
+        } break;
+        default:
+            GGML_ABORT("snake: unsupported type");
+    }
+}
+
+// Fusion entry: caller supplies x/a/inv_b explicitly from the matched
+// mul -> sin -> sqr -> mul -> add pattern. The dst is the trailing add output.
+void ggml_cuda_op_snake_fused(ggml_backend_cuda_context & ctx,
+                              const ggml_tensor * x,
+                              const ggml_tensor * a,
+                              const ggml_tensor * inv_b,
+                              ggml_tensor *       dst) {
+    launch_snake(ctx, x, a, inv_b, dst);
+}
diff --git a/ggml/src/ggml-cuda/snake.cuh b/ggml/src/ggml-cuda/snake.cuh
new file mode 100644
index 00000000000..7f6f1cb3b41
--- /dev/null
+++ b/ggml/src/ggml-cuda/snake.cuh
@@ -0,0 +1,8 @@
+#include "common.cuh"
+
+// Fusion entry point. Caller supplies x/a/inv_b explicitly.
+void ggml_cuda_op_snake_fused(ggml_backend_cuda_context & ctx,
+                              const ggml_tensor * x,
+                              const ggml_tensor * a,
+                              const ggml_tensor * inv_b,
+                              ggml_tensor *       dst);
diff --git a/ggml/src/ggml-cuda/ssm-conv.cu b/ggml/src/ggml-cuda/ssm-conv.cu
index b77cdc1c137..4841389fbc8 100644
--- a/ggml/src/ggml-cuda/ssm-conv.cu
+++ b/ggml/src/ggml-cuda/ssm-conv.cu
@@ -3,6 +3,7 @@
 
 template <bool apply_silu, size_t split_d_inner, size_t d_conv>
 static __global__ void ssm_conv_f32(const float * __restrict__ src0, const float * __restrict__ src1,
+                                    const float * __restrict__ bias,
                                     const int src0_nb0, const int src0_nb1, const int src0_nb2, const int src1_nb1,
                                     float * __restrict__ dst, const int dst_nb0, const int dst_nb1, const int dst_nb2,
                                     const int64_t n_t) {
@@ -27,6 +28,8 @@ static __global__ void ssm_conv_f32(const float * __restrict__ src0, const float
         w[j] = w_block[tid * stride_w + j];
     }
 
+    float b = bias != nullptr ? bias[bidy * split_d_inner + tid] : 0.0f;
+
     for (int64_t i = 0; i < n_t; i++) {
         float sumf = 0.0f;
 
@@ -42,12 +45,14 @@ static __global__ void ssm_conv_f32(const float * __restrict__ src0, const float
         for (size_t j = 0; j < d_conv; j++) {
             sumf += x[(i + j) % d_conv] * w[j];
         }
+        sumf += b;
         y_block[i * stride_y + tid] = apply_silu ? ggml_cuda_op_silu_single(sumf) : sumf;
     }
 }
 
 template <bool apply_silu, size_t split_d_inner, size_t d_conv, int64_t split_n_t>
 static __global__ void ssm_conv_long_token_f32(const float * __restrict__ src0, const float * __restrict__ src1,
+                                               const float * __restrict__ bias,
                                                const int src0_nb0, const int src0_nb1, const int src0_nb2,
                                                const int src1_nb1, float * __restrict__ dst, const int dst_nb0,
                                                const int dst_nb1, const int dst_nb2, const int64_t n_t) {
@@ -97,6 +102,8 @@ static __global__ void ssm_conv_long_token_f32(const float * __restrict__ src0,
         w[j] = w_block[tid * stride_w + j];
     }
 
+    float b = bias != nullptr ? bias[bidy * split_d_inner + tid] : 0.0f;
+
     // Compute from shared memory
     for (int64_t i = 0; i < local_n_t; i++) {
         float sumf = 0.0f;
@@ -104,12 +111,13 @@ static __global__ void ssm_conv_long_token_f32(const float * __restrict__ src0,
         for (size_t j = 0; j < d_conv; j++) {
             sumf += smem[tid * n_cols + i + j] * w[j];
         }
+        sumf += b;
         y_block[i * stride_y + tid] = apply_silu ? ggml_cuda_op_silu_single(sumf) : sumf;
     }
 }
 
 template <bool apply_silu>
-static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int src0_nb0, const int src0_nb1,
+static void ssm_conv_f32_cuda(const float * src0, const float * src1, const float * bias, const int src0_nb0, const int src0_nb1,
                               const int src0_nb2, const int src1_nb1, float * dst, const int dst_nb0, const int dst_nb1,
                               const int dst_nb2, const int64_t nc, const int64_t nr, const int64_t n_t,
                               const int64_t n_s, cudaStream_t stream) {
@@ -120,14 +128,14 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int
         constexpr int kNC = decltype(NC)::value;
         if (n_t <= 32) {
             const dim3 blocks(n_s, (nr + threads - 1) / threads, 1);
-            ssm_conv_f32<apply_silu, threads, kNC><<<blocks, threads, 0, stream>>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
+            ssm_conv_f32<apply_silu, threads, kNC><<<blocks, threads, 0, stream>>>(src0, src1, bias, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
                                                                        dst, dst_nb0, dst_nb1, dst_nb2, n_t);
         } else {
             const int64_t split_n_t = 32;
             dim3          blocks(n_s, (nr + threads - 1) / threads, (n_t + split_n_t - 1) / split_n_t);
             const size_t  smem_size = threads * (kNC - 1 + split_n_t) * sizeof(float);
             ssm_conv_long_token_f32<apply_silu, threads, kNC, split_n_t><<<blocks, threads, smem_size, stream>>>(
-                src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t);
+                src0, src1, bias, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t);
         }
     };
 
@@ -140,11 +148,18 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int
     }
 }
 
-void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * silu_dst) {
+void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * bias_add_node, ggml_tensor * silu_dst) {
     const struct ggml_tensor * src0 = dst->src[0];  // conv_x
     const struct ggml_tensor * src1 = dst->src[1];  // conv1d.weight
+    const bool fuse_bias = bias_add_node != nullptr;
     const bool fuse_silu = silu_dst != nullptr;
 
+    // bias always comes with silu.
+    GGML_ASSERT(!fuse_bias || fuse_silu);
+
+    // The bias (when fused) is the non-conv operand of the ADD node.
+    const struct ggml_tensor * bias = fuse_bias ? (bias_add_node->src[0] == dst ? bias_add_node->src[1] : bias_add_node->src[0]) : nullptr;
+
     // When fusing, write to silu_dst (the node downstream references).
     const struct ggml_tensor * out = fuse_silu ? silu_dst : dst;
 
@@ -160,16 +175,23 @@ void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, g
 
     const float * src0_d = (const float *) src0->data;
     const float * src1_d = (const float *) src1->data;
+    const float * bias_d = fuse_bias ? (const float *) bias->data : nullptr;
     float *       dst_d  = (float *) out->data;
     cudaStream_t  stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(out->type == GGML_TYPE_F32);
+    if (fuse_bias) {
+        GGML_ASSERT(bias->type == GGML_TYPE_F32);
+        GGML_ASSERT(ggml_is_contiguous(bias));
+        GGML_ASSERT(ggml_nelements(bias) == nr);
+    }
+
     if (fuse_silu) {
-        ssm_conv_f32_cuda<true>(src0_d, src1_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1],
+        ssm_conv_f32_cuda<true>(src0_d, src1_d, bias_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1],
                           out->nb[2], nc, nr, n_t, n_s, stream);
     } else {
-        ssm_conv_f32_cuda<false>(src0_d, src1_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1],
+        ssm_conv_f32_cuda<false>(src0_d, src1_d, bias_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1],
                           out->nb[2], nc, nr, n_t, n_s, stream);
     }
 }
diff --git a/ggml/src/ggml-cuda/ssm-conv.cuh b/ggml/src/ggml-cuda/ssm-conv.cuh
index f96a1cd2484..8514ca84920 100644
--- a/ggml/src/ggml-cuda/ssm-conv.cuh
+++ b/ggml/src/ggml-cuda/ssm-conv.cuh
@@ -1,3 +1,3 @@
 #include "common.cuh"
 
-void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * silu_dst = nullptr);
+void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * bias_add_node = nullptr, ggml_tensor * silu_dst = nullptr);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_16.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_16.cu
index b643d1d2daa..b572da280d4 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_16.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_16.cu
@@ -2,5 +2,6 @@
 
 #include "../fattn-mma-f16.cuh"
 
+DECL_FATTN_MMA_F16_CASE(192, 128, 1, 16);
 DECL_FATTN_MMA_F16_CASE(576, 512, 1, 16);
 DECL_FATTN_MMA_F16_CASE(640, 512, 1, 16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu
index 1f554d81e5e..8fc3b17976e 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_32.cu
@@ -2,4 +2,5 @@
 
 #include "../fattn-mma-f16.cuh"
 
+DECL_FATTN_MMA_F16_CASE(320, 256, 1, 32);
 DECL_FATTN_MMA_F16_CASE(576, 512, 1, 32);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_8.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_8.cu
index 22d383173f3..6ae77bec895 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_8.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_1-ncols2_8.cu
@@ -7,5 +7,6 @@ DECL_FATTN_MMA_F16_CASE(80, 80, 1, 8);
 DECL_FATTN_MMA_F16_CASE(96, 96, 1, 8);
 DECL_FATTN_MMA_F16_CASE(112, 112, 1, 8);
 DECL_FATTN_MMA_F16_CASE(128, 128, 1, 8);
+DECL_FATTN_MMA_F16_CASE(192, 128, 1, 8);
 DECL_FATTN_MMA_F16_CASE(256, 256, 1, 8);
 DECL_FATTN_MMA_F16_CASE(512, 512, 1, 8);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_16.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_16.cu
index 2580167449c..c868bfbd613 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_16.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_16.cu
@@ -2,5 +2,6 @@
 
 #include "../fattn-mma-f16.cuh"
 
+DECL_FATTN_MMA_F16_CASE(192, 128, 2, 16);
 DECL_FATTN_MMA_F16_CASE(576, 512, 2, 16);
 DECL_FATTN_MMA_F16_CASE(640, 512, 2, 16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu
index 264751d65ec..abd2b21ce04 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_32.cu
@@ -2,4 +2,5 @@
 
 #include "../fattn-mma-f16.cuh"
 
+DECL_FATTN_MMA_F16_CASE(320, 256, 2, 32);
 DECL_FATTN_MMA_F16_CASE(576, 512, 2, 32);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_8.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_8.cu
index 84b674cd05a..9f4bef11a44 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_8.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_2-ncols2_8.cu
@@ -7,5 +7,6 @@ DECL_FATTN_MMA_F16_CASE(80, 80, 2, 8);
 DECL_FATTN_MMA_F16_CASE(96, 96, 2, 8);
 DECL_FATTN_MMA_F16_CASE(112, 112, 2, 8);
 DECL_FATTN_MMA_F16_CASE(128, 128, 2, 8);
+DECL_FATTN_MMA_F16_CASE(192, 128, 2, 8);
 DECL_FATTN_MMA_F16_CASE(256, 256, 2, 8);
 DECL_FATTN_MMA_F16_CASE(512, 512, 2, 8);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_16.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_16.cu
index 585e13d42ff..221a566ead6 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_16.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_16.cu
@@ -2,5 +2,6 @@
 
 #include "../fattn-mma-f16.cuh"
 
+DECL_FATTN_MMA_F16_CASE(192, 128, 4, 16);
 DECL_FATTN_MMA_F16_CASE(576, 512, 4, 16);
 DECL_FATTN_MMA_F16_CASE(640, 512, 4, 16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_8.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_8.cu
index 5906398db91..859bea5c525 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_8.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_8.cu
@@ -7,5 +7,6 @@ DECL_FATTN_MMA_F16_CASE(80, 80, 4, 8);
 DECL_FATTN_MMA_F16_CASE(96, 96, 4, 8);
 DECL_FATTN_MMA_F16_CASE(112, 112, 4, 8);
 DECL_FATTN_MMA_F16_CASE(128, 128, 4, 8);
+DECL_FATTN_MMA_F16_CASE(192, 128, 4, 8);
 DECL_FATTN_MMA_F16_CASE(256, 256, 4, 8);
 DECL_FATTN_MMA_F16_CASE(512, 512, 4, 8);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_8.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_8.cu
index 4bc60d62f91..c975ce6b9b7 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_8.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_8.cu
@@ -7,5 +7,6 @@ DECL_FATTN_MMA_F16_CASE(80, 80, 8, 8);
 DECL_FATTN_MMA_F16_CASE(96, 96, 8, 8);
 DECL_FATTN_MMA_F16_CASE(112, 112, 8, 8);
 DECL_FATTN_MMA_F16_CASE(128, 128, 8, 8);
+DECL_FATTN_MMA_F16_CASE(192, 128, 8, 8);
 DECL_FATTN_MMA_F16_CASE(256, 256, 8, 8);
 DECL_FATTN_MMA_F16_CASE(512, 512, 8, 8);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq192-dv128.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq192-dv128.cu
new file mode 100644
index 00000000000..b571cca0df2
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq192-dv128.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.cuh"
+
+DECL_FATTN_TILE_CASE(192, 128);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq320-dv256.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq320-dv256.cu
new file mode 100644
index 00000000000..c91f508079d
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq320-dv256.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-tile.cuh"
+
+DECL_FATTN_TILE_CASE(320, 256);
diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
index 841059c15b5..af05a9eff71 100755
--- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
+++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
@@ -3,7 +3,10 @@
 from glob import glob
 import os
 
-HEAD_SIZES_KQ = [40, 64, 72, 80, 96, 112, 128, 256, 512, 576]
+HEAD_SIZES_KQ = [40, 64, 72, 80, 96, 112, 128, 192, 256, 320, 512, 576]
+
+# DKQ -> DV override for asymmetric head dims.
+HEAD_SIZES_V_OVERRIDE = {576: 512, 320: 256, 192: 128}
 
 TYPES_KV = ["GGML_TYPE_F16", "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_BF16"]
 
@@ -62,7 +65,7 @@ def get_short_name(long_quant_name):
     os.remove(filename)
 
 for head_size_kq in HEAD_SIZES_KQ:
-    head_size_v = head_size_kq if head_size_kq != 576 else 512
+    head_size_v = HEAD_SIZES_V_OVERRIDE.get(head_size_kq, head_size_kq)
     with open(f"fattn-tile-instance-dkq{head_size_kq}-dv{head_size_v}.cu", "w") as f:
         f.write(SOURCE_FATTN_TILE.format(head_size_kq=head_size_kq, head_size_v=head_size_v))
 
@@ -84,13 +87,18 @@ def get_short_name(long_quant_name):
                     continue
                 if head_size_kq == 72:
                     continue
-                if head_size_kq == 512 and ncols2 not in (4, 8):
+                # Skip compilation of unused ncols2 values for niche head sizes:
+                if head_size_kq == 192 and ncols2 not in (8, 16): # MiMo-V2.5
+                    continue
+                if head_size_kq == 320 and ncols2 != 32: # Mistral Small 4
+                    continue
+                if head_size_kq == 512 and ncols2 not in (4, 8): # Gemma 4
                     continue
-                if head_size_kq != 576 and ncols2 in (16, 32):
+                if head_size_kq == 576 and ncols2 not in (4, 16, 32): # Deepseek, GLM 4.7 Flash
                     continue
-                if head_size_kq == 576 and ncols2 not in (4, 16, 32):
+                if head_size_kq not in (192, 320, 576) and ncols2 in (16, 32):
                     continue
-                head_size_v = head_size_kq if head_size_kq != 576 else 512
+                head_size_v = HEAD_SIZES_V_OVERRIDE.get(head_size_kq, head_size_kq)
                 f.write(SOURCE_FATTN_MMA_CASE.format(ncols1=ncols1, ncols2=ncols2, head_size_kq=head_size_kq, head_size_v=head_size_v))
 
 for type in TYPES_MMQ:
diff --git a/ggml/src/ggml-cuda/unary.cu b/ggml/src/ggml-cuda/unary.cu
index 4ad30fa1f35..2aeba26f414 100644
--- a/ggml/src/ggml-cuda/unary.cu
+++ b/ggml/src/ggml-cuda/unary.cu
@@ -65,6 +65,11 @@ static __device__ __forceinline__ float op_sqr(float x) {
     return x * x;
 }
 
+static __device__ __forceinline__ float op_relu_sqr(float x) {
+    const float r = fmaxf(x, 0.0f);
+    return r * r;
+}
+
 static __device__ __forceinline__ float op_sqrt(float x) {
     return sqrtf(x);
 }
@@ -615,3 +620,21 @@ void ggml_cuda_op_unary_mul(ggml_backend_cuda_context & ctx, ggml_tensor * unary
             GGML_ABORT("Unsupported unary op for fused unary+mul");
     }
 }
+
+/* fused relu + sqr */
+
+void ggml_cuda_op_relu_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * relu_node, ggml_tensor * sqr_node) {
+    const ggml_tensor * src = relu_node->src[0];
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(src));
+    GGML_ASSERT(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16);
+    GGML_ASSERT(src->type == sqr_node->type);
+
+    const int k = ggml_nelements(src);
+    if (src->type == GGML_TYPE_F16) {
+        unary_cuda<op_relu_sqr>((const half *)src->data, (half *)sqr_node->data, k, stream);
+    } else {
+        unary_cuda<op_relu_sqr>((const float *)src->data, (float *)sqr_node->data, k, stream);
+    }
+}
diff --git a/ggml/src/ggml-cuda/unary.cuh b/ggml/src/ggml-cuda/unary.cuh
index f1dd2183a6c..81ed873ecc3 100644
--- a/ggml/src/ggml-cuda/unary.cuh
+++ b/ggml/src/ggml-cuda/unary.cuh
@@ -91,6 +91,8 @@ void ggml_cuda_op_xielu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_unary_mul(ggml_backend_cuda_context & ctx, ggml_tensor * unary_node, ggml_tensor * mul_node);
 
+void ggml_cuda_op_relu_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * relu_node, ggml_tensor * sqr_node);
+
 __device__ __forceinline__ float ggml_cuda_op_silu_single(float x) {
     return x / (1.0f + expf(-x));
 }
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 4dadf942a1d..f2ad40dc4a3 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -67,6 +67,7 @@
 #define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
 #define cublasSetStream hipblasSetStream
 #define cublasSgemm hipblasSgemm
+#define cublasSgemmStridedBatched hipblasSgemmStridedBatched
 #define cublasStatus_t hipblasStatus_t
 #define cublasOperation_t hipblasOperation_t
 #define cudaDevAttrCooperativeLaunch hipDeviceAttributeCooperativeLaunch
@@ -74,6 +75,7 @@
 #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
 #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
 #define cudaDeviceGetAttribute hipDeviceGetAttribute
+#define cudaDeviceGetPCIBusId hipDeviceGetPCIBusId
 #define cudaDeviceProp hipDeviceProp_t
 #define cudaDeviceSynchronize hipDeviceSynchronize
 #define cudaError_t hipError_t
diff --git a/ggml/src/ggml-cuda/vendors/musa.h b/ggml/src/ggml-cuda/vendors/musa.h
index 8aa056e9174..118d7e6079a 100644
--- a/ggml/src/ggml-cuda/vendors/musa.h
+++ b/ggml/src/ggml-cuda/vendors/musa.h
@@ -32,6 +32,7 @@
 #define cublasSetMathMode mublasSetMathMode
 #define cublasSetStream mublasSetStream
 #define cublasSgemm mublasSgemm
+#define cublasSgemmStridedBatched mublasSgemmStridedBatched
 #define cublasStatus_t mublasStatus_t
 #define cublasOperation_t mublasOperation_t
 #define cublasGetStatusString mublasGetStatusString
@@ -39,6 +40,7 @@
 #define cudaDeviceCanAccessPeer musaDeviceCanAccessPeer
 #define cudaDeviceDisablePeerAccess musaDeviceDisablePeerAccess
 #define cudaDeviceEnablePeerAccess musaDeviceEnablePeerAccess
+#define cudaDeviceGetPCIBusId musaDeviceGetPCIBusId
 #define cudaDeviceProp musaDeviceProp
 #define cudaDeviceSynchronize musaDeviceSynchronize
 #define cudaError_t musaError_t
@@ -74,7 +76,9 @@
 #define cudaMemcpyDeviceToDevice musaMemcpyDeviceToDevice
 #define cudaMemcpyDeviceToHost musaMemcpyDeviceToHost
 #define cudaMemcpyHostToDevice musaMemcpyHostToDevice
+#define cudaMemcpyFromSymbol musaMemcpyFromSymbol
 #define cudaMemcpyKind musaMemcpyKind
+#define cudaMemcpyToSymbol musaMemcpyToSymbol
 #define cudaMemset musaMemset
 #define cudaMemsetAsync musaMemsetAsync
 #define cudaMemGetInfo musaMemGetInfo
diff --git a/ggml/src/ggml-hexagon/CMakeLists.txt b/ggml/src/ggml-hexagon/CMakeLists.txt
index f3a583543c6..b82bae0c103 100644
--- a/ggml/src/ggml-hexagon/CMakeLists.txt
+++ b/ggml/src/ggml-hexagon/CMakeLists.txt
@@ -22,7 +22,8 @@ message(STATUS "hexagon: using ${HEXAGON_SDK_ROOT} and ${HEXAGON_TOOLS_ROOT} for
 include(${HEXAGON_SDK_ROOT}/build/cmake/hexagon_fun.cmake)
 include(ExternalProject)
 
-option(GGML_HEXAGON_HTP_DEBUG "ggml-hexagon: enable HTP debug output" OFF)
+option(GGML_HEXAGON_HTP_DEBUG  "ggml-hexagon: enable HTP debug output" OFF)
+option(GGML_HEXAGON_FA_EXP2_HF "ggml-hexagon: use FP16 exp2 polynomial in FA softmax instead of F32 exp round-trip" OFF)
 set(GGML_HEXAGON_HTP_CERT  "$ENV{HEXAGON_HTP_CERT}" CACHE PATH "ggml-hexagon: enable HTP library signing using certificate")
 set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml-hexagon: quantize group size (32, 64, or 128)")
 
diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
index 3d68b80048f..3d1c9da8329 100644
--- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp
+++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
@@ -12,9 +12,12 @@
 #include <cstddef>
 #include <stdexcept>
 #include <string>
+#include <sstream>
+#include <iomanip>
 #include <unordered_set>
 #include <unordered_map>
 #include <regex>
+#include <queue>
 
 #ifdef _WIN32
 #    include <sal.h>
@@ -41,20 +44,31 @@
 #include "htp_iface.h"
 #include "htp-drv.h"
 
-static size_t opt_ndev         = 1;
-static size_t opt_nhvx         = 0; // use all
-static int    opt_arch         = 0; // autodetect
-static int    opt_etm          = 0;
-static int    opt_verbose      = 0;
-static int    opt_profile      = 0;
-static int    opt_hostbuf      = 1; // hostbuf ON by default
-static int    opt_use_hmx      = 1; // when set, enable HMX; when 0, use HVX only
+using intvec  = std::vector<int>;
+using uintvec = std::vector<unsigned int>;
+using u32vec  = std::vector<uint32_t>;
+
+static int    opt_arch    = 0; // autodetect
+static size_t opt_ndev    = 1;
+static size_t opt_nhvx    = 0; // use all
+static int    opt_use_hmx = 1; // when set, enable HMX; when 0, use HVX only
+static size_t opt_vmem    = HTP_OP_MAX_VMEM_DEFAULT;  // max available va space for buffer mappings
+static size_t opt_mbuf    = 1ul * 1024 * 1024 * 1024; // max buffer size
+static int    opt_etm     = 0;
+static int    opt_verbose = 0;
+static int    opt_profile = 0; // profiling mode (0-disabled, 1-basic, 2-pmu)
+static int    opt_hostbuf = 1; // hostbuf ON by default
+
+// Default PMU events, if profiling with PMU (mode=2) is enabled
+// See https://docs.qualcomm.com/doc/80-N2040-60/topic/pmu-events.html
+//     https://docs.qualcomm.com/doc/80-N2040-61/topic/hvx-pmu-events.html
+static u32vec opt_pmu_evt { 0x3, 0x111, 0x100, 0x105, 0x240, 0x256, 0x7D, 0x8C };
 
 // Enable all stages by default
-static int opt_opmask   = HTP_OPMASK_QUEUE | HTP_OPMASK_COMPUTE;
-static int opt_opsync   = 0;  // synchronous ops
+static int opt_opstage  = HTP_OPSTAGE_QUEUE | HTP_OPSTAGE_COMPUTE;
 static int opt_opbatch  = 1024; // max number of ops in a batch
 static int opt_opqueue  = 16;   // max number of pending batches
+
 static std::regex* opt_opfilter = NULL; // regex of ops to not claim
 
 #define HEX_VERBOSE(...) \
@@ -99,24 +113,30 @@ static void ggml_hexagon_dump_op_supp(const std::string &sess_name, const struct
     if (!opt_verbose) return;
 
     op_desc desc(op);
-    GGML_LOG_DEBUG("ggml-hex: %s supports-op %s : %s : %s : %s : %s : %s : %s\n", sess_name.c_str(),
+    GGML_LOG_DEBUG("ggml-hex: %s supports-op %s: %s : %s : %s : %s : %s : %s\n", sess_name.c_str(),
                 ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, supp ? "yes" : "no");
 }
 
 static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const ggml_tensor * op,
-                                      uint32_t op_usec, uint32_t op_cycles, uint32_t op_pkts, uint64_t call_usec) {
+                                      uint32_t op_usec, uint32_t op_cycles, const uint32_t pmu[]) {
     if (!opt_profile) return;
 
+    char pmu_str[256] = "";
+    if (opt_profile > 1) {
+        static_assert(HTP_PROF_PMU_NCNT == 8, "current implementation assumes 8 PMU counters");
+        sprintf(pmu_str, " pmu [%u,%u,%u,%u,%u,%u,%u,%u]",
+                pmu[0], pmu[1], pmu[2], pmu[3], pmu[4], pmu[5], pmu[6], pmu[7]);
+    }
+
     op_desc desc(op);
-    GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : %s : op-usec %u op-cycles %u op-pkts %u (%f) call-usec %llu\n", sess_name.c_str(),
-                ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs,
-                op_usec, op_cycles, op_pkts, (float) op_cycles / op_pkts, (unsigned long long) call_usec);
+    GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : usec %u cycles %u%s\n", sess_name.c_str(),
+            ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, op_usec, op_cycles, pmu_str);
 }
 
 // ** backend sessions
 
 struct ggml_hexagon_opbatch;
-struct ggml_hexagon_opshm;
+struct ggml_hexagon_opqueue;
 
 struct ggml_hexagon_session {
     std::string      name;
@@ -132,8 +152,8 @@ struct ggml_hexagon_session {
     bool             valid_iface;
 
     std::atomic<int>      op_pending;
-    ggml_hexagon_opbatch *op_batch;
-    ggml_hexagon_opshm   *op_shm;
+    ggml_hexagon_opbatch* op_batch;
+    ggml_hexagon_opqueue* op_queue;
 
     ggml_backend_buffer_type buffer_type        = {};
     ggml_backend_buffer_type repack_buffer_type = {};
@@ -173,33 +193,30 @@ struct ggml_hexagon_shared_buffer {
     bool                   mapped;
     bool                   pinned;
 
-    void mmap(bool pinned = false) {
-        int err = fastrpc_mmap(sess->domain_id, this->fd, (void *) this->base, 0, this->size, FASTRPC_MAP_FD_DELAYED);
+    void mmap() {
+        fastrpc_map_flags flags = this->pinned ? FASTRPC_MAP_FD : FASTRPC_MAP_FD_DELAYED;
+
+        int err = fastrpc_mmap(sess->domain_id, this->fd, (void *) this->base, 0, this->size, flags);
         if (err != 0) {
             GGML_LOG_ERROR("ggml-hex: %s buffer mapping failed : domain_id %d size %zu fd %d error 0x%08x\n", sess->c_name(),
                     sess->domain_id, this->size, this->fd, (unsigned) err);
             throw std::runtime_error("ggml-hex: fastrpc_mmap failed (see log for details)");
         }
 
-        if (pinned) {
-            err = htp_iface_mmap(sess->handle, this->fd, this->size, pinned);
-            if (err != 0) {
-                GGML_LOG_ERROR("ggml-hex: %s buffer pinning failed : domain_id %d size %zu fd %d error 0x%08x\n", sess->c_name(),
-                        sess->domain_id, this->size, this->fd, (unsigned) err);
-                throw std::runtime_error("ggml-hex: htp_iface_mmap failed (see log for details)");
-            }
-        }
-
-        this->mapped = true;
-        this->pinned = pinned;
         HEX_VERBOSE("ggml-hex: %s mapped buffer: base %p size %zu fd %d pinned %u\n",
                 sess->c_name(), (void *) this->base, this->size, this->fd, pinned);
+
+        this->mapped = true;
     }
 
     void unmap() {
         if (!this->mapped) return;
 
-        htp_iface_munmap(sess->handle, this->fd);
+        if (!this->pinned) {
+            // HTP might still hold a reference, tell it drop it
+            htp_iface_munmap(sess->handle, this->fd);
+        }
+
         fastrpc_munmap(sess->domain_id, this->fd, (void *) this->base, this->size);
 
         HEX_VERBOSE("ggml-hex: %s unmapped buffer: base %p size %zu fd %d\n", sess->c_name(),
@@ -209,7 +226,7 @@ struct ggml_hexagon_shared_buffer {
         this->fd     = -1;
     }
 
-    void alloc(size_t size, bool pinned = false) {
+    void alloc(size_t size) {
         if (this->base) return;
 
         this->base = (uint8_t *) rpcmem_alloc2(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS, size);
@@ -227,8 +244,7 @@ struct ggml_hexagon_shared_buffer {
 
         HEX_VERBOSE("ggml-hex: %s allocated buffer: base %p size %zu fd %d pinned %d\n", sess->c_name(),
                     (void *) this->base, this->size, this->fd, (int) pinned);
-
-        mmap(pinned);
+        mmap();
     }
 
     void free() {
@@ -244,15 +260,14 @@ struct ggml_hexagon_shared_buffer {
     }
 
     ggml_hexagon_shared_buffer(ggml_hexagon_session * sess, size_t size, bool pinned = false) {
-        size += 4 * 1024;  // extra page for padding
-
         this->sess   = sess;
         this->size   = 0;
         this->base   = nullptr;
         this->fd     = -1;
         this->mapped = false;
+        this->pinned = pinned;
 
-        alloc(size, pinned);
+        alloc(size);
     }
 
     ~ggml_hexagon_shared_buffer() {
@@ -1457,6 +1472,7 @@ static ggml_backend_buffer_t ggml_backend_hexagon_buffer_type_alloc_buffer(
             ggml_backend_buffer_type_t buffer_type, size_t size) {
     auto sess = static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type->context)->sess;
     try {
+        size += 4 * 1024;  // guard page
         ggml_hexagon_shared_buffer * sbuf = new ggml_hexagon_shared_buffer(sess, size);
         return ggml_backend_buffer_init(buffer_type, ggml_backend_hexagon_buffer_interface, sbuf, size);
     } catch (const std::exception & exc) {
@@ -1469,6 +1485,7 @@ static ggml_backend_buffer_t ggml_backend_hexagon_repack_buffer_type_alloc_buffe
             ggml_backend_buffer_type_t buffer_type, size_t size) {
     auto sess = static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type->context)->sess;
     try {
+        size += 4 * 1024;  // guard page
         ggml_hexagon_shared_buffer * sbuf = new ggml_hexagon_shared_buffer(sess, size);
         return ggml_backend_buffer_init(buffer_type, ggml_backend_hexagon_buffer_interface, sbuf, size);
     } catch (const std::exception & exc) {
@@ -1487,7 +1504,7 @@ static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffe
 }
 
 static size_t ggml_backend_hexagon_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
-    return 1UL * 1024 * 1024 * 1024;  // 1GB per buffer
+    return opt_mbuf; // typically 1GB per buffer
     GGML_UNUSED(buffer_type);
 }
 
@@ -1521,65 +1538,14 @@ static ggml_backend_buffer_type_i ggml_backend_hexagon_repack_buffer_type_interf
 
 // Backend session implementation
 
-struct ggml_hexagon_opshm {
-    ggml_hexagon_shared_buffer *sbuf;
-
-    std::vector<bool> block_mask;
-    size_t            block_size;
-
-    uint8_t * base()     const { return this->sbuf->base; }
-    int       fd()       const { return this->sbuf->fd;   }
-    size_t    n_blocks() const { return this->block_mask.size(); }
-
-    ggml_hexagon_opshm(ggml_hexagon_session *sess, size_t max_batch, size_t max_pending) {
-        size_t n_bufs    = HTP_OP_MAX_BUFS;
-        size_t n_ops     = max_batch;
-        size_t n_tensors = n_ops + n_ops * HTP_OP_MAX_INPUTS;
-
-        block_mask.resize(max_pending, true);
-
-        block_size = sizeof(htp_buf_desc) * n_bufs    +
-                     sizeof(htp_tensor)   * n_tensors +
-                     sizeof(htp_op_desc)  * n_ops;
-
-        sbuf = new ggml_hexagon_shared_buffer(sess, block_size * block_mask.size(), true /* pinned */);
-
-        if (opt_verbose) {
-            GGML_LOG_INFO("ggml-hex: %s allocated shared buf %zu : block-size %zu max-batch %zu max-pending %zu\n",
-                    sess->c_name(), (size_t) sbuf->size, block_size, max_batch, max_pending);
-        }
-    }
-
-    ~ggml_hexagon_opshm() {
-        delete sbuf;
-    }
-
-    uint8_t * allocate() {
-        auto it = std::find(block_mask.begin(), block_mask.end(), true);
-        if (it == block_mask.end())
-            return nullptr;
-
-        unsigned int i = std::distance(block_mask.begin(), it);
-        uint8_t*  addr = sbuf->base + (i * block_size);
-        block_mask[i]  = false;
-
-        HEX_VERBOSE("ggml-hex: %s allocated op shm #%u %p\n", sbuf->sess->c_name(), i, (void*) addr);
-        return addr;
-    }
-
-    void release(uint8_t * addr) {
-        int i = (addr - sbuf->base) / block_size;
-        block_mask[i] = true;
-        HEX_VERBOSE("ggml-hex: %s released op shm #%u %p\n", sbuf->sess->c_name(), i, (void*) addr);
-    }
-};
-
 struct ggml_hexagon_opbatch {
-    const char* name;
+    ggml_hexagon_session*            sess;
 
-    std::vector<htp_buf_desc> buffers;
-    std::vector<htp_tensor>   tensors;
-    std::vector<htp_op_desc>  ops;
+    std::vector<const ggml_tensor*>  ops;       // pointers to original ops
+
+    std::vector<htp_buf_desc>        h_bufs;    // htp buffer descriptors
+    std::vector<htp_tensor>          h_tens;    // htp tensor descriptors
+    std::vector<htp_op_desc>         h_ops;     // htp op descriptors
 
     std::unordered_map<int, int>                b_map; // buffer fd   to index
     std::unordered_map<const ggml_tensor*, int> t_map; // tensor ptr  to index
@@ -1606,23 +1572,28 @@ struct ggml_hexagon_opbatch {
         d_map.clear();
     }
 
-    ggml_hexagon_opbatch(ggml_hexagon_session *sess, size_t max_batch) {
-        name = sess->c_name();
+    ggml_hexagon_opbatch(ggml_hexagon_session *sess, size_t batch_size, size_t max_vmem) {
+        this->sess = sess;
 
         n_bufs_max = HTP_OP_MAX_BUFS;
-        n_ops_max  = max_batch;
+        n_ops_max  = batch_size;
         n_tens_max = n_ops_max + n_ops_max * HTP_OP_MAX_INPUTS;
 
-        b_vmem_max = HTP_OP_MAX_VMEM;
+        b_vmem_max = max_vmem;
 
-        buffers.resize(n_bufs_max);
-        tensors.resize(n_tens_max);
         ops.resize(n_ops_max);
 
+        h_bufs.resize(n_bufs_max);
+        h_tens.resize(n_tens_max);
+        h_ops.resize(n_ops_max);
+
         b_map.reserve(n_bufs_max);
         t_map.reserve(n_tens_max);
         d_map.reserve(n_tens_max);
 
+        GGML_LOG_INFO("ggml-hex: %s op batching: n-bufs %u n-tensors %u n-ops %u vmem %zu\n",
+                sess->c_name(), n_bufs_max, n_tens_max, n_ops_max, b_vmem_max);
+
         reset();
     }
 
@@ -1640,7 +1611,7 @@ struct ggml_hexagon_opbatch {
 
         b_map.insert({sbuf->fd, bi});
 
-        htp_buf_desc &b = buffers[bi];
+        htp_buf_desc &b = h_bufs[bi];
         b.base = (uint64_t) sbuf->base;
         b.fd   = sbuf->fd;
         b.size = sbuf->size;
@@ -1664,7 +1635,7 @@ struct ggml_hexagon_opbatch {
         // First lookup by tensor data
         auto range = d_map.equal_range(t->data);
         for (auto it = range.first; it != range.second; ++it) {
-            htp_tensor * h = &tensors[it->second];
+            htp_tensor * h = &h_tens[it->second];
             if (same_shape(h, t)) { return it->second; }
         }
 
@@ -1682,7 +1653,7 @@ struct ggml_hexagon_opbatch {
         uint64_t t_offset = (uint8_t *) t->data - sbuf->base;
         size_t   t_size   = ggml_nbytes(t);
 
-        htp_tensor &h = tensors[ti];
+        htp_tensor &h = h_tens[ti];
         h.bi    = add_buffer(sbuf);
         h.data  = t_offset;
         h.size  = t_size;
@@ -1737,65 +1708,170 @@ struct ggml_hexagon_opbatch {
     // assumes that fit_op() was called first and returned true
     void add_op(htp_op_code opcode, const struct ggml_tensor * t) {
         // Add new op
-        htp_op_desc &o = ops[n_ops++];
+
+        unsigned int n = n_ops++;
         GGML_ASSERT(n_ops <= n_ops_max);
 
+        ops[n] = t;
+
+        htp_op_desc &o = h_ops[n];
         memcpy(&o.params, &t->op_params, sizeof(t->op_params));
         o.opcode = opcode;
         o.flags  = 0;
 
-        if (!(opt_opmask & HTP_OPMASK_COMPUTE)) {
+        if (!(opt_opstage & HTP_OPSTAGE_COMPUTE)) {
             o.flags |= HTP_OPFLAGS_SKIP_COMPUTE;
         }
 
-        ggml_hexagon_dump_op_exec(name, t, o.flags);
+        ggml_hexagon_dump_op_exec(sess->c_name(), t, o.flags);
 
         for (unsigned int i=0; i < HTP_OP_MAX_INPUTS; i++) {
             o.src[i] = t->src[i] ? add_tensor(t->src[i]) : 0xffff;
         }
         o.dst = add_tensor(t);
     }
+};
+
+struct ggml_hexagon_opqueue {
+    // Shared buffer for storing batches
+    ggml_hexagon_shared_buffer *shm_buf;
+    size_t                      shm_blk_size;
+
+    using opvec = std::vector<const ggml_tensor*>;
+
+    std::queue<unsigned int>    done;       // completed batch ids
+    std::vector<opvec>          op_cache;   // per batch op cache
+    std::vector<uint64_t>       start_usec; // per batch start time
+
+    ggml_hexagon_opqueue(ggml_hexagon_session *sess, size_t batch_size, size_t depth) {
+        size_t n_bufs    = HTP_OP_MAX_BUFS;
+        size_t n_ops     = batch_size;
+        size_t n_tensors = n_ops + n_ops * HTP_OP_MAX_INPUTS;
+
+        shm_blk_size = sizeof(htp_buf_desc)  * n_bufs    +
+                       sizeof(htp_tensor)    * n_tensors +
+                       sizeof(htp_op_desc)   * n_ops     +
+                       sizeof(htp_prof_desc) * n_ops;
+
+        shm_buf = new ggml_hexagon_shared_buffer(sess, shm_blk_size * depth, true /* pinned */);
+
+        op_cache.resize(depth);
+        start_usec.resize(depth, 0);
+
+        // init done queue
+        for (unsigned int i = 0; i < depth; i++) { done.push(i); }
+
+        if (opt_verbose) {
+            GGML_LOG_INFO("ggml-hex: %s allocated op-queue : batch-size %zu depth %zu shm-size %zu shm-block-size %zu\n",
+                    sess->c_name(), batch_size, depth, shm_buf->size, shm_blk_size);
+        }
+    }
+
+    ~ggml_hexagon_opqueue() {
+        delete shm_buf;
+    }
+
+    // push new batch
+    bool push(htp_opbatch_req& req, dspqueue_buffer& dbuf, ggml_hexagon_opbatch* op_batch) {
+        static_assert(sizeof(htp_opbatch_req) % 8 == 0, "sizeof(htp_opbatch_req) must be multiple of 8");
+        static_assert(sizeof(htp_opbatch_rsp) % 8 == 0, "sizeof(htp_opbatch_rsp) must be multiple of 8");
+        static_assert(sizeof(htp_buf_desc)    % 8 == 0, "sizeof(htp_buf_desc) must be multiple of 8");
+        static_assert(sizeof(htp_tensor)      % 8 == 0, "sizeof(htp_tensor) must be multiple of 8");
+        static_assert(sizeof(htp_op_desc)     % 8 == 0, "sizeof(htp_op_desc) must be multiple of 8");
+        static_assert(sizeof(htp_prof_desc)   % 8 == 0, "sizeof(htp_prof_desc) must be multiple of 8");
+
+        if (done.empty()) { return false; }
+
+        req.id        = done.front(); done.pop(); // batch id
+        req.n_bufs    = op_batch->n_bufs;
+        req.n_tensors = op_batch->n_tens;
+        req.n_ops     = op_batch->n_ops;
 
-    size_t flush(uint8_t * mem_addr, size_t mem_size) {
-        static_assert(sizeof(htp_buf_desc) % 8 == 0, "sizeof(htp_buf_desc) must be multiple of 8");
-        static_assert(sizeof(htp_tensor)   % 8 == 0, "sizeof(htp_tensor) must be multiple of 8");
-        static_assert(sizeof(htp_op_desc)  % 8 == 0, "sizeof(htp_op_desc) must be multiple of 8");
+        op_cache[req.id]   = op_batch->ops;
+        start_usec[req.id] = ggml_time_us();
 
-        const size_t b_size = sizeof(htp_buf_desc) * n_bufs;
-        const size_t t_size = sizeof(htp_tensor)   * n_tens;
-        const size_t o_size = sizeof(htp_op_desc)  * n_ops;
+        const size_t b_size = sizeof(htp_buf_desc)  * req.n_bufs;
+        const size_t t_size = sizeof(htp_tensor)    * req.n_tensors;
+        const size_t o_size = sizeof(htp_op_desc)   * req.n_ops;
+        const size_t p_size = sizeof(htp_prof_desc) * req.n_ops;
 
-        const size_t m_size = b_size + t_size + o_size;
-        GGML_ASSERT(m_size <= mem_size);
+        dbuf.ptr      = shm_buf->base + (req.id * shm_blk_size);
+        dbuf.fd       = shm_buf->fd;
+        dbuf.flags    = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT;
+        dbuf.offset   = (uint8_t*) dbuf.ptr - (uint8_t*) shm_buf->base;
+        dbuf.size     = b_size + t_size + o_size + p_size;
 
-        uint8_t * b_ptr = (uint8_t *) mem_addr;
-        uint8_t * t_ptr = (uint8_t *) b_ptr + b_size;
-        uint8_t * o_ptr = (uint8_t *) t_ptr + t_size;
+        GGML_ASSERT(dbuf.size <= shm_blk_size);
 
-        memcpy(b_ptr, (void *) buffers.data(), b_size);
-        memcpy(t_ptr, (void *) tensors.data(), t_size);
-        memcpy(o_ptr, (void *) ops.data(),     o_size);
+        uint8_t * m_ptr = (uint8_t*) dbuf.ptr;
+        uint8_t * b_ptr = m_ptr; m_ptr += b_size;
+        uint8_t * t_ptr = m_ptr; m_ptr += t_size;
+        uint8_t * o_ptr = m_ptr;
 
-        HEX_VERBOSE("ggml-hex: %s flush-opbatch : n-bufs %u n-tensors %u n-ops %u vmem %zu : b-size %zu t-size %zu o-size %zu\n",
-                name, n_bufs, n_tens, n_ops, b_vmem, b_size, t_size, o_size);
+        memcpy(b_ptr, (void *) op_batch->h_bufs.data(), b_size);
+        memcpy(t_ptr, (void *) op_batch->h_tens.data(), t_size);
+        memcpy(o_ptr, (void *) op_batch->h_ops.data(),  o_size);
+
+        HEX_VERBOSE("ggml-hex: %s op-queue push batch #%u : n-bufs %u n-tensors %u n-ops %u vmem %zu : b-size %zu t-size %zu o-size %zu m-size %zu\n",
+                shm_buf->sess->c_name(), req.id, req.n_bufs, req.n_tensors, req.n_ops, op_batch->b_vmem,
+                b_size, t_size, o_size, (size_t) dbuf.size);
+
+        op_batch->reset();
 
         if (opt_verbose > 1) {
             htp_buf_desc *b = (htp_buf_desc*) b_ptr;
-            for (unsigned int i=0; i < n_bufs; i++) {
-                GGML_LOG_DEBUG("ggml-hex: %s htp-buf #%u : fd %d base %p size %zu\n", name, i,
+            for (unsigned int i=0; i < req.n_bufs; i++) {
+                GGML_LOG_DEBUG("ggml-hex: %s htp-buf #%u : fd %d base %p size %zu\n", shm_buf->sess->c_name(), i,
                             b[i].fd, (void *) b[i].base, (size_t) b[i].size);
             }
             htp_tensor *t = (htp_tensor*) t_ptr;
-            for (unsigned int i=0; i < n_tens; i++) {
+            for (unsigned int i=0; i < req.n_tensors; i++) {
                 GGML_LOG_DEBUG("ggml-hex: %s htp-tensor #%u : bi %u offset %u size %u : %zu:%zu:%zu:%zu\n",
-                            name, i, t[i].bi, t[i].data, t[i].size,
+                            shm_buf->sess->c_name(), i, t[i].bi, t[i].data, t[i].size,
                             (size_t) t[i].ne[0], (size_t) t[i].ne[1], (size_t) t[i].ne[2], (size_t) t[i].ne[3]);
             }
         }
 
-        reset();
+        return true;
+    }
+
+    void pop(htp_opbatch_rsp rsp, dspqueue_buffer dbuf) {
+        GGML_ASSERT(rsp.id < op_cache.size());
+
+        done.push(rsp.id);
+
+        const size_t b_size = sizeof(htp_buf_desc)  * rsp.n_bufs;
+        const size_t t_size = sizeof(htp_tensor)    * rsp.n_tensors;
+        const size_t o_size = sizeof(htp_op_desc)   * rsp.n_ops;
+        const size_t p_size = sizeof(htp_prof_desc) * rsp.n_ops;
+
+        const size_t m_size = b_size + t_size + o_size + p_size;
+        GGML_ASSERT(m_size <= shm_blk_size);
+
+        HEX_VERBOSE("ggml-hex: %s op-queue pop batch #%u : n-bufs %u n-tensors %u n-ops %u : m-size %zu b-size %zu t-size %zu o-size %zu\n",
+                shm_buf->sess->c_name(), rsp.id, rsp.n_bufs, rsp.n_tensors, rsp.n_ops,
+                (size_t) dbuf.size, b_size, t_size, o_size);
+
+        uint8_t * m_ptr = (uint8_t*) dbuf.ptr;
+        uint8_t * p_ptr = m_ptr + (b_size + t_size + o_size);
+
+        if (opt_profile && rsp.n_ops > 0) {
+            auto & ops = op_cache[rsp.id];
+
+            uint64_t batch_usec = ggml_time_us() - start_usec[rsp.id];
+            uint32_t htp_usec   = 0;
+
+            GGML_ASSERT(rsp.n_ops <= ops.size());
+
+            const htp_prof_desc * pd = (const htp_prof_desc *) p_ptr;
+            for (uint32_t i = 0; i < rsp.n_ops; i++) {
+                htp_usec += pd[i].usecs;
+                ggml_hexagon_dump_op_prof(shm_buf->sess->name, ops[i], pd[i].usecs, pd[i].cycles, pd[i].pmu);
+            }
 
-        return m_size;
+            GGML_LOG_DEBUG("ggml-hex: %s profile-batch n-ops %u batch-dur-usec %lld htp-ops-usec %u\n",
+                           shm_buf->sess->c_name(), rsp.n_ops, (long long) batch_usec, htp_usec);
+        }
     }
 };
 
@@ -1824,17 +1900,12 @@ void ggml_hexagon_session::flush_pending(bool all) {
             GGML_ABORT("ggml-hex: %s dspcall : bad response : size %u dspbufs %u\n", this->c_name(), rsp_size, n_dbufs);
         }
 
-        op_shm->release((uint8_t*) dbuf.ptr);
-
         if (rsp.status != HTP_STATUS_OK) {
             GGML_LOG_ERROR("ggml-hex: %s dspcall : dsp-rsp: %s\n", this->c_name(), status_to_str(rsp.status));
             // TODO: handle errors
         }
 
-        // FIXME: profile will be per opreq
-        // this->prof_usecs  = rsp.prof_usecs;
-        // this->prof_cycles = rsp.prof_cycles;
-        // this->prof_pkts   = rsp.prof_pkts;
+        op_queue->pop(rsp, dbuf);
 
         this->op_pending--;  // atomic dec
 
@@ -1845,27 +1916,18 @@ void ggml_hexagon_session::flush_pending(bool all) {
 void ggml_hexagon_session::flush_batch() {
     if (op_batch->empty()) { return; }
 
-    htp_opbatch_req req;
-    req.n_bufs    = op_batch->n_bufs;
-    req.n_tensors = op_batch->n_tens;
-    req.n_ops     = op_batch->n_ops;
+    htp_opbatch_req req {};
+    dspqueue_buffer dbuf{};
 
-    dspqueue_buffer dbuf;
-    dbuf.fd     = op_shm->fd();
-    dbuf.flags  = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT;
-    dbuf.ptr    = op_shm->allocate();
-    if (!dbuf.ptr) {
+    if (!op_queue->push(req, dbuf, op_batch)) {
         flush_pending(false);
-        dbuf.ptr = op_shm->allocate();
+        op_queue->push(req, dbuf, op_batch);
     }
 
-    dbuf.offset = (uint8_t*) dbuf.ptr - (uint8_t*) op_shm->base();
-    dbuf.size   = op_batch->flush((uint8_t*) dbuf.ptr, op_shm->block_size);
-
     // Bump pending flag (cleared in the session::flush once we get the response)
     this->op_pending++;  // atomic inc
 
-    HEX_VERBOSE("ggml-hex: %s: queue-opbatch : %p size %u\n", this->c_name(), dbuf.ptr, dbuf.size);
+    HEX_VERBOSE("ggml-hex: %s queue-opbatch: %p size %u\n", this->c_name(), dbuf.ptr, dbuf.size);
 
     int err = dspqueue_write(this->queue, 0, 1, &dbuf, sizeof(req), (const uint8_t*) &req, DSPQUEUE_TIMEOUT);
     if (err != 0) {
@@ -1886,6 +1948,35 @@ void ggml_hexagon_session::flush(bool all) {
     flush_pending(all);
 }
 
+static size_t ggml_hexagon_measure_max_vmem(ggml_hexagon_session *sess) {
+    // Allocate a bunch pinned buffers till failure.
+    // This is kind of expensive but handy for figuring out exactly how much we can mmap on a specific device.
+    // Typically we're going to allocate all/most of these buffers anyway for the model weights.
+
+    std::vector<ggml_hexagon_shared_buffer *> sbufs;
+
+    const size_t MiB = 1024 * 1024;
+    const size_t GiB = MiB  * 1024;
+
+    size_t vmem = 0;
+    size_t step = 256u * MiB;
+
+    try {
+        sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
+        sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
+        sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
+
+        while (1) {
+            sbufs.push_back(new ggml_hexagon_shared_buffer(sess, step, true));
+            vmem += step;
+        }
+    } catch (...) { }
+
+    for (auto b : sbufs) { delete b; }
+
+    return vmem - step; // backoff to account for overhead from internal mappings
+}
+
 void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
     this->valid_session = false;
     this->valid_handle  = false;
@@ -1899,7 +1990,7 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
 
     this->op_pending  = 0;
 
-    GGML_LOG_INFO("ggml-hex: allocating new session: %s\n", this->name.c_str());
+    GGML_LOG_DEBUG("ggml-hex: %s allocating new session\n", this->name.c_str());
 
     domain * my_domain = get_domain(this->domain_id);
     if (my_domain == NULL) {
@@ -1975,9 +2066,6 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
 
     this->valid_handle = true;
 
-    GGML_LOG_INFO("ggml-hex: new session: %s : session-id %d domain-id %d uri %s handle 0x%lx\n", this->name.c_str(),
-                  this->session_id, this->domain_id, session_uri, (unsigned long) this->handle);
-
     // Enable FastRPC QoS mode
     {
         struct remote_rpc_control_latency l;
@@ -1989,6 +2077,9 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
         }
     }
 
+    GGML_LOG_INFO("ggml-hex: %s new session : session-id %d domain-id %d uri %s handle 0x%lx\n", this->c_name(),
+                  this->session_id, this->domain_id, session_uri, (unsigned long) this->handle);
+
     const size_t req_q_size = (sizeof(htp_opbatch_req) * opt_opqueue * 2) + 1024;
     const size_t rsp_q_size = (sizeof(htp_opbatch_rsp) * opt_opqueue * 2) + 1024;
 
@@ -2016,25 +2107,39 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
     }
 
     if (opt_etm) {
-        err = htp_iface_enable_etm(this->handle);
+        err = htp_iface_etm(this->handle, 1);
         if (err != 0) {
             GGML_LOG_ERROR("ggml-hex: failed to enable ETM tracing: 0x%08x\n", (unsigned) err);
         }
     }
 
-    // Start the DSP-side service. We need to pass the queue ID to the
-    // DSP in a FastRPC call; the DSP side will import the queue and start
-    // listening for packets in a callback.
-    err = htp_iface_start(this->handle, dev_id, this->queue_id, opt_nhvx, opt_use_hmx);
+    if (opt_profile) {
+        htp_iface_pmu_conf pmu_conf{};
+        std::copy(opt_pmu_evt.begin(), opt_pmu_evt.end(), pmu_conf.events);
+
+        err = htp_iface_profiler(this->handle, opt_profile, &pmu_conf);
+        if (err != 0) {
+            GGML_LOG_ERROR("ggml-hex: failed to enable profiling: 0x%08x\n", (unsigned) err);
+        }
+    }
+
+    // Allocate buffers and state for op batching
+    this->op_queue = new ggml_hexagon_opqueue(this, opt_opbatch, opt_opqueue);
+
+    if (!opt_vmem) {
+        opt_vmem = ggml_hexagon_measure_max_vmem(this);
+        GGML_LOG_INFO("ggml-hex: %s measured max vmem %zu\n", this->c_name(), opt_vmem);
+    }
+
+    this->op_batch = new ggml_hexagon_opbatch(this, opt_opbatch, opt_vmem);
+
+    // Start dspqueue/opbatch processing
+    err = htp_iface_start(this->handle, dev_id, this->queue_id, opt_nhvx, opt_use_hmx, opt_vmem);
     if (err != 0) {
-        GGML_LOG_ERROR("ggml-hex: failed to start session: 0x%08x\n", (unsigned) err);
+        GGML_LOG_ERROR("ggml-hex: %s failed to start session: 0x%08x\n", this->c_name(), (unsigned) err);
         throw std::runtime_error("ggml-hex: iface start failed (see log for details)");
     }
     this->valid_iface = true;
-
-    // Allocate buffers and state for op batching
-    this->op_batch = new ggml_hexagon_opbatch(this, opt_opbatch);
-    this->op_shm   = new ggml_hexagon_opshm(this, opt_opbatch, opt_opqueue);
 }
 
 void ggml_hexagon_session::release() noexcept(true) {
@@ -2042,24 +2147,32 @@ void ggml_hexagon_session::release() noexcept(true) {
 
     int err;
 
-    delete this->op_batch;
-    delete this->op_shm;
-
-    // Stop the DSP-side service and close the queue
     if (this->valid_iface) {
+        // Stop dspqueue/opbatch processing
         err = htp_iface_stop(this->handle);
         if (err != 0) {
             GGML_ABORT("ggml-hex: htp_iface_stop failed: 0x%08x\n", (unsigned) err);
         }
     }
 
+    delete this->op_batch;
+    delete this->op_queue;
+
     if (opt_etm) {
-        err = htp_iface_disable_etm(this->handle);
+        err = htp_iface_etm(this->handle, 0);
         if (err != 0) {
             GGML_LOG_ERROR("ggml-hex: warn : failed to disable ETM tracing: 0x%08x\n", (unsigned) err);
         }
     }
 
+    if (opt_profile) {
+        htp_iface_pmu_conf pmu_conf{};
+        err = htp_iface_profiler(this->handle, 0, &pmu_conf);
+        if (err != 0) {
+            GGML_LOG_ERROR("ggml-hex: warn : failed to disable profiling: 0x%08x\n", (unsigned) err);
+        }
+    }
+
     if (this->valid_queue) {
         err = dspqueue_close(queue);
         if (err != 0) {
@@ -2077,7 +2190,7 @@ ggml_hexagon_session::ggml_hexagon_session(int dev_id, ggml_backend_dev_t dev) n
     repack_buffer_type.device = dev;
 
     op_batch = nullptr;
-    op_shm   = nullptr;
+    op_queue = nullptr;
 
     try {
         allocate(dev_id);
@@ -2141,14 +2254,65 @@ static bool ggml_hexagon_supported_flash_attn_ext(const struct ggml_hexagon_sess
         return false;
     }
 
-    if (dst->ne[2] != 1 || dst->ne[3] != 1) {
-        // FA during prompt still needs work
+    if (dst->ne[3] != 1) {
         return false;
     }
 
     return true;
 }
 
+static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * q     = op->src[0];
+    const struct ggml_tensor * k     = op->src[1];
+    const struct ggml_tensor * v     = op->src[2];
+    const struct ggml_tensor * g     = op->src[3];
+    const struct ggml_tensor * beta  = op->src[4];
+    const struct ggml_tensor * state = op->src[5];
+    const struct ggml_tensor * dst   = op;
+
+    if (!q || !k || !v || !g || !beta || !state) {
+        return false;
+    }
+
+    if (q->type != GGML_TYPE_F32 || k->type != GGML_TYPE_F32 || v->type != GGML_TYPE_F32 ||
+        g->type != GGML_TYPE_F32 || beta->type != GGML_TYPE_F32 || state->type != GGML_TYPE_F32 ||
+        dst->type != GGML_TYPE_F32) {
+        return false;
+    }
+
+    if (!ggml_is_contiguous_rows(q) || !ggml_is_contiguous_rows(k) || !ggml_is_contiguous_rows(v) ||
+        !ggml_is_contiguous(g) || !ggml_is_contiguous(beta) || !ggml_is_contiguous(state) ||
+        !ggml_is_contiguous(dst)) {
+        return false;
+    }
+
+    const int64_t S_v      = v->ne[0];
+    const int64_t H        = v->ne[1];
+    const int64_t n_tokens = v->ne[2];
+    const int64_t n_seqs   = v->ne[3];
+
+    if (S_v <= 0 || S_v > 128 || H <= 0 || n_tokens <= 0 || n_seqs <= 0) {
+        return false;
+    }
+    if (q->ne[0] != S_v || k->ne[0] != S_v || q->ne[1] <= 0 || k->ne[1] <= 0 ||
+        q->ne[2] != n_tokens || k->ne[2] != n_tokens || q->ne[3] <= 0 || k->ne[3] <= 0 ||
+        (n_seqs % q->ne[3]) != 0 || (n_seqs % k->ne[3]) != 0) {
+        return false;
+    }
+    if ((g->ne[0] != 1 && g->ne[0] != S_v) || beta->ne[0] != 1) {
+        return false;
+    }
+    if (ggml_nelements(state) != S_v * S_v * H * n_seqs) {
+        return false;
+    }
+    if (dst->ne[0] != S_v * H || dst->ne[1] != n_tokens * n_seqs + S_v * n_seqs) {
+        return false;
+    }
+
+    GGML_UNUSED(sess);
+    return true;
+}
+
 static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * sess, const struct ggml_tensor * dst) {
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
@@ -2308,8 +2472,8 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses
         return false;
     }
 
-    // TODO: add support for non-contigiuos tensors
-    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
+    // dst must be contiguous; src0 may be non-contiguous
+    if (!ggml_is_contiguous(dst)) {
         return false;
     }
 
@@ -2596,6 +2760,62 @@ static bool ggml_hexagon_supported_cumsum(const struct ggml_hexagon_session * se
     return true;
 }
 
+static bool ggml_hexagon_supported_diag(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * src0 = op->src[0];
+    const struct ggml_tensor * dst  = op;
+
+    // diag only supports F32 currently
+    if (src0->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
+        return false;
+    }
+
+    // Input must have ne[1] == 1 (vector input)
+    if (src0->ne[1] != 1) {
+        return false;
+    }
+
+    // Output must be square in first two dimensions
+    if (dst->ne[0] != dst->ne[1] || dst->ne[0] != src0->ne[0]) {
+        return false;
+    }
+
+    GGML_UNUSED(sess);
+    return true;
+}
+
+static bool ggml_hexagon_supported_solve_tri(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * src0 = op->src[0]; // A
+    const struct ggml_tensor * src1 = op->src[1]; // B
+    const struct ggml_tensor * dst  = op;         // X
+
+    if (!src0 || !src1) {
+        return false;
+    }
+
+    if (src0->type != GGML_TYPE_F32 || src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
+        return false;
+    }
+
+    if (src0->ne[0] != src0->ne[1]) {
+        return false;
+    }
+
+    if (src0->ne[1] != src1->ne[1]) {
+        return false;
+    }
+
+    if (src0->ne[2] != src1->ne[2] || src0->ne[3] != src1->ne[3]) {
+        return false;
+    }
+
+    if (dst->ne[0] != src1->ne[0] || dst->ne[1] != src1->ne[1] || dst->ne[2] != src1->ne[2] || dst->ne[3] != src1->ne[3]) {
+        return false;
+    }
+
+    GGML_UNUSED(sess);
+    return true;
+}
+
 static const char * ggml_backend_hexagon_name(ggml_backend_t backend) {
     auto sess = static_cast<ggml_hexagon_session *>(backend->context);
     return sess->c_name();
@@ -2609,30 +2829,34 @@ static void ggml_backend_hexagon_free(ggml_backend_t backend) {
 
 static htp_op_code op_remap_to_htp(const ggml_tensor * t) {
     switch (t->op) {
-        case GGML_OP_FLASH_ATTN_EXT: return HTP_OP_FLASH_ATTN_EXT;
-        case GGML_OP_MUL_MAT:        return HTP_OP_MUL_MAT;
-        case GGML_OP_MUL_MAT_ID:     return HTP_OP_MUL_MAT_ID;
-        case GGML_OP_MUL:            return HTP_OP_MUL;
-        case GGML_OP_ADD:            return HTP_OP_ADD;
-        case GGML_OP_ADD_ID:         return HTP_OP_ADD_ID;
-        case GGML_OP_SUB:            return HTP_OP_SUB;
-        case GGML_OP_DIV:            return HTP_OP_DIV;
-        case GGML_OP_CPY:            return HTP_OP_CPY;
-        case GGML_OP_CONT:           return HTP_OP_CPY;
-        case GGML_OP_GET_ROWS:       return HTP_OP_GET_ROWS;
-        case GGML_OP_SET_ROWS:       return HTP_OP_SET_ROWS;
-        case GGML_OP_SUM_ROWS:       return HTP_OP_SUM_ROWS;
-        case GGML_OP_ARGSORT:        return HTP_OP_ARGSORT;
-        case GGML_OP_RMS_NORM:       return HTP_OP_RMS_NORM;
-        case GGML_OP_SCALE:          return HTP_OP_SCALE;
-        case GGML_OP_SQR:            return HTP_OP_SQR;
-        case GGML_OP_SQRT:           return HTP_OP_SQRT;
-        case GGML_OP_SOFT_MAX:       return HTP_OP_SOFTMAX;
-        case GGML_OP_SSM_CONV:       return HTP_OP_SSM_CONV;
-        case GGML_OP_ROPE:           return HTP_OP_ROPE;
-        case GGML_OP_REPEAT:         return HTP_OP_REPEAT;
-        case GGML_OP_CUMSUM:         return HTP_OP_CUMSUM;
-
+        case GGML_OP_FLASH_ATTN_EXT:  return HTP_OP_FLASH_ATTN_EXT;
+        case GGML_OP_MUL_MAT:         return HTP_OP_MUL_MAT;
+        case GGML_OP_MUL_MAT_ID:      return HTP_OP_MUL_MAT_ID;
+        case GGML_OP_MUL:             return HTP_OP_MUL;
+        case GGML_OP_ADD:             return HTP_OP_ADD;
+        case GGML_OP_ADD_ID:          return HTP_OP_ADD_ID;
+        case GGML_OP_SUB:             return HTP_OP_SUB;
+        case GGML_OP_DIV:             return HTP_OP_DIV;
+        case GGML_OP_CPY:             return HTP_OP_CPY;
+        case GGML_OP_CONT:            return HTP_OP_CPY;
+        case GGML_OP_GET_ROWS:        return HTP_OP_GET_ROWS;
+        case GGML_OP_SET_ROWS:        return HTP_OP_SET_ROWS;
+        case GGML_OP_SUM_ROWS:        return HTP_OP_SUM_ROWS;
+        case GGML_OP_ARGSORT:         return HTP_OP_ARGSORT;
+        case GGML_OP_L2_NORM:         return HTP_OP_L2_NORM;
+        case GGML_OP_RMS_NORM:        return HTP_OP_RMS_NORM;
+        case GGML_OP_SCALE:           return HTP_OP_SCALE;
+        case GGML_OP_SQR:             return HTP_OP_SQR;
+        case GGML_OP_SQRT:            return HTP_OP_SQRT;
+        case GGML_OP_SOFT_MAX:        return HTP_OP_SOFTMAX;
+        case GGML_OP_SSM_CONV:        return HTP_OP_SSM_CONV;
+        case GGML_OP_GATED_DELTA_NET: return HTP_OP_GATED_DELTA_NET;
+        case GGML_OP_ROPE:            return HTP_OP_ROPE;
+        case GGML_OP_REPEAT:          return HTP_OP_REPEAT;
+        case GGML_OP_CUMSUM:          return HTP_OP_CUMSUM;
+        case GGML_OP_FILL:            return HTP_OP_FILL;
+        case GGML_OP_DIAG:            return HTP_OP_DIAG;
+        case GGML_OP_SOLVE_TRI:       return HTP_OP_SOLVE_TRI;
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(t)) {
                 case GGML_UNARY_OP_SILU:     return HTP_OP_UNARY_SILU;
@@ -2641,6 +2865,7 @@ static htp_op_code op_remap_to_htp(const ggml_tensor * t) {
                 case GGML_UNARY_OP_NEG:      return HTP_OP_UNARY_NEG;
                 case GGML_UNARY_OP_EXP:      return HTP_OP_UNARY_EXP;
                 case GGML_UNARY_OP_SOFTPLUS: return HTP_OP_UNARY_SOFTPLUS;
+                case GGML_UNARY_OP_TANH:     return HTP_OP_UNARY_TANH;
             default:
                 break;
             }
@@ -2673,7 +2898,7 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
 
     for (int i = 0; i < graph->n_nodes; ++i) {
         ggml_tensor * n = graph->nodes[i];
-        if (op_is_compute(n)) {
+        if (op_is_compute(n) && (opt_opstage & HTP_OPSTAGE_QUEUE)) {
             sess->enqueue_op(op_remap_to_htp(n), n);
         }
     }
@@ -2865,8 +3090,8 @@ static struct ggml_backend_i hexagon_backend_i = {
     /* .free                    = */ ggml_backend_hexagon_free,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ ggml_backend_hexagon_synchronize,
     /* .graph_plan_create       = */ NULL,
@@ -3029,6 +3254,17 @@ static bool ggml_hexagon_supported_repeat(const struct ggml_hexagon_session * se
     return true;
 }
 
+static bool ggml_hexagon_supported_fill(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
+    const struct ggml_tensor * dst = op;
+
+    if (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) {
+        return false;
+    }
+
+    GGML_UNUSED(sess);
+    return true;
+}
+
 static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
     auto sess = static_cast<ggml_hexagon_session *>(dev->context);
 
@@ -3072,6 +3308,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
             supp = ggml_hexagon_supported_add_id(sess, op);
             break;
 
+        case GGML_OP_L2_NORM:
+            supp = ggml_hexagon_supported_unary(sess, op);
+            break;
+
         case GGML_OP_RMS_NORM:
         case GGML_OP_SCALE:
             supp = ggml_hexagon_supported_unary(sess, op);
@@ -3096,6 +3336,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
                 case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_SIGMOID:
                 case GGML_UNARY_OP_SOFTPLUS:
+                case GGML_UNARY_OP_TANH:
                     supp = ggml_hexagon_supported_unary(sess, op);
                     break;
                 case GGML_UNARY_OP_SILU:
@@ -3155,10 +3396,26 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
             supp = ggml_hexagon_supported_ssm_conv(sess, op);
             break;
 
+        case GGML_OP_GATED_DELTA_NET:
+            supp = ggml_hexagon_supported_gated_delta_net(sess, op);
+            break;
+
         case GGML_OP_CUMSUM:
             supp = ggml_hexagon_supported_cumsum(sess, op);
             break;
 
+        case GGML_OP_FILL:
+            supp = ggml_hexagon_supported_fill(sess, op);
+            break;
+
+        case GGML_OP_DIAG:
+            supp = ggml_hexagon_supported_diag(sess, op);
+            break;
+
+        case GGML_OP_SOLVE_TRI:
+            supp = ggml_hexagon_supported_solve_tri(sess, op);
+            break;
+
         default:
             break;
     }
@@ -3225,21 +3482,6 @@ struct ggml_hexagon_registry {
 ggml_hexagon_registry::ggml_hexagon_registry(ggml_backend_reg_t reg) {
     GGML_LOG_INFO("ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev %zu\n", opt_ndev);
 
-    if (!opt_arch) {
-        int err = get_hex_arch_ver(CDSP_DOMAIN_ID, &opt_arch);
-        if (err != 0) {
-            GGML_LOG_ERROR("ggml-hex: failed to query HTP version (err %d) defaulting to v73\n", err);
-            opt_arch = 73;
-        }
-    }
-
-#if defined(__ANDROID__)
-    if (opt_arch < 75) {
-        opt_ndev = 1;
-        GGML_LOG_WARN("ggml-hex: forcing ndev to 1 for SoCs archs lower than v75.\n");
-    }
-#endif
-
     GGML_LOG_INFO("ggml-hex: Hexagon Arch version v%d\n", opt_arch);
 
     // Create devices / sessions
@@ -3294,6 +3536,26 @@ static void * ggml_backend_hexagon_get_proc_address(ggml_backend_reg_t reg, cons
     return NULL;
 }
 
+template<typename T> std::vector<T> str_to_vec(const char* str) {
+    std::stringstream ss(str);
+    std::vector<T> v;
+    std::string    t;
+
+    while (std::getline(ss, t, ',')) {
+        v.push_back(std::stoul(t, nullptr, 0));
+    }
+
+    return v;
+}
+
+template<typename T, int BASE=10> std::string vec_to_str(std::vector<T> v) {
+    std::stringstream ss;
+    ss << std::setbase(BASE) << std::showbase;
+    for (auto i : v) { ss << i << ','; }
+    auto str = ss.str(); str.pop_back(); // drop last comma
+    return str;
+}
+
 static void ggml_hexagon_init(ggml_backend_reg * reg) {
     // Basic sanity checks to make sure definitions match
     static_assert((unsigned int) HTP_TYPE_Q4_0 == (unsigned int) GGML_TYPE_Q4_0,
@@ -3305,45 +3567,79 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     static_assert((unsigned int) HTP_TYPE_IQ4_NL == (unsigned int) GGML_TYPE_IQ4_NL,
                   "please update hexagon_type to match ggml_type");
 
-    const char * str_verbose = getenv("GGML_HEXAGON_VERBOSE");
-    const char * str_hostbuf = getenv("GGML_HEXAGON_HOSTBUF");
-    const char * str_opmask  = getenv("GGML_HEXAGON_OPMASK");
-    const char * str_opsync  = getenv("GGML_HEXAGON_OPSYNC");
-    const char * str_opbatch = getenv("GGML_HEXAGON_OPBATCH");
-    const char * str_opqueue = getenv("GGML_HEXAGON_OPQUEUE");
-    const char * str_opfilter= getenv("GGML_HEXAGON_OPFILTER");
-    const char * str_profile = getenv("GGML_HEXAGON_PROFILE");
-    const char * str_etm     = getenv("GGML_HEXAGON_ETM");
-    const char * str_nhvx    = getenv("GGML_HEXAGON_NHVX");
-    const char * str_use_hmx = getenv("GGML_HEXAGON_USE_HMX");
-    const char * str_ndev    = getenv("GGML_HEXAGON_NDEV");
-    const char * str_arch    = getenv("GGML_HEXAGON_ARCH");
+    const char * str_verbose  = getenv("GGML_HEXAGON_VERBOSE");
+    const char * str_hostbuf  = getenv("GGML_HEXAGON_HOSTBUF");
+    const char * str_opstage  = getenv("GGML_HEXAGON_OPSTAGE");
+    const char * str_opbatch  = getenv("GGML_HEXAGON_OPBATCH");
+    const char * str_opqueue  = getenv("GGML_HEXAGON_OPQUEUE");
+    const char * str_opfilter = getenv("GGML_HEXAGON_OPFILTER");
+    const char * str_profile  = getenv("GGML_HEXAGON_PROFILE");
+    const char * str_etm      = getenv("GGML_HEXAGON_ETM");
+    const char * str_nhvx     = getenv("GGML_HEXAGON_NHVX");
+    const char * str_use_hmx  = getenv("GGML_HEXAGON_USE_HMX");
+    const char * str_ndev     = getenv("GGML_HEXAGON_NDEV");
+    const char * str_arch     = getenv("GGML_HEXAGON_ARCH");
+    const char * str_vmem     = getenv("GGML_HEXAGON_VMEM");
+    const char * str_mbuf     = getenv("GGML_HEXAGON_MBUF");
+
+    // Init Arch first since it affects other defaults
+    if (!str_arch) {
+        int err = get_hex_arch_ver(CDSP_DOMAIN_ID, &opt_arch);
+        if (err != 0) {
+            GGML_LOG_ERROR("ggml-hex: failed to query HTP version (err %d) defaulting to v73\n", err);
+            opt_arch = 73;
+        }
+    } else {
+        if (str_arch[0] == 'v' || str_arch[0] == 'V') {
+            str_arch++;
+        }
+        opt_arch = strtoul(str_arch, NULL, 0);
+    }
+
+    size_t MiB = 1024 * 1024;
+
+    // Update vmem default
+    opt_vmem = opt_arch >= 75 ? HTP_OP_MAX_VMEM_DEFAULT : 3000 * MiB;
 
     auto RE_ICASE = std::regex_constants::icase;
 
-    opt_opfilter     = str_opfilter     ? new std::regex(str_opfilter, RE_ICASE) : NULL;
-    opt_verbose      = str_verbose ? atoi(str_verbose) : 0;
-    opt_hostbuf      = str_hostbuf ? atoi(str_hostbuf) : opt_hostbuf;
-    opt_opmask       = str_opmask  ? strtoul(str_opmask, NULL, 0)  : opt_opmask;
-    opt_opsync       = str_opsync  ? atoi(str_opsync)              : opt_opsync;
-    opt_opbatch      = str_opbatch ? strtoul(str_opbatch, NULL, 0) : opt_opbatch;
-    opt_opqueue      = str_opqueue ? strtoul(str_opqueue, NULL, 0) : opt_opqueue;
-    opt_profile      = str_profile ? atoi(str_profile) : 0;
-    opt_etm          = str_etm     ? atoi(str_etm)     : 0;
-    opt_nhvx         = str_nhvx    ? strtoul(str_nhvx, NULL, 0) : opt_nhvx;
-    opt_use_hmx      = str_use_hmx ? atoi(str_use_hmx) : opt_use_hmx;
-    opt_ndev         = str_ndev    ? strtoul(str_ndev, NULL, 0) : opt_ndev;
-    opt_hostbuf      = str_hostbuf ? atoi(str_hostbuf) : opt_hostbuf;
+    opt_opfilter  = str_opfilter ? new std::regex(str_opfilter, RE_ICASE) : NULL;
+    opt_verbose   = str_verbose  ? atoi(str_verbose)                      : 0;
+    opt_hostbuf   = str_hostbuf  ? atoi(str_hostbuf)                      : opt_hostbuf;
+    opt_opstage   = str_opstage  ? strtoul(str_opstage, NULL, 0)          : opt_opstage;
+    opt_opbatch   = str_opbatch  ? strtoul(str_opbatch, NULL, 0)          : opt_opbatch;
+    opt_opqueue   = str_opqueue  ? strtoul(str_opqueue, NULL, 0)          : opt_opqueue;
+    opt_profile   = str_profile  ? atoi(str_profile)                      : 0;
+    opt_etm       = str_etm      ? atoi(str_etm)                          : 0;
+    opt_nhvx      = str_nhvx     ? strtoul(str_nhvx, NULL, 0)             : opt_nhvx;
+    opt_use_hmx   = str_use_hmx  ? atoi(str_use_hmx)                      : opt_use_hmx;
+    opt_ndev      = str_ndev     ? strtoul(str_ndev, NULL, 0)             : opt_ndev;
+    opt_hostbuf   = str_hostbuf  ? atoi(str_hostbuf)                      : opt_hostbuf;
+    opt_mbuf      = str_mbuf     ? strtoul(str_mbuf, NULL, 0) * MiB       : opt_mbuf;
+    opt_vmem      = str_vmem     ? strtoul(str_vmem, NULL, 0) * MiB       : opt_vmem;
 
     if (opt_ndev > GGML_HEXAGON_MAX_SESSIONS) {
         opt_ndev = GGML_HEXAGON_MAX_SESSIONS;
     }
 
-    if (str_arch) {
-        if (str_arch[0] == 'v') {
-            str_arch++;
-        }
-        opt_arch = strtoul(str_arch, NULL, 0);
+#if defined(__ANDROID__)
+    if (opt_arch < 75) {
+        opt_ndev = 1;
+        GGML_LOG_WARN("ggml-hex: forcing ndev to 1 for SoCs archs lower than v75.\n");
+    }
+#endif
+
+    if (str_profile) {
+        opt_pmu_evt = [&]() -> std::vector<uint32_t> {
+            auto v  = str_to_vec<uint32_t>(str_profile);
+            switch (v.size()) {
+                case 1:  opt_profile = v[0]; return opt_pmu_evt; // mode with default pmu events
+                case 8:  opt_profile = 2;    return v;           // mode with custom  pmu events
+                default: opt_profile = 0;    return {};          // garbage input
+            }}();
+        if (opt_profile == 1) opt_pmu_evt = {};
+        GGML_LOG_INFO("ggml-hex: Profiling mode %u : pmu-evt [ %s ]\n", opt_profile,
+                vec_to_str<uint32_t, 16>(opt_pmu_evt).c_str());
     }
 
     reg->context = new ggml_hexagon_registry(reg);
diff --git a/ggml/src/ggml-hexagon/htp/CMakeLists.txt b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
index 9ca759459d4..bcadac11f95 100644
--- a/ggml/src/ggml-hexagon/htp/CMakeLists.txt
+++ b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
@@ -34,6 +34,10 @@ add_library(${HTP_LIB} SHARED
     argsort-ops.c
     ssm-conv.c
     cumsum-ops.c
+    fill-ops.c
+    diag-ops.c
+    solve-tri-ops.c
+    gated-delta-net-ops.c
 )
 
 target_compile_definitions(${HTP_LIB} PRIVATE
@@ -41,6 +45,11 @@ target_compile_definitions(${HTP_LIB} PRIVATE
     $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,FARF_HIGH=1,>
     FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE})
 
+if (GGML_HEXAGON_FA_EXP2_HF)
+    message(STATUS "ggml-htp: HMX_FA_USE_EXP2_HF=1 (use FP16 exp2 polynomial in FA softmax)")
+    target_compile_definitions(${HTP_LIB} PRIVATE HMX_FA_USE_EXP2_HF=1)
+endif()
+
 # HMX acceleration: available on v73+ architectures
 set(HTP_HMX_VERSIONS v73 v75 v79 v81)
 list(FIND HTP_HMX_VERSIONS ${DSP_VERSION} _hmx_idx)
@@ -49,11 +58,13 @@ if (_hmx_idx GREATER_EQUAL 0)
     target_sources(${HTP_LIB} PRIVATE
         hmx-queue.c
         hmx-matmul-ops.c
+        hmx-flash-attn-ops.c
     )
 
     # -mhmx enables HMX instruction set (needed by files that include hmx-utils.h)
     set_source_files_properties(
         hmx-matmul-ops.c
+        hmx-flash-attn-ops.c
         PROPERTIES COMPILE_OPTIONS "-mhmx"
     )
 
diff --git a/ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake b/ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake
index 7fa236e328f..ed5c198468c 100644
--- a/ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake
+++ b/ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake
@@ -138,15 +138,15 @@ set(CMAKE_SHARED_LIBRARY_SONAME_C_FLAG   "-Wl,-soname,")
 set(CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG "-Wl,-soname,")
 
 #Compiler Options
-set(COMMON_FLAGS "-mcpu=hexagon${V_ARCH} -m${V_ARCH} -mhvx=${V_ARCH} -fvectorize -Wall -Werror -fno-zero-initialized-in-bss -G0 -fdata-sections -fpic ${XQF_ARGS}")
+set(COMMON_FLAGS "-mcpu=hexagon${V_ARCH} -m${V_ARCH} -mhvx=${V_ARCH} -fvectorize -flto -Wall -Werror -fno-zero-initialized-in-bss -G0 -fdata-sections -fpic ${XQF_ARGS}")
 
 set(CMAKE_CXX_FLAGS_DEBUG          "${COMMON_FLAGS} -O0 -D_DEBUG -g")
-set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${COMMON_FLAGS} -O3 -g")
-set(CMAKE_CXX_FLAGS_RELEASE        "${COMMON_FLAGS} -O3")
+set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${COMMON_FLAGS} -O2 -g")
+set(CMAKE_CXX_FLAGS_RELEASE        "${COMMON_FLAGS} -O2")
 
 set(CMAKE_C_FLAGS_DEBUG            "${COMMON_FLAGS} -O0 -D_DEBUG -g")
-set(CMAKE_C_FLAGS_RELWITHDEBINFO   "${COMMON_FLAGS} -O3 -g")
-set(CMAKE_C_FLAGS_RELEASE          "${COMMON_FLAGS} -O3")
+set(CMAKE_C_FLAGS_RELWITHDEBINFO   "${COMMON_FLAGS} -O2 -g")
+set(CMAKE_C_FLAGS_RELEASE          "${COMMON_FLAGS} -O2")
 
 set(CMAKE_ASM_FLAGS_DEBUG          "${COMMON_FLAGS} ${CMAKE_CXX_FLAGS_DEBUG}")
 set(CMAKE_ASM_FLAGS_RELEASE        "${COMMON_FLAGS} ${CMAKE_CXX_FLAGS_RELEASE}")
diff --git a/ggml/src/ggml-hexagon/htp/cpy-ops.c b/ggml/src/ggml-hexagon/htp/cpy-ops.c
index e5b9d350fd7..5c040a32224 100644
--- a/ggml/src/ggml-hexagon/htp/cpy-ops.c
+++ b/ggml/src/ggml-hexagon/htp/cpy-ops.c
@@ -88,6 +88,29 @@ static void cpy_thread_sametype_reshape(struct htp_copy_context * ct, struct htp
     const uint32_t ir0 = dr * ith;
     const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr;
 
+    // Fast path: when both src0 and dst are contiguous in memory
+    // Replace the element-by-element loop with a single bulk HVX copy per (i03, i02) slice.
+    const bool src0_contig = (nb00 == ct->src0_type_size) &&
+                             (nb01 == ne00 * nb00) &&
+                             (nb02 == ne01 * nb01) &&
+                             (nb03 == ne02 * nb02);
+    const bool dst_contig  = (nb0  == ct->dst_type_size)  &&
+                             (nb1  == ne0  * nb0)  &&
+                             (nb2  == ne1  * nb1)  &&
+                             (nb3  == ne2  * nb2);
+
+    if (src0_contig && dst_contig) {
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                uint8_t * src_ptr = (uint8_t *) src0->data + i03*nb03 + i02*nb02 + ir0*nb01;
+                uint32_t  flat    = ((i03*ne02 + i02)*ne01 + ir0) * ne00;
+                uint8_t * dst_ptr = (uint8_t *) dst->data  + flat * ct->src0_type_size;
+                hvx_copy_uu(dst_ptr, src_ptr, (ir1 - ir0) * ne00, ct->src0_type_size);
+            }
+        }
+        return;
+    }
+
     // dst counters
     int64_t k10 = 0;
     int64_t i11 = 0;
diff --git a/ggml/src/ggml-hexagon/htp/diag-ops.c b/ggml/src/ggml-hexagon/htp/diag-ops.c
new file mode 100644
index 00000000000..9b3194d9084
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/diag-ops.c
@@ -0,0 +1,216 @@
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <HAP_farf.h>
+#include <HAP_perf.h>
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-ops.h"
+#include "hvx-types.h"
+#include "hex-utils.h"
+#include "hvx-copy.h"
+#include "hex-dma.h"
+
+#define htp_diag_tensors_preamble                           \
+    const struct htp_tensor * restrict src0 = octx->src[0]; \
+    const struct htp_tensor * restrict dst  = octx->dst;    \
+                                                     \
+    const uint32_t ne02 = src0->ne[2];               \
+                                                     \
+    const uint32_t ne0 = dst->ne[0];                 \
+    const uint32_t ne1 = dst->ne[1];                 \
+                                                     \
+    const uint32_t nb02 = src0->nb[2];               \
+    const uint32_t nb03 = src0->nb[3];               \
+                                                     \
+    const uint32_t nb1 = dst->nb[1];                 \
+    const uint32_t nb2 = dst->nb[2];                 \
+    const uint32_t nb3 = dst->nb[3];
+
+struct htp_diag_context {
+    struct htp_ops_context * octx;
+    size_t          src_batch_size;
+    size_t          dst_row_size;
+    size_t          src_batch_size_aligned;
+    size_t          dst_row_size_aligned;
+    uint32_t        batches_per_thread;
+    uint32_t        total_batches;
+};
+
+#define htp_diag_preamble                                              \
+    struct htp_diag_context * dctx = (struct htp_diag_context *) data; \
+    struct htp_ops_context *  octx = dctx->octx;                       \
+    htp_diag_tensors_preamble;
+
+static inline void hvx_diag_row_f32(const float * restrict src, float * restrict dst,
+                                    uint32_t row_idx, uint32_t n) {
+    hvx_splat_f32_a((uint8_t *) dst, 0.0f, n);
+    dst[row_idx] = src[row_idx];
+}
+
+// ---------------------------------------------------------------------------
+// Per thread worker: DMA src fetch, compute in VTCM, DMA dst writeback
+// ---------------------------------------------------------------------------
+
+static void diag_thread_f32_dma(unsigned int nth, unsigned int ith, void * data) {
+    htp_diag_preamble;
+    dma_queue * dma_queue = octx->ctx->dma[ith];
+
+    uint64_t t1, t2;
+    t1 = HAP_perf_get_qtimer_count();
+
+    const uint32_t ib0 = dctx->batches_per_thread * ith;
+    const uint32_t ib1 = MIN(ib0 + dctx->batches_per_thread, dctx->total_batches);
+
+    if (ib0 >= ib1) {
+        return;
+    }
+
+    const size_t src_batch_size         = dctx->src_batch_size;
+    const size_t dst_row_size           = dctx->dst_row_size;
+    const size_t src_batch_size_aligned = dctx->src_batch_size_aligned;
+    const size_t dst_row_size_aligned   = dctx->dst_row_size_aligned;
+
+    const uint8_t * src_data = (const uint8_t *) src0->data;
+    uint8_t *       dst_data = (uint8_t *) dst->data;
+
+    // 1 src buffer + 1 dst row buffer per thread in VTCM
+    uint8_t * src_spad = octx->src0_spad.data + (ith * src_batch_size_aligned);
+    uint8_t * dst_spad = octx->dst_spad.data  + (ith * dst_row_size_aligned);
+
+    for (uint32_t ib = ib0; ib < ib1; ib++) {
+        const uint32_t i3 = ib / ne02;
+        const uint32_t i2 = ib % ne02;
+
+        const uint8_t * src_batch = src_data + i3 * nb03 + i2 * nb02;
+
+        // Fetch source vector into VTCM
+        dma_queue_push_ddr_to_vtcm(dma_queue,
+                                   dma_make_ptr(src_spad, src_batch),
+                                   src_batch_size_aligned, src_batch_size, 1);
+        dma_queue_flush(dma_queue);
+
+        const float * src_spad_f32 = (const float *) src_spad;
+        float       * dst_spad_f32 = (float *) dst_spad;
+
+        for (uint32_t i1 = 0; i1 < ne1; i1++) {
+            // Compute row in VTCM
+            hvx_diag_row_f32(src_spad_f32, dst_spad_f32, i1, ne0);
+
+            // Write completed row back to DDR
+            uint8_t * dst_row = dst_data + i3 * nb3 + i2 * nb2 + i1 * nb1;
+            dma_queue_push_vtcm_to_ddr(dma_queue,
+                                       dma_make_ptr(dst_row, dst_spad),
+                                       dst_row_size, dst_row_size_aligned, 1);
+            dma_queue_flush(dma_queue);
+        }
+    }
+
+    t2 = HAP_perf_get_qtimer_count();
+
+    FARF(HIGH, "diag-f32-dma %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n",
+         ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], ib0, ib1,
+         dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
+// ---------------------------------------------------------------------------
+// Per thread worker: Direct HVX (no DMA)
+// ---------------------------------------------------------------------------
+
+static void diag_thread_f32(unsigned int nth, unsigned int ith, void * data) {
+    htp_diag_preamble;
+
+    uint64_t t1, t2;
+    t1 = HAP_perf_get_qtimer_count();
+
+    const uint8_t * src_data = (const uint8_t *) src0->data;
+    uint8_t *       dst_data = (uint8_t *) dst->data;
+
+    const uint32_t ib0 = dctx->batches_per_thread * ith;
+    const uint32_t ib1 = MIN(ib0 + dctx->batches_per_thread, dctx->total_batches);
+
+    for (uint32_t ib = ib0; ib < ib1; ib++) {
+        const uint32_t i3 = ib / ne02;
+        const uint32_t i2 = ib % ne02;
+
+        const float * restrict src_batch = (const float *)(src_data + i3 * nb03 + i2 * nb02);
+
+        for (uint32_t i1 = 0; i1 < ne1; i1++) {
+            float * restrict dst_row = (float *)(dst_data + i3 * nb3 + i2 * nb2 + i1 * nb1);
+            hvx_diag_row_f32(src_batch, dst_row, i1, ne0);
+        }
+    }
+
+    t2 = HAP_perf_get_qtimer_count();
+
+    FARF(HIGH, "diag-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n",
+         ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], ib0, ib1,
+         dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
+int op_diag_f32(struct htp_ops_context * octx) {
+    const struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * dst  = octx->dst;
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    const uint32_t total_batches = src0->ne[2] * src0->ne[3];
+    const uint32_t n_threads     = MIN(octx->n_threads, total_batches);
+
+    const size_t src_batch_size         = src0->ne[0] * sizeof(float);
+    const size_t dst_row_size           = dst->ne[0] * sizeof(float);
+    const size_t src_batch_size_aligned = hex_round_up(src_batch_size, VLEN);
+    const size_t dst_row_size_aligned   = hex_round_up(dst_row_size, VLEN);
+
+    // 1 src buffer + 1 dst row buffer per thread
+    const size_t spad_per_thread = src_batch_size_aligned + dst_row_size_aligned;
+
+    octx->src0_spad.size_per_thread = src_batch_size_aligned;
+    octx->dst_spad.size_per_thread  = dst_row_size_aligned;
+
+    octx->src0_spad.size = n_threads * octx->src0_spad.size_per_thread;
+    octx->dst_spad.size  = n_threads * octx->dst_spad.size_per_thread;
+
+    octx->src0_spad.data = octx->ctx->vtcm_base;                        octx->src0_spad.src = NULL;
+    octx->dst_spad.data  = octx->src0_spad.data + octx->src0_spad.size; octx->dst_spad.src  = NULL;
+
+    struct htp_diag_context dctx = {
+        .octx                   = octx,
+        .src_batch_size         = src_batch_size,
+        .dst_row_size           = dst_row_size,
+        .src_batch_size_aligned = src_batch_size_aligned,
+        .dst_row_size_aligned   = dst_row_size_aligned,
+        .batches_per_thread     = (total_batches + n_threads - 1) / n_threads,
+        .total_batches          = total_batches,
+    };
+
+    if (octx->ctx->vtcm_size < spad_per_thread * n_threads) {
+        worker_pool_run_func(octx->ctx->worker_pool, diag_thread_f32, &dctx, n_threads);
+    } else {
+        worker_pool_run_func(octx->ctx->worker_pool, diag_thread_f32_dma, &dctx, n_threads);
+    }
+
+    return HTP_STATUS_OK;
+}
+
+int op_diag(struct htp_ops_context * octx) {
+    const struct htp_tensor * dst = octx->dst;
+
+    int err = HTP_STATUS_OK;
+
+    switch (dst->type) {
+        case HTP_TYPE_F32:
+            err = op_diag_f32(octx);
+            break;
+        default:
+            err = HTP_STATUS_NO_SUPPORT;
+            break;
+    }
+
+    return err;
+}
diff --git a/ggml/src/ggml-hexagon/htp/fill-ops.c b/ggml/src/ggml-hexagon/htp/fill-ops.c
new file mode 100644
index 00000000000..3ccfbe74ee4
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/fill-ops.c
@@ -0,0 +1,123 @@
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <HAP_farf.h>
+#include <HAP_perf.h>
+
+#include <string.h>
+
+#include "hvx-copy.h"
+#include "hvx-utils.h"
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-ops.h"
+
+// ggml op_params layout for FILL:
+//   op_params[0] (as float) - the scalar fill value
+
+#define fill_preamble \
+    const struct htp_tensor * dst = octx->dst; \
+    \
+    const uint32_t ne0 = dst->ne[0]; \
+    const uint32_t ne1 = dst->ne[1]; \
+    const uint32_t ne2 = dst->ne[2]; \
+    const uint32_t ne3 = dst->ne[3]; \
+    \
+    const uint32_t nb1 = dst->nb[1]; \
+    const uint32_t nb2 = dst->nb[2]; \
+    const uint32_t nb3 = dst->nb[3]; \
+    \
+    const uint32_t nr = ne1 * ne2 * ne3;
+
+struct htp_fill_context {
+    struct htp_ops_context * octx;
+    uint32_t nrows_per_thread;
+    uint32_t total_rows;  // ne1 * ne2 * ne3
+    bool     opt_path;
+    HVX_Vector splat_vec;
+    uint32_t   elem_size;
+};
+
+static void fill_thread(unsigned int nth, unsigned int ith, void * data) {
+    const struct htp_fill_context * fctx = (const struct htp_fill_context *) data;
+    struct htp_ops_context        * octx = fctx->octx;
+    fill_preamble;
+
+    // Parallelise over the flat row index spanning ne1*ne2*ne3
+    const uint32_t ir0 = fctx->nrows_per_thread * ith;
+    const uint32_t ir1 = MIN(ir0 + fctx->nrows_per_thread, fctx->total_rows);
+
+    uint64_t t1 = HAP_perf_get_qtimer_count();
+
+    if (fctx->opt_path) {
+        // Opt path: tensor is fully contiguous, treat as flat array
+        const uint32_t elem_start = ir0 * ne0;
+        const uint32_t elem_end = ir1 * ne0;
+        uint8_t * dst_ptr = (uint8_t *) dst->data + elem_start * fctx->elem_size;
+        hvx_splat_u(dst_ptr, fctx->splat_vec, elem_end - elem_start, fctx->elem_size);
+    } else {
+        // Non-contiguous path: must respect strides
+        for (uint32_t ir = ir0; ir < ir1; ++ir) {
+            const uint32_t i1 = ir % ne1;
+            const uint32_t i2 = (ir / ne1) % ne2;
+            const uint32_t i3 = ir / (ne1 * ne2);
+            uint8_t * dst_ptr = (uint8_t *) dst->data + i1*nb1 + i2*nb2 + i3*nb3;
+            hvx_splat_u(dst_ptr, fctx->splat_vec, ne0, fctx->elem_size);
+        }
+    }
+
+    uint64_t t2 = HAP_perf_get_qtimer_count();
+    FARF(HIGH, "fill %u/%u: rows %u:%u usec %u\n",
+         ith, nth, ir0, ir1, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
+int op_fill(struct htp_ops_context * octx) {
+    fill_preamble;
+
+    if (dst->type != HTP_TYPE_F32 && dst->type != HTP_TYPE_F16) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    // nr = ne1*ne2*ne3 (flat row count across all outer dims); parallelise over it.
+    const uint32_t n_threads = MIN(nr, octx->n_threads);
+
+    // Optimize if fully contiguous: skip stride arithmetic, treat as flat array
+    const bool opt_path = (nb2 == nb1 * ne1) && (nb3 == nb2 * ne2);
+
+    FARF(HIGH, "fill: (%ux%ux%ux%u) type=%u opt=%d\n",
+         dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], dst->type, (int) opt_path);
+
+    float val_f32 = 0.f;
+    memcpy(&val_f32, &octx->op_params[0], sizeof(float));
+
+    struct htp_fill_context fctx = {
+        .octx             = octx,
+        .nrows_per_thread = (nr + n_threads - 1) / n_threads,
+        .total_rows       = nr,
+        .opt_path         = opt_path,
+    };
+
+    switch (dst->type) {
+    case HTP_TYPE_F32:
+        fctx.splat_vec = hvx_vec_splat_f32(val_f32);
+        fctx.elem_size = sizeof(float);
+        break;
+    case HTP_TYPE_F16:
+        fctx.splat_vec = hvx_vec_splat_f16((_Float16) val_f32);
+        fctx.elem_size = sizeof(_Float16);
+        break;
+    default:
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    worker_pool_run_func(octx->ctx->worker_pool, fill_thread, &fctx, n_threads);
+
+    return HTP_STATUS_OK;
+}
diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
index d296a322589..d95df6ac9d5 100644
--- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
+++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
@@ -17,13 +17,14 @@
 #include "htp-ctx.h"
 #include "htp-ops.h"
 #include "htp-ops.h"
+#include "hmx-ops.h"
 
 // Must be multiple of 32
 #define FLASH_ATTN_BLOCK_SIZE (32 * 2)
 
 // This is a bit of a hack because the compiler is strugling to properly inline
 // the default hvx_vec_f32_to_f16 with output into the local array.
-static void __attribute__((noinline)) hvx_vec_f32_to_f16_a(void *ptr, HVX_Vector v0, HVX_Vector v1)
+static __attribute__((noinline)) void hvx_vec_f32_to_f16_a(void *ptr, HVX_Vector v0, HVX_Vector v1)
 {
     *(HVX_Vector *) ptr = hvx_vec_f32_to_f16(v0, v1);
 }
@@ -621,6 +622,17 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
+#ifdef HTP_HAS_HMX
+    // HMX path: prefill (neq1 >= 32), head_dim multiple of 32, F16 KV
+    if (k->type == HTP_TYPE_F16 && v->type == HTP_TYPE_F16 && k->ne[0] % 32 == 0 && q->ne[1] >= 32) {
+        int ret = hmx_flash_attn_ext(octx);
+        if (ret == HTP_STATUS_OK) {
+            return ret;
+        }
+        // VTCM too small or other failure -> fall through to HVX path
+    }
+#endif
+
     struct htp_fa_context factx;
     factx.octx = octx;
 
diff --git a/ggml/src/ggml-hexagon/htp/gated-delta-net-ops.c b/ggml/src/ggml-hexagon/htp/gated-delta-net-ops.c
new file mode 100644
index 00000000000..2e84badc9b7
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/gated-delta-net-ops.c
@@ -0,0 +1,955 @@
+#include <math.h>
+#include <stdint.h>
+#include <string.h>
+
+#include "hvx-utils.h"
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+
+#ifndef MIN
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#endif
+
+#define HTP_GDN_MAX_SV 128
+
+struct htp_gdn_context {
+    struct htp_ops_context * octx;
+    uint32_t rows_per_thread;
+    size_t state_bytes;
+    bool use_vtcm;
+    uint8_t * vtcm_state_base;
+    size_t vtcm_state_per_thread;
+};
+
+static inline float gdn_mul_dot_f32(float * restrict dst, const float * restrict mul,
+        const float * restrict dot, uint32_t n) {
+    HVX_Vector acc = Q6_V_vzero();
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vd = hvx_vmemu(dst + i * epv);
+        HVX_Vector vm = hvx_vmem(mul + i * epv);
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+        HVX_Vector out = hvx_vec_mul_f32_f32(vd, vm);
+        hvx_vmemu(dst + i * epv) = out;
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(out, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vd = hvx_vmemu(dst + off);
+        HVX_Vector vm = hvx_vmem(mul + off);
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_Vector out = hvx_vec_mul_f32_f32(vd, vm);
+        hvx_vec_store_u(dst + off, tail * sizeof(float), out);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector prod = hvx_vec_mul_f32_f32(out, vdot);
+        acc = hvx_vec_add_f32_f32(acc, Q6_V_vmux_QVV(mask, prod, Q6_V_vzero()));
+    }
+
+    return hvx_vec_get_f32(hvx_vec_reduce_sum_f32(acc));
+}
+
+static inline float gdn_mul_scalar_dot_f32(float * restrict dst, float mul,
+        const float * restrict dot, uint32_t n) {
+    HVX_Vector acc = Q6_V_vzero();
+    const HVX_Vector vmul = hvx_vec_splat_f32(mul);
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vd = hvx_vmemu(dst + i * epv);
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+        HVX_Vector out = hvx_vec_mul_f32_f32(vd, vmul);
+        hvx_vmemu(dst + i * epv) = out;
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(out, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vd = hvx_vmemu(dst + off);
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_Vector out = hvx_vec_mul_f32_f32(vd, vmul);
+        hvx_vec_store_u(dst + off, tail * sizeof(float), out);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector prod = hvx_vec_mul_f32_f32(out, vdot);
+        acc = hvx_vec_add_f32_f32(acc, Q6_V_vmux_QVV(mask, prod, Q6_V_vzero()));
+    }
+
+    return hvx_vec_get_f32(hvx_vec_reduce_sum_f32(acc));
+}
+
+static inline float gdn_add_scaled_dot_f32(float * restrict dst, const float * restrict src,
+        float scale, const float * restrict dot, uint32_t n) {
+    HVX_Vector acc = Q6_V_vzero();
+    const HVX_Vector vscale = hvx_vec_splat_f32(scale);
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vd = hvx_vmemu(dst + i * epv);
+        HVX_Vector vs = hvx_vmem(src + i * epv);
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+        HVX_Vector out = hvx_vec_add_f32_f32(vd, hvx_vec_mul_f32_f32(vs, vscale));
+        hvx_vmemu(dst + i * epv) = out;
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(out, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vd = hvx_vmemu(dst + off);
+        HVX_Vector vs = hvx_vmem(src + off);
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_Vector out = hvx_vec_add_f32_f32(vd, hvx_vec_mul_f32_f32(vs, vscale));
+        hvx_vec_store_u(dst + off, tail * sizeof(float), out);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector prod = hvx_vec_mul_f32_f32(out, vdot);
+        acc = hvx_vec_add_f32_f32(acc, Q6_V_vmux_QVV(mask, prod, Q6_V_vzero()));
+    }
+
+    return hvx_vec_get_f32(hvx_vec_reduce_sum_f32(acc));
+}
+
+static inline void gdn_mul_dot4_f32(float * restrict dst0, float * restrict dst1,
+        float * restrict dst2, float * restrict dst3, const float * restrict mul,
+        const float * restrict dot, uint32_t n, float * restrict sums) {
+    HVX_Vector acc0 = Q6_V_vzero();
+    HVX_Vector acc1 = Q6_V_vzero();
+    HVX_Vector acc2 = Q6_V_vzero();
+    HVX_Vector acc3 = Q6_V_vzero();
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vm = hvx_vmem(mul + i * epv);
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+
+        HVX_Vector out0 = hvx_vec_mul_f32_f32(hvx_vmemu(dst0 + i * epv), vm);
+        HVX_Vector out1 = hvx_vec_mul_f32_f32(hvx_vmemu(dst1 + i * epv), vm);
+        HVX_Vector out2 = hvx_vec_mul_f32_f32(hvx_vmemu(dst2 + i * epv), vm);
+        HVX_Vector out3 = hvx_vec_mul_f32_f32(hvx_vmemu(dst3 + i * epv), vm);
+
+        hvx_vmemu(dst0 + i * epv) = out0;
+        hvx_vmemu(dst1 + i * epv) = out1;
+        hvx_vmemu(dst2 + i * epv) = out2;
+        hvx_vmemu(dst3 + i * epv) = out3;
+
+        acc0 = hvx_vec_add_f32_f32(acc0, hvx_vec_mul_f32_f32(out0, vdot));
+        acc1 = hvx_vec_add_f32_f32(acc1, hvx_vec_mul_f32_f32(out1, vdot));
+        acc2 = hvx_vec_add_f32_f32(acc2, hvx_vec_mul_f32_f32(out2, vdot));
+        acc3 = hvx_vec_add_f32_f32(acc3, hvx_vec_mul_f32_f32(out3, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vm = hvx_vmem(mul + off);
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector zero = Q6_V_vzero();
+
+        HVX_Vector out0 = hvx_vec_mul_f32_f32(hvx_vmemu(dst0 + off), vm);
+        HVX_Vector out1 = hvx_vec_mul_f32_f32(hvx_vmemu(dst1 + off), vm);
+        HVX_Vector out2 = hvx_vec_mul_f32_f32(hvx_vmemu(dst2 + off), vm);
+        HVX_Vector out3 = hvx_vec_mul_f32_f32(hvx_vmemu(dst3 + off), vm);
+
+        hvx_vec_store_u(dst0 + off, tail * sizeof(float), out0);
+        hvx_vec_store_u(dst1 + off, tail * sizeof(float), out1);
+        hvx_vec_store_u(dst2 + off, tail * sizeof(float), out2);
+        hvx_vec_store_u(dst3 + off, tail * sizeof(float), out3);
+
+        acc0 = hvx_vec_add_f32_f32(acc0, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out0, vdot), zero));
+        acc1 = hvx_vec_add_f32_f32(acc1, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out1, vdot), zero));
+        acc2 = hvx_vec_add_f32_f32(acc2, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out2, vdot), zero));
+        acc3 = hvx_vec_add_f32_f32(acc3, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out3, vdot), zero));
+    }
+
+    HVX_Vector_x4 acc = { .v = { acc0, acc1, acc2, acc3 } };
+    hvx_vec_store_u(sums, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(acc));
+}
+
+static inline void gdn_mul_scalar_dot4_f32(float * restrict dst0, float * restrict dst1,
+        float * restrict dst2, float * restrict dst3, float mul,
+        const float * restrict dot, uint32_t n, float * restrict sums) {
+    HVX_Vector acc0 = Q6_V_vzero();
+    HVX_Vector acc1 = Q6_V_vzero();
+    HVX_Vector acc2 = Q6_V_vzero();
+    HVX_Vector acc3 = Q6_V_vzero();
+    const HVX_Vector vmul = hvx_vec_splat_f32(mul);
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+
+        HVX_Vector out0 = hvx_vec_mul_f32_f32(hvx_vmemu(dst0 + i * epv), vmul);
+        HVX_Vector out1 = hvx_vec_mul_f32_f32(hvx_vmemu(dst1 + i * epv), vmul);
+        HVX_Vector out2 = hvx_vec_mul_f32_f32(hvx_vmemu(dst2 + i * epv), vmul);
+        HVX_Vector out3 = hvx_vec_mul_f32_f32(hvx_vmemu(dst3 + i * epv), vmul);
+
+        hvx_vmemu(dst0 + i * epv) = out0;
+        hvx_vmemu(dst1 + i * epv) = out1;
+        hvx_vmemu(dst2 + i * epv) = out2;
+        hvx_vmemu(dst3 + i * epv) = out3;
+
+        acc0 = hvx_vec_add_f32_f32(acc0, hvx_vec_mul_f32_f32(out0, vdot));
+        acc1 = hvx_vec_add_f32_f32(acc1, hvx_vec_mul_f32_f32(out1, vdot));
+        acc2 = hvx_vec_add_f32_f32(acc2, hvx_vec_mul_f32_f32(out2, vdot));
+        acc3 = hvx_vec_add_f32_f32(acc3, hvx_vec_mul_f32_f32(out3, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector zero = Q6_V_vzero();
+
+        HVX_Vector out0 = hvx_vec_mul_f32_f32(hvx_vmemu(dst0 + off), vmul);
+        HVX_Vector out1 = hvx_vec_mul_f32_f32(hvx_vmemu(dst1 + off), vmul);
+        HVX_Vector out2 = hvx_vec_mul_f32_f32(hvx_vmemu(dst2 + off), vmul);
+        HVX_Vector out3 = hvx_vec_mul_f32_f32(hvx_vmemu(dst3 + off), vmul);
+
+        hvx_vec_store_u(dst0 + off, tail * sizeof(float), out0);
+        hvx_vec_store_u(dst1 + off, tail * sizeof(float), out1);
+        hvx_vec_store_u(dst2 + off, tail * sizeof(float), out2);
+        hvx_vec_store_u(dst3 + off, tail * sizeof(float), out3);
+
+        acc0 = hvx_vec_add_f32_f32(acc0, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out0, vdot), zero));
+        acc1 = hvx_vec_add_f32_f32(acc1, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out1, vdot), zero));
+        acc2 = hvx_vec_add_f32_f32(acc2, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out2, vdot), zero));
+        acc3 = hvx_vec_add_f32_f32(acc3, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out3, vdot), zero));
+    }
+
+    HVX_Vector_x4 acc = { .v = { acc0, acc1, acc2, acc3 } };
+    hvx_vec_store_u(sums, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(acc));
+}
+
+static inline void gdn_add_scaled_dot4_f32(float * restrict dst0, float * restrict dst1,
+        float * restrict dst2, float * restrict dst3, const float * restrict src,
+        const float * restrict scale, const float * restrict dot, uint32_t n,
+        float * restrict sums) {
+    HVX_Vector acc0 = Q6_V_vzero();
+    HVX_Vector acc1 = Q6_V_vzero();
+    HVX_Vector acc2 = Q6_V_vzero();
+    HVX_Vector acc3 = Q6_V_vzero();
+    const HVX_Vector scale0 = hvx_vec_splat_f32(scale[0]);
+    const HVX_Vector scale1 = hvx_vec_splat_f32(scale[1]);
+    const HVX_Vector scale2 = hvx_vec_splat_f32(scale[2]);
+    const HVX_Vector scale3 = hvx_vec_splat_f32(scale[3]);
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vs = hvx_vmem(src + i * epv);
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+
+        HVX_Vector out0 = hvx_vec_add_f32_f32(hvx_vmemu(dst0 + i * epv), hvx_vec_mul_f32_f32(vs, scale0));
+        HVX_Vector out1 = hvx_vec_add_f32_f32(hvx_vmemu(dst1 + i * epv), hvx_vec_mul_f32_f32(vs, scale1));
+        HVX_Vector out2 = hvx_vec_add_f32_f32(hvx_vmemu(dst2 + i * epv), hvx_vec_mul_f32_f32(vs, scale2));
+        HVX_Vector out3 = hvx_vec_add_f32_f32(hvx_vmemu(dst3 + i * epv), hvx_vec_mul_f32_f32(vs, scale3));
+
+        hvx_vmemu(dst0 + i * epv) = out0;
+        hvx_vmemu(dst1 + i * epv) = out1;
+        hvx_vmemu(dst2 + i * epv) = out2;
+        hvx_vmemu(dst3 + i * epv) = out3;
+
+        acc0 = hvx_vec_add_f32_f32(acc0, hvx_vec_mul_f32_f32(out0, vdot));
+        acc1 = hvx_vec_add_f32_f32(acc1, hvx_vec_mul_f32_f32(out1, vdot));
+        acc2 = hvx_vec_add_f32_f32(acc2, hvx_vec_mul_f32_f32(out2, vdot));
+        acc3 = hvx_vec_add_f32_f32(acc3, hvx_vec_mul_f32_f32(out3, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vs = hvx_vmem(src + off);
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector zero = Q6_V_vzero();
+
+        HVX_Vector out0 = hvx_vec_add_f32_f32(hvx_vmemu(dst0 + off), hvx_vec_mul_f32_f32(vs, scale0));
+        HVX_Vector out1 = hvx_vec_add_f32_f32(hvx_vmemu(dst1 + off), hvx_vec_mul_f32_f32(vs, scale1));
+        HVX_Vector out2 = hvx_vec_add_f32_f32(hvx_vmemu(dst2 + off), hvx_vec_mul_f32_f32(vs, scale2));
+        HVX_Vector out3 = hvx_vec_add_f32_f32(hvx_vmemu(dst3 + off), hvx_vec_mul_f32_f32(vs, scale3));
+
+        hvx_vec_store_u(dst0 + off, tail * sizeof(float), out0);
+        hvx_vec_store_u(dst1 + off, tail * sizeof(float), out1);
+        hvx_vec_store_u(dst2 + off, tail * sizeof(float), out2);
+        hvx_vec_store_u(dst3 + off, tail * sizeof(float), out3);
+
+        acc0 = hvx_vec_add_f32_f32(acc0, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out0, vdot), zero));
+        acc1 = hvx_vec_add_f32_f32(acc1, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out1, vdot), zero));
+        acc2 = hvx_vec_add_f32_f32(acc2, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out2, vdot), zero));
+        acc3 = hvx_vec_add_f32_f32(acc3, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out3, vdot), zero));
+    }
+
+    HVX_Vector_x4 acc = { .v = { acc0, acc1, acc2, acc3 } };
+    hvx_vec_store_u(sums, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(acc));
+}
+
+static inline void gdn_mul_dot8_f32(float * restrict dst0, float * restrict dst1,
+        float * restrict dst2, float * restrict dst3, float * restrict dst4,
+        float * restrict dst5, float * restrict dst6, float * restrict dst7,
+        const float * restrict mul, const float * restrict dot, uint32_t n,
+        float * restrict sums) {
+    HVX_Vector acc0 = Q6_V_vzero();
+    HVX_Vector acc1 = Q6_V_vzero();
+    HVX_Vector acc2 = Q6_V_vzero();
+    HVX_Vector acc3 = Q6_V_vzero();
+    HVX_Vector acc4 = Q6_V_vzero();
+    HVX_Vector acc5 = Q6_V_vzero();
+    HVX_Vector acc6 = Q6_V_vzero();
+    HVX_Vector acc7 = Q6_V_vzero();
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vm = hvx_vmem(mul + i * epv);
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+
+        HVX_Vector out0 = hvx_vec_mul_f32_f32(hvx_vmemu(dst0 + i * epv), vm);
+        HVX_Vector out1 = hvx_vec_mul_f32_f32(hvx_vmemu(dst1 + i * epv), vm);
+        HVX_Vector out2 = hvx_vec_mul_f32_f32(hvx_vmemu(dst2 + i * epv), vm);
+        HVX_Vector out3 = hvx_vec_mul_f32_f32(hvx_vmemu(dst3 + i * epv), vm);
+        HVX_Vector out4 = hvx_vec_mul_f32_f32(hvx_vmemu(dst4 + i * epv), vm);
+        HVX_Vector out5 = hvx_vec_mul_f32_f32(hvx_vmemu(dst5 + i * epv), vm);
+        HVX_Vector out6 = hvx_vec_mul_f32_f32(hvx_vmemu(dst6 + i * epv), vm);
+        HVX_Vector out7 = hvx_vec_mul_f32_f32(hvx_vmemu(dst7 + i * epv), vm);
+
+        hvx_vmemu(dst0 + i * epv) = out0;
+        hvx_vmemu(dst1 + i * epv) = out1;
+        hvx_vmemu(dst2 + i * epv) = out2;
+        hvx_vmemu(dst3 + i * epv) = out3;
+        hvx_vmemu(dst4 + i * epv) = out4;
+        hvx_vmemu(dst5 + i * epv) = out5;
+        hvx_vmemu(dst6 + i * epv) = out6;
+        hvx_vmemu(dst7 + i * epv) = out7;
+
+        acc0 = hvx_vec_add_f32_f32(acc0, hvx_vec_mul_f32_f32(out0, vdot));
+        acc1 = hvx_vec_add_f32_f32(acc1, hvx_vec_mul_f32_f32(out1, vdot));
+        acc2 = hvx_vec_add_f32_f32(acc2, hvx_vec_mul_f32_f32(out2, vdot));
+        acc3 = hvx_vec_add_f32_f32(acc3, hvx_vec_mul_f32_f32(out3, vdot));
+        acc4 = hvx_vec_add_f32_f32(acc4, hvx_vec_mul_f32_f32(out4, vdot));
+        acc5 = hvx_vec_add_f32_f32(acc5, hvx_vec_mul_f32_f32(out5, vdot));
+        acc6 = hvx_vec_add_f32_f32(acc6, hvx_vec_mul_f32_f32(out6, vdot));
+        acc7 = hvx_vec_add_f32_f32(acc7, hvx_vec_mul_f32_f32(out7, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vm = hvx_vmem(mul + off);
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector zero = Q6_V_vzero();
+
+        HVX_Vector out0 = hvx_vec_mul_f32_f32(hvx_vmemu(dst0 + off), vm);
+        HVX_Vector out1 = hvx_vec_mul_f32_f32(hvx_vmemu(dst1 + off), vm);
+        HVX_Vector out2 = hvx_vec_mul_f32_f32(hvx_vmemu(dst2 + off), vm);
+        HVX_Vector out3 = hvx_vec_mul_f32_f32(hvx_vmemu(dst3 + off), vm);
+        HVX_Vector out4 = hvx_vec_mul_f32_f32(hvx_vmemu(dst4 + off), vm);
+        HVX_Vector out5 = hvx_vec_mul_f32_f32(hvx_vmemu(dst5 + off), vm);
+        HVX_Vector out6 = hvx_vec_mul_f32_f32(hvx_vmemu(dst6 + off), vm);
+        HVX_Vector out7 = hvx_vec_mul_f32_f32(hvx_vmemu(dst7 + off), vm);
+
+        hvx_vec_store_u(dst0 + off, tail * sizeof(float), out0);
+        hvx_vec_store_u(dst1 + off, tail * sizeof(float), out1);
+        hvx_vec_store_u(dst2 + off, tail * sizeof(float), out2);
+        hvx_vec_store_u(dst3 + off, tail * sizeof(float), out3);
+        hvx_vec_store_u(dst4 + off, tail * sizeof(float), out4);
+        hvx_vec_store_u(dst5 + off, tail * sizeof(float), out5);
+        hvx_vec_store_u(dst6 + off, tail * sizeof(float), out6);
+        hvx_vec_store_u(dst7 + off, tail * sizeof(float), out7);
+
+        acc0 = hvx_vec_add_f32_f32(acc0, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out0, vdot), zero));
+        acc1 = hvx_vec_add_f32_f32(acc1, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out1, vdot), zero));
+        acc2 = hvx_vec_add_f32_f32(acc2, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out2, vdot), zero));
+        acc3 = hvx_vec_add_f32_f32(acc3, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out3, vdot), zero));
+        acc4 = hvx_vec_add_f32_f32(acc4, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out4, vdot), zero));
+        acc5 = hvx_vec_add_f32_f32(acc5, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out5, vdot), zero));
+        acc6 = hvx_vec_add_f32_f32(acc6, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out6, vdot), zero));
+        acc7 = hvx_vec_add_f32_f32(acc7, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out7, vdot), zero));
+    }
+
+    HVX_Vector_x4 accA = { .v = { acc0, acc1, acc2, acc3 } };
+    HVX_Vector_x4 accB = { .v = { acc4, acc5, acc6, acc7 } };
+    hvx_vec_store_u(sums + 0, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(accA));
+    hvx_vec_store_u(sums + 4, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(accB));
+}
+
+static inline void gdn_mul_scalar_dot8_f32(float * restrict dst0, float * restrict dst1,
+        float * restrict dst2, float * restrict dst3, float * restrict dst4,
+        float * restrict dst5, float * restrict dst6, float * restrict dst7,
+        float mul, const float * restrict dot, uint32_t n, float * restrict sums) {
+    HVX_Vector acc0 = Q6_V_vzero();
+    HVX_Vector acc1 = Q6_V_vzero();
+    HVX_Vector acc2 = Q6_V_vzero();
+    HVX_Vector acc3 = Q6_V_vzero();
+    HVX_Vector acc4 = Q6_V_vzero();
+    HVX_Vector acc5 = Q6_V_vzero();
+    HVX_Vector acc6 = Q6_V_vzero();
+    HVX_Vector acc7 = Q6_V_vzero();
+    const HVX_Vector vmul = hvx_vec_splat_f32(mul);
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+
+        HVX_Vector out0 = hvx_vec_mul_f32_f32(hvx_vmemu(dst0 + i * epv), vmul);
+        HVX_Vector out1 = hvx_vec_mul_f32_f32(hvx_vmemu(dst1 + i * epv), vmul);
+        HVX_Vector out2 = hvx_vec_mul_f32_f32(hvx_vmemu(dst2 + i * epv), vmul);
+        HVX_Vector out3 = hvx_vec_mul_f32_f32(hvx_vmemu(dst3 + i * epv), vmul);
+        HVX_Vector out4 = hvx_vec_mul_f32_f32(hvx_vmemu(dst4 + i * epv), vmul);
+        HVX_Vector out5 = hvx_vec_mul_f32_f32(hvx_vmemu(dst5 + i * epv), vmul);
+        HVX_Vector out6 = hvx_vec_mul_f32_f32(hvx_vmemu(dst6 + i * epv), vmul);
+        HVX_Vector out7 = hvx_vec_mul_f32_f32(hvx_vmemu(dst7 + i * epv), vmul);
+
+        hvx_vmemu(dst0 + i * epv) = out0;
+        hvx_vmemu(dst1 + i * epv) = out1;
+        hvx_vmemu(dst2 + i * epv) = out2;
+        hvx_vmemu(dst3 + i * epv) = out3;
+        hvx_vmemu(dst4 + i * epv) = out4;
+        hvx_vmemu(dst5 + i * epv) = out5;
+        hvx_vmemu(dst6 + i * epv) = out6;
+        hvx_vmemu(dst7 + i * epv) = out7;
+
+        acc0 = hvx_vec_add_f32_f32(acc0, hvx_vec_mul_f32_f32(out0, vdot));
+        acc1 = hvx_vec_add_f32_f32(acc1, hvx_vec_mul_f32_f32(out1, vdot));
+        acc2 = hvx_vec_add_f32_f32(acc2, hvx_vec_mul_f32_f32(out2, vdot));
+        acc3 = hvx_vec_add_f32_f32(acc3, hvx_vec_mul_f32_f32(out3, vdot));
+        acc4 = hvx_vec_add_f32_f32(acc4, hvx_vec_mul_f32_f32(out4, vdot));
+        acc5 = hvx_vec_add_f32_f32(acc5, hvx_vec_mul_f32_f32(out5, vdot));
+        acc6 = hvx_vec_add_f32_f32(acc6, hvx_vec_mul_f32_f32(out6, vdot));
+        acc7 = hvx_vec_add_f32_f32(acc7, hvx_vec_mul_f32_f32(out7, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector zero = Q6_V_vzero();
+
+        HVX_Vector out0 = hvx_vec_mul_f32_f32(hvx_vmemu(dst0 + off), vmul);
+        HVX_Vector out1 = hvx_vec_mul_f32_f32(hvx_vmemu(dst1 + off), vmul);
+        HVX_Vector out2 = hvx_vec_mul_f32_f32(hvx_vmemu(dst2 + off), vmul);
+        HVX_Vector out3 = hvx_vec_mul_f32_f32(hvx_vmemu(dst3 + off), vmul);
+        HVX_Vector out4 = hvx_vec_mul_f32_f32(hvx_vmemu(dst4 + off), vmul);
+        HVX_Vector out5 = hvx_vec_mul_f32_f32(hvx_vmemu(dst5 + off), vmul);
+        HVX_Vector out6 = hvx_vec_mul_f32_f32(hvx_vmemu(dst6 + off), vmul);
+        HVX_Vector out7 = hvx_vec_mul_f32_f32(hvx_vmemu(dst7 + off), vmul);
+
+        hvx_vec_store_u(dst0 + off, tail * sizeof(float), out0);
+        hvx_vec_store_u(dst1 + off, tail * sizeof(float), out1);
+        hvx_vec_store_u(dst2 + off, tail * sizeof(float), out2);
+        hvx_vec_store_u(dst3 + off, tail * sizeof(float), out3);
+        hvx_vec_store_u(dst4 + off, tail * sizeof(float), out4);
+        hvx_vec_store_u(dst5 + off, tail * sizeof(float), out5);
+        hvx_vec_store_u(dst6 + off, tail * sizeof(float), out6);
+        hvx_vec_store_u(dst7 + off, tail * sizeof(float), out7);
+
+        acc0 = hvx_vec_add_f32_f32(acc0, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out0, vdot), zero));
+        acc1 = hvx_vec_add_f32_f32(acc1, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out1, vdot), zero));
+        acc2 = hvx_vec_add_f32_f32(acc2, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out2, vdot), zero));
+        acc3 = hvx_vec_add_f32_f32(acc3, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out3, vdot), zero));
+        acc4 = hvx_vec_add_f32_f32(acc4, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out4, vdot), zero));
+        acc5 = hvx_vec_add_f32_f32(acc5, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out5, vdot), zero));
+        acc6 = hvx_vec_add_f32_f32(acc6, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out6, vdot), zero));
+        acc7 = hvx_vec_add_f32_f32(acc7, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out7, vdot), zero));
+    }
+
+    HVX_Vector_x4 accA = { .v = { acc0, acc1, acc2, acc3 } };
+    HVX_Vector_x4 accB = { .v = { acc4, acc5, acc6, acc7 } };
+    hvx_vec_store_u(sums + 0, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(accA));
+    hvx_vec_store_u(sums + 4, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(accB));
+}
+
+static inline void gdn_add_scaled_dot8_f32(float * restrict dst0, float * restrict dst1,
+        float * restrict dst2, float * restrict dst3, float * restrict dst4,
+        float * restrict dst5, float * restrict dst6, float * restrict dst7,
+        const float * restrict src, const float * restrict scale,
+        const float * restrict dot, uint32_t n, float * restrict sums) {
+    HVX_Vector acc0 = Q6_V_vzero();
+    HVX_Vector acc1 = Q6_V_vzero();
+    HVX_Vector acc2 = Q6_V_vzero();
+    HVX_Vector acc3 = Q6_V_vzero();
+    HVX_Vector acc4 = Q6_V_vzero();
+    HVX_Vector acc5 = Q6_V_vzero();
+    HVX_Vector acc6 = Q6_V_vzero();
+    HVX_Vector acc7 = Q6_V_vzero();
+    const HVX_Vector scale0 = hvx_vec_splat_f32(scale[0]);
+    const HVX_Vector scale1 = hvx_vec_splat_f32(scale[1]);
+    const HVX_Vector scale2 = hvx_vec_splat_f32(scale[2]);
+    const HVX_Vector scale3 = hvx_vec_splat_f32(scale[3]);
+    const HVX_Vector scale4 = hvx_vec_splat_f32(scale[4]);
+    const HVX_Vector scale5 = hvx_vec_splat_f32(scale[5]);
+    const HVX_Vector scale6 = hvx_vec_splat_f32(scale[6]);
+    const HVX_Vector scale7 = hvx_vec_splat_f32(scale[7]);
+
+    const uint32_t epv = 128 / sizeof(float);
+    const uint32_t nvec = n / epv;
+    const uint32_t tail = n % epv;
+    for (uint32_t i = 0; i < nvec; ++i) {
+        HVX_Vector vs = hvx_vmem(src + i * epv);
+        HVX_Vector vdot = hvx_vmem(dot + i * epv);
+
+        HVX_Vector out0 = hvx_vec_add_f32_f32(hvx_vmemu(dst0 + i * epv), hvx_vec_mul_f32_f32(vs, scale0));
+        HVX_Vector out1 = hvx_vec_add_f32_f32(hvx_vmemu(dst1 + i * epv), hvx_vec_mul_f32_f32(vs, scale1));
+        HVX_Vector out2 = hvx_vec_add_f32_f32(hvx_vmemu(dst2 + i * epv), hvx_vec_mul_f32_f32(vs, scale2));
+        HVX_Vector out3 = hvx_vec_add_f32_f32(hvx_vmemu(dst3 + i * epv), hvx_vec_mul_f32_f32(vs, scale3));
+        HVX_Vector out4 = hvx_vec_add_f32_f32(hvx_vmemu(dst4 + i * epv), hvx_vec_mul_f32_f32(vs, scale4));
+        HVX_Vector out5 = hvx_vec_add_f32_f32(hvx_vmemu(dst5 + i * epv), hvx_vec_mul_f32_f32(vs, scale5));
+        HVX_Vector out6 = hvx_vec_add_f32_f32(hvx_vmemu(dst6 + i * epv), hvx_vec_mul_f32_f32(vs, scale6));
+        HVX_Vector out7 = hvx_vec_add_f32_f32(hvx_vmemu(dst7 + i * epv), hvx_vec_mul_f32_f32(vs, scale7));
+
+        hvx_vmemu(dst0 + i * epv) = out0;
+        hvx_vmemu(dst1 + i * epv) = out1;
+        hvx_vmemu(dst2 + i * epv) = out2;
+        hvx_vmemu(dst3 + i * epv) = out3;
+        hvx_vmemu(dst4 + i * epv) = out4;
+        hvx_vmemu(dst5 + i * epv) = out5;
+        hvx_vmemu(dst6 + i * epv) = out6;
+        hvx_vmemu(dst7 + i * epv) = out7;
+
+        acc0 = hvx_vec_add_f32_f32(acc0, hvx_vec_mul_f32_f32(out0, vdot));
+        acc1 = hvx_vec_add_f32_f32(acc1, hvx_vec_mul_f32_f32(out1, vdot));
+        acc2 = hvx_vec_add_f32_f32(acc2, hvx_vec_mul_f32_f32(out2, vdot));
+        acc3 = hvx_vec_add_f32_f32(acc3, hvx_vec_mul_f32_f32(out3, vdot));
+        acc4 = hvx_vec_add_f32_f32(acc4, hvx_vec_mul_f32_f32(out4, vdot));
+        acc5 = hvx_vec_add_f32_f32(acc5, hvx_vec_mul_f32_f32(out5, vdot));
+        acc6 = hvx_vec_add_f32_f32(acc6, hvx_vec_mul_f32_f32(out6, vdot));
+        acc7 = hvx_vec_add_f32_f32(acc7, hvx_vec_mul_f32_f32(out7, vdot));
+    }
+
+    if (tail) {
+        const uint32_t off = nvec * epv;
+        HVX_Vector vs = hvx_vmem(src + off);
+        HVX_Vector vdot = hvx_vmem(dot + off);
+        HVX_VectorPred mask = Q6_Q_vsetq2_R(tail * sizeof(float));
+        HVX_Vector zero = Q6_V_vzero();
+
+        HVX_Vector out0 = hvx_vec_add_f32_f32(hvx_vmemu(dst0 + off), hvx_vec_mul_f32_f32(vs, scale0));
+        HVX_Vector out1 = hvx_vec_add_f32_f32(hvx_vmemu(dst1 + off), hvx_vec_mul_f32_f32(vs, scale1));
+        HVX_Vector out2 = hvx_vec_add_f32_f32(hvx_vmemu(dst2 + off), hvx_vec_mul_f32_f32(vs, scale2));
+        HVX_Vector out3 = hvx_vec_add_f32_f32(hvx_vmemu(dst3 + off), hvx_vec_mul_f32_f32(vs, scale3));
+        HVX_Vector out4 = hvx_vec_add_f32_f32(hvx_vmemu(dst4 + off), hvx_vec_mul_f32_f32(vs, scale4));
+        HVX_Vector out5 = hvx_vec_add_f32_f32(hvx_vmemu(dst5 + off), hvx_vec_mul_f32_f32(vs, scale5));
+        HVX_Vector out6 = hvx_vec_add_f32_f32(hvx_vmemu(dst6 + off), hvx_vec_mul_f32_f32(vs, scale6));
+        HVX_Vector out7 = hvx_vec_add_f32_f32(hvx_vmemu(dst7 + off), hvx_vec_mul_f32_f32(vs, scale7));
+
+        hvx_vec_store_u(dst0 + off, tail * sizeof(float), out0);
+        hvx_vec_store_u(dst1 + off, tail * sizeof(float), out1);
+        hvx_vec_store_u(dst2 + off, tail * sizeof(float), out2);
+        hvx_vec_store_u(dst3 + off, tail * sizeof(float), out3);
+        hvx_vec_store_u(dst4 + off, tail * sizeof(float), out4);
+        hvx_vec_store_u(dst5 + off, tail * sizeof(float), out5);
+        hvx_vec_store_u(dst6 + off, tail * sizeof(float), out6);
+        hvx_vec_store_u(dst7 + off, tail * sizeof(float), out7);
+
+        acc0 = hvx_vec_add_f32_f32(acc0, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out0, vdot), zero));
+        acc1 = hvx_vec_add_f32_f32(acc1, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out1, vdot), zero));
+        acc2 = hvx_vec_add_f32_f32(acc2, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out2, vdot), zero));
+        acc3 = hvx_vec_add_f32_f32(acc3, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out3, vdot), zero));
+        acc4 = hvx_vec_add_f32_f32(acc4, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out4, vdot), zero));
+        acc5 = hvx_vec_add_f32_f32(acc5, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out5, vdot), zero));
+        acc6 = hvx_vec_add_f32_f32(acc6, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out6, vdot), zero));
+        acc7 = hvx_vec_add_f32_f32(acc7, Q6_V_vmux_QVV(mask, hvx_vec_mul_f32_f32(out7, vdot), zero));
+    }
+
+    HVX_Vector_x4 accA = { .v = { acc0, acc1, acc2, acc3 } };
+    HVX_Vector_x4 accB = { .v = { acc4, acc5, acc6, acc7 } };
+    hvx_vec_store_u(sums + 0, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(accA));
+    hvx_vec_store_u(sums + 4, 4 * sizeof(float), hvx_vec_reduce_sum_f32x4(accB));
+}
+
+static void gated_delta_net_f32_pp_thread(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_gdn_context * gctx = (struct htp_gdn_context *) data;
+    struct htp_ops_context * octx = gctx->octx;
+
+    const struct htp_tensor * q     = octx->src[0];
+    const struct htp_tensor * k     = octx->src[1];
+    const struct htp_tensor * v     = octx->src[2];
+    const struct htp_tensor * g     = octx->src[3];
+    const struct htp_tensor * beta  = octx->src[4];
+    const struct htp_tensor * state = octx->src[5];
+    const struct htp_tensor * dst   = octx->dst;
+
+    const uint32_t S_v      = v->ne[0];
+    const uint32_t H        = v->ne[1];
+    const uint32_t n_tokens = v->ne[2];
+    const uint32_t n_seqs   = v->ne[3];
+
+    const uint32_t total_rows = H * n_seqs;
+    if (ith >= total_rows) {
+        return;
+    }
+
+    const uint32_t rq3 = n_seqs / q->ne[3];
+    const uint32_t rk3 = n_seqs / k->ne[3];
+    const float scale = 1.0f / sqrtf((float) S_v);
+
+    float * dst_base       = (float *) (uintptr_t) dst->data;
+    float * state_out_base = dst_base + (uint64_t) S_v * H * n_tokens * n_seqs;
+    const float * state_in_base = (const float *) (uintptr_t) state->data;
+
+    const bool kda = (g->ne[0] == S_v);
+    float local_gate[HTP_GDN_MAX_SV] __attribute__((aligned(128)));
+    float local_q[HTP_GDN_MAX_SV] __attribute__((aligned(128)));
+    float local_k[HTP_GDN_MAX_SV] __attribute__((aligned(128)));
+    float local_sums[4] __attribute__((aligned(128)));
+
+    for (uint32_t ir = ith; ir < total_rows; ir += nth) {
+        const uint32_t iv1 = ir % H;
+        const uint32_t iv3 = ir / H;
+
+        const uint32_t iq1 = iv1 % q->ne[1];
+        const uint32_t ik1 = iv1 % k->ne[1];
+        const uint32_t iq3 = iv3 / rq3;
+        const uint32_t ik3 = iv3 / rk3;
+
+        float * s_out = state_out_base + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
+        const float * s_in = state_in_base + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
+
+        memcpy(s_out, s_in, gctx->state_bytes);
+        float * s_work = s_out;
+
+        float * attn_data = dst_base + ((uint64_t) iv3 * n_tokens * H + iv1) * S_v;
+
+        for (uint32_t t = 0; t < n_tokens; ++t) {
+            const float * q_t = (const float *) ((const uint8_t *) (uintptr_t) q->data +
+                    (uint64_t) iq3 * q->nb[3] + (uint64_t) t * q->nb[2] + (uint64_t) iq1 * q->nb[1]);
+            const float * k_t = (const float *) ((const uint8_t *) (uintptr_t) k->data +
+                    (uint64_t) ik3 * k->nb[3] + (uint64_t) t * k->nb[2] + (uint64_t) ik1 * k->nb[1]);
+            const float * v_t = (const float *) ((const uint8_t *) (uintptr_t) v->data +
+                    (uint64_t) iv3 * v->nb[3] + (uint64_t) t * v->nb[2] + (uint64_t) iv1 * v->nb[1]);
+            const float * g_t = (const float *) ((const uint8_t *) (uintptr_t) g->data +
+                    (uint64_t) iv3 * g->nb[3] + (uint64_t) t * g->nb[2] + (uint64_t) iv1 * g->nb[1]);
+            const float beta_val = *(const float *) ((const uint8_t *) (uintptr_t) beta->data +
+                    (uint64_t) iv3 * beta->nb[3] + (uint64_t) t * beta->nb[2] + (uint64_t) iv1 * beta->nb[1]);
+
+            memcpy(local_q, q_t, (size_t) S_v * sizeof(float));
+            memcpy(local_k, k_t, (size_t) S_v * sizeof(float));
+
+            if (kda) {
+                hvx_exp_f32((uint8_t *) local_gate, (const uint8_t *) g_t, S_v, false);
+
+                uint32_t j = 0;
+                for (; j + 4 <= S_v; j += 4) {
+                    float * row0 = s_work + (uint64_t) (j + 0) * S_v;
+                    float * row1 = s_work + (uint64_t) (j + 1) * S_v;
+                    float * row2 = s_work + (uint64_t) (j + 2) * S_v;
+                    float * row3 = s_work + (uint64_t) (j + 3) * S_v;
+                    gdn_mul_dot4_f32(row0, row1, row2, row3, local_gate, local_k, S_v, local_sums);
+                    float local_delta_b[4] __attribute__((aligned(128)));
+                    for (uint32_t r = 0; r < 4; ++r) {
+                        local_delta_b[r] = (v_t[j + r] - local_sums[r]) * beta_val;
+                    }
+                    gdn_add_scaled_dot4_f32(row0, row1, row2, row3, local_k, local_delta_b, local_q, S_v, local_sums);
+                    for (uint32_t r = 0; r < 4; ++r) {
+                        attn_data[j + r] = local_sums[r] * scale;
+                    }
+                }
+                for (; j < S_v; ++j) {
+                    float * row = s_work + (uint64_t) j * S_v;
+                    const float sum = gdn_mul_dot_f32(row, local_gate, local_k, S_v);
+                    const float dj = (v_t[j] - sum) * beta_val;
+                    attn_data[j] = gdn_add_scaled_dot_f32(row, local_k, dj, local_q, S_v) * scale;
+                }
+            } else {
+                const float gate = expf(g_t[0]);
+                uint32_t j = 0;
+                for (; j + 4 <= S_v; j += 4) {
+                    float * row0 = s_work + (uint64_t) (j + 0) * S_v;
+                    float * row1 = s_work + (uint64_t) (j + 1) * S_v;
+                    float * row2 = s_work + (uint64_t) (j + 2) * S_v;
+                    float * row3 = s_work + (uint64_t) (j + 3) * S_v;
+                    gdn_mul_scalar_dot4_f32(row0, row1, row2, row3, gate, local_k, S_v, local_sums);
+                    float local_delta_b[4] __attribute__((aligned(128)));
+                    for (uint32_t r = 0; r < 4; ++r) {
+                        local_delta_b[r] = (v_t[j + r] - local_sums[r]) * beta_val;
+                    }
+                    gdn_add_scaled_dot4_f32(row0, row1, row2, row3, local_k, local_delta_b, local_q, S_v, local_sums);
+                    for (uint32_t r = 0; r < 4; ++r) {
+                        attn_data[j + r] = local_sums[r] * scale;
+                    }
+                }
+                for (; j < S_v; ++j) {
+                    float * row = s_work + (uint64_t) j * S_v;
+                    const float sum = gdn_mul_scalar_dot_f32(row, gate, local_k, S_v);
+                    const float dj = (v_t[j] - sum) * beta_val;
+                    attn_data[j] = gdn_add_scaled_dot_f32(row, local_k, dj, local_q, S_v) * scale;
+                }
+            }
+
+            attn_data += (uint64_t) S_v * H;
+        }
+    }
+}
+
+static void gated_delta_net_f32_tg_thread(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_gdn_context * gctx = (struct htp_gdn_context *) data;
+    struct htp_ops_context * octx = gctx->octx;
+
+    const struct htp_tensor * q     = octx->src[0];
+    const struct htp_tensor * k     = octx->src[1];
+    const struct htp_tensor * v     = octx->src[2];
+    const struct htp_tensor * g     = octx->src[3];
+    const struct htp_tensor * beta  = octx->src[4];
+    const struct htp_tensor * state = octx->src[5];
+    const struct htp_tensor * dst   = octx->dst;
+
+    const uint32_t S_v      = v->ne[0];
+    const uint32_t H        = v->ne[1];
+    const uint32_t n_seqs   = v->ne[3];
+
+    const uint32_t total_rows = H * n_seqs;
+    if (ith >= total_rows) {
+        return;
+    }
+
+    const uint32_t rq3 = n_seqs / q->ne[3];
+    const uint32_t rk3 = n_seqs / k->ne[3];
+    const float scale = 1.0f / sqrtf((float) S_v);
+
+    float * dst_base       = (float *) (uintptr_t) dst->data;
+    float * state_out_base = dst_base + (uint64_t) S_v * H * n_seqs;
+    const float * state_in_base = (const float *) (uintptr_t) state->data;
+
+    const bool kda = (g->ne[0] == S_v);
+    float local_gate[HTP_GDN_MAX_SV] __attribute__((aligned(128)));
+    float local_q[HTP_GDN_MAX_SV] __attribute__((aligned(128)));
+    float local_k[HTP_GDN_MAX_SV] __attribute__((aligned(128)));
+    float local_sums[8] __attribute__((aligned(128)));
+
+    dma_queue * dma = octx->ctx->dma[ith];
+
+    uint8_t * spad = NULL;
+    if (gctx->use_vtcm) {
+        spad = gctx->vtcm_state_base + gctx->vtcm_state_per_thread * ith;
+    }
+
+    for (uint32_t ir = ith; ir < total_rows; ir += nth) {
+        const uint32_t iv1 = ir % H;
+        const uint32_t iv3 = ir / H;
+
+        const uint32_t iq1 = iv1 % q->ne[1];
+        const uint32_t ik1 = iv1 % k->ne[1];
+        const uint32_t iq3 = iv3 / rq3;
+        const uint32_t ik3 = iv3 / rk3;
+
+        float * s_out = state_out_base + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
+        const float * s_in = state_in_base + ((uint64_t) iv3 * H + iv1) * S_v * S_v;
+        float * s_work;
+
+        if (spad) {
+            dma_queue_push(dma, dma_make_ptr(spad, s_in),
+                           S_v * sizeof(float), S_v * sizeof(float),
+                           S_v * sizeof(float), S_v);
+            dma_queue_pop(dma);
+            s_work = (float *) spad;
+        } else {
+            s_work = s_out;
+            memcpy(s_work, s_in, gctx->state_bytes);
+        }
+
+        float * attn_data = dst_base + ((uint64_t) iv3 * H + iv1) * S_v;
+
+        const float * q_t = (const float *) ((const uint8_t *) (uintptr_t) q->data +
+                (uint64_t) iq3 * q->nb[3] + (uint64_t) iq1 * q->nb[1]);
+        const float * k_t = (const float *) ((const uint8_t *) (uintptr_t) k->data +
+                (uint64_t) ik3 * k->nb[3] + (uint64_t) ik1 * k->nb[1]);
+        const float * v_t = (const float *) ((const uint8_t *) (uintptr_t) v->data +
+                (uint64_t) iv3 * v->nb[3] + (uint64_t) iv1 * v->nb[1]);
+        const float * g_t = (const float *) ((const uint8_t *) (uintptr_t) g->data +
+                (uint64_t) iv3 * g->nb[3] + (uint64_t) iv1 * g->nb[1]);
+        const float beta_val = *(const float *) ((const uint8_t *) (uintptr_t) beta->data +
+                (uint64_t) iv3 * beta->nb[3] + (uint64_t) iv1 * beta->nb[1]);
+
+        memcpy(local_q, q_t, (size_t) S_v * sizeof(float));
+        memcpy(local_k, k_t, (size_t) S_v * sizeof(float));
+
+        if (kda) {
+            hvx_exp_f32((uint8_t *) local_gate, (const uint8_t *) g_t, S_v, false);
+
+            uint32_t j = 0;
+            for (; j + 8 <= S_v; j += 8) {
+                float * row0 = s_work + (uint64_t) (j + 0) * S_v;
+                float * row1 = s_work + (uint64_t) (j + 1) * S_v;
+                float * row2 = s_work + (uint64_t) (j + 2) * S_v;
+                float * row3 = s_work + (uint64_t) (j + 3) * S_v;
+                float * row4 = s_work + (uint64_t) (j + 4) * S_v;
+                float * row5 = s_work + (uint64_t) (j + 5) * S_v;
+                float * row6 = s_work + (uint64_t) (j + 6) * S_v;
+                float * row7 = s_work + (uint64_t) (j + 7) * S_v;
+                gdn_mul_dot8_f32(row0, row1, row2, row3, row4, row5, row6, row7,
+                                 local_gate, local_k, S_v, local_sums);
+                float local_delta_b[8] __attribute__((aligned(128)));
+                for (uint32_t r = 0; r < 8; ++r) {
+                    local_delta_b[r] = (v_t[j + r] - local_sums[r]) * beta_val;
+                }
+                gdn_add_scaled_dot8_f32(row0, row1, row2, row3, row4, row5, row6, row7,
+                                        local_k, local_delta_b, local_q, S_v, local_sums);
+                for (uint32_t r = 0; r < 8; ++r) {
+                    attn_data[j + r] = local_sums[r] * scale;
+                }
+            }
+            for (; j + 4 <= S_v; j += 4) {
+                float * row0 = s_work + (uint64_t) (j + 0) * S_v;
+                float * row1 = s_work + (uint64_t) (j + 1) * S_v;
+                float * row2 = s_work + (uint64_t) (j + 2) * S_v;
+                float * row3 = s_work + (uint64_t) (j + 3) * S_v;
+                gdn_mul_dot4_f32(row0, row1, row2, row3, local_gate, local_k, S_v, local_sums);
+                float local_delta_b[4] __attribute__((aligned(128)));
+                for (uint32_t r = 0; r < 4; ++r) {
+                    local_delta_b[r] = (v_t[j + r] - local_sums[r]) * beta_val;
+                }
+                gdn_add_scaled_dot4_f32(row0, row1, row2, row3, local_k, local_delta_b, local_q, S_v, local_sums);
+                for (uint32_t r = 0; r < 4; ++r) {
+                    attn_data[j + r] = local_sums[r] * scale;
+                }
+            }
+            for (; j < S_v; ++j) {
+                float * row = s_work + (uint64_t) j * S_v;
+                const float sum = gdn_mul_dot_f32(row, local_gate, local_k, S_v);
+                const float dj = (v_t[j] - sum) * beta_val;
+                attn_data[j] = gdn_add_scaled_dot_f32(row, local_k, dj, local_q, S_v) * scale;
+            }
+        } else {
+            const float gate = expf(g_t[0]);
+            uint32_t j = 0;
+            for (; j + 8 <= S_v; j += 8) {
+                float * row0 = s_work + (uint64_t) (j + 0) * S_v;
+                float * row1 = s_work + (uint64_t) (j + 1) * S_v;
+                float * row2 = s_work + (uint64_t) (j + 2) * S_v;
+                float * row3 = s_work + (uint64_t) (j + 3) * S_v;
+                float * row4 = s_work + (uint64_t) (j + 4) * S_v;
+                float * row5 = s_work + (uint64_t) (j + 5) * S_v;
+                float * row6 = s_work + (uint64_t) (j + 6) * S_v;
+                float * row7 = s_work + (uint64_t) (j + 7) * S_v;
+                gdn_mul_scalar_dot8_f32(row0, row1, row2, row3, row4, row5, row6, row7,
+                                        gate, local_k, S_v, local_sums);
+                float local_delta_b[8] __attribute__((aligned(128)));
+                for (uint32_t r = 0; r < 8; ++r) {
+                    local_delta_b[r] = (v_t[j + r] - local_sums[r]) * beta_val;
+                }
+                gdn_add_scaled_dot8_f32(row0, row1, row2, row3, row4, row5, row6, row7,
+                                        local_k, local_delta_b, local_q, S_v, local_sums);
+                for (uint32_t r = 0; r < 8; ++r) {
+                    attn_data[j + r] = local_sums[r] * scale;
+                }
+            }
+            for (; j + 4 <= S_v; j += 4) {
+                float * row0 = s_work + (uint64_t) (j + 0) * S_v;
+                float * row1 = s_work + (uint64_t) (j + 1) * S_v;
+                float * row2 = s_work + (uint64_t) (j + 2) * S_v;
+                float * row3 = s_work + (uint64_t) (j + 3) * S_v;
+                gdn_mul_scalar_dot4_f32(row0, row1, row2, row3, gate, local_k, S_v, local_sums);
+                float local_delta_b[4] __attribute__((aligned(128)));
+                for (uint32_t r = 0; r < 4; ++r) {
+                    local_delta_b[r] = (v_t[j + r] - local_sums[r]) * beta_val;
+                }
+                gdn_add_scaled_dot4_f32(row0, row1, row2, row3, local_k, local_delta_b, local_q, S_v, local_sums);
+                for (uint32_t r = 0; r < 4; ++r) {
+                    attn_data[j + r] = local_sums[r] * scale;
+                }
+            }
+            for (; j < S_v; ++j) {
+                float * row = s_work + (uint64_t) j * S_v;
+                const float sum = gdn_mul_scalar_dot_f32(row, gate, local_k, S_v);
+                const float dj = (v_t[j] - sum) * beta_val;
+                attn_data[j] = gdn_add_scaled_dot_f32(row, local_k, dj, local_q, S_v) * scale;
+            }
+        }
+
+        if (spad) {
+            dma_queue_push(dma, dma_make_ptr(s_out, spad),
+                           S_v * sizeof(float), S_v * sizeof(float),
+                           S_v * sizeof(float), S_v);
+            dma_queue_pop(dma);
+        }
+    }
+}
+
+int op_gated_delta_net(struct htp_ops_context * octx) {
+    const struct htp_tensor * q     = octx->src[0];
+    const struct htp_tensor * k     = octx->src[1];
+    const struct htp_tensor * v     = octx->src[2];
+    const struct htp_tensor * g     = octx->src[3];
+    const struct htp_tensor * beta  = octx->src[4];
+    const struct htp_tensor * state = octx->src[5];
+    const struct htp_tensor * dst   = octx->dst;
+
+    if (!q || !k || !v || !g || !beta || !state || !dst) {
+        return HTP_STATUS_INVAL_PARAMS;
+    }
+
+    if (q->type != HTP_TYPE_F32 || k->type != HTP_TYPE_F32 || v->type != HTP_TYPE_F32 ||
+        g->type != HTP_TYPE_F32 || beta->type != HTP_TYPE_F32 || state->type != HTP_TYPE_F32 ||
+        dst->type != HTP_TYPE_F32) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    const uint32_t S_v      = v->ne[0];
+    const uint32_t H        = v->ne[1];
+    const uint32_t n_tokens = v->ne[2];
+    const uint32_t n_seqs   = v->ne[3];
+
+    if (S_v == 0 || S_v > HTP_GDN_MAX_SV || H == 0 || n_tokens == 0 || n_seqs == 0) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+    if ((g->ne[0] != 1 && g->ne[0] != S_v) || beta->ne[0] != 1) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+    if (q->ne[0] != S_v || k->ne[0] != S_v || q->ne[1] == 0 || k->ne[1] == 0 ||
+        q->ne[2] != n_tokens || k->ne[2] != n_tokens || q->ne[3] == 0 || k->ne[3] == 0 ||
+        (n_seqs % q->ne[3]) != 0 || (n_seqs % k->ne[3]) != 0) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+    if (state->ne[0] * state->ne[1] * state->ne[2] * state->ne[3] != S_v * S_v * H * n_seqs) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+    if (dst->ne[0] != S_v * H || dst->ne[1] != n_tokens * n_seqs + S_v * n_seqs) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    struct htp_gdn_context gctx;
+    gctx.octx = octx;
+    gctx.rows_per_thread = (H * n_seqs + octx->n_threads - 1) / octx->n_threads;
+    gctx.state_bytes = (size_t) S_v * S_v * sizeof(float);
+
+    size_t state_aligned = (size_t) S_v * S_v * sizeof(float);
+    state_aligned = (state_aligned + 127) & ~(size_t)127;
+
+    gctx.use_vtcm = false;
+    gctx.vtcm_state_base = NULL;
+    gctx.vtcm_state_per_thread = 0;
+
+    if (n_tokens == 1 && octx->ctx->vtcm_base) {
+        size_t vtcm_total = state_aligned * octx->n_threads;
+        if (octx->ctx->vtcm_size >= vtcm_total) {
+            gctx.use_vtcm = true;
+            gctx.vtcm_state_base = octx->ctx->vtcm_base;
+            gctx.vtcm_state_per_thread = state_aligned;
+        }
+    }
+
+    if (n_tokens == 1) {
+        worker_pool_run_func(octx->ctx->worker_pool, gated_delta_net_f32_tg_thread, &gctx, octx->n_threads);
+    } else {
+        worker_pool_run_func(octx->ctx->worker_pool, gated_delta_net_f32_pp_thread, &gctx, octx->n_threads);
+    }
+
+    return HTP_STATUS_OK;
+}
diff --git a/ggml/src/ggml-hexagon/htp/hex-utils.h b/ggml/src/ggml-hexagon/htp/hex-utils.h
index f6713c5cf8f..6239ceff4b4 100644
--- a/ggml/src/ggml-hexagon/htp/hex-utils.h
+++ b/ggml/src/ggml-hexagon/htp/hex-utils.h
@@ -4,6 +4,7 @@
 #include <stdbool.h>
 #include <stdint.h>
 #include <qurt_memory.h>
+#include <qurt.h>
 
 #include "hexagon_types.h"
 #include "hexagon_protos.h"
@@ -73,6 +74,12 @@ static inline size_t hex_smax(size_t a, size_t b) {
     return a > b ? a : b;
 }
 
+static inline void hex_swap_ptr(void ** p1, void ** p2) {
+    void * t = *p1;
+    *p1      = *p2;
+    *p2      = t;
+}
+
 static inline void hex_l2fetch(const void * p, uint32_t width, uint32_t stride, uint32_t height) {
     const uint64_t control = Q6_P_combine_RR(stride, Q6_R_combine_RlRl(width, height));
     Q6_l2fetch_AP((void *) p, control);
@@ -100,4 +107,31 @@ static inline void hex_pause() {
     asm volatile(" pause(#255)\n");
 }
 
+#ifndef HEX_NUM_PMU_COUNTERS
+#define HEX_NUM_PMU_COUNTERS 8
+#endif
+
+static inline void hex_get_pmu(uint32_t counters[]) {
+#if __HVX_ARCH__ >= 79
+    asm volatile("%0 = upmucnt0" : "=r"(counters[0]));
+    asm volatile("%0 = upmucnt1" : "=r"(counters[1]));
+    asm volatile("%0 = upmucnt2" : "=r"(counters[2]));
+    asm volatile("%0 = upmucnt3" : "=r"(counters[3]));
+    asm volatile("%0 = upmucnt4" : "=r"(counters[4]));
+    asm volatile("%0 = upmucnt5" : "=r"(counters[5]));
+    asm volatile("%0 = upmucnt6" : "=r"(counters[6]));
+    asm volatile("%0 = upmucnt7" : "=r"(counters[7]));
+#else
+    counters[0] = qurt_pmu_get(QURT_PMUCNT0);
+    counters[1] = qurt_pmu_get(QURT_PMUCNT1);
+    counters[2] = qurt_pmu_get(QURT_PMUCNT2);
+    counters[3] = qurt_pmu_get(QURT_PMUCNT3);
+    counters[4] = qurt_pmu_get(QURT_PMUCNT4);
+    counters[5] = qurt_pmu_get(QURT_PMUCNT5);
+    counters[6] = qurt_pmu_get(QURT_PMUCNT6);
+    counters[7] = qurt_pmu_get(QURT_PMUCNT7);
+    // qurt_pmu_get_pmucnt(counters);
+#endif
+}
+
 #endif /* HEX_UTILS_H */
diff --git a/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
new file mode 100644
index 00000000000..4a4ff0b331d
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
@@ -0,0 +1,1841 @@
+// HMX-accelerated Flash Attention for prefill (neq1 >= 32).
+// Ported from htp-ops-lib/src/dsp/ops/flash_attn.c, adapted to the htp/ codebase.
+
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <assert.h>
+#include <HAP_compute_res.h>
+#include <HAP_farf.h>
+#include <math.h>
+#include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
+#include <string.h>
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "hex-dma.h"
+#include "hmx-profile.h"
+#include "hmx-queue.h"
+#include "hmx-utils.h"
+#include "htp-ctx.h"
+#include "htp-ops.h"
+#include "hvx-dump.h"
+#include "hvx-reduce.h"
+#include "hvx-utils.h"
+#include "vtcm-utils.h"
+#include "worker-pool.h"
+
+// ============================================================================
+// Constants
+// ============================================================================
+
+// Tile constants from hmx-utils.h
+// HMX_FP16_TILE_N_ROWS  = 32
+// HMX_FP16_TILE_N_COLS  = 32
+// HMX_FP16_TILE_N_ELMS  = 1024
+// HMX_FP16_TILE_SIZE    = 2048
+
+// ============================================================================
+// Dynamic block size computation (GQA-aware)
+// ============================================================================
+
+// Exact VTCM usage for a given (gqa_factor, DK, DV, Br, Bc) configuration.
+// g_br = hex_align_up(gqa_factor * Br, 32) replaces Br for all Q/O/S/P/D dimensions.
+// Layout: Q + O_ping + O_pong + K_dma*2 + V_dma*2 + K_tile + V_tile + S + P + D + vectors + scales
+// Mask is DMA'd into a VTCM buffer (Br rows per KV block) to avoid DDR reads in softmax.
+static size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads) {
+    const size_t g_br         = hex_align_up(gqa_factor * Br, HMX_FP16_TILE_N_ROWS);
+    const size_t q_tile_size  = hex_align_up(g_br * DK * sizeof(__fp16), 4096);    // Q:  [g_br, DK]
+    const size_t o_tile_size  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);    // O:  [g_br, DV] x2 ping-pong
+    const size_t k_dma_size   = hex_align_up(Bc * DK * sizeof(__fp16), 4096);      // K DMA: [Bc, DK] x2 double-buf
+    const size_t v_dma_size   = hex_align_up(Bc * DV * sizeof(__fp16), 4096);      // V DMA: [Bc, DV] x2 double-buf
+    const size_t k_tile_size  = hex_align_up(Bc * DK * sizeof(__fp16), 4096);      // K tiles: [Bc, DK] interleaved
+    const size_t v_tile_size  = hex_align_up(Bc * DV * sizeof(__fp16), 4096);      // V tiles: [Bc, DV] interleaved
+    const size_t s_tile_size  = hex_align_up(g_br * Bc * sizeof(__fp16), 4096);    // S/P:[g_br, Bc]
+    const size_t d_tile_size  = hex_align_up(g_br * g_br * sizeof(__fp16), 4096);  // D:  [g_br, g_br]
+    const size_t col_vec_size = hex_align_up(g_br * sizeof(__fp16), 256);          // m, l, etc.
+    const size_t row_vec_size = hex_align_up(Bc * sizeof(__fp16), 256);
+    const size_t m_line_size  = hex_align_up(Bc * sizeof(__fp16), 128);
+    const size_t m_buf_size   = hex_align_up(Br * m_line_size, 4096);
+    const size_t slopes_size  = hex_align_up(g_br * sizeof(__fp16), 128);
+
+    return   q_tile_size * 1               // Q tiles
+           + o_tile_size * 2               // O ping-pong
+           + k_dma_size  * 2               // K DMA x2
+           + v_dma_size  * 2               // V DMA x2
+           + k_tile_size * 1               // K tiles
+           + v_tile_size * 1               // V tiles
+           + s_tile_size * 2               // S + P
+           + d_tile_size * 1               // D (diagonal matrix)
+           + col_vec_size * 4              // m_vec, l_vec, s_rowmax, p_rowsum
+           + row_vec_size * 2 * n_threads  // per-thread softmax row scratch
+           + m_buf_size * 1                // mask VTCM buffer [Br rows]
+           + slopes_size                   // Slopes
+           + 256 * 2;                      // HMX scales (id + qk)
+}
+
+// ============================================================================
+// FP16 exp2 polynomial (ported from htp-ops-lib/include/dsp/hvx_math.h)
+// ============================================================================
+// 5th-order Horner polynomial for exp2(x) in qf16/hf16 domain.  Input must be
+// ≤ 0 (safe softmax invariant — overflow handling omitted).  ~18 ALU ops per
+// 64 fp16 lanes, fully parallel across HVX threads (no scatter/gather engine).
+// Replaces the F32 round-trip (qf16→f32→exp→f32→f16, ~44 ops for 2×32 lanes).
+static inline HVX_Vector hvx_exp2_hf(HVX_Vector x_v) {
+    const HVX_Vector zero_v    = Q6_V_vzero();
+    const HVX_Vector half_hf_v = Q6_Vh_vsplat_R(0x3800);  // fp16 0.5
+
+    // k = round_toward_neg_inf(x);  f = (float)k;  frac = x - f
+    HVX_Vector x_minus_half = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vsub_VhfVhf(x_v, half_hf_v));
+    HVX_Vector k_v          = Q6_Vh_equals_Vhf(x_minus_half);  // truncate to int16
+    HVX_Vector f_v          = Q6_Vhf_equals_Vh(k_v);           // back to fp16
+
+    HVX_Vector x_qf16 = Q6_Vqf16_vsub_VhfVhf(x_v, f_v);        // fractional part in qf16
+
+    // Horner: y = ((((E5*x + E4)*x + E3)*x + E2)*x + E1)*x + E0
+    HVX_Vector y = Q6_Vqf16_vmpy_Vqf16Vqf16(Q6_Vh_vsplat_R(0x5082), x_qf16); // E5*x
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x157d));        // + E4
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x20ed));        // + E3
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x2b1b));        // + E2
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x33b0));        // + E1
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x398c));        // + E0
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);                      // y = y * x
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x3c00));        // + 1.0
+
+    // Combine polynomial (mantissa) with integer part (exponent): result = y * 2^k
+    y                          = Q6_Vhf_equals_Vqf16(y);
+    HVX_Vector y_exp           = Q6_Vuh_vlsr_VuhR(Q6_Vh_vasl_VhR(y, 1), 11);
+    y_exp                      = Q6_Vh_vadd_VhVh(k_v, y_exp);
+    HVX_VectorPred q_underflow = Q6_Q_vcmp_gt_VhVh(zero_v, y_exp);
+    y                          = Q6_Vh_vaslacc_VhVhR(y, k_v, 10);
+    return Q6_V_vmux_QVV(q_underflow, zero_v, y);
+}
+
+#define FA_MIN_KV_BLOCKS 3
+
+// Cost-based (Br, Bc) search for flash attention with pipeline constraint.
+//
+// VTCM model (same as before):
+//   overhead + g_br * per_gbr + g_br² * per_gbr2 + Bc * per_bc + g_br * Bc * per_gbr_bc
+//
+// Cost model (minimization objective):
+//   Q * (c_q_fixed + K * c_iter_fixed),  where Q = ceil(qo/Br), K = ceil(kv/Bc)
+static int hmx_fa_find_chunk_size(size_t * Br_out,
+                                  size_t * Bc_out,
+                                  size_t   gqa_factor,
+                                  size_t   DK,
+                                  size_t   DV,
+                                  size_t   qo_len,
+                                  size_t   kv_len,
+                                  size_t   vtcm_budget,
+                                  size_t   n_threads) {
+    const size_t T       = HMX_FP16_TILE_N_ROWS;  // 32
+    const size_t br_unit = hmx_ceil_div(T, gqa_factor);
+    // Bc must be a multiple of 64 so that n_tiles_per_bc is even.  The softmax
+    // P-tile write uses a dual-tile pattern (vshuff + two stores 16 slots apart)
+    // that would race across r0 blocks if the last dual-tile is half-occupied.
+    // See .cursor/todos/hmx-flash-attn-bc-search-space.md for the perf trade-off.
+    const size_t bc_unit = HMX_FP16_TILE_N_COLS * 2;  // 64
+    const size_t fp16    = sizeof(__fp16);
+
+    // Approximate per-unit VTCM costs (without per-buffer alignment padding).
+    const size_t per_gbr  = (DK + 2 * DV) * fp16 + 4 * fp16;  // Q + O×2 + 4 col vectors
+    const size_t per_gbr2 = fp16;                             // D diagonal matrix
+    const size_t per_bc =
+        3 * (DK + DV) * fp16 + 2 * n_threads * fp16;          // K_dma×2 + V_dma×2 + K_tile + V_tile + row bufs
+    const size_t per_gbr_bc = 2 * fp16;                       // S + P
+
+    const size_t overhead = 256 * 2 + 13 * 4096;
+
+    if (vtcm_budget <= overhead) {
+        return -1;
+    }
+    const size_t usable = vtcm_budget - overhead;
+
+    // Br_max: largest Br aligned to br_unit that does not exceed qo_len.
+    const size_t Br_max = qo_len >= br_unit ? hex_align_down(qo_len, br_unit) : br_unit;
+
+    // Pipeline constraint: cap Bc so n_kv_blocks >= FA_MIN_KV_BLOCKS.
+    // Only relax when kv_len is too short to form enough blocks.
+    const bool   can_pipeline = (kv_len >= FA_MIN_KV_BLOCKS * bc_unit && n_threads >= 2);
+    const size_t Bc_limit     = can_pipeline ? hex_align_down(kv_len / FA_MIN_KV_BLOCKS, bc_unit) :
+                                               (kv_len >= bc_unit ? hex_align_down(kv_len, bc_unit) : bc_unit);
+    // Cost coefficients calibrated from profiling
+    const size_t c_q_fixed    = 1400;  // per-Q-block: q_load + epilogue o_update + o_norm + o_store
+    const size_t c_iter_fixed = 200;   // per-KV-iter: HMX queue push/pop + DMA pop + barriers
+
+    size_t best_cost = SIZE_MAX, best_mn = 0;
+    size_t best_Br = 0, best_Bc = 0;
+
+    for (size_t Br = Br_max; Br >= br_unit; Br -= br_unit) {
+        const size_t g_br = hex_align_up(gqa_factor * Br, T);
+
+        // g_br-dependent VTCM cost: g_br * per_gbr + g_br² * per_gbr2
+        const size_t gbr_cost = g_br * per_gbr + g_br * g_br * per_gbr2;
+        if (gbr_cost >= usable) {
+            if (Br == br_unit) {
+                break;
+            }
+            continue;
+        }
+
+        // Analytically solve for max Bc:
+        //   remain >= Bc * (per_bc + g_br * per_gbr_bc + Br * fp16_mask)
+        // The Br * fp16 term accounts for the VTCM mask buffer [Br × Bc].
+        const size_t remain   = usable - gbr_cost;
+        const size_t bc_denom = per_bc + g_br * per_gbr_bc + Br * fp16;
+        size_t       Bc       = hex_smin(hex_align_down(remain / bc_denom, bc_unit), Bc_limit);
+        if (Bc < bc_unit) {
+            if (Br == br_unit) {
+                break;
+            }
+            continue;
+        }
+
+        // Exact VTCM verification (alignment padding may push over budget)
+        while (Bc >= bc_unit && hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads) > vtcm_budget) {
+            Bc -= bc_unit;
+        }
+        if (Bc < bc_unit) {
+            if (Br == br_unit) {
+                break;
+            }
+            continue;
+        }
+
+        const size_t q_blocks  = (qo_len + Br - 1) / Br;
+        const size_t kv_blocks = (kv_len + Bc - 1) / Bc;
+        const size_t cost      = q_blocks * (c_q_fixed + kv_blocks * c_iter_fixed);
+        const size_t mn        = Br * Bc;
+
+        if (cost < best_cost || (cost == best_cost && mn > best_mn)) {
+            best_cost = cost;
+            best_mn   = mn;
+            best_Br   = Br;
+            best_Bc   = Bc;
+        }
+
+        if (Br == br_unit) {
+            break;
+        }
+    }
+
+    if (best_Br == 0) {
+        return -1;
+    }
+
+    *Br_out = best_Br;
+    *Bc_out = best_Bc;
+    return 0;
+}
+
+// ============================================================================
+// Tile interleave / extract helpers
+// ============================================================================
+
+// transpose scatter offsets moved to hmx-utils.h as hmx_transpose_scatter_offsets
+
+// Scatter offsets for diagonal tile: entry[2i] = i*136, entry[2i+1] = i*136+6
+// 136 = 4 * 32 + 8 = byte offset to diagonal in a 32x32 fp16 interleaved tile
+static const int16_t d_tile_scatter_offsets[64] __attribute__((aligned(128))) = {
+    0 * 136,  0 * 136 + 6,
+    1 * 136,  1 * 136 + 6,
+    2 * 136,  2 * 136 + 6,
+    3 * 136,  3 * 136 + 6,
+    4 * 136,  4 * 136 + 6,
+    5 * 136,  5 * 136 + 6,
+    6 * 136,  6 * 136 + 6,
+    7 * 136,  7 * 136 + 6,
+    8 * 136,  8 * 136 + 6,
+    9 * 136,  9 * 136 + 6,
+    10 * 136, 10 * 136 + 6,
+    11 * 136, 11 * 136 + 6,
+    12 * 136, 12 * 136 + 6,
+    13 * 136, 13 * 136 + 6,
+    14 * 136, 14 * 136 + 6,
+    15 * 136, 15 * 136 + 6,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+};
+
+// hmx_interleave_rows_to_tiles and hmx_interleave_cols_to_tiles are in hmx-utils.h
+
+// ============================================================================
+// HMX Flash Attention context (GQA-merged)
+// ============================================================================
+
+struct hmx_fa_context {
+    const struct htp_ops_context * octx;
+    bool         use_pipeline;  // true when n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads >= 2
+    uint32_t     n_threads;
+
+    // Op parameters
+    float        scale;
+    float        max_bias;
+    float        logit_softcap;
+    uint32_t     n_head_log2;
+    float        m0, m1;
+
+    // Dimensions
+    uint32_t     DK, DV;
+    uint32_t     n_kv;        // kv_len
+    uint32_t     n_kv_heads;  // number of KV heads
+    uint32_t     n_heads;     // number of Q heads
+    uint32_t     G;           // GQA factor = n_heads / n_kv_heads
+    uint32_t     n_kv_blocks;
+    uint32_t     neq1;        // Q token count
+
+    // Types
+    bool         is_q_fp32;
+    bool         is_dst_fp32;
+
+    // Dynamic block sizes
+    uint32_t     Br;    // Q tokens per block (before GQA expansion)
+    uint32_t     Bc;
+    uint32_t     g_br;  // hex_align_up(G * Br, 32) - actual tile row dim
+
+    // VTCM buffers (allocated by vtcm_seq_alloc)
+    __fp16 *     vtcm_q_tiles;         // Q tile format [g_br, D]
+    __fp16 *     vtcm_o_tiles[2];      // O ping-pong [g_br, D]
+    __fp16 *     vtcm_k_fp16[2];       // K DMA double-buffer [Bc, D]
+    __fp16 *     vtcm_v_fp16[2];       // V DMA double-buffer [Bc, D]
+    __fp16 *     vtcm_k_tiles;         // K tiles (transposed)
+    __fp16 *     vtcm_v_tiles;         // V tiles (column-major)
+    __fp16 *     vtcm_s_tiles;         // S = QK^T [g_br, Bc]
+    __fp16 *     vtcm_p_tiles;         // P = softmax(S) [g_br, Bc]
+    __fp16 *     vtcm_d_tiles;         // Diagonal rescale [g_br, g_br]
+    HVX_Vector * vtcm_m_vec;           // Row max [g_br]
+    HVX_Vector * vtcm_l_vec;           // Row sum [g_br]
+    HVX_Vector * vtcm_s_rowmax;        // Softmax intermediate [g_br]
+    HVX_Vector * vtcm_p_rowsum;        // Softmax intermediate [g_br]
+    HVX_Vector * vtcm_row_bufs;        // Per-thread softmax row scratch [n_threads][2][Bc/64]
+    uint8_t *    vtcm_hmx_scales_id;   // HMX output scales (identity)
+    uint8_t *    vtcm_hmx_scales_qk;   // HMX output scales (qk_scale)
+    __fp16 *     vtcm_mask_buf;        // VTCM mask buffer [Br × m_line], DMA'd per KV block
+    __fp16 *     vtcm_slopes;          // ALiBi slopes [g_br]
+    size_t       row_buf_stride;       // HVX vectors per row buffer (Bc/64)
+    size_t       mask_buf_row_stride;  // elements (__fp16) per row in mask buffer
+    bool         mask_broadcast;       // true when mask->ne[2] == 1 (head-independent, single 2D DMA)
+};
+
+// ============================================================================
+// Multi-thread K interleave phase
+// ============================================================================
+
+typedef struct {
+    struct hmx_fa_context * factx;
+    int                     kv_rows;
+    size_t                  src_stride;
+    size_t                  buf_idx;
+} fa_k_int_args_t;
+
+static void fa_k_interleave_thread(unsigned int n, unsigned int i, void * data) {
+    fa_k_int_args_t *       args  = (fa_k_int_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const int total_rows = args->kv_rows;
+    const int rows_per_t = hex_align_up(hmx_ceil_div(total_rows, n), 2);  // ensure even (row pairs)
+    const int start      = i * rows_per_t;
+    const int end        = hex_smin(start + rows_per_t, total_rows);
+
+    if (start >= total_rows) {
+        return;
+    }
+
+    hmx_interleave_rows_to_tiles(factx->vtcm_k_tiles, factx->vtcm_k_fp16[args->buf_idx], total_rows, (int) factx->DK,
+                             (int) args->src_stride, start, end);
+}
+
+static void fa_phase_k_interleave(struct hmx_fa_context * factx, int kv_rows, size_t src_stride, size_t buf_idx) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    fa_k_int_args_t args = { factx, kv_rows, src_stride, buf_idx };
+    if (factx->n_threads > 1 && kv_rows >= (int) (factx->n_threads * 2)) {
+        worker_pool_run_func(wp, fa_k_interleave_thread, &args, factx->n_threads);
+    } else {
+        fa_k_interleave_thread(1, 0, &args);
+    }
+}
+
+// ============================================================================
+// Multi-thread V interleave phase
+// ============================================================================
+
+typedef struct {
+    struct hmx_fa_context * factx;
+    int                     kv_rows;
+    size_t                  src_stride;
+    size_t                  buf_idx;
+    size_t                  n_col_tiles;
+} fa_v_int_args_t;
+
+static void fa_v_interleave_thread(unsigned int n, unsigned int i, void * data) {
+    fa_v_int_args_t *       args  = (fa_v_int_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const int total_rows = args->kv_rows;
+    const int rows_per_t = hex_align_up(hmx_ceil_div(total_rows, n), 2);
+    const int start      = i * rows_per_t;
+    const int end        = hex_smin(start + rows_per_t, total_rows);
+
+    if (start >= total_rows) {
+        return;
+    }
+
+    hmx_interleave_cols_to_tiles(factx->vtcm_v_tiles, factx->vtcm_v_fp16[args->buf_idx], total_rows, (int) factx->DV,
+                             (int) args->src_stride, (int) args->n_col_tiles, start, end);
+}
+
+static void fa_phase_v_interleave(struct hmx_fa_context * factx,
+                                  int                     kv_rows,
+                                  size_t                  src_stride,
+                                  size_t                  buf_idx,
+                                  size_t                  n_col_tiles) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    fa_v_int_args_t args = { factx, kv_rows, src_stride, buf_idx, n_col_tiles };
+    if (factx->n_threads > 1 && kv_rows >= (int) (factx->n_threads * 2)) {
+        worker_pool_run_func(wp, fa_v_interleave_thread, &args, factx->n_threads);
+    } else {
+        fa_v_interleave_thread(1, 0, &args);
+    }
+}
+
+// ============================================================================
+// Multi-thread Q load phase: read Q[G × neq1, DK] from DDR, convert F32→F16
+// (or deal F16 pairs), and write interleaved into vtcm_q_tiles.
+// Each thread owns a disjoint range of row pairs; writes target distinct tile
+// slots (r0 selects tile row, r1 selects intra-tile slot), so there is no
+// write conflict.  Padding fill (when n_rows_g < g_br) is done single-threaded
+// by the caller before dispatching.
+// ============================================================================
+
+typedef struct {
+    struct hmx_fa_context *   factx;
+    const struct htp_tensor * q;
+    uint32_t                  q_start;
+    uint32_t                  kv_head;
+    uint32_t                  ib3;
+    size_t                    n_rows_g;
+} fa_q_load_args_t;
+
+static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) {
+    fa_q_load_args_t *      args  = (fa_q_load_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const size_t n_rows_g = args->n_rows_g;
+    const size_t G        = factx->G;
+    const size_t DK       = factx->DK;
+
+    // Partition row pairs across threads.  Keep each thread's start even so r/r+1
+    // are always in the same thread's range.
+    const size_t rows_per_t = hex_align_up(hmx_ceil_div(n_rows_g, n), 2);
+    const size_t start      = (size_t) i * rows_per_t;
+    const size_t end        = hex_smin(start + rows_per_t, n_rows_g);
+
+    if (start >= n_rows_g) {
+        return;
+    }
+
+    const struct htp_tensor * q       = args->q;
+    const uint32_t            q_start = args->q_start;
+    const uint32_t            kv_head = args->kv_head;
+    const uint32_t            ib3     = args->ib3;
+
+    for (size_t r = start; r < end; r += 2) {
+        const bool next_row_valid = (r + 1) < n_rows_g;
+
+        const size_t q_idx0 = (r + 0) / G;
+        const size_t h_idx0 = (r + 0) % G;
+        const size_t q_idx1 = (r + 1) / G;
+        const size_t h_idx1 = (r + 1) % G;
+
+        const uint8_t * q_ptr0 = (const uint8_t *) q->data + (q_start + q_idx0) * q->nb[1] +
+                                                  (kv_head * G + h_idx0) * q->nb[2] + ib3 * q->nb[3];
+        const uint8_t * q_ptr1 = next_row_valid ? ((const uint8_t *) q->data + (q_start + q_idx1) * q->nb[1] +
+                                                  (kv_head * G + h_idx1) * q->nb[2] + ib3 * q->nb[3]) :
+                                                  NULL;
+
+        size_t   r0       = r / HMX_FP16_TILE_N_ROWS;
+        size_t   r1       = r % HMX_FP16_TILE_N_ROWS;
+        __fp16 * out_base = factx->vtcm_q_tiles + r0 * HMX_FP16_TILE_N_ROWS * DK;
+
+        if (factx->is_q_fp32) {
+            const HVX_Vector * pv_in0 = (const HVX_Vector *) q_ptr0;
+            const HVX_Vector * pv_in1 = q_ptr1 ? (const HVX_Vector *) q_ptr1 : NULL;
+
+            for (uint32_t d = 0; d < DK / 32; ++d) {
+                HVX_Vector v0   = pv_in0[d];
+                HVX_Vector v1   = pv_in1 ? pv_in1[d] : Q6_V_vzero();
+                HVX_Vector v_hf = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+                HVX_Vector * out_tile = (HVX_Vector *) (out_base + d * HMX_FP16_TILE_N_ELMS);
+                out_tile[r1 / 2]      = v_hf;
+            }
+        } else {
+            const HVX_Vector * pv_in0 = (const HVX_Vector *) q_ptr0;
+            const HVX_Vector * pv_in1 = q_ptr1 ? (const HVX_Vector *) q_ptr1 : NULL;
+
+            for (uint32_t d = 0; d < DK / 64; ++d) {
+                HVX_Vector     v0 = pv_in0[d];
+                HVX_Vector     v1 = pv_in1 ? pv_in1[d] : Q6_V_vzero();
+                HVX_VectorPair vp = Q6_W_vshuff_VVR(v1, v0, -2);
+
+                __fp16 *     out_dual_tile = out_base + d * HMX_FP16_TILE_N_ELMS * 2;
+                HVX_Vector * pv_out0       = ((HVX_Vector *) out_dual_tile) + r1 / 2;
+                HVX_Vector * pv_out1       = pv_out0 + 16;
+
+                *pv_out0 = Q6_V_lo_W(vp);
+                *pv_out1 = Q6_V_hi_W(vp);
+            }
+        }
+    }
+}
+
+static void fa_phase_q_load(struct hmx_fa_context *   factx,
+                            const struct htp_tensor * q,
+                            uint32_t                  q_start,
+                            uint32_t                  kv_head,
+                            uint32_t                  ib3,
+                            size_t                    n_rows_g) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    fa_q_load_args_t args = { factx, q, q_start, kv_head, ib3, n_rows_g };
+    // Require >= 2 row pairs per thread so partitioning is worthwhile.
+    if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) {
+        worker_pool_run_func(wp, fa_q_load_thread, &args, factx->n_threads);
+    } else {
+        fa_q_load_thread(1, 0, &args);
+    }
+}
+
+// ============================================================================
+// Multi-thread O store phase: read O tiles from VTCM, convert F16->F32 (or
+// deal F16 pairs), and write to strided DDR dst tensor.  Each thread owns a
+// disjoint row range; writes target distinct dst rows (different q_idx/h_idx
+// pairs produced by r/G and r%G), so there is no write conflict.
+// ============================================================================
+
+typedef struct {
+    struct hmx_fa_context *   factx;
+    const struct htp_tensor * dst;
+    const __fp16 *            o_tile_src;
+    uint32_t                  q_start;
+    uint32_t                  kv_head;
+    uint32_t                  ib3;
+    size_t                    n_rows_g;
+} fa_o_store_args_t;
+
+static void fa_o_store_thread(unsigned int n, unsigned int i, void * data) {
+    fa_o_store_args_t *     args  = (fa_o_store_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const size_t n_rows_g = args->n_rows_g;
+    const size_t G        = factx->G;
+    const size_t DV       = factx->DV;
+
+    const size_t rows_per_t = hmx_ceil_div(n_rows_g, n);
+    const size_t start      = (size_t) i * rows_per_t;
+    const size_t end        = hex_smin(start + rows_per_t, n_rows_g);
+
+    if (start >= n_rows_g) {
+        return;
+    }
+
+    const struct htp_tensor * dst        = args->dst;
+    const __fp16 *            o_tile_src = args->o_tile_src;
+    const uint32_t            q_start    = args->q_start;
+    const uint32_t            kv_head    = args->kv_head;
+    const uint32_t            ib3        = args->ib3;
+
+    for (size_t r = start; r < end; ++r) {
+        const size_t q_idx = r / G;
+        const size_t h_idx = r % G;
+
+        // FIX(dst-indexing): ggml_flash_attn_ext() creates dst as permute(0,2,1,3) ->
+        // [DV, n_heads, n_tokens, n_seq], so head stride is nb[1] and token stride is nb[2].
+        uint8_t * dst_row = (uint8_t *) dst->data + (kv_head * G + h_idx) * dst->nb[1] +
+                            (q_start + q_idx) * dst->nb[2] + ib3 * dst->nb[3];
+
+        size_t         r0            = r / HMX_FP16_TILE_N_ROWS;
+        size_t         r1            = r % HMX_FP16_TILE_N_ROWS;
+        const __fp16 * tile_row_base = o_tile_src + r0 * HMX_FP16_TILE_N_ROWS * DV;
+
+        if (factx->is_dst_fp32) {
+            float * out = (float *) dst_row;
+            for (uint32_t d = 0; d < DV / 32; ++d) {
+                const HVX_Vector * in_tile = (const HVX_Vector *) (tile_row_base + d * HMX_FP16_TILE_N_ELMS);
+                HVX_VectorPair     vp      = hvx_vec_f16_to_f32_shuff(in_tile[r1 / 2]);
+                if (r1 % 2 == 0) {
+                    *(HVX_UVector *) (out + d * 32) = Q6_V_lo_W(vp);
+                } else {
+                    *(HVX_UVector *) (out + d * 32) = Q6_V_hi_W(vp);
+                }
+            }
+        } else {
+            __fp16 * out = (__fp16 *) dst_row;
+            for (uint32_t d = 0; d < DV / 64; ++d) {
+                const __fp16 *     in_dual_tile = tile_row_base + d * HMX_FP16_TILE_N_ELMS * 2;
+                const HVX_Vector * pv_in0       = ((const HVX_Vector *) in_dual_tile) + r1 / 2;
+                const HVX_Vector * pv_in1       = pv_in0 + 16;
+                HVX_VectorPair     vp           = Q6_W_vdeal_VVR(*pv_in1, *pv_in0, -2);
+                if (r1 % 2 == 0) {
+                    *(HVX_UVector *) (out + d * 64) = Q6_V_lo_W(vp);
+                } else {
+                    *(HVX_UVector *) (out + d * 64) = Q6_V_hi_W(vp);
+                }
+            }
+        }
+    }
+}
+
+static void fa_phase_o_store(struct hmx_fa_context *   factx,
+                             const struct htp_tensor * dst,
+                             const __fp16 *            o_tile_src,
+                             uint32_t                  q_start,
+                             uint32_t                  kv_head,
+                             uint32_t                  ib3,
+                             size_t                    n_rows_g) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    fa_o_store_args_t args = { factx, dst, o_tile_src, q_start, kv_head, ib3, n_rows_g };
+    if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) {
+        worker_pool_run_func(wp, fa_o_store_thread, &args, factx->n_threads);
+    } else {
+        fa_o_store_thread(1, 0, &args);
+    }
+}
+
+// ============================================================================
+// Multi-thread softmax phase + serial m/l update + build_D
+// ============================================================================
+
+typedef struct {
+    struct hmx_fa_context *   factx;
+    size_t                    kv_rows;
+    size_t                    n_rows_g;
+    size_t                    n_col_tiles;
+    size_t                    n_tiles_per_bc;
+    size_t                    n_row_tiles;
+    size_t                    n_row_tiles_g_br;
+    uint32_t                  Bc;
+    uint32_t                  G;
+    uint32_t                  kv_head;
+    uint32_t                  kv_start;
+    uint32_t                  q_start;
+    uint32_t                  ib3;
+    bool                      has_alibi;  // true when max_bias != 0 (need slope * mask + add)
+
+    // ALiBi per-head slopes (indexed by GQA-merged row: slope[r] for r in [0, n_rows_g))
+    // slope[r] = 1.0 when max_bias == 0 (no ALiBi)
+    // Pointer into hmx_fa_context.vtcm_slopes (sized to g_br)
+    __fp16 *                  slopes;
+
+    // Mask info (preloaded before softmax)
+    const struct htp_tensor * mask;
+    const __fp16 *            mask_vtcm;             // VTCM mask buffer base (NULL = DDR fallback)
+    size_t                    mask_vtcm_row_stride;  // elements (__fp16) per row in VTCM mask buffer
+} fa_softmax_args_t;
+
+static void fa_softmax_thread(unsigned int n, unsigned int i, void * data) {
+    fa_softmax_args_t *     args  = (fa_softmax_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const size_t n_rows_g       = args->n_rows_g;
+    const size_t kv_rows        = args->kv_rows;
+    const size_t Bc             = args->Bc;
+    const size_t G              = args->G;
+    const size_t n_tiles_per_bc = args->n_tiles_per_bc;
+    const size_t n_row_vec_cnt  = hmx_ceil_div(n_rows_g, 64);
+
+    // Partition r_vec_idx across threads
+    const size_t vecs_per_t = hmx_ceil_div(n_row_vec_cnt, n);
+    const size_t vec_start  = i * vecs_per_t;
+    const size_t vec_end    = hex_smin(vec_start + vecs_per_t, n_row_vec_cnt);
+
+    if (vec_start >= n_row_vec_cnt) {
+        return;
+    }
+
+    // Per-thread row scratch: thread i uses bufs at offset i * 2 * stride
+    const size_t row_buf_stride = factx->row_buf_stride;
+    HVX_Vector * my_row_buf0    = factx->vtcm_row_bufs + i * 2 * row_buf_stride;
+    HVX_Vector * my_row_buf1    = my_row_buf0 + row_buf_stride;
+
+    const HVX_Vector v_neg_inf = Q6_Vh_vsplat_R(0xfbff);
+
+    // Per-row accumulators: each fp16 lane in a 64-lane vector holds one row's scalar.
+    // CONTRACT: lane bits must be IEEE fp16 (hf), never qf16 — qf16 uses a different
+    // bit layout, so a later hf-domain read would silently produce wrong values.
+    // Convert first via Q6_Vhf_equals_Vqf16(). For reference: vtcm_m_vec/vtcm_s_rowmax
+    // are hf; vtcm_l_vec is qf16 — don't mix them up.
+
+    for (size_t r_vec_idx = vec_start; r_vec_idx < vec_end; ++r_vec_idx) {
+        HVX_Vector rowmax_acc_v = v_neg_inf;
+        HVX_Vector rowsum_acc_v = Q6_V_vzero();
+        HVX_Vector m_prev_v     = factx->vtcm_m_vec[r_vec_idx];
+
+        for (int r_vec_off = 0; r_vec_off < 64; r_vec_off += 2) {
+            int r = r_vec_idx * 64 + r_vec_off;
+            if (r >= (int) hex_align_up(n_rows_g, 2)) {
+                break;
+            }
+
+            int r0 = r / HMX_FP16_TILE_N_ROWS;
+            int r1 = r % HMX_FP16_TILE_N_ROWS;
+
+            const __fp16 * s_ld_base = factx->vtcm_s_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc;
+            __fp16 *       p_st_base = factx->vtcm_p_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc;
+
+            // Decode 2 rows from S tiles into per-thread row buffers
+            HVX_Vector * pv_row_buf0 = my_row_buf0;
+            HVX_Vector * pv_row_buf1 = my_row_buf1;
+            for (size_t c = 0; c < kv_rows; c += 64) {
+                const __fp16 *     in_dual_tile = s_ld_base + (c / 64) * HMX_FP16_TILE_N_ELMS * 2;
+                const HVX_Vector * pv_s_in0     = ((const HVX_Vector *) in_dual_tile) + r1 / 2;
+                const HVX_Vector * pv_s_in1     = pv_s_in0 + 16;
+
+                HVX_VectorPair vp_s_dual_row = Q6_W_vdeal_VVR(*pv_s_in1, *pv_s_in0, -2);
+                *pv_row_buf0++               = Q6_V_lo_W(vp_s_dual_row);
+                *pv_row_buf1++               = Q6_V_hi_W(vp_s_dual_row);
+            }
+
+            // Apply softcap if enabled (in F32 precision)
+            if (factx->logit_softcap != 0.0f) {
+                // When EXP2_HF is on, fold log2(e) into v_cap so the output lands in
+                // log2(e)-scaled space for the downstream exp2.  log2(e) is kept OUT
+                // of qk_scale in this configuration (see scale setup) so tanh sees
+                // the physical QK/(√d·c) argument.
+                float cap = factx->logit_softcap;
+#ifdef HMX_FA_USE_EXP2_HF
+                cap *= 1.44269504f;  // log2(e)
+#endif
+                const HVX_Vector v_cap = hvx_vec_splat_f32(cap);
+                for (size_t c = 0; c < kv_rows; c += 64) {
+                    size_t ci = c / 64;
+
+                    HVX_VectorPair r0_f32 = hvx_vec_f16_to_f32(my_row_buf0[ci]);
+                    HVX_Vector     t0_lo  = hvx_vec_tanh_f32(Q6_V_lo_W(r0_f32));
+                    HVX_Vector     t0_hi  = hvx_vec_tanh_f32(Q6_V_hi_W(r0_f32));
+                    t0_lo                 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(t0_lo, v_cap));
+                    t0_hi                 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(t0_hi, v_cap));
+                    my_row_buf0[ci]       = hvx_vec_f32_to_f16(t0_lo, t0_hi);
+
+                    HVX_VectorPair r1_f32 = hvx_vec_f16_to_f32(my_row_buf1[ci]);
+                    HVX_Vector     t1_lo  = hvx_vec_tanh_f32(Q6_V_lo_W(r1_f32));
+                    HVX_Vector     t1_hi  = hvx_vec_tanh_f32(Q6_V_hi_W(r1_f32));
+                    t1_lo                 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(t1_lo, v_cap));
+                    t1_hi                 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(t1_hi, v_cap));
+                    my_row_buf1[ci]       = hvx_vec_f32_to_f16(t1_lo, t1_hi);
+                }
+            }
+
+            // Apply mask & compute rowmax(S)
+            //
+            // Optimizations over baseline:
+            //   A. No-ALiBi fast path: when max_bias==0 (slope≡1.0), skip the
+            //      slope multiplication — still add mask (additive bias) but
+            //      avoid the mul_f16_f16.  Saves 2 ops/dual-row vs ALiBi path.
+            //   B. GQA mask row dedup: G consecutive Q rows share one mask row
+            //      (qi = r / G).  Reuse mask vector when qi is unchanged between
+            //      row0 and row1 (saves ~75% of VTCM loads for G=4).
+
+            // ALiBi slopes — only needed when has_alibi (scheme A)
+            HVX_Vector v_slope0, v_slope1;
+            if (args->has_alibi) {
+                HVX_Vector v_s = hvx_vmemu(args->slopes + r);
+                v_slope0 = hvx_vec_repl_f16(v_s);
+                v_slope1 = (r + 1 < (int) n_rows_g) ? hvx_vec_repl_f16(Q6_V_vror_VR(v_s, 2)) : Q6_V_vzero();
+            }
+
+            const HVX_Vector v_threshold = Q6_Vh_vsplat_R(0xcc00);  // fp16 -16.0 (hoisted outside for-c)
+
+            HVX_Vector v_s_rowmax0 = v_neg_inf;
+            HVX_Vector v_s_rowmax1 = v_neg_inf;
+            for (size_t c = 0; c < kv_rows; c += 64) {
+                size_t         ci          = c / 64;
+                const size_t   ne          = hex_smin(kv_rows - c, 64);
+                HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16));
+
+                if (args->mask) {
+                    HVX_Vector v_mask0, v_mask1;
+
+                    if (args->mask_vtcm) {
+                        // Read mask from VTCM buffer (DMA'd per KV block).
+                        // GQA dedup (scheme B): skip load when qi unchanged.
+                        const size_t qi0 = (r + 0) / G;
+                        v_mask0 = *(const HVX_UVector *) (args->mask_vtcm + qi0 * args->mask_vtcm_row_stride + c);
+                        v_mask1 = v_neg_inf;
+                        if (r + 1 < (int) n_rows_g) {
+                            const size_t qi1 = (r + 1) / G;
+                            if (qi1 == qi0) {
+                                v_mask1 = v_mask0;  // scheme B: reuse — same mask row
+                            } else {
+                                v_mask1 = *(const HVX_UVector *) (args->mask_vtcm + qi1 * args->mask_vtcm_row_stride + c);
+                            }
+                        }
+                    } else {
+                        // Fallback: read mask directly from DDR (when mask->ne[2] > 1).
+                        const struct htp_tensor * mask   = args->mask;
+                        const size_t              q_idx0 = args->q_start + ((r + 0) / G);
+                        const size_t              h_idx0 = args->kv_head * G + (r + 0) % G;
+                        const uint32_t            im2_0  = h_idx0 % mask->ne[2];
+                        const uint32_t            im3_0  = args->ib3 % mask->ne[3];
+
+                        const __fp16 * m0_ptr = (const __fp16 *) ((const uint8_t *) mask->data + q_idx0 * mask->nb[1] +
+                                                        im2_0 * mask->nb[2] + im3_0 * mask->nb[3]) + args->kv_start + c;
+                        v_mask0 = *(const HVX_UVector *) m0_ptr;
+                        v_mask1 = v_neg_inf;
+
+                        if (r + 1 < (int) n_rows_g) {
+                            const size_t q_idx1 = args->q_start + ((r + 1) / G);
+                            if (q_idx1 == q_idx0) {
+                                // scheme B: same mask row in DDR path
+                                v_mask1 = v_mask0;
+                            } else {
+                                const size_t   h_idx1 = args->kv_head * G + (r + 1) % G;
+                                const uint32_t im2_1  = h_idx1 % mask->ne[2];
+                                const uint32_t im3_1  = args->ib3 % mask->ne[3];
+                                const __fp16 * m1_ptr = (const __fp16 *) ((const uint8_t *) mask->data + q_idx1 * mask->nb[1] +
+                                                                im2_1 * mask->nb[2] + im3_1 * mask->nb[3]) + args->kv_start + c;
+                                v_mask1 = *(const HVX_UVector *) m1_ptr;
+                            }
+                        }
+                    }
+
+                    // Threshold: mask values below -16.0 are treated as -inf (causal mask).
+                    HVX_VectorPred q_keep0 = Q6_Q_and_QQ(Q6_Q_vcmp_gt_VhfVhf(v_mask0, v_threshold), q_tail_keep);
+                    HVX_VectorPred q_keep1 = Q6_Q_and_QQ(Q6_Q_vcmp_gt_VhfVhf(v_mask1, v_threshold), q_tail_keep);
+
+                    if (args->has_alibi) {
+                        // ALiBi path: S += slope * mask (full mul + add)
+                        HVX_Vector v_sm0 = hvx_vec_mul_f16_f16(v_mask0, v_slope0);
+                        HVX_Vector v_sm1 = hvx_vec_mul_f16_f16(v_mask1, v_slope1);
+                        my_row_buf0[ci]  = Q6_V_vmux_QVV(q_keep0, hvx_vec_add_f16_f16(my_row_buf0[ci], v_sm0), v_neg_inf);
+                        my_row_buf1[ci]  = Q6_V_vmux_QVV(q_keep1, hvx_vec_add_f16_f16(my_row_buf1[ci], v_sm1), v_neg_inf);
+                    } else {
+                        // No-ALiBi fast path (scheme A): slope≡1.0, skip the mul
+                        // but still add mask (additive positional bias).  vmux
+                        // clamps mask < -16 to -inf as a numerical safeguard.
+                        my_row_buf0[ci] = Q6_V_vmux_QVV(q_keep0, hvx_vec_add_f16_f16(my_row_buf0[ci], v_mask0), v_neg_inf);
+                        my_row_buf1[ci] = Q6_V_vmux_QVV(q_keep1, hvx_vec_add_f16_f16(my_row_buf1[ci], v_mask1), v_neg_inf);
+                    }
+                } else {
+                    if (ne < 64) {
+                        my_row_buf0[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf0[ci], v_neg_inf);
+                        my_row_buf1[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf1[ci], v_neg_inf);
+                    }
+                }
+
+                v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]);
+                v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]);
+            }
+
+            v_s_rowmax0 = hvx_vec_reduce_max_f16(v_s_rowmax0);
+            v_s_rowmax1 = hvx_vec_reduce_max_f16(v_s_rowmax1);
+
+            // Splat m_prev[r], m_prev[r+1] from the per-row accumulator.
+            // vror brings the target lane to lane 0, then extract + re-splat.
+            HVX_Vector v_m_prev0 = hvx_vec_splat_f16(hvx_vec_get_f16(Q6_V_vror_VR(m_prev_v, r_vec_off * 2)));
+            HVX_Vector v_m_prev1 = hvx_vec_splat_f16(hvx_vec_get_f16(Q6_V_vror_VR(m_prev_v, (r_vec_off + 1) * 2)));
+
+            // HVX max — both operands are splats, so result is splat of m_new.
+            HVX_Vector v_dup_m0 = Q6_Vhf_vmax_VhfVhf(v_m_prev0, v_s_rowmax0);
+            HVX_Vector v_dup_m1 = Q6_Vhf_vmax_VhfVhf(v_m_prev1, v_s_rowmax1);
+
+            // Insert row r, r+1 rowmax into rowmax_acc_v via 2-byte-wide vmux.
+            // Byte ranges: lane0 = [r_vec_off*2 .. r_vec_off*2+1], lane1 shifted by 2.
+            // vsetq2 handles the n=128 corner case when r_vec_off reaches 62.
+            {
+                HVX_VectorPred p_start = Q6_Q_vsetq_R(r_vec_off * 2);
+                HVX_VectorPred p_mid   = Q6_Q_vsetq_R((r_vec_off + 1) * 2);
+                HVX_VectorPred p_end   = Q6_Q_vsetq2_R((r_vec_off + 2) * 2);
+                HVX_VectorPred p_lane0 = Q6_Q_and_QQn(p_mid, p_start);
+                HVX_VectorPred p_lane1 = Q6_Q_and_QQn(p_end, p_mid);
+                rowmax_acc_v           = Q6_V_vmux_QVV(p_lane0, v_dup_m0, rowmax_acc_v);
+                rowmax_acc_v           = Q6_V_vmux_QVV(p_lane1, v_dup_m1, rowmax_acc_v);
+            }
+
+            // Compute P = exp(S - m_new), using HVX exp
+            const HVX_Vector v_zero      = Q6_V_vzero();
+            HVX_Vector       v_p_rowsum0 = v_zero;
+            HVX_Vector       v_p_rowsum1 = v_zero;
+
+#ifdef HMX_FA_USE_EXP2_HF
+            // FP16 exp2 polynomial path (matches htp-ops-lib flash_attn.c):
+            // P = exp2(S - m_new)
+            for (size_t c = 0; c < kv_rows; c += 64) {
+                size_t     ci           = c / 64;
+                HVX_Vector v_s_minus_m0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci], v_dup_m0);
+                HVX_Vector v_s_minus_m1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci], v_dup_m1);
+
+                HVX_Vector v_p_row0_hf  = hvx_exp2_hf(Q6_Vhf_equals_Vqf16(v_s_minus_m0));
+                HVX_Vector v_p_row1_hf  = hvx_exp2_hf(Q6_Vhf_equals_Vqf16(v_s_minus_m1));
+#else
+            // F32 exp path: qf16 → f32 → exp → f32 → f16.  Higher precision,
+            for (size_t c = 0; c < kv_rows; c += 64) {
+                size_t     ci           = c / 64;
+                HVX_Vector v_s_minus_m0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci], v_dup_m0);
+                HVX_Vector v_s_minus_m1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci], v_dup_m1);
+
+                HVX_VectorPair vp0         = hvx_vec_f16_to_f32_shuff(Q6_Vhf_equals_Vqf16(v_s_minus_m0));
+                HVX_Vector     p0_lo       = hvx_vec_exp_f32(Q6_V_lo_W(vp0));
+                HVX_Vector     p0_hi       = hvx_vec_exp_f32(Q6_V_hi_W(vp0));
+                HVX_Vector     v_p_row0_hf = hvx_vec_f32_to_f16_shuff(p0_lo, p0_hi);
+
+                HVX_VectorPair vp1         = hvx_vec_f16_to_f32_shuff(Q6_Vhf_equals_Vqf16(v_s_minus_m1));
+                HVX_Vector     p1_lo       = hvx_vec_exp_f32(Q6_V_lo_W(vp1));
+                HVX_Vector     p1_hi       = hvx_vec_exp_f32(Q6_V_hi_W(vp1));
+                HVX_Vector     v_p_row1_hf = hvx_vec_f32_to_f16_shuff(p1_lo, p1_hi);
+#endif
+                // Write P to tile format.  Dual-tile pattern assumes Bc is a
+                // multiple of 64 (enforced by bc_unit=64 in hmx_fa_find_chunk_size),
+                // so both tile halves are always in the current r0 block.
+                __fp16 *     out_dual_tile = p_st_base + (c / 64) * HMX_FP16_TILE_N_ELMS * 2;
+                HVX_Vector * pv_p_out0     = ((HVX_Vector *) out_dual_tile) + r1 / 2;
+                HVX_Vector * pv_p_out1     = pv_p_out0 + 16;
+
+                HVX_VectorPair vp_p_dual = Q6_W_vshuff_VVR(v_p_row1_hf, v_p_row0_hf, -2);
+                *pv_p_out0               = Q6_V_lo_W(vp_p_dual);
+                *pv_p_out1               = Q6_V_hi_W(vp_p_dual);
+
+                HVX_VectorPair vp_p0 = hvx_vec_f16_to_f32_shuff(v_p_row0_hf);
+                HVX_VectorPair vp_p1 = hvx_vec_f16_to_f32_shuff(v_p_row1_hf);
+
+                v_p_rowsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum0, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p0), Q6_V_hi_W(vp_p0)));
+                v_p_rowsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum1, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p1), Q6_V_hi_W(vp_p1)));
+            }
+
+            HVX_Vector rowsum0_sf = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(v_p_rowsum0));
+            HVX_Vector rowsum1_sf = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(v_p_rowsum1));
+            {
+                // Both inputs are f32 splats, so the f32->f16 output is an fp16 splat.
+                HVX_Vector rv0_v = hvx_vec_f32_to_f16(rowsum0_sf, rowsum0_sf);
+                HVX_Vector rv1_v = hvx_vec_f32_to_f16(rowsum1_sf, rowsum1_sf);
+
+                HVX_VectorPred p_start = Q6_Q_vsetq_R(r_vec_off * 2);
+                HVX_VectorPred p_mid   = Q6_Q_vsetq_R((r_vec_off + 1) * 2);
+                HVX_VectorPred p_end   = Q6_Q_vsetq2_R((r_vec_off + 2) * 2);
+                HVX_VectorPred p_lane0 = Q6_Q_and_QQn(p_mid, p_start);
+                HVX_VectorPred p_lane1 = Q6_Q_and_QQn(p_end, p_mid);
+                rowsum_acc_v           = Q6_V_vmux_QVV(p_lane0, rv0_v, rowsum_acc_v);
+                rowsum_acc_v           = Q6_V_vmux_QVV(p_lane1, rv1_v, rowsum_acc_v);
+            }
+        }
+
+        factx->vtcm_s_rowmax[r_vec_idx] = rowmax_acc_v;
+        factx->vtcm_p_rowsum[r_vec_idx] = rowsum_acc_v;
+    }
+}
+
+// Serial m/l update + build_D.  Must run after softmax barrier (s_rowmax written by all threads).
+//
+// noinline: function boundary acts as a hard compiler barrier so the (size_t)addr scatter
+// intrinsics inside cannot be hoisted past the call site.  Mirrors the structural protection
+// matmul gets for free via worker_pool function-pointer dispatch.  Without this, the compiler
+// can reorder the scatter past the subsequent hmx_queue_push and the HMX-queue worker thread
+// reads stale VTCM (PPL → ~vocab-size).
+static __attribute__((noinline)) void fa_ml_update_and_build_d(struct hmx_fa_context * factx,
+                                                               size_t                  n_rows_g,
+                                                               size_t                  n_row_tiles,
+                                                               size_t                  n_row_tiles_g_br) {
+    // Reuse s_rowmax buffer for exp(m_diff) — safe because softmax is fully complete
+    HVX_Vector * const mvec_exp_m_diff = factx->vtcm_s_rowmax;
+
+    const size_t n_row_vec_cnt = hmx_ceil_div(n_rows_g, 64);
+    for (size_t i = 0; i < n_row_vec_cnt; ++i) {
+        HVX_Vector v_m_prev = factx->vtcm_m_vec[i];
+        HVX_Vector v_m_curr = Q6_Vhf_vmax_VhfVhf(v_m_prev, factx->vtcm_s_rowmax[i]);
+        HVX_Vector v_m_diff = Q6_Vqf16_vsub_VhfVhf(v_m_prev, v_m_curr);
+
+#ifdef HMX_FA_USE_EXP2_HF
+        // Base-2 path: must match P = exp2(S - m_new) in fa_softmax_thread.
+        HVX_Vector v_exp_m_diff      = hvx_exp2_hf(Q6_Vhf_equals_Vqf16(v_m_diff));
+#else
+        HVX_VectorPair vp_diff       = hvx_vec_f16_to_f32_shuff(Q6_Vhf_equals_Vqf16(v_m_diff));
+        HVX_Vector     exp_lo        = hvx_vec_exp_f32(Q6_V_lo_W(vp_diff));
+        HVX_Vector     exp_hi        = hvx_vec_exp_f32(Q6_V_hi_W(vp_diff));
+        HVX_Vector     v_exp_m_diff  = hvx_vec_f32_to_f16_shuff(exp_lo, exp_hi);
+#endif
+
+        HVX_Vector v_l_curr = Q6_Vqf16_vmpy_Vqf16Vhf(factx->vtcm_l_vec[i], v_exp_m_diff);
+        v_l_curr            = Q6_Vqf16_vadd_Vqf16Vhf(v_l_curr, factx->vtcm_p_rowsum[i]);
+
+        factx->vtcm_m_vec[i] = v_m_curr;
+        factx->vtcm_l_vec[i] = v_l_curr;
+        mvec_exp_m_diff[i]   = v_exp_m_diff;
+    }
+
+    // Build diagonal tile D = diag(exp(m_diff))
+    const HVX_Vector     v_offsets = *(const HVX_Vector *) d_tile_scatter_offsets;
+    const HVX_VectorPred q_32_mask = Q6_Q_vsetq_R(32 * sizeof(__fp16));
+    for (size_t i = 0; i < n_row_tiles; ++i) {
+        const HVX_Vector v_content = Q6_V_vror_VR(mvec_exp_m_diff[i / 2], (i % 2) * 64);
+        __fp16 *         out_base  = factx->vtcm_d_tiles + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+        Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content);
+        // Compiler barrier — Q6_vscatter takes (size_t)addr; without this the
+        // compiler may not recognize the volatile read below as aliasing and
+        // could reorder it before the scatter, defeating the HW drain.
+        __asm__ __volatile__("" ::: "memory");
+        // Per-tile drain: scatter regions are disjoint (stride > tile size),
+        // so a single drain at tile 0 does NOT retire later tiles' entries.
+        (void) *(volatile HVX_Vector *) out_base;
+    }
+}
+
+// Build D = diag(1/l) tile for the final O = D @ O normalization.
+//
+// noinline: same rationale as fa_ml_update_and_build_d — keeps Q6_vscatter from
+// being hoisted past the subsequent hmx_queue_push at the o_norm call site.
+static __attribute__((noinline)) void fa_build_d_diag_inv_l(struct hmx_fa_context * factx,
+                                                            size_t                  n_row_tiles,
+                                                            size_t                  n_row_tiles_g_br) {
+    const HVX_Vector     v_offsets = *(const HVX_Vector *) d_tile_scatter_offsets;
+    const HVX_VectorPred q_32_mask = Q6_Q_vsetq_R(32 * sizeof(__fp16));
+    const HVX_Vector     one       = hvx_vec_splat_f32(1.0f);
+
+    HVX_Vector v_content = Q6_V_vzero();
+    for (size_t i = 0; i < n_row_tiles; ++i) {
+        if ((i % 2) == 0) {
+            HVX_Vector     v_l_hf = Q6_Vhf_equals_Vqf16(factx->vtcm_l_vec[i / 2]);
+            HVX_VectorPair vp_l   = hvx_vec_f16_to_f32_shuff(v_l_hf);
+            HVX_Vector     inv_lo = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(one, hvx_vec_inverse_f32(Q6_V_lo_W(vp_l))));
+            HVX_Vector     inv_hi = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(one, hvx_vec_inverse_f32(Q6_V_hi_W(vp_l))));
+            v_content = hvx_vec_f32_to_f16_shuff(inv_lo, inv_hi);
+        } else {
+            v_content = Q6_V_vror_VR(v_content, 64);
+        }
+
+        __fp16 * out_base = factx->vtcm_d_tiles + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+        Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content);
+        // Compiler barrier — see fa_ml_update_and_build_d for rationale.
+        __asm__ __volatile__("" ::: "memory");
+        (void) *(volatile HVX_Vector *) out_base;
+    }
+}
+
+// Combined: multi-thread softmax -> barrier -> serial m/l update + build_D
+static void fa_phase_softmax_and_build_d(struct hmx_fa_context * factx,
+                                         fa_softmax_args_t *     sargs,
+                                         size_t                  n_row_tiles,
+                                         size_t                  n_row_tiles_g_br) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    const size_t n_row_vec_cnt = hmx_ceil_div(sargs->n_rows_g, 64);
+
+    if (factx->n_threads > 1 && n_row_vec_cnt >= 2) {
+        uint32_t n_use = (uint32_t) hex_smin((size_t) factx->n_threads, n_row_vec_cnt);
+        worker_pool_run_func(wp, fa_softmax_thread, sargs, n_use);
+    } else {
+        fa_softmax_thread(1, 0, sargs);
+    }
+    // barrier implicit in worker_pool_run_func return
+
+    fa_ml_update_and_build_d(factx, sargs->n_rows_g, n_row_tiles, n_row_tiles_g_br);
+}
+
+// ============================================================================
+// HMX job structs and worker functions
+// ============================================================================
+
+typedef struct {
+    const __fp16 * q_tiles;
+    const __fp16 * k_tiles;
+    __fp16 *       s_tiles;
+    size_t         n_row_tiles;
+    size_t         n_col_tiles;
+    size_t         n_dot_tiles;  // DK / 32
+    size_t         n_tiles_per_bc;
+    uint8_t *      hmx_scales;
+} hmx_fa_qk_job_t;
+
+static void hmx_fa_qk_dot_worker(void * data) {
+    hmx_fa_qk_job_t * job            = (hmx_fa_qk_job_t *) data;
+    const size_t      n_row_tiles    = job->n_row_tiles;
+    const size_t      n_col_tiles    = job->n_col_tiles;
+    const size_t      n_dot_tiles    = job->n_dot_tiles;
+    const size_t      n_tiles_per_bc = job->n_tiles_per_bc;
+    const __fp16 * restrict q_tiles  = job->q_tiles;
+    const __fp16 * restrict k_tiles  = job->k_tiles;
+    __fp16 * restrict s_tiles        = job->s_tiles;
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(n_dot_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *) job->hmx_scales);
+    for (size_t r = 0; r < n_row_tiles; ++r) {
+        for (size_t c = 0; c < n_col_tiles; ++c) {
+            const __fp16 * row_tiles = q_tiles + r * HMX_FP16_TILE_N_ROWS * n_dot_tiles * HMX_FP16_TILE_N_COLS;
+            const __fp16 * col_tiles = k_tiles + c * HMX_FP16_TILE_N_COLS * n_dot_tiles * HMX_FP16_TILE_N_COLS;
+            __fp16 *       out_tile  = s_tiles + (r * n_tiles_per_bc + c) * HMX_FP16_TILE_N_ELMS;
+
+            for (size_t k = 0; k < n_dot_tiles; ++k) {
+                Q6_activation_hf_mxmem_RR((unsigned int) row_tiles, 2047);
+                Q6_weight_hf_mxmem_RR((unsigned int) col_tiles, 2047);
+                row_tiles += HMX_FP16_TILE_N_ELMS;
+                col_tiles += HMX_FP16_TILE_N_ELMS;
+            }
+            Q6_mxmem_AR_after_hf(out_tile, 0);
+        }
+    }
+}
+
+typedef struct {
+    __fp16 *       o_curr;
+    const __fp16 * o_prev;
+    const __fp16 * p_tiles;
+    const __fp16 * v_tiles;
+    const __fp16 * d_tiles;
+    uint8_t *      hmx_scales;
+    size_t         n_row_tiles;
+    size_t         n_col_tiles;
+    size_t         n_row_tiles_g_br;
+    size_t         n_tiles_per_bc;
+    size_t         DV;
+} hmx_fa_o_update_job_t;
+
+static void hmx_fa_o_update_worker(void * data) {
+    hmx_fa_o_update_job_t * job              = (hmx_fa_o_update_job_t *) data;
+    const size_t            n_row_tiles      = job->n_row_tiles;
+    const size_t            n_col_tiles      = job->n_col_tiles;
+    const size_t            n_row_tiles_g_br = job->n_row_tiles_g_br;
+    const size_t            n_tiles_per_bc   = job->n_tiles_per_bc;
+    const size_t            DV_tiles         = job->DV / 32;
+    const __fp16 * restrict d_tiles          = job->d_tiles;
+    const __fp16 * restrict p_tiles          = job->p_tiles;
+    const __fp16 * restrict v_tiles          = job->v_tiles;
+    const __fp16 * restrict o_prev           = job->o_prev;
+    __fp16 * restrict o_curr                 = job->o_curr;
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(DV_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *) job->hmx_scales);
+    for (size_t r = 0; r < n_row_tiles; ++r) {
+        for (size_t c = 0; c < DV_tiles; ++c) {
+            // D[r,r] @ O_prev[r,c] — only the diagonal tile
+            const __fp16 * d_diag = d_tiles + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+            const __fp16 * o_rc   = o_prev + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+            Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
+            Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
+
+            // P @ V (accumulate on same accumulator)
+            const __fp16 * p_tile_in = p_tiles + (r * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
+            const __fp16 * v_tile_in = v_tiles + (c * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
+            for (size_t k = 0; k < n_col_tiles; ++k) {
+                Q6_activation_hf_mxmem_RR((unsigned int) p_tile_in, 2047);
+                Q6_weight_hf_mxmem_RR((unsigned int) v_tile_in, 2047);
+                p_tile_in += HMX_FP16_TILE_N_ELMS;
+                v_tile_in += HMX_FP16_TILE_N_ELMS;
+            }
+
+            __fp16 * o_tile_out = o_curr + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+            Q6_mxmem_AR_after_hf(o_tile_out, 0);
+        }
+    }
+}
+
+typedef struct {
+    __fp16 *       o_curr;   // output (row-major tile layout)
+    const __fp16 * o_prev;   // input (column-major tile layout)
+    const __fp16 * d_tiles;  // diag(1/l) tiles
+    uint8_t *      hmx_scales;
+    size_t         n_row_tiles;
+    size_t         n_row_tiles_g_br;
+    size_t         DV;
+} hmx_fa_o_norm_job_t;
+
+static void hmx_fa_o_norm_worker(void * data) {
+    hmx_fa_o_norm_job_t * job              = (hmx_fa_o_norm_job_t *) data;
+    const size_t          n_row_tiles      = job->n_row_tiles;
+    const size_t          n_row_tiles_g_br = job->n_row_tiles_g_br;
+    const size_t          DV_tiles         = job->DV / 32;
+    const __fp16 * restrict d_tiles        = job->d_tiles;
+    const __fp16 * restrict o_prev         = job->o_prev;
+    __fp16 * restrict o_curr               = job->o_curr;
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(DV_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *) job->hmx_scales);
+    for (size_t r = 0; r < n_row_tiles; ++r) {
+        for (size_t c = 0; c < DV_tiles; ++c) {
+            const __fp16 * d_diag = d_tiles + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+            const __fp16 * o_rc   = o_prev + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+            __fp16 *       o_out  = o_curr + (r * DV_tiles + c) * HMX_FP16_TILE_N_ELMS;
+
+            Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
+            Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
+            Q6_mxmem_AR_after_hf(o_out, 0);
+        }
+    }
+}
+
+// Populate per-GQA-row ALiBi slopes for a given KV head.
+// Row r in the GQA-merged block maps to Q head h = kv_head * G + r % G.
+// slope(h) = m0^(h+1) when h < n_head_log2, else m1^(2*(h-n_head_log2)+1).
+// When max_bias == 0, all slopes are 1.0 (no ALiBi).
+static __attribute__((noinline)) void fa_compute_slopes(fa_softmax_args_t * sargs,
+                              const struct hmx_fa_context * factx,
+                              uint32_t                      kv_head,
+                              size_t                        n_rows_g) {
+    if (factx->max_bias == 0.0f) {
+        for (size_t r = 0; r < n_rows_g; ++r) {
+            sargs->slopes[r] = 1.0f;
+        }
+        return;
+    }
+
+    const uint32_t G           = factx->G;
+    const uint32_t n_head_log2 = factx->n_head_log2;
+    const float    m0          = factx->m0;
+    const float    m1          = factx->m1;
+
+    for (size_t r = 0; r < n_rows_g; ++r) {
+        const uint32_t h = kv_head * G + r % G;
+        sargs->slopes[r] = (h < n_head_log2) ? powf(m0, h + 1) : powf(m1, 2 * (h - n_head_log2) + 1);
+    }
+}
+
+// ============================================================================
+// Core HMX flash attention algorithm (GQA-merged)
+// ============================================================================
+
+int hmx_flash_attn_ext(struct htp_ops_context * octx) {
+    const struct htp_tensor * q    = octx->src[0];
+    const struct htp_tensor * k    = octx->src[1];
+    const struct htp_tensor * v    = octx->src[2];
+    const struct htp_tensor * mask = (octx->src[3] && octx->src[3]->data) ? octx->src[3] : NULL;
+    const struct htp_tensor * dst  = octx->dst;
+
+    struct htp_context * const ctx = octx->ctx;
+
+    if (!ctx->hmx_enabled) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    // Dimensions
+    const uint32_t neq0 = q->ne[0];  // head_dim (DK)
+    const uint32_t neq1 = q->ne[1];  // n_tokens
+    const uint32_t neq2 = q->ne[2];  // n_heads
+    const uint32_t neq3 = q->ne[3];  // n_seqs
+
+    const uint32_t nek0 = k->ne[0];  // head_dim
+    const uint32_t nek1 = k->ne[1];  // kv_len
+
+    const uint32_t nev0 = v->ne[0];  // head_dim (DV)
+
+    const uint32_t DK = neq0;
+    const uint32_t DV = nev0;
+
+    // HMX requires head_dim to be multiple of 32
+    if (DK % 32 != 0 || DV % 32 != 0) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+    if (neq1 < 32) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    // GQA factor
+    const uint32_t n_kv_heads = k->ne[2];
+    const uint32_t G          = neq2 / n_kv_heads;
+
+    // Thread count for multi-thread HVX phases
+    const uint32_t n_threads = octx->n_threads;
+
+    // Compute dynamic block sizes (GQA-aware, accounting for per-thread row bufs)
+    size_t       Br, Bc;
+    const size_t vtcm_budget = ctx->vtcm_size;
+    if (hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, vtcm_budget, n_threads) != 0) {
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    const size_t g_br = hex_align_up(G * Br, HMX_FP16_TILE_N_ROWS);
+
+    const uint32_t n_kv_blocks  = (nek1 + Bc - 1) / Bc;
+    const bool     use_pipeline = (n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads >= 2);
+
+    FARF(HIGH, "hmx-fa: neq1=%u nek1=%u DK=%u DV=%u G=%u Br=%zu Bc=%zu g_br=%zu n_kv_blocks=%u pipeline=%d vtcm=%zu",
+         neq1, nek1, DK, DV, G, Br, Bc, g_br, n_kv_blocks, use_pipeline, vtcm_budget);
+
+    // ======== Build context ========
+    struct hmx_fa_context factx;
+    memset(&factx, 0, sizeof(factx));
+    factx.octx           = octx;
+    factx.n_threads      = octx->ctx->n_threads;
+    factx.DK             = DK;
+    factx.DV             = DV;
+    factx.n_kv           = nek1;
+    factx.n_kv_heads     = n_kv_heads;
+    factx.n_heads        = neq2;
+    factx.G              = G;
+    factx.neq1           = neq1;
+    factx.Br             = (uint32_t) Br;
+    factx.Bc             = (uint32_t) Bc;
+    factx.g_br           = (uint32_t) g_br;
+    factx.n_kv_blocks    = n_kv_blocks;
+    factx.is_q_fp32      = (q->type == HTP_TYPE_F32);
+    factx.is_dst_fp32    = (dst->type == HTP_TYPE_F32);
+    factx.use_pipeline   = use_pipeline;
+    factx.mask_broadcast = (mask != NULL && mask->ne[2] == 1);
+
+    // Extract op parameters (mutable during softcap adjustment, then stored as const in factx)
+    float scale = 1.0f, max_bias = 0.0f, logit_softcap = 0.0f;
+    memcpy(&scale, (float *) octx->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) octx->op_params + 1, sizeof(float));
+    memcpy(&logit_softcap, (float *) octx->op_params + 2, sizeof(float));
+
+    if (logit_softcap != 0.0f) {
+        scale /= logit_softcap;
+    }
+
+#ifdef HMX_FA_USE_EXP2_HF
+    // Pre-bake log2(e) into qk_scale so HMX-produced S tiles are in log2(e)-scaled
+    // space.  Then exp2(S - m) in the softmax equals base-e exp((S - m) / log2(e)),
+    // preserving ggml's base-e softmax semantics.  Matches htp-ops-lib flash_attn.c.
+    //
+    // When softcap is active we cannot pre-bake log2(e) here — it would land inside
+    // the tanh argument and shift the softcap knee from x≈c to x≈c/log2(e), giving
+    // numerically wrong softcapped values.  Instead fold log2(e) into the post-tanh
+    // multiplier (see softcap block: v_cap absorbs log2(e)).
+    if (logit_softcap == 0.0f) {
+        scale *= 1.44269504f;  // log2(e)
+    }
+#endif
+
+    factx.scale         = scale;
+    factx.max_bias      = max_bias;
+    factx.logit_softcap = logit_softcap;
+
+    factx.n_head_log2 = 1u << (uint32_t) floor(log2(neq2));
+    factx.m0          = powf(2.0f, -(max_bias) / factx.n_head_log2);
+    factx.m1          = powf(2.0f, -(max_bias / 2.0f) / factx.n_head_log2);
+
+    // ======== VTCM allocation (GQA-aware) ========
+    const size_t q_tile_bytes  = hex_align_up(g_br * DK * sizeof(__fp16), 4096);
+    const size_t o_tile_bytes  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);
+    const size_t k_dma_bytes   = hex_align_up(Bc * DK * sizeof(__fp16), 4096);
+    const size_t v_dma_bytes   = hex_align_up(Bc * DV * sizeof(__fp16), 4096);
+    const size_t k_tile_bytes  = hex_align_up(Bc * DK * sizeof(__fp16), 4096);
+    const size_t v_tile_bytes  = hex_align_up(Bc * DV * sizeof(__fp16), 4096);
+    const size_t s_tile_bytes  = hex_align_up(g_br * Bc * sizeof(__fp16), 4096);
+    const size_t d_tile_bytes  = hex_align_up(g_br * g_br * sizeof(__fp16), 4096);
+    const size_t col_vec_bytes = hex_align_up(g_br * sizeof(__fp16), 256);
+    const size_t row_vec_bytes = hex_align_up(Bc * sizeof(__fp16), 256);
+    const size_t m_line_bytes  = hex_align_up(Bc * sizeof(__fp16), 128);
+    const size_t m_buf_bytes   = hex_align_up(Br * m_line_bytes, 4096);
+    const size_t slopes_bytes  = hex_align_up(g_br * sizeof(__fp16), 128);
+
+    uint8_t * vtcm_cur = ctx->vtcm_base;
+
+    factx.vtcm_q_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, q_tile_bytes);
+    factx.vtcm_o_tiles[0]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, o_tile_bytes);
+    factx.vtcm_o_tiles[1]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, o_tile_bytes);
+    factx.vtcm_k_fp16[0]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_dma_bytes);
+    factx.vtcm_k_fp16[1]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_dma_bytes);
+    factx.vtcm_v_fp16[0]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
+    factx.vtcm_v_fp16[1]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
+    factx.vtcm_k_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_tile_bytes);
+    factx.vtcm_v_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
+    factx.vtcm_s_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, s_tile_bytes);
+    factx.vtcm_p_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, s_tile_bytes);
+    factx.vtcm_d_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, d_tile_bytes);
+    factx.vtcm_m_vec          = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
+    factx.vtcm_l_vec          = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
+    factx.vtcm_s_rowmax       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
+    factx.vtcm_p_rowsum       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
+    factx.vtcm_row_bufs       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, row_vec_bytes * 2 * n_threads);
+    factx.row_buf_stride      = row_vec_bytes / sizeof(HVX_Vector);
+    factx.vtcm_hmx_scales_id  = vtcm_seq_alloc(&vtcm_cur, 256);
+    factx.vtcm_hmx_scales_qk  = vtcm_seq_alloc(&vtcm_cur, 256);
+    factx.vtcm_mask_buf       = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, m_buf_bytes);
+    factx.mask_buf_row_stride = m_line_bytes / sizeof(__fp16);
+    factx.vtcm_slopes         = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, slopes_bytes);
+
+    if ((size_t) (vtcm_cur - ctx->vtcm_base) > ctx->vtcm_size) {
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    // ======== Initialize HMX output scales ========
+    // Identity scale (1.0) for O updates and normalization
+    hmx_init_column_scales(factx.vtcm_hmx_scales_id, Q6_V_vsplat_R(0x3c00)); // 1.0
+
+    // QK scale embedded in HMX output
+    hmx_init_column_scales(factx.vtcm_hmx_scales_qk, hvx_vec_splat_f16(factx.scale));
+
+    // ======== Skip compute if profiling ========
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    // Profiling timers
+    TIMER_DEFINE(total);
+    TIMER_DEFINE(q_load);
+    TIMER_DEFINE(kv_dma);
+    TIMER_DEFINE(k_interleave);
+    TIMER_DEFINE(v_interleave);
+    TIMER_DEFINE(qk_dot);
+    TIMER_DEFINE(softmax);
+    TIMER_DEFINE(o_update);
+    TIMER_DEFINE(o_norm);
+    TIMER_DEFINE(o_store);
+
+    TIMER_START(total);
+
+    // ======== DMA setup ========
+    dma_queue * const dma = ctx->dma[0];
+
+    // Padded row sizes for DMA
+    const size_t size_k_row        = nek0 * sizeof(__fp16);
+    const size_t size_v_row        = nev0 * sizeof(__fp16);
+    const size_t size_k_row_padded = hex_round_up(nek0 * sizeof(__fp16), 128);
+    const size_t size_v_row_padded = hex_round_up(nev0 * sizeof(__fp16), 128);
+
+    const size_t n_row_tiles_g_br = g_br / HMX_FP16_TILE_N_ROWS;
+    const size_t n_tiles_per_bc   = Bc / HMX_FP16_TILE_N_COLS;
+
+    // Q/O element size for Q load and O store
+    const size_t qo_element_size = factx.is_q_fp32 ? sizeof(float) : sizeof(__fp16);
+
+    // ======== HMX lock strategy ========
+    // Pipeline: queue thread auto-acquires HMX lock on first push; released by suspend.
+    // Fallback: main thread holds the lock (original behavior).
+    if (!factx.use_pipeline) {
+        HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
+    }
+
+    // ======== Reusable job descriptors for pipeline ========
+    hmx_fa_qk_job_t       qk_job;
+    hmx_fa_o_update_job_t ou_job;
+    hmx_fa_o_norm_job_t   on_job;
+
+    // ======== Main loop: per batch, per KV head, per Q block ========
+    for (uint32_t ib3 = 0; ib3 < neq3; ++ib3) {
+        for (uint32_t kv_head = 0; kv_head < n_kv_heads; ++kv_head) {
+            const uint32_t ik2 = kv_head;
+            const uint32_t ik3 = ib3 / (neq3 / k->ne[3]);
+            const uint32_t iv2 = kv_head;
+            const uint32_t iv3 = ib3 / (neq3 / v->ne[3]);
+
+            for (uint32_t q_start = 0; q_start < neq1; q_start += Br) {
+                const uint32_t n_q_rows    = hex_smin(Br, neq1 - q_start);
+                const size_t   n_rows_g    = n_q_rows * G;
+                const size_t   g_br_actual = hex_align_up(n_rows_g, HMX_FP16_TILE_N_ROWS);
+                const size_t   n_row_tiles = g_br_actual / HMX_FP16_TILE_N_ROWS;
+
+                // ---- Load Q block [g_br, D] -> tiles, interleaving G heads ----
+                TIMER_START(q_load);
+                if (n_rows_g < g_br) {
+                    hvx_splat_u8_a(factx.vtcm_q_tiles, 0, q_tile_bytes);
+                }
+                fa_phase_q_load(&factx, q, q_start, kv_head, ib3, n_rows_g);
+                TIMER_STOP(q_load);
+
+                // ---- Initialize per-block state ----
+                hvx_splat_u8_a(factx.vtcm_l_vec,   0,      col_vec_bytes);
+                hvx_splat_u8_a(factx.vtcm_d_tiles, 0,      d_tile_bytes);
+                hvx_splat_u16_a(factx.vtcm_m_vec,  0xfbff, col_vec_bytes/2);
+
+                __fp16 * o_tile_prev = factx.vtcm_o_tiles[0];
+                __fp16 * o_tile_curr = factx.vtcm_o_tiles[1];
+                hvx_splat_u8_a(o_tile_prev, 0, o_tile_bytes);
+
+                // ---- KV block loop with DMA double-buffering ----
+                size_t buf_idx = 0;
+
+                // Prefetch first KV block
+                if (factx.n_kv_blocks > 0) {
+                    const uint32_t kv_rows0 = hex_smin(Bc, nek1);
+
+                    const uint8_t * k_src = (const uint8_t *) k->data + ik2 * k->nb[2] + ik3 * k->nb[3];
+                    dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], k_src), size_k_row_padded, k->nb[1],
+                                   size_k_row, kv_rows0);
+
+                    const uint8_t * v_src = (const uint8_t *) v->data + iv2 * v->nb[2] + iv3 * v->nb[3];
+                    dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], v_src), size_v_row_padded, v->nb[1],
+                                   size_v_row, kv_rows0);
+                }
+
+                // Mask DMA: single 2D transfer of n_q_rows unique mask rows into VTCM buffer.
+                // Only when mask is head-broadcast (ne[2]==1); otherwise softmax reads DDR directly.
+                #define MASK_DMA_PUSH(kv_start_val, kv_rows_val, has_mask_dma_var)                                             \
+                    do {                                                                                                       \
+                        has_mask_dma_var = false;                                                                              \
+                        if (mask && factx.mask_broadcast) {                                                                    \
+                            const uint32_t  _im3 = ib3 % mask->ne[3];                                                          \
+                            const uint8_t * _ms  = (const uint8_t *) mask->data + q_start * mask->nb[1] + _im3 * mask->nb[3] + \
+                                                  (kv_start_val) * sizeof(__fp16);                                             \
+                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_mask_buf, _ms), m_line_bytes, mask->nb[1],             \
+                                           (kv_rows_val) * sizeof(__fp16), n_q_rows);                                          \
+                            has_mask_dma_var = true;                                                                           \
+                        }                                                                                                      \
+                    } while (0)
+
+                #define MASK_DMA_POP(has_mask_dma_var) \
+                    do {                               \
+                        if (has_mask_dma_var) {        \
+                            dma_queue_pop(dma);        \
+                        }                              \
+                    } while (0)
+
+                #define DMA_PREFETCH_KV(blk_val)                                                                                          \
+                    do {                                                                                                                  \
+                        if ((blk_val) < factx.n_kv_blocks) {                                                                              \
+                            const uint32_t  _ns = (blk_val) * Bc;                                                                         \
+                            const uint32_t  _nr = hex_smin(Bc, nek1 - _ns);                                                               \
+                            size_t          _nb = 1 - buf_idx;                                                                            \
+                            const uint8_t * _ks = (const uint8_t *) k->data + _ns * k->nb[1] + ik2 * k->nb[2] + ik3 * k->nb[3];           \
+                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[_nb], _ks), size_k_row_padded, k->nb[1], size_k_row, _nr); \
+                            const uint8_t * _vs = (const uint8_t *) v->data + _ns * v->nb[1] + iv2 * v->nb[2] + iv3 * v->nb[3];           \
+                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[_nb], _vs), size_v_row_padded, v->nb[1], size_v_row, _nr); \
+                        }                                                                                                                 \
+                    } while (0)
+
+                const size_t k_src_stride = size_k_row_padded / sizeof(__fp16);
+                const size_t v_src_stride = size_v_row_padded / sizeof(__fp16);
+
+                if (factx.use_pipeline) {
+                    // ==================================================================
+                    // Pipeline path: HVX phases ‖ HMX queue worker
+                    // ==================================================================
+                    struct hmx_queue * hmx_q = ctx->hmx_queue;
+
+                    for (uint32_t kv_blk = 0; kv_blk < factx.n_kv_blocks; ++kv_blk) {
+                        const uint32_t kv_start    = kv_blk * Bc;
+                        const uint32_t kv_rows     = hex_smin(Bc, nek1 - kv_start);
+                        const size_t   n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS);
+
+                        // Wait for current KV DMA
+                        TIMER_START(kv_dma);
+                        dma_queue_pop(dma);  // K
+                        dma_queue_pop(dma);  // V
+                        TIMER_STOP(kv_dma);
+
+                        // Push mask DMA for this block (single 2D DMA when broadcast)
+                        bool has_mask_dma = false;
+                        MASK_DMA_PUSH(kv_start, kv_rows, has_mask_dma);
+
+                        // ---- Phase 1: K_int(blk) ‖ O_update(blk-1) ----
+                        if (kv_blk > 0) {
+                            // Submit O_update for previous block (HMX worker)
+                            ou_job.o_curr           = o_tile_curr;
+                            ou_job.o_prev           = o_tile_prev;
+                            ou_job.p_tiles          = factx.vtcm_p_tiles;
+                            ou_job.v_tiles          = factx.vtcm_v_tiles;
+                            ou_job.d_tiles          = factx.vtcm_d_tiles;
+                            ou_job.hmx_scales       = factx.vtcm_hmx_scales_id;
+                            ou_job.n_row_tiles      = n_row_tiles;
+                            ou_job.n_col_tiles      = hmx_ceil_div(hex_smin(Bc, nek1 - (kv_blk - 1) * Bc), HMX_FP16_TILE_N_COLS);
+                            ou_job.n_row_tiles_g_br = n_row_tiles_g_br;
+                            ou_job.n_tiles_per_bc   = n_tiles_per_bc;
+                            ou_job.DV               = DV;
+                            hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job));
+                        }
+
+                        TIMER_START(k_interleave);
+                        fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx);
+                        TIMER_STOP(k_interleave);
+
+                        if (kv_blk > 0) {
+                            hmx_queue_pop(hmx_q);
+                            hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
+                        }
+
+                        // ---- Phase 2: qk_dot(blk) on HMX ‖ V_int(blk) + DMA prefetch on HVX ----
+                        qk_job.q_tiles        = factx.vtcm_q_tiles;
+                        qk_job.k_tiles        = factx.vtcm_k_tiles;
+                        qk_job.s_tiles        = factx.vtcm_s_tiles;
+                        qk_job.n_row_tiles    = n_row_tiles;
+                        qk_job.n_col_tiles    = n_col_tiles;
+                        qk_job.n_dot_tiles    = DK / 32;
+                        qk_job.n_tiles_per_bc = n_tiles_per_bc;
+                        qk_job.hmx_scales     = factx.vtcm_hmx_scales_qk;
+                        TIMER_START(qk_dot);
+                        hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job));
+
+                        // DMA push next block (non-blocking, before worker_pool)
+                        DMA_PREFETCH_KV(kv_blk + 1);
+
+                        TIMER_START(v_interleave);
+                        fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc);
+                        TIMER_STOP(v_interleave);
+
+                        hmx_queue_pop(hmx_q);
+                        TIMER_STOP(qk_dot);
+
+                        // ---- Phase 3: softmax(blk) + build_D(blk) | HMX idle ----
+                        // Pop mask DMA before softmax (ensures VTCM buffer is ready)
+                        MASK_DMA_POP(has_mask_dma);
+
+                        fa_softmax_args_t sargs;
+                        memset(&sargs, 0, sizeof(sargs));
+                        sargs.factx                = &factx;
+                        sargs.kv_rows              = kv_rows;
+                        sargs.n_rows_g             = n_rows_g;
+                        sargs.n_col_tiles          = n_col_tiles;
+                        sargs.n_tiles_per_bc       = n_tiles_per_bc;
+                        sargs.n_row_tiles          = n_row_tiles;
+                        sargs.n_row_tiles_g_br     = n_row_tiles_g_br;
+                        sargs.Bc                   = Bc;
+                        sargs.G                    = G;
+                        sargs.kv_head              = kv_head;
+                        sargs.kv_start             = kv_start;
+                        sargs.q_start              = q_start;
+                        sargs.ib3                  = ib3;
+                        sargs.has_alibi            = (factx.max_bias != 0.0f);
+                        sargs.mask                 = mask;
+                        sargs.mask_vtcm            = has_mask_dma ? (const __fp16 *) factx.vtcm_mask_buf : NULL;
+                        sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
+                        sargs.slopes               = factx.vtcm_slopes;
+                        fa_compute_slopes(&sargs, &factx, kv_head, n_rows_g);
+
+                        TIMER_START(softmax);
+                        fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
+                        TIMER_STOP(softmax);
+
+                        buf_idx = 1 - buf_idx;
+                    }  // end KV block loop (pipeline)
+
+                    // Epilogue: O_update for last block
+                    if (factx.n_kv_blocks > 0) {
+                        const uint32_t last_blk = factx.n_kv_blocks - 1;
+                        const size_t last_cols  = hmx_ceil_div(hex_smin(Bc, nek1 - last_blk * Bc), HMX_FP16_TILE_N_COLS);
+                        ou_job.o_curr           = o_tile_curr;
+                        ou_job.o_prev           = o_tile_prev;
+                        ou_job.p_tiles          = factx.vtcm_p_tiles;
+                        ou_job.v_tiles          = factx.vtcm_v_tiles;
+                        ou_job.d_tiles          = factx.vtcm_d_tiles;
+                        ou_job.hmx_scales       = factx.vtcm_hmx_scales_id;
+                        ou_job.n_row_tiles      = n_row_tiles;
+                        ou_job.n_col_tiles      = last_cols;
+                        ou_job.n_row_tiles_g_br = n_row_tiles_g_br;
+                        ou_job.n_tiles_per_bc   = n_tiles_per_bc;
+                        ou_job.DV               = DV;
+
+                        TIMER_START(o_update);
+                        hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job));
+                        hmx_queue_pop(hmx_q);
+                        TIMER_STOP(o_update);
+
+                        hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
+                    }
+
+                } else {
+                    // ==================================================================
+                    // Fallback path: sequential with multi-thread HVX phases
+                    // Main thread holds HMX lock, runs HMX inline.
+                    // ==================================================================
+
+                    for (uint32_t kv_blk = 0; kv_blk < factx.n_kv_blocks; ++kv_blk) {
+                        const uint32_t kv_start    = kv_blk * Bc;
+                        const uint32_t kv_rows     = hex_smin(Bc, nek1 - kv_start);
+                        const size_t   n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS);
+
+                        TIMER_START(kv_dma);
+                        dma_queue_pop(dma);  // K
+                        dma_queue_pop(dma);  // V
+                        TIMER_STOP(kv_dma);
+
+                        bool has_mask_dma = false;
+                        MASK_DMA_PUSH(kv_start, kv_rows, has_mask_dma);
+                        DMA_PREFETCH_KV(kv_blk + 1);
+
+                        // K interleave (multi-thread HVX)
+                        TIMER_START(k_interleave);
+                        fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx);
+                        TIMER_STOP(k_interleave);
+
+                        // QK dot (inline HMX on main thread)
+                        TIMER_START(qk_dot);
+                        {
+                            const size_t n_dot_tiles       = (size_t) (DK / 32);
+                            const __fp16 * restrict q_base = factx.vtcm_q_tiles;
+                            const __fp16 * restrict k_base = factx.vtcm_k_tiles;
+                            __fp16 * restrict s_base       = factx.vtcm_s_tiles;
+                            __builtin_assume(n_row_tiles > 0);
+                            __builtin_assume(n_col_tiles > 0);
+                            __builtin_assume(n_dot_tiles > 0);
+
+                            Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_qk);
+                            for (size_t r = 0; r < n_row_tiles; ++r) {
+                                for (size_t c = 0; c < n_col_tiles; ++c) {
+                                    const __fp16 * row_tiles = q_base + r * HMX_FP16_TILE_N_ROWS * DK;
+                                    const __fp16 * col_tiles = k_base + c * HMX_FP16_TILE_N_COLS * DK;
+                                    __fp16 *       out_tile  = s_base + (r * n_tiles_per_bc + c) * HMX_FP16_TILE_N_ELMS;
+                                    for (size_t k = 0; k < n_dot_tiles; ++k) {
+                                        Q6_activation_hf_mxmem_RR((unsigned int) row_tiles, 2047);
+                                        Q6_weight_hf_mxmem_RR((unsigned int) col_tiles, 2047);
+                                        row_tiles += HMX_FP16_TILE_N_ELMS;
+                                        col_tiles += HMX_FP16_TILE_N_ELMS;
+                                    }
+                                    Q6_mxmem_AR_after_hf(out_tile, 0);
+                                }
+                            }
+                        }
+                        TIMER_STOP(qk_dot);
+
+                        // Pop mask DMA
+                        MASK_DMA_POP(has_mask_dma);
+
+                        // Softmax + build_D (multi-thread HVX + serial m/l update)
+                        fa_softmax_args_t sargs;
+                        memset(&sargs, 0, sizeof(sargs));
+                        sargs.factx                = &factx;
+                        sargs.kv_rows              = kv_rows;
+                        sargs.n_rows_g             = n_rows_g;
+                        sargs.n_col_tiles          = n_col_tiles;
+                        sargs.n_tiles_per_bc       = n_tiles_per_bc;
+                        sargs.n_row_tiles          = n_row_tiles;
+                        sargs.n_row_tiles_g_br     = n_row_tiles_g_br;
+                        sargs.Bc                   = Bc;
+                        sargs.G                    = G;
+                        sargs.kv_head              = kv_head;
+                        sargs.kv_start             = kv_start;
+                        sargs.q_start              = q_start;
+                        sargs.ib3                  = ib3;
+                        sargs.has_alibi            = (factx.max_bias != 0.0f);
+                        sargs.mask                 = mask;
+                        sargs.mask_vtcm            = has_mask_dma ? (const __fp16 *) factx.vtcm_mask_buf : NULL;
+                        sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
+                        sargs.slopes               = factx.vtcm_slopes;
+                        fa_compute_slopes(&sargs, &factx, kv_head, n_rows_g);
+
+                        TIMER_START(softmax);
+                        fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
+                        TIMER_STOP(softmax);
+
+                        // V interleave (multi-thread HVX)
+                        TIMER_START(v_interleave);
+                        // FIX(v-stride): use n_tiles_per_bc (block-invariant) as V tile layout
+                        // stride to match o_update's v_tile access.  Using per-block n_col_tiles
+                        // misplaces DV_tile 1..3 in the last partial KV block.
+                        fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc);
+                        TIMER_STOP(v_interleave);
+
+                        // O update (inline HMX on main thread)
+                        TIMER_START(o_update);
+                        {
+                            const size_t DV_tiles           = (size_t) (DV / 32);
+                            const __fp16 * restrict d_base  = factx.vtcm_d_tiles;
+                            const __fp16 * restrict p_base  = factx.vtcm_p_tiles;
+                            const __fp16 * restrict v_base  = factx.vtcm_v_tiles;
+                            const __fp16 * restrict op_base = o_tile_prev;
+                            __fp16 * restrict oc_base       = o_tile_curr;
+                            __builtin_assume(n_row_tiles > 0);
+                            __builtin_assume(n_col_tiles > 0);
+                            __builtin_assume(DV_tiles > 0);
+
+                            Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_id);
+                            for (size_t r = 0; r < n_row_tiles; ++r) {
+                                for (size_t c = 0; c < DV_tiles; ++c) {
+                                    const __fp16 * d_diag = d_base  + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+                                    const __fp16 * o_rc   = op_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+                                    Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
+                                    Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
+
+                                    const __fp16 * p_tile_in = p_base + (r * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
+                                    const __fp16 * v_tile_in = v_base + (c * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
+                                    for (size_t k = 0; k < n_col_tiles; ++k) {
+                                        Q6_activation_hf_mxmem_RR((unsigned int) p_tile_in, 2047);
+                                        Q6_weight_hf_mxmem_RR((unsigned int) v_tile_in, 2047);
+                                        p_tile_in += HMX_FP16_TILE_N_ELMS;
+                                        v_tile_in += HMX_FP16_TILE_N_ELMS;
+                                    }
+
+                                    __fp16 * o_tile_out = oc_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+                                    Q6_mxmem_AR_after_hf(o_tile_out, 0);
+                                }
+                            }
+                            hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
+                        }
+                        TIMER_STOP(o_update);
+
+                        buf_idx = 1 - buf_idx;
+                    }  // end KV block loop (fallback)
+                }
+
+                // ---- Final normalization: O = diag(1/l) @ O ----
+                TIMER_START(o_norm);
+                {
+                    fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br);
+
+                    // HMX: O_final = diag(1/l) @ O_prev
+                    if (factx.use_pipeline) {
+                        on_job.o_curr           = o_tile_curr;
+                        on_job.o_prev           = o_tile_prev;
+                        on_job.d_tiles          = factx.vtcm_d_tiles;
+                        on_job.hmx_scales       = factx.vtcm_hmx_scales_id;
+                        on_job.n_row_tiles      = n_row_tiles;
+                        on_job.n_row_tiles_g_br = n_row_tiles_g_br;
+                        on_job.DV               = DV;
+                        hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_fa_o_norm_worker, &on_job));
+                        hmx_queue_pop(ctx->hmx_queue);
+                    } else {
+                        const size_t DV_tiles           = (size_t) (DV / 32);
+                        const __fp16 * restrict d_base  = factx.vtcm_d_tiles;
+                        const __fp16 * restrict op_base = o_tile_prev;
+                        __fp16 * restrict oc_base       = o_tile_curr;
+                        __builtin_assume(n_row_tiles > 0);
+                        __builtin_assume(DV_tiles > 0);
+
+                        Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_id);
+                        for (size_t r = 0; r < n_row_tiles; ++r) {
+                            for (size_t c = 0; c < DV_tiles; ++c) {
+                                const __fp16 * d_diag = d_base  + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+                                const __fp16 * o_rc   = op_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+                                __fp16 *       o_out  = oc_base + (r * DV_tiles + c) * HMX_FP16_TILE_N_ELMS;
+
+                                Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
+                                Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
+                                Q6_mxmem_AR_after_hf(o_out, 0);
+                            }
+                        }
+                    }
+                }
+                TIMER_STOP(o_norm);
+
+                // ---- Store O block ----
+                TIMER_START(o_store);
+                fa_phase_o_store(&factx, dst, o_tile_curr, q_start, kv_head, ib3, n_rows_g);
+                TIMER_STOP(o_store);
+
+#undef MASK_DMA_PUSH
+#undef MASK_DMA_POP
+#undef DMA_PREFETCH_KV
+
+            }  // end Q block loop
+        }  // end KV head loop
+    }  // end batch loop
+
+    if (factx.use_pipeline) {
+        hmx_queue_suspend(ctx->hmx_queue);
+    } else {
+        HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
+    }
+
+    TIMER_STOP(total);
+
+#if defined(ENABLE_PROFILE_TIMERS)
+    FARF(HIGH, "hmx-fa: %lld us, q_load=%lld kv_dma=%lld k_interleave=%lld v_interleave=%lld", TIMER_US(total),
+         TIMER_US(q_load), TIMER_US(kv_dma), TIMER_US(k_interleave), TIMER_US(v_interleave));
+    FARF(HIGH, "  qk_dot=%lld softmax=%lld o_update=%lld o_norm=%lld o_store=%lld", TIMER_US(qk_dot), TIMER_US(softmax),
+         TIMER_US(o_update), TIMER_US(o_norm), TIMER_US(o_store));
+#endif
+
+    return HTP_STATUS_OK;
+}
diff --git a/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c b/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
index dbca8220fab..e05ccfd5fc7 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
+++ b/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
@@ -28,6 +28,8 @@
 #include "hmx-queue.h"
 #include "hmx-profile.h"
 
+#include "vtcm-utils.h"
+
 static const __fp16 q4_0_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
     -8, 0, -7, 0, -6, 0, -5, 0, -4, 0, -3, 0, -2, 0, -1, 0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0,
 };
@@ -43,40 +45,11 @@ static const __fp16 iq4_nl_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
     1,    0, 13,   0, 25,  0, 38,  0, 53,  0, 69,  0, 89,  0, 113, 0,
 };
 
-// vscatter offsets for fused dequant+transpose: write K-values directly to [K][N] tile.
-// word[i] = i*128 maps K-row-pair i to byte offset i*128 in the tile.
-// Column offset (n*4) is added at runtime.  Only entries 0..15 are used (masked by predicate).
-static const int32_t weight_transpose_scatter_offsets[32] __attribute__((aligned(VLEN))) = {
-    0*128,  1*128,  2*128,  3*128,  4*128,  5*128,  6*128,  7*128,
-    8*128,  9*128, 10*128, 11*128, 12*128, 13*128, 14*128, 15*128,
-    16*128, 17*128, 18*128, 19*128, 20*128, 21*128, 22*128, 23*128,
-    24*128, 25*128, 26*128, 27*128, 28*128, 29*128, 30*128, 31*128
-};
-
 // Scales per x4x2 logical block: 8 × sizeof(__fp16) = 16 bytes
 #define HMX_X4X2_SCALES_PER_BLK  8
 #define HMX_X4X2_DBLK_SIZE       16  // 8 * 2 bytes (fp16 scales for Q4_0/Q8_0/IQ4_NL)
 #define HMX_X4X2_MXFP4_EBLK_SIZE 8   // 8 * 1 byte  (E8M0 scales for MXFP4)
 
-static inline void swap_ptr(void **p1, void **p2) {
-    void *t = *p1;
-    *p1     = *p2;
-    *p2     = t;
-}
-
-typedef struct {
-    uint8_t       *dst;
-    const uint8_t *src;
-    dma_queue     *dma;
-    size_t         n_rows;
-    size_t         src_stride;   // DDR row stride (full row_stride)
-    size_t         dst_stride;   // VTCM sub-block row stride
-    size_t         quant_off;    // quant byte offset in each DDR row
-    size_t         quant_width;  // quant bytes to copy per row
-    size_t         scale_off;    // scale byte offset in each DDR row
-    size_t         scale_width;  // scale bytes to copy per row
-} qweight_fetch_task_state_t;
-
 // Compute the byte stride of one row in x4x2 format.
 // Numerically equals ggml_row_size(type, k) when k is 256-aligned, because
 // x4x2 packing has the same density as block_q4_0 / block_q8_0.
@@ -202,57 +175,15 @@ static int hmx_compute_chunks(size_t   vtcm_total,
     return 0;
 }
 
-// forward declaration – defined after transfer_activation_chunk_fp32_to_fp16
-void transfer_activation_chunk_threaded(struct htp_context *ctx, __fp16 *dst, const float *src, int n_rows, int k_block, int k_stride);
-
-// Scatter row-major FP16 weight (already in VTCM scratch) directly into transposed [K][N] tiles.
-// vtcm_src: [n_cols][k] row-major fp16 in VTCM scratch buffer
-// vtcm_dst: [n_col_tiles][n_k_tiles][HMX_FP16_TILE_N_ELMS] tile-major interleaved fp16
-static void interleave_fp16_weight_chunk_to_tiles(__fp16 *restrict vtcm_dst,
-                                                   const __fp16 *restrict vtcm_src,
-                                                   int n_cols, int k) {
-    assert(n_cols % HMX_FP16_TILE_N_COLS == 0);
-    assert(k % HMX_FP16_TILE_N_COLS == 0);
-
-    const int n_k_tiles = k / HMX_FP16_TILE_N_COLS;
-    const HVX_Vector v_scat_base = hvx_vmem(weight_transpose_scatter_offsets);
-    const HVX_Vector v_scat_step = Q6_V_vsplat_R(4);
-    const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);
-
-    for (int r = 0; r < n_cols; r += 2) {
-        int ct = r / HMX_FP16_TILE_N_ROWS;       // N-dimension tile index
-        int local_r = r % HMX_FP16_TILE_N_ROWS;  // intra-tile row index
-        const bool next_row_valid = (r + 1) < n_cols;
-
-        // Offset vectors for N-columns local_r and local_r+1, reused across K-tiles.
-        HVX_Vector v_off0 = Q6_Vw_vadd_VwVw(v_scat_base, Q6_V_vsplat_R(local_r * 4));
-        HVX_Vector v_off1 = Q6_Vw_vadd_VwVw(v_off0, v_scat_step);
-
-        for (int c = 0; c < k; c += HMX_FP16_TILE_N_COLS) {
-            int kt       = c / HMX_FP16_TILE_N_COLS;
-            int tile_idx = ct * n_k_tiles + kt;
-            __fp16 *tile_base = vtcm_dst + tile_idx * HMX_FP16_TILE_N_ELMS;
-
-            HVX_Vector v0 = hvx_vmemu(vtcm_src + r * k + c);
-            HVX_Vector v1 = next_row_valid ? hvx_vmemu(vtcm_src + (r + 1) * k + c) : Q6_V_vzero();
-
-            Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off0, v0);
-            Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off1, v1);
-        }
-    }
-}
-
 // --- x4x2 format dequantizers ---
 
 // Dequantize one x4x2 Q4_0 group (32 elements from 32 packed bytes) -> 32 FP16 in first 64 bytes.
 // In x4x2, sub-blocks 0..3 use lower nibbles, sub-blocks 4..7 use upper nibbles
 // of the same 32 packed bytes.
-static inline HVX_Vector dequantize_x4x2_q4_0_group_hvx(
-         const uint8_t *packed_32, bool upper_nibbles,
-         const __fp16 *scale, const HVX_Vector vlut_cvt) {
+static inline HVX_Vector dequantize_x4x2_q4_0_group_hvx(const uint8_t *packed_32, bool upper_nibbles, const __fp16 *scale, const HVX_Vector vlut_cvt) {
     HVX_Vector vq = hvx_vmemu(packed_32);
     const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector v_scales = hvx_vec_splat_f16(*scale);
+    HVX_Vector v_scales = hvx_vec_repl_f16(hvx_vmemu(scale));
     // q4x4x2 stores two int4 values per byte. Keep only the selected nibble.
     HVX_Vector v_quants =  Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
     v_quants = Q6_V_vand_VV(v_quants, mask_h4);
@@ -290,9 +221,10 @@ static inline void dequantize_x4x2_q4_0_x4groups_hvx(
     HVX_Vector v_hi = Q6_V_hi_W(vp);  // [group2: 32 fp16 | group3: 32 fp16]
 
     // Build per-group scale vectors: first 64 bytes use scale_a, last 64 use scale_b
-    HVX_VectorPred q64 = Q6_Q_vsetq_R(64);
-    HVX_Vector v_sc01 = Q6_V_vmux_QVV(q64, hvx_vec_splat_f16(scales_4[0]), hvx_vec_splat_f16(scales_4[1]));
-    HVX_Vector v_sc23 = Q6_V_vmux_QVV(q64, hvx_vec_splat_f16(scales_4[2]), hvx_vec_splat_f16(scales_4[3]));
+    volatile HVX_Vector vscale = hvx_vmemu(scales_4);
+
+    HVX_Vector v_sc01 = hvx_vec_repl_2x_f16(vscale);
+    HVX_Vector v_sc23 = hvx_vec_repl_2x_f16(Q6_V_vror_VR(vscale, 4));
 
     v_lo = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_lo, v_sc01));
     v_hi = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hi, v_sc23));
@@ -303,12 +235,11 @@ static inline void dequantize_x4x2_q4_0_x4groups_hvx(
 }
 
 // Dequantize one x4x2 Q8_0 group (32 int8 quants) -> 32 FP16 in first 64 bytes.
-static inline HVX_Vector dequantize_x4x2_q8_0_group_hvx(
-        const int8_t *quants_32, const __fp16 *scale) {
-    HVX_Vector vq = hvx_vmemu(quants_32);
-    HVX_Vector v_scales = hvx_vec_splat_f16(*scale);
-    HVX_Vector v0 = Q6_V_lo_W(Q6_Wh_vunpack_Vb(vq));
-    HVX_Vector v_hf = Q6_Vhf_equals_Vh(v0);
+static inline HVX_Vector dequantize_x4x2_q8_0_group_hvx(const int8_t *quants_32, const __fp16 *scale) {
+    HVX_Vector vq       = hvx_vmemu(quants_32);
+    HVX_Vector v_scales = hvx_vec_repl_f16(hvx_vmemu(scale));
+    HVX_Vector v0       = Q6_V_lo_W(Q6_Wh_vunpack_Vb(vq));
+    HVX_Vector v_hf     = Q6_Vhf_equals_Vh(v0);
     return Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hf, v_scales));
 }
 
@@ -414,8 +345,8 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
     // vscatter setup: write dequantized K-values directly to transposed [K][N] tile positions.
     // Each int32 element holds a K-row-pair (2 adjacent fp16 values).  word[i] at offset i*128
     // maps to K-rows 2i and 2i+1.  Column offset (n*4) added per row.
-    const HVX_Vector v_scat_base = hvx_vmem(weight_transpose_scatter_offsets);
-    const HVX_Vector v_scat_step = Q6_V_vsplat_R(4);  // 4 bytes = 1 column step
+    const HVX_Vector v_scat_base  = hvx_vmem(hmx_transpose_scatter_offsets);
+    const HVX_Vector v_scat_step  = Q6_V_vsplat_R(4);  // 4 bytes = 1 column step
     const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);  // first 16 words (64 bytes)
 
     unsigned ct = (unsigned)start_tile / n_k_tiles;  // column tile index
@@ -589,12 +520,8 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
                 const uint8_t *r0 = vtcm_src + row0 * row_stride;
                 const uint8_t *r1 = vtcm_src + row1 * row_stride;
 
-                HVX_Vector v0 = dequantize_x4x2_q8_0_group_hvx(
-                    (const int8_t *)(r0 + byte_off), (const __fp16 *)(r0 + scale_off));
-                HVX_Vector v1 = (row1 < n_cols)
-                    ? dequantize_x4x2_q8_0_group_hvx(
-                        (const int8_t *)(r1 + byte_off), (const __fp16 *)(r1 + scale_off))
-                    : Q6_V_vzero();
+                HVX_Vector v0 = dequantize_x4x2_q8_0_group_hvx((const int8_t *)(r0 + byte_off), (const __fp16 *)(r0 + scale_off));
+                HVX_Vector v1 = (row1 < n_cols) ? dequantize_x4x2_q8_0_group_hvx((const int8_t *)(r1 + byte_off), (const __fp16 *)(r1 + scale_off)) : Q6_V_vzero();
 
                 Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v0);
                 v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
@@ -658,12 +585,12 @@ static void dequantize_x4x2_weight_chunk_to_fp16_tiles(
     state.n_tasks          = (n_tot_tiles + n_tiles_per_task - 1) / n_tiles_per_task;
     state.n_tot_tiles      = n_tot_tiles;
     state.n_tiles_per_task = n_tiles_per_task;
-    state.dst         = vtcm_dst;
-    state.src         = (const uint8_t *)vtcm_src;
-    state.n_cols      = n_cols;
-    state.k_block     = k_block;
-    state.row_stride  = row_stride;
-    state.weight_type = weight_type;
+    state.dst              = vtcm_dst;
+    state.src              = (const uint8_t *)vtcm_src;
+    state.n_cols           = n_cols;
+    state.k_block          = k_block;
+    state.row_stride       = row_stride;
+    state.weight_type      = weight_type;
 
     worker_pool_run_func(ctx->worker_pool, dequantize_x4x2_worker_loop, &state, ctx->n_threads);
 }
@@ -733,7 +660,7 @@ static inline void hmx_matmul_job_init(hmx_matmul_job_t * job,
     job->n_dot_tiles = n_dot_tiles;
 }
 
-// --- End async HMX matmul job ---
+// output : fp16 -> f32p
 
 static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16 *restrict vtcm_src, int n_rows, int n_cols, int n) {
     assert(n_cols % HMX_FP16_TILE_N_COLS == 0);
@@ -807,421 +734,334 @@ static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst,
     worker_pool_run_func(ctx->worker_pool, transfer_output_chunk_worker_fn, &state, ctx->n_threads);
 }
 
-static inline int hmx_matmul_batch_r2(const hmx_matmul_w16a32_batched_params_t *params) {
-    return params->ne02 > 0 ? params->ne12 / params->ne02 : 1;
-}
-
-static inline int hmx_matmul_batch_r3(const hmx_matmul_w16a32_batched_params_t *params) {
-    return params->ne03 > 0 ? params->ne13 / params->ne03 : 1;
-}
-
-static inline const __fp16 *hmx_matmul_weight_batch_ptr(const hmx_matmul_w16a32_batched_params_t *params,
-                                                        int dst_b2, int dst_b3) {
-    const int r2 = hmx_matmul_batch_r2(params);
-    const int r3 = hmx_matmul_batch_r3(params);
-    return (const __fp16 *) ((const uint8_t *) params->permuted_weight +
-                             (size_t) (dst_b2 / r2) * params->src0_nb2 +
-                             (size_t) (dst_b3 / r3) * params->src0_nb3);
-}
+// activations : fp32 -> fp16
 
-static inline const float *hmx_matmul_activation_batch_ptr(const hmx_matmul_w16a32_batched_params_t *params,
-                                                           int dst_b2, int dst_b3) {
-    return (const float *) ((const uint8_t *) params->activation +
-                            (size_t) dst_b2 * params->src1_nb2 +
-                            (size_t) dst_b3 * params->src1_nb3);
-}
+static void transfer_activation_chunk_fp32_to_fp16(__fp16 *restrict vtcm_dst, const float *restrict src, int n_rows, int k_block, int k_stride) {
+    const int n_rows_padded = hex_align_up(n_rows, HMX_FP16_TILE_N_ROWS);
+    const int n_rows_tiled  = (n_rows / HMX_FP16_TILE_N_ROWS) * HMX_FP16_TILE_N_ROWS;
 
-static inline float *hmx_matmul_dst_batch_ptr(const hmx_matmul_w16a32_batched_params_t *params,
-                                              int dst_b2, int dst_b3) {
-    return (float *) ((uint8_t *) params->dst +
-                      (size_t) dst_b2 * params->dst_nb2 +
-                      (size_t) dst_b3 * params->dst_nb3);
-}
+    int r = 0;
 
-static int hmx_mat_mul_permuted_w16a32_batched_legacy(struct htp_context *ctx,
-                                                      const hmx_matmul_w16a32_batched_params_t *params) {
-    int ret = 0;
-    for (int b3 = 0; b3 < params->ne13 && ret == 0; ++b3) {
-        for (int b2 = 0; b2 < params->ne12 && ret == 0; ++b2) {
-            ret = hmx_mat_mul_permuted_w16a32(ctx,
-                                              hmx_matmul_dst_batch_ptr(params, b2, b3),
-                                              hmx_matmul_activation_batch_ptr(params, b2, b3),
-                                              hmx_matmul_weight_batch_ptr(params, b2, b3),
-                                              params->m, params->k, params->n,
-                                              params->act_stride, params->weight_stride);
-        }
-    }
-    return ret;
-}
+    #pragma unroll(2)
+    for (r = 0; r < n_rows_tiled; r += 2) {
+        int r0 = r / HMX_FP16_TILE_N_ROWS;  // tile row index
+        int r1 = r % HMX_FP16_TILE_N_ROWS;  // intra-tile row idx
 
-int hmx_mat_mul_permuted_w16a32_batched(struct htp_context *ctx, const hmx_matmul_w16a32_batched_params_t *params) {
-    if (!ctx || !params || !params->dst || !params->activation || !params->permuted_weight) { return -1; }
-    if (!params->m || !params->k || !params->n) { return -1; }
-    if (params->act_stride < params->k || params->weight_stride < params->k || params->dst_stride < params->n) { return -1; }
-    if (params->ne02 <= 0 || params->ne03 <= 0 || params->ne12 <= 0 || params->ne13 <= 0) { return -1; }
-    if (params->ne12 % params->ne02 != 0 || params->ne13 % params->ne03 != 0) { return -1; }
-    if (params->k % 32 != 0 || params->n % 32 != 0) { return -1; }
+        const HVX_Vector *pv_in0 = (const HVX_Vector *) (src + (r + 0) * k_stride);
+        const HVX_Vector *pv_in1 = (const HVX_Vector *) (src + (r + 1) * k_stride);
+        for (int c = 0; c < k_block; c += 32) {
+            HVX_Vector v0 = *pv_in0++;
+            HVX_Vector v1 = *pv_in1++;
 
-    if (!hex_is_aligned(params->dst, VLEN) ||
-        !hex_is_aligned(params->activation, VLEN) ||
-        !hex_is_aligned(params->permuted_weight, VLEN)) {
-        return -1;
-    }
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
 
-    const int group_size = hmx_matmul_batch_r2(params);
+            // compute output position
+            int c0       = c / HMX_FP16_TILE_N_COLS;  // tile column index
+            int tile_idx = r0 * (k_block / HMX_FP16_TILE_N_COLS) + c0;
 
-    if (group_size <= 1) {
-        FARF(MEDIUM, "%s: no dim2 GQA reuse (group=%d), using legacy batched loop", __func__, group_size);
-        return hmx_mat_mul_permuted_w16a32_batched_legacy(ctx, params);
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HMX_FP16_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
     }
 
-    // Grouped path: reuse interleaved weight across all q_heads sharing a
-    // kv_head.  Each q_head gets its own activation buffer in VTCM (so
-    // activation is loaded once per m_chunk and reused across all n_chunks),
-    // and each q_head is computed individually to avoid tile-major packing
-    // issues.  m_chunk_n_rows is always a multiple of 32 (from
-    // hmx_compute_chunks), so per-head tile arrays don't overlap.
-    const size_t vtcm_budget  = ctx->vtcm_size;
-    const size_t vec_dot_size = params->k * sizeof(__fp16);
+    for (; r < n_rows_padded; r += 2) {
+        int r0 = r / HMX_FP16_TILE_N_ROWS;  // tile row index
+        int r1 = r % HMX_FP16_TILE_N_ROWS;  // intra-tile row idx
 
-    // When the activation has a large stride (e.g. permuted Q tensor with
-    // act_stride >> k), HVX vector loads from strided DDR thrash L2 cache.
-    // Allocate an F32 scratch buffer in VTCM and use 2D DMA to gather
-    // strided rows into a contiguous block before the F32->F16 conversion.
-    const bool use_dma_activation = (params->act_stride > params->k);
-    const size_t f32_scratch_per_m = use_dma_activation ? (size_t) params->k * sizeof(float) : 0;
+        const bool row0_valid = r       < n_rows;
+        const bool row1_valid = (r + 1) < n_rows;
 
-    size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
-    // FP16 weight: interleave and activation load have similar per-element cost.
-    if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256,
-                           /*per_n=*/3 * vec_dot_size,
-                           /*per_m=*/group_size * vec_dot_size + f32_scratch_per_m,
-                           /*per_mn=*/sizeof(__fp16), params->m, params->n,
-                           /*m_block_cost=*/(size_t) params->n,
-                           /*n_block_cost=*/(size_t) params->m, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
-        FARF(HIGH, "%s: grouped path does not fit VTCM, falling back to legacy batched loop", __func__);
-        return hmx_mat_mul_permuted_w16a32_batched_legacy(ctx, params);
-    }
+        const HVX_Vector *pv_in0 = row0_valid ? (const HVX_Vector *) (src + (r + 0) * k_stride) : NULL;
+        const HVX_Vector *pv_in1 = row1_valid ? (const HVX_Vector *) (src + (r + 1) * k_stride) : NULL;
+        for (int c = 0; c < k_block; c += 32) {
+            HVX_Vector v0 = row0_valid ? *pv_in0++ : Q6_V_vzero();
+            HVX_Vector v1 = row1_valid ? *pv_in1++ : Q6_V_vzero();
 
-    const size_t act_head_stride      = m_chunk_n_rows * (size_t) params->k;  // fp16 elements between heads
-    const size_t weight_area_size     = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t activation_area_size = hex_align_up(group_size * m_chunk_n_rows * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t output_area_size     = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HMX_FP16_TILE_SIZE);
-    const size_t scratch_area_size    = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t f32_scratch_size     = use_dma_activation
-        ? hex_align_up(m_chunk_n_rows * (size_t) params->k * sizeof(float), HMX_FP16_TILE_SIZE) : 0;
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
 
-    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
-    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
-    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, activation_area_size);
-    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
-    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
-    void    *vtcm_scratch1   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
-    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
-    float   *vtcm_f32_act    = use_dma_activation ? (float *) vtcm_seq_alloc(&vtcm_ptr, f32_scratch_size) : NULL;
+            // compute output position
+            int c0       = c / HMX_FP16_TILE_N_COLS;  // tile column index
+            int tile_idx = r0 * (k_block / HMX_FP16_TILE_N_COLS) + c0;
 
-    if ((size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base) > vtcm_budget) {
-        FARF(HIGH, "%s: grouped layout overflowed VTCM, falling back to legacy batched loop", __func__);
-        return hmx_mat_mul_permuted_w16a32_batched_legacy(ctx, params);
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HMX_FP16_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
     }
+}
 
-    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
+typedef struct {
+    __fp16      *dst;
+    const float *src;
+    int          n_tasks;
+    int          n_tot_chunks;
+    int          n_chunks_per_task;
+    int          k_block;
+    int          k_stride;
+} activation_transfer_task_state_t;
 
-    FARF(MEDIUM, "%s: grouped path m=%d k=%d n=%d group=%d streams=%d mc=%zu nc=%zu vtcm=%zu/%zu",
-            __func__, params->m, params->k, params->n, group_size, params->ne13,
-            m_chunk_n_rows, n_chunk_n_cols,
-            (size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base), vtcm_budget);
+static void transfer_activation_chunk_worker_fn(unsigned int n, unsigned int i, void *data) {
+    activation_transfer_task_state_t *st = (activation_transfer_task_state_t *) data;
 
-    TIMER_DEFINE(activation_load);
-    TIMER_DEFINE(weight_load);
-    TIMER_DEFINE(hmx_core);
-    TIMER_DEFINE(output_store);
-    TIMER_DEFINE(total);
+    for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) {
+        // one chunk: one row
+        int    chunk_idx  = task_id * st->n_chunks_per_task;
+        size_t chunk_size = hex_smin(st->n_tot_chunks - chunk_idx, st->n_chunks_per_task);
 
-    TIMER_START(total);
+        __fp16      *dst = st->dst + chunk_idx * st->k_block;
+        const float *src = st->src + chunk_idx * st->k_stride;
+        transfer_activation_chunk_fp32_to_fp16(dst, src, chunk_size, st->k_block, st->k_stride);
+    }
+}
 
-    const size_t fp16_row_bytes   = (size_t) params->k * sizeof(__fp16);
-    const size_t weight_row_bytes = (size_t) params->weight_stride * sizeof(__fp16);
+static void transfer_activation_chunk_threaded(struct htp_context *ctx, __fp16 *dst, const float *src, int n_rows, int k_block, int k_stride) {
+    assert(k_block % HMX_FP16_TILE_N_COLS == 0 && k_stride % HMX_FP16_TILE_N_COLS == 0);
+    assert(VLEN == 32 * sizeof(float));
 
-    HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
+    size_t n_tot_chunks      = n_rows;
+    size_t n_chunks_per_task = 32;  // must be multiple of 32 to ensure correct destination address
 
-    for (int b3 = 0; b3 < params->ne13; ++b3) {
-        for (int b2_base = 0; b2_base < params->ne12; b2_base += group_size) {
-            const __fp16 *weight_group = hmx_matmul_weight_batch_ptr(params, b2_base, b3);
+    activation_transfer_task_state_t state;
+    state.n_tasks           = (n_tot_chunks + n_chunks_per_task - 1) / n_chunks_per_task;
+    state.n_tot_chunks      = n_tot_chunks;
+    state.n_chunks_per_task = n_chunks_per_task;
+    state.dst               = dst;
+    state.src               = src;
+    state.k_block           = k_block;
+    state.k_stride          = k_stride;
 
-            for (size_t mr = 0; mr < (size_t) params->m; mr += m_chunk_n_rows) {
-                const size_t n_rows = hex_smin((size_t) params->m - mr, m_chunk_n_rows);
-                const size_t n_row_tiles = hmx_ceil_div((int) n_rows, HMX_FP16_TILE_N_ROWS);
+    worker_pool_run_func(ctx->worker_pool, transfer_activation_chunk_worker_fn, &state, ctx->n_threads);
+}
 
-                // Pre-load activations for all heads in the group (once per m_chunk).
-                // When the source is strided (permuted Q), use 2D DMA to gather
-                // contiguous rows into a VTCM scratch buffer first, then HVX
-                // converts from the contiguous VTCM buffer.  This avoids L2 cache
-                // thrashing from HVX loads at large strides.
-                TIMER_START(activation_load);
-                for (int g = 0; g < group_size; ++g) {
-                    const float *activation_chunk = hmx_matmul_activation_batch_ptr(params, b2_base + g, b3) + mr * params->act_stride;
-                    __fp16 *vtcm_act_g = vtcm_activation + (size_t) g * act_head_stride;
-                    if (use_dma_activation) {
-                        const size_t row_bytes    = (size_t) params->k * sizeof(float);
-                        const size_t stride_bytes = (size_t) params->act_stride * sizeof(float);
-                        dma_queue_push(ctx->dma[0],
-                                          dma_make_ptr(vtcm_f32_act, activation_chunk),
-                                          row_bytes, stride_bytes, row_bytes, n_rows);
-                        dma_queue_pop(ctx->dma[0]);
-                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
-                                                              vtcm_f32_act, (int) n_rows,
-                                                              params->k, params->k);
-                    } else {
-                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
-                                                              activation_chunk, (int) n_rows,
-                                                              params->k, params->act_stride);
-                    }
-                }
-                TIMER_STOP(activation_load);
+//
 
-                void *buf_curr = vtcm_scratch0;
-                void *buf_next = vtcm_scratch1;
+#define FALLBACK_TO_STANDARD 1
 
-                {
-                    const size_t n_cols_first = hex_smin((size_t) params->n, n_chunk_n_cols);
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, weight_group),
-                                      fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_first);
-                }
+// C += AB
+static void core_mma_chunk_fp16(__fp16 *restrict c, const __fp16 *restrict a, const __fp16 *restrict b,
+                                const __fp16 *restrict col_scales, const __fp16 *restrict eye_tile,
+                                int n_row_tiles, int n_col_tiles, int n_dot_tiles, bool zero_init) {
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(n_dot_tiles > 0);
 
-                for (size_t nc = 0; nc < (size_t) params->n; nc += n_chunk_n_cols) {
-                    const size_t n_cols = hex_smin((size_t) params->n - nc, n_chunk_n_cols);
-                    const size_t n_col_tiles = hmx_ceil_div((int) n_cols, HMX_FP16_TILE_N_COLS);
+    Q6_bias_mxmem2_A((void *)col_scales);
 
-                    TIMER_START(weight_load);
-                    {
-                        dma_queue_pop(ctx->dma[0]);
+    const size_t dot_tile_stride = n_dot_tiles * HMX_FP16_TILE_N_ELMS;
+    for (size_t i = 0; i < n_row_tiles; ++i) {
+        const __fp16 *row_base = a + i * dot_tile_stride;
+        __fp16 *res_base = c + i * n_col_tiles * HMX_FP16_TILE_N_ELMS;
+        for (size_t j = 0; j < n_col_tiles; ++j) {
+            Q6_mxclracc_hf();
 
-                        const size_t nc_next = nc + n_chunk_n_cols;
-                        if (nc_next < (size_t) params->n) {
-                            const size_t n_cols_next = hex_smin((size_t) params->n - nc_next, n_chunk_n_cols);
-                            const __fp16 *next_weight_chunk = weight_group + nc_next * params->weight_stride;
-
-                            dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
-                                              fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_next);
-                        }
-
-                        interleave_fp16_weight_chunk_to_tiles(vtcm_weight, (const __fp16 *) buf_curr, n_cols, params->k);
-                        swap_ptr(&buf_curr, &buf_next);
-                    }
-                    TIMER_STOP(weight_load);
-
-                    // Reuse the interleaved weight for every q_head in this GQA group
-                    for (int g = 0; g < group_size; ++g) {
-                        TIMER_START(hmx_core);
-                        {
-                            const __fp16 * vtcm_act_g = vtcm_activation + (size_t) g * act_head_stride;
-                            core_dot_chunk_fp16(vtcm_output, vtcm_act_g, vtcm_weight, vtcm_scales, n_row_tiles, n_col_tiles,
-                                                params->k / 32);
-                        }
-                        TIMER_STOP(hmx_core);
+            const __fp16 *col_tiles = b + j * dot_tile_stride;
+            const __fp16 *row_tiles = row_base;
+            __fp16 *accum_tile = res_base + j * HMX_FP16_TILE_N_ELMS;
+            if (!zero_init) {
+                Q6_activation_hf_mxmem_RR((unsigned int)accum_tile, 2047);
+                Q6_weight_hf_mxmem_RR((unsigned int)eye_tile, 2047);
+            }
 
-                        TIMER_START(output_store);
-                        {
-                            float *output = hmx_matmul_dst_batch_ptr(params, b2_base + g, b3) + mr * params->dst_stride + nc;
-                            transfer_output_chunk_threaded(ctx, output, vtcm_output, (int) n_rows, (int) n_cols, params->dst_stride);
-                        }
-                        TIMER_STOP(output_store);
-                    }
-                }
+            for (int k = 0; k < n_dot_tiles; ++k) {
+                Q6_activation_hf_mxmem_RR((unsigned int)row_tiles, 2047);
+                Q6_weight_hf_mxmem_RR((unsigned int)col_tiles, 2047);
+                row_tiles += HMX_FP16_TILE_N_ELMS;
+                col_tiles += HMX_FP16_TILE_N_ELMS;
             }
+            Q6_mxmem_AR_after_hf(accum_tile, 0);
         }
     }
+}
 
-    HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
-
-    TIMER_STOP(total);
-
-#if defined(ENABLE_PROFILE_TIMERS)
-    FARF(HIGH, "%s: %lld us, m=%d k=%d n=%d group=%d", __func__, TIMER_US(total),
-         params->m, params->k, params->n, group_size);
-    FARF(HIGH, "  activation_load: %lld us, weight_load: %lld us, hmx_core: %lld us, output_store: %lld us",
-         TIMER_US(activation_load), TIMER_US(weight_load), TIMER_US(hmx_core), TIMER_US(output_store));
-#endif
+static __attribute__((noinline)) int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx,
+                                       float *restrict out, const float *restrict x, const uint8_t *restrict w,
+                                       int m, int k, int n, int weight_type) {
+    // assume k % 32 == 0 && n % 32 == 0
+    const size_t row_stride = get_x4x2_row_stride(weight_type, k);
+    if (row_stride == 0) {
+        return -1;
+    }
 
-  return 0;
-}
+    const size_t vtcm_budget = ctx->vtcm_size;
 
-int hmx_mat_mul_permuted_w16a32(struct htp_context *ctx, float *restrict dst, const float *restrict activation,
-                                const __fp16 *restrict permuted_weight, int m, int k, int n,
-                                int act_stride, int weight_stride) {
-    if (!dst || !activation || !permuted_weight || !m || !n || !k) { return -1; }
-    if (act_stride < k || weight_stride < k) { return -1; }
-    if (k % 32 != 0 || n % 32 != 0) { return -1; }
+    const size_t K_BLOCK_SIZE = 1024;
 
-    if (!hex_is_aligned(dst, VLEN) || !hex_is_aligned(activation, VLEN) || !hex_is_aligned(permuted_weight, VLEN)) {
-      return -1;
+    // Fallback: if k doesn't need K-blocking, out-stationary has no advantage
+    const size_t k_iters_check = (k + K_BLOCK_SIZE - 1) / K_BLOCK_SIZE;
+    if (k_iters_check <= 1) {
+        FARF(HIGH, "%s: K_BLK=%zu >= k=%d, fallback to standard path", __func__, K_BLOCK_SIZE, k);
+        return FALLBACK_TO_STANDARD;
     }
 
-    // --- Dynamic VTCM layout ---
-    const size_t vtcm_budget  = ctx->vtcm_size;
-    const size_t vec_dot_size = k * sizeof(__fp16);
+    // Dynamic M,N search via hmx_compute_chunks
+    const size_t sub_row_stride_alloc = get_x4x2_row_stride(weight_type, K_BLOCK_SIZE);
+    const size_t per_m  = K_BLOCK_SIZE * sizeof(float)   // scratch1: M×K×4 (act DMA staging F32)
+                        + K_BLOCK_SIZE * sizeof(__fp16); // activation: M×K×2 (F16 tiles)
+    const size_t per_n  = sub_row_stride_alloc           // scratch0: N×sub_row(K) (packed quant)
+                        + K_BLOCK_SIZE * sizeof(__fp16); // weight: N×K×2 (F16 tiles)
+    const size_t per_mn = sizeof(__fp16);                // output: M×N×2 (out-stationary)
 
-    // DMA-based activation gather for strided tensors (see batched path comment).
-    const bool use_dma_activation = (act_stride > k);
-    const size_t f32_scratch_per_m = use_dma_activation ? (size_t) k * sizeof(float) : 0;
+    // Alignment margin: hex_align_up can add up to 2047 bytes per buffer;
+    // scratch1 (mc×6144) is naturally 2048-aligned, remaining 4 buffers need margin
+    const size_t align_margin = 4 * HMX_FP16_TILE_SIZE;
+    const size_t overhead     = HMX_FP16_TILE_SIZE + 256 + align_margin;  // eye_tile + scales + alignment
 
-    size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
-    // FP16 weight: interleave and activation load have similar per-element cost.
-    if (hmx_compute_chunks(vtcm_budget,
-                           /*overhead=*/256,
-                           /*per_n=*/3 * vec_dot_size,                  // W + S0 + S1
-                           /*per_m=*/vec_dot_size + f32_scratch_per_m,  // A + optional F32 scratch
-                           /*per_mn=*/sizeof(__fp16),                   // O
-                           m, n,
-                           /*m_block_cost=*/(size_t) n,
-                           /*n_block_cost=*/(size_t) m, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
+    size_t       M_BLOCK_SIZE, N_BLOCK_SIZE, vtcm_used;
+    // Cost-based search: minimize ceil(m/mc)*m_block_cost + ceil(n/nc)*n_block_cost.
+    // From profiling: wt_dequant per element ≈ 1.5× activation load per element.
+    // m_block_cost = n*3: each extra M-block re-dequants all N×K weight (expensive).
+    // n_block_cost = m*2: each extra N-block re-loads all M×K activation (cheaper).
+    const size_t m_block_cost = (size_t) n * 3;
+    const size_t n_block_cost = (size_t) m * 2;
+    if (hmx_compute_chunks(vtcm_budget, overhead, per_n, per_m, per_mn,
+                           hex_align_up(m, HMX_FP16_TILE_N_ROWS), n,
+                           m_block_cost, n_block_cost, &M_BLOCK_SIZE,
+                           &N_BLOCK_SIZE, &vtcm_used) != 0) {
         FARF(HIGH, "%s: VTCM too small (m=%d k=%d n=%d budget=%zu)", __func__, m, k, n, vtcm_budget);
         return -1;
     }
 
-    const size_t weight_area_size     = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t activation_area_size = hex_align_up(m_chunk_n_rows * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t output_area_size     = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HMX_FP16_TILE_SIZE);
-    const size_t scratch_area_size    = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t f32_scratch_size     = use_dma_activation
-        ? hex_align_up(m_chunk_n_rows * (size_t) k * sizeof(float), HMX_FP16_TILE_SIZE) : 0;
+    // Compute precise buffer sizes from searched M,N and fixed K
+    const size_t weight_size  = hex_align_up(N_BLOCK_SIZE * K_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
+    const size_t act_size     = hex_align_up(M_BLOCK_SIZE * K_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
+    const size_t out_size     = hex_align_up(M_BLOCK_SIZE * N_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
+    const size_t scratch0_sz  = hex_align_up(N_BLOCK_SIZE * sub_row_stride_alloc, HMX_FP16_TILE_SIZE);
+    const size_t scratch1_sz  = hex_align_up(M_BLOCK_SIZE * K_BLOCK_SIZE * sizeof(float), HMX_FP16_TILE_SIZE);
 
-    // VTCM layout: weight | activation | output | scratch0 | scratch1 | scales | [f32_scratch]
-    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
-    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
-    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, activation_area_size);
-    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
-    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
-    void    *vtcm_scratch1   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
-    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
-    float   *vtcm_f32_act    = use_dma_activation ? (float *) vtcm_seq_alloc(&vtcm_ptr, f32_scratch_size) : NULL;
-    if ((size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base) > vtcm_budget) {
-        FARF(ERROR, "%s: vtcm overflow: used=%zu limit=%zu", __func__,
-             (size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base), vtcm_budget);
+    const size_t total_vtcm = weight_size + act_size + out_size + scratch0_sz + scratch1_sz + HMX_FP16_TILE_SIZE + 256;
+    if (total_vtcm > vtcm_budget) {
+        FARF(HIGH, "%s: VTCM overflow after search: need %zu have %zu (M=%zu N=%zu K=%zu)", __func__, total_vtcm,
+                    vtcm_budget, M_BLOCK_SIZE, N_BLOCK_SIZE, K_BLOCK_SIZE);
         return -1;
     }
 
-    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
+    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
+    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_size);
+    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_size);
+    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, out_size);
+    uint8_t *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch0_sz);
+    uint8_t *vtcm_scratch1   = vtcm_seq_alloc(&vtcm_ptr, scratch1_sz);
+    __fp16  *vtcm_eye_tile   = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, HMX_FP16_TILE_SIZE);
+    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
+    assert((size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base) <= vtcm_budget);
 
-    FARF(MEDIUM, "%s: m=%d k=%d n=%d mc=%zu nc=%zu vtcm=%zu/%zu",
-         __func__, m, k, n, m_chunk_n_rows, n_chunk_n_cols,
-         (size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base), vtcm_budget);
+    FARF(HIGH, "hmx-mm: m=%d k=%d n=%d wtype=%d block M=%zu N=%zu K=%zu vtcm=%zu/%zu", m, k, n, weight_type,
+         M_BLOCK_SIZE, N_BLOCK_SIZE, K_BLOCK_SIZE, (size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base), vtcm_budget);
 
-    TIMER_DEFINE(activation_load);
-    TIMER_DEFINE(weight_load);
-    TIMER_DEFINE(hmx_core);
-    TIMER_DEFINE(output_store);
+    // initialize eye tile (32x32 identity matrix)
+    {
+        HVX_Vector v;
+        v = Q6_V_vzero();
+        v = Q6_Vw_vinsert_VwR(v, 0x3c000000);
+        v = Q6_V_vror_VR(v, VLEN - 4);
+        v = Q6_Vw_vinsert_VwR(v, 0x00003c00);
+        for (int i = 0; i < 16; ++i) {
+            ((HVX_Vector *) vtcm_eye_tile)[i] = v;
+            v = Q6_V_vror_VR(v, VLEN - 8);
+        }
+    }
+    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
 
-    TIMER_DEFINE(total);
-    TIMER_START(total);
+    TIMER_DEFINE(fetch);
+    TIMER_DEFINE(act_load);
+    TIMER_DEFINE(wt_dequant);
+    TIMER_DEFINE(core);
 
     HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
 
-    for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) {
-        // transfer activation matrix chunk into VTCM
-        const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows);
-        const size_t n_row_tiles = hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS);
-
-        TIMER_START(activation_load);
-        {
-            const float *activation_chunk = activation + mr * act_stride;
-            if (use_dma_activation) {
-                const size_t row_bytes    = (size_t) k * sizeof(float);
-                const size_t stride_bytes = (size_t) act_stride * sizeof(float);
-                dma_queue_push(ctx->dma[0],
-                                  dma_make_ptr(vtcm_f32_act, activation_chunk),
-                                  row_bytes, stride_bytes, row_bytes, n_rows);
-                dma_queue_pop(ctx->dma[0]);
-                transfer_activation_chunk_threaded(ctx, vtcm_activation,
-                                                      vtcm_f32_act, n_rows, k, k);
-            } else {
-                transfer_activation_chunk_threaded(ctx, vtcm_activation,
-                                                    activation_chunk, n_rows, k, act_stride);
-            }
-        }
-        TIMER_STOP(activation_load);
-
-        const size_t fp16_row_bytes    = (size_t) k * sizeof(__fp16);
-        const size_t weight_row_bytes  = (size_t) weight_stride * sizeof(__fp16);
+    for (size_t mr = 0; mr < m; mr += M_BLOCK_SIZE) {
+        size_t m_blk_sz = hex_smin(m - mr, M_BLOCK_SIZE);
+        for (size_t nc = 0; nc < n; nc += N_BLOCK_SIZE) {
+            size_t n_blk_sz = hex_smin(n - nc, N_BLOCK_SIZE);
 
-        void *buf_curr = vtcm_scratch0;
-        void *buf_next = vtcm_scratch1;
+            const int n_row_tiles = hmx_ceil_div(m_blk_sz, HMX_FP16_TILE_N_ROWS);
+            const int n_col_tiles = hmx_ceil_div(n_blk_sz, HMX_FP16_TILE_N_COLS);
 
-        // issue async DMA for the first weight chunk
-        // NOTE: use 2D DMA (n_cols rows x fp16_row_bytes) to avoid 16-bit roiwidth overflow.
-        // The source rows can be strided (e.g. KV-cache K after ggml_permute).
-        {
-            const size_t n_cols_first = hex_smin(n, n_chunk_n_cols);
+            for (size_t kk = 0; kk < k; kk += K_BLOCK_SIZE) {
+                const size_t k_blk_sz = hex_smin(k - kk, K_BLOCK_SIZE);
 
-            dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, permuted_weight),
-                              fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_first);
-        }
+                TIMER_START(fetch);
+                // fetch activation block into VTCM
+                {
+                    const float *activation_block = x + mr * k + kk;
 
-        for (size_t nc = 0; nc < n; nc += n_chunk_n_cols) {
-            const size_t n_cols = hex_smin(n - nc, n_chunk_n_cols);
-            const size_t n_col_tiles = hmx_ceil_div(n_cols, HMX_FP16_TILE_N_COLS);
+                    dma_queue_push(ctx->dma[0],
+                                     dma_make_ptr(vtcm_scratch1, activation_block),
+                                     k_blk_sz * sizeof(float),
+                                     k * sizeof(float),
+                                     k_blk_sz * sizeof(float),
+                                     m_blk_sz);
+                }
 
-            TIMER_START(weight_load);
-            {
-                dma_queue_pop(ctx->dma[0]);  // wait until current weight chunk is ready
+                // fetch weight block into VTCM (x4x2 sub-block: quants + scales)
+                const size_t sub_row_stride = get_x4x2_row_stride(weight_type, k_blk_sz);
+                {
+                    const int blk_start       = kk / QK_Q4_0x4x2;
+                    const int nb_sub          = (k_blk_sz + QK_Q4_0x4x2 - 1) / QK_Q4_0x4x2;
+                    const int  full_qrow      = (weight_type == HTP_TYPE_Q8_0) ? k : (k / 2);
+                    const int  scale_blk_size = (weight_type == HTP_TYPE_MXFP4) ? HMX_X4X2_MXFP4_EBLK_SIZE : HMX_X4X2_DBLK_SIZE;
+                    uint8_t       *dst        = vtcm_scratch0;
+                    const uint8_t *src        = w + nc * row_stride;
+                    const size_t  n_rows      = n_blk_sz;
+                    const size_t  src_stride  = row_stride;
+                    const size_t  dst_stride  = sub_row_stride;
+                    const size_t  quant_off   = (weight_type == HTP_TYPE_Q8_0) ? (blk_start * QK_Q8_0x4x2) : (blk_start * (QK_Q4_0x4x2 / 2));
+                    const size_t  quant_width = (weight_type == HTP_TYPE_Q8_0) ? (nb_sub    * QK_Q8_0x4x2) : (nb_sub    * (QK_Q4_0x4x2 / 2));
+                    const size_t  scale_off   = full_qrow + blk_start * scale_blk_size;
+                    const size_t  scale_width = nb_sub * scale_blk_size;
 
-                // issue async DMA for the next weight chunk (double buffering)
-                const size_t nc_next = nc + n_chunk_n_cols;
-                if (nc_next < n) {
-                    const size_t n_cols_next       = hex_smin(n - nc_next, n_chunk_n_cols);
-                    const __fp16 *next_weight_chunk = permuted_weight + nc_next * weight_stride;
+                    // 2D DMA: quants sub-range
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(dst, src + quant_off), dst_stride, src_stride, quant_width, n_rows);
+                    // 2D DMA: scales sub-range
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(dst + quant_width, src + scale_off), dst_stride, src_stride, scale_width, n_rows);
+                }
+                TIMER_STOP(fetch);
 
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
-                                      fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_next);
+                TIMER_START(act_load);
+                // load activation block
+                {
+                    dma_queue_pop(ctx->dma[0]); // wait for act DNA
+                    transfer_activation_chunk_threaded(ctx, vtcm_activation, (float *) vtcm_scratch1, m_blk_sz, k_blk_sz, k_blk_sz);
                 }
+                TIMER_STOP(act_load);
 
-                // interleave row-major fp16 from scratch into tile-major in vtcm_weight
-                interleave_fp16_weight_chunk_to_tiles(vtcm_weight, (const __fp16 *)buf_curr, n_cols, k);
+                TIMER_START(wt_dequant);
+                // dequantize weight block
+                {
+                    dma_queue_pop(ctx->dma[0]);
+                    dma_queue_pop(ctx->dma[0]);
+                    // vtcm_scratch0 is used to store the qweight chunk
+                    // worker_pool_run_func already returned, so fetch is done
+                    dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight, vtcm_scratch0,
+                                                                n_blk_sz, k_blk_sz, sub_row_stride, weight_type);
+                }
+                TIMER_STOP(wt_dequant);
 
-                swap_ptr(&buf_curr, &buf_next);
-            }
-            TIMER_STOP(weight_load);
-
-            TIMER_START(hmx_core);
-            {
-                core_dot_chunk_fp16(vtcm_output, vtcm_activation, vtcm_weight, vtcm_scales, n_row_tiles, n_col_tiles, k / 32);
+                // core mma
+                TIMER_START(core);
+                {
+                    core_mma_chunk_fp16(vtcm_output, vtcm_activation, vtcm_weight, vtcm_scales, vtcm_eye_tile, n_row_tiles,
+                                        n_col_tiles, k_blk_sz / HMX_FP16_TILE_N_COLS, kk == 0);
+                }
+                TIMER_STOP(core);
             }
-            TIMER_STOP(hmx_core);
 
-            TIMER_START(output_store);
+            // store output block
             {
-                float *output = dst + (mr * n + nc);
-                transfer_output_chunk_threaded(ctx, output, vtcm_output, n_rows, n_cols, n);
+                float *output_block = out + (mr * n + nc);
+                transfer_output_chunk_threaded(ctx, output_block, vtcm_output, m_blk_sz, n_blk_sz, n);
             }
-            TIMER_STOP(output_store);
         }
-
     }
 
     HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
 
-    TIMER_STOP(total);
-
 #if defined(ENABLE_PROFILE_TIMERS)
-    FARF(HIGH, "%s: %lld us, m=%d k=%d n=%d", __func__, TIMER_US(total), m, k, n);
-    FARF(HIGH, "  activation_load: %lld us, weight_load: %lld us, hmx_core: %lld us, output_store: %lld us",
-         TIMER_US(activation_load), TIMER_US(weight_load), TIMER_US(hmx_core), TIMER_US(output_store));
-    {
-        size_t weight_size = (size_t)k * n * sizeof(__fp16);
-        float  bandwidth   = 1e-3f * weight_size / (float)TIMER_US(weight_load);
-        FARF(HIGH, "  weight load bandwidth: %.2f GB/s", bandwidth);
-    }
+    FARF(HIGH, "fetch: %lld us, act_load: %lld us, wt_dequant: %lld us, core: %lld us",
+         TIMER_US(fetch), TIMER_US(act_load), TIMER_US(wt_dequant), TIMER_US(core));
 #endif
-
     return 0;
 }
 
-int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict out, const float *restrict x, const uint8_t *restrict w, int m,
-                                       int k, int n, int w_type);
-
-#define FALLBACK_TO_STANDARD 1
-
 int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict dst, const float *restrict activation,
                                      const uint8_t *restrict permuted_weight, int m, int k, int n,
                                      int weight_type) {
@@ -1238,7 +1078,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
         if (rc != FALLBACK_TO_STANDARD) {
             return rc;  // 0 success, -1 error
         }
-        FARF(MEDIUM, "hmx_matmul_qk: out-stationary fallback to standard m=%d k=%d n=%d", m, k, n);
+        FARF(HIGH, "hmx_matmul_qk: out-stationary fallback to standard m=%d k=%d n=%d", m, k, n);
         // fall through to standard path
     }
 
@@ -1247,31 +1087,48 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
         return -1;
     }
 
-    FARF(MEDIUM, "hmx_matmul_qk: STANDARD path m=%d k=%d n=%d type=%d", m, k, n, weight_type);
+    FARF(HIGH, "hmx_matmul_qk: STANDARD path m=%d k=%d n=%d type=%d", m, k, n, weight_type);
 
     // --- Dynamic VTCM layout ---
     const size_t vtcm_budget   = ctx->vtcm_size;
     const size_t vec_dot_size  = k * sizeof(__fp16);
-    const bool   use_pipeline  = (m >= 128) && (k <= n);
 
-    // Select cost parameters based on execution path
-    size_t per_n_cost, per_mn_cost;
-    if (use_pipeline) {
-        per_n_cost  = row_stride + 2 * vec_dot_size;  // Q + S0 + S1 (dequant bufs)
-        per_mn_cost = 2 * sizeof(__fp16);              // O x 2 (output double buffer)
-    } else {
-        per_n_cost  = vec_dot_size + 2 * row_stride;   // W + S0 + S1 (x4x2 DMA bufs)
-        per_mn_cost = sizeof(__fp16);                   // O x 1
-    }
+    // Pipeline = 4-stage DMA→dequant→HMX→store with HMX worker overlap.
+    // Only pays off when the chunker yields >=2 n-chunks, so the main loop can
+    // overlap HMX (C) with HVX (B/D); with a single n-chunk the extra VTCM for
+    // double-buffered output and the worker-dispatch overhead are pure loss.
+    // Try pipeline costs first; fall back to sequential if the layout collapses
+    // to one n-chunk. m >= 128 floor keeps HMX utilization reasonable.
+    const size_t pipe_per_n  = row_stride + 2 * vec_dot_size;  // Q + S0 + S1 (dequant bufs)
+    const size_t pipe_per_mn = 2 * sizeof(__fp16);             // O x 2 (output double buffer)
+    const size_t seq_per_n   = vec_dot_size + 2 * row_stride;  // W + S0 + S1 (x4x2 DMA bufs)
+    const size_t seq_per_mn  = sizeof(__fp16);                 // O x 1
 
     size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
-    // Quantized weight: dequant ~1.5x more expensive per element than activation load.
-    if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256, per_n_cost, /*per_m=*/vec_dot_size, per_mn_cost, m, n,
-                           /*m_block_cost=*/(size_t) n * 3,
-                           /*n_block_cost=*/(size_t) m * 2, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
-        FARF(HIGH, "%s: VTCM too small (m=%d k=%d n=%d pipe=%d budget=%zu)",
-             __func__, m, k, n, use_pipeline, vtcm_budget);
-        return -1;
+    bool   use_pipeline = false;
+
+    if (m >= 128) {
+        size_t mc = 0, nc = 0, used = 0;
+        if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256, pipe_per_n, /*per_m=*/vec_dot_size, pipe_per_mn,
+                               hex_align_up(m, HMX_FP16_TILE_N_ROWS), n,
+                               /*m_block_cost=*/(size_t) n * 3,
+                               /*n_block_cost=*/(size_t) m * 2, &mc, &nc, &used) == 0 &&
+            hmx_ceil_div((size_t) n, nc) >= 2) {
+            m_chunk_n_rows = mc;
+            n_chunk_n_cols = nc;
+            vtcm_used      = used;
+            use_pipeline   = true;
+        }
+    }
+
+    if (!use_pipeline) {
+        if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256, seq_per_n, /*per_m=*/vec_dot_size, seq_per_mn,
+                               hex_align_up(m, HMX_FP16_TILE_N_ROWS), n,
+                               /*m_block_cost=*/(size_t) n * 3,
+                               /*n_block_cost=*/(size_t) m * 2, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
+            FARF(HIGH, "%s: VTCM too small (m=%d k=%d n=%d budget=%zu)", __func__, m, k, n, vtcm_budget);
+            return -1;
+        }
     }
 
     // Compute precise buffer sizes per execution path
@@ -1308,7 +1165,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
 
     hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
 
-    FARF(MEDIUM, "%s: m=%d k=%d n=%d wtype=%d pipe=%d mc=%zu nc=%zu vtcm=%zu/%zu",
+    FARF(HIGH, "%s: m=%d k=%d n=%d wtype=%d pipe=%d mc=%zu nc=%zu vtcm=%zu/%zu",
          __func__, m, k, n, weight_type, use_pipeline,
          m_chunk_n_rows, n_chunk_n_cols,
          (size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base), vtcm_budget);
@@ -1321,7 +1178,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
     TIMER_DEFINE(total);
     TIMER_START(total);
 
-    FARF(MEDIUM, "hmx_matmul_qk: %s mc=%zu nc=%zu vtcm=%zu/%zu",
+    FARF(HIGH, "hmx_matmul_qk: %s mc=%zu nc=%zu vtcm=%zu/%zu",
          use_pipeline ? "PIPELINE" : "SEQUENTIAL", m_chunk_n_rows, n_chunk_n_cols,
          (size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base), vtcm_budget);
 
@@ -1368,7 +1225,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
                     // HMX computes C = A x B, where A=[M,K] activation, B=[K,N] weight.
                     dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight, buf_curr, n_cols, k, row_stride, weight_type);
 
-                    swap_ptr(&buf_curr, &buf_next);
+                    hex_swap_ptr(&buf_curr, &buf_next);
                 }
                 TIMER_STOP(weight_load);
 
@@ -1511,300 +1368,418 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
     return 0;
 }
 
-// C += AB
-void core_mma_chunk_fp16(__fp16 *restrict c, const __fp16 *restrict a, const __fp16 *restrict b, const __fp16 *restrict col_scales, const __fp16 *restrict eye_tile,
-                         int n_row_tiles, int n_col_tiles, int n_dot_tiles, bool zero_init) {
-    __builtin_assume(n_row_tiles > 0);
-    __builtin_assume(n_col_tiles > 0);
-    __builtin_assume(n_dot_tiles > 0);
+//
 
-    Q6_bias_mxmem2_A((void *)col_scales);
+static inline int hmx_matmul_batch_r2(const hmx_matmul_w16a32_batched_params_t *params) {
+    return params->ne02 > 0 ? params->ne12 / params->ne02 : 1;
+}
 
-    const size_t dot_tile_stride = n_dot_tiles * HMX_FP16_TILE_N_ELMS;
-    for (size_t i = 0; i < n_row_tiles; ++i) {
-        const __fp16 *row_base = a + i * dot_tile_stride;
-        __fp16 *res_base = c + i * n_col_tiles * HMX_FP16_TILE_N_ELMS;
-        for (size_t j = 0; j < n_col_tiles; ++j) {
-            Q6_mxclracc_hf();
+static inline int hmx_matmul_batch_r3(const hmx_matmul_w16a32_batched_params_t *params) {
+    return params->ne03 > 0 ? params->ne13 / params->ne03 : 1;
+}
 
-            const __fp16 *col_tiles = b + j * dot_tile_stride;
-            const __fp16 *row_tiles = row_base;
-            __fp16 *accum_tile = res_base + j * HMX_FP16_TILE_N_ELMS;
-            if (!zero_init) {
-                Q6_activation_hf_mxmem_RR((unsigned int)accum_tile, 2047);
-                Q6_weight_hf_mxmem_RR((unsigned int)eye_tile, 2047);
-            }
+static inline const __fp16 *hmx_matmul_weight_batch_ptr(const hmx_matmul_w16a32_batched_params_t *params,
+                                                        int dst_b2, int dst_b3) {
+    const int r2 = hmx_matmul_batch_r2(params);
+    const int r3 = hmx_matmul_batch_r3(params);
+    return (const __fp16 *) ((const uint8_t *) params->permuted_weight +
+                             (size_t) (dst_b2 / r2) * params->src0_nb2 +
+                             (size_t) (dst_b3 / r3) * params->src0_nb3);
+}
 
-            for (int k = 0; k < n_dot_tiles; ++k) {
-                Q6_activation_hf_mxmem_RR((unsigned int)row_tiles, 2047);
-                Q6_weight_hf_mxmem_RR((unsigned int)col_tiles, 2047);
-                row_tiles += HMX_FP16_TILE_N_ELMS;
-                col_tiles += HMX_FP16_TILE_N_ELMS;
-            }
-            Q6_mxmem_AR_after_hf(accum_tile, 0);
+static inline const float *hmx_matmul_activation_batch_ptr(const hmx_matmul_w16a32_batched_params_t *params,
+                                                           int dst_b2, int dst_b3) {
+    return (const float *) ((const uint8_t *) params->activation +
+                            (size_t) dst_b2 * params->src1_nb2 +
+                            (size_t) dst_b3 * params->src1_nb3);
+}
+
+static inline float *hmx_matmul_dst_batch_ptr(const hmx_matmul_w16a32_batched_params_t *params,
+                                              int dst_b2, int dst_b3) {
+    return (float *) ((uint8_t *) params->dst +
+                      (size_t) dst_b2 * params->dst_nb2 +
+                      (size_t) dst_b3 * params->dst_nb3);
+}
+
+static int hmx_mat_mul_permuted_w16a32_batched_legacy(struct htp_context *ctx,
+                                                      const hmx_matmul_w16a32_batched_params_t *params) {
+    int ret = 0;
+    for (int b3 = 0; b3 < params->ne13 && ret == 0; ++b3) {
+        for (int b2 = 0; b2 < params->ne12 && ret == 0; ++b2) {
+            ret = hmx_mat_mul_permuted_w16a32(ctx,
+                                              hmx_matmul_dst_batch_ptr(params, b2, b3),
+                                              hmx_matmul_activation_batch_ptr(params, b2, b3),
+                                              hmx_matmul_weight_batch_ptr(params, b2, b3),
+                                              params->m, params->k, params->n,
+                                              params->act_stride, params->weight_stride);
         }
     }
+    return ret;
 }
 
-static void transfer_activation_chunk_fp32_to_fp16(__fp16 *restrict vtcm_dst, const float *restrict src, int n_rows,
-                                           int k_block, int k_stride) {
-    for (int r = 0; r < n_rows; r += 2) {
-        int r0 = r / HMX_FP16_TILE_N_ROWS;  // tile row index
-        int r1 = r % HMX_FP16_TILE_N_ROWS;  // intra-tile row idx
+int hmx_mat_mul_permuted_w16a32_batched(struct htp_context *ctx, const hmx_matmul_w16a32_batched_params_t *params) {
+    if (!ctx || !params || !params->dst || !params->activation || !params->permuted_weight) { return -1; }
+    if (!params->m || !params->k || !params->n) { return -1; }
+    if (params->act_stride < params->k || params->weight_stride < params->k || params->dst_stride < params->n) { return -1; }
+    if (params->ne02 <= 0 || params->ne03 <= 0 || params->ne12 <= 0 || params->ne13 <= 0) { return -1; }
+    if (params->ne12 % params->ne02 != 0 || params->ne13 % params->ne03 != 0) { return -1; }
+    if (params->k % 32 != 0 || params->n % 32 != 0) { return -1; }
 
-        const bool next_row_valid = (r + 1) < n_rows;
+    if (!hex_is_aligned(params->dst, VLEN) ||
+        !hex_is_aligned(params->activation, VLEN) ||
+        !hex_is_aligned(params->permuted_weight, VLEN)) {
+        return -1;
+    }
 
-        const HVX_Vector *pv_in0 = (const HVX_Vector *) (src + (r + 0) * k_stride);
-        const HVX_Vector *pv_in1 = (const HVX_Vector *) (src + (r + 1) * k_stride);
-        for (int c = 0; c < k_block; c += 32) {
-            HVX_Vector v0 = *pv_in0++;
-            HVX_Vector v1 = next_row_valid ? *pv_in1++ : Q6_V_vzero();
+    const int group_size = hmx_matmul_batch_r2(params);
 
-            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+    if (group_size <= 1) {
+        FARF(HIGH, "%s: no dim2 GQA reuse (group=%d), using legacy batched loop", __func__, group_size);
+        return hmx_mat_mul_permuted_w16a32_batched_legacy(ctx, params);
+    }
 
-            // compute output position
-            int c0       = c / HMX_FP16_TILE_N_COLS;  // tile column index
-            int tile_idx = r0 * (k_block / HMX_FP16_TILE_N_COLS) + c0;
+    // Grouped path: reuse interleaved weight across all q_heads sharing a
+    // kv_head.  Each q_head gets its own activation buffer in VTCM (so
+    // activation is loaded once per m_chunk and reused across all n_chunks),
+    // and each q_head is computed individually to avoid tile-major packing
+    // issues.  m_chunk_n_rows is always a multiple of 32 (from
+    // hmx_compute_chunks), so per-head tile arrays don't overlap.
+    const size_t vtcm_budget  = ctx->vtcm_size;
+    const size_t vec_dot_size = params->k * sizeof(__fp16);
 
-            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HMX_FP16_TILE_N_ELMS);
-            tile[r1 / 2]     = v_out;
-        }
-    }
-}
+    // When the activation has a large stride (e.g. permuted Q tensor with
+    // act_stride >> k), HVX vector loads from strided DDR thrash L2 cache.
+    // Allocate an F32 scratch buffer in VTCM and use 2D DMA to gather
+    // strided rows into a contiguous block before the F32->F16 conversion.
+    const bool use_dma_activation = (params->act_stride > params->k);
+    const size_t f32_scratch_per_m = use_dma_activation ? (size_t) params->k * sizeof(float) : 0;
 
-typedef struct {
-    __fp16      *dst;
-    const float *src;
-    int          n_tasks;
-    int          n_tot_chunks;
-    int          n_chunks_per_task;
-    int          k_block;
-    int          k_stride;
-} activation_transfer_task_state_t;
+    size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
+    // FP16 weight: interleave and activation load have similar per-element cost.
+    if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256,
+                           /*per_n=*/3 * vec_dot_size,
+                           /*per_m=*/group_size * vec_dot_size + f32_scratch_per_m,
+                           /*per_mn=*/sizeof(__fp16),
+                           hex_align_up(params->m, HMX_FP16_TILE_N_ROWS), params->n,
+                           /*m_block_cost=*/(size_t) params->n,
+                           /*n_block_cost=*/(size_t) params->m, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
+        FARF(HIGH, "%s: grouped path does not fit VTCM, falling back to legacy batched loop", __func__);
+        return hmx_mat_mul_permuted_w16a32_batched_legacy(ctx, params);
+    }
 
-static void transfer_activation_chunk_worker_fn(unsigned int n, unsigned int i, void *data) {
-    activation_transfer_task_state_t *st = (activation_transfer_task_state_t *) data;
+    const size_t act_head_stride      = m_chunk_n_rows * (size_t) params->k;  // fp16 elements between heads
+    const size_t weight_area_size     = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
+    const size_t activation_area_size = hex_align_up(group_size * m_chunk_n_rows * vec_dot_size, HMX_FP16_TILE_SIZE);
+    const size_t output_area_size     = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HMX_FP16_TILE_SIZE);
+    const size_t scratch_area_size    = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
+    const size_t f32_scratch_size     = use_dma_activation
+        ? hex_align_up(m_chunk_n_rows * (size_t) params->k * sizeof(float), HMX_FP16_TILE_SIZE) : 0;
 
-    for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) {
-        // one chunk: one row
-        int    chunk_idx  = task_id * st->n_chunks_per_task;
-        size_t chunk_size = hex_smin(st->n_tot_chunks - chunk_idx, st->n_chunks_per_task);
+    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
+    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
+    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, activation_area_size);
+    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
+    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
+    void    *vtcm_scratch1   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
+    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
+    float   *vtcm_f32_act    = use_dma_activation ? (float *) vtcm_seq_alloc(&vtcm_ptr, f32_scratch_size) : NULL;
 
-        __fp16      *dst = st->dst + chunk_idx * st->k_block;
-        const float *src = st->src + chunk_idx * st->k_stride;
-        transfer_activation_chunk_fp32_to_fp16(dst, src, chunk_size, st->k_block, st->k_stride);
+    if ((size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base) > vtcm_budget) {
+        FARF(HIGH, "%s: grouped layout overflowed VTCM, falling back to legacy batched loop", __func__);
+        return hmx_mat_mul_permuted_w16a32_batched_legacy(ctx, params);
     }
-}
 
-void transfer_activation_chunk_threaded(struct htp_context *ctx, __fp16 *dst, const float *src, int n_rows, int k_block, int k_stride) {
-    assert(k_block % HMX_FP16_TILE_N_COLS == 0 && k_stride % HMX_FP16_TILE_N_COLS == 0);
-    assert(VLEN == 32 * sizeof(float));
+    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
 
-    size_t n_tot_chunks      = n_rows;
-    size_t n_chunks_per_task = 32;  // must be multiple of 32 to ensure correct destination address
+    FARF(HIGH, "%s: grouped path m=%d k=%d n=%d group=%d streams=%d mc=%zu nc=%zu vtcm=%zu/%zu",
+            __func__, params->m, params->k, params->n, group_size, params->ne13,
+            m_chunk_n_rows, n_chunk_n_cols,
+            (size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base), vtcm_budget);
 
-    activation_transfer_task_state_t state;
-    state.n_tasks           = (n_tot_chunks + n_chunks_per_task - 1) / n_chunks_per_task;
-    state.n_tot_chunks      = n_tot_chunks;
-    state.n_chunks_per_task = n_chunks_per_task;
-    state.dst               = dst;
-    state.src               = src;
-    state.k_block           = k_block;
-    state.k_stride          = k_stride;
+    TIMER_DEFINE(activation_load);
+    TIMER_DEFINE(weight_load);
+    TIMER_DEFINE(hmx_core);
+    TIMER_DEFINE(output_store);
+    TIMER_DEFINE(total);
+
+    TIMER_START(total);
+
+    const size_t fp16_row_bytes   = (size_t) params->k * sizeof(__fp16);
+    const size_t weight_row_bytes = (size_t) params->weight_stride * sizeof(__fp16);
+
+    HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
+
+    for (int b3 = 0; b3 < params->ne13; ++b3) {
+        for (int b2_base = 0; b2_base < params->ne12; b2_base += group_size) {
+            const __fp16 *weight_group = hmx_matmul_weight_batch_ptr(params, b2_base, b3);
+
+            for (size_t mr = 0; mr < (size_t) params->m; mr += m_chunk_n_rows) {
+                const size_t n_rows = hex_smin((size_t) params->m - mr, m_chunk_n_rows);
+                const size_t n_row_tiles = hmx_ceil_div((int) n_rows, HMX_FP16_TILE_N_ROWS);
+
+                // Pre-load activations for all heads in the group (once per m_chunk).
+                // When the source is strided (permuted Q), use 2D DMA to gather
+                // contiguous rows into a VTCM scratch buffer first, then HVX
+                // converts from the contiguous VTCM buffer.  This avoids L2 cache
+                // thrashing from HVX loads at large strides.
+                TIMER_START(activation_load);
+                for (int g = 0; g < group_size; ++g) {
+                    const float *activation_chunk = hmx_matmul_activation_batch_ptr(params, b2_base + g, b3) + mr * params->act_stride;
+                    __fp16 *vtcm_act_g = vtcm_activation + (size_t) g * act_head_stride;
+                    if (use_dma_activation) {
+                        const size_t row_bytes    = (size_t) params->k * sizeof(float);
+                        const size_t stride_bytes = (size_t) params->act_stride * sizeof(float);
+                        dma_queue_push(ctx->dma[0],
+                                          dma_make_ptr(vtcm_f32_act, activation_chunk),
+                                          row_bytes, stride_bytes, row_bytes, n_rows);
+                        dma_queue_pop(ctx->dma[0]);
+                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
+                                                              vtcm_f32_act, (int) n_rows,
+                                                              params->k, params->k);
+                    } else {
+                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
+                                                              activation_chunk, (int) n_rows,
+                                                              params->k, params->act_stride);
+                    }
+                }
+                TIMER_STOP(activation_load);
+
+                void *buf_curr = vtcm_scratch0;
+                void *buf_next = vtcm_scratch1;
+
+                {
+                    const size_t n_cols_first = hex_smin((size_t) params->n, n_chunk_n_cols);
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, weight_group),
+                                      fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_first);
+                }
+
+                for (size_t nc = 0; nc < (size_t) params->n; nc += n_chunk_n_cols) {
+                    const size_t n_cols = hex_smin((size_t) params->n - nc, n_chunk_n_cols);
+                    const size_t n_col_tiles = hmx_ceil_div((int) n_cols, HMX_FP16_TILE_N_COLS);
+
+                    TIMER_START(weight_load);
+                    {
+                        dma_queue_pop(ctx->dma[0]);
+
+                        const size_t nc_next = nc + n_chunk_n_cols;
+                        if (nc_next < (size_t) params->n) {
+                            const size_t n_cols_next = hex_smin((size_t) params->n - nc_next, n_chunk_n_cols);
+                            const __fp16 *next_weight_chunk = weight_group + nc_next * params->weight_stride;
+
+                            dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
+                                              fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_next);
+                        }
+
+                        hmx_interleave_rows_to_tiles(vtcm_weight, (const __fp16 *) buf_curr, n_cols, params->k, params->k,
+                                                 0, n_cols);
+                        hex_swap_ptr(&buf_curr, &buf_next);
+                    }
+                    TIMER_STOP(weight_load);
+
+                    // Reuse the interleaved weight for every q_head in this GQA group
+                    for (int g = 0; g < group_size; ++g) {
+                        TIMER_START(hmx_core);
+                        {
+                            const __fp16 * vtcm_act_g = vtcm_activation + (size_t) g * act_head_stride;
+                            core_dot_chunk_fp16(vtcm_output, vtcm_act_g, vtcm_weight, vtcm_scales, n_row_tiles, n_col_tiles,
+                                                params->k / 32);
+                        }
+                        TIMER_STOP(hmx_core);
+
+                        TIMER_START(output_store);
+                        {
+                            float *output = hmx_matmul_dst_batch_ptr(params, b2_base + g, b3) + mr * params->dst_stride + nc;
+                            transfer_output_chunk_threaded(ctx, output, vtcm_output, (int) n_rows, (int) n_cols, params->dst_stride);
+                        }
+                        TIMER_STOP(output_store);
+                    }
+                }
+            }
+        }
+    }
 
-    worker_pool_run_func(ctx->worker_pool, transfer_activation_chunk_worker_fn, &state, ctx->n_threads);
-}
+    HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
 
-int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict out, const float *restrict x, const uint8_t *restrict w,
-                                       int m, int k, int n, int weight_type) {
-    // assume k % 32 == 0 && n % 32 == 0
-    const size_t row_stride = get_x4x2_row_stride(weight_type, k);
-    if (row_stride == 0) {
-        return -1;
-    }
+    TIMER_STOP(total);
 
-    const size_t vtcm_budget = ctx->vtcm_size;
+#if defined(ENABLE_PROFILE_TIMERS)
+    FARF(HIGH, "%s: %lld us, m=%d k=%d n=%d group=%d", __func__, TIMER_US(total),
+         params->m, params->k, params->n, group_size);
+    FARF(HIGH, "  activation_load: %lld us, weight_load: %lld us, hmx_core: %lld us, output_store: %lld us",
+         TIMER_US(activation_load), TIMER_US(weight_load), TIMER_US(hmx_core), TIMER_US(output_store));
+#endif
 
-    const size_t K_BLOCK_SIZE = 1024;
+  return 0;
+}
 
-    // Fallback: if k doesn't need K-blocking, out-stationary has no advantage
-    const size_t k_iters_check = (k + K_BLOCK_SIZE - 1) / K_BLOCK_SIZE;
-    if (k_iters_check <= 1) {
-        FARF(MEDIUM, "%s: K_BLK=%zu >= k=%d, fallback to standard path", __func__, K_BLOCK_SIZE, k);
-        return FALLBACK_TO_STANDARD;
+//
+
+int hmx_mat_mul_permuted_w16a32(struct htp_context *ctx, float *restrict dst, const float *restrict activation,
+                                const __fp16 *restrict permuted_weight, int m, int k, int n,
+                                int act_stride, int weight_stride) {
+    if (!dst || !activation || !permuted_weight || !m || !n || !k) { return -1; }
+    if (act_stride < k || weight_stride < k) { return -1; }
+    if (k % 32 != 0 || n % 32 != 0) { return -1; }
+
+    if (!hex_is_aligned(dst, VLEN) || !hex_is_aligned(activation, VLEN) || !hex_is_aligned(permuted_weight, VLEN)) {
+      return -1;
     }
 
-    // Dynamic M,N search via hmx_compute_chunks
-    const size_t sub_row_stride_alloc = get_x4x2_row_stride(weight_type, K_BLOCK_SIZE);
-    const size_t per_m                = K_BLOCK_SIZE * sizeof(float)  // scratch1: M×K×4 (act DMA staging F32)
-                         + K_BLOCK_SIZE * sizeof(__fp16);             // activation: M×K×2 (F16 tiles)
-    const size_t per_n = sub_row_stride_alloc                         // scratch0: N×sub_row(K) (packed quant)
-                         + K_BLOCK_SIZE * sizeof(__fp16);             // weight: N×K×2 (F16 tiles)
-    const size_t per_mn       = sizeof(__fp16);                       // output: M×N×2 (out-stationary)
-    // Alignment margin: hex_align_up can add up to 2047 bytes per buffer;
-    // scratch1 (mc×6144) is naturally 2048-aligned, remaining 4 buffers need margin
-    const size_t align_margin = 4 * HMX_FP16_TILE_SIZE;
-    const size_t overhead     = HMX_FP16_TILE_SIZE + 256 + align_margin;  // eye_tile + scales + alignment
+    // --- Dynamic VTCM layout ---
+    const size_t vtcm_budget  = ctx->vtcm_size;
+    const size_t vec_dot_size = k * sizeof(__fp16);
 
-    size_t       M_BLOCK_SIZE, N_BLOCK_SIZE, vtcm_used;
-    // Cost-based search: minimize ceil(m/mc)*m_block_cost + ceil(n/nc)*n_block_cost.
-    // From profiling: wt_dequant per element ≈ 1.5× activation load per element.
-    // m_block_cost = n*3: each extra M-block re-dequants all N×K weight (expensive).
-    // n_block_cost = m*2: each extra N-block re-loads all M×K activation (cheaper).
-    const size_t m_block_cost = (size_t) n * 3;
-    const size_t n_block_cost = (size_t) m * 2;
-    if (hmx_compute_chunks(vtcm_budget, overhead, per_n, per_m, per_mn, m, n, m_block_cost, n_block_cost, &M_BLOCK_SIZE,
-                           &N_BLOCK_SIZE, &vtcm_used) != 0) {
+    // DMA-based activation gather for strided tensors (see batched path comment).
+    const bool use_dma_activation = (act_stride > k);
+    const size_t f32_scratch_per_m = use_dma_activation ? (size_t) k * sizeof(float) : 0;
+
+    size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
+    // FP16 weight: interleave and activation load have similar per-element cost.
+    if (hmx_compute_chunks(vtcm_budget,
+                           /*overhead=*/256,
+                           /*per_n=*/3 * vec_dot_size,                  // W + S0 + S1
+                           /*per_m=*/vec_dot_size + f32_scratch_per_m,  // A + optional F32 scratch
+                           /*per_mn=*/sizeof(__fp16),                   // O
+                           hex_align_up(m, HMX_FP16_TILE_N_ROWS), n,
+                           /*m_block_cost=*/(size_t) n,
+                           /*n_block_cost=*/(size_t) m, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
         FARF(HIGH, "%s: VTCM too small (m=%d k=%d n=%d budget=%zu)", __func__, m, k, n, vtcm_budget);
         return -1;
     }
 
-    // Compute precise buffer sizes from searched M,N and fixed K
-    const size_t weight_size  = hex_align_up(N_BLOCK_SIZE * K_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
-    const size_t act_size     = hex_align_up(M_BLOCK_SIZE * K_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
-    const size_t out_size     = hex_align_up(M_BLOCK_SIZE * N_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
-    const size_t scratch0_sz  = hex_align_up(N_BLOCK_SIZE * sub_row_stride_alloc, HMX_FP16_TILE_SIZE);
-    const size_t scratch1_sz  = hex_align_up(M_BLOCK_SIZE * K_BLOCK_SIZE * sizeof(float), HMX_FP16_TILE_SIZE);
+    const size_t weight_area_size     = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
+    const size_t activation_area_size = hex_align_up(m_chunk_n_rows * vec_dot_size, HMX_FP16_TILE_SIZE);
+    const size_t output_area_size     = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HMX_FP16_TILE_SIZE);
+    const size_t scratch_area_size    = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
+    const size_t f32_scratch_size     = use_dma_activation
+        ? hex_align_up(m_chunk_n_rows * (size_t) k * sizeof(float), HMX_FP16_TILE_SIZE) : 0;
 
-    const size_t total_vtcm = weight_size + act_size + out_size + scratch0_sz + scratch1_sz + HMX_FP16_TILE_SIZE + 256;
-    if (total_vtcm > vtcm_budget) {
-        FARF(HIGH, "%s: VTCM overflow after search: need %zu have %zu (M=%zu N=%zu K=%zu)", __func__, total_vtcm,
-             vtcm_budget, M_BLOCK_SIZE, N_BLOCK_SIZE, K_BLOCK_SIZE);
+    // VTCM layout: weight | activation | output | scratch0 | scratch1 | scales | [f32_scratch]
+    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
+    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
+    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, activation_area_size);
+    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
+    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
+    void    *vtcm_scratch1   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
+    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
+    float   *vtcm_f32_act    = use_dma_activation ? (float *) vtcm_seq_alloc(&vtcm_ptr, f32_scratch_size) : NULL;
+    if ((size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base) > vtcm_budget) {
+        FARF(ERROR, "%s: vtcm overflow: used=%zu limit=%zu", __func__,
+             (size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base), vtcm_budget);
         return -1;
     }
 
-    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
-    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_size);
-    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_size);
-    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, out_size);
-    uint8_t *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch0_sz);
-    uint8_t *vtcm_scratch1   = vtcm_seq_alloc(&vtcm_ptr, scratch1_sz);
-    __fp16  *vtcm_eye_tile   = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, HMX_FP16_TILE_SIZE);
-    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
-    assert((size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base) <= vtcm_budget);
+    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
 
-    FARF(HIGH, "hmx-mm: m=%d k=%d n=%d wtype=%d block M=%zu N=%zu K=%zu vtcm=%zu/%zu", __func__, m, k, n, weight_type,
-         M_BLOCK_SIZE, N_BLOCK_SIZE, K_BLOCK_SIZE, (size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base), vtcm_budget);
+    FARF(HIGH, "%s: m=%d k=%d n=%d mc=%zu nc=%zu vtcm=%zu/%zu",
+         __func__, m, k, n, m_chunk_n_rows, n_chunk_n_cols,
+         (size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base), vtcm_budget);
 
-    // initialize eye tile (32x32 identity matrix)
-    {
-        HVX_Vector v;
-        v = Q6_V_vzero();
-        v = Q6_Vw_vinsert_VwR(v, 0x3c000000);
-        v = Q6_V_vror_VR(v, VLEN - 4);
-        v = Q6_Vw_vinsert_VwR(v, 0x00003c00);
-        for (int i = 0; i < 16; ++i) {
-            ((HVX_Vector *) vtcm_eye_tile)[i] = v;
-            v = Q6_V_vror_VR(v, VLEN - 8);
-        }
-    }
-    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
+    TIMER_DEFINE(activation_load);
+    TIMER_DEFINE(weight_load);
+    TIMER_DEFINE(hmx_core);
+    TIMER_DEFINE(output_store);
 
-    TIMER_DEFINE(fetch);
-    TIMER_DEFINE(act_load);
-    TIMER_DEFINE(wt_dequant);
-    TIMER_DEFINE(core);
+    TIMER_DEFINE(total);
+    TIMER_START(total);
 
     HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
 
-    for (size_t mr = 0; mr < m; mr += M_BLOCK_SIZE) {
-        size_t m_blk_sz = hex_smin(m - mr, M_BLOCK_SIZE);
-        for (size_t nc = 0; nc < n; nc += N_BLOCK_SIZE) {
-            size_t n_blk_sz = hex_smin(n - nc, N_BLOCK_SIZE);
+    for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) {
+        // transfer activation matrix chunk into VTCM
+        const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows);
+        const size_t n_row_tiles = hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS);
 
-            const int n_row_tiles = hmx_ceil_div(m_blk_sz, HMX_FP16_TILE_N_ROWS);
-            const int n_col_tiles = hmx_ceil_div(n_blk_sz, HMX_FP16_TILE_N_COLS);
+        TIMER_START(activation_load);
+        {
+            const float *activation_chunk = activation + mr * act_stride;
+            if (use_dma_activation) {
+                const size_t row_bytes    = (size_t) k * sizeof(float);
+                const size_t stride_bytes = (size_t) act_stride * sizeof(float);
+                dma_queue_push(ctx->dma[0],
+                                  dma_make_ptr(vtcm_f32_act, activation_chunk),
+                                  row_bytes, stride_bytes, row_bytes, n_rows);
+                dma_queue_pop(ctx->dma[0]);
+                transfer_activation_chunk_threaded(ctx, vtcm_activation,
+                                                      vtcm_f32_act, n_rows, k, k);
+            } else {
+                transfer_activation_chunk_threaded(ctx, vtcm_activation,
+                                                    activation_chunk, n_rows, k, act_stride);
+            }
+        }
+        TIMER_STOP(activation_load);
 
-            for (size_t kk = 0; kk < k; kk += K_BLOCK_SIZE) {
-                const size_t k_blk_sz = hex_smin(k - kk, K_BLOCK_SIZE);
+        const size_t fp16_row_bytes    = (size_t) k * sizeof(__fp16);
+        const size_t weight_row_bytes  = (size_t) weight_stride * sizeof(__fp16);
 
-                TIMER_START(fetch);
-                // fetch activation block into VTCM
-                {
-                    const float *activation_block = x + mr * k + kk;
+        void *buf_curr = vtcm_scratch0;
+        void *buf_next = vtcm_scratch1;
 
-                    dma_queue_push(ctx->dma[0],
-                                     dma_make_ptr(vtcm_scratch1, activation_block),
-                                     k_blk_sz * sizeof(float),
-                                     k * sizeof(float),
-                                     k_blk_sz * sizeof(float),
-                                     m_blk_sz);
-                }
+        // issue async DMA for the first weight chunk
+        // NOTE: use 2D DMA (n_cols rows x fp16_row_bytes) to avoid 16-bit roiwidth overflow.
+        // The source rows can be strided (e.g. KV-cache K after ggml_permute).
+        {
+            const size_t n_cols_first = hex_smin(n, n_chunk_n_cols);
 
-                // fetch weight block into VTCM (x4x2 sub-block: quants + scales)
-                const size_t sub_row_stride = get_x4x2_row_stride(weight_type, k_blk_sz);
-                {
-                    qweight_fetch_task_state_t s;
-
-                    const int blk_start = kk / QK_Q4_0x4x2;
-                    const int nb_sub = (k_blk_sz + QK_Q4_0x4x2 - 1) / QK_Q4_0x4x2;
-                    const int    full_qrow      = (weight_type == HTP_TYPE_Q8_0) ? k : (k / 2);
-                    const int    scale_blk_size =
-                        (weight_type == HTP_TYPE_MXFP4) ? HMX_X4X2_MXFP4_EBLK_SIZE : HMX_X4X2_DBLK_SIZE;
-
-                    s.dst         = vtcm_scratch0;
-                    s.src         = w + nc * row_stride;
-                    s.n_rows      = n_blk_sz;
-                    s.src_stride  = row_stride;
-                    s.dst_stride  = sub_row_stride;
-                    s.quant_off =
-                        (weight_type == HTP_TYPE_Q8_0) ? (blk_start * QK_Q8_0x4x2) : (blk_start * (QK_Q4_0x4x2 / 2));
-                    s.quant_width =
-                        (weight_type == HTP_TYPE_Q8_0) ? (nb_sub * QK_Q8_0x4x2) : (nb_sub * (QK_Q4_0x4x2 / 2));
-                    s.scale_off   = full_qrow + blk_start * scale_blk_size;
-                    s.scale_width = nb_sub * scale_blk_size;
+            dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, permuted_weight),
+                              fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_first);
+        }
 
-                    // 2D DMA: quants sub-range
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(s.dst, s.src + s.quant_off),
-                                      s.dst_stride, s.src_stride, s.quant_width, s.n_rows);
-                    // 2D DMA: scales sub-range
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(s.dst + s.quant_width, s.src + s.scale_off),
-                                      s.dst_stride, s.src_stride, s.scale_width, s.n_rows);
-                }
-                TIMER_STOP(fetch);
+        for (size_t nc = 0; nc < n; nc += n_chunk_n_cols) {
+            const size_t n_cols = hex_smin(n - nc, n_chunk_n_cols);
+            const size_t n_col_tiles = hmx_ceil_div(n_cols, HMX_FP16_TILE_N_COLS);
 
-                TIMER_START(act_load);
-                // load activation block
-                {
-                    dma_queue_pop(ctx->dma[0]); // wait for act DNA
-                    transfer_activation_chunk_threaded(ctx, vtcm_activation, (float *) vtcm_scratch1, m_blk_sz, k_blk_sz, k_blk_sz);
-                }
-                TIMER_STOP(act_load);
+            TIMER_START(weight_load);
+            {
+                dma_queue_pop(ctx->dma[0]);  // wait until current weight chunk is ready
 
-                TIMER_START(wt_dequant);
-                // dequantize weight block
-                {
-                    dma_queue_pop(ctx->dma[0]);
-                    dma_queue_pop(ctx->dma[0]);
-                    // vtcm_scratch0 is used to store the qweight chunk
-                    // worker_pool_run_func already returned, so fetch is done
-                    dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight, vtcm_scratch0,
-                                                                n_blk_sz, k_blk_sz, sub_row_stride, weight_type);
-                }
-                TIMER_STOP(wt_dequant);
+                // issue async DMA for the next weight chunk (double buffering)
+                const size_t nc_next = nc + n_chunk_n_cols;
+                if (nc_next < n) {
+                    const size_t n_cols_next       = hex_smin(n - nc_next, n_chunk_n_cols);
+                    const __fp16 *next_weight_chunk = permuted_weight + nc_next * weight_stride;
 
-                // core mma
-                TIMER_START(core);
-                {
-                    core_mma_chunk_fp16(vtcm_output, vtcm_activation, vtcm_weight, vtcm_scales, vtcm_eye_tile, n_row_tiles,
-                                        n_col_tiles, k_blk_sz / HMX_FP16_TILE_N_COLS, kk == 0);
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
+                                      fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_next);
                 }
-                TIMER_STOP(core);
+
+                // interleave row-major fp16 from scratch into tile-major in vtcm_weight
+                hmx_interleave_rows_to_tiles(vtcm_weight, (const __fp16 *) buf_curr, n_cols, k, k, 0, n_cols);
+
+                hex_swap_ptr(&buf_curr, &buf_next);
             }
+            TIMER_STOP(weight_load);
 
-            // store output block
+            TIMER_START(hmx_core);
             {
-                float *output_block = out + (mr * n + nc);
-                transfer_output_chunk_threaded(ctx, output_block, vtcm_output, m_blk_sz, n_blk_sz, n);
+                core_dot_chunk_fp16(vtcm_output, vtcm_activation, vtcm_weight, vtcm_scales, n_row_tiles, n_col_tiles, k / 32);
+            }
+            TIMER_STOP(hmx_core);
+
+            TIMER_START(output_store);
+            {
+                float *output = dst + (mr * n + nc);
+                transfer_output_chunk_threaded(ctx, output, vtcm_output, n_rows, n_cols, n);
             }
+            TIMER_STOP(output_store);
         }
+
     }
 
     HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
 
+    TIMER_STOP(total);
+
 #if defined(ENABLE_PROFILE_TIMERS)
-    FARF(HIGH, "fetch: %lld us, act_load: %lld us, wt_dequant: %lld us, core: %lld us",
-         TIMER_US(fetch), TIMER_US(act_load), TIMER_US(wt_dequant), TIMER_US(core));
+    FARF(HIGH, "%s: %lld us, m=%d k=%d n=%d", __func__, TIMER_US(total), m, k, n);
+    FARF(HIGH, "  activation_load: %lld us, weight_load: %lld us, hmx_core: %lld us, output_store: %lld us",
+         TIMER_US(activation_load), TIMER_US(weight_load), TIMER_US(hmx_core), TIMER_US(output_store));
+    {
+        size_t weight_size = (size_t)k * n * sizeof(__fp16);
+        float  bandwidth   = 1e-3f * weight_size / (float)TIMER_US(weight_load);
+        FARF(HIGH, "  weight load bandwidth: %.2f GB/s", bandwidth);
+    }
 #endif
+
     return 0;
 }
diff --git a/ggml/src/ggml-hexagon/htp/hmx-ops.h b/ggml/src/ggml-hexagon/htp/hmx-ops.h
index fb95d36f5a9..1c78ffadd1c 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-ops.h
+++ b/ggml/src/ggml-hexagon/htp/hmx-ops.h
@@ -61,6 +61,9 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx,
                                       int m, int k, int n,
                                       int weight_type);
 
+// HMX flash attention
+int hmx_flash_attn_ext(struct htp_ops_context * octx);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/ggml/src/ggml-hexagon/htp/hmx-utils.h b/ggml/src/ggml-hexagon/htp/hmx-utils.h
index af04619cebb..f448ee3372a 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-utils.h
+++ b/ggml/src/ggml-hexagon/htp/hmx-utils.h
@@ -4,6 +4,9 @@
 #ifndef HMX_UTILS_H
 #define HMX_UTILS_H
 
+#include "hvx-base.h"
+
+#include <assert.h>
 #include <hexagon_types.h>
 #include <stddef.h>
 
@@ -12,21 +15,186 @@
 #define HMX_FP16_TILE_N_ELMS 1024
 #define HMX_FP16_TILE_SIZE   2048
 
-#define HMX_INLINE_ALWAYS inline __attribute__((unused, always_inline))
-
 // Initialise aligned 256-byte area with scale vector + zero padding.
-static HMX_INLINE_ALWAYS void hmx_init_column_scales(void *out_scales, HVX_Vector v_scale) {
-    HVX_Vector *pv = (HVX_Vector *)out_scales;
-    *pv++ = v_scale;
-    *pv   = Q6_V_vzero();
+static inline void hmx_init_column_scales(void *out_scales, HVX_Vector v_scale) {
+    volatile HVX_Vector *pv = (HVX_Vector *) out_scales;
+    pv[0] = v_scale;
+    pv[1] = Q6_V_vzero();
 }
 
-// --- VTCM sequential allocator (from htp-ops-lib/include/dsp/vtcm_mgr.h) ---
+// --- Shared scatter offsets and interleave helper ---
+
+// vscatter offsets for fused dequant+transpose: write K-values directly to [K][N] tile.
+// word[i] = i*128 maps K-row-pair i to byte offset i*128.
+// Column offset (n*4) is added at runtime.  Entries 0..15 cover one tile (region 2047);
+// entries 16..31 cover the next adjacent tile (region 4095) — pick region size at the
+// call site to scatter into one tile (masked) or two contiguous tiles (unmasked).
+static const int32_t hmx_transpose_scatter_offsets[32] __attribute__((aligned(VLEN))) = {
+    0 * 128,  1 * 128,  2 * 128,  3 * 128,  4 * 128,  5 * 128,  6 * 128,  7 * 128,  8 * 128,  9 * 128,  10 * 128,
+    11 * 128, 12 * 128, 13 * 128, 14 * 128, 15 * 128, 16 * 128, 17 * 128, 18 * 128, 19 * 128, 20 * 128, 21 * 128,
+    22 * 128, 23 * 128, 24 * 128, 25 * 128, 26 * 128, 27 * 128, 28 * 128, 29 * 128, 30 * 128, 31 * 128,
+};
+
+// Scatter row-major FP16 data (in VTCM scratch) into transposed [K][N] tiles.
+// vtcm_src: [n_cols][src_stride] row-major fp16 (only first k elements per row are used)
+// vtcm_dst: [n_col_tiles][n_k_tiles][HMX_FP16_TILE_N_ELMS] tile-major interleaved fp16
+// Processes rows [start_row, end_row) for multi-thread slicing.
+// Full range: start_row=0, end_row=n_cols.
+static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
+                                            const __fp16 * restrict vtcm_src,
+                                            int n_cols,
+                                            int k,
+                                            int src_stride,
+                                            int start_row,
+                                            int end_row) {
+    assert(k % HMX_FP16_TILE_N_COLS == 0);
+
+    const int            n_k_tiles     = k / HMX_FP16_TILE_N_COLS;
+    const HVX_Vector     v_scat_base   = hvx_vmem(hmx_transpose_scatter_offsets);
+    const HVX_Vector     v_scat_step   = Q6_V_vsplat_R(4);
+    const HVX_VectorPred q_mask64      = Q6_Q_vsetq_R(64);
+    // Each hvx_vmemu load brings 64 fp16 = 128 bytes covering 2 adjacent K-tiles.
+    // When n_k_tiles is even, scatter into 2 K-tiles per call (region 4095, no mask)
+    // using the upper half of hmx_transpose_scatter_offsets.  Tail one K-tile (when
+    // n_k_tiles is odd) falls back to single-tile masked scatter.
+    const bool           pair_scatter  = (n_k_tiles & 1) == 0;
+    const size_t         pair_region   = (size_t) (2 * HMX_FP16_TILE_SIZE - 1);
+    const size_t         single_region = (size_t) (HMX_FP16_TILE_SIZE - 1);
+    __builtin_assume(k > 0);
+    __builtin_assume(end_row > start_row);
+
+    if (pair_scatter) {
+        // Step c by 64 fp16 (two K-tiles per scatter), advance dst by 2 tiles per iter.
+        const int    c_step      = 2 * HMX_FP16_TILE_N_COLS;
+        const size_t c_byte_step = (size_t) c_step * sizeof(__fp16);
+        const size_t dst_step    = 2 * (size_t) HMX_FP16_TILE_N_ELMS;
+        const int    n_c_iters   = k / c_step;
+
+        for (int r = start_row; r < end_row; r += 2) {
+            const int        ct             = r / HMX_FP16_TILE_N_ROWS;
+            const int        local_r        = r % HMX_FP16_TILE_N_ROWS;
+            const bool       next_row_valid = (r + 1) < end_row && (r + 1) < n_cols;
+            const HVX_Vector v_off0         = Q6_Vw_vadd_VwVw(v_scat_base, Q6_V_vsplat_R(local_r * 4));
+            const HVX_Vector v_off1         = Q6_Vw_vadd_VwVw(v_off0, v_scat_step);
+
+            __fp16 * tile_base = vtcm_dst + (size_t) ct * n_k_tiles * HMX_FP16_TILE_N_ELMS;
+            const uint8_t * p0 = (const uint8_t *) (vtcm_src + r * src_stride);
+            const uint8_t * p1 = next_row_valid ? (const uint8_t *) (vtcm_src + (r + 1) * src_stride) : NULL;
+
+            assert(hex_is_aligned(p0, 128));
+            assert(hex_is_aligned(p1, 128));
+            assert(c_byte_step % 128 == 0);
+
+            if (p1) {
+                for (int i = 0; i < n_c_iters; ++i) {
+                    HVX_Vector v0 = hvx_vmem(p0); p0 += c_byte_step;
+                    HVX_Vector v1 = hvx_vmem(p1); p1 += c_byte_step;
+                    Q6_vscatter_RMVwV((size_t) tile_base, pair_region, v_off0, v0);
+                    Q6_vscatter_RMVwV((size_t) tile_base, pair_region, v_off1, v1);
+                    tile_base += dst_step;
+                }
+            } else {
+                const HVX_Vector vzero = Q6_V_vzero();
+                for (int i = 0; i < n_c_iters; ++i) {
+                    HVX_Vector v0 = hvx_vmem(p0); p0 += c_byte_step;
+                    Q6_vscatter_RMVwV((size_t) tile_base, pair_region, v_off0, v0);
+                    Q6_vscatter_RMVwV((size_t) tile_base, pair_region, v_off1, vzero);
+                    tile_base += dst_step;
+                }
+            }
+        }
+    } else {
+        // Fallback: scatter one K-tile per call (region 2047, masked).
+        const int    c_step      = HMX_FP16_TILE_N_COLS;
+        const size_t c_byte_step = (size_t) c_step * sizeof(__fp16);
+        const size_t dst_step    = (size_t) HMX_FP16_TILE_N_ELMS;
+        const int    n_c_iters   = k / c_step;
+
+        for (int r = start_row; r < end_row; r += 2) {
+            const int        ct             = r / HMX_FP16_TILE_N_ROWS;
+            const int        local_r        = r % HMX_FP16_TILE_N_ROWS;
+            const bool       next_row_valid = (r + 1) < end_row && (r + 1) < n_cols;
+            const HVX_Vector v_off0         = Q6_Vw_vadd_VwVw(v_scat_base, Q6_V_vsplat_R(local_r * 4));
+            const HVX_Vector v_off1         = Q6_Vw_vadd_VwVw(v_off0, v_scat_step);
+
+            __fp16 * tile_base = vtcm_dst + (size_t) ct * n_k_tiles * HMX_FP16_TILE_N_ELMS;
+            const uint8_t * p0 = (const uint8_t *) (vtcm_src + r * src_stride);
+            const uint8_t * p1 = next_row_valid ? (const uint8_t *) (vtcm_src + (r + 1) * src_stride) : NULL;
+
+            if (p1) {
+                for (int i = 0; i < n_c_iters; ++i) {
+                    HVX_Vector v0 = hvx_vmemu(p0); p0 += c_byte_step;
+                    HVX_Vector v1 = hvx_vmemu(p1); p1 += c_byte_step;
+                    Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, single_region, v_off0, v0);
+                    Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, single_region, v_off1, v1);
+                    tile_base += dst_step;
+                }
+            } else {
+                const HVX_Vector vzero = Q6_V_vzero();
+                for (int i = 0; i < n_c_iters; ++i) {
+                    HVX_Vector v0 = hvx_vmemu(p0); p0 += c_byte_step;
+                    Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, single_region, v_off0, v0);
+                    Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, single_region, v_off1, vzero);
+                    tile_base += dst_step;
+                }
+            }
+        }
+    }
+}
+
+// Interleave row-major FP16 data into column-major tile format.
+// Input: [n_rows, head_dim] row-major.  Output: tile[dim_tile][row_tile].
+// Processes rows [start_row, end_row) for multi-thread slicing.
+// Full range: start_row=0, end_row=n_rows.
+static inline void hmx_interleave_cols_to_tiles(__fp16 * restrict tiles_out,
+                                            const __fp16 * restrict src,
+                                            int n_rows,
+                                            int head_dim,
+                                            int src_stride,
+                                            int n_row_tiles,
+                                            int start_row,
+                                            int end_row) {
+    __builtin_assume(head_dim > 0);
+    const size_t tile_stride_elms = (size_t) n_row_tiles * HMX_FP16_TILE_N_ELMS;
+
+    for (int r = start_row; r < end_row; r += 2) {
+        const bool next_row_valid = (r + 1) < end_row && (r + 1) < n_rows;
+
+        const HVX_Vector * pv_in0 = (const HVX_Vector *) (src + r * src_stride);
+        const HVX_Vector * pv_in1 = next_row_valid ? (const HVX_Vector *) (src + (r + 1) * src_stride) : NULL;
+
+        // Row-pair invariants hoisted out of the c loop.
+        const int r0      = r / HMX_FP16_TILE_N_ROWS;
+        const int r1_half = (r % HMX_FP16_TILE_N_ROWS) / 2;
+
+        // tb0 starts at tile (c0=0, r0); tb1 at the adjacent dim-tile (c0=1, r0).
+        // Each c step (+= 64) advances both by 2 dim-tiles worth of fp16.
+        __fp16 *     tb0     = tiles_out + (size_t) r0 * HMX_FP16_TILE_N_ELMS;
+        __fp16 *     tb1     = tb0 + tile_stride_elms;
+        const size_t tb_step = 2 * tile_stride_elms;
 
-static inline uint8_t *vtcm_seq_alloc(uint8_t **vtcm_ptr, size_t size) {
-    uint8_t *p = *vtcm_ptr;
-    *vtcm_ptr += size;
-    return p;
+        if (pv_in1) {
+            for (int c = 0; c < head_dim; c += 64) {
+                HVX_Vector     v0             = *pv_in0++;
+                HVX_Vector     v1             = *pv_in1++;
+                HVX_VectorPair vp             = Q6_W_vshuff_VVR(v1, v0, -2);
+                ((HVX_Vector *) tb0)[r1_half] = Q6_V_lo_W(vp);
+                ((HVX_Vector *) tb1)[r1_half] = Q6_V_hi_W(vp);
+                tb0 += tb_step;
+                tb1 += tb_step;
+            }
+        } else {
+            const HVX_Vector vzero = Q6_V_vzero();
+            for (int c = 0; c < head_dim; c += 64) {
+                HVX_Vector     v0             = *pv_in0++;
+                HVX_VectorPair vp             = Q6_W_vshuff_VVR(vzero, v0, -2);
+                ((HVX_Vector *) tb0)[r1_half] = Q6_V_lo_W(vp);
+                ((HVX_Vector *) tb1)[r1_half] = Q6_V_hi_W(vp);
+                tb0 += tb_step;
+                tb1 += tb_step;
+            }
+        }
+    }
 }
 
 #endif // HMX_UTILS_H
diff --git a/ggml/src/ggml-hexagon/htp/htp-ctx.h b/ggml/src/ggml-hexagon/htp/htp-ctx.h
index 8b5e47adef8..92f02eac6e3 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ctx.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ctx.h
@@ -10,6 +10,7 @@
 #include <dspqueue.h>
 #include <stdatomic.h>
 #include <stdint.h>
+#include <stdbool.h>
 
 #define HTP_MAX_NTHREADS 10
 #define HTP_MAX_MMAPS    16
@@ -19,7 +20,7 @@ struct htp_mmap {
     uint64_t size;
     uint64_t base;
     uint32_t fd;
-    uint32_t pinned;
+    uint32_t reserved;
 };
 
 // Scratchpad state
@@ -66,7 +67,9 @@ struct htp_context {
     int                    thread_id;
     int                    thread_prio;
 
-    int                    hmx_enabled;
+    bool                   hmx_enabled;
+    bool                   etm;
+    uint32_t               profiler;
 
     uint8_t *              vtcm_base;
     size_t                 vtcm_size;
@@ -74,6 +77,8 @@ struct htp_context {
     atomic_bool            vtcm_valid;
     atomic_bool            vtcm_needs_release;
 
+    uint64_t               max_vmem;
+
     struct htp_ops_context octx;
 
 #ifdef HTP_HAS_HMX
@@ -98,5 +103,9 @@ int op_repeat(struct htp_ops_context * octx);
 int op_argsort(struct htp_ops_context * octx);
 int op_ssm_conv(struct htp_ops_context * octx);
 int op_cumsum(struct htp_ops_context * octx);
+int op_fill(struct htp_ops_context * octx);
+int op_diag(struct htp_ops_context * octx);
+int op_solve_tri(struct htp_ops_context * octx);
+int op_gated_delta_net(struct htp_ops_context * octx);
 
 #endif /* HTP_CTX_H */
diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h
index 79b5ecd2270..98db864dd42 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ops.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ops.h
@@ -42,9 +42,9 @@ enum htp_data_type {
 
 // Mask to enable various stages of the Ops.
 // Used for debugging and profiling.
-enum htp_op_mask {
-    HTP_OPMASK_QUEUE    = (1 << 0),  // Enable Queueing (ie calls into the DSP)
-    HTP_OPMASK_COMPUTE  = (1 << 1),  // Enable Compute
+enum htp_op_stage {
+    HTP_OPSTAGE_QUEUE    = (1 << 0),  // Enable Queueing (ie calls into NPU)
+    HTP_OPSTAGE_COMPUTE  = (1 << 1),  // Enable Compute
 };
 
 // Do not reorder first 4 (used as an index)
@@ -62,6 +62,7 @@ enum htp_op_code {
     HTP_OP_UNARY_EXP,
     HTP_OP_UNARY_NEG,
     HTP_OP_UNARY_SOFTPLUS,
+    HTP_OP_UNARY_TANH,
     HTP_OP_GLU_SWIGLU,
     HTP_OP_GLU_SWIGLU_OAI,
     HTP_OP_GLU_GEGLU,
@@ -80,6 +81,11 @@ enum htp_op_code {
     HTP_OP_SSM_CONV,
     HTP_OP_REPEAT,
     HTP_OP_CUMSUM,
+    HTP_OP_FILL,
+    HTP_OP_DIAG,
+    HTP_OP_SOLVE_TRI,
+    HTP_OP_L2_NORM,
+    HTP_OP_GATED_DELTA_NET,
 
     HTP_OP_INVALID
 };
@@ -88,15 +94,11 @@ enum htp_op_code {
 #define HTP_OP_MAX_INPUTS  6    // aka GGML_MAX_SRCS
 #define HTP_OP_MAX_PARAMS  16   // aka GGML_MAX_OP_PARAMS
 
-#define HTP_OP_MAX_BUFS    8
+#define HTP_OP_MAX_BUFS    16
 #define HTP_OP_MAX_REQS    256
 #define HTP_OP_MAX_TENSORS (HTP_OP_MAX_REQS * HTP_OP_MAX_INPUTS + HTP_OP_MAX_REQS)
 
-#if __HVX_ARCH__ < 75
-#define HTP_OP_MAX_VMEM    (3167538380u)
-#else
-#define HTP_OP_MAX_VMEM    (3221225472u)
-#endif
+#define HTP_OP_MAX_VMEM_DEFAULT (3355443200u)
 
 #define HTP_MMAP_MAX_VMEM  (2147483648u)
 
@@ -135,27 +137,45 @@ struct htp_op_desc {
     int32_t  params[HTP_OP_MAX_PARAMS]; // Params for the op, e.g. epsilon of RMS norm
     uint16_t src[HTP_OP_MAX_INPUTS];    // Input tensors indices
     uint16_t dst;                       // Output tensor index
+};
+
+enum htp_profiler_mode {
+    HTP_PROF_DISABLED = 0,
+    HTP_PROF_BASIC    = 1,
+    HTP_PROF_PMU      = 2,
+};
+
+#define HTP_PROF_PMU_NCNT 8
 
-    // the rest is filled in-place by the NPU
-    uint32_t prof_usecs;                // Number of usec per request
-    uint32_t prof_cycles;               // Number of cycles per request
-    uint32_t prof_pkts;                 // Number of instruction packets per request
-    uint32_t unused;
+// Profile descriptor
+struct htp_prof_desc {
+    uint32_t opcode;                 // GGML/HTP Op
+    uint32_t usecs;                  // Number of usec
+    uint32_t cycles;                 // Number of cycles
+    uint32_t pad;                    // Unused
+    uint32_t pmu[HTP_PROF_PMU_NCNT]; // PMU counters
 };
 
 struct htp_opbatch_req {
+    uint32_t id;          // Batch id
     uint32_t n_bufs;      // Number of buffers
     uint32_t n_tensors;   // Number of tensors
     uint32_t n_ops;       // Number of ops
     uint32_t flags;       // unused
+    uint32_t pad;         // unused
     // struct htp_buf_desc  bufs[];    -- dspqueue buf 0
     // struct htp_tensor    tensors[]; -- dspqueue buf 0
     // struct htp_op_desc   ops[];     -- dspqueue buf 0
 };
 
 struct htp_opbatch_rsp {
+    uint32_t id;         // Batch id
     uint32_t status;     // HTP_STATUS_...
-    // struct htp_op_req ops[];     -- dspqueue buf 0
+    uint32_t n_bufs;     // Number of buffers
+    uint32_t n_tensors;  // Number of tensors
+    uint32_t n_ops;      // Number of op profile descriptors
+    uint32_t pad;        // unused
+    // struct htp_prof_desc profs[];  -- dspqueue buf 0
 };
 
 #endif /* HTP_OPS_H */
diff --git a/ggml/src/ggml-hexagon/htp/htp_iface.idl b/ggml/src/ggml-hexagon/htp/htp_iface.idl
index 3eb5d5a6912..d696a5fba0c 100644
--- a/ggml/src/ggml-hexagon/htp/htp_iface.idl
+++ b/ggml/src/ggml-hexagon/htp/htp_iface.idl
@@ -6,13 +6,17 @@
 #include "AEEStdDef.idl"
 #include "remote.idl"
 
+struct htp_iface_pmu_conf {
+    uint32 events[8];
+};
+
 interface htp_iface : remote_handle64 {
-    AEEResult start(in uint32 sess_id, in uint64 dsp_queue_id, in uint32 n_hvx, in uint32 use_hmx);
+    AEEResult start(in uint32 sess_id, in uint64 dsp_queue_id, in uint32 n_hvx, in uint32 use_hmx, in uint64 max_vmem);
     AEEResult stop();
-    AEEResult mmap(in uint32 fd, in uint32 size, in uint32 pinned);
+    AEEResult mmap(in uint32 fd, in uint32 size);
     AEEResult munmap(in uint32 fd);
-    AEEResult enable_etm();
-    AEEResult disable_etm();
+    AEEResult profiler(in uint32 mode, in htp_iface_pmu_conf pmu);
+    AEEResult etm(in uint32 enable);
 };
 
 #endif /* HTP_IDL */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-base.h b/ggml/src/ggml-hexagon/htp/hvx-base.h
index ed6026e762a..f6cb02951d0 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-base.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-base.h
@@ -77,6 +77,12 @@ static inline int32_t hvx_vec_get_i32(HVX_Vector v) {
     return x;
 }
 
+static inline _Float16 hvx_vec_get_f16(HVX_Vector v) {
+    _Float16 __attribute__((aligned(128))) x;
+    hvx_vec_store_a(&x, 2, v);
+    return x;
+}
+
 static inline HVX_Vector hvx_vec_abs_f16(HVX_Vector v) {
     // abs by clearing the fp16 sign bit
     HVX_Vector mask = Q6_Vh_vsplat_R(0x7fff);
@@ -256,6 +262,18 @@ static inline HVX_Vector hvx_vec_mul_f16_f16(HVX_Vector a, HVX_Vector b)
     return Q6_Vhf_equals_Wqf32(Q6_Wqf32_vmpy_VhfVhf(a, b));
 }
 
+static inline HVX_Vector hvx_vec_add_f32_f32(HVX_Vector a, HVX_Vector b) {
+    return Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b));
+}
+
+static inline HVX_Vector hvx_vec_sub_f32_f32(HVX_Vector a, HVX_Vector b) {
+    return Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a, b));
+}
+
+static inline HVX_Vector hvx_vec_mul_f32_f32(HVX_Vector a, HVX_Vector b) {
+    return Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b));
+}
+
 #else
 
 static inline HVX_Vector hvx_vec_add_f16_f16(HVX_Vector a, HVX_Vector b)
@@ -273,6 +291,18 @@ static inline HVX_Vector hvx_vec_mul_f16_f16(HVX_Vector a, HVX_Vector b)
     return Q6_Vhf_vmpy_VhfVhf(a, b);
 }
 
+static inline HVX_Vector hvx_vec_add_f32_f32(HVX_Vector a, HVX_Vector b) {
+    return Q6_Vsf_vadd_VsfVsf(a, b);
+}
+
+static inline HVX_Vector hvx_vec_sub_f32_f32(HVX_Vector a, HVX_Vector b) {
+    return Q6_Vsf_vsub_VsfVsf(a, b);
+}
+
+static inline HVX_Vector hvx_vec_mul_f32_f32(HVX_Vector a, HVX_Vector b) {
+    return Q6_Vsf_vmpy_VsfVsf(a, b);
+}
+
 #endif // __HVX_ARCH__ < 79
 
 #endif /* HVX_BASE_H */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-copy.h b/ggml/src/ggml-hexagon/htp/hvx-copy.h
index 851482e01b2..a3e33c3b3af 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-copy.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-copy.h
@@ -7,7 +7,8 @@
 
 #include "hvx-base.h"
 
-#define hvx_splat_loop_body(dst_type, vec_store)                 \
+#define hvx_splat_pragma(x) _Pragma(#x)
+#define hvx_splat_loop_body(dst_type, vec_store, unroll_cnt)     \
     do {                                                         \
         dst_type * restrict vdst = (dst_type *) dst;             \
                                                                  \
@@ -16,7 +17,7 @@
                                                                  \
         uint32_t i = 0;                                          \
                                                                  \
-        _Pragma("unroll(4)")                                     \
+        hvx_splat_pragma(unroll(unroll_cnt))                     \
         for (; i < nvec; i++) {                                  \
             vdst[i] = src;                                       \
         }                                                        \
@@ -25,31 +26,47 @@
         }                                                        \
     } while(0)
 
-static inline void hvx_splat_a(uint8_t * restrict dst, HVX_Vector src, uint32_t n, uint32_t elem_size) {
+static inline void hvx_splat_a(void * restrict dst, HVX_Vector src, uint32_t n, uint32_t elem_size) {
     assert((unsigned long) dst % 128 == 0);
-    hvx_splat_loop_body(HVX_Vector, hvx_vec_store_a);
+    hvx_splat_loop_body(HVX_Vector, hvx_vec_store_a, 4);
 }
 
-static inline void hvx_splat_u(uint8_t * restrict dst, HVX_Vector src, uint32_t n, uint32_t elem_size) {
-    hvx_splat_loop_body(HVX_UVector, hvx_vec_store_u);
+static inline void hvx_splat_u(void * restrict dst, HVX_Vector src, uint32_t n, uint32_t elem_size) {
+    hvx_splat_loop_body(HVX_UVector, hvx_vec_store_u, 4);
 }
 
-static inline void hvx_splat_f32_a(uint8_t * restrict dst, float v, uint32_t n) {
+static inline void hvx_splat_f32_a(void * restrict dst, float v, uint32_t n) {
     hvx_splat_a(dst,  hvx_vec_splat_f32(v), n, sizeof(float));
 }
 
-static inline void hvx_splat_f32_u(uint8_t * restrict dst, float v, uint32_t n) {
+static inline void hvx_splat_f32_u(void * restrict dst, float v, uint32_t n) {
     hvx_splat_u(dst,  hvx_vec_splat_f32(v), n, sizeof(float));
 }
 
-static inline void hvx_splat_f16_a(uint8_t * restrict dst, _Float16 v, uint32_t n) {
+static inline void hvx_splat_f16_a(void * restrict dst, _Float16 v, uint32_t n) {
     hvx_splat_u(dst,  hvx_vec_splat_f16(v), n, sizeof(__fp16));
 }
 
-static inline void hvx_splat_f16_u(uint8_t * restrict dst, _Float16 v, uint32_t n) {
+static inline void hvx_splat_f16_u(void * restrict dst, _Float16 v, uint32_t n) {
     hvx_splat_u(dst,  hvx_vec_splat_f16(v), n, sizeof(__fp16));
 }
 
+static inline void hvx_splat_u16_a(void * restrict dst, uint16_t v, uint32_t n) {
+    hvx_splat_a(dst,  Q6_Vh_vsplat_R(v), n, sizeof(uint16_t));
+}
+
+static inline void hvx_splat_u16_u(void * restrict dst, uint16_t v, uint32_t n) {
+    hvx_splat_u(dst,  Q6_Vh_vsplat_R(v), n, sizeof(uint16_t));
+}
+
+static inline void hvx_splat_u8_a(void * restrict dst, uint8_t v, uint32_t n) {
+    hvx_splat_a(dst,  Q6_Vb_vsplat_R(v), n, 1);
+}
+
+static inline void hvx_splat_u8_u(void * restrict dst, uint8_t v, uint32_t n) {
+    hvx_splat_u(dst,  Q6_Vb_vsplat_R(v), n, 1);
+}
+
 #define hvx_copy_loop_body(dst_type, src_type, vec_store)            \
     do {                                                             \
         dst_type * restrict vdst = (dst_type *) dst;                 \
diff --git a/ggml/src/ggml-hexagon/htp/hvx-exp.h b/ggml/src/ggml-hexagon/htp/hvx-exp.h
index 84e4836dc92..e71ec4909a6 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-exp.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-exp.h
@@ -17,7 +17,7 @@
 #define EXP_LOGN2   (0x3F317218)  // ln(2)   = 0.6931471805
 #define EXP_LOG2E   (0x3FB8AA3B)  // log2(e) = 1/ln(2) = 1.4426950408
 #define EXP_ONE     (0x3f800000)  // 1.0
-#define EXP_RANGE_R (0x42B16666)  // 88.7
+#define EXP_RANGE_R (0x42B17218)  // ln(FLT_MAX) approx = 88.7228
 #define EXP_RANGE_L (0xC2B00000)  // -88.0 (approx log(FLT_MIN))
 
 static inline HVX_Vector hvx_vec_exp_f32(HVX_Vector in_vec) {
@@ -163,7 +163,7 @@ static inline void hvx_exp_f32(uint8_t * restrict dst, const uint8_t * restrict
     HVX_Vector vec_out = Q6_V_vzero();
 
     static const float kInf    = INFINITY;
-    static const float kMaxExp = 88.7f;
+    static const float kMaxExp = 88.7228f;
 
     const HVX_Vector max_exp = hvx_vec_splat_f32(kMaxExp);
     const HVX_Vector inf     = hvx_vec_splat_f32(kInf);
diff --git a/ggml/src/ggml-hexagon/htp/hvx-repl.h b/ggml/src/ggml-hexagon/htp/hvx-repl.h
new file mode 100644
index 00000000000..fdc7e6c7d2f
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hvx-repl.h
@@ -0,0 +1,74 @@
+#ifndef HVX_REPL_H
+#define HVX_REPL_H
+
+#include <assert.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#include "hvx-base.h"
+
+static inline HVX_Vector hvx_vec_repl(HVX_Vector v, const uint8_t * ctrl) {
+    return Q6_V_vdelta_VV(v, hvx_vmem(ctrl));
+}
+
+static inline HVX_Vector hvx_vec_repl_u32(HVX_Vector v) {
+    // vdelta control to replicate first 4 bytes across all lanes
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    return hvx_vec_repl(v, repl);
+}
+
+static inline HVX_Vector hvx_vec_repl_f32(HVX_Vector v) {
+    // vdelta control to replicate first 4 bytes across all lanes
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    return hvx_vec_repl(v, repl);
+}
+
+static inline HVX_Vector hvx_vec_repl_f16(HVX_Vector v) {
+    // vdelta control to replicate first two bytes across all lanes
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x40, 0x40, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+    };
+    return hvx_vec_repl(v, repl);
+}
+
+static inline HVX_Vector hvx_vec_repl_2x_f16(HVX_Vector v) {
+    // vdelta control to splat a pair of f16s: first half = f16[0], second half = f16[1]
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
+        0x02, 0x02, 0x40, 0x40, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04,
+        0x02, 0x02, 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04,
+        0x02, 0x02, 0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04,
+        0x02, 0x02, 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04,
+    };
+    return hvx_vec_repl(v, repl);
+}
+
+#endif // HVX_REPL_H
diff --git a/ggml/src/ggml-hexagon/htp/hvx-utils.h b/ggml/src/ggml-hexagon/htp/hvx-utils.h
index a518ad37331..e0452811ec3 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-utils.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-utils.h
@@ -5,6 +5,7 @@
 
 #include "hvx-types.h"
 #include "hvx-copy.h"
+#include "hvx-repl.h"
 #include "hvx-scale.h"
 #include "hvx-exp.h"
 #include "hvx-inverse.h"
diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c
index 5091623a653..883a31d6163 100644
--- a/ggml/src/ggml-hexagon/htp/main.c
+++ b/ggml/src/ggml-hexagon/htp/main.c
@@ -27,6 +27,7 @@
 #include "htp-ctx.h"
 #include "htp-ops.h"
 #include "htp-ops.h"
+#include "htp_iface.h"
 #include "worker-pool.h"
 
 AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) {
@@ -100,6 +101,74 @@ AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) {
         }
     }
 
+#if __HVX_ARCH__ >= 75
+    {
+        // Set HMX clock
+        HAP_power_request_t request;
+        memset(&request, 0, sizeof(HAP_power_request_t));
+        request.type = HAP_power_set_HMX_v2;
+        request.hmx_v2.set_clock = TRUE;
+        request.hmx_v2.target_corner = HAP_DCVS_EXP_VCORNER_MAX;
+        request.hmx_v2.min_corner = HAP_DCVS_EXP_VCORNER_MAX;
+        request.hmx_v2.max_corner = HAP_DCVS_EXP_VCORNER_MAX;
+        request.hmx_v2.perf_mode = HAP_CLK_PERF_HIGH;
+        FARF(ALWAYS, "Setting HMX clock\n");
+        err = HAP_power_set((void *) &ctx, &request);
+        if (err != AEE_SUCCESS) {
+            FARF(ERROR, "Error setting HMX clock.");
+            return err;
+        }
+    }
+#endif
+
+    return AEE_SUCCESS;
+}
+
+AEEResult htp_iface_etm(remote_handle64 handle, uint32_t enable) {
+    int err = enable ? HAP_user_etm_enable() : HAP_user_etm_disable();
+    if (err) {
+        if (err == AEE_EVERSIONNOTSUPPORT) {
+            FARF(ERROR, "API HAP_user_etm_enable/disable is not supported\n");
+        } else {
+            FARF(ERROR, "Error executing HAP_user_etm_enable/disable with error code : 0x%x\n", err);
+        }
+    }
+    return err;
+}
+
+AEEResult htp_iface_profiler(remote_handle64 handle, uint32_t mode, const htp_iface_pmu_conf* pmu_conf) {
+    struct htp_context * ctx = (struct htp_context *) handle;
+    if (!ctx) {
+        return AEE_EBADPARM;
+    }
+
+    if (mode == HTP_PROF_PMU) {
+        const uint32_t* events = pmu_conf->events;
+
+        // Pack 4 event IDs (low 8 bits) into each 32-bit config register
+        uint32_t evtcfg = 0, evtcfg1 = 0, cfg = 0, i = 0;
+        for (; i < HEX_NUM_PMU_COUNTERS/2; i++) {
+            evtcfg  |= ((events[i + 0] & 0xFF) << (i * 8));
+            evtcfg1 |= ((events[i + 4] & 0xFF) << (i * 8));
+        }
+
+        // For events >255 pack high 2 bits of all 8 event IDs into cfg register
+        // 2 bits per counter: bits [1:0] for counter 0, [3:2] for counter 1, etc.
+        for (i = 0; i < HEX_NUM_PMU_COUNTERS; i++) {
+            cfg |= (((events[i] >> 8) & 3) << (i * 2));
+        }
+
+        FARF(ALWAYS, "Configuring PMU registers: evtcfg = 0x%x, evtcfg1 = 0x%x, pmucfg = 0x%x", evtcfg, evtcfg1, cfg);
+
+        // Configure PMU registers
+        qurt_pmu_set(QURT_PMUCFG,     cfg);
+        qurt_pmu_set(QURT_PMUEVTCFG,  evtcfg);
+        qurt_pmu_set(QURT_PMUEVTCFG1, evtcfg1);
+        qurt_pmu_enable(1);
+    }
+
+    ctx->profiler = mode;
+
     return AEE_SUCCESS;
 }
 
@@ -129,35 +198,19 @@ AEEResult htp_iface_close(remote_handle64 handle) {
         }
     }
 
-    free(ctx);
-    return AEE_SUCCESS;
-}
-
-AEEResult htp_iface_enable_etm(remote_handle64 handle) {
-    int err = HAP_user_etm_enable();
-    if (err) {
-        if (err == AEE_EVERSIONNOTSUPPORT) {
-            FARF(ERROR, "API HAP_user_etm_enable is not supported\n");
-        } else {
-            FARF(ERROR, "Error executing HAP_user_etm_enable with error code : 0x%x\n", err);
-        }
+    if (ctx->profiler) {
+        qurt_pmu_enable(1);
     }
-    return err;
-}
 
-AEEResult htp_iface_disable_etm(remote_handle64 handle) {
-    int err = HAP_user_etm_disable();
-    if (err) {
-        if (err == AEE_EVERSIONNOTSUPPORT) {
-            FARF(ERROR, "API HAP_user_etm_disable is not supported\n");
-        } else {
-            FARF(ERROR, "Error executing HAP_user_etm_disable with error code : 0x%x\n", err);
-        }
+    if (ctx->etm) {
+        HAP_user_etm_disable();
     }
-    return err;
+
+    free(ctx);
+    return AEE_SUCCESS;
 }
 
-AEEResult htp_iface_mmap(remote_handle64 handle, int fd, uint32_t size, uint32_t pinned) {
+AEEResult htp_iface_mmap(remote_handle64 handle, uint32_t fd, uint32_t size) {
     struct htp_context * ctx = (struct htp_context *) handle;
     if (!ctx) {
         return AEE_EBADPARM;
@@ -167,7 +220,6 @@ AEEResult htp_iface_mmap(remote_handle64 handle, int fd, uint32_t size, uint32_t
     for (uint32_t i=0; i<HTP_MAX_MMAPS; i++) {
         struct htp_mmap *m = &ctx->mmap[i];
         if (m->fd == fd) {
-            m->pinned = pinned;
             return AEE_SUCCESS;
         }
     }
@@ -176,7 +228,7 @@ AEEResult htp_iface_mmap(remote_handle64 handle, int fd, uint32_t size, uint32_t
     for (uint32_t i=0; i<HTP_MAX_MMAPS; i++) {
         struct htp_mmap *m = &ctx->mmap[i];
         if (!m->size) {
-            FARF(HIGH, "mmap : fd %u size %u pinned %u", fd, size, pinned);
+            FARF(HIGH, "mmap : fd %u size %u", fd, size);
 #if __HVX_ARCH__ > 73
             void *va = HAP_mmap2(NULL, size, HAP_PROT_READ | HAP_PROT_WRITE, 0, fd, 0);
 #else
@@ -195,7 +247,6 @@ AEEResult htp_iface_mmap(remote_handle64 handle, int fd, uint32_t size, uint32_t
             m->base   = (uint64_t) va;
             m->fd     = fd;
             m->size   = size;
-            m->pinned = pinned;
 
             return AEE_SUCCESS;
         }
@@ -204,7 +255,7 @@ AEEResult htp_iface_mmap(remote_handle64 handle, int fd, uint32_t size, uint32_t
     return AEE_ENOMEMORY;
 }
 
-AEEResult htp_iface_munmap(remote_handle64 handle, int fd) {
+AEEResult htp_iface_munmap(remote_handle64 handle, uint32 fd) {
     struct htp_context * ctx = (struct htp_context *) handle;
     if (!ctx) {
         return AEE_EBADPARM;
@@ -222,7 +273,6 @@ AEEResult htp_iface_munmap(remote_handle64 handle, int fd) {
             m->size   = 0;
             m->base   = NULL;
             m->fd     = -1;
-            m->pinned = 0;
         }
     }
 
@@ -305,7 +355,7 @@ static void vtcm_free(struct htp_context * ctx) {
 static void htp_packet_callback(dspqueue_t queue, int error, void * context);
 static void htp_error_callback(dspqueue_t queue, int error, void * context);
 
-AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_queue_id, uint32 n_hvx, uint32 use_hmx) {
+AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_queue_id, uint32 n_hvx, uint32 use_hmx, uint64_t max_vmem) {
     struct htp_context * ctx = (struct htp_context *) handle;
 
     if (!ctx) {
@@ -323,12 +373,12 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_que
                               htp_error_callback,   // Error callback; no errors expected on the DSP
                               (void *) ctx,         // Callback context
                               &ctx->queue);
-
     if (err) {
         FARF(ERROR, "Queue import failed with 0x%08x", (unsigned) err);
         return err;
     }
 
+    ctx->max_vmem    = max_vmem;
     ctx->thread_id   = qurt_thread_get_id();
     ctx->thread_prio = qurt_thread_get_priority(ctx->thread_id);
 
@@ -434,19 +484,39 @@ static void htp_error_callback(dspqueue_t queue, int error, void * context) {
 struct profile_data {
     uint64_t usecs;
     uint64_t cycles;
-    uint64_t pkts;
+    uint32_t pmu_counters[HEX_NUM_PMU_COUNTERS];
 };
 
-static inline void profile_start(struct profile_data * d) {
-    d->usecs  = HAP_perf_get_qtimer_count();
-    d->cycles = hex_get_cycles();
-    d->pkts   = hex_get_pktcnt();
+static inline void profile_start(uint32_t mode, struct profile_data * d) {
+    switch (mode) {
+        case HTP_PROF_PMU:
+            hex_get_pmu(d->pmu_counters);
+            // fallthrough
+        case HTP_PROF_BASIC:
+            d->usecs  = HAP_perf_get_qtimer_count();
+            d->cycles = hex_get_cycles();
+            break;
+        default:
+            break;
+    }
 }
 
-static inline void profile_stop(struct profile_data * d) {
-    d->usecs  = HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - d->usecs);
-    d->cycles = hex_get_cycles() - d->cycles;
-    d->pkts   = hex_get_pktcnt() - d->pkts;
+static inline void profile_stop(uint32_t mode, struct profile_data * d) {
+    uint32_t pmu_counters[HEX_NUM_PMU_COUNTERS];
+    switch (mode) {
+        case HTP_PROF_PMU:
+            hex_get_pmu(pmu_counters);
+            for (int i = 0; i < HEX_NUM_PMU_COUNTERS; i++) {
+                d->pmu_counters[i] = pmu_counters[i] - d->pmu_counters[i];
+            }
+            // fallthrough
+        case HTP_PROF_BASIC:
+            d->usecs  = HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - d->usecs);
+            d->cycles = hex_get_cycles() - d->cycles;
+            break;
+        default:
+            break;
+    }
 }
 
 static int execute_op(struct htp_ops_context * octx) {
@@ -472,6 +542,8 @@ static int execute_op(struct htp_ops_context * octx) {
         case HTP_OP_UNARY_SIGMOID:
         case HTP_OP_UNARY_NEG:
         case HTP_OP_UNARY_EXP:
+        case HTP_OP_UNARY_TANH:
+        case HTP_OP_L2_NORM:
             return op_unary(octx);
 
         case HTP_OP_UNARY_SILU:
@@ -514,6 +586,18 @@ static int execute_op(struct htp_ops_context * octx) {
         case HTP_OP_CUMSUM:
             return op_cumsum(octx);
 
+        case HTP_OP_FILL:
+            return op_fill(octx);
+
+        case HTP_OP_DIAG:
+            return op_diag(octx);
+
+        case HTP_OP_SOLVE_TRI:
+            return op_solve_tri(octx);
+
+        case HTP_OP_GATED_DELTA_NET:
+            return op_gated_delta_net(octx);
+
         case HTP_OP_INVALID:
             break;
 
@@ -540,8 +624,8 @@ static inline bool reuse_buf(struct htp_context *ctx, uint32_t *m_reuse, struct
 }
 
 static inline void drop_mmap(struct htp_context *ctx, struct htp_mmap *m) {
-    if (m->size && !m->pinned) {
-        FARF(HIGH, "unmap : fd %u base %p size %u pinned %u", m->fd, (void*) m->base, (uint32_t) m->size, m->pinned);
+    if (m->size) {
+        FARF(HIGH, "unmap : fd %u base %p size %u", m->fd, (void*) m->base, (uint32_t) m->size);
 #if __HVX_ARCH__ > 73
         HAP_munmap2((void *) m->base, m->size);
 #else
@@ -578,9 +662,8 @@ static inline void mmap_buf(struct htp_context *ctx, struct htp_buf_desc *b) {
             m->base   = b->base = (uint64_t) va;
             m->fd     = b->fd;
             m->size   = b->size;
-            m->pinned = 0;
 
-            FARF(HIGH, "mmap : fd %u base %p size %u pinned %u", m->fd, (void*) m->base, (uint32_t) m->size, m->pinned);
+            FARF(HIGH, "mmap : fd %u base %p size %u", m->fd, (void*) m->base, (uint32_t) m->size);
             return;
         }
     }
@@ -590,8 +673,8 @@ static void prep_op_bufs(struct htp_context *ctx, struct htp_buf_desc *bufs, uin
     uint32_t m_reuse = 0; // mmap reuse mask (index from ctx->mmap array)
     uint32_t b_reuse = 0; // buf reuse count
 
-    size_t   m_vmem  = 0; // mapped vmem
-    size_t   e_vmem  = 0; // extra  vmem
+    uint64_t m_vmem  = 0; // mapped vmem
+    uint64_t e_vmem  = 0; // extra  vmem
 
     // See what we can reuse
     for (uint32_t i=0; i < n_bufs; i++) {
@@ -605,9 +688,10 @@ static void prep_op_bufs(struct htp_context *ctx, struct htp_buf_desc *bufs, uin
     // See how much vmem we have mmaped right now
     for (uint32_t i=0; i<HTP_MAX_MMAPS; i++) { m_vmem += ctx->mmap[i].size; }
 
-    FARF(HIGH, "prep-bufs : pass1 mmap-vmem %zu extra-vmem %zu n-bufs %u b-reuse %u", m_vmem, e_vmem, n_bufs, b_reuse);
+    FARF(HIGH, "prep-bufs : pass1 mmap-vmem %zu extra-vmem %zu max-vmem %zu : n-bufs %u b-reuse %u",
+            (size_t) m_vmem, (size_t) e_vmem, (size_t) ctx->max_vmem, n_bufs, b_reuse);
 
-    if ((m_vmem + e_vmem) > HTP_OP_MAX_VMEM) {
+    if ((m_vmem + e_vmem) > ctx->max_vmem) {
         // Drop unused mappings
         for (uint32_t i=0; i < HTP_MAX_MMAPS; i++) {
             bool used = m_reuse & (1<<i);
@@ -720,29 +804,32 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
             continue;
         }
 
+        // Reset poll count for valid requests
+        poll_count = DSPQUEUE_POLL_COUNT;
+
         const uint32_t n_bufs = req.n_bufs;
         const uint32_t n_tens = req.n_tensors;
         const uint32_t n_ops  = req.n_ops;
 
-        const uint32_t b_size = sizeof(struct htp_buf_desc) * n_bufs;
-        const uint32_t t_size = sizeof(struct htp_tensor)   * n_tens;
-        const uint32_t o_size = sizeof(struct htp_op_desc)  * n_ops;
+        const uint32_t b_size = sizeof(struct htp_buf_desc)  * n_bufs;
+        const uint32_t t_size = sizeof(struct htp_tensor)    * n_tens;
+        const uint32_t o_size = sizeof(struct htp_op_desc)   * n_ops;
+        const uint32_t p_size = sizeof(struct htp_prof_desc) * n_ops;
 
-        if (dbuf.size < b_size + t_size + o_size) {
+        if (dbuf.size < b_size + t_size + o_size + p_size) {
             FARF(ERROR, "invalid opbatch memory block size %u", dbuf.size);
             break;
         }
 
-        // Reset poll count for valid requests
-        poll_count = DSPQUEUE_POLL_COUNT;
+        FARF(HIGH, "processing opbatch #%u: n-bufs %u n-tensors %u n-ops %u : m-size %u b-size %u t-size %u o-size %u", req.id,
+                n_bufs, n_tens, n_ops, dbuf.size, b_size, t_size, o_size);
 
+        // Setup descriptor pointers
         uint8_t * m_ptr = dbuf.ptr;
-        struct htp_buf_desc* bufs = (struct htp_buf_desc*) m_ptr; m_ptr += b_size;
-        struct htp_tensor*   tens = (struct htp_tensor*)   m_ptr; m_ptr += t_size;
-        struct htp_op_desc*   ops = (struct htp_op_desc*)  m_ptr;
-
-        FARF(HIGH, "processing opbatch: n-bufs %u n-tensors %u n-ops %u : m-size %u b-size %u t-size %u o-size %u",
-                n_bufs, n_tens, n_ops, dbuf.size, b_size, t_size, o_size);
+        struct htp_buf_desc* bufs = (struct htp_buf_desc*)  m_ptr; m_ptr += b_size;
+        struct htp_tensor*   tens = (struct htp_tensor*)    m_ptr; m_ptr += t_size;
+        struct htp_op_desc*   ops = (struct htp_op_desc*)   m_ptr; m_ptr += o_size;
+        struct htp_prof_desc* pds = (struct htp_prof_desc*) m_ptr;
 
         prep_op_bufs(ctx, bufs, n_bufs);
         prep_tensors(ctx, bufs, tens, n_tens);
@@ -754,22 +841,34 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
 
         for (uint32_t i=0; i < n_ops; i++) {
             struct profile_data prof;
-            profile_start(&prof);
+
+            profile_start(ctx->profiler, &prof);
 
             proc_op_req(octx, tens, i, &ops[i]);
 
-            profile_stop(&prof);
-            ops[i].prof_usecs  = prof.usecs;
-            ops[i].prof_cycles = prof.cycles;
-            ops[i].prof_pkts   = prof.pkts;
+            profile_stop(ctx->profiler, &prof);
+
+            if (ctx->profiler) {
+                pds[i].opcode = ops[i].opcode;
+                pds[i].usecs  = prof.usecs;
+                pds[i].cycles = prof.cycles;
+                for (int j = 0; j < HEX_NUM_PMU_COUNTERS; j++) {
+                    pds[i].pmu[j] = prof.pmu_counters[j];
+                }
+            }
         }
 
         // dspqueue_write_early_wakeup_noblock(ctx->queue, 10, 0);
 
         struct htp_opbatch_rsp rsp;
-        rsp.status = HTP_STATUS_OK; // FIXME
+        rsp.id        = req.id;
+        rsp.status    = HTP_STATUS_OK;
+        rsp.n_bufs    = n_bufs;
+        rsp.n_tensors = n_tens;
+        rsp.n_ops     = n_ops;
 
         dbuf.flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT;
+
         err = dspqueue_write(queue, 0, 1, &dbuf, sizeof(rsp), (const uint8_t *) &rsp, DSPQUEUE_TIMEOUT_NONE);
         if (err != 0) {
             FARF(ERROR, "dspqueue_write failed: 0x%08x", (unsigned) err);
diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c
index bac06693d81..2461ae617fa 100644
--- a/ggml/src/ggml-hexagon/htp/matmul-ops.c
+++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c
@@ -2991,12 +2991,10 @@ int op_matmul(struct htp_ops_context * octx) {
         return op_matmul_hvx(octx);
     }
 
-    // M alignment: when M > 32 but not 32-aligned, we split into
-    // HMX (first m_hmx = M & ~31 rows) + HVX (remaining m_tail rows).
-    // When M <= 32 and not 32-aligned, fall back entirely to HVX.
+    // M alignment: Use HMX when M >= 32, the last partial tile (m_total % 32 rows)
+    //  is handled by HMX itself; when M < 32  fall back to HVX.
     const int m_total = (int) src1->ne[1];
-    const int m_tail  = m_total % 32;
-    const int m_hmx   = m_total - m_tail;
+    const int m_hmx   = m_total & ~31;   // 0 when M < 32
 
     if (m_hmx == 0) {
         return op_matmul_hvx(octx);
@@ -3009,7 +3007,6 @@ int op_matmul(struct htp_ops_context * octx) {
     int k = (int) src0->ne[0];  // inner dimension
     int n = (int) src0->ne[1];  // weight columns
 
-    // --- Phase 1: HMX on the first m_hmx (32-aligned) rows ---
     int ret = -1;
 
     // Row strides in elements. For compact tensors these equal k; for
@@ -3017,13 +3014,17 @@ int op_matmul(struct htp_ops_context * octx) {
     const int act_stride = (int)(src1->nb[1] / sizeof(float));
     const int wgt_stride = (int)(src0->nb[1] / sizeof(__fp16));
 
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
     if (src0->type == HTP_TYPE_F16) {
         if (is_batched) {
             hmx_matmul_w16a32_batched_params_t batch_params = {
                 .dst             = (float *) dst->data,
                 .activation      = (float *) src1->data,
                 .permuted_weight = (const __fp16 *) src0->data,
-                .m               = m_hmx,
+                .m               = m_total,
                 .k               = k,
                 .n               = n,
                 .act_stride      = act_stride,
@@ -3044,12 +3045,12 @@ int op_matmul(struct htp_ops_context * octx) {
         } else {
             ret = hmx_mat_mul_permuted_w16a32(octx->ctx,
                     (float*) dst->data, (float*) src1->data, (const __fp16 *) src0->data,
-                    m_hmx, k, n, act_stride, wgt_stride);
+                    m_total, k, n, act_stride, wgt_stride);
         }
     } else {
         ret = hmx_mat_mul_permuted_qk_0_d16a32(octx->ctx,
                     (float*) dst->data, (float*) src1->data, (const uint8_t *) src0->data,
-                    m_hmx, k, n, (int) src0->type);
+                    m_total, k, n, (int) src0->type);
     }
 
     if (ret != 0) {
@@ -3057,27 +3058,6 @@ int op_matmul(struct htp_ops_context * octx) {
         return op_matmul(octx);
     }
 
-    // --- Phase 2: HVX on the remaining m_tail rows ---
-    if (m_tail > 0) {
-        // copy of src1 and dst
-        struct htp_tensor src1_tail = *src1;
-        struct htp_tensor dst_tail  = *dst;
-
-        src1_tail.ne[1] = m_tail; // only tail rows
-        dst_tail.ne[1]  = m_tail; // only tail rows
-
-        // Offset activation and dst pointers past the HMX-processed rows.
-        // Use nb[1] (row stride in bytes) to compute the byte offset.
-        src1_tail.data += (uint32_t) m_hmx * src1->nb[1];
-        dst_tail.data  += (uint32_t) m_hmx * dst->nb[1];
-
-        octx->src[1] = &src1_tail;
-        octx->dst    = &dst_tail;
-
-        FARF(HIGH, "hmx-matmul: HVX tail m_tail %d src1 %p dst %p", m_tail, (void *) src1_tail.data, (void *) dst_tail.data);
-        return op_matmul_hvx(octx);
-    }
-
     return 0;
 #endif // HTP_HAS_HMX
 }
diff --git a/ggml/src/ggml-hexagon/htp/solve-tri-ops.c b/ggml/src/ggml-hexagon/htp/solve-tri-ops.c
new file mode 100644
index 00000000000..ae8e1a50495
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/solve-tri-ops.c
@@ -0,0 +1,267 @@
+#pragma clang diagnostic ignored "-Wunused-but-set-variable"
+
+#include <HAP_farf.h>
+#include <HAP_perf.h>
+#include <string.h>
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+#include "htp-ctx.h"
+#include "htp-ops.h"
+#include "hvx-types.h"
+#include "hvx-utils.h"
+
+struct htp_solve_tri_context {
+    struct htp_ops_context * octx;
+    uint32_t                 jobs_per_thread;
+    uint32_t                 total_jobs;
+    uint32_t                 k_chunks;
+    uint32_t                 col_block;
+};
+
+static inline void solve_tri_row_scalar(const float * A_row,
+                                        const float * B_row,
+                                        float *       X,
+                                        uint32_t      row,
+                                        uint32_t      k,
+                                        uint32_t      col0,
+                                        uint32_t      coln,
+                                        float         inv_diag) {
+    for (uint32_t col = col0; col < col0 + coln; ++col) {
+        float sum = 0.0f;
+        for (uint32_t t = 0; t < row; ++t) {
+            sum += A_row[t] * X[t * k + col];
+        }
+        X[row * k + col] = (B_row[col] - sum) * inv_diag;
+    }
+}
+
+static inline HVX_Vector hvx_load_partial_f32(const float * src, uint32_t n) {
+    HVX_Vector v = *((const HVX_UVector *) src);
+    HVX_VectorPred mask = Q6_Q_vsetq2_R(n * sizeof(float));
+    return Q6_V_vmux_QVV(mask, v, Q6_V_vzero());
+}
+
+static inline void solve_tri_row_hvx(const float * A_row,
+                                     const float * B_row,
+                                     float *       X,
+                                     uint32_t      row,
+                                     uint32_t      k,
+                                     uint32_t      col0,
+                                     uint32_t      coln,
+                                     float         inv_diag) {
+    const bool full = (coln == VLEN_FP32);
+
+    HVX_Vector sum_v = Q6_V_vzero();
+    for (uint32_t t = 0; t < row; ++t) {
+        const float   a         = A_row[t];
+        const float * x_row_col = X + t * k + col0;
+
+        HVX_Vector x_v = full ? *((const HVX_UVector *) x_row_col) : hvx_load_partial_f32(x_row_col, coln);
+        HVX_Vector a_v = hvx_vec_splat_f32(a);
+        sum_v          = hvx_vec_add_f32_f32(sum_v, hvx_vec_mul_f32_f32(x_v, a_v));
+    }
+
+    const float * b_row_col = B_row + col0;
+    float *       x_out_col = X + row * k + col0;
+
+    HVX_Vector b_v        = full ? *((const HVX_UVector *) b_row_col) : hvx_load_partial_f32(b_row_col, coln);
+    HVX_Vector inv_diag_v = hvx_vec_splat_f32(inv_diag);
+
+    HVX_Vector out_v = hvx_vec_mul_f32_f32(hvx_vec_sub_f32_f32(b_v, sum_v), inv_diag_v);
+    hvx_vec_store_u((void *) x_out_col, coln * sizeof(float), out_v);
+}
+
+// Batch-level thread: each job is one full batch.
+static void solve_tri_batch_thread_f32(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_solve_tri_context * sctx = (struct htp_solve_tri_context *) data;
+    struct htp_ops_context *       octx = sctx->octx;
+
+    const struct htp_tensor * src0 = octx->src[0];  // A
+    const struct htp_tensor * src1 = octx->src[1];  // B
+    const struct htp_tensor * dst  = octx->dst;     // X
+
+    const uint32_t n = src0->ne[0];
+    const uint32_t k = src1->ne[0];
+
+    const uint32_t ne02 = src0->ne[2];
+
+    const uint32_t col_block = VLEN_FP32;
+    const uint32_t k_full    = (k / col_block) * col_block;
+
+    const uint32_t start_batch = sctx->jobs_per_thread * ith;
+    const uint32_t end_batch   = MIN(start_batch + sctx->jobs_per_thread, sctx->total_jobs);
+
+    uint64_t t1, t2;
+    t1 = HAP_perf_get_qtimer_count();
+
+    for (uint32_t batch = start_batch; batch < end_batch; ++batch) {
+        const uint32_t i03 = batch / ne02;
+        const uint32_t i02 = batch - i03 * ne02;
+
+        const float * A_batch =
+            (const float *) ((const uint8_t *) (uintptr_t) src0->data + i02 * src0->nb[2] + i03 * src0->nb[3]);
+        const float * B_batch =
+            (const float *) ((const uint8_t *) (uintptr_t) src1->data + i02 * src1->nb[2] + i03 * src1->nb[3]);
+        float * X_batch = (float *) ((uint8_t *) (uintptr_t) dst->data + i02 * dst->nb[2] + i03 * dst->nb[3]);
+
+        for (uint32_t row = 0; row < n; ++row) {
+            const float   diag     = A_batch[row * n + row];
+            const float   inv_diag = 1.0f / diag;
+            const float * A_row    = A_batch + row * n;
+            const float * B_row    = B_batch + row * k;
+
+            uint32_t col0 = 0;
+            for (; col0 < k_full; col0 += col_block) {
+                solve_tri_row_hvx(A_row, B_row, X_batch, row, k, col0, col_block, inv_diag);
+            }
+
+            if (col0 < k) {
+                const uint32_t coln = k - col0;
+                if (coln >= 8) {
+                    solve_tri_row_hvx(A_row, B_row, X_batch, row, k, col0, coln, inv_diag);
+                } else {
+                    solve_tri_row_scalar(A_row, B_row, X_batch, row, k, col0, coln, inv_diag);
+                }
+            }
+        }
+    }
+
+    t2 = HAP_perf_get_qtimer_count();
+
+    FARF(HIGH, "solve-tri-batch %d/%d: A=(%ux%u) B=(%ux%u) batch %u:%u usec %u\n",
+         ith, nth, n, n, k, n, start_batch, end_batch,
+         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
+// Chunk-level thread: each job is one (batch, col_chunk) pair.
+static void solve_tri_chunk_thread_f32(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_solve_tri_context * sctx = (struct htp_solve_tri_context *) data;
+    struct htp_ops_context *       octx = sctx->octx;
+
+    const struct htp_tensor * src0 = octx->src[0];  // A
+    const struct htp_tensor * src1 = octx->src[1];  // B
+    const struct htp_tensor * dst  = octx->dst;     // X
+
+    const uint32_t n = src0->ne[0];
+    const uint32_t k = src1->ne[0];
+
+    const uint32_t ne02 = src0->ne[2];
+
+    const uint32_t start_job = sctx->jobs_per_thread * ith;
+    const uint32_t end_job   = MIN(start_job + sctx->jobs_per_thread, sctx->total_jobs);
+
+    uint64_t t1, t2;
+    t1 = HAP_perf_get_qtimer_count();
+
+    for (uint32_t job = start_job; job < end_job; ++job) {
+        const uint32_t batch = job / sctx->k_chunks;
+        const uint32_t chunk = job - batch * sctx->k_chunks;
+
+        const uint32_t i03 = batch / ne02;
+        const uint32_t i02 = batch - i03 * ne02;
+
+        const uint32_t col0 = chunk * sctx->col_block;
+        const uint32_t coln = MIN(sctx->col_block, k - col0);
+
+        const float * A_batch =
+            (const float *) ((const uint8_t *) (uintptr_t) src0->data + i02 * src0->nb[2] + i03 * src0->nb[3]);
+        const float * B_batch =
+            (const float *) ((const uint8_t *) (uintptr_t) src1->data + i02 * src1->nb[2] + i03 * src1->nb[3]);
+        float * X_batch = (float *) ((uint8_t *) (uintptr_t) dst->data + i02 * dst->nb[2] + i03 * dst->nb[3]);
+
+        const bool use_hvx = (coln >= 8);
+
+        for (uint32_t row = 0; row < n; ++row) {
+            const float diag     = A_batch[row * n + row];
+            const float inv_diag = 1.0f / diag;
+
+            const float * A_row = A_batch + row * n;
+            const float * B_row = B_batch + row * k;
+
+            if (use_hvx) {
+                solve_tri_row_hvx(A_row, B_row, X_batch, row, k, col0, coln, inv_diag);
+            } else {
+                solve_tri_row_scalar(A_row, B_row, X_batch, row, k, col0, coln, inv_diag);
+            }
+        }
+    }
+
+    t2 = HAP_perf_get_qtimer_count();
+
+    FARF(HIGH, "solve-tri-chunk %d/%d: A=(%ux%u) B=(%ux%u) job %u:%u usec %u\n",
+         ith, nth, n, n, k, n, start_job, end_job,
+         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+}
+
+int op_solve_tri(struct htp_ops_context * octx) {
+    const struct htp_tensor * src0 = octx->src[0];  // A
+    const struct htp_tensor * src1 = octx->src[1];  // B
+    const struct htp_tensor * dst  = octx->dst;     // X
+
+    if (src0->type != HTP_TYPE_F32 || src1->type != HTP_TYPE_F32 || dst->type != HTP_TYPE_F32) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    // left=true, lower=true, uni=false only
+    if (src0->ne[0] != src0->ne[1]) {
+        return HTP_STATUS_INVAL_PARAMS;
+    }
+    if (src0->ne[1] != src1->ne[1]) {
+        return HTP_STATUS_INVAL_PARAMS;
+    }
+    if (src0->ne[2] != src1->ne[2] || src0->ne[3] != src1->ne[3]) {
+        return HTP_STATUS_INVAL_PARAMS;
+    }
+    if (dst->ne[0] != src1->ne[0] || dst->ne[1] != src1->ne[1] || dst->ne[2] != src1->ne[2] ||
+        dst->ne[3] != src1->ne[3]) {
+        return HTP_STATUS_INVAL_PARAMS;
+    }
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    const uint32_t k = src1->ne[0];
+
+    const uint32_t col_block     = VLEN_FP32;
+    const uint32_t k_chunks      = (k + col_block - 1) / col_block;
+    const uint32_t total_batches = src0->ne[2] * src0->ne[3];
+    const bool     batched       = total_batches >= (uint32_t) octx->n_threads;
+
+    FARF(HIGH, "solve-tri: (%ux%ux%ux%u) x (%ux%ux%ux%u) -> (%ux%ux%ux%u) : batched %d\n",
+         src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+         src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
+         dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], batched);
+
+    if (batched) {
+        // Batch-level parallelism
+        const uint32_t n_threads = MIN((uint32_t) octx->n_threads, total_batches);
+
+        struct htp_solve_tri_context sctx = {
+            .octx            = octx,
+            .jobs_per_thread = (total_batches + n_threads - 1) / n_threads,
+            .total_jobs      = total_batches,
+            .k_chunks        = k_chunks,
+            .col_block       = col_block,
+        };
+
+        worker_pool_run_func(octx->ctx->worker_pool, solve_tri_batch_thread_f32, &sctx, n_threads);
+    } else {
+        // Chunk-level parallelism
+        const uint32_t total_jobs = total_batches * k_chunks;
+        const uint32_t n_threads  = MIN((uint32_t) octx->n_threads, MAX(total_jobs, 1));
+
+        struct htp_solve_tri_context sctx = {
+            .octx            = octx,
+            .jobs_per_thread = (total_jobs + n_threads - 1) / n_threads,
+            .total_jobs      = total_jobs,
+            .k_chunks        = k_chunks,
+            .col_block       = col_block,
+        };
+
+        worker_pool_run_func(octx->ctx->worker_pool, solve_tri_chunk_thread_f32, &sctx, n_threads);
+    }
+
+    return HTP_STATUS_OK;
+}
diff --git a/ggml/src/ggml-hexagon/htp/unary-ops.c b/ggml/src/ggml-hexagon/htp/unary-ops.c
index 03eccfd55e3..d4ae89ee6f0 100644
--- a/ggml/src/ggml-hexagon/htp/unary-ops.c
+++ b/ggml/src/ggml-hexagon/htp/unary-ops.c
@@ -26,8 +26,8 @@ struct htp_unary_context {
     const uint8_t *           data_src0;
     uint8_t *                 data_dst;
 
-    size_t                    src0_row_size;
-    size_t                    dst_row_size;
+    size_t                    src0_data_row_size;   // actual data bytes per row
+    size_t                    dst_data_row_size;    // actual data bytes per row
 
     size_t                    src0_row_size_aligned;
     size_t                    dst_row_size_aligned;
@@ -41,6 +41,40 @@ struct htp_unary_context {
     uint32_t                  nc;
 };
 
+// Convert flat row index to DDR byte offset using the tensor's actual strides.
+// ir = i1 + ne1*(i2 + ne2*i3)  =>  offset = i1*nb1 + i2*nb2 + i3*nb3
+static inline size_t unary_row_offset(uint32_t ir,
+                                      uint32_t ne1, uint32_t ne2,
+                                      size_t nb1, size_t nb2, size_t nb3) {
+    const uint32_t i1 = ir % ne1;
+    const uint32_t i2 = (ir / ne1) % ne2;
+    const uint32_t i3 = ir / (ne1 * ne2);
+    return i1 * nb1 + i2 * nb2 + i3 * nb3;
+}
+// Safe DMA block size from row `ir`: clamp to the tighter dim-1 slice
+// boundary of src and dst so the nb1 stride stays valid for all rows.
+static inline uint32_t unary_block_size(uint32_t ir,
+                                        uint32_t end_row,
+                                        uint32_t block,
+                                        bool src_contig,
+                                        bool dst_contig,
+                                        uint32_t src_ne1,
+                                        uint32_t dst_ne1) {
+    uint32_t limit = MIN(block, end_row - ir);
+
+    if (!src_contig) {
+        const uint32_t src_slice_end = (ir / src_ne1 + 1) * src_ne1;
+        limit = MIN(limit, src_slice_end - ir);
+    }
+
+    if (!dst_contig) {
+        const uint32_t dst_slice_end = (ir / dst_ne1 + 1) * dst_ne1;
+        limit = MIN(limit, dst_slice_end - ir);
+    }
+
+    return limit;
+}
+
 #define htp_unary_preamble            \
     const uint32_t ne00 = src->ne[0]; \
     const uint32_t ne01 = src->ne[1]; \
@@ -264,6 +298,96 @@ static void softplus_f32(const float * restrict src,
     }
 }
 
+// --- L2_NORM HVX kernel ---
+// Computes y[i] = x[i] / fmax(sqrt(sum(x[j]^2)), epsilon) for each row.
+// scale = 1/fmax(sqrt(sum), epsilon) is computed entirely in HVX registers
+// using rsqrt + inverse to avoid scalar extraction.
+static void hvx_fast_l2_norm_f32(const uint8_t * restrict src,
+                                 uint8_t * restrict dst,
+                                 uint8_t * restrict pad,
+                                 const int num_elems,
+                                 float     epsilon) {
+    (void)pad;
+
+    const HVX_Vector * restrict v_src = (HVX_Vector *) src;
+    HVX_Vector * restrict v_dst       = (HVX_Vector *) dst;
+
+    HVX_Vector sum_v = hvx_vec_splat_f32(0.0f);
+
+    const int nvec = num_elems / VLEN_FP32;
+    const int nloe = num_elems % VLEN_FP32;
+
+    #pragma unroll(4)
+    for (int i = 0; i < nvec; i++) {
+        HVX_Vector v1 = v_src[i];
+        HVX_Vector sq = Q6_Vqf32_vmpy_VsfVsf(v1, v1);
+        sum_v         = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, sq);
+    }
+
+    // Include tail elements in the sum-of-squares using a predicate mask
+    if (nloe > 0) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        HVX_Vector v1 = Q6_V_vand_QV(bmask, v_src[nvec]);
+        HVX_Vector sq = Q6_Vqf32_vmpy_VsfVsf(v1, v1);
+        sum_v         = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, sq);
+    }
+
+    // Compute scale = 1/fmax(sqrt(sum), epsilon) entirely in HVX registers.
+    // hvx_vec_rsqrt_f32 + hvx_vec_inverse_f32 avoids scalar extraction.
+    HVX_Vector sum_sf    = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_v));
+    HVX_Vector rsqrt_v   = hvx_vec_rsqrt_f32(sum_sf);              // 1/sqrt(sum)
+    HVX_Vector sqrt_v    = hvx_vec_inverse_f32(rsqrt_v);            // sqrt(sum)
+    HVX_Vector epsilon_v = hvx_vec_splat_f32(epsilon);
+    HVX_Vector denom_v   = Q6_Vsf_vmax_VsfVsf(sqrt_v, epsilon_v);  // fmax(sqrt(sum), epsilon)
+    HVX_Vector scale_v   = hvx_vec_inverse_f32(denom_v);            // 1/fmax(sqrt(sum), epsilon)
+
+    #pragma unroll(4)
+    for (int i = 0; i < nvec; i++) {
+        HVX_Vector v1 = v_src[i];
+        v_dst[i]      = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(v1, scale_v));
+    }
+
+    if (nloe > 0) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        HVX_Vector v1 = Q6_V_vand_QV(bmask, v_src[nvec]);
+        HVX_Vector result = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(v1, scale_v));
+        hvx_vec_store_a(&v_dst[nvec], nloe * 4, result);
+    }
+}
+
+static void l2_norm_f32(const float * restrict src,
+                        float * restrict dst,
+                        uint8_t * restrict spad,
+                        const uint32_t num_rows,
+                        const uint32_t row_elems,
+                        const size_t   row_size,
+                        int32_t *      op_params) {
+    float epsilon = 0.f;
+    memcpy(&epsilon, op_params, sizeof(float));
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const float * restrict src_f = (const float *)((const uint8_t *)src + (ir * row_size));
+        float * restrict dst_f       = (float *)((uint8_t *)dst + (ir * row_size));
+
+        hvx_fast_l2_norm_f32((const uint8_t *)src_f, (uint8_t *)dst_f, spad, row_elems, epsilon);
+    }
+}
+
+static void tanh_f32(const float * restrict src,
+                     float * restrict dst,
+                     uint8_t * restrict spad,
+                     const uint32_t num_rows,
+                     const uint32_t row_elems,
+                     const size_t   row_size,
+                     int32_t *      op_params) {
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const uint8_t * restrict src_local = (const uint8_t *)src + (ir * row_size);
+        uint8_t * restrict dst_local       = (uint8_t *)dst + (ir * row_size);
+
+        hvx_tanh_f32_aa(dst_local, src_local, row_elems);
+    }
+}
+
 static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void * data) {
     const struct htp_unary_context * uctx = (const struct htp_unary_context *) data;
     struct htp_ops_context * octx = uctx->octx;
@@ -276,8 +400,8 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *
     int32_t *                 op_params = octx->op_params;
     uint32_t                  src0_nrows_per_thread = uctx->src0_nrows_per_thread;
 
-    const size_t src0_row_size = uctx->src0_row_size;
-    const size_t dst_row_size  = uctx->dst_row_size;
+    const size_t src0_data_row_size = uctx->src0_data_row_size;
+    const size_t dst_data_row_size  = uctx->dst_data_row_size;
 
     const size_t src0_row_size_aligned = uctx->src0_row_size_aligned;
     const size_t dst_row_size_aligned  = uctx->dst_row_size_aligned;
@@ -303,7 +427,16 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *
     size_t src0_spad_half_size = uctx->src0_spad_half_size;
     size_t dst_spad_half_size  = uctx->dst_spad_half_size;
 
-    const int BLOCK = uctx->block;
+    // Non-contiguous tensors have gaps at dim-2/3 boundaries that a single-stride
+    // 2D DMA descriptor cannot span. Clamp BLOCK to ne1 (one dim-1 slice) so every
+    // transfer stays within a nb1-uniform region. Skipped for contiguous tensors.
+    const bool src0_contig = (nb02 == (size_t)ne01 * nb01) &&
+                             (nb03 == (size_t)ne02 * nb02);
+    const bool dst_contig  = (nb2  == (size_t)ne1  * nb1)  &&
+                             (nb3  == (size_t)ne2  * nb2);
+    const uint32_t src0_max_block = src0_contig ? uctx->block : MIN((uint32_t)uctx->block, ne01);
+    const uint32_t dst_max_block  = dst_contig  ? uctx->block : MIN((uint32_t)uctx->block, ne1);
+    const uint32_t BLOCK = MIN(src0_max_block, dst_max_block);
     if (BLOCK == 0) {
         FARF(ERROR, "unary-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n",
              octx->src0_spad.size_per_thread, src0_row_size_aligned);
@@ -312,21 +445,23 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *
 
     dma_queue * dma_queue = octx->ctx->dma[ith];
 
-    for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) {
-        const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
+    for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; spad_idx++) {
+        const uint32_t block_size = unary_block_size(ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01, ne1);
 
         // Dummy DMA transation for sequencing (interleaving dst,src,dst,...)
-        dma_queue_push_vtcm_to_ddr(dma_queue,
+        dma_queue_push(dma_queue,
             dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)),
-            dst_row_size, dst_row_size_aligned, 0);
+            nb1, dst_row_size_aligned, dst_data_row_size, 0);
 
-        dma_queue_push_ddr_to_vtcm(dma_queue,
-            dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src + (ir * src0_row_size)),
-            src0_row_size_aligned, src0_row_size, block_size);
+        const size_t src0_off = unary_row_offset(ir, ne01, ne02, nb01, nb02, nb03);
+        dma_queue_push(dma_queue,
+            dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src + src0_off),
+            src0_row_size_aligned, nb01, src0_data_row_size, block_size);
+        ir += block_size;
     }
 
-    for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) {
-        const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
+    for (uint32_t ir = src0_start_row; ir < src0_end_row; ) {
+        const uint32_t block_size = unary_block_size(ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01, ne1);
 
         float * dst_spad  = (float *) dma_queue_pop(dma_queue).src;
         float * src0_spad = (float *) dma_queue_pop(dma_queue).dst;
@@ -357,22 +492,35 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *
             case HTP_OP_UNARY_SOFTPLUS:
                 softplus_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params);
                 break;
+            case HTP_OP_UNARY_TANH:
+                tanh_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params);
+                break;
+            case HTP_OP_L2_NORM:
+                l2_norm_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params);
+                break;
             default:
                 break;
         }
 
-        dma_queue_push_vtcm_to_ddr(dma_queue,
-            dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad),
-            dst_row_size, dst_row_size_aligned, block_size);
+        const size_t dst_off = unary_row_offset(ir, ne1, ne2, nb1, nb2, nb3);
+        dma_queue_push(dma_queue,
+            dma_make_ptr(data_dst + dst_off, dst_spad),
+            nb1, dst_row_size_aligned, dst_data_row_size, block_size);
 
         // prefetch N+2 loop iteration if any
-        const uint32_t pref_block = (ir + BLOCK * 2);
-        if (pref_block < src0_end_row) {
-            const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block);
-            dma_queue_push_ddr_to_vtcm(dma_queue,
-                dma_make_ptr(src0_spad, data_src + (pref_block * src0_row_size)),
-                src0_row_size_aligned, src0_row_size, pref_block_size);
+        const uint32_t next_ir = ir + block_size;
+        if (next_ir < src0_end_row) {
+            const uint32_t next_block_size = unary_block_size(next_ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01, ne1);
+            const uint32_t pref_ir = next_ir + next_block_size;
+            if (pref_ir < src0_end_row) {
+                const uint32_t pref_block_size = unary_block_size(pref_ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01, ne1);
+                const size_t src0_pref_off = unary_row_offset(pref_ir, ne01, ne02, nb01, nb02, nb03);
+            dma_queue_push(dma_queue,
+                dma_make_ptr(src0_spad, data_src + src0_pref_off),
+                src0_row_size_aligned, nb01, src0_data_row_size, pref_block_size);
+            }
         }
+        ir += block_size;
     }
 
     dma_queue_flush(dma_queue);
@@ -417,7 +565,12 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
         case HTP_OP_UNARY_SOFTPLUS:
             op_type = "softplus-f32";
             break;
-
+        case HTP_OP_UNARY_TANH:
+            op_type = "tanh-f32";
+            break;
+        case HTP_OP_L2_NORM:
+            op_type = "l2norm-f32";
+            break;
         default:
             FARF(ERROR, "Unsupported unary Op %u\n", octx->op);
             return HTP_STATUS_NO_SUPPORT;
@@ -426,11 +579,11 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
     const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3];
     const uint32_t n_threads  = MIN(octx->n_threads, src0_nrows);
 
-    const size_t src0_row_size = src0->nb[1];
-    const size_t dst_row_size  = dst->nb[1];
+    const size_t src0_data_row_size = src0->ne[0] * sizeof(float);
+    const size_t dst_data_row_size  = dst->ne[0]  * sizeof(float);
 
-    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
-    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+    const size_t src0_row_size_aligned = hex_round_up(src0_data_row_size, VLEN);
+    const size_t dst_row_size_aligned  = hex_round_up(dst_data_row_size,  VLEN);
 
     // VTCM scratchpads for all tensors
     // N rows per thread, padded to HVX vector size
@@ -468,8 +621,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
             .data_src0             = (const uint8_t *)src0->data,
             .data_dst              = (uint8_t *)dst->data,
 
-            .src0_row_size         = src0_row_size,
-            .dst_row_size          = dst_row_size,
+            .src0_data_row_size    = src0_data_row_size,
+            .dst_data_row_size     = dst_data_row_size,
 
             .src0_row_size_aligned = src0_row_size_aligned,
             .dst_row_size_aligned  = dst_row_size_aligned,
diff --git a/ggml/src/ggml-hexagon/htp/vtcm-utils.h b/ggml/src/ggml-hexagon/htp/vtcm-utils.h
new file mode 100644
index 00000000000..b129fb74e31
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/vtcm-utils.h
@@ -0,0 +1,16 @@
+#ifndef VTCM_UTILS_H
+#define VTCM_UTILS_H
+
+#include "hex-utils.h"
+
+#include <assert.h>
+#include <stdint.h>
+#include <hexagon_types.h>
+
+static inline uint8_t *vtcm_seq_alloc(uint8_t **vtcm_ptr, size_t size) {
+    uint8_t *p = *vtcm_ptr;
+    *vtcm_ptr += size;
+    return p;
+}
+
+#endif // VTCM_UTILS_H
diff --git a/ggml/src/ggml-hexagon/libggml-htp.inf b/ggml/src/ggml-hexagon/libggml-htp.inf
index 656d2d9ab26..39cefcdda38 100644
--- a/ggml/src/ggml-hexagon/libggml-htp.inf
+++ b/ggml/src/ggml-hexagon/libggml-htp.inf
@@ -8,7 +8,7 @@ CatalogFile = libggml-htp.cat
 PnpLockDown = 1
 
 [DestinationDirs]
-Drivers_Dir = 6
+Drivers_Dir = 13
 
 [SourceDisksNames]
 1 = %DiskId%
@@ -18,6 +18,7 @@ libggml-htp-v68.so = 1
 libggml-htp-v69.so = 1
 libggml-htp-v73.so = 1
 libggml-htp-v75.so = 1
+libggml-htp-v79.so = 1
 libggml-htp-v81.so = 1
 
 [ControlFlags]
@@ -31,6 +32,7 @@ libggml-htp-v68.so,,,0x10 ;COPYFLG_NO_OVERWRITE
 libggml-htp-v69.so,,,0x10 ;COPYFLG_NO_OVERWRITE
 libggml-htp-v73.so,,,0x10 ;COPYFLG_NO_OVERWRITE
 libggml-htp-v75.so,,,0x10 ;COPYFLG_NO_OVERWRITE
+libggml-htp-v79.so,,,0x10 ;COPYFLG_NO_OVERWRITE
 libggml-htp-v81.so,,,0x10 ;COPYFLG_NO_OVERWRITE
 
 [Strings]
diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index a0fc781916a..610b68c2f26 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -590,6 +590,8 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_gated_delta_net(
     const int ne20 = op->src[2]->ne[0]; // S_v
     const int ne21 = op->src[2]->ne[1]; // H
     const int ne30 = op->src[3]->ne[0]; // G
+    // state is src[5], 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
+    const int K = op->src[5]->ne[1];
 
     const int nsg = op->src[2]->ne[0]/32;
 
@@ -598,7 +600,7 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_gated_delta_net(
     GGML_ASSERT(ne20 % 32 == 0);
 
     snprintf(base, 256, "kernel_gated_delta_net_%s_%d", ggml_type_name(op->src[0]->type), nsg);
-    snprintf(name, 256, "%s_ne20=%d_ne30=%d", base, ne20, ne30);
+    snprintf(name, 256, "%s_ne20=%d_ne30=%d_K=%d", base, ne20, ne30, K);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
@@ -606,6 +608,7 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_gated_delta_net(
 
         ggml_metal_cv_set_int16(cv, ne20, FC_GATED_DELTA_NET + 0);
         ggml_metal_cv_set_int16(cv, ne30, FC_GATED_DELTA_NET + 1);
+        ggml_metal_cv_set_int16(cv, K,    FC_GATED_DELTA_NET + 2);
 
         res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
 
@@ -664,19 +667,30 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_turbo_wht(ggml_m
     return res;
 }
 
-ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext(ggml_metal_library_t lib, ggml_type tsrc0, ggml_type tsrc1, int nsg, int nxpsg, int r1ptg) {
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext(ggml_metal_library_t lib, const ggml_tensor * op, int nsg, int nxpsg, int r1ptg) {
     char base[256];
     char name[256];
 
+    const ggml_type tsrc0 = op->src[0]->type;
+    const ggml_type tsrc1 = op->src[1]->type;
+    const int       ne12  = op->src[1]->ne[2];
+    const int       r2    = ne12 / op->src[0]->ne[2];
+    const int       r3    = op->src[1]->ne[3] / op->src[0]->ne[3];
+
+    GGML_ASSERT(ne12 <= INT16_MAX && r2 <= INT16_MAX && r3 <= INT16_MAX);
+
     snprintf(base, 256, "kernel_mul_mv_ext_%s_%s_r1_%d", ggml_type_name(tsrc0), ggml_type_name(tsrc1), r1ptg);
-    snprintf(name, 256, "%s_nsg=%d_nxpsg=%d", base, nsg, nxpsg);
+    snprintf(name, 256, "%s_nsg=%d_nxpsg=%d_ne12=%d_r2=%d_r3=%d", base, nsg, nxpsg, ne12, r2, r3);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
         ggml_metal_cv_t cv = ggml_metal_cv_init();
 
-        ggml_metal_cv_set_int16(cv, nsg,   FC_MUL_MV + 0);
-        ggml_metal_cv_set_int16(cv, nxpsg, FC_MUL_MV + 1);
+        ggml_metal_cv_set_int16(cv, nsg,            FC_MUL_MV + 0);
+        ggml_metal_cv_set_int16(cv, nxpsg,          FC_MUL_MV + 1);
+        ggml_metal_cv_set_int16(cv, (int16_t) ne12, FC_MUL_MV + 2);
+        ggml_metal_cv_set_int16(cv, (int16_t) r2,   FC_MUL_MV + 3);
+        ggml_metal_cv_set_int16(cv, (int16_t) r3,   FC_MUL_MV + 4);
 
         res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
 
@@ -694,10 +708,25 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm(ggml_meta
     const ggml_type tsrc1 = op->src[1]->type;
 
     const bool bc_inp = op->src[0]->ne[0] % 32 != 0;
-    const bool bc_out = op->ne[0] % 64 != 0 || op->ne[1] % 32 != 0;
+
+    constexpr int NRA = SZ_SIMDGROUP * N_MM_BLOCK_Y * N_MM_SIMD_GROUP_Y;
+    constexpr int NRB = SZ_SIMDGROUP * N_MM_BLOCK_X * N_MM_SIMD_GROUP_X;
+
+    const bool has_tensor = ggml_metal_device_get_props(ggml_metal_library_get_device(lib))->has_tensor;
+
+    const bool bc_out = has_tensor
+        ? (op->ne[0] % NRA != 0 || op->ne[1] % NRB != 0)
+        : (op->ne[0] % 64  != 0 || op->ne[1] % 32  != 0);
+
+    GGML_ASSERT(op->src[1]->ne[2] <= INT16_MAX && op->src[1]->ne[3] <= INT16_MAX);
+    const int16_t ne12 = (int16_t) op->src[1]->ne[2];
+    const int16_t ne13 = (int16_t) op->src[1]->ne[3];
+    const int16_t r2   = (int16_t) (ne12 / op->src[0]->ne[2]);
+    const int16_t r3   = (int16_t) (ne13 / op->src[0]->ne[3]);
 
     snprintf(base, 256, "kernel_mul_mm_%s_%s", ggml_type_name(tsrc0), ggml_type_name(tsrc1));
-    snprintf(name, 256, "%s_bci=%d_bco=%d", base, bc_inp, bc_out);
+    snprintf(name, 256, "%s_bci=%d_bco=%d_ne12=%d_ne13=%d_r2=%d_r3=%d",
+             base, bc_inp, bc_out, ne12, ne13, r2, r3);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
@@ -705,14 +734,30 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm(ggml_meta
 
         ggml_metal_cv_set_bool(cv, bc_inp, FC_MUL_MM + 0);
         ggml_metal_cv_set_bool(cv, bc_out, FC_MUL_MM + 1);
+        ggml_metal_cv_set_int16(cv, ne12,  FC_MUL_MM + 2);
+        ggml_metal_cv_set_int16(cv, ne13,  FC_MUL_MM + 3);
+        ggml_metal_cv_set_int16(cv, r2,    FC_MUL_MM + 4);
+        ggml_metal_cv_set_int16(cv, r3,    FC_MUL_MM + 5);
 
         res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
 
         ggml_metal_cv_free(cv);
     }
 
-    // when the output size is not multiple of 64x32, we need extra smem to prevent out-of-bounds writes
-    res.smem = bc_out ? 8192 : 4096 + 2048;
+    if (has_tensor) {
+        res.nr0 = NRA;
+        res.nr1 = NRB;
+
+        const size_t smem_a = NRA * N_MM_NK_TOTAL * sizeof(ggml_fp16_t);
+        res.smem = smem_a;
+    } else {
+        res.nr0 = 64;
+        res.nr1 = 32;
+
+        res.smem = bc_out ? 8192 : (4096 + 2048);
+    }
+
+    res.nsg = N_MM_SIMD_GROUP_X * N_MM_SIMD_GROUP_Y;
 
     return res;
 }
@@ -886,14 +931,21 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv(ggml_meta
             }
     };
 
+    GGML_ASSERT(ne12 <= INT16_MAX && ne13 <= INT16_MAX);
+    const int16_t r2 = (int16_t) (ne12 / ne02);
+    const int16_t r3 = (int16_t) (ne13 / ne03);
+
     snprintf(base, 256, "kernel_mul_mv_%s_%s%s", ggml_type_name(tsrc0), ggml_type_name(tsrc1), suffix);
-    snprintf(name, 256, "%s_nsg=%d", base, nsg);
+    snprintf(name, 256, "%s_nsg=%d_ne12=%d_r2=%d_r3=%d", base, nsg, ne12, r2, r3);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
         ggml_metal_cv_t cv = ggml_metal_cv_init();
 
-        ggml_metal_cv_set_int16(cv, nsg, FC_MUL_MV + 0);
+        ggml_metal_cv_set_int16(cv, nsg,            FC_MUL_MV + 0);
+        ggml_metal_cv_set_int16(cv, (int16_t) ne12, FC_MUL_MV + 2);
+        ggml_metal_cv_set_int16(cv, r2,             FC_MUL_MV + 3);
+        ggml_metal_cv_set_int16(cv, r3,             FC_MUL_MV + 4);
 
         res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
 
@@ -919,8 +971,15 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_tq_rotate
     const bool bc_inp = op->src[0]->ne[0] % 32 != 0;
     const bool bc_out = op->ne[0] % 64 != 0 || op->ne[1] % 32 != 0;
 
+    GGML_ASSERT(op->src[1]->ne[2] <= INT16_MAX && op->src[1]->ne[3] <= INT16_MAX);
+    const int16_t ne12 = (int16_t) op->src[1]->ne[2];
+    const int16_t ne13 = (int16_t) op->src[1]->ne[3];
+    const int16_t r2   = (int16_t) (ne12 / op->src[0]->ne[2]);
+    const int16_t r3   = (int16_t) (ne13 / op->src[0]->ne[3]);
+
     snprintf(base, 256, "kernel_mul_mm_%s_rotated_%s", ggml_type_name(tsrc0), ggml_type_name(tsrc1));
-    snprintf(name, 256, "%s_bci=%d_bco=%d", base, bc_inp, bc_out);
+    snprintf(name, 256, "%s_bci=%d_bco=%d_ne12=%d_ne13=%d_r2=%d_r3=%d",
+             base, bc_inp, bc_out, ne12, ne13, r2, r3);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
@@ -928,6 +987,10 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_tq_rotate
 
         ggml_metal_cv_set_bool(cv, bc_inp, FC_MUL_MM + 0);
         ggml_metal_cv_set_bool(cv, bc_out, FC_MUL_MM + 1);
+        ggml_metal_cv_set_int16(cv, ne12,  FC_MUL_MM + 2);
+        ggml_metal_cv_set_int16(cv, ne13,  FC_MUL_MM + 3);
+        ggml_metal_cv_set_int16(cv, r2,    FC_MUL_MM + 4);
+        ggml_metal_cv_set_int16(cv, r3,    FC_MUL_MM + 5);
 
         res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
 
@@ -1205,6 +1268,9 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_id(ggml_m
         ggml_metal_cv_t cv = ggml_metal_cv_init();
 
         ggml_metal_cv_set_int16(cv, nsg, FC_MUL_MV + 0);
+        ggml_metal_cv_set_int16(cv, 1,   FC_MUL_MV + 2);
+        ggml_metal_cv_set_int16(cv, 1,   FC_MUL_MV + 3);
+        ggml_metal_cv_set_int16(cv, 1,   FC_MUL_MV + 4);
 
         res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
 
diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h
index 20cc92841e7..9328ab2abf4 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.h
+++ b/ggml/src/ggml-metal/ggml-metal-device.h
@@ -102,6 +102,8 @@ ggml_metal_library_t ggml_metal_library_init_from_source(ggml_metal_device_t dev
 
 void ggml_metal_library_free(ggml_metal_library_t lib);
 
+ggml_metal_device_t ggml_metal_library_get_device(ggml_metal_library_t lib);
+
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline    (ggml_metal_library_t lib, const char * name);
 struct ggml_metal_pipeline_with_params ggml_metal_library_compile_pipeline(ggml_metal_library_t lib, const char * base, const char * name, ggml_metal_cv_t cv);
 
@@ -128,7 +130,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rwkv
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_gated_delta_net   (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri         (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_turbo_wht         (ggml_metal_library_t lib);
-struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext        (ggml_metal_library_t lib, enum ggml_type tsrc0, enum ggml_type tsrc1, int nsg, int nxpsg, int r1ptg);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext        (ggml_metal_library_t lib, const struct ggml_tensor * op, int nsg, int nxpsg, int r1ptg);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm            (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_tq_rotated(ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id_tq_rotated(ggml_metal_library_t lib, const struct ggml_tensor * op);
@@ -284,6 +286,7 @@ bool   ggml_metal_buffer_is_shared(ggml_metal_buffer_t buf);
 void   ggml_metal_buffer_memset_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
 void   ggml_metal_buffer_set_tensor   (ggml_metal_buffer_t buf, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
 void   ggml_metal_buffer_get_tensor   (ggml_metal_buffer_t buf, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
+bool   ggml_metal_buffer_cpy_tensor   (ggml_metal_buffer_t buf, const struct ggml_tensor * src, struct ggml_tensor * dst);
 void   ggml_metal_buffer_clear        (ggml_metal_buffer_t buf, uint8_t value);
 
 // finds the Metal buffer that contains the tensor data on the GPU device
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index eb74f6d3ee4..58d6b3f746a 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1,6 +1,7 @@
 #import "ggml-metal-device.h"
 
 #import "ggml-impl.h"
+#import "ggml-backend-impl.h"
 
 #include <Foundation/Foundation.h>
 
@@ -95,8 +96,8 @@ int ggml_metal_pipeline_max_theads_per_threadgroup(struct ggml_metal_pipeline_wi
 
 struct ggml_metal_library {
     id<MTLLibrary> obj;
-    id<MTLDevice> device;
 
+    ggml_metal_device_t dev;
     ggml_metal_pipelines_t pipelines; // cache of compiled pipelines
 
     NSLock * lock;
@@ -288,7 +289,7 @@ ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev) {
     ggml_metal_library_t res = calloc(1, sizeof(struct ggml_metal_library));
 
     res->obj       = library;
-    res->device    = device;
+    res->dev       = dev;
     res->pipelines = ggml_metal_pipelines_init();
     res->lock      = [NSLock new];
 
@@ -355,7 +356,7 @@ ggml_metal_library_t ggml_metal_library_init_from_source(ggml_metal_device_t dev
     }
 
     res->obj       = library;
-    res->device    = device;
+    res->dev       = dev;
     res->pipelines = ggml_metal_pipelines_init();
     res->lock      = [NSLock new];
 
@@ -378,6 +379,10 @@ void ggml_metal_library_free(ggml_metal_library_t lib) {
     free(lib);
 }
 
+ggml_metal_device_t ggml_metal_library_get_device(ggml_metal_library_t lib) {
+    return lib->dev;
+}
+
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline(ggml_metal_library_t lib, const char * name) {
     [lib->lock lock];
 
@@ -442,7 +447,8 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_compile_pipeline(ggml_
             return res;
         }
 
-        id<MTLComputePipelineState> obj = [lib->device newComputePipelineStateWithFunction:mtl_function error:&error];
+        id<MTLDevice> device = ggml_metal_device_get_obj(lib->dev);
+        id<MTLComputePipelineState> obj = [device newComputePipelineStateWithFunction:mtl_function error:&error];
 
         [mtl_function release];
 
@@ -703,7 +709,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
                 ![[dev->mtl_device name] containsString:@"M6"] &&
                 ![[dev->mtl_device name] containsString:@"A19"] &&
                 ![[dev->mtl_device name] containsString:@"A20"]) {
-                GGML_LOG_WARN("%s: tensor API disabled for pre-M5 and pre-A19 devices\n", __func__);
+                GGML_LOG_INFO("%s: tensor API disabled for pre-M5 and pre-A19 devices\n", __func__);
                 dev->props.has_tensor = false;
             }
 
@@ -736,7 +742,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
                     "    auto sB = tB.slice(0, 0); \n"
                     "    mm.run(sB, sA, cT); \n"
                     " \n"
-                    "    auto tC = tensor<device float, dextents<int32_t, 2>, tensor_inline>(C, dextents<int32_t, 2>(4, 4)); \n"
+                    "    auto tC = tensor<device float, dextents<int32_t, 2>, tensor_inline>(C, dextents<int32_t, 2>(16, 16)); \n"
                     " \n"
                     "    cT.store(tC); \n"
                     "}";
@@ -786,7 +792,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
                     "    auto sB = tB.slice(0, 0); \n"
                     "    mm.run(sB, sA, cT); \n"
                     " \n"
-                    "    auto tC = tensor<device float, dextents<int32_t, 2>, tensor_inline>(C, dextents<int32_t, 2>(4, 4)); \n"
+                    "    auto tC = tensor<device float, dextents<int32_t, 2>, tensor_inline>(C, dextents<int32_t, 2>(16, 16)); \n"
                     " \n"
                     "    cT.store(tC); \n"
                     "}";
@@ -851,7 +857,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
             }
 
             // print MTL GPU family:
-            GGML_LOG_INFO("%s: GPU name:   %s\n", __func__, dev->props.name);
+            GGML_LOG_INFO("%s: GPU name:   %s (%s)\n", __func__, dev->props.name, dev->props.desc);
 
             // determine max supported GPU family
             // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
@@ -968,13 +974,13 @@ void ggml_metal_device_rsets_keep_alive(ggml_metal_device_t dev) {
 }
 
 struct ggml_metal_event {
-    void * obj; // id<MTLEvent>
+    void * obj; // id<MTLSharedEvent>
 
     atomic_int value;
 };
 
 void ggml_metal_event_encode_signal(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf_raw) {
-    id<MTLEvent> event = (id<MTLEvent>)ev->obj;
+    id<MTLSharedEvent> event = (id<MTLSharedEvent>)ev->obj;
 
     id<MTLCommandBuffer> cmd_buf = (id<MTLCommandBuffer>) cmd_buf_raw;
 
@@ -982,7 +988,7 @@ void ggml_metal_event_encode_signal(ggml_metal_event_t ev, ggml_metal_cmd_buf_t
 }
 
 void ggml_metal_event_encode_wait(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cmd_buf_raw) {
-    id<MTLEvent> event = (id<MTLEvent>)ev->obj;
+    id<MTLSharedEvent> event = (id<MTLSharedEvent>)ev->obj;
 
     id<MTLCommandBuffer> cmd_buf = (id<MTLCommandBuffer>) cmd_buf_raw;
 
@@ -990,7 +996,7 @@ void ggml_metal_event_encode_wait(ggml_metal_event_t ev, ggml_metal_cmd_buf_t cm
 }
 
 ggml_metal_event_t ggml_metal_device_event_init(ggml_metal_device_t dev) {
-    id<MTLEvent> event = [dev->mtl_device newEvent];
+    id<MTLSharedEvent> event = [dev->mtl_device newSharedEvent];
 
     ggml_metal_event_t ev = calloc(1, sizeof(struct ggml_metal_event));
 
@@ -1001,7 +1007,7 @@ ggml_metal_event_t ggml_metal_device_event_init(ggml_metal_device_t dev) {
 }
 
 void ggml_metal_device_event_free(ggml_metal_device_t dev, ggml_metal_event_t ev) {
-    id<MTLEvent> event = ev->obj;
+    id<MTLSharedEvent> event = ev->obj;
     [event release];
 
     free(ev);
@@ -1010,14 +1016,13 @@ void ggml_metal_device_event_free(ggml_metal_device_t dev, ggml_metal_event_t ev
 }
 
 void ggml_metal_device_event_synchronize(ggml_metal_device_t dev, ggml_metal_event_t ev) {
-    @autoreleasepool {
-        id<MTLEvent> event = ev->obj;
-
-        id<MTLCommandBuffer> cmd_buf = [dev->mtl_queue commandBuffer];
-        [cmd_buf encodeWaitForEvent:event value:atomic_load_explicit(&ev->value, memory_order_relaxed)];
-        [cmd_buf commit];
-        [cmd_buf waitUntilCompleted];
+    id<MTLSharedEvent> event = ev->obj;
+    const bool res = [event waitUntilSignaledValue:atomic_load_explicit(&ev->value, memory_order_relaxed) timeoutMS:60000];
+    if (!res) {
+        GGML_ABORT("%s: failed to wait for event\n", __func__);
     }
+
+    GGML_UNUSED(dev);
 }
 
 void ggml_metal_device_get_memory(ggml_metal_device_t dev, size_t * free, size_t * total) {
@@ -1799,6 +1804,47 @@ void ggml_metal_buffer_get_tensor(ggml_metal_buffer_t buf, const struct ggml_ten
     }
 }
 
+bool ggml_metal_buffer_cpy_tensor(ggml_metal_buffer_t buf_dst, const struct ggml_tensor * src, struct ggml_tensor * dst) {
+    ggml_metal_buffer_t buf_src = (ggml_metal_buffer_t)src->buffer->context;
+
+    const size_t size = ggml_nbytes(src);
+
+    // if both buffers are shared, we can use memcpy directly
+    if (buf_dst->is_shared && buf_src->is_shared) {
+        memcpy(dst->data, src->data, size);
+        return true;
+    }
+
+    // for private buffers, we need to use Metal blit commands
+    @autoreleasepool {
+        struct ggml_metal_buffer_id bid_src = ggml_metal_buffer_get_id(buf_src, src);
+        struct ggml_metal_buffer_id bid_dst = ggml_metal_buffer_get_id(buf_dst, dst);
+
+        if (bid_src.metal == nil || bid_dst.metal == nil) {
+            return false;
+        }
+
+        id<MTLCommandBuffer> cmd_buf = [buf_dst->dev->mtl_queue commandBufferWithUnretainedReferences];
+
+        {
+            id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
+
+            [encoder copyFromBuffer:bid_src.metal
+                       sourceOffset:bid_src.offs
+                           toBuffer:bid_dst.metal
+                  destinationOffset:bid_dst.offs
+                               size:size];
+
+            [encoder endEncoding];
+        }
+
+        [cmd_buf commit];
+        [cmd_buf waitUntilCompleted];
+    }
+
+    return true;
+}
+
 void ggml_metal_buffer_clear(ggml_metal_buffer_t buf, uint8_t value) {
     if (buf->is_shared) {
         memset(buf->all_data, value, buf->all_size);
diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index c911a5bc254..7596cc7a76d 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -1,6 +1,19 @@
 #ifndef GGML_METAL_IMPL
 #define GGML_METAL_IMPL
 
+// kernel parameters for mat-mat threadgroups
+//
+// TODO: become function constants
+
+#define SZ_SIMDGROUP 16
+#define N_MM_NK 2
+#define N_MM_NK_TOTAL (SZ_SIMDGROUP * N_MM_NK)
+
+#define N_MM_BLOCK_X 4
+#define N_MM_BLOCK_Y 2
+#define N_MM_SIMD_GROUP_X 2
+#define N_MM_SIMD_GROUP_Y 2
+
 // kernel parameters for mat-vec threadgroups
 //
 // N_R0: number of src0 rows to process per simdgroup
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index 373206ddd4e..727e489ebfa 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -2186,7 +2186,7 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) {
                 GGML_ABORT("unsupported ne11");
         };
 
-        auto pipeline = ggml_metal_library_get_pipeline_mul_mv_ext(lib, op->src[0]->type, op->src[1]->type, nsg, nxpsg, r1ptg);
+        auto pipeline = ggml_metal_library_get_pipeline_mul_mv_ext(lib, op, nsg, nxpsg, r1ptg);
 
         ggml_metal_kargs_mul_mv_ext args = {
             /*.ne00  =*/ ne00,
@@ -2288,7 +2288,7 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) {
             // Without this, mixing TQ and non-TQ tensors in the same layer corrupts src1
             ggml_metal_op_concurrency_reset(ctx);
         } else {
-            // Non-TQ weight or unaligned: use standard mul_mm path
+            // Non-TQ weight or unaligned: standard mul_mm via upstream pipeline-param dispatch
             auto pipeline = ggml_metal_library_get_pipeline_mul_mm(lib, op);
 
             ggml_metal_kargs_mul_mm args = {
@@ -2315,9 +2315,12 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) {
             ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         3);
 
             const size_t smem = pipeline.smem;
-
             ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
-            ggml_metal_encoder_dispatch_threadgroups(enc, ((ne11 + 31)/32), ((ne01 + 63)/64), ne12*ne13, 128, 1, 1);
+
+            const int nr0 = pipeline.nr0;
+            const int nr1 = pipeline.nr1;
+            const int nsg = pipeline.nsg;
+            ggml_metal_encoder_dispatch_threadgroups(enc, ((ne11 + nr1 - 1) / nr1), ((ne01 + nr0 - 1) / nr0), ne12 * ne13, 32, nsg, 1);
         }
     } else {
         auto pipeline = ggml_metal_library_get_pipeline_mul_mv(lib, op);
diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp
index 6a836e45908..a1003b3acff 100644
--- a/ggml/src/ggml-metal/ggml-metal.cpp
+++ b/ggml/src/ggml-metal/ggml-metal.cpp
@@ -17,6 +17,9 @@
 // note: can be overridden with GGML_METAL_DEVICES env to simulate virtual devices
 static int g_devices = 1;
 
+// forward declaration
+static bool ggml_backend_buffer_is_metal(ggml_backend_buffer_t buffer);
+
 ////////////////////////////////////////////////////////////////////////////////
 // backend interface
 ////////////////////////////////////////////////////////////////////////////////
@@ -68,11 +71,11 @@ static bool ggml_backend_metal_buffer_shared_cpy_tensor(ggml_backend_buffer_t bu
 
     GGML_ASSERT(ggml_metal_buffer_is_shared(ctx));
 
-    GGML_UNUSED(buffer);
-    GGML_UNUSED(src);
-    GGML_UNUSED(dst);
+    if (!ggml_backend_buffer_is_metal(src->buffer)) {
+        return false;
+    }
 
-    return false;
+    return ggml_metal_buffer_cpy_tensor(ctx, src, dst);
 }
 
 static void ggml_backend_metal_buffer_shared_clear(ggml_backend_buffer_t buffer, uint8_t value) {
@@ -84,17 +87,17 @@ static void ggml_backend_metal_buffer_shared_clear(ggml_backend_buffer_t buffer,
 }
 
 static ggml_backend_buffer_i ggml_backend_metal_buffer_shared_i = {
-    /* .free_buffer     = */ ggml_backend_metal_buffer_shared_free_buffer,
-    /* .get_base        = */ ggml_backend_metal_buffer_shared_get_base,
-    /* .init_tensor     = */ NULL,
-    /* .memset_tensor   = */ ggml_backend_metal_buffer_shared_memset_tensor,
-    /* .set_tensor      = */ ggml_backend_metal_buffer_shared_set_tensor,
-    /* .get_tensor      = */ ggml_backend_metal_buffer_shared_get_tensor,
-    /* .set_tensor_2d   = */ NULL,
-    /* .get_tensor_2d   = */ NULL,
-    /* .cpy_tensor      = */ ggml_backend_metal_buffer_shared_cpy_tensor,
-    /* .clear           = */ ggml_backend_metal_buffer_shared_clear,
-    /* .reset           = */ NULL,
+    /* .free_buffer   = */ ggml_backend_metal_buffer_shared_free_buffer,
+    /* .get_base      = */ ggml_backend_metal_buffer_shared_get_base,
+    /* .init_tensor   = */ NULL,
+    /* .memset_tensor = */ ggml_backend_metal_buffer_shared_memset_tensor,
+    /* .set_tensor    = */ ggml_backend_metal_buffer_shared_set_tensor,
+    /* .get_tensor    = */ ggml_backend_metal_buffer_shared_get_tensor,
+    /* .set_tensor_2d = */ NULL,
+    /* .get_tensor_2d = */ NULL,
+    /* .cpy_tensor    = */ ggml_backend_metal_buffer_shared_cpy_tensor,
+    /* .clear         = */ ggml_backend_metal_buffer_shared_clear,
+    /* .reset         = */ NULL,
 };
 
 // private buffer
@@ -144,11 +147,11 @@ static bool ggml_backend_metal_buffer_private_cpy_tensor(ggml_backend_buffer_t b
 
     GGML_ASSERT(!ggml_metal_buffer_is_shared(ctx));
 
-    GGML_UNUSED(buffer);
-    GGML_UNUSED(src);
-    GGML_UNUSED(dst);
+    if (!ggml_backend_buffer_is_metal(src->buffer)) {
+        return false;
+    }
 
-    return false;
+    return ggml_metal_buffer_cpy_tensor(ctx, src, dst);
 }
 
 static void ggml_backend_metal_buffer_private_clear(ggml_backend_buffer_t buffer, uint8_t value) {
@@ -160,17 +163,17 @@ static void ggml_backend_metal_buffer_private_clear(ggml_backend_buffer_t buffer
 }
 
 static ggml_backend_buffer_i ggml_backend_metal_buffer_private_i = {
-    /* .free_buffer             = */ ggml_backend_metal_buffer_private_free_buffer,
-    /* .get_base                = */ ggml_backend_metal_buffer_private_get_base,
-    /* .init_tensor             = */ NULL,
-    /* .memset_tensor           = */ ggml_backend_metal_buffer_private_memset_tensor,
-    /* .set_tensor              = */ ggml_backend_metal_buffer_private_set_tensor,
-    /* .get_tensor              = */ ggml_backend_metal_buffer_private_get_tensor,
-    /* .get_tensor_2d_async     = */ NULL,
-    /* .set_tensor_2d_async     = */ NULL,
-    /* .cpy_tensor              = */ ggml_backend_metal_buffer_private_cpy_tensor,
-    /* .clear                   = */ ggml_backend_metal_buffer_private_clear,
-    /* .reset                   = */ NULL,
+    /* .free_buffer   = */ ggml_backend_metal_buffer_private_free_buffer,
+    /* .get_base      = */ ggml_backend_metal_buffer_private_get_base,
+    /* .init_tensor   = */ NULL,
+    /* .memset_tensor = */ ggml_backend_metal_buffer_private_memset_tensor,
+    /* .set_tensor    = */ ggml_backend_metal_buffer_private_set_tensor,
+    /* .get_tensor    = */ ggml_backend_metal_buffer_private_get_tensor,
+    /* .set_tensor_2d = */ NULL,
+    /* .get_tensor_2d = */ NULL,
+    /* .cpy_tensor    = */ ggml_backend_metal_buffer_private_cpy_tensor,
+    /* .clear         = */ ggml_backend_metal_buffer_private_clear,
+    /* .reset         = */ NULL,
 };
 
 static bool ggml_backend_buffer_is_metal(ggml_backend_buffer_t buffer) {
@@ -567,8 +570,8 @@ static ggml_backend_i ggml_backend_metal_i = {
     /* .free                    = */ ggml_backend_metal_free,
     /* .set_tensor_async        = */ ggml_backend_metal_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_metal_get_tensor_async,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ ggml_backend_metal_cpy_tensor_async, // only needed for multi-GPU setups
     /* .synchronize             = */ ggml_backend_metal_synchronize,
     /* .graph_plan_create       = */ NULL,
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 3dfed68ca19..ddf9acfcfb5 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -3323,6 +3323,7 @@ kernel void kernel_rwkv_wkv7_f32(
 
 constant short FC_gated_delta_net_ne20 [[function_constant(FC_GATED_DELTA_NET + 0)]];
 constant short FC_gated_delta_net_ne30 [[function_constant(FC_GATED_DELTA_NET + 1)]];
+constant short FC_gated_delta_net_K    [[function_constant(FC_GATED_DELTA_NET + 2)]];
 
 #if 1
 template<short NSG>
@@ -3340,21 +3341,24 @@ kernel void kernel_gated_delta_net_impl(
         uint3   ntg[[threads_per_threadgroup]])  {
 #define S_v FC_gated_delta_net_ne20
 #define G   FC_gated_delta_net_ne30
+#define K   FC_gated_delta_net_K
 
     const uint tx = tpitg.x;
     const uint ty = tpitg.y;
 
-    const uint i23 = tgpig.z; // B
-    const uint i21 = tgpig.y; // H
-    const uint i20 = tgpig.x*NSG + ty;
+    const uint i23 = tgpig.z; // B (n_seqs)
+    const uint i21 = tgpig.y; // H (head)
+    const uint i20 = tgpig.x*NSG + ty; // row within S_v
 
     const uint i01 = i21 % args.ne01;
     const uint i11 = i21 % args.ne11;
 
     const float scale = 1.0f / sqrt((float)S_v);
 
+    // input state layout (D, K, n_seqs): per-seq stride is K*H*D; we read slot 0.
     // state is stored transposed: M[i20][is] = S[is][i20], so row i20 is contiguous
-    device const float * s_ptr = (device const float *) (s) + (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
+    const uint state_in_base = (i23*K*args.ne21 + i21)*S_v*S_v + i20*S_v;
+    device const float * s_ptr = (device const float *) (s) + state_in_base;
 
     float ls[NSG];
 
@@ -3372,6 +3376,17 @@ kernel void kernel_gated_delta_net_impl(
     device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21);
     device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G;
 
+    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K, only the last
+    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
+    const int shift = (int)args.ne22 - (int)K;
+
+    // output state base offset: after attention scores
+    const uint attn_size = args.ne22 * args.ne21 * S_v * args.ne23;
+    // output state per-slot size: S_v * S_v * H * n_seqs
+    const uint state_size_per_snap = S_v * S_v * args.ne21 * args.ne23;
+    // per-(seq,head) offset within a slot
+    const uint state_out_base = (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
+
     for (short t = 0; t < args.ne22; t++) {
         float s_k = 0.0f;
 
@@ -3419,17 +3434,30 @@ kernel void kernel_gated_delta_net_impl(
 
         b_ptr += args.ne21;
         g_ptr += args.ne21*G;
-    }
 
-    device float * dst_state = (device float *) (dst) + args.ne23*args.ne22*args.ne21*S_v + (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
+        if (K > 1u) {
+            const int target_slot = (int)t - shift;
+            if (target_slot >= 0 && target_slot < (int)K) {
+                device float * dst_state = (device float *) (dst) + attn_size + (uint)target_slot * state_size_per_snap + state_out_base;
+                FOR_UNROLL (short j = 0; j < NSG; j++) {
+                    const short is = tx*NSG + j;
+                    dst_state[is] = ls[j];
+                }
+            }
+        }
+    }
 
-    FOR_UNROLL (short j = 0; j < NSG; j++) {
-        const short is = tx*NSG + j;
-        dst_state[is] = ls[j];
+    if (K == 1u) {
+        device float * dst_state = (device float *) (dst) + attn_size + state_out_base;
+        FOR_UNROLL (short j = 0; j < NSG; j++) {
+            const short is = tx*NSG + j;
+            dst_state[is] = ls[j];
+        }
     }
 
 #undef S_v
 #undef G
+#undef K
 }
 
 typedef decltype(kernel_gated_delta_net_impl<4>) kernel_gated_delta_net_t;
@@ -4238,6 +4266,9 @@ static inline void helper_mv_reduce_and_write(
 
 constant short FC_mul_mv_nsg   [[function_constant(FC_MUL_MV + 0)]];
 constant short FC_mul_mv_nxpsg [[function_constant(FC_MUL_MV + 1)]];
+constant short FC_mul_mv_ne12  [[function_constant(FC_MUL_MV + 2)]];
+constant short FC_mul_mv_r2    [[function_constant(FC_MUL_MV + 3)]];
+constant short FC_mul_mv_r3    [[function_constant(FC_MUL_MV + 4)]];
 
 template<typename block_q_type, short NR0, typename args_t>
 void mul_vec_q_n_f32_impl(
@@ -4261,10 +4292,10 @@ void mul_vec_q_n_f32_impl(
     const int r1 =  tgpig.y;
     const int im =  tgpig.z;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-  //const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+  //const uint64_t offset0 = r0*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 = r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
   //device const block_q_type * x = (device const block_q_type *) (src0 + offset0);
@@ -4273,7 +4304,7 @@ void mul_vec_q_n_f32_impl(
     // pointers to src0 rows
     device const block_q_type * ax[NR0];
     FOR_UNROLL (int row = 0; row < NR0; ++row) {
-        const uint64_t offset0 = (r0 + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+        const uint64_t offset0 = (r0 + row)*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
 
         ax[row] = (device const block_q_type *) ((device char *) src0 + offset0);
     }
@@ -4347,8 +4378,8 @@ void kernel_mul_mv_q1_0_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
     const uint64_t offset1 = r1*args.nb11 + (i12)*args.nb12 + (i13)*args.nb13;
 
@@ -4356,7 +4387,7 @@ void kernel_mul_mv_q1_0_f32_impl(
 
     device const block_q1_0 * ax[nr0];
     for (int row = 0; row < nr0; ++row) {
-        const uint64_t offset0 = (first_row + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+        const uint64_t offset0 = (first_row + row)*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
         ax[row] = (device const block_q1_0 *) ((device char *) src0 + offset0);
     }
 
@@ -4475,10 +4506,10 @@ void kernel_mul_mv_q8_0_f32_impl(
     const int r1 = tgpig.y;
     const int im = tgpig.z;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-  //const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+  //const uint64_t offset0 = r0*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 = r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
   //device const block_q8_0 * x = (device const block_q8_0 *) (src0 + offset0);
@@ -4487,7 +4518,7 @@ void kernel_mul_mv_q8_0_f32_impl(
     // pointers to src0 rows
     device const block_q8_0 * ax[NR0];
     FOR_UNROLL (short row = 0; row < NR0; ++row) {
-        const uint64_t offset0 = (r0 + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+        const uint64_t offset0 = (r0 + row)*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
 
         ax[row] = (device const block_q8_0 *) ((device char *) src0 + offset0);
     }
@@ -4741,10 +4772,10 @@ void kernel_mul_mv_ext_q4_f32_impl(
     const int i11 = tgpig.y*r1ptg;
     const int i1m = tgpig.z;
 
-    const int i12 = i1m%args.ne12;
-    const int i13 = i1m/args.ne12;
+    const int i12 = i1m%FC_mul_mv_ne12;
+    const int i13 = i1m/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = i01*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = i01*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 = i11*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const q_t * xq = (i01 < args.ne01) ? (device const q_t *) (src0 + offset0) + tx/chpb : (device const q_t *) src0;
@@ -4844,10 +4875,10 @@ void kernel_mul_mv_ext_q4x4_f32_impl(
     const int i11 = tgpig.y*r1ptg;
     const int i1m = tgpig.z;
 
-    const int i12 = i1m%args.ne12;
-    const int i13 = i1m/args.ne12;
+    const int i12 = i1m%FC_mul_mv_ne12;
+    const int i13 = i1m/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = i01*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = i01*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 = i11*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const q_t * xq = (i01 < args.ne01) ? (device const q_t *) (src0 + offset0) + tx/chpb : (device const q_t *) src0;
@@ -5069,10 +5100,10 @@ void kernel_mul_mv_t_t_impl(
     const int r1 = tgpig.y;
     const int im = tgpig.z;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-  //const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+  //const uint64_t offset0 = r0*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 = r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
   //device const T0 * x = (device const T0 *) (src0 + offset0);
@@ -5081,7 +5112,7 @@ void kernel_mul_mv_t_t_impl(
     // pointers to src0 rows
     device const T0 * ax [NR0];
     FOR_UNROLL (short row = 0; row < NR0; ++row) {
-        const uint64_t offset0 = (r0 + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+        const uint64_t offset0 = (r0 + row)*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
 
         ax[row] = (device const T0 *) ((device char *) src0 + offset0);
     }
@@ -5191,10 +5222,10 @@ void kernel_mul_mv_t_t_4_impl(
     const int r1 = tgpig.y;
     const int im = tgpig.z;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-  //const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+  //const uint64_t offset0 = r0*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 = r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const T1  * y  = (device const T1  *) (src1 + offset1);
@@ -5204,7 +5235,7 @@ void kernel_mul_mv_t_t_4_impl(
     device const T0  * ax [NR0];
     device const T04 * ax4[NR0];
     FOR_UNROLL (short row = 0; row < NR0; ++row) {
-        const uint64_t offset0 = (r0 + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+        const uint64_t offset0 = (r0 + row)*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
 
         ax [row] = (device const T0  *) ((device char *) src0 + offset0);
         ax4[row] = (device const T04 *) ((device char *) src0 + offset0);
@@ -5308,10 +5339,10 @@ void kernel_mul_mv_t_t_short_impl(
         return;
     }
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = r0*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
 
     device const T0 * x = (device const T0 *) (src0 + offset0);
 
@@ -5919,15 +5950,32 @@ kernel void kernel_conv_transpose_1d(
         uint3   tgpig[[threadgroup_position_in_grid]],
         uint3   tgpg[[threadgroups_per_grid]]) {
 
-    float v = 0.0f;
+    // For output position j on the time axis, only input positions
+    //   i such that i*s0 <= j < i*s0 + K
+    // contribute -- i.e. i in [ceil((j - K + 1)/s0), floor(j/s0)]
+    // intersected with [0, IL-1]. That's at most ceil(K/s0) values
+    // (typically 2 for stride==K/2 transposed convs).
+    const int32_t j  = tgpig[0];
+    const int32_t s0 = args.s0;
+    const int32_t K  = args.K;
+    const int32_t IL = args.IL;
+
+    int32_t i_min;
+    {
+        int32_t a = j - K + 1;
+        i_min = a <= 0 ? 0 : (a + s0 - 1) / s0; // ceil(a/s0) for a>0
+    }
+    int32_t i_max = j / s0;
+    if (i_max > IL - 1) i_max = IL - 1;
 
-    for (int64_t c = 0; c < args.IC; c++) {
-        const int32_t kernel_offset = c * tgpg[1] * args.K + args.K * tgpig[1];
-        const int32_t input_offset = c * args.IL;
+    float v = 0.0f;
+    if (i_min <= i_max) {
+        for (int64_t c = 0; c < args.IC; c++) {
+            const int32_t kernel_offset = c * tgpg[1] * K + K * tgpig[1];
+            const int32_t input_offset  = c * IL;
 
-        for (int64_t i = 0; i < args.IL; i++) {
-            if (tgpig[0] >= i * args.s0 && tgpig[0] < i * args.s0 + args.K) {
-                v += src0[kernel_offset + tgpig[0] - i * args.s0] * src1[input_offset + i];
+            for (int32_t i = i_min; i <= i_max; i++) {
+                v += float(src0[kernel_offset + j - i * s0]) * src1[input_offset + i];
             }
         }
     }
@@ -9363,10 +9411,10 @@ void kernel_mul_mv_q2_K_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_q2_K * x = (device const block_q2_K *) (src0 + offset0);
@@ -9468,10 +9516,10 @@ void kernel_mul_mv_q3_K_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_q3_K * x = (device const block_q3_K *) (src0 + offset0);
@@ -9642,10 +9690,10 @@ void kernel_mul_mv_q4_K_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_q4_K * x = (device const block_q4_K *) (src0 + offset0);
@@ -9754,10 +9802,10 @@ void kernel_mul_mv_q5_K_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_q5_K * x = (device const block_q5_K *) (src0 + offset0);
@@ -9890,10 +9938,10 @@ void kernel_mul_mv_q6_K_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_q6_K * x = (device const block_q6_K *) (src0 + offset0);
@@ -9995,10 +10043,10 @@ void kernel_mul_mv_iq2_xxs_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq2_xxs * x = (device const block_iq2_xxs *) (src0 + offset0);
@@ -10103,10 +10151,10 @@ void kernel_mul_mv_iq2_xs_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq2_xs * x = (device const block_iq2_xs *) (src0 + offset0);
@@ -10222,10 +10270,10 @@ void kernel_mul_mv_iq3_xxs_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq3_xxs * x = (device const block_iq3_xxs *) (src0 + offset0);
@@ -10334,10 +10382,10 @@ void kernel_mul_mv_iq3_s_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq3_s * x = (device const block_iq3_s *) (src0 + offset0);
@@ -10446,10 +10494,10 @@ void kernel_mul_mv_iq2_s_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq2_s * x = (device const block_iq2_s *) (src0 + offset0);
@@ -10559,10 +10607,10 @@ void kernel_mul_mv_iq1_s_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq1_s * x = (device const block_iq1_s *) (src0 + offset0);
@@ -10658,10 +10706,10 @@ void kernel_mul_mv_iq1_m_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * nr0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq1_m * x = (device const block_iq1_m *) (src0 + offset0);
@@ -10767,10 +10815,10 @@ void kernel_mul_mv_iq4_nl_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * NR0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq4_nl * x = (device const block_iq4_nl *) (src0 + offset0);
@@ -10876,10 +10924,10 @@ void kernel_mul_mv_iq4_xs_f32_impl(
     const int im = tgpig.z;
     const int first_row = (r0 * NSG + sgitg) * NR0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_iq4_xs * x = (device const block_iq4_xs *) (src0 + offset0);
@@ -10987,10 +11035,10 @@ void kernel_mul_mv_mxfp4_f32_impl(
 
     const int first_row = (r0 * NSG + sgitg) * NR0;
 
-    const uint i12 = im%args.ne12;
-    const uint i13 = im/args.ne12;
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
 
-    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = first_row*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
     const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
 
     device const block_mxfp4 * x = (device const block_mxfp4 *) (src0 + offset0);
@@ -11454,9 +11502,143 @@ kernel void kernel_diag_f32(
 
 constant bool FC_mul_mm_bc_inp [[function_constant(FC_MUL_MM + 0)]];
 constant bool FC_mul_mm_bc_out [[function_constant(FC_MUL_MM + 1)]];
+constant short FC_mul_mm_ne12  [[function_constant(FC_MUL_MM + 2)]];
+constant short FC_mul_mm_ne13  [[function_constant(FC_MUL_MM + 3)]];
+constant short FC_mul_mm_r2    [[function_constant(FC_MUL_MM + 4)]];
+constant short FC_mul_mm_r3    [[function_constant(FC_MUL_MM + 5)]];
 
 // each block_q contains 16*nl weights
-template<typename S0, typename S0_4x4, typename S0_8x8, typename S1, typename S1_2x4, typename S1_8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread S0_4x4 &), typename T0, typename T0_4x4, typename T1, typename T1_2x4>
+#ifdef GGML_METAL_HAS_TENSOR
+template<
+    typename SA, typename SA_4x4, typename SA_8x8,
+    typename SB, typename SB_2x4, typename SB_8x8,
+    typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread SA_4x4 &),
+    typename T0, typename T0_4x4, typename T1, typename T1_2x4>
+kernel void kernel_mul_mm(
+        constant ggml_metal_kargs_mul_mm & args,
+        device const char * srcA,
+        device const char * srcB,
+        device       char * dst,
+        threadgroup  char * shmem [[threadgroup(0)]],
+        uint3  tgpig [[threadgroup_position_in_grid]],
+        ushort tiitg [[thread_index_in_threadgroup]],
+        ushort sgitg [[simdgroup_index_in_threadgroup]]) {
+    (void) sgitg;
+
+    // Matrix dimensions: A(M,K) x B(K,N) -> C(M,N)
+    const int K = args.ne00;
+    const int M = args.ne0;
+    const int N = args.ne1;
+
+    // Batch dimension handling
+    const int im = tgpig.z;
+    const int i12 = im % FC_mul_mm_ne12;
+    const int i13 = im / FC_mul_mm_ne12;
+
+    // Batch offsets for srcA and srcB
+    const uint64_t offset0 = (i12/FC_mul_mm_r2)*args.nb02 + (i13/FC_mul_mm_r3)*args.nb03;
+
+    // Tile dimensions
+    constexpr int NRB = SZ_SIMDGROUP * N_MM_BLOCK_X * N_MM_SIMD_GROUP_X;
+    constexpr int NRA = SZ_SIMDGROUP * N_MM_BLOCK_Y * N_MM_SIMD_GROUP_Y;
+
+    // Tile offsets in output matrix
+    const int ra = tgpig.y * NRA;
+    const int rb = tgpig.x * NRB;
+
+    // Threadgroup memory for dequantized A tile only
+    threadgroup SA * sa = (threadgroup SA *)(shmem);
+
+    // Work-item count for A loading
+    constexpr int A_WORK_ITEMS = NRA * N_MM_NK;
+    constexpr int NUM_THREADS = N_SIMDWIDTH * N_MM_SIMD_GROUP_X * N_MM_SIMD_GROUP_Y;
+
+    // tA wraps threadgroup memory
+    auto tA = tensor(sa, dextents<int32_t, 2>(N_MM_NK_TOTAL, NRA));
+
+    // tB wraps device memory directly
+    device T1 * ptrB = (device T1 *)(srcB + args.nb12*i12 + args.nb13*i13);
+    const int strideB = args.nb11 / sizeof(T1);
+    auto tB = tensor(ptrB, dextents<int32_t, 2>(K, N), array<int, 2>({1, strideB}));
+
+    // Configure matmul operation
+    mpp::tensor_ops::matmul2d<
+        mpp::tensor_ops::matmul2d_descriptor(
+            NRB, NRA, N_MM_NK_TOTAL, false, true, true,
+            mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate),
+        execution_simdgroups<N_MM_SIMD_GROUP_X * N_MM_SIMD_GROUP_Y>> mm;
+
+    auto cT = mm.get_destination_cooperative_tensor<decltype(tB), decltype(tA), float>();
+
+    // Accumulate partial results over K dimension
+    for (int loop_k = 0; loop_k < K; loop_k += N_MM_NK_TOTAL) {
+        // === PHASE 1: Dequantization of A into threadgroup memory ===
+        for (int work = tiitg; work < A_WORK_ITEMS; work += NUM_THREADS) {
+            const int row = work / N_MM_NK;
+            const int k_chunk = work % N_MM_NK;
+            const int k_pos = loop_k + k_chunk * 16;
+            const short k_base = k_chunk * 16;
+
+            // Bounds check: skip device read if row is out of matrix bounds
+            if (ra + row < M) {
+                if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
+                    // Element-wise reads when K is not aligned (nb01 not aligned for half4x4/float4x4).
+                    // MSL spec Table 2.5: half4x4 requires 8-byte alignment. When K is odd,
+                    // nb01 = K*2 is not 8-byte aligned, so odd-row pointers are misaligned.
+                    // Mirrors the legacy kernel's existing guard.
+                    device const T0 * row_ptr = (device const T0 *)(srcA + args.nb01 * (ra + row) + offset0);
+
+                    FOR_UNROLL (short i = 0; i < 16; i++) {
+                        sa[row * N_MM_NK_TOTAL + (k_base + i)] = (k_pos + i < K) ? (SA) row_ptr[k_pos + i] : (SA)0;
+                    }
+                } else {
+                    const int block_idx = k_pos / (16 * nl);
+                    const short il = (k_pos / 16) % nl;
+
+                    device const block_q * row_ptr = (device const block_q *)(srcA + args.nb01 * (ra + row) + offset0);
+
+                    SA_4x4 temp_a;
+                    dequantize_func(row_ptr + block_idx, il, temp_a);
+
+                    FOR_UNROLL (short i = 0; i < 16; i++) {
+                        // Zero-pad A for K positions beyond valid range (handles partial K iterations)
+                        sa[row * N_MM_NK_TOTAL + (k_base + i)] = (k_pos + i < K) ? temp_a[i/4][i%4] : (SA)0;
+                    }
+                }
+            } else {
+                // Zero-pad rows beyond matrix bounds
+                FOR_UNROLL (short i = 0; i < 16; i++) {
+                    sa[row * N_MM_NK_TOTAL + (k_base + i)] = (SA)0;
+                }
+            }
+        }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        // === PHASE 2: Tensor matmul ===
+        auto mA = tA.slice(0, 0);
+        auto mB = tB.slice(loop_k, rb);
+
+        mm.run(mB, mA, cT);
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+    }
+
+    // Store result tile to output matrix (with batch offset)
+    // cT.store handles bounds checking via tD's extents (M, N)
+    device float * dstBatch = (device float *)dst + im * N * M;
+
+    auto tD = tensor(dstBatch, dextents<int32_t, 2>(M, N), array<int, 2>({1, M}));
+    cT.store(tD.slice(ra, rb));
+}
+
+#else
+
+template<
+    typename S0, typename S0_4x4, typename S0_8x8,
+    typename S1, typename S1_2x4, typename S1_8x8,
+    typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread S0_4x4 &),
+    typename T0, typename T0_4x4, typename T1, typename T1_2x4>
 kernel void kernel_mul_mm(
         constant ggml_metal_kargs_mul_mm & args,
         device const char * src0,
@@ -11470,10 +11652,6 @@ kernel void kernel_mul_mm(
     threadgroup S0 * sa = (threadgroup S0 *)(shmem);
     threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
 
-#ifdef GGML_METAL_HAS_TENSOR
-    threadgroup float * sc = (threadgroup float *)(shmem);
-#endif
-
     constexpr int NR0 = 64;
     constexpr int NR1 = 32;
 
@@ -11497,10 +11675,10 @@ kernel void kernel_mul_mm(
 
     short il = il0;
 
-    const int i12 = im%args.ne12;
-    const int i13 = im/args.ne12;
+    const int i12 = im % FC_mul_mm_ne12;
+    const int i13 = im / FC_mul_mm_ne12;
 
-    const uint64_t offset0 = (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset0 = (i12/FC_mul_mm_r2)*args.nb02 + (i13/FC_mul_mm_r3)*args.nb03;
     const short    offset1 = il0/nl;
 
     device const block_q * x = (device const block_q *)(src0 + args.nb01*(r0 + lr0) + offset0) + offset1;
@@ -11513,7 +11691,6 @@ kernel void kernel_mul_mm(
         + args.nb11*(r1 + lr1)
         + args.nb10*iy);
 
-#ifndef GGML_METAL_HAS_TENSOR
     S0_8x8 ma[4];
     S1_8x8 mb[2];
 
@@ -11522,19 +11699,8 @@ kernel void kernel_mul_mm(
     for (short i = 0; i < 8; i++){
         mc[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
     }
-#else
-    auto tA = tensor<threadgroup S0, dextents<int32_t, 2>, tensor_inline>(sa, dextents<int32_t, 2>(NK,  NR0));
-    auto tB = tensor<threadgroup S1, dextents<int32_t, 2>, tensor_inline>(sb, dextents<int32_t, 2>(NR1, NK ));
-
-    mpp::tensor_ops::matmul2d<
-        mpp::tensor_ops::matmul2d_descriptor(NR1, NR0, NK, false, true, false, mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate),
-        execution_simdgroups<4>> mm;
-
-    auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>();
-#endif
 
     for (int loop_k = 0; loop_k < args.ne00; loop_k += NK) {
-#ifndef GGML_METAL_HAS_TENSOR
         // load data and store to threadgroup memory
         if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
             threadgroup_barrier(mem_flags::mem_threadgroup);
@@ -11604,66 +11770,6 @@ kernel void kernel_mul_mm(
 
             *(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)(*((device T1_2x4 *) y));
         }
-#else
-        // load data and store to threadgroup memory
-        if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
-            threadgroup_barrier(mem_flags::mem_threadgroup);
-
-            // no need for dequantization
-            for (short i = 0; i < 16; i++) {
-                const short sx = 2*il0 + i/8;
-                const short sy = (tiitg/NL0)/8;
-
-                const short lx = i%8;
-                const short ly = (tiitg/NL0)%8;
-                //const short lx = (tiitg/NL0)%8;
-                //const short ly = i%8;
-
-                *(sa + NK*(8*sy + ly) + 8*sx + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
-            }
-        } else {
-            S0_4x4 temp_a;
-            dequantize_func(x, il, temp_a);
-
-            threadgroup_barrier(mem_flags::mem_threadgroup);
-
-            FOR_UNROLL (short i = 0; i < 16; i++) {
-                const short sx = 2*il0 + i/8;
-                const short sy = (tiitg/NL0)/8;
-
-                const short lx = i%8;
-                const short ly = (tiitg/NL0)%8;
-                //const short lx = (tiitg/NL0)%8;
-                //const short ly = i%8;
-
-                *(sa + NK*(8*sy + ly) + 8*sx + lx) = temp_a[i/4][i%4];
-            }
-        }
-
-        if (FC_mul_mm_bc_inp) {
-            for (short i = 0; i < 8; ++i) {
-                const short sx = (tiitg%NL1);
-                const short sy = (tiitg/NL1)/8;
-
-                const short lx = i;
-                const short ly = (tiitg/NL1)%8;
-                //const short lx = (tiitg/NL1)%8;
-                //const short ly = i;
-
-                *(sb + NK*(8*sy + ly) + 8*sx + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
-            }
-        } else {
-            const short sx = (tiitg%NL1);
-            const short sy = (tiitg/NL1)/8;
-
-            //const short lx = i;
-            const short ly = (tiitg/NL1)%8;
-            //const short lx = (tiitg/NL1)%8;
-            //const short ly = i;
-
-            *(threadgroup S1_2x4 *)(sb + NK*(8*sy + ly) + 8*sx) = (S1_2x4)(*((device T1_2x4 *) y));
-        }
-#endif
 
         il = (il + 2 < nl) ? il + 2 : il % 2;
         x  = (il < 2) ? x + (2 + nl - 1)/nl : x;
@@ -11672,7 +11778,6 @@ kernel void kernel_mul_mm(
 
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
-#ifndef GGML_METAL_HAS_TENSOR
         // load matrices from threadgroup memory and conduct outer products
         threadgroup const S0 * lsma = (sa + 4*64*(sgitg%2));
         threadgroup const S1 * lsmb = (sb + 2*64*(sgitg/2));
@@ -11699,24 +11804,10 @@ kernel void kernel_mul_mm(
             lsma += 8*64;
             lsmb += 4*64;
         }
-#else
-        auto sA = tA.slice(0, 0);
-        auto sB = tB.slice(0, 0);
-
-        mm.run(sB, sA, cT);
-#endif
     }
 
     if (!FC_mul_mm_bc_out || (r0 + NR0 <= args.ne0 && r1 + NR1 <= args.ne1)) {
         // if no bounds checks on the output are needed, we can directly write to device memory
-#ifdef GGML_METAL_HAS_TENSOR
-        device float * C = (device float *) dst +
-            r0 + \
-            r1 * args.ne0 + im*args.ne1*args.ne0;
-
-        auto tC = tensor<device float, dextents<int32_t, 2>, tensor_inline>(C, dextents<int32_t, 2>(args.ne0, NR1));
-        cT.store(tC);
-#else
         device float * C = (device float *) dst +
             (r0 + 32*(sgitg &  1)) + \
             (r1 + 16*(sgitg >> 1)) * args.ne0 + im*args.ne1*args.ne0;
@@ -11724,21 +11815,15 @@ kernel void kernel_mul_mm(
         for (short i = 0; i < 8; i++) {
             simdgroup_store(mc[i], C + 8*(i%4) + 8*args.ne0*(i/4), args.ne0, 0, false);
         }
-#endif
     } else {
         // block is smaller than 64x32, we should avoid writing data outside of the matrix
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
         threadgroup float * temp_str = ((threadgroup float *) shmem) + 32*(sgitg&1) + (16*(sgitg >> 1))*NR0;
 
-#ifdef GGML_METAL_HAS_TENSOR
-        auto tC = tensor<threadgroup float, dextents<int32_t, 2>, tensor_inline>(sc, dextents<int32_t, 2>(NR0, NR1));
-        cT.store(tC);
-#else
         for (short i = 0; i < 8; i++) {
             simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*NR0*(i/4), NR0, 0, false);
         }
-#endif
 
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
@@ -11764,6 +11849,8 @@ kernel void kernel_mul_mm(
     }
 }
 
+#endif // GGML_METAL_HAS_TENSOR
+
 template<short ne20> // n_expert_used
 kernel void kernel_mul_mm_id_map0(
         constant ggml_metal_kargs_mul_mm_id_map0 & args,
@@ -11946,7 +12033,7 @@ kernel void kernel_mul_mm_id(
 
                 const short ib = 8*sx + sy;
 
-                *(sa + 64*ib + 8*ly + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
+                *(sa + 64*ib + 8*ly + lx) = loop_k + 16*il + i < args.ne00 ? (S0) *((device T0 *) x + i) : (S0) 0;
             }
         } else {
             S0_4x4 temp_a;
diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index 772fc537494..c6aba608736 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -66,8 +66,6 @@ set(GGML_OPENCL_KERNELS
     diag
     div
     gelu
-    gemv_noshuffle_general
-    gemv_noshuffle
     get_rows
     glu
     group_norm
@@ -75,7 +73,6 @@ set(GGML_OPENCL_KERNELS
     im2col_f32
     im2col_f16
     mean
-    mul_mat_Ab_Bi_8x4
     mul_mv_f16_f16
     mul_mv_f16_f32_1row
     mul_mv_f16_f32_l4
@@ -96,6 +93,8 @@ set(GGML_OPENCL_KERNELS
     mul_mv_q6_k_f32_flat
     mul_mv_q8_0_f32
     mul_mv_q8_0_f32_flat
+    mul_mv_iq4_nl_f32
+    mul_mv_iq4_nl_f32_flat
     mul_mv_mxfp4_f32
     mul_mv_mxfp4_f32_flat
     mul_mv_id_q4_0_f32_8x_flat
@@ -103,20 +102,38 @@ set(GGML_OPENCL_KERNELS
     mul_mv_id_q8_0_f32_flat
     mul_mv_id_mxfp4_f32
     mul_mv_id_mxfp4_f32_flat
+    gemm_moe_q4_0_f32_ns
+    gemv_moe_q4_0_f32_ns
+    gemm_moe_q4_1_f32_ns
+    gemv_moe_q4_1_f32_ns
+    gemm_moe_q5_0_f32_ns
+    gemv_moe_q5_0_f32_ns
+    gemm_moe_q5_1_f32_ns
+    gemv_moe_q5_1_f32_ns
     gemm_moe_mxfp4_f32
     gemv_moe_mxfp4_f32
+    gemm_moe_mxfp4_f32_ns
+    gemv_moe_mxfp4_f32_ns
+    moe_reorder_b
+    moe_sort_by_expert
     mul_mm_f32_f32_l4_lm
     mul_mm_f16_f32_l4_lm
     mul_mm_q4_0_f32_l4_lm
     mul_mm_q4_1_f32_l4_lm
     mul_mm_q8_0_f32_l4_lm
+    mul_mm_iq4_nl_f32_l4_lm
     mul_mm_q4_k_f32_l4_lm
     mul_mm_q5_k_f32_l4_lm
     mul_mm_q6_k_f32_l4_lm
-    mul_mm_q8_0_f32_8x4
+    gemv_noshuffle_q4_0_f32
+    gemv_noshuffle_q4_0_f32_spec
+    gemm_noshuffle_q4_0_f32
     gemv_noshuffle_q4_1_f32
     gemm_noshuffle_q4_1_f32
-    gemv_noshuffle_general_q8_0_f32
+    gemv_noshuffle_iq4_nl_f32
+    gemm_noshuffle_iq4_nl_f32
+    gemv_noshuffle_q8_0_f32
+    gemm_noshuffle_q8_0_f32
     gemv_noshuffle_q4_k_f32
     gemm_noshuffle_q4_k_f32
     gemv_noshuffle_q6_k_f32
@@ -163,6 +180,10 @@ set(GGML_OPENCL_KERNELS
     flash_attn_f32
 )
 
+if (GGML_OPENCL_USE_ADRENO_KERNELS)
+    list(APPEND GGML_OPENCL_KERNELS gemm_xmem_f16_f32_os8)
+endif ()
+
 foreach (K ${GGML_OPENCL_KERNELS})
     ggml_opencl_add_kernel(${K})
 endforeach()
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 8bc7ae65a6d..0e511592d53 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -28,6 +28,7 @@
 #include <memory>
 #include <charconv>
 #include <mutex>
+#include <regex>
 
 #undef MIN
 #undef MAX
@@ -389,12 +390,15 @@ struct ggml_backend_opencl_context {
     ADRENO_GPU_GEN adreno_gen;
 
     cl_int alignment;
+    size_t global_mem_size;
     size_t max_alloc_size;
     size_t max_workgroup_size;
     bool fp16_support;
     bool has_vector_subgroup_broadcast;
     bool disable_fusion;
 
+    std::regex *opfilter = nullptr; // regex of ops to not claim
+
     bool adreno_has_large_buffer;
     bool adreno_use_large_buffer;
     ggml_cl_compiler_version adreno_cl_compiler_version;
@@ -403,6 +407,8 @@ struct ggml_backend_opencl_context {
 
     cl_bool non_uniform_workgroups;
     size_t  image_max_buffer_size;
+    size_t  image2d_max_width;
+    size_t  image2d_max_height;
 
     cl_context context;
     cl_command_queue queue;
@@ -416,6 +422,20 @@ struct ggml_backend_opencl_context {
     ggml_cl_buffer prealloc_src0;
     ggml_cl_buffer prealloc_src1;
 
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    ggml_cl_buffer prealloc_adreno_xmem_const;
+    bool adreno_xmem_gemm_enabled = false;
+#endif
+
+    // prealloc buffers for MoE router table preprocess
+    bool toggle_reorder = false;
+    ggml_cl_buffer prealloc_post_router;
+    ggml_cl_buffer prealloc_emap;
+    ggml_cl_buffer prealloc_hist;
+    ggml_cl_buffer prealloc_tile_offset;
+    ggml_cl_buffer prealloc_total_tiles;
+    ggml_cl_buffer prealloc_slot_counter;
+
     cl_program program_add;
     cl_program program_add_id;
     cl_program program_clamp;
@@ -525,12 +545,21 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mat_f16_f32;
     cl_kernel kernel_mul_mat_f16_f32_l4;
     cl_kernel kernel_mul_mat_f16_f32_tiled;
+    cl_kernel kernel_adreno_xmem_pack_src_f32;
+    cl_kernel kernel_adreno_xmem_prepack_weight_f16;
+    cl_kernel kernel_gemm_xmem_f16_f32_os8;
+    cl_kernel kernel_adreno_xmem_store_dst_f32;
     cl_kernel kernel_mul_mm_f16_f32_kqv;
     cl_kernel kernel_mul_mm_f16_f32_kq;
     cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
     cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0;
+    cl_kernel kernel_convert_block_q4_0_trans4_ns, kernel_restore_block_q4_0_trans4_ns;
     cl_kernel kernel_convert_block_q4_1, kernel_restore_block_q4_1;
+    cl_kernel kernel_convert_block_q4_1_trans4_ns, kernel_restore_block_q4_1_trans4_ns;
+    cl_kernel kernel_convert_block_q5_0_trans4_ns, kernel_restore_block_q5_0_trans4_ns;
+    cl_kernel kernel_convert_block_q5_1_trans4_ns, kernel_restore_block_q5_1_trans4_ns;
     cl_kernel kernel_convert_block_mxfp4, kernel_convert_block_mxfp4_trans, kernel_restore_block_mxfp4, kernel_restore_block_mxfp4_trans;
+    cl_kernel kernel_convert_block_mxfp4_trans4_ns, kernel_restore_block_mxfp4_trans4_ns;
     cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0, kernel_restore_block_q8_0_trans;
     cl_kernel kernel_convert_block_q6_K_noshuffle, kernel_restore_block_q6_K_noshuffle;
     cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
@@ -545,6 +574,9 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_convert_block_q5_K_noshuffle;
     cl_kernel kernel_restore_block_q5_K_noshuffle;
     cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
+    cl_kernel kernel_convert_block_iq4_nl, kernel_restore_block_iq4_nl;
+    cl_kernel kernel_convert_block_iq4_nl_noshuffle;
+    cl_kernel kernel_restore_block_iq4_nl_noshuffle;
     cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
     cl_kernel kernel_mul_mv_q4_1_f32;
     cl_kernel kernel_mul_mv_q4_1_f32_flat;
@@ -556,6 +588,8 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mv_q6_K_f32_flat;
     cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat;
     cl_kernel kernel_mul_mv_q8_0_f32, kernel_mul_mv_q8_0_f32_flat;
+    cl_kernel kernel_mul_mv_iq4_nl_f32;
+    cl_kernel kernel_mul_mv_iq4_nl_f32_flat;
     cl_kernel kernel_solve_tri_f32;
     cl_kernel kernel_im2col_f32, kernel_im2col_f16;
     cl_kernel kernel_argsort_f32_i32;
@@ -581,7 +615,14 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_conv_2d_f16_f32;
     cl_kernel kernel_ssm_conv_f32_f32, kernel_ssm_conv_f32_f32_4;
     cl_kernel kernel_timestep_embedding;
+    cl_kernel kernel_gemv_moe_q4_0_f32_ns, kernel_gemm_moe_q4_0_f32_ns;
+    cl_kernel kernel_gemv_moe_q4_1_f32_ns, kernel_gemm_moe_q4_1_f32_ns;
+    cl_kernel kernel_gemv_moe_q5_0_f32_ns, kernel_gemm_moe_q5_0_f32_ns;
+    cl_kernel kernel_gemv_moe_q5_1_f32_ns, kernel_gemm_moe_q5_1_f32_ns;
     cl_kernel kernel_gemv_moe_mxfp4_f32, kernel_gemm_moe_mxfp4_f32;
+    cl_kernel kernel_gemv_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns;
+    cl_kernel kernel_moe_reorder_b;
+    cl_kernel kernel_moe_histogram, kernel_moe_scan, kernel_moe_fill, kernel_moe_scatter;
     cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat;
     cl_kernel kernel_mul_mv_id_q8_0_f32, kernel_mul_mv_id_q8_0_f32_flat;
     cl_kernel kernel_mul_mv_id_mxfp4_f32;
@@ -594,6 +635,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mm_q4_k_f32_l4_lm;
     cl_kernel kernel_mul_mm_q5_k_f32_l4_lm;
     cl_kernel kernel_mul_mm_q6_k_f32_l4_lm;
+    cl_kernel kernel_mul_mm_iq4_nl_f32_l4_lm;
 
     std::vector<ProfilingInfo> profiling_info;
 
@@ -712,28 +754,24 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_transpose_16_4x1;
 
     // Gemm and Gemv related programs, kernels, etc
-    cl_program program_CL_gemm;
-    cl_program program_CL_gemv_general;
-    cl_program program_CL_gemv_4096_1_11008;
-    cl_program program_CL_gemv_4096_1_4096;
-    cl_program program_CL_gemv_11008_1_4096;
-    cl_program program_CL_gemv_32000_1_4096;
-    cl_kernel CL_mul_mat_Ab_Bi_8x4;
-    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general;
-    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008;
-    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096;
-    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096;
-    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096;
+    cl_kernel kernel_gemm_noshuffle_q4_0_f32;
+    cl_kernel kernel_gemv_noshuffle_q4_0_f32;
+    cl_kernel kernel_gemv_noshuffle_q4_0_f32_4096_1_11008;
+    cl_kernel kernel_gemv_noshuffle_q4_0_f32_4096_1_4096;
+    cl_kernel kernel_gemv_noshuffle_q4_0_f32_11008_1_4096;
+    cl_kernel kernel_gemv_noshuffle_q4_0_f32_32000_1_4096;
     cl_kernel kernel_gemv_noshuffle_q4_1_f32;
     cl_kernel kernel_gemm_noshuffle_q4_1_f32;
-    cl_kernel kernel_mul_mm_q8_0_f32_8x4;
-    cl_kernel CL_mul_mat_vec_q8_0_f32;
+    cl_kernel kernel_gemm_noshuffle_q8_0_f32;
+    cl_kernel kernel_gemv_noshuffle_q8_0_f32;
     cl_kernel kernel_gemv_noshuffle_q4_k_f32;
     cl_kernel kernel_gemm_noshuffle_q4_k_f32;
     cl_kernel kernel_gemv_noshuffle_q6_K_f32;
     cl_kernel kernel_gemm_noshuffle_q6_K_f32;
     cl_kernel kernel_gemv_noshuffle_q5_k_f32;
     cl_kernel kernel_gemm_noshuffle_q5_k_f32;
+    cl_kernel kernel_gemv_noshuffle_iq4_nl_f32;
+    cl_kernel kernel_gemm_noshuffle_iq4_nl_f32;
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
 
     void free() {
@@ -931,12 +969,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_restore_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q4_0_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0_trans4_ns", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q4_0_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_trans4_ns", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_1_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_1_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_1  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_1  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q4_1_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1_trans4_ns", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q4_1_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1_trans4_ns", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q5_0_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q5_0_trans4_ns", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q5_0_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q5_0_trans4_ns", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q5_1_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q5_1_trans4_ns", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q5_1_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q5_1_trans4_ns", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4_trans", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_mxfp4_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4_trans4_ns", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_mxfp4_trans4_ns = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4_trans4_ns", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4_trans", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q8_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q8_0", &err), err));
@@ -954,6 +1002,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_restore_block_q6_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q6_K_noshuffle  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q6_K_noshuffle  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_iq4_nl = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_iq4_nl", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_iq4_nl = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_iq4_nl", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_iq4_nl_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_iq4_nl_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_iq4_nl_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_iq4_nl_noshuffle", &err), err));
         GGML_LOG_CONT(".");
     }
 
@@ -1359,6 +1411,40 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_iq4_nl_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_iq4_nl_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_iq4_nl_f32.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_iq4_nl_f32 = clCreateKernel(prog, "kernel_mul_mv_iq4_nl_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // mul_mv_iq4_nl_f32_flat
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_iq4_nl_f32_flat.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_iq4_nl_f32_flat.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_iq4_nl_f32_flat = clCreateKernel(prog, "kernel_mul_mv_iq4_nl_f32_flat", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mv_mxfp4_f32
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1487,6 +1573,32 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    // gemm_xmem_f16_f32_os8
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_xmem_f16_f32_os8.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_xmem_f16_f32_os8.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_adreno_xmem_pack_src_f32 =
+            clCreateKernel(prog, "adreno_xmem_pack_src_f32", &err), err));
+        CL_CHECK((backend_ctx->kernel_adreno_xmem_prepack_weight_f16 =
+            clCreateKernel(prog, "adreno_xmem_prepack_weight_f16", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemm_xmem_f16_f32_os8 =
+            clCreateKernel(prog, "kernel_gemm_xmem_f16_f32_os8", &err), err));
+        CL_CHECK((backend_ctx->kernel_adreno_xmem_store_dst_f32 =
+            clCreateKernel(prog, "adreno_xmem_store_dst_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
     // mul_mm_f32_f32_l4_lm
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1567,6 +1679,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mm_iq4_nl_f32_l4_lm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mm_iq4_nl_f32_l4_lm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mm_iq4_nl_f32_l4_lm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mm_iq4_nl_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_iq4_nl_f32_l4_lm", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mm_q4_k_f32_l4_lm
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2500,21 +2629,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
                                        " -DSIMDGROUP_WIDTH=" +
                                        std::to_string(backend_ctx->adreno_wave_size);
         if (backend_ctx->has_vector_subgroup_broadcast) {
-            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
         }
 
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src_CL_gemv_general {
-            #include "gemv_noshuffle_general.cl.h"
+            #include "gemv_noshuffle_q4_0_f32.cl.h"
         };
 #else
-        const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_general.cl");
+        const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q4_0_f32.cl");
 #endif
 
-        backend_ctx->program_CL_gemv_general = build_program_from_source(
+        cl_program prog = build_program_from_source(
             backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts);
 
-        CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general = clCreateKernel(backend_ctx->program_CL_gemv_general, "kernel_gemv_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
 
@@ -2528,20 +2658,21 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
             " -DSIMDGROUP_WIDTH=" +
             std::to_string(backend_ctx->adreno_wave_size);
         if (backend_ctx->has_vector_subgroup_broadcast) {
-            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
         }
 
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src_CL_gemv {
-            #include "gemv_noshuffle.cl.h"
+            #include "gemv_noshuffle_q4_0_f32_spec.cl.h"
         };
 #else
-        const std::string kernel_src_CL_gemv = read_file("gemv_noshuffle.cl");
+        const std::string kernel_src_CL_gemv = read_file("gemv_noshuffle_q4_0_f32_spec.cl");
 #endif
 
-        backend_ctx->program_CL_gemv_4096_1_4096 = build_program_from_source(
+        cl_program prog = build_program_from_source(
             backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
-        CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_4096_1_4096, "kernel_gemv_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_4096_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
 
         // Gemv 2048, 16384
@@ -2552,12 +2683,13 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
             " -DSIMDGROUP_WIDTH=" +
             std::to_string(backend_ctx->adreno_wave_size);
         if (backend_ctx->has_vector_subgroup_broadcast) {
-            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
         }
 
-        backend_ctx->program_CL_gemv_4096_1_11008 = build_program_from_source(
+        prog = build_program_from_source(
             backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
-        CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008 = clCreateKernel(backend_ctx->program_CL_gemv_4096_1_11008, "kernel_gemv_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_4096_1_11008 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
 
         // Gemv 5504, 44032
@@ -2568,12 +2700,13 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
             " -DSIMDGROUP_WIDTH=" +
             std::to_string(backend_ctx->adreno_wave_size);
         if (backend_ctx->has_vector_subgroup_broadcast) {
-            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
         }
 
-        backend_ctx->program_CL_gemv_11008_1_4096 = build_program_from_source(
+        prog = build_program_from_source(
             backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
-        CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_11008_1_4096, "kernel_gemv_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_11008_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
 
         // Gemv 16000, 128000
@@ -2585,12 +2718,13 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
             std::to_string(backend_ctx->adreno_wave_size);
 
         if (backend_ctx->has_vector_subgroup_broadcast) {
-            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
         }
 
-        backend_ctx->program_CL_gemv_32000_1_4096 = build_program_from_source(
+        prog = build_program_from_source(
             backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
-        CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_32000_1_4096, "kernel_gemv_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_32000_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
 
@@ -2598,13 +2732,14 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src_CL_gemm {
-            #include "mul_mat_Ab_Bi_8x4.cl.h"
+            #include "gemm_noshuffle_q4_0_f32.cl.h"
         };
 #else
-        const std::string kernel_src_CL_gemm = read_file("mul_mat_Ab_Bi_8x4.cl");
+        const std::string kernel_src_CL_gemm = read_file("gemm_noshuffle_q4_0_f32.cl");
 #endif
-        backend_ctx->program_CL_gemm = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_CL_gemm.c_str(), compile_opts);
-        CL_CHECK((backend_ctx->CL_mul_mat_Ab_Bi_8x4 = clCreateKernel(backend_ctx->program_CL_gemm, "kernel_mul_mat_Ab_Bi_8x4", &err), err));
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_CL_gemm.c_str(), compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
 
@@ -2647,17 +2782,57 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // gemm_noshuffle_iq4_nl_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_noshuffle_iq4_nl_f32.cl.h"
+       };
+#else
+        const std::string kernel_src = read_file("gemm_noshuffle_iq4_nl_f32.cl");
+#endif
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_iq4_nl_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_iq4_nl_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemv_noshuffle_iq4_nl_f32
+    {
+        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                                       " -cl-mad-enable ";
+        if (backend_ctx->has_vector_subgroup_broadcast) {
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
+        }
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_noshuffle_iq4_nl_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_noshuffle_iq4_nl_f32.cl");
+#endif
+
+        cl_program prog = build_program_from_source(
+            backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_iq4_nl_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_iq4_nl_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mm_q8_0_f32_8x4
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
-        const std::string kernel_src_q8_8x4_gemm {
-            #include "mul_mm_q8_0_f32_8x4.cl.h"
+        const std::string kernel_src {
+            #include "gemm_noshuffle_q8_0_f32.cl.h"
        };
 #else
-        const std::string kernel_src_q8_8x4_gemm = read_file("mul_mm_q8_0_f32_8x4.cl");
+        const std::string kernel_src = read_file("gemm_noshuffle_q8_0_f32.cl");
 #endif
-        backend_ctx->program_CL_gemm = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_q8_8x4_gemm.c_str(), compile_opts);
-        CL_CHECK((backend_ctx->kernel_mul_mm_q8_0_f32_8x4 = clCreateKernel(backend_ctx->program_CL_gemm, "kernel_mul_mm_q8_0_f32_8x4", &err), err));
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
 
@@ -2673,16 +2848,16 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
 
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src_CL_gemv_general {
-            #include "gemv_noshuffle_general_q8_0_f32.cl.h"
+            #include "gemv_noshuffle_q8_0_f32.cl.h"
         };
 #else
-        const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_general_q8_0_f32.cl");
+        const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q8_0_f32.cl");
 #endif
 
         cl_program prog = build_program_from_source(
             backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts);
 
-        CL_CHECK((backend_ctx->CL_mul_mat_vec_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q8_0_f32", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q8_0_f32", &err), err));
         CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
@@ -2730,6 +2905,38 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
             " -cl-mad-enable "
             " -cl-fast-relaxed-math";
 
+    // gemv_moe_q4_1_f32_ns
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_moe_q4_1_f32_ns.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_moe_q4_1_f32_ns.cl");
+#endif
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemv_moe_q4_1_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q4_1_f32_ns", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemm_moe_q4_1_f32_ns
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_moe_q4_1_f32_ns.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_moe_q4_1_f32_ns.cl");
+#endif
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemm_moe_q4_1_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q4_1_f32_ns", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // gemv_moe_mxfp4_f32
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2762,120 +2969,293 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
-    // gemv_noshuffle_q6_k_f32
+    // gemv_moe_q4_0_f32_ns
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src {
-            #include "gemv_noshuffle_q6_k_f32.cl.h"
+            #include "gemv_moe_q4_0_f32_ns.cl.h"
         };
 #else
-        const std::string kernel_src = read_file("gemv_noshuffle_q6_k_f32.cl");
+        const std::string kernel_src = read_file("gemv_moe_q4_0_f32_ns.cl");
 #endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
 
-        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
-                                       " -cl-mad-enable ";
-        if (backend_ctx->has_vector_subgroup_broadcast) {
-            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
-        }
+        CL_CHECK((backend_ctx->kernel_gemv_moe_q4_0_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q4_0_f32_ns", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
 
+    // gemm_moe_q4_0_f32_ns
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_moe_q4_0_f32_ns.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_moe_q4_0_f32_ns.cl");
+#endif
         cl_program prog =
-            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
 
-        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q6_K_f32", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemm_moe_q4_0_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q4_0_f32_ns", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
 
-    // gemm_noshuffle_q6_k_f32
+    // gemv_moe_q5_0_f32_ns
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src {
-            #include "gemm_noshuffle_q6_k_f32.cl.h"
+            #include "gemv_moe_q5_0_f32_ns.cl.h"
         };
 #else
-        const std::string kernel_src = read_file("gemm_noshuffle_q6_k_f32.cl");
+        const std::string kernel_src = read_file("gemv_moe_q5_0_f32_ns.cl");
 #endif
         cl_program prog =
             build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
 
-        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_K_f32", &err), err));
+        CL_CHECK((backend_ctx->kernel_gemv_moe_q5_0_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q5_0_f32_ns", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
 
-    // gemv_noshuffle_q5_k_f32
+    // gemm_moe_q5_0_f32_ns
     {
-        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
-                                       " -cl-mad-enable ";
-        if (backend_ctx->has_vector_subgroup_broadcast) {
-            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
-        }
-
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src {
-            #include "gemv_noshuffle_q5_k_f32.cl.h"
+            #include "gemm_moe_q5_0_f32_ns.cl.h"
         };
 #else
-        const std::string kernel_src = read_file("gemv_noshuffle_q5_k_f32.cl");
+        const std::string kernel_src = read_file("gemm_moe_q5_0_f32_ns.cl");
 #endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
 
-        cl_program prog = build_program_from_source(
-            backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemm_moe_q5_0_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q5_0_f32_ns", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
 
-        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_k_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_k_f32", &err), err));
+    // gemv_moe_q5_1_f32_ns
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_moe_q5_1_f32_ns.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_moe_q5_1_f32_ns.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemv_moe_q5_1_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q5_1_f32_ns", &err), err));
         CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
 
-    // gemm_noshuffle_q5_k_f32
+    // gemm_moe_q5_1_f32_ns
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
         const std::string kernel_src {
-            #include "gemm_noshuffle_q5_k_f32.cl.h"
+            #include "gemm_moe_q5_1_f32_ns.cl.h"
         };
 #else
-        const std::string kernel_src = read_file("gemm_noshuffle_q5_k_f32.cl");
+        const std::string kernel_src = read_file("gemm_moe_q5_1_f32_ns.cl");
 #endif
-        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
-        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_k_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_k_f32", &err), err));
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemm_moe_q5_1_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q5_1_f32_ns", &err), err));
         CL_CHECK(clReleaseProgram(prog));
         GGML_LOG_CONT(".");
     }
-#endif // GGML_OPENCL_USE_ADRENO_KERNELS
-    GGML_LOG_CONT("\n");
-}
 
-// XXX static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
-// XXX    static bool initialized = false;
-// XXX    static ggml_backend_opencl_context *backend_ctx = nullptr;
+    // gemv_moe_mxfp4_f32_ns
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_moe_mxfp4_f32_ns.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_moe_mxfp4_f32_ns.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
 
-static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev);
+        CL_CHECK((backend_ctx->kernel_gemv_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_mxfp4_f32_ns", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
 
-namespace /* anonymous */ {
-extern struct ggml_backend_device_i ggml_backend_opencl_device_i;
-}
+    // gemm_moe_mxfp4_f32_ns
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_moe_mxfp4_f32_ns.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_moe_mxfp4_f32_ns.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
 
-// Look for available and suitable devices.
-static std::vector<ggml_backend_device> ggml_opencl_probe_devices(ggml_backend_reg * reg) {
-    std::vector<ggml_backend_device> found_devices;
+        CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
 
-#ifdef GGML_OPENCL_PROFILING
-    GGML_LOG_INFO("ggml_opencl: OpenCL profiling enabled\n");
+    // moe_reorder_b
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "moe_reorder_b.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("moe_reorder_b.cl");
 #endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
 
-    struct cl_device;
-    struct cl_platform {
-        cl_platform_id id;
-        unsigned number;
-        char name[128];
-        char vendor[128];
-        struct cl_device * devices;
-        unsigned n_devices;
-        struct cl_device * default_device;
-    };
+        CL_CHECK((backend_ctx->kernel_moe_reorder_b = clCreateKernel(prog, "kernel_moe_reorder_b", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
 
-    struct cl_device {
-        struct cl_platform * platform;
-        cl_device_id id;
-        unsigned number;
+    // moe_sort_by_expert
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "moe_sort_by_expert.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("moe_sort_by_expert.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_moe_histogram = clCreateKernel(prog, "kernel_moe_histogram", &err), err));
+        CL_CHECK((backend_ctx->kernel_moe_scan = clCreateKernel(prog, "kernel_moe_scan", &err), err));
+        CL_CHECK((backend_ctx->kernel_moe_fill = clCreateKernel(prog, "kernel_moe_fill", &err), err));
+        CL_CHECK((backend_ctx->kernel_moe_scatter = clCreateKernel(prog, "kernel_moe_scatter", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemv_noshuffle_q6_k_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_noshuffle_q6_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_noshuffle_q6_k_f32.cl");
+#endif
+
+        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                                       " -cl-mad-enable ";
+        if (backend_ctx->has_vector_subgroup_broadcast) {
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+        }
+
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q6_K_f32", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemm_noshuffle_q6_k_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_noshuffle_q6_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_noshuffle_q6_k_f32.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_K_f32", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemv_noshuffle_q5_k_f32
+    {
+        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                                       " -cl-mad-enable ";
+        if (backend_ctx->has_vector_subgroup_broadcast) {
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
+        }
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_noshuffle_q5_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_noshuffle_q5_k_f32.cl");
+#endif
+
+        cl_program prog = build_program_from_source(
+            backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_k_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_k_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemm_noshuffle_q5_k_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_noshuffle_q5_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_noshuffle_q5_k_f32.cl");
+#endif
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_k_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_k_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+    GGML_LOG_CONT("\n");
+}
+
+// XXX static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
+// XXX    static bool initialized = false;
+// XXX    static ggml_backend_opencl_context *backend_ctx = nullptr;
+
+static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev);
+
+namespace /* anonymous */ {
+extern struct ggml_backend_device_i ggml_backend_opencl_device_i;
+}
+
+// Look for available and suitable devices.
+static std::vector<ggml_backend_device> ggml_opencl_probe_devices(ggml_backend_reg * reg) {
+    std::vector<ggml_backend_device> found_devices;
+
+#ifdef GGML_OPENCL_PROFILING
+    GGML_LOG_INFO("ggml_opencl: OpenCL profiling enabled\n");
+#endif
+
+    struct cl_device;
+    struct cl_platform {
+        cl_platform_id id;
+        unsigned number;
+        char name[128];
+        char vendor[128];
+        struct cl_device * devices;
+        unsigned n_devices;
+        struct cl_device * default_device;
+    };
+
+    struct cl_device {
+        struct cl_platform * platform;
+        cl_device_id id;
+        unsigned number;
         cl_device_type type;
         char name[128];
         char version[128];
@@ -3197,12 +3577,19 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
     backend_ctx->alignment = base_align_in_bits / 8u;
     GGML_LOG_INFO("ggml_opencl: mem base addr align: %u\n", backend_ctx->alignment);
 
+    clGetDeviceInfo(device, CL_DEVICE_GLOBAL_MEM_SIZE, sizeof(size_t), &backend_ctx->global_mem_size, NULL);
+    GGML_LOG_INFO("ggml_opencl: global mem size: %zu MB\n", backend_ctx->global_mem_size/1024/1024);
+
     clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &backend_ctx->max_alloc_size, NULL);
     GGML_LOG_INFO("ggml_opencl: max mem alloc size: %zu MB\n", backend_ctx->max_alloc_size/1024/1024);
 
     clGetDeviceInfo(device, CL_DEVICE_IMAGE_MAX_BUFFER_SIZE, sizeof(size_t), &backend_ctx->image_max_buffer_size, NULL);
     GGML_LOG_INFO("ggml_opencl: device max image buffer size (pixels): %lu\n", backend_ctx->image_max_buffer_size);
 
+    clGetDeviceInfo(device, CL_DEVICE_IMAGE2D_MAX_WIDTH, sizeof(size_t), &backend_ctx->image2d_max_width, NULL);
+    clGetDeviceInfo(device, CL_DEVICE_IMAGE2D_MAX_HEIGHT, sizeof(size_t), &backend_ctx->image2d_max_height, NULL);
+    GGML_LOG_INFO("ggml_opencl: device max image2d size: %lu x %lu\n", backend_ctx->image2d_max_width, backend_ctx->image2d_max_height);
+
     clGetDeviceInfo(device, CL_DEVICE_MAX_WORK_GROUP_SIZE, sizeof(size_t), &backend_ctx->max_workgroup_size, NULL);
     GGML_LOG_INFO("ggml_opencl: device max workgroup size: %lu\n", backend_ctx->max_workgroup_size);
 
@@ -3241,6 +3628,16 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
     GGML_LOG_INFO("ggml_opencl: using kernels optimized for Adreno (GGML_OPENCL_USE_ADRENO_KERNELS)\n");
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
 
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    backend_ctx->adreno_xmem_gemm_enabled = getenv("GGML_OPENCL_ADRENO_XMEM_GEMM") != nullptr &&
+                                             backend_ctx->gpu_family == GPU_FAMILY::ADRENO;
+    if (getenv("GGML_OPENCL_ADRENO_XMEM_GEMM") != nullptr) {
+        GGML_LOG_INFO("ggml_opencl: Adreno xmem F16xF32 GEMM %s\n",
+                      backend_ctx->adreno_xmem_gemm_enabled ?
+                      "enabled (temporary weight prepack)" : "requested but unsupported by this driver");
+    }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
     // determine whether to use large buffer for Adreno
     backend_ctx->adreno_use_large_buffer = getenv("GGML_OPENCL_ADRENO_USE_LARGE_BUFFER") != nullptr &&
                                            backend_ctx->gpu_family == GPU_FAMILY::ADRENO;
@@ -3301,6 +3698,12 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
 
     backend_ctx->disable_fusion = getenv("GGML_OPENCL_DISABLE_FUSION") != nullptr;
 
+    const char * str_opfilter = getenv("GGML_OPENCL_OPFILTER");
+    if (str_opfilter) {
+        backend_ctx->opfilter = new std::regex(str_opfilter, std::regex_constants::icase);
+        GGML_LOG_INFO("ggml_opencl: opfilter regex = \"%s\"\n", str_opfilter);
+    }
+
     dev_ctx->backend_ctx = backend_ctx.release();
     return dev_ctx->backend_ctx;
 }
@@ -3455,11 +3858,14 @@ struct ggml_tensor_extra_cl_q4_0 {
             CL_CHECK(clReleaseMemObject(d));
             d = nullptr;
         }
+        if (q_img != nullptr) {
+            CL_CHECK(clReleaseMemObject(q_img));
+            q_img = nullptr;
+        }
         // Currently, q_img and d_img are only initialized when SMALL_ALLOC is
         // enabled. They point to the images in ggml_backend_opencl_buffer_context.
         // So, there is no need to release them here.
         // TODO: initialize them for non SMALL_PATH path, or remove them.
-        q_img = nullptr;
         d_img = nullptr;
         size_q = 0;
         size_d = 0;
@@ -3506,11 +3912,14 @@ struct ggml_tensor_extra_cl_q4_1 {
             CL_CHECK(clReleaseMemObject(m));
             m = nullptr;
         }
+        if (q_img != nullptr) {
+            CL_CHECK(clReleaseMemObject(q_img));
+            q_img = nullptr;
+        }
         // Currently, q_img and d_img are only initialized when SMALL_ALLOC is
         // enabled. They point to the images in ggml_backend_opencl_buffer_context.
         // So, there is no need to release them here.
         // TODO: initialize them for non SMALL_PATH path, or remove them.
-        q_img = nullptr;
         d_img = nullptr;
         m_img = nullptr;
         size_q = 0;
@@ -3519,6 +3928,122 @@ struct ggml_tensor_extra_cl_q4_1 {
     }
 };
 
+struct ggml_tensor_extra_cl_q5_0 {
+    // Quantized values.
+    cl_mem qs = nullptr;
+    // Quantized values in image1d_buffer_t.
+    cl_mem qs_img = nullptr;
+    // 5-th bit values.
+    cl_mem qh = nullptr;
+    // 5-th bit values in image1d_buffer_t.
+    cl_mem qh_img = nullptr;
+    // Scales.
+    cl_mem d = nullptr;
+    // Scales in image1d_buffer_t.
+    cl_mem d_img = nullptr;
+    // Size of quantized values.
+    size_t size_qs = 0;
+    // Size of 5-th bit values.
+    size_t size_qh = 0;
+    // Size of scales.
+    size_t size_d = 0;
+
+    ~ggml_tensor_extra_cl_q5_0() {
+        reset();
+    }
+
+    void reset() {
+        if (qs != nullptr) {
+            CL_CHECK(clReleaseMemObject(qs));
+            qs = nullptr;
+        }
+        if (qh != nullptr) {
+            CL_CHECK(clReleaseMemObject(qh));
+            qh = nullptr;
+        }
+        if (d != nullptr) {
+            CL_CHECK(clReleaseMemObject(d));
+            d = nullptr;
+        }
+        if (qs_img != nullptr) {
+            CL_CHECK(clReleaseMemObject(qs_img));
+            qs_img = nullptr;
+        }
+
+        qh_img = nullptr;
+        d_img = nullptr;
+        size_qs = 0;
+        size_qh = 0;
+        size_d = 0;
+    }
+};
+
+struct ggml_tensor_extra_cl_q5_1 {
+    // Quantized values.
+    cl_mem qs = nullptr;
+    // Quantized values in image1d_buffer_t.
+    cl_mem qs_img = nullptr;
+    // 5-th bit values.
+    cl_mem qh = nullptr;
+    // 5-th bit values in image1d_buffer_t.
+    cl_mem qh_img = nullptr;
+    // Scales.
+    cl_mem d = nullptr;
+    // Scales in image1d_buffer_t.
+    cl_mem d_img = nullptr;
+    // Min
+    cl_mem m = nullptr;
+    // Min in image1d_buffer_t.
+    cl_mem m_img = nullptr;
+    // Size of quantized values.
+    size_t size_qs = 0;
+    // Size of 5-th bit values.
+    size_t size_qh = 0;
+    // Size of scales.
+    size_t size_d = 0;
+    // Size of min values.
+    size_t size_m = 0;
+
+    ~ggml_tensor_extra_cl_q5_1() {
+        reset();
+    }
+
+    void reset() {
+        // q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
+        // They must be properly released so that the original buffer can be
+        // properly released to avoid memory leak.
+        if (qs != nullptr) {
+            CL_CHECK(clReleaseMemObject(qs));
+            qs = nullptr;
+        }
+        if (qh != nullptr) {
+            CL_CHECK(clReleaseMemObject(qh));
+            qh = nullptr;
+        }
+        if (d != nullptr) {
+            CL_CHECK(clReleaseMemObject(d));
+            d = nullptr;
+        }
+        if (m != nullptr) {
+            CL_CHECK(clReleaseMemObject(m));
+            m = nullptr;
+        }
+        if (qs_img != nullptr) {
+            CL_CHECK(clReleaseMemObject(qs_img));
+            qs_img = nullptr;
+        }
+        // qh_img, d_img, and m_img are not currently allocated separately.
+        // TODO: initialize them for non SMALL_PATH path, or remove them.
+        qh_img = nullptr;
+        d_img = nullptr;
+        m_img = nullptr;
+        size_qs = 0;
+        size_qh = 0;
+        size_d = 0;
+        size_m = 0;
+    }
+};
+
 struct ggml_tensor_extra_cl_mxfp4 {
     // Quantized values.
     cl_mem q = nullptr;
@@ -3549,13 +4074,12 @@ struct ggml_tensor_extra_cl_mxfp4 {
             CL_CHECK(clReleaseMemObject(e));
             e = nullptr;
         }
-        if (q != nullptr) {
+        if (q_img != nullptr) {
             CL_CHECK(clReleaseMemObject(q_img));
-            q = nullptr;
+            q_img = nullptr;
         }
-        // Currently, q_img and d_img are not used. They can be image1d_buffer_t
+        // Currently, e_img is not used. They can be image1d_buffer_t
         // that wraps around q and d to utilize image access path.
-        q_img = nullptr;
         e_img = nullptr;
         size_q = 0;
         size_e = 0;
@@ -3597,6 +4121,30 @@ struct ggml_tensor_extra_cl_q8_0 {
     }
 };
 
+struct ggml_tensor_extra_cl_iq4_nl {
+    cl_mem q = nullptr;
+    cl_mem q_img = nullptr;
+
+    cl_mem d = nullptr;
+    cl_mem d_img = nullptr;
+
+    size_t size_q = 0;
+    size_t size_d = 0;
+
+    ~ggml_tensor_extra_cl_iq4_nl() {
+        reset();
+    }
+
+    void reset() {
+        if (q != nullptr) { CL_CHECK(clReleaseMemObject(q)); q = nullptr; }
+        if (d != nullptr) { CL_CHECK(clReleaseMemObject(d)); d = nullptr; }
+        q_img = nullptr;
+        d_img = nullptr;
+        size_q = 0;
+        size_d = 0;
+    }
+};
+
 struct ggml_tensor_extra_cl_q4_K {
     // Quantized values
     cl_mem q = nullptr;
@@ -3923,16 +4471,50 @@ static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggm
     return GGML_STATUS_SUCCESS;
 }
 
-static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
-    ggml_backend_opencl_device_context * dev_ctx     = (ggml_backend_opencl_device_context *)dev->context;
-    ggml_backend_opencl_context *        backend_ctx = dev_ctx->backend_ctx;
-
-    switch (op->op) {
-        case GGML_OP_NONE:
-            return true;
-        case GGML_OP_GET_ROWS:
-            switch (op->src[0]->type) {
-                case GGML_TYPE_F32:
+// The optimized gemm and gemv kernels are used for large matrices without batch.
+// tensor is the quantized weights matrix.
+inline bool use_adreno_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
+    int64_t threshold_ne0 = 512;
+    int64_t threshold_ne1 = 512;
+    if (!backend_ctx->adreno_cl_compiler_version.newer_than_or_same(E031, 38, 11, 0) &&
+         backend_ctx->adreno_cl_compiler_version.type != DX) {
+        threshold_ne0 = 128;
+        threshold_ne1 = 128;
+    }
+    return tensor->ne[0] >= threshold_ne0 && tensor->ne[1] >= threshold_ne1 &&
+            tensor->ne[2] == 1 && tensor->ne[3] == 1;
+}
+
+inline bool use_adreno_moe_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
+    GGML_UNUSED(backend_ctx);
+    int ne01 = tensor->ne[1];
+    return (((strstr(tensor->name, "ffn") != NULL) && (strstr(tensor->name, "exps") != NULL)) || (strstr(tensor->name, "as") != NULL)) && (ne01 % 64 == 0);
+}
+
+inline bool enable_adreno_trans_weight(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
+
+    bool adreno_kernel = use_adreno_kernels(backend_ctx, tensor);
+
+    size_t elem_num = tensor->ne[0] * tensor->ne[1] * tensor->ne[2] * tensor->ne[3];
+
+    return ((elem_num < 128 * 1024 * 1024) && adreno_kernel);  // max element num: 2**27
+}
+
+static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
+    ggml_backend_opencl_device_context * dev_ctx     = (ggml_backend_opencl_device_context *)dev->context;
+    ggml_backend_opencl_context *        backend_ctx = dev_ctx->backend_ctx;
+
+    // reject ops that match the opfilter regex
+    if (backend_ctx->opfilter && std::regex_match(std::string(ggml_op_desc(op)), *backend_ctx->opfilter)) {
+        return false;
+    }
+
+    switch (op->op) {
+        case GGML_OP_NONE:
+            return true;
+        case GGML_OP_GET_ROWS:
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
                 case GGML_TYPE_F16:
                     return true;
                 case GGML_TYPE_Q4_0:
@@ -4097,6 +4679,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 return op->src[1]->type == GGML_TYPE_F32;
             } else if (op->src[0]->type == GGML_TYPE_Q4_0  || op->src[0]->type == GGML_TYPE_Q4_1 ||
                        op->src[0]->type == GGML_TYPE_MXFP4 ||
+                       op->src[0]->type == GGML_TYPE_IQ4_NL ||
                        op->src[0]->type == GGML_TYPE_Q4_K  ||
                        op->src[0]->type == GGML_TYPE_Q5_K  ||
                        op->src[0]->type == GGML_TYPE_Q6_K) {
@@ -4113,6 +4696,20 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                     return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
                 }
             }
+            // q4_0, q8_0 and mxfp4 have general MUL_MAT_ID support,
+            // the quantizations here currently do not - they are only supported by Adreno with certain shapes
+            if (op->src[0]->type == GGML_TYPE_Q4_1 ||
+                op->src[0]->type == GGML_TYPE_Q5_0 ||
+                op->src[0]->type == GGML_TYPE_Q5_1) {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+                if (op->src[1]->type == GGML_TYPE_F32) {
+                    return use_adreno_moe_kernels(backend_ctx, op->src[0])
+                        && ggml_is_contiguous(op->src[0])
+                        && ggml_is_contiguous(op->src[1]);
+                }
+#endif
+                return false;
+            }
             return false;
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
@@ -4216,9 +4813,9 @@ static ggml_backend_i ggml_backend_opencl_i = {
     /* .free                    = */ ggml_backend_opencl_free,
     /* .set_tensor_async        = */ NULL,  /* ggml_backend_opencl_set_tensor_async */
     /* .get_tensor_async        = */ NULL,  /* ggml_backend_opencl_get_tensor_async */
-    /* .cpy_tensor_async        = */ NULL,  /* ggml_backend_opencl_cpy_tensor_async */
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,  /* ggml_backend_opencl_cpy_tensor_async */
     /* .synchronize             = */ ggml_backend_opencl_synchronize,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
@@ -4283,6 +4880,24 @@ struct ggml_backend_opencl_buffer_context {
         for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0_in_use) {
             delete e;
         }
+        for (ggml_tensor_extra_cl_q4_1 * e : temp_tensor_extras_q4_1) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_q4_1 * e : temp_tensor_extras_q4_1_in_use) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_q5_0 * e : temp_tensor_extras_q5_0) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_q5_0 * e : temp_tensor_extras_q5_0_in_use) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_q5_1 * e : temp_tensor_extras_q5_1) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_q5_1 * e : temp_tensor_extras_q5_1_in_use) {
+            delete e;
+        }
         for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4) {
             delete e;
         }
@@ -4295,6 +4910,12 @@ struct ggml_backend_opencl_buffer_context {
         for (ggml_tensor_extra_cl_q8_0 * e : temp_tensor_extras_q8_0_in_use) {
             delete e;
         }
+        for (ggml_tensor_extra_cl_iq4_nl * e : temp_tensor_extras_iq4_nl) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_iq4_nl * e : temp_tensor_extras_iq4_nl_in_use) {
+            delete e;
+        }
         for (ggml_tensor_extra_cl_q4_K * e : temp_tensor_extras_q4_K) {
             delete e;
         }
@@ -4360,6 +4981,36 @@ struct ggml_backend_opencl_buffer_context {
         return extra;
     }
 
+    ggml_tensor_extra_cl_q5_0 * ggml_opencl_alloc_temp_tensor_extra_q5_0() {
+        ggml_tensor_extra_cl_q5_0 * extra;
+        if (temp_tensor_extras_q5_0.empty()) {
+            extra = new ggml_tensor_extra_cl_q5_0();
+        } else {
+            extra = temp_tensor_extras_q5_0.back();
+            temp_tensor_extras_q5_0.pop_back();
+        }
+
+        temp_tensor_extras_q5_0_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
+    ggml_tensor_extra_cl_q5_1 * ggml_opencl_alloc_temp_tensor_extra_q5_1() {
+        ggml_tensor_extra_cl_q5_1 * extra;
+        if (temp_tensor_extras_q5_1.empty()) {
+            extra = new ggml_tensor_extra_cl_q5_1();
+        } else {
+            extra = temp_tensor_extras_q5_1.back();
+            temp_tensor_extras_q5_1.pop_back();
+        }
+
+        temp_tensor_extras_q5_1_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
     ggml_tensor_extra_cl_mxfp4 * ggml_opencl_alloc_temp_tensor_extra_mxfp4() {
         ggml_tensor_extra_cl_mxfp4 * extra;
         if (temp_tensor_extras_mxfp4.empty()) {
@@ -4390,6 +5041,21 @@ struct ggml_backend_opencl_buffer_context {
         return extra;
     }
 
+    ggml_tensor_extra_cl_iq4_nl * ggml_opencl_alloc_temp_tensor_extra_iq4_nl() {
+        ggml_tensor_extra_cl_iq4_nl * extra;
+        if (temp_tensor_extras_iq4_nl.empty()) {
+            extra = new ggml_tensor_extra_cl_iq4_nl();
+        } else {
+            extra = temp_tensor_extras_iq4_nl.back();
+            temp_tensor_extras_iq4_nl.pop_back();
+        }
+
+        temp_tensor_extras_iq4_nl_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
     ggml_tensor_extra_cl_q4_K * ggml_opencl_alloc_temp_tensor_extra_q4_K() {
         ggml_tensor_extra_cl_q4_K * extra;
         if (temp_tensor_extras_q4_K.empty()) {
@@ -4451,6 +5117,16 @@ struct ggml_backend_opencl_buffer_context {
         }
         temp_tensor_extras_q4_1_in_use.clear();
 
+        for (ggml_tensor_extra_cl_q5_0 * e : temp_tensor_extras_q5_0_in_use) {
+            temp_tensor_extras_q5_0.push_back(e);
+        }
+        temp_tensor_extras_q5_0_in_use.clear();
+
+        for (ggml_tensor_extra_cl_q5_1 * e : temp_tensor_extras_q5_1_in_use) {
+            temp_tensor_extras_q5_1.push_back(e);
+        }
+        temp_tensor_extras_q5_1_in_use.clear();
+
         for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4_in_use) {
             temp_tensor_extras_mxfp4.push_back(e);
         }
@@ -4461,6 +5137,11 @@ struct ggml_backend_opencl_buffer_context {
         }
         temp_tensor_extras_q8_0_in_use.clear();
 
+        for (ggml_tensor_extra_cl_iq4_nl * e : temp_tensor_extras_iq4_nl_in_use) {
+            temp_tensor_extras_iq4_nl.push_back(e);
+        }
+        temp_tensor_extras_iq4_nl_in_use.clear();
+
         for (ggml_tensor_extra_cl_q4_K * e : temp_tensor_extras_q4_K_in_use) {
             temp_tensor_extras_q4_K.push_back(e);
         }
@@ -4488,10 +5169,16 @@ struct ggml_backend_opencl_buffer_context {
     std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0_in_use;
     std::vector<ggml_tensor_extra_cl_q4_1 *> temp_tensor_extras_q4_1;
     std::vector<ggml_tensor_extra_cl_q4_1 *> temp_tensor_extras_q4_1_in_use;
+    std::vector<ggml_tensor_extra_cl_q5_0 *> temp_tensor_extras_q5_0;
+    std::vector<ggml_tensor_extra_cl_q5_0 *> temp_tensor_extras_q5_0_in_use;
+    std::vector<ggml_tensor_extra_cl_q5_1 *> temp_tensor_extras_q5_1;
+    std::vector<ggml_tensor_extra_cl_q5_1 *> temp_tensor_extras_q5_1_in_use;
     std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4;
     std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4_in_use;
     std::vector<ggml_tensor_extra_cl_q8_0 *> temp_tensor_extras_q8_0;
     std::vector<ggml_tensor_extra_cl_q8_0 *> temp_tensor_extras_q8_0_in_use;
+    std::vector<ggml_tensor_extra_cl_iq4_nl *> temp_tensor_extras_iq4_nl;
+    std::vector<ggml_tensor_extra_cl_iq4_nl *> temp_tensor_extras_iq4_nl_in_use;
     std::vector<ggml_tensor_extra_cl_q4_K *> temp_tensor_extras_q4_K;
     std::vector<ggml_tensor_extra_cl_q4_K *> temp_tensor_extras_q4_K_in_use;
     std::vector<ggml_tensor_extra_cl_q5_K *> temp_tensor_extras_q5_K;
@@ -4568,35 +5255,6 @@ static enum ggml_status ggml_backend_opencl_buffer_init_tensor(ggml_backend_buff
     return GGML_STATUS_SUCCESS;
 }
 
-// The optimized gemm and gemv kernels are used for large matrices without batch.
-// tensor is the quantized weights matrix.
-inline bool use_adreno_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
-    int64_t threshold_ne0 = 512;
-    int64_t threshold_ne1 = 512;
-    if (!backend_ctx->adreno_cl_compiler_version.newer_than_or_same(E031, 38, 11, 0) &&
-         backend_ctx->adreno_cl_compiler_version.type != DX) {
-        threshold_ne0 = 128;
-        threshold_ne1 = 128;
-    }
-    return tensor->ne[0] >= threshold_ne0 && tensor->ne[1] >= threshold_ne1 &&
-            tensor->ne[2] == 1 && tensor->ne[3] == 1;
-}
-
-inline bool use_adreno_moe_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
-    GGML_UNUSED(backend_ctx);
-    int ne01 = tensor->ne[1];
-    return ((strstr(tensor->name, "ffn") != NULL) || (strstr(tensor->name, "as") != NULL)) && (ne01 % 64 == 0);
-}
-
-inline bool enable_adreno_trans_weight(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
-
-    bool adreno_kernel = use_adreno_kernels(backend_ctx, tensor);
-
-    size_t elem_num = tensor->ne[0] * tensor->ne[1] * tensor->ne[2] * tensor->ne[3];
-
-    return ((elem_num < 128 * 1024 * 1024) && adreno_kernel);  // max element num: 2**27
-}
-
 static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer->buft->device);
 
@@ -4667,17 +5325,53 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
         CL_CHECK(err);
 
-        //cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;
-    #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        // Adreno moe q4_0 kernel needs special transpose and unshuffling
+        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0_trans4_ns;
+
+            int ne00 = tensor->ne[0];
+            int ne01 = tensor->ne[1];
+            int ne02 = tensor->ne[2];
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &ne00));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne01));
+
+            size_t global_work_size[3] = {static_cast<size_t>(((ne01 + 63) / 64) * 64), static_cast<size_t>(ne00 / 32), static_cast<size_t>(ne02)};
+            size_t local_work_size[3] = {64, 2, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clReleaseMemObject(data_device));
+
+            // Create image for Q
+            cl_image_format img_format_q = {CL_R, CL_UNSIGNED_INT32};
+            cl_image_desc img_desc_q = {
+                CL_MEM_OBJECT_IMAGE1D_BUFFER,
+                static_cast<size_t>(ggml_nelements(tensor) / 8),
+                0, 0, 0, 0, 0, 0, 0,
+                { extra->q }
+            };
+            extra->q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_format_q, &img_desc_q, NULL, &err);
+            tensor->extra = extra;
+
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
         cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;
 
         // The optimized kernels need weights in natural order, so unshuffle.
         if (use_adreno_kernels(backend_ctx, tensor)) {
             kernel = backend_ctx->kernel_convert_block_q4_0_noshuffle;
         }
-    #else
+#else
         cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;
-    #endif // GGML_OPENCL_USE_ADRENO_KERNELS
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
         CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
         CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
@@ -4693,173 +5387,22 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         tensor->extra = extra;
 
         // transpose the weights and scales
-    #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
         // Only do transpose for large, non batched matrix
         // TODO: use preallocated images instead of sub-buffer then image
         if (use_adreno_kernels(backend_ctx, tensor)) {
-        // <----------------------------------------------------------------------------------> //
-        // start transpose
-        // <----------------------------------------------------------------------------------> //
-        int M = tensor->ne[1];   // ne01
-        int K = tensor->ne[0];   // ne00
-
-        //For matrix-vector multiplication kernel, we assume K is a multiple of 32
-        GGML_ASSERT(K % 32 == 0);
-        //For transpose kernels, we assume K is a multiple of 4 (satisfied by prior assert), and M is a multiple of 4
-        GGML_ASSERT(M % 4 == 0);
-
-        // transpose is out of place, so we need to allocate transposed buffers
-        // <----------------------------------------------------------------------------------> //
-        // use sub_buffer of max buffer size instead
-
-        size_t q_size_bytes = K * M / 8 * sizeof(float);
-        backend_ctx->prealloc_quant_trans.allocate(context, q_size_bytes);
-
-        cl_buffer_region region;
-        region.origin = 0;
-        region.size = q_size_bytes;
-        cl_mem qT_d = clCreateSubBuffer(
-            backend_ctx->prealloc_quant_trans.buffer,
-            0,
-            CL_BUFFER_CREATE_TYPE_REGION,
-            &region,
-            &err);
-        CL_CHECK(err);
-
-        bool K_tile_trans = true;
-        if ((K / 32) % 4 != 0){
-            K_tile_trans =false;
-        }
-
-        size_t d_size_bytes = M * (K / 32) * 2;
-        backend_ctx->prealloc_scales_trans.allocate(context, d_size_bytes);
-
-        region.origin = 0;
-        region.size = d_size_bytes;
-        cl_mem dT_d = clCreateSubBuffer(
-            backend_ctx->prealloc_scales_trans.buffer,
-            0,
-            CL_BUFFER_CREATE_TYPE_REGION,
-            &region,
-            &err);
-        CL_CHECK(err);
-
-        // <----------------------------------------------------------------------------------> //
-
-
-        // create images from the buffers
-        // <----------------------------------------------------------------------------------> //
-        cl_mem q_d_image1D;
-        cl_mem d_d_image1D;
-        cl_mem qT_d_image1D;
-        cl_mem dT_d_image1D;
-
-        cl_image_format img_fmt_1d = { CL_RGBA, CL_HALF_FLOAT };
-        cl_image_desc img_desc_1d;
-
-        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
-        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
-        img_desc_1d.image_width = M * K / 4 / 4;
-        img_desc_1d.buffer = extra->q;
-        q_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
-        CL_CHECK(err);
-
-        img_fmt_1d = { CL_RGBA, CL_HALF_FLOAT };
-        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
-        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
-        img_desc_1d.image_width = M * K / 4 / 4;
-        img_desc_1d.buffer = qT_d;
-        qT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
-        CL_CHECK(err);
-
-        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
-        if (K_tile_trans) {
-            img_fmt_1d = { CL_RGBA, CL_HALF_FLOAT };
-            img_desc_1d.image_width = M * K / 32 / 4;
-        } else {
-            img_fmt_1d = { CL_R, CL_HALF_FLOAT };
-            img_desc_1d.image_width = M * K / 32;
-        }
-        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
-        img_desc_1d.buffer = extra->d;
-        d_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
-        CL_CHECK(err);
-
-        img_fmt_1d = { CL_RGBA, CL_HALF_FLOAT };
-        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
-        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
-        img_desc_1d.image_width = M * K / 32 / 4;
-        img_desc_1d.buffer = dT_d;
-        dT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
-        CL_CHECK(err);
-        // <----------------------------------------------------------------------------------> //
-
-        // set up and call the transpose kernels
-        // <----------------------------------------------------------------------------------> //
-        // weights
-        int height_q = M / 4;
-        int width_q = K / 4 / 4;
-        kernel = backend_ctx->kernel_transpose_16;
-
-        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_d_image1D));
-        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &qT_d_image1D));
-        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_q));
-        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_q));
-
-        size_t local_size_q[3] = {4, 16, 1};
-        size_t global_size_q[3] = {static_cast<size_t>(width_q), static_cast<size_t>(height_q), 1};
-        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_size_q, local_size_q, 0, NULL, &evt));
-        CL_CHECK(clWaitForEvents(1, &evt));
-
-        // scales
-        int height_s = M / 4;
-        int width_s = K / 32 / 4;
-
-        kernel = backend_ctx->kernel_transpose_16;
-        if (!K_tile_trans) {
-            kernel = backend_ctx->kernel_transpose_16_4x1;
-            width_s = K / 32;
-        }
-        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &d_d_image1D));
-        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &dT_d_image1D));
-        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_s));
-        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_s));
-
-        size_t local_size_s[3] = {4, 16, 1};
-        size_t global_size_s[3] = {static_cast<size_t>(width_s), static_cast<size_t>(height_s), 1};
-        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_size_s, local_size_s, 0, NULL, &evt));
-        CL_CHECK(clWaitForEvents(1, &evt));
-        // <----------------------------------------------------------------------------------> //
-
-        // copy transposed buffer contents to original buffers
-        // <----------------------------------------------------------------------------------> //
-        // weights
-        CL_CHECK(clEnqueueCopyBuffer(queue, qT_d, extra->q, 0, 0, q_size_bytes, 0, NULL, &evt));
-        CL_CHECK(clWaitForEvents(1, &evt));
-
-        // scales
-        CL_CHECK(clEnqueueCopyBuffer(queue, dT_d, extra->d, 0, 0, d_size_bytes, 0, NULL, &evt));
-        CL_CHECK(clWaitForEvents(1, &evt));
-        // <----------------------------------------------------------------------------------> //
+        int M = tensor->ne[1];
+            int K = tensor->ne[0];
 
-        // deallocate transpose buffers
-        // <----------------------------------------------------------------------------------> //
-        CL_CHECK(clReleaseMemObject(qT_d));
-        CL_CHECK(clReleaseMemObject(dT_d));
+            GGML_ASSERT(K % 32 == 0);
 
-        // deallocate temporary images
-        CL_CHECK(clReleaseMemObject(q_d_image1D));
-        CL_CHECK(clReleaseMemObject(d_d_image1D));
-        CL_CHECK(clReleaseMemObject(qT_d_image1D));
-        CL_CHECK(clReleaseMemObject(dT_d_image1D));
-        // <----------------------------------------------------------------------------------> //
-        // end transpose
-        // <----------------------------------------------------------------------------------> //
+            // Transpose q as ushort
+            transpose_2d_as_16b(backend_ctx, extra->q, extra->q, size_q, K/4, M);
+            // Transpose d as ushort
+            transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/32, M);
         }
-    #endif // GGML_OPENCL_USE_ADRENO_KERNELS
-
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
         return;
-
     }
     if (tensor->type == GGML_TYPE_Q4_1) {
         ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
@@ -4912,15 +5455,54 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
         CL_CHECK(err);
 
-    #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        // Adreno moe q4_1 kernel needs special transpose and unshuffling
+        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1_trans4_ns;
+
+            int ne00 = tensor->ne[0];
+            int ne01 = tensor->ne[1];
+            int ne02 = tensor->ne[2];
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->m));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne01));
+
+            size_t global_work_size[3] = {static_cast<size_t>(((ne01 + 63) / 64) * 64), static_cast<size_t>(ne00 / 32), static_cast<size_t>(ne02)};
+            size_t local_work_size[3] = {64, 2, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clReleaseMemObject(data_device));
+
+            // Create image for Q
+            cl_image_format img_format_q = {CL_R, CL_UNSIGNED_INT32};
+            cl_image_desc img_desc_q = {
+                CL_MEM_OBJECT_IMAGE1D_BUFFER,
+                static_cast<size_t>(ggml_nelements(tensor) / 8),
+                0, 0, 0, 0, 0, 0, 0,
+                { extra->q }
+            };
+            extra->q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_format_q, &img_desc_q, NULL, &err);
+            tensor->extra = extra;
+
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
+        // normal q4_1 repack
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
         cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1;
 
         if (use_adreno_kernels(backend_ctx, tensor)) {
             kernel = backend_ctx->kernel_convert_block_q4_1_noshuffle;
         }
-    #else
+#else
         cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1;
-    #endif // GGML_OPENCL_USE_ADRENO_KERNELS
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
         CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
         CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
@@ -4954,17 +5536,18 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
         return;
     }
-    if (tensor->type == GGML_TYPE_MXFP4) {
+    if (tensor->type == GGML_TYPE_Q5_0) {
         ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
         GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
 
         // Allocate the new extra and create aliases from the original.
         ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
-        ggml_tensor_extra_cl_mxfp4 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_mxfp4();
+        ggml_tensor_extra_cl_q5_0 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q5_0();
 
-        size_t size_e = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(char);
-        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
-        GGML_ASSERT(size_e + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+        size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_qs = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+        size_t size_qh = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(int32_t);
+        GGML_ASSERT(size_d + size_qs + size_qh == ggml_nbytes(tensor) && "Incorrect tensor size");
 
         cl_int err;
         cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
@@ -4974,30 +5557,219 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             queue, data_device, CL_TRUE, 0,
             ggml_nbytes(tensor), data, 0, NULL, NULL));
 
-        // The original tensor memory is divided into scales and quants, i.e.,
-        // we first store scales, then quants.
         cl_buffer_region region;
 
         // Create subbuffer for scales.
         region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
-        region.size = size_e;
-        extra->e = clCreateSubBuffer(
+        region.size = size_d;
+        extra->d = clCreateSubBuffer(
             extra_orig->data_device, CL_MEM_READ_WRITE,
             CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
         CL_CHECK(err);
         auto previous_origin = region.origin;
 
-        // Create subbuffer for quants.
-        region.origin = align_to(previous_origin + size_e, backend_ctx->alignment);
-        region.size = size_q;
-        extra->q = clCreateSubBuffer(
+        // Create subbuffer for qh.
+        region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
+        region.size = size_qh;
+        extra->qh = clCreateSubBuffer(
             extra_orig->data_device, CL_MEM_READ_WRITE,
             CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
         CL_CHECK(err);
+        previous_origin = region.origin;
 
-#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        // Create subbuffer for qs.
+        region.origin = align_to(previous_origin + size_qh, backend_ctx->alignment);
+        region.size = size_qs;
+        extra->qs = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        // Adreno moe q5_0 kernel needs special transpose and unshuffling
+        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_kernel kernel = backend_ctx->kernel_convert_block_q5_0_trans4_ns;
+
+            int ne00 = tensor->ne[0];
+            int ne01 = tensor->ne[1];
+            int ne02 = tensor->ne[2];
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qs));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->qh));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne01));
+
+            size_t global_work_size[3] = {static_cast<size_t>(((ne01 + 63) / 64) * 64), static_cast<size_t>(ne00 / 32), static_cast<size_t>(ne02)};
+            size_t local_work_size[3] = {64, 2, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clReleaseMemObject(data_device));
+
+            // Create image for Q
+            cl_image_format img_format_qs = {CL_R, CL_UNSIGNED_INT32};
+            cl_image_desc img_desc_qs = {
+                CL_MEM_OBJECT_IMAGE1D_BUFFER,
+                static_cast<size_t>(ggml_nelements(tensor) / 8),
+                0, 0, 0, 0, 0, 0, 0,
+                { extra->qs }
+            };
+            extra->qs_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_format_qs, &img_desc_qs, NULL, &err);
+            tensor->extra = extra;
+
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+        return;
+    }
+    if (tensor->type == GGML_TYPE_Q5_1) {
+        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
+        GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
+
+        // Allocate the new extra and create aliases from the original.
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        ggml_tensor_extra_cl_q5_1 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q5_1();
+
+        size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_m = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_qs = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+        size_t size_qh = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(int32_t);
+        GGML_ASSERT(size_d + size_m + size_qs + size_qh == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+        CL_CHECK(clEnqueueWriteBuffer(
+            queue, data_device, CL_TRUE, 0,
+            ggml_nbytes(tensor), data, 0, NULL, NULL));
+
+        cl_buffer_region region;
+
+        // The original tensor memory is divided into scales and quants, i.e.,
+        // we first store scales, mins, then quants.
+        // Create subbuffer for scales.
+        region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
+        region.size = size_d;
+        extra->d = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        auto previous_origin = region.origin;
+
+        // Create subbuffer for mins.
+        region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
+        region.size = size_m;
+        extra->m = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        previous_origin = region.origin;
+
+        // Create subbuffer for qh.
+        region.origin = align_to(previous_origin + size_m, backend_ctx->alignment);
+        region.size = size_qh;
+        extra->qh = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        previous_origin = region.origin;
+
+        // Create subbuffer for qs.
+        region.origin = align_to(previous_origin + size_qh, backend_ctx->alignment);
+        region.size = size_qs;
+        extra->qs = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        // Adreno moe q5_1 kernel needs special transpose and unshuffling
+        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_kernel kernel = backend_ctx->kernel_convert_block_q5_1_trans4_ns;
+
+            int ne00 = tensor->ne[0];
+            int ne01 = tensor->ne[1];
+            int ne02 = tensor->ne[2];
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qs));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->qh));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra->m));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne00));
+            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne01));
+
+            size_t global_work_size[3] = {static_cast<size_t>(((ne01 + 63) / 64) * 64), static_cast<size_t>(ne00 / 32), static_cast<size_t>(ne02)};
+            size_t local_work_size[3] = {64, 2, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clReleaseMemObject(data_device));
+
+            // Create image for Q
+            cl_image_format img_format_qs = {CL_R, CL_UNSIGNED_INT32};
+            cl_image_desc img_desc_qs = {
+                CL_MEM_OBJECT_IMAGE1D_BUFFER,
+                static_cast<size_t>(ggml_nelements(tensor) / 8),
+                0, 0, 0, 0, 0, 0, 0,
+                { extra->qs }
+            };
+            extra->qs_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_format_qs, &img_desc_qs, NULL, &err);
+            tensor->extra = extra;
+
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+        return;
+    }
+    if (tensor->type == GGML_TYPE_MXFP4) {
+        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
+        GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
+
+        // Allocate the new extra and create aliases from the original.
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        ggml_tensor_extra_cl_mxfp4 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_mxfp4();
+
+        size_t size_e = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(char);
+        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+        GGML_ASSERT(size_e + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+        CL_CHECK(clEnqueueWriteBuffer(
+            queue, data_device, CL_TRUE, 0,
+            ggml_nbytes(tensor), data, 0, NULL, NULL));
+
+        // The original tensor memory is divided into scales and quants, i.e.,
+        // we first store scales, then quants.
+        cl_buffer_region region;
+
+        // Create subbuffer for scales.
+        region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
+        region.size = size_e;
+        extra->e = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        auto previous_origin = region.origin;
+
+        // Create subbuffer for quants.
+        region.origin = align_to(previous_origin + size_e, backend_ctx->alignment);
+        region.size = size_q;
+        extra->q = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        // Adreno moe mxfp4 kernel needs special transpose and unshuffling
         if (use_adreno_moe_kernels(backend_ctx, tensor)) {
-            cl_kernel kernel = backend_ctx->kernel_convert_block_mxfp4_trans;
+            cl_kernel kernel = backend_ctx->kernel_convert_block_mxfp4_trans4_ns;
 
             int ne00 = tensor->ne[0];
             int ne01 = tensor->ne[1];
@@ -5017,9 +5789,21 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             CL_CHECK(clReleaseMemObject(data_device));
             tensor->extra = extra;
 
+            // Create image for Q
+            cl_image_format img_format_q = {CL_R, CL_UNSIGNED_INT32};
+            cl_image_desc img_desc_q = {
+                CL_MEM_OBJECT_IMAGE1D_BUFFER,
+                static_cast<size_t>(ggml_nelements(tensor) / 8),
+                0, 0, 0, 0, 0, 0, 0,
+                { extra->q }
+            };
+            extra->q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_format_q, &img_desc_q, NULL, &err);
+            tensor->extra = extra;
+
             return;
         }
-#endif
+
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
         cl_kernel kernel = backend_ctx->kernel_convert_block_mxfp4;
 
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
@@ -5123,6 +5907,87 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
 
         return;
     }
+    if (tensor->type == GGML_TYPE_IQ4_NL) {
+        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
+        GGML_ASSERT(extra_orig && "Tensors in OpenCL backend should have been allocated and initialized");
+
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        ggml_tensor_extra_cl_iq4_nl * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_iq4_nl();
+
+        size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*(ggml_blck_size(tensor->type)/2);
+        GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+        CL_CHECK(clEnqueueWriteBuffer(
+            queue, data_device, CL_TRUE, 0,
+            ggml_nbytes(tensor), data, 0, NULL, NULL));
+
+        cl_buffer_region region;
+
+        // Create subbuffer for scales.
+        region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
+        region.size = size_d;
+        extra->d = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        auto previous_origin = region.origin;
+
+        // Create subbuffer for quants.
+        region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
+        region.size = size_q;
+        extra->q = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+    #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        cl_kernel kernel = backend_ctx->kernel_convert_block_iq4_nl;
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            kernel = backend_ctx->kernel_convert_block_iq4_nl_noshuffle;
+        }
+    #else
+        cl_kernel kernel = backend_ctx->kernel_convert_block_iq4_nl;
+    #endif
+        cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
+        cl_uchar mask_0F = 0x0F;
+        cl_uchar mask_F0 = 0xF0;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_uchar), &mask_0F));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_F0));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &n_blk));
+
+        size_t global_work_size[] = {(size_t)CEIL_DIV(n_blk, 64)*64, 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clReleaseMemObject(data_device));
+
+        tensor->extra = extra;
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            int M = tensor->ne[1];
+            int K = tensor->ne[0];
+            GGML_ASSERT(K % 32 == 0);
+
+            // Transpose q as ushort
+            transpose_2d_as_16b(backend_ctx, extra->q, extra->q, size_q, K/4, M);
+            // Transpose d as ushort
+            transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/32, M);
+        }
+#endif
+        return;
+    }
     if (tensor->type == GGML_TYPE_Q4_K) {
         ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
         GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
@@ -5485,9 +6350,40 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
         ggml_tensor_extra_cl_q4_0 * extra = (ggml_tensor_extra_cl_q4_0 *)tensor->extra;
 
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
-        if (use_adreno_kernels(backend_ctx, tensor)) {
+        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
             cl_int err;
-            cl_kernel kernel;
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q4_0_trans4_ns;
+
+            cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+                ggml_nbytes(tensor), NULL, &err);
+            CL_CHECK(err);
+
+            int ne00 = tensor->ne[0];
+            int ne01 = tensor->ne[1];
+            int ne02 = tensor->ne[2];
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &ne00));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &ne01));
+
+            size_t global_work_size[3] = {static_cast<size_t>(((ne01 + 63) / 64) * 64), static_cast<size_t>(ne00 / 32), static_cast<size_t>(ne02)};
+            size_t local_work_size[3] = {64, 2, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+                global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clEnqueueReadBuffer(
+                queue, data_device, CL_TRUE, offset,
+                size, data, 0, NULL, NULL));
+            CL_CHECK(clReleaseMemObject(data_device));
+            return;
+        }
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            ggml_cl_buffer buf_trans_q;
+            ggml_cl_buffer buf_trans_d;
+            ggml_cl_buffer buf_unpacked;
 
             cl_int M = tensor->ne[1];   // ne01
             cl_int K = tensor->ne[0];   // ne00
@@ -5499,46 +6395,12 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
             size_t size_d = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t);
             GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
 
-            cl_mem buf_trans_q;
-            cl_mem buf_trans_d;
-
-            CL_CHECK((buf_trans_q = clCreateBuffer(context, CL_MEM_READ_WRITE,
-                size_q, NULL, &err), err));
-            CL_CHECK((buf_trans_d = clCreateBuffer(context, CL_MEM_READ_WRITE,
-                size_d, NULL, &err), err));
-
-            kernel = backend_ctx->kernel_transpose_16_buf;
-
-            // transpose q back
-            cl_int stride_k_q = K/4;
-            size_t local_size_q[3] = {64, 1, 1};
-            size_t global_size_q[3] = {(size_t)M, (size_t)stride_k_q, 1};
-
-            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
-            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_q));
-            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_int), &M));
-            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &stride_k_q));
-
-            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
-                global_size_q, local_size_q, 0, NULL, NULL));
-
-            // transpose scales back
-            cl_int stride_k_d = K/32;
-            size_t local_size_d[3] = {64, 1, 1};
-            size_t global_size_d[3] = {(size_t)M, (size_t)stride_k_d, 1};
-
-            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->d));
-            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_d));
-            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_int), &M));
-            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &stride_k_d));
-
-            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
-                global_size_d, local_size_d, 0, NULL, NULL));
+            buf_trans_q.allocate(backend_ctx->context, size_q);
+            buf_trans_d.allocate(backend_ctx->context, size_d);
+            buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));
 
-            // unpack
-            cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
-                ggml_nbytes(tensor), NULL, &err);
-            CL_CHECK(err);
+            transpose_2d_as_16b(backend_ctx, extra->q, buf_trans_q.buffer, size_q, M, K/4);
+            transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/32);
 
             cl_uchar mask_0F = 0x0F;
             cl_uchar mask_F0 = 0xF0;
@@ -5546,25 +6408,15 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
             size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
             size_t local_work_size[] = {1, 1, 1};
 
-            kernel = backend_ctx->kernel_restore_block_q4_0_noshuffle;
-            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &buf_trans_q));
-            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &buf_trans_d));
-            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &data_device));
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q4_0_noshuffle;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &buf_trans_q.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &buf_trans_d.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &buf_unpacked.buffer));
             CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_uchar), &mask_0F));
             CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_F0));
 
-            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
-                global_work_size, local_work_size, 0, NULL, NULL));
-
-            // read back to host
-            CL_CHECK(clEnqueueReadBuffer(
-                queue, data_device, CL_TRUE, offset,
-                size, data, 0, NULL, NULL));
-
-            CL_CHECK(clReleaseMemObject(data_device));
-            CL_CHECK(clReleaseMemObject(buf_trans_q));
-            CL_CHECK(clReleaseMemObject(buf_trans_d));
-
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+            CL_CHECK(clEnqueueReadBuffer(queue, buf_unpacked.buffer, CL_TRUE, offset, size, data, 0, NULL, NULL));
             return;
         }
 #endif
@@ -5596,6 +6448,36 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
         ggml_tensor_extra_cl_q4_1 * extra = (ggml_tensor_extra_cl_q4_1 *)tensor->extra;
 
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_int err;
+            cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+                ggml_nbytes(tensor), NULL, &err);
+            CL_CHECK(err);
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q4_1_trans4_ns;
+
+            int ne00 = tensor->ne[0];
+            int ne01 = tensor->ne[1];
+            int ne02 = tensor->ne[2];
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->m));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &ne00));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int), &ne01));
+
+            size_t global_work_size[3] = {static_cast<size_t>(((ne01 + 63) / 64) * 64), static_cast<size_t>(ne00 / 32), static_cast<size_t>(ne02)};
+            size_t local_work_size[3] = {64, 2, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+                global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clEnqueueReadBuffer(
+                queue, data_device, CL_TRUE, offset,
+                size, data, 0, NULL, NULL));
+            CL_CHECK(clReleaseMemObject(data_device));
+            return;
+        }
         if (use_adreno_kernels(backend_ctx, tensor)) {
             static ggml_cl_buffer buf_trans_q;
             static ggml_cl_buffer buf_trans_m;
@@ -5666,6 +6548,89 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
         CL_CHECK(clReleaseMemObject(data_device));
         return;
     }
+    if (tensor->type == GGML_TYPE_Q5_0) {
+        ggml_tensor_extra_cl_q5_0 * extra = (ggml_tensor_extra_cl_q5_0 *)tensor->extra;
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_int err;
+            // TODO: use ggml_cl_buffer to manage this temporary buffer
+            cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+                ggml_nbytes(tensor), NULL, &err);
+            CL_CHECK(err);
+
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q5_0_trans4_ns;
+
+            int ne00 = tensor->ne[0];
+            int ne01 = tensor->ne[1];
+            int ne02 = tensor->ne[2];
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->qs));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qh));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &ne00));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int), &ne01));
+
+            size_t global_work_size[3] = {static_cast<size_t>(((ne01 + 63) / 64) * 64), static_cast<size_t>(ne00 / 32), static_cast<size_t>(ne02)};
+            size_t local_work_size[3] = {64, 2, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+                global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clEnqueueReadBuffer(
+                queue, data_device, CL_TRUE, offset,
+                size, data, 0, NULL, NULL));
+            CL_CHECK(clReleaseMemObject(data_device));
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+        // TODO: normal q5_0
+        (void) extra;
+        return;
+    }
+    if (tensor->type == GGML_TYPE_Q5_1) {
+        ggml_tensor_extra_cl_q5_1 * extra = (ggml_tensor_extra_cl_q5_1 *)tensor->extra;
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_int err;
+            // TODO: use ggml_cl_buffer to manage this temporary buffer
+            cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+                ggml_nbytes(tensor), NULL, &err);
+            CL_CHECK(err);
+
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q5_1_trans4_ns;
+
+            int ne00 = tensor->ne[0];
+            int ne01 = tensor->ne[1];
+            int ne02 = tensor->ne[2];
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->qs));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qh));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->m));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int), &ne00));
+            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int), &ne01));
+
+            size_t global_work_size[3] = {static_cast<size_t>(((ne01 + 63) / 64) * 64), static_cast<size_t>(ne00 / 32), static_cast<size_t>(ne02)};
+            size_t local_work_size[3] = {64, 2, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+                global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clEnqueueReadBuffer(
+                queue, data_device, CL_TRUE, offset,
+                size, data, 0, NULL, NULL));
+            CL_CHECK(clReleaseMemObject(data_device));
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+        // TODO: normal q5_1
+        (void) extra;
+        return;
+    }
     if (tensor->type == GGML_TYPE_MXFP4) {
         ggml_tensor_extra_cl_mxfp4 * extra = (ggml_tensor_extra_cl_mxfp4 *)tensor->extra;
 
@@ -5676,7 +6641,7 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
 
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
         if (use_adreno_moe_kernels(backend_ctx, tensor)) {
-            cl_kernel kernel = backend_ctx->kernel_restore_block_mxfp4_trans;
+            cl_kernel kernel = backend_ctx->kernel_restore_block_mxfp4_trans4_ns;
 
             int ne00 = tensor->ne[0];
             int ne01 = tensor->ne[1];
@@ -5700,7 +6665,8 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
             CL_CHECK(clReleaseMemObject(data_device));
             return;
         }
-#endif
+
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
         cl_kernel kernel = backend_ctx->kernel_restore_block_mxfp4;
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
         CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->e));
@@ -5775,6 +6741,78 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
         CL_CHECK(clReleaseMemObject(data_device));
         return;
     }
+    if (tensor->type == GGML_TYPE_IQ4_NL) {
+        ggml_tensor_extra_cl_iq4_nl * extra = (ggml_tensor_extra_cl_iq4_nl *)tensor->extra;
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            static ggml_cl_buffer buf_trans_q;
+            static ggml_cl_buffer buf_trans_d;
+            static ggml_cl_buffer buf_unpacked;
+
+            cl_int M = tensor->ne[1];
+            cl_int K = tensor->ne[0];
+            GGML_ASSERT(K % 32 == 0);
+
+            size_t size_q = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*(ggml_blck_size(tensor->type)/2);
+            size_t size_d = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t);
+            GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+            buf_trans_q.allocate(backend_ctx->context, size_q);
+            buf_trans_d.allocate(backend_ctx->context, size_d);
+            buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));
+
+            // transpose q, d back
+            transpose_2d_as_16b(backend_ctx, extra->q, buf_trans_q.buffer, size_q, M, K/4);
+            transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/32);
+
+            cl_uchar mask_0F = 0x0F;
+            cl_uchar mask_F0 = 0xF0;
+
+            cl_kernel kernel = backend_ctx->kernel_restore_block_iq4_nl_noshuffle;
+            cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
+
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &buf_trans_q.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &buf_trans_d.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &buf_unpacked.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_uchar), &mask_0F));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_F0));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &n_blk));
+
+            size_t global_work_size[] = {(size_t)n_blk, 1, 1};
+            size_t local_work_size[] = {1, 1, 1};
+
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+            CL_CHECK(clEnqueueReadBuffer(queue, buf_unpacked.buffer, CL_TRUE, offset, size, data, 0, NULL, NULL));
+            return;
+        }
+#endif
+        cl_kernel kernel = backend_ctx->kernel_restore_block_iq4_nl;
+        cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &n_blk));
+
+        size_t global_work_size[] = {(size_t)n_blk, 1, 1};
+        size_t local_work_size[] = {1, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+            global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clEnqueueReadBuffer(
+            queue, data_device, CL_TRUE, offset,
+            size, data, 0, NULL, NULL));
+        CL_CHECK(clReleaseMemObject(data_device));
+        return;
+    }
     if (tensor->type == GGML_TYPE_Q4_K) {
         ggml_tensor_extra_cl_q4_K * extra = (ggml_tensor_extra_cl_q4_K *)tensor->extra;
 
@@ -6140,11 +7178,16 @@ static const char * ggml_backend_opencl_device_get_description(ggml_backend_dev_
 }
 
 static void ggml_backend_opencl_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
-    // no memory to report
-    *free  = 0;
-    *total = 0;
+    ggml_backend_opencl_device_context * dev_ctx = (ggml_backend_opencl_device_context *) dev->context;
+    ggml_backend_opencl_context * backend_ctx = (ggml_backend_opencl_context *) dev_ctx->backend_ctx;
 
-    GGML_UNUSED(dev);
+    static const size_t opencl_extra_margin = 1024ull*1024ull*1024ull;
+
+    // OpenCL does not provide reliable currently-free device memory.
+    // Use total/global memory as a best-effort upper bound.
+    // Improved safety: Reduce by a 1GiB extra margin for common --fit
+    *total = backend_ctx->global_mem_size;
+    *free  = *total > opencl_extra_margin ? *total - opencl_extra_margin : 0;
 }
 
 static enum ggml_backend_dev_type ggml_backend_opencl_device_get_type(ggml_backend_dev_t dev) {
@@ -9450,6 +10493,169 @@ static void ggml_cl_mul_mat_f16_f32_tiled(ggml_backend_t backend, const ggml_ten
     backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
 }
 
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+static bool ggml_cl_can_use_adreno_xmem_gemm_f16_f32(
+        const ggml_backend_opencl_context * backend_ctx,
+        const ggml_tensor * src0,
+        const ggml_tensor * src1,
+        const ggml_tensor * dst) {
+    if (!backend_ctx->adreno_xmem_gemm_enabled) {
+        return false;
+    }
+    if (backend_ctx->gpu_family != GPU_FAMILY::ADRENO) {
+        return false;
+    }
+    if (src0->type != GGML_TYPE_F16 || src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
+        return false;
+    }
+    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) {
+        return false;
+    }
+    if (src0->ne[2] != 1 || src0->ne[3] != 1 ||
+        src1->ne[2] != 1 || src1->ne[3] != 1 ||
+        dst->ne[2]  != 1 || dst->ne[3]  != 1) {
+        return false;
+    }
+    const int K = src0->ne[0];
+    const int M = src0->ne[1];
+    const int N = src1->ne[1];
+    if (src1->ne[0] != K || dst->ne[0] != M || dst->ne[1] != N) {
+        return false;
+    }
+    if (N <= 1 || M < 64 || N < 16 || K < 64) {
+        return false;
+    }
+    if ((K % 8) != 0) {
+        return false;
+    }
+    const int kpack = K / 4;
+    const int npack = CEIL_DIV(M, 4);
+    if (static_cast<size_t>(N) > backend_ctx->image2d_max_width ||
+        static_cast<size_t>(kpack) > backend_ctx->image2d_max_height) {
+        return false;
+    }
+    if (static_cast<size_t>(N) > backend_ctx->image2d_max_width ||
+        static_cast<size_t>(npack) > backend_ctx->image2d_max_height) {
+        return false;
+    }
+    return true;
+}
+
+static void ggml_cl_mul_mat_f16_f32_adreno_xmem(
+        ggml_backend_t backend,
+        const ggml_tensor * src0,
+        const ggml_tensor * src1,
+        ggml_tensor * dst) {
+    ggml_backend_opencl_context * backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    const cl_ulong offset0 = extra0->offset + src0->view_offs;
+    const cl_ulong offset1 = extra1->offset + src1->view_offs;
+    const cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const int K = src0->ne[0];
+    const int M = src0->ne[1];
+    const int N = src1->ne[1];
+    const int kpack = K / 4;
+    const int npack = CEIL_DIV(M, 4);
+    const int os = 8;
+
+    const size_t xmem_bytes = 6144;
+    const size_t weight_bytes = static_cast<size_t>(kpack) * static_cast<size_t>(npack) * 4u * sizeof(cl_half4);
+
+    backend_ctx->prealloc_adreno_xmem_const.allocate(backend_ctx->context, xmem_bytes);
+
+    cl_int err = CL_SUCCESS;
+    cl_image_format fmt = {};
+    fmt.image_channel_order = CL_RGBA;
+    fmt.image_channel_data_type = CL_HALF_FLOAT;
+
+    cl_image_desc desc_src = {};
+    desc_src.image_type = CL_MEM_OBJECT_IMAGE2D;
+    desc_src.image_width = static_cast<size_t>(N);
+    desc_src.image_height = static_cast<size_t>(kpack);
+    cl_mem src_img = clCreateImage(backend_ctx->context, CL_MEM_READ_WRITE, &fmt, &desc_src, nullptr, &err);
+    CL_CHECK(err);
+
+    cl_image_desc desc_dst = {};
+    desc_dst.image_type = CL_MEM_OBJECT_IMAGE2D;
+    desc_dst.image_width = static_cast<size_t>(N);
+    desc_dst.image_height = static_cast<size_t>(npack);
+    cl_mem dst_img = clCreateImage(backend_ctx->context, CL_MEM_READ_WRITE, &fmt, &desc_dst, nullptr, &err);
+    CL_CHECK(err);
+
+    cl_mem weights = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, weight_bytes, nullptr, &err);
+    CL_CHECK(err);
+
+    cl_kernel prepack = backend_ctx->kernel_adreno_xmem_prepack_weight_f16;
+    CL_CHECK(clSetKernelArg(prepack, 0, sizeof(cl_mem),   &weights));
+    CL_CHECK(clSetKernelArg(prepack, 1, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(prepack, 2, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(prepack, 3, sizeof(int),      &K));
+    CL_CHECK(clSetKernelArg(prepack, 4, sizeof(int),      &M));
+    CL_CHECK(clSetKernelArg(prepack, 5, sizeof(int),      &kpack));
+    CL_CHECK(clSetKernelArg(prepack, 6, sizeof(int),      &npack));
+    CL_CHECK(clSetKernelArg(prepack, 7, sizeof(int),      &os));
+    size_t lws = 256;
+    size_t max_wg = backend_ctx->get_kernel_workgroup_size(prepack);
+    if (lws > max_wg) {
+        lws = max_wg;
+    }
+    size_t gws = CEIL_DIV(static_cast<size_t>(kpack) * static_cast<size_t>(npack), lws) * lws;
+    backend_ctx->enqueue_ndrange_kernel(prepack, 1, &gws, &lws, dst);
+
+    cl_kernel pack_src = backend_ctx->kernel_adreno_xmem_pack_src_f32;
+    CL_CHECK(clSetKernelArg(pack_src, 0, sizeof(cl_mem),   &extra1->data_device));
+    CL_CHECK(clSetKernelArg(pack_src, 1, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(pack_src, 2, sizeof(cl_mem),   &src_img));
+    CL_CHECK(clSetKernelArg(pack_src, 3, sizeof(int),      &K));
+    CL_CHECK(clSetKernelArg(pack_src, 4, sizeof(int),      &N));
+    size_t pack_src_lws[2] = { 16, 16 };
+    size_t pack_src_gws[2] = {
+        CEIL_DIV(static_cast<size_t>(N), pack_src_lws[0])*pack_src_lws[0],
+        CEIL_DIV(static_cast<size_t>(kpack), pack_src_lws[1])*pack_src_lws[1]
+    };
+    backend_ctx->enqueue_ndrange_kernel(pack_src, 2, pack_src_gws, pack_src_lws, dst);
+
+    cl_kernel gemm = backend_ctx->kernel_gemm_xmem_f16_f32_os8;
+    CL_CHECK(clSetKernelArg(gemm, 0, sizeof(cl_mem), &weights));
+    CL_CHECK(clSetKernelArg(gemm, 1, sizeof(cl_mem), &backend_ctx->prealloc_adreno_xmem_const.buffer));
+    CL_CHECK(clSetKernelArg(gemm, 2, sizeof(cl_mem), &src_img));
+    CL_CHECK(clSetKernelArg(gemm, 3, sizeof(cl_mem), &dst_img));
+    CL_CHECK(clSetKernelArg(gemm, 4, sizeof(int),    &N));
+    CL_CHECK(clSetKernelArg(gemm, 5, sizeof(int),    &npack));
+    CL_CHECK(clSetKernelArg(gemm, 6, sizeof(int),    &kpack));
+    const size_t z_values = CEIL_DIV(static_cast<size_t>(npack), static_cast<size_t>(os));
+    size_t gemm_lws[3] = { 64, 1, 1 };
+    size_t gemm_gws[3] = {
+        z_values*gemm_lws[0],
+        CEIL_DIV(static_cast<size_t>(N), gemm_lws[0]),
+        1
+    };
+    backend_ctx->enqueue_ndrange_kernel(gemm, 3, gemm_gws, gemm_lws, dst);
+
+    cl_kernel store_dst = backend_ctx->kernel_adreno_xmem_store_dst_f32;
+    CL_CHECK(clSetKernelArg(store_dst, 0, sizeof(cl_mem),   &dst_img));
+    CL_CHECK(clSetKernelArg(store_dst, 1, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(store_dst, 2, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(store_dst, 3, sizeof(int),      &M));
+    CL_CHECK(clSetKernelArg(store_dst, 4, sizeof(int),      &N));
+    size_t store_lws[2] = { 16, 16 };
+    size_t store_gws[2] = {
+        CEIL_DIV(static_cast<size_t>(N), store_lws[0])*store_lws[0],
+        CEIL_DIV(static_cast<size_t>(npack), store_lws[1])*store_lws[1]
+    };
+    backend_ctx->enqueue_ndrange_kernel(store_dst, 2, store_gws, store_lws, dst);
+
+    CL_CHECK(clReleaseMemObject(weights));
+    CL_CHECK(clReleaseMemObject(dst_img));
+    CL_CHECK(clReleaseMemObject(src_img));
+}
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
 static void ggml_cl_conv_2d(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_TENSOR_BINARY_OP_LOCALS;
     ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
@@ -9657,16 +10863,419 @@ static void ggml_cl_mul_mat_kq_kqv_adreno(ggml_backend_t backend, const ggml_ten
 
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
 
-    // deallocate sub buffers and images
-    // <--------------------------------------------> //
-    CL_CHECK(clReleaseMemObject(A_image1d));
-    CL_CHECK(clReleaseMemObject(D_image1d));
-    CL_CHECK(clReleaseMemObject(A_sub_buffer));
-    CL_CHECK(clReleaseMemObject(B_sub_buffer));
-    CL_CHECK(clReleaseMemObject(D_sub_buffer));
+    // deallocate sub buffers and images
+    // <--------------------------------------------> //
+    CL_CHECK(clReleaseMemObject(A_image1d));
+    CL_CHECK(clReleaseMemObject(D_image1d));
+    CL_CHECK(clReleaseMemObject(A_sub_buffer));
+    CL_CHECK(clReleaseMemObject(B_sub_buffer));
+    CL_CHECK(clReleaseMemObject(D_sub_buffer));
+}
+
+static void ggml_cl_mul_mat_q4_0_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+    ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
+
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+
+    const int ne10 = src1->ne[0];
+    const int ne12 = src1->ne[2];
+
+    const int ne0 = dst->ne[0];
+    const int ne1 = dst->ne[1];
+
+    GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
+
+    cl_context context = backend_ctx->context;
+    cl_kernel kernel;
+
+    cl_int              err;
+    cl_image_format     img_fmt;
+    cl_image_desc       img_desc;
+    cl_buffer_region    region;
+
+    int M = ne01;
+    int N = ne1;
+    int K = ne00;
+
+    if (ne1 == 1) {
+        cl_mem q_img = nullptr;
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_img = nullptr;
+
+        // image for q
+        img_fmt = { CL_R, CL_UNSIGNED_INT32};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = M * K / 2 / 4;
+        img_desc.buffer = extra0_q4_0->q;
+        CL_CHECK((q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        kernel = backend_ctx->kernel_gemv_noshuffle_q4_0_f32;
+        if (M == 4096 && K == 4096) {
+            kernel = backend_ctx->kernel_gemv_noshuffle_q4_0_f32_4096_1_4096;
+        } else if (M == 4096 && K == 11008) {
+            kernel = backend_ctx->kernel_gemv_noshuffle_q4_0_f32_4096_1_11008;
+        } else if (M == 11008 && K == 4096) {
+            kernel = backend_ctx->kernel_gemv_noshuffle_q4_0_f32_11008_1_4096;
+        } else if (M == 32000 && K == 4096) {
+            kernel = backend_ctx->kernel_gemv_noshuffle_q4_0_f32_32000_1_4096;
+        }
+
+        int r2 = 1;
+        int r3 = 1;
+
+        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &q_img));
+        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_0->d));
+        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &b_img));
+        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+
+        size_t local_work_size[3] = {64, 4, 1};
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(q_img));
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_img));
+    } else {
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_sub_buf_trans = nullptr;
+        cl_mem b_img = nullptr;
+        cl_mem b_img_trans = nullptr;
+        cl_mem d_sub_buf = nullptr;
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // pad N to multiple of 8
+        int extra_elements = N % 8;
+        int padding = 0;
+        if (extra_elements > 0){
+            padding = 8 - extra_elements;
+        }
+
+        // subbuffer for transposed activations
+        region.origin = 0;
+        region.size = K * (N + padding) * sizeof(float)/2;
+        backend_ctx->prealloc_act_trans.allocate(context, region.size);
+        CL_CHECK((b_sub_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for transposed activations
+        img_fmt = {CL_RGBA, CL_HALF_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * (N + padding) / 4;
+        img_desc.buffer = b_sub_buf_trans;
+        CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err));
+
+        // subbuffer for output
+        region.origin = extrad->offset; // Specify the starting offset (in bytes)
+        region.size = M * N * sizeof(float); // Specify the size of the sub-buffer
+        CL_CHECK((d_sub_buf = clCreateSubBuffer(extrad->data_device, CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // transpose activations
+        int height_B = N/4;
+        if (height_B == 0) {
+            height_B = 1;
+        }
+        int width_B = K/4;
+        int padded_height_B = (N + padding)/4;
+
+        kernel = backend_ctx->kernel_transpose_32_16;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));
+
+        size_t local_work_size_t[2] = { 1, 16 };
+        size_t global_work_size_t[2] = { (size_t)width_B, (size_t)padded_height_B };
+        if (ne0 == 4096 && ne1 == 128 && ne10 == 4096) {
+            local_work_size_t[0]=4;
+            local_work_size_t[1]=8;
+        } else if (ne0 == 11008 && ne1 == 128 && ne10 == 4096) {
+            local_work_size_t[0]=2;
+            local_work_size_t[1]=8;
+        } else if(ne0 == 4096 && ne1 == 128 && ne10 == 11008) {
+            local_work_size_t[0]=1;
+            local_work_size_t[1]=8;
+        } else if(ne0 == 32000 && ne1 == 128 && ne10 == 4096) {
+            local_work_size_t[0]=2;
+            local_work_size_t[1]=8;
+        }
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
+
+        // gemm
+        kernel = backend_ctx->kernel_gemm_noshuffle_q4_0_f32;
+        int padded_N = N + padding;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0_q4_0->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q4_0->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &d_sub_buf));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int),   &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int),   &padded_N));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne1));
+
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1};
+        size_t local_work_size[3] = {1, 128, 1};
+        if (ne0 == 4096 && ne1 == 128 && ne10 == 4096) {
+            local_work_size[0] = 1;
+            local_work_size[1] = 128;
+        } else if (ne0 == 11008 && ne1 == 128 && ne10 == 4096) {
+            local_work_size[0] = 2;
+            local_work_size[1] = 64;
+        } else if (ne0 == 4096 && ne1 == 128 && ne10 == 11008) {
+            local_work_size[0] = 2;
+            local_work_size[1] = 64;
+        } else if (ne0 == 32000 && ne1 == 128 && ne10 == 4096) {
+            local_work_size[0] = 2;
+            local_work_size[1] = 64;
+        }
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_sub_buf_trans));
+        CL_CHECK(clReleaseMemObject(b_img));
+        CL_CHECK(clReleaseMemObject(b_img_trans));
+        CL_CHECK(clReleaseMemObject(d_sub_buf));
+    }
+#else
+    GGML_UNUSED(backend);
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+#endif
+}
+
+static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
+
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const int  ne00 = src0->ne[0];
+    const int  ne01 = src0->ne[1];
+
+    const int  ne1 = dst->ne[1];
+
+    GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
+
+    cl_context context = backend_ctx->context;
+    cl_kernel kernel;
+
+    cl_int              err;
+    cl_image_format     img_fmt;
+    cl_image_desc       img_desc;
+    cl_buffer_region    region;
+
+    int M = ne01;
+    int N = ne1;
+    int K = ne00;
+
+    if (ne1 == 1) {
+        cl_mem q_img = nullptr;
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_img = nullptr;
+
+        // image for q
+        img_fmt = { CL_R, CL_UNSIGNED_INT32};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = M * K / 2 / 4;
+        img_desc.buffer = extra0_q4_1->q;
+        CL_CHECK((q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        kernel = backend_ctx->kernel_gemv_noshuffle_q4_1_f32;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &q_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q4_1->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q4_1->m));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne01));
+
+        size_t local_work_size[3] = {64, 4, 1};
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(q_img));
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_img));
+    } else {
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_sub_buf_trans = nullptr;
+        cl_mem b_img = nullptr;
+        cl_mem b_img_trans = nullptr;
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // pad N to multiple of 8
+        int extra_elements = N % 8;
+        int padding = 0;
+        if (extra_elements > 0){
+            padding = 8 - extra_elements;
+        }
+
+        // subbuffer for transposed activations
+        region.origin = 0;
+        region.size = K * (N + padding) * sizeof(float)/2;
+        backend_ctx->prealloc_act_trans.allocate(context, region.size);
+        CL_CHECK((b_sub_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for transposed activations
+        img_fmt = {CL_RGBA, CL_HALF_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * (N + padding) / 4;
+        img_desc.buffer = b_sub_buf_trans;
+        CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err));
+
+        // transpose activations
+        int height_B = N/4;
+        if (height_B == 0) {
+            height_B = 1;
+        }
+        int width_B = K/4;
+        int padded_height_B = (N + padding)/4;
+
+        kernel = backend_ctx->kernel_transpose_32_16;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));
+
+        size_t local_work_size_t[2] = { 1, 16 };
+        size_t global_work_size_t[2] = { (size_t)width_B, (size_t)padded_height_B };
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
+
+        // gemm
+        kernel = backend_ctx->kernel_gemm_noshuffle_q4_1_f32;
+        int padded_N = N + padding;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0_q4_1->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q4_1->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q4_1->m));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &padded_N));
+        CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_int),   &ne1));
+
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1};
+        size_t local_work_size[3] = {1, 128, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_sub_buf_trans));
+        CL_CHECK(clReleaseMemObject(b_img));
+        CL_CHECK(clReleaseMemObject(b_img_trans));
+    }
+#else
+    GGML_UNUSED(backend);
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+#endif
 }
 
-static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_cl_mul_mat_iq4_nl_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -9679,7 +11288,7 @@ static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_t
 
     ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
     ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
-    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
+    ggml_tensor_extra_cl_iq4_nl * extra0_iq4_nl = (ggml_tensor_extra_cl_iq4_nl *)src0->extra;
 
     cl_ulong offset1 = extra1->offset + src1->view_offs;
     cl_ulong offsetd = extrad->offset + dst->view_offs;
@@ -9689,7 +11298,7 @@ static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_t
 
     const int  ne1 = dst->ne[1];
 
-    GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
+    GGML_ASSERT(ne00 % 32 == 0);
 
     cl_context context = backend_ctx->context;
     cl_kernel kernel;
@@ -9713,7 +11322,7 @@ static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_t
         memset(&img_desc, 0, sizeof(img_desc));
         img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
         img_desc.image_width = M * K / 2 / 4;
-        img_desc.buffer = extra0_q4_1->q;
+        img_desc.buffer = extra0_iq4_nl->q;
         CL_CHECK((q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
 
         // subbuffer for activations
@@ -9729,16 +11338,15 @@ static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_t
         img_desc.buffer = b_sub_buf;
         CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
 
-        kernel = backend_ctx->kernel_gemv_noshuffle_q4_1_f32;
+        kernel = backend_ctx->kernel_gemv_noshuffle_iq4_nl_f32;
 
         CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &q_img));
-        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q4_1->d));
-        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q4_1->m));
-        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &b_img));
-        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
-        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
-        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne00));
-        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_iq4_nl->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne01));
 
         size_t local_work_size[3] = {64, 4, 1};
         size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1};
@@ -9808,19 +11416,18 @@ static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_t
         backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
 
         // gemm
-        kernel = backend_ctx->kernel_gemm_noshuffle_q4_1_f32;
+        kernel = backend_ctx->kernel_gemm_noshuffle_iq4_nl_f32;
         int padded_N = N + padding;
 
-        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0_q4_1->q));
-        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_q4_1->d));
-        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q4_1->m));
-        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &b_img_trans));
-        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
-        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
-        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne01));
-        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &padded_N));
-        CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &ne00));
-        CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_int),   &ne1));
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0_iq4_nl->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra0_iq4_nl->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int),   &ne01));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &padded_N));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &ne1));
 
         size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1};
         size_t local_work_size[3] = {1, 128, 1};
@@ -9916,7 +11523,7 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t
         img_desc.buffer = b_sub_buf;
         CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
 
-        kernel = backend_ctx->CL_mul_mat_vec_q8_0_f32;
+        kernel = backend_ctx->kernel_gemv_noshuffle_q8_0_f32;
 
         int r2 = 1;
         int r3 = 1;
@@ -10006,7 +11613,7 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t
         backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
 
         // gemm
-        kernel = backend_ctx->kernel_mul_mm_q8_0_f32_8x4;
+        kernel = backend_ctx->kernel_gemm_noshuffle_q8_0_f32;
         int padded_N = N + padding;
 
         CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q8_0->q));
@@ -10616,8 +12223,8 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     GGML_ASSERT(dst);
     GGML_ASSERT(dst->extra);
 
-    const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
-    const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
+    const enum ggml_type src0t = src0->type;
+    const enum ggml_type src1t = src1->type;
 
     ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
 
@@ -10634,33 +12241,18 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
     ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
     ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
+    ggml_tensor_extra_cl_iq4_nl * extra0_iq4_nl = (ggml_tensor_extra_cl_iq4_nl *)src0->extra;
     ggml_tensor_extra_cl_q4_K * extra0_q4_K = (ggml_tensor_extra_cl_q4_K *)src0->extra;
     ggml_tensor_extra_cl_q5_K * extra0_q5_K = (ggml_tensor_extra_cl_q5_K *)src0->extra;
     ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
 #endif
 
-    const int  ne00 = src0 ? src0->ne[0] : 0;
-    const int  ne01 = src0 ? src0->ne[1] : 0;
-    const int  ne02 = src0 ? src0->ne[2] : 0;
-    const int  ne03 = src0 ? src0->ne[3] : 0;
-
-    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
-    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
-    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
-    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
-
-    const int  ne10 = src1 ? src1->ne[0] : 0;
-    const int  ne11 = src1 ? src1->ne[1] : 0;
-    const int  ne12 = src1 ? src1->ne[2] : 0;
-    const int  ne13 = src1 ? src1->ne[3] : 0;
-
-    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
-    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
-    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
-    const cl_ulong nb13 = src1 ? src1->nb[3] : 0;
-
-    const int  ne0 = dst ? dst->ne[0] : 0;
-    const int  ne1 = dst ? dst->ne[1] : 0;
+    GGML_TENSOR_LOCALS(int,      ne0, src0, ne);
+    GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb);
+    GGML_TENSOR_LOCALS(int,      ne1, src1, ne);
+    GGML_TENSOR_LOCALS(cl_ulong, nb1, src1, nb);
+    GGML_TENSOR_LOCALS(int,      ne,  dst,  ne);
+    GGML_TENSOR_LOCALS(cl_ulong, nb,  dst,  nb);
 
     int r2 = ne12/ne02;
     int r3 = ne13/ne03;
@@ -10676,8 +12268,6 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     cl_kernel kernel;
 
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
-    cl_context context = backend_ctx->context;
-
     if(src0t == GGML_TYPE_F16 && src1t == GGML_TYPE_F32){
         if (ne01 >= 64 && ne1 >= 32 && ne00 >= 16 && (ne12 % ne02) == 0  &&
             // dst is wrapped with image1d_buffer, the size limit applies, also src0
@@ -10704,334 +12294,52 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     }
 
     if (ne01 && ne1 && use_adreno_kernels(backend_ctx, src0)) {
+        // NOTE: Kernels using image1d_buffer_t (e.g., src0_q) would normally require
+        // a limit check, but q4_0 / q4_1 tensors are very unlikely to exceed that
+        // limit, so the check is omitted.
 
-    // init CL objects
-    // <--------------------------------------------> //
-    cl_int              status;
-    cl_image_format     img_fmt_1d;
-    cl_image_desc       img_desc_1d;
-    cl_buffer_region    region;
-    cl_mem              A_image1d = nullptr;
-    cl_mem              B_image1d = nullptr;
-    cl_mem              B_sub_buffer = nullptr;
-    cl_mem              C_d = nullptr;
-    // for B transpose
-    cl_mem B_d = nullptr;
-    cl_mem B_d_input_image = nullptr;
-    // <--------------------------------------------> //
-
-    // define matrix dimensions
-    // <--------------------------------------------> //
-    int M = ne01;
-    int N = ne1;
-    int K = ne00;
-    int padding;
-    // <--------------------------------------------> //
-
-    // NOTE: Kernels using image1d_buffer_t (e.g., src0_q) would normally require
-    // a limit check, but q4_0 / q4_1 tensors are very unlikely to exceed that
-    // limit, so the check is omitted.
+        // q4_0 x fp32
+        if(src0t == GGML_TYPE_Q4_0 && src1t == GGML_TYPE_F32) {
+            ggml_cl_mul_mat_q4_0_f32_adreno(backend, src0, src1, dst);
+            return;
+        }
 
-    // q4_1 x fp32
-    if (src0t == GGML_TYPE_Q4_1 && src1t == GGML_TYPE_F32) {
+        // q4_1 x fp32
+        if (src0t == GGML_TYPE_Q4_1 && src1t == GGML_TYPE_F32) {
             ggml_cl_mul_mat_q4_1_f32_adreno(backend, src0, src1, dst);
             return;
-    }
+        }
 
-    // q8_0 x fp32
-    if (src0t == GGML_TYPE_Q8_0 && src1t == GGML_TYPE_F32 &&
-        enable_adreno_trans_weight(backend_ctx, src0)) {
-            ggml_cl_mul_mat_q8_0_f32_adreno(backend, src0, src1, dst);
+        // iq4_nl x fp32
+        if (src0t == GGML_TYPE_IQ4_NL && src1t == GGML_TYPE_F32) {
+            ggml_cl_mul_mat_iq4_nl_f32_adreno(backend, src0, src1, dst);
             return;
-    }
+        }
+
+        // q8_0 x fp32
+        if (src0t == GGML_TYPE_Q8_0 && src1t == GGML_TYPE_F32 &&
+            enable_adreno_trans_weight(backend_ctx, src0)) {
+                ggml_cl_mul_mat_q8_0_f32_adreno(backend, src0, src1, dst);
+                return;
+        }
 
-    // q4_k x fp32
-    if (src0t == GGML_TYPE_Q4_K && src1t == GGML_TYPE_F32) {
+        // q4_k x fp32
+        if (src0t == GGML_TYPE_Q4_K && src1t == GGML_TYPE_F32) {
             ggml_cl_mul_mat_q4_k_f32_adreno(backend, src0, src1, dst);
             return;
-    }
-
-    // q6_K x fp32
-    if (src0t == GGML_TYPE_Q6_K && src1t == GGML_TYPE_F32) {
-        ggml_cl_mul_mat_q6_K_f32_adreno(backend, src0, src1, dst);
-        return;
-    }
-
-    // q5_K x fp32
-    if (src0t == GGML_TYPE_Q5_K && src1t == GGML_TYPE_F32) {
-        ggml_cl_mul_mat_q5_K_f32_adreno(backend, src0, src1, dst);
-        return;
-    }
-
-    // q4_0 x fp32
-    if(src0t == GGML_TYPE_Q4_0 && src1t == GGML_TYPE_F32) {
-        // TODO: remove duplicate definitions of image description + format -- move to top
-
-        // create an image for A
-        // <--------------------------------------------> //
-        if (N == 1) {
-            img_fmt_1d = { CL_R, CL_UNSIGNED_INT32};
-        } else {
-            img_fmt_1d = { CL_R, CL_FLOAT};
-        }
-        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
-        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
-        img_desc_1d.image_width = M * K / 2 / 4;    // Divide by 4 for char -> float
-        img_desc_1d.buffer = extra0_q4_0->q;
-        A_image1d = clCreateImage(
-            context,
-            CL_MEM_READ_ONLY,
-            &img_fmt_1d,
-            &img_desc_1d,
-            NULL,
-            &status);
-        CL_CHECK(status);
-        // <--------------------------------------------> //
-
-
-        // create a sub_buffer for B
-        // <--------------------------------------------> //
-        region.origin = (extra1->offset);
-        region.size = K * N * sizeof(float);
-        B_sub_buffer = clCreateSubBuffer(
-            extra1->data_device,
-            0,
-            CL_BUFFER_CREATE_TYPE_REGION,
-            &region,
-            &status);
-        CL_CHECK(status);
-        // <--------------------------------------------> //
-
-        // transpose activation for Skyler's gemm
-        if (N != 1) {
-            //how many extra elements beyond multiple of 8
-            int extra_elements = N % 8;
-
-            //how much padding to add
-            padding = 0;
-            if (extra_elements > 0){
-                padding = 8 - extra_elements;
-            }
-
-            // Specify the starting offset (in bytes)
-            region.origin = 0;
-            // Specify the size of the sub-buffer (divide by 2 for FP16)
-            region.size = K * (N + padding) * sizeof(float)/2;
-            backend_ctx->prealloc_act_trans.allocate(context, region.size);
-
-            B_d = clCreateSubBuffer(
-                backend_ctx->prealloc_act_trans.buffer,
-                0,
-                CL_BUFFER_CREATE_TYPE_REGION,
-                &region,
-                &status);
-            CL_CHECK(status);
-
-            cl_image_format image_format_B_d_input = { CL_RGBA, CL_FLOAT };
-            cl_image_desc image_desc_B_d_input = {
-                CL_MEM_OBJECT_IMAGE1D_BUFFER,
-                static_cast<size_t>(K * N / 4),
-                0, 0, 0, 0, 0, 0, 0, { B_sub_buffer }
-            };
-            B_d_input_image = clCreateImage(
-                context,
-                0,
-                &image_format_B_d_input,
-                &image_desc_B_d_input,
-                NULL,
-                &status);
-            CL_CHECK(status);
-
-            cl_image_format image_format_B_d_output = { CL_RGBA, CL_HALF_FLOAT }; //(CL_HALF_FLOAT for FP16)
-            cl_image_desc image_desc_B_d_output = {
-                CL_MEM_OBJECT_IMAGE1D_BUFFER,
-                static_cast<size_t>(K * (N + padding)/4),
-                0, 0, 0, 0, 0, 0, 0, { B_d }
-            };
-            B_image1d = clCreateImage(
-                context,
-                0,
-                &image_format_B_d_output,
-                &image_desc_B_d_output,
-                NULL,
-                &status);
-            CL_CHECK(status);
-
-            int height_B = N/4;
-            if (height_B == 0) {
-                height_B = 1;
-            }
-            int width_B = K/4;
-            int padded_height_B = (N + padding)/4;
-
-            kernel = backend_ctx->kernel_transpose_32_16;
-            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &B_d_input_image));
-            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &B_image1d));
-            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
-            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
-            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));
-
-            size_t local_size_t[2] = { 1, 16 };
-            //WGS tuning
-            if (ne0 == 4096 && ne1 == 128 && ne10 == 4096) {
-                local_size_t[0]=4;
-                local_size_t[1]=8;
-            } else if (ne0 == 11008 && ne1 == 128 && ne10 == 4096) {
-                local_size_t[0]=2;
-                local_size_t[1]=8;
-            } else if(ne0 == 4096 && ne1 == 128 && ne10 == 11008) {
-                local_size_t[0]=1;
-                local_size_t[1]=8;
-            } else if(ne0 == 32000 && ne1 == 128 && ne10 == 4096) {
-                local_size_t[0]=2;
-                local_size_t[1]=8;
-            }
-
-            size_t global_size_t[2] = {
-                static_cast<size_t>(width_B),
-                static_cast<size_t>(padded_height_B)
-            };
-
-            backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_size_t, local_size_t, dst);
-        } else {
-            // no need to transpose B in other cases
-            // create an image for B from sub_buffer
-            // <--------------------------------------------> //
-            img_fmt_1d = {CL_RGBA, CL_FLOAT};
-
-            memset(&img_desc_1d, 0, sizeof(img_desc_1d));
-            img_desc_1d.image_width = K * N / 4;
-            img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
-            img_desc_1d.buffer = B_sub_buffer;
-            B_image1d = clCreateImage(
-                context,
-                CL_MEM_READ_ONLY,
-                &img_fmt_1d,
-                &img_desc_1d,
-                NULL,
-                &status);
-            CL_CHECK(status);
-            // <--------------------------------------------> //
-        }
-
-        // choose gemm or gemv kernel
-        // <--------------------------------------------> //
-        if (N == 1) {
-            kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general;
-            if (M == 4096 && K == 4096) {
-                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096;
-            } else if (M == 4096 && K == 11008) {
-                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008;
-            } else if (M == 11008 && K == 4096) {
-                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096;
-            } else if (M == 32000 && K == 4096) {
-                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096;
-            }
-        } else {
-            kernel = backend_ctx->CL_mul_mat_Ab_Bi_8x4;
-        }
-        // <--------------------------------------------> //
-
-        // set kernel args
-        // <--------------------------------------------> //
-        cl_uint k_arg = 0;
-
-        if (N == 1) {
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &A_image1d));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &extra0_q4_0->d));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &B_image1d));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_ulong), &extra1->offset));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &extrad->data_device));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_ulong), &extrad->offset));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne00));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne01));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne02));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne10));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne12));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne0));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne1));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &r2));
-            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &r3));
-        } else {
-            region.origin = extrad->offset; // Specify the starting offset (in bytes)
-            region.size = M * N * sizeof(float); // Specify the size of the sub-buffer
-            C_d = clCreateSubBuffer(extrad->data_device, CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
-            CL_CHECK(status);
-
-            int padded_N = ne1 + padding;
-
-            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_0->q)); //A_q_dextra0_q4_0->q
-            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_0->d)); //A_s_d
-            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &B_image1d)); //B_d
-            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &C_d)); //C_d
-            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &ne01)); //M
-            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),    &padded_N)); //N with padding
-            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),    &ne00)); //K
-            CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int),    &ne1)); //N without padding
-        }
-        // <--------------------------------------------> //
-
-        // choose workgroup size
-        // <--------------------------------------------> //
-        size_t global_work_size[3] = {
-            64, static_cast<size_t>((M+63)/64), static_cast<size_t>((N+31)/32)};
-        size_t local_work_size[3] = {64, 2, 4};
-
-        global_work_size[0] = (size_t)(ceil((float)ne1/8));
-        global_work_size[1] = (size_t)(ne01/4);
-        global_work_size[2] = (size_t)(1);
-
-        local_work_size[0]  = (size_t)(1); //4x32 for FP32
-        local_work_size[1]  = (size_t)(128);
-        local_work_size[2]  = (size_t)(1);
-
-        //WGS tuning
-        if (ne0 == 4096 && ne1 == 128 && ne10 == 4096) {
-            local_work_size[0] = 1;
-            local_work_size[1] = 128;
-        } else if (ne0 == 11008 && ne1 == 128 && ne10 == 4096) {
-            local_work_size[0] = 2;
-            local_work_size[1] = 64;
-        } else if (ne0 == 4096 && ne1 == 128 && ne10 == 11008) {
-            local_work_size[0] = 2;
-            local_work_size[1] = 64;
-        } else if (ne0 == 32000 && ne1 == 128 && ne10 == 4096) {
-            local_work_size[0] = 2;
-            local_work_size[1] = 64;
         }
 
-        if (N == 1) {
-            size_t wavesize = backend_ctx->adreno_wave_size;
-            local_work_size[0] = wavesize; // localsize
-            local_work_size[1] = 4; // reduce factor
-            local_work_size[2] = 1;
-
-            global_work_size[0] = (((M / 2) + wavesize - 1) / wavesize) * wavesize;
-            global_work_size[1] = 4; // reduce factor
-            global_work_size[2] = 1;
+        // q6_K x fp32
+        if (src0t == GGML_TYPE_Q6_K && src1t == GGML_TYPE_F32) {
+            ggml_cl_mul_mat_q6_K_f32_adreno(backend, src0, src1, dst);
+            return;
         }
-        // <--------------------------------------------> //
-
-        // enqueue kernel with profiling
-        // <--------------------------------------------> //
-        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
-        // <--------------------------------------------> //
 
-        // deallocate sub buffers and images
-        // <--------------------------------------------> //
-        CL_CHECK(clReleaseMemObject(A_image1d));
-        CL_CHECK(clReleaseMemObject(B_sub_buffer));
-        CL_CHECK(clReleaseMemObject(B_image1d));
-
-        if (N != 1) {
-            CL_CHECK(clReleaseMemObject(B_d));
-            CL_CHECK(clReleaseMemObject(B_d_input_image));
-            CL_CHECK(clReleaseMemObject(C_d));
+        // q5_K x fp32
+        if (src0t == GGML_TYPE_Q5_K && src1t == GGML_TYPE_F32) {
+            ggml_cl_mul_mat_q5_K_f32_adreno(backend, src0, src1, dst);
+            return;
         }
-        // <--------------------------------------------> //
-
-        return;
-    }
     } // if (ne01 && ne1)
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
 
@@ -11109,6 +12417,12 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
                 return;
             }
             case GGML_TYPE_F16: {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+                if (ggml_cl_can_use_adreno_xmem_gemm_f16_f32(backend_ctx, src0, src1, dst)) {
+                    ggml_cl_mul_mat_f16_f32_adreno_xmem(backend, src0, src1, dst);
+                    return;
+                }
+#endif
                 kernel = backend_ctx->kernel_mul_mm_f16_f32_l4_lm;
                 nth0 = 128; // calculated as (BM*BN)/(TM*TN)
 
@@ -11302,6 +12616,48 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
                 backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
                 return;
             }
+            case GGML_TYPE_IQ4_NL: {
+                if (ne11 < 32) {
+                    break;
+                }
+                if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+                    break;
+                }
+
+                kernel = backend_ctx->kernel_mul_mm_iq4_nl_f32_l4_lm;
+                nth0 = 128; // calculated as (BM*BN)/(TM*TN)
+
+                int batch_stride_a = ne00*ne01;
+                int batch_stride_b = ne10*ne11;
+                int batch_stride_d = ne0*ne1;
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_iq4_nl->q));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_iq4_nl->d));
+                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+                CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+                CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+                CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+                CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne11));
+                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne10)); // stride_a
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne10)); // stride_b
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne01)); // stride_d
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &batch_stride_a));
+                CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &batch_stride_b));
+                CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &batch_stride_d));
+                CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r3));
+
+                // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
+                size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
+                size_t local_work_size[] = {(size_t)nth0, 1, 1};
+
+                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+                return;
+            }
             case GGML_TYPE_Q4_K: {
                 if (ne11 < 32) {
                     break;
@@ -11829,6 +13185,70 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne1));
             CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r2));
             CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r3));
+#endif // GGML_OPENCL_SOA_Q
+            break;
+        }
+        case GGML_TYPE_IQ4_NL: {
+#ifdef GGML_OPENCL_SOA_Q
+            kernel = backend_ctx->kernel_mul_mv_iq4_nl_f32_flat;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+                ndst = 8;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+                ndst = 8;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_iq4_nl->q));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_iq4_nl->d));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+#else
+            kernel = backend_ctx->kernel_mul_mv_iq4_nl_f32;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 1;
+                ndst = 4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 1;
+                ndst = 4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
 #endif // GGML_OPENCL_SOA_Q
             break;
         }
@@ -12131,6 +13551,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_MXFP4 ||
         src0t == GGML_TYPE_Q4_1 ||
         src0t == GGML_TYPE_Q8_0 ||
+        src0t == GGML_TYPE_IQ4_NL ||
         src0t == GGML_TYPE_Q2_K) {
         // Each SIMD group produces N_DST values in the result. Assuming each
         // workgroup has N_SIMDGROUP SIMD groups, then each workgroup will
@@ -12169,6 +13590,118 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     }
 }
 
+static void moe_router_reoerder(ggml_backend_t backend, const ggml_tensor * src, int ne20) {
+    cl_int err;
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *)src->extra;
+    cl_ulong offset = extra->offset + src->view_offs;
+
+    const int ne21 = src->ne[1];
+    const int nb21 = src->nb[1];
+    const int ne02 = nb21 / src->nb[0];
+    const int n_tile_size = 32;
+    const int max_post_router_tile = (ne20 * ne21 / n_tile_size) + ne02;
+
+    cl_buffer_region region;
+    region.origin = offset;
+    region.size = nb21 * ne21;
+    cl_mem original_router_buf = clCreateSubBuffer(extra->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+    CL_CHECK(err);
+
+    backend_ctx->prealloc_post_router.allocate(backend_ctx->context, sizeof(int) * max_post_router_tile * n_tile_size);
+    region.origin = 0;
+    region.size = sizeof(int) * max_post_router_tile * n_tile_size;
+    cl_mem post_router_buf = clCreateSubBuffer(backend_ctx->prealloc_post_router.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+    CL_CHECK(err);
+
+    backend_ctx->prealloc_emap.allocate(backend_ctx->context, sizeof(short) * max_post_router_tile);
+    region.origin = 0;
+    region.size = sizeof(short) * max_post_router_tile;
+    cl_mem emap_buf = clCreateSubBuffer(backend_ctx->prealloc_emap.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+    CL_CHECK(err);
+
+    backend_ctx->prealloc_hist.allocate(backend_ctx->context, sizeof(int) * ne02);
+    region.origin = 0;
+    region.size = sizeof(int) * ne02;
+    cl_mem hist_buf = clCreateSubBuffer(backend_ctx->prealloc_hist.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+    CL_CHECK(err);
+
+    backend_ctx->prealloc_tile_offset.allocate(backend_ctx->context, sizeof(int) * ne02);
+    region.origin = 0;
+    region.size = sizeof(int) * ne02;
+    cl_mem tile_offset_buf = clCreateSubBuffer(backend_ctx->prealloc_tile_offset.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+    CL_CHECK(err);
+
+    backend_ctx->prealloc_slot_counter.allocate(backend_ctx->context, sizeof(int) * ne02);
+    region.origin = 0;
+    region.size = sizeof(int) * ne02;
+    cl_mem slot_counter_buf = clCreateSubBuffer(backend_ctx->prealloc_slot_counter.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+    CL_CHECK(err);
+
+    backend_ctx->prealloc_total_tiles.allocate(backend_ctx->context, sizeof(int));
+    region.origin = 0;
+    region.size = sizeof(int);
+    cl_mem total_tiles_buf = clCreateSubBuffer(backend_ctx->prealloc_total_tiles.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+    CL_CHECK(err);
+
+    // Histogram
+    cl_kernel kernel = backend_ctx->kernel_moe_histogram;
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &original_router_buf));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &hist_buf));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &ne21));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &ne20));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne02));
+
+    size_t histogram_global_size[] = {(size_t)(((ne21 + 63) / 64) * 64), static_cast<size_t>(ne20), 1};
+    size_t histogram_local_size[] = {64, 1, 1};
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, histogram_global_size, histogram_local_size, src);
+
+    // Scan
+    kernel = backend_ctx->kernel_moe_scan;
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &hist_buf));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &tile_offset_buf));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &total_tiles_buf));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &slot_counter_buf));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &n_tile_size));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne02));
+
+    size_t scan_global_size[] = {1};
+    size_t scan_local_size[] = {1};
+    backend_ctx->enqueue_ndrange_kernel(kernel, 1, scan_global_size, scan_local_size, src);
+
+    // Fill
+    kernel = backend_ctx->kernel_moe_fill;
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &post_router_buf));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &total_tiles_buf));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &n_tile_size));
+
+    size_t fill_global_size[] = {(size_t)(((max_post_router_tile + 63) / 64) * 64), n_tile_size, 1};
+    size_t fill_local_size[] = {64, 1, 1};
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, fill_global_size, fill_local_size, src);
+
+    // Scatter
+    kernel = backend_ctx->kernel_moe_scatter;
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &original_router_buf));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &post_router_buf));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &emap_buf));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &tile_offset_buf));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &slot_counter_buf));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne21));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne20));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne02));
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, histogram_global_size, histogram_local_size, src);
+
+    CL_CHECK(clReleaseMemObject(original_router_buf));
+    CL_CHECK(clReleaseMemObject(hist_buf));
+    CL_CHECK(clReleaseMemObject(tile_offset_buf));
+    CL_CHECK(clReleaseMemObject(total_tiles_buf));
+    CL_CHECK(clReleaseMemObject(slot_counter_buf));
+    CL_CHECK(clReleaseMemObject(post_router_buf));
+    CL_CHECK(clReleaseMemObject(emap_buf));
+}
+
 static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -12197,10 +13730,18 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
 #ifdef GGML_OPENCL_SOA_Q
     ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
+    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
+    ggml_tensor_extra_cl_q5_0 * extra0_q5_0 = (ggml_tensor_extra_cl_q5_0 *)src0->extra;
+    ggml_tensor_extra_cl_q5_1 * extra0_q5_1 = (ggml_tensor_extra_cl_q5_1 *)src0->extra;
     ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
     ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
 #endif
 
+    // TODO: general MoE for the following types
+    (void)extra0_q4_1;
+    (void)extra0_q5_0;
+    (void)extra0_q5_1;
+
     const int ne00 = src0->ne[0];
     const int ne01 = src0->ne[1];
     const int ne02 = src0->ne[2];
@@ -12230,6 +13771,7 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
     const int ne0 = dst->ne[0];
     const int ne1 = dst->ne[1];
+    const int ne2 = dst->ne[2];
 
     const int r2 = ne12/ne02;
     const int r3 = ne13/ne03;
@@ -12242,11 +13784,187 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
     int nrows = 1;  // number of row in src1
     int ndst  = 4;  // number of values produced by each subgroup
 
+    const int n_tile_size = 32;
+    const int max_post_router_tile = (ne20 * ne21 / n_tile_size) + ne02;
+
     cl_kernel kernel;
 
     // subgroup mat vec
     switch (src0->type) {
         case GGML_TYPE_Q4_0: {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+            if (use_adreno_moe_kernels(backend_ctx, src0)) {
+                cl_int status;
+
+                size_t local_size[3] = {64, 2, 1};
+                size_t global_size[3] = {64, 2, 1};
+
+                if (ne12 == 1) { // for gemv
+                    kernel = backend_ctx->kernel_gemv_moe_q4_0_f32_ns;
+
+                    cl_mem src1_sub_buffer, buf_src1_image, buf_src2;
+
+                    // create a sub_buffer for src2
+                    cl_buffer_region region;
+                    region.origin = offset2;
+                    region.size = ne20 * ne21 * sizeof(int);
+                    buf_src2 = clCreateSubBuffer(extra2->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // set thread grid
+                    global_size[0] = static_cast<size_t>(ne01);
+                    global_size[1] = 4;
+                    global_size[2] = static_cast<size_t>(ne20);
+                    local_size[1] = 4;
+
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    src1_sub_buffer = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // create image for src1
+                    cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne10 * ne11 * ne12 / 4), 0,0,0,0,0,0,0, {src1_sub_buffer}};
+                    buf_src1_image = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_0->q));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_0->d));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src1_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extrad->data_device));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_ulong),  &offsetd));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne11));
+
+                    // launch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    // deallocate sub buffers and images
+                    CL_CHECK(clReleaseMemObject(src1_sub_buffer));
+                    CL_CHECK(clReleaseMemObject(buf_src1_image));
+                    CL_CHECK(clReleaseMemObject(buf_src2));
+
+                } else { // for gemm
+                    kernel = backend_ctx->kernel_gemm_moe_q4_0_f32_ns;
+
+                    // Reorder router if called from test-backend-ops or when new router is generated.
+                    // Otherwise reuse the reordered result from previous mul_mat_id call.
+                    if ((strstr(src0->name, "as") != NULL) || backend_ctx->toggle_reorder) {
+                        moe_router_reoerder(backend, src2, ne20);
+                        backend_ctx->toggle_reorder = false;
+                    }
+
+                    cl_mem sub_buf_src1_pre, buf_src1_reordered, image_src1_reordered, sub_buf_dst, buf_dst_image;
+                    cl_mem buf_src2, buf_src2_emap;
+
+                    cl_buffer_region region;
+                    region.origin = 0;
+                    region.size = sizeof(int) * max_post_router_tile * n_tile_size;
+                    buf_src2 = clCreateSubBuffer(backend_ctx->prealloc_post_router.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    region.origin = 0;
+                    region.size = sizeof(short) * max_post_router_tile;
+                    buf_src2_emap = clCreateSubBuffer(backend_ctx->prealloc_emap.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Reorder activations
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    sub_buf_src1_pre = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Create image for reordered src1
+                    // Use pre-allocated placeholder
+                    region.origin = 0;
+                    region.size = ne00 * max_post_router_tile * n_tile_size * sizeof(float);
+                    backend_ctx->prealloc_act_trans.allocate(backend_ctx->context, region.size);
+                    buf_src1_reordered = clCreateSubBuffer(
+                        backend_ctx->prealloc_act_trans.buffer,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    cl_image_format image_format_buf_src1;
+                    cl_image_desc image_desc_buf_src1;
+                    image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    unsigned short map_ratio = ne20 / ne11;
+                    GGML_ASSERT(((map_ratio == 1) || (map_ratio == ne20)) && "Map ratio not supported\n");
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 0, sizeof(cl_mem),        &sub_buf_src1_pre));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 1, sizeof(cl_mem),        &buf_src2));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 2, sizeof(cl_mem),        &buf_src1_reordered));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 3, sizeof(cl_mem),        &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 4, sizeof(unsigned int),  &ne00));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 5, sizeof(unsigned short),  &map_ratio));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 6, sizeof(unsigned int),  &n_tile_size));
+
+                    size_t reorder_b_local_size[3] = {256, 1, 1};
+                    size_t reorder_b_global_size[3] = {static_cast<size_t>(((ne00 / 4) + 255) / 256 * 256), static_cast<size_t>(max_post_router_tile * n_tile_size), 1};
+
+                    // Dispatch reorder kernel
+                    backend_ctx->enqueue_ndrange_kernel(backend_ctx->kernel_moe_reorder_b, 3, reorder_b_global_size, reorder_b_local_size, dst);
+
+                    // MoE kernel prepare
+                    // Create sub buffer for dst
+                    region.origin = offsetd;
+                    region.size = ne0 * ne1 * ne2 * sizeof(float);
+                    sub_buf_dst = clCreateSubBuffer(
+                        extrad->data_device,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    // Create image for dst
+                    cl_image_format image_format_buf_dst = {CL_R, CL_FLOAT};
+                    cl_image_desc image_desc_buf_dst = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne0 * ne1 * ne2), 0,0,0,0,0,0,0, {sub_buf_dst}};
+                    buf_dst_image = clCreateImage(backend_ctx->context, CL_MEM_WRITE_ONLY, &image_format_buf_dst, &image_desc_buf_dst, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_0->q_img));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_0->d));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &image_src1_reordered));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2_emap));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_dst_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+
+                    // set thread grid
+                    global_size[1] = static_cast<size_t>((ne01 + 63) / 64);
+                    global_size[2] = static_cast<size_t>(max_post_router_tile);
+                    local_size[1] = 1;
+                    local_size[2] = 1;
+
+                    // Dispatch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    clReleaseMemObject(sub_buf_src1_pre);
+                    clReleaseMemObject(buf_src1_reordered);
+                    clReleaseMemObject(image_src1_reordered);
+                    clReleaseMemObject(buf_src2);
+                    clReleaseMemObject(buf_src2_emap);
+                    clReleaseMemObject(sub_buf_dst);
+                    clReleaseMemObject(buf_dst_image);
+                }
+                return;
+            } // fallback to generic Q4_0 MoE kernel
+
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
             kernel = backend_ctx->kernel_mul_mv_id_q4_0_f32_8x_flat;
 
             if (backend_ctx->gpu_family == INTEL) {
@@ -12289,6 +14007,535 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
             break;
         }
+        case GGML_TYPE_Q4_1: {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+            if (use_adreno_moe_kernels(backend_ctx, src0)) {
+                cl_int status;
+
+                size_t local_size[3] = {64, 2, 1};
+                size_t global_size[3] = {64, 2, 1};
+
+                if (ne12 == 1) { // for gemv
+                    kernel = backend_ctx->kernel_gemv_moe_q4_1_f32_ns;
+
+                    cl_mem src1_sub_buffer, buf_src1_image, buf_src2;
+
+                    // create a sub_buffer for src2
+                    cl_buffer_region region;
+                    region.origin = offset2;
+                    region.size = ne20 * ne21 * sizeof(int);
+                    buf_src2 = clCreateSubBuffer(extra2->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // set thread grid
+                    global_size[0] = static_cast<size_t>(ne01);
+                    global_size[1] = 4;
+                    global_size[2] = static_cast<size_t>(ne20);
+                    local_size[1] = 4;
+
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    src1_sub_buffer = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // create image for src1
+                    cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne10 * ne11 * ne12 / 4), 0,0,0,0,0,0,0, {src1_sub_buffer}};
+                    buf_src1_image = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_1->q));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_1->d));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_1->m));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src1_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extrad->data_device));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_ulong),  &offsetd));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne11));
+
+                    // launch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    // deallocate sub buffers and images
+                    CL_CHECK(clReleaseMemObject(src1_sub_buffer));
+                    CL_CHECK(clReleaseMemObject(buf_src1_image));
+                    CL_CHECK(clReleaseMemObject(buf_src2));
+
+                } else { // for gemm
+                    kernel = backend_ctx->kernel_gemm_moe_q4_1_f32_ns;
+
+                    // Reorder router if called from test-backend-ops or when new router is generated.
+                    // Otherwise reuse the reordered result from previous mul_mat_id call.
+                    if ((strstr(src0->name, "as") != NULL) || backend_ctx->toggle_reorder) {
+                        moe_router_reoerder(backend, src2, ne20);
+                        backend_ctx->toggle_reorder = false;
+                    }
+
+                    cl_mem sub_buf_src1_pre, buf_src1_reordered, image_src1_reordered, sub_buf_dst, buf_dst_image;
+                    cl_mem buf_src2, buf_src2_emap;
+
+                    cl_buffer_region region;
+                    region.origin = 0;
+                    region.size = sizeof(int) * max_post_router_tile * n_tile_size;
+                    buf_src2 = clCreateSubBuffer(backend_ctx->prealloc_post_router.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    region.origin = 0;
+                    region.size = sizeof(short) * max_post_router_tile;
+                    buf_src2_emap = clCreateSubBuffer(backend_ctx->prealloc_emap.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Reorder activations
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    sub_buf_src1_pre = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Create image for reordered src1
+                    // Use pre-allocated placeholder
+                    region.origin = 0;
+                    region.size = ne00 * max_post_router_tile * n_tile_size * sizeof(float);
+                    backend_ctx->prealloc_act_trans.allocate(backend_ctx->context, region.size);
+                    buf_src1_reordered = clCreateSubBuffer(
+                        backend_ctx->prealloc_act_trans.buffer,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    cl_image_format image_format_buf_src1;
+                    cl_image_desc image_desc_buf_src1;
+                    image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    unsigned short map_ratio = ne20 / ne11;
+                    GGML_ASSERT(((map_ratio == 1) || (map_ratio == ne20)) && "Map ratio not supported\n");
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 0, sizeof(cl_mem),        &sub_buf_src1_pre));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 1, sizeof(cl_mem),        &buf_src2));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 2, sizeof(cl_mem),        &buf_src1_reordered));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 3, sizeof(cl_mem),        &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 4, sizeof(unsigned int),  &ne00));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 5, sizeof(unsigned short),  &map_ratio));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 6, sizeof(unsigned int),  &n_tile_size));
+
+                    size_t reorder_b_local_size[3] = {256, 1, 1};
+                    size_t reorder_b_global_size[3] = {static_cast<size_t>(((ne00 / 4) + 255) / 256 * 256), static_cast<size_t>(max_post_router_tile * n_tile_size), 1};
+
+                    // Dispatch reorder kernel
+                    backend_ctx->enqueue_ndrange_kernel(backend_ctx->kernel_moe_reorder_b, 3, reorder_b_global_size, reorder_b_local_size, dst);
+
+                    // MoE kernel prepare
+                    // Create sub buffer for dst
+                    region.origin = offsetd;
+                    region.size = ne0 * ne1 * ne2 * sizeof(float);
+                    sub_buf_dst = clCreateSubBuffer(
+                        extrad->data_device,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    // Create image for dst
+                    cl_image_format image_format_buf_dst = {CL_R, CL_FLOAT};
+                    cl_image_desc image_desc_buf_dst = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne0 * ne1 * ne2), 0,0,0,0,0,0,0, {sub_buf_dst}};
+                    buf_dst_image = clCreateImage(backend_ctx->context, CL_MEM_WRITE_ONLY, &image_format_buf_dst, &image_desc_buf_dst, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_1->q_img));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_1->d));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q4_1->m));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &image_src1_reordered));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2_emap));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_dst_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+
+                    // set thread grid
+                    global_size[1] = static_cast<size_t>((ne01 + 63) / 64);
+                    global_size[2] = static_cast<size_t>(max_post_router_tile);
+                    local_size[1] = 1;
+                    local_size[2] = 1;
+
+                    // Dispatch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    clReleaseMemObject(sub_buf_src1_pre);
+                    clReleaseMemObject(buf_src1_reordered);
+                    clReleaseMemObject(image_src1_reordered);
+                    clReleaseMemObject(buf_src2);
+                    clReleaseMemObject(buf_src2_emap);
+                    clReleaseMemObject(sub_buf_dst);
+                    clReleaseMemObject(buf_dst_image);
+                }
+                return;
+            }
+#endif //GGML_OPENCL_USE_ADRENO_KERNELS
+        }
+        case GGML_TYPE_Q5_0: {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+            if (use_adreno_moe_kernels(backend_ctx, src0)) {
+                cl_int status;
+
+                size_t local_size[3] = {64, 2, 1};
+                size_t global_size[3] = {64, 2, 1};
+
+                if (ne12 == 1) { // for gemv
+                    kernel = backend_ctx->kernel_gemv_moe_q5_0_f32_ns;
+
+                    cl_mem src1_sub_buffer, buf_src1_image, buf_src2;
+
+                    // create a sub_buffer for src2
+                    cl_buffer_region region;
+                    region.origin = offset2;
+                    region.size = ne20 * ne21 * sizeof(int);
+                    buf_src2 = clCreateSubBuffer(extra2->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // set thread grid
+                    global_size[0] = static_cast<size_t>(ne01);
+                    global_size[1] = 4;
+                    global_size[2] = static_cast<size_t>(ne20);
+                    local_size[1] = 4;
+
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    src1_sub_buffer = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // create image for src1
+                    cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne10 * ne11 * ne12 / 4), 0,0,0,0,0,0,0, {src1_sub_buffer}};
+                    buf_src1_image = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_0->qs));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_0->qh));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_0->d));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src1_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extrad->data_device));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_ulong),  &offsetd));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne11));
+
+                    // launch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    // deallocate sub buffers and images
+                    CL_CHECK(clReleaseMemObject(src1_sub_buffer));
+                    CL_CHECK(clReleaseMemObject(buf_src1_image));
+                    CL_CHECK(clReleaseMemObject(buf_src2));
+
+                } else { // for gemm
+                    kernel = backend_ctx->kernel_gemm_moe_q5_0_f32_ns;
+
+                    // Reorder router if called from test-backend-ops or when new router is generated.
+                    // Otherwise reuse the reordered result from previous mul_mat_id call.
+                    if ((strstr(src0->name, "as") != NULL) || backend_ctx->toggle_reorder) {
+                        moe_router_reoerder(backend, src2, ne20);
+                        backend_ctx->toggle_reorder = false;
+                    }
+
+                    cl_mem sub_buf_src1_pre, buf_src1_reordered, image_src1_reordered, sub_buf_dst, buf_dst_image;
+                    cl_mem buf_src2, buf_src2_emap;
+
+                    cl_buffer_region region;
+                    region.origin = 0;
+                    region.size = sizeof(int) * max_post_router_tile * n_tile_size;
+                    buf_src2 = clCreateSubBuffer(backend_ctx->prealloc_post_router.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    region.origin = 0;
+                    region.size = sizeof(short) * max_post_router_tile;
+                    buf_src2_emap = clCreateSubBuffer(backend_ctx->prealloc_emap.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Reorder activations
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    sub_buf_src1_pre = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Create image for reordered src1
+                    // Use pre-allocated placeholder
+                    region.origin = 0;
+                    region.size = ne00 * max_post_router_tile * n_tile_size * sizeof(float);
+                    backend_ctx->prealloc_act_trans.allocate(backend_ctx->context, region.size);
+                    buf_src1_reordered = clCreateSubBuffer(
+                        backend_ctx->prealloc_act_trans.buffer,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    cl_image_format image_format_buf_src1;
+                    cl_image_desc image_desc_buf_src1;
+                    image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    unsigned short map_ratio = ne20 / ne11;
+                    GGML_ASSERT(((map_ratio == 1) || (map_ratio == ne20)) && "Map ratio not supported\n");
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 0, sizeof(cl_mem),        &sub_buf_src1_pre));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 1, sizeof(cl_mem),        &buf_src2));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 2, sizeof(cl_mem),        &buf_src1_reordered));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 3, sizeof(cl_mem),        &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 4, sizeof(unsigned int),  &ne00));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 5, sizeof(unsigned short),  &map_ratio));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 6, sizeof(unsigned int),  &n_tile_size));
+
+                    size_t reorder_b_local_size[3] = {256, 1, 1};
+                    size_t reorder_b_global_size[3] = {static_cast<size_t>(((ne00 / 4) + 255) / 256 * 256), static_cast<size_t>(max_post_router_tile * n_tile_size), 1};
+
+                    // Dispatch reorder kernel
+                    backend_ctx->enqueue_ndrange_kernel(backend_ctx->kernel_moe_reorder_b, 3, reorder_b_global_size, reorder_b_local_size, dst);
+
+                    // MoE kernel prepare
+                    // Create sub buffer for dst
+                    region.origin = offsetd;
+                    region.size = ne0 * ne1 * ne2 * sizeof(float);
+                    sub_buf_dst = clCreateSubBuffer(
+                        extrad->data_device,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    // Create image for dst
+                    cl_image_format image_format_buf_dst = {CL_R, CL_FLOAT};
+                    cl_image_desc image_desc_buf_dst = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne0 * ne1 * ne2), 0,0,0,0,0,0,0, {sub_buf_dst}};
+                    buf_dst_image = clCreateImage(backend_ctx->context, CL_MEM_WRITE_ONLY, &image_format_buf_dst, &image_desc_buf_dst, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_0->qs_img));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_0->qh));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_0->d));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &image_src1_reordered));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2_emap));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_dst_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+
+                    // set thread grid
+                    global_size[1] = static_cast<size_t>((ne01 + 63) / 64);
+                    global_size[2] = static_cast<size_t>(max_post_router_tile);
+                    local_size[1] = 1;
+                    local_size[2] = 1;
+
+                    // Dispatch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    clReleaseMemObject(sub_buf_src1_pre);
+                    clReleaseMemObject(buf_src1_reordered);
+                    clReleaseMemObject(image_src1_reordered);
+                    clReleaseMemObject(buf_src2);
+                    clReleaseMemObject(buf_src2_emap);
+                    clReleaseMemObject(sub_buf_dst);
+                    clReleaseMemObject(buf_dst_image);
+                }
+                return;
+            }
+#endif //GGML_OPENCL_USE_ADRENO_KERNELS
+        }
+        case GGML_TYPE_Q5_1: {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+            if (use_adreno_moe_kernels(backend_ctx, src0)) {
+                cl_int status;
+
+                size_t local_size[3] = {64, 2, 1};
+                size_t global_size[3] = {64, 2, 1};
+
+                if (ne12 == 1) { // for gemv
+                    kernel = backend_ctx->kernel_gemv_moe_q5_1_f32_ns;
+
+                    cl_mem src1_sub_buffer, buf_src1_image, buf_src2;
+
+                    // create a sub_buffer for src2
+                    cl_buffer_region region;
+                    region.origin = offset2;
+                    region.size = ne20 * ne21 * sizeof(int);
+                    buf_src2 = clCreateSubBuffer(extra2->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // set thread grid
+                    global_size[0] = static_cast<size_t>(ne01);
+                    global_size[1] = 4;
+                    global_size[2] = static_cast<size_t>(ne20);
+                    local_size[1] = 4;
+
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    src1_sub_buffer = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // create image for src1
+                    cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne10 * ne11 * ne12 / 4), 0,0,0,0,0,0,0, {src1_sub_buffer}};
+                    buf_src1_image = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_1->qs));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_1->qh));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_1->d));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_1->m));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src1_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extrad->data_device));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_ulong),  &offsetd));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne11));
+
+                    // launch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    // deallocate sub buffers and images
+                    CL_CHECK(clReleaseMemObject(src1_sub_buffer));
+                    CL_CHECK(clReleaseMemObject(buf_src1_image));
+                    CL_CHECK(clReleaseMemObject(buf_src2));
+                } else { // for gemm
+                    kernel = backend_ctx->kernel_gemm_moe_q5_1_f32_ns;
+
+                    // Reorder router if called from test-backend-ops or when new router is generated.
+                    // Otherwise reuse the reordered result from previous mul_mat_id call.
+                    if ((strstr(src0->name, "as") != NULL) || backend_ctx->toggle_reorder) {
+                        moe_router_reoerder(backend, src2, ne20);
+                        backend_ctx->toggle_reorder = false;
+                    }
+
+                    cl_mem sub_buf_src1_pre, buf_src1_reordered, image_src1_reordered, sub_buf_dst, buf_dst_image;
+                    cl_mem buf_src2, buf_src2_emap;
+
+                    cl_buffer_region region;
+                    region.origin = 0;
+                    region.size = sizeof(int) * max_post_router_tile * n_tile_size;
+                    buf_src2 = clCreateSubBuffer(backend_ctx->prealloc_post_router.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    region.origin = 0;
+                    region.size = sizeof(short) * max_post_router_tile;
+                    buf_src2_emap = clCreateSubBuffer(backend_ctx->prealloc_emap.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Reorder activations
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    sub_buf_src1_pre = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Create image for reordered src1
+                    // Use pre-allocated placeholder
+                    region.origin = 0;
+                    region.size = ne00 * max_post_router_tile * n_tile_size * sizeof(float);
+                    backend_ctx->prealloc_act_trans.allocate(backend_ctx->context, region.size);
+                    buf_src1_reordered = clCreateSubBuffer(
+                        backend_ctx->prealloc_act_trans.buffer,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    cl_image_format image_format_buf_src1;
+                    cl_image_desc image_desc_buf_src1;
+                    image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    unsigned short map_ratio = ne20 / ne11;
+                    GGML_ASSERT(((map_ratio == 1) || (map_ratio == ne20)) && "Map ratio not supported\n");
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 0, sizeof(cl_mem),        &sub_buf_src1_pre));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 1, sizeof(cl_mem),        &buf_src2));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 2, sizeof(cl_mem),        &buf_src1_reordered));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 3, sizeof(cl_mem),        &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 4, sizeof(unsigned int),  &ne00));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 5, sizeof(unsigned short),  &map_ratio));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 6, sizeof(unsigned int),  &n_tile_size));
+
+                    size_t reorder_b_local_size[3] = {256, 1, 1};
+                    size_t reorder_b_global_size[3] = {static_cast<size_t>(((ne00 / 4) + 255) / 256 * 256), static_cast<size_t>(max_post_router_tile * n_tile_size), 1};
+
+                    // Dispatch reorder kernel
+                    backend_ctx->enqueue_ndrange_kernel(backend_ctx->kernel_moe_reorder_b, 3, reorder_b_global_size, reorder_b_local_size, dst);
+
+                    // MoE kernel prepare
+                    // Create sub buffer for dst
+                    region.origin = offsetd;
+                    region.size = ne0 * ne1 * ne2 * sizeof(float);
+                    sub_buf_dst = clCreateSubBuffer(
+                        extrad->data_device,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    // Create image for dst
+                    cl_image_format image_format_buf_dst = {CL_R, CL_FLOAT};
+                    cl_image_desc image_desc_buf_dst = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne0 * ne1 * ne2), 0,0,0,0,0,0,0, {sub_buf_dst}};
+                    buf_dst_image = clCreateImage(backend_ctx->context, CL_MEM_WRITE_ONLY, &image_format_buf_dst, &image_desc_buf_dst, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_1->qs_img));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_1->qh));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_1->d));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_q5_1->m));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &image_src1_reordered));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2_emap));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_dst_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+
+                    // set thread grid
+                    global_size[1] = static_cast<size_t>((ne01 + 63) / 64);
+                    global_size[2] = static_cast<size_t>(max_post_router_tile);
+                    local_size[1] = 1;
+                    local_size[2] = 1;
+
+                    // Dispatch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    clReleaseMemObject(sub_buf_src1_pre);
+                    clReleaseMemObject(buf_src1_reordered);
+                    clReleaseMemObject(image_src1_reordered);
+                    clReleaseMemObject(buf_src2);
+                    clReleaseMemObject(buf_src2_emap);
+                    clReleaseMemObject(sub_buf_dst);
+                    clReleaseMemObject(buf_dst_image);
+                }
+                return;
+            }
+#endif //GGML_OPENCL_USE_ADRENO_KERNELS
+        }
         case GGML_TYPE_Q8_0: {
 #ifdef GGML_OPENCL_SOA_Q
             kernel = backend_ctx->kernel_mul_mv_id_q8_0_f32_flat;
@@ -12373,11 +14620,10 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                 size_t local_size[3] = {64, 2, 1};
                 size_t global_size[3] = {64, 2, 1};
 
-                cl_mem src1_sub_buffer, buf_src1_image, buf_src2;
-
-                int tile_size = 320;
                 if (ne12 == 1) { // for gemv
-                    kernel = backend_ctx->kernel_gemv_moe_mxfp4_f32;
+                    kernel = backend_ctx->kernel_gemv_moe_mxfp4_f32_ns;
+
+                    cl_mem src1_sub_buffer, buf_src1_image, buf_src2;
 
                     // create a sub_buffer for src2
                     cl_buffer_region region;
@@ -12391,78 +14637,154 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                     global_size[1] = 4;
                     global_size[2] = static_cast<size_t>(ne20);
                     local_size[1] = 4;
+
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    src1_sub_buffer = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // create image for src1
+                    cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne10 * ne11 * ne12 / 4), 0,0,0,0,0,0,0, {src1_sub_buffer}};
+                    buf_src1_image = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_mxfp4->q));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_mxfp4->e));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src1_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extrad->data_device));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_ulong),  &offsetd));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne11));
+
+                    // launch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    // deallocate sub buffers and images
+                    CL_CHECK(clReleaseMemObject(src1_sub_buffer));
+                    CL_CHECK(clReleaseMemObject(buf_src1_image));
+                    CL_CHECK(clReleaseMemObject(buf_src2));
+
                 } else { // for gemm
-                    kernel = backend_ctx->kernel_gemm_moe_mxfp4_f32;
-
-                    // preprocess router table
-                    int num_tiles_per_expert = (ne01 + tile_size - 1) / tile_size;
-                    void * host_src2_reorder = malloc(ne20 * ne21 * 4 * num_tiles_per_expert * sizeof(short));
-                    void * host_src2 = malloc(ne21 * nb21);
-                    CL_CHECK(clEnqueueReadBuffer(backend_ctx->queue, extra2->data_device, CL_TRUE, offset2, ne21 * nb21, host_src2, 0, NULL, NULL));
-                    int total_experts = nb21 / nb20;
-                    int out_idx = 0;
-                    for (int i_expert = 0; i_expert < ne02; i_expert++) {
-                        for (int i_tile = 0; i_tile < num_tiles_per_expert; i_tile++) {
-                            for (int j = 0; j < ne21; j++) {
-                                for (int i = 0; i < ne20; i++) {
-                                    int expert = ((int *)host_src2)[j * total_experts + i];
-                                    if (i_expert == expert) {
-                                        ((short *)host_src2_reorder)[out_idx] = static_cast<short>(expert);
-                                        ((short *)host_src2_reorder)[out_idx + 1] = static_cast<short>(j * ne11 + (i % ne11));
-                                        ((short *)host_src2_reorder)[out_idx + 2] = static_cast<short>(j * ne20 + i);
-                                        ((short *)host_src2_reorder)[out_idx + 3] = static_cast<short>(i_tile);
-                                        out_idx += 4;
-                                    }
-                                }
-                            }
-                        }
+                    kernel = backend_ctx->kernel_gemm_moe_mxfp4_f32_ns;
+
+                    // Reorder router if called from test-backend-ops or when new router is generated.
+                    // Otherwise reuse the reordered result from previous mul_mat_id call.
+                    if ((strstr(src0->name, "as") != NULL) || backend_ctx->toggle_reorder) {
+                        moe_router_reoerder(backend, src2, ne20);
+                        backend_ctx->toggle_reorder = false;
                     }
-                    buf_src2 = clCreateBuffer(backend_ctx->context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, ne20 * ne21 * 4 * num_tiles_per_expert * sizeof(short), host_src2_reorder, &status);
+
+                    cl_mem sub_buf_src1_pre, buf_src1_reordered, image_src1_reordered, sub_buf_dst, buf_dst_image;
+                    cl_mem buf_src2, buf_src2_emap;
+
+                    cl_buffer_region region;
+                    region.origin = 0;
+                    region.size = sizeof(int) * max_post_router_tile * n_tile_size;
+                    GGML_ASSERT(backend_ctx->prealloc_post_router.buffer);
+                    buf_src2 = clCreateSubBuffer(backend_ctx->prealloc_post_router.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
                     CL_CHECK(status);
 
-                    // set thread grid
-                    global_size[0] = static_cast<size_t>(tile_size);
-                    global_size[2] = static_cast<size_t>(ne20 * ne21 * num_tiles_per_expert);
-                }
+                    region.origin = 0;
+                    region.size = sizeof(short) * max_post_router_tile;
+                    buf_src2_emap = clCreateSubBuffer(backend_ctx->prealloc_emap.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
 
-                // create a sub_buffer for src1
-                cl_buffer_region region;
-                region.origin = offset1;
-                region.size = ne10 * ne11 * ne12 * sizeof(float);
-                src1_sub_buffer = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
-                CL_CHECK(status);
-
-                // create image for src1
-                cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
-                cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne10 * ne11 * ne12 / 4), 0,0,0,0,0,0,0, {src1_sub_buffer}};
-                buf_src1_image = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
-                CL_CHECK(status);
-
-                // Set kernel args
-                int arg_idx = 0;
-                CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_mxfp4->q));
-                CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_mxfp4->e));
-                CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src1_image));
-                CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
-                CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extrad->data_device));
-                CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_ulong),  &offsetd));
-                CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
-                CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
-                if (ne12 == 1) {
-                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne11));
-                } else {
-                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &tile_size));
-                }
+                    // Reorder activations
+                    // create a sub_buffer for src1
+                    region.origin = offset1;
+                    region.size = ne10 * ne11 * ne12 * sizeof(float);
+                    sub_buf_src1_pre = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
+                    CL_CHECK(status);
+
+                    // Create image for reordered src1
+                    // Use pre-allocated placeholder
+                    region.origin = 0;
+                    region.size = ne00 * max_post_router_tile * n_tile_size * sizeof(float);
+                    backend_ctx->prealloc_act_trans.allocate(backend_ctx->context, region.size);
+                    buf_src1_reordered = clCreateSubBuffer(
+                        backend_ctx->prealloc_act_trans.buffer,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    cl_image_format image_format_buf_src1;
+                    cl_image_desc image_desc_buf_src1;
+                    image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
+                    image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
+                    CL_CHECK(status);
+
+                    unsigned short map_ratio = ne20 / ne11;
+                    GGML_ASSERT(((map_ratio == 1) || (map_ratio == ne20)) && "Map ratio not supported\n");
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 0, sizeof(cl_mem),        &sub_buf_src1_pre));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 1, sizeof(cl_mem),        &buf_src2));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 2, sizeof(cl_mem),        &buf_src1_reordered));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 3, sizeof(cl_mem),        &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 4, sizeof(unsigned int),  &ne00));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 5, sizeof(unsigned short), &map_ratio));
+                    CL_CHECK(clSetKernelArg(backend_ctx->kernel_moe_reorder_b, 6, sizeof(unsigned int),  &n_tile_size));
+
+                    size_t reorder_b_local_size[3] = {256, 1, 1};
+                    size_t reorder_b_global_size[3] = {static_cast<size_t>(((ne00 / 4) + 255) / 256 * 256), static_cast<size_t>(max_post_router_tile * n_tile_size), 1};
+
+                    // Dispatch reorder kernel
+                    backend_ctx->enqueue_ndrange_kernel(backend_ctx->kernel_moe_reorder_b, 3, reorder_b_global_size, reorder_b_local_size, dst);
+
+                    // MoE kernel prepare
+                    // Create sub buffer for dst
+                    region.origin = offsetd;
+                    region.size = ne0 * ne1 * ne2 * sizeof(float);
+                    sub_buf_dst = clCreateSubBuffer(
+                        extrad->data_device,
+                        0,
+                        CL_BUFFER_CREATE_TYPE_REGION,
+                        &region,
+                        &status);
+                    CL_CHECK(status);
+                    // Create image for dst
+                    cl_image_format image_format_buf_dst = {CL_R, CL_FLOAT};
+                    cl_image_desc image_desc_buf_dst = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne0 * ne1 * ne2), 0,0,0,0,0,0,0, {sub_buf_dst}};
+                    buf_dst_image = clCreateImage(backend_ctx->context, CL_MEM_WRITE_ONLY, &image_format_buf_dst, &image_desc_buf_dst, NULL, &status);
+                    CL_CHECK(status);
 
-                // launch kernel
-                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+                    // Set kernel args
+                    int arg_idx = 0;
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_mxfp4->q_img));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &extra0_mxfp4->e));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &image_src1_reordered));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_src2_emap));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &buf_dst_image));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem),    &(backend_ctx->prealloc_total_tiles.buffer)));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne00));
+                    CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int),       &ne01));
 
-                // deallocate sub buffers and images
-                CL_CHECK(clReleaseMemObject(src1_sub_buffer));
-                CL_CHECK(clReleaseMemObject(buf_src1_image));
-                CL_CHECK(clReleaseMemObject(buf_src2));
+                    // set thread grid
+                    global_size[1] = static_cast<size_t>((ne01 + 63) / 64);
+                    global_size[2] = static_cast<size_t>(max_post_router_tile);
+                    local_size[1] = 1;
+                    local_size[2] = 1;
+
+                    // Dispatch kernel
+                    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst);
+
+                    clReleaseMemObject(sub_buf_src1_pre);
+                    clReleaseMemObject(buf_src1_reordered);
+                    clReleaseMemObject(image_src1_reordered);
+                    clReleaseMemObject(buf_src2);
+                    clReleaseMemObject(buf_src2_emap);
+                    clReleaseMemObject(sub_buf_dst);
+                    clReleaseMemObject(buf_dst_image);
+                }
                 return;
-            } // else fallback to generic kernel
+            } // fallback to generic MoE mxfp4 kernel
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
 
 #ifdef GGML_OPENCL_SOA_Q
@@ -13408,6 +15730,13 @@ static void ggml_cl_argsort(ggml_backend_t backend, const ggml_tensor * src0, co
     size_t local_work_size[] = {(size_t)ne00_padded, 1, 1};
 
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    const int ne21 = dst->ne[1];
+    if ((strstr(src0->name, "_moe") != NULL) && (ne21 != 1)) {
+        backend_ctx->toggle_reorder = true;
+    }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
 }
 
 static void ggml_cl_sum_rows(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl
index 39af32d282b..8f06d570587 100644
--- a/ggml/src/ggml-opencl/kernels/cvt.cl
+++ b/ggml/src/ggml-opencl/kernels/cvt.cl
@@ -56,6 +56,25 @@ struct block_q4_1 {
     uchar qs[QK4_1 / 2]; // nibbles / quants
 };
 
+//------------------------------------------------------------------------------
+// block_q5_0
+//------------------------------------------------------------------------------
+struct block_q5_0 {
+    half d; // delta
+    uchar qh[4]; // 5-th bit of quants
+    uchar qs[QK5_0 / 2]; // nibbles / quants
+};
+
+//------------------------------------------------------------------------------
+// block_q5_1
+//------------------------------------------------------------------------------
+struct block_q5_1 {
+    half d; // delta
+    half m; // min
+    uchar qh[4]; // 5-th bit of quants
+    uchar qs[QK5_1 / 2]; // nibbles / quants
+};
+
 //------------------------------------------------------------------------------
 // block_q4_k
 //------------------------------------------------------------------------------
@@ -87,6 +106,17 @@ struct block_q6_K {
     half d;                  // super-block scale
 };
 
+//------------------------------------------------------------------------------
+// block_iq4_nl
+//------------------------------------------------------------------------------
+#define QK4_NL 32
+
+struct block_iq4_nl
+{
+    half d;
+    uint8_t qs[QK4_NL / 2];
+};
+
 //------------------------------------------------------------------------------
 // kernel_convert_block_q4_0
 // Convert the block_q4_0 format to 2 separate arrays (AOS -> SOA).
@@ -179,6 +209,92 @@ kernel void kernel_restore_block_q4_0_noshuffle(
     }
 }
 
+kernel void kernel_convert_block_q4_0_trans4_ns(
+    global struct block_q4_0 * src0,
+    __global uint * dst_q,
+    __global half * dst_d,
+    uint ne00,
+    uint ne01
+) {
+    uint i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK4_0;
+    uint src_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint dst_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    global struct block_q4_0 * b = src0 + src_blk_offset;
+    dst_d[dst_blk_offset] = b->d;
+
+    // extract quantization and unshuffle
+    ushort8 pre_block = ((global ushort8 *)(&(b->qs[0])))[0];
+
+    ushort8 post_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK4_0 / 4; ++i) {
+        uchar x0 = pre_block_ptr[2*i + 0];
+        uchar x1 = pre_block_ptr[2*i + 1];
+
+        post_block_ptr[i + 0        ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        post_block_ptr[i + QK4_0 / 4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    uint4 q_block = as_uint4(post_block);
+
+    uint offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    dst_q[offset] = q_block.x;
+    dst_q[offset + ne01] = q_block.y;
+    dst_q[offset + ne01 * 2] = q_block.z;
+    dst_q[offset + ne01 * 3] = q_block.w;
+}
+
+kernel void kernel_restore_block_q4_0_trans4_ns(
+    __global uint * src_q,
+    __global half * src_d,
+    __global struct block_q4_0 * dst0,
+    uint ne00,
+    uint ne01
+) {
+    uint i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK4_0;
+    uint dst_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint src_d_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    __global struct block_q4_0 * b = dst0 + dst_blk_offset;
+    b->d = src_d[src_d_offset];
+
+    // collect transposed quantization parts for a block
+    uint src_q_offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    uint4 q_block;
+    q_block.x = src_q[src_q_offset];
+    q_block.y = src_q[src_q_offset + ne01];
+    q_block.z = src_q[src_q_offset + ne01 * 2];
+    q_block.w = src_q[src_q_offset + ne01 * 3];
+
+    ushort8 post_block = as_ushort8(q_block);
+    ushort8 pre_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK4_0 / 4; ++i) {
+        uchar x0 = post_block_ptr[i + 0];
+        uchar x1 = post_block_ptr[i + QK4_0 / 4];
+
+        pre_block_ptr[2 * i + 0] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        pre_block_ptr[2 * i + 1] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    ((__global ushort8 *)(&(b->qs[0])))[0] = pre_block;
+}
+
 //------------------------------------------------------------------------------
 // kernel_convert_block_q4_1
 // Convert the block_q4_1 format to 2 separate arrays (AOS -> SOA).
@@ -273,6 +389,281 @@ kernel void kernel_restore_block_q4_1_noshuffle(
     }
 }
 
+kernel void kernel_convert_block_q4_1_trans4_ns(
+    __global struct block_q4_1 * src0,
+    __global uint * dst_q,
+    __global half * dst_d,
+    __global half * dst_m,
+    uint ne00,
+    uint ne01
+) {
+    uint i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK4_1;
+    uint src_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint dst_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    global struct block_q4_1 * b = src0 + src_blk_offset;
+    dst_d[dst_blk_offset] = b->d;
+    dst_m[dst_blk_offset] = b->m;
+
+    // extract quantization and unshuffle
+    ushort8 pre_block = ((global ushort8 *)(&(b->qs[0])))[0];
+
+    ushort8 post_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK4_1 / 4; ++i) {
+        uchar x0 = pre_block_ptr[2*i + 0];
+        uchar x1 = pre_block_ptr[2*i + 1];
+
+        post_block_ptr[i + 0        ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        post_block_ptr[i + QK4_1 / 4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    uint4 q_block = as_uint4(post_block);
+
+    uint offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    dst_q[offset] = q_block.x;
+    dst_q[offset + ne01] = q_block.y;
+    dst_q[offset + ne01 * 2] = q_block.z;
+    dst_q[offset + ne01 * 3] = q_block.w;
+}
+
+kernel void kernel_restore_block_q4_1_trans4_ns(
+    __global uint * src_q,
+    __global half * src_d,
+    __global half * src_m,
+    __global struct block_q4_1 * dst0,
+    uint ne00,
+    uint ne01
+) {
+    int i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK4_1;
+    uint dst_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint src_dm_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    __global struct block_q4_1 * b = dst0 + dst_blk_offset;
+    b->d = src_d[src_dm_offset];
+    b->m = src_m[src_dm_offset];
+
+    // collect transposed quantization parts for a block
+    uint src_q_offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    uint4 q_block;
+    q_block.x = src_q[src_q_offset];
+    q_block.y = src_q[src_q_offset + ne01];
+    q_block.z = src_q[src_q_offset + ne01 * 2];
+    q_block.w = src_q[src_q_offset + ne01 * 3];
+
+    ushort8 post_block = as_ushort8(q_block);
+    ushort8 pre_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK4_0 / 4; ++i) {
+        uchar x0 = post_block_ptr[i + 0];
+        uchar x1 = post_block_ptr[i + QK4_0 / 4];
+
+        pre_block_ptr[2 * i + 0] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        pre_block_ptr[2 * i + 1] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    ((__global ushort8 *)(&(b->qs[0])))[0] = pre_block;
+}
+
+kernel void kernel_convert_block_q5_0_trans4_ns(
+    __global struct block_q5_0 * src0,
+    __global uint * dst_qs,
+    __global uint * dst_qh,
+    __global half * dst_d,
+    uint ne00,
+    uint ne01
+) {
+    uint i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK5_0;
+    uint src_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint dst_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    global struct block_q5_0 * b = src0 + src_blk_offset;
+    dst_d[dst_blk_offset] = b->d;
+
+    dst_qh[dst_blk_offset] = ((global uint *)(&(b->qh[0])))[0];
+
+    // extract quantization and unshuffle
+    ushort8 pre_block = ((global ushort8 *)(&(b->qs[0])))[0];
+    ushort8 post_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK5_0 / 4; ++i) {
+        uchar x0 = pre_block_ptr[2*i + 0];
+        uchar x1 = pre_block_ptr[2*i + 1];
+
+        post_block_ptr[i + 0        ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        post_block_ptr[i + QK5_0 / 4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    uint4 q_block = as_uint4(post_block);
+
+    uint offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    dst_qs[offset] = q_block.x;
+    dst_qs[offset + ne01] = q_block.y;
+    dst_qs[offset + ne01 * 2] = q_block.z;
+    dst_qs[offset + ne01 * 3] = q_block.w;
+}
+
+kernel void kernel_restore_block_q5_0_trans4_ns(
+    __global uint * src_qs,
+    __global uint * src_qh,
+    __global half * src_d,
+    __global struct block_q5_0 * dst0,
+    uint ne00,
+    uint ne01
+) {
+    int i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK5_0;
+    uint dst_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint src_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    __global struct block_q5_0 * b = dst0 + dst_blk_offset;
+    b->d = src_d[src_blk_offset];
+
+    ((__global uint *)(&(b->qh[0])))[0] = src_qh[src_blk_offset];
+
+    // collect transposed quantization parts for a block
+    uint src_q_offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    uint4 q_block;
+    q_block.x = src_qs[src_q_offset];
+    q_block.y = src_qs[src_q_offset + ne01];
+    q_block.z = src_qs[src_q_offset + ne01 * 2];
+    q_block.w = src_qs[src_q_offset + ne01 * 3];
+
+    ushort8 post_block = as_ushort8(q_block);
+    ushort8 pre_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK5_0 / 4; ++i) {
+        uchar x0 = post_block_ptr[i + 0];
+        uchar x1 = post_block_ptr[i + QK5_0 / 4];
+
+        pre_block_ptr[2 * i + 0] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        pre_block_ptr[2 * i + 1] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    ((__global ushort8 *)(&(b->qs[0])))[0] = pre_block;
+}
+
+kernel void kernel_convert_block_q5_1_trans4_ns(
+    __global struct block_q5_1 * src0,
+    __global uint * dst_qs,
+    __global uint * dst_qh,
+    __global half * dst_d,
+    __global half * dst_m,
+    uint ne00,
+    uint ne01
+) {
+    uint i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK5_1;
+    uint src_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint dst_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    global struct block_q5_1 * b = src0 + src_blk_offset;
+    dst_d[dst_blk_offset] = b->d;
+    dst_m[dst_blk_offset] = b->m;
+
+    dst_qh[dst_blk_offset] = ((global uint *)(&(b->qh[0])))[0];
+
+    // extract quantization and unshuffle
+    ushort8 pre_block = ((global ushort8 *)(&(b->qs[0])))[0];
+    ushort8 post_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK5_1 / 4; ++i) {
+        uchar x0 = pre_block_ptr[2*i + 0];
+        uchar x1 = pre_block_ptr[2*i + 1];
+
+        post_block_ptr[i + 0        ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        post_block_ptr[i + QK5_1 / 4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    uint4 q_block = as_uint4(post_block);
+
+    uint offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    dst_qs[offset] = q_block.x;
+    dst_qs[offset + ne01] = q_block.y;
+    dst_qs[offset + ne01 * 2] = q_block.z;
+    dst_qs[offset + ne01 * 3] = q_block.w;
+}
+
+kernel void kernel_restore_block_q5_1_trans4_ns(
+    __global uint * src_qs,
+    __global uint * src_qh,
+    __global half * src_d,
+    __global half * src_m,
+    __global struct block_q5_1 * dst0,
+    uint ne00,
+    uint ne01
+) {
+    int i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK5_1;
+    uint dst_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint src_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    __global struct block_q5_1 * b = dst0 + dst_blk_offset;
+    b->d = src_d[src_blk_offset];
+    b->m = src_m[src_blk_offset];
+
+    ((__global uint *)(&(b->qh[0])))[0] = src_qh[src_blk_offset];
+
+    // collect transposed quantization parts for a block
+    uint src_q_offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    uint4 q_block;
+    q_block.x = src_qs[src_q_offset];
+    q_block.y = src_qs[src_q_offset + ne01];
+    q_block.z = src_qs[src_q_offset + ne01 * 2];
+    q_block.w = src_qs[src_q_offset + ne01 * 3];
+
+    ushort8 post_block = as_ushort8(q_block);
+    ushort8 pre_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK5_1 / 4; ++i) {
+        uchar x0 = post_block_ptr[i + 0];
+        uchar x1 = post_block_ptr[i + QK5_1 / 4];
+
+        pre_block_ptr[2 * i + 0] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        pre_block_ptr[2 * i + 1] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+    ((__global ushort8 *)(&(b->qs[0])))[0] = pre_block;
+}
+
 //------------------------------------------------------------------------------
 // block_mxfp4
 //------------------------------------------------------------------------------
@@ -360,6 +751,93 @@ kernel void kernel_restore_block_mxfp4_trans(
     b->e = src_e[src_blk_offset];
 }
 
+kernel void kernel_convert_block_mxfp4_trans4_ns(
+    global struct block_mxfp4 * src0,
+    __global uint * dst_q,
+    __global uchar * dst_e,
+    uint ne00,
+    uint ne01
+) {
+    uint i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK_MXFP4;
+    uint src_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint dst_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    global struct block_mxfp4 * b = src0 + src_blk_offset;
+    dst_e[dst_blk_offset] = b->e;
+
+    // extract quantization and unshuffle
+    ushort8 pre_block = ((global ushort8 *)(&(b->qs[0])))[0];
+
+    ushort8 post_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK_MXFP4 / 4; ++i) {
+        uchar x0 = pre_block_ptr[2*i + 0];
+        uchar x1 = pre_block_ptr[2*i + 1];
+
+        post_block_ptr[i + 0        ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        post_block_ptr[i + QK_MXFP4 / 4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    uint4 q_block = as_uint4(post_block);
+
+    uint offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    dst_q[offset] = q_block.x;
+    dst_q[offset + ne01] = q_block.y;
+    dst_q[offset + ne01 * 2] = q_block.z;
+    dst_q[offset + ne01 * 3] = q_block.w;
+}
+
+kernel void kernel_restore_block_mxfp4_trans4_ns(
+    __global uint * src_q,
+    __global uchar * src_e,
+    __global struct block_mxfp4 * dst0,
+    uint ne00,
+    uint ne01
+) {
+    uint i00 = get_global_id(1);
+    uint i01 = get_global_id(0);
+    uint i02 = get_global_id(2);
+
+    uint ne00_blk = ne00 / QK_MXFP4;
+    uint dst_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01;
+    uint src_d_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01;
+
+    __global struct block_mxfp4 * b = dst0 + dst_blk_offset;
+    b->e = src_e[src_d_offset];
+
+    // collect transposed quantization parts for a block
+    uint src_q_offset = i02 * ne00_blk * ne01 * 4 + i00 * ne01 * 4 + i01;
+    uint4 q_block;
+    q_block.x = src_q[src_q_offset];
+    q_block.y = src_q[src_q_offset + ne01];
+    q_block.z = src_q[src_q_offset + ne01 * 2];
+    q_block.w = src_q[src_q_offset + ne01 * 3];
+
+    ushort8 post_block = as_ushort8(q_block);
+    ushort8 pre_block = (ushort8)(0);
+
+    uchar * pre_block_ptr = (uchar *)(&pre_block);
+    uchar * post_block_ptr = (uchar *)(&post_block);
+
+    for (int i = 0; i < QK_MXFP4 / 4; ++i) {
+        uchar x0 = post_block_ptr[i + 0];
+        uchar x1 = post_block_ptr[i + QK_MXFP4 / 4];
+
+        pre_block_ptr[2 * i + 0] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4);
+        pre_block_ptr[2 * i + 1] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0);
+    }
+
+    ((__global ushort8 *)(&(b->qs[0])))[0] = pre_block;
+}
+
+
 //------------------------------------------------------------------------------
 // block_q8_0
 //------------------------------------------------------------------------------
@@ -895,3 +1373,99 @@ kernel void kernel_restore_block_q6_K_noshuffle(
         b->scales[i] = s[i];
     }
 }
+
+//------------------------------------------------------------------------------
+// kernel_convert_block_iq4_nl
+// Convert the block_iq4_nl format to 2 separate arrays (AOS -> SOA).
+//------------------------------------------------------------------------------
+kernel void kernel_convert_block_iq4_nl(
+    global struct block_iq4_nl * src0,
+    global uchar * dst_q,
+    global half  * dst_d,
+    uchar          mask_0F,
+    uchar          mask_F0,
+    ulong          n_blk
+) {
+    if (get_global_id(0) >= n_blk) {
+        return;
+    }
+    global struct block_iq4_nl * b = (global struct block_iq4_nl *) src0 + get_global_id(0);
+    global uchar * q = (global uchar *) dst_q + QK4_NL/2*get_global_id(0);
+    global half  * d = (global half *) dst_d + get_global_id(0);
+
+    *d = b->d;
+
+    for (int i = 0; i < QK4_NL/2; ++i) {
+        q[i] = b->qs[i];
+    }
+}
+
+kernel void kernel_restore_block_iq4_nl(
+    global uchar * src_q,
+    global half  * src_d,
+    global struct block_iq4_nl * dst,
+    ulong          n_blk
+) {
+    if (get_global_id(0) >= n_blk) {
+        return;
+    }
+    global struct block_iq4_nl * b = (global struct block_iq4_nl *) dst + get_global_id(0);
+    global uchar * q = (global uchar *) src_q + QK4_NL/2*get_global_id(0);
+    global half  * d = (global half *) src_d + get_global_id(0);
+
+    b->d = *d;
+
+    for (int i = 0; i < QK4_NL/2; ++i) {
+        b->qs[i] = q[i];
+    }
+}
+
+kernel void kernel_convert_block_iq4_nl_noshuffle(
+    global struct block_iq4_nl * src0,
+    global uchar * dst_q,
+    global half  * dst_d,
+    uchar          mask_0F,
+    uchar          mask_F0,
+    ulong          n_blk
+) {
+    if (get_global_id(0) >= n_blk) {
+        return;
+    }
+    global struct block_iq4_nl * b = (global struct block_iq4_nl *) src0 + get_global_id(0);
+    global uchar * q = (global uchar *) dst_q + QK4_NL/2*get_global_id(0);
+    global half  * d = (global half *) dst_d + get_global_id(0);
+
+    *d = b->d;
+    for (int i = 0; i < QK4_NL/4; ++i) {
+        uchar x0 = b->qs[2*i + 0];
+        uchar x1 = b->qs[2*i + 1];
+
+        q[i + 0       ] = convert_uchar(x0 & mask_0F) | convert_uchar((x1 & mask_0F) << 4);
+        q[i + QK4_NL/4] = convert_uchar((x0 & mask_F0) >> 4) | convert_uchar(x1 & mask_F0);
+    }
+}
+
+kernel void kernel_restore_block_iq4_nl_noshuffle(
+    global uchar * src_q,
+    global half  * src_d,
+    global struct block_iq4_nl * dst,
+    uchar mask_0F,
+    uchar mask_F0,
+    ulong n_blk
+) {
+    if (get_global_id(0) >= n_blk) {
+        return;
+    }
+    global struct block_iq4_nl * b = (global struct block_iq4_nl *) dst + get_global_id(0);
+    global uchar * q = (global uchar *) src_q + QK4_NL/2*get_global_id(0);
+    global half  * d = (global half *) src_d + get_global_id(0);
+
+    b->d = *d;
+    for (int i = 0; i < QK4_NL/4; ++i) {
+        uchar x0 = q[i + 0       ];
+        uchar x1 = q[i + QK4_NL/4];
+
+        b->qs[2*i + 0] = convert_uchar((x0 & mask_0F) | ((x1 & mask_0F) << 4));
+        b->qs[2*i + 1] = convert_uchar(((x0 & mask_F0) >> 4) | (x1 & mask_F0));
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl
new file mode 100644
index 00000000000..e404f392bdd
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl
@@ -0,0 +1,302 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_uniform_load: enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_constant_load: enable
+#pragma OPENCL EXTENSION cl_qcom_extra_vector_types : enable
+
+#define TILESIZE_K 16
+#define TILESIZE_M 64
+#define TILESIZE_N 32
+
+
+static inline half8 mxfp4_to_fp16_packed8(ushort2 fp4x8) {
+    ushort2 fp16_packed_a_0, fp16_packed_b_0, bias_a, bias_b, sign_a, sign_b;
+    fp16_packed_a_0.lo = (fp4x8.s0 << 9) & 0x0E00;
+    fp16_packed_a_0.hi = (fp4x8.s0 << 5) & 0x0E00;
+    fp16_packed_b_0.lo = (fp4x8.s0 << 1) & 0x0E00;
+    fp16_packed_b_0.hi = (fp4x8.s0 >> 3) & 0x0E00;
+
+    bias_a.lo = (fp16_packed_a_0.lo != 0) ? 0x3800 : 0x0;
+    bias_a.hi = (fp16_packed_a_0.hi != 0) ? 0x3800 : 0x0;
+    bias_b.lo = (fp16_packed_b_0.lo != 0) ? 0x3800 : 0x0;
+    bias_b.hi = (fp16_packed_b_0.hi != 0) ? 0x3800 : 0x0;
+
+    fp16_packed_a_0.lo = (fp16_packed_a_0.lo != 0x0200) ? fp16_packed_a_0.lo : 0x0;
+    fp16_packed_a_0.hi = (fp16_packed_a_0.hi != 0x0200) ? fp16_packed_a_0.hi : 0x0;
+    fp16_packed_b_0.lo = (fp16_packed_b_0.lo != 0x0200) ? fp16_packed_b_0.lo : 0x0;
+    fp16_packed_b_0.hi = (fp16_packed_b_0.hi != 0x0200) ? fp16_packed_b_0.hi : 0x0;
+
+    sign_a.lo = (fp4x8.s0 << 12) & 0x8000;
+    sign_a.hi = (fp4x8.s0 << 8) & 0x8000;
+    sign_b.lo = (fp4x8.s0 << 4) & 0x8000;
+    sign_b.hi = fp4x8.s0 & 0x8000;
+
+    fp16_packed_a_0 = sign_a + bias_a + fp16_packed_a_0;
+    fp16_packed_b_0 = sign_b + bias_b + fp16_packed_b_0;
+
+    ushort2 fp16_packed_a_1, fp16_packed_b_1;
+    fp16_packed_a_1.lo = (fp4x8.s1 << 9) & 0x0E00;
+    fp16_packed_a_1.hi = (fp4x8.s1 << 5) & 0x0E00;
+    fp16_packed_b_1.lo = (fp4x8.s1 << 1) & 0x0E00;
+    fp16_packed_b_1.hi = (fp4x8.s1 >> 3) & 0x0E00;
+
+    bias_a.lo = (fp16_packed_a_1.lo != 0) ? 0x3800 : 0x0;
+    bias_a.hi = (fp16_packed_a_1.hi != 0) ? 0x3800 : 0x0;
+    bias_b.lo = (fp16_packed_b_1.lo != 0) ? 0x3800 : 0x0;
+    bias_b.hi = (fp16_packed_b_1.hi != 0) ? 0x3800 : 0x0;
+
+    fp16_packed_a_1.lo = (fp16_packed_a_1.lo != 0x0200) ? fp16_packed_a_1.lo : 0x0;
+    fp16_packed_a_1.hi = (fp16_packed_a_1.hi != 0x0200) ? fp16_packed_a_1.hi : 0x0;
+    fp16_packed_b_1.lo = (fp16_packed_b_1.lo != 0x0200) ? fp16_packed_b_1.lo : 0x0;
+    fp16_packed_b_1.hi = (fp16_packed_b_1.hi != 0x0200) ? fp16_packed_b_1.hi : 0x0;
+
+    sign_a.lo = (fp4x8.s1 << 12) & 0x8000;
+    sign_a.hi = (fp4x8.s1 << 8) & 0x8000;
+    sign_b.lo = (fp4x8.s1 << 4) & 0x8000;
+    sign_b.hi = fp4x8.s1 & 0x8000;
+
+    fp16_packed_a_1 = sign_a + bias_a + fp16_packed_a_1;
+    fp16_packed_b_1 = sign_b + bias_b + fp16_packed_b_1;
+
+    return as_half8((ushort8)(fp16_packed_a_0, fp16_packed_b_0, fp16_packed_a_1, fp16_packed_b_1));
+}
+
+
+#define dotx16_reduce8(a_reg, b_lm, c_reg, lm_offset) \
+    acc.s0 = dot(a_reg.s0123, b_lm[lm_offset + 0]); \
+    acc.s1 = dot(a_reg.s0123, b_lm[lm_offset + 1]); \
+    acc.s2 = dot(a_reg.s0123, b_lm[lm_offset + 2]); \
+    acc.s3 = dot(a_reg.s0123, b_lm[lm_offset + 3]); \
+    acc.s4 = dot(a_reg.s0123, b_lm[lm_offset + 4]); \
+    acc.s5 = dot(a_reg.s0123, b_lm[lm_offset + 5]); \
+    acc.s6 = dot(a_reg.s0123, b_lm[lm_offset + 6]); \
+    acc.s7 = dot(a_reg.s0123, b_lm[lm_offset + 7]); \
+    acc.s8 = dot(a_reg.s0123, b_lm[lm_offset + 8]); \
+    acc.s9 = dot(a_reg.s0123, b_lm[lm_offset + 9]); \
+    acc.sa = dot(a_reg.s0123, b_lm[lm_offset + 10]); \
+    acc.sb = dot(a_reg.s0123, b_lm[lm_offset + 11]); \
+    acc.sc = dot(a_reg.s0123, b_lm[lm_offset + 12]); \
+    acc.sd = dot(a_reg.s0123, b_lm[lm_offset + 13]); \
+    acc.se = dot(a_reg.s0123, b_lm[lm_offset + 14]); \
+    acc.sf = dot(a_reg.s0123, b_lm[lm_offset + 15]); \
+    acc.s0 += dot(a_reg.s4567, b_lm[lm_offset + 32]); \
+    acc.s1 += dot(a_reg.s4567, b_lm[lm_offset + 33]); \
+    acc.s2 += dot(a_reg.s4567, b_lm[lm_offset + 34]); \
+    acc.s3 += dot(a_reg.s4567, b_lm[lm_offset + 35]); \
+    acc.s4 += dot(a_reg.s4567, b_lm[lm_offset + 36]); \
+    acc.s5 += dot(a_reg.s4567, b_lm[lm_offset + 37]); \
+    acc.s6 += dot(a_reg.s4567, b_lm[lm_offset + 38]); \
+    acc.s7 += dot(a_reg.s4567, b_lm[lm_offset + 39]); \
+    acc.s8 += dot(a_reg.s4567, b_lm[lm_offset + 40]); \
+    acc.s9 += dot(a_reg.s4567, b_lm[lm_offset + 41]); \
+    acc.sa += dot(a_reg.s4567, b_lm[lm_offset + 42]); \
+    acc.sb += dot(a_reg.s4567, b_lm[lm_offset + 43]); \
+    acc.sc += dot(a_reg.s4567, b_lm[lm_offset + 44]); \
+    acc.sd += dot(a_reg.s4567, b_lm[lm_offset + 45]); \
+    acc.se += dot(a_reg.s4567, b_lm[lm_offset + 46]); \
+    acc.sf += dot(a_reg.s4567, b_lm[lm_offset + 47]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+    acc.s0 = dot(a_reg.s89ab, b_lm[lm_offset + 64]); \
+    acc.s1 = dot(a_reg.s89ab, b_lm[lm_offset + 65]); \
+    acc.s2 = dot(a_reg.s89ab, b_lm[lm_offset + 66]); \
+    acc.s3 = dot(a_reg.s89ab, b_lm[lm_offset + 67]); \
+    acc.s4 = dot(a_reg.s89ab, b_lm[lm_offset + 68]); \
+    acc.s5 = dot(a_reg.s89ab, b_lm[lm_offset + 69]); \
+    acc.s6 = dot(a_reg.s89ab, b_lm[lm_offset + 70]); \
+    acc.s7 = dot(a_reg.s89ab, b_lm[lm_offset + 71]); \
+    acc.s8 = dot(a_reg.s89ab, b_lm[lm_offset + 72]); \
+    acc.s9 = dot(a_reg.s89ab, b_lm[lm_offset + 73]); \
+    acc.sa = dot(a_reg.s89ab, b_lm[lm_offset + 74]); \
+    acc.sb = dot(a_reg.s89ab, b_lm[lm_offset + 75]); \
+    acc.sc = dot(a_reg.s89ab, b_lm[lm_offset + 76]); \
+    acc.sd = dot(a_reg.s89ab, b_lm[lm_offset + 77]); \
+    acc.se = dot(a_reg.s89ab, b_lm[lm_offset + 78]); \
+    acc.sf = dot(a_reg.s89ab, b_lm[lm_offset + 79]); \
+    acc.s0 += dot(a_reg.scdef, b_lm[lm_offset + 96]); \
+    acc.s1 += dot(a_reg.scdef, b_lm[lm_offset + 97]); \
+    acc.s2 += dot(a_reg.scdef, b_lm[lm_offset + 98]); \
+    acc.s3 += dot(a_reg.scdef, b_lm[lm_offset + 99]); \
+    acc.s4 += dot(a_reg.scdef, b_lm[lm_offset + 100]); \
+    acc.s5 += dot(a_reg.scdef, b_lm[lm_offset + 101]); \
+    acc.s6 += dot(a_reg.scdef, b_lm[lm_offset + 102]); \
+    acc.s7 += dot(a_reg.scdef, b_lm[lm_offset + 103]); \
+    acc.s8 += dot(a_reg.scdef, b_lm[lm_offset + 104]); \
+    acc.s9 += dot(a_reg.scdef, b_lm[lm_offset + 105]); \
+    acc.sa += dot(a_reg.scdef, b_lm[lm_offset + 106]); \
+    acc.sb += dot(a_reg.scdef, b_lm[lm_offset + 107]); \
+    acc.sc += dot(a_reg.scdef, b_lm[lm_offset + 108]); \
+    acc.sd += dot(a_reg.scdef, b_lm[lm_offset + 109]); \
+    acc.se += dot(a_reg.scdef, b_lm[lm_offset + 110]); \
+    acc.sf += dot(a_reg.scdef, b_lm[lm_offset + 111]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+
+
+static inline half e8m0_to_fp16(uchar x) {
+    ushort bits;
+    bits = (ushort)(x) - (ushort)(112);
+    bits = ((bits & 0x00E0) != 0) ? 0x7C00 : (bits << 10);
+    return as_half(bits);
+}
+
+static inline float e8m0_to_fp32(uchar x) {
+    int bits;
+    bits = (x == 0) ? 0x00400000 : ((uint) x << 23);
+    return as_float(bits);
+}
+
+
+__attribute__((qcom_wave_pair_mode(1))) // 1=force single 2=force pair
+kernel void kernel_gemm_moe_mxfp4_f32_ns(
+        __read_only  image1d_buffer_t src0_q,
+        __global     uchar *          src0_d,
+        __read_only  image1d_buffer_t src1,
+        __global     uint *           src2,
+        __global     ushort *         src2_emap,
+        __write_only image1d_buffer_t dst,
+        __global     int *            total_tiles,
+        uint ne00,
+        uint ne01
+) {
+    uint block_id_m = get_global_id(1); // m_tile
+    uint block_id_n = get_global_id(2); // n_tile
+
+    // Boundary check
+    if (((get_global_id(0) + block_id_m * TILESIZE_M) >= ne01) || (block_id_n >= total_tiles[0])) {
+        return;
+    }
+
+    __private half16 reg_a;
+    __private float32 reg_c = (float32)(0);
+    __local half4 shared_b[128];
+
+    const ushort expert_id = src2_emap[block_id_n];
+
+    const uint row = block_id_m * TILESIZE_M;
+    const uint col = block_id_n * TILESIZE_N;
+
+    uint sub_block_id_m = get_local_id(0);
+    uint2 b_global_offset;
+    b_global_offset.x = ((sub_block_id_m & 3) << 2) + (sub_block_id_m >> 2) * ne00;
+    b_global_offset.y = b_global_offset.x + (16 * ne00);
+    uint2 b_local_offset;
+    b_local_offset.x = (sub_block_id_m & 3) * 32 + (sub_block_id_m >> 2);
+    b_local_offset.y = b_local_offset.x + 16;
+
+    // Loop along K axis, 32 elements (one block) for each iteration, divided into 2 sub-blocks
+    for (uint step = 0; step < ne00; step += TILESIZE_K * 2) {
+        // First sub-block
+        uint q_sub_offset = row + ((ne01 * step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        uint s_sub_offset = row + ((ne01 * step) >> 5) + ((expert_id * ne00 * ne01) >> 5);
+        uint b_sub_offset = col * ne00 + step;
+
+        // Load scale for current mxfp4 block
+        uint s_offset = s_sub_offset + get_global_id(0);
+        float s = e8m0_to_fp32(src0_d[s_offset]);
+
+        // Load 16 fp4 (64-bits) in transposed layout
+        uint2 mxfp4x16;
+        mxfp4x16.x = read_imageui(src0_q, q_sub_offset + sub_block_id_m).x;
+        mxfp4x16.y = read_imageui(src0_q, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        float8 bx8_f32;
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        half8 bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * s;
+        reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * s;
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 8 elements reduction for better precision
+        half16 acc;
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+
+        // Repeat for second sub-block
+        uint half_step = step + TILESIZE_K;
+        q_sub_offset = row + ((ne01 * half_step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        b_sub_offset = col * ne00 + half_step;
+
+        // Load next 16 fp4 (64-bits) in transposed layout
+        mxfp4x16.x = read_imageui(src0_q, q_sub_offset + sub_block_id_m).x;
+        mxfp4x16.y = read_imageui(src0_q, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * s;
+        reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * s;
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 3-levels reduction for better precision
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+    }
+
+    // Load poster router and share in LM
+    __local uint out_idx[TILESIZE_N];
+
+    if (get_local_id(0) < TILESIZE_N) {
+        uint idx = src2[block_id_n * TILESIZE_N + get_local_id(0)];
+        if (idx == 0xFFFFFFFF) {
+            idx = src2[block_id_n * TILESIZE_N + 0];
+        }
+        out_idx[get_local_id(0)] = idx * ne01;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // Scatter results back to original position in output grid
+    uint m_offset = row + get_local_id(0);
+
+    write_imagef(dst, out_idx[1] + m_offset, (reg_c.s1));
+    write_imagef(dst, out_idx[2] + m_offset, (reg_c.s2));
+    write_imagef(dst, out_idx[3] + m_offset, (reg_c.s3));
+    write_imagef(dst, out_idx[4] + m_offset, (reg_c.s4));
+    write_imagef(dst, out_idx[5] + m_offset, (reg_c.s5));
+    write_imagef(dst, out_idx[6] + m_offset, (reg_c.s6));
+    write_imagef(dst, out_idx[7] + m_offset, (reg_c.s7));
+    write_imagef(dst, out_idx[8] + m_offset, (reg_c.s8));
+    write_imagef(dst, out_idx[9] + m_offset, (reg_c.s9));
+    write_imagef(dst, out_idx[10] + m_offset, (reg_c.sa));
+    write_imagef(dst, out_idx[11] + m_offset, (reg_c.sb));
+    write_imagef(dst, out_idx[12] + m_offset, (reg_c.sc));
+    write_imagef(dst, out_idx[13] + m_offset, (reg_c.sd));
+    write_imagef(dst, out_idx[14] + m_offset, (reg_c.se));
+    write_imagef(dst, out_idx[15] + m_offset, (reg_c.sf));
+    write_imagef(dst, out_idx[16] + m_offset, (reg_c.sg));
+    write_imagef(dst, out_idx[17] + m_offset, (reg_c.sh));
+    write_imagef(dst, out_idx[18] + m_offset, (reg_c.si));
+    write_imagef(dst, out_idx[19] + m_offset, (reg_c.sj));
+    write_imagef(dst, out_idx[20] + m_offset, (reg_c.sk));
+    write_imagef(dst, out_idx[21] + m_offset, (reg_c.sl));
+    write_imagef(dst, out_idx[22] + m_offset, (reg_c.sm));
+    write_imagef(dst, out_idx[23] + m_offset, (reg_c.sn));
+    write_imagef(dst, out_idx[24] + m_offset, (reg_c.so));
+    write_imagef(dst, out_idx[25] + m_offset, (reg_c.sp));
+    write_imagef(dst, out_idx[26] + m_offset, (reg_c.sq));
+    write_imagef(dst, out_idx[27] + m_offset, (reg_c.sr));
+    write_imagef(dst, out_idx[28] + m_offset, (reg_c.ss));
+    write_imagef(dst, out_idx[29] + m_offset, (reg_c.st));
+    write_imagef(dst, out_idx[30] + m_offset, (reg_c.su));
+    write_imagef(dst, out_idx[31] + m_offset, (reg_c.sv));
+
+    // Store zero padding parts to the index of first output in tile, override correct result in the end
+    barrier(CLK_GLOBAL_MEM_FENCE);
+    write_imagef(dst, out_idx[0] + m_offset, (reg_c.s0));
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_f32_ns.cl
new file mode 100644
index 00000000000..02290c17eb1
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_f32_ns.cl
@@ -0,0 +1,252 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_uniform_load: enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_constant_load: enable
+#pragma OPENCL EXTENSION cl_qcom_extra_vector_types : enable
+
+#define TILESIZE_K 16
+#define TILESIZE_M 64
+#define TILESIZE_N 32
+
+
+#define dequantize_q4_0(q4, a_f16, scale) \
+    a_f16.s0 = (half)((q4.s0 & 0x000F) - 8) * scale; \
+    a_f16.s1 = (half)(((q4.s0 & 0x00F0) >> 4) - 8) * scale; \
+    a_f16.s2 = (half)(((q4.s0 & 0x0F00) >> 8) - 8) * scale; \
+    a_f16.s3 = (half)(((q4.s0 & 0xF000) >> 12) - 8) * scale; \
+    a_f16.s4 = (half)((q4.s1 & 0x000F) - 8) * scale; \
+    a_f16.s5 = (half)(((q4.s1 & 0x00F0) >> 4) - 8) * scale; \
+    a_f16.s6 = (half)(((q4.s1 & 0x0F00) >> 8) - 8) * scale; \
+    a_f16.s7 = (half)(((q4.s1 & 0xF000) >> 12) - 8) * scale; \
+    a_f16.s8 = (half)((q4.s2 & 0x000F) - 8) * scale; \
+    a_f16.s9 = (half)(((q4.s2 & 0x00F0) >> 4) - 8) * scale; \
+    a_f16.sa = (half)(((q4.s2 & 0x0F00) >> 8) - 8) * scale; \
+    a_f16.sb = (half)(((q4.s2 & 0xF000) >> 12) - 8) * scale; \
+    a_f16.sc = (half)((q4.s3 & 0x000F) - 8) * scale; \
+    a_f16.sd = (half)(((q4.s3 & 0x00F0) >> 4) - 8) * scale; \
+    a_f16.se = (half)(((q4.s3 & 0x0F00) >> 8) - 8) * scale; \
+    a_f16.sf = (half)(((q4.s3 & 0xF000) >> 12) - 8) * scale; \
+
+
+#define dotx16_reduce8(a_reg, b_lm, c_reg, lm_offset) \
+    acc.s0 = dot(a_reg.s0123, b_lm[lm_offset + 0]); \
+    acc.s1 = dot(a_reg.s0123, b_lm[lm_offset + 1]); \
+    acc.s2 = dot(a_reg.s0123, b_lm[lm_offset + 2]); \
+    acc.s3 = dot(a_reg.s0123, b_lm[lm_offset + 3]); \
+    acc.s4 = dot(a_reg.s0123, b_lm[lm_offset + 4]); \
+    acc.s5 = dot(a_reg.s0123, b_lm[lm_offset + 5]); \
+    acc.s6 = dot(a_reg.s0123, b_lm[lm_offset + 6]); \
+    acc.s7 = dot(a_reg.s0123, b_lm[lm_offset + 7]); \
+    acc.s8 = dot(a_reg.s0123, b_lm[lm_offset + 8]); \
+    acc.s9 = dot(a_reg.s0123, b_lm[lm_offset + 9]); \
+    acc.sa = dot(a_reg.s0123, b_lm[lm_offset + 10]); \
+    acc.sb = dot(a_reg.s0123, b_lm[lm_offset + 11]); \
+    acc.sc = dot(a_reg.s0123, b_lm[lm_offset + 12]); \
+    acc.sd = dot(a_reg.s0123, b_lm[lm_offset + 13]); \
+    acc.se = dot(a_reg.s0123, b_lm[lm_offset + 14]); \
+    acc.sf = dot(a_reg.s0123, b_lm[lm_offset + 15]); \
+    acc.s0 += dot(a_reg.s4567, b_lm[lm_offset + 32]); \
+    acc.s1 += dot(a_reg.s4567, b_lm[lm_offset + 33]); \
+    acc.s2 += dot(a_reg.s4567, b_lm[lm_offset + 34]); \
+    acc.s3 += dot(a_reg.s4567, b_lm[lm_offset + 35]); \
+    acc.s4 += dot(a_reg.s4567, b_lm[lm_offset + 36]); \
+    acc.s5 += dot(a_reg.s4567, b_lm[lm_offset + 37]); \
+    acc.s6 += dot(a_reg.s4567, b_lm[lm_offset + 38]); \
+    acc.s7 += dot(a_reg.s4567, b_lm[lm_offset + 39]); \
+    acc.s8 += dot(a_reg.s4567, b_lm[lm_offset + 40]); \
+    acc.s9 += dot(a_reg.s4567, b_lm[lm_offset + 41]); \
+    acc.sa += dot(a_reg.s4567, b_lm[lm_offset + 42]); \
+    acc.sb += dot(a_reg.s4567, b_lm[lm_offset + 43]); \
+    acc.sc += dot(a_reg.s4567, b_lm[lm_offset + 44]); \
+    acc.sd += dot(a_reg.s4567, b_lm[lm_offset + 45]); \
+    acc.se += dot(a_reg.s4567, b_lm[lm_offset + 46]); \
+    acc.sf += dot(a_reg.s4567, b_lm[lm_offset + 47]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+    acc.s0 = dot(a_reg.s89ab, b_lm[lm_offset + 64]); \
+    acc.s1 = dot(a_reg.s89ab, b_lm[lm_offset + 65]); \
+    acc.s2 = dot(a_reg.s89ab, b_lm[lm_offset + 66]); \
+    acc.s3 = dot(a_reg.s89ab, b_lm[lm_offset + 67]); \
+    acc.s4 = dot(a_reg.s89ab, b_lm[lm_offset + 68]); \
+    acc.s5 = dot(a_reg.s89ab, b_lm[lm_offset + 69]); \
+    acc.s6 = dot(a_reg.s89ab, b_lm[lm_offset + 70]); \
+    acc.s7 = dot(a_reg.s89ab, b_lm[lm_offset + 71]); \
+    acc.s8 = dot(a_reg.s89ab, b_lm[lm_offset + 72]); \
+    acc.s9 = dot(a_reg.s89ab, b_lm[lm_offset + 73]); \
+    acc.sa = dot(a_reg.s89ab, b_lm[lm_offset + 74]); \
+    acc.sb = dot(a_reg.s89ab, b_lm[lm_offset + 75]); \
+    acc.sc = dot(a_reg.s89ab, b_lm[lm_offset + 76]); \
+    acc.sd = dot(a_reg.s89ab, b_lm[lm_offset + 77]); \
+    acc.se = dot(a_reg.s89ab, b_lm[lm_offset + 78]); \
+    acc.sf = dot(a_reg.s89ab, b_lm[lm_offset + 79]); \
+    acc.s0 += dot(a_reg.scdef, b_lm[lm_offset + 96]); \
+    acc.s1 += dot(a_reg.scdef, b_lm[lm_offset + 97]); \
+    acc.s2 += dot(a_reg.scdef, b_lm[lm_offset + 98]); \
+    acc.s3 += dot(a_reg.scdef, b_lm[lm_offset + 99]); \
+    acc.s4 += dot(a_reg.scdef, b_lm[lm_offset + 100]); \
+    acc.s5 += dot(a_reg.scdef, b_lm[lm_offset + 101]); \
+    acc.s6 += dot(a_reg.scdef, b_lm[lm_offset + 102]); \
+    acc.s7 += dot(a_reg.scdef, b_lm[lm_offset + 103]); \
+    acc.s8 += dot(a_reg.scdef, b_lm[lm_offset + 104]); \
+    acc.s9 += dot(a_reg.scdef, b_lm[lm_offset + 105]); \
+    acc.sa += dot(a_reg.scdef, b_lm[lm_offset + 106]); \
+    acc.sb += dot(a_reg.scdef, b_lm[lm_offset + 107]); \
+    acc.sc += dot(a_reg.scdef, b_lm[lm_offset + 108]); \
+    acc.sd += dot(a_reg.scdef, b_lm[lm_offset + 109]); \
+    acc.se += dot(a_reg.scdef, b_lm[lm_offset + 110]); \
+    acc.sf += dot(a_reg.scdef, b_lm[lm_offset + 111]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+
+
+__attribute__((qcom_wave_pair_mode(1))) // 1=force single 2=force pair
+kernel void kernel_gemm_moe_q4_0_f32_ns(
+        __read_only  image1d_buffer_t src0_q,
+        __global     half *           src0_d,
+        __read_only  image1d_buffer_t src1,
+        __global     uint *           src2,
+        __global     ushort *         src2_emap,
+        __write_only image1d_buffer_t dst,
+        __global     int *            total_tiles,
+        uint ne00,
+        uint ne01
+) {
+    uint block_id_m = get_global_id(1); // m_tile
+    uint block_id_n = get_global_id(2); // n_tile
+
+    // Boundary check
+    if (((get_global_id(0) + block_id_m * TILESIZE_M) >= ne01) || (block_id_n >= total_tiles[0])) {
+        return;
+    }
+
+    __private half16 reg_a;
+    __private float32 reg_c = (float32)(0);
+    __local half4 shared_b[128];
+
+    const ushort expert_id = src2_emap[block_id_n];
+
+    const uint row = block_id_m * TILESIZE_M;
+    const uint col = block_id_n * TILESIZE_N;
+
+    uint sub_block_id_m = get_local_id(0);
+    uint2 b_global_offset;
+    b_global_offset.x = ((sub_block_id_m & 3) << 2) + (sub_block_id_m >> 2) * ne00;
+    b_global_offset.y = b_global_offset.x + (16 * ne00);
+    uint2 b_local_offset;
+    b_local_offset.x = (sub_block_id_m & 3) * 32 + (sub_block_id_m >> 2);
+    b_local_offset.y = b_local_offset.x + 16;
+
+    // Loop along K axis, 32 elements (one block) for each iteration, divided into 2 sub-blocks
+    for (uint step = 0; step < ne00; step += TILESIZE_K * 2) {
+        // First sub-block
+        uint q_sub_offset = row + ((ne01 * step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        uint s_sub_offset = row + ((ne01 * step) >> 5) + ((expert_id * ne00 * ne01) >> 5);
+        uint b_sub_offset = col * ne00 + step;
+
+        // Load scale for current Q4_0 block
+        uint s_offset = s_sub_offset + get_global_id(0);
+        half s = src0_d[s_offset];
+
+        // Load 16 q (64-bits) in transposed layout
+        uint2 q4x16;
+        q4x16.x = read_imageui(src0_q, q_sub_offset + sub_block_id_m).x;
+        q4x16.y = read_imageui(src0_q, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        float8 bx8_f32;
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        half8 bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        dequantize_q4_0(as_ushort4(q4x16), reg_a, s);
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 8 elements reduction for better precision
+        half16 acc;
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+
+        // Repeat for second sub-block
+        uint half_step = step + TILESIZE_K;
+        q_sub_offset = row + ((ne01 * half_step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        b_sub_offset = col * ne00 + half_step;
+
+        // Load next 16 q (64-bits) in transposed layout
+        q4x16.x = read_imageui(src0_q, q_sub_offset + sub_block_id_m).x;
+        q4x16.y = read_imageui(src0_q, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        dequantize_q4_0(as_ushort4(q4x16), reg_a, s);
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 3-levels reduction for better precision
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+    }
+
+    // Load poster router and share in LM
+    __local uint out_idx[TILESIZE_N];
+
+    if (get_local_id(0) < TILESIZE_N) {
+        uint idx = src2[block_id_n * TILESIZE_N + get_local_id(0)];
+        if (idx == 0xFFFFFFFF) {
+            idx = src2[block_id_n * TILESIZE_N + 0];
+        }
+        out_idx[get_local_id(0)] = idx * ne01;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // Scatter results back to original position in output grid
+    uint m_offset = row + get_local_id(0);
+
+    write_imagef(dst, out_idx[1] + m_offset, (reg_c.s1));
+    write_imagef(dst, out_idx[2] + m_offset, (reg_c.s2));
+    write_imagef(dst, out_idx[3] + m_offset, (reg_c.s3));
+    write_imagef(dst, out_idx[4] + m_offset, (reg_c.s4));
+    write_imagef(dst, out_idx[5] + m_offset, (reg_c.s5));
+    write_imagef(dst, out_idx[6] + m_offset, (reg_c.s6));
+    write_imagef(dst, out_idx[7] + m_offset, (reg_c.s7));
+    write_imagef(dst, out_idx[8] + m_offset, (reg_c.s8));
+    write_imagef(dst, out_idx[9] + m_offset, (reg_c.s9));
+    write_imagef(dst, out_idx[10] + m_offset, (reg_c.sa));
+    write_imagef(dst, out_idx[11] + m_offset, (reg_c.sb));
+    write_imagef(dst, out_idx[12] + m_offset, (reg_c.sc));
+    write_imagef(dst, out_idx[13] + m_offset, (reg_c.sd));
+    write_imagef(dst, out_idx[14] + m_offset, (reg_c.se));
+    write_imagef(dst, out_idx[15] + m_offset, (reg_c.sf));
+    write_imagef(dst, out_idx[16] + m_offset, (reg_c.sg));
+    write_imagef(dst, out_idx[17] + m_offset, (reg_c.sh));
+    write_imagef(dst, out_idx[18] + m_offset, (reg_c.si));
+    write_imagef(dst, out_idx[19] + m_offset, (reg_c.sj));
+    write_imagef(dst, out_idx[20] + m_offset, (reg_c.sk));
+    write_imagef(dst, out_idx[21] + m_offset, (reg_c.sl));
+    write_imagef(dst, out_idx[22] + m_offset, (reg_c.sm));
+    write_imagef(dst, out_idx[23] + m_offset, (reg_c.sn));
+    write_imagef(dst, out_idx[24] + m_offset, (reg_c.so));
+    write_imagef(dst, out_idx[25] + m_offset, (reg_c.sp));
+    write_imagef(dst, out_idx[26] + m_offset, (reg_c.sq));
+    write_imagef(dst, out_idx[27] + m_offset, (reg_c.sr));
+    write_imagef(dst, out_idx[28] + m_offset, (reg_c.ss));
+    write_imagef(dst, out_idx[29] + m_offset, (reg_c.st));
+    write_imagef(dst, out_idx[30] + m_offset, (reg_c.su));
+    write_imagef(dst, out_idx[31] + m_offset, (reg_c.sv));
+
+    // Store zero padding parts to the index of first output in tile, override correct result in the end
+    barrier(CLK_GLOBAL_MEM_FENCE);
+    write_imagef(dst, out_idx[0] + m_offset, (reg_c.s0));
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_1_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_1_f32_ns.cl
new file mode 100644
index 00000000000..e2574ae0187
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_1_f32_ns.cl
@@ -0,0 +1,254 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_uniform_load: enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_constant_load: enable
+#pragma OPENCL EXTENSION cl_qcom_extra_vector_types : enable
+
+#define TILESIZE_K 16
+#define TILESIZE_M 64
+#define TILESIZE_N 32
+
+
+#define dequantize_q4_1(q4, a_f16, scale, m) \
+    a_f16.s0 = (half)(q4.s0 & 0x000F) * scale + m; \
+    a_f16.s1 = (half)((q4.s0 & 0x00F0) >> 4) * scale + m; \
+    a_f16.s2 = (half)((q4.s0 & 0x0F00) >> 8) * scale + m; \
+    a_f16.s3 = (half)((q4.s0 & 0xF000) >> 12) * scale + m; \
+    a_f16.s4 = (half)(q4.s1 & 0x000F) * scale + m; \
+    a_f16.s5 = (half)((q4.s1 & 0x00F0) >> 4) * scale + m; \
+    a_f16.s6 = (half)((q4.s1 & 0x0F00) >> 8) * scale + m; \
+    a_f16.s7 = (half)((q4.s1 & 0xF000) >> 12) * scale + m; \
+    a_f16.s8 = (half)(q4.s2 & 0x000F) * scale + m; \
+    a_f16.s9 = (half)((q4.s2 & 0x00F0) >> 4) * scale + m; \
+    a_f16.sa = (half)((q4.s2 & 0x0F00) >> 8) * scale + m; \
+    a_f16.sb = (half)((q4.s2 & 0xF000) >> 12) * scale + m; \
+    a_f16.sc = (half)(q4.s3 & 0x000F) * scale + m; \
+    a_f16.sd = (half)((q4.s3 & 0x00F0) >> 4) * scale + m; \
+    a_f16.se = (half)((q4.s3 & 0x0F00) >> 8) * scale + m; \
+    a_f16.sf = (half)((q4.s3 & 0xF000) >> 12) * scale + m; \
+
+
+#define dotx16_reduce8(a_reg, b_lm, c_reg, lm_offset) \
+    acc.s0 = dot(a_reg.s0123, b_lm[lm_offset + 0]); \
+    acc.s1 = dot(a_reg.s0123, b_lm[lm_offset + 1]); \
+    acc.s2 = dot(a_reg.s0123, b_lm[lm_offset + 2]); \
+    acc.s3 = dot(a_reg.s0123, b_lm[lm_offset + 3]); \
+    acc.s4 = dot(a_reg.s0123, b_lm[lm_offset + 4]); \
+    acc.s5 = dot(a_reg.s0123, b_lm[lm_offset + 5]); \
+    acc.s6 = dot(a_reg.s0123, b_lm[lm_offset + 6]); \
+    acc.s7 = dot(a_reg.s0123, b_lm[lm_offset + 7]); \
+    acc.s8 = dot(a_reg.s0123, b_lm[lm_offset + 8]); \
+    acc.s9 = dot(a_reg.s0123, b_lm[lm_offset + 9]); \
+    acc.sa = dot(a_reg.s0123, b_lm[lm_offset + 10]); \
+    acc.sb = dot(a_reg.s0123, b_lm[lm_offset + 11]); \
+    acc.sc = dot(a_reg.s0123, b_lm[lm_offset + 12]); \
+    acc.sd = dot(a_reg.s0123, b_lm[lm_offset + 13]); \
+    acc.se = dot(a_reg.s0123, b_lm[lm_offset + 14]); \
+    acc.sf = dot(a_reg.s0123, b_lm[lm_offset + 15]); \
+    acc.s0 += dot(a_reg.s4567, b_lm[lm_offset + 32]); \
+    acc.s1 += dot(a_reg.s4567, b_lm[lm_offset + 33]); \
+    acc.s2 += dot(a_reg.s4567, b_lm[lm_offset + 34]); \
+    acc.s3 += dot(a_reg.s4567, b_lm[lm_offset + 35]); \
+    acc.s4 += dot(a_reg.s4567, b_lm[lm_offset + 36]); \
+    acc.s5 += dot(a_reg.s4567, b_lm[lm_offset + 37]); \
+    acc.s6 += dot(a_reg.s4567, b_lm[lm_offset + 38]); \
+    acc.s7 += dot(a_reg.s4567, b_lm[lm_offset + 39]); \
+    acc.s8 += dot(a_reg.s4567, b_lm[lm_offset + 40]); \
+    acc.s9 += dot(a_reg.s4567, b_lm[lm_offset + 41]); \
+    acc.sa += dot(a_reg.s4567, b_lm[lm_offset + 42]); \
+    acc.sb += dot(a_reg.s4567, b_lm[lm_offset + 43]); \
+    acc.sc += dot(a_reg.s4567, b_lm[lm_offset + 44]); \
+    acc.sd += dot(a_reg.s4567, b_lm[lm_offset + 45]); \
+    acc.se += dot(a_reg.s4567, b_lm[lm_offset + 46]); \
+    acc.sf += dot(a_reg.s4567, b_lm[lm_offset + 47]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+    acc.s0 = dot(a_reg.s89ab, b_lm[lm_offset + 64]); \
+    acc.s1 = dot(a_reg.s89ab, b_lm[lm_offset + 65]); \
+    acc.s2 = dot(a_reg.s89ab, b_lm[lm_offset + 66]); \
+    acc.s3 = dot(a_reg.s89ab, b_lm[lm_offset + 67]); \
+    acc.s4 = dot(a_reg.s89ab, b_lm[lm_offset + 68]); \
+    acc.s5 = dot(a_reg.s89ab, b_lm[lm_offset + 69]); \
+    acc.s6 = dot(a_reg.s89ab, b_lm[lm_offset + 70]); \
+    acc.s7 = dot(a_reg.s89ab, b_lm[lm_offset + 71]); \
+    acc.s8 = dot(a_reg.s89ab, b_lm[lm_offset + 72]); \
+    acc.s9 = dot(a_reg.s89ab, b_lm[lm_offset + 73]); \
+    acc.sa = dot(a_reg.s89ab, b_lm[lm_offset + 74]); \
+    acc.sb = dot(a_reg.s89ab, b_lm[lm_offset + 75]); \
+    acc.sc = dot(a_reg.s89ab, b_lm[lm_offset + 76]); \
+    acc.sd = dot(a_reg.s89ab, b_lm[lm_offset + 77]); \
+    acc.se = dot(a_reg.s89ab, b_lm[lm_offset + 78]); \
+    acc.sf = dot(a_reg.s89ab, b_lm[lm_offset + 79]); \
+    acc.s0 += dot(a_reg.scdef, b_lm[lm_offset + 96]); \
+    acc.s1 += dot(a_reg.scdef, b_lm[lm_offset + 97]); \
+    acc.s2 += dot(a_reg.scdef, b_lm[lm_offset + 98]); \
+    acc.s3 += dot(a_reg.scdef, b_lm[lm_offset + 99]); \
+    acc.s4 += dot(a_reg.scdef, b_lm[lm_offset + 100]); \
+    acc.s5 += dot(a_reg.scdef, b_lm[lm_offset + 101]); \
+    acc.s6 += dot(a_reg.scdef, b_lm[lm_offset + 102]); \
+    acc.s7 += dot(a_reg.scdef, b_lm[lm_offset + 103]); \
+    acc.s8 += dot(a_reg.scdef, b_lm[lm_offset + 104]); \
+    acc.s9 += dot(a_reg.scdef, b_lm[lm_offset + 105]); \
+    acc.sa += dot(a_reg.scdef, b_lm[lm_offset + 106]); \
+    acc.sb += dot(a_reg.scdef, b_lm[lm_offset + 107]); \
+    acc.sc += dot(a_reg.scdef, b_lm[lm_offset + 108]); \
+    acc.sd += dot(a_reg.scdef, b_lm[lm_offset + 109]); \
+    acc.se += dot(a_reg.scdef, b_lm[lm_offset + 110]); \
+    acc.sf += dot(a_reg.scdef, b_lm[lm_offset + 111]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+
+
+__attribute__((qcom_wave_pair_mode(1))) // 1=force single 2=force pair
+kernel void kernel_gemm_moe_q4_1_f32_ns(
+        __read_only  image1d_buffer_t src0_q,
+        __global     half *           src0_d,
+        __global     half *           src0_m,
+        __read_only  image1d_buffer_t src1,
+        __global     uint *           src2,
+        __global     ushort *         src2_emap,
+        __write_only image1d_buffer_t dst,
+        __global     int *            total_tiles,
+        uint ne00,
+        uint ne01
+) {
+    uint block_id_m = get_global_id(1); // m_tile
+    uint block_id_n = get_global_id(2); // n_tile
+
+    // Boundary check
+    if (((get_global_id(0) + block_id_m * TILESIZE_M) >= ne01) || (block_id_n >= total_tiles[0])) {
+        return;
+    }
+
+    __private half16 reg_a;
+    __private float32 reg_c = (float32)(0);
+    __local half4 shared_b[128];
+
+    const ushort expert_id = src2_emap[block_id_n];
+
+    const uint row = block_id_m * TILESIZE_M;
+    const uint col = block_id_n * TILESIZE_N;
+
+    uint sub_block_id_m = get_local_id(0);
+    uint2 b_global_offset;
+    b_global_offset.x = ((sub_block_id_m & 3) << 2) + (sub_block_id_m >> 2) * ne00;
+    b_global_offset.y = b_global_offset.x + (16 * ne00);
+    uint2 b_local_offset;
+    b_local_offset.x = (sub_block_id_m & 3) * 32 + (sub_block_id_m >> 2);
+    b_local_offset.y = b_local_offset.x + 16;
+
+    // Loop along K axis, 32 elements (one block) for each iteration, divided into 2 sub-blocks
+    for (uint step = 0; step < ne00; step += TILESIZE_K * 2) {
+        // First sub-block
+        uint q_sub_offset = row + ((ne01 * step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        uint s_sub_offset = row + ((ne01 * step) >> 5) + ((expert_id * ne00 * ne01) >> 5);
+        uint b_sub_offset = col * ne00 + step;
+
+        // Load scale and m for current Q4_1 block
+        uint sm_offset = s_sub_offset + get_global_id(0);
+        half s = src0_d[sm_offset];
+        half m = src0_m[sm_offset];
+
+        // Load 16 q (64-bits) in transposed layout
+        uint2 q4x16;
+        q4x16.x = read_imageui(src0_q, q_sub_offset + sub_block_id_m).x;
+        q4x16.y = read_imageui(src0_q, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        float8 bx8_f32;
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        half8 bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        dequantize_q4_1(as_ushort4(q4x16), reg_a, s, m);
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 8 elements reduction for better precision
+        half16 acc;
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+
+        // Repeat for second sub-block
+        uint half_step = step + TILESIZE_K;
+        q_sub_offset = row + ((ne01 * half_step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        b_sub_offset = col * ne00 + half_step;
+
+        // Load next 16 q (64-bits) in transposed layout
+        q4x16.x = read_imageui(src0_q, q_sub_offset + sub_block_id_m).x;
+        q4x16.y = read_imageui(src0_q, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        dequantize_q4_1(as_ushort4(q4x16), reg_a, s, m);
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 3-levels reduction for better precision
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+    }
+
+    // Load poster router and share in LM
+    __local uint out_idx[TILESIZE_N];
+
+    if (get_local_id(0) < TILESIZE_N) {
+        uint idx = src2[block_id_n * TILESIZE_N + get_local_id(0)];
+        if (idx == 0xFFFFFFFF) {
+            idx = src2[block_id_n * TILESIZE_N + 0];
+        }
+        out_idx[get_local_id(0)] = idx * ne01;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // Scatter results back to original position in output grid
+    uint m_offset = row + get_local_id(0);
+
+    write_imagef(dst, out_idx[1] + m_offset, (reg_c.s1));
+    write_imagef(dst, out_idx[2] + m_offset, (reg_c.s2));
+    write_imagef(dst, out_idx[3] + m_offset, (reg_c.s3));
+    write_imagef(dst, out_idx[4] + m_offset, (reg_c.s4));
+    write_imagef(dst, out_idx[5] + m_offset, (reg_c.s5));
+    write_imagef(dst, out_idx[6] + m_offset, (reg_c.s6));
+    write_imagef(dst, out_idx[7] + m_offset, (reg_c.s7));
+    write_imagef(dst, out_idx[8] + m_offset, (reg_c.s8));
+    write_imagef(dst, out_idx[9] + m_offset, (reg_c.s9));
+    write_imagef(dst, out_idx[10] + m_offset, (reg_c.sa));
+    write_imagef(dst, out_idx[11] + m_offset, (reg_c.sb));
+    write_imagef(dst, out_idx[12] + m_offset, (reg_c.sc));
+    write_imagef(dst, out_idx[13] + m_offset, (reg_c.sd));
+    write_imagef(dst, out_idx[14] + m_offset, (reg_c.se));
+    write_imagef(dst, out_idx[15] + m_offset, (reg_c.sf));
+    write_imagef(dst, out_idx[16] + m_offset, (reg_c.sg));
+    write_imagef(dst, out_idx[17] + m_offset, (reg_c.sh));
+    write_imagef(dst, out_idx[18] + m_offset, (reg_c.si));
+    write_imagef(dst, out_idx[19] + m_offset, (reg_c.sj));
+    write_imagef(dst, out_idx[20] + m_offset, (reg_c.sk));
+    write_imagef(dst, out_idx[21] + m_offset, (reg_c.sl));
+    write_imagef(dst, out_idx[22] + m_offset, (reg_c.sm));
+    write_imagef(dst, out_idx[23] + m_offset, (reg_c.sn));
+    write_imagef(dst, out_idx[24] + m_offset, (reg_c.so));
+    write_imagef(dst, out_idx[25] + m_offset, (reg_c.sp));
+    write_imagef(dst, out_idx[26] + m_offset, (reg_c.sq));
+    write_imagef(dst, out_idx[27] + m_offset, (reg_c.sr));
+    write_imagef(dst, out_idx[28] + m_offset, (reg_c.ss));
+    write_imagef(dst, out_idx[29] + m_offset, (reg_c.st));
+    write_imagef(dst, out_idx[30] + m_offset, (reg_c.su));
+    write_imagef(dst, out_idx[31] + m_offset, (reg_c.sv));
+
+    // Store zero padding parts to the index of first output in tile, override correct result in the end
+    barrier(CLK_GLOBAL_MEM_FENCE);
+    write_imagef(dst, out_idx[0] + m_offset, (reg_c.s0));
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q5_0_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q5_0_f32_ns.cl
new file mode 100644
index 00000000000..3524cb1bdbd
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q5_0_f32_ns.cl
@@ -0,0 +1,256 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_uniform_load: enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_constant_load: enable
+#pragma OPENCL EXTENSION cl_qcom_extra_vector_types : enable
+
+#define TILESIZE_K 16
+#define TILESIZE_M 64
+#define TILESIZE_N 32
+
+
+#define dequantize_q5_0(qs5x16, qh5x16, a_f16, scale) \
+    a_f16.s0 = (half)((( qs5x16.s0 & 0x000F)        | (( qh5x16.s0       & 0x01) << 4)) - 16) * scale; \
+    a_f16.s1 = (half)((((qs5x16.s0 & 0x00F0) >> 4 ) | (((qh5x16.s0 >> 1) & 0x01) << 4)) - 16) * scale; \
+    a_f16.s2 = (half)((((qs5x16.s0 & 0x0F00) >> 8 ) | (((qh5x16.s0 >> 2) & 0x01) << 4)) - 16) * scale; \
+    a_f16.s3 = (half)((((qs5x16.s0 & 0xF000) >> 12) | (((qh5x16.s0 >> 3) & 0x01) << 4)) - 16) * scale; \
+    a_f16.s4 = (half)((( qs5x16.s1 & 0x000F)        | (((qh5x16.s0 >> 4) & 0x01) << 4)) - 16) * scale; \
+    a_f16.s5 = (half)((((qs5x16.s1 & 0x00F0) >> 4 ) | (((qh5x16.s0 >> 5) & 0x01) << 4)) - 16) * scale; \
+    a_f16.s6 = (half)((((qs5x16.s1 & 0x0F00) >> 8 ) | (((qh5x16.s0 >> 6) & 0x01) << 4)) - 16) * scale; \
+    a_f16.s7 = (half)((((qs5x16.s1 & 0xF000) >> 12) | (((qh5x16.s0 >> 7) & 0x01) << 4)) - 16) * scale; \
+    a_f16.s8 = (half)((( qs5x16.s2 & 0x000F)        | (( qh5x16.s1       & 0x01) << 4)) - 16) * scale; \
+    a_f16.s9 = (half)((((qs5x16.s2 & 0x00F0) >> 4 ) | (((qh5x16.s1 >> 1) & 0x01) << 4)) - 16) * scale; \
+    a_f16.sa = (half)((((qs5x16.s2 & 0x0F00) >> 8 ) | (((qh5x16.s1 >> 2) & 0x01) << 4)) - 16) * scale; \
+    a_f16.sb = (half)((((qs5x16.s2 & 0xF000) >> 12) | (((qh5x16.s1 >> 3) & 0x01) << 4)) - 16) * scale; \
+    a_f16.sc = (half)((( qs5x16.s3 & 0x000F)        | (((qh5x16.s1 >> 4) & 0x01) << 4)) - 16) * scale; \
+    a_f16.sd = (half)((((qs5x16.s3 & 0x00F0) >> 4 ) | (((qh5x16.s1 >> 5) & 0x01) << 4)) - 16) * scale; \
+    a_f16.se = (half)((((qs5x16.s3 & 0x0F00) >> 8 ) | (((qh5x16.s1 >> 6) & 0x01) << 4)) - 16) * scale; \
+    a_f16.sf = (half)((((qs5x16.s3 & 0xF000) >> 12) | (((qh5x16.s1 >> 7) & 0x01) << 4)) - 16) * scale; \
+
+
+#define dotx16_reduce8(a_reg, b_lm, c_reg, lm_offset) \
+    acc.s0 = dot(a_reg.s0123, b_lm[lm_offset + 0]); \
+    acc.s1 = dot(a_reg.s0123, b_lm[lm_offset + 1]); \
+    acc.s2 = dot(a_reg.s0123, b_lm[lm_offset + 2]); \
+    acc.s3 = dot(a_reg.s0123, b_lm[lm_offset + 3]); \
+    acc.s4 = dot(a_reg.s0123, b_lm[lm_offset + 4]); \
+    acc.s5 = dot(a_reg.s0123, b_lm[lm_offset + 5]); \
+    acc.s6 = dot(a_reg.s0123, b_lm[lm_offset + 6]); \
+    acc.s7 = dot(a_reg.s0123, b_lm[lm_offset + 7]); \
+    acc.s8 = dot(a_reg.s0123, b_lm[lm_offset + 8]); \
+    acc.s9 = dot(a_reg.s0123, b_lm[lm_offset + 9]); \
+    acc.sa = dot(a_reg.s0123, b_lm[lm_offset + 10]); \
+    acc.sb = dot(a_reg.s0123, b_lm[lm_offset + 11]); \
+    acc.sc = dot(a_reg.s0123, b_lm[lm_offset + 12]); \
+    acc.sd = dot(a_reg.s0123, b_lm[lm_offset + 13]); \
+    acc.se = dot(a_reg.s0123, b_lm[lm_offset + 14]); \
+    acc.sf = dot(a_reg.s0123, b_lm[lm_offset + 15]); \
+    acc.s0 += dot(a_reg.s4567, b_lm[lm_offset + 32]); \
+    acc.s1 += dot(a_reg.s4567, b_lm[lm_offset + 33]); \
+    acc.s2 += dot(a_reg.s4567, b_lm[lm_offset + 34]); \
+    acc.s3 += dot(a_reg.s4567, b_lm[lm_offset + 35]); \
+    acc.s4 += dot(a_reg.s4567, b_lm[lm_offset + 36]); \
+    acc.s5 += dot(a_reg.s4567, b_lm[lm_offset + 37]); \
+    acc.s6 += dot(a_reg.s4567, b_lm[lm_offset + 38]); \
+    acc.s7 += dot(a_reg.s4567, b_lm[lm_offset + 39]); \
+    acc.s8 += dot(a_reg.s4567, b_lm[lm_offset + 40]); \
+    acc.s9 += dot(a_reg.s4567, b_lm[lm_offset + 41]); \
+    acc.sa += dot(a_reg.s4567, b_lm[lm_offset + 42]); \
+    acc.sb += dot(a_reg.s4567, b_lm[lm_offset + 43]); \
+    acc.sc += dot(a_reg.s4567, b_lm[lm_offset + 44]); \
+    acc.sd += dot(a_reg.s4567, b_lm[lm_offset + 45]); \
+    acc.se += dot(a_reg.s4567, b_lm[lm_offset + 46]); \
+    acc.sf += dot(a_reg.s4567, b_lm[lm_offset + 47]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+    acc.s0 = dot(a_reg.s89ab, b_lm[lm_offset + 64]); \
+    acc.s1 = dot(a_reg.s89ab, b_lm[lm_offset + 65]); \
+    acc.s2 = dot(a_reg.s89ab, b_lm[lm_offset + 66]); \
+    acc.s3 = dot(a_reg.s89ab, b_lm[lm_offset + 67]); \
+    acc.s4 = dot(a_reg.s89ab, b_lm[lm_offset + 68]); \
+    acc.s5 = dot(a_reg.s89ab, b_lm[lm_offset + 69]); \
+    acc.s6 = dot(a_reg.s89ab, b_lm[lm_offset + 70]); \
+    acc.s7 = dot(a_reg.s89ab, b_lm[lm_offset + 71]); \
+    acc.s8 = dot(a_reg.s89ab, b_lm[lm_offset + 72]); \
+    acc.s9 = dot(a_reg.s89ab, b_lm[lm_offset + 73]); \
+    acc.sa = dot(a_reg.s89ab, b_lm[lm_offset + 74]); \
+    acc.sb = dot(a_reg.s89ab, b_lm[lm_offset + 75]); \
+    acc.sc = dot(a_reg.s89ab, b_lm[lm_offset + 76]); \
+    acc.sd = dot(a_reg.s89ab, b_lm[lm_offset + 77]); \
+    acc.se = dot(a_reg.s89ab, b_lm[lm_offset + 78]); \
+    acc.sf = dot(a_reg.s89ab, b_lm[lm_offset + 79]); \
+    acc.s0 += dot(a_reg.scdef, b_lm[lm_offset + 96]); \
+    acc.s1 += dot(a_reg.scdef, b_lm[lm_offset + 97]); \
+    acc.s2 += dot(a_reg.scdef, b_lm[lm_offset + 98]); \
+    acc.s3 += dot(a_reg.scdef, b_lm[lm_offset + 99]); \
+    acc.s4 += dot(a_reg.scdef, b_lm[lm_offset + 100]); \
+    acc.s5 += dot(a_reg.scdef, b_lm[lm_offset + 101]); \
+    acc.s6 += dot(a_reg.scdef, b_lm[lm_offset + 102]); \
+    acc.s7 += dot(a_reg.scdef, b_lm[lm_offset + 103]); \
+    acc.s8 += dot(a_reg.scdef, b_lm[lm_offset + 104]); \
+    acc.s9 += dot(a_reg.scdef, b_lm[lm_offset + 105]); \
+    acc.sa += dot(a_reg.scdef, b_lm[lm_offset + 106]); \
+    acc.sb += dot(a_reg.scdef, b_lm[lm_offset + 107]); \
+    acc.sc += dot(a_reg.scdef, b_lm[lm_offset + 108]); \
+    acc.sd += dot(a_reg.scdef, b_lm[lm_offset + 109]); \
+    acc.se += dot(a_reg.scdef, b_lm[lm_offset + 110]); \
+    acc.sf += dot(a_reg.scdef, b_lm[lm_offset + 111]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+
+
+__attribute__((qcom_wave_pair_mode(1))) // 1=force single 2=force pair
+kernel void kernel_gemm_moe_q5_0_f32_ns(
+        __read_only  image1d_buffer_t src0_qs,
+        __global     uint *           src0_qh,
+        __global     half *           src0_d,
+        __read_only  image1d_buffer_t src1,
+        __global     uint *           src2,
+        __global     ushort *         src2_emap,
+        __write_only image1d_buffer_t dst,
+        __global     int *            total_tiles,
+        uint ne00,
+        uint ne01
+) {
+    uint block_id_m = get_global_id(1); // m_tile
+    uint block_id_n = get_global_id(2); // n_tile
+
+    // Boundary check
+    if (((get_global_id(0) + block_id_m * TILESIZE_M) >= ne01) || (block_id_n >= total_tiles[0])) {
+        return;
+    }
+
+    __private half16 reg_a;
+    __private float32 reg_c = (float32)(0);
+    __local half4 shared_b[128];
+
+    const ushort expert_id = src2_emap[block_id_n];
+
+    const uint row = block_id_m * TILESIZE_M;
+    const uint col = block_id_n * TILESIZE_N;
+
+    uint sub_block_id_m = get_local_id(0);
+    uint2 b_global_offset;
+    b_global_offset.x = ((sub_block_id_m & 3) << 2) + (sub_block_id_m >> 2) * ne00;
+    b_global_offset.y = b_global_offset.x + (16 * ne00);
+    uint2 b_local_offset;
+    b_local_offset.x = (sub_block_id_m & 3) * 32 + (sub_block_id_m >> 2);
+    b_local_offset.y = b_local_offset.x + 16;
+
+    // Loop along K axis, 32 elements (one block) for each iteration, divided into 2 sub-blocks
+    for (uint step = 0; step < ne00; step += TILESIZE_K * 2) {
+        // First sub-block
+        uint q_sub_offset = row + ((ne01 * step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        uint s_sub_offset = row + ((ne01 * step) >> 5) + ((expert_id * ne00 * ne01) >> 5);
+        uint b_sub_offset = col * ne00 + step;
+
+        // Load scale for current Q5_0 block
+        uint blk_offset = s_sub_offset + get_global_id(0);
+        half s = src0_d[blk_offset];
+
+        // Load 32 qh (5-th bit of each Q5) for the entire block
+        uchar4 qhx32 = as_uchar4(src0_qh[blk_offset]);
+
+        // Load 16 qs (half block) in transposed layout
+        uint2 qsx16;
+        qsx16.x = read_imageui(src0_qs, q_sub_offset + sub_block_id_m).x;
+        qsx16.y = read_imageui(src0_qs, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        float8 bx8_f32;
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        half8 bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        dequantize_q5_0(as_ushort4(qsx16), qhx32.lo, reg_a, s);
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 8 elements reduction for better precision
+        half16 acc;
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+
+        // Repeat for second sub-block
+        uint half_step = step + TILESIZE_K;
+        q_sub_offset = row + ((ne01 * half_step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        b_sub_offset = col * ne00 + half_step;
+
+        // Load next 16 qs in transposed layout
+        qsx16.x = read_imageui(src0_qs, q_sub_offset + sub_block_id_m).x;
+        qsx16.y = read_imageui(src0_qs, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        dequantize_q5_0(as_ushort4(qsx16), qhx32.hi, reg_a, s);
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 3-levels reduction for better precision
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+    }
+
+    // Load poster router and share in LM
+    __local uint out_idx[TILESIZE_N];
+
+    if (get_local_id(0) < TILESIZE_N) {
+        uint idx = src2[block_id_n * TILESIZE_N + get_local_id(0)];
+        if (idx == 0xFFFFFFFF) {
+            idx = src2[block_id_n * TILESIZE_N + 0];
+        }
+        out_idx[get_local_id(0)] = idx * ne01;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // Scatter results back to original position in output grid
+    uint m_offset = row + get_local_id(0);
+
+    write_imagef(dst, out_idx[1] + m_offset, (reg_c.s1));
+    write_imagef(dst, out_idx[2] + m_offset, (reg_c.s2));
+    write_imagef(dst, out_idx[3] + m_offset, (reg_c.s3));
+    write_imagef(dst, out_idx[4] + m_offset, (reg_c.s4));
+    write_imagef(dst, out_idx[5] + m_offset, (reg_c.s5));
+    write_imagef(dst, out_idx[6] + m_offset, (reg_c.s6));
+    write_imagef(dst, out_idx[7] + m_offset, (reg_c.s7));
+    write_imagef(dst, out_idx[8] + m_offset, (reg_c.s8));
+    write_imagef(dst, out_idx[9] + m_offset, (reg_c.s9));
+    write_imagef(dst, out_idx[10] + m_offset, (reg_c.sa));
+    write_imagef(dst, out_idx[11] + m_offset, (reg_c.sb));
+    write_imagef(dst, out_idx[12] + m_offset, (reg_c.sc));
+    write_imagef(dst, out_idx[13] + m_offset, (reg_c.sd));
+    write_imagef(dst, out_idx[14] + m_offset, (reg_c.se));
+    write_imagef(dst, out_idx[15] + m_offset, (reg_c.sf));
+    write_imagef(dst, out_idx[16] + m_offset, (reg_c.sg));
+    write_imagef(dst, out_idx[17] + m_offset, (reg_c.sh));
+    write_imagef(dst, out_idx[18] + m_offset, (reg_c.si));
+    write_imagef(dst, out_idx[19] + m_offset, (reg_c.sj));
+    write_imagef(dst, out_idx[20] + m_offset, (reg_c.sk));
+    write_imagef(dst, out_idx[21] + m_offset, (reg_c.sl));
+    write_imagef(dst, out_idx[22] + m_offset, (reg_c.sm));
+    write_imagef(dst, out_idx[23] + m_offset, (reg_c.sn));
+    write_imagef(dst, out_idx[24] + m_offset, (reg_c.so));
+    write_imagef(dst, out_idx[25] + m_offset, (reg_c.sp));
+    write_imagef(dst, out_idx[26] + m_offset, (reg_c.sq));
+    write_imagef(dst, out_idx[27] + m_offset, (reg_c.sr));
+    write_imagef(dst, out_idx[28] + m_offset, (reg_c.ss));
+    write_imagef(dst, out_idx[29] + m_offset, (reg_c.st));
+    write_imagef(dst, out_idx[30] + m_offset, (reg_c.su));
+    write_imagef(dst, out_idx[31] + m_offset, (reg_c.sv));
+
+    // Store zero padding parts to the index of first output in tile, override correct result in the end
+    barrier(CLK_GLOBAL_MEM_FENCE);
+    write_imagef(dst, out_idx[0] + m_offset, (reg_c.s0));
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q5_1_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q5_1_f32_ns.cl
new file mode 100644
index 00000000000..5fc2a523234
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q5_1_f32_ns.cl
@@ -0,0 +1,258 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_uniform_load: enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_constant_load: enable
+#pragma OPENCL EXTENSION cl_qcom_extra_vector_types : enable
+
+#define TILESIZE_K 16
+#define TILESIZE_M 64
+#define TILESIZE_N 32
+
+
+#define dequantize_q5_1(qs5x16, qh5x16, a_f16, scale, m) \
+    a_f16.s0 = (half)((( qs5x16.s0 & 0x000F)        | (( qh5x16.s0       & 0x01) << 4)) * scale + m); \
+    a_f16.s1 = (half)((((qs5x16.s0 & 0x00F0) >> 4 ) | (((qh5x16.s0 >> 1) & 0x01) << 4)) * scale + m); \
+    a_f16.s2 = (half)((((qs5x16.s0 & 0x0F00) >> 8 ) | (((qh5x16.s0 >> 2) & 0x01) << 4)) * scale + m); \
+    a_f16.s3 = (half)((((qs5x16.s0 & 0xF000) >> 12) | (((qh5x16.s0 >> 3) & 0x01) << 4)) * scale + m); \
+    a_f16.s4 = (half)((( qs5x16.s1 & 0x000F)        | (((qh5x16.s0 >> 4) & 0x01) << 4)) * scale + m); \
+    a_f16.s5 = (half)((((qs5x16.s1 & 0x00F0) >> 4 ) | (((qh5x16.s0 >> 5) & 0x01) << 4)) * scale + m); \
+    a_f16.s6 = (half)((((qs5x16.s1 & 0x0F00) >> 8 ) | (((qh5x16.s0 >> 6) & 0x01) << 4)) * scale + m); \
+    a_f16.s7 = (half)((((qs5x16.s1 & 0xF000) >> 12) | (((qh5x16.s0 >> 7) & 0x01) << 4)) * scale + m); \
+    a_f16.s8 = (half)((( qs5x16.s2 & 0x000F)        | (( qh5x16.s1       & 0x01) << 4)) * scale + m); \
+    a_f16.s9 = (half)((((qs5x16.s2 & 0x00F0) >> 4 ) | (((qh5x16.s1 >> 1) & 0x01) << 4)) * scale + m); \
+    a_f16.sa = (half)((((qs5x16.s2 & 0x0F00) >> 8 ) | (((qh5x16.s1 >> 2) & 0x01) << 4)) * scale + m); \
+    a_f16.sb = (half)((((qs5x16.s2 & 0xF000) >> 12) | (((qh5x16.s1 >> 3) & 0x01) << 4)) * scale + m); \
+    a_f16.sc = (half)((( qs5x16.s3 & 0x000F)        | (((qh5x16.s1 >> 4) & 0x01) << 4)) * scale + m); \
+    a_f16.sd = (half)((((qs5x16.s3 & 0x00F0) >> 4 ) | (((qh5x16.s1 >> 5) & 0x01) << 4)) * scale + m); \
+    a_f16.se = (half)((((qs5x16.s3 & 0x0F00) >> 8 ) | (((qh5x16.s1 >> 6) & 0x01) << 4)) * scale + m); \
+    a_f16.sf = (half)((((qs5x16.s3 & 0xF000) >> 12) | (((qh5x16.s1 >> 7) & 0x01) << 4)) * scale + m); \
+
+
+#define dotx16_reduce8(a_reg, b_lm, c_reg, lm_offset) \
+    acc.s0 = dot(a_reg.s0123, b_lm[lm_offset + 0]); \
+    acc.s1 = dot(a_reg.s0123, b_lm[lm_offset + 1]); \
+    acc.s2 = dot(a_reg.s0123, b_lm[lm_offset + 2]); \
+    acc.s3 = dot(a_reg.s0123, b_lm[lm_offset + 3]); \
+    acc.s4 = dot(a_reg.s0123, b_lm[lm_offset + 4]); \
+    acc.s5 = dot(a_reg.s0123, b_lm[lm_offset + 5]); \
+    acc.s6 = dot(a_reg.s0123, b_lm[lm_offset + 6]); \
+    acc.s7 = dot(a_reg.s0123, b_lm[lm_offset + 7]); \
+    acc.s8 = dot(a_reg.s0123, b_lm[lm_offset + 8]); \
+    acc.s9 = dot(a_reg.s0123, b_lm[lm_offset + 9]); \
+    acc.sa = dot(a_reg.s0123, b_lm[lm_offset + 10]); \
+    acc.sb = dot(a_reg.s0123, b_lm[lm_offset + 11]); \
+    acc.sc = dot(a_reg.s0123, b_lm[lm_offset + 12]); \
+    acc.sd = dot(a_reg.s0123, b_lm[lm_offset + 13]); \
+    acc.se = dot(a_reg.s0123, b_lm[lm_offset + 14]); \
+    acc.sf = dot(a_reg.s0123, b_lm[lm_offset + 15]); \
+    acc.s0 += dot(a_reg.s4567, b_lm[lm_offset + 32]); \
+    acc.s1 += dot(a_reg.s4567, b_lm[lm_offset + 33]); \
+    acc.s2 += dot(a_reg.s4567, b_lm[lm_offset + 34]); \
+    acc.s3 += dot(a_reg.s4567, b_lm[lm_offset + 35]); \
+    acc.s4 += dot(a_reg.s4567, b_lm[lm_offset + 36]); \
+    acc.s5 += dot(a_reg.s4567, b_lm[lm_offset + 37]); \
+    acc.s6 += dot(a_reg.s4567, b_lm[lm_offset + 38]); \
+    acc.s7 += dot(a_reg.s4567, b_lm[lm_offset + 39]); \
+    acc.s8 += dot(a_reg.s4567, b_lm[lm_offset + 40]); \
+    acc.s9 += dot(a_reg.s4567, b_lm[lm_offset + 41]); \
+    acc.sa += dot(a_reg.s4567, b_lm[lm_offset + 42]); \
+    acc.sb += dot(a_reg.s4567, b_lm[lm_offset + 43]); \
+    acc.sc += dot(a_reg.s4567, b_lm[lm_offset + 44]); \
+    acc.sd += dot(a_reg.s4567, b_lm[lm_offset + 45]); \
+    acc.se += dot(a_reg.s4567, b_lm[lm_offset + 46]); \
+    acc.sf += dot(a_reg.s4567, b_lm[lm_offset + 47]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+    acc.s0 = dot(a_reg.s89ab, b_lm[lm_offset + 64]); \
+    acc.s1 = dot(a_reg.s89ab, b_lm[lm_offset + 65]); \
+    acc.s2 = dot(a_reg.s89ab, b_lm[lm_offset + 66]); \
+    acc.s3 = dot(a_reg.s89ab, b_lm[lm_offset + 67]); \
+    acc.s4 = dot(a_reg.s89ab, b_lm[lm_offset + 68]); \
+    acc.s5 = dot(a_reg.s89ab, b_lm[lm_offset + 69]); \
+    acc.s6 = dot(a_reg.s89ab, b_lm[lm_offset + 70]); \
+    acc.s7 = dot(a_reg.s89ab, b_lm[lm_offset + 71]); \
+    acc.s8 = dot(a_reg.s89ab, b_lm[lm_offset + 72]); \
+    acc.s9 = dot(a_reg.s89ab, b_lm[lm_offset + 73]); \
+    acc.sa = dot(a_reg.s89ab, b_lm[lm_offset + 74]); \
+    acc.sb = dot(a_reg.s89ab, b_lm[lm_offset + 75]); \
+    acc.sc = dot(a_reg.s89ab, b_lm[lm_offset + 76]); \
+    acc.sd = dot(a_reg.s89ab, b_lm[lm_offset + 77]); \
+    acc.se = dot(a_reg.s89ab, b_lm[lm_offset + 78]); \
+    acc.sf = dot(a_reg.s89ab, b_lm[lm_offset + 79]); \
+    acc.s0 += dot(a_reg.scdef, b_lm[lm_offset + 96]); \
+    acc.s1 += dot(a_reg.scdef, b_lm[lm_offset + 97]); \
+    acc.s2 += dot(a_reg.scdef, b_lm[lm_offset + 98]); \
+    acc.s3 += dot(a_reg.scdef, b_lm[lm_offset + 99]); \
+    acc.s4 += dot(a_reg.scdef, b_lm[lm_offset + 100]); \
+    acc.s5 += dot(a_reg.scdef, b_lm[lm_offset + 101]); \
+    acc.s6 += dot(a_reg.scdef, b_lm[lm_offset + 102]); \
+    acc.s7 += dot(a_reg.scdef, b_lm[lm_offset + 103]); \
+    acc.s8 += dot(a_reg.scdef, b_lm[lm_offset + 104]); \
+    acc.s9 += dot(a_reg.scdef, b_lm[lm_offset + 105]); \
+    acc.sa += dot(a_reg.scdef, b_lm[lm_offset + 106]); \
+    acc.sb += dot(a_reg.scdef, b_lm[lm_offset + 107]); \
+    acc.sc += dot(a_reg.scdef, b_lm[lm_offset + 108]); \
+    acc.sd += dot(a_reg.scdef, b_lm[lm_offset + 109]); \
+    acc.se += dot(a_reg.scdef, b_lm[lm_offset + 110]); \
+    acc.sf += dot(a_reg.scdef, b_lm[lm_offset + 111]); \
+    c_reg.lo += convert_float8(acc.lo); \
+    c_reg.hi += convert_float8(acc.hi); \
+
+
+__attribute__((qcom_wave_pair_mode(1))) // 1=force single 2=force pair
+kernel void kernel_gemm_moe_q5_1_f32_ns(
+        __read_only  image1d_buffer_t src0_qs,
+        __global     uint *           src0_qh,
+        __global     half *           src0_d,
+        __global     half *           src0_m,
+        __read_only  image1d_buffer_t src1,
+        __global     uint *           src2,
+        __global     ushort *         src2_emap,
+        __write_only image1d_buffer_t dst,
+        __global     int *            total_tiles,
+        uint ne00,
+        uint ne01
+) {
+    uint block_id_m = get_global_id(1); // m_tile
+    uint block_id_n = get_global_id(2); // n_tile
+
+    // Boundary check
+    if (((get_global_id(0) + block_id_m * TILESIZE_M) >= ne01) || (block_id_n >= total_tiles[0])) {
+        return;
+    }
+
+    __private half16 reg_a;
+    __private float32 reg_c = (float32)(0);
+    __local half4 shared_b[128];
+
+    const ushort expert_id = src2_emap[block_id_n];
+
+    const uint row = block_id_m * TILESIZE_M;
+    const uint col = block_id_n * TILESIZE_N;
+
+    uint sub_block_id_m = get_local_id(0);
+    uint2 b_global_offset;
+    b_global_offset.x = ((sub_block_id_m & 3) << 2) + (sub_block_id_m >> 2) * ne00;
+    b_global_offset.y = b_global_offset.x + (16 * ne00);
+    uint2 b_local_offset;
+    b_local_offset.x = (sub_block_id_m & 3) * 32 + (sub_block_id_m >> 2);
+    b_local_offset.y = b_local_offset.x + 16;
+
+    // Loop along K axis, 32 elements (one block) for each iteration, divided into 2 sub-blocks
+    for (uint step = 0; step < ne00; step += TILESIZE_K * 2) {
+        // First sub-block
+        uint q_sub_offset = row + ((ne01 * step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        uint s_sub_offset = row + ((ne01 * step) >> 5) + ((expert_id * ne00 * ne01) >> 5);
+        uint b_sub_offset = col * ne00 + step;
+
+        // Load scale and m for current Q5_1 block
+        uint blk_offset = s_sub_offset + get_global_id(0);
+        half s = src0_d[blk_offset];
+        half m = src0_m[blk_offset];
+
+        // Load 32 qh (5-th bit of each Q5) for the entire block
+        uchar4 qhx32 = as_uchar4(src0_qh[blk_offset]);
+
+        // Load 16 qs (half block) in transposed layout
+        uint2 qsx16;
+        qsx16.x = read_imageui(src0_qs, q_sub_offset + sub_block_id_m).x;
+        qsx16.y = read_imageui(src0_qs, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        float8 bx8_f32;
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        half8 bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        dequantize_q5_1(as_ushort4(qsx16), qhx32.lo, reg_a, s, m);
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 8 elements reduction for better precision
+        half16 acc;
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+
+        // Repeat for second sub-block
+        uint half_step = step + TILESIZE_K;
+        q_sub_offset = row + ((ne01 * half_step) >> 3) + ((expert_id * ne00 * ne01) >> 3);
+        b_sub_offset = col * ne00 + half_step;
+
+        // Load next 16 qs in transposed layout
+        qsx16.x = read_imageui(src0_qs, q_sub_offset + sub_block_id_m).x;
+        qsx16.y = read_imageui(src0_qs, q_sub_offset + sub_block_id_m + ne01).x;
+
+        // Load 16x32 floats from matrix B, each fiber out of 64 in a sub-group loads 8 elements
+        bx8_f32.lo = read_imagef(src1, (b_sub_offset + b_global_offset.x) / 4);
+        bx8_f32.hi = read_imagef(src1, (b_sub_offset + b_global_offset.y) / 4);
+        // Convert to half and store to LM to share within the subgroup
+        bx8_f16 = convert_half8(bx8_f32);
+        shared_b[b_local_offset.x] = bx8_f16.lo;
+        shared_b[b_local_offset.y] = bx8_f16.hi;
+
+        // Dequantization
+        dequantize_q5_1(as_ushort4(qsx16), qhx32.hi, reg_a, s, m);
+
+        sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+
+        // 32 16x16 fp16 dot product with 3-levels reduction for better precision
+        dotx16_reduce8(reg_a, shared_b, reg_c.lo, 0);
+        dotx16_reduce8(reg_a, shared_b, reg_c.hi, 16);
+    }
+
+    // Load poster router and share in LM
+    __local uint out_idx[TILESIZE_N];
+
+    if (get_local_id(0) < TILESIZE_N) {
+        uint idx = src2[block_id_n * TILESIZE_N + get_local_id(0)];
+        if (idx == 0xFFFFFFFF) {
+            idx = src2[block_id_n * TILESIZE_N + 0];
+        }
+        out_idx[get_local_id(0)] = idx * ne01;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    // Scatter results back to original position in output grid
+    uint m_offset = row + get_local_id(0);
+
+    write_imagef(dst, out_idx[1] + m_offset, (reg_c.s1));
+    write_imagef(dst, out_idx[2] + m_offset, (reg_c.s2));
+    write_imagef(dst, out_idx[3] + m_offset, (reg_c.s3));
+    write_imagef(dst, out_idx[4] + m_offset, (reg_c.s4));
+    write_imagef(dst, out_idx[5] + m_offset, (reg_c.s5));
+    write_imagef(dst, out_idx[6] + m_offset, (reg_c.s6));
+    write_imagef(dst, out_idx[7] + m_offset, (reg_c.s7));
+    write_imagef(dst, out_idx[8] + m_offset, (reg_c.s8));
+    write_imagef(dst, out_idx[9] + m_offset, (reg_c.s9));
+    write_imagef(dst, out_idx[10] + m_offset, (reg_c.sa));
+    write_imagef(dst, out_idx[11] + m_offset, (reg_c.sb));
+    write_imagef(dst, out_idx[12] + m_offset, (reg_c.sc));
+    write_imagef(dst, out_idx[13] + m_offset, (reg_c.sd));
+    write_imagef(dst, out_idx[14] + m_offset, (reg_c.se));
+    write_imagef(dst, out_idx[15] + m_offset, (reg_c.sf));
+    write_imagef(dst, out_idx[16] + m_offset, (reg_c.sg));
+    write_imagef(dst, out_idx[17] + m_offset, (reg_c.sh));
+    write_imagef(dst, out_idx[18] + m_offset, (reg_c.si));
+    write_imagef(dst, out_idx[19] + m_offset, (reg_c.sj));
+    write_imagef(dst, out_idx[20] + m_offset, (reg_c.sk));
+    write_imagef(dst, out_idx[21] + m_offset, (reg_c.sl));
+    write_imagef(dst, out_idx[22] + m_offset, (reg_c.sm));
+    write_imagef(dst, out_idx[23] + m_offset, (reg_c.sn));
+    write_imagef(dst, out_idx[24] + m_offset, (reg_c.so));
+    write_imagef(dst, out_idx[25] + m_offset, (reg_c.sp));
+    write_imagef(dst, out_idx[26] + m_offset, (reg_c.sq));
+    write_imagef(dst, out_idx[27] + m_offset, (reg_c.sr));
+    write_imagef(dst, out_idx[28] + m_offset, (reg_c.ss));
+    write_imagef(dst, out_idx[29] + m_offset, (reg_c.st));
+    write_imagef(dst, out_idx[30] + m_offset, (reg_c.su));
+    write_imagef(dst, out_idx[31] + m_offset, (reg_c.sv));
+
+    // Store zero padding parts to the index of first output in tile, override correct result in the end
+    barrier(CLK_GLOBAL_MEM_FENCE);
+    write_imagef(dst, out_idx[0] + m_offset, (reg_c.s0));
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_iq4_nl_f32.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_iq4_nl_f32.cl
new file mode 100644
index 00000000000..6869d822862
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_iq4_nl_f32.cl
@@ -0,0 +1,150 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+constant half kvalues_iq4nl[16] = {
+    (half)-127.f, (half)-104.f, (half)-83.f, (half)-65.f,
+    (half) -49.f, (half) -35.f, (half)-22.f, (half)-10.f,
+    (half)   1.f, (half)  13.f, (half) 25.f, (half) 38.f,
+    (half)  53.f, (half)  69.f, (half) 89.f, (half)113.f
+};
+
+// Packed LUT: 2 FP16 values per uint, 8 unique constant loads instead of 16
+constant uint iq4nl_packed[8] = {
+    0xD680D7F0u,  // idx 0,1: -127, -104
+    0xD410D530u,  // idx 2,3: -83, -65
+    0xD060D220u,  // idx 4,5: -49, -35
+    0xC900CD80u,  // idx 6,7: -22, -10
+    0x4A803C00u,  // idx 8,9: 1, 13
+    0x50C04E40u,  // idx 10,11: 25, 38
+    0x545052A0u,  // idx 12,13: 53, 69
+    0x57105590u   // idx 14,15: 89, 113
+};
+
+// Packed dequant: 1 uint constant load (8-way divergence) + shift + as_half
+#define IQ4_NL_DEQUANT(nibble) as_half((ushort)(iq4nl_packed[(nibble) >> 1] >> (((nibble) & 1u) << 4)))
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_128
+#endif
+
+kernel void kernel_gemm_noshuffle_iq4_nl_f32(
+        global const ushort * src0_q,
+        global const half  * src0_d,
+        read_only image1d_buffer_t src1,
+        global float * dst,
+        ulong offsetd,
+        int m,
+        int n,
+        int k,
+        int n_no_padding
+) {
+    dst = (global float *)((global char *)dst + offsetd);
+
+    int m_4 = m >> 2;
+    int n_4 = n >> 2;
+
+    int gy = get_global_id(0);
+    int gx = get_global_id(1);
+    int gx_2 = gx << 2;
+
+    half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0;
+    half8 B;
+    half4 dequantized_weights;
+
+    global const ushort * weight_ptr = src0_q + gx_2;
+    global const half * scale_ptr = src0_d + gx_2;
+
+    for (int i = 0; i < k; i += 4) {
+        B.s0123 = read_imageh(src1, gy*2 + (i)*(n_4));
+        B.s4567 = read_imageh(src1, gy*2 + (i)*(n_4)+1);
+
+        ushort4 bits4 = vload4(0, weight_ptr + (i/4)*(m));
+
+        half4 scale = vload4(0, scale_ptr + (i/32)*(m));
+
+        // j=0
+        dequantized_weights.s0 = IQ4_NL_DEQUANT(bits4.s0 & 0x000Fu) * scale.s0;
+        dequantized_weights.s1 = IQ4_NL_DEQUANT(bits4.s1 & 0x000Fu) * scale.s1;
+        dequantized_weights.s2 = IQ4_NL_DEQUANT(bits4.s2 & 0x000Fu) * scale.s2;
+        dequantized_weights.s3 = IQ4_NL_DEQUANT(bits4.s3 & 0x000Fu) * scale.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=1
+        B.s0123 = read_imageh(src1, gy*2 + (i+1)*(n_4));
+        B.s4567 = read_imageh(src1, gy*2 + (i+1)*(n_4)+1);
+        dequantized_weights.s0 = IQ4_NL_DEQUANT((bits4.s0 >> 4) & 0x000Fu) * scale.s0;
+        dequantized_weights.s1 = IQ4_NL_DEQUANT((bits4.s1 >> 4) & 0x000Fu) * scale.s1;
+        dequantized_weights.s2 = IQ4_NL_DEQUANT((bits4.s2 >> 4) & 0x000Fu) * scale.s2;
+        dequantized_weights.s3 = IQ4_NL_DEQUANT((bits4.s3 >> 4) & 0x000Fu) * scale.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=2
+        B.s0123 = read_imageh(src1, gy*2 + (i+2)*(n_4));
+        B.s4567 = read_imageh(src1, gy*2 + (i+2)*(n_4)+1);
+        dequantized_weights.s0 = IQ4_NL_DEQUANT((bits4.s0 >> 8) & 0x000Fu) * scale.s0;
+        dequantized_weights.s1 = IQ4_NL_DEQUANT((bits4.s1 >> 8) & 0x000Fu) * scale.s1;
+        dequantized_weights.s2 = IQ4_NL_DEQUANT((bits4.s2 >> 8) & 0x000Fu) * scale.s2;
+        dequantized_weights.s3 = IQ4_NL_DEQUANT((bits4.s3 >> 8) & 0x000Fu) * scale.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=3
+        B.s0123 = read_imageh(src1, gy*2 + (i+3)*(n_4));
+        B.s4567 = read_imageh(src1, gy*2 + (i+3)*(n_4)+1);
+        dequantized_weights.s0 = IQ4_NL_DEQUANT((bits4.s0 >> 12) & 0x000Fu) * scale.s0;
+        dequantized_weights.s1 = IQ4_NL_DEQUANT((bits4.s1 >> 12) & 0x000Fu) * scale.s1;
+        dequantized_weights.s2 = IQ4_NL_DEQUANT((bits4.s2 >> 12) & 0x000Fu) * scale.s2;
+        dequantized_weights.s3 = IQ4_NL_DEQUANT((bits4.s3 >> 12) & 0x000Fu) * scale.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+    }
+
+    int idx = (gy<<3)*m + (gx<<2);
+
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx);
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mat_Ab_Bi_8x4.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_0_f32.cl
similarity index 99%
rename from ggml/src/ggml-opencl/kernels/mul_mat_Ab_Bi_8x4.cl
rename to ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_0_f32.cl
index ecb577b9933..159378049fb 100644
--- a/ggml/src/ggml-opencl/kernels/mul_mat_Ab_Bi_8x4.cl
+++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_0_f32.cl
@@ -17,7 +17,7 @@
 REQD_SUBGROUP_SIZE_128
 #endif
 
-kernel void kernel_mul_mat_Ab_Bi_8x4(
+kernel void kernel_gemm_noshuffle_q4_0_f32(
         global const ushort * src0_q,       // quantized A
         global const half  * src0_d,        // A scales
         __read_only image1d_buffer_t src1,  // B (1d image)
diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_8x4.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q8_0_f32.cl
similarity index 98%
rename from ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_8x4.cl
rename to ggml/src/ggml-opencl/kernels/gemm_noshuffle_q8_0_f32.cl
index 51ce2121ce2..7f06a22a2cb 100644
--- a/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_8x4.cl
+++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q8_0_f32.cl
@@ -11,7 +11,7 @@
 REQD_SUBGROUP_SIZE_128
 #endif
 
-kernel void kernel_mul_mm_q8_0_f32_8x4(
+kernel void kernel_gemm_noshuffle_q8_0_f32(
         global const uint * src0_q,
         global const half  * src0_d,
         __read_only image1d_buffer_t src1,
diff --git a/ggml/src/ggml-opencl/kernels/gemm_xmem_f16_f32_os8.cl b/ggml/src/ggml-opencl/kernels/gemm_xmem_f16_f32_os8.cl
new file mode 100644
index 00000000000..df9d9aed067
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemm_xmem_f16_f32_os8.cl
@@ -0,0 +1,233 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_uniform_load : enable
+#pragma OPENCL EXTENSION cl_qcom_subgroup_constant_load : enable
+
+__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+
+__kernel void adreno_xmem_pack_src_f32(
+    __global const void * src_void,
+    ulong offset,
+    __write_only image2d_t src_img,
+    int K,
+    int N) {
+    const int x = get_global_id(0);
+    const int y = get_global_id(1);
+    const int kpack = K / 4;
+
+    if (x >= N || y >= kpack) {
+        return;
+    }
+
+    __global const float * src = (__global const float *)((__global const char *)src_void + offset);
+    const int base = x*K + y*4;
+    const half4 v = (half4)((half)src[base + 0], (half)src[base + 1], (half)src[base + 2], (half)src[base + 3]);
+    write_imageh(src_img, (int2)(x, y), v);
+}
+
+__kernel void adreno_xmem_prepack_weight_f16(
+    __global half4 * dst,
+    __global const void * src_void,
+    ulong offset,
+    int K,
+    int M,
+    int kpack,
+    int npack,
+    int os) {
+    const int linear = get_global_id(0);
+    const int total = kpack*npack;
+    if (linear >= total) {
+        return;
+    }
+
+    __global const half * src = (__global const half *)((__global const char *)src_void + offset);
+
+    const int dst_ogroup = linear % os;
+    const int dst_o_sp_i = linear / os;
+    const int dst_i = dst_o_sp_i % kpack;
+    const int dst_o = dst_o_sp_i / kpack;
+    const int o_slice = dst_o*os + dst_ogroup;
+    const int k_base = dst_i*4;
+
+    half4 w0 = (half4)(0.0h);
+    half4 w1 = (half4)(0.0h);
+    half4 w2 = (half4)(0.0h);
+    half4 w3 = (half4)(0.0h);
+
+    const int o0 = o_slice*4 + 0;
+    const int o1 = o_slice*4 + 1;
+    const int o2 = o_slice*4 + 2;
+    const int o3 = o_slice*4 + 3;
+
+    if (k_base + 0 < K) {
+        if (o0 < M) w0.s0 = src[o0*K + k_base + 0];
+        if (o1 < M) w0.s1 = src[o1*K + k_base + 0];
+        if (o2 < M) w0.s2 = src[o2*K + k_base + 0];
+        if (o3 < M) w0.s3 = src[o3*K + k_base + 0];
+    }
+    if (k_base + 1 < K) {
+        if (o0 < M) w1.s0 = src[o0*K + k_base + 1];
+        if (o1 < M) w1.s1 = src[o1*K + k_base + 1];
+        if (o2 < M) w1.s2 = src[o2*K + k_base + 1];
+        if (o3 < M) w1.s3 = src[o3*K + k_base + 1];
+    }
+    if (k_base + 2 < K) {
+        if (o0 < M) w2.s0 = src[o0*K + k_base + 2];
+        if (o1 < M) w2.s1 = src[o1*K + k_base + 2];
+        if (o2 < M) w2.s2 = src[o2*K + k_base + 2];
+        if (o3 < M) w2.s3 = src[o3*K + k_base + 2];
+    }
+    if (k_base + 3 < K) {
+        if (o0 < M) w3.s0 = src[o0*K + k_base + 3];
+        if (o1 < M) w3.s1 = src[o1*K + k_base + 3];
+        if (o2 < M) w3.s2 = src[o2*K + k_base + 3];
+        if (o3 < M) w3.s3 = src[o3*K + k_base + 3];
+    }
+
+    dst[linear*4 + 0] = w0;
+    dst[linear*4 + 1] = w1;
+    dst[linear*4 + 2] = w2;
+    dst[linear*4 + 3] = w3;
+}
+
+__attribute__((qcom_max_concurrent_subgroups(12)))
+__kernel void kernel_gemm_xmem_f16_f32_os8(
+    __constant half8 * weights_buffer __attribute__((sub_group_uniform)),
+    __constant half8 * xmem_buffer __attribute__((max_constant_size((6144)))),
+    __read_only image2d_t src_img,
+    __write_only image2d_t dst_img,
+    int N,
+    int npack,
+    int kpack) {
+    const int X = get_group_id(1)*get_local_size(0) + get_local_id(0);
+    const int Z = get_group_id(0)*get_local_size(2) + get_local_id(2);
+
+    if (X >= N || Z*8 >= npack) {
+        return;
+    }
+
+    half4 r0 = (half4)(0.0h);
+    half4 r1 = (half4)(0.0h);
+    half4 r2 = (half4)(0.0h);
+    half4 r3 = (half4)(0.0h);
+    half4 r4 = (half4)(0.0h);
+    half4 r5 = (half4)(0.0h);
+    half4 r6 = (half4)(0.0h);
+    half4 r7 = (half4)(0.0h);
+
+    int f_offset = Z*kpack*32;
+    int subgroup_id = (int)(0x1F & qcom_get_physical_sub_group_id());
+    subgroup_id = subgroup_id % 12;
+    const int c_offset = subgroup_id*32;
+    __constant half16 * weights_cache = (__constant half16 *)&xmem_buffer[c_offset];
+
+    int coord_s = 0;
+    do {
+        const half4 src0 = read_imageh(src_img, smp_zero, (int2)(X, coord_s));
+        coord_s++;
+        const half4 src1 = read_imageh(src_img, smp_zero, (int2)(X, coord_s));
+        coord_s++;
+
+        qcom_sub_group_constant_load8(xmem_buffer, weights_buffer, c_offset, f_offset >> 1, 32);
+        f_offset += 64;
+        qcom_sub_group_sync(QCOM_CLK_CONST_LOAD_SYNC);
+
+        r0 += src0.x * weights_cache[0].s0123;
+        r0 += src0.y * weights_cache[0].s4567;
+        r0 += src0.z * weights_cache[0].s89ab;
+        r0 += src0.w * weights_cache[0].scdef;
+        r1 += src0.x * weights_cache[1].s0123;
+        r1 += src0.y * weights_cache[1].s4567;
+        r1 += src0.z * weights_cache[1].s89ab;
+        r1 += src0.w * weights_cache[1].scdef;
+        r2 += src0.x * weights_cache[2].s0123;
+        r2 += src0.y * weights_cache[2].s4567;
+        r2 += src0.z * weights_cache[2].s89ab;
+        r2 += src0.w * weights_cache[2].scdef;
+        r3 += src0.x * weights_cache[3].s0123;
+        r3 += src0.y * weights_cache[3].s4567;
+        r3 += src0.z * weights_cache[3].s89ab;
+        r3 += src0.w * weights_cache[3].scdef;
+        r4 += src0.x * weights_cache[4].s0123;
+        r4 += src0.y * weights_cache[4].s4567;
+        r4 += src0.z * weights_cache[4].s89ab;
+        r4 += src0.w * weights_cache[4].scdef;
+        r5 += src0.x * weights_cache[5].s0123;
+        r5 += src0.y * weights_cache[5].s4567;
+        r5 += src0.z * weights_cache[5].s89ab;
+        r5 += src0.w * weights_cache[5].scdef;
+        r6 += src0.x * weights_cache[6].s0123;
+        r6 += src0.y * weights_cache[6].s4567;
+        r6 += src0.z * weights_cache[6].s89ab;
+        r6 += src0.w * weights_cache[6].scdef;
+        r7 += src0.x * weights_cache[7].s0123;
+        r7 += src0.y * weights_cache[7].s4567;
+        r7 += src0.z * weights_cache[7].s89ab;
+        r7 += src0.w * weights_cache[7].scdef;
+
+        r0 += src1.x * weights_cache[8].s0123;
+        r0 += src1.y * weights_cache[8].s4567;
+        r0 += src1.z * weights_cache[8].s89ab;
+        r0 += src1.w * weights_cache[8].scdef;
+        r1 += src1.x * weights_cache[9].s0123;
+        r1 += src1.y * weights_cache[9].s4567;
+        r1 += src1.z * weights_cache[9].s89ab;
+        r1 += src1.w * weights_cache[9].scdef;
+        r2 += src1.x * weights_cache[10].s0123;
+        r2 += src1.y * weights_cache[10].s4567;
+        r2 += src1.z * weights_cache[10].s89ab;
+        r2 += src1.w * weights_cache[10].scdef;
+        r3 += src1.x * weights_cache[11].s0123;
+        r3 += src1.y * weights_cache[11].s4567;
+        r3 += src1.z * weights_cache[11].s89ab;
+        r3 += src1.w * weights_cache[11].scdef;
+        r4 += src1.x * weights_cache[12].s0123;
+        r4 += src1.y * weights_cache[12].s4567;
+        r4 += src1.z * weights_cache[12].s89ab;
+        r4 += src1.w * weights_cache[12].scdef;
+        r5 += src1.x * weights_cache[13].s0123;
+        r5 += src1.y * weights_cache[13].s4567;
+        r5 += src1.z * weights_cache[13].s89ab;
+        r5 += src1.w * weights_cache[13].scdef;
+        r6 += src1.x * weights_cache[14].s0123;
+        r6 += src1.y * weights_cache[14].s4567;
+        r6 += src1.z * weights_cache[14].s89ab;
+        r6 += src1.w * weights_cache[14].scdef;
+        r7 += src1.x * weights_cache[15].s0123;
+        r7 += src1.y * weights_cache[15].s4567;
+        r7 += src1.z * weights_cache[15].s89ab;
+        r7 += src1.w * weights_cache[15].scdef;
+    } while (coord_s < kpack);
+
+    int coord_s_out = Z*8;
+    if (coord_s_out < npack) { write_imageh(dst_img, (int2)(X, coord_s_out), r0); coord_s_out++; }
+    if (coord_s_out < npack) { write_imageh(dst_img, (int2)(X, coord_s_out), r1); coord_s_out++; }
+    if (coord_s_out < npack) { write_imageh(dst_img, (int2)(X, coord_s_out), r2); coord_s_out++; }
+    if (coord_s_out < npack) { write_imageh(dst_img, (int2)(X, coord_s_out), r3); coord_s_out++; }
+    if (coord_s_out < npack) { write_imageh(dst_img, (int2)(X, coord_s_out), r4); coord_s_out++; }
+    if (coord_s_out < npack) { write_imageh(dst_img, (int2)(X, coord_s_out), r5); coord_s_out++; }
+    if (coord_s_out < npack) { write_imageh(dst_img, (int2)(X, coord_s_out), r6); coord_s_out++; }
+    if (coord_s_out < npack) { write_imageh(dst_img, (int2)(X, coord_s_out), r7); }
+}
+
+__kernel void adreno_xmem_store_dst_f32(
+    __read_only image2d_t dst_img,
+    __global void * dst_void,
+    ulong offset,
+    int M,
+    int N) {
+    const int x = get_global_id(0);
+    const int y = get_global_id(1);
+    const int npack = (M + 3) / 4;
+
+    if (x >= N || y >= npack) {
+        return;
+    }
+
+    __global float * dst = (__global float *)((__global char *)dst_void + offset);
+    const half4 hv = read_imageh(dst_img, smp_zero, (int2)(x, y));
+    const int m = y*4;
+    if (m + 0 < M) dst[x*M + m + 0] = (float)hv.s0;
+    if (m + 1 < M) dst[x*M + m + 1] = (float)hv.s1;
+    if (m + 2 < M) dst[x*M + m + 2] = (float)hv.s2;
+    if (m + 3 < M) dst[x*M + m + 3] = (float)hv.s3;
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemv_moe_mxfp4_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemv_moe_mxfp4_f32_ns.cl
new file mode 100644
index 00000000000..e4b44c1a56a
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemv_moe_mxfp4_f32_ns.cl
@@ -0,0 +1,161 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#define QK_MXFP4 32
+#define N_SIMDGROUP 4
+#define SIMDGROUP_WIDTH 64
+
+static inline half8 mxfp4_to_fp16_packed8(ushort2 fp4x8) {
+    ushort2 fp16_packed_a_0, fp16_packed_b_0, bias_a, bias_b, sign_a, sign_b;
+    fp16_packed_a_0.lo = (fp4x8.s0 << 9) & 0x0E00;
+    fp16_packed_a_0.hi = (fp4x8.s0 << 5) & 0x0E00;
+    fp16_packed_b_0.lo = (fp4x8.s0 << 1) & 0x0E00;
+    fp16_packed_b_0.hi = (fp4x8.s0 >> 3) & 0x0E00;
+
+    bias_a.lo = (fp16_packed_a_0.lo != 0) ? 0x3800 : 0x0;
+    bias_a.hi = (fp16_packed_a_0.hi != 0) ? 0x3800 : 0x0;
+    bias_b.lo = (fp16_packed_b_0.lo != 0) ? 0x3800 : 0x0;
+    bias_b.hi = (fp16_packed_b_0.hi != 0) ? 0x3800 : 0x0;
+
+    fp16_packed_a_0.lo = (fp16_packed_a_0.lo != 0x0200) ? fp16_packed_a_0.lo : 0x0;
+    fp16_packed_a_0.hi = (fp16_packed_a_0.hi != 0x0200) ? fp16_packed_a_0.hi : 0x0;
+    fp16_packed_b_0.lo = (fp16_packed_b_0.lo != 0x0200) ? fp16_packed_b_0.lo : 0x0;
+    fp16_packed_b_0.hi = (fp16_packed_b_0.hi != 0x0200) ? fp16_packed_b_0.hi : 0x0;
+
+    sign_a.lo = (fp4x8.s0 << 12) & 0x8000;
+    sign_a.hi = (fp4x8.s0 << 8) & 0x8000;
+    sign_b.lo = (fp4x8.s0 << 4) & 0x8000;
+    sign_b.hi = fp4x8.s0 & 0x8000;
+
+    fp16_packed_a_0 = sign_a + bias_a + fp16_packed_a_0;
+    fp16_packed_b_0 = sign_b + bias_b + fp16_packed_b_0;
+
+    ushort2 fp16_packed_a_1, fp16_packed_b_1;
+    fp16_packed_a_1.lo = (fp4x8.s1 << 9) & 0x0E00;
+    fp16_packed_a_1.hi = (fp4x8.s1 << 5) & 0x0E00;
+    fp16_packed_b_1.lo = (fp4x8.s1 << 1) & 0x0E00;
+    fp16_packed_b_1.hi = (fp4x8.s1 >> 3) & 0x0E00;
+
+    bias_a.lo = (fp16_packed_a_1.lo != 0) ? 0x3800 : 0x0;
+    bias_a.hi = (fp16_packed_a_1.hi != 0) ? 0x3800 : 0x0;
+    bias_b.lo = (fp16_packed_b_1.lo != 0) ? 0x3800 : 0x0;
+    bias_b.hi = (fp16_packed_b_1.hi != 0) ? 0x3800 : 0x0;
+
+    fp16_packed_a_1.lo = (fp16_packed_a_1.lo != 0x0200) ? fp16_packed_a_1.lo : 0x0;
+    fp16_packed_a_1.hi = (fp16_packed_a_1.hi != 0x0200) ? fp16_packed_a_1.hi : 0x0;
+    fp16_packed_b_1.lo = (fp16_packed_b_1.lo != 0x0200) ? fp16_packed_b_1.lo : 0x0;
+    fp16_packed_b_1.hi = (fp16_packed_b_1.hi != 0x0200) ? fp16_packed_b_1.hi : 0x0;
+
+    sign_a.lo = (fp4x8.s1 << 12) & 0x8000;
+    sign_a.hi = (fp4x8.s1 << 8) & 0x8000;
+    sign_b.lo = (fp4x8.s1 << 4) & 0x8000;
+    sign_b.hi = fp4x8.s1 & 0x8000;
+
+    fp16_packed_a_1 = sign_a + bias_a + fp16_packed_a_1;
+    fp16_packed_b_1 = sign_b + bias_b + fp16_packed_b_1;
+
+    return as_half8((ushort8)(fp16_packed_a_0, fp16_packed_b_0, fp16_packed_a_1, fp16_packed_b_1));
+}
+
+static inline float e8m0_to_fp32(uchar x) {
+    int bits;
+    bits = (x == 0) ? 0x00400000 : ((uint) x << 23);
+    return as_float(bits);
+}
+
+
+__attribute__((qcom_reqd_sub_group_size("half")))
+__kernel void kernel_gemv_moe_mxfp4_f32_ns(
+    __global uint * src0_q,
+    __global uchar * src0_e,
+    __read_only image1d_buffer_t src1,
+    __global uint * src2,
+    __global float * dst,
+    ulong         offsetd,
+    int           ne00,
+    int           ne01,
+    int           ne11
+) {
+    uint i01  = get_global_id(0);
+    uint i20  = get_global_id(2);
+    uint sgid = get_local_id(1);
+    uint slid = get_sub_group_local_id();
+
+    uint i11 = i20 % ne11;
+
+    uint expert_id = src2[i20];
+    uint expert_offset = expert_id * ne00 * ne01 / 32;
+
+    __private float sum = 0.0f; // each thread calculate partial sum of one output
+
+    // loop along ne00 in block granularity, skip 4 blocks every iter
+    for (uint ib00 = sgid; ib00 < (ne00 / QK_MXFP4); ib00 += N_SIMDGROUP) {
+
+        // load one block of q
+        uint4 regQ;
+        uint block_offset = expert_offset * 4 + ib00 * ne01 * 4 + i01;
+
+        regQ.s0 = src0_q[block_offset];
+        regQ.s1 = src0_q[block_offset + ne01];
+        regQ.s2 = src0_q[block_offset + ne01 * 2];
+        regQ.s3 = src0_q[block_offset + ne01 * 3];
+
+        uint offset = i11 * ne00 / 4 + ib00 * 8;
+
+        half8 fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s0));
+
+        float4 shared_y4;
+        shared_y4 = read_imagef(src1, (offset + 0));
+        float4 acc = shared_y4 * convert_float4(fp16x8.lo);
+
+        shared_y4 = read_imagef(src1, (offset + 1));
+        acc += shared_y4 * convert_float4(fp16x8.hi);
+
+        fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s1));
+
+        shared_y4 = read_imagef(src1, (offset + 2));
+        acc += shared_y4 * convert_float4(fp16x8.lo);
+
+        shared_y4 = read_imagef(src1, (offset + 3));
+        acc += shared_y4 * convert_float4(fp16x8.hi);
+
+
+        fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s2));
+
+        shared_y4 = read_imagef(src1, (offset + 4));
+        acc += shared_y4 * convert_float4(fp16x8.lo);
+
+        shared_y4 = read_imagef(src1, (offset + 5));
+        acc += shared_y4 * convert_float4(fp16x8.hi);
+
+
+        fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s3));
+
+        shared_y4 = read_imagef(src1, (offset + 6));
+        acc += shared_y4 * convert_float4(fp16x8.lo);
+
+        shared_y4 = read_imagef(src1, (offset + 7));
+        acc += shared_y4 * convert_float4(fp16x8.hi);
+
+        uchar regE = src0_e[ib00 * ne01 + i01 + expert_offset];
+        sum += e8m0_to_fp32(regE) * ((acc.s0 + acc.s1) + (acc.s2 + acc.s3));
+    }
+
+    // reduction in local memory, assumes #subgroups=4
+    __local float reduceLM[SIMDGROUP_WIDTH * (N_SIMDGROUP - 1)];
+    if (sgid == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = sum;
+    if (sgid == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = sum;
+    if (sgid == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = sum;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 0 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 1 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 2 + slid];
+
+    // 1 outputs per thread in subgroup 0
+    if (sgid == 0) {
+        dst = dst + (offsetd >> 2);
+        dst[i01 + i20 * ne01] = sum;
+    }
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemv_moe_q4_0_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemv_moe_q4_0_f32_ns.cl
new file mode 100644
index 00000000000..6f4d3f53216
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemv_moe_q4_0_f32_ns.cl
@@ -0,0 +1,116 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#define QK_Q4_0 32
+#define N_SIMDGROUP 4
+#define SIMDGROUP_WIDTH 64
+
+static inline float8 q4_0_to_fp32_packed8(ushort2 q4x8) {
+    float8 fp32x8;
+    fp32x8.s0 = (float)((q4x8.s0 & 0x000F) - 8);
+    fp32x8.s1 = (float)(((q4x8.s0 & 0x00F0) >> 4) - 8);
+    fp32x8.s2 = (float)(((q4x8.s0 & 0x0F00) >> 8) - 8);
+    fp32x8.s3 = (float)(((q4x8.s0 & 0xF000) >> 12) - 8);
+    fp32x8.s4 = (float)((q4x8.s1 & 0x000F) - 8);
+    fp32x8.s5 = (float)(((q4x8.s1 & 0x00F0) >> 4) - 8);
+    fp32x8.s6 = (float)(((q4x8.s1 & 0x0F00) >> 8) - 8);
+    fp32x8.s7 = (float)(((q4x8.s1 & 0xF000) >> 12) - 8);
+    return fp32x8;
+}
+
+
+__attribute__((qcom_reqd_sub_group_size("half")))
+__kernel void kernel_gemv_moe_q4_0_f32_ns(
+    __global uint * src0_q,
+    __global half * src0_d,
+    __read_only image1d_buffer_t src1,
+    __global uint * src2,
+    __global float * dst,
+    ulong         offsetd,
+    int           ne00,
+    int           ne01,
+    int           ne11
+) {
+    uint i01  = get_global_id(0);
+    uint i20  = get_global_id(2);
+    uint sgid = get_local_id(1);
+    uint slid = get_sub_group_local_id();
+
+    uint i11 = i20 % ne11;
+
+    uint expert_id = src2[i20];
+    uint expert_offset = expert_id * ne00 * ne01 / 32;
+
+    __private float sum = 0.0f; // each thread calculate partial sum of one output
+
+    // loop along ne00 in block granularity, skip 4 blocks every iter
+    for (uint ib00 = sgid; ib00 < (ne00 / QK_Q4_0); ib00 += N_SIMDGROUP) {
+
+        // load one block of q
+        uint4 regQ;
+        uint block_offset = expert_offset * 4 + ib00 * ne01 * 4 + i01;
+
+        regQ.s0 = src0_q[block_offset];
+        regQ.s1 = src0_q[block_offset + ne01];
+        regQ.s2 = src0_q[block_offset + ne01 * 2];
+        regQ.s3 = src0_q[block_offset + ne01 * 3];
+
+        uint offset = i11 * ne00 / 4 + ib00 * 8;
+
+        float8 fp32x8 = q4_0_to_fp32_packed8(as_ushort2(regQ.s0));
+
+        float4 shared_y4;
+        shared_y4 = read_imagef(src1, (offset + 0));
+        float4 acc = shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 1));
+        acc += shared_y4 * fp32x8.hi;
+
+        fp32x8 = q4_0_to_fp32_packed8(as_ushort2(regQ.s1));
+
+        shared_y4 = read_imagef(src1, (offset + 2));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 3));
+        acc += shared_y4 * fp32x8.hi;
+
+
+        fp32x8 = q4_0_to_fp32_packed8(as_ushort2(regQ.s2));
+
+        shared_y4 = read_imagef(src1, (offset + 4));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 5));
+        acc += shared_y4 * fp32x8.hi;
+
+
+        fp32x8 = q4_0_to_fp32_packed8(as_ushort2(regQ.s3));
+
+        shared_y4 = read_imagef(src1, (offset + 6));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 7));
+        acc += shared_y4 * fp32x8.hi;
+
+        half regS = src0_d[ib00 * ne01 + i01 + expert_offset];
+        sum += (float)(regS) * ((acc.s0 + acc.s1) + (acc.s2 + acc.s3));
+    }
+
+    // reduction in local memory, assumes #subgroups=4
+    __local float reduceLM[SIMDGROUP_WIDTH * (N_SIMDGROUP - 1)];
+    if (sgid == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = sum;
+    if (sgid == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = sum;
+    if (sgid == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = sum;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 0 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 1 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 2 + slid];
+
+    // 1 outputs per thread in subgroup 0
+    if (sgid == 0) {
+        dst = dst + (offsetd >> 2);
+        dst[i01 + i20 * ne01] = sum;
+    }
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemv_moe_q4_1_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemv_moe_q4_1_f32_ns.cl
new file mode 100644
index 00000000000..3739a215705
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemv_moe_q4_1_f32_ns.cl
@@ -0,0 +1,119 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#define QK_Q4_1 32
+#define N_SIMDGROUP 4
+#define SIMDGROUP_WIDTH 64
+
+static inline float8 q4_1_to_fp32_packed8(ushort2 q4x8, half s, half m) {
+    float8 fp32x8;
+    fp32x8.s0 = (float)((q4x8.s0 & 0x000F) * s + m);
+    fp32x8.s1 = (float)(((q4x8.s0 & 0x00F0) >> 4) * s + m);
+    fp32x8.s2 = (float)(((q4x8.s0 & 0x0F00) >> 8) * s + m);
+    fp32x8.s3 = (float)(((q4x8.s0 & 0xF000) >> 12) * s + m);
+    fp32x8.s4 = (float)((q4x8.s1 & 0x000F) * s + m);
+    fp32x8.s5 = (float)(((q4x8.s1 & 0x00F0) >> 4) * s + m);
+    fp32x8.s6 = (float)(((q4x8.s1 & 0x0F00) >> 8) * s + m);
+    fp32x8.s7 = (float)(((q4x8.s1 & 0xF000) >> 12) * s + m);
+    return fp32x8;
+}
+
+
+__attribute__((qcom_reqd_sub_group_size("half")))
+__kernel void kernel_gemv_moe_q4_1_f32_ns(
+    __global uint * src0_q,
+    __global half * src0_d,
+    __global half * src0_m,
+    __read_only image1d_buffer_t src1,
+    __global uint * src2,
+    __global float * dst,
+    ulong         offsetd,
+    int           ne00,
+    int           ne01,
+    int           ne11
+) {
+    uint i01  = get_global_id(0);
+    uint i20  = get_global_id(2);
+    uint sgid = get_local_id(1);
+    uint slid = get_sub_group_local_id();
+
+    uint i11 = i20 % ne11;
+
+    uint expert_id = src2[i20];
+    uint expert_offset = expert_id * ne00 * ne01 / 32;
+
+    __private float sum = 0.0f; // each thread calculate partial sum of one output
+
+    // loop along ne00 in block granularity, skip 4 blocks every iter
+    for (uint ib00 = sgid; ib00 < (ne00 / QK_Q4_1); ib00 += N_SIMDGROUP) {
+
+        // load one block of q
+        uint4 regQ;
+        uint block_offset = expert_offset * 4 + ib00 * ne01 * 4 + i01;
+
+        regQ.s0 = src0_q[block_offset];
+        regQ.s1 = src0_q[block_offset + ne01];
+        regQ.s2 = src0_q[block_offset + ne01 * 2];
+        regQ.s3 = src0_q[block_offset + ne01 * 3];
+
+        uint offset = i11 * ne00 / 4 + ib00 * 8;
+
+        half regM = src0_m[ib00 * ne01 + i01 + expert_offset];
+        half regS = src0_d[ib00 * ne01 + i01 + expert_offset];
+
+        float8 fp32x8 = q4_1_to_fp32_packed8(as_ushort2(regQ.s0), regS, regM);
+
+        float4 shared_y4;
+        shared_y4 = read_imagef(src1, (offset + 0));
+        float4 acc = shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 1));
+        acc += shared_y4 * fp32x8.hi;
+
+        fp32x8 = q4_1_to_fp32_packed8(as_ushort2(regQ.s1), regS, regM);
+
+        shared_y4 = read_imagef(src1, (offset + 2));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 3));
+        acc += shared_y4 * fp32x8.hi;
+
+
+        fp32x8 = q4_1_to_fp32_packed8(as_ushort2(regQ.s2), regS, regM);
+
+        shared_y4 = read_imagef(src1, (offset + 4));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 5));
+        acc += shared_y4 * fp32x8.hi;
+
+
+        fp32x8 = q4_1_to_fp32_packed8(as_ushort2(regQ.s3), regS, regM);
+
+        shared_y4 = read_imagef(src1, (offset + 6));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 7));
+        acc += shared_y4 * fp32x8.hi;
+
+        sum += ((acc.s0 + acc.s1) + (acc.s2 + acc.s3));
+    }
+
+    // reduction in local memory, assumes #subgroups=4
+    __local float reduceLM[SIMDGROUP_WIDTH * (N_SIMDGROUP - 1)];
+    if (sgid == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = sum;
+    if (sgid == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = sum;
+    if (sgid == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = sum;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 0 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 1 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 2 + slid];
+
+    // 1 outputs per thread in subgroup 0
+    if (sgid == 0) {
+        dst = dst + (offsetd >> 2);
+        dst[i01 + i20 * ne01] = sum;
+    }
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemv_moe_q5_0_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemv_moe_q5_0_f32_ns.cl
new file mode 100644
index 00000000000..938054cf982
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemv_moe_q5_0_f32_ns.cl
@@ -0,0 +1,119 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#define QK_Q5_0 32
+#define N_SIMDGROUP 4
+#define SIMDGROUP_WIDTH 64
+
+static inline float8 q5_0_to_fp32_packed8(ushort2 qs5x8, uchar qh5x8) {
+    float8 fp32x8;
+    fp32x8.s0 = (float)((( qs5x8.s0 & 0x000F)        | (( qh5x8       & 0x01) << 4)) - 16);
+    fp32x8.s1 = (float)((((qs5x8.s0 & 0x00F0) >> 4 ) | (((qh5x8 >> 1) & 0x01) << 4)) - 16);
+    fp32x8.s2 = (float)((((qs5x8.s0 & 0x0F00) >> 8 ) | (((qh5x8 >> 2) & 0x01) << 4)) - 16);
+    fp32x8.s3 = (float)((((qs5x8.s0 & 0xF000) >> 12) | (((qh5x8 >> 3) & 0x01) << 4)) - 16);
+    fp32x8.s4 = (float)((( qs5x8.s1 & 0x000F)        | (((qh5x8 >> 4) & 0x01) << 4)) - 16);
+    fp32x8.s5 = (float)((((qs5x8.s1 & 0x00F0) >> 4 ) | (((qh5x8 >> 5) & 0x01) << 4)) - 16);
+    fp32x8.s6 = (float)((((qs5x8.s1 & 0x0F00) >> 8 ) | (((qh5x8 >> 6) & 0x01) << 4)) - 16);
+    fp32x8.s7 = (float)((((qs5x8.s1 & 0xF000) >> 12) | (((qh5x8 >> 7) & 0x01) << 4)) - 16);
+    return fp32x8;
+}
+
+
+__attribute__((qcom_reqd_sub_group_size("half")))
+__kernel void kernel_gemv_moe_q5_0_f32_ns(
+    __global    uint *           src0_qs,
+    __global    uint *           src0_qh,
+    __global    half *           src0_d,
+    __read_only image1d_buffer_t src1,
+    __global    uint *           src2,
+    __global    float *          dst,
+    ulong offsetd,
+    uint  ne00,
+    uint  ne01,
+    uint  ne11
+) {
+    uint i01  = get_global_id(0);
+    uint i20  = get_global_id(2);
+    uint sgid = get_local_id(1);
+    uint slid = get_sub_group_local_id();
+
+    uint i11 = i20 % ne11;
+
+    uint expert_id = src2[i20];
+    uint expert_offset = expert_id * ne00 * ne01 / 32;
+
+    __private float sum = 0.0f; // each thread calculate partial sum of one output
+
+    // loop along ne00 in block granularity, skip 4 blocks every iter
+    for (uint ib00 = sgid; ib00 < (ne00 / QK_Q5_0); ib00 += N_SIMDGROUP) {
+
+        // load one block of q
+        uint4 regQ;
+        uint block_offset = expert_offset * 4 + ib00 * ne01 * 4 + i01;
+
+        regQ.s0 = src0_qs[block_offset];
+        regQ.s1 = src0_qs[block_offset + ne01];
+        regQ.s2 = src0_qs[block_offset + ne01 * 2];
+        regQ.s3 = src0_qs[block_offset + ne01 * 3];
+
+        uint offset = i11 * ne00 / 4 + ib00 * 8;
+
+        uchar4 regQh = as_uchar4(src0_qh[ib00 * ne01 + i01 + expert_offset]);
+        half regS = src0_d[ib00 * ne01 + i01 + expert_offset];
+
+        float8 fp32x8 = q5_0_to_fp32_packed8(as_ushort2(regQ.s0), regQh.s0);
+
+        float4 shared_y4;
+        shared_y4 = read_imagef(src1, (offset + 0));
+        float4 acc = shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 1));
+        acc += shared_y4 * fp32x8.hi;
+
+        fp32x8 = q5_0_to_fp32_packed8(as_ushort2(regQ.s1), regQh.s1);
+
+        shared_y4 = read_imagef(src1, (offset + 2));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 3));
+        acc += shared_y4 * fp32x8.hi;
+
+
+        fp32x8 = q5_0_to_fp32_packed8(as_ushort2(regQ.s2), regQh.s2);
+
+        shared_y4 = read_imagef(src1, (offset + 4));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 5));
+        acc += shared_y4 * fp32x8.hi;
+
+
+        fp32x8 = q5_0_to_fp32_packed8(as_ushort2(regQ.s3), regQh.s3);
+
+        shared_y4 = read_imagef(src1, (offset + 6));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 7));
+        acc += shared_y4 * fp32x8.hi;
+
+        sum += (float)(regS) * ((acc.s0 + acc.s1) + (acc.s2 + acc.s3));
+    }
+
+    // reduction in local memory, assumes #subgroups=4
+    __local float reduceLM[SIMDGROUP_WIDTH * (N_SIMDGROUP - 1)];
+    if (sgid == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = sum;
+    if (sgid == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = sum;
+    if (sgid == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = sum;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 0 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 1 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 2 + slid];
+
+    // 1 outputs per thread in subgroup 0
+    if (sgid == 0) {
+        dst = dst + (offsetd >> 2);
+        dst[i01 + i20 * ne01] = sum;
+    }
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemv_moe_q5_1_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemv_moe_q5_1_f32_ns.cl
new file mode 100644
index 00000000000..f33a4ef2757
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemv_moe_q5_1_f32_ns.cl
@@ -0,0 +1,121 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#define QK_Q5_1 32
+#define N_SIMDGROUP 4
+#define SIMDGROUP_WIDTH 64
+
+static inline float8 q5_1_to_fp32_packed8(ushort2 qs5x8, uchar qh5x8, half s, half m) {
+    float8 fp32x8;
+    fp32x8.s0 = (float)((( qs5x8.s0 & 0x000F)        | (( qh5x8       & 0x01) << 4)) * s + m);
+    fp32x8.s1 = (float)((((qs5x8.s0 & 0x00F0) >> 4 ) | (((qh5x8 >> 1) & 0x01) << 4)) * s + m);
+    fp32x8.s2 = (float)((((qs5x8.s0 & 0x0F00) >> 8 ) | (((qh5x8 >> 2) & 0x01) << 4)) * s + m);
+    fp32x8.s3 = (float)((((qs5x8.s0 & 0xF000) >> 12) | (((qh5x8 >> 3) & 0x01) << 4)) * s + m);
+    fp32x8.s4 = (float)((( qs5x8.s1 & 0x000F)        | (((qh5x8 >> 4) & 0x01) << 4)) * s + m);
+    fp32x8.s5 = (float)((((qs5x8.s1 & 0x00F0) >> 4 ) | (((qh5x8 >> 5) & 0x01) << 4)) * s + m);
+    fp32x8.s6 = (float)((((qs5x8.s1 & 0x0F00) >> 8 ) | (((qh5x8 >> 6) & 0x01) << 4)) * s + m);
+    fp32x8.s7 = (float)((((qs5x8.s1 & 0xF000) >> 12) | (((qh5x8 >> 7) & 0x01) << 4)) * s + m);
+    return fp32x8;
+}
+
+
+__attribute__((qcom_reqd_sub_group_size("half")))
+__kernel void kernel_gemv_moe_q5_1_f32_ns(
+    __global    uint *           src0_qs,
+    __global    uint *           src0_qh,
+    __global    half *           src0_d,
+    __global    half *           src0_m,
+    __read_only image1d_buffer_t src1,
+    __global    uint *           src2,
+    __global    float *          dst,
+    ulong offsetd,
+    uint  ne00,
+    uint  ne01,
+    uint  ne11
+) {
+    uint i01  = get_global_id(0);
+    uint i20  = get_global_id(2);
+    uint sgid = get_local_id(1);
+    uint slid = get_sub_group_local_id();
+
+    uint i11 = i20 % ne11;
+
+    uint expert_id = src2[i20];
+    uint expert_offset = expert_id * ne00 * ne01 / 32;
+
+    __private float sum = 0.0f; // each thread calculate partial sum of one output
+
+    // loop along ne00 in block granularity, skip 4 blocks every iter
+    for (uint ib00 = sgid; ib00 < (ne00 / QK_Q5_1); ib00 += N_SIMDGROUP) {
+
+        // load one block of q
+        uint4 regQ;
+        uint block_offset = expert_offset * 4 + ib00 * ne01 * 4 + i01;
+
+        regQ.s0 = src0_qs[block_offset];
+        regQ.s1 = src0_qs[block_offset + ne01];
+        regQ.s2 = src0_qs[block_offset + ne01 * 2];
+        regQ.s3 = src0_qs[block_offset + ne01 * 3];
+
+        uint offset = i11 * ne00 / 4 + ib00 * 8;
+
+        uchar4 regQh = as_uchar4(src0_qh[ib00 * ne01 + i01 + expert_offset]);
+        half regM = src0_m[ib00 * ne01 + i01 + expert_offset];
+        half regS = src0_d[ib00 * ne01 + i01 + expert_offset];
+
+        float8 fp32x8 = q5_1_to_fp32_packed8(as_ushort2(regQ.s0), regQh.s0, regS, regM);
+
+        float4 shared_y4;
+        shared_y4 = read_imagef(src1, (offset + 0));
+        float4 acc = shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 1));
+        acc += shared_y4 * fp32x8.hi;
+
+        fp32x8 = q5_1_to_fp32_packed8(as_ushort2(regQ.s1), regQh.s1, regS, regM);
+
+        shared_y4 = read_imagef(src1, (offset + 2));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 3));
+        acc += shared_y4 * fp32x8.hi;
+
+
+        fp32x8 = q5_1_to_fp32_packed8(as_ushort2(regQ.s2), regQh.s2, regS, regM);
+
+        shared_y4 = read_imagef(src1, (offset + 4));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 5));
+        acc += shared_y4 * fp32x8.hi;
+
+
+        fp32x8 = q5_1_to_fp32_packed8(as_ushort2(regQ.s3), regQh.s3, regS, regM);
+
+        shared_y4 = read_imagef(src1, (offset + 6));
+        acc += shared_y4 * fp32x8.lo;
+
+        shared_y4 = read_imagef(src1, (offset + 7));
+        acc += shared_y4 * fp32x8.hi;
+
+        sum += ((acc.s0 + acc.s1) + (acc.s2 + acc.s3));
+    }
+
+    // reduction in local memory, assumes #subgroups=4
+    __local float reduceLM[SIMDGROUP_WIDTH * (N_SIMDGROUP - 1)];
+    if (sgid == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = sum;
+    if (sgid == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = sum;
+    if (sgid == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = sum;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 0 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 1 + slid];
+    if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 2 + slid];
+
+    // 1 outputs per thread in subgroup 0
+    if (sgid == 0) {
+        dst = dst + (offsetd >> 2);
+        dst[i01 + i20 * ne01] = sum;
+    }
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_iq4_nl_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_iq4_nl_f32.cl
new file mode 100644
index 00000000000..9386bf25a6f
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_iq4_nl_f32.cl
@@ -0,0 +1,302 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
+#endif
+
+#define QK4_NL 32
+#define NSUBGROUPS 4
+#define SUBGROUP_SIZE 64
+
+constant half kvalues_iq4nl[16] = {
+    (half)-127.f, (half)-104.f, (half)-83.f, (half)-65.f,
+    (half) -49.f, (half) -35.f, (half)-22.f, (half)-10.f,
+    (half)   1.f, (half)  13.f, (half) 25.f, (half) 38.f,
+    (half)  53.f, (half)  69.f, (half) 89.f, (half)113.f
+};
+
+// Packed LUT: 2 FP16 values per uint, 8 unique constant loads instead of 16
+constant uint iq4nl_packed[8] = {
+    0xD680D7F0u,  // idx 0,1: -127, -104
+    0xD410D530u,  // idx 2,3: -83, -65
+    0xD060D220u,  // idx 4,5: -49, -35
+    0xC900CD80u,  // idx 6,7: -22, -10
+    0x4A803C00u,  // idx 8,9: 1, 13
+    0x50C04E40u,  // idx 10,11: 25, 38
+    0x545052A0u,  // idx 12,13: 53, 69
+    0x57105590u   // idx 14,15: 89, 113
+};
+
+// Packed dequant: 1 uint constant load (8-way divergence) + shift + as_half
+#define IQ4_NL_DEQUANT(nibble) as_half((ushort)(iq4nl_packed[(nibble) >> 1] >> (((nibble) & 1u) << 4)))
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_hi(total_sums, bits4, scale, y) \
+    float shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s0 & 0x000F)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s1 & 0x000F)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0xF000) >> 12)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0xF000) >> 12)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s2 & 0x000F)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s3 & 0x000F)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0xF000) >> 12)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0xF000) >> 12)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s4 & 0x000F)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s5 & 0x000F)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0xF000) >> 12)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0xF000) >> 12)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s6 & 0x000F)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s7 & 0x000F)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0xF000) >> 12)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0xF000) >> 12)) * scale.s1 * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_lo(total_sums, bits4, scale, y) \
+    shared_y = sub_group_broadcast(y.s0, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s0 & 0x000F)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s1 & 0x000F)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0xF000) >> 12)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0xF000) >> 12)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s2 & 0x000F)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s3 & 0x000F)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0xF000) >> 12)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0xF000) >> 12)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s4 & 0x000F)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s5 & 0x000F)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0xF000) >> 12)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0xF000) >> 12)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s6 & 0x000F)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s7 & 0x000F)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0xF000) >> 12)) * scale.s0 * shared_y; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0xF000) >> 12)) * scale.s1 * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_hi(total_sums, bits4, scale, y) \
+    float8 shared_y; \
+    shared_y = sub_group_broadcast(y, 0); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s0 & 0x000F))         * scale.s0 * shared_y.s0; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x00F0) >> 4))  * scale.s0 * shared_y.s1; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x0F00) >> 8))  * scale.s0 * shared_y.s2; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0xF000) >> 12)) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s2 & 0x000F))         * scale.s0 * shared_y.s4; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x00F0) >> 4))  * scale.s0 * shared_y.s5; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x0F00) >> 8))  * scale.s0 * shared_y.s6; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0xF000) >> 12)) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s1 & 0x000F))         * scale.s1 * shared_y.s0; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x00F0) >> 4))  * scale.s1 * shared_y.s1; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x0F00) >> 8))  * scale.s1 * shared_y.s2; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0xF000) >> 12)) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s3 & 0x000F))         * scale.s1 * shared_y.s4; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x00F0) >> 4))  * scale.s1 * shared_y.s5; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x0F00) >> 8))  * scale.s1 * shared_y.s6; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0xF000) >> 12)) * scale.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 1); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s4 & 0x000F))         * scale.s0 * shared_y.s0; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x00F0) >> 4))  * scale.s0 * shared_y.s1; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x0F00) >> 8))  * scale.s0 * shared_y.s2; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0xF000) >> 12)) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s6 & 0x000F))         * scale.s0 * shared_y.s4; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x00F0) >> 4))  * scale.s0 * shared_y.s5; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x0F00) >> 8))  * scale.s0 * shared_y.s6; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0xF000) >> 12)) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s5 & 0x000F))         * scale.s1 * shared_y.s0; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x00F0) >> 4))  * scale.s1 * shared_y.s1; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x0F00) >> 8))  * scale.s1 * shared_y.s2; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0xF000) >> 12)) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s7 & 0x000F))         * scale.s1 * shared_y.s4; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x00F0) >> 4))  * scale.s1 * shared_y.s5; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x0F00) >> 8))  * scale.s1 * shared_y.s6; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0xF000) >> 12)) * scale.s1 * shared_y.s7; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_lo(total_sums, bits4, scale, y) \
+    shared_y = sub_group_broadcast(y, 2); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s0 & 0x000F))         * scale.s0 * shared_y.s0; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x00F0) >> 4))  * scale.s0 * shared_y.s1; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x0F00) >> 8))  * scale.s0 * shared_y.s2; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0xF000) >> 12)) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s2 & 0x000F))         * scale.s0 * shared_y.s4; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x00F0) >> 4))  * scale.s0 * shared_y.s5; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x0F00) >> 8))  * scale.s0 * shared_y.s6; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0xF000) >> 12)) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s1 & 0x000F))         * scale.s1 * shared_y.s0; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x00F0) >> 4))  * scale.s1 * shared_y.s1; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x0F00) >> 8))  * scale.s1 * shared_y.s2; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0xF000) >> 12)) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s3 & 0x000F))         * scale.s1 * shared_y.s4; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x00F0) >> 4))  * scale.s1 * shared_y.s5; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x0F00) >> 8))  * scale.s1 * shared_y.s6; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0xF000) >> 12)) * scale.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 3); \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s4 & 0x000F))         * scale.s0 * shared_y.s0; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x00F0) >> 4))  * scale.s0 * shared_y.s1; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x0F00) >> 8))  * scale.s0 * shared_y.s2; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0xF000) >> 12)) * scale.s0 * shared_y.s3; \
+    total_sums.s0 += IQ4_NL_DEQUANT((bits4.s6 & 0x000F))         * scale.s0 * shared_y.s4; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x00F0) >> 4))  * scale.s0 * shared_y.s5; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x0F00) >> 8))  * scale.s0 * shared_y.s6; \
+    total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0xF000) >> 12)) * scale.s0 * shared_y.s7; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s5 & 0x000F))         * scale.s1 * shared_y.s0; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x00F0) >> 4))  * scale.s1 * shared_y.s1; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x0F00) >> 8))  * scale.s1 * shared_y.s2; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0xF000) >> 12)) * scale.s1 * shared_y.s3; \
+    total_sums.s1 += IQ4_NL_DEQUANT((bits4.s7 & 0x000F))         * scale.s1 * shared_y.s4; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x00F0) >> 4))  * scale.s1 * shared_y.s5; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x0F00) >> 8))  * scale.s1 * shared_y.s6; \
+    total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0xF000) >> 12)) * scale.s1 * shared_y.s7; \
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_gemv_noshuffle_iq4_nl_f32(
+        read_only  image1d_buffer_t src0_q,
+        global half2  * src0_d,
+        read_only  image1d_buffer_t src1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01)
+{
+    uint groupId = get_local_id(1);
+    uint gid     = get_global_id(0);
+    ushort slid    = get_sub_group_local_id();
+
+    uint K = ne00;
+    uint M = ne01;
+
+    uint LINE_STRIDE_A = M / 2;
+    uint BLOCK_STRIDE_A = NSUBGROUPS * M;
+
+    private uint4     regA;
+    private half2     regS;
+    private float8    regB;
+
+    private float2 totalSum = (float2)(0.0f);
+
+    // loop along K in block granularity, skip 4 blocks every iter
+    for (uint k = groupId; k < (K / QK4_NL); k += NSUBGROUPS) {
+        regS = src0_d[gid + k * LINE_STRIDE_A]; // each fiber loads scale of two rows
+        // first 4 fibers in each wave load 8 B values to its private scope
+        if (slid < 4) {
+            regB.s0123 = read_imagef(src1, (slid * 2 + k * 8));
+            regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8));
+        }
+
+        // load half weights for two blocks in consecutive rows
+        regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
+        regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
+        regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
+        regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAST
+        dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regB);
+#else
+        dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAST
+
+        regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
+        regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
+        regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
+        regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
+#ifdef VECTOR_SUB_GROUP_BROADCAST
+        dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regB);
+#else
+        dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAST
+    }
+
+    // reduction in local memory, assumes #wave=4
+    local float2 reduceLM[SUBGROUP_SIZE * 3];
+    if (groupId == 1) {
+        reduceLM[SUBGROUP_SIZE * 0 + slid] = totalSum;
+    }
+    if (groupId == 2) {
+        reduceLM[SUBGROUP_SIZE * 1 + slid] = totalSum;
+    }
+    if (groupId == 3) {
+        reduceLM[SUBGROUP_SIZE * 2 + slid] = totalSum;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 0 + slid];
+    }
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 1 + slid];
+    }
+    if (groupId == 0) {
+        totalSum += reduceLM[SUBGROUP_SIZE * 2 + slid];
+    }
+
+    // 2 outputs per fiber in wave 0
+    if (groupId == 0) {
+        dst = (global float*)((global char*)dst + offsetd);
+        vstore2(totalSum, 0, &(dst[gid * 2]));
+    }
+
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_0_f32.cl
similarity index 98%
rename from ggml/src/ggml-opencl/kernels/gemv_noshuffle_general.cl
rename to ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_0_f32.cl
index 469d3edef00..10683206919 100644
--- a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general.cl
+++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_0_f32.cl
@@ -191,7 +191,7 @@
 #ifdef ADRENO_GPU
 REQD_SUBGROUP_SIZE_64
 #endif
-__kernel void kernel_gemv_noshuffle(
+__kernel void kernel_gemv_noshuffle_q4_0_f32(
         __read_only  image1d_buffer_t src0_q,  // quantized A
         global half2  * src0_d,  // A scales
         __read_only  image1d_buffer_t src1,    // B
@@ -238,21 +238,21 @@ __kernel void kernel_gemv_noshuffle(
         regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
         regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
         regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
-#ifdef VECTOR_SUB_GROUP_BROADCAT
+#ifdef VECTOR_SUB_GROUP_BROADCAST
         dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regB);
 #else
         dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regB);
-#endif // VECTOR_SUB_GROUP_BROADCAT
+#endif // VECTOR_SUB_GROUP_BROADCAST
 
         regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
         regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
         regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
         regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
-#ifdef VECTOR_SUB_GROUP_BROADCAT
+#ifdef VECTOR_SUB_GROUP_BROADCAST
         dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regB);
 #else
         dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regB);
-#endif // VECTOR_SUB_GROUP_BROADCAT
+#endif // VECTOR_SUB_GROUP_BROADCAST
     }
 
     // reduction in local memory, assumes #wave=4
diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_0_f32_spec.cl
similarity index 98%
rename from ggml/src/ggml-opencl/kernels/gemv_noshuffle.cl
rename to ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_0_f32_spec.cl
index ee5c79f000d..571a375da7f 100644
--- a/ggml/src/ggml-opencl/kernels/gemv_noshuffle.cl
+++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_0_f32_spec.cl
@@ -191,7 +191,7 @@
 #ifdef ADRENO_GPU
 REQD_SUBGROUP_SIZE_64
 #endif
-__kernel void kernel_gemv_noshuffle(
+__kernel void kernel_gemv_noshuffle_q4_0_f32(
         __read_only  image1d_buffer_t src0_q,  // quantized A
         global half2  * src0_d,  // A scales
         __read_only  image1d_buffer_t src1,    // B
@@ -232,21 +232,21 @@ __kernel void kernel_gemv_noshuffle(
         regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
         regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
         regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
-#ifdef VECTOR_SUB_GROUP_BROADCAT
+#ifdef VECTOR_SUB_GROUP_BROADCAST
         dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regB);
 #else
         dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regB);
-#endif // VECTOR_SUB_GROUP_BROADCAT
+#endif // VECTOR_SUB_GROUP_BROADCAST
 
         regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
         regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
         regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
         regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
-#ifdef VECTOR_SUB_GROUP_BROADCAT
+#ifdef VECTOR_SUB_GROUP_BROADCAST
         dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regB);
 #else
         dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regB);
-#endif // VECTOR_SUB_GROUP_BROADCAT
+#endif // VECTOR_SUB_GROUP_BROADCAST
     }
 
     // reduction in local memory, assumes #wave=4
diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q8_0_f32.cl
similarity index 100%
rename from ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl
rename to ggml/src/ggml-opencl/kernels/gemv_noshuffle_q8_0_f32.cl
diff --git a/ggml/src/ggml-opencl/kernels/moe_reorder_b.cl b/ggml/src/ggml-opencl/kernels/moe_reorder_b.cl
new file mode 100644
index 00000000000..e6295c81648
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/moe_reorder_b.cl
@@ -0,0 +1,30 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define QK4_0 32
+
+kernel void kernel_moe_reorder_b(
+    global float4 * src,
+    global uint * router,
+    global float4 * dst,
+    global int * total_tiles,
+    uint K,
+    ushort map_ratio,
+    uint tile_size
+) {
+    uint k_4 = get_global_id(0);
+    uint post_router_idx = get_global_id(1);
+
+    if ((k_4 >= (K / 4)) || (post_router_idx >= total_tiles[0] * tile_size)) {
+        return;
+    }
+
+    uint router_idx = router[post_router_idx];
+
+    float4 out = (float4)(0);
+    if (router_idx != 0xFFFFFFFF) {
+        ushort activation_idx = router_idx / map_ratio;
+        out = src[activation_idx * K / 4 + k_4];
+    }
+
+    dst[post_router_idx * K / 4 + k_4] = out;
+}
diff --git a/ggml/src/ggml-opencl/kernels/moe_sort_by_expert.cl b/ggml/src/ggml-opencl/kernels/moe_sort_by_expert.cl
new file mode 100644
index 00000000000..d9703429b11
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/moe_sort_by_expert.cl
@@ -0,0 +1,82 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+__kernel void kernel_moe_histogram(
+    __global const int * input,
+    __global int * hist,
+    uint N,
+    uint topK,
+    uint n_experts
+) {
+    uint n = get_global_id(0);
+    uint k = get_global_id(1);
+
+    if (n >= N || k >= topK) {
+        return;
+    }
+
+    int expert_id = input[n * n_experts + k];
+    atomic_inc(&hist[expert_id]);
+}
+
+__kernel void kernel_moe_scan(
+    __global int * hist,
+    __global int * tile_offset,
+    __global int * total_tiles,
+    __global int * slot_counter,
+    int tile_size,
+    uint n_experts
+) {
+    int offset = 0;
+    for (int v = 0; v < n_experts; v++) {
+        int count = hist[v];
+        int tiles = (count + tile_size - 1) / tile_size;
+        tile_offset[v] = offset;
+        offset += tiles;
+        hist[v] = 0;
+        slot_counter[v] = 0;
+    }
+
+    *total_tiles = offset;
+}
+
+__kernel void kernel_moe_scatter(
+    __global const int * input,
+    __global int * post_router,
+    __global ushort * emap,
+    __global const int * tile_offset,
+    __global int * slot_counter,
+    int N,
+    int topK,
+    uint n_experts
+) {
+    uint n = get_global_id(0);
+    uint k = get_global_id(1);
+
+    if (n >= N || k >= topK) {
+        return;
+    }
+
+    int val = input[n * n_experts + k];
+
+    int local_slot = atomic_inc(&slot_counter[val]);
+
+    int tile_idx  = tile_offset[val] + (local_slot / 32);
+    int lane      = local_slot % 32;
+    int out_pos   = tile_idx * 32 + lane;
+
+    post_router[out_pos] = n * topK + k;
+    emap[tile_idx] = val;
+}
+
+__kernel void kernel_moe_fill(
+    __global int * post_router,
+    __global int * total_tiles,
+    int tile_size
+) {
+    int tile_id = get_global_id(0);
+    int vec_id_in_tile = get_global_id(1);
+
+    if (tile_id < total_tiles[0]) {
+        post_router[tile_id * tile_size + vec_id_in_tile] = 0xFFFFFFFF;
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_iq4_nl_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_iq4_nl_f32_l4_lm.cl
new file mode 100644
index 00000000000..11ff7f8d9dc
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mm_iq4_nl_f32_l4_lm.cl
@@ -0,0 +1,171 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#define LOAD_VEC_A 8
+#define LOAD_VEC_B 4
+
+#define BM 64
+#define BN 64
+#define BK 32
+#define TM 4
+#define TN 8
+
+constant float kvalues_iq4nl[16] = {
+    -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f,
+      1.f,   13.f,  25.f,  38.f,  53.f,  69.f,  89.f, 113.f
+};
+
+kernel void kernel_mul_mm_iq4_nl_f32_l4_lm(
+    global uchar4 * src0_q,
+    global half   * src0_d,
+    global float4 * src1,
+    ulong offset1,
+    global float  * dst,
+    ulong offsetd,
+
+    int ne00,
+    int ne01,
+    int ne02,
+    int ne11,
+    int ne12,
+
+    int stride_a,
+    int stride_b,
+    int stride_d,
+
+    int batch_stride_a,
+    int batch_stride_b,
+    int batch_stride_d,
+
+    int r2,
+    int r3
+) {
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    local float buf_a[BM * BK];
+    local float buf_b[BN * BK];
+
+    const int batch_idx = get_global_id(2);
+
+    const int i13 = batch_idx / ne12;
+    const int i12 = batch_idx % ne12;
+
+    const int i03 = i13 / r3;
+    const int i02 = i12 / r2;
+
+    const int batch_idx_a = i03 * ne02 + i02;
+
+    const int ir = get_group_id(0);
+    const int ic = get_group_id(1);
+
+    const int tid = get_local_id(0);
+    const int th_r  = tid % (BM / TM);
+    const int th_c  = tid / (BM / TM);
+
+    const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
+    const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
+    const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
+    const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
+
+    const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
+    const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
+
+    int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
+    int pos_b = (batch_idx   * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
+
+    float sums[TM * TN];
+    float cache_a[TM];
+    float cache_b[TN];
+
+    for (int i = 0; i < TM * TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    for (int block = 0; block < ne00; block += BK) {
+        for (int l = 0; l < BM; l += loadstride_a) {
+            if (ir*BM + loadc_a + l < ne01) {
+                int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+                int ib  = idx / 4;
+                int iqs = idx % 4;
+
+                float d = (float)src0_d[ib];
+                global uchar4 * qs = src0_q + ib*4 + iqs;
+                uchar4 q = *qs;
+                // IQ4_NL: use lookup table instead of linear (nibble - 8)
+                float4 v1 = (float4)(kvalues_iq4nl[(q.s0   )&0x0F], kvalues_iq4nl[(q.s1   )&0x0F],
+                                     kvalues_iq4nl[(q.s2   )&0x0F], kvalues_iq4nl[(q.s3   )&0x0F])*d;
+                float4 v2 = (float4)(kvalues_iq4nl[(q.s0>>4)&0x0F], kvalues_iq4nl[(q.s1>>4)&0x0F],
+                                     kvalues_iq4nl[(q.s2>>4)&0x0F], kvalues_iq4nl[(q.s3>>4)&0x0F])*d;
+
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = v1.s0;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = v1.s1;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = v1.s2;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = v1.s3;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = v2.s0;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = v2.s1;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = v2.s2;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = v2.s3;
+            } else {
+                buf_a[(loadr_a * 4 +  0) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  1) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  2) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 +  3) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = 0.0f;
+                buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = 0.0f;
+            }
+        }
+
+        for (int l = 0; l < BN; l += loadstride_b) {
+            if (ic*BN + loadc_b + l < ne11) {
+                int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
+            } else {
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
+            }
+        }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (int i = 0; i < BK; i++) {
+            for (int j = 0; j < TM; j++) {
+                cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
+            }
+
+            for (int j = 0; j < TN; j++) {
+                cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
+            }
+
+            for (int cc = 0; cc < TN; cc++) {
+                for (int cr = 0; cr < TM; cr++) {
+                    const int sums_idx = cc*TM + cr;
+                    sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const int dr = ir * BM + th_r * TM;
+    const int dc = ic * BN + th_c * TN;
+
+    const int offsets = batch_idx * batch_stride_d;
+
+    for (int cc = 0; cc < TN; cc++) {
+        for (int cr = 0; cr < TM; cr++) {
+            if (dr + cr < ne01 && dc + cc < ne11) {
+                dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
+            }
+        }
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_iq4_nl_f32.cl b/ggml/src/ggml-opencl/kernels/mul_mv_iq4_nl_f32.cl
new file mode 100644
index 00000000000..a6a325cd729
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_iq4_nl_f32.cl
@@ -0,0 +1,164 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_NL 32
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+constant float kvalues_iq4nl[16] = {
+    -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f,
+      1.f,   13.f,  25.f,  38.f,  53.f,  69.f,  89.f, 113.f
+};
+
+//------------------------------------------------------------------------------
+// block_iq4_nl
+//------------------------------------------------------------------------------
+struct block_iq4_nl
+{
+    half d;
+    uint8_t qs[QK4_NL / 2];
+};
+
+//------------------------------------------------------------------------------
+// mul_vec_q_n_f32
+//------------------------------------------------------------------------------
+// Compute inner product between half a block of iq4_nl and 16 floats (yl).
+// il indicates where the quants begin (0 or 8).
+inline float block_iq4_nl_dot_y(
+        global struct block_iq4_nl * qb_curr,
+        private float * yl,
+        int il
+) {
+    float d = qb_curr->d;
+    float acc = 0.f;
+    global uchar * qs = qb_curr->qs + il;
+    for (int i = 0; i < 8; ++i) {
+        acc += yl[i]   * kvalues_iq4nl[qs[i] & 0x0F];
+        acc += yl[i+8] * kvalues_iq4nl[qs[i] >> 4];
+    }
+    return d * acc;
+}
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each subgroup group works on 4 rows
+#define N_SUBGROUP 1 // number of subgroups in a thread group
+#define N_SUBGROUP_SIZE 16 // assuming subgroup size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SUBGROUP 1
+#define N_SUBGROUP_SIZE 64
+#endif
+
+inline void mul_vec_q_n_f32(
+        global void * src0,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+
+    const ulong nb = ne00/QK4_NL;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SUBGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global struct block_iq4_nl * x = (global struct block_iq4_nl *) src0 + offset0;
+    global float               * y = (global float               *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[16];       // src1 vector cache
+    float sumf[N_DST]={0.f};
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_NL + il;
+
+    // each thread in a SIMD group deals with half a block.
+    for (int ib = ix; ib < nb; ib += N_SUBGROUP_SIZE/2) {
+        for (int i = 0; i < 8; ++i) {
+            yl[i]   = yb[i];
+            yl[i+8] = yb[i+16];
+        }
+
+        for (int row = 0; row < N_DST; row++) {
+            sumf[row] += block_iq4_nl_dot_y(x+ib+row*nb, yl, il);
+        }
+
+        yb += QK4_NL * (N_SUBGROUP_SIZE/2);
+    }
+
+    float tot[N_DST] = {
+        sub_group_reduce_add(sumf[0]), sub_group_reduce_add(sumf[1]),
+        sub_group_reduce_add(sumf[2]), sub_group_reduce_add(sumf[3])};
+    for (int row = 0; row < N_DST; ++row) {
+        if (get_sub_group_local_id() == 0 && first_row + row < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot[row];
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_iq4_nl_f32(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_iq4_nl_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_iq4_nl_f32_flat.cl
new file mode 100644
index 00000000000..8c5b3f52e42
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_iq4_nl_f32_flat.cl
@@ -0,0 +1,202 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_NL 32
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+constant float kvalues_iq4nl[16] = {
+    -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f,
+      1.f,   13.f,  25.f,  38.f,  53.f,  69.f,  89.f, 113.f
+};
+
+//------------------------------------------------------------------------------
+// block_iq4_nl
+//------------------------------------------------------------------------------
+struct block_iq4_nl
+{
+    half d;
+    uint8_t qs[QK4_NL / 2];
+};
+
+// Compute dot product between half a block of iq4_nl quants and activations.
+// x points to the quant bytes, dh points to the scale.
+// yl has 16 activation values: [0..7] for low nibbles, [8..15] for high nibbles.
+// il indicates offset into the quant bytes (0 or 8).
+inline float block_iq4_nl_dot_y_flat(
+        global uchar * x,
+        global half  * dh,
+        private float * yl,
+        int il
+) {
+    float d = *dh;
+    global uchar * qs = x + il;
+    float acc = 0.f;
+    for (int i = 0; i < 8; ++i) {
+        acc += yl[i]   * kvalues_iq4nl[qs[i] & 0x0F];
+        acc += yl[i+8] * kvalues_iq4nl[qs[i] >> 4];
+    }
+    return d * acc;
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 8 // each subgroup works on 8 rows
+#define N_SUBGROUP 1 // number of subgroups in a thread group
+#define N_SUBGROUP_SIZE 16 // assuming subgroup size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 8
+#define N_SUBGROUP 1
+#define N_SUBGROUP_SIZE 64
+#endif
+
+inline void mul_vec_q_n_f32_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_NL;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SUBGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_NL/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_NL/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float yl[16];
+    float8 sumf = 0.f;
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_NL + il;
+
+    for (int ib = ix; ib < nb; ib += N_SUBGROUP_SIZE/2) {
+        for (int i = 0; i < 8; ++i) {
+            yl[i]   = yb[i];
+            yl[i+8] = yb[i+16];
+        }
+
+        sumf.s0 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 0*nb*QK4_NL/2, d + ib + 0*nb, yl, il);
+        sumf.s1 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 1*nb*QK4_NL/2, d + ib + 1*nb, yl, il);
+        sumf.s2 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 2*nb*QK4_NL/2, d + ib + 2*nb, yl, il);
+        sumf.s3 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 3*nb*QK4_NL/2, d + ib + 3*nb, yl, il);
+
+        sumf.s4 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 4*nb*QK4_NL/2, d + ib + 4*nb, yl, il);
+        sumf.s5 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 5*nb*QK4_NL/2, d + ib + 5*nb, yl, il);
+        sumf.s6 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 6*nb*QK4_NL/2, d + ib + 6*nb, yl, il);
+        sumf.s7 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 7*nb*QK4_NL/2, d + ib + 7*nb, yl, il);
+
+        yb += QK4_NL * (N_SUBGROUP_SIZE/2);
+    }
+
+    float8 tot = (float8)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
+        sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
+        sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_iq4_nl_f32_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32_8x_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}
diff --git a/ggml/src/ggml-openvino/ggml-decoder.cpp b/ggml/src/ggml-openvino/ggml-decoder.cpp
index 0938d2273e9..5095e799849 100644
--- a/ggml/src/ggml-openvino/ggml-decoder.cpp
+++ b/ggml/src/ggml-openvino/ggml-decoder.cpp
@@ -19,7 +19,6 @@
 #include <iomanip>
 #include <map>
 #include <memory>
-#include <mutex>
 #include <openvino/core/dimension.hpp>
 #include <openvino/core/except.hpp>
 #include <openvino/core/node.hpp>
@@ -207,8 +206,22 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
         break;
     }
     case GGML_OP_ROPE: {
+        const int mode = node->op_params[2];
+        switch (mode) {
+       case GGML_ROPE_TYPE_NEOX: {
+            op_case = 0x00010000;
+            break;
+        }
+       case GGML_ROPE_TYPE_IMROPE: {
+            op_case = 0x00020000;
+            break;
+        }
+        default:
+            op_case = 0x00000000;
+            break;
+        }
         if (node->src[0]->op == GGML_OP_VIEW) {
-            op_case = 2;
+            op_case = (op_case | 0x00000002);
         }
         break;
     }
@@ -573,9 +586,6 @@ std::map<std::string, std::string> GgmlOvDecoder::get_kv_param_res_names() const
 }
 
 std::map<std::string, std::shared_ptr<ov::Node>> GgmlOvDecoder::create_weight_nodes(ggml_cgraph * cgraph, bool naive) {
-    static std::mutex weights_mutex;
-    std::lock_guard<std::mutex> lock(weights_mutex);
-
     std::map<std::string, std::shared_ptr<ov::Node>> model_weights;
     auto * nodes = cgraph->nodes;
     auto n_nodes = cgraph->n_nodes;
diff --git a/ggml/src/ggml-openvino/ggml-openvino-extra.cpp b/ggml/src/ggml-openvino/ggml-openvino-extra.cpp
index cc3cb4583cd..4140136aca2 100644
--- a/ggml/src/ggml-openvino/ggml-openvino-extra.cpp
+++ b/ggml/src/ggml-openvino/ggml-openvino-extra.cpp
@@ -6,6 +6,7 @@
 #include <cstring>
 #include <openvino/runtime/intel_gpu/ocl/ocl.hpp>
 #include <openvino/runtime/intel_npu/level_zero/level_zero.hpp>
+#include <openvino/runtime/properties.hpp>
 #include <optional>
 
 ov::Core & ov_singleton_core() {
@@ -42,11 +43,13 @@ void ggml_openvino_device_config::init() {
             {"NPUW_DQ",                           "YES"   },
             {"NPUW_DQ_FULL",                      "NO"    },
         };
-        if (cache_dir) {
+        if (cache_dir && strlen(cache_dir) > 0) {
             compile_config["NPUW_CACHE_DIR"] = cache_dir;
+            compile_config.insert(ov::cache_mode(ov::CacheMode::OPTIMIZE_SIZE));
         }
-    } else if (cache_dir) {
-        ov_singleton_core().set_property(ov::cache_dir(cache_dir));
+    } else if (cache_dir && strlen(cache_dir) > 0) {
+        compile_config.insert(ov::cache_dir(cache_dir));
+        compile_config.insert(ov::cache_mode(ov::CacheMode::OPTIMIZE_SIZE));
     }
 
     // Initialize remote context with queue sharing for GPU
@@ -259,10 +262,12 @@ ggml_openvino_extracted_layout ggml_openvino_get_extracted_layout(const ggml_ten
             layout.weights_size = layout.is_u4 ? (n_elements / 2) : n_elements;
             int64_t n_blocks = n_elements / layout.weights_per_block;
             layout.scales_size = n_blocks * sizeof(uint16_t);
-            // For symmetric quantization, we only need one zp value (not one per block)
-            // Zero points are stored in U4 or U8 format matching the weight type
-            size_t n_zp_elements = layout.is_symmetric ? 1 : n_blocks;
-            layout.zp_size = layout.is_u4 ? ((n_zp_elements + 1) / 2) : n_zp_elements;
+            // For symmetric quantization, no zp needed (weights stored as signed)
+            if (layout.is_symmetric) {
+                layout.zp_size = 0;
+            } else {
+                layout.zp_size = layout.is_u4 ? ((n_blocks + 1) / 2) : n_blocks;
+            }
 
             layout.weights_offset = 0;
             layout.scales_offset = ((layout.weights_size + alignment - 1) / alignment) * alignment;
@@ -313,10 +318,12 @@ ggml_openvino_extracted_layout ggml_openvino_get_extracted_layout(const ggml_ten
     // Scales: F16 per block
     int64_t n_blocks = n_elements / layout.weights_per_block;
     layout.scales_size = n_blocks * sizeof(uint16_t);  // F16 = 2 bytes
-    // Zero points: U4 or U8 matching weight type
-    // For symmetric quantization, we only need one zp value (not one per block)
-    size_t n_zp_elements = layout.is_symmetric ? 1 : n_blocks;
-    layout.zp_size = layout.is_u4 ? ((n_zp_elements + 1) / 2) : n_zp_elements;
+    // For symmetric quantization, no zp needed (weights stored as signed)
+    if (layout.is_symmetric) {
+        layout.zp_size = 0;
+    } else {
+        layout.zp_size = layout.is_u4 ? ((n_blocks + 1) / 2) : n_blocks;
+    }
 
     // Layout in buffer: [weights | scales | zp] with alignment
     layout.weights_offset = 0;
diff --git a/ggml/src/ggml-openvino/ggml-openvino.cpp b/ggml/src/ggml-openvino/ggml-openvino.cpp
index 0c8d3508e87..4f3ebf2536b 100644
--- a/ggml/src/ggml-openvino/ggml-openvino.cpp
+++ b/ggml/src/ggml-openvino/ggml-openvino.cpp
@@ -145,13 +145,18 @@ static void * ggml_backend_openvino_buffer_get_base(ggml_backend_buffer_t buffer
     return ctx->data;
 }
 
+static bool is_stateful_enabled() {
+    static const auto * stateful = getenv("GGML_OPENVINO_STATEFUL_EXECUTION");
+    return stateful && *stateful != '\0' && strcmp(stateful, "0") != 0;
+}
+
 static enum ggml_status ggml_backend_openvino_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
     // GGML_LOG_DEBUG("%s: buffer usage=%d, tensor name=%s\n", __func__, buffer->usage, tensor->name);
     ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context;
 
     // Put kvcache on device memory for GPU (NPU memory is too small even for kvcache)
     if (strncmp(tensor->name, "cache_", 6) == 0 && !ctx->is_remote && ggml_openvino_get_device_name() == "GPU" &&
-        !getenv("GGML_OPENVINO_STATEFUL_EXECUTION")) {
+        !is_stateful_enabled()) {
         GGML_ASSERT(ctx->tensor_extras.empty());
         auto device = ctx->device;
         auto size = ctx->size;
@@ -600,6 +605,14 @@ bool ggml_backend_buft_is_openvino_host(ggml_backend_buffer_type_t buft) {
 
 static void ggml_backend_openvino_free(ggml_backend_t backend) {
     ggml_backend_openvino_context * ctx = (ggml_backend_openvino_context *) backend->context;
+
+    if (ctx->runtime_context) {
+        auto r_ctx = std::static_pointer_cast<ov_runtime_context>(ctx->runtime_context);
+        if (--r_ctx->backend_count == 0) {
+            r_ctx->clear_caches();
+        }
+    }
+
     delete ctx;
     delete backend;
 }
@@ -644,7 +657,12 @@ static ggml_guid_t ggml_backend_openvino_guid(void) {
 }
 
 static std::shared_ptr<ov_runtime_context> get_ov_runtime_context_ptr() {
-    static std::shared_ptr<ov_runtime_context> r_ctx = std::make_shared<ov_runtime_context>();
+    static std::shared_ptr<ov_runtime_context> r_ctx = [] {
+        auto ctx = std::make_shared<ov_runtime_context>();
+        ctx->device = ggml_openvino_get_device_name();
+        ctx->stateful = is_stateful_enabled() && !ggml_openvino_is_npu();
+        return ctx;
+    }();
     return r_ctx;
 }
 
@@ -669,8 +687,7 @@ GGML_BACKEND_API ggml_backend_t ggml_backend_openvino_init(int device) {
     }
 
     std::shared_ptr<ov_runtime_context> r_ctx = std::static_pointer_cast<ov_runtime_context>(ctx->runtime_context);
-    r_ctx->device = ggml_openvino_get_device_name();
-    r_ctx->stateful = getenv("GGML_OPENVINO_STATEFUL_EXECUTION") && !ggml_openvino_is_npu();
+    r_ctx->backend_count++;
 
     ggml_backend_t openvino_backend = new ggml_backend{
         /* .guid      = */ ggml_backend_openvino_guid(),
@@ -883,7 +900,7 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
         const int32_t * op_params = op->op_params;
         const int n_dims = op_params[1];
         const int mode = op_params[2];
-        if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX) {
+        if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX && mode != GGML_ROPE_TYPE_IMROPE) {
             // GGML_LOG_WARN("OpenVINO backend does not support ROPE with mode %d\n", mode);
             return true;
         }
@@ -896,14 +913,6 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
             // GGML_LOG_WARN("OpenVINO backend does not support ROPE with type %s\n", ggml_type_name(op->type));
             return true;
         }
-        float freq_scale;
-        float ext_factor;
-        memcpy(&freq_scale, op_params + 6, sizeof(float));
-        memcpy(&ext_factor, op_params + 7, sizeof(float));
-        if (ext_factor != 0.0f) {
-            // GGML_LOG_WARN("OpenVINO backend does not support ROPE with ext_factor %f != 0.0f\n", ext_factor);
-            return true;
-        }
         if (op->src[0]->op == GGML_OP_VIEW) {
             if (op->src[0]->view_src->ne[1] != op->src[0]->ne[2]) {
                 // GGML_LOG_WARN(
@@ -913,6 +922,12 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
                 return true;
             }
         }
+        if (mode == GGML_ROPE_TYPE_IMROPE &&
+            (op->src[2] != 0 || ((const float *) op_params)[6] != 1 || ((const float *) op_params)[7] != 0 ||
+             ((const float *) op_params)[8] != 1)) {
+            // GGML_LOG_WARN("OpenVINO backend does not support IMROPE with freq_factors, freq_scale, ext_factor, and attn_factor\n");
+            return true;
+        }
         break;
     }
     default:
@@ -942,6 +957,7 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
                                                  // GGML_OP_SOFT_MAX,
                                                  GGML_OP_SET_ROWS, GGML_OP_FLASH_ATTN_EXT, GGML_OP_CPY};
     static const std::set<ggml_unary_op> supported_unary_ops{
+        GGML_UNARY_OP_GELU,
         GGML_UNARY_OP_SILU,
     };
     static const std::set<ggml_glu_op> supported_glu_ops{
diff --git a/ggml/src/ggml-openvino/ggml-quants.cpp b/ggml/src/ggml-openvino/ggml-quants.cpp
index dbf38646ddd..57d66df4f01 100644
--- a/ggml/src/ggml-openvino/ggml-quants.cpp
+++ b/ggml/src/ggml-openvino/ggml-quants.cpp
@@ -46,6 +46,7 @@ void unpack_32_4(const uint8_t * data, uint8_t * dst) {
 
 // Extracts (weight, scales, zp) from Q4_0 tensors.
 // Data layout is: |16 bit scale|32 x 4bit weights|.
+// When zp_arr is empty (symmetric), weights are stored as signed i4 (value - 8).
 void extract_q4_0_data(const ggml_tensor * tensor,
                        ov::Tensor & weights_arr,
                        ov::Tensor & scales_arr,
@@ -55,28 +56,32 @@ void extract_q4_0_data(const ggml_tensor * tensor,
     auto * data = static_cast<uint8_t *>(tensor->data);
     auto * weights = static_cast<uint8_t *>(weights_arr.data());
     auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
-    auto * zp = static_cast<uint8_t *>(zp_arr.data());
-
-    bool is_scalar_zp = (zp_arr.get_size() == 1);  // Symmetric quantization
 
-    // For Q4_0, zero point is always 8
-    if (is_scalar_zp) {
-        zp[0] = 8 | (8 << 4);  // Pack two 4-bit values
-    }
+    bool is_symmetric = (weights_arr.get_element_type() == ov::element::i4);  // Signed i4 path
 
-    ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
-        scales[i] = ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block)));
-        // For asymmetric quantization, compute per-block zero points
-        if (!is_scalar_zp) {
+    if (!is_symmetric) {
+        auto * zp = static_cast<uint8_t *>(zp_arr.data());
+        ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
+            scales[i] = ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block)));
             // Pack two 4-bit zero points per byte
             if (i % 2 == 0) {
                 zp[i / 2] = 8;          // Lower nibble
             } else {
                 zp[i / 2] |= (8 << 4);  // Upper nibble
             }
-        }
-        unpack_32_4(data + i * bytes_per_block + 2, weights + i * 16);
-    });
+            unpack_32_4(data + i * bytes_per_block + 2, weights + i * 16);
+        });
+    } else {
+        // Symmetric: unpack as u4 then convert to i4 by subtracting 8 (XOR each nibble)
+        ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
+            scales[i] = ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block)));
+            unpack_32_4(data + i * bytes_per_block + 2, weights + i * 16);
+            // Convert u4 to i4: subtract 8 from each nibble. XOR 0x88 flips each nibble by 8.
+            for (int j = 0; j < 16; ++j) {
+                weights[i * 16 + j] ^= 0x88;
+            }
+        });
+    }
 }
 
 // Extracts (weight, scales, zp) from Q4_1 tensors.
@@ -123,6 +128,7 @@ void extract_q4_1_data(const ggml_tensor * tensor,
 
 // Extracts (weight, scales, zp) from Q8_0 tensors.
 // Data layout is: |16 bit scale|32 x 8bit weights|.
+// When zp_arr is empty (symmetric), weights are stored as signed i8 directly.
 void extract_q8_0_data(const ggml_tensor * tensor,
                        ov::Tensor & weights_arr,
                        ov::Tensor & scales_arr,
@@ -133,29 +139,30 @@ void extract_q8_0_data(const ggml_tensor * tensor,
     auto * data = static_cast<uint8_t *>(tensor->data);
     auto * weights = static_cast<uint8_t *>(weights_arr.data());
     auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
-    auto * zp = static_cast<uint8_t *>(zp_arr.data());
-
-    bool is_scalar_zp = (zp_arr.get_size() == 1);  // Symmetric quantization
 
-    // For Q8_0, zero point is always 128
-    if (is_scalar_zp) {
-        zp[0] = 128;
-    }
+    bool is_symmetric = (weights_arr.get_element_type() == ov::element::i8);  // Signed i8 path
 
-    ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
-        uint8_t * block_data = data + i * bytes_per_block;
-        scales[i] = ov::float16::from_bits(*(uint16_t *) block_data);
-        // For asymmetric quantization, store per-block zero points
-        if (!is_scalar_zp) {
+    if (!is_symmetric) {
+        auto * zp = static_cast<uint8_t *>(zp_arr.data());
+        ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
+            uint8_t * block_data = data + i * bytes_per_block;
+            scales[i] = ov::float16::from_bits(*(uint16_t *) block_data);
             zp[i] = 128;
-        }
-        for (size_t j = 0; j < weights_per_block; ++j) {
-            uint8_t x = block_data[j + 2];  // j+2 to skip the scale bytes.
-            // Original data is in int8_t, so we add a bias of -128 and invert the first bit.
-            x ^= 1 << 7;
-            weights[i * weights_per_block + j] = x;
-        }
-    });
+            for (size_t j = 0; j < weights_per_block; ++j) {
+                uint8_t x = block_data[j + 2];
+                x ^= 1 << 7;  // Convert int8 to uint8 by flipping sign bit
+                weights[i * weights_per_block + j] = x;
+            }
+        });
+    } else {
+        // Symmetric: store original int8 values directly (no unsigned bias)
+        ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
+            uint8_t * block_data = data + i * bytes_per_block;
+            scales[i] = ov::float16::from_bits(*(uint16_t *) block_data);
+            // Copy int8 weights as-is (the tensor element type is i8)
+            memcpy(weights + i * weights_per_block, block_data + 2, weights_per_block);
+        });
+    }
 }
 
 void unpack_256_4(const uint8_t * data, uint8_t * dst) {
@@ -256,44 +263,62 @@ void extract_q6_k_data(const ggml_tensor * tensor,
     auto * data = static_cast<uint8_t *>(tensor->data);
     auto * weights = static_cast<uint8_t *>(weights_arr.data());
     auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
-    auto * zp = static_cast<uint8_t *>(zp_arr.data());
-
-    bool is_scalar_zp = (zp_arr.get_size() == 1);  // Symmetric quantization
-
-    // For Q6_K, zero point is always 32
-    if (is_scalar_zp) {
-        zp[0] = 32;
-    }
-
-    ov::parallel_for(n_super_block, [&](size_t i) {
-        uint8_t * block_data = data + i * bytes_per_block;
 
-        float scale_factor =
-            static_cast<float>(ov::float16::from_bits(*((uint16_t *) block_data + 104)));  // (128+64+16)/2
+    bool is_symmetric = (weights_arr.get_element_type() == ov::element::i8);  // Signed i8 path
 
-        for (size_t j = 0; j < 16; j++) {
-            scales[j + i * 16] =
-                ov::float16(scale_factor * static_cast<float>(*((int8_t *) (block_data + 128 + 64 + j))));
-            // For asymmetric quantization, store per-block zero points
-            if (!is_scalar_zp) {
+    if (!is_symmetric) {
+        auto * zp = static_cast<uint8_t *>(zp_arr.data());
+        ov::parallel_for(n_super_block, [&](size_t i) {
+            uint8_t * block_data = data + i * bytes_per_block;
+            float scale_factor = static_cast<float>(ov::float16::from_bits(*((uint16_t *) block_data + 104)));
+            for (size_t j = 0; j < 16; j++) {
+                scales[j + i * 16] =
+                    ov::float16(scale_factor * static_cast<float>(*((int8_t *) (block_data + 128 + 64 + j))));
                 zp[j + i * 16] = 32;
             }
-        }
-
-        uint8_t * ql = block_data;
-        uint8_t * qh = block_data + 128;
-
-        for (int64_t j = 0; j < 32; ++j) {
-            weights[i * 256 + j] = (ql[j] & 0xF) | (((qh[j] >> 0) & 3) << 4);
-            weights[i * 256 + j + 32] = (ql[32 + j] & 0xF) | (((qh[j] >> 2) & 3) << 4);
-            weights[i * 256 + j + 64] = (ql[j] >> 4) | (((qh[j] >> 4) & 3) << 4);
-            weights[i * 256 + j + 96] = (ql[32 + j] >> 4) | (((qh[j] >> 6) & 3) << 4);
-            weights[i * 256 + j + 128] = (ql[64 + j] & 0xF) | (((qh[32 + j] >> 0) & 3) << 4);
-            weights[i * 256 + j + 160] = (ql[96 + j] & 0xF) | (((qh[32 + j] >> 2) & 3) << 4);
-            weights[i * 256 + j + 192] = (ql[64 + j] >> 4) | (((qh[32 + j] >> 4) & 3) << 4);
-            weights[i * 256 + j + 224] = (ql[96 + j] >> 4) | (((qh[32 + j] >> 6) & 3) << 4);
-        }
-    });
+            uint8_t * ql = block_data;
+            uint8_t * qh = block_data + 128;
+            for (int64_t j = 0; j < 32; ++j) {
+                weights[i * 256 + j] = (ql[j] & 0xF) | (((qh[j] >> 0) & 3) << 4);
+                weights[i * 256 + j + 32] = (ql[32 + j] & 0xF) | (((qh[j] >> 2) & 3) << 4);
+                weights[i * 256 + j + 64] = (ql[j] >> 4) | (((qh[j] >> 4) & 3) << 4);
+                weights[i * 256 + j + 96] = (ql[32 + j] >> 4) | (((qh[j] >> 6) & 3) << 4);
+                weights[i * 256 + j + 128] = (ql[64 + j] & 0xF) | (((qh[32 + j] >> 0) & 3) << 4);
+                weights[i * 256 + j + 160] = (ql[96 + j] & 0xF) | (((qh[32 + j] >> 2) & 3) << 4);
+                weights[i * 256 + j + 192] = (ql[64 + j] >> 4) | (((qh[32 + j] >> 4) & 3) << 4);
+                weights[i * 256 + j + 224] = (ql[96 + j] >> 4) | (((qh[32 + j] >> 6) & 3) << 4);
+            }
+        });
+    } else {
+        // Symmetric: subtract 32 from each weight to store as signed i8
+        ov::parallel_for(n_super_block, [&](size_t i) {
+            uint8_t * block_data = data + i * bytes_per_block;
+            float scale_factor = static_cast<float>(ov::float16::from_bits(*((uint16_t *) block_data + 104)));
+            for (size_t j = 0; j < 16; j++) {
+                scales[j + i * 16] =
+                    ov::float16(scale_factor * static_cast<float>(*((int8_t *) (block_data + 128 + 64 + j))));
+            }
+            uint8_t * ql = block_data;
+            uint8_t * qh = block_data + 128;
+            auto * signed_weights = reinterpret_cast<int8_t *>(weights);
+            for (int64_t j = 0; j < 32; ++j) {
+                signed_weights[i * 256 + j] = static_cast<int8_t>((ql[j] & 0xF) | (((qh[j] >> 0) & 3) << 4)) - 32;
+                signed_weights[i * 256 + j + 32] =
+                    static_cast<int8_t>((ql[32 + j] & 0xF) | (((qh[j] >> 2) & 3) << 4)) - 32;
+                signed_weights[i * 256 + j + 64] = static_cast<int8_t>((ql[j] >> 4) | (((qh[j] >> 4) & 3) << 4)) - 32;
+                signed_weights[i * 256 + j + 96] =
+                    static_cast<int8_t>((ql[32 + j] >> 4) | (((qh[j] >> 6) & 3) << 4)) - 32;
+                signed_weights[i * 256 + j + 128] =
+                    static_cast<int8_t>((ql[64 + j] & 0xF) | (((qh[32 + j] >> 0) & 3) << 4)) - 32;
+                signed_weights[i * 256 + j + 160] =
+                    static_cast<int8_t>((ql[96 + j] & 0xF) | (((qh[32 + j] >> 2) & 3) << 4)) - 32;
+                signed_weights[i * 256 + j + 192] =
+                    static_cast<int8_t>((ql[64 + j] >> 4) | (((qh[32 + j] >> 4) & 3) << 4)) - 32;
+                signed_weights[i * 256 + j + 224] =
+                    static_cast<int8_t>((ql[96 + j] >> 4) | (((qh[32 + j] >> 6) & 3) << 4)) - 32;
+            }
+        });
+    }
 }
 
 static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t * d, uint8_t * m) {
@@ -389,11 +414,10 @@ ov::Output<ov::Node> make_int8_weights(ov::Tensor & weight,
                                        size_t group_size,
                                        bool use_bias) {
     ov::Shape orig_shape = weight.get_shape();
+    bool is_signed = (weight.get_element_type() == ov::element::i8);  // Symmetric: signed weights, no ZP
 
     // Expand dimensions for scales and zp/bias
     auto scale_shape = scales.get_shape();
-    auto zp_shape = zp.get_shape();
-    bool is_scalar_zp = zp_shape.empty();  // Symmetric quantization
 
     ov::Shape packed_shape = {orig_shape[0], orig_shape[1] / group_size, group_size};
 
@@ -403,37 +427,48 @@ ov::Output<ov::Node> make_int8_weights(ov::Tensor & weight,
     } else {
         scale_shape.push_back(1);
         scales.set_shape(scale_shape);
-        // For symmetric quantization, zp remains scalar (don't resize)
-        if (!is_scalar_zp) {
+        if (!is_signed && zp.get_size() > 0) {
+            auto zp_shape = zp.get_shape();
             zp_shape.push_back(1);
             zp.set_shape(zp_shape);
         }
     }
 
-    // Create graph nodes
-    auto weights_node = std::make_shared<ov::op::v0::Constant>(ov::element::u8, packed_shape,
-                                                               static_cast<uint8_t *>(weight.data()), nullptr);
-    weights_node->get_rt_info()["__gguf_tensor_holder"] = weight;
     auto scales_f16 = std::make_shared<ov::op::v0::Constant>(scales);
-    auto weights_f16 = std::make_shared<ov::op::v0::Convert>(weights_node, ov::element::f16);
 
     ov::Output<ov::Node> result;
-    if (use_bias && !is_scalar_zp) {
-        // Bias path: w * s + b (zp tensor holds f16 bias values)
-        auto bias_f16 = std::make_shared<ov::op::v0::Constant>(zp);
-        auto w_s = std::make_shared<ov::op::v1::Multiply>(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY);
-        result = std::make_shared<ov::op::v1::Add>(w_s, bias_f16, ov::op::AutoBroadcastType::NUMPY);
+    if (is_signed) {
+        // Signed path: q * s (no zero point subtraction needed)
+        auto weights_node = std::make_shared<ov::op::v0::Constant>(ov::element::i8, packed_shape,
+                                                                   static_cast<uint8_t *>(weight.data()), nullptr);
+        weights_node->get_rt_info()["__gguf_tensor_holder"] = weight;
+        auto weights_f16 = std::make_shared<ov::op::v0::Convert>(weights_node, ov::element::f16);
+        result = std::make_shared<ov::op::v1::Multiply>(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY);
     } else {
-        // Zero point path: (w - zp) * s
-        auto zero_point = std::make_shared<ov::op::v0::Constant>(zp);
-        float zp_value;
-        if (ov::op::util::get_single_value(zero_point, zp_value)) {
-            zero_point = ov::op::v0::Constant::create(zero_point->get_element_type(), {}, {zp_value});
+        // Unsigned path
+        auto weights_node = std::make_shared<ov::op::v0::Constant>(ov::element::u8, packed_shape,
+                                                                   static_cast<uint8_t *>(weight.data()), nullptr);
+        weights_node->get_rt_info()["__gguf_tensor_holder"] = weight;
+        auto weights_f16 = std::make_shared<ov::op::v0::Convert>(weights_node, ov::element::f16);
+
+        if (use_bias && zp.get_size() > 0) {
+            // Bias path: w * s + b (zp tensor holds f16 bias values)
+            auto bias_f16 = std::make_shared<ov::op::v0::Constant>(zp);
+            auto w_s =
+                std::make_shared<ov::op::v1::Multiply>(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY);
+            result = std::make_shared<ov::op::v1::Add>(w_s, bias_f16, ov::op::AutoBroadcastType::NUMPY);
+        } else {
+            // Zero point path: (w - zp) * s
+            auto zero_point = std::make_shared<ov::op::v0::Constant>(zp);
+            float zp_value;
+            if (ov::op::util::get_single_value(zero_point, zp_value)) {
+                zero_point = ov::op::v0::Constant::create(zero_point->get_element_type(), {}, {zp_value});
+            }
+            auto zero_point_f16 = std::make_shared<ov::op::v0::Convert>(zero_point, ov::element::f16);
+            auto w_zp =
+                std::make_shared<ov::op::v1::Subtract>(weights_f16, zero_point_f16, ov::op::AutoBroadcastType::NUMPY);
+            result = std::make_shared<ov::op::v1::Multiply>(w_zp, scales_f16, ov::op::AutoBroadcastType::NUMPY);
         }
-        auto zero_point_f16 = std::make_shared<ov::op::v0::Convert>(zero_point, ov::element::f16);
-        auto w_zp =
-            std::make_shared<ov::op::v1::Subtract>(weights_f16, zero_point_f16, ov::op::AutoBroadcastType::NUMPY);
-        result = std::make_shared<ov::op::v1::Multiply>(w_zp, scales_f16, ov::op::AutoBroadcastType::NUMPY);
     }
 
     if (packed_shape.size() != 2) {
@@ -452,11 +487,10 @@ ov::Output<ov::Node> make_int4_weights(ov::Tensor & weight,
                                        size_t group_size,
                                        bool use_bias) {
     ov::Shape orig_weight_shape = weight.get_shape();
+    bool is_signed = (weight.get_element_type() == ov::element::i4);  // Symmetric: signed weights, no ZP
 
     // Expand dimensions for scales and zp/bias
     ov::Shape scale_shape = scales.get_shape();
-    auto zp_shape = zp.get_shape();
-    bool is_scalar_zp = zp_shape.empty();  // Symmetric quantization
 
     // Create INT4 weight tensor
     ov::Shape packed_shape = {orig_weight_shape[0], orig_weight_shape[1] / group_size, group_size};
@@ -467,36 +501,48 @@ ov::Output<ov::Node> make_int4_weights(ov::Tensor & weight,
     } else {
         scale_shape.push_back(1);
         scales.set_shape(scale_shape);
-        // For symmetric quantization, zp remains scalar (don't resize)
-        if (!is_scalar_zp) {
+        if (!is_signed && zp.get_size() > 0) {
+            auto zp_shape = zp.get_shape();
             zp_shape.push_back(1);
             zp.set_shape(zp_shape);
         }
     }
 
-    auto weights_node = std::make_shared<ov::op::v0::Constant>(ov::element::u4, packed_shape,
-                                                               static_cast<uint8_t *>(weight.data()), nullptr);
-    weights_node->get_rt_info()["__gguf_tensor_holder"] = weight;
-    auto weights_f16 = std::make_shared<ov::op::v0::Convert>(weights_node, ov::element::f16);
     auto scales_f16 = std::make_shared<ov::op::v0::Constant>(scales);
 
     ov::Output<ov::Node> result;
-    if (use_bias && !is_scalar_zp) {
-        // Bias path: w * s + b (zp tensor holds f16 bias values)
-        auto bias_f16 = std::make_shared<ov::op::v0::Constant>(zp);
-        auto w_s = std::make_shared<ov::op::v1::Multiply>(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY);
-        result = std::make_shared<ov::op::v1::Add>(w_s, bias_f16, ov::op::AutoBroadcastType::NUMPY);
+    if (is_signed) {
+        // Signed path: q * s (no zero point subtraction needed)
+        auto weights_node = std::make_shared<ov::op::v0::Constant>(ov::element::i4, packed_shape,
+                                                                   static_cast<uint8_t *>(weight.data()), nullptr);
+        weights_node->get_rt_info()["__gguf_tensor_holder"] = weight;
+        auto weights_f16 = std::make_shared<ov::op::v0::Convert>(weights_node, ov::element::f16);
+        result = std::make_shared<ov::op::v1::Multiply>(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY);
     } else {
-        // Zero point path: (w - zp) * s
-        auto zero_points_node = std::make_shared<ov::op::v0::Constant>(zp);
-        float zp_value;
-        if (ov::op::util::get_single_value(zero_points_node, zp_value)) {
-            zero_points_node = ov::op::v0::Constant::create(zero_points_node->get_element_type(), {}, {zp_value});
+        // Unsigned path
+        auto weights_node = std::make_shared<ov::op::v0::Constant>(ov::element::u4, packed_shape,
+                                                                   static_cast<uint8_t *>(weight.data()), nullptr);
+        weights_node->get_rt_info()["__gguf_tensor_holder"] = weight;
+        auto weights_f16 = std::make_shared<ov::op::v0::Convert>(weights_node, ov::element::f16);
+
+        if (use_bias && zp.get_size() > 0) {
+            // Bias path: w * s + b (zp tensor holds f16 bias values)
+            auto bias_f16 = std::make_shared<ov::op::v0::Constant>(zp);
+            auto w_s =
+                std::make_shared<ov::op::v1::Multiply>(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY);
+            result = std::make_shared<ov::op::v1::Add>(w_s, bias_f16, ov::op::AutoBroadcastType::NUMPY);
+        } else {
+            // Zero point path: (w - zp) * s
+            auto zero_points_node = std::make_shared<ov::op::v0::Constant>(zp);
+            float zp_value;
+            if (ov::op::util::get_single_value(zero_points_node, zp_value)) {
+                zero_points_node = ov::op::v0::Constant::create(zero_points_node->get_element_type(), {}, {zp_value});
+            }
+            auto zero_points_f16 = std::make_shared<ov::op::v0::Convert>(zero_points_node, ov::element::f16);
+            auto w_zp =
+                std::make_shared<ov::op::v1::Subtract>(weights_f16, zero_points_f16, ov::op::AutoBroadcastType::NUMPY);
+            result = std::make_shared<ov::op::v1::Multiply>(w_zp, scales_f16, ov::op::AutoBroadcastType::NUMPY);
         }
-        auto zero_points_f16 = std::make_shared<ov::op::v0::Convert>(zero_points_node, ov::element::f16);
-        auto w_zp =
-            std::make_shared<ov::op::v1::Subtract>(weights_f16, zero_points_f16, ov::op::AutoBroadcastType::NUMPY);
-        result = std::make_shared<ov::op::v1::Multiply>(w_zp, scales_f16, ov::op::AutoBroadcastType::NUMPY);
     }
 
     if (packed_shape.size() != 2) {
@@ -699,24 +745,32 @@ OvWeight process_weight_tensor(const ggml_tensor * tensor, const void * data, vo
 
     // Quantized path (normal extraction or quantized requant)
     // Create weight/scale/zp tensors - shared between both paths
-    ov::element::Type weight_type = layout.is_u4 ? ov::element::u4 : ov::element::u8;
+    // For symmetric quantization, use signed types (i4/i8) and no ZP tensor
+    ov::element::Type weight_type = layout.is_symmetric ? (layout.is_u4 ? ov::element::i4 : ov::element::i8) :
+                                                          (layout.is_u4 ? ov::element::u4 : ov::element::u8);
     ov::Shape scale_shape = {node_shape[0], node_shape[1] / layout.weights_per_block};
-    ov::Shape zp_shape = layout.is_symmetric ? ov::Shape{} : scale_shape;
 
     if (output_base_ptr) {
         uint8_t * buf_base = static_cast<uint8_t *>(output_base_ptr);
         result.weights = ov::Tensor(weight_type, node_shape, buf_base + layout.weights_offset);
         result.scales = ov::Tensor(ov::element::f16, scale_shape, buf_base + layout.scales_offset);
-        result.zp = ov::Tensor(weight_type, zp_shape, buf_base + layout.zp_offset);
+        if (!layout.is_symmetric) {
+            ov::element::Type zp_type = layout.is_u4 ? ov::element::u4 : ov::element::u8;
+            result.zp = ov::Tensor(zp_type, scale_shape, buf_base + layout.zp_offset);
+        }
+        // else: result.zp remains default-constructed (empty) for symmetric
     } else {
         result.weights = ov::Tensor(weight_type, node_shape);
         result.scales = ov::Tensor(ov::element::f16, scale_shape);
-        if (use_bias && !layout.is_symmetric) {
-            // bias only has effect for asymmetric quant
-            result.zp = ov::Tensor(ov::element::f16, zp_shape);
-        } else {
-            result.zp = ov::Tensor(weight_type, zp_shape);
+        if (!layout.is_symmetric) {
+            if (use_bias) {
+                result.zp = ov::Tensor(ov::element::f16, scale_shape);
+            } else {
+                ov::element::Type zp_type = layout.is_u4 ? ov::element::u4 : ov::element::u8;
+                result.zp = ov::Tensor(zp_type, scale_shape);
+            }
         }
+        // else: result.zp remains default-constructed (empty) for symmetric
     }
 
     if (layout.is_requant && layout.requant_type.has_value()) {
@@ -741,59 +795,75 @@ void quantize_q4_0(const float * x,
 
     auto * weights = static_cast<uint8_t *>(weights_arr.data());
     auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
-    auto * zp = static_cast<uint8_t *>(zp_arr.data());
-    bool is_scalar_zp = (zp_arr.get_size() == 1);  // Symmetric quantization
-
-    // For Q4_0, zero point is always 8
-    if (is_scalar_zp) {
-        zp[0] = 8 | (8 << 4);  // Pack two 4-bit values
-    }
+    bool is_symmetric = (weights_arr.get_element_type() == ov::element::i4);  // Signed i4 path
 
-    for (int i = 0; i < nb; i++) {
-        float amax = 0.0f;  // absolute max
-        float max = 0.0f;
-
-        for (int j = 0; j < qk; j++) {
-            const float v = x[i * qk + j];
-            if (amax < fabsf(v)) {
-                amax = fabsf(v);
-                max = v;
+    if (!is_symmetric) {
+        auto * zp = static_cast<uint8_t *>(zp_arr.data());
+        for (int i = 0; i < nb; i++) {
+            float amax = 0.0f;
+            float max = 0.0f;
+            for (int j = 0; j < qk; j++) {
+                const float v = x[i * qk + j];
+                if (amax < fabsf(v)) {
+                    amax = fabsf(v);
+                    max = v;
+                }
             }
-        }
-
-        const float d = max / -8;
-
-        if (d == 0) {
-            scales[i] = ov::float16(1.0f);
-            // zp is already set to 8 for symmetric, or set per-block for asymmetric
-            if (!is_scalar_zp) {
+            const float d = max / -8;
+            if (d == 0) {
+                scales[i] = ov::float16(1.0f);
                 if (i % 2 == 0) {
                     zp[i / 2] = 8;
                 } else {
                     zp[i / 2] |= (8 << 4);
                 }
+                memset(weights + i * qk / 2, 8 | (8 << 4), qk / 2);
+                continue;
             }
-            memset(weights + i * qk / 2, 8 | (8 << 4), qk / 2);
-            continue;
-        }
-
-        const float id = 1.0f / d;
-        scales[i] = ov::float16(d);
-        // For asymmetric quantization, store per-block zero points
-        if (!is_scalar_zp) {
+            const float id = 1.0f / d;
+            scales[i] = ov::float16(d);
             if (i % 2 == 0) {
                 zp[i / 2] = 8;
             } else {
                 zp[i / 2] |= (8 << 4);
             }
+            for (int j = 0; j < qk / 2; ++j) {
+                const float x0 = x[i * qk + 2 * j] * id;
+                const float x1 = x[i * qk + 2 * j + 1] * id;
+                const uint8_t xi0 = MIN(15, (int8_t) (x0 + 8.5f));
+                const uint8_t xi1 = MIN(15, (int8_t) (x1 + 8.5f));
+                weights[i * qk / 2 + j] = xi0 | (xi1 << 4);
+            }
         }
-
-        for (int j = 0; j < qk / 2; ++j) {
-            const float x0 = x[i * qk + 2 * j] * id;
-            const float x1 = x[i * qk + 2 * j + 1] * id;
-            const uint8_t xi0 = MIN(15, (int8_t) (x0 + 8.5f));
-            const uint8_t xi1 = MIN(15, (int8_t) (x1 + 8.5f));
-            weights[i * qk / 2 + j] = xi0 | (xi1 << 4);
+    } else {
+        // Symmetric: produce signed i4 values in [-8, 7]
+        for (int i = 0; i < nb; i++) {
+            float amax = 0.0f;
+            float max = 0.0f;
+            for (int j = 0; j < qk; j++) {
+                const float v = x[i * qk + j];
+                if (amax < fabsf(v)) {
+                    amax = fabsf(v);
+                    max = v;
+                }
+            }
+            const float d = max / -8;
+            if (d == 0) {
+                scales[i] = ov::float16(1.0f);
+                // i4 value 0 packed: 0x00
+                memset(weights + i * qk / 2, 0, qk / 2);
+                continue;
+            }
+            const float id = 1.0f / d;
+            scales[i] = ov::float16(d);
+            for (int j = 0; j < qk / 2; ++j) {
+                const float x0 = x[i * qk + 2 * j] * id;
+                const float x1 = x[i * qk + 2 * j + 1] * id;
+                // Signed i4: range [-8, 7]. Quantize as round(x*id), then pack as 4-bit two's complement.
+                int8_t si0 = (int8_t) std::max(-8, std::min(7, (int) roundf(x0)));
+                int8_t si1 = (int8_t) std::max(-8, std::min(7, (int) roundf(x1)));
+                weights[i * qk / 2 + j] = (si0 & 0x0F) | ((si1 & 0x0F) << 4);
+            }
         }
     }
 }
@@ -809,36 +879,42 @@ void quantize_q8_0(const float * x,
 
     auto * weights = static_cast<uint8_t *>(weights_arr.data());
     auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
-    auto * zp = static_cast<uint8_t *>(zp_arr.data());
-    bool is_scalar_zp = (zp_arr.get_size() == 1);  // Symmetric quantization
-
-    // For Q8_0, zero point is always 128
-    if (is_scalar_zp) {
-        zp[0] = 128;
-    }
-
-    for (int i = 0; i < nb; i++) {
-        float amax = 0.0f;  // absolute max
+    bool is_symmetric = (weights_arr.get_element_type() == ov::element::i8);  // Signed i8 path
 
-        for (int j = 0; j < qk; j++) {
-            const float v = x[i * qk + j];
-            if (amax < fabsf(v)) {
-                amax = fabsf(v);
+    if (!is_symmetric) {
+        auto * zp = static_cast<uint8_t *>(zp_arr.data());
+        for (int i = 0; i < nb; i++) {
+            float amax = 0.0f;
+            for (int j = 0; j < qk; j++) {
+                const float v = x[i * qk + j];
+                amax = std::max(amax, fabsf(v));
             }
-        }
-
-        const float d = amax / 127.0f;
-        const float id = d ? 1.0f / d : 0.0f;
-        scales[i] = ov::float16(d);
-        // For asymmetric quantization, store per-block zero points
-        if (!is_scalar_zp) {
+            const float d = amax / 127.0f;
+            const float id = d ? 1.0f / d : 0.0f;
+            scales[i] = ov::float16(d);
             zp[i] = 128;
+            for (int j = 0; j < qk; ++j) {
+                const float x0 = x[i * qk + j] * id;
+                const int8_t xi0 = roundf(x0);
+                weights[i * qk + j] = (uint8_t) (xi0 + 128);
+            }
         }
-
-        for (int j = 0; j < qk; ++j) {
-            const float x0 = x[i * qk + j] * id;
-            const int8_t xi0 = roundf(x0);
-            weights[i * qk + j] = (uint8_t) (xi0 + 128);
+    } else {
+        // Symmetric: store signed int8 values directly
+        auto * signed_weights = reinterpret_cast<int8_t *>(weights);
+        for (int i = 0; i < nb; i++) {
+            float amax = 0.0f;
+            for (int j = 0; j < qk; j++) {
+                const float v = x[i * qk + j];
+                amax = std::max(amax, fabsf(v));
+            }
+            const float d = amax / 127.0f;
+            const float id = d ? 1.0f / d : 0.0f;
+            scales[i] = ov::float16(d);
+            for (int j = 0; j < qk; ++j) {
+                const float x0 = x[i * qk + j] * id;
+                signed_weights[i * qk + j] = (int8_t) roundf(x0);
+            }
         }
     }
 }
@@ -861,12 +937,8 @@ void quantize_q8_1(const float * x,
 
         for (int j = 0; j < qk; j++) {
             const float v = x[i * qk + j];
-            if (v < min) {
-                min = v;
-            }
-            if (v > max) {
-                max = v;
-            }
+            min = std::min(v, min);
+            max = std::max(v, max);
         }
 
         const float d = (max - min) / ((1 << 8) - 1);
diff --git a/ggml/src/ggml-openvino/openvino/op/rope.cpp b/ggml/src/ggml-openvino/openvino/op/rope.cpp
index 26dc2d24f82..a8db9b38930 100644
--- a/ggml/src/ggml-openvino/openvino/op/rope.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/rope.cpp
@@ -9,12 +9,17 @@
 #include <openvino/op/add.hpp>
 #include <openvino/op/concat.hpp>
 #include <openvino/op/constant.hpp>
+#include <openvino/op/convert.hpp>
+#include <openvino/op/cos.hpp>
+#include <openvino/op/gather.hpp>
 #include <openvino/op/multiply.hpp>
 #include <openvino/op/reshape.hpp>
 #include <openvino/op/shape_of.hpp>
+#include <openvino/op/sin.hpp>
 #include <openvino/op/slice.hpp>
 #include <openvino/op/split.hpp>
 #include <openvino/op/subtract.hpp>
+#include <openvino/op/transpose.hpp>
 #include <openvino/op/unsqueeze.hpp>
 #include <vector>
 
@@ -33,6 +38,12 @@ OutputVector translate_rope(const NodeContext & context) {
     auto data_node = context.get_input(0).get_node_shared_ptr();
     auto output_shape = context.get_output_shape().to_shape();
     int32_t * op_params = context.get_output_op_params();
+    const int mode = (op_case & 0xFFFF0000) >> 16;
+    op_case = (op_case & 0x0000FFFF);
+
+    constexpr int TYPE_NORMAL = 0;
+    constexpr int TYPE_NEOX = 1;
+    constexpr int TYPE_IMROPE = 2;
 
     Output<Node> cos_theta_node;
     Output<Node> sin_theta_node;
@@ -45,7 +56,7 @@ OutputVector translate_rope(const NodeContext & context) {
         if (context.get_input_size() == 3) {
             rope_freqs_weight = context.get_input(2).get_node_shared_ptr();
         }
-        auto sin_cos = make_sin_cos(op_params, inp_pos, rope_freqs_weight);
+        auto sin_cos = make_sin_cos(op_params, inp_pos, rope_freqs_weight, mode == TYPE_IMROPE);
         sin_theta_node = sin_cos.first;
         cos_theta_node = sin_cos.second;
     }
@@ -65,11 +76,7 @@ OutputVector translate_rope(const NodeContext & context) {
         }
     }
 
-    const int mode = op_params[2];
-    constexpr int ROPE_TYPE_NORMAL = 0;
-    constexpr int ROPE_TYPE_NEOX = 2;
-
-    if (mode == ROPE_TYPE_NORMAL) {
+    if (mode == TYPE_NORMAL) {
         auto neg_one = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
         auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
         auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
@@ -97,7 +104,7 @@ OutputVector translate_rope(const NodeContext & context) {
         auto data_shape = ov::op::v0::Constant::create(
             ov::element::i64, {4}, std::vector<int64_t>{1, -1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
         res = std::make_shared<ov::op::v1::Reshape>(stack, data_shape, false);
-    } else if (mode == ROPE_TYPE_NEOX) {
+    } else if (mode == TYPE_NEOX) {
         auto data_split = std::make_shared<ov::op::v1::Split>(
             data_node, ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {-1}), 2);
         Output<Node> slice_data_node_0 = data_split->outputs()[0];
@@ -112,6 +119,25 @@ OutputVector translate_rope(const NodeContext & context) {
             std::make_shared<ov::op::v1::Multiply>(slice_data_node_1, cos_theta_node));
 
         res = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{first_half_node, second_half_node}, -1);
+    } else if (mode == TYPE_IMROPE) {
+        int64_t n_dims = data_node->get_shape()[3];
+        auto cos_sin_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{1,-1,1,(n_dims >> 1)});
+        auto cos_reshaped = std::make_shared<ov::op::v1::Reshape>(cos_theta_node, cos_sin_shape, true);
+        auto sin_reshaped = std::make_shared<ov::op::v1::Reshape>(sin_theta_node, cos_sin_shape, true);
+
+        auto split_axis = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {3});
+        auto split_a = std::make_shared<ov::op::v1::Split>(data_node, split_axis, 2);
+        auto x0 = split_a->output(0);
+        auto x1 = split_a->output(1);
+        auto mul_a = std::make_shared<ov::op::v1::Multiply>(x0, cos_reshaped);
+        auto mul_b = std::make_shared<ov::op::v1::Multiply>(x1, sin_reshaped);
+        auto sub = std::make_shared<ov::op::v1::Subtract>(mul_a, mul_b);
+
+        auto mul_c = std::make_shared<ov::op::v1::Multiply>(x0, sin_reshaped);
+        auto mul_d = std::make_shared<ov::op::v1::Multiply>(x1, cos_reshaped);
+        auto add = std::make_shared<ov::op::v1::Add>(mul_c, mul_d);
+
+        res = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{sub, add}, 3);
     }
 
     return rename_outputs_with_suffix({res}, context.get_name());
diff --git a/ggml/src/ggml-openvino/openvino/op/unary_gelu.cpp b/ggml/src/ggml-openvino/openvino/op/unary_gelu.cpp
new file mode 100644
index 00000000000..d1e9efc33a5
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/unary_gelu.cpp
@@ -0,0 +1,25 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <openvino/core/node_output.hpp>
+#include <openvino/op/gelu.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_unary_gelu(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+
+    auto input = context.get_input(0);
+    auto res = std::make_shared<ov::op::v7::Gelu>(input);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op_table.cpp b/ggml/src/ggml-openvino/openvino/op_table.cpp
index beadafe8103..1385539279c 100644
--- a/ggml/src/ggml-openvino/openvino/op_table.cpp
+++ b/ggml/src/ggml-openvino/openvino/op_table.cpp
@@ -31,6 +31,7 @@ std::unordered_map<std::string, CreatorFunction> get_supported_ops() {
         {"GGML_OP_SOFT_MAX",       op::translate_soft_max                         },
         {"GGML_OP_SUB",            op::translate_1to1_match_2_inputs<v1::Subtract>},
         {"GGML_OP_TRANSPOSE",      op::translate_transpose                        },
+        {"GGML_UNARY_OP_GELU",     op::translate_unary_gelu                       },
         {"GGML_UNARY_OP_SILU",     op::translate_unary_silu                       },
         {"GGML_OP_VIEW",           op::translate_view                             },
         {"GGML_GLU_OP_SWIGLU",     op::translate_glu_swiglu                       },
diff --git a/ggml/src/ggml-openvino/openvino/op_table.h b/ggml/src/ggml-openvino/openvino/op_table.h
index 37f763117aa..f546796d2ee 100644
--- a/ggml/src/ggml-openvino/openvino/op_table.h
+++ b/ggml/src/ggml-openvino/openvino/op_table.h
@@ -21,6 +21,7 @@ GGML_OP_CONVERTER(translate_rms_norm);
 GGML_OP_CONVERTER(translate_rope);
 GGML_OP_CONVERTER(translate_scale);
 GGML_OP_CONVERTER(translate_unary_silu);
+GGML_OP_CONVERTER(translate_unary_gelu);
 GGML_OP_CONVERTER(translate_soft_max);
 GGML_OP_CONVERTER(translate_transpose);
 GGML_OP_CONVERTER(translate_view);
diff --git a/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.cpp b/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.cpp
deleted file mode 100644
index ed2a3ab6d1b..00000000000
--- a/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.cpp
+++ /dev/null
@@ -1,123 +0,0 @@
-#include "eliminate_zp.h"
-
-#include <openvino/core/graph_util.hpp>
-#include <openvino/core/parallel.hpp>
-#include <openvino/core/rt_info.hpp>
-#include <openvino/op/constant.hpp>
-#include <openvino/op/convert.hpp>
-#include <openvino/op/multiply.hpp>
-#include <openvino/op/subtract.hpp>
-#include <openvino/pass/pattern/op/label.hpp>
-#include <openvino/pass/pattern/op/pattern.hpp>
-#include <openvino/pass/pattern/op/wrap_type.hpp>
-
-namespace ov {
-namespace frontend {
-namespace ggml {
-namespace pass {
-
-EliminateZeroPoints::EliminateZeroPoints() {
-    // Find pattern:
-    // (Multiply Any(scale)
-    //           (Subtract (Convert Constant(data)))
-    //                     (Convert Constant(zero_point)))
-    // where zero_point is a scalar
-    // If data is u4 and zp value is 8 (q4_0), Replace the Subtract with an i4 Constant whose value is data - zp_val
-    // If data is u8 and zp value is 128 (q8_0) or 32 (q6_k), Replace the Subtract with an i8 Constant
-
-    auto m_data_constant = ov::pass::pattern::wrap_type<ov::op::v0::Constant>();
-    auto m_data_convert = ov::pass::pattern::wrap_type<ov::op::v0::Convert>({m_data_constant});
-
-    auto m_zp_constant = ov::pass::pattern::wrap_type<ov::op::v0::Constant>();
-    auto m_zp_convert = ov::pass::pattern::wrap_type<ov::op::v0::Convert>({m_zp_constant});
-
-    auto m_subtract = ov::pass::pattern::wrap_type<ov::op::v1::Subtract>({m_data_convert, m_zp_convert});
-    auto m_scale = ov::pass::pattern::any_input();
-    auto m_multiply = ov::pass::pattern::wrap_type<ov::op::v1::Multiply>({m_scale, m_subtract});
-
-    const auto callback = [=](ov::pass::pattern::Matcher & m) {
-        const auto & pattern_map = m.get_pattern_value_map();
-
-        auto multiply_node =
-            std::dynamic_pointer_cast<ov::op::v1::Multiply>(pattern_map.at(m_multiply).get_node_shared_ptr());
-        auto subtract_node =
-            std::dynamic_pointer_cast<ov::op::v1::Subtract>(pattern_map.at(m_subtract).get_node_shared_ptr());
-        auto data_constant =
-            std::dynamic_pointer_cast<ov::op::v0::Constant>(pattern_map.at(m_data_constant).get_node_shared_ptr());
-        auto zp_constant =
-            std::dynamic_pointer_cast<ov::op::v0::Constant>(pattern_map.at(m_zp_constant).get_node_shared_ptr());
-
-        if (!multiply_node || !subtract_node || !data_constant || !zp_constant) {
-            return false;
-        }
-
-        if (ov::shape_size(zp_constant->get_shape()) != 1) {
-            return false;
-        }
-
-        auto data_type = data_constant->get_element_type();
-        auto zp_data = zp_constant->cast_vector<int>();
-
-        if (zp_data.empty()) {
-            return false;
-        }
-
-        int zp_value = zp_data[0];
-
-        bool should_eliminate = false;
-        ov::element::Type target_type;
-
-        if (data_type == ov::element::u4 && zp_value == 8) {
-            should_eliminate = true;
-            target_type = ov::element::i4;
-        } else if (data_type == ov::element::u8 && (zp_value == 128 || zp_value == 32)) {
-            should_eliminate = true;
-            target_type = ov::element::i8;
-        }
-
-        if (!should_eliminate) {
-            return false;
-        }
-
-        auto data_shape = data_constant->get_shape();
-        size_t total_elements = ov::shape_size(data_shape);
-
-        std::shared_ptr<ov::op::v0::Constant> new_constant;
-
-        // TODO improve performance
-        if (data_type == ov::element::u4) {
-            auto data_values = data_constant->cast_vector<uint8_t>();
-            std::vector<int8_t> adjusted_values(total_elements);
-
-            ov::parallel_for(total_elements, [&](size_t i) {
-                adjusted_values[i] = static_cast<int8_t>(static_cast<int>(data_values[i]) - 8);
-            });
-
-            new_constant = std::make_shared<ov::op::v0::Constant>(target_type, data_shape, adjusted_values);
-        } else if (data_type == ov::element::u8) {
-            auto data_values = data_constant->cast_vector<uint8_t>();
-            std::vector<int8_t> adjusted_values(total_elements);
-
-            ov::parallel_for(total_elements, [&, zp_value](size_t i) {
-                adjusted_values[i] = static_cast<int8_t>(static_cast<int>(data_values[i]) - zp_value);
-            });
-
-            new_constant = std::make_shared<ov::op::v0::Constant>(target_type, data_shape, adjusted_values);
-        }
-
-        auto new_convert =
-            std::make_shared<ov::op::v0::Convert>(new_constant, subtract_node->get_output_element_type(0));
-        ov::replace_node(subtract_node, new_convert);
-
-        return true;
-    };
-
-    register_matcher(
-        std::make_shared<ov::pass::pattern::Matcher>(m_multiply, "ov::frontend::ggml::pass::EliminateZeroPoints"),
-        callback);
-}
-
-}  // namespace pass
-}  // namespace ggml
-}  // namespace frontend
-}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.h b/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.h
deleted file mode 100644
index edd3cd718d9..00000000000
--- a/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.h
+++ /dev/null
@@ -1,17 +0,0 @@
-#include "openvino/pass/matcher_pass.hpp"
-
-namespace ov {
-namespace frontend {
-namespace ggml {
-namespace pass {
-
-class EliminateZeroPoints : public ov::pass::MatcherPass {
-public:
-    OPENVINO_MATCHER_PASS_RTTI("ov::frontend::ggml::pass::EliminateZeroPoints")
-    EliminateZeroPoints();
-};
-
-}  // namespace pass
-}  // namespace ggml
-}  // namespace frontend
-}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/rt_info/weightless_caching_attributes.hpp b/ggml/src/ggml-openvino/openvino/rt_info/weightless_caching_attributes.hpp
new file mode 100644
index 00000000000..f051891c481
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/rt_info/weightless_caching_attributes.hpp
@@ -0,0 +1,41 @@
+// Copyright (C) 2018-2026 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <openvino/core/core_visibility.hpp>
+#include <openvino/core/node.hpp>
+#include <openvino/core/runtime_attribute.hpp>
+
+namespace ov {
+
+/**
+ * @brief Holds weightless caching attributes of a single constant.
+ *
+ * WeightlessCacheAttribute class represents runtime info attribute that holds
+ * the values of original size of the constant in bytes and the binary offset of the
+ * constant's data in the weights file used by the weightless caching mechanism. It's
+ * not copyable in case the data was changed (the original node was replaced by a new
+ * one produced during the tranformation pipeline) - in that case weightless caching
+ * can't be used for that constant.
+ */
+class OPENVINO_API WeightlessCacheAttribute : public RuntimeAttribute {
+public:
+    OPENVINO_RTTI("WeightlessCacheAttribute", "0", RuntimeAttribute)
+
+    WeightlessCacheAttribute() = delete;
+
+    WeightlessCacheAttribute(size_t original_size, size_t bin_offset, ov::element::Type original_dtype)
+        : original_size(original_size),
+          bin_offset(bin_offset),
+          original_dtype(original_dtype) {}
+
+    bool is_copyable() const override;
+
+    size_t original_size;
+    size_t bin_offset;
+    ov::element::Type original_dtype;
+};
+
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/translate_session.cpp b/ggml/src/ggml-openvino/openvino/translate_session.cpp
index 23a1dea2496..0f68a1f5062 100644
--- a/ggml/src/ggml-openvino/openvino/translate_session.cpp
+++ b/ggml/src/ggml-openvino/openvino/translate_session.cpp
@@ -3,15 +3,16 @@
 #include "ggml-openvino/openvino/node_context.h"
 #include "ggml-openvino/openvino/utils.h"
 #include "input_model.h"
-#include "pass/eliminate_zp.h"
 #include "pass/mark_decompression_convert_constant_folding.h"
 #include "pass/squeeze_matmul.h"
+#include "rt_info/weightless_caching_attributes.hpp"
 
 #include <cstdint>
 #include <cstdlib>
 #include <map>
 #include <memory>
 #include <openvino/core/node.hpp>
+#include <openvino/core/preprocess/pre_post_process.hpp>
 #include <openvino/op/add.hpp>
 #include <openvino/op/broadcast.hpp>
 #include <openvino/op/concat.hpp>
@@ -33,7 +34,6 @@
 #include <openvino/op/unsqueeze.hpp>
 #include <openvino/pass/constant_folding.hpp>
 #include <openvino/pass/make_stateful.hpp>
-#include <openvino/core/preprocess/pre_post_process.hpp>
 
 namespace ov {
 namespace frontend {
@@ -240,6 +240,31 @@ std::shared_ptr<Model> TranslateSession::translate_graph(const frontend::InputMo
     resulting_model = std::make_shared<Model>(results, used_params);
 
     apply_transformations(resulting_model);
+
+    // Set WeightlessCacheAttribute on large constants to avoid unnecessary memory copies
+    // in the NPUW plugin. Without this attribute, NPUW's LazyTensor constructor
+    // (lazy_tensor.cpp, op::Const::Const) will memcpy every constant "in case export
+    // occurs", doubling memory usage per compile_model call.
+    //
+    // The bin_offset field serves as a unique key (not a real file offset) — this is
+    // the same convention the GPU plugin uses for non-IR models (see
+    // Plugin::set_weightless_cache_attributes in intel_gpu/src/plugin/plugin.cpp).
+    // Each constant must have a distinct bin_offset, otherwise GPU's weightless cache
+    // import will map multiple constants to the same data.
+    //
+    // Small constants (< 16 elements) are excluded since they may be introduced by
+    // optimization patterns and the overhead is negligible.
+    size_t offset = 0;
+    for (auto & node : resulting_model->get_ordered_ops()) {
+        if (auto cnst = ov::as_type_ptr<ov::op::v0::Constant>(node);
+            cnst && cnst->get_byte_size() / cnst->get_element_type().size() >= 16) {
+            auto & rt_info = cnst->get_rt_info();
+            if (rt_info.find(ov::WeightlessCacheAttribute::get_type_info_static()) == rt_info.end()) {
+                rt_info[ov::WeightlessCacheAttribute::get_type_info_static()] =
+                    ov::WeightlessCacheAttribute(cnst->get_byte_size(), offset++, cnst->get_element_type());
+            }
+        }
+    }
     return resulting_model;
 }
 
@@ -257,7 +282,6 @@ std::shared_ptr<Model> TranslateSession::apply_transformations(std::shared_ptr<M
         }
 
         if (ggml_model_decoder->is_static()) {
-            manager.register_pass<pass::EliminateZeroPoints>();
             manager.register_pass<pass::SqueezeMatmul>();
         }
         manager.run_passes(model);
diff --git a/ggml/src/ggml-openvino/openvino/utils.cpp b/ggml/src/ggml-openvino/openvino/utils.cpp
index 65356a51b51..0baaf88e17a 100644
--- a/ggml/src/ggml-openvino/openvino/utils.cpp
+++ b/ggml/src/ggml-openvino/openvino/utils.cpp
@@ -2,6 +2,7 @@
 
 #include "ggml-impl.h"
 
+#include <cmath>
 #include <cstddef>
 #include <ctime>
 #include <memory>
@@ -13,6 +14,7 @@
 #include <openvino/op/gather.hpp>
 #include <openvino/op/maximum.hpp>
 #include <openvino/op/multiply.hpp>
+#include <openvino/op/reshape.hpp>
 #include <openvino/op/shape_of.hpp>
 #include <openvino/op/sin.hpp>
 #include <openvino/op/squeeze.hpp>
@@ -87,8 +89,11 @@ ov::Output<ov::Node> rope_yarn_ramp_mix(int n_dims, const float corr_dims[2], fl
     auto ramp_y =
         std::make_shared<ov::op::v1::Divide>(std::make_shared<ov::op::v1::Subtract>(dim_ids, corr_low), denom);
     auto ramp_clamped = std::make_shared<ov::op::v0::Clamp>(ramp_y, 0.0f, 1.0f);
+    // rope_yarn_ramp returns (1 - clamp(y)), so invert before scaling
+    auto one = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1, 1}, {1.0f});
+    auto ramp_inverted = std::make_shared<ov::op::v1::Subtract>(one, ramp_clamped);
     auto ext_factor_node = ov::op::v0::Constant::create(ov::element::f32, Shape{}, {ext_factor});
-    auto ramp_mix = std::make_shared<ov::op::v1::Multiply>(ramp_clamped, ext_factor_node);
+    auto ramp_mix = std::make_shared<ov::op::v1::Multiply>(ramp_inverted, ext_factor_node);
     return ramp_mix;
 }
 
@@ -115,6 +120,7 @@ void ggml_rope_yarn_corr_dims(int n_dims,
 std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t * rope_params,
                                                            std::shared_ptr<ov::Node> inp_pos,
                                                            std::shared_ptr<ov::Node> rope_freqs_weight,
+                                                           bool imrope,
                                                            bool stateful) {
     if (stateful) {
         inp_pos = std::make_shared<ov::op::v0::Squeeze>(inp_pos, ov::op::v0::Constant::create(ov::element::i64, {1}, {0}));
@@ -122,6 +128,13 @@ std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t * rope_params
         auto pos_perm =
             std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{3}, std::vector<int64_t>{2, 1, 0});
         inp_pos = std::make_shared<ov::op::v1::Transpose>(inp_pos, pos_perm);
+    } else if (imrope) {
+        inp_pos = std::make_shared<ov::op::v0::Convert>(inp_pos, ov::element::f32);
+        auto pos_shape = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{5}, {0, 0, 0, 4, -1});
+        inp_pos = std::make_shared<ov::op::v1::Reshape>(inp_pos, pos_shape, true);
+        auto pos_transpose_shape =
+            std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{5}, std::vector<int64_t>{0, 1, 2, 4, 3});
+        inp_pos = std::make_shared<ov::op::v1::Transpose>(inp_pos, pos_transpose_shape);
     } else {
         inp_pos = std::make_shared<ov::op::v0::Convert>(inp_pos, ov::element::f32);
         auto pos_perm =
@@ -136,6 +149,7 @@ std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t * rope_params
     float beta_fast;
     float beta_slow;
     const int n_dims = rope_params[1];
+    const size_t n_dims_half = n_dims >> 1;
     const int n_ctx_orig = rope_params[4];
     memcpy(&freq_base, rope_params + 5, sizeof(float));
     memcpy(&freq_scale, rope_params + 6, sizeof(float));
@@ -146,57 +160,74 @@ std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t * rope_params
 
     const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
-    float corr_dims[2];
-    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
-
-    std::vector<float> factor(n_dims / 2);
-    factor[0] = 1.0f;
-    for (size_t i = 1; i < factor.size(); i++) {
-        factor[i] = theta_scale * factor[i - 1];
-    }
+    std::vector<float> factor(n_dims_half);
 
     Output<Node> freq_factors;
-    if (stateful) {
-        freq_factors =
-            std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1, 1, factor.size()}, factor);
-    } else {
-        freq_factors =
-            std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1, 1, 1, factor.size()}, factor);
-    }
-    if (rope_freqs_weight) {
-        freq_factors = std::make_shared<ov::op::v1::Divide>(freq_factors, rope_freqs_weight);
-    }
-
-    auto theta_extrap = std::make_shared<ov::op::v1::Multiply>(freq_factors, inp_pos);
-    auto theta_interp = std::make_shared<ov::op::v1::Multiply>(
-        theta_extrap, ov::op::v0::Constant::create(ov::element::f32, {1}, {freq_scale}));
 
     Output<Node> theta;
     float mscale = attn_factor;
-    if (ext_factor == 0.0f) {
-        theta = theta_interp;
+    if (imrope) {
+        std::vector<int64_t> gather_indices(n_dims_half);
+        for (size_t j = 0; j < n_dims_half; j++) {
+            gather_indices[j] = j % 3;
+            factor[j] = std::pow(theta_scale, j);
+        }
+        auto gather_indices_const =
+            std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{n_dims_half}, gather_indices);
+        auto gather_axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {4});
+        inp_pos = std::make_shared<ov::op::v8::Gather>(inp_pos, gather_indices_const, gather_axis);
+        auto factor_const = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{n_dims_half}, factor);
+        theta = std::make_shared<ov::op::v1::Multiply>(inp_pos, factor_const);
     } else {
-        auto ramp_mix = rope_yarn_ramp_mix(n_dims, corr_dims, ext_factor);
-        Output<Node> one;
+        float corr_dims[2];
+        ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
+        factor[0] = 1.0f;
+        for (size_t i = 1; i < factor.size(); i++) {
+            factor[i] = theta_scale * factor[i - 1];
+        }
         if (stateful) {
-            one = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1}, {1.0f});
+            freq_factors =
+                std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1, 1, factor.size()}, factor);
         } else {
-            one = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1, 1}, {1.0f});
+            freq_factors =
+                std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1, 1, 1, factor.size()}, factor);
+        }
+        if (rope_freqs_weight) {
+            freq_factors = std::make_shared<ov::op::v1::Divide>(freq_factors, rope_freqs_weight);
         }
-        auto one_minus_ramp = std::make_shared<ov::op::v1::Subtract>(one, ramp_mix);
 
-        theta = std::make_shared<ov::op::v1::Add>(std::make_shared<ov::op::v1::Multiply>(theta_interp, one_minus_ramp),
-                                                  std::make_shared<ov::op::v1::Multiply>(theta_extrap, ramp_mix));
-        mscale *= (1.0f + 0.1f * std::log(1.0f / freq_scale));
+        auto theta_extrap = std::make_shared<ov::op::v1::Multiply>(freq_factors, inp_pos);
+        auto theta_interp = std::make_shared<ov::op::v1::Multiply>(
+            theta_extrap, ov::op::v0::Constant::create(ov::element::f32, {1}, {freq_scale}));
+
+        if (ext_factor == 0.0f) {
+            theta = theta_interp;
+        } else {
+            auto ramp_mix = rope_yarn_ramp_mix(n_dims, corr_dims, ext_factor);
+            Output<Node> one;
+            if (stateful) {
+                one = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1}, {1.0f});
+            } else {
+                one = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1, 1}, {1.0f});
+            }
+            auto one_minus_ramp = std::make_shared<ov::op::v1::Subtract>(one, ramp_mix);
+
+            theta = std::make_shared<ov::op::v1::Add>(std::make_shared<ov::op::v1::Multiply>(theta_interp, one_minus_ramp),
+                                                      std::make_shared<ov::op::v1::Multiply>(theta_extrap, ramp_mix));
+            mscale *= (1.0f + 0.1f * std::log(1.0f / freq_scale));
+        }
     }
 
     Output<Node> cos_theta = std::make_shared<ov::op::v0::Cos>(theta);
     Output<Node> sin_theta = std::make_shared<ov::op::v0::Sin>(theta);
 
-    auto mscale_node = ov::op::v0::Constant::create(ov::element::f32, Shape{}, {mscale});
+    if (!imrope) {
+        auto mscale_node = ov::op::v0::Constant::create(ov::element::f32, Shape{}, {mscale});
+
+        cos_theta = std::make_shared<ov::op::v1::Multiply>(cos_theta, mscale_node);
+        sin_theta = std::make_shared<ov::op::v1::Multiply>(sin_theta, mscale_node);
+    }
 
-    cos_theta = std::make_shared<ov::op::v1::Multiply>(cos_theta, mscale_node);
-    sin_theta = std::make_shared<ov::op::v1::Multiply>(sin_theta, mscale_node);
     return std::make_pair(sin_theta, cos_theta);
 }
 
diff --git a/ggml/src/ggml-openvino/openvino/utils.h b/ggml/src/ggml-openvino/openvino/utils.h
index 88dcad4c906..767dd4c53ea 100644
--- a/ggml/src/ggml-openvino/openvino/utils.h
+++ b/ggml/src/ggml-openvino/openvino/utils.h
@@ -67,6 +67,7 @@ OutputVector rename_outputs_with_suffix(const OutputVector& outputs, const std::
 std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t* rope_params,
                                                            std::shared_ptr<ov::Node> inp_pos,
                                                            std::shared_ptr<ov::Node> rope_freqs_weight = nullptr,
+                                                           bool imrope = false,
                                                            bool stateful = false);
 
 ov::Output<ov::Node> process_view_input(const NodeContext& context, int input_index, int slice_len = 0);
diff --git a/ggml/src/ggml-openvino/utils.cpp b/ggml/src/ggml-openvino/utils.cpp
index 1b553a0de00..998ef7c9eb4 100644
--- a/ggml/src/ggml-openvino/utils.cpp
+++ b/ggml/src/ggml-openvino/utils.cpp
@@ -81,8 +81,8 @@ ov::Tensor create_ov_output_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
 enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<ov_runtime_context> r_ctx) {
     auto & core = ov_singleton_core();
     const auto & config = ggml_openvino_get_compile_config();
-    auto device = r_ctx->device;
-    bool stateful = r_ctx->stateful;
+    const auto & device = r_ctx->device;
+    const auto & stateful = r_ctx->stateful;
     static auto is_static = false;
 
     if (is_naive(cgraph)) {
@@ -106,14 +106,26 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
     int64_t infer_end_time;
 
     {
-        std::lock_guard<std::mutex> lock(r_ctx->ov_compute_mutex);
+        std::shared_ptr<decoder_runtime_ctx> entry;
+        ModelParams old_m_params;
 
-        auto it = r_ctx->decoder_cache.find(key);
+        {
+            std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+            auto it = r_ctx->decoder_cache.find(key);
+            cache_hit = it != r_ctx->decoder_cache.end();
+            if (cache_hit) {
+                entry = it->second;
+            } else {
+                auto mutex = std::make_shared<std::mutex>();
+                entry = std::make_shared<decoder_runtime_ctx>(mutex);
+                r_ctx->decoder_cache[key] = entry;
+            }
+        }
+
+        std::lock_guard<std::mutex> lock(*(entry->mutex));
 
-        cache_hit = it != r_ctx->decoder_cache.end();
-        ModelParams old_m_params;
         if (cache_hit) {
-            ggml_decoder = it->second;
+            ggml_decoder = entry->ptr;
             old_m_params = ggml_decoder->get_model_params();
             cache_hit = old_m_params.can_reuse_dynamically(m_params);
         }
@@ -126,7 +138,10 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
                 ggml_decoder->update_io(cgraph);
             }
             ggml_decoder->add_extra_inputs();
-            infer_request = r_ctx->infer_request_cache.at(key);
+            {
+                std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+                infer_request = r_ctx->infer_request_cache.at(key);
+            }
 
             if (stateful) {
                 const auto * inp_pos = get_inp_pos_tensor(cgraph);
@@ -170,7 +185,10 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
             conversion_end_time = decoder_end_time;
             compile_end_time = decoder_end_time;
         } else {
-            r_ctx->infer_request_cache.erase(key);
+            {
+                std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+                r_ctx->infer_request_cache.erase(key);
+            }
 
             std::shared_ptr<ov::Model> model;
             auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph);
@@ -199,8 +217,7 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
             }
             compile_end_time = ggml_time_us();
             infer_request = std::make_shared<ov::InferRequest>(compiled_model.create_infer_request());
-            r_ctx->infer_request_cache[key] = infer_request;
-            r_ctx->decoder_cache[key] = ggml_decoder;
+            entry->ptr = ggml_decoder;
 
             std::vector<std::string> ov_input_names;
             std::vector<std::string> ov_output_names;
@@ -210,8 +227,13 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
             for (const auto & ov_output : model->get_results()) {
                 ov_output_names.push_back(ov_output->get_friendly_name());
             }
-            r_ctx->ov_input_names_cache[key] = std::move(ov_input_names);
-            r_ctx->ov_output_names_cache[key] = std::move(ov_output_names);
+
+            {
+                std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+                r_ctx->infer_request_cache[key] = infer_request;
+                r_ctx->ov_input_names_cache[key] = std::move(ov_input_names);
+                r_ctx->ov_output_names_cache[key] = std::move(ov_output_names);
+            }
 
             if (stateful) {
                 const auto * inp_pos = get_inp_pos_tensor(cgraph);
@@ -224,8 +246,13 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
             }
         }
 
-        auto ov_input_names = r_ctx->ov_input_names_cache[key];
-        auto ov_output_names = r_ctx->ov_output_names_cache[key];
+        std::vector<std::string> ov_input_names;
+        std::vector<std::string> ov_output_names;
+        {
+            std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+            ov_input_names = r_ctx->ov_input_names_cache[key];
+            ov_output_names = r_ctx->ov_output_names_cache[key];
+        }
 
         for (size_t i = 0; i < ov_input_names.size(); i++) {
             auto param_name = ov_input_names[i];
@@ -306,12 +333,26 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
     int64_t compile_end_time;
     int64_t infer_end_time;
 
-    auto it = r_ctx->decoder_cache.find(key);
-
-    cache_hit = it != r_ctx->decoder_cache.end();
+    std::shared_ptr<decoder_runtime_ctx> entry;
     ModelParams old_m_params;
+
+    {
+        std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+        auto it = r_ctx->decoder_cache.find(key);
+        cache_hit = it != r_ctx->decoder_cache.end();
+        if (cache_hit) {
+            entry = it->second;
+        } else {
+            auto mutex = std::make_shared<std::mutex>();
+            entry = std::make_shared<decoder_runtime_ctx>(mutex);
+            r_ctx->decoder_cache[key] = entry;
+        }
+    }
+
+    std::lock_guard<std::mutex> lock(*(entry->mutex));
+
     if (cache_hit) {
-        ggml_decoder = it->second;
+        ggml_decoder = entry->ptr;
         old_m_params = ggml_decoder->get_model_params();
         cache_hit = old_m_params.can_reuse_statically(m_params);
     }
@@ -325,14 +366,21 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
             ggml_decoder->update_io(cgraph);
         }
         ggml_decoder->add_extra_inputs();
-        infer_request = is_prefill ? r_ctx->infer_request_cache_prefill.at(key) : r_ctx->infer_request_cache.at(key);
+        {
+            std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+            infer_request =
+                is_prefill ? r_ctx->infer_request_cache_prefill.at(key) : r_ctx->infer_request_cache.at(key);
+        }
 
         decoder_end_time = ggml_time_us();
         conversion_end_time = decoder_end_time;
         compile_end_time = decoder_end_time;
     } else {
-        r_ctx->infer_request_cache.erase(key);
-        r_ctx->infer_request_cache_prefill.erase(key);
+        {
+            std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+            r_ctx->infer_request_cache.erase(key);
+            r_ctx->infer_request_cache_prefill.erase(key);
+        }
 
         std::shared_ptr<ov::Model> model;
         auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph);
@@ -372,16 +420,14 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
             compiled_model_decode = core.compile_model(model_decode, device, config);
         }
 
-        r_ctx->infer_request_cache_prefill[key] =
-            std::make_shared<ov::InferRequest>(compiled_model_prefill.create_infer_request());
-        r_ctx->infer_request_cache[key] =
-            std::make_shared<ov::InferRequest>(compiled_model_decode.create_infer_request());
+        auto infer_request_prefill = std::make_shared<ov::InferRequest>(compiled_model_prefill.create_infer_request());
+        auto infer_request_decode = std::make_shared<ov::InferRequest>(compiled_model_decode.create_infer_request());
         compile_end_time = ggml_time_us();
 
         model = is_prefill ? model_prefill : model_decode;
         ggml_decoder = is_prefill ? ggml_decoder_prefill : ggml_decoder_decode;
-        infer_request = is_prefill ? r_ctx->infer_request_cache_prefill[key] : r_ctx->infer_request_cache[key];
-        r_ctx->decoder_cache[key] = ggml_decoder;
+        infer_request = is_prefill ? infer_request_prefill : infer_request_decode;
+        entry->ptr = ggml_decoder;
 
         std::vector<std::string> ov_input_names;
         std::vector<std::string> ov_output_names;
@@ -391,18 +437,29 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
         for (const auto & ov_output : model->get_results()) {
             ov_output_names.push_back(ov_output->get_friendly_name());
         }
-        r_ctx->ov_input_names_cache[key] = std::move(ov_input_names);
-        r_ctx->ov_output_names_cache[key] = std::move(ov_output_names);
+
+        {
+            std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+            r_ctx->infer_request_cache_prefill[key] = infer_request_prefill;
+            r_ctx->infer_request_cache[key] = infer_request_decode;
+            r_ctx->ov_input_names_cache[key] = std::move(ov_input_names);
+            r_ctx->ov_output_names_cache[key] = std::move(ov_output_names);
+        }
     }
 
-    auto ov_input_names = r_ctx->ov_input_names_cache[key];
-    auto ov_output_names = r_ctx->ov_output_names_cache[key];
+    std::vector<std::string> ov_input_names_local;
+    std::vector<std::string> ov_output_names_local;
+    {
+        std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
+        ov_input_names_local = r_ctx->ov_input_names_cache[key];
+        ov_output_names_local = r_ctx->ov_output_names_cache[key];
+    }
 
     if (is_prefill) {
         auto inp_len = inp_pos->ne[0];
         for (int chunk_index = 0; chunk_index * prefill_chunk_size < inp_len; chunk_index++) {
-            for (size_t i = 0; i < ov_input_names.size(); i++) {
-                auto param_name = ov_input_names[i];
+            for (size_t i = 0; i < ov_input_names_local.size(); i++) {
+                auto param_name = ov_input_names_local[i];
                 auto input_tensor = get_ov_input_tensor_static_prefill(ggml_decoder, param_name, chunk_index);
                 infer_request->set_input_tensor(i, input_tensor);
 
@@ -412,8 +469,8 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
                 }
             }
 
-            for (size_t i = 0; i < ov_output_names.size(); i++) {
-                auto * ggml_tensor = ggml_decoder->get_model_outputs().at(ov_output_names[i]);
+            for (size_t i = 0; i < ov_output_names_local.size(); i++) {
+                auto * ggml_tensor = ggml_decoder->get_model_outputs().at(ov_output_names_local[i]);
                 auto output_tensor = create_ov_output_tensor(ggml_decoder, infer_request, i, ggml_tensor);
                 infer_request->set_output_tensor(i, output_tensor);
             }
@@ -421,16 +478,16 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
             infer_request->infer();
 
             if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) {
-                for (size_t i = 0; i < ov_output_names.size(); i++) {
+                for (size_t i = 0; i < ov_output_names_local.size(); i++) {
                     const auto output_tensor = infer_request->get_output_tensor(i);
-                    print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data());
+                    print_output_tensor_info(ov_output_names_local[i], output_tensor, output_tensor.data());
                 }
             }
         }
         infer_end_time = ggml_time_us();
     } else {
-        for (size_t i = 0; i < ov_input_names.size(); i++) {
-            auto param_name = ov_input_names[i];
+        for (size_t i = 0; i < ov_input_names_local.size(); i++) {
+            auto param_name = ov_input_names_local[i];
             auto input_tensor = get_ov_input_tensor_static_decode(ggml_decoder, param_name);
             infer_request->set_input_tensor(i, input_tensor);
 
@@ -440,8 +497,8 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
             }
         }
 
-        for (size_t i = 0; i < ov_output_names.size(); i++) {
-            auto * ggml_tensor = ggml_decoder->get_model_outputs().at(ov_output_names[i]);
+        for (size_t i = 0; i < ov_output_names_local.size(); i++) {
+            auto * ggml_tensor = ggml_decoder->get_model_outputs().at(ov_output_names_local[i]);
             auto output_tensor = create_ov_output_tensor(ggml_decoder, infer_request, i, ggml_tensor);
             infer_request->set_output_tensor(i, output_tensor);
         }
@@ -450,9 +507,9 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
         infer_end_time = ggml_time_us();
 
         if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) {
-            for (size_t i = 0; i < ov_output_names.size(); i++) {
+            for (size_t i = 0; i < ov_output_names_local.size(); i++) {
                 const auto output_tensor = infer_request->get_output_tensor(i);
-                print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data());
+                print_output_tensor_info(ov_output_names_local[i], output_tensor, output_tensor.data());
             }
         }
     }
diff --git a/ggml/src/ggml-openvino/utils.h b/ggml/src/ggml-openvino/utils.h
index 656573d1389..2c72e33c352 100644
--- a/ggml/src/ggml-openvino/utils.h
+++ b/ggml/src/ggml-openvino/utils.h
@@ -3,12 +3,15 @@
 #include "ggml-impl.h"
 
 #include <algorithm>
+#include <atomic>
 #include <cstddef>
 #include <memory>
+#include <mutex>
 #include <openvino/runtime/core.hpp>
 #include <openvino/runtime/infer_request.hpp>
 #include <string>
 #include <unordered_map>
+#include <utility>
 #include <vector>
 
 struct graph_key {
@@ -40,11 +43,17 @@ struct graph_key_hash {
     }
 };
 
+struct decoder_runtime_ctx {
+    decoder_runtime_ctx(std::shared_ptr<std::mutex> mutex) : mutex(std::move(mutex)) {}
+    std::shared_ptr<std::mutex> mutex;
+    std::shared_ptr<GgmlOvDecoder> ptr;
+};
+
 struct ov_runtime_context {
-    std::mutex ov_compute_mutex;
+    mutable std::mutex ctx_mutex;
     std::string device;
     bool stateful;
-    std::unordered_map<graph_key, std::shared_ptr<GgmlOvDecoder>, graph_key_hash> decoder_cache;
+    std::unordered_map<graph_key, std::shared_ptr<decoder_runtime_ctx>, graph_key_hash> decoder_cache;
     std::unordered_map<graph_key, std::shared_ptr<ov::InferRequest>, graph_key_hash> infer_request_cache;
     std::unordered_map<graph_key, std::shared_ptr<ov::InferRequest>, graph_key_hash> infer_request_cache_prefill;
     std::unordered_map<graph_key, std::vector<std::string>, graph_key_hash> ov_input_names_cache;
@@ -53,11 +62,22 @@ struct ov_runtime_context {
     //      Simultanous stateful inference request support to be added.
     size_t stateful_kv_size;
     std::map<std::string, std::string> kv_state_input_name_map;
+    std::atomic<int> backend_count;
 
     ov_runtime_context() :
         device("CPU"),
         stateful(false),
-        stateful_kv_size(0) {}
+        stateful_kv_size(0),
+        backend_count(0) {}
+
+    void clear_caches() {
+        std::lock_guard<std::mutex> lock(ctx_mutex);
+        decoder_cache.clear();
+        infer_request_cache.clear();
+        infer_request_cache_prefill.clear();
+        ov_input_names_cache.clear();
+        ov_output_names_cache.clear();
+    }
 };
 
 enum ggml_status ov_graph_compute(struct ggml_cgraph * cgraph, ggml_backend_t backend);
diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index 2ded7397868..1cb8f563d85 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -207,35 +207,11 @@ struct ggml_backend_rpc_buffer_type_context {
     size_t      max_size;
 };
 
-struct graph_cache {
-
-    bool is_cached(const ggml_cgraph * cgraph) {
-        if ((int)last_graph.size() != cgraph->n_nodes) {
-            return false;
-        }
-        for (int i = 0; i < cgraph->n_nodes; i++) {
-            if (memcmp(&last_graph[i], cgraph->nodes[i], sizeof(ggml_tensor)) != 0) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-    void add(const ggml_cgraph * cgraph) {
-        last_graph.resize(cgraph->n_nodes);
-        for (int i = 0; i < cgraph->n_nodes; i++) {
-            memcpy(&last_graph[i], cgraph->nodes[i], sizeof(ggml_tensor));
-        }
-    }
-
-    std::vector<ggml_tensor> last_graph;
-};
-
 struct ggml_backend_rpc_context {
     std::string endpoint;
     uint32_t    device;
     std::string name;
-    graph_cache gc;
+    uint64_t    last_graph_uid;
 };
 
 struct ggml_backend_rpc_buffer_context {
@@ -717,7 +693,7 @@ static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, g
     ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
 
     GGML_ASSERT(cgraph->n_nodes > 0);
-    bool reuse = rpc_ctx->gc.is_cached(cgraph);
+    bool reuse = cgraph->uid != 0 && rpc_ctx->last_graph_uid == cgraph->uid;
     if (reuse) {
         rpc_msg_graph_recompute_req request;
         request.device = rpc_ctx->device;
@@ -725,7 +701,7 @@ static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, g
         bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_RECOMPUTE, &request, sizeof(request));
         RPC_STATUS_ASSERT(status);
     } else {
-        rpc_ctx->gc.add(cgraph);
+        rpc_ctx->last_graph_uid = cgraph->uid;
         std::vector<uint8_t> input;
         serialize_graph(rpc_ctx->device, cgraph, input);
         auto sock = get_socket(rpc_ctx->endpoint);
@@ -740,9 +716,9 @@ static ggml_backend_i ggml_backend_rpc_interface = {
     /* .free                    = */ ggml_backend_rpc_free,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
-    /* .cpy_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ ggml_backend_rpc_synchronize,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
@@ -791,10 +767,10 @@ ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint, u
 ggml_backend_t ggml_backend_rpc_init(const char * endpoint, uint32_t device) {
     std::string dev_name = "RPC" + std::to_string(device) + "[" + std::string(endpoint) + "]";
     ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context {
-        /* .endpoint = */ endpoint,
-        /* .device   = */ device,
-        /* .name     = */ dev_name,
-        /* .gc       = */ {},
+        /* .endpoint       = */ endpoint,
+        /* .device         = */ device,
+        /* .name           = */ dev_name,
+        /* .last_graph_uid = */ 0,
     };
     auto reg = ggml_backend_rpc_add_server(endpoint);
     ggml_backend_t backend = new ggml_backend {
@@ -1101,7 +1077,7 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
         fs::path cache_file = fs::path(cache_dir) / hash_str;
         std::ofstream ofs(cache_file, std::ios::binary);
         ofs.write((const char *)data, size);
-        GGML_LOG_INFO("[%s] saved to '%s'\n", __func__, cache_file.c_str());
+        GGML_LOG_INFO("[%s] saved to '%s'\n", __func__, cache_file.string().c_str());
     }
     ggml_backend_tensor_set(tensor, data, offset, size);
     return true;
diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt
index 8e589fa238d..180de92202d 100644
--- a/ggml/src/ggml-sycl/CMakeLists.txt
+++ b/ggml/src/ggml-sycl/CMakeLists.txt
@@ -39,6 +39,18 @@ if (WIN32)
         set(CMAKE_CXX_COMPILER "icx")
         set(CMAKE_CXX_COMPILER_ID "IntelLLVM")
     endif()
+    # Level Zero SDK path for Windows (only when GGML_SYCL_SUPPORT_LEVEL_ZERO is enabled)
+    if(GGML_SYCL_SUPPORT_LEVEL_ZERO)
+        if(DEFINED ENV{LEVEL_ZERO_V1_SDK_PATH})
+            set(LEVEL_ZERO_V1_SDK_PATH $ENV{LEVEL_ZERO_V1_SDK_PATH})
+            if(EXISTS "${LEVEL_ZERO_V1_SDK_PATH}")
+                target_include_directories(ggml-sycl PRIVATE "${LEVEL_ZERO_V1_SDK_PATH}/include")
+                set(LEVEL_ZERO_V1_SDK_LIB_PATH "${LEVEL_ZERO_V1_SDK_PATH}/lib")
+            else()
+                message(WARNING "LEVEL_ZERO_V1_SDK_PATH set but folder not found: ${LEVEL_ZERO_V1_SDK_PATH}")
+            endif()
+        endif()
+    endif()
 endif()
 
 macro(detect_and_find_package package_name)
@@ -93,6 +105,23 @@ endif()
 
 target_compile_options(ggml-sycl PRIVATE "-Wno-narrowing")
 
+message(STATUS "GGML_SYCL_SUPPORT_LEVEL_ZERO ${GGML_SYCL_SUPPORT_LEVEL_ZERO}")
+if (GGML_SYCL_SUPPORT_LEVEL_ZERO)
+    # Link against Level Zero loader for direct device memory allocation.
+    # Avoids sycl::malloc_device triggering DMA-buf/TTM system RAM staging
+    # in the xe kernel driver during multi-GPU inference.
+    find_path(LEVEL_ZERO_INCLUDE_DIR level_zero/ze_api.h HINTS ${ONEAPI_ROOT}/include ${LEVEL_ZERO_V1_SDK_PATH}/include)
+    find_library(ZE_LOADER_LIB ze_loader HINTS ${ONEAPI_ROOT}/lib ${LEVEL_ZERO_V1_SDK_LIB_PATH} ENV LD_LIBRARY_PATH)
+    if(ZE_LOADER_LIB AND LEVEL_ZERO_INCLUDE_DIR)
+        target_link_libraries(ggml-sycl PRIVATE ${ZE_LOADER_LIB})
+        target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_SUPPORT_LEVEL_ZERO)
+        message(STATUS "Level Zero loader found: ${ZE_LOADER_LIB}")
+        message(STATUS "Level Zero headers found: ${LEVEL_ZERO_INCLUDE_DIR}")
+    else()
+        message(WARNING "Level Zero loader or headers not found, Level Zero support disabled")
+    endif()
+endif()
+
 # Link against oneDNN
 set(GGML_SYCL_DNNL 0)
 if(GGML_SYCL_DNN)
@@ -135,7 +164,11 @@ endif()
 
 if (GGML_SYCL_TARGET STREQUAL "INTEL")
     add_compile_definitions(GGML_SYCL_WARP_SIZE=16)
-    target_link_options(ggml-sycl PRIVATE  -Xs   -ze-intel-greater-than-4GB-buffer-required)
+    if (NOT GGML_SYCL_DEVICE_ARCH)
+        target_link_options(ggml-sycl PRIVATE -Xs -ze-intel-greater-than-4GB-buffer-required)
+    else()
+        message(STATUS "Skipping -ze-intel-greater-than-4GB-buffer-required for spir64_gen AOT")
+    endif()
 
     # Link against Intel oneMKL
     if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
@@ -160,7 +193,15 @@ if (GGML_SYCL_HOST_MEM_FALLBACK)
 endif()
 
 if (GGML_SYCL_DEVICE_ARCH)
-    target_compile_options(ggml-sycl PRIVATE -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH})
-    target_link_options(ggml-sycl PRIVATE -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH})
+    message(STATUS "GGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} (AOT via spir64_gen)")
+    target_compile_options(
+        ggml-sycl PRIVATE
+        -fsycl-targets=spir64_gen
+        "SHELL:-Xsycl-target-backend=spir64_gen \"-device ${GGML_SYCL_DEVICE_ARCH}\""
+    )
+    target_link_options(
+        ggml-sycl PRIVATE
+        -fsycl-targets=spir64_gen
+        "SHELL:-Xsycl-target-backend=spir64_gen \"-device ${GGML_SYCL_DEVICE_ARCH}\""
+    )
 endif()
-
diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp
index 05fd5ef46c7..ae08abad81b 100644
--- a/ggml/src/ggml-sycl/common.cpp
+++ b/ggml/src/ggml-sycl/common.cpp
@@ -11,6 +11,10 @@
 //
 
 #include "common.hpp"
+#include <sycl/backend.hpp>
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#include <level_zero/ze_api.h>
+#endif
 
 #include "ggml-backend-impl.h"
 #include "ggml-impl.h"
@@ -55,6 +59,20 @@ bool gpu_has_xmx(sycl::device &dev) {
     return dev.has(sycl::aspect::ext_intel_matrix);
 }
 
+static int ggml_sycl_get_env(const char *env_name, int default_val) {
+    char *user_device_string = getenv(env_name);
+    int user_number = default_val;
+
+    unsigned n;
+    if (user_device_string != NULL &&
+        sscanf(user_device_string, " %u", &n) == 1) {
+        user_number = (int)n;
+    } else {
+        user_number = default_val;
+    }
+    return user_number;
+}
+
 int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block_size) {
   const int64_t max_range = std::numeric_limits<int>::max();
   int64_t sycl_down_blk_size = block_size;
@@ -66,6 +84,61 @@ int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block
   return sycl_down_blk_size;
 }
 
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+static bool ggml_sycl_use_level_zero_device_alloc(sycl::queue &q) {
+    return ggml_sycl_get_env("GGML_SYCL_ENABLE_LEVEL_ZERO", 1) &&
+        q.get_device().is_gpu() &&
+        q.get_backend() == sycl::backend::ext_oneapi_level_zero;
+}
+#endif
+
+// Use Level Zero zeMemAllocDevice to avoid sycl::malloc_device triggering
+// DMA-buf/TTM system RAM staging in the xe kernel driver during multi-GPU inference.
+// The decision is made from the queue and runtime env because large buffers can be
+// allocated before ggml_check_sycl() initializes g_ggml_sycl_enable_level_zero.
+void * ggml_sycl_malloc_device(size_t size, sycl::queue &q) {
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+    if (ggml_sycl_use_level_zero_device_alloc(q)) {
+        void *ptr = nullptr;
+        auto ze_ctx = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q.get_context());
+        auto ze_dev = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q.get_device());
+#ifdef ZE_RELAXED_ALLOCATION_LIMITS_EXP_NAME
+        ze_relaxed_allocation_limits_exp_desc_t relaxed_desc = {
+            ZE_STRUCTURE_TYPE_RELAXED_ALLOCATION_LIMITS_EXP_DESC,
+            nullptr,
+            ZE_RELAXED_ALLOCATION_LIMITS_EXP_FLAG_MAX_SIZE,
+        };
+        ze_device_mem_alloc_desc_t alloc_desc = {
+            ZE_STRUCTURE_TYPE_DEVICE_MEM_ALLOC_DESC,
+            &relaxed_desc,
+            0,
+            0,
+        };
+#else
+        ze_device_mem_alloc_desc_t alloc_desc = {ZE_STRUCTURE_TYPE_DEVICE_MEM_ALLOC_DESC, nullptr, 0, 0};
+#endif
+        ze_result_t r = zeMemAllocDevice(ze_ctx, &alloc_desc, size, 64, ze_dev, &ptr);
+        if (r == ZE_RESULT_SUCCESS && ptr) {
+            return ptr;
+        }
+        return nullptr;
+    }
+#endif
+    return sycl::malloc_device(size, q);
+}
+
+void ggml_sycl_free_device(void *ptr, sycl::queue &q) {
+    if (!ptr) return;
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+    if (ggml_sycl_use_level_zero_device_alloc(q)) {
+        auto ze_ctx = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q.get_context());
+        zeMemFree(ze_ctx, ptr);
+        return;
+    }
+#endif
+    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, q)));
+}
+
 void release_extra_gpu(ggml_tensor_extra_gpu * extra, std::vector<queue_ptr> streams) {
     for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
         for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) {
@@ -75,8 +148,7 @@ void release_extra_gpu(ggml_tensor_extra_gpu * extra, std::vector<queue_ptr> str
         }
         if (extra->data_device[i] != nullptr && streams.size()>0) {
             ggml_sycl_set_device(i);
-            SYCL_CHECK(
-                CHECK_TRY_ERROR(sycl::free(extra->data_device[i], *(streams[i]))));
+            SYCL_CHECK(CHECK_TRY_ERROR(ggml_sycl_free_device(extra->data_device[i], *(streams[i]))));
         }
     }
     delete extra;
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index fd84c917853..96bc1c98bd9 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -25,9 +25,17 @@
 #include "presets.hpp"
 #include "type.hpp"
 #include "sycl_hw.hpp"
+#include "fattn-buffers.hpp"
 
 namespace syclexp = sycl::ext::oneapi::experimental;
 
+#if defined(__INTEL_LLVM_COMPILER) && __has_include(<sycl/ext/oneapi/bfloat16.hpp>)
+    #include <sycl/ext/oneapi/bfloat16.hpp>
+    #ifndef GGML_SYCL_HAS_BF16
+        #define GGML_SYCL_HAS_BF16
+    #endif
+#endif
+
 #if GGML_SYCL_DNNL
 #include "dnnl.hpp"
 #include "dnnl_sycl.hpp"
@@ -217,7 +225,7 @@ struct sycl_device_info {
                        // cudaOccupancyMaxActiveBlocksPerMultiprocessor
     bool    vmm;                // virtual memory support
     size_t  total_vram;
-    //sycl_hw_info hw_info;     \\ device id and aarch, currently not used
+    sycl_hw_info hw_info;
     optimize_feature opt_feature;
 };
 
@@ -302,6 +310,10 @@ struct ggml_tensor_extra_gpu {
   optimize_feature optimized_feature;
 };
 
+extern int g_ggml_sycl_enable_level_zero;
+void * ggml_sycl_malloc_device(size_t size, sycl::queue &q);
+void ggml_sycl_free_device(void *ptr, sycl::queue &q);
+
 void release_extra_gpu(ggml_tensor_extra_gpu * extra, std::vector<queue_ptr> streams={});
 
 namespace sycl_ex = sycl::ext::oneapi::experimental;
@@ -397,12 +409,16 @@ struct ggml_backend_sycl_context {
     std::unique_ptr<ggml_sycl_pool> pools[GGML_SYCL_MAX_DEVICES];
     std::unordered_map<sycl::queue *, std::unique_ptr<ggml_sycl_pool_alloc<uint8_t>>> scratchpad_map;
 
+    std::unique_ptr<ggml_sycl_fattn_kv_buffers> fattn_bufs[GGML_SYCL_MAX_DEVICES];
+
     std::unique_ptr<ggml_sycl_pool> host_pools[GGML_SYCL_MAX_DEVICES];
 
     static std::unique_ptr<ggml_sycl_pool> new_pool_for_device(queue_ptr qptr, int device);
 
     static std::unique_ptr<ggml_sycl_pool> new_pool_for_host(queue_ptr qptr, int device);
 
+    static std::unique_ptr<ggml_sycl_fattn_kv_buffers> new_fattn_kv_buffers(queue_ptr qptr, int device);
+
     ggml_sycl_pool & pool(int device) {
         if (pools[device] == nullptr) {
             pools[device] = new_pool_for_device(stream(device,0), device);
@@ -414,6 +430,17 @@ struct ggml_backend_sycl_context {
         return pool(device);
     }
 
+    ggml_sycl_fattn_kv_buffers & fattn_buffers(int device) {
+        if (fattn_bufs[device] == nullptr) {
+            fattn_bufs[device] = new_fattn_kv_buffers(stream(device, 0), device);
+        }
+        return *fattn_bufs[device];
+    }
+
+    ggml_sycl_fattn_kv_buffers & fattn_buffers() {
+        return fattn_buffers(device);
+    }
+
 #ifdef GGML_SYCL_GRAPH
     std::unique_ptr<sycl_ex::command_graph<sycl_ex::graph_state::executable>> exec_graph = nullptr;
 #endif
diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp
index f3c521b45f6..576f19d79ae 100644
--- a/ggml/src/ggml-sycl/convert.cpp
+++ b/ggml/src/ggml-sycl/convert.cpp
@@ -2,13 +2,6 @@
 #include "dequantize.hpp"
 #include "presets.hpp"
 
-#if defined(__INTEL_LLVM_COMPILER)
-    #if __has_include(<sycl/ext/oneapi/bfloat16.hpp>)
-        #include <sycl/ext/oneapi/bfloat16.hpp>
-        #define GGML_SYCL_HAS_BF16
-    #endif
-#endif
-
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k,
                              const sycl::nd_item<3> &item_ct1) {
@@ -259,6 +252,23 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int64_t k,
 #endif
 }
 
+template <typename dst_t>
+static void dequantize_row_q5_K_sycl_reorder(const void * vx, dst_t * y, const int64_t k, dpct::queue_ptr stream) {
+    const int64_t nb = k / QK_K;
+
+    dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
+
+    stream->submit([&](sycl::handler & cgh) {
+        sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(K_SCALE_SIZE), cgh);
+
+        cgh.parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
+            [=](sycl::nd_item<3> item_ct1) {
+                dequantize_block_q5_K_reorder(vx, y, get_pointer(scale_local_acc), item_ct1, nb);
+            });
+    });
+}
+
 template <typename dst_t>
 static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int64_t k,
                                      dpct::queue_ptr stream) {
@@ -650,7 +660,11 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
                 return dequantize_row_q4_K_sycl;
             }
         case GGML_TYPE_Q5_K:
-            return dequantize_row_q5_K_sycl;
+            if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
+                return dequantize_row_q5_K_sycl_reorder;
+            } else {
+                return dequantize_row_q5_K_sycl;
+            }
         case GGML_TYPE_Q6_K:
             if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
                 return dequantize_row_q6_K_sycl_reorder;
@@ -725,7 +739,11 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
                 return dequantize_row_q4_K_sycl;
             }
         case GGML_TYPE_Q5_K:
-            return dequantize_row_q5_K_sycl;
+            if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
+                return dequantize_row_q5_K_sycl_reorder;
+            } else {
+                return dequantize_row_q5_K_sycl;
+            }
         case GGML_TYPE_Q6_K:
             if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
                 return dequantize_row_q6_K_sycl_reorder;
@@ -767,6 +785,22 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
 }
 
 
+#ifdef GGML_SYCL_HAS_BF16
+to_bf16_sycl_t ggml_get_to_bf16_sycl(ggml_type type, ggml_tensor * /*dst*/) {
+    switch (type) {
+        case GGML_TYPE_F32:
+            return convert_unary_sycl<float>;
+        case GGML_TYPE_F16:
+            return convert_unary_sycl<sycl::half>;
+        case GGML_TYPE_BF16:
+            return convert_unary_sycl<sycl::ext::oneapi::bfloat16>;
+        default:
+            GGML_ABORT("fatal error: unsupport data type=%s\n", ggml_type_name(type));
+            return nullptr;
+    }
+}
+#endif
+
 to_fp16_nc_sycl_t ggml_get_to_fp16_nc_sycl(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F32:
diff --git a/ggml/src/ggml-sycl/convert.hpp b/ggml/src/ggml-sycl/convert.hpp
index 6e621f2154d..8de79d10ff6 100644
--- a/ggml/src/ggml-sycl/convert.hpp
+++ b/ggml/src/ggml-sycl/convert.hpp
@@ -23,6 +23,11 @@ typedef to_t_sycl_t<sycl::half> to_fp16_sycl_t;
 to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst);
 to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor * dst);
 
+#ifdef GGML_SYCL_HAS_BF16
+typedef to_t_sycl_t<sycl::ext::oneapi::bfloat16> to_bf16_sycl_t;
+to_bf16_sycl_t ggml_get_to_bf16_sycl(ggml_type type, ggml_tensor * dst);
+#endif
+
 // Nc = Non-contiguous
 template <typename T>
 using to_t_nc_sycl_t = void (*)(const void * x, T * y, int64_t ne00, int64_t ne01, int64_t ne02, int64_t ne03,
@@ -35,15 +40,19 @@ template<typename dst_t, typename src_t>
  inline dst_t ggml_sycl_cast(src_t x) {
     if constexpr (std::is_same_v<dst_t, src_t>) {
         return x;
+#ifdef GGML_SYCL_HAS_BF16
     } else if constexpr (std::is_same_v<dst_t, sycl::ext::oneapi::bfloat16>) {
         return sycl::ext::oneapi::bfloat16(float(x));
     } else if constexpr (std::is_same_v<src_t, sycl::ext::oneapi::bfloat16>) {
         return static_cast<float>(x);
+#endif
     } else if constexpr (std::is_same_v<src_t, sycl::float2> && std::is_same_v<dst_t, sycl::half2>) {
         return x.template convert<sycl::half, sycl::rounding_mode::rte>();
+#ifdef GGML_SYCL_HAS_BF16
     } else if constexpr (std::is_same_v<src_t, sycl::float2> &&
                          std::is_same_v<dst_t, sycl::vec<sycl::ext::oneapi::bfloat16, 2>>) {
         return {x.x, x.y};
+#endif
     } else if constexpr(std::is_same_v<dst_t, int32_t>) {
         return int32_t(x);
     } else {
diff --git a/ggml/src/ggml-sycl/cumsum.cpp b/ggml/src/ggml-sycl/cumsum.cpp
new file mode 100644
index 00000000000..c1c5fe4fe4a
--- /dev/null
+++ b/ggml/src/ggml-sycl/cumsum.cpp
@@ -0,0 +1,148 @@
+#include "cumsum.hpp"
+#include "common.hpp"
+
+#include <algorithm>
+
+#define SYCL_CUMSUM_BLOCK_SIZE 256
+
+static __dpct_inline__ float warp_prefix_inclusive_sum_f32(float x, const sycl::nd_item<3> & item) {
+    return sycl::inclusive_scan_over_group(item.get_sub_group(), x, sycl::plus<float>());
+}
+
+static void cumsum_f32_kernel(
+        const float * __restrict__ src, float * __restrict__ dst,
+        const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
+        const int64_t s01, const int64_t s02, const int64_t s03,
+        const int64_t  d1, const int64_t  d2, const int64_t  d3,
+        const sycl::nd_item<3> & item, float * smem) {
+
+    const int tid = item.get_local_id(2);
+    const int block_size = item.get_local_range(2);
+    const int lane = tid % WARP_SIZE;
+    const int warp = tid / WARP_SIZE;
+    const int warps_per_block = block_size / WARP_SIZE;
+
+    float * s_vals      = smem;
+    float * s_warp_sums = smem + block_size;
+    float * s_carry     = smem + block_size + warps_per_block;
+
+    if (tid == 0) {
+        s_carry[0] = 0.0f;
+    }
+    item.barrier(sycl::access::fence_space::local_space);
+
+    const int64_t i3 = item.get_group(0);
+    const int64_t i2 = item.get_group(1);
+    const int64_t i1 = item.get_group(2);
+    if (i3 >= ne03 || i2 >= ne02 || i1 >= ne01) {
+        return;
+    }
+
+    const float * src_row = src + i1 * s01 + i2 * s02 + i3 * s03;
+    float       * dst_row = dst + i1 * d1  + i2 * d2  + i3 * d3;
+
+    constexpr int num_unroll = 4;
+    float temp[num_unroll];
+
+    for (int64_t i = 0; i < ne00; i += num_unroll * block_size) {
+        int64_t idx = i + tid * num_unroll;
+
+        temp[0] = (idx < ne00 ? src_row[idx] : 0.0f);
+#pragma unroll
+        for (int j = 1; j < num_unroll; j++) {
+            temp[j] = temp[j - 1];
+            if (idx + j < ne00) {
+                temp[j] += src_row[idx + j];
+            }
+        }
+
+        float val = (idx < ne00) ? temp[num_unroll - 1] : 0.0f;
+
+        val = warp_prefix_inclusive_sum_f32(val, item);
+        s_vals[tid] = val;
+
+        if (lane == WARP_SIZE - 1) {
+            s_warp_sums[warp] = val;
+        }
+        item.barrier(sycl::access::fence_space::local_space);
+
+        if (warp == 0) {
+            float w = (tid < warps_per_block) ? s_warp_sums[tid] : 0.0f;
+            float inc = warp_prefix_inclusive_sum_f32(w, item);
+            if (tid < warps_per_block) {
+                s_warp_sums[tid] = inc - w;
+            }
+            if (tid == warps_per_block - 1) {
+                s_carry[1] = inc;
+            }
+        }
+        item.barrier(sycl::access::fence_space::local_space);
+
+        float carry = s_carry[0];
+        float final_offset = s_vals[tid] + s_warp_sums[warp] + carry - temp[num_unroll - 1];
+
+#pragma unroll
+        for (int j = 0; j < num_unroll; j++) {
+            if (idx + j < ne00) {
+                dst_row[idx + j] = temp[j] + final_offset;
+            }
+        }
+
+        item.barrier(sycl::access::fence_space::local_space);
+
+        if (tid == 0) {
+            s_carry[0] += s_carry[1];
+        }
+    }
+}
+
+inline void ggml_sycl_op_cumsum(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    dpct::queue_ptr stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    const float * src_d = static_cast<const float *>(src0->data);
+    float       * dst_d = static_cast<float *>(dst->data);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    const size_t ts = sizeof(float);
+    const int64_t s01 = src0->nb[1] / ts;
+    const int64_t s02 = src0->nb[2] / ts;
+    const int64_t s03 = src0->nb[3] / ts;
+    const int64_t d1  = dst->nb[1] / ts;
+    const int64_t d2  = dst->nb[2] / ts;
+    const int64_t d3  = dst->nb[3] / ts;
+
+    const int num_warps = (ne00 + WARP_SIZE - 1) / WARP_SIZE;
+    int block_size = num_warps * WARP_SIZE;
+    block_size = std::min(block_size, SYCL_CUMSUM_BLOCK_SIZE);
+    const int warps_per_block = block_size / WARP_SIZE;
+    const int smem_size = block_size + warps_per_block + 2;
+
+    const sycl::range<3> grid(ne03, ne02, ne01);
+    const sycl::range<3> block(1, 1, block_size);
+
+    stream->submit([&](sycl::handler & cgh) {
+        sycl::local_accessor<float, 1> smem_acc(sycl::range<1>(smem_size), cgh);
+        cgh.parallel_for(
+            sycl::nd_range<3>(grid * block, block),
+            [=](sycl::nd_item<3> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                cumsum_f32_kernel(src_d, dst_d, ne00, ne01, ne02, ne03,
+                                  s01, s02, s03, d1, d2, d3,
+                                  item, get_pointer(smem_acc));
+            });
+    });
+}
+
+void ggml_sycl_cumsum(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
+    ggml_sycl_op_cumsum(ctx, dst);
+}
diff --git a/ggml/src/ggml-sycl/cumsum.hpp b/ggml/src/ggml-sycl/cumsum.hpp
new file mode 100644
index 00000000000..f1a564472c5
--- /dev/null
+++ b/ggml/src/ggml-sycl/cumsum.hpp
@@ -0,0 +1,5 @@
+#pragma once
+
+#include "common.hpp"
+
+void ggml_sycl_cumsum(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp
index 19fa88680d6..2324bfacd22 100644
--- a/ggml/src/ggml-sycl/dequantize.hpp
+++ b/ggml/src/ggml-sycl/dequantize.hpp
@@ -537,6 +537,63 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri
 #endif
 }
 
+template <typename dst_t>
+static void dequantize_block_q5_K_reorder(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                          uint8_t * scales_local, const sycl::nd_item<3> & item_ct1, int64_t n_blocks) {
+    const int64_t ib = item_ct1.get_group(2);
+
+#if QK_K == 256
+    // assume 64 threads
+    const int64_t tid = item_ct1.get_local_id(2);
+    const int64_t il  = tid / 16;   // 0...3
+    const int64_t ir  = tid % 16;   // 0...15
+    const int64_t is  = 2 * il;
+
+    dst_t * y = yy + ib * QK_K + 64 * il + 2 * ir;
+
+    const uint8_t * base = static_cast<const uint8_t *>(vx);
+
+    // Reordered layout: [qs (QK_K/2 per block)] [qh (QK_K/8 per block)] [scales (K_SCALE_SIZE per block)] [dm (half2 per block)]
+    const size_t qs_offset     = ib * (QK_K / 2);
+    const size_t qh_offset     = n_blocks * (QK_K / 2) + ib * (QK_K / 8);
+    const size_t scales_offset = n_blocks * (QK_K / 2) + n_blocks * (QK_K / 8) + ib * K_SCALE_SIZE;
+    const size_t dm_offset     = n_blocks * (QK_K / 2) + n_blocks * (QK_K / 8) + n_blocks * K_SCALE_SIZE + ib * sizeof(ggml_half2);
+
+    const uint8_t *  qs_ptr     = base + qs_offset;
+    const uint8_t *  qh_ptr     = base + qh_offset;
+    const uint8_t *  scales_ptr = base + scales_offset;
+    const ggml_half2 dm_values  = *reinterpret_cast<const ggml_half2 *>(base + dm_offset);
+
+    const float dall = dm_values.x();
+    const float dmin = dm_values.y();
+
+    const uint8_t * ql = qs_ptr + 32 * il + 2 * ir;
+    const uint8_t * qh = qh_ptr + 2 * ir;
+
+    if (tid < K_SCALE_SIZE) {
+        scales_local[tid] = scales_ptr[tid];
+    }
+
+    item_ct1.barrier(sycl::access::fence_space::local_space);
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, scales_local, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, scales_local, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+
+    uint8_t hm  = 1 << (2 * il);
+    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
+    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
+    hm <<= 1;
+    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
+    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
+#else
+    GGML_UNUSED(ib); GGML_UNUSED(tid); GGML_UNUSED(yy); GGML_UNUSED(scales_local); GGML_UNUSED(n_blocks);
+    GGML_ABORT("Q5_K reorder dequantize not supported for QK_K != 256");
+#endif
+}
+
 template<typename dst_t>
 static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
                                   const sycl::nd_item<3> &item_ct1) {
diff --git a/ggml/src/ggml-sycl/diag.cpp b/ggml/src/ggml-sycl/diag.cpp
new file mode 100644
index 00000000000..c4264fee342
--- /dev/null
+++ b/ggml/src/ggml-sycl/diag.cpp
@@ -0,0 +1,67 @@
+#include "diag.hpp"
+#include "common.hpp"
+
+#define SYCL_DIAG_BLOCK_SIZE 256
+
+template <typename T>
+static void diag_kernel(T * __restrict__ dst, const T * __restrict__ src,
+                        const int64_t ne0, const int64_t ne1,
+                        const int64_t ne2, const int64_t ne3,
+                        const int64_t total_elements,
+                        const sycl::nd_item<1> & item) {
+    const int64_t i = item.get_global_id(0);
+    if (i >= total_elements) {
+        return;
+    }
+
+    const int64_t i0 = i % ne0;
+    const int64_t i1 = (i / ne0) % ne1;
+    const int64_t i2 = (i / (ne0 * ne1)) % ne2;
+    const int64_t i3 = i / (ne0 * ne1 * ne2);
+
+    const int64_t dst_idx = ((i3 * ne2 + i2) * ne1 + i1) * ne0 + i0;
+
+    if (i0 == i1) {
+        const int64_t batch_idx = i3 * ne2 + i2;
+        dst[dst_idx] = src[batch_idx * ne0 + i0];
+    } else {
+        dst[dst_idx] = T(0);
+    }
+
+    (void)ne3;
+}
+
+inline void ggml_sycl_op_diag(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(src0->ne[1] == 1);
+
+    dpct::queue_ptr stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    const void * src0_d = src0->data;
+    void * dst_d = dst->data;
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    const int64_t ne3 = dst->ne[3];
+    const int64_t n_elems = ggml_nelements(dst);
+    const int64_t num_blocks = (n_elems + SYCL_DIAG_BLOCK_SIZE - 1) / SYCL_DIAG_BLOCK_SIZE;
+
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    stream->parallel_for(
+        sycl::nd_range<1>(num_blocks * SYCL_DIAG_BLOCK_SIZE, SYCL_DIAG_BLOCK_SIZE),
+        [=](sycl::nd_item<1> item) {
+            diag_kernel(static_cast<float *>(dst_d),
+                        static_cast<const float *>(src0_d),
+                        ne0, ne1, ne2, ne3, n_elems, item);
+        });
+}
+
+void ggml_sycl_diag(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
+    ggml_sycl_op_diag(ctx, dst);
+}
diff --git a/ggml/src/ggml-sycl/diag.hpp b/ggml/src/ggml-sycl/diag.hpp
new file mode 100644
index 00000000000..20d7ce4895d
--- /dev/null
+++ b/ggml/src/ggml-sycl/diag.hpp
@@ -0,0 +1,5 @@
+#pragma once
+
+#include "common.hpp"
+
+void ggml_sycl_diag(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-sycl/fattn-buffers.cpp b/ggml/src/ggml-sycl/fattn-buffers.cpp
new file mode 100644
index 00000000000..46cf6d551f1
--- /dev/null
+++ b/ggml/src/ggml-sycl/fattn-buffers.cpp
@@ -0,0 +1,56 @@
+//
+// MIT license
+// Copyright (C) 2025 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include "common.hpp"
+
+sycl::half * ggml_sycl_fattn_kv_buffers::kv_buffer::ensure_half(size_t n_elems) {
+    const size_t need_bytes = n_elems * sizeof(sycl::half);
+
+    if (capacity >= need_bytes) {
+        return ptr;
+    }
+
+    if (ptr) {
+        SYCL_CHECK(CHECK_TRY_ERROR(qptr->wait()));
+        SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr)));
+        ptr = nullptr;
+        capacity = 0;
+    }
+
+    size_t cap = 0;
+    while (cap < need_bytes) {
+        cap += CHUNK_SIZE;
+    }
+
+    void * dev_ptr;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(dev_ptr = sycl::malloc_device(
+                        cap, *qptr)));
+
+    if (!dev_ptr) {
+        GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on device\n", __func__, cap);
+        GGML_ABORT("fattn buffer alloc failed");
+    }
+
+    ptr = static_cast<sycl::half *>(dev_ptr);
+    capacity = cap;
+    return ptr;
+}
+
+ggml_sycl_fattn_kv_buffers::kv_buffer::~kv_buffer() {
+#ifdef DEBUG_SYCL_POOL
+    GGML_LOG_INFO("ggml_sycl_fattn_kv_buffer[%d]: %.2f MiB\n", device, capacity / 1024.0 / 1024.0);
+#endif
+    if (ptr) {
+        SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr)));
+    }
+}
diff --git a/ggml/src/ggml-sycl/fattn-buffers.hpp b/ggml/src/ggml-sycl/fattn-buffers.hpp
new file mode 100644
index 00000000000..c00461de620
--- /dev/null
+++ b/ggml/src/ggml-sycl/fattn-buffers.hpp
@@ -0,0 +1,63 @@
+//
+// MIT license
+// Copyright (C) 2025 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_FATTN_BUFFERS_HPP
+#define GGML_SYCL_FATTN_BUFFERS_HPP
+
+#include <sycl/sycl.hpp>
+
+typedef sycl::queue *queue_ptr;
+
+struct ggml_sycl_fattn_kv_buffers {
+    // buffers grow in chunks of this size
+    static constexpr size_t CHUNK_SIZE = 16ull << 20; // 16 MiB
+
+    struct kv_buffer {
+        kv_buffer(queue_ptr qptr_, int device_) : qptr(qptr_), device(device_) {}
+        ~kv_buffer();
+
+        kv_buffer(const kv_buffer &) = delete;
+        kv_buffer & operator=(const kv_buffer &) = delete;
+
+        sycl::half * ensure_half(size_t n_elems);
+
+    private:
+        sycl::half * ptr      = nullptr;
+        size_t       capacity = 0;
+        queue_ptr    qptr     = nullptr;
+        [[maybe_unused]] int device = 0;
+    };
+
+    kv_buffer K;
+    kv_buffer V;
+
+    ggml_sycl_fattn_kv_buffers(queue_ptr qptr, int device) : K(qptr, device), V(qptr, device) {}
+
+    ggml_sycl_fattn_kv_buffers(const ggml_sycl_fattn_kv_buffers &) = delete;
+    ggml_sycl_fattn_kv_buffers & operator=(const ggml_sycl_fattn_kv_buffers &) = delete;
+};
+
+/**
+ * Imitates `ggml_sycl_pool_alloc` to keep the code calling alloc unchanged.
+ */
+struct ggml_sycl_fattn_alloc {
+    ggml_sycl_fattn_kv_buffers::kv_buffer & buf;
+    sycl::half *                         ptr = nullptr;
+
+    explicit ggml_sycl_fattn_alloc(ggml_sycl_fattn_kv_buffers::kv_buffer & buf_) : buf(buf_) {}
+
+    sycl::half * alloc(size_t n_elems) {
+        ptr = buf.ensure_half(n_elems);
+        return ptr;
+    }
+};
+#endif
diff --git a/ggml/src/ggml-sycl/fattn-common.hpp b/ggml/src/ggml-sycl/fattn-common.hpp
index ed00d03c3b6..03f0c2623c8 100644
--- a/ggml/src/ggml-sycl/fattn-common.hpp
+++ b/ggml/src/ggml-sycl/fattn-common.hpp
@@ -5,6 +5,7 @@
 #include "common.hpp"
 #include "convert.hpp"
 #include "vecdotq.hpp"
+#include "fattn-buffers.hpp"
 
 #include "ggml.h"
 
@@ -918,12 +919,13 @@ void launch_fattn(
     GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
 
     ggml_sycl_pool & pool = ctx.pool();
+    ggml_sycl_fattn_kv_buffers & fbuf = ctx.fattn_buffers();
     dpct::queue_ptr  main_stream = ctx.stream();
     const int id  = ggml_sycl_get_device();
     const int nsm = ggml_sycl_info().devices[id].nsm;
 
-    ggml_sycl_pool_alloc<sycl::half>   K_f16(pool);
-    ggml_sycl_pool_alloc<sycl::half>   V_f16(pool);
+    ggml_sycl_fattn_alloc        K_f16(fbuf.K);
+    ggml_sycl_fattn_alloc        V_f16(fbuf.V);
     ggml_sycl_pool_alloc<int>    KV_max(pool);
     ggml_sycl_pool_alloc<float>  dst_tmp(pool);
     ggml_sycl_pool_alloc<sycl::float2> dst_tmp_meta(pool);
diff --git a/ggml/src/ggml-sycl/fill.cpp b/ggml/src/ggml-sycl/fill.cpp
new file mode 100644
index 00000000000..28e618e4ef5
--- /dev/null
+++ b/ggml/src/ggml-sycl/fill.cpp
@@ -0,0 +1,55 @@
+#include "fill.hpp"
+#include "common.hpp"
+
+#define SYCL_FILL_BLOCK_SIZE 256
+
+template <typename T>
+static void fill_kernel(T * dst, const int64_t k, const T value,
+                        const sycl::nd_item<1> & item) {
+    const int64_t i = (int64_t)item.get_global_id(0);
+    if (i >= k) {
+        return;
+    }
+    dst[i] = value;
+}
+
+inline void ggml_sycl_op_fill(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    dpct::queue_ptr stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    float value;
+    memcpy(&value, dst->op_params, sizeof(float));
+
+    const int64_t k = ggml_nelements(dst);
+    const int64_t num_blocks = (k + SYCL_FILL_BLOCK_SIZE - 1) / SYCL_FILL_BLOCK_SIZE;
+    void * dst_d = dst->data;
+
+    switch (dst->type) {
+        case GGML_TYPE_F32:
+            stream->parallel_for(
+                sycl::nd_range<1>(num_blocks * SYCL_FILL_BLOCK_SIZE, SYCL_FILL_BLOCK_SIZE),
+                [=](sycl::nd_item<1> item) {
+                    fill_kernel(static_cast<float *>(dst_d), k, value, item);
+                });
+            break;
+        case GGML_TYPE_F16:
+            {
+                sycl::half h_value = sycl::half(value);
+                stream->parallel_for(
+                    sycl::nd_range<1>(num_blocks * SYCL_FILL_BLOCK_SIZE, SYCL_FILL_BLOCK_SIZE),
+                    [=](sycl::nd_item<1> item) {
+                        fill_kernel(static_cast<sycl::half *>(dst_d), k, h_value, item);
+                    });
+            }
+            break;
+        default:
+            GGML_ABORT("unsupported type");
+    }
+}
+
+void ggml_sycl_fill(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/0);
+    ggml_sycl_op_fill(ctx, dst);
+}
diff --git a/ggml/src/ggml-sycl/fill.hpp b/ggml/src/ggml-sycl/fill.hpp
new file mode 100644
index 00000000000..b2adb94ff52
--- /dev/null
+++ b/ggml/src/ggml-sycl/fill.hpp
@@ -0,0 +1,5 @@
+#pragma once
+
+#include "common.hpp"
+
+void ggml_sycl_fill(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-sycl/gated_delta_net.hpp b/ggml/src/ggml-sycl/gated_delta_net.hpp
index a3308ee8763..350b4ce2f66 100644
--- a/ggml/src/ggml-sycl/gated_delta_net.hpp
+++ b/ggml/src/ggml-sycl/gated_delta_net.hpp
@@ -5,4 +5,5 @@
 #include "common.hpp"
 #include "ggml.h"
 
+void ggml_sycl_op_gated_delta_net(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 void ggml_sycl_gated_delta_net(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-sycl/gemm.hpp b/ggml/src/ggml-sycl/gemm.hpp
index dcf6c7aeeb4..c202da110be 100644
--- a/ggml/src/ggml-sycl/gemm.hpp
+++ b/ggml/src/ggml-sycl/gemm.hpp
@@ -29,6 +29,9 @@ class DnnlGemmWrapper {
     static constexpr dt to_dt() {
         if constexpr (std::is_same_v<T, float>) return dt::f32;
         else if constexpr (std::is_same_v<T, sycl::half>) return dt::f16;
+#ifdef GGML_SYCL_HAS_BF16
+        else if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) return dt::bf16;
+#endif
         else static_assert(0);
     }
 
diff --git a/ggml/src/ggml-sycl/getrows.cpp b/ggml/src/ggml-sycl/getrows.cpp
index 03f8dd90748..ca457454775 100644
--- a/ggml/src/ggml-sycl/getrows.cpp
+++ b/ggml/src/ggml-sycl/getrows.cpp
@@ -183,6 +183,10 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
             get_rows_sycl_float(ctx, dst->src[0], dst->src[1], dst, (const sycl::half *)dst->src[0]->data,
                                 src1_i32, (float *)dst->data, ctx.stream());
             break;
+        case GGML_TYPE_BF16:
+            get_rows_sycl_float(ctx, dst->src[0], dst->src[1], dst, (const sycl::ext::oneapi::bfloat16 *)dst->src[0]->data,
+                                src1_i32, (float *)dst->data, ctx.stream());
+            break;
         case GGML_TYPE_F32:
             get_rows_sycl_float(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data,
             src1_i32, (float *)dst->data, ctx.stream());
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index c02a41ad862..f5d10b56de0 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -30,6 +30,10 @@
 #include <regex>
 
 #include <sycl/sycl.hpp>
+#include <sycl/backend.hpp>
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#include <level_zero/ze_api.h>
+#endif
 #if defined(GGML_SYCL_GRAPH) && SYCL_EXT_ONEAPI_ASYNC_MEMORY_ALLOC
 #    include <sycl/ext/oneapi/experimental/async_alloc/async_alloc.hpp>
 #endif
@@ -54,7 +58,12 @@
 #include "ggml-sycl/set.hpp"
 #include "ggml-sycl/ssm_conv.hpp"
 #include "ggml-sycl/sycl_hw.hpp"
-
+#include "ggml-sycl/ssm_scan.hpp"
+#include "ggml-sycl/fill.hpp"
+#include "ggml-sycl/cumsum.hpp"
+#include "ggml-sycl/diag.hpp"
+#include "ggml-sycl/solve_tri.hpp"
+#include "ggml-sycl/gated_delta_net.hpp"
 
 static bool g_sycl_loaded = false;
 int g_ggml_sycl_debug = 0;
@@ -63,6 +72,7 @@ int g_ggml_sycl_disable_graph = 0;
 int g_ggml_sycl_disable_dnn = 0;
 int g_ggml_sycl_prioritize_dmmv = 0;
 int g_ggml_sycl_use_async_mem_op = 0;
+int g_ggml_sycl_enable_level_zero = 0;
 int g_ggml_sycl_enable_flash_attention = 1;
 
 
@@ -104,6 +114,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
 
         info.max_work_group_sizes[i] = prop.get_max_work_group_size();
         info.devices[i].max_wg_per_cu = info.max_work_group_sizes[i] / prop.get_max_compute_units();
+        info.devices[i].hw_info = get_device_hw_info(&device);
 
     }
 
@@ -217,6 +228,27 @@ static void ggml_check_sycl() try {
         g_ggml_sycl_disable_graph = get_sycl_env("GGML_SYCL_DISABLE_GRAPH", 1);
         g_ggml_sycl_disable_dnn = get_sycl_env("GGML_SYCL_DISABLE_DNN", 0);
         g_ggml_sycl_prioritize_dmmv = get_sycl_env("GGML_SYCL_PRIORITIZE_DMMV", 0);
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+        g_ggml_sycl_enable_level_zero = get_sycl_env("GGML_SYCL_ENABLE_LEVEL_ZERO", 1);
+#else
+        g_ggml_sycl_enable_level_zero = 0;
+#endif
+        if (g_ggml_sycl_enable_level_zero) {
+            // Verify all GPU devices use the Level Zero backend before enabling L0 APIs.
+            // Only check GPU devices; CPU devices use OpenCL and would otherwise
+            // disable Level Zero for the GPUs on systems without ONEAPI_DEVICE_SELECTOR set.
+            for (unsigned int i = 0; i < dpct::dev_mgr::instance().device_count(); i++) {
+                auto & q = dpct::dev_mgr::instance().get_device(i).default_queue();
+                if (!q.get_device().is_gpu()) {
+                    continue;
+                }
+                if (q.get_backend() != sycl::backend::ext_oneapi_level_zero) {
+                    GGML_LOG_WARN("SYCL GPU device %d does not use Level Zero backend, disabling Level Zero memory API\n", i);
+                    g_ggml_sycl_enable_level_zero = 0;
+                    break;
+                }
+            }
+        }
 
 #ifdef SYCL_FLASH_ATTN
         g_ggml_sycl_enable_flash_attention = get_sycl_env("GGML_SYCL_ENABLE_FLASH_ATTN", 1);
@@ -247,6 +279,11 @@ static void ggml_check_sycl() try {
 #else
         GGML_LOG_INFO("  GGML_SYCL_DNNL: no\n");
 #endif
+#if defined(GGML_SYCL_SUPPORT_LEVEL_ZERO)
+        GGML_LOG_INFO("  GGML_SYCL_SUPPORT_LEVEL_ZERO: yes\n");
+#else
+        GGML_LOG_INFO("  GGML_SYCL_SUPPORT_LEVEL_ZERO: no\n");
+#endif
 
         GGML_LOG_INFO("Running with Environment Variables:\n");
         GGML_LOG_INFO("  GGML_SYCL_DEBUG: %d\n", g_ggml_sycl_debug);
@@ -256,6 +293,11 @@ static void ggml_check_sycl() try {
 #else
         GGML_LOG_INFO("  GGML_SYCL_DISABLE_GRAPH: graph disabled by compile flag\n");
 #endif
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_LEVEL_ZERO: %d\n", g_ggml_sycl_enable_level_zero);
+#else
+        GGML_LOG_INFO("  GGML_SYCL_ENABLE_LEVEL_ZERO: Level Zero disabled by compile flag\n");
+#endif
 #if GGML_SYCL_DNNL
         GGML_LOG_INFO("  GGML_SYCL_DISABLE_DNN: %d\n", g_ggml_sycl_disable_dnn);
 #else
@@ -365,7 +407,7 @@ struct ggml_backend_sycl_buffer_context {
     ~ggml_backend_sycl_buffer_context() {
         if (dev_ptr != nullptr) {
             ggml_sycl_set_device(device);
-            SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(dev_ptr, *stream)));
+            SYCL_CHECK(CHECK_TRY_ERROR(ggml_sycl_free_device(dev_ptr, *stream)));
         }
 
         //release extra used by tensors
@@ -498,8 +540,43 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+static bool ggml_sycl_is_l0_discrete_gpu(sycl::queue &q) {
+    if (!q.get_device().is_gpu() || q.get_backend() != sycl::backend::ext_oneapi_level_zero) {
+        return false;
+    }
+
+    ze_device_handle_t ze_dev = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q.get_device());
+    ze_device_properties_t props = {};
+    props.stype = ZE_STRUCTURE_TYPE_DEVICE_PROPERTIES;
+    ze_result_t r = zeDeviceGetProperties(ze_dev, &props);
+    return r == ZE_RESULT_SUCCESS && !(props.flags & ZE_DEVICE_PROPERTY_FLAG_INTEGRATED);
+}
+#endif
+
 static void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst,
                     const void *ptr_src, size_t size) {
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+    // Use Level Zero direct copy for dGPU-to-dGPU transfers.
+    const bool l0_copy_supported =
+        ggml_sycl_is_l0_discrete_gpu(q_dst) && ggml_sycl_is_l0_discrete_gpu(q_src);
+    if (g_ggml_sycl_enable_level_zero && l0_copy_supported) {
+        auto ze_ctx = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q_dst.get_context());
+        auto ze_dev = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q_dst.get_device());
+        ze_command_queue_desc_t cq_desc = {ZE_STRUCTURE_TYPE_COMMAND_QUEUE_DESC, nullptr, 0, 0,
+                                           0, ZE_COMMAND_QUEUE_MODE_SYNCHRONOUS, ZE_COMMAND_QUEUE_PRIORITY_NORMAL};
+        ze_command_list_handle_t cl;
+        ze_result_t r = zeCommandListCreateImmediate(ze_ctx, ze_dev, &cq_desc, &cl);
+        if (r == ZE_RESULT_SUCCESS) {
+            r = zeCommandListAppendMemoryCopy(cl, ptr_dst, ptr_src, size, nullptr, 0, nullptr);
+            zeCommandListDestroy(cl);
+            if (r == ZE_RESULT_SUCCESS) {
+                return;
+            }
+        }
+    }
+#endif
+    // Host-staged copy
     char *host_buf = (char *)malloc(size);
     q_src.memcpy(host_buf, (const char *)ptr_src, size).wait();
     q_dst.memcpy((char *)ptr_dst, host_buf, size).wait();
@@ -669,8 +746,7 @@ ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
     size = std::max(size, (size_t)1); // syclMalloc returns null for size 0
 
     void * dev_ptr;
-    SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device(
-                                    size, *stream)));
+    SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)ggml_sycl_malloc_device(size, *stream)));
     if (!dev_ptr) {
       GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on device\n", __func__, size);
       return nullptr;
@@ -911,18 +987,10 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
             size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
         }
 
-        // FIXME: do not crash if SYCL Buffer alloc fails
-        // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
         ggml_sycl_set_device(i);
         const queue_ptr stream = ctx->streams[i];
         char * buf;
-        /*
-        DPCT1009:208: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-        SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device(
-                                        size, *stream)));
+        SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)ggml_sycl_malloc_device(size, *stream)));
         if (!buf) {
             char err_buf[1024];
             snprintf(err_buf, 1023, "%s: can't allocate %lu Bytes of memory on device\n", __func__, size);
@@ -1280,16 +1348,53 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool {
     explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : device(device_), qptr(qptr_) {}
 
     ~ggml_sycl_pool_leg() {
+#ifdef DEBUG_SYCL_POOL
+        int    n_cached    = 0;
+        size_t bytes_cached = 0;
+        for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
+            if (buffer_pool[i].ptr != nullptr) {
+                ++n_cached;
+                bytes_cached += buffer_pool[i].size;
+            }
+        }
+        GGML_LOG_INFO("%s: %d buffers, cached = %.2f MiB\n", __func__,
+                      n_cached, bytes_cached / 1024.0 / 1024.0);
+        const auto slots = format_slots_in_alloc_order();
+        if (!slots.empty()) {
+            GGML_LOG_INFO("%s: slots MiB: %s\n", __func__, slots.c_str());
+        }
+#endif
+
         for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
             ggml_sycl_buffer & b = buffer_pool[i];
             if (b.ptr != nullptr) {
-                SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr)));
+                SYCL_CHECK(CHECK_TRY_ERROR(ggml_sycl_free_device(b.ptr, *qptr)));
                 pool_size -= b.size;
             }
         }
         GGML_ASSERT(pool_size == 0);
     }
 
+#ifdef DEBUG_SYCL_POOL
+    std::string format_slots_in_alloc_order() const {
+        std::string line;
+        char buf[32];
+        bool first = true;
+        for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
+            if (buffer_pool[i].ptr == nullptr) {
+                continue;
+            }
+            if (!first) {
+                line += '/';
+            }
+            first = false;
+            snprintf(buf, sizeof(buf), "%.2f", buffer_pool[i].size / 1024.0 / 1024.0);
+            line += buf;
+        }
+        return line;
+    }
+#endif
+
     void * alloc(size_t size, size_t * actual_size) override {
 #ifdef DEBUG_sycl_MALLOC
         int nnz = 0;
@@ -1331,9 +1436,7 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool {
         void * ptr;
         size_t look_ahead_size = (size_t) (1.05 * size);
 
-        SYCL_CHECK(
-            CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device(
-                                look_ahead_size, *qptr)));
+        SYCL_CHECK(CHECK_TRY_ERROR(ptr = (void *)ggml_sycl_malloc_device(look_ahead_size, *qptr)));
         if (!ptr) {
             GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on device/GPU\n", __func__, look_ahead_size);
             return nullptr;
@@ -1361,7 +1464,7 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool {
             }
         }
         GGML_LOG_WARN("WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n");
-        SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr)));
+        SYCL_CHECK(CHECK_TRY_ERROR(ggml_sycl_free_device(ptr, *qptr)));
         pool_size -= size;
     }
 };
@@ -1453,6 +1556,10 @@ std::unique_ptr<ggml_sycl_pool> ggml_backend_sycl_context::new_pool_for_device(q
    return std::unique_ptr<ggml_sycl_pool>(new ggml_sycl_pool_leg(qptr, device));
 }
 
+std::unique_ptr<ggml_sycl_fattn_kv_buffers> ggml_backend_sycl_context::new_fattn_kv_buffers(queue_ptr qptr, int device) {
+    return std::unique_ptr<ggml_sycl_fattn_kv_buffers>(new ggml_sycl_fattn_kv_buffers(qptr, device));
+}
+
 // TBD pool with virtual memory management
 // struct ggml_sycl_pool_vmm : public ggml_sycl_pool
 
@@ -2176,6 +2283,31 @@ inline void ggml_sycl_op_mul_mat_sycl(
 #else
     bool use_fp16 = false;
 #endif
+
+#if GGML_SYCL_DNNL && defined(GGML_SYCL_HAS_BF16)
+    // Fast path for bf16 src0
+    if (src0->type == GGML_TYPE_BF16 && !g_ggml_sycl_disable_dnn && ggml_is_contiguous(src0) &&
+        row_diff == src0->ne[1]) {
+        using bf16_t = sycl::ext::oneapi::bfloat16;
+        ggml_sycl_pool_alloc<bf16_t> src1_as_bf16(ctx.pool(), src1_ncols*ne10);
+        if (src1->type != GGML_TYPE_BF16) {
+            const to_bf16_sycl_t to_bf16_sycl = ggml_get_to_bf16_sycl(src1->type, dst);
+            GGML_ASSERT(to_bf16_sycl != nullptr);
+            to_bf16_sycl(src1_ddf_i, src1_as_bf16.get(), src1_ncols*ne10, stream);
+        } else {
+            stream->memcpy(src1_as_bf16.get(), src1_ddf_i, src1_ncols*ne10*sizeof(bf16_t));
+        }
+        DnnlGemmWrapper::row_gemm(ctx, row_diff, src1_ncols, ne10,
+                                  src0_dd_i, DnnlGemmWrapper::to_dt<bf16_t>(),
+                                  src1_as_bf16.get(), DnnlGemmWrapper::to_dt<bf16_t>(),
+                                  dst_dd_i, DnnlGemmWrapper::to_dt<float>(), stream);
+        GGML_UNUSED(dst);
+        GGML_UNUSED(src1_ddq_i);
+        GGML_UNUSED(src1_padded_row_size);
+        return;
+    }
+#endif
+
     if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && use_fp16 && ggml_is_contiguous(src0) &&
         row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
         ggml_sycl_pool_alloc<sycl::half> src0_as_f16(ctx.pool());
@@ -3272,6 +3404,7 @@ inline bool ggml_sycl_supports_reorder_mul_mat_sycl(enum ggml_type type) {
         case GGML_TYPE_Q8_0:
             return true;
         case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
             return !g_ggml_sycl_prioritize_dmmv;
         default:
@@ -3294,6 +3427,7 @@ inline bool ggml_sycl_supports_reorder_mmvq(enum ggml_type type) {
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
             return true;
         default:
@@ -3331,7 +3465,7 @@ static inline void * sycl_ext_malloc_device(dpct::queue_ptr stream, size_t size)
     // If async allocation extension is not available, use_async should always be false.
     GGML_ASSERT(!use_async);
 #endif
-    return sycl::malloc(size, *stream, sycl::usm::alloc::device);
+    return ggml_sycl_malloc_device(size, *stream);
 }
 
 static inline void sycl_ext_free(dpct::queue_ptr stream, void * ptr) {
@@ -3345,7 +3479,7 @@ static inline void sycl_ext_free(dpct::queue_ptr stream, void * ptr) {
     // If async allocation extension is not available, use_async should always be false.
     GGML_ASSERT(!use_async);
 #endif
-    sycl::free(ptr, *stream);
+    ggml_sycl_free_device(ptr, *stream);
 }
 
 // RAII wrapper for temporary reorder buffers with optional host memory fallback.
@@ -3510,6 +3644,54 @@ static bool reorder_qw_q4_k(uint8_t * data_device, size_t size, size_t offset, d
     return true;
 }
 
+static bool reorder_qw_q5_k(uint8_t * data_device, size_t size, size_t offset, dpct::queue_ptr stream) {
+    GGML_ASSERT(size % sizeof(block_q5_K) == 0);
+    GGML_ASSERT(offset % sizeof(block_q5_K) == 0);
+
+    const int nblocks = size / sizeof(block_q5_K);
+
+    sycl_reorder_temp_buffer tmp(stream, size);
+    if (!tmp) {
+        GGML_LOG_WARN("%s: failed to allocate %zu bytes for reorder temp buffer, skipping reorder\n", __func__, size);
+        return false;
+    }
+    uint8_t * tmp_buf = static_cast<uint8_t *>(tmp.ptr);
+
+    sycl::event copy_event;
+    SYCL_CHECK(CHECK_TRY_ERROR(copy_event = stream->memcpy(tmp_buf, data_device, size)));
+    if (!g_ggml_sycl_use_async_mem_op) {
+        copy_event.wait();
+    }
+
+    auto * qs_ptr     = data_device;
+    auto * qh_ptr     = qs_ptr + (QK_K / 2) * nblocks;
+    auto * scales_ptr = qh_ptr + (QK_K / 8) * nblocks;
+    auto * dm_ptr     = (sycl::half2 *) (scales_ptr + K_SCALE_SIZE * nblocks);
+
+    auto reorder_event = stream->parallel_for(nblocks, [=](auto i) {
+        const block_q5_K * x  = (const block_q5_K *) tmp_buf;
+        const int          ib = i;
+
+        for (int j = 0; j < QK_K / 2; ++j) {
+            qs_ptr[ib * (QK_K / 2) + j] = x[ib].qs[j];
+        }
+
+        for (int j = 0; j < QK_K / 8; ++j) {
+            qh_ptr[ib * (QK_K / 8) + j] = x[ib].qh[j];
+        }
+
+        for (int j = 0; j < K_SCALE_SIZE; ++j) {
+            scales_ptr[ib * K_SCALE_SIZE + j] = x[ib].scales[j];
+        }
+
+        dm_ptr[ib] = x[ib].dm;
+    });
+    if (!g_ggml_sycl_use_async_mem_op) {
+        reorder_event.wait_and_throw();
+    }
+    return true;
+}
+
 static bool reorder_qw_q6_k(uint8_t * data_device, size_t size, size_t offset, dpct::queue_ptr stream) {
     GGML_ASSERT(size % sizeof(block_q6_K) == 0);
     GGML_ASSERT(offset % sizeof(block_q6_K) == 0);
@@ -3576,6 +3758,8 @@ static bool reorder_qw(const ggml_tensor * src0, dpct::queue_ptr stream) {
             return reorder_qw_q8_0(data_device, ncols, nrows, size, 0, stream);
         case GGML_TYPE_Q4_K:
             return reorder_qw_q4_k(data_device, size, 0, stream);
+        case GGML_TYPE_Q5_K:
+            return reorder_qw_q5_k(data_device, size, 0, stream);
         case GGML_TYPE_Q6_K:
             return reorder_qw_q6_k(data_device, size, 0, stream);
         default:
@@ -3678,9 +3862,16 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
     // Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization
     // is enabled takes precedence over DMMV, the current if-else implementation
     // requires disabling DMMV if both conditions are met
+
     if (!g_ggml_sycl_prioritize_dmmv && ((should_reorder_tensor(ctx, dst) &&
                                           ggml_sycl_supports_reorder_mmvq(src0->type)))) {
-        use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
+      // Arc770 get benefit with Q4_0 by skipping it.
+      if (!(ggml_sycl_info().devices[ctx.device].hw_info.arch ==
+                gpu_arch::intel_gpu_acm_g10 &&
+            src0->type == GGML_TYPE_Q4_0)) {
+        use_dequantize_mul_mat_vec =
+            use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
+      }
     }
 
     if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
@@ -3783,6 +3974,51 @@ __dpct_inline__ static void k_copy_dst_from_contiguous(
     }
 }
 
+// Fused MoE TG fast path. Returns false to fall back to the per-expert loop below.
+static bool ggml_sycl_mul_mat_id_mmvq_fused(
+    ggml_backend_sycl_context & ctx, const ggml_tensor * src0,
+    const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst)
+{
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    if (ne12 != 1) return false;
+    if (src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) return false;
+    if (ne10 != src0->ne[0] || ne10 % QK8_1 != 0) return false;
+    if (!ggml_is_contiguous(src1)) return false;
+
+    // Reorder layout not supported; fall back.
+    const ggml_tensor_extra_gpu * src0_extra =
+        static_cast<const ggml_tensor_extra_gpu *>(src0->extra);
+    if (src0_extra && src0_extra->optimized_feature.reorder) return false;
+
+    const int64_t n_ids_per_group = ids->ne[0];
+    if (ids->ne[1] != 1) return false;
+    if (ne11 != 1 && ne11 != n_ids_per_group) return false;
+
+    const queue_ptr stream           = ctx.stream();
+    const int       src1_padded_cols = GGML_PAD((int) ne10, MATRIX_ROW_PADDING);
+    const int       n_experts_used   = (int) n_ids_per_group;
+    const int       nrows            = (int) src0->ne[1];
+
+    ggml_sycl_pool_alloc<char> src1_q8_alloc(ctx.pool(),
+        (size_t) ne11 * src1_padded_cols * sizeof(block_q8_1) / QK8_1);
+    char * src1_ddq = src1_q8_alloc.get();
+    quantize_row_q8_1_sycl<quantize_q8_1>(
+        (const float *) src1->data, src1_ddq, (int) ne10, (int) ne11,
+        src1_padded_cols, stream);
+
+    const size_t bytes_per_qrow = (size_t) src1_padded_cols * sizeof(block_q8_1) / QK8_1;
+    const size_t src1_row_stride = (ne11 == 1) ? 0 : bytes_per_qrow;
+
+    return ggml_sycl_mul_mat_vec_q_id(
+        src0->type, src0->data, src1_ddq, (const int32_t *) ids->data,
+        (float *) dst->data, (int) ne10, nrows, n_experts_used,
+        /*expert_weight_stride=*/ src0->nb[2],
+        /*dst_row_stride=*/ dst->nb[1],
+        src1_row_stride, stream);
+}
+
 static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
                                  ggml_tensor *dst) try {
     scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3);
@@ -3798,6 +4034,12 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
     const int64_t n_as = ne02;
     const int64_t n_ids = ids->ne[0];
 
+    if (ne12 == 1) {
+        if (ggml_sycl_mul_mat_id_mmvq_fused(ctx, src0, src1, ids, dst)) {
+            return;
+        }
+    }
+
     std::vector<char> ids_host(ggml_nbytes(ids));
     const char * ids_dev = (const char *) ids->data;
 
@@ -3848,8 +4090,9 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
             }
         }
     } else {
-        ggml_sycl_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
-        ggml_sycl_pool_alloc<char>  dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst));
+        const int64_t n_routed_rows = ids->ne[1] * n_ids;
+        ggml_sycl_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*n_routed_rows*ne10);
+        ggml_sycl_pool_alloc<char>  dst_contiguous(ctx.pool(), sizeof(float)*n_routed_rows*ne0);
 
         src1_row.data = src1_contiguous.get();
         dst_row.data  =  dst_contiguous.get();
@@ -3976,6 +4219,11 @@ static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, ggml_tensor * dst)
     ggml_sycl_op_im2col(ctx, dst);
 }
 
+static void ggml_sycl_im2col_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    ggml_sycl_op_im2col_3d(ctx, dst);
+}
+
 static void ggml_sycl_sum(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
     GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
@@ -4273,6 +4521,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_IM2COL:
             ggml_sycl_im2col(ctx, dst);
             break;
+        case GGML_OP_IM2COL_3D:
+            ggml_sycl_im2col_3d(ctx, dst);
+            break;
         case GGML_OP_POOL_2D:
             ggml_sycl_pool2d(ctx, dst);
             break;
@@ -4309,6 +4560,21 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_SSM_CONV:
             ggml_sycl_ssm_conv(ctx, dst);
             break;
+        case GGML_OP_SSM_SCAN:
+            ggml_sycl_ssm_scan(ctx, dst);
+            break;
+        case GGML_OP_FILL:
+            ggml_sycl_fill(ctx, dst);
+            break;
+        case GGML_OP_CUMSUM:
+            ggml_sycl_cumsum(ctx, dst);
+            break;
+        case GGML_OP_DIAG:
+            ggml_sycl_diag(ctx, dst);
+            break;
+        case GGML_OP_SOLVE_TRI:
+            ggml_sycl_solve_tri(ctx, dst);
+            break;
         case GGML_OP_ROLL:
             ggml_sycl_roll(ctx, dst);
             break;
@@ -4615,8 +4881,8 @@ static ggml_backend_i ggml_backend_sycl_interface = {
     /* .free                    = */ ggml_backend_sycl_free,
     /* .set_tensor_async        = */ ggml_backend_sycl_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_sycl_get_tensor_async,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL, // ggml_backend_sycl_cpy_tensor_async,
                                            // // TODO: update for the new
                                            // interface
@@ -4817,6 +5083,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
             {
                 switch (op->src[0]->type) {
                     case GGML_TYPE_F16:
+                    case GGML_TYPE_BF16:
                     case GGML_TYPE_F32:
                     case GGML_TYPE_Q4_0:
                     case GGML_TYPE_Q4_1:
@@ -4976,6 +5243,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_ROPE:
         case GGML_OP_ROPE_BACK:
         case GGML_OP_IM2COL:
+        case GGML_OP_IM2COL_3D:
         case GGML_OP_UPSCALE:
             return true;
         case GGML_OP_SUM:
@@ -4999,11 +5267,10 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_ACC:
             return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
         case GGML_OP_PAD:
-            // TODO: add circular padding support for syscl, see https://github.com/ggml-org/llama.cpp/pull/16985
             if (ggml_get_op_params_i32(op, 8) != 0) {
                 return false;
             }
-            return ggml_is_contiguous(op->src[0]);
+            return true;
         case GGML_OP_LEAKY_RELU:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_RWKV_WKV6:
@@ -5019,6 +5286,21 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
             return op->type == GGML_TYPE_F32;
         case GGML_OP_ARANGE:
             return op->type == GGML_TYPE_F32;
+        case GGML_OP_SSM_SCAN:
+            if (op->src[3]->ne[0] == 1) {
+                // Mamba2
+                // (kernel only supports (d_state == 128 || d_state == 256) && d_head % WARP_SIZE == 0)
+                return (op->src[0]->ne[0] == 128 || op->src[0]->ne[0] == 256) && op->src[0]->ne[1] % WARP_SIZE == 0;
+            } else {
+                // TODO Mamba-1 not yet ported to SYCL
+                return false;
+            }
+        case GGML_OP_FILL:
+        case GGML_OP_CUMSUM:
+        case GGML_OP_DIAG:
+            return true;
+        case GGML_OP_SOLVE_TRI:
+            return op->src[0]->ne[0] <= SYCL_SOLVE_TRI_MAX_N && op->src[1]->ne[0] <= SYCL_SOLVE_TRI_MAX_K;
         case GGML_OP_FLASH_ATTN_EXT:
             return ggml_sycl_flash_attn_ext_supported(device, op);
         default:
diff --git a/ggml/src/ggml-sycl/im2col.cpp b/ggml/src/ggml-sycl/im2col.cpp
index 6d75d34d83f..7bf3584fb97 100644
--- a/ggml/src/ggml-sycl/im2col.cpp
+++ b/ggml/src/ggml-sycl/im2col.cpp
@@ -1,6 +1,6 @@
 //
 // MIT license
-// Copyright (C) 2024 Intel Corporation
+// Copyright (C) 2026 Intel Corporation
 // SPDX-License-Identifier: MIT
 //
 
@@ -12,125 +12,389 @@
 
 #include "im2col.hpp"
 
-#include <sycl/sycl.hpp>
-#include <type_traits>  // For std::is_same_v
-
-#include "ggml.h"
+#define MAX_GRIDDIM_Z 65535
 
 template <typename T>
-static void im2col_kernel(const float * x, T * dst, int64_t batch_offset, int64_t offset_delta, int64_t IC, int64_t IW,
-                          int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW,
-                          int s0, int s1, int p0, int p1, int d0, int d1, const sycl::nd_item<3> & item_ct1) {
-    const int64_t work_group_size = item_ct1.get_local_range(2);
-    const int64_t global_id       = item_ct1.get_local_id(2) + (work_group_size * item_ct1.get_group(2));
-
-    // make each work-item deal with more elements since sycl global range can not exceed max int
-    for (int64_t i = global_id; i < pelements; i += (work_group_size * item_ct1.get_group_range(2))) {
-        const int64_t ksize = OW * KH;
-        const int64_t kx    = i / ksize;
-        const int64_t kd    = kx * ksize;
-        const int64_t ky    = (i - kd) / OW;
-        const int64_t ix    = i % OW;
-
-        const int64_t oh    = item_ct1.get_group(1);
-        const int64_t batch = item_ct1.get_group(0) / IC;
-        const int64_t ic    = item_ct1.get_group(0) % IC;
-
-        const int64_t iiw = (ix * s0) + (kx * d0) - p0;
-        const int64_t iih = (oh * s1) + (ky * d1) - p1;
-
-        const int64_t offset_dst = (((batch * OH + oh) * OW + ix) * CHW) + (ic * (KW * KH) + ky * KW + kx);
-
-        const int64_t offset_src_base = (ic * offset_delta) + (batch * batch_offset);
-        const int64_t offset_src      = offset_src_base + (iih * IW) + iiw;
-
-        const bool  out_of_bounds = (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW);
-        const float src_val       = out_of_bounds ? 0.0f : x[offset_src];
-
-        if constexpr (std::is_same_v<T, sycl::half>) {
-            dst[offset_dst] = sycl::half(src_val);
-        } else if constexpr (std::is_same_v<T, float>) {
-            dst[offset_dst] = src_val;
-        }
+static  void im2col_kernel(
+        const float * x, T * dst,
+        int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH,
+        int64_t IC_IH_IW, int64_t IH_IW, int64_t N_OH, int64_t KH_KW, int64_t IC_KH_KW,
+        int s0, int s1, int p0, int p1, int d0, int d1) {
+    auto          item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    const int64_t i        = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (i >= IC_KH_KW) {
+        return;
     }
-}
 
-template <typename T>
-static void im2col_sycl_internal(const float * x, T * dst, int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW,
-                                 int64_t KH, int64_t IC, int64_t batch, int64_t batch_offset, int64_t offset_delta,
-                                 int s0, int s1, int p0, int p1, int d0, int d1, queue_ptr stream) {
-    const int64_t parallel_elements = OW * KW * KH;
-    const int64_t num_blocks        = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE;
+    const int64_t iic = i / (KH_KW);
+    const int64_t rem = i - iic * KH_KW;
+    const int64_t ikh = rem / KW;
+    const int64_t ikw = rem - ikh * KW;
 
-    // decrease global range when it exceeds the max int
-    int64_t local_size = downsample_sycl_global_range(batch * IC * OH * num_blocks, SYCL_IM2COL_BLOCK_SIZE);
+    const int64_t iow = item_ct1.get_group(1);
+    for (int64_t iz = item_ct1.get_group(0); iz < N_OH; iz += MAX_GRIDDIM_Z) {
+        const int64_t  in = iz / OH;
+        const int64_t  ioh = iz - in * OH;
 
-    sycl::range<3> block_nums(batch * IC, OH, num_blocks);
-    sycl::range<3> local_range(1, 1, local_size);
+        const int64_t iiw = iow * s0 + ikw * d0 - p0;
+        const int64_t iih = ioh * s1 + ikh * d1 - p1;
 
-    const int64_t CHW = IC * KH * KW;
+        const int64_t offset_dst =
+            ((in * OH + ioh) * OW + iow) * IC_KH_KW + iic * KH_KW + ikh * KW + ikw;
 
-    stream->parallel_for(sycl::nd_range<3>(block_nums * local_range, local_range), [=](sycl::nd_item<3> item_ct1) {
-        im2col_kernel<T>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, CHW, s0, s1,
-                         p0, p1, d0, d1, item_ct1);
-    });
+        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+            dst[offset_dst] = 0.0f;
+        } else {
+            const int64_t offset_src = iic * IC_IH_IW + in * IH_IW;
+            dst[offset_dst] = x[offset_src + iih * IW + iiw];
+        }
+    }
+
+    GGML_UNUSED(IC);
+    GGML_UNUSED(KH);
 }
 
-static void im2col_sycl_f16(const float * x, sycl::half * dst, int64_t IW, int64_t IH, int64_t OW, int64_t OH,
-                            int64_t KW, int64_t KH, int64_t IC, int64_t batch, int64_t batch_offset,
-                            int64_t offset_delta, int s0, int s1, int p0, int p1, int d0, int d1, queue_ptr stream) {
-    if (!stream->get_device().has(sycl::aspect::fp16)) {
-        throw sycl::exception(sycl::make_error_code(sycl::errc::kernel_not_supported),
-                              "Device does not support half precision (fp16) operations!");
-    }
-    im2col_sycl_internal<sycl::half>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0,
-                                     p1, d0, d1, stream);
+// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
+template <typename T>
+static void im2col_sycl(const float *   x,
+                        T *             dst,
+                        int64_t         IW,
+                        int64_t         IH,
+                        int64_t         OW,
+                        int64_t         OH,
+                        int64_t         KW,
+                        int64_t         KH,
+                        int64_t         IC,
+                        int64_t         N,
+                        int64_t         IC_IH_IW,
+                        int64_t         IH_IW,
+                        int             s0,
+                        int             s1,
+                        int             p0,
+                        int             p1,
+                        int             d0,
+                        int             d1,
+                        dpct::queue_ptr stream) {
+    const int64_t IC_KH_KW = IC * KH * KW;
+    const int64_t num_blocks = (IC_KH_KW + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE;
+    const int64_t N_OH = N * OH;
+    const int64_t KH_KW = KW*KH;
+    dpct::dim3    block_nums(num_blocks, OW, MIN(N_OH, MAX_GRIDDIM_Z));
+    /*
+    DPCT1049:73: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed.
+    */
+    stream->parallel_for(sycl::nd_range<3>(block_nums * sycl::range<3>(1, 1, MIN(IC_KH_KW, SYCL_IM2COL_BLOCK_SIZE)),
+                                           sycl::range<3>(1, 1, MIN(IC_KH_KW, SYCL_IM2COL_BLOCK_SIZE))),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             im2col_kernel(x, dst, IC, IW, IH, OH, OW, KW, KH, IC_IH_IW, IH_IW, N_OH, KH_KW, IC_KH_KW,
+                                           s0, s1, p0, p1, d0, d1);
+                         });
+}
+
+static void im2col_sycl_f16(const float *   x,
+                            sycl::half *    dst,
+                            int64_t         IW,
+                            int64_t         IH,
+                            int64_t         OW,
+                            int64_t         OH,
+                            int64_t         KW,
+                            int64_t         KH,
+                            int64_t         IC,
+                            int64_t         N,
+                            int64_t         IC_IH_IW,
+                            int64_t         IH_IW,
+                            int             s0,
+                            int             s1,
+                            int             p0,
+                            int             p1,
+                            int             d0,
+                            int             d1,
+                            dpct::queue_ptr stream) {
+    im2col_sycl<sycl::half>(x, dst, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
 }
 
-static void im2col_sycl_f32(const float * x, float * dst, int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW,
-                            int64_t KH, int64_t IC, int64_t batch, int64_t batch_offset, int64_t offset_delta, int s0,
-                            int s1, int p0, int p1, int d0, int d1, queue_ptr stream) {
-    im2col_sycl_internal<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1,
-                                d0, d1, stream);
+static void im2col_sycl_f32(const float *   x,
+                            float *         dst,
+                            int64_t         IW,
+                            int64_t         IH,
+                            int64_t         OW,
+                            int64_t         OH,
+                            int64_t         KW,
+                            int64_t         KH,
+                            int64_t         IC,
+                            int64_t         N,
+                            int64_t         IC_IH_IW,
+                            int64_t         IH_IW,
+                            int             s0,
+                            int             s1,
+                            int             p0,
+                            int             p1,
+                            int             d0,
+                            int             d1,
+                            dpct::queue_ptr stream) {
+    im2col_sycl<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
 }
 
 void ggml_sycl_op_im2col(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    dpct::queue_ptr     stream = ctx.stream();
 
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
 
-    const int32_t s0 = ((const int32_t *) (dst->op_params))[0];
-    const int32_t s1 = ((const int32_t *) (dst->op_params))[1];
-    const int32_t p0 = ((const int32_t *) (dst->op_params))[2];
-    const int32_t p1 = ((const int32_t *) (dst->op_params))[3];
-    const int32_t d0 = ((const int32_t *) (dst->op_params))[4];
-    const int32_t d1 = ((const int32_t *) (dst->op_params))[5];
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
 
-    const bool is_2D = ((const int32_t *) (dst->op_params))[6] == 1;
+    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
 
     const int64_t IC = src1->ne[is_2D ? 2 : 1];
     const int64_t IH = is_2D ? src1->ne[1] : 1;
-    const int64_t IW = src1->ne[0];
+    const int64_t IW =         src1->ne[0];
 
     const int64_t KH = is_2D ? src0->ne[1] : 1;
-    const int64_t KW = src0->ne[0];
+    const int64_t KW =         src0->ne[0];
 
     const int64_t OH = is_2D ? dst->ne[2] : 1;
-    const int64_t OW = dst->ne[1];
+    const int64_t OW =         dst->ne[1];
+
+    const int64_t IC_IH_IW = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+    const int64_t N        = src1->ne[is_2D ? 3 : 2];
+    const int64_t IH_IW    = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
+
+    if(dst->type == GGML_TYPE_F16) {
+        im2col_sycl_f16(src1_d, (sycl::half *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1,
+                        d0, d1, stream);
+    } else {
+        im2col_sycl_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
+    }
+}
+
+// [N*IC, ID, IH, IW] => [N*OD, OH, OW, IC * KD * KH * KW]
+template <typename T>
+static  void im2col_3d_kernel(
+        const float * src, T * dst,
+        int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW, int64_t OC,
+        int64_t KD, int64_t KH, int64_t KW, int64_t OD, int64_t OH, int64_t OW,
+        int64_t OH_OW, int64_t KD_KH_KW, int64_t ID_IH_IW, int64_t KH_KW, int64_t IH_IW, int64_t IC_ID_IH_IW,
+        int64_t IC_KD_KH_KW, int64_t OW_KD_KH_KW, int64_t OD_OH_OW_IC_KD_KH_KW, int64_t OH_OW_IC_KD_KH_KW,
+        int64_t OW_IC_KD_KH_KW, int64_t N_OD_OH, int64_t OD_OH,
+        int64_t stride_q, int64_t stride_z, int64_t stride_y, int64_t stride_x,
+        int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2) {
+    auto          item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    const int64_t i        = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (i >= IC_KD_KH_KW) {
+        return;
+    }
+    GGML_UNUSED(N); GGML_UNUSED(OC); GGML_UNUSED(OH_OW); GGML_UNUSED(OD); GGML_UNUSED(OW); GGML_UNUSED(KD); GGML_UNUSED(KH);
+    GGML_UNUSED(ID_IH_IW); GGML_UNUSED(IH_IW); GGML_UNUSED(IC_ID_IH_IW); GGML_UNUSED(OW_KD_KH_KW);
+
+    const int64_t iic = i / KD_KH_KW;
+    const int64_t ikd = (i - iic * KD_KH_KW) / KH_KW;
+    const int64_t ikh = (i - iic * KD_KH_KW - ikd * KH_KW) / KW;
+    const int64_t ikw = i % KW;
+
+    const int64_t iow = item_ct1.get_group(1);
+    for (int64_t iz = item_ct1.get_group(0); iz < N_OD_OH; iz += MAX_GRIDDIM_Z) {
+        const int64_t in  = iz / OD_OH;
+        const int64_t iod = (iz - in*OD_OH) / OH;
+        const int64_t ioh = iz % OH;
+
+        const int64_t iiw = iow * s0 + ikw * d0 - p0;
+        const int64_t iih = ioh * s1 + ikh * d1 - p1;
+        const int64_t iid = iod * s2 + ikd * d2 - p2;
+
+        const int64_t offset_dst = in*OD_OH_OW_IC_KD_KH_KW + iod*OH_OW_IC_KD_KH_KW + ioh*OW_IC_KD_KH_KW + iow*IC_KD_KH_KW + iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw;
+
+        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) {
+            dst[offset_dst] = 0.0f;
+        } else {
+            const int64_t offset_src = ((in * IC + iic) * stride_q) + (iid * stride_z) + (iih * stride_y) + (iiw * stride_x);
+            dst[offset_dst] = src[offset_src];
+        }
+    }
+}
+
+// [N*IC, ID, IH, IW] => [N*OD, OH, OW, IC * KD * KH * KW]
+template <typename T>
+static void im2col_3d_sycl(const float *   src,
+                           T *             dst,
+                           int64_t         N,
+                           int64_t         IC,
+                           int64_t         ID,
+                           int64_t         IH,
+                           int64_t         IW,
+                           int64_t         OC,
+                           int64_t         KD,
+                           int64_t         KH,
+                           int64_t         KW,
+                           int64_t         OD,
+                           int64_t         OH,
+                           int64_t         OW,
+                           int64_t         stride_q,
+                           int64_t         stride_z,
+                           int64_t         stride_y,
+                           int64_t         stride_x,
+                           int             s0,
+                           int             s1,
+                           int             s2,
+                           int             p0,
+                           int             p1,
+                           int             p2,
+                           int             d0,
+                           int             d1,
+                           int             d2,
+                           dpct::queue_ptr stream) {
+    const int64_t OH_OW = OH*OW;
+    const int64_t KD_KH_KW = KD*KH*KW;
+    const int64_t ID_IH_IW = ID*IH*IW;
+    const int64_t KH_KW = KH*KW;
+    const int64_t IH_IW = IH*IW;
+    const int64_t IC_KD_KH_KW = IC*KD*KH*KW;
+    const int64_t OW_KD_KH_KW = OW*KD*KH*KW;
+    const int64_t N_OD_OH = N*OD*OH;
+    const int64_t OD_OH = OD*OH;
+    const int64_t IC_ID_IH_IW = IC*ID*IH*IW;
+    const int64_t OD_OH_OW_IC_KD_KH_KW = OD*OH*OW*IC*KD*KH*KW;
+    const int64_t OH_OW_IC_KD_KH_KW = OH*OW*IC*KD*KH*KW;
+    const int64_t OW_IC_KD_KH_KW = OW*IC*KD*KH*KW;
+    const int64_t num_blocks = (IC_KD_KH_KW + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE;
+    dpct::dim3    block_nums(num_blocks, OW, MIN(N_OD_OH, MAX_GRIDDIM_Z));
+    /*
+    DPCT1049:74: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed.
+    */
+    stream->parallel_for(sycl::nd_range<3>(block_nums * sycl::range<3>(1, 1, MIN(IC_KD_KH_KW, SYCL_IM2COL_BLOCK_SIZE)),
+                                           sycl::range<3>(1, 1, MIN(IC_KD_KH_KW, SYCL_IM2COL_BLOCK_SIZE))),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             im2col_3d_kernel(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, OH_OW, KD_KH_KW,
+                                              ID_IH_IW, KH_KW, IH_IW, IC_ID_IH_IW, IC_KD_KH_KW, OW_KD_KH_KW,
+                                              OD_OH_OW_IC_KD_KH_KW, OH_OW_IC_KD_KH_KW, OW_IC_KD_KH_KW, N_OD_OH, OD_OH,
+                                              stride_q, stride_z, stride_y, stride_x, s0, s1, s2, p0, p1, p2, d0, d1,
+                                              d2);
+                         });
+}
+
+static void im2col_3d_sycl_f16(const float *   src,
+                               sycl::half *    dst,
+                               int64_t         N,
+                               int64_t         IC,
+                               int64_t         ID,
+                               int64_t         IH,
+                               int64_t         IW,
+                               int64_t         OC,
+                               int64_t         KD,
+                               int64_t         KH,
+                               int64_t         KW,
+                               int64_t         OD,
+                               int64_t         OH,
+                               int64_t         OW,
+                               int64_t         stride_q,
+                               int64_t         stride_z,
+                               int64_t         stride_y,
+                               int64_t         stride_x,
+                               int             s0,
+                               int             s1,
+                               int             s2,
+                               int             p0,
+                               int             p1,
+                               int             p2,
+                               int             d0,
+                               int             d1,
+                               int             d2,
+                               dpct::queue_ptr stream) {
+    im2col_3d_sycl<sycl::half>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, stride_q, stride_z, stride_y,
+                               stride_x, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+}
+
+static void im2col_3d_sycl_f32(const float *   src,
+                               float *         dst,
+                               int64_t         N,
+                               int64_t         IC,
+                               int64_t         ID,
+                               int64_t         IH,
+                               int64_t         IW,
+                               int64_t         OC,
+                               int64_t         KD,
+                               int64_t         KH,
+                               int64_t         KW,
+                               int64_t         OD,
+                               int64_t         OH,
+                               int64_t         OW,
+                               int64_t         stride_q,
+                               int64_t         stride_z,
+                               int64_t         stride_y,
+                               int64_t         stride_x,
+                               int             s0,
+                               int             s1,
+                               int             s2,
+                               int             p0,
+                               int             p1,
+                               int             p2,
+                               int             d0,
+                               int             d1,
+                               int             d2,
+                               dpct::queue_ptr stream) {
+    im2col_3d_sycl<float>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
+                          stride_q, stride_z, stride_y, stride_x,
+                          s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+}
+
+void ggml_sycl_op_im2col_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    dpct::queue_ptr     stream = ctx.stream();
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
+    const int32_t s2 = ((const int32_t *)(dst->op_params))[2];
+    const int32_t p0 = ((const int32_t *)(dst->op_params))[3];
+    const int32_t p1 = ((const int32_t *)(dst->op_params))[4];
+    const int32_t p2 = ((const int32_t *)(dst->op_params))[5];
+    const int32_t d0 = ((const int32_t *)(dst->op_params))[6];
+    const int32_t d1 = ((const int32_t *)(dst->op_params))[7];
+    const int32_t d2 = ((const int32_t *)(dst->op_params))[8];
+    const int32_t IC = ((const int32_t *)(dst->op_params))[9];
+
+    const int64_t N  = ne13 / IC;
+    const int64_t ID = ne12;
+    const int64_t IH = ne11;
+    const int64_t IW = ne10;
+
+    const int64_t OC = ne03 / IC;
+    const int64_t KD = ne02;
+    const int64_t KH = ne01;
+    const int64_t KW = ne00;
 
-    const size_t  delta_offset = src1->nb[is_2D ? 2 : 1] / sizeof(float);
-    const int64_t batch        = src1->ne[is_2D ? 3 : 2];
-    const size_t  batch_offset = src1->nb[is_2D ? 3 : 2] / sizeof(float);
+    const int64_t OD = ne3 / N;
+    const int64_t OH = ne2;
+    const int64_t OW = ne1;
 
-    queue_ptr stream = ctx.stream();
+    const size_t  es       = ggml_element_size(src1);
+    const int64_t stride_x = src1->nb[0] / es;
+    const int64_t stride_y = src1->nb[1] / es;
+    const int64_t stride_z = src1->nb[2] / es;
+    const int64_t stride_q = src1->nb[3] / es;
 
-    if (dst->type == GGML_TYPE_F16) {
-        im2col_sycl_f16((const float *) src1->data, (sycl::half *) dst->data, IW, IH, OW, OH, KW, KH, IC, batch,
-                        batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+    if(dst->type == GGML_TYPE_F16) {
+        im2col_3d_sycl_f16(src1_d, (sycl::half *) dst_d, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
+                           stride_q, stride_z, stride_y, stride_x,
+                           s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
     } else {
-        im2col_sycl_f32((const float *) src1->data, (float *) dst->data, IW, IH, OW, OH, KW, KH, IC, batch,
-                        batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+        im2col_3d_sycl_f32(src1_d, (float *) dst_d, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
+                           stride_q, stride_z, stride_y, stride_x,
+                           s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
     }
 }
diff --git a/ggml/src/ggml-sycl/im2col.hpp b/ggml/src/ggml-sycl/im2col.hpp
index dbbb248ddb4..976d1094636 100644
--- a/ggml/src/ggml-sycl/im2col.hpp
+++ b/ggml/src/ggml-sycl/im2col.hpp
@@ -1,6 +1,6 @@
 //
 // MIT license
-// Copyright (C) 2024 Intel Corporation
+// Copyright (C) 2026 Intel Corporation
 // SPDX-License-Identifier: MIT
 //
 
@@ -15,7 +15,9 @@
 
 #include "common.hpp"
 
-void ggml_sycl_op_im2col(
-        ggml_backend_sycl_context & ctx, ggml_tensor *dst);
+#define SYCL_IM2COL_BLOCK_SIZE 256
+
+void ggml_sycl_op_im2col(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+void ggml_sycl_op_im2col_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 #endif // GGML_SYCL_IM2COL_HPP
diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp
index 3a4577ecbbc..49998f13ba8 100644
--- a/ggml/src/ggml-sycl/mmvq.cpp
+++ b/ggml/src/ggml-sycl/mmvq.cpp
@@ -839,6 +839,26 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
     }
 }
 
+static void reorder_mul_mat_vec_q5_k_q8_1_sycl(const void * vx, const void * vy, float * dst, const int ncols,
+                                               const int nrows, dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+
+    const int        block_num_y   = ceil_div(nrows, GGML_SYCL_MMV_Y);
+    constexpr size_t num_subgroups = 16;
+    GGML_ASSERT(block_num_y % num_subgroups == 0);
+
+    const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, block_num_y * WARP_SIZE);
+    const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
+
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
+                            [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                                mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q5_K>>(vx, vy, dst, ncols,
+                                                                                            nrows, nd_item);
+                            });
+    });
+}
+
 static void reorder_mul_mat_vec_q6_k_q8_1_sycl(const void * vx, const void * vy, float * dst, const int ncols,
                                                const int nrows, dpct::queue_ptr stream) {
     GGML_ASSERT(ncols % QK_K == 0);
@@ -1125,6 +1145,7 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
                     GGML_SYCL_DEBUG("Calling reorder_mul_mat_vec_q8_0_q8_1_sycl\n");
                     reorder_mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
                 } else {
+                    GGML_SYCL_DEBUG("Calling mul_mat_vec_q8_0_q8_1_sycl\n");
                     mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
                 }
                 break;
@@ -1145,7 +1166,14 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
                 }
                 break;
             case GGML_TYPE_Q5_K:
-                mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+                if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
+                    ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
+                    GGML_SYCL_DEBUG("Calling reorder_mul_mat_vec_q5_k_q8_1_sycl\n");
+                    reorder_mul_mat_vec_q5_k_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+                } else {
+                    GGML_SYCL_DEBUG("Calling mul_mat_vec_q5_K_q8_1_sycl\n");
+                    mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+                }
                 break;
             case GGML_TYPE_Q6_K:
                 if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
@@ -1199,3 +1227,154 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
     GGML_UNUSED(src1_ddf_i);
     GGML_UNUSED(ctx);
 }
+
+// src1_row_stride: 0 for shared src1 (gate/up proj), else per-expert stride (down proj).
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_sycl_t vec_dot_q_sycl>
+static void mul_mat_vec_q_moe(
+    const void * __restrict__ vx_base, const void * __restrict__ vy_base,
+    float * __restrict__ dst_base, const int32_t * __restrict__ ids_dev,
+    const int ncols, const int nrows,
+    const size_t expert_weight_stride, const size_t dst_row_stride,
+    const size_t src1_row_stride,
+    const sycl::nd_item<3> & item_ct1) {
+
+    const int expert_idx = item_ct1.get_group(1);
+    const int i02        = ids_dev[expert_idx];
+
+    const char * vx = (const char *) vx_base + (size_t) i02 * expert_weight_stride;
+    const char * vy = (const char *) vy_base + (size_t) expert_idx * src1_row_stride;
+    float *      dst = (float *) ((char *) dst_base + (size_t) expert_idx * dst_row_stride);
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int     blocks_per_row  = ncols / qk;
+    constexpr int blocks_per_warp = (vdr * WARP_SIZE + qi - 1) / qi;
+
+    float tmp = 0.0f;
+
+    const block_q_t *  x = (const block_q_t *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; i += blocks_per_warp) {
+        const int ibx = row * blocks_per_row + i;
+        const int iby = i * (qk / QK8_1);
+
+        for (size_t elem = 0; elem < qi / vdr; elem += WARP_SIZE) {
+            const int iqs = elem + vdr * (item_ct1.get_local_id(2) % (qi / vdr));
+            tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
+        }
+    }
+
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_sycl_t vec_dot_q_sycl>
+static void launch_mul_mat_vec_q_moe(
+    const void * vx_base, const void * vy, const int32_t * ids_dev,
+    float * dst_base, const int ncols, const int nrows, const int n_experts_used,
+    const size_t expert_weight_stride, const size_t dst_row_stride,
+    const size_t src1_row_stride,
+    dpct::queue_ptr stream) {
+    const int            block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, (unsigned) n_experts_used, (unsigned) block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                mul_mat_vec_q_moe<qk, qi, block_q_t, vdr, vec_dot_q_sycl>(
+                    vx_base, vy, dst_base, ids_dev, ncols, nrows,
+                    expert_weight_stride, dst_row_stride, src1_row_stride, item);
+            });
+    });
+}
+
+bool ggml_sycl_mul_mat_vec_q_id(
+    enum ggml_type     src0_type,
+    const void *       vx_base,
+    const void *       vy,
+    const int32_t *    ids_dev,
+    float *            dst_base,
+    int                ncols,
+    int                nrows,
+    int                n_experts_used,
+    size_t             expert_weight_stride,
+    size_t             dst_row_stride,
+    size_t             src1_row_stride,
+    dpct::queue_ptr    stream) {
+    switch (src0_type) {
+        case GGML_TYPE_Q4_0:
+            launch_mul_mat_vec_q_moe<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q4_1:
+            launch_mul_mat_vec_q_moe<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q5_0:
+            launch_mul_mat_vec_q_moe<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q5_1:
+            launch_mul_mat_vec_q_moe<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q8_0:
+            launch_mul_mat_vec_q_moe<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q2_K:
+            launch_mul_mat_vec_q_moe<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q3_K:
+            launch_mul_mat_vec_q_moe<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q4_K:
+            launch_mul_mat_vec_q_moe<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q5_K:
+            launch_mul_mat_vec_q_moe<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_Q6_K:
+            launch_mul_mat_vec_q_moe<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_MXFP4:
+            launch_mul_mat_vec_q_moe<QK_MXFP4, QI_MXFP4, block_mxfp4, VDR_MXFP4_Q8_1_MMVQ, vec_dot_mxfp4_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        case GGML_TYPE_NVFP4:
+            launch_mul_mat_vec_q_moe<QK_NVFP4, QI_NVFP4, block_nvfp4, VDR_NVFP4_Q8_1_MMVQ, vec_dot_nvfp4_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
+        default:
+            return false;
+    }
+}
diff --git a/ggml/src/ggml-sycl/mmvq.hpp b/ggml/src/ggml-sycl/mmvq.hpp
index 049b43d4535..d674dc1d61e 100644
--- a/ggml/src/ggml-sycl/mmvq.hpp
+++ b/ggml/src/ggml-sycl/mmvq.hpp
@@ -24,4 +24,20 @@ void ggml_sycl_op_mul_mat_vec_q(
     const int64_t src1_ncols, const int64_t src1_padded_row_size,
     const dpct::queue_ptr &stream);
 
+// Requires standard (non-reorder) block layout for src0.
+// Returns false if src0_type isn't handled; caller should fall back.
+bool ggml_sycl_mul_mat_vec_q_id(
+    enum ggml_type     src0_type,
+    const void *       vx_base,             // start of stacked expert weights
+    const void *       vy,                  // pre-quantized src1 (Q8_1)
+    const int32_t *    ids_dev,             // device-side int32, length n_experts_used
+    float *            dst_base,
+    int                ncols,
+    int                nrows,
+    int                n_experts_used,
+    size_t             expert_weight_stride, // bytes between experts in vx_base
+    size_t             dst_row_stride,       // bytes between dst rows
+    size_t             src1_row_stride,      // 0 = shared src1, else per-expert stride in bytes
+    dpct::queue_ptr    stream);
+
 #endif // GGML_SYCL_MMVQ_HPP
diff --git a/ggml/src/ggml-sycl/pad.cpp b/ggml/src/ggml-sycl/pad.cpp
index f989c5e4b8b..ee93bb51801 100644
--- a/ggml/src/ggml-sycl/pad.cpp
+++ b/ggml/src/ggml-sycl/pad.cpp
@@ -13,7 +13,8 @@
 //#include "common.hpp"
 #include "pad.hpp"
 
-static void pad_f32(const float * src, float * dst,
+static void pad_f32(const float * src, size_t s00, size_t s01, size_t s02, size_t s03,
+                    float * dst,
                     const int lp0, const int rp0, const int lp1, const int rp1,
                     const int lp2, const int rp2, const int lp3, const int rp3,
                     const int ne0, const int ne1, const int ne2, const int ne3,
@@ -27,7 +28,6 @@ static void pad_f32(const float * src, float * dst,
         return;
     }
 
-    // operation
     const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
     if ((i0 >= lp0 && i0 < ne0 - rp0) &&
         (i1 >= lp1 && i1 < ne1 - rp1) &&
@@ -37,12 +37,8 @@ static void pad_f32(const float * src, float * dst,
         const int64_t i01 = i1 - lp1;
         const int64_t i02 = i2 - lp2;
         const int64_t i03 = i3 - lp3;
-        const int64_t ne02 = ne2 - lp2 - rp2;
-        const int64_t ne01 = ne1 - lp1 - rp1;
-        const int64_t ne00 = ne0 - lp0 - rp0;
 
-        const int64_t src_idx = i03 * (ne00 * ne01 * ne02) +
-                                i02 * (ne00 * ne01) + i01 * ne00 + i00;
+        const int64_t src_idx = i03 * s03 + i02 * s02 + i01 * s01 + i00 * s00;
 
         dst[dst_idx] = src[src_idx];
     } else {
@@ -50,20 +46,19 @@ static void pad_f32(const float * src, float * dst,
     }
 }
 
-static void pad_f32_sycl(const float *src, float *dst, const int lp0,
-                         const int rp0, const int lp1, const int rp1,
-                         const int lp2, const int rp2, const int lp3,
-                         const int rp3, const int ne0, const int ne1,
-                         const int ne2, const int ne3,
+static void pad_f32_sycl(const float * src, size_t s00, size_t s01, size_t s02, size_t s03,
+                         float * dst, const int lp0, const int rp0, const int lp1, const int rp1,
+                         const int lp2, const int rp2, const int lp3, const int rp3,
+                         const int ne0, const int ne1, const int ne2, const int ne3,
                          dpct::queue_ptr stream) {
     int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE;
-    dpct::dim3 gridDim(num_blocks, ne1, ne2 * ne3);
+    sycl::range<3> grid(ne2 * ne3, ne1, num_blocks);
     stream->parallel_for(
-        sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE),
+        sycl::nd_range<3>(grid * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE),
                           sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
         [=](sycl::nd_item<3> item_ct1) {
-            pad_f32(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1,
-                    ne2, ne3, item_ct1);
+            pad_f32(src, s00, s01, s02, s03, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
+                    ne0, ne1, ne2, ne3, item_ct1);
         });
 }
 
@@ -71,22 +66,27 @@ void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const float * src0_d = (const float *)src0->data;
     float * dst_d = (float *)dst->data;
-    dpct::queue_ptr     stream = ctx.stream();
+    dpct::queue_ptr stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_is_contiguous(src0));
 
-    const int32_t lp0 = ((const int32_t*)(dst->op_params))[0];
-    const int32_t rp0 = ((const int32_t*)(dst->op_params))[1];
-    const int32_t lp1 = ((const int32_t*)(dst->op_params))[2];
-    const int32_t rp1 = ((const int32_t*)(dst->op_params))[3];
-    const int32_t lp2 = ((const int32_t*)(dst->op_params))[4];
-    const int32_t rp2 = ((const int32_t*)(dst->op_params))[5];
-    const int32_t lp3 = ((const int32_t*)(dst->op_params))[6];
-    const int32_t rp3 = ((const int32_t*)(dst->op_params))[7];
+    const size_t ts = ggml_type_size(src0->type);
+    const size_t s00 = src0->nb[0] / ts;
+    const size_t s01 = src0->nb[1] / ts;
+    const size_t s02 = src0->nb[2] / ts;
+    const size_t s03 = src0->nb[3] / ts;
 
-    pad_f32_sycl(src0_d, dst_d,
+    const int32_t lp0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t rp0 = ((const int32_t *)(dst->op_params))[1];
+    const int32_t lp1 = ((const int32_t *)(dst->op_params))[2];
+    const int32_t rp1 = ((const int32_t *)(dst->op_params))[3];
+    const int32_t lp2 = ((const int32_t *)(dst->op_params))[4];
+    const int32_t rp2 = ((const int32_t *)(dst->op_params))[5];
+    const int32_t lp3 = ((const int32_t *)(dst->op_params))[6];
+    const int32_t rp3 = ((const int32_t *)(dst->op_params))[7];
+
+    pad_f32_sycl(src0_d, s00, s01, s02, s03, dst_d,
                  lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
                  dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
 }
diff --git a/ggml/src/ggml-sycl/quants.hpp b/ggml/src/ggml-sycl/quants.hpp
index 1f5b62740a8..806028ef3a3 100644
--- a/ggml/src/ggml-sycl/quants.hpp
+++ b/ggml/src/ggml-sycl/quants.hpp
@@ -79,6 +79,31 @@ template <> struct block_q_t<GGML_TYPE_Q4_K> {
     static constexpr int block_to_q8_1_ratio() { return traits::qk / QK8_1; }
 };
 
+template <> struct block_q_t<GGML_TYPE_Q5_K> {
+    struct traits {
+        static constexpr uint32_t qk       = QK_K;
+        static constexpr uint32_t qi       = QI5_K;
+        static constexpr uint32_t qr       = QR5_K;
+        static constexpr uint32_t vdr_mmvq = 2;
+    };
+
+    // Reordered layout: [qs (QK_K/2 per block)] [qh (QK_K/8 per block)] [scales] [dm]
+    static constexpr std::pair<int, int> get_block_offset(const int block_index, const int n_blocks) {
+        auto qs_offset = block_index * (QK_K / 2);
+        auto qh_offset = n_blocks * (QK_K / 2) + block_index * (QK_K / 8);
+        return { qs_offset, qh_offset };
+    }
+
+    static constexpr std::pair<int, int> get_d_offset(int nrows, int ncols, const int block_index) {
+        auto nblocks        = (nrows * (ncols / QK_K));
+        auto total_qs_bytes = nblocks * (QK_K / 2) + nblocks * (QK_K / 8);
+        return { total_qs_bytes + block_index * K_SCALE_SIZE,
+                 total_qs_bytes + nblocks * K_SCALE_SIZE + block_index * sizeof(ggml_half2) };
+    }
+
+    static constexpr int block_to_q8_1_ratio() { return traits::qk / QK8_1; }
+};
+
 template <> struct block_q_t<GGML_TYPE_Q6_K> {
     struct traits {
         static constexpr uint32_t qk       = QK_K;
diff --git a/ggml/src/ggml-sycl/set_rows.cpp b/ggml/src/ggml-sycl/set_rows.cpp
index a641c100913..8fb41943525 100644
--- a/ggml/src/ggml-sycl/set_rows.cpp
+++ b/ggml/src/ggml-sycl/set_rows.cpp
@@ -4,7 +4,11 @@
 namespace utils {
 template<typename T>
 static constexpr bool is_arithmetic_v() {
-    return std::is_arithmetic_v<T> || std::is_same_v<T, sycl::half> || std::is_same_v<T, sycl::ext::oneapi::bfloat16>;
+    return std::is_arithmetic_v<T> || std::is_same_v<T, sycl::half>
+#ifdef GGML_SYCL_HAS_BF16
+        || std::is_same_v<T, sycl::ext::oneapi::bfloat16>
+#endif
+        ;
 }
 }
 
@@ -181,6 +185,7 @@ static void set_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor * s
                 stream
             );
             break;
+#ifdef GGML_SYCL_HAS_BF16
         case GGML_TYPE_BF16:
             set_rows_sycl<TIn, TIdx, sycl::ext::oneapi::bfloat16>(
                 src0_d, src1_d, (char *)dst->data,
@@ -193,6 +198,7 @@ static void set_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor * s
                 stream
             );
             break;
+#endif
         case GGML_TYPE_Q8_0:
             set_rows_sycl_q<TIdx, block_q8_0, QK8_0, cpy_blck_f32_q8_0>(src0_d, src1_d, (block_q8_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
diff --git a/ggml/src/ggml-sycl/solve_tri.cpp b/ggml/src/ggml-sycl/solve_tri.cpp
new file mode 100644
index 00000000000..39326deee44
--- /dev/null
+++ b/ggml/src/ggml-sycl/solve_tri.cpp
@@ -0,0 +1,172 @@
+#include "solve_tri.hpp"
+#include "common.hpp"
+#include <oneapi/mkl/blas.hpp>
+
+template <int n_template, int k_template>
+static void solve_tri_f32_fast(const float * __restrict__ A,
+                               const float * __restrict__ B,
+                               float * __restrict__ X,
+                               const int64_t ne02, [[maybe_unused]] const int64_t ne03,
+                               const int64_t nb02, const int64_t nb03,
+                               const int64_t nb12, const int64_t nb13,
+                               const int64_t nb2,  const int64_t nb3,
+                               const int n_arg, const int k_arg,
+                               const sycl::nd_item<2> & item, float * sA) {
+
+    const int n = n_template == 0 ? n_arg : n_template;
+    const int k = k_template == 0 ? k_arg : k_template;
+
+    const int batch_idx = item.get_group(1);
+    const int lane      = item.get_local_id(1) % WARP_SIZE;
+    const int col_idx   = item.get_local_id(0);
+
+    if (col_idx >= k) {
+        return;
+    }
+
+    const int64_t i03 = batch_idx / ne02;
+    const int64_t i02 = batch_idx % ne02;
+
+    const float * A_batch = (const float *) ((const char *) A + i02 * nb02 + i03 * nb03);
+    const float * B_batch = (const float *) ((const char *) B + i02 * nb12 + i03 * nb13);
+    float *       X_batch = (float *)       ((char *)       X + i02 * nb2  + i03 * nb3);
+
+    const int offset = item.get_local_id(1) + item.get_local_id(0) * item.get_local_range(1);
+
+#pragma unroll
+    for (int i = 0; i < n * n; i += k * WARP_SIZE) {
+        const int i0 = i + offset;
+        if (i0 < n * n) {
+            sA[i0] = A_batch[i0];
+        }
+    }
+
+    item.barrier(sycl::access::fence_space::local_space);
+
+    float x_low  = (lane < n) ? B_batch[lane * k + col_idx] : 0.0f;
+    float x_high = (WARP_SIZE + lane < n) ? B_batch[(WARP_SIZE + lane) * k + col_idx] : 0.0f;
+
+    const int half      = WARP_SIZE;
+    const int nrows_low = (n < half) ? n : half;
+
+#pragma unroll
+    for (int row = 0; row < nrows_low; ++row) {
+        float sum = 0.0f;
+        if (lane < row) {
+            sum += sA[row * n + lane] * x_low;
+        }
+        sum = warp_reduce_sum<WARP_SIZE>(sum);
+        if (lane == row) {
+            x_low = (x_low - sum) / sA[row * n + row];
+        }
+    }
+
+#pragma unroll
+    for (int row = half; row < n; ++row) {
+        float     sum = sA[row * n + lane] * x_low;
+        const int j   = half + lane;
+        if (j < row) {
+            sum += sA[row * n + j] * x_high;
+        }
+        sum = warp_reduce_sum<WARP_SIZE>(sum);
+        if (lane == row - half) {
+            x_high = (x_high - sum) / sA[row * n + row];
+        }
+    }
+
+#pragma unroll
+    for (int rr = 0; rr < 2; ++rr) {
+        const int row = rr * WARP_SIZE + lane;
+        if (row < n) {
+            const float val            = (row < half) ? x_low : x_high;
+            X_batch[row * k + col_idx] = val;
+        }
+    }
+}
+
+static void solve_tri_f32_mkl(dpct::queue_ptr stream,
+                               const float * A, float * X,
+                               int n, int k,
+                               int64_t ne02, [[maybe_unused]] int64_t ne03,
+                               int64_t nb02, [[maybe_unused]] int64_t nb03,
+                               int64_t nb2,  [[maybe_unused]] int64_t nb3) {
+    const float alpha = 1.0f;
+    const int64_t total_batches = ne02 * ne03;
+    if (total_batches == 0) {
+        return;
+    }
+
+    const int64_t stride_a = nb02 / sizeof(float);
+    const int64_t stride_x = nb2 / sizeof(float);
+
+    oneapi::mkl::blas::trsm_batch(
+        *stream,
+        oneapi::mkl::side::right,
+        oneapi::mkl::uplo::upper,
+        oneapi::mkl::transpose::nontrans,
+        oneapi::mkl::diag::nonunit,
+        k, n, alpha,
+        A, n, stride_a,
+        X, k, stride_x,
+        total_batches);
+}
+
+inline void ggml_sycl_op_solve_tri(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+    dpct::queue_ptr stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    const int n    = src0->ne[0];
+    const int k    = src1->ne[0];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    GGML_ASSERT(n <= SYCL_SOLVE_TRI_MAX_N && k <= SYCL_SOLVE_TRI_MAX_K);
+
+    const float * A_d = static_cast<const float *>(src0->data);
+    const float * B_d = static_cast<const float *>(src1->data);
+    float * X_d       = static_cast<float *>(dst->data);
+
+    if (X_d != B_d) {
+        const int64_t total_elements = (int64_t)n * k * ne02 * ne03;
+        stream->memcpy(X_d, B_d, total_elements * sizeof(float));
+    }
+
+    const int64_t nb02 = src0->nb[2];
+    const int64_t nb03 = src0->nb[3];
+    const int64_t nb12 = src1->nb[2];
+    const int64_t nb13 = src1->nb[3];
+    const int64_t nb2  = dst->nb[2];
+    const int64_t nb3  = dst->nb[3];
+
+    const int64_t total_batches = ne02 * ne03;
+
+    if (n <= 2 * WARP_SIZE && k <= 32) {
+        const int smem_size = 2 * WARP_SIZE * 2 * WARP_SIZE;
+        const sycl::range<2> grid(1, total_batches);
+        const sycl::range<2> block(k, WARP_SIZE);
+        stream->submit([&](sycl::handler & cgh) {
+            sycl::local_accessor<float, 1> smem_acc(sycl::range<1>(smem_size), cgh);
+            cgh.parallel_for(
+                sycl::nd_range<2>(grid * block, block),
+                [=](sycl::nd_item<2> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    solve_tri_f32_fast<0, 0>(A_d, B_d, X_d, ne02, ne03,
+                                              nb02, nb03, nb12, nb13, nb2, nb3,
+                                              n, k, item, get_pointer(smem_acc));
+                });
+        });
+    } else {
+        solve_tri_f32_mkl(stream, A_d, X_d, n, k, ne02, ne03, nb02, nb03, nb2, nb3);
+    }
+}
+
+void ggml_sycl_solve_tri(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    ggml_sycl_op_solve_tri(ctx, dst);
+}
diff --git a/ggml/src/ggml-sycl/solve_tri.hpp b/ggml/src/ggml-sycl/solve_tri.hpp
new file mode 100644
index 00000000000..c7c34cfa2bb
--- /dev/null
+++ b/ggml/src/ggml-sycl/solve_tri.hpp
@@ -0,0 +1,8 @@
+#pragma once
+
+#include "common.hpp"
+
+#define SYCL_SOLVE_TRI_MAX_N 64
+#define SYCL_SOLVE_TRI_MAX_K 64
+
+void ggml_sycl_solve_tri(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-sycl/ssm_conv.cpp b/ggml/src/ggml-sycl/ssm_conv.cpp
index eea9a73d67e..e55223586a1 100644
--- a/ggml/src/ggml-sycl/ssm_conv.cpp
+++ b/ggml/src/ggml-sycl/ssm_conv.cpp
@@ -63,7 +63,7 @@ static void kernel_ssm_conv(
     });
 }
 
-void ggml_sycl_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+inline void ggml_sycl_op_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     ggml_tensor * src0 = dst->src[0];
     ggml_tensor * src1 = dst->src[1];
 
@@ -125,3 +125,8 @@ void ggml_sycl_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
         throw;
     }
 }
+
+void ggml_sycl_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    ggml_sycl_op_ssm_conv(ctx, dst);
+}
diff --git a/ggml/src/ggml-sycl/ssm_scan.cpp b/ggml/src/ggml-sycl/ssm_scan.cpp
new file mode 100644
index 00000000000..ae652981384
--- /dev/null
+++ b/ggml/src/ggml-sycl/ssm_scan.cpp
@@ -0,0 +1,156 @@
+#include "ssm_scan.hpp"
+#include "common.hpp"
+
+template <int c_factor, int d_state>
+static void ssm_scan_f32_group(
+        const float * __restrict__ src0, const float * __restrict__ src1, const float * __restrict__ src2,
+        const float * __restrict__ src3, const float * __restrict__ src4, const float * __restrict__ src5,
+        const int32_t * __restrict__ src6, float * __restrict__ dst,
+        const int src0_nb2, const int src0_nb3, const int src1_nb2, const int src1_nb3,
+        const int src2_nb1, const int src2_nb2, const int src3_nb1,
+        const int src4_nb2, const int src4_nb3, const int src5_nb2, const int src5_nb3,
+        const int64_t s_off, const int64_t n_head, const int64_t d_head, const int64_t n_group, const int64_t n_tok,
+        const sycl::nd_item<2> & item) {
+
+    const int lane     = item.get_local_id(1) % WARP_SIZE;
+    const int warp     = item.get_local_id(1) / WARP_SIZE;
+    const int warp_idx = item.get_group(1) * c_factor + warp;
+    const int seq_idx  = item.get_group(0);
+
+    const int head_idx = warp_idx / d_head;
+    const int head_off = (warp_idx % d_head) * sizeof(float);
+    const int group_off = (head_idx / (n_head / n_group)) * d_state * sizeof(float);
+
+    const float * s0_warp = (const float *) ((const char *) src0 + src6[seq_idx] * src0_nb3 + head_idx * src0_nb2 + head_off * d_state);
+    const float * x_warp  = (const float *) ((const char *) src1 + (seq_idx * src1_nb3) + (warp_idx * sizeof(float)));
+    const float * dt_warp = (const float *) ((const char *) src2 + (seq_idx * src2_nb2) + head_idx * sizeof(float));
+    const float * A_warp  = (const float *) ((const char *) src3 + head_idx * src3_nb1);
+    const float * B_warp  = (const float *) ((const char *) src4 + (seq_idx * src4_nb3) + (group_off));
+    const float * C_warp  = (const float *) ((const char *) src5 + (seq_idx * src5_nb3) + (group_off));
+    float *       y_warp  = dst + (seq_idx * n_tok * n_head * d_head) + warp_idx;
+    float *       s_warp  = (float *) ((char *) dst + s_off + seq_idx * src0_nb3 + head_idx * src0_nb2 + head_off * d_state);
+
+    const int stride_x  = src1_nb2 / sizeof(float);
+    const int stride_dt = src2_nb1 / sizeof(float);
+    const int stride_B  = src4_nb2 / sizeof(float);
+    const int stride_C  = src5_nb2 / sizeof(float);
+    const int stride_y  = n_head * d_head;
+
+    float state[c_factor];
+    float state_sum = 0.0f;
+
+#pragma unroll
+    for (int j = 0; j < c_factor; j++) {
+        state[j] = s0_warp[WARP_SIZE * j + lane];
+    }
+
+    for (int64_t i = 0; i < n_tok; i++) {
+        const float dt_val = dt_warp[i * stride_dt];
+        const float dt_soft_plus = (dt_val <= 20.0f ? sycl::log1p(sycl::exp(dt_val)) : dt_val);
+
+        state_sum = 0.0f;
+        const float dA   = sycl::exp(dt_soft_plus * A_warp[0]);
+        const float x_dt = x_warp[i * stride_x] * dt_soft_plus;
+#pragma unroll
+        for (int j = 0; j < c_factor; j++) {
+            const float B_val = B_warp[i * stride_B + WARP_SIZE * j + lane];
+            const float C_val = C_warp[i * stride_C + WARP_SIZE * j + lane];
+            state[j] = (state[j] * dA) + (B_val * x_dt);
+            state_sum += state[j] * C_val;
+        }
+
+        state_sum = warp_reduce_sum<WARP_SIZE>(state_sum);
+
+        if (lane == 0) {
+            y_warp[i * stride_y] = state_sum;
+        }
+    }
+
+#pragma unroll
+    for (int j = 0; j < c_factor; j++) {
+        s_warp[WARP_SIZE * j + lane] = state[j];
+    }
+}
+
+static void ssm_scan_f32_sycl(
+        const float * src0, const float * src1, const float * src2, const float * src3,
+        const float * src4, const float * src5, const int32_t * src6, float * dst,
+        const int src0_nb2, const int src0_nb3, const int src1_nb2, const int src1_nb3, const int src2_nb1,
+        const int src2_nb2, const int src3_nb1, const int src4_nb2, const int src4_nb3, const int src5_nb2,
+        const int src5_nb3, const int64_t s_off, const int64_t d_state, const int64_t head_dim,
+        const int64_t n_head, const int64_t n_group, const int64_t n_tok, const int64_t n_seq,
+        dpct::queue_ptr stream) {
+
+    // NOTE: if you change conditions here, be sure to update the corresponding supports_op condition!
+    GGML_ASSERT(src3_nb1 == sizeof(float));
+    if (d_state == 128) {
+        constexpr int threads   = 128;
+        constexpr int num_warps = threads / WARP_SIZE;
+        const sycl::range<2> grid(n_seq, (n_head * head_dim + num_warps - 1) / num_warps);
+        const sycl::range<2> block(1, threads);
+        stream->parallel_for(
+            sycl::nd_range<2>(grid * block, block),
+            [=](sycl::nd_item<2> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                ssm_scan_f32_group<128 / WARP_SIZE, 128>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                    src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2, src3_nb1,
+                    src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok, item);
+            });
+    } else if (d_state == 256) {
+        constexpr int threads   = 256;
+        constexpr int num_warps = threads / WARP_SIZE;
+        const sycl::range<2> grid(n_seq, (n_head * head_dim + num_warps - 1) / num_warps);
+        const sycl::range<2> block(1, threads);
+        stream->parallel_for(
+            sycl::nd_range<2>(grid * block, block),
+            [=](sycl::nd_item<2> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                ssm_scan_f32_group<256 / WARP_SIZE, 256>(
+                    src0, src1, src2, src3, src4, src5, src6, dst,
+                    src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2, src3_nb1,
+                    src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok, item);
+            });
+    } else {
+        GGML_ABORT("ssm_scan: unsupported d_state (must be 128 or 256)");
+    }
+}
+
+inline void ggml_sycl_op_ssm_scan(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];
+    const ggml_tensor * src3 = dst->src[3];
+    const ggml_tensor * src4 = dst->src[4];
+    const ggml_tensor * src5 = dst->src[5];
+    const ggml_tensor * src6 = dst->src[6];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src6->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+
+    const int64_t nc  = src0->ne[0];
+    const int64_t nr  = src0->ne[1];
+    const int64_t nh  = src1->ne[1];
+    const int64_t ng  = src4->ne[1];
+    const int64_t n_t = src1->ne[2];
+    const int64_t n_s = src1->ne[3];
+    const int64_t s_off = ggml_nelements(src1) * sizeof(float);
+
+    GGML_ASSERT(ggml_nelements(src1) + nc * nr * nh * n_s == ggml_nelements(dst));
+
+    dpct::queue_ptr stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    ssm_scan_f32_sycl(
+        static_cast<const float *>(src0->data), static_cast<const float *>(src1->data),
+        static_cast<const float *>(src2->data), static_cast<const float *>(src3->data),
+        static_cast<const float *>(src4->data), static_cast<const float *>(src5->data),
+        static_cast<const int32_t *>(src6->data), static_cast<float *>(dst->data),
+        src0->nb[2], src0->nb[3], src1->nb[2], src1->nb[3], src2->nb[1], src2->nb[2],
+        src3->nb[1], src4->nb[2], src4->nb[3], src5->nb[2], src5->nb[3],
+        s_off, nc, nr, nh, ng, n_t, n_s, stream);
+}
+
+void ggml_sycl_ssm_scan(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/7);
+    ggml_sycl_op_ssm_scan(ctx, dst);
+}
diff --git a/ggml/src/ggml-sycl/ssm_scan.hpp b/ggml/src/ggml-sycl/ssm_scan.hpp
new file mode 100644
index 00000000000..1f9731fb6fd
--- /dev/null
+++ b/ggml/src/ggml-sycl/ssm_scan.hpp
@@ -0,0 +1,5 @@
+#pragma once
+
+#include "common.hpp"
+
+void ggml_sycl_ssm_scan(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-sycl/sycl_hw.cpp b/ggml/src/ggml-sycl/sycl_hw.cpp
index 7041140034b..03b0c37a3cd 100644
--- a/ggml/src/ggml-sycl/sycl_hw.cpp
+++ b/ggml/src/ggml-sycl/sycl_hw.cpp
@@ -1,15 +1,67 @@
 #include "sycl_hw.hpp"
 
-// TODO: currently not used
-/*
-sycl_hw_info get_device_hw_info(sycl::device *device_ptr) {
-  sycl_hw_info res;
-  int32_t id = device_ptr->get_info<sycl::ext::intel::info::device::device_id>();
-  res.device_id = id;
+using namespace std;
 
-  syclex::architecture arch = device_ptr->get_info<syclex::info::device::architecture>();
-  res.arch = arch;
+/*defined in
+* /opt/intel/oneapi/compiler/latest/include/sycl/ext/oneapi/experimental/device_architecture.def
+*/
+static map<gpu_arch, std::pair<const char*, sycl_intel_gpu_family>> arch2name = {
+    {gpu_arch::intel_gpu_bdw,     {"intel_gpu_bdw",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_skl,     {"intel_gpu_skl",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_kbl,     {"intel_gpu_kbl",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_cfl,     {"intel_gpu_cfl",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_apl,     {"intel_gpu_apl",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_glk,     {"intel_gpu_glk",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_whl,     {"intel_gpu_whl",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_aml,     {"intel_gpu_aml",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_cml,     {"intel_gpu_cml",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_icllp,   {"intel_gpu_icllp",   GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_ehl,     {"intel_gpu_ehl",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_tgllp,   {"intel_gpu_tgllp",   GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_rkl,     {"intel_gpu_rkl",     GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_adl_s,   {"intel_gpu_adl_s",   GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_adl_p,   {"intel_gpu_adl_p",   GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_adl_n,   {"intel_gpu_adl_n",   GPU_FAMILY_IGPU_NON_XE}},
+    {gpu_arch::intel_gpu_dg1,     {"intel_gpu_dg1",     GPU_FAMILY_DGPU_CLIENT_GAME}},
+    {gpu_arch::intel_gpu_acm_g10, {"intel_gpu_acm_g10", GPU_FAMILY_DGPU_CLIENT_GAME}},
+    {gpu_arch::intel_gpu_acm_g11, {"intel_gpu_acm_g11", GPU_FAMILY_DGPU_CLIENT_GAME}},
+    {gpu_arch::intel_gpu_acm_g12, {"intel_gpu_acm_g12", GPU_FAMILY_DGPU_CLIENT_GAME}},
+    {gpu_arch::intel_gpu_pvc,     {"intel_gpu_pvc",     GPU_FAMILY_DGPU_CLOUD}},
+    {gpu_arch::intel_gpu_pvc_vg,  {"intel_gpu_pvc_vg",  GPU_FAMILY_DGPU_CLOUD}},
+    {gpu_arch::intel_gpu_mtl_u,   {"intel_gpu_mtl_u",   GPU_FAMILY_IGPU_XE}},
+    {gpu_arch::intel_gpu_mtl_h,   {"intel_gpu_mtl_h",   GPU_FAMILY_IGPU_XE}},
+    {gpu_arch::intel_gpu_arl_h,   {"intel_gpu_arl_h",   GPU_FAMILY_IGPU_XE}},
+    {gpu_arch::intel_gpu_bmg_g21, {"intel_gpu_bmg_g21", GPU_FAMILY_DGPU_CLIENT_GAME}},
+    {gpu_arch::intel_gpu_bmg_g31, {"intel_gpu_bmg_g31", GPU_FAMILY_DGPU_CLIENT_GAME}},
+    {gpu_arch::intel_gpu_lnl_m,   {"intel_gpu_lnl_m",   GPU_FAMILY_IGPU_XE}},
+    {gpu_arch::intel_gpu_ptl_h,   {"intel_gpu_ptl_h",   GPU_FAMILY_IGPU_XE}},
+    {gpu_arch::intel_gpu_ptl_u,   {"intel_gpu_ptl_u",   GPU_FAMILY_IGPU_XE}},
+    {gpu_arch::intel_gpu_wcl,     {"intel_gpu_wcl",     GPU_FAMILY_IGPU_XE}}
+};
+
+
+sycl_hw_info get_device_hw_info(sycl::device* device_ptr) {
+    sycl_hw_info res;
+    int32_t id =
+        device_ptr->get_info<sycl::ext::intel::info::device::device_id>();
+    res.device_id = id;
+
+    res.name = device_ptr->get_info<sycl::info::device::name>();
 
-  return res;
+    syclex::architecture arch =
+        device_ptr->get_info<syclex::info::device::architecture>();
+    res.arch = arch;
+
+    map<syclex::architecture,
+        std::pair<const char*, sycl_intel_gpu_family>>::iterator it =
+        arch2name.find(res.arch);
+    if (it != arch2name.end()) {
+        res.arch_name = it->second.first;
+        res.gpu_family = it->second.second;
+    } else {
+        res.arch_name = "unknown";
+        res.gpu_family = GPU_FAMILY_UKNOWN;
+    }
+
+    return res;
 }
-*/
diff --git a/ggml/src/ggml-sycl/sycl_hw.hpp b/ggml/src/ggml-sycl/sycl_hw.hpp
index 36b140bf037..a5d20462572 100644
--- a/ggml/src/ggml-sycl/sycl_hw.hpp
+++ b/ggml/src/ggml-sycl/sycl_hw.hpp
@@ -9,18 +9,30 @@
 #include <sycl/sycl.hpp>
 
 namespace syclex = sycl::ext::oneapi::experimental;
+using gpu_arch = sycl::ext::oneapi::experimental::architecture;
+
+// It's used to mark the GPU computing capacity
+// The value must flow the order of performance.
+enum sycl_intel_gpu_family {
+  GPU_FAMILY_UKNOWN = -1,
+  // iGPU without Xe core, before Meteor Lake iGPU(Xe)
+  GPU_FAMILY_IGPU_NON_XE = 0,
+  // iGPU with Xe core, Meteor Lake iGPU or newer.
+  GPU_FAMILY_IGPU_XE = 1,
+  // dGPU for gaming in client/data center (DG1/FLex 140 or newer).
+  GPU_FAMILY_DGPU_CLIENT_GAME = 2,
+  // dGPU for AI in cloud, PVC or newer.
+  GPU_FAMILY_DGPU_CLOUD = 3
+};
 
-// TODO: currently not used
-/*
 struct sycl_hw_info {
   syclex::architecture arch;
+  const char* arch_name;
   int32_t device_id;
+  std::string name;
+  sycl_intel_gpu_family gpu_family;
 };
 
-bool is_in_vector(std::vector<int> &vec, int item);
-
 sycl_hw_info get_device_hw_info(sycl::device *device_ptr);
-*/
-
 
 #endif // SYCL_HW_HPP
diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp
index 9253168e5ea..d7770047424 100644
--- a/ggml/src/ggml-sycl/vecdotq.hpp
+++ b/ggml/src/ggml-sycl/vecdotq.hpp
@@ -357,38 +357,31 @@ template <> struct reorder_vec_dot_q_sycl<GGML_TYPE_Q8_0> {
     using q8_0_block  = ggml_sycl_reordered::block_q_t<GGML_TYPE_Q8_0>;
     using q8_0_traits = typename q8_0_block::traits;
 
-    __dpct_inline__ float vec_dot_q8_0_q8_1_impl(const int * v, const int * u, const float & d8_0, const sycl::half2 & ds8) {
-        int sumi = 0;
-
-#pragma unroll
-        for (size_t i = 0; i < q8_0_traits::vdr_mmvq; ++i) {
-            // Q8_0 values are signed int8, no nibble extraction needed
-            // Direct dp4a: each int packs 4 int8 values
-            sumi = dpct::dp4a(v[i], u[i], sumi);
-        }
-
-        const sycl::float2 ds8f = ds8.convert<float, sycl::rounding_mode::automatic>();
-
-        // Q8_0 has no bias term (values are signed), so just scale
-        return d8_0 * sumi * ds8f.x();
-    }
-
     __dpct_inline__ float operator()(const void * __restrict__ vbq, const std::pair<int, int> ibx_offset,
                                      const std::pair<int, int> d_offset, const int8_t * q8_1_quant_ptr,
                                      const sycl::half2 * q8_1_ds, const int & iqs) {
-        const int8_t * bq8_0 = static_cast<const int8_t *>(vbq) + ibx_offset.first;
-        const ggml_half d = *(reinterpret_cast<const ggml_half *>(static_cast<const uint8_t *>(vbq) + d_offset.first));
-        int             v[q8_0_traits::vdr_mmvq];
-        int             u[q8_0_traits::vdr_mmvq];
+        const uint8_t * base = static_cast<const uint8_t *>(vbq);
+        const int8_t *  qs   = reinterpret_cast<const int8_t *>(base + ibx_offset.first);
+        const ggml_half  d   = *reinterpret_cast<const ggml_half *>(base + d_offset.first);
+
+        int v[q8_0_traits::vdr_mmvq];
+        int u[q8_0_traits::vdr_mmvq];
 
 #pragma unroll
         for (size_t i = 0; i < q8_0_traits::vdr_mmvq; ++i) {
-            v[i] = get_int_from_int8(bq8_0, iqs + i);
+            v[i] = get_int_from_int8(qs, iqs + i);
             u[i] = get_int_from_int8_aligned(q8_1_quant_ptr, iqs + i);
         }
 
-        return vec_dot_q8_0_q8_1_impl(v, u, d, *q8_1_ds);
-    };
+        int sumi = 0;
+#pragma unroll
+        for (size_t i = 0; i < q8_0_traits::vdr_mmvq; ++i) {
+            sumi = dpct::dp4a(v[i], u[i], sumi);
+        }
+
+        const sycl::half2 ds_values = *q8_1_ds;
+        return static_cast<float>(d) * static_cast<float>(ds_values[0]) * sumi;
+    }
 };
 
 static inline float vec_dot_q4_K_q8_1_common(const int * __restrict__ q4, const uint16_t * __restrict__ scales,
@@ -481,6 +474,65 @@ template <> struct reorder_vec_dot_q_sycl<GGML_TYPE_Q4_K> {
     }
 };
 
+template <> struct reorder_vec_dot_q_sycl<GGML_TYPE_Q5_K> {
+    static constexpr ggml_type gtype = GGML_TYPE_Q5_K;
+
+    using q5_k_block  = ggml_sycl_reordered::block_q_t<GGML_TYPE_Q5_K>;
+    using q5_k_traits = typename q5_k_block::traits;
+
+    __dpct_inline__ float operator()(const void * __restrict__ vbq, const std::pair<int, int> ibx_offset,
+                                     const std::pair<int, int> d_offset, const int8_t * q8_1_quant_ptr,
+                                     const sycl::half2 * q8_1_ds, const int & iqs) {
+        const uint8_t *    base           = static_cast<const uint8_t *>(vbq);
+        const uint8_t *    qs             = base + ibx_offset.first;   // low 4 bits
+        const uint8_t *    qh_base        = base + ibx_offset.second;  // high bit
+        const uint8_t *    scs            = base + d_offset.first;
+        const ggml_half2 * dms            = reinterpret_cast<const ggml_half2 *>(base + d_offset.second);
+
+        const int        bq8_offset = QR5_K * ((iqs / 2) / (QI8_1 / 2));
+        const int *      ql_ptr     = (const int *) (qs + 16 * bq8_offset + 4 * ((iqs / 2) % 4));
+        const int *      qh_ptr     = (const int *) (qh_base + 4 * ((iqs / 2) % 4));
+        const uint16_t * scales     = (const uint16_t *) scs;
+
+        int   vl[2];
+        int   vh[2];
+        int   u[2 * QR5_K];
+        float d8[QR5_K];
+
+        vl[0] = ql_ptr[0];
+        vl[1] = ql_ptr[4];
+
+        vh[0] = qh_ptr[0] >> bq8_offset;
+        vh[1] = qh_ptr[4] >> bq8_offset;
+
+        uint16_t  aux[2];
+        const int j = (QR5_K * ((iqs / 2) / (QI8_1 / 2))) / 2;
+        if (j < 2) {
+            aux[0] = scales[j + 0] & 0x3f3f;
+            aux[1] = scales[j + 2] & 0x3f3f;
+        } else {
+            aux[0] = ((scales[j + 2] >> 0) & 0x0f0f) | ((scales[j - 2] & 0xc0c0) >> 2);
+            aux[1] = ((scales[j + 2] >> 4) & 0x0f0f) | ((scales[j - 0] & 0xc0c0) >> 2);
+        }
+
+        const uint8_t * sc = (const uint8_t *) aux;
+        const uint8_t * m  = sc + 2;
+
+        for (int i = 0; i < QR5_K; ++i) {
+            const int8_t* quant_base_ptr = q8_1_quant_ptr + (bq8_offset + i) * QK8_1;
+            sycl::half2 ds_values = *(q8_1_ds + bq8_offset + i);
+
+            d8[i]                   = ds_values[0];
+
+            const int * q8 = (const int *) quant_base_ptr + ((iqs / 2) % 4);
+            u[2 * i + 0]   = q8[0];
+            u[2 * i + 1]   = q8[4];
+        }
+
+        return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, *dms, d8);
+    }
+};
+
 template <> struct reorder_vec_dot_q_sycl<GGML_TYPE_Q6_K> {
     static constexpr ggml_type gtype = GGML_TYPE_Q6_K;
 
diff --git a/ggml/src/ggml-turbo-quant.c b/ggml/src/ggml-turbo-quant.c
index 8cbc83fd1d9..83c9eaf74e0 100644
--- a/ggml/src/ggml-turbo-quant.c
+++ b/ggml/src/ggml-turbo-quant.c
@@ -20,6 +20,10 @@
 #define M_PI 3.14159265358979323846
 #endif
 
+/* Forward declarations for GGML_API symbols defined in this file (satisfies
+ * -Wmissing-prototypes under upstream CI's -Werror policy). */
+GGML_API void turbo_cpu_fwht_inverse(float * x, int group_size);
+
 /* Global: WHT group size for CPU quantize path (set by CPU SET_ROWS handler) */
 GGML_API int turbo3_cpu_wht_group_size = 0;
 
diff --git a/ggml/src/ggml-virtgpu/ggml-backend-device.cpp b/ggml/src/ggml-virtgpu/ggml-backend-device.cpp
index ec8156bb868..a978812cd90 100644
--- a/ggml/src/ggml-virtgpu/ggml-backend-device.cpp
+++ b/ggml/src/ggml-virtgpu/ggml-backend-device.cpp
@@ -1,5 +1,7 @@
 #include "ggml-remoting.h"
 
+#include <mutex>
+
 static const char * ggml_backend_remoting_device_get_name(ggml_backend_dev_t dev) {
     virtgpu * gpu = DEV_TO_GPU(dev);
 
diff --git a/ggml/src/ggml-virtgpu/ggml-backend.cpp b/ggml/src/ggml-virtgpu/ggml-backend.cpp
index 2b978556228..12756c9282f 100644
--- a/ggml/src/ggml-virtgpu/ggml-backend.cpp
+++ b/ggml/src/ggml-virtgpu/ggml-backend.cpp
@@ -34,8 +34,8 @@ static ggml_backend_i ggml_backend_remoting_interface = {
     /* .free                    = */ ggml_backend_remoting_free,
     /* .set_tensor_async        = */ NULL,  // ggml_backend_remoting_set_tensor_async,
     /* .get_tensor_async        = */ NULL,  // ggml_backend_remoting_get_tensor_async,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,  // ggml_backend_remoting_cpy_tensor_async,
     /* .synchronize             = */ NULL,  // ggml_backend_remoting_synchronize,
     /* .graph_plan_create       = */ NULL,
diff --git a/ggml/src/ggml-virtgpu/virtgpu-shm.cpp b/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
index ce6b3b3e607..7f2c2322d91 100644
--- a/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
+++ b/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
@@ -1,6 +1,7 @@
 #include "virtgpu-shm.h"
 
 #include "virtgpu.h"
+#include "ggml-remoting.h"
 
 #include <assert.h>
 
diff --git a/ggml/src/ggml-virtgpu/virtgpu.cpp b/ggml/src/ggml-virtgpu/virtgpu.cpp
index a84a77399d9..e3ae1cc75e0 100644
--- a/ggml/src/ggml-virtgpu/virtgpu.cpp
+++ b/ggml/src/ggml-virtgpu/virtgpu.cpp
@@ -1,4 +1,5 @@
 #include "virtgpu.h"
+#include "ggml-remoting.h"
 
 #include <stdio.h>
 #include <unistd.h>
diff --git a/ggml/src/ggml-virtgpu/virtgpu.h b/ggml/src/ggml-virtgpu/virtgpu.h
index f82d8fb50ba..6b8de583893 100644
--- a/ggml/src/ggml-virtgpu/virtgpu.h
+++ b/ggml/src/ggml-virtgpu/virtgpu.h
@@ -18,8 +18,6 @@
 
 #include <cstring>
 
-#include "ggml-remoting.h"
-
 #define VIRGL_RENDERER_UNSTABLE_APIS 1
 #include "apir_hw.h"
 #include <drm/virtgpu_drm.h>
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index cedd7132cfa..a296d0ab446 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -20,12 +20,19 @@ DispatchLoaderDynamic & ggml_vk_default_dispatcher();
 #define VULKAN_HPP_DEFAULT_DISPATCHER ggml_vk_default_dispatcher()
 
 #include <vulkan/vulkan.hpp>
-// SPIRV-Headers: LunarG Windows SDK uses Include/spirv-headers/spirv.hpp (not spirv/unified1/). MinGW/MSYS2 and
-// Linux packages use Khronos layout spirv/unified1/spirv.hpp. See docs/build.md#vulkan.
-#if defined(_WIN32) && !defined(__MINGW32__)
-#include <spirv-headers/spirv.hpp>
+
+// SPIR-V Headers: different SDK installations expose different include paths.
+// LunarG Vulkan SDK on Windows typically provides <spirv-headers/spirv.hpp>.
+// Linux packages, MSYS2 and MinGW often use the Khronos layout <spirv/unified1/spirv.hpp>.
+#if __has_include(<spirv/unified1/spirv.hpp>)
+#    include <spirv/unified1/spirv.hpp>
+#elif __has_include(<spirv-headers/spirv.hpp>)
+#    include <spirv-headers/spirv.hpp>
+#elif __has_include(<spirv.hpp>)
+#    include <spirv.hpp>
 #else
-#include <spirv/unified1/spirv.hpp>
+     // Fallback to let the compiler throw a standard "file not found" error
+#    include <spirv/unified1/spirv.hpp>
 #endif
 
 #include <algorithm>
@@ -42,7 +49,6 @@ DispatchLoaderDynamic & ggml_vk_default_dispatcher();
 #include <map>
 #include <set>
 #include <unordered_map>
-#include <memory>
 #include <mutex>
 #include <future>
 #include <thread>
@@ -104,8 +110,6 @@ static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }
 
 #define VK_DEVICE_DESCRIPTOR_POOL_SIZE 256
 
-#define GGML_VK_MAX_NODES 8192
-
 #define VK_CHECK(err, msg)                                          \
     do {                                                            \
         vk::Result err_ = (err);                                    \
@@ -433,10 +437,12 @@ struct vk_fa_pipeline_state {
     bool f32acc;
     uint32_t flags;
     uint32_t limit_occupancy_shmem;
+    ggml_type k_type;
+    ggml_type v_type;
 
     bool operator<(const vk_fa_pipeline_state &b) const {
-        return std::tie(HSK, HSV, Br, Bc, D_split, row_split, shmem_staging, path, workgroup_size, subgroup_size, aligned, f32acc, flags, limit_occupancy_shmem) <
-               std::tie(b.HSK, b.HSV, b.Br, b.Bc, b.D_split, b.row_split, b.shmem_staging, b.path, b.workgroup_size, b.subgroup_size, b.aligned, b.f32acc, b.flags, b.limit_occupancy_shmem);
+        return std::tie(HSK, HSV, Br, Bc, D_split, row_split, shmem_staging, path, workgroup_size, subgroup_size, aligned, f32acc, flags, limit_occupancy_shmem, k_type, v_type) <
+               std::tie(b.HSK, b.HSV, b.Br, b.Bc, b.D_split, b.row_split, b.shmem_staging, b.path, b.workgroup_size, b.subgroup_size, b.aligned, b.f32acc, b.flags, b.limit_occupancy_shmem, b.k_type, b.v_type);
     }
 };
 
@@ -674,6 +680,15 @@ struct vk_device_struct {
     bool mul_mat_id_m[GGML_TYPE_COUNT];
     bool mul_mat_id_s[GGML_TYPE_COUNT];
 
+    // Separate flags for the q8_1 (integer dot) mmq path, whose shader uses
+    // a different shared-memory layout than the float matmul shaders.
+    bool mul_mat_l_int[GGML_TYPE_COUNT];
+    bool mul_mat_m_int[GGML_TYPE_COUNT];
+    bool mul_mat_s_int[GGML_TYPE_COUNT];
+    bool mul_mat_id_l_int[GGML_TYPE_COUNT];
+    bool mul_mat_id_m_int[GGML_TYPE_COUNT];
+    bool mul_mat_id_s_int[GGML_TYPE_COUNT];
+
     vk::DescriptorSetLayout dsl;
 
     vk_matmul_pipeline pipeline_matmul_f32 {};
@@ -832,7 +847,6 @@ struct vk_device_struct {
     vk_pipeline pipeline_timestep_embedding_f32;
     vk_pipeline pipeline_conv_transpose_1d_f32;
     vk_pipeline pipeline_pool2d_f32;
-    vk_pipeline pipeline_turbo_wht;
     vk_pipeline pipeline_rwkv_wkv6_f32;
     vk_pipeline pipeline_rwkv_wkv7_f32;
     // [size_idx][kda] where size_idx: 0=d32, 1=d64, 2=d128
@@ -849,7 +863,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_conv2d_dw_whcn_f32, pipeline_conv2d_dw_whcn_f16_f32;
     vk_pipeline pipeline_conv2d_dw_cwhn_f32, pipeline_conv2d_dw_cwhn_f16_f32;
 
-    std::map<vk_fa_pipeline_state, vk_pipeline> pipeline_flash_attn_f32_f16[GGML_TYPE_COUNT];
+    std::map<vk_fa_pipeline_state, vk_pipeline> pipeline_flash_attn_f32_f16;
 
     std::map<std::pair<uint32_t, uint32_t>, vk_pipeline> pipeline_fa_mask_opt;
 
@@ -1491,6 +1505,7 @@ struct vk_op_gated_delta_net_push_constants {
     uint32_t sb1, sb2, sb3;
     uint32_t neq1, rq3;
     float scale;
+    uint32_t K;
 };
 
 struct vk_op_ssm_scan_push_constants {
@@ -2143,11 +2158,11 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
 
     // Patch SPIR-V to enable RTE rounding for FP16, avoiding the need for
     // separate shader variants compiled with -DRTE16.
-    std::vector<uint32_t> spv;
+    std::vector<uint32_t> spirv;
     if (device->float_controls_rte_fp16) {
         const uint32_t* spv_words = reinterpret_cast<const uint32_t *>(spv_data);
         size_t word_count = spv_size / sizeof(uint32_t);
-        spv.assign(spv_words, spv_words + word_count);
+        spirv.assign(spv_words, spv_words + word_count);
 
         // Find insertion points respecting SPIR-V layout order:
         //   Header(5) -> OpCapability -> OpExtension -> ... -> OpEntryPoint -> OpExecutionMode -> ...
@@ -2157,9 +2172,9 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
         size_t exec_insert_pos = pos;
         uint32_t entry_point_id = 0;
 
-        while (pos < spv.size()) {
-            uint32_t opcode = spv[pos] & spv::OpCodeMask;
-            uint32_t len    = spv[pos] >> spv::WordCountShift;
+        while (pos < spirv.size()) {
+            uint32_t opcode = spirv[pos] & spv::OpCodeMask;
+            uint32_t len    = spirv[pos] >> spv::WordCountShift;
             if (len == 0) break;
 
             if (opcode == spv::OpCapability) {
@@ -2168,7 +2183,7 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
             } else if (opcode == spv::OpExtension) {
                 ext_insert_pos = pos + len;
             } else if (opcode == spv::OpEntryPoint) {
-                entry_point_id = spv[pos + 2];
+                entry_point_id = spirv[pos + 2];
                 exec_insert_pos = pos + len;
             } else if (opcode == spv::OpExecutionMode || opcode == spv::OpExecutionModeId) {
                 exec_insert_pos = pos + len;
@@ -2183,7 +2198,7 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
 
         // OpExecutionMode %entrypoint RoundingModeRTE 16
         uint32_t exec_mode[] = { (4u << spv::WordCountShift) | spv::OpExecutionMode, entry_point_id, spv::ExecutionModeRoundingModeRTE, 16 };
-        spv.insert(spv.begin() + exec_insert_pos, std::begin(exec_mode), std::end(exec_mode));
+        spirv.insert(spirv.begin() + exec_insert_pos, std::begin(exec_mode), std::end(exec_mode));
 
         // OpExtension "SPV_KHR_float_controls"
         const char ext_str[] = "SPV_KHR_float_controls";
@@ -2191,13 +2206,13 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
         std::vector<uint32_t> extension(1 + ext_str_words, 0);
         extension[0] = (uint32_t)((1 + ext_str_words) << spv::WordCountShift) | spv::OpExtension;
         memcpy(&extension[1], ext_str, sizeof(ext_str));
-        spv.insert(spv.begin() + ext_insert_pos, extension.begin(), extension.end());
+        spirv.insert(spirv.begin() + ext_insert_pos, extension.begin(), extension.end());
 
         // OpCapability RoundingModeRTE
         uint32_t capability[] = { (2u << spv::WordCountShift) | spv::OpCapability, spv::CapabilityRoundingModeRTE };
-        spv.insert(spv.begin() + cap_insert_pos, std::begin(capability), std::end(capability));
+        spirv.insert(spirv.begin() + cap_insert_pos, std::begin(capability), std::end(capability));
 
-        shader_module_create_info = vk::ShaderModuleCreateInfo({}, spv.size() * sizeof(uint32_t), spv.data());
+        shader_module_create_info = vk::ShaderModuleCreateInfo({}, spirv.size() * sizeof(uint32_t), spirv.data());
     }
 
     pipeline->shader_module = device->device.createShaderModule(shader_module_create_info);
@@ -2927,10 +2942,10 @@ struct vk_fa_tuning_params {
     }
 };
 
-static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type kv_type);
+static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type);
 static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc);
 
-static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type kv_type, bool f32acc) {
+static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {
 
     vk_fa_tuning_params result{};
     result.path = FA_SCALAR;
@@ -2982,7 +2997,7 @@ static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device,
 
     result.shmem_staging = (device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 && hsv < 256) ? 1 : 0;
 
-    if (!reduce_block_rows && !ggml_vk_flash_attn_scalar_shmem_support(device, result, hsk, hsv, f32acc, kv_type)) {
+    if (!reduce_block_rows && !ggml_vk_flash_attn_scalar_shmem_support(device, result, hsk, hsv, f32acc, k_type, v_type)) {
         result.block_rows /= 2;
     }
 
@@ -3005,10 +3020,11 @@ static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device,
     return result;
 }
 
-static vk_fa_tuning_params get_fa_tuning_params_coopmat1(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type kv_type, bool f32acc) {
+static vk_fa_tuning_params get_fa_tuning_params_coopmat1(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {
     GGML_UNUSED(n_rows);
     GGML_UNUSED(n_kv);
-    GGML_UNUSED(kv_type);
+    GGML_UNUSED(k_type);
+    GGML_UNUSED(v_type);
     GGML_UNUSED(f32acc);
 
     vk_fa_tuning_params result{};
@@ -3035,7 +3051,7 @@ static vk_fa_tuning_params get_fa_tuning_params_coopmat1(const vk_device& device
     return result;
 }
 
-static vk_fa_tuning_params get_fa_tuning_params_coopmat2(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type kv_type, bool f32acc) {
+static vk_fa_tuning_params get_fa_tuning_params_coopmat2(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {
     GGML_UNUSED(n_kv);
     GGML_UNUSED(f32acc);
 
@@ -3049,7 +3065,7 @@ static vk_fa_tuning_params get_fa_tuning_params_coopmat2(const vk_device& device
     if (small_rows) {
         result.block_rows = 32;
         result.block_cols = 32;
-    } else if (ggml_is_quantized(kv_type) || hsk >= 256 || hsv >= 256) {
+    } else if (ggml_is_quantized(k_type) || ggml_is_quantized(v_type) || hsk >= 256 || hsv >= 256) {
         result.block_rows = (hsk >= 512 || hsv >= 512) ? 32 : 64;
         result.block_cols = 32;
     } else {
@@ -3063,7 +3079,7 @@ static vk_fa_tuning_params get_fa_tuning_params_coopmat2(const vk_device& device
     return result;
 }
 
-static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type kv_type, bool f32acc) {
+static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {
     FaCodePath path = device->coopmat2 ? FA_COOPMAT2 :
                       device->coopmat1_fa_support ? FA_COOPMAT1 : FA_SCALAR;
 
@@ -3075,7 +3091,7 @@ static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_
     if (path == FA_COOPMAT1) {
         bool shape_ok = (f32acc && device->coopmat_support_16x16x16_f32acc) ||
                         (!f32acc && device->coopmat_support_16x16x16_f16acc);
-        const vk_fa_tuning_params params = get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, kv_type, f32acc);
+        const vk_fa_tuning_params params = get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
         bool shmem_ok = ggml_vk_flash_attn_coopmat_shmem_support(device, params, hsk, hsv, f32acc);
 
         if (!shape_ok || !shmem_ok) {
@@ -3088,20 +3104,25 @@ static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_
         path = FA_SCALAR;
     }
 
+    // Q1_0 K/V is only implemented on coopmat2 (flash_attn_cm2); there is no scalar FA shader for it.
+    if ((k_type == GGML_TYPE_Q1_0 || v_type == GGML_TYPE_Q1_0) && device->coopmat2) {
+        path = FA_COOPMAT2;
+    }
+
     switch (path) {
     case FA_SCALAR:
-        return get_fa_tuning_params_scalar(device, hsk, hsv, n_rows, n_kv, kv_type, f32acc);
+        return get_fa_tuning_params_scalar(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
     case FA_COOPMAT1:
-        return get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, kv_type, f32acc);
+        return get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
     case FA_COOPMAT2:
-        return get_fa_tuning_params_coopmat2(device, hsk, hsv, n_rows, n_kv, kv_type, f32acc);
+        return get_fa_tuning_params_coopmat2(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
     default:
         throw std::runtime_error("unsupported FaCodePath");
     }
 }
 
 static vk_fa_pipeline_state get_fa_pipeline_state(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool aligned, bool f32acc,
-                                                  bool use_mask, bool use_mask_opt, bool use_logit_softcap) {
+                                                  bool use_mask, bool use_mask_opt, bool use_logit_softcap, ggml_type k_type, ggml_type v_type) {
     const bool old_amd_windows = device->vendor_id == VK_VENDOR_ID_AMD && device->driver_id == vk::DriverId::eAmdProprietary &&
                                  (device->architecture == AMD_GCN || device->architecture == AMD_RDNA1 || device->architecture == AMD_RDNA2);
 
@@ -3112,12 +3133,32 @@ static vk_fa_pipeline_state get_fa_pipeline_state(const vk_device& device, const
 
     const uint32_t subgroup_size = params.disable_subgroups ? 0 : params.subgroup_size;
 
-    return vk_fa_pipeline_state{hsk, hsv, params.block_rows, params.block_cols, params.d_split, params.row_split, params.shmem_staging, params.path, params.workgroup_size, subgroup_size, aligned, f32acc, flags, params.limit_occupancy_shmem};
+    return vk_fa_pipeline_state{hsk, hsv, params.block_rows, params.block_cols, params.d_split, params.row_split, params.shmem_staging, params.path, params.workgroup_size, subgroup_size, aligned, f32acc, flags, params.limit_occupancy_shmem, k_type, v_type};
 }
 
 static std::vector<uint32_t> get_fa_spec_constants(const vk_fa_pipeline_state& state) {
-    return {state.workgroup_size, state.Br, state.Bc, state.HSK, state.HSV, !state.aligned, state.D_split,
-            state.row_split, state.subgroup_size, state.shmem_staging ? 1u : 0u, state.flags, state.limit_occupancy_shmem};
+    const auto fa_block_bytes = [](ggml_type t) -> uint32_t {
+        // decodeBufF32 uses a block of vec4s for a better memory access pattern.
+        return t == GGML_TYPE_F32 ? 16u : (uint32_t) ggml_type_size(t);
+    };
+    return {
+        /* 0 WorkGroupSize   */ state.workgroup_size,
+        /* 1 Br              */ state.Br,
+        /* 2 Bc              */ state.Bc,
+        /* 3 HSK             */ state.HSK,
+        /* 4 HSV             */ state.HSV,
+        /* 5 Clamp           */ static_cast<uint32_t>(!state.aligned),
+        /* 6 D_split         */ state.D_split,
+        /* 7 row_split       */ state.row_split,
+        /* 8 SubGroupSize    */ state.subgroup_size,
+        /* 9 SHMEM_STAGING   */ state.shmem_staging ? 1u : 0u,
+        /*10 Flags           */ state.flags,
+        /*11 LIMIT_OCCUPANCY_SHMEM */ state.limit_occupancy_shmem,
+        /*12 FaTypeK         */ static_cast<uint32_t>(state.k_type),
+        /*13 FaTypeV         */ static_cast<uint32_t>(state.v_type),
+        /*14 FaBlockBytesK   */ fa_block_bytes(state.k_type),
+        /*15 FaBlockBytesV   */ fa_block_bytes(state.v_type),
+    };
 }
 
 static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vector<uint32_t>& warptile, bool mul_mat_id, ggml_type src0_type) {
@@ -3175,6 +3216,70 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
     return supported;
 }
 
+// Shmem usage for the q8_1 mmq shader (mul_mmq.comp), which uses
+// block_a_cache / block_b_cache layouts (see mul_mmq_shmem_types.glsl) rather
+// than the float load buffers checked by ggml_vk_matmul_shmem_support.
+// Sizes follow std430 rules. Returns false for types without a q8_1 pipeline.
+static bool ggml_vk_matmul_int_shmem_support(const vk_device& device, const std::vector<uint32_t>& warptile, bool mul_mat_id, ggml_type src0_type) {
+
+    // FLOAT_TYPE in the shader is float16_t with fp16 support, otherwise float.
+    const uint32_t fp_size   = device->fp16 ? 2u : 4u;
+    const uint32_t fp_align  = fp_size;
+    const uint32_t fp2_size  = 2u * fp_size;
+    const uint32_t fp2_align = device->fp16 ? 4u : 8u;
+
+    struct member { uint32_t size, align; };
+    auto std430_size = [](std::initializer_list<member> members) {
+        uint32_t off = 0, struct_align = 1;
+        for (const auto &m : members) {
+            off = (off + m.align - 1) & ~(m.align - 1);
+            off += m.size;
+            struct_align = std::max(struct_align, m.align);
+        }
+        return (off + struct_align - 1) & ~(struct_align - 1);
+    };
+
+    uint32_t block_a_size = 0;
+    switch (src0_type) {
+        case GGML_TYPE_Q4_0:    block_a_size = std430_size({{16, 4}, {fp_size,  fp_align}});                  break; // qs[16/4] + dm
+        case GGML_TYPE_Q4_1:    block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}});                 break; // qs[16/4] + dm(vec2)
+        case GGML_TYPE_Q5_0:    block_a_size = std430_size({{16, 4}, {4, 4}, {fp_size,  fp_align}});          break; // qs[16/4] + qh + dm
+        case GGML_TYPE_Q5_1:    block_a_size = std430_size({{16, 4}, {4, 4}, {fp2_size, fp2_align}});         break; // qs[16/4] + qh + dm(vec2)
+        case GGML_TYPE_Q8_0:    block_a_size = std430_size({{32, 4}, {fp_size,  fp_align}});                  break; // qs[8] + dm
+        case GGML_TYPE_MXFP4:   block_a_size = std430_size({{32, 4}, {fp_size,  fp_align}});                  break; // qs[8] + d
+        case GGML_TYPE_Q2_K:    block_a_size = std430_size({{ 8, 4}, {2, 2}, {fp2_size, fp2_align}});         break; // qs[2] + scales(u8vec2) + dm(vec2)
+        case GGML_TYPE_Q3_K:    block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}});                 break; // qs[4] + d_scales(vec2)
+        case GGML_TYPE_Q4_K:    block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}});                 break; // qs[4] + dm(vec2)
+        case GGML_TYPE_Q5_K:    block_a_size = std430_size({{32, 4}, {fp2_size, fp2_align}});                 break; // qs[8] + dm(vec2)
+        case GGML_TYPE_Q6_K:    block_a_size = std430_size({{32, 4}, {fp2_size, fp2_align}});                 break; // qs[8] + d_scales(vec2)
+        default:
+            return false;
+    }
+
+    // block_b_cache: { int32_t qs[8]; FLOAT_TYPEV2 ds; }
+    const uint32_t block_b_size = std430_size({{32, 4}, {fp2_size, fp2_align}});
+
+    const uint32_t BM = warptile[1];
+    const uint32_t BN = warptile[2];
+    // mul_mmq.comp: BK_STEP=1 for MUL_MAT_ID, 4 otherwise.
+    const uint32_t BK_STEP = mul_mat_id ? 1u : 4u;
+
+    const uint32_t buf_a_size = BM * BK_STEP * block_a_size;
+    const uint32_t buf_b_size = BN * BK_STEP * block_b_size;
+    const uint32_t mmid_row_ids = mul_mat_id ? (BN * 2u * (uint32_t)sizeof(uint16_t)) : 0u;
+
+    const uint32_t warps = warptile[0] / warptile[10];
+    const uint32_t ballots_sh = mul_mat_id ? (warps * 4u * (uint32_t)sizeof(uint32_t)) : 0u;
+
+    const uint32_t total_size = buf_a_size + buf_b_size + mmid_row_ids + ballots_sh;
+    const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
+
+    VK_LOG_DEBUG("ggml_vk_matmul_int_shmem_support(warptile=(" << warptile[0] << "," << warptile[1] << "," << warptile[2] << "), "
+                 "mul_mat_id=" << mul_mat_id << ", src0_type=" << ggml_type_name(src0_type) << ", total=" << total_size << ", supported=" << supported);
+
+    return supported;
+}
+
 struct GpuPipelineConfig {
     // GPU architecture identifier.
     // Example: vk_device_architecture::AMD_GCN
@@ -3242,6 +3347,20 @@ static uint32_t get_subgroup_size(const std::string &pipeline_name, const vk_dev
     return 0; // If no matching configuration is found
 }
 
+// Whether scalar flash attention will use the MMQ path for the given k_type.
+static bool ggml_vk_fa_scalar_uses_mmq(const vk_device& device, ggml_type k_type) {
+#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
+    return device->integer_dot_product && device->subgroup_clustered &&
+           (k_type == GGML_TYPE_Q4_0 || k_type == GGML_TYPE_Q4_1 ||
+            k_type == GGML_TYPE_Q5_0 || k_type == GGML_TYPE_Q5_1 ||
+            k_type == GGML_TYPE_Q8_0);
+#else
+    GGML_UNUSED(device);
+    GGML_UNUSED(k_type);
+    return false;
+#endif
+}
+
 static void ggml_vk_load_shaders(vk_device& device) {
     VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")");
 
@@ -3407,6 +3526,40 @@ static void ggml_vk_load_shaders(vk_device& device) {
             } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmqid, true, t)) {
                 device->mul_mat_id_l[i] = false;
             }
+
+            // The q8_1 mmq path has its own (larger) shmem layout, check it separately.
+            // K-quants use the _int_k warptiles, others use _int.
+            const bool is_k_quant = (t == GGML_TYPE_Q2_K || t == GGML_TYPE_Q3_K ||
+                                     t == GGML_TYPE_Q4_K || t == GGML_TYPE_Q5_K ||
+                                     t == GGML_TYPE_Q6_K);
+            const auto & s_int   = is_k_quant ? s_warptile_mmq_int_k   : s_warptile_mmq_int;
+            const auto & m_int   = is_k_quant ? m_warptile_mmq_int_k   : m_warptile_mmq_int;
+            const auto & l_int   = is_k_quant ? l_warptile_mmq_int_k   : l_warptile_mmq_int;
+            const auto & s_intid = is_k_quant ? s_warptile_mmqid_int_k : s_warptile_mmqid_int;
+            const auto & m_intid = is_k_quant ? m_warptile_mmqid_int_k : m_warptile_mmqid_int;
+            const auto & l_intid = is_k_quant ? l_warptile_mmqid_int_k : l_warptile_mmqid_int;
+
+            if (!ggml_vk_matmul_int_shmem_support(device, s_int, false, t)) {
+                device->mul_mat_s_int[i] = false;
+                device->mul_mat_m_int[i] = false;
+                device->mul_mat_l_int[i] = false;
+            } else if (!ggml_vk_matmul_int_shmem_support(device, m_int, false, t)) {
+                device->mul_mat_m_int[i] = false;
+                device->mul_mat_l_int[i] = false;
+            } else if (!ggml_vk_matmul_int_shmem_support(device, l_int, false, t)) {
+                device->mul_mat_l_int[i] = false;
+            }
+
+            if (!ggml_vk_matmul_int_shmem_support(device, s_intid, true, t)) {
+                device->mul_mat_id_s_int[i] = false;
+                device->mul_mat_id_m_int[i] = false;
+                device->mul_mat_id_l_int[i] = false;
+            } else if (!ggml_vk_matmul_int_shmem_support(device, m_intid, true, t)) {
+                device->mul_mat_id_m_int[i] = false;
+                device->mul_mat_id_l_int[i] = false;
+            } else if (!ggml_vk_matmul_int_shmem_support(device, l_intid, true, t)) {
+                device->mul_mat_id_l_int[i] = false;
+            }
         }
     }
 
@@ -3488,106 +3641,96 @@ static void ggml_vk_load_shaders(vk_device& device) {
                                        align, disable_robustness, require_full_subgroups, required_subgroup_size);
     };
 
-#define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \
-        for (auto &fa : device->pipeline_flash_attn_f32_f16[TYPE]) { \
-            FaCodePath path = fa.first.path; \
-            uint32_t Br = fa.first.Br; \
-            uint32_t Bc = fa.first.Bc; \
-            bool aligned = fa.first.aligned; \
-            bool f32acc = fa.first.f32acc; \
-            uint32_t fa_sgs = fa.first.subgroup_size; \
-            bool fa_ds = fa.first.subgroup_size == 0; \
-            if (path == FAPATH) { \
-                if (aligned) { \
-                    if (f32acc) { \
-                        ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ##            SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ##            SUFFIX ## _data,  "main", 7, sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, get_fa_spec_constants(fa.first), Bc, true, (!fa_ds && (FAPATH!=FA_COOPMAT2)), ((!fa_ds && (FAPATH!=FA_COOPMAT2)) ? fa_sgs : 0));     \
-                    } else { \
-                        ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 7, sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, get_fa_spec_constants(fa.first), Bc, true, (!fa_ds && (FAPATH!=FA_COOPMAT2)), ((!fa_ds && (FAPATH!=FA_COOPMAT2)) ? fa_sgs : 0));     \
-                    } \
-                } else { \
-                    if (f32acc) { \
-                        ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f32acc"         #NAMELC, flash_attn_f32_f16_ ## NAMELC ##            SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ##            SUFFIX ## _data,  "main", 7, sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, get_fa_spec_constants(fa.first), 1,  true, (!fa_ds && (FAPATH!=FA_COOPMAT2)), ((!fa_ds && (FAPATH!=FA_COOPMAT2)) ? fa_sgs : 0));     \
-                    } else { \
-                        ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f16acc"         #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 7, sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, get_fa_spec_constants(fa.first), 1,  true, (!fa_ds && (FAPATH!=FA_COOPMAT2)), ((!fa_ds && (FAPATH!=FA_COOPMAT2)) ? fa_sgs : 0));     \
-                    } \
-                } \
-            } \
-        }
-
-    if (device->fp16) {
-        CREATE_FA(GGML_TYPE_F32, f32, FA_SCALAR, )
-        CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, )
-
+    // FA scalar has two SPIR-V modules (MMQ vs non-MMQ); FA cm1 has one. K/V
+    // quant type is selected at runtime via the FaTypeK / FaTypeV spec constants.
+
+    for (auto &fa : device->pipeline_flash_attn_f32_f16) {
+        if (fa.first.path != FA_SCALAR) continue;
+        const uint32_t Br = fa.first.Br;
+        const uint32_t Bc = fa.first.Bc;
+        const bool aligned = fa.first.aligned;
+        const bool f32acc = fa.first.f32acc;
+        const uint32_t fa_sgs = fa.first.subgroup_size;
+        const bool fa_ds = fa.first.subgroup_size == 0;
+
+        const bool use_mmq = ggml_vk_fa_scalar_uses_mmq(device, fa.first.k_type);
+        const void * spv_data = nullptr;
+        size_t spv_size = 0;
+        if (use_mmq) {
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
-        if (device->integer_dot_product && device->subgroup_clustered) {
-            CREATE_FA(GGML_TYPE_Q4_0,     q4_0, FA_SCALAR, _int8)
-            CREATE_FA(GGML_TYPE_Q8_0,     q8_0, FA_SCALAR, _int8)
-            CREATE_FA(GGML_TYPE_Q4_1,     q4_1, FA_SCALAR, _int8)
-            CREATE_FA(GGML_TYPE_Q5_0,     q5_0, FA_SCALAR, _int8)
-            CREATE_FA(GGML_TYPE_Q5_1,     q5_1, FA_SCALAR, _int8)
-            CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, FA_SCALAR, _int8)
-        } else
-#endif
-        {
-            CREATE_FA(GGML_TYPE_Q4_0,     q4_0, FA_SCALAR, )
-            CREATE_FA(GGML_TYPE_Q8_0,     q8_0, FA_SCALAR, )
-            CREATE_FA(GGML_TYPE_Q4_1,     q4_1, FA_SCALAR, )
-            CREATE_FA(GGML_TYPE_Q5_0,     q5_0, FA_SCALAR, )
-            CREATE_FA(GGML_TYPE_Q5_1,     q5_1, FA_SCALAR, )
-            CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, FA_SCALAR, )
-        }
-        CREATE_FA(GGML_TYPE_TURBO3_0, turbo3_0, FA_SCALAR, )
-    } else {
-        CREATE_FA(GGML_TYPE_F32, f32, FA_SCALAR, _fp32)
-        CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, _fp32)
-
-#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
-        if (device->integer_dot_product && device->subgroup_clustered) {
-            CREATE_FA(GGML_TYPE_Q4_0,     q4_0, FA_SCALAR, _fp32_int8)
-            CREATE_FA(GGML_TYPE_Q8_0,     q8_0, FA_SCALAR, _fp32_int8)
-            CREATE_FA(GGML_TYPE_Q4_1,     q4_1, FA_SCALAR, _fp32_int8)
-            CREATE_FA(GGML_TYPE_Q5_0,     q5_0, FA_SCALAR, _fp32_int8)
-            CREATE_FA(GGML_TYPE_Q5_1,     q5_1, FA_SCALAR, _fp32_int8)
-            CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, FA_SCALAR, _fp32_int8)
-        } else
+            if (device->fp16) {
+                if (f32acc) { spv_data = flash_attn_f32_f16_int8_data;        spv_size = flash_attn_f32_f16_int8_len; }
+                else        { spv_data = flash_attn_f32_f16_f16acc_int8_data; spv_size = flash_attn_f32_f16_f16acc_int8_len; }
+            } else {
+                spv_data = flash_attn_f32_f16_fp32_int8_data;
+                spv_size = flash_attn_f32_f16_fp32_int8_len;
+            }
 #endif
-        {
-            CREATE_FA(GGML_TYPE_Q4_0,     q4_0, FA_SCALAR, _fp32)
-            CREATE_FA(GGML_TYPE_Q8_0,     q8_0, FA_SCALAR, _fp32)
-            CREATE_FA(GGML_TYPE_Q4_1,     q4_1, FA_SCALAR, _fp32)
-            CREATE_FA(GGML_TYPE_Q5_0,     q5_0, FA_SCALAR, _fp32)
-            CREATE_FA(GGML_TYPE_Q5_1,     q5_1, FA_SCALAR, _fp32)
-            CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, FA_SCALAR, _fp32)
+        } else {
+            if (device->fp16) {
+                if (f32acc) { spv_data = flash_attn_f32_f16_data;        spv_size = flash_attn_f32_f16_len; }
+                else        { spv_data = flash_attn_f32_f16_f16acc_data; spv_size = flash_attn_f32_f16_f16acc_len; }
+            } else {
+                spv_data = flash_attn_f32_f16_fp32_data;
+                spv_size = flash_attn_f32_f16_fp32_len;
+            }
         }
-        CREATE_FA(GGML_TYPE_TURBO3_0, turbo3_0, FA_SCALAR, _fp32)
+        const char *name = aligned ? "flash_attn_f32_f16_aligned" : "flash_attn_f32_f16";
+        ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7,
+                                sizeof(vk_flash_attn_push_constants), {Br, 1, 1},
+                                get_fa_spec_constants(fa.first), aligned ? Bc : 1, true,
+                                !fa_ds, !fa_ds ? fa_sgs : 0);
     }
+
 #if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
     if (device->coopmat1_fa_support) {
-        CREATE_FA(GGML_TYPE_F32, f32, FA_COOPMAT1, _cm1)
-        CREATE_FA(GGML_TYPE_F16, f16, FA_COOPMAT1, _cm1)
-        CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_COOPMAT1, _cm1)
-        CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_COOPMAT1, _cm1)
-        CREATE_FA(GGML_TYPE_Q4_1, q4_1, FA_COOPMAT1, _cm1)
-        CREATE_FA(GGML_TYPE_Q5_0, q5_0, FA_COOPMAT1, _cm1)
-        CREATE_FA(GGML_TYPE_Q5_1, q5_1, FA_COOPMAT1, _cm1)
-        CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, FA_COOPMAT1, _cm1)
-        CREATE_FA(GGML_TYPE_TURBO3_0, turbo3_0, FA_COOPMAT1, _cm1)
+        for (auto &fa : device->pipeline_flash_attn_f32_f16) {
+            if (fa.first.path != FA_COOPMAT1) continue;
+            const uint32_t Br = fa.first.Br;
+            const uint32_t Bc = fa.first.Bc;
+            const bool aligned = fa.first.aligned;
+            const bool f32acc = fa.first.f32acc;
+            const uint32_t fa_sgs = fa.first.subgroup_size;
+            const bool fa_ds = fa.first.subgroup_size == 0;
+
+            const void * spv_data;
+            size_t spv_size;
+            if (f32acc) { spv_data = flash_attn_f32_f16_cm1_data;        spv_size = flash_attn_f32_f16_cm1_len; }
+            else        { spv_data = flash_attn_f32_f16_f16acc_cm1_data; spv_size = flash_attn_f32_f16_f16acc_cm1_len; }
+            const char *name = aligned ? "flash_attn_f32_f16_aligned_cm1" : "flash_attn_f32_f16_cm1";
+            ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7,
+                                    sizeof(vk_flash_attn_push_constants), {Br, 1, 1},
+                                    get_fa_spec_constants(fa.first), aligned ? Bc : 1, true,
+                                    !fa_ds, !fa_ds ? fa_sgs : 0);
+        }
     }
 #endif
+
 #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
     if (device->coopmat2) {
-        CREATE_FA(GGML_TYPE_F32, f32, FA_COOPMAT2, _cm2)
-        CREATE_FA(GGML_TYPE_F16, f16, FA_COOPMAT2, _cm2)
-        CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_COOPMAT2, _cm2)
-        CREATE_FA(GGML_TYPE_Q4_1, q4_1, FA_COOPMAT2, _cm2)
-        CREATE_FA(GGML_TYPE_Q5_0, q5_0, FA_COOPMAT2, _cm2)
-        CREATE_FA(GGML_TYPE_Q5_1, q5_1, FA_COOPMAT2, _cm2)
-        CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_COOPMAT2, _cm2)
-        CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, FA_COOPMAT2, _cm2)
-        CREATE_FA(GGML_TYPE_TURBO3_0, turbo3_0, FA_COOPMAT2, _cm2)
+        for (auto &fa : device->pipeline_flash_attn_f32_f16) {
+            if (fa.first.path != FA_COOPMAT2) continue;
+            const uint32_t Br = fa.first.Br;
+            const uint32_t Bc = fa.first.Bc;
+            const bool aligned = fa.first.aligned;
+            const bool f32acc = fa.first.f32acc;
+
+            const void * spv_data;
+            size_t spv_size;
+            const char * name;
+            if (aligned) {
+                if (f32acc) { spv_data = flash_attn_f32_f16_cm2_data;        spv_size = flash_attn_f32_f16_cm2_len;        name = "flash_attn_f32_f16_aligned_f32acc_cm2"; }
+                else        { spv_data = flash_attn_f32_f16_f16acc_cm2_data; spv_size = flash_attn_f32_f16_f16acc_cm2_len; name = "flash_attn_f32_f16_aligned_f16acc_cm2"; }
+            } else {
+                if (f32acc) { spv_data = flash_attn_f32_f16_cm2_data;        spv_size = flash_attn_f32_f16_cm2_len;        name = "flash_attn_f32_f16_f32acc_cm2"; }
+                else        { spv_data = flash_attn_f32_f16_f16acc_cm2_data; spv_size = flash_attn_f32_f16_f16acc_cm2_len; name = "flash_attn_f32_f16_f16acc_cm2"; }
+            }
+            ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7,
+                                    sizeof(vk_flash_attn_push_constants), {Br, 1, 1},
+                                    get_fa_spec_constants(fa.first), aligned ? Bc : 1, true, false, 0);
+        }
     }
 #endif
-#undef CREATE_FA
 
     const int mul_mat_id_param_count = 5;
 
@@ -3811,13 +3954,13 @@ static void ggml_vk_load_shaders(vk_device& device) {
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
 
 #define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
-        if (device->mul_mat ## ID ## _l[TYPE]) { \
+        if (device->mul_mat ## ID ## _l_int[TYPE]) { \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->l, #NAMELC        "_l", NAMELC ## _len,        NAMELC ##  _data,        "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         } \
-        if (device->mul_mat ## ID ## _m[TYPE]) { \
+        if (device->mul_mat ## ID ## _m_int[TYPE]) { \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->m, #NAMELC        "_m", NAMELC ## _len,        NAMELC ##  _data,        "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         } \
-        if (device->mul_mat ## ID ## _s[TYPE]) { \
+        if (device->mul_mat ## ID ## _s_int[TYPE]) { \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->s, #NAMELC        "_s", NAMELC ## _len,        NAMELC ##  _data,        "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         } \
 
@@ -3988,11 +4131,11 @@ static void ggml_vk_load_shaders(vk_device& device) {
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
 
 #define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
-        if (device->mul_mat ## ID ## _l[TYPE]) \
+        if (device->mul_mat ## ID ## _l_int[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC "_l", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1);   \
-        if (device->mul_mat ## ID ## _m[TYPE]) \
+        if (device->mul_mat ## ID ## _m_int[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC "_m", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1);   \
-        if (device->mul_mat ## ID ## _s[TYPE]) \
+        if (device->mul_mat ## ID ## _s_int[TYPE]) \
             ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC "_s", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1);   \
 
         CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
@@ -4117,11 +4260,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
         m_wg_denoms = { 64,  64, 1 };
         s_wg_denoms = { 32,  32, 1 };
 
-        if (device->vendor_id == VK_VENDOR_ID_INTEL && device->architecture == INTEL_XE2) {
-            // Xe2/Xe3 - bf16 warptile performance tuning
-            l_warptile = { 512, 128, 128, 16, subgroup_size_8, 32, 2, 4, 4, 1, subgroup_size_8 };
-        }
-
         CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
         CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
     }
@@ -4156,30 +4294,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     const uint32_t force_subgroup_size = use_subgroups ? subgroup_size : 0;
     const uint32_t force_subgroup_size16 = use_subgroups16 ? subgroup_size16 : 0;
-
-    // TQ4_1S uses a dedicated pipeline whose workgroup size is always 32 and
-    // whose reduction path is always the shared-memory variant.
-    //
-    // The Walsh-Hadamard butterfly inside the shader operates on 32-element
-    // blocks with one element per thread, so the workgroup contract is fixed
-    // regardless of what the rest of the mul_mat_vec family picks for the
-    // current DMMV_WG_SIZE bucket.  We always use 32 threads per workgroup.
-    //
-    // Reduction choice: the shader uses the SHMEM tree reduction even when
-    // subgroup arithmetic is available.  A subgroup-shuffle butterfly + pure
-    // subgroupAdd reduction variant was tried and measured ~70 %% slower on
-    // Intel Arc (Mesa Xe HPG), where subgroup shuffles and subgroup adds are
-    // emulated over LDS and end up doing the same amount of LDS traffic as
-    // the explicit shared-memory path but with extra driver overhead.  Going
-    // through SHMEM directly is always correct and is fastest on the devices
-    // we can actually measure.  Future vendor-specific heuristics can switch
-    // to the hybrid reduction variant on NVIDIA / AMD RDNA if hardware
-    // subgroup shuffles beat the LDS roundtrip there.
-    const uint32_t tq4_1s_wg_size            = 32u;
-    const uint32_t tq4_1s_force_sg_size      = 0u;
-    const bool     tq4_1s_use_subgroups      = false;
-    const shader_reduction_mode tq4_1s_reduc = SHADER_REDUCTION_MODE_SHMEM;
-
     static constexpr uint32_t mul_mat_vec_num_bindings = 5;
     static constexpr uint32_t mul_mat_vec_id_num_bindings = 6;
 
@@ -4221,10 +4335,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32",  arr_dmmv_iq4_nl_f32_f32_len[reduc16],  arr_dmmv_iq4_nl_f32_f32_data[reduc16],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32",   arr_dmmv_mxfp4_f32_f32_len[reduc16],   arr_dmmv_mxfp4_f32_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_NVFP4][i],   "mul_mat_vec_nvfp4_f32_f32",   arr_dmmv_nvfp4_f32_f32_len[reduc16],   arr_dmmv_nvfp4_f32_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            // TQ4_1S: fixed 32-thread workgroup, shared-memory WHT butterfly,
-            // shared-memory reduction.  NUM_ROWS=8 amortises the butterfly cost
-            // across 8 output rows per workgroup.
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_TQ4_1S][i],  "mul_mat_vec_tq4_1s_f32_f32",  arr_dmmv_tq4_1s_f32_f32_len[tq4_1s_reduc],  arr_dmmv_tq4_1s_f32_f32_data[tq4_1s_reduc],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {8, 1, 1}, {tq4_1s_wg_size, 8, i+1}, 1, true, tq4_1s_use_subgroups, tq4_1s_force_sg_size);
 
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32",  arr_dmmv_f32_f16_f32_len[reduc],  arr_dmmv_f32_f16_f32_data[reduc],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {wg_size_subgroup, 1, i+1}, 1, false, use_subgroups, force_subgroup_size);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32",  arr_dmmv_f16_f16_f32_len[reduc],  arr_dmmv_f16_f16_f32_data[reduc],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
@@ -4251,7 +4361,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32",  arr_dmmv_iq4_nl_f16_f32_len[reduc16],  arr_dmmv_iq4_nl_f16_f32_data[reduc16],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32",   arr_dmmv_mxfp4_f16_f32_len[reduc16],   arr_dmmv_mxfp4_f16_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
             ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_NVFP4][i],   "mul_mat_vec_nvfp4_f16_f32",   arr_dmmv_nvfp4_f16_f32_len[reduc16],   arr_dmmv_nvfp4_f16_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_TQ4_1S][i],  "mul_mat_vec_tq4_1s_f16_f32",  arr_dmmv_tq4_1s_f16_f32_len[tq4_1s_reduc],  arr_dmmv_tq4_1s_f16_f32_data[tq4_1s_reduc],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {8, 1, 1}, {tq4_1s_wg_size, 8, i+1}, 1, true, tq4_1s_use_subgroups, tq4_1s_force_sg_size);
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
             if (device->integer_dot_product) {
@@ -4360,11 +4469,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL],  "dequant_iq4_nl",  dequant_iq4_nl_len,  dequant_iq4_nl_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_MXFP4],   "dequant_mxfp4",   dequant_mxfp4_len,   dequant_mxfp4_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_NVFP4],   "dequant_nvfp4",   dequant_nvfp4_len,   dequant_nvfp4_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_TURBO3_0], "dequant_turbo3_0", dequant_turbo3_0_len, dequant_turbo3_0_data, "main", 2, 5 * sizeof(uint32_t), {128, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_TQ4_1S],  "dequant_tq4_1s",  dequant_tq4_1s_len,  dequant_tq4_1s_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
-
-    // TurboQuant WHT
-    ggml_vk_create_pipeline(device, device->pipeline_turbo_wht, "turbo_wht", turbo_wht_len, turbo_wht_data, "main", 2, 3 * sizeof(uint32_t), {128, 1, 1}, {}, 1);
 
     // get_rows
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -4392,7 +4496,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl",  get_rows_iq4_nl_len,  get_rows_iq4_nl_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4],   "get_rows_mxfp4",   get_rows_mxfp4_len,   get_rows_mxfp4_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_NVFP4],   "get_rows_nvfp4",   get_rows_nvfp4_len,   get_rows_nvfp4_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_TURBO3_0], "get_rows_turbo3_0", get_rows_turbo3_0_len, get_rows_turbo3_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_I32],     "get_rows_i32",     get_rows_i32_len,     get_rows_i32_data,     "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -4420,7 +4523,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl_f32",  get_rows_iq4_nl_f32_len,  get_rows_iq4_nl_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_MXFP4],   "get_rows_mxfp4_f32",   get_rows_mxfp4_f32_len,   get_rows_mxfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_NVFP4],   "get_rows_nvfp4_f32",   get_rows_nvfp4_f32_len,   get_rows_nvfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_TURBO3_0], "get_rows_turbo3_0_f32", get_rows_turbo3_0_f32_len, get_rows_turbo3_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, sizeof(vk_op_flash_attn_split_k_reduce_push_constants), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
@@ -4480,32 +4582,25 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_cpy_transpose_32, "cpy_transpose_32", cpy_transpose_32_len, cpy_transpose_32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_cpy_transpose_16, "cpy_transpose_16", cpy_transpose_16_len, cpy_transpose_16_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1);
 
-    // RTE fp16 rounding is now patched into SPIR-V programmatically (see
-    // ggml_vk_create_pipeline_func), so we only compile a single variant of
-    // each cpy_f32_quant / set_rows shader rather than _rte/non-_rte pairs.
-    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q1_0],   "cpy_f32_q1_0",   cpy_f32_q1_0_len,   cpy_f32_q1_0_data,   "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_0],   "cpy_f32_q4_0",   cpy_f32_q4_0_len,   cpy_f32_q4_0_data,   "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_1],   "cpy_f32_q4_1",   cpy_f32_q4_1_len,   cpy_f32_q4_1_data,   "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_0],   "cpy_f32_q5_0",   cpy_f32_q5_0_len,   cpy_f32_q5_0_data,   "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_1],   "cpy_f32_q5_1",   cpy_f32_q5_1_len,   cpy_f32_q5_1_data,   "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q8_0],   "cpy_f32_q8_0",   cpy_f32_q8_0_len,   cpy_f32_q8_0_data,   "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q1_0], "cpy_f32_q1_0", cpy_f32_q1_0_len, cpy_f32_q1_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_0], "cpy_f32_q4_0", cpy_f32_q4_0_len, cpy_f32_q4_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_1], "cpy_f32_q4_1", cpy_f32_q4_1_len, cpy_f32_q4_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_0], "cpy_f32_q5_0", cpy_f32_q5_0_len, cpy_f32_q5_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_1], "cpy_f32_q5_1", cpy_f32_q5_1_len, cpy_f32_q5_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q8_0], "cpy_f32_q8_0", cpy_f32_q8_0_len, cpy_f32_q8_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_IQ4_NL], "cpy_f32_iq4_nl", cpy_f32_iq4_nl_len, cpy_f32_iq4_nl_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
 
 #define SET_ROWS(itype) \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F32],      "set_rows_f32" #itype,      set_rows_f32 ## itype ## _len,      set_rows_f32 ## itype ## _data,      "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F16],      "set_rows_f16" #itype,      set_rows_f16 ## itype ## _len,      set_rows_f16 ## itype ## _data,      "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_BF16],     "set_rows_bf16" #itype,     set_rows_bf16 ## itype ## _len,     set_rows_bf16 ## itype ## _data,     "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q1_0],     "set_rows_q1_0" #itype,     set_rows_q1_0 ## itype ## _len,     set_rows_q1_0 ## itype ## _data,     "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_0],     "set_rows_q4_0" #itype,     set_rows_q4_0 ## itype ## _len,     set_rows_q4_0 ## itype ## _data,     "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_1],     "set_rows_q4_1" #itype,     set_rows_q4_1 ## itype ## _len,     set_rows_q4_1 ## itype ## _data,     "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_0],     "set_rows_q5_0" #itype,     set_rows_q5_0 ## itype ## _len,     set_rows_q5_0 ## itype ## _data,     "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_1],     "set_rows_q5_1" #itype,     set_rows_q5_1 ## itype ## _len,     set_rows_q5_1 ## itype ## _data,     "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q8_0],     "set_rows_q8_0" #itype,     set_rows_q8_0 ## itype ## _len,     set_rows_q8_0 ## itype ## _data,     "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_IQ4_NL],   "set_rows_iq4_nl" #itype,   set_rows_iq4_nl ## itype ## _len,   set_rows_iq4_nl ## itype ## _data,   "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_TURBO2_0], "set_rows_turbo2_0" #itype, set_rows_turbo2_0 ## itype ## _len, set_rows_turbo2_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_TURBO3_0], "set_rows_turbo3_0" #itype, set_rows_turbo3_0 ## itype ## _len, set_rows_turbo3_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_TURBO4_0], "set_rows_turbo4_0" #itype, set_rows_turbo4_0 ## itype ## _len, set_rows_turbo4_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
-        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_TQ4_1S],   "set_rows_tq4_1s" #itype,   set_rows_tq4_1s ## itype ## _len,   set_rows_tq4_1s ## itype ## _data,   "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F32],  "set_rows_f32" #itype,  set_rows_f32 ## itype ## _len,  set_rows_f32 ## itype ## _data,  "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F16],  "set_rows_f16" #itype,  set_rows_f16 ## itype ## _len,  set_rows_f16 ## itype ## _data,  "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_BF16], "set_rows_bf16" #itype, set_rows_bf16 ## itype ## _len, set_rows_bf16 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q1_0], "set_rows_q1_0" #itype, set_rows_q1_0 ## itype ## _len, set_rows_q1_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_0], "set_rows_q4_0" #itype, set_rows_q4_0 ## itype ## _len, set_rows_q4_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_1], "set_rows_q4_1" #itype, set_rows_q4_1 ## itype ## _len, set_rows_q4_1 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_0], "set_rows_q5_0" #itype, set_rows_q5_0 ## itype ## _len, set_rows_q5_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_1], "set_rows_q5_1" #itype, set_rows_q5_1 ## itype ## _len, set_rows_q5_1 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q8_0], "set_rows_q8_0" #itype, set_rows_q8_0 ## itype ## _len, set_rows_q8_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
+        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_IQ4_NL], "set_rows_iq4_nl" #itype, set_rows_iq4_nl ## itype ## _len, set_rows_iq4_nl ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
 
     SET_ROWS(_i32)
     SET_ROWS(_i64)
@@ -4519,8 +4614,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_1], "cpy_q5_1_f32", cpy_q5_1_f32_len, cpy_q5_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_1), 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q8_0], "cpy_q8_0_f32", cpy_q8_0_f32_len, cpy_q8_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q8_0), 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_IQ4_NL], "cpy_iq4_nl_f32", cpy_iq4_nl_f32_len, cpy_iq4_nl_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_IQ4_NL), 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_TURBO3_0], "cpy_turbo3_0_f32", cpy_turbo3_0_f32_len, cpy_turbo3_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_TURBO3_0), 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_TQ4_1S], "cpy_tq4_1s_f32", cpy_tq4_1s_f32_len, cpy_tq4_1s_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_TQ4_1S), 1, 1}, {}, 1);
 
     auto get_suffix = [](bool src0_f16, bool src1_f16, bool dst_f16) {
         std::string s;
@@ -5591,19 +5684,19 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 device->mul_mat_id_m[i] = true;
                 device->mul_mat_id_s[i] = true;
                 break;
-            case VK_VENDOR_ID_INTEL:
-                if (!device->coopmat_support || device->architecture != INTEL_XE2) {
-                    device->mul_mat_l[i] = false;
-                    device->mul_mat_id_l[i] = false;
-                } else {
-                    device->mul_mat_l[i] = true;  // if coopmat & XE2+, allow large matmul warptile config for Intel
-                    device->mul_mat_id_l[i] = true;
-                }
+            case VK_VENDOR_ID_INTEL: {
+                // Current Windows driver does not expose BF16 support.
+                // We only want to use l_warptile if coopmat is available and is Xe2+
+                const bool xe2_with_coopmat = device->coopmat_support && device->architecture == INTEL_XE2;
+                const bool use_l_warptile = (i == GGML_TYPE_BF16) ? (device->coopmat_bf16_support && xe2_with_coopmat) : xe2_with_coopmat;
+                device->mul_mat_l[i] = use_l_warptile;
+                device->mul_mat_id_l[i] = use_l_warptile;
                 device->mul_mat_m[i] = true;
                 device->mul_mat_s[i] = true;
                 device->mul_mat_id_m[i] = true;
                 device->mul_mat_id_s[i] = true;
                 break;
+            }
             case VK_VENDOR_ID_APPLE:
                 device->mul_mat_l[i] = false;
                 device->mul_mat_m[i] = true;
@@ -5622,6 +5715,13 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 device->mul_mat_id_s[i] = true;
                 break;
             }
+
+            device->mul_mat_l_int[i]    = device->mul_mat_l[i];
+            device->mul_mat_m_int[i]    = device->mul_mat_m[i];
+            device->mul_mat_s_int[i]    = device->mul_mat_s[i];
+            device->mul_mat_id_l_int[i] = device->mul_mat_id_l[i];
+            device->mul_mat_id_m_int[i] = device->mul_mat_id_m[i];
+            device->mul_mat_id_s_int[i] = device->mul_mat_id_s[i];
         }
 
 
@@ -6177,7 +6277,6 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_MXFP4:
         case GGML_TYPE_NVFP4:
-        case GGML_TYPE_TQ4_1S:
             break;
         default:
             return nullptr;
@@ -6318,7 +6417,6 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_MXFP4:
         case GGML_TYPE_NVFP4:
-        case GGML_TYPE_TQ4_1S:
             break;
         default:
             return nullptr;
@@ -6334,10 +6432,6 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
             if (m < 4096 && k >= 1024) {
                 dmmv_wg = DMMV_WG_SIZE_LARGE;
             }
-        } else if (a_type == GGML_TYPE_TQ4_1S) {
-            // TQ4_1S needs exactly 32 threads (one subgroup) to cooperate on the
-            // 32-element WHT butterfly in shared memory. Force SUBGROUP-sized wg.
-            dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
         } else {
             if (m <= 8192 && k >= 1024) {
                 dmmv_wg = DMMV_WG_SIZE_LARGE;
@@ -6894,7 +6988,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont
     }
 }
 
-static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) {
+static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
     VK_LOG_DEBUG("ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")");
     // Check if src is pinned memory
     vk_buffer buf = nullptr;
@@ -6904,7 +6998,7 @@ static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz
     if (buf != nullptr) {
         // Memory is pinned, use as staging buffer
         std::vector<vk::BufferCopy> slices(1);
-        if (width == spitch) {
+        if (width == spitch && width == dpitch) {
             // Only do single write if stride is equal
             slices[0].srcOffset = buf_offset;
             slices[0].dstOffset = offset;
@@ -6913,7 +7007,7 @@ static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz
             slices.resize(height);
             for (size_t i = 0; i < height; i++) {
                 slices[i].srcOffset = buf_offset + i * spitch;
-                slices[i].dstOffset = offset + i * width;
+                slices[i].dstOffset = offset + i * dpitch;
                 slices[i].size = width;
             }
         }
@@ -6930,21 +7024,30 @@ static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz
     }
 
     // Staging buffer required
-    const size_t copy_size = width*height;
-    ggml_vk_ensure_sync_staging_buffer(dst->device, copy_size);
+    const size_t staging_size = width * height;
+    ggml_vk_ensure_sync_staging_buffer(dst->device, staging_size);
 
     vk_buffer& staging_buffer = dst->device->sync_staging;
 
-    VkBufferCopy buf_copy = {
-        0,
-        offset,
-        copy_size};
+    std::vector<vk::BufferCopy> slices(1);
+    if (width == dpitch) {
+        slices[0].srcOffset = 0;
+        slices[0].dstOffset = offset;
+        slices[0].size = staging_size;
+    } else {
+        slices.resize(height);
+        for (size_t i = 0; i < height; i++) {
+            slices[i].srcOffset = i * width;
+            slices[i].dstOffset = offset + i * dpitch;
+            slices[i].size = width;
+        }
+    }
 
     ggml_vk_sync_buffers(nullptr, subctx);
-    vkCmdCopyBuffer(subctx->s->buffer->buf, (VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, 1, &buf_copy);
+    subctx->s->buffer->buf.copyBuffer((VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, slices);
 
     if (width == spitch) {
-        deferred_memcpy((uint8_t *)staging_buffer->ptr, src, width * height, &subctx->in_memcpys);
+        deferred_memcpy((uint8_t *)staging_buffer->ptr, src, staging_size, &subctx->in_memcpys);
     } else {
         for (size_t i = 0; i < height; i++) {
             deferred_memcpy((uint8_t *)staging_buffer->ptr + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys);
@@ -6955,24 +7058,24 @@ static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz
 
 static bool ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) {
     VK_LOG_DEBUG("ggml_vk_buffer_write_async(" << size << ")");
-    return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, 1, sync_staging);
+    return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, size, 1, sync_staging);
 }
 
-static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) {
+static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height) {
     VK_LOG_DEBUG("ggml_vk_buffer_write_2d(" << width << ", " << height << ")");
     // Buffer is already mapped
     if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
         GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
 
         for (size_t i = 0; i < height; i++) {
-            memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width);
+            memcpy((uint8_t *)dst->ptr + offset + i * dpitch, (const uint8_t *) src + i * spitch, width);
         }
     } else {
         std::lock_guard<std::recursive_mutex> guard(dst->device->mutex);
 
         vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool);
         ggml_vk_ctx_begin(dst->device, subctx);
-        bool ret = ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, width, height, true);
+        bool ret = ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, dpitch, width, height, true);
         GGML_ASSERT(ret);
         ggml_vk_ctx_end(subctx);
 
@@ -6993,7 +7096,7 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void *
 
 static void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src, size_t size) {
     VK_LOG_DEBUG("ggml_vk_buffer_write(" << size << ")");
-    ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1);
+    ggml_vk_buffer_write_2d(dst, offset, src, size, size, size, 1);
 }
 
 static bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
@@ -7039,15 +7142,35 @@ static bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size
     }
 
     // Fall back to staging buffer
-    const size_t copy_size = dpitch * height;
-    ggml_vk_ensure_sync_staging_buffer(src->device, copy_size);
+    const size_t staging_size = width * height;
+    ggml_vk_ensure_sync_staging_buffer(src->device, staging_size);
 
     vk_buffer& staging_buffer = src->device->sync_staging;
 
+    std::vector<vk::BufferCopy> staging_slices(1);
+    if (width == spitch) {
+        staging_slices[0].srcOffset = offset;
+        staging_slices[0].dstOffset = 0;
+        staging_slices[0].size = staging_size;
+    } else {
+        staging_slices.resize(height);
+        for (size_t i = 0; i < height; i++) {
+            staging_slices[i].srcOffset = offset + i * spitch;
+            staging_slices[i].dstOffset = i * width;
+            staging_slices[i].size = width;
+        }
+    }
+
     ggml_vk_sync_buffers(nullptr, subctx);
-    subctx->s->buffer->buf.copyBuffer(src->buffer, staging_buffer->buffer, slices);
+    subctx->s->buffer->buf.copyBuffer(src->buffer, staging_buffer->buffer, staging_slices);
 
-    deferred_memcpy(dst, staging_buffer->ptr, copy_size, &subctx->out_memcpys);
+    if (width == dpitch) {
+        deferred_memcpy(dst, staging_buffer->ptr, staging_size, &subctx->out_memcpys);
+    } else {
+        for (size_t i = 0; i < height; i++) {
+            deferred_memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) staging_buffer->ptr + i * width, width, &subctx->out_memcpys);
+        }
+    }
     return true;
 }
 
@@ -7055,8 +7178,8 @@ static bool ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t
     return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, sync_staging);
 }
 
-static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) {
-    VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")");
+static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height) {
+    VK_LOG_DEBUG("ggml_vk_buffer_read_2d(" << src->buffer << ", " << offset << ", " << width << ", " << height << ")");
 
     // If the device is not an UMA device the memory is host-accessible through rebar. While writing
     // through PCIe is sufficient fast reading back data from PCIe is slower than going through
@@ -7064,18 +7187,20 @@ static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_
     if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) {
         GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
 
-        memcpy(dst, (uint8_t *) src->ptr + offset, size);
+        for (size_t i = 0; i < height; i++) {
+            memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) src->ptr + offset + i * spitch, width);
+        }
     } else {
         std::lock_guard<std::recursive_mutex> guard(src->device->mutex);
 
         vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool);
         ggml_vk_ctx_begin(src->device, subctx);
-        bool ret = ggml_vk_buffer_read_async(subctx, src, offset, dst, size, true);
+        bool ret = ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, spitch, dpitch, width, height, true);
         GGML_ASSERT(ret);
         ggml_vk_ctx_end(subctx);
 
         ggml_vk_submit(subctx, src->device->fence);
-        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences");
+        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read_2d waitForFences");
         src->device->device.resetFences({ src->device->fence });
         ggml_vk_queue_command_pools_cleanup(src->device);
 
@@ -7085,6 +7210,11 @@ static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_
     }
 }
 
+static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) {
+    VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")");
+    ggml_vk_buffer_read_2d(src, offset, dst, size, size, size, 1);
+}
+
 static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
     VK_LOG_DEBUG("ggml_vk_buffer_copy_async(" << size << ")");
     // Make sure both buffers are on same device
@@ -7116,7 +7246,7 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr
         // Copy to src staging buffer
         ggml_vk_buffer_copy(src->device->sync_staging, 0, src, src_offset, size);
         // Copy to dst buffer
-        ggml_vk_buffer_write_2d(dst, dst_offset, src->device->sync_staging->ptr, 0, size, 1);
+        ggml_vk_buffer_write(dst, dst_offset, src->device->sync_staging->ptr, size);
     }
 }
 
@@ -7199,6 +7329,13 @@ static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, uint32_t m,
 static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type, ggml_type src1_type) {
     VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");
 
+    // The q8_1 (integer dot) mmq path uses a different shader with its own
+    // shared-memory layout, so use the int-specific availability flags.
+    const bool is_q8_1 = (src1_type == GGML_TYPE_Q8_1);
+    const bool mm_l = is_q8_1 ? ctx->device->mul_mat_l_int[src0_type] : ctx->device->mul_mat_l[src0_type];
+    const bool mm_m = is_q8_1 ? ctx->device->mul_mat_m_int[src0_type] : ctx->device->mul_mat_m[src0_type];
+    const bool mm_s = is_q8_1 ? ctx->device->mul_mat_s_int[src0_type] : ctx->device->mul_mat_s[src0_type];
+
     if (ctx->device->coopmat2) {
         const uint32_t shader_core_count = ctx->device->shader_core_count;
         const uint32_t tiles_l = CEIL_DIV(m, mmp->a_l->wg_denoms[0]) * CEIL_DIV(n, mmp->a_l->wg_denoms[1]);
@@ -7215,26 +7352,24 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx,
                             // split_k==3 with large tiles likely better than medium tiles with no split_k.
                             (tiles_l <= shader_core_count / 3 && tiles_m > shader_core_count / 2);
 
-        if ((ctx->device->mul_mat_l[src0_type] && (n > crossover_large && prefer_large)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_s[src0_type])) {
+        if ((mm_l && (n > crossover_large && prefer_large)) || (!mm_m && !mm_s)) {
             return aligned ? mmp->a_l : mmp->l;
         }
         // Use medium shader when the N dimension is greater than the small shader's tile size
         uint32_t crossover_medium = mmp->s->wg_denoms[1];
-        if ((ctx->device->mul_mat_m[src0_type] && (n > crossover_medium)) || !ctx->device->mul_mat_s[src0_type]) {
+        if ((mm_m && (n > crossover_medium)) || !mm_s) {
             return aligned ? mmp->a_m : mmp->m;
         }
         return aligned ? mmp->a_s : mmp->s;
     }
 
-    if ((ctx->device->mul_mat_s[src0_type] && (m <= 32 || n <= 32)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_l[src0_type])) {
+    if ((mm_s && (m <= 32 || n <= 32)) || (!mm_m && !mm_l)) {
         return aligned ? mmp->a_s : mmp->s;
     }
-    if ((ctx->device->mul_mat_m[src0_type] && (m <= 64 || n <= 64)) || !ctx->device->mul_mat_l[src0_type]) {
+    if ((mm_m && (m <= 64 || n <= 64)) || !mm_l) {
         return aligned ? mmp->a_m : mmp->m;
     }
     return aligned ? mmp->a_l : mmp->l;
-
-    GGML_UNUSED(src1_type);
 }
 
 static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) {
@@ -7291,35 +7426,42 @@ static void ggml_vk_matmul(
     ctx->prealloc_split_k_need_sync = true;
 }
 
-static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type) {
-    VK_LOG_DEBUG("ggml_vk_guess_matmul_id_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ")");
+static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type, ggml_type src1_type) {
+    VK_LOG_DEBUG("ggml_vk_guess_matmul_id_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");
+
+    // The q8_1 (integer dot) mmq path uses a different shader with its own
+    // shared-memory layout, so use the int-specific availability flags.
+    const bool is_q8_1 = (src1_type == GGML_TYPE_Q8_1);
+    const bool mm_l = is_q8_1 ? ctx->device->mul_mat_id_l_int[src0_type] : ctx->device->mul_mat_id_l[src0_type];
+    const bool mm_m = is_q8_1 ? ctx->device->mul_mat_id_m_int[src0_type] : ctx->device->mul_mat_id_m[src0_type];
+    const bool mm_s = is_q8_1 ? ctx->device->mul_mat_id_s_int[src0_type] : ctx->device->mul_mat_id_s[src0_type];
 
     if (ctx->device->coopmat2) {
         // Use large shader when the N dimension is greater than the medium shader's tile size
         uint32_t crossover_large = mmp->m->wg_denoms[1];
-        if ((ctx->device->mul_mat_id_l[src0_type] && (n > crossover_large)) || (!ctx->device->mul_mat_id_m[src0_type] && !ctx->device->mul_mat_id_s[src0_type])) {
+        if ((mm_l && (n > crossover_large)) || (!mm_m && !mm_s)) {
             return aligned ? mmp->a_l : mmp->l;
         }
         // Use medium shader when the N dimension is greater than the small shader's tile size
         uint32_t crossover_medium = mmp->s->wg_denoms[1];
-        if ((ctx->device->mul_mat_id_m[src0_type] && (n > crossover_medium)) || !ctx->device->mul_mat_id_s[src0_type]) {
+        if ((mm_m && (n > crossover_medium)) || !mm_s) {
             return aligned ? mmp->a_m : mmp->m;
         }
         return aligned ? mmp->a_s : mmp->s;
     }
 
-    if ((ctx->device->mul_mat_id_s[src0_type] && (m <= 32 || n <= 32)) || (!ctx->device->mul_mat_id_m[src0_type] && !ctx->device->mul_mat_id_l[src0_type])) {
+    if ((mm_s && (m <= 32 || n <= 32)) || (!mm_m && !mm_l)) {
         return aligned ? mmp->a_s : mmp->s;
     }
-    if ((ctx->device->mul_mat_id_m[src0_type] && (m <= 64 || n <= 64)) || !ctx->device->mul_mat_id_l[src0_type]) {
+    if ((mm_m && (m <= 64 || n <= 64)) || !mm_l) {
         return aligned ? mmp->a_m : mmp->m;
     }
     return aligned ? mmp->a_l : mmp->l;
 }
 
-static uint32_t ggml_vk_guess_matmul_id_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type) {
-    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ")");
-    return ggml_vk_guess_matmul_id_pipeline(ctx, mmp, m, n, true, src0_type)->align;
+static uint32_t ggml_vk_guess_matmul_id_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) {
+    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");
+    return ggml_vk_guess_matmul_id_pipeline(ctx, mmp, m, n, true, src0_type, src1_type)->align;
 }
 
 static void ggml_vk_matmul_id(
@@ -7434,8 +7576,6 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_IQ4_NL:
-        case GGML_TYPE_TURBO3_0:
-        case GGML_TYPE_TQ4_1S:
             return ctx->device->pipeline_cpy_quant_f32[src->type];
         default:
             break;
@@ -7617,10 +7757,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
     // Not implemented
     GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
 
-    const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? f16_type : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type)));
+    const ggml_type effective_src1_type = quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type);
+
+    const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? f16_type : src0->type, effective_src1_type));
     const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && ne11 > 8;
 
-    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? f16_type : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type));
+    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? f16_type : src0->type, effective_src1_type);
 
     if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
         pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
@@ -8452,10 +8594,12 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
     // Not implemented
     GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
 
-    const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? f16_type : src0->type));
+    const ggml_type effective_src1_type = quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type);
+
+    const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? f16_type : src0->type, effective_src1_type));
     const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && nei1 > 8;
 
-    vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? f16_type : src0->type);
+    vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? f16_type : src0->type, effective_src1_type);
 
     if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
         pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
@@ -8905,8 +9049,9 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx
     }
 }
 
-static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type kv_type) {
+static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type) {
     GGML_UNUSED(f32acc);
+    GGML_UNUSED(v_type);
     // Needs to be kept up to date on shader changes
     const uint32_t wg_size = params.workgroup_size;
     const uint32_t Br = params.block_rows;
@@ -8914,10 +9059,7 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con
 
     const uint32_t float_type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float);
 
-    const bool mmq = device->integer_dot_product && device->subgroup_clustered &&
-                     (kv_type == GGML_TYPE_Q4_0 || kv_type == GGML_TYPE_Q4_1 ||
-                      kv_type == GGML_TYPE_Q5_0 || kv_type == GGML_TYPE_Q5_1 ||
-                      kv_type == GGML_TYPE_Q8_0 || kv_type == GGML_TYPE_IQ4_NL);
+    const bool mmq = ggml_vk_fa_scalar_uses_mmq(device, k_type);
 
     // tmpsh is overestimated slightly
     const uint32_t tmpsh = wg_size * sizeof(float);
@@ -8934,17 +9076,10 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con
         // kvsh uses D = HSV (K goes through kblocksh instead)
         kvsh = params.shmem_staging ? Bc * (hsv / 4 + 1) * 4 * float_type_size : 4 * float_type_size;
 
-        // block_a_cache size depends on quant type
-        uint32_t block_a_size;
-        switch (kv_type) {
-            case GGML_TYPE_Q4_0:  block_a_size = 4 * sizeof(uint32_t) + float_type_size; break;
-            case GGML_TYPE_Q4_1:  block_a_size = 4 * sizeof(uint32_t) + 2 * float_type_size; break;
-            case GGML_TYPE_Q5_0:  block_a_size = 4 * sizeof(uint32_t) + sizeof(uint32_t) + float_type_size; break;
-            case GGML_TYPE_Q5_1:  block_a_size = 4 * sizeof(uint32_t) + sizeof(uint32_t) + 2 * float_type_size; break;
-            case GGML_TYPE_Q8_0:
-            case GGML_TYPE_IQ4_NL: block_a_size = 8 * sizeof(int32_t) + float_type_size; break;
-            default: block_a_size = 0; break;
-        }
+        // The mixed MMQ shader uses a superset block_a_cache that fits every
+        // FA-supported quant: int32_t qs[8] + uint32_t qh + FLOAT_TYPEV2 dm.
+        // Single-scale types leave dm.y unused; non-Q5_* leave qh unused.
+        const uint32_t block_a_size = 8 * sizeof(int32_t) + sizeof(uint32_t) + 2 * float_type_size;
         kblocksh_size = params.shmem_staging ? Bc * (hsk / 32) * block_a_size : block_a_size;
     } else {
         Qf = Br * (hsk / 4 + 1) * 4 * float_type_size;
@@ -9057,8 +9192,6 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
 
     assert(dst->type == GGML_TYPE_F32);
     assert(q->type == GGML_TYPE_F32);
-    assert(k->type == v->type);
-
     uint32_t gqa_ratio = 1;
     uint32_t qk_ratio = neq2 / nek2;
     uint32_t workgroups_x = (uint32_t)neq1;
@@ -9069,7 +9202,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
 
     // For scalar/coopmat1 FA, we can use the "large" size to accommodate qga.
     // For coopmat2 FA, we always use the small size (which is still pretty large for gqa).
-    vk_fa_tuning_params tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, 512, KV, k->type, f32acc);
+    vk_fa_tuning_params tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, 512, KV, k->type, v->type, f32acc);
     const uint32_t max_gqa = std::min(tuning_params.block_rows, 32u);
 
     if (N <= 8 && qk_ratio > 1 && qk_ratio <= max_gqa &&
@@ -9082,7 +9215,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
         workgroups_y /= gqa_ratio;
     }
 
-    tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, N, KV, k->type, f32acc);
+    tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, N, KV, k->type, v->type, f32acc);
 
     const uint32_t q_stride = (uint32_t)(nbq1 / ggml_type_size(q->type));
     uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type));
@@ -9121,13 +9254,13 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     // Only use mask opt when the mask is fairly large. This hasn't been tuned extensively.
     bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768 && nem0 >= tuning_params.block_cols * 16;
     vk_fa_pipeline_state fa_pipeline_state = get_fa_pipeline_state(ctx->device, tuning_params, HSK, HSV, aligned, f32acc,
-                                                                   mask != nullptr, use_mask_opt, logit_softcap != 0);
+                                                                   mask != nullptr, use_mask_opt, logit_softcap != 0, k->type, v->type);
 
     vk_pipeline pipeline = nullptr;
 
     {
         std::lock_guard<std::recursive_mutex> guard(ctx->device->mutex);
-        auto &pipelines = ctx->device->pipeline_flash_attn_f32_f16[k->type];
+        auto &pipelines = ctx->device->pipeline_flash_attn_f32_f16;
         auto it = pipelines.find(fa_pipeline_state);
         if (it != pipelines.end()) {
             pipeline = it->second;
@@ -10260,13 +10393,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     case GGML_OP_SET_ROWS:
         {
             uint32_t ne = ggml_nelements(src0);
-            if (dst->type == GGML_TYPE_TURBO2_0 ||
-                dst->type == GGML_TYPE_TURBO3_0 ||
-                dst->type == GGML_TYPE_TURBO4_0) {
-                ne = ne / 128;
-            } else if (dst->type == GGML_TYPE_TQ4_1S) {
-                ne = ne / 32;
-            } else if (ggml_is_quantized(dst->type)) {
+            if (ggml_is_quantized(dst->type)) {
                 // quants run 32 threads each doing QUANT_K elements
                 ne = CEIL_DIV(ne, 32 * ggml_blck_size(dst->type));
             } else {
@@ -10640,6 +10767,7 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
     const ggml_tensor * src_q     = dst->src[0];
     const ggml_tensor * src_v     = dst->src[2];
     const ggml_tensor * src_beta  = dst->src[4];
+    const ggml_tensor * src_state = dst->src[5];
 
     GGML_ASSERT(dst->buffer != nullptr);
 
@@ -10648,6 +10776,9 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
     const uint32_t n_tokens = (uint32_t)src_v->ne[2];
     const uint32_t n_seqs   = (uint32_t)src_v->ne[3];
 
+    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
+    const uint32_t K = (uint32_t)src_state->ne[1];
+
     const uint32_t s_off = S_v * H * n_tokens * n_seqs;
 
     vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op);
@@ -10681,7 +10812,8 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
         sv1, sv2, sv3,
         sb1, sb2, sb3,
         neq1, rq3,
-        scale
+        scale,
+        K
     };
 
     ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
@@ -11037,32 +11169,6 @@ static void ggml_vk_set_rows(ggml_backend_vk_context * ctx, vk_context& subctx,
     });
 }
 
-static void ggml_vk_turbo_wht(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    int direction, group_size;
-    memcpy(&direction, dst->op_params + 0, sizeof(int));
-    memcpy(&group_size, dst->op_params + sizeof(int), sizeof(int));
-    struct { uint32_t ne; uint32_t direction; uint32_t group_size; } pc = {
-        (uint32_t)ggml_nelements(src0), (uint32_t)direction, (uint32_t)group_size,
-    };
-    vk_pipeline pipeline = ctx->device->pipeline_turbo_wht;
-    GGML_ASSERT(pipeline != nullptr);
-    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
-    vk_subbuffer src_buf = ggml_vk_tensor_subbuffer(ctx, src0, false);
-    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, false);
-    // Spread workgroups across Y/Z to stay within maxComputeWorkGroupCount[0].
-    // elements[0] / group_size = wg0; each row of 512 workgroups uses one Y slice.
-    const uint32_t n_groups = pc.ne / (uint32_t)group_size;
-    std::array<uint32_t, 3> elements;
-    if (n_groups > 262144) {
-        elements = { 512 * (uint32_t)group_size, 512, CEIL_DIV(n_groups, 262144) };
-    } else if (n_groups > 512) {
-        elements = { 512 * (uint32_t)group_size, CEIL_DIV(n_groups, 512), 1 };
-    } else {
-        elements = { pc.ne, 1, 1 };
-    }
-    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src_buf, dst_buf }, pc, elements);
-}
-
 static void ggml_vk_silu_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SILU_BACK, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f, 0.0f, 0.0f });
 }
@@ -13097,6 +13203,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
             if (vk_perf_logger_enabled && vk_perf_logger_concurrent) {
                 ctx->query_node_idx[ctx->query_idx] = node_idx;
                 compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++);
+                ggml_vk_sync_buffers(ctx, compute_ctx);
             }
         }
         // Add all fused nodes to the unsynchronized lists.
@@ -13243,10 +13350,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_SET_ROWS:
         ggml_vk_set_rows(ctx, compute_ctx, src0, src1, node);
 
-        break;
-    case GGML_OP_TURBO_WHT:
-        ggml_vk_turbo_wht(ctx, compute_ctx, src0, node);
-
         break;
     case GGML_OP_SILU_BACK:
         ggml_vk_silu_back(ctx, compute_ctx, src0, src1, node);
@@ -13701,6 +13804,20 @@ static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml
     ggml_vk_buffer_write(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size);
 }
 
+static void ggml_backend_vk_buffer_set_tensor_2d(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset,
+                                                 size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    VK_LOG_DEBUG("ggml_backend_vk_buffer_set_tensor_2d(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ", " <<
+                 n_copies << ", " << stride_tensor << ", " << stride_data << ")");
+    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+    vk_buffer buf = buf_ctx->dev_buffer;
+
+    if (size == 0) {
+        return;
+    }
+
+    ggml_vk_buffer_write_2d(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, stride_data, stride_tensor, size, n_copies);
+}
+
 static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")");
     ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
@@ -13714,6 +13831,21 @@ static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, cons
     ggml_vk_buffer_read(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size);
 }
 
+static void ggml_backend_vk_buffer_get_tensor_2d(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset,
+                                                 size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor_2d(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ", " <<
+                 n_copies << ", " << stride_tensor << ", " << stride_data << ")");
+    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+
+    if (size == 0) {
+        return;
+    }
+
+    vk_buffer buf = buf_ctx->dev_buffer;
+
+    ggml_vk_buffer_read_2d(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, stride_tensor, stride_data, size, n_copies);
+}
+
 static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
     if (ggml_nbytes(src) == 0) {
         return true;
@@ -13748,8 +13880,8 @@ static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
     /* .memset_tensor   = */ ggml_backend_vk_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_vk_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_vk_buffer_get_tensor,
-    /* .set_tensor_2d   = */ NULL,
-    /* .get_tensor_2d   = */ NULL,
+    /* .set_tensor_2d   = */ ggml_backend_vk_buffer_set_tensor_2d,
+    /* .get_tensor_2d   = */ ggml_backend_vk_buffer_get_tensor_2d,
     /* .cpy_tensor      = */ ggml_backend_vk_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_vk_buffer_clear,
     /* .reset           = */ NULL,
@@ -13905,8 +14037,9 @@ static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_b
     return &ctx->device->buffer_type;
 }
 
-static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    VK_LOG_DEBUG("ggml_backend_vk_set_tensor_async(" << size << ")");
+static void ggml_backend_vk_set_tensor_2d_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset,
+                                                size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    VK_LOG_DEBUG("ggml_backend_vk_set_tensor_2d_async(" << size << ", " << n_copies << ")");
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
     GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
 
@@ -13920,7 +14053,6 @@ static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor
 
     if (ctx->device->async_use_transfer_queue) {
         if (ctx->transfer_ctx.expired()) {
-            // Initialize new transfer context
             cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
             ctx->transfer_ctx = cpy_ctx;
             ggml_vk_ctx_begin(ctx->device, cpy_ctx);
@@ -13935,25 +14067,48 @@ static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor
 
     auto dst_offset = vk_tensor_offset(tensor) + tensor->view_offs + offset;
 
-    bool ret = ggml_vk_buffer_write_async(cpy_ctx, buf, dst_offset, data, size);
+    bool ret = ggml_vk_buffer_write_2d_async(cpy_ctx, buf, dst_offset, data, stride_data, stride_tensor, size, n_copies);
 
     if (!ret) {
-        ggml_vk_ensure_sync_staging_buffer(ctx, size);
+        const size_t staging_size = size * n_copies;
+        ggml_vk_ensure_sync_staging_buffer(ctx, staging_size);
         ggml_vk_sync_buffers(nullptr, cpy_ctx);
 
-        vk::BufferCopy buffer_cpy;
-        buffer_cpy.srcOffset = 0;
-        buffer_cpy.dstOffset = dst_offset;
-        buffer_cpy.size = size;
+        std::vector<vk::BufferCopy> slices(1);
+        if (size == stride_tensor) {
+            slices[0].srcOffset = 0;
+            slices[0].dstOffset = dst_offset;
+            slices[0].size = staging_size;
+        } else {
+            slices.resize(n_copies);
+            for (size_t i = 0; i < n_copies; i++) {
+                slices[i].srcOffset = i * size;
+                slices[i].dstOffset = dst_offset + i * stride_tensor;
+                slices[i].size = size;
+            }
+        }
+
+        cpy_ctx->s->buffer->buf.copyBuffer(ctx->sync_staging->buffer, buf->buffer, slices);
 
-        cpy_ctx->s->buffer->buf.copyBuffer(ctx->sync_staging->buffer, buf->buffer, { buffer_cpy });
-        deferred_memcpy(ctx->sync_staging->ptr, data, size, &cpy_ctx->in_memcpys);
+        if (size == stride_data) {
+            deferred_memcpy(ctx->sync_staging->ptr, data, staging_size, &cpy_ctx->in_memcpys);
+        } else {
+            for (size_t i = 0; i < n_copies; i++) {
+                deferred_memcpy((uint8_t *)ctx->sync_staging->ptr + i * size, (const uint8_t *)data + i * stride_data, size, &cpy_ctx->in_memcpys);
+            }
+        }
         ggml_vk_synchronize(ctx);
     }
 }
 
-static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    VK_LOG_DEBUG("ggml_backend_vk_get_tensor_async(" << size << ")");
+static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    VK_LOG_DEBUG("ggml_backend_vk_set_tensor_async(" << size << ")");
+    ggml_backend_vk_set_tensor_2d_async(backend, tensor, data, offset, size, 1, size, size);
+}
+
+static void ggml_backend_vk_get_tensor_2d_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset,
+                                                size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    VK_LOG_DEBUG("ggml_backend_vk_get_tensor_2d_async(" << size << ", " << n_copies << ")");
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
     GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
 
@@ -13968,24 +14123,45 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_
     vk_buffer buf = buf_ctx->dev_buffer;
 
     auto src_offset = vk_tensor_offset(tensor) + tensor->view_offs + offset;
-    bool ret = ggml_vk_buffer_read_async(compute_ctx, buf, src_offset, data, size);
+    bool ret = ggml_vk_buffer_read_2d_async(compute_ctx, buf, src_offset, data, stride_tensor, stride_data, size, n_copies);
 
-    // If that failed, copy synchronously through a staging buffer
     if (!ret) {
-        ggml_vk_ensure_sync_staging_buffer(ctx, size);
+        const size_t staging_size = size * n_copies;
+        ggml_vk_ensure_sync_staging_buffer(ctx, staging_size);
         ggml_vk_sync_buffers(nullptr, compute_ctx);
 
-        vk::BufferCopy buffer_cpy;
-        buffer_cpy.srcOffset = src_offset;
-        buffer_cpy.dstOffset = 0;
-        buffer_cpy.size = size;
+        std::vector<vk::BufferCopy> slices(1);
+        if (size == stride_tensor) {
+            slices[0].srcOffset = src_offset;
+            slices[0].dstOffset = 0;
+            slices[0].size = staging_size;
+        } else {
+            slices.resize(n_copies);
+            for (size_t i = 0; i < n_copies; i++) {
+                slices[i].srcOffset = src_offset + i * stride_tensor;
+                slices[i].dstOffset = i * size;
+                slices[i].size = size;
+            }
+        }
+
+        compute_ctx->s->buffer->buf.copyBuffer(buf->buffer, ctx->sync_staging->buffer, slices);
 
-        compute_ctx->s->buffer->buf.copyBuffer(buf->buffer, ctx->sync_staging->buffer, { buffer_cpy });
-        deferred_memcpy(data, ctx->sync_staging->ptr, size, &compute_ctx->out_memcpys);
+        if (size == stride_data) {
+            deferred_memcpy(data, ctx->sync_staging->ptr, staging_size, &compute_ctx->out_memcpys);
+        } else {
+            for (size_t i = 0; i < n_copies; i++) {
+                deferred_memcpy((uint8_t *)data + i * stride_data, (const uint8_t *)ctx->sync_staging->ptr + i * size, size, &compute_ctx->out_memcpys);
+            }
+        }
         ggml_vk_synchronize(ctx);
     }
 }
 
+static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    VK_LOG_DEBUG("ggml_backend_vk_get_tensor_async(" << size << ")");
+    ggml_backend_vk_get_tensor_2d_async(backend, tensor, data, offset, size, 1, size, size);
+}
+
 static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
     VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async(" << src << " -> " << dst << ", size=" << ggml_nbytes(src) << ")");
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend_dst->context;
@@ -14590,6 +14766,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         compute_ctx = ggml_vk_get_compute_ctx(ctx);
         ctx->query_idx = 0;
         compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++);
+        ggml_vk_sync_buffers(ctx, compute_ctx);
     }
 
     ctx->prealloc_y_last_pipeline_used = nullptr;
@@ -14826,6 +15003,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
                 ctx->query_nodes[ctx->query_idx] = cgraph->nodes[i];
                 ctx->query_fusion_names[ctx->query_idx] = fusion_string;
                 compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++);
+                ggml_vk_sync_buffers(ctx, compute_ctx);
             } else {
                 // track a fusion string and number of fused ops for the current node_idx
                 ctx->query_fusion_names[i] = fusion_string;
@@ -15207,8 +15385,8 @@ static ggml_backend_i ggml_backend_vk_interface = {
     /* .free                    = */ ggml_backend_vk_free,
     /* .set_tensor_async        = */ ggml_backend_vk_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_vk_get_tensor_async,
-    /* .get_tensor_2d_async     = */ NULL,
-    /* .set_tensor_2d_async     = */ NULL,
+    /* .set_tensor_2d_async     = */ ggml_backend_vk_set_tensor_2d_async,
+    /* .get_tensor_2d_async     = */ ggml_backend_vk_get_tensor_2d_async,
     /* .cpy_tensor_async        = */ ggml_backend_vk_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_vk_synchronize,
     /* .graph_plan_create       = */ NULL,
@@ -15517,7 +15695,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ4_NL:
                     case GGML_TYPE_MXFP4:
                     case GGML_TYPE_NVFP4:
-                    case GGML_TYPE_TQ4_1S:
                         break;
                     default:
                         return false;
@@ -15566,41 +15743,23 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) {
                     return false;
                 }
-                // It's straightforward to support different K/V dequant, but would
-                // significantly increase the number of pipelines
-                if (op->src[1]->type != op->src[2]->type) {
-                    return false;
-                }
-                switch (op->src[1]->type) {
-                case GGML_TYPE_F16:
-                case GGML_TYPE_F32:
-                case GGML_TYPE_Q4_0:
-                case GGML_TYPE_Q8_0:
-                case GGML_TYPE_TURBO3_0:
-                    // supported in scalar and coopmat2 paths
-                    break;
-                case GGML_TYPE_Q4_1:
-                case GGML_TYPE_Q5_0:
-                case GGML_TYPE_Q5_1:
-                case GGML_TYPE_IQ4_NL:
-                    // supported in scalar and coopmat2 paths
-                    break;
-                // K dequants currently disabled because D dimension is rounded up to 256 and runs inefficiently
-                //case GGML_TYPE_Q2_K:
-                //case GGML_TYPE_Q3_K:
-                //case GGML_TYPE_Q4_K:
-                //case GGML_TYPE_Q5_K:
-                //case GGML_TYPE_Q6_K:
-                //case GGML_TYPE_IQ1_S:
-                //case GGML_TYPE_IQ1_M:
-                //case GGML_TYPE_IQ2_XXS:
-                //case GGML_TYPE_IQ2_XS:
-                //case GGML_TYPE_IQ2_S:
-                //case GGML_TYPE_IQ3_XXS:
-                //case GGML_TYPE_IQ3_S:
-                //case GGML_TYPE_IQ4_XS:
-
-                default:
+                auto fa_kv_ok = [coopmat2](ggml_type t) {
+                    switch (t) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q4_0:
+                        return true;
+                    case GGML_TYPE_Q1_0:
+                        return coopmat2;
+                    default:
+                        return false;
+                    }
+                };
+                if (!fa_kv_ok(op->src[1]->type) || !fa_kv_ok(op->src[2]->type)) {
                     return false;
                 }
                 if (!coopmat2 && !(device->subgroup_shuffle && device->subgroup_vote)) {
@@ -15637,7 +15796,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ4_NL:
                     case GGML_TYPE_MXFP4:
                     case GGML_TYPE_NVFP4:
-                    case GGML_TYPE_TURBO3_0:
                     case GGML_TYPE_I32:
                         return true;
                     default:
@@ -15657,10 +15815,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
                     case GGML_TYPE_IQ4_NL:
-                    case GGML_TYPE_TURBO2_0:
-                    case GGML_TYPE_TURBO3_0:
-                    case GGML_TYPE_TURBO4_0:
-                    case GGML_TYPE_TQ4_1S:
                         return true;
                     default:
                         return false;
@@ -15700,8 +15854,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
                     case GGML_TYPE_IQ4_NL:
-                    case GGML_TYPE_TURBO3_0:
-                    case GGML_TYPE_TQ4_1S:
                         return true;
                     default:
                         break;
@@ -15956,8 +16108,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     ggml_is_contiguous(op->src[1]) &&
                     ggml_is_contiguous(op));
             }
-        case GGML_OP_TURBO_WHT:
-            return op->src[0]->type == GGML_TYPE_F32 && op->src[0]->ne[0] % 128 == 0;
         default:
             return false;
     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
index 35dfd0dffdb..4a60fea387d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
@@ -22,6 +22,7 @@
 
 #include "types.glsl"
 #include "flash_attn_base.glsl"
+#include "flash_attn_dequant.glsl"
 
 const uint32_t HSK_per_thread = HSK / D_split;
 const uint32_t HSV_per_thread = HSV / D_split;
@@ -130,18 +131,20 @@ void main() {
 
         Qf[buf_ib].qs[buf_iqs] = pack32(i8vec4(vals));
 
-#if defined(DATA_A_Q8_0) || defined(DATA_A_IQ4_NL)
-        if (buf_iqs == 0) {
-            Qf[buf_ib].ds = FLOAT_TYPEV2(qd, 0.0);
-        }
-#else // Q4_0, Q4_1, Q5_0, Q5_1
-        const FLOAT_TYPE thread_sum = vals.x + vals.y + vals.z + vals.w;
-        const FLOAT_TYPE sum = subgroupClusteredAdd(thread_sum, 8);
+        // Q8_0 K only needs (qd, _); the asymmetric Q4_*/Q5_* family also stores
+        // the row-sum scaled by qd, used in k_dot_correction.
+        if (FaTypeK == FA_TYPE_Q8_0) {
+            if (buf_iqs == 0) {
+                Qf[buf_ib].ds = FLOAT_TYPEV2(qd, 0.0);
+            }
+        } else {
+            const FLOAT_TYPE thread_sum = vals.x + vals.y + vals.z + vals.w;
+            const FLOAT_TYPE sum = subgroupClusteredAdd(thread_sum, 8);
 
-        if (buf_iqs == 0) {
-            Qf[buf_ib].ds = FLOAT_TYPEV2(qd, sum * qd);
+            if (buf_iqs == 0) {
+                Qf[buf_ib].ds = FLOAT_TYPEV2(qd, sum * qd);
+            }
         }
-#endif
 #endif
     }
     barrier();
@@ -179,13 +182,9 @@ void main() {
     // mo_offset will point to the tile starting at row i*Br and col 0
     uint32_t mo_offset = mo_stride * i;
 
-#if BLOCK_SIZE > 1
-    uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE;
-    uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE;
-#else
-    uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / 2;
-    uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / 2;
-#endif
+    // FaBlockBytesK/V == 2 for f16, 16 for f32, ggml block byte size for quants.
+    uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / FaBlockBytesK;
+    uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / FaBlockBytesV;
     uint32_t m_offset = gqa_iq1*KV;
     if (p.nem2 != 1 || p.nem3 != 1) {
         m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV;
@@ -259,21 +258,21 @@ void main() {
                 if (idx + gl_WorkGroupSize.x <= Bc * HSK / 4 || c < Bc) {
                     FLOAT_TYPEV4 K_Tf = FLOAT_TYPEV4(0);
                     if (!KV_bounds_check || j * Bc + c < KV) {
-#if BLOCK_SIZE > 1
-                        uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE + 4 * d;
-                        uint ib = coord / BLOCK_SIZE;
-                        uint iqs = (coord % BLOCK_SIZE);
-                        K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K);
-#else
-                        K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]);
-#endif
+                        if (USE_DECODE_K) {
+                            uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE_K + 4 * d;
+                            uint ib = coord / BLOCK_SIZE_K;
+                            uint iqs = (coord % BLOCK_SIZE_K);
+                            K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K);
+                        } else {
+                            K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]);
+                        }
                     }
 
                     kvsh[c * kvsh_stride + d] = K_Tf;
                 }
             }
 #else // MMQ
-            const uint ints_per_block = 8 / QUANT_R_MMQ;
+            const uint ints_per_block = 8u / fa_quant_r_mmq(FaTypeK);
             const uint quant_iters = Bc * HSK / 32 * ints_per_block;
             [[unroll]] for (uint32_t idx = 0; idx < quant_iters; idx += gl_WorkGroupSize.x) {
                 const uint32_t iqs = (idx + tid) % ints_per_block;
@@ -312,15 +311,13 @@ void main() {
                     FLOAT_TYPEV4 K_Tf;
                     if (SHMEM_STAGING != 0) {
                         K_Tf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)];
-                    } else {
-#if BLOCK_SIZE > 1
-                        uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid);
-                        uint ib = coord / BLOCK_SIZE;
-                        uint iqs = (coord % BLOCK_SIZE);
+                    } else if (USE_DECODE_K) {
+                        uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE_K + 4 * (d * D_split + d_tid);
+                        uint ib = coord / BLOCK_SIZE_K;
+                        uint iqs = (coord % BLOCK_SIZE_K);
                         K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K);
-#else
+                    } else {
                         K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]);
-#endif
                     }
                     [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
                         Sf[r][c] += dot(ACC_TYPEV4(Q_cache[r]), ACC_TYPEV4(K_Tf));
@@ -337,15 +334,13 @@ void main() {
                     FLOAT_TYPEV4 K_Tf;
                     if (SHMEM_STAGING != 0) {
                         K_Tf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)];
-                    } else {
-#if BLOCK_SIZE > 1
-                        uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid);
-                        uint ib = coord / BLOCK_SIZE;
-                        uint iqs = (coord % BLOCK_SIZE);
+                    } else if (USE_DECODE_K) {
+                        uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE_K + 4 * (d * D_split + d_tid);
+                        uint ib = coord / BLOCK_SIZE_K;
+                        uint iqs = (coord % BLOCK_SIZE_K);
                         K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K);
-#else
+                    } else {
                         K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]);
-#endif
                     }
                     [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
                         Sf[r][c] += dot(ACC_TYPEV4(Qf[tile_row(r) * qf_stride + d * D_split + d_tid]), ACC_TYPEV4(K_Tf));
@@ -368,72 +363,47 @@ void main() {
                 int32_t k_quants[d_per_step];
                 ACC_TYPEV2 k_dm;
 
+                // Q4_*/Q5_* take the block-8 fast path when one step covers a full
+                // block; Q8_0 always goes through the per-int get_k_qs* helpers
+                // (its qs is byte-packed, not nibble-packed).
+                const bool block8_fast = (d_per_step == 8) && (FaTypeK != FA_TYPE_Q8_0);
+
                 if (SHMEM_STAGING != 0) {
                     const uint k_block_idx = (d_tid * (HSK_per_thread / 4) + d_block) / 8;
                     const uint buf_ib = (c * cols_per_iter + col_tid) * qf_stride + k_block_idx;
-#if QUANT_AUXF == 1
-                    k_dm = ACC_TYPEV2(kblocksh[buf_ib].dm, 0.0);
-#else
                     k_dm = ACC_TYPEV2(kblocksh[buf_ib].dm);
-#endif
 
-#if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1) || defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
-                    if (d_per_step == 8) {
+                    if (block8_fast) {
+                        const bool has_qh = (FaTypeK == FA_TYPE_Q5_0) || (FaTypeK == FA_TYPE_Q5_1);
                         [[unroll]] for (uint32_t d = 0; d < 4; d++) {
                             uint vui = kblocksh[buf_ib].qs[d];
                             k_quants[d    ] = int32_t( vui       & 0x0F0F0F0F);
                             k_quants[d + 4] = int32_t((vui >> 4) & 0x0F0F0F0F);
-#if defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
-                            uint qh_lo = (kblocksh[buf_ib].qh >> (d * 4)) & 0xF;
-                            uint qh_hi = (kblocksh[buf_ib].qh >> (d * 4 + 16)) & 0xF;
-                            k_quants[d    ] |= int32_t((qh_lo * 0x02040810u) & 0x10101010u);
-                            k_quants[d + 4] |= int32_t((qh_hi * 0x02040810u) & 0x10101010u);
-#endif
+                            if (has_qh) {
+                                uint qh_lo = (kblocksh[buf_ib].qh >> (d * 4)) & 0xF;
+                                uint qh_hi = (kblocksh[buf_ib].qh >> (d * 4 + 16)) & 0xF;
+                                k_quants[d    ] |= int32_t((qh_lo * 0x02040810u) & 0x10101010u);
+                                k_quants[d + 4] |= int32_t((qh_hi * 0x02040810u) & 0x10101010u);
+                            }
                         }
-                    } else
-#endif
-                    {
+                    } else {
                         [[unroll]] for (uint32_t d = 0; d < d_per_step; d++) {
                             k_quants[d] = get_k_qs_shmem(buf_ib, (d_tid * (HSK_per_thread / 4) + d_block) % 8 + d);
                         }
                     }
                 } else {
-                    const uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE + 4 * (d_tid * (HSK_per_thread / 4) + d_block);
-                    const uint ib = coord / BLOCK_SIZE;
-                    const uint iqs = (coord % BLOCK_SIZE);
+                    const uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE_K + 4 * (d_tid * (HSK_per_thread / 4) + d_block);
+                    const uint ib = coord / BLOCK_SIZE_K;
+                    const uint iqs = (coord % BLOCK_SIZE_K);
 
-#if QUANT_AUXF == 1
-                    k_dm = ACC_TYPEV2(get_k_d(ib, k_offset), 0.0);
-#else
-                    k_dm = ACC_TYPEV2(get_k_dm(ib, k_offset));
-#endif
-#if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1) || defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
-                    if (d_per_step == 8) {
-#if defined(DATA_A_Q5_0)
-                        uint qh = pack32(u16vec2(k_packed.k_data_packed16[k_offset + ib].qh[0],
-                                                 k_packed.k_data_packed16[k_offset + ib].qh[1]));
-#elif defined(DATA_A_Q5_1)
-                        uint qh = k_packed.k_data_packed16[k_offset + ib].qh;
-#endif
-                        [[unroll]] for (uint32_t d = 0; d < 4; d++) {
-#if defined(A_TYPE_PACKED32)
-                            uint vui = k_packed32.k_data_packed32[k_offset + ib].qs[d];
-#else
-                            uint vui = pack32(u16vec2(k_packed.k_data_packed16[k_offset + ib].qs[iqs / 2 + d * 2 + 0],
-                                                      k_packed.k_data_packed16[k_offset + ib].qs[iqs / 2 + d * 2 + 1]));
-#endif
-                            k_quants[d    ] = int32_t( vui       & 0x0F0F0F0F);
-                            k_quants[d + 4] = int32_t((vui >> 4) & 0x0F0F0F0F);
-#if defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
-                            uint qh_lo = (qh >> (d * 4)) & 0xF;
-                            uint qh_hi = (qh >> (d * 4 + 16)) & 0xF;
-                            k_quants[d    ] |= int32_t((qh_lo * 0x02040810u) & 0x10101010u);
-                            k_quants[d + 4] |= int32_t((qh_hi * 0x02040810u) & 0x10101010u);
-#endif
+                    k_dm = ACC_TYPEV2(get_k_scale(ib, k_offset));
+
+                    if (block8_fast) {
+                        fa_k_qs_block8 blk = get_k_qs_block8(ib, k_offset);
+                        [[unroll]] for (uint32_t d = 0; d < 8; d++) {
+                            k_quants[d] = blk.qs[d];
                         }
-                    } else
-#endif
-                    {
+                    } else {
                         [[unroll]] for (uint32_t d = 0; d < d_per_step; d++) {
                             k_quants[d] = get_k_qs(ib, iqs + d * 4, k_offset);
                         }
@@ -518,14 +488,14 @@ void main() {
                 if (idx + gl_WorkGroupSize.x <= Bc * HSV / 4 || c < Bc) {
                     FLOAT_TYPEV4 V_Tf = FLOAT_TYPEV4(0);
                     if (!KV_bounds_check || j * Bc + c < KV) {
-#if BLOCK_SIZE > 1
-                        uint coord = (j * Bc + c) * v_stride * BLOCK_SIZE + 4 * d;
-                        uint ib = coord / BLOCK_SIZE;
-                        uint iqs = (coord % BLOCK_SIZE);
-                        V_Tf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V);
-#else
-                        V_Tf = FLOAT_TYPEV4(data_vv4[v_offset / 4 + (j * Bc + c) * v_stride / 4 + d]);
-#endif
+                        if (USE_DECODE_V) {
+                            uint coord = (j * Bc + c) * v_stride * BLOCK_SIZE_V + 4 * d;
+                            uint ib = coord / BLOCK_SIZE_V;
+                            uint iqs = (coord % BLOCK_SIZE_V);
+                            V_Tf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V);
+                        } else {
+                            V_Tf = FLOAT_TYPEV4(data_vv4[v_offset / 4 + (j * Bc + c) * v_stride / 4 + d]);
+                        }
                     }
 
                     kvsh[c * kvsh_stride + d] = V_Tf;
@@ -549,15 +519,13 @@ void main() {
                 FLOAT_TYPEV4 Vf;
                 if (SHMEM_STAGING != 0) {
                     Vf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)];
-                } else {
-#if BLOCK_SIZE > 1
-                    uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid);
-                    uint ib = coord / BLOCK_SIZE;
-                    uint iqs = (coord % BLOCK_SIZE);
+                } else if (USE_DECODE_V) {
+                    uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE_V + 4 * (d * D_split + d_tid);
+                    uint ib = coord / BLOCK_SIZE_V;
+                    uint iqs = (coord % BLOCK_SIZE_V);
                     Vf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V);
-#else
+                } else {
                     Vf = FLOAT_TYPEV4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]);
-#endif
                 }
                 [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
                     Of[r][d] += FLOAT_TYPEV4(Pf[r] * Vf);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
index 1878eb7e1aa..9a7957da97b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl
@@ -13,6 +13,12 @@ layout (constant_id =  8) const uint32_t SubGroupSize = 32;
 layout (constant_id =  9) const uint32_t SHMEM_STAGING = 0;
 layout (constant_id = 10) const uint32_t Flags = 0;
 layout (constant_id = 11) const uint32_t LIMIT_OCCUPANCY_SHMEM = 0;
+// ggml_type enumerant for K/V
+layout (constant_id = 12) const uint32_t FaTypeK = 0;
+layout (constant_id = 13) const uint32_t FaTypeV = 0;
+// sizeof(decode buffer): quants -> ggml block size; F32 -> 16 (decodeBufF32 vec4).
+layout (constant_id = 14) const uint32_t FaBlockBytesK = 2;
+layout (constant_id = 15) const uint32_t FaBlockBytesV = 2;
 
 const bool USE_MASK_OPT    = (Flags & 1) != 0;
 const bool MASK_ENABLE     = (Flags & 2) != 0;
@@ -81,213 +87,58 @@ layout (binding = 6) readonly buffer MO {uint32_t data_mask_opt[];};
 
 #define BINDING_IDX_K 0
 #define BINDING_IDX_V 1
-#if defined(DATA_A_F32)
-layout (binding = 1) readonly buffer K_PACKED {vec4 k_data_packed[];} k_packed;
-layout (binding = 2) readonly buffer V_PACKED {vec4 v_data_packed[];} v_packed;
-#elif defined(DATA_A_TURBO3_0)
-layout (binding = 1) readonly buffer K_T3 {block_turbo3_0 data_k_t3[];};
-layout (binding = 2) readonly buffer V_T3 {block_turbo3_0 data_v_t3[];};
-#elif defined(A_TYPE_PACKED16)
-layout (binding = 1) readonly buffer K_PACKED16 {A_TYPE_PACKED16 k_data_packed16[];} k_packed;
-layout (binding = 2) readonly buffer V_PACKED16 {A_TYPE_PACKED16 v_data_packed16[];} v_packed;
-#endif
-
-#if defined(A_TYPE_PACKED32)
-layout (binding = 1) readonly buffer K_PACKED32 {A_TYPE_PACKED32 k_data_packed32[];} k_packed32;
-layout (binding = 2) readonly buffer V_PACKED32 {A_TYPE_PACKED32 v_data_packed32[];} v_packed32;
-#endif
-
-#ifndef BLOCK_SIZE
-#define BLOCK_SIZE 1
-#endif
-
-// turbo3: define BLOCK_BYTE_SIZE early (before first use in FA offset computation)
-#if defined(DATA_A_TURBO3_0) && !defined(BLOCK_BYTE_SIZE)
-#define BLOCK_BYTE_SIZE 50 // block_turbo3_0: 2 (norm) + 32 (qs) + 16 (signs) = 50 bytes
-#endif
-
-#if defined(DATA_A_F32)
-#undef BLOCK_SIZE
-#define BLOCK_SIZE 4
-#define BLOCK_BYTE_SIZE 16
-
-FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
-    // iqs is currently always zero in the flash attention shaders
-    if (binding_idx == BINDING_IDX_K) {
-        return FLOAT_TYPEV4(k_packed.k_data_packed[a_offset + ib]);
-    } else {
-        return FLOAT_TYPEV4(v_packed.v_data_packed[a_offset + ib]);
-    }
-}
-#endif
-
-#if defined(DATA_A_Q4_0)
-#define BLOCK_BYTE_SIZE 18
-#elif defined(DATA_A_Q4_1)
-#define BLOCK_BYTE_SIZE 20
-#endif
-
-#if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1)
-FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
-    if (binding_idx == BINDING_IDX_K) {
-        uint vui_lo = uint(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);
-        uint vui_hi = uint(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);
-        uint shift = (iqs & 0x10) >> 2;
-        vui_lo >>= shift;
-        vui_hi >>= shift;
-
-        FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF);
-#ifdef DATA_A_Q4_1
-        return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d) * nibbles + FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].m);
-#else
-        return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d) * (nibbles - FLOAT_TYPE(8.0f));
-#endif
-    } else {
-        uint vui_lo = uint(v_packed.v_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);
-        uint vui_hi = uint(v_packed.v_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);
-        uint shift = (iqs & 0x10) >> 2;
-        vui_lo >>= shift;
-        vui_hi >>= shift;
-
-        FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF);
-#ifdef DATA_A_Q4_1
-        return FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].d) * nibbles + FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].m);
-#else
-        return FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].d) * (nibbles - FLOAT_TYPE(8.0f));
-#endif
-    }
-}
-#endif
-
-#if defined(DATA_A_Q5_0)
-#define BLOCK_BYTE_SIZE 22
-#elif defined(DATA_A_Q5_1)
-#define BLOCK_BYTE_SIZE 24
-#endif
-
-#if defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
-FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
-    if (binding_idx == BINDING_IDX_K) {
-        uint vui_lo = uint(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);
-        uint vui_hi = uint(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);
-        uint shift = (iqs & 0x10) >> 2;
-        vui_lo >>= shift;
-        vui_hi >>= shift;
-
-#ifdef DATA_A_Q5_1
-        uint qh = k_packed.k_data_packed16[a_offset + ib].qh;
-#else
-        uint qh = uint(k_packed.k_data_packed16[a_offset + ib].qh[0]) | (uint(k_packed.k_data_packed16[a_offset + ib].qh[1]) << 16);
-#endif
-        FLOAT_TYPEV4 hb = FLOAT_TYPEV4((qh >> iqs) & 1, (qh >> (iqs + 1)) & 1, (qh >> (iqs + 2)) & 1, (qh >> (iqs + 3)) & 1) * FLOAT_TYPE(16.0f);
-
-        FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF);
-#ifdef DATA_A_Q5_1
-        return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d) * (nibbles + hb) + FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].m);
-#else
-        return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d) * (nibbles + hb - FLOAT_TYPE(16.0f));
-#endif
-    } else {
-        uint vui_lo = uint(v_packed.v_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);
-        uint vui_hi = uint(v_packed.v_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);
-        uint shift = (iqs & 0x10) >> 2;
-        vui_lo >>= shift;
-        vui_hi >>= shift;
-
-#ifdef DATA_A_Q5_1
-        uint qh = v_packed.v_data_packed16[a_offset + ib].qh;
-#else
-        uint qh = uint(v_packed.v_data_packed16[a_offset + ib].qh[0]) | (uint(v_packed.v_data_packed16[a_offset + ib].qh[1]) << 16);
-#endif
-        FLOAT_TYPEV4 hb = FLOAT_TYPEV4((qh >> iqs) & 1, (qh >> (iqs + 1)) & 1, (qh >> (iqs + 2)) & 1, (qh >> (iqs + 3)) & 1) * FLOAT_TYPE(16.0f);
-
-        FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF);
-#ifdef DATA_A_Q5_1
-        return FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].d) * (nibbles + hb) + FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].m);
-#else
-        return FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].d) * (nibbles + hb - FLOAT_TYPE(16.0f));
-#endif
-    }
-}
-#endif
-
 
-#if defined(DATA_A_IQ4_NL)
-#define BLOCK_BYTE_SIZE 18
-
-FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
-    if (binding_idx == BINDING_IDX_K) {
-        uint vui_lo = uint(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);
-        uint vui_hi = uint(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);
-        uint shift = (iqs & 0x10) >> 2;
-        vui_lo >>= shift;
-        vui_hi >>= shift;
-
-        return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d) * FLOAT_TYPEV4(
-            kvalues_iq4nl[vui_lo & 0xF],
-            kvalues_iq4nl[(vui_lo >> 8) & 0xF],
-            kvalues_iq4nl[vui_hi & 0xF],
-            kvalues_iq4nl[(vui_hi >> 8) & 0xF]);
-    } else {
-        uint vui_lo = uint(v_packed.v_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);
-        uint vui_hi = uint(v_packed.v_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);
-        uint shift = (iqs & 0x10) >> 2;
-        vui_lo >>= shift;
-        vui_hi >>= shift;
-
-        return FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].d) * FLOAT_TYPEV4(
-            kvalues_iq4nl[vui_lo & 0xF],
-            kvalues_iq4nl[(vui_lo >> 8) & 0xF],
-            kvalues_iq4nl[vui_hi & 0xF],
-            kvalues_iq4nl[(vui_hi >> 8) & 0xF]);
+// FaTypeK / FaTypeV spec constant values. These mirror enum ggml_type so the
+// host can pass the type directly. Keep in sync with ggml.h.
+#define FA_TYPE_F32   0u
+#define FA_TYPE_F16   1u
+#define FA_TYPE_Q4_0  2u
+#define FA_TYPE_Q4_1  3u
+#define FA_TYPE_Q5_0  6u
+#define FA_TYPE_Q5_1  7u
+#define FA_TYPE_Q8_0  8u
+#define FA_TYPE_Q1_0 41u
+
+// Number of matrix elements per buffer block, derived from the K/V type spec
+// constant. F32 is treated as a vec4 "block" of 4 floats. F16 uses block size 1
+// and bypasses the dequant path entirely. Quants follow their ggml block sizes.
+uint fa_block_elems(uint ty) {
+    switch (ty) {
+        case FA_TYPE_F32:  return 4u;
+        case FA_TYPE_F16:  return 1u;
+        case FA_TYPE_Q4_0: return uint(QUANT_K_Q4_0);
+        case FA_TYPE_Q4_1: return uint(QUANT_K_Q4_1);
+        case FA_TYPE_Q5_0: return uint(QUANT_K_Q5_0);
+        case FA_TYPE_Q5_1: return uint(QUANT_K_Q5_1);
+        case FA_TYPE_Q8_0: return uint(QUANT_K_Q8_0);
+        case FA_TYPE_Q1_0: return uint(QUANT_K_Q1_0); // cm2-only, harmless elsewhere
+        default:           return 1u;
     }
 }
-#endif
-#if defined(DATA_A_Q8_0)
-#define BLOCK_BYTE_SIZE 34
-FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
-    if (binding_idx == BINDING_IDX_K) {
-        const i8vec2 v0 = unpack8(int32_t(k_packed.k_data_packed16[a_offset + ib].qs[iqs / 2])).xy; // vec4 used due to #12147
-        const i8vec2 v1 = unpack8(int32_t(k_packed.k_data_packed16[a_offset + ib].qs[iqs / 2 + 1])).xy;
-
-        return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d) * FLOAT_TYPEV4(v0.x, v0.y, v1.x, v1.y);
-    } else {
-        const i8vec2 v0 = unpack8(int32_t(v_packed.v_data_packed16[a_offset + ib].qs[iqs / 2])).xy; // vec4 used due to #12147
-        const i8vec2 v1 = unpack8(int32_t(v_packed.v_data_packed16[a_offset + ib].qs[iqs / 2 + 1])).xy;
 
-        return FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].d) * FLOAT_TYPEV4(v0.x, v0.y, v1.x, v1.y);
+// QUANT_R_MMQ for FA-eligible K types. Q4_*/Q5_* store two nibbles per byte
+// (R==2); Q8_0 stores one byte per element (R==1). Used to derive the number
+// of int32s per 32-element block on the MMQ K path: ints_per_block == 8 / R.
+uint fa_quant_r_mmq(uint ty) {
+    switch (ty) {
+        case FA_TYPE_Q4_0: return uint(QUANT_R_Q4_0);
+        case FA_TYPE_Q4_1: return uint(QUANT_R_Q4_1);
+        case FA_TYPE_Q5_0: return uint(QUANT_R_Q5_0);
+        case FA_TYPE_Q5_1: return uint(QUANT_R_Q5_1);
+        case FA_TYPE_Q8_0: return uint(QUANT_R_Q8_0);
+        default:           return 1u;
     }
 }
-#endif
-
-#if defined(DATA_A_TURBO3_0)
-const float T3C[8] = float[8](
-    -0.190685, -0.117832, -0.065717, -0.021460,
-     0.021460,  0.065717,  0.117832,  0.190685
-);
-FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
-    FLOAT_TYPEV4 r;
-    for (int k = 0; k < 4; k++) {
-        uint  j  = iqs + uint(k);
-        float nm;
-        uint  qb;
-        uint  sb;
-        if (binding_idx == BINDING_IDX_K) {
-            nm = float(data_k_t3[a_offset + ib].norm);
-            qb = uint(data_k_t3[a_offset + ib].qs[j / 4]);
-            sb = uint(data_k_t3[a_offset + ib].signs[j / 8]);
-        } else {
-            nm = float(data_v_t3[a_offset + ib].norm);
-            qb = uint(data_v_t3[a_offset + ib].qs[j / 4]);
-            sb = uint(data_v_t3[a_offset + ib].signs[j / 8]);
-        }
-        uint lo = (qb >> ((j % 4) * 2)) & 0x3;
-        uint hi = (sb >> (j % 8)) & 0x1;
-        r[k] = FLOAT_TYPE(T3C[lo | (hi << 2)] * nm);
-    }
-    return r;
-}
-#endif
+
+// These can't be `const` globals because GLSL forbids function calls in global
+// const initializers, even when the spec constants would let the driver fold
+// them. Macros expand at the use site and fold after specialization.
+#define BLOCK_SIZE_K fa_block_elems(FaTypeK)
+#define BLOCK_SIZE_V fa_block_elems(FaTypeV)
+// F16 reads f16 elements directly from the binding; everything else routes
+// through dequantize4 / the MMQ helpers to unpack from the packed block layout.
+#define USE_DECODE_K (FaTypeK != FA_TYPE_F16)
+#define USE_DECODE_V (FaTypeV != FA_TYPE_F16)
 
 #define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
index 526e8da384e..bffcc095be3 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -14,6 +14,7 @@
 
 #include "types.glsl"
 #include "flash_attn_base.glsl"
+#include "flash_attn_dequant.glsl"
 
 // These need to be supported N,M values for a MatBc x MatBr x 16 coopmatmuladd
 const uint32_t MatBr = 16;
@@ -127,13 +128,9 @@ void main() {
     // mo_offset will point to the tile starting at row i*Br and col 0
     uint32_t mo_offset = mo_stride * i;
 
-#if BLOCK_SIZE > 1
-    uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE;
-    uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE;
-#else
-    uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / 2;
-    uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / 2;
-#endif
+    // FaBlockBytesK/V == 2 for f16 (sizeof f16) and == 16 for f32 (vec4) and == ggml block size for quants.
+    uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / FaBlockBytesK;
+    uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / FaBlockBytesV;
     uint32_t m_offset = gqa_iq1*KV;
     if (p.nem2 != 1 || p.nem3 != 1) {
         m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV;
@@ -227,14 +224,14 @@ void main() {
                 if (idx + gl_WorkGroupSize.x <= Bc * HSK_pad / 4 || c < Bc) {
                     f16vec4 K_Tf = f16vec4(0);
                     if ((!KV_bounds_check || j * Bc + c < KV) && (HSK == HSK_pad || d < HSK / 4)) {
-#if BLOCK_SIZE > 1
-                        uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE + 4 * d;
-                        uint ib = coord / BLOCK_SIZE;
-                        uint iqs = (coord % BLOCK_SIZE);
-                        K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K);
-#else
-                        K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]);
-#endif
+                        if (USE_DECODE_K) {
+                            uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE_K + 4 * d;
+                            uint ib = coord / BLOCK_SIZE_K;
+                            uint iqs = (coord % BLOCK_SIZE_K);
+                            K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K);
+                        } else {
+                            K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]);
+                        }
                     }
 
                     kvsh[c * kvsh_stride + d] = K_Tf;
@@ -256,47 +253,40 @@ void main() {
             // staged through a Bc * MatBr size staging buffer.
             // If K is not type f16, then it is always staged for dequantization.
             if (SHMEM_STAGING == 0) {
-#if BLOCK_SIZE == 1
-            if (KV_bounds_check || d * 16 + 16 > HSK) {
-#endif
-            barrier();
-            [[unroll]] for (uint32_t idx = 0; idx < Bc * MatBr / 4; idx += gl_WorkGroupSize.x) {
-                uint32_t col_vec = (idx + tid) % (MatBr / 4);
-                uint32_t row = (idx + tid) / (MatBr / 4);
-                if (idx + tid < Bc * MatBr / 4) {
-                    f16vec4 K_Tf = f16vec4(0);
-                    if ((!KV_bounds_check || j * Bc + row < KV) && (HSK == HSK_pad || d * 16 + col_vec * 4 < HSK)) {
-#if BLOCK_SIZE > 1
-                        uint coord = (j * Bc + row) * k_stride * BLOCK_SIZE + d * 16 + col_vec * 4;
-                        uint ib = coord / BLOCK_SIZE;
-                        uint iqs = (coord % BLOCK_SIZE);
-                        K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K);
-#else
-                        K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + row) * k_stride / 4 + d * 16 / 4 + col_vec]);
-#endif
-                    }
+            // For quants we always need to dequant into kvsh; for f16 we can load
+            // directly from global memory when alignment / bounds allow it.
+            const bool stage_k = USE_DECODE_K || KV_bounds_check || d * 16 + 16 > HSK;
+            if (stage_k) {
+                barrier();
+                [[unroll]] for (uint32_t idx = 0; idx < Bc * MatBr / 4; idx += gl_WorkGroupSize.x) {
+                    uint32_t col_vec = (idx + tid) % (MatBr / 4);
+                    uint32_t row = (idx + tid) / (MatBr / 4);
+                    if (idx + tid < Bc * MatBr / 4) {
+                        f16vec4 K_Tf = f16vec4(0);
+                        if ((!KV_bounds_check || j * Bc + row < KV) && (HSK == HSK_pad || d * 16 + col_vec * 4 < HSK)) {
+                            if (USE_DECODE_K) {
+                                uint coord = (j * Bc + row) * k_stride * BLOCK_SIZE_K + d * 16 + col_vec * 4;
+                                uint ib = coord / BLOCK_SIZE_K;
+                                uint iqs = (coord % BLOCK_SIZE_K);
+                                K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K);
+                            } else {
+                                K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + row) * k_stride / 4 + d * 16 / 4 + col_vec]);
+                            }
+                        }
 
-                    kvsh[row * kvsh_stride + col_vec] = K_Tf;
+                        kvsh[row * kvsh_stride + col_vec] = K_Tf;
+                    }
                 }
+                barrier();
             }
-            barrier();
-#if BLOCK_SIZE == 1
-            }
-#endif
 
-#if BLOCK_SIZE == 1
-            if (KV_bounds_check || d * 16 + 16 > HSK)
-#endif
-            {
+            if (stage_k) {
                 uint coord = (gl_SubgroupID * MatBc) * kvsh_stride;
                 coopMatLoad(KMat, kvsh, coord, kvsh_stride, gl_CooperativeMatrixLayoutRowMajor);
-            }
-#if BLOCK_SIZE == 1
-            else {
+            } else {
                 const uint coord = k_offset / 4 + (j * Bc + gl_SubgroupID * MatBc) * k_stride / 4 + d * 16 / 4;
                 coopMatLoad(KMat, data_kv4, coord, k_stride / 4, gl_CooperativeMatrixLayoutRowMajor);
             }
-#endif
             } else {
                 uint coord = (gl_SubgroupID * MatBc) * kvsh_stride + d * 16 / 4;
                 coopMatLoad(KMat, kvsh, coord, kvsh_stride, gl_CooperativeMatrixLayoutRowMajor);
@@ -397,14 +387,14 @@ void main() {
                 if (idx + gl_WorkGroupSize.x <= Bc * HSV_pad / 4 || c < Bc) {
                     f16vec4 V_Tf = f16vec4(0);
                     if ((!KV_bounds_check || j * Bc + c < KV) && (HSV == HSV_pad || d < HSV / 4)) {
-#if BLOCK_SIZE > 1
-                        uint coord = (j * Bc + c) * v_stride * BLOCK_SIZE + 4 * d;
-                        uint ib = coord / BLOCK_SIZE;
-                        uint iqs = (coord % BLOCK_SIZE);
-                        V_Tf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V);
-#else
-                        V_Tf = f16vec4(data_vv4[v_offset / 4 + (j * Bc + c) * v_stride / 4 + d]);
-#endif
+                        if (USE_DECODE_V) {
+                            uint coord = (j * Bc + c) * v_stride * BLOCK_SIZE_V + 4 * d;
+                            uint ib = coord / BLOCK_SIZE_V;
+                            uint iqs = (coord % BLOCK_SIZE_V);
+                            V_Tf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V);
+                        } else {
+                            V_Tf = f16vec4(data_vv4[v_offset / 4 + (j * Bc + c) * v_stride / 4 + d]);
+                        }
                     }
 
                     kvsh[c * kvsh_stride + d] = V_Tf;
@@ -431,36 +421,33 @@ void main() {
             // staged through a Bc * MatBr size staging buffer.
             // If V is not type f16, then it is always staged for dequantization.
             if (SHMEM_STAGING == 0) {
-#if BLOCK_SIZE == 1
-            // For f16, only preload if not aligned
-            if (KV_bounds_check) {
-#endif
-            [[unroll]] for (uint32_t i = 0; i < v_loads_per_thread; ++i) {
-                const uint idx = i * gl_WorkGroupSize.x + tid;
-                const uint row = idx / v_cols;
-                const uint col = idx % v_cols;
-
-                const uint v_row = j * Bc + row;
-                const uint v_col = hsv_tile * MatBc * row_split + col * 4;
-
-                const uint coord = v_row * v_stride * BLOCK_SIZE + v_col;
-                const uint ib = coord / BLOCK_SIZE;
-                const uint iqs = coord % BLOCK_SIZE;
-
-                if (!KV_bounds_check || (v_row < KV && v_col < HSV)) {
-#if BLOCK_SIZE > 1
-                    kvsh[row * vsh_stride + col] = dequantize4(ib, iqs, v_offset, BINDING_IDX_V);
-#else
-                    kvsh[row * vsh_stride + col] = data_vv4[(v_offset + v_row * v_stride + v_col) / 4];
-#endif
-                } else {
-                    kvsh[row * vsh_stride + col] = f16vec4(0.0f);
+            // For quants we always preload via kvsh. For f16 we only preload when
+            // alignment / bounds force it (otherwise we coopMatLoad direct from data_vv4).
+            const bool stage_v = USE_DECODE_V || KV_bounds_check;
+            if (stage_v) {
+                [[unroll]] for (uint32_t i = 0; i < v_loads_per_thread; ++i) {
+                    const uint idx = i * gl_WorkGroupSize.x + tid;
+                    const uint row = idx / v_cols;
+                    const uint col = idx % v_cols;
+
+                    const uint v_row = j * Bc + row;
+                    const uint v_col = hsv_tile * MatBc * row_split + col * 4;
+
+                    const uint coord = v_row * v_stride * BLOCK_SIZE_V + v_col;
+                    const uint ib = coord / BLOCK_SIZE_V;
+                    const uint iqs = coord % BLOCK_SIZE_V;
+
+                    if (!KV_bounds_check || (v_row < KV && v_col < HSV)) {
+                        if (USE_DECODE_V) {
+                            kvsh[row * vsh_stride + col] = dequantize4(ib, iqs, v_offset, BINDING_IDX_V);
+                        } else {
+                            kvsh[row * vsh_stride + col] = data_vv4[(v_offset + v_row * v_stride + v_col) / 4];
+                        }
+                    } else {
+                        kvsh[row * vsh_stride + col] = f16vec4(0.0f);
+                    }
                 }
             }
-
-#if BLOCK_SIZE == 1
-            }
-#endif
             }
             barrier();
 
@@ -471,15 +458,12 @@ void main() {
                     coopMatLoad(KMat, Psh, bc_chunk * MatBc * psh_stride, psh_stride, gl_CooperativeMatrixLayoutColumnMajor);
 
                     if (SHMEM_STAGING == 0) {
-#if BLOCK_SIZE == 1
-                    if (!KV_bounds_check) {
+                    if (!USE_DECODE_V && !KV_bounds_check) {
                         // F16 values can be loaded directly from global memory
                         const uint v_tile_row = j * Bc + bc_chunk * MatBc;
                         const uint v_tile_offset = v_offset / 4 + v_tile_row * v_stride / 4 + hsv_offset / 4;
                         coopMatLoad(QMat, data_vv4, v_tile_offset, v_stride / 4, gl_CooperativeMatrixLayoutRowMajor);
-                    } else
-#endif
-                    {
+                    } else {
                         const uint v_tile_offset = bc_chunk * MatBr * v_cols + gl_SubgroupID * (MatBc / 4);
                         coopMatLoad(QMat, kvsh, v_tile_offset, vsh_stride, gl_CooperativeMatrixLayoutRowMajor);
                     }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
index 0ea181342ce..141bb870883 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
@@ -17,8 +17,42 @@
 #extension GL_EXT_null_initializer : enable
 
 #include "types.glsl"
-#include "dequant_funcs_cm2.glsl"
 #include "flash_attn_base.glsl"
+#include "dequant_funcs_cm2.glsl"
+
+// buffer_reference stride = sizeof(struct) = FaBlockBytesK/V.
+layout(buffer_reference, std430, buffer_reference_align = 1) buffer decodeBufFA_K {
+    uint8_t raw[FaBlockBytesK];
+};
+layout(buffer_reference, std430, buffer_reference_align = 1) buffer decodeBufFA_V {
+    uint8_t raw[FaBlockBytesV];
+};
+
+float16_t faDecodeK(const decodeBufFA_K bl_in, const uint blockCoords[2], const uint coordInBlock[2]) {
+    switch (FaTypeK) {
+        case FA_TYPE_F32:  return dequantFuncF32 (decodeBufF32 (bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q4_0: return dequantFuncQ4_0(decodeBufQ4_0(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q4_1: return dequantFuncQ4_1(decodeBufQ4_1(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q5_0: return dequantFuncQ5_0(decodeBufQ5_0(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q5_1: return dequantFuncQ5_1(decodeBufQ5_1(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q8_0: return dequantFuncQ8_0(decodeBufQ8_0(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q1_0: return dequantFuncQ1_0(decodeBufQ1_0(bl_in), blockCoords, coordInBlock);
+        default: return float16_t(0);
+    }
+}
+
+float16_t faDecodeV(const decodeBufFA_V bl_in, const uint blockCoords[2], const uint coordInBlock[2]) {
+    switch (FaTypeV) {
+        case FA_TYPE_F32:  return dequantFuncF32 (decodeBufF32 (bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q4_0: return dequantFuncQ4_0(decodeBufQ4_0(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q4_1: return dequantFuncQ4_1(decodeBufQ4_1(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q5_0: return dequantFuncQ5_0(decodeBufQ5_0(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q5_1: return dequantFuncQ5_1(decodeBufQ5_1(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q8_0: return dequantFuncQ8_0(decodeBufQ8_0(bl_in), blockCoords, coordInBlock);
+        case FA_TYPE_Q1_0: return dequantFuncQ1_0(decodeBufQ1_0(bl_in), blockCoords, coordInBlock);
+        default: return float16_t(0);
+    }
+}
 
 layout (binding = 0) readonly buffer Q {uint8_t data_q[];};
 layout (binding = 1) readonly buffer K {uint8_t data_k[];};
@@ -55,12 +89,6 @@ ACC_TYPE Max(const in uint32_t row, const in uint32_t col, const in ACC_TYPE ele
     return max(elem0, elem1);
 }
 
-#if BLOCK_SIZE > 1
-#define DECODEFUNC , DEQUANTFUNC
-#else
-#define DECODEFUNC
-#endif
-
 // Store the output when doing grouped query attention.
 // Rows index by Q's dimension 2, and the first N rows are valid.
 D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N)
@@ -95,10 +123,6 @@ ACC_TYPE perElemOpNonGqaSplitKStoreCol0(const in uint32_t r, const in uint32_t c
 }
 
 void main() {
-#ifdef NEEDS_INIT_IQ_SHMEM
-    init_iq_shmem(gl_WorkGroupSize);
-#endif
-
     init_indices();
 
     tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutQ = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
@@ -107,10 +131,10 @@ void main() {
 
     tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0);
 
-#if BLOCK_SIZE > 1
-    tensorLayoutK = setTensorLayoutBlockSizeNV(tensorLayoutK, 1, BLOCK_SIZE);
-    tensorLayoutV = setTensorLayoutBlockSizeNV(tensorLayoutV, 1, BLOCK_SIZE);
-#endif
+    const uint bs_k = fa_block_elems(FaTypeK);
+    const uint bs_v = fa_block_elems(FaTypeV);
+    tensorLayoutK = setTensorLayoutBlockSizeNV(tensorLayoutK, 1, bs_k);
+    tensorLayoutV = setTensorLayoutBlockSizeNV(tensorLayoutV, 1, bs_v);
 
     tensorLayoutQ = setTensorLayoutDimensionNV(tensorLayoutQ, N, HSK);
     tensorLayoutK = setTensorLayoutDimensionNV(tensorLayoutK, KV, HSK);
@@ -120,10 +144,12 @@ void main() {
     if (Clamp != gl_CooperativeMatrixClampModeConstantNV)
     {
         q_stride &= ~7;
-#if BLOCK_SIZE == 1
-        k_stride &= ~7;
-        v_stride &= ~7;
-#endif
+        if (bs_k == 1u) {
+            k_stride &= ~7;
+        }
+        if (bs_v == 1u) {
+            v_stride &= ~7;
+        }
         m_stride &= ~7;
     }
     tensorLayoutQ = setTensorLayoutStrideNV(tensorLayoutQ, q_stride, 1);
@@ -230,7 +256,13 @@ void main() {
         coopmat<float16_t, gl_ScopeWorkgroup, HSK_pad, Bc, gl_MatrixUseB> K_T;
 
         uint32_t k_offset = ik2*p.nb12 + ik3*p.nb13;
-        coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose DECODEFUNC);
+        // F16: bs_k==1 (direct load). F32: bs_k==4 (vec4 / dequantFuncF32). Q4/Q8 family: bs_k==32. Q1_0: bs_k==128.
+        const bool k_use_decode = (bs_k > 1u);
+        if (k_use_decode) {
+            coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose, faDecodeK);
+        } else {
+            coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose);
+        }
         S = coopMatMulAdd(Qf16, K_T, S);
 
         if (LOGIT_SOFTCAP) {
@@ -291,7 +323,12 @@ void main() {
 
         coopmat<float16_t, gl_ScopeWorkgroup, Bc, HSV_pad, gl_MatrixUseB> V;
         uint32_t v_offset = iv2*p.nb22 + iv3*p.nb23;
-        coopMatLoadTensorNV(V,  data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, HSV_pad) DECODEFUNC);
+        const bool v_use_decode = (bs_v > 1u);
+        if (v_use_decode) {
+            coopMatLoadTensorNV(V, data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, HSV_pad), faDecodeV);
+        } else {
+            coopMatLoadTensorNV(V, data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, HSV_pad));
+        }
 
         L = eM*L + rowsum;
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl
new file mode 100644
index 00000000000..02106f33cbe
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl
@@ -0,0 +1,123 @@
+// Asymmetric K/V flash attention: aliased SSBO views of bindings 1 (K) and 2 (V)
+// covering every supported FA element type, plus an uber dequantize4() that
+// switches on FaTypeK / FaTypeV. After spec-constant specialization the driver
+// folds away every path except the one matching the K/V type for this pipeline.
+//
+// Included by flash_attn.comp and flash_attn_cm1.comp. Not included by
+// flash_attn_cm2.comp, which has its own buffer_reference-based decode path.
+//
+// We use macros (rather than per-quant decode functions taking a struct) on
+// purpose: the FA shaders don't enable GL_EXT_shader_explicit_arithmetic_types_float16
+// when FLOAT16 isn't defined, which makes float16-containing struct values
+// illegal to return from / pass to functions. Macros expand inline where the
+// float16 stays in storage and is converted to FLOAT_TYPE at use.
+
+// F32 is fed as a vec4 "block" (4 floats), matching what dequant_funcs_cm2.glsl
+// does for F32 in the cm2 shader. FaBlockBytesK/V == 16 for F32.
+layout (binding = 1) readonly buffer K_PACKED_F32  { vec4 data[]; }                k_packed_f32;
+layout (binding = 2) readonly buffer V_PACKED_F32  { vec4 data[]; }                v_packed_f32;
+
+layout (binding = 1) readonly buffer K_PACKED_Q4_0 { block_q4_0_packed16 data[]; } k_packed_q4_0;
+layout (binding = 2) readonly buffer V_PACKED_Q4_0 { block_q4_0_packed16 data[]; } v_packed_q4_0;
+layout (binding = 1) readonly buffer K_PACKED_Q4_1 { block_q4_1_packed16 data[]; } k_packed_q4_1;
+layout (binding = 2) readonly buffer V_PACKED_Q4_1 { block_q4_1_packed16 data[]; } v_packed_q4_1;
+layout (binding = 1) readonly buffer K_PACKED_Q5_0 { block_q5_0_packed16 data[]; } k_packed_q5_0;
+layout (binding = 2) readonly buffer V_PACKED_Q5_0 { block_q5_0_packed16 data[]; } v_packed_q5_0;
+layout (binding = 1) readonly buffer K_PACKED_Q5_1 { block_q5_1_packed16 data[]; } k_packed_q5_1;
+layout (binding = 2) readonly buffer V_PACKED_Q5_1 { block_q5_1_packed16 data[]; } v_packed_q5_1;
+layout (binding = 1) readonly buffer K_PACKED_Q8_0 { block_q8_0_packed16 data[]; } k_packed_q8_0;
+layout (binding = 2) readonly buffer V_PACKED_Q8_0 { block_q8_0_packed16 data[]; } v_packed_q8_0;
+
+// Q4_1 and Q5_1 packed32 views: aliased to the same memory as the packed16
+// views, used by the MMQ K-side hot path for fast 4-uint loads.
+layout (binding = 1) readonly buffer K_PACKED_Q4_1_P32 { block_q4_1_packed32 data[]; } k_packed_q4_1_p32;
+layout (binding = 1) readonly buffer K_PACKED_Q5_1_P32 { block_q5_1_packed32 data[]; } k_packed_q5_1_p32;
+
+// Per-quant decode bodies are expanded once for the K view set and once for
+// the V view set. The macros take the buffer name as a parameter.
+#define FA_DEQUANT4_F32(BUF) \
+    return FLOAT_TYPEV4(BUF.data[a_offset + ib]);
+
+#define FA_DEQUANT4_Q4_0(BUF) {                                                                   \
+    uint vui_lo = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);                          \
+    uint vui_hi = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);                          \
+    uint shift = (iqs & 0x10) >> 2;                                                               \
+    vui_lo >>= shift;                                                                             \
+    vui_hi >>= shift;                                                                             \
+    FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF,                        \
+                                        vui_hi & 0xF, (vui_hi >> 8) & 0xF);                       \
+    return FLOAT_TYPE(BUF.data[a_offset + ib].d) * (nibbles - FLOAT_TYPE(8.0f));                  \
+}
+
+#define FA_DEQUANT4_Q4_1(BUF) {                                                                   \
+    uint vui_lo = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);                          \
+    uint vui_hi = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);                          \
+    uint shift = (iqs & 0x10) >> 2;                                                               \
+    vui_lo >>= shift;                                                                             \
+    vui_hi >>= shift;                                                                             \
+    FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF,                        \
+                                        vui_hi & 0xF, (vui_hi >> 8) & 0xF);                       \
+    return FLOAT_TYPE(BUF.data[a_offset + ib].d) * nibbles                                        \
+         + FLOAT_TYPE(BUF.data[a_offset + ib].m);                                                 \
+}
+
+#define FA_DEQUANT4_Q5_0(BUF) {                                                                   \
+    uint vui_lo = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);                          \
+    uint vui_hi = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);                          \
+    uint shift = (iqs & 0x10) >> 2;                                                               \
+    vui_lo >>= shift;                                                                             \
+    vui_hi >>= shift;                                                                             \
+    uint qh = uint(BUF.data[a_offset + ib].qh[0])                                                 \
+            | (uint(BUF.data[a_offset + ib].qh[1]) << 16);                                        \
+    FLOAT_TYPEV4 hb = FLOAT_TYPEV4((qh >> iqs)       & 1, (qh >> (iqs + 1)) & 1,                  \
+                                   (qh >> (iqs + 2)) & 1, (qh >> (iqs + 3)) & 1)                  \
+                      * FLOAT_TYPE(16.0f);                                                        \
+    FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF,                        \
+                                        vui_hi & 0xF, (vui_hi >> 8) & 0xF);                       \
+    return FLOAT_TYPE(BUF.data[a_offset + ib].d) * (nibbles + hb - FLOAT_TYPE(16.0f));            \
+}
+
+#define FA_DEQUANT4_Q5_1(BUF) {                                                                   \
+    uint vui_lo = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);                          \
+    uint vui_hi = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);                          \
+    uint shift = (iqs & 0x10) >> 2;                                                               \
+    vui_lo >>= shift;                                                                             \
+    vui_hi >>= shift;                                                                             \
+    uint qh = BUF.data[a_offset + ib].qh;                                                         \
+    FLOAT_TYPEV4 hb = FLOAT_TYPEV4((qh >> iqs)       & 1, (qh >> (iqs + 1)) & 1,                  \
+                                   (qh >> (iqs + 2)) & 1, (qh >> (iqs + 3)) & 1)                  \
+                      * FLOAT_TYPE(16.0f);                                                        \
+    FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF,                        \
+                                        vui_hi & 0xF, (vui_hi >> 8) & 0xF);                       \
+    return FLOAT_TYPE(BUF.data[a_offset + ib].d) * (nibbles + hb)                                 \
+         + FLOAT_TYPE(BUF.data[a_offset + ib].m);                                                 \
+}
+
+#define FA_DEQUANT4_Q8_0(BUF) {                                                                   \
+    const i8vec2 v0 = unpack8(int32_t(BUF.data[a_offset + ib].qs[iqs / 2    ])).xy;               \
+    const i8vec2 v1 = unpack8(int32_t(BUF.data[a_offset + ib].qs[iqs / 2 + 1])).xy;               \
+    return FLOAT_TYPE(BUF.data[a_offset + ib].d) * FLOAT_TYPEV4(v0.x, v0.y, v1.x, v1.y);          \
+}
+
+FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
+    if (binding_idx == BINDING_IDX_K) {
+        switch (FaTypeK) {
+            case FA_TYPE_F32:  FA_DEQUANT4_F32 (k_packed_f32)
+            case FA_TYPE_Q4_0: FA_DEQUANT4_Q4_0(k_packed_q4_0)
+            case FA_TYPE_Q4_1: FA_DEQUANT4_Q4_1(k_packed_q4_1)
+            case FA_TYPE_Q5_0: FA_DEQUANT4_Q5_0(k_packed_q5_0)
+            case FA_TYPE_Q5_1: FA_DEQUANT4_Q5_1(k_packed_q5_1)
+            case FA_TYPE_Q8_0: FA_DEQUANT4_Q8_0(k_packed_q8_0)
+        }
+    } else {
+        switch (FaTypeV) {
+            case FA_TYPE_F32:  FA_DEQUANT4_F32 (v_packed_f32)
+            case FA_TYPE_Q4_0: FA_DEQUANT4_Q4_0(v_packed_q4_0)
+            case FA_TYPE_Q4_1: FA_DEQUANT4_Q4_1(v_packed_q4_1)
+            case FA_TYPE_Q5_0: FA_DEQUANT4_Q5_0(v_packed_q5_0)
+            case FA_TYPE_Q5_1: FA_DEQUANT4_Q5_1(v_packed_q5_1)
+            case FA_TYPE_Q8_0: FA_DEQUANT4_Q8_0(v_packed_q8_0)
+        }
+    }
+    return FLOAT_TYPEV4(0);
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mmq_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mmq_funcs.glsl
index e14e62d546a..6bf10a7cffd 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mmq_funcs.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mmq_funcs.glsl
@@ -1,149 +1,203 @@
-#if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1)
-int32_t get_k_qs(uint ib, uint iqs, uint a_offset) {
-#ifdef DATA_A_Q4_0
-    uint vui = pack32(u16vec2(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0],
-                              k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]));
-#else
-    uint vui = k_packed32.k_data_packed32[a_offset + ib].qs[(iqs & 0xF) / 4];
-#endif
-
-    uint shift = (iqs & 0x10) >> 2;
-    vui >>= shift;
-
-    return int32_t(vui & 0x0F0F0F0F);
-}
-#endif
-
-#if defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
-int32_t get_k_qs(uint ib, uint iqs, uint a_offset) {
-#ifdef DATA_A_Q5_0
-    uint vui = pack32(u16vec2(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0],
-                              k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]));
-    uint qh = pack32(u16vec2(k_packed.k_data_packed16[a_offset + ib].qh[0],
-                             k_packed.k_data_packed16[a_offset + ib].qh[1]));
-#else
-    uint vui = k_packed32.k_data_packed32[a_offset + ib].qs[(iqs & 0xF) / 4];
-    uint qh = k_packed.k_data_packed16[a_offset + ib].qh;
-#endif
-
-    uint shift = (iqs & 0x10) >> 2;
-    vui >>= shift;
-
-    uint qh_bits = (qh >> iqs) & 0xF;
-    return int32_t(vui & 0x0F0F0F0F) | int32_t((qh_bits * 0x02040810u) & 0x10101010u);
-}
-#endif
-
-#if defined(DATA_A_Q8_0)
-int32_t get_k_qs(uint ib, uint iqs, uint a_offset) {
-    return pack32(i16vec2(k_packed.k_data_packed16[a_offset + ib].qs[iqs / 2], k_packed.k_data_packed16[a_offset + ib].qs[iqs / 2 + 1]));
-}
-#endif
+// MMQ K-side helpers, asymmetric form. Each function dispatches on FaTypeK and
+// reads from the matching aliased K binding declared in flash_attn_dequant.glsl.
+// Spec-constant specialization folds the unused paths.
 
-#if defined(DATA_A_IQ4_NL)
 int32_t get_k_qs(uint ib, uint iqs, uint a_offset) {
-    uint vui = pack32(u16vec2(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0],
-                              k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]));
-    uint shift = (iqs & 0x10) >> 2;
-    vui >>= shift;
-
-    u8vec4 idx = unpack8(vui & 0x0F0F0F0F);
-    return pack32(i8vec4(kvalues_iq4nl_const[idx.x],
-                         kvalues_iq4nl_const[idx.y],
-                         kvalues_iq4nl_const[idx.z],
-                         kvalues_iq4nl_const[idx.w]));
+    switch (FaTypeK) {
+        case FA_TYPE_Q4_0: {
+            uint vui = pack32(u16vec2(k_packed_q4_0.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0],
+                                      k_packed_q4_0.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]));
+            uint shift = (iqs & 0x10) >> 2;
+            vui >>= shift;
+            return int32_t(vui & 0x0F0F0F0F);
+        }
+        case FA_TYPE_Q4_1: { // uses packed32 alias
+            uint vui = k_packed_q4_1_p32.data[a_offset + ib].qs[(iqs & 0xF) / 4];
+            uint shift = (iqs & 0x10) >> 2;
+            vui >>= shift;
+            return int32_t(vui & 0x0F0F0F0F);
+        }
+        case FA_TYPE_Q5_0: {
+            uint vui = pack32(u16vec2(k_packed_q5_0.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0],
+                                      k_packed_q5_0.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]));
+            uint qh = pack32(u16vec2(k_packed_q5_0.data[a_offset + ib].qh[0],
+                                     k_packed_q5_0.data[a_offset + ib].qh[1]));
+            uint shift = (iqs & 0x10) >> 2;
+            vui >>= shift;
+            uint qh_bits = (qh >> iqs) & 0xF;
+            return int32_t(vui & 0x0F0F0F0F) | int32_t((qh_bits * 0x02040810u) & 0x10101010u);
+        }
+        case FA_TYPE_Q5_1: { // qs via packed32, qh via packed16
+            uint vui = k_packed_q5_1_p32.data[a_offset + ib].qs[(iqs & 0xF) / 4];
+            uint qh  = k_packed_q5_1.data[a_offset + ib].qh;
+            uint shift = (iqs & 0x10) >> 2;
+            vui >>= shift;
+            uint qh_bits = (qh >> iqs) & 0xF;
+            return int32_t(vui & 0x0F0F0F0F) | int32_t((qh_bits * 0x02040810u) & 0x10101010u);
+        }
+        case FA_TYPE_Q8_0: {
+            return pack32(i16vec2(k_packed_q8_0.data[a_offset + ib].qs[iqs / 2],
+                                  k_packed_q8_0.data[a_offset + ib].qs[iqs / 2 + 1]));
+        }
+        default: return 0;
+    }
 }
-#endif
 
-#if QUANT_AUXF == 1
-FLOAT_TYPE get_k_d(uint ib, uint a_offset) {
-    return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d);
-}
-#else
-FLOAT_TYPEV2 get_k_dm(uint ib, uint a_offset) {
-    return FLOAT_TYPEV2(k_packed32.k_data_packed32[a_offset + ib].dm);
+// Per-block scale/min, packed as (d, m). Single-scale types (Q4_0, Q5_0, Q8_0)
+// return (d, 0) so call sites always see the same shape.
+FLOAT_TYPEV2 get_k_scale(uint ib, uint a_offset) {
+    switch (FaTypeK) {
+        case FA_TYPE_Q4_0: return FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q4_0.data[a_offset + ib].d), 0.0);
+        case FA_TYPE_Q4_1: return FLOAT_TYPEV2(k_packed_q4_1_p32.data[a_offset + ib].dm);
+        case FA_TYPE_Q5_0: return FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q5_0.data[a_offset + ib].d), 0.0);
+        case FA_TYPE_Q5_1: return FLOAT_TYPEV2(k_packed_q5_1_p32.data[a_offset + ib].dm);
+        case FA_TYPE_Q8_0: return FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q8_0.data[a_offset + ib].d), 0.0);
+        default: return FLOAT_TYPEV2(0);
+    }
 }
-#endif
 
 void k_block_to_shmem(const uint buf_ib, const uint global_ib, const uint iqs, const uint a_offset) {
-#if defined(DATA_A_Q4_0)
-    kblocksh[buf_ib].qs[iqs] = pack32(u16vec2(k_packed.k_data_packed16[a_offset + global_ib].qs[iqs * 2],
-                                              k_packed.k_data_packed16[a_offset + global_ib].qs[iqs * 2 + 1]));
-#elif defined(DATA_A_Q4_1)
-    kblocksh[buf_ib].qs[iqs] = k_packed32.k_data_packed32[a_offset + global_ib].qs[iqs];
-#elif defined(DATA_A_Q5_0)
-    kblocksh[buf_ib].qs[iqs] = pack32(u16vec2(k_packed.k_data_packed16[a_offset + global_ib].qs[iqs * 2],
-                                              k_packed.k_data_packed16[a_offset + global_ib].qs[iqs * 2 + 1]));
-    if (iqs == 0) {
-        kblocksh[buf_ib].qh = pack32(u16vec2(k_packed.k_data_packed16[a_offset + global_ib].qh[0],
-                                             k_packed.k_data_packed16[a_offset + global_ib].qh[1]));
+    // kblocksh[].qs is int32_t for the unified MMQ struct; uint sources need
+    // explicit casts. The bit pattern is what we care about here -- the actual
+    // signed/unsigned interpretation happens downstream in the dot product.
+    switch (FaTypeK) {
+        case FA_TYPE_Q4_0: {
+            kblocksh[buf_ib].qs[iqs] = int32_t(pack32(u16vec2(k_packed_q4_0.data[a_offset + global_ib].qs[iqs * 2],
+                                                              k_packed_q4_0.data[a_offset + global_ib].qs[iqs * 2 + 1])));
+            break;
+        }
+        case FA_TYPE_Q4_1: {
+            kblocksh[buf_ib].qs[iqs] = int32_t(k_packed_q4_1_p32.data[a_offset + global_ib].qs[iqs]);
+            break;
+        }
+        case FA_TYPE_Q5_0: {
+            kblocksh[buf_ib].qs[iqs] = int32_t(pack32(u16vec2(k_packed_q5_0.data[a_offset + global_ib].qs[iqs * 2],
+                                                              k_packed_q5_0.data[a_offset + global_ib].qs[iqs * 2 + 1])));
+            if (iqs == 0) {
+                kblocksh[buf_ib].qh = pack32(u16vec2(k_packed_q5_0.data[a_offset + global_ib].qh[0],
+                                                     k_packed_q5_0.data[a_offset + global_ib].qh[1]));
+            }
+            break;
+        }
+        case FA_TYPE_Q5_1: {
+            kblocksh[buf_ib].qs[iqs] = int32_t(k_packed_q5_1_p32.data[a_offset + global_ib].qs[iqs]);
+            if (iqs == 0) {
+                kblocksh[buf_ib].qh = k_packed_q5_1.data[a_offset + global_ib].qh;
+            }
+            break;
+        }
+        case FA_TYPE_Q8_0: {
+            kblocksh[buf_ib].qs[iqs] = pack32(i16vec2(k_packed_q8_0.data[a_offset + global_ib].qs[iqs * 2],
+                                                      k_packed_q8_0.data[a_offset + global_ib].qs[iqs * 2 + 1]));
+            break;
+        }
     }
-#elif defined(DATA_A_Q5_1)
-    kblocksh[buf_ib].qs[iqs] = k_packed32.k_data_packed32[a_offset + global_ib].qs[iqs];
+
     if (iqs == 0) {
-        kblocksh[buf_ib].qh = k_packed.k_data_packed16[a_offset + global_ib].qh;
+        // Q4_0/Q5_0/Q8_0 store dm.x = d; Q4_1/Q5_1 store dm = (d, m) pair.
+        switch (FaTypeK) {
+            case FA_TYPE_Q4_0: kblocksh[buf_ib].dm = FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q4_0.data[a_offset + global_ib].d), 0.0); break;
+            case FA_TYPE_Q4_1: kblocksh[buf_ib].dm = FLOAT_TYPEV2(k_packed_q4_1_p32.data[a_offset + global_ib].dm); break;
+            case FA_TYPE_Q5_0: kblocksh[buf_ib].dm = FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q5_0.data[a_offset + global_ib].d), 0.0); break;
+            case FA_TYPE_Q5_1: kblocksh[buf_ib].dm = FLOAT_TYPEV2(k_packed_q5_1_p32.data[a_offset + global_ib].dm); break;
+            case FA_TYPE_Q8_0: kblocksh[buf_ib].dm = FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q8_0.data[a_offset + global_ib].d), 0.0); break;
+        }
     }
-#elif defined(DATA_A_Q8_0)
-    kblocksh[buf_ib].qs[iqs] = pack32(i16vec2(k_packed.k_data_packed16[a_offset + global_ib].qs[iqs * 2],
-                                              k_packed.k_data_packed16[a_offset + global_ib].qs[iqs * 2 + 1]));
-#elif defined(DATA_A_IQ4_NL)
-    const uint qs = pack32(u16vec2(k_packed.k_data_packed16[a_offset + global_ib].qs[iqs * 2],
-                                   k_packed.k_data_packed16[a_offset + global_ib].qs[iqs * 2 + 1]));
-    const u8vec4 i_a0 = unpack8( qs       & 0x0F0F0F0F);
-    const u8vec4 i_a1 = unpack8((qs >> 4) & 0x0F0F0F0F);
-    kblocksh[buf_ib].qs[iqs    ] = pack32(i8vec4(kvalues_iq4nl_const[i_a0.x], kvalues_iq4nl_const[i_a0.y],
-                                                 kvalues_iq4nl_const[i_a0.z], kvalues_iq4nl_const[i_a0.w]));
-    kblocksh[buf_ib].qs[iqs + 4] = pack32(i8vec4(kvalues_iq4nl_const[i_a1.x], kvalues_iq4nl_const[i_a1.y],
-                                                 kvalues_iq4nl_const[i_a1.z], kvalues_iq4nl_const[i_a1.w]));
-#endif
+}
 
-    if (iqs == 0) {
-#if QUANT_AUXF == 1
-        kblocksh[buf_ib].dm = FLOAT_TYPE(k_packed.k_data_packed16[a_offset + global_ib].d);
-#else
-        kblocksh[buf_ib].dm = FLOAT_TYPEV2(k_packed32.k_data_packed32[a_offset + global_ib].dm);
-#endif
+// d_per_step==8 hot path: read one full 32-element block worth of nibble-packed
+// int32 quants. Equivalent to 8 calls to get_k_qs(ib, d*4, a_offset) but reads
+// qh (Q5_*) and runs pack32 (Q4_0/Q5_0) once per block instead of per nibble
+// quad. iqs is always 0 in this path (hsk4 % 8 == 0 implies block-aligned).
+// Q8_0 takes the generic get_k_qs path because its qs layout (i8 pairs) doesn't
+// share this nibble shape.
+//
+// Returned via a struct so the caller's k_quants array (sized from spec
+// constants) doesn't need to match a fixed[8] out-parameter type.
+struct fa_k_qs_block8 {
+    int32_t qs[8];
+};
+
+fa_k_qs_block8 get_k_qs_block8(uint ib, uint a_offset) {
+    fa_k_qs_block8 r;
+    uint qh = 0;
+    if (FaTypeK == FA_TYPE_Q5_0) {
+        qh = pack32(u16vec2(k_packed_q5_0.data[a_offset + ib].qh[0],
+                            k_packed_q5_0.data[a_offset + ib].qh[1]));
+    } else if (FaTypeK == FA_TYPE_Q5_1) {
+        qh = k_packed_q5_1.data[a_offset + ib].qh;
     }
+    const bool has_qh = (FaTypeK == FA_TYPE_Q5_0) || (FaTypeK == FA_TYPE_Q5_1);
+    [[unroll]] for (uint32_t d = 0; d < 4; d++) {
+        uint vui = 0;
+        switch (FaTypeK) {
+            case FA_TYPE_Q4_0: { // packed16
+                vui = pack32(u16vec2(k_packed_q4_0.data[a_offset + ib].qs[d * 2 + 0],
+                                     k_packed_q4_0.data[a_offset + ib].qs[d * 2 + 1]));
+                break;
+            }
+            case FA_TYPE_Q4_1: { // packed32 alias
+                vui = k_packed_q4_1_p32.data[a_offset + ib].qs[d];
+                break;
+            }
+            case FA_TYPE_Q5_0: { // packed16
+                vui = pack32(u16vec2(k_packed_q5_0.data[a_offset + ib].qs[d * 2 + 0],
+                                     k_packed_q5_0.data[a_offset + ib].qs[d * 2 + 1]));
+                break;
+            }
+            case FA_TYPE_Q5_1: { // packed32 alias
+                vui = k_packed_q5_1_p32.data[a_offset + ib].qs[d];
+                break;
+            }
+        }
+        r.qs[d    ] = int32_t( vui       & 0x0F0F0F0F);
+        r.qs[d + 4] = int32_t((vui >> 4) & 0x0F0F0F0F);
+        if (has_qh) {
+            uint qh_lo = (qh >> (d * 4))      & 0xFu;
+            uint qh_hi = (qh >> (d * 4 + 16)) & 0xFu;
+            r.qs[d    ] |= int32_t((qh_lo * 0x02040810u) & 0x10101010u);
+            r.qs[d + 4] |= int32_t((qh_hi * 0x02040810u) & 0x10101010u);
+        }
+    }
+    return r;
 }
 
 int32_t get_k_qs_shmem(const uint buf_ib, const uint pos) {
-#if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1)
-    uint sub = pos % 4;
-    uint shift = ((pos % 8) >= 4) ? 4 : 0;
-    return int32_t((kblocksh[buf_ib].qs[sub] >> shift) & 0x0F0F0F0F);
-#elif defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
-    uint sub = pos % 4;
-    uint shift = ((pos % 8) >= 4) ? 4 : 0;
-    int32_t result = int32_t((kblocksh[buf_ib].qs[sub] >> shift) & 0x0F0F0F0F);
-    uint qh_bits = (kblocksh[buf_ib].qh >> (pos * 4)) & 0xF;
-    return result | int32_t((qh_bits * 0x02040810u) & 0x10101010u);
-#elif defined(DATA_A_Q8_0) || defined(DATA_A_IQ4_NL)
-    return kblocksh[buf_ib].qs[pos];
-#endif
+    switch (FaTypeK) {
+        case FA_TYPE_Q4_0:
+        case FA_TYPE_Q4_1: {
+            uint sub = pos % 4;
+            uint shift = ((pos % 8) >= 4) ? 4u : 0u;
+            return int32_t((uint(kblocksh[buf_ib].qs[sub]) >> shift) & 0x0F0F0F0Fu);
+        }
+        case FA_TYPE_Q5_0:
+        case FA_TYPE_Q5_1: {
+            uint sub = pos % 4;
+            uint shift = ((pos % 8) >= 4) ? 4u : 0u;
+            int32_t result = int32_t((uint(kblocksh[buf_ib].qs[sub]) >> shift) & 0x0F0F0F0Fu);
+            uint qh_bits = (kblocksh[buf_ib].qh >> (pos * 4u)) & 0xFu;
+            return result | int32_t((qh_bits * 0x02040810u) & 0x10101010u);
+        }
+        case FA_TYPE_Q8_0: {
+            return kblocksh[buf_ib].qs[pos];
+        }
+        default: return 0;
+    }
 }
 
 ACC_TYPE k_dot_correction(const uint qib, const ACC_TYPEV2 k_dm) {
-#if defined(DATA_A_Q4_0)
-    return -ACC_TYPE(8.0) * ACC_TYPE(Qf[qib].ds.y) * k_dm.x;
-#elif defined(DATA_A_Q5_0)
-    return -ACC_TYPE(16.0) * ACC_TYPE(Qf[qib].ds.y) * k_dm.x;
-#elif defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1)
-    return ACC_TYPE(Qf[qib].ds.y) * k_dm.y;
-#else
-    return ACC_TYPE(0.0);
-#endif
+    switch (FaTypeK) {
+        case FA_TYPE_Q4_0: return -ACC_TYPE(8.0)  * ACC_TYPE(Qf[qib].ds.y) * k_dm.x;
+        case FA_TYPE_Q5_0: return -ACC_TYPE(16.0) * ACC_TYPE(Qf[qib].ds.y) * k_dm.x;
+        case FA_TYPE_Q4_1:
+        case FA_TYPE_Q5_1: return ACC_TYPE(Qf[qib].ds.y) * k_dm.y;
+        default: return ACC_TYPE(0.0);
+    }
 }
 
 void k_block_to_shmem_zero(const uint buf_ib, const uint iqs) {
     kblocksh[buf_ib].qs[iqs] = 0;
-#if defined(DATA_A_IQ4_NL)
-    kblocksh[buf_ib].qs[iqs + 4] = 0;
-#endif
     if (iqs == 0) {
-#if QUANT_AUXF == 1
-        kblocksh[buf_ib].dm = FLOAT_TYPE(0.0f);
-#else
         kblocksh[buf_ib].dm = FLOAT_TYPEV2(0.0f);
-#endif
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp b/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp
index 5e9f8308c1d..33c3202dbb7 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp
@@ -31,6 +31,7 @@ layout(push_constant) uniform Parameters {
     uint sb1, sb2, sb3;
     uint neq1, rq3;
     float scale;
+    uint K;
 };
 
 layout(binding = 0) readonly  buffer QBuf     { FLOAT_TYPE data_q[];     };
@@ -101,13 +102,21 @@ void main() {
     const uint iq3 = seq_id / rq3;
 
     const uint state_size = S_V * S_V;
-    const uint state_base = (seq_id * H + head_id) * state_size;
+    // input state layout (D, K, n_seqs): per-seq stride is K*H*D; we read slot 0.
+    const uint state_in_base       = (seq_id * K * H + head_id) * state_size;
+    // output state layout per slot: same per-(seq,head) offset as the single-slot case.
+    const uint state_out_base      = (seq_id * H + head_id) * state_size;
+    const uint state_size_per_snap = state_size * H * n_seqs;
 
     FLOAT_TYPE s_shard[ROWS_PER_LANE];
     [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) {
-        s_shard[r] = FLOAT_TYPE(data_state[state_base + col * S_V + r * LANES_PER_COLUMN + lane]);
+        s_shard[r] = FLOAT_TYPE(data_state[state_in_base + col * S_V + r * LANES_PER_COLUMN + lane]);
     }
 
+    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K, only the last
+    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
+    const int shift = int(n_tokens) - int(K);
+
     uint attn_off = (seq_id * n_tokens * H + head_id) * S_V;
 
     for (uint t = 0; t < n_tokens; t++) {
@@ -161,9 +170,21 @@ void main() {
         }
 
         attn_off += S_V * H;
+
+        if (K > 1u) {
+            const int target_slot = int(t) - shift;
+            if (target_slot >= 0 && target_slot < int(K)) {
+                const uint slot_base = s_off + uint(target_slot) * state_size_per_snap + state_out_base;
+                [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) {
+                    data_dst[slot_base + col * S_V + r * LANES_PER_COLUMN + lane] = s_shard[r];
+                }
+            }
+        }
     }
 
-    [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) {
-        data_dst[s_off + state_base + col * S_V + r * LANES_PER_COLUMN + lane] = s_shard[r];
+    if (K == 1u) {
+        [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) {
+            data_dst[s_off + state_out_base + col * S_V + r * LANES_PER_COLUMN + lane] = s_shard[r];
+        }
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl
index e99108dc50c..bc580aeeb83 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl
@@ -296,13 +296,22 @@ vec2 get_dm_scale(uint ib, uint iqs) {
     const uint ib_k = ib / 8;
     const uint iqs_k = (ib % 8) * 8 + iqs;
     const uint is = iqs_k / 8;
-    u8vec2 scale_dm;
-    if (is < 4) {
-        scale_dm = u8vec2(data_a[ib_k].scales[is] & 0x3F, data_a[ib_k].scales[is + 4] & 0x3F);
-    } else {
-        scale_dm = u8vec2((data_a[ib_k].scales[is+4] & 0xF) | ((data_a[ib_k].scales[is-4] & 0xC0) >> 2),
-                          (data_a[ib_k].scales[is+4] >>  4) | ((data_a[ib_k].scales[is  ] & 0xC0) >> 2));
-    }
+
+    const uvec3 scales = uvec3(data_a_packed32[ib_k].scales[0],
+                               data_a_packed32[ib_k].scales[1],
+                               data_a_packed32[ib_k].scales[2]);
+    const uint scalesoffs = (is & 3) * 8;
+
+    const uint scidx0 = (is < 4) ? 0 : 2;
+    const uint scidxshift0 = scalesoffs;
+    const uint scidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2;
+    const uint mbidx0 = (is < 4) ? 1 : 2;
+    const uint mbidxshift0 = (is < 4) ? scalesoffs : scalesoffs + 4;
+    const uint mbidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2;
+
+    const uint8_t sc    = uint8_t(((scales[scidx0] >> scidxshift0) & 0xF) | ((scales[0] >> scidxshift1) & 0x30));
+    const uint8_t mbyte = uint8_t(((scales[mbidx0] >> mbidxshift0) & 0xF) | ((scales[1] >> mbidxshift1) & 0x30));
+    u8vec2 scale_dm = u8vec2(sc, mbyte);
 
     return FLOAT_TYPEV2(data_a_packed32[ib_k].dm) * FLOAT_TYPEV2(scale_dm);
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
index 6e4a29d2fdd..73595168984 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
@@ -201,19 +201,20 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 
             const vec2 loadd = vec2(data_a[ib].dm);
 
-            const uint scidx0 = (is < 4) ? is : (is + 4);
-            const uint scidx1 = (is < 4) ? is : (is - 4);
-            const uint scidxmask1 = (is < 4) ? 0x30 : 0xC0;
-            const uint scidxshift1 = (is < 4) ? 0 : 2;
-            const uint mbidx0 = is + 4;
-            const uint mbidx1 = (is < 4) ? is + 4 : is;
-            const uint mbidxmask0 = (is < 4) ? 0xF : 0xF0;
-            const uint mbidxshift0 = (is < 4) ? 0 : 4;
-            const uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
-            const uint mbidxshift1 = (is < 4) ? 0 : 2;
-
-            const uint8_t sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
-            const uint8_t mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
+            const uvec3 scales = uvec3(data_a_packed32[ib].scales[0],
+                                       data_a_packed32[ib].scales[1],
+                                       data_a_packed32[ib].scales[2]);
+            const uint scalesoffs = (is & 3) * 8;
+
+            const uint scidx0 = (is < 4) ? 0 : 2;
+            const uint scidxshift0 = scalesoffs;
+            const uint scidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2;
+            const uint mbidx0 = (is < 4) ? 1 : 2;
+            const uint mbidxshift0 = (is < 4) ? scalesoffs : scalesoffs + 4;
+            const uint mbidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2;
+
+            const uint8_t sc    = uint8_t(((scales[scidx0] >> scidxshift0) & 0xF) | ((scales[0] >> scidxshift1) & 0x30));
+            const uint8_t mbyte = uint8_t(((scales[mbidx0] >> mbidxshift0) & 0xF) | ((scales[1] >> mbidxshift1) & 0x30));
 
             const float d = loadd.x * sc;
             const float m = -loadd.y * mbyte;
@@ -237,19 +238,20 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 
             const vec2 loadd = vec2(data_a[ib].dm);
 
-            const uint scidx0 = (is < 4) ? is : (is + 4);
-            const uint scidx1 = (is < 4) ? is : (is - 4);
-            const uint scidxmask1 = (is < 4) ? 0x30 : 0xC0;
-            const uint scidxshift1 = (is < 4) ? 0 : 2;
-            const uint mbidx0 = is + 4;
-            const uint mbidx1 = (is < 4) ? is + 4 : is;
-            const uint mbidxmask0 = (is < 4) ? 0xF : 0xF0;
-            const uint mbidxshift0 = (is < 4) ? 0 : 4;
-            const uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
-            const uint mbidxshift1 = (is < 4) ? 0 : 2;
-
-            const uint8_t sc    = uint8_t((data_a[ib].scales[scidx0] & 0xF)                         | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
-            const uint8_t mbyte = uint8_t(((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
+            const uvec3 scales = uvec3(data_a_packed32[ib].scales[0],
+                                       data_a_packed32[ib].scales[1],
+                                       data_a_packed32[ib].scales[2]);
+            const uint scalesoffs = (is & 3) * 8;
+
+            const uint scidx0 = (is < 4) ? 0 : 2;
+            const uint scidxshift0 = scalesoffs;
+            const uint scidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2;
+            const uint mbidx0 = (is < 4) ? 1 : 2;
+            const uint mbidxshift0 = (is < 4) ? scalesoffs : scalesoffs + 4;
+            const uint mbidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2;
+
+            const uint8_t sc    = uint8_t(((scales[scidx0] >> scidxshift0) & 0xF) | ((scales[0] >> scidxshift1) & 0x30));
+            const uint8_t mbyte = uint8_t(((scales[mbidx0] >> mbidxshift0) & 0xF) | ((scales[1] >> mbidxshift1) & 0x30));
 
             const float d = loadd.x * sc;
             const float m = -loadd.y * mbyte;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl
index 10552d013a2..79c933f40cf 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl
@@ -1,4 +1,13 @@
-#if defined(DATA_A_Q4_0)
+#if defined(FA_MMQ_MIXED)
+// Mixed-K flash attention MMQ: superset cache that fits Q4_0/Q4_1/Q5_0/Q5_1/Q8_0.
+// Q4_*/Q5_* only use qs[0..3] and (for Q5_*) qh. Q8_0 uses qs[0..7]. Single-scale
+// types (Q4_0/Q5_0/Q8_0) leave dm.y unused.
+struct block_a_cache {
+    int32_t qs[8];
+    uint32_t qh;
+    FLOAT_TYPEV2 dm;
+};
+#elif defined(DATA_A_Q4_0)
 #define QUANT_R_MMQ 2
 struct block_a_cache {
     uint32_t qs[16/4];
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index e3e7952b93a..d99b2b5d802 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -68,8 +68,6 @@ const std::vector<std::string> type_names = {
     "mxfp4",
     "nvfp4",
     "bf16",
-    "turbo3_0",
-    "tq4_1s",
 };
 
 enum MatMulIdType {
@@ -565,11 +563,6 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
         if (tname == "bf16") {
             continue;
         }
-        // TQ4_1S uses a specialized mul_mat_vec shader for small N and
-        // the dequant+f16 matmul fallback for large N. No dedicated mul_mm needed.
-        if (tname == "tq4_1s") {
-            continue;
-        }
 
         std::string data_a_key = "DATA_A_" + to_uppercase(tname);
         // For unaligned, load one at a time for f32/f16, or two at a time for quants
@@ -648,50 +641,24 @@ void process_shaders() {
                 fa_base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)";
             }
 
-            for (const auto& tname : type_names) {
-                if (tname == "bf16") continue;
-                // TQ4_1S is a weight-only format; flash attention isn't defined for it.
-                if (tname == "tq4_1s") continue;
-
-                if (fp16) {
+            if (fp16) {
 #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
-                if (tname == "f16") {
-                    string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp",
-                        merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), fp16, false, true, f16acc);
-                } else {
-                    std::string data_a_key = "DATA_A_" + to_uppercase(tname);
-                    string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp",
-                        merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), fp16, false, true, f16acc);
-                }
+                string_to_spv("flash_attn_f32_f16", "flash_attn_cm2.comp",
+                    merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), fp16, false, true, f16acc);
 #endif
+
 #if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
-                if (tname == "f16") {
-                    string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp",
-                        merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"COOPMAT", "1"}}), fp16, true, false, f16acc);
-                } else if (tname == "q4_0" || tname == "q4_1" || tname == "q5_0" || tname == "q5_1" || tname == "iq4_nl" || tname == "q8_0" || tname == "f32" || tname == "turbo3_0") {
-                    std::string data_a_key = "DATA_A_" + to_uppercase(tname);
-                    string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp",
-                        merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname)}, {"COOPMAT", "1"}}), fp16, true, false, f16acc);
-                }
+                string_to_spv("flash_attn_f32_f16", "flash_attn_cm1.comp",
+                    merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"COOPMAT", "1"}}), fp16, true, false, f16acc);
 #endif
-                }
+            }
 
-                if (tname == "f16") {
-                    string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp",
-                        merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), fp16, false, false, f16acc);
-                } else if (tname == "q4_0" || tname == "q4_1" || tname == "q5_0" || tname == "q5_1" || tname == "iq4_nl" || tname == "q8_0" || tname == "f32" || tname == "turbo3_0") {
-                    std::string data_a_key = "DATA_A_" + to_uppercase(tname);
-                    string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp",
-                        merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), fp16, false, false, f16acc);
+            string_to_spv("flash_attn_f32_f16", "flash_attn.comp",
+                merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), fp16, false, false, f16acc);
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
-                    // MMQ path has no turbo3_0 code path; skip.
-                    if (tname != "f32" && tname != "turbo3_0") {
-                        string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp",
-                            merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }, {"MMQ", "1"}}), fp16, false, false, f16acc, "_int8");
-                    }
+            string_to_spv("flash_attn_f32_f16", "flash_attn.comp",
+                merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"MMQ", "1"}, {"FA_MMQ_MIXED", "1"}}), fp16, false, false, f16acc, "_int8");
 #endif
-                }
-            }
         }
     }
 
@@ -700,7 +667,7 @@ void process_shaders() {
     for (const auto& tname : type_names) {
         // mul mat vec
         std::string data_a_key = "DATA_A_" + to_uppercase(tname);
-        std::string shader = (string_ends_with(tname, "_k") || string_starts_with(tname, "iq1_") || string_starts_with(tname, "iq2_") || string_starts_with(tname, "iq3_") || tname == "tq4_1s") ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp";
+        std::string shader = (string_ends_with(tname, "_k") || string_starts_with(tname, "iq1_") || string_starts_with(tname, "iq2_") || string_starts_with(tname, "iq3_")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp";
 
         string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPEV2", "vec2"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}}));
         string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPEV2", "f16vec2"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}));
@@ -781,19 +748,12 @@ void process_shaders() {
         string_to_spv("cpy_f32_" + t, "copy_to_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
         string_to_spv("cpy_" + t + "_f32", "copy_from_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
     }
-    // turbo3_0 copy-from-quant only; copy-to-quant (cpy_f32_turbo3_0) omitted because the non-SET_ROWS quantize() path lacks the WHT transform
-    string_to_spv("cpy_turbo3_0_f32", "copy_from_quant.comp", {{"DATA_A_TURBO3_0", "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
-    // tq4_1s copy-from-quant only; copy-to-quant requires WHT forward (handled in SET_ROWS path)
-    string_to_spv("cpy_tq4_1s_f32", "copy_from_quant.comp", {{"DATA_A_TQ4_1S", "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
 
-    for (std::string t : {"f32", "f16", "bf16", "q1_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl", "turbo2_0", "turbo3_0", "turbo4_0", "tq4_1s"}) {
+    for (std::string t : {"f32", "f16", "bf16", "q1_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) {
         string_to_spv("set_rows_" + t + "_i32", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uint"}, {"B_SIZE", "32"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
         string_to_spv("set_rows_" + t + "_i64", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uvec2"}, {"B_SIZE", "64"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
     }
 
-    // TurboQuant WHT operation
-    string_to_spv("turbo_wht", "turbo_wht.comp", {});
-
     auto get_type_str = [](bool f16) {
         return f16 ? "float16_t" : "float";
     };
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
index 9d88f98050e..4c4eda1cbe5 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
@@ -1,6 +1,7 @@
 #ifndef GGML_WEBGPU_SHADER_LIB_HPP
 #define GGML_WEBGPU_SHADER_LIB_HPP
 
+#include "ggml-impl.h"
 #include "ggml-wgsl-shaders.hpp"
 #include "ggml.h"
 #include "pre_wgsl.hpp"
@@ -26,20 +27,23 @@
 // Matrix multiplication parameters
 
 // Register tiling parameters
-#define WEBGPU_MUL_MAT_TILE_M    8
-#define WEBGPU_MUL_MAT_TILE_N    8
-#define WEBGPU_MUL_MAT_WG_SIZE_M 8
-#define WEBGPU_MUL_MAT_WG_SIZE_N 8
-#define WEBGPU_MUL_MAT_TILE_K    32
+#define WEBGPU_MUL_MAT_TILE_M           4
+#define WEBGPU_MUL_MAT_TILE_N           4
+#define WEBGPU_MUL_MAT_WG_SIZE_M        8
+#define WEBGPU_MUL_MAT_WG_SIZE_N        8
+#define WEBGPU_MUL_MAT_REG_TILE_K_FLOAT 8
+#define WEBGPU_MUL_MAT_REG_TILE_K_QUANT 32
 
 // Subgroup matrix parameters
 // The number of subgroups in the M dimension
-#define WEBGPU_MUL_MAT_SUBGROUP_M        2
+#define WEBGPU_MUL_MAT_SUBGROUP_M            2
 // The number of subgroups in the N dimension
-#define WEBGPU_MUL_MAT_SUBGROUP_N        2
+#define WEBGPU_MUL_MAT_SUBGROUP_N            4
 // The number of subgroup matrices each subgroup accumulates over
-#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M 4
-#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N 2
+#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M     4
+#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N     2
+#define WEBGPU_MUL_MAT_SUBGROUP_TILE_K_FLOAT 32
+#define WEBGPU_MUL_MAT_SUBGROUP_TILE_K_QUANT 32
 
 // Matrix-vector multiplication parameters
 #define WEBGPU_MUL_MAT_VEC_WG_SIZE 256
@@ -56,24 +60,38 @@ template <typename T> inline void ggml_webgpu_hash_combine(size_t & seed, const
     seed ^= std::hash<T>{}(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
 }
 
+// Calculates base address of a tensor ignoring the fake base pointer
+inline uintptr_t ggml_webgpu_tensor_addr(const ggml_tensor * tensor) {
+    const ggml_tensor * base_tensor = tensor->view_src ? tensor->view_src : tensor;
+    return (uintptr_t) base_tensor->data + tensor->view_offs;
+}
+
+inline bool ggml_webgpu_tensor_equal(const ggml_tensor * a, const ggml_tensor * b) {
+    return a->buffer == b->buffer && ggml_webgpu_tensor_addr(a) == ggml_webgpu_tensor_addr(b);
+}
+
+inline bool ggml_webgpu_tensor_overlap(const ggml_tensor * a, const ggml_tensor * b) {
+    return a->buffer == b->buffer && ggml_webgpu_tensor_addr(a) < ggml_webgpu_tensor_addr(b) + ggml_nbytes(b) &&
+           ggml_webgpu_tensor_addr(b) < ggml_webgpu_tensor_addr(a) + ggml_nbytes(a);
+}
+
 struct ggml_webgpu_shader_lib_context {
     ggml_tensor * src0;
     ggml_tensor * src1;
     ggml_tensor * src2;
     ggml_tensor * src3;
     ggml_tensor * src4;
+    ggml_tensor * src5;
     ggml_tensor * dst;
 
     uint32_t max_wg_size;
     size_t   wg_mem_limit_bytes       = 0;
-    bool     inplace                  = false;
-    bool     overlap                  = false;
-    bool     src_overlap              = false;
     bool     supports_subgroups       = false;
     bool     supports_subgroup_matrix = false;
     uint32_t sg_mat_m                 = 0;
     uint32_t sg_mat_n                 = 0;
     uint32_t sg_mat_k                 = 0;
+    uint32_t min_subgroup_size        = 0;
     uint32_t max_subgroup_size        = 0;
 };
 
@@ -85,6 +103,14 @@ struct webgpu_pipeline {
 
 struct ggml_webgpu_generic_shader_decisions {
     uint32_t wg_size = 0;
+    bool     inplace = false;
+};
+
+struct ggml_webgpu_binary_shader_decisions {
+    uint32_t wg_size     = 0;
+    bool     inplace     = false;
+    bool     overlap     = false;
+    bool     src_overlap = false;
 };
 
 struct ggml_webgpu_processed_shader {
@@ -98,6 +124,32 @@ struct ggml_webgpu_ssm_conv_shader_decisions {
     uint32_t tokens_per_wg;
 };
 
+struct ggml_webgpu_ssm_scan_pipeline_key {
+    int  type;
+    int  d_state;
+    bool xbc_overlap;
+
+    bool operator==(const ggml_webgpu_ssm_scan_pipeline_key & other) const {
+        return type == other.type && d_state == other.d_state && xbc_overlap == other.xbc_overlap;
+    }
+};
+
+struct ggml_webgpu_ssm_scan_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_ssm_scan_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.type);
+        ggml_webgpu_hash_combine(seed, key.d_state);
+        ggml_webgpu_hash_combine(seed, key.xbc_overlap);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_ssm_scan_shader_decisions {
+    uint32_t wg_size;
+    uint32_t tokens_per_tile;
+    bool     xbc_overlap = false;
+};
+
 /** Argsort **/
 
 struct ggml_webgpu_argsort_shader_lib_context {
@@ -177,11 +229,13 @@ struct ggml_webgpu_get_rows_pipeline_key_hash {
 /** Row Norm **/
 
 struct ggml_webgpu_row_norm_pipeline_key {
-    ggml_op op;
-    bool    inplace;
+    ggml_op   op;
+    ggml_type src_type;
+    ggml_type dst_type;
+    bool      inplace;
 
     bool operator==(const ggml_webgpu_row_norm_pipeline_key & other) const {
-        return op == other.op && inplace == other.inplace;
+        return op == other.op && src_type == other.src_type && dst_type == other.dst_type && inplace == other.inplace;
     }
 };
 
@@ -189,11 +243,42 @@ struct ggml_webgpu_row_norm_pipeline_key_hash {
     size_t operator()(const ggml_webgpu_row_norm_pipeline_key & key) const {
         size_t seed = 0;
         ggml_webgpu_hash_combine(seed, key.op);
+        ggml_webgpu_hash_combine(seed, key.src_type);
+        ggml_webgpu_hash_combine(seed, key.dst_type);
+        ggml_webgpu_hash_combine(seed, key.inplace);
+        return seed;
+    }
+};
+
+/** RMS_NORM + MUL **/
+
+struct ggml_webgpu_rms_norm_mul_pipeline_key {
+    bool inplace;      // rn_src == dst
+    bool overlap;      // mul_src == dst
+    bool src_overlap;  // rn_src == mul_src
+
+    bool operator==(const ggml_webgpu_rms_norm_mul_pipeline_key & other) const {
+        return inplace == other.inplace && overlap == other.overlap && src_overlap == other.src_overlap;
+    }
+};
+
+struct ggml_webgpu_rms_norm_mul_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_rms_norm_mul_pipeline_key & key) const {
+        size_t seed = 0;
         ggml_webgpu_hash_combine(seed, key.inplace);
+        ggml_webgpu_hash_combine(seed, key.overlap);
+        ggml_webgpu_hash_combine(seed, key.src_overlap);
         return seed;
     }
 };
 
+struct ggml_webgpu_rms_norm_mul_shader_decisions {
+    uint32_t wg_size     = 0;
+    bool     inplace     = false;
+    bool     overlap     = false;
+    bool     src_overlap = false;
+};
+
 /** Pad **/
 struct ggml_webgpu_pad_pipeline_key {
     bool circular;
@@ -240,6 +325,46 @@ struct ggml_webgpu_ssm_conv_pipeline_key {
     }
 };
 
+/** CONV 2D */
+struct ggml_webgpu_conv2d_pipeline_key {
+    ggml_type weight_type;
+    ggml_type input_type;
+    ggml_type output_type;
+
+    bool operator==(const ggml_webgpu_conv2d_pipeline_key & other) const {
+        return weight_type == other.weight_type && input_type == other.input_type && output_type == other.output_type;
+    }
+};
+
+struct ggml_webgpu_conv2d_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_conv2d_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.weight_type);
+        ggml_webgpu_hash_combine(seed, key.input_type);
+        ggml_webgpu_hash_combine(seed, key.output_type);
+        return seed;
+    }
+};
+
+/** Im2Col **/
+struct ggml_webgpu_im2col_pipeline_key {
+    ggml_type input_type;
+    ggml_type output_type;
+
+    bool operator==(const ggml_webgpu_im2col_pipeline_key & other) const {
+        return input_type == other.input_type && output_type == other.output_type;
+    }
+};
+
+struct ggml_webgpu_im2col_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_im2col_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.input_type);
+        ggml_webgpu_hash_combine(seed, key.output_type);
+        return seed;
+    }
+};
+
 /** Gated Delta Net **/
 struct ggml_webgpu_gated_delta_net_pipeline_key {
     int type;
@@ -286,6 +411,31 @@ struct ggml_webgpu_scale_pipeline_key_hash {
     }
 };
 
+/** Upscale **/
+
+struct ggml_webgpu_upscale_pipeline_key {
+    ggml_type input_type;
+    ggml_type output_type;
+    uint32_t  base_mode;
+    bool      antialias;
+
+    bool operator==(const ggml_webgpu_upscale_pipeline_key & other) const {
+        return input_type == other.input_type && output_type == other.output_type && base_mode == other.base_mode &&
+               antialias == other.antialias;
+    }
+};
+
+struct ggml_webgpu_upscale_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_upscale_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.input_type);
+        ggml_webgpu_hash_combine(seed, key.output_type);
+        ggml_webgpu_hash_combine(seed, key.base_mode);
+        ggml_webgpu_hash_combine(seed, key.antialias);
+        return seed;
+    }
+};
+
 /** Concat **/
 
 struct ggml_webgpu_concat_pipeline_key {
@@ -345,6 +495,22 @@ struct ggml_webgpu_binary_pipeline_key_hash {
     }
 };
 
+/* Add_Id */
+
+struct ggml_webgpu_add_id_pipeline_key {
+    bool inplace;
+
+    bool operator==(const ggml_webgpu_add_id_pipeline_key & other) const { return inplace == other.inplace; }
+};
+
+struct ggml_webgpu_add_id_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_add_id_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.inplace);
+        return seed;
+    }
+};
+
 /** Unary **/
 
 struct ggml_webgpu_unary_pipeline_key {
@@ -374,68 +540,117 @@ struct ggml_webgpu_unary_pipeline_key_hash {
 
 /** FlashAttention */
 
+enum ggml_webgpu_flash_attn_path : uint32_t {
+    GGML_WEBGPU_FLASH_ATTN_PATH_NONE            = 0u,
+    GGML_WEBGPU_FLASH_ATTN_PATH_SUBGROUP_MATRIX = 1u,
+    GGML_WEBGPU_FLASH_ATTN_PATH_TILE            = 2u,
+    GGML_WEBGPU_FLASH_ATTN_PATH_VEC             = 3u,
+};
+
 struct ggml_webgpu_flash_attn_pipeline_key {
+    ggml_type q_type;
     ggml_type kv_type;
+    ggml_type dst_type;
     uint32_t  head_dim_qk;
     uint32_t  head_dim_v;
     bool      kv_direct;
+    bool      kv_overlap;
     bool      has_mask;
     bool      has_sinks;
     bool      uses_logit_softcap;
+    uint32_t  path;
 
     bool operator==(const ggml_webgpu_flash_attn_pipeline_key & other) const {
-        return kv_type == other.kv_type && head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v &&
-               kv_direct == other.kv_direct && has_mask == other.has_mask && has_sinks == other.has_sinks &&
-               uses_logit_softcap == other.uses_logit_softcap;
+        return q_type == other.q_type && kv_type == other.kv_type && dst_type == other.dst_type &&
+               head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v && kv_direct == other.kv_direct &&
+               kv_overlap == other.kv_overlap && has_mask == other.has_mask && has_sinks == other.has_sinks &&
+               uses_logit_softcap == other.uses_logit_softcap && path == other.path;
     }
 };
 
 struct ggml_webgpu_flash_attn_pipeline_key_hash {
     size_t operator()(const ggml_webgpu_flash_attn_pipeline_key & key) const {
         size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.q_type);
         ggml_webgpu_hash_combine(seed, key.kv_type);
+        ggml_webgpu_hash_combine(seed, key.dst_type);
         ggml_webgpu_hash_combine(seed, key.head_dim_qk);
         ggml_webgpu_hash_combine(seed, key.head_dim_v);
         ggml_webgpu_hash_combine(seed, key.kv_direct);
+        ggml_webgpu_hash_combine(seed, key.kv_overlap);
         ggml_webgpu_hash_combine(seed, key.has_mask);
         ggml_webgpu_hash_combine(seed, key.has_sinks);
         ggml_webgpu_hash_combine(seed, key.uses_logit_softcap);
+        ggml_webgpu_hash_combine(seed, key.path);
         return seed;
     }
 };
 
 struct ggml_webgpu_flash_attn_decisions {
-    uint32_t q_tile  = 0;
-    uint32_t kv_tile = 0;
-    uint32_t wg_size = 0;
+    uint32_t path       = GGML_WEBGPU_FLASH_ATTN_PATH_NONE;
+    uint32_t q_tile     = 0;
+    uint32_t kv_tile    = 0;
+    uint32_t wg_size    = 0;
+    bool     kv_direct  = false;
+    bool     kv_overlap = false;
 };
 
-struct ggml_webgpu_flash_attn_vec_decisions {
-    uint32_t kv_tile = 0;
-    uint32_t wg_size = 0;
-};
+inline constexpr uint32_t GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH = 4u;
+inline constexpr uint32_t GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE       = 4u;
+
+inline uint32_t ggml_webgpu_flash_attn_pick_vec_ne(const ggml_webgpu_flash_attn_pipeline_key & key) {
+    if (key.path != GGML_WEBGPU_FLASH_ATTN_PATH_VEC || key.kv_type != GGML_TYPE_F16 ||
+        key.head_dim_qk != key.head_dim_v) {
+        return 1u;
+    }
+
+    switch (key.head_dim_qk) {
+        case 64:
+        case 192:
+        case 576:
+            return 2u;
+        case 96:
+            return 4u;
+        default:
+            return 1u;
+    }
+}
 
 inline ggml_webgpu_flash_attn_pipeline_key ggml_webgpu_flash_attn_make_pipeline_key(
-    const ggml_webgpu_shader_lib_context & context) {
+    const ggml_webgpu_shader_lib_context &   context,
+    const ggml_webgpu_flash_attn_decisions & decisions) {
     const bool has_mask  = context.src3 != nullptr;
     const bool has_sinks = context.src4 != nullptr;
-    const bool kv_direct = (context.src1->type == GGML_TYPE_F16) && (context.src0->ne[0] % context.sg_mat_k == 0) &&
-                           (context.src1->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0);
+    bool       kv_direct = false;
+    if (decisions.path != GGML_WEBGPU_FLASH_ATTN_PATH_TILE) {
+        uint32_t kv_direct_align = GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH;
+        if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_SUBGROUP_MATRIX) {
+            kv_direct_align = context.sg_mat_k;
+        }
+        kv_direct = (context.src1->type == GGML_TYPE_F16) &&
+                    (context.src0->ne[0] % std::max(1u, kv_direct_align) == 0) &&
+                    (context.src1->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0);
+    }
 
     ggml_webgpu_flash_attn_pipeline_key key = {};
+    key.q_type                              = context.src0->type;
     key.kv_type                             = context.src1->type;
+    key.dst_type                            = context.dst->type;
     key.head_dim_qk                         = (uint32_t) context.src0->ne[0];
     key.head_dim_v                          = (uint32_t) context.src2->ne[0];
     key.kv_direct                           = kv_direct;
+    key.kv_overlap                          = ggml_webgpu_tensor_overlap(context.src1, context.src2);
     key.has_mask                            = has_mask;
     key.has_sinks                           = has_sinks;
     key.uses_logit_softcap                  = ggml_get_op_params_f32(context.dst, 2) != 0.0f;
+    key.path                                = decisions.path;
     return key;
 }
 
 struct ggml_webgpu_flash_attn_vec_reduce_pipeline_key {
-    uint32_t head_dim_v;
-    uint32_t wg_size;
+    uint32_t  head_dim_v;
+    uint32_t  wg_size;
+    ggml_type dst_type;
 };
 
 struct ggml_webgpu_flash_attn_vec_reduce_pipeline_key_hash {
@@ -443,13 +658,14 @@ struct ggml_webgpu_flash_attn_vec_reduce_pipeline_key_hash {
         size_t seed = 0;
         ggml_webgpu_hash_combine(seed, key.head_dim_v);
         ggml_webgpu_hash_combine(seed, key.wg_size);
+        ggml_webgpu_hash_combine(seed, key.dst_type);
         return seed;
     }
 };
 
 inline bool operator==(const ggml_webgpu_flash_attn_vec_reduce_pipeline_key & lhs,
                        const ggml_webgpu_flash_attn_vec_reduce_pipeline_key & rhs) {
-    return lhs.head_dim_v == rhs.head_dim_v && lhs.wg_size == rhs.wg_size;
+    return lhs.head_dim_v == rhs.head_dim_v && lhs.wg_size == rhs.wg_size && lhs.dst_type == rhs.dst_type;
 }
 
 struct ggml_webgpu_flash_attn_blk_pipeline_key {
@@ -472,19 +688,32 @@ inline size_t ggml_webgpu_flash_attn_wg_mem_bytes(uint32_t q_tile,
                                                   uint32_t head_dim_qk,
                                                   uint32_t head_dim_v,
                                                   bool     has_mask,
-                                                  bool     kv_direct) {
+                                                  bool     kv_direct,
+                                                  uint32_t path = GGML_WEBGPU_FLASH_ATTN_PATH_SUBGROUP_MATRIX) {
     const uint32_t max_head_dim = std::max(head_dim_qk, head_dim_v);
     size_t         f16_elems    = 0;
     size_t         f32_elems    = 0;
-    f16_elems += q_tile * head_dim_qk;        // q_shmem
+    if (path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) {
+        f32_elems += head_dim_qk;                 // q_shmem
+        if (!kv_direct) {
+            f32_elems += kv_tile * max_head_dim;  // kv_shmem
+        }
+        f32_elems += head_dim_v;                  // o_shmem
+        if (has_mask) {
+            f32_elems += kv_tile;                 // mask_shmem
+        }
+        f32_elems += kv_tile;                     // inter_shmem
+        return f32_elems * GGML_WEBGPU_F32_SIZE_BYTES;
+    }
+    f32_elems += q_tile * head_dim_qk;        // q_shmem
     if (!kv_direct) {
-        f16_elems += kv_tile * max_head_dim;  // kv_shmem
+        f32_elems += kv_tile * max_head_dim;  // kv_shmem
     }
-    f16_elems += q_tile * head_dim_v;         // o_shmem
+    f32_elems += q_tile * head_dim_v;         // o_shmem
     if (has_mask) {
-        f16_elems += q_tile * kv_tile;        // mask_shmem
+        f32_elems += q_tile * kv_tile;        // mask_shmem
     }
-    f16_elems += q_tile * kv_tile;            // inter_shmem
+    f32_elems += q_tile * kv_tile;            // inter_shmem
     f32_elems += q_tile;                      // row_max_shmem
     f32_elems += q_tile;                      // exp_sum_shmem
     return f16_elems * GGML_WEBGPU_F16_SIZE_BYTES + f32_elems * GGML_WEBGPU_F32_SIZE_BYTES;
@@ -492,37 +721,131 @@ inline size_t ggml_webgpu_flash_attn_wg_mem_bytes(uint32_t q_tile,
 
 inline uint32_t ggml_webgpu_flash_attn_max_kv_tile(const ggml_webgpu_shader_lib_context &      context,
                                                    const ggml_webgpu_flash_attn_pipeline_key & key) {
-    const size_t limit_bytes  = context.wg_mem_limit_bytes;
-    const size_t q_tile       = context.sg_mat_m;
-    const size_t base_q_bytes = (key.head_dim_qk + key.head_dim_v) * q_tile * GGML_WEBGPU_F16_SIZE_BYTES +
-                                2 * q_tile * GGML_WEBGPU_F32_SIZE_BYTES;
-    size_t bytes_per_kv = 0;
-    if (!key.kv_direct) {
-        bytes_per_kv += std::max(key.head_dim_qk, key.head_dim_v);
-    }
-    if (key.has_mask) {
-        bytes_per_kv += q_tile;
-    }
-    bytes_per_kv += q_tile;
-    bytes_per_kv *= GGML_WEBGPU_F16_SIZE_BYTES;
-    const uint32_t max_kv_tile = (limit_bytes - base_q_bytes) / bytes_per_kv;
-    return (max_kv_tile / context.sg_mat_n) * context.sg_mat_n;
+    const size_t limit_bytes    = context.wg_mem_limit_bytes;
+    uint32_t     q_tile         = context.sg_mat_m;
+    uint32_t     kv_granularity = std::max(1u, context.sg_mat_n);
+    if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE) {
+        q_tile         = GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE;
+        kv_granularity = 1u;
+    } else if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) {
+        q_tile         = 1u;
+        kv_granularity = 8u;
+    }
+    const size_t base_q_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(q_tile, 0, key.head_dim_qk, key.head_dim_v,
+                                                                    key.has_mask, key.kv_direct, key.path);
+    if (limit_bytes <= base_q_bytes) {
+        return 0;
+    }
+    const size_t one_kv_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(q_tile, 1, key.head_dim_qk, key.head_dim_v,
+                                                                    key.has_mask, key.kv_direct, key.path);
+    const size_t bytes_per_kv = one_kv_bytes - base_q_bytes;
+    if (bytes_per_kv == 0) {
+        return 0;
+    }
+    const size_t max_kv_tile = (limit_bytes - base_q_bytes) / bytes_per_kv;
+    return (uint32_t) ((max_kv_tile / kv_granularity) * kv_granularity);
 }
 
-inline uint32_t ggml_webgpu_flash_attn_vec_get_kv_tile(const ggml_webgpu_shader_lib_context & context) {
-    const ggml_webgpu_flash_attn_pipeline_key key         = ggml_webgpu_flash_attn_make_pipeline_key(context);
-    const uint32_t                            min_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(context, key);
-    uint32_t                                  kv_tile     = std::max(context.sg_mat_n, std::min(32u, min_kv_tile));
-    kv_tile                                               = (kv_tile / context.sg_mat_n) * context.sg_mat_n;
-
-    if (key.kv_direct) {
-        kv_tile = std::min(kv_tile, GGML_WEBGPU_KV_SEQ_PAD);
-        while (GGML_WEBGPU_KV_SEQ_PAD % kv_tile != 0) {
-            kv_tile -= context.sg_mat_n;
+inline ggml_webgpu_flash_attn_decisions ggml_webgpu_flash_attn_get_decisions(
+    const ggml_webgpu_shader_lib_context & context,
+    size_t                                 storage_offset_alignment) {
+    ggml_webgpu_flash_attn_decisions decisions = {};
+    const size_t                     alignment = std::max<size_t>(1u, storage_offset_alignment);
+    const auto *                     K         = context.src1;
+    const auto *                     V         = context.src2;
+    GGML_ASSERT(K != nullptr);
+    GGML_ASSERT(V != nullptr);
+
+    const auto flash_attn_tensor_offset = [](const ggml_tensor * tensor) -> size_t {
+        constexpr uintptr_t ptr_base_addr = 0x1000u;
+        const ggml_tensor * base          = tensor->view_src != nullptr ? tensor->view_src : tensor;
+        return reinterpret_cast<uintptr_t>(base->data) - ptr_base_addr + tensor->view_offs;
+    };
+
+    const uint32_t k_offset_elems =
+        (uint32_t) ((flash_attn_tensor_offset(K) & (alignment - 1)) / ggml_type_size(K->type));
+    const uint32_t v_offset_elems =
+        (uint32_t) ((flash_attn_tensor_offset(V) & (alignment - 1)) / ggml_type_size(V->type));
+    const bool f16_vec4_aligned = (k_offset_elems % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0u) &&
+                                  (v_offset_elems % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0u);
+    const bool kv_vec_type_supported =
+        K->type == GGML_TYPE_F16 || K->type == GGML_TYPE_Q4_0 || K->type == GGML_TYPE_Q8_0;
+    const uint32_t kv_vec_head_align = K->type == GGML_TYPE_F16 ? GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH :
+                                                                  (uint32_t) ggml_blck_size(K->type);
+    const bool kv_vec_head_dims_aligned = context.src0->ne[0] % kv_vec_head_align == 0 &&
+                                          context.src2->ne[0] % kv_vec_head_align == 0;
+    // Compile with enough invocations to cover the largest reported subgroup.
+    const bool use_vec = context.supports_subgroups && (context.src0->ne[1] < 20) &&
+                         kv_vec_head_dims_aligned && kv_vec_type_supported &&
+                         (K->type != GGML_TYPE_F16 || f16_vec4_aligned) &&
+                         (context.src2->type == K->type);
+    const bool tile_can_dispatch_all_q_rows =
+        context.max_subgroup_size > 0 &&
+        context.max_wg_size >= GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * context.max_subgroup_size;
+    const bool use_subgroup_matrix =
+        context.supports_subgroup_matrix && context.sg_mat_k > 0 && context.sg_mat_n > 0 &&
+        context.src0->ne[0] % context.sg_mat_k == 0 && context.src2->ne[0] % context.sg_mat_n == 0;
+    const bool use_tile = context.supports_subgroups && !use_subgroup_matrix && K->type == GGML_TYPE_F16 &&
+                          V->type == GGML_TYPE_F16 && f16_vec4_aligned &&
+                          (context.src0->ne[0] % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0) &&
+                          (context.src2->ne[0] % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0) &&
+                          tile_can_dispatch_all_q_rows && !use_vec;
+
+    decisions.path = use_vec                          ? GGML_WEBGPU_FLASH_ATTN_PATH_VEC :
+                     use_tile                         ? GGML_WEBGPU_FLASH_ATTN_PATH_TILE :
+                     use_subgroup_matrix              ? GGML_WEBGPU_FLASH_ATTN_PATH_SUBGROUP_MATRIX :
+                                                        GGML_WEBGPU_FLASH_ATTN_PATH_NONE;
+
+    if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_NONE) {
+        return decisions;
+    }
+
+    const ggml_webgpu_flash_attn_pipeline_key key = ggml_webgpu_flash_attn_make_pipeline_key(context, decisions);
+    decisions.kv_direct                           = key.kv_direct;
+    const uint32_t max_kv_tile                    = ggml_webgpu_flash_attn_max_kv_tile(context, key);
+    // invalidate if even the smallest kv_tile doesn't fit in shared memory
+    if (max_kv_tile == 0) {
+        decisions.path = GGML_WEBGPU_FLASH_ATTN_PATH_NONE;
+        return decisions;
+    }
+
+    if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) {
+        decisions.q_tile  = 1u;
+        decisions.kv_tile = std::max(8u, std::min(32u, max_kv_tile));
+        decisions.kv_tile = (decisions.kv_tile / 8u) * 8u;
+        decisions.wg_size = context.max_subgroup_size;
+        if (decisions.kv_direct) {
+            decisions.kv_tile = std::min(decisions.kv_tile, GGML_WEBGPU_KV_SEQ_PAD);
+            while (GGML_WEBGPU_KV_SEQ_PAD % decisions.kv_tile != 0) {
+                decisions.kv_tile -= 8u;
+            }
         }
+        return decisions;
+    }
+
+    decisions.q_tile =
+        decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE ? GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE : context.sg_mat_m;
+    decisions.kv_tile = decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE ?
+                            std::min(64u, max_kv_tile) :
+                            std::min(max_kv_tile, context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES);
+    decisions.wg_size = decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE ?
+                            std::min(std::max(1u, context.max_wg_size),
+                                     std::max(GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE,
+                                              GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * context.max_subgroup_size)) :
+                            std::max(context.max_subgroup_size, GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE);
+
+    if (decisions.kv_tile == 0) {
+        return decisions;
     }
 
-    return kv_tile;
+    if (decisions.kv_direct) {
+        GGML_ASSERT(decisions.kv_tile <= GGML_WEBGPU_KV_SEQ_PAD);
+        while (GGML_WEBGPU_KV_SEQ_PAD % decisions.kv_tile != 0) {
+            decisions.kv_tile -=
+                decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE ? context.min_subgroup_size : context.sg_mat_n;
+        }
+    }
+    return decisions;
 }
 
 /** Matrix Multiplication **/
@@ -619,9 +942,12 @@ struct ggml_webgpu_mul_mat_shader_decisions {
 struct ggml_webgpu_mul_mat_id_pipeline_key {
     ggml_type src0_type;
     ggml_type src1_type;
+    uint32_t  n_experts;
+    int       vectorized;
 
     bool operator==(const ggml_webgpu_mul_mat_id_pipeline_key & other) const {
-        return src0_type == other.src0_type && src1_type == other.src1_type;
+        return src0_type == other.src0_type && src1_type == other.src1_type && n_experts == other.n_experts &&
+               vectorized == other.vectorized;
     }
 };
 
@@ -630,6 +956,8 @@ struct ggml_webgpu_mul_mat_id_pipeline_key_hash {
         size_t seed = 0;
         ggml_webgpu_hash_combine(seed, key.src0_type);
         ggml_webgpu_hash_combine(seed, key.src1_type);
+        ggml_webgpu_hash_combine(seed, key.n_experts);
+        ggml_webgpu_hash_combine(seed, key.vectorized);
         return seed;
     }
 };
@@ -734,16 +1062,19 @@ class ggml_webgpu_shader_lib {
     std::unordered_map<int, webgpu_pipeline> cumsum_pipelines;         // key is fixed, no variants yet
     std::unordered_map<ggml_webgpu_row_norm_pipeline_key, webgpu_pipeline, ggml_webgpu_row_norm_pipeline_key_hash>
         row_norm_pipelines;                                            // op/inplace
+
     std::unordered_map<ggml_webgpu_get_rows_pipeline_key, webgpu_pipeline, ggml_webgpu_get_rows_pipeline_key_hash>
-        get_rows_pipelines;                                            // src_type, vectorized
+        get_rows_pipelines;   // src_type, vectorized
     std::unordered_map<ggml_webgpu_unary_pipeline_key, webgpu_pipeline, ggml_webgpu_unary_pipeline_key_hash>
-        unary_pipelines;                                               // type/op/inplace
+        unary_pipelines;      // type/op/inplace
     std::unordered_map<ggml_webgpu_scale_pipeline_key, webgpu_pipeline, ggml_webgpu_scale_pipeline_key_hash>
-        scale_pipelines;                                               // inplace
+        scale_pipelines;      // inplace
     std::unordered_map<ggml_webgpu_solve_tri_pipeline_key, webgpu_pipeline, ggml_webgpu_solve_tri_pipeline_key_hash>
-        solve_tri_pipelines;                                           // type
+        solve_tri_pipelines;  // type
     std::unordered_map<ggml_webgpu_ssm_conv_pipeline_key, webgpu_pipeline, ggml_webgpu_ssm_conv_pipeline_key_hash>
-        ssm_conv_pipelines;                                            // type/vectorized
+        ssm_conv_pipelines;   // type/vectorized
+    std::unordered_map<ggml_webgpu_ssm_scan_pipeline_key, webgpu_pipeline, ggml_webgpu_ssm_scan_pipeline_key_hash>
+        ssm_scan_pipelines;   // type/d_state
     std::unordered_map<ggml_webgpu_gated_delta_net_pipeline_key,
                        webgpu_pipeline,
                        ggml_webgpu_gated_delta_net_pipeline_key_hash>
@@ -751,15 +1082,15 @@ class ggml_webgpu_shader_lib {
     std::unordered_map<ggml_webgpu_pad_pipeline_key, webgpu_pipeline, ggml_webgpu_pad_pipeline_key_hash>
         pad_pipelines;              // circular/non-circular
     std::unordered_map<ggml_webgpu_binary_pipeline_key, webgpu_pipeline, ggml_webgpu_binary_pipeline_key_hash>
-        binary_pipelines;           // type/op/inplace/overlap
+        binary_pipelines;           // type/op/inplace/overlap/src_overlap
+    std::unordered_map<ggml_webgpu_add_id_pipeline_key, webgpu_pipeline, ggml_webgpu_add_id_pipeline_key_hash>
+        add_id_pipelines;           // inplace
     std::unordered_map<ggml_webgpu_concat_pipeline_key, webgpu_pipeline, ggml_webgpu_concat_pipeline_key_hash>
         concat_pipelines;           // type
     std::unordered_map<ggml_webgpu_repeat_pipeline_key, webgpu_pipeline, ggml_webgpu_repeat_pipeline_key_hash>
         repeat_pipelines;           // type
     std::unordered_map<ggml_webgpu_flash_attn_pipeline_key, webgpu_pipeline, ggml_webgpu_flash_attn_pipeline_key_hash>
         flash_attn_pipelines;
-    std::unordered_map<ggml_webgpu_flash_attn_pipeline_key, webgpu_pipeline, ggml_webgpu_flash_attn_pipeline_key_hash>
-        flash_attn_vec_pipelines;
     std::unordered_map<ggml_webgpu_flash_attn_vec_reduce_pipeline_key,
                        webgpu_pipeline,
                        ggml_webgpu_flash_attn_vec_reduce_pipeline_key_hash>
@@ -779,6 +1110,8 @@ class ggml_webgpu_shader_lib {
     std::unordered_map<int, webgpu_pipeline> mul_mat_id_gather_pipelines;  // key is fixed
     std::unordered_map<ggml_webgpu_mul_mat_id_pipeline_key, webgpu_pipeline, ggml_webgpu_mul_mat_id_pipeline_key_hash>
         mul_mat_id_pipelines;                                              // src0_type/src1_type
+    std::unordered_map<ggml_webgpu_mul_mat_id_pipeline_key, webgpu_pipeline, ggml_webgpu_mul_mat_id_pipeline_key_hash>
+        mul_mat_id_vec_pipelines;                                          // src0_type/src1_type
 
     std::unordered_map<ggml_webgpu_set_rows_pipeline_key, webgpu_pipeline, ggml_webgpu_set_rows_pipeline_key_hash>
         set_rows_pipelines;
@@ -789,6 +1122,17 @@ class ggml_webgpu_shader_lib {
         rope_pipelines;
     std::unordered_map<ggml_webgpu_soft_max_pipeline_key, webgpu_pipeline, ggml_webgpu_soft_max_pipeline_key_hash>
         soft_max_pipelines;
+    std::unordered_map<ggml_webgpu_conv2d_pipeline_key, webgpu_pipeline, ggml_webgpu_conv2d_pipeline_key_hash>
+        conv2d_pipelines;
+    std::unordered_map<ggml_webgpu_im2col_pipeline_key, webgpu_pipeline, ggml_webgpu_im2col_pipeline_key_hash>
+        im2col_pipelines;
+
+    std::unordered_map<ggml_webgpu_rms_norm_mul_pipeline_key,
+                       webgpu_pipeline,
+                       ggml_webgpu_rms_norm_mul_pipeline_key_hash>
+        rms_norm_mul_pipelines;
+    std::unordered_map<ggml_webgpu_upscale_pipeline_key, webgpu_pipeline, ggml_webgpu_upscale_pipeline_key_hash>
+        upscale_pipelines;
 
   public:
     ggml_webgpu_shader_lib(wgpu::Device device) { this->device = device; }
@@ -809,7 +1153,9 @@ class ggml_webgpu_shader_lib {
     webgpu_pipeline get_row_norm_pipeline(const ggml_webgpu_shader_lib_context & context) {
         ggml_webgpu_row_norm_pipeline_key key = {};
         key.op                                = context.dst->op;
-        key.inplace                           = context.inplace;
+        key.src_type                          = context.src0->type;
+        key.dst_type                          = context.dst->type;
+        key.inplace                           = ggml_webgpu_tensor_equal(context.src0, context.dst);
 
         auto it = row_norm_pipelines.find(key);
         if (it != row_norm_pipelines.end()) {
@@ -823,6 +1169,10 @@ class ggml_webgpu_shader_lib {
                 defines.push_back("RMS_NORM");
                 variant = "rms_norm";
                 break;
+            case GGML_OP_NORM:
+                defines.push_back("NORM");
+                variant = "norm";
+                break;
             case GGML_OP_L2_NORM:
                 defines.push_back("L2_NORM");
                 variant = "l2_norm";
@@ -836,11 +1186,31 @@ class ggml_webgpu_shader_lib {
             variant += "_inplace";
         }
 
+        if (key.src_type == GGML_TYPE_F32) {
+            defines.push_back("SRC_F32");
+            variant += "_src_f32";
+        } else if (key.src_type == GGML_TYPE_F16) {
+            defines.push_back("SRC_F16");
+            variant += "_src_f16";
+        }
+
+        if (key.dst_type == GGML_TYPE_F32) {
+            defines.push_back("DST_F32");
+            variant += "_dst_f32";
+        } else if (key.dst_type == GGML_TYPE_F16) {
+            defines.push_back("DST_F16");
+            variant += "_dst_f16";
+        }
+
         const uint32_t row_norm_wg_size = 128u;
         uint32_t       wg_size          = std::min(context.max_wg_size, row_norm_wg_size);
         defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
-        auto processed          = preprocessor.preprocess(wgsl_row_norm, defines);
-        row_norm_pipelines[key] = ggml_webgpu_create_pipeline(device, processed, variant);
+        auto processed                  = preprocessor.preprocess(wgsl_row_norm, defines);
+        auto decisions                  = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+        decisions->wg_size              = wg_size;
+        decisions->inplace              = key.inplace;
+        row_norm_pipelines[key]         = ggml_webgpu_create_pipeline(device, processed, variant);
+        row_norm_pipelines[key].context = decisions;
         return row_norm_pipelines[key];
     }
 
@@ -915,7 +1285,7 @@ class ggml_webgpu_shader_lib {
     webgpu_pipeline get_set_pipeline(const ggml_webgpu_shader_lib_context & context) {
         ggml_webgpu_set_pipeline_key key = {};
         key.type                         = context.dst->type;
-        key.inplace                      = context.inplace;
+        key.inplace                      = ggml_webgpu_tensor_equal(context.src0, context.dst);
 
         auto it = set_pipelines.find(key);
         if (it != set_pipelines.end()) {
@@ -948,6 +1318,7 @@ class ggml_webgpu_shader_lib {
         auto processed           = preprocessor.preprocess(wgsl_set, defines);
         auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
         decisions->wg_size       = context.max_wg_size;
+        decisions->inplace       = key.inplace;
         webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
         pipeline.context         = decisions;
         set_pipelines[key]       = pipeline;
@@ -1075,6 +1446,7 @@ class ggml_webgpu_shader_lib {
                     std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper);
 
                     switch (key.src_type) {
+                        case GGML_TYPE_Q1_0:
                         case GGML_TYPE_Q4_0:
                         case GGML_TYPE_Q5_0:
                         case GGML_TYPE_Q8_0:
@@ -1087,6 +1459,7 @@ class ggml_webgpu_shader_lib {
                         case GGML_TYPE_IQ3_S:
                         case GGML_TYPE_IQ1_S:
                         case GGML_TYPE_IQ4_NL:
+                        case GGML_TYPE_MXFP4:
                             {
                                 // Quantized types using u32 buffers for portability.
                                 defines.push_back("SRC_TYPE=u32");
@@ -1105,14 +1478,17 @@ class ggml_webgpu_shader_lib {
                     defines.push_back(type_upper + "_SCALE_MIN");
                     defines.push_back(type_upper + "_TABLES");
                     defines.push_back(type_upper + "_GRID");
+                    defines.push_back(type_upper + "_LUT");
 
                     variant += "_";
                     variant += type_str;
 
                     defines.push_back("DST_TYPE=f32");
 
-                    if ((key.src_type >= GGML_TYPE_Q4_0 && key.src_type <= GGML_TYPE_Q8_1) ||
-                        key.src_type == GGML_TYPE_IQ4_NL) {
+                    if (key.src_type == GGML_TYPE_Q1_0) {
+                        defines.push_back("BLOCK_SIZE=128u");
+                    } else if ((key.src_type >= GGML_TYPE_Q4_0 && key.src_type <= GGML_TYPE_Q8_1) ||
+                               key.src_type == GGML_TYPE_IQ4_NL || key.src_type == GGML_TYPE_MXFP4) {
                         defines.push_back("BLOCK_SIZE=32u");
                     } else if (key.src_type >= GGML_TYPE_Q2_K) {
                         defines.push_back("BLOCK_SIZE=256u");
@@ -1140,7 +1516,7 @@ class ggml_webgpu_shader_lib {
 
     webgpu_pipeline get_scale_pipeline(const ggml_webgpu_shader_lib_context & context) {
         ggml_webgpu_scale_pipeline_key key = {};
-        key.inplace                        = context.inplace;
+        key.inplace                        = ggml_webgpu_tensor_equal(context.src0, context.dst);
 
         auto it = scale_pipelines.find(key);
         if (it != scale_pipelines.end()) {
@@ -1160,6 +1536,7 @@ class ggml_webgpu_shader_lib {
         auto processed           = preprocessor.preprocess(wgsl_scale, defines);
         auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
         decisions->wg_size       = context.max_wg_size;
+        decisions->inplace       = key.inplace;
         webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
         pipeline.context         = decisions;
         scale_pipelines[key]     = pipeline;
@@ -1249,6 +1626,60 @@ class ggml_webgpu_shader_lib {
         return ssm_conv_pipelines[key];
     }
 
+    webgpu_pipeline get_ssm_scan_pipeline(const ggml_webgpu_shader_lib_context & context) {
+        ggml_webgpu_ssm_scan_pipeline_key key = {};
+        key.type                              = context.dst->type;
+        key.d_state                           = (int) context.src0->ne[0];
+        key.xbc_overlap                       = ggml_webgpu_tensor_overlap(context.src1, context.src4) &&
+                          ggml_webgpu_tensor_overlap(context.src1, context.src5);
+
+        auto it = ssm_scan_pipelines.find(key);
+        if (it != ssm_scan_pipelines.end()) {
+            return it->second;
+        }
+
+        std::vector<std::string> defines;
+        std::string              variant = "ssm_scan";
+
+        switch (key.type) {
+            case GGML_TYPE_F32:
+                variant += "_f32";
+                break;
+            default:
+                GGML_ABORT("Unsupported type for ssm_scan shader");
+        }
+
+        const uint32_t wg_size = (uint32_t) key.d_state;
+
+        constexpr uint32_t tokens_per_tile = 4u;
+
+        defines.push_back("WG_SIZE=" + std::to_string(wg_size) + "u");
+        defines.push_back("TOKENS_PER_TILE=" + std::to_string(tokens_per_tile) + "u");
+
+        if (context.supports_subgroups) {
+            defines.push_back("USE_SUBGROUP_REDUCTION");
+            variant += "_sg_reduce";
+        } else {
+            variant += "_wg_reduce";
+        }
+
+        if (key.xbc_overlap) {
+            defines.push_back("XBC_OVERLAP");
+        }
+
+        variant += "_d" + std::to_string(key.d_state);
+
+        auto processed             = preprocessor.preprocess(wgsl_ssm_scan, defines);
+        auto decisions             = std::make_shared<ggml_webgpu_ssm_scan_shader_decisions>();
+        decisions->wg_size         = wg_size;
+        decisions->tokens_per_tile = tokens_per_tile;
+        decisions->xbc_overlap     = key.xbc_overlap;
+        webgpu_pipeline pipeline   = ggml_webgpu_create_pipeline(device, processed, variant);
+        pipeline.context           = decisions;
+        ssm_scan_pipelines[key]    = pipeline;
+        return ssm_scan_pipelines[key];
+    }
+
     webgpu_pipeline get_gated_delta_net_pipeline(const ggml_webgpu_shader_lib_context & context) {
         ggml_webgpu_gated_delta_net_pipeline_key key = {};
         key.type                                     = context.dst->type;
@@ -1356,6 +1787,27 @@ class ggml_webgpu_shader_lib {
                     defines.push_back("MUL_ACC_" + type_upper);
                     defines.push_back("U32_DEQUANT_HELPERS");
                     defines.push_back("SRC0_INNER_TYPE=u32");
+                    switch (context.src0->type) {
+                        case GGML_TYPE_IQ1_S:
+                        case GGML_TYPE_IQ1_M:
+                        case GGML_TYPE_IQ2_S:
+                        case GGML_TYPE_IQ3_S:
+                        case GGML_TYPE_IQ4_NL:
+                        case GGML_TYPE_IQ4_XS:
+                            defines.push_back(type_upper + "_GRID");
+                            break;
+                        case GGML_TYPE_IQ2_XXS:
+                        case GGML_TYPE_IQ2_XS:
+                        case GGML_TYPE_IQ3_XXS:
+                            defines.push_back(type_upper + "_GRID");
+                            defines.push_back(type_upper + "_TABLES");
+                            break;
+                        case GGML_TYPE_MXFP4:
+                            defines.push_back(type_upper + "_LUT");
+                            break;
+                        default:
+                            break;
+                    }
                     break;
                 }
         }
@@ -1380,7 +1832,9 @@ class ggml_webgpu_shader_lib {
         uint32_t wg_size        = WEBGPU_MUL_MAT_VEC_WG_SIZE;
         uint32_t outputs_per_wg = WEBGPU_MUL_MAT_VEC_FLOAT_OUTPUTS_PER_WG;
 
-        if (key.src0_type >= GGML_TYPE_Q2_K) {
+        if (key.src0_type == GGML_TYPE_Q1_0) {
+            outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG;
+        } else if (key.src0_type >= GGML_TYPE_Q2_K) {
             outputs_per_wg = WEBGPU_MUL_MAT_VEC_K_Q_OUTPUTS_PER_WG;
         } else if (key.src0_type >= GGML_TYPE_Q4_0) {
             outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG;
@@ -1410,11 +1864,11 @@ class ggml_webgpu_shader_lib {
         ggml_webgpu_mul_mat_pipeline_key key = {};
         key.src0_type                        = context.src0->type;
         key.src1_type                        = context.src1->type;
-        key.vectorized = (context.src0->ne[0] % 4 == 0 && context.dst->ne[0] % 4 == 0 && context.dst->ne[1] % 4 == 0 &&
+        key.vectorized                       = (context.src0->ne[0] % 4 == 0 && context.dst->ne[0] % 4 == 0 &&
                           (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ?
-                             1 :
-                             0;
-        key.use_subgroup_matrix = context.supports_subgroup_matrix;
+                                                   1 :
+                                                   0;
+        key.use_subgroup_matrix              = context.supports_subgroup_matrix;
 
         auto it = mul_mat_fast_pipelines.find(key);
         if (it != mul_mat_fast_pipelines.end()) {
@@ -1470,6 +1924,28 @@ class ggml_webgpu_shader_lib {
                     defines.push_back("U32_DEQUANT_HELPERS");
                     defines.push_back("SRC0_INNER_TYPE=u32");
 
+                    switch (context.src0->type) {
+                        case GGML_TYPE_IQ1_S:
+                        case GGML_TYPE_IQ1_M:
+                        case GGML_TYPE_IQ4_NL:
+                        case GGML_TYPE_IQ4_XS:
+                            defines.push_back(type_upper + "_GRID");
+                            break;
+                        case GGML_TYPE_IQ2_XXS:
+                        case GGML_TYPE_IQ2_XS:
+                        case GGML_TYPE_IQ2_S:
+                        case GGML_TYPE_IQ3_XXS:
+                        case GGML_TYPE_IQ3_S:
+                            defines.push_back(type_upper + "_GRID");
+                            defines.push_back(type_upper + "_TABLES");
+                            break;
+                        case GGML_TYPE_MXFP4:
+                            defines.push_back(type_upper + "_LUT");
+                            break;
+                        default:
+                            break;
+                    }
+
                     variant += std::string("_") + src0_name;
                     break;
                 }
@@ -1478,13 +1954,22 @@ class ggml_webgpu_shader_lib {
         // VEC/SCALAR controls
         defines.push_back(key.vectorized ? "VEC" : "SCALAR");
 
+        const bool is_quant = ggml_is_quantized(context.src0->type);
+
+        uint32_t tile_k;
+        if (key.use_subgroup_matrix) {
+            tile_k = is_quant ? WEBGPU_MUL_MAT_SUBGROUP_TILE_K_QUANT : WEBGPU_MUL_MAT_SUBGROUP_TILE_K_FLOAT;
+        } else {
+            tile_k = is_quant ? WEBGPU_MUL_MAT_REG_TILE_K_QUANT : WEBGPU_MUL_MAT_REG_TILE_K_FLOAT;
+        }
+
         // Tiles
         defines.push_back("TILE_M=" + std::to_string(WEBGPU_MUL_MAT_TILE_M) + "u");
         defines.push_back("TILE_N=" + std::to_string(WEBGPU_MUL_MAT_TILE_N) + "u");
-        defines.push_back("TILE_K=" + std::to_string(WEBGPU_MUL_MAT_TILE_K) + "u");
 
         // Subgroup matrix specifics
         if (key.use_subgroup_matrix) {
+            defines.push_back("TILE_K=" + std::to_string(tile_k) + "u");
             defines.push_back("MAX_SUBGROUP_SIZE=" + std::to_string(context.max_subgroup_size) + "u");
             defines.push_back("SUBGROUP_M=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_M) + "u");
             defines.push_back("SUBGROUP_N=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_N) + "u");
@@ -1504,12 +1989,13 @@ class ggml_webgpu_shader_lib {
         if (!key.use_subgroup_matrix) {
             defines.push_back("WORKGROUP_SIZE_M=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_M) + "u");
             defines.push_back("WORKGROUP_SIZE_N=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_N) + "u");
+            defines.push_back("TILE_K=" + std::to_string(tile_k) + "u");
         }
 
         auto processed = preprocessor.preprocess(shader_src, defines);
 
         auto decisions                 = std::make_shared<ggml_webgpu_mul_mat_shader_decisions>();
-        decisions->tile_k              = WEBGPU_MUL_MAT_TILE_K;
+        decisions->tile_k              = tile_k;
         decisions->tile_m              = WEBGPU_MUL_MAT_TILE_M;
         decisions->tile_n              = WEBGPU_MUL_MAT_TILE_N;
         decisions->use_subgroup_matrix = key.use_subgroup_matrix;
@@ -1590,6 +2076,7 @@ class ggml_webgpu_shader_lib {
                         case GGML_TYPE_IQ3_S:
                         case GGML_TYPE_IQ1_S:
                         case GGML_TYPE_IQ4_NL:
+                        case GGML_TYPE_MXFP4:
                             {
                                 // Quantized types using u32 buffers for portability.
                                 defines.push_back("SRC0_TYPE=u32");
@@ -1647,6 +2134,11 @@ class ggml_webgpu_shader_lib {
         ggml_webgpu_mul_mat_id_pipeline_key key = {};
         key.src0_type                           = context.src0->type;
         key.src1_type                           = context.src1->type;
+        key.n_experts                           = context.src0->ne[2];
+        key.vectorized                          = (context.src0->ne[0] % 4 == 0 && context.src0->ne[1] % 4 == 0 &&
+                          (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ?
+                                                      1 :
+                                                      0;
 
         auto it = mul_mat_id_pipelines.find(key);
         if (it != mul_mat_id_pipelines.end()) {
@@ -1676,14 +2168,12 @@ class ggml_webgpu_shader_lib {
         switch (context.src0->type) {
             case GGML_TYPE_F32:
                 defines.push_back("SRC0_INNER_TYPE=f32");
-                defines.push_back("FLOAT");
                 defines.push_back("INIT_SRC0_SHMEM_FLOAT");
                 defines.push_back("INIT_SRC1_SHMEM_FLOAT");
                 variant += "_f32";
                 break;
             case GGML_TYPE_F16:
                 defines.push_back("SRC0_INNER_TYPE=f16");
-                defines.push_back("FLOAT");
                 defines.push_back("INIT_SRC0_SHMEM_FLOAT");
                 defines.push_back("INIT_SRC1_SHMEM_FLOAT");
                 variant += "_f16";
@@ -1699,28 +2189,58 @@ class ggml_webgpu_shader_lib {
                     defines.push_back("U32_DEQUANT_HELPERS");
                     defines.push_back("SRC0_INNER_TYPE=u32");
 
+                    switch (context.src0->type) {
+                        case GGML_TYPE_IQ1_S:
+                        case GGML_TYPE_IQ1_M:
+                        case GGML_TYPE_IQ4_NL:
+                        case GGML_TYPE_IQ4_XS:
+                            defines.push_back(type_upper + "_GRID");
+                            break;
+                        case GGML_TYPE_IQ2_XXS:
+                        case GGML_TYPE_IQ2_XS:
+                        case GGML_TYPE_IQ2_S:
+                        case GGML_TYPE_IQ3_XXS:
+                        case GGML_TYPE_IQ3_S:
+                            defines.push_back(type_upper + "_GRID");
+                            defines.push_back(type_upper + "_TABLES");
+                            break;
+                        case GGML_TYPE_MXFP4:
+                            defines.push_back(type_upper + "_LUT");
+                            break;
+                        default:
+                            break;
+                    }
+
                     variant += std::string("_") + src0_name;
                     break;
                 }
         }
 
-        defines.push_back("SCALAR");
+        // VEC/SCALAR controls
+        defines.push_back(key.vectorized ? "VEC" : "SCALAR");
+
+        // mul_mat_id is register-tile only.
+        const uint32_t tile_k =
+            ggml_is_quantized(context.src0->type) ? WEBGPU_MUL_MAT_REG_TILE_K_QUANT : WEBGPU_MUL_MAT_REG_TILE_K_FLOAT;
 
         // Tiles
         defines.push_back("TILE_M=" + std::to_string(WEBGPU_MUL_MAT_TILE_M) + "u");
         defines.push_back("TILE_N=" + std::to_string(WEBGPU_MUL_MAT_TILE_N) + "u");
-        defines.push_back("TILE_K=" + std::to_string(WEBGPU_MUL_MAT_TILE_K) + "u");
+        defines.push_back("TILE_K=" + std::to_string(tile_k) + "u");
 
         defines.push_back("WORKGROUP_SIZE_M=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_M) + "u");
         defines.push_back("WORKGROUP_SIZE_N=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_N) + "u");
 
         // variant suffix for src1 type
         variant += std::string("_") + (context.src1->type == GGML_TYPE_F32 ? "f32" : "f16");
+        if (key.vectorized) {
+            variant += "_vectorized";
+        }
 
         auto processed = preprocessor.preprocess(wgsl_mul_mat_id, defines);
 
         auto decisions       = std::make_shared<ggml_webgpu_mul_mat_shader_decisions>();
-        decisions->tile_k    = WEBGPU_MUL_MAT_TILE_K;
+        decisions->tile_k    = tile_k;
         decisions->tile_m    = WEBGPU_MUL_MAT_TILE_M;
         decisions->tile_n    = WEBGPU_MUL_MAT_TILE_N;
         decisions->wg_size_m = WEBGPU_MUL_MAT_WG_SIZE_M;
@@ -1733,6 +2253,126 @@ class ggml_webgpu_shader_lib {
         return mul_mat_id_pipelines[key];
     }
 
+    webgpu_pipeline get_mul_mat_id_vec_pipeline(const ggml_webgpu_shader_lib_context & context) {
+        ggml_webgpu_mul_mat_id_pipeline_key key = {};
+        key.src0_type                           = context.src0->type;
+        key.src1_type                           = context.src1->type;
+        key.n_experts                           = context.src0->ne[2];
+        key.vectorized                          = (context.src0->ne[0] % 4 == 0 &&
+                          (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ?
+                                                      1 :
+                                                      0;
+
+        auto it = mul_mat_id_vec_pipelines.find(key);
+        if (it != mul_mat_id_vec_pipelines.end()) {
+            return it->second;
+        }
+
+        std::vector<std::string> defines;
+        std::string              variant    = "mul_mat_id_vec";
+        const char *             shader_src = wgsl_mul_mat_id_vec;
+
+        // src1 type
+        switch (context.src1->type) {
+            case GGML_TYPE_F32:
+                defines.push_back("SRC1_INNER_TYPE=f32");
+                break;
+            case GGML_TYPE_F16:
+                defines.push_back("SRC1_INNER_TYPE=f16");
+                break;
+            default:
+                GGML_ABORT("Unsupported src1 type for mul_mat fast shader");
+        }
+
+        // src0 type
+        switch (context.src0->type) {
+            case GGML_TYPE_F32:
+                defines.push_back("SRC0_INNER_TYPE=f32");
+                defines.push_back("MUL_ACC_FLOAT");
+                variant += "_f32";
+                break;
+            case GGML_TYPE_F16:
+                defines.push_back("SRC0_INNER_TYPE=f16");
+                defines.push_back("MUL_ACC_FLOAT");
+                variant += "_f16";
+                break;
+            default:
+                {
+                    // Quantized types: use helpers but accumulate in f16
+                    const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type);
+                    std::string                     src0_name   = src0_traits->type_name;
+                    std::string                     type_upper  = src0_name;
+                    variant += "_" + src0_name;
+                    std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper);
+
+                    defines.push_back("BYTE_HELPERS");
+                    defines.push_back("MUL_ACC_" + type_upper);
+                    defines.push_back("U32_DEQUANT_HELPERS");
+                    defines.push_back("SRC0_INNER_TYPE=u32");
+                    switch (context.src0->type) {
+                        case GGML_TYPE_IQ1_S:
+                        case GGML_TYPE_IQ1_M:
+                        case GGML_TYPE_IQ2_S:
+                        case GGML_TYPE_IQ3_S:
+                        case GGML_TYPE_IQ4_NL:
+                        case GGML_TYPE_IQ4_XS:
+                            defines.push_back(type_upper + "_GRID");
+                            break;
+                        case GGML_TYPE_IQ2_XXS:
+                        case GGML_TYPE_IQ2_XS:
+                        case GGML_TYPE_IQ3_XXS:
+                            defines.push_back(type_upper + "_GRID");
+                            defines.push_back(type_upper + "_TABLES");
+                            break;
+                        case GGML_TYPE_MXFP4:
+                            defines.push_back(type_upper + "_LUT");
+                            break;
+                        default:
+                            break;
+                    }
+                    break;
+                }
+        }
+
+        // VEC/SCALAR controls
+        defines.push_back(key.vectorized ? "VEC" : "SCALAR");
+
+        uint32_t wg_size        = WEBGPU_MUL_MAT_VEC_WG_SIZE;
+        uint32_t outputs_per_wg = WEBGPU_MUL_MAT_VEC_FLOAT_OUTPUTS_PER_WG;
+
+        if (key.src0_type == GGML_TYPE_Q1_0) {
+            outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG;
+        } else if (key.src0_type >= GGML_TYPE_Q2_K) {
+            outputs_per_wg = WEBGPU_MUL_MAT_VEC_K_Q_OUTPUTS_PER_WG;
+        } else if (key.src0_type >= GGML_TYPE_Q4_0) {
+            outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG;
+        }
+
+        // variant suffix for src1 type
+        variant += std::string("_") + (context.src1->type == GGML_TYPE_F32 ? "f32" : "f16");
+
+        defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
+        defines.push_back(std::string("OUTPUTS_PER_WG=") + std::to_string(outputs_per_wg));
+        defines.push_back(context.supports_subgroups ? "USE_SUBGROUP_REDUCTION" : "USE_WORKGROUP_REDUCTION");
+        variant += context.supports_subgroups ? "_sg_reduce" : "_wg_reduce";
+        if (key.vectorized) {
+            variant += "_vectorized";
+        }
+
+        defines.push_back(std::string("N_EXPERTS=") + std::to_string(key.n_experts));
+
+        auto processed = preprocessor.preprocess(shader_src, defines);
+
+        auto decisions            = std::make_shared<ggml_webgpu_mul_mat_vec_shader_decisions>();
+        decisions->wg_size        = wg_size;
+        decisions->outputs_per_wg = outputs_per_wg;
+
+        webgpu_pipeline pipeline      = ggml_webgpu_create_pipeline(device, processed, variant);
+        pipeline.context              = decisions;
+        mul_mat_id_vec_pipelines[key] = pipeline;
+        return mul_mat_id_vec_pipelines[key];
+    }
+
     webgpu_pipeline get_unary_pipeline(const ggml_webgpu_shader_lib_context & context) {
         const bool                     is_unary = context.dst->op == GGML_OP_UNARY;
         const int                      op       = is_unary ? (int) ggml_get_unary_op(context.dst) : context.dst->op;
@@ -1740,8 +2380,8 @@ class ggml_webgpu_shader_lib {
         key.type                                = context.dst->type;
         key.op                                  = op;
         key.is_unary                            = is_unary;
-        key.inplace                             = context.inplace;
-        key.ttype                               = (ggml_tri_type) ggml_get_op_params_i32(context.dst, 0);
+        key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst) || context.dst->op == GGML_OP_FILL;
+        key.ttype   = (ggml_tri_type) ggml_get_op_params_i32(context.dst, 0);
 
         auto it = unary_pipelines.find(key);
         if (it != unary_pipelines.end()) {
@@ -1799,19 +2439,60 @@ class ggml_webgpu_shader_lib {
         auto processed           = preprocessor.preprocess(wgsl_unary, defines);
         auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
         decisions->wg_size       = context.max_wg_size;
+        decisions->inplace       = key.inplace;
         webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
         pipeline.context         = decisions;
         unary_pipelines[key]     = pipeline;
         return unary_pipelines[key];
     }
 
+    webgpu_pipeline get_rms_norm_mul_pipeline(const ggml_webgpu_shader_lib_context & context) {
+        ggml_webgpu_rms_norm_mul_pipeline_key key = {};
+        key.inplace                               = ggml_webgpu_tensor_equal(context.src0, context.dst);
+        key.overlap                               = ggml_webgpu_tensor_equal(context.src1, context.dst);
+        key.src_overlap                           = ggml_webgpu_tensor_overlap(context.src0, context.src1);
+
+        auto it = rms_norm_mul_pipelines.find(key);
+        if (it != rms_norm_mul_pipelines.end()) {
+            return it->second;
+        }
+
+        std::vector<std::string> defines;
+        std::string              op_name = "RMS_NORM_MUL";
+        std::string              variant = op_name;
+
+        if (key.inplace) {
+            defines.push_back("INPLACE");
+            variant += "_inplace";
+        } else if (key.overlap) {
+            defines.push_back("OVERLAP");
+            variant += "_overlap";
+        } else if (key.src_overlap) {
+            defines.push_back("SRC_OVERLAP");
+            variant += "_src_overlap";
+        }
+
+        defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+        auto processed                  = preprocessor.preprocess(wgsl_rms_norm_mul, defines);
+        auto pipeline_decisions         = std::make_shared<ggml_webgpu_rms_norm_mul_shader_decisions>();
+        pipeline_decisions->wg_size     = context.max_wg_size;
+        pipeline_decisions->inplace     = key.inplace;
+        pipeline_decisions->overlap     = key.overlap;
+        pipeline_decisions->src_overlap = key.src_overlap;
+        webgpu_pipeline pipeline        = ggml_webgpu_create_pipeline(device, processed, variant);
+        pipeline.context                = pipeline_decisions;
+        rms_norm_mul_pipelines[key]     = pipeline;
+        return rms_norm_mul_pipelines[key];
+    }
+
     webgpu_pipeline get_binary_pipeline(const ggml_webgpu_shader_lib_context & context) {
         ggml_webgpu_binary_pipeline_key key = {};
         key.type                            = context.dst->type;
         key.op                              = context.dst->op;
-        key.inplace                         = context.inplace;
-        key.overlap                         = context.overlap;
-        key.src_overlap                     = context.src_overlap;
+        key.inplace                         = ggml_webgpu_tensor_equal(context.src0, context.dst);
+        key.overlap                         = ggml_webgpu_tensor_equal(context.src1, context.dst);
+        key.src_overlap                     = ggml_webgpu_tensor_overlap(context.src0, context.src1);
 
         auto it = binary_pipelines.find(key);
         if (it != binary_pipelines.end()) {
@@ -1850,15 +2531,50 @@ class ggml_webgpu_shader_lib {
 
         defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
 
-        auto processed           = preprocessor.preprocess(wgsl_binary, defines);
-        auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
-        decisions->wg_size       = context.max_wg_size;
+        auto processed                  = preprocessor.preprocess(wgsl_binary, defines);
+        auto pipeline_decisions         = std::make_shared<ggml_webgpu_binary_shader_decisions>();
+        pipeline_decisions->wg_size     = context.max_wg_size;
+        pipeline_decisions->inplace     = key.inplace;
+        pipeline_decisions->overlap     = key.overlap;
+        pipeline_decisions->src_overlap = key.src_overlap;
+
         webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
-        pipeline.context         = decisions;
+        pipeline.context         = pipeline_decisions;
         binary_pipelines[key]    = pipeline;
         return binary_pipelines[key];
     }
 
+    webgpu_pipeline get_add_id_pipeline(const ggml_webgpu_shader_lib_context & context) {
+        ggml_webgpu_add_id_pipeline_key key = {};
+        key.inplace                         = ggml_webgpu_tensor_equal(context.src0, context.dst);
+
+        auto it = add_id_pipelines.find(key);
+        if (it != add_id_pipelines.end()) {
+            return it->second;
+        }
+
+        std::vector<std::string> defines;
+        std::string              variant    = "add_id";
+        const char *             shader_src = wgsl_add_id;
+
+        if (key.inplace) {
+            defines.push_back("INPLACE");
+            variant += "_inplace";
+        }
+
+        defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+        auto processed              = preprocessor.preprocess(shader_src, defines);
+        auto pipeline_decisions     = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+        pipeline_decisions->wg_size = context.max_wg_size;
+        pipeline_decisions->inplace = key.inplace;
+
+        webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
+        pipeline.context         = pipeline_decisions;
+        add_id_pipelines[key]    = pipeline;
+        return pipeline;
+    }
+
     webgpu_pipeline get_concat_pipeline(const ggml_webgpu_shader_lib_context & context) {
         ggml_webgpu_concat_pipeline_key key = {};
         key.type                            = context.dst->type;
@@ -1935,14 +2651,20 @@ class ggml_webgpu_shader_lib {
         return repeat_pipelines[key];
     }
 
-    webgpu_pipeline get_flash_attn_pipeline(const ggml_webgpu_shader_lib_context & context) {
-        const ggml_webgpu_flash_attn_pipeline_key key = ggml_webgpu_flash_attn_make_pipeline_key(context);
-        auto                                      it  = flash_attn_pipelines.find(key);
+    webgpu_pipeline get_flash_attn_pipeline(const ggml_webgpu_shader_lib_context & context,
+                                            size_t                                 storage_offset_alignment) {
+        const ggml_webgpu_flash_attn_decisions decisions =
+            ggml_webgpu_flash_attn_get_decisions(context, storage_offset_alignment);
+        GGML_ASSERT(decisions.path != GGML_WEBGPU_FLASH_ATTN_PATH_NONE);
+        ggml_webgpu_flash_attn_pipeline_key key = ggml_webgpu_flash_attn_make_pipeline_key(context, decisions);
+        auto                                it  = flash_attn_pipelines.find(key);
         if (it != flash_attn_pipelines.end()) {
             return it->second;
         }
         std::vector<std::string> defines;
-        std::string              variant = "flash_attn";
+        std::string              variant = decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC  ? "flash_attn_vec" :
+                                           decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE ? "flash_attn_tile" :
+                                                                                                "flash_attn";
 
         switch (key.kv_type) {
             case GGML_TYPE_F32:
@@ -1962,92 +2684,38 @@ class ggml_webgpu_shader_lib {
         }
         variant += std::string("_") + ggml_type_name(key.kv_type);
 
-        if (key.has_mask) {
-            defines.push_back("MASK");
-            variant += "_mask";
-        }
-        if (key.has_sinks) {
-            defines.push_back("SINKS");
-            variant += "_sinks";
-        }
-        if (key.uses_logit_softcap) {
-            defines.push_back("LOGIT_SOFTCAP");
-            variant += "_lgsc";
-        }
-        if (key.kv_direct) {
-            defines.push_back("KV_DIRECT");
-            variant += "_kvdirect";
-        }
-
-        defines.push_back(std::string("HEAD_DIM_QK=") + std::to_string(key.head_dim_qk));
-        variant += std::string("_hsqk") + std::to_string(key.head_dim_qk);
-
-        defines.push_back(std::string("HEAD_DIM_V=") + std::to_string(key.head_dim_v));
-        variant += std::string("_hsv") + std::to_string(key.head_dim_v);
-
-        defines.push_back(std::string("SG_MAT_M=") + std::to_string(context.sg_mat_m));
-        defines.push_back(std::string("SG_MAT_N=") + std::to_string(context.sg_mat_n));
-        defines.push_back(std::string("SG_MAT_K=") + std::to_string(context.sg_mat_k));
-
-        auto decisions    = std::make_shared<ggml_webgpu_flash_attn_decisions>();
-        decisions->q_tile = context.sg_mat_m;
-
-        const uint32_t min_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(context, key);
-        uint32_t       kv_tile = std::min(min_kv_tile, context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES);
-
-        if (key.kv_direct) {
-            kv_tile = std::min(kv_tile, GGML_WEBGPU_KV_SEQ_PAD);
-            while (GGML_WEBGPU_KV_SEQ_PAD % kv_tile != 0) {
-                kv_tile -= context.sg_mat_n;
-            }
-        }
-
-        decisions->kv_tile = kv_tile;
-        decisions->wg_size = std::max(context.max_subgroup_size, GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE);
-
-        defines.push_back(std::string("Q_TILE=") + std::to_string(decisions->q_tile));
-        defines.push_back(std::string("KV_TILE=") + std::to_string(decisions->kv_tile));
-        defines.push_back(std::string("WG_SIZE=") + std::to_string(decisions->wg_size));
-
-        webgpu_pipeline pipeline =
-            ggml_webgpu_create_pipeline(device, preprocessor.preprocess(wgsl_flash_attn, defines), variant);
-        pipeline.context          = decisions;
-        flash_attn_pipelines[key] = pipeline;
-        return flash_attn_pipelines[key];
-    }
-
-    webgpu_pipeline get_flash_attn_vec_pipeline(const ggml_webgpu_shader_lib_context & context) {
-        const ggml_webgpu_flash_attn_pipeline_key key = ggml_webgpu_flash_attn_make_pipeline_key(context);
-        auto                                      it  = flash_attn_vec_pipelines.find(key);
-        if (it != flash_attn_vec_pipelines.end()) {
-            return it->second;
-        }
-
-        std::vector<std::string> defines;
-        std::string              variant = "flash_attn_vec";
-
-        switch (key.kv_type) {
+        switch (key.q_type) {
             case GGML_TYPE_F32:
-                defines.push_back("KV_F32");
+                defines.push_back("Q_F32");
                 break;
             case GGML_TYPE_F16:
-                defines.push_back("KV_F16");
+                defines.push_back("Q_F16");
                 break;
-            case GGML_TYPE_Q4_0:
-                defines.push_back("KV_Q4_0");
+            default:
+                GGML_ABORT("Unsupported Q type for flash attention shader");
+        }
+        variant += std::string("_q") + ggml_type_name(key.q_type);
+
+        switch (key.dst_type) {
+            case GGML_TYPE_F32:
+                defines.push_back("DST_F32");
                 break;
-            case GGML_TYPE_Q8_0:
-                defines.push_back("KV_Q8_0");
+            case GGML_TYPE_F16:
+                defines.push_back("DST_F16");
                 break;
             default:
-                GGML_ABORT("Unsupported KV type for flash attention shader");
+                GGML_ABORT("Unsupported dst type for flash attention shader");
         }
-        variant += std::string("_") + ggml_type_name(key.kv_type);
+        variant += std::string("_dst") + ggml_type_name(key.dst_type);
 
         if (key.has_mask) {
             defines.push_back("MASK");
-            defines.push_back("BLK");
-            variant += "_mask_blk";
+            if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) {
+                defines.push_back("BLK");
+                variant += "_mask_blk";
+            } else {
+                variant += "_mask";
+            }
         }
         if (key.has_sinks) {
             defines.push_back("SINKS");
@@ -2061,6 +2729,10 @@ class ggml_webgpu_shader_lib {
             defines.push_back("KV_DIRECT");
             variant += "_kvdirect";
         }
+        if (key.kv_overlap) {
+            defines.push_back("KV_OVERLAP");
+            variant += "_kv_overlap";
+        }
 
         defines.push_back(std::string("HEAD_DIM_QK=") + std::to_string(key.head_dim_qk));
         variant += std::string("_hsqk") + std::to_string(key.head_dim_qk);
@@ -2068,46 +2740,40 @@ class ggml_webgpu_shader_lib {
         defines.push_back(std::string("HEAD_DIM_V=") + std::to_string(key.head_dim_v));
         variant += std::string("_hsv") + std::to_string(key.head_dim_v);
 
-        defines.push_back(std::string("SG_MAT_M=") + std::to_string(context.sg_mat_m));
-        defines.push_back(std::string("SG_MAT_N=") + std::to_string(context.sg_mat_n));
-        defines.push_back(std::string("SG_MAT_K=") + std::to_string(context.sg_mat_k));
-        defines.push_back("Q_TILE=1");
-
-        auto decisions     = std::make_shared<ggml_webgpu_flash_attn_vec_decisions>();
-        decisions->kv_tile = ggml_webgpu_flash_attn_vec_get_kv_tile(context);
-        decisions->wg_size = std::max(1u, std::min<uint32_t>(32u, context.max_subgroup_size));
-        uint32_t vec_ne    = 1u;
-
-        // Keep conservative defaults unless this is the f16 vec-split shape family.
-        if (key.kv_type == GGML_TYPE_F16 && key.head_dim_qk == key.head_dim_v) {
-            switch (key.head_dim_qk) {
-                case 64:
-                case 192:
-                case 576:
-                    vec_ne = 2u;
-                    break;
-                case 96:
-                    vec_ne = 4u;
-                    break;
-                default:
-                    break;
-            }
+        const char * shader_src = wgsl_flash_attn;
+        if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) {
+            defines.push_back("KV_GRANULARITY=8");
+            defines.push_back(std::string("VEC_NE=") + std::to_string(ggml_webgpu_flash_attn_pick_vec_ne(key)) + "u");
+            shader_src = wgsl_flash_attn_vec_split;
+        } else if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE) {
+            shader_src = wgsl_flash_attn_tile;
+            defines.push_back("MIN_SUBGROUP_SIZE=" + std::to_string(context.min_subgroup_size) + "u");
+            defines.push_back("MAX_SUBGROUP_SIZE=" + std::to_string(context.max_subgroup_size) + "u");
+            defines.push_back("KV_STAGE_STRIDE=" + std::to_string(std::max(key.head_dim_qk, key.head_dim_v)));
+            variant += "_tile_sg" + std::to_string(context.min_subgroup_size) + "_" +
+                       std::to_string(context.max_subgroup_size);
+        } else {
+            defines.push_back(std::string("SG_MAT_M=") + std::to_string(context.sg_mat_m));
+            defines.push_back(std::string("SG_MAT_N=") + std::to_string(context.sg_mat_n));
+            defines.push_back(std::string("SG_MAT_K=") + std::to_string(context.sg_mat_k));
         }
 
-        defines.push_back(std::string("KV_TILE=") + std::to_string(decisions->kv_tile));
-        defines.push_back(std::string("WG_SIZE=") + std::to_string(decisions->wg_size));
-        defines.push_back(std::string("VEC_NE=") + std::to_string(vec_ne) + "u");
+        auto pipeline_decisions        = std::make_shared<ggml_webgpu_flash_attn_decisions>(decisions);
+        pipeline_decisions->kv_overlap = key.kv_overlap;
+        defines.push_back(std::string("Q_TILE=") + std::to_string(decisions.q_tile));
+        defines.push_back(std::string("KV_TILE=") + std::to_string(decisions.kv_tile));
+        defines.push_back(std::string("WG_SIZE=") + std::to_string(decisions.wg_size));
 
         webgpu_pipeline pipeline =
-            ggml_webgpu_create_pipeline(device, preprocessor.preprocess(wgsl_flash_attn_vec_split, defines), variant);
-        pipeline.context              = decisions;
-        flash_attn_vec_pipelines[key] = pipeline;
-        return flash_attn_vec_pipelines[key];
+            ggml_webgpu_create_pipeline(device, preprocessor.preprocess(shader_src, defines), variant);
+        pipeline.context          = pipeline_decisions;
+        flash_attn_pipelines[key] = pipeline;
+        return flash_attn_pipelines[key];
     }
 
-    webgpu_pipeline get_flash_attn_blk_pipeline(const ggml_webgpu_shader_lib_context & context) {
+    webgpu_pipeline get_flash_attn_blk_pipeline(const ggml_webgpu_shader_lib_context & context, uint32_t kv_tile) {
         ggml_webgpu_flash_attn_blk_pipeline_key key = {};
-        key.kv_tile                                 = ggml_webgpu_flash_attn_vec_get_kv_tile(context);
+        key.kv_tile                                 = kv_tile;
         auto it                                     = flash_attn_blk_pipelines.find(key);
         if (it != flash_attn_blk_pipelines.end()) {
             return it->second;
@@ -2135,6 +2801,7 @@ class ggml_webgpu_shader_lib {
     webgpu_pipeline get_flash_attn_vec_reduce_pipeline(const ggml_webgpu_shader_lib_context & context) {
         ggml_webgpu_flash_attn_vec_reduce_pipeline_key key = {};
         key.head_dim_v                                     = (uint32_t) context.src2->ne[0];
+        key.dst_type                                       = context.dst->type;
         key.wg_size                                        = context.max_wg_size;
         auto it                                            = flash_attn_vec_reduce_pipelines.find(key);
         if (it != flash_attn_vec_reduce_pipelines.end()) {
@@ -2144,6 +2811,18 @@ class ggml_webgpu_shader_lib {
         std::vector<std::string> defines;
         std::string              variant = "flash_attn_vec_reduce";
 
+        switch (key.dst_type) {
+            case GGML_TYPE_F32:
+                defines.push_back("DST_F32");
+                break;
+            case GGML_TYPE_F16:
+                defines.push_back("DST_F16");
+                break;
+            default:
+                GGML_ABORT("Unsupported dst type for flash attention vec reduce shader");
+        }
+        variant += std::string("_dst") + ggml_type_name(key.dst_type);
+
         defines.push_back(std::string("HEAD_DIM_V=") + std::to_string(key.head_dim_v));
         variant += std::string("_hsv") + std::to_string(key.head_dim_v);
 
@@ -2285,7 +2964,7 @@ class ggml_webgpu_shader_lib {
     webgpu_pipeline get_rope_pipeline(const ggml_webgpu_shader_lib_context & context) {
         ggml_webgpu_rope_pipeline_key key = {};
         key.type                          = context.dst->type;
-        key.inplace                       = context.inplace;
+        key.inplace                       = ggml_webgpu_tensor_equal(context.src0, context.dst);
         key.has_ff                        = (context.src2 != nullptr);
 
         auto it = rope_pipelines.find(key);
@@ -2324,6 +3003,7 @@ class ggml_webgpu_shader_lib {
         auto processed           = preprocessor.preprocess(wgsl_rope, defines);
         auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
         decisions->wg_size       = context.max_wg_size;
+        decisions->inplace       = key.inplace;
         webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
         pipeline.context         = decisions;
         rope_pipelines[key]      = pipeline;
@@ -2335,7 +3015,7 @@ class ggml_webgpu_shader_lib {
         key.mask_type                         = context.src1 ? context.src1->type : GGML_TYPE_F32;
         key.has_mask                          = (context.src1 != nullptr);
         key.has_sink                          = (context.src2 != nullptr);
-        key.inplace                           = context.inplace;
+        key.inplace                           = ggml_webgpu_tensor_equal(context.src0, context.dst);
 
         auto it = soft_max_pipelines.find(key);
         if (it != soft_max_pipelines.end()) {
@@ -2376,12 +3056,157 @@ class ggml_webgpu_shader_lib {
         auto processed           = preprocessor.preprocess(wgsl_soft_max, defines);
         auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
         decisions->wg_size       = context.max_wg_size;
+        decisions->inplace       = key.inplace;
         webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
         pipeline.context         = decisions;
         soft_max_pipelines[key]  = pipeline;
         return soft_max_pipelines[key];
     }
 
+    webgpu_pipeline get_conv2d_pipeline(const ggml_webgpu_shader_lib_context & context) {
+        ggml_webgpu_conv2d_pipeline_key key = {};
+        key.weight_type                     = context.src0->type;
+        key.input_type                      = context.src1->type;
+        key.output_type                     = context.dst->type;
+
+        auto it = conv2d_pipelines.find(key);
+        if (it != conv2d_pipelines.end()) {
+            return it->second;
+        }
+
+        std::vector<std::string> defines;
+        std::string              variant = "conv_2d";
+
+        auto push_type_defines = [&](const char * prefix, ggml_type type) {
+            std::string s_prefix = prefix;
+            if (type == GGML_TYPE_F32) {
+                defines.push_back(s_prefix + "_F32");
+            } else if (type == GGML_TYPE_F16) {
+                defines.push_back(s_prefix + "_F16");
+            } else {
+                GGML_ABORT("Unsupported type for CONV_2D shader");
+            }
+        };
+
+        push_type_defines("WEIGHT", key.weight_type);
+        push_type_defines("INPUT", key.input_type);
+        push_type_defines("OUTPUT", key.output_type);
+
+        defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+        auto processed           = preprocessor.preprocess(wgsl_conv2d, defines);
+        auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+        decisions->wg_size       = context.max_wg_size;
+        webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
+        pipeline.context         = decisions;
+        conv2d_pipelines[key]    = pipeline;
+        return conv2d_pipelines[key];
+    }
+
+    webgpu_pipeline get_im2col_pipeline(const ggml_webgpu_shader_lib_context & context) {
+        ggml_webgpu_im2col_pipeline_key key = {};
+        key.input_type                      = context.src1->type;
+        key.output_type                     = context.dst->type;
+
+        auto it = im2col_pipelines.find(key);
+        if (it != im2col_pipelines.end()) {
+            return it->second;
+        }
+
+        std::vector<std::string> defines;
+        std::string              variant = "im2col";
+
+        auto push_type_defines = [&](const char * prefix, ggml_type type) {
+            std::string s_prefix = prefix;
+            if (type == GGML_TYPE_F32) {
+                defines.push_back(s_prefix + "_F32");
+            } else if (type == GGML_TYPE_F16) {
+                defines.push_back(s_prefix + "_F16");
+            } else {
+                GGML_ABORT("Unsupported type for IM2COL shader");
+            }
+        };
+
+        push_type_defines("INPUT", key.input_type);
+        push_type_defines("OUTPUT", key.output_type);
+
+        defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+        auto processed           = preprocessor.preprocess(wgsl_im2col, defines);
+        auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+        decisions->wg_size       = context.max_wg_size;
+        webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
+        pipeline.context         = decisions;
+        im2col_pipelines[key]    = pipeline;
+        return im2col_pipelines[key];
+    }
+
+    webgpu_pipeline get_upscale_pipeline(const ggml_webgpu_shader_lib_context & context) {
+        const uint32_t mode_flags = (uint32_t) ggml_get_op_params_i32(context.dst, 0);
+        const uint32_t base_mode  = mode_flags & 0xFFu;
+        const bool     antialias  = (mode_flags & GGML_SCALE_FLAG_ANTIALIAS) != 0u;
+
+        ggml_webgpu_upscale_pipeline_key key = {};
+        key.input_type                       = context.src0->type;
+        key.output_type                      = context.dst->type;
+        key.base_mode                        = base_mode;
+        key.antialias                        = antialias;
+
+        auto it = upscale_pipelines.find(key);
+        if (it != upscale_pipelines.end()) {
+            return it->second;
+        }
+
+        std::vector<std::string> defines;
+        std::string              variant = "upscale";
+
+        if (key.input_type == GGML_TYPE_F16) {
+            defines.push_back("SRC_F16");
+            variant += "_src_f16";
+        } else {
+            variant += "_src_f32";
+        }
+
+        if (key.output_type == GGML_TYPE_F16) {
+            defines.push_back("DST_F16");
+            variant += "_dst_f16";
+        } else {
+            variant += "_dst_f32";
+        }
+
+        switch (base_mode) {
+            case GGML_SCALE_MODE_NEAREST:
+                defines.push_back("NEAREST");
+                variant += "_nearest";
+                break;
+            case GGML_SCALE_MODE_BILINEAR:
+                defines.push_back("BILINEAR");
+                variant += "_bilinear";
+                break;
+            case GGML_SCALE_MODE_BICUBIC:
+                defines.push_back("BICUBIC");
+                variant += "_bicubic";
+                break;
+            default:
+                GGML_ABORT("Unsupported upscale mode");
+        }
+
+        if (antialias) {
+            defines.push_back("ANTIALIAS");
+            variant += "_aa";
+        }
+
+        defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+        auto processed           = preprocessor.preprocess(wgsl_upscale, defines);
+        auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+        decisions->wg_size       = context.max_wg_size;
+        webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
+        pipeline.context         = decisions;
+        upscale_pipelines[key]   = pipeline;
+        return upscale_pipelines[key];
+    }
+
   private:
     static webgpu_pipeline ggml_webgpu_create_pipeline(wgpu::Device & device,
                                                        std::string    shader_code,
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index aa20a745e0a..78cb02be06d 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -8,6 +8,7 @@
 #include "ggml-backend-impl.h"
 #include "ggml-impl.h"
 #include "ggml-webgpu-shader-lib.hpp"
+#include "ggml.h"
 
 #ifdef __EMSCRIPTEN__
 #    include <emscripten/emscripten.h>
@@ -107,12 +108,9 @@ static inline uint32_t ggml_webgpu_u32_from_f32(float value) {
 // their locations.
 static void * const webgpu_ptr_base = (void *) (uintptr_t) 0x1000;  // NOLINT
 
-// Always returns the base offset of a tensor, regardless of views.
-static uint64_t webgpu_tensor_offset(const ggml_tensor * tensor) {
-    if (tensor->view_src) {
-        return (uint8_t *) tensor->view_src->data - (uint8_t *) webgpu_ptr_base;
-    }
-    return (uint8_t *) tensor->data - (uint8_t *) webgpu_ptr_base;
+static size_t ggml_webgpu_tensor_offset(const ggml_tensor * tensor) {
+    const ggml_tensor * base_tensor = tensor->view_src ? tensor->view_src : tensor;
+    return (size_t) ((uintptr_t) base_tensor->data - (uintptr_t) webgpu_ptr_base) + tensor->view_offs;
 }
 
 /* Struct definitions */
@@ -189,6 +187,7 @@ struct webgpu_capabilities {
     uint32_t sg_mat_k = 0;
 
     uint32_t subgroup_size     = 0;
+    uint32_t min_subgroup_size = 0;
     uint32_t max_subgroup_size = 0;
     size_t   memset_bytes_per_thread;
 };
@@ -211,6 +210,7 @@ struct webgpu_global_context_struct {
     wgpu::Buffer    memset_params_buf;
     webgpu_pipeline memset_pipeline;
 
+    // TODO: We should rework the CPU profiling time handling to make it more useful. ref: https://github.com/ggml-org/llama.cpp/pull/22050
 #ifdef GGML_WEBGPU_CPU_PROFILE
     // Profiling: labeled CPU time in ms (total)
     std::unordered_map<std::string, double> cpu_time_ms;
@@ -218,11 +218,6 @@ struct webgpu_global_context_struct {
     std::unordered_map<std::string, double> cpu_detail_ms;
 #endif
 
-#ifdef GGML_WEBGPU_GPU_PROFILE
-    // Profiling: per-shader GPU time in ms
-    std::unordered_map<std::string, double> shader_gpu_time_ms;
-#endif
-
 #ifdef GGML_WEBGPU_DEBUG
     wgpu::Buffer debug_host_buf;
     wgpu::Buffer debug_dev_buf;
@@ -268,10 +263,12 @@ struct webgpu_context_struct {
     size_t memset_bytes_per_thread;
 
 #ifdef GGML_WEBGPU_GPU_PROFILE
-    wgpu::Buffer   profile_timestamp_dev_buf;
-    wgpu::Buffer   profile_timestamp_host_buf;
-    wgpu::QuerySet profile_timestamp_query_set;
-    uint32_t       profile_timestamp_query_count = 0;
+    // Profiling: per-shader GPU time in ms
+    std::unordered_map<std::string, double> shader_gpu_time_ms;
+    wgpu::Buffer                            profile_timestamp_dev_buf;
+    wgpu::Buffer                            profile_timestamp_host_buf;
+    wgpu::QuerySet                          profile_timestamp_query_set;
+    uint32_t                                profile_timestamp_query_count = 0;
 #endif
 
     ~webgpu_context_struct() {
@@ -376,10 +373,6 @@ static void ggml_webgpu_create_buffer(wgpu::Device &    device,
     buffer = device.CreateBuffer(&buffer_desc);
 }
 
-static size_t ggml_webgpu_tensor_offset(const ggml_tensor * tensor) {
-    return webgpu_tensor_offset(tensor) + tensor->view_offs;
-}
-
 static wgpu::Buffer ggml_webgpu_tensor_buf(const ggml_tensor * tensor) {
     ggml_backend_webgpu_buffer_context * ctx = (ggml_backend_webgpu_buffer_context *) tensor->buffer->context;
     return ctx->buffer;
@@ -390,23 +383,6 @@ static size_t ggml_webgpu_tensor_misalignment(webgpu_context & ctx, const ggml_t
     return offset & (ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1);
 }
 
-static bool ggml_webgpu_flash_attn_use_vec(webgpu_global_context & global_ctx,
-                                           const ggml_tensor *     Q,
-                                           const ggml_tensor *     K,
-                                           const ggml_tensor *     V) {
-    const size_t   alignment = global_ctx->capabilities.limits.minStorageBufferOffsetAlignment;
-    const uint32_t k_offset_elems =
-        (uint32_t) ((ggml_webgpu_tensor_offset(K) & (alignment - 1)) / ggml_type_size(K->type));
-    const uint32_t v_offset_elems =
-        (uint32_t) ((ggml_webgpu_tensor_offset(V) & (alignment - 1)) / ggml_type_size(V->type));
-    const bool f16_vec4_aligned = (k_offset_elems % 4u == 0u) && (v_offset_elems % 4u == 0u);
-    const bool kv_vec_type_supported =
-        K->type == GGML_TYPE_F16 || K->type == GGML_TYPE_Q4_0 || K->type == GGML_TYPE_Q8_0;
-
-    return (Q->ne[1] < 20) && (Q->ne[0] % 32 == 0) && (V->ne[0] % 4 == 0) && kv_vec_type_supported &&
-           (K->type != GGML_TYPE_F16 || f16_vec4_aligned) && (V->type == K->type);
-}
-
 static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, const ggml_tensor * t) {
     size_t offset = ggml_webgpu_tensor_offset(t);
     return offset & ~(ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1);
@@ -416,34 +392,31 @@ static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor
     return ROUNDUP_POW2(ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t), WEBGPU_STORAGE_BUF_BINDING_MULT);
 }
 
-// Used to determine if two tensors are the same for in-place operations
-static bool ggml_webgpu_tensor_equal(ggml_tensor * a, ggml_tensor * b) {
-    return (ggml_webgpu_tensor_buf(a).Get() == ggml_webgpu_tensor_buf(b).Get()) &&
-           (ggml_webgpu_tensor_offset(a) == ggml_webgpu_tensor_offset(b));
-}
+struct ggml_webgpu_merged_binding_range {
+    size_t offset;
+    size_t size;
+};
 
-// Used to determine if two tensors share the same buffer and their byte ranges overlap,
-static bool ggml_webgpu_tensor_overlap(ggml_tensor * a, ggml_tensor * b) {
-    return (ggml_webgpu_tensor_buf(a).Get() == ggml_webgpu_tensor_buf(b).Get()) &&
-           ggml_webgpu_tensor_offset(a) < (ggml_webgpu_tensor_offset(b) + ggml_nbytes(b)) &&
-           ggml_webgpu_tensor_offset(b) < (ggml_webgpu_tensor_offset(a) + ggml_nbytes(a));
-}
+static ggml_webgpu_merged_binding_range ggml_webgpu_tensor_merged_binding_range(
+    webgpu_context &                     ctx,
+    std::initializer_list<ggml_tensor *> tensors) {
+    size_t merged_offset = SIZE_MAX;
+    size_t merged_end    = 0;
 
-struct binary_overlap_flags {
-    bool inplace;  // src0 == dst
-    bool overlap;  // src1 == dst
-    bool src_overlap;
-};
+    for (ggml_tensor * tensor : tensors) {
+        const size_t bind_offset = ggml_webgpu_tensor_align_offset(ctx, tensor);
+        const size_t bind_end    = bind_offset + ggml_webgpu_tensor_binding_size(ctx, tensor);
 
-static binary_overlap_flags ggml_webgpu_detect_binary_overlap(ggml_tensor * src0,
-                                                              ggml_tensor * src1,
-                                                              ggml_tensor * dst) {
-    binary_overlap_flags flags = {};
-    flags.inplace              = ggml_webgpu_tensor_equal(src0, dst);
-    flags.overlap              = ggml_webgpu_tensor_overlap(src1, dst);
-    flags.src_overlap          = ggml_webgpu_tensor_overlap(src0, src1);
+        merged_offset = std::min(merged_offset, bind_offset);
+        merged_end    = std::max(merged_end, bind_end);
+    }
+
+    return { merged_offset, merged_end - merged_offset };
+}
 
-    return flags;
+static uint32_t ggml_webgpu_tensor_merged_element_offset(const ggml_tensor *                      tensor,
+                                                         const ggml_webgpu_merged_binding_range & merged_range) {
+    return (uint32_t) ((ggml_webgpu_tensor_offset(tensor) - merged_range.offset) / ggml_type_size(tensor->type));
 }
 
 static wgpu::BindGroupEntry ggml_webgpu_make_bind_group_entry(uint32_t     binding,
@@ -713,12 +686,12 @@ static void ggml_backend_webgpu_free(ggml_backend_t backend) {
 #ifdef GGML_WEBGPU_GPU_PROFILE
     std::cout << "\n[ggml_webgpu gpu profiling summary]\n";
     double total_gpu = 0.0;
-    for (const auto & kv : ctx->webgpu_ctx->global_ctx->shader_gpu_time_ms) {
+    for (const auto & kv : ctx->webgpu_ctx->shader_gpu_time_ms) {
         total_gpu += kv.second;
     }
     std::cout << "ggml_webgpu: total gpu time (all shaders): " << total_gpu << " ms\n";
     std::cout << "\nggml_webgpu: gpu breakdown:\n";
-    for (const auto & kv : ctx->webgpu_ctx->global_ctx->shader_gpu_time_ms) {
+    for (const auto & kv : ctx->webgpu_ctx->shader_gpu_time_ms) {
         double pct = (total_gpu > 0.0) ? (kv.second / total_gpu * 100.0) : 0.0;
         std::cout << "ggml_webgpu:  " << kv.first << ": " << kv.second << " ms (" << std::fixed << std::setprecision(2)
                   << pct << "%)\n";
@@ -771,18 +744,16 @@ static webgpu_encoded_op ggml_webgpu_set(webgpu_context & ctx,
                                          ggml_tensor *    src0,
                                          ggml_tensor *    src1,
                                          ggml_tensor *    dst) {
-    const bool inplace = ggml_webgpu_tensor_equal(src0, dst);
-
     ggml_webgpu_shader_lib_context shader_lib_ctx = {};
     shader_lib_ctx.src0                           = src0;
     shader_lib_ctx.src1                           = src1;
     shader_lib_ctx.dst                            = dst;
     shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
-    shader_lib_ctx.inplace     = inplace;
 
     webgpu_pipeline pipeline = ctx->shader_lib->get_set_pipeline(shader_lib_ctx);
 
-    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+    auto *     decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+    const bool inplace   = decisions->inplace;
 
     const uint32_t ne            = inplace ? (uint32_t) ggml_nelements(src1) : (uint32_t) ggml_nelements(dst);
     const uint32_t dst_type_size = (uint32_t) ggml_type_size(dst->type);
@@ -923,6 +894,170 @@ static webgpu_encoded_op ggml_webgpu_solve_tri(webgpu_context & ctx,
     return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
 }
 
+static webgpu_encoded_op ggml_webgpu_conv_2d(webgpu_context & ctx,
+                                             ggml_tensor *    src0,
+                                             ggml_tensor *    src1,
+                                             ggml_tensor *    dst) {
+    const int32_t s0 = ggml_get_op_params_i32(dst, 0);
+    const int32_t s1 = ggml_get_op_params_i32(dst, 1);
+    const int32_t p0 = ggml_get_op_params_i32(dst, 2);
+    const int32_t p1 = ggml_get_op_params_i32(dst, 3);
+    const int32_t d0 = ggml_get_op_params_i32(dst, 4);
+    const int32_t d1 = ggml_get_op_params_i32(dst, 5);
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+
+        (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),
+
+        (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),
+
+        (uint32_t) (dst->nb[0] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+
+        (uint32_t) src0->ne[0],
+        (uint32_t) src0->ne[1],
+        (uint32_t) src0->ne[2],
+
+        (uint32_t) src1->ne[0],
+        (uint32_t) src1->ne[1],
+
+        (uint32_t) dst->ne[0],
+        (uint32_t) dst->ne[1],
+        (uint32_t) dst->ne[2],
+        (uint32_t) dst->ne[3],
+
+        (uint32_t) s0,
+        (uint32_t) s1,
+        (uint32_t) p0,
+        (uint32_t) p1,
+        (uint32_t) d0,
+        (uint32_t) d1,
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0),
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1),
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst),
+    };
+
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = src0;
+    shader_lib_ctx.src1                           = src1;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+
+    webgpu_pipeline pipeline = ctx->shader_lib->get_conv2d_pipeline(shader_lib_ctx);
+
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+
+    uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size);
+    uint32_t wg_x     = std::min(ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, total_wg);
+    uint32_t wg_y     = CEIL_DIV(total_wg, wg_x);
+
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
+}
+
+static webgpu_encoded_op ggml_webgpu_im2col(webgpu_context & ctx,
+                                            ggml_tensor *    src0,
+                                            ggml_tensor *    src1,
+                                            ggml_tensor *    dst) {
+    const int32_t s0    = ggml_get_op_params_i32(dst, 0);
+    const int32_t s1    = ggml_get_op_params_i32(dst, 1);
+    const int32_t p0    = ggml_get_op_params_i32(dst, 2);
+    const int32_t p1    = ggml_get_op_params_i32(dst, 3);
+    const int32_t d0    = ggml_get_op_params_i32(dst, 4);
+    const int32_t d1    = ggml_get_op_params_i32(dst, 5);
+    const bool    is_2D = ggml_get_op_params_i32(dst, 6) == 1;
+
+    const uint32_t KW = src0->ne[0];
+    const uint32_t KH = is_2D ? src0->ne[1] : 1;
+    const uint32_t IC = is_2D ? src0->ne[2] : src0->ne[1];
+
+    const uint32_t IW = src1->ne[0];
+    const uint32_t IH = is_2D ? src1->ne[1] : 1;
+    const uint32_t N  = is_2D ? src1->ne[3] : src1->ne[2];
+
+    const uint32_t OW = dst->ne[1];
+    const uint32_t OH = is_2D ? dst->ne[2] : 1;
+
+    const uint32_t si0 = (uint32_t) (src1->nb[0] / ggml_type_size(src1->type));
+    const uint32_t si1 = is_2D ? (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)) : 0;
+    const uint32_t si2 = is_2D ? (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)) :
+                                 (uint32_t) (src1->nb[1] / ggml_type_size(src1->type));
+    const uint32_t si3 = is_2D ? (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)) :
+                                 (uint32_t) (src1->nb[2] / ggml_type_size(src1->type));
+
+    const uint32_t so0 = (uint32_t) (dst->nb[0] / ggml_type_size(dst->type));
+    const uint32_t so1 = (uint32_t) (dst->nb[1] / ggml_type_size(dst->type));
+    const uint32_t so2 = is_2D ? (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)) : 0;
+    const uint32_t so3 = is_2D ? (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)) :
+                                 (uint32_t) (dst->nb[2] / ggml_type_size(dst->type));
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+
+        si0,
+        si1,
+        si2,
+        si3,
+        so0,
+        so1,
+        so2,
+        so3,
+
+        KW,
+        KH,
+        IC,
+
+        IW,
+        IH,
+        N,
+
+        OW,
+        OH,
+
+        (uint32_t) s0,
+        (uint32_t) s1,
+        (uint32_t) p0,
+        (uint32_t) p1,
+        (uint32_t) d0,
+        (uint32_t) d1,
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src1),
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst),
+    };
+
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = src0;
+    shader_lib_ctx.src1                           = src1;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+
+    webgpu_pipeline pipeline = ctx->shader_lib->get_im2col_pipeline(shader_lib_ctx);
+
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+
+    uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size);
+    uint32_t wg_x     = std::min(ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, total_wg);
+    uint32_t wg_y     = CEIL_DIV(total_wg, wg_x);
+
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
+}
+
 static webgpu_encoded_op ggml_webgpu_ssm_conv(webgpu_context & ctx,
                                               ggml_tensor *    src0,
                                               ggml_tensor *    src1,
@@ -969,6 +1104,113 @@ static webgpu_encoded_op ggml_webgpu_ssm_conv(webgpu_context & ctx,
     return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
 }
 
+static webgpu_encoded_op ggml_webgpu_ssm_scan(webgpu_context & ctx,
+                                              ggml_tensor *    src0,
+                                              ggml_tensor *    src1,
+                                              ggml_tensor *    src2,
+                                              ggml_tensor *    src3,
+                                              ggml_tensor *    src4,
+                                              ggml_tensor *    src5,
+                                              ggml_tensor *    src6,
+                                              ggml_tensor *    dst) {
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = src0;
+    shader_lib_ctx.src1                           = src1;
+    shader_lib_ctx.src4                           = src4;
+    shader_lib_ctx.src5                           = src5;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.max_wg_size        = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+    shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups;
+
+    webgpu_pipeline pipeline    = ctx->shader_lib->get_ssm_scan_pipeline(shader_lib_ctx);
+    auto *          decisions   = static_cast<ggml_webgpu_ssm_scan_shader_decisions *>(pipeline.context.get());
+    const bool      xbc_overlap = decisions->xbc_overlap;
+
+    uint32_t offset_x        = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type));
+    uint32_t offset_B        = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src4) / ggml_type_size(src4->type));
+    uint32_t offset_C        = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src5) / ggml_type_size(src5->type));
+    size_t   xbc_bind_offset = 0;
+    size_t   xbc_bind_size   = 0;
+    if (xbc_overlap) {
+        const ggml_webgpu_merged_binding_range merged_range =
+            ggml_webgpu_tensor_merged_binding_range(ctx, { src1, src4, src5 });
+        xbc_bind_offset = merged_range.offset;
+        xbc_bind_size   = merged_range.size;
+        offset_x        = ggml_webgpu_tensor_merged_element_offset(src1, merged_range);
+        offset_B        = ggml_webgpu_tensor_merged_element_offset(src4, merged_range);
+        offset_C        = ggml_webgpu_tensor_merged_element_offset(src5, merged_range);
+    }
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        offset_x,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src3) / ggml_type_size(src3->type)),
+        offset_B,
+        offset_C,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src6) / ggml_type_size(src6->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),
+
+        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),
+
+        (uint32_t) (src2->nb[1] / ggml_type_size(src2->type)),
+        (uint32_t) (src2->nb[2] / ggml_type_size(src2->type)),
+
+        (uint32_t) src3->ne[0],
+        (uint32_t) (src3->nb[1] / ggml_type_size(src3->type)),
+
+        (uint32_t) (src4->nb[1] / ggml_type_size(src4->type)),
+        (uint32_t) (src4->nb[2] / ggml_type_size(src4->type)),
+        (uint32_t) (src4->nb[3] / ggml_type_size(src4->type)),
+
+        (uint32_t) (src5->nb[1] / ggml_type_size(src5->type)),
+        (uint32_t) (src5->nb[2] / ggml_type_size(src5->type)),
+        (uint32_t) (src5->nb[3] / ggml_type_size(src5->type)),
+
+        (uint32_t) src0->ne[0],
+        (uint32_t) src0->ne[1],
+        (uint32_t) src0->ne[2],
+        (uint32_t) src4->ne[1],
+        (uint32_t) src1->ne[2],
+        (uint32_t) src1->ne[3],
+        (uint32_t) ggml_nelements(src1),
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0),
+    };
+    if (xbc_overlap) {
+        entries.push_back(
+            ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(src1), xbc_bind_offset, xbc_bind_size));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, src3));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 4, src6));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 5, dst));
+    } else {
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, src3));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 4, src4));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 5, src5));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 6, src6));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 7, dst));
+    }
+
+    const uint32_t total_wg       = (uint32_t) (src0->ne[1] * src0->ne[2] * src1->ne[3]);
+    const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
+    uint32_t       wg_x;
+    uint32_t       wg_y;
+    compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y);
+
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
+}
+
 static webgpu_encoded_op ggml_webgpu_gated_delta_net(webgpu_context & ctx,
                                                      ggml_tensor *    src0,
                                                      ggml_tensor *    src1,
@@ -1163,12 +1405,22 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx,
                 case GGML_TYPE_Q5_0:
                 case GGML_TYPE_Q5_1:
                 case GGML_TYPE_Q8_0:
-                case GGML_TYPE_Q8_1:
                 case GGML_TYPE_Q6_K:
                 case GGML_TYPE_Q4_K:
                 case GGML_TYPE_Q5_K:
                 case GGML_TYPE_Q3_K:
                 case GGML_TYPE_Q2_K:
+                case GGML_TYPE_Q1_0:
+                case GGML_TYPE_IQ1_S:
+                case GGML_TYPE_IQ1_M:
+                case GGML_TYPE_IQ2_XXS:
+                case GGML_TYPE_IQ2_XS:
+                case GGML_TYPE_IQ2_S:
+                case GGML_TYPE_IQ3_XXS:
+                case GGML_TYPE_IQ3_S:
+                case GGML_TYPE_IQ4_NL:
+                case GGML_TYPE_IQ4_XS:
+                case GGML_TYPE_MXFP4:
                     use_fast = true;
                     break;
                 default:
@@ -1190,6 +1442,7 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx,
     shader_lib_ctx.sg_mat_m                 = ctx->global_ctx->capabilities.sg_mat_m;
     shader_lib_ctx.sg_mat_n                 = ctx->global_ctx->capabilities.sg_mat_n;
     shader_lib_ctx.sg_mat_k                 = ctx->global_ctx->capabilities.sg_mat_k;
+    shader_lib_ctx.min_subgroup_size        = ctx->global_ctx->capabilities.min_subgroup_size;
     shader_lib_ctx.max_subgroup_size        = ctx->global_ctx->capabilities.max_subgroup_size;
 
     // Get or create pipeline
@@ -1274,11 +1527,74 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx,
     return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
 }
 
+static webgpu_encoded_op ggml_webgpu_mul_mat_id_vec(webgpu_context & ctx,
+                                                    ggml_tensor *    src0,
+                                                    ggml_tensor *    src1,
+                                                    ggml_tensor *    src2,
+                                                    ggml_tensor *    dst) {
+    const uint32_t param_n_expert      = (uint32_t) src0->ne[2];
+    const uint32_t param_n_expert_used = (uint32_t) dst->ne[1];
+
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = src0;
+    shader_lib_ctx.src1                           = src1;
+    shader_lib_ctx.src2                           = src2;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.supports_subgroups             = ctx->global_ctx->capabilities.supports_subgroups;
+    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+
+    webgpu_pipeline pipeline = ctx->shader_lib->get_mul_mat_id_vec_pipeline(shader_lib_ctx);
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) src0->ne[0],
+        (uint32_t) src0->ne[1],
+        param_n_expert,
+        param_n_expert_used,
+        (uint32_t) src1->ne[1],
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
+        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
+        (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), ggml_webgpu_tensor_align_offset(ctx, src0),
+                                          ggml_webgpu_tensor_binding_size(ctx, src0)),
+        ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(src1), ggml_webgpu_tensor_align_offset(ctx, src1),
+                                          ggml_webgpu_tensor_binding_size(ctx, src1)),
+        ggml_webgpu_make_bind_group_entry(2, ggml_webgpu_tensor_buf(src2), ggml_webgpu_tensor_align_offset(ctx, src2),
+                                          ggml_webgpu_tensor_binding_size(ctx, src2)),
+        ggml_webgpu_make_bind_group_entry(3, ggml_webgpu_tensor_buf(dst), ggml_webgpu_tensor_align_offset(ctx, dst),
+                                          ggml_webgpu_tensor_binding_size(ctx, dst)),
+    };
+
+    uint32_t wg_x = 1;
+    uint32_t wg_y = 1;
+
+    auto * decisions = static_cast<ggml_webgpu_mul_mat_vec_shader_decisions *>(pipeline.context.get());
+
+    const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
+    uint32_t       output_groups  = CEIL_DIV(dst->ne[0], decisions->outputs_per_wg);
+    uint32_t       total_wg       = output_groups * param_n_expert_used;
+    compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y);
+
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
+}
+
 static webgpu_encoded_op ggml_webgpu_mul_mat_id(webgpu_context & ctx,
                                                 ggml_tensor *    src0,
                                                 ggml_tensor *    src1,
                                                 ggml_tensor *    src2,
                                                 ggml_tensor *    dst) {
+    // we can use mat-vec fast path
+    if (dst->ne[2] == 1) {
+        return ggml_webgpu_mul_mat_id_vec(ctx, src0, src1, src2, dst);
+    }
+
     ggml_webgpu_shader_lib_context shader_lib_ctx = {};
     shader_lib_ctx.src0                           = src0;
     shader_lib_ctx.src1                           = src1;
@@ -1404,7 +1720,6 @@ static webgpu_encoded_op ggml_webgpu_mul_mat_id(webgpu_context & ctx,
     return ggml_backend_webgpu_build_multi(ctx, dispatches);
 }
 
-#ifndef __EMSCRIPTEN__
 static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
                                                 ggml_tensor *    Q,
                                                 ggml_tensor *    K,
@@ -1422,13 +1737,45 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
     float m0          = powf(2.0f, -(max_bias) / n_head_log2);
     float m1          = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
-    const int has_mask  = (mask != nullptr);
-    const int has_sinks = (sinks != nullptr);
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = Q;
+    shader_lib_ctx.src1                           = K;
+    shader_lib_ctx.src2                           = V;
+    shader_lib_ctx.src3                           = mask;
+    shader_lib_ctx.src4                           = sinks;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.supports_subgroups             = ctx->global_ctx->capabilities.supports_subgroups;
+    shader_lib_ctx.supports_subgroup_matrix       = ctx->global_ctx->capabilities.supports_subgroup_matrix;
+    shader_lib_ctx.max_wg_size        = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+    shader_lib_ctx.wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize;
+    shader_lib_ctx.sg_mat_m           = ctx->global_ctx->capabilities.sg_mat_m;
+    shader_lib_ctx.sg_mat_n           = ctx->global_ctx->capabilities.sg_mat_n;
+    shader_lib_ctx.sg_mat_k           = ctx->global_ctx->capabilities.sg_mat_k;
+    shader_lib_ctx.min_subgroup_size  = ctx->global_ctx->capabilities.min_subgroup_size;
+    shader_lib_ctx.max_subgroup_size  = ctx->global_ctx->capabilities.max_subgroup_size;
+    webgpu_pipeline pipeline          = ctx->shader_lib->get_flash_attn_pipeline(
+        shader_lib_ctx, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment);
+    auto *     decisions  = static_cast<ggml_webgpu_flash_attn_decisions *>(pipeline.context.get());
+    const int  has_mask   = (mask != nullptr);
+    const int  has_sinks  = (sinks != nullptr);
+    const bool kv_overlap = decisions->kv_overlap;
+
+    uint32_t offset_k       = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, K) / ggml_type_size(K->type));
+    uint32_t offset_v       = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, V) / ggml_type_size(V->type));
+    size_t   kv_bind_offset = 0;
+    size_t   kv_bind_size   = 0;
+    if (kv_overlap) {
+        const ggml_webgpu_merged_binding_range merged_range = ggml_webgpu_tensor_merged_binding_range(ctx, { K, V });
+        kv_bind_offset                                      = merged_range.offset;
+        kv_bind_size                                        = merged_range.size;
+        offset_k                                            = ggml_webgpu_tensor_merged_element_offset(K, merged_range);
+        offset_v                                            = ggml_webgpu_tensor_merged_element_offset(V, merged_range);
+    }
 
     std::vector<uint32_t> params = {
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, Q) / ggml_type_size(Q->type)),
-        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, K) / ggml_type_size(K->type)),
-        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, V) / ggml_type_size(V->type)),
+        offset_k,
+        offset_v,
         has_mask ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, mask) / ggml_type_size(mask->type)) : 0,
         has_sinks ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, sinks) / ggml_type_size(sinks->type)) : 0,
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
@@ -1456,10 +1803,15 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
     };
     std::vector<wgpu::BindGroupEntry> entries = {
         ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, Q),
-        ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, K),
-        ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, V),
     };
-    uint32_t binding_index = 3;
+    if (kv_overlap) {
+        entries.push_back(
+            ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(K), kv_bind_offset, kv_bind_size));
+    } else {
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, K));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, V));
+    }
+    uint32_t binding_index = kv_overlap ? 2u : 3u;
     if (has_mask) {
         entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_index++, mask));
     }
@@ -1468,39 +1820,19 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
     }
     entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_index++, dst));
 
-    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
-    shader_lib_ctx.src0                           = Q;
-    shader_lib_ctx.src1                           = K;
-    shader_lib_ctx.src2                           = V;
-    shader_lib_ctx.src3                           = mask;
-    shader_lib_ctx.src4                           = sinks;
-    shader_lib_ctx.dst                            = dst;
-    shader_lib_ctx.max_wg_size        = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
-    shader_lib_ctx.wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize;
-    shader_lib_ctx.sg_mat_m           = ctx->global_ctx->capabilities.sg_mat_m;
-    shader_lib_ctx.sg_mat_n           = ctx->global_ctx->capabilities.sg_mat_n;
-    shader_lib_ctx.sg_mat_k           = ctx->global_ctx->capabilities.sg_mat_k;
-    shader_lib_ctx.max_subgroup_size  = ctx->global_ctx->capabilities.max_subgroup_size;
-    const bool      use_vec           = ggml_webgpu_flash_attn_use_vec(ctx->global_ctx, Q, K, V);
-    webgpu_pipeline pipeline          = use_vec ? ctx->shader_lib->get_flash_attn_vec_pipeline(shader_lib_ctx) :
-                                                  ctx->shader_lib->get_flash_attn_pipeline(shader_lib_ctx);
-
-    if (!use_vec) {
-        auto *   decisions   = static_cast<ggml_webgpu_flash_attn_decisions *>(pipeline.context.get());
+    if (decisions->path != GGML_WEBGPU_FLASH_ATTN_PATH_VEC) {
         uint32_t wg_per_head = CEIL_DIV(Q->ne[1], decisions->q_tile);
         uint32_t wg_x        = wg_per_head * Q->ne[2] * Q->ne[3];  // wg per head * number of heads * number of batches
         return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x);
     }
 
-    auto * decisions = static_cast<ggml_webgpu_flash_attn_vec_decisions *>(pipeline.context.get());
-
     wgpu::Buffer blk_buf         = {};
     uint64_t     blk_size_bytes  = 0;
     uint32_t     blk_nblk0       = 0;
     uint32_t     blk_nblk1       = 0;
     uint32_t     blk_batch_count = 0;
 
-    const uint32_t vec_nwg_cap = std::max(1u, std::min<uint32_t>(32u, ctx->global_ctx->capabilities.max_subgroup_size));
+    const uint32_t vec_nwg_cap = ctx->global_ctx->capabilities.min_subgroup_size;
     uint32_t       nwg         = 1u;
     const uint64_t kv_span     = (uint64_t) std::max(1u, decisions->kv_tile);
     while ((2u * nwg * kv_span) < (uint64_t) K->ne[1] && nwg < vec_nwg_cap) {
@@ -1532,10 +1864,12 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
         tmp_bind_size   = tmp_size_bytes;
         scratch_offset  = ROUNDUP_POW2(scratch_offset + tmp_size_bytes, align_bytes);
     } else {
-        // nwg==1 writes final dst directly in vec-split; keep tmp binding valid without extra allocation.
+        // nwg==1 writes final dst directly in vec-split; bind tmp to a tiny non-overlapping scratch region.
+        tmp_size_bytes  = WEBGPU_STORAGE_BUF_BINDING_MULT;
         tmp_buf         = ggml_webgpu_tensor_buf(dst);
-        tmp_bind_offset = ggml_webgpu_tensor_align_offset(ctx, dst);
-        tmp_bind_size   = ggml_webgpu_tensor_binding_size(ctx, dst);
+        tmp_bind_offset = scratch_offset;
+        tmp_bind_size   = tmp_size_bytes;
+        scratch_offset  = ROUNDUP_POW2(scratch_offset + tmp_size_bytes, align_bytes);
     }
 
     webgpu_pipeline                   blk_pipeline;
@@ -1550,7 +1884,7 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
         const uint64_t blk_elems    = (uint64_t) blk_nblk0 * blk_nblk1 * blk_batch_count;
         blk_size_bytes              = ROUNDUP_POW2(blk_elems * sizeof(uint32_t), WEBGPU_STORAGE_BUF_BINDING_MULT);
         const ggml_webgpu_shader_lib_context blk_shader_ctx = shader_lib_ctx;
-        blk_pipeline = ctx->shader_lib->get_flash_attn_blk_pipeline(blk_shader_ctx);
+        blk_pipeline = ctx->shader_lib->get_flash_attn_blk_pipeline(blk_shader_ctx, decisions->kv_tile);
 
         blk_params = {
             (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, mask) / ggml_type_size(mask->type)),  // offset_mask
@@ -1582,12 +1916,19 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
     std::vector<wgpu::BindGroupEntry> split_entries = {
         ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(Q), ggml_webgpu_tensor_align_offset(ctx, Q),
                                           ggml_webgpu_tensor_binding_size(ctx, Q)),
-        ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(K), ggml_webgpu_tensor_align_offset(ctx, K),
-                                          ggml_webgpu_tensor_binding_size(ctx, K)),
-        ggml_webgpu_make_bind_group_entry(2, ggml_webgpu_tensor_buf(V), ggml_webgpu_tensor_align_offset(ctx, V),
-                                          ggml_webgpu_tensor_binding_size(ctx, V)),
     };
-    uint32_t split_binding_index = 3;
+    if (kv_overlap) {
+        split_entries.push_back(
+            ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(K), kv_bind_offset, kv_bind_size));
+    } else {
+        split_entries.push_back(ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(K),
+                                                                  ggml_webgpu_tensor_align_offset(ctx, K),
+                                                                  ggml_webgpu_tensor_binding_size(ctx, K)));
+        split_entries.push_back(ggml_webgpu_make_bind_group_entry(2, ggml_webgpu_tensor_buf(V),
+                                                                  ggml_webgpu_tensor_align_offset(ctx, V),
+                                                                  ggml_webgpu_tensor_binding_size(ctx, V)));
+    }
+    uint32_t split_binding_index = kv_overlap ? 2u : 3u;
     if (has_mask) {
         split_entries.push_back(ggml_webgpu_make_bind_group_entry(split_binding_index++, ggml_webgpu_tensor_buf(mask),
                                                                   ggml_webgpu_tensor_align_offset(ctx, mask),
@@ -1612,8 +1953,11 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
     std::vector<uint32_t>             reduce_params;
     std::vector<wgpu::BindGroupEntry> reduce_entries;
     if (use_vec_reduce) {
-        const uint32_t reduce_wg_size = std::max(
-            32u, std::min<uint32_t>(nwg * 32u, ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup));
+        const uint32_t reduce_sg_size = ctx->global_ctx->capabilities.max_subgroup_size;
+        const uint32_t reduce_wg_size =
+            std::max(reduce_sg_size, (uint32_t) std::min<uint64_t>(
+                                         (uint64_t) nwg * reduce_sg_size,
+                                         ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup));
         ggml_webgpu_shader_lib_context reduce_shader_ctx = shader_lib_ctx;
         reduce_shader_ctx.max_wg_size                    = reduce_wg_size;
         reduce_pipeline = ctx->shader_lib->get_flash_attn_vec_reduce_pipeline(reduce_shader_ctx);
@@ -1657,22 +2001,20 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx,
 
     return ggml_backend_webgpu_build_multi(ctx, dispatches);
 }
-#endif  // __EMSCRIPTEN__
 
 static webgpu_encoded_op ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
     bool is_unary = dst->op == GGML_OP_UNARY;
-    bool inplace  = ggml_webgpu_tensor_equal(src, dst) || (dst->op == GGML_OP_FILL);
 
     ggml_webgpu_shader_lib_context shader_lib_ctx = {};
     shader_lib_ctx.src0                           = src;
     shader_lib_ctx.src1                           = nullptr;
     shader_lib_ctx.dst                            = dst;
     shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
-    shader_lib_ctx.inplace     = inplace;
 
     webgpu_pipeline pipeline = ctx->shader_lib->get_unary_pipeline(shader_lib_ctx);
 
-    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+    auto *     decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+    const bool inplace   = decisions->inplace;
 
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -1734,41 +2076,38 @@ static webgpu_encoded_op ggml_webgpu_binary_op(webgpu_context & ctx,
                                                ggml_tensor *    src0,
                                                ggml_tensor *    src1,
                                                ggml_tensor *    dst) {
-    binary_overlap_flags flags = ggml_webgpu_detect_binary_overlap(src0, src1, dst);
-
     ggml_webgpu_shader_lib_context shader_lib_ctx = {};
     shader_lib_ctx.src0                           = src0;
     shader_lib_ctx.src1                           = src1;
     shader_lib_ctx.dst                            = dst;
     shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
-    shader_lib_ctx.inplace     = flags.inplace;
-    shader_lib_ctx.overlap     = flags.overlap;
-    shader_lib_ctx.src_overlap = flags.src_overlap;
 
-    webgpu_pipeline pipeline = ctx->shader_lib->get_binary_pipeline(shader_lib_ctx);
-
-    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+    webgpu_pipeline pipeline  = ctx->shader_lib->get_binary_pipeline(shader_lib_ctx);
+    auto *          decisions = static_cast<ggml_webgpu_binary_shader_decisions *>(pipeline.context.get());
 
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
     size_t src0_webgpu_tensor_align_offset = ggml_webgpu_tensor_align_offset(ctx, src0);
     size_t src1_webgpu_tensor_align_offset = ggml_webgpu_tensor_align_offset(ctx, src1);
 
-    uint32_t offset_merged_src0 = 0;
-    uint32_t offset_merged_src1 = 0;
-    if (flags.src_overlap) {
-        size_t min_off     = std::min(src0_webgpu_tensor_align_offset, src1_webgpu_tensor_align_offset);
-        offset_merged_src0 = (uint32_t) ((src0_webgpu_tensor_align_offset - min_off) / ggml_type_size(src0->type));
-        offset_merged_src1 = (uint32_t) ((src1_webgpu_tensor_align_offset - min_off) / ggml_type_size(src0->type));
+    uint32_t offset_src0   = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type));
+    uint32_t offset_src1   = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type));
+    size_t   merged_offset = 0;
+    size_t   merged_size   = 0;
+    if (decisions->src_overlap) {
+        const ggml_webgpu_merged_binding_range merged_range =
+            ggml_webgpu_tensor_merged_binding_range(ctx, { src0, src1 });
+        merged_offset = merged_range.offset;
+        merged_size   = merged_range.size;
+        offset_src0   = ggml_webgpu_tensor_merged_element_offset(src0, merged_range);
+        offset_src1   = ggml_webgpu_tensor_merged_element_offset(src1, merged_range);
     }
 
     std::vector<uint32_t> params = {
         ne,
-        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
-        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        offset_src0,
+        offset_src1,
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
-        offset_merged_src0,
-        offset_merged_src1,
         (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)),
         (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
         (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
@@ -1788,12 +2127,9 @@ static webgpu_encoded_op ggml_webgpu_binary_op(webgpu_context & ctx,
 
     std::vector<wgpu::BindGroupEntry> entries;
 
-    if (flags.src_overlap) {
-        size_t merged_offset = std::min(src0_webgpu_tensor_align_offset, src1_webgpu_tensor_align_offset);
-        size_t merged_end    = std::max(src0_webgpu_tensor_align_offset + ggml_webgpu_tensor_binding_size(ctx, src0),
-                                        src1_webgpu_tensor_align_offset + ggml_webgpu_tensor_binding_size(ctx, src1));
-        entries.push_back(ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), merged_offset,
-                                                            merged_end - merged_offset));
+    if (decisions->src_overlap) {
+        entries.push_back(
+            ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), merged_offset, merged_size));
         entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst));
     } else {
         entries.push_back(ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0),
@@ -1802,7 +2138,7 @@ static webgpu_encoded_op ggml_webgpu_binary_op(webgpu_context & ctx,
         entries.push_back(ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(src1),
                                                             src1_webgpu_tensor_align_offset,
                                                             ggml_webgpu_tensor_binding_size(ctx, src1)));
-        if (!flags.inplace && !flags.overlap) {
+        if (!decisions->inplace && !decisions->overlap) {
             entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst));
         }
     }
@@ -1811,6 +2147,56 @@ static webgpu_encoded_op ggml_webgpu_binary_op(webgpu_context & ctx,
     return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x);
 }
 
+static webgpu_encoded_op ggml_webgpu_add_id(webgpu_context & ctx,
+                                            ggml_tensor *    src0,
+                                            ggml_tensor *    src1,
+                                            ggml_tensor *    src2,
+                                            ggml_tensor *    dst) {
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = src0;
+    shader_lib_ctx.src1                           = src1;
+    shader_lib_ctx.src2                           = src2;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+
+    webgpu_pipeline pipeline = ctx->shader_lib->get_add_id_pipeline(shader_lib_ctx);
+
+    auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
+        (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
+        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),
+        (uint32_t) (src2->nb[0] / ggml_type_size(src2->type)),
+        (uint32_t) (src2->nb[1] / ggml_type_size(src2->type)),
+        (uint32_t) dst->ne[0],
+        (uint32_t) dst->ne[1],
+        (uint32_t) dst->ne[2],
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries;
+
+    entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0));
+    entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1));
+    entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2));
+
+    if (!decisions->inplace) {
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, dst));
+    }
+
+    uint32_t       wg_x           = 1;
+    uint32_t       wg_y           = 1;
+    uint32_t       total_wg       = ggml_nrows(dst);
+    const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
+    compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y);
+
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
+}
+
 static webgpu_encoded_op ggml_webgpu_concat(webgpu_context & ctx,
                                             ggml_tensor *    src0,
                                             ggml_tensor *    src1,
@@ -1892,9 +2278,91 @@ static webgpu_encoded_op ggml_webgpu_repeat(webgpu_context & ctx, ggml_tensor *
     return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x);
 }
 
-static webgpu_encoded_op ggml_webgpu_row_norm(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
-    bool inplace = ggml_webgpu_tensor_equal(src, dst);
+static std::optional<webgpu_encoded_op> ggml_webgpu_rms_norm_mul(webgpu_context & ctx,
+                                                                 ggml_tensor *    rn_src,
+                                                                 ggml_tensor *    rn_dst,
+                                                                 ggml_tensor *    mul_src0,
+                                                                 ggml_tensor *    mul_src1,
+                                                                 ggml_tensor *    dst) {
+    ggml_tensor * mul_src;
+
+    if (ggml_webgpu_tensor_equal(rn_dst, mul_src0)) {
+        mul_src = mul_src1;
+    } else if (ggml_webgpu_tensor_equal(rn_dst, mul_src1)) {
+        mul_src = mul_src0;
+    } else {
+        GGML_ABORT("rms_norm must be equal to the one of mul_src0 and mul_src1");
+    }
+
+    uint32_t offset_rn_src = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, rn_src) / ggml_type_size(rn_src->type));
+    uint32_t offset_mul_src =
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, mul_src) / ggml_type_size(mul_src->type));
+    size_t merged_offset = 0;
+    size_t merged_size   = 0;
 
+    std::vector<uint32_t> params = {
+        offset_rn_src,
+        offset_mul_src,
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+        (uint32_t) (rn_src->nb[1] / ggml_type_size(rn_src->type)),
+        (uint32_t) (rn_src->nb[2] / ggml_type_size(rn_src->type)),
+        (uint32_t) (rn_src->nb[3] / ggml_type_size(rn_src->type)),
+        (uint32_t) (mul_src->nb[1] / ggml_type_size(mul_src->type)),
+        (uint32_t) (mul_src->nb[2] / ggml_type_size(mul_src->type)),
+        (uint32_t) (mul_src->nb[3] / ggml_type_size(mul_src->type)),
+        (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+        (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+        (uint32_t) mul_src->ne[0],
+        (uint32_t) mul_src->ne[1],
+        (uint32_t) mul_src->ne[2],
+        (uint32_t) mul_src->ne[3],
+        (uint32_t) dst->ne[0],
+        (uint32_t) dst->ne[1],
+        (uint32_t) dst->ne[2],
+        (uint32_t) dst->ne[3],
+        ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(rn_dst, 0))  // epsilon, treated as f32 in the shader
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries;
+
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = rn_src;
+    shader_lib_ctx.src1                           = mul_src;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+
+    webgpu_pipeline pipeline  = ctx->shader_lib->get_rms_norm_mul_pipeline(shader_lib_ctx);
+    auto *          decisions = static_cast<ggml_webgpu_rms_norm_mul_shader_decisions *>(pipeline.context.get());
+
+    if (decisions->src_overlap) {
+        const ggml_webgpu_merged_binding_range merged_range =
+            ggml_webgpu_tensor_merged_binding_range(ctx, { rn_src, mul_src });
+        merged_offset  = merged_range.offset;
+        merged_size    = merged_range.size;
+        offset_rn_src  = ggml_webgpu_tensor_merged_element_offset(rn_src, merged_range);
+        offset_mul_src = ggml_webgpu_tensor_merged_element_offset(mul_src, merged_range);
+        params[0]      = offset_rn_src;
+        params[1]      = offset_mul_src;
+    }
+
+    if (decisions->inplace || decisions->overlap) {
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, rn_src));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, mul_src));
+    } else if (decisions->src_overlap) {
+        entries.push_back(
+            ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(rn_src), merged_offset, merged_size));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst));
+    } else {
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, rn_src));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, mul_src));
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst));
+    }
+
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, ggml_nrows(dst));
+}
+
+static webgpu_encoded_op ggml_webgpu_row_norm(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
     std::vector<uint32_t> params = {
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
@@ -1911,18 +2379,18 @@ static webgpu_encoded_op ggml_webgpu_row_norm(webgpu_context & ctx, ggml_tensor
         ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(dst, 0))  // epsilon, treated as f32 in the shader
     };
 
-    std::vector<wgpu::BindGroupEntry> entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src) };
-    if (!inplace) {
-        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst));
-    }
-
     ggml_webgpu_shader_lib_context shader_lib_ctx = {};
     shader_lib_ctx.src0                           = src;
     shader_lib_ctx.dst                            = dst;
     shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
-    shader_lib_ctx.inplace     = inplace;
 
-    webgpu_pipeline pipeline = ctx->shader_lib->get_row_norm_pipeline(shader_lib_ctx);
+    webgpu_pipeline pipeline  = ctx->shader_lib->get_row_norm_pipeline(shader_lib_ctx);
+    auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+
+    std::vector<wgpu::BindGroupEntry> entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src) };
+    if (!decisions->inplace) {
+        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst));
+    }
     return ggml_backend_webgpu_build(ctx, pipeline, params, entries, ggml_nrows(src));
 }
 
@@ -1937,14 +2405,13 @@ static webgpu_encoded_op ggml_webgpu_rope(webgpu_context & ctx,
     shader_lib_ctx.src2                           = src2;
     shader_lib_ctx.dst                            = dst;
     shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
-    shader_lib_ctx.inplace     = ggml_webgpu_tensor_equal(src0, dst);
 
     webgpu_pipeline pipeline = ctx->shader_lib->get_rope_pipeline(shader_lib_ctx);
 
     auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
 
-    const int inplace         = ggml_webgpu_tensor_equal(src0, dst);
-    const int has_freq_factor = (src2 != nullptr);
+    const bool inplace         = decisions->inplace;
+    const int  has_freq_factor = (src2 != nullptr);
 
     const int n_dims     = ((int32_t *) dst->op_params)[1];
     const int mode       = ((int32_t *) dst->op_params)[2];
@@ -2071,14 +2538,11 @@ static webgpu_encoded_op ggml_webgpu_glu(webgpu_context & ctx,
 }
 
 static webgpu_encoded_op ggml_webgpu_scale(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
-    bool inplace = ggml_webgpu_tensor_equal(src, dst);
-
     ggml_webgpu_shader_lib_context shader_lib_ctx = {};
     shader_lib_ctx.src0                           = src;
     shader_lib_ctx.src1                           = nullptr;
     shader_lib_ctx.dst                            = dst;
     shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
-    shader_lib_ctx.inplace     = inplace;
 
     webgpu_pipeline pipeline  = ctx->shader_lib->get_scale_pipeline(shader_lib_ctx);
     auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
@@ -2104,7 +2568,7 @@ static webgpu_encoded_op ggml_webgpu_scale(webgpu_context & ctx, ggml_tensor * s
     // bindgroups unchanged
     std::vector<wgpu::BindGroupEntry> entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src) };
 
-    if (!inplace) {
+    if (!decisions->inplace) {
         entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst));
     }
 
@@ -2123,17 +2587,17 @@ static webgpu_encoded_op ggml_webgpu_soft_max(webgpu_context & ctx,
     shader_lib_ctx.src2                           = src2;
     shader_lib_ctx.dst                            = dst;
     shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
-    shader_lib_ctx.inplace     = ggml_webgpu_tensor_equal(src0, dst);
 
-    webgpu_pipeline pipeline = ctx->shader_lib->get_soft_max_pipeline(shader_lib_ctx);
+    webgpu_pipeline pipeline  = ctx->shader_lib->get_soft_max_pipeline(shader_lib_ctx);
+    auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
 
-    const int inplace     = ggml_webgpu_tensor_equal(src0, dst);
-    const int has_mask    = (src1 != nullptr);
-    const int has_sink    = (src2 != nullptr);
-    float     max_bias    = ggml_get_op_params_f32(dst, 1);
-    float     n_head_log2 = float(1u << (uint32_t) floor(log2(src0->ne[2])));
-    float     m0          = powf(2.0f, -(max_bias) / n_head_log2);
-    float     m1          = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+    const bool inplace     = decisions->inplace;
+    const int  has_mask    = (src1 != nullptr);
+    const int  has_sink    = (src2 != nullptr);
+    float      max_bias    = ggml_get_op_params_f32(dst, 1);
+    float      n_head_log2 = float(1u << (uint32_t) floor(log2(src0->ne[2])));
+    float      m0          = powf(2.0f, -(max_bias) / n_head_log2);
+    float      m1          = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
     std::vector<uint32_t> params = {
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
@@ -2388,15 +2852,91 @@ static webgpu_encoded_op ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor
     return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x);
 }
 
+static bool ggml_webgpu_can_fuse_rms_norm_mul(const struct ggml_cgraph * cgraph, int node_idx) {
+    if (!ggml_can_fuse(cgraph, node_idx, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+        return false;
+    }
+
+    // additional constraints specific to this fusion
+    const ggml_tensor * rms_norm = cgraph->nodes[node_idx];
+    const ggml_tensor * mul      = cgraph->nodes[node_idx + 1];
+
+    GGML_ASSERT(rms_norm->src[0]->type == GGML_TYPE_F32);
+    GGML_ASSERT(rms_norm->type == GGML_TYPE_F32);
+    // rms_norm only supports f32
+    if (mul->src[0]->type != GGML_TYPE_F32 || mul->src[1]->type != GGML_TYPE_F32 || mul->type != GGML_TYPE_F32) {
+        return false;
+    }
+    // if rms_norm is the B operand, then we don't handle broadcast
+    if (rms_norm == mul->src[1] && !ggml_are_same_shape(mul->src[0], rms_norm)) {
+        return false;
+    }
+    // rms_norm shader assumes contiguous rows
+    if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) {
+        return false;
+    }
+
+    return true;
+}
+
+static webgpu_encoded_op ggml_webgpu_upscale(webgpu_context ctx, ggml_tensor * src, ggml_tensor * dst) {
+    const uint32_t        mode_flags = (uint32_t) ggml_get_op_params_i32(dst, 0);
+    std::vector<uint32_t> params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+                                     (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+
+                                     (uint32_t) (src->nb[0] / ggml_type_size(src->type)),
+                                     (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
+                                     (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
+                                     (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+
+                                     (uint32_t) (dst->nb[0] / ggml_type_size(dst->type)),
+                                     (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
+                                     (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
+                                     (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
+
+                                     (uint32_t) src->ne[0],
+                                     (uint32_t) src->ne[1],
+                                     (uint32_t) src->ne[2],
+                                     (uint32_t) src->ne[3],
+
+                                     (uint32_t) dst->ne[0],
+                                     (uint32_t) dst->ne[1],
+                                     (uint32_t) dst->ne[2],
+                                     (uint32_t) dst->ne[3],
+
+                                     mode_flags };
+
+    std::vector<wgpu::BindGroupEntry> entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src),
+                                                  ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst) };
+
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = src;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+
+    webgpu_pipeline pipeline  = ctx->shader_lib->get_upscale_pipeline(shader_lib_ctx);
+    auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+    uint32_t        total_wg  = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size);
+    uint32_t        wg_x = std::min(ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, total_wg);
+    uint32_t        wg_y = CEIL_DIV(total_wg, wg_x);
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
+}
+
 // Returns the encoded command, or std::nullopt if the operation is a no-op
-static std::optional<webgpu_encoded_op> ggml_webgpu_encode_node(webgpu_context ctx, ggml_tensor * node) {
+static std::optional<webgpu_encoded_op> ggml_webgpu_encode(webgpu_context ctx,
+                                                           ggml_cgraph *  cgraph,
+                                                           int            node_idx,
+                                                           int &          num_encoded_ops) {
+    ggml_tensor ** nodes = cgraph->nodes;
+    ggml_tensor *  node  = nodes[node_idx];
+
     if (ggml_is_empty(node)) {
         return std::nullopt;
     }
     if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
         return std::nullopt;
     }
-    WEBGPU_LOG_DEBUG("ggml_webgpu_encode_node(" << node << ", " << ggml_op_name(node->op) << ")");
+    WEBGPU_LOG_DEBUG("ggml_webgpu_encode(" << node << ", " << ggml_op_name(node->op) << ")");
 
     ggml_tensor * src0 = node->src[0];
     ggml_tensor * src1 = node->src[1];
@@ -2424,21 +2964,27 @@ static std::optional<webgpu_encoded_op> ggml_webgpu_encode_node(webgpu_context c
         case GGML_OP_MUL_MAT_ID:
             return ggml_webgpu_mul_mat_id(ctx, src0, src1, src2, node);
         case GGML_OP_FLASH_ATTN_EXT:
-#ifndef __EMSCRIPTEN__
             return ggml_webgpu_flash_attn(ctx, src0, src1, src2, node->src[3], node->src[4], node);
-#else
-            return std::nullopt;
-#endif
         case GGML_OP_ADD:
         case GGML_OP_SUB:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
             return ggml_webgpu_binary_op(ctx, src0, src1, node);
+        case GGML_OP_ADD_ID:
+            return ggml_webgpu_add_id(ctx, src0, src1, src2, node);
         case GGML_OP_CONCAT:
             return ggml_webgpu_concat(ctx, src0, src1, node);
         case GGML_OP_REPEAT:
             return ggml_webgpu_repeat(ctx, src0, node);
         case GGML_OP_RMS_NORM:
+            if (ggml_webgpu_can_fuse_rms_norm_mul(cgraph, node_idx)) {
+                num_encoded_ops        = 2;
+                ggml_tensor * mul_node = nodes[node_idx + 1];
+                return ggml_webgpu_rms_norm_mul(ctx, src0, node, mul_node->src[0], mul_node->src[1], mul_node);
+            } else {
+                return ggml_webgpu_row_norm(ctx, src0, node);
+            }
+        case GGML_OP_NORM:
         case GGML_OP_L2_NORM:
             return ggml_webgpu_row_norm(ctx, src0, node);
         case GGML_OP_ROPE:
@@ -2464,6 +3010,9 @@ static std::optional<webgpu_encoded_op> ggml_webgpu_encode_node(webgpu_context c
             return ggml_webgpu_solve_tri(ctx, src0, src1, node);
         case GGML_OP_SSM_CONV:
             return ggml_webgpu_ssm_conv(ctx, src0, src1, node);
+        case GGML_OP_SSM_SCAN:
+            return ggml_webgpu_ssm_scan(ctx, src0, src1, src2, node->src[3], node->src[4], node->src[5], node->src[6],
+                                        node);
         case GGML_OP_GATED_DELTA_NET:
             return ggml_webgpu_gated_delta_net(ctx, src0, src1, src2, node->src[3], node->src[4], node->src[5], node);
         case GGML_OP_PAD:
@@ -2479,6 +3028,12 @@ static std::optional<webgpu_encoded_op> ggml_webgpu_encode_node(webgpu_context c
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
             return ggml_webgpu_sum_rows(ctx, src0, node);
+        case GGML_OP_CONV_2D:
+            return ggml_webgpu_conv_2d(ctx, src0, src1, node);
+        case GGML_OP_IM2COL:
+            return ggml_webgpu_im2col(ctx, src0, src1, node);
+        case GGML_OP_UPSCALE:
+            return ggml_webgpu_upscale(ctx, src0, node);
         default:
             return std::nullopt;
     }
@@ -2511,14 +3066,17 @@ static void ggml_backend_webgpu_collect_profile_results(webgpu_context &
     for (size_t i = 0; i < pipeline_names.size(); ++i) {
         // WebGPU timestamps are in ns; convert to ms.
         const double elapsed_ms = double(ts_data[2 * i + 1] - ts_data[2 * i]) * 1e-6;
-        ctx->global_ctx->shader_gpu_time_ms[pipeline_names[i]] += elapsed_ms;
+        ctx->shader_gpu_time_ms[pipeline_names[i]] += elapsed_ms;
     }
 
     ctx->profile_timestamp_host_buf.Unmap();
 }
 #endif
 
+// Don't bother checking set_rows index overflow for now, since practically the WebGPU doesn't need to support
+// models that would require it right now.
 static void ggml_backend_webgpu_check_set_rows(webgpu_context & ctx, uint32_t & num_inflight_batches) {
+#ifdef GGML_WEBGPU_CHECK_SET_ROWS
     wgpu::CommandEncoder encoder = ctx->global_ctx->device.CreateCommandEncoder();
     encoder.CopyBufferToBuffer(ctx->set_rows_dev_error_buf, 0, ctx->set_rows_host_error_buf, 0,
                                ctx->set_rows_host_error_buf.GetSize());
@@ -2531,6 +3089,10 @@ static void ggml_backend_webgpu_check_set_rows(webgpu_context & ctx, uint32_t &
         GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported.");
     }
     ctx->set_rows_host_error_buf.Unmap();
+#else
+    GGML_UNUSED(ctx);
+    GGML_UNUSED(num_inflight_batches);
+#endif
 }
 
 static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
@@ -2547,6 +3109,8 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
     uint32_t num_inflight_batches = 0;
     bool     contains_set_rows    = false;
     bool     batch_compute_passes = true;
+    int      num_encoded_ops      = 1;
+    int      node_idx             = 0;
 
 #ifdef GGML_WEBGPU_GPU_PROFILE
     ctx->profile_timestamp_query_count = 0;
@@ -2559,11 +3123,11 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
         ctx->active_compute_pass = ctx->active_command_encoder.BeginComputePass();
     }
 
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        if (cgraph->nodes[i]->op == GGML_OP_SET_ROWS) {
+    while (node_idx < cgraph->n_nodes) {
+        if (cgraph->nodes[node_idx]->op == GGML_OP_SET_ROWS) {
             contains_set_rows = true;
         }
-        if (auto cmd = ggml_webgpu_encode_node(ctx, cgraph->nodes[i])) {
+        if (auto cmd = ggml_webgpu_encode(ctx, cgraph, node_idx, num_encoded_ops)) {
             commands.push_back(*cmd);
             num_batched_kernels += cmd.value().num_kernels;
 #ifdef GGML_WEBGPU_GPU_PROFILE
@@ -2587,7 +3151,20 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
             }
             ctx->param_arena.reset();
             commands.clear();
+#ifdef GGML_WEBGPU_GPU_PROFILE
+            // flush before the next batch can overflow the QuerySet
+            if (ctx->profile_timestamp_query_count + 2 * ctx->global_ctx->command_submit_batch_size >=
+                WEBGPU_MAX_PROFILE_QUERY_COUNT) {
+                ggml_backend_webgpu_collect_profile_results(ctx, profile_pipeline_names, num_inflight_batches);
+                // reset profile timestamp state
+                ctx->profile_timestamp_query_count = 0;
+                profile_pipeline_names.clear();
+            }
+#endif
         }
+
+        node_idx += num_encoded_ops;
+        num_encoded_ops = 1;
     }
 
     if (ctx->active_compute_pass) {
@@ -2611,28 +3188,111 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
         ggml_backend_webgpu_check_set_rows(ctx, num_inflight_batches);
     }
 
-    ggml_backend_webgpu_wait_queue(ctx->global_ctx);
-
     WEBGPU_CPU_PROFILE_TOTAL_END(graph_compute, ctx->global_ctx);
     return GGML_STATUS_SUCCESS;
 }
 
+struct ggml_backend_webgpu_event_context {
+    webgpu_global_context global_ctx;
+    wgpu::Future          future;
+    bool                  recorded = false;
+};
+
+static ggml_backend_event_t ggml_backend_webgpu_device_event_new(ggml_backend_dev_t device) {
+    ggml_backend_webgpu_device_context * dev_ctx = (ggml_backend_webgpu_device_context *) device->context;
+
+    auto * event_ctx      = new ggml_backend_webgpu_event_context();
+    event_ctx->global_ctx = dev_ctx->webgpu_global_ctx;
+
+    auto * event   = new ggml_backend_event;
+    event->device  = device;
+    event->context = event_ctx;
+    return event;
+}
+
+static void ggml_backend_webgpu_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) {
+    GGML_UNUSED(dev);
+    delete static_cast<ggml_backend_webgpu_event_context *>(event->context);
+    delete event;
+}
+
+static void ggml_backend_webgpu_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) {
+    GGML_UNUSED(dev);
+    ggml_backend_webgpu_event_context * event_ctx = (ggml_backend_webgpu_event_context *) event->context;
+    if (!event_ctx->recorded) {
+        return;
+    }
+    wgpu::WaitStatus status =
+        event_ctx->global_ctx->instance.WaitAny(event_ctx->future, WEBGPU_RUNTIME_WAIT_TIMEOUT_NS);
+    if (status == wgpu::WaitStatus::TimedOut) {
+        GGML_ABORT("ggml_webgpu: event_synchronize timed out after %u ms\n", WEBGPU_RUNTIME_WAIT_TIMEOUT_MS);
+    }
+    event_ctx->recorded = false;
+}
+
+static void ggml_backend_webgpu_event_record(ggml_backend_t backend, ggml_backend_event_t event) {
+    ggml_backend_webgpu_context *       backend_ctx = (ggml_backend_webgpu_context *) backend->context;
+    ggml_backend_webgpu_event_context * event_ctx   = (ggml_backend_webgpu_event_context *) event->context;
+
+    event_ctx->future = backend_ctx->webgpu_ctx->global_ctx->queue.OnSubmittedWorkDone(
+        wgpu::CallbackMode::AllowSpontaneous, [](wgpu::QueueWorkDoneStatus, wgpu::StringView) {});
+    event_ctx->recorded = true;
+}
+
+static void ggml_backend_webgpu_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
+    GGML_UNUSED(backend);
+    ggml_backend_webgpu_device_event_synchronize(nullptr, event);
+}
+
+static void ggml_backend_webgpu_set_tensor_async(ggml_backend_t backend,
+                                                 ggml_tensor *  tensor,
+                                                 const void *   data,
+                                                 size_t         offset,
+                                                 size_t         size) {
+    GGML_UNUSED(backend);
+    auto * buf_ctx      = (ggml_backend_webgpu_buffer_context *) tensor->buffer->context;
+    size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset;
+
+    // Write aligned portion
+    buf_ctx->global_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4);
+
+    if (size % 4 != 0) {
+        // If size is not a multiple of 4, we need to memset the remaining bytes
+        size_t remaining_size = size % 4;
+
+        // pack the remaining bytes into a uint32_t
+        uint32_t val32 = 0;
+
+        for (size_t i = 0; i < remaining_size; i++) {
+            ((uint8_t *) &val32)[i] = ((const uint8_t *) data)[size - remaining_size + i];
+        }
+        // memset the remaining bytes
+        ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, val32,
+                                          total_offset + (size - remaining_size), remaining_size);
+    }
+}
+
+static void ggml_backend_webgpu_synchronize(ggml_backend_t backend) {
+    ggml_backend_webgpu_context * backend_ctx = (ggml_backend_webgpu_context *) backend->context;
+    ggml_backend_webgpu_wait_queue(backend_ctx->webgpu_ctx->global_ctx);
+}
+
 static ggml_backend_i ggml_backend_webgpu_i = {
     /* .get_name                = */ ggml_backend_webgpu_name,
     /* .free                    = */ ggml_backend_webgpu_free,
-    /* .set_tensor_async        = */ NULL,
+    /* .set_tensor_async        = */ ggml_backend_webgpu_set_tensor_async,
     /* .get_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
-    /* .synchronize             = */ NULL,
+    /* .synchronize             = */ ggml_backend_webgpu_synchronize,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
     /* .graph_plan_update       = */ NULL,
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_webgpu_graph_compute,
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
+    /* .event_record            = */ ggml_backend_webgpu_event_record,
+    /* .event_wait              = */ ggml_backend_webgpu_event_wait,
     /* .graph_optimize          = */ NULL,
 };
 
@@ -2673,7 +3333,7 @@ static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffe
     WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor(" << buf_ctx->label << ", " << tensor << ", " << value
                                                                  << ", " << offset << ", " << size << ")");
 
-    size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;
+    size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset;
 
     // This is a trick to set all bytes of a u32 to the same 1 byte value.
     uint32_t val32 = (uint32_t) value * 0x01010101;
@@ -2692,7 +3352,7 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer,
     WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buf_ctx->label << ", " << tensor << ", " << data
                                                               << ", " << offset << ", " << size << ")");
 
-    size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;
+    size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset;
 
     buf_ctx->global_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4);
 
@@ -2724,7 +3384,7 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer,
                                                               << ", " << offset << ", " << size << ")");
     wgpu::Device device = buf_ctx->global_ctx->device;
 
-    size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;
+    size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset;
 
     size_t final_size = size;
     if (size % 4 != 0) {
@@ -2870,16 +3530,22 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer
                         ctx->webgpu_global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
                     shader_lib_ctx.wg_mem_limit_bytes =
                         ctx->webgpu_global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize;
+                    shader_lib_ctx.supports_subgroups = ctx->webgpu_global_ctx->capabilities.supports_subgroups;
+                    shader_lib_ctx.supports_subgroup_matrix =
+                        ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix;
                     shader_lib_ctx.sg_mat_m          = ctx->webgpu_global_ctx->capabilities.sg_mat_m;
                     shader_lib_ctx.sg_mat_n          = ctx->webgpu_global_ctx->capabilities.sg_mat_n;
                     shader_lib_ctx.sg_mat_k          = ctx->webgpu_global_ctx->capabilities.sg_mat_k;
+                    shader_lib_ctx.min_subgroup_size = ctx->webgpu_global_ctx->capabilities.min_subgroup_size;
                     shader_lib_ctx.max_subgroup_size = ctx->webgpu_global_ctx->capabilities.max_subgroup_size;
 
-                    if (ggml_webgpu_flash_attn_use_vec(ctx->webgpu_global_ctx, Q, K, V)) {
-                        const uint32_t kv_tile = ggml_webgpu_flash_attn_vec_get_kv_tile(shader_lib_ctx);
+                    const ggml_webgpu_flash_attn_decisions decisions = ggml_webgpu_flash_attn_get_decisions(
+                        shader_lib_ctx, ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment);
 
-                        const uint32_t vec_nwg_cap = std::max(
-                            1u, std::min<uint32_t>(32u, ctx->webgpu_global_ctx->capabilities.max_subgroup_size));
+                    if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) {
+                        const uint32_t kv_tile = decisions.kv_tile;
+
+                        const uint32_t vec_nwg_cap = ctx->webgpu_global_ctx->capabilities.min_subgroup_size;
                         uint32_t       nwg     = 1u;
                         const uint64_t kv_span = (uint64_t) std::max(1u, kv_tile);
                         while ((2u * nwg * kv_span) < (uint64_t) K->ne[1] && nwg < vec_nwg_cap) {
@@ -2896,6 +3562,8 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer
                             const size_t   tmp_size_bytes  = ROUNDUP_POW2(
                                 (tmp_data_elems + tmp_stats_elems) * sizeof(float), WEBGPU_STORAGE_BUF_BINDING_MULT);
                             res += tmp_size_bytes + align;
+                        } else {
+                            res += WEBGPU_STORAGE_BUF_BINDING_MULT + align;
                         }
                         if (mask != nullptr) {
                             const uint32_t blk_nblk0       = CEIL_DIV((uint32_t) K->ne[1], kv_tile);
@@ -3044,12 +3712,12 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
     ctx->webgpu_global_ctx->capabilities.supports_subgroups =
         ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::Subgroups);
 
+    bool valid_subgroup_matrix_config = false;
 #ifndef __EMSCRIPTEN__
     // Accept f16 subgroup matrix configurations (square or non-square).
     // NVIDIA GPUs typically report square configs (e.g. 16x16x16),
     // while Intel Xe2 GPUs report non-square configs (e.g. 8x16x16).
     // The shaders are already parameterized to handle any M/N/K dimensions.
-    bool valid_subgroup_matrix_config = false;
     if (ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) {
         for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) {
             const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i];
@@ -3063,11 +3731,12 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
             }
         }
     }
-    ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix = valid_subgroup_matrix_config;
 #endif
+    ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix = valid_subgroup_matrix_config;
 
-    // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate.
-    // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter.
+    // Runtime subgroup size can be any supported size in this range. Shaders
+    // that allocate per-lane register arrays must size them for the minimum.
+    ctx->webgpu_global_ctx->capabilities.min_subgroup_size = info.subgroupMinSize;
     ctx->webgpu_global_ctx->capabilities.max_subgroup_size = info.subgroupMaxSize;
     // Initialize device
     std::vector<wgpu::FeatureName> required_features       = { wgpu::FeatureName::ShaderF16 };
@@ -3112,12 +3781,12 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
     // Enable Dawn-specific toggles to increase native performance
     // TODO: Maybe WebGPU needs a "fast" mode where you can request compilers skip adding checks like these,
     //       only for native performance?
-    const char * const deviceEnabledToggles[]  = { "skip_validation", "disable_robustness", "disable_workgroup_init",
-                                                   "disable_polyfills_on_integer_div_and_mod" };
-    const char * const deviceDisabledToggles[] = { "timestamp_quantization" };
+    const char * const          deviceEnabledToggles[]  = { "disable_robustness", "disable_workgroup_init",
+                                                            "disable_polyfills_on_integer_div_and_mod" };
+    const char * const          deviceDisabledToggles[] = { "timestamp_quantization" };
     wgpu::DawnTogglesDescriptor deviceTogglesDesc;
     deviceTogglesDesc.enabledToggles      = deviceEnabledToggles;
-    deviceTogglesDesc.enabledToggleCount  = 4;
+    deviceTogglesDesc.enabledToggleCount  = 3;
     deviceTogglesDesc.disabledToggles     = deviceDisabledToggles;
     deviceTogglesDesc.disabledToggleCount = 1;
 
@@ -3241,6 +3910,7 @@ static bool ggml_backend_webgpu_device_supports_buft(ggml_backend_dev_t dev, ggm
 
 static bool ggml_webgpu_supported_qtype(ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -3260,6 +3930,7 @@ static bool ggml_webgpu_supported_qtype(ggml_type type) {
         case GGML_TYPE_IQ1_M:
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_MXFP4:
             return true;
         default:
             return false;
@@ -3298,6 +3969,9 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
             supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type) &&
                           (src1->type == op->type);
             break;
+        case GGML_OP_ADD_ID:
+            supports_op = src0->type == GGML_TYPE_F32;
+            break;
         case GGML_OP_CONCAT:
             supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32);
             break;
@@ -3335,6 +4009,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
                         switch (src0->type) {
                             case GGML_TYPE_F32:
                             case GGML_TYPE_F16:
+                            case GGML_TYPE_Q1_0:
                             case GGML_TYPE_Q4_0:
                             case GGML_TYPE_Q4_1:
                             case GGML_TYPE_Q5_0:
@@ -3354,6 +4029,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
                             case GGML_TYPE_IQ1_M:
                             case GGML_TYPE_IQ4_NL:
                             case GGML_TYPE_IQ4_XS:
+                            case GGML_TYPE_MXFP4:
                                 supports_op = true;
                                 break;
                             default:
@@ -3373,6 +4049,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
                     switch (src0->type) {
                         case GGML_TYPE_F32:
                         case GGML_TYPE_F16:
+                        case GGML_TYPE_Q1_0:
                         case GGML_TYPE_Q4_0:
                         case GGML_TYPE_Q4_1:
                         case GGML_TYPE_Q5_0:
@@ -3383,6 +4060,16 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
                         case GGML_TYPE_Q4_K:
                         case GGML_TYPE_Q5_K:
                         case GGML_TYPE_Q6_K:
+                        case GGML_TYPE_IQ1_S:
+                        case GGML_TYPE_IQ1_M:
+                        case GGML_TYPE_IQ2_XXS:
+                        case GGML_TYPE_IQ2_XS:
+                        case GGML_TYPE_IQ2_S:
+                        case GGML_TYPE_IQ3_XXS:
+                        case GGML_TYPE_IQ3_S:
+                        case GGML_TYPE_IQ4_NL:
+                        case GGML_TYPE_IQ4_XS:
+                        case GGML_TYPE_MXFP4:
                             supports_op = true;
                             break;
                         default:
@@ -3395,36 +4082,74 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
             break;
         case GGML_OP_FLASH_ATTN_EXT:
             {
-#ifndef __EMSCRIPTEN__
-                if (!ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix) {
+                supports_op = src0->type == GGML_TYPE_F32 &&
+                              (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 ||
+                               src1->type == GGML_TYPE_Q4_0 || src1->type == GGML_TYPE_Q8_0) &&
+                              src2->type == src1->type && op->type == GGML_TYPE_F32;
+                if (!supports_op) {
                     break;
                 }
-                // Head dimensions must be divisible by subgroup matrix dimensions
-                if (src0->ne[0] % ctx->webgpu_global_ctx->capabilities.sg_mat_k != 0 ||
-                    src2->ne[0] % ctx->webgpu_global_ctx->capabilities.sg_mat_n != 0) {
+                ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+                shader_lib_ctx.src0                           = src0;
+                shader_lib_ctx.src1                           = src1;
+                shader_lib_ctx.src2                           = src2;
+                shader_lib_ctx.src3                           = op->src[3];
+                shader_lib_ctx.src4                           = op->src[4];
+                shader_lib_ctx.dst                            = const_cast<ggml_tensor *>(op);
+                shader_lib_ctx.supports_subgroups             = ctx->webgpu_global_ctx->capabilities.supports_subgroups;
+                shader_lib_ctx.supports_subgroup_matrix = ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix;
+                shader_lib_ctx.max_wg_size =
+                    ctx->webgpu_global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+                shader_lib_ctx.wg_mem_limit_bytes =
+                    ctx->webgpu_global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize;
+                shader_lib_ctx.sg_mat_m          = ctx->webgpu_global_ctx->capabilities.sg_mat_m;
+                shader_lib_ctx.sg_mat_n          = ctx->webgpu_global_ctx->capabilities.sg_mat_n;
+                shader_lib_ctx.sg_mat_k          = ctx->webgpu_global_ctx->capabilities.sg_mat_k;
+                shader_lib_ctx.min_subgroup_size = ctx->webgpu_global_ctx->capabilities.min_subgroup_size;
+                shader_lib_ctx.max_subgroup_size = ctx->webgpu_global_ctx->capabilities.max_subgroup_size;
+
+                const ggml_webgpu_flash_attn_decisions decisions = ggml_webgpu_flash_attn_get_decisions(
+                    shader_lib_ctx, ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment);
+                const size_t limit_bytes = ctx->webgpu_global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize;
+                const bool   has_mask    = op->src[3] != nullptr;
+                if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_NONE) {
+                    supports_op = false;
                     break;
                 }
-                // Head dimensions must fit in workgroup memory with minimum tile sizes
-                size_t     limit_bytes = ctx->webgpu_global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize;
-                const bool has_mask    = op->src[3] != nullptr;
-                const bool kv_direct   = src1->type == GGML_TYPE_F16 &&
-                                       (src0->ne[0] % ctx->webgpu_global_ctx->capabilities.sg_mat_k) == 0 &&
-                                       (src1->ne[1] % GGML_WEBGPU_KV_SEQ_PAD) == 0;
-                const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(
-                    ctx->webgpu_global_ctx->capabilities.sg_mat_m, ctx->webgpu_global_ctx->capabilities.sg_mat_n,
-                    (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask, kv_direct);
-                if (min_bytes > limit_bytes) {
+                if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) {
+                    const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(
+                        decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask,
+                        decisions.kv_direct, decisions.path);
+                    if (min_bytes > limit_bytes) {
+                        supports_op = false;
+                    }
                     break;
                 }
 
-                supports_op = src0->type == GGML_TYPE_F32 &&
-                              (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 ||
-                               src1->type == GGML_TYPE_Q4_0 || src1->type == GGML_TYPE_Q8_0) &&
-                              src2->type == src1->type && op->type == GGML_TYPE_F32;
-#endif
+                if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE) {
+                    const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(
+                        decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask,
+                        decisions.kv_direct, decisions.path);
+                    if (min_bytes > limit_bytes) {
+                        supports_op = false;
+                    }
+                    break;
+                }
+
+                if (!ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix) {
+                    supports_op = false;
+                    break;
+                }
+                const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(
+                    decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask,
+                    decisions.kv_direct, decisions.path);
+                if (min_bytes > limit_bytes) {
+                    supports_op = false;
+                }
                 break;
             }
         case GGML_OP_RMS_NORM:
+        case GGML_OP_NORM:
         case GGML_OP_L2_NORM:
             supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32;
             break;
@@ -3497,9 +4222,22 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
         case GGML_OP_SOLVE_TRI:
             supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
             break;
+        case GGML_OP_CONV_2D:
+            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
+                          (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) &&
+                          (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
+            break;
+        case GGML_OP_IM2COL:
+            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
+                          (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+            break;
         case GGML_OP_SSM_CONV:
             supports_op = op->type == GGML_TYPE_F32;
             break;
+        case GGML_OP_SSM_SCAN:
+            supports_op = op->type == GGML_TYPE_F32 &&
+                          src0->ne[0] <= ctx->webgpu_global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+            break;
         case GGML_OP_GATED_DELTA_NET:
             {
                 const uint32_t s_v = (uint32_t) src2->ne[0];
@@ -3549,6 +4287,10 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
         case GGML_OP_SUM_ROWS:
             supports_op = op->type == GGML_TYPE_F32 && src0->type == op->type && ggml_is_contiguous_rows(src0);
             break;
+        case GGML_OP_UPSCALE:
+            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
+                          (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+            break;
         default:
             break;
     }
@@ -3590,9 +4332,9 @@ static struct ggml_backend_device_i ggml_backend_webgpu_device_i = {
     /* .supports_op          = */ ggml_backend_webgpu_device_supports_op,
     /* .supports_buft        = */ ggml_backend_webgpu_device_supports_buft,
     /* .offload_op           = */ NULL,
-    /* .event_new            = */ NULL,
-    /* .event_free           = */ NULL,
-    /* .event_synchronize    = */ NULL,
+    /* .event_new            = */ ggml_backend_webgpu_device_event_new,
+    /* .event_free           = */ ggml_backend_webgpu_device_event_free,
+    /* .event_synchronize    = */ ggml_backend_webgpu_device_event_synchronize,
 };
 
 /* End GGML Backend Device Interface */
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl
new file mode 100644
index 00000000000..2573926cb89
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl
@@ -0,0 +1,64 @@
+struct Params {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_ids: u32,
+    offset_dst: u32,
+
+    nb01: u32,
+    nb02: u32,
+    nb11: u32,
+    nb20: u32,
+    nb21: u32,
+
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<f32>; // [n_embd, n_experts_used, n_token]
+@group(0) @binding(1) var<storage, read_write> src1: array<f32>; // [n_embd, n_experts]
+@group(0) @binding(2) var<storage, read_write> ids:  array<i32>; // [n_experts_used, n_token]
+
+#ifdef INPLACE
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+#else
+
+@group(0) @binding(3)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(4)
+var<uniform> params: Params;
+
+#endif
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
+        @builtin(num_workgroups) num_wg: vec3<u32>,
+        @builtin(local_invocation_id) local_id: vec3<u32>) {
+
+    let wg_linear = wg_id.x + wg_id.y * num_wg.x;
+
+    if (wg_linear < params.ne1 * params.ne2) {
+        let thread_id = local_id.x;
+        let i2 = wg_linear / params.ne1;
+        let i1 = wg_linear % params.ne1;
+
+        let i11 = u32(ids[params.offset_ids + i1 * params.nb20 + i2 * params.nb21]);
+
+        let src0_row = params.offset_src0 + i1 * params.nb01 + i2 * params.nb02;
+        let src1_row = params.offset_src1 + i11 * params.nb11;
+        let dst_row = params.offset_dst + i1 * params.ne0 + i2 * (params.ne0 * params.ne1);
+
+        for (var i = thread_id;i < params.ne0; i += WG_SIZE) {
+#ifdef INPLACE
+            src0[src0_row + i] = src0[src0_row + i] + src1[src1_row + i];
+#else
+            dst[dst_row + i] = src0[src0_row + i] + src1[src1_row + i];
+#endif
+        }
+    }
+
+}
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl
index a748dc1b86c..605de7aa7be 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl
@@ -7,8 +7,6 @@ struct Params {
     offset_src0: u32,
     offset_src1: u32,
     offset_dst: u32,
-    offset_merged_src0: u32,
-    offset_merged_src1: u32,
 
     stride_src0_0: u32,
     stride_src0_1: u32,
@@ -134,8 +132,8 @@ fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
 @compute @workgroup_size(WG_SIZE)
 fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
     if (gid.x < params.ne) {
-        let src0_i = params.offset_src0 + params.offset_merged_src0 + src0_index(gid.x);
-        let src1_i = params.offset_src1 + params.offset_merged_src1 + src1_index(gid.x);
+        let src0_i = params.offset_src0 + src0_index(gid.x);
+        let src1_i = params.offset_src1 + src1_index(gid.x);
         update(params.offset_dst + gid.x, src0_i, src1_i);
     }
 }
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
index 14c045b0ba6..372ea79bf9d 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
@@ -896,3 +896,10 @@ const kvalues_iq4nl = array<i32, 16>(
 );
 
 #endif
+
+#ifdef MXFP4_LUT
+const kvalues_mxfp4 = array<i32, 16>(
+    0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12
+);
+#endif
+
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/conv2d.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/conv2d.wgsl
new file mode 100644
index 00000000000..9eb131dc221
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/conv2d.wgsl
@@ -0,0 +1,165 @@
+#include "common_decls.tmpl"
+enable f16;
+
+@group(0) @binding(0)
+#if defined(WEIGHT_F32)
+var<storage, read_write> weights: array<f32>;
+#elif defined(WEIGHT_F16)
+var<storage, read_write> weights: array<f16>;
+#endif
+
+@group(0) @binding(1)
+#if defined(INPUT_F32)
+var<storage, read_write> input: array<f32>;
+#elif defined(INPUT_F16)
+var<storage, read_write> input: array<f16>;
+#endif
+
+@group(0) @binding(2)
+#if defined(OUTPUT_F32)
+var<storage, read_write> output: array<f32>;
+#elif defined(OUTPUT_F16)
+var<storage, read_write> output: array<f16>;
+#endif
+
+struct Params {
+    offset_w: u32,
+    offset_i: u32,
+    offset_o: u32,
+
+    // element strides
+    sw0: u32, sw1: u32, sw2: u32, sw3: u32,
+    si0: u32, si1: u32, si2: u32, si3: u32,
+    so0: u32, so1: u32, so2: u32, so3: u32,
+
+    // kernel dimensions
+    KW: u32, KH: u32, IC: u32,
+    // input dimensions
+    IW: u32, IH: u32,
+    // output dimensions
+    OW: u32, OH: u32, OC_out: u32, N_out: u32,
+
+    // stride
+    s0: u32, s1: u32,
+    // padding
+    p0: u32, p1: u32,
+    // dilation
+    d0: u32, d1: u32,
+};
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+fn load_weight(idx: u32) -> f32 {
+    #if defined(WEIGHT_F32)
+        return weights[idx];
+    #elif defined(WEIGHT_F16)
+        return f32(weights[idx]);
+    #endif
+}
+
+fn load_input(idx: u32) -> f32 {
+    #if defined(INPUT_F32)
+        return input[idx];
+    #elif defined(INPUT_F16)
+        return f32(input[idx]);
+    #endif
+}
+
+fn store_output(idx: u32, val: f32) {
+    #if defined(OUTPUT_F32)
+        output[idx] = val;
+    #elif defined(OUTPUT_F16)
+        output[idx] = f16(val);
+    #endif
+}
+
+fn ceil_div_u32(x: u32, y: u32) -> u32 {
+    return (x + y - 1) / y;
+}
+
+// returns the first valid kernel index k such that base + k * step >= 0
+fn first_valid_k(base: i32, step: u32) -> u32 {
+    if (base >= 0) {
+        return 0;
+    }
+
+    return ceil_div_u32(u32(-base), step);
+}
+
+// returns the first invalid kernel index k such that base + k * step >= limit so valid k are in [0, end_valid_k)
+fn end_valid_k(base: i32, step: u32, limit: u32, k_max: u32) -> u32 {
+    let remaining = i32(limit) - base;
+    if (remaining <= 0) {
+        return 0;
+    }
+
+    return min(k_max, ceil_div_u32(u32(remaining), step));
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(
+    @builtin(global_invocation_id) gid: vec3<u32>,
+    @builtin(num_workgroups) num_wg: vec3<u32>
+) {
+
+    let threads_per_group = u32(WG_SIZE);
+    let i_out = gid.x + (num_wg.x * threads_per_group) * gid.y;
+    let n_out = params.OW * params.OH * params.OC_out * params.N_out;
+
+    var sum: f32 = 0.0;
+    if (i_out >= n_out) {
+        return;
+    }
+
+    // Kernel layout: [KW, KH, IC, ..]
+    // Input layout:  [IW, IH, .., ..]
+    // Output layout: [OW, OH, OC, N]
+
+    var i = i_out;
+    let n = i / (params.OC_out * params.OH * params.OW);
+    i = i % (params.OC_out * params.OH * params.OW);
+    let oc = i / (params.OH * params.OW);
+    i = i % (params.OH * params.OW);
+    let oh = i / params.OW;
+    let ow = i % params.OW;
+
+    let ow_base = i32(ow * params.s0) - i32(params.p0);
+    let oh_base = i32(oh * params.s1) - i32(params.p1);
+
+    // clip the valid kernel window once
+    let kw_begin = first_valid_k(ow_base, params.d0);
+    let kw_end = end_valid_k(ow_base, params.d0, params.IW, params.KW);
+    let kh_begin = first_valid_k(oh_base, params.d1);
+    let kh_end = end_valid_k(oh_base, params.d1, params.IH, params.KH);
+
+    // entire receptive field is out of bounds
+    if (kw_begin >= kw_end || kh_begin >= kh_end) {
+        let out_idx = params.offset_o + ow * params.so0 + oh * params.so1 + oc * params.so2 + n * params.so3;
+        store_output(out_idx, 0.0);
+        return;
+    }
+
+    let weight_oc_base = params.offset_w + oc * params.sw3;
+    let input_n_base = params.offset_i + n * params.si3;
+
+    for (var ic: u32 = 0; ic < params.IC; ic += 1) {
+        let w_base_ic = ic * params.sw2 + weight_oc_base;
+        let in_base = ic * params.si2 + input_n_base;
+
+        for (var kh: u32 = kh_begin; kh < kh_end; kh += 1) {
+            let ih = u32(oh_base + i32(kh * params.d1));
+            let w_row_base = w_base_ic + kh * params.sw1;
+            let in_row_base = in_base + ih * params.si1;
+            for (var kw: u32 = kw_begin; kw < kw_end; kw += 1) {
+                let iw = u32(ow_base + i32(kw * params.d0));
+                let w_idx = w_row_base + kw * params.sw0;
+                let in_idx = in_row_base + iw * params.si0;
+                sum += load_weight(w_idx) * load_input(in_idx);
+            }
+        }
+    }
+
+    let out_idx = params.offset_o + ow * params.so0 + oh * params.so1 + oc * params.so2 + n * params.so3;
+    store_output(out_idx, sum);
+}
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl
index aa2d2e54db9..6d5d69fb8de 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl
@@ -138,26 +138,55 @@ struct Params {
 };
 
 @group(0) @binding(0) var<storage, read_write> Q: array<f32>;
+#ifdef KV_OVERLAP
+@group(0) @binding(1) var<storage, read_write> K: array<KV_TYPE>;
+#define V K
+#else
 @group(0) @binding(1) var<storage, read_write> K: array<KV_TYPE>;
 @group(0) @binding(2) var<storage, read_write> V: array<KV_TYPE>;
+#endif
 
 #if defined(MASK) && defined(SINKS)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> mask: array<f16>;
+@group(0) @binding(3) var<storage, read_write> sinks: array<f32>;
+#define DST_BINDING 4
+#define PARAMS_BINDING 5
+#else
 @group(0) @binding(3) var<storage, read_write> mask: array<f16>;
 @group(0) @binding(4) var<storage, read_write> sinks: array<f32>;
 #define DST_BINDING 5
 #define PARAMS_BINDING 6
+#endif
 #elif defined(MASK)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> mask: array<f16>;
+#define DST_BINDING 3
+#define PARAMS_BINDING 4
+#else
 @group(0) @binding(3) var<storage, read_write> mask: array<f16>;
 #define DST_BINDING 4
 #define PARAMS_BINDING 5
+#endif
 #elif defined(SINKS)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> sinks: array<f32>;
+#define DST_BINDING 3
+#define PARAMS_BINDING 4
+#else
 @group(0) @binding(3) var<storage, read_write> sinks: array<f32>;
 #define DST_BINDING 4
 #define PARAMS_BINDING 5
+#endif
+#else
+#ifdef KV_OVERLAP
+#define DST_BINDING 2
+#define PARAMS_BINDING 3
 #else
 #define DST_BINDING 3
 #define PARAMS_BINDING 4
 #endif
+#endif
 
 @group(0) @binding(DST_BINDING) var<storage, read_write> dst: array<vec4<f32>>;
 @group(0) @binding(PARAMS_BINDING) var<uniform> params: Params;
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl
new file mode 100644
index 00000000000..ae8036b9ac5
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl
@@ -0,0 +1,351 @@
+enable f16;
+enable subgroups;
+
+#ifdef Q_F16
+#define Q_TYPE f16
+#else
+#define Q_TYPE f32
+#endif
+
+#ifdef KV_F32
+#define KV_TYPE f32
+#else
+#define KV_TYPE f16
+#endif
+
+#ifdef DST_F16
+#define DST_TYPE f16
+#else
+#define DST_TYPE f32
+#endif
+
+#define HEAD_DIM_QK 64
+#define HEAD_DIM_V 64
+#define KV_STAGE_STRIDE 64
+#define Q_TILE 4
+#define KV_TILE 64
+#define WG_SIZE 128
+#ifndef MIN_SUBGROUP_SIZE
+#define MIN_SUBGROUP_SIZE MAX_SUBGROUP_SIZE
+#endif
+
+struct Params {
+    offset_q: u32,
+    offset_k: u32,
+    offset_v: u32,
+    offset_mask: u32,
+    offset_sinks: u32,
+    offset_dst: u32,
+
+    n_heads: u32,
+    seq_len_q: u32,
+    seq_len_kv: u32,
+
+    stride_q1: u32,
+    stride_q2: u32,
+    stride_q3: u32,
+    stride_k1: u32,
+    stride_k2: u32,
+    stride_k3: u32,
+    stride_v1: u32,
+    stride_v2: u32,
+    stride_v3: u32,
+    stride_mask3: u32,
+
+    q_per_kv: u32,
+
+    scale: f32,
+    max_bias: f32,
+    logit_softcap: f32,
+    n_head_log2: f32,
+    m0: f32,
+    m1: f32,
+};
+
+@group(0) @binding(0) var<storage, read_write> Q: array<Q_TYPE>;
+#ifdef KV_OVERLAP
+@group(0) @binding(1) var<storage, read_write> K: array<vec4<KV_TYPE>>;
+#define V K
+#else
+@group(0) @binding(1) var<storage, read_write> K: array<vec4<KV_TYPE>>;
+@group(0) @binding(2) var<storage, read_write> V: array<vec4<KV_TYPE>>;
+#endif
+
+#if defined(MASK) && defined(SINKS)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> mask: array<f16>;
+@group(0) @binding(3) var<storage, read_write> sinks: array<f32>;
+#define DST_BINDING 4
+#define PARAMS_BINDING 5
+#else
+@group(0) @binding(3) var<storage, read_write> mask: array<f16>;
+@group(0) @binding(4) var<storage, read_write> sinks: array<f32>;
+#define DST_BINDING 5
+#define PARAMS_BINDING 6
+#endif
+#elif defined(MASK)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> mask: array<f16>;
+#define DST_BINDING 3
+#define PARAMS_BINDING 4
+#else
+@group(0) @binding(3) var<storage, read_write> mask: array<f16>;
+#define DST_BINDING 4
+#define PARAMS_BINDING 5
+#endif
+#elif defined(SINKS)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> sinks: array<f32>;
+#define DST_BINDING 3
+#define PARAMS_BINDING 4
+#else
+@group(0) @binding(3) var<storage, read_write> sinks: array<f32>;
+#define DST_BINDING 4
+#define PARAMS_BINDING 5
+#endif
+#else
+#ifdef KV_OVERLAP
+#define DST_BINDING 2
+#define PARAMS_BINDING 3
+#else
+#define DST_BINDING 3
+#define PARAMS_BINDING 4
+#endif
+#endif
+
+@group(0) @binding(DST_BINDING) var<storage, read_write> dst: array<vec4<DST_TYPE>>;
+@group(0) @binding(PARAMS_BINDING) var<uniform> params: Params;
+
+const FLOAT_MIN: f32 = -1.0e9;
+const Q_CHUNKS: u32 = HEAD_DIM_QK / 4u;
+const V_CHUNKS: u32 = HEAD_DIM_V / 4u;
+const SCORE_REGS_PER_LANE: u32 = (KV_TILE + MIN_SUBGROUP_SIZE - 1u) / MIN_SUBGROUP_SIZE;
+const OUT_REGS_PER_LANE: u32 = (V_CHUNKS + MIN_SUBGROUP_SIZE - 1u) / MIN_SUBGROUP_SIZE;
+
+var<workgroup> q_shmem: array<f32, Q_TILE * HEAD_DIM_QK>;
+var<workgroup> kv_shmem: array<f32, KV_TILE * KV_STAGE_STRIDE>;
+var<workgroup> p_shmem: array<f32, Q_TILE * KV_TILE>;
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
+        @builtin(local_invocation_id) local_id: vec3<u32>,
+        @builtin(subgroup_id) subgroup_id: u32,
+        @builtin(subgroup_size) subgroup_size: u32,
+        @builtin(num_subgroups) num_subgroups: u32,
+        @builtin(subgroup_invocation_id) sg_inv_id: u32) {
+    if (subgroup_size == 0u || num_subgroups < Q_TILE) {
+        return;
+    }
+
+    let wg_per_head = (params.seq_len_q + Q_TILE - 1u) / Q_TILE;
+    let wg_per_batch = wg_per_head * params.n_heads;
+
+    let dst2_stride = HEAD_DIM_V * params.n_heads;
+    let dst3_stride = dst2_stride * params.seq_len_q;
+
+    let batch_idx = wg_id.x / wg_per_batch;
+    let q_batch_offset = params.offset_q + batch_idx * params.stride_q3;
+    let k_batch_offset = params.offset_k + batch_idx * params.stride_k3;
+    let v_batch_offset = params.offset_v + batch_idx * params.stride_v3;
+    let dst_batch_offset = params.offset_dst + batch_idx * dst3_stride;
+    let wg_in_batch = wg_id.x % wg_per_batch;
+
+    let head_idx = wg_in_batch / wg_per_head;
+    let q_head_offset = q_batch_offset + head_idx * params.stride_q2;
+    let k_head_idx = head_idx / params.q_per_kv;
+    let v_head_offset = v_batch_offset + k_head_idx * params.stride_v2;
+    let k_head_offset = k_batch_offset + k_head_idx * params.stride_k2;
+
+    let wg_in_head = wg_in_batch % wg_per_head;
+    let q_row_start = wg_in_head * Q_TILE;
+    let global_q_row = q_row_start + subgroup_id;
+    let row_active = subgroup_id < Q_TILE && global_q_row < params.seq_len_q;
+
+#ifdef MASK
+    let mask_global_offset = params.offset_mask + batch_idx * params.stride_mask3 + q_row_start * params.seq_len_kv;
+#endif
+
+    let dst_global_offset = dst_batch_offset + q_row_start * dst2_stride + head_idx * HEAD_DIM_V;
+
+    let head = f32(head_idx);
+    let slope = select(1.0,
+                       select(pow(params.m1, 2.0 * (head - params.n_head_log2) + 1.0),
+                              pow(params.m0, head + 1.0),
+                              head < params.n_head_log2),
+                       params.max_bias > 0.0);
+
+    for (var elem_idx = local_id.x; elem_idx < Q_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) {
+        let q_tile_row = elem_idx / HEAD_DIM_QK;
+        let q_col = elem_idx % HEAD_DIM_QK;
+        let head_q_row = q_row_start + q_tile_row;
+        let global_q_row_offset = q_head_offset + head_q_row * params.stride_q1;
+        q_shmem[elem_idx] = select(
+            0.0,
+            f32(Q[global_q_row_offset + q_col]) * params.scale,
+            head_q_row < params.seq_len_q);
+    }
+
+    workgroupBarrier();
+
+    var row_max = FLOAT_MIN;
+    var exp_sum = 0.0;
+    var out_regs: array<vec4<f32>, OUT_REGS_PER_LANE>;
+    for (var reg_idx = 0u; reg_idx < OUT_REGS_PER_LANE; reg_idx += 1u) {
+        out_regs[reg_idx] = vec4<f32>(0.0);
+    }
+
+    let q_base = subgroup_id * HEAD_DIM_QK;
+    let subgroup_p_offset = subgroup_id * KV_TILE;
+
+    for (var kv_tile = 0u; kv_tile < params.seq_len_kv; kv_tile += KV_TILE) {
+        let kv_count = min(KV_TILE, params.seq_len_kv - kv_tile);
+        let score_slots = min(SCORE_REGS_PER_LANE, (kv_count + subgroup_size - 1u) / subgroup_size);
+        let out_slots = min(OUT_REGS_PER_LANE, (V_CHUNKS + subgroup_size - 1u) / subgroup_size);
+        var local_scores: array<f32, SCORE_REGS_PER_LANE>;
+        for (var slot = 0u; slot < SCORE_REGS_PER_LANE; slot += 1u) {
+            local_scores[slot] = FLOAT_MIN;
+        }
+
+        for (var vec_idx_local = local_id.x; vec_idx_local < kv_count * Q_CHUNKS; vec_idx_local += WG_SIZE) {
+            let kv_local = vec_idx_local / Q_CHUNKS;
+            let chunk = vec_idx_local % Q_CHUNKS;
+            let global_k_row = kv_tile + kv_local;
+            let k_vec_index = (k_head_offset + global_k_row * params.stride_k1 + chunk * 4u) >> 2u;
+            let k4 = K[k_vec_index];
+            let kv_off = kv_local * KV_STAGE_STRIDE + chunk * 4u;
+            kv_shmem[kv_off + 0u] = f32(k4.x);
+            kv_shmem[kv_off + 1u] = f32(k4.y);
+            kv_shmem[kv_off + 2u] = f32(k4.z);
+            kv_shmem[kv_off + 3u] = f32(k4.w);
+        }
+
+        workgroupBarrier();
+
+        var local_max = FLOAT_MIN;
+        if (row_active) {
+            for (var slot = 0u; slot < score_slots; slot += 1u) {
+                let kv_local = sg_inv_id + slot * subgroup_size;
+                if (kv_local >= kv_count) {
+                    continue;
+                }
+
+                let global_k_row = kv_tile + kv_local;
+                var dot_val = 0.0;
+                for (var chunk = 0u; chunk < Q_CHUNKS; chunk += 1u) {
+                    let q_off = q_base + chunk * 4u;
+                    let qv = vec4<f32>(
+                        q_shmem[q_off + 0u],
+                        q_shmem[q_off + 1u],
+                        q_shmem[q_off + 2u],
+                        q_shmem[q_off + 3u]);
+                    let kv_off = kv_local * KV_STAGE_STRIDE + chunk * 4u;
+                    let kv = vec4<f32>(
+                        kv_shmem[kv_off + 0u],
+                        kv_shmem[kv_off + 1u],
+                        kv_shmem[kv_off + 2u],
+                        kv_shmem[kv_off + 3u]);
+                    dot_val += dot(qv, kv);
+                }
+#ifdef LOGIT_SOFTCAP
+                dot_val = params.logit_softcap * tanh(dot_val);
+#endif
+#ifdef MASK
+                let mask_idx = mask_global_offset + subgroup_id * params.seq_len_kv + global_k_row;
+                dot_val += slope * f32(mask[mask_idx]);
+#endif
+                local_scores[slot] = dot_val;
+                local_max = max(local_max, dot_val);
+            }
+        }
+
+        let tile_max = subgroupMax(local_max);
+        let new_max = max(row_max, tile_max);
+        let cur_exp = exp(row_max - new_max);
+        exp_sum *= cur_exp;
+        for (var reg_idx = 0u; reg_idx < OUT_REGS_PER_LANE; reg_idx += 1u) {
+            out_regs[reg_idx] *= cur_exp;
+        }
+
+        var local_sum = 0.0;
+        for (var slot = 0u; slot < score_slots; slot += 1u) {
+            let kv_local = sg_inv_id + slot * subgroup_size;
+            if (row_active && kv_local < kv_count) {
+                let p = exp(local_scores[slot] - new_max);
+                p_shmem[subgroup_p_offset + kv_local] = p;
+                local_sum += p;
+            }
+        }
+
+        workgroupBarrier();
+
+        for (var vec_idx_local = local_id.x; vec_idx_local < kv_count * V_CHUNKS; vec_idx_local += WG_SIZE) {
+            let kv_local = vec_idx_local / V_CHUNKS;
+            let chunk = vec_idx_local % V_CHUNKS;
+            let global_v_row = kv_tile + kv_local;
+            let v_vec_index = (v_head_offset + global_v_row * params.stride_v1 + chunk * 4u) >> 2u;
+            let v4 = V[v_vec_index];
+            let kv_off = kv_local * KV_STAGE_STRIDE + chunk * 4u;
+            kv_shmem[kv_off + 0u] = f32(v4.x);
+            kv_shmem[kv_off + 1u] = f32(v4.y);
+            kv_shmem[kv_off + 2u] = f32(v4.z);
+            kv_shmem[kv_off + 3u] = f32(v4.w);
+        }
+
+        workgroupBarrier();
+
+        let tile_sum = subgroupAdd(local_sum);
+        exp_sum += tile_sum;
+        row_max = new_max;
+
+        if (row_active) {
+            for (var reg_idx = 0u; reg_idx < out_slots; reg_idx += 1u) {
+                let chunk = sg_inv_id + reg_idx * subgroup_size;
+                if (chunk >= V_CHUNKS) {
+                    continue;
+                }
+
+                var acc = out_regs[reg_idx];
+                for (var kv_local = 0u; kv_local < kv_count; kv_local += 1u) {
+                    let p = p_shmem[subgroup_p_offset + kv_local];
+                    let kv_off = kv_local * KV_STAGE_STRIDE + chunk * 4u;
+                    let v4 = vec4<f32>(
+                        kv_shmem[kv_off + 0u],
+                        kv_shmem[kv_off + 1u],
+                        kv_shmem[kv_off + 2u],
+                        kv_shmem[kv_off + 3u]);
+                    acc += p * v4;
+                }
+                out_regs[reg_idx] = acc;
+            }
+        }
+
+        workgroupBarrier();
+    }
+
+#ifdef SINKS
+    if (row_active) {
+        let sink_score = sinks[params.offset_sinks + head_idx];
+        let sink_max = max(row_max, sink_score);
+        let sink_scale = exp(row_max - sink_max);
+        for (var reg_idx = 0u; reg_idx < OUT_REGS_PER_LANE; reg_idx += 1u) {
+            out_regs[reg_idx] *= sink_scale;
+        }
+        exp_sum = exp_sum * sink_scale + exp(sink_score - sink_max);
+        row_max = sink_max;
+    }
+#endif
+
+    if (row_active) {
+        let inv_exp_sum = select(0.0, 1.0 / exp_sum, exp_sum != 0.0);
+        let row_base = dst_global_offset + subgroup_id * dst2_stride;
+        let out_slots = min(OUT_REGS_PER_LANE, (V_CHUNKS + subgroup_size - 1u) / subgroup_size);
+        for (var reg_idx = 0u; reg_idx < out_slots; reg_idx += 1u) {
+            let chunk = sg_inv_id + reg_idx * subgroup_size;
+            if (chunk >= V_CHUNKS) {
+                continue;
+            }
+            let dst_vec_index = (row_base + chunk * 4u) >> 2u;
+            dst[dst_vec_index] = vec4<DST_TYPE>(out_regs[reg_idx] * inv_exp_sum);
+        }
+    }
+}
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_blk.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_blk.wgsl
index 61107c6a985..b4f7c16c35d 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_blk.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_blk.wgsl
@@ -15,7 +15,7 @@ struct Params {
     nblk1: u32,
 };
 
-@group(0) @binding(0) var<storage, read> mask: array<f16>;
+@group(0) @binding(0) var<storage, read_write> mask: array<f16>;
 @group(0) @binding(1) var<storage, read_write> blk: array<u32>;
 @group(0) @binding(2) var<uniform> params: Params;
 
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl
index 9a0de82a56a..1091d744073 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl
@@ -2,6 +2,12 @@ diagnostic(off, subgroup_uniformity);
 enable f16;
 enable subgroups;
 
+#ifdef DST_F16
+#define DST_TYPE f16
+#else
+#define DST_TYPE f32
+#endif
+
 // Default values
 #define HEAD_DIM_V 64
 #define WG_SIZE 128
@@ -17,7 +23,7 @@ struct Params {
 };
 
 @group(0) @binding(0) var<storage, read_write> tmp: array<f32>;
-@group(0) @binding(1) var<storage, read_write> dst: array<vec4<f32>>;
+@group(0) @binding(1) var<storage, read_write> dst: array<vec4<DST_TYPE>>;
 @group(0) @binding(2) var<uniform> params: Params;
 
 const FLOAT_MIN: f32 = -1.0e9;
@@ -72,7 +78,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
 
         if (thread == 0u) {
             let dst_vec_index = (row_base + elem_base) >> 2u;
-            dst[dst_vec_index] = vec4<f32>(sum_x, sum_y, sum_z, sum_w) * inv_s;
+            dst[dst_vec_index] = vec4<DST_TYPE>(vec4<f32>(sum_x, sum_y, sum_z, sum_w) * inv_s);
         }
     }
 }
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl
index a52575871ae..30ebbebe772 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl
@@ -1,8 +1,6 @@
-diagnostic(off, chromium.subgroup_matrix_uniformity);
 diagnostic(off, subgroup_uniformity);
 enable f16;
 enable subgroups;
-enable chromium_experimental_subgroup_matrix;
 
 #ifdef KV_F32
 #define KV_TYPE f32
@@ -10,22 +8,29 @@ enable chromium_experimental_subgroup_matrix;
 #define KV_TYPE f16
 #endif
 
-#define HEAD_DIM_QK 64
-#define HEAD_DIM_V 64
+#ifdef Q_F16
+#define Q_TYPE f16
+#else
+#define Q_TYPE f32
+#endif
 
+#ifdef DST_F16
+#define DST_TYPE f16
+#else
+#define DST_TYPE f32
+#endif
 
-#define SG_MAT_M 8
-#define SG_MAT_N 8
-#define SG_MAT_K 8
+#define HEAD_DIM_QK 64
+#define HEAD_DIM_V 64
 
-#define Q_TILE SG_MAT_M
+#define KV_GRANULARITY 8
 #define KV_TILE 16
 #define WG_SIZE 64
 #ifndef VEC_NE
 #define VEC_NE 4u
 #endif
 
-#define KV_BLOCKS (KV_TILE / SG_MAT_N)
+#define KV_BLOCKS (KV_TILE / KV_GRANULARITY)
 
 #define BLOCK_SIZE 32
 #define BLOCKS_K ((HEAD_DIM_QK + BLOCK_SIZE - 1) / BLOCK_SIZE)
@@ -96,7 +101,15 @@ struct Params {
     nwg: u32,
 };
 
-@group(0) @binding(0) var<storage, read_write> Q: array<f32>;
+@group(0) @binding(0) var<storage, read_write> Q: array<Q_TYPE>;
+#ifdef KV_OVERLAP
+#if defined(KV_Q4_0) || defined(KV_Q8_0)
+@group(0) @binding(1) var<storage, read_write> K: array<KV_TYPE>;
+#else
+@group(0) @binding(1) var<storage, read_write> K: array<vec4<KV_TYPE>>;
+#endif
+#define V K
+#else
 #if defined(KV_Q4_0) || defined(KV_Q8_0)
 @group(0) @binding(1) var<storage, read_write> K: array<KV_TYPE>;
 #else
@@ -107,7 +120,22 @@ struct Params {
 #else
 @group(0) @binding(2) var<storage, read_write> V: array<vec4<KV_TYPE>>;
 #endif
+#endif
 #if defined(MASK) && defined(SINKS)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> mask: array<f16>;
+@group(0) @binding(3) var<storage, read_write> sinks: array<f32>;
+#ifdef BLK
+#define BLK_BINDING 4
+#define TMP_BINDING 5
+#define DST_BINDING 6
+#define PARAMS_BINDING 7
+#else
+#define TMP_BINDING 4
+#define DST_BINDING 5
+#define PARAMS_BINDING 6
+#endif
+#else
 @group(0) @binding(3) var<storage, read_write> mask: array<f16>;
 @group(0) @binding(4) var<storage, read_write> sinks: array<f32>;
 #ifdef BLK
@@ -120,7 +148,21 @@ struct Params {
 #define DST_BINDING 6
 #define PARAMS_BINDING 7
 #endif
+#endif
 #elif defined(MASK)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> mask: array<f16>;
+#ifdef BLK
+#define BLK_BINDING 3
+#define TMP_BINDING 4
+#define DST_BINDING 5
+#define PARAMS_BINDING 6
+#else
+#define TMP_BINDING 3
+#define DST_BINDING 4
+#define PARAMS_BINDING 5
+#endif
+#else
 @group(0) @binding(3) var<storage, read_write> mask: array<f16>;
 #ifdef BLK
 #define BLK_BINDING 4
@@ -132,60 +174,70 @@ struct Params {
 #define DST_BINDING 5
 #define PARAMS_BINDING 6
 #endif
+#endif
 #elif defined(SINKS)
+#ifdef KV_OVERLAP
+@group(0) @binding(2) var<storage, read_write> sinks: array<f32>;
+#define TMP_BINDING 3
+#define DST_BINDING 4
+#define PARAMS_BINDING 5
+#else
 @group(0) @binding(3) var<storage, read_write> sinks: array<f32>;
 #define TMP_BINDING 4
 #define DST_BINDING 5
 #define PARAMS_BINDING 6
+#endif
+#else
+#ifdef KV_OVERLAP
+#define TMP_BINDING 2
+#define DST_BINDING 3
+#define PARAMS_BINDING 4
 #else
 #define TMP_BINDING 3
 #define DST_BINDING 4
 #define PARAMS_BINDING 5
 #endif
+#endif
 
 #ifdef BLK
 @group(0) @binding(BLK_BINDING) var<storage, read_write> blk: array<u32>;
 #endif
 @group(0) @binding(TMP_BINDING) var<storage, read_write> tmp: array<f32>;
-@group(0) @binding(DST_BINDING) var<storage, read_write> dst: array<vec4<f32>>;
+@group(0) @binding(DST_BINDING) var<storage, read_write> dst: array<vec4<DST_TYPE>>;
 @group(0) @binding(PARAMS_BINDING) var<uniform> params: Params;
 
 // Just a very small float value.
 const FLOAT_MIN: f32 = -1.0e9;
 
-var<workgroup> q_shmem: array<f16, Q_TILE * HEAD_DIM_QK>;
+var<workgroup> q_shmem: array<f32, HEAD_DIM_QK>;
 
 #ifndef KV_DIRECT
 const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V);
 // we can reuse the same shmem for K and V since we only need one at a time
-var<workgroup> kv_shmem: array<f16, kv_shmem_size>;
+var<workgroup> kv_shmem: array<f32, kv_shmem_size>;
 #endif
 
-var<workgroup> o_shmem: array<f16, Q_TILE * HEAD_DIM_V>;
+var<workgroup> o_shmem: array<f32, HEAD_DIM_V>;
 
 #ifdef MASK
 // storage for mask values
-var<workgroup> mask_shmem: array<f16, Q_TILE * KV_TILE>;
+var<workgroup> mask_shmem: array<f32, KV_TILE>;
 #endif
 
 // note that we reuse the same storage for both since we only need one at a time
-var<workgroup> inter_shmem: array<f16, Q_TILE * KV_TILE>;
+var<workgroup> inter_shmem: array<f32, KV_TILE>;
 
 // Storage for row max and exp sum during online softmax
-var<workgroup> row_max_shmem: array<f32, Q_TILE>;
-var<workgroup> exp_sum_shmem: array<f32, Q_TILE>;
-var<workgroup> blk_state_wg: u32;
-
-fn calc_softmax_term(kv_idx: u32, q_tile_row: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 {
+fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 {
     var v = select(FLOAT_MIN,
-                   f32(inter_shmem[kv_idx + q_tile_row * KV_TILE]) * params.scale,
+                   inter_shmem[kv_idx] * params.scale,
                    kv_idx < KV_TILE);
 #ifdef LOGIT_SOFTCAP
     v = params.logit_softcap * tanh(v);
 #endif
 #ifdef MASK
     if (apply_mask) {
-        var mask_val = select(0.0,f32(mask_shmem[q_tile_row * KV_TILE + kv_idx]), kv_idx < KV_TILE);
+        var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE);
         v += select(mask_val, slope * mask_val, has_bias);
     }
 #endif
@@ -199,19 +251,17 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
     @builtin(subgroup_size) subgroup_size: u32,
     @builtin(num_subgroups) num_subgroups: u32,
     @builtin(subgroup_invocation_id) sg_inv_id: u32) {
+    // Vec path processes exactly one query row per workgroup, so subgroup 0 can
+    // keep the running softmax state in private storage.
+    var row_max = FLOAT_MIN;
+    var exp_sum = 0.0;
 
-    // initialize row max for online softmax
-    for (var i = local_id.x; i < Q_TILE; i += WG_SIZE) {
-        row_max_shmem[i] = FLOAT_MIN;
-        exp_sum_shmem[i] = 0.0;
-    }
-
-    for (var i = local_id.x; i < Q_TILE * HEAD_DIM_V; i += WG_SIZE) {
+    for (var i = local_id.x; i < HEAD_DIM_V; i += WG_SIZE) {
         o_shmem[i] = 0.0;
     }
 
     // workgroups per head/batch
-    let wg_per_head = (params.seq_len_q + Q_TILE - 1u) / Q_TILE;
+    let wg_per_head = params.seq_len_q;
     let wg_per_batch = wg_per_head * params.n_heads;
 
     let dst2_stride = HEAD_DIM_V * params.n_heads;
@@ -235,9 +285,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
     let k_head_offset = k_batch_offset + k_head_idx * params.stride_k2;
     let v_head_offset = v_batch_offset + v_head_idx * params.stride_v2;
 
-    // starting Q row for this workgroup
+    // Vec path handles one Q row per workgroup.
     let wg_in_head = wg_in_batch % wg_per_head;
-    let q_row_start = wg_in_head * Q_TILE;
+    let q_row_start = wg_in_head;
 
 #ifdef MASK
     // mask offset
@@ -248,21 +298,18 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
     let has_bias = params.max_bias > 0.0;
     let slope = select(1.0, select(pow(params.m1, 2.0 * (head - params.n_head_log2) + 1.0), pow(params.m0, head + 1.0), head < params.n_head_log2), has_bias);
 
-    // load q tile into shared memory
-    for (var elem_idx = local_id.x; elem_idx < Q_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) {
-        let q_row = elem_idx / HEAD_DIM_QK;
-        let q_col = elem_idx % HEAD_DIM_QK;
-        let head_q_row = q_row_start + q_row;
-        let global_q_row_offset = q_head_offset + head_q_row * params.stride_q1;
-        q_shmem[elem_idx] = f16(select(
+    // load the single Q row into shared memory
+    for (var elem_idx = local_id.x; elem_idx < HEAD_DIM_QK; elem_idx += WG_SIZE) {
+        let global_q_row_offset = q_head_offset + q_row_start * params.stride_q1;
+        q_shmem[elem_idx] = select(
             0.0,
-            Q[global_q_row_offset + q_col],
-            head_q_row < params.seq_len_q && q_col < HEAD_DIM_QK));
+            f32(Q[global_q_row_offset + elem_idx]),
+            q_row_start < params.seq_len_q);
     }
 
     for (var kv_tile = iwg * KV_TILE; kv_tile < params.seq_len_kv; kv_tile += KV_TILE * params.nwg) {
 #ifdef BLK
-        let q_blk = q_row_start / Q_TILE;
+        let q_blk = q_row_start;
         let kv_blk = kv_tile / KV_TILE;
         let blk_batch = select(0u, batch_idx, params.stride_mask3 > 0u);
         let blk_idx = params.blk_base + (blk_batch * params.blk_nblk1 + q_blk) * params.blk_nblk0 + kv_blk;
@@ -270,14 +317,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
 #else
         let blk_state_local = 1u;
 #endif
-        if (local_id.x == 0u) {
-            blk_state_wg = blk_state_local;
-        }
-        workgroupBarrier();
-        let blk_state = blk_state_wg;
+        let blk_state = blk_state_local;
         let skip_tile = blk_state == 0u;
-        for (var elem_idx = local_id.x; elem_idx < Q_TILE * KV_TILE; elem_idx += WG_SIZE) {
-            inter_shmem[elem_idx] = f16(0.0);
+        for (var elem_idx = local_id.x; elem_idx < KV_TILE; elem_idx += WG_SIZE) {
+            inter_shmem[elem_idx] = 0.0;
         }
 
       // load k tile into shared memory
@@ -300,8 +343,8 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                   let q_packed = bitcast<u32>(vec2(q_0, q_1));
                   for (var k = 0u; k < 4u; k++) {
                       let q_byte = get_byte(q_packed, k);
-                      let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d;
-                      let q_lo = (f16(q_byte & 0xF) - 8.0) * d;
+                      let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0) * f32(d);
+                      let q_lo = (f32(q_byte & 0xF) - 8.0) * f32(d);
                       let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k;
                       kv_shmem[row_offset + idx] = q_lo;
                       kv_shmem[row_offset + idx + 16u] = q_hi;
@@ -328,7 +371,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                   let q_packed = bitcast<u32>(vec2(q_0, q_1));
                   for (var k = 0u; k < 4u; k++) {
                       let q_byte = get_byte_i32(q_packed, k);
-                      let q_val = f16(q_byte) * d;
+                      let q_val = f32(q_byte) * f32(d);
                       let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k;
                       kv_shmem[row_offset + idx] = q_val;
                   }
@@ -346,10 +389,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
           let in_bounds = global_k_row < params.seq_len_kv && (k_col + 3u) < HEAD_DIM_QK;
           let vec_idx = (global_k_row_offset + k_col) >> 2u;
           let k4 = select(vec4<KV_TYPE>(0.0), K[vec_idx], in_bounds);
-          kv_shmem[elem_idx + 0u] = f16(k4.x);
-          kv_shmem[elem_idx + 1u] = f16(k4.y);
-          kv_shmem[elem_idx + 2u] = f16(k4.z);
-          kv_shmem[elem_idx + 3u] = f16(k4.w);
+          kv_shmem[elem_idx + 0u] = f32(k4.x);
+          kv_shmem[elem_idx + 1u] = f32(k4.y);
+          kv_shmem[elem_idx + 2u] = f32(k4.z);
+          kv_shmem[elem_idx + 3u] = f32(k4.w);
       }
 #endif
 
@@ -360,36 +403,30 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
         let num_of_threads = subgroup_size / VEC_NE;
         let tx = sg_inv_id % num_of_threads;
         let ty = sg_inv_id / num_of_threads;
-          for (var q_tile_row = subgroup_id; q_tile_row < Q_TILE; q_tile_row += num_subgroups) {
-              let global_q_row = q_row_start + q_tile_row;
-              if (global_q_row >= params.seq_len_q) {
-                  continue;
-              }
-              let local_q_row_offset = q_tile_row * HEAD_DIM_QK;
-
+          if (subgroup_id == 0u && q_row_start < params.seq_len_q) {
               for (var kv_base : u32 = 0u; kv_base < KV_TILE; kv_base += VEC_NE) {
                   let kv_idx = kv_base + ty;
                   var partial_sum: f32 = 0.0;
                   let kv_valid = kv_idx < KV_TILE && (kv_tile + kv_idx) < params.seq_len_kv;
                   if (kv_valid) {
                     for (var i = tx; i < (HEAD_DIM_QK / 4u); i += num_of_threads) {
-                        let q_off = local_q_row_offset + i * 4u;
+                        let q_off = i * 4u;
 
                         let qv = vec4<f32>(
-                            f32(q_shmem[q_off + 0u]),
-                            f32(q_shmem[q_off + 1u]),
-                            f32(q_shmem[q_off + 2u]),
-                            f32(q_shmem[q_off + 3u]));
+                            q_shmem[q_off + 0u],
+                            q_shmem[q_off + 1u],
+                            q_shmem[q_off + 2u],
+                            q_shmem[q_off + 3u]);
 #ifdef KV_DIRECT
                         let idx = k_head_offset + (kv_tile + kv_idx) * params.stride_k1 + (i * 4u);
                         let kv = vec4<f32>(K[idx >> 2u]);
 #else
                         let idx = kv_idx * HEAD_DIM_QK + (i * 4u);
                         let kv = vec4<f32>(
-                            f32(kv_shmem[idx + 0u]),
-                            f32(kv_shmem[idx + 1u]),
-                            f32(kv_shmem[idx + 2u]),
-                            f32(kv_shmem[idx + 3u]));
+                            kv_shmem[idx + 0u],
+                            kv_shmem[idx + 1u],
+                            kv_shmem[idx + 2u],
+                            kv_shmem[idx + 3u]);
 #endif
                         partial_sum += dot(qv, kv);
                     }
@@ -410,8 +447,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
 
                   let sum_bcast = subgroupShuffle(sum, num_of_threads * ty);
                   if (tx == 0u && kv_valid) {
-                      let dst_idx = q_tile_row * KV_TILE + kv_idx;
-                      inter_shmem[dst_idx] = f16(sum_bcast);
+                      inter_shmem[kv_idx] = sum_bcast;
                   }
               }
           }
@@ -422,14 +458,11 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
       let apply_mask = !skip_tile && (blk_state != 2u);
       if (apply_mask) {
           // load mask tile into shared memory for this KV block
-          for (var elem_idx = local_id.x; elem_idx < Q_TILE * KV_TILE; elem_idx += WG_SIZE) {
-              let mask_row = elem_idx / KV_TILE;
-              let mask_col = elem_idx % KV_TILE;
-              let global_q_row = q_row_start + mask_row;
-              let global_k_col = kv_tile + mask_col;
-              let mask_in_bounds = global_q_row < params.seq_len_q && global_k_col < params.seq_len_kv;
-              let mask_idx = mask_global_offset + mask_row * params.seq_len_kv + global_k_col;
-              mask_shmem[elem_idx] = select(0.0, mask[mask_idx], mask_in_bounds);
+          for (var elem_idx = local_id.x; elem_idx < KV_TILE; elem_idx += WG_SIZE) {
+              let global_k_col = kv_tile + elem_idx;
+              let mask_in_bounds = q_row_start < params.seq_len_q && global_k_col < params.seq_len_kv;
+              let mask_idx = mask_global_offset + global_k_col;
+              mask_shmem[elem_idx] = select(0.0f, f32(mask[mask_idx]), mask_in_bounds);
           }
       }
 #else
@@ -439,50 +472,40 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
       workgroupBarrier();
 
       // online softmax
-      if (!skip_tile) {
-          for (var q_tile_row = subgroup_id; q_tile_row < Q_TILE; q_tile_row += num_subgroups) {
-              let global_q_row = q_row_start + q_tile_row;
-              if (global_q_row >= params.seq_len_q) {
-                  break;
-              }
-
-              var prev_max = row_max_shmem[q_tile_row];
-              var final_max = prev_max;
-              // pass 1: compute final max across the full KV tile in chunks
-              for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) {
-                  let kv_idx = kv_offset + sg_inv_id;
-                  let kv_valid = kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE;
-                  let softmax_term = select(FLOAT_MIN,
-                                            calc_softmax_term(kv_idx, q_tile_row, slope, has_bias, apply_mask),
-                                            kv_valid);
-                  final_max = subgroupMax(max(final_max, softmax_term));
-              }
+      if (!skip_tile && subgroup_id == 0u && q_row_start < params.seq_len_q) {
+          var prev_max = row_max;
+          var final_max = prev_max;
+          // pass 1: compute final max across the full KV tile in chunks
+          for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) {
+              let kv_idx = kv_offset + sg_inv_id;
+              let kv_valid = kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE;
+              let softmax_term = select(FLOAT_MIN,
+                                        calc_softmax_term(kv_idx, slope, has_bias, apply_mask),
+                                        kv_valid);
+              final_max = subgroupMax(max(final_max, softmax_term));
+          }
 
-              var total_exp_term: f32 = 0.0;
-              // pass 2: compute exp sum and write P using final_max
-              for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) {
-                  let kv_idx = kv_offset + sg_inv_id;
-                  let softmax_term = calc_softmax_term(kv_idx, q_tile_row, slope, has_bias, apply_mask);
-                  let cur_p = select(0.0,
-                                     exp(softmax_term - final_max),
-                                     kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE);
-                  total_exp_term += subgroupAdd(cur_p);
-                  if (kv_idx < KV_TILE) {
-                      inter_shmem[kv_idx + q_tile_row * KV_TILE] = f16(cur_p);
-                  }
+          var total_exp_term: f32 = 0.0;
+          // pass 2: compute exp sum and write P using final_max
+          for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) {
+              let kv_idx = kv_offset + sg_inv_id;
+              let softmax_term = calc_softmax_term(kv_idx, slope, has_bias, apply_mask);
+              let cur_p = select(0.0,
+                                 exp(softmax_term - final_max),
+                                 kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE);
+              total_exp_term += subgroupAdd(cur_p);
+              if (kv_idx < KV_TILE) {
+                  inter_shmem[kv_idx] = cur_p;
               }
+          }
 
-              let cur_exp = exp(prev_max - final_max);
+          let cur_exp = exp(prev_max - final_max);
 
-              if (sg_inv_id == 0) {
-                  row_max_shmem[q_tile_row] = final_max;
-                  exp_sum_shmem[q_tile_row] = exp_sum_shmem[q_tile_row] * cur_exp + total_exp_term;
-              }
+          row_max = final_max;
+          exp_sum = exp_sum * cur_exp + total_exp_term;
 
-              for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) {
-                  let idx = q_tile_row * HEAD_DIM_V + elem_idx;
-                  o_shmem[idx] = f16(f32(o_shmem[idx]) * cur_exp);
-              }
+          for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) {
+              o_shmem[elem_idx] = o_shmem[elem_idx] * cur_exp;
           }
       }
 
@@ -506,8 +529,8 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                   let q_packed = bitcast<u32>(vec2(q_0, q_1));
                   for (var k = 0u; k < 4u; k++) {
                       let q_byte = get_byte(q_packed, k);
-                      let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d;
-                      let q_lo = (f16(q_byte & 0xF) - 8.0) * d;
+                      let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0) * f32(d);
+                      let q_lo = (f32(q_byte & 0xF) - 8.0) * f32(d);
                       let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k;
                       kv_shmem[row_offset + idx] = q_lo;
                       kv_shmem[row_offset + idx + 16u] = q_hi;
@@ -534,7 +557,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                   let q_packed = bitcast<u32>(vec2(q_0, q_1));
                   for (var k = 0u; k < 4u; k++) {
                       let q_byte = get_byte_i32(q_packed, k);
-                      let q_val = f16(q_byte) * d;
+                      let q_val = f32(q_byte) * f32(d);
                       let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k;
                       kv_shmem[row_offset + idx] = q_val;
                   }
@@ -552,25 +575,23 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
           let in_bounds = global_v_row < params.seq_len_kv && (v_col + 3u) < HEAD_DIM_V;
           let vec_idx = (global_v_row_offset + v_col) >> 2u;
           let v4 = select(vec4<KV_TYPE>(0.0), V[vec_idx], in_bounds);
-          kv_shmem[elem_idx + 0u] = f16(v4.x);
-          kv_shmem[elem_idx + 1u] = f16(v4.y);
-          kv_shmem[elem_idx + 2u] = f16(v4.z);
-          kv_shmem[elem_idx + 3u] = f16(v4.w);
+          kv_shmem[elem_idx + 0u] = f32(v4.x);
+          kv_shmem[elem_idx + 1u] = f32(v4.y);
+          kv_shmem[elem_idx + 2u] = f32(v4.z);
+          kv_shmem[elem_idx + 3u] = f32(v4.w);
       }
 #endif
 
       workgroupBarrier();
 
       if (!skip_tile) {
-          // we have P (Q_TILE x KV_TILE) in inter_shmem and V (KV_TILE x head_dim_v) in kv_shmem
+          // we have P (KV_TILE) in inter_shmem and V (KV_TILE x head_dim_v) in kv_shmem
           // we want to compute O += P * V across the full KV tile
           let ne_threads : u32 = VEC_NE;
           let nl_threads = max(1u, subgroup_size / ne_threads);
           let tx_pv = sg_inv_id % nl_threads;
           let ty_pv = sg_inv_id / nl_threads;
-          for (var q_tile_row = subgroup_id;
-               q_tile_row < Q_TILE;
-               q_tile_row += num_subgroups) {
+          if (subgroup_id == 0u && q_row_start < params.seq_len_q) {
               for (var vec_col = tx_pv; vec_col < (HEAD_DIM_V / 4u); vec_col += nl_threads) {
                   var lo = vec4<f32>(0.0, 0.0, 0.0, 0.0);
                   for (var cc = 0u; cc < KV_TILE / ne_threads; cc += 1u) {
@@ -580,17 +601,17 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                           continue;
                       }
 
-                      let p = f32(inter_shmem[kv_idx + q_tile_row * KV_TILE]);
+                      let p = inter_shmem[kv_idx];
 #ifdef KV_DIRECT
                       let v_idx = v_head_offset + v_row * params.stride_v1 + vec_col * 4u;
                       let v4 = vec4<f32>(V[v_idx >> 2u]);
 #else
                       let v_idx = kv_idx * HEAD_DIM_V + vec_col * 4u;
                       let v4 = vec4<f32>(
-                          f32(kv_shmem[v_idx + 0u]),
-                          f32(kv_shmem[v_idx + 1u]),
-                          f32(kv_shmem[v_idx + 2u]),
-                          f32(kv_shmem[v_idx + 3u]));
+                          kv_shmem[v_idx + 0u],
+                          kv_shmem[v_idx + 1u],
+                          kv_shmem[v_idx + 2u],
+                          kv_shmem[v_idx + 3u]);
 #endif
                       lo += p * v4;
                   }
@@ -621,11 +642,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
 
                   if (ty_pv == 0u) {
                       let elem_base = vec_col * 4u;
-                      let o_base_idx = q_tile_row * HEAD_DIM_V + elem_base;
-                      o_shmem[o_base_idx + 0u] = f16(f32(o_shmem[o_base_idx + 0u]) + lo_x);
-                      o_shmem[o_base_idx + 1u] = f16(f32(o_shmem[o_base_idx + 1u]) + lo_y);
-                      o_shmem[o_base_idx + 2u] = f16(f32(o_shmem[o_base_idx + 2u]) + lo_z);
-                      o_shmem[o_base_idx + 3u] = f16(f32(o_shmem[o_base_idx + 3u]) + lo_w);
+                      o_shmem[elem_base + 0u] = o_shmem[elem_base + 0u] + lo_x;
+                      o_shmem[elem_base + 1u] = o_shmem[elem_base + 1u] + lo_y;
+                      o_shmem[elem_base + 2u] = o_shmem[elem_base + 2u] + lo_z;
+                      o_shmem[elem_base + 3u] = o_shmem[elem_base + 3u] + lo_w;
                   }
               }
           }
@@ -637,70 +657,46 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
 
 #ifdef SINKS
     // Sinks are global terms and must be applied exactly once across split workgroups.
-    if (iwg == 0u) {
-        for (var q_tile_row = subgroup_id;
-             q_tile_row < Q_TILE;
-             q_tile_row += num_subgroups) {
-                let global_q_row = q_row_start + q_tile_row;
-                if (global_q_row >= params.seq_len_q) {
-                    break;
-                }
-
-                var prev_max = row_max_shmem[q_tile_row];
-
-                // for non-sink threads, exp(FLOAT_MIN) effectively zeroes out their contribution to the sum
-                let sink_val = select(FLOAT_MIN, sinks[params.offset_sinks + head_idx], sg_inv_id == 0);
-                let new_max = subgroupMax(max(prev_max, sink_val));
-                let max_exp = exp(prev_max - new_max);
-                let sink_exp = exp(sink_val - new_max);
-
-                let sink_exp_sum = subgroupAdd(sink_exp);
-
-                if (sg_inv_id == 0) {
-                    row_max_shmem[q_tile_row] = new_max;
-                    exp_sum_shmem[q_tile_row] = exp_sum_shmem[q_tile_row] * max_exp + sink_exp_sum;
-                }
-
-            for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) {
-                let idx = q_tile_row * HEAD_DIM_V + elem_idx;
-                o_shmem[idx] = f16(f32(o_shmem[idx]) * max_exp);
-            }
+    if (iwg == 0u && subgroup_id == 0u && q_row_start < params.seq_len_q) {
+        var prev_max = row_max;
+
+        // for non-sink threads, exp(FLOAT_MIN) effectively zeroes out their contribution to the sum
+        let sink_val = select(FLOAT_MIN, sinks[params.offset_sinks + head_idx], sg_inv_id == 0u);
+        let new_max = subgroupMax(max(prev_max, sink_val));
+        let max_exp = exp(prev_max - new_max);
+        let sink_exp = exp(sink_val - new_max);
+
+        let sink_exp_sum = subgroupAdd(sink_exp);
+
+        row_max = new_max;
+        exp_sum = exp_sum * max_exp + sink_exp_sum;
+
+        for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) {
+            o_shmem[elem_idx] = o_shmem[elem_idx] * max_exp;
         }
-        workgroupBarrier();
     }
+    workgroupBarrier();
 #endif
     let rows_per_batch = params.n_heads * params.seq_len_q;
-    for (var q_tile_row = subgroup_id;
-         q_tile_row < Q_TILE;
-         q_tile_row += num_subgroups) {
-
-        let global_q_row = q_row_start + q_tile_row;
-        if (global_q_row >= params.seq_len_q) { break; }
-
+    if (subgroup_id == 0u && q_row_start < params.seq_len_q) {
         if (params.nwg == 1u) {
-            let exp_sum = exp_sum_shmem[q_tile_row];
             let scale = select(0.0, 1.0 / exp_sum, exp_sum != 0.0);
-            let row_base: u32 =
-                params.offset_dst + batch_idx * dst3_stride + global_q_row * dst2_stride + head_idx * HEAD_DIM_V;
+            let row_base: u32 = params.offset_dst + batch_idx * dst3_stride + q_row_start * dst2_stride +
+                                head_idx * HEAD_DIM_V;
 
             for (var elem_base = sg_inv_id * 4u; elem_base < HEAD_DIM_V; elem_base += subgroup_size * 4u) {
-                let i0 = q_tile_row * HEAD_DIM_V + (elem_base + 0u);
-                let i1 = q_tile_row * HEAD_DIM_V + (elem_base + 1u);
-                let i2 = q_tile_row * HEAD_DIM_V + (elem_base + 2u);
-                let i3 = q_tile_row * HEAD_DIM_V + (elem_base + 3u);
-
                 let v = vec4<f32>(
-                    f32(o_shmem[i0]) * scale,
-                    f32(o_shmem[i1]) * scale,
-                    f32(o_shmem[i2]) * scale,
-                    f32(o_shmem[i3]) * scale
+                    f32(o_shmem[elem_base + 0u]) * scale,
+                    f32(o_shmem[elem_base + 1u]) * scale,
+                    f32(o_shmem[elem_base + 2u]) * scale,
+                    f32(o_shmem[elem_base + 3u]) * scale
                 );
 
                 let dst_vec_index: u32 = (row_base + elem_base) >> 2u;
-                dst[dst_vec_index] = v;
+                dst[dst_vec_index] = vec4<DST_TYPE>(v);
             }
         } else {
-            let rid = batch_idx * rows_per_batch + head_idx * params.seq_len_q + global_q_row;
+            let rid = batch_idx * rows_per_batch + head_idx * params.seq_len_q + q_row_start;
             let tmp_row_data_base = params.tmp_data_base + rid * (HEAD_DIM_V * params.nwg) + iwg * HEAD_DIM_V;
             let tmp_row_stats_base = params.tmp_stats_base + rid * (2u * params.nwg) + 2u * iwg;
 
@@ -708,21 +704,16 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                 elem_base < HEAD_DIM_V;
                 elem_base += subgroup_size * 4u) {
 
-                let i0 = q_tile_row * HEAD_DIM_V + (elem_base + 0u);
-                let i1 = q_tile_row * HEAD_DIM_V + (elem_base + 1u);
-                let i2 = q_tile_row * HEAD_DIM_V + (elem_base + 2u);
-                let i3 = q_tile_row * HEAD_DIM_V + (elem_base + 3u);
-
                 let tbase = tmp_row_data_base + elem_base;
-                tmp[tbase + 0u] = f32(o_shmem[i0]);
-                tmp[tbase + 1u] = f32(o_shmem[i1]);
-                tmp[tbase + 2u] = f32(o_shmem[i2]);
-                tmp[tbase + 3u] = f32(o_shmem[i3]);
+                tmp[tbase + 0u] = f32(o_shmem[elem_base + 0u]);
+                tmp[tbase + 1u] = f32(o_shmem[elem_base + 1u]);
+                tmp[tbase + 2u] = f32(o_shmem[elem_base + 2u]);
+                tmp[tbase + 3u] = f32(o_shmem[elem_base + 3u]);
             }
 
             if (sg_inv_id == 0u) {
-                tmp[tmp_row_stats_base + 0u] = exp_sum_shmem[q_tile_row];
-                tmp[tmp_row_stats_base + 1u] = row_max_shmem[q_tile_row];
+                tmp[tmp_row_stats_base + 0u] = exp_sum;
+                tmp[tmp_row_stats_base + 1u] = row_max;
             }
         }
     }
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
index 1415798fa6b..78d61a93d28 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
@@ -27,6 +27,24 @@ fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
 }
 #endif
 
+#ifdef Q1_0
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block_byte_base = (src_base + offset) * 18;
+    let d = load_f16_as_f32_at_src(block_byte_base);
+    for (var j: u32 = 0u; j < 4u; j++) {
+        let q_packed = load_u32_at_src(block_byte_base + 2u + j * 4u);
+        let dst_base128 = dst_base + offset * 128u + j * 32u;
+        for (var k: u32 = 0; k < 4u; k++) {
+            let q_byte = get_byte(q_packed, k);
+            for (var bit: u32 = 0; bit < 8u; bit++) {
+                let w = select(-d, d, ((q_byte >> bit) & 1u) != 0u);
+                dst[dst_base128 + k * 8u + bit] = w;
+            }
+        }
+    }
+}
+#endif
+
 #ifdef Q4_0
 fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
     let block_byte_base = (src_base + offset) * 18; // Block stride: 18 bytes
@@ -634,6 +652,27 @@ fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
 }
 #endif
 
+#ifdef MXFP4
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block_byte_base = (src_base + offset) * 17;
+    let eu8 = get_byte(load_u32_at_src(block_byte_base), 0);
+    let d = ldexp(1.0, i32(eu8) - 128);
+    for (var j: u32 = 0u; j < 4; j++) {
+        let q_byte_offset = block_byte_base + 1 + j * 4;
+        let q_packed = load_u32_at_src(q_byte_offset);
+        for (var k: u32 = 0; k < 4; k++) {
+            let q_byte = get_byte(q_packed, k);
+            let q_hi = f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * d;
+            let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * d;
+            let dst_offset = dst_base + offset * 32 + j * 4 + k;
+            dst[dst_offset] = q_lo;
+            dst[dst_offset + 16u] = q_hi;
+        }
+    }
+}
+#endif
+
+
 @group(0) @binding(0)
 var<storage, read_write> src: array<SRC_TYPE>;
 
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/im2col.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/im2col.wgsl
new file mode 100644
index 00000000000..386ebab879f
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/im2col.wgsl
@@ -0,0 +1,101 @@
+#include "common_decls.tmpl"
+enable f16;
+
+@group(0) @binding(0)
+#if defined(INPUT_F32)
+var<storage, read_write> input: array<f32>;
+#elif defined(INPUT_F16)
+var<storage, read_write> input: array<f16>;
+#endif
+
+@group(0) @binding(1)
+#if defined(OUTPUT_F32)
+var<storage, read_write> output: array<f32>;
+#elif defined(OUTPUT_F16)
+var<storage, read_write> output: array<f16>;
+#endif
+
+struct Params {
+    offset_i: u32,
+    offset_o: u32,
+
+    // element strides
+    si0: u32, si1: u32, si2: u32, si3: u32,
+    so0: u32, so1: u32, so2: u32, so3: u32,
+
+    KW: u32, KH: u32, IC: u32,
+    IW: u32, IH: u32, N: u32,
+    OW: u32, OH: u32,
+
+    // stride
+    s0: u32, s1: u32,
+    // padding
+    p0: u32, p1: u32,
+    // dilation
+    d0: u32, d1: u32,
+}
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+fn load_input(idx: u32) -> f32 {
+    #if defined(INPUT_F32)
+        return input[idx];
+    #elif defined(INPUT_F16)
+        return f32(input[idx]);
+    #endif
+}
+
+fn store_output(idx: u32, val: f32) {
+    #if defined(OUTPUT_F32)
+        output[idx] = val;
+    #elif defined(OUTPUT_F16)
+        output[idx] = f16(val);
+    #endif
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(
+    @builtin(global_invocation_id) gid: vec3<u32>,
+    @builtin(num_workgroups) num_wg: vec3<u32>
+) {
+
+    let threads_per_group = u32(WG_SIZE);
+    let i_out = gid.x + (num_wg.x * threads_per_group) * gid.y;
+    let K = params.KW * params.KH * params.IC;
+    let M = params.OW * params.OH;
+    let total = K * M * params.N;
+
+    if (i_out >= total) {
+        return;
+    }
+
+    // decode (k, m, n)
+    var i = i_out;
+    let n = i / (K * M);
+    i = i % (K * M);
+    let m = i / K;
+    let k = i % K;
+
+    // decode (oh, ow)
+    let oh = m / params.OW;
+    let ow = m % params.OW;
+
+    // decode (kw, kh, ic)
+    let kw = k % params.KW;
+    let tmp = k / params.KW;
+    let kh = tmp % params.KH;
+    let ic = tmp / params.KH;
+
+    let iw_i32 = i32(ow * params.s0 + kw * params.d0) - i32(params.p0);
+    let ih_i32 = i32(oh * params.s1 + kh * params.d1) - i32(params.p1);
+
+    if (iw_i32 >= 0 && iw_i32 < i32(params.IW) && ih_i32 >= 0 && ih_i32 < i32(params.IH)) {
+        let iw = u32(iw_i32);
+        let ih = u32(ih_i32);
+        let in_idx = params.offset_i + iw * params.si0 + ih * params.si1 + ic * params.si2 + n * params.si3;
+        store_output(params.offset_o + k * params.so0 + ow * params.so1 + oh * params.so2 + n * params.so3, load_input(in_idx));
+    } else {
+        store_output(params.offset_o + k * params.so0 + ow * params.so1 + oh * params.so2 + n * params.so3, 0.0);
+    }
+}
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
index 5a323818260..eb2a8368f43 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
@@ -61,40 +61,76 @@ fn init_shmem_src1(thread_id: u32, batch_offset: u32, offset_n: u32, k_outer: u3
 #endif // INIT_SRC1_SHMEM_FLOAT
 #endif
 
+#ifdef INIT_SRC0_SHMEM_Q1_0
+const BLOCK_SIZE = 128u;
+const BLOCK_SIZE_BYTES = 18u;
+const NQ = 8u; // 8 weights (1 byte of qs) per thread per iteration
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
+        let tile_m = i / TILE_K;
+        let tile_k_start = i % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k_start = k_outer + tile_k_start;
+
+        if (global_m >= params.m) {
+            break;
+        }
+
+        let block_k = global_k_start / BLOCK_SIZE;
+        let byte_in_block = (global_k_start % BLOCK_SIZE) / 8u;
+        let src0_idx = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d = load_f16_at_src0(block_byte_base);
+        let q_byte = load_u32_at_src0(block_byte_base + 2u + byte_in_block) & 0xFFu;
+
+        for (var bit = 0u; bit < NQ; bit++) {
+            let global_k = global_k_start + bit;
+            if (global_k < params.k) {
+                shmem[i + bit] = select(-d, d, ((q_byte >> bit) & 1u) != 0u);
+            }
+        }
+    }
+}
+#endif // INIT_SRC0_SHMEM_Q1_0
+
 #ifdef INIT_SRC0_SHMEM_Q4_0
 const BLOCK_SIZE = 32u;
 const BLOCK_SIZE_BYTES = 18u;
 // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
 override BLOCKS_K = TILE_K/BLOCK_SIZE;
 const NQ = 16u;
-const WEIGHTS_PER_F16 = 4u; // 4 weights per f16
-const F16_PER_THREAD = NQ / WEIGHTS_PER_F16;
+const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q
+const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed)
 
 fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
     for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
         let blck_idx = i / BLOCK_SIZE;
-        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
-        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u;
+        let block_offset = (i % BLOCK_SIZE) / NQ;
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD;
 
         let tile_m = blck_idx / BLOCKS_K;
         let global_m = offset_m + tile_m;
         let block_k = blck_idx % BLOCKS_K;
-        let global_k = k_outer / BLOCK_SIZE + block_k;
+        let global_block_k = k_outer / BLOCK_SIZE + block_k;
 
-        if (global_m < params.m && global_k < params.k / BLOCK_SIZE) {
-            let src0_idx = batch_offset + global_m * params.stride_01 + global_k;
+        if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) {
+            let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k;
             let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
             let d = load_f16_at_src0(block_byte_base);
 
-            for (var j = 0u; j < F16_PER_THREAD; j += 2) {
-                let q_byte_offset = block_byte_base + 2u + 2u * (block_offset + j);
+            // store NQ(16) weights
+            for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) {
+
+                let q_byte_offset = block_byte_base + 2u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP;
                 let q_packed = load_u32_at_src0(q_byte_offset);
-                for (var k = 0u; k < 4u; k++) {
+
+                for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) {
                     let q_byte = get_byte(q_packed, k);
                     let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d;
                     let q_lo = (f16(q_byte & 0xF) - 8.0) * d;
-                    shmem[shmem_idx + j * 2 + k] = q_lo;
-                    shmem[shmem_idx + j * 2 + k + 16u] = q_hi;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi;
                 }
             }
         }
@@ -108,35 +144,38 @@ const BLOCK_SIZE_BYTES = 20u;
 // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
 override BLOCKS_K = TILE_K/BLOCK_SIZE;
 const NQ = 16u;
-const WEIGHTS_PER_F16 = 4u; // 4 weights per f16
-const F16_PER_THREAD = NQ / WEIGHTS_PER_F16;
+const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q
+const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed)
 
 fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
     for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
         let blck_idx = i / BLOCK_SIZE;
-        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
-        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u;
+        let block_offset = (i % BLOCK_SIZE) / NQ;
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD;
 
         let tile_m = blck_idx / BLOCKS_K;
         let global_m = offset_m + tile_m;
         let block_k = blck_idx % BLOCKS_K;
-        let global_k = k_outer / BLOCK_SIZE + block_k;
+        let global_block_k = k_outer / BLOCK_SIZE + block_k;
 
-        if (global_m < params.m && global_k < params.k / BLOCK_SIZE) {
-            let src0_idx = batch_offset + global_m * params.stride_01 + global_k;
+        if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) {
+            let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k;
             let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
             let d = load_f16_at_src0(block_byte_base);
             let m = load_f16_at_src0(block_byte_base + 2u);
 
-            for (var j = 0u; j < F16_PER_THREAD; j += 2) {
-                let q_byte_offset = block_byte_base + 4u + 2u * (block_offset + j);
+            // store NQ(16) weights
+            for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) {
+
+                let q_byte_offset = block_byte_base + 4u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP;
                 let q_packed = load_u32_at_src0(q_byte_offset);
-                for (var k = 0u; k < 4u; k++) {
+
+                for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) {
                     let q_byte = get_byte(q_packed, k);
                     let q_lo = f16(q_byte & 0xF) * d + m;
                     let q_hi = f16((q_byte >> 4) & 0xF) * d + m;
-                    shmem[shmem_idx + j * 2 + k] = q_lo;
-                    shmem[shmem_idx + j * 2 + k + 16u] = q_hi;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi;
                 }
             }
         }
@@ -145,52 +184,49 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3
 #endif // INIT_SRC0_SHMEM_Q4_1
 
 #ifdef INIT_SRC0_SHMEM_Q5_0
-// 32 weights per block, each at 4 bits each = 32 * 4 = 128 bits / 16 = 8 f16s per block
 const BLOCK_SIZE = 32u;
 const BLOCK_SIZE_BYTES = 22u;
 // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
 // tile_k is defined as 32u, so blocks_k ends up being 1 always
 override BLOCKS_K = TILE_K / BLOCK_SIZE;
 const NQ = 16u;
-const WEIGHTS_PER_F16 = 4u; // 4 weights per f16
-const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 16 / 4 = 4 f16s per thread, each thread should handle 4 f16s * 4 weights per = 16 weights
+const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q
+const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed)
 
 fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
 
     for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
         let blck_idx    = i / BLOCK_SIZE;
-        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
-        let shmem_idx   = blck_idx * BLOCK_SIZE + block_offset * 2u;
+        let block_offset = (i % BLOCK_SIZE) / NQ;
+        let shmem_idx   = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD;
 
         let tile_m   = blck_idx / BLOCKS_K;
         let global_m = offset_m + tile_m;
         let block_k  = blck_idx % BLOCKS_K;
-        let global_k = k_outer / BLOCK_SIZE + block_k;
+        let global_block_k = k_outer / BLOCK_SIZE + block_k;
 
-        if (global_m < params.m && global_k < params.k / BLOCK_SIZE) {
-            let src0_idx  = batch_offset + global_m * params.stride_01 + global_k;
+        if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) {
+            let src0_idx  = batch_offset + global_m * params.stride_01 + global_block_k;
             let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
 
             let d  = load_f16_at_src0(block_byte_base);
             let qh_packed = load_u32_at_src0(block_byte_base + 2u);
 
-            for (var j = 0u; j < 2; j++) {
-                let q_byte_offset = block_byte_base + 6u + 2u * (block_offset + j * 2u);
+            // store NQ(16) weights
+            for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) {
+                let q_byte_offset = block_byte_base + 6u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP;
                 let q_packed = load_u32_at_src0(q_byte_offset);
 
-                let j_adjusted = j + (block_offset / 2u);
-
-
-                for (var k = 0u; k < 4u; k++) {
+                for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) {
                     let q_byte = get_byte(q_packed, k);
 
-                    let qh_hi = (qh_packed >> (j_adjusted * 4 + k + 12)) & 0x10;
+                    let byte_idx = block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP + k;
+                    let qh_hi = (qh_packed >> (byte_idx + 12u)) & 0x10;
                     let q_hi = (f16(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d;
-                    let qh_lo = ((qh_packed >> (j_adjusted * 4 + k)) << 4) & 0x10;
+                    let qh_lo = ((qh_packed >> byte_idx) << 4) & 0x10;
                     let q_lo = (f16((q_byte & 0xF) | qh_lo) - 16.0) * d;
-
-                    shmem[shmem_idx + j * 4u + k]        = q_lo; // store first weight
-                    shmem[shmem_idx + j * 4u + k + 16u]  = q_hi; // store second weight
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi;
                 }
             }
         }
@@ -199,54 +235,49 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3
 #endif // INIT_SRC0_SHMEM_Q5_0
 
 #ifdef INIT_SRC0_SHMEM_Q5_1
-// 32 weights per block, each at 4 bits each = 32 * 4 = 128 bits / 16 = 8 f16s per block
 const BLOCK_SIZE = 32u;
 const BLOCK_SIZE_BYTES = 24u;
 // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
-// tile_k is defined as 32u, so blocks_k ends up being 1 always
 override BLOCKS_K = TILE_K / BLOCK_SIZE;
 const NQ = 16u;
-const WEIGHTS_PER_F16 = 4u; // 4 weights per f16
-const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 16 / 4 = 4 f16s per thread, each thread should handle 4 f16s * 4 weights per = 16 weights
+const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q
+const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed)
 
 fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
 
     for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
         let blck_idx    = i / BLOCK_SIZE;
-        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
-        let shmem_idx   = blck_idx * BLOCK_SIZE + block_offset * 2u;
+        let block_offset = (i % BLOCK_SIZE) / NQ;
+        let shmem_idx   = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD;
 
         let tile_m   = blck_idx / BLOCKS_K;
         let global_m = offset_m + tile_m;
         let block_k  = blck_idx % BLOCKS_K;
-        let global_k = k_outer / BLOCK_SIZE + block_k;
+        let global_block_k = k_outer / BLOCK_SIZE + block_k;
 
-        if (global_m < params.m && global_k < params.k / BLOCK_SIZE) {
-            let src0_idx  = batch_offset + global_m * params.stride_01 + global_k;
+        if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) {
+            let src0_idx  = batch_offset + global_m * params.stride_01 + global_block_k;
             let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
 
             let d  = load_f16_at_src0(block_byte_base);
             let m = load_f16_at_src0(block_byte_base + 2u);
             let qh_packed = load_u32_at_src0(block_byte_base + 4u);
 
-            for (var j = 0u; j < 2; j++) {
-
-                let q_byte_offset = block_byte_base + 8u + 2u * (block_offset + j * 2u);
+            // store NQ(16) weights
+            for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) {
+                let q_byte_offset = block_byte_base + 8u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP;
                 let q_packed = load_u32_at_src0(q_byte_offset);
 
-                let j_adjusted = j + (block_offset / 2u);
-
-
-                for (var k = 0u; k < 4u; k++) {
+                for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) {
                     let q_byte = get_byte(q_packed, k);
 
-                    let qh_hi = (qh_packed >> (j_adjusted * 4 + k + 12)) & 0x10;
-                    let q_hi = (f16(((q_byte >> 4) & 0xF) | qh_hi)) * d + m;
-                    let qh_lo = ((qh_packed >> (j_adjusted * 4 + k)) << 4) & 0x10;
-                    let q_lo = (f16((q_byte & 0xF) | qh_lo)) * d + m;
-
-                    shmem[shmem_idx + j * 4u + k]        = q_lo; // store first weight
-                    shmem[shmem_idx + j * 4u + k + 16u]  = q_hi; // store second weight
+                    let byte_idx = block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP + k;
+                    let qh_hi = (qh_packed >> (byte_idx + 12u)) & 0x10;
+                    let q_hi = f16(((q_byte >> 4) & 0xF) | qh_hi) * d + m;
+                    let qh_lo = ((qh_packed >> byte_idx) << 4) & 0x10;
+                    let q_lo = f16((q_byte & 0xF) | qh_lo) * d + m;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi;
                 }
             }
         }
@@ -260,33 +291,34 @@ const BLOCK_SIZE_BYTES = 34u;
 // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
 override BLOCKS_K = TILE_K/BLOCK_SIZE;
 const NQ = 16u;
-const WEIGHTS_PER_F16 = 2u; // 2 8-bit weights per f16
-const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 8 f16s per thread
+const BYTES_PER_THREAD = 16u; // NQ(16) weights use 16 bytes of q
+const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed)
 
 fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
     for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
         let blck_idx = i / BLOCK_SIZE;
-        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
-        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u;
+        let block_offset = (i % BLOCK_SIZE) / NQ;
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD;
 
         let tile_m = blck_idx / BLOCKS_K;
         let global_m = offset_m + tile_m;
         let block_k = blck_idx % BLOCKS_K;
-        let global_k = k_outer / BLOCK_SIZE + block_k;
+        let global_block_k = k_outer / BLOCK_SIZE + block_k;
 
-        if (global_m < params.m && global_k < params.k / BLOCK_SIZE) {
-            let src0_idx = batch_offset + global_m * params.stride_01 + global_k;
+        if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) {
+            let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k;
             let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
             let d = load_f16_at_src0(block_byte_base);
 
-            for (var j = 0u; j < F16_PER_THREAD; j+=2) {
-                let q_byte_offset = block_byte_base + 2u + 2u * (block_offset + j);
+            // store NQ(16) weights
+            for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) {
+                let q_byte_offset = block_byte_base + 2u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP;
                 let q_packed = load_u32_at_src0(q_byte_offset);
-                for (var k = 0u; k < 4u; k++) {
+                for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) {
                     let q_byte = get_byte_i32(q_packed, k);
 
                     let q_val = f16(q_byte) * d;
-                    shmem[shmem_idx + j * 2 + k] = q_val;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_val;
                 }
             }
         }
@@ -300,34 +332,35 @@ const BLOCK_SIZE_BYTES = 36u;
 // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
 override BLOCKS_K = TILE_K/BLOCK_SIZE;
 const NQ = 16u;
-const WEIGHTS_PER_F16 = 2u; // 2 8-bit weights per f16
-const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 8 f16s per thread, 2 threads per block
+const BYTES_PER_THREAD = 16u; // NQ(16) weights use 16 bytes of q
+const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed)
 
 fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
     for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
         let blck_idx = i / BLOCK_SIZE;
-        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
-        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u;
+        let block_offset = (i % BLOCK_SIZE) / NQ;
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD;
 
         let tile_m = blck_idx / BLOCKS_K;
         let global_m = offset_m + tile_m;
         let block_k = blck_idx % BLOCKS_K;
-        let global_k = k_outer / BLOCK_SIZE + block_k;
+        let global_block_k = k_outer / BLOCK_SIZE + block_k;
 
-        if (global_m < params.m && global_k < params.k / BLOCK_SIZE) {
-            let src0_idx = batch_offset + global_m * params.stride_01 + global_k;
+        if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) {
+            let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k;
             let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
             let d = load_f16_at_src0(block_byte_base);
             let m = load_f16_at_src0(block_byte_base + 2u);
 
-            for (var j = 0u; j < F16_PER_THREAD; j+=2) {
-                let q_byte_offset = block_byte_base + 4u + 2u * (block_offset + j);
+            // store NQ(16) weights
+            for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) {
+                let q_byte_offset = block_byte_base + 4u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP;
                 let q_packed = load_u32_at_src0(q_byte_offset);
-                for (var k = 0u; k < 4u; k++) {
+                for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) {
                     let q_byte = get_byte_i32(q_packed, k);
 
                     let q_val = f16(q_byte) * d + m;
-                    shmem[shmem_idx + j * 2 + k] = q_val;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_val;
                 }
             }
         }
@@ -707,3 +740,471 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3
     }
 }
 #endif // INIT_SRC0_SHMEM_Q6_K
+
+#ifdef INIT_SRC0_SHMEM_IQ4_NL
+const BLOCK_SIZE = 32u;
+const BLOCK_SIZE_BYTES = 18u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d               = load_f16_at_src0(block_byte_base);
+
+        let pos       = k_in_block % 16u;
+        let nib_shift = (k_in_block / 16u) * 4u;
+        let q_packed  = load_u32_at_src0(block_byte_base + 2u + (pos / 4u) * 4u);
+        let nib       = (get_byte(q_packed, pos % 4u) >> nib_shift) & 0xFu;
+
+        shmem[elem_idx] = d * f16(kvalues_iq4nl[nib]);
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ4_NL
+
+#ifdef INIT_SRC0_SHMEM_IQ4_XS
+const BLOCK_SIZE = 256u;
+const BLOCK_SIZE_BYTES = 136u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+
+        let d_scales_h = load_u32_at_src0(block_byte_base);
+        let d          = bitcast<vec2<f16>>(d_scales_h).x;
+        let scales_h   = d_scales_h >> 16u;
+
+        let ib  = k_in_block / 32u;
+        let pos = k_in_block % 32u;
+
+        let scales_l_word = load_u32_at_src0(block_byte_base + 4u);
+        let ls_lo         = (get_byte(scales_l_word, ib / 2u) >> ((ib & 1u) * 4u)) & 0xFu;
+        let ls_hi         = ((scales_h >> (2u * ib)) & 3u) << 4u;
+        let dl            = d * f16(i32(ls_lo | ls_hi) - 32);
+
+        let iqs       = ib * 16u + (pos % 16u);
+        let nib_shift = (pos / 16u) * 4u;
+        let q_packed  = load_u32_at_src0(block_byte_base + 8u + (iqs / 4u) * 4u);
+        let nib       = (get_byte(q_packed, iqs % 4u) >> nib_shift) & 0xFu;
+
+        shmem[elem_idx] = dl * f16(kvalues_iq4nl[nib]);
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ4_XS
+
+#ifdef INIT_SRC0_SHMEM_IQ1_S
+const BLOCK_SIZE = 256u;
+const BLOCK_SIZE_BYTES = 50u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d               = load_f16_as_f32_at_src0(block_byte_base);
+
+        let ib  = k_in_block / 32u;
+        let pos = k_in_block % 32u;
+        let l   = pos / 8u;
+        let j   = pos % 8u;
+
+        let qh    = load_u32_at_src0(block_byte_base + 34u + ib * 2u) & 0xFFFFu;
+        let dl    = d * (2.0 * f32((qh >> 12u) & 7u) + 1.0);
+        let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x8000u) != 0u);
+
+        let qs_w = load_u32_at_src0(block_byte_base + 2u + ib * 4u);
+        let ig   = (get_byte(qs_w, l) | (((qh >> (3u * l)) & 7u) << 8u)) * 8u;
+
+        let gw = iq1_grid[(ig + j) / 16u];
+        let g  = (gw >> (((ig + j) % 16u) * 2u)) & 3u;
+        let gs = bitcast<i32>(g << 30u) >> 30u;
+
+        shmem[elem_idx] = f16(dl * (f32(gs) + delta));
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ1_S
+
+#ifdef INIT_SRC0_SHMEM_IQ1_M
+const BLOCK_SIZE = 256u;
+const BLOCK_SIZE_BYTES = 56u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+
+        let scales0 = load_u32_at_src0(block_byte_base + 48u);
+        let scales1 = load_u32_at_src0(block_byte_base + 52u);
+        let scale_packed = ((scales0 >> 12u) & 0xFu) |
+                           ((scales0 >> 24u) & 0x00F0u) |
+                           ((scales1 >>  4u) & 0x0F00u) |
+                           ((scales1 >> 16u) & 0xF000u);
+        let d = f32(bitcast<vec2<f16>>(scale_packed).x);
+
+        let ib  = k_in_block / 32u;
+        let pos = k_in_block % 32u;
+        let l   = pos / 8u;
+        let j   = pos % 8u;
+
+        let scales = select(scales0, scales1, ib >= 4u);
+        let sw = (scales >> (16u * ((ib / 2u) % 2u))) & 0xFFFFu;
+        let s_pair = (sw >> (6u * (ib % 2u) + 3u * (l / 2u))) & 0x7u;
+        let dl     = d * f32(2u * s_pair + 1u);
+
+        let qh_word = load_u32_at_src0(block_byte_base + 32u + (ib / 2u) * 4u);
+        let qh      = qh_word >> (16u * (ib % 2u));
+        let qh_nib  = (qh >> (4u * l)) & 0xFu;
+
+        let qs_w = load_u32_at_src0(block_byte_base + ib * 4u);
+        let idx  = get_byte(qs_w, l) | ((qh_nib & 7u) << 8u);
+        let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh_nib & 0x8u) != 0u);
+
+        let ig = idx * 8u;
+        let gw = iq1_grid[(ig + j) / 16u];
+        let g  = (gw >> (((ig + j) % 16u) * 2u)) & 3u;
+        let gs = bitcast<i32>(g << 30u) >> 30u;
+
+        shmem[elem_idx] = f16(dl * (f32(gs) + delta));
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ1_M
+
+#ifdef INIT_SRC0_SHMEM_IQ2_XXS
+const BLOCK_SIZE = 256u;
+const BLOCK_SIZE_BYTES = 66u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d               = load_f16_as_f32_at_src0(block_byte_base);
+
+        let entry_idx = k_in_block / 8u;
+        let j         = k_in_block % 8u;
+
+        let ib = entry_idx & ~3u;
+        let l  = entry_idx & 3u;
+
+        let aux0 = load_u32_at_src0(block_byte_base + 2u + ib * 2u);
+        let aux1 = load_u32_at_src0(block_byte_base + 2u + (ib + 2u) * 2u);
+        let db   = d * (0.5 + f32(aux1 >> 28u)) * 0.25;
+
+        let ig    = get_byte(aux0, l) * 8u;
+        let is    = (aux1 >> (7u * l)) & 127u;
+        let signs = get_byte(ksigns_iq2xs[is / 4u], is % 4u);
+
+        let g = get_byte(iq2xxs_grid[(ig + j) / 4u], (ig + j) % 4u);
+        let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4u], j % 4u) & signs) != 0u);
+
+        shmem[elem_idx] = f16(db * f32(g) * m);
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ2_XXS
+
+#ifdef INIT_SRC0_SHMEM_IQ2_XS
+const BLOCK_SIZE = 256u;
+const BLOCK_SIZE_BYTES = 74u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d               = load_f16_as_f32_at_src0(block_byte_base);
+
+        let entry_idx = k_in_block / 8u;
+        let j         = k_in_block % 8u;
+
+        let ib = entry_idx & ~3u;
+        let l  = entry_idx & 3u;
+
+        let scales_word = load_u32_at_src0(block_byte_base + 66u + (ib / 16u) * 4u);
+        let s           = get_byte(scales_word, (ib % 16u) / 4u);
+        let s_nib       = select(s & 0xFu, (s >> 4u) & 0xFu, (l / 2u) != 0u);
+        let dl          = d * (0.5 + f32(s_nib)) * 0.25;
+
+        let qs_word = load_u32_at_src0(block_byte_base + 2u + (ib + l) * 2u);
+        let qs_val  = qs_word & 0xFFFFu;
+        let ig      = (qs_val & 511u) * 8u;
+        let is      = qs_val >> 9u;
+        let signs   = get_byte(ksigns_iq2xs[is / 4u], is % 4u);
+
+        let g = get_byte(iq2xs_grid[(ig + j) / 4u], (ig + j) % 4u);
+        let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4u], j % 4u) & signs) != 0u);
+
+        shmem[elem_idx] = f16(dl * f32(g) * m);
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ2_XS
+
+#ifdef INIT_SRC0_SHMEM_IQ2_S
+const BLOCK_SIZE = 256u;
+const BLOCK_SIZE_BYTES = 82u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d               = load_f16_as_f32_at_src0(block_byte_base);
+
+        let ib = k_in_block / 32u;
+        let l  = (k_in_block % 32u) / 8u;
+        let j  = k_in_block % 8u;
+
+        let scales_word = load_u32_at_src0(block_byte_base + 74u + (ib / 4u) * 4u);
+        let s           = get_byte(scales_word, ib % 4u);
+        let s_nib       = select(s & 0xFu, (s >> 4u) & 0xFu, (l / 2u) != 0u);
+        let dl          = d * (0.5 + f32(s_nib)) * 0.25;
+
+        let qs_word = load_u32_at_src0(block_byte_base + 2u + ib * 4u);
+        let qh_word = load_u32_at_src0(block_byte_base + 66u + (ib / 4u) * 4u);
+        let qh_b    = (get_byte(qh_word, ib % 4u) << (8u - 2u * l)) & 0x300u;
+        let ig      = (get_byte(qs_word, l) | qh_b) * 8u;
+
+        let signs_word = load_u32_at_src0(block_byte_base + 34u + ib * 4u);
+        let signs      = get_byte(signs_word, l);
+
+        let g = get_byte(iq2s_grid[(ig + j) / 4u], (ig + j) % 4u);
+        let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4u], j % 4u) & signs) != 0u);
+
+        shmem[elem_idx] = f16(dl * f32(g) * m);
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ2_S
+
+#ifdef INIT_SRC0_SHMEM_IQ3_XXS
+const BLOCK_SIZE = 256u;
+const BLOCK_SIZE_BYTES = 98u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d               = load_f16_as_f32_at_src0(block_byte_base);
+
+        let ib_pair = k_in_block / 32u;
+        let in_pair = k_in_block % 32u;
+        let l       = in_pair / 8u;
+        let in_l    = in_pair % 8u;
+        let k2      = in_l / 4u;
+        let j       = in_l % 4u;
+
+        let ib            = ib_pair * 2u;
+        let sc_sign_off   = block_byte_base + 2u + (ib + 32u) * 2u;
+        let sc_sign       = load_u32_at_src0(sc_sign_off);
+        let db            = d * (0.5 + f32(sc_sign >> 28u)) * 0.5;
+        let is            = (sc_sign >> (7u * l)) & 127u;
+        let signs         = get_byte(ksigns_iq2xs[is / 4u], is % 4u);
+
+        let ig_word = load_u32_at_src0(block_byte_base + 2u + (ib * 2u + l) * 2u) & 0xFFFFu;
+        let ig_byte = get_byte(ig_word, k2);
+        let g       = get_byte(iq3xxs_grid[ig_byte], j);
+        let m       = select(1.0, -1.0, (get_byte(kmask_iq2xs[k2], j) & signs) != 0u);
+
+        shmem[elem_idx] = f16(db * f32(g) * m);
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ3_XXS
+
+#ifdef INIT_SRC0_SHMEM_IQ3_S
+const BLOCK_SIZE = 256u;
+const BLOCK_SIZE_BYTES = 110u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+
+        if (global_m >= params.m || global_k >= params.k) {
+            shmem[elem_idx] = f16(0.0);
+            continue;
+        }
+
+        let block_k    = global_k / BLOCK_SIZE;
+        let k_in_block = global_k % BLOCK_SIZE;
+
+        let src0_idx        = batch_offset + global_m * params.stride_01 + block_k;
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d               = load_f16_as_f32_at_src0(block_byte_base);
+
+        let ib   = k_in_block / 64u;
+        let rest = k_in_block % 64u;
+        let k    = rest / 32u;
+        let in_k = rest % 32u;
+        let l    = in_k / 8u;
+        let in_l = in_k % 8u;
+        let k2   = in_l / 4u;
+        let j    = in_l % 4u;
+
+        let scales_word = load_u32_at_src0(block_byte_base + 106u);
+        let s           = get_byte(scales_word, ib);
+        let s_nib       = select(s & 0xFu, (s >> 4u) & 0xFu, k != 0u);
+        let dl          = d * (1.0 + 2.0 * f32(s_nib));
+
+        let qh_word = load_u32_at_src0(block_byte_base + 66u + (ib / 2u) * 4u);
+        let qh_byte = get_byte(qh_word, (ib % 2u) * 2u + k);
+
+        let ig_word = load_u32_at_src0(block_byte_base + 2u + (ib * 8u + k * 4u + l) * 2u) & 0xFFFFu;
+        let ig_lo   = get_byte(ig_word, 0u) | ((qh_byte << (8u - 2u * l)) & 256u);
+        let ig_hi   = get_byte(ig_word, 1u) | ((qh_byte << (7u - 2u * l)) & 256u);
+        let ig      = select(ig_lo, ig_hi, k2 != 0u);
+
+        let signs_word = load_u32_at_src0(block_byte_base + 74u + (ib * 2u + k) * 4u);
+        let signs      = get_byte(signs_word, l);
+
+        let g = get_byte(iq3s_grid[ig], j);
+        let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[k2], j) & signs) != 0u);
+
+        shmem[elem_idx] = f16(dl * f32(g) * m);
+    }
+}
+#endif // INIT_SRC0_SHMEM_IQ3_S
+
+#ifdef INIT_SRC0_SHMEM_MXFP4
+const BLOCK_SIZE = 32u;
+const BLOCK_SIZE_BYTES = 17u;
+// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
+override BLOCKS_K = TILE_K/BLOCK_SIZE;
+const NQ = 16u;
+const BYTES_PER_THREAD = 8u; // NQ(16) weights uses 8 bytes of q
+const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed)
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
+        let blck_idx = i / BLOCK_SIZE;
+        let block_offset = (i % BLOCK_SIZE) / NQ;
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD;
+
+        let tile_m = blck_idx / BLOCKS_K;
+        let global_m = offset_m + tile_m;
+        let block_k = blck_idx % BLOCKS_K;
+        let global_block_k = k_outer / BLOCK_SIZE + block_k;
+
+        if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) {
+            let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k;
+            let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+            let eu8 = get_byte(load_u32_at_src0(block_byte_base), 0);
+            let e = ldexp(1.0, i32(eu8) - 128);
+
+            // store NQ(16) weights
+            for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) {
+
+                let q_byte_offset = block_byte_base + 1u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP;
+                let q_packed = load_u32_at_src0(q_byte_offset);
+
+                for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) {
+                    let q_byte = get_byte(q_packed, k);
+                    let q_hi = f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * e;
+                    let q_lo = f32(kvalues_mxfp4[q_byte & 0xF]) * e;
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = f16(q_lo);
+                    shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = f16(q_hi);
+                }
+            }
+        }
+    }
+}
+#endif // INIT_SRC0_SHMEM_MXFP4
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl
new file mode 100644
index 00000000000..6ff9bcf2df0
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl
@@ -0,0 +1,154 @@
+#ifdef USE_SUBGROUP_REDUCTION
+enable subgroups;
+#endif
+enable f16;
+
+#define DECLARE_BYTE_LOADERS_SRC0
+#include "common_decls.tmpl"
+
+#include "mul_mat_vec_acc.tmpl"
+
+struct MulMatIdVecParams {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_ids: u32,
+    offset_dst: u32,
+
+    k: u32,
+    m: u32,
+    n_expert: u32,
+    n_expert_used: u32,
+    b_ne1: u32,
+
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<SRC0_TYPE>; // [cols, rows, n_expert]
+@group(0) @binding(1) var<storage, read_write> src1: array<SRC1_TYPE>; // [cols, b_ne1, n_tokens(1)]
+@group(0) @binding(2) var<storage, read_write> ids: array<u32>;        // [n_experd_used, n_tokens(1)]
+@group(0) @binding(3) var<storage, read_write> dst: array<f32>;   // [rows, n_expert_used, n_tokens(1)]
+
+// "mul_mat_vec_acc.tmpl" requires params.k, params.m, params.stride_01
+@group(0) @binding(4) var<uniform> params: MulMatIdVecParams;
+
+// Flattened as [row][thread] to keep each row's reduction contiguous in memory.
+var<workgroup> partial_sums: array<f32, OUTPUTS_PER_WG * WG_SIZE>;
+
+fn partial_index(row: u32, thread: u32) -> u32 {
+    return row * WG_SIZE + thread;
+}
+
+var<workgroup> gathered_count_ids: array<u32, N_EXPERTS>;
+var<workgroup> gathered_expert_used: array<u32, N_EXPERTS>;
+
+@compute @workgroup_size(WG_SIZE)
+fn main(
+    @builtin(local_invocation_id) local_id: vec3<u32>,
+    @builtin(workgroup_id) wg_id: vec3<u32>,
+    @builtin(num_workgroups) num_wg: vec3<u32>
+#ifdef USE_SUBGROUP_REDUCTION
+  , @builtin(subgroup_id) subgroup_id: u32,
+    @builtin(subgroup_invocation_id) subgroup_invocation_id: u32,
+    @builtin(num_subgroups) num_subgroups: u32,
+    @builtin(subgroup_size) subgroup_size: u32
+#endif
+) {
+
+    let thread_id = local_id.x;
+
+    for (var i = thread_id;i < params.n_expert;i += WG_SIZE) {
+        gathered_count_ids[i] = 0;
+    }
+
+    workgroupBarrier();
+
+    // gather the selected experts for the target token.
+    for (var col = thread_id;col < params.n_expert_used;col += WG_SIZE) {
+        let expert = ids[params.offset_ids + col];
+        gathered_count_ids[expert] = 1;
+        gathered_expert_used[expert] = col;
+    }
+
+    workgroupBarrier();
+
+    let output_groups:u32 = (params.m + OUTPUTS_PER_WG - 1u) / OUTPUTS_PER_WG;
+    let wg_linear = wg_id.y * num_wg.x + wg_id.x;
+
+    var own_expert:u32 = 0;
+    var wg_in_batch:u32 = 0;
+    var wg_sum:u32 = 0;
+
+    for (var i = 0u;i < params.n_expert;i += 1) {
+        let wg_vec_count = gathered_count_ids[i]; // 1 or 0
+        let wg_per_matrix = output_groups * wg_vec_count;
+        if (wg_sum <= wg_linear && wg_linear < wg_sum + wg_per_matrix) {
+            own_expert = i;
+            wg_in_batch = wg_linear - wg_sum;
+            break;
+        }
+        wg_sum += wg_per_matrix;
+    }
+
+    let row_base = (wg_linear % output_groups) * OUTPUTS_PER_WG;
+    let dst1_stride = params.m;
+
+    let src0_batch_offset = params.offset_src0 + own_expert * params.stride_02;
+    let src1_idx_base = params.offset_src1 + (gathered_expert_used[own_expert] % params.b_ne1) * params.stride_11;
+    let dst_idx_base = params.offset_dst + gathered_expert_used[own_expert] * dst1_stride + row_base;
+
+    let acc = accumulate_vec_dot(thread_id, row_base, src0_batch_offset, src1_idx_base);
+
+#ifdef USE_SUBGROUP_REDUCTION
+    for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+        let subgroup_total = subgroupAdd(acc[row]);
+        if (subgroup_invocation_id == 0u) {
+            partial_sums[partial_index(row, subgroup_id)] = subgroup_total;
+        }
+    }
+
+    workgroupBarrier();
+
+    for (var row = subgroup_id; (row < OUTPUTS_PER_WG) && (row_base + row < params.m); row += num_subgroups) {
+        let output_row = row_base + row;
+        var row_acc = 0.0f;
+        for (var k = subgroup_invocation_id; k < num_subgroups; k += subgroup_size) {
+            row_acc += partial_sums[partial_index(row, k)];
+        }
+        let row_total = subgroupAdd(row_acc);
+        if (subgroup_invocation_id == 0) {
+            dst[dst_idx_base + row] = row_total;
+        }
+    }
+#endif
+
+#ifdef USE_WORKGROUP_REDUCTION
+    for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+        partial_sums[partial_index(row, thread_id)] = acc[row];
+    }
+
+    workgroupBarrier();
+
+    var stride:u32 = WG_SIZE / 2u;
+
+    while (stride > 0) {
+        if (thread_id < stride) {
+            for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+                partial_sums[partial_index(row, thread_id)] += partial_sums[partial_index(row, thread_id + stride)];
+            }
+        }
+
+        workgroupBarrier();
+        stride = stride / 2;
+    }
+
+    if (thread_id < OUTPUTS_PER_WG) {
+        let output_row = row_base + thread_id;
+        if (output_row < params.m) {
+            dst[dst_idx_base + thread_id] = partial_sums[partial_index(thread_id, 0)];
+        }
+    }
+#endif
+}
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
index 97c9f6d7a09..a194cf40468 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
@@ -6,38 +6,7 @@ enable f16;
 #define DECLARE_BYTE_LOADERS_SRC0
 #include "common_decls.tmpl"
 
-#ifdef U32_DEQUANT_HELPERS
-#define SRC0_TYPE u32
-
-fn byte_of(v: u32, b: u32) -> u32 {
-    return (v >> (b * 8u)) & 0xFFu;
-}
-
-fn sbyte_of(v: u32, b: u32) -> i32 {
-    let raw = i32((v >> (b * 8u)) & 0xFFu);
-    return select(raw, raw - 256, raw >= 128);
-}
-#endif
-
-#ifdef VEC
-#define VEC_SIZE 4u
-#define SRC0_TYPE vec4<SRC0_INNER_TYPE>
-#define SRC1_TYPE vec4<SRC1_INNER_TYPE>
-
-fn inner_dot(src0_val: SRC0_TYPE, src1_val: SRC1_TYPE) -> f32 {
-    return f32(dot(SRC1_TYPE(src0_val), src1_val));
-}
-#endif
-
-#ifdef SCALAR
-#define VEC_SIZE 1u
-#define SRC0_TYPE SRC0_INNER_TYPE
-#define SRC1_TYPE SRC1_INNER_TYPE
-
-fn inner_dot(src0_val: SRC0_TYPE, src1_val: SRC1_TYPE) -> f32 {
-    return f32(src0_val) * f32(src1_val);
-}
-#endif
+#include "mul_mat_vec_acc.tmpl"
 
 struct MulMatParams {
     offset_src0: u32,
@@ -62,6 +31,7 @@ struct MulMatParams {
 @group(0) @binding(1) var<storage, read_write> src1: array<SRC1_TYPE>;
 @group(0) @binding(2) var<storage, read_write> dst: array<f32>;
 
+// "mul_mat_vec_acc.tmpl" requires params.k, params.m, params.stride_01
 @group(0) @binding(3) var<uniform> params: MulMatParams;
 
 // Flattened as [row][thread] to keep each row's reduction contiguous in memory.
@@ -108,709 +78,7 @@ fn main(
     let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
     let dst_idx_base = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + row_base;
 
-    var acc: array<f32, OUTPUTS_PER_WG>;
-
-#ifdef MUL_ACC_FLOAT
-    let k_vec = params.k / VEC_SIZE;
-    let src1_idx_base_vec = src1_idx_base / VEC_SIZE;
-
-    // Each thread walks K, loads from the vector, and updates
-    // a small block of output rows held in registers.
-    for (var k = thread_id; k < k_vec; k += WG_SIZE) {
-        let x = src1[src1_idx_base_vec + k];
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let src0_idx = (src0_batch_offset + output_row * params.stride_01) / VEC_SIZE + k;
-                acc[row] += inner_dot(src0[src0_idx], x);
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q4_0
-#define BLOCK_SIZE 32
-#define BLOCK_SIZE_BYTES 18
-#define THREADS_PER_BLOCK 4
-#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-
-    let num_blocks = params.k / BLOCK_SIZE;
-    let thread_within_block = thread_id % 4;
-    for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-                let d = f32(load_f16_at_src0(block_byte_base));
-                var row_sum = 0.0;
-
-                let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * thread_within_block);
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let q_lo = (f32(q_byte & 0xFu) - 8.0) * d;
-                    let q_hi = (f32((q_byte >> 4u) & 0xFu) - 8.0) * d;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
-                }
-                acc[row] += row_sum;
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q4_1
-#define BLOCK_SIZE 32
-#define BLOCK_SIZE_BYTES 20
-#define THREADS_PER_BLOCK 4
-#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-
-    let num_blocks = params.k / BLOCK_SIZE;
-    let thread_within_block = thread_id % THREADS_PER_BLOCK;
-    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-                let d = f32(load_f16_at_src0(block_byte_base));
-                let m = f32(load_f16_at_src0(block_byte_base + 2u));
-                var row_sum = 0.0;
-
-                let q_packed = load_u32_at_src0(block_byte_base + 4u + 4u * thread_within_block);
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let q_lo = f32(q_byte & 0xFu) * d + m;
-                    let q_hi = f32((q_byte >> 4u) & 0xFu) * d + m;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
-                }
-                acc[row] += row_sum;
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q5_0
-#define BLOCK_SIZE 32
-#define BLOCK_SIZE_BYTES 22
-#define THREADS_PER_BLOCK 4
-#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-
-    let num_blocks = params.k / BLOCK_SIZE;
-    let thread_within_block = thread_id % THREADS_PER_BLOCK;
-    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-                let d = f32(load_f16_at_src0(block_byte_base));
-                let qh_packed = load_u32_at_src0(block_byte_base + 2u);
-                let q_packed = load_u32_at_src0(block_byte_base + 6u + 4u * thread_within_block);
-                let qh_shift = thread_within_block * 4u;
-                var row_sum = 0.0;
-
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u;
-                    let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u;
-                    let q_lo = (f32((q_byte & 0xFu) | qh_lo) - 16.0) * d;
-                    let q_hi = (f32(((q_byte >> 4u) & 0xFu) | qh_hi) - 16.0) * d;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
-                }
-                acc[row] += row_sum;
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q5_1
-#define BLOCK_SIZE 32
-#define BLOCK_SIZE_BYTES 24
-#define THREADS_PER_BLOCK 4
-#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-
-    let num_blocks = params.k / BLOCK_SIZE;
-    let thread_within_block = thread_id % THREADS_PER_BLOCK;
-    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-                let d = f32(load_f16_at_src0(block_byte_base));
-                let m = f32(load_f16_at_src0(block_byte_base + 2u));
-                let qh_packed = load_u32_at_src0(block_byte_base + 4u);
-                let q_packed = load_u32_at_src0(block_byte_base + 8u + 4u * thread_within_block);
-                let qh_shift = thread_within_block * 4u;
-                var row_sum = 0.0;
-
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u;
-                    let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u;
-                    let q_lo = f32((q_byte & 0xFu) | qh_lo) * d + m;
-                    let q_hi = f32(((q_byte >> 4u) & 0xFu) | qh_hi) * d + m;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
-                }
-                acc[row] += row_sum;
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q8_0
-#define BLOCK_SIZE 32
-#define BLOCK_SIZE_BYTES 34
-#define THREADS_PER_BLOCK 4
-#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-
-    let num_blocks = params.k / BLOCK_SIZE;
-    let thread_within_block = thread_id % THREADS_PER_BLOCK;
-    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-                let d = f32(load_f16_at_src0(block_byte_base));
-                var row_sum = 0.0;
-
-                for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) {
-                    let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * (thread_within_block * 2u + packed_idx));
-                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                        let q_val = f32(get_byte_i32(q_packed, byte_idx)) * d;
-                        row_sum += q_val * x_block[packed_idx * 4u + byte_idx];
-                    }
-                }
-                acc[row] += row_sum;
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q8_1
-#define BLOCK_SIZE 32
-#define BLOCK_SIZE_BYTES 36
-#define THREADS_PER_BLOCK 4
-#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-
-    let num_blocks = params.k / BLOCK_SIZE;
-    let thread_within_block = thread_id % THREADS_PER_BLOCK;
-    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-                let d = f32(load_f16_at_src0(block_byte_base));
-                let m = f32(load_f16_at_src0(block_byte_base + 2u));
-                var row_sum = 0.0;
-
-                for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) {
-                    let q_packed = load_u32_at_src0(block_byte_base + 4u + 4u * (thread_within_block * 2u + packed_idx));
-                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                        let q_val = f32(get_byte_i32(q_packed, byte_idx)) * d + m;
-                        row_sum += q_val * x_block[packed_idx * 4u + byte_idx];
-                    }
-                }
-                acc[row] += row_sum;
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q2_K
-#define BLOCK_SIZE 256
-#define BLOCK_SIZE_BYTES 84
-#define THREADS_PER_BLOCK 16
-
-    let tid = thread_id % THREADS_PER_BLOCK;
-    let block_group = thread_id / THREADS_PER_BLOCK;
-    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
-
-    let lane = tid / 2u;
-    let phase = tid % 2u;
-    let iq = lane / 4u;
-    let ir = lane % 4u;
-    let is = ir / 2u;
-
-    let y_offset = 128u * iq + 8u * ir + 4u * phase;
-    let sc0_byte = 8u * iq + is;
-    let sc2_byte = 8u * iq + is + 2u;
-    let sc4_byte = 8u * iq + is + 4u;
-    let sc6_byte = 8u * iq + is + 6u;
-    let qs_byte = 16u + (16u * iq + 4u * ir) * 2u + 4u * phase;
-
-    let num_blocks = params.k / BLOCK_SIZE;
-
-    for (var block = block_group; block < num_blocks; block += num_block_groups) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 4u; i++) {
-            x_block[i]       = f32(src1[x_base + i]);
-            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
-            x_block[i + 8u]  = f32(src1[x_base + 64u + i]);
-            x_block[i + 12u] = f32(src1[x_base + 96u + i]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-
-                let dall = f32(load_f16_at_src0(block_byte_base + 80u));
-                let dmin = f32(load_f16_at_src0(block_byte_base + 82u)) * (1.0 / 16.0);
-
-                let sc0 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc0_byte), sc0_byte & 3u);
-                let sc2 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc2_byte), sc2_byte & 3u);
-                let sc4 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc4_byte), sc4_byte & 3u);
-                let sc6 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc6_byte), sc6_byte & 3u);
-
-                let q_u32 = load_u32_at_src0_aligned(block_byte_base + qs_byte);
-                let qs0 = q_u32 & 0xFFFFu;
-                let qs1 = q_u32 >> 16u;
-
-                var sumy = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                var acc1 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                var acc2 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-
-                sumy[0] = x_block[0] + x_block[1] + x_block[2] + x_block[3];
-                sumy[1] = x_block[4] + x_block[5] + x_block[6] + x_block[7];
-                sumy[2] = x_block[8] + x_block[9] + x_block[10] + x_block[11];
-                sumy[3] = x_block[12] + x_block[13] + x_block[14] + x_block[15];
-
-                acc1[0] = x_block[0] * f32(qs0 & 0x0003u) + x_block[2] * f32(qs1 & 0x0003u);
-                acc2[0] = x_block[1] * f32(qs0 & 0x0300u) + x_block[3] * f32(qs1 & 0x0300u);
-                acc1[1] = x_block[4] * f32(qs0 & 0x000Cu) + x_block[6] * f32(qs1 & 0x000Cu);
-                acc2[1] = x_block[5] * f32(qs0 & 0x0C00u) + x_block[7] * f32(qs1 & 0x0C00u);
-                acc1[2] = x_block[8] * f32(qs0 & 0x0030u) + x_block[10] * f32(qs1 & 0x0030u);
-                acc2[2] = x_block[9] * f32(qs0 & 0x3000u) + x_block[11] * f32(qs1 & 0x3000u);
-                acc1[3] = x_block[12] * f32(qs0 & 0x00C0u) + x_block[14] * f32(qs1 & 0x00C0u);
-                acc2[3] = x_block[13] * f32(qs0 & 0xC000u) + x_block[15] * f32(qs1 & 0xC000u);
-
-                acc[row] += dall * ((acc1[0] + (1.0/256.0) * acc2[0]) * f32(sc0 & 0xFu) +
-                                    (acc1[1] + (1.0/256.0) * acc2[1]) * f32(sc2 & 0xFu) / 4.0 +
-                                    (acc1[2] + (1.0/256.0) * acc2[2]) * f32(sc4 & 0xFu) / 16.0 +
-                                    (acc1[3] + (1.0/256.0) * acc2[3]) * f32(sc6 & 0xFu) / 64.0)
-                          - dmin * (sumy[0] * f32(sc0 & 0xF0u) + sumy[1] * f32(sc2 & 0xF0u) +
-                                    sumy[2] * f32(sc4 & 0xF0u) + sumy[3] * f32(sc6 & 0xF0u));
-            }
-        }
-    }
-#endif
-
-
-#ifdef MUL_ACC_Q3_K
-#define BLOCK_SIZE 256
-#define BLOCK_SIZE_BYTES 110
-#define THREADS_PER_BLOCK 16
-
-    let tid = thread_id % THREADS_PER_BLOCK;
-    let block_group = thread_id / THREADS_PER_BLOCK;
-    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
-
-    let lane = tid / 2u;
-    let phase = tid % 2u;
-    let ip = lane / 4u;
-    let il = 2u * ((lane % 4u) / 2u);
-    let ir = lane % 2u;
-    let l0 = 8u * ir;
-
-    let q_byte = 32u + 32u * ip + l0 + 16u * phase;
-    let h_byte = l0 + 16u * phase;
-    let y_offset = 128u * ip + 32u * il + l0 + 16u * phase;
-
-    let s_shift1 = 4u * ip;
-    let s_shift2 = s_shift1 + il;
-
-    let v1 = select(64.0, 4.0, il == 0u);
-    let v2 = 4.0 * v1;
-    let shift = 2u * il;
-
-    var qm0: u32; var qm1: u32; var qm2: u32; var qm3: u32;
-    if (il == 0u) {
-        qm0 = 0x0003u; qm1 = 0x0300u; qm2 = 0x000Cu; qm3 = 0x0C00u;
-    } else {
-        qm0 = 0x0030u; qm1 = 0x3000u; qm2 = 0x00C0u; qm3 = 0xC000u;
-    }
-
-    let mm_idx = 2u * ip + il / 2u;
-    var hm0: u32; var hm1: u32; var hm2: u32; var hm3: u32;
-    switch (mm_idx) {
-        case 0u: { hm0=0x0001u; hm1=0x0100u; hm2=0x0002u; hm3=0x0200u; }
-        case 1u: { hm0=0x0004u; hm1=0x0400u; hm2=0x0008u; hm3=0x0800u; }
-        case 2u: { hm0=0x0010u; hm1=0x1000u; hm2=0x0020u; hm3=0x2000u; }
-        default: { hm0=0x0040u; hm1=0x4000u; hm2=0x0080u; hm3=0x8000u; }
-    }
-
-    let num_blocks = params.k / BLOCK_SIZE;
-
-    for (var block = block_group; block < num_blocks; block += num_block_groups) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 8u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 8u] = f32(src1[x_base + 32u + i]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-
-                let d = f32(load_f16_at_src0(block_byte_base + 108u));
-                let a_base = 96u;
-                let a_il0 = load_u16_at_src0(block_byte_base + a_base + il * 2u);
-                let a_il1 = load_u16_at_src0(block_byte_base + a_base + (il + 1u) * 2u);
-                let a_4 = load_u16_at_src0(block_byte_base + a_base + 8u);
-                let a_5 = load_u16_at_src0(block_byte_base + a_base + 10u);
-
-                var scales32 = a_4 | (a_5 << 16u);
-                let aux32 = ((scales32 >> s_shift2) << 4u) & 0x30303030u;
-                scales32 = a_il0 | (a_il1 << 16u);
-                scales32 = ((scales32 >> s_shift1) & 0x0F0F0F0Fu) | aux32;
-
-                let scale0 = f32(i32(byte_of(scales32, phase + 0u)) - 32);
-                let scale1 = f32(i32(byte_of(scales32, phase + 2u)) - 32);
-
-                let q_u32_0 = load_u32_at_src0(block_byte_base + q_byte + 0u);
-                let q_u32_1 = load_u32_at_src0(block_byte_base + q_byte + 4u);
-                let h_u32_0 = load_u32_at_src0(block_byte_base + h_byte + 0u);
-                let h_u32_1 = load_u32_at_src0(block_byte_base + h_byte + 4u);
-
-                var s1 = 0.0; var s2 = 0.0; var s3 = 0.0;
-                var s4 = 0.0; var s5 = 0.0; var s6 = 0.0;
-
-                for (var l = 0u; l < 8u; l += 2u) {
-                    let q_u32 = select(q_u32_0, q_u32_1, l >= 4u);
-                    let qs = select(q_u32 & 0xFFFFu, q_u32 >> 16u, (l & 2u) != 0u);
-                    let h_u32 = select(h_u32_0, h_u32_1, l >= 4u);
-                    let hv = select(h_u32 & 0xFFFFu, h_u32 >> 16u, (l & 2u) != 0u);
-
-                    s1 += x_block[l + 0u] * f32(qs & qm0);
-                    s2 += x_block[l + 1u] * f32(qs & qm1);
-                    s3 += select(0.0, x_block[l + 0u], (hv & hm0) == 0u) +
-                          select(0.0, x_block[l + 1u], (hv & hm1) == 0u);
-                    s4 += x_block[l + 8u] * f32(qs & qm2);
-                    s5 += x_block[l + 9u] * f32(qs & qm3);
-                    s6 += select(0.0, x_block[l + 8u], (hv & hm2) == 0u) +
-                          select(0.0, x_block[l + 9u], (hv & hm3) == 0u);
-                }
-
-                let d1 = d * (s1 + (1.0/256.0) * s2 - s3 * v1);
-                let d2 = d * (s4 + (1.0/256.0) * s5 - s6 * v2);
-                acc[row] += (d1 * scale0 + 0.25 * d2 * scale1) / f32(1u << shift);
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q4_K
-#define BLOCK_SIZE 256
-#define BLOCK_SIZE_BYTES 144
-#define THREADS_PER_BLOCK 16
-
-    let tid = thread_id % THREADS_PER_BLOCK;
-    let block_group = thread_id / THREADS_PER_BLOCK;
-    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
-
-    let il = tid / 4u;
-    let ir = tid % 4u;
-    let im = il / 2u;
-    let in = il % 2u;
-    let l0 = 4u * (2u * ir + in);
-
-    let y_offset = 64u * im + l0;
-    let q_offset = 32u * im + l0;
-    let sc0_byte = 4u + im * 2u;
-    let sc2_byte = 4u + (im + 2u) * 2u;
-    let sc4_byte = 4u + (im + 4u) * 2u;
-
-    let num_blocks = params.k / BLOCK_SIZE;
-
-    for (var block = block_group; block < num_blocks; block += num_block_groups) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 4u; i++) {
-            x_block[i]       = f32(src1[x_base + i]);
-            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
-            x_block[i + 8u]  = f32(src1[x_base + 128u + i]);
-            x_block[i + 12u] = f32(src1[x_base + 160u + i]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-
-                let d = f32(load_f16_at_src0(block_byte_base + 0u));
-                let dmin = f32(load_f16_at_src0(block_byte_base + 2u));
-
-                let sc0_u32 = load_u32_at_src0_aligned(block_byte_base + sc0_byte);
-                let sc0 = select(sc0_u32 & 0xFFFFu, sc0_u32 >> 16u, (sc0_byte & 2u) != 0u);
-                let sc2_u32 = load_u32_at_src0_aligned(block_byte_base + sc2_byte);
-                let sc2 = select(sc2_u32 & 0xFFFFu, sc2_u32 >> 16u, (sc2_byte & 2u) != 0u);
-                let sc4_u32 = load_u32_at_src0_aligned(block_byte_base + sc4_byte);
-                let sc4 = select(sc4_u32 & 0xFFFFu, sc4_u32 >> 16u, (sc4_byte & 2u) != 0u);
-
-                let sc16_0 = sc0 & 0x3F3Fu;
-                let sc16_1 = sc2 & 0x3F3Fu;
-                let sc16_2 = (sc4 & 0x0F0Fu) | ((sc0 & 0xC0C0u) >> 2u);
-                let sc16_3 = ((sc4 >> 4u) & 0x0F0Fu) | ((sc2 & 0xC0C0u) >> 2u);
-
-                let scale0 = f32(sc16_0 & 0xFFu);
-                let scale1 = f32((sc16_0 >> 8u) & 0xFFu);
-                let min0 = f32(sc16_1 & 0xFFu);
-                let min1 = f32((sc16_1 >> 8u) & 0xFFu);
-                let scale2 = f32(sc16_2 & 0xFFu);
-                let scale3 = f32((sc16_2 >> 8u) & 0xFFu);
-                let min2 = f32(sc16_3 & 0xFFu);
-                let min3 = f32((sc16_3 >> 8u) & 0xFFu);
-
-                let q1_u32 = load_u32_at_src0_aligned(block_byte_base + 16u + q_offset);
-                let q2_u32 = load_u32_at_src0_aligned(block_byte_base + 80u + q_offset);
-
-                var dot = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                var sumx = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                for (var i = 0u; i < 4u; i++) {
-                    let q1b = byte_of(q1_u32, i);
-                    let q2b = byte_of(q2_u32, i);
-                    dot[0] += x_block[i] * f32(q1b & 0x0Fu);
-                    dot[1] += x_block[i + 4u] * f32(q1b >> 4u);
-                    dot[2] += x_block[i + 8u] * f32(q2b & 0x0Fu);
-                    dot[3] += x_block[i + 12u] * f32(q2b >> 4u);
-                    sumx[0] += x_block[i];
-                    sumx[1] += x_block[i + 4u];
-                    sumx[2] += x_block[i + 8u];
-                    sumx[3] += x_block[i + 12u];
-                }
-
-                acc[row] += d * (dot[0] * scale0 + dot[1] * scale1 + dot[2] * scale2 + dot[3] * scale3)
-                          - dmin * (sumx[0] * min0 + sumx[1] * min1 + sumx[2] * min2 + sumx[3] * min3);
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q5_K
-#define BLOCK_SIZE 256
-#define BLOCK_SIZE_BYTES 176
-#define THREADS_PER_BLOCK 16
-
-    let tid = thread_id % THREADS_PER_BLOCK;
-    let block_group = thread_id / THREADS_PER_BLOCK;
-    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
-
-    let il = tid / 4u;
-    let ir = tid % 4u;
-    let im = il / 2u;
-    let in = il % 2u;
-    let l0 = 4u * (2u * ir + in);
-
-    let y_offset = 64u * im + l0;
-    let q_offset = 48u + 32u * im + l0;
-    let qh_offset = 16u + 8u * ir + 4u * in;
-    let sc0_byte = 4u + im * 2u;
-    let sc2_byte = 4u + (im + 2u) * 2u;
-    let sc4_byte = 4u + (im + 4u) * 2u;
-
-    let hm1 = 1u << (2u * im);
-    let hm2 = hm1 << 1u;
-    let hm3 = hm1 << 4u;
-    let hm4 = hm2 << 4u;
-
-    let num_blocks = params.k / BLOCK_SIZE;
-
-    for (var block = block_group; block < num_blocks; block += num_block_groups) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 4u; i++) {
-            x_block[i]       = f32(src1[x_base + i]);
-            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
-            x_block[i + 8u]  = f32(src1[x_base + 128u + i]);
-            x_block[i + 12u] = f32(src1[x_base + 160u + i]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-
-                let d = f32(load_f16_at_src0(block_byte_base + 0u));
-                let dmin = f32(load_f16_at_src0(block_byte_base + 2u));
-
-                let sc0_u32 = load_u32_at_src0_aligned(block_byte_base + sc0_byte);
-                let sc0 = select(sc0_u32 & 0xFFFFu, sc0_u32 >> 16u, (sc0_byte & 2u) != 0u);
-                let sc2_u32 = load_u32_at_src0_aligned(block_byte_base + sc2_byte);
-                let sc2 = select(sc2_u32 & 0xFFFFu, sc2_u32 >> 16u, (sc2_byte & 2u) != 0u);
-                let sc4_u32 = load_u32_at_src0_aligned(block_byte_base + sc4_byte);
-                let sc4 = select(sc4_u32 & 0xFFFFu, sc4_u32 >> 16u, (sc4_byte & 2u) != 0u);
-
-                let sc16_0 = sc0 & 0x3F3Fu;
-                let sc16_1 = sc2 & 0x3F3Fu;
-                let sc16_2 = (sc4 & 0x0F0Fu) | ((sc0 & 0xC0C0u) >> 2u);
-                let sc16_3 = ((sc4 >> 4u) & 0x0F0Fu) | ((sc2 & 0xC0C0u) >> 2u);
-
-                let f0 = f32(sc16_0 & 0xFFu);
-                let f1 = f32((sc16_0 >> 8u) & 0xFFu);
-                let m0 = f32(sc16_1 & 0xFFu);
-                let m1 = f32((sc16_1 >> 8u) & 0xFFu);
-                let f4 = f32(sc16_2 & 0xFFu);
-                let f5 = f32((sc16_2 >> 8u) & 0xFFu);
-                let m4 = f32(sc16_3 & 0xFFu);
-                let m5 = f32((sc16_3 >> 8u) & 0xFFu);
-
-                let q1_u32 = load_u32_at_src0_aligned(block_byte_base + q_offset);
-                let q2_u32 = load_u32_at_src0_aligned(block_byte_base + q_offset + 64u);
-                let qh_u32 = load_u32_at_src0_aligned(block_byte_base + qh_offset);
-
-                var vals = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                var sumy = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                for (var i = 0u; i < 4u; i++) {
-                    let q1b = byte_of(q1_u32, i);
-                    let q2b = byte_of(q2_u32, i);
-                    let qhb = byte_of(qh_u32, i);
-
-                    let yl0 = x_block[i];
-                    let yl8 = x_block[i + 4u];
-                    let yh0 = x_block[i + 8u];
-                    let yh8 = x_block[i + 12u];
-
-                    sumy[0] += yl0;
-                    sumy[1] += yl8;
-                    sumy[2] += yh0;
-                    sumy[3] += yh8;
-
-                    let q0 = f32((q1b & 0x0Fu) | select(0u, 0x10u, (qhb & hm1) != 0u));
-                    let q1 = f32((q1b >> 4u) | select(0u, 0x10u, (qhb & hm2) != 0u));
-                    let q2 = f32((q2b & 0x0Fu) | select(0u, 0x10u, (qhb & hm3) != 0u));
-                    let q3 = f32((q2b >> 4u) | select(0u, 0x10u, (qhb & hm4) != 0u));
-
-                    vals[0] += yl0 * q0;
-                    vals[1] += yl8 * q1;
-                    vals[2] += yh0 * q2;
-                    vals[3] += yh8 * q3;
-                }
-
-                acc[row] += d * (f0 * vals[0] + f1 * vals[1] + f4 * vals[2] + f5 * vals[3])
-                          - dmin * (sumy[0] * m0 + sumy[1] * m1 +
-                                    sumy[2] * m4 + sumy[3] * m5);
-            }
-        }
-    }
-#endif
-
-#ifdef MUL_ACC_Q6_K
-#define BLOCK_SIZE 256
-#define BLOCK_SIZE_BYTES 210
-#define THREADS_PER_BLOCK 16
-
-    let tid = thread_id % THREADS_PER_BLOCK;
-    let block_group = thread_id / THREADS_PER_BLOCK;
-    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
-
-    let ip = tid / 8u;
-    let il = tid % 8u;
-    let l0 = 4u * il;
-    let is = 8u * ip + l0 / 16u;
-
-    let y_offset = 128u * ip + l0;
-    let q_offset_l = 64u * ip + l0;
-    let q_offset_h = 32u * ip + l0;
-
-    let num_blocks = params.k / BLOCK_SIZE;
-    let sc_base_byte = 192u + (is & ~3u);
-    let sc_byte_pos = is & 3u;
-
-    for (var block = block_group; block < num_blocks; block += num_block_groups) {
-        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var l = 0u; l < 4u; l++) {
-            x_block[l]       = f32(src1[x_base + l]);
-            x_block[l + 4u]  = f32(src1[x_base + 32u + l]);
-            x_block[l + 8u]  = f32(src1[x_base + 64u + l]);
-            x_block[l + 12u] = f32(src1[x_base + 96u + l]);
-        }
-
-        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-            let output_row = row_base + row;
-            if (output_row < params.m) {
-                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-
-                let d = f32(load_f16_at_src0(block_byte_base + 208u));
-                let ql1_u32 = load_u32_at_src0(block_byte_base + q_offset_l);
-                let ql2_u32 = load_u32_at_src0(block_byte_base + q_offset_l + 32u);
-                let qh_u32 = load_u32_at_src0(block_byte_base + 128u + q_offset_h);
-                let sc_u32_0 = load_u32_at_src0(block_byte_base + sc_base_byte);
-                let sc_u32_1 = load_u32_at_src0(block_byte_base + sc_base_byte + 4u);
-
-                let sc0 = sbyte_of(sc_u32_0, sc_byte_pos);
-                let sc2 = sbyte_of(sc_u32_0, sc_byte_pos + 2u);
-                let sc4 = sbyte_of(sc_u32_1, sc_byte_pos);
-                let sc6 = sbyte_of(sc_u32_1, sc_byte_pos + 2u);
-
-                var sums = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-
-                for (var l = 0u; l < 4u; l++) {
-                    let q1b = byte_of(ql1_u32, l);
-                    let q2b = byte_of(ql2_u32, l);
-                    let qhb = byte_of(qh_u32, l);
-
-                    let dq0 = f32(i32((q1b & 0x0Fu) | ((qhb & 0x03u) << 4u)) - 32);
-                    let dq1 = f32(i32((q2b & 0x0Fu) | ((qhb & 0x0Cu) << 2u)) - 32);
-                    let dq2 = f32(i32((q1b >> 4u) | (qhb & 0x30u)) - 32);
-                    let dq3 = f32(i32((q2b >> 4u) | ((qhb & 0xC0u) >> 2u)) - 32);
-
-                    sums[0] += x_block[l] * dq0;
-                    sums[1] += x_block[l + 4u] * dq1;
-                    sums[2] += x_block[l + 8u] * dq2;
-                    sums[3] += x_block[l + 12u] * dq3;
-                }
-
-                acc[row] += d * (sums[0] * f32(sc0) + sums[1] * f32(sc2) +
-                                 sums[2] * f32(sc4) + sums[3] * f32(sc6));
-            }
-        }
-    }
-#endif
+    let acc = accumulate_vec_dot(thread_id, row_base, src0_batch_offset, src1_idx_base);
 
 #ifdef USE_SUBGROUP_REDUCTION
     for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
new file mode 100644
index 00000000000..711c7e829d8
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
@@ -0,0 +1,1433 @@
+#ifdef U32_DEQUANT_HELPERS
+#define SRC0_TYPE u32
+
+fn byte_of(v: u32, b: u32) -> u32 {
+    return (v >> (b * 8u)) & 0xFFu;
+}
+
+fn sbyte_of(v: u32, b: u32) -> i32 {
+    let raw = i32((v >> (b * 8u)) & 0xFFu);
+    return select(raw, raw - 256, raw >= 128);
+}
+#endif
+
+#ifdef VEC
+#define VEC_SIZE 4u
+#define SRC0_TYPE vec4<SRC0_INNER_TYPE>
+#define SRC1_TYPE vec4<SRC1_INNER_TYPE>
+
+fn inner_dot(src0_val: SRC0_TYPE, src1_val: SRC1_TYPE) -> f32 {
+    return f32(dot(SRC1_TYPE(src0_val), src1_val));
+}
+#endif
+
+#ifdef SCALAR
+#define VEC_SIZE 1u
+#define SRC0_TYPE SRC0_INNER_TYPE
+#define SRC1_TYPE SRC1_INNER_TYPE
+
+fn inner_dot(src0_val: SRC0_TYPE, src1_val: SRC1_TYPE) -> f32 {
+    return f32(src0_val) * f32(src1_val);
+}
+#endif
+
+#ifdef MUL_ACC_FLOAT
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let k_vec = params.k / VEC_SIZE;
+    let src1_idx_base_vec = src1_idx_base / VEC_SIZE;
+
+    // Each thread walks K, loads from the vector, and updates
+    // a small block of output rows held in registers.
+    for (var k = thread_id; k < k_vec; k += WG_SIZE) {
+        let x = src1[src1_idx_base_vec + k];
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let src0_idx = (src0_batch_offset + output_row * params.stride_01) / VEC_SIZE + k;
+                acc[row] += inner_dot(src0[src0_idx], x);
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q1_0
+#define BLOCK_SIZE 128
+#define BLOCK_SIZE_BYTES 18
+#define THREADS_PER_BLOCK 16
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % THREADS_PER_BLOCK;
+    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let q_byte = load_u32_at_src0(block_byte_base + 2u + thread_within_block) & 0xFFu;
+                var row_sum = 0.0;
+                for (var bit = 0u; bit < 8u; bit++) {
+                    let w = select(-d, d, ((q_byte >> bit) & 1u) != 0u);
+                    row_sum += w * x_block[bit];
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q4_0
+#define BLOCK_SIZE 32
+#define BLOCK_SIZE_BYTES 18
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % 4;
+    for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                var row_sum = 0.0;
+
+                let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * thread_within_block);
+                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                    let q_byte = get_byte(q_packed, byte_idx);
+                    let q_lo = (f32(q_byte & 0xFu) - 8.0) * d;
+                    let q_hi = (f32((q_byte >> 4u) & 0xFu) - 8.0) * d;
+                    row_sum += q_lo * x_block[byte_idx];
+                    row_sum += q_hi * x_block[byte_idx + 4u];
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q4_1
+#define BLOCK_SIZE 32
+#define BLOCK_SIZE_BYTES 20
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % THREADS_PER_BLOCK;
+    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let m = f32(load_f16_at_src0(block_byte_base + 2u));
+                var row_sum = 0.0;
+
+                let q_packed = load_u32_at_src0(block_byte_base + 4u + 4u * thread_within_block);
+                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                    let q_byte = get_byte(q_packed, byte_idx);
+                    let q_lo = f32(q_byte & 0xFu) * d + m;
+                    let q_hi = f32((q_byte >> 4u) & 0xFu) * d + m;
+                    row_sum += q_lo * x_block[byte_idx];
+                    row_sum += q_hi * x_block[byte_idx + 4u];
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q5_0
+#define BLOCK_SIZE 32
+#define BLOCK_SIZE_BYTES 22
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % THREADS_PER_BLOCK;
+    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let qh_packed = load_u32_at_src0(block_byte_base + 2u);
+                let q_packed = load_u32_at_src0(block_byte_base + 6u + 4u * thread_within_block);
+                let qh_shift = thread_within_block * 4u;
+                var row_sum = 0.0;
+
+                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                    let q_byte = get_byte(q_packed, byte_idx);
+                    let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u;
+                    let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u;
+                    let q_lo = (f32((q_byte & 0xFu) | qh_lo) - 16.0) * d;
+                    let q_hi = (f32(((q_byte >> 4u) & 0xFu) | qh_hi) - 16.0) * d;
+                    row_sum += q_lo * x_block[byte_idx];
+                    row_sum += q_hi * x_block[byte_idx + 4u];
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q5_1
+#define BLOCK_SIZE 32
+#define BLOCK_SIZE_BYTES 24
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % THREADS_PER_BLOCK;
+    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let m = f32(load_f16_at_src0(block_byte_base + 2u));
+                let qh_packed = load_u32_at_src0(block_byte_base + 4u);
+                let q_packed = load_u32_at_src0(block_byte_base + 8u + 4u * thread_within_block);
+                let qh_shift = thread_within_block * 4u;
+                var row_sum = 0.0;
+
+                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                    let q_byte = get_byte(q_packed, byte_idx);
+                    let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u;
+                    let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u;
+                    let q_lo = f32((q_byte & 0xFu) | qh_lo) * d + m;
+                    let q_hi = f32(((q_byte >> 4u) & 0xFu) | qh_hi) * d + m;
+                    row_sum += q_lo * x_block[byte_idx];
+                    row_sum += q_hi * x_block[byte_idx + 4u];
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q8_0
+#define BLOCK_SIZE 32
+#define BLOCK_SIZE_BYTES 34
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % THREADS_PER_BLOCK;
+    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                var row_sum = 0.0;
+
+                for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) {
+                    let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * (thread_within_block * 2u + packed_idx));
+                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                        let q_val = f32(get_byte_i32(q_packed, byte_idx)) * d;
+                        row_sum += q_val * x_block[packed_idx * 4u + byte_idx];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q8_1
+#define BLOCK_SIZE 32
+#define BLOCK_SIZE_BYTES 36
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % THREADS_PER_BLOCK;
+    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let m = f32(load_f16_at_src0(block_byte_base + 2u));
+                var row_sum = 0.0;
+
+                for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) {
+                    let q_packed = load_u32_at_src0(block_byte_base + 4u + 4u * (thread_within_block * 2u + packed_idx));
+                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                        let q_val = f32(get_byte_i32(q_packed, byte_idx)) * d + m;
+                        row_sum += q_val * x_block[packed_idx * 4u + byte_idx];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q2_K
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 84
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let lane = tid / 2u;
+    let phase = tid % 2u;
+    let iq = lane / 4u;
+    let ir = lane % 4u;
+    let is = ir / 2u;
+
+    let y_offset = 128u * iq + 8u * ir + 4u * phase;
+    let sc0_byte = 8u * iq + is;
+    let sc2_byte = 8u * iq + is + 2u;
+    let sc4_byte = 8u * iq + is + 4u;
+    let sc6_byte = 8u * iq + is + 6u;
+    let qs_byte = 16u + (16u * iq + 4u * ir) * 2u + 4u * phase;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 4u; i++) {
+            x_block[i]       = f32(src1[x_base + i]);
+            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
+            x_block[i + 8u]  = f32(src1[x_base + 64u + i]);
+            x_block[i + 12u] = f32(src1[x_base + 96u + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+
+                let dall = f32(load_f16_at_src0(block_byte_base + 80u));
+                let dmin = f32(load_f16_at_src0(block_byte_base + 82u)) * (1.0 / 16.0);
+
+                let sc0 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc0_byte), sc0_byte & 3u);
+                let sc2 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc2_byte), sc2_byte & 3u);
+                let sc4 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc4_byte), sc4_byte & 3u);
+                let sc6 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc6_byte), sc6_byte & 3u);
+
+                let q_u32 = load_u32_at_src0_aligned(block_byte_base + qs_byte);
+                let qs0 = q_u32 & 0xFFFFu;
+                let qs1 = q_u32 >> 16u;
+
+                var sumy = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                var acc1 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                var acc2 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+
+                sumy[0] = x_block[0] + x_block[1] + x_block[2] + x_block[3];
+                sumy[1] = x_block[4] + x_block[5] + x_block[6] + x_block[7];
+                sumy[2] = x_block[8] + x_block[9] + x_block[10] + x_block[11];
+                sumy[3] = x_block[12] + x_block[13] + x_block[14] + x_block[15];
+
+                acc1[0] = x_block[0] * f32(qs0 & 0x0003u) + x_block[2] * f32(qs1 & 0x0003u);
+                acc2[0] = x_block[1] * f32(qs0 & 0x0300u) + x_block[3] * f32(qs1 & 0x0300u);
+                acc1[1] = x_block[4] * f32(qs0 & 0x000Cu) + x_block[6] * f32(qs1 & 0x000Cu);
+                acc2[1] = x_block[5] * f32(qs0 & 0x0C00u) + x_block[7] * f32(qs1 & 0x0C00u);
+                acc1[2] = x_block[8] * f32(qs0 & 0x0030u) + x_block[10] * f32(qs1 & 0x0030u);
+                acc2[2] = x_block[9] * f32(qs0 & 0x3000u) + x_block[11] * f32(qs1 & 0x3000u);
+                acc1[3] = x_block[12] * f32(qs0 & 0x00C0u) + x_block[14] * f32(qs1 & 0x00C0u);
+                acc2[3] = x_block[13] * f32(qs0 & 0xC000u) + x_block[15] * f32(qs1 & 0xC000u);
+
+                acc[row] += dall * ((acc1[0] + (1.0/256.0) * acc2[0]) * f32(sc0 & 0xFu) +
+                                    (acc1[1] + (1.0/256.0) * acc2[1]) * f32(sc2 & 0xFu) / 4.0 +
+                                    (acc1[2] + (1.0/256.0) * acc2[2]) * f32(sc4 & 0xFu) / 16.0 +
+                                    (acc1[3] + (1.0/256.0) * acc2[3]) * f32(sc6 & 0xFu) / 64.0)
+                          - dmin * (sumy[0] * f32(sc0 & 0xF0u) + sumy[1] * f32(sc2 & 0xF0u) +
+                                    sumy[2] * f32(sc4 & 0xF0u) + sumy[3] * f32(sc6 & 0xF0u));
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+
+#ifdef MUL_ACC_Q3_K
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 110
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let lane = tid / 2u;
+    let phase = tid % 2u;
+    let ip = lane / 4u;
+    let il = 2u * ((lane % 4u) / 2u);
+    let ir = lane % 2u;
+    let l0 = 8u * ir;
+
+    let q_byte = 32u + 32u * ip + l0 + 16u * phase;
+    let h_byte = l0 + 16u * phase;
+    let y_offset = 128u * ip + 32u * il + l0 + 16u * phase;
+
+    let s_shift1 = 4u * ip;
+    let s_shift2 = s_shift1 + il;
+
+    let v1 = select(64.0, 4.0, il == 0u);
+    let v2 = 4.0 * v1;
+    let shift = 2u * il;
+
+    var qm0: u32; var qm1: u32; var qm2: u32; var qm3: u32;
+    if (il == 0u) {
+        qm0 = 0x0003u; qm1 = 0x0300u; qm2 = 0x000Cu; qm3 = 0x0C00u;
+    } else {
+        qm0 = 0x0030u; qm1 = 0x3000u; qm2 = 0x00C0u; qm3 = 0xC000u;
+    }
+
+    let mm_idx = 2u * ip + il / 2u;
+    var hm0: u32; var hm1: u32; var hm2: u32; var hm3: u32;
+    switch (mm_idx) {
+        case 0u: { hm0=0x0001u; hm1=0x0100u; hm2=0x0002u; hm3=0x0200u; }
+        case 1u: { hm0=0x0004u; hm1=0x0400u; hm2=0x0008u; hm3=0x0800u; }
+        case 2u: { hm0=0x0010u; hm1=0x1000u; hm2=0x0020u; hm3=0x2000u; }
+        default: { hm0=0x0040u; hm1=0x4000u; hm2=0x0080u; hm3=0x8000u; }
+    }
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 8u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+            x_block[i + 8u] = f32(src1[x_base + 32u + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+
+                let d = f32(load_f16_at_src0(block_byte_base + 108u));
+                let a_base = 96u;
+                let a_il0 = load_u16_at_src0(block_byte_base + a_base + il * 2u);
+                let a_il1 = load_u16_at_src0(block_byte_base + a_base + (il + 1u) * 2u);
+                let a_4 = load_u16_at_src0(block_byte_base + a_base + 8u);
+                let a_5 = load_u16_at_src0(block_byte_base + a_base + 10u);
+
+                var scales32 = a_4 | (a_5 << 16u);
+                let aux32 = ((scales32 >> s_shift2) << 4u) & 0x30303030u;
+                scales32 = a_il0 | (a_il1 << 16u);
+                scales32 = ((scales32 >> s_shift1) & 0x0F0F0F0Fu) | aux32;
+
+                let scale0 = f32(i32(byte_of(scales32, phase + 0u)) - 32);
+                let scale1 = f32(i32(byte_of(scales32, phase + 2u)) - 32);
+
+                let q_u32_0 = load_u32_at_src0(block_byte_base + q_byte + 0u);
+                let q_u32_1 = load_u32_at_src0(block_byte_base + q_byte + 4u);
+                let h_u32_0 = load_u32_at_src0(block_byte_base + h_byte + 0u);
+                let h_u32_1 = load_u32_at_src0(block_byte_base + h_byte + 4u);
+
+                var s1 = 0.0; var s2 = 0.0; var s3 = 0.0;
+                var s4 = 0.0; var s5 = 0.0; var s6 = 0.0;
+
+                for (var l = 0u; l < 8u; l += 2u) {
+                    let q_u32 = select(q_u32_0, q_u32_1, l >= 4u);
+                    let qs = select(q_u32 & 0xFFFFu, q_u32 >> 16u, (l & 2u) != 0u);
+                    let h_u32 = select(h_u32_0, h_u32_1, l >= 4u);
+                    let hv = select(h_u32 & 0xFFFFu, h_u32 >> 16u, (l & 2u) != 0u);
+
+                    s1 += x_block[l + 0u] * f32(qs & qm0);
+                    s2 += x_block[l + 1u] * f32(qs & qm1);
+                    s3 += select(0.0, x_block[l + 0u], (hv & hm0) == 0u) +
+                          select(0.0, x_block[l + 1u], (hv & hm1) == 0u);
+                    s4 += x_block[l + 8u] * f32(qs & qm2);
+                    s5 += x_block[l + 9u] * f32(qs & qm3);
+                    s6 += select(0.0, x_block[l + 8u], (hv & hm2) == 0u) +
+                          select(0.0, x_block[l + 9u], (hv & hm3) == 0u);
+                }
+
+                let d1 = d * (s1 + (1.0/256.0) * s2 - s3 * v1);
+                let d2 = d * (s4 + (1.0/256.0) * s5 - s6 * v2);
+                acc[row] += (d1 * scale0 + 0.25 * d2 * scale1) / f32(1u << shift);
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q4_K
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 144
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let il = tid / 4u;
+    let ir = tid % 4u;
+    let im = il / 2u;
+    let in = il % 2u;
+    let l0 = 4u * (2u * ir + in);
+
+    let y_offset = 64u * im + l0;
+    let q_offset = 32u * im + l0;
+    let sc0_byte = 4u + im * 2u;
+    let sc2_byte = 4u + (im + 2u) * 2u;
+    let sc4_byte = 4u + (im + 4u) * 2u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 4u; i++) {
+            x_block[i]       = f32(src1[x_base + i]);
+            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
+            x_block[i + 8u]  = f32(src1[x_base + 128u + i]);
+            x_block[i + 12u] = f32(src1[x_base + 160u + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+
+                let d = f32(load_f16_at_src0(block_byte_base + 0u));
+                let dmin = f32(load_f16_at_src0(block_byte_base + 2u));
+
+                let sc0_u32 = load_u32_at_src0_aligned(block_byte_base + sc0_byte);
+                let sc0 = select(sc0_u32 & 0xFFFFu, sc0_u32 >> 16u, (sc0_byte & 2u) != 0u);
+                let sc2_u32 = load_u32_at_src0_aligned(block_byte_base + sc2_byte);
+                let sc2 = select(sc2_u32 & 0xFFFFu, sc2_u32 >> 16u, (sc2_byte & 2u) != 0u);
+                let sc4_u32 = load_u32_at_src0_aligned(block_byte_base + sc4_byte);
+                let sc4 = select(sc4_u32 & 0xFFFFu, sc4_u32 >> 16u, (sc4_byte & 2u) != 0u);
+
+                let sc16_0 = sc0 & 0x3F3Fu;
+                let sc16_1 = sc2 & 0x3F3Fu;
+                let sc16_2 = (sc4 & 0x0F0Fu) | ((sc0 & 0xC0C0u) >> 2u);
+                let sc16_3 = ((sc4 >> 4u) & 0x0F0Fu) | ((sc2 & 0xC0C0u) >> 2u);
+
+                let scale0 = f32(sc16_0 & 0xFFu);
+                let scale1 = f32((sc16_0 >> 8u) & 0xFFu);
+                let min0 = f32(sc16_1 & 0xFFu);
+                let min1 = f32((sc16_1 >> 8u) & 0xFFu);
+                let scale2 = f32(sc16_2 & 0xFFu);
+                let scale3 = f32((sc16_2 >> 8u) & 0xFFu);
+                let min2 = f32(sc16_3 & 0xFFu);
+                let min3 = f32((sc16_3 >> 8u) & 0xFFu);
+
+                let q1_u32 = load_u32_at_src0_aligned(block_byte_base + 16u + q_offset);
+                let q2_u32 = load_u32_at_src0_aligned(block_byte_base + 80u + q_offset);
+
+                var dot = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                var sumx = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                for (var i = 0u; i < 4u; i++) {
+                    let q1b = byte_of(q1_u32, i);
+                    let q2b = byte_of(q2_u32, i);
+                    dot[0] += x_block[i] * f32(q1b & 0x0Fu);
+                    dot[1] += x_block[i + 4u] * f32(q1b >> 4u);
+                    dot[2] += x_block[i + 8u] * f32(q2b & 0x0Fu);
+                    dot[3] += x_block[i + 12u] * f32(q2b >> 4u);
+                    sumx[0] += x_block[i];
+                    sumx[1] += x_block[i + 4u];
+                    sumx[2] += x_block[i + 8u];
+                    sumx[3] += x_block[i + 12u];
+                }
+
+                acc[row] += d * (dot[0] * scale0 + dot[1] * scale1 + dot[2] * scale2 + dot[3] * scale3)
+                          - dmin * (sumx[0] * min0 + sumx[1] * min1 + sumx[2] * min2 + sumx[3] * min3);
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q5_K
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 176
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let il = tid / 4u;
+    let ir = tid % 4u;
+    let im = il / 2u;
+    let in = il % 2u;
+    let l0 = 4u * (2u * ir + in);
+
+    let y_offset = 64u * im + l0;
+    let q_offset = 48u + 32u * im + l0;
+    let qh_offset = 16u + 8u * ir + 4u * in;
+    let sc0_byte = 4u + im * 2u;
+    let sc2_byte = 4u + (im + 2u) * 2u;
+    let sc4_byte = 4u + (im + 4u) * 2u;
+
+    let hm1 = 1u << (2u * im);
+    let hm2 = hm1 << 1u;
+    let hm3 = hm1 << 4u;
+    let hm4 = hm2 << 4u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 4u; i++) {
+            x_block[i]       = f32(src1[x_base + i]);
+            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
+            x_block[i + 8u]  = f32(src1[x_base + 128u + i]);
+            x_block[i + 12u] = f32(src1[x_base + 160u + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+
+                let d = f32(load_f16_at_src0(block_byte_base + 0u));
+                let dmin = f32(load_f16_at_src0(block_byte_base + 2u));
+
+                let sc0_u32 = load_u32_at_src0_aligned(block_byte_base + sc0_byte);
+                let sc0 = select(sc0_u32 & 0xFFFFu, sc0_u32 >> 16u, (sc0_byte & 2u) != 0u);
+                let sc2_u32 = load_u32_at_src0_aligned(block_byte_base + sc2_byte);
+                let sc2 = select(sc2_u32 & 0xFFFFu, sc2_u32 >> 16u, (sc2_byte & 2u) != 0u);
+                let sc4_u32 = load_u32_at_src0_aligned(block_byte_base + sc4_byte);
+                let sc4 = select(sc4_u32 & 0xFFFFu, sc4_u32 >> 16u, (sc4_byte & 2u) != 0u);
+
+                let sc16_0 = sc0 & 0x3F3Fu;
+                let sc16_1 = sc2 & 0x3F3Fu;
+                let sc16_2 = (sc4 & 0x0F0Fu) | ((sc0 & 0xC0C0u) >> 2u);
+                let sc16_3 = ((sc4 >> 4u) & 0x0F0Fu) | ((sc2 & 0xC0C0u) >> 2u);
+
+                let f0 = f32(sc16_0 & 0xFFu);
+                let f1 = f32((sc16_0 >> 8u) & 0xFFu);
+                let m0 = f32(sc16_1 & 0xFFu);
+                let m1 = f32((sc16_1 >> 8u) & 0xFFu);
+                let f4 = f32(sc16_2 & 0xFFu);
+                let f5 = f32((sc16_2 >> 8u) & 0xFFu);
+                let m4 = f32(sc16_3 & 0xFFu);
+                let m5 = f32((sc16_3 >> 8u) & 0xFFu);
+
+                let q1_u32 = load_u32_at_src0_aligned(block_byte_base + q_offset);
+                let q2_u32 = load_u32_at_src0_aligned(block_byte_base + q_offset + 64u);
+                let qh_u32 = load_u32_at_src0_aligned(block_byte_base + qh_offset);
+
+                var vals = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                var sumy = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                for (var i = 0u; i < 4u; i++) {
+                    let q1b = byte_of(q1_u32, i);
+                    let q2b = byte_of(q2_u32, i);
+                    let qhb = byte_of(qh_u32, i);
+
+                    let yl0 = x_block[i];
+                    let yl8 = x_block[i + 4u];
+                    let yh0 = x_block[i + 8u];
+                    let yh8 = x_block[i + 12u];
+
+                    sumy[0] += yl0;
+                    sumy[1] += yl8;
+                    sumy[2] += yh0;
+                    sumy[3] += yh8;
+
+                    let q0 = f32((q1b & 0x0Fu) | select(0u, 0x10u, (qhb & hm1) != 0u));
+                    let q1 = f32((q1b >> 4u) | select(0u, 0x10u, (qhb & hm2) != 0u));
+                    let q2 = f32((q2b & 0x0Fu) | select(0u, 0x10u, (qhb & hm3) != 0u));
+                    let q3 = f32((q2b >> 4u) | select(0u, 0x10u, (qhb & hm4) != 0u));
+
+                    vals[0] += yl0 * q0;
+                    vals[1] += yl8 * q1;
+                    vals[2] += yh0 * q2;
+                    vals[3] += yh8 * q3;
+                }
+
+                acc[row] += d * (f0 * vals[0] + f1 * vals[1] + f4 * vals[2] + f5 * vals[3])
+                          - dmin * (sumy[0] * m0 + sumy[1] * m1 +
+                                    sumy[2] * m4 + sumy[3] * m5);
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_Q6_K
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 210
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let ip = tid / 8u;
+    let il = tid % 8u;
+    let l0 = 4u * il;
+    let is = 8u * ip + l0 / 16u;
+
+    let y_offset = 128u * ip + l0;
+    let q_offset_l = 64u * ip + l0;
+    let q_offset_h = 32u * ip + l0;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let sc_base_byte = 192u + (is & ~3u);
+    let sc_byte_pos = is & 3u;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var l = 0u; l < 4u; l++) {
+            x_block[l]       = f32(src1[x_base + l]);
+            x_block[l + 4u]  = f32(src1[x_base + 32u + l]);
+            x_block[l + 8u]  = f32(src1[x_base + 64u + l]);
+            x_block[l + 12u] = f32(src1[x_base + 96u + l]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+
+                let d = f32(load_f16_at_src0(block_byte_base + 208u));
+                let ql1_u32 = load_u32_at_src0(block_byte_base + q_offset_l);
+                let ql2_u32 = load_u32_at_src0(block_byte_base + q_offset_l + 32u);
+                let qh_u32 = load_u32_at_src0(block_byte_base + 128u + q_offset_h);
+                let sc_u32_0 = load_u32_at_src0(block_byte_base + sc_base_byte);
+                let sc_u32_1 = load_u32_at_src0(block_byte_base + sc_base_byte + 4u);
+
+                let sc0 = sbyte_of(sc_u32_0, sc_byte_pos);
+                let sc2 = sbyte_of(sc_u32_0, sc_byte_pos + 2u);
+                let sc4 = sbyte_of(sc_u32_1, sc_byte_pos);
+                let sc6 = sbyte_of(sc_u32_1, sc_byte_pos + 2u);
+
+                var sums = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+
+                for (var l = 0u; l < 4u; l++) {
+                    let q1b = byte_of(ql1_u32, l);
+                    let q2b = byte_of(ql2_u32, l);
+                    let qhb = byte_of(qh_u32, l);
+
+                    let dq0 = f32(i32((q1b & 0x0Fu) | ((qhb & 0x03u) << 4u)) - 32);
+                    let dq1 = f32(i32((q2b & 0x0Fu) | ((qhb & 0x0Cu) << 2u)) - 32);
+                    let dq2 = f32(i32((q1b >> 4u) | (qhb & 0x30u)) - 32);
+                    let dq3 = f32(i32((q2b >> 4u) | ((qhb & 0xC0u) >> 2u)) - 32);
+
+                    sums[0] += x_block[l] * dq0;
+                    sums[1] += x_block[l + 4u] * dq1;
+                    sums[2] += x_block[l + 8u] * dq2;
+                    sums[3] += x_block[l + 12u] * dq3;
+                }
+
+                acc[row] += d * (sums[0] * f32(sc0) + sums[1] * f32(sc2) +
+                                 sums[2] * f32(sc4) + sums[3] * f32(sc6));
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ1_S
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 50
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let sub_blk = tid / 2u;
+    let half    = tid % 2u;
+    let slot0   = half * 2u;
+    let y_offset = sub_blk * 32u + slot0 * 8u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 16u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+
+                let d     = f32(load_f16_at_src0(block_byte_base));
+                let qh    = load_u32_at_src0(block_byte_base + 34u + sub_blk * 2u) & 0xFFFFu;
+                let dl    = d * f32(2u * ((qh >> 12u) & 7u) + 1u);
+                let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x8000u) != 0u);
+                let qs_w  = load_u32_at_src0(block_byte_base + 2u + sub_blk * 4u);
+
+                var row_sum = 0.0;
+                for (var ll = 0u; ll < 2u; ll++) {
+                    let l       = slot0 + ll;
+                    let qs_byte = get_byte(qs_w, l);
+                    let ig      = (qs_byte | (((qh >> (3u * l)) & 7u) << 8u)) * 8u;
+                    let gw      = iq1_grid[ig / 16u];
+                    let bit_base = (ig % 16u) * 2u;
+                    for (var j = 0u; j < 8u; j++) {
+                        let g  = (gw >> (bit_base + j * 2u)) & 3u;
+                        let gs = select(f32(g), f32(g) - 4.0, (g & 2u) != 0u);
+                        row_sum += dl * (gs + delta) * x_block[ll * 8u + j];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ1_M
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 56
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let sub_blk = tid / 2u;
+    let half    = tid % 2u;
+    let slot0   = half * 2u;
+    let y_offset = sub_blk * 32u + slot0 * 8u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 16u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+
+                let sc_lo = load_u32_at_src0(block_byte_base + 48u);
+                let sc_hi = load_u32_at_src0(block_byte_base + 52u);
+                let sc0 = sc_lo & 0xFFFFu;
+                let sc1 = (sc_lo >> 16u) & 0xFFFFu;
+                let sc2 = sc_hi & 0xFFFFu;
+                let sc3 = (sc_hi >> 16u) & 0xFFFFu;
+                let d_bits = (sc0 >> 12u) | ((sc1 >> 8u) & 0xF0u) | ((sc2 >> 4u) & 0xF00u) | (sc3 & 0xF000u);
+                let d = f32(bitcast<vec2<f16>>(d_bits)[0]);
+
+                let sc_u16 = select(select(sc2, sc3, sub_blk >= 6u),
+                                    select(sc0, sc1, sub_blk >= 2u),
+                                    sub_blk < 4u);
+
+                let qs_w = load_u32_at_src0(block_byte_base + sub_blk * 4u);
+                let qh = load_u32_at_src0(block_byte_base + 32u + sub_blk * 2u) & 0xFFFFu;
+                let qh_lo = qh & 0xFFu;
+                let qh_hi = (qh >> 8u) & 0xFFu;
+
+                var row_sum = 0.0;
+                for (var ll = 0u; ll < 2u; ll++) {
+                    let l = slot0 + ll;
+                    let bit_off = 6u * (sub_blk % 2u) + 3u * (l / 2u);
+                    let sub_scale = (sc_u16 >> bit_off) & 0x7u;
+                    let dl = d * f32(2u * sub_scale + 1u);
+                    let qh_byte = select(qh_lo, qh_hi, l >= 2u);
+                    let ll2 = l % 2u;
+                    let grid_idx = get_byte(qs_w, l) | (((qh_byte >> (4u * ll2)) & 7u) << 8u);
+                    let delta = select(IQ1_DELTA, -IQ1_DELTA, ((qh_byte >> (3u + 4u * ll2)) & 1u) != 0u);
+                    let ig = grid_idx * 8u;
+                    let gw = iq1_grid[ig / 16u];
+                    let bit_base = (ig % 16u) * 2u;
+                    for (var j = 0u; j < 8u; j++) {
+                        let g  = (gw >> (bit_base + j * 2u)) & 3u;
+                        let gs = select(f32(g), f32(g) - 4.0, (g & 2u) != 0u);
+                        row_sum += dl * (gs + delta) * x_block[ll * 8u + j];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ2_XXS
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 66
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let sub_blk = tid / 2u;
+    let half    = tid % 2u;
+    let slot0   = half * 2u;
+    let y_offset = sub_blk * 32u + slot0 * 8u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 16u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let aux_lo = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u);
+                let aux_hi = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u + 4u);
+                let ls = aux_hi >> 28u;
+                let db = d * (0.5 + f32(ls)) * 0.25;
+
+                var row_sum = 0.0;
+                for (var ll = 0u; ll < 2u; ll++) {
+                    let l = slot0 + ll;
+                    let grid_idx = (aux_lo >> (8u * l)) & 0xFFu;
+                    let signs_idx = (aux_hi >> (7u * l)) & 0x7Fu;
+                    let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
+                    let gw_lo = iq2xxs_grid[grid_idx * 2u];
+                    let gw_hi = iq2xxs_grid[grid_idx * 2u + 1u];
+                    for (var j = 0u; j < 8u; j++) {
+                        let gw = select(gw_hi, gw_lo, j < 4u);
+                        let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
+                        let s = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
+                        row_sum += db * b * s * x_block[ll * 8u + j];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ2_XS
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 74
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let sub_blk = tid / 2u;
+    let half    = tid % 2u;
+    let slot0   = half * 2u;
+    let y_offset = sub_blk * 32u + slot0 * 8u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 16u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let qs_lo = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u);
+                let qs_hi = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u + 4u);
+                let scales_word = load_u32_at_src0(block_byte_base + 66u + (sub_blk / 4u) * 4u);
+                let scales_byte = get_byte(scales_word, sub_blk % 4u);
+
+                var row_sum = 0.0;
+                for (var ll = 0u; ll < 2u; ll++) {
+                    let l = slot0 + ll;
+                    let qs_word = select(qs_hi, qs_lo, l < 2u);
+                    let half2 = (l % 2u) * 16u;
+                    let qs_val = (qs_word >> half2) & 0xFFFFu;
+                    let grid_idx = qs_val & 0x1FFu;
+                    let signs_idx = (qs_val >> 9u) & 0x7Fu;
+                    let sub_scale = (scales_byte >> (4u * (l / 2u))) & 0xFu;
+                    let db = d * (0.5 + f32(sub_scale)) * 0.25;
+                    let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
+                    let gw_lo = iq2xs_grid[grid_idx * 2u];
+                    let gw_hi = iq2xs_grid[grid_idx * 2u + 1u];
+                    for (var j = 0u; j < 8u; j++) {
+                        let gw = select(gw_hi, gw_lo, j < 4u);
+                        let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
+                        let s = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
+                        row_sum += db * b * s * x_block[ll * 8u + j];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ2_S
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 82
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let sub_blk = tid / 2u;
+    let half    = tid % 2u;
+    let slot0   = half * 2u;
+    let y_offset = sub_blk * 32u + slot0 * 8u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 16u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let qs_w = load_u32_at_src0(block_byte_base + 2u + sub_blk * 4u);
+                let sg_w = load_u32_at_src0(block_byte_base + 34u + sub_blk * 4u);
+                let qh_word = load_u32_at_src0(block_byte_base + 66u + (sub_blk / 4u) * 4u);
+                let qh_byte = get_byte(qh_word, sub_blk % 4u);
+                let sc_word = load_u32_at_src0(block_byte_base + 74u + (sub_blk / 4u) * 4u);
+                let scales_byte = get_byte(sc_word, sub_blk % 4u);
+
+                var row_sum = 0.0;
+                for (var ll = 0u; ll < 2u; ll++) {
+                    let l = slot0 + ll;
+                    let qs_byte = get_byte(qs_w, l);
+                    let sign_byte = get_byte(sg_w, l);
+                    let grid_idx = qs_byte | (((qh_byte >> (2u * l)) & 3u) << 8u);
+                    let sub_scale = (scales_byte >> (4u * (l / 2u))) & 0xFu;
+                    let db = d * (0.5 + f32(sub_scale)) * 0.25;
+                    let gw_lo = iq2s_grid[grid_idx * 2u];
+                    let gw_hi = iq2s_grid[grid_idx * 2u + 1u];
+                    for (var j = 0u; j < 8u; j++) {
+                        let gw = select(gw_hi, gw_lo, j < 4u);
+                        let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
+                        let s = select(1.0, -1.0, ((sign_byte >> j) & 1u) != 0u);
+                        row_sum += db * b * s * x_block[ll * 8u + j];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ3_XXS
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 98
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let sub_blk = tid / 2u;
+    let half    = tid % 2u;
+    let slot0   = half * 2u;
+    let y_offset = sub_blk * 32u + slot0 * 8u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 16u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let qs_lo = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u);
+                let qs_hi = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u + 4u);
+                let aux = load_u32_at_src0(block_byte_base + 66u + sub_blk * 4u);
+                let ls = aux >> 28u;
+                let db = d * (0.5 + f32(ls)) * 0.5;
+
+                var row_sum = 0.0;
+                for (var ll = 0u; ll < 2u; ll++) {
+                    let l = slot0 + ll;
+                    let qs_word = select(qs_hi, qs_lo, l < 2u);
+                    let byte_pos = (l % 2u) * 2u;
+                    let grid_idx_0 = (qs_word >> (byte_pos * 8u)) & 0xFFu;
+                    let grid_idx_1 = (qs_word >> ((byte_pos + 1u) * 8u)) & 0xFFu;
+                    let signs_idx = (aux >> (7u * l)) & 0x7Fu;
+                    let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
+                    let grid1 = iq3xxs_grid[grid_idx_0];
+                    let grid2 = iq3xxs_grid[grid_idx_1];
+                    for (var j = 0u; j < 4u; j++) {
+                        let b1 = f32((grid1 >> (j * 8u)) & 0xFFu);
+                        let b2 = f32((grid2 >> (j * 8u)) & 0xFFu);
+                        let s1 = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
+                        let s2 = select(1.0, -1.0, ((signs >> (j + 4u)) & 1u) != 0u);
+                        row_sum += db * b1 * s1 * x_block[ll * 8u + j];
+                        row_sum += db * b2 * s2 * x_block[ll * 8u + j + 4u];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ3_S
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 110
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let sub_blk = tid / 2u;
+    let half    = tid % 2u;
+    let slot0   = half * 2u;
+    let y_offset = sub_blk * 32u + slot0 * 8u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 16u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let qs_lo = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u);
+                let qs_hi = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u + 4u);
+                let qh_word = load_u32_at_src0(block_byte_base + 66u + (sub_blk / 4u) * 4u);
+                let qh_byte = get_byte(qh_word, sub_blk % 4u);
+                let sg_w = load_u32_at_src0(block_byte_base + 74u + sub_blk * 4u);
+                let sc_word = load_u32_at_src0(block_byte_base + 106u);
+                let scales_byte = get_byte(sc_word, sub_blk / 2u);
+                let sub_scale = (scales_byte >> (4u * (sub_blk % 2u))) & 0xFu;
+                let db = d * (1.0 + 2.0 * f32(sub_scale));
+
+                var row_sum = 0.0;
+                for (var ll = 0u; ll < 2u; ll++) {
+                    let l = slot0 + ll;
+                    let qs_word = select(qs_hi, qs_lo, l < 2u);
+                    let byte_pos = (l % 2u) * 2u;
+                    let qs0 = (qs_word >> (byte_pos * 8u)) & 0xFFu;
+                    let qs1 = (qs_word >> ((byte_pos + 1u) * 8u)) & 0xFFu;
+                    let grid_idx_1 = qs0 | (((qh_byte >> (2u * l)) & 1u) << 8u);
+                    let grid_idx_2 = qs1 | (((qh_byte >> (2u * l + 1u)) & 1u) << 8u);
+                    let sign_byte = get_byte(sg_w, l);
+                    let grid1 = iq3s_grid[grid_idx_1];
+                    let grid2 = iq3s_grid[grid_idx_2];
+                    for (var j = 0u; j < 4u; j++) {
+                        let b1 = f32((grid1 >> (j * 8u)) & 0xFFu);
+                        let b2 = f32((grid2 >> (j * 8u)) & 0xFFu);
+                        let s1 = select(1.0, -1.0, ((sign_byte >> j) & 1u) != 0u);
+                        let s2 = select(1.0, -1.0, ((sign_byte >> (j + 4u)) & 1u) != 0u);
+                        row_sum += db * b1 * s1 * x_block[ll * 8u + j];
+                        row_sum += db * b2 * s2 * x_block[ll * 8u + j + 4u];
+                    }
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ4_NL
+#define BLOCK_SIZE 32
+#define BLOCK_SIZE_BYTES 18
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % THREADS_PER_BLOCK;
+    for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4u;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD / 2u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+            x_block[i + 4u] = f32(src1[x_base + i + 16u]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                var row_sum = 0.0;
+
+                let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * thread_within_block);
+                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                    let q_byte = get_byte(q_packed, byte_idx);
+                    let q_lo = f32(kvalues_iq4nl[q_byte & 0xFu]) * d;
+                    let q_hi = f32(kvalues_iq4nl[(q_byte >> 4u) & 0xFu]) * d;
+                    row_sum += q_lo * x_block[byte_idx];
+                    row_sum += q_hi * x_block[byte_idx + 4u];
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_IQ4_XS
+#define BLOCK_SIZE 256
+#define BLOCK_SIZE_BYTES 136
+#define THREADS_PER_BLOCK 16
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let tid = thread_id % THREADS_PER_BLOCK;
+    let block_group = thread_id / THREADS_PER_BLOCK;
+    let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK;
+
+    let sub_blk = tid / 2u;
+    let half    = tid % 2u;
+    let y_offset = sub_blk * 32u + half * 16u;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+
+    for (var block = block_group; block < num_blocks; block += num_block_groups) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
+        var x_block: array<f32, 16>;
+        for (var i = 0u; i < 16u; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = f32(load_f16_at_src0(block_byte_base));
+                let scales_h = load_u16_at_src0(block_byte_base + 2u);
+                let scales_l_word = load_u32_at_src0(block_byte_base + 4u);
+                let sl_byte = get_byte(scales_l_word, sub_blk / 2u);
+                let sl = (sl_byte >> (4u * (sub_blk % 2u))) & 0xFu;
+                let sh_bits = (scales_h >> (2u * sub_blk)) & 3u;
+                let ls = i32(sl | (sh_bits << 4u));
+                let dl = d * f32(ls - 32);
+
+                let qs_byte_off = 8u + sub_blk * 16u;
+                let q_w0 = load_u32_at_src0(block_byte_base + qs_byte_off);
+                let q_w1 = load_u32_at_src0(block_byte_base + qs_byte_off + 4u);
+                let q_w2 = load_u32_at_src0(block_byte_base + qs_byte_off + 8u);
+                let q_w3 = load_u32_at_src0(block_byte_base + qs_byte_off + 12u);
+
+                var row_sum = 0.0;
+                for (var i = 0u; i < 16u; i++) {
+                    let q_word = select(
+                        select(q_w0, q_w1, i >= 4u),
+                        select(q_w2, q_w3, i >= 12u),
+                        i >= 8u);
+                    let q_byte = get_byte(q_word, i % 4u);
+                    let nib = select(q_byte & 0xFu, (q_byte >> 4u) & 0xFu, half == 1u);
+                    row_sum += f32(kvalues_iq4nl[nib]) * dl * x_block[i];
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
+
+#ifdef MUL_ACC_MXFP4
+#define BLOCK_SIZE 32
+#define BLOCK_SIZE_BYTES 17
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
+    var acc: array<f32, OUTPUTS_PER_WG>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let thread_within_block = thread_id % 4;
+    for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
+        var x_block: array<f32, ELEMS_PER_THREAD>;
+        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+            x_block[i] = f32(src1[x_base + i]);
+            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        }
+
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let eu8 = get_byte(load_u32_at_src0(block_byte_base), 0);
+                let e = ldexp(1.0, i32(eu8) - 128);
+                var row_sum = 0.0;
+                let q_packed = load_u32_at_src0(block_byte_base + 1u + 4u * thread_within_block);
+                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                    let q_byte = get_byte(q_packed, byte_idx);
+                    let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * e;
+                    let q_hi = f32(kvalues_mxfp4[(q_byte >> 4u) & 0xFu]) * e;
+                    row_sum += q_lo * x_block[byte_idx];
+                    row_sum += q_hi * x_block[byte_idx + 4u];
+                }
+                acc[row] += row_sum;
+            }
+        }
+    }
+
+    return acc;
+}
+#endif
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm_mul.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm_mul.wgsl
new file mode 100644
index 00000000000..fd20a4e54c9
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm_mul.wgsl
@@ -0,0 +1,152 @@
+#ifdef OVERLAP
+
+@group(0) @binding(0)
+var<storage, read_write> rn_src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> mul_src: array<f32>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+fn update(rn_src_offset: u32, dst_offset: u32, scale: f32, mul_src_offset: u32) {
+    mul_src[dst_offset] = scale * rn_src[rn_src_offset] * mul_src[mul_src_offset];
+}
+
+#elif INPLACE
+
+@group(0) @binding(0)
+var<storage, read_write> rn_src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> mul_src: array<f32>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+fn update(rn_src_offset: u32, dst_offset: u32, scale: f32, mul_src_offset: u32) {
+    rn_src[dst_offset] = scale * rn_src[rn_src_offset] * mul_src[mul_src_offset];
+}
+
+#elif SRC_OVERLAP
+
+@group(0) @binding(0)
+var<storage, read_write> merged_src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+fn update(rn_src_offset: u32, dst_offset: u32, scale: f32, mul_src_offset: u32) {
+    dst[dst_offset] = scale * merged_src[rn_src_offset] * merged_src[mul_src_offset];
+}
+
+#else
+
+@group(0) @binding(0)
+var<storage, read_write> rn_src: array<f32>;
+
+@group(0) @binding(1)
+var<storage, read_write> mul_src: array<f32>;
+
+@group(0) @binding(2)
+var<storage, read_write> dst: array<f32>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+fn update(rn_src_offset: u32, dst_offset: u32, scale: f32, mul_src_offset: u32) {
+    dst[dst_offset] = scale * rn_src[rn_src_offset] * mul_src[mul_src_offset];
+}
+
+#endif
+
+struct Params {
+    offset_rn_src: u32,
+    offset_mul_src: u32,
+    offset_dst: u32,
+
+    stride_rn_src1: u32,
+    stride_rn_src2: u32,
+    stride_rn_src3: u32,
+
+    stride_mul_src1: u32,
+    stride_mul_src2: u32,
+    stride_mul_src3: u32,
+
+    stride_dst1: u32,
+    stride_dst2: u32,
+    stride_dst3: u32,
+
+    mul_src_ne0: u32,
+    mul_src_ne1: u32,
+    mul_src_ne2: u32,
+    mul_src_ne3: u32,
+
+    ne0: u32,
+    ne1: u32,
+    ne2: u32,
+    ne3: u32,
+
+    eps: f32
+};
+
+var<workgroup> scratch: array<f32, WG_SIZE>;
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(local_invocation_id) lid: vec3<u32>) {
+
+    // one thread per row
+    var i = wid.x;
+    let i3 = i / (params.ne2 * params.ne1);
+    i = i % (params.ne2 * params.ne1);
+    let i2 = i / params.ne1;
+    let i1 = i % params.ne1;
+    let i_rn_src_row = params.offset_rn_src + i3 * params.stride_rn_src3 + i2 * params.stride_rn_src2 + i1 * params.stride_rn_src1;
+    let i_mul_src_row = params.offset_mul_src + (i3 % params.mul_src_ne3) * params.stride_mul_src3 + (i2 % params.mul_src_ne2) * params.stride_mul_src2 + (i1 % params.mul_src_ne1) * params.stride_mul_src1;
+    let i_dst_row = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1;
+
+    let elems = (params.ne0 + WG_SIZE - 1) / WG_SIZE;
+
+    var sum = 0.0f;
+    var col = lid.x;
+    for (var j: u32 = 0; j < elems; j++) {
+        if (col >= params.ne0) {
+            break;
+        }
+#ifdef SRC_OVERLAP
+        sum += pow(merged_src[i_rn_src_row + col], 2.0);
+#else
+        sum += pow(rn_src[i_rn_src_row + col], 2.0);
+#endif
+        col += WG_SIZE;
+    }
+
+    scratch[lid.x] = sum;
+
+    workgroupBarrier();
+
+    var offset: u32 = WG_SIZE / 2;
+    while (offset > 0) {
+        if (lid.x < offset) {
+            scratch[lid.x] += scratch[lid.x + offset];
+        }
+        offset = offset / 2;
+        workgroupBarrier();
+    }
+    sum = scratch[0];
+
+    let scale = 1.0/sqrt(sum/f32(params.ne0) + params.eps);
+
+    col = lid.x;
+    for (var j: u32 = 0; j < elems; j++) {
+        if (col >= params.ne0) {
+            break;
+        }
+        update(i_rn_src_row + col, i_dst_row + col, scale, i_mul_src_row + col % params.mul_src_ne0);
+        col += WG_SIZE;
+    }
+}
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/row_norm.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/row_norm.wgsl
index bd8d32bded7..5eaf5e7bbe5 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/row_norm.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/row_norm.wgsl
@@ -1,20 +1,17 @@
-#ifdef INPLACE
-fn update(src_offset: u32, dst_offset: u32, scale: f32) {
-    src[dst_offset] = scale * src[src_offset];
-}
+#if defined(SRC_F16) || defined(DST_F16)
+enable f16;
+#endif
 
-@group(0) @binding(1)
-var<uniform> params: Params;
+#ifdef SRC_F16
+#define SRC_TYPE f16
 #else
-fn update(src_offset: u32, dst_offset: u32, scale: f32) {
-    dst[dst_offset] = scale * src[src_offset];
-}
-
-@group(0) @binding(1)
-var<storage, read_write> dst: array<f32>;
+#define SRC_TYPE f32
+#endif
 
-@group(0) @binding(2)
-var<uniform> params: Params;
+#ifdef DST_F16
+#define DST_TYPE f16
+#else
+#define DST_TYPE f32
 #endif
 
 struct Params {
@@ -40,9 +37,20 @@ struct Params {
 };
 
 @group(0) @binding(0)
-var<storage, read_write> src: array<f32>;
+var<storage, read_write> src: array<SRC_TYPE>;
 
-var<workgroup> scratch: array<f32, WG_SIZE>;
+#ifdef INPLACE
+@group(0) @binding(1)
+var<uniform> params: Params;
+#else
+@group(0) @binding(1)
+var<storage, read_write> dst: array<DST_TYPE>;
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+#endif
+
+var<workgroup> scratch: array<f32, WG_SIZE * 2u>;
 
 @compute @workgroup_size(WG_SIZE)
 fn main(@builtin(workgroup_id) wid: vec3<u32>,
@@ -65,34 +73,81 @@ fn main(@builtin(workgroup_id) wid: vec3<u32>,
         if (col >= params.ne0) {
             break;
         }
-        sum += pow(src[i_src_row + col], 2.0);
+        let v = f32(src[i_src_row + col]);
+#ifdef NORM
+        sum += v;
+#else
+        sum += v * v;
+#endif
         col += WG_SIZE;
     }
 
     scratch[lid.x] = sum;
     workgroupBarrier();
-    var offset: u32 = WG_SIZE / 2;
+
+    var offset: u32 = WG_SIZE / 2u;
     while (offset > 0) {
         if (lid.x < offset) {
             scratch[lid.x] += scratch[lid.x + offset];
         }
-        offset = offset / 2;
+        offset /= 2u;
         workgroupBarrier();
     }
     sum = scratch[0];
 
-#ifdef RMS_NORM
+#ifdef NORM
+    let mean = sum / f32(params.ne0);
+    var sq_sum = 0.0f;
+    col = lid.x;
+    for (var j: u32 = 0; j < elems; j++) {
+        if (col >= params.ne0) {
+            break;
+        }
+        let v = f32(src[i_src_row + col]);
+        let d = v - mean;
+        sq_sum += d * d;
+        col += WG_SIZE;
+    }
+
+    workgroupBarrier();
+    scratch[lid.x] = sq_sum;
+    workgroupBarrier();
+    offset = WG_SIZE / 2u;
+    while (offset > 0) {
+        if (lid.x < offset) {
+            scratch[lid.x] += scratch[lid.x + offset];
+        }
+        offset /= 2u;
+        workgroupBarrier();
+    }
+
+    let variance = scratch[0] / f32(params.ne0);
+    let scale = 1.0 / sqrt(variance + params.eps);
+#elif defined(RMS_NORM)
     let scale = 1.0/sqrt(sum/f32(params.ne0) + params.eps);
 #elif defined(L2_NORM)
     let scale = 1.0/max(sqrt(sum), params.eps);
 #endif
 
+#ifdef NORM
+    let mean_val = mean;
+#else
+    let mean_val = 0.0f;
+#endif
+
     col = lid.x;
     for (var j: u32 = 0; j < elems; j++) {
         if (col >= params.ne0) {
             break;
         }
-        update(i_src_row + col, i_dst_row + col, scale);
+        let i_src = i_src_row + col;
+        let i_dst = i_dst_row + col;
+        let v = src[i_src];
+#ifdef INPLACE
+        src[i_dst] = scale * (v - mean_val);
+#else
+        dst[i_dst] = scale * (v - mean_val);
+#endif
         col += WG_SIZE;
     }
 }
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl
new file mode 100644
index 00000000000..05761dec353
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl
@@ -0,0 +1,193 @@
+#ifdef USE_SUBGROUP_REDUCTION
+enable subgroups;
+#endif
+
+struct Params {
+    offset_s: u32,
+    offset_x: u32,
+    offset_dt: u32,
+    offset_A: u32,
+    offset_B: u32,
+    offset_C: u32,
+    offset_ids: u32,
+    offset_dst: u32,
+
+    stride_s1: u32,
+    stride_s2: u32,
+    stride_s3: u32,
+
+    stride_x1: u32,
+    stride_x2: u32,
+    stride_x3: u32,
+
+    stride_dt1: u32,
+    stride_dt2: u32,
+
+    a_ne0: u32,
+    stride_A1: u32,
+
+    stride_B1: u32,
+    stride_B2: u32,
+    stride_B3: u32,
+
+    stride_C1: u32,
+    stride_C2: u32,
+    stride_C3: u32,
+
+    d_state: u32,
+    d_inner: u32,
+    n_head: u32,
+    n_group: u32,
+    n_seq_tokens: u32,
+    n_seqs: u32,
+
+    y_elems: u32,
+};
+
+@group(0) @binding(0) var<storage, read_write> s_in: array<f32>;
+#ifdef XBC_OVERLAP
+@group(0) @binding(1) var<storage, read_write> x_B_C_merged: array<f32>;
+@group(0) @binding(2) var<storage, read_write> dt: array<f32>;
+@group(0) @binding(3) var<storage, read_write> A: array<f32>;
+@group(0) @binding(4) var<storage, read_write> ids: array<i32>;
+@group(0) @binding(5) var<storage, read_write> dst: array<f32>;
+@group(0) @binding(6) var<uniform> params: Params;
+#else
+@group(0) @binding(1) var<storage, read_write> x: array<f32>;
+@group(0) @binding(2) var<storage, read_write> dt: array<f32>;
+@group(0) @binding(3) var<storage, read_write> A: array<f32>;
+@group(0) @binding(4) var<storage, read_write> B: array<f32>;
+@group(0) @binding(5) var<storage, read_write> C: array<f32>;
+@group(0) @binding(6) var<storage, read_write> ids: array<i32>;
+@group(0) @binding(7) var<storage, read_write> dst: array<f32>;
+@group(0) @binding(8) var<uniform> params: Params;
+#endif
+
+var<workgroup> shared_x_dt: array<f32, TOKENS_PER_TILE>;
+var<workgroup> shared_dtsp: array<f32, TOKENS_PER_TILE>;
+var<workgroup> shared_reduce: array<f32, TOKENS_PER_TILE * WG_SIZE>;
+
+fn reduce_base(token_in_tile: u32) -> u32 {
+    return token_in_tile * WG_SIZE;
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(
+    @builtin(local_invocation_id) local_id: vec3<u32>,
+    @builtin(workgroup_id) wg_id: vec3<u32>,
+    @builtin(num_workgroups) num_wg: vec3<u32>
+#ifdef USE_SUBGROUP_REDUCTION
+  , @builtin(subgroup_id) subgroup_id: u32,
+    @builtin(subgroup_invocation_id) subgroup_invocation_id: u32,
+    @builtin(num_subgroups) num_subgroups: u32
+#endif
+) {
+    let tid = local_id.x;
+    let wg_linear = wg_id.y * num_wg.x + wg_id.x;
+
+    let i1 = wg_linear % params.d_inner;
+    let head_seq = wg_linear / params.d_inner;
+    let ir = head_seq % params.n_head;
+    let i3 = head_seq / params.n_head;
+
+    let state_slot = u32(ids[params.offset_ids + i3]);
+    let g = ir / (params.n_head / params.n_group);
+
+    let s_idx = params.offset_s + tid + i1 * params.stride_s1 + ir * params.stride_s2 + state_slot * params.stride_s3;
+    var s_prev = s_in[s_idx];
+
+    let A0 = A[params.offset_A + (tid % params.a_ne0) + ir * params.stride_A1];
+
+    for (var token_base = 0u; token_base < params.n_seq_tokens; token_base += TOKENS_PER_TILE) {
+        if (tid < TOKENS_PER_TILE) {
+            let token = token_base + tid;
+            if (token < params.n_seq_tokens) {
+                let x_idx = params.offset_x + i1 + ir * params.stride_x1 + token * params.stride_x2 + i3 * params.stride_x3;
+                let dt_idx = params.offset_dt + ir + token * params.stride_dt1 + i3 * params.stride_dt2;
+                let dt0 = dt[dt_idx];
+                let dtsp = select(log(1.0 + exp(dt0)), dt0, dt0 > 20.0);
+                shared_dtsp[tid] = dtsp;
+#ifdef XBC_OVERLAP
+                shared_x_dt[tid] = x_B_C_merged[x_idx] * dtsp;
+#else
+                shared_x_dt[tid] = x[x_idx] * dtsp;
+#endif
+            }
+        }
+
+        workgroupBarrier();
+
+        for (var token_in_tile = 0u; token_in_tile < TOKENS_PER_TILE; token_in_tile++) {
+            let token = token_base + token_in_tile;
+            if (token >= params.n_seq_tokens) {
+                break;
+            }
+
+            let x_dt = shared_x_dt[token_in_tile];
+            let dA = exp(shared_dtsp[token_in_tile] * A0);
+            let reduce_idx = reduce_base(token_in_tile) + tid;
+
+            let b_idx = params.offset_B + tid + g * params.stride_B1 + token * params.stride_B2 + i3 * params.stride_B3;
+            let c_idx = params.offset_C + tid + g * params.stride_C1 + token * params.stride_C2 + i3 * params.stride_C3;
+#ifdef XBC_OVERLAP
+            let s = s_prev * dA + x_B_C_merged[b_idx] * x_dt;
+#else
+            let s = s_prev * dA + B[b_idx] * x_dt;
+#endif
+            s_prev = s;
+
+#ifdef USE_SUBGROUP_REDUCTION
+#ifdef XBC_OVERLAP
+            let subgroup_partial = subgroupAdd(s * x_B_C_merged[c_idx]);
+#else
+            let subgroup_partial = subgroupAdd(s * C[c_idx]);
+#endif
+            if (subgroup_invocation_id == 0u) {
+                shared_reduce[reduce_idx - tid + subgroup_id] = subgroup_partial;
+            }
+#else
+#ifdef XBC_OVERLAP
+            shared_reduce[reduce_idx] = s * x_B_C_merged[c_idx];
+#else
+            shared_reduce[reduce_idx] = s * C[c_idx];
+#endif
+#endif
+
+            workgroupBarrier();
+
+#ifdef USE_SUBGROUP_REDUCTION
+            if (tid == 0u) {
+                var sum = 0.0;
+                for (var sg = 0u; sg < num_subgroups; sg++) {
+                    sum += shared_reduce[reduce_base(token_in_tile) + sg];
+                }
+                let y_idx =
+                    params.offset_dst + i1 + ir * params.d_inner + token * (params.n_head * params.d_inner) +
+                    i3 * (params.n_seq_tokens * params.n_head * params.d_inner);
+                dst[y_idx] = sum;
+            }
+#else
+            for (var stride = WG_SIZE / 2u; stride > 0u; stride >>= 1u) {
+                if (tid < stride) {
+                    shared_reduce[reduce_idx] += shared_reduce[reduce_idx + stride];
+                }
+                workgroupBarrier();
+            }
+
+            if (tid == 0u) {
+                let y_idx =
+                    params.offset_dst + i1 + ir * params.d_inner + token * (params.n_head * params.d_inner) +
+                    i3 * (params.n_seq_tokens * params.n_head * params.d_inner);
+                dst[y_idx] = shared_reduce[reduce_base(token_in_tile)];
+            }
+#endif
+
+            workgroupBarrier();
+        }
+    }
+
+    let state_idx =
+        params.offset_dst + params.y_elems + tid + i1 * params.d_state + ir * (params.d_state * params.d_inner) +
+        i3 * (params.d_state * params.d_inner * params.n_head);
+    dst[state_idx] = s_prev;
+}
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl
index b8f1bca1284..8e34e1c9ca0 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl
@@ -50,10 +50,25 @@ struct Params {
 @group(0) @binding(PARAMS_BINDING)
 var<uniform> params: Params;
 
+fn erf_approx(x: TYPE) -> TYPE {
+    let x_f32 = f32(x);
+    let s = select(-1.0, 1.0, x_f32 >= 0.0);
+    let ax = abs(x_f32);
+
+    let t = 1.0 / (1.0 + 0.3275911 * ax);
+
+    let y = 1.0 -
+        (((((1.061405429 * t - 1.453152027) * t + 1.421413741) * t
+            - 0.284496736) * t + 0.254829592) * t) *
+        exp(-ax * ax);
+
+    return TYPE(s * y);
+}
+
 @compute @workgroup_size(WG_SIZE)
 fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
     if (gid.x >= params.ne) {
-      return;
+        return;
     }
     var i = gid.x;
     let ne2 = params.ne2;
@@ -71,15 +86,13 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
     let i1 = i / ne0;
     let i0 = i % ne0;
 
-    let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 +
-                  i2 * params.stride_src2 + i3 * params.stride_src3;
+    let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 + i2 * params.stride_src2 + i3 * params.stride_src3;
 
 #ifdef ABS
     let res = abs(src[params.offset_src + src_idx]);
 #endif
 #ifdef SGN
-    let res = select(TYPE(select(0.0, -1.0, src[params.offset_src + src_idx] < 0.0)), TYPE(1.0),
-                     src[params.offset_src + src_idx] > 0.0);
+    let res = select(TYPE(select(0.0, -1.0, src[params.offset_src + src_idx] < 0.0)), TYPE(1.0), src[params.offset_src + src_idx] > 0.0);
 #endif
 #ifdef NEG
     let res = -src[params.offset_src + src_idx];
@@ -94,8 +107,7 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
     let res = select(0.0, src[params.offset_src + src_idx], src[params.offset_src + src_idx] > 0.0);
 #endif
 #ifdef ELU
-    let res = select(exp(src[params.offset_src + src_idx]) - 1.0, src[params.offset_src + src_idx],
-                     src[params.offset_src + src_idx] > 0.0);
+    let res = select(exp(src[params.offset_src + src_idx]) - 1.0, src[params.offset_src + src_idx], src[params.offset_src + src_idx] > 0.0);
 #endif
 #ifdef HARDSIGMOID
     let res = min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0));
@@ -120,31 +132,16 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
     let res = TYPE(params.fill_val);
 #endif
 #ifdef HARDSWISH
-    let res = src[params.offset_src + src_idx] *
-              min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0));
+    let res = src[params.offset_src + src_idx] * min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0));
 #endif
 #ifdef GELU
-    let res = 0.5 * src[params.offset_src + src_idx] *
-              (1.0 + tanh(clamp(sqrt(2.0 / 3.14159265) *
-                               (src[params.offset_src + src_idx] +
-                                0.044715 * pow(src[params.offset_src + src_idx], 3.0)),
-                               -9.010913, 9.010913)));
+    let res = 0.5 * src[params.offset_src + src_idx] * (1.0 + tanh(clamp(0.7978845608028654 * (src[params.offset_src + src_idx] + 0.044715 * src[params.offset_src + src_idx] * src[params.offset_src + src_idx] * src[params.offset_src + src_idx]), -9.010913, 9.010913)));
 #endif
 #ifdef GELU_QUICK
-    let res = src[params.offset_src + src_idx] * 0.5 *
-              (1.0 + tanh(clamp(0.79788456 *
-                               (src[params.offset_src + src_idx] +
-                                0.044715 * src[params.offset_src + src_idx] *
-                                    src[params.offset_src + src_idx] * src[params.offset_src + src_idx]),
-                               -9.010913, 9.010913)));
+    let res = src[params.offset_src + src_idx] * (1.0 / (1.0 + exp(clamp(-1.702 * src[params.offset_src + src_idx], -80.0, 80.0))));
 #endif
 #ifdef GELU_ERF
-    let res = 0.5 * src[params.offset_src + src_idx] *
-              (1.0 + tanh(clamp(0.79788456 *
-                               (src[params.offset_src + src_idx] +
-                                0.044715 * src[params.offset_src + src_idx] *
-                                    src[params.offset_src + src_idx] * src[params.offset_src + src_idx]),
-                               -9.010913, 9.010913)));
+    let res = 0.5 * src[params.offset_src + src_idx] * (1.0 + erf_approx(src[params.offset_src + src_idx] * 0.7071067811865476));
 #endif
 #ifdef XIELU
     let val = f32(src[params.offset_src + src_idx]);
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/upscale.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/upscale.wgsl
new file mode 100644
index 00000000000..e9ef8822644
--- /dev/null
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/upscale.wgsl
@@ -0,0 +1,240 @@
+#if defined(SRC_F16) || defined(DST_F16)
+enable f16;
+#endif
+
+#ifdef SRC_F16
+#define SRC_TYPE f16
+#else
+#define SRC_TYPE f32
+#endif
+
+#ifdef DST_F16
+#define DST_TYPE f16
+#else
+#define DST_TYPE f32
+#endif
+
+@group(0) @binding(0)
+var<storage, read_write> input: array<SRC_TYPE>;
+
+@group(0) @binding(1)
+var<storage, read_write> output: array<DST_TYPE>;
+
+struct Params {
+    offset_i: u32,
+    offset_o: u32,
+
+    // element strides
+    si0: u32, si1: u32, si2: u32, si3: u32,
+    so0: u32, so1: u32, so2: u32, so3: u32,
+
+    src_w: u32,
+    src_h: u32,
+    src_z: u32,
+    src_n: u32,
+
+    dst_w: u32,
+    dst_h: u32,
+    dst_z: u32,
+    dst_n: u32,
+
+    mode_flags: u32,
+};
+
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+const GGML_SCALE_FLAG_ALIGN_CORNERS: u32 = 1u << 8u;
+
+fn get_clamped_input(x: i32, y: i32, z: u32, n: u32) -> f32 {
+    let cx = u32(clamp(x, 0, i32(params.src_w) - 1));
+    let cy = u32(clamp(y, 0, i32(params.src_h) - 1));
+    let i = params.offset_i + cx * params.si0 + cy * params.si1 + z * params.si2 + n * params.si3;
+    return f32(input[i]);
+}
+
+fn cubic_weight(t: f32, a: f32) -> f32 {
+    let at = abs(t);
+    if (at <= 1.0) {
+        return (a + 2.0) * at * at * at - (a + 3.0) * at * at + 1.0;
+    } else if (at <= 2.0) {
+        return a * at * at * at - 5.0 * a * at * at + 8.0 * a * at - 4.0 * a;
+    } else {
+        return 0.0;
+    }
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(
+    @builtin(global_invocation_id) gid: vec3<u32>,
+    @builtin(num_workgroups) num_wg: vec3<u32>
+) {
+
+    let i_out = gid.x + (num_wg.x * u32(WG_SIZE)) * gid.y;
+    let total = params.dst_w * params.dst_h * params.dst_z * params.dst_n;
+
+    if (i_out >= total) {
+        return;
+    }
+
+    // decode (x, y, z, n)
+    var i = i_out;
+    let x_dst = i % params.dst_w;
+    i = i / params.dst_w;
+    let y_dst = i % params.dst_h;
+    i = i / params.dst_h;
+    let z_dst = i % params.dst_z;
+    let n_dst = i / params.dst_z;
+
+    // scale factors
+    var sf0 = f32(params.dst_w) / f32(params.src_w);
+    var sf1 = f32(params.dst_h) / f32(params.src_h);
+    var sf2 = f32(params.dst_z) / f32(params.src_z);
+    var sf3 = f32(params.dst_n) / f32(params.src_n);
+
+    let align_corners = (params.mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) != 0;
+
+    // pixel_offset: 0.5 for half-pixel-center (default), 0.0 for align_corners
+    var pixel_offset = 0.5;
+    if (align_corners) {
+        pixel_offset = 0.0;
+        if (params.dst_w > 1 && params.src_w > 1) {
+            sf0 = f32(params.dst_w - 1) / f32(params.src_w - 1);
+        }
+        if (params.dst_h > 1 && params.src_h > 1) {
+            sf1 = f32(params.dst_h - 1) / f32(params.src_h - 1);
+        }
+    }
+
+    let z_src = min(params.src_z - 1, u32(floor(f32(z_dst) / sf2)));
+    let n_src = min(params.src_n - 1, u32(floor(f32(n_dst) / sf3)));
+
+    var result = 0.0;
+
+#if defined(NEAREST)
+
+    let x_src = min(params.src_w - 1, u32(floor(f32(x_dst) / sf0)));
+    let y_src = min(params.src_h - 1, u32(floor(f32(y_dst) / sf1)));
+
+    result = get_clamped_input(i32(x_src), i32(y_src), z_src, n_src);
+
+#elif defined(BILINEAR)
+
+#if defined(ANTIALIAS)
+
+    // Antialiased bilinear: triangle filter over a variable support region.
+    let support0 = max(1.0f / sf0, 1.0f);
+    let support1 = max(1.0f / sf1, 1.0f);
+    let invscale0 = 1.0 / support0;
+    let invscale1 = 1.0 / support1;
+
+    let fx = (f32(x_dst) + pixel_offset) / sf0;
+    let fy = (f32(y_dst) + pixel_offset) / sf1;
+
+    let x_min = max(i32(fx - support0 + pixel_offset), 0);
+    let y_min = max(i32(fy - support1 + pixel_offset), 0);
+    let x_max = min(i32(fx + support0 + pixel_offset), i32(params.src_w));
+    let y_max = min(i32(fy + support1 + pixel_offset), i32(params.src_h));
+
+    var weighted_sum = 0.0;
+    var total_weight = 0.0;
+
+    for (var x = x_min; x < x_max; x += 1) {
+        let wx = max(1.0 - abs(f32(x) - fx + pixel_offset) * invscale0, 0.0);
+        for (var y = y_min; y < y_max; y += 1) {
+            let wy = max(1.0 - abs(f32(y) - fy + pixel_offset) * invscale1, 0.0);
+            let w = wx * wy;
+            if (w > 0.0) {
+                weighted_sum += get_clamped_input(x, y, z_src, n_src) * w;
+                total_weight += w;
+            }
+        }
+    }
+
+    if (total_weight > 0.0) {
+        result = weighted_sum / total_weight;
+    }
+
+#else
+
+    let fx = (f32(x_dst) + pixel_offset) / sf0 - pixel_offset;
+    let fy = (f32(y_dst) + pixel_offset) / sf1 - pixel_offset;
+    let x0 = i32(floor(fx));
+    let y0 = i32(floor(fy));
+    let dx = clamp(fx - f32(x0), 0.0, 1.0);
+    let dy = clamp(fy - f32(y0), 0.0, 1.0);
+    let a = get_clamped_input(x0, y0, z_src, n_src);
+    let b = get_clamped_input(x0 + 1, y0, z_src, n_src);
+    let c = get_clamped_input(x0, y0 + 1, z_src, n_src);
+    let d = get_clamped_input(x0 + 1, y0 + 1, z_src, n_src);
+
+    let wa = (1.0 - dx) * (1.0 - dy);
+    let wb = dx * (1.0 - dy);
+    let wc = (1.0 - dx) * dy;
+    let wd = dx * dy;
+
+    result = a * wa + b * wb + c * wc + d * wd;
+
+#endif
+
+#elif defined(BICUBIC)
+
+    // bicubic convolution with alpha = -0.75 (PyTorch default)
+    let alpha = -0.75;
+    let fx = (f32(x_dst) + pixel_offset) / sf0 - pixel_offset;
+    let fy = (f32(y_dst) + pixel_offset) / sf1 - pixel_offset;
+
+    let x0 = i32(floor(fx));
+    let y0 = i32(floor(fy));
+    let dx = fx - f32(x0);
+    let dy = fy - f32(y0);
+
+    // horizontal weights for offsets -1, 0, 1, 2
+    let wx0 = cubic_weight(dx + 1.0, alpha);
+    let wx1 = cubic_weight(dx, alpha);
+    let wx2 = cubic_weight(1.0 - dx, alpha);
+    let wx3 = cubic_weight(2.0 - dx, alpha);
+
+    // vertical weights for offsets -1, 0, 1, 2
+    let wy0 = cubic_weight(dy + 1.0, alpha);
+    let wy1 = cubic_weight(dy, alpha);
+    let wy2 = cubic_weight(1.0 - dy, alpha);
+    let wy3 = cubic_weight(2.0 - dy, alpha);
+
+    // intermediate horizontal interpolation for 4x4 grid of pixels
+    // x0-1, x0, x0+1, x0+2, y0-1
+    let p0 = get_clamped_input(x0 - 1, y0 - 1, z_src, n_src);
+    let p1 = get_clamped_input(x0, y0 - 1, z_src, n_src);
+    let p2 = get_clamped_input(x0 + 1, y0 - 1, z_src, n_src);
+    let p3 = get_clamped_input(x0 + 2, y0 - 1, z_src, n_src);
+    let row0 = p0 * wx0 + p1 * wx1 + p2 * wx2 + p3 * wx3;
+
+    // x0-1, x0, x0+1, x0+2, y0
+    let q0 = get_clamped_input(x0 - 1, y0, z_src, n_src);
+    let q1 = get_clamped_input(x0, y0, z_src, n_src);
+    let q2 = get_clamped_input(x0 + 1, y0, z_src, n_src);
+    let q3 = get_clamped_input(x0 + 2, y0, z_src, n_src);
+    let row1 = q0 * wx0 + q1 * wx1 + q2 * wx2 + q3 * wx3;
+
+    // x0-1, x0, x0+1, x0+2, y0+1
+    let r0 = get_clamped_input(x0 - 1, y0 + 1, z_src, n_src);
+    let r1 = get_clamped_input(x0, y0 + 1, z_src, n_src);
+    let r2 = get_clamped_input(x0 + 1, y0 + 1, z_src, n_src);
+    let r3 = get_clamped_input(x0 + 2, y0 + 1, z_src, n_src);
+    let row2 = r0 * wx0 + r1 * wx1 + r2 * wx2 + r3 * wx3;
+
+    // x0-1, x0, x0+1, x0+2, y0+2
+    let s0 = get_clamped_input(x0 - 1, y0 + 2, z_src, n_src);
+    let s1 = get_clamped_input(x0, y0 + 2, z_src, n_src);
+    let s2 = get_clamped_input(x0 + 1, y0 + 2, z_src, n_src);
+    let s3 = get_clamped_input(x0 + 2, y0 + 2, z_src, n_src);
+    let row3 = s0 * wx0 + s1 * wx1 + s2 * wx2 + s3 * wx3;
+
+    // final vertical interpolation
+    result = row0 * wy0 + row1 * wy1 + row2 * wy2 + row3 * wy3;
+
+#endif
+
+    let dst_idx = params.offset_o + x_dst * params.so0 + y_dst * params.so1 + z_dst * params.so2 + n_dst * params.so3;
+    output[dst_idx] = DST_TYPE(result);
+}
diff --git a/ggml/src/ggml-zdnn/ggml-zdnn.cpp b/ggml/src/ggml-zdnn/ggml-zdnn.cpp
index e6b6fc24fd7..639b818d128 100644
--- a/ggml/src/ggml-zdnn/ggml-zdnn.cpp
+++ b/ggml/src/ggml-zdnn/ggml-zdnn.cpp
@@ -423,8 +423,8 @@ static ggml_backend_i ggml_backend_zdnn_i = {
     /* .free                   = */ ggml_backend_zdnn_free,
     /* .set_tensor_async       = */ NULL,
     /* .get_tensor_async       = */ NULL,
-    /* .get_tensor_2d_async    = */ NULL,
     /* .set_tensor_2d_async    = */ NULL,
+    /* .get_tensor_2d_async    = */ NULL,
     /* .cpy_tensor_async       = */ NULL,
     /* .synchronize            = */ NULL,
     /* .graph_plan_create      = */ NULL,
diff --git a/ggml/src/ggml-zendnn/CMakeLists.txt b/ggml/src/ggml-zendnn/CMakeLists.txt
index 4f321a25257..f1e4f991fae 100644
--- a/ggml/src/ggml-zendnn/CMakeLists.txt
+++ b/ggml/src/ggml-zendnn/CMakeLists.txt
@@ -28,7 +28,7 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
     ExternalProject_Add(
         zendnn
         GIT_REPOSITORY https://github.com/amd/ZenDNN.git
-        GIT_TAG f79f7321a1add65ced6397a6bfab7edba6e3e14e    # ZenDNN-2026-WW13
+        GIT_TAG ac9e580d9434b7b98985f2627a7ebfb5eba4bb0d    # ZenDNN-2026-WW17
         PREFIX      ${ZENDNN_PREFIX}
         SOURCE_DIR  ${ZENDNN_SOURCE_DIR}
         BINARY_DIR  ${ZENDNN_BUILD_DIR}
diff --git a/ggml/src/ggml-zendnn/ggml-zendnn.cpp b/ggml/src/ggml-zendnn/ggml-zendnn.cpp
index fc1df4dbef4..6a83bb6b1ec 100644
--- a/ggml/src/ggml-zendnn/ggml-zendnn.cpp
+++ b/ggml/src/ggml-zendnn/ggml-zendnn.cpp
@@ -47,6 +47,7 @@ static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int
     params.dtypes.dst = ggml_to_zendnn_type<TC>();
     params.num_threads = ctx->n_threads;
 
+    zendnnl::lowoha::matmul::matmul_batch_params_t batch_params;
     zendnnl::error_handling::status_t status = zendnnl::lowoha::matmul::matmul_direct(
         'r', false, true,   // row-major, don't transpose B, transpose A (because it's column-major)
         n,                  // M: rows of B and C
@@ -59,7 +60,7 @@ static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int
         0.0f,               // beta
         C, ldc,             // output C[n,m]
         true,               // is_weights_const
-        {},                 // batch_params
+        batch_params,       // batch_params
         params              // params
     );
 
@@ -407,8 +408,8 @@ static struct ggml_backend_i ggml_backend_zendnn_i = {
     /* .free                    = */ ggml_backend_zendnn_free,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
-    /* .get_tensor_2d_async     = */ NULL,
     /* .set_tensor_2d_async     = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ NULL,
@@ -520,6 +521,12 @@ static ggml_backend_buffer_t ggml_backend_zendnn_device_buffer_from_host_ptr(ggm
     GGML_UNUSED(max_tensor_size);
 }
 
+static bool ggml_zendnn_adaptive_fallback_enabled() {
+    static const bool enabled = std::getenv("GGML_ZENDNN_ADAPTIVE_FALLBACK") == nullptr ||
+                                std::atoi(std::getenv("GGML_ZENDNN_ADAPTIVE_FALLBACK")) != 0;
+    return enabled;
+}
+
 static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_NONE:
@@ -538,12 +545,24 @@ static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const
             const int64_t ne10 = inputs->ne[0];
             const int64_t ne0 = op->ne[0];
             const int64_t ne1 = op->ne[1];
-
             const int64_t min_batch = 1;
-            if (!ggml_is_contiguous(weights) || !ggml_is_contiguous(inputs) ||
-                ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) {
+
+            if(!ggml_is_contiguous(weights) || !ggml_is_contiguous(inputs)) {
+                return false;
+            }
+
+            if (ggml_zendnn_adaptive_fallback_enabled()) {
+                const int64_t K = inputs->ne[0];
+                const int64_t N = (inputs->ne[1]*inputs->ne[2]*inputs->ne[3]);
+                const int64_t M = weights->ne[1];
+                if(K <= 256 || N <= 128 || M <= 96) {
                     return false;
+                }
             }
+            else if (ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) {
+                return false;
+            }
+
             // MUL_MAT_ID performs best with a moderate number of experts due to its
             // gather + batched matmul + scatter approach. Future versions will leverage
             // ZenDNN's grouped_gemm for better scalability with larger expert counts:
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 7234d7f3b09..3f399921f6c 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -55,8 +55,13 @@
 
 uint64_t ggml_graph_next_uid(void) {
 #ifdef _MSC_VER
+#if defined(_WIN32)
+    static volatile LONG counter = 1;
+    return (uint64_t) InterlockedIncrement(&counter) - 1;
+#else
     static volatile long long counter = 1;
     return (uint64_t) _InterlockedIncrement64(&counter) - 1;
+#endif
 #else
     static uint64_t counter = 1;
     return __atomic_fetch_add(&counter, 1, __ATOMIC_RELAXED);
@@ -3301,6 +3306,16 @@ void ggml_mul_mat_set_prec(
     ggml_set_op_params_i32(a, 0, prec_i32);
 }
 
+void ggml_mul_mat_set_hint(
+        struct ggml_tensor * a,
+        enum ggml_op_hint    hint) {
+    GGML_ASSERT(a->op == GGML_OP_MUL_MAT);
+
+    const int32_t hint_i32 = (int32_t) hint;
+
+    ggml_set_op_params_i32(a, 1, hint_i32);
+}
+
 // ggml_mul_mat_id
 
 /*
@@ -6237,11 +6252,13 @@ struct ggml_tensor * ggml_gated_delta_net(
     GGML_ASSERT(g->ne[0] == 1 || g->ne[0] == S_v);
     GGML_ASSERT(beta->ne[0] == 1);
 
-    GGML_ASSERT(ggml_nelements(state) == S_v * S_v * H * n_seqs);
-
-    // concat output and new_state into a single tensor
-    // output: S_v * H * n_tokens * n_seqs, state: S_v * S_v * H * n_seqs
-    const int64_t ne[4] = { S_v * H, n_tokens * n_seqs + S_v * n_seqs, 1, 1 };
+    // state is a 3D tensor (S_v*S_v*H, K, n_seqs). K is the snapshot slot count.
+    GGML_ASSERT(state->ne[0] == S_v * S_v * H);
+    GGML_ASSERT(state->ne[2] == n_seqs);
+    GGML_ASSERT(state->ne[3] == 1);
+    const int64_t K = state->ne[1];
+    const int64_t state_rows = K * S_v * n_seqs;
+    const int64_t ne[4] = { S_v * H, n_tokens * n_seqs + state_rows, 1, 1 };
     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
     result->op     = GGML_OP_GATED_DELTA_NET;
@@ -7730,7 +7747,7 @@ size_t ggml_quantize_chunk(
                int64_t   nrows,
                int64_t   n_per_row,
            const float * imatrix) {
-    const int64_t n = (int64_t) nrows * n_per_row;
+    const int64_t n = nrows * n_per_row;
 
     if (ggml_quantize_requires_imatrix(type)) {
         GGML_ASSERT(imatrix != NULL);
@@ -7747,21 +7764,21 @@ size_t ggml_quantize_chunk(
     size_t result = 0;
 
     switch (type) {
-        case GGML_TYPE_Q1_0:    result = quantize_q1_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q4_0:    result = quantize_q4_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q4_1:    result = quantize_q4_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q5_0:    result = quantize_q5_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q5_1:    result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q8_0:    result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_MXFP4:   result = quantize_mxfp4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_NVFP4:   result = quantize_nvfp4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q2_K:    result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q3_K:    result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q4_K:    result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q5_K:    result = quantize_q5_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_Q6_K:    result = quantize_q6_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_TQ1_0:   result = quantize_tq1_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-        case GGML_TYPE_TQ2_0:   result = quantize_tq2_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q1_0:    result = quantize_q1_0   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_0:    result = quantize_q4_0   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_1:    result = quantize_q4_1   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_0:    result = quantize_q5_0   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_1:    result = quantize_q5_1   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q8_0:    result = quantize_q8_0   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_MXFP4:   result = quantize_mxfp4  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_NVFP4:   result = quantize_nvfp4  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q2_K:    result = quantize_q2_K   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q3_K:    result = quantize_q3_K   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_K:    result = quantize_q4_K   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_K:    result = quantize_q5_K   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q6_K:    result = quantize_q6_K   (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_TQ1_0:   result = quantize_tq1_0  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_TQ2_0:   result = quantize_tq2_0  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ2_XS:  result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
@@ -7831,9 +7848,9 @@ struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads) {
 }
 
 bool ggml_threadpool_params_match(const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1) {
-    if (p0->n_threads      != p1->n_threads  )    return false;
-    if (p0->prio           != p1->prio       )    return false;
-    if (p0->poll           != p1->poll       )    return false;
-    if (p0->strict_cpu     != p1->strict_cpu )    return false;
+    if (p0->n_threads  != p1->n_threads  ) return false;
+    if (p0->prio       != p1->prio       ) return false;
+    if (p0->poll       != p1->poll       ) return false;
+    if (p0->strict_cpu != p1->strict_cpu ) return false;
     return memcmp(p0->cpumask, p1->cpumask, GGML_MAX_N_THREADS) == 0;
 }
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 997744f34c3..13636f92911 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -175,6 +175,7 @@ class Attention:
         SLIDING_WINDOW               = "{arch}.attention.sliding_window"
         SCALE                        = "{arch}.attention.scale"
         OUTPUT_SCALE                 = "{arch}.attention.output_scale"
+        VALUE_SCALE                  = "{arch}.attention.value_scale"
         TEMPERATURE_LENGTH           = "{arch}.attention.temperature_length"
         KEY_LENGTH_MLA               = "{arch}.attention.key_length_mla"
         VALUE_LENGTH_MLA             = "{arch}.attention.value_length_mla"
@@ -197,6 +198,7 @@ class Rope:
         FREQ_BASE_SWA             = "{arch}.rope.freq_base_swa"
         SCALING_TYPE              = "{arch}.rope.scaling.type"
         SCALING_FACTOR            = "{arch}.rope.scaling.factor"
+        SCALING_ALPHA             = "{arch}.rope.scaling.alpha"
         SCALING_ATTN_FACTOR       = "{arch}.rope.scaling.attn_factor"
         SCALING_ORIG_CTX_LEN      = "{arch}.rope.scaling.original_context_length"
         SCALING_FINETUNED         = "{arch}.rope.scaling.finetuned"
@@ -297,30 +299,32 @@ class Clip:
         HAS_LLAVA_PROJECTOR   = "clip.has_llava_projector"
 
     class ClipVision:
-        PROJECTOR_TYPE      = "clip.vision.projector_type" # for mixed modality models
-        IMAGE_SIZE          = "clip.vision.image_size"
-        IMAGE_MIN_PIXELS    = "clip.vision.image_min_pixels"
-        IMAGE_MAX_PIXELS    = "clip.vision.image_max_pixels"
-        PREPROC_MIN_TILES   = "clip.vision.preproc_min_tiles"
-        PREPROC_MAX_TILES   = "clip.vision.preproc_max_tiles"
-        PREPROC_IMAGE_SIZE  = "clip.vision.preproc_image_size"
-        PATCH_SIZE          = "clip.vision.patch_size"
-        EMBEDDING_LENGTH    = "clip.vision.embedding_length"
-        FEED_FORWARD_LENGTH = "clip.vision.feed_forward_length"
-        PROJECTION_DIM      = "clip.vision.projection_dim"
-        BLOCK_COUNT         = "clip.vision.block_count"
-        IMAGE_MEAN          = "clip.vision.image_mean"
-        IMAGE_STD           = "clip.vision.image_std"
-        SPATIAL_MERGE_SIZE  = "clip.vision.spatial_merge_size"
-        USE_GELU            = "clip.use_gelu"
-        USE_SILU            = "clip.use_silu"
-        N_WA_PATTERN        = "clip.vision.n_wa_pattern" # used by qwen2.5vl
-        WA_LAYER_INDEXES    = "clip.vision.wa_layer_indexes" # used by youtuvl
-        IS_DEEPSTACK_LAYERS = "clip.vision.is_deepstack_layers"
-        WINDOW_SIZE         = "clip.vision.window_size"
+        PROJECTOR_TYPE        = "clip.vision.projector_type" # for mixed modality models
+        IMAGE_SIZE            = "clip.vision.image_size"
+        IMAGE_MIN_PIXELS      = "clip.vision.image_min_pixels"
+        IMAGE_MAX_PIXELS      = "clip.vision.image_max_pixels"
+        PREPROC_MIN_TILES     = "clip.vision.preproc_min_tiles"
+        PREPROC_MAX_TILES     = "clip.vision.preproc_max_tiles"
+        PREPROC_IMAGE_SIZE    = "clip.vision.preproc_image_size"
+        PATCH_SIZE            = "clip.vision.patch_size"
+        EMBEDDING_LENGTH      = "clip.vision.embedding_length"
+        FEED_FORWARD_LENGTH   = "clip.vision.feed_forward_length"
+        PROJECTION_DIM        = "clip.vision.projection_dim"
+        BLOCK_COUNT           = "clip.vision.block_count"
+        IMAGE_MEAN            = "clip.vision.image_mean"
+        IMAGE_STD             = "clip.vision.image_std"
+        SPATIAL_MERGE_SIZE    = "clip.vision.spatial_merge_size"
+        USE_GELU              = "clip.use_gelu"
+        USE_SILU              = "clip.use_silu"
+        N_WA_PATTERN          = "clip.vision.n_wa_pattern"     # used by qwen2.5vl
+        WA_LAYER_INDEXES      = "clip.vision.wa_layer_indexes" # used by youtuvl
+        WA_PATTERN_MODE       = "clip.vision.wa_pattern_mode"  # used by mimovl, per-layer -1/0/1
+        IS_DEEPSTACK_LAYERS   = "clip.vision.is_deepstack_layers"
+        WINDOW_SIZE           = "clip.vision.window_size"
 
         class Attention:
             HEAD_COUNT      = "clip.vision.attention.head_count"
+            HEAD_COUNT_KV   = "clip.vision.attention.head_count_kv" # used by mimovl (GQA)
             LAYERNORM_EPS   = "clip.vision.attention.layer_norm_epsilon"
 
         class Projector:
@@ -338,6 +342,9 @@ class ClipAudio:
         FEED_FORWARD_LENGTH = "clip.audio.feed_forward_length"
         PROJECTION_DIM      = "clip.audio.projection_dim"
         BLOCK_COUNT         = "clip.audio.block_count"
+        CHUNK_SIZE          = "clip.audio.chunk_size"
+        CONV_KERNEL_SIZE    = "clip.audio.conv_kernel_size"
+        MAX_POS_EMB         = "clip.audio.max_pos_emb"
 
         class Attention:
             HEAD_COUNT      = "clip.audio.attention.head_count"
@@ -345,6 +352,9 @@ class Attention:
 
         class Projector:
             STACK_FACTOR    = "clip.audio.projector.stack_factor"
+            WINDOW_SIZE     = "clip.audio.projector.window_size"
+            DOWNSAMPLE_RATE = "clip.audio.projector.downsample_rate"
+            HEAD_COUNT      = "clip.audio.projector.head_count"
 
     class Diffusion:
         SHIFT_LOGITS        = "diffusion.shift_logits"
@@ -471,6 +481,7 @@ class MODEL_ARCH(IntEnum):
     ERNIE4_5_MOE     = auto()
     HUNYUAN_MOE      = auto()
     HUNYUAN_DENSE    = auto()
+    HUNYUAN_VL       = auto()
     SMOLLM3          = auto()
     GPT_OSS          = auto()
     LFM2             = auto()
@@ -724,6 +735,7 @@ class MODEL_TENSOR(IntEnum):
     V_ENC_ATTN_V         = auto()
     V_ENC_ATTN_O         = auto()
     V_ENC_ATTN_O_NORM    = auto()
+    V_ENC_ATTN_SINKS     = auto() # mimovl
     V_ENC_POST_ATTN_NORM = auto()
     V_ENC_FFN_UP         = auto()
     V_ENC_FFN_GATE       = auto()
@@ -765,6 +777,14 @@ class MODEL_TENSOR(IntEnum):
     V_DS_NORM            = auto() # qwen3vl
     V_DS_FC1             = auto() # qwen3vl
     V_DS_FC2             = auto() # qwen3vl
+    V_MERGER_LN1         = auto() # minicpmv4_6
+    V_MERGER_ATTN_Q      = auto() # minicpmv4_6
+    V_MERGER_ATTN_K      = auto() # minicpmv4_6
+    V_MERGER_ATTN_V      = auto() # minicpmv4_6
+    V_MERGER_ATTN_O      = auto() # minicpmv4_6
+    V_MERGER_DS_LN       = auto() # minicpmv4_6
+    V_MERGER_DS_UP       = auto() # minicpmv4_6
+    V_MERGER_DS_DOWN     = auto() # minicpmv4_6
     V_MM_POST_FC_NORM    = auto() # cogvlm
     V_MM_UP              = auto() # cogvlm
     V_MM_DOWN            = auto() # cogvlm
@@ -852,6 +872,26 @@ class MODEL_TENSOR(IntEnum):
     A_ENC_CONV_NORM        = auto() # SSM conv
     A_ENC_CONV_PW1         = auto()
     A_ENC_CONV_PW2         = auto()
+    A_CTC_OUT              = auto()
+    A_CTC_OUT_MID          = auto()
+    A_ENC_ATTN_REL_POS_EMB = auto()
+    # qformer projector
+    A_QF_PROJ_QUERY        = auto()
+    A_QF_PROJ_NORM         = auto()
+    A_QF_PROJ_LINEAR       = auto()
+    A_QF_SELF_ATTN_Q       = auto()
+    A_QF_SELF_ATTN_K       = auto()
+    A_QF_SELF_ATTN_V       = auto()
+    A_QF_SELF_ATTN_O       = auto()
+    A_QF_SELF_ATTN_NORM    = auto()
+    A_QF_CROSS_ATTN_Q      = auto()
+    A_QF_CROSS_ATTN_K      = auto()
+    A_QF_CROSS_ATTN_V      = auto()
+    A_QF_CROSS_ATTN_O      = auto()
+    A_QF_CROSS_ATTN_NORM   = auto()
+    A_QF_FFN_UP            = auto()
+    A_QF_FFN_DOWN          = auto()
+    A_QF_FFN_NORM          = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -957,6 +997,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.FALCON_H1:        "falcon-h1",
     MODEL_ARCH.HUNYUAN_MOE:      "hunyuan-moe",
     MODEL_ARCH.HUNYUAN_DENSE:    "hunyuan-dense",
+    MODEL_ARCH.HUNYUAN_VL:       "hunyuan_vl",
     MODEL_ARCH.SMOLLM3:          "smollm3",
     MODEL_ARCH.GPT_OSS:          "gpt-oss",
     MODEL_ARCH.LFM2:             "lfm2",
@@ -1208,6 +1249,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.V_ENC_INPUT_NORM:          "v.blk.{bid}.ln1",
     MODEL_TENSOR.V_ENC_ATTN_O:              "v.blk.{bid}.attn_out",
     MODEL_TENSOR.V_ENC_ATTN_O_NORM:         "v.blk.{bid}.attn_out_norm",
+    MODEL_TENSOR.V_ENC_ATTN_SINKS:          "v.blk.{bid}.attn_sinks",
     MODEL_TENSOR.V_ENC_POST_ATTN_NORM:      "v.blk.{bid}.ln2",
     MODEL_TENSOR.V_ENC_FFN_UP:              "v.blk.{bid}.ffn_up",
     MODEL_TENSOR.V_ENC_FFN_GATE:            "v.blk.{bid}.ffn_gate",
@@ -1248,6 +1290,14 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.V_DS_NORM:                 "v.deepstack.{bid}.norm",
     MODEL_TENSOR.V_DS_FC1:                  "v.deepstack.{bid}.fc1",
     MODEL_TENSOR.V_DS_FC2:                  "v.deepstack.{bid}.fc2",
+    MODEL_TENSOR.V_MERGER_LN1:              "v.vit_merger.ln1",
+    MODEL_TENSOR.V_MERGER_ATTN_Q:           "v.vit_merger.attn_q",
+    MODEL_TENSOR.V_MERGER_ATTN_K:           "v.vit_merger.attn_k",
+    MODEL_TENSOR.V_MERGER_ATTN_V:           "v.vit_merger.attn_v",
+    MODEL_TENSOR.V_MERGER_ATTN_O:           "v.vit_merger.attn_out",
+    MODEL_TENSOR.V_MERGER_DS_LN:            "v.vit_merger.ds_ln",
+    MODEL_TENSOR.V_MERGER_DS_UP:            "v.vit_merger.ds_ffn_up",
+    MODEL_TENSOR.V_MERGER_DS_DOWN:          "v.vit_merger.ds_ffn_down",
     MODEL_TENSOR.V_MM_POST_FC_NORM:         "mm.post_fc_norm", # cogvlm
     MODEL_TENSOR.V_MM_UP:                   "mm.up",
     MODEL_TENSOR.V_MM_DOWN:                 "mm.down",
@@ -1330,6 +1380,26 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.A_ENC_CONV_NORM:           "a.blk.{bid}.conv_norm",
     MODEL_TENSOR.A_ENC_CONV_PW1:            "a.blk.{bid}.conv_pw1",
     MODEL_TENSOR.A_ENC_CONV_PW2:            "a.blk.{bid}.conv_pw2",
+    MODEL_TENSOR.A_CTC_OUT:                 "a.enc_ctc_out",
+    MODEL_TENSOR.A_CTC_OUT_MID:             "a.enc_ctc_out_mid",
+    MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB:    "a.blk.{bid}.attn_rel_pos_emb",
+    # qformer projector
+    MODEL_TENSOR.A_QF_PROJ_QUERY:           "a.proj_query",
+    MODEL_TENSOR.A_QF_PROJ_NORM:            "a.proj_norm",
+    MODEL_TENSOR.A_QF_PROJ_LINEAR:          "a.proj_linear",
+    MODEL_TENSOR.A_QF_SELF_ATTN_Q:          "a.proj_blk.{bid}.self_attn_q",
+    MODEL_TENSOR.A_QF_SELF_ATTN_K:          "a.proj_blk.{bid}.self_attn_k",
+    MODEL_TENSOR.A_QF_SELF_ATTN_V:          "a.proj_blk.{bid}.self_attn_v",
+    MODEL_TENSOR.A_QF_SELF_ATTN_O:          "a.proj_blk.{bid}.self_attn_out",
+    MODEL_TENSOR.A_QF_SELF_ATTN_NORM:       "a.proj_blk.{bid}.self_attn_norm",
+    MODEL_TENSOR.A_QF_CROSS_ATTN_Q:         "a.proj_blk.{bid}.cross_attn_q",
+    MODEL_TENSOR.A_QF_CROSS_ATTN_K:         "a.proj_blk.{bid}.cross_attn_k",
+    MODEL_TENSOR.A_QF_CROSS_ATTN_V:         "a.proj_blk.{bid}.cross_attn_v",
+    MODEL_TENSOR.A_QF_CROSS_ATTN_O:         "a.proj_blk.{bid}.cross_attn_out",
+    MODEL_TENSOR.A_QF_CROSS_ATTN_NORM:      "a.proj_blk.{bid}.cross_attn_norm",
+    MODEL_TENSOR.A_QF_FFN_UP:               "a.proj_blk.{bid}.ffn_up",
+    MODEL_TENSOR.A_QF_FFN_DOWN:             "a.proj_blk.{bid}.ffn_down",
+    MODEL_TENSOR.A_QF_FFN_NORM:             "a.proj_blk.{bid}.ffn_norm",
     # NextN/MTP
     MODEL_TENSOR.NEXTN_EH_PROJ:             "blk.{bid}.nextn.eh_proj",
     MODEL_TENSOR.NEXTN_EMBED_TOKENS:        "blk.{bid}.nextn.embed_tokens",
@@ -1360,6 +1430,7 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.V_ENC_ATTN_V,
         MODEL_TENSOR.V_ENC_ATTN_O,
         MODEL_TENSOR.V_ENC_ATTN_O_NORM,
+        MODEL_TENSOR.V_ENC_ATTN_SINKS,
         MODEL_TENSOR.V_ENC_POST_ATTN_NORM,
         MODEL_TENSOR.V_ENC_FFN_UP,
         MODEL_TENSOR.V_ENC_FFN_GATE,
@@ -1400,6 +1471,14 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.V_DS_NORM,
         MODEL_TENSOR.V_DS_FC1,
         MODEL_TENSOR.V_DS_FC2,
+        MODEL_TENSOR.V_MERGER_LN1,
+        MODEL_TENSOR.V_MERGER_ATTN_Q,
+        MODEL_TENSOR.V_MERGER_ATTN_K,
+        MODEL_TENSOR.V_MERGER_ATTN_V,
+        MODEL_TENSOR.V_MERGER_ATTN_O,
+        MODEL_TENSOR.V_MERGER_DS_LN,
+        MODEL_TENSOR.V_MERGER_DS_UP,
+        MODEL_TENSOR.V_MERGER_DS_DOWN,
         MODEL_TENSOR.V_MM_POST_FC_NORM,
         MODEL_TENSOR.V_MM_UP,
         MODEL_TENSOR.V_MM_DOWN,
@@ -1477,6 +1556,26 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.A_MM_HARD_EMB_NORM,
         MODEL_TENSOR.A_PER_DIM_K_SCALE,
         MODEL_TENSOR.A_PER_DIM_SCALE,
+        MODEL_TENSOR.A_CTC_OUT,
+        MODEL_TENSOR.A_CTC_OUT_MID,
+        MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB,
+        # qformer projector
+        MODEL_TENSOR.A_QF_PROJ_QUERY,
+        MODEL_TENSOR.A_QF_PROJ_NORM,
+        MODEL_TENSOR.A_QF_PROJ_LINEAR,
+        MODEL_TENSOR.A_QF_SELF_ATTN_Q,
+        MODEL_TENSOR.A_QF_SELF_ATTN_K,
+        MODEL_TENSOR.A_QF_SELF_ATTN_V,
+        MODEL_TENSOR.A_QF_SELF_ATTN_O,
+        MODEL_TENSOR.A_QF_SELF_ATTN_NORM,
+        MODEL_TENSOR.A_QF_CROSS_ATTN_Q,
+        MODEL_TENSOR.A_QF_CROSS_ATTN_K,
+        MODEL_TENSOR.A_QF_CROSS_ATTN_V,
+        MODEL_TENSOR.A_QF_CROSS_ATTN_O,
+        MODEL_TENSOR.A_QF_CROSS_ATTN_NORM,
+        MODEL_TENSOR.A_QF_FFN_UP,
+        MODEL_TENSOR.A_QF_FFN_DOWN,
+        MODEL_TENSOR.A_QF_FFN_NORM,
     ],
     MODEL_ARCH.LLAMA: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -2015,7 +2114,14 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.SSM_NORM,
         MODEL_TENSOR.SSM_BETA,
         MODEL_TENSOR.SSM_ALPHA,
-        MODEL_TENSOR.SSM_OUT
+        MODEL_TENSOR.SSM_OUT,
+        # NextN/MTP tensors - preserved but unused
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
     MODEL_ARCH.QWEN35MOE: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -2046,7 +2152,14 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.SSM_NORM,
         MODEL_TENSOR.SSM_BETA,
         MODEL_TENSOR.SSM_ALPHA,
-        MODEL_TENSOR.SSM_OUT
+        MODEL_TENSOR.SSM_OUT,
+        # NextN/MTP tensors - preserved but unused
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
     MODEL_ARCH.PLAMO: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -2349,6 +2462,8 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.FFN_GATE_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.ATTN_NORM,
         MODEL_TENSOR.ATTN_POST_NORM,
@@ -3489,6 +3604,22 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.HUNYUAN_VL: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.SMOLLM3: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -3759,6 +3890,7 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.OUTPUT_NORM,
         MODEL_TENSOR.OUTPUT,
         MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_K,
         MODEL_TENSOR.ATTN_V,
@@ -3773,6 +3905,10 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_EXP_PROBS_B,
+        MODEL_TENSOR.LAYER_OUT_NORM,
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
     ],
     MODEL_ARCH.STEP35: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -4142,6 +4278,10 @@ class VisionProjectorType:
     YOUTUVL = "youtuvl"
     NEMOTRON_V2_VL = "nemotron_v2_vl"
     HUNYUANOCR     = "hunyuanocr"
+    HUNYUANVL      = "hunyuanvl"
+    MINICPMV4_6    = "minicpmv4_6"
+    GRANITE_SPEECH = "granite_speech"  # audio
+    MIMOVL         = "mimovl"
 
 
 # Items here are (block size, type size)
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 90d500dc771..a101382719d 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -943,6 +943,9 @@ def add_attention_scale(self, value: float) -> None:
     def add_attn_output_scale(self, value: float) -> None:
         self.add_float32(Keys.Attention.OUTPUT_SCALE.format(arch=self.arch), value)
 
+    def add_attn_value_scale(self, value: float) -> None:
+        self.add_float32(Keys.Attention.VALUE_SCALE.format(arch=self.arch), value)
+
     def add_attn_temperature_length(self, value: int) -> None:
         self.add_uint32(Keys.Attention.TEMPERATURE_LENGTH.format(arch=self.arch), value)
 
@@ -973,6 +976,9 @@ def add_rope_scaling_type(self, value: RopeScalingType) -> None:
     def add_rope_scaling_factor(self, value: float) -> None:
         self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value)
 
+    def add_rope_scaling_alpha(self, value: float) -> None:
+        self.add_float32(Keys.Rope.SCALING_ALPHA.format(arch=self.arch), value)
+
     def add_rope_scaling_attn_factors(self, value: float) -> None:
         self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value)
 
@@ -1145,6 +1151,9 @@ def add_vision_block_count(self, value: int) -> None:
     def add_vision_head_count(self, value: int) -> None:
         self.add_uint32(Keys.ClipVision.Attention.HEAD_COUNT, value)
 
+    def add_vision_head_count_kv(self, value: int) -> None:
+        self.add_uint32(Keys.ClipVision.Attention.HEAD_COUNT_KV, value)
+
     def add_vision_attention_layernorm_eps(self, value: float) -> None:
         self.add_float32(Keys.ClipVision.Attention.LAYERNORM_EPS, value)
 
@@ -1216,6 +1225,9 @@ def add_vision_wa_layer_indexes(self, layers: Sequence[int]) -> None:
     def add_vision_is_deepstack_layers(self, layers: Sequence[bool]) -> None:
         self.add_array(Keys.ClipVision.IS_DEEPSTACK_LAYERS, layers)
 
+    def add_vision_wa_pattern_mode(self, modes: Sequence[int]) -> None:
+        self.add_array(Keys.ClipVision.WA_PATTERN_MODE, modes)
+
     def add_vision_window_size(self, value: int) -> None:
         self.add_uint32(Keys.ClipVision.WINDOW_SIZE, value)
 
@@ -1257,6 +1269,24 @@ def add_audio_num_mel_bins(self, value: int) -> None:
     def add_audio_stack_factor(self, value: int) -> None:
         self.add_uint32(Keys.ClipAudio.Projector.STACK_FACTOR, value)
 
+    def add_audio_chunk_size(self, value: int) -> None:
+        self.add_uint32(Keys.ClipAudio.CHUNK_SIZE, value)
+
+    def add_audio_conv_kernel_size(self, value: int) -> None:
+        self.add_uint32(Keys.ClipAudio.CONV_KERNEL_SIZE, value)
+
+    def add_audio_max_pos_emb(self, value: int) -> None:
+        self.add_uint32(Keys.ClipAudio.MAX_POS_EMB, value)
+
+    def add_audio_projector_window_size(self, value: int) -> None:
+        self.add_uint32(Keys.ClipAudio.Projector.WINDOW_SIZE, value)
+
+    def add_audio_projector_downsample_rate(self, value: int) -> None:
+        self.add_uint32(Keys.ClipAudio.Projector.DOWNSAMPLE_RATE, value)
+
+    def add_audio_projector_head_count(self, value: int) -> None:
+        self.add_uint32(Keys.ClipAudio.Projector.HEAD_COUNT, value)
+
     def add_xielu_alpha_p(self, values: Sequence[float]):
         self.add_array(Keys.xIELU.ALPHA_P, values)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 01a9b236000..f40cb828201 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -18,7 +18,6 @@ class TensorNameMap:
             "tok_embeddings",                            # llama-pth
             "embeddings.word_embeddings",                # bert nomic-bert
             "embeddings.tok_embeddings",                 # modern-bert
-            "language_model.embedding.word_embeddings",  # persimmon
             "wte",                                       # gpt2
             "transformer.embd.wte",                      # phi2
             "model.tok_embeddings",                      # internlm2
@@ -32,7 +31,6 @@ class TensorNameMap:
             "rwkv.embeddings",                           # rwkv6
             "model.embeddings",                          # rwkv7
             "model.word_embeddings",                     # bailingmoe
-            "language_model.model.embed_tokens",         # llama4
             "encoder",                                   # neobert
             "model.transformer.wte",                     # llada
             "embed_tokens",                              # qwen3-embedding
@@ -94,7 +92,6 @@ class TensorNameMap:
             "norm",                                    # llama-pth
             "transformer.norm_f",                      # mpt dbrx
             "ln_f",                                    # refact bloom qwen gpt2
-            "language_model.encoder.final_layernorm",  # persimmon
             "model.final_layernorm",                   # persimmon
             "lm_head.ln",                              # phi2
             "model.norm_f",                            # mamba-qbert
@@ -158,6 +155,21 @@ class TensorNameMap:
         MODEL_TENSOR.V_ENC_MSFA_NORM: (
             "model.vision_tower.timm_model.msfa.norm", # gemma3n
         ),
+        MODEL_TENSOR.A_CTC_OUT: (
+            "encoder.out",
+        ),
+        MODEL_TENSOR.A_CTC_OUT_MID: (
+            "encoder.out_mid",
+        ),
+        MODEL_TENSOR.A_QF_PROJ_QUERY: (
+            "projector.query",
+        ),
+        MODEL_TENSOR.A_QF_PROJ_NORM: (
+            "projector.qformer.layernorm",
+        ),
+        MODEL_TENSOR.A_QF_PROJ_LINEAR: (
+            "projector.linear",
+        ),
     }
 
     block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
@@ -171,7 +183,6 @@ class TensorNameMap:
             "transformer.h.{bid}.ln_mlp",                           # falcon40b
             "model.layers.{bid}.input_layernorm",                   # llama-hf nemotron olmoe phimoe granite-hybrid
             "layers.{bid}.attention_norm",                          # llama-pth
-            "language_model.encoder.layers.{bid}.input_layernorm",  # persimmon
             "model.layers.{bid}.ln1",                               # yi
             "h.{bid}.ln_1",                                         # gpt2
             "transformer.h.{bid}.ln",                               # phi2
@@ -215,7 +226,6 @@ class TensorNameMap:
             "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv",                   # dbrx
             "transformer.h.{bid}.self_attention.query_key_value",                  # falcon
             "h.{bid}.self_attention.query_key_value",                              # bloom
-            "language_model.encoder.layers.{bid}.self_attention.query_key_value",  # persimmon
             "model.layers.{bid}.self_attn.query_key_value",                        # persimmon
             "model.layers.{bid}.attention.query_key_value",                        # bailingmoe2
             "h.{bid}.attn.c_attn",                                                 # gpt2
@@ -306,7 +316,6 @@ class TensorNameMap:
             "layers.{bid}.attn.Wo",                                         # modern-bert
             "transformer.layer.{bid}.attention.out_lin",                    # distillbert
             "transformer.h.{bid}.attn.out_proj",                            # gpt-j
-            "language_model.encoder.layers.{bid}.self_attention.dense",     # persimmon
             "model.layers.{bid}.self_attn.dense",                           # persimmon
             "model.layers.{bid}.attention.dense",                           # bailingmoe2
             "h.{bid}.attn.c_proj",                                          # gpt2
@@ -373,7 +382,6 @@ class TensorNameMap:
             "transformer.blocks.{bid}.norm_2",                               # mpt
             "model.layers.{bid}.post_attention_layernorm",                   # llama-hf nemotron olmoe phimoe
             "layers.{bid}.ffn_norm",                                         # llama-pth
-            "language_model.encoder.layers.{bid}.post_attention_layernorm",  # persimmon
             "model.layers.{bid}.ln2",                                        # yi
             "h.{bid}.ln_2",                                                  # gpt2
             "model.layers.{bid}.ffn_norm",                                   # internlm2
@@ -475,7 +483,6 @@ class TensorNameMap:
             "transformer.layer.{bid}.ffn.lin1",                       # distillbert
             "transformer.h.{bid}.mlp.fc_in",                          # gpt-j
             "transformer.h.{bid}.mlp.linear_3",                       # refact
-            "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h",  # persimmon
             "model.layers.{bid}.mlp.dense_h_to_4h",                   # persimmon
             "transformer.h.{bid}.mlp.w1",                             # qwen
             "h.{bid}.mlp.c_fc",                                       # gpt2
@@ -608,7 +615,6 @@ class TensorNameMap:
             "layers.{bid}.mlp.Wo",                                    # modern-bert
             "transformer.layer.{bid}.ffn.lin2",                       # distillbert
             "transformer.h.{bid}.mlp.fc_out",                         # gpt-j
-            "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h",  # persimmon
             "model.layers.{bid}.mlp.dense_4h_to_h",                   # persimmon
             "h.{bid}.mlp.c_proj",                                     # gpt2
             "transformer.h.{bid}.mlp.fc2",                            # phi2
@@ -663,7 +669,7 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
-            "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
+            "encoder.layers.{bid}.self_attention.q_layernorm",
             "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon
             "model.layers.{bid}.self_attn.query_layernorm",                   # hunyuan
             "model.layers.{bid}.attention.query_layernorm",                   # bailingmoe2
@@ -679,7 +685,7 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_K_NORM: (
-            "language_model.encoder.layers.{bid}.self_attention.k_layernorm",
+            "encoder.layers.{bid}.self_attention.k_layernorm",
             "model.layers.{bid}.self_attn.k_layernorm",                       # persimmon
             "model.layers.{bid}.self_attn.key_layernorm",                     # hunyuan
             "model.layers.{bid}.attention.key_layernorm",                     # bailingmoe2
@@ -695,7 +701,7 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ROPE_FREQS: (
-            "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq",  # persimmon
+            "encoder.layers.{bid}.self_attention.rotary_emb.inv_freq",  # persimmon
         ),
 
         MODEL_TENSOR.LAYER_OUT_NORM: (
@@ -1393,6 +1399,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_EMBD_PATCH: (
             "vision_tower.vision_model.embeddings.patch_embedding",
+            "model.vision_tower.embeddings.patch_embedding", # minicpmv4_6
             "model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1
             "vpm.embeddings.patch_embedding",
             "model.vision_model.embeddings.patch_embedding", # SmolVLM
@@ -1418,6 +1425,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_EMBD_POS: (
             "vision_tower.vision_model.embeddings.position_embedding",
+            "model.vision_tower.embeddings.position_embedding", # minicpmv4_6
             "model.vision_tower.embeddings.position_embeddings", # Intern-S1
             "vpm.embeddings.position_embedding",
             "model.vision_model.embeddings.position_embedding", # SmolVLM
@@ -1454,6 +1462,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_Q: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj",
+            "model.vision_tower.encoder.layers.{bid}.self_attn.q_proj", # minicpmv4_6
             "model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.q_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM
@@ -1477,6 +1486,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_K: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj",
+            "model.vision_tower.encoder.layers.{bid}.self_attn.k_proj", # minicpmv4_6
             "model.vision_tower.encoder.layer.{bid}.attention.k_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.k_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM
@@ -1500,6 +1510,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_V: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj",
+            "model.vision_tower.encoder.layers.{bid}.self_attn.v_proj", # minicpmv4_6
             "model.vision_tower.encoder.layer.{bid}.attention.v_proj", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.v_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM
@@ -1516,6 +1527,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_INPUT_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1",
+            "model.vision_tower.encoder.layers.{bid}.layer_norm1", # minicpmv4_6
             "vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL
             "model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1
             "vpm.encoder.layers.{bid}.layer_norm1",
@@ -1536,6 +1548,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_ATTN_O: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
+            "model.vision_tower.encoder.layers.{bid}.self_attn.out_proj", # minicpmv4_6
             "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL
             "model.vision_tower.encoder.layer.{bid}.attention.projection_layer", # Intern-S1
             "vpm.encoder.layers.{bid}.self_attn.out_proj",
@@ -1556,8 +1569,13 @@ class TensorNameMap:
             "vision_model.transformer.resblocks.{bid}.attn.out_proj", # Step3-VL
         ),
 
+        MODEL_TENSOR.V_ENC_ATTN_SINKS: (
+            "visual.blocks.{bid}.attn.sinks", # mimovl
+        ),
+
         MODEL_TENSOR.V_ENC_POST_ATTN_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
+            "model.vision_tower.encoder.layers.{bid}.layer_norm2", # minicpmv4_6
             "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL
             "model.vision_tower.encoder.layer.{bid}.layernorm_after", # Intern-S1
             "vpm.encoder.layers.{bid}.layer_norm2",
@@ -1579,6 +1597,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_FFN_UP: (
             "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1",
+            "model.vision_tower.encoder.layers.{bid}.mlp.fc1", # minicpmv4_6
             "model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1
             "vpm.encoder.layers.{bid}.mlp.fc1",
             "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3
@@ -1607,6 +1626,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_FFN_DOWN: (
             "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2",
+            "model.vision_tower.encoder.layers.{bid}.mlp.fc2", # minicpmv4_6
             "model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1
             "vpm.encoder.layers.{bid}.mlp.fc2",
             "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3
@@ -1662,6 +1682,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_POST_NORM: (
             "vision_tower.vision_model.post_layernorm",
+            "model.vision_tower.post_layernorm", # minicpmv4_6
             "model.vision_model.post_layernorm", # SmolVLM
             "vision_model.layernorm_post", # llama4
             "visual.merger.ln_q", # qwen2vl
@@ -1690,6 +1711,7 @@ class TensorNameMap:
             "mlp_AR.pre_norm", # PaddleOCR-VL
             "merger.ln_q",
             "vision_tower.merger.ln_q", # dots.ocr
+            "model.merger.mlp.0.pre_norm", # minicpmv4_6
         ),
 
         MODEL_TENSOR.V_MM_SOFT_EMB_NORM: (
@@ -1763,6 +1785,38 @@ class TensorNameMap:
             "model.visual.deepstack_merger_list.{bid}.linear_fc2", # deepstack in qwen3vl
         ),
 
+        MODEL_TENSOR.V_MERGER_LN1: (
+            "model.vision_tower.vit_merger.layer_norm1", # minicpmv4_6
+        ),
+
+        MODEL_TENSOR.V_MERGER_ATTN_Q: (
+            "model.vision_tower.vit_merger.self_attn.q_proj", # minicpmv4_6
+        ),
+
+        MODEL_TENSOR.V_MERGER_ATTN_K: (
+            "model.vision_tower.vit_merger.self_attn.k_proj", # minicpmv4_6
+        ),
+
+        MODEL_TENSOR.V_MERGER_ATTN_V: (
+            "model.vision_tower.vit_merger.self_attn.v_proj", # minicpmv4_6
+        ),
+
+        MODEL_TENSOR.V_MERGER_ATTN_O: (
+            "model.vision_tower.vit_merger.self_attn.out_proj", # minicpmv4_6
+        ),
+
+        MODEL_TENSOR.V_MERGER_DS_LN: (
+            "model.vision_tower.vit_merger.pre_norm", # minicpmv4_6
+        ),
+
+        MODEL_TENSOR.V_MERGER_DS_UP: (
+            "model.vision_tower.vit_merger.linear_1", # minicpmv4_6
+        ),
+
+        MODEL_TENSOR.V_MERGER_DS_DOWN: (
+            "model.vision_tower.vit_merger.linear_2", # minicpmv4_6
+        ),
+
         MODEL_TENSOR.V_SAM_POS_EMBD: (
             "model.sam_model.pos_embed",
         ),
@@ -1822,11 +1876,13 @@ class TensorNameMap:
         MODEL_TENSOR.V_MM_UP: (
             "model.vision.linear_proj.dense_h_to_4h", # cogvlm
             "visual.merger.up_proj", # glm4v
+            "model.merger.mlp.0.linear_1", # minicpmv4_6
         ),
 
         MODEL_TENSOR.V_MM_DOWN: (
             "model.vision.linear_proj.dense_4h_to_h", # cogvlm
             "visual.merger.down_proj", # glm4v
+            "model.merger.mlp.0.linear_2", # minicpmv4_6
         ),
 
         MODEL_TENSOR.V_MM_GATE: (
@@ -1890,6 +1946,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.A_ENC_INP_PROJ: (
             "conformer.subsample_conv_projection.input_proj_linear", # gemma4
+            "encoder.input_linear",
         ),
 
         MODEL_TENSOR.A_ENC_CONV2D: (
@@ -1912,6 +1969,7 @@ class TensorNameMap:
             "conformer.layers.{bid}.self_attn.linear_q", # lfm2
             "conformer.layers.{bid}.attention.attn.q_proj", # gemma3n
             "conformer.layers.{bid}.self_attn.q_proj", # gemma4
+            "encoder.layers.{bid}.attn.to_q", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_ATTN_K: (
@@ -1919,6 +1977,7 @@ class TensorNameMap:
             "conformer.layers.{bid}.self_attn.linear_k", # lfm2
             "conformer.layers.{bid}.attention.attn.k_proj", # gemma3n
             "conformer.layers.{bid}.self_attn.k_proj", # gemma4
+            "encoder.layers.{bid}.attn.to_k", # granite_speech (split from to_kv)
         ),
 
         MODEL_TENSOR.A_ENC_ATTN_V: (
@@ -1926,6 +1985,7 @@ class TensorNameMap:
             "conformer.layers.{bid}.self_attn.linear_v", # lfm2
             "conformer.layers.{bid}.attention.attn.v_proj", # gemma3n
             "conformer.layers.{bid}.self_attn.v_proj", # gemma4
+            "encoder.layers.{bid}.attn.to_v", # granite_speech (split from to_kv)
         ),
 
         MODEL_TENSOR.A_ENC_ATTN_K_REL: (
@@ -1953,6 +2013,7 @@ class TensorNameMap:
             "audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox
             "conformer.layers.{bid}.norm_self_att", # lfm2
             "conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n
+            "encoder.layers.{bid}.attn.pre_norm", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_OUTPUT: (
@@ -1960,18 +2021,21 @@ class TensorNameMap:
             "conformer.layers.{bid}.self_attn.linear_out", # lfm2
             "conformer.layers.{bid}.attention.post", # gemma3n
             "conformer.layers.{bid}.self_attn.post", # gemma4
+            "encoder.layers.{bid}.attn.to_out", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_OUTPUT_NORM: (
             "audio_tower.layers.{bid}.final_layer_norm", # ultravox
             "conformer.layers.{bid}.norm_out", # lfm2
             "conformer.layers.{bid}.attention.post_norm", # gemma3n
+            "encoder.layers.{bid}.post_norm", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_FFN_NORM: (
             "conformer.layers.{bid}.norm_feed_forward1", # lfm2
             "conformer.layers.{bid}.ffw_layer_start.pre_layer_norm", # gemma3n
             "conformer.layers.{bid}.feed_forward1.pre_layer_norm", # gemma4
+            "encoder.layers.{bid}.ff1.pre_norm", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_FFN_POST_NORM: (
@@ -1988,6 +2052,7 @@ class TensorNameMap:
             "conformer.layers.{bid}.feed_forward1.linear1", # lfm2
             "conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n
             "conformer.layers.{bid}.feed_forward1.ffw_layer_1", # gemma4
+            "encoder.layers.{bid}.ff1.up_proj", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_FFN_GATE: (),
@@ -1997,24 +2062,28 @@ class TensorNameMap:
             "conformer.layers.{bid}.feed_forward1.linear2", # lfm2
             "conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n
             "conformer.layers.{bid}.feed_forward1.ffw_layer_2", # gemma4
+            "encoder.layers.{bid}.ff1.down_proj", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_FFN_UP_1: (
             "conformer.layers.{bid}.feed_forward2.linear1", # lfm2
             "conformer.layers.{bid}.ffw_layer_end.ffw_layer_1", # gemma3n
             "conformer.layers.{bid}.feed_forward2.ffw_layer_1", # gemma4
+            "encoder.layers.{bid}.ff2.up_proj", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_FFN_DOWN_1: (
             "conformer.layers.{bid}.feed_forward2.linear2", # lfm2
             "conformer.layers.{bid}.ffw_layer_end.ffw_layer_2", # gemma3n
             "conformer.layers.{bid}.feed_forward2.ffw_layer_2", # gemma4
+            "encoder.layers.{bid}.ff2.down_proj", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_FFN_NORM_1: (
             "conformer.layers.{bid}.norm_feed_forward2", # lfm2
             "conformer.layers.{bid}.ffw_layer_end.pre_layer_norm", # gemma3n
             "conformer.layers.{bid}.feed_forward2.pre_layer_norm", # gemma4
+            "encoder.layers.{bid}.ff2.pre_norm", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_FFN_POST_NORM_1: (
@@ -2071,26 +2140,31 @@ class TensorNameMap:
         MODEL_TENSOR.A_ENC_CONV_DW: (
             "conformer.layers.{bid}.conv.depthwise_conv", # lfm2
             "conformer.layers.{bid}.lconv1d.depthwise_conv1d", # gemma3n
+            "encoder.layers.{bid}.conv.depth_conv.conv", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_CONV_NORM: (
             "conformer.layers.{bid}.conv.batch_norm", # lfm2
             "conformer.layers.{bid}.lconv1d.pre_layer_norm", # gemma3n
+            "encoder.layers.{bid}.conv.batch_norm", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_CONV_PW1: (
             "conformer.layers.{bid}.conv.pointwise_conv1", # lfm2
             "conformer.layers.{bid}.lconv1d.linear_start", # gemma3n
+            "encoder.layers.{bid}.conv.up_conv", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_CONV_PW2: (
             "conformer.layers.{bid}.conv.pointwise_conv2", # lfm2
             "conformer.layers.{bid}.lconv1d.linear_end", # gemma3n
+            "encoder.layers.{bid}.conv.down_conv", # granite_speech
         ),
 
         MODEL_TENSOR.A_ENC_NORM_CONV: (
             "conformer.layers.{bid}.norm_conv", # lfm2
             "conformer.layers.{bid}.lconv1d.conv_norm", # gemma3n
+            "encoder.layers.{bid}.conv.norm", # granite_speech
         ),
 
         MODEL_TENSOR.A_PER_DIM_K_SCALE: (
@@ -2114,6 +2188,62 @@ class TensorNameMap:
             "model.embed_audio.soft_embedding_norm", # gemma3n
         ),
 
+        MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB: (
+            "encoder.layers.{bid}.attn.rel_pos_emb.weight",
+        ),
+
+        MODEL_TENSOR.A_QF_SELF_ATTN_Q: (
+            "projector.qformer.encoder.layer.{bid}.attention.attention.query",
+        ),
+
+        MODEL_TENSOR.A_QF_SELF_ATTN_K: (
+            "projector.qformer.encoder.layer.{bid}.attention.attention.key",
+        ),
+
+        MODEL_TENSOR.A_QF_SELF_ATTN_V: (
+            "projector.qformer.encoder.layer.{bid}.attention.attention.value",
+        ),
+
+        MODEL_TENSOR.A_QF_SELF_ATTN_O: (
+            "projector.qformer.encoder.layer.{bid}.attention.output.dense",
+        ),
+
+        MODEL_TENSOR.A_QF_SELF_ATTN_NORM: (
+            "projector.qformer.encoder.layer.{bid}.attention.output.LayerNorm",
+        ),
+
+        MODEL_TENSOR.A_QF_CROSS_ATTN_Q: (
+            "projector.qformer.encoder.layer.{bid}.crossattention.attention.query",
+        ),
+
+        MODEL_TENSOR.A_QF_CROSS_ATTN_K: (
+            "projector.qformer.encoder.layer.{bid}.crossattention.attention.key",
+        ),
+
+        MODEL_TENSOR.A_QF_CROSS_ATTN_V: (
+            "projector.qformer.encoder.layer.{bid}.crossattention.attention.value",
+        ),
+
+        MODEL_TENSOR.A_QF_CROSS_ATTN_O: (
+            "projector.qformer.encoder.layer.{bid}.crossattention.output.dense",
+        ),
+
+        MODEL_TENSOR.A_QF_CROSS_ATTN_NORM: (
+            "projector.qformer.encoder.layer.{bid}.crossattention.output.LayerNorm",
+        ),
+
+        MODEL_TENSOR.A_QF_FFN_UP: (
+            "projector.qformer.encoder.layer.{bid}.intermediate_query.dense",
+        ),
+
+        MODEL_TENSOR.A_QF_FFN_DOWN: (
+            "projector.qformer.encoder.layer.{bid}.output_query.dense",
+        ),
+
+        MODEL_TENSOR.A_QF_FFN_NORM: (
+            "projector.qformer.encoder.layer.{bid}.output_query.LayerNorm",
+        ),
+
         # NextN/MTP tensors
         MODEL_TENSOR.NEXTN_EH_PROJ: (
             "model.layers.{bid}.eh_proj",
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 5fb2755f1ae..b2a36460ad8 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,44 +1,45 @@
-[tool.poetry]
+[project]
 name = "gguf"
-version = "0.18.0"
+version = "0.19.0"
 description = "Read and write ML models in GGUF for GGML"
-authors = ["GGML <ggml@ggml.ai>"]
-packages = [
-    {include = "gguf"},
-    {include = "gguf/py.typed"},
-]
-readme = "README.md"
-homepage = "https://ggml.ai"
-repository = "https://github.com/ggml-org/llama.cpp"
 keywords = ["ggml", "gguf", "llama.cpp"]
+dynamic = ["classifiers"]
+readme = "README.md"
+authors = [{name = "GGML", email = "ggml@ggml.ai"}]
+requires-python = '>=3.10'
+dependencies = ['numpy (>=1.17)', 'tqdm (>=4.27)', 'pyyaml (>=5.1)', 'requests (>=2.25)']
 classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
 ]
 
+[project.urls]
+homepage = "https://ggml.ai"
+repository = "https://github.com/ggml-org/llama.cpp"
+
+[project.scripts]
+gguf-convert-endian = "gguf.scripts.gguf_convert_endian:main"
+gguf-dump = "gguf.scripts.gguf_dump:main"
+gguf-set-metadata = "gguf.scripts.gguf_set_metadata:main"
+gguf-new-metadata = "gguf.scripts.gguf_new_metadata:main"
+gguf-editor-gui = "gguf.scripts.gguf_editor_gui:main"
+
+[project.optional-dependencies]
+gui = ['PySide6 (>=6.9,<7.0) ; python_version >= "3.9" and python_version < "3.14"']
+
+[tool.poetry]
+packages = [
+    {include = "gguf"},
+    {include = "gguf/py.typed"},
+]
+
 [tool.poetry.dependencies]
-python = ">=3.8"
-numpy = ">=1.17"
-tqdm = ">=4.27"
-pyyaml = ">=5.1"
-requests = ">=2.25"
-sentencepiece = { version = ">=0.1.98,<0.3.0", optional = true }
-PySide6 = { version = "^6.9", python = ">=3.9,<3.14", optional = true }
+python = ">=3.10"
 
 [tool.poetry.dev-dependencies]
 pytest = "^5.2"
 
-[tool.poetry.extras]
-gui = ["PySide6"]
-
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
-
-[tool.poetry.scripts]
-gguf-convert-endian = "gguf.scripts.gguf_convert_endian:main"
-gguf-dump = "gguf.scripts.gguf_dump:main"
-gguf-set-metadata = "gguf.scripts.gguf_set_metadata:main"
-gguf-new-metadata = "gguf.scripts.gguf_new_metadata:main"
-gguf-editor-gui = "gguf.scripts.gguf_editor_gui:main"
diff --git a/grammars/README.md b/grammars/README.md
index dcd28648b15..99c6bd912a7 100644
--- a/grammars/README.md
+++ b/grammars/README.md
@@ -148,7 +148,6 @@ You can use GBNF grammars:
 - In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--json` / `-j` flag
 - To convert to a grammar ahead of time:
     - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py)
-    - in JavaScript with [json-schema-to-grammar.mjs](../tools/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../tools/server)'s Web UI)
 
 > [!NOTE]
 > The JSON schema is only used to constrain the model output and is not injected into the prompt. The model has no visibility into the schema, so if you want it to understand the expected structure, describe it explicitly in your prompt. This does not apply to tool calling, where schemas are injected into the prompt.
diff --git a/include/llama.h b/include/llama.h
index 49bd8e74150..52eb212466b 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -200,6 +200,11 @@ extern "C" {
         LLAMA_SPLIT_MODE_TENSOR = 3,
     };
 
+    enum llama_context_type {
+        LLAMA_CONTEXT_TYPE_DEFAULT = 0,
+        LLAMA_CONTEXT_TYPE_MTP     = 1,
+    };
+
     // TODO: simplify (https://github.com/ggml-org/llama.cpp/pull/9294#pullrequestreview-2286561979)
     typedef struct llama_token_data {
         llama_token id; // token id
@@ -335,9 +340,11 @@ extern "C" {
         uint32_t n_batch;           // logical maximum batch size that can be submitted to llama_decode
         uint32_t n_ubatch;          // physical maximum batch size
         uint32_t n_seq_max;         // max number of sequences (i.e. distinct states for recurrent models)
+        uint32_t n_rs_seq;          // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL]
         int32_t  n_threads;         // number of threads to use for generation
         int32_t  n_threads_batch;   // number of threads to use for batch processing
 
+        enum llama_context_type      ctx_type;          // set the context type (e.g. MTP)
         enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
         enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
         enum llama_attention_type    attention_type;    // attention type to use for embeddings
@@ -532,6 +539,7 @@ extern "C" {
     LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_ubatch   (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_seq_max  (const struct llama_context * ctx);
+    LLAMA_API uint32_t llama_n_rs_seq   (const struct llama_context * ctx);
 
     DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead");
     DEPRECATED(LLAMA_API int32_t llama_n_embd     (const struct llama_model * model), "use llama_model_n_embd instead");
@@ -860,12 +868,17 @@ extern "C" {
                           size_t   n_token_capacity,
                           size_t * n_token_count_out);
 
+#define LLAMA_STATE_SEQ_FLAGS_NONE 0
+
 // for backwards-compat
 #define LLAMA_STATE_SEQ_FLAGS_SWA_ONLY 1
 
 // work only with partial states, such as SWA KV cache or recurrent cache (e.g. Mamba)
 #define LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY 1
 
+// keeps the tensor data on device buffers (i.e. not accessible in host memory, but faster save/load)
+#define LLAMA_STATE_SEQ_FLAGS_ON_DEVICE 2
+
     typedef uint32_t llama_state_seq_flags;
 
     LLAMA_API size_t llama_state_seq_get_size_ext(
diff --git a/models/ggml-vocab-qwen35.gguf b/models/ggml-vocab-qwen35.gguf
new file mode 100644
index 00000000000..c06d1c17e67
Binary files /dev/null and b/models/ggml-vocab-qwen35.gguf differ
diff --git a/models/ggml-vocab-qwen35.gguf.inp b/models/ggml-vocab-qwen35.gguf.inp
new file mode 100644
index 00000000000..65307375499
--- /dev/null
+++ b/models/ggml-vocab-qwen35.gguf.inp
@@ -0,0 +1,120 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Äpfel
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
+é
+__ggml_vocab_test__
+résumé
+__ggml_vocab_test__
+àààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààààà
+__ggml_vocab_test__
+Vieết Nam
+__ggml_vocab_test__
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-[package.dependencies]
-colorama = {version = "*", markers = "platform_system == \"Windows\""}
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-[package.extras]
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-audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-codecarbon = ["codecarbon (==1.2.0)"]
-deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
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-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
-docs-specific = ["hf-doc-builder"]
-flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
-flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-ftfy = ["ftfy"]
-integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
-ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
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-ray = ["ray[tune] (>=2.7.0)"]
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-sagemaker = ["sagemaker (>=2.31.0)"]
-sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
-sigopt = ["sigopt"]
-sklearn = ["scikit-learn"]
-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.19)"]
-torch = ["accelerate (>=0.21.0)", "torch"]
-torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
-video = ["av (==9.2.0)", "decord (==0.6.0)"]
-vision = ["Pillow (>=10.0.1,<=15.0)"]
-
-[[package]]
-name = "typing-extensions"
-version = "4.9.0"
-description = "Backported and Experimental Type Hints for Python 3.8+"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
-]
-
-[[package]]
-name = "urllib3"
-version = "2.2.1"
-description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
-    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
-]
-
-[package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
-h2 = ["h2 (>=4,<5)"]
-socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
-zstd = ["zstandard (>=0.18.0)"]
-
-[[package]]
-name = "wcwidth"
-version = "0.2.13"
-description = "Measures the displayed width of unicode strings in a terminal"
-optional = false
-python-versions = "*"
-files = [
-    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
-    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
-]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.9"
-content-hash = "c8c4cc87637266a7b85debcbafa8887c5ad81cc8ef40e98a3f52c7c50af05c03"
diff --git a/pyproject.toml b/pyproject.toml
index 35cd067083b..a39c4f010bb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,31 +1,48 @@
-[tool.poetry]
+[project]
 name = "llama-cpp-scripts"
-version = "0.0.0"
 description = "Scripts that ship with llama.cpp"
-authors = ["GGML <ggml@ggml.ai>"]
-readme = "README.md"
-homepage = "https://ggml.ai"
-repository = "https://github.com/ggml-org/llama.cpp"
 keywords = ["ggml", "gguf", "llama.cpp"]
-packages = [{ include = "*.py", from = "." }]
+version = "0.0.0"
+dynamic = ["classifiers"]
+readme = "README.md"
+authors = [{name = "GGML", email = "ggml@ggml.ai"}]
+requires-python = '>=3.10'
+dependencies = [
+    'numpy (>=1.25.0,<2.0.0)',
+    'sentencepiece (>=0.1.98,<0.3.0)',
+    'transformers (==5.5.1)',
+    'protobuf (>=4.21.0)',
+    'torch (>=2.2.0,<3.0.0)',
+    'gguf @ ./gguf-py',
+]
 classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
 ]
 
+[project.urls]
+homepage = "https://ggml.ai"
+repository = "https://github.com/ggml-org/llama.cpp"
+
+[project.scripts]
+llama-convert-hf-to-gguf = "convert_hf_to_gguf:main"
+llama-convert-lora-to-gguf = "convert_lora_to_gguf:main"
+llama-convert-llama-ggml-to-gguf = "convert_llama_ggml_to_gguf:main"
+llama-ggml-vk-generate-shaders = "ggml_vk_generate_shaders:main"
+
+[tool.poetry]
+packages = [{ include = "*.py", from = "." }]
+
 [tool.poetry.dependencies]
-python = ">=3.9"
-numpy = "^1.25.0"
-sentencepiece = ">=0.1.98,<0.3.0"
-transformers = "==5.5.1"
-protobuf = ">=4.21.0,<5.0.0"
-gguf = { path = "./gguf-py" }
-torch = { version = "^2.2.0", source = "pytorch" }
-
-[tool.poetry.dev-dependencies]
-pytest = "^5.2"
+torch = [
+    { version = "~=2.6.0", source = "pypi", markers = "sys_platform == 'darwin'" },
+    { version = "~=2.6.0+cpu", source = "pytorch", markers = "sys_platform == 'linux'" },
+    { version = "~=2.6.0", source = "pypi", markers = "sys_platform == 'win32'" }
+]
 
+[tool.poetry.group.dev.dependencies]
+pytest = "^5.2"
 
 # Force wheel + cpu
 # For discussion and context see https://github.com/python-poetry/poetry#6409
@@ -34,12 +51,14 @@ name = "pytorch"
 url = "https://download.pytorch.org/whl/cpu"
 priority = "explicit"
 
+[tool.uv.sources]
+torch = { index = "pytorch" }
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
-
-[tool.poetry.scripts]
-llama-convert-hf-to-gguf = "convert_hf_to_gguf:main"
-llama-convert-lora-to-gguf = "convert_lora_to_gguf:main"
-llama-convert-llama-ggml-to-gguf = "convert_llama_ggml_to_gguf:main"
-llama-ggml-vk-generate-shaders = "ggml_vk_generate_shaders:main"
diff --git a/scripts/jinja/jinja-tester.py b/scripts/jinja/jinja-tester.py
index 4f79b8da3db..a83f025411a 100755
--- a/scripts/jinja/jinja-tester.py
+++ b/scripts/jinja/jinja-tester.py
@@ -20,6 +20,7 @@
 from jinja2 import TemplateSyntaxError
 from jinja2.sandbox import ImmutableSandboxedEnvironment
 from datetime import datetime
+from typing import Callable
 
 
 def format_template_content(template_content):
@@ -395,7 +396,7 @@ def raise_exception(text: str) -> str:
                 ensure_ascii=ensure_ascii,
             )
         )
-        env.globals["strftime_now"] = lambda format: datetime.now().strftime(format)  # ty: ignore[invalid-assignment]
+        env.globals["strftime_now"]: Callable[[str], str] = lambda format: datetime.now().strftime(format)
         env.globals["raise_exception"] = raise_exception  # ty: ignore[invalid-assignment]
         try:
             template = env.from_string(template_str)
diff --git a/scripts/pr2wt.sh b/scripts/pr2wt.sh
index e028814c431..ae03a888cb6 100755
--- a/scripts/pr2wt.sh
+++ b/scripts/pr2wt.sh
@@ -68,11 +68,19 @@ dir=$(basename $(pwd))
 git branch -D pr/$PR 2> /dev/null
 git worktree add -b pr/$PR ../$dir-pr-$PR pr/$PR/$head_ref 2> /dev/null
 
+og_path=$(pwd)
 wt_path=$(cd ../$dir-pr-$PR && pwd)
 
 echo "git worktree created in $wt_path"
 
 cd $wt_path
+
+# pi agent setup in the worktree
+if [[ -f "$og_path/.pi/SYSTEM.md" && ! -f ".pi/SYSTEM.md" ]]; then
+    mkdir -p .pi
+    ln -sfn "$og_path/.pi/SYSTEM.md" .pi/SYSTEM.md
+fi
+
 git branch --set-upstream-to=pr/$PR/$head_ref
 git pull   --ff-only || {
     echo "error: failed to pull pr/$PR"
diff --git a/scripts/server-test-function-call.py b/scripts/server-test-function-call.py
index b3aae1a961e..c32f17b5e3b 100755
--- a/scripts/server-test-function-call.py
+++ b/scripts/server-test-function-call.py
@@ -79,7 +79,7 @@ def print_info(msg):
 # ---------------------------------------------------------------------------
 
 
-def chat_completion(url, messages, tools=None, stream=False):
+def chat_completion(url, messages, tools=None, stream=False, force_tools=False):
     payload = {
         "messages": messages,
         "stream": stream,
@@ -87,7 +87,10 @@ def chat_completion(url, messages, tools=None, stream=False):
     }
     if tools:
         payload["tools"] = tools
-        payload["tool_choice"] = "auto"
+        if force_tools:
+            payload["tool_choice"] = "required"
+        else:
+            payload["tool_choice"] = "auto"
 
     try:
         response = requests.post(url, json=payload, stream=stream)
@@ -160,7 +163,13 @@ def chat_completion(url, messages, tools=None, stream=False):
     return result
 
 
-def run_agentic_loop(url, messages, tools, mock_tool_responses, stream, max_turns=6):
+def all_tools_called(tools, all_tool_calls):
+    all_tool_names = set([tc["function"]["name"] for tc in tools])
+    all_called_tool_names = set([tc["function"]["name"] for tc in all_tool_calls])
+    return all_tool_names == all_called_tool_names
+
+
+def run_agentic_loop(url, messages, tools, mock_tool_responses, stream, max_turns=6, force_tools=False):
     """
     Drive the multi-turn tool-call loop:
       1. Send messages to model.
@@ -172,8 +181,8 @@ def run_agentic_loop(url, messages, tools, mock_tool_responses, stream, max_turn
     msgs = list(messages)
     all_tool_calls: list[dict] = []
 
-    for _ in range(max_turns):
-        result = chat_completion(url, msgs, tools=tools, stream=stream)
+    for t in range(max_turns):
+        result = chat_completion(url, msgs, tools=tools, stream=stream, force_tools=(force_tools and not all_tools_called(tools, all_tool_calls)))
         if result is None:
             return all_tool_calls, None
 
@@ -235,10 +244,10 @@ def run_agentic_loop(url, messages, tools, mock_tool_responses, stream, max_turn
 # ---------------------------------------------------------------------------
 
 
-def run_test(url, test_case, stream):
+def run_test(url, test_case, stream, force_tools):
     name = test_case["name"]
     mode = f"{'stream' if stream else 'non-stream'}"
-    print_header(f"{name}  [{mode}]")
+    print_header(f"{name} [{mode}, force_tools={force_tools}] ")
 
     all_tool_calls, final_content = run_agentic_loop(
         url,
@@ -246,6 +255,7 @@ def run_test(url, test_case, stream):
         tools=test_case["tools"],
         mock_tool_responses=test_case["mock_tool_responses"],
         stream=stream,
+        force_tools=force_tools
     )
 
     if final_content is None and not all_tool_calls:
@@ -1093,6 +1103,9 @@ def main():
     parser.add_argument(
         "--stream-only", action="store_true", help="Only run streaming mode tests"
     )
+    parser.add_argument(
+        "--force-tools", action="store_true", help="Change tool mode to forced instead of auto"
+    )
     parser.add_argument(
         "--test",
         help="Run only the test whose name contains this substring (case-insensitive)",
@@ -1103,10 +1116,13 @@ def main():
     print_info(f"Testing server at {url}")
 
     modes = []
+    force_tools = False
     if not args.stream_only:
         modes.append(False)
     if not args.no_stream:
         modes.append(True)
+    if args.force_tools:
+        force_tools = True
 
     cases: list[dict] = ALL_TEST_CASES
     if args.test:
@@ -1121,7 +1137,7 @@ def main():
     for stream in modes:
         for case in cases:
             total += 1
-            if run_test(url, case, stream=stream):
+            if run_test(url, case, stream=stream, force_tools=force_tools):
                 passed += 1
 
     color = GREEN if passed == total else RED
diff --git a/scripts/server-test-parallel-tc.py b/scripts/server-test-parallel-tc.py
new file mode 100755
index 00000000000..a166c6d7208
--- /dev/null
+++ b/scripts/server-test-parallel-tc.py
@@ -0,0 +1,991 @@
+#!/usr/bin/env python3
+"""
+Test parallel tool-calling capability via chat completions endpoint.
+
+Only run this against models that actually support parallel tool calls — this
+script does not attempt to toggle that setting on the server. Each scenario is
+explicitly worded so that a capable model SHOULD emit multiple tool calls in a
+single assistant turn (either the same tool N times, or several different
+tools at once).
+
+Each test case contains:
+  - tools: list of tool definitions (OpenAI-compatible)
+  - messages: initial conversation messages
+  - mock_tool_responses: dict mapping tool_name -> callable(arguments) -> str (JSON)
+  - expected_parallel: dict describing what constitutes a successful parallel turn
+        {"min_parallel": int,                # minimum tool_calls in one turn
+         "require_same_tool": Optional[str], # all parallel calls must be this tool
+         "require_distinct_tools": Optional[int], # >= N distinct tool names in one turn
+         "min_distinct_args_key": Optional[str]}  # parallel calls must span this
+                                                   # many distinct values of this arg key
+  - validate: callable(turns, all_tool_calls, final_content) -> (passed, reason)
+"""
+
+import argparse
+import json
+import requests
+import sys
+
+# ---------------------------------------------------------------------------
+# Color / formatting helpers
+# ---------------------------------------------------------------------------
+
+RESET = "\x1b[0m"
+BOLD = "\x1b[1m"
+DIM = "\x1b[2m"
+CYAN = "\x1b[36m"
+YELLOW = "\x1b[33m"
+GREEN = "\x1b[32m"
+RED = "\x1b[31m"
+BLUE = "\x1b[34m"
+WHITE = "\x1b[97m"
+MAGENTA = "\x1b[35m"
+
+
+def _print(text="", end="\n"):
+    sys.stdout.write(text + end)
+    sys.stdout.flush()
+
+
+def print_header(title):
+    bar = "─" * 60
+    _print(f"\n{BOLD}{CYAN}┌{bar}┐{RESET}")
+    _print(
+        f"{BOLD}{CYAN}│  {WHITE}{title}{CYAN}{' ' * max(0, 58 - len(title))}│{RESET}"
+    )
+    _print(f"{BOLD}{CYAN}└{bar}┘{RESET}")
+
+
+def print_turn_banner(turn_idx, n_calls):
+    color = MAGENTA if n_calls >= 2 else DIM
+    _print(f"\n  {BOLD}{color}▶ turn {turn_idx} — {n_calls} tool call(s){RESET}")
+
+
+def print_tool_call(name, args):
+    args_str = json.dumps(args)
+    _print(
+        f"    {BOLD}{YELLOW}⚙ {name}{RESET}{DIM}({args_str}){RESET}"
+    )
+
+
+def print_tool_result(result):
+    preview = result[:140] + ("…" if len(result) > 140 else "")
+    _print(f"      {DIM}{BLUE}↳ {preview}{RESET}")
+
+
+def print_model_output(text):
+    sys.stdout.write(text)
+    sys.stdout.flush()
+
+
+def print_pass(reason):
+    _print(f"\n{BOLD}{GREEN}✔ PASS{RESET}  {reason}")
+
+
+def print_fail(reason):
+    _print(f"\n{BOLD}{RED}✘ FAIL{RESET}  {reason}")
+
+
+def print_info(msg):
+    _print(f"{DIM}{msg}{RESET}")
+
+
+def print_warn(msg):
+    _print(f"{BOLD}{YELLOW}⚠ {msg}{RESET}")
+
+
+# ---------------------------------------------------------------------------
+# HTTP helpers
+# ---------------------------------------------------------------------------
+
+
+def chat_completion(url, messages, tools=None, stream=False):
+    payload = {
+        "messages": messages,
+        "stream": stream,
+        "max_tokens": 4096,
+    }
+    if tools:
+        payload["tools"] = tools
+        payload["tool_choice"] = "auto"
+
+    try:
+        response = requests.post(url, json=payload, stream=stream)
+        response.raise_for_status()
+    except requests.exceptions.RequestException as e:
+        body = e.response.content if (e.response is not None) else b""
+        print_fail(f"Request error: {e} | body: {body}")
+        return None
+
+    full_content = ""
+    reasoning_content = ""
+    tool_calls: list[dict] = []
+
+    if stream:
+        for line in response.iter_lines():
+            if not line:
+                continue
+            decoded = line.decode("utf-8")
+            if not decoded.startswith("data: "):
+                continue
+            data_str = decoded[6:]
+            if data_str == "[DONE]":
+                break
+            try:
+                data = json.loads(data_str)
+            except json.JSONDecodeError:
+                continue
+            choices = data.get("choices", [])
+            if not choices:
+                continue
+            delta = choices[0].get("delta", {})
+            if delta.get("reasoning_content"):
+                reasoning_content += delta["reasoning_content"]
+            if delta.get("content"):
+                full_content += delta["content"]
+                print_model_output(delta["content"])
+            for tc in delta.get("tool_calls", []):
+                idx = tc.get("index", 0)
+                while len(tool_calls) <= idx:
+                    tool_calls.append(
+                        {
+                            "id": "",
+                            "type": "function",
+                            "function": {"name": "", "arguments": ""},
+                        }
+                    )
+                if "id" in tc:
+                    tool_calls[idx]["id"] += tc["id"]
+                if "function" in tc:
+                    if "name" in tc["function"]:
+                        tool_calls[idx]["function"]["name"] += tc["function"]["name"]
+                    if "arguments" in tc["function"]:
+                        tool_calls[idx]["function"]["arguments"] += tc["function"][
+                            "arguments"
+                        ]
+    else:
+        data = response.json()
+        choices = data.get("choices", [])
+        if choices:
+            msg = choices[0].get("message", {})
+            full_content = msg.get("content") or ""
+            reasoning_content = msg.get("reasoning_content") or ""
+            tool_calls = msg.get("tool_calls") or []
+            if full_content:
+                print_model_output(full_content)
+
+    result = {"content": full_content, "tool_calls": tool_calls}
+    if reasoning_content:
+        result["reasoning_content"] = reasoning_content
+    return result
+
+
+def run_agentic_loop(url, messages, tools, mock_tool_responses, stream, max_turns=6):
+    """
+    Drive the multi-turn tool-call loop, but record each turn's tool calls
+    separately so parallelism can be validated.
+
+    Returns (turns, all_tool_calls, final_content) where `turns` is a list
+    of dicts: {"index": int, "tool_calls": [...], "content": str}.
+    """
+    msgs = list(messages)
+    turns: list[dict] = []
+    all_tool_calls: list[dict] = []
+
+    for turn_idx in range(max_turns):
+        result = chat_completion(url, msgs, tools=tools, stream=stream)
+        if result is None:
+            return turns, all_tool_calls, None
+
+        tcs = result.get("tool_calls") or []
+        content = result.get("content") or ""
+
+        turns.append(
+            {"index": turn_idx, "tool_calls": list(tcs), "content": content}
+        )
+
+        if not tcs:
+            if content:
+                _print(f"\n{DIM}{'·' * 60}{RESET}")
+                _print(f"{DIM}  model response:{RESET}\n")
+            return turns, all_tool_calls, content
+
+        print_turn_banner(turn_idx, len(tcs))
+        all_tool_calls.extend(tcs)
+
+        assistant_msg: dict = {
+            "role": "assistant",
+            "content": content,
+            "tool_calls": tcs,
+        }
+        reasoning = result.get("reasoning_content")
+        if reasoning:
+            assistant_msg["reasoning_content"] = reasoning
+        msgs.append(assistant_msg)
+
+        for tc in tcs:
+            tool_name = tc["function"]["name"]
+            try:
+                args = json.loads(tc["function"]["arguments"])
+            except json.JSONDecodeError:
+                args = {}
+
+            print_tool_call(tool_name, args)
+
+            mock_fn = mock_tool_responses.get(tool_name)
+            if mock_fn:
+                tool_result = mock_fn(args)
+            else:
+                tool_result = json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+            print_tool_result(tool_result)
+
+            msgs.append(
+                {
+                    "role": "tool",
+                    "tool_call_id": tc.get("id", ""),
+                    "content": tool_result,
+                }
+            )
+
+    return turns, all_tool_calls, None
+
+
+# ---------------------------------------------------------------------------
+# Parallelism helpers
+# ---------------------------------------------------------------------------
+
+
+def _best_parallel_turn(turns):
+    """Return the turn (dict) with the most tool calls, or None if no tools."""
+    tool_turns = [t for t in turns if t["tool_calls"]]
+    if not tool_turns:
+        return None
+    return max(tool_turns, key=lambda t: len(t["tool_calls"]))
+
+
+def _distinct_tool_names(turn):
+    return {tc["function"]["name"] for tc in turn["tool_calls"]}
+
+
+def _distinct_arg_values(turn, key):
+    values = set()
+    for tc in turn["tool_calls"]:
+        try:
+            args = json.loads(tc["function"]["arguments"])
+        except json.JSONDecodeError:
+            continue
+        v = args.get(key)
+        if v is not None:
+            if isinstance(v, str):
+                values.add(v.strip().lower())
+            else:
+                values.add(v)
+    return values
+
+
+def _check_parallel(turns, expected):
+    """
+    Check that at least one turn satisfies the parallel-call expectations.
+    Returns (ok, reason).
+    """
+    best = _best_parallel_turn(turns)
+    if best is None:
+        return False, "No tool calls were made at all"
+
+    min_parallel = expected.get("min_parallel", 2)
+    if len(best["tool_calls"]) < min_parallel:
+        by_turn = [len(t["tool_calls"]) for t in turns]
+        return False, (
+            f"No turn had >= {min_parallel} parallel tool calls "
+            f"(per-turn counts: {by_turn})"
+        )
+
+    require_same = expected.get("require_same_tool")
+    if require_same is not None:
+        names = [tc["function"]["name"] for tc in best["tool_calls"]]
+        if any(n != require_same for n in names):
+            return False, (
+                f"Parallel turn mixed tools; expected all {require_same!r}, got {names}"
+            )
+
+    require_distinct = expected.get("require_distinct_tools")
+    if require_distinct is not None:
+        distinct = _distinct_tool_names(best)
+        if len(distinct) < require_distinct:
+            return False, (
+                f"Parallel turn had only {len(distinct)} distinct tool names "
+                f"({distinct}); need >= {require_distinct}"
+            )
+
+    distinct_key = expected.get("min_distinct_args_key")
+    distinct_count = expected.get("min_distinct_args_count", min_parallel)
+    if distinct_key is not None:
+        values = _distinct_arg_values(best, distinct_key)
+        if len(values) < distinct_count:
+            return False, (
+                f"Parallel turn had only {len(values)} distinct {distinct_key!r} "
+                f"values ({values}); need >= {distinct_count}"
+            )
+
+    return True, (
+        f"Parallel turn had {len(best['tool_calls'])} calls across "
+        f"{len(_distinct_tool_names(best))} distinct tool(s)"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Test case runner
+# ---------------------------------------------------------------------------
+
+
+def run_test(url, test_case, stream):
+    name = test_case["name"]
+    mode = f"{'stream' if stream else 'non-stream'}"
+    print_header(f"{name}  [{mode}]")
+
+    turns, all_tool_calls, final_content = run_agentic_loop(
+        url,
+        messages=test_case["messages"],
+        tools=test_case["tools"],
+        mock_tool_responses=test_case["mock_tool_responses"],
+        stream=stream,
+    )
+
+    if not turns:
+        print_fail("No response from server.")
+        return False
+
+    parallel_ok, parallel_reason = _check_parallel(turns, test_case["expected_parallel"])
+    if not parallel_ok:
+        print_fail(parallel_reason)
+        return False
+
+    passed, reason = test_case["validate"](turns, all_tool_calls, final_content)
+    if passed:
+        print_pass(f"{parallel_reason}; {reason}")
+    else:
+        print_fail(reason)
+    return passed
+
+
+# ---------------------------------------------------------------------------
+# Test case definitions
+# ---------------------------------------------------------------------------
+
+# ---- Test 1: Multi-file read (same tool, multiple distinct paths) ----
+
+_FILE_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "read_file",
+            "description": (
+                "Read the full contents of a file from the local filesystem. "
+                "Call this tool in parallel when asked to read several files — "
+                "each path needs its own call."
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "path": {
+                        "type": "string",
+                        "description": "Absolute or repo-relative path to a file",
+                    },
+                },
+                "required": ["path"],
+            },
+        },
+    },
+]
+
+_FILE_CONTENTS = {
+    "config/database.yml": "host: db.internal\nport: 5432\nuser: svc_app\n",
+    "config/redis.yml":    "host: cache.internal\nport: 6379\ndb: 0\n",
+    "config/queue.yml":    "broker: rabbitmq.internal\nport: 5672\nvhost: prod\n",
+    "config/auth.yml":     "provider: oidc\nissuer: https://auth.internal\n",
+}
+
+
+def _read_file_mock(args):
+    path = args.get("path", "")
+    norm = path.lstrip("./").lstrip("/")
+    content = _FILE_CONTENTS.get(norm)
+    if content is None:
+        for k, v in _FILE_CONTENTS.items():
+            if path.endswith(k):
+                content = v
+                break
+    if content is None:
+        return json.dumps({"path": path, "error": "not found"})
+    return json.dumps({"path": path, "content": content})
+
+
+MULTIFILE_READ_TEST = {
+    "name": "Parallel multi-file read (same tool, 4 distinct paths)",
+    "tools": _FILE_TOOLS,
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "Please read all four of these config files so I can review them "
+                "together: config/database.yml, config/redis.yml, config/queue.yml, "
+                "and config/auth.yml. Call read_file for every path in parallel in "
+                "a single batch — do NOT read them one by one sequentially across "
+                "turns. After you have all four, give me a one-line summary of each."
+            ),
+        }
+    ],
+    "mock_tool_responses": {"read_file": _read_file_mock},
+    "expected_parallel": {
+        "min_parallel": 4,
+        "require_same_tool": "read_file",
+        "min_distinct_args_key": "path",
+        "min_distinct_args_count": 4,
+    },
+    "validate": lambda turns, tcs, content: _validate_multifile(turns, tcs, content),
+}
+
+
+def _validate_multifile(turns, tcs, content):
+    del turns
+    if not content:
+        return False, "No final summary produced"
+    return True, f"{len(tcs)} total read_file calls; content length={len(content)}"
+
+
+# ---- Test 2: Batch TODO marking (same tool, N calls in one turn) ----
+
+_TODO_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "mark_todo_complete",
+            "description": (
+                "Mark a single TODO item as complete by ID. When the user wants "
+                "several items marked at once, call this tool in parallel — "
+                "one call per item — rather than sequentially across turns."
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "todo_id": {
+                        "type": "string",
+                        "description": "Identifier of the TODO item",
+                    },
+                    "note": {
+                        "type": "string",
+                        "description": "Optional completion note",
+                    },
+                },
+                "required": ["todo_id"],
+            },
+        },
+    },
+]
+
+_TODO_DB = {
+    "T-101": "Draft onboarding doc",
+    "T-102": "Update dependency lockfile",
+    "T-103": "Fix flaky login test",
+    "T-104": "Rotate service credentials",
+    "T-105": "Archive Q4 reports",
+}
+
+
+def _mark_todo_mock(args):
+    tid = args.get("todo_id", "")
+    if tid in _TODO_DB:
+        return json.dumps({"todo_id": tid, "title": _TODO_DB[tid], "status": "done"})
+    return json.dumps({"todo_id": tid, "error": "unknown id"})
+
+
+TODO_BATCH_TEST = {
+    "name": "Batch TODO completion (same tool, 5 IDs in one turn)",
+    "tools": _TODO_TOOLS,
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "I finished every item on today's list. Please mark all of the "
+                "following TODOs as complete, in one parallel batch: T-101, T-102, "
+                "T-103, T-104, T-105. Don't mark them one at a time across separate "
+                "turns — issue all five mark_todo_complete calls at once. Afterwards "
+                "confirm which ones succeeded."
+            ),
+        }
+    ],
+    "mock_tool_responses": {"mark_todo_complete": _mark_todo_mock},
+    "expected_parallel": {
+        "min_parallel": 5,
+        "require_same_tool": "mark_todo_complete",
+        "min_distinct_args_key": "todo_id",
+        "min_distinct_args_count": 5,
+    },
+    "validate": lambda turns, tcs, content: _validate_todo(turns, tcs, content),
+}
+
+
+def _validate_todo(turns, tcs, content):
+    del turns
+    if not content:
+        return False, "No confirmation summary produced"
+    return True, f"{len(tcs)} total mark_todo_complete calls"
+
+
+# ---- Test 3: Multi-city weather (same tool, N parallel locations) ----
+
+_WEATHER_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": (
+                "Fetch current weather for ONE city. When the user asks about "
+                "several cities, call this tool in parallel — one call per city — "
+                "instead of sequentially."
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "city":  {"type": "string", "description": "City name"},
+                    "units": {
+                        "type": "string",
+                        "enum": ["metric", "imperial"],
+                        "default": "metric",
+                    },
+                },
+                "required": ["city"],
+            },
+        },
+    },
+]
+
+_WEATHER_DB = {
+    "tokyo":  {"city": "Tokyo",  "temp_c": 18.4, "condition": "partly cloudy", "humidity": 64},
+    "london": {"city": "London", "temp_c":  9.1, "condition": "overcast",       "humidity": 81},
+    "new york": {"city": "New York", "temp_c": 12.7, "condition": "clear",      "humidity": 55},
+    "paris":  {"city": "Paris",  "temp_c": 11.3, "condition": "light rain",     "humidity": 78},
+}
+
+
+def _weather_mock(args):
+    city = args.get("city", "").strip().lower()
+    if city.startswith("new york"):
+        city = "new york"
+    if city in _WEATHER_DB:
+        return json.dumps(_WEATHER_DB[city])
+    return json.dumps({"city": args.get("city", ""), "error": "unknown city"})
+
+
+MULTI_WEATHER_TEST = {
+    "name": "Parallel multi-city weather (same tool, 4 cities)",
+    "tools": _WEATHER_TOOLS,
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "I'm comparing today's weather across four cities for a travel "
+                "decision: Tokyo, London, New York, and Paris. Please call "
+                "get_weather for all four in parallel in a single turn — don't "
+                "fetch them one at a time. Then rank them from warmest to coolest."
+            ),
+        }
+    ],
+    "mock_tool_responses": {"get_weather": _weather_mock},
+    "expected_parallel": {
+        "min_parallel": 4,
+        "require_same_tool": "get_weather",
+        "min_distinct_args_key": "city",
+        "min_distinct_args_count": 4,
+    },
+    "validate": lambda turns, tcs, content: _validate_weather(turns, tcs, content),
+}
+
+
+def _validate_weather(turns, tcs, content):
+    del turns
+    if not content or not any(
+        kw in content.lower() for kw in ("warmest", "rank", "hot", "cool")
+    ):
+        return False, f"Final content missing a ranking: {content!r}"
+    return True, f"{len(tcs)} total get_weather calls; ranking produced"
+
+
+# ---- Test 4: Trip planning (different tools, parallel in one turn) ----
+
+_TRIP_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "search_flights",
+            "description": "Search one-way flights between two airports on a given date.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "from_airport": {"type": "string", "description": "IATA code, e.g. SFO"},
+                    "to_airport":   {"type": "string", "description": "IATA code, e.g. JFK"},
+                    "date":         {"type": "string", "description": "YYYY-MM-DD"},
+                },
+                "required": ["from_airport", "to_airport", "date"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "search_hotels",
+            "description": "Search hotels in a city for a date range.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "city":       {"type": "string"},
+                    "check_in":   {"type": "string", "description": "YYYY-MM-DD"},
+                    "check_out":  {"type": "string", "description": "YYYY-MM-DD"},
+                    "max_price":  {"type": "integer"},
+                },
+                "required": ["city", "check_in", "check_out"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "search_restaurants",
+            "description": "Search restaurants in a city by cuisine.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "city":    {"type": "string"},
+                    "cuisine": {"type": "string"},
+                },
+                "required": ["city"],
+            },
+        },
+    },
+]
+
+_FLIGHTS_RESULT = {
+    "results": [
+        {"flight": "UA 1552", "depart": "08:15", "arrive": "16:45", "price": 389},
+        {"flight": "AA  20",  "depart": "10:00", "arrive": "18:35", "price": 412},
+    ]
+}
+_HOTELS_RESULT = {
+    "results": [
+        {"name": "Midtown Grand",    "nightly_rate": 245, "rating": 4.3},
+        {"name": "Harbour Boutique", "nightly_rate": 312, "rating": 4.6},
+    ]
+}
+_RESTAURANTS_RESULT = {
+    "results": [
+        {"name": "Trattoria Nona", "cuisine": "italian", "rating": 4.5},
+        {"name": "Osteria Blu",    "cuisine": "italian", "rating": 4.4},
+    ]
+}
+
+TRIP_PLAN_TEST = {
+    "name": "Trip planning (3 different tools in parallel)",
+    "tools": _TRIP_TOOLS,
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "I'm flying from SFO to JFK on 2026-06-12 and staying four nights "
+                "(check out 2026-06-16). I'd also like some Italian restaurant "
+                "suggestions in New York. Please call search_flights, search_hotels, "
+                "and search_restaurants in parallel — all three in a single turn, "
+                "since they don't depend on each other. Then give me a concise "
+                "travel summary."
+            ),
+        }
+    ],
+    "mock_tool_responses": {
+        "search_flights": lambda _: json.dumps(_FLIGHTS_RESULT),
+        "search_hotels": lambda _: json.dumps(_HOTELS_RESULT),
+        "search_restaurants": lambda _: json.dumps(_RESTAURANTS_RESULT),
+    },
+    "expected_parallel": {
+        "min_parallel": 3,
+        "require_distinct_tools": 3,
+    },
+    "validate": lambda turns, tcs, content: _validate_trip(turns, tcs, content),
+}
+
+
+def _validate_trip(turns, tcs, content):
+    del turns
+    names = {tc["function"]["name"] for tc in tcs}
+    required = {"search_flights", "search_hotels", "search_restaurants"}
+    missing = required - names
+    if missing:
+        return False, f"Missing tool calls: {missing}"
+    if not content:
+        return False, "No travel summary produced"
+    return True, f"All three tools called; summary length={len(content)}"
+
+
+# ---- Test 5: Portfolio check (same tool, parallel tickers) ----
+
+_STOCK_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_stock_quote",
+            "description": (
+                "Get the latest quote for ONE ticker. When the user asks about "
+                "multiple tickers, call this tool in parallel — one per symbol — "
+                "rather than sequentially."
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "symbol": {"type": "string", "description": "Ticker symbol"},
+                },
+                "required": ["symbol"],
+            },
+        },
+    },
+]
+
+_STOCK_DB = {
+    "AAPL": {"symbol": "AAPL", "price": 218.45, "change_pct": "+0.8%"},
+    "MSFT": {"symbol": "MSFT", "price": 421.10, "change_pct": "+1.2%"},
+    "GOOGL":{"symbol": "GOOGL","price": 175.22, "change_pct": "-0.3%"},
+    "AMZN": {"symbol": "AMZN", "price": 189.76, "change_pct": "+0.5%"},
+    "NVDA": {"symbol": "NVDA", "price": 140.88, "change_pct": "+2.4%"},
+}
+
+
+def _stock_mock(args):
+    sym = args.get("symbol", "").strip().upper()
+    if sym in _STOCK_DB:
+        return json.dumps(_STOCK_DB[sym])
+    return json.dumps({"symbol": sym, "error": "unknown ticker"})
+
+
+PORTFOLIO_TEST = {
+    "name": "Portfolio check (same tool, 5 tickers in parallel)",
+    "tools": _STOCK_TOOLS,
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "Pull the latest quote for every ticker in my portfolio — AAPL, "
+                "MSFT, GOOGL, AMZN, and NVDA — in a single parallel batch. These "
+                "lookups are independent, so please don't chain them across turns. "
+                "Once you have all five, tell me which ticker had the biggest "
+                "percentage change today."
+            ),
+        }
+    ],
+    "mock_tool_responses": {"get_stock_quote": _stock_mock},
+    "expected_parallel": {
+        "min_parallel": 5,
+        "require_same_tool": "get_stock_quote",
+        "min_distinct_args_key": "symbol",
+        "min_distinct_args_count": 5,
+    },
+    "validate": lambda turns, tcs, content: _validate_portfolio(turns, tcs, content),
+}
+
+
+def _validate_portfolio(turns, tcs, content):
+    del turns
+    if not content or ("nvda" not in content.lower() and "NVDA" not in content):
+        return False, f"Expected NVDA to be identified as the biggest mover: {content!r}"
+    return True, f"{len(tcs)} total quotes pulled"
+
+
+# ---- Test 6: Mixed — translate + dictionary in parallel for the same word ----
+
+_LANG_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "translate_text",
+            "description": "Translate a short text into a target language.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "text":            {"type": "string"},
+                    "target_language": {"type": "string",
+                                        "description": "ISO 639-1 language code, e.g. 'es'"},
+                },
+                "required": ["text", "target_language"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_definition",
+            "description": "Get the English dictionary definition of a word.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "word": {"type": "string"},
+                },
+                "required": ["word"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_synonyms",
+            "description": "Get English synonyms for a word.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "word": {"type": "string"},
+                },
+                "required": ["word"],
+            },
+        },
+    },
+]
+
+
+def _translate_mock(args):
+    t = args.get("text", "")
+    lang = args.get("target_language", "")
+    return json.dumps({"source": t, "target_language": lang, "translation": f"[{lang}] {t}"})
+
+
+def _definition_mock(args):
+    w = args.get("word", "")
+    return json.dumps({
+        "word": w,
+        "definition": f"A standard dictionary definition of {w!r}.",
+    })
+
+
+def _synonyms_mock(args):
+    w = args.get("word", "")
+    return json.dumps({
+        "word": w,
+        "synonyms": ["synonym_a", "synonym_b", "synonym_c"],
+    })
+
+
+LANG_TOOLKIT_TEST = {
+    "name": "Language toolkit (translate + definition + synonyms in parallel)",
+    "tools": _LANG_TOOLS,
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "For the English word 'resilient', I need three independent "
+                "look-ups at once: (a) translate it into Spanish, (b) fetch its "
+                "dictionary definition, and (c) list its synonyms. These three "
+                "calls don't depend on each other — please issue them in parallel "
+                "in a single turn. Then present the combined results as a short "
+                "language note."
+            ),
+        }
+    ],
+    "mock_tool_responses": {
+        "translate_text":  _translate_mock,
+        "get_definition":  _definition_mock,
+        "get_synonyms":    _synonyms_mock,
+    },
+    "expected_parallel": {
+        "min_parallel": 3,
+        "require_distinct_tools": 3,
+    },
+    "validate": lambda turns, tcs, content: _validate_lang(turns, tcs, content),
+}
+
+
+def _validate_lang(turns, tcs, content):
+    del turns
+    names = {tc["function"]["name"] for tc in tcs}
+    required = {"translate_text", "get_definition", "get_synonyms"}
+    missing = required - names
+    if missing:
+        return False, f"Missing tool calls: {missing}"
+    if not content:
+        return False, "No language note produced"
+    return True, f"All three lookup tools called; note length={len(content)}"
+
+
+# ---------------------------------------------------------------------------
+# All test cases
+# ---------------------------------------------------------------------------
+
+ALL_TEST_CASES = [
+    MULTIFILE_READ_TEST,
+    TODO_BATCH_TEST,
+    MULTI_WEATHER_TEST,
+    TRIP_PLAN_TEST,
+    PORTFOLIO_TEST,
+    LANG_TOOLKIT_TEST,
+]
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Test llama-server parallel tool-calling capability. Run this only "
+            "against models configured for parallel tool calls — this script "
+            "does not configure that itself."
+        )
+    )
+    parser.add_argument("--host", default="localhost")
+    parser.add_argument("--port", default=8080, type=int)
+    parser.add_argument(
+        "--no-stream", action="store_true", help="Disable streaming mode tests"
+    )
+    parser.add_argument(
+        "--stream-only", action="store_true", help="Only run streaming mode tests"
+    )
+    parser.add_argument(
+        "--test",
+        help="Run only the test whose name contains this substring (case-insensitive)",
+    )
+    args = parser.parse_args()
+
+    url = f"http://{args.host}:{args.port}/v1/chat/completions"
+    print_info(f"Testing server at {url}")
+    print_warn(
+        "This script expects the target model to emit multiple tool calls in a "
+        "single assistant turn. Run it only against parallel-tool-capable models."
+    )
+
+    modes: list[bool] = []
+    if not args.stream_only:
+        modes.append(False)
+    if not args.no_stream:
+        modes.append(True)
+
+    cases: list[dict] = ALL_TEST_CASES
+    if args.test:
+        name_filter = args.test.lower()
+        cases = [c for c in cases if name_filter in str(c["name"]).lower()]
+        if not cases:
+            print_fail(f"No test cases matched '{args.test}'")
+            sys.exit(1)
+
+    total = 0
+    passed = 0
+    for stream in modes:
+        for case in cases:
+            total += 1
+            if run_test(url, case, stream=stream):
+                passed += 1
+
+    color = GREEN if passed == total else RED
+    _print(f"\n{BOLD}{color}{'─' * 60}{RESET}")
+    _print(f"{BOLD}{color}  Results: {passed}/{total} passed{RESET}")
+    _print(f"{BOLD}{color}{'─' * 60}{RESET}\n")
+    sys.exit(0 if passed == total else 1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/server-test-structured.py b/scripts/server-test-structured.py
new file mode 100755
index 00000000000..da217fc46c8
--- /dev/null
+++ b/scripts/server-test-structured.py
@@ -0,0 +1,1040 @@
+#!/usr/bin/env python3
+"""
+Test structured output capability via chat completions endpoint.
+
+Each test case contains:
+  - response_format: OpenAI-compatible response_format specification.
+                     Both "json_schema" and "json_object" are accepted; with
+                     "json_object" a schema can be supplied via extra_body.
+  - extra_body (optional): dict of extra top-level request fields merged into
+                     the request payload (mirrors the OpenAI SDK's extra_body
+                     feature; llama.cpp reads a top-level "json_schema" here).
+  - messages: initial conversation messages
+  - tools (optional): tool definitions (for mixed tool + structured tests)
+  - mock_tool_responses (optional): dict mapping tool_name -> callable(arguments) -> str (JSON)
+  - apply_stage: "always" to apply response_format to every request,
+                 "after_tools" to run the tool loop plain, then request a
+                 structured summary in a follow-up user turn.
+  - followup (optional, for after_tools): user message appended before the
+                 final structured call.
+  - validate: callable(parsed_json, tool_calls_history, raw_content) -> (passed: bool, reason: str)
+"""
+
+import argparse
+import json
+import requests
+import sys
+from typing import Any, cast
+
+# ---------------------------------------------------------------------------
+# Color / formatting helpers
+# ---------------------------------------------------------------------------
+
+RESET = "\x1b[0m"
+BOLD = "\x1b[1m"
+DIM = "\x1b[2m"
+CYAN = "\x1b[36m"
+YELLOW = "\x1b[33m"
+GREEN = "\x1b[32m"
+RED = "\x1b[31m"
+BLUE = "\x1b[34m"
+WHITE = "\x1b[97m"
+MAGENTA = "\x1b[35m"
+
+
+def _print(text="", end="\n"):
+    sys.stdout.write(text + end)
+    sys.stdout.flush()
+
+
+def print_header(title):
+    bar = "─" * 60
+    _print(f"\n{BOLD}{CYAN}┌{bar}┐{RESET}")
+    _print(
+        f"{BOLD}{CYAN}│  {WHITE}{title}{CYAN}{' ' * max(0, 58 - len(title))}│{RESET}"
+    )
+    _print(f"{BOLD}{CYAN}└{bar}┘{RESET}")
+
+
+def print_tool_call(name, args):
+    args_str = json.dumps(args)
+    _print(
+        f"\n  {BOLD}{YELLOW}⚙ tool call{RESET}  {CYAN}{name}{RESET}{DIM}({args_str}){RESET}"
+    )
+
+
+def print_tool_result(result):
+    preview = result[:160] + ("…" if len(result) > 160 else "")
+    _print(f"  {DIM}{BLUE}↳ result{RESET}    {DIM}{preview}{RESET}")
+
+
+def print_model_output(text):
+    sys.stdout.write(text)
+    sys.stdout.flush()
+
+
+def print_pass(reason):
+    _print(f"\n{BOLD}{GREEN}✔ PASS{RESET}  {reason}")
+
+
+def print_fail(reason):
+    _print(f"\n{BOLD}{RED}✘ FAIL{RESET}  {reason}")
+
+
+def print_info(msg):
+    _print(f"{DIM}{msg}{RESET}")
+
+
+def print_schema_note(label, rf, extra_body=None):
+    kind = rf.get("type", "?")
+    name = ""
+    if kind == "json_schema":
+        name = rf.get("json_schema", {}).get("name", "")
+    elif kind == "json_object" and extra_body and "json_schema" in extra_body:
+        extra_schema = extra_body["json_schema"] or {}
+        name = extra_schema.get("title") or "extra_body.json_schema"
+    _print(f"{DIM}{MAGENTA}  ⟐ response_format [{label}]: {kind}"
+           f"{(' / ' + name) if name else ''}{RESET}")
+
+
+# ---------------------------------------------------------------------------
+# HTTP helpers
+# ---------------------------------------------------------------------------
+
+
+def chat_completion(url, messages, tools=None, response_format=None, stream=False,
+                    extra_body=None):
+    payload = {
+        "messages": messages,
+        "stream": stream,
+        "max_tokens": 8192,
+    }
+    if tools:
+        payload["tools"] = tools
+        payload["tool_choice"] = "auto"
+    if response_format is not None:
+        payload["response_format"] = response_format
+    if extra_body:
+        payload.update(extra_body)
+
+    try:
+        response = requests.post(url, json=payload, stream=stream)
+        response.raise_for_status()
+    except requests.exceptions.RequestException as e:
+        body = e.response.content if (e.response is not None) else b""
+        print_fail(f"Request error: {e} | body: {body}")
+        return None
+
+    full_content = ""
+    reasoning_content = ""
+    tool_calls: list[dict] = []
+
+    if stream:
+        for line in response.iter_lines():
+            if not line:
+                continue
+            decoded = line.decode("utf-8")
+            if not decoded.startswith("data: "):
+                continue
+            data_str = decoded[6:]
+            if data_str == "[DONE]":
+                break
+            try:
+                data = json.loads(data_str)
+            except json.JSONDecodeError:
+                continue
+            choices = data.get("choices", [])
+            if not choices:
+                continue
+            delta = choices[0].get("delta", {})
+            if delta.get("reasoning_content"):
+                reasoning_content += delta["reasoning_content"]
+            if delta.get("content"):
+                full_content += delta["content"]
+                print_model_output(delta["content"])
+            for tc in delta.get("tool_calls", []):
+                idx = tc.get("index", 0)
+                while len(tool_calls) <= idx:
+                    tool_calls.append(
+                        {
+                            "id": "",
+                            "type": "function",
+                            "function": {"name": "", "arguments": ""},
+                        }
+                    )
+                if "id" in tc:
+                    tool_calls[idx]["id"] += tc["id"]
+                if "function" in tc:
+                    if "name" in tc["function"]:
+                        tool_calls[idx]["function"]["name"] += tc["function"]["name"]
+                    if "arguments" in tc["function"]:
+                        tool_calls[idx]["function"]["arguments"] += tc["function"][
+                            "arguments"
+                        ]
+    else:
+        data = response.json()
+        choices = data.get("choices", [])
+        if choices:
+            msg = choices[0].get("message", {})
+            full_content = msg.get("content") or ""
+            reasoning_content = msg.get("reasoning_content") or ""
+            tool_calls = msg.get("tool_calls") or []
+            if full_content:
+                print_model_output(full_content)
+
+    result = {"content": full_content, "tool_calls": tool_calls}
+    if reasoning_content:
+        result["reasoning_content"] = reasoning_content
+    return result
+
+
+def run_tool_loop(
+    url, messages, tools, mock_tool_responses, stream, response_format=None,
+    extra_body=None, max_turns=6,
+):
+    """
+    Drive the tool-call loop. If response_format is provided it is applied to
+    every request. Returns (all_tool_calls, final_messages, final_content).
+    """
+    msgs = list(messages)
+    all_tool_calls: list[dict] = []
+
+    for _ in range(max_turns):
+        result = chat_completion(
+            url, msgs, tools=tools, response_format=response_format, stream=stream,
+            extra_body=extra_body,
+        )
+        if result is None:
+            return all_tool_calls, msgs, None
+
+        tcs = result.get("tool_calls") or []
+        content = result.get("content") or ""
+
+        if not tcs:
+            if content:
+                _print(f"\n{DIM}{'·' * 60}{RESET}")
+            return all_tool_calls, msgs, content
+
+        all_tool_calls.extend(tcs)
+
+        assistant_msg: dict = {
+            "role": "assistant",
+            "content": content,
+            "tool_calls": tcs,
+        }
+        reasoning = result.get("reasoning_content")
+        if reasoning:
+            assistant_msg["reasoning_content"] = reasoning
+        msgs.append(assistant_msg)
+
+        for tc in tcs:
+            tool_name = tc["function"]["name"]
+            try:
+                args = json.loads(tc["function"]["arguments"])
+            except json.JSONDecodeError:
+                args = {}
+
+            print_tool_call(tool_name, args)
+
+            mock_fn = mock_tool_responses.get(tool_name) if mock_tool_responses else None
+            if mock_fn:
+                tool_result = mock_fn(args)
+            else:
+                tool_result = json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+            print_tool_result(tool_result)
+
+            msgs.append(
+                {
+                    "role": "tool",
+                    "tool_call_id": tc.get("id", ""),
+                    "content": tool_result,
+                }
+            )
+
+    return all_tool_calls, msgs, None
+
+
+# ---------------------------------------------------------------------------
+# Test case runner
+# ---------------------------------------------------------------------------
+
+
+def _try_parse_json(text):
+    """Attempt to parse text as JSON, trimming common markdown fences."""
+    if text is None:
+        return None
+    stripped = text.strip()
+    if stripped.startswith("```"):
+        lines = stripped.splitlines()
+        if lines and lines[0].startswith("```"):
+            lines = lines[1:]
+        if lines and lines[-1].strip().startswith("```"):
+            lines = lines[:-1]
+        stripped = "\n".join(lines).strip()
+    try:
+        return json.loads(stripped)
+    except json.JSONDecodeError:
+        return None
+
+
+def run_test(url, test_case, stream):
+    name = test_case["name"]
+    mode = f"{'stream' if stream else 'non-stream'}"
+    apply_stage = test_case.get("apply_stage", "always")
+    print_header(f"{name}  [{mode}] ({apply_stage})")
+
+    response_format = test_case["response_format"]
+    extra_body = test_case.get("extra_body")
+    print_schema_note(apply_stage, response_format, extra_body)
+
+    tools = test_case.get("tools")
+    mocks = test_case.get("mock_tool_responses") or {}
+
+    all_tcs: list[dict] = []
+    final_content = None
+
+    if apply_stage == "always":
+        all_tcs, _msgs, final_content = run_tool_loop(
+            url,
+            messages=list(test_case["messages"]),
+            tools=tools,
+            mock_tool_responses=mocks,
+            stream=stream,
+            response_format=response_format,
+            extra_body=extra_body,
+        )
+    elif apply_stage == "after_tools":
+        # Phase 1: plain tool loop, no response_format applied yet.
+        all_tcs, msgs, interim_content = run_tool_loop(
+            url,
+            messages=list(test_case["messages"]),
+            tools=tools,
+            mock_tool_responses=mocks,
+            stream=stream,
+            response_format=None,
+        )
+        if interim_content:
+            msgs.append({"role": "assistant", "content": interim_content})
+        followup = test_case.get(
+            "followup",
+            "Now output the answer strictly as JSON matching the provided schema. "
+            "Do not include commentary.",
+        )
+        msgs.append({"role": "user", "content": followup})
+
+        # Phase 2: request final structured output. Tools are not passed so the
+        # model focuses on producing the schema-constrained answer.
+        _print(f"\n{DIM}{MAGENTA}  ⟐ follow-up turn with response_format applied{RESET}")
+        result = chat_completion(
+            url, msgs, tools=None, response_format=response_format, stream=stream,
+            extra_body=extra_body,
+        )
+        final_content = result["content"] if result else None
+    else:
+        print_fail(f"Unknown apply_stage: {apply_stage}")
+        return False
+
+    if final_content is None:
+        print_fail("No final content from server.")
+        return False
+
+    parsed = _try_parse_json(final_content)
+    if parsed is None:
+        print_fail(f"Final content is not valid JSON: {final_content[:200]!r}")
+        return False
+
+    passed, reason = test_case["validate"](parsed, all_tcs, final_content)
+    if passed:
+        print_pass(reason)
+    else:
+        print_fail(reason)
+    return passed
+
+
+# ---------------------------------------------------------------------------
+# Test case definitions
+# ---------------------------------------------------------------------------
+
+# ---- Test 1: Book metadata extraction (always / json_schema) ----
+
+_BOOK_SCHEMA = {
+    "type": "json_schema",
+    "json_schema": {
+        "name": "book_metadata",
+        "strict": True,
+        "schema": {
+            "type": "object",
+            "additionalProperties": False,
+            "properties": {
+                "title": {"type": "string"},
+                "author": {"type": "string"},
+                "year": {"type": "integer"},
+                "genre": {
+                    "type": "string",
+                    "enum": [
+                        "fiction",
+                        "non-fiction",
+                        "fantasy",
+                        "sci-fi",
+                        "mystery",
+                        "biography",
+                        "history",
+                        "other",
+                    ],
+                },
+                "page_count": {"type": "integer"},
+            },
+            "required": ["title", "author", "year", "genre", "page_count"],
+        },
+    },
+}
+
+BOOK_TEST_CASE = {
+    "name": "Book metadata extraction (json_schema, always)",
+    "response_format": _BOOK_SCHEMA,
+    "apply_stage": "always",
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "Extract book metadata from this description: "
+                "'Dune is a 1965 science fiction epic by Frank Herbert, spanning roughly "
+                "688 pages in its first edition, set on the desert planet Arrakis.' "
+                "Return the data as JSON."
+            ),
+        }
+    ],
+    "validate": lambda parsed, tcs, raw: _validate_book(parsed),
+}
+
+
+def _validate_book(parsed):
+    required = {"title", "author", "year", "genre", "page_count"}
+    missing = required - parsed.keys()
+    if missing:
+        return False, f"Missing fields: {missing}"
+    if not isinstance(parsed["title"], str) or not parsed["title"]:
+        return False, "title must be a non-empty string"
+    if not isinstance(parsed["author"], str) or "herbert" not in parsed["author"].lower():
+        return False, f"author unexpected: {parsed['author']!r}"
+    if not isinstance(parsed["year"], int) or parsed["year"] != 1965:
+        return False, f"year should be 1965, got {parsed['year']!r}"
+    if parsed["genre"] not in {
+        "fiction", "non-fiction", "fantasy", "sci-fi", "mystery",
+        "biography", "history", "other",
+    }:
+        return False, f"genre not in enum: {parsed['genre']!r}"
+    if not isinstance(parsed["page_count"], int) or parsed["page_count"] <= 0:
+        return False, f"page_count should be positive int: {parsed['page_count']!r}"
+    return True, f"Book: {parsed['title']} ({parsed['year']}) / {parsed['genre']}"
+
+
+# ---- Test 2: Sentiment classification (always / enum-constrained) ----
+
+_SENTIMENT_SCHEMA = {
+    "type": "json_schema",
+    "json_schema": {
+        "name": "sentiment_analysis",
+        "strict": True,
+        "schema": {
+            "type": "object",
+            "additionalProperties": False,
+            "properties": {
+                "sentiment": {
+                    "type": "string",
+                    "enum": ["positive", "negative", "neutral"],
+                },
+                "confidence": {"type": "number"},
+                "keywords": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "minItems": 1,
+                    "maxItems": 5,
+                },
+            },
+            "required": ["sentiment", "confidence", "keywords"],
+        },
+    },
+}
+
+SENTIMENT_TEST_CASE = {
+    "name": "Sentiment analysis with enum and array",
+    "response_format": _SENTIMENT_SCHEMA,
+    "apply_stage": "always",
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "Analyse the sentiment of this review and return JSON with the "
+                "detected sentiment label, a confidence score between 0 and 1, "
+                "and up to five keyword strings that drove the classification:\n\n"
+                "'This product completely exceeded my expectations. The build "
+                "quality is phenomenal, it arrived a day early, and customer "
+                "support was delightful when I had a setup question.'"
+            ),
+        }
+    ],
+    "validate": lambda parsed, tcs, raw: _validate_sentiment(parsed),
+}
+
+
+def _validate_sentiment(parsed):
+    if parsed.get("sentiment") not in {"positive", "negative", "neutral"}:
+        return False, f"sentiment not in enum: {parsed.get('sentiment')!r}"
+    if parsed["sentiment"] != "positive":
+        return False, f"expected positive sentiment, got {parsed['sentiment']}"
+    conf = parsed.get("confidence")
+    if not isinstance(conf, (int, float)) or not (0.0 <= conf <= 1.0):
+        return False, f"confidence not in [0,1]: {conf!r}"
+    kws = parsed.get("keywords")
+    if not isinstance(kws, list) or not (1 <= len(kws) <= 5):
+        return False, f"keywords length out of range: {kws!r}"
+    if not all(isinstance(k, str) and k for k in kws):
+        return False, f"keywords must be non-empty strings: {kws!r}"
+    return True, f"sentiment={parsed['sentiment']} conf={conf} kws={kws}"
+
+
+# ---- Test: json_object + extra_body.json_schema (always) ----
+#
+# Exercises the llama.cpp-specific path where the OpenAI SDK would send
+# response_format={"type": "json_object"} and tunnel the schema through
+# extra_body.json_schema (which becomes a top-level "json_schema" field on
+# the request body).
+
+_PRODUCT_JSON_OBJECT_SCHEMA = {
+    "$schema": "https://json-schema.org/draft/2020-12/schema",
+    "$id": "https://example.com/product.schema.json",
+    "title": "Product",
+    "description": "A product in the catalog",
+    "type": "object",
+}
+
+PRODUCT_JSON_OBJECT_TEST_CASE = {
+    "name": "json_object response_format with extra_body json_schema",
+    "response_format": {"type": "json_object"},
+    "extra_body": {"json_schema": _PRODUCT_JSON_OBJECT_SCHEMA},
+    "apply_stage": "always",
+    "messages": [
+        {
+            "role": "system",
+            "content": (
+                "Extract structured data from the provided text according to the "
+                "JSON schema. Return only valid JSON matching the schema exactly."
+            ),
+        },
+        {
+            "role": "user",
+            "content": "Product: Wireless Headphones, ID: 101, In Stock: Yes",
+        },
+    ],
+    "validate": lambda parsed, tcs, raw: _validate_product_json_object(parsed),
+}
+
+
+def _validate_product_json_object(parsed):
+    if not isinstance(parsed, dict):
+        return False, f"expected JSON object, got {type(parsed).__name__}: {parsed!r}"
+    if not parsed:
+        return False, f"expected non-empty object, got {parsed!r}"
+    return True, f"product object with {len(parsed)} field(s): {sorted(parsed.keys())}"
+
+
+# ---- Test 3: Nested recipe schema (always) ----
+
+_RECIPE_SCHEMA = {
+    "type": "json_schema",
+    "json_schema": {
+        "name": "recipe",
+        "strict": True,
+        "schema": {
+            "type": "object",
+            "additionalProperties": False,
+            "properties": {
+                "name": {"type": "string"},
+                "servings": {"type": "integer"},
+                "ingredients": {
+                    "type": "array",
+                    "minItems": 2,
+                    "items": {
+                        "type": "object",
+                        "additionalProperties": False,
+                        "properties": {
+                            "item": {"type": "string"},
+                            "quantity": {"type": "string"},
+                        },
+                        "required": ["item", "quantity"],
+                    },
+                },
+                "steps": {
+                    "type": "array",
+                    "minItems": 2,
+                    "items": {"type": "string"},
+                },
+                "prep_time_minutes": {"type": "integer"},
+            },
+            "required": ["name", "servings", "ingredients", "steps", "prep_time_minutes"],
+        },
+    },
+}
+
+RECIPE_TEST_CASE = {
+    "name": "Nested recipe with arrays of objects",
+    "response_format": _RECIPE_SCHEMA,
+    "apply_stage": "always",
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "Give me a simple 4-serving scrambled eggs recipe as structured JSON. "
+                "Include the recipe name, servings, ingredients (each with item and "
+                "quantity), preparation steps, and total prep time in minutes."
+            ),
+        }
+    ],
+    "validate": lambda parsed, tcs, raw: _validate_recipe(parsed),
+}
+
+
+def _validate_recipe(parsed):
+    required = {"name", "servings", "ingredients", "steps", "prep_time_minutes"}
+    missing = required - parsed.keys()
+    if missing:
+        return False, f"Missing fields: {missing}"
+    if not isinstance(parsed["name"], str) or not parsed["name"]:
+        return False, "name must be a non-empty string"
+    if not isinstance(parsed["servings"], int) or parsed["servings"] <= 0:
+        return False, f"servings must be positive int: {parsed['servings']!r}"
+    ings = parsed["ingredients"]
+    if not isinstance(ings, list) or len(ings) < 2:
+        return False, f"ingredients must be array of >=2: got {ings!r}"
+    for i, ing in enumerate(ings):
+        if not isinstance(ing, dict):
+            return False, f"ingredient[{i}] is not an object: {ing!r}"
+        ing_d = cast(dict[str, Any], ing)
+        item_val = ing_d.get("item")
+        qty_val = ing_d.get("quantity")
+        if item_val is None or qty_val is None:
+            return False, f"ingredient[{i}] missing item/quantity: {ing!r}"
+        if not isinstance(item_val, str) or not isinstance(qty_val, str):
+            return False, f"ingredient[{i}] fields must be strings: {ing!r}"
+    steps = parsed["steps"]
+    if not isinstance(steps, list) or len(steps) < 2:
+        return False, f"steps must be array of >=2 strings: got {steps!r}"
+    if not all(isinstance(s, str) and s for s in steps):
+        return False, "all steps must be non-empty strings"
+    pt = parsed["prep_time_minutes"]
+    if not isinstance(pt, int) or pt <= 0:
+        return False, f"prep_time_minutes must be positive int: {pt!r}"
+    return True, f"recipe '{parsed['name']}' with {len(ings)} ingredients, {len(steps)} steps"
+
+
+# ---- Test 4: Tool call -> structured product comparison (after_tools) ----
+
+_SHOP_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "search_products",
+            "description": "Search a product catalogue by keyword.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "query": {"type": "string"},
+                },
+                "required": ["query"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_product_details",
+            "description": "Get detailed specs for a product by ID.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "product_id": {"type": "string"},
+                },
+                "required": ["product_id"],
+            },
+        },
+    },
+]
+
+_SHOP_SEARCH_RESULT = {
+    "results": [
+        {"product_id": "LAP-001", "title": "AeroBook 13 Pro",       "price": 1399.0, "rating": 4.7},
+        {"product_id": "LAP-002", "title": "QuantumSlim 14",        "price": 1199.0, "rating": 4.4},
+        {"product_id": "LAP-003", "title": "NimbusWork Ultra 15",   "price":  999.0, "rating": 4.2},
+    ],
+}
+_SHOP_PRODUCT_DETAILS = {
+    "LAP-001": {
+        "product_id": "LAP-001",
+        "title": "AeroBook 13 Pro",
+        "cpu": "M-series 10-core",
+        "ram_gb": 16,
+        "storage_gb": 512,
+        "battery_hours": 18,
+        "weight_kg": 1.24,
+        "price": 1399.0,
+    },
+    "LAP-002": {
+        "product_id": "LAP-002",
+        "title": "QuantumSlim 14",
+        "cpu": "Core i7 12-core",
+        "ram_gb": 16,
+        "storage_gb": 512,
+        "battery_hours": 12,
+        "weight_kg": 1.35,
+        "price": 1199.0,
+    },
+    "LAP-003": {
+        "product_id": "LAP-003",
+        "title": "NimbusWork Ultra 15",
+        "cpu": "Ryzen 7 8-core",
+        "ram_gb": 16,
+        "storage_gb": 1024,
+        "battery_hours": 10,
+        "weight_kg": 1.70,
+        "price": 999.0,
+    },
+}
+
+
+def _shop_details_mock(args):
+    pid = args.get("product_id", "")
+    if pid in _SHOP_PRODUCT_DETAILS:
+        return json.dumps(_SHOP_PRODUCT_DETAILS[pid])
+    return json.dumps({"error": f"unknown product_id: {pid}"})
+
+
+_SHOP_COMPARISON_SCHEMA = {
+    "type": "json_schema",
+    "json_schema": {
+        "name": "laptop_comparison",
+        "strict": True,
+        "schema": {
+            "type": "object",
+            "additionalProperties": False,
+            "properties": {
+                "recommendation": {"type": "string"},
+                "ranked_candidates": {
+                    "type": "array",
+                    "minItems": 2,
+                    "items": {
+                        "type": "object",
+                        "additionalProperties": False,
+                        "properties": {
+                            "product_id": {"type": "string"},
+                            "title":      {"type": "string"},
+                            "score":      {"type": "number"},
+                            "reason":     {"type": "string"},
+                        },
+                        "required": ["product_id", "title", "score", "reason"],
+                    },
+                },
+            },
+            "required": ["recommendation", "ranked_candidates"],
+        },
+    },
+}
+
+SHOP_COMPARISON_TEST_CASE = {
+    "name": "Tool calls then structured laptop comparison (after_tools)",
+    "response_format": _SHOP_COMPARISON_SCHEMA,
+    "apply_stage": "after_tools",
+    "tools": _SHOP_TOOLS,
+    "mock_tool_responses": {
+        "search_products": lambda _: json.dumps(_SHOP_SEARCH_RESULT),
+        "get_product_details": _shop_details_mock,
+    },
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "I need a lightweight laptop for travel. Please search the catalogue "
+                "for 'ultraportable laptop', then fetch detailed specs for at least two "
+                "of the top candidates. Once you've gathered the data I'll ask you to "
+                "produce a structured comparison."
+            ),
+        }
+    ],
+    "followup": (
+        "Thanks. Now produce the final comparison strictly as JSON matching the "
+        "laptop_comparison schema: your single best recommendation (the product_id), "
+        "and a ranked_candidates array of at least two laptops, each with "
+        "product_id, title, a numeric score, and a short reason."
+    ),
+    "validate": lambda parsed, tcs, raw: _validate_shop_comparison(parsed, tcs),
+}
+
+
+def _validate_shop_comparison(parsed, tcs):
+    names = [tc["function"]["name"] for tc in tcs]
+    if "search_products" not in names:
+        return False, f"expected search_products tool call, got {names}"
+    if "get_product_details" not in names:
+        return False, f"expected get_product_details tool call, got {names}"
+    if "recommendation" not in parsed or not isinstance(parsed["recommendation"], str):
+        return False, f"recommendation missing or not a string: {parsed!r}"
+    cands = parsed.get("ranked_candidates")
+    if not isinstance(cands, list) or len(cands) < 2:
+        return False, f"ranked_candidates must be >=2: {cands!r}"
+    valid_ids = set(_SHOP_PRODUCT_DETAILS.keys())
+    candidate_pids: list = []
+    for i, c in enumerate(cands):
+        if not isinstance(c, dict):
+            return False, f"candidate[{i}] not an object: {c!r}"
+        c_d = cast(dict[str, Any], c)
+        pid = c_d.get("product_id")
+        title = c_d.get("title")
+        score = c_d.get("score")
+        reason = c_d.get("reason")
+        for k, v in (("product_id", pid), ("title", title),
+                     ("score", score), ("reason", reason)):
+            if v is None:
+                return False, f"candidate[{i}] missing {k}: {c!r}"
+        if pid not in valid_ids:
+            return False, f"candidate[{i}].product_id not in catalogue: {pid!r}"
+        if not isinstance(score, (int, float)):
+            return False, f"candidate[{i}].score not numeric: {score!r}"
+        candidate_pids.append(pid)
+    recommendation = parsed["recommendation"]
+    if recommendation not in valid_ids and recommendation not in candidate_pids:
+        return False, f"recommendation {recommendation!r} not in candidates"
+    return True, (
+        f"tools={names}; recommended={parsed['recommendation']}; "
+        f"{len(cands)} ranked candidates"
+    )
+
+
+# ---- Test 5: Multi-step research then structured report (after_tools) ----
+
+_RESEARCH_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_country_stats",
+            "description": "Fetch basic statistics for a country (population, GDP, capital).",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "country": {"type": "string"},
+                },
+                "required": ["country"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_climate_info",
+            "description": "Fetch climate information for a country.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "country": {"type": "string"},
+                },
+                "required": ["country"],
+            },
+        },
+    },
+]
+
+_COUNTRY_STATS = {
+    "norway": {
+        "country": "Norway",
+        "capital": "Oslo",
+        "population": 5_480_000,
+        "gdp_usd_trillion": 0.48,
+        "currency": "NOK",
+    }
+}
+_CLIMATE_INFO = {
+    "norway": {
+        "country": "Norway",
+        "climate_zone": "subarctic / temperate coastal",
+        "avg_winter_temp_c": -4.5,
+        "avg_summer_temp_c": 16.0,
+        "annual_precipitation_mm": 1400,
+    }
+}
+
+
+def _country_stats_mock(args):
+    c = args.get("country", "").strip().lower()
+    if c in _COUNTRY_STATS:
+        return json.dumps(_COUNTRY_STATS[c])
+    return json.dumps({"error": f"unknown country: {c}"})
+
+
+def _climate_info_mock(args):
+    c = args.get("country", "").strip().lower()
+    if c in _CLIMATE_INFO:
+        return json.dumps(_CLIMATE_INFO[c])
+    return json.dumps({"error": f"unknown country: {c}"})
+
+
+_RESEARCH_REPORT_SCHEMA = {
+    "type": "json_schema",
+    "json_schema": {
+        "name": "country_report",
+        "strict": True,
+        "schema": {
+            "type": "object",
+            "additionalProperties": False,
+            "properties": {
+                "country": {"type": "string"},
+                "capital": {"type": "string"},
+                "population": {"type": "integer"},
+                "climate_summary": {"type": "string"},
+                "highlights": {
+                    "type": "array",
+                    "minItems": 2,
+                    "maxItems": 5,
+                    "items": {"type": "string"},
+                },
+                "suitable_for_tourism": {"type": "boolean"},
+            },
+            "required": [
+                "country", "capital", "population",
+                "climate_summary", "highlights", "suitable_for_tourism",
+            ],
+        },
+    },
+}
+
+COUNTRY_REPORT_TEST_CASE = {
+    "name": "Research pipeline then structured country report (after_tools)",
+    "response_format": _RESEARCH_REPORT_SCHEMA,
+    "apply_stage": "after_tools",
+    "tools": _RESEARCH_TOOLS,
+    "mock_tool_responses": {
+        "get_country_stats": _country_stats_mock,
+        "get_climate_info": _climate_info_mock,
+    },
+    "messages": [
+        {
+            "role": "user",
+            "content": (
+                "I'm preparing a short briefing on Norway. Please call the "
+                "get_country_stats and get_climate_info tools to gather data "
+                "first. Afterwards I'll ask for a structured summary."
+            ),
+        }
+    ],
+    "followup": (
+        "Based on the tool results, produce the briefing as JSON matching the "
+        "country_report schema. Populate every required field and provide between "
+        "two and five highlights."
+    ),
+    "validate": lambda parsed, tcs, raw: _validate_country_report(parsed, tcs),
+}
+
+
+def _validate_country_report(parsed, tcs):
+    names = [tc["function"]["name"] for tc in tcs]
+    for required_tool in ("get_country_stats", "get_climate_info"):
+        if required_tool not in names:
+            return False, f"missing tool call {required_tool!r}: got {names}"
+    required = {
+        "country", "capital", "population",
+        "climate_summary", "highlights", "suitable_for_tourism",
+    }
+    missing = required - parsed.keys()
+    if missing:
+        return False, f"missing report fields: {missing}"
+    if "norway" not in parsed["country"].lower():
+        return False, f"country should reference Norway: {parsed['country']!r}"
+    if "oslo" not in parsed["capital"].lower():
+        return False, f"capital should be Oslo: {parsed['capital']!r}"
+    if not isinstance(parsed["population"], int) or parsed["population"] < 1_000_000:
+        return False, f"population implausible: {parsed['population']!r}"
+    if not isinstance(parsed["climate_summary"], str) or not parsed["climate_summary"]:
+        return False, "climate_summary must be a non-empty string"
+    hls = parsed["highlights"]
+    if not isinstance(hls, list) or not (2 <= len(hls) <= 5):
+        return False, f"highlights length out of range: {hls!r}"
+    if not all(isinstance(h, str) and h for h in hls):
+        return False, "each highlight must be a non-empty string"
+    if not isinstance(parsed["suitable_for_tourism"], bool):
+        return False, f"suitable_for_tourism must be bool: {parsed['suitable_for_tourism']!r}"
+    return True, (
+        f"tools={names}; report for {parsed['country']} "
+        f"(pop {parsed['population']}, {len(hls)} highlights)"
+    )
+
+
+# ---------------------------------------------------------------------------
+# All test cases
+# ---------------------------------------------------------------------------
+
+ALL_TEST_CASES = [
+    BOOK_TEST_CASE,
+    SENTIMENT_TEST_CASE,
+    PRODUCT_JSON_OBJECT_TEST_CASE,
+    RECIPE_TEST_CASE,
+    SHOP_COMPARISON_TEST_CASE,
+    COUNTRY_REPORT_TEST_CASE,
+]
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Test llama-server structured-output capability."
+    )
+    parser.add_argument("--host", default="localhost")
+    parser.add_argument("--port", default=8080, type=int)
+    parser.add_argument(
+        "--no-stream", action="store_true", help="Disable streaming mode tests"
+    )
+    parser.add_argument(
+        "--stream-only", action="store_true", help="Only run streaming mode tests"
+    )
+    parser.add_argument(
+        "--test",
+        help="Run only the test whose name contains this substring (case-insensitive)",
+    )
+    args = parser.parse_args()
+
+    url = f"http://{args.host}:{args.port}/v1/chat/completions"
+    print_info(f"Testing server at {url}")
+
+    modes: list[bool] = []
+    if not args.stream_only:
+        modes.append(False)
+    if not args.no_stream:
+        modes.append(True)
+
+    cases: list[dict] = ALL_TEST_CASES
+    if args.test:
+        name_filter = args.test.lower()
+        cases = [c for c in cases if name_filter in str(c["name"]).lower()]
+        if not cases:
+            print_fail(f"No test cases matched '{args.test}'")
+            sys.exit(1)
+
+    total = 0
+    passed = 0
+    for stream in modes:
+        for case in cases:
+            total += 1
+            if run_test(url, case, stream=stream):
+                passed += 1
+
+    color = GREEN if passed == total else RED
+    _print(f"\n{BOLD}{color}{'─' * 60}{RESET}")
+    _print(f"{BOLD}{color}  Results: {passed}/{total} passed{RESET}")
+    _print(f"{BOLD}{color}{'─' * 60}{RESET}\n")
+    sys.exit(0 if passed == total else 1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/snapdragon/adb/run-bench.sh b/scripts/snapdragon/adb/run-bench.sh
index 36c908da74e..27459df241b 100755
--- a/scripts/snapdragon/adb/run-bench.sh
+++ b/scripts/snapdragon/adb/run-bench.sh
@@ -23,10 +23,10 @@ verbose=
 [ "$V" != "" ] && verbose="GGML_HEXAGON_VERBOSE=$V" cli_opts="$cli_opts -v"
 
 profile=
-[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1" cli_opts="$cli_opts -v"
+[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF" cli_opts="$cli_opts -v"
 
 opmask=
-[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK"
+[ "$OPSTAGE" != "" ] && opmask="GGML_HEXAGON_OPSTAGE=$OPSTAGE"
 
 nhvx=
 [ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX"
diff --git a/scripts/snapdragon/adb/run-cli.sh b/scripts/snapdragon/adb/run-cli.sh
index 901d7eff13f..3f89a7777c6 100755
--- a/scripts/snapdragon/adb/run-cli.sh
+++ b/scripts/snapdragon/adb/run-cli.sh
@@ -28,10 +28,10 @@ sched=
 [ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v"
 
 profile=
-[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1" cli_opts="$cli_opts -v"
+[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF" cli_opts="$cli_opts -v"
 
 opmask=
-[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK"
+[ "$OPSTAGE" != "" ] && opmask="GGML_HEXAGON_OPSTAGE=$OPSTAGE"
 
 nhvx=
 [ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX"
@@ -54,13 +54,23 @@ opqueue=
 opflt=
 [ "$OF" != "" ] && opflt="GGML_HEXAGON_OPFILTER=$OF"
 
+vmem=
+[ "$VM" != "" ] && opflt="GGML_HEXAGON_VMEM=$VM"
+
+mbuf=
+[ "$MB" != "" ] && opflt="GGML_HEXAGON_MBUF=$MB"
+vmem=
+[ "$VM" != "" ] && vmem="GGML_HEXAGON_VMEM=$VM"
+
+mbuf=
+[ "$MB" != "" ] && mbuf="GGML_HEXAGON_MBUF=$MB"
 set -x
 
 adb $adbserial $adbhost shell " \
   cd $basedir; ulimit -c unlimited;        \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
-    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $opflt \
+    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $opflt $vmem $mbuf \
       ./$branch/bin/llama-cli --no-mmap -m $basedir/../gguf/$model \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1           \
          --ctx-size 8192 --ubatch-size 256 -fa on                  \
diff --git a/scripts/snapdragon/adb/run-completion.sh b/scripts/snapdragon/adb/run-completion.sh
index f7290825ad5..1b904c77b3f 100755
--- a/scripts/snapdragon/adb/run-completion.sh
+++ b/scripts/snapdragon/adb/run-completion.sh
@@ -28,10 +28,10 @@ sched=
 [ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v"
 
 profile=
-[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1" cli_opts="$cli_opts -v"
+[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF" cli_opts="$cli_opts -v"
 
 opmask=
-[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK"
+[ "$OPSTAGE" != "" ] && opmask="GGML_HEXAGON_OPSTAGE=$OPSTAGE"
 
 nhvx=
 [ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX"
@@ -54,15 +54,21 @@ opqueue=
 opflt=
 [ "$OF" != "" ] && opflt="GGML_HEXAGON_OPFILTER=$OF"
 
+vmem=
+[ "$VM" != "" ] && vmem="GGML_HEXAGON_VMEM=$VM"
+
+mbuf=
+[ "$MB" != "" ] && mbuf="GGML_HEXAGON_MBUF=$MB"
+
 set -x
 
 adb $adbserial $adbhost shell " \
   cd $basedir; ulimit -c unlimited;        \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
-    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $opflt \
+    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $opflt $vmem $mbuf \
       ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1                  \
          --ctx-size 8192 --ubatch-size 256 -fa on                         \
-         -ngl 99 -no-cnv --device $device $cli_opts $@                    \
+         -ngl 99 --device $device $cli_opts $@                    \
 "
diff --git a/scripts/snapdragon/adb/run-mtmd.sh b/scripts/snapdragon/adb/run-mtmd.sh
index 0c1cf892800..38467beba3d 100755
--- a/scripts/snapdragon/adb/run-mtmd.sh
+++ b/scripts/snapdragon/adb/run-mtmd.sh
@@ -37,10 +37,10 @@ sched=
 [ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v"
 
 profile=
-[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1"
+[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF"
 
 opmask=
-[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK"
+[ "$OPSTAGE" != "" ] && opmask="GGML_HEXAGON_OPSTAGE=$OPSTAGE"
 
 nhvx=
 [ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX"
diff --git a/scripts/snapdragon/adb/run-tool.sh b/scripts/snapdragon/adb/run-tool.sh
index 70ed407e87b..27cbb2b6d05 100755
--- a/scripts/snapdragon/adb/run-tool.sh
+++ b/scripts/snapdragon/adb/run-tool.sh
@@ -25,10 +25,10 @@ sched=
 [ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v"
 
 profile=
-[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1"
+[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF"
 
 opmask=
-[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK"
+[ "$OPSTAGE" != "" ] && opmask="GGML_HEXAGON_OPSTAGE=$OPSTAGE"
 
 nhvx=
 [ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX"
diff --git a/scripts/snapdragon/ggml-hexagon-profile.py b/scripts/snapdragon/ggml-hexagon-profile.py
new file mode 100755
index 00000000000..3edaacd2749
--- /dev/null
+++ b/scripts/snapdragon/ggml-hexagon-profile.py
@@ -0,0 +1,188 @@
+#!/usr/bin/env python3
+
+import sys
+import os
+import re
+import argparse
+import statistics
+import logging
+
+from collections import defaultdict
+
+# Mapping of cli-friendly names to (internal_data_key, Display Header, numeric_sort_key)
+COL_MAP = {
+    "op":         ("op",         "Op",         "op"),
+    "dims":       ("dims",       "Dims",       "dims"),
+    "dtypes":     ("dtypes",     "DTypes",     "dtypes"),
+    "count":      ("count",      "Count",      "_sort_count"),
+    "max-usec":   ("max_usec",   "Max usec",   "_sort_max_usec"),
+    "avg-usec":   ("avg_usec",   "Avg usec",   "_sort_avg_usec"),
+    "max-cycles": ("max_cycles", "Max Cycles", "_sort_max_cycles"),
+    "avg-cycles": ("avg_cycles", "Avg Cycles", "_sort_avg_cycles"),
+    "max-pmu":    ("max_pmu",    "Max PMU",    "_sort_max_pmu"),
+    "avg-pmu":    ("avg_pmu",    "Avg PMU",    "_sort_avg_pmu"),
+}
+
+op_pattern = re.compile(
+    r"profile-op\s+(?P<op_name>[A-Z_0-9]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+.*?\s+usec\s+(?P<usec>\d+)\s+cycles\s+(?P<cycles>\d+)(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?"
+)
+
+logger = logging.getLogger("ggml-hexagon-profile")
+
+
+def parse_log(file_path, pmu_index=None):
+    try:
+        if file_path != "-":
+            f = open(file_path, 'r', encoding='utf-8', errors='ignore')
+        else:
+            f = os.fdopen(0, 'r', encoding='utf-8', errors='ignore')
+    except FileNotFoundError:
+        logger.error(f"file '{file_path}' not found.")
+        sys.exit(1)
+
+    all_ops = []
+    for line in f:
+        match = op_pattern.search(line)
+        if not match: continue
+
+        pmu_raw = match.group('pmu')
+        pmu_val = None
+        if pmu_raw and pmu_index is not None:
+            try:
+                pmu_list = [int(x.strip()) for x in pmu_raw.split(',')]
+                if len(pmu_list) > pmu_index:
+                    pmu_val = pmu_list[pmu_index]
+            except (ValueError, IndexError):
+                pmu_val = None
+
+        all_ops.append({
+            'name':    match.group('op_name'),
+            'dims':    match.group('dims').strip(),
+            'types':   match.group('types').strip(),
+            'usec':    int(match.group('usec')),
+            'cycles':  int(match.group('cycles')),
+            'pmu_val': pmu_val
+        })
+
+    f.close()
+
+    return all_ops
+
+
+def generate_report(ops, top_n, width_overrides, sort_col, pmu_name=None):
+    if not ops:
+        logger.info("No valid records found.")
+        return
+
+    grouped = defaultdict(list)
+    for op in ops:
+        key = (op['name'], op['dims'], op['types'])
+        grouped[key].append(op)
+
+    group_stats = []
+    for (name, dims, types), group_ops in grouped.items():
+        usecs = [o['usec'] for o in group_ops]
+        cycles = [o['cycles'] for o in group_ops]
+        pmu_vals = [o['pmu_val'] for o in group_ops if o['pmu_val'] is not None]
+
+        group_stats.append({
+            'op':               name,
+            'dims':             dims,
+            'dtypes':           types,
+            'count':            str(len(group_ops)),
+            'max_usec':         str(max(usecs)),
+            'avg_usec':         f"{statistics.mean(usecs):.2f}",
+            'max_cycles':       str(max(cycles)),
+            'avg_cycles':       f"{statistics.mean(cycles):.2f}",
+            'max_pmu':          str(max(pmu_vals)) if pmu_vals else "0",
+            'avg_pmu':          f"{statistics.mean(pmu_vals):.2f}" if pmu_vals else "0.00",
+            # Numeric values for accurate sorting
+            '_sort_count':      len(group_ops),
+            '_sort_max_usec':   max(usecs),
+            '_sort_avg_usec':   statistics.mean(usecs),
+            '_sort_max_cycles': max(cycles),
+            '_sort_avg_cycles': statistics.mean(cycles),
+            '_sort_max_pmu':    max(pmu_vals) if pmu_vals else 0,
+            '_sort_avg_pmu':    statistics.mean(pmu_vals) if pmu_vals else 0
+        })
+
+    # Sorting logic
+    actual_sort_key = COL_MAP[sort_col][2]
+    # We sort numeric fields descending, strings (op/dims) ascending
+    is_numeric    = actual_sort_key.startswith("_") or actual_sort_key == "count"
+    sorted_groups = sorted(group_stats, key=lambda x: x[actual_sort_key], reverse=is_numeric)[:top_n]
+
+    # Define initial column order
+    active_cols = ["op", "dims", "dtypes"]
+    if pmu_name:
+        active_cols += ["max-pmu", "avg-pmu"]
+    active_cols += ["max-usec", "avg-usec", "max-cycles", "avg-cycles", "count"]
+
+    final_headers, final_keys, final_widths = [], [], []
+
+    for col_name in active_cols:
+        data_key, header_text, _ = COL_MAP[col_name]
+        if "pmu" in col_name and pmu_name:
+            header_text = header_text.replace("PMU", pmu_name)
+
+        natural_width = max([len(row[data_key]) for row in sorted_groups] + [len(header_text)])
+        target_width  = width_overrides.get(col_name, natural_width)
+
+        if target_width == 0:
+            continue
+
+        final_headers.append(header_text)
+        final_keys.append(data_key)
+        final_widths.append(target_width)
+
+    # Print Report
+    logger.info(f"\n# Profile Report (Top {top_n} Ops sorted by {sort_col})\n")
+    header_line = "| " + " | ".join(f"{h:<{final_widths[i]}}" for i, h in enumerate(final_headers)) + " |"
+    sep_line    = "| " + " | ".join("-" * final_widths[i] for i in range(len(final_headers))) + " |"
+    logger.info(header_line)
+    logger.info(sep_line)
+
+    for group in sorted_groups:
+        row_vals = []
+        for i, key in enumerate(final_keys):
+            val = group[key]
+            if len(val) > final_widths[i]:
+                val = val[:final_widths[i] - 3] + "..."
+            row_vals.append(f"{val:<{final_widths[i]}}")
+        logger.info("| " + " | ".join(row_vals) + " |")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Post-process Op profile info.")
+    parser.add_argument("logfile")
+    parser.add_argument("-n", "--top", type=int, default=100)
+    parser.add_argument("--sort", type=str, default="max-usec", choices=list(COL_MAP.keys()))
+    parser.add_argument("--pmu-index", type=int)
+    parser.add_argument("--pmu-name", type=str)
+    parser.add_argument("--width", action='append', default=['dims:40'], help="Override column width, e.g. --width dims:50")
+
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format='%(message)s')
+
+    # Sort validation: can't sort by PMU if index isn't provided
+    if "pmu" in args.sort and args.pmu_index is None:
+        logger.error(f"Cannot sort by '{args.sort}' without --pmu-index.")
+        sys.exit(1)
+
+    overrides = {}
+    if args.width:
+        for w in args.width:
+            try:
+                name, val = w.split(':')
+                overrides[name.lower()] = int(val)
+            except ValueError:
+                logger.warning(f"Invalid width format '{w}'")
+
+    final_pmu_name = (args.pmu_name or f"#{args.pmu_index}") if args.pmu_index is not None else None
+    ops = parse_log(args.logfile, pmu_index=args.pmu_index)
+    generate_report(ops, args.top, overrides, args.sort, pmu_name=final_pmu_name)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/snapdragon/qdc/readme.md b/scripts/snapdragon/qdc/readme.md
deleted file mode 100644
index b92cf243aaa..00000000000
--- a/scripts/snapdragon/qdc/readme.md
+++ /dev/null
@@ -1 +0,0 @@
-This directory includes pytest based scripts for running CI jobs on Qualcomm Device Cloud (QDC).
diff --git a/scripts/snapdragon/qdc/requirements.txt b/scripts/snapdragon/qdc/requirements.txt
index f04bd682ea0..5e0f85917e3 100644
--- a/scripts/snapdragon/qdc/requirements.txt
+++ b/scripts/snapdragon/qdc/requirements.txt
@@ -8,12 +8,9 @@ iniconfig==2.1.0
 outcome==1.3.0.post0
 packaging==25.0
 pluggy==1.6.0
-Pygments==2.19.2
 PySocks==1.7.1
 pytest==8.4.2
-pytest-dependency==0.6.0
 selenium==4.36.0
-setuptools==80.9.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 tomli==2.3.0
diff --git a/scripts/snapdragon/qdc/run_qdc_jobs.py b/scripts/snapdragon/qdc/run_qdc_jobs.py
new file mode 100644
index 00000000000..f1b0453eec4
--- /dev/null
+++ b/scripts/snapdragon/qdc/run_qdc_jobs.py
@@ -0,0 +1,684 @@
+"""Run llama.cpp Hexagon tests in a single QDC job.
+
+Bundles test scripts into one artifact and submits a single QDC job:
+
+  1. run_bench_tests_posix.py — llama-cli and llama-bench on CPU / GPU / NPU
+                                (from scripts/snapdragon/qdc/)
+
+Results are written to $GITHUB_STEP_SUMMARY when set (GitHub Actions).
+
+Prerequisites:
+  pip install /path/to/qualcomm_device_cloud_sdk*.whl
+
+Platform is inferred from --device:
+  android  Appium + pytest (Android phones: SM8750 / SM8650 / SM8850)
+  linux    BASH (Linux IoT: QCS9075M)
+
+Required environment variables:
+  QDC_API_KEY   API key from QDC UI -> Users -> Settings -> API Keys
+
+Usage:
+  python run_qdc_jobs.py \\
+      --pkg-dir    pkg-snapdragon/llama.cpp \\
+      --model-url  https://.../Llama-3.2-1B-Instruct-Q4_0.gguf \\
+      --device     SM8750
+"""
+
+from __future__ import annotations
+
+import argparse
+import enum
+import logging
+import os
+import re
+import shutil
+import sys
+import tempfile
+import time
+import urllib.request
+import xml.etree.ElementTree as ET
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Callable
+
+from qualcomm_device_cloud_sdk.api import qdc_api
+from qualcomm_device_cloud_sdk.logging import configure_logging
+from qualcomm_device_cloud_sdk.models import (
+    ArtifactType,
+    JobMode,
+    JobState,
+    JobSubmissionParameter,
+    JobType,
+    TestFramework,
+)
+
+# configure_logging only sets up the SDK logger; basicConfig is needed for
+# our own log.info to reach stdout.
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(name)s %(levelname)s - %(message)s",
+    handlers=[logging.StreamHandler()],
+)
+configure_logging(level=logging.INFO, handlers=[logging.StreamHandler()])
+# Silence per-poll GET/status spam from the SDK and its HTTP client.
+logging.getLogger("qualcomm_device_cloud").setLevel(logging.WARNING)
+logging.getLogger("httpx").setLevel(logging.WARNING)
+logging.getLogger("httpcore").setLevel(logging.WARNING)
+log = logging.getLogger(__name__)
+
+POLL_INTERVAL        = 30
+JOB_TIMEOUT          = 3600
+LOG_UPLOAD_TIMEOUT   = 600
+CAPACITY_TIMEOUT     = 1800
+CAPACITY_POLL        = 60
+MAX_CONCURRENT_JOBS  = 5
+DEFAULT_RETRIES      = 0
+RETRY_DELAY          = 300
+TERMINAL_STATES     = {JobState.COMPLETED, JobState.CANCELED}
+NON_TERMINAL_STATES = {JobState.DISPATCHED, JobState.RUNNING, JobState.SETUP, JobState.SUBMITTED}
+
+
+class DeviceUnavailableError(Exception):
+    """Raised when the QDC device resource is not available (retryable)."""
+
+
+_SCRIPTS_DIR = Path(__file__).parent
+_TESTS_DIR = _SCRIPTS_DIR / "tests"
+
+# --- Shared test assets -------------------------------------------------------
+_UTILS = _TESTS_DIR / "utils.py"
+_CONFTEST = _TESTS_DIR / "conftest.py"
+_PYTEST_LINE_RE = re.compile(
+    r"(?:[\w/]+\.py::)?(?:\w+::)?([\w\[\].-]+)\s+(PASSED|FAILED|ERROR|SKIPPED)"
+)
+_EXCLUDED_LOGS = {
+    "qdc_android_whole_host-000.log",
+    "qdc_kernel_host-000.log",
+    "qdc_LE_whole_host-000.log",
+    "qdc_LE_kernel_host-000.log",
+    "script.log",
+}
+_NON_TERMINAL_STATE_VALUES = {s.value for s in NON_TERMINAL_STATES}
+
+# --- Android (Appium + pytest) assets ----------------------------------------
+_RUN_BENCH = _TESTS_DIR / "run_bench_tests_posix.py"
+_RUN_BACKEND_OPS = _TESTS_DIR / "run_backend_ops_posix.py"
+_REQUIREMENTS = _SCRIPTS_DIR / "requirements.txt"
+_UPSTREAM_ADB_SCRIPTS = (
+    "https://raw.githubusercontent.com/ggml-org/llama.cpp/master/scripts/snapdragon/adb"
+)
+_ADB_SCRIPT_NAMES = [
+    "run-bench.sh",
+    "run-cli.sh",
+    "run-completion.sh",
+    "run-tool.sh",
+]
+
+# --- Linux (BASH) assets ------------------------------------------------------
+_RUN_LINUX_TEMPLATE = _TESTS_DIR / "linux" / "run_linux.sh"
+_LINUX_ENTRY_SCRIPT = "/bin/bash /data/local/tmp/TestContent/run_linux.sh"
+
+# =============================================================================
+# Artifact builders (per platform)
+# =============================================================================
+
+
+@dataclass
+class JobResult:
+    passed: bool
+    tests: dict[str, bool] = field(default_factory=dict)
+    raw_logs: dict[str, str] = field(default_factory=dict)
+    failure_details: dict[str, str] = field(default_factory=dict)
+
+
+def _write_lf(path: Path, content: str) -> None:
+    """Write text with LF line endings (required by /bin/bash on Linux)."""
+    with open(path, "w", encoding="utf-8", newline="\n") as f:
+        f.write(content)
+
+
+def _build_android_artifact(
+    pkg_dir: Path,
+    stage_dir: Path,
+    test_mode: str,
+    model_url: str | None,
+) -> Path:
+    """Android zip (Appium/pytest). Extracted by QDC under /qdc/appium/.
+
+    Zip structure:
+      llama_cpp_bundle/            installed package (adb pushed to /data/local/tmp/)
+      run-{bench,cli,completion,tool}.sh  upstream adb wrappers (patched)
+      tests/
+        utils.py                   shared adb helpers
+        conftest.py                Appium pytest fixtures
+        test_bench_posix.py        bench + cli tests (for --test bench or all)
+        test_backend_ops_posix.py  test-backend-ops on HTP0
+      requirements.txt
+      pytest.ini                   addopts = --junitxml=results.xml
+    """
+    bundle_dir = stage_dir / "llama_cpp_bundle"
+    shutil.copytree(pkg_dir, bundle_dir)
+
+    # Download upstream adb scripts so they land at /qdc/appium/ on the QDC
+    # runner. They wrap `adb shell` internally. Patch in `chmod +x bin/* lib/*`
+    # right after `cd $basedir` so device binaries are executable.
+    for name in _ADB_SCRIPT_NAMES:
+        url = f"{_UPSTREAM_ADB_SCRIPTS}/{name}"
+        dest = stage_dir / name
+        log.info("Downloading %s", url)
+        urllib.request.urlretrieve(url, str(dest))
+        content = dest.read_text()
+        content = content.replace(
+            "cd $basedir;",
+            "cd $basedir; chmod +x bin/* lib/* 2>/dev/null;",
+        )
+        dest.write_text(content)
+        dest.chmod(0o755)
+
+    tests_dir = stage_dir / "tests"
+    tests_dir.mkdir()
+
+    shutil.copy(_UTILS, tests_dir / "utils.py")
+    shutil.copy(_CONFTEST, tests_dir / "conftest.py")
+
+    if test_mode in ("bench", "all"):
+        assert model_url is not None
+        (tests_dir / "test_bench_posix.py").write_text(
+            _RUN_BENCH.read_text().replace("<<MODEL_URL>>", model_url)
+        )
+    if test_mode in ("backend-ops", "all"):
+        shutil.copy(_RUN_BACKEND_OPS, tests_dir / "test_backend_ops_posix.py")
+
+    shutil.copy(_REQUIREMENTS, stage_dir / "requirements.txt")
+    (stage_dir / "pytest.ini").write_text(
+        "[pytest]\naddopts = --junitxml=results.xml\n"
+    )
+
+    zip_base = str(stage_dir / "artifact")
+    shutil.make_archive(zip_base, "zip", stage_dir)
+    return Path(f"{zip_base}.zip")
+
+
+def _build_linux_artifact(
+    pkg_dir: Path,
+    stage_dir: Path,
+    test_mode: str,
+    model_url: str | None,
+) -> Path:
+    """Linux IoT zip (BASH framework). Extracted by QDC to /data/local/tmp/TestContent/.
+
+    Zip structure:
+      run_linux.sh               entry script (placeholder-substituted, LF line endings)
+      llama_cpp_bundle/          installed package
+    """
+    bundle_dir = stage_dir / "llama_cpp_bundle"
+    shutil.copytree(pkg_dir, bundle_dir)
+
+    template = _RUN_LINUX_TEMPLATE.read_text(encoding="utf-8")
+    rendered = template.replace("{MODEL_URL}", model_url or "").replace(
+        "{TEST_MODE}", test_mode
+    )
+    script_path = stage_dir / "run_linux.sh"
+    _write_lf(script_path, rendered)
+    script_path.chmod(0o755)
+
+    zip_base = str(stage_dir / "artifact")
+    shutil.make_archive(zip_base, "zip", stage_dir)
+    return Path(f"{zip_base}.zip")
+
+
+# =============================================================================
+# Platform enum + strategy table
+# =============================================================================
+
+
+class Platform(enum.Enum):
+    ANDROID = "android"
+    LINUX = "linux"
+
+
+@dataclass(frozen=True)
+class PlatformSpec:
+    test_framework: TestFramework
+    entry_script: str | None
+    build_artifact: Callable[[Path, Path, str, str | None], Path]
+    job_name_fmt: str
+
+
+PLATFORM_SPECS: dict[Platform, PlatformSpec] = {
+    Platform.ANDROID: PlatformSpec(
+        test_framework=TestFramework.APPIUM,
+        entry_script=None,
+        build_artifact=_build_android_artifact,
+        job_name_fmt="{base}",
+    ),
+    Platform.LINUX: PlatformSpec(
+        test_framework=TestFramework.BASH,
+        entry_script=_LINUX_ENTRY_SCRIPT,
+        build_artifact=_build_linux_artifact,
+        job_name_fmt="{base} (Linux)",
+    ),
+}
+
+DEVICE_PLATFORM: dict[str, Platform] = {
+    "SM8750": Platform.ANDROID,
+    "SM8650": Platform.ANDROID,
+    "SM8850": Platform.ANDROID,
+    "QCS9075M": Platform.LINUX,
+}
+
+
+# =============================================================================
+# Shared QDC job plumbing
+# =============================================================================
+
+
+def wait_for_job(client, job_id: str, timeout: int) -> str:
+    elapsed = 0
+    last_state = None
+    consecutive_errors = 0
+    max_consecutive_errors = 5
+    while elapsed < timeout:
+        try:
+            raw = qdc_api.get_job_status(client, job_id)
+            consecutive_errors = 0
+        except Exception as e:
+            consecutive_errors += 1
+            log.warning(
+                "Transient error polling job %s (%d/%d): %s",
+                job_id,
+                consecutive_errors,
+                max_consecutive_errors,
+                e,
+            )
+            if consecutive_errors >= max_consecutive_errors:
+                raise
+            time.sleep(POLL_INTERVAL)
+            elapsed += POLL_INTERVAL
+            continue
+        try:
+            status = JobState(raw)
+        except ValueError:
+            status = raw
+        if status in TERMINAL_STATES:
+            return raw.lower()
+        if raw != last_state:
+            log.info("Job %s: %s", job_id, raw)
+            last_state = raw
+        time.sleep(POLL_INTERVAL)
+        elapsed += POLL_INTERVAL
+    # Abort to free the QDC concurrency slot instead of leaking it.
+    try:
+        qdc_api.abort_job(client, job_id)
+        log.warning("Aborted job %s after timeout to free concurrency slot", job_id)
+    except Exception as e:
+        log.warning("Failed to abort job %s: %s", job_id, e)
+    raise TimeoutError(f"Job {job_id} did not finish within {timeout}s")
+
+
+def wait_for_log_upload(client, job_id: str) -> None:
+    elapsed = 0
+    while elapsed <= LOG_UPLOAD_TIMEOUT:
+        try:
+            status = (qdc_api.get_job_log_upload_status(client, job_id) or "").lower()
+        except Exception as e:
+            log.warning("get_job_log_upload_status failed: %s — will retry", e)
+            status = ""
+        if status in {"completed", "failed"}:
+            return
+        log.info("Waiting for log upload (status=%s) ...", status)
+        time.sleep(POLL_INTERVAL)
+        elapsed += POLL_INTERVAL
+    log.warning("Timed out waiting for log upload after %ds", LOG_UPLOAD_TIMEOUT)
+
+
+def wait_for_capacity(client, max_jobs: int = MAX_CONCURRENT_JOBS) -> None:
+    """Block until the user's active (non-terminal) QDC job count is below max_jobs."""
+    elapsed = 0
+    while elapsed < CAPACITY_TIMEOUT:
+        jobs_page = qdc_api.get_jobs_list(client, page_number=0, page_size=50)
+        if jobs_page is None:
+            log.warning(
+                "Could not retrieve job list; proceeding without capacity check"
+            )
+            return
+        items = getattr(jobs_page, "data", []) or []
+        active = sum(
+            1 for j in items if getattr(j, "state", None) in _NON_TERMINAL_STATE_VALUES
+        )
+        if active < max_jobs:
+            log.info("Active QDC jobs: %d / %d — proceeding", active, max_jobs)
+            return
+        log.info(
+            "Active QDC jobs: %d / %d — waiting %ds ...",
+            active,
+            max_jobs,
+            CAPACITY_POLL,
+        )
+        time.sleep(CAPACITY_POLL)
+        elapsed += CAPACITY_POLL
+    raise TimeoutError(
+        f"Capacity wait timed out after {CAPACITY_TIMEOUT}s"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Log parsing helpers
+# ---------------------------------------------------------------------------
+
+
+def _parse_junit_xml(content: str) -> tuple[dict[str, bool], dict[str, str]]:
+    try:
+        root = ET.fromstring(content)
+    except ET.ParseError:
+        return {}, {}
+    results: dict[str, bool] = {}
+    failures: dict[str, str] = {}
+    for tc in root.iter("testcase"):
+        name = tc.get("name", "")
+        if classname := tc.get("classname", ""):
+            name = f"{classname}.{name}"
+        failure_el = tc.find("failure")
+        if failure_el is None:
+            failure_el = tc.find("error")
+        results[name] = failure_el is None
+        if failure_el is not None:
+            parts = [failure_el.get("message", ""), failure_el.text or ""]
+            failures[name] = "\n".join(p for p in parts if p).strip()
+    return results, failures
+
+
+def _parse_pytest_output(content: str) -> dict[str, bool]:
+    results: dict[str, bool] = {}
+    for m in _PYTEST_LINE_RE.finditer(content):
+        results[m.group(1)] = m.group(2) == "PASSED"
+    return results
+
+
+def fetch_logs_and_parse_tests(
+    client, job_id: str, max_retries: int = 5, retry_delay: int = 30
+) -> tuple[dict[str, bool], dict[str, str], dict[str, str]]:
+    """Returns (test_results, raw_logs, failure_details)."""
+    log_files = None
+    for attempt in range(1, max_retries + 1):
+        try:
+            log_files = qdc_api.get_job_log_files(client, job_id)
+            break
+        except Exception as e:
+            if attempt < max_retries:
+                log.warning(
+                    "get_job_log_files failed (attempt %d/%d): %s — retrying in %ds",
+                    attempt, max_retries, e, retry_delay,
+                )
+                time.sleep(retry_delay)
+            else:
+                log.error(
+                    "get_job_log_files failed after %d attempts: %s", max_retries, e
+                )
+                return {}, {}, {}
+    if not log_files:
+        log.warning("No log files returned for job %s", job_id)
+        return {}, {}, {}
+
+    test_results: dict[str, bool] = {}
+    pytest_fallback: dict[str, bool] = {}
+    raw_logs: dict[str, str] = {}
+    failure_details: dict[str, str] = {}
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        for lf in log_files:
+            zip_path = os.path.join(tmpdir, "log.zip")
+            log.info("Downloading log file: %s", lf.filename)
+            qdc_api.download_job_log_files(client, lf.filename, zip_path)
+            try:
+                shutil.unpack_archive(zip_path, tmpdir, "zip")
+            except Exception as e:
+                log.warning("Could not unpack %s as zip: %s", lf.filename, e)
+
+        for root_dir, _, files in os.walk(tmpdir):
+            for fname in sorted(files):
+                fpath = os.path.join(root_dir, fname)
+                content = Path(fpath).read_text(errors="replace")
+                if fname.endswith(".xml"):
+                    results, failures = _parse_junit_xml(content)
+                    test_results.update(results)
+                    failure_details.update(failures)
+                elif fname.endswith(".log"):
+                    if fname in _EXCLUDED_LOGS:
+                        continue
+                    log.info("--- %s ---\n%s", fname, content)
+                    raw_logs[fname] = content
+                    pytest_fallback.update(_parse_pytest_output(content))
+
+    return (
+        (test_results if test_results else pytest_fallback),
+        raw_logs,
+        failure_details,
+    )
+
+
+def write_summary(result: JobResult, title: str = "QDC Test Results") -> None:
+    summary_path = os.environ.get("GITHUB_STEP_SUMMARY")
+    if not summary_path:
+        return
+
+    icon = "✅" if result.passed else "❌"
+
+    lines = [
+        f"## {title}\n",
+        f"Overall: {icon} {'PASSED' if result.passed else 'FAILED'}\n",
+    ]
+    reportable = {n: ok for n, ok in result.tests.items() if "test_install" not in n}
+    if reportable:
+        lines += ["| Test | Result |", "| ---- | ------ |"]
+        for name, ok in reportable.items():
+            lines.append(f"| `{name}` | {'✅' if ok else '❌'} |")
+        passed_n = sum(1 for v in reportable.values() if v)
+        failed_n = sum(1 for v in reportable.values() if not v)
+        lines += ["", f"**{passed_n} passed, {failed_n} failed**"]
+    else:
+        lines.append("_No per-test data available._")
+
+    failed_names = [n for n, ok in reportable.items() if not ok]
+    if failed_names:
+        lines += ["", "### Failures"]
+        for name in failed_names:
+            detail = result.failure_details.get(name)
+            if detail:
+                lines += [
+                    f"<details><summary><code>{name}</code></summary>",
+                    "",
+                    "```",
+                    detail,
+                    "```",
+                    "",
+                    "</details>",
+                ]
+
+    if result.raw_logs:
+        lines += ["", "### Raw Logs"]
+        for fname, content in sorted(result.raw_logs.items()):
+            lines += [
+                f"<details><summary>{fname}</summary>",
+                "",
+                "```",
+                content.rstrip(),
+                "```",
+                "",
+                "</details>",
+            ]
+
+    with open(summary_path, "a") as f:
+        f.write("\n".join(lines) + "\n")
+
+
+# =============================================================================
+# CLI + main
+# =============================================================================
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    p.add_argument("--pkg-dir", required=True, type=Path,
+                   help="Installed llama.cpp package directory (contains bin/ and lib/)")
+    p.add_argument("--model-url",
+                   help="Direct URL to the GGUF model file (required for --test bench)")
+    p.add_argument("--device", required=True,
+                   help="QDC chipset name, e.g. SM8750")
+    p.add_argument("--test", choices=["bench", "backend-ops", "all"], default="bench",
+                   help="Test suite to run (default: bench)")
+    p.add_argument("--job-timeout", type=int, default=JOB_TIMEOUT, metavar="SECONDS",
+                   help=f"Max seconds to wait for job completion (default: {JOB_TIMEOUT})")
+    p.add_argument("--retries", type=int, default=DEFAULT_RETRIES, metavar="N",
+                   help="Number of retries when device is unavailable (default: 0)")
+    p.add_argument("--retry-delay", type=int, default=RETRY_DELAY, metavar="SECONDS",
+                   help=f"Seconds to wait between retries (default: {RETRY_DELAY})")
+    args = p.parse_args()
+    if args.test in ("bench", "all") and not args.model_url:
+        p.error("--model-url is required when --test bench or --test all")
+    return args
+
+
+def _submit_and_run_job(client, args, spec, target_id, artifact_id) -> JobResult:
+    """Submit a QDC job and wait for results.
+
+    Raises DeviceUnavailableError for transient device/resource issues that
+    are worth retrying. Returns JobResult for definitive outcomes (pass or
+    test failure).
+    """
+    try:
+        wait_for_capacity(client)
+    except TimeoutError:
+        raise DeviceUnavailableError("Capacity wait timed out — device busy")
+
+    job_name = spec.job_name_fmt.format(base="llama.cpp Hexagon tests")
+
+    job_id = qdc_api.submit_job(
+        public_api_client=client,
+        target_id=target_id,
+        job_name=job_name,
+        external_job_id=None,
+        job_type=JobType.AUTOMATED,
+        job_mode=JobMode.APPLICATION,
+        timeout=max(1, args.job_timeout // 60),
+        test_framework=spec.test_framework,
+        entry_script=spec.entry_script,
+        job_artifacts=[artifact_id],
+        monkey_events=None,
+        monkey_session_timeout=None,
+        job_parameters=[JobSubmissionParameter.WIFIENABLED],
+    )
+    if job_id is None:
+        raise DeviceUnavailableError("Job submission failed — device may be unavailable")
+    log.info("Job submitted: %s  (device=%s)", job_id, args.device)
+
+    try:
+        job_status = wait_for_job(client, job_id, timeout=args.job_timeout)
+    except TimeoutError as e:
+        raise DeviceUnavailableError(str(e))
+    log.info("Job %s finished: %s", job_id, job_status)
+
+    wait_for_log_upload(client, job_id)
+    tests, raw_logs, failure_details = fetch_logs_and_parse_tests(client, job_id)
+
+    job_ok = job_status == JobState.COMPLETED.value.lower()
+
+    if not job_ok and not tests:
+        raise DeviceUnavailableError(
+            f"Job did not complete (status={job_status}) and produced no test results"
+        )
+
+    passed = job_ok and all(tests.values()) if tests else job_ok
+    if spec.test_framework == TestFramework.BASH and not tests:
+        log.error("No test results recovered (state=%s). Script likely never ran.", job_status)
+        passed = False
+    if not passed:
+        log.error("Job did not complete successfully or tests failed (status=%s)", job_status)
+
+    return JobResult(passed=passed, tests=tests, raw_logs=raw_logs, failure_details=failure_details)
+
+
+def main() -> int:
+    args = parse_args()
+
+    platform = DEVICE_PLATFORM.get(args.device)
+    if platform is None:
+        log.error(
+            "Unknown device %r. Known: %s",
+            args.device, ", ".join(sorted(DEVICE_PLATFORM.keys())),
+        )
+        return 1
+    spec = PLATFORM_SPECS[platform]
+
+    api_key = os.environ.get("QDC_API_KEY")
+    if not api_key:
+        log.error("QDC_API_KEY environment variable must be set")
+        return 1
+    if not args.pkg_dir.is_dir():
+        log.error("--pkg-dir %s does not exist", args.pkg_dir)
+        return 1
+
+    client = qdc_api.get_public_api_client_using_api_key(
+        api_key_header=api_key,
+        app_name_header="llama-cpp-ci",
+        on_behalf_of_header="llama-cpp-ci",
+        client_type_header="Python",
+    )
+
+    target_id = qdc_api.get_target_id(client, args.device)
+    if target_id is None:
+        log.error("Could not find QDC target for device %r", args.device)
+        return 1
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        log.info("Building %s artifact (test=%s) ...", platform.value, args.test)
+        zip_path = spec.build_artifact(
+            args.pkg_dir, Path(tmpdir), args.test, args.model_url
+        )
+        log.info("Uploading artifact (%d MB) ...", zip_path.stat().st_size // 1_000_000)
+        artifact_id = qdc_api.upload_file(client, str(zip_path), ArtifactType.TESTSCRIPT)
+
+    if artifact_id is None:
+        log.error("Artifact upload failed")
+        return 1
+
+    max_attempts = 1 + args.retries
+    for attempt in range(1, max_attempts + 1):
+        try:
+            result = _submit_and_run_job(client, args, spec, target_id, artifact_id)
+            break
+        except DeviceUnavailableError as e:
+            if attempt < max_attempts:
+                log.warning(
+                    "Attempt %d/%d failed (device unavailable): %s — retrying in %ds",
+                    attempt, max_attempts, e, args.retry_delay,
+                )
+                time.sleep(args.retry_delay)
+            else:
+                log.error(
+                    "Attempt %d/%d failed (device unavailable): %s — no retries left",
+                    attempt, max_attempts, e,
+                )
+                write_summary(
+                    JobResult(passed=False, tests={}),
+                    title=f"QDC Device Unavailable ({args.device})",
+                )
+                return 1
+    else:
+        return 1
+
+    if args.test == "backend-ops":
+        title = f"Backend Ops — HTP0 ({args.device})"
+    elif args.test == "all":
+        title = f"QDC Tests ({args.device})"
+    else:
+        title = f"QDC Test Results ({args.device})"
+    write_summary(result, title=title)
+
+    return 0 if result.passed else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/snapdragon/qdc/tests/conftest.py b/scripts/snapdragon/qdc/tests/conftest.py
new file mode 100644
index 00000000000..0fc5b3e5fa7
--- /dev/null
+++ b/scripts/snapdragon/qdc/tests/conftest.py
@@ -0,0 +1,20 @@
+"""Shared pytest fixtures for QDC on-device test runners."""
+
+import os
+
+import pytest
+from appium import webdriver
+
+from utils import options, write_qdc_log
+
+
+@pytest.fixture(scope="session", autouse=True)
+def driver():
+    return webdriver.Remote(command_executor="http://127.0.0.1:4723/wd/hub", options=options)
+
+
+def pytest_sessionfinish(session, exitstatus):
+    xml_path = getattr(session.config.option, "xmlpath", None) or "results.xml"
+    if os.path.exists(xml_path):
+        with open(xml_path) as f:
+            write_qdc_log("results.xml", f.read())
diff --git a/scripts/snapdragon/qdc/tests/linux/run_linux.sh b/scripts/snapdragon/qdc/tests/linux/run_linux.sh
new file mode 100644
index 00000000000..a6abf8ec301
--- /dev/null
+++ b/scripts/snapdragon/qdc/tests/linux/run_linux.sh
@@ -0,0 +1,232 @@
+#!/bin/bash
+# llama.cpp Hexagon test entry script for QDC Linux IoT (BASH framework).
+#
+# Placeholders substituted by run_qdc_jobs.py (--platform linux) before upload:
+#   {MODEL_URL}   direct URL to a .gguf model file
+#   {TEST_MODE}   bench | backend-ops | all
+#
+# QDC extracts the artifact zip to /data/local/tmp/TestContent/ and invokes
+# this script via: /bin/bash /data/local/tmp/TestContent/run_linux.sh
+# Any files written under /data/local/tmp/QDC_logs/ are auto-uploaded.
+
+set +e
+umask 022
+
+LOG_DIR=/data/local/tmp/QDC_logs
+BUNDLE_DIR=/data/local/tmp/TestContent/llama_cpp_bundle
+MODEL_DIR=/data/local/tmp/gguf
+MODEL_PATH="$MODEL_DIR/model.gguf"
+RESULTS_XML="$LOG_DIR/results.xml"
+
+mkdir -p "$LOG_DIR" "$MODEL_DIR"
+# Redirect all parent-shell output to script.log so QDC auto-uploads it;
+# per-case runs still capture their own stdout/stderr into dedicated logs.
+exec > "$LOG_DIR/script.log" 2>&1
+
+echo "=== env ==="
+date -u
+uname -a
+pwd
+
+mount -o rw,remount / 2>/dev/null || true
+
+cd "$BUNDLE_DIR" || { echo "FATAL: bundle missing at $BUNDLE_DIR"; exit 1; }
+chmod +x bin/* 2>/dev/null
+export LD_LIBRARY_PATH="$BUNDLE_DIR/lib:$LD_LIBRARY_PATH"
+export ADSP_LIBRARY_PATH="$BUNDLE_DIR/lib"
+export GGML_HEXAGON_EXPERIMENTAL=1
+
+echo "=== download model ==="
+MODEL_URL="{MODEL_URL}"
+if [ -z "$MODEL_URL" ]; then
+  echo "No model URL provided, skipping download"
+elif [ ! -f "$MODEL_PATH" ]; then
+  curl -L -fS --retry 3 --retry-delay 5 -o "$MODEL_PATH" "$MODEL_URL"
+  curl_rc=$?
+  if [ $curl_rc -ne 0 ]; then
+    echo "FATAL: model download failed (rc=$curl_rc)"
+    exit 1
+  fi
+  ls -la "$MODEL_PATH"
+fi
+
+# ---------------------------------------------------------------------------
+# JUnit XML helpers
+# ---------------------------------------------------------------------------
+
+xml_open() {
+  printf '%s\n' \
+    '<?xml version="1.0" encoding="utf-8"?>' \
+    "<testsuites>" \
+    "<testsuite name=\"llama_cpp_linux\">" \
+    > "$RESULTS_XML"
+}
+
+xml_close() {
+  printf '%s\n' '</testsuite>' '</testsuites>' >> "$RESULTS_XML"
+}
+
+xml_case_pass() {
+  local classname=$1 name=$2
+  printf '<testcase classname="%s" name="%s"/>\n' "$classname" "$name" >> "$RESULTS_XML"
+}
+
+xml_case_fail() {
+  local classname=$1 name=$2 rc=$3 logfile=$4
+  {
+    printf '<testcase classname="%s" name="%s">\n' "$classname" "$name"
+    printf '<failure message="exit %s"><![CDATA[\n' "$rc"
+    tail -c 4096 "$logfile" 2>/dev/null | sed 's/]]>/]] >/g'
+    printf '\n]]></failure>\n</testcase>\n'
+  } >> "$RESULTS_XML"
+}
+
+# Map backend name -> "NDEV --device" pair. "none" means no offload (CPU).
+backend_env() {
+  case "$1" in
+    cpu) echo "0 none" ;;
+    gpu) echo "0 GPUOpenCL" ;;
+    npu) echo "1 HTP0" ;;
+  esac
+}
+
+backend_log_name() {
+  case "$1" in
+    cpu) echo "cpu" ;;
+    gpu) echo "gpu" ;;
+    npu) echo "htp" ;;
+  esac
+}
+
+
+backend_device_name() {
+  case "$1" in
+    cpu) echo "none" ;;
+    gpu) echo "GPUOpenCL" ;;
+    npu) echo "HTP0" ;;
+  esac
+}
+
+# Append a diagnostic block when a per-case `timeout N` fires (rc=124). The
+# naked log file at that point usually just ends mid-OpenCL-init with no
+# stderr, which is hard to read in CI summaries.
+note_timeout_if_triggered() {
+  local rc=$1 budget=$2 log=$3
+  [ "$rc" -eq 124 ] || return 0
+  {
+    printf '\n'
+    printf '=== TIMEOUT after %ss ===\n' "$budget"
+    printf 'uptime: '; uptime 2>/dev/null
+    printf 'free -m:\n'; free -m 2>/dev/null
+    printf 'loadavg: '; cat /proc/loadavg 2>/dev/null
+  } >> "$log"
+}
+
+completion_extra_args() {
+  case "$1" in
+    cpu) echo "--device none --ctx-size 128 -no-cnv -n 32 --seed 42 --batch-size 128" ;;
+    gpu) echo "--device GPUOpenCL --ctx-size 128 -no-cnv -n 32 --seed 42 --ubatch-size 512" ;;
+    npu) echo "--device HTP0 --ctx-size 128 -no-cnv -n 32 --seed 42 --ubatch-size 1024" ;;
+  esac
+}
+
+run_completion_case() {
+  local name=$1
+  local parts=($(backend_env "$name"))
+  local ndev=${parts[0]} device=${parts[1]}
+  local device_log_name=$(backend_device_name "$name")
+  local log="$LOG_DIR/llama_completion_${device_log_name}.log"
+  local prompt="$LOG_DIR/bench_prompt.txt"
+  echo 'What is the capital of France?' > "$prompt"
+  local extra
+  extra=$(completion_extra_args "$name")
+  echo "=== [completion:$name] llama-completion --device $device (NDEV=$ndev) ==="
+  timeout 600 env GGML_HEXAGON_NDEV=$ndev ./bin/llama-completion \
+      -m "$MODEL_PATH" \
+      -f "$prompt" \
+      $extra \
+      > "$log" 2>&1 < /dev/null
+  local rc=$?
+  note_timeout_if_triggered "$rc" 600 "$log"
+  if [ $rc -eq 0 ]; then
+    xml_case_pass "tests.test_bench_posix" "test_llama_completion[$name]"
+  else
+    xml_case_fail "tests.test_bench_posix" "test_llama_completion[$name]" "$rc" "$log"
+  fi
+}
+
+run_bench_case() {
+  local name=$1
+  local parts=($(backend_env "$name"))
+  local ndev=${parts[0]} device=${parts[1]}
+  local log_suffix=$(backend_log_name "$name")
+  local log="$LOG_DIR/llama_bench_${log_suffix}.log"
+  echo "=== [bench:$name] llama-bench --device $device (NDEV=$ndev) ==="
+  timeout 600 env GGML_HEXAGON_NDEV=$ndev ./bin/llama-bench \
+      -m "$MODEL_PATH" \
+      --device "$device" \
+      -ngl 99 \
+      --batch-size 128 \
+      -t 4 \
+      -p 128 \
+      -n 32 \
+      > "$log" 2>&1
+  local rc=$?
+  note_timeout_if_triggered "$rc" 600 "$log"
+  if [ $rc -eq 0 ]; then
+    xml_case_pass "tests.test_bench_posix" "test_llama_bench[$name]"
+  else
+    xml_case_fail "tests.test_bench_posix" "test_llama_bench[$name]" "$rc" "$log"
+  fi
+}
+
+run_backend_ops_case() {
+  local dtype=$1
+  local log="$LOG_DIR/backend_ops_${dtype}.log"
+  local pattern
+  case "$dtype" in
+    q4_0)
+      # Matches Android: exclude a known-broken shape on NPU.
+      pattern='^(?=.*type_a=q4_0)(?!.*type_b=f32,m=576,n=512,k=576).*$'
+      ;;
+    *)
+      pattern="type_a=${dtype}"
+      ;;
+  esac
+  echo "=== [backend-ops:$dtype] test-backend-ops -b HTP0 -o MUL_MAT ==="
+  timeout 600 env GGML_HEXAGON_NDEV=1 GGML_HEXAGON_HOSTBUF=0 ./bin/test-backend-ops \
+      -b HTP0 -o MUL_MAT -p "$pattern" \
+      > "$log" 2>&1
+  local rc=$?
+  note_timeout_if_triggered "$rc" 600 "$log"
+  if [ $rc -eq 0 ]; then
+    xml_case_pass "tests.test_backend_ops_posix" "test_backend_ops_htp0[$dtype]"
+  else
+    xml_case_fail "tests.test_backend_ops_posix" "test_backend_ops_htp0[$dtype]" "$rc" "$log"
+  fi
+}
+
+xml_open
+
+case "{TEST_MODE}" in
+  bench)
+    for b in cpu gpu npu; do run_completion_case "$b"; done
+    for b in cpu gpu npu; do run_bench_case "$b"; done
+    ;;
+  backend-ops)
+    for d in mxfp4 fp16 q4_0; do run_backend_ops_case "$d"; done
+    ;;
+  all)
+    for b in cpu gpu npu; do run_completion_case "$b"; done
+    for b in cpu gpu npu; do run_bench_case "$b"; done
+    for d in mxfp4 fp16 q4_0; do run_backend_ops_case "$d"; done
+    ;;
+  *)
+    echo "FATAL: unsupported TEST_MODE={TEST_MODE}"
+    ;;
+esac
+
+xml_close
+echo "=== done ==="
+# Host parses results.xml to decide pass/fail.
+exit 0
diff --git a/scripts/snapdragon/qdc/tests/run_backend_ops_posix.py b/scripts/snapdragon/qdc/tests/run_backend_ops_posix.py
new file mode 100644
index 00000000000..355bf6c6a5b
--- /dev/null
+++ b/scripts/snapdragon/qdc/tests/run_backend_ops_posix.py
@@ -0,0 +1,51 @@
+"""
+On-device test-backend-ops runner for llama.cpp (HTP0 backend).
+
+On Android: executed by QDC's Appium test framework on the QDC runner.
+The runner has ADB access to the allocated device.
+On Linux: runs test-backend-ops directly via run_linux.sh (BASH framework).
+"""
+
+import os
+import sys
+
+import pytest
+
+from utils import (
+    BIN_PATH,
+    push_bundle_if_needed,
+    run_script,
+    write_qdc_log,
+)
+
+
+@pytest.fixture(scope="session", autouse=True)
+def install(driver):
+    push_bundle_if_needed(f"{BIN_PATH}/test-backend-ops")
+
+
+@pytest.mark.parametrize("type_a", ["mxfp4", "fp16", "q4_0"])
+def test_backend_ops_htp0(type_a):
+    if type_a == "q4_0":
+        pattern = r'^(?=.*type_a=q4_0)(?!.*type_b=f32,m=576,n=512,k=576).*$'
+    else:
+        pattern = f"type_a={type_a}"
+
+    quoted_pattern = f'"{pattern}"' if type_a == "q4_0" else pattern
+    result = run_script(
+        "run-tool.sh",
+        extra_env={"HB": "0"},
+        extra_args=["test-backend-ops", "-b", "HTP0", "-o", "MUL_MAT", "-p", quoted_pattern],
+    )
+    write_qdc_log(f"backend_ops_{type_a}.log", result.stdout or "")
+    assert result.returncode == 0, (
+        f"test-backend-ops type_a={type_a} failed (exit {result.returncode})"
+    )
+
+
+if __name__ == "__main__":
+    ret = pytest.main(["-s", "--junitxml=results.xml", os.path.realpath(__file__)])
+    if os.path.exists("results.xml"):
+        with open("results.xml") as f:
+            write_qdc_log("results.xml", f.read())
+    sys.exit(ret)
diff --git a/scripts/snapdragon/qdc/tests/run_bench_tests_posix.py b/scripts/snapdragon/qdc/tests/run_bench_tests_posix.py
new file mode 100644
index 00000000000..f42227c9f6e
--- /dev/null
+++ b/scripts/snapdragon/qdc/tests/run_bench_tests_posix.py
@@ -0,0 +1,95 @@
+"""
+On-device bench and completion test runner for llama.cpp (CPU, GPU, NPU backends).
+
+On Android: calls upstream run-*.sh scripts from llama.cpp/scripts/snapdragon/adb/
+on the QDC runner host (scripts wrap commands in ``adb shell`` internally).
+
+On Linux: runs llama-bench directly via run_linux.sh (BASH framework).
+
+Placeholders replaced at artifact creation time by run_qdc_jobs.py:
+  <<MODEL_URL>>  Direct URL to the GGUF model file (downloaded on-device)
+"""
+
+import os
+import subprocess
+import sys
+
+import pytest
+
+from utils import (
+    BIN_PATH,
+    MODEL_DEVICE_PATH,
+    MODEL_NAME,
+    PROMPT_DIR,
+    push_bundle_if_needed,
+    run_adb_command,
+    run_script,
+    write_qdc_log,
+)
+
+MODEL_URL = "<<MODEL_URL>>"
+
+
+@pytest.fixture(scope="session", autouse=True)
+def install(driver):
+    push_bundle_if_needed(f"{BIN_PATH}/llama-cli")
+    run_adb_command(f"mkdir -p /data/local/tmp/gguf {PROMPT_DIR}")
+    run_adb_command(f"echo 'What is the capital of France?' > {PROMPT_DIR}/bench_prompt.txt")
+    check = subprocess.run(
+        ["adb", "shell", f"ls {MODEL_DEVICE_PATH}"],
+        text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
+    )
+    if check.returncode != 0:
+        run_adb_command(f'curl -L -J --output {MODEL_DEVICE_PATH} "{MODEL_URL}"')
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        pytest.param("none", id="cpu"),
+        pytest.param("GPUOpenCL", id="gpu"),
+        pytest.param("HTP0", id="npu"),
+    ],
+)
+def test_llama_completion(device):
+    result = run_script(
+        "run-completion.sh",
+        extra_env={"D": device, "M": MODEL_NAME},
+        extra_args=["--batch-size", "128", "-n", "128", "--seed", "42",
+                    "-f", f"{PROMPT_DIR}/bench_prompt.txt"],
+    )
+    write_qdc_log(f"llama_completion_{device}.log", result.stdout or "")
+    assert result.returncode == 0, (
+        f"llama-completion {device} failed (exit {result.returncode})"
+    )
+
+
+_DEVICE_LOG_NAME = {"none": "cpu", "GPUOpenCL": "gpu", "HTP0": "htp"}
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        pytest.param("none", id="cpu"),
+        pytest.param("GPUOpenCL", id="gpu"),
+        pytest.param("HTP0", id="npu"),
+    ],
+)
+def test_llama_bench(device):
+    result = run_script(
+        "run-bench.sh",
+        extra_env={"D": device, "M": MODEL_NAME},
+        extra_args=["--batch-size", "128", "-p", "128", "-n", "32"],
+    )
+    write_qdc_log(f"llama_bench_{_DEVICE_LOG_NAME[device]}.log", result.stdout or "")
+    assert result.returncode == 0, (
+        f"llama-bench {device} failed (exit {result.returncode})"
+    )
+
+
+if __name__ == "__main__":
+    ret = pytest.main(["-s", "--junitxml=results.xml", os.path.realpath(__file__)])
+    if os.path.exists("results.xml"):
+        with open("results.xml") as f:
+            write_qdc_log("results.xml", f.read())
+    sys.exit(ret)
diff --git a/scripts/snapdragon/qdc/tests/test_bench.py b/scripts/snapdragon/qdc/tests/test_bench.py
deleted file mode 100644
index 651ab5b7172..00000000000
--- a/scripts/snapdragon/qdc/tests/test_bench.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import pytest
-import subprocess
-import sys
-
-tmp_path='/data/local/tmp'
-pkg_path=f'{tmp_path}/llama.cpp'
-lib_path=f'{pkg_path}/lib'
-bin_path=f'{pkg_path}/bin'
-
-model='../gguf/Llama-3.2-1B-Instruct-Q4_0.gguf'
-cli_pref=f'cd {pkg_path} && LD_LIBRARY_PATH={lib_path} ADSP_LIBRARY_PATH={lib_path} {bin_path}'
-
-
-def run_cmd(cmd):
-    p = subprocess.run(cmd, text = True, stdout = subprocess.PIPE, stderr = subprocess.STDOUT)
-    sys.stdout.write(p.stdout)
-    assert(p.returncode == 0)
-
-
-@pytest.mark.dependency()
-def test_install():
-    run_cmd(['adb', 'push', 'llama.cpp', f'{tmp_path}'])
-    run_cmd(['adb', 'shell', f'chmod 755 {bin_path}/*'])
-
-
-## Basic cli tests
-def run_llama_cli(dev, opts):
-    prompt='what is the most popular cookie in the world?\nPlease provide a very brief bullet point summary.\nBegin your answer with **BEGIN**.'
-    opts = '--batch-size 128 -n 128 -no-cnv --seed 42 ' + opts
-    run_cmd(['adb', 'shell', f'{cli_pref}/llama-cli -m {model} --device {dev} -ngl 99 -t 4 {opts} -p "{prompt}"'])
-
-
-@pytest.mark.dependency(depends=['test_install'])
-def test_llama_cli_cpu():
-    run_llama_cli('none', '-ctk q8_0 -ctv q8_0 -fa on')
-
-
-@pytest.mark.dependency(depends=['test_install'])
-def test_llama_cli_gpu():
-    run_llama_cli('GPUOpenCL', '-fa on')
-
-
-@pytest.mark.dependency(depends=['test_install'])
-def test_llama_cli_npu():
-    run_llama_cli('HTP0', '-ctk q8_0 -ctv q8_0 -fa on')
-
-
-## Basic bench tests
-def run_llama_bench(dev):
-    run_cmd(['adb', 'shell', f'{cli_pref}/llama-bench -m {model} --device {dev} -ngl 99 --batch-size 128 -t 4 -p 128 -n 32'])
-
-
-@pytest.mark.dependency(depends=['test_install'])
-def test_llama_bench_cpu():
-    run_llama_bench('none')
-
-
-def test_llama_bench_gpu():
-    run_llama_bench('GPUOpenCL')
-
-
-def test_llama_bench_npu():
-    run_llama_bench('HTP0')
diff --git a/scripts/snapdragon/qdc/tests/utils.py b/scripts/snapdragon/qdc/tests/utils.py
new file mode 100644
index 00000000000..fad6a923295
--- /dev/null
+++ b/scripts/snapdragon/qdc/tests/utils.py
@@ -0,0 +1,143 @@
+"""Shared helpers for QDC on-device test runners."""
+
+from __future__ import annotations
+
+import logging
+import os
+import subprocess
+import tempfile
+
+from appium.options.common import AppiumOptions
+
+log = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# On-device paths
+# ---------------------------------------------------------------------------
+
+BUNDLE_PATH = "/data/local/tmp/llama.cpp"
+BIN_PATH = f"{BUNDLE_PATH}/bin"
+LIB_PATH = f"{BUNDLE_PATH}/lib"
+QDC_LOGS_PATH = "/data/local/tmp/QDC_logs"
+SCRIPTS_DIR = "/qdc/appium"
+MODEL_NAME = "model.gguf"
+MODEL_DEVICE_PATH = "/data/local/tmp/gguf/model.gguf"
+PROMPT_DIR = "/data/local/tmp/scorecard_prompts"
+
+# ---------------------------------------------------------------------------
+# Appium session options
+# ---------------------------------------------------------------------------
+
+options = AppiumOptions()
+options.set_capability("automationName", "UiAutomator2")
+options.set_capability("platformName", "Android")
+options.set_capability("deviceName", os.getenv("ANDROID_DEVICE_VERSION"))
+
+# ---------------------------------------------------------------------------
+# Shell / process helpers
+# ---------------------------------------------------------------------------
+
+
+def write_qdc_log(filename: str, content: str) -> None:
+    """Write content as a log file for QDC log collection."""
+    subprocess.run(
+        ["adb", "shell", f"mkdir -p {QDC_LOGS_PATH}"],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+    )
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".log", delete=False) as f:
+        f.write(content)
+        tmp_path = f.name
+    try:
+        subprocess.run(
+            ["adb", "push", tmp_path, f"{QDC_LOGS_PATH}/{filename}"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+        )
+    finally:
+        os.unlink(tmp_path)
+
+
+def ensure_bundle(check_binary: str | None = None) -> None:
+    """Ensure the llama_cpp_bundle is available on the target device."""
+    push_bundle_if_needed(check_binary or f"{BIN_PATH}/llama-cli")
+
+
+# ---------------------------------------------------------------------------
+# Android / Linux host helpers
+# ---------------------------------------------------------------------------
+
+
+def run_adb_command(cmd: str, *, check: bool = True) -> subprocess.CompletedProcess:
+    """Run a command on-device via ``adb shell`` with exit-code sentinel."""
+    raw = subprocess.run(
+        ["adb", "shell", f"{cmd}; echo __RC__:$?"],
+        text=True,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+    )
+    stdout = raw.stdout
+    returncode = raw.returncode
+    if stdout:
+        lines = stdout.rstrip("\n").split("\n")
+        if lines and lines[-1].startswith("__RC__:"):
+            try:
+                returncode = int(lines[-1][7:])
+                stdout = "\n".join(lines[:-1]) + "\n"
+            except ValueError:
+                pass
+    log.info(stdout)
+    result = subprocess.CompletedProcess(raw.args, returncode, stdout=stdout)
+    if check:
+        assert returncode == 0, f"Command failed (exit {returncode})"
+    return result
+
+
+def run_script(
+    script: str,
+    extra_env: dict[str, str] | None = None,
+    extra_args: list[str] | None = None,
+) -> subprocess.CompletedProcess:
+    """Run an upstream shell script from /qdc/appium/ on the QDC runner host."""
+    env = os.environ.copy()
+    env["GGML_HEXAGON_EXPERIMENTAL"] = "1"
+    if extra_env:
+        env.update(extra_env)
+    cmd = [f"{SCRIPTS_DIR}/{script}"] + (extra_args or [])
+    result = subprocess.run(
+        cmd, env=env,
+        text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
+    )
+    log.info(result.stdout)
+    return result
+
+
+def adb_shell(cmd: str) -> None:
+    """Run a command via adb shell (fire-and-forget, no error check)."""
+    subprocess.run(
+        ["adb", "shell", "sh", "-c", cmd],
+        capture_output=True, encoding="utf-8", errors="replace", check=False,
+    )
+
+
+def push_bundle_if_needed(check_binary: str) -> None:
+    """Push llama_cpp_bundle to the device if check_binary is not already present."""
+    result = subprocess.run(
+        ["adb", "shell", f"ls {check_binary}"],
+        text=True,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+    )
+    if result.returncode != 0:
+        subprocess.run(
+            ["adb", "push", "/qdc/appium/llama_cpp_bundle/", BUNDLE_PATH],
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+        )
+        subprocess.run(
+            ["adb", "shell", f"find {BUNDLE_PATH}/bin -type f -exec chmod 755 {{}} +"],
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+        )
diff --git a/scripts/snapdragon/windows/run-bench.ps1 b/scripts/snapdragon/windows/run-bench.ps1
index 5a3a9074dfd..8bf6939d2c0 100644
--- a/scripts/snapdragon/windows/run-bench.ps1
+++ b/scripts/snapdragon/windows/run-bench.ps1
@@ -21,11 +21,11 @@ if ($null -ne $env:V) {
 }
 
 if ($null -ne $env:PROF) {
-    $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
+    $env:GGML_HEXAGON_PROFILE=$env:PROF
 }
 
-if ($null -ne $env:OPMASK) {
-    $env:GGML_HEXAGON_OPMASK=$env:OPMASK
+if ($null -ne $env:OPSTAGE) {
+    $env:GGML_HEXAGON_OPSTAGE=$env:OPSTAGE
 }
 
 if ($null -ne $env:NHVX) {
diff --git a/scripts/snapdragon/windows/run-cli.ps1 b/scripts/snapdragon/windows/run-cli.ps1
index c64aaf725cf..104452f9ba7 100644
--- a/scripts/snapdragon/windows/run-cli.ps1
+++ b/scripts/snapdragon/windows/run-cli.ps1
@@ -25,11 +25,11 @@ if ($null -ne $env:SCHED) {
 }
 
 if ($null -ne $env:PROF) {
-    $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
+    $env:GGML_HEXAGON_PROFILE=$env:PROF
 }
 
-if ($null -ne $env:OPMASK) {
-    $env:GGML_HEXAGON_OPMASK=$env:OPMASK
+if ($null -ne $env:OPSTAGE) {
+    $env:GGML_HEXAGON_OPSTAGE=$env:OPSTAGE
 }
 
 if ($null -ne $env:NHVX) {
diff --git a/scripts/snapdragon/windows/run-completion.ps1 b/scripts/snapdragon/windows/run-completion.ps1
index a896cd3524d..5841a82fa99 100644
--- a/scripts/snapdragon/windows/run-completion.ps1
+++ b/scripts/snapdragon/windows/run-completion.ps1
@@ -25,11 +25,11 @@ if ($null -ne $env:SCHED) {
 }
 
 if ($null -ne $env:PROF) {
-    $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
+    $env:GGML_HEXAGON_PROFILE=$env:PROF
 }
 
-if ($null -ne $env:OPMASK) {
-    $env:GGML_HEXAGON_OPMASK=$env:OPMASK
+if ($null -ne $env:OPSTAGE) {
+    $env:GGML_HEXAGON_OPSTAGE=$env:OPSTAGE
 }
 
 if ($null -ne $env:NHVX) {
diff --git a/scripts/snapdragon/windows/run-mtmd.ps1 b/scripts/snapdragon/windows/run-mtmd.ps1
index f230ac5a6b7..be817875142 100644
--- a/scripts/snapdragon/windows/run-mtmd.ps1
+++ b/scripts/snapdragon/windows/run-mtmd.ps1
@@ -34,11 +34,11 @@ if ($null -ne $env:SCHED) {
 }
 
 if ($null -ne $env:PROF) {
-    $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
+    $env:GGML_HEXAGON_PROFILE=$env:PROF
 }
 
-if ($null -ne $env:OPMASK) {
-    $env:GGML_HEXAGON_OPMASK=$env:OPMASK
+if ($null -ne $env:OPSTAGE) {
+    $env:GGML_HEXAGON_OPSTAGE=$env:OPSTAGE
 }
 
 if ($null -ne $env:NHVX) {
diff --git a/scripts/snapdragon/windows/run-tool.ps1 b/scripts/snapdragon/windows/run-tool.ps1
index 39edbfcf76c..15c880f2dbd 100644
--- a/scripts/snapdragon/windows/run-tool.ps1
+++ b/scripts/snapdragon/windows/run-tool.ps1
@@ -31,11 +31,11 @@ if ($null -ne $env:SCHED) {
 }
 
 if ($null -ne $env:PROF) {
-    $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
+    $env:GGML_HEXAGON_PROFILE=$env:PROF
 }
 
-if ($null -ne $env:OPMASK) {
-    $env:GGML_HEXAGON_OPMASK=$env:OPMASK
+if ($null -ne $env:OPSTAGE) {
+    $env:GGML_HEXAGON_OPSTAGE=$env:OPSTAGE
 }
 
 if ($null -ne $env:NHVX) {
diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index de0140cfe24..0fa47782fd9 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-1c40d85a4dcfcd62176f649b8682433bb1a6caef
+0ce7ad348a3151e1da9f65d962044546bcaad421
diff --git a/scripts/sync_vendor.py b/scripts/sync_vendor.py
index 7ce09332360..658f7326b96 100755
--- a/scripts/sync_vendor.py
+++ b/scripts/sync_vendor.py
@@ -5,7 +5,7 @@
 import sys
 import subprocess
 
-HTTPLIB_VERSION = "refs/tags/v0.42.0"
+HTTPLIB_VERSION = "refs/tags/v0.45.0"
 
 vendor = {
     "https://github.com/nlohmann/json/releases/latest/download/json.hpp":     "vendor/nlohmann/json.hpp",
diff --git a/scripts/ui-download.cmake b/scripts/ui-download.cmake
new file mode 100644
index 00000000000..65143642a2c
--- /dev/null
+++ b/scripts/ui-download.cmake
@@ -0,0 +1,223 @@
+# Download UI assets from Hugging Face Bucket at build time
+# Usage: cmake -DPUBLIC_DIR=... -DHF_BUCKET=... -DHF_VERSION=... -DASSETS="a;b;c" -P scripts/ui-download.cmake
+#
+# Asset provisioning priority:
+#   1. Pre-built assets already in PUBLIC_DIR (cached from a previous run)
+#   2. Local npm build (if NPM_DIR is provided and has package.json)
+#   3. Hugging Face Bucket download (version-specific, then 'latest' fallback)
+
+cmake_minimum_required(VERSION 3.16)
+
+set(PUBLIC_DIR   "" CACHE STRING "Directory to store/download assets")
+set(HF_BUCKET    "" CACHE STRING "Hugging Face bucket name")
+set(HF_VERSION   "" CACHE STRING "Version to download (empty = resolve from git)")
+set(ASSETS       "" CACHE STRING "Plus-separated list of asset filenames (+)")
+set(STAMP_FILE   "" CACHE STRING "Stamp file to create on success (optional)")
+set(SOURCE_DIR   "" CACHE STRING "Project source root (to resolve version from git)")
+set(NPM_DIR      "" CACHE STRING "UI source directory (to run npm build)")
+set(HF_ENABLED   "" CACHE STRING "Whether to allow HF Bucket download (ON/OFF)")
+
+# ---------------------------------------------------------------------------
+# 1. Resolve version from git if not provided at configure time
+# ---------------------------------------------------------------------------
+set(RESOLVED_VERSION "${HF_VERSION}")
+if("${RESOLVED_VERSION}" STREQUAL "" AND NOT "${SOURCE_DIR}" STREQUAL "")
+    if(EXISTS "${SOURCE_DIR}/cmake/build-info.cmake")
+        include("${SOURCE_DIR}/cmake/build-info.cmake")
+        if(NOT "${BUILD_NUMBER}" STREQUAL "" AND NOT BUILD_NUMBER EQUAL 0)
+            set(RESOLVED_VERSION "b${BUILD_NUMBER}")
+            message(STATUS "UI: resolved version from git: ${RESOLVED_VERSION}")
+        endif()
+    endif()
+endif()
+
+# Convert + back to CMake list (+ is used as separator instead of ; to
+# avoid platform-specific escaping issues when passing via -D arguments)
+string(REGEX REPLACE "\\+" ";" ASSETS "${ASSETS}")
+
+# ---------------------------------------------------------------------------
+# 2. Check stamp freshness — re-download if resolved version changed
+# ---------------------------------------------------------------------------
+set(FORCE_REBUILD FALSE)
+if(NOT "${STAMP_FILE}" STREQUAL "" AND EXISTS "${STAMP_FILE}")
+    file(READ "${STAMP_FILE}" STAMPED_VERSION)
+    string(STRIP "${STAMPED_VERSION}" STAMPED_VERSION)
+    if(NOT "${STAMPED_VERSION}" STREQUAL "${RESOLVED_VERSION}")
+        message(STATUS "UI: version changed (${STAMPED_VERSION} -> ${RESOLVED_VERSION}), re-building")
+        set(FORCE_REBUILD TRUE)
+    endif()
+endif()
+
+# ---------------------------------------------------------------------------
+# 3. Check if assets already exist (cached from a previous run)
+# ---------------------------------------------------------------------------
+set(ALL_EXISTS TRUE)
+foreach(asset ${ASSETS})
+    if(NOT EXISTS "${PUBLIC_DIR}/${asset}")
+        set(ALL_EXISTS FALSE)
+        break()
+    endif()
+endforeach()
+
+if(ALL_EXISTS AND NOT FORCE_REBUILD)
+    message(STATUS "UI: all assets already exist in ${PUBLIC_DIR}, skipping")
+    return()
+endif()
+
+file(MAKE_DIRECTORY "${PUBLIC_DIR}")
+
+# ---------------------------------------------------------------------------
+# 4. Priority 2: build from source via npm (fast path for developers)
+# ---------------------------------------------------------------------------
+set(PROVISION_SUCCESS FALSE)
+
+if(NOT PROVISION_SUCCESS AND NOT "${NPM_DIR}" STREQUAL "")
+    if(EXISTS "${NPM_DIR}/package.json")
+        # Check if npm is available before attempting npm build
+        find_program(NPM_EXECUTABLE npm)
+        if(NPM_EXECUTABLE)
+            message(STATUS "UI: building from source in ${NPM_DIR}")
+
+            # Run npm install if node_modules is missing
+            if(NOT EXISTS "${NPM_DIR}/node_modules")
+                message(STATUS "UI: running npm install (first time)")
+                execute_process(
+                    COMMAND ${NPM_EXECUTABLE} install
+                    WORKING_DIRECTORY "${NPM_DIR}"
+                    RESULT_VARIABLE NPM_INSTALL_RESULT
+                    OUTPUT_VARIABLE NPM_OUT
+                    ERROR_VARIABLE  NPM_ERR
+                )
+                if(NOT NPM_INSTALL_RESULT EQUAL 0)
+                    message(STATUS "UI: npm install failed (${NPM_INSTALL_RESULT}), falling back to download")
+                    message(STATUS "  stderr: ${NPM_ERR}")
+                endif()
+            endif()
+
+            # Run the build
+            execute_process(
+                COMMAND ${NPM_EXECUTABLE} run build
+                WORKING_DIRECTORY "${NPM_DIR}"
+                RESULT_VARIABLE NPM_BUILD_RESULT
+                OUTPUT_VARIABLE NPM_OUT
+                ERROR_VARIABLE  NPM_ERR
+            )
+
+            if(NPM_BUILD_RESULT EQUAL 0)
+                # Verify that the expected assets were produced
+                set(ALL_BUILT TRUE)
+                foreach(asset ${ASSETS})
+                    if(NOT EXISTS "${PUBLIC_DIR}/${asset}")
+                        set(ALL_BUILT FALSE)
+                        break()
+                    endif()
+                endforeach()
+
+                if(ALL_BUILT)
+                    message(STATUS "UI: local npm build succeeded")
+                    set(PROVISION_SUCCESS TRUE)
+                else()
+                    message(STATUS "UI: npm build completed but assets missing from ${PUBLIC_DIR}, falling back to download")
+                endif()
+            else()
+                message(STATUS "UI: npm build failed (${NPM_BUILD_RESULT}), falling back to download")
+                message(STATUS "  stderr: ${NPM_ERR}")
+            endif()
+        else()
+            message(STATUS "UI: npm not found, skipping npm build and trying HF Bucket download")
+        endif()
+    else()
+        message(STATUS "UI: NPM_DIR (${NPM_DIR}) has no package.json, skipping npm build")
+    endif()
+endif()
+
+# ---------------------------------------------------------------------------
+# 5. Priority 3: download from Hugging Face Bucket (if enabled)
+# ---------------------------------------------------------------------------
+if(NOT PROVISION_SUCCESS AND HF_ENABLED)
+    # Build list of URLs to try — version-specific first, then 'latest'
+    set(URL_ENTRIES "")
+    if(NOT "${RESOLVED_VERSION}" STREQUAL "")
+        list(APPEND URL_ENTRIES
+            "version:https://huggingface.co/buckets/ggml-org/${HF_BUCKET}/resolve/${RESOLVED_VERSION}")
+    endif()
+    list(APPEND URL_ENTRIES
+        "latest:https://huggingface.co/buckets/ggml-org/${HF_BUCKET}/resolve/latest")
+
+    foreach(entry ${URL_ENTRIES})
+        string(REGEX REPLACE "^([^:]+):.*$" "\\1" url_label "${entry}")
+        string(REGEX REPLACE "^[^:]+:(.*)$" "\\1" base_url "${entry}")
+
+        message(STATUS "UI: downloading assets from ${url_label}: ${base_url}")
+
+        # Download each asset
+        set(ALL_OK TRUE)
+        foreach(asset ${ASSETS})
+            set(download_url "${base_url}/${asset}?download=true")
+            set(download_path "${PUBLIC_DIR}/${asset}")
+            file(DOWNLOAD "${download_url}" "${download_path}"
+                STATUS download_status TIMEOUT 60
+            )
+            list(GET download_status 0 download_result)
+            if(NOT download_result EQUAL 0)
+                list(GET download_status 1 error_message)
+                message(STATUS "UI: failed to download ${asset} from ${url_label}: ${error_message}")
+                set(ALL_OK FALSE)
+                break()
+            endif()
+            message(STATUS "UI: downloaded ${asset}")
+        endforeach()
+
+        if(NOT ALL_OK)
+            continue()
+        endif()
+
+        # Verify checksums if the server provides them
+        file(DOWNLOAD "${base_url}/checksums.txt?download=true"
+            "${PUBLIC_DIR}/checksums.txt"
+            STATUS checksum_status TIMEOUT 30
+        )
+        list(GET checksum_status 0 checksum_result)
+        if(checksum_result EQUAL 0)
+            message(STATUS "UI: verifying checksums...")
+            file(STRINGS "${PUBLIC_DIR}/checksums.txt" CHECKSUMS_CONTENT)
+            foreach(asset ${ASSETS})
+                set(download_path "${PUBLIC_DIR}/${asset}")
+                file(SHA256 "${download_path}" asset_hash)
+                string(TOLOWER "${asset_hash}" EXPECTED_HASH_LOWER)
+                string(REGEX MATCH "${EXPECTED_HASH_LOWER}[ \\t]+${asset}" CHECKSUM_LINE "${CHECKSUMS_CONTENT}")
+                if(NOT CHECKSUM_LINE)
+                    message(WARNING "UI: checksum verification failed for ${asset}")
+                    set(ALL_OK FALSE)
+                    break()
+                endif()
+            endforeach()
+            if(ALL_OK)
+                message(STATUS "UI: all checksums verified")
+            endif()
+        endif()
+
+        if(ALL_OK)
+            set(PROVISION_SUCCESS TRUE)
+            break()
+        endif()
+    endforeach()
+
+    if(PROVISION_SUCCESS)
+        message(STATUS "UI: provisioning complete")
+    else()
+        message(WARNING "UI: failed to download assets from HF Bucket (${HF_BUCKET})")
+    endif()
+endif()
+
+# ---------------------------------------------------------------------------
+# 6. Write stamp file on success (stores resolved version for freshness check)
+# ---------------------------------------------------------------------------
+if(PROVISION_SUCCESS)
+    if(NOT "${STAMP_FILE}" STREQUAL "")
+        file(WRITE "${STAMP_FILE}" "${RESOLVED_VERSION}")
+    endif()
+else()
+    message(WARNING "UI: no source available. Neither local build (${NPM_DIR}) nor HF Bucket download succeeded.")
+    message(WARNING "UI: building server without embedded UI. Set LLAMA_BUILD_UI=OFF to suppress this warning.")
+endif()
diff --git a/scripts/wc2wt.sh b/scripts/wc2wt.sh
new file mode 100755
index 00000000000..157881b458f
--- /dev/null
+++ b/scripts/wc2wt.sh
@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# initialize a new worktree from a branch name:
+#
+# - creates a new branch from current HEAD
+# - creates a new worktree in a parent folder, suffixed with the branch name
+#
+# sample usage:
+#   ./scripts/wc2wt.sh gg/new-feature-foo-bar
+#   ./scripts/wc2wt.sh gg/new-feature-foo-bar opencode
+#   ./scripts/wc2wt.sh gg/new-feature-foo-bar "cmake -B build && cmake --build build"
+#   ./scripts/wc2wt.sh gg/new-feature-foo-bar "bash -l"
+
+function usage() {
+    echo "usage: $0 <branch_name> [cmd]"
+    exit 1
+}
+
+# check we are in the right directory
+if [[ ! -f "scripts/wc2wt.sh" ]]; then
+    echo "error: this script must be run from the root of the repository"
+    exit 1
+fi
+
+if [[ $# -lt 1 || $# -gt 2 ]]; then
+    usage
+fi
+
+BRANCH=$1
+
+if [[ -z "$BRANCH" ]]; then
+    echo "error: branch name must not be empty"
+    exit 1
+fi
+
+dir=$(basename $(pwd))
+# sanitize branch name for directory name (replace / with -)
+dir_suffix=$(echo "$BRANCH" | tr '/' '-')
+
+git worktree add -b "$BRANCH" "../$dir-$dir_suffix" HEAD
+
+og_path=$(pwd)
+wt_path=$(cd "../$dir-$dir_suffix" && pwd)
+
+echo "git worktree created in $wt_path"
+
+cd "$wt_path"
+
+# pi agent setup in the worktree
+if [[ -f "$og_path/.pi/SYSTEM.md" && ! -f ".pi/SYSTEM.md" ]]; then
+    mkdir -p .pi
+    ln -sfn "$og_path/.pi/SYSTEM.md" .pi/SYSTEM.md
+fi
+
+if [[ $# -eq 2 ]]; then
+    echo "executing: $2"
+    eval "$2"
+fi
diff --git a/scripts/xxd.cmake b/scripts/xxd.cmake
index 14d2753808a..9c5dacd628c 100644
--- a/scripts/xxd.cmake
+++ b/scripts/xxd.cmake
@@ -1,5 +1,5 @@
 # CMake equivalent of `xxd -i ${INPUT} ${OUTPUT}`
-# Usage: cmake -DINPUT=tools/server/public/index.html -DOUTPUT=tools/server/index.html.hpp -P scripts/xxd.cmake
+# Usage: cmake -DINPUT=build/tools/ui/dist/index.html -DOUTPUT=build/tools/ui/dist/index.html.hpp -P scripts/xxd.cmake
 
 SET(INPUT "" CACHE STRING "Input File")
 SET(OUTPUT "" CACHE STRING "Output File")
@@ -7,10 +7,25 @@ SET(OUTPUT "" CACHE STRING "Output File")
 get_filename_component(filename "${INPUT}" NAME)
 string(REGEX REPLACE "\\.|-" "_" name "${filename}")
 
-file(READ "${INPUT}" hex_data HEX)
-string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," hex_sequence "${hex_data}")
+# Robust handling for the offline / nix-sandbox build path where the UI
+# provisioning flow (npm build → HF Bucket fallback) can leave assets either
+# missing entirely or present-but-empty. Without these guards xxd.cmake hits
+# either "file failed to open for reading" (missing) or "string sub-command
+# LENGTH requires two arguments" (empty). Emit a valid 0-byte symbol in both
+# cases so the build completes; the server side already handles a server
+# build with LLAMA_UI_DEFAULT_ENABLED=0 cleanly.
+if(NOT EXISTS "${INPUT}")
+    file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {0};\nunsigned int ${name}_len = 0;\n")
+    return()
+endif()
 
-string(LENGTH ${hex_data} hex_len)
+file(READ "${INPUT}" hex_data HEX)
+string(LENGTH "${hex_data}" hex_len)
 math(EXPR len "${hex_len} / 2")
 
-file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {${hex_sequence}};\nunsigned int ${name}_len = ${len};\n")
+if(len EQUAL 0)
+    file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {0};\nunsigned int ${name}_len = 0;\n")
+else()
+    string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," hex_sequence "${hex_data}")
+    file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {${hex_sequence}};\nunsigned int ${name}_len = ${len};\n")
+endif()
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 6904b9c1a64..c9eead18aa3 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -109,6 +109,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_ERNIE4_5_MOE,     "ernie4_5-moe"     },
     { LLM_ARCH_HUNYUAN_MOE,      "hunyuan-moe"      },
     { LLM_ARCH_HUNYUAN_DENSE,    "hunyuan-dense"    },
+    { LLM_ARCH_HUNYUAN_VL,       "hunyuan_vl"       },
     { LLM_ARCH_SMOLLM3,          "smollm3"          },
     { LLM_ARCH_OPENAI_MOE,       "gpt-oss"          },
     { LLM_ARCH_LFM2,             "lfm2"             },
@@ -231,6 +232,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,       "%s.attention.sliding_window_pattern"       },
     { LLM_KV_ATTENTION_SCALE,                        "%s.attention.scale"                        },
     { LLM_KV_ATTENTION_OUTPUT_SCALE,                 "%s.attention.output_scale"                 },
+    { LLM_KV_ATTENTION_VALUE_SCALE,                  "%s.attention.value_scale"                  },
     { LLM_KV_ATTENTION_TEMPERATURE_LENGTH,           "%s.attention.temperature_length"           },
     { LLM_KV_ATTENTION_TEMPERATURE_SCALE,            "%s.attention.temperature_scale"            },
     { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
@@ -250,6 +252,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ROPE_SCALE_LINEAR,              "%s.rope.scale_linear"                    },
     { LLM_KV_ROPE_SCALING_TYPE,              "%s.rope.scaling.type"                    },
     { LLM_KV_ROPE_SCALING_FACTOR,            "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ALPHA,             "%s.rope.scaling.alpha"                   },
     { LLM_KV_ROPE_SCALING_ATTN_FACTOR,       "%s.rope.scaling.attn_factor"             },
     { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,      "%s.rope.scaling.original_context_length" },
     { LLM_KV_ROPE_SCALING_FINETUNED,         "%s.rope.scaling.finetuned"               },
@@ -754,14 +757,15 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_INDEXER_PROJ,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_INDEXER_ATTN_K,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_INDEXER_ATTN_Q_B,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    // NextN/MTP tensors are currently ignored (reserved for future MTP support)
-    // These tensors only exist in the last layer(s) and are treated as output tensors
-    {LLM_TENSOR_NEXTN_EH_PROJ,              {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_NEXTN_EMBED_TOKENS,         {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_NEXTN_ENORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_NEXTN_HNORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
-    {LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
+    // NextN/MTP tensors are stored per-block (blk.%d.nextn.*) even though only the
+    // last nextn_predict_layers blocks carry them. Classify as LAYER_REPEATING so
+    // the model loader doesn't fault on the block index.
+    {LLM_TENSOR_NEXTN_EH_PROJ,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_NEXTN_EMBED_TOKENS,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_NEXTN_ENORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_NEXTN_HNORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     // Nemotron 3 Super
     {LLM_TENSOR_FFN_LATENT_DOWN,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_FFN_LATENT_UP,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
@@ -874,6 +878,16 @@ bool llm_arch_is_diffusion(const llm_arch & arch) {
     }
 }
 
+bool llm_arch_supports_rs_rollback(const llm_arch & arch) {
+    switch (arch) {
+        case LLM_ARCH_QWEN35:
+        case LLM_ARCH_QWEN35MOE:
+            return true;
+        default:
+            return false;
+    }
+}
+
 bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
     switch (arch) {
         case LLM_ARCH_GROK:
diff --git a/src/llama-arch.h b/src/llama-arch.h
index c4aabab7e0c..89cf16cc37c 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -113,6 +113,7 @@ enum llm_arch {
     LLM_ARCH_ERNIE4_5_MOE,
     LLM_ARCH_HUNYUAN_MOE,
     LLM_ARCH_HUNYUAN_DENSE,
+    LLM_ARCH_HUNYUAN_VL,
     LLM_ARCH_SMOLLM3,
     LLM_ARCH_OPENAI_MOE,
     LLM_ARCH_LFM2,
@@ -235,6 +236,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,
     LLM_KV_ATTENTION_SCALE,
     LLM_KV_ATTENTION_OUTPUT_SCALE,
+    LLM_KV_ATTENTION_VALUE_SCALE,
     LLM_KV_ATTENTION_TEMPERATURE_LENGTH,
     LLM_KV_ATTENTION_TEMPERATURE_SCALE,
     LLM_KV_ATTENTION_KEY_LENGTH_MLA,
@@ -254,6 +256,7 @@ enum llm_kv {
     LLM_KV_ROPE_SCALE_LINEAR,
     LLM_KV_ROPE_SCALING_TYPE,
     LLM_KV_ROPE_SCALING_FACTOR,
+    LLM_KV_ROPE_SCALING_ALPHA,
     LLM_KV_ROPE_SCALING_ATTN_FACTOR,
     LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
     LLM_KV_ROPE_SCALING_FINETUNED,
@@ -634,3 +637,4 @@ bool llm_arch_is_recurrent      (const llm_arch & arch);
 bool llm_arch_is_hybrid         (const llm_arch & arch);
 bool llm_arch_is_diffusion      (const llm_arch & arch);
 bool llm_arch_supports_sm_tensor(const llm_arch & arch);
+bool llm_arch_supports_rs_rollback(const llm_arch & arch);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index debcf0b71a6..083cb673ef7 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2,6 +2,7 @@
 
 #include "ggml.h"
 #include "llama-arch.h"
+#include "llama-graph.h"
 #include "llama-impl.h"
 #include "llama-batch.h"
 #include "llama-io.h"
@@ -21,6 +22,14 @@
 // llama_context
 //
 
+static llm_graph_type ctx_type_to_graph_type(llama_context_type ctx_type) {
+    switch (ctx_type) {
+        case LLAMA_CONTEXT_TYPE_DEFAULT: return LLM_GRAPH_TYPE_DEFAULT;
+        case LLAMA_CONTEXT_TYPE_MTP    : return LLM_GRAPH_TYPE_DECODER_MTP;
+    }
+    throw std::runtime_error("Unsupported ctx type");
+}
+
 llama_context::llama_context(
         const llama_model & model,
               llama_context_params params) :
@@ -42,6 +51,13 @@ llama_context::llama_context(
         throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_SEQ));
     }
 
+    cparams.n_rs_seq = params.n_rs_seq;
+    if (cparams.n_rs_seq > 0 && !llm_arch_supports_rs_rollback(model.arch)) {
+        LLAMA_LOG_DEBUG("%s: n_rs_seq=%u requested but model arch does not support recurrent partial rollback; clamping to 0\n",
+                        __func__, cparams.n_rs_seq);
+        cparams.n_rs_seq = 0;
+    }
+
     cparams.n_threads        = params.n_threads;
     cparams.n_threads_batch  = params.n_threads_batch;
     cparams.yarn_ext_factor  = params.yarn_ext_factor  >= 0.0f ? params.yarn_ext_factor  : hparams.yarn_ext_factor;
@@ -49,6 +65,7 @@ llama_context::llama_context(
     cparams.yarn_beta_fast   = params.yarn_beta_fast   >= 0.0f ? params.yarn_beta_fast   : hparams.yarn_beta_fast;
     cparams.yarn_beta_slow   = params.yarn_beta_slow   >= 0.0f ? params.yarn_beta_slow   : hparams.yarn_beta_slow;
     cparams.embeddings       = params.embeddings;
+    cparams.embeddings_pre_norm = false;
     cparams.offload_kqv      = params.offload_kqv;
     cparams.no_perf          = params.no_perf;
     cparams.pooling_type     = params.pooling_type;
@@ -65,6 +82,8 @@ llama_context::llama_context(
     cparams.cb_eval           = params.cb_eval;
     cparams.cb_eval_user_data = params.cb_eval_user_data;
 
+    cparams.ctx_type          = params.ctx_type;
+
     // Initialize backend samplers here so they are part of the sampling graph
     // before the reserve passes run later in this function. This avoids a later
     // re-reserve when graph nodes change.
@@ -206,6 +225,7 @@ llama_context::llama_context(
     LLAMA_LOG_INFO("%s: kv_unified    = %s\n",   __func__, cparams.kv_unified ? "true" : "false");
     LLAMA_LOG_INFO("%s: freq_base     = %.1f\n", __func__, cparams.rope_freq_base);
     LLAMA_LOG_INFO("%s: freq_scale    = %g\n",   __func__, cparams.rope_freq_scale);
+    LLAMA_LOG_INFO("%s: n_rs_seq      = %u\n",   __func__, cparams.n_rs_seq);
 
     if (cparams.n_ctx_seq < hparams.n_ctx_train) {
         LLAMA_LOG_WARN("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
@@ -278,6 +298,7 @@ llama_context::llama_context(
             /*.type_k   =*/ params.type_k,
             /*.type_v   =*/ params.type_v,
             /*.swa_full =*/ params.swa_full,
+            /*.ctx_type= */ cparams.ctx_type,
         };
 
         memory.reset(model.create_memory(params_mem, cparams));
@@ -860,6 +881,33 @@ float * llama_context::get_embeddings_seq(llama_seq_id seq_id) {
     return it->second.data();
 }
 
+float * llama_context::get_embeddings_pre_norm() {
+    output_reorder();
+
+    return embd_pre_norm.data;
+}
+
+float * llama_context::get_embeddings_pre_norm_ith(int32_t i) {
+    output_reorder();
+
+    try {
+        if (embd_pre_norm.data == nullptr) {
+            throw std::runtime_error("no pre-norm embeddings");
+        }
+
+        const int64_t j = output_resolve_row(i);
+        const uint32_t n_embd = model.hparams.n_embd;
+        return embd_pre_norm.data + j*n_embd;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid pre-norm embeddings id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+        GGML_ABORT("fatal error");
+#else
+        return nullptr;
+#endif
+    }
+}
+
 llama_token llama_context::get_sampled_token_ith(int32_t idx) {
     output_reorder();
 
@@ -1040,6 +1088,12 @@ void llama_context::set_embeddings(bool value) {
     //sched_need_reserve = true;
 }
 
+void llama_context::set_embeddings_pre_norm(bool value) {
+    LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
+
+    cparams.embeddings_pre_norm = value;
+}
+
 void llama_context::set_causal_attn(bool value) {
     LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
 
@@ -1241,7 +1295,9 @@ llm_graph_result * llama_context::process_ubatch(const llama_ubatch & ubatch, ll
 }
 
 int llama_context::encode(const llama_batch & batch_inp) {
-    GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT
+    // MTP hook batches carry both token (next-token id) and embd (h_pre_norm row),
+    // so accept either present rather than requiring exactly one.
+    GGML_ASSERT(batch_inp.token || batch_inp.embd);
 
     if (batch_inp.n_tokens == 0) {
         LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
@@ -1312,8 +1368,9 @@ int llama_context::encode(const llama_batch & batch_inp) {
         }
     }
 
-    auto * t_logits = res->get_logits();
-    auto * t_embd = res->get_embd_pooled() ? res->get_embd_pooled() : res->get_embd();
+    auto * t_logits        = res->get_logits();
+    auto * t_embd          = res->get_embd_pooled() ? res->get_embd_pooled() : res->get_embd();
+    auto * t_h_pre_norm    = cparams.embeddings_pre_norm ? res->get_h_pre_norm() : nullptr;
 
     // extract logits
     if (logits.data && t_logits) {
@@ -1379,6 +1436,16 @@ int llama_context::encode(const llama_batch & batch_inp) {
         }
     }
 
+    // extract pre-norm embeddings (hidden state before the final output norm)
+    if (embd_pre_norm.data && t_h_pre_norm && cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        ggml_backend_t backend_h = ggml_backend_sched_get_tensor_backend(sched.get(), t_h_pre_norm);
+        GGML_ASSERT(backend_h != nullptr);
+
+        const uint32_t n_embd = hparams.n_embd;
+        GGML_ASSERT(n_tokens*n_embd <= (int64_t) embd_pre_norm.size);
+        ggml_backend_tensor_get_async(backend_h, t_h_pre_norm, embd_pre_norm.data, 0, n_tokens*n_embd*sizeof(float));
+    }
+
     // TODO: hacky solution
     if (model.arch == LLM_ARCH_T5 && t_embd) {
         //cross.t_embd = t_embd;
@@ -1531,7 +1598,9 @@ static bool needs_raw_logits(const llama_ubatch & ubatch, const std::map<llama_s
 }
 
 int llama_context::decode(const llama_batch & batch_inp) {
-    GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT
+    // MTP hook batches carry both token (next-token id) and embd (h_pre_norm row),
+    // so accept either present rather than requiring exactly one.
+    GGML_ASSERT(batch_inp.token || batch_inp.embd);
 
     if (!memory) {
         LLAMA_LOG_DEBUG("%s: cannot decode batches with this context (calling encode() instead)\n", __func__);
@@ -1689,7 +1758,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
         }
 
         ggml_status status;
-        const auto * res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mctx.get(), status);
+
+        const auto * res = process_ubatch(ubatch, ctx_type_to_graph_type(cparams.ctx_type), mctx.get(), status);
 
         if (!res) {
             // the last ubatch failed or was aborted -> remove all positions of that ubatch from the memory module
@@ -1727,8 +1797,9 @@ int llama_context::decode(const llama_batch & batch_inp) {
         //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
         //}
 
-        auto * t_logits = res->get_logits();
-        auto * t_embd   = cparams.embeddings ? res->get_embd() : nullptr;
+        auto * t_logits        = res->get_logits();
+        auto * t_embd          = cparams.embeddings          ? res->get_embd()        : nullptr;
+        auto * t_h_pre_norm    = cparams.embeddings_pre_norm ? res->get_h_pre_norm()  : nullptr;
 
         if (t_embd && res->get_embd_pooled()) {
             t_embd = res->get_embd_pooled();
@@ -1809,6 +1880,20 @@ int llama_context::decode(const llama_batch & batch_inp) {
             }
         }
 
+        // extract pre-norm embeddings (hidden state before the final output norm)
+        // only meaningful in LLAMA_POOLING_TYPE_NONE (per-token); other pooling modes are ignored.
+        if (embd_pre_norm.data && t_h_pre_norm && n_outputs > 0 && cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
+            ggml_backend_t backend_h = ggml_backend_sched_get_tensor_backend(sched.get(), t_h_pre_norm);
+            GGML_ASSERT(backend_h != nullptr);
+
+            const uint32_t n_embd = hparams.n_embd;
+            float * embd_pre_norm_out = embd_pre_norm.data + n_outputs_prev*n_embd;
+
+            GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
+            GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd <= (int64_t) embd_pre_norm.size);
+            ggml_backend_tensor_get_async(backend_h, t_h_pre_norm, embd_pre_norm_out, 0, n_outputs*n_embd*sizeof(float));
+        }
+
         // Copy backend sampling output if this ubatch produced any sampling tensors.
         if (has_samplers && (!res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty())) {
             const auto seq_to_output_row = build_seq_to_output_row(ubatch, n_outputs_prev);
@@ -1893,10 +1978,12 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const auto n_batch    = cparams.n_batch;
     const auto n_vocab    = vocab.n_tokens();
+    const auto n_embd     = hparams.n_embd;
     const auto n_embd_out = hparams.n_embd_out();
 
-    bool has_logits = true;
-    bool has_embd   = cparams.embeddings;
+    bool has_logits        = true;
+    bool has_embd          = cparams.embeddings;
+    bool has_embd_pre_norm = cparams.embeddings_pre_norm;
 
     // TODO: hacky enc-dec support
     if (model.arch == LLM_ARCH_T5) {
@@ -1908,8 +1995,9 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     size_t backend_float_count = 0;
     size_t backend_token_count = 0;
 
-    logits.size = has_logits ? n_vocab*n_outputs_max : 0;
-    embd.size   = has_embd ? n_embd_out*n_outputs_max : 0;
+    logits.size        = has_logits        ? n_vocab*n_outputs_max     : 0;
+    embd.size          = has_embd          ? n_embd_out*n_outputs_max  : 0;
+    embd_pre_norm.size = has_embd_pre_norm ? n_embd*n_outputs_max      : 0;
 
     // Allocate backend sampling output buffers if there are backend samplers configured.
     const bool has_sampling = !sampling.samplers.empty();
@@ -1925,8 +2013,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
     const size_t new_size  =
-        (logits.size + embd.size + backend_float_count) * sizeof(float) +
-        (                          backend_token_count) * sizeof(llama_token);
+        (logits.size + embd.size + embd_pre_norm.size + backend_float_count) * sizeof(float) +
+        (                                               backend_token_count) * sizeof(llama_token);
 
     // alloc only when more than the current capacity is required
     // TODO: also consider shrinking the buffer
@@ -1942,6 +2030,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
             buf_output = nullptr;
             logits.data = nullptr;
             embd.data = nullptr;
+            embd_pre_norm.data = nullptr;
         }
 
         auto * buft = ggml_backend_cpu_buffer_type();
@@ -1970,6 +2059,9 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     embd = has_embd ? buffer_view<float>{(float *) (base + offset), embd.size} : buffer_view<float>{nullptr, 0};
     offset += embd.size * sizeof(float);
 
+    embd_pre_norm = has_embd_pre_norm ? buffer_view<float>{(float *) (base + offset), embd_pre_norm.size} : buffer_view<float>{nullptr, 0};
+    offset += embd_pre_norm.size * sizeof(float);
+
     if (has_sampling) {
         sampling.logits = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
         offset += sampling.logits.size * sizeof(float);
@@ -2034,6 +2126,12 @@ void llama_context::output_reorder() {
             }
         }
 
+        if (embd_pre_norm.size > 0) {
+            for (uint64_t k = 0; k < n_embd; k++) {
+                std::swap(embd_pre_norm.data[i0*n_embd + k], embd_pre_norm.data[i1*n_embd + k]);
+            }
+        }
+
         if (!sampling.samplers.empty()) {
             assert(sampling.logits.size > 0);
             assert(sampling.probs.size > 0);
@@ -2121,7 +2219,7 @@ ggml_cgraph * llama_context::graph_reserve(
 
     auto * res = gf_res_reserve.get();
 
-    const auto gparams = graph_params(res, ubatch, mctx, LLM_GRAPH_TYPE_DEFAULT);
+    const auto gparams = graph_params(res, ubatch, mctx, ctx_type_to_graph_type(cparams.ctx_type));
 
     res->reset();
 
@@ -2230,13 +2328,17 @@ llm_graph_cb llama_context::graph_get_cb() const {
 
 class llama_io_write_dummy : public llama_io_write_i {
 public:
-    llama_io_write_dummy() = default;
+    llama_io_write_dummy(bool skip_tensors) : skip_tensors(skip_tensors) {}
 
     void write(const void * /* src */, size_t size) override {
         size_written += size;
     }
 
-    void write_tensor(const ggml_tensor * /* tensor */, size_t /* offset */, size_t size) override {
+    void write_tensor(ggml_tensor * /* tensor */, size_t /* offset */, size_t size) override {
+        if (skip_tensors) {
+            return;
+        }
+
         size_written += size;
     }
 
@@ -2245,14 +2347,23 @@ class llama_io_write_dummy : public llama_io_write_i {
     }
 
 private:
+    const bool skip_tensors;
+
     size_t size_written = 0;
 };
 
-class llama_io_write_buffer : public llama_io_write_i {
+class llama_io_write_host : public llama_io_write_i {
 public:
-    llama_io_write_buffer(
+    llama_io_write_host(
             uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
 
+    ~llama_io_write_host() {
+        // TODO: add backend support to batch tensor_get? or some other way to speed this up
+        for (const auto & winfo : winfos) {
+            ggml_backend_tensor_get(winfo.tensor, winfo.ptr, winfo.offset, winfo.size);
+        }
+    }
+
     void write(const void * src, size_t size) override {
         if (size > buf_size) {
             throw std::runtime_error("unexpectedly reached end of buffer");
@@ -2263,11 +2374,14 @@ class llama_io_write_buffer : public llama_io_write_i {
         buf_size -= size;
     }
 
-    void write_tensor(const ggml_tensor * tensor, size_t offset, size_t size) override {
+    void write_tensor(ggml_tensor * tensor, size_t offset, size_t size) override {
         if (size > buf_size) {
             throw std::runtime_error("unexpectedly reached end of buffer");
         }
-        ggml_backend_tensor_get(tensor, ptr, offset, size);
+
+        // save the write for later during destruction
+        winfos.push_back({tensor, ptr, size, offset});
+
         ptr += size;
         size_written += size;
         buf_size -= size;
@@ -2281,25 +2395,48 @@ class llama_io_write_buffer : public llama_io_write_i {
     uint8_t * ptr;
     size_t buf_size = 0;
     size_t size_written = 0;
+
+    struct write_info {
+        ggml_tensor * tensor;
+        uint8_t * ptr;
+        size_t size;
+        size_t offset;
+    };
+    std::vector<write_info> winfos;
 };
 
-class llama_io_read_buffer : public llama_io_read_i {
+class llama_io_read_host : public llama_io_read_i {
 public:
-    llama_io_read_buffer(const uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
+    llama_io_read_host(const uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
+
+    ~llama_io_read_host() {
+        // flush the reads
+        for (const auto & rinfo : rinfos) {
+            ggml_backend_tensor_set(rinfo.tensor, rinfo.ptr, rinfo.offset, rinfo.size);
+        }
+    }
 
-    const uint8_t * read(size_t size) override {
-        const uint8_t * base_ptr = ptr;
+    void read(void * dst, size_t size) override {
         if (size > buf_size) {
             throw std::runtime_error("unexpectedly reached end of buffer");
         }
+        memcpy(dst, ptr, size);
         ptr += size;
         size_read += size;
         buf_size -= size;
-        return base_ptr;
     }
 
-    void read_to(void * dst, size_t size) override {
-        memcpy(dst, read(size), size);
+    void read_tensor(ggml_tensor * tensor, size_t offset, size_t size) override {
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+
+        // save for later during destruction
+        rinfos.push_back({tensor, ptr, size, offset});
+
+        ptr += size;
+        size_read += size;
+        buf_size -= size;
     }
 
     size_t n_bytes() override {
@@ -2310,6 +2447,14 @@ class llama_io_read_buffer : public llama_io_read_i {
     const uint8_t * ptr;
     size_t buf_size = 0;
     size_t size_read = 0;
+
+    struct read_info {
+        ggml_tensor * tensor;
+        const uint8_t * ptr;
+        size_t size;
+        size_t offset;
+    };
+    std::vector<read_info> rinfos;
 };
 
 class llama_io_write_file : public llama_io_write_i {
@@ -2321,7 +2466,7 @@ class llama_io_write_file : public llama_io_write_i {
         size_written += size;
     }
 
-    void write_tensor(const ggml_tensor * tensor, size_t offset, size_t size) override {
+    void write_tensor(ggml_tensor * tensor, size_t offset, size_t size) override {
         temp_buffer.resize(size);
         ggml_backend_tensor_get(tensor, temp_buffer.data(), offset, size);
         write(temp_buffer.data(), temp_buffer.size());
@@ -2341,15 +2486,15 @@ class llama_io_read_file : public llama_io_read_i {
 public:
     llama_io_read_file(llama_file * f) : file(f) {}
 
-    void read_to(void * dst, size_t size) override {
+    void read(void * dst, size_t size) override {
         file->read_raw(dst, size);
         size_read += size;
     }
 
-    const uint8_t * read(size_t size) override {
+    void read_tensor(ggml_tensor * tensor, size_t offset, size_t size) override {
         temp_buffer.resize(size);
-        read_to(temp_buffer.data(), size);
-        return temp_buffer.data();
+        read(temp_buffer.data(), size);
+        ggml_backend_tensor_set(tensor, temp_buffer.data(), offset, size);
     }
 
     size_t n_bytes() override {
@@ -2362,8 +2507,229 @@ class llama_io_read_file : public llama_io_read_i {
     std::vector<uint8_t> temp_buffer;
 };
 
+class llama_io_write_device : public llama_io_write_i {
+public:
+    llama_io_write_device(uint8_t * p, size_t len, llama_memory_buffers & mbufs) : ptr(p), buf_size(len), mbufs(mbufs)  {
+    }
+
+    ~llama_io_write_device() {
+        llama_memory_buffers mbufs_new;
+
+        for (const auto & winfo : winfos) {
+            auto * buft = ggml_backend_buffer_get_type(winfo.tensor->buffer);
+
+            mbufs_new[buft].n_tensors++;
+            mbufs_new[buft].total_size += winfo.size;
+        }
+
+        for (auto & [buft, mbuf] : mbufs_new) {
+            ggml_init_params params = {
+                /*.mem_size   =*/ 2*mbuf.n_tensors*ggml_tensor_overhead(),
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+
+            mbuf.ctx.reset(ggml_init(params));
+
+            mbuf.org.reserve(mbuf.n_tensors);
+            mbuf.cpy.reserve(mbuf.n_tensors);
+        }
+
+        for (const auto & winfo : winfos) {
+            auto * buft = ggml_backend_buffer_get_type(winfo.tensor->buffer);
+
+            const int64_t n = winfo.size/ggml_element_size(winfo.tensor);
+
+            auto & mbuf = mbufs_new[buft];
+
+            mbuf.org.push_back(ggml_view_1d      (mbuf.ctx.get(), winfo.tensor, n, winfo.offset));
+            mbuf.cpy.push_back(ggml_new_tensor_1d(mbuf.ctx.get(), winfo.tensor->type, n));
+        }
+
+        for (auto & [buft, mbuf] : mbufs_new) {
+            auto & mbuf_cur = mbufs[buft];
+
+            bool need_alloc = false;
+
+            need_alloc = need_alloc || (!mbuf_cur.buf);
+            need_alloc = need_alloc || (mbuf_cur.org.size() != mbuf.org.size());
+            need_alloc = need_alloc || (mbuf_cur.total_size != mbuf.total_size);
+
+            if (!need_alloc) {
+                for (size_t i = 0; i < mbuf_cur.org.size(); ++i) {
+                    auto * org0 = mbuf_cur.org[i];
+                    auto * org1 = mbuf.org[i];
+
+                    if (!ggml_are_same_shape(org0, org1)) {
+                        need_alloc = true;
+                        break;
+                    }
+
+                    if (org0->view_src != org1->view_src || org0->view_offs != org1->view_offs) {
+                        need_alloc = true;
+                        break;
+                    }
+                }
+            }
+
+            if (need_alloc) {
+                if (!mbuf_cur.buf || mbuf_cur.total_size != mbuf.total_size) {
+                    mbuf_cur = std::move(mbuf);
+
+                    mbuf_cur.buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(mbuf_cur.ctx.get(), buft));
+
+                    LLAMA_LOG_INFO("%s: allocated '%s' buffer %.3f MiB\n", __func__, ggml_backend_buft_name(buft), mbuf.total_size/1024.0/1024.0);
+                } else {
+                    //LLAMA_LOG_INFO("%s: reallocating tensors in '%s' buffer %.3f MiB\n", __func__, ggml_backend_buft_name(buft), mbuf.total_size/1024.0/1024.0);
+
+                    // save the old buffer and allocate the new tensors in it
+                    auto buf = std::move(mbuf_cur.buf);
+
+                    mbuf_cur = std::move(mbuf);
+
+                    ggml_tallocr talloc = ggml_tallocr_new(buf.get());
+
+                    for (size_t i = 0; i < mbuf_cur.org.size(); ++i) {
+                        ggml_backend_view_init(mbuf_cur.org[i]);
+                        ggml_tallocr_alloc(&talloc, mbuf_cur.cpy[i]);
+                    }
+
+                    mbuf_cur.buf = std::move(buf);
+                }
+            }
+
+            for (size_t i = 0; i < mbuf_cur.org.size(); ++i) {
+                ggml_backend_tensor_copy(mbuf_cur.org[i], mbuf_cur.cpy[i]);
+            }
+        }
+    }
+
+    void write(const void * src, size_t size) override {
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+        memcpy(ptr, src, size);
+        ptr += size;
+        size_written += size;
+        buf_size -= size;
+    }
+
+    void write_tensor(ggml_tensor * tensor, size_t offset, size_t size) override {
+        // save the write for later during destruction
+        winfos.push_back({tensor, ptr, size, offset});
+    }
+
+    size_t n_bytes() override {
+        return size_written;
+    }
+
+private:
+    uint8_t * ptr;
+    size_t buf_size = 0;
+    size_t size_written = 0;
+
+    struct write_info {
+        ggml_tensor * tensor;
+        uint8_t * ptr;
+        size_t size;
+        size_t offset;
+    };
+    std::vector<write_info> winfos;
+
+    llama_memory_buffers & mbufs;
+};
+
+class llama_io_read_device : public llama_io_read_i {
+public:
+    llama_io_read_device(const uint8_t * p, size_t len, const llama_memory_buffers & mbufs) : ptr(p), buf_size(len), mbufs(mbufs) {
+    }
+
+    ~llama_io_read_device() {
+        llama_memory_buffers mbufs_new;
+
+        for (const auto & rinfo : rinfos) {
+            auto * buft = ggml_backend_buffer_get_type(rinfo.tensor->buffer);
+
+            mbufs_new[buft].n_tensors++;
+            mbufs_new[buft].total_size += rinfo.size;
+        }
+
+        for (auto & [buft, mbuf] : mbufs_new) {
+            ggml_init_params params = {
+                /*.mem_size   =*/ mbuf.n_tensors*ggml_tensor_overhead(),
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+
+            mbuf.ctx.reset(ggml_init(params));
+
+            mbuf.org.reserve(mbuf.n_tensors);
+        }
+
+        for (const auto & rinfo : rinfos) {
+            auto * buft = ggml_backend_buffer_get_type(rinfo.tensor->buffer);
+
+            const int64_t n = rinfo.size/ggml_element_size(rinfo.tensor);
+
+            auto & mbuf = mbufs_new[buft];
+
+            mbuf.org.push_back(ggml_view_1d(mbuf.ctx.get(), rinfo.tensor, n, rinfo.offset));
+
+            ggml_backend_view_init(mbuf.org.back());
+        }
+
+        for (auto & [buft, mbuf] : mbufs_new) {
+            const auto & mbuf_cur = mbufs.at(buft);
+
+            if (!mbuf_cur.buf || mbuf_cur.n_tensors != mbuf.n_tensors || mbuf_cur.total_size != mbuf.total_size) {
+                GGML_ABORT("%s: memory buffer mismatch\n", __func__);
+            }
+
+            for (size_t i = 0; i < mbuf_cur.org.size(); ++i) {
+                ggml_backend_tensor_copy(mbuf_cur.cpy[i], mbuf.org[i]);
+            }
+        }
+
+        GGML_ASSERT(buf_size == 0);
+    }
+
+    void read(void * dst, size_t size) override {
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+        memcpy(dst, ptr, size);
+        ptr += size;
+        size_read += size;
+        buf_size -= size;
+    }
+
+    void read_tensor(ggml_tensor * tensor, size_t offset, size_t size) override {
+        // save for later during destruction
+        rinfos.push_back({tensor, ptr, size, offset});
+    }
+
+    size_t n_bytes() override {
+        return size_read;
+    }
+
+private:
+    const uint8_t * ptr;
+    size_t buf_size = 0;
+    size_t size_read = 0;
+
+    struct read_info {
+        ggml_tensor * tensor;
+        const uint8_t * ptr;
+        size_t size;
+        size_t offset;
+    };
+    std::vector<read_info> rinfos;
+
+    const llama_memory_buffers & mbufs;
+};
+
 size_t llama_context::state_get_size() {
-    llama_io_write_dummy io;
+    llama_io_write_dummy io(false);
     try {
         return state_write_data(io);
     } catch (const std::exception & err) {
@@ -2373,7 +2739,7 @@ size_t llama_context::state_get_size() {
 }
 
 size_t llama_context::state_get_data(uint8_t * dst, size_t size) {
-    llama_io_write_buffer io(dst, size);
+    llama_io_write_host io(dst, size);
     try {
         return state_write_data(io);
     } catch (const std::exception & err) {
@@ -2383,7 +2749,7 @@ size_t llama_context::state_get_data(uint8_t * dst, size_t size) {
 }
 
 size_t llama_context::state_set_data(const uint8_t * src, size_t size) {
-    llama_io_read_buffer io(src, size);
+    llama_io_read_host io(src, size);
     try {
         return state_read_data(io);
     } catch (const std::exception & err) {
@@ -2392,9 +2758,14 @@ size_t llama_context::state_set_data(const uint8_t * src, size_t size) {
     }
 }
 
+static constexpr uint32_t io_magic = 0xaf143cd8;
+
 size_t llama_context::state_seq_get_size(llama_seq_id seq_id, llama_state_seq_flags flags) {
-    llama_io_write_dummy io;
+    llama_io_write_dummy io(flags & LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
     try {
+        io.write(&io_magic, sizeof(io_magic));
+        io.write(&seq_id, sizeof(seq_id));
+
         return state_seq_write_data(io, seq_id, flags);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: error getting state size: %s\n", __func__, err.what());
@@ -2403,9 +2774,18 @@ size_t llama_context::state_seq_get_size(llama_seq_id seq_id, llama_state_seq_fl
 }
 
 size_t llama_context::state_seq_get_data(llama_seq_id seq_id, uint8_t * dst, size_t size, llama_state_seq_flags flags) {
-    llama_io_write_buffer io(dst, size);
+    std::unique_ptr<llama_io_write_i> io;
+    if (flags & LLAMA_STATE_SEQ_FLAGS_ON_DEVICE) {
+        io = std::make_unique<llama_io_write_device>(dst, size, mem_storage[seq_id]);
+    } else {
+        io = std::make_unique<llama_io_write_host>(dst, size);
+    }
+
     try {
-        return state_seq_write_data(io, seq_id, flags);
+        io->write(&io_magic, sizeof(io_magic));
+        io->write(&seq_id, sizeof(seq_id));
+
+        return state_seq_write_data(*io, seq_id, flags);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: error saving state: %s\n", __func__, err.what());
         return 0;
@@ -2413,9 +2793,38 @@ size_t llama_context::state_seq_get_data(llama_seq_id seq_id, uint8_t * dst, siz
 }
 
 size_t llama_context::state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size, llama_state_seq_flags flags) {
-    llama_io_read_buffer io(src, size);
+    std::unique_ptr<llama_io_read_i> io;
+    if (flags & LLAMA_STATE_SEQ_FLAGS_ON_DEVICE) {
+        // create a temporary io to read the magic and the src seq_id
+        io = std::make_unique<llama_io_read_host>(src, size);
+
+        uint32_t magic_read;
+        io->read(&magic_read, sizeof(magic_read));
+        if (io_magic != magic_read) {
+            throw std::runtime_error("wrong sequence state magic");
+        }
+
+        llama_seq_id seq_id_read;
+        io->read(&seq_id_read, sizeof(seq_id_read));
+
+        GGML_ASSERT(mem_storage.find(seq_id_read) != mem_storage.end());
+
+        io = std::make_unique<llama_io_read_device>(src, size, mem_storage[seq_id_read]);
+    } else {
+        io = std::make_unique<llama_io_read_host>(src, size);
+    }
+
     try {
-        return state_seq_read_data(io, seq_id, flags);
+        uint32_t magic_read;
+        io->read(&magic_read, sizeof(magic_read));
+        if (io_magic != magic_read) {
+            throw std::runtime_error("wrong sequence state magic");
+        }
+
+        llama_seq_id seq_id_read;
+        io->read(&seq_id_read, sizeof(seq_id_read));
+
+        return state_seq_read_data(*io, seq_id, flags);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: error loading state: %s\n", __func__, err.what());
         return 0;
@@ -2789,7 +3198,7 @@ void llama_context::opt_epoch_iter(
 
             auto * res = gf_res_prev.get();
 
-            const auto gparams = graph_params(res, ubatch, mctx.get(), LLM_GRAPH_TYPE_DEFAULT);
+            const auto gparams = graph_params(res, ubatch, mctx.get(), ctx_type_to_graph_type(cparams.ctx_type));
 
             res->reset();
 
@@ -2890,8 +3299,10 @@ llama_context_params llama_context_default_params() {
         /*.n_batch                     =*/ 2048,
         /*.n_ubatch                    =*/ 512,
         /*.n_seq_max                   =*/ 1,
+        /*.n_rs_seq                    =*/ 0,
         /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
         /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
+        /*.ctx_type                    =*/ LLAMA_CONTEXT_TYPE_DEFAULT,
         /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
         /*.pooling_type                =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
         /*.attention_type              =*/ LLAMA_ATTENTION_TYPE_UNSPECIFIED,
@@ -3020,6 +3431,13 @@ llama_context * llama_init_from_model(
                        model->hparams.pooling_type, params.pooling_type);
     }
 
+    if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP &&
+        model->hparams.nextn_predict_layers == 0) {
+        LLAMA_LOG_WARN("%s: context type MTP requested but model doesn't contain MTP layers\n", __func__);
+        return nullptr;
+    }
+
+
     try {
         auto * ctx = new llama_context(*model, params);
         return ctx;
@@ -3061,6 +3479,10 @@ uint32_t llama_n_seq_max(const llama_context * ctx) {
     return ctx->n_seq_max();
 }
 
+uint32_t llama_n_rs_seq(const llama_context * ctx) {
+    return ctx->get_cparams().n_rs_seq;
+}
+
 const llama_model * llama_get_model(const llama_context * ctx) {
     return &ctx->get_model();
 }
@@ -3150,6 +3572,22 @@ float * llama_get_embeddings_seq(llama_context * ctx, llama_seq_id seq_id) {
     return ctx->get_embeddings_seq(seq_id);
 }
 
+void llama_set_embeddings_pre_norm(llama_context * ctx, bool value) {
+    ctx->set_embeddings_pre_norm(value);
+}
+
+float * llama_get_embeddings_pre_norm(llama_context * ctx) {
+    ctx->synchronize();
+
+    return ctx->get_embeddings_pre_norm();
+}
+
+float * llama_get_embeddings_pre_norm_ith(llama_context * ctx, int32_t i) {
+    ctx->synchronize();
+
+    return ctx->get_embeddings_pre_norm_ith(i);
+}
+
 bool llama_set_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
     return ctx->set_sampler(seq_id, smpl);
 }
@@ -3431,7 +3869,6 @@ size_t llama_state_seq_get_data_ext(llama_context * ctx, uint8_t * dst, size_t s
 
     return ctx->state_seq_get_data(seq_id, dst, size, flags);
 }
-
 size_t llama_state_seq_set_data_ext(llama_context * ctx, const uint8_t * src, size_t size, llama_seq_id seq_id, llama_state_seq_flags flags) {
     ctx->synchronize();
 
diff --git a/src/llama-context.h b/src/llama-context.h
index 53c705eaffc..e16ac4c618b 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -23,6 +23,21 @@ class llama_io_write_i;
 struct llama_memory_i;
 struct llama_memory_context_i;
 
+// stores copy of the memory in device buffer. used for fast state save/load
+struct llama_memory_buffer {
+    int n_tensors = 0;
+    size_t total_size = 0;
+
+    ggml_backend_buffer_ptr buf;
+
+    ggml_context_ptr ctx;
+
+    std::vector<ggml_tensor *> org;
+    std::vector<ggml_tensor *> cpy;
+};
+
+using llama_memory_buffers = std::map<ggml_backend_buffer_type_t, llama_memory_buffer>;
+
 struct llama_context {
     // init scheduler and compute buffers, reserve worst-case graphs
     llama_context(
@@ -69,6 +84,9 @@ struct llama_context {
     float * get_embeddings_ith(int32_t i);
     float * get_embeddings_seq(llama_seq_id seq_id);
 
+    float * get_embeddings_pre_norm();
+    float * get_embeddings_pre_norm_ith(int32_t i);
+
     llama_token * get_sampled_tokens() const;
     llama_token   get_sampled_token_ith(int32_t idx);
 
@@ -92,6 +110,7 @@ struct llama_context {
     void set_abort_callback(bool (*abort_callback)(void * data), void * abort_callback_data);
 
     void set_embeddings (bool value);
+    void set_embeddings_pre_norm(bool value);
     void set_causal_attn(bool value);
     void set_warmup(bool value);
 
@@ -128,6 +147,7 @@ struct llama_context {
     size_t state_set_data(const uint8_t * src, size_t size);
 
     size_t state_seq_get_size(llama_seq_id seq_id, llama_state_seq_flags flags);
+
     size_t state_seq_get_data(llama_seq_id seq_id,       uint8_t * dst, size_t size, llama_state_seq_flags flags);
     size_t state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size, llama_state_seq_flags flags);
 
@@ -262,6 +282,11 @@ struct llama_context {
     // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
     buffer_view<float> embd = {nullptr, 0};
 
+    // hidden state before the final output norm (2-dimensional array: [n_outputs][n_embd])
+    // populated only when cparams.embeddings_pre_norm is enabled and the model graph
+    // sets llm_graph_result::t_h_pre_norm
+    buffer_view<float> embd_pre_norm = {nullptr, 0};
+
     struct sampling_info {
         // !samplers.empty() to check if any samplers are active
         std::map<llama_seq_id, llama_sampler *> samplers;
@@ -328,6 +353,9 @@ struct llama_context {
     // host buffer for the model output (logits and embeddings)
     ggml_backend_buffer_ptr buf_output;
 
+    // keep copies of the per-sequence memory on the device
+    std::map<llama_seq_id, llama_memory_buffers> mem_storage;
+
     bool has_evaluated_once = false;
 
     // env: LLAMA_GRAPH_REUSE_DISABLE
diff --git a/src/llama-cparams.h b/src/llama-cparams.h
index 9d359474132..5898a1c38d5 100644
--- a/src/llama-cparams.h
+++ b/src/llama-cparams.h
@@ -12,6 +12,7 @@ struct llama_cparams {
     uint32_t n_batch;
     uint32_t n_ubatch;
     uint32_t n_seq_max;
+    uint32_t n_rs_seq;        // number of recurrent-state snapshots per seq for rollback
     int32_t  n_threads;       // number of threads to use for generation
     int32_t  n_threads_batch; // number of threads to use for batch processing
 
@@ -27,6 +28,7 @@ struct llama_cparams {
     float yarn_beta_slow;
 
     bool embeddings;
+    bool embeddings_pre_norm; // also extract the hidden state before the final output norm
     bool causal_attn;
     bool offload_kqv;
     bool flash_attn;
@@ -40,6 +42,7 @@ struct llama_cparams {
     bool kv_unified;
     bool pipeline_parallel;
 
+    enum llama_context_type ctx_type;
     enum llama_pooling_type pooling_type;
 
     ggml_backend_sched_eval_callback cb_eval;
diff --git a/src/llama-ext.h b/src/llama-ext.h
index 8ce29d217cb..11f1986676a 100644
--- a/src/llama-ext.h
+++ b/src/llama-ext.h
@@ -88,3 +88,19 @@ LLAMA_API int32_t llama_model_n_devices(const struct llama_model * model);
 LLAMA_API ggml_backend_dev_t llama_model_get_device(const struct llama_model * model, int i);
 
 LLAMA_API llama_memory_breakdown llama_get_memory_breakdown(const struct llama_context * ctx);
+
+//
+// pre-norm embeddings (hidden state before the final output norm)
+//
+
+// mirrors:
+// LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
+LLAMA_API void llama_set_embeddings_pre_norm(struct llama_context * ctx, bool value);
+
+// mirrors:
+// LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+LLAMA_API float * llama_get_embeddings_pre_norm(struct llama_context * ctx);
+
+// mirrors:
+// LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
+LLAMA_API float * llama_get_embeddings_pre_norm_ith(struct llama_context * ctx, int32_t i);
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 5e5b9c9054e..f4ca37c5160 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -65,8 +65,13 @@ static ggml_tensor * ggml_mul_mat_aux(
 
     ggml_tensor * res;
 
-    res = ggml_reshape_2d(ctx, cur, n, ggml_nelements(cur)/n);
+    if (!ggml_is_contiguous(cur)) {
+        res = ggml_cont_2d   (ctx, cur, n, ggml_nelements(cur)/n);
+    } else {
+        res = ggml_reshape_2d(ctx, cur, n, ggml_nelements(cur)/n);
+    }
     res = ggml_mul_mat   (ctx, rot, res);
+    ggml_mul_mat_set_hint(res, GGML_HINT_SRC0_IS_HADAMARD);
     res = ggml_reshape_4d(ctx, res, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3]);
 
     return res;
@@ -2661,7 +2666,8 @@ ggml_tensor * llm_graph_context::build_rs(
             int32_t   rs_zero,
         const llm_graph_get_rows_fn & get_state_rows) const {
 
-    ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, rs_size);
+    GGML_UNUSED(rs_size);
+    ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, s->ne[1]);
 
     // Clear a single state which will then be copied to the other cleared states.
     // Note that this is a no-op when the view is zero-sized.
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 5cb1756c6a9..9e55d0a675e 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -32,6 +32,7 @@ enum llm_graph_type {
     LLM_GRAPH_TYPE_DEFAULT,
     LLM_GRAPH_TYPE_ENCODER,
     LLM_GRAPH_TYPE_DECODER,
+    LLM_GRAPH_TYPE_DECODER_MTP,
 };
 
 enum llm_ffn_op_type {
@@ -644,6 +645,7 @@ class llm_graph_result {
     ggml_tensor * get_logits()      const { return t_logits; }
     ggml_tensor * get_embd()        const { return t_embd; }
     ggml_tensor * get_embd_pooled() const { return t_embd_pooled; }
+    ggml_tensor * get_h_pre_norm()  const { return t_h_pre_norm; }
 
     ggml_cgraph  * get_gf()  const { return gf; }
     ggml_context * get_ctx() const { return ctx_compute.get(); }
@@ -672,6 +674,7 @@ class llm_graph_result {
     ggml_tensor * t_logits      = nullptr;
     ggml_tensor * t_embd        = nullptr;
     ggml_tensor * t_embd_pooled = nullptr;
+    ggml_tensor * t_h_pre_norm  = nullptr; // [n_embd, n_outputs] hidden state before final output norm
 
     std::map<llama_seq_id, ggml_tensor*> t_sampled_logits;
     std::map<llama_seq_id, ggml_tensor*> t_candidates;
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 002d15d415f..2239309c8fb 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -229,6 +229,12 @@ uint32_t llama_hparams::n_embd_head_v_mla() const {
 }
 
 bool llama_hparams::has_kv(uint32_t il) const {
+    if (kv_only_nextn) {
+        // MTP head: only the trailing nextn_predict_layers blocks own a KV cache;
+        // the leading trunk blocks are not executed in this graph.
+        return nextn_predict_layers > 0 && il >= (n_layer - nextn_predict_layers);
+    }
+
     if (n_layer_kv_from_start >= 0) {
         if (il < (uint32_t) n_layer_kv_from_start) {
             return true;
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index c2000c77c37..e2d051edc6c 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -92,6 +92,8 @@ struct llama_hparams {
     uint32_t moe_latent_size      = 0;
     uint32_t nextn_predict_layers = 0;
 
+    bool kv_only_nextn = false; // if true, only the last nextn_predict_layers blocks have a KV cache (MTP head arches)
+
     float f_norm_eps;
     float f_norm_rms_eps;
     float f_norm_group_eps;
@@ -116,6 +118,7 @@ struct llama_hparams {
     float    rope_freq_base_train_swa  = 10000.0f;
     float    rope_freq_scale_train;
     float    rope_freq_scale_train_swa = 1.0f;
+    float    rope_scaling_alpha        = 0.0f;  // NTK-aware alpha for XDRoPE
 
     uint32_t n_ctx_orig_yarn;
     float    rope_yarn_log_mul = 0.0f;
@@ -165,6 +168,8 @@ struct llama_hparams {
     float    f_attn_out_scale = 0.0f;
     uint32_t attn_temp_length = 0;
 
+    float    f_attn_value_scale = 0.0f;
+
     bool causal_attn   = true;
     bool use_alibi     = false;
     bool attn_soft_cap = false;
diff --git a/src/llama-io.cpp b/src/llama-io.cpp
index 7ad70d16334..5ec4634943f 100644
--- a/src/llama-io.cpp
+++ b/src/llama-io.cpp
@@ -1,5 +1,7 @@
 #include "llama-io.h"
 
+#include <vector>
+
 void llama_io_write_i::write_string(const std::string & str) {
     uint32_t str_size = str.size();
 
@@ -9,7 +11,10 @@ void llama_io_write_i::write_string(const std::string & str) {
 
 void llama_io_read_i::read_string(std::string & str) {
     uint32_t str_size;
-    read_to(&str_size, sizeof(str_size));
+    read(&str_size, sizeof(str_size));
+
+    std::vector<char> buf(str_size);
+    read(buf.data(), str_size);
 
-    str.assign((const char *) read(str_size), str_size);
+    str.assign(buf.data(), str_size);
 }
diff --git a/src/llama-io.h b/src/llama-io.h
index ce9216b83b1..f276af4fb96 100644
--- a/src/llama-io.h
+++ b/src/llama-io.h
@@ -12,7 +12,7 @@ class llama_io_write_i {
     virtual ~llama_io_write_i() = default;
 
     virtual void write(const void * src, size_t size) = 0;
-    virtual void write_tensor(const ggml_tensor * tensor, size_t offset, size_t size) = 0;
+    virtual void write_tensor(ggml_tensor * tensor, size_t offset, size_t size) = 0;
 
     // bytes written so far
     virtual size_t n_bytes() = 0;
@@ -25,8 +25,8 @@ class llama_io_read_i {
     llama_io_read_i() = default;
     virtual ~llama_io_read_i() = default;
 
-    virtual const uint8_t * read(size_t size) = 0;
-    virtual void read_to(void * dst, size_t size) = 0;
+    virtual void read(void * dst, size_t size) = 0;
+    virtual void read_tensor(ggml_tensor * tensor, size_t offset, size_t size) = 0;
 
     // bytes read so far
     virtual size_t n_bytes() = 0;
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index bbfb01a9055..5a1a1c2e88b 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -67,6 +67,7 @@ static ggml_tensor * ggml_mul_mat_aux(
 
     res = ggml_reshape_2d(ctx, cur, n, ggml_nelements(cur)/n);
     res = ggml_mul_mat   (ctx, rot, res);
+    ggml_mul_mat_set_hint(res, GGML_HINT_SRC0_IS_HADAMARD);
     res = ggml_reshape_4d(ctx, res, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3]);
 
     return res;
@@ -90,10 +91,10 @@ extern TURBO_IQ_IMPORT float g_innerq_scale_inv_host[INNERQ_MAX_CHANNELS];
 TURBO_IQ_IMPORT bool turbo_innerq_needs_tensor_update(void);
 TURBO_IQ_IMPORT void turbo_innerq_mark_tensor_updated(void);
 #else
-static bool  g_innerq_finalized = false;
-static float g_innerq_scale_inv_host[INNERQ_MAX_CHANNELS] = {};
-static bool turbo_innerq_needs_tensor_update(void) { return false; }
-static void turbo_innerq_mark_tensor_updated(void) {}
+[[maybe_unused]] static bool  g_innerq_finalized = false;
+[[maybe_unused]] static float g_innerq_scale_inv_host[INNERQ_MAX_CHANNELS] = {};
+[[maybe_unused]] static bool turbo_innerq_needs_tensor_update(void) { return false; }
+[[maybe_unused]] static void turbo_innerq_mark_tensor_updated(void) {}
 #endif
 
 //
@@ -2196,14 +2197,14 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama
     GGML_ASSERT(seq_id == -1 || (seq_id >= 0 && (size_t) seq_id < seq_to_stream.size()));
 
     uint32_t n_stream_cur;
-    io.read_to(&n_stream_cur, sizeof(n_stream_cur));
+    io.read(&n_stream_cur, sizeof(n_stream_cur));
     if (n_stream_cur != n_stream) {
         throw std::runtime_error("n_stream mismatch");
     }
 
     for (uint32_t s = 0; s < n_stream; ++s) {
         uint32_t cell_count;
-        io.read_to(&cell_count, sizeof(cell_count));
+        io.read(&cell_count, sizeof(cell_count));
 
         if (cell_count == 0) {
             continue;
@@ -2273,8 +2274,6 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
     // Iterate and write all the keys first, each row is a cell
     // Get whole range at a time
     for (const auto & layer : layers) {
-        const uint32_t il = layer.il;
-
         auto * k = layer.k_stream[cr.strm];
 
         // Use actual tensor width (may be padded for turbo types: e.g. 576→640)
@@ -2298,8 +2297,6 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
 
     if (!v_trans) {
         for (const auto & layer : layers) {
-            const uint32_t il = layer.il;
-
             auto * v = layer.v_stream[cr.strm];
             if (!v) {
                 continue;
@@ -2380,8 +2377,8 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
             llama_pos pos;
             uint32_t n_seq_id;
 
-            io.read_to(&pos,      sizeof(pos));
-            io.read_to(&n_seq_id, sizeof(n_seq_id));
+            io.read(&pos,      sizeof(pos));
+            io.read(&n_seq_id, sizeof(n_seq_id));
 
             if (n_seq_id != 1) {
                 LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
@@ -2390,7 +2387,7 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
 
             if (hparams.n_pos_per_embd() > 1) {
                 llama_kv_cell_ext ext;
-                io.read_to(&ext, sizeof(ext));
+                io.read(&ext, sizeof(ext));
 
                 ubatch.pos[i + ubatch.n_tokens]   = ext.y;
                 ubatch.pos[i + ubatch.n_tokens*2] = ext.x;
@@ -2399,7 +2396,7 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
             // read the sequence id, but directly discard it - we will use dest_seq_id instead
             {
                 llama_seq_id seq_id;
-                io.read_to(&seq_id, sizeof(seq_id));
+                io.read(&seq_id, sizeof(seq_id));
             }
 
             ubatch.pos[i]      = pos;
@@ -2441,20 +2438,20 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
             llama_pos pos;
             uint32_t  n_seq_id;
 
-            io.read_to(&pos,      sizeof(pos));
-            io.read_to(&n_seq_id, sizeof(n_seq_id));
+            io.read(&pos,      sizeof(pos));
+            io.read(&n_seq_id, sizeof(n_seq_id));
 
             cells.pos_set(i, pos);
 
             if (hparams.n_pos_per_embd() > 1) {
                 llama_kv_cell_ext ext;
-                io.read_to(&ext, sizeof(ext));
+                io.read(&ext, sizeof(ext));
                 cells.ext_set(i, ext);
             }
 
             for (uint32_t j = 0; j < n_seq_id; ++j) {
                 llama_seq_id seq_id;
-                io.read_to(&seq_id, sizeof(seq_id));
+                io.read(&seq_id, sizeof(seq_id));
 
                 if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
                     LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, n_seq_max);
@@ -2487,8 +2484,8 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
     uint32_t v_trans;
     uint32_t n_layer;
 
-    io.read_to(&v_trans, sizeof(v_trans));
-    io.read_to(&n_layer, sizeof(n_layer));
+    io.read(&v_trans, sizeof(v_trans));
+    io.read(&n_layer, sizeof(n_layer));
 
     if (n_layer != layers.size()) {
         LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, (uint32_t) layers.size());
@@ -2516,7 +2513,7 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
 
         // Read type of key
         int32_t k_type_i_ref;
-        io.read_to(&k_type_i_ref, sizeof(k_type_i_ref));
+        io.read(&k_type_i_ref, sizeof(k_type_i_ref));
         const int32_t k_type_i = (int32_t) k->type;
         if (k_type_i != k_type_i_ref) {
             LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
@@ -2525,7 +2522,7 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
 
         // Read row size of key
         uint64_t k_size_row_ref;
-        io.read_to(&k_size_row_ref, sizeof(k_size_row_ref));
+        io.read(&k_size_row_ref, sizeof(k_size_row_ref));
         const size_t k_size_row = ggml_row_size(k->type, n_embd_k_gqa);
         if (k_size_row != k_size_row_ref) {
             LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, (size_t) k_size_row_ref, il);
@@ -2535,13 +2532,12 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
         if (cell_count) {
             if (sinfo.is_contiguous()) {
                 // Fast path: contiguous cells, single memcpy
-                ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), sinfo.head() * k_size_row, cell_count * k_size_row);
+                io.read_tensor(k, sinfo.head() * k_size_row, cell_count * k_size_row);
             } else {
                 // Slow path: scatter to non-contiguous positions
-                const void * src = io.read(cell_count * k_size_row);
                 for (uint32_t i = 0; i < cell_count; ++i) {
                     const size_t dst_offset = sinfo.idxs[0][i] * k_size_row;
-                    ggml_backend_tensor_set(k, (const char*)src + i * k_size_row, dst_offset, k_size_row);
+                    io.read_tensor(k, dst_offset, k_size_row);
                 }
             }
         }
@@ -2561,7 +2557,7 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
 
             // Read type of value
             int32_t v_type_i_ref;
-            io.read_to(&v_type_i_ref, sizeof(v_type_i_ref));
+            io.read(&v_type_i_ref, sizeof(v_type_i_ref));
             const int32_t v_type_i = (int32_t) v->type;
             if (v_type_i != v_type_i_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
@@ -2570,7 +2566,7 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
 
             // Read row size of value
             uint64_t v_size_row_ref;
-            io.read_to(&v_size_row_ref, sizeof(v_size_row_ref));
+            io.read(&v_size_row_ref, sizeof(v_size_row_ref));
             const size_t v_size_row = ggml_row_size(v->type, n_embd_v_gqa);
             if (v_size_row != v_size_row_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, (size_t) v_size_row_ref, il);
@@ -2580,13 +2576,12 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
             if (cell_count) {
                 if (sinfo.is_contiguous()) {
                     // Fast path: contiguous cells, single memcpy
-                    ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), sinfo.head() * v_size_row, cell_count * v_size_row);
+                    io.read_tensor(v, sinfo.head() * v_size_row, cell_count * v_size_row);
                 } else {
                     // Slow path: scatter to non-contiguous positions
-                    const void * src = io.read(cell_count * v_size_row);
                     for (uint32_t i = 0; i < cell_count; ++i) {
                         const size_t dst_offset = sinfo.idxs[0][i] * v_size_row;
-                        ggml_backend_tensor_set(v, (const char*)src + i * v_size_row, dst_offset, v_size_row);
+                        io.read_tensor(v, dst_offset, v_size_row);
                     }
                 }
             }
@@ -2605,7 +2600,7 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
 
             // Read type of value
             int32_t v_type_i_ref;
-            io.read_to(&v_type_i_ref, sizeof(v_type_i_ref));
+            io.read(&v_type_i_ref, sizeof(v_type_i_ref));
             const int32_t v_type_i = (int32_t) v->type;
             if (v_type_i != v_type_i_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
@@ -2614,7 +2609,7 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
 
             // Read element size of value
             uint32_t v_size_el_ref;
-            io.read_to(&v_size_el_ref, sizeof(v_size_el_ref));
+            io.read(&v_size_el_ref, sizeof(v_size_el_ref));
             const size_t v_size_el = ggml_type_size(v->type);
             if (v_size_el != v_size_el_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, (size_t) v_size_el_ref, il);
@@ -2623,7 +2618,7 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
 
             // Read GQA embedding size
             uint32_t n_embd_v_gqa_ref;
-            io.read_to(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
+            io.read(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
             if (n_embd_v_gqa != n_embd_v_gqa_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched GQA embedding size (%u != %u, layer %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref, il);
                 return false;
@@ -2635,15 +2630,14 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
                     const uint32_t h = sinfo.head();
                     for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
                         const size_t dst_offset = (h + j * cells.size()) * v_size_el;
-                        ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+                        io.read_tensor(v, dst_offset, cell_count * v_size_el);
                     }
                 } else {
                     // Slow path: scatter to non-contiguous positions
                     for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                        const void * src = io.read(cell_count * v_size_el);
                         for (uint32_t i = 0; i < cell_count; ++i) {
                             const size_t dst_offset = (sinfo.idxs[0][i] + j * cells.size()) * v_size_el;
-                            ggml_backend_tensor_set(v, (const char*)src + i * v_size_el, dst_offset, v_size_el);
+                            io.read_tensor(v, dst_offset, v_size_el);
                         }
                     }
                 }
diff --git a/src/llama-memory-hybrid-iswa.cpp b/src/llama-memory-hybrid-iswa.cpp
index 10e6b459797..a59561ea54d 100644
--- a/src/llama-memory-hybrid-iswa.cpp
+++ b/src/llama-memory-hybrid-iswa.cpp
@@ -24,6 +24,7 @@ llama_memory_hybrid_iswa::llama_memory_hybrid_iswa(
                  uint32_t   rs_size,
                             /* common */
                  uint32_t   n_seq_max,
+                 uint32_t   n_rs_seq,
                      bool   offload,
                      bool   unified,
                             /* layer filters */
@@ -54,6 +55,7 @@ llama_memory_hybrid_iswa::llama_memory_hybrid_iswa(
         offload,
         rs_size,
         n_seq_max,
+        n_rs_seq,
         filter_recr == nullptr ?
             [&](int32_t il) { return hparams.is_recurrent(il); }
             : filter_recr
diff --git a/src/llama-memory-hybrid-iswa.h b/src/llama-memory-hybrid-iswa.h
index 807c8aac96c..c9d3f9f57c5 100644
--- a/src/llama-memory-hybrid-iswa.h
+++ b/src/llama-memory-hybrid-iswa.h
@@ -34,6 +34,7 @@ class llama_memory_hybrid_iswa : public llama_memory_i {
                  uint32_t   rs_size,
                             /* common */
                  uint32_t   n_seq_max,
+                 uint32_t   n_rs_seq,
                      bool   offload,
                      bool   unified,
                             /* layer filters */
diff --git a/src/llama-memory-hybrid.cpp b/src/llama-memory-hybrid.cpp
index b3755d5768b..5c2e2afdbef 100644
--- a/src/llama-memory-hybrid.cpp
+++ b/src/llama-memory-hybrid.cpp
@@ -24,6 +24,7 @@ llama_memory_hybrid::llama_memory_hybrid(
                  uint32_t   rs_size,
                             /* common */
                  uint32_t   n_seq_max,
+                 uint32_t   n_rs_seq,
                      bool   offload,
                      bool   unified,
                             /* layer filters */
@@ -54,6 +55,7 @@ llama_memory_hybrid::llama_memory_hybrid(
         offload,
         rs_size,
         n_seq_max,
+        n_rs_seq,
         filter_recr == nullptr ?
             [&](int32_t il) { return hparams.is_recurrent(il); }
             : filter_recr
diff --git a/src/llama-memory-hybrid.h b/src/llama-memory-hybrid.h
index 87dd3487692..af205875b71 100644
--- a/src/llama-memory-hybrid.h
+++ b/src/llama-memory-hybrid.h
@@ -34,6 +34,7 @@ class llama_memory_hybrid : public llama_memory_i {
                  uint32_t   rs_size,
                             /* common */
                  uint32_t   n_seq_max,
+                 uint32_t   n_rs_seq,
                      bool   offload,
                      bool   unified,
                             /* layer filters */
diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp
index 9287fe45e96..aeb866657f2 100644
--- a/src/llama-memory-recurrent.cpp
+++ b/src/llama-memory-recurrent.cpp
@@ -24,6 +24,7 @@ llama_memory_recurrent::llama_memory_recurrent(
                      bool   offload,
                  uint32_t   mem_size,
                  uint32_t   n_seq_max,
+                 uint32_t   n_rs_seq,
     const layer_filter_cb & filter) : hparams(model.hparams), n_seq_max(n_seq_max) {
     const int32_t n_layer = hparams.n_layer;
 
@@ -31,6 +32,9 @@ llama_memory_recurrent::llama_memory_recurrent(
     size = mem_size;
     used = 0;
 
+    this->n_rs_seq = n_rs_seq;
+    rs_idx.assign(n_seq_max, 0);
+
     cells.clear();
     cells.resize(mem_size);
 
@@ -92,8 +96,9 @@ llama_memory_recurrent::llama_memory_recurrent(
             throw std::runtime_error("failed to create ggml context for rs cache");
         }
 
-        ggml_tensor * r = ggml_new_tensor_2d(ctx, type_r, hparams.n_embd_r(), mem_size);
-        ggml_tensor * s = ggml_new_tensor_2d(ctx, type_s, hparams.n_embd_s(), mem_size);
+        const uint32_t n_rows = mem_size * (1 + n_rs_seq);
+        ggml_tensor * r = ggml_new_tensor_2d(ctx, type_r, hparams.n_embd_r(), n_rows);
+        ggml_tensor * s = ggml_new_tensor_2d(ctx, type_s, hparams.n_embd_s(), n_rows);
         ggml_format_name(r, "cache_r_l%d", i);
         ggml_format_name(s, "cache_s_l%d", i);
         r_l[i] = r;
@@ -115,8 +120,8 @@ llama_memory_recurrent::llama_memory_recurrent(
         const size_t memory_size_r = size_r_bytes();
         const size_t memory_size_s = size_s_bytes();
 
-        LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u seqs), R (%s): %7.2f MiB, S (%s): %7.2f MiB\n", __func__,
-                (float)(memory_size_r + memory_size_s) / (1024.0f * 1024.0f), mem_size, n_layer, n_seq_max,
+        LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u seqs %2u rs_seq), R (%s): %7.2f MiB, S (%s): %7.2f MiB\n", __func__,
+                (float)(memory_size_r + memory_size_s) / (1024.0f * 1024.0f), mem_size, n_layer, n_seq_max, n_rs_seq,
                 ggml_type_name(type_r), (float)memory_size_r / (1024.0f * 1024.0f),
                 ggml_type_name(type_s), (float)memory_size_s / (1024.0f * 1024.0f));
     }
@@ -138,10 +143,11 @@ void llama_memory_recurrent::clear(bool data) {
             ggml_backend_buffer_clear(buf.get(), 0);
         }
     }
+
+    std::fill(rs_idx.begin(), rs_idx.end(), 0);
 }
 
 bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
-    //printf("[DEBUG] calling llama_memory_recurrent::seq_rm` with `seq_id=%d, p0=%d, p1=%d`\n", seq_id, p0, p1);
     uint32_t new_head = size;
 
     if (p0 < 0) {
@@ -152,6 +158,15 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
         p1 = std::numeric_limits<llama_pos>::max();
     }
 
+    const bool rm_all = p0 == 0 && p1 == std::numeric_limits<llama_pos>::max();
+    if (rm_all) {
+        if (seq_id >= 0) {
+            set_rs_idx(seq_id, 0);
+        } else {
+            std::fill(rs_idx.begin(), rs_idx.end(), 0);
+        }
+    }
+
     // models like Mamba or RWKV can't have a state partially erased at the end
     // of the sequence because their state isn't preserved for previous tokens
     if (seq_id >= (int64_t) size) {
@@ -161,10 +176,16 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
     if (0 <= seq_id) {
         int32_t & tail_id = cells[seq_id].tail;
         if (tail_id >= 0) {
-            const auto & cell = cells[tail_id];
-            // partial intersection is invalid if it includes the final pos
+            auto & cell = cells[tail_id];
+
+            // partial rollback via per-token snapshot index (bounded by n_rs_seq)
             if (0 < p0 && p0 <= cell.pos && p1 > cell.pos) {
-                //printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: partial intersection is invalid, so returning false, p0 = %d, cell.pos = %d, p1 = %d\n", p0, cell.pos, p1);
+                const llama_pos rollback = cell.pos - (p0 - 1);
+                if (rollback >= 1 && rollback <= (llama_pos) n_rs_seq) {
+                    set_rs_idx(seq_id, (uint32_t) rollback);
+                    cell.pos = p0 - 1;
+                    return true;
+                }
                 return false;
             }
             // invalidate tails which will be cleared
@@ -368,6 +389,13 @@ llama_pos llama_memory_recurrent::seq_pos_max(llama_seq_id seq_id) const {
     return result;
 }
 
+void llama_memory_recurrent::set_rs_idx(llama_seq_id seq_id, uint32_t idx) {
+    if (seq_id < 0 || (size_t) seq_id >= rs_idx.size()) {
+        return;
+    }
+    rs_idx[seq_id] = (idx > n_rs_seq) ? n_rs_seq : idx;
+}
+
 std::map<ggml_backend_buffer_type_t, size_t> llama_memory_recurrent::memory_breakdown() const {
     std::map<ggml_backend_buffer_type_t, size_t> ret;
     for (const auto & [_, buf] : ctxs_bufs) {
@@ -703,6 +731,7 @@ void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq
     GGML_UNUSED(flags);
 
     std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
+    std::vector<std::pair<uint32_t, uint32_t>> cell_ranges_data; // logical source row ranges
     uint32_t cell_count = 0;
 
     // Count the number of cells with the specified seq_id
@@ -712,6 +741,35 @@ void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq
         const auto & cell = cells[i];
         if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
             ++cell_count;
+            uint32_t rs_idx_cur = 0;
+
+            if (n_rs_seq != 0) {
+                if (seq_id != -1) {
+                    GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < rs_idx.size());
+                    rs_idx_cur = rs_idx[seq_id];
+                } else {
+                    bool has_rs_idx = false;
+                    for (const llama_seq_id cell_seq_id : cell.seq_id) {
+                        GGML_ASSERT(cell_seq_id >= 0 && (size_t) cell_seq_id < rs_idx.size());
+
+                        const uint32_t seq_rs_idx = rs_idx[cell_seq_id];
+                        if (!has_rs_idx) {
+                            rs_idx_cur = seq_rs_idx;
+                            has_rs_idx = true;
+                        } else if (rs_idx_cur != seq_rs_idx) {
+                            GGML_ABORT("cannot write shared recurrent state with different rollback indices");
+                        }
+                    }
+                }
+            }
+
+            const uint32_t cell_id = rs_idx_cur * size + (cell.src >= 0 ? cell.src : (int32_t) i);
+            if (cell_ranges_data.empty() || cell_ranges_data.back().second != cell_id) {
+                cell_ranges_data.emplace_back(cell_id, cell_id + 1);
+            } else {
+                cell_ranges_data.back().second++;
+            }
+
             if (cell_range_begin == size) {
                 cell_range_begin = i;
             }
@@ -726,6 +784,10 @@ void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq
         cell_ranges.emplace_back(cell_range_begin, size);
     }
 
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_ON_DEVICE) && cell_ranges.size() > 1) {
+        GGML_ABORT("cannot save/load multiple ranges of cells to/from device memory\n");
+    }
+
     // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
     uint32_t cell_count_check = 0;
     for (const auto & range : cell_ranges) {
@@ -733,17 +795,23 @@ void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq
     }
     GGML_ASSERT(cell_count == cell_count_check);
 
+    cell_count_check = 0;
+    for (const auto & range : cell_ranges_data) {
+        cell_count_check += range.second - range.first;
+    }
+    GGML_ASSERT(cell_count == cell_count_check);
+
     io.write(&cell_count, sizeof(cell_count));
 
     state_write_meta(io, cell_ranges, seq_id);
-    state_write_data(io, cell_ranges);
+    state_write_data(io, cell_ranges_data);
 }
 
 void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
     GGML_UNUSED(flags);
 
     uint32_t cell_count;
-    io.read_to(&cell_count, sizeof(cell_count));
+    io.read(&cell_count, sizeof(cell_count));
 
     bool res = true;
 
@@ -758,6 +826,14 @@ void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_i
         }
         throw std::runtime_error("failed to restore kv cache");
     }
+
+    if (n_rs_seq != 0) {
+        if (seq_id == -1) {
+            std::fill(rs_idx.begin(), rs_idx.end(), 0);
+        } else {
+            set_rs_idx(seq_id, 0);
+        }
+    }
 }
 
 void llama_memory_recurrent::state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id) const {
@@ -784,7 +860,7 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
     const uint32_t n_layer = hparams.n_layer;
 
     io.write(&s_trans, sizeof(s_trans));
-    io.write(&n_layer,   sizeof(n_layer));
+    io.write(&n_layer, sizeof(n_layer));
 
     // Iterate and write all the R tensors first, each row is a cell
     // Get whole range at a time
@@ -800,7 +876,8 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
         const uint64_t r_size_row = ggml_row_size(r_l[il]->type, hparams.n_embd_r());
         io.write(&r_size_row, sizeof(r_size_row));
 
-        // Write each range of cells of r_size_row length
+        // Write each logical cell row range. With pending recurrent rollback,
+        // the logical current state may live in a rollback snapshot plane.
         for (const auto & range : cell_ranges) {
             const size_t range_size = range.second - range.first;
             const size_t buf_size = range_size * r_size_row;
@@ -821,7 +898,8 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
             const uint64_t s_size_row = ggml_row_size(s_l[il]->type, hparams.n_embd_s());
             io.write(&s_size_row, sizeof(s_size_row));
 
-            // Write each range of S tensor rows
+            // Write each logical cell row range. With pending recurrent rollback,
+            // the logical current state may live in a rollback snapshot plane.
             for (const auto & range : cell_ranges) {
                 const size_t range_size = range.second - range.first;
                 const size_t buf_size = range_size * s_size_row;
@@ -848,9 +926,8 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
             // Write GQA embedding size
             io.write(&n_embd_s, sizeof(n_embd_s));
 
-            // For each row, we get the element values of each cell
+            // For each row, we get the element values of each logical cell
             for (uint32_t j = 0; j < n_embd_s; ++j) {
-                // Write each range of cells of s_size_el length
                 for (const auto & range : cell_ranges) {
                     const size_t range_size = range.second - range.first;
                     const size_t src_offset = (range.first + j * mem_size) * s_size_el;
@@ -879,8 +956,8 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell
             llama_pos pos;
             uint32_t n_seq_id;
 
-            io.read_to(&pos,      sizeof(pos));
-            io.read_to(&n_seq_id, sizeof(n_seq_id));
+            io.read(&pos,      sizeof(pos));
+            io.read(&n_seq_id, sizeof(n_seq_id));
 
             if (n_seq_id != 0) {
                 LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
@@ -920,14 +997,14 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell
             llama_pos pos;
             uint32_t  n_seq_id;
 
-            io.read_to(&pos,      sizeof(pos));
-            io.read_to(&n_seq_id, sizeof(n_seq_id));
+            io.read(&pos,      sizeof(pos));
+            io.read(&n_seq_id, sizeof(n_seq_id));
 
             cell.pos = pos;
 
             for (uint32_t j = 0; j < n_seq_id; ++j) {
                 llama_seq_id seq_id;
-                io.read_to(&seq_id, sizeof(seq_id));
+                io.read(&seq_id, sizeof(seq_id));
 
                 if (seq_id < 0 || (uint32_t) seq_id >= this->n_seq_max) {
                     LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, this->n_seq_max);
@@ -961,8 +1038,8 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell
 bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell_count) {
     uint32_t s_trans;
     uint32_t n_layer;
-    io.read_to(&s_trans, sizeof(s_trans));
-    io.read_to(&n_layer, sizeof(n_layer));
+    io.read(&s_trans, sizeof(s_trans));
+    io.read(&n_layer, sizeof(n_layer));
 
     if (n_layer != hparams.n_layer) {
         LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer);
@@ -984,7 +1061,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
         // Read type of key
         int32_t r_type_i_ref;
-        io.read_to(&r_type_i_ref, sizeof(r_type_i_ref));
+        io.read(&r_type_i_ref, sizeof(r_type_i_ref));
         const int32_t r_type_i = (int32_t) r_l[il]->type;
         if (r_type_i != r_type_i_ref) {
             LLAMA_LOG_ERROR("%s: mismatched r type (%d != %d, layer %d)\n", __func__, r_type_i, r_type_i_ref, il);
@@ -993,7 +1070,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
         // Read row size of key
         uint64_t r_size_row_ref;
-        io.read_to(&r_size_row_ref, sizeof(r_size_row_ref));
+        io.read(&r_size_row_ref, sizeof(r_size_row_ref));
         const size_t r_size_row = ggml_row_size(r_l[il]->type, hparams.n_embd_r());
         if (r_size_row != r_size_row_ref) {
             LLAMA_LOG_ERROR("%s: mismatched r row size (%zu != %zu, layer %d)\n", __func__, r_size_row, (size_t) r_size_row_ref, il);
@@ -1002,7 +1079,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
         if (cell_count) {
             // Read and set the keys for the whole cell range
-            ggml_backend_tensor_set(r_l[il], io.read(cell_count * r_size_row), head * r_size_row, cell_count * r_size_row);
+            io.read_tensor(r_l[il], head * r_size_row, cell_count * r_size_row);
         }
     }
 
@@ -1013,7 +1090,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
             // Read type of value
             int32_t s_type_i_ref;
-            io.read_to(&s_type_i_ref, sizeof(s_type_i_ref));
+            io.read(&s_type_i_ref, sizeof(s_type_i_ref));
             const int32_t s_type_i = (int32_t)s_l[il]->type;
 
             if (s_type_i != s_type_i_ref) {
@@ -1023,7 +1100,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
             // Read row size of value
             uint64_t s_size_row_ref;
-            io.read_to(&s_size_row_ref, sizeof(s_size_row_ref));
+            io.read(&s_size_row_ref, sizeof(s_size_row_ref));
             const size_t s_size_row = ggml_row_size(s_l[il]->type, hparams.n_embd_s());
             if (s_size_row != s_size_row_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched s row size (%zu != %zu, layer %d)\n", __func__, s_size_row, (size_t) s_size_row_ref, il);
@@ -1032,7 +1109,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
             if (cell_count) {
                 // Read and set the values for the whole cell range
-                ggml_backend_tensor_set(s_l[il], io.read(cell_count * s_size_row), head * s_size_row, cell_count * s_size_row);
+                io.read_tensor(s_l[il], head * s_size_row, cell_count * s_size_row);
             }
         }
     } else {
@@ -1045,7 +1122,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
             // Read type of value
             int32_t s_type_i_ref;
-            io.read_to(&s_type_i_ref, sizeof(s_type_i_ref));
+            io.read(&s_type_i_ref, sizeof(s_type_i_ref));
             const int32_t s_type_i = (int32_t)s_l[il]->type;
             if (s_type_i != s_type_i_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched s type (%d != %d, layer %d)\n", __func__, s_type_i, s_type_i_ref, il);
@@ -1054,7 +1131,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
             // Read element size of value
             uint32_t s_size_el_ref;
-            io.read_to(&s_size_el_ref, sizeof(s_size_el_ref));
+            io.read(&s_size_el_ref, sizeof(s_size_el_ref));
             const size_t s_size_el = ggml_type_size(s_l[il]->type);
             if (s_size_el != s_size_el_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched s element size (%zu != %zu, layer %d)\n", __func__, s_size_el, (size_t) s_size_el_ref, il);
@@ -1063,7 +1140,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 
             // Read state embedding size
             uint32_t n_embd_s_ref;
-            io.read_to(&n_embd_s_ref, sizeof(n_embd_s_ref));
+            io.read(&n_embd_s_ref, sizeof(n_embd_s_ref));
             if (n_embd_s != n_embd_s_ref) {
                 LLAMA_LOG_ERROR("%s: mismatched s embedding size (%u != %u, layer %d)\n", __func__, n_embd_s, n_embd_s_ref, il);
                 return false;
@@ -1073,7 +1150,7 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
                 // For each row in the transposed matrix, read the values for the whole cell range
                 for (uint32_t j = 0; j < n_embd_s; ++j) {
                     const size_t dst_offset = (head + j * size) * s_size_el;
-                    ggml_backend_tensor_set(s_l[il], io.read(cell_count * s_size_el), dst_offset, cell_count * s_size_el);
+                    io.read_tensor(s_l[il], dst_offset, cell_count * s_size_el);
                 }
             }
         }
@@ -1159,5 +1236,21 @@ ggml_tensor * llama_memory_recurrent_context::get_s_l(int32_t il) const {
 }
 
 int32_t llama_memory_recurrent_context::s_copy(int i) const {
-    return  mem->cells[i + mem->head].src0;
+    const uint32_t cell_idx = i + mem->head;
+    const int32_t  src0     = mem->cells[cell_idx].src0;
+
+    if (mem->n_rs_seq == 0) {
+        return src0;
+    }
+
+    uint32_t idx = 0;
+    if (!mem->cells[cell_idx].seq_id.empty()) {
+        const llama_seq_id seq = *mem->cells[cell_idx].seq_id.begin();
+        if (seq >= 0 && (size_t) seq < mem->rs_idx.size()) {
+            idx = mem->rs_idx[seq];
+            // reset rollback idx
+            mem->rs_idx[seq] = 0;
+        }
+    }
+    return (int32_t)(idx * mem->size) + src0;
 }
diff --git a/src/llama-memory-recurrent.h b/src/llama-memory-recurrent.h
index 47f01d73912..29c58afc9c2 100644
--- a/src/llama-memory-recurrent.h
+++ b/src/llama-memory-recurrent.h
@@ -23,6 +23,7 @@ class llama_memory_recurrent : public llama_memory_i {
                          bool   offload,
                      uint32_t   mem_size,
                      uint32_t   n_seq_max,
+                     uint32_t   n_rs_seq,
         const layer_filter_cb & filter);
 
     ~llama_memory_recurrent() = default;
@@ -69,6 +70,13 @@ class llama_memory_recurrent : public llama_memory_i {
     uint32_t size = 0; // total number of cells, shared across all sequences
     uint32_t used = 0; // used cells (i.e. at least one seq_id)
 
+    // number of recurrent-state snapshots per seq for rollback; tensors are widened to (1 + n_rs_seq) groups
+    uint32_t n_rs_seq = 0;
+    // per-seq rollback index
+    std::vector<uint32_t> rs_idx;
+
+    void set_rs_idx(llama_seq_id seq_id, uint32_t idx);
+
     // computed before each graph build
     uint32_t n = 0;
 
diff --git a/src/llama-memory.h b/src/llama-memory.h
index 2704096da71..1ab412261f3 100644
--- a/src/llama-memory.h
+++ b/src/llama-memory.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "llama.h"
+#include "llama-graph.h"
 
 #include <map>
 #include <memory>
@@ -20,6 +21,8 @@ struct llama_memory_params {
 
     // use full-size SWA cache
     bool swa_full;
+
+    llama_context_type ctx_type;
 };
 
 enum llama_memory_status {
diff --git a/src/llama-mmap.cpp b/src/llama-mmap.cpp
index ccc29c1302e..ed572da7fb5 100644
--- a/src/llama-mmap.cpp
+++ b/src/llama-mmap.cpp
@@ -40,6 +40,14 @@
 #include <TargetConditionals.h>
 #endif
 
+#ifdef _WIN32
+#    define llama_mmap_ftell _ftelli64
+#    define llama_mmap_fseek _fseeki64
+#else
+#    define llama_mmap_ftell ftello
+#    define llama_mmap_fseek fseeko
+#endif
+
 // TODO: consider moving to llama-impl.h if needed in more places
 #if defined(_WIN32)
 static std::string llama_format_win_err(DWORD err) {
@@ -226,7 +234,7 @@ struct llama_file::impl {
 
     size_t tell() const {
         if (fd == -1) {
-            long ret = std::ftell(fp);
+            off_t ret = llama_mmap_ftell(fp);
             if (ret == -1) {
                 throw std::runtime_error(format("ftell error: %s", strerror(errno)));
             }
@@ -244,7 +252,7 @@ struct llama_file::impl {
     void seek(size_t offset, int whence) const {
         off_t ret = 0;
         if (fd == -1) {
-            ret = std::fseek(fp, (long) offset, whence);
+            ret = llama_mmap_fseek(fp, offset, whence);
         } else {
             ret = lseek(fd, offset, whence);
         }
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 1adff5810b4..54e5974e62c 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -1316,9 +1316,16 @@ struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_conte
     return tensor;
 }
 
-void llama_model_loader::done_getting_tensors() const {
-    if (n_created != n_tensors) {
-        throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
+void llama_model_loader::done_getting_tensors(bool partial) const {
+    if (n_created > n_tensors) {
+        throw std::runtime_error(format("%s: too many tensors created; expected %d, got %d", __func__, n_tensors, n_created));
+    }
+    if (n_created < n_tensors) {
+        if (!partial) {
+            throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
+        }
+        LLAMA_LOG_INFO("%s: partial load — used %d of %d tensors in the file (rest belong to a sibling model on the same .gguf)\n",
+                __func__, n_created, n_tensors);
     }
     if (n_tensors_moved > 0) {
         LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %zu others) cannot be used with preferred buffer type %s, using %s instead\n",
diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h
index 7b3d6703c03..c476026d3e5 100644
--- a/src/llama-model-loader.h
+++ b/src/llama-model-loader.h
@@ -184,7 +184,7 @@ struct llama_model_loader {
 
     struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required = true);
 
-    void done_getting_tensors() const;
+    void done_getting_tensors(bool partial = false) const;
 
     void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr);
 
diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp
index 26864c18e97..528e4c9c069 100644
--- a/src/llama-model-saver.cpp
+++ b/src/llama-model-saver.cpp
@@ -268,6 +268,7 @@ void llama_model_saver::add_kv_from_model() {
     // add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,  ???);
     add_kv(LLM_KV_ATTENTION_SCALE,                   hparams.f_attention_scale);
     add_kv(LLM_KV_ATTENTION_OUTPUT_SCALE,            hparams.f_attn_out_scale);
+    add_kv(LLM_KV_ATTENTION_VALUE_SCALE,             hparams.f_attn_value_scale);
     add_kv(LLM_KV_ATTENTION_TEMPERATURE_LENGTH,      hparams.attn_temp_length);
     add_kv(LLM_KV_ATTENTION_TEMPERATURE_SCALE,       hparams.f_attn_temp_scale);
     add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA,          hparams.n_embd_head_k_mla_impl);
@@ -392,6 +393,8 @@ void llama_model_saver::add_tensors_from_model() {
     add_tensor(model->output);
     add_tensor(model->output_b);
     add_tensor(model->output_norm_enc);
+    add_tensor(model->output_s);
+    add_tensor(model->output_in_s);
     add_tensor(model->cls);
     add_tensor(model->cls_b);
     add_tensor(model->cls_out);
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index f77b2e9217f..8bf20a716eb 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -34,6 +34,285 @@
 #include <string>
 #include <vector>
 
+static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params & params) {
+    switch (arch) {
+        case LLM_ARCH_LLAMA:
+            return new llama_model_llama(params);
+        case LLM_ARCH_LLAMA4:
+            return new llama_model_llama4(params);
+        case LLM_ARCH_LLAMA_EMBED:
+            return new llama_model_llama_embed(params);
+        case LLM_ARCH_MAINCODER:
+            return new llama_model_maincoder(params);
+        case LLM_ARCH_DECI:
+            return new llama_model_deci(params);
+        case LLM_ARCH_BAICHUAN:
+            return new llama_model_baichuan(params);
+        case LLM_ARCH_FALCON:
+            return new llama_model_falcon(params);
+        case LLM_ARCH_GROK:
+            return new llama_model_grok(params);
+        case LLM_ARCH_STARCODER:
+            return new llama_model_starcoder(params);
+        case LLM_ARCH_REFACT:
+            return new llama_model_refact(params);
+        case LLM_ARCH_BERT:
+            return new llama_model_bert(params);
+        case LLM_ARCH_JINA_BERT_V2:
+            return new llama_model_jina_bert_v2(params);
+        case LLM_ARCH_JINA_BERT_V3:
+            return new llama_model_jina_bert_v3(params);
+        case LLM_ARCH_NOMIC_BERT:
+            return new llama_model_nomic_bert(params);
+        case LLM_ARCH_NOMIC_BERT_MOE:
+            return new llama_model_nomic_bert_moe(params);
+        case LLM_ARCH_MODERN_BERT:
+            return new llama_model_modern_bert(params);
+        case LLM_ARCH_NEO_BERT:
+            return new llama_model_neo_bert(params);
+        case LLM_ARCH_EUROBERT:
+            return new llama_model_eurobert(params);
+        case LLM_ARCH_BLOOM:
+            return new llama_model_bloom(params);
+        case LLM_ARCH_MPT:
+            return new llama_model_mpt(params);
+        case LLM_ARCH_STABLELM:
+            return new llama_model_stablelm(params);
+        case LLM_ARCH_QWEN:
+            return new llama_model_qwen(params);
+        case LLM_ARCH_QWEN2:
+            return new llama_model_qwen2(params);
+        case LLM_ARCH_DREAM:
+            return new llama_model_dream(params);
+        case LLM_ARCH_LLADA:
+            return new llama_model_llada(params);
+        case LLM_ARCH_LLADA_MOE:
+            return new llama_model_llada_moe(params);
+        case LLM_ARCH_RND1:
+            return new llama_model_rnd1(params);
+        case LLM_ARCH_QWEN2VL:
+            return new llama_model_qwen2vl(params);
+        case LLM_ARCH_QWEN2MOE:
+            return new llama_model_qwen2moe(params);
+        case LLM_ARCH_QWEN3:
+            return new llama_model_qwen3(params);
+        case LLM_ARCH_QWEN3MOE:
+            return new llama_model_qwen3moe(params);
+        case LLM_ARCH_QWEN3VL:
+            return new llama_model_qwen3vl(params);
+        case LLM_ARCH_QWEN3VLMOE:
+            return new llama_model_qwen3vlmoe(params);
+        case LLM_ARCH_PHI2:
+            return new llama_model_phi2(params);
+        case LLM_ARCH_PHI3:
+            return new llama_model_phi3(params);
+        case LLM_ARCH_PHIMOE:
+            return new llama_model_phimoe(params);
+        case LLM_ARCH_PLAMO:
+            return new llama_model_plamo(params);
+        case LLM_ARCH_PLAMO2:
+            return new llama_model_plamo2(params);
+        case LLM_ARCH_PLAMO3:
+            return new llama_model_plamo3(params);
+        case LLM_ARCH_GPT2:
+            return new llama_model_gpt2(params);
+        case LLM_ARCH_CODESHELL:
+            return new llama_model_codeshell(params);
+        case LLM_ARCH_ORION:
+            return new llama_model_orion(params);
+        case LLM_ARCH_INTERNLM2:
+            return new llama_model_internlm2(params);
+        case LLM_ARCH_MINICPM3:
+            return new llama_model_minicpm3(params);
+        case LLM_ARCH_GEMMA:
+            return new llama_model_gemma(params);
+        case LLM_ARCH_GEMMA2:
+            return new llama_model_gemma2(params);
+        case LLM_ARCH_GEMMA3:
+            return new llama_model_gemma3(params);
+        case LLM_ARCH_GEMMA3N:
+            return new llama_model_gemma3n(params);
+        case LLM_ARCH_GEMMA4:
+            return new llama_model_gemma4(params);
+        case LLM_ARCH_GEMMA_EMBEDDING:
+            return new llama_model_gemma_embedding(params);
+        case LLM_ARCH_STARCODER2:
+            return new llama_model_starcoder2(params);
+        case LLM_ARCH_MAMBA:
+            return new llama_model_mamba(params);
+        case LLM_ARCH_MAMBA2:
+            return new llama_model_mamba2(params);
+        case LLM_ARCH_JAMBA:
+            return new llama_model_jamba(params);
+        case LLM_ARCH_XVERSE:
+            return new llama_model_xverse(params);
+        case LLM_ARCH_COMMAND_R:
+            return new llama_model_command_r(params);
+        case LLM_ARCH_COHERE2:
+            return new llama_model_cohere2(params);
+        case LLM_ARCH_DBRX:
+            return new llama_model_dbrx(params);
+        case LLM_ARCH_OLMO:
+            return new llama_model_olmo(params);
+        case LLM_ARCH_OLMO2:
+            return new llama_model_olmo2(params);
+        case LLM_ARCH_OLMOE:
+            return new llama_model_olmoe(params);
+        case LLM_ARCH_OPENELM:
+            return new llama_model_openelm(params);
+        case LLM_ARCH_GPTNEOX:
+            return new llama_model_gptneox(params);
+        case LLM_ARCH_ARCTIC:
+            return new llama_model_arctic(params);
+        case LLM_ARCH_DEEPSEEK:
+            return new llama_model_deepseek(params);
+        case LLM_ARCH_DEEPSEEK2:
+            return new llama_model_deepseek2(params);
+        case LLM_ARCH_DEEPSEEK2OCR:
+            return new llama_model_deepseek2ocr(params);
+        case LLM_ARCH_GLM_DSA:
+            return new llama_model_glm_dsa(params);
+        case LLM_ARCH_MISTRAL4:
+            return new llama_model_mistral4(params);
+        case LLM_ARCH_CHATGLM:
+            return new llama_model_chatglm(params);
+        case LLM_ARCH_GLM4:
+            return new llama_model_glm4(params);
+        case LLM_ARCH_GLM4_MOE:
+            return new llama_model_glm4_moe(params);
+        case LLM_ARCH_BITNET:
+            return new llama_model_bitnet(params);
+        case LLM_ARCH_T5:
+            return new llama_model_t5(params);
+        case LLM_ARCH_T5ENCODER:
+            return new llama_model_t5encoder(params);
+        case LLM_ARCH_JAIS:
+            return new llama_model_jais(params);
+        case LLM_ARCH_JAIS2:
+            return new llama_model_jais2(params);
+        case LLM_ARCH_NEMOTRON:
+            return new llama_model_nemotron(params);
+        case LLM_ARCH_NEMOTRON_H:
+            return new llama_model_nemotron_h(params);
+        case LLM_ARCH_NEMOTRON_H_MOE:
+            return new llama_model_nemotron_h_moe(params);
+        case LLM_ARCH_EXAONE:
+            return new llama_model_exaone(params);
+        case LLM_ARCH_EXAONE4:
+            return new llama_model_exaone4(params);
+        case LLM_ARCH_EXAONE_MOE:
+            return new llama_model_exaone_moe(params);
+        case LLM_ARCH_RWKV6:
+            return new llama_model_rwkv6(params);
+        case LLM_ARCH_RWKV6QWEN2:
+            return new llama_model_rwkv6qwen2(params);
+        case LLM_ARCH_RWKV7:
+            return new llama_model_rwkv7(params);
+        case LLM_ARCH_ARWKV7:
+            return new llama_model_arwkv7(params);
+        case LLM_ARCH_GRANITE:
+            return new llama_model_granite(params);
+        case LLM_ARCH_GRANITE_MOE:
+            return new llama_model_granite_moe(params);
+        case LLM_ARCH_MINICPM:
+            return new llama_model_minicpm(params);
+        case LLM_ARCH_GRANITE_HYBRID:
+            return new llama_model_granite_hybrid(params);
+        case LLM_ARCH_CHAMELEON:
+            return new llama_model_chameleon(params);
+        case LLM_ARCH_WAVTOKENIZER_DEC:
+            return new llama_model_wavtokenizer_dec(params);
+        case LLM_ARCH_PLM:
+            return new llama_model_plm(params);
+        case LLM_ARCH_BAILINGMOE:
+            return new llama_model_bailingmoe(params);
+        case LLM_ARCH_BAILINGMOE2:
+            return new llama_model_bailingmoe2(params);
+        case LLM_ARCH_SEED_OSS:
+            return new llama_model_seed_oss(params);
+        case LLM_ARCH_DOTS1:
+            return new llama_model_dots1(params);
+        case LLM_ARCH_ARCEE:
+            return new llama_model_arcee(params);
+        case LLM_ARCH_AFMOE:
+            return new llama_model_afmoe(params);
+        case LLM_ARCH_ERNIE4_5:
+            return new llama_model_ernie4_5(params);
+        case LLM_ARCH_ERNIE4_5_MOE:
+            return new llama_model_ernie4_5_moe(params);
+        case LLM_ARCH_PADDLEOCR:
+            return new llama_model_paddleocr(params);
+        case LLM_ARCH_HUNYUAN_MOE:
+            return new llama_model_hunyuan_moe(params);
+        case LLM_ARCH_HUNYUAN_VL:
+            return new llama_model_hunyuan_vl(params);
+        case LLM_ARCH_HUNYUAN_DENSE:
+            return new llama_model_hunyuan_dense(params);
+        case LLM_ARCH_SMOLLM3:
+            return new llama_model_smollm3(params);
+        case LLM_ARCH_OPENAI_MOE:
+            return new llama_model_openai_moe(params);
+        case LLM_ARCH_FALCON_H1:
+            return new llama_model_falcon_h1(params);
+        case LLM_ARCH_LFM2:
+            return new llama_model_lfm2(params);
+        case LLM_ARCH_LFM2MOE:
+            return new llama_model_lfm2moe(params);
+        case LLM_ARCH_SMALLTHINKER:
+            return new llama_model_smallthinker(params);
+        case LLM_ARCH_GROVEMOE:
+            return new llama_model_grovemoe(params);
+        case LLM_ARCH_APERTUS:
+            return new llama_model_apertus(params);
+        case LLM_ARCH_MINIMAX_M2:
+            return new llama_model_minimax_m2(params);
+        case LLM_ARCH_COGVLM:
+            return new llama_model_cogvlm(params);
+        case LLM_ARCH_PANGU_EMBED:
+            return new llama_model_pangu_embed(params);
+        case LLM_ARCH_QWEN3NEXT:
+            return new llama_model_qwen3next(params);
+        case LLM_ARCH_QWEN35:
+            return new llama_model_qwen35(params);
+        case LLM_ARCH_QWEN35MOE:
+            return new llama_model_qwen35moe(params);
+        case LLM_ARCH_MISTRAL3:
+            return new llama_model_mistral3(params);
+        case LLM_ARCH_MIMO2:
+            return new llama_model_mimo2(params);
+        case LLM_ARCH_KIMI_LINEAR:
+            return new llama_model_kimi_linear(params);
+        case LLM_ARCH_STEP35:
+            return new llama_model_step35(params);
+        default:
+            throw std::runtime_error(std::string("unsupported model architecture: '") + llm_arch_name(arch) + "'");
+    }
+
+}
+
+llama_model * llama_model_create(llm_arch arch, const llama_model_params & params) {
+    llama_model * model = llama_model_mapping(arch, params);
+
+    if (model != nullptr) {
+        model->arch = arch;
+        auto & devices = model->devices;
+        if (!devices.empty() && devices[0].is_meta && !llm_arch_supports_sm_tensor(arch)) {
+            throw std::runtime_error(std::string("LLAMA_SPLIT_MODE_TENSOR not implemented for architecture '") + llm_arch_name(arch) + "'");
+        }
+    }
+
+    return model;
+}
+
+llama_model * llama_model_create(llama_model_loader & ml, const llama_model_params & params) {
+    llm_arch arch = ml.get_arch();
+    if (arch == LLM_ARCH_UNKNOWN) {
+        throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
+    }
+
+    return llama_model_create(arch, params);
+}
+
 struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const struct ggml_tensor * tensor, void * userdata) {
     const llama_meta_device_get_split_state_userdata * ud = (const llama_meta_device_get_split_state_userdata *) userdata;
     const llama_hparams & hparams = ud->model->hparams;
@@ -688,22 +967,12 @@ llama_model::~llama_model() {
     }
 }
 
-void llama_model::load_stats(llama_model_loader & ml) {
+void llama_model_base::load_stats(llama_model_loader & ml) {
     pimpl->n_elements = ml.n_elements;
     pimpl->n_bytes = ml.n_bytes;
 }
 
-void llama_model::load_arch(llama_model_loader & ml) {
-    arch = ml.get_arch();
-    if (arch == LLM_ARCH_UNKNOWN) {
-        throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
-    }
-    if (!devices.empty() && devices[0].is_meta && !llm_arch_supports_sm_tensor(arch)) {
-        throw std::runtime_error(std::string("LLAMA_SPLIT_MODE_TENSOR not implemented for architecture '") + llm_arch_name(arch) + "'");
-    }
-}
-
-void llama_model::load_hparams(llama_model_loader & ml) {
+void llama_model_base::load_hparams(llama_model_loader & ml) {
     const gguf_context * ctx = ml.metadata;
 
     // get metadata as string
@@ -737,6 +1006,13 @@ void llama_model::load_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_EXPERT_GROUP_COUNT,      hparams.n_expert_groups, false);
     ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used,    false);
 
+    if (arch == LLM_ARCH_HUNYUAN_VL || arch == LLM_ARCH_HUNYUAN_DENSE) {
+        if (hparams.n_expert <= 1) {
+            hparams.n_expert      = 0;
+            hparams.n_expert_used = 0;
+        }
+    }
+
     if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
         ml.get_key(LLM_KV_FEATURES_LENGTH,  hparams.n_embd);
         ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd_out_impl);
@@ -815,6 +1091,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
     hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
 
     ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
+    ml.get_key(LLM_KV_ROPE_SCALING_ALPHA,       hparams.rope_scaling_alpha, false);
 
     // non-transformer models do not have attention heads
     if (hparams.n_head() > 0) {
@@ -854,8204 +1131,967 @@ void llama_model::load_hparams(llama_model_loader & ml) {
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
     }
 
-    // for differentiating model types
-    uint32_t n_vocab = 0;
-    ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
-
     // for classifier models
     ml.get_arr(LLM_KV_CLASSIFIER_OUTPUT_LABELS, classifier_labels, false);
     if (!classifier_labels.empty()) {
         hparams.n_cls_out = classifier_labels.size();
     }
 
-    // arch-specific KVs
-    switch (arch) {
-        case LLM_ARCH_LLAMA:
-        case LLM_ARCH_LLAMA_EMBED:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                if (hparams.n_expert == 8) {
-                    switch (hparams.n_layer) {
-                        case 32: type = LLM_TYPE_8x7B; break;
-                        case 56: type = LLM_TYPE_8x22B; break;
-                        default: type = LLM_TYPE_UNKNOWN;
-                    }
-                } else {
-                    switch (hparams.n_layer) {
-                        case 16: type = LLM_TYPE_1B; break; // Llama 3.2 1B
-                        case 22: type = LLM_TYPE_1B; break;
-                        case 26: type = LLM_TYPE_3B; break;
-                        case 28: type = LLM_TYPE_3B; break; // Llama 3.2 3B
-                        case 30: type = LLM_TYPE_256M; break; // smoldocling 256M
-                        // granite uses a vocab with len 49152
-                        case 32: type = n_vocab == 49152 ? LLM_TYPE_3B : (n_vocab < 40000 ? LLM_TYPE_7B : LLM_TYPE_8B); break;
-                        case 36: type = LLM_TYPE_8B; break; // granite
-                        case 40: type = LLM_TYPE_13B; break;
-                        case 48: type = LLM_TYPE_34B; break;
-                        case 60: type = LLM_TYPE_30B; break;
-                        case 80: type = hparams.n_head() == hparams.n_head_kv() ? LLM_TYPE_65B : LLM_TYPE_70B; break;
-                        default: type = LLM_TYPE_UNKNOWN;
-                    }
-                }
-            } break;
-        case LLM_ARCH_LLAMA4:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,   hparams.n_moe_layer_step);
-
-                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-                if (found_swa && hparams.n_swa == 0) {
-                    hparams.swa_type             = LLAMA_SWA_TYPE_NONE;
-                    hparams.n_no_rope_layer_step = hparams.n_layer; // always use rope
-                } else {
-                    hparams.swa_type                = LLAMA_SWA_TYPE_CHUNKED;
-                    hparams.n_swa                   = 8192;
-                    hparams.n_attn_temp_floor_scale = 8192;
-                    hparams.f_attn_temp_scale       = 0.1f;
-                    hparams.f_attn_temp_offset      = 1.0f;
-                    uint32_t swa_period             = 4; // pattern: 3 chunked - 1 full
-                    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                    hparams.set_swa_pattern(swa_period);
-
-                    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
-                    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                }
-
-                switch (hparams.n_expert) {
-                    case 0: {
-                        // MobileLLM (no MoE)
-                        switch (hparams.n_embd) {
-                            case 2048: type = LLM_TYPE_140M; break;
-                            case 4096: type = LLM_TYPE_360M; break;
-                            case 6144: type = LLM_TYPE_950M; break;
-                            default:   type = LLM_TYPE_UNKNOWN;
-                        }
-                    } break;
-                    case 16:  type = LLM_TYPE_17B_16E; break;
-                    case 128: type = LLM_TYPE_17B_128E; break;
-                    default:  type = LLM_TYPE_UNKNOWN;
-                }
-
-                hparams.use_kq_norm = type != LLM_TYPE_17B_128E;
-            } break;
-        case LLM_ARCH_ARCEE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    // per-arch hparams
+    load_arch_hparams(ml);
 
-                // Arcee uses the same structure as Llama
-                switch (hparams.n_layer) {
-                    case 36: type = LLM_TYPE_4B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_AFMOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
-
-                // Set up interleaved sliding window attention (ISWA)
-                // Pattern: 3 sliding - 1 full (global_attn_every_n_layers = 4)
-                if (hparams.n_swa > 0) {
-                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                    uint32_t swa_period = 4;
-                    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                    hparams.set_swa_pattern(swa_period);
-
-                    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
-                    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                } else {
-                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
-                }
+    pimpl->n_bytes = ml.n_bytes;
 
-                // Default to sigmoid if not set
-                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
-                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
-                }
+    pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name();
 
-                switch (hparams.n_layer) {
-                    case 56: type = LLM_TYPE_6B; break;
-                    case 32: type = LLM_TYPE_26B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DECI:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 80: type = LLM_TYPE_70B; break;
-                    case 162: type = LLM_TYPE_405B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MINICPM:
-            {
-                // Backward-compatible defaults for older MiniCPM GGUFs
-                hparams.f_embedding_scale = 12.0f;
-                hparams.f_residual_scale  = 1.4f / sqrtf(float(hparams.n_layer));
-                hparams.f_logit_scale     = hparams.n_embd ? (256.0f / float(hparams.n_embd)) : 1.0f;
+    if (hparams.f_max_alibi_bias > 0.0f) {
+        hparams.use_alibi = true;
+    }
 
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    hparams.rope_type = llama_model_rope_type(this);
+}
 
-                // Optional KV reads, override defaults if present in newer GGUF exports
-                ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale, /*required=*/false);
-                ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale, /*required=*/false);
-                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale, /*required=*/false);
+void llama_model_base::load_vocab(llama_model_loader & ml) {
+    const auto kv = LLM_KV(arch);
 
-                // MiniCPM uses rope by default, unlike Granite which uses it as a switch
-                hparams.rope_finetuned = true;
+    vocab.load(ml, kv);
+}
 
-                switch (hparams.n_layer) {
-                    case 52: type = LLM_TYPE_1B; break;
-                    case 40: type = LLM_TYPE_2B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MINICPM3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,       hparams.n_lora_q);
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
+bool llama_model_base::load_tensors(llama_model_loader & ml) {
+    const auto & split_mode   = params.split_mode;
+    const auto & use_mlock    = params.use_mlock;
+    const auto & tensor_split = params.tensor_split;
 
-                switch (hparams.n_layer) {
-                    case 62: type = LLM_TYPE_4B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GROK:
-            {
-                // defaults for old GGUFs
-                hparams.yarn_beta_fast = 8.0f;
-                hparams.f_logit_scale = 0.5773502691896257f;
-                hparams.f_embedding_scale = 78.38367176906169f;
-                hparams.f_attn_out_scale = 0.08838834764831845f;
-                hparams.f_attn_logit_softcapping = 30.0f;
-                hparams.f_router_logit_softcapping = 30.0f;
-                // no final_logit_softcapping in grok-1
-                hparams.f_final_logit_softcapping = 0.0f;
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,  hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,   hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_LOGIT_SCALE,                  hparams.f_logit_scale, false);
-                ml.get_key(LLM_KV_EMBEDDING_SCALE,              hparams.f_embedding_scale, false);
-                ml.get_key(LLM_KV_ATTENTION_OUTPUT_SCALE,       hparams.f_attn_out_scale, false);
-                ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING,       hparams.f_attn_logit_softcapping, false);
-                ml.get_key(LLM_KV_ROUTER_LOGIT_SOFTCAPPING,     hparams.f_router_logit_softcapping, false);
-                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,      hparams.f_final_logit_softcapping, false);
-
-                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH,  hparams.attn_temp_length, false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,  hparams.yarn_ext_factor, false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, hparams.yarn_attn_factor, false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST,   hparams.yarn_beta_fast, false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,   hparams.yarn_beta_slow, false);
-
-                switch (hparams.n_layer) {
-                    case 64: type = LLM_TYPE_314B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_FALCON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    const int n_layer      = hparams.n_layer;
+    const int n_gpu_layers = this->n_gpu_layers();
 
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 60: type = LLM_TYPE_40B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_BAICHUAN:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 40: type = LLM_TYPE_13B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
+    const bool use_mmap_buffer = true;
 
-                if (type == LLM_TYPE_13B) {
-                    // TODO: become GGUF KV parameter
-                    hparams.f_max_alibi_bias = 8.0f;
-                }
-            } break;
-        case LLM_ARCH_STARCODER:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_1B; break;
-                    case 36: type = LLM_TYPE_3B; break;
-                    case 42: type = LLM_TYPE_7B; break;
-                    case 40: type = LLM_TYPE_15B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_REFACT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_1B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
+    this->ml = &ml; // to be used by create_tensor() and load_arch_tensors()
 
-                // TODO: become GGUF KV parameter
-                hparams.f_max_alibi_bias = 8.0f;
-            } break;
-        case LLM_ARCH_BERT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 3:
-                        type = LLM_TYPE_17M; break; // bge-micro
-                    case 6:
-                        type = LLM_TYPE_22M; break; // MiniLM-L6
-                    case 12:
-                        switch (hparams.n_embd) {
-                            case 384: type = LLM_TYPE_33M; break; // MiniLM-L12, bge-small
-                            case 768: type = LLM_TYPE_109M; break; // bge-base
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 24:
-                        type = LLM_TYPE_335M; break; // bge-large
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MODERN_BERT:
-            {
-                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-                if (found_swa && hparams.n_swa > 0) {
-                    hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
-                    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                    uint32_t swa_period = 3;
-                    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                    hparams.set_swa_pattern(swa_period, true);
-                } else {
-                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
-                }
+    LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s, direct_io = %s)\n",
+        __func__, ml.use_mmap ? "true" : "false", ml.use_direct_io ? "true" : "false");
 
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    // build a list of buffer types for the CPU and GPU devices
+    pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host);
+    for (const auto & dev : devices) {
+        buft_list_t buft_list = make_gpu_buft_list(dev.dev, split_mode, tensor_split);
+        // add CPU buffer types as a fallback
+        buft_list.insert(buft_list.end(), pimpl->cpu_buft_list.begin(), pimpl->cpu_buft_list.end());
+        pimpl->gpu_buft_list.emplace(dev.dev, std::move(buft_list));
+    }
 
-                switch (hparams.n_layer) {
-                    case 12:
-                        type = LLM_TYPE_47M; break; // granite-embedding-small
-                    case 22:
-                        type = LLM_TYPE_149M; break; // modern-bert-base
-                    case 28:
-                        type = LLM_TYPE_395M; break; // modern-bert-large
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_JINA_BERT_V2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                hparams.f_max_alibi_bias = 8.0f;
+    ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+    if (cpu_dev == nullptr) {
+        throw std::runtime_error(format("%s: no CPU backend found", __func__));
+    }
 
-                switch (hparams.n_layer) {
-                    case 4:  type = LLM_TYPE_33M;  break; // jina-embeddings-small
-                    case 12: type = LLM_TYPE_137M; break; // jina-embeddings-base
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_JINA_BERT_V3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 24:
-                        type = LLM_TYPE_558M; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_NOMIC_BERT:
-        case LLM_ARCH_NOMIC_BERT_MOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS,         hparams.moe_every_n_layers, 0);
-
-                if (hparams.n_layer == 12 && hparams.n_embd == 768) {
-                    if (arch == LLM_ARCH_NOMIC_BERT) {
-                        type = LLM_TYPE_137M;
-                    } else if (arch == LLM_ARCH_NOMIC_BERT_MOE && hparams.moe_every_n_layers == 2) {
-                        type = LLM_TYPE_475M;
-                    }
-                }
-            } break;
-        case LLM_ARCH_NEO_BERT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                if (hparams.n_layer == 28) {
-                    type = LLM_TYPE_250M;
-                }
-            } break;
-        case LLM_ARCH_EUROBERT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                if (hparams.n_layer == 12) {
-                    type = LLM_TYPE_SMALL;  // 0.2B
-                }
-            } break;
-        case LLM_ARCH_BLOOM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_1B; break;
-                    case 30:
-                        switch (hparams.n_embd) {
-                            case 2560: type = LLM_TYPE_3B; break;
-                            case 4096: type = LLM_TYPE_7B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-
-                // TODO: become GGUF KV parameter
-                hparams.f_max_alibi_bias = 8.0f;
-            } break;
-        case LLM_ARCH_MPT:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv, false);
-                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias, false);
-
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 48: type = LLM_TYPE_30B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_STABLELM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_1B; break;
-                    case 32: type = LLM_TYPE_3B; break;
-                    case 40: type = LLM_TYPE_12B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_QWEN:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    // calculate the split points
+    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + n_devices(), [](float x) { return x == 0.0f; });
+    std::vector<float> splits(n_devices());
+    if (all_zero) {
+        // default split, by free memory
+        for (size_t i = 0; i < n_devices(); ++i) {
+            ggml_backend_dev_t dev = devices[i].dev;
+            size_t total;
+            size_t free;
+            ggml_backend_dev_memory(dev, &free, &total);
 
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 40: type = LLM_TYPE_13B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN2VL:
-            {
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
+            // devices can return 0 bytes for free and total memory if they do not
+            // have any to report. in this case, we will use the host memory as a fallback
+            // fixes: https://github.com/ggml-org/llama.cpp/issues/18577
+            if (free == 0 && total == 0) {
+                ggml_backend_dev_memory(cpu_dev, &free, &total);
             }
-            // fall through
-        case LLM_ARCH_QWEN2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break;
-                    case 28: type = hparams.n_embd == 1536 ? LLM_TYPE_1_5B : LLM_TYPE_7B; break;
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 36: type = LLM_TYPE_3B; break;
-                    case 40: type = hparams.n_head() == 20 ? LLM_TYPE_4B : LLM_TYPE_13B; break;
-                    case 48: type = LLM_TYPE_14B; break;
-                    case 64: type = LLM_TYPE_32B; break;
-                    case 80: type = LLM_TYPE_70B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DREAM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                // Dream models are primarily 7B with 28 layers
-                switch (hparams.n_layer) {
-                    case 28:
-                        type = LLM_TYPE_7B;
-                        break;
-                    default:
-                        type = LLM_TYPE_UNKNOWN;
-                }
-                // Set non-causal attention for diffusion models
-                hparams.causal_attn = false;
-            } break;
-        case LLM_ARCH_LLADA:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                // LLaDA-8B has 32 layers, similar to LLaMA but for diffusion
-                switch (hparams.n_layer) {
-                    case 32:
-                        type = LLM_TYPE_8B;
-                        break;
-                    default:
-                        type = LLM_TYPE_UNKNOWN;
-                }
-                // Set non-causal attention for diffusion models
-                hparams.causal_attn = false;
-            } break;
-        case LLM_ARCH_LLADA_MOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                // diffusion language model uses non-causal attention
-                hparams.causal_attn = false;
-                switch (hparams.n_layer) {
-                    case 16: type = LLM_TYPE_A1_7B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_RND1:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 48: type = LLM_TYPE_30B_A3B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-                // Set non-causal attention for diffusion models
-                hparams.causal_attn = false;
-            } break;
-        case LLM_ARCH_QWEN2MOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_A2_7B; break;
-                    case 28: type = LLM_TYPE_57B_A14B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break;
-                    case 36: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_8B; break;
-                    case 40: type = LLM_TYPE_14B; break;
-                    case 64: type = LLM_TYPE_32B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MAINCODER:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_1B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN3VL:
-            {
-                ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 28: type = LLM_TYPE_1_7B; break;
-                    case 36: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_8B; break;
-                    case 64: type = LLM_TYPE_32B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN3MOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 48: type = LLM_TYPE_30B_A3B; break;
-                    case 94: type = LLM_TYPE_235B_A22B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN3VLMOE:
-            {
-                ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 48: type = LLM_TYPE_30B_A3B; break;
-                    case 94: type = LLM_TYPE_235B_A22B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_PHI2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+            splits[i] = free;
+        }
+    } else {
+        std::copy(tensor_split, tensor_split + n_devices(), splits.begin());
+    }
 
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_1B; break;
-                    case 32: type = LLM_TYPE_3B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_PHI3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    // sum and normalize the splits to get the split points
+    float split_sum = 0.0f;
+    for (size_t i = 0; i < n_devices(); ++i) {
+        split_sum += splits[i];
+        splits[i] = split_sum;
+    }
+    for (size_t i = 0; i < n_devices(); ++i) {
+        splits[i] /= split_sum;
+    }
 
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_1B; break;
-                    case 32: type = LLM_TYPE_3B; break;
-                    case 40: type = LLM_TYPE_14B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
+    const int i_gpu_start = std::max(int(hparams.n_layer) + 1 - n_gpu_layers, 0);
+    const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, int(n_layer) + 1);
+    auto get_layer_buft_list = [&](int il) -> llama_model::impl::layer_dev {
+        const bool is_swa = il < int(hparams.n_layer) && hparams.is_swa(il);
+        if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
+            LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(cpu_dev), is_swa);
+            return {cpu_dev, &pimpl->cpu_buft_list};
+        }
+        const int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + n_devices(), float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
+        auto * dev = devices.at(layer_gpu).dev;
+        LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(dev), is_swa);
+        return {dev, &pimpl->gpu_buft_list.at(dev)};
+    };
 
-                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+    // assign the input layer
+    // there is very little benefit to offloading the input layer, so always keep it on the CPU
+    pimpl->dev_input = { cpu_dev, &pimpl->cpu_buft_list };
 
-                if (found_swa && hparams.n_swa > 0) {
-                    LLAMA_LOG_WARN("%s: Phi SWA is currently disabled - results might be suboptimal for some models (see %s)\n",
-                            __func__, "https://github.com/ggml-org/llama.cpp/pull/13676");
+    // assign the repeating layers to the devices according to the splits
+    pimpl->dev_layer.resize(n_layer);
+    for (int il = 0; il < n_layer; ++il) {
+        pimpl->dev_layer[il] = get_layer_buft_list(il);
+    }
 
-                    // TODO: fix conversion scripts to correctly populate `n_swa` and `n_swa_pattern`
-                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+    // assign the output layer
+    pimpl->dev_output = get_layer_buft_list(n_layer);
 
-                    hparams.n_swa         = 0;
-                    hparams.set_swa_pattern(1);
-                }
-            } break;
-        case LLM_ARCH_PHIMOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED;
 
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_16x3_8B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_PLAMO:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    // create tensors for the weights
+    {
+        // TODO: move to a separate function
+        const auto tn = LLM_TN(arch);
 
-                switch (hparams.n_layer) {
-                    case 40: type = LLM_TYPE_13B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_PLAMO2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+        const int64_t n_expert      = hparams.n_expert;
+        const int64_t n_expert_used = hparams.n_expert_used;
 
-                // Load Mamba SSM parameters
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+        if (n_expert > 0 && n_expert_used == 0) {
+            throw std::runtime_error("model has expert layers but no expert layers are used");
+        }
 
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
-                }
+        layers.resize(n_layer);
 
-                switch (hparams.n_layer) {
-                    case 16: type = LLM_TYPE_1B; break;
-                    case 32:
-                        if (hparams.n_embd == 2048) {
-                            type = LLM_TYPE_2B;
-                        } else if (hparams.n_embd == 4096) {
-                            type = LLM_TYPE_8B;
-                        }
-                        break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_PLAMO3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-                if (found_swa && hparams.n_swa > 0) {
-                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                    uint32_t swa_period = 8;
-                    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                    hparams.set_swa_pattern(swa_period);
-                } else {
-                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
-                }
+        // call the per-model loading function
+        load_arch_tensors(ml);
 
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_2B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GPT2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 12: type = LLM_TYPE_SMALL; break;
-                    case 24: type = LLM_TYPE_MEDIUM; break;
-                    case 36: type = LLM_TYPE_LARGE; break;
-                    case 48: type = LLM_TYPE_XL; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_CODESHELL:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 42: type = LLM_TYPE_7B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_ORION:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+        // generic pass: load optional per-tensor/per-expert ".scale" tensors (e.g. NVFP4 scale2)
+        // this avoids having to add scale loading to every architecture
+        for (int i = 0; i < n_layer; ++i) {
+            auto & layer = layers[i];
 
-                switch (hparams.n_layer) {
-                    case 40: type = LLM_TYPE_14B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_INTERNLM2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 48: type = LLM_TYPE_20B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GEMMA:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+            // attention weight scales (per-tensor, shape {1})
+            if (!layer.wq_s && layer.wq) {
+                layer.wq_s = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wk_s && layer.wk) {
+                layer.wk_s = create_tensor(tn(LLM_TENSOR_ATTN_K,   "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wv_s && layer.wv) {
+                layer.wv_s = create_tensor(tn(LLM_TENSOR_ATTN_V,   "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wo_s && layer.wo) {
+                layer.wo_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wqkv_s && layer.wqkv) {
+                layer.wqkv_s = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wqkv_gate_s && layer.wqkv_gate) {
+                layer.wqkv_gate_s = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
 
-                switch (hparams.n_layer) {
-                    case 18: type = LLM_TYPE_2B; break;
-                    case 28: type = LLM_TYPE_7B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_GEMMA2:
-            {
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                hparams.n_swa = 4096; // default value of gemma 2
-                uint32_t swa_period = 2;
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                hparams.set_swa_pattern(swa_period);
-                hparams.attn_soft_cap = true;
-                hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
-
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,          hparams.rope_freq_base_train_swa, false);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING,      hparams.f_attn_logit_softcapping, false);
-                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);
-
-                switch (hparams.n_layer) {
-                    case 26: type = LLM_TYPE_2B; break;
-                    case 42: type = LLM_TYPE_9B; break;
-                    case 46: type = LLM_TYPE_27B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-               }
-
-                // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L173
-                hparams.f_attention_scale = type == LLM_TYPE_27B
-                    ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
-                    : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
-            } break;
-        case LLM_ARCH_GEMMA3:
-            {
-                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-                if (found_swa && hparams.n_swa > 0) {
-                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                    uint32_t swa_period = 6;
-                    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                    hparams.set_swa_pattern(swa_period);
-
-                    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                } else {
-                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
-                }
+            // dense FFN weight scales (per-tensor, shape {1})
+            if (!layer.ffn_gate_s && layer.ffn_gate) {
+                layer.ffn_gate_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_down_s && layer.ffn_down) {
+                layer.ffn_down_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_up_s && layer.ffn_up) {
+                layer.ffn_up_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_gate_shexp_s && layer.ffn_gate_shexp) {
+                layer.ffn_gate_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_down_shexp_s && layer.ffn_down_shexp) {
+                layer.ffn_down_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_up_shexp_s && layer.ffn_up_shexp) {
+                layer.ffn_up_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
 
-                hparams.f_final_logit_softcapping = 0.0f;
-                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 18: type = LLM_TYPE_270M; break;
-                    case 26: type = LLM_TYPE_1B; break;
-                    case 32: type = LLM_TYPE_8B; break; // Rnj-1
-                    case 34: type = LLM_TYPE_4B; break;
-                    case 48: type = LLM_TYPE_12B; break;
-                    case 62: type = LLM_TYPE_27B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
+            // MoE expert weight scales (per-expert, shape {n_expert})
+            if (!layer.ffn_gate_exps_s && layer.ffn_gate_exps) {
+                layer.ffn_gate_exps_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_down_exps_s && layer.ffn_down_exps) {
+                layer.ffn_down_exps_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_up_exps_s && layer.ffn_up_exps) {
+                layer.ffn_up_exps_s = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
+            }
 
-                // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L289
-                hparams.f_attention_scale = type == LLM_TYPE_27B
-                    ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
-                    : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
-            } break;
-        case LLM_ARCH_GEMMA3N:
-            {
-                uint32_t swa_period = 5;
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                hparams.set_swa_pattern(swa_period);
-
-                hparams.n_layer_kv_from_start     = 20;
-                hparams.f_attention_scale         = 1.0f;
-
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,          hparams.rope_freq_base_train_swa, false);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 30: type = LLM_TYPE_E2B; break;
-                    case 35: type = LLM_TYPE_E4B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GEMMA4:
-            {
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
-
-                uint32_t n_kv_shared_layers = 0;
-                ml.get_key(LLM_KV_ATTENTION_SHARED_KV_LAYERS, n_kv_shared_layers, false);
-
-                hparams.n_layer_kv_from_start = hparams.n_layer - (int32_t)n_kv_shared_layers;
-                hparams.f_attention_scale     = 1.0f; // Gemma4 uses self.scaling = 1.0 (no pre-attn scaling)
-
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,          hparams.rope_freq_base_train_swa, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER,  hparams.n_embd_per_layer);
-                ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA,    hparams.n_embd_head_k_swa);
-                ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,  hparams.n_embd_head_v_swa);
-                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);
-
-                switch (hparams.n_layer) {
-                    case 30: type = LLM_TYPE_26B_A4B; break;
-                    case 35: type = LLM_TYPE_E2B; break;
-                    case 42: type = LLM_TYPE_E4B; break;
-                    case 60: type = LLM_TYPE_31B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GEMMA_EMBEDDING:
-            {
-                hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
-                uint32_t swa_period = 6;
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                hparams.set_swa_pattern(swa_period);
-
-                hparams.causal_attn = false; // embeddings do not use causal attention
+            // recurrent / linear-attention weight scales (per-tensor, shape {1})
+            if (!layer.ssm_in_s && layer.ssm_in) {
+                layer.ssm_in_s = create_tensor(tn(LLM_TENSOR_SSM_IN, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ssm_out_s && layer.ssm_out) {
+                layer.ssm_out_s = create_tensor(tn(LLM_TENSOR_SSM_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ssm_alpha_s && layer.ssm_alpha) {
+                layer.ssm_alpha_s = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ssm_beta_s && layer.ssm_beta) {
+                layer.ssm_beta_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
 
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+            // input scales
+            if (!layer.wq_in_s && layer.wq) {
+                layer.wq_in_s = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wk_in_s && layer.wk) {
+                layer.wk_in_s = create_tensor(tn(LLM_TENSOR_ATTN_K,   "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wv_in_s && layer.wv) {
+                layer.wv_in_s = create_tensor(tn(LLM_TENSOR_ATTN_V,   "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wo_in_s && layer.wo) {
+                layer.wo_in_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wqkv_in_s && layer.wqkv) {
+                layer.wqkv_in_s = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.wqkv_gate_in_s && layer.wqkv_gate) {
+                layer.wqkv_gate_in_s = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_gate_in_s && layer.ffn_gate) {
+                layer.ffn_gate_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_down_in_s && layer.ffn_down) {
+                layer.ffn_down_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_up_in_s && layer.ffn_up) {
+                layer.ffn_up_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_gate_exps_in_s && layer.ffn_gate_exps) {
+                layer.ffn_gate_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_down_exps_in_s && layer.ffn_down_exps) {
+                layer.ffn_down_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_up_exps_in_s && layer.ffn_up_exps) {
+                layer.ffn_up_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_gate_shexp_in_s && layer.ffn_gate_shexp) {
+                layer.ffn_gate_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_down_shexp_in_s && layer.ffn_down_shexp) {
+                layer.ffn_down_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ffn_up_shexp_in_s && layer.ffn_up_shexp) {
+                layer.ffn_up_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ssm_in_in_s && layer.ssm_in) {
+                layer.ssm_in_in_s = create_tensor(tn(LLM_TENSOR_SSM_IN, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ssm_out_in_s && layer.ssm_out) {
+                layer.ssm_out_in_s = create_tensor(tn(LLM_TENSOR_SSM_OUT, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ssm_alpha_in_s && layer.ssm_alpha) {
+                layer.ssm_alpha_in_s = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+            if (!layer.ssm_beta_in_s && layer.ssm_beta) {
+                layer.ssm_beta_in_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
+            }
+        }
+        // output scales
+        if (output && output->type == GGML_TYPE_NVFP4) {
+            // weight scale
+            if (!output_s) {
+                output_s = create_tensor(tn(LLM_TENSOR_OUTPUT, "scale"), {1}, TENSOR_NOT_REQUIRED);
+            }
+            // input scale
+            if (!output_in_s) {
+                output_in_s = create_tensor(tn(LLM_TENSOR_OUTPUT, "input_scale"), {1}, TENSOR_NOT_REQUIRED);
+            }
+        }
+    }
+    ml.done_getting_tensors();
 
-                //applied only if model converted with --sentence-transformers-dense-modules
-                ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
-                ml.get_key(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out, false);
-                ml.get_key(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in, false);
-                ml.get_key(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out, false);
+    GGML_ASSERT(!(output && tok_embd &&
+            strcmp(output->name, tok_embd->name) == 0 &&
+            output->type == GGML_TYPE_NVFP4));
+    // populate tensors_by_name
+    for (auto & [_, ctx_ptr] : ml.ctx_map) {
+        for (auto * cur = ggml_get_first_tensor(ctx_ptr.get()); cur != NULL; cur = ggml_get_next_tensor(ctx_ptr.get(), cur)) {
+            tensors_by_name.emplace_back(ggml_get_name(cur), cur);
+        }
+    }
 
-                GGML_ASSERT((hparams.dense_2_feat_in == 0 || hparams.dense_2_feat_in == hparams.n_embd) && "dense_2_feat_in must be equal to n_embd");
-                GGML_ASSERT((hparams.dense_3_feat_out == 0 || hparams.dense_3_feat_out == hparams.n_embd) && "dense_3_feat_out must be equal to n_embd");
+    ml.init_mappings(true, use_mlock ? &pimpl->mlock_mmaps : nullptr);
+    pimpl->mappings.reserve(ml.mappings.size());
 
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_0_3B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-                hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
+    // create the backend buffers
+    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_buf_maps;
+    ctx_buf_maps.reserve(ml.ctx_map.size());
 
-            } break;
-        case LLM_ARCH_STARCODER2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 30: type = LLM_TYPE_3B; break;
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 40: type = LLM_TYPE_15B; break;
-                    case 52: type = LLM_TYPE_20B; break; // granite
-                    case 88: type = LLM_TYPE_34B; break; // granite
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MAMBA:
-            {
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_DT_B_C_RMS,     hparams.ssm_dt_b_c_rms, false);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 24:
-                        switch (hparams.n_embd) {
-                            case 768: type = LLM_TYPE_SMALL; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 48:
-                        switch (hparams.n_embd) {
-                            case 1024: type = LLM_TYPE_MEDIUM; break;
-                            case 1536: type = LLM_TYPE_LARGE; break;
-                            case 2048: type = LLM_TYPE_XL; break;
-                            default:   type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 64:
-                        switch (hparams.n_embd) {
-                            case 2560: type = LLM_TYPE_3B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MAMBA2:
-            {
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 24:
-                        switch (hparams.n_embd) {
-                            case 768: type = LLM_TYPE_SMALL; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 48:
-                        switch (hparams.n_embd) {
-                            case 1024: type = LLM_TYPE_MEDIUM; break;
-                            case 1536: type = LLM_TYPE_LARGE; break;
-                            case 2048: type = LLM_TYPE_XL; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 64:
-                        switch (hparams.n_embd) {
-                            case 2560: type = LLM_TYPE_3B; break;
-                            case 4096: type = LLM_TYPE_7B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_JAMBA:
-            {
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
+    const size_t n_max_backend_buffer = ml.ctx_map.size() * ml.files.size();
+    pimpl->ctxs_bufs.reserve(n_max_backend_buffer);
 
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    for (auto & [buft, ctx_ptr] : ml.ctx_map) {
+        ggml_context * ctx = ctx_ptr.get();
 
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
-                }
+        // skip contexts without tensors
+        if (ggml_get_first_tensor(ctx) == nullptr) {
+            continue;
+        }
 
-                switch (hparams.n_layer) {
-                    // TODO: Jamba layers are a bit heterogeneous, so naming this is hard.
-                    case 12: // 900M  8x???M
-                    case 32: // 51B  16x?B
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_XVERSE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 40: type = LLM_TYPE_13B; break;
-                    case 80: type = LLM_TYPE_65B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_COMMAND_R:
-            {
-                ml.get_key(LLM_KV_LOGIT_SCALE,             hparams.f_logit_scale, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 40: type = LLM_TYPE_35B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_COHERE2:
-            {
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                uint32_t swa_period = 4;
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                hparams.set_swa_pattern(swa_period);
-                hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
-
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,       hparams.rope_freq_base_train_swa, false);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
-                ml.get_key(LLM_KV_LOGIT_SCALE,              hparams.f_logit_scale);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_8B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DBRX:
-        {
-            ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-            ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv);
+        llama_buf_map buf_map;
+        buf_map.reserve(n_max_backend_buffer);
 
-            switch (hparams.n_layer) {
-                case 40: type = LLM_TYPE_16x12B; break;
-                default: type = LLM_TYPE_UNKNOWN;
+        // check if it is possible to use buffer_from_host_ptr with this buffer type
+        ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
+        if (!dev) {
+            // FIXME: workaround for CPU backend buft having a NULL device
+            dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+            if (!dev) {
+                throw std::runtime_error(format("%s: no CPU backend found", __func__));
             }
-        } break;
-        case LLM_ARCH_OLMO:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv, false);
-
-                switch (hparams.n_layer) {
-                    case 22: type = LLM_TYPE_1B; break;
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 80: type = LLM_TYPE_70B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_OLMO2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-                if (found_swa && hparams.n_swa > 0) {
-                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                    uint32_t swa_period = 4;
-                    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                    hparams.set_swa_pattern(swa_period);
-
-                    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                    hparams.rope_freq_scale_train_swa = 1.0; // See olmo2.cpp
-                    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                } else {
-                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
-                }
+        }
+        ggml_backend_dev_props props;
+        ggml_backend_dev_get_props(dev, &props);
+        bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
+        bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev);
 
-                switch (hparams.n_layer) {
-                    case 16: type = LLM_TYPE_1B; break;
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 40: type = LLM_TYPE_13B; break;
-                    case 64: type = LLM_TYPE_32B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_SEED_OSS:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 64: type = LLM_TYPE_36B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_OLMOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 16: type = LLM_TYPE_A1_7B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
+        std::vector<ggml_backend_buffer_ptr> bufs;
+        if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
+            GGML_ASSERT(!ml.no_alloc);
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                // only the mmap region containing the tensors in the model is mapped to the backend buffer
+                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer,
+                //     then we could just use metal for all layers
+                // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
+                void * addr = nullptr;
+                size_t first, last; // NOLINT
+                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+                if (first >= last) {
+                    continue;
                 }
-            } break;
-        case LLM_ARCH_OPENELM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                case 16: type = LLM_TYPE_270M; break;
-                case 20: type = LLM_TYPE_450M; break;
-                case 28: type = LLM_TYPE_1B; break;
-                case 36: type = LLM_TYPE_3B; break;
-                default: type = LLM_TYPE_UNKNOWN;
+                const size_t max_size = ggml_get_max_tensor_size(ctx);
+                ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
+                if (buf == nullptr) {
+                    throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
                 }
-            } break;
-        case LLM_ARCH_GPTNEOX:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL,   hparams.use_par_res);
-                switch (hparams.n_layer) {
-                    case 6:
-                        switch (hparams.n_ff()) {
-                            case 512:  type = LLM_TYPE_14M; break;
-                            case 2048: type = LLM_TYPE_70M; break;
-                            default:   type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 12:
-                        switch (hparams.n_ff()) {
-                            case 3072: type = LLM_TYPE_160M; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 16:
-                        switch (hparams.n_ff()) {
-                            case 8192: type = LLM_TYPE_1B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 24:
-                        switch (hparams.n_ff()) {
-                            case 4096: type = LLM_TYPE_410M; break;
-                            case 8192: type = LLM_TYPE_1_4B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 32:
-                        switch (hparams.n_ff()) {
-                            case 10240: type = LLM_TYPE_2_8B; break;
-                            case 16384: type = LLM_TYPE_6_9B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 36:
-                        switch (hparams.n_ff()) {
-                            case 20480: type = LLM_TYPE_12B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 44:
-                        switch (hparams.n_ff()) {
-                            case 24576: type = LLM_TYPE_20B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    default: type = LLM_TYPE_UNKNOWN;
+                bufs.emplace_back(buf);
+                buf_map.emplace(idx, buf);
+            }
+        } else {
+            ggml_backend_buffer_t buf;
+            if (ml.no_alloc) {
+                buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
+                for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+                    t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them
                 }
-            } break;
-        case LLM_ARCH_ARCTIC:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+            } else {
+                buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
+            }
+            if (buf == nullptr) {
+                throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
+            }
+            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
+                pimpl->mlock_bufs.emplace_back(new llama_mlock);
+                auto & mlock_buf = pimpl->mlock_bufs.back();
+                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
+                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
+            }
+            bufs.emplace_back(buf);
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                buf_map.emplace(idx, buf);
+            }
+        }
 
-                if (hparams.n_expert == 128) {
-                    switch (hparams.n_layer) {
-                        case 35: type = LLM_TYPE_10B_128x3_66B; break;
-                        default: type = LLM_TYPE_UNKNOWN;
-                    }
-                } else {
-                    type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DEEPSEEK:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
-
-                switch (hparams.n_ff_exp) {
-                    case 1408: type = LLM_TYPE_16B; break;
-                    case 1792: type = LLM_TYPE_20B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DEEPSEEK2:
-        case LLM_ARCH_MISTRAL4:
-            {
-                // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B, Kanana-2-30B-A3B
-                const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26 || (hparams.n_layer == 48 && n_vocab == 128256));
+        for (auto & buf : bufs) {
+            // indicate that this buffer contains weights
+            // this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
+            ggml_backend_buffer_set_usage(buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+        }
 
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                if (!is_lite) {
-                    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
-                }
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,     hparams.n_lora_kv);
-                ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,   hparams.n_embd_head_k_mla_impl, false);
-                ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,       hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,        hparams.expert_weights_norm, false);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,         hparams.expert_gating_func, false);
-                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
-                    // for compatibility with existing DeepSeek V2 and V2.5 GGUFs
-                    // that have no expert_gating_func model parameter set
-                    if ((hparams.n_layer == 47 || hparams.n_layer == 48) && n_vocab == 154880) {
-                        // GLM 4.7 Lite
-                        hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
-                    } else {
-                        hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
-                    }
-                }
+        pimpl->ctxs_bufs.emplace_back(std::move(ctx_ptr), std::move(bufs));
 
-                if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) {
-                    // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
-                    // cancel the factor from the convert script
-                    hparams.rope_yarn_log_mul /= 0.1f;
-                }
+        ctx_buf_maps.emplace_back(ctx, buf_map);
+    }
 
-                // (optional) temperature tuning - used by mistral-large
-                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE,  hparams.f_attn_temp_scale,       false);
-                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); // FIXME why not use temperature_length?
+    if (llama_supports_gpu_offload()) {
+        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
 
-                hparams.f_attn_temp_offset = 0.0f;
+        int n_repeating = n_gpu;
+        if (n_repeating > 0) {
+            LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__);
+            n_repeating--;
+        }
+        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_repeating);
 
-                switch (hparams.n_layer) {
-                    case 27: type = LLM_TYPE_16B; break;
-                    case 47: type = LLM_TYPE_30B_A3B; break;
-                    case 60: type = LLM_TYPE_236B; break;
-                    case 61: type = LLM_TYPE_671B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DEEPSEEK2OCR:
-            {
-                // similar to deepseek2, but without MLA
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
-
-                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
-                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
-                }
-
-                switch (hparams.n_layer) {
-                    case 12: type = LLM_TYPE_3B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_PLM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_1_8B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_CHATGLM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 28: {
-                        if (hparams.n_head(0) == 16) {
-                            type = LLM_TYPE_1_5B;
-                        } else {
-                            type = LLM_TYPE_6B;
-                        }
-                    } break;
-                    case 40: {
-                        if (hparams.n_head(0) == 24) {
-                            type = LLM_TYPE_4B;
-                        } else {
-                            type = LLM_TYPE_9B;
-                        }
-                    } break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GLM4:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
-
-                // NextN/MTP parameters (GLM-OCR)
-                ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
-                GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
-
-                // TODO: when MTP is implemented, this should probably be updated if needed
-                hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
-
-                switch (hparams.n_layer) {
-                    case 17: type = LLM_TYPE_1B; break; // GLM-OCR
-                    case 40: type = LLM_TYPE_9B; break;
-                    case 61: type = LLM_TYPE_32B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GLM4_MOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,     hparams.n_ff_exp);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
-
-                // MoE parameters
-                ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
-                ml.get_key(LLM_KV_EXPERT_USED_COUNT,           hparams.n_expert_used);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
-
-                // Expert gating function (GLM-4.5 uses sigmoid)
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
-                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
-                    hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
-                }
+        const int max_backend_supported_layers = hparams.n_layer + 1;
+        const int max_offloadable_layers       = hparams.n_layer + 1;
 
-                // NextN/MTP parameters
-                ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.nextn_predict_layers, false);
-                GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
+    }
 
-                // TODO: when MTP is implemented, this should probably be updated if needed
-                hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+    // print memory requirements per buffer type
+    for (auto & [_, bufs] : pimpl->ctxs_bufs) {
+        for (auto & buf: bufs) {
+            LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n",
+                __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0);
+        }
+    }
 
-                switch (hparams.n_layer) {
-                    case 47: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air (46 layers + 1 NextN layer)
-                    case 48: type = LLM_TYPE_102B_A12B; break; // Solar Open
-                    case 93: type = LLM_TYPE_355B_A32B; break; // GLM-4.5 (92 layers + 1 NextN layer)
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GLM_DSA:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,     hparams.n_ff_exp);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
-
-                // MoE parameters
-                ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
-                ml.get_key(LLM_KV_EXPERT_USED_COUNT,           hparams.n_expert_used);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
-
-                // deepseek MLA parameters
-                ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,      hparams.n_lora_q);
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,     hparams.n_lora_kv);
-                ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,   hparams.n_embd_head_k_mla_impl, false);
-                ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);
-
-                // DSA parameters
-                ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
-                ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
-                ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K,      hparams.indexer_top_k);
-
-                // Expert gating function (GLM-4.5 uses sigmoid)
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
-                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
-                    hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
-                }
+    if (ml.no_alloc) {
+        return true;
+    }
 
-                // NextN/MTP parameters
-                ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.nextn_predict_layers, false);
-                GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+    // load tensor data
+    for (auto & [ctx, buf_map] : ctx_buf_maps) {
+        if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) {
+            return false;
+        }
+    }
 
-                // TODO: when MTP is implemented, this should probably be updated if needed
-                hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+    if (use_mmap_buffer) {
+        for (auto & mapping : ml.mappings) {
+            pimpl->mappings.emplace_back(std::move(mapping));
+        }
+    }
 
-                switch (hparams.n_layer) {
-                    case 79: type = LLM_TYPE_744B_A40B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_BITNET:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    return true;
+}
 
-                switch (hparams.n_layer) {
-                    case 26: type = LLM_TYPE_3B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_T5:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+ggml_tensor * llama_model_base::create_tensor(llama_model_loader & ml, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) {
+    const buft_list_t * buft_list_layer = tn.bid == -1 ? nullptr : pimpl->dev_layer.at(tn.bid).buft_list;
+    return ml.create_tensor(
+        hparams, &pimpl->cpu_buft_list, pimpl->dev_input.buft_list, pimpl->dev_output.buft_list, buft_list_layer,
+        tn, ne, flags);
+}
 
-                uint32_t dec_start_token_id;
-                if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
-                    hparams.dec_start_token_id = dec_start_token_id;
-                }
+std::string llama_model::arch_name() const {
+    return llm_arch_name(arch);
+}
 
-                hparams.dec_n_layer = hparams.n_layer;
-                ml.get_key(LLM_KV_DECODER_BLOCK_COUNT, hparams.dec_n_layer, false);
-
-                switch (hparams.n_layer) {
-                    case 6:  type = LLM_TYPE_60M;  break; // t5-small
-                    case 8:  type = LLM_TYPE_80M;  break; // flan-t5-small
-                    case 12:
-                        switch (hparams.n_ff()) {
-                            case 3072: type = LLM_TYPE_220M; break; // t5-base
-                            case 2048: type = LLM_TYPE_250M; break; // flan-t5-base
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 24:
-                        switch (hparams.n_ff()) {
-                            case 4096:  type = LLM_TYPE_770M; break; // t5-large
-                            case 2816:  type = LLM_TYPE_780M; break; // flan-t5-large
-                            case 16384: type = LLM_TYPE_3B;   break; // t5-3b
-                            case 5120:  type = LLM_TYPE_3B;   break; // flan-t5-xl
-                            case 65536: type = LLM_TYPE_11B;  break; // t5-11b
-                            case 10240: type = LLM_TYPE_11B;  break; // flan-t5-xxl
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    default: type = LLM_TYPE_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_T5ENCODER:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
-                type = LLM_TYPE_UNKNOWN;
-            } break;
-        case LLM_ARCH_JAIS:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias, false);
-
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_1_3B; break;
-                    case 40: type = LLM_TYPE_13B; break;
-                    /* TODO: add variants */
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_JAIS2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+std::string llama_model::type_name() const {
+    return llm_type_name(type);
+}
 
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_8B; break;
-                    case 68: type = LLM_TYPE_70B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_NEMOTRON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_4B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_NEMOTRON_H:
-        case LLM_ARCH_NEMOTRON_H_MOE:
-            {
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
-
-                // A layer is recurrent IFF the n_head_kv value is set to 0 and
-                // the n_ff value is set to 0
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = (hparams.n_head_kv(i) == 0 && hparams.n_ff(i) == 0);
-                }
+std::string llama_model::desc() const {
+    return pimpl->desc_str;
+}
 
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+size_t llama_model::size() const {
+    return pimpl->n_bytes;
+}
 
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp,        false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp,      false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_MOE_LATENT_SIZE,                   hparams.moe_latent_size, false);
+size_t llama_model::n_tensors() const {
+    return tensors_by_name.size();
+}
 
-                switch (hparams.n_layer) {
-                    case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B
-                    case 56: type = LLM_TYPE_9B; break;
-                    case 88: type = LLM_TYPE_120B_A12B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_EXAONE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+size_t llama_model::n_devices() const {
+    return devices.size();
+}
 
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_8B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_EXAONE4:
-            {
-                if (hparams.n_layer == 64) {    // 32B
-                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                    hparams.n_swa = 4096;
-                    uint32_t swa_period = 4;
-                    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                    hparams.set_swa_pattern(swa_period);
-
-                    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
-                    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                }
+const float * llama_model::tensor_split() const {
+    return params.tensor_split;
+}
 
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+uint32_t llama_model::n_gpu_layers() const {
+    return params.n_gpu_layers >= 0 ? params.n_gpu_layers : hparams.n_layer + 1;
+}
 
-                switch (hparams.n_layer) {
-                    case 30: type = LLM_TYPE_1_2B; break;
-                    case 64: type = LLM_TYPE_32B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_EXAONE_MOE:
-            {
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                hparams.n_swa = 128;
-                uint32_t swa_period = 4;
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                hparams.set_swa_pattern(swa_period);
-                hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
-
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,                hparams.rope_freq_base_train_swa, false);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,          hparams.n_swa);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
-
-                ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,              hparams.nextn_predict_layers, false);
-                GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
-
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_30B_A3B; break;
-                    case 48:
-                    case 49: type = LLM_TYPE_235B_A22B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_RWKV6:
-        case LLM_ARCH_RWKV6QWEN2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,     hparams.f_norm_eps, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
-                ml.get_key(LLM_KV_WKV_HEAD_SIZE,               hparams.wkv_head_size);
-                ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM,          hparams.time_mix_extra_dim);
-                ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM,        hparams.time_decay_extra_dim);
-                ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS,      hparams.rescale_every_n_layers, false);
-                ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,           hparams.token_shift_count, false);
-
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_1_6B; break;
-                    case 32:
-                        switch (hparams.n_embd) {
-                            case 2560: type = LLM_TYPE_3B; break;
-                            case 4096: type = LLM_TYPE_7B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 61: type = LLM_TYPE_14B; break;
-                    case 64: type = LLM_TYPE_32B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_RWKV7:
-        case LLM_ARCH_ARWKV7:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,                hparams.f_norm_eps, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,            hparams.f_norm_rms_eps, false);
-                ml.get_key(LLM_KV_WKV_HEAD_SIZE,                          hparams.wkv_head_size);
-                ml.get_key(LLM_KV_ATTENTION_DECAY_LORA_RANK,              hparams.n_lora_decay);
-                ml.get_key(LLM_KV_ATTENTION_ICLR_LORA_RANK,               hparams.n_lora_iclr);
-                ml.get_key(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);
-                ml.get_key(LLM_KV_ATTENTION_GATE_LORA_RANK,               hparams.n_lora_gate, false);
-                ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,                      hparams.token_shift_count, false);
-
-                switch (hparams.n_layer) {
-                    case 12:
-                        switch (hparams.n_embd) {
-                            case 768: type = LLM_TYPE_190M; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 24:
-                        switch (hparams.n_embd) {
-                            case 1024: type = LLM_TYPE_450M; break;
-                            case 2048: type = LLM_TYPE_1_5B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 28:
-                        switch (hparams.n_embd) {
-                            case 1536: type = LLM_TYPE_1_5B; break;
-                            case 3584: type = LLM_TYPE_7B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 32:
-                        switch (hparams.n_embd) {
-                            case 2560: type = LLM_TYPE_2_9B; break;
-                            case 4096: type = LLM_TYPE_7B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    case 61:
-                        switch (hparams.n_embd) {
-                            case 4096: type = LLM_TYPE_14B; break;
-                            default: type = LLM_TYPE_UNKNOWN;
-                        } break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GRANITE:
-        case LLM_ARCH_GRANITE_MOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);
-                ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale, false);
-                ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, false);
-                ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, false);
-
-                // Granite uses rope_finetuned as a switch for rope, so default to true
-                bool rope_finetuned = true;
-                ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
-                hparams.rope_finetuned = rope_finetuned;
-
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_3B; break;
-                    case 40: type = LLM_TYPE_3B; break;
-                    // Add additional layer/vocab/etc checks here for other model sizes
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
+llama_split_mode llama_model::split_mode() const {
+    return params.split_mode;
+}
 
-                // For Granite MoE Shared
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
-            } break;
-        case LLM_ARCH_GRANITE_HYBRID:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale, /* required */ false);
-                ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale, /* required */ false);
-                ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, /* required */ false);
-                ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, /* required */ false);
-
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
-
-                // Granite uses rope_finetuned as a switch for rope, so default to true
-                bool rope_finetuned = true;
-                ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
-                hparams.rope_finetuned = rope_finetuned;
-
-                // A layer is recurrent IFF the n_head_kv value is set to 0
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
-                }
+std::map<ggml_backend_buffer_type_t, size_t> llama_model::memory_breakdown() const {
+    std::map<ggml_backend_buffer_type_t, size_t> ret;
+    for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) {
+        if (hparams.no_alloc) {
+            GGML_ASSERT(bufs.size() == 1);
+            ggml_backend_buffer_t buf = bufs[0].get();
+            GGML_ASSERT(ggml_backend_buffer_get_base(buf) == nullptr);
+            ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf);
+            ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
+        } else {
+            for (const auto & buf : bufs) {
+                // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
+                ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
+            }
+        }
+    }
+    return ret;
+}
 
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+uint64_t llama_model::n_elements() const {
+    return pimpl->n_elements;
+}
 
-                switch (hparams.n_embd) {
-                    case 768: type = LLM_TYPE_350M; break;
-                    case 1536: type = (hparams.n_ff() == 512 ? LLM_TYPE_7B_A1B : LLM_TYPE_1B); break;
-                    case 2048: case 2560: type = LLM_TYPE_3B; break;
-                    case 4096: type = LLM_TYPE_32B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
+void llama_model::print_info() const {
+    const std::string rope_scaling_type = llama_rope_scaling_type_name(hparams.rope_scaling_type_train);
 
-                // For Granite MoE Shared
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
-            } break;
-        case LLM_ARCH_CHAMELEON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                hparams.f_norm_eps = 1e-5;  // eps for qk-norm, torch default
-                ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm, false);
-
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_7B; break;
-                    case 48: type = LLM_TYPE_34B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-               }
-            } break;
-        case LLM_ARCH_WAVTOKENIZER_DEC:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS,    hparams.f_norm_group_eps);
-                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
-            } break;
-        case LLM_ARCH_BAILINGMOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
-
-                switch (hparams.n_layer) {
-                    case 28: type = LLM_TYPE_16B; break;
-                    case 88: type = LLM_TYPE_290B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_BAILINGMOE2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
-                ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,              hparams.nextn_predict_layers, false);
-                GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
-
-                // TODO: when MTP is implemented, this should probably be updated if needed
-                hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
-
-                switch (hparams.n_layer) {
-                    case 20: type = LLM_TYPE_16B_A1B; break;
-                    case 21: type = LLM_TYPE_16B_A1B; break;
-                    case 32: type = LLM_TYPE_100B_A6B; break;
-                    case 33: type = LLM_TYPE_100B_A6B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_DOTS1:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
-                switch (hparams.n_layer) {
-                    case 62: type = LLM_TYPE_142B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_ERNIE4_5:
-        case LLM_ARCH_ERNIE4_5_MOE:
-        case LLM_ARCH_PADDLEOCR:
-            {
-                // paddleocr need mrope_section
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
-
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                if (arch == LLM_ARCH_ERNIE4_5_MOE) {
-                    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-                    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-                    ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         hparams.n_moe_layer_step);
-                    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
-                }
+    auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
+        bool is_var = false;
 
-                switch (hparams.n_layer) {
-                    case 18: type = LLM_TYPE_0_3B; break;
-                    case 28: type = LLM_TYPE_21B_A3B; break;
-                    case 54: type = LLM_TYPE_300B_A47B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_FALCON_H1:
-            {
-                // Common parameters
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                // SSM parameters
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
-
-                std::fill(hparams.recurrent_layer_arr.begin(), hparams.recurrent_layer_arr.end(), true);
-
-                switch (hparams.n_layer) {
-                    case 36:
-                        type = LLM_TYPE_0_5B; break;
-                    case 24:
-                        type = LLM_TYPE_1_5B; break;
-                    case 66:
-                        type = LLM_TYPE_1B; break;
-                    case 32:
-                        type = LLM_TYPE_3B; break;
-                    case 44:
-                        type = LLM_TYPE_7B; break;
-                    case 72:
-                        type = LLM_TYPE_34B; break;
-                    default:
-                        type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_HUNYUAN_MOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+        std::vector<uint32_t> v;
+        for (uint32_t i = 0; i < n; ++i) {
+            v.push_back(f(i));
+            if (v[i] != v[0]) {
+                is_var = true;
+            }
+        }
 
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_A13B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_HUNYUAN_DENSE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-                switch (hparams.n_embd) {
-                    case 1024: type = LLM_TYPE_0_5B; break;
-                    case 2048: type = LLM_TYPE_1_8B; break;
-                    case 3072: type = LLM_TYPE_4B; break;
-                    case 4096: type = LLM_TYPE_7B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_SMOLLM3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                hparams.n_no_rope_layer_step = 4;
+        std::stringstream ss;
 
-                switch (hparams.n_layer) {
-                    case 36: type = LLM_TYPE_3B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_OPENAI_MOE:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
-
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                uint32_t swa_period = 2;
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                hparams.set_swa_pattern(swa_period);
-
-                hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_20B; break;
-                    case 36: type = LLM_TYPE_120B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_LFM2:
-            {
-                ml.get_key(LLM_KV_SHORTCONV_L_CACHE,           hparams.n_shortconv_l_cache);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                for (uint32_t il = 0; il < hparams.n_layer; ++il) {
-                    hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
-                }
-                hparams.n_layer_dense_lead = hparams.n_layer;
-                switch (hparams.n_ff()) {
-                    case  4608: type = LLM_TYPE_350M; break;
-                    case  6912: type = LLM_TYPE_700M; break;
-                    case  8192: type = LLM_TYPE_1_2B; break;
-                    case 10752: type = LLM_TYPE_2_6B; break;
-                    default:    type = LLM_TYPE_UNKNOWN;
-                }
-                if (const auto is_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); is_swa && hparams.n_swa > 0) {
-                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                    for (uint32_t il = 0; il < hparams.n_layer; ++il) {
-                        hparams.swa_layers[il] = !hparams.recurrent_layer_arr[il];
-                    }
-                }
-            } break;
-        case LLM_ARCH_LFM2MOE:
-            {
-                ml.get_key(LLM_KV_SHORTCONV_L_CACHE,           hparams.n_shortconv_l_cache);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func);
-
-                for (uint32_t il = 0; il < hparams.n_layer; ++il) {
-                    hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
+        if (is_var) {
+            ss << "[";
+            for (uint32_t i = 0; i < n; ++i) {
+                ss << v[i];
+                if (i < n - 1) {
+                    ss << ", ";
                 }
+            }
+            ss << "]";
+        } else {
+            ss << v[0];
+        }
 
-                switch (hparams.n_layer) {
-                    case 24: type = LLM_TYPE_8B_A1B;  break;
-                    case 40: type = LLM_TYPE_24B_A2B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_SMALLTHINKER:
-            {
-                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-
-                if (found_swa && hparams.n_swa > 0) {
-                    hparams.swa_type    = LLAMA_SWA_TYPE_STANDARD;
-                    hparams.n_swa       = 4096;
-                    uint32_t swa_period = 4;
-                    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
-                    hparams.set_swa_pattern(swa_period, true);
-
-                    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
-                    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
-                    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
-                } else {
-                    hparams.swa_type             = LLAMA_SWA_TYPE_NONE;
-                    hparams.n_no_rope_layer_step = hparams.n_layer;
-                }
+        return ss.str();
+    };
 
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+    // hparams
+    LLAMA_LOG_INFO("%s: arch                  = %s\n",     __func__, arch_name().c_str());
+    LLAMA_LOG_INFO("%s: vocab_only            = %d\n",     __func__, hparams.vocab_only);
+    LLAMA_LOG_INFO("%s: no_alloc              = %d\n",     __func__, hparams.no_alloc);
 
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_4B;  break;
-                    case 52: type = LLM_TYPE_20B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_GROVEMOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,  hparams.n_ff_chexp, false);
-                ml.get_key(LLM_KV_EXPERT_GROUP_SCALE,                hparams.expert_group_scale);
-                ml.get_key(LLM_KV_EXPERTS_PER_GROUP,                 hparams.n_group_experts);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-
-                switch (hparams.n_layer) {
-                    case 48: type = LLM_TYPE_30B_A3B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_APERTUS:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key_or_arr(LLM_KV_XIELU_ALPHA_N,        hparams.xielu_alpha_n, hparams.n_layer);
-                ml.get_key_or_arr(LLM_KV_XIELU_ALPHA_P,        hparams.xielu_alpha_p, hparams.n_layer);
-                ml.get_key_or_arr(LLM_KV_XIELU_BETA,           hparams.xielu_beta,    hparams.n_layer);
-                ml.get_key_or_arr(LLM_KV_XIELU_EPS,            hparams.xielu_eps,     hparams.n_layer);
-
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_8B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MINIMAX_M2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,  hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,   hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,           hparams.expert_gating_func, false);
-
-                switch (hparams.n_layer) {
-                    case 62: type = LLM_TYPE_230B_A10B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_COGVLM:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 32: type = LLM_TYPE_13B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_PANGU_EMBED:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                switch (hparams.n_layer) {
-                    case 26: type = LLM_TYPE_1B; break; // openPangu-Embedded-1B-V1.1
-                    case 34: type = LLM_TYPE_7B; break; // openPangu-Embedded-7B-V1.1
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN3NEXT:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-
-                // Load linear attention (gated delta net) parameters
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
-
-                // Mark recurrent layers (linear attention layers)
-                {
-                    uint32_t full_attn_interval = 4;
-                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
-                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
-                    }
-                }
+    if (!hparams.vocab_only) {
+        LLAMA_LOG_INFO("%s: n_ctx_train           = %u\n",     __func__, hparams.n_ctx_train);
+        LLAMA_LOG_INFO("%s: n_embd                = %u\n",     __func__, hparams.n_embd);
+        LLAMA_LOG_INFO("%s: n_embd_inp            = %u\n",     __func__, hparams.n_embd_inp());
+        LLAMA_LOG_INFO("%s: n_layer               = %u\n",     __func__, hparams.n_layer);
+        LLAMA_LOG_INFO("%s: n_head                = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head(il);    }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_head_kv             = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_rot                 = %u\n",     __func__, hparams.n_rot_full);
+        LLAMA_LOG_INFO("%s: n_swa                 = %u\n",     __func__, hparams.n_swa);
+        LLAMA_LOG_INFO("%s: is_swa_any            = %u\n",     __func__, hparams.is_swa_any());
+        LLAMA_LOG_INFO("%s: n_embd_head_k         = %u\n",     __func__, hparams.n_embd_head_k_full);
+        LLAMA_LOG_INFO("%s: n_embd_head_v         = %u\n",     __func__, hparams.n_embd_head_v_full);
+        LLAMA_LOG_INFO("%s: n_gqa                 = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il);        }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_embd_k_gqa          = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_embd_v_gqa          = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: f_norm_eps            = %.1e\n",   __func__, hparams.f_norm_eps);
+        LLAMA_LOG_INFO("%s: f_norm_rms_eps        = %.1e\n",   __func__, hparams.f_norm_rms_eps);
+        LLAMA_LOG_INFO("%s: f_clamp_kqv           = %.1e\n",   __func__, hparams.f_clamp_kqv);
+        LLAMA_LOG_INFO("%s: f_max_alibi_bias      = %.1e\n",   __func__, hparams.f_max_alibi_bias);
+        LLAMA_LOG_INFO("%s: f_logit_scale         = %.1e\n",   __func__, hparams.f_logit_scale);
+        LLAMA_LOG_INFO("%s: f_attn_scale          = %.1e\n",   __func__, hparams.f_attention_scale);
+        LLAMA_LOG_INFO("%s: f_attn_value_scale    = %.4f\n",   __func__, hparams.f_attn_value_scale);
+        LLAMA_LOG_INFO("%s: n_ff                  = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_expert              = %u\n",     __func__, hparams.n_expert);
+        LLAMA_LOG_INFO("%s: n_expert_used         = %u\n",     __func__, hparams.n_expert_used);
+        LLAMA_LOG_INFO("%s: n_expert_groups       = %d\n",     __func__, hparams.n_expert_groups);
+        LLAMA_LOG_INFO("%s: n_group_used          = %d\n",     __func__, hparams.n_group_used);
+        LLAMA_LOG_INFO("%s: causal attn           = %d\n",     __func__, hparams.causal_attn);
+        LLAMA_LOG_INFO("%s: pooling type          = %d\n",     __func__, hparams.pooling_type);
+        LLAMA_LOG_INFO("%s: rope type             = %d\n",     __func__, hparams.rope_type);
+        LLAMA_LOG_INFO("%s: rope scaling          = %s\n",     __func__, rope_scaling_type.c_str());
+        LLAMA_LOG_INFO("%s: freq_base_train       = %.1f\n",   __func__, hparams.rope_freq_base_train);
+        LLAMA_LOG_INFO("%s: freq_scale_train      = %g\n",     __func__, hparams.rope_freq_scale_train);
+        if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
+            LLAMA_LOG_INFO("%s: freq_base_swa         = %.1f\n",   __func__, hparams.rope_freq_base_train_swa);
+            LLAMA_LOG_INFO("%s: freq_scale_swa        = %g\n",     __func__, hparams.rope_freq_scale_train_swa);
+            LLAMA_LOG_INFO("%s: n_embd_head_k_swa     = %u\n",     __func__, hparams.n_embd_head_k_swa);
+            LLAMA_LOG_INFO("%s: n_embd_head_v_swa     = %u\n",     __func__, hparams.n_embd_head_v_swa);
+            LLAMA_LOG_INFO("%s: n_rot_swa             = %u\n",     __func__, hparams.n_rot_swa);
+        }
+        LLAMA_LOG_INFO("%s: n_ctx_orig_yarn       = %u\n",     __func__, hparams.n_ctx_orig_yarn);
+        LLAMA_LOG_INFO("%s: rope_yarn_log_mul     = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
+        LLAMA_LOG_INFO("%s: rope_finetuned        = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
+        // MRoPE (Multi-axis Rotary Position Embedding) sections
+        if (const auto & s = hparams.rope_sections; s[0] || s[1] || s[2] || s[3]) {
+            LLAMA_LOG_INFO("%s: mrope sections        = [%d, %d, %d, %d]\n", __func__, s[0], s[1], s[2], s[3]);
+        }
+        if (!classifier_labels.empty()) {
+            LLAMA_LOG_INFO("%s: n_cls_out             = %u\n", __func__, hparams.n_cls_out);
 
-                switch (hparams.n_layer) {
-                    case 48: type = LLM_TYPE_80B_A3B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN35:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS,    hparams.rope_sections, 4, true);
-
-                // Load linear attention (gated delta net) parameters
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
-
-                // Mark recurrent layers (linear attention layers)
-                {
-                    uint32_t full_attn_interval = 4;
-                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
-                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
-                    }
-                }
+            size_t i = 0;
+            for (const auto & label : classifier_labels) {
+                LLAMA_LOG_INFO("%s: cls_label[%2zu]         = %s\n", __func__, i++, label.c_str());
+            }
+        }
 
-                switch (hparams.n_layer) {
-                    case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_8B : LLM_TYPE_2B; break;
-                    case 32: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_9B; break;
-                    case 64: type = LLM_TYPE_27B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_QWEN35MOE:
-            {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
-
-                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS,    hparams.rope_sections, 4, true);
-
-                // Load linear attention (gated delta net) parameters
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
-                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
-                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
-                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
-
-                // Mark recurrent layers (linear attention layers)
-                {
-                    uint32_t full_attn_interval = 4;
-                    ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
-                    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                        hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
-                    }
-                }
+        if (arch == LLM_ARCH_MAMBA ||
+                arch == LLM_ARCH_MAMBA2 ||
+                arch == LLM_ARCH_JAMBA ||
+                arch == LLM_ARCH_FALCON_H1 ||
+                arch == LLM_ARCH_PLAMO2 ||
+                arch == LLM_ARCH_GRANITE_HYBRID ||
+                arch == LLM_ARCH_QWEN3NEXT ||
+                arch == LLM_ARCH_QWEN35 ||
+                arch == LLM_ARCH_QWEN35MOE ||
+                arch == LLM_ARCH_NEMOTRON_H ||
+                arch == LLM_ARCH_NEMOTRON_H_MOE) {
+            LLAMA_LOG_INFO("%s: ssm_d_conv            = %u\n",     __func__, hparams.ssm_d_conv);
+            LLAMA_LOG_INFO("%s: ssm_d_inner           = %u\n",     __func__, hparams.ssm_d_inner);
+            LLAMA_LOG_INFO("%s: ssm_d_state           = %u\n",     __func__, hparams.ssm_d_state);
+            LLAMA_LOG_INFO("%s: ssm_dt_rank           = %u\n",     __func__, hparams.ssm_dt_rank);
+            LLAMA_LOG_INFO("%s: ssm_n_group           = %u\n",     __func__, hparams.ssm_n_group);
+            LLAMA_LOG_INFO("%s: ssm_dt_b_c_rms        = %d\n",     __func__, hparams.ssm_dt_b_c_rms);
+        }
 
-                switch (hparams.n_layer) {
-                    case 40: type = LLM_TYPE_35B_A3B; break;
-                    case 48: type = LLM_TYPE_122B_A10B; break;
-                    case 60: type = LLM_TYPE_397B_A17B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MISTRAL3:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
+        LLAMA_LOG_INFO("%s: model type            = %s\n",     __func__, type_name().c_str());
+        if (pimpl->n_elements >= 1e12) {
+            LLAMA_LOG_INFO("%s: model params          = %.2f T\n", __func__, pimpl->n_elements*1e-12);
+        } else if (pimpl->n_elements >= 1e9) {
+            LLAMA_LOG_INFO("%s: model params          = %.2f B\n", __func__, pimpl->n_elements*1e-9);
+        } else if (pimpl->n_elements >= 1e6) {
+            LLAMA_LOG_INFO("%s: model params          = %.2f M\n", __func__, pimpl->n_elements*1e-6);
+        } else {
+            LLAMA_LOG_INFO("%s: model params          = %.2f K\n", __func__, pimpl->n_elements*1e-3);
+        }
 
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast,    false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow,    false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,   hparams.rope_yarn_log_mul, 0.0f);
+        // general kv
+        LLAMA_LOG_INFO("%s: general.name          = %s\n",    __func__, name.c_str());
 
-                hparams.f_attn_temp_offset = 0.0f;
+        if (arch == LLM_ARCH_DEEPSEEK) {
+            LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
+            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
+            LLAMA_LOG_INFO("%s: n_expert_shared       = %d\n",     __func__, hparams.n_expert_shared);
+            LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
+        }
 
-                // TODO: maybe add n_attn_temp_floor_scale as a separate KV?
-                if (hparams.f_attn_temp_scale != 0.0f) {
-                    hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn;
-                    if (hparams.n_attn_temp_floor_scale == 0) {
-                        throw std::runtime_error("invalid n_ctx_orig_yarn for attention temperature scaling");
-                    }
-                }
+        if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
+            LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
+            LLAMA_LOG_INFO("%s: n_lora_q              = %d\n",     __func__, hparams.n_lora_q);
+            LLAMA_LOG_INFO("%s: n_lora_kv             = %d\n",     __func__, hparams.n_lora_kv);
+            LLAMA_LOG_INFO("%s: n_embd_head_k_mla     = %d\n",     __func__, hparams.n_embd_head_k_mla());
+            LLAMA_LOG_INFO("%s: n_embd_head_v_mla     = %d\n",     __func__, hparams.n_embd_head_v_mla());
+            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
+            LLAMA_LOG_INFO("%s: n_expert_shared       = %d\n",     __func__, hparams.n_expert_shared);
+            LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
+            LLAMA_LOG_INFO("%s: expert_weights_norm   = %d\n",     __func__, hparams.expert_weights_norm);
+            LLAMA_LOG_INFO("%s: expert_gating_func    = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
+        }
 
-                switch (hparams.n_layer) {
-                    case 26: type = LLM_TYPE_3B; break;
-                    case 34: type = LLM_TYPE_8B; break;
-                    case 40: type = LLM_TYPE_14B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_MIMO2:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+        if (arch == LLM_ARCH_QWEN2MOE) {
+            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
+            LLAMA_LOG_INFO("%s: n_ff_shexp            = %d\n",     __func__, hparams.n_ff_shexp);
+        }
 
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        if (arch == LLM_ARCH_QWEN3MOE || arch == LLM_ARCH_OPENAI_MOE || arch == LLM_ARCH_QWEN3VLMOE || arch == LLM_ARCH_RND1) {
+            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
+        }
 
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,   hparams.n_swa);
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,         hparams.rope_freq_base_train_swa, false);
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+        if (arch == LLM_ARCH_MINICPM ||
+                arch == LLM_ARCH_GRANITE ||
+                arch == LLM_ARCH_GRANITE_MOE ||
+                arch == LLM_ARCH_GRANITE_HYBRID ||
+                arch == LLM_ARCH_NEMOTRON_H_MOE) {
+            LLAMA_LOG_INFO("%s: f_embedding_scale     = %f\n", __func__, hparams.f_embedding_scale);
+            LLAMA_LOG_INFO("%s: f_residual_scale      = %f\n", __func__, hparams.f_residual_scale);
+            LLAMA_LOG_INFO("%s: f_attention_scale     = %f\n", __func__, hparams.f_attention_scale);
+            LLAMA_LOG_INFO("%s: n_ff_shexp            = %d\n", __func__, hparams.n_ff_shexp);
+        }
 
-                switch (hparams.n_layer) {
-                    case 48: type = LLM_TYPE_310B_A15B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_KIMI_LINEAR:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,    hparams.n_embd_head_k_mla_impl);
-                ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,  hparams.n_embd_head_v_mla_impl);
-                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
-                ml.get_key(LLM_KV_SSM_CONV_KERNEL,             hparams.ssm_d_conv);
-                ml.get_key(LLM_KV_KDA_HEAD_DIM,                hparams.n_embd_head_kda);
-
-                // MLA qk_rope_head_dim (for reference)
-                // qk_rope_head_dim = 64, qk_nope_head_dim = 128, qk_head_dim = 192
-
-                // Mark KDA layers as recurrent using n_head_kv pattern (like Jamba)
-                // Set n_head_kv = 0 for KDA layers (recurrent), n_head_kv = n_head for MLA layers (attention)
-                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-                    hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;  // KDA layers are recurrent
-                }
+        if (arch == LLM_ARCH_BAILINGMOE) {
+            LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
+            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
+            LLAMA_LOG_INFO("%s: n_expert_shared       = %d\n",     __func__, hparams.n_expert_shared);
+            LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
+            LLAMA_LOG_INFO("%s: expert_weights_norm   = %d\n",     __func__, hparams.expert_weights_norm);
+        }
 
-                // MoE parameters - Kimi uses moe_intermediate_size = 1024
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
+        if (arch == LLM_ARCH_BAILINGMOE2) {
+            LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
+            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
+            LLAMA_LOG_INFO("%s: n_ff_shexp            = %d\n",     __func__, hparams.n_ff_shexp);
+            LLAMA_LOG_INFO("%s: n_expert_shared       = %d\n",     __func__, hparams.n_expert_shared);
+            LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
+            LLAMA_LOG_INFO("%s: expert_weights_norm   = %d\n",     __func__, hparams.expert_weights_norm);
+            LLAMA_LOG_INFO("%s: expert_gating_func    = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
+            LLAMA_LOG_INFO("%s: nextn_predict_layers  = %d\n",     __func__, hparams.nextn_predict_layers);
+        }
 
-                switch (hparams.n_layer) {
-                    case 27: type = LLM_TYPE_48B_A3B; break; // Kimi-Linear-48B-A3B
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        case LLM_ARCH_STEP35:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+        if (arch == LLM_ARCH_SMALLTHINKER || arch == LLM_ARCH_LFM2MOE) {
+            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
+            LLAMA_LOG_INFO("%s: expert_gating_func    = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
+        }
 
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        if (arch == LLM_ARCH_GROVEMOE) {
+            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
+            LLAMA_LOG_INFO("%s: n_ff_chexp            = %d\n",     __func__, hparams.n_ff_chexp);
+            LLAMA_LOG_INFO("%s: n_group_experts       = %d\n",     __func__, hparams.n_group_experts);
+            LLAMA_LOG_INFO("%s: expert_group_scale    = %.2f\n",   __func__, hparams.expert_group_scale);
+        }
+    }
 
-                // full_attention layer only use half of the RoPE dimensions
-                hparams.n_rot_full = hparams.n_rot_full / 2;
+    vocab.print_info();
+}
 
-                // MoE + SWA parameters
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
-                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+ggml_backend_dev_t llama_model::dev_layer(int il) const {
+    return pimpl->dev_layer.at(il).dev;
+}
 
-                // Step35 uses sigmoid gating by default (if not set in GGUF)
-                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
-                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
-                }
+ggml_backend_dev_t llama_model::dev_output() const {
+    return pimpl->dev_output.dev;
+}
 
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,  hparams.n_swa);
-                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,        hparams.rope_freq_base_train_swa, false);
-                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
-                ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,   hparams.swiglu_clamp_exp,   hparams.n_layer, false);
-                ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false);
+template<typename F>
+static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
+    ggml_init_params params = {
+        /*.mem_size   =*/ ggml_tensor_overhead()*8,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
 
-                switch (hparams.n_layer) {
-                    case 45: type = LLM_TYPE_196B_A11B; break;
-                    default: type = LLM_TYPE_UNKNOWN;
-                }
-            } break;
-        default: throw std::runtime_error("unsupported model architecture: " + arch_name());
+    ggml_context_ptr ctx { ggml_init(params) };
+    if (!ctx) {
+        throw std::runtime_error(format("failed to create ggml context"));
     }
 
-    pimpl->n_bytes = ml.n_bytes;
-
-    pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name();
-
-    if (hparams.f_max_alibi_bias > 0.0f) {
-        hparams.use_alibi = true;
+    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
+    ggml_tensor * op_tensor = fn(ctx.get());
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (op_tensor->src[i] != nullptr) {
+            assert(op_tensor->src[i]->buffer == nullptr);
+            op_tensor->src[i]->buffer = buf.get();
+        }
     }
 
-    hparams.rope_type = llama_model_rope_type(this);
+    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
+
+    return op_supported;
 }
 
-void llama_model::load_vocab(llama_model_loader & ml) {
-    const auto kv = LLM_KV(arch);
+template<typename F>
+static ggml_backend_buffer_type_t select_buft(const buft_list_t & buft_list, const F & fn) {
+    for (const auto & cur : buft_list) {
+        ggml_backend_dev_t cur_dev = cur.first;
+        ggml_backend_buffer_type_t cur_buft = cur.second;
+        if (buft_supported(cur_buft, cur_dev, fn)) {
+            return cur_buft;
+        }
+    }
 
-    vocab.load(ml, kv);
+    throw std::runtime_error(format("no suitable buffer type found"));
 }
 
-bool llama_model::load_tensors(llama_model_loader & ml) {
-    const auto & split_mode   = params.split_mode;
-    const auto & use_mlock    = params.use_mlock;
-    const auto & tensor_split = params.tensor_split;
-
-    const int n_layer      = hparams.n_layer;
-    const int n_gpu_layers = this->n_gpu_layers();
-
-    const bool use_mmap_buffer = true;
-
-    LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s, direct_io = %s)\n",
-        __func__, ml.use_mmap ? "true" : "false", ml.use_direct_io ? "true" : "false");
+ggml_backend_buffer_type_t llama_model::select_buft(int il) const {
+    return ::select_buft(
+            *pimpl->dev_layer.at(il).buft_list,
+            [&](ggml_context * ctx) {
+                ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
+                ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
+                return ggml_add(ctx, cur, layer_dir);
+            });
+}
 
-    // build a list of buffer types for the CPU and GPU devices
-    pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host);
-    for (const auto & dev : devices) {
-        buft_list_t buft_list = make_gpu_buft_list(dev.dev, split_mode, tensor_split);
-        // add CPU buffer types as a fallback
-        buft_list.insert(buft_list.end(), pimpl->cpu_buft_list.begin(), pimpl->cpu_buft_list.end());
-        pimpl->gpu_buft_list.emplace(dev.dev, std::move(buft_list));
-    }
+bool llama_model::has_tensor_overrides() const {
+    return pimpl->has_tensor_overrides;
+}
 
-    ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-    if (cpu_dev == nullptr) {
-        throw std::runtime_error(format("%s: no CPU backend found", __func__));
+const ggml_tensor * llama_model::get_tensor(const char * name) const {
+    auto it = std::find_if(tensors_by_name.begin(), tensors_by_name.end(),
+            [name](const std::pair<std::string, ggml_tensor *> & it) {
+                return it.first == name;
+            });
+    if (it == tensors_by_name.end()) {
+        return nullptr;
     }
 
-    // calculate the split points
-    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + n_devices(), [](float x) { return x == 0.0f; });
-    std::vector<float> splits(n_devices());
-    if (all_zero) {
-        // default split, by free memory
-        for (size_t i = 0; i < n_devices(); ++i) {
-            ggml_backend_dev_t dev = devices[i].dev;
-            size_t total;
-            size_t free;
-            ggml_backend_dev_memory(dev, &free, &total);
-
-            // devices can return 0 bytes for free and total memory if they do not
-            // have any to report. in this case, we will use the host memory as a fallback
-            // fixes: https://github.com/ggml-org/llama.cpp/issues/18577
-            if (free == 0 && total == 0) {
-                ggml_backend_dev_memory(cpu_dev, &free, &total);
-            }
-            splits[i] = free;
-        }
-    } else {
-        std::copy(tensor_split, tensor_split + n_devices(), splits.begin());
-    }
+    return it->second;
+}
 
-    // sum and normalize the splits to get the split points
-    float split_sum = 0.0f;
-    for (size_t i = 0; i < n_devices(); ++i) {
-        split_sum += splits[i];
-        splits[i] = split_sum;
-    }
-    for (size_t i = 0; i < n_devices(); ++i) {
-        splits[i] /= split_sum;
-    }
+float llama_model::get_rope_freq_base (const llama_cparams & cparams, int il) const {
+    return hparams.is_swa(il) ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base;
+}
 
-    const int i_gpu_start = std::max(int(hparams.n_layer) + 1 - n_gpu_layers, 0);
-    const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, int(n_layer) + 1);
-    auto get_layer_buft_list = [&](int il) -> llama_model::impl::layer_dev {
-        const bool is_swa = il < int(hparams.n_layer) && hparams.is_swa(il);
-        if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
-            LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(cpu_dev), is_swa);
-            return {cpu_dev, &pimpl->cpu_buft_list};
-        }
-        const int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + n_devices(), float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
-        auto * dev = devices.at(layer_gpu).dev;
-        LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(dev), is_swa);
-        return {dev, &pimpl->gpu_buft_list.at(dev)};
-    };
+float llama_model::get_rope_freq_scale(const llama_cparams & cparams, int il) const {
+    return hparams.is_swa(il) ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale;
+}
 
-    // assign the input layer
-    // there is very little benefit to offloading the input layer, so always keep it on the CPU
-    pimpl->dev_input = { cpu_dev, &pimpl->cpu_buft_list };
+ggml_tensor * llama_model::get_rope_factors(const llama_cparams & cparams, int il) const {
+    const uint32_t n_ctx_seq = cparams.n_ctx_seq;
 
-    // assign the repeating layers to the devices according to the splits
-    pimpl->dev_layer.resize(n_layer);
-    for (int il = 0; il < n_layer; ++il) {
-        pimpl->dev_layer[il] = get_layer_buft_list(il);
+    // choose long/short freq factors based on the context size
+    if (layers[il].rope_freqs != nullptr) {
+        return layers[il].rope_freqs;
     }
-
-    // assign the output layer
-    pimpl->dev_output = get_layer_buft_list(n_layer);
-
-    const auto TENSOR_DUPLICATED      = llama_model_loader::TENSOR_DUPLICATED;
-    const auto TENSOR_NOT_REQUIRED    = llama_model_loader::TENSOR_NOT_REQUIRED;
-    const auto TENSOR_SKIP            = llama_model_loader::TENSOR_SKIP;
-    const auto TENSOR_SKIP_IF_VIRTUAL = llama_model_loader::TENSOR_SKIP_IF_VIRTUAL;
-
-    // create tensors for the weights
-    {
-        // note: cast to int64_t since we will use these for the tensor dimensions
-        const int64_t n_head        = hparams.n_head();
-        const int64_t n_head_kv     = hparams.n_head_kv();
-        const int64_t n_embd        = hparams.n_embd;
-        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
-        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
-        const int64_t n_embd_head_k = hparams.n_embd_head_k();
-        const int64_t n_embd_head_v = hparams.n_embd_head_v();
-        const int64_t n_ff          = hparams.n_ff();
-        const int64_t n_embd_gqa    = n_embd_v_gqa;
-        const int64_t n_vocab       = vocab.n_tokens();
-        const int64_t n_token_types = vocab.n_token_types();
-        const int64_t n_rot         = hparams.n_rot();
-        const int64_t n_expert      = hparams.n_expert;
-        const int64_t n_expert_used = hparams.n_expert_used;
-        const int64_t n_ctx_train   = hparams.n_ctx_train;
-
-        if (n_expert > 0 && hparams.n_expert_used == 0) {
-            throw std::runtime_error("model has expert layers but no expert layers are used");
-        }
-
-        auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) -> ggml_tensor * {
-            const buft_list_t * buft_list_layer = tn.bid == -1 ? nullptr : pimpl->dev_layer.at(tn.bid).buft_list;
-            return ml.create_tensor(
-                hparams, &pimpl->cpu_buft_list, pimpl->dev_input.buft_list, pimpl->dev_output.buft_list, buft_list_layer,
-                tn, ne, flags);
-        };
-
-        layers.resize(n_layer);
-
-        // TODO: move to a separate function
-        const auto tn = LLM_TN(arch);
-
-        // helper: try merged gate_up_exps first, fall back to separate gate and up
-        auto create_tensor_gate_up_exps = [&](llama_layer & layer, int bid, int64_t n_embd_, int64_t n_ff_, int64_t n_expert_, int flags) {
-            layer.ffn_gate_up_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS, "weight", bid), {n_embd_, n_ff_ * 2, n_expert_}, TENSOR_NOT_REQUIRED);
-            if (layer.ffn_gate_up_exps == nullptr) {
-                layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", bid), {n_embd_, n_ff_, n_expert_}, flags);
-                layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", bid), {n_embd_, n_ff_, n_expert_}, flags);
-            }
-        };
-
-        // helper: try to load merged qkv first, fall back to separate q, k, v
-        auto create_tensor_qkv = [&](llama_layer & layer, int bid,
-                int64_t n_embd_, int64_t n_embd_q_, int64_t n_embd_k_, int64_t n_embd_v_,
-                int flags) {
-            const int64_t n_embd_qkv = n_embd_q_ + n_embd_k_ + n_embd_v_;
-            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", bid), {n_embd_, n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
-            if (layer.wqkv) {
-                layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", bid), {n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
-            } else {
-                layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", bid), {n_embd_, n_embd_q_}, flags);
-                layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", bid), {n_embd_, n_embd_k_}, flags);
-                layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", bid), {n_embd_, n_embd_v_}, flags);
-                layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", bid), {n_embd_q_}, TENSOR_NOT_REQUIRED);
-                layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", bid), {n_embd_k_}, TENSOR_NOT_REQUIRED);
-                layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", bid), {n_embd_v_}, TENSOR_NOT_REQUIRED);
-            }
-        };
-
-        switch (arch) {
-            case LLM_ARCH_LLAMA:
-            case LLM_ARCH_REFACT:
-            case LLM_ARCH_MINICPM:
-            case LLM_ARCH_GRANITE:
-            case LLM_ARCH_GRANITE_MOE:
-            case LLM_ARCH_MISTRAL3:
-            case LLM_ARCH_LLAMA_EMBED:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
-                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        }
-                        else {
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        }
-
-                        if (n_expert == 0) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                            // optional MLP bias
-                            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
-                        } else {
-                            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
-
-                            // For Granite MoE Shared
-                            if (hparams.n_ff_shexp > 0) {
-                                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
-                                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
-                                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
-                            }
-                        }
-                    }
-                } break;
-            case LLM_ARCH_LLADA:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output =
-                            create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-
-                        // Use separate Q, K, V projections without bias, matching LLaDALlamaBlock
-                        layer.wq =
-                            create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
-                        // No bias for QKV projections as per config: include_bias=false, include_qkv_bias=false
-                        layer.wo =
-                            create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
-
-                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot / 2 },
-                                                         TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
-
-                        // optional MLP bias
-                        layer.ffn_gate_b =
-                            create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), { n_ff }, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_b =
-                            create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), { n_ff }, TENSOR_NOT_REQUIRED);
-                    }
-                }
-                break;
-            case LLM_ARCH_LLADA_MOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for llada-moe");
-                    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for llada-moe");
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_LLAMA4:
-                {
-                    if (n_expert == 0) {
-                        throw std::runtime_error(arch_name() + " model cannot have zero experts");
-                    }
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        const bool is_moe_layer = hparams.n_moe_layer_step > 0 && (i + 1) % hparams.n_moe_layer_step == 0;
-
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-
-                        if (is_moe_layer) {
-                            const int64_t n_ff_exp = hparams.n_ff_exp;
-
-                            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff_exp, n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
-
-                            // Shared expert
-                            const int64_t n_ff_shexp = n_ff_exp;
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd    }, 0);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);
-                        } else {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_DECI:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-                        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
-                        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
-                        const int64_t n_ff          = hparams.n_ff(i);
-                        const int64_t n_head        = hparams.n_head(i);
-                        const int64_t n_head_kv     = hparams.n_head_kv(i);
-
-                        if (n_head_kv == 0 && n_head > 0) {
-                            // linear attention for DeciLMCausalModel
-                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        }
-                        else if (n_head_kv > 0) {
-                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-                        }
-
-                        // optional bias tensors
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        if (n_ff > 0) {
-                            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        }
-
-                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
-                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        }
-                        else {
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        }
-
-                        if (n_ff > 0) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        }
-
-                        // optional MLP bias
-                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_MINICPM3:
-                {
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot();
-                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();
-
-                    const int64_t q_lora_rank  = hparams.n_lora_q;
-                    const int64_t kv_lora_rank = hparams.n_lora_kv;
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
-
-                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
-
-                        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
-                        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
-
-                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
-                        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
-                        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head_qk_rope/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head_qk_rope/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                    }
-                } break;
-            case LLM_ARCH_GROK:
-                {
-                    if (n_expert == 0) {
-                        throw std::runtime_error(arch_name() + " model cannot have zero experts");
-                    }
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff/* / n_expert_used*/; // grok-1 n_ff_exp == n_ff
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff,   n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd,   n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
-
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        if (!layer.ffn_post_norm) {
-                            layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_DBRX:
-                {
-                    if (n_expert == 0) {
-                        throw std::runtime_error("DBRX model cannot have zero experts");
-                    }
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_BAICHUAN:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    {
-                        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_FALCON:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    {
-                        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-
-                        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                        if (!output) {
-                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU
-                        }
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_STARCODER:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);
-
-                    // output
-                    {
-                        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                        output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                        if (!output) {
-                            // needs to be on GPU
-                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                        }
-
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
-                        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_BERT:
-            case LLM_ARCH_NOMIC_BERT:
-            case LLM_ARCH_NOMIC_BERT_MOE:
-            case LLM_ARCH_JINA_BERT_V3:
-                {
-                    if (n_token_types == 0) {
-                        throw std::runtime_error(arch_name() + " model needs to define token type count");
-                    }
-                    tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
-                    type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
-
-                    if (arch == LLM_ARCH_BERT) {
-                        pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train}, 0);
-
-                        cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
-                        cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
-
-                        cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
-                        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
-                    }
-
-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);
-
-                        if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff,   n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff,   n_embd, n_expert}, 0);
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,   "weight", i), {n_embd, n_expert}, 0);
-                        } else {
-                            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
-
-                            if (arch == LLM_ARCH_NOMIC_BERT) {
-                                layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                            }
-                        }
-
-                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
-                        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_MODERN_BERT:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    for(int i = 0; i < n_layer; ++i) {
-                        auto& layer = layers[i];
-
-                        if ( i != 0 ) {
-                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        } else{
-                            // layer 0 uses identity
-                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        }
-
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd }, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,   "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, 2 * n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                    }
-
-                    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT,  "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
-                    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT,  "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
-                    cls       = create_tensor(tn(LLM_TENSOR_CLS,      "weight"), {n_embd, n_embd},            TENSOR_NOT_REQUIRED);
-                    cls_norm  = create_tensor(tn(LLM_TENSOR_CLS_NORM, "weight"), {n_embd},                    TENSOR_NOT_REQUIRED);
-
-                } break;
-            case LLM_ARCH_NEO_BERT:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
-
-                    cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
-                    cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
-
-                    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
-                    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
-
-                    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff*2}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_EUROBERT:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_JINA_BERT_V2:
-                {
-                    tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
-                    type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0); // token_type_embeddings
-
-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0); // LayerNorm
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0); // LayerNorm bias
-
-                    cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
-                    cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {1},         TENSOR_NOT_REQUIRED);
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i]; // JinaBertLayer
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-
-                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); //output_dens
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); //output_dens
-
-                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); //output_norm
-                        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias",   i), {n_embd}, 0);
-
-                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
-
-                        const auto tn_ffn_up_weight = tn(LLM_TENSOR_FFN_UP, "weight", i);
-                        ggml_tensor * t_ffn_up = ml.get_tensor_meta(tn_ffn_up_weight.str().c_str());
-                        const int64_t n_ffn_up = t_ffn_up ? t_ffn_up->ne[1] : n_ff;
-
-                        GGML_ASSERT(n_ffn_up == n_ff || n_ffn_up == n_ff * 2);
-                        layer.ffn_up   = create_tensor(tn_ffn_up_weight, {n_embd, n_ffn_up}, 0);
-                        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ffn_up}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);
-
-                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
-                        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias",   i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_BLOOM:
-                {
-                    tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias",   i), {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_MPT:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, TENSOR_NOT_REQUIRED);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, TENSOR_NOT_REQUIRED);
-
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    if (!output) {
-                        output    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
-
-                        // FIXME test-llama-archs crashes if q_norm is created
-                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
-                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
-
-                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        // AWQ ScaleActivation layer
-                        layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_STABLELM:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm =   create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // optional q and k layernorms, present in StableLM 2 12B
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head},    TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);
-
-                        // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}, 0);
-                        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}, 0);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff/2}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN2:
-            case LLM_ARCH_QWEN2VL:
-            case LLM_ARCH_DREAM:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    output_b    = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN2MOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                        if (n_expert == 0) {
-                            throw std::runtime_error("n_expert must be > 0 for QWEN2MOE");
-                        }
-                        if (n_expert_used == 0) {
-                            throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE");
-                        }
-
-                        // MoE branch
-                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                        // Shared expert branch
-                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
-
-                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);
-                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp,     n_embd}, 0);
-                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN3:
-            case LLM_ARCH_QWEN3VL:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    // output rerank head
-                    cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN3MOE:
-            case LLM_ARCH_QWEN3VLMOE:
-            case LLM_ARCH_RND1:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                        if (n_expert == 0) {
-                            throw std::runtime_error("n_expert must be > 0 for QWEN3MOE");
-                        }
-                        if (n_expert_used == 0) {
-                            throw std::runtime_error("n_expert_used must be > 0 for QWEN3MOE");
-                        }
-
-                        // MoE branch
-                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_PHI2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-                    output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_PHI3:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, TENSOR_NOT_REQUIRED);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
-                        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
-
-                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                    }
-                } break;
-            case LLM_ARCH_PHIMOE:
-                {
-                    const int64_t n_embd_head = n_embd / n_head;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, 0);
-                    output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   { n_vocab }, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), { n_embd }, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), { n_embd }, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), { n_embd }, 0);
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert},         0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
-
-                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                     }
-                } break;
-            case LLM_ARCH_PLAMO:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_PLAMO2:
-                {
-                    // mamba parameters
-                    const uint32_t d_conv             = hparams.ssm_d_conv;
-                    const uint32_t d_state            = hparams.ssm_d_state;
-                    const uint32_t num_heads          = hparams.ssm_dt_rank;
-                    const uint32_t intermediate_size  = hparams.ssm_d_inner;
-                    const int64_t dt_dim              = std::max(64, int(hparams.n_embd / 16));
-
-                    // attention parameters
-                    const uint32_t qk_dim = hparams.n_embd_head_k();
-                    const uint32_t v_dim  = hparams.n_embd_head_v();
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-                        bool is_mamba_layer = hparams.is_recurrent(i);
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (is_mamba_layer) {
-                            layer.ssm_in       = create_tensor(tn(LLM_TENSOR_SSM_IN,     "weight", i), {n_embd, 2 * intermediate_size}, 0);
-                            layer.ssm_conv1d   = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, intermediate_size}, 0);
-
-                            layer.ssm_x    = create_tensor(tn(LLM_TENSOR_SSM_X,  "weight", i), {intermediate_size, dt_dim + 2*d_state}, 0);
-                            layer.ssm_dt   = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_dim, num_heads}, 0);
-                            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {num_heads}, 0);
-
-                            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {num_heads}, 0);
-                            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {num_heads}, 0);
-
-                            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {intermediate_size, n_embd}, 0);
-
-                            layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, i), {dt_dim}, 0);
-                            layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, i), {d_state}, 0);
-                            layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, i), {d_state}, 0);
-                        } else {
-                            const int64_t num_attention_heads = hparams.n_head(i);
-                            const int64_t q_num_heads         = num_attention_heads;
-                            const int64_t num_key_value_heads = hparams.n_head_kv(i);
-                            const int64_t k_num_heads         = num_key_value_heads;
-                            const int64_t v_num_heads         = num_key_value_heads;
-                            const int64_t q_proj_dim          = q_num_heads * qk_dim;
-                            const int64_t k_proj_dim          = k_num_heads * qk_dim;
-                            const int64_t v_proj_dim          = v_num_heads * v_dim;
-
-                            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, q_proj_dim + k_proj_dim + v_proj_dim}, 0);
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {qk_dim, num_attention_heads}, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {qk_dim, k_num_heads}, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {q_num_heads * v_dim, n_embd}, 0);
-                        }
-
-                        // All layers have post-attention norm, FFN norm, and FFN tensors
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, i), {n_embd}, 0);
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_PLAMO3:
-                {
-                    const int64_t head_dim_q = hparams.n_embd_head_k();
-                    const int64_t head_dim_v = hparams.n_embd_head_v();
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        const int64_t num_attention_heads = hparams.n_head(i);
-                        const int64_t num_key_value_heads = hparams.n_head_kv(i);
-                        const int64_t q_proj_dim = num_attention_heads * head_dim_q;
-                        const int64_t k_proj_dim = num_key_value_heads * head_dim_q;
-                        const int64_t v_proj_dim = num_key_value_heads * head_dim_v;
-                        const int64_t n_ff_cur   = hparams.n_ff(i);
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i),
-                                {n_embd,q_proj_dim + k_proj_dim + v_proj_dim}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim_q}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim_q}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {num_attention_heads * head_dim_v, n_embd}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
-
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff_cur * 2}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_cur, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GPT2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_CODESHELL:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if tok embd is NULL, init from output
-                    if (tok_embd == NULL) {
-                        tok_embd = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_ORION:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_INTERNLM2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        // layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GEMMA:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GEMMA2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GEMMA3:
-            case LLM_ARCH_GEMMA_EMBEDDING:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,   "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    // Dense linear weights
-                    dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);
-                    dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);
-
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GEMMA3N:
-                {
-                    const int64_t n_altup      = hparams.n_altup;
-                    const int64_t laurel_rank  = hparams.laurel_rank;
-                    const int64_t n_embd_altup = hparams.n_embd_altup;
-
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    altup_proj        = create_tensor(tn(LLM_TENSOR_ALTUP_PROJ,        "weight"), {n_embd, n_embd, n_altup - 1}, 0);
-                    altup_unembd_proj = create_tensor(tn(LLM_TENSOR_ALTUP_UNEMBD_PROJ, "weight"), {n_embd, n_embd, n_altup - 1}, 0);
-
-                    per_layer_tok_embd   = create_tensor(tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight"), {n_embd_altup * n_layer, n_vocab}, 0);
-                    per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_altup * n_layer}, 0);
-                    per_layer_proj_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM,  "weight", 0), {n_embd_altup}, 0);
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        // altup & laurel
-                        layer.per_layer_inp_gate   = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE,  "weight", i), {n_embd, n_embd_altup}, 0);
-                        layer.per_layer_proj       = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ,      "weight", i), {n_embd_altup, n_embd}, 0);
-                        layer.per_layer_post_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0);
-                        layer.altup_correct_coef   = create_tensor(tn(LLM_TENSOR_ALTUP_CORRECT_COEF,  "weight", i), {n_altup, n_altup}, 0);
-                        layer.altup_correct_scale  = create_tensor(tn(LLM_TENSOR_ALTUP_CORRECT_SCALE, "weight", i), {n_embd}, 0);
-                        layer.altup_predict_coef   = create_tensor(tn(LLM_TENSOR_ALTUP_PREDICT_COEF,  "weight", i), {n_altup, n_altup * n_altup}, 0);
-                        layer.altup_router         = create_tensor(tn(LLM_TENSOR_ALTUP_ROUTER,        "weight", i), {n_embd, n_altup}, 0);
-                        layer.altup_router_norm    = create_tensor(tn(LLM_TENSOR_ALTUP_ROUTER_NORM,   "weight", i), {n_embd}, 0);
-                        layer.laurel_l             = create_tensor(tn(LLM_TENSOR_LAUREL_L,            "weight", i), {n_embd, laurel_rank}, 0);
-                        layer.laurel_r             = create_tensor(tn(LLM_TENSOR_LAUREL_R,            "weight", i), {laurel_rank, n_embd}, 0);
-                        layer.laurel_post_norm     = create_tensor(tn(LLM_TENSOR_LAUREL_POST_NORM,    "weight", i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GEMMA4:
-                {
-                    const uint32_t n_embd_per_layer = hparams.n_embd_per_layer;
-                    const int64_t  n_ff_exp         = hparams.n_ff_exp;
-
-                    if (n_embd_head_k != n_embd_head_v) {
-                        throw std::runtime_error("Gemma 4 requires n_embd_head_k == n_embd_head_v");
-                    }
-                    if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) {
-                        throw std::runtime_error("Gemma 4 requires n_embd_head_k_swa == n_embd_head_v_swa");
-                    }
-
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    if (n_embd_per_layer > 0) {
-                        per_layer_tok_embd   = create_tensor(tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight"),    {n_embd_per_layer * n_layer, n_vocab}, 0);
-                        per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_per_layer * n_layer}, 0);
-                        per_layer_proj_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM,  "weight", 0), {n_embd_per_layer}, 0);
-                    }
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    int rope_freqs_flag = 0;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-                        const int64_t n_head      = hparams.n_head(i);
-                        const int64_t n_embd_head = hparams.n_embd_head_k(i);
-                        const int64_t n_embd_k    = hparams.n_embd_k_gqa(i);
-                        const int64_t n_embd_v    = hparams.n_embd_v_gqa(i);
-                        const int     kv_flags    = hparams.has_kv(i) ? 0 : TENSOR_NOT_REQUIRED;
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        // note: use_alternative_attention (v_proj is optional, if it's not present, use k_proj)
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head * n_head}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k}, kv_flags);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0);
-
-                        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), {n_embd_head}, 0);
-                        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head}, kv_flags);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1u}, TENSOR_NOT_REQUIRED);
-
-                        if (!hparams.is_swa(i)) {
-                            // full_attention layers use rope_freqs for proportional rope
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_embd_head/2}, rope_freqs_flag);
-                            rope_freqs_flag = TENSOR_DUPLICATED;
-                        }
-
-                        // handle use_double_wide_mlp
-                        int64_t n_ff_cur = hparams.n_ff(i);
-
-                        // for expert layers, we use normal FFN as shared expert (same as python code)
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff_cur}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff_cur}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_cur, n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        // MoE router
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
-                        bool has_expert = layer.ffn_gate_inp != nullptr;
-
-                        // norm
-                        if (has_expert) {
-                            layer.ffn_gate_inp_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "scale", i), {n_embd}, 0);
-
-                            layer.ffn_pre_norm_2  = create_tensor(tn(LLM_TENSOR_FFN_PRE_NORM_2,  "weight", i), {n_embd}, 0);
-                            layer.ffn_post_norm_1 = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM_1, "weight", i), {n_embd}, 0);
-                            layer.ffn_post_norm_2 = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM_2, "weight", i), {n_embd}, 0);
-
-                            // MoE FFN
-                            layer.ffn_gate_up_exps  = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS,  "weight", i), {n_embd, n_ff_exp * 2, n_expert}, 0);
-                            layer.ffn_down_exps     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS,     "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
-
-                            // per-expert scale will be loaded as down_exps_s at the end of the current switch case
-                        }
-
-                        // per-layer embeddings
-                        if (n_embd_per_layer > 0) {
-                            layer.per_layer_inp_gate   = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE,  "weight", i), {n_embd, n_embd_per_layer}, 0);
-                            layer.per_layer_proj       = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ,      "weight", i), {n_embd_per_layer, n_embd}, 0);
-                            layer.per_layer_post_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_STARCODER2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        // optional bias tensors
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP ,  "bias", i), {  n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_MAMBA:
-                {
-                    const int64_t d_conv  = hparams.ssm_d_conv;
-                    const int64_t d_inner = hparams.ssm_d_inner;
-                    const int64_t d_state = hparams.ssm_d_state;
-                    const int64_t dt_rank = hparams.ssm_dt_rank;
-
-                    // only an expansion factor of 2 is supported for now
-                    if (2 * n_embd != d_inner) {
-                        throw std::runtime_error("only an expansion factor of 2 is supported for now");
-                    }
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed, duplicated to allow offloading
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        // norm
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);
-
-                        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);
-                        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);
-
-                        layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);
-
-                        layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);
-                        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
-
-                        // no "weight" suffix for these
-                        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);
-                        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);
-
-                        // out_proj
-                        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_MAMBA2:
-                {
-                    const int64_t d_conv  = hparams.ssm_d_conv;
-                    const int64_t d_inner = hparams.ssm_d_inner;
-                    const int64_t d_state = hparams.ssm_d_state;
-                    const int64_t n_head  = hparams.ssm_dt_rank;
-                    const int64_t n_group = hparams.ssm_n_group;
-                    const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_head;
-
-                    // only an expansion factor of 2 is supported for now
-                    GGML_ASSERT(2 * n_embd == d_inner);
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    {
-                        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                        // if output is NULL, init from the input tok embed, duplicated to allow offloading
-                        if (output == NULL) {
-                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                        }
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        // norm
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
-
-                        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
-                        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, 0);
-
-                        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_head}, 0);
-
-                        // no "weight" suffix for these
-                        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
-                        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_head}, 0);
-
-                        layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
-
-                        // out_proj
-                        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_JAMBA:
-                {
-                    const int64_t d_conv  = hparams.ssm_d_conv;
-                    const int64_t d_inner = hparams.ssm_d_inner;
-                    const int64_t d_state = hparams.ssm_d_state;
-                    const int64_t dt_rank = hparams.ssm_dt_rank;
-
-                    // only an expansion factor of 2 is supported for now
-                    GGML_ASSERT(2 * n_embd == d_inner);
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    {
-                        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                        // if output is NULL, init from the input tok embed, duplicated to allow offloading
-                        if (output == NULL) {
-                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                        }
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        const int64_t n_head_kv = hparams.n_head_kv(i);
-                        const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i);
-
-                        auto & layer = layers[i];
-
-                        // norm
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (n_head_kv == 0) {
-                            // Mamba layer
-                            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);
-
-                            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);
-                            layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);
-
-                            layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);
-
-                            layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, "weight", i), {dt_rank}, 0);
-
-                            layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);
-                            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
-
-                            layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, "weight", i), {d_state}, 0);
-                            layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, "weight", i), {d_state}, 0);
-
-                            // no "weight" suffix for these
-                            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);
-                            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);
-
-                            // out_proj
-                            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
-                        } else {
-                            // Attention layers
-
-                            create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        }
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
-
-                        if (layer.ffn_gate_inp) {
-                            // MoE
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff, n_expert}, 0);
-                        } else {
-                            // FFN (no MoE)
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_GRANITE_HYBRID:
-                {
-                    // mamba2 Mixer SSM params
-                    // NOTE: int64_t for tensor dimensions
-                    const int64_t d_conv     = hparams.ssm_d_conv;
-                    const int64_t d_inner    = hparams.ssm_d_inner;
-                    const int64_t d_state    = hparams.ssm_d_state;
-                    const int64_t n_ssm_head = hparams.ssm_dt_rank;
-                    const int64_t n_group    = hparams.ssm_n_group;
-                    const int64_t d_in_proj  = 2*d_inner + 2*n_group*d_state + n_ssm_head;
-
-                    // only an expansion factor of 2 is supported for now
-                    GGML_ASSERT(2 * n_embd == d_inner);
-
-                    // embeddings
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    {
-                        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                        // if output is NULL, init from the input tok embed, duplicated to allow offloading
-                        if (output == NULL) {
-                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                        }
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        // norm
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (hparams.is_recurrent(i)) {
-                            // ssm layers
-                            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
-
-                            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
-                            layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, TENSOR_NOT_REQUIRED);
-
-                            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_ssm_head}, 0);
-
-                            // no "weight" suffix for these
-                            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_ssm_head}, 0);
-                            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_ssm_head}, 0);
-
-                            layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
-
-                            // out_proj
-                            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
-                        } else {
-                            // attention layers (with optional bias)
-                            const int64_t n_head_i = hparams.n_head(i);
-                            const int64_t n_embd_k_gqa_i = hparams.n_embd_k_gqa(i);
-                            const int64_t n_embd_v_gqa_i = hparams.n_embd_v_gqa(i);
-                            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_i, n_embd_k_gqa_i, n_embd_v_gqa_i, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, 0);
-                            layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        }
-
-                        // feed forward (w/ optional biases)
-                        if (n_expert > 0) {
-                            // MoE FFN
-                            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
-
-                            // For Granite MoE Shared
-                            if (hparams.n_ff_shexp > 0) {
-                                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
-                                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
-                                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
-                            }
-                        } else {
-                            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_XVERSE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_COMMAND_R:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // init output from the input tok embed
-                    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (n_layer >= 64){
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
-                        }
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_COHERE2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    // init output from the input tok embed
-                    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
-                                                      TENSOR_DUPLICATED);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
-                    }
-                }
-                break;
-            case LLM_ARCH_OLMO:  // adapted from LLM_ARCH_LLAMA with norm params removed
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_OLMO2:
-                {
-                    const int64_t n_embd_head = n_embd / n_head;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_head_kv * n_embd_head}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_SEED_OSS:
-                {
-                    const uint32_t head_dim             = hparams.n_embd_head_k();
-                    const int64_t n_qo_dim              = n_head * head_dim;
-                    const int64_t n_kv_dim              = n_head_kv * head_dim;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        create_tensor_qkv(layer, i, n_embd, n_qo_dim, n_kv_dim, n_kv_dim, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_qo_dim, n_embd}, 0);
-
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                    }
-                } break;
-
-            case LLM_ARCH_OLMOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                        if (n_expert == 0) {
-                            throw std::runtime_error("n_expert must be > 0");
-                        }
-                        if (n_expert_used == 0) {
-                            throw std::runtime_error("n_expert_used must be > 0");
-                        }
-
-                        // MoE branch
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_OPENELM:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // init output from the input tok embed
-                    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        const int64_t n_head      =   hparams.n_head(i);
-                        const int64_t n_head_qkv  = 2*hparams.n_head_kv(i) + n_head;
-                        const int64_t n_ff        =   hparams.n_ff(i);
-
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GPTNEOX:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_ARCTIC:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                        layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_DEEPSEEK:
-                {
-
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // try to load output.weight, if not found, use token_embd (tied embeddings)
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    if (!output) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        } else {
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            // MoE branch
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                            // Shared expert branch
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_DEEPSEEK2:
-            case LLM_ARCH_MISTRAL4:
-                {
-                    const bool is_mla = hparams.is_mla();
-
-                    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
-                    const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
-                    const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
-
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot();
-                    const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
-                    GGML_ASSERT(n_embd_head_qk_nope >= 1);
-
-                    const int64_t q_lora_rank  = hparams.n_lora_q;
-                    const int64_t kv_lora_rank = hparams.n_lora_kv;
-
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // try to load output.weight, if not found, use token_embd (tied embeddings)
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    if (!output) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        if (q_lora_rank > 0) {
-                            layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
-                        }
-
-                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
-
-                        if (q_lora_rank > 0) {
-                            layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
-                            layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
-                        } else {
-                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0);
-                        }
-
-                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, 0);
-
-                        // note: only old legacy GGUF files will have the unsplit wkv_b tensor in
-                        if (is_mla) {
-                            layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, 0);
-                            layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
-                        } else {
-                            layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, 0);
-                        }
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        } else {
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            // MoE branch
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                            create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
-
-                            // Shared expert branch
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_DEEPSEEK2OCR:
-                {
-                    // similar to deepseek2, but without MLA
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // try to load output.weight, if not found, use token_embd (tied embeddings)
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    if (!output) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // norm
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        } else {
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            // MoE branch
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                            create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
-
-                            // Shared expert branch
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_PLM:
-                {
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot();
-                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();
-                    const int64_t kv_lora_rank = hparams.n_lora_kv;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-                    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq        = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
-                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
-                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
-                        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
-                        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_BITNET:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm     = create_tensor(tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd}, 0);
-                        layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq       = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wq_s     = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
-                        layer.wk       = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wk_s     = create_tensor(tn(LLM_TENSOR_ATTN_K,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
-                        layer.wv       = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv_s     = create_tensor(tn(LLM_TENSOR_ATTN_V,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
-                        layer.wo       = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_s     = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm     = create_tensor(tn(LLM_TENSOR_FFN_NORM,     "weight", i), {n_embd}, 0);
-                        layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0);
-
-                        layer.ffn_gate       = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_gate_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down       = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up         = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_s   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_T5:
-                {
-                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm     = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0);
-
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    // n_layer:     number of encoder_layers
-                    // dec_n_layer: number of decoder_layers
-                    const int dec_n_layer = hparams.dec_n_layer;
-                    if (dec_n_layer > n_layer) {
-                        layers.resize(dec_n_layer);
-                    }
-
-                    // load encoder layers
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
-
-                        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
-
-                        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-
-                    // load decoder layers
-                    for (int i = 0; i < dec_n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
-
-                        layer.attn_norm_cross  = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        // this tensor seems to be unused in HF transformers implementation
-                        layer.attn_rel_b_cross = create_tensor(
-                            tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
-
-                        layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_T5ENCODER:
-                {
-                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
-
-                        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
-
-                        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_JAIS:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
-                        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-
-                        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "bias", i),   {n_ff}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_JAIS2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    if (!output) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        // attention biases - all have shape n_embd (output dimension of projections)
-                        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
-                        layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd}, 0);
-                        layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
-
-                        // Jais-2 uses simple MLP (no gate) with biases
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_CHATGLM:
-                {
-                    tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, 0);
-
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_GLM4:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        int flags = 0;
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            // skip all tensors in the NextN layers
-                            flags |= TENSOR_SKIP;
-                        }
-
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
-
-                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
-
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, flags);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, flags);
-
-                        layer.ffn_post_norm  = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, flags);
-
-                        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
-                            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
-                            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
-
-                            // Optional tensors
-                            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
-                            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
-                            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_GLM4_MOE:
-                {
-                    const int64_t n_expert        = hparams.n_expert;
-                    const int64_t n_expert_used   = hparams.n_expert_used;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
-                    GGML_ASSERT(hparams.n_expert_used > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    // Load ALL tensors including NextN layer to satisfy total tensor count
-                    // but only PROCESS up to last layer (skipping final NextN layer) in forward pass
-                    for (int i = 0; i < n_layer; ++i) {
-                        int flags = 0;
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            // skip all tensors in the NextN layers
-                            flags |= TENSOR_SKIP;
-                        }
-
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
-
-                        // GLM-style attention with bias terms
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
-
-                        // K/Q norm tensors (optional for GLM-4.5 355B variant)
-                        layer.attn_q_norm = create_tensor(
-                            tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
-                        layer.attn_k_norm = create_tensor(
-                            tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
-
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, flags);
-
-                        // Check if this layer uses MoE or dense FFN based on n_layer_dense_lead
-                        // GLM 4.5 uses hybrid architecture: layer 0 is dense, layers 1+ are MoE
-                        const bool use_moe = (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead);
-
-                        if (use_moe) {
-                            // MoE layers
-                            layer.ffn_gate_inp =
-                                create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, flags);
-
-                            // MoE branch
-                            const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                            layer.ffn_gate_exps = create_tensor(
-                                tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
-                            layer.ffn_down_exps = create_tensor(
-                                tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
-                            layer.ffn_up_exps = create_tensor(
-                                tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
-
-                            // Shared expert
-                            if (n_expert_shared > 0) {
-                                const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
-                                layer.ffn_gate_shexp = create_tensor(
-                                    tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
-                                layer.ffn_down_shexp = create_tensor(
-                                    tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
-                                layer.ffn_up_shexp = create_tensor(
-                                    tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
-                            }
-                        } else {
-                            // Dense layers (first k layers) - GLM uses separate gate/up projections
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, flags);
-                        }
-
-                        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
-                            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
-                            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
-
-                            // Optional tensors
-                            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
-                            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
-                            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
-                        }
-                    }
-                }
-                break;
-            case LLM_ARCH_GLM_DSA:
-                {
-                    const bool is_mla = hparams.is_mla();
-                    if (!is_mla) {
-                        throw std::runtime_error("GLM_DSA architecture requires MLA");
-                    }
-
-                    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
-                    const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
-                    const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
-
-                    const int64_t n_embd_head_qk_rope = hparams.n_rot();
-                    const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
-
-                    const int64_t q_lora_rank  = hparams.n_lora_q;
-                    const int64_t kv_lora_rank = hparams.n_lora_kv;
-
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    // try to load output.weight, if not found, use token_embd (tied embeddings)
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    if (!output) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        int flags = 0;
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            // skip all tensors in the NextN layers
-                            // TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
-                            flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
-                        }
-
-                        auto & layer = layers[i];
-
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
-                        layer.attn_q_a_norm  = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
-                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
-
-                        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
-                        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);
-
-                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
-
-                        // note: only old legacy GGUF files will have the unsplit wkv_b tensor in
-                        layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);
-                        layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);
-
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
-
-                        // DSA indexer
-                        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags);
-                        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags);
-                        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags);
-                        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags);
-                        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, flags);
-                        } else {
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            // MoE branch
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, flags);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
-
-                            // Shared expert branch
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, flags);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
-                        }
-
-                        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
-                            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
-                            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
-
-                            // Optional tensors
-                            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
-                            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
-                            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_NEMOTRON:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
-                    output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        // optional MLP bias
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_NEMOTRON_H:
-            case LLM_ARCH_NEMOTRON_H_MOE:
-                {
-                    // mamba2 Mixer SSM params
-                    // NOTE: int64_t for tensor dimensions
-                    const int64_t d_conv     = hparams.ssm_d_conv;
-                    const int64_t d_inner    = hparams.ssm_d_inner;
-                    const int64_t d_state    = hparams.ssm_d_state;
-                    const int64_t n_ssm_head = hparams.ssm_dt_rank;
-                    const int64_t n_group    = hparams.ssm_n_group;
-                    const int64_t d_in_proj  = 2*d_inner + 2*n_group*d_state + n_ssm_head;
-                    const int64_t moe_n_embd = hparams.moe_latent_size > 0 ? hparams.moe_latent_size : n_embd;
-
-                    // embeddings
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    {
-                        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                        // if output is NULL, init from the input tok embed, duplicated to allow offloading
-                        if (output == NULL) {
-                            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                        }
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        // all blocks use the attn norm
-                        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (hparams.is_recurrent(i)) {
-                            // ssm layers
-                            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
-
-                            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
-                            layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, TENSOR_NOT_REQUIRED);
-
-                            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_ssm_head}, 0);
-
-                            // no "weight" suffix for these
-                            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_ssm_head}, 0);
-                            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_ssm_head}, 0);
-
-                            layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
-
-                            // out_proj
-                            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
-                        } else if (hparams.n_ff(i) == 0) {
-                            // attention layers (with optional bias)
-                            const int64_t n_head_i = hparams.n_head(i);
-                            const int64_t n_embd_k_gqa_i = hparams.n_embd_k_gqa(i);
-                            const int64_t n_embd_v_gqa_i = hparams.n_embd_v_gqa(i);
-                            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_i, n_embd_k_gqa_i, n_embd_v_gqa_i, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, 0);
-                            layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        }  else {
-                            if (n_expert != 0) {
-                                const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-                                const int64_t n_ff_shexp = hparams.n_ff_shexp;
-
-                                layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert}, 0);
-                                layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert         }, 0);
-
-                                // MoE branch
-                                layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, TENSOR_NOT_REQUIRED);
-                                layer.ffn_latent_up   = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP,   "weight", i), {moe_n_embd, n_embd}, TENSOR_NOT_REQUIRED);
-
-                                layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   moe_n_embd, n_expert}, 0);
-                                layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {moe_n_embd, n_ff_exp, n_expert}, 0);
-
-                                // Shared expert branch
-                                layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
-                                layer.ffn_up_shexp    = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
-
-                            } else {
-                                // mlp layers
-                                layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, 0);
-                                layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, 0);
-                                layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
-                                layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias",   i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
-                            }
-                        }
-                    }
-                } break;
-            case LLM_ARCH_EXAONE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM,   "weight", i), {n_embd}, 0);
-                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN,   "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,     "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_EXAONE4:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_post_norm  = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_EXAONE_MOE:
-                {
-                    const int64_t n_ff_exp       = hparams.n_ff_exp;
-                    const int64_t n_expert       = hparams.n_expert;
-                    const int64_t n_expert_used  = hparams.n_expert_used;
-                    const int64_t n_ff_shexp     = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : n_ff_exp;
-                    const int64_t head_dim       = hparams.n_embd_head_k();
-                    const int64_t n_qo_dim       = n_head * head_dim;
-                    const int64_t n_kv_dim       = n_head_kv * head_dim;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        int flags = 0;
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            // skip all tensors in the NextN layers
-                            flags |= TENSOR_SKIP;
-                        }
-
-                        auto & layer = layers[i];
-                        create_tensor_qkv(layer, i, n_embd, n_qo_dim, n_kv_dim, n_kv_dim, flags);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_qo_dim, n_embd}, flags);
-
-                        layer.rope_freqs   = create_tensor(tn(LLM_TENSOR_ROPE_FREQS,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0) | flags);
-
-                        layer.attn_norm    = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, flags);
-                        layer.attn_q_norm  = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
-                        layer.attn_k_norm  = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
-
-                        layer.ffn_norm     = create_tensor(tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd}, flags);
-
-                        // dense layers for first n_layer_dense_lead layers or nextn_predict_layers layers at the end
-                        if (i < (int) hparams.n_layer_dense_lead || (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers)) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, flags);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, flags);
-                        } else {
-                            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, flags);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            layer.ffn_gate_exps  = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS,  "weight", i), {n_embd, n_ff_exp, n_expert}, flags);
-                            layer.ffn_down_exps  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS,  "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
-                            layer.ffn_up_exps    = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,    "weight", i), {n_embd, n_ff_exp, n_expert}, flags);
-
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, flags);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, flags);
-                        }
-
-                        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags);
-                            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,   "weight", i), {n_embd}, flags);
-                            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,   "weight", i), {n_embd}, flags);
-
-                            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, flags | TENSOR_NOT_REQUIRED);
-                            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", i), {n_embd, n_vocab}, flags | TENSOR_NOT_REQUIRED);
-                            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), {n_embd, n_vocab}, flags | TENSOR_NOT_REQUIRED);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_RWKV6:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // Block 0, LN0
-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
-
-                    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
-                    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
-                    const int head_size = hparams.wkv_head_size;
-                    const int attn_hidden_size = n_embd;
-                    const int ffn_size = hparams.n_ff_arr[0];
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, 0);
-
-                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
-                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
-
-                        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, TENSOR_NOT_REQUIRED);
-                        GGML_ASSERT(!(layer.time_mix_lerp_fused == NULL && layer.time_mix_lerp_w == NULL));
-
-                        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0);
-                        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
-                        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
-                        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
-                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
-
-                        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
-                        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
-                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
-
-                        layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.channel_mix_lerp_r = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0);
-
-                        layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
-                        layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
-                        layer.channel_mix_receptance = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}, 0);
-                    }
-
-                } break;
-            case LLM_ARCH_RWKV6QWEN2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, TENSOR_NOT_REQUIRED);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
-
-                    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
-                    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
-                    const int head_size = hparams.wkv_head_size;
-                    const int attn_hidden_size = n_embd;
-                    const int n_head_kv = hparams.n_head_kv();
-                    int attn_key_value_size;
-                    if (n_head_kv == 0 || attn_hidden_size / head_size == n_head_kv) {
-                        attn_key_value_size = attn_hidden_size;
-                    } else {
-                        attn_key_value_size = n_head_kv * head_size;
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
-                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
-
-                        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
-                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
-
-                        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
-                        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
-                        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
-                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {n_embd, attn_key_value_size}, 0);
-                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {n_embd, attn_key_value_size}, 0);
-                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        // optional bias tensors
-                        layer.time_mix_key_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "bias", i), {attn_key_value_size}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_value_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "bias", i), {attn_key_value_size}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_receptance_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "bias", i), {attn_hidden_size}, TENSOR_NOT_REQUIRED);
-
-                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_RWKV7:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // Block 0, LN0
-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
-
-                    const int n_lora_decay = hparams.n_lora_decay;
-                    const int n_lora_iclr = hparams.n_lora_iclr;
-                    const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;
-                    const int n_lora_gate = hparams.n_lora_gate;
-                    const int attn_hidden_size = n_embd;
-                    const int ffn_size = hparams.n_ff_arr[0];
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
-
-                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
-                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, 0);
-
-                        layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);
-                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);
-                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);
-
-                        layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);
-                        layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);
-                        layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);
-
-                        if (i == 0) {
-                            // actually not used
-                            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
-                            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);
-                            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);
-                        } else {
-                            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
-                            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);
-                            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);
-                        }
-
-                        layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, 0);
-                        layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, 0);
-
-                        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);
-
-                        layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);
-                        layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);
-                        layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);
-
-                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
-
-                        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
-                        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
-                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
-
-                        layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
-
-                        layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
-                        layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
-                    }
-
-                } break;
-            case LLM_ARCH_ARWKV7:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
-
-                    const int n_lora_decay = hparams.n_lora_decay;
-                    const int n_lora_iclr = hparams.n_lora_iclr;
-                    const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;
-                    const int n_lora_gate = hparams.n_lora_gate;
-                    const int attn_hidden_size = n_embd;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);
-                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);
-                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);
-
-                        layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);
-                        layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);
-                        layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);
-
-                        if (i == 0) {
-                            // actually not used
-                            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
-                            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);
-                            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);
-                        } else {
-                            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
-                            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);
-                            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);
-                        }
-
-                        layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, TENSOR_NOT_REQUIRED);
-
-                        try {
-                            layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);
-                        } catch(std::runtime_error & e) {
-                            // ARWKV models may not have gate tensors
-                            layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
-                        }
-
-                        layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);
-                        layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);
-                        layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);
-
-                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
-                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
-
-                        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-
-                } break;
-            case LLM_ARCH_CHAMELEON:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
-                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),  {n_embd_head_k, n_head}, TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),  {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_WAVTOKENIZER_DEC:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd, n_vocab}, 0);
-
-                    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight", 0), {7, hparams.n_embd, hparams.posnet.n_embd}, 0);
-                    conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias",   0), {1, hparams.posnet.n_embd}, 0);
-
-                    // posnet
-                    {
-                        const int64_t n_embd = hparams.posnet.n_embd;
-
-                        for (uint32_t i = 0; i < hparams.posnet.n_layer; ++i) {
-                            auto & layer = layers[i].posnet;
-
-                            // posnet:
-                            //
-                            //  - resnet
-                            //  - resnet
-                            //  - attn
-                            //  - resnet
-                            //  - resnet
-                            //  - norm
-                            //
-                            switch (i) {
-                                case 0:
-                                case 1:
-                                case 3:
-                                case 4:
-                                    {
-                                        layer.norm1   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", i), {1, n_embd}, 0);
-                                        layer.norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias",   i), {1, n_embd}, 0);
-
-                                        layer.conv1   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", i), {3, n_embd, n_embd}, 0);
-                                        layer.conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias",   i), {1, n_embd}, 0);
-
-                                        layer.norm2   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", i), {1, n_embd}, 0);
-                                        layer.norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias",   i), {1, n_embd}, 0);
-
-                                        layer.conv2   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", i), {3, n_embd, n_embd}, 0);
-                                        layer.conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias",   i), {1, n_embd}, 0);
-                                    } break;
-                                case 2:
-                                    {
-                                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
-                                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
-
-                                        layer.attn_q      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "weight", i), {1, n_embd, n_embd}, 0);
-                                        layer.attn_q_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "bias",   i), {1, n_embd}, 0);
-
-                                        layer.attn_k      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "weight", i), {1, n_embd, n_embd}, 0);
-                                        layer.attn_k_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "bias",   i), {1, n_embd}, 0);
-
-                                        layer.attn_v      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "weight", i), {1, n_embd, n_embd}, 0);
-                                        layer.attn_v_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "bias",   i), {1, n_embd}, 0);
-
-                                        layer.attn_o      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "weight", i), {1, n_embd, n_embd}, 0);
-                                        layer.attn_o_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "bias",   i), {1, n_embd}, 0);
-                                    } break;
-                                case 5:
-                                    {
-                                        layer.norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
-                                        layer.norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
-                                    } break;
-                                default: GGML_ABORT("unknown posnet layer");
-                            };
-                        }
-                    }
-
-                    GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd);
-
-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {hparams.posnet.n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {hparams.posnet.n_embd}, 0);
-
-                    // convnext
-                    {
-                        const int64_t n_embd = hparams.convnext.n_embd;
-
-                        for (uint32_t i = 0; i < hparams.convnext.n_layer; ++i) {
-                            auto & layer = layers[i].convnext;
-
-                            layer.dw     = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "weight", i), {7, 1, n_embd}, 0);
-                            layer.dw_b   = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "bias",   i), {1, n_embd}, 0);
-
-                            layer.norm   = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "weight", i), {n_embd}, 0);
-                            layer.norm_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "bias",   i), {n_embd}, 0);
-
-                            layer.pw1    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "weight", i), {n_embd, n_ff}, 0);
-                            layer.pw1_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "bias",   i), {n_ff}, 0);
-
-                            layer.pw2    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "weight", i), {n_ff, n_embd}, 0);
-                            layer.pw2_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "bias",   i), {n_embd}, 0);
-
-                            layer.gamma  = create_tensor(tn(LLM_TENSOR_CONVNEXT_GAMMA, "weight", i), {n_embd}, 0);
-                        }
-
-                        // output
-                        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
-                    }
-
-                    output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, hparams.n_embd_out()}, 0);
-                    output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {hparams.n_embd_out()}, 0);
-                } break;
-            case LLM_ARCH_BAILINGMOE:
-                {
-                    const int64_t n_ff_exp            = hparams.n_ff_exp;
-                    const int64_t n_expert_shared     = hparams.n_expert_shared;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_head * n_rot, n_head_kv * n_rot, n_head_kv * n_rot, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_rot, n_embd}, 0);
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                        if (n_expert == 0) {
-                            throw std::runtime_error("n_expert must be > 0");
-                        }
-                        if (n_expert_used == 0) {
-                            throw std::runtime_error("n_expert_used must be > 0");
-                        }
-
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                    }
-                } break;
-            case LLM_ARCH_BAILINGMOE2:
-                {
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for bailingmoe2");
-                    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for bailingmoe2");
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        int flags = 0;
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            // skip all tensors in the NextN layers
-                            flags |= TENSOR_SKIP;
-                        }
-
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
-
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, flags);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
-
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
-
-                        if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
-                            const int64_t n_ff_shexp = (hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp) * n_expert_shared;
-
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
-
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, flags);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
-
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, flags);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, flags);
-                        } else { // Dense layers
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, flags);
-                        }
-
-                        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
-                        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
-                            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
-                            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
-                            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
-                            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
-                            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
-                            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
-                            layer.layer_out_norm         = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, flags);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_DOTS1:
-                {
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_head_k * n_head, n_embd_head_k * n_head, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        } else {
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            // MoE branch
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                            // Shared expert branch
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_ARCEE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_AFMOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    const int64_t n_ff_exp = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        // dual attention normalization
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        // attention projections
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        // Q/K normalization
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        // attention gating
-                        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
-
-                        // dual ffn normalization
-                        layer.ffn_norm      = create_tensor(tn(LLM_TENSOR_FFN_NORM,      "weight", i), {n_embd}, 0);
-                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) {
-                            // MoE layers
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
-
-                            // grouped expert weights
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
-
-                            // shared expert
-                            if (n_expert_shared > 0) {
-                                const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
-                                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
-                                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
-                                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
-                            }
-                        } else {
-                            // Dense layers
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_ERNIE4_5:
-            case LLM_ARCH_ERNIE4_5_MOE:
-            case LLM_ARCH_PADDLEOCR:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        // optional bias tensors
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (arch == LLM_ARCH_ERNIE4_5_MOE && static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
-                            int n_ff_exp = hparams.n_ff_exp;
-
-                            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff_exp, n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
-
-                            // Shared expert (if present)
-                            if (hparams.n_ff_shexp > 0) {
-                                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, hparams.n_ff_shexp}, 0);
-                                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd    }, 0);
-                                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, hparams.n_ff_shexp}, 0);
-                            }
-                        } else { // Dense layers
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_FALCON_H1:
-                {
-                    // Common
-                    const int64_t hidden_size = hparams.n_embd; // hidden_size
-
-                    // mamba2 Mixer SSM params
-                    const int64_t ssm_conv_kernel_size  = hparams.ssm_d_conv; // ssm_conv_kernel_size
-                    const int64_t ssm_n_groups          = hparams.ssm_n_group; // ssm_n_groups
-                    const int64_t ssm_state_size        = hparams.ssm_d_state; // ssm_state_size
-                    const int64_t ssm_intermediate_size = hparams.ssm_d_inner; // TODO expand
-                    const int64_t ssm_num_heads         = hparams.ssm_dt_rank; // ssm_num_heads
-                    const int64_t ssm_conv_dim          = ssm_intermediate_size + 2 * ssm_n_groups * ssm_state_size;
-                    const int64_t ssm_projection_size   = ssm_intermediate_size + ssm_conv_dim + ssm_num_heads;
-
-                    // attn params
-                    const int64_t attn_num_attention_head = hparams.n_head(0); // rename to: attn_num_attention_head
-                    const int64_t attn_num_key_value_head = hparams.n_head_kv(0);
-
-                    // ffn params
-                    const int64_t ffn_intermediate_size = hparams.n_ff(0);
-
-                    // embeddings
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, 0);
-
-                    // output
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hidden_size, n_vocab}, TENSOR_NOT_REQUIRED);
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {hidden_size}, 0);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        /*SSM LAYERS*/
-                        // ssm in
-                        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {hidden_size, ssm_projection_size}, 0);
-                        // ssm 1d conv
-                        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {ssm_conv_kernel_size, ssm_conv_dim}, 0);
-                        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {ssm_conv_dim}, TENSOR_NOT_REQUIRED);
-                        // ssm_dt
-                        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {ssm_num_heads}, 0);
-                        // no "weight" suffix for these
-                        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, ssm_num_heads}, 0);
-                        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, ssm_num_heads}, 0);
-                        // ssm_norm
-                        layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {ssm_intermediate_size / ssm_n_groups, ssm_n_groups}, TENSOR_NOT_REQUIRED);
-                        // out_proj
-                        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {ssm_intermediate_size, hidden_size}, 0);
-
-                        /*ATTENTION LAYERS*/
-                        // attention layers (with optional bias)
-                        create_tensor_qkv(layer, i, hidden_size, n_embd_head_k * attn_num_attention_head, attn_num_key_value_head * n_embd_head_k, attn_num_key_value_head * n_embd_head_v, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * attn_num_attention_head, hidden_size}, 0);
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {hidden_size}, 0);
-
-
-                        // feed forward (w/ optional biases)
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, i), {hidden_size}, 0);
-                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {hidden_size,   ffn_intermediate_size}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  ffn_intermediate_size, hidden_size}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {hidden_size,   ffn_intermediate_size}, 0);
-
-                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_HUNYUAN_MOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-                        const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(i);
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
-
-                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
-                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
-                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
-                    }
-                } break;
-            case LLM_ARCH_HUNYUAN_DENSE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                    }
-                } break;
-            case LLM_ARCH_SMOLLM3:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_OPENAI_MOE:
-                {
-                    const int64_t n_ff_exp = hparams.n_ff_exp;
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_head * n_rot, n_head_kv * n_rot, n_head_kv * n_rot, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_rot, n_embd}, 0);
-
-                        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {  n_embd, n_expert}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp_b  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "bias", i), {n_expert}, 0);
-                        layer.ffn_gate_exps_b = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "bias", i), {n_ff_exp, n_expert}, 0);
-                        layer.ffn_down_exps_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "bias", i), {  n_embd, n_expert}, 0);
-                        layer.ffn_up_exps_b   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "bias", i), {n_ff_exp, n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_LFM2:
-            case LLM_ARCH_LFM2MOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM_LFM2, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,           "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        const bool is_moe_layer = i >= static_cast<int>(hparams.n_layer_dense_lead);
-
-                        // ffn/moe is same for transformer and conv layers
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        if (is_moe_layer) {
-                            GGML_ASSERT(n_expert && n_expert_used);
-                            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i),  {n_embd, n_expert}, 0);
-                            layer.ffn_gate_exps   = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp,   n_embd, n_expert}, 0);
-                            layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i),   {n_embd, hparams.n_ff_exp, n_expert}, 0);
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
-                        } else {  // dense
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        }
-
-                        // for operator_norm
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (!hparams.is_recurrent(i)) {
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                            GGML_ASSERT(n_embd_v_gqa == n_embd_k_gqa);
-
-                            create_tensor_qkv(layer, i, n_embd, n_embd, hparams.n_embd_k_gqa(i), hparams.n_embd_v_gqa(i), 0);
-
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        } else {
-                            layer.shortconv.conv     = create_tensor(tn(LLM_TENSOR_SHORTCONV_CONV,    "weight", i), {hparams.n_shortconv_l_cache, n_embd}, 0);
-                            layer.shortconv.in_proj  = create_tensor(tn(LLM_TENSOR_SHORTCONV_INPROJ,  "weight", i), {n_embd, 3 * n_embd}, 0);
-                            layer.shortconv.out_proj = create_tensor(tn(LLM_TENSOR_SHORTCONV_OUTPROJ, "weight", i), {n_embd, n_embd}, 0);
-                        }
-                    }
-
-                    // for LFM2-ColBert-350M
-                    dense_2_out_layers   = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.n_embd_out()}, TENSOR_NOT_REQUIRED);
-                    dense_2_out_layers_b = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "bias"),   {hparams.n_embd_out()        }, TENSOR_NOT_REQUIRED);
-                } break;
-            case LLM_ARCH_SMALLTHINKER:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
-
-                        GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for SMALLTHINKER");
-                        GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for SMALLTHINKER");
-
-                        // MoE branch
-                        const int64_t n_ff_exp = hparams.n_ff_exp;
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
-                    }
-                } break;
-            case LLM_ARCH_GROVEMOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for GROVEMOE");
-                    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for GROVEMOE");
-                    GGML_ASSERT(hparams.n_group_experts > 0 && "n_group_experts must be > 0 for GROVEMOE");
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                        // MoE branch
-                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-                        const int64_t n_ff_chexp = hparams.n_ff_chexp ? hparams.n_ff_chexp : n_embd_head_k;
-                        const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
-
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                        layer.ffn_gate_chexps = create_tensor(tn(LLM_TENSOR_FFN_GATE_CHEXPS, "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
-                        layer.ffn_down_chexps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_CHEXPS, "weight", i), {n_ff_chexp,   n_embd, n_chunk_expert}, 0);
-                        layer.ffn_up_chexps   = create_tensor(tn(LLM_TENSOR_FFN_UP_CHEXPS,   "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_APERTUS:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
-
-                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
-                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        } else {
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        }
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                        // optional bias tensors
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
-                        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
-
-                        // Q and K layernorms for Apertus
-                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_MINIMAX_M2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k * n_head}, 0);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_k_gqa}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
-                        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
-                    }
-                } break;
-            case LLM_ARCH_KIMI_LINEAR:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        // Check for KDA specific tensors to determine layer type or if it's a mixed model
-                        // Assuming KDA layer if KDA tensors are present
-
-                        // KDA uses head_dim = 128 (from linear_attn_config.head_dim)
-                        const int64_t n_embd_head_k_kda = hparams.n_embd_head_kda;
-                        const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda;
-                        const int64_t ssm_d_conv = hparams.ssm_d_conv;
-
-                        if (hparams.is_recurrent(i)) {
-                            // Conv1d weights: try 4D first, then 3D (quantization may remove trailing 1)
-                            // 4D: [d_conv, 1, d_inner, 1], 3D: [d_conv, 1, d_inner]
-                            layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
-                            if (!layer.ssm_q_conv) {
-                                layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0);
-                            }
-
-                             // KDA Layer - Conv1d weights may be 3D or 4D
-                             layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
-                             if (!layer.ssm_k_conv) {
-                                 layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0);
-                             }
-                             layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
-                             if (!layer.ssm_v_conv) {
-                                 layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head}, 0);
-                             }
-
-                             // q, k, v projections
-                             // Python: q_proj, k_proj, v_proj
-                             create_tensor_qkv(layer, i, n_embd, n_embd_head_k_kda * n_head, n_embd_head_k_kda * n_head, n_embd_head_v_kda * n_head, 0);
-
-                             // KDA specific projections
-                             // f_a_proj, f_b_proj
-                             layer.ssm_f_a = create_tensor(tn(LLM_TENSOR_SSM_F_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0); // head_dim
-                             layer.ssm_f_b = create_tensor(tn(LLM_TENSOR_SSM_F_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0); // projection_size
-
-                             // b_proj (beta mixing coefficient)
-                             layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0);
-
-                             // A_log - Shape in GGUF: [1, num_heads, 1, 1] (4D) or [1, num_heads] (2D after quantization) Note: -exp(A_log) is applied in convert_hf_to_gguf.py
-                             layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head, 1, 1}, TENSOR_NOT_REQUIRED);
-                             if (!layer.ssm_a) {
-                                 layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
-                             }
-
-                             // dt_bias - shape [n_embd_head_k_kda * n_head] = [4096]
-                             layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_embd_head_k_kda * n_head}, 0);
-
-                             // g_a_proj, g_b_proj (output gate)
-                             layer.ssm_g_a = create_tensor(tn(LLM_TENSOR_SSM_G_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0);
-                             layer.ssm_g_b = create_tensor(tn(LLM_TENSOR_SSM_G_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0);
-
-                             // o_norm (reusing SSM_NORM)
-                             layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {n_embd_head_k_kda}, 0); // FusedRMSNormGated
-
-                             // o_proj
-                             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v_kda * n_head, n_embd}, 0);
-
-                        } else {
-                             // MLA Layer - use MLA-specific head dimensions
-                             const int64_t q_lora_rank  = hparams.n_lora_q;
-                             const int64_t kv_lora_rank = hparams.n_lora_kv;
-                             const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
-                             const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
-
-                             layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, TENSOR_NOT_REQUIRED);
-                             layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
-
-                             if (layer.attn_q_a_norm) {
-                                 layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
-                                 layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
-                             } else {
-                                 // Kimi MLA without Q compression: wq = [n_embd, n_head * n_embd_head_k_mla]
-                                 layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0);
-                             }
-
-                             // Kimi: qk_rope_head_dim = 64 (actual RoPE dimension for MLA)
-                             // Note: hparams.n_rot may be 72 (from conversion) but actual is 64
-                             const int64_t qk_rope_head_dim = hparams.n_rot();  // From config: qk_rope_head_dim
-                             layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0);
-                             // Support Legacy GGUFs that don't split wkv_b (MLA KV cache disabled)
-                             layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i),
-                                {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
-                             if (!layer.wkv_b) { // MLA KV cache enabled
-                                 layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, 0);
-                                 layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
-                             }
-                             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
-                        }
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        // MoE intermediate size (different from dense FFN)
-                        const int64_t n_ff_exp = hparams.n_ff_exp;
-
-                        // Kimi uses n_layer_dense_lead to determine which layers use dense FFN vs MoE
-                        // first_k_dense_replace = 1 means layer 0 uses dense FFN, layers 1+ use MoE
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            // Dense FFN layer - use normal n_ff
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
-                        } else {
-                            // MoE layer - use n_ff_exp (1024) instead of n_ff (9216)
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
-
-                            // Shared experts use moe_intermediate_size * num_shared_experts
-                            // Kimi: shared_expert_intermediate_size = 1024 * 1 = 1024
-                            // Tensors are 2D: [n_embd, n_ff_shexp] or [n_ff_shexp, n_embd]
-                            const int64_t n_ff_shexp_actual = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1);
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp_actual, n_embd}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
-
-                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
-                        }
-                    }
-                } break;
-            case LLM_ARCH_COGVLM:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd_head_k * n_head * 3}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.visexp_attn_wqkv = create_tensor(tn(LLM_TENSOR_VISEXP_ATTN_QKV, "weight", i), {n_embd, n_embd_head_k * n_head * 3}, 0);
-                        layer.visexp_attn_wo = create_tensor(tn(LLM_TENSOR_VISEXP_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-
-                        layer.visexp_ffn_gate = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.visexp_ffn_down = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.visexp_ffn_up   = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_PANGU_EMBED:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        // weight tensors
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        // bias tensors
-                        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
-                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        } else {
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        }
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN3NEXT:
-                {
-                    if (n_expert == 0) {
-                        throw std::runtime_error(arch_name() + " model cannot have zero experts");
-                    }
-
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                    // Calculate dimensions from hyperparameters
-                    const int64_t head_k_dim = hparams.ssm_d_state;
-                    const int64_t head_v_dim = hparams.ssm_d_state;
-                    const int64_t n_k_heads  = hparams.ssm_n_group;
-                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
-                    const int64_t key_dim    = head_k_dim * n_k_heads;
-                    const int64_t value_dim  = head_v_dim * n_v_heads;
-                    const int64_t conv_dim   = key_dim * 2 + value_dim;
-
-                    // Calculate projection sizes
-                    const int64_t qkvz_dim = key_dim * 2 + value_dim * 2;
-                    const int64_t ba_dim   = n_v_heads * 2;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-                        const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(i);
-
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
-
-                        if (!hparams.is_recurrent(i)) {
-                            // Attention layers
-                            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                            // Q/K normalization for attention layers
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        } else {
-                            // Linear attention (gated delta net) specific tensors
-                            // Create tensors with calculated dimensions
-                            // note: ssm_in is used by legacy GGUF
-                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, qkvz_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
-                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
-                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
-                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
-                        }
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
-                        create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
-
-                        // Shared experts
-                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
-                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, n_ff_shexp }, 0);
-                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, n_ff_shexp }, 0);
-                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { n_ff_shexp, n_embd }, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN35MOE:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
-
-                    // Calculate dimensions from hyperparameters
-                    const int64_t head_k_dim = hparams.ssm_d_state;
-                    const int64_t head_v_dim = hparams.ssm_d_state;
-                    const int64_t n_k_heads  = hparams.ssm_n_group;
-                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
-                    const int64_t key_dim    = head_k_dim * n_k_heads;
-                    const int64_t value_dim  = head_v_dim * n_v_heads;
-                    const int64_t conv_dim   = key_dim * 2 + value_dim;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
-
-                        if (!hparams.is_recurrent(i)) {
-                            // Attention layers
-                            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                            // Q/K normalization for attention layers
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        } else {
-                            // Linear attention (gated delta net) specific tensors
-                            // Create tensors with calculated dimensions
-                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
-                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", i), { n_embd, n_v_heads }, 0);
-                            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", i), { n_embd, n_v_heads }, 0);
-                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
-                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
-                        }
-
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
-                        create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
-
-                        // Shared experts
-                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
-
-                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
-                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, n_ff_shexp }, 0);
-                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, n_ff_shexp }, 0);
-                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { n_ff_shexp, n_embd }, 0);
-                    }
-                } break;
-            case LLM_ARCH_QWEN35:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
-
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
-                    }
-
-                    // Calculate dimensions from hyperparameters
-                    const int64_t head_k_dim = hparams.ssm_d_state;
-                    const int64_t head_v_dim = hparams.ssm_d_state;
-                    const int64_t n_k_heads  = hparams.ssm_n_group;
-                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
-                    const int64_t key_dim    = head_k_dim * n_k_heads;
-                    const int64_t value_dim  = head_v_dim * n_v_heads;
-                    const int64_t conv_dim   = key_dim * 2 + value_dim;
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
-                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
-
-                        if (!hparams.is_recurrent(i)) {
-                            // Attention layers
-                            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
-                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
-
-                            // Q/K normalization for attention layers
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
-                        } else {
-                            // Linear attention (gated delta net) specific tensors
-                            // Create tensors with calculated dimensions
-                            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
-                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
-                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
-                            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", i), { n_embd, n_v_heads }, 0);
-                            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", i), { n_embd, n_v_heads }, 0);
-                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
-                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
-                        }
-
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            case LLM_ARCH_MIMO2:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-                        uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
-                        uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
-                        uint32_t n_head = hparams.n_head(i);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, 0);
-
-                        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM,  "weight", i), {n_embd}, 0);
-                        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        // non-MoE branch
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
-
-                        // MoE branch
-                        int64_t n_ff_exp = hparams.n_ff_exp;
-                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_STEP35:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    // STEP35 supports per-layer partial RoPE dims; rope factors are stored as a single shared tensor
-                    // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer.
-                    uint32_t n_rot_max = 0;
-                    for (int i = 0; i < n_layer; ++i) {
-                        n_rot_max = std::max(n_rot_max, hparams.n_rot(i));
-                    }
-                    if (n_rot_max == 0) {
-                        n_rot_max = n_rot;
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        const uint32_t n_head_l      = hparams.n_head(i);
-                        const uint32_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
-                        const uint32_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
-
-                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
-
-                        // optional rope factors (llama3) / longrope tensors
-                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
-                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        } else {
-                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        }
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, 0);
-
-                        // head-wise attention gate (Step35 self_attn.g_proj)
-                        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        // dense MLP (leading dense blocks)
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
-
-                        // MoE routed experts + selection bias (router_bias)
-                        const int64_t n_ff_exp = hparams.n_ff_exp;
-                        layer.ffn_gate_inp      = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_gate_exps     = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_exps     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_exps       = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_exp_probs_b   = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
-
-                        // shared expert MLP
-                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
-                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
-                    }
-                } break;
-            case LLM_ARCH_MAINCODER:
-                {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-                    // if output is NULL, init from the input tok embed
-                    if (output == NULL) {
-                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
-
-                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                    }
-                } break;
-            default:
-                throw std::runtime_error("unknown architecture");
-        }
-
-        // generic pass: load optional per-tensor/per-expert ".scale" tensors (e.g. NVFP4 scale2)
-        // this avoids having to add scale loading to every architecture
-        for (int i = 0; i < n_layer; ++i) {
-            auto & layer = layers[i];
-
-            // attention weight scales (per-tensor, shape {1})
-            if (!layer.wq_s && layer.wq) {
-                layer.wq_s = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wk_s && layer.wk) {
-                layer.wk_s = create_tensor(tn(LLM_TENSOR_ATTN_K,   "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wv_s && layer.wv) {
-                layer.wv_s = create_tensor(tn(LLM_TENSOR_ATTN_V,   "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wo_s && layer.wo) {
-                layer.wo_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wqkv_s && layer.wqkv) {
-                layer.wqkv_s = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wqkv_gate_s && layer.wqkv_gate) {
-                layer.wqkv_gate_s = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-
-            // dense FFN weight scales (per-tensor, shape {1})
-            if (!layer.ffn_gate_s && layer.ffn_gate) {
-                layer.ffn_gate_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_down_s && layer.ffn_down) {
-                layer.ffn_down_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_up_s && layer.ffn_up) {
-                layer.ffn_up_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_gate_shexp_s && layer.ffn_gate_shexp) {
-                layer.ffn_gate_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_down_shexp_s && layer.ffn_down_shexp) {
-                layer.ffn_down_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_up_shexp_s && layer.ffn_up_shexp) {
-                layer.ffn_up_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-
-            // MoE expert weight scales (per-expert, shape {n_expert})
-            if (!layer.ffn_gate_exps_s && layer.ffn_gate_exps) {
-                layer.ffn_gate_exps_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_down_exps_s && layer.ffn_down_exps) {
-                layer.ffn_down_exps_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_up_exps_s && layer.ffn_up_exps) {
-                layer.ffn_up_exps_s = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
-            }
-
-            // recurrent / linear-attention weight scales (per-tensor, shape {1})
-            if (!layer.ssm_in_s && layer.ssm_in) {
-                layer.ssm_in_s = create_tensor(tn(LLM_TENSOR_SSM_IN, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ssm_out_s && layer.ssm_out) {
-                layer.ssm_out_s = create_tensor(tn(LLM_TENSOR_SSM_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ssm_alpha_s && layer.ssm_alpha) {
-                layer.ssm_alpha_s = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ssm_beta_s && layer.ssm_beta) {
-                layer.ssm_beta_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-
-            // input scales
-            if (!layer.wq_in_s && layer.wq) {
-                layer.wq_in_s = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wk_in_s && layer.wk) {
-                layer.wk_in_s = create_tensor(tn(LLM_TENSOR_ATTN_K,   "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wv_in_s && layer.wv) {
-                layer.wv_in_s = create_tensor(tn(LLM_TENSOR_ATTN_V,   "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wo_in_s && layer.wo) {
-                layer.wo_in_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wqkv_in_s && layer.wqkv) {
-                layer.wqkv_in_s = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.wqkv_gate_in_s && layer.wqkv_gate) {
-                layer.wqkv_gate_in_s = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_gate_in_s && layer.ffn_gate) {
-                layer.ffn_gate_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_down_in_s && layer.ffn_down) {
-                layer.ffn_down_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_up_in_s && layer.ffn_up) {
-                layer.ffn_up_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_gate_exps_in_s && layer.ffn_gate_exps) {
-                layer.ffn_gate_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_down_exps_in_s && layer.ffn_down_exps) {
-                layer.ffn_down_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_up_exps_in_s && layer.ffn_up_exps) {
-                layer.ffn_up_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_gate_shexp_in_s && layer.ffn_gate_shexp) {
-                layer.ffn_gate_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_down_shexp_in_s && layer.ffn_down_shexp) {
-                layer.ffn_down_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ffn_up_shexp_in_s && layer.ffn_up_shexp) {
-                layer.ffn_up_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ssm_in_in_s && layer.ssm_in) {
-                layer.ssm_in_in_s = create_tensor(tn(LLM_TENSOR_SSM_IN, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ssm_out_in_s && layer.ssm_out) {
-                layer.ssm_out_in_s = create_tensor(tn(LLM_TENSOR_SSM_OUT, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ssm_alpha_in_s && layer.ssm_alpha) {
-                layer.ssm_alpha_in_s = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-            if (!layer.ssm_beta_in_s && layer.ssm_beta) {
-                layer.ssm_beta_in_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
-            }
-        }
-    }
-
-    ml.done_getting_tensors();
-
-    // populate tensors_by_name
-    for (auto & [_, ctx_ptr] : ml.ctx_map) {
-        for (auto * cur = ggml_get_first_tensor(ctx_ptr.get()); cur != NULL; cur = ggml_get_next_tensor(ctx_ptr.get(), cur)) {
-            tensors_by_name.emplace_back(ggml_get_name(cur), cur);
-        }
-    }
-
-    ml.init_mappings(true, use_mlock ? &pimpl->mlock_mmaps : nullptr);
-    pimpl->mappings.reserve(ml.mappings.size());
-
-    // create the backend buffers
-    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_buf_maps;
-    ctx_buf_maps.reserve(ml.ctx_map.size());
-
-    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
-    const size_t n_max_backend_buffer = ml.ctx_map.size() * ml.files.size();
-    pimpl->ctxs_bufs.reserve(n_max_backend_buffer);
-
-    for (auto & [buft, ctx_ptr] : ml.ctx_map) {
-        ggml_context * ctx = ctx_ptr.get();
-
-        // skip contexts without tensors
-        if (ggml_get_first_tensor(ctx) == nullptr) {
-            continue;
-        }
-
-        llama_buf_map buf_map;
-        buf_map.reserve(n_max_backend_buffer);
-
-        // check if it is possible to use buffer_from_host_ptr with this buffer type
-        ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
-        if (!dev) {
-            // FIXME: workaround for CPU backend buft having a NULL device
-            dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-            if (!dev) {
-                throw std::runtime_error(format("%s: no CPU backend found", __func__));
-            }
-        }
-        ggml_backend_dev_props props;
-        ggml_backend_dev_get_props(dev, &props);
-        bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
-        bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev);
-
-        std::vector<ggml_backend_buffer_ptr> bufs;
-        if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
-            GGML_ASSERT(!ml.no_alloc);
-            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
-                // only the mmap region containing the tensors in the model is mapped to the backend buffer
-                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer,
-                //     then we could just use metal for all layers
-                // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
-                void * addr = nullptr;
-                size_t first, last; // NOLINT
-                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
-                if (first >= last) {
-                    continue;
-                }
-                const size_t max_size = ggml_get_max_tensor_size(ctx);
-                ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
-                if (buf == nullptr) {
-                    throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
-                }
-                bufs.emplace_back(buf);
-                buf_map.emplace(idx, buf);
-            }
-        } else {
-            ggml_backend_buffer_t buf;
-            if (ml.no_alloc) {
-                buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
-                for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
-                    t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them
-                }
-            } else {
-                buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
-            }
-            if (buf == nullptr) {
-                throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
-            }
-            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
-                pimpl->mlock_bufs.emplace_back(new llama_mlock);
-                auto & mlock_buf = pimpl->mlock_bufs.back();
-                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
-                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
-            }
-            bufs.emplace_back(buf);
-            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
-                buf_map.emplace(idx, buf);
-            }
-        }
-
-        for (auto & buf : bufs) {
-            // indicate that this buffer contains weights
-            // this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
-            ggml_backend_buffer_set_usage(buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-        }
-
-        pimpl->ctxs_bufs.emplace_back(std::move(ctx_ptr), std::move(bufs));
-
-        ctx_buf_maps.emplace_back(ctx, buf_map);
-    }
-
-    if (llama_supports_gpu_offload()) {
-        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
-
-        int n_repeating = n_gpu;
-        if (n_repeating > 0) {
-            LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__);
-            n_repeating--;
-        }
-        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_repeating);
-
-        const int max_backend_supported_layers = hparams.n_layer + 1;
-        const int max_offloadable_layers       = hparams.n_layer + 1;
-
-        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
-    }
-
-    // print memory requirements per buffer type
-    for (auto & [_, bufs] : pimpl->ctxs_bufs) {
-        for (auto & buf: bufs) {
-            LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n",
-                __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0);
-        }
-    }
-
-    if (ml.no_alloc) {
-        return true;
-    }
-
-    // load tensor data
-    for (auto & [ctx, buf_map] : ctx_buf_maps) {
-        if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) {
-            return false;
-        }
-    }
-
-    if (use_mmap_buffer) {
-        for (auto & mapping : ml.mappings) {
-            pimpl->mappings.emplace_back(std::move(mapping));
-        }
-    }
-
-    return true;
-}
-
-std::string llama_model::arch_name() const {
-    return llm_arch_name(arch);
-}
-
-std::string llama_model::type_name() const {
-    return llm_type_name(type);
-}
-
-std::string llama_model::desc() const {
-    return pimpl->desc_str;
-}
-
-size_t llama_model::size() const {
-    return pimpl->n_bytes;
-}
-
-size_t llama_model::n_tensors() const {
-    return tensors_by_name.size();
-}
-
-size_t llama_model::n_devices() const {
-    return devices.size();
-}
-
-const float * llama_model::tensor_split() const {
-    return params.tensor_split;
-}
-
-uint32_t llama_model::n_gpu_layers() const {
-    return params.n_gpu_layers >= 0 ? params.n_gpu_layers : hparams.n_layer + 1;
-}
-
-llama_split_mode llama_model::split_mode() const {
-    return params.split_mode;
-}
-
-std::map<ggml_backend_buffer_type_t, size_t> llama_model::memory_breakdown() const {
-    std::map<ggml_backend_buffer_type_t, size_t> ret;
-    for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) {
-        if (hparams.no_alloc) {
-            GGML_ASSERT(bufs.size() == 1);
-            ggml_backend_buffer_t buf = bufs[0].get();
-            GGML_ASSERT(ggml_backend_buffer_get_base(buf) == nullptr);
-            ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf);
-            ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
-        } else {
-            for (const auto & buf : bufs) {
-                // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
-                ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
-            }
-        }
-    }
-    return ret;
-}
-
-uint64_t llama_model::n_elements() const {
-    return pimpl->n_elements;
-}
-
-void llama_model::print_info() const {
-    const std::string rope_scaling_type = llama_rope_scaling_type_name(hparams.rope_scaling_type_train);
-
-    auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
-        bool is_var = false;
-
-        std::vector<uint32_t> v;
-        for (uint32_t i = 0; i < n; ++i) {
-            v.push_back(f(i));
-            if (v[i] != v[0]) {
-                is_var = true;
-            }
-        }
-
-        std::stringstream ss;
-
-        if (is_var) {
-            ss << "[";
-            for (uint32_t i = 0; i < n; ++i) {
-                ss << v[i];
-                if (i < n - 1) {
-                    ss << ", ";
-                }
-            }
-            ss << "]";
-        } else {
-            ss << v[0];
-        }
-
-        return ss.str();
-    };
-
-    // hparams
-    LLAMA_LOG_INFO("%s: arch                  = %s\n",     __func__, arch_name().c_str());
-    LLAMA_LOG_INFO("%s: vocab_only            = %d\n",     __func__, hparams.vocab_only);
-    LLAMA_LOG_INFO("%s: no_alloc              = %d\n",     __func__, hparams.no_alloc);
-
-    if (!hparams.vocab_only) {
-        LLAMA_LOG_INFO("%s: n_ctx_train           = %u\n",     __func__, hparams.n_ctx_train);
-        LLAMA_LOG_INFO("%s: n_embd                = %u\n",     __func__, hparams.n_embd);
-        LLAMA_LOG_INFO("%s: n_embd_inp            = %u\n",     __func__, hparams.n_embd_inp());
-        LLAMA_LOG_INFO("%s: n_layer               = %u\n",     __func__, hparams.n_layer);
-        LLAMA_LOG_INFO("%s: n_head                = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head(il);    }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_head_kv             = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_rot                 = %u\n",     __func__, hparams.n_rot_full);
-        LLAMA_LOG_INFO("%s: n_swa                 = %u\n",     __func__, hparams.n_swa);
-        LLAMA_LOG_INFO("%s: is_swa_any            = %u\n",     __func__, hparams.is_swa_any());
-        LLAMA_LOG_INFO("%s: n_embd_head_k         = %u\n",     __func__, hparams.n_embd_head_k_full);
-        LLAMA_LOG_INFO("%s: n_embd_head_v         = %u\n",     __func__, hparams.n_embd_head_v_full);
-        LLAMA_LOG_INFO("%s: n_gqa                 = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il);        }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_embd_k_gqa          = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_embd_v_gqa          = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: f_norm_eps            = %.1e\n",   __func__, hparams.f_norm_eps);
-        LLAMA_LOG_INFO("%s: f_norm_rms_eps        = %.1e\n",   __func__, hparams.f_norm_rms_eps);
-        LLAMA_LOG_INFO("%s: f_clamp_kqv           = %.1e\n",   __func__, hparams.f_clamp_kqv);
-        LLAMA_LOG_INFO("%s: f_max_alibi_bias      = %.1e\n",   __func__, hparams.f_max_alibi_bias);
-        LLAMA_LOG_INFO("%s: f_logit_scale         = %.1e\n",   __func__, hparams.f_logit_scale);
-        LLAMA_LOG_INFO("%s: f_attn_scale          = %.1e\n",   __func__, hparams.f_attention_scale);
-        LLAMA_LOG_INFO("%s: n_ff                  = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
-        LLAMA_LOG_INFO("%s: n_expert              = %u\n",     __func__, hparams.n_expert);
-        LLAMA_LOG_INFO("%s: n_expert_used         = %u\n",     __func__, hparams.n_expert_used);
-        LLAMA_LOG_INFO("%s: n_expert_groups       = %d\n",     __func__, hparams.n_expert_groups);
-        LLAMA_LOG_INFO("%s: n_group_used          = %d\n",     __func__, hparams.n_group_used);
-        LLAMA_LOG_INFO("%s: causal attn           = %d\n",     __func__, hparams.causal_attn);
-        LLAMA_LOG_INFO("%s: pooling type          = %d\n",     __func__, hparams.pooling_type);
-        LLAMA_LOG_INFO("%s: rope type             = %d\n",     __func__, hparams.rope_type);
-        LLAMA_LOG_INFO("%s: rope scaling          = %s\n",     __func__, rope_scaling_type.c_str());
-        LLAMA_LOG_INFO("%s: freq_base_train       = %.1f\n",   __func__, hparams.rope_freq_base_train);
-        LLAMA_LOG_INFO("%s: freq_scale_train      = %g\n",     __func__, hparams.rope_freq_scale_train);
-        if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
-            LLAMA_LOG_INFO("%s: freq_base_swa         = %.1f\n",   __func__, hparams.rope_freq_base_train_swa);
-            LLAMA_LOG_INFO("%s: freq_scale_swa        = %g\n",     __func__, hparams.rope_freq_scale_train_swa);
-            LLAMA_LOG_INFO("%s: n_embd_head_k_swa     = %u\n",     __func__, hparams.n_embd_head_k_swa);
-            LLAMA_LOG_INFO("%s: n_embd_head_v_swa     = %u\n",     __func__, hparams.n_embd_head_v_swa);
-            LLAMA_LOG_INFO("%s: n_rot_swa             = %u\n",     __func__, hparams.n_rot_swa);
-        }
-        LLAMA_LOG_INFO("%s: n_ctx_orig_yarn       = %u\n",     __func__, hparams.n_ctx_orig_yarn);
-        LLAMA_LOG_INFO("%s: rope_yarn_log_mul     = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
-        LLAMA_LOG_INFO("%s: rope_finetuned        = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
-        // MRoPE (Multi-axis Rotary Position Embedding) sections
-        if (const auto & s = hparams.rope_sections; s[0] || s[1] || s[2] || s[3]) {
-            LLAMA_LOG_INFO("%s: mrope sections        = [%d, %d, %d, %d]\n", __func__, s[0], s[1], s[2], s[3]);
-        }
-        if (!classifier_labels.empty()) {
-            LLAMA_LOG_INFO("%s: n_cls_out             = %u\n", __func__, hparams.n_cls_out);
-
-            size_t i = 0;
-            for (const auto & label : classifier_labels) {
-                LLAMA_LOG_INFO("%s: cls_label[%2zu]         = %s\n", __func__, i++, label.c_str());
-            }
-        }
-
-        if (arch == LLM_ARCH_MAMBA ||
-                arch == LLM_ARCH_MAMBA2 ||
-                arch == LLM_ARCH_JAMBA ||
-                arch == LLM_ARCH_FALCON_H1 ||
-                arch == LLM_ARCH_PLAMO2 ||
-                arch == LLM_ARCH_GRANITE_HYBRID ||
-                arch == LLM_ARCH_QWEN3NEXT ||
-                arch == LLM_ARCH_QWEN35 ||
-                arch == LLM_ARCH_QWEN35MOE ||
-                arch == LLM_ARCH_NEMOTRON_H ||
-                arch == LLM_ARCH_NEMOTRON_H_MOE) {
-            LLAMA_LOG_INFO("%s: ssm_d_conv            = %u\n",     __func__, hparams.ssm_d_conv);
-            LLAMA_LOG_INFO("%s: ssm_d_inner           = %u\n",     __func__, hparams.ssm_d_inner);
-            LLAMA_LOG_INFO("%s: ssm_d_state           = %u\n",     __func__, hparams.ssm_d_state);
-            LLAMA_LOG_INFO("%s: ssm_dt_rank           = %u\n",     __func__, hparams.ssm_dt_rank);
-            LLAMA_LOG_INFO("%s: ssm_n_group           = %u\n",     __func__, hparams.ssm_n_group);
-            LLAMA_LOG_INFO("%s: ssm_dt_b_c_rms        = %d\n",     __func__, hparams.ssm_dt_b_c_rms);
-        }
-
-        LLAMA_LOG_INFO("%s: model type            = %s\n",     __func__, type_name().c_str());
-        if (pimpl->n_elements >= 1e12) {
-            LLAMA_LOG_INFO("%s: model params          = %.2f T\n", __func__, pimpl->n_elements*1e-12);
-        } else if (pimpl->n_elements >= 1e9) {
-            LLAMA_LOG_INFO("%s: model params          = %.2f B\n", __func__, pimpl->n_elements*1e-9);
-        } else if (pimpl->n_elements >= 1e6) {
-            LLAMA_LOG_INFO("%s: model params          = %.2f M\n", __func__, pimpl->n_elements*1e-6);
-        } else {
-            LLAMA_LOG_INFO("%s: model params          = %.2f K\n", __func__, pimpl->n_elements*1e-3);
-        }
-
-        // general kv
-        LLAMA_LOG_INFO("%s: general.name          = %s\n",    __func__, name.c_str());
-
-        if (arch == LLM_ARCH_DEEPSEEK) {
-            LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
-            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
-            LLAMA_LOG_INFO("%s: n_expert_shared       = %d\n",     __func__, hparams.n_expert_shared);
-            LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
-        }
-
-        if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
-            LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
-            LLAMA_LOG_INFO("%s: n_lora_q              = %d\n",     __func__, hparams.n_lora_q);
-            LLAMA_LOG_INFO("%s: n_lora_kv             = %d\n",     __func__, hparams.n_lora_kv);
-            LLAMA_LOG_INFO("%s: n_embd_head_k_mla     = %d\n",     __func__, hparams.n_embd_head_k_mla());
-            LLAMA_LOG_INFO("%s: n_embd_head_v_mla     = %d\n",     __func__, hparams.n_embd_head_v_mla());
-            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
-            LLAMA_LOG_INFO("%s: n_expert_shared       = %d\n",     __func__, hparams.n_expert_shared);
-            LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
-            LLAMA_LOG_INFO("%s: expert_weights_norm   = %d\n",     __func__, hparams.expert_weights_norm);
-            LLAMA_LOG_INFO("%s: expert_gating_func    = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
-        }
-
-        if (arch == LLM_ARCH_QWEN2MOE) {
-            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
-            LLAMA_LOG_INFO("%s: n_ff_shexp            = %d\n",     __func__, hparams.n_ff_shexp);
-        }
-
-        if (arch == LLM_ARCH_QWEN3MOE || arch == LLM_ARCH_OPENAI_MOE || arch == LLM_ARCH_QWEN3VLMOE || arch == LLM_ARCH_RND1) {
-            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
-        }
-
-        if (arch == LLM_ARCH_MINICPM ||
-                arch == LLM_ARCH_GRANITE ||
-                arch == LLM_ARCH_GRANITE_MOE ||
-                arch == LLM_ARCH_GRANITE_HYBRID ||
-                arch == LLM_ARCH_NEMOTRON_H_MOE) {
-            LLAMA_LOG_INFO("%s: f_embedding_scale     = %f\n", __func__, hparams.f_embedding_scale);
-            LLAMA_LOG_INFO("%s: f_residual_scale      = %f\n", __func__, hparams.f_residual_scale);
-            LLAMA_LOG_INFO("%s: f_attention_scale     = %f\n", __func__, hparams.f_attention_scale);
-            LLAMA_LOG_INFO("%s: n_ff_shexp            = %d\n", __func__, hparams.n_ff_shexp);
-        }
-
-        if (arch == LLM_ARCH_BAILINGMOE) {
-            LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
-            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
-            LLAMA_LOG_INFO("%s: n_expert_shared       = %d\n",     __func__, hparams.n_expert_shared);
-            LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
-            LLAMA_LOG_INFO("%s: expert_weights_norm   = %d\n",     __func__, hparams.expert_weights_norm);
-        }
-
-        if (arch == LLM_ARCH_BAILINGMOE2) {
-            LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
-            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
-            LLAMA_LOG_INFO("%s: n_ff_shexp            = %d\n",     __func__, hparams.n_ff_shexp);
-            LLAMA_LOG_INFO("%s: n_expert_shared       = %d\n",     __func__, hparams.n_expert_shared);
-            LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
-            LLAMA_LOG_INFO("%s: expert_weights_norm   = %d\n",     __func__, hparams.expert_weights_norm);
-            LLAMA_LOG_INFO("%s: expert_gating_func    = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
-            LLAMA_LOG_INFO("%s: nextn_predict_layers  = %d\n",     __func__, hparams.nextn_predict_layers);
-        }
-
-        if (arch == LLM_ARCH_SMALLTHINKER || arch == LLM_ARCH_LFM2MOE) {
-            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
-            LLAMA_LOG_INFO("%s: expert_gating_func    = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
-        }
-
-        if (arch == LLM_ARCH_GROVEMOE) {
-            LLAMA_LOG_INFO("%s: n_ff_exp              = %d\n",     __func__, hparams.n_ff_exp);
-            LLAMA_LOG_INFO("%s: n_ff_chexp            = %d\n",     __func__, hparams.n_ff_chexp);
-            LLAMA_LOG_INFO("%s: n_group_experts       = %d\n",     __func__, hparams.n_group_experts);
-            LLAMA_LOG_INFO("%s: expert_group_scale    = %.2f\n",   __func__, hparams.expert_group_scale);
-        }
-    }
-
-    vocab.print_info();
-}
-
-ggml_backend_dev_t llama_model::dev_layer(int il) const {
-    return pimpl->dev_layer.at(il).dev;
-}
-
-ggml_backend_dev_t llama_model::dev_output() const {
-    return pimpl->dev_output.dev;
-}
-
-template<typename F>
-static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
-    ggml_init_params params = {
-        /*.mem_size   =*/ ggml_tensor_overhead()*8,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
-
-    ggml_context_ptr ctx { ggml_init(params) };
-    if (!ctx) {
-        throw std::runtime_error(format("failed to create ggml context"));
-    }
-
-    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
-    ggml_tensor * op_tensor = fn(ctx.get());
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (op_tensor->src[i] != nullptr) {
-            assert(op_tensor->src[i]->buffer == nullptr);
-            op_tensor->src[i]->buffer = buf.get();
-        }
-    }
-
-    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
-
-    return op_supported;
-}
-
-template<typename F>
-static ggml_backend_buffer_type_t select_buft(const buft_list_t & buft_list, const F & fn) {
-    for (const auto & cur : buft_list) {
-        ggml_backend_dev_t cur_dev = cur.first;
-        ggml_backend_buffer_type_t cur_buft = cur.second;
-        if (buft_supported(cur_buft, cur_dev, fn)) {
-            return cur_buft;
-        }
-    }
-
-    throw std::runtime_error(format("no suitable buffer type found"));
-}
-
-ggml_backend_buffer_type_t llama_model::select_buft(int il) const {
-    return ::select_buft(
-            *pimpl->dev_layer.at(il).buft_list,
-            [&](ggml_context * ctx) {
-                ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
-                ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
-                return ggml_add(ctx, cur, layer_dir);
-            });
-}
-
-bool llama_model::has_tensor_overrides() const {
-    return pimpl->has_tensor_overrides;
-}
-
-const ggml_tensor * llama_model::get_tensor(const char * name) const {
-    auto it = std::find_if(tensors_by_name.begin(), tensors_by_name.end(),
-            [name](const std::pair<std::string, ggml_tensor *> & it) {
-                return it.first == name;
-            });
-    if (it == tensors_by_name.end()) {
-        return nullptr;
-    }
-
-    return it->second;
-}
-
-float llama_model::get_rope_freq_base (const llama_cparams & cparams, int il) const {
-    return hparams.is_swa(il) ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base;
-}
-
-float llama_model::get_rope_freq_scale(const llama_cparams & cparams, int il) const {
-    return hparams.is_swa(il) ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale;
-}
-
-ggml_tensor * llama_model::get_rope_factors(const llama_cparams & cparams, int il) const {
-    const uint32_t n_ctx_seq = cparams.n_ctx_seq;
-
-    // choose long/short freq factors based on the context size
-    if (layers[il].rope_freqs != nullptr) {
-        return layers[il].rope_freqs;
-    }
-
-    if (n_ctx_seq > hparams.n_ctx_orig_yarn) {
-        return layers[il].rope_long;
-    }
-
-    return layers[il].rope_short;
-}
-
-llama_memory_i * llama_model::create_memory(const llama_memory_params & params, const llama_cparams & cparams) const {
-    llama_memory_i * res;
-
-    switch (arch) {
-        // Models that need specific instantiation should be handled in the
-        // switch statement
-        case LLM_ARCH_BERT:
-        case LLM_ARCH_JINA_BERT_V2:
-        case LLM_ARCH_JINA_BERT_V3:
-        case LLM_ARCH_NOMIC_BERT:
-        case LLM_ARCH_NOMIC_BERT_MOE:
-        case LLM_ARCH_NEO_BERT:
-        case LLM_ARCH_EUROBERT:
-        case LLM_ARCH_WAVTOKENIZER_DEC:
-        case LLM_ARCH_MODERN_BERT:
-        case LLM_ARCH_GEMMA_EMBEDDING:
-        case LLM_ARCH_DREAM:
-        case LLM_ARCH_LLADA:
-        case LLM_ARCH_LLADA_MOE:
-        case LLM_ARCH_RND1:
-            {
-                res = nullptr;
-            } break;
-        // Models that need standard caching should rely on recurrent/hybrid
-        // checks
-        default:
-            {
-                if (llm_arch_is_recurrent(arch)) {
-                    res = new llama_memory_recurrent(
-                            *this,
-                            GGML_TYPE_F32,
-                            GGML_TYPE_F32,
-                            cparams.offload_kqv,
-                            std::max((uint32_t) 1, cparams.n_seq_max),
-                            cparams.n_seq_max,
-                            nullptr);
-                } else if (llm_arch_is_hybrid(arch)) {
-                    // The main difference between hybrid architectures is the
-                    // layer filters, so pick the right one here
-                    llama_memory_hybrid::layer_filter_cb filter_attn = nullptr;
-                    llama_memory_hybrid::layer_filter_cb filter_recr = nullptr;
-                    if (arch == LLM_ARCH_FALCON_H1) {
-                        filter_attn = [&](int32_t) { return true; };
-                        filter_recr = [&](int32_t) { return true; };
-                    } else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) {
-                        filter_attn = [&](int32_t il) {
-                            return !hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
-                        };
-                        filter_recr = [&](int32_t il) {
-                            return hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
-                        };
-                    }
-
-                    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
-                        // Use hybrid-iswa for hybrid models with SWA
-                        res = new llama_memory_hybrid_iswa(
-                            /* model             */ *this,
-                            /* attn_type_k       */ params.type_k,
-                            /* attn_type_v       */ params.type_v,
-                            /* attn_v_trans      */ !cparams.flash_attn,
-                            /* attn_swa_full     */ params.swa_full,
-                            /* attn_kv_size      */ cparams.n_ctx_seq,
-                            /* attn_n_ubatch     */ cparams.n_ubatch,
-                            /* attn_n_pad        */ 1,
-                            /* recurrent_type_r  */ GGML_TYPE_F32,
-                            /* recurrent_type_s  */ GGML_TYPE_F32,
-                            /* recurrent_rs_size */ std::max((uint32_t) 1, cparams.n_seq_max),
-                            /* n_seq_max         */ cparams.n_seq_max,
-                            /* offload           */ cparams.offload_kqv,
-                            /* unified           */ cparams.kv_unified,
-                            /* filter_attn       */ std::move(filter_attn),
-                            /* filter_recr       */ std::move(filter_recr));
-                    } else {
-                        res = new llama_memory_hybrid(
-                            /* model             */ *this,
-                            /* attn_type_k       */ params.type_k,
-                            /* attn_type_v       */ params.type_v,
-                            /* attn_v_trans      */ !cparams.flash_attn,
-                            /* attn_kv_size      */ cparams.n_ctx_seq,
-                            /* attn_n_pad        */ 1,
-                            /* attn_n_swa        */ hparams.n_swa,
-                            /* attn_swa_type     */ hparams.swa_type,
-                            /* recurrent_type_k  */ GGML_TYPE_F32,
-                            /* recurrent_type_v  */ GGML_TYPE_F32,
-                            /* recurrent_kv_size */ std::max((uint32_t) 1, cparams.n_seq_max),
-                            /* n_seq_max         */ cparams.n_seq_max,
-                            /* offload           */ cparams.offload_kqv,
-                            /* unified           */ cparams.kv_unified,
-                            /* filter_attn       */ std::move(filter_attn),
-                            /* filter_recr       */ std::move(filter_recr));
-                    }
-                } else {
-                    llama_memory_i::layer_reuse_cb reuse = nullptr;
-
-                    if (arch == LLM_ARCH_GEMMA3N || arch == LLM_ARCH_GEMMA4) {
-                        reuse = [&](int32_t il) {
-                            if (il >= (int32_t) hparams.n_layer_kv_from_start) {
-                                return (int32_t) hparams.n_layer_kv_from_start - (hparams.is_swa(il) ? 2 : 1);
-                            }
-
-                            return -1;
-                        };
-                    }
-
-                    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
-                        GGML_ASSERT(hparams.is_swa_any());
-
-                        res = new llama_kv_cache_iswa(
-                                *this,
-                                params.type_k,
-                                params.type_v,
-                                !cparams.flash_attn,
-                                cparams.offload_kqv,
-                                params.swa_full,
-                                cparams.kv_unified,
-                                cparams.n_ctx_seq,
-                                cparams.n_seq_max,
-                                cparams.n_ubatch,
-                                1,
-                                nullptr,
-                                reuse);
-                    } else {
-                        GGML_ASSERT(!hparams.is_swa_any());
-
-                        res = new llama_kv_cache(
-                                *this,
-                                params.type_k,
-                                params.type_v,
-                                !cparams.flash_attn,
-                                cparams.offload_kqv,
-                                cparams.kv_unified,
-                                cparams.n_ctx_seq,
-                                cparams.n_seq_max,
-                                1,
-                                hparams.n_swa,
-                                hparams.swa_type,
-                                nullptr,
-                                nullptr);
-                    }
-                }
-            }
-    }
-
-    return res;
-}
-
-ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
-    std::unique_ptr<llm_graph_context> llm;
-
-    switch (arch) {
-        case LLM_ARCH_LLAMA:
-            {
-                llm = std::make_unique<llm_build_llama<false>>(*this, params);
-            } break;
-        case LLM_ARCH_LLAMA4:
-            {
-                if (hparams.swa_type == LLAMA_SWA_TYPE_NONE) {
-                    llm = std::make_unique<llm_build_llama4<false>>(*this, params);
-                } else {
-                    llm = std::make_unique<llm_build_llama4<true>>(*this, params);
-                }
-            } break;
-        case LLM_ARCH_LLAMA_EMBED:
-            {
-                llm = std::make_unique<llm_build_llama<true>>(*this, params);
-            } break;
-        case LLM_ARCH_MAINCODER:
-            {
-                llm = std::make_unique<llm_build_maincoder>(*this, params);
-            } break;
-        case LLM_ARCH_DECI:
-            {
-                llm = std::make_unique<llm_build_deci>(*this, params);
-            } break;
-        case LLM_ARCH_BAICHUAN:
-            {
-                llm = std::make_unique<llm_build_baichuan>(*this, params);
-            } break;
-        case LLM_ARCH_FALCON:
-            {
-                llm = std::make_unique<llm_build_falcon>(*this, params);
-            } break;
-        case LLM_ARCH_GROK:
-            {
-                llm = std::make_unique<llm_build_grok>(*this, params);
-            } break;
-        case LLM_ARCH_STARCODER:
-            {
-                llm = std::make_unique<llm_build_starcoder>(*this, params);
-            } break;
-        case LLM_ARCH_REFACT:
-            {
-                llm = std::make_unique<llm_build_refact>(*this, params);
-            } break;
-        case LLM_ARCH_BERT:
-        case LLM_ARCH_JINA_BERT_V2:
-        case LLM_ARCH_JINA_BERT_V3:
-        case LLM_ARCH_NOMIC_BERT:
-        case LLM_ARCH_NOMIC_BERT_MOE:
-            {
-                llm = std::make_unique<llm_build_bert>(*this, params);
-            } break;
-        case LLM_ARCH_MODERN_BERT:
-            {
-                llm = std::make_unique<llm_build_modern_bert>(*this, params);
-            } break;
-        case LLM_ARCH_NEO_BERT:
-            {
-                llm = std::make_unique<llm_build_neo_bert>(*this, params);
-            } break;
-        case LLM_ARCH_EUROBERT:
-            {
-                llm = std::make_unique<llm_build_eurobert>(*this, params);
-            } break;
-        case LLM_ARCH_BLOOM:
-            {
-                llm = std::make_unique<llm_build_bloom>(*this, params);
-            } break;
-        case LLM_ARCH_MPT:
-            {
-                llm = std::make_unique<llm_build_mpt>(*this, params);
-            } break;
-        case LLM_ARCH_STABLELM:
-            {
-                llm = std::make_unique<llm_build_stablelm>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN:
-            {
-                llm = std::make_unique<llm_build_qwen>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN2:
-            {
-                llm = std::make_unique<llm_build_qwen2>(*this, params);
-            } break;
-        case LLM_ARCH_DREAM:
-            {
-                llm = std::make_unique<llm_build_dream>(*this, params);
-            } break;
-        case LLM_ARCH_LLADA:
-            {
-                llm = std::make_unique<llm_build_llada>(*this, params);
-            } break;
-        case LLM_ARCH_LLADA_MOE:
-            {
-                llm = std::make_unique<llm_build_llada_moe>(*this, params);
-            } break;
-        case LLM_ARCH_RND1:
-            {
-                llm = std::make_unique<llm_build_rnd1>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN2VL:
-            {
-                llm = std::make_unique<llm_build_qwen2vl>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN2MOE:
-            {
-                llm = std::make_unique<llm_build_qwen2moe>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN3:
-            {
-                llm = std::make_unique<llm_build_qwen3>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN3MOE:
-            {
-                llm = std::make_unique<llm_build_qwen3moe>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN3VL:
-            {
-                llm = std::make_unique<llm_build_qwen3vl>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN3VLMOE:
-            {
-                llm = std::make_unique<llm_build_qwen3vlmoe>(*this, params);
-            } break;
-        case LLM_ARCH_PHI2:
-            {
-                llm = std::make_unique<llm_build_phi2>(*this, params);
-            } break;
-        case LLM_ARCH_PHI3:
-        case LLM_ARCH_PHIMOE:
-            {
-                if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
-                    llm = std::make_unique<llm_build_phi3<true>> (*this, params);
-                } else {
-                    llm = std::make_unique<llm_build_phi3<false>>(*this, params);
-                }
-            } break;
-        case LLM_ARCH_PLAMO:
-            {
-                llm = std::make_unique<llm_build_plamo>(*this, params);
-            } break;
-        case LLM_ARCH_PLAMO2:
-            {
-                llm = std::make_unique<llm_build_plamo2>(*this, params);
-            } break;
-        case LLM_ARCH_PLAMO3:
-            {
-                if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
-                    llm = std::make_unique<llm_build_plamo3<true>> (*this, params);
-                } else {
-                    llm = std::make_unique<llm_build_plamo3<false>>(*this, params);
-                }
-            } break;
-        case LLM_ARCH_GPT2:
-            {
-                llm = std::make_unique<llm_build_gpt2>(*this, params);
-            } break;
-        case LLM_ARCH_CODESHELL:
-            {
-                llm = std::make_unique<llm_build_codeshell>(*this, params);
-            } break;
-        case LLM_ARCH_ORION:
-            {
-                llm = std::make_unique<llm_build_orion>(*this, params);
-            } break;
-        case LLM_ARCH_INTERNLM2:
-            {
-                llm = std::make_unique<llm_build_internlm2>(*this, params);
-            } break;
-        case LLM_ARCH_MINICPM3:
-            {
-                llm = std::make_unique<llm_build_minicpm3>(*this, params);
-            } break;
-        case LLM_ARCH_GEMMA:
-            {
-                llm = std::make_unique<llm_build_gemma>(*this, params);
-            } break;
-        case LLM_ARCH_GEMMA2:
-            {
-                llm = std::make_unique<llm_build_gemma2_iswa>(*this, params);
-            } break;
-        case LLM_ARCH_GEMMA3:
-            {
-                if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
-                    llm = std::make_unique<llm_build_gemma3<true>>(*this, params);
-                } else {
-                    llm = std::make_unique<llm_build_gemma3<false>>(*this, params);
-                }
-            } break;
-        case LLM_ARCH_GEMMA3N:
-            {
-                llm = std::make_unique<llm_build_gemma3n_iswa>(*this, params);
-            } break;
-        case LLM_ARCH_GEMMA4:
-            {
-                llm = std::make_unique<llm_build_gemma4_iswa>(*this, params);
-            } break;
-        case LLM_ARCH_GEMMA_EMBEDDING:
-            {
-                llm = std::make_unique<llm_build_gemma_embedding>(*this, params);
-            } break;
-        case LLM_ARCH_STARCODER2:
-            {
-                llm = std::make_unique<llm_build_starcoder2>(*this, params);
-            } break;
-        case LLM_ARCH_MAMBA:
-        case LLM_ARCH_MAMBA2:
-            {
-                llm = std::make_unique<llm_build_mamba>(*this, params);
-            } break;
-        case LLM_ARCH_JAMBA:
-            {
-                llm = std::make_unique<llm_build_jamba>(*this, params);
-            } break;
-        case LLM_ARCH_XVERSE:
-            {
-                llm = std::make_unique<llm_build_xverse>(*this, params);
-            } break;
-        case LLM_ARCH_COMMAND_R:
-            {
-                llm = std::make_unique<llm_build_command_r>(*this, params);
-            } break;
-        case LLM_ARCH_COHERE2:
-            {
-                llm = std::make_unique<llm_build_cohere2_iswa>(*this, params);
-            } break;
-        case LLM_ARCH_DBRX:
-            {
-                llm = std::make_unique<llm_build_dbrx>(*this, params);
-            } break;
-        case LLM_ARCH_OLMO:
-            {
-                llm = std::make_unique<llm_build_olmo>(*this, params);
-            } break;
-        case LLM_ARCH_OLMO2:
-            {
-                if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
-                    llm = std::make_unique<llm_build_olmo2<true>>(*this, params);
-                } else {
-                    llm = std::make_unique<llm_build_olmo2<false>>(*this, params);
-                }
-            } break;
-        case LLM_ARCH_OLMOE:
-            {
-                llm = std::make_unique<llm_build_olmoe>(*this, params);
-            } break;
-        case LLM_ARCH_OPENELM:
-            {
-                llm = std::make_unique<llm_build_openelm>(*this, params);
-            } break;
-        case LLM_ARCH_GPTNEOX:
-            {
-                llm = std::make_unique<llm_build_gptneox>(*this, params);
-            } break;
-        case LLM_ARCH_ARCTIC:
-            {
-                llm = std::make_unique<llm_build_arctic>(*this, params);
-            } break;
-        case LLM_ARCH_DEEPSEEK:
-            {
-                llm = std::make_unique<llm_build_deepseek>(*this, params);
-            } break;
-        case LLM_ARCH_DEEPSEEK2:
-        case LLM_ARCH_DEEPSEEK2OCR:
-        case LLM_ARCH_GLM_DSA:
-        case LLM_ARCH_MISTRAL4:
-            {
-                llm = std::make_unique<llm_build_deepseek2>(*this, params);
-            } break;
-        case LLM_ARCH_CHATGLM:
-            {
-                llm = std::make_unique<llm_build_chatglm>(*this, params);
-            } break;
-        case LLM_ARCH_GLM4:
-            {
-                llm = std::make_unique<llm_build_glm4>(*this, params);
-            } break;
-        case LLM_ARCH_GLM4_MOE:
-            {
-                llm = std::make_unique<llm_build_glm4_moe>(*this, params);
-            } break;
-        case LLM_ARCH_BITNET:
-            {
-                llm = std::make_unique<llm_build_bitnet>(*this, params);
-            } break;
-        case LLM_ARCH_T5:
-            {
-                switch (params.gtype) {
-                    case LLM_GRAPH_TYPE_ENCODER:
-                        llm = std::make_unique<llm_build_t5<true>>(*this, params);
-                        break;
-                    case LLM_GRAPH_TYPE_DEFAULT:
-                    case LLM_GRAPH_TYPE_DECODER:
-                        llm = std::make_unique<llm_build_t5<false>>(*this, params);
-                        break;
-                    default:
-                        GGML_ABORT("invalid graph type");
-                };
-            } break;
-        case LLM_ARCH_T5ENCODER:
-            {
-                llm = std::make_unique<llm_build_t5encoder>(*this, params);
-            } break;
-        case LLM_ARCH_JAIS:
-            {
-                llm = std::make_unique<llm_build_jais>(*this, params);
-            } break;
-        case LLM_ARCH_JAIS2:
-            {
-                llm = std::make_unique<llm_build_jais2>(*this, params);
-            } break;
-        case LLM_ARCH_NEMOTRON:
-            {
-                llm = std::make_unique<llm_build_nemotron>(*this, params);
-            } break;
-        case LLM_ARCH_NEMOTRON_H:
-        case LLM_ARCH_NEMOTRON_H_MOE:
-            {
-                llm = std::make_unique<llm_build_nemotron_h>(*this, params);
-            } break;
-        case LLM_ARCH_EXAONE:
-            {
-                llm = std::make_unique<llm_build_exaone>(*this, params);
-            } break;
-        case LLM_ARCH_EXAONE4:
-            {
-                if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
-                    llm = std::make_unique<llm_build_exaone4<true>>(*this, params);
-                } else {
-                    llm = std::make_unique<llm_build_exaone4<false>>(*this, params);
-                }
-            } break;
-        case LLM_ARCH_EXAONE_MOE:
-            {
-                llm = std::make_unique<llm_build_exaone_moe>(*this, params);
-            } break;
-        case LLM_ARCH_RWKV6:
-            {
-                llm = std::make_unique<llm_build_rwkv6>(*this, params);
-            } break;
-        case LLM_ARCH_RWKV6QWEN2:
-            {
-                llm = std::make_unique<llm_build_rwkv6qwen2>(*this, params);
-            } break;
-        case LLM_ARCH_RWKV7:
-            {
-                llm = std::make_unique<llm_build_rwkv7>(*this, params);
-            } break;
-        case LLM_ARCH_ARWKV7:
-            {
-                llm = std::make_unique<llm_build_arwkv7>(*this, params);
-            } break;
-        case LLM_ARCH_GRANITE:
-        case LLM_ARCH_GRANITE_MOE:
-        case LLM_ARCH_MINICPM:
-            {
-                llm = std::make_unique<llm_build_granite>(*this, params);
-            } break;
-        case LLM_ARCH_GRANITE_HYBRID:
-            {
-                llm = std::make_unique<llm_build_granite_hybrid>(*this, params);
-            } break;
-        case LLM_ARCH_CHAMELEON:
-            {
-                llm = std::make_unique<llm_build_chameleon>(*this, params);
-            } break;
+
+    if (n_ctx_seq > hparams.n_ctx_orig_yarn) {
+        return layers[il].rope_long;
+    }
+
+    return layers[il].rope_short;
+}
+
+llama_memory_i * llama_model::create_memory(const llama_memory_params & params, const llama_cparams & cparams) const {
+    llama_memory_i * res;
+
+    switch (arch) {
+        // Models that need specific instantiation should be handled in the
+        // switch statement
+        case LLM_ARCH_BERT:
+        case LLM_ARCH_JINA_BERT_V2:
+        case LLM_ARCH_JINA_BERT_V3:
+        case LLM_ARCH_NOMIC_BERT:
+        case LLM_ARCH_NOMIC_BERT_MOE:
+        case LLM_ARCH_NEO_BERT:
+        case LLM_ARCH_EUROBERT:
         case LLM_ARCH_WAVTOKENIZER_DEC:
+        case LLM_ARCH_MODERN_BERT:
+        case LLM_ARCH_GEMMA_EMBEDDING:
+        case LLM_ARCH_DREAM:
+        case LLM_ARCH_LLADA:
+        case LLM_ARCH_LLADA_MOE:
+        case LLM_ARCH_RND1:
             {
-                llm = std::make_unique<llm_build_wavtokenizer_dec>(*this, params);
-            } break;
-        case LLM_ARCH_PLM:
-            {
-                llm = std::make_unique<llm_build_plm>(*this, params);
-            } break;
-        case LLM_ARCH_BAILINGMOE:
-            {
-                llm = std::make_unique<llm_build_bailingmoe>(*this, params);
-            } break;
-        case LLM_ARCH_BAILINGMOE2:
-            {
-                llm = std::make_unique<llm_build_bailingmoe2>(*this, params);
-            } break;
-        case LLM_ARCH_SEED_OSS:
-            {
-                llm = std::make_unique<llm_build_seed_oss>(*this, params);
-            } break;
-        case LLM_ARCH_DOTS1:
-            {
-                llm = std::make_unique<llm_build_dots1>(*this, params);
-            } break;
-        case LLM_ARCH_ARCEE:
-            {
-                llm = std::make_unique<llm_build_arcee>(*this, params);
-            } break;
-        case LLM_ARCH_AFMOE:
-            {
-                llm = std::make_unique<llm_build_afmoe>(*this, params);
-            } break;
-        case LLM_ARCH_ERNIE4_5:
-            {
-                llm = std::make_unique<llm_build_ernie4_5>(*this, params);
-            } break;
-        case LLM_ARCH_ERNIE4_5_MOE:
-            {
-                llm = std::make_unique<llm_build_ernie4_5_moe>(*this, params);
-            } break;
-        case LLM_ARCH_PADDLEOCR:
-            {
-                llm = std::make_unique<llm_build_paddleocr>(*this, params);
-            } break;
-        case LLM_ARCH_HUNYUAN_MOE:
-            {
-                llm = std::make_unique<llm_build_hunyuan_moe>(*this, params);
-            } break;
-        case LLM_ARCH_HUNYUAN_DENSE:
-            {
-                llm = std::make_unique<llm_build_hunyuan_dense>(*this, params);
-            } break;
-        case LLM_ARCH_SMOLLM3:
-            {
-                llm = std::make_unique<llm_build_smollm3>(*this, params);
-            } break;
-        case LLM_ARCH_OPENAI_MOE:
-            {
-                llm = std::make_unique<llm_build_openai_moe_iswa>(*this, params);
-            } break;
-        case LLM_ARCH_FALCON_H1:
-            {
-                llm = std::make_unique<llm_build_falcon_h1>(*this, params);
-            } break;
-        case LLM_ARCH_LFM2:
-        case LLM_ARCH_LFM2MOE:
-            {
-                if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
-                    llm = std::make_unique<llm_build_lfm2<true>>(*this, params);
-                } else {
-                    llm = std::make_unique<llm_build_lfm2<false>>(*this, params);
-                }
+                res = nullptr;
             } break;
-        case LLM_ARCH_SMALLTHINKER:
+        // Models that need standard caching should rely on recurrent/hybrid
+        // checks
+        default:
             {
-                if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
-                    llm = std::make_unique<llm_build_smallthinker<true>> (*this, params);
+                // The MTP head is dense-attention only on hybrid Qwen3.5/3.6, so use a plain
+                // attention KV cache for the MTP context instead of the hybrid wrapper.
+                const bool mtp_on_hybrid_qwen35 =
+                    params.ctx_type == LLAMA_CONTEXT_TYPE_MTP &&
+                    (arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE);
+
+                if (llm_arch_is_recurrent(arch)) {
+                    res = new llama_memory_recurrent(
+                            *this,
+                            GGML_TYPE_F32,
+                            GGML_TYPE_F32,
+                            cparams.offload_kqv,
+                            std::max((uint32_t) 1, cparams.n_seq_max),
+                            cparams.n_seq_max,
+                            cparams.n_rs_seq,
+                            nullptr);
+                } else if (llm_arch_is_hybrid(arch) && !mtp_on_hybrid_qwen35) {
+                    // The main difference between hybrid architectures is the
+                    // layer filters, so pick the right one here
+                    llama_memory_hybrid::layer_filter_cb filter_attn = nullptr;
+                    llama_memory_hybrid::layer_filter_cb filter_recr = nullptr;
+                    if (arch == LLM_ARCH_FALCON_H1) {
+                        filter_attn = [&](int32_t) { return true; };
+                        filter_recr = [&](int32_t) { return true; };
+                    } else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) {
+                        filter_attn = [&](int32_t il) {
+                            return !hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
+                        };
+                        filter_recr = [&](int32_t il) {
+                            return hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
+                        };
+                    } else if (arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE) {
+                        const uint32_t n_main = hparams.n_layer - hparams.nextn_predict_layers;
+                        filter_attn = [&, n_main](int32_t il) {
+                            return (uint32_t)il < n_main && !hparams.is_recurrent(il);
+                        };
+                        filter_recr = [&, n_main](int32_t il) {
+                            return (uint32_t)il < n_main && hparams.is_recurrent(il);
+                        };
+                    }
+
+                    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
+                        // Use hybrid-iswa for hybrid models with SWA
+                        res = new llama_memory_hybrid_iswa(
+                            /* model             */ *this,
+                            /* attn_type_k       */ params.type_k,
+                            /* attn_type_v       */ params.type_v,
+                            /* attn_v_trans      */ !cparams.flash_attn,
+                            /* attn_swa_full     */ params.swa_full,
+                            /* attn_kv_size      */ cparams.n_ctx_seq,
+                            /* attn_n_ubatch     */ cparams.n_ubatch,
+                            /* attn_n_pad        */ 1,
+                            /* recurrent_type_r  */ GGML_TYPE_F32,
+                            /* recurrent_type_s  */ GGML_TYPE_F32,
+                            /* recurrent_rs_size */ std::max((uint32_t) 1, cparams.n_seq_max),
+                            /* n_seq_max         */ cparams.n_seq_max,
+                            /* n_rs_seq          */ cparams.n_rs_seq,
+                            /* offload           */ cparams.offload_kqv,
+                            /* unified           */ cparams.kv_unified,
+                            /* filter_attn       */ std::move(filter_attn),
+                            /* filter_recr       */ std::move(filter_recr));
+                    } else {
+                        res = new llama_memory_hybrid(
+                            /* model             */ *this,
+                            /* attn_type_k       */ params.type_k,
+                            /* attn_type_v       */ params.type_v,
+                            /* attn_v_trans      */ !cparams.flash_attn,
+                            /* attn_kv_size      */ cparams.n_ctx_seq,
+                            /* attn_n_pad        */ 1,
+                            /* attn_n_swa        */ hparams.n_swa,
+                            /* attn_swa_type     */ hparams.swa_type,
+                            /* recurrent_type_k  */ GGML_TYPE_F32,
+                            /* recurrent_type_v  */ GGML_TYPE_F32,
+                            /* recurrent_kv_size */ std::max((uint32_t) 1, cparams.n_seq_max),
+                            /* n_seq_max         */ cparams.n_seq_max,
+                            /* n_rs_seq          */ cparams.n_rs_seq,
+                            /* offload           */ cparams.offload_kqv,
+                            /* unified           */ cparams.kv_unified,
+                            /* filter_attn       */ std::move(filter_attn),
+                            /* filter_recr       */ std::move(filter_recr));
+                    }
                 } else {
-                    llm = std::make_unique<llm_build_smallthinker<false>>(*this, params);
+                    llama_memory_i::layer_reuse_cb reuse = nullptr;
+                    llama_kv_cache::layer_filter_cb filter = nullptr;
+
+                    if (arch == LLM_ARCH_GEMMA3N || arch == LLM_ARCH_GEMMA4) {
+                        reuse = [&](int32_t il) {
+                            if (il >= (int32_t) hparams.n_layer_kv_from_start) {
+                                return (int32_t) hparams.n_layer_kv_from_start - (hparams.is_swa(il) ? 2 : 1);
+                            }
+
+                            return -1;
+                        };
+                    }
+
+                    if (mtp_on_hybrid_qwen35) {
+                        const uint32_t n_main = hparams.n_layer - hparams.nextn_predict_layers;
+                        filter = [n_main](int32_t il) { return (uint32_t)il >= n_main; };
+                    }
+
+                    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
+                        GGML_ASSERT(hparams.is_swa_any());
+
+                        res = new llama_kv_cache_iswa(
+                                *this,
+                                params.type_k,
+                                params.type_v,
+                                !cparams.flash_attn,
+                                cparams.offload_kqv,
+                                params.swa_full,
+                                cparams.kv_unified,
+                                cparams.n_ctx_seq,
+                                cparams.n_seq_max,
+                                cparams.n_ubatch,
+                                1,
+                                filter,
+                                reuse);
+                    } else {
+                        GGML_ASSERT(!hparams.is_swa_any());
+
+                        res = new llama_kv_cache(
+                                *this,
+                                params.type_k,
+                                params.type_v,
+                                !cparams.flash_attn,
+                                cparams.offload_kqv,
+                                cparams.kv_unified,
+                                cparams.n_ctx_seq,
+                                cparams.n_seq_max,
+                                1,
+                                hparams.n_swa,
+                                hparams.swa_type,
+                                filter,
+                                nullptr);
+                    }
                 }
-            } break;
-        case LLM_ARCH_GROVEMOE:
-            {
-                llm = std::make_unique<llm_build_grovemoe>(*this, params);
-            } break;
-        case LLM_ARCH_APERTUS:
-            {
-                llm = std::make_unique<llm_build_apertus>(*this, params);
-            } break;
-        case LLM_ARCH_MINIMAX_M2:
-            {
-                llm = std::make_unique<llm_build_minimax_m2>(*this, params);
-            } break;
-        case LLM_ARCH_COGVLM:
-            {
-                llm = std::make_unique<llm_build_cogvlm>(*this, params);
-            } break;
-        case LLM_ARCH_PANGU_EMBED:
-            {
-                llm = std::make_unique<llm_build_pangu_embedded>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN3NEXT:
-            {
-                llm = std::make_unique<llm_build_qwen3next>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN35:
-            {
-                llm = std::make_unique<llm_build_qwen35>(*this, params);
-            } break;
-        case LLM_ARCH_QWEN35MOE:
-            {
-                llm = std::make_unique<llm_build_qwen35moe>(*this, params);
-            } break;
-        case LLM_ARCH_MISTRAL3:
-            {
-                llm = std::make_unique<llm_build_mistral3>(*this, params);
-            } break;
-        case LLM_ARCH_MIMO2:
-            {
-                llm = std::make_unique<llm_build_mimo2_iswa>(*this, params);
-            } break;
-        case LLM_ARCH_KIMI_LINEAR:
-            {
-                llm = std::make_unique<llm_build_kimi_linear>(*this, params);
-            } break;
-        case LLM_ARCH_STEP35:
-            {
-                llm = std::make_unique<llm_build_step35_iswa>(*this, params);
-            } break;
-        default:
-            GGML_ABORT("fatal error");
+            }
     }
 
+    return res;
+}
+
+ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
+    std::unique_ptr<llm_graph_context> llm = build_arch_graph(params);
+
     // add on pooling layer
     llm->build_pooling(cls, cls_b, cls_out, cls_out_b, cls_norm);
 
@@ -9142,6 +2182,7 @@ int32_t llama_model_n_swa(const llama_model * model) {
     return model->hparams.n_swa;
 }
 
+
 uint32_t llama_model_n_cls_out(const struct llama_model * model) {
     return model->hparams.n_cls_out;
 }
@@ -9316,6 +2357,9 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_GLM4_MOE:
             return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NEOX;
 
+        case LLM_ARCH_HUNYUAN_VL:
+            return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NEOX;
+
         // all model arches should be listed explicitly here
         case LLM_ARCH_UNKNOWN:
             GGML_ABORT("unknown architecture");
@@ -9465,3 +2509,43 @@ ggml_backend_dev_t llama_model_get_device(const struct llama_model * model, int
     }
     return model->devices[i].dev;
 }
+
+//
+// llama_model_base
+//
+
+llama_model_base::llama_model_base(const struct llama_model_params & params) : llama_model(params), model(this), tn(model->arch),
+    TENSOR_DUPLICATED     (llama_model_loader::TENSOR_DUPLICATED),
+    TENSOR_NOT_REQUIRED   (llama_model_loader::TENSOR_NOT_REQUIRED),
+    TENSOR_SKIP           (llama_model_loader::TENSOR_SKIP),
+    TENSOR_SKIP_IF_VIRTUAL(llama_model_loader::TENSOR_SKIP_IF_VIRTUAL) {}
+
+ggml_tensor * llama_model_base::create_tensor(const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) {
+    GGML_ASSERT(ml != nullptr);
+    return create_tensor(*ml, tn, ne, flags);
+}
+
+void llama_model_base::create_tensor_gate_up_exps(llama_layer & layer, int bid, int64_t n_embd_, int64_t n_ff_, int64_t n_expert_, int flags) {
+    layer.ffn_gate_up_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS, "weight", bid), {n_embd_, n_ff_ * 2, n_expert_}, TENSOR_NOT_REQUIRED);
+    if (layer.ffn_gate_up_exps == nullptr) {
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", bid), {n_embd_, n_ff_, n_expert_}, flags);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", bid), {n_embd_, n_ff_, n_expert_}, flags);
+    }
+}
+
+void llama_model_base::create_tensor_qkv(llama_layer & layer, int bid,
+        int64_t n_embd_, int64_t n_embd_q_, int64_t n_embd_k_, int64_t n_embd_v_,
+        int flags) {
+    const int64_t n_embd_qkv = n_embd_q_ + n_embd_k_ + n_embd_v_;
+    layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", bid), {n_embd_, n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
+    if (layer.wqkv) {
+        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", bid), {n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
+    } else {
+        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", bid), {n_embd_, n_embd_q_}, flags);
+        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", bid), {n_embd_, n_embd_k_}, flags);
+        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", bid), {n_embd_, n_embd_v_}, flags);
+        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", bid), {n_embd_q_}, TENSOR_NOT_REQUIRED);
+        layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", bid), {n_embd_k_}, TENSOR_NOT_REQUIRED);
+        layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", bid), {n_embd_v_}, TENSOR_NOT_REQUIRED);
+    }
+}
diff --git a/src/llama-model.h b/src/llama-model.h
index 5f101bd6374..01c87a75271 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -533,6 +533,11 @@ struct llama_model {
     struct ggml_tensor * output_b        = nullptr;
     struct ggml_tensor * output_norm_enc = nullptr;
 
+
+    // NVFP4 per-tensor scale2, input_scale for LM head
+    struct ggml_tensor * output_s    = nullptr;
+    struct ggml_tensor * output_in_s = nullptr;
+
     // classifier
     struct ggml_tensor * cls       = nullptr;
     struct ggml_tensor * cls_b     = nullptr;
@@ -577,14 +582,8 @@ struct llama_model {
     int64_t t_load_us  = 0;
     int64_t t_start_us = 0;
 
-    explicit llama_model(const struct llama_model_params & params);
-    ~llama_model();
-
-    void load_stats  (llama_model_loader & ml);
-    void load_arch   (llama_model_loader & ml);
-    void load_hparams(llama_model_loader & ml);
-    void load_vocab  (llama_model_loader & ml);
-    bool load_tensors(llama_model_loader & ml); // returns false if cancelled by progress_callback
+    explicit llama_model(const llama_model_params & params);
+    virtual ~llama_model();
 
     std::string arch_name() const;
     std::string type_name() const;
@@ -620,21 +619,94 @@ struct llama_model {
 
     ggml_tensor * get_rope_factors(const llama_cparams & cparams, int il) const;
 
-    // TODO: move this to new llm_arch_model_i interface
     llama_memory_i * create_memory(const llama_memory_params & params, const llama_cparams & cparams) const;
 
-    // TODO: move this to new llm_arch_model_i interface
     ggml_cgraph * build_graph(const llm_graph_params & params) const;
 
-private:
+    virtual void load_stats  (llama_model_loader & ml) = 0;
+    virtual void load_hparams(llama_model_loader & ml) = 0;
+    virtual void load_vocab  (llama_model_loader & ml) = 0;
+    virtual bool load_tensors(llama_model_loader & ml) = 0; // returns false if cancelled by progress_callback
+
+    // model must define these
+    virtual void load_arch_hparams(llama_model_loader & ml) = 0;
+    virtual void load_arch_tensors(llama_model_loader & ml) = 0;
+    virtual std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const = 0;
+
+protected:
     llama_model_params params;
 
     struct impl;
     std::unique_ptr<impl> pimpl;
 };
 
+llama_model * llama_model_create(llm_arch arch, const llama_model_params & params);
+llama_model * llama_model_create(llama_model_loader & ml, const llama_model_params & params);
+
+// model must inherit from this
+struct llama_model_base : public llama_model {
+    friend struct llama_model;
+
+    llama_model * model;
+    llama_model_loader * ml = nullptr;
+    const LLM_TN tn;
+
+    // llama_model_loader is not yet defined at this point, so we will set it after construction
+    const int TENSOR_DUPLICATED;
+    const int TENSOR_NOT_REQUIRED;
+    const int TENSOR_SKIP;
+    const int TENSOR_SKIP_IF_VIRTUAL;
+
+    explicit llama_model_base(const llama_model_params & params);
+    virtual ~llama_model_base() = default;
+
+    ggml_tensor * create_tensor(llama_model_loader & ml, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags);
+
+    // convenience overload of create_tensor that doesn't require llama_model_loader
+    ggml_tensor * create_tensor(const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags);
+
+    // helper: try merged gate_up_exps first, fall back to separate gate and up
+    void create_tensor_gate_up_exps(llama_layer & layer, int bid, int64_t n_embd_,
+                int64_t n_ff_, int64_t n_expert_, int flags);
+
+    // helper: try to load merged qkv first, fall back to separate q, k, v
+    void create_tensor_qkv(llama_layer & layer, int bid,
+                int64_t n_embd_, int64_t n_embd_q_, int64_t n_embd_k_, int64_t n_embd_v_,
+                int flags);
+
+    void load_stats  (llama_model_loader & ml) override;
+    void load_hparams(llama_model_loader & ml) override;
+    void load_vocab  (llama_model_loader & ml) override;
+    bool load_tensors(llama_model_loader & ml) override;
+
+    // model must define these
+    void load_arch_hparams(llama_model_loader & ml) override = 0;
+    void load_arch_tensors(llama_model_loader & ml) override = 0;
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override = 0;
+};
+
 const char * llm_type_name(llm_type type);
 
+// convenience macro for loading local variables for load_tensors() in llama_model_base
+// note: cast to int64_t since we will use these for the tensor dimensions
+#define LLAMA_LOAD_LOCALS \
+    const int     n_layer        = hparams.n_layer;          GGML_UNUSED(n_layer); \
+    const int64_t n_head         = hparams.n_head();         GGML_UNUSED(n_head); \
+    const int64_t n_head_kv      = hparams.n_head_kv();      GGML_UNUSED(n_head_kv); \
+    const int64_t n_embd         = hparams.n_embd;           GGML_UNUSED(n_embd); \
+    const int64_t n_embd_k_gqa   = hparams.n_embd_k_gqa();   GGML_UNUSED(n_embd_k_gqa); \
+    const int64_t n_embd_v_gqa   = hparams.n_embd_v_gqa();   GGML_UNUSED(n_embd_v_gqa); \
+    const int64_t n_embd_head_k  = hparams.n_embd_head_k();  GGML_UNUSED(n_embd_head_k); \
+    const int64_t n_embd_head_v  = hparams.n_embd_head_v();  GGML_UNUSED(n_embd_head_v); \
+    const int64_t n_ff           = hparams.n_ff();           GGML_UNUSED(n_ff); \
+    const int64_t n_embd_gqa     = n_embd_v_gqa;             GGML_UNUSED(n_embd_gqa); \
+    const int64_t n_vocab        = vocab.n_tokens();         GGML_UNUSED(n_vocab); \
+    const int64_t n_token_types  = vocab.n_token_types();    GGML_UNUSED(n_token_types); \
+    const int64_t n_rot          = hparams.n_rot();          GGML_UNUSED(n_rot); \
+    const int64_t n_expert       = hparams.n_expert;         GGML_UNUSED(n_expert); \
+    const int64_t n_expert_used  = hparams.n_expert_used;    GGML_UNUSED(n_expert_used); \
+    const int64_t n_ctx_train    = hparams.n_ctx_train;      GGML_UNUSED(n_ctx_train);
+
 // For internal test use
 // TODO: remove
 const std::vector<std::pair<std::string, ggml_tensor *>> & llama_internal_get_tensor_map(const llama_model * model);
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 632a4a2199c..f86c9cce55e 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -683,9 +683,9 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_mod
                         LLAMA_LOG_WARN("%s: %-36s - applying manual override: %s -> %s\n",
                                        __func__, tensor_name.c_str(), ggml_type_name(new_type), ggml_type_name(qtype));
                         new_type = qtype;
-                        manual = true;
-                        break;
                     }
+                    manual = true;
+                    break;
                 }
             }
         }
@@ -884,13 +884,18 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         fname_inp, splits, /*file*/ nullptr, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
     ml.init_mappings(false); // no prefetching
 
-    llama_model model(llama_model_default_params());
+    auto mparams = llama_model_default_params();
+    std::unique_ptr<llama_model> model_ptr(llama_model_create(ml, mparams));
 
-    model.load_arch   (ml);
-    model.load_hparams(ml);
-    model.load_stats  (ml);
+    auto * model = dynamic_cast<llama_model_base *>(model_ptr.get());
+    if (model == nullptr) {
+        GGML_ABORT("fatal error: model does not implement llama_model_base");
+    }
+
+    model->load_hparams(ml);
+    model->load_stats  (ml);
 
-    quantize_state_impl qs(model, params);
+    quantize_state_impl qs(*model, params);
 
     if (params->only_copy) {
         ftype = ml.ftype;
@@ -1025,7 +1030,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         }
         gguf_add_tensor(ctx_outs[i_split].get(), tensor);
 
-        metadata[i].allows_quantization = tensor_allows_quantization(params, model.arch, tensor);
+        metadata[i].allows_quantization = tensor_allows_quantization(params, model->arch, tensor);
 
         if (metadata[i].allows_quantization) {
             metadata[i].target_type = llama_tensor_get_type(qs, params, tensor, default_type, metadata[i]);
@@ -1285,7 +1290,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
 llama_model_quantize_params llama_model_quantize_default_params() {
     llama_model_quantize_params result = {
         /*.nthread                     =*/ 0,
-        /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
+        /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q8_0,
         /*.output_tensor_type          =*/ GGML_TYPE_COUNT,
         /*.token_embedding_type        =*/ GGML_TYPE_COUNT,
         /*.allow_requantize            =*/ false,
@@ -1333,9 +1338,9 @@ void llama_quant_free(quantize_state_impl * qs) {
 
 llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * desc) {
     struct llama_model_params mparams = llama_model_default_params();
-    auto * model = new llama_model(mparams);
-
-    model->arch = llm_arch_from_string(desc->architecture);
+    auto arch = llm_arch_from_string(desc->architecture);
+    auto * model = llama_model_create(arch, mparams);
+    model->arch = arch;
 
     // infer llm_type: only LLM_TYPE_70B matters for quantization logic
     if (model->arch == LLM_ARCH_LLAMA && desc->n_layer == 80 && desc->n_head != desc->n_head_kv) {
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 163f222ef61..f43cf546ca0 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -503,6 +503,14 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                 };
                 byte_encode = false; // uses raw UTF-8, not GPT-2 byte encoding
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE:
+                // Sarvam uses SPM-style BPE (same shape as Gemma4): spaces replaced with U+2581
+                // by the normalizer, BPE merges over the whole text on raw UTF-8.
+                regex_exprs = {
+                    "[^\\n]+|[\\n]+",
+                };
+                byte_encode = false;
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -2005,6 +2013,11 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "gemma4") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GEMMA4;
                 escape_whitespaces = true;
+            } else if (
+                    tokenizer_pre == "sarvam-moe") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE;
+                escape_whitespaces = true;
+                clean_spaces = false;
             } else if (
                     tokenizer_pre == "jina-v1-en" ||
                     tokenizer_pre == "jina-v2-code" ||
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index dd38f45d3a2..8b040b912e2 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -59,6 +59,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM       = 48,
     LLAMA_VOCAB_PRE_TYPE_JAIS2           = 49,
     LLAMA_VOCAB_PRE_TYPE_GEMMA4          = 50,
+    LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE      = 51,
 };
 
 struct LLM_KV;
diff --git a/src/llama.cpp b/src/llama.cpp
index e9c3028585d..dfe30ce8f61 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -71,12 +71,18 @@ bool llama_supports_mlock(void) {
 }
 
 bool llama_supports_gpu_offload(void) {
+    if (!ggml_backend_reg_count()) {
+        ggml_backend_load_all();
+    }
     return ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU) != nullptr ||
            ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU) != nullptr ||
            llama_supports_rpc();
 }
 
 bool llama_supports_rpc(void) {
+    if (!ggml_backend_reg_count()) {
+        ggml_backend_load_all();
+    }
     return ggml_backend_reg_by_name("RPC") != nullptr;
 }
 
@@ -89,6 +95,10 @@ void llama_backend_init(void) {
         struct ggml_context * ctx = ggml_init(params);
         ggml_free(ctx);
     }
+
+    if (!ggml_backend_reg_count()) {
+        ggml_backend_load_all();
+    }
 }
 
 void llama_numa_init(enum ggml_numa_strategy numa) {
@@ -111,113 +121,8 @@ int64_t llama_time_us(void) {
     return ggml_time_us();
 }
 
-// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
-static int llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud,
-        const std::string & fname, std::vector<std::string> & splits, FILE * file, llama_model & model, llama_model_params & params) {
-    // loading time will be recalculated after the first eval, so
-    // we take page faults deferred by mmap() into consideration
-    model.t_load_us = 0;
-    time_meas tm(model.t_load_us);
-
-    model.t_start_us = tm.t_start_us;
-
-    try {
-        llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.use_mmap, params.use_direct_io,
-            params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
-
-        ml.print_info();
-
-        model.hparams.vocab_only = params.vocab_only;
-        model.hparams.no_alloc   = params.no_alloc;
-
-        try {
-            model.load_arch(ml);
-        } catch(const std::exception & e) {
-            throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
-        }
-        try {
-            model.load_hparams(ml);
-        } catch(const std::exception & e) {
-            throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
-        }
-        if (model.arch == LLM_ARCH_CLIP) {
-            throw std::runtime_error("CLIP cannot be used as main model, use it with --mmproj instead");
-        }
-        try {
-            model.load_vocab(ml);
-        } catch(const std::exception & e) {
-            throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
-        }
-
-        model.load_stats(ml);
-        model.print_info();
-
-        if (params.vocab_only) {
-            LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
-            return 0;
-        }
-
-        if (!model.load_tensors(ml)) {
-            return -2;
-        }
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
-        return -1;
-    }
-
-    return 0;
-}
-
-static struct llama_model * llama_model_load_from_file_impl(
-        struct gguf_context * metadata,
-        llama_model_set_tensor_data_t set_tensor_data,
-        void * set_tensor_data_ud,
-        const std::string & path_model,
-        std::vector<std::string> & splits,
-        FILE * file,
-        struct llama_model_params params) {
-    {
-        int n_sources_defined = 0;
-        if (metadata != nullptr) {
-            n_sources_defined++;
-        }
-        if (!path_model.empty()) {
-            n_sources_defined++;
-        }
-        if (file != nullptr) {
-            n_sources_defined++;
-        }
-        if (n_sources_defined != 1) {
-            LLAMA_LOG_ERROR("%s: exactly one out metadata, path_model, and file must be defined\n", __func__);
-            return nullptr;
-        }
-    }
-    ggml_time_init();
-
-    if (!params.vocab_only && ggml_backend_reg_count() == 0) {
-        LLAMA_LOG_ERROR("%s: no backends are loaded. hint: use ggml_backend_load() or ggml_backend_load_all() to load a backend before calling this function\n", __func__);
-        return nullptr;
-    }
-
-    unsigned cur_percentage = 0;
-    if (params.progress_callback == NULL) {
-        params.progress_callback_user_data = &cur_percentage;
-        params.progress_callback = [](float progress, void * ctx) {
-            unsigned * cur_percentage_p = (unsigned *) ctx;
-            unsigned percentage = (unsigned) (100 * progress);
-            while (percentage > *cur_percentage_p) {
-                *cur_percentage_p = percentage;
-                LLAMA_LOG_CONT(".");
-                if (percentage >= 100) {
-                    LLAMA_LOG_CONT("\n");
-                }
-            }
-            return true;
-        };
-    }
-
-    llama_model * model = new llama_model(params);
-
+// returns true on success
+static bool llama_prepare_model_devices(const llama_model_params & params, llama_model * model) {
     // create list of devices to use with this model
     if (params.devices) {
         if (params.split_mode == LLAMA_SPLIT_MODE_TENSOR) {
@@ -227,7 +132,7 @@ static struct llama_model * llama_model_load_from_file_impl(
             }
             if (n_devs == 0) {
                 LLAMA_LOG_ERROR("%s: LLAMA_SPLIT_MODE_TENSOR needs >= 1 devices\n", __func__);
-                return nullptr;
+                return false;
             }
             LLAMA_LOG_INFO("%s: creating a Meta device with %zu devices\n", __func__, n_devs);
             for (size_t i = 0; i < n_devs; ++i) {
@@ -265,7 +170,7 @@ static struct llama_model * llama_model_load_from_file_impl(
             }
             if (devs.empty()) {
                 LLAMA_LOG_ERROR("%s: LLAMA_SPLIT_MODE_TENSOR needs >= 1 devices\n", __func__);
-                return nullptr;
+                return false;
             }
 
             LLAMA_LOG_INFO("%s: creating a Meta device for tensor parallelism from %zu devices:\n", __func__, devs.size());
@@ -347,8 +252,7 @@ static struct llama_model * llama_model_load_from_file_impl(
         } else {
             if (params.main_gpu >= (int)model->devices.size()) {
                 LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %zu)\n", __func__, params.main_gpu, model->devices.size());
-                llama_model_free(model);
-                return nullptr;
+                return false;
             }
             llama_device main_gpu = model->devices[params.main_gpu];
             model->devices.clear();
@@ -365,7 +269,121 @@ static struct llama_model * llama_model_load_from_file_impl(
                 props.memory_free/1024/1024);
     }
 
-    const int status = llama_model_load(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, file, *model, params);
+    return true;
+}
+
+// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
+static std::pair<int, llama_model *> llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud,
+        const std::string & fname, std::vector<std::string> & splits, FILE * file, llama_model_params & params) {
+    try {
+        llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.use_mmap, params.use_direct_io,
+            params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
+
+        ml.print_info();
+        std::unique_ptr<llama_model> model_ptr(llama_model_create(ml, params));
+
+        bool ok = llama_prepare_model_devices(params, model_ptr.get());
+        if (!ok) {
+            return {-1, nullptr};
+        }
+
+        auto * model = dynamic_cast<llama_model_base *>(model_ptr.get());
+        if (model == nullptr) {
+            GGML_ABORT("fatal error: model does not implement llama_model_base");
+        }
+
+        // loading time will be recalculated after the first eval, so
+        // we take page faults deferred by mmap() into consideration
+        model->t_load_us = 0;
+        time_meas tm(model->t_load_us);
+
+        model->t_start_us = tm.t_start_us;
+
+        model->hparams.vocab_only = params.vocab_only;
+        model->hparams.no_alloc   = params.no_alloc;
+
+        try {
+            model->load_hparams(ml);
+        } catch(const std::exception & e) {
+            throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
+        }
+        if (model->arch == LLM_ARCH_CLIP) {
+            throw std::runtime_error("CLIP cannot be used as main model, use it with --mmproj instead");
+        }
+        try {
+            model->load_vocab(ml);
+        } catch(const std::exception & e) {
+            throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
+        }
+
+        model->load_stats(ml);
+        model->print_info();
+
+        if (params.vocab_only) {
+            LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
+            return {0, model_ptr.release()};
+        }
+
+        if (!model->load_tensors(ml)) {
+            return {-2, nullptr};
+        }
+
+        return {0, model_ptr.release()};
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
+        return {-1, nullptr};
+    }
+}
+
+static struct llama_model * llama_model_load_from_file_impl(
+        struct gguf_context * metadata,
+        llama_model_set_tensor_data_t set_tensor_data,
+        void * set_tensor_data_ud,
+        const std::string & path_model,
+        std::vector<std::string> & splits,
+        FILE * file,
+        struct llama_model_params params) {
+    {
+        int n_sources_defined = 0;
+        if (metadata != nullptr) {
+            n_sources_defined++;
+        }
+        if (!path_model.empty()) {
+            n_sources_defined++;
+        }
+        if (file != nullptr) {
+            n_sources_defined++;
+        }
+        if (n_sources_defined != 1) {
+            LLAMA_LOG_ERROR("%s: exactly one out metadata, path_model, and file must be defined\n", __func__);
+            return nullptr;
+        }
+    }
+    ggml_time_init();
+
+    if (!params.vocab_only && ggml_backend_reg_count() == 0) {
+        LLAMA_LOG_ERROR("%s: no backends are loaded. hint: use ggml_backend_load() or ggml_backend_load_all() to load a backend before calling this function\n", __func__);
+        return nullptr;
+    }
+
+    unsigned cur_percentage = 0;
+    if (params.progress_callback == NULL) {
+        params.progress_callback_user_data = &cur_percentage;
+        params.progress_callback = [](float progress, void * ctx) {
+            unsigned * cur_percentage_p = (unsigned *) ctx;
+            unsigned percentage = (unsigned) (100 * progress);
+            while (percentage > *cur_percentage_p) {
+                *cur_percentage_p = percentage;
+                LLAMA_LOG_CONT(".");
+                if (percentage >= 100) {
+                    LLAMA_LOG_CONT("\n");
+                }
+            }
+            return true;
+        };
+    }
+
+    const auto [status, model] = llama_model_load(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, file, params);
     GGML_ASSERT(status <= 0);
     if (status < 0) {
         if (status == -1) {
@@ -374,7 +392,9 @@ static struct llama_model * llama_model_load_from_file_impl(
             LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
         }
 
-        llama_model_free(model);
+        if (model) {
+            llama_model_free(model);
+        }
         return nullptr;
     }
 
diff --git a/src/models/afmoe.cpp b/src/models/afmoe.cpp
index 2790b12111d..a7c77ee5d28 100644
--- a/src/models/afmoe.cpp
+++ b/src/models/afmoe.cpp
@@ -1,6 +1,112 @@
 #include "models.h"
 
-llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_afmoe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
+
+    // Set up interleaved sliding window attention (ISWA)
+    // Pattern: 3 sliding - 1 full (global_attn_every_n_layers = 4)
+    if (hparams.n_swa > 0) {
+        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        uint32_t swa_period = 4;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period);
+
+        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+        hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+    } else {
+        hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+    }
+
+    // Default to sigmoid if not set
+    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+        hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+    }
+
+    switch (hparams.n_layer) {
+        case 56: type = LLM_TYPE_6B; break;
+        case 32: type = LLM_TYPE_26B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_afmoe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    const int64_t n_ff_exp = hparams.n_ff_exp;
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // dual attention normalization
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        // attention projections
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // Q/K normalization
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        // attention gating
+        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+
+        // dual ffn normalization
+        layer.ffn_norm      = create_tensor(tn(LLM_TENSOR_FFN_NORM,      "weight", i), {n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) {
+            // MoE layers
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+
+            // grouped expert weights
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+
+            // shared expert
+            if (n_expert_shared > 0) {
+                const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
+            }
+        } else {
+            // Dense layers
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_afmoe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_afmoe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
 
@@ -171,7 +277,7 @@ llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
diff --git a/src/models/apertus.cpp b/src/models/apertus.cpp
index af44cea6054..bec7136521c 100644
--- a/src/models/apertus.cpp
+++ b/src/models/apertus.cpp
@@ -1,6 +1,62 @@
 #include "models.h"
 
-llm_build_apertus::llm_build_apertus(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_apertus::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key_or_arr(LLM_KV_XIELU_ALPHA_N,        hparams.xielu_alpha_n, hparams.n_layer);
+    ml.get_key_or_arr(LLM_KV_XIELU_ALPHA_P,        hparams.xielu_alpha_p, hparams.n_layer);
+    ml.get_key_or_arr(LLM_KV_XIELU_BETA,           hparams.xielu_beta,    hparams.n_layer);
+    ml.get_key_or_arr(LLM_KV_XIELU_EPS,            hparams.xielu_eps,     hparams.n_layer);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_8B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_apertus::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        } else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
+
+        // Q and K layernorms for Apertus
+        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
+        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_apertus::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_apertus::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -104,7 +160,7 @@ llm_build_apertus::llm_build_apertus(const llama_model & model, const llm_graph_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/arcee.cpp b/src/models/arcee.cpp
index 2e71f5d9e2a..d086c4717ff 100644
--- a/src/models/arcee.cpp
+++ b/src/models/arcee.cpp
@@ -1,6 +1,51 @@
 #include "models.h"
 
-llm_build_arcee::llm_build_arcee(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_arcee::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    // Arcee uses the same structure as Llama
+    switch (hparams.n_layer) {
+        case 36: type = LLM_TYPE_4B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_arcee::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_arcee::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_arcee::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -103,7 +148,7 @@ llm_build_arcee::llm_build_arcee(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/arctic.cpp b/src/models/arctic.cpp
index f8ca6aff6ab..27deadffeb7 100644
--- a/src/models/arctic.cpp
+++ b/src/models/arctic.cpp
@@ -1,6 +1,59 @@
 #include "models.h"
 
-llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_arctic::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    if (hparams.n_expert == 128) {
+        switch (hparams.n_layer) {
+            case 35: type = LLM_TYPE_10B_128x3_66B; break;
+            default: type = LLM_TYPE_UNKNOWN;
+        }
+    } else {
+        type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_arctic::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+        layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_arctic::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_arctic::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -118,7 +171,7 @@ llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_pa
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/arwkv7.cpp b/src/models/arwkv7.cpp
index 107a3bef8da..9bd04127b25 100644
--- a/src/models/arwkv7.cpp
+++ b/src/models/arwkv7.cpp
@@ -1,7 +1,123 @@
 #include "models.h"
 
+void llama_model_arwkv7::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,                hparams.f_norm_eps, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,            hparams.f_norm_rms_eps, false);
+    ml.get_key(LLM_KV_WKV_HEAD_SIZE,                          hparams.wkv_head_size);
+    ml.get_key(LLM_KV_ATTENTION_DECAY_LORA_RANK,              hparams.n_lora_decay);
+    ml.get_key(LLM_KV_ATTENTION_ICLR_LORA_RANK,               hparams.n_lora_iclr);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);
+    ml.get_key(LLM_KV_ATTENTION_GATE_LORA_RANK,               hparams.n_lora_gate, false);
+    ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,                      hparams.token_shift_count, false);
+
+    switch (hparams.n_layer) {
+        case 12:
+            switch (hparams.n_embd) {
+                case 768: type = LLM_TYPE_190M; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 24:
+            switch (hparams.n_embd) {
+                case 1024: type = LLM_TYPE_450M; break;
+                case 2048: type = LLM_TYPE_1_5B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 28:
+            switch (hparams.n_embd) {
+                case 1536: type = LLM_TYPE_1_5B; break;
+                case 3584: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 32:
+            switch (hparams.n_embd) {
+                case 2560: type = LLM_TYPE_2_9B; break;
+                case 4096: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 61:
+            switch (hparams.n_embd) {
+                case 4096: type = LLM_TYPE_14B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_arwkv7::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+    const int n_lora_decay = hparams.n_lora_decay;
+    const int n_lora_iclr = hparams.n_lora_iclr;
+    const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;
+    const int n_lora_gate = hparams.n_lora_gate;
+    const int attn_hidden_size = n_embd;
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);
+        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);
+        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);
+
+        layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);
+        layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);
+        layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);
+
+        if (i == 0) {
+            // actually not used
+            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
+            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);
+            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);
+        } else {
+            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
+            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);
+            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);
+        }
+
+        layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, TENSOR_NOT_REQUIRED);
+
+        try {
+            layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);
+        } catch(std::runtime_error & e) {
+            // ARWKV models may not have gate tensors
+            layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
+        }
+
+        layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);
+        layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);
+        layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);
+
+        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
+        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
+        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
+
+        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+
+}
+
+std::unique_ptr<llm_graph_context> llama_model_arwkv7::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
 
-llm_build_arwkv7::llm_build_arwkv7(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {
+llama_model_arwkv7::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {
     GGML_ASSERT(n_embd == hparams.n_embd_r());
 
     ggml_tensor * cur;
@@ -77,7 +193,7 @@ llm_build_arwkv7::llm_build_arwkv7(const llama_model & model, const llm_graph_pa
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/baichuan.cpp b/src/models/baichuan.cpp
index 2d0d05df485..4d26081cd5d 100644
--- a/src/models/baichuan.cpp
+++ b/src/models/baichuan.cpp
@@ -1,6 +1,49 @@
 #include "models.h"
 
-llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_baichuan::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_7B; break;
+        case 40: type = LLM_TYPE_13B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    if (type == LLM_TYPE_13B) {
+        // TODO: become GGUF KV parameter
+        hparams.f_max_alibi_bias = 8.0f;
+    }
+}
+
+void llama_model_baichuan::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+    {
+        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+        output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_baichuan::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_baichuan::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -103,7 +146,7 @@ llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/bailingmoe.cpp b/src/models/bailingmoe.cpp
index 67a7120d622..fe1ae10864b 100644
--- a/src/models/bailingmoe.cpp
+++ b/src/models/bailingmoe.cpp
@@ -1,6 +1,65 @@
 #include "models.h"
 
-llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_bailingmoe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+
+    switch (hparams.n_layer) {
+        case 28: type = LLM_TYPE_16B; break;
+        case 88: type = LLM_TYPE_290B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_bailingmoe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    const int64_t n_ff_exp            = hparams.n_ff_exp;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_head * n_rot, n_head_kv * n_rot, n_head_kv * n_rot, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_rot, n_embd}, 0);
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+        if (n_expert == 0) {
+            throw std::runtime_error("n_expert must be > 0");
+        }
+        if (n_expert_used == 0) {
+            throw std::runtime_error("n_expert_used must be > 0");
+        }
+
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_bailingmoe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_bailingmoe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -112,7 +171,7 @@ llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/bailingmoe2.cpp b/src/models/bailingmoe2.cpp
index 497b4babd0c..2f0d44a6259 100644
--- a/src/models/bailingmoe2.cpp
+++ b/src/models/bailingmoe2.cpp
@@ -1,6 +1,100 @@
 #include "models.h"
 
-llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) :
+void llama_model_bailingmoe2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,              hparams.nextn_predict_layers, false);
+    GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+
+    // TODO: when MTP is implemented, this should probably be updated if needed
+    hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+
+    switch (hparams.n_layer) {
+        case 20: type = LLM_TYPE_16B_A1B; break;
+        case 21: type = LLM_TYPE_16B_A1B; break;
+        case 32: type = LLM_TYPE_100B_A6B; break;
+        case 33: type = LLM_TYPE_100B_A6B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_bailingmoe2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    const int64_t n_ff_exp        = hparams.n_ff_exp;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for bailingmoe2");
+    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for bailingmoe2");
+
+    for (int i = 0; i < n_layer; ++i) {
+        int flags = 0;
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            // skip all tensors in the NextN layers
+            flags |= TENSOR_SKIP;
+        }
+
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, flags);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
+
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
+
+        if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
+            const int64_t n_ff_shexp = (hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp) * n_expert_shared;
+
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
+
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, flags);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
+
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, flags);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, flags);
+        } else { // Dense layers
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, flags);
+        }
+
+        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
+            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
+            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
+            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
+            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
+            layer.layer_out_norm         = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, flags);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_bailingmoe2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_bailingmoe2::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -116,7 +210,7 @@ llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const ll
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/bert.cpp b/src/models/bert.cpp
index 7e046cfd2a4..3c28f419ccf 100644
--- a/src/models/bert.cpp
+++ b/src/models/bert.cpp
@@ -1,6 +1,83 @@
 #include "models.h"
 
-llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_bert::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 3:
+            type = LLM_TYPE_17M; break; // bge-micro
+        case 6:
+            type = LLM_TYPE_22M; break; // MiniLM-L6
+        case 12:
+            switch (hparams.n_embd) {
+                case 384: type = LLM_TYPE_33M; break; // MiniLM-L12, bge-small
+                case 768: type = LLM_TYPE_109M; break; // bge-base
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 24:
+            type = LLM_TYPE_335M; break; // bge-large
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_bert::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_token_types == 0) {
+        throw std::runtime_error(arch_name() + " model needs to define token type count");
+    }
+    tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+    type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
+
+    if (arch == LLM_ARCH_BERT) {
+        pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train}, 0);
+
+        cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+        cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
+
+        cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
+    }
+
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);
+
+        if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff,   n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff,   n_embd, n_expert}, 0);
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,   "weight", i), {n_embd, n_expert}, 0);
+        } else {
+            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+            if (arch == LLM_ARCH_NOMIC_BERT) {
+                layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            }
+        }
+
+        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_bert::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_bert::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
diff --git a/src/models/bitnet.cpp b/src/models/bitnet.cpp
index 71526354ca6..7e8125deec4 100644
--- a/src/models/bitnet.cpp
+++ b/src/models/bitnet.cpp
@@ -1,7 +1,54 @@
 #include "models.h"
 
+void llama_model_bitnet::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
-llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    switch (hparams.n_layer) {
+        case 26: type = LLM_TYPE_3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_bitnet::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm     = create_tensor(tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd}, 0);
+        layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0);
+
+        layer.wq       = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
+        layer.wq_s     = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+        layer.wk       = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
+        layer.wk_s     = create_tensor(tn(LLM_TENSOR_ATTN_K,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+        layer.wv       = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
+        layer.wv_s     = create_tensor(tn(LLM_TENSOR_ATTN_V,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+        layer.wo       = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_s     = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm     = create_tensor(tn(LLM_TENSOR_FFN_NORM,     "weight", i), {n_embd}, 0);
+        layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0);
+
+        layer.ffn_gate       = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_gate_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down       = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+        layer.ffn_up         = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_up_s   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "scale",  i), {1}, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_bitnet::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_bitnet::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
diff --git a/src/models/bloom.cpp b/src/models/bloom.cpp
index f3b0999bf54..30b0f3d07d0 100644
--- a/src/models/bloom.cpp
+++ b/src/models/bloom.cpp
@@ -1,6 +1,68 @@
 #include "models.h"
 
-llm_build_bloom::llm_build_bloom(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_bloom::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_1B; break;
+        case 30:
+            switch (hparams.n_embd) {
+                case 2560: type = LLM_TYPE_3B; break;
+                case 4096: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    // TODO: become GGUF KV parameter
+    hparams.f_max_alibi_bias = 8.0f;
+}
+
+void llama_model_bloom::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
+
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), {n_embd}, 0);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);
+
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias",   i), {n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_bloom::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_bloom::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -80,7 +142,7 @@ llm_build_bloom::llm_build_bloom(const llama_model & model, const llm_graph_para
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/chameleon.cpp b/src/models/chameleon.cpp
index 21deaba1a6d..4bceaefd63b 100644
--- a/src/models/chameleon.cpp
+++ b/src/models/chameleon.cpp
@@ -1,8 +1,56 @@
 #include "models.h"
-
 #include <float.h>
 
-llm_build_chameleon::llm_build_chameleon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_chameleon::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    hparams.f_norm_eps = 1e-5;  // eps for qk-norm, torch default
+    ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm, false);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_7B; break;
+        case 48: type = LLM_TYPE_34B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+   }
+}
+
+void llama_model_chameleon::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
+        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),  {n_embd_head_k, n_head}, TENSOR_NOT_REQUIRED);
+        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),  {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_chameleon::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_chameleon::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -133,7 +181,7 @@ llm_build_chameleon::llm_build_chameleon(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output_with_img_logits", -1);
 
     // TODO: this suppresses the output of image tokens, which is required to enable text-only outputs.
diff --git a/src/models/chatglm.cpp b/src/models/chatglm.cpp
index 7d4a43fdca5..6766fa71c15 100644
--- a/src/models/chatglm.cpp
+++ b/src/models/chatglm.cpp
@@ -1,7 +1,60 @@
 #include "models.h"
 
+void llama_model_chatglm::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 28: {
+            if (hparams.n_head(0) == 16) {
+                type = LLM_TYPE_1_5B;
+            } else {
+                type = LLM_TYPE_6B;
+            }
+        } break;
+        case 40: {
+            if (hparams.n_head(0) == 24) {
+                type = LLM_TYPE_4B;
+            } else {
+                type = LLM_TYPE_9B;
+            }
+        } break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_chatglm::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, 0);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_chatglm::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
 
-llm_build_chatglm::llm_build_chatglm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_chatglm::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -98,7 +151,7 @@ llm_build_chatglm::llm_build_chatglm(const llama_model & model, const llm_graph_
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/codeshell.cpp b/src/models/codeshell.cpp
index 3ceb5835b85..274dd3342a7 100644
--- a/src/models/codeshell.cpp
+++ b/src/models/codeshell.cpp
@@ -1,6 +1,55 @@
 #include "models.h"
 
-llm_build_codeshell::llm_build_codeshell(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_codeshell::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    switch (hparams.n_layer) {
+        case 42: type = LLM_TYPE_7B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_codeshell::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if tok embd is NULL, init from output
+    if (tok_embd == NULL) {
+        tok_embd = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_codeshell::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_codeshell::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -94,7 +143,7 @@ llm_build_codeshell::llm_build_codeshell(const llama_model & model, const llm_gr
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/cogvlm.cpp b/src/models/cogvlm.cpp
index be3eeeddac7..2e231bb3f93 100644
--- a/src/models/cogvlm.cpp
+++ b/src/models/cogvlm.cpp
@@ -1,6 +1,55 @@
 #include "models.h"
 
-llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) :
+void llama_model_cogvlm::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_13B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_cogvlm::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd_head_k * n_head * 3}, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.visexp_attn_wqkv = create_tensor(tn(LLM_TENSOR_VISEXP_ATTN_QKV, "weight", i), {n_embd, n_embd_head_k * n_head * 3}, 0);
+        layer.visexp_attn_wo = create_tensor(tn(LLM_TENSOR_VISEXP_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+        layer.visexp_ffn_gate = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.visexp_ffn_down = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.visexp_ffn_up   = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_cogvlm::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_cogvlm::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
     const float   kq_scale    = 1.0f / sqrtf(float(n_embd_head));
@@ -101,7 +150,7 @@ llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_pa
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
     res->t_logits = cur;
     ggml_build_forward_expand(gf, cur);
diff --git a/src/models/cohere2-iswa.cpp b/src/models/cohere2.cpp
similarity index 59%
rename from src/models/cohere2-iswa.cpp
rename to src/models/cohere2.cpp
index 670b08e7d97..a514cf88fc6 100644
--- a/src/models/cohere2-iswa.cpp
+++ b/src/models/cohere2.cpp
@@ -1,6 +1,53 @@
 #include "models.h"
 
-llm_build_cohere2_iswa::llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_cohere2::load_arch_hparams(llama_model_loader & ml) {
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    uint32_t swa_period = 4;
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+    hparams.set_swa_pattern(swa_period);
+    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,       hparams.rope_freq_base_train_swa, false);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+    ml.get_key(LLM_KV_LOGIT_SCALE,              hparams.f_logit_scale);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_8B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_cohere2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    // init output from the input tok embed
+    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
+                                      TENSOR_DUPLICATED);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_cohere2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_cohere2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -99,7 +146,7 @@ llm_build_cohere2_iswa::llm_build_cohere2_iswa(const llama_model & model, const
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     if (f_logit_scale) {
         cur = ggml_scale(ctx0, cur, f_logit_scale);
diff --git a/src/models/command-r.cpp b/src/models/command-r.cpp
index 067961caa08..adf7fcaa20f 100644
--- a/src/models/command-r.cpp
+++ b/src/models/command-r.cpp
@@ -1,8 +1,48 @@
 #include "models.h"
 
+void llama_model_command_r::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_LOGIT_SCALE,             hparams.f_logit_scale, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    switch (hparams.n_layer) {
+        case 40: type = LLM_TYPE_35B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_command_r::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    // init output from the input tok embed
+    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
 
+        if (n_layer >= 64){
+            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
+            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
+        }
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_command_r::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
 
-llm_build_command_r::llm_build_command_r(const llama_model & model, const llm_graph_params & params) :
+llama_model_command_r::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -91,7 +131,7 @@ llm_build_command_r::llm_build_command_r(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     if (f_logit_scale) {
         cur = ggml_scale(ctx0, cur, f_logit_scale);
diff --git a/src/models/dbrx.cpp b/src/models/dbrx.cpp
index 0e882721807..af71c775365 100644
--- a/src/models/dbrx.cpp
+++ b/src/models/dbrx.cpp
@@ -1,6 +1,50 @@
 #include "models.h"
 
-llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_dbrx::load_arch_hparams(llama_model_loader & ml) {
+ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv);
+
+switch (hparams.n_layer) {
+    case 40: type = LLM_TYPE_16x12B; break;
+    default: type = LLM_TYPE_UNKNOWN;
+}
+        }
+
+void llama_model_dbrx::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_expert == 0) {
+        throw std::runtime_error("DBRX model cannot have zero experts");
+    }
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_dbrx::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_dbrx::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -101,7 +145,7 @@ llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/deci.cpp b/src/models/deci.cpp
index 30272eabd69..567e3535276 100644
--- a/src/models/deci.cpp
+++ b/src/models/deci.cpp
@@ -1,6 +1,82 @@
 #include "models.h"
 
-llm_build_deci::llm_build_deci(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_deci::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_7B; break;
+        case 80: type = LLM_TYPE_70B; break;
+        case 162: type = LLM_TYPE_405B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_deci::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
+        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
+        const int64_t n_ff          = hparams.n_ff(i);
+        const int64_t n_head        = hparams.n_head(i);
+        const int64_t n_head_kv     = hparams.n_head_kv(i);
+
+        if (n_head_kv == 0 && n_head > 0) {
+            // linear attention for DeciLMCausalModel
+            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        }
+        else if (n_head_kv > 0) {
+            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+        }
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        if (n_ff > 0) {
+            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        }
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+        else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        if (n_ff > 0) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        }
+
+        // optional MLP bias
+        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_deci::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_deci::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -105,7 +181,7 @@ llm_build_deci::llm_build_deci(const llama_model & model, const llm_graph_params
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/deepseek.cpp b/src/models/deepseek.cpp
index 671b72dfead..f52ec9518b6 100644
--- a/src/models/deepseek.cpp
+++ b/src/models/deepseek.cpp
@@ -1,6 +1,77 @@
 #include "models.h"
 
-llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_graph_params & params) :
+void llama_model_deepseek::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+
+    switch (hparams.n_ff_exp) {
+        case 1408: type = LLM_TYPE_16B; break;
+        case 1792: type = LLM_TYPE_20B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_deepseek::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+
+    const int64_t n_ff_exp        = hparams.n_ff_exp;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    // try to load output.weight, if not found, use token_embd (tied embeddings)
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (!output) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (i < (int) hparams.n_layer_dense_lead) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        } else {
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+            if (n_expert == 0) {
+                throw std::runtime_error("n_expert must be > 0");
+            }
+            if (n_expert_used == 0) {
+                throw std::runtime_error("n_expert_used must be > 0");
+            }
+
+            // MoE branch
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+            // Shared expert branch
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_deepseek::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_deepseek::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -114,7 +185,7 @@ llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp
index 303fc72c610..1fe54adc13e 100644
--- a/src/models/deepseek2.cpp
+++ b/src/models/deepseek2.cpp
@@ -1,6 +1,149 @@
 #include "models.h"
 
-llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
+void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
+    uint32_t n_vocab = 0;
+    ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
+
+    // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B, Kanana-2-30B-A3B
+    const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26 || (hparams.n_layer == 48 && n_vocab == 128256));
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    if (!is_lite) {
+        ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
+    }
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,     hparams.n_lora_kv);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,   hparams.n_embd_head_k_mla_impl, false);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,       hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,        hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,         hparams.expert_gating_func, false);
+    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+        // for compatibility with existing DeepSeek V2 and V2.5 GGUFs
+        // that have no expert_gating_func model parameter set
+        if ((hparams.n_layer == 47 || hparams.n_layer == 48) && n_vocab == 154880) {
+            // GLM 4.7 Lite
+            hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+        } else {
+            hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
+        }
+    }
+
+    if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false)) {
+        // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+        // cancel the factor from the convert script
+        hparams.rope_yarn_log_mul /= 0.1f;
+    }
+
+    // (optional) temperature tuning - used by mistral-large
+    ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE,  hparams.f_attn_temp_scale,       false);
+    ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); // FIXME why not use temperature_length?
+
+    hparams.f_attn_temp_offset = 0.0f;
+
+    switch (hparams.n_layer) {
+        case 27: type = LLM_TYPE_16B; break;
+        case 47: type = LLM_TYPE_30B_A3B; break;
+        case 60: type = LLM_TYPE_236B; break;
+        case 61: type = LLM_TYPE_671B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    const bool is_mla = hparams.is_mla();
+
+    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
+    const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
+    const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
+
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+    const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
+    GGML_ASSERT(n_embd_head_qk_nope >= 1);
+
+    const int64_t q_lora_rank  = hparams.n_lora_q;
+    const int64_t kv_lora_rank = hparams.n_lora_kv;
+
+    const int64_t n_ff_exp        = hparams.n_ff_exp;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    // try to load output.weight, if not found, use token_embd (tied embeddings)
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (!output) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        if (q_lora_rank > 0) {
+            layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
+        }
+
+        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
+
+        if (q_lora_rank > 0) {
+            layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
+            layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
+        } else {
+            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0);
+        }
+
+        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, 0);
+
+        // note: only old legacy GGUF files will have the unsplit wkv_b tensor in
+        if (is_mla) {
+            layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, 0);
+            layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
+        } else {
+            layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, 0);
+        }
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (i < (int) hparams.n_layer_dense_lead) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        } else {
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+            if (n_expert == 0) {
+                throw std::runtime_error("n_expert must be > 0");
+            }
+            if (n_expert_used == 0) {
+                throw std::runtime_error("n_expert_used must be > 0");
+            }
+
+            // MoE branch
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+            create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
+
+            // Shared expert branch
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_deepseek2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
     bool is_ocr = model.arch == LLM_ARCH_DEEPSEEK2OCR;
diff --git a/src/models/deepseek2ocr.cpp b/src/models/deepseek2ocr.cpp
new file mode 100644
index 00000000000..f9e4c98785c
--- /dev/null
+++ b/src/models/deepseek2ocr.cpp
@@ -0,0 +1,82 @@
+#include "models.h"
+
+void llama_model_deepseek2ocr::load_arch_hparams(llama_model_loader & ml) {
+    // similar to deepseek2, but without MLA
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+
+    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+        hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
+    }
+
+    switch (hparams.n_layer) {
+        case 12: type = LLM_TYPE_3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_deepseek2ocr::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    // similar to deepseek2, but without MLA
+    const int64_t n_ff_exp        = hparams.n_ff_exp;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    // try to load output.weight, if not found, use token_embd (tied embeddings)
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (!output) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
+        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd}, 0);
+        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        // norm
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (i < (int) hparams.n_layer_dense_lead) {
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        } else {
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+            if (n_expert == 0) {
+                throw std::runtime_error("n_expert must be > 0");
+            }
+            if (n_expert_used == 0) {
+                throw std::runtime_error("n_expert_used must be > 0");
+            }
+
+            // MoE branch
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+            create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
+
+            // Shared expert branch
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_deepseek2ocr::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/delta-net-base.cpp b/src/models/delta-net-base.cpp
index 6bc989c9509..2a4e00384e9 100644
--- a/src/models/delta-net-base.cpp
+++ b/src/models/delta-net-base.cpp
@@ -1,6 +1,7 @@
 #include "models.h"
 
 #include "llama-impl.h"
+#include "llama-memory-recurrent.h"
 
 // utility to get one slice from the third dimension
 // input dim:  [x, y, c, b]
@@ -397,7 +398,9 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_delta_net_base::build_delta_ne
     GGML_ASSERT(b->ne[0] == 1   && b->ne[1] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
     GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v      && s->ne[3] == n_seqs);
 
-    ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s);
+    // K=1 (final state only): reshape to 3D (S_v*S_v*H_v, 1, n_seqs) for ggml_gated_delta_net.
+    ggml_tensor * s_3d = ggml_reshape_3d(ctx0, s, S_v * S_v * H_v, 1, n_seqs);
+    ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s_3d);
     if (n_tokens == 1) {
         cb(result, LLAMA_TENSOR_NAME_FGDN_AR, il);
     } else {
@@ -443,3 +446,141 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_delta_net_base::build_delta_ne
 
     return build_delta_net_chunking(q, k, v, g, b, s, il);
 }
+
+bool llm_build_delta_net_base::keep_rs() const {
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+    return cparams.n_rs_seq > 0
+        && n_seq_tokens > 1
+        && (uint32_t) n_seq_tokens <= 1 + cparams.n_rs_seq;
+}
+
+ggml_tensor * llm_build_delta_net_base::build_conv_state(
+        llm_graph_input_rs * inp,
+        ggml_tensor *        conv_states_all,
+        ggml_tensor *        qkv_mixed,
+        int64_t              conv_kernel_size,
+        int64_t              conv_channels,
+        int                  il) {
+    const auto * mctx_cur   = inp->mctx;
+    const auto   kv_head    = mctx_cur->get_head();
+    const uint32_t mem_size = mctx_cur->get_size();
+    const int64_t n_seqs       = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+    const bool    keep         = keep_rs();
+
+    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    cb(conv_states, "conv_states", il);
+
+    conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+    cb(conv_states, "conv_states_reshaped", il);
+
+    qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
+    cb(qkv_mixed, "qkv_mixed_transposed", il);
+
+    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+    cb(conv_input, "conv_input", il);
+
+    if (!keep) {
+        ggml_tensor * last_conv_states =
+            ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+                         conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+        cb(last_conv_states, "last_conv_states", il);
+
+        ggml_tensor * state_update_target =
+            ggml_view_2d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels, n_seqs, conv_states_all->nb[1],
+                         kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+        cb(state_update_target, "state_update_target", il);
+
+        ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    } else {
+        const int64_t row_count = (conv_kernel_size - 1) * conv_channels;
+        const size_t  row_size  = row_count * ggml_element_size(conv_states_all);
+        for (int64_t t = 1; t <= n_seq_tokens; ++t) {
+            const uint32_t slot = (uint32_t)(n_seq_tokens - t);
+            ggml_tensor * src =
+                ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs,
+                             conv_input->nb[1], conv_input->nb[2],
+                             t * ggml_element_size(conv_input));
+            ggml_tensor * dst =
+                ggml_view_2d(ctx0, conv_states_all, row_count, n_seqs,
+                             conv_states_all->nb[1],
+                             ((size_t) slot * mem_size + kv_head) * row_size);
+            ggml_build_forward_expand(gf, ggml_cpy(ctx0, src, dst));
+        }
+    }
+
+    return conv_input;
+}
+
+ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
+        llm_graph_input_rs * inp,
+        ggml_tensor *        ssm_states_all,
+        ggml_tensor *        q,
+        ggml_tensor *        k,
+        ggml_tensor *        v,
+        ggml_tensor *        g,
+        ggml_tensor *        b,
+        ggml_tensor *        s,
+        int                  il) {
+    const auto * mctx_cur   = inp->mctx;
+    const auto   kv_head    = mctx_cur->get_head();
+    const uint32_t mem_size = mctx_cur->get_size();
+
+    const int64_t S_v          = s->ne[0];
+    const int64_t H_v          = s->ne[2];
+    const int64_t n_seqs       = s->ne[3];
+    const int64_t n_seq_tokens = q->ne[2];
+
+    if (!keep_rs()) {
+        auto attn_out = build_delta_net(q, k, v, g, b, s, il);
+        ggml_tensor * output    = attn_out.first;
+        ggml_tensor * new_state = attn_out.second;
+        cb(output, "attn_output", il);
+        cb(new_state, "new_state", il);
+
+        ggml_build_forward_expand(gf,
+                ggml_cpy(ctx0, new_state,
+                    ggml_view_2d(ctx0, ssm_states_all, hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
+                        kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+        return output;
+    }
+
+    const int64_t D = S_v * S_v * H_v;
+    const int64_t K = (int64_t) cparams.n_rs_seq + 1;
+
+    // TODO: remove pad + simplify
+    ggml_tensor * state_in_3d = ggml_reshape_3d(ctx0, s, D, 1, n_seqs);
+    ggml_tensor * state_3d    = ggml_pad(ctx0, state_in_3d, 0, K - 1, 0, 0);
+
+    ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, state_3d);
+    cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_CH, il);
+
+    const int64_t attn_score_elems    = S_v * H_v * n_seq_tokens * n_seqs;
+    const int64_t state_size_per_snap = S_v * S_v * H_v * n_seqs;
+
+    ggml_tensor * output = ggml_view_4d(ctx0, gdn_out,
+        S_v, H_v, n_seq_tokens, n_seqs,
+        ggml_row_size(gdn_out->type, S_v),
+        ggml_row_size(gdn_out->type, S_v * H_v),
+        ggml_row_size(gdn_out->type, S_v * H_v * n_seq_tokens),
+        0);
+    cb(output, "attn_output", il);
+
+    const size_t row_size = hparams.n_embd_s() * ggml_element_size(ssm_states_all);
+    for (int64_t k_i = 0; k_i < K; ++k_i) {
+        const uint32_t cache_slot = (uint32_t) (K - 1 - k_i);
+        ggml_tensor * src = ggml_view_4d(ctx0, gdn_out,
+            S_v, S_v, H_v, n_seqs,
+            ggml_row_size(gdn_out->type, S_v),
+            ggml_row_size(gdn_out->type, S_v * S_v),
+            ggml_row_size(gdn_out->type, S_v * S_v * H_v),
+            ggml_row_size(gdn_out->type, attn_score_elems + k_i * state_size_per_snap));
+        ggml_tensor * dst = ggml_view_2d(ctx0, ssm_states_all,
+            hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
+            ((size_t) cache_slot * mem_size + kv_head) * row_size);
+        ggml_build_forward_expand(gf, ggml_cpy(ctx0, src, dst));
+    }
+
+    return output;
+}
diff --git a/src/models/dots1.cpp b/src/models/dots1.cpp
index 5d1750fedda..435d27281c6 100644
--- a/src/models/dots1.cpp
+++ b/src/models/dots1.cpp
@@ -1,6 +1,76 @@
 #include "models.h"
 
-llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_params & params) :
+void llama_model_dots1::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+    switch (hparams.n_layer) {
+        case 62: type = LLM_TYPE_142B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_dots1::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    const int64_t n_ff_exp        = hparams.n_ff_exp;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_head_k * n_head, n_embd_head_k * n_head, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (i < (int) hparams.n_layer_dense_lead) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        } else {
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+            if (n_expert == 0) {
+                throw std::runtime_error("n_expert must be > 0");
+            }
+            if (n_expert_used == 0) {
+                throw std::runtime_error("n_expert_used must be > 0");
+            }
+
+            // MoE branch
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+            // Shared expert branch
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_dots1::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_dots1::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -113,7 +183,7 @@ llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/dream.cpp b/src/models/dream.cpp
index 8e7d9ae64c7..12ac6f1ce88 100644
--- a/src/models/dream.cpp
+++ b/src/models/dream.cpp
@@ -1,6 +1,54 @@
 #include "models.h"
 
-llm_build_dream::llm_build_dream(const llama_model & model, const llm_graph_params & params) :
+void llama_model_dream::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    // Dream models are primarily 7B with 28 layers
+    switch (hparams.n_layer) {
+        case 28:
+            type = LLM_TYPE_7B;
+            break;
+        default:
+            type = LLM_TYPE_UNKNOWN;
+    }
+    // Set non-causal attention for diffusion models
+    hparams.causal_attn = false;
+}
+
+void llama_model_dream::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    output_b    = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_dream::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_dream::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     //copied from qwen2
     const int64_t n_embd_head = hparams.n_embd_head_v();
@@ -80,7 +128,7 @@ llm_build_dream::llm_build_dream(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/ernie4-5-moe.cpp b/src/models/ernie4-5-moe.cpp
index fc6a3e17a09..8d9ff138676 100644
--- a/src/models/ernie4-5-moe.cpp
+++ b/src/models/ernie4-5-moe.cpp
@@ -1,6 +1,10 @@
 #include "models.h"
 
-llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params) :
+std::unique_ptr<llm_graph_context> llama_model_ernie4_5_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_ernie4_5_moe::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -120,7 +124,7 @@ llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/ernie4-5.cpp b/src/models/ernie4-5.cpp
index 033ba409eab..9b39c605e35 100644
--- a/src/models/ernie4-5.cpp
+++ b/src/models/ernie4-5.cpp
@@ -1,6 +1,79 @@
 #include "models.h"
 
-llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params) :
+void llama_model_ernie4_5::load_arch_hparams(llama_model_loader & ml) {
+    // paddleocr need mrope_section
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    if (arch == LLM_ARCH_ERNIE4_5_MOE) {
+        ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+        ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+        ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         hparams.n_moe_layer_step);
+        ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
+    }
+
+    switch (hparams.n_layer) {
+        case 18: type = LLM_TYPE_0_3B; break;
+        case 28: type = LLM_TYPE_21B_A3B; break;
+        case 54: type = LLM_TYPE_300B_A47B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_ernie4_5::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (arch == LLM_ARCH_ERNIE4_5_MOE && static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
+            int n_ff_exp = hparams.n_ff_exp;
+
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff_exp, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+
+            // Shared expert (if present)
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd    }, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, hparams.n_ff_shexp}, 0);
+            }
+        } else { // Dense layers
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_ernie4_5::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_ernie4_5::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -82,7 +155,7 @@ llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/eurobert.cpp b/src/models/eurobert.cpp
index 43fff4daf3a..ddf13c3028f 100644
--- a/src/models/eurobert.cpp
+++ b/src/models/eurobert.cpp
@@ -1,6 +1,41 @@
 #include "models.h"
 
-llm_build_eurobert::llm_build_eurobert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_eurobert::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    if (hparams.n_layer == 12) {
+        type = LLM_TYPE_SMALL;  // 0.2B
+    }
+}
+
+void llama_model_eurobert::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_eurobert::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_eurobert::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
diff --git a/src/models/exaone-moe.cpp b/src/models/exaone-moe.cpp
index 7b88a31d39d..76d91982fc5 100644
--- a/src/models/exaone-moe.cpp
+++ b/src/models/exaone-moe.cpp
@@ -1,6 +1,117 @@
 #include "models.h"
 
-llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params) :
+void llama_model_exaone_moe::load_arch_hparams(llama_model_loader & ml) {
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    hparams.n_swa = 128;
+    uint32_t swa_period = 4;
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+    hparams.set_swa_pattern(swa_period);
+    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,                hparams.rope_freq_base_train_swa, false);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,          hparams.n_swa);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
+
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,              hparams.nextn_predict_layers, false);
+    GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_30B_A3B; break;
+        case 48:
+        case 49: type = LLM_TYPE_235B_A22B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_exaone_moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_ff_exp       = hparams.n_ff_exp;
+    const int64_t n_ff_shexp     = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : n_ff_exp;
+    const int64_t head_dim       = hparams.n_embd_head_k();
+    const int64_t n_qo_dim       = n_head * head_dim;
+    const int64_t n_kv_dim       = n_head_kv * head_dim;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        int flags = 0;
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            // skip all tensors in the NextN layers
+            flags |= TENSOR_SKIP;
+        }
+
+        auto & layer = layers[i];
+        create_tensor_qkv(layer, i, n_embd, n_qo_dim, n_kv_dim, n_kv_dim, flags);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_qo_dim, n_embd}, flags);
+
+        layer.rope_freqs   = create_tensor(tn(LLM_TENSOR_ROPE_FREQS,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0) | flags);
+
+        layer.attn_norm    = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, flags);
+        layer.attn_q_norm  = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
+        layer.attn_k_norm  = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
+
+        layer.ffn_norm     = create_tensor(tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd}, flags);
+
+        // dense layers for first n_layer_dense_lead layers or nextn_predict_layers layers at the end
+        if (i < (int) hparams.n_layer_dense_lead || (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers)) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, flags);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, flags);
+        } else {
+            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, flags);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
+
+            if (n_expert == 0) {
+                throw std::runtime_error("n_expert must be > 0");
+            }
+            if (n_expert_used == 0) {
+                throw std::runtime_error("n_expert_used must be > 0");
+            }
+
+            layer.ffn_gate_exps  = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS,  "weight", i), {n_embd, n_ff_exp, n_expert}, flags);
+            layer.ffn_down_exps  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS,  "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
+            layer.ffn_up_exps    = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,    "weight", i), {n_embd, n_ff_exp, n_expert}, flags);
+
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, flags);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, flags);
+        }
+
+        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags);
+            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,   "weight", i), {n_embd}, flags);
+            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,   "weight", i), {n_embd}, flags);
+
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", i), {n_embd, n_vocab}, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), {n_embd, n_vocab}, flags | TENSOR_NOT_REQUIRED);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_exaone_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_exaone_moe::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_k();
 
@@ -126,7 +237,7 @@ llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/exaone.cpp b/src/models/exaone.cpp
index 4f845bf4106..c7e9960d718 100644
--- a/src/models/exaone.cpp
+++ b/src/models/exaone.cpp
@@ -1,6 +1,49 @@
 #include "models.h"
 
-llm_build_exaone::llm_build_exaone(const llama_model & model, const llm_graph_params & params) :
+void llama_model_exaone::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_8B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_exaone::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM,   "weight", i), {n_embd}, 0);
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN,   "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,     "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_exaone::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_exaone::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -84,7 +127,7 @@ llm_build_exaone::llm_build_exaone(const llama_model & model, const llm_graph_pa
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/exaone4.cpp b/src/models/exaone4.cpp
index 34bee3b8fe9..499e22dde81 100644
--- a/src/models/exaone4.cpp
+++ b/src/models/exaone4.cpp
@@ -1,7 +1,71 @@
 #include "models.h"
 
+void llama_model_exaone4::load_arch_hparams(llama_model_loader & ml) {
+    if (hparams.n_layer == 64) {    // 32B
+        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        hparams.n_swa = 4096;
+        uint32_t swa_period = 4;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period);
+
+        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+        hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+    }
+
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 30: type = LLM_TYPE_1_2B; break;
+        case 64: type = LLM_TYPE_32B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_exaone4::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_post_norm  = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_exaone4::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+        return std::make_unique<graph<true>>(*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
 template <bool iswa>
-llm_build_exaone4<iswa>::llm_build_exaone4(const llama_model & model, const llm_graph_params & params) :
+llama_model_exaone4::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_k();
 
@@ -99,7 +163,7 @@ llm_build_exaone4<iswa>::llm_build_exaone4(const llama_model & model, const llm_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
@@ -108,5 +172,5 @@ llm_build_exaone4<iswa>::llm_build_exaone4(const llama_model & model, const llm_
 }
 
 // Explicit template instantiations
-template struct llm_build_exaone4<false>;
-template struct llm_build_exaone4<true>;
+template struct llama_model_exaone4::graph<false>;
+template struct llama_model_exaone4::graph<true>;
diff --git a/src/models/falcon-h1.cpp b/src/models/falcon-h1.cpp
index 05accf90fad..94b65a3c7c9 100644
--- a/src/models/falcon-h1.cpp
+++ b/src/models/falcon-h1.cpp
@@ -1,6 +1,115 @@
 #include "models.h"
 
-llm_build_falcon_h1::llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params) :
+void llama_model_falcon_h1::load_arch_hparams(llama_model_loader & ml) {
+    // Common parameters
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    // SSM parameters
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    std::fill(hparams.recurrent_layer_arr.begin(), hparams.recurrent_layer_arr.end(), true);
+
+    switch (hparams.n_layer) {
+        case 36:
+            type = LLM_TYPE_0_5B; break;
+        case 24:
+            type = LLM_TYPE_1_5B; break;
+        case 66:
+            type = LLM_TYPE_1B; break;
+        case 32:
+            type = LLM_TYPE_3B; break;
+        case 44:
+            type = LLM_TYPE_7B; break;
+        case 72:
+            type = LLM_TYPE_34B; break;
+        default:
+            type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_falcon_h1::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    // Common
+    const int64_t hidden_size = hparams.n_embd; // hidden_size
+
+    // mamba2 Mixer SSM params
+    const int64_t ssm_conv_kernel_size  = hparams.ssm_d_conv; // ssm_conv_kernel_size
+    const int64_t ssm_n_groups          = hparams.ssm_n_group; // ssm_n_groups
+    const int64_t ssm_state_size        = hparams.ssm_d_state; // ssm_state_size
+    const int64_t ssm_intermediate_size = hparams.ssm_d_inner; // TODO expand
+    const int64_t ssm_num_heads         = hparams.ssm_dt_rank; // ssm_num_heads
+    const int64_t ssm_conv_dim          = ssm_intermediate_size + 2 * ssm_n_groups * ssm_state_size;
+    const int64_t ssm_projection_size   = ssm_intermediate_size + ssm_conv_dim + ssm_num_heads;
+
+    // attn params
+    const int64_t attn_num_attention_head = hparams.n_head(0); // rename to: attn_num_attention_head
+    const int64_t attn_num_key_value_head = hparams.n_head_kv(0);
+
+    // ffn params
+    const int64_t ffn_intermediate_size = hparams.n_ff(0);
+
+    // embeddings
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, 0);
+
+    // output
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hidden_size, n_vocab}, TENSOR_NOT_REQUIRED);
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {hidden_size}, 0);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        /*SSM LAYERS*/
+        // ssm in
+        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {hidden_size, ssm_projection_size}, 0);
+        // ssm 1d conv
+        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {ssm_conv_kernel_size, ssm_conv_dim}, 0);
+        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {ssm_conv_dim}, TENSOR_NOT_REQUIRED);
+        // ssm_dt
+        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {ssm_num_heads}, 0);
+        // no "weight" suffix for these
+        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, ssm_num_heads}, 0);
+        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, ssm_num_heads}, 0);
+        // ssm_norm
+        layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {ssm_intermediate_size / ssm_n_groups, ssm_n_groups}, TENSOR_NOT_REQUIRED);
+        // out_proj
+        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {ssm_intermediate_size, hidden_size}, 0);
+
+        /*ATTENTION LAYERS*/
+        // attention layers (with optional bias)
+        create_tensor_qkv(layer, i, hidden_size, n_embd_head_k * attn_num_attention_head, attn_num_key_value_head * n_embd_head_k, attn_num_key_value_head * n_embd_head_v, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * attn_num_attention_head, hidden_size}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {hidden_size}, 0);
+
+
+        // feed forward (w/ optional biases)
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, i), {hidden_size}, 0);
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {hidden_size,   ffn_intermediate_size}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  ffn_intermediate_size, hidden_size}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {hidden_size,   ffn_intermediate_size}, 0);
+
+        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_falcon_h1::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_falcon_h1::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_mamba_base(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -91,7 +200,7 @@ llm_build_falcon_h1::llm_build_falcon_h1(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/falcon.cpp b/src/models/falcon.cpp
index 2f65fa56e1f..ad546ef2db5 100644
--- a/src/models/falcon.cpp
+++ b/src/models/falcon.cpp
@@ -1,6 +1,53 @@
 #include "models.h"
 
-llm_build_falcon::llm_build_falcon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_falcon::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_7B; break;
+        case 60: type = LLM_TYPE_40B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_falcon::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    {
+        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+
+        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+        if (!output) {
+            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU
+        }
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_falcon::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_falcon::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -105,7 +152,7 @@ llm_build_falcon::llm_build_falcon(const llama_model & model, const llm_graph_pa
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/gemma-embedding.cpp b/src/models/gemma-embedding.cpp
index b6de9551c52..4e07f5f2bda 100644
--- a/src/models/gemma-embedding.cpp
+++ b/src/models/gemma-embedding.cpp
@@ -1,6 +1,78 @@
 #include "models.h"
 
-llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params) :
+void llama_model_gemma_embedding::load_arch_hparams(llama_model_loader & ml) {
+    hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
+    uint32_t swa_period = 6;
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+    hparams.set_swa_pattern(swa_period);
+
+    hparams.causal_attn = false; // embeddings do not use causal attention
+
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    //applied only if model converted with --sentence-transformers-dense-modules
+    ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
+    ml.get_key(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out, false);
+    ml.get_key(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in, false);
+    ml.get_key(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out, false);
+
+    GGML_ASSERT((hparams.dense_2_feat_in == 0 || hparams.dense_2_feat_in == hparams.n_embd) && "dense_2_feat_in must be equal to n_embd");
+    GGML_ASSERT((hparams.dense_3_feat_out == 0 || hparams.dense_3_feat_out == hparams.n_embd) && "dense_3_feat_out must be equal to n_embd");
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_0_3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+    hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
+
+}
+
+void llama_model_gemma_embedding::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,   "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    // Dense linear weights
+    dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);
+    dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);
+
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gemma_embedding::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_gemma_embedding::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_k();
 
diff --git a/src/models/gemma.cpp b/src/models/gemma.cpp
index 09d2ff8bae7..1519682fdf6 100644
--- a/src/models/gemma.cpp
+++ b/src/models/gemma.cpp
@@ -1,6 +1,44 @@
 #include "models.h"
 
-llm_build_gemma::llm_build_gemma(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_gemma::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 18: type = LLM_TYPE_2B; break;
+        case 28: type = LLM_TYPE_7B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+   }
+}
+
+void llama_model_gemma::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gemma::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_gemma::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     ggml_tensor * cur;
@@ -92,7 +130,7 @@ llm_build_gemma::llm_build_gemma(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/gemma2-iswa.cpp b/src/models/gemma2.cpp
similarity index 52%
rename from src/models/gemma2-iswa.cpp
rename to src/models/gemma2.cpp
index 0ef07df8d01..ae3f9ffb530 100644
--- a/src/models/gemma2-iswa.cpp
+++ b/src/models/gemma2.cpp
@@ -1,6 +1,65 @@
 #include "models.h"
 
-llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_gemma2::load_arch_hparams(llama_model_loader & ml) {
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    hparams.n_swa = 4096; // default value of gemma 2
+    uint32_t swa_period = 2;
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+    hparams.set_swa_pattern(swa_period);
+    hparams.attn_soft_cap = true;
+    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,          hparams.rope_freq_base_train_swa, false);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING,      hparams.f_attn_logit_softcapping, false);
+    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);
+
+    switch (hparams.n_layer) {
+        case 26: type = LLM_TYPE_2B; break;
+        case 42: type = LLM_TYPE_9B; break;
+        case 46: type = LLM_TYPE_27B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+   }
+
+    // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L173
+    hparams.f_attention_scale = type == LLM_TYPE_27B
+        ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
+        : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
+}
+
+void llama_model_gemma2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gemma2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_gemma2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_k();
 
     ggml_tensor * cur;
@@ -104,7 +163,7 @@ llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const ll
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     // final logit soft-capping
     cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
diff --git a/src/models/gemma3.cpp b/src/models/gemma3.cpp
index 0da4af21c17..63a2b380e71 100644
--- a/src/models/gemma3.cpp
+++ b/src/models/gemma3.cpp
@@ -1,7 +1,87 @@
 #include "models.h"
 
+void llama_model_gemma3::load_arch_hparams(llama_model_loader & ml) {
+    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+    if (found_swa && hparams.n_swa > 0) {
+        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        uint32_t swa_period = 6;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period);
+
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+    } else {
+        hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+    }
+
+    hparams.f_final_logit_softcapping = 0.0f;
+    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 18: type = LLM_TYPE_270M; break;
+        case 26: type = LLM_TYPE_1B; break;
+        case 32: type = LLM_TYPE_8B; break; // Rnj-1
+        case 34: type = LLM_TYPE_4B; break;
+        case 48: type = LLM_TYPE_12B; break;
+        case 62: type = LLM_TYPE_27B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L289
+    hparams.f_attention_scale = type == LLM_TYPE_27B
+        ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
+        : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
+}
+
+void llama_model_gemma3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,   "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    // Dense linear weights
+    dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);
+    dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);
+
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gemma3::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+        return std::make_unique<graph<true>>(*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
 template <bool iswa>
-llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_gemma3::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_k();
 
     ggml_tensor * cur;
@@ -127,7 +207,7 @@ llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     if (hparams.f_final_logit_softcapping) {
         cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
@@ -141,5 +221,5 @@ llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_gr
     ggml_build_forward_expand(gf, cur);
 }
 
-template struct llm_build_gemma3<false>;
-template struct llm_build_gemma3<true>;
+template struct llama_model_gemma3::graph<false>;
+template struct llama_model_gemma3::graph<true>;
diff --git a/src/models/gemma3n-iswa.cpp b/src/models/gemma3n.cpp
similarity index 75%
rename from src/models/gemma3n-iswa.cpp
rename to src/models/gemma3n.cpp
index f8095417e06..6ec3a006081 100644
--- a/src/models/gemma3n-iswa.cpp
+++ b/src/models/gemma3n.cpp
@@ -1,5 +1,86 @@
 #include "models.h"
 
+void llama_model_gemma3n::load_arch_hparams(llama_model_loader & ml) {
+    uint32_t swa_period = 5;
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    hparams.set_swa_pattern(swa_period);
+
+    hparams.n_layer_kv_from_start     = 20;
+    hparams.f_attention_scale         = 1.0f;
+
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,          hparams.rope_freq_base_train_swa, false);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 30: type = LLM_TYPE_E2B; break;
+        case 35: type = LLM_TYPE_E4B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_gemma3n::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_altup      = hparams.n_altup;
+    const int64_t laurel_rank  = hparams.laurel_rank;
+    const int64_t n_embd_altup = hparams.n_embd_altup;
+
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    altup_proj        = create_tensor(tn(LLM_TENSOR_ALTUP_PROJ,        "weight"), {n_embd, n_embd, n_altup - 1}, 0);
+    altup_unembd_proj = create_tensor(tn(LLM_TENSOR_ALTUP_UNEMBD_PROJ, "weight"), {n_embd, n_embd, n_altup - 1}, 0);
+
+    per_layer_tok_embd   = create_tensor(tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight"), {n_embd_altup * n_layer, n_vocab}, 0);
+    per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_altup * n_layer}, 0);
+    per_layer_proj_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM,  "weight", 0), {n_embd_altup}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), {n_embd_head_k}, 0);
+        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head_k}, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        // altup & laurel
+        layer.per_layer_inp_gate   = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE,  "weight", i), {n_embd, n_embd_altup}, 0);
+        layer.per_layer_proj       = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ,      "weight", i), {n_embd_altup, n_embd}, 0);
+        layer.per_layer_post_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0);
+        layer.altup_correct_coef   = create_tensor(tn(LLM_TENSOR_ALTUP_CORRECT_COEF,  "weight", i), {n_altup, n_altup}, 0);
+        layer.altup_correct_scale  = create_tensor(tn(LLM_TENSOR_ALTUP_CORRECT_SCALE, "weight", i), {n_embd}, 0);
+        layer.altup_predict_coef   = create_tensor(tn(LLM_TENSOR_ALTUP_PREDICT_COEF,  "weight", i), {n_altup, n_altup * n_altup}, 0);
+        layer.altup_router         = create_tensor(tn(LLM_TENSOR_ALTUP_ROUTER,        "weight", i), {n_embd, n_altup}, 0);
+        layer.altup_router_norm    = create_tensor(tn(LLM_TENSOR_ALTUP_ROUTER_NORM,   "weight", i), {n_embd}, 0);
+        layer.laurel_l             = create_tensor(tn(LLM_TENSOR_LAUREL_L,            "weight", i), {n_embd, laurel_rank}, 0);
+        layer.laurel_r             = create_tensor(tn(LLM_TENSOR_LAUREL_R,            "weight", i), {laurel_rank, n_embd}, 0);
+        layer.laurel_post_norm     = create_tensor(tn(LLM_TENSOR_LAUREL_POST_NORM,    "weight", i), {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gemma3n::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
 // get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
 static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {
     GGML_ASSERT(idx < (int) x->ne[2]);
@@ -7,7 +88,7 @@ static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, in
                         idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
 }
 
-llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params) :
+llama_model_gemma3n::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params),
     model(model),
     n_embd_head(model.hparams.n_embd_head_k()),
@@ -215,7 +296,7 @@ llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     {
         // final logit soft-capping
@@ -229,13 +310,13 @@ llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const
     ggml_build_forward_expand(gf, cur);
 }
 
-ggml_tensor * llm_build_gemma3n_iswa::calc_magnitude(ggml_tensor * x) {
+ggml_tensor * llama_model_gemma3n::graph::calc_magnitude(ggml_tensor * x) {
     return ggml_sqrt(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, x)));
 }
 
 // equivalent to get_per_layer_inputs() in python code
 // output shape: [n_embd_altup, n_layer, n_tokens]
-ggml_tensor * llm_build_gemma3n_iswa::build_inp_per_layer() {
+ggml_tensor * llama_model_gemma3n::graph::build_inp_per_layer() {
     auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
     ggml_tensor * inp_per_layer;
     float tok_embd_scale = sqrtf((float) n_embd_altup);
@@ -268,7 +349,7 @@ ggml_tensor * llm_build_gemma3n_iswa::build_inp_per_layer() {
 // equivalent to project_per_layer_inputs() in python code
 // this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim
 // output shape: [n_embd_altup, n_tokens, n_layer]
-ggml_tensor * llm_build_gemma3n_iswa::project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer) {
+ggml_tensor * llama_model_gemma3n::graph::project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer) {
     const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);
     const float per_layer_input_scale      = 1.0f / sqrtf(2.0f);
 
@@ -291,7 +372,7 @@ ggml_tensor * llm_build_gemma3n_iswa::project_per_layer_inputs(ggml_tensor * inp
 
 // input cur shape: [n_altup, n_tokens]
 // output    shape: [n_altup, n_tokens]
-ggml_tensor * llm_build_gemma3n_iswa::laurel(ggml_tensor * cur, int il) {
+ggml_tensor * llama_model_gemma3n::graph::laurel(ggml_tensor * cur, int il) {
     ggml_tensor * tmp = cur;
     tmp               = build_lora_mm(model.layers[il].laurel_l, tmp);
     tmp               = build_lora_mm(model.layers[il].laurel_r, tmp);
@@ -303,7 +384,7 @@ ggml_tensor * llm_build_gemma3n_iswa::laurel(ggml_tensor * cur, int il) {
 
 // input x shape: [n_embd, n_tokens]
 // output  shape: [n_embd, n_tokens]
-ggml_tensor * llm_build_gemma3n_iswa::gaussian_topk(ggml_tensor * x) {
+ggml_tensor * llama_model_gemma3n::graph::gaussian_topk(ggml_tensor * x) {
     ggml_tensor * mean = ggml_mean(ctx0, x);
     ggml_tensor * std  = ggml_sqrt(ctx0, ggml_scale(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x, mean))),
                                                     1.0f / (float) (x->ne[0] - 1)));
@@ -318,7 +399,7 @@ ggml_tensor * llm_build_gemma3n_iswa::gaussian_topk(ggml_tensor * x) {
 // equivalent to compute_router_modalities() in python code
 // input x shape: [n_embd,  n_tokens]
 // output  shape: [n_altup, n_tokens]
-ggml_tensor * llm_build_gemma3n_iswa::altup_compute_router_modalities(ggml_tensor * x, int il) {
+ggml_tensor * llama_model_gemma3n::graph::altup_compute_router_modalities(ggml_tensor * x, int il) {
     ggml_tensor * router_inputs = build_norm(x, model.layers[il].altup_router_norm, NULL, LLM_NORM_RMS, il);
 
     // router_input_scale
@@ -330,7 +411,7 @@ ggml_tensor * llm_build_gemma3n_iswa::altup_compute_router_modalities(ggml_tenso
 
 // input cur shape: [n_embd, n_tokens, n_altup]
 // output    shape: [n_embd, n_tokens, n_altup]
-ggml_tensor * llm_build_gemma3n_iswa::altup_predict(ggml_tensor * cur, int il) {
+ggml_tensor * llama_model_gemma3n::graph::altup_predict(ggml_tensor * cur, int il) {
     ggml_tensor * activated  = ggml_view_2d_slice(ctx0, cur, i_altup_act);      // [n_embd, n_tokens]
     ggml_tensor * modalities = altup_compute_router_modalities(activated, il);  // [n_altup, n_tokens]
     cb(modalities, "modalities", il);
@@ -355,7 +436,7 @@ ggml_tensor * llm_build_gemma3n_iswa::altup_predict(ggml_tensor * cur, int il) {
 // input predictions       shape: [n_embd, n_tokens, n_altup]
 // input activated         shape: [n_embd, n_tokens]
 // output                  shape: [n_embd, n_tokens, n_altup]
-ggml_tensor * llm_build_gemma3n_iswa::altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il) {
+ggml_tensor * llama_model_gemma3n::graph::altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il) {
     ggml_tensor * modalities = altup_compute_router_modalities(activated, il);  // [n_altup, n_tokens]
     cb(modalities, "modalities", il);
 
diff --git a/src/models/gemma4-iswa.cpp b/src/models/gemma4.cpp
similarity index 62%
rename from src/models/gemma4-iswa.cpp
rename to src/models/gemma4.cpp
index c7fb7747414..4f9d8b18bc7 100644
--- a/src/models/gemma4-iswa.cpp
+++ b/src/models/gemma4.cpp
@@ -1,5 +1,140 @@
 #include "models.h"
 
+void llama_model_gemma4::load_arch_hparams(llama_model_loader & ml) {
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+
+    uint32_t n_kv_shared_layers = 0;
+    ml.get_key(LLM_KV_ATTENTION_SHARED_KV_LAYERS, n_kv_shared_layers, false);
+
+    hparams.n_layer_kv_from_start = hparams.n_layer - (int32_t)n_kv_shared_layers;
+    hparams.f_attention_scale     = 1.0f; // Gemma4 uses self.scaling = 1.0 (no pre-attn scaling)
+
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,          hparams.rope_freq_base_train_swa, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp, false);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER,  hparams.n_embd_per_layer);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA,    hparams.n_embd_head_k_swa);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,  hparams.n_embd_head_v_swa);
+    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);
+
+    switch (hparams.n_layer) {
+        case 30: type = LLM_TYPE_26B_A4B; break;
+        case 35: type = LLM_TYPE_E2B; break;
+        case 42: type = LLM_TYPE_E4B; break;
+        case 60: type = LLM_TYPE_31B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_gemma4::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const uint32_t n_embd_per_layer = hparams.n_embd_per_layer;
+    const int64_t  n_ff_exp         = hparams.n_ff_exp;
+
+    if (n_embd_head_k != n_embd_head_v) {
+        throw std::runtime_error("Gemma 4 requires n_embd_head_k == n_embd_head_v");
+    }
+    if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) {
+        throw std::runtime_error("Gemma 4 requires n_embd_head_k_swa == n_embd_head_v_swa");
+    }
+
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    if (n_embd_per_layer > 0) {
+        per_layer_tok_embd   = create_tensor(tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight"),    {n_embd_per_layer * n_layer, n_vocab}, 0);
+        per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_per_layer * n_layer}, 0);
+        per_layer_proj_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM,  "weight", 0), {n_embd_per_layer}, 0);
+    }
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+    int rope_freqs_flag = 0;
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+        const int64_t n_head      = hparams.n_head(i);
+        const int64_t n_embd_head = hparams.n_embd_head_k(i);
+        const int64_t n_embd_k    = hparams.n_embd_k_gqa(i);
+        const int64_t n_embd_v    = hparams.n_embd_v_gqa(i);
+        const int     kv_flags    = hparams.has_kv(i) ? 0 : TENSOR_NOT_REQUIRED;
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        // note: use_alternative_attention (v_proj is optional, if it's not present, use k_proj)
+        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head * n_head}, 0);
+        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k}, kv_flags);
+        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0);
+
+        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), {n_embd_head}, 0);
+        layer.attn_k_norm    = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM,    "weight", i), {n_embd_head}, kv_flags);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1u}, TENSOR_NOT_REQUIRED);
+
+        if (!hparams.is_swa(i)) {
+            // full_attention layers use rope_freqs for proportional rope
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_embd_head/2}, rope_freqs_flag);
+            rope_freqs_flag = TENSOR_DUPLICATED;
+        }
+
+        // handle use_double_wide_mlp
+        int64_t n_ff_cur = hparams.n_ff(i);
+
+        // for expert layers, we use normal FFN as shared expert (same as python code)
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff_cur}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff_cur}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_cur, n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        // MoE router
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+        bool has_expert = layer.ffn_gate_inp != nullptr;
+
+        // norm
+        if (has_expert) {
+            layer.ffn_gate_inp_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "scale", i), {n_embd}, 0);
+
+            layer.ffn_pre_norm_2  = create_tensor(tn(LLM_TENSOR_FFN_PRE_NORM_2,  "weight", i), {n_embd}, 0);
+            layer.ffn_post_norm_1 = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM_1, "weight", i), {n_embd}, 0);
+            layer.ffn_post_norm_2 = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM_2, "weight", i), {n_embd}, 0);
+
+            // MoE FFN
+            layer.ffn_gate_up_exps  = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS,  "weight", i), {n_embd, n_ff_exp * 2, n_expert}, TENSOR_NOT_REQUIRED);
+
+            if (layer.ffn_gate_up_exps == nullptr) {
+                layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+                layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+            }
+
+            layer.ffn_down_exps     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS,     "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
+
+            // per-expert scale will be loaded as down_exps_s at the end of the current switch case
+        }
+
+        // per-layer embeddings
+        if (n_embd_per_layer > 0) {
+            layer.per_layer_inp_gate   = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE,  "weight", i), {n_embd, n_embd_per_layer}, 0);
+            layer.per_layer_proj       = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ,      "weight", i), {n_embd_per_layer, n_embd}, 0);
+            layer.per_layer_post_norm  = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gemma4::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
 // get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
 static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {
     GGML_ASSERT(idx < (int) x->ne[2]);
@@ -7,7 +142,7 @@ static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, in
                         idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
 }
 
-llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const llm_graph_params & params) :
+llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_params & params) :
         llm_graph_context(params),
         model(model),
         n_embd_per_layer(model.hparams.n_embd_per_layer) {
@@ -157,8 +292,8 @@ llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const ll
 
             cur_moe = build_moe_ffn(cur_moe,
                     nullptr, // gate_inp
-                    nullptr, // up_exps
-                    nullptr, // gate_exps
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
                     model.layers[il].ffn_down_exps,
                     nullptr, // exp_probs_b (not used for gemma4)
                     n_expert, n_expert_used,
@@ -167,8 +302,8 @@ llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const ll
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     il, logits,
                     model.layers[il].ffn_gate_up_exps,
-                    nullptr, // up_exps_s
-                    nullptr, // gate_exps_s
+                    model.layers[il].ffn_up_exps_s,
+                    model.layers[il].ffn_gate_exps_s,
                     model.layers[il].ffn_down_exps_s);
             cur_moe = build_norm(cur_moe,
                     model.layers[il].ffn_post_norm_2, nullptr,
@@ -245,7 +380,7 @@ llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const ll
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     if (hparams.f_final_logit_softcapping) {
         cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
@@ -261,7 +396,7 @@ llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const ll
 
 // equivalent to get_per_layer_inputs() in python code
 // output shape: [n_embd_per_layer, n_layer, n_tokens]
-ggml_tensor * llm_build_gemma4_iswa::build_inp_per_layer() {
+ggml_tensor * llama_model_gemma4::graph::build_inp_per_layer() {
     auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
 
     ggml_tensor * inp_per_layer;
@@ -299,7 +434,7 @@ ggml_tensor * llm_build_gemma4_iswa::build_inp_per_layer() {
 // inp_batch     shape: [n_embd, n_tokens]
 // inp_per_layer shape: [n_embd_per_layer, n_layer, n_tokens] (from build_inp_per_layer)
 // output shape: [n_embd_per_layer, n_tokens, n_layer]
-ggml_tensor * llm_build_gemma4_iswa::project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer) {
+ggml_tensor * llama_model_gemma4::graph::project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer) {
     const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);
     const float per_layer_input_scale      = 1.0f / sqrtf(2.0f);
 
diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp
new file mode 100644
index 00000000000..af2b55ef563
--- /dev/null
+++ b/src/models/glm-dsa.cpp
@@ -0,0 +1,155 @@
+#include "models.h"
+
+void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,     hparams.n_ff_exp);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
+
+    // MoE parameters
+    ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
+    ml.get_key(LLM_KV_EXPERT_USED_COUNT,           hparams.n_expert_used);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+
+    // deepseek MLA parameters
+    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,      hparams.n_lora_q);
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,     hparams.n_lora_kv);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,   hparams.n_embd_head_k_mla_impl, false);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);
+
+    // DSA parameters
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K,      hparams.indexer_top_k);
+
+    // Expert gating function (GLM-4.5 uses sigmoid)
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+        hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+    }
+
+    // NextN/MTP parameters
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.nextn_predict_layers, false);
+    GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+
+    // TODO: when MTP is implemented, this should probably be updated if needed
+    hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+
+    switch (hparams.n_layer) {
+        case 79: type = LLM_TYPE_744B_A40B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    const bool is_mla = hparams.is_mla();
+    if (!is_mla) {
+        throw std::runtime_error("GLM_DSA architecture requires MLA");
+    }
+
+    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
+    const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
+    const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
+
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+    const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
+
+    const int64_t q_lora_rank  = hparams.n_lora_q;
+    const int64_t kv_lora_rank = hparams.n_lora_kv;
+
+    const int64_t n_ff_exp        = hparams.n_ff_exp;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    // try to load output.weight, if not found, use token_embd (tied embeddings)
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (!output) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        int flags = 0;
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            // skip all tensors in the NextN layers
+            // TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
+            flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
+        }
+
+        auto & layer = layers[i];
+
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
+        layer.attn_q_a_norm  = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
+        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
+
+        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
+        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);
+
+        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
+
+        // note: only old legacy GGUF files will have the unsplit wkv_b tensor in
+        layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);
+        layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
+
+        // DSA indexer
+        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags);
+        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags);
+        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags);
+        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags);
+        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
+        if (i < (int) hparams.n_layer_dense_lead) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, flags);
+        } else {
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+            if (n_expert == 0) {
+                throw std::runtime_error("n_expert must be > 0");
+            }
+            if (n_expert_used == 0) {
+                throw std::runtime_error("n_expert_used must be > 0");
+            }
+
+            // MoE branch
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, flags);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);
+
+            // Shared expert branch
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, flags);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
+        }
+
+        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
+            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
+            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
+
+            // Optional tensors
+            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_glm_dsa::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/glm4-moe.cpp b/src/models/glm4-moe.cpp
index 8d4f4a01553..27654b8cba3 100644
--- a/src/models/glm4-moe.cpp
+++ b/src/models/glm4-moe.cpp
@@ -1,6 +1,139 @@
 #include "models.h"
 
-llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_glm4_moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,     hparams.n_ff_exp);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
+
+    // MoE parameters
+    ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
+    ml.get_key(LLM_KV_EXPERT_USED_COUNT,           hparams.n_expert_used);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+
+    // Expert gating function (GLM-4.5 uses sigmoid)
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+        hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+    }
+
+    // NextN/MTP parameters
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.nextn_predict_layers, false);
+    GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+
+    // TODO: when MTP is implemented, this should probably be updated if needed
+    hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+
+    switch (hparams.n_layer) {
+        case 47: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air (46 layers + 1 NextN layer)
+        case 48: type = LLM_TYPE_102B_A12B; break; // Solar Open
+        case 93: type = LLM_TYPE_355B_A32B; break; // GLM-4.5 (92 layers + 1 NextN layer)
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+
+    GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
+    GGML_ASSERT(hparams.n_expert_used > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+    }
+
+    // Load ALL tensors including NextN layer to satisfy total tensor count
+    // but only PROCESS up to last layer (skipping final NextN layer) in forward pass
+    for (int i = 0; i < n_layer; ++i) {
+        int flags = 0;
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            // skip all tensors in the NextN layers
+            flags |= TENSOR_SKIP;
+        }
+
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
+
+        // GLM-style attention with bias terms
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
+
+        // K/Q norm tensors (optional for GLM-4.5 355B variant)
+        layer.attn_q_norm = create_tensor(
+            tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
+        layer.attn_k_norm = create_tensor(
+            tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
+
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, flags);
+
+        // Check if this layer uses MoE or dense FFN based on n_layer_dense_lead
+        // GLM 4.5 uses hybrid architecture: layer 0 is dense, layers 1+ are MoE
+        const bool use_moe = (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead);
+
+        if (use_moe) {
+            // MoE layers
+            layer.ffn_gate_inp =
+                create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, flags);
+
+            // MoE branch
+            const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+            layer.ffn_gate_exps = create_tensor(
+                tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
+            layer.ffn_down_exps = create_tensor(
+                tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
+            layer.ffn_up_exps = create_tensor(
+                tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
+
+            // Shared expert
+            if (n_expert_shared > 0) {
+                const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
+                layer.ffn_gate_shexp = create_tensor(
+                    tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
+                layer.ffn_down_shexp = create_tensor(
+                    tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
+                layer.ffn_up_shexp = create_tensor(
+                    tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
+            }
+        } else {
+            // Dense layers (first k layers) - GLM uses separate gate/up projections
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, flags);
+        }
+
+        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
+            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
+            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
+
+            // Optional tensors
+            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_glm4_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -142,7 +275,7 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/glm4.cpp b/src/models/glm4.cpp
index f0bfda393fa..7c242fed298 100644
--- a/src/models/glm4.cpp
+++ b/src/models/glm4.cpp
@@ -1,6 +1,78 @@
 #include "models.h"
 
-llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_glm4::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
+
+    // NextN/MTP parameters (GLM-OCR)
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
+    GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+
+    // TODO: when MTP is implemented, this should probably be updated if needed
+    hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+
+    switch (hparams.n_layer) {
+        case 17: type = LLM_TYPE_1B; break; // GLM-OCR
+        case 40: type = LLM_TYPE_9B; break;
+        case 61: type = LLM_TYPE_32B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_glm4::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        int flags = 0;
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            // skip all tensors in the NextN layers
+            flags |= TENSOR_SKIP;
+        }
+
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
+
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
+
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, flags);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, flags);
+
+        layer.ffn_post_norm  = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, flags);
+
+        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
+        if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
+            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
+            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
+
+            // Optional tensors
+            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_glm4::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_glm4::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -113,7 +185,7 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params
     res->t_embd = cur;
 
     // Output projection
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/gpt2.cpp b/src/models/gpt2.cpp
index f8dc53eb723..e2dcc8b1521 100644
--- a/src/models/gpt2.cpp
+++ b/src/models/gpt2.cpp
@@ -1,6 +1,60 @@
 #include "models.h"
 
-llm_build_gpt2::llm_build_gpt2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_gpt2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    switch (hparams.n_layer) {
+        case 12: type = LLM_TYPE_SMALL; break;
+        case 24: type = LLM_TYPE_MEDIUM; break;
+        case 36: type = LLM_TYPE_LARGE; break;
+        case 48: type = LLM_TYPE_XL; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_gpt2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
+
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gpt2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_gpt2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -84,7 +138,7 @@ llm_build_gpt2::llm_build_gpt2(const llama_model & model, const llm_graph_params
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/gptneox.cpp b/src/models/gptneox.cpp
index 0016ddede43..443e35addf2 100644
--- a/src/models/gptneox.cpp
+++ b/src/models/gptneox.cpp
@@ -1,6 +1,89 @@
 #include "models.h"
 
-llm_build_gptneox::llm_build_gptneox(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_gptneox::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL,   hparams.use_par_res);
+    switch (hparams.n_layer) {
+        case 6:
+            switch (hparams.n_ff()) {
+                case 512:  type = LLM_TYPE_14M; break;
+                case 2048: type = LLM_TYPE_70M; break;
+                default:   type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 12:
+            switch (hparams.n_ff()) {
+                case 3072: type = LLM_TYPE_160M; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 16:
+            switch (hparams.n_ff()) {
+                case 8192: type = LLM_TYPE_1B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 24:
+            switch (hparams.n_ff()) {
+                case 4096: type = LLM_TYPE_410M; break;
+                case 8192: type = LLM_TYPE_1_4B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 32:
+            switch (hparams.n_ff()) {
+                case 10240: type = LLM_TYPE_2_8B; break;
+                case 16384: type = LLM_TYPE_6_9B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 36:
+            switch (hparams.n_ff()) {
+                case 20480: type = LLM_TYPE_12B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 44:
+            switch (hparams.n_ff()) {
+                case 24576: type = LLM_TYPE_20B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_gptneox::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
+
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gptneox::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_gptneox::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -126,7 +209,7 @@ llm_build_gptneox::llm_build_gptneox(const llama_model & model, const llm_graph_
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/granite-hybrid.cpp b/src/models/granite-hybrid.cpp
index e983742bef5..27f6706ea10 100644
--- a/src/models/granite-hybrid.cpp
+++ b/src/models/granite-hybrid.cpp
@@ -1,6 +1,137 @@
 #include "models.h"
 
-llm_build_granite_hybrid::llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params) :
+void llama_model_granite_hybrid::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale, /* required */ false);
+    ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale, /* required */ false);
+    ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, /* required */ false);
+    ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, /* required */ false);
+
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    // Granite uses rope_finetuned as a switch for rope, so default to true
+    bool rope_finetuned = true;
+    ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
+    hparams.rope_finetuned = rope_finetuned;
+
+    // A layer is recurrent IFF the n_head_kv value is set to 0
+    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+        hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
+    }
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_embd) {
+        case 768: type = LLM_TYPE_350M; break;
+        case 1536: type = (hparams.n_ff() == 512 ? LLM_TYPE_7B_A1B : LLM_TYPE_1B); break;
+        case 2048: case 2560: type = LLM_TYPE_3B; break;
+        case 4096: type = LLM_TYPE_32B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    // For Granite MoE Shared
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
+}
+
+void llama_model_granite_hybrid::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    // mamba2 Mixer SSM params
+    // NOTE: int64_t for tensor dimensions
+    const int64_t d_conv     = hparams.ssm_d_conv;
+    const int64_t d_inner    = hparams.ssm_d_inner;
+    const int64_t d_state    = hparams.ssm_d_state;
+    const int64_t n_ssm_head = hparams.ssm_dt_rank;
+    const int64_t n_group    = hparams.ssm_n_group;
+    const int64_t d_in_proj  = 2*d_inner + 2*n_group*d_state + n_ssm_head;
+
+    // only an expansion factor of 2 is supported for now
+    GGML_ASSERT(2 * n_embd == d_inner);
+
+    // embeddings
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    {
+        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+        // if output is NULL, init from the input tok embed, duplicated to allow offloading
+        if (output == NULL) {
+            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+        }
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // norm
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.is_recurrent(i)) {
+            // ssm layers
+            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
+
+            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
+            layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, TENSOR_NOT_REQUIRED);
+
+            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_ssm_head}, 0);
+
+            // no "weight" suffix for these
+            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_ssm_head}, 0);
+            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_ssm_head}, 0);
+
+            layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
+
+            // out_proj
+            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+        } else {
+            // attention layers (with optional bias)
+            const int64_t n_head_i = hparams.n_head(i);
+            const int64_t n_embd_k_gqa_i = hparams.n_embd_k_gqa(i);
+            const int64_t n_embd_v_gqa_i = hparams.n_embd_v_gqa(i);
+            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_i, n_embd_k_gqa_i, n_embd_v_gqa_i, 0);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, 0);
+            layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        }
+
+        // feed forward (w/ optional biases)
+        if (n_expert > 0) {
+            // MoE FFN
+            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+            // For Granite MoE Shared
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+            }
+        } else {
+            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_granite_hybrid::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_granite_hybrid::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_mamba_base(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -55,7 +186,7 @@ llm_build_granite_hybrid::llm_build_granite_hybrid(const llama_model & model, co
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     // For Granite architectures - scale logits
     if (hparams.f_logit_scale) {
@@ -67,7 +198,7 @@ llm_build_granite_hybrid::llm_build_granite_hybrid(const llama_model & model, co
     ggml_build_forward_expand(gf, cur);
 }
 
-ggml_tensor * llm_build_granite_hybrid::build_attention_layer(ggml_tensor *             cur,
+ggml_tensor * llama_model_granite_hybrid::graph::build_attention_layer(ggml_tensor *             cur,
                                                               ggml_tensor *             inp_pos,
                                                               llm_graph_input_attn_kv * inp_attn,
                                                               const llama_model &       model,
@@ -98,7 +229,7 @@ ggml_tensor * llm_build_granite_hybrid::build_attention_layer(ggml_tensor *
     return cur;
 }
 
-ggml_tensor * llm_build_granite_hybrid::build_layer_ffn(ggml_tensor *       cur,
+ggml_tensor * llama_model_granite_hybrid::graph::build_layer_ffn(ggml_tensor *       cur,
                                                         ggml_tensor *       inpSA,
                                                         const llama_model & model,
                                                         const int           il) {
diff --git a/src/models/granite-moe.cpp b/src/models/granite-moe.cpp
new file mode 100644
index 00000000000..0d89bc1f340
--- /dev/null
+++ b/src/models/granite-moe.cpp
@@ -0,0 +1,89 @@
+#include "models.h"
+
+void llama_model_granite_moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);
+    ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale, false);
+    ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, false);
+    ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, false);
+
+    // Granite uses rope_finetuned as a switch for rope, so default to true
+    bool rope_finetuned = true;
+    ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
+    hparams.rope_finetuned = rope_finetuned;
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_3B; break;
+        case 40: type = LLM_TYPE_3B; break;
+        // Add additional layer/vocab/etc checks here for other model sizes
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    // For Granite MoE Shared
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
+}
+
+void llama_model_granite_moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+        else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        if (n_expert == 0) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+            // optional MLP bias
+            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        } else {
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+            // For Granite MoE Shared
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_granite_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/granite.cpp b/src/models/granite.cpp
index 6ea90285225..cda4aa231fa 100644
--- a/src/models/granite.cpp
+++ b/src/models/granite.cpp
@@ -1,6 +1,93 @@
 #include "models.h"
 
-llm_build_granite::llm_build_granite(
+void llama_model_granite::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);
+    ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale, false);
+    ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, false);
+    ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, false);
+
+    // Granite uses rope_finetuned as a switch for rope, so default to true
+    bool rope_finetuned = true;
+    ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
+    hparams.rope_finetuned = rope_finetuned;
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_3B; break;
+        case 40: type = LLM_TYPE_3B; break;
+        // Add additional layer/vocab/etc checks here for other model sizes
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    // For Granite MoE Shared
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
+}
+
+void llama_model_granite::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+        else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        if (n_expert == 0) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+            // optional MLP bias
+            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        } else {
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+            // For Granite MoE Shared
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_granite::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_granite::graph::graph(
     const llama_model & model,
     const llm_graph_params & params)
     : llm_graph_context(params) {
@@ -58,7 +145,7 @@ llm_build_granite::llm_build_granite(
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     // For Granite architectures - scale logits
     cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
@@ -68,7 +155,7 @@ llm_build_granite::llm_build_granite(
     ggml_build_forward_expand(gf, cur);
 }
 
-ggml_tensor * llm_build_granite::build_attention_layer(
+ggml_tensor * llama_model_granite::graph::build_attention_layer(
           ggml_tensor             * cur,
           ggml_tensor             * inp_pos,
           llm_graph_input_attn_kv * inp_attn,
@@ -107,7 +194,7 @@ ggml_tensor * llm_build_granite::build_attention_layer(
     return cur;
 }
 
-ggml_tensor * llm_build_granite::build_layer_ffn(
+ggml_tensor * llama_model_granite::graph::build_layer_ffn(
           ggml_tensor       * cur,
           ggml_tensor       * inpSA,
     const llama_model       & model,
diff --git a/src/models/grok.cpp b/src/models/grok.cpp
index b8f35afdc03..7c46ec1c0f2 100644
--- a/src/models/grok.cpp
+++ b/src/models/grok.cpp
@@ -1,6 +1,89 @@
 #include "models.h"
 
-llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_grok::load_arch_hparams(llama_model_loader & ml) {
+    // defaults for old GGUFs
+    hparams.yarn_beta_fast = 8.0f;
+    hparams.f_logit_scale = 0.5773502691896257f;
+    hparams.f_embedding_scale = 78.38367176906169f;
+    hparams.f_attn_out_scale = 0.08838834764831845f;
+    hparams.f_attn_logit_softcapping = 30.0f;
+    hparams.f_router_logit_softcapping = 30.0f;
+    // no final_logit_softcapping in grok-1
+    hparams.f_final_logit_softcapping = 0.0f;
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,  hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,   hparams.n_ff_exp, false);
+    ml.get_key(LLM_KV_LOGIT_SCALE,                  hparams.f_logit_scale, false);
+    ml.get_key(LLM_KV_EMBEDDING_SCALE,              hparams.f_embedding_scale, false);
+    ml.get_key(LLM_KV_ATTENTION_OUTPUT_SCALE,       hparams.f_attn_out_scale, false);
+    ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING,       hparams.f_attn_logit_softcapping, false);
+    ml.get_key(LLM_KV_ROUTER_LOGIT_SOFTCAPPING,     hparams.f_router_logit_softcapping, false);
+    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,      hparams.f_final_logit_softcapping, false);
+
+    ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH,  hparams.attn_temp_length, false);
+    ml.get_key(LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,  hparams.yarn_ext_factor, false);
+    ml.get_key(LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, hparams.yarn_attn_factor, false);
+    ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST,   hparams.yarn_beta_fast, false);
+    ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,   hparams.yarn_beta_slow, false);
+
+    switch (hparams.n_layer) {
+        case 64: type = LLM_TYPE_314B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_grok::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_expert == 0) {
+        throw std::runtime_error(arch_name() + " model cannot have zero experts");
+    }
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff/* / n_expert_used*/; // grok-1 n_ff_exp == n_ff
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff,   n_embd}, TENSOR_NOT_REQUIRED);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd,   n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        if (!layer.ffn_post_norm) {
+            layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_grok::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_grok::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -123,7 +206,7 @@ llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
 
diff --git a/src/models/grovemoe.cpp b/src/models/grovemoe.cpp
index 151108a2a71..1cab75adc7f 100644
--- a/src/models/grovemoe.cpp
+++ b/src/models/grovemoe.cpp
@@ -1,6 +1,70 @@
 #include "models.h"
 
-llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_graph_params & params) :
+void llama_model_grovemoe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,  hparams.n_ff_chexp, false);
+    ml.get_key(LLM_KV_EXPERT_GROUP_SCALE,                hparams.expert_group_scale);
+    ml.get_key(LLM_KV_EXPERTS_PER_GROUP,                 hparams.n_group_experts);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 48: type = LLM_TYPE_30B_A3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_grovemoe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for GROVEMOE");
+    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for GROVEMOE");
+    GGML_ASSERT(hparams.n_group_experts > 0 && "n_group_experts must be > 0 for GROVEMOE");
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+        // MoE branch
+        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+        const int64_t n_ff_chexp = hparams.n_ff_chexp ? hparams.n_ff_chexp : n_embd_head_k;
+        const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
+
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+        layer.ffn_gate_chexps = create_tensor(tn(LLM_TENSOR_FFN_GATE_CHEXPS, "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
+        layer.ffn_down_chexps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_CHEXPS, "weight", i), {n_ff_chexp,   n_embd, n_chunk_expert}, 0);
+        layer.ffn_up_chexps   = create_tensor(tn(LLM_TENSOR_FFN_UP_CHEXPS,   "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_grovemoe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_grovemoe::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head    = hparams.n_embd_head_v();
     const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
@@ -120,7 +184,7 @@ llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/hunyuan-dense.cpp b/src/models/hunyuan-dense.cpp
index e4e837eb4f1..c137bd37c02 100644
--- a/src/models/hunyuan-dense.cpp
+++ b/src/models/hunyuan-dense.cpp
@@ -1,113 +1,6 @@
 #include "models.h"
 
-llm_build_hunyuan_dense::llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v();
-
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
-    GGML_ASSERT(n_embd_head == n_rot);
-
-    ggml_tensor * cur;
-    ggml_tensor * inpL;
-
-    inpL = build_inp_embd(model.tok_embd);
-
-    // inp_pos - contains the positions
-    ggml_tensor * inp_pos = build_inp_pos();
-
-    auto * inp_attn = build_attn_inp_kv();
-
-    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
-
-    ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-    for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-
-        // norm
-        cur = build_norm(inpL,
-                model.layers[il].attn_norm, NULL,
-                LLM_NORM_RMS, il);
-        cb(cur, "attn_norm", il);
-        // self-attention
-        {
-            // rope freq factors for llama3; may return nullptr for llama2 and other models
-            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-            // compute Q and K and RoPE them
-            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
-                    n_embd_head, n_head, n_head_kv, il);
-
-            Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
-
-            Kcur = ggml_rope_ext(
-                        ctx0, Kcur, inp_pos, rope_factors,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-
-            Kcur = build_norm(Kcur,
-                        model.layers[il].attn_k_norm, nullptr,
-                        LLM_NORM_RMS, il);
-            cb(Kcur, "Kcur_norm", il);
-
-            Qcur = build_norm(Qcur,
-                        model.layers[il].attn_q_norm, nullptr,
-                        LLM_NORM_RMS, il);
-            cb(Qcur, "Qcur_norm", il);
-
-            cur = build_attn(inp_attn,
-                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
-                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-            cb(cur, "attn_out", il);
-        }
-        if (il == n_layer - 1 && inp_out_ids) {
-            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-        }
-        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
-
-        cur = build_norm(ffn_inp,
-                model.layers[il].ffn_norm, NULL,
-                LLM_NORM_RMS, il);
-        cb(cur, "ffn_norm", il);
-        // feed-forward network (non-MoE)
-        ggml_tensor * cur_mlp = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-        cb(cur_mlp, "ffn_out", il);
-
-        cur = ggml_add(ctx0, cur_mlp, ffn_inp);
-
-        cur = build_cvec(cur, il);
-        cb(cur, "l_out", il);
-
-        // input for next layer
-        inpL = cur;
-    }
-    cur = inpL;
-
-    cur = build_norm(cur,
-            model.output_norm, NULL,
-            LLM_NORM_RMS, -1);
-
-    cb(cur, "result_norm", -1);
-    res->t_embd = cur;
-    // lm_head
-    cur = build_lora_mm(model.output, cur);
-    cb(cur, "result_output", -1);
-    res->t_logits = cur;
-
-    ggml_build_forward_expand(gf, cur);
+std::unique_ptr<llm_graph_context> llama_model_hunyuan_dense::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
 }
+
diff --git a/src/models/hunyuan-moe.cpp b/src/models/hunyuan-moe.cpp
index ffe1664b0e1..deb3c9671f3 100644
--- a/src/models/hunyuan-moe.cpp
+++ b/src/models/hunyuan-moe.cpp
@@ -1,6 +1,59 @@
 #include "models.h"
 
-llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_hunyuan_moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_A13B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_hunyuan_moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+        const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(i);
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
+        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
+        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_hunyuan_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_hunyuan_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -126,7 +179,7 @@ llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const ll
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
diff --git a/src/models/hunyuan-vl.cpp b/src/models/hunyuan-vl.cpp
new file mode 100644
index 00000000000..da9bb74de7e
--- /dev/null
+++ b/src/models/hunyuan-vl.cpp
@@ -0,0 +1,189 @@
+#include "models.h"
+
+void llama_model_hunyuan_vl::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
+
+    // XDRoPE / NTK-aware scaling: base = rope_theta * alpha^(dim / (dim - 2))
+    if (hparams.rope_scaling_alpha > 0.0f) {
+        const int dim = hparams.n_embd_head_k();
+        hparams.rope_freq_base_train = hparams.rope_freq_base_train
+            * powf(hparams.rope_scaling_alpha, (float)dim / (float)(dim - 2));
+    }
+
+    switch (hparams.n_embd) {
+        case 1024: type = LLM_TYPE_0_5B; break;
+        case 2048: type = LLM_TYPE_1_8B; break;
+        case 3072: type = LLM_TYPE_4B; break;
+        case 4096: type = LLM_TYPE_7B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_hunyuan_vl::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_hunyuan_vl::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_hunyuan_vl::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(n_embd_head == n_rot);
+
+    const bool use_mrope = hparams.use_mrope();
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+        // self-attention
+        {
+            // rope freq factors for llama3; may return nullptr for llama2 and other models
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+                    n_embd_head, n_head, n_head_kv, il);
+
+            if (use_mrope) {
+                Qcur = ggml_rope_multi(
+                            ctx0, Qcur, inp_pos, rope_factors,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                            );
+
+                Kcur = ggml_rope_multi(
+                            ctx0, Kcur, inp_pos, rope_factors,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                            );
+            } else {
+                Qcur = ggml_rope_ext(
+                            ctx0, Qcur, inp_pos, rope_factors,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                            );
+
+                Kcur = ggml_rope_ext(
+                            ctx0, Kcur, inp_pos, rope_factors,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                            );
+            }
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            Kcur = build_norm(Kcur,
+                        model.layers[il].attn_k_norm, nullptr,
+                        LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_norm", il);
+
+            Qcur = build_norm(Qcur,
+                        model.layers[il].attn_q_norm, nullptr,
+                        LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_norm", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+        // feed-forward network (non-MoE)
+        ggml_tensor * cur_mlp = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur_mlp, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur_mlp, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+    // lm_head
+    cur = build_lora_mm(model.output, cur, model.output_s);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/internlm2.cpp b/src/models/internlm2.cpp
index 83be2ca0aee..f9ee37a24b6 100644
--- a/src/models/internlm2.cpp
+++ b/src/models/internlm2.cpp
@@ -1,6 +1,43 @@
 #include "models.h"
 
-llm_build_internlm2::llm_build_internlm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_internlm2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_7B; break;
+        case 48: type = LLM_TYPE_20B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_internlm2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        // layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_internlm2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_internlm2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -92,7 +129,7 @@ llm_build_internlm2::llm_build_internlm2(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/jais.cpp b/src/models/jais.cpp
index 31101f3c14b..2ba162605f1 100644
--- a/src/models/jais.cpp
+++ b/src/models/jais.cpp
@@ -1,6 +1,58 @@
 #include "models.h"
 
-llm_build_jais::llm_build_jais(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_jais::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias, false);
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_1_3B; break;
+        case 40: type = LLM_TYPE_13B; break;
+        /* TODO: add variants */
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_jais::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "bias", i),   {n_ff}, 0);
+
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_jais::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_jais::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -71,7 +123,7 @@ llm_build_jais::llm_build_jais(const llama_model & model, const llm_graph_params
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/jais2.cpp b/src/models/jais2.cpp
index 507e04fa4aa..8966131441c 100644
--- a/src/models/jais2.cpp
+++ b/src/models/jais2.cpp
@@ -1,8 +1,63 @@
 #include "models.h"
 
+void llama_model_jais2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_8B; break;
+        case 68: type = LLM_TYPE_70B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_jais2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (!output) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // attention biases - all have shape n_embd (output dimension of projections)
+        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
+        layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd}, 0);
+        layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+        // Jais-2 uses simple MLP (no gate) with biases
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_jais2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
 // JAIS-2 model graph builder
 // Uses: LayerNorm (not RMSNorm), relu2 activation, separate Q/K/V, RoPE embeddings
-llm_build_jais2::llm_build_jais2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_jais2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -97,7 +152,7 @@ llm_build_jais2::llm_build_jais2(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // Output projection
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
 
     res->t_logits = cur;
diff --git a/src/models/jamba.cpp b/src/models/jamba.cpp
index f82b7795c87..84ea63c3136 100644
--- a/src/models/jamba.cpp
+++ b/src/models/jamba.cpp
@@ -1,6 +1,111 @@
 #include "models.h"
 
-llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) {
+void llama_model_jamba::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+        hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
+    }
+
+    switch (hparams.n_layer) {
+        // TODO: Jamba layers are a bit heterogeneous, so naming this is hard.
+        case 12: // 900M  8x???M
+        case 32: // 51B  16x?B
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_jamba::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t d_conv  = hparams.ssm_d_conv;
+    const int64_t d_inner = hparams.ssm_d_inner;
+    const int64_t d_state = hparams.ssm_d_state;
+    const int64_t dt_rank = hparams.ssm_dt_rank;
+
+    // only an expansion factor of 2 is supported for now
+    GGML_ASSERT(2 * n_embd == d_inner);
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    {
+        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+        // if output is NULL, init from the input tok embed, duplicated to allow offloading
+        if (output == NULL) {
+            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+        }
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        const int64_t n_head_kv = hparams.n_head_kv(i);
+        const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i);
+
+        auto & layer = layers[i];
+
+        // norm
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (n_head_kv == 0) {
+            // Mamba layer
+            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);
+
+            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);
+            layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);
+
+            layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);
+
+            layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, "weight", i), {dt_rank}, 0);
+
+            layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);
+            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
+
+            layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, "weight", i), {d_state}, 0);
+            layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, "weight", i), {d_state}, 0);
+
+            // no "weight" suffix for these
+            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);
+            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);
+
+            // out_proj
+            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+        } else {
+            // Attention layers
+
+            create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        }
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+
+        if (layer.ffn_gate_inp) {
+            // MoE
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff, n_expert}, 0);
+        } else {
+            // FFN (no MoE)
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_jamba::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_jamba::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     ggml_tensor * cur;
@@ -84,7 +189,7 @@ llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/jina-bert-v2.cpp b/src/models/jina-bert-v2.cpp
new file mode 100644
index 00000000000..4f8866ece4d
--- /dev/null
+++ b/src/models/jina-bert-v2.cpp
@@ -0,0 +1,66 @@
+#include "models.h"
+
+void llama_model_jina_bert_v2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+    hparams.f_max_alibi_bias = 8.0f;
+
+    switch (hparams.n_layer) {
+        case 4:  type = LLM_TYPE_33M;  break; // jina-embeddings-small
+        case 12: type = LLM_TYPE_137M; break; // jina-embeddings-base
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_jina_bert_v2::load_arch_tensors(llama_model_loader & ml) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
+    type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0); // token_type_embeddings
+
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0); // LayerNorm
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0); // LayerNorm bias
+
+    cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
+    cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {1},         TENSOR_NOT_REQUIRED);
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i]; // JinaBertLayer
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+
+        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); //output_dens
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); //output_dens
+
+        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); //output_norm
+        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias",   i), {n_embd}, 0);
+
+        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
+
+        const auto tn_ffn_up_weight = tn(LLM_TENSOR_FFN_UP, "weight", i);
+        ggml_tensor * t_ffn_up = ml.get_tensor_meta(tn_ffn_up_weight.str().c_str());
+        const int64_t n_ffn_up = t_ffn_up ? t_ffn_up->ne[1] : n_ff;
+
+        GGML_ASSERT(n_ffn_up == n_ff || n_ffn_up == n_ff * 2);
+        layer.ffn_up   = create_tensor(tn_ffn_up_weight, {n_embd, n_ffn_up}, 0);
+        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ffn_up}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);
+
+        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias",   i), {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_jina_bert_v2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/jina-bert-v3.cpp b/src/models/jina-bert-v3.cpp
new file mode 100644
index 00000000000..e0527529f56
--- /dev/null
+++ b/src/models/jina-bert-v3.cpp
@@ -0,0 +1,69 @@
+#include "models.h"
+
+void llama_model_jina_bert_v3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 24:
+            type = LLM_TYPE_558M; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_jina_bert_v3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_token_types == 0) {
+        throw std::runtime_error(arch_name() + " model needs to define token type count");
+    }
+    tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+    type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
+
+    if (arch == LLM_ARCH_BERT) {
+        pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train}, 0);
+
+        cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+        cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
+
+        cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
+    }
+
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);
+
+        if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff,   n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff,   n_embd, n_expert}, 0);
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,   "weight", i), {n_embd, n_expert}, 0);
+        } else {
+            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+            if (arch == LLM_ARCH_NOMIC_BERT) {
+                layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            }
+        }
+
+        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_jina_bert_v3::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp
index 58c89c417fc..ecffb105496 100644
--- a/src/models/kimi-linear.cpp
+++ b/src/models/kimi-linear.cpp
@@ -1,7 +1,175 @@
 #include "models.h"
-
 #include "llama-memory-recurrent.h"
 
+void llama_model_kimi_linear::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,    hparams.n_embd_head_k_mla_impl);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,  hparams.n_embd_head_v_mla_impl);
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,             hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_KDA_HEAD_DIM,                hparams.n_embd_head_kda);
+
+    // MLA qk_rope_head_dim (for reference)
+    // qk_rope_head_dim = 64, qk_nope_head_dim = 128, qk_head_dim = 192
+
+    // Mark KDA layers as recurrent using n_head_kv pattern (like Jamba)
+    // Set n_head_kv = 0 for KDA layers (recurrent), n_head_kv = n_head for MLA layers (attention)
+    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+        hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;  // KDA layers are recurrent
+    }
+
+    // MoE parameters - Kimi uses moe_intermediate_size = 1024
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
+
+    switch (hparams.n_layer) {
+        case 27: type = LLM_TYPE_48B_A3B; break; // Kimi-Linear-48B-A3B
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_kimi_linear::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        // Check for KDA specific tensors to determine layer type or if it's a mixed model
+        // Assuming KDA layer if KDA tensors are present
+
+        // KDA uses head_dim = 128 (from linear_attn_config.head_dim)
+        const int64_t n_embd_head_k_kda = hparams.n_embd_head_kda;
+        const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda;
+        const int64_t ssm_d_conv = hparams.ssm_d_conv;
+
+        if (hparams.is_recurrent(i)) {
+            // Conv1d weights: try 4D first, then 3D (quantization may remove trailing 1)
+            // 4D: [d_conv, 1, d_inner, 1], 3D: [d_conv, 1, d_inner]
+            layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
+            if (!layer.ssm_q_conv) {
+                layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0);
+            }
+
+             // KDA Layer - Conv1d weights may be 3D or 4D
+             layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
+             if (!layer.ssm_k_conv) {
+                 layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0);
+             }
+             layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
+             if (!layer.ssm_v_conv) {
+                 layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head}, 0);
+             }
+
+             // q, k, v projections
+             // Python: q_proj, k_proj, v_proj
+             create_tensor_qkv(layer, i, n_embd, n_embd_head_k_kda * n_head, n_embd_head_k_kda * n_head, n_embd_head_v_kda * n_head, 0);
+
+             // KDA specific projections
+             // f_a_proj, f_b_proj
+             layer.ssm_f_a = create_tensor(tn(LLM_TENSOR_SSM_F_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0); // head_dim
+             layer.ssm_f_b = create_tensor(tn(LLM_TENSOR_SSM_F_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0); // projection_size
+
+             // b_proj (beta mixing coefficient)
+             layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0);
+
+             // A_log - Shape in GGUF: [1, num_heads, 1, 1] (4D) or [1, num_heads] (2D after quantization) Note: -exp(A_log) is applied in convert_hf_to_gguf.py
+             layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head, 1, 1}, TENSOR_NOT_REQUIRED);
+             if (!layer.ssm_a) {
+                 layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
+             }
+
+             // dt_bias - shape [n_embd_head_k_kda * n_head] = [4096]
+             layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_embd_head_k_kda * n_head}, 0);
+
+             // g_a_proj, g_b_proj (output gate)
+             layer.ssm_g_a = create_tensor(tn(LLM_TENSOR_SSM_G_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0);
+             layer.ssm_g_b = create_tensor(tn(LLM_TENSOR_SSM_G_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0);
+
+             // o_norm (reusing SSM_NORM)
+             layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {n_embd_head_k_kda}, 0); // FusedRMSNormGated
+
+             // o_proj
+             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v_kda * n_head, n_embd}, 0);
+
+        } else {
+             // MLA Layer - use MLA-specific head dimensions
+             const int64_t q_lora_rank  = hparams.n_lora_q;
+             const int64_t kv_lora_rank = hparams.n_lora_kv;
+             const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
+             const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
+
+             layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, TENSOR_NOT_REQUIRED);
+             layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
+
+             if (layer.attn_q_a_norm) {
+                 layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
+                 layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
+             } else {
+                 // Kimi MLA without Q compression: wq = [n_embd, n_head * n_embd_head_k_mla]
+                 layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0);
+             }
+
+             // Kimi: qk_rope_head_dim = 64 (actual RoPE dimension for MLA)
+             // Note: hparams.n_rot may be 72 (from conversion) but actual is 64
+             const int64_t qk_rope_head_dim = hparams.n_rot();  // From config: qk_rope_head_dim
+             layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0);
+             // Support Legacy GGUFs that don't split wkv_b (MLA KV cache disabled)
+             layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i),
+                {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
+             if (!layer.wkv_b) { // MLA KV cache enabled
+                 layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, 0);
+                 layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
+             }
+             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
+        }
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        // MoE intermediate size (different from dense FFN)
+        const int64_t n_ff_exp = hparams.n_ff_exp;
+
+        // Kimi uses n_layer_dense_lead to determine which layers use dense FFN vs MoE
+        // first_k_dense_replace = 1 means layer 0 uses dense FFN, layers 1+ use MoE
+        if (i < (int) hparams.n_layer_dense_lead) {
+            // Dense FFN layer - use normal n_ff
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        } else {
+            // MoE layer - use n_ff_exp (1024) instead of n_ff (9216)
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+
+            // Shared experts use moe_intermediate_size * num_shared_experts
+            // Kimi: shared_expert_intermediate_size = 1024 * 1 = 1024
+            // Tensors are 2D: [n_embd, n_ff_shexp] or [n_ff_shexp, n_embd]
+            const int64_t n_ff_shexp_actual = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1);
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp_actual, n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
+
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_kimi_linear::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
 // Causal Conv1d function for Q,K,V
 // When qkv is 0, it is Q, 1 is K, 2 is V
 static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * conv_states_all, ggml_tensor * conv_state_all, int64_t qkv, ggml_tensor * x, ggml_tensor * proj_w, ggml_tensor * conv_w, int64_t d_conv, int64_t head_dim, int64_t n_head, int64_t n_seq_tokens, int64_t n_seqs, int64_t n_tokens, int64_t kv_head) {
@@ -63,7 +231,7 @@ static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_t
     return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs);
 }
 
-llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const llm_graph_params & params) :
+llama_model_kimi_linear::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_delta_net_base(params), model(model) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
diff --git a/src/models/lfm2.cpp b/src/models/lfm2.cpp
index eb8ec3c803a..29081344b24 100644
--- a/src/models/lfm2.cpp
+++ b/src/models/lfm2.cpp
@@ -1,10 +1,94 @@
 #include "models.h"
-
 #include "../llama-memory-hybrid-iswa.h"
 #include "../llama-memory-hybrid.h"
 
+void llama_model_lfm2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_SHORTCONV_L_CACHE,           hparams.n_shortconv_l_cache);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    for (uint32_t il = 0; il < hparams.n_layer; ++il) {
+        hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
+    }
+    hparams.n_layer_dense_lead = hparams.n_layer;
+    switch (hparams.n_ff()) {
+        case  4608: type = LLM_TYPE_350M; break;
+        case  6912: type = LLM_TYPE_700M; break;
+        case  8192: type = LLM_TYPE_1_2B; break;
+        case 10752: type = LLM_TYPE_2_6B; break;
+        default:    type = LLM_TYPE_UNKNOWN;
+    }
+    if (const auto is_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); is_swa && hparams.n_swa > 0) {
+        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        for (uint32_t il = 0; il < hparams.n_layer; ++il) {
+            hparams.swa_layers[il] = !hparams.recurrent_layer_arr[il];
+        }
+    }
+}
+
+void llama_model_lfm2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM_LFM2, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,           "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        const bool is_moe_layer = i >= static_cast<int>(hparams.n_layer_dense_lead);
+
+        // ffn/moe is same for transformer and conv layers
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        if (is_moe_layer) {
+            GGML_ASSERT(n_expert && n_expert_used);
+            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i),  {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps   = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp,   n_embd, n_expert}, 0);
+            layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i),   {n_embd, hparams.n_ff_exp, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+        } else {  // dense
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        }
+
+        // for operator_norm
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (!hparams.is_recurrent(i)) {
+            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+            GGML_ASSERT(n_embd_v_gqa == n_embd_k_gqa);
+
+            create_tensor_qkv(layer, i, n_embd, n_embd, hparams.n_embd_k_gqa(i), hparams.n_embd_v_gqa(i), 0);
+
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        } else {
+            layer.shortconv.conv     = create_tensor(tn(LLM_TENSOR_SHORTCONV_CONV,    "weight", i), {hparams.n_shortconv_l_cache, n_embd}, 0);
+            layer.shortconv.in_proj  = create_tensor(tn(LLM_TENSOR_SHORTCONV_INPROJ,  "weight", i), {n_embd, 3 * n_embd}, 0);
+            layer.shortconv.out_proj = create_tensor(tn(LLM_TENSOR_SHORTCONV_OUTPROJ, "weight", i), {n_embd, n_embd}, 0);
+        }
+    }
+
+    // for LFM2-ColBert-350M
+    dense_2_out_layers   = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.n_embd_out()}, TENSOR_NOT_REQUIRED);
+    dense_2_out_layers_b = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "bias"),   {hparams.n_embd_out()        }, TENSOR_NOT_REQUIRED);
+}
+
+std::unique_ptr<llm_graph_context> llama_model_lfm2::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+        return std::make_unique<graph<true>>(*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
 template <bool iswa>
-llm_build_lfm2<iswa>::llm_build_lfm2(const llama_model & model, const llm_graph_params & params) :
+llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     using inp_hybrid_type = std::conditional_t<iswa, llm_graph_input_mem_hybrid_iswa,  llm_graph_input_mem_hybrid>;
     using inp_attn_type   = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa,     llm_graph_input_attn_kv>;
@@ -178,7 +262,7 @@ llm_build_lfm2<iswa>::llm_build_lfm2(const llama_model & model, const llm_graph_
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
 
     res->t_logits = cur;
@@ -187,5 +271,5 @@ llm_build_lfm2<iswa>::llm_build_lfm2(const llama_model & model, const llm_graph_
 }
 
 // Explicit template instantiations
-template struct llm_build_lfm2<true>;
-template struct llm_build_lfm2<false>;
+template struct llama_model_lfm2::graph<true>;
+template struct llama_model_lfm2::graph<false>;
diff --git a/src/models/lfm2moe.cpp b/src/models/lfm2moe.cpp
new file mode 100644
index 00000000000..12a66c05c7d
--- /dev/null
+++ b/src/models/lfm2moe.cpp
@@ -0,0 +1,85 @@
+#include "models.h"
+#include "../llama-memory-hybrid-iswa.h"
+#include "../llama-memory-hybrid.h"
+
+void llama_model_lfm2moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_SHORTCONV_L_CACHE,           hparams.n_shortconv_l_cache);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func);
+
+    for (uint32_t il = 0; il < hparams.n_layer; ++il) {
+        hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
+    }
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_8B_A1B;  break;
+        case 40: type = LLM_TYPE_24B_A2B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_lfm2moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM_LFM2, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,           "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        const bool is_moe_layer = i >= static_cast<int>(hparams.n_layer_dense_lead);
+
+        // ffn/moe is same for transformer and conv layers
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        if (is_moe_layer) {
+            GGML_ASSERT(n_expert && n_expert_used);
+            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i),  {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps   = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp,   n_embd, n_expert}, 0);
+            layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i),   {n_embd, hparams.n_ff_exp, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+        } else {  // dense
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        }
+
+        // for operator_norm
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (!hparams.is_recurrent(i)) {
+            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+            GGML_ASSERT(n_embd_v_gqa == n_embd_k_gqa);
+
+            create_tensor_qkv(layer, i, n_embd, n_embd, hparams.n_embd_k_gqa(i), hparams.n_embd_v_gqa(i), 0);
+
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        } else {
+            layer.shortconv.conv     = create_tensor(tn(LLM_TENSOR_SHORTCONV_CONV,    "weight", i), {hparams.n_shortconv_l_cache, n_embd}, 0);
+            layer.shortconv.in_proj  = create_tensor(tn(LLM_TENSOR_SHORTCONV_INPROJ,  "weight", i), {n_embd, 3 * n_embd}, 0);
+            layer.shortconv.out_proj = create_tensor(tn(LLM_TENSOR_SHORTCONV_OUTPROJ, "weight", i), {n_embd, n_embd}, 0);
+        }
+    }
+
+    // for LFM2-ColBert-350M
+    dense_2_out_layers   = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.n_embd_out()}, TENSOR_NOT_REQUIRED);
+    dense_2_out_layers_b = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "bias"),   {hparams.n_embd_out()        }, TENSOR_NOT_REQUIRED);
+}
+
+std::unique_ptr<llm_graph_context> llama_model_lfm2moe::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+        return std::make_unique<graph<true>>(*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
diff --git a/src/models/llada-moe.cpp b/src/models/llada-moe.cpp
index c756d6fde5f..9722dde9f17 100644
--- a/src/models/llada-moe.cpp
+++ b/src/models/llada-moe.cpp
@@ -1,6 +1,56 @@
 #include "models.h"
 
-llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_llada_moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    // diffusion language model uses non-causal attention
+    hparams.causal_attn = false;
+    switch (hparams.n_layer) {
+        case 16: type = LLM_TYPE_A1_7B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_llada_moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for llada-moe");
+    GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for llada-moe");
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_llada_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_llada_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -103,7 +153,7 @@ llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/llada.cpp b/src/models/llada.cpp
index 501df3c7eaf..58b2c466e17 100644
--- a/src/models/llada.cpp
+++ b/src/models/llada.cpp
@@ -1,6 +1,72 @@
 #include "models.h"
 
-llm_build_llada::llm_build_llada(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_llada::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    // LLaDA-8B has 32 layers, similar to LLaMA but for diffusion
+    switch (hparams.n_layer) {
+        case 32:
+            type = LLM_TYPE_8B;
+            break;
+        default:
+            type = LLM_TYPE_UNKNOWN;
+    }
+    // Set non-causal attention for diffusion models
+    hparams.causal_attn = false;
+}
+
+void llama_model_llada::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output =
+            create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+        // Use separate Q, K, V projections without bias, matching LLaDALlamaBlock
+        layer.wq =
+            create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
+        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
+        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
+        // No bias for QKV projections as per config: include_bias=false, include_qkv_bias=false
+        layer.wo =
+            create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot / 2 },
+                                         TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
+
+        // optional MLP bias
+        layer.ffn_gate_b =
+            create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), { n_ff }, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_b =
+            create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
+        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), { n_ff }, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_llada::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_llada::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     // LLaDA is similar to LLaMA but uses non-causal attention for diffusion
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -81,7 +147,7 @@ llm_build_llada::llm_build_llada(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/llama-embed.cpp b/src/models/llama-embed.cpp
new file mode 100644
index 00000000000..0699e744461
--- /dev/null
+++ b/src/models/llama-embed.cpp
@@ -0,0 +1,6 @@
+#include "models.h"
+
+std::unique_ptr<llm_graph_context> llama_model_llama_embed::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph<true>>(*this, params);
+}
+
diff --git a/src/models/llama.cpp b/src/models/llama.cpp
index ddaa6c40f59..cef66d054b0 100644
--- a/src/models/llama.cpp
+++ b/src/models/llama.cpp
@@ -1,7 +1,102 @@
 #include "models.h"
 
+void llama_model_llama::load_arch_hparams(llama_model_loader & ml) {
+    uint32_t n_vocab = 0;
+    ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    if (hparams.n_expert == 8) {
+        switch (hparams.n_layer) {
+            case 32: type = LLM_TYPE_8x7B; break;
+            case 56: type = LLM_TYPE_8x22B; break;
+            default: type = LLM_TYPE_UNKNOWN;
+        }
+    } else {
+        switch (hparams.n_layer) {
+            case 16: type = LLM_TYPE_1B; break; // Llama 3.2 1B
+            case 22: type = LLM_TYPE_1B; break;
+            case 26: type = LLM_TYPE_3B; break;
+            case 28: type = LLM_TYPE_3B; break; // Llama 3.2 3B
+            case 30: type = LLM_TYPE_256M; break; // smoldocling 256M
+            // granite uses a vocab with len 49152
+            case 32: type = n_vocab == 49152 ? LLM_TYPE_3B : (n_vocab < 40000 ? LLM_TYPE_7B : LLM_TYPE_8B); break;
+            case 36: type = LLM_TYPE_8B; break; // granite
+            case 40: type = LLM_TYPE_13B; break;
+            case 48: type = LLM_TYPE_34B; break;
+            case 60: type = LLM_TYPE_30B; break;
+            case 80: type = hparams.n_head() == hparams.n_head_kv() ? LLM_TYPE_65B : LLM_TYPE_70B; break;
+            default: type = LLM_TYPE_UNKNOWN;
+        }
+    }
+}
+
+void llama_model_llama::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+        else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        if (n_expert == 0) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+            // optional MLP bias
+            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        } else {
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+            // For Granite MoE Shared
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_llama::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph<false>>(*this, params);
+}
+
 template <bool embed>
-llm_build_llama<embed>::llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_llama::graph<embed>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -72,9 +167,6 @@ llm_build_llama<embed>::llm_build_llama(const llama_model & model, const llm_gra
             cur = build_attn(inp_attn,
                     model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
                     Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-            if (model.layers[il].wo_s) {
-                cur = ggml_mul(ctx0, cur, model.layers[il].wo_s);
-            }
             cb(cur, "attn_out", il);
         }
         if (il == n_layer - 1 && inp_out_ids) {
@@ -143,7 +235,7 @@ llm_build_llama<embed>::llm_build_llama(const llama_model & model, const llm_gra
 
     if constexpr (!embed) {
         // lm_head
-        cur = build_lora_mm(model.output, cur);
+        cur = build_lora_mm(model.output, cur, model.output_s);
 
         cb(cur, "result_output", -1);
         res->t_logits = cur;
@@ -152,5 +244,5 @@ llm_build_llama<embed>::llm_build_llama(const llama_model & model, const llm_gra
     ggml_build_forward_expand(gf, cur);
 }
 
-template struct llm_build_llama<false>;
-template struct llm_build_llama<true>;
+template struct llama_model_llama::graph<false>;
+template struct llama_model_llama::graph<true>;
diff --git a/src/models/llama4.cpp b/src/models/llama4.cpp
index 4e4bfb43f33..0ff5376d571 100644
--- a/src/models/llama4.cpp
+++ b/src/models/llama4.cpp
@@ -1,7 +1,109 @@
 #include "models.h"
 
+void llama_model_llama4::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,   hparams.n_moe_layer_step);
+
+    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+    if (found_swa && hparams.n_swa == 0) {
+        hparams.swa_type             = LLAMA_SWA_TYPE_NONE;
+        hparams.n_no_rope_layer_step = hparams.n_layer; // always use rope
+    } else {
+        hparams.swa_type                = LLAMA_SWA_TYPE_CHUNKED;
+        hparams.n_swa                   = 8192;
+        hparams.n_attn_temp_floor_scale = 8192;
+        hparams.f_attn_temp_scale       = 0.1f;
+        hparams.f_attn_temp_offset      = 1.0f;
+        uint32_t swa_period             = 4; // pattern: 3 chunked - 1 full
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period);
+
+        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+        hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+    }
+
+    switch (hparams.n_expert) {
+        case 0: {
+            // MobileLLM (no MoE)
+            switch (hparams.n_embd) {
+                case 2048: type = LLM_TYPE_140M; break;
+                case 4096: type = LLM_TYPE_360M; break;
+                case 6144: type = LLM_TYPE_950M; break;
+                default:   type = LLM_TYPE_UNKNOWN;
+            }
+        } break;
+        case 16:  type = LLM_TYPE_17B_16E; break;
+        case 128: type = LLM_TYPE_17B_128E; break;
+        default:  type = LLM_TYPE_UNKNOWN;
+    }
+
+    hparams.use_kq_norm = type != LLM_TYPE_17B_128E;
+}
+
+void llama_model_llama4::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_expert == 0) {
+        throw std::runtime_error(arch_name() + " model cannot have zero experts");
+    }
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        const bool is_moe_layer = hparams.n_moe_layer_step > 0 && (i + 1) % hparams.n_moe_layer_step == 0;
+
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+        if (is_moe_layer) {
+            const int64_t n_ff_exp = hparams.n_ff_exp;
+
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff_exp, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+
+            // Shared expert
+            const int64_t n_ff_shexp = n_ff_exp;
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd    }, 0);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);
+        } else {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_llama4::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type == LLAMA_SWA_TYPE_NONE) {
+        return std::make_unique<graph<false>>(*this, params);
+    } else {
+        return std::make_unique<graph<true>>(*this, params);
+    }
+}
+
 template <bool iswa>
-llm_build_llama4<iswa>::llm_build_llama4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_llama4::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -158,7 +260,7 @@ llm_build_llama4<iswa>::llm_build_llama4(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
@@ -167,5 +269,5 @@ llm_build_llama4<iswa>::llm_build_llama4(const llama_model & model, const llm_gr
 }
 
 // Explicit template instantiations
-template struct llm_build_llama4<false>;
-template struct llm_build_llama4<true>;
+template struct llama_model_llama4::graph<false>;
+template struct llama_model_llama4::graph<true>;
diff --git a/src/models/maincoder.cpp b/src/models/maincoder.cpp
index 8a76931c007..84cfe399027 100644
--- a/src/models/maincoder.cpp
+++ b/src/models/maincoder.cpp
@@ -1,6 +1,49 @@
 #include "models.h"
 
-llm_build_maincoder::llm_build_maincoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_maincoder::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_1B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_maincoder::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_maincoder::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_maincoder::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -98,7 +141,7 @@ llm_build_maincoder::llm_build_maincoder(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/mamba.cpp b/src/models/mamba.cpp
index 55fd2e055c4..887a1fa509a 100644
--- a/src/models/mamba.cpp
+++ b/src/models/mamba.cpp
@@ -1,6 +1,90 @@
 #include "models.h"
 
-llm_build_mamba::llm_build_mamba(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) {
+void llama_model_mamba::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_DT_B_C_RMS,     hparams.ssm_dt_b_c_rms, false);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 24:
+            switch (hparams.n_embd) {
+                case 768: type = LLM_TYPE_SMALL; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 48:
+            switch (hparams.n_embd) {
+                case 1024: type = LLM_TYPE_MEDIUM; break;
+                case 1536: type = LLM_TYPE_LARGE; break;
+                case 2048: type = LLM_TYPE_XL; break;
+                default:   type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 64:
+            switch (hparams.n_embd) {
+                case 2560: type = LLM_TYPE_3B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_mamba::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t d_conv  = hparams.ssm_d_conv;
+    const int64_t d_inner = hparams.ssm_d_inner;
+    const int64_t d_state = hparams.ssm_d_state;
+    const int64_t dt_rank = hparams.ssm_dt_rank;
+
+    // only an expansion factor of 2 is supported for now
+    if (2 * n_embd != d_inner) {
+        throw std::runtime_error("only an expansion factor of 2 is supported for now");
+    }
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed, duplicated to allow offloading
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // norm
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);
+
+        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);
+        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);
+
+        layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);
+
+        layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);
+        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
+
+        // no "weight" suffix for these
+        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);
+        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);
+
+        // out_proj
+        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_mamba::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_mamba::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -44,11 +128,10 @@ llm_build_mamba::llm_build_mamba(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
     ggml_build_forward_expand(gf, cur);
 }
-
diff --git a/src/models/mamba2.cpp b/src/models/mamba2.cpp
new file mode 100644
index 00000000000..3277ca53ec4
--- /dev/null
+++ b/src/models/mamba2.cpp
@@ -0,0 +1,87 @@
+#include "models.h"
+
+void llama_model_mamba2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 24:
+            switch (hparams.n_embd) {
+                case 768: type = LLM_TYPE_SMALL; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 48:
+            switch (hparams.n_embd) {
+                case 1024: type = LLM_TYPE_MEDIUM; break;
+                case 1536: type = LLM_TYPE_LARGE; break;
+                case 2048: type = LLM_TYPE_XL; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 64:
+            switch (hparams.n_embd) {
+                case 2560: type = LLM_TYPE_3B; break;
+                case 4096: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_mamba2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t d_conv  = hparams.ssm_d_conv;
+    const int64_t d_inner = hparams.ssm_d_inner;
+    const int64_t d_state = hparams.ssm_d_state;
+    const int64_t n_group = hparams.ssm_n_group;
+    const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_head;
+
+    // only an expansion factor of 2 is supported for now
+    GGML_ASSERT(2 * n_embd == d_inner);
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    {
+        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+        // if output is NULL, init from the input tok embed, duplicated to allow offloading
+        if (output == NULL) {
+            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+        }
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // norm
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
+
+        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
+        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, 0);
+
+        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_head}, 0);
+
+        // no "weight" suffix for these
+        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
+        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_head}, 0);
+
+        layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
+
+        // out_proj
+        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_mamba2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/mimo2-iswa.cpp b/src/models/mimo2-iswa.cpp
deleted file mode 100644
index 52c6acfe214..00000000000
--- a/src/models/mimo2-iswa.cpp
+++ /dev/null
@@ -1,129 +0,0 @@
-#include "models.h"
-
-llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    ggml_tensor * cur;
-    ggml_tensor * inpL;
-
-    inpL = build_inp_embd(model.tok_embd);
-
-    ggml_tensor * inp_pos = build_inp_pos();
-    auto * inp_attn = build_attn_inp_kv_iswa();
-    ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-    for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-
-        uint32_t n_head_l    = hparams.n_head(il);
-        uint32_t n_head_kv_l = hparams.n_head_kv(il);
-        const float freq_base_l  = model.get_rope_freq_base(cparams, il);
-        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
-
-        cur = inpL;
-
-        // self_attention
-        {
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // compute Q and K and RoPE them
-            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-            cb(Qcur, "Qcur", il);
-
-            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-            cb(Kcur, "Kcur", il);
-
-            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-            cb(Vcur, "Vcur", il);
-
-            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);
-            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
-            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
-
-            Qcur = ggml_rope_ext(
-                ctx0, Qcur, inp_pos, nullptr,
-                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
-                ext_factor, attn_factor, beta_fast, beta_slow
-                );
-
-            Kcur = ggml_rope_ext(
-                ctx0, Kcur, inp_pos, nullptr,
-                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
-                ext_factor, attn_factor, beta_fast, beta_slow
-                );
-
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
-
-            ggml_tensor * sinks = model.layers[il].attn_sinks;
-
-            cur = build_attn(inp_attn,
-                    model.layers[il].wo, NULL, model.layers[il].wo_s,
-                    Qcur, Kcur, Vcur, nullptr, sinks, nullptr, 1.0f/sqrtf(float(n_embd_head_k)), il);
-        }
-
-        if (il == n_layer - 1 && inp_out_ids) {
-            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-        }
-
-        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
-
-        cur = build_norm(ffn_inp,
-                model.layers[il].ffn_norm, NULL,
-                LLM_NORM_RMS, il);
-        cb(cur, "ffn_norm", il);
-
-        // feed-forward network
-        if (model.layers[il].ffn_gate_inp == nullptr) {
-            // dense branch
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
-                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-        } else {
-            // MoE branch
-            cur = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    model.layers[il].ffn_exp_probs_b,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    hparams.expert_weights_scale,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
-                    il);
-            cb(cur, "ffn_moe_out", il);
-        }
-
-        cur = ggml_add(ctx0, cur, ffn_inp);
-
-        cur = build_cvec(cur, il);
-        cb(cur, "l_out", il);
-
-        // input for next layer
-        inpL = cur;
-    }
-
-    cur = inpL;
-
-    cur = build_norm(cur,
-            model.output_norm, NULL,
-            LLM_NORM_RMS, -1);
-
-    cb(cur, "result_norm", -1);
-    res->t_embd = cur;
-
-    // lm_head
-    cur = build_lora_mm(model.output, cur);
-
-    cb(cur, "result_output", -1);
-    res->t_logits = cur;
-
-    ggml_build_forward_expand(gf, cur);
-}
diff --git a/src/models/mimo2.cpp b/src/models/mimo2.cpp
new file mode 100644
index 00000000000..d0295ec116f
--- /dev/null
+++ b/src/models/mimo2.cpp
@@ -0,0 +1,240 @@
+#include "models.h"
+
+void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,   hparams.n_swa);
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,         hparams.rope_freq_base_train_swa, false);
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+
+    float value_scale = 0.0f;
+    if (ml.get_key(LLM_KV_ATTENTION_VALUE_SCALE, value_scale, false) && value_scale != 1.0f) {
+        hparams.f_attn_value_scale = value_scale;
+    }
+
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
+    GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+    hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+
+    switch (hparams.n_layer - hparams.nextn_predict_layers) {
+        case 48: type = LLM_TYPE_310B_A15B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    const uint32_t n_nextn = hparams.nextn_predict_layers;
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+        uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
+        uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
+        uint32_t n_head = hparams.n_head(i);
+
+        // NextN/MTP layers (the last n_nextn blocks) are preserved but disabled pending support
+        const bool is_nextn = (n_nextn > 0) && (static_cast<uint32_t>(i) >= n_layer - n_nextn);
+        const int  skip     = is_nextn ? TENSOR_SKIP : 0;
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, skip);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, skip);
+
+        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM,  "weight", i), {n_embd}, skip);
+        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | skip);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, skip);
+
+        // non-MoE branch
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED | skip);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED | skip);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED | skip);
+
+        // MoE branch
+        int64_t n_ff_exp = hparams.n_ff_exp;
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED | skip);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED | skip);
+        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | skip);
+
+        if (is_nextn) {
+            layer.nextn.eh_proj  = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, skip);
+            layer.nextn.enorm    = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,   "weight", i), {n_embd}, skip);
+            layer.nextn.hnorm    = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,   "weight", i), {n_embd}, skip);
+            layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, skip);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_mimo2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    auto * inp_attn = build_attn_inp_kv_iswa();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    const float v_scale = hparams.f_attn_value_scale;
+
+    // The last hparams.nextn_predict_layers blocks are MTP heads, currently inactive
+    const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
+
+    for (int il = 0; il < n_transformer_layers; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        uint32_t n_head_l    = hparams.n_head(il);
+        uint32_t n_head_kv_l = hparams.n_head_kv(il);
+        const float freq_base_l  = model.get_rope_freq_base(cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+        cur = inpL;
+
+        // self_attention
+        {
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            ggml_tensor * Qcur;
+            ggml_tensor * Kcur;
+            ggml_tensor * Vcur;
+
+            if (model.layers[il].wqkv) {
+                // Fused qkv_proj - Q/K share head_dim_k, V uses head_dim_v
+                ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(qkv, "wqkv", il);
+
+                const size_t row_k    = ggml_row_size(qkv->type, n_embd_head_k);
+                const size_t row_v    = ggml_row_size(qkv->type, n_embd_head_v);
+                const size_t row_full = qkv->nb[1];
+                const size_t k_off    = row_k * n_head_l;
+                const size_t v_off    = k_off + row_k * n_head_kv_l;
+
+                Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_l,    n_tokens, row_k, row_full, 0);
+                Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv_l, n_tokens, row_k, row_full, k_off);
+                Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv_l, n_tokens, row_v, row_full, v_off);
+            } else {
+                // Split path
+                Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
+            }
+
+            Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+            Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            ggml_tensor * sinks = model.layers[il].attn_sinks;
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL, model.layers[il].wo_s,
+                    Qcur, Kcur, Vcur, nullptr, sinks, nullptr, 1.0f/sqrtf(float(n_embd_head_k)), il);
+            cb(cur, "attn_out", il);
+
+            if (v_scale) {
+                cur = ggml_scale(ctx0, cur, v_scale);
+                cb(cur, "attn_out_scaled", il);
+            }
+        }
+
+        if (il == n_transformer_layers - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // feed-forward network
+        if (model.layers[il].ffn_gate_inp == nullptr) {
+            // dense branch
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE branch
+            cur = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, true,
+                    hparams.expert_weights_scale,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
+                    il);
+            cb(cur, "ffn_moe_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur, model.output_s);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/minicpm.cpp b/src/models/minicpm.cpp
new file mode 100644
index 00000000000..966d3af615c
--- /dev/null
+++ b/src/models/minicpm.cpp
@@ -0,0 +1,89 @@
+#include "models.h"
+
+void llama_model_minicpm::load_arch_hparams(llama_model_loader & ml) {
+    // Backward-compatible defaults for older MiniCPM GGUFs
+    hparams.f_embedding_scale = 12.0f;
+    hparams.f_residual_scale  = 1.4f / sqrtf(float(hparams.n_layer));
+    hparams.f_logit_scale     = hparams.n_embd ? (256.0f / float(hparams.n_embd)) : 1.0f;
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    // Optional KV reads, override defaults if present in newer GGUF exports
+    ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale, /*required=*/false);
+    ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale, /*required=*/false);
+    ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale, /*required=*/false);
+
+    // MiniCPM uses rope by default, unlike Granite which uses it as a switch
+    hparams.rope_finetuned = true;
+
+    switch (hparams.n_layer) {
+        case 52: type = LLM_TYPE_1B; break;
+        case 40: type = LLM_TYPE_2B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_minicpm::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+        else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        if (n_expert == 0) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+            // optional MLP bias
+            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        } else {
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+            // For Granite MoE Shared
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_minicpm::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/minicpm3.cpp b/src/models/minicpm3.cpp
index bf12ab73c74..1ffc54fa7c6 100644
--- a/src/models/minicpm3.cpp
+++ b/src/models/minicpm3.cpp
@@ -1,6 +1,66 @@
 #include "models.h"
 
-llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_minicpm3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,       hparams.n_lora_q);
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
+
+    switch (hparams.n_layer) {
+        case 62: type = LLM_TYPE_4B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_minicpm3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();
+
+    const int64_t q_lora_rank  = hparams.n_lora_q;
+    const int64_t kv_lora_rank = hparams.n_lora_kv;
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
+
+        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
+
+        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
+        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
+
+        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
+        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
+        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head_qk_rope/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head_qk_rope/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_minicpm3::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_minicpm3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     //TODO: if the model varies, these parameters need to be read from the model
     const int64_t n_embd_base = 256;
     const float scale_embd  = 12.0f;
@@ -191,7 +251,7 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap
     cb(cur, "lmhead_scaling", -1);
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/minimax-m2.cpp b/src/models/minimax-m2.cpp
index b809b79f2b9..22e291d73a3 100644
--- a/src/models/minimax-m2.cpp
+++ b/src/models/minimax-m2.cpp
@@ -1,6 +1,50 @@
 #include "models.h"
 
-llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_minimax_m2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,  hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,   hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,           hparams.expert_gating_func, false);
+
+    switch (hparams.n_layer) {
+        case 62: type = LLM_TYPE_230B_A10B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_minimax_m2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k * n_head}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_k_gqa}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_minimax_m2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_minimax_m2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -114,7 +158,7 @@ llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/mistral3.cpp b/src/models/mistral3.cpp
index b5ae72a2ee1..4e6ebef82cb 100644
--- a/src/models/mistral3.cpp
+++ b/src/models/mistral3.cpp
@@ -1,6 +1,96 @@
 #include "models.h"
 
-llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_mistral3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
+
+    ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast,    false);
+    ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow,    false);
+    ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,   hparams.rope_yarn_log_mul, false);
+
+    hparams.f_attn_temp_offset = 0.0f;
+
+    // TODO: maybe add n_attn_temp_floor_scale as a separate KV?
+    if (hparams.f_attn_temp_scale != 0.0f) {
+        hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn;
+        if (hparams.n_attn_temp_floor_scale == 0) {
+            throw std::runtime_error("invalid n_ctx_orig_yarn for attention temperature scaling");
+        }
+    }
+
+    switch (hparams.n_layer) {
+        case 26: type = LLM_TYPE_3B; break;
+        case 34: type = LLM_TYPE_8B; break;
+        case 40: type = LLM_TYPE_14B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_mistral3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+        else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        if (n_expert == 0) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+            // optional MLP bias
+            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        } else {
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+            // For Granite MoE Shared
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_mistral3::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_mistral3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -132,7 +222,7 @@ llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/mistral4.cpp b/src/models/mistral4.cpp
new file mode 100644
index 00000000000..3d9190650e3
--- /dev/null
+++ b/src/models/mistral4.cpp
@@ -0,0 +1,6 @@
+#include "models.h"
+
+std::unique_ptr<llm_graph_context> llama_model_mistral4::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/models.h b/src/models/models.h
index 94991c55fe8..4e40536a5ea 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -2,6 +2,7 @@
 
 #include "llama-model.h"
 #include "llama-graph.h"
+#include "llama-model-loader.h"
 
 // note: almost all graphs require at least sqrtf, so include cmath globally
 #include <cmath>
@@ -45,7 +46,7 @@ struct llm_build_delta_net_base : public llm_graph_context {
                 ggml_tensor * s,
                 int           il);
 
-    // use the ggml_gated_delta_net fused operator
+    // use the ggml_gated_delta_net fused operator (K=1; state has shape (D, 1, n_seqs))
     std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_fused(
                 ggml_tensor * q,
                 ggml_tensor * k,
@@ -64,6 +65,32 @@ struct llm_build_delta_net_base : public llm_graph_context {
                 ggml_tensor * b,
                 ggml_tensor * s,
                         int   il);
+
+    // true when speculative rollback is enabled and the batch fits in the rs cache
+    bool keep_rs() const;
+
+    // read conv state from cache, concat with qkv_mixed, write back (single slot or per-token)
+    // qkv_mixed: (qkv_dim, n_seq_tokens, n_seqs); returns conv_input: (kernel_size + n_seq_tokens - 1, channels, n_seqs)
+    ggml_tensor * build_conv_state(
+            llm_graph_input_rs * inp,
+            ggml_tensor *        conv_states_all,
+            ggml_tensor *        qkv_mixed,
+            int64_t              conv_kernel_size,
+            int64_t              conv_channels,
+            int                  il);
+
+    // run delta-net attention and write the new recurrent state(s) back to ssm_states_all
+    // s: (head_v_dim, head_v_dim, num_v_heads, n_seqs); returns output: (head_v_dim, num_v_heads, n_seq_tokens, n_seqs)
+    ggml_tensor * build_recurrent_attn(
+            llm_graph_input_rs * inp,
+            ggml_tensor *        ssm_states_all,
+            ggml_tensor *        q,
+            ggml_tensor *        k,
+            ggml_tensor *        v,
+            ggml_tensor *        g,
+            ggml_tensor *        b,
+            ggml_tensor *        s,
+            int                  il);
 };
 
 struct llm_build_rwkv6_base : public llm_graph_context {
@@ -110,611 +137,1758 @@ struct llm_build_rwkv7_base : public llm_graph_context {
 // models
 //
 
-struct llm_build_afmoe : public llm_graph_context {
-    llm_build_afmoe(const llama_model & model, const llm_graph_params & params);
+struct llama_model_llama : public llama_model_base {
+    llama_model_llama(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool embed>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_apertus : public llm_graph_context {
-    llm_build_apertus(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_llama4 : public llama_model_base {
+    llama_model_llama4(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool iswa>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_arcee : public llm_graph_context {
-    llm_build_arcee(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_llama_embed : public llama_model_llama {
+    llama_model_llama_embed(const struct llama_model_params & params) : llama_model_llama(params) {}
+    // reuse load_arch_hparams and load_arch_tensors from llama_model_llama
+
+    template <bool embed>
+    using graph = llama_model_llama::graph<embed>;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_arctic : public llm_graph_context {
-    llm_build_arctic(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_maincoder : public llama_model_base {
+    llama_model_maincoder(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_arwkv7 : public llm_build_rwkv7_base {
-    llm_build_arwkv7(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_deci : public llama_model_base {
+    llama_model_deci(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_baichuan : public llm_graph_context {
-    llm_build_baichuan(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_baichuan : public llama_model_base {
+    llama_model_baichuan(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_bailingmoe2 : public llm_graph_context {
-    llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_falcon : public llama_model_base {
+    llama_model_falcon(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_bailingmoe : public llm_graph_context {
-    llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_grok : public llama_model_base {
+    llama_model_grok(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_bert : public llm_graph_context {
-    llm_build_bert(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_starcoder : public llama_model_base {
+    llama_model_starcoder(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_bitnet : public llm_graph_context {
-    llm_build_bitnet(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_refact : public llama_model_base {
+    llama_model_refact(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_bloom : public llm_graph_context {
-    llm_build_bloom(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_bert : public llama_model_base {
+    llama_model_bert(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_chameleon : public llm_graph_context {
-    llm_build_chameleon(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_jina_bert_v2 : public llama_model_base {
+    llama_model_jina_bert_v2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_bert::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_chatglm : public llm_graph_context {
-    llm_build_chatglm(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_jina_bert_v3 : public llama_model_base {
+    llama_model_jina_bert_v3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_bert::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_codeshell : public llm_graph_context {
-    llm_build_codeshell(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_nomic_bert : public llama_model_base {
+    llama_model_nomic_bert(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_bert::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_cogvlm : public llm_graph_context {
-    llm_build_cogvlm(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_nomic_bert_moe : public llama_model_base {
+    llama_model_nomic_bert_moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_bert::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_cohere2_iswa : public llm_graph_context {
-    llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_modern_bert : public llama_model_base {
+    llama_model_modern_bert(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_command_r : public llm_graph_context {
-    llm_build_command_r(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_neo_bert : public llama_model_base {
+    llama_model_neo_bert(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_dbrx : public llm_graph_context {
-    llm_build_dbrx(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_eurobert : public llama_model_base {
+    llama_model_eurobert(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_deci : public llm_graph_context {
-    llm_build_deci(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_bloom : public llama_model_base {
+    llama_model_bloom(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_deepseek2 : public llm_graph_context {
-    llm_build_deepseek2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_mpt : public llama_model_base {
+    llama_model_mpt(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_deepseek : public llm_graph_context {
-    llm_build_deepseek(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_stablelm : public llama_model_base {
+    llama_model_stablelm(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_dots1 : public llm_graph_context {
-    llm_build_dots1(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen : public llama_model_base {
+    llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_dream : public llm_graph_context {
-    llm_build_dream(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen2 : public llama_model_base {
+    llama_model_qwen2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_ernie4_5 : public llm_graph_context {
-    llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_dream : public llama_model_base {
+    llama_model_dream(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_ernie4_5_moe : public llm_graph_context {
-    llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_llada : public llama_model_base {
+    llama_model_llada(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_paddleocr : public llm_graph_context {
-    llm_build_paddleocr(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_llada_moe : public llama_model_base {
+    llama_model_llada_moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool iswa>
-struct llm_build_exaone4 : public llm_graph_context {
-    llm_build_exaone4(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_rnd1 : public llama_model_base {
+    llama_model_rnd1(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_exaone : public llm_graph_context {
-    llm_build_exaone(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen2vl : public llama_model_base {
+    llama_model_qwen2vl(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_exaone_moe : public llm_graph_context {
-    llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen2moe : public llama_model_base {
+    llama_model_qwen2moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_falcon : public llm_graph_context {
-    llm_build_falcon(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen3 : public llama_model_base {
+    llama_model_qwen3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_falcon_h1 : public llm_build_mamba_base {
-    llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen3moe : public llama_model_base {
+    llama_model_qwen3moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_gemma2_iswa : public llm_graph_context {
-    llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen3vl : public llama_model_base {
+    llama_model_qwen3vl(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool iswa>
-struct llm_build_gemma3 : public llm_graph_context {
-    llm_build_gemma3(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen3vlmoe : public llama_model_base {
+    llama_model_qwen3vlmoe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_gemma3n_iswa : public llm_graph_context {
-    const llama_model & model;
 
-    const int64_t n_embd_head;
-    const int64_t n_embd_altup;
-    const int64_t n_altup;
-    const int     i_altup_act;
-    const int     n_layer_sparsity = 10; // number of layers using activation sparsity
-    const float   f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95)
+struct llama_model_phi2 : public llama_model_base {
+    llama_model_phi2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
 
-    llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params);
-    ggml_tensor * calc_magnitude(ggml_tensor * x);
 
-    // TODO: refactor in common "per-layer" functionality [TAG_PER_LAYER]
-    ggml_tensor * build_inp_per_layer();
-    ggml_tensor * project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer);
+struct llama_model_phi3 : public llama_model_base {
+    llama_model_phi3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
 
-    ggml_tensor * gaussian_topk(ggml_tensor * x);
-    ggml_tensor * altup_compute_router_modalities(ggml_tensor * x, int il);
-    ggml_tensor * altup_predict(ggml_tensor * cur, int il);
-    ggml_tensor * laurel(ggml_tensor * cur, int il);
-    ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il);
+    template <bool iswa>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_gemma4_iswa : public llm_graph_context {
-    const llama_model & model;
 
-    const int64_t n_embd_per_layer;
+struct llama_model_phimoe : public llama_model_base {
+    llama_model_phimoe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
 
-    llm_build_gemma4_iswa(const llama_model & model, const llm_graph_params & params);
+    template <bool iswa>
+    using graph = llama_model_phi3::graph<iswa>;
 
-    // TODO: refactor in common "per-layer" functionality [TAG_PER_LAYER]
-    ggml_tensor * build_inp_per_layer();
-    ggml_tensor * project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer);
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_gemma_embedding : public llm_graph_context {
-    llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_plamo : public llama_model_base {
+    llama_model_plamo(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_gemma : public llm_graph_context {
-    llm_build_gemma(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_plamo2 : public llama_model_base {
+    llama_model_plamo2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_mamba_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+        private:
+            ggml_tensor * build_plamo2_mamba_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il);
+            ggml_tensor * build_plamo2_attn_layer(llm_graph_input_attn_kv * inp, ggml_tensor * inp_pos, ggml_tensor * cur,
+                                                    const llama_model & model, int il);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_glm4 : public llm_graph_context {
-    llm_build_glm4(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_plamo3 : public llama_model_base {
+    llama_model_plamo3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool iswa>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_glm4_moe : public llm_graph_context {
-    llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_gpt2 : public llama_model_base {
+    llama_model_gpt2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_gpt2 : public llm_graph_context {
-    llm_build_gpt2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_codeshell : public llama_model_base {
+    llama_model_codeshell(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_gptneox : public llm_graph_context {
-    llm_build_gptneox(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_orion : public llama_model_base {
+    llama_model_orion(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_granite : public llm_graph_context {
-    llm_build_granite(const llama_model & model, const llm_graph_params & params);
 
-private:
-    ggml_tensor * build_attention_layer(
-              ggml_tensor             * cur,
-              ggml_tensor             * inp_pos,
-              llm_graph_input_attn_kv * inp_attn,
-        const llama_model             & model,
-        const int64_t                 n_embd_head,
-        const int                     il);
+struct llama_model_internlm2 : public llama_model_base {
+    llama_model_internlm2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
 
-    ggml_tensor * build_layer_ffn(
-              ggml_tensor       * cur,
-              ggml_tensor       * inpSA,
-        const llama_model       & model,
-        const int                 il);
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_granite_hybrid : public llm_build_mamba_base {
-    llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params);
-    ggml_tensor * build_layer_ffn(ggml_tensor * cur, ggml_tensor * inpSA, const llama_model & model, const int il);
-    ggml_tensor * build_attention_layer(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn,
-        const llama_model & model,const int64_t n_embd_head, const int il);
+
+struct llama_model_minicpm3 : public llama_model_base {
+    llama_model_minicpm3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_grok : public llm_graph_context {
-    llm_build_grok(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_gemma : public llama_model_base {
+    llama_model_gemma(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_grovemoe : public llm_graph_context {
-    llm_build_grovemoe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_gemma2 : public llama_model_base {
+    llama_model_gemma2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_hunyuan_dense : public llm_graph_context {
-    llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_gemma3 : public llama_model_base {
+    llama_model_gemma3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool iswa>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_hunyuan_moe : public llm_graph_context {
-    llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_gemma3n : public llama_model_base {
+    llama_model_gemma3n(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        const llama_model & model;
+
+        const int64_t n_embd_head;
+        const int64_t n_embd_altup;
+        const int64_t n_altup;
+        const int     i_altup_act;
+        const int     n_layer_sparsity = 10; // number of layers using activation sparsity
+        const float   f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95)
+
+        graph(const llama_model & model, const llm_graph_params & params);
+        ggml_tensor * calc_magnitude(ggml_tensor * x);
+
+        // TODO: refactor in common "per-layer" functionality [TAG_PER_LAYER]
+        ggml_tensor * build_inp_per_layer();
+        ggml_tensor * project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer);
+
+        ggml_tensor * gaussian_topk(ggml_tensor * x);
+        ggml_tensor * altup_compute_router_modalities(ggml_tensor * x, int il);
+        ggml_tensor * altup_predict(ggml_tensor * cur, int il);
+        ggml_tensor * laurel(ggml_tensor * cur, int il);
+        ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_internlm2 : public llm_graph_context {
-    llm_build_internlm2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_gemma4 : public llama_model_base {
+    llama_model_gemma4(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        const llama_model & model;
+
+        const int64_t n_embd_per_layer;
+
+        graph(const llama_model & model, const llm_graph_params & params);
+
+        // TODO: refactor in common "per-layer" functionality [TAG_PER_LAYER]
+        ggml_tensor * build_inp_per_layer();
+        ggml_tensor * project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_jais : public llm_graph_context {
-    llm_build_jais(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_gemma_embedding : public llama_model_base {
+    llama_model_gemma_embedding(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_jais2 : public llm_graph_context {
-    llm_build_jais2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_starcoder2 : public llama_model_base {
+    llama_model_starcoder2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_jamba : public llm_build_mamba_base {
-    llm_build_jamba(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_mamba : public llama_model_base {
+    llama_model_mamba(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_mamba_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_kimi_linear : public llm_build_delta_net_base {
-    llm_build_kimi_linear(const llama_model & model, const llm_graph_params & params);
 
-    std::pair<ggml_tensor *, ggml_tensor *> build_kda_autoregressive(
-                ggml_tensor * q,
-                ggml_tensor * k,
-                ggml_tensor * v,
-                ggml_tensor * gk,
-                ggml_tensor * beta,
-                ggml_tensor * state,
-                        int   il);
+struct llama_model_mamba2 : public llama_model_base {
+    llama_model_mamba2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
 
-    std::pair<ggml_tensor *, ggml_tensor *> build_kda_chunking(
-                ggml_tensor * q,
-                ggml_tensor * k,
-                ggml_tensor * v,
-                ggml_tensor * gk,
-                ggml_tensor * beta,
-                ggml_tensor * state,
-                ggml_tensor * causal_mask,
-                ggml_tensor * identity,
-                ggml_tensor * diag_mask,
-                        int   il);
+    using graph = llama_model_mamba::graph;
 
-    const llama_model & model;
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool iswa>
-struct llm_build_lfm2 : public llm_graph_context {
-    llm_build_lfm2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_jamba : public llama_model_base {
+    llama_model_jamba(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_mamba_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_llada : public llm_graph_context {
-    llm_build_llada(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_xverse : public llama_model_base {
+    llama_model_xverse(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_llada_moe : public llm_graph_context {
-    llm_build_llada_moe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_command_r : public llama_model_base {
+    llama_model_command_r(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool embed>
-struct llm_build_llama : public llm_graph_context {
-    llm_build_llama(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_cohere2 : public llama_model_base {
+    llama_model_cohere2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool iswa>
-struct llm_build_llama4 : public llm_graph_context {
-    llm_build_llama4(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_dbrx : public llama_model_base {
+    llama_model_dbrx(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_maincoder : public llm_graph_context {
-    llm_build_maincoder(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_olmo : public llama_model_base {
+    llama_model_olmo(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_mamba : public llm_build_mamba_base {
-    llm_build_mamba(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_olmo2 : public llama_model_base {
+    llama_model_olmo2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool iswa>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_mimo2_iswa : public llm_graph_context {
-    llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_olmoe : public llama_model_base {
+    llama_model_olmoe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_minicpm3 : public llm_graph_context {
-    llm_build_minicpm3(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_openelm : public llama_model_base {
+    llama_model_openelm(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_minimax_m2 : public llm_graph_context {
-    llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_gptneox : public llama_model_base {
+    llama_model_gptneox(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_mistral3 : public llm_graph_context {
-    llm_build_mistral3(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_arctic : public llama_model_base {
+    llama_model_arctic(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_modern_bert : public llm_graph_context {
-    llm_build_modern_bert(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_deepseek : public llama_model_base {
+    llama_model_deepseek(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_mpt : public llm_graph_context {
-    llm_build_mpt(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_deepseek2 : public llama_model_base {
+    llama_model_deepseek2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_nemotron : public llm_graph_context {
-    llm_build_nemotron(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_deepseek2ocr : public llama_model_base {
+    llama_model_deepseek2ocr(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_deepseek2::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_nemotron_h : public llm_build_mamba_base {
-    llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params);
-    ggml_tensor * build_ffn_layer(ggml_tensor * cur, const llama_model & model, int il);
-    ggml_tensor * build_attention_layer(ggml_tensor * cur, llm_graph_input_attn_kv * inp_attn,
-        const llama_model & model, int64_t n_embd_head, int il);
+
+struct llama_model_glm_dsa : public llama_model_base {
+    llama_model_glm_dsa(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_deepseek2::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_neo_bert : public llm_graph_context {
-    llm_build_neo_bert(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_mistral4 : public llama_model_deepseek2 {
+    llama_model_mistral4(const struct llama_model_params & params) : llama_model_deepseek2(params) {}
+    // reuse load_arch_hparams and load_arch_tensors from llama_model_deepseek2
+
+    using graph = llama_model_deepseek2::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_eurobert : public llm_graph_context {
-    llm_build_eurobert(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_chatglm : public llama_model_base {
+    llama_model_chatglm(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_glm4 : public llama_model_base {
+    llama_model_glm4(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_glm4_moe : public llama_model_base {
+    llama_model_glm4_moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_bitnet : public llama_model_base {
+    llama_model_bitnet(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_t5 : public llama_model_base {
+    llama_model_t5(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool is_enc>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_t5encoder : public llama_model_base {
+    llama_model_t5encoder(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_t5::graph<true>;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_jais : public llama_model_base {
+    llama_model_jais(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool iswa>
-struct llm_build_olmo2 : public llm_graph_context {
-    llm_build_olmo2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_jais2 : public llama_model_base {
+    llama_model_jais2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_olmoe : public llm_graph_context {
-    llm_build_olmoe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_nemotron : public llama_model_base {
+    llama_model_nemotron(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_olmo : public llm_graph_context {
-    llm_build_olmo(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_nemotron_h : public llama_model_base {
+    llama_model_nemotron_h(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_mamba_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+        ggml_tensor * build_ffn_layer(ggml_tensor * cur, const llama_model & model, int il);
+        ggml_tensor * build_attention_layer(ggml_tensor * cur, llm_graph_input_attn_kv * inp_attn,
+            const llama_model & model, int64_t n_embd_head, int il);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_openai_moe_iswa : public llm_graph_context {
-    llm_build_openai_moe_iswa(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_nemotron_h_moe : public llama_model_nemotron_h {
+    llama_model_nemotron_h_moe(const struct llama_model_params & params) : llama_model_nemotron_h(params) {}
+    // reuse load_arch_hparams and load_arch_tensors from llama_model_nemotron_h
+
+    using graph = llama_model_nemotron_h::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_exaone : public llama_model_base {
+    llama_model_exaone(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_openelm : public llm_graph_context {
-    llm_build_openelm(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_exaone4 : public llama_model_base {
+    llama_model_exaone4(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool iswa>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_exaone_moe : public llama_model_base {
+    llama_model_exaone_moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_orion : public llm_graph_context {
-    llm_build_orion(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_rwkv6 : public llama_model_base {
+    llama_model_rwkv6(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_rwkv6_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_pangu_embedded : public llm_graph_context {
-    llm_build_pangu_embedded(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_rwkv6qwen2 : public llama_model_base {
+    llama_model_rwkv6qwen2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_rwkv6_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_phi2 : public llm_graph_context {
-    llm_build_phi2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_rwkv7 : public llama_model_base {
+    llama_model_rwkv7(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_rwkv7_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool iswa>
-struct llm_build_phi3 : public llm_graph_context {
-    llm_build_phi3(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_arwkv7 : public llama_model_base {
+    llama_model_arwkv7(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_rwkv7_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_plamo2 : public llm_build_mamba_base {
-    llm_build_plamo2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_granite : public llama_model_base {
+    llama_model_granite(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+
     private:
-        ggml_tensor * build_plamo2_mamba_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il);
-        ggml_tensor * build_plamo2_attn_layer(llm_graph_input_attn_kv * inp, ggml_tensor * inp_pos, ggml_tensor * cur,
-                                                const llama_model & model, int il);
+        ggml_tensor * build_attention_layer(
+                  ggml_tensor             * cur,
+                  ggml_tensor             * inp_pos,
+                  llm_graph_input_attn_kv * inp_attn,
+            const llama_model             & model,
+            const int64_t                 n_embd_head,
+            const int                     il);
+
+        ggml_tensor * build_layer_ffn(
+                  ggml_tensor       * cur,
+                  ggml_tensor       * inpSA,
+            const llama_model       & model,
+            const int                 il);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_plamo : public llm_graph_context {
-    llm_build_plamo(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_granite_moe : public llama_model_base {
+    llama_model_granite_moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_granite::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool iswa>
-struct llm_build_plamo3 : public llm_graph_context {
-    llm_build_plamo3(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_minicpm : public llama_model_base {
+    llama_model_minicpm(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    using graph = llama_model_granite::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_plm : public llm_graph_context {
-    llm_build_plm(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_granite_hybrid : public llama_model_base {
+    llama_model_granite_hybrid(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_mamba_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+        ggml_tensor * build_layer_ffn(ggml_tensor * cur, ggml_tensor * inpSA, const llama_model & model, const int il);
+        ggml_tensor * build_attention_layer(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn,
+            const llama_model & model,const int64_t n_embd_head, const int il);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen2 : public llm_graph_context {
-    llm_build_qwen2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_chameleon : public llama_model_base {
+    llama_model_chameleon(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen2moe : public llm_graph_context {
-    llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_wavtokenizer_dec : public llama_model_base {
+    llama_model_wavtokenizer_dec(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen2vl : public llm_graph_context {
-    llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_plm : public llama_model_base {
+    llama_model_plm(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen3 : public llm_graph_context {
-    llm_build_qwen3(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_bailingmoe : public llama_model_base {
+    llama_model_bailingmoe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen3moe : public llm_graph_context {
-    llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_bailingmoe2 : public llama_model_base {
+    llama_model_bailingmoe2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen3vl : public llm_graph_context {
-    llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_seed_oss : public llama_model_base {
+    llama_model_seed_oss(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen3vlmoe : public llm_graph_context {
-    llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_dots1 : public llama_model_base {
+    llama_model_dots1(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen3next : public llm_build_delta_net_base {
-    llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
-private:
-    ggml_tensor * build_layer_attn(
-    llm_graph_input_attn_kv * inp_attn,
-                ggml_tensor * cur,
-                ggml_tensor * inp_pos,
-                        int   il);
 
-    ggml_tensor * build_layer_attn_linear(
-         llm_graph_input_rs * inp,
-                ggml_tensor * cur,
-                        int   il);
+struct llama_model_arcee : public llama_model_base {
+    llama_model_arcee(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
 
-    ggml_tensor * build_layer_ffn(
-                ggml_tensor * cur,
-                        int   il);
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
 
-    ggml_tensor * build_norm_gated(
-                ggml_tensor * input,
-                ggml_tensor * weights,
-                ggml_tensor * gate,
-                        int   layer);
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
 
-    // returns pair of qkv, z
-    std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
-                ggml_tensor * input,
-                        int   il);
 
-    const llama_model & model;
+struct llama_model_afmoe : public llama_model_base {
+    llama_model_afmoe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen35 : public llm_build_delta_net_base {
-    llm_build_qwen35(const llama_model & model, const llm_graph_params & params);
-private:
-    ggml_tensor * build_layer_attn(
-    llm_graph_input_attn_kv * inp_attn,
-                ggml_tensor * cur,
-                ggml_tensor * inp_pos,
-                        int * sections,
-                        int   il);
 
-    ggml_tensor * build_layer_attn_linear(
-         llm_graph_input_rs * inp,
-                ggml_tensor * cur,
-                        int   il);
+struct llama_model_ernie4_5 : public llama_model_base {
+    llama_model_ernie4_5(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
 
-    ggml_tensor * build_layer_ffn(
-                ggml_tensor * cur,
-                        int   il);
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
 
-    ggml_tensor * build_norm_gated(
-                ggml_tensor * input,
-                ggml_tensor * weights,
-                ggml_tensor * gate,
-                        int   layer);
 
-    // returns pair of qkv, z
-    std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
-                ggml_tensor * input,
-                        int   il);
+struct llama_model_ernie4_5_moe : public llama_model_ernie4_5 {
+    llama_model_ernie4_5_moe(const struct llama_model_params & params) : llama_model_ernie4_5(params) {}
+    // reuse load_arch_hparams and load_arch_tensors from llama_model_ernie4_5
 
-    const llama_model & model;
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-// TODO: derive llm_build_delta_net_base instead
-struct llm_build_qwen35moe : public llm_build_delta_net_base {
-    llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params);
-private:
-    ggml_tensor * build_layer_attn(
-    llm_graph_input_attn_kv * inp_attn,
-                ggml_tensor * cur,
-                ggml_tensor * inp_pos,
-                        int * sections,
-                        int   il);
 
-    ggml_tensor * build_layer_attn_linear(
-         llm_graph_input_rs * inp,
-                ggml_tensor * cur,
-                        int   il);
+struct llama_model_paddleocr : public llama_model_ernie4_5 {
+    llama_model_paddleocr(const struct llama_model_params & params) : llama_model_ernie4_5(params) {}
+    // reuse load_arch_hparams and load_arch_tensors from llama_model_ernie4_5
 
-    ggml_tensor * build_layer_ffn(
-                ggml_tensor * cur,
-                        int   il);
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
 
-    ggml_tensor * build_norm_gated(
-                ggml_tensor * input,
-                ggml_tensor * weights,
-                ggml_tensor * gate,
-                        int   layer);
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
 
-    // returns pair of qkv, z
-    std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
-                ggml_tensor * input,
-                        int   il);
 
-    const llama_model & model;
+struct llama_model_hunyuan_moe : public llama_model_base {
+    llama_model_hunyuan_moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_hunyuan_vl : public llama_model_base {
+    llama_model_hunyuan_vl(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_hunyuan_dense : public llama_model_hunyuan_vl {
+    llama_model_hunyuan_dense(const struct llama_model_params & params) : llama_model_hunyuan_vl(params) {}
+    // reuse load_arch_hparams and load_arch_tensors from llama_model_hunyuan_vl
+
+    using graph = llama_model_hunyuan_vl::graph;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_qwen : public llm_graph_context {
-    llm_build_qwen(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_smollm3 : public llama_model_base {
+    llama_model_smollm3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_refact : public llm_graph_context {
-    llm_build_refact(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_openai_moe : public llama_model_base {
+    llama_model_openai_moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_rnd1 : public llm_graph_context {
-    llm_build_rnd1(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_falcon_h1 : public llama_model_base {
+    llama_model_falcon_h1(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_mamba_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_rwkv6 : public llm_build_rwkv6_base {
-    llm_build_rwkv6(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_lfm2 : public llama_model_base {
+    llama_model_lfm2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool iswa>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
-    llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_lfm2moe : public llama_model_base {
+    llama_model_lfm2moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool iswa>
+    using graph = llama_model_lfm2::graph<iswa>;
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_rwkv7 : public llm_build_rwkv7_base {
-    llm_build_rwkv7(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_smallthinker : public llama_model_base {
+    llama_model_smallthinker(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool iswa>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_seed_oss : public llm_graph_context {
-    llm_build_seed_oss(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_grovemoe : public llama_model_base {
+    llama_model_grovemoe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
+struct llama_model_apertus : public llama_model_base {
+    llama_model_apertus(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool iswa>
-struct llm_build_smallthinker : public llm_graph_context {
-    llm_build_smallthinker(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_minimax_m2 : public llama_model_base {
+    llama_model_minimax_m2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_smollm3 : public llm_graph_context {
-    llm_build_smollm3(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_cogvlm : public llama_model_base {
+    llama_model_cogvlm(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_stablelm : public llm_graph_context {
-    llm_build_stablelm(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_pangu_embed : public llama_model_base {
+    llama_model_pangu_embed(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_starcoder2 : public llm_graph_context {
-    llm_build_starcoder2(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen3next : public llama_model_base {
+    llama_model_qwen3next(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_delta_net_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    private:
+        ggml_tensor * build_layer_attn(
+        llm_graph_input_attn_kv * inp_attn,
+                    ggml_tensor * cur,
+                    ggml_tensor * inp_pos,
+                            int   il);
+
+        ggml_tensor * build_layer_attn_linear(
+             llm_graph_input_rs * inp,
+                    ggml_tensor * cur,
+                            int   il);
+
+        ggml_tensor * build_layer_ffn(
+                    ggml_tensor * cur,
+                            int   il);
+
+        ggml_tensor * build_norm_gated(
+                    ggml_tensor * input,
+                    ggml_tensor * weights,
+                    ggml_tensor * gate,
+                            int   layer);
+
+        // returns pair of qkv, z
+        std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+                    ggml_tensor * input,
+                            int   il);
+
+        const llama_model & model;
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_starcoder : public llm_graph_context {
-    llm_build_starcoder(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen35 : public llama_model_base {
+    llama_model_qwen35(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_delta_net_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    private:
+        ggml_tensor * build_layer_attn(
+        llm_graph_input_attn_kv * inp_attn,
+                    ggml_tensor * cur,
+                    ggml_tensor * inp_pos,
+                            int * sections,
+                            int   il);
+
+        ggml_tensor * build_layer_attn_linear(
+             llm_graph_input_rs * inp,
+                    ggml_tensor * cur,
+                            int   il);
+
+        ggml_tensor * build_layer_ffn(
+                    ggml_tensor * cur,
+                            int   il);
+
+        ggml_tensor * build_norm_gated(
+                    ggml_tensor * input,
+                    ggml_tensor * weights,
+                    ggml_tensor * gate,
+                            int   layer);
+
+        // returns pair of qkv, z
+        std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+                    ggml_tensor * input,
+                            int   il);
+
+        const llama_model & model;
+    };
+
+    struct graph_mtp : public llm_graph_context {
+        graph_mtp(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_step35_iswa : public llm_graph_context {
-    llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_qwen35moe : public llama_model_base {
+    llama_model_qwen35moe(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_delta_net_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+    private:
+        ggml_tensor * build_layer_attn(
+        llm_graph_input_attn_kv * inp_attn,
+                    ggml_tensor * cur,
+                    ggml_tensor * inp_pos,
+                            int * sections,
+                            int   il);
+
+        ggml_tensor * build_layer_attn_linear(
+             llm_graph_input_rs * inp,
+                    ggml_tensor * cur,
+                            int   il);
+
+        ggml_tensor * build_layer_ffn(
+                    ggml_tensor * cur,
+                            int   il);
+
+        ggml_tensor * build_norm_gated(
+                    ggml_tensor * input,
+                    ggml_tensor * weights,
+                    ggml_tensor * gate,
+                            int   layer);
+
+        // returns pair of qkv, z
+        std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+                    ggml_tensor * input,
+                            int   il);
+
+        const llama_model & model;
+    };
+
+    struct graph_mtp : public llm_graph_context {
+        graph_mtp(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-template <bool is_enc>
-struct llm_build_t5 : public llm_graph_context {
-    llm_build_t5(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_mistral3 : public llama_model_base {
+    llama_model_mistral3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_t5encoder : public llm_build_t5<true> {
-    llm_build_t5encoder(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_mimo2 : public llama_model_base {
+    llama_model_mimo2(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_wavtokenizer_dec : public llm_graph_context {
-    llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_kimi_linear : public llama_model_base {
+    llama_model_kimi_linear(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_delta_net_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+
+        std::pair<ggml_tensor *, ggml_tensor *> build_kda_autoregressive(
+                    ggml_tensor * q,
+                    ggml_tensor * k,
+                    ggml_tensor * v,
+                    ggml_tensor * gk,
+                    ggml_tensor * beta,
+                    ggml_tensor * state,
+                            int   il);
+
+        std::pair<ggml_tensor *, ggml_tensor *> build_kda_chunking(
+                    ggml_tensor * q,
+                    ggml_tensor * k,
+                    ggml_tensor * v,
+                    ggml_tensor * gk,
+                    ggml_tensor * beta,
+                    ggml_tensor * state,
+                    ggml_tensor * causal_mask,
+                    ggml_tensor * identity,
+                    ggml_tensor * diag_mask,
+                            int   il);
+
+        const llama_model & model;
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
-struct llm_build_xverse : public llm_graph_context {
-    llm_build_xverse(const llama_model & model, const llm_graph_params & params);
+
+struct llama_model_step35 : public llama_model_base {
+    llama_model_step35(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
diff --git a/src/models/modern-bert.cpp b/src/models/modern-bert.cpp
index 5c6a1b5e1bc..e9b79ffc6dc 100644
--- a/src/models/modern-bert.cpp
+++ b/src/models/modern-bert.cpp
@@ -1,6 +1,69 @@
 #include "models.h"
 
-llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_modern_bert::load_arch_hparams(llama_model_loader & ml) {
+    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+    if (found_swa && hparams.n_swa > 0) {
+        hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+        uint32_t swa_period = 3;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period, true);
+    } else {
+        hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+    }
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 12:
+            type = LLM_TYPE_47M; break; // granite-embedding-small
+        case 22:
+            type = LLM_TYPE_149M; break; // modern-bert-base
+        case 28:
+            type = LLM_TYPE_395M; break; // modern-bert-large
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_modern_bert::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+    for(int i = 0; i < n_layer; ++i) {
+        auto& layer = layers[i];
+
+        if ( i != 0 ) {
+            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        } else{
+            // layer 0 uses identity
+            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        }
+
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd }, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,   "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, 2 * n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+    }
+
+    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT,  "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT,  "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
+    cls       = create_tensor(tn(LLM_TENSOR_CLS,      "weight"), {n_embd, n_embd},            TENSOR_NOT_REQUIRED);
+    cls_norm  = create_tensor(tn(LLM_TENSOR_CLS_NORM, "weight"), {n_embd},                    TENSOR_NOT_REQUIRED);
+
+}
+
+std::unique_ptr<llm_graph_context> llama_model_modern_bert::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_modern_bert::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
diff --git a/src/models/mpt.cpp b/src/models/mpt.cpp
index 8596bbb2024..0229d20ed36 100644
--- a/src/models/mpt.cpp
+++ b/src/models/mpt.cpp
@@ -1,6 +1,70 @@
 #include "models.h"
 
-llm_build_mpt::llm_build_mpt(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_mpt::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
+    ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv, false);
+    ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias, false);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_7B; break;
+        case 48: type = LLM_TYPE_30B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_mpt::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, TENSOR_NOT_REQUIRED);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (!output) {
+        output    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
+
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
+
+        // FIXME test-llama-archs crashes if q_norm is created
+        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
+        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
+
+        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        // AWQ ScaleActivation layer
+        layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_mpt::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_mpt::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -97,7 +161,7 @@ llm_build_mpt::llm_build_mpt(const llama_model & model, const llm_graph_params &
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/nemotron-h-moe.cpp b/src/models/nemotron-h-moe.cpp
new file mode 100644
index 00000000000..a59cc6c9fbd
--- /dev/null
+++ b/src/models/nemotron-h-moe.cpp
@@ -0,0 +1,6 @@
+#include "models.h"
+
+std::unique_ptr<llm_graph_context> llama_model_nemotron_h_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp
index dc07d43df58..a82f9c170b4 100644
--- a/src/models/nemotron-h.cpp
+++ b/src/models/nemotron-h.cpp
@@ -1,6 +1,127 @@
 #include "models.h"
 
-llm_build_nemotron_h::llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params) :
+void llama_model_nemotron_h::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    // A layer is recurrent IFF the n_head_kv value is set to 0 and
+    // the n_ff value is set to 0
+    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+        hparams.recurrent_layer_arr[i] = (hparams.n_head_kv(i) == 0 && hparams.n_ff(i) == 0);
+    }
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp,        false);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp,      false);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_MOE_LATENT_SIZE,                   hparams.moe_latent_size, false);
+
+    switch (hparams.n_layer) {
+        case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B
+        case 56: type = LLM_TYPE_9B; break;
+        case 88: type = LLM_TYPE_120B_A12B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_nemotron_h::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    // mamba2 Mixer SSM params
+    // NOTE: int64_t for tensor dimensions
+    const int64_t d_conv     = hparams.ssm_d_conv;
+    const int64_t d_inner    = hparams.ssm_d_inner;
+    const int64_t d_state    = hparams.ssm_d_state;
+    const int64_t n_ssm_head = hparams.ssm_dt_rank;
+    const int64_t n_group    = hparams.ssm_n_group;
+    const int64_t d_in_proj  = 2*d_inner + 2*n_group*d_state + n_ssm_head;
+    const int64_t moe_n_embd = hparams.moe_latent_size > 0 ? hparams.moe_latent_size : n_embd;
+
+    // embeddings
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    {
+        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+        // if output is NULL, init from the input tok embed, duplicated to allow offloading
+        if (output == NULL) {
+            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+        }
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // all blocks use the attn norm
+        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.is_recurrent(i)) {
+            // ssm layers
+            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
+
+            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
+            layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, TENSOR_NOT_REQUIRED);
+
+            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_ssm_head}, 0);
+
+            // no "weight" suffix for these
+            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_ssm_head}, 0);
+            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_ssm_head}, 0);
+
+            layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
+
+            // out_proj
+            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+        } else if (hparams.n_ff(i) == 0) {
+            // attention layers (with optional bias)
+            const int64_t n_head_i = hparams.n_head(i);
+            const int64_t n_embd_k_gqa_i = hparams.n_embd_k_gqa(i);
+            const int64_t n_embd_v_gqa_i = hparams.n_embd_v_gqa(i);
+            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_i, n_embd_k_gqa_i, n_embd_v_gqa_i, 0);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, 0);
+            layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        }  else {
+            if (n_expert != 0) {
+                const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+                const int64_t n_ff_shexp = hparams.n_ff_shexp;
+
+                layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert}, 0);
+                layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert         }, 0);
+
+                // MoE branch
+                layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, TENSOR_NOT_REQUIRED);
+                layer.ffn_latent_up   = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP,   "weight", i), {moe_n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+
+                layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   moe_n_embd, n_expert}, 0);
+                layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {moe_n_embd, n_ff_exp, n_expert}, 0);
+
+                // Shared expert branch
+                layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
+                layer.ffn_up_shexp    = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
+
+            } else {
+                // mlp layers
+                layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, 0);
+                layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, 0);
+                layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+                layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias",   i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_nemotron_h::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_mamba_base(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -53,14 +174,14 @@ llm_build_nemotron_h::llm_build_nemotron_h(const llama_model & model, const llm_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
     ggml_build_forward_expand(gf, cur);
 }
 
-ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor *             cur,
+ggml_tensor * llama_model_nemotron_h::graph::build_attention_layer(ggml_tensor *             cur,
                                                           llm_graph_input_attn_kv * inp_attn,
                                                           const llama_model &       model,
                                                                 int64_t             n_embd_head,
@@ -76,7 +197,7 @@ ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor *
     return cur;
 }
 
-ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const llama_model & model, int il) {
+ggml_tensor * llama_model_nemotron_h::graph::build_ffn_layer(ggml_tensor * cur, const llama_model & model, int il) {
     if (model.layers[il].ffn_gate_inp == nullptr) {
         cur = build_ffn(cur,
                 model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   model.layers[il].ffn_up_s,
diff --git a/src/models/nemotron.cpp b/src/models/nemotron.cpp
index 054b16fe0ef..5d4a3b5c69e 100644
--- a/src/models/nemotron.cpp
+++ b/src/models/nemotron.cpp
@@ -1,6 +1,52 @@
 #include "models.h"
 
-llm_build_nemotron::llm_build_nemotron(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_nemotron::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_4B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_nemotron::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
+
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+        // optional MLP bias
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_nemotron::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_nemotron::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -94,7 +140,7 @@ llm_build_nemotron::llm_build_nemotron(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/neo-bert.cpp b/src/models/neo-bert.cpp
index da68024a34d..f00d6eddfc9 100644
--- a/src/models/neo-bert.cpp
+++ b/src/models/neo-bert.cpp
@@ -1,6 +1,46 @@
 #include "models.h"
 
-llm_build_neo_bert::llm_build_neo_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_neo_bert::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    if (hparams.n_layer == 28) {
+        type = LLM_TYPE_250M;
+    }
+}
+
+void llama_model_neo_bert::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+
+    cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+    cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
+
+    cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+    cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
+
+    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff*2}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_neo_bert::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_neo_bert::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
diff --git a/src/models/nomic-bert-moe.cpp b/src/models/nomic-bert-moe.cpp
new file mode 100644
index 00000000000..a17abe2c269
--- /dev/null
+++ b/src/models/nomic-bert-moe.cpp
@@ -0,0 +1,72 @@
+#include "models.h"
+
+void llama_model_nomic_bert_moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+    ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS,         hparams.moe_every_n_layers, 0);
+
+    if (hparams.n_layer == 12 && hparams.n_embd == 768) {
+        if (arch == LLM_ARCH_NOMIC_BERT) {
+            type = LLM_TYPE_137M;
+        } else if (arch == LLM_ARCH_NOMIC_BERT_MOE && hparams.moe_every_n_layers == 2) {
+            type = LLM_TYPE_475M;
+        }
+    }
+}
+
+void llama_model_nomic_bert_moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_token_types == 0) {
+        throw std::runtime_error(arch_name() + " model needs to define token type count");
+    }
+    tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+    type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
+
+    if (arch == LLM_ARCH_BERT) {
+        pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train}, 0);
+
+        cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+        cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
+
+        cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
+    }
+
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);
+
+        if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff,   n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff,   n_embd, n_expert}, 0);
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,   "weight", i), {n_embd, n_expert}, 0);
+        } else {
+            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+            if (arch == LLM_ARCH_NOMIC_BERT) {
+                layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            }
+        }
+
+        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_nomic_bert_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/nomic-bert.cpp b/src/models/nomic-bert.cpp
new file mode 100644
index 00000000000..5a8a5584457
--- /dev/null
+++ b/src/models/nomic-bert.cpp
@@ -0,0 +1,72 @@
+#include "models.h"
+
+void llama_model_nomic_bert::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+    ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS,         hparams.moe_every_n_layers, 0);
+
+    if (hparams.n_layer == 12 && hparams.n_embd == 768) {
+        if (arch == LLM_ARCH_NOMIC_BERT) {
+            type = LLM_TYPE_137M;
+        } else if (arch == LLM_ARCH_NOMIC_BERT_MOE && hparams.moe_every_n_layers == 2) {
+            type = LLM_TYPE_475M;
+        }
+    }
+}
+
+void llama_model_nomic_bert::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_token_types == 0) {
+        throw std::runtime_error(arch_name() + " model needs to define token type count");
+    }
+    tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+    type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
+
+    if (arch == LLM_ARCH_BERT) {
+        pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train}, 0);
+
+        cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
+        cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
+
+        cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
+    }
+
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);
+
+        if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff,   n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff,   n_embd, n_expert}, 0);
+            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,   "weight", i), {n_embd, n_expert}, 0);
+        } else {
+            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+            if (arch == LLM_ARCH_NOMIC_BERT) {
+                layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            }
+        }
+
+        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
+        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_nomic_bert::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
diff --git a/src/models/olmo.cpp b/src/models/olmo.cpp
index a9974025f07..cfcf17bcb03 100644
--- a/src/models/olmo.cpp
+++ b/src/models/olmo.cpp
@@ -1,6 +1,46 @@
 #include "models.h"
 
-llm_build_olmo::llm_build_olmo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_olmo::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,     hparams.f_clamp_kqv, false);
+
+    switch (hparams.n_layer) {
+        case 22: type = LLM_TYPE_1B; break;
+        case 32: type = LLM_TYPE_7B; break;
+        case 80: type = LLM_TYPE_70B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_olmo::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_olmo::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_olmo::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -93,7 +133,7 @@ llm_build_olmo::llm_build_olmo(const llama_model & model, const llm_graph_params
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/olmo2.cpp b/src/models/olmo2.cpp
index 308d2a600c2..7cc262f5504 100644
--- a/src/models/olmo2.cpp
+++ b/src/models/olmo2.cpp
@@ -1,7 +1,68 @@
 #include "models.h"
 
+void llama_model_olmo2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+    if (found_swa && hparams.n_swa > 0) {
+        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        uint32_t swa_period = 4;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period);
+
+        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+        hparams.rope_freq_scale_train_swa = 1.0; // See olmo2.cpp
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+    } else {
+        hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+    }
+
+    switch (hparams.n_layer) {
+        case 16: type = LLM_TYPE_1B; break;
+        case 32: type = LLM_TYPE_7B; break;
+        case 40: type = LLM_TYPE_13B; break;
+        case 64: type = LLM_TYPE_32B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_olmo2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_embd_head = n_embd / n_head;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_head_kv * n_embd_head}, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_olmo2::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+        return std::make_unique<graph<true>>(*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
 template <bool iswa>
-llm_build_olmo2<iswa>::llm_build_olmo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_olmo2::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -137,7 +198,7 @@ llm_build_olmo2<iswa>::llm_build_olmo2(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
@@ -146,5 +207,5 @@ llm_build_olmo2<iswa>::llm_build_olmo2(const llama_model & model, const llm_grap
 }
 
 // Explicit template instantiations
-template struct llm_build_olmo2<false>;
-template struct llm_build_olmo2<true>;
+template struct llama_model_olmo2::graph<false>;
+template struct llama_model_olmo2::graph<true>;
diff --git a/src/models/olmoe.cpp b/src/models/olmoe.cpp
index ed46a00ef90..7976ae44a51 100644
--- a/src/models/olmoe.cpp
+++ b/src/models/olmoe.cpp
@@ -1,6 +1,55 @@
 #include "models.h"
 
-llm_build_olmoe::llm_build_olmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_olmoe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 16: type = LLM_TYPE_A1_7B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_olmoe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+        if (n_expert == 0) {
+            throw std::runtime_error("n_expert must be > 0");
+        }
+        if (n_expert_used == 0) {
+            throw std::runtime_error("n_expert_used must be > 0");
+        }
+
+        // MoE branch
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_olmoe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_olmoe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -115,7 +164,7 @@ llm_build_olmoe::llm_build_olmoe(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/openai-moe-iswa.cpp b/src/models/openai-moe.cpp
similarity index 51%
rename from src/models/openai-moe-iswa.cpp
rename to src/models/openai-moe.cpp
index 50992b8d506..15b6c8c1205 100644
--- a/src/models/openai-moe-iswa.cpp
+++ b/src/models/openai-moe.cpp
@@ -1,6 +1,67 @@
 #include "models.h"
 
-llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_openai_moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    uint32_t swa_period = 2;
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+    hparams.set_swa_pattern(swa_period);
+
+    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_20B; break;
+        case 36: type = LLM_TYPE_120B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_openai_moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_ff_exp = hparams.n_ff_exp;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_head * n_rot, n_head_kv * n_rot, n_head_kv * n_rot, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_rot, n_embd}, 0);
+
+        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);
+
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {  n_embd, n_expert}, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp_b  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "bias", i), {n_expert}, 0);
+        layer.ffn_gate_exps_b = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "bias", i), {n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "bias", i), {  n_embd, n_expert}, 0);
+        layer.ffn_up_exps_b   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "bias", i), {n_ff_exp, n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_openai_moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -99,7 +160,7 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model,
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/openelm.cpp b/src/models/openelm.cpp
index 514ac33517f..9f76350fd4d 100644
--- a/src/models/openelm.cpp
+++ b/src/models/openelm.cpp
@@ -1,6 +1,53 @@
 #include "models.h"
 
-llm_build_openelm::llm_build_openelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_openelm::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+    case 16: type = LLM_TYPE_270M; break;
+    case 20: type = LLM_TYPE_450M; break;
+    case 28: type = LLM_TYPE_1B; break;
+    case 36: type = LLM_TYPE_3B; break;
+    default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_openelm::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    // init output from the input tok embed
+    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+
+    for (int i = 0; i < n_layer; ++i) {
+        const int64_t n_head      =   hparams.n_head(i);
+        const int64_t n_head_qkv  = 2*hparams.n_head_kv(i) + n_head;
+        const int64_t n_ff        =   hparams.n_ff(i);
+
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_openelm::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_openelm::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -115,7 +162,7 @@ llm_build_openelm::llm_build_openelm(const llama_model & model, const llm_graph_
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/orion.cpp b/src/models/orion.cpp
index a5874b6dee7..bcb4bbba4b1 100644
--- a/src/models/orion.cpp
+++ b/src/models/orion.cpp
@@ -1,6 +1,46 @@
 #include "models.h"
 
-llm_build_orion::llm_build_orion(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_orion::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 40: type = LLM_TYPE_14B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_orion::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_orion::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_orion::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -92,7 +132,7 @@ llm_build_orion::llm_build_orion(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/paddleocr.cpp b/src/models/paddleocr.cpp
index 56cb1d94c5f..d39220bd778 100644
--- a/src/models/paddleocr.cpp
+++ b/src/models/paddleocr.cpp
@@ -1,6 +1,10 @@
 #include "models.h"
 
-llm_build_paddleocr::llm_build_paddleocr(const llama_model & model, const llm_graph_params & params) :
+std::unique_ptr<llm_graph_context> llama_model_paddleocr::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_paddleocr::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
 
     // NOTE: same with qwen2vl.cpp, but bias tensors are optional
@@ -94,7 +98,7 @@ llm_build_paddleocr::llm_build_paddleocr(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/pangu-embedded.cpp b/src/models/pangu-embed.cpp
similarity index 52%
rename from src/models/pangu-embedded.cpp
rename to src/models/pangu-embed.cpp
index 53464f21d22..7593f879b24 100644
--- a/src/models/pangu-embedded.cpp
+++ b/src/models/pangu-embed.cpp
@@ -1,6 +1,60 @@
 #include "models.h"
 
-llm_build_pangu_embedded::llm_build_pangu_embedded(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_pangu_embed::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 26: type = LLM_TYPE_1B; break; // openPangu-Embedded-1B-V1.1
+        case 34: type = LLM_TYPE_7B; break; // openPangu-Embedded-7B-V1.1
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_pangu_embed::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        // weight tensors
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        } else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_pangu_embed::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_pangu_embed::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -94,7 +148,7 @@ llm_build_pangu_embedded::llm_build_pangu_embedded(const llama_model & model, co
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     if (model.output_b != nullptr) {
         cur = ggml_add(ctx0, cur, model.output_b);
diff --git a/src/models/phi2.cpp b/src/models/phi2.cpp
index 0fb3ffa2e63..8f3ed5f7b7d 100644
--- a/src/models/phi2.cpp
+++ b/src/models/phi2.cpp
@@ -1,6 +1,50 @@
 #include "models.h"
 
-llm_build_phi2::llm_build_phi2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_phi2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_1B; break;
+        case 32: type = LLM_TYPE_3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_phi2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+    output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_phi2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_phi2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -86,7 +130,7 @@ llm_build_phi2::llm_build_phi2(const llama_model & model, const llm_graph_params
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output_no_bias", -1);
 
     cur = ggml_add(ctx0, cur, model.output_b);
diff --git a/src/models/phi3.cpp b/src/models/phi3.cpp
index 39af285d3c5..f8a4a4d5aa5 100644
--- a/src/models/phi3.cpp
+++ b/src/models/phi3.cpp
@@ -1,7 +1,71 @@
 #include "models.h"
 
+void llama_model_phi3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_1B; break;
+        case 32: type = LLM_TYPE_3B; break;
+        case 40: type = LLM_TYPE_14B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+
+    if (found_swa && hparams.n_swa > 0) {
+        LLAMA_LOG_WARN("%s: Phi SWA is currently disabled - results might be suboptimal for some models (see %s)\n",
+                __func__, "https://github.com/ggml-org/llama.cpp/pull/13676");
+
+        // TODO: fix conversion scripts to correctly populate `n_swa` and `n_swa_pattern`
+        hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+
+        hparams.n_swa         = 0;
+        hparams.set_swa_pattern(1);
+    }
+}
+
+void llama_model_phi3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, TENSOR_NOT_REQUIRED);
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
+
+        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_phi3::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
+        return std::make_unique<graph<true>> (*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
 template<bool iswa>
-llm_build_phi3<iswa>::llm_build_phi3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_phi3::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -115,7 +179,7 @@ llm_build_phi3<iswa>::llm_build_phi3(const llama_model & model, const llm_graph_
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     if (model.output_b != nullptr) {
         cb(cur, "result_output_no_bias", -1);
@@ -128,5 +192,5 @@ llm_build_phi3<iswa>::llm_build_phi3(const llama_model & model, const llm_graph_
 }
 
 // Explicit template instantiations
-template struct llm_build_phi3<false>;
-template struct llm_build_phi3<true>;
+template struct llama_model_phi3::graph<false>;
+template struct llama_model_phi3::graph<true>;
diff --git a/src/models/phimoe.cpp b/src/models/phimoe.cpp
new file mode 100644
index 00000000000..4575d6139cf
--- /dev/null
+++ b/src/models/phimoe.cpp
@@ -0,0 +1,55 @@
+#include "models.h"
+
+void llama_model_phimoe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_16x3_8B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_phimoe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_embd_head = n_embd / n_head;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), { n_embd, n_vocab }, 0);
+    output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   { n_vocab }, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), { n_embd }, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), { n_embd }, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), { n_embd }, 0);
+
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert},         0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+
+        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+     }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_phimoe::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
+        return std::make_unique<graph<true>> (*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
diff --git a/src/models/plamo.cpp b/src/models/plamo.cpp
index 4d5c84506c2..c7ed1211c31 100644
--- a/src/models/plamo.cpp
+++ b/src/models/plamo.cpp
@@ -1,6 +1,42 @@
 #include "models.h"
 
-llm_build_plamo::llm_build_plamo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_plamo::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 40: type = LLM_TYPE_13B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+   }
+}
+
+void llama_model_plamo::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_plamo::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_plamo::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -91,7 +127,7 @@ llm_build_plamo::llm_build_plamo(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/plamo2.cpp b/src/models/plamo2.cpp
index b6142daebd9..b713889fe72 100644
--- a/src/models/plamo2.cpp
+++ b/src/models/plamo2.cpp
@@ -1,8 +1,109 @@
 #include "models.h"
-
 #include "llama-memory-recurrent.h"
 
-llm_build_plamo2::llm_build_plamo2(const llama_model & model, const llm_graph_params & params) :
+void llama_model_plamo2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    // Load Mamba SSM parameters
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+        hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0;
+    }
+
+    switch (hparams.n_layer) {
+        case 16: type = LLM_TYPE_1B; break;
+        case 32:
+            if (hparams.n_embd == 2048) {
+                type = LLM_TYPE_2B;
+            } else if (hparams.n_embd == 4096) {
+                type = LLM_TYPE_8B;
+            }
+            break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_plamo2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    // mamba parameters
+    const uint32_t d_conv             = hparams.ssm_d_conv;
+    const uint32_t d_state            = hparams.ssm_d_state;
+    const uint32_t num_heads          = hparams.ssm_dt_rank;
+    const uint32_t intermediate_size  = hparams.ssm_d_inner;
+    const int64_t dt_dim              = std::max(64, int(hparams.n_embd / 16));
+
+    // attention parameters
+    const uint32_t qk_dim = hparams.n_embd_head_k();
+    const uint32_t v_dim  = hparams.n_embd_head_v();
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+        bool is_mamba_layer = hparams.is_recurrent(i);
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (is_mamba_layer) {
+            layer.ssm_in       = create_tensor(tn(LLM_TENSOR_SSM_IN,     "weight", i), {n_embd, 2 * intermediate_size}, 0);
+            layer.ssm_conv1d   = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, intermediate_size}, 0);
+
+            layer.ssm_x    = create_tensor(tn(LLM_TENSOR_SSM_X,  "weight", i), {intermediate_size, dt_dim + 2*d_state}, 0);
+            layer.ssm_dt   = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_dim, num_heads}, 0);
+            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {num_heads}, 0);
+
+            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {num_heads}, 0);
+            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {num_heads}, 0);
+
+            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {intermediate_size, n_embd}, 0);
+
+            layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, i), {dt_dim}, 0);
+            layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, i), {d_state}, 0);
+            layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, i), {d_state}, 0);
+        } else {
+            const int64_t num_attention_heads = hparams.n_head(i);
+            const int64_t q_num_heads         = num_attention_heads;
+            const int64_t num_key_value_heads = hparams.n_head_kv(i);
+            const int64_t k_num_heads         = num_key_value_heads;
+            const int64_t v_num_heads         = num_key_value_heads;
+            const int64_t q_proj_dim          = q_num_heads * qk_dim;
+            const int64_t k_proj_dim          = k_num_heads * qk_dim;
+            const int64_t v_proj_dim          = v_num_heads * v_dim;
+
+            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, q_proj_dim + k_proj_dim + v_proj_dim}, 0);
+            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {qk_dim, num_attention_heads}, 0);
+            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {qk_dim, k_num_heads}, 0);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {q_num_heads * v_dim, n_embd}, 0);
+        }
+
+        // All layers have post-attention norm, FFN norm, and FFN tensors
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, i), {n_embd}, 0);
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_plamo2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_plamo2::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_mamba_base(params) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -84,7 +185,7 @@ llm_build_plamo2::llm_build_plamo2(const llama_model & model, const llm_graph_pa
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
 
     // Explicitly mark as output tensor to ensure proper backend assignment
@@ -95,7 +196,7 @@ llm_build_plamo2::llm_build_plamo2(const llama_model & model, const llm_graph_pa
     ggml_build_forward_expand(gf, cur);
 }
 
-ggml_tensor * llm_build_plamo2::build_plamo2_attn_layer(llm_graph_input_attn_kv * inp,
+ggml_tensor * llama_model_plamo2::graph::build_plamo2_attn_layer(llm_graph_input_attn_kv * inp,
                                                         ggml_tensor *             inp_pos,
                                                         ggml_tensor *             cur,
                                                         const llama_model &       model,
@@ -150,7 +251,7 @@ ggml_tensor * llm_build_plamo2::build_plamo2_attn_layer(llm_graph_input_attn_kv
     return cur;
 }
 
-ggml_tensor * llm_build_plamo2::build_plamo2_mamba_layer(llm_graph_input_rs * inp,
+ggml_tensor * llama_model_plamo2::graph::build_plamo2_mamba_layer(llm_graph_input_rs * inp,
                                                          ggml_tensor *        cur,
                                                          const llama_model &  model,
                                                          const llama_ubatch & ubatch,
diff --git a/src/models/plamo3.cpp b/src/models/plamo3.cpp
index 67844c09f24..29f3e803d68 100644
--- a/src/models/plamo3.cpp
+++ b/src/models/plamo3.cpp
@@ -1,7 +1,74 @@
 #include "models.h"
 
+void llama_model_plamo3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+    if (found_swa && hparams.n_swa > 0) {
+        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+        uint32_t swa_period = 8;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period);
+    } else {
+        hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+    }
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_2B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_plamo3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t head_dim_q = hparams.n_embd_head_k();
+    const int64_t head_dim_v = hparams.n_embd_head_v();
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        const int64_t num_attention_heads = hparams.n_head(i);
+        const int64_t num_key_value_heads = hparams.n_head_kv(i);
+        const int64_t q_proj_dim = num_attention_heads * head_dim_q;
+        const int64_t k_proj_dim = num_key_value_heads * head_dim_q;
+        const int64_t v_proj_dim = num_key_value_heads * head_dim_v;
+        const int64_t n_ff_cur   = hparams.n_ff(i);
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i),
+                {n_embd,q_proj_dim + k_proj_dim + v_proj_dim}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim_q}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim_q}, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {num_attention_heads * head_dim_v, n_embd}, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, i), {n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
+
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff_cur * 2}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_cur, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_plamo3::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
+        return std::make_unique<graph<true>> (*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
 template <bool iswa>
-llm_build_plamo3<iswa>::llm_build_plamo3(const llama_model & model, const llm_graph_params & params) :
+llama_model_plamo3::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t head_dim_q = hparams.n_embd_head_k();
     const int64_t head_dim_v = hparams.n_embd_head_v();
@@ -119,12 +186,12 @@ llm_build_plamo3<iswa>::llm_build_plamo3(const llama_model & model, const llm_gr
     cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     res->t_logits = cur;
 
     ggml_build_forward_expand(gf, cur);
 }
 
 // Explicit template instantiations
-template struct llm_build_plamo3<false>;
-template struct llm_build_plamo3<true>;
+template struct llama_model_plamo3::graph<false>;
+template struct llama_model_plamo3::graph<true>;
diff --git a/src/models/plm.cpp b/src/models/plm.cpp
index abce6b34d04..ce050919e6a 100644
--- a/src/models/plm.cpp
+++ b/src/models/plm.cpp
@@ -1,6 +1,50 @@
 #include "models.h"
 
-llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_plm::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_1_8B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_plm::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();
+    const int64_t kv_lora_rank = hparams.n_lora_kv;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    // output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+    output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.wq        = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
+        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
+        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
+        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_plm::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_plm::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k()));
 
     const uint32_t n_embd_head_qk_rope = hparams.n_rot();
@@ -160,7 +204,7 @@ llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params &
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/qwen.cpp b/src/models/qwen.cpp
index 44e75d87437..00467dbad7d 100644
--- a/src/models/qwen.cpp
+++ b/src/models/qwen.cpp
@@ -1,6 +1,46 @@
 #include "models.h"
 
-llm_build_qwen::llm_build_qwen(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_qwen::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_7B; break;
+        case 40: type = LLM_TYPE_13B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}, 0);
+        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}, 0);
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff/2}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -91,7 +131,7 @@ llm_build_qwen::llm_build_qwen(const llama_model & model, const llm_graph_params
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp
index 2892dd75087..a5147460bae 100644
--- a/src/models/qwen2.cpp
+++ b/src/models/qwen2.cpp
@@ -1,6 +1,55 @@
 #include "models.h"
 
-llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_qwen2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break;
+        case 28: type = hparams.n_embd == 1536 ? LLM_TYPE_1_5B : LLM_TYPE_7B; break;
+        case 32: type = LLM_TYPE_7B; break;
+        case 36: type = LLM_TYPE_3B; break;
+        case 40: type = hparams.n_head() == 20 ? LLM_TYPE_4B : LLM_TYPE_13B; break;
+        case 48: type = LLM_TYPE_14B; break;
+        case 64: type = LLM_TYPE_32B; break;
+        case 80: type = LLM_TYPE_70B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    output_b    = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -92,7 +141,7 @@ llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     if (model.output_b != nullptr) {
         cur = ggml_add(ctx0, cur, model.output_b);
diff --git a/src/models/qwen2moe.cpp b/src/models/qwen2moe.cpp
index 5f0a6861b68..7cb03859deb 100644
--- a/src/models/qwen2moe.cpp
+++ b/src/models/qwen2moe.cpp
@@ -1,6 +1,67 @@
 #include "models.h"
 
-llm_build_qwen2moe::llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_qwen2moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_A2_7B; break;
+        case 28: type = LLM_TYPE_57B_A14B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen2moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+        if (n_expert == 0) {
+            throw std::runtime_error("n_expert must be > 0 for QWEN2MOE");
+        }
+        if (n_expert_used == 0) {
+            throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE");
+        }
+
+        // MoE branch
+        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+        // Shared expert branch
+        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
+        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0);
+        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);
+        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp,     n_embd}, 0);
+        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen2moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen2moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -123,7 +184,7 @@ llm_build_qwen2moe::llm_build_qwen2moe(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/qwen2vl.cpp b/src/models/qwen2vl.cpp
index da7937c7667..d79db682cd4 100644
--- a/src/models/qwen2vl.cpp
+++ b/src/models/qwen2vl.cpp
@@ -1,6 +1,45 @@
 #include "models.h"
 
-llm_build_qwen2vl::llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_qwen2vl::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
+}
+// fall through
+
+void llama_model_qwen2vl::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    output_b    = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen2vl::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen2vl::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -95,7 +134,7 @@ llm_build_qwen2vl::llm_build_qwen2vl(const llama_model & model, const llm_graph_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/qwen3.cpp b/src/models/qwen3.cpp
index e6f1fc81d88..41b97fed956 100644
--- a/src/models/qwen3.cpp
+++ b/src/models/qwen3.cpp
@@ -1,6 +1,55 @@
 #include "models.h"
 
-llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_qwen3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break;
+        case 36: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_8B; break;
+        case 40: type = LLM_TYPE_14B; break;
+        case 64: type = LLM_TYPE_32B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    // output rerank head
+    cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen3::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -58,9 +107,6 @@ llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_para
             cur = build_attn(inp_attn,
                     model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
                     Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            if (model.layers[il].wo_s) {
-                cur = ggml_mul(ctx0, cur, model.layers[il].wo_s);
-            }
         }
         if (il == n_layer - 1 && inp_out_ids) {
             cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
@@ -101,7 +147,7 @@ llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_para
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp
index 87790f08e4e..2b4d5b14cd4 100644
--- a/src/models/qwen35.cpp
+++ b/src/models/qwen35.cpp
@@ -1,8 +1,142 @@
 #include "models.h"
-
 #include "llama-memory-recurrent.h"
 
-llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_params & params) :
+void llama_model_qwen35::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS,    hparams.rope_sections, 4, true);
+
+    // Load linear attention (gated delta net) parameters
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    // NextN/MTP (Qwen3.5/3.6): extra decoder block appended beyond the main stack
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
+    GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+
+    // Mark recurrent layers (linear attention layers). MTP layers are dense
+    // attention-only and must be flagged non-recurrent.
+    {
+        const uint32_t n_main = hparams.n_layer - hparams.nextn_predict_layers;
+        uint32_t full_attn_interval = 4;
+        ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+        for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+            hparams.recurrent_layer_arr[i] = (i < n_main) && ((i + 1) % full_attn_interval != 0);
+        }
+    }
+
+    switch (hparams.n_layer - hparams.nextn_predict_layers) {
+        case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_8B : LLM_TYPE_2B; break;
+        case 32: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_9B; break;
+        case 64: type = LLM_TYPE_27B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen35::load_arch_tensors(llama_model_loader & ml) {
+    LLAMA_LOAD_LOCALS;
+
+    const uint32_t n_main = n_layer - hparams.nextn_predict_layers;
+    const bool mtp_only   = (hparams.nextn_predict_layers > 0) &&
+                            (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
+    const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+    }
+
+    auto load_block_trunk = [&](int il, int flags) {
+        auto & layer = layers[il];
+
+        // Calculate dimensions from hyperparameters
+        const int64_t head_k_dim = hparams.ssm_d_state;
+        const int64_t head_v_dim = hparams.ssm_d_state;
+        const int64_t n_k_heads  = hparams.ssm_n_group;
+        const int64_t n_v_heads  = hparams.ssm_dt_rank;
+        const int64_t key_dim    = head_k_dim * n_k_heads;
+        const int64_t value_dim  = head_v_dim * n_v_heads;
+        const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", il), { n_embd }, flags);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, flags);
+
+        if (!hparams.is_recurrent(il)) {
+            // Attention layers
+            create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, flags);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, flags);
+
+            // Q/K normalization for attention layers
+            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, flags);
+            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, flags);
+        } else {
+            // Linear attention (gated delta net) specific tensors
+            // Create tensors with calculated dimensions
+            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", il), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", il), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", il), { hparams.ssm_d_conv, conv_dim }, flags);
+            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   il), { hparams.ssm_dt_rank }, flags);
+            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             il), { hparams.ssm_dt_rank }, flags);
+            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", il), { n_embd, n_v_heads }, flags);
+            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", il), { n_embd, n_v_heads }, flags);
+            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", il), { head_v_dim }, flags);
+            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", il), { value_dim, n_embd }, flags);
+        }
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", il), {n_embd,   n_ff}, flags);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", il), {  n_ff, n_embd}, flags);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", il), {n_embd,   n_ff}, flags);
+    };
+
+    auto load_block_mtp = [&](int il) {
+        auto & layer = layers[il];
+
+        // MTP block looks like a full-attention Qwen3.5 decoder block.
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", il), { n_embd }, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, 0);
+
+        create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo          = create_tensor(tn(LLM_TENSOR_ATTN_OUT,    "weight", il), { n_embd_head_k * n_head, n_embd }, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", il), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", il), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", il), {n_embd,   n_ff}, 0);
+
+        // NextN-specific tensors that define the MTP block.
+        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", il), { 2 * n_embd, n_embd }, 0);
+        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", il), { n_embd },              0);
+        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", il), { n_embd },              0);
+        layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", il), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
+        layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
+        layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd },              TENSOR_NOT_REQUIRED);
+    };
+
+    for (int i = 0; i < (int) n_main; ++i) {
+        load_block_trunk(i, trunk_flags);
+    }
+    for (int i = (int) n_main; i < n_layer; ++i) {
+        load_block_mtp(i);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen35::build_arch_graph(const llm_graph_params & params) const {
+    if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
+        return std::make_unique<graph_mtp>(*this, params);
+    }
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen35::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_delta_net_base(params), model(model) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -23,7 +157,9 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa
     ggml_tensor * inp_pos     = build_inp_pos();
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
-    for (int il = 0; il < n_layer; ++il) {
+    // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
+    const int n_transformer_layers = n_layer - (int) hparams.nextn_predict_layers;
+    for (int il = 0; il < n_transformer_layers; ++il) {
         ggml_tensor * inpSA = inpL;
 
         cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
@@ -40,7 +176,7 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa
             cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids) {
+        if (il == n_transformer_layers - 1 && inp_out_ids) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -72,6 +208,9 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa
     }
     cur = inpL;
 
+    cb(cur, "h_pre_norm", -1);
+    res->t_h_pre_norm = cur;
+
     // Final norm
     cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
 
@@ -79,7 +218,7 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa
     res->t_embd = cur;
 
     // LM head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
@@ -87,7 +226,7 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa
     ggml_build_forward_expand(gf, cur);
 }
 
-std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_qkvz(
+std::pair<ggml_tensor *, ggml_tensor *> llama_model_qwen35::graph::build_qkvz(
                 ggml_tensor * input,
                         int   il) {
     const int64_t n_seqs       = ubatch.n_seqs;
@@ -103,7 +242,7 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_qkvz(
     return { qkv_mixed, z };
 }
 
-ggml_tensor * llm_build_qwen35::build_norm_gated(
+ggml_tensor * llama_model_qwen35::graph::build_norm_gated(
         ggml_tensor * input,
         ggml_tensor * weights,
         ggml_tensor * gate,
@@ -114,7 +253,7 @@ ggml_tensor * llm_build_qwen35::build_norm_gated(
     return ggml_mul(ctx0, normalized, gated_silu);
 }
 
-ggml_tensor * llm_build_qwen35::build_layer_attn(
+ggml_tensor * llama_model_qwen35::graph::build_layer_attn(
         llm_graph_input_attn_kv * inp,
         ggml_tensor *             cur,
         ggml_tensor *             inp_pos,
@@ -195,7 +334,7 @@ ggml_tensor * llm_build_qwen35::build_layer_attn(
     return cur;
 }
 
-ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
+ggml_tensor * llama_model_qwen35::graph::build_layer_attn_linear(
         llm_graph_input_rs * inp,
         ggml_tensor *        cur,
         int                  il) {
@@ -209,8 +348,6 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
     const int64_t head_v_dim   = d_inner / num_v_heads;
     const int64_t n_seq_tokens = ubatch.n_seq_tokens;
 
-    const auto kv_head = mctx_cur->get_head();
-
     GGML_ASSERT(n_seqs != 0);
     GGML_ASSERT(ubatch.equal_seqs());
     GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
@@ -240,41 +377,14 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
 
     gate = ggml_reshape_4d(ctx0, gate, 1, num_v_heads, n_seq_tokens, n_seqs);
 
-    // Get convolution states from cache
     ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
     ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
 
-    // Build the convolution states tensor
-    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-    cb(conv_states, "conv_states", il);
-
-    // Calculate convolution kernel size
     ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
     const int64_t conv_kernel_size = conv_kernel->ne[0];
     const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
 
-    conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
-    cb(conv_states, "conv_states_reshaped", il);
-
-    qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
-    cb(qkv_mixed, "qkv_mixed_transposed", il);
-
-    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
-    cb(conv_input, "conv_input", il);
-
-    // Update convolution state cache
-    // Extract the last (conv_kernel_size - 1) states from conv_input
-    ggml_tensor * last_conv_states =
-        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
-                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
-    cb(last_conv_states, "last_conv_states", il);
-
-    ggml_tensor * state_update_target =
-        ggml_view_2d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels, n_seqs, conv_states_all->nb[1],
-                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
-    cb(state_update_target, "state_update_target", il);
-
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    ggml_tensor * conv_input = build_conv_state(inp, conv_states_all, qkv_mixed, conv_kernel_size, conv_channels, il);
 
     ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
     state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
@@ -325,7 +435,7 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
     //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
 
     // if head keys and value keys are different, repeat to force tensors into matching shapes
-    // note: need explicit repeat only if we are not using the fused GDN
+    // note: need explicit repeat only if we are not using the fused GDN.
     if (num_k_heads != num_v_heads && (!cparams.fused_gdn_ar || !cparams.fused_gdn_ch)) {
         GGML_ASSERT(num_v_heads % num_k_heads == 0);
         q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
@@ -336,18 +446,7 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    auto attn_out = build_delta_net(q_conv, k_conv, v_conv, gate, beta, state, il);
-
-    ggml_tensor * output    = attn_out.first;
-    ggml_tensor * new_state = attn_out.second;
-    cb(output, "attn_output", il);
-    cb(new_state, "new_state", il);
-
-    // Update the recurrent states
-    ggml_build_forward_expand(gf,
-            ggml_cpy(ctx0, new_state,
-                ggml_view_2d(ctx0, ssm_states_all, hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
-                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+    ggml_tensor * output = build_recurrent_attn(inp, ssm_states_all, q_conv, k_conv, v_conv, gate, beta, state, il);
 
     // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
     ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
@@ -369,7 +468,7 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
     return cur;
 }
 
-ggml_tensor * llm_build_qwen35::build_layer_ffn(ggml_tensor * cur, const int il) {
+ggml_tensor * llama_model_qwen35::graph::build_layer_ffn(ggml_tensor * cur, const int il) {
     // Qwen3.5 does not use MoE FFN
     GGML_ASSERT(model.layers[il].ffn_gate_inp == nullptr);
 
@@ -383,3 +482,146 @@ ggml_tensor * llm_build_qwen35::build_layer_ffn(ggml_tensor * cur, const int il)
 
     return cur;
 }
+
+// LLM_GRAPH_TYPE_DECODER_MTP draft head for Qwen3.5/3.6 dense series
+llama_model_qwen35::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
+    : llm_graph_context(params) {
+    GGML_ASSERT(hparams.nextn_predict_layers > 0 && "QWEN35 MTP requires nextn_predict_layers > 0");
+    GGML_ASSERT(hparams.nextn_predict_layers == 1 && "QWEN35 MTP currently only supports a single MTP block");
+
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+    // The MTP block lives at the source file's original layer index.
+    const int il = (int) hparams.n_layer - (int) hparams.nextn_predict_layers;
+    const auto & layer = model.layers[il];
+
+    GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
+    GGML_ASSERT(layer.nextn.enorm   && "MTP block missing nextn.enorm");
+    GGML_ASSERT(layer.nextn.hnorm   && "MTP block missing nextn.hnorm");
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    auto inp = std::make_unique<llm_graph_input_embd>(hparams.n_embd);
+
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    ggml_set_input(inp->tokens);
+
+    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
+    ggml_set_input(inp->embd);
+    ggml_set_name(inp->embd, "mtp_h_input");
+
+    ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
+
+    ggml_tensor * h_input  = inp->embd;
+    ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
+    cb(tok_embd, "mtp_tok_embd", il);
+
+    res->add_input(std::move(inp));
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    auto * inp_attn       = build_attn_inp_kv();
+
+    ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
+    cb(h_norm, "mtp_hnorm", il);
+
+    ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
+    cb(e_norm, "mtp_enorm", il);
+
+    ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
+    cb(concat, "mtp_concat", il);
+
+    ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat);
+    cb(cur, "mtp_eh_proj", il);
+
+    ggml_tensor * inpSA = cur;
+
+    cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_attn_norm", il);
+
+    ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
+    cb(Qcur_full, "mtp_Qcur_full", il);
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
+            n_embd_head, n_head, n_tokens,
+            ggml_element_size(Qcur_full) * n_embd_head * 2,
+            ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+            0);
+    Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Qcur, "mtp_Qcur_normed", il);
+
+    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full,
+            n_embd_head, n_head, n_tokens,
+            ggml_element_size(Qcur_full) * n_embd_head * 2,
+            ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+            ggml_element_size(Qcur_full) * n_embd_head);
+    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+    cb(gate, "mtp_gate", il);
+
+    ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+    Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Kcur, "mtp_Kcur_normed", il);
+
+    ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+    cb(Vcur, "mtp_Vcur", il);
+
+    Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+    Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+
+    const float kq_scale = hparams.f_attention_scale == 0.0f
+            ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    cur = build_attn(inp_attn,
+            nullptr, nullptr, nullptr,
+            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "mtp_attn_pregate", il);
+
+    cur = ggml_mul(ctx0, cur, ggml_sigmoid(ctx0, gate));
+    cur = build_lora_mm(layer.wo, cur, layer.wo_s);
+    cb(cur, "mtp_attn_out", il);
+
+    cur = ggml_add(ctx0, cur, inpSA);
+    cb(cur, "mtp_attn_residual", il);
+
+    ggml_tensor * ffn_residual = cur;
+    cur = build_norm(cur, layer.attn_post_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_attn_post_norm", il);
+
+    cur = build_ffn(cur,
+            layer.ffn_up,   nullptr, layer.ffn_up_s,
+            layer.ffn_gate, nullptr, layer.ffn_gate_s,
+            layer.ffn_down, nullptr, layer.ffn_down_s,
+            nullptr,
+            LLM_FFN_SILU, LLM_FFN_PAR, il);
+    cb(cur, "mtp_ffn_out", il);
+
+    cur = ggml_add(ctx0, cur, ffn_residual);
+    cb(cur, "mtp_post_ffn", il);
+
+    // Pre-norm hidden state: used by the AR draft loop to seed the next MTP step.
+    // (In the trunk graph this is `t_h_pre_norm`; the MTP head reuses the same slot.)
+    cb(cur, "h_pre_norm", -1);
+    res->t_h_pre_norm = cur;
+
+    ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
+            ? layer.nextn.shared_head_norm
+            : model.output_norm;
+    GGML_ASSERT(head_norm_w && "QWEN35 MTP: missing both nextn.shared_head_norm and output_norm");
+    cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "mtp_shared_head_norm", -1);
+
+    ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
+    GGML_ASSERT(head_w && "QWEN35 MTP: missing LM head (nextn.shared_head_head or model.output)");
+    cur = build_lora_mm(head_w, cur);
+    cb(cur, "result_output", -1);
+
+    res->t_logits = cur;
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp
index 7dc6a23c751..22e3e110765 100644
--- a/src/models/qwen35moe.cpp
+++ b/src/models/qwen35moe.cpp
@@ -1,8 +1,165 @@
 #include "models.h"
-
 #include "llama-memory-recurrent.h"
 
-llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) :
+void llama_model_qwen35moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS,    hparams.rope_sections, 4, true);
+
+    // Load linear attention (gated delta net) parameters
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    // NextN/MTP (Qwen3.5/3.6): extra decoder block appended beyond the main stack
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
+    GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
+
+    // Mark recurrent layers (linear attention layers). MTP layers are dense
+    // attention-only and must be flagged non-recurrent.
+    {
+        const uint32_t n_main = hparams.n_layer - hparams.nextn_predict_layers;
+        uint32_t full_attn_interval = 4;
+        ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+        for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+            hparams.recurrent_layer_arr[i] = (i < n_main) && ((i + 1) % full_attn_interval != 0);
+        }
+    }
+
+    switch (hparams.n_layer - hparams.nextn_predict_layers) {
+        case 40: type = LLM_TYPE_35B_A3B; break;
+        case 48: type = LLM_TYPE_122B_A10B; break;
+        case 60: type = LLM_TYPE_397B_A17B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {
+    LLAMA_LOAD_LOCALS;
+
+    const uint32_t n_main = n_layer - hparams.nextn_predict_layers;
+    const bool mtp_only   = (hparams.nextn_predict_layers > 0) &&
+                            (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
+    const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+    }
+
+    auto load_block_trunk = [&](int il, int flags) {
+        auto & layer = layers[il];
+
+        const int64_t n_ff_exp   = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
+        // Calculate dimensions from hyperparameters
+        const int64_t head_k_dim = hparams.ssm_d_state;
+        const int64_t head_v_dim = hparams.ssm_d_state;
+        const int64_t n_k_heads  = hparams.ssm_n_group;
+        const int64_t n_v_heads  = hparams.ssm_dt_rank;
+        const int64_t key_dim    = head_k_dim * n_k_heads;
+        const int64_t value_dim  = head_v_dim * n_v_heads;
+        const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", il), { n_embd }, flags);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, flags);
+
+        if (!hparams.is_recurrent(il)) {
+            // Attention layers
+            create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, flags);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, flags);
+
+            // Q/K normalization for attention layers
+            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, flags);
+            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, flags);
+        } else {
+            // Linear attention (gated delta net) specific tensors
+            // Create tensors with calculated dimensions
+            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", il), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", il), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", il), { hparams.ssm_d_conv, conv_dim }, flags);
+            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   il), { hparams.ssm_dt_rank }, flags);
+            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             il), { hparams.ssm_dt_rank }, flags);
+            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", il), { n_embd, n_v_heads }, flags);
+            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", il), { n_embd, n_v_heads }, flags);
+            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", il), { head_v_dim }, flags);
+            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", il), { value_dim, n_embd }, flags);
+        }
+
+        // Routed experts
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", il), { n_embd, n_expert }, flags);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, flags);
+        create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, flags);
+
+        // Shared experts
+        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, flags);
+        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", il), { n_embd, n_ff_shexp }, flags);
+        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", il), { n_embd, n_ff_shexp }, flags);
+        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", il), { n_ff_shexp, n_embd }, flags);
+    };
+
+    auto load_block_mtp = [&](int il) {
+        auto & layer = layers[il];
+
+        const int64_t n_ff_exp   = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
+        // MTP block looks like a full-attention Qwen3.5 decoder block with MoE FFN.
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", il), { n_embd }, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, 0);
+
+        create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo          = create_tensor(tn(LLM_TENSOR_ATTN_OUT,    "weight", il), { n_embd_head_k * n_head, n_embd }, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, 0);
+
+        // Routed experts
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", il), { n_embd, n_expert }, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, 0);
+        create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, 0);
+
+        // Shared experts
+        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, 0);
+        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", il), { n_embd, n_ff_shexp }, 0);
+        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", il), { n_embd, n_ff_shexp }, 0);
+        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", il), { n_ff_shexp, n_embd }, 0);
+
+        // NextN-specific tensors that define the MTP block.
+        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", il), { 2 * n_embd, n_embd }, 0);
+        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", il), { n_embd },              0);
+        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", il), { n_embd },              0);
+        layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", il), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
+        layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
+        layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd },              TENSOR_NOT_REQUIRED);
+    };
+
+    for (int i = 0; i < (int) n_main; ++i) {
+        load_block_trunk(i, trunk_flags);
+    }
+    for (int i = (int) n_main; i < n_layer; ++i) {
+        load_block_mtp(i);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen35moe::build_arch_graph(const llm_graph_params & params) const {
+    if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
+        return std::make_unique<graph_mtp>(*this, params);
+    }
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen35moe::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_delta_net_base(params), model(model) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
@@ -23,7 +180,9 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr
     ggml_tensor * inp_pos     = build_inp_pos();
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
-    for (int il = 0; il < n_layer; ++il) {
+    // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
+    const int n_transformer_layers = n_layer - (int) hparams.nextn_predict_layers;
+    for (int il = 0; il < n_transformer_layers; ++il) {
         ggml_tensor * inpSA = inpL;
 
         cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
@@ -40,7 +199,7 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr
             cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids) {
+        if (il == n_transformer_layers - 1 && inp_out_ids) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -72,6 +231,9 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr
     }
     cur = inpL;
 
+    cb(cur, "h_pre_norm", -1);
+    res->t_h_pre_norm = cur;
+
     // Final norm
     cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
 
@@ -79,7 +241,7 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // LM head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
@@ -87,7 +249,7 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr
     ggml_build_forward_expand(gf, cur);
 }
 
-std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_qkvz(
+std::pair<ggml_tensor *, ggml_tensor *> llama_model_qwen35moe::graph::build_qkvz(
                 ggml_tensor * input,
                         int   il) {
     const int64_t n_seqs       = ubatch.n_seqs;
@@ -103,7 +265,7 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_qkvz(
     return { qkv_mixed, z };
 }
 
-ggml_tensor * llm_build_qwen35moe::build_norm_gated(
+ggml_tensor * llama_model_qwen35moe::graph::build_norm_gated(
         ggml_tensor * input,
         ggml_tensor * weights,
         ggml_tensor * gate,
@@ -114,7 +276,7 @@ ggml_tensor * llm_build_qwen35moe::build_norm_gated(
     return ggml_mul(ctx0, normalized, gated_silu);
 }
 
-ggml_tensor * llm_build_qwen35moe ::build_layer_attn(
+ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn(
         llm_graph_input_attn_kv * inp,
         ggml_tensor *             cur,
         ggml_tensor *             inp_pos,
@@ -195,7 +357,7 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn(
     return cur;
 }
 
-ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
+ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn_linear(
         llm_graph_input_rs * inp,
         ggml_tensor *        cur,
         int                  il) {
@@ -209,8 +371,6 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
     const int64_t head_v_dim   = d_inner / num_v_heads;
     const int64_t n_seq_tokens = ubatch.n_seq_tokens;
 
-    const auto kv_head = mctx_cur->get_head();
-
     GGML_ASSERT(n_seqs != 0);
     GGML_ASSERT(ubatch.equal_seqs());
     GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
@@ -240,41 +400,14 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
 
     gate = ggml_reshape_4d(ctx0, gate, 1, num_v_heads, n_seq_tokens, n_seqs);
 
-    // Get convolution states from cache
     ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
     ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
 
-    // Build the convolution states tensor
-    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-    cb(conv_states, "conv_states", il);
-
-    // Calculate convolution kernel size
     ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
     const int64_t conv_kernel_size = conv_kernel->ne[0];
     const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
 
-    conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
-    cb(conv_states, "conv_states_reshaped", il);
-
-    qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
-    cb(qkv_mixed, "qkv_mixed_transposed", il);
-
-    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
-    cb(conv_input, "conv_input", il);
-
-    // Update convolution state cache
-    // Extract the last (conv_kernel_size - 1) states from conv_input
-    ggml_tensor * last_conv_states =
-        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
-                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
-    cb(last_conv_states, "last_conv_states", il);
-
-    ggml_tensor * state_update_target =
-        ggml_view_2d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels, n_seqs, conv_states_all->nb[1],
-                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
-    cb(state_update_target, "state_update_target", il);
-
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    ggml_tensor * conv_input = build_conv_state(inp, conv_states_all, qkv_mixed, conv_kernel_size, conv_channels, il);
 
     ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
     state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
@@ -325,7 +458,7 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
     //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
 
     // if head keys and value keys are different, repeat to force tensors into matching shapes
-    // note: need explicit repeat only if we are not using the fused GDN
+    // note: need explicit repeat only if we are not using the fused GDN.
     if (num_k_heads != num_v_heads && (!cparams.fused_gdn_ar || !cparams.fused_gdn_ch)) {
         GGML_ASSERT(num_v_heads % num_k_heads == 0);
         q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
@@ -336,18 +469,7 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    auto attn_out = build_delta_net(q_conv, k_conv, v_conv, gate, beta, state, il);
-
-    ggml_tensor * output    = attn_out.first;
-    ggml_tensor * new_state = attn_out.second;
-    cb(output, "attn_output", il);
-    cb(new_state, "new_state", il);
-
-    // Update the recurrent states
-    ggml_build_forward_expand(gf,
-            ggml_cpy(ctx0, new_state,
-                ggml_view_2d(ctx0, ssm_states_all, hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
-                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+    ggml_tensor * output = build_recurrent_attn(inp, ssm_states_all, q_conv, k_conv, v_conv, gate, beta, state, il);
 
     // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
     ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
@@ -369,7 +491,7 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
     return cur;
 }
 
-ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int il) {
+ggml_tensor * llama_model_qwen35moe::graph::build_layer_ffn(ggml_tensor * cur, const int il) {
     // Check if this is an MoE layer
     GGML_ASSERT(model.layers[il].ffn_gate_inp != nullptr);
 
@@ -424,3 +546,178 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int
 
     return cur;
 }
+
+// LLM_GRAPH_TYPE_DECODER_MTP draft head for Qwen3.5/3.6 MoE
+llama_model_qwen35moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
+    : llm_graph_context(params) {
+    GGML_ASSERT(hparams.nextn_predict_layers > 0 && "QWEN35MOE MTP requires nextn_predict_layers > 0");
+    GGML_ASSERT(hparams.nextn_predict_layers == 1 && "QWEN35MOE MTP currently only supports a single MTP block");
+
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+    const int il = (int) hparams.n_layer - (int) hparams.nextn_predict_layers;
+    const auto & layer = model.layers[il];
+
+    GGML_ASSERT(layer.nextn.eh_proj    && "MTP block missing nextn.eh_proj");
+    GGML_ASSERT(layer.nextn.enorm      && "MTP block missing nextn.enorm");
+    GGML_ASSERT(layer.nextn.hnorm      && "MTP block missing nextn.hnorm");
+    GGML_ASSERT(layer.ffn_gate_inp     && "MTP block missing ffn_gate_inp");
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    auto inp = std::make_unique<llm_graph_input_embd>(hparams.n_embd);
+
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    ggml_set_input(inp->tokens);
+
+    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
+    ggml_set_input(inp->embd);
+    ggml_set_name(inp->embd, "mtp_h_input");
+
+    ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
+
+    ggml_tensor * h_input  = inp->embd;
+    ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
+    cb(tok_embd, "mtp_tok_embd", il);
+
+    res->add_input(std::move(inp));
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    auto * inp_attn       = build_attn_inp_kv();
+
+    ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
+    cb(h_norm, "mtp_hnorm", il);
+
+    ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
+    cb(e_norm, "mtp_enorm", il);
+
+    ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
+    cb(concat, "mtp_concat", il);
+
+    ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat);
+    cb(cur, "mtp_eh_proj", il);
+
+    ggml_tensor * inpSA = cur;
+
+    cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_attn_norm", il);
+
+    ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
+    cb(Qcur_full, "mtp_Qcur_full", il);
+
+    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
+            n_embd_head, n_head, n_tokens,
+            ggml_element_size(Qcur_full) * n_embd_head * 2,
+            ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+            0);
+    Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Qcur, "mtp_Qcur_normed", il);
+
+    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full,
+            n_embd_head, n_head, n_tokens,
+            ggml_element_size(Qcur_full) * n_embd_head * 2,
+            ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
+            ggml_element_size(Qcur_full) * n_embd_head);
+    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+    cb(gate, "mtp_gate", il);
+
+    ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+    Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
+    cb(Kcur, "mtp_Kcur_normed", il);
+
+    ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+    cb(Vcur, "mtp_Vcur", il);
+
+    Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+    Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
+            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+
+    const float kq_scale = hparams.f_attention_scale == 0.0f
+            ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    cur = build_attn(inp_attn,
+            nullptr, nullptr, nullptr,
+            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "mtp_attn_pregate", il);
+
+    cur = ggml_mul(ctx0, cur, ggml_sigmoid(ctx0, gate));
+    cur = build_lora_mm(layer.wo, cur, layer.wo_s);
+    cb(cur, "mtp_attn_out", il);
+
+    cur = ggml_add(ctx0, cur, inpSA);
+    cb(cur, "mtp_attn_residual", il);
+
+    ggml_tensor * ffn_residual = cur;
+    cur = build_norm(cur, layer.attn_post_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_attn_post_norm", il);
+
+    // MoE FFN — routed experts plus gated shared expert (mirrors qwen35moe).
+    ggml_tensor * moe_out =
+        build_moe_ffn(cur,
+            layer.ffn_gate_inp,
+            layer.ffn_up_exps,
+            layer.ffn_gate_exps,
+            layer.ffn_down_exps,
+            nullptr,
+            n_expert, n_expert_used,
+            LLM_FFN_SILU, true,
+            hparams.expert_weights_scale,
+            LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il,
+            nullptr, layer.ffn_gate_up_exps,
+            layer.ffn_up_exps_s,
+            layer.ffn_gate_exps_s,
+            layer.ffn_down_exps_s);
+    cb(moe_out, "mtp_ffn_moe_out", il);
+
+    if (layer.ffn_up_shexp != nullptr) {
+        ggml_tensor * ffn_shexp =
+            build_ffn(cur,
+                layer.ffn_up_shexp,   nullptr, layer.ffn_up_shexp_s,
+                layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
+                layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
+                nullptr,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "mtp_ffn_shexp", il);
+
+        ggml_tensor * shared_gate = build_lora_mm(layer.ffn_gate_inp_shexp, cur);
+        shared_gate = ggml_sigmoid(ctx0, shared_gate);
+        cb(shared_gate, "mtp_shared_expert_gate_sigmoid", il);
+
+        ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
+        cb(ffn_shexp, "mtp_ffn_shexp_gated", il);
+
+        cur = ggml_add(ctx0, moe_out, ffn_shexp);
+    } else {
+        cur = moe_out;
+    }
+    cb(cur, "mtp_ffn_out", il);
+
+    cur = ggml_add(ctx0, cur, ffn_residual);
+    cb(cur, "mtp_post_ffn", il);
+
+    // Pre-norm hidden state: used by the AR draft loop to seed the next MTP step.
+    cb(cur, "h_pre_norm", -1);
+    res->t_h_pre_norm = cur;
+
+    ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
+            ? layer.nextn.shared_head_norm
+            : model.output_norm;
+    GGML_ASSERT(head_norm_w && "QWEN35MOE MTP: missing both nextn.shared_head_norm and output_norm");
+    cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "mtp_shared_head_norm", -1);
+
+    ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
+    GGML_ASSERT(head_w && "QWEN35MOE MTP: missing LM head (nextn.shared_head_head or model.output)");
+    cur = build_lora_mm(head_w, cur);
+    cb(cur, "result_output", -1);
+
+    res->t_logits = cur;
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen3moe.cpp b/src/models/qwen3moe.cpp
index dc554b5b3a9..a4f8e1379c9 100644
--- a/src/models/qwen3moe.cpp
+++ b/src/models/qwen3moe.cpp
@@ -1,6 +1,65 @@
 #include "models.h"
 
-llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_qwen3moe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 48: type = LLM_TYPE_30B_A3B; break;
+        case 94: type = LLM_TYPE_235B_A22B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen3moe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+        if (n_expert == 0) {
+            throw std::runtime_error("n_expert must be > 0 for QWEN3MOE");
+        }
+        if (n_expert_used == 0) {
+            throw std::runtime_error("n_expert_used must be > 0 for QWEN3MOE");
+        }
+
+        // MoE branch
+        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen3moe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen3moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -58,9 +117,6 @@ llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_grap
             cur = build_attn(inp_attn,
                     model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
                     Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            if (model.layers[il].wo_s) {
-                cur = ggml_mul(ctx0, cur, model.layers[il].wo_s);
-            }
         }
         if (il == n_layer - 1 && inp_out_ids) {
             cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
@@ -112,7 +168,7 @@ llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 1beda70b7cf..1d873427db5 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -1,8 +1,113 @@
 #include "models.h"
-
 #include "llama-memory-recurrent.h"
 
-llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
+void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+    // Load linear attention (gated delta net) parameters
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+    // Mark recurrent layers (linear attention layers)
+    {
+        uint32_t full_attn_interval = 4;
+        ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+        for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+            hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+        }
+    }
+
+    switch (hparams.n_layer) {
+        case 48: type = LLM_TYPE_80B_A3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen3next::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_expert == 0) {
+        throw std::runtime_error(arch_name() + " model cannot have zero experts");
+    }
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+    }
+
+    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+    // Calculate dimensions from hyperparameters
+    const int64_t head_k_dim = hparams.ssm_d_state;
+    const int64_t head_v_dim = hparams.ssm_d_state;
+    const int64_t n_k_heads  = hparams.ssm_n_group;
+    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+    const int64_t key_dim    = head_k_dim * n_k_heads;
+    const int64_t value_dim  = head_v_dim * n_v_heads;
+    const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+    // Calculate projection sizes
+    const int64_t qkvz_dim = key_dim * 2 + value_dim * 2;
+    const int64_t ba_dim   = n_v_heads * 2;
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+        const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(i);
+
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
+
+        if (!hparams.is_recurrent(i)) {
+            // Attention layers
+            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+            // Q/K normalization for attention layers
+            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+        } else {
+            // Linear attention (gated delta net) specific tensors
+            // Create tensors with calculated dimensions
+            // note: ssm_in is used by legacy GGUF
+            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, qkvz_dim }, TENSOR_NOT_REQUIRED);
+            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
+            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
+            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             i), { hparams.ssm_dt_rank }, 0);
+            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
+            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+        }
+
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
+        create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
+
+        // Shared experts
+        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
+        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, n_ff_shexp }, 0);
+        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, n_ff_shexp }, 0);
+        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { n_ff_shexp, n_embd }, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen3next::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_delta_net_base(params), model(model) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
@@ -71,7 +176,7 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
     res->t_embd = cur;
 
     // LM head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
@@ -87,7 +192,7 @@ static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t
         t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
 }
 
-ggml_tensor * llm_build_qwen3next::build_norm_gated(
+ggml_tensor * llama_model_qwen3next::graph::build_norm_gated(
         ggml_tensor * input,
         ggml_tensor * weights,
         ggml_tensor * gate,
@@ -98,7 +203,7 @@ ggml_tensor * llm_build_qwen3next::build_norm_gated(
     return ggml_mul(ctx0, normalized, gated_silu);
 }
 
-ggml_tensor * llm_build_qwen3next::build_layer_attn(
+ggml_tensor * llama_model_qwen3next::graph::build_layer_attn(
         llm_graph_input_attn_kv * inp,
         ggml_tensor *             cur,
         ggml_tensor *             inp_pos,
@@ -178,7 +283,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn(
     return cur;
 }
 
-std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_qkvz(
+std::pair<ggml_tensor *, ggml_tensor *> llama_model_qwen3next::graph::build_qkvz(
                 ggml_tensor * input,
                         int   il) {
     const int64_t d_inner      = hparams.ssm_d_inner;
@@ -259,7 +364,7 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_qkvz(
     }
 }
 
-ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
+ggml_tensor * llama_model_qwen3next::graph::build_layer_attn_linear(
         llm_graph_input_rs * inp,
         ggml_tensor *        cur,
         int                  il) {
@@ -273,8 +378,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     const int64_t head_v_dim   = d_inner / num_v_heads;
     const int64_t n_seq_tokens = ubatch.n_seq_tokens;
 
-    const auto kv_head = mctx_cur->get_head();
-
     GGML_ASSERT(n_seqs != 0);
     GGML_ASSERT(ubatch.equal_seqs());
     GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
@@ -324,41 +427,14 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     beta = ggml_reshape_4d(ctx0, beta, 1, num_v_heads, n_seq_tokens, n_seqs);
     gate = ggml_reshape_4d(ctx0, gate, 1, num_v_heads, n_seq_tokens, n_seqs);
 
-    // Get convolution states from cache
     ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
     ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
 
-    // Build the convolution states tensor
-    ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-    cb(conv_states, "conv_states", il);
-
-    // Calculate convolution kernel size
     ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;
     const int64_t conv_kernel_size = conv_kernel->ne[0];
     const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
 
-    conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
-    cb(conv_states, "conv_states_reshaped", il);
-
-    qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
-    cb(qkv_mixed, "qkv_mixed_transposed", il);
-
-    ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
-    cb(conv_input, "conv_input", il);
-
-    // Update convolution state cache
-    // Extract the last (conv_kernel_size - 1) states from conv_input
-    ggml_tensor * last_conv_states =
-        ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
-                     conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
-    cb(last_conv_states, "last_conv_states", il);
-
-    ggml_tensor * state_update_target =
-        ggml_view_2d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels, n_seqs, conv_states_all->nb[1],
-                     kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
-    cb(state_update_target, "state_update_target", il);
-
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+    ggml_tensor * conv_input = build_conv_state(inp, conv_states_all, qkv_mixed, conv_kernel_size, conv_channels, il);
 
     ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
     state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
@@ -435,18 +511,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    auto attn_out = build_delta_net(q_conv, k_conv, v_conv, gate, beta, state, il);
-
-    ggml_tensor * output    = attn_out.first;
-    ggml_tensor * new_state = attn_out.second;
-    cb(output, "attn_output", il);
-    cb(new_state, "new_state", il);
-
-    // Update the recurrent states
-    ggml_build_forward_expand(gf,
-            ggml_cpy(ctx0, new_state,
-                ggml_view_2d(ctx0, ssm_states_all, hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
-                    kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+    ggml_tensor * output = build_recurrent_attn(inp, ssm_states_all, q_conv, k_conv, v_conv, gate, beta, state, il);
 
     // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
     ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
@@ -468,7 +533,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     return cur;
 }
 
-ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const int il) {
+ggml_tensor * llama_model_qwen3next::graph::build_layer_ffn(ggml_tensor * cur, const int il) {
     // Check if this is an MoE layer
     if (model.layers[il].ffn_gate_inp != nullptr) {
         // MoE branch
diff --git a/src/models/qwen3vl.cpp b/src/models/qwen3vl.cpp
index faa5f2ef3c8..5defd893944 100644
--- a/src/models/qwen3vl.cpp
+++ b/src/models/qwen3vl.cpp
@@ -1,6 +1,56 @@
 #include "models.h"
 
-llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_qwen3vl::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 28: type = LLM_TYPE_1_7B; break;
+        case 36: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_8B; break;
+        case 64: type = LLM_TYPE_32B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen3vl::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    // output rerank head
+    cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen3vl::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen3vl::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const size_t n_deepstack_layers = hparams.n_deepstack_layers;
 
     const int64_t n_embd      = hparams.n_embd;
@@ -113,7 +163,7 @@ llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/qwen3vl-moe.cpp b/src/models/qwen3vlmoe.cpp
similarity index 57%
rename from src/models/qwen3vl-moe.cpp
rename to src/models/qwen3vlmoe.cpp
index 29ee8278a4d..5b77df57122 100644
--- a/src/models/qwen3vl-moe.cpp
+++ b/src/models/qwen3vlmoe.cpp
@@ -1,6 +1,66 @@
 #include "models.h"
 
-llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_qwen3vlmoe::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
+    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 48: type = LLM_TYPE_30B_A3B; break;
+        case 94: type = LLM_TYPE_235B_A22B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_qwen3vlmoe::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+        if (n_expert == 0) {
+            throw std::runtime_error("n_expert must be > 0 for QWEN3MOE");
+        }
+        if (n_expert_used == 0) {
+            throw std::runtime_error("n_expert_used must be > 0 for QWEN3MOE");
+        }
+
+        // MoE branch
+        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_qwen3vlmoe::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_qwen3vlmoe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const size_t n_deepstack_layers = hparams.n_deepstack_layers;
 
     const int64_t n_embd      = hparams.n_embd;
@@ -120,11 +180,10 @@ llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
     ggml_build_forward_expand(gf, cur);
 }
-
diff --git a/src/models/refact.cpp b/src/models/refact.cpp
index 398eb368db0..bf3949a9092 100644
--- a/src/models/refact.cpp
+++ b/src/models/refact.cpp
@@ -1,6 +1,81 @@
 #include "models.h"
 
-llm_build_refact::llm_build_refact(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_refact::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_1B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+
+    // TODO: become GGUF KV parameter
+    hparams.f_max_alibi_bias = 8.0f;
+}
+
+void llama_model_refact::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+        else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        if (n_expert == 0) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+            // optional MLP bias
+            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
+        } else {
+            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+            // For Granite MoE Shared
+            if (hparams.n_ff_shexp > 0) {
+                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_refact::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_refact::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -75,7 +150,7 @@ llm_build_refact::llm_build_refact(const llama_model & model, const llm_graph_pa
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/rnd1.cpp b/src/models/rnd1.cpp
index a917c19f25a..ca8e009615e 100644
--- a/src/models/rnd1.cpp
+++ b/src/models/rnd1.cpp
@@ -1,7 +1,67 @@
 #include "models.h"
 
+void llama_model_rnd1::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 48: type = LLM_TYPE_30B_A3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+    // Set non-causal attention for diffusion models
+    hparams.causal_attn = false;
+}
+
+void llama_model_rnd1::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+        if (n_expert == 0) {
+            throw std::runtime_error("n_expert must be > 0 for QWEN3MOE");
+        }
+        if (n_expert_used == 0) {
+            throw std::runtime_error("n_expert_used must be > 0 for QWEN3MOE");
+        }
+
+        // MoE branch
+        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_rnd1::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
 // RND1 is a Qwen3Moe AR model converted to diffusion model.
-llm_build_rnd1::llm_build_rnd1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_rnd1::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -107,7 +167,7 @@ llm_build_rnd1::llm_build_rnd1(const llama_model & model, const llm_graph_params
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/rwkv6.cpp b/src/models/rwkv6.cpp
index 032b219d6cb..ba2a9dfa0db 100644
--- a/src/models/rwkv6.cpp
+++ b/src/models/rwkv6.cpp
@@ -1,6 +1,97 @@
 #include "models.h"
 
-llm_build_rwkv6::llm_build_rwkv6(const llama_model & model, const llm_graph_params & params) :
+void llama_model_rwkv6::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,     hparams.f_norm_eps, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
+    ml.get_key(LLM_KV_WKV_HEAD_SIZE,               hparams.wkv_head_size);
+    ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM,          hparams.time_mix_extra_dim);
+    ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM,        hparams.time_decay_extra_dim);
+    ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS,      hparams.rescale_every_n_layers, false);
+    ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,           hparams.token_shift_count, false);
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_1_6B; break;
+        case 32:
+            switch (hparams.n_embd) {
+                case 2560: type = LLM_TYPE_3B; break;
+                case 4096: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 61: type = LLM_TYPE_14B; break;
+        case 64: type = LLM_TYPE_32B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_rwkv6::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // Block 0, LN0
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
+    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
+    const int head_size = hparams.wkv_head_size;
+    const int attn_hidden_size = n_embd;
+    const int ffn_size = hparams.n_ff_arr[0];
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
+        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, 0);
+
+        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
+        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
+
+        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
+        layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, TENSOR_NOT_REQUIRED);
+        GGML_ASSERT(!(layer.time_mix_lerp_fused == NULL && layer.time_mix_lerp_w == NULL));
+
+        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0);
+        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
+        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
+        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
+        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
+        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
+        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
+        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
+
+        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
+        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
+        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
+
+        layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
+        layer.channel_mix_lerp_r = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0);
+
+        layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
+        layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
+        layer.channel_mix_receptance = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}, 0);
+    }
+
+}
+
+std::unique_ptr<llm_graph_context> llama_model_rwkv6::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_rwkv6::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_rwkv6_base(model, params) {
     GGML_ASSERT(hparams.token_shift_count == 2);
 
@@ -85,7 +176,7 @@ llm_build_rwkv6::llm_build_rwkv6(const llama_model & model, const llm_graph_para
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/rwkv6qwen2.cpp b/src/models/rwkv6qwen2.cpp
index e84e5973820..566b8cdcb54 100644
--- a/src/models/rwkv6qwen2.cpp
+++ b/src/models/rwkv6qwen2.cpp
@@ -1,6 +1,87 @@
 #include "models.h"
 
-llm_build_rwkv6qwen2::llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) {
+void llama_model_rwkv6qwen2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,     hparams.f_norm_eps, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
+    ml.get_key(LLM_KV_WKV_HEAD_SIZE,               hparams.wkv_head_size);
+    ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM,          hparams.time_mix_extra_dim);
+    ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM,        hparams.time_decay_extra_dim);
+    ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS,      hparams.rescale_every_n_layers, false);
+    ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,           hparams.token_shift_count, false);
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_1_6B; break;
+        case 32:
+            switch (hparams.n_embd) {
+                case 2560: type = LLM_TYPE_3B; break;
+                case 4096: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 61: type = LLM_TYPE_14B; break;
+        case 64: type = LLM_TYPE_32B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_rwkv6qwen2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, TENSOR_NOT_REQUIRED);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
+    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
+    const int head_size = hparams.wkv_head_size;
+    const int attn_hidden_size = n_embd;
+    int attn_key_value_size;
+    if (n_head_kv == 0 || attn_hidden_size / head_size == n_head_kv) {
+        attn_key_value_size = attn_hidden_size;
+    } else {
+        attn_key_value_size = n_head_kv * head_size;
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
+        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
+
+        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
+        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
+
+        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
+        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
+        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
+        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {n_embd, attn_key_value_size}, 0);
+        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {n_embd, attn_key_value_size}, 0);
+        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
+        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
+        // optional bias tensors
+        layer.time_mix_key_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "bias", i), {attn_key_value_size}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_value_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "bias", i), {attn_key_value_size}, TENSOR_NOT_REQUIRED);
+        layer.time_mix_receptance_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "bias", i), {attn_hidden_size}, TENSOR_NOT_REQUIRED);
+
+        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_rwkv6qwen2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_rwkv6qwen2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) {
     GGML_ASSERT(n_embd == hparams.n_embd_r());
 
     ggml_tensor * cur;
@@ -77,7 +158,7 @@ llm_build_rwkv6qwen2::llm_build_rwkv6qwen2(const llama_model & model, const llm_
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/rwkv7.cpp b/src/models/rwkv7.cpp
index 16ffa6901b9..7574b252621 100644
--- a/src/models/rwkv7.cpp
+++ b/src/models/rwkv7.cpp
@@ -1,6 +1,127 @@
 #include "models.h"
 
-llm_build_rwkv7::llm_build_rwkv7(const llama_model & model, const llm_graph_params & params) :
+void llama_model_rwkv7::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,                hparams.f_norm_eps, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,            hparams.f_norm_rms_eps, false);
+    ml.get_key(LLM_KV_WKV_HEAD_SIZE,                          hparams.wkv_head_size);
+    ml.get_key(LLM_KV_ATTENTION_DECAY_LORA_RANK,              hparams.n_lora_decay);
+    ml.get_key(LLM_KV_ATTENTION_ICLR_LORA_RANK,               hparams.n_lora_iclr);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);
+    ml.get_key(LLM_KV_ATTENTION_GATE_LORA_RANK,               hparams.n_lora_gate, false);
+    ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,                      hparams.token_shift_count, false);
+
+    switch (hparams.n_layer) {
+        case 12:
+            switch (hparams.n_embd) {
+                case 768: type = LLM_TYPE_190M; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 24:
+            switch (hparams.n_embd) {
+                case 1024: type = LLM_TYPE_450M; break;
+                case 2048: type = LLM_TYPE_1_5B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 28:
+            switch (hparams.n_embd) {
+                case 1536: type = LLM_TYPE_1_5B; break;
+                case 3584: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 32:
+            switch (hparams.n_embd) {
+                case 2560: type = LLM_TYPE_2_9B; break;
+                case 4096: type = LLM_TYPE_7B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 61:
+            switch (hparams.n_embd) {
+                case 4096: type = LLM_TYPE_14B; break;
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_rwkv7::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // Block 0, LN0
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+    const int n_lora_decay = hparams.n_lora_decay;
+    const int n_lora_iclr = hparams.n_lora_iclr;
+    const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;
+    const int n_lora_gate = hparams.n_lora_gate;
+    const int attn_hidden_size = n_embd;
+    const int ffn_size = hparams.n_ff_arr[0];
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
+        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, 0);
+
+        layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);
+        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);
+        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);
+
+        layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);
+        layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);
+        layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);
+
+        if (i == 0) {
+            // actually not used
+            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
+            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);
+            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);
+        } else {
+            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
+            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);
+            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);
+        }
+
+        layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, 0);
+        layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, 0);
+
+        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);
+
+        layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);
+        layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);
+        layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);
+
+        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
+        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
+        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
+
+        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
+        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
+        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
+
+        layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
+
+        layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
+        layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
+    }
+
+}
+
+std::unique_ptr<llm_graph_context> llama_model_rwkv7::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_rwkv7::graph::graph(const llama_model & model, const llm_graph_params & params) :
     llm_build_rwkv7_base(model, params) {
     GGML_ASSERT(hparams.token_shift_count == 2);
 
@@ -81,7 +202,7 @@ llm_build_rwkv7::llm_build_rwkv7(const llama_model & model, const llm_graph_para
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/seed-oss.cpp b/src/models/seed-oss.cpp
index 6db8d9781fe..806cba574be 100644
--- a/src/models/seed-oss.cpp
+++ b/src/models/seed-oss.cpp
@@ -1,6 +1,51 @@
 #include "models.h"
 
-llm_build_seed_oss::llm_build_seed_oss(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_seed_oss::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 64: type = LLM_TYPE_36B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_seed_oss::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const uint32_t head_dim             = hparams.n_embd_head_k();
+    const int64_t n_qo_dim              = n_head * head_dim;
+    const int64_t n_kv_dim              = n_head_kv * head_dim;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        create_tensor_qkv(layer, i, n_embd, n_qo_dim, n_kv_dim, n_kv_dim, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_qo_dim, n_embd}, 0);
+
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_seed_oss::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_seed_oss::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -96,7 +141,7 @@ llm_build_seed_oss::llm_build_seed_oss(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/smallthinker.cpp b/src/models/smallthinker.cpp
index 55d09ec325d..4231cccc666 100644
--- a/src/models/smallthinker.cpp
+++ b/src/models/smallthinker.cpp
@@ -1,7 +1,80 @@
 #include "models.h"
 
+void llama_model_smallthinker::load_arch_hparams(llama_model_loader & ml) {
+    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+
+    if (found_swa && hparams.n_swa > 0) {
+        hparams.swa_type    = LLAMA_SWA_TYPE_STANDARD;
+        hparams.n_swa       = 4096;
+        uint32_t swa_period = 4;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period, true);
+
+        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+        hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+    } else {
+        hparams.swa_type             = LLAMA_SWA_TYPE_NONE;
+        hparams.n_no_rope_layer_step = hparams.n_layer;
+    }
+
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp, false);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_4B;  break;
+        case 52: type = LLM_TYPE_20B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_smallthinker::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+
+        GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for SMALLTHINKER");
+        GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for SMALLTHINKER");
+
+        // MoE branch
+        const int64_t n_ff_exp = hparams.n_ff_exp;
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_smallthinker::build_arch_graph(const llm_graph_params & params) const {
+    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+        return std::make_unique<graph<true>> (*this, params);
+    } else {
+        return std::make_unique<graph<false>>(*this, params);
+    }
+}
+
 template <bool iswa>
-llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){
+llama_model_smallthinker::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -105,7 +178,7 @@ llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model,
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
@@ -113,5 +186,5 @@ llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model,
 }
 
 // Explicit template instantiations
-template struct llm_build_smallthinker<false>;
-template struct llm_build_smallthinker<true>;
+template struct llama_model_smallthinker::graph<false>;
+template struct llama_model_smallthinker::graph<true>;
diff --git a/src/models/smollm3.cpp b/src/models/smollm3.cpp
index 83636dbf546..90e7d473eaf 100644
--- a/src/models/smollm3.cpp
+++ b/src/models/smollm3.cpp
@@ -1,6 +1,49 @@
 #include "models.h"
 
-llm_build_smollm3::llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_smollm3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    hparams.n_no_rope_layer_step = 4;
+
+    switch (hparams.n_layer) {
+        case 36: type = LLM_TYPE_3B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_smollm3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_smollm3::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_smollm3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -100,7 +143,7 @@ llm_build_smollm3::llm_build_smollm3(const llama_model & model, const llm_graph_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/stablelm.cpp b/src/models/stablelm.cpp
index 9c19abd8835..4da7f7aefcf 100644
--- a/src/models/stablelm.cpp
+++ b/src/models/stablelm.cpp
@@ -1,6 +1,54 @@
 #include "models.h"
 
-llm_build_stablelm::llm_build_stablelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_stablelm::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_1B; break;
+        case 32: type = LLM_TYPE_3B; break;
+        case 40: type = LLM_TYPE_12B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+   }
+}
+
+void llama_model_stablelm::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm =   create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        // optional q and k layernorms, present in StableLM 2 12B
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head},    TENSOR_NOT_REQUIRED);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);
+
+        // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_stablelm::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_stablelm::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -115,7 +163,7 @@ llm_build_stablelm::llm_build_stablelm(const llama_model & model, const llm_grap
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/starcoder.cpp b/src/models/starcoder.cpp
index cf9fe95c35b..e131af058bc 100644
--- a/src/models/starcoder.cpp
+++ b/src/models/starcoder.cpp
@@ -1,6 +1,62 @@
 #include "models.h"
 
-llm_build_starcoder::llm_build_starcoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_starcoder::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    switch (hparams.n_layer) {
+        case 24: type = LLM_TYPE_1B; break;
+        case 36: type = LLM_TYPE_3B; break;
+        case 42: type = LLM_TYPE_7B; break;
+        case 40: type = LLM_TYPE_15B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_starcoder::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+    pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);
+
+    // output
+    {
+        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+        output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+        if (!output) {
+            // needs to be on GPU
+            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+        }
+
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
+        layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
+
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
+        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_starcoder::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_starcoder::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -79,7 +135,7 @@ llm_build_starcoder::llm_build_starcoder(const llama_model & model, const llm_gr
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/starcoder2.cpp b/src/models/starcoder2.cpp
index b6d4d5aac1a..9c207c02885 100644
--- a/src/models/starcoder2.cpp
+++ b/src/models/starcoder2.cpp
@@ -1,6 +1,61 @@
 #include "models.h"
 
-llm_build_starcoder2::llm_build_starcoder2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_starcoder2::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+    switch (hparams.n_layer) {
+        case 30: type = LLM_TYPE_3B; break;
+        case 32: type = LLM_TYPE_7B; break;
+        case 40: type = LLM_TYPE_15B; break;
+        case 52: type = LLM_TYPE_20B; break; // granite
+        case 88: type = LLM_TYPE_34B; break; // granite
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_starcoder2::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        // optional bias tensors
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
+
+        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);
+
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+        // optional bias tensors
+        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
+        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP ,  "bias", i), {  n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_starcoder2::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_starcoder2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -93,7 +148,7 @@ llm_build_starcoder2::llm_build_starcoder2(const llama_model & model, const llm_
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/models/step35-iswa.cpp b/src/models/step35.cpp
similarity index 52%
rename from src/models/step35-iswa.cpp
rename to src/models/step35.cpp
index 86aa98909e7..3b68e68707a 100644
--- a/src/models/step35-iswa.cpp
+++ b/src/models/step35.cpp
@@ -1,6 +1,108 @@
 #include "models.h"
 
-llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_step35::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+    // full_attention layer only use half of the RoPE dimensions
+    hparams.n_rot_full = hparams.n_rot_full / 2;
+
+    // MoE + SWA parameters
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+
+    // Step35 uses sigmoid gating by default (if not set in GGUF)
+    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+        hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+    }
+
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,  hparams.n_swa);
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,        hparams.rope_freq_base_train_swa, false);
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,   hparams.swiglu_clamp_exp,   hparams.n_layer, false);
+    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false);
+
+    switch (hparams.n_layer) {
+        case 45: type = LLM_TYPE_196B_A11B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_step35::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    // STEP35 supports per-layer partial RoPE dims; rope factors are stored as a single shared tensor
+    // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer.
+    uint32_t n_rot_max = 0;
+    for (int i = 0; i < n_layer; ++i) {
+        n_rot_max = std::max(n_rot_max, hparams.n_rot(i));
+    }
+    if (n_rot_max == 0) {
+        n_rot_max = n_rot;
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        const uint32_t n_head_l      = hparams.n_head(i);
+        const uint32_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
+        const uint32_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
+
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+
+        // optional rope factors (llama3) / longrope tensors
+        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        } else {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, 0);
+
+        // head-wise attention gate (Step35 self_attn.g_proj)
+        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        // dense MLP (leading dense blocks)
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+
+        // MoE routed experts + selection bias (router_bias)
+        const int64_t n_ff_exp = hparams.n_ff_exp;
+        layer.ffn_gate_inp      = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+        layer.ffn_gate_exps     = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_exps     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+        layer.ffn_up_exps       = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
+        layer.ffn_exp_probs_b   = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+        // shared expert MLP
+        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_step35::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_step35::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -159,7 +261,7 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
diff --git a/src/models/t5.cpp b/src/models/t5.cpp
index 9f9dfef4012..73e32741406 100644
--- a/src/models/t5.cpp
+++ b/src/models/t5.cpp
@@ -1,7 +1,125 @@
 #include "models.h"
 
+void llama_model_t5::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+
+    uint32_t dec_start_token_id;
+    if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
+        hparams.dec_start_token_id = dec_start_token_id;
+    }
+
+    hparams.dec_n_layer = hparams.n_layer;
+    ml.get_key(LLM_KV_DECODER_BLOCK_COUNT, hparams.dec_n_layer, false);
+
+    switch (hparams.n_layer) {
+        case 6:  type = LLM_TYPE_60M;  break; // t5-small
+        case 8:  type = LLM_TYPE_80M;  break; // flan-t5-small
+        case 12:
+            switch (hparams.n_ff()) {
+                case 3072: type = LLM_TYPE_220M; break; // t5-base
+                case 2048: type = LLM_TYPE_250M; break; // flan-t5-base
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        case 24:
+            switch (hparams.n_ff()) {
+                case 4096:  type = LLM_TYPE_770M; break; // t5-large
+                case 2816:  type = LLM_TYPE_780M; break; // flan-t5-large
+                case 16384: type = LLM_TYPE_3B;   break; // t5-3b
+                case 5120:  type = LLM_TYPE_3B;   break; // flan-t5-xl
+                case 65536: type = LLM_TYPE_11B;  break; // t5-11b
+                case 10240: type = LLM_TYPE_11B;  break; // flan-t5-xxl
+                default: type = LLM_TYPE_UNKNOWN;
+            } break;
+        default: type = LLM_TYPE_UNKNOWN;
+   }
+}
+
+void llama_model_t5::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output_norm     = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    // n_layer:     number of encoder_layers
+    // dec_n_layer: number of decoder_layers
+    const int dec_n_layer = hparams.dec_n_layer;
+    if (dec_n_layer > n_layer) {
+        layers.resize(dec_n_layer);
+    }
+
+    // load encoder layers
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
+        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
+
+        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
+
+        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+
+    // load decoder layers
+    for (int i = 0; i < dec_n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd}, 0);
+        layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
+
+        layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
+
+        layer.attn_norm_cross  = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "weight", i), {n_embd}, 0);
+        // this tensor seems to be unused in HF transformers implementation
+        layer.attn_rel_b_cross = create_tensor(
+            tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
+
+        layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+        layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_t5::build_arch_graph(const llm_graph_params & params) const {
+    switch (params.gtype) {
+        case LLM_GRAPH_TYPE_ENCODER:
+            return std::make_unique<graph<true>>(*this, params);
+        case LLM_GRAPH_TYPE_DEFAULT:
+        case LLM_GRAPH_TYPE_DECODER:
+            return std::make_unique<graph<false>>(*this, params);
+        default:
+            GGML_ABORT("invalid graph type");
+    };
+}
+
 template <>
-llm_build_t5<false>::llm_build_t5(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_t5::graph<false>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
     //const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
 
@@ -147,7 +265,7 @@ llm_build_t5<false>::llm_build_t5(const llama_model & model, const llm_graph_par
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
@@ -156,7 +274,7 @@ llm_build_t5<false>::llm_build_t5(const llama_model & model, const llm_graph_par
 }
 
 template <>
-llm_build_t5<true>::llm_build_t5(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llama_model_t5::graph<true>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
diff --git a/src/models/t5encoder.cpp b/src/models/t5encoder.cpp
index 5c1f9eb4030..23c5f9b6a1c 100644
--- a/src/models/t5encoder.cpp
+++ b/src/models/t5encoder.cpp
@@ -1,3 +1,44 @@
 #include "models.h"
 
-llm_build_t5encoder::llm_build_t5encoder(const llama_model & model, const llm_graph_params & params) : llm_build_t5<true>(model, params) {}
+void llama_model_t5encoder::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+    type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_t5encoder::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
+        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
+
+        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
+
+        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_t5encoder::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
diff --git a/src/models/wavtokenizer-dec.cpp b/src/models/wavtokenizer-dec.cpp
index a7776d9cdc9..214fed99bad 100644
--- a/src/models/wavtokenizer-dec.cpp
+++ b/src/models/wavtokenizer-dec.cpp
@@ -1,6 +1,121 @@
 #include "models.h"
 
-llm_build_wavtokenizer_dec::llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_wavtokenizer_dec::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+    ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS,    hparams.f_norm_group_eps);
+    ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
+}
+
+void llama_model_wavtokenizer_dec::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd, n_vocab}, 0);
+
+    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight", 0), {7, hparams.n_embd, hparams.posnet.n_embd}, 0);
+    conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias",   0), {1, hparams.posnet.n_embd}, 0);
+
+    // posnet
+    {
+        const int64_t n_embd = hparams.posnet.n_embd;
+
+        for (uint32_t i = 0; i < hparams.posnet.n_layer; ++i) {
+            auto & layer = layers[i].posnet;
+
+            // posnet:
+            //
+            //  - resnet
+            //  - resnet
+            //  - attn
+            //  - resnet
+            //  - resnet
+            //  - norm
+            //
+            switch (i) {
+                case 0:
+                case 1:
+                case 3:
+                case 4:
+                    {
+                        layer.norm1   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", i), {1, n_embd}, 0);
+                        layer.norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias",   i), {1, n_embd}, 0);
+
+                        layer.conv1   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", i), {3, n_embd, n_embd}, 0);
+                        layer.conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias",   i), {1, n_embd}, 0);
+
+                        layer.norm2   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", i), {1, n_embd}, 0);
+                        layer.norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias",   i), {1, n_embd}, 0);
+
+                        layer.conv2   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", i), {3, n_embd, n_embd}, 0);
+                        layer.conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias",   i), {1, n_embd}, 0);
+                    } break;
+                case 2:
+                    {
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
+                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
+
+                        layer.attn_q      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "weight", i), {1, n_embd, n_embd}, 0);
+                        layer.attn_q_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "bias",   i), {1, n_embd}, 0);
+
+                        layer.attn_k      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "weight", i), {1, n_embd, n_embd}, 0);
+                        layer.attn_k_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "bias",   i), {1, n_embd}, 0);
+
+                        layer.attn_v      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "weight", i), {1, n_embd, n_embd}, 0);
+                        layer.attn_v_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "bias",   i), {1, n_embd}, 0);
+
+                        layer.attn_o      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "weight", i), {1, n_embd, n_embd}, 0);
+                        layer.attn_o_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "bias",   i), {1, n_embd}, 0);
+                    } break;
+                case 5:
+                    {
+                        layer.norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
+                        layer.norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
+                    } break;
+                default: GGML_ABORT("unknown posnet layer");
+            };
+        }
+    }
+
+    GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd);
+
+    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {hparams.posnet.n_embd}, 0);
+    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {hparams.posnet.n_embd}, 0);
+
+    // convnext
+    {
+        const int64_t n_embd = hparams.convnext.n_embd;
+
+        for (uint32_t i = 0; i < hparams.convnext.n_layer; ++i) {
+            auto & layer = layers[i].convnext;
+
+            layer.dw     = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "weight", i), {7, 1, n_embd}, 0);
+            layer.dw_b   = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "bias",   i), {1, n_embd}, 0);
+
+            layer.norm   = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "weight", i), {n_embd}, 0);
+            layer.norm_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "bias",   i), {n_embd}, 0);
+
+            layer.pw1    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "weight", i), {n_embd, n_ff}, 0);
+            layer.pw1_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "bias",   i), {n_ff}, 0);
+
+            layer.pw2    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "weight", i), {n_ff, n_embd}, 0);
+            layer.pw2_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "bias",   i), {n_embd}, 0);
+
+            layer.gamma  = create_tensor(tn(LLM_TENSOR_CONVNEXT_GAMMA, "weight", i), {n_embd}, 0);
+        }
+
+        // output
+        output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+        output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
+    }
+
+    output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, hparams.n_embd_out()}, 0);
+    output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {hparams.n_embd_out()}, 0);
+}
+
+std::unique_ptr<llm_graph_context> llama_model_wavtokenizer_dec::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_wavtokenizer_dec::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
@@ -138,7 +253,7 @@ llm_build_wavtokenizer_dec::llm_build_wavtokenizer_dec(const llama_model & model
             LLM_NORM, -1);
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cur = ggml_add(ctx0, cur, model.output_b);
 
diff --git a/src/models/xverse.cpp b/src/models/xverse.cpp
index 53085ec80f6..d6d1c7a2e5d 100644
--- a/src/models/xverse.cpp
+++ b/src/models/xverse.cpp
@@ -1,6 +1,43 @@
 #include "models.h"
 
-llm_build_xverse::llm_build_xverse(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+void llama_model_xverse::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    switch (hparams.n_layer) {
+        case 32: type = LLM_TYPE_7B; break;
+        case 40: type = LLM_TYPE_13B; break;
+        case 80: type = LLM_TYPE_65B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_xverse::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_xverse::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_xverse::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v();
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -89,7 +126,7 @@ llm_build_xverse::llm_build_xverse(const llama_model & model, const llm_graph_pa
     res->t_embd = cur;
 
     // lm_head
-    cur = build_lora_mm(model.output, cur);
+    cur = build_lora_mm(model.output, cur, model.output_s);
 
     cb(cur, "result_output", -1);
     res->t_logits = cur;
diff --git a/src/unicode.cpp b/src/unicode.cpp
index dc13e53f09f..b02ecdc930f 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -605,6 +605,136 @@ static std::vector<size_t> unicode_regex_split_custom_qwen2(const std::string &
     return bpe_offsets;
 }
 
+// Qwen3.5 system regex: "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+// Compared to Qwen2, letter-runs also consume Unicode combining marks (\p{M}): [\p{L}\p{M}]+ instead of \p{L}+
+static std::vector<size_t> unicode_regex_split_custom_qwen35(const std::string & text, const std::vector<size_t> & offsets) {
+    std::vector<size_t> bpe_offsets; // store the offset of each word
+    bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+
+    size_t start = 0;
+    for (auto offset : offsets) {
+        const size_t offset_ini = start;
+        const size_t offset_end = start + offset;
+        assert(offset_end <= cpts.size());
+        start = offset_end;
+
+        static const uint32_t OUT_OF_RANGE = 0xFFFFFFFF;
+        auto _get_cpt = [&] (const size_t pos) -> uint32_t {
+            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
+        };
+
+        auto _get_flags = [&] (const size_t pos) -> unicode_cpt_flags {
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags_from_cpt(cpts[pos]) : unicode_cpt_flags{};
+        };
+
+        size_t _prev_end = offset_ini;
+        auto _add_token = [&] (const size_t end) -> size_t {
+            assert(_prev_end <= end && end <= offset_end);
+            size_t len = end - _prev_end;
+            if (len > 0) {
+                bpe_offsets.push_back(len);
+            }
+            _prev_end = end;
+            return len;
+        };
+
+        for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
+            const uint32_t cpt = _get_cpt(pos);
+            const auto flags = _get_flags(pos);
+
+            // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive
+            if (cpt == '\'' && pos+1 < offset_end) {
+                uint32_t cpt_next = unicode_tolower(_get_cpt(pos+1));
+                if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
+                    pos += _add_token(pos+2);
+                    continue;
+                }
+                if (pos+2 < offset_end) {
+                    uint32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2));
+                    if ((cpt_next == 'r' && cpt_next_next == 'e') ||
+                        (cpt_next == 'v' && cpt_next_next == 'e') ||
+                        (cpt_next == 'l' && cpt_next_next == 'l')) {
+                        pos += _add_token(pos+3);
+                        continue;
+                    }
+                }
+            }
+
+            // regex: [^\r\n\p{L}\p{N}]?[\p{L}\p{M}]+
+            if (!(cpt == '\r' || cpt == '\n' || flags.is_number)) {
+                if (flags.is_letter || flags.is_accent_mark || _get_flags(pos + 1).is_accent_mark || _get_flags(pos+1).is_letter) {
+                    pos++;
+                    while (_get_flags(pos).is_letter || _get_flags(pos).is_accent_mark) {
+                        pos++;
+                    }
+                    _add_token(pos);
+                    continue;
+                }
+            }
+
+            // regex: \p{N}
+            if (flags.is_number) {
+                pos++;
+                _add_token(pos);
+                continue;
+            }
+
+            // regex: <space>?[^\s\p{L}\p{M}\p{N}]+[\r\n]*
+            auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags);
+            if (!(flags2.is_whitespace | flags2.is_letter | flags2.is_accent_mark | flags2.is_number) && flags.as_uint()) {
+                pos += (cpt == ' ');
+                while (!(flags2.is_whitespace | flags2.is_letter | flags2.is_accent_mark | flags2.is_number) && flags2.as_uint()) {
+                    flags2 = _get_flags(++pos);
+                }
+                uint32_t cpt2 = _get_cpt(pos);
+                while (cpt2 == '\r' || cpt2 == '\n') {
+                    cpt2 = _get_cpt(++pos);
+                }
+                _add_token(pos);
+                continue;
+            }
+
+            size_t num_whitespaces = 0;
+            size_t last_end_r_or_n = 0;
+            while (_get_flags(pos+num_whitespaces).is_whitespace) {
+                uint32_t cpt2 = _get_cpt(pos+num_whitespaces);
+                if (cpt2 == '\r' || cpt2 == '\n') {
+                    last_end_r_or_n = pos + num_whitespaces + 1;
+                }
+                num_whitespaces++;
+            }
+
+            // regex: \s*[\r\n]+
+            if (last_end_r_or_n > 0) {
+                pos = last_end_r_or_n;
+                _add_token(pos);
+                continue;
+            }
+
+            // regex: \s+(?!\S)
+            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != OUT_OF_RANGE) {
+                pos += num_whitespaces - 1;
+                _add_token(pos);
+                continue;
+            }
+
+            // regex: \s+
+            if (num_whitespaces > 0) {
+                pos += num_whitespaces;
+                _add_token(pos);
+                continue;
+            }
+
+            // no matches
+            _add_token(++pos);
+        }
+    }
+
+    return bpe_offsets;
+}
+
 template <typename CharT>
 static std::vector<size_t> unicode_regex_split_stl(const std::basic_string<CharT> & text, const std::basic_string<CharT> & regex, const std::vector<size_t> & offsets) {
     using BidirIt = typename std::basic_string<CharT>::const_iterator;
@@ -929,6 +1059,9 @@ static std::vector<size_t> unicode_regex_split_custom(const std::string & text,
     } else if (
            regex_expr == "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+") {
         bpe_offsets = unicode_regex_split_custom_qwen2(text, offsets);
+    } else if (
+           regex_expr == "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+") {
+        bpe_offsets = unicode_regex_split_custom_qwen35(text, offsets);
     } else if (regex_expr == "\\p{Han}+") {
         // K2's first pattern - handle all K2 patterns together
         bpe_offsets = unicode_regex_split_custom_kimi_k2(text, offsets);
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index b282c3239f0..0fdbd39c94a 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -131,6 +131,7 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${PROJE
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-mpt.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-phi-3.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-qwen2.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen35            ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-qwen35.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-refact.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-starcoder.gguf)
 
@@ -155,6 +156,8 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
     llama_build_and_test(test-grammar-integration.cpp)
     llama_build_and_test(test-llama-grammar.cpp)
     llama_build_and_test(test-chat.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
+    target_include_directories(test-chat PRIVATE ${PROJECT_SOURCE_DIR}/tools/server)
+    target_link_libraries(test-chat PRIVATE server-context)
     # TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
     if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
         llama_build_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
@@ -249,6 +252,9 @@ llama_build_and_test(test-backend-sampler.cpp   LABEL "model")
 llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -m "${MODEL_DEST}")
 set_tests_properties(test-state-restore-fragmented PROPERTIES FIXTURES_REQUIRED test-download-model)
 
+llama_build_and_test(test-recurrent-state-rollback.cpp LABEL "model" ARGS -m "${MODEL_DEST}")
+set_tests_properties(test-recurrent-state-rollback PROPERTIES FIXTURES_REQUIRED test-download-model)
+
 if (NOT GGML_BACKEND_DL)
     # these tests use the backends directly and cannot be built with dynamic loading
     llama_build_and_test(test-barrier.cpp)
diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp
index 67f8ca632c9..0dd8422e736 100644
--- a/tests/test-arg-parser.cpp
+++ b/tests/test-arg-parser.cpp
@@ -40,8 +40,12 @@ int main(void) {
                     }
                 }
 
+                // exclude spec args from this check
+                // ref: https://github.com/ggml-org/llama.cpp/pull/22397
+                const bool skip = opt.is_spec;
+
                 // ensure shorter argument precedes longer argument
-                if (opt.args.size() > 1) {
+                if (!skip && opt.args.size() > 1) {
                     const std::string first(opt.args.front());
                     const std::string last(opt.args.back());
 
@@ -124,9 +128,9 @@ int main(void) {
     assert(params.n_batch == 9090);
 
     // --draft cannot be used outside llama-speculative
-    argv = {"binary_name", "--draft", "123"};
+    argv = {"binary_name", "--spec-draft-n-max", "123"};
     assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
-    assert(params.speculative.n_max == 123);
+    assert(params.speculative.draft.n_max == 123);
 
     // multi-value args (CSV)
     argv = {"binary_name", "--lora", "file1.gguf,\"file2,2.gguf\",\"file3\"\"3\"\".gguf\",file4\".gguf"};
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 6207a736501..fdcf2aa44d4 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1128,7 +1128,11 @@ struct test_case {
     }
 
     virtual double max_nmse_err(ggml_backend_t backend) {
-        GGML_UNUSED(backend);
+        ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend));
+        // See https://github.com/ggml-org/llama.cpp/pull/22976 for explanation.
+        if (contains_f16 && strcmp(ggml_backend_reg_name(reg), "WebGPU") == 0) {
+            return std::max(max_nmse_err(), 1e-6);
+        }
         return max_nmse_err();
     }
 
@@ -1205,6 +1209,18 @@ struct test_case {
     std::vector<ggml_tensor *> sentinels;
 
     std::string current_op_name;
+    bool contains_f16 = false;
+
+    // Used by the WebGPU backend to relax error thresholds on ops on f16 tensors
+    void check_for_f16_tensor(ggml_context * ctx) {
+        contains_f16 = false;
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->type == GGML_TYPE_F16) {
+                contains_f16 = true;
+                break;
+            }
+        }
+    }
 
     void add_sentinel(ggml_context * ctx) {
         if (mode == MODE_PERF || mode == MODE_GRAD || mode == MODE_SUPPORT) {
@@ -1298,6 +1314,7 @@ struct test_case {
 
         ggml_tensor * out = build_graph(ctx);
         current_op_name   = op_desc(out);
+        check_for_f16_tensor(ctx);
 
         if (!matches_filter(out, op_names_filter)) {
             //printf("  %s: skipping\n", op_desc(out).c_str());
@@ -1973,9 +1990,19 @@ struct test_unary : public test_case {
     }
 
     void initialize_tensors(ggml_context * ctx) override {
+        float min = -150.f;
+        float max =  150.f;
+
+        // Keep FP16 exp/expm1 inputs in-range so all backends stay finite instead of
+        // disagreeing on whether overflow saturates to max-F16 or produces +inf.
+        if (type == GGML_TYPE_F16 && (op == GGML_UNARY_OP_EXP || op == GGML_UNARY_OP_EXPM1)) {
+            min = -10.f;
+            max =  10.f;
+        }
+
         for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
             // test extended range of values to check for NaNs in GELU
-            init_tensor_uniform(t, -150.f, 150.f);
+            init_tensor_uniform(t, min, max);
         }
     }
 
@@ -2989,7 +3016,7 @@ struct test_bin_bcast : public test_case {
     bool run_whole_graph() override { return nf > 1; }
 
     std::string vars() override {
-        return VARS_TO_STR5(type, ne, nr, nf, perm1);
+        return VARS_TO_STR6(type, ne, nr, nf, perm1, src_overlap);
     }
 
     size_t op_size(ggml_tensor * t) override {
@@ -3527,6 +3554,107 @@ struct test_add_rms_norm : public test_case {
     }
 };
 
+// GGML_OP_UNARY(RELU) + GGML_OP_SQR (fused operation)
+struct test_relu_sqr : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "RELU_SQR";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    std::string vars() override {
+        return VARS_TO_STR2(type, ne);
+    }
+
+    test_relu_sqr(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {128, 2, 2, 2})
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(a, "a");
+
+        ggml_tensor * r = ggml_relu(ctx, a);
+        ggml_set_name(r, "relu");
+
+        ggml_tensor * out = ggml_sqr(ctx, r);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
+// SNAKE activation fusion: y = x + sin(a*x)^2 * inv_b
+// CUDA backend matches the naive 5-op chain (mul, sin, sqr, mul, add)
+// and dispatches a single fused kernel.
+struct test_snake_fuse : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;   // [T, C, D2, D3]
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "SNAKE_FUSE";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    double max_nmse_err() override {
+        // BF16 epsilon ~ 7.8e-3, F16 epsilon ~ 9.7e-4: relax tolerance to match
+        // the natural roundoff drift between the naive CPU chain and the fused
+        // CUDA kernel. F32 keeps the default tight bound.
+        switch (type) {
+            case GGML_TYPE_BF16: return 5e-3;
+            case GGML_TYPE_F16:  return 5e-5;
+            default:             return 1e-7;
+        }
+    }
+
+    std::string vars() override {
+        return VARS_TO_STR2(type, ne);
+    }
+
+    test_snake_fuse(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {256, 192, 1, 1})
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * x = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
+        ggml_set_name(x, "x");
+
+        ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, ne[1]);
+        ggml_set_name(a, "a");
+
+        ggml_tensor * inv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, ne[1]);
+        ggml_set_name(inv_b, "inv_b");
+
+        // exact 5-op chain that BigVGAN / Vocos frontends emit
+        ggml_tensor * ax     = ggml_mul(ctx, x, a);
+        ggml_tensor * sin_ax = ggml_sin(ctx, ax);
+        ggml_tensor * sin_sq = ggml_sqr(ctx, sin_ax);
+        ggml_tensor * scaled = ggml_mul(ctx, sin_sq, inv_b);
+        ggml_tensor * out    = ggml_add(ctx, x, scaled);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        // x in [-pi, pi] to exercise sin periodicity, params in default [-1, 1]
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            const std::string name = ggml_get_name(t);
+            if (name == "x") {
+                init_tensor_uniform(t, -3.14159f, 3.14159f);
+            } else {
+                init_tensor_uniform(t);
+            }
+        }
+    }
+};
+
 // GGML_OP_SSM_CONV
 struct test_ssm_conv : public test_case {
     const ggml_type type;
@@ -3550,6 +3678,49 @@ struct test_ssm_conv : public test_case {
     }
 };
 
+// GGML_OP_SSM_CONV + GGML_OP_ADD (channel-wise bias, optional) + GGML_OP_UNARY(SILU) (fused operation)
+struct test_ssm_conv_bias_silu : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne_a;
+    const std::array<int64_t, 4> ne_b;
+    const bool fuse_bias;
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "SSM_CONV_BIAS_SILU";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, ne_a, ne_b, fuse_bias);
+    }
+
+    test_ssm_conv_bias_silu(ggml_type type, std::array<int64_t, 4> ne_a, std::array<int64_t, 4> ne_b,
+            bool fuse_bias)
+        : type(type), ne_a(ne_a), ne_b(ne_b), fuse_bias(fuse_bias) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
+        ggml_set_name(a, "a");
+        ggml_set_name(b, "b");
+
+        ggml_tensor * out = ggml_ssm_conv(ctx, a, b);
+
+        if (fuse_bias) {
+            ggml_tensor * bias = ggml_new_tensor_1d(ctx, type, out->ne[0]);
+            ggml_set_name(bias, "bias");
+            out = ggml_add(ctx, out, bias);
+        }
+
+        out = ggml_silu(ctx, out);
+
+        ggml_set_name(out, "out");
+        return out;
+    }
+};
+
 // GGML_OP_SSM_SCAN
 struct test_ssm_scan : public test_case {
     const ggml_type type;
@@ -3560,9 +3731,10 @@ struct test_ssm_scan : public test_case {
     const int64_t n_group;
     const int64_t n_seq_tokens;
     const int64_t n_seqs;
+    const bool    xbc_overlap;
 
     std::string vars() override {
-        return VARS_TO_STR7(type, d_state, head_dim, n_head, n_group, n_seq_tokens, n_seqs);
+        return VARS_TO_STR8(type, d_state, head_dim, n_head, n_group, n_seq_tokens, n_seqs, xbc_overlap);
     }
 
     test_ssm_scan(ggml_type type = GGML_TYPE_F32,
@@ -3571,16 +3743,31 @@ struct test_ssm_scan : public test_case {
             int64_t n_head  = 32,
             int64_t n_group = 1,
             int64_t n_seq_tokens = 32,
-            int64_t n_seqs = 32)
-        : type(type), d_state(d_state), head_dim(head_dim), n_head(n_head), n_group(n_group), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {}
+            int64_t n_seqs = 32,
+            bool xbc_overlap = false)
+        : type(type), d_state(d_state), head_dim(head_dim), n_head(n_head), n_group(n_group), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs), xbc_overlap(xbc_overlap) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * s   = ggml_new_tensor_4d(ctx, type, d_state,  head_dim,     n_head,       n_seqs);
-        ggml_tensor * x   = ggml_new_tensor_4d(ctx, type, head_dim, n_head,       n_seq_tokens, n_seqs);
         ggml_tensor * dt  = ggml_new_tensor_3d(ctx, type, n_head,   n_seq_tokens, n_seqs);
         ggml_tensor * A   = ggml_new_tensor_2d(ctx, type, (head_dim > 1) ? 1 : d_state, n_head);
-        ggml_tensor * B   = ggml_new_tensor_4d(ctx, type, d_state,  n_group,      n_seq_tokens, n_seqs);
-        ggml_tensor * C   = ggml_new_tensor_4d(ctx, type, d_state,  n_group,      n_seq_tokens, n_seqs);
+        ggml_tensor * x;
+        ggml_tensor * B;
+        ggml_tensor * C;
+
+        if (xbc_overlap) {
+            ggml_tensor * xbc = ggml_new_tensor_4d(ctx, type, d_state, n_head, n_seq_tokens, 2 * n_seqs);
+            x = ggml_view_4d(ctx, xbc, head_dim, n_head, n_seq_tokens, n_seqs,
+                             xbc->nb[1], xbc->nb[2], xbc->nb[3], xbc->nb[3]);
+            B = ggml_view_4d(ctx, xbc, d_state, n_group, n_seq_tokens, n_seqs,
+                             xbc->nb[1], xbc->nb[2], xbc->nb[3], 0);
+            C = ggml_view_4d(ctx, xbc, d_state, n_group, n_seq_tokens, n_seqs,
+                             xbc->nb[1], xbc->nb[2], xbc->nb[3], 2 * xbc->nb[3]);
+        } else {
+            x = ggml_new_tensor_4d(ctx, type, head_dim, n_head, n_seq_tokens, n_seqs);
+            B = ggml_new_tensor_4d(ctx, type, d_state,  n_group, n_seq_tokens, n_seqs);
+            C = ggml_new_tensor_4d(ctx, type, d_state,  n_group, n_seq_tokens, n_seqs);
+        }
         ggml_tensor * ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32,  n_seqs);
         ggml_tensor * out = ggml_ssm_scan(ctx, s, x, dt, A, B, C, ids);
         return out;
@@ -3650,16 +3837,17 @@ struct test_gated_delta_net : public test_case {
     const int     v_repeat;
     const bool    permuted;
     const bool    kda;
+    const int64_t K; // snapshot slot count: 1 = final-only, >1 = last K states
 
     std::string vars() override {
-        return VARS_TO_STR8(type, head_count, head_size, n_seq_tokens, n_seqs, v_repeat, permuted, kda);
+        return VARS_TO_STR9(type, head_count, head_size, n_seq_tokens, n_seqs, v_repeat, permuted, kda, K);
     }
 
     test_gated_delta_net(ggml_type type = GGML_TYPE_F32,
             int64_t head_count = 4, int64_t head_size = 16, int64_t n_seq_tokens = 1, int64_t n_seqs = 1,
-            int v_repeat = 1, bool permuted = false, bool kda = false)
+            int v_repeat = 1, bool permuted = false, bool kda = false, int64_t K = 1)
         : type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs),
-          v_repeat(v_repeat), permuted(permuted), kda(kda) {}
+          v_repeat(v_repeat), permuted(permuted), kda(kda), K(K) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * q;
@@ -3675,13 +3863,37 @@ struct test_gated_delta_net : public test_case {
             k = ggml_new_tensor_4d(ctx, type, head_size, head_count, n_seq_tokens, n_seqs);
             v = ggml_new_tensor_4d(ctx, type, head_size, head_count * v_repeat, n_seq_tokens, n_seqs);
         }
+        ggml_set_name(q, "q");
+        ggml_set_name(k, "k");
+        ggml_set_name(v, "v");
         const int64_t g_ne0 = kda ? head_size : 1;
         ggml_tensor * g     = ggml_new_tensor_4d(ctx, type, g_ne0, head_count * v_repeat, n_seq_tokens, n_seqs);
         ggml_tensor * beta  = ggml_new_tensor_4d(ctx, type, 1, head_count * v_repeat, n_seq_tokens, n_seqs);
-        ggml_tensor * state = ggml_new_tensor_2d(ctx, type, head_size * v_repeat * head_size * head_count, n_seqs);
+        ggml_tensor * state = ggml_new_tensor_3d(ctx, type, head_size * v_repeat * head_size * head_count, K, n_seqs);
+        ggml_set_name(g,     "g");
+        ggml_set_name(beta,  "beta");
+        ggml_set_name(state, "state");
+        // q/k are L2-normalised in qwen35/kimi-linear before delta_net
+        q = ggml_l2_norm(ctx, q, 1e-6f);
+        k = ggml_l2_norm(ctx, k, 1e-6f);
         ggml_tensor * out   = ggml_gated_delta_net(ctx, q, k, v, g, beta, state);
         return out;
     }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            if (ggml_is_view_op(t->op)) { continue; }
+            if (strcmp(t->name, "g") == 0) {
+                init_tensor_uniform(t, -20.0f, -1e-4f);
+            } else if (strcmp(t->name, "beta") == 0) {
+                init_tensor_uniform(t, 0.0f, 1.0f);
+            } else if (strcmp(t->name, "v") == 0) {
+                init_tensor_uniform(t, -0.3f, 5.0f);
+            } else {
+                init_tensor_uniform(t);
+            }
+        }
+    }
 };
 
 // GGML_OP_GATED_LINEAR_ATTN
@@ -3770,7 +3982,7 @@ struct test_mul_mat : public test_case {
 
     double max_nmse_err(ggml_backend_t backend) override {
         // for blackwell we quantize activations to mxfp4 instead of q8_1 so we add higher tolerance
-        if (type_a == GGML_TYPE_MXFP4 && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
+        if ((type_a == GGML_TYPE_MXFP4 || type_a == GGML_TYPE_NVFP4) && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
             return 2e-2;
         }
         return max_nmse_err();
@@ -3864,6 +4076,59 @@ struct test_mul_mat : public test_case {
     }
 };
 
+// GGML_HINT_SRC0_IS_HADAMARD
+struct test_mul_mat_hadamard : public test_mul_mat {
+    test_mul_mat_hadamard(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
+            int64_t m = 32, int64_t n = 32, int64_t k = 32,
+            std::array<int64_t, 2> bs = {1, 1},
+            std::array<int64_t, 2> nr = {1, 1})
+        : test_mul_mat(type_a, type_b, m, n, k, bs, nr) {
+            GGML_ASSERT(type_a == GGML_TYPE_F32);
+        }
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * out = test_mul_mat::build_graph(ctx);
+        // Find the mul_mat op in the graph and set the hint
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->op == GGML_OP_MUL_MAT) {
+                ggml_mul_mat_set_hint(t, GGML_HINT_SRC0_IS_HADAMARD);
+            }
+        }
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            if (strcmp(t->name, "a") == 0) {
+                const int64_t n_cols = t->ne[0];
+                const int64_t n_rows = ggml_nrows(t);
+                std::vector<float> data(n_cols * n_rows);
+                float scale = 1.0f / sqrtf((float)n_cols);
+                for (int64_t r = 0; r < n_rows; r++) {
+                    float * row_data = data.data() + r * n_cols;
+                    for (int64_t i = 0; i < n_cols; i++) {
+                        int pop = 0;
+                        int64_t val = r & i;
+                        while (val) {
+                            pop += (val & 1);
+                            val >>= 1;
+                        }
+                        row_data[i] = (pop % 2 == 0) ? scale : -scale;
+                    }
+                }
+                ggml_backend_tensor_set(t, data.data(), 0, data.size() * sizeof(float));
+            } else if (t->type == GGML_TYPE_F32 || t->type == GGML_TYPE_F16) {
+                init_tensor_uniform(t);
+            }
+        }
+    }
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "MUL_MAT_HADAMARD";
+    }
+};
+
 static void init_mul_mat_id_tensors(ggml_context * ctx, int n_mats) {
     std::random_device rd;
     std::default_random_engine rng(rd());
@@ -3906,7 +4171,7 @@ struct test_mul_mat_id : public test_case {
 
     double max_nmse_err(ggml_backend_t backend) override {
         // for blackwell we quantize activations to mxfp4 instead of q8_1 so we add higher tolerance
-        if (type_a == GGML_TYPE_MXFP4 && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
+        if ((type_a == GGML_TYPE_MXFP4 || type_a == GGML_TYPE_NVFP4) && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
             return 2e-2;
         }
         return max_nmse_err();
@@ -6345,11 +6610,12 @@ struct test_flash_attn_ext : public test_case {
     const float logit_softcap; // Gemma 2
 
     const ggml_prec prec;
-    const ggml_type type_KV;
+    const ggml_type type_K;
+    const ggml_type type_V;
     std::array<int32_t, 4> permute;
 
     std::string vars() override {
-        return VARS_TO_STR13(hsk, hsv, nh, nr23, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV, permute);
+        return VARS_TO_STR14(hsk, hsv, nh, nr23, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_K, type_V, permute);
     }
 
     double max_nmse_err() override {
@@ -6365,12 +6631,13 @@ struct test_flash_attn_ext : public test_case {
 
     test_flash_attn_ext(int64_t hsk = 128, int64_t hsv = 128, int64_t nh = 32, std::array<int64_t, 2> nr23 = {1, 1}, int64_t kv = 96, int64_t nb = 8,
                         bool mask = true, bool sinks = false, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_prec prec = GGML_PREC_F32,
-                        ggml_type type_KV = GGML_TYPE_F16, std::array<int32_t, 4> permute = {0, 1, 2, 3})
-        : hsk(hsk), hsv(hsv), nh(nh), nr23(nr23), kv(kv), nb(nb), mask(mask), sinks(sinks), max_bias(max_bias), logit_softcap(logit_softcap), prec(prec), type_KV(type_KV), permute(permute) {}
+                        ggml_type type_K = GGML_TYPE_F16, ggml_type type_V = GGML_TYPE_F16, std::array<int32_t, 4> permute = {0, 1, 2, 3})
+        : hsk(hsk), hsv(hsv), nh(nh), nr23(nr23), kv(kv), nb(nb), mask(mask), sinks(sinks), max_bias(max_bias), logit_softcap(logit_softcap), prec(prec),
+          type_K(type_K), type_V(type_V), permute(permute) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        const int64_t hsk_padded = GGML_PAD(hsk, ggml_blck_size(type_KV));
-        const int64_t hsv_padded = GGML_PAD(hsv, ggml_blck_size(type_KV));
+        const int64_t hsk_padded = GGML_PAD(hsk, ggml_blck_size(type_K));
+        const int64_t hsv_padded = GGML_PAD(hsv, ggml_blck_size(type_V));
 
         auto const &create_permuted = [&](ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, bool is_view) -> ggml_tensor * {
             int64_t ne[4] = {ne0, ne1, ne2, ne3};
@@ -6394,11 +6661,11 @@ struct test_flash_attn_ext : public test_case {
         ggml_tensor * q = create_permuted(GGML_TYPE_F32, hsk_padded, nb, nh*nr23[0], nr23[1], false);
         ggml_set_name(q, "q");
 
-        ggml_tensor * k = create_permuted(type_KV,       hsk_padded, kv, nh,         nr23[1], true); // the K tensor is usually a view of the K cache
+        ggml_tensor * k = create_permuted(type_K,        hsk_padded, kv, nh,         nr23[1], true); // the K tensor is usually a view of the K cache
         ggml_set_name(k, "k");
 
         ggml_tensor * v = nullptr;
-        if (hsk_padded == 576 && hsv_padded == 512) {
+        if (type_K == type_V && hsk_padded == 576 && hsv_padded == 512) {
             // TODO: this branch should become a separate test case parameter instead of hardcoding this for these head shapes
 
             // in this branch, the V cache is sub-view of the K cache. this is used by some MLA-based models
@@ -6408,7 +6675,7 @@ struct test_flash_attn_ext : public test_case {
             //   - https://github.com/ggml-org/llama.cpp/pull/18986
             v = ggml_view_4d(ctx, k, hsv_padded, kv, nh, nr23[1], k->nb[1], k->nb[2], k->nb[3], 0);
         } else {
-            v = create_permuted(type_KV,       hsv_padded, kv, nh,         nr23[1], true); // the V tensor is usually a view of the V cache
+            v = create_permuted(type_V,        hsv_padded, kv, nh,         nr23[1], true); // the V tensor is usually a view of the V cache
         }
         ggml_set_name(v, "v");
 
@@ -7506,6 +7773,25 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    // fused relu + sqr (squared ReLU)
+    for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+        test_cases.emplace_back(new test_relu_sqr(type, { 128, 2, 2, 2 }));
+        test_cases.emplace_back(new test_relu_sqr(type, { 5, 7, 11, 13 }));
+    }
+
+    // SNAKE activation fusion: x + sin(a*x)^2 * inv_b
+    for (ggml_type type : { GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16 }) {
+        test_cases.emplace_back(new test_snake_fuse(type, {   5,   7, 1, 1}));   // primes sub-block
+        test_cases.emplace_back(new test_snake_fuse(type, {  33,  32, 1, 1}));   // boundary
+        test_cases.emplace_back(new test_snake_fuse(type, {1025,  13, 1, 1}));   // large prime, grid-stride
+        test_cases.emplace_back(new test_snake_fuse(type, { 128,  16, 1, 1}));   // power-of-two
+        test_cases.emplace_back(new test_snake_fuse(type, { 256, 192, 1, 1}));   // BigVGAN-ish
+        // higher-rank shapes: matcher must reject fusion, fallback to naive chain
+        test_cases.emplace_back(new test_snake_fuse(type, {  64,  32, 2, 1}));   // ne[2] > 1
+        test_cases.emplace_back(new test_snake_fuse(type, {  64,  32, 1, 2}));   // ne[3] > 1
+        test_cases.emplace_back(new test_snake_fuse(type, {  64,  32, 2, 3}));   // ne[2] > 1 and ne[3] > 1
+    }
+
     // glu ops
     for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
         for (int v : {0, 1}) {
@@ -8116,9 +8402,31 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    // fused ssm_conv + (optional) bias_add + silu. The bias-only graph (no silu) is intentionally
+    // not tested since there's no fusion for that pattern in ggml_cuda_can_fuse.
+    for (int64_t d_conv : {3, 4, 9}) {
+        for (int64_t d_inner : {1024, 1536, 2048}) {
+            for (bool fuse_bias : {false, true}) {
+                // short token path (n_t <= 32)
+                test_cases.emplace_back(new test_ssm_conv_bias_silu(
+                    GGML_TYPE_F32, {d_conv, d_inner, 1, 1}, {d_conv, d_inner, 1, 1}, fuse_bias));
+                test_cases.emplace_back(new test_ssm_conv_bias_silu(
+                    GGML_TYPE_F32, {2 * d_conv, d_inner, 1, 1}, {d_conv, d_inner, 1, 1}, fuse_bias));
+                test_cases.emplace_back(new test_ssm_conv_bias_silu(
+                    GGML_TYPE_F32, {d_conv, d_inner, 4, 1}, {d_conv, d_inner, 1, 1}, fuse_bias));
+                // long token path (n_t > 32)
+                test_cases.emplace_back(new test_ssm_conv_bias_silu(
+                    GGML_TYPE_F32, {d_conv - 1 + 64, d_inner, 1, 1}, {d_conv, d_inner, 1, 1}, fuse_bias));
+                test_cases.emplace_back(new test_ssm_conv_bias_silu(
+                    GGML_TYPE_F32, {d_conv - 1 + 64, d_inner, 4, 1}, {d_conv, d_inner, 1, 1}, fuse_bias));
+            }
+        }
+    }
+
     test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 16, 1, 1024, 1, 32, 4)); // Mamba-1
     test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 16, 2, 32, 4)); // Mamba-2
     test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 256, 64,  8, 2, 32, 4)); // Falcon-H1
+    test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 128, 4, 4, 16, 2, true)); // x/B/C overlap
 
     test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 1, 1));
     test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 32, 1));
@@ -8135,6 +8443,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 32, 4));
     test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 128, 4));
 
+    // FWHT tests
+    test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 1, 128));
+    test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 64, 1, 64));
+    test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 1, 256));
+    test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 32, 128));
+
 #if 0
     // > 4GB A matrix. Too slow to be enabled by default.
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F16,  900000,  3, 2592, {1, 1}, {1, 1}));
@@ -8244,6 +8558,18 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {1536, 1}, {1, 1}));
         }
     }
+
+    // BF16 is absent from base_types: add the 3 standard non-contig permutations explicitly
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16,  1, 256, {2, 3}, {1, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16,  1, 256, {2, 3}, {1, 1}, {0, 1, 3, 2}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16,  1, 256, {2, 3}, {1, 1}, {0, 3, 2, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16,  8, 256, {2, 3}, {1, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16,  8, 256, {2, 3}, {1, 1}, {0, 1, 3, 2}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16,  8, 256, {2, 3}, {1, 1}, {0, 3, 2, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16, 16, 256, {2, 3}, {1, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16, 16, 256, {2, 3}, {1, 1}, {0, 1, 3, 2}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_BF16, GGML_TYPE_F32, 16, 16, 256, {2, 3}, {1, 1}, {0, 3, 2, 1}));
+
     for (ggml_type type_a : other_types) {
         for (ggml_type type_b : {GGML_TYPE_F32}) {
             if (ggml_blck_size(type_a) != 256) {
@@ -8399,6 +8725,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    // ne2 sweep to cover the cublasSgemmStridedBatched path (dps2 == 1, ne2 > 1)
+    for (int64_t ne2 : {1, 8, 16, 32}) {
+        test_cases.emplace_back(new test_out_prod(GGML_TYPE_F32, GGML_TYPE_F32,
+                                                  256, 16, 16, {ne2, 1}, {1, 1}));
+    }
+
     // add_id
     for (ggml_type type_a : {GGML_TYPE_F32}) {
         for (ggml_type type_b : {GGML_TYPE_F32}) {
@@ -8836,8 +9168,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                                 if (nh == 1 && hsk != 320 && hsk != 576) continue;
                                 for (int nr3 : { 1, 3, }) {
                                     if (hsk > 64 && nr3 > 1) continue; // skip broadcast for large head sizes
-                                    for (int nr2 : { 1, 4, 12, 20, 32 }) {
+                                    for (int nr2 : { 1, 4, 8, 12, 16, 20, 32 }) {
+                                        if (nr2 ==  8 && hsk != 192) continue;
                                         if (nr2 == 12 && hsk != 128) continue;
+                                        if (nr2 == 16 && hsk != 192) continue;
                                         if (nr2 == 20 && (nh != 1 || hsk != 576)) continue;
                                         if (nr2 == 32 && (nh != 1 || hsk != 320)) continue;
                                         //for (int kv : { 1, 17, 31, 33, 61, 113, 65, 127, 129, 130, 255, 260, 371, 380, 407, 512, 1024, }) {
@@ -8850,11 +9184,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                                                         if (type_KV == GGML_TYPE_TURBO3_0 && hsk < 128) continue;
                                                         if (type_KV != GGML_TYPE_F16 && hsk != 64 && hsk != 72 && hsk != 128) continue;
                                                         test_cases.emplace_back(new test_flash_attn_ext(
-                                                                    hsk, hsv, nh, {nr2, nr3}, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV));
+                                                                    hsk, hsv, nh, {nr2, nr3}, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV, type_KV));
                                                         // run fewer test cases permuted
                                                         if (mask == true && max_bias == 0.0f && logit_softcap == 0 && kv == 512) {
                                                             test_cases.emplace_back(new test_flash_attn_ext(
-                                                                        hsk, hsv, nh, {nr2, nr3}, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV, {0, 2, 1, 3}));
+                                                                        hsk, hsv, nh, {nr2, nr3}, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV, type_KV, {0, 2, 1, 3}));
                                                         }
                                                     }
                                                 }
@@ -8870,6 +9204,16 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    // mixed quant and Q1_0 test cases
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_F16));
+    test_cases.emplace_back(new test_flash_attn_ext(72, 72, 4, {1, 1}, 96, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 4, {1, 1}, 96, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16, GGML_TYPE_F32));
+    test_cases.emplace_back(new test_flash_attn_ext(128, 128, 4, {1, 1}, 96, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_Q1_0));
+    test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_Q4_0));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 128, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q1_0));
+    test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 64, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_F16));
+
     test_cases.emplace_back(new test_cross_entropy_loss     (GGML_TYPE_F32, {   10, 5, 4, 3}));
     test_cases.emplace_back(new test_cross_entropy_loss     (GGML_TYPE_F32, {30000, 1, 1, 1}));
     test_cases.emplace_back(new test_cross_entropy_loss_back(GGML_TYPE_F32, {   10, 5, 4, 3}));
@@ -8943,6 +9287,29 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 8, 32, 4, 2, 2, false, true));
     test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 2, 1, true,  true));
     test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 16, 4, 2, 1, true,  true));
+    // chunked path: multi-chunk and non-multiple-of-chunk-size (chunk_size=64 GDN, 16 KDA)
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  64, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 127, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 256, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  65, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 100, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 200, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 127, 2));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  64, 1, 1, false, true));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  33, 1, 1, false, true));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 100, 1, 1, false, true));
+
+    // K > 1: output keeps the last min(n_tokens, K) per-token snapshots in the trailing K-token region.
+    // exact-match cases (K == n_seq_tokens):
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 16,   2, 1, 1, false, false, /*K=*/2));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 32,   4, 1, 1, false, false, /*K=*/4));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,   4, 2, 1, false, false, /*K=*/4));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 8, 128,  4, 1, 1, false, false, /*K=*/4));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,   4, 2, 1, false, true,  /*K=*/4));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 8, 32,   4, 2, 2, false, true,  /*K=*/4));
+    // overflow: n_tokens > K — only the last K snapshots kept.
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 32,   8, 1, 1, false, false, /*K=*/3));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  16, 2, 1, false, false, /*K=*/4));
 
 #if 0
     // these tests are disabled to save execution time, sbut they can be handy for debugging
@@ -9045,9 +9412,20 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     test_cases.emplace_back(new test_pad_reflect_1d(GGML_TYPE_F32, {3000, 384, 1, 1}));
     test_cases.emplace_back(new test_pad_reflect_1d(GGML_TYPE_F32, {3000, 384, 4, 1}));
 
+    // SNAKE activation fusion at BigVGAN scale (T=7680 = 24 kHz x 320 ms, C=192)
+    test_cases.emplace_back(new test_snake_fuse(GGML_TYPE_F32,  {7680, 192, 1, 1}));
+    test_cases.emplace_back(new test_snake_fuse(GGML_TYPE_F16,  {7680, 192, 1, 1}));
+    test_cases.emplace_back(new test_snake_fuse(GGML_TYPE_BF16, {7680, 192, 1, 1}));
+
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16416, 1, 128, {8,  1}, {4, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8,  1}, {4, 1}, {0, 1, 2, 3}, 2*16416));
 
+    // FWHT tests
+    test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 1, 128));
+    test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 64, 1, 64));
+    test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 1, 256));
+    test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 32, 128));
+
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 32, 64, 4, 4 }));
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 }));
     // qwen3next with CHUNK_SIZE 64
@@ -9114,15 +9492,19 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     }
 
     // Qwen3-VL-8B https://github.com/ggml-org/llama.cpp/issues/17012
-    test_cases.emplace_back(new test_flash_attn_ext(72, 72, 16, {1, 1}, 5776, 5776, false, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16));
+    test_cases.emplace_back(new test_flash_attn_ext(72, 72, 16, {1, 1}, 5776, 5776, false, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16, GGML_TYPE_F16));
 
-    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 8, {8, 1}, 7680, 1, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16));
-    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 8, {8, 1}, 7680, 4, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 8, {8, 1}, 7680, 1, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16, GGML_TYPE_F16));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 8, {8, 1}, 7680, 4, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16, GGML_TYPE_F16));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 8, {8, 1}, 7680,   1, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 8, {8, 1}, 7680, 512, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 8, {8, 1}, 7680,   1, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 64, 8, {8, 1}, 7680, 512, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0));
 
     for (int kv : { 4096, 8192, 16384, }) {
         for (int hs : { 64, 128, }) {
             for (int nr : { 1, 4, }) {
-                test_cases.emplace_back(new test_flash_attn_ext(hs, hs, 8, {nr, 1}, kv, 1, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16));
+                test_cases.emplace_back(new test_flash_attn_ext(hs, hs, 8, {nr, 1}, kv, 1, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16, GGML_TYPE_F16));
             }
         }
     }
@@ -9200,6 +9582,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     // Examples from granite-4.0-h-1b/ggml-model-Q8_0.gguf
     test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {515, 3328, 1, 1}, {4, 3328, 1, 1})); // prefill
     test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {4,   3328, 1, 1}, {4, 3328, 1, 1})); // generate
+    test_cases.emplace_back(new test_ssm_conv_bias_silu(GGML_TYPE_F32, {515, 3328, 1, 1}, {4, 3328, 1, 1}, true));  // prefill
+    test_cases.emplace_back(new test_ssm_conv_bias_silu(GGML_TYPE_F32, {4,   3328, 1, 1}, {4, 3328, 1, 1}, true));  // generate
     test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 512, 1)); // prefill
     test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 1,   1)); // generate
 
diff --git a/tests/test-chat-auto-parser.cpp b/tests/test-chat-auto-parser.cpp
index bb23b7f2aae..1d96de718e2 100644
--- a/tests/test-chat-auto-parser.cpp
+++ b/tests/test-chat-auto-parser.cpp
@@ -1331,7 +1331,7 @@ static void test_nemotron_reasoning_detection(testing & t) {
 
     // Check reasoning markers
     t.assert_equal("reasoning_start should be '<think>\\n'", "<think>\n", analysis.reasoning.start);
-    t.assert_equal("reasoning_end should be '</think>'", "</think>", analysis.reasoning.end);
+    t.assert_equal("reasoning_end should be '\\n</think>\\n'", "\n</think>\n", analysis.reasoning.end);
 
     // Check reasoning mode detection
     // Nemotron uses tag-based reasoning; prefill handles the template's forced markers
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index eee28271be2..05c60d29743 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -7,6 +7,7 @@
 //
 #include "../src/llama-grammar.h"
 #include "../src/unicode.h"
+#include "../tools/server/server-chat.h"
 #include "chat-auto-parser.h"
 #include "chat.h"
 #include "common.h"
@@ -541,6 +542,36 @@ static common_chat_tool edit_tool{
     })",
 };
 
+static common_chat_tool manage_todo_list_tool{
+    /* .name = */ "manage_todo_list",
+    /* .description = */ "Create or update the todo list",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "todos": {
+                "type": "array",
+                "description": "List of TODO list items"
+            }
+        },
+        "required": ["todos"]
+    })",
+};
+
+static common_chat_tool run_in_terminal_tool{
+    /* .name = */ "run_in_terminal",
+    /* .description = */ "Run a shell command.",
+    /* .parameters = */ R"({
+        "type": "object",
+        "properties": {
+            "command": {
+                "type": "string",
+                "description": "Shell command to run"
+            }
+        },
+        "required": ["command"]
+    })",
+};
+
 static common_chat_tool magic_tool{
     /* .name = */ "magic",
     /* .description = */ "Magic tool that takes a hash",
@@ -1378,6 +1409,16 @@ class peg_test_builder {
         return *this;
     }
 
+    peg_test_builder & tool_choice(common_chat_tool_choice choice) {
+        tc_.params.tool_choice = choice;
+        return *this;
+    }
+
+    peg_test_builder & messages(std::vector<common_chat_msg> messages) {
+        tc_.params.messages = std::move(messages);
+        return *this;
+    }
+
     // Execute the test
     void run() {
         // Check template filter
@@ -1514,6 +1555,194 @@ static void test_tools_oaicompat_json_conversion() {
                   common_chat_tools_to_json_oaicompat({ special_function_tool }).dump(2));
 }
 
+static void test_convert_responses_to_chatcmpl() {
+    LOG_DBG("%s\n", __func__);
+
+    // Test basic conversion with input messages (user/assistant alternating)
+    {
+        json input = json::parse(R"({
+            "input": [
+                {
+                    "type": "message",
+                    "role": "user",
+                    "content": "hi wassup"
+                },
+                {
+                    "type": "message",
+                    "role": "assistant",
+                    "content": "Hey! 👋 Not much, just here ready to chat. What's up with you? Anything I can help you with today?"
+                },
+                {
+                    "type": "message",
+                    "role": "user",
+                    "content": "hi"
+                }
+            ],
+            "model": "gpt-5-mini",
+            "stream": false,
+            "text": {},
+            "reasoning": {
+                "effort": "medium"
+            }
+        })");
+
+        json result = server_chat_convert_responses_to_chatcmpl(input);
+
+        // Verify messages were converted correctly
+        assert_equals(true, result.contains("messages"));
+        assert_equals(true, result.at("messages").is_array());
+        assert_equals((size_t)3, result.at("messages").size());
+
+        // Check first message (user)
+        const auto & msg0 = result.at("messages")[0];
+        assert_equals(std::string("user"), msg0.at("role").get<std::string>());
+        assert_equals(true, msg0.at("content").is_array());
+        assert_equals(std::string("text"), msg0.at("content")[0].at("type").get<std::string>());
+        assert_equals(std::string("hi wassup"), msg0.at("content")[0].at("text").get<std::string>());
+
+        // Check second message (assistant)
+        const auto & msg1 = result.at("messages")[1];
+        assert_equals(std::string("assistant"), msg1.at("role").get<std::string>());
+        assert_equals(true, msg1.at("content").is_array());
+        assert_equals(std::string("text"), msg1.at("content")[0].at("type").get<std::string>());
+        assert_equals(std::string("Hey! 👋 Not much, just here ready to chat. What's up with you? Anything I can help you with today?"), msg1.at("content")[0].at("text").get<std::string>());
+
+        // Check third message (user)
+        const auto & msg2 = result.at("messages")[2];
+        assert_equals(std::string("user"), msg2.at("role").get<std::string>());
+        assert_equals(true, msg2.at("content").is_array());
+        assert_equals(std::string("text"), msg2.at("content")[0].at("type").get<std::string>());
+        assert_equals(std::string("hi"), msg2.at("content")[0].at("text").get<std::string>());
+
+        // Verify other fields preserved
+        assert_equals(std::string("gpt-5-mini"), result.at("model").get<std::string>());
+        assert_equals(false, result.at("stream").get<bool>());
+    }
+
+    // Test string input
+    {
+        json input = json::parse(R"({
+            "input": "Hello, world!",
+            "model": "test-model"
+        })");
+
+        json result = server_chat_convert_responses_to_chatcmpl(input);
+
+        assert_equals((size_t)1, result.at("messages").size());
+        const auto & msg = result.at("messages")[0];
+        assert_equals(std::string("user"), msg.at("role").get<std::string>());
+        assert_equals(std::string("Hello, world!"), msg.at("content").get<std::string>());
+    }
+
+    // Test with instructions (system message)
+    {
+        json input = json::parse(R"({
+            "input": "Hello",
+            "instructions": "You are a helpful assistant.",
+            "model": "test-model"
+        })");
+
+        json result = server_chat_convert_responses_to_chatcmpl(input);
+
+        assert_equals((size_t)2, result.at("messages").size());
+        const auto & sys_msg = result.at("messages")[0];
+        assert_equals(std::string("system"), sys_msg.at("role").get<std::string>());
+        assert_equals(std::string("You are a helpful assistant."), sys_msg.at("content").get<std::string>());
+    }
+
+    // Test with max_output_tokens conversion
+    {
+        json input = json::parse(R"({
+            "input": "Hello",
+            "model": "test-model",
+            "max_output_tokens": 100
+        })");
+
+        json result = server_chat_convert_responses_to_chatcmpl(input);
+
+        assert_equals(true, result.contains("max_tokens"));
+        assert_equals(false, result.contains("max_output_tokens"));
+        assert_equals(100, result.at("max_tokens").get<int>());
+    }
+
+    // Test mixed Responses tools: convert only function tools
+    {
+        json input = json::parse(R"({
+            "input": "Hello",
+            "model": "test-model",
+            "tools": [
+                {
+                    "type": "web_search"
+                },
+                {
+                    "type": "function",
+                    "name": "get_weather",
+                    "description": "Get weather for a location",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string"
+                            }
+                        },
+                        "required": ["location"]
+                    }
+                },
+                {
+                    "type": "image_generation"
+                },
+                {
+                    "type": "mcp",
+                    "server_label": "test-server"
+                },
+                {
+                    "type": "namespace",
+                    "name": "browser"
+                }
+            ]
+        })");
+
+        json result = server_chat_convert_responses_to_chatcmpl(input);
+
+        assert_equals(true, result.contains("tools"));
+        assert_equals(true, result.at("tools").is_array());
+        assert_equals((size_t)1, result.at("tools").size());
+
+        const auto & tool = result.at("tools")[0];
+        assert_equals(std::string("function"), tool.at("type").get<std::string>());
+        assert_equals(std::string("get_weather"), tool.at("function").at("name").get<std::string>());
+        assert_equals(true, tool.at("function").at("strict").get<bool>());
+    }
+
+    // Test non-function Responses tools are ignored
+    {
+        json input = json::parse(R"({
+            "input": "Hello",
+            "model": "test-model",
+            "tools": [
+                {
+                    "type": "web_search"
+                },
+                {
+                    "type": "image_generation"
+                },
+                {
+                    "type": "mcp",
+                    "server_label": "test-server"
+                },
+                {
+                    "type": "namespace",
+                    "name": "browser"
+                }
+            ]
+        })");
+
+        json result = server_chat_convert_responses_to_chatcmpl(input);
+
+        assert_equals(false, result.contains("tools"));
+    }
+}
+
 static void test_template_output_peg_parsers(bool detailed_debug) {
     LOG_DBG("%s\n", __func__);
 
@@ -1530,22 +1759,16 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
         // Qwen3.5 (basically same as Nemotron, but keeping separate tests just in case)
         auto tst = peg_tester("models/templates/Qwen3.5-4B.jinja", detailed_debug);
 
-        tst.test("I'm\nthinking</think>Hello, world!\nWhat's up?")
+        tst.test("I'm\nthinking\n</think>\n\nHello, world!\nWhat's up?")
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
             .enable_thinking(true)
             .expect(message_assist_thoughts)
             .run();
 
-                tst.test("I'm\nthinking\n</think>\nHello, world!\nWhat's up?")
+        tst.test("I'm\nthinking\n</think>\n\nHello, world!\nWhat's up?")
             .enable_thinking(true)
             .reasoning_format(COMMON_REASONING_FORMAT_NONE)
-            .expect_content("<think>\nI'm\nthinking\n</think>\nHello, world!\nWhat's up?")
-            .run();
-
-        tst.test("I'm\nthinking\n</think>\nHello, world!\nWhat's up?")
-            .enable_thinking(true)
-            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
-            .expect(message_assist_thoughts)
+            .expect_content("<think>\nI'm\nthinking\n</think>\n\nHello, world!\nWhat's up?")
             .run();
 
         tst.test(
@@ -1561,7 +1784,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .run();
 
         tst.test(
-               "I'm\nthinking\n</think>\n"
+               "I'm\nthinking\n</think>\n\n"
                "<tool_call>\n"
                "<function=special_function>\n"
                "<parameter=arg1>\n1\n</parameter>\n"
@@ -1619,7 +1842,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
 
         tst.test(
                "I need to output the invoice details in JSON\n"
-               "</think>\n"
+               "</think>\n\n"
                R"({"amount": 123.45, "date": "2025-12-03"})")
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
             .enable_thinking(true)
@@ -1639,7 +1862,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
                "hello()\n"
                "</parameter>\n"
                "</function>\n"
-               "</tool_call></think>\n"
+               "</tool_call>\n</think>\n\n"
                "<tool_call>\n"
                "<function=python>\n"
                "<parameter=code>\n"
@@ -1649,23 +1872,23 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
                "hello()\n"
                "</parameter>\n"
                "</function>\n"
-               "</tool_call>"
-            )
+               "</tool_call>")
             .enable_thinking(true)
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
             .tools({
                 python_tool
         })
-            .expect_reasoning("Let's call a tool: <tool_call>\n"
-               "<function=python>\n"
-               "<parameter=code>\n"
-               "def hello():\n"
-               "    print(\"Not the real call!\")\n"
-               "\n"
-               "hello()\n"
-               "</parameter>\n"
-               "</function>\n"
-               "</tool_call>")
+            .expect_reasoning(
+                "Let's call a tool: <tool_call>\n"
+                "<function=python>\n"
+                "<parameter=code>\n"
+                "def hello():\n"
+                "    print(\"Not the real call!\")\n"
+                "\n"
+                "hello()\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>")
             .expect_tool_calls({
                 { "python", "{\"code\": \"def hello():\\n    print(\\\"Hello, world!\\\")\\n\\nhello()\"}", {} },
             })
@@ -1694,6 +1917,219 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .tools({ empty_args_tool_no_properties })
             .expect(message_with_tool_calls("empty_args_no_props", "{}"))
             .run();
+
+        // Edge cases when reasoning traces are not sent
+        tst.test(
+               "<think>\n\n</think>\n\n"
+               "<tool_call>\n"
+               "<function=special_function>\n"
+               "<parameter=arg1>\n1\n</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .tools({
+                special_function_tool
+        })
+            .expect_reasoning("<think>\n\n")
+            .expect_tool_calls({ { "special_function", "{\"arg1\": 1}", "" } })
+            .run();
+
+        tst.test(
+               "</think>\n\n"
+               "<tool_call>\n"
+               "<function=special_function>\n"
+               "<parameter=arg1>\n1\n</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .tools({
+                special_function_tool
+        })
+            .expect_reasoning("")
+            .expect_tool_calls({ { "special_function", "{\"arg1\": 1}", "" } })
+            .run();
+
+        tst.test(
+               "</think>\n\n"
+               "<tool_call>\n"
+               "<function=run_in_terminal>\n"
+               "<parameter=command>\n"
+               "pwd\n"
+               "</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .enable_thinking(true)
+            .tools({
+                run_in_terminal_tool
+        })
+            .expect_tool_calls({
+                { "run_in_terminal", R"({"command": "pwd"})", {} },
+            })
+            .run();
+
+        tst.test(
+               "</think>\n\n"
+               "Let me inspect the current directory.\n"
+               "<tool_call>\n"
+               "<function=run_in_terminal>\n"
+               "<parameter=command>\n"
+               "pwd\n"
+               "</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .enable_thinking(true)
+            .tools({
+                run_in_terminal_tool
+        })
+            .expect_content("Let me inspect the current directory.\n")
+            .expect_tool_calls({
+                { "run_in_terminal", R"({"command": "pwd"})", {} },
+            })
+            .run();
+
+        tst.test(
+               "</think>\n\n"
+               "Let me inspect the current directory.\n"
+               "<tool_call>\n"
+               "<function=run_in_terminal>\n"
+               "<parameter=command>\n"
+               "pwd\n"
+               "</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .enable_thinking(true)
+            .tools({
+                run_in_terminal_tool
+        })
+            .tool_choice(COMMON_CHAT_TOOL_CHOICE_REQUIRED)
+            .expect_content("Let me inspect the current directory.\n")
+            .expect_tool_calls({
+                { "run_in_terminal", R"({"command": "pwd"})", {} },
+            })
+            .run();
+
+        tst.test(
+               "I should inspect the directory.\n"
+               "</think>\n\n"
+               "Let me inspect it now.\n"
+               "<tool_call>\n"
+               "<function=run_in_terminal>\n"
+               "<parameter=command>\n"
+               "pwd\n"
+               "</parameter>\n"
+               "</function>\n"
+               "</tool_call>")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .enable_thinking(true)
+            .tools({
+                run_in_terminal_tool
+        })
+            .expect_reasoning("I should inspect the directory.")
+            .expect_content("Let me inspect it now.\n")
+            .expect_tool_calls({
+                { "run_in_terminal", R"({"command": "pwd"})", {} },
+            })
+            .run();
+
+        tst.test(
+               "I might call <tool_call> later, but I am still thinking.\n"
+               "</think>\n\n"
+               "Final answer without tools.")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .enable_thinking(true)
+            .tools({ run_in_terminal_tool })
+            .expect_reasoning("I might call <tool_call> later, but I am still thinking.")
+            .expect_content("Final answer without tools.")
+            .run();
+
+        {
+            common_chat_msg user_start;
+            user_start.role    = "user";
+            user_start.content = "Create a todo list, then inspect the repository.";
+
+            common_chat_msg assistant_todos =
+                simple_assist_msg("", "", "manage_todo_list",
+                                  R"({"todos":[{"item":"Inspect repository","selected":false}]})", "call_todos");
+
+            common_chat_msg tool_result;
+            tool_result.role         = "tool";
+            tool_result.content      = "Successfully wrote todo list";
+            tool_result.tool_call_id = "call_todos";
+
+            common_chat_msg user_continue;
+            user_continue.role    = "user";
+            user_continue.content = "Proceed.";
+
+            tst.test(
+                   "I need to run a terminal command.\n"
+                   "</think>\n\n"
+                   "<tool_call>\n"
+                   "<function=run_in_terminal>\n"
+                   "<parameter=command>\n"
+                   "pwd\n"
+                   "</parameter>\n"
+                   "</function>\n"
+                   "</tool_call>")
+                .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+                .enable_thinking(true)
+                .tools({
+                    manage_todo_list_tool, run_in_terminal_tool
+            })
+                .messages({ user_start, assistant_todos, tool_result, user_continue })
+                .expect_reasoning("I need to run a terminal command.")
+                .expect_tool_calls({
+                    { "run_in_terminal", R"({"command": "pwd"})", {} },
+                })
+                .run();
+
+            tst.test(
+                   "I need to run a terminal command.\n"
+                   "</think>\n\n"
+                   "Let me inspect the current directory.\n"
+                   "<tool_call>\n"
+                   "<function=run_in_terminal>\n"
+                   "<parameter=command>\n"
+                   "pwd\n"
+                   "</parameter>\n"
+                   "</function>\n"
+                   "</tool_call>")
+                .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+                .enable_thinking(true)
+                .tools({
+                    manage_todo_list_tool, run_in_terminal_tool
+            })
+                .tool_choice(COMMON_CHAT_TOOL_CHOICE_REQUIRED)
+                .messages({ user_start, assistant_todos, tool_result, user_continue })
+                .expect_reasoning("I need to run a terminal command.")
+                .expect_content("Let me inspect the current directory.\n")
+                .expect_tool_calls({
+                    { "run_in_terminal", R"({"command": "pwd"})", {} },
+                })
+                .run();
+
+            tst.test(
+                   "</think>\n\n"
+                   "<tool_call>\n"
+                   "<function=run_in_terminal>\n"
+                   "<parameter=command>\n"
+                   "pwd\n"
+                   "</parameter>\n"
+                   "</function>\n"
+                   "</tool_call>")
+                .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+                .enable_thinking(true)
+                .tools({
+                    manage_todo_list_tool, run_in_terminal_tool
+            })
+                .messages({ user_start, assistant_todos, tool_result, user_continue })
+                .expect_tool_calls({
+                    { "run_in_terminal", R"({"command": "pwd"})", {} },
+                })
+                .run();
+        }
     }
 
     {
@@ -1882,7 +2318,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
                "hello()\n"
                "</parameter>\n"
                "</function>\n"
-               "</tool_call></think>\n"
+               "</tool_call>\n</think>\n"
                "<tool_call>\n"
                "<function=python>\n"
                "<parameter=code>\n"
@@ -2143,6 +2579,46 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
             .expect(message_assist)
             .run();
+
+        {
+            // additional tests for https://github.com/ggml-org/llama.cpp/pull/21760
+            auto tmpls = read_templates("models/templates/google-gemma-4-31B-it.jinja");
+
+            common_chat_msg tool_call_msg = simple_assist_msg(
+                "Let me check.", "", "special_function", "{\"arg1\": 1}","c0");
+
+            common_chat_msg tool_msg;
+            tool_msg.role         = "tool";
+            tool_msg.tool_name    = "special_function";
+            tool_msg.tool_call_id = "c0";
+            tool_msg.content      = "{\"r\":\"ok\"}";
+
+            {
+                common_chat_templates_inputs inputs;
+                inputs.messages              = { message_user, tool_call_msg, tool_msg };
+                inputs.tools                 = { special_function_tool };
+                inputs.add_generation_prompt = true;
+
+                auto params = common_chat_templates_apply(tmpls.get(), inputs);
+
+                if (!string_ends_with(params.prompt, "<turn|>\n<|turn>model\n")) {
+                    throw std::runtime_error("Missing generation prompt for Gemma 4");
+                }
+            }
+
+            {
+                common_chat_templates_inputs inputs;
+                inputs.messages              = { message_user, tool_call_msg, tool_msg };
+                inputs.tools                 = { special_function_tool };
+                inputs.add_generation_prompt = false;
+
+                auto params = common_chat_templates_apply(tmpls.get(), inputs);
+
+                if (string_ends_with(params.prompt, "<|turn>model\n")) {
+                    throw std::runtime_error("Gemma 4: generation prompt was modified despite add_generation_prompt=false");
+                }
+            }
+        }
     }
 
     {
@@ -3351,7 +3827,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .run();
 
         // Tool call with reasoning (enable_thinking=true)
-        tst.test("I'm\nthinking</think><tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}</tool_call>")
+        tst.test("I'm\nthinking\n</think>\n\n<tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}</tool_call>")
             .enable_thinking(true)
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
             .tools({ special_function_tool })
@@ -3375,7 +3851,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .run();
 
         // Tool call with reasoning and content
-        tst.test("I need to call a function</think>"
+        tst.test("I need to call a function\n</think>\n\n"
                  "Let me check the time.<tool_call>\n{\"name\": \"get_time\", \"arguments\": {\"city\": \"XYZCITY\"}}</tool_call>")
             .enable_thinking(true)
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
@@ -3402,7 +3878,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
 
         // fake tool call marker in reasoning
         tst.test(
-               "Let me think about <tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 2}}</tool_call> hmm</think>"
+               "Let me think about <tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 2}}</tool_call> hmm\n</think>\n\n"
                "<tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}</tool_call>")
             .enable_thinking(true)
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
@@ -3430,11 +3906,11 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
     // Format: <minimax:tool_call><invoke name="func"><parameter name="key">value</parameter></invoke></minimax:tool_call>
     {
         auto tst = peg_tester("models/templates/MiniMax-M2.jinja", detailed_debug);
-        tst.test("</think>Hello, world!\nWhat's up?").enable_thinking(true).reasoning_format(COMMON_REASONING_FORMAT_AUTO).expect(message_assist).run();
+        tst.test("\n</think>\n\nHello, world!\nWhat's up?").enable_thinking(true).reasoning_format(COMMON_REASONING_FORMAT_AUTO).expect(message_assist).run();
 
-        tst.test("I'm\nthinking</think>Hello, world!\nWhat's up?").enable_thinking(true).reasoning_format(COMMON_REASONING_FORMAT_AUTO).expect(message_assist_thoughts).run();
+        tst.test("I'm\nthinking\n</think>\n\nHello, world!\nWhat's up?").enable_thinking(true).reasoning_format(COMMON_REASONING_FORMAT_AUTO).expect(message_assist_thoughts).run();
 
-        tst.test("Let's call a tool:</think><minimax:tool_call>\n<invoke name=\"empty_args\">\n</invoke>\n</minimax:tool_call>").
+        tst.test("Let's call a tool:\n</think>\n\n<minimax:tool_call>\n<invoke name=\"empty_args\">\n</invoke>\n</minimax:tool_call>").
             enable_thinking(true).
             reasoning_format(COMMON_REASONING_FORMAT_AUTO).
             tools({ empty_args_tool }).
@@ -3442,7 +3918,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             run();
 
         tst.test(
-               "</think><minimax:tool_call>\n<invoke name=\"special_function\">\n<parameter "
+               "\n</think>\n\n<minimax:tool_call>\n<invoke name=\"special_function\">\n<parameter "
                "name=\"arg1\">1</parameter>\n</invoke>\n</minimax:tool_call>")
             .tools({ special_function_tool })
             .expect(message_assist_call)
@@ -3595,6 +4071,51 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .run();
     }
 
+    // Reka Edge
+    {
+        auto tst = peg_tester("models/templates/Reka-Edge.jinja", detailed_debug);
+        tst.test("Hello, world!\nWhat's up?")
+            .enable_thinking(false)
+            .expect(message_assist)
+            .run();
+        tst.test("I'm\nthinking\n</think>\n\nHello, world!\nWhat's up?")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .expect(message_assist_thoughts)
+            .run();
+        tst.test("<tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n</tool_call>")
+            .enable_thinking(false)
+            .tools({ special_function_tool })
+            .expect(message_assist_call)
+            .run();
+        tst.test("Hello, world!\nWhat's up?\n<tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n</tool_call>")
+            .enable_thinking(false)
+            .tools({ special_function_tool })
+            .expect(message_assist_call_content)
+            .run();
+        tst.test("I'm\nthinking\n</think>\n\n<tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n</tool_call>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ special_function_tool })
+            .expect(message_assist_call_thoughts)
+            .run();
+        tst.test("<tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n</tool_call>\n<tool_call>\n{\"name\": \"special_function_with_opt\", \"arguments\": {\"arg1\": 1, \"arg2\": 2}}\n</tool_call>")
+            .enable_thinking(false)
+            .parallel_tool_calls(true)
+            .tools({ special_function_tool, special_function_tool_with_optional_param })
+            .expect_tool_calls({
+                { "special_function", R"({"arg1": 1})", {} },
+                { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} },
+            })
+            .run();
+        tst.test("<tool_call>\n{\"name\": \"special_function\", \"arguments\": {\"arg")
+            .enable_thinking(false)
+            .tools({ special_function_tool })
+            .is_partial(true)
+            .expect(message_assist_call_cutoff_args)
+            .run();
+    }
+
     // Apriel 1.5
     {
         auto tst = peg_tester("models/templates/unsloth-Apriel-1.5.jinja", detailed_debug);
@@ -3849,7 +4370,8 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
 
     {
         auto tst = peg_tester("models/templates/StepFun3.5-Flash.jinja", detailed_debug);
-        tst.test("I was thinking</think>\nNow I'm not.").
+
+        tst.test("I was thinking\n</think>\nNow I'm not.").
             enable_thinking(true).
             reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK).
             expect_reasoning("I was thinking").
@@ -4077,6 +4599,55 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
     }
 }
 
+static void test_reka_edge_common_path() {
+    auto tmpls = read_templates("models/templates/Reka-Edge.jinja");
+
+    {
+        common_chat_templates_inputs inputs;
+        common_chat_msg system_msg;
+        system_msg.role = "system";
+        system_msg.content = "Use tools when needed.";
+
+        common_chat_msg tool_call_msg = simple_assist_msg("", "", "special_function", "{\"arg1\": 1}");
+
+        common_chat_msg tool_msg;
+        tool_msg.role = "tool";
+        tool_msg.tool_name = "special_function";
+        tool_msg.tool_call_id = "call0";
+        tool_msg.content = "Sunny";
+
+        inputs.messages = { system_msg, message_user, tool_call_msg, tool_msg, message_user };
+        inputs.tools = { special_function_tool };
+        inputs.enable_thinking = true;
+        inputs.add_generation_prompt = true;
+
+        auto params = common_chat_templates_apply(tmpls.get(), inputs);
+
+        if (params.prompt.find("<tool_response>\nSunny\n</tool_response><sep>") == std::string::npos) {
+            throw std::runtime_error("Reka Edge prompt did not render tool response history");
+        }
+        if (params.prompt.rfind("assistant: <think>\n") == std::string::npos) {
+            throw std::runtime_error("Reka Edge prompt did not render thinking generation prompt");
+        }
+    }
+
+    {
+        common_chat_templates_inputs inputs;
+        inputs.messages = {
+            message_user,
+            simple_assist_msg("The first point is")
+        };
+        inputs.add_generation_prompt = false;
+        inputs.enable_thinking = false;
+        inputs.chat_template_kwargs["continue_final_message"] = "true";
+
+        auto params = common_chat_templates_apply(tmpls.get(), inputs);
+        if (string_ends_with(params.prompt, "<sep>")) {
+            throw std::runtime_error("Reka Edge continue_final_message unexpectedly closed the assistant turn");
+        }
+    }
+}
+
 // Test the developer role to system workaround with a simple mock template
 static void test_developer_role_to_system_workaround() {
     LOG_DBG("%s\n", __func__);
@@ -4197,7 +4768,7 @@ int main(int argc, char ** argv) {
     bool detailed_debug    = false;
     bool only_run_filtered = false;
 
-    // Check for --template flag
+    // Check for --template and --detailed flags
     for (int i = 1; i < argc; i++) {
         std::string arg = argv[i];
         if (arg == "--template" && i + 1 < argc) {
@@ -4222,7 +4793,20 @@ int main(int argc, char ** argv) {
     }
 
 #ifndef _WIN32
-    if (argc > 1) {
+    // Check if any argument is a .jinja file (for template format detection mode)
+    bool has_jinja_files = false;
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+        if (arg == "--detailed") {
+            continue;
+        }
+        if (arg.size() >= 6 && arg.rfind(".jinja") == arg.size() - 6) {
+            has_jinja_files = true;
+            break;
+        }
+    }
+
+    if (has_jinja_files) {
         common_chat_templates_inputs inputs;
         common_chat_msg              msg;
         msg.role        = "user";
@@ -4255,7 +4839,9 @@ int main(int argc, char ** argv) {
         test_msg_diffs_compute();
         test_msgs_oaicompat_json_conversion();
         test_tools_oaicompat_json_conversion();
+        test_convert_responses_to_chatcmpl();
         test_developer_role_to_system_workaround();
+        test_reka_edge_common_path();
         test_template_output_peg_parsers(detailed_debug);
         std::cout << "\n[chat] All tests passed!" << '\n';
     }
diff --git a/tests/test-log.cpp b/tests/test-log.cpp
index 306f28c6191..ae4a6606bd3 100644
--- a/tests/test-log.cpp
+++ b/tests/test-log.cpp
@@ -35,5 +35,9 @@ int main() {
         threads[i].join();
     }
 
+    common_log_flush(common_log_main());
+    // We explicitly free the logger singleton to avoid hanging on Windows
+    // related to timing issues of thread startup and DLL teardown
+    common_log_free(common_log_main());
     return 0;
 }
diff --git a/tests/test-reasoning-budget.cpp b/tests/test-reasoning-budget.cpp
index 3028fb4d8f0..10d0ac21da8 100644
--- a/tests/test-reasoning-budget.cpp
+++ b/tests/test-reasoning-budget.cpp
@@ -124,6 +124,66 @@ static void test_reasoning_budget(
     (void)sequence;
 }
 
+static llama_token get_forced_token(struct llama_sampler * sampler, llama_token max_token) {
+    std::vector<llama_token_data> cur;
+    const size_t n_vocab = (size_t) max_token + 1;
+    for (size_t i = 0; i < n_vocab; i++) {
+        cur.emplace_back(llama_token_data{(llama_token) i, logf((float) (i + 1)), 0.0f});
+    }
+
+    llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
+    llama_sampler_apply(sampler, &cur_p);
+
+    size_t finite_count = 0;
+    llama_token finite_token = LLAMA_TOKEN_NULL;
+    for (size_t i = 0; i < cur.size(); i++) {
+        if (std::isfinite(cur[i].logit)) {
+            finite_count++;
+            finite_token = cur[i].id;
+        }
+    }
+
+    GGML_ASSERT(finite_count == 1 && "sampler is not forcing exactly one token");
+    return finite_token;
+}
+
+static void test_reasoning_budget_clone_mid_counting() {
+    const std::vector<llama_token> start = {100};
+    const std::vector<llama_token> end = {101};
+    const std::vector<llama_token> forced = {102, 101};
+
+    auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 2, REASONING_BUDGET_IDLE);
+
+    llama_sampler_accept(sampler, 100); // COUNTING, remaining=2
+    llama_sampler_accept(sampler, 50);  // COUNTING, remaining=1
+
+    auto * clone = llama_sampler_clone(sampler);
+    llama_sampler_accept(clone, 51); // should exhaust the cloned remaining budget
+
+    GGML_ASSERT(get_forced_token(clone, 102) == 102 && "cloned counting state lost remaining budget");
+
+    llama_sampler_free(clone);
+    llama_sampler_free(sampler);
+}
+
+static void test_reasoning_budget_clone_mid_forcing() {
+    const std::vector<llama_token> start = {100};
+    const std::vector<llama_token> end = {101};
+    const std::vector<llama_token> forced = {102, 101};
+
+    auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 0, REASONING_BUDGET_FORCING);
+
+    GGML_ASSERT(get_forced_token(sampler, 102) == 102);
+    llama_sampler_accept(sampler, 102); // advance to the second forced token
+
+    auto * clone = llama_sampler_clone(sampler);
+
+    GGML_ASSERT(get_forced_token(clone, 102) == 101 && "cloned forcing state lost force position");
+
+    llama_sampler_free(clone);
+    llama_sampler_free(sampler);
+}
+
 // UTF-8 boundary detection unit test
 // Tests common_utf8_is_complete() from reasoning-budget.h
 static void test_utf8_boundary_detection() {
@@ -227,7 +287,33 @@ int main(void) {
             3);     // forcing continues through i=3
     }
 
-    printf("OK (5 tests passed)\n");
+    // Test 6: Multi-block thinking. First block ends naturally at i=2, second
+    // start tag at i=3 re-arms the budget, which then exhausts at i=5.
+    // Regression: before this fix, DONE absorbed all subsequent tokens and a
+    // second <think> block ran unbudgeted.
+    // Flow: i=0 accept(100)->COUNTING rem=2; i=1 accept(50)->rem=1;
+    //       i=2 accept(101)->end_matcher matches, DONE;
+    //       i=3 accept(100)->re-arm, COUNTING rem=2;
+    //       i=4 accept(60)->rem=1; i=5 accept(61)->rem=0->FORCING;
+    //       i=6 apply()->forces token[0]=102, accept(62)->force_pos=1, stay FORCING;
+    //       i=7 apply()->forces token[1]=101, accept(63)->force_pos=2->DONE.
+    {
+        const std::vector<llama_token> start = {100};
+        const std::vector<llama_token> end = {101};
+        const std::vector<llama_token> forced = {102, 101};
+        const std::vector<llama_token> sequence = {100, 50, 101, 100, 60, 61, 62, 63};
+
+        test_reasoning_budget("multi-block re-arms budget after DONE", sequence, start, end, forced,
+            2,      // budget of 2 tokens (per block)
+            REASONING_BUDGET_IDLE,
+            6,      // forcing starts at i=6 (after second block exhausts at i=5)
+            7);     // forcing continues through i=7
+    }
+
+    test_reasoning_budget_clone_mid_counting();
+    test_reasoning_budget_clone_mid_forcing();
+
+    printf("OK (8 tests passed)\n");
 
     printf("Testing UTF-8 boundary detection... ");
     test_utf8_boundary_detection();
diff --git a/tests/test-recurrent-state-rollback.cpp b/tests/test-recurrent-state-rollback.cpp
new file mode 100644
index 00000000000..be19316db8a
--- /dev/null
+++ b/tests/test-recurrent-state-rollback.cpp
@@ -0,0 +1,185 @@
+#include "arg.h"
+#include "common.h"
+#include "llama.h"
+
+#include <algorithm>
+#include <clocale>
+#include <cmath>
+#include <cstdio>
+#include <vector>
+
+static llama_context * make_ctx(const common_params & params, llama_model * model) {
+    auto cparams = common_context_params_to_llama(params);
+    cparams.n_seq_max = 1;
+    cparams.n_rs_seq  = 8;
+    cparams.n_batch   = std::max(cparams.n_batch,  (uint32_t) (cparams.n_rs_seq + 1));
+    cparams.n_ubatch  = std::max(cparams.n_ubatch, (uint32_t) (cparams.n_rs_seq + 1));
+    return llama_init_from_model(model, cparams);
+}
+
+static bool decode_tokens(llama_context * ctx, const std::vector<llama_token> & tokens, uint32_t count) {
+    llama_batch batch = llama_batch_init(count, 0, 1);
+    for (uint32_t pos = 0; pos < count; ++pos) {
+        common_batch_add(batch, tokens[pos], pos, { 0 }, false);
+    }
+    const bool ok = llama_decode(ctx, batch) == 0;
+    llama_batch_free(batch);
+    return ok;
+}
+
+static bool decode_one(llama_context * ctx, llama_token tok, llama_pos pos) {
+    llama_batch batch = llama_batch_init(1, 0, 1);
+    common_batch_add(batch, tok, pos, { 0 }, true);
+    const bool ok = llama_decode(ctx, batch) == 0;
+    llama_batch_free(batch);
+    return ok;
+}
+
+int main(int argc, char ** argv) {
+    std::setlocale(LC_NUMERIC, "C");
+
+    common_params params;
+    params.sampling.seed = 1234;
+    params.n_predict = 1;
+
+    common_init();
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
+        return 1;
+    }
+
+    ggml_backend_load_all();
+
+    common_init_result_ptr llama_init = common_init_from_params(params);
+    llama_model * model = llama_init->model();
+    if (model == nullptr) {
+        fprintf(stderr, "%s : failed to init model\n", __func__);
+        return 1;
+    }
+
+    if (!llama_model_is_recurrent(model) && !llama_model_is_hybrid(model)) {
+        fprintf(stderr, "%s : skipping for non-recurrent model\n", __func__);
+        return 0;
+    }
+
+    const llama_vocab * vocab   = llama_model_get_vocab(model);
+    const int           n_vocab = llama_vocab_n_tokens(vocab);
+
+    llama_context * ctx_src = make_ctx(params, model);
+    llama_context * ctx_dst = make_ctx(params, model);
+    if (ctx_src == nullptr || ctx_dst == nullptr) {
+        fprintf(stderr, "%s : failed to init contexts\n", __func__);
+        return 1;
+    }
+
+    if (llama_n_rs_seq(ctx_src) == 0) {
+        fprintf(stderr, "%s : skipping because n_rs_seq is disabled\n", __func__);
+        llama_free(ctx_src);
+        llama_free(ctx_dst);
+        return 0;
+    }
+
+    std::vector<llama_token> tokens = common_tokenize(ctx_src, "The quick brown fox jumps", true);
+    const uint32_t n_rs_seq = llama_n_rs_seq(ctx_src);
+    if (tokens.size() > n_rs_seq + 1) {
+        tokens.resize(n_rs_seq + 1);
+    }
+    if (tokens.size() < 2) {
+        fprintf(stderr, "%s : not enough prompt tokens\n", __func__);
+        return 1;
+    }
+    const uint32_t    n_tokens = tokens.size();
+    const llama_token last_tok = tokens.back();
+    const llama_pos   last_pos = (llama_pos) n_tokens - 2;
+
+    // Decode the full prompt on the source, then roll back the last position.
+    // Rollback leaves the recurrent memory in a snapshot state (rs_idx != 0).
+    if (!decode_tokens(ctx_src, tokens, n_tokens)) {
+        fprintf(stderr, "%s : failed to decode prompt\n", __func__);
+        return 1;
+    }
+    if (!llama_memory_seq_rm(llama_get_memory(ctx_src), 0, last_pos, -1)) {
+        fprintf(stderr, "%s : rollback failed\n", __func__);
+        return 1;
+    }
+
+    // Save the rolled-back state and restore it into a fresh context.
+    common_prompt_checkpoint ckpt;
+    ckpt.update_tgt(ctx_src, 0, 0);
+    ckpt.load_tgt(ctx_dst, 0, 0);
+
+    // Replay the rolled-back token on both contexts and compare logits.
+    if (!decode_one(ctx_src, last_tok, last_pos) ||
+        !decode_one(ctx_dst, last_tok, last_pos)) {
+        fprintf(stderr, "%s : replay failed\n", __func__);
+        return 1;
+    }
+
+    const float * logits_src = llama_get_logits_ith(ctx_src, 0);
+    const float * logits_dst = llama_get_logits_ith(ctx_dst, 0);
+    if (logits_src == nullptr || logits_dst == nullptr) {
+        fprintf(stderr, "%s : missing logits\n", __func__);
+        return 1;
+    }
+
+    constexpr float eps = 1e-5f;
+    for (int i = 0; i < n_vocab; ++i) {
+        if (std::fabs(logits_src[i] - logits_dst[i]) > eps) {
+            fprintf(stderr, "%s : logits mismatch at token %d (%g != %g)\n",
+                    __func__, i, (double) logits_src[i], (double) logits_dst[i]);
+            return 1;
+        }
+    }
+
+    // Repeat the load into a context that already has its own rollback state:
+    // groups 1..n_rs_seq hold a *different* prompt's history, and rs_idx[0] is
+    // non-zero at load time. The restore must wipe that state and still match.
+    llama_context * ctx_dirty = make_ctx(params, model);
+    if (ctx_dirty == nullptr) {
+        fprintf(stderr, "%s : failed to init dirty ctx\n", __func__);
+        return 1;
+    }
+
+    std::vector<llama_token> noise = tokens;
+    for (auto & t : noise) {
+        t = (t + 1) % n_vocab;
+        if (t < 0) {
+            t = 0;
+        }
+    }
+    if (!decode_tokens(ctx_dirty, noise, n_tokens)) {
+        fprintf(stderr, "%s : dirty prompt decode failed\n", __func__);
+        return 1;
+    }
+    if (!llama_memory_seq_rm(llama_get_memory(ctx_dirty), 0, last_pos, -1)) {
+        fprintf(stderr, "%s : dirty rollback failed\n", __func__);
+        return 1;
+    }
+
+    ckpt.load_tgt(ctx_dirty, 0, 0);
+
+    if (!decode_one(ctx_dirty, last_tok, last_pos)) {
+        fprintf(stderr, "%s : dirty replay failed\n", __func__);
+        return 1;
+    }
+
+    const float * logits_dirty = llama_get_logits_ith(ctx_dirty, 0);
+    if (logits_dirty == nullptr) {
+        fprintf(stderr, "%s : missing dirty logits\n", __func__);
+        return 1;
+    }
+
+    for (int i = 0; i < n_vocab; ++i) {
+        if (std::fabs(logits_src[i] - logits_dirty[i]) > eps) {
+            fprintf(stderr, "%s : dirty-ctx logits mismatch at token %d (%g != %g)\n",
+                    __func__, i, (double) logits_src[i], (double) logits_dirty[i]);
+            return 1;
+        }
+    }
+
+    fprintf(stderr, "%s : recurrent rollback checkpoint restored successfully\n", __func__);
+    llama_free(ctx_src);
+    llama_free(ctx_dst);
+    llama_free(ctx_dirty);
+    return 0;
+}
diff --git a/tests/test-state-restore-fragmented.cpp b/tests/test-state-restore-fragmented.cpp
index 8a9bfaf5dcc..d5548afba17 100644
--- a/tests/test-state-restore-fragmented.cpp
+++ b/tests/test-state-restore-fragmented.cpp
@@ -29,6 +29,9 @@ int main(int argc, char ** argv) {
     }
 
     // init
+
+    ggml_backend_load_all();
+
     common_init_result_ptr llama_init = common_init_from_params(params);
 
     llama_model * model = llama_init->model();
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index b433c91d85e..a60d3dab469 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -22,6 +22,7 @@ else()
     add_subdirectory(perplexity)
     add_subdirectory(quantize)
     if (LLAMA_BUILD_SERVER)
+        add_subdirectory(ui)
         add_subdirectory(cli)
         add_subdirectory(server)
     endif()
diff --git a/tools/cli/README.md b/tools/cli/README.md
index de0b7804091..c40b5a21cc0 100644
--- a/tools/cli/README.md
+++ b/tools/cli/README.md
@@ -15,7 +15,6 @@
 | `--license` | show source code license and dependencies |
 | `-cl, --cache-list` | show list of models in cache |
 | `--completion-bash` | print source-able bash completion script for llama.cpp |
-| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
 | `-t, --threads N` | number of CPU threads to use during generation (default: -1)<br/>(env: LLAMA_ARG_THREADS) |
 | `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) |
 | `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") |
@@ -66,7 +65,7 @@
 | `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU<br/>(env: LLAMA_ARG_CPU_MOE) |
 | `-ncmoe, --n-cpu-moe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU<br/>(env: LLAMA_ARG_N_CPU_MOE) |
 | `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
-| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
+| `-sm, --split-mode {none,layer,row,tensor}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs (pipelined)<br/>- row: split weight across GPUs by rows (parallelized)<br/>- tensor: split weights and KV across GPUs (parallelized, EXPERIMENTAL)<br/>(env: LLAMA_ARG_SPLIT_MODE) |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)<br/>(env: LLAMA_ARG_MAIN_GPU) |
 | `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')<br/>(env: LLAMA_ARG_FIT) |
@@ -84,7 +83,6 @@
 | `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(env: LLAMA_ARG_MODEL_URL) |
 | `-dr, --docker-repo [<repo>/]<model>[:quant]` | Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.<br/>example: gemma3<br/>(default: unused)<br/>(env: LLAMA_ARG_DOCKER_REPO) |
 | `-hf, -hfr, --hf-repo <user>/<model>[:quant]` | Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.<br/>mmproj is also downloaded automatically if available. to disable, add --no-mmproj<br/>example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M<br/>(default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
-| `-hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_HFD_REPO) |
 | `-hff, --hf-file FILE` | Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
 | `-hfv, -hfrv, --hf-repo-v <user>/<model>[:quant]` | Hugging Face model repository for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_REPO_V) |
 | `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_FILE_V) |
@@ -95,10 +93,10 @@
 | `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) |
 | `--offline` | Offline mode: forces use of cache, prevents network access<br/>(env: LLAMA_OFFLINE) |
 | `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:<br/> - 0: generic output<br/> - 1: error<br/> - 2: warning<br/> - 3: info<br/> - 4: debug<br/>(default: 3)<br/><br/>(env: LLAMA_LOG_VERBOSITY) |
-| `--log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_LOG_PREFIX) |
-| `--log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_LOG_TIMESTAMPS) |
-| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) |
-| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) |
+| `--log-prefix, --no-log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_ARG_LOG_PREFIX) |
+| `--log-timestamps, --no-log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_ARG_LOG_TIMESTAMPS) |
+| `--spec-draft-type-k, -ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K) |
+| `--spec-draft-type-v, -ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V) |
 
 
 ### Sampling params
@@ -145,6 +143,7 @@
 
 | Argument | Explanation |
 | -------- | ----------- |
+| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
 | `--display-prompt, --no-display-prompt` | whether to print prompt at generation (default: true) |
 | `-co, --color [on\|off\|auto]` | Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')<br/>'auto' enables colors when output is to a terminal |
 | `-ctxcp, --ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 32)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_CTX_CHECKPOINTS) |
@@ -167,30 +166,57 @@
 | `--image, --audio FILE` | path to an image or audio file. use with multimodal models, use comma-separated values for multiple files |
 | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MIN_TOKENS) |
 | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MAX_TOKENS) |
-| `-otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
-| `-cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_CPU_MOE_DRAFT) |
-| `-ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_N_CPU_MOE_DRAFT) |
 | `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'<br/>(env: LLAMA_CHAT_TEMPLATE_KWARGS) |
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-ocr, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-ocr, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)<br/>(env: LLAMA_ARG_SKIP_CHAT_PARSING) |
 | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles |
-| `--draft, --draft-n, --draft-max N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_DRAFT_MAX) |
-| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_DRAFT_MIN) |
-| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)<br/>(env: LLAMA_ARG_DRAFT_P_MIN) |
-| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE_DRAFT) |
-| `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
-| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
-| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
-| `--spec-replace TARGET DRAFT` | translate the string in TARGET into DRAFT if the draft model and main model are not compatible |
+| `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) |
+| `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
+| `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |
+| `--spec-draft-cpu-mask, -Cd, --cpu-mask-draft M` | Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask) |
+| `--spec-draft-cpu-range, -Crd, --cpu-range-draft lo-hi` | Ranges of CPUs for affinity. Complements --cpu-mask-draft |
+| `--spec-draft-cpu-strict, --cpu-strict-draft <0\|1>` | Use strict CPU placement for draft model (default: same as --cpu-strict) |
+| `--spec-draft-prio, --prio-draft N` | set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) |
+| `--spec-draft-poll, --poll-draft <0\|1>` | Use polling to wait for draft model work (default: same as --poll) |
+| `--spec-draft-cpu-mask-batch, -Cbd, --cpu-mask-batch-draft M` | Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask) |
+| `--spec-draft-cpu-strict-batch, --cpu-strict-batch-draft <0\|1>` | Use strict CPU placement for draft model (default: --cpu-strict-draft) |
+| `--spec-draft-prio-batch, --prio-batch-draft N` | set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) |
+| `--spec-draft-poll-batch, --poll-batch-draft <0\|1>` | Use polling to wait for draft model work (default: --poll-draft) |
+| `--spec-draft-override-tensor, -otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
+| `--spec-draft-cpu-moe, -cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_CPU_MOE) |
+| `--spec-draft-n-cpu-moe, --spec-draft-ncmoe, -ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE) |
+| `--spec-draft-n-max N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MAX) |
+| `--spec-draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MIN) |
+| `--spec-draft-p-split, --draft-p-split P` | speculative decoding split probability (default: 0.10)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT) |
+| `--spec-draft-p-min, --draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_MIN) |
+| `--spec-draft-device, -devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
+| `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
+| `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_MODEL) |
+| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)<br/><br/>(env: LLAMA_ARG_SPEC_TYPE) |
+| `--spec-ngram-mod-n-min N` | minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48) |
+| `--spec-ngram-mod-n-max N` | maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64) |
+| `--spec-ngram-mod-n-match N` | ngram-mod lookup length (default: 24) |
+| `--spec-ngram-simple-size-n N` | ngram size N for ngram-simple speculative decoding, length of lookup n-gram (default: 12) |
+| `--spec-ngram-simple-size-m N` | ngram size M for ngram-simple speculative decoding, length of draft m-gram (default: 48) |
+| `--spec-ngram-simple-min-hits N` | minimum hits for ngram-simple speculative decoding (default: 1) |
+| `--spec-ngram-map-k-size-n N` | ngram size N for ngram-map-k speculative decoding, length of lookup n-gram (default: 12) |
+| `--spec-ngram-map-k-size-m N` | ngram size M for ngram-map-k speculative decoding, length of draft m-gram (default: 48) |
+| `--spec-ngram-map-k-min-hits N` | minimum hits for ngram-map-k speculative decoding (default: 1) |
+| `--spec-ngram-map-k4v-size-n N` | ngram size N for ngram-map-k4v speculative decoding, length of lookup n-gram (default: 12) |
+| `--spec-ngram-map-k4v-size-m N` | ngram size M for ngram-map-k4v speculative decoding, length of draft m-gram (default: 48) |
+| `--spec-ngram-map-k4v-min-hits N` | minimum hits for ngram-map-k4v speculative decoding (default: 1) |
+| `--draft, --draft-n, --draft-max N` | the argument has been removed. use --spec-draft-n-max or --spec-ngram-mod-n-max<br/>(env: LLAMA_ARG_DRAFT_MAX) |
+| `--draft-min, --draft-n-min N` | the argument has been removed. use --spec-draft-n-min or --spec-ngram-mod-n-min<br/>(env: LLAMA_ARG_DRAFT_MIN) |
 | `--gpt-oss-20b-default` | use gpt-oss-20b (note: can download weights from the internet) |
 | `--gpt-oss-120b-default` | use gpt-oss-120b (note: can download weights from the internet) |
 | `--vision-gemma-4b-default` | use Gemma 3 4B QAT (note: can download weights from the internet) |
 | `--vision-gemma-12b-default` | use Gemma 3 12B QAT (note: can download weights from the internet) |
+| `--spec-default` | enable default speculative decoding config |
 
 <!-- HELP_END -->
diff --git a/tools/cli/cli.cpp b/tools/cli/cli.cpp
index 6ee00012dc9..369c24216b7 100644
--- a/tools/cli/cli.cpp
+++ b/tools/cli/cli.cpp
@@ -59,8 +59,6 @@ struct cli_context {
     std::vector<raw_buffer> input_files;
     task_params defaults;
     bool verbose_prompt;
-    int reasoning_budget = -1;
-    std::string reasoning_budget_message;
 
     // thread for showing "loading" animation
     std::atomic<bool> loading_show;
@@ -77,8 +75,6 @@ struct cli_context {
         // defaults.return_progress = true; // TODO: show progress
 
         verbose_prompt = params.verbose_prompt;
-        reasoning_budget = params.reasoning_budget;
-        reasoning_budget_message = params.reasoning_budget_message;
     }
 
     std::string generate_completion(result_timings & out_timings) {
@@ -106,7 +102,7 @@ struct cli_context {
                 const llama_vocab * vocab = llama_model_get_vocab(
                     llama_get_model(ctx_server.get_llama_context()));
 
-                task.params.sampling.reasoning_budget_tokens = reasoning_budget;
+                task.params.sampling.reasoning_budget_tokens = defaults.sampling.reasoning_budget_tokens;
                 task.params.sampling.generation_prompt = chat_params.generation_prompt;
 
                 if (!chat_params.thinking_start_tag.empty()) {
@@ -116,7 +112,7 @@ struct cli_context {
                 task.params.sampling.reasoning_budget_end =
                     common_tokenize(vocab, chat_params.thinking_end_tag, false, true);
                 task.params.sampling.reasoning_budget_forced =
-                    common_tokenize(vocab, reasoning_budget_message + chat_params.thinking_end_tag, false, true);
+                    common_tokenize(vocab, defaults.sampling.reasoning_budget_message + chat_params.thinking_end_tag, false, true);
             }
 
             rd.post_task({std::move(task)});
@@ -207,6 +203,8 @@ struct cli_context {
         auto meta = ctx_server.get_meta();
         auto & chat_params = meta.chat_params;
 
+        auto caps = common_chat_templates_get_caps(chat_params.tmpls.get());
+
         common_chat_templates_inputs inputs;
         inputs.messages              = common_chat_msgs_parse_oaicompat(messages);
         inputs.tools                 = {}; // TODO
@@ -214,7 +212,7 @@ struct cli_context {
         inputs.json_schema           = ""; // TODO
         inputs.grammar               = ""; // TODO
         inputs.use_jinja             = chat_params.use_jinja;
-        inputs.parallel_tool_calls   = false;
+        inputs.parallel_tool_calls   = caps["supports_parallel_tool_calls"];
         inputs.add_generation_prompt = true;
         inputs.reasoning_format      = COMMON_REASONING_FORMAT_DEEPSEEK;
         inputs.force_pure_content    = chat_params.force_pure_content;
@@ -226,7 +224,7 @@ struct cli_context {
 };
 
 // TODO?: Make this reusable, enums, docs
-static const std::array<const std::string, 7> cmds = {
+static const std::array<std::string_view, 7> cmds = {
     "/audio ",
     "/clear",
     "/exit",
@@ -240,19 +238,19 @@ static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std:
     std::vector<std::pair<std::string, size_t>> matches;
     std::string cmd;
 
-    if (line.length() > 1 && line[0] == '/' && !std::any_of(cmds.begin(), cmds.end(), [line](const std::string & prefix) {
+    if (line.length() > 1 && line.front() == '/' && !std::any_of(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
         return string_starts_with(line, prefix);
     })) {
         auto it = cmds.begin();
 
-        while ((it = std::find_if(it, cmds.end(), [line](const std::string & cmd_line) {
+        while ((it = std::find_if(it, cmds.end(), [line](std::string_view cmd_line) {
             return string_starts_with(cmd_line, line);
         })) != cmds.end()) {
-            matches.emplace_back(*it, (*it).length());
+            matches.emplace_back(*it, it->length());
             ++it;
         }
     } else {
-        auto it = std::find_if(cmds.begin(), cmds.end(), [line](const std::string & prefix) {
+        auto it = std::find_if(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
             return prefix.back() == ' ' && string_starts_with(line, prefix);
         });
 
@@ -269,18 +267,18 @@ static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std:
         std::string expanded_prefix = path_prefix;
 
 #if !defined(_WIN32)
-        if (string_starts_with(path_prefix, "~")) {
+        if (string_starts_with(path_prefix, '~')) {
             const char * home = std::getenv("HOME");
             if (home && home[0]) {
-                expanded_prefix = std::string(home) + path_prefix.substr(1);
+                expanded_prefix = home + path_prefix.substr(1);
             }
         }
-        if (string_starts_with(expanded_prefix, "/")) {
+        if (string_starts_with(expanded_prefix, '/')) {
 #else
         if (std::isalpha(expanded_prefix[0]) && expanded_prefix.find(':') == 1) {
 #endif
             cur_dir = std::filesystem::path(expanded_prefix).parent_path();
-            cur_dir_str = "";
+            cur_dir_str.clear();
         } else if (!path_prefix.empty()) {
             cur_dir /= std::filesystem::path(path_prefix).parent_path();
         }
@@ -303,7 +301,7 @@ static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std:
             }
 
             if (expanded_prefix.empty() || string_starts_with(path_entry, expanded_prefix)) {
-                std::string updated_line = cmd + path_entry;
+                const std::string updated_line = cmd + path_entry;
                 matches.emplace_back(updated_line + path_postfix, updated_line.length());
             }
 
@@ -313,7 +311,7 @@ static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std:
         }
 
         if (matches.empty()) {
-            std::string updated_line = cmd + path_prefix;
+            const std::string updated_line = cmd + path_prefix;
             matches.emplace_back(updated_line + path_postfix, updated_line.length());
         }
 
@@ -330,7 +328,7 @@ static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std:
                 len = std::min(len, static_cast<size_t>(cmp.first - match0.begin()));
             }
 
-            std::string updated_line = std::string(match0.substr(0, len));
+            const std::string updated_line = std::string(match0.substr(0, len));
             matches.emplace_back(updated_line + path_postfix, updated_line.length());
         }
 
@@ -567,10 +565,10 @@ int main(int argc, char ** argv) {
                 if (endpath != std::string::npos) {
                     std::string rel_pattern = pattern.substr(0, endpath);
 #if !defined(_WIN32)
-                    if (string_starts_with(rel_pattern, "~")) {
+                    if (string_starts_with(rel_pattern, '~')) {
                         const char * home = std::getenv("HOME");
                         if (home && home[0]) {
-                            rel_pattern = std::string(home) + rel_pattern.substr(1);
+                            rel_pattern = home + rel_pattern.substr(1);
                         }
                     }
 #endif
diff --git a/tools/completion/README.md b/tools/completion/README.md
index fe1a036a38c..e5dd7f6f4e7 100644
--- a/tools/completion/README.md
+++ b/tools/completion/README.md
@@ -98,7 +98,6 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `--license` | show source code license and dependencies |
 | `-cl, --cache-list` | show list of models in cache |
 | `--completion-bash` | print source-able bash completion script for llama.cpp |
-| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
 | `-t, --threads N` | number of CPU threads to use during generation (default: -1)<br/>(env: LLAMA_ARG_THREADS) |
 | `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) |
 | `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") |
@@ -149,7 +148,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU<br/>(env: LLAMA_ARG_CPU_MOE) |
 | `-ncmoe, --n-cpu-moe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU<br/>(env: LLAMA_ARG_N_CPU_MOE) |
 | `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
-| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
+| `-sm, --split-mode {none,layer,row,tensor}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs (pipelined)<br/>- row: split weight across GPUs by rows (parallelized)<br/>- tensor: split weights and KV across GPUs (parallelized, EXPERIMENTAL)<br/>(env: LLAMA_ARG_SPLIT_MODE) |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)<br/>(env: LLAMA_ARG_MAIN_GPU) |
 | `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')<br/>(env: LLAMA_ARG_FIT) |
@@ -167,7 +166,6 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(env: LLAMA_ARG_MODEL_URL) |
 | `-dr, --docker-repo [<repo>/]<model>[:quant]` | Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.<br/>example: gemma3<br/>(default: unused)<br/>(env: LLAMA_ARG_DOCKER_REPO) |
 | `-hf, -hfr, --hf-repo <user>/<model>[:quant]` | Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.<br/>mmproj is also downloaded automatically if available. to disable, add --no-mmproj<br/>example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M<br/>(default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
-| `-hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_HFD_REPO) |
 | `-hff, --hf-file FILE` | Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
 | `-hfv, -hfrv, --hf-repo-v <user>/<model>[:quant]` | Hugging Face model repository for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_REPO_V) |
 | `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_FILE_V) |
@@ -178,10 +176,10 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) |
 | `--offline` | Offline mode: forces use of cache, prevents network access<br/>(env: LLAMA_OFFLINE) |
 | `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:<br/> - 0: generic output<br/> - 1: error<br/> - 2: warning<br/> - 3: info<br/> - 4: debug<br/>(default: 3)<br/><br/>(env: LLAMA_LOG_VERBOSITY) |
-| `--log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_LOG_PREFIX) |
-| `--log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_LOG_TIMESTAMPS) |
-| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) |
-| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) |
+| `--log-prefix, --no-log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_ARG_LOG_PREFIX) |
+| `--log-timestamps, --no-log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_ARG_LOG_TIMESTAMPS) |
+| `--spec-draft-type-k, -ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K) |
+| `--spec-draft-type-v, -ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V) |
 
 
 ### Sampling params
@@ -228,6 +226,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 
 | Argument | Explanation |
 | -------- | ----------- |
+| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
 | `--display-prompt, --no-display-prompt` | whether to print prompt at generation (default: true) |
 | `-co, --color [on\|off\|auto]` | Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')<br/>'auto' enables colors when output is to a terminal |
 | `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
@@ -255,8 +254,8 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-ocr, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-ocr, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)<br/>(env: LLAMA_ARG_SKIP_CHAT_PARSING) |
 | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles |
 
diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp
index 3c8e8fc6873..5c1e4275644 100644
--- a/tools/llama-bench/llama-bench.cpp
+++ b/tools/llama-bench/llama-bench.cpp
@@ -372,7 +372,7 @@ static const cmd_params cmd_params_defaults = {
     /* n_ubatch             */ { 512 },
     /* type_k               */ { GGML_TYPE_F16 },
     /* type_v               */ { GGML_TYPE_F16 },
-    /* n_threads            */ { cpu_get_num_math() },
+    /* n_threads            */ { common_cpu_get_num_math() },
     /* cpu_mask             */ { "0x0" },
     /* cpu_strict           */ { false },
     /* poll                 */ { 50 },
diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index 399876128ef..a76adc9b80b 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -21,6 +21,7 @@ add_library(mtmd
             models/gemma4a.cpp
             models/gemma4v.cpp
             models/glm4v.cpp
+            models/granite-speech.cpp
             models/hunyuanocr.cpp
             models/internvl.cpp
             models/kimivl.cpp
@@ -33,6 +34,7 @@ add_library(mtmd
             models/pixtral.cpp
             models/qwen2vl.cpp
             models/qwen3vl.cpp
+            models/mimovl.cpp
             models/qwen3a.cpp
             models/step3vl.cpp
             models/siglip.cpp
@@ -40,6 +42,7 @@ add_library(mtmd
             models/deepseekocr.cpp
             models/mobilenetv5.cpp
             models/youtuvl.cpp
+            models/yasa2.cpp
             )
 
 set_target_properties(mtmd PROPERTIES
diff --git a/tools/mtmd/README.md b/tools/mtmd/README.md
index ef31d1957cd..70194194718 100644
--- a/tools/mtmd/README.md
+++ b/tools/mtmd/README.md
@@ -49,6 +49,7 @@ For the following models, you can use `convert_hf_to_gguf.py` with `--mmproj` fl
 - Qwen 2 VL and Qwen 2.5 VL (from [Qwen](https://huggingface.co/Qwen))
 - [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)
 - InternVL 2.5 and InternVL 3 from [OpenGVLab](https://huggingface.co/OpenGVLab) (note: we don't support conversion of `InternVL3-*-hf` model, only non-HF version is supported ; `InternLM2Model` **text** model is not supported)
+- [MiniCPM-V 4.6](https://huggingface.co/openbmb/MiniCPM-V-4_6) ; See the guide [here](../../docs/multimodal/minicpmv4.6.md) - requires the standard `transformers` v5.7.0+ checkpoint
 
 For older models, please refer to the relevant guide for instructions on how to obtain or create them:
 
@@ -60,4 +61,7 @@ NOTE: conversion scripts are located under `tools/mtmd/legacy-models`
 - [MiniCPM-V 2.5](../../docs/multimodal/minicpmv2.5.md)
 - [MiniCPM-V 2.6](../../docs/multimodal/minicpmv2.6.md)
 - [MiniCPM-o 2.6](../../docs/multimodal/minicpmo2.6.md)
+- [MiniCPM-V 4.0](../../docs/multimodal/minicpmv4.0.md)
+- [MiniCPM-o 4.0](../../docs/multimodal/minicpmo4.0.md)
+- [MiniCPM-V 4.5](../../docs/multimodal/minicpmv4.5.md)
 - [IBM Granite Vision](../../docs/multimodal/granitevision.md)
diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h
index d3e7b1ed044..c5e880c71ec 100644
--- a/tools/mtmd/clip-graph.h
+++ b/tools/mtmd/clip-graph.h
@@ -11,6 +11,10 @@
 
 #define DEFAULT_INTERPOLATION_MODE (GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS)
 
+struct build_vit_opts {
+    ggml_tensor * attn_mask = nullptr;
+};
+
 struct clip_graph {
     const clip_model & model;
     const clip_hparams & hparams;
@@ -63,7 +67,8 @@ struct clip_graph {
                 norm_type norm_t,
                 ffn_op_type ffn_t,
                 ggml_tensor * learned_pos_embd,
-                std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos);
+                std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos,
+                const build_vit_opts & opts = {});
 
     // build the input after conv2d (inp_raw --> patches)
     // returns tensor with shape [n_embd, n_patches]
@@ -98,7 +103,8 @@ struct clip_graph {
             ggml_tensor * v_cur,
             ggml_tensor * kq_mask,
             float kq_scale,
-            int il) const;
+            int il,
+            ggml_tensor * sinks = nullptr) const;
 
     // implementation of the 2D RoPE without adding a new op in ggml
     // this is not efficient (use double the memory), but works on all backends
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index 17cb703f7fb..c359851999f 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -31,6 +31,7 @@
 #define KEY_N_BLOCK             "clip.%s.block_count"
 #define KEY_PROJ_DIM            "clip.%s.projection_dim"
 #define KEY_N_HEAD              "clip.%s.attention.head_count"
+#define KEY_N_HEAD_KV           "clip.%s.attention.head_count_kv"
 #define KEY_LAYER_NORM_EPS      "clip.%s.attention.layer_norm_epsilon"
 
 // vision-specific
@@ -53,6 +54,7 @@
 #define KEY_IMAGE_GRID_PINPOINTS   "clip.vision.image_grid_pinpoints"
 #define KEY_WIN_ATTN_PATTERN       "clip.vision.n_wa_pattern"
 #define KEY_WIN_ATTN_LAYER_INDEXES "clip.vision.wa_layer_indexes"
+#define KEY_WA_PATTERN_MODE        "clip.vision.wa_pattern_mode"
 #define KEY_ATTN_WINDOW_SIZE       "clip.vision.window_size"
 #define KEY_MINICPMV_VERSION       "clip.minicpmv_version"
 #define KEY_MINICPMV_QUERY_NUM     "clip.minicpmv_query_num"
@@ -60,9 +62,15 @@
 #define KEY_SAM_N_BLOCK            "clip.vision.sam.block_count"
 #define KEY_SAM_N_EMBD             "clip.vision.sam.embedding_length"
 // audio-specific
-#define KEY_AUDIO_PROJ_TYPE     "clip.audio.projector_type" // for models with mixed modalities
-#define KEY_A_NUM_MEL_BINS      "clip.audio.num_mel_bins"
-#define KEY_A_PROJ_STACK_FACTOR "clip.audio.projector.stack_factor"
+#define KEY_AUDIO_PROJ_TYPE        "clip.audio.projector_type" // for models with mixed modalities
+#define KEY_A_NUM_MEL_BINS         "clip.audio.num_mel_bins"
+#define KEY_A_PROJ_STACK_FACTOR    "clip.audio.projector.stack_factor"
+#define KEY_A_CHUNK_SIZE           "clip.audio.chunk_size"
+#define KEY_A_CONV_KERNEL_SIZE     "clip.audio.conv_kernel_size"
+#define KEY_A_MAX_POS_EMB          "clip.audio.max_pos_emb"
+#define KEY_A_PROJ_WINDOW_SIZE     "clip.audio.projector.window_size"
+#define KEY_A_PROJ_DOWNSAMPLE_RATE "clip.audio.projector.downsample_rate"
+#define KEY_A_PROJ_HEAD_COUNT      "clip.audio.projector.head_count"
 
 
 //
@@ -80,6 +88,7 @@
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
 #define TN_ATTN_V          "%s.blk.%d.attn_v.%s"
 #define TN_ATTN_OUTPUT     "%s.blk.%d.attn_out.%s"
+#define TN_ATTN_SINKS      "%s.blk.%d.attn_sinks"
 #define TN_ATTN_K_NORM     "%s.blk.%d.attn_k_norm.%s"
 #define TN_ATTN_Q_NORM     "%s.blk.%d.attn_q_norm.%s"
 #define TN_FFN_DOWN        "%s.blk.%d.ffn_down.%s"
@@ -126,6 +135,17 @@
 #define TN_MINICPMV_ATTN       "resampler.attn.%s.%s"
 #define TN_MINICPMV_LN         "resampler.ln_%s.%s"
 
+// MiniCPM-V 4.6 ViT merger (window attention + MLP downsample),
+// matching the upstream `vit_merger` module name in transformers.
+#define TN_VIT_MERGER_LN1      "v.vit_merger.ln1.%s"
+#define TN_VIT_MERGER_ATTN_Q   "v.vit_merger.attn_q.%s"
+#define TN_VIT_MERGER_ATTN_K   "v.vit_merger.attn_k.%s"
+#define TN_VIT_MERGER_ATTN_V   "v.vit_merger.attn_v.%s"
+#define TN_VIT_MERGER_ATTN_O   "v.vit_merger.attn_out.%s"
+#define TN_VIT_MERGER_DS_LN    "v.vit_merger.ds_ln.%s"
+#define TN_VIT_MERGER_DS_UP    "v.vit_merger.ds_ffn_up.%s"
+#define TN_VIT_MERGER_DS_DOWN  "v.vit_merger.ds_ffn_down.%s"
+
 #define TN_GLM_ADAPER_CONV      "adapter.conv.%s"
 #define TN_GLM_ADAPTER_LINEAR   "adapter.linear.linear.%s"
 #define TN_GLM_ADAPTER_NORM_1   "adapter.linear.norm1.%s"
@@ -150,7 +170,7 @@
 #define TN_TOK_BOI         "v.boi"
 #define TN_TOK_EOI         "v.eoi"
 
-// hunyuanocr
+// hunyuanocr / hunyuanvl (shared GGUF tensor names)
 #define TN_MM_PRE_NORM     "mm.pre_norm.%s"
 #define TN_TOK_IMG_BEGIN   "mm.image_begin"
 #define TN_TOK_IMG_END     "mm.image_end"
@@ -182,6 +202,27 @@
 #define TN_CONV_NORM       "%s.blk.%d.conv_norm.%s"
 #define TN_CONV_PW1        "%s.blk.%d.conv_pw1.%s"
 #define TN_CONV_PW2        "%s.blk.%d.conv_pw2.%s"
+#define TN_INP_PROJ        "a.input_projection.%s"
+#define TN_CTC_OUT         "a.enc_ctc_out.%s"
+#define TN_CTC_OUT_MID     "a.enc_ctc_out_mid.%s"
+#define TN_ATTN_REL_POS_EMB "%s.blk.%d.attn_rel_pos_emb"
+// qformer projector
+#define TN_QF_PROJ_QUERY   "a.proj_query"
+#define TN_QF_PROJ_NORM    "a.proj_norm.%s"
+#define TN_QF_PROJ_LINEAR  "a.proj_linear.%s"
+#define TN_QF_SELF_ATTN_Q  "a.proj_blk.%d.self_attn_q.%s"
+#define TN_QF_SELF_ATTN_K  "a.proj_blk.%d.self_attn_k.%s"
+#define TN_QF_SELF_ATTN_V  "a.proj_blk.%d.self_attn_v.%s"
+#define TN_QF_SELF_ATTN_O  "a.proj_blk.%d.self_attn_out.%s"
+#define TN_QF_SELF_ATTN_N  "a.proj_blk.%d.self_attn_norm.%s"
+#define TN_QF_CROSS_ATTN_Q "a.proj_blk.%d.cross_attn_q.%s"
+#define TN_QF_CROSS_ATTN_K "a.proj_blk.%d.cross_attn_k.%s"
+#define TN_QF_CROSS_ATTN_V "a.proj_blk.%d.cross_attn_v.%s"
+#define TN_QF_CROSS_ATTN_O "a.proj_blk.%d.cross_attn_out.%s"
+#define TN_QF_CROSS_ATTN_N "a.proj_blk.%d.cross_attn_norm.%s"
+#define TN_QF_FFN_UP       "a.proj_blk.%d.ffn_up.%s"
+#define TN_QF_FFN_DOWN     "a.proj_blk.%d.ffn_down.%s"
+#define TN_QF_FFN_NORM     "a.proj_blk.%d.ffn_norm.%s"
 
 // gemma4 audio conformer
 #define TN_A_MM_INP_PROJ     "mm.a.input_projection.%s"
@@ -242,6 +283,15 @@
 #define TN_STD_BIAS              "v.std_bias"
 #define TN_STD_SCALE             "v.std_scale"
 
+// yasa2
+#define TN_YASA_PATCH_LN_W       "v.patch_ln.weight"
+#define TN_YASA_PATCH_LN_B       "v.patch_ln.bias"
+#define TN_YASA_BACKBONE_LN_W    "v.backbone_ln.weight"
+#define TN_YASA_BACKBONE_LN_B    "v.backbone_ln.bias"
+#define TN_YASA_POS_EMBD         "v.vision_pos_embed"
+#define TN_YASA_STAGE_DOWN_LN    "v.stage.%d.down.ln.%s"
+#define TN_YASA_STAGE_DOWN_CONV  "v.stage.%d.down.conv.%s"
+#define TN_YASA_STAGE_BLK        "v.stage.%d.blk.%d.%s.%s"
 
 // align x to upper multiple of n
 #define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
@@ -290,9 +340,14 @@ enum projector_type {
     PROJECTOR_TYPE_LFM2A,
     PROJECTOR_TYPE_GLM4V,
     PROJECTOR_TYPE_YOUTUVL,
+    PROJECTOR_TYPE_YASA2,
     PROJECTOR_TYPE_KIMIK25,
     PROJECTOR_TYPE_NEMOTRON_V2_VL,
     PROJECTOR_TYPE_HUNYUANOCR,
+    PROJECTOR_TYPE_HUNYUANVL,
+    PROJECTOR_TYPE_MINICPMV4_6,
+    PROJECTOR_TYPE_GRANITE_SPEECH,
+    PROJECTOR_TYPE_MIMOVL,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -335,9 +390,14 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_LFM2A,     "lfm2a"},
     { PROJECTOR_TYPE_GLM4V,     "glm4v"},
     { PROJECTOR_TYPE_YOUTUVL,   "youtuvl"},
+    { PROJECTOR_TYPE_YASA2,     "yasa2"},
     { PROJECTOR_TYPE_KIMIK25,   "kimik25"},
     { PROJECTOR_TYPE_NEMOTRON_V2_VL, "nemotron_v2_vl"},
     { PROJECTOR_TYPE_HUNYUANOCR, "hunyuanocr"},
+    { PROJECTOR_TYPE_HUNYUANVL,  "hunyuanvl"},
+    { PROJECTOR_TYPE_MINICPMV4_6, "minicpmv4_6"},
+    { PROJECTOR_TYPE_GRANITE_SPEECH, "granite_speech"},
+    { PROJECTOR_TYPE_MIMOVL,     "mimovl"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {
@@ -413,10 +473,10 @@ static void clip_log_internal(enum ggml_log_level level, const char * format, ..
     va_end(args);
 }
 
+#define LOG_DBG(...) clip_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
 #define LOG_INF(...) clip_log_internal(GGML_LOG_LEVEL_INFO,  __VA_ARGS__)
 #define LOG_WRN(...) clip_log_internal(GGML_LOG_LEVEL_WARN,  __VA_ARGS__)
 #define LOG_ERR(...) clip_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
-#define LOG_DBG(...) clip_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
 #define LOG_CNT(...) clip_log_internal(GGML_LOG_LEVEL_CONT,  __VA_ARGS__)
 
 //
diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h
index 9a93584d9be..ce15dbcd11e 100644
--- a/tools/mtmd/clip-model.h
+++ b/tools/mtmd/clip-model.h
@@ -42,6 +42,7 @@ struct clip_hparams {
     int32_t n_ff = 0;
     int32_t projection_dim = 0;
     int32_t n_head = 0;
+    int32_t n_head_kv = 0;
     int32_t n_layer = 0;
     // idefics3
     int32_t n_merge = 0; // number of patch merges **per-side**
@@ -83,6 +84,7 @@ struct clip_hparams {
     int32_t attn_window_size = 0;
     int32_t n_wa_pattern = 0;
     std::unordered_set<int32_t> wa_layer_indexes; // explicit layer indexes that use full attention (for irregular patterns like YoutuVL)
+    std::vector<int32_t> wa_pattern_mode; // mimovl: per-layer window-attention mode
 
     // deepseek-ocr (sam)
     int32_t sam_n_layer = 0;
@@ -92,6 +94,12 @@ struct clip_hparams {
     // audio
     int32_t n_mel_bins = 0; // whisper preprocessor
     int32_t proj_stack_factor = 0; // ultravox
+    int32_t audio_chunk_size           = 0;
+    int32_t audio_conv_kernel_size     = 0;
+    int32_t audio_max_pos_emb          = 0;
+    int32_t audio_proj_window_size     = 0;
+    int32_t audio_proj_downsample_rate = 0;
+    int32_t audio_proj_head_count      = 0;
 
     // audio-to-mel preprocessor params
     int32_t audio_chunk_len   = -1; // in seconds
@@ -104,6 +112,7 @@ struct clip_hparams {
     bool has_llava_projector = false;
     int minicpmv_version = 0;
     int32_t minicpmv_query_num = 0;         // MiniCPM-V query number
+    int32_t insert_layer_id   = 0;          // MiniCPM-V 4.6 ViT merger insertion layer
 
     // custom value provided by user, can be undefined if not set
     int32_t custom_image_min_tokens = -1;
@@ -159,6 +168,8 @@ struct clip_layer {
     ggml_tensor * o_w = nullptr;
     ggml_tensor * o_b = nullptr;
 
+    ggml_tensor * attn_sinks = nullptr;
+
     ggml_tensor * k_norm = nullptr;
     ggml_tensor * q_norm = nullptr;
 
@@ -224,6 +235,21 @@ struct clip_layer {
     ggml_tensor * per_dim_k_scale_w = nullptr;
     ggml_tensor * ff_post_norm_1_w  = nullptr;
 
+    // granite_speech conformer per-layer
+    ggml_tensor * attn_rel_pos_emb = nullptr;
+
+    // granite_speech qformer cross-attention
+    ggml_tensor * cross_attn_q_w    = nullptr;
+    ggml_tensor * cross_attn_q_b    = nullptr;
+    ggml_tensor * cross_attn_k_w    = nullptr;
+    ggml_tensor * cross_attn_k_b    = nullptr;
+    ggml_tensor * cross_attn_v_w    = nullptr;
+    ggml_tensor * cross_attn_v_b    = nullptr;
+    ggml_tensor * cross_attn_o_w    = nullptr;
+    ggml_tensor * cross_attn_o_b    = nullptr;
+    ggml_tensor * cross_attn_norm_w = nullptr;
+    ggml_tensor * cross_attn_norm_b = nullptr;
+
     bool has_deepstack() const {
         return deepstack_fc1_w != nullptr;
     }
@@ -268,6 +294,27 @@ struct mobilenetv5_block {
     ggml_tensor * attn_norm_w   = nullptr;
 };
 
+struct yasa2_block {
+    ggml_tensor * dw_w  = nullptr;
+    ggml_tensor * dw_b  = nullptr;
+    ggml_tensor * ln_w  = nullptr;
+    ggml_tensor * ln_b  = nullptr;
+    ggml_tensor * pw1_w = nullptr;
+    ggml_tensor * pw1_b = nullptr;
+    ggml_tensor * grn_w = nullptr;
+    ggml_tensor * grn_b = nullptr;
+    ggml_tensor * pw2_w = nullptr;
+    ggml_tensor * pw2_b = nullptr;
+};
+
+struct yasa2_stage {
+    ggml_tensor * down_ln_w   = nullptr;
+    ggml_tensor * down_ln_b   = nullptr;
+    ggml_tensor * down_conv_w = nullptr;
+    ggml_tensor * down_conv_b = nullptr;
+    std::vector<yasa2_block> blocks;
+};
+
 struct clip_model {
     clip_modality modality = CLIP_MODALITY_VISION;
     projector_type proj_type = PROJECTOR_TYPE_MLP;
@@ -382,6 +429,24 @@ struct clip_model {
     ggml_tensor * mm_model_ln_post_w = nullptr;
     ggml_tensor * mm_model_ln_post_b = nullptr;
 
+    // MiniCPM-V 4.6 ViT merger (window self-attention + ViT MLP downsample)
+    ggml_tensor * vit_merger_ln1_w     = nullptr;
+    ggml_tensor * vit_merger_ln1_b     = nullptr;
+    ggml_tensor * vit_merger_attn_q_w  = nullptr;
+    ggml_tensor * vit_merger_attn_q_b  = nullptr;
+    ggml_tensor * vit_merger_attn_k_w  = nullptr;
+    ggml_tensor * vit_merger_attn_k_b  = nullptr;
+    ggml_tensor * vit_merger_attn_v_w  = nullptr;
+    ggml_tensor * vit_merger_attn_v_b  = nullptr;
+    ggml_tensor * vit_merger_attn_o_w  = nullptr;
+    ggml_tensor * vit_merger_attn_o_b  = nullptr;
+    ggml_tensor * vit_merger_ds_ln_w   = nullptr;
+    ggml_tensor * vit_merger_ds_ln_b   = nullptr;
+    ggml_tensor * vit_merger_ds_up_w   = nullptr;
+    ggml_tensor * vit_merger_ds_up_b   = nullptr;
+    ggml_tensor * vit_merger_ds_down_w = nullptr;
+    ggml_tensor * vit_merger_ds_down_b = nullptr;
+
     // gemma3
     ggml_tensor * mm_input_proj_w = nullptr;
     ggml_tensor * mm_soft_emb_norm_w = nullptr;
@@ -402,6 +467,15 @@ struct clip_model {
     ggml_tensor * msfa_ffn_expand_bn = nullptr;
     ggml_tensor * msfa_ffn_project_bn = nullptr;
 
+    // yasa2
+    ggml_tensor * yasa_patch_w = nullptr;
+    ggml_tensor * yasa_patch_b = nullptr;
+    ggml_tensor * yasa_patch_ln_w = nullptr;
+    ggml_tensor * yasa_patch_ln_b = nullptr;
+    ggml_tensor * yasa_backbone_ln_w = nullptr;
+    ggml_tensor * yasa_backbone_ln_b = nullptr;
+    ggml_tensor * yasa_vision_pos_embed = nullptr;
+    std::vector<yasa2_stage> yasa_stages;
 
     // pixtral, glm4v
     ggml_tensor * token_embd_img_break = nullptr;
@@ -485,6 +559,21 @@ struct clip_model {
     ggml_tensor * audio_out_proj_w = nullptr;
     ggml_tensor * audio_out_proj_b = nullptr;
 
+    // granite_speech encoder
+    ggml_tensor * inp_proj_w    = nullptr;
+    ggml_tensor * inp_proj_b    = nullptr;
+    ggml_tensor * ctc_out_w     = nullptr;
+    ggml_tensor * ctc_out_b     = nullptr;
+    ggml_tensor * ctc_out_mid_w = nullptr;
+    ggml_tensor * ctc_out_mid_b = nullptr;
+    // qformer projector
+    ggml_tensor * qf_proj_query    = nullptr;
+    ggml_tensor * qf_proj_norm_w   = nullptr;
+    ggml_tensor * qf_proj_norm_b   = nullptr;
+    ggml_tensor * qf_proj_linear_w = nullptr;
+    ggml_tensor * qf_proj_linear_b = nullptr;
+    std::vector<clip_layer> qf_proj_layers;
+
     bool audio_has_avgpool() const {
         return proj_type == PROJECTOR_TYPE_QWEN2A
             || proj_type == PROJECTOR_TYPE_VOXTRAL
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index f0e8786b660..9727a738ed8 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -169,7 +169,7 @@ struct clip_ctx {
             throw std::runtime_error("failed to initialize CPU backend");
         }
         if (ctx_params.use_gpu) {
-            auto backend_name = std::getenv("MTMD_BACKEND_DEVICE");
+            auto * backend_name = std::getenv("MTMD_BACKEND_DEVICE");
             if (backend_name != nullptr) {
                 backend = ggml_backend_init_by_name(backend_name, nullptr);
                 if (!backend) {
@@ -300,7 +300,8 @@ ggml_tensor * clip_graph::build_vit(
             norm_type norm_t,
             ffn_op_type ffn_t,
             ggml_tensor * learned_pos_embd,
-            std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos
+            std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos,
+            const build_vit_opts & opts
         ) {
     if (learned_pos_embd) {
         inp = ggml_add(ctx0, inp, learned_pos_embd);
@@ -427,7 +428,7 @@ ggml_tensor * clip_graph::build_vit(
             }
 
             cur = build_attn(layer.o_w, layer.o_b,
-                Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+                Qcur, Kcur, Vcur, opts.attn_mask, kq_scale, il);
             cb(cur, "attn_out", il);
         }
 
@@ -642,7 +643,8 @@ ggml_tensor * clip_graph::build_attn(
         ggml_tensor * v_cur,
         ggml_tensor * kq_mask,
         float kq_scale,
-        int il) const {
+        int il,
+        ggml_tensor * sinks) const {
     // these nodes are added to the graph together so that they are not reordered
     // by doing so, the number of splits in the graph is reduced
     ggml_build_forward_expand(gf, q_cur);
@@ -662,9 +664,15 @@ ggml_tensor * clip_graph::build_attn(
 
         k = ggml_cast(ctx0, k, GGML_TYPE_F16);
         v = ggml_cast(ctx0, v, GGML_TYPE_F16);
+        if (kq_mask) {
+            kq_mask = ggml_cast(ctx0, kq_mask, GGML_TYPE_F16);
+        }
 
         cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f);
         ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
+        if (sinks != nullptr) {
+            ggml_flash_attn_ext_add_sinks(cur, sinks);
+        }
 
         cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
 
@@ -677,6 +685,9 @@ ggml_tensor * clip_graph::build_attn(
         // ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
 
         kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f);
+        if (sinks != nullptr) {
+            ggml_soft_max_add_sinks(kq, sinks);
+        }
 
         ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
         cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
@@ -866,6 +877,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 builder = std::make_unique<clip_graph_qwen3vl>(ctx, img);
             } break;
+        case PROJECTOR_TYPE_MIMOVL:
+            {
+                builder = std::make_unique<clip_graph_mimovl>(ctx, img);
+            } break;
         case PROJECTOR_TYPE_STEP3VL:
             {
                 builder = std::make_unique<clip_graph_step3vl>(ctx, img);
@@ -874,6 +889,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 builder = std::make_unique<clip_graph_minicpmv>(ctx, img);
             } break;
+        case PROJECTOR_TYPE_MINICPMV4_6:
+            {
+                builder = std::make_unique<clip_graph_minicpmv4_6>(ctx, img);
+            } break;
         case PROJECTOR_TYPE_INTERNVL:
             {
                 builder = std::make_unique<clip_graph_internvl>(ctx, img);
@@ -912,6 +931,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
                 builder = std::make_unique<clip_graph_cogvlm>(ctx, img);
             } break;
         case PROJECTOR_TYPE_HUNYUANOCR:
+        case PROJECTOR_TYPE_HUNYUANVL:
             {
                 builder = std::make_unique<clip_graph_hunyuanocr>(ctx, img);
             } break;
@@ -935,6 +955,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 builder = std::make_unique<clip_graph_gemma4a>(ctx, img);
             } break;
+        case PROJECTOR_TYPE_GRANITE_SPEECH:
+            {
+                builder = std::make_unique<clip_graph_granite_speech>(ctx, img);
+            } break;
         case PROJECTOR_TYPE_GLM4V:
             {
                 builder = std::make_unique<clip_graph_glm4v>(ctx, img);
@@ -947,6 +971,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 builder = std::make_unique<clip_graph_youtuvl>(ctx, img);
             } break;
+        case PROJECTOR_TYPE_YASA2:
+            {
+                builder = std::make_unique<clip_graph_yasa2>(ctx, img);
+            } break;
         default:
             GGML_ABORT("missing cgraph builder");
     }
@@ -970,7 +998,7 @@ struct clip_model_loader {
     bool has_audio  = false;
 
     // TODO @ngxson : we should not pass clip_ctx here, it should be clip_model
-    clip_model_loader(const char * fname) : fname(fname) {
+    clip_model_loader(const char * fname, bool skip_tensors = false) : fname(fname) {
         struct ggml_context * meta = nullptr;
 
         struct gguf_init_params params = {
@@ -1016,7 +1044,7 @@ struct clip_model_loader {
         }
 
         // tensors
-        {
+        if (!skip_tensors) {
             for (int i = 0; i < n_tensors; ++i) {
                 const char * name = gguf_get_tensor_name(ctx_gguf.get(), i);
                 const size_t offset = gguf_get_tensor_offset(ctx_gguf.get(), i);
@@ -1222,6 +1250,20 @@ struct clip_model_loader {
                             hparams.minicpmv_version = 2; // default to 2 if not set
                         }
                     } break;
+                case PROJECTOR_TYPE_MINICPMV4_6:
+                    {
+                        // MiniCPM-V 4.6 unified merger projector
+                        // ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension
+                        hparams.n_merge = 4;
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
+
+                        // borrow wa_layer_indexes for vit_merger insertion point
+                        std::vector<int> wa_layer_indexes_vec;
+                        get_arr_int(KEY_WIN_ATTN_LAYER_INDEXES, wa_layer_indexes_vec, false);
+                        if (!wa_layer_indexes_vec.empty()) {
+                            hparams.insert_layer_id = wa_layer_indexes_vec[0];
+                        }
+                    } break;
                 case PROJECTOR_TYPE_INTERNVL:
                     {
                         // use default llava-uhd preprocessing params
@@ -1362,6 +1404,22 @@ struct clip_model_loader {
                             LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__);
                         }
                     } break;
+                case PROJECTOR_TYPE_MIMOVL:
+                    {
+                        hparams.n_merge = 2; // spatial_merge_size
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
+                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
+                        get_u32(string_format(KEY_N_HEAD_KV, "vision"), hparams.n_head_kv);
+                        // 1D banded sliding-window radius (visual_token_window_size); required
+                        get_u32(KEY_ATTN_WINDOW_SIZE, hparams.attn_window_size);
+                        std::vector<int> pat;
+                        get_arr_int(KEY_WA_PATTERN_MODE, pat, true);
+                        GGML_ASSERT((int) pat.size() == hparams.n_layer && "mimovl wa_pattern_mode length must equal n_layer");
+                        hparams.wa_pattern_mode.assign(pat.begin(), pat.end());
+                        get_u32(KEY_IMAGE_MIN_PIXELS, hparams.image_min_pixels);
+                        get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels);
+                        hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup
+                    } break;
                 case PROJECTOR_TYPE_STEP3VL:
                     {
                         hparams.n_merge = 4; // two stride-2 downsamplers after patching
@@ -1389,6 +1447,16 @@ struct clip_model_loader {
                         hparams.set_limit_image_tokens(1, 62500);
                         hparams.set_warmup_n_tokens(16*16); // avoid OOM on warmup
                     } break;
+                case PROJECTOR_TYPE_YASA2:
+                    {
+                        hparams.ffn_op = FFN_GELU_ERF;
+                        log_ffn_op = "gelu_erf";
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC;
+
+                        // reka model performs better when using resize_bicubic, which stretches
+                        // the image to fit fixed square size
+                        hparams.image_resize_pad = false;
+                    } break;
                 case PROJECTOR_TYPE_GLM4V:
                     {
                         hparams.rope_theta = 10000.0f;
@@ -1459,6 +1527,16 @@ struct clip_model_loader {
                         get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels);
                         hparams.set_warmup_n_tokens(28*28);
                     } break;
+                case PROJECTOR_TYPE_HUNYUANVL:
+                    {
+                        hparams.n_merge = 2;
+                        hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
+                        hparams.image_resize_pad = false;
+                        hparams.ffn_op = FFN_GELU;
+                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
+                        hparams.set_limit_image_tokens(256, 16384);
+                        hparams.set_warmup_n_tokens(32*32);
+                    } break;
                 case PROJECTOR_TYPE_LFM2A:
                     {
                         // audio preprocessing params
@@ -1478,6 +1556,20 @@ struct clip_model_loader {
                         hparams.audio_window_len       = 320;  // 20ms frame (NOT 25ms/400)
                         hparams.audio_hop_len          = 160;
                     } break;
+                case PROJECTOR_TYPE_GRANITE_SPEECH:
+                    {
+                        hparams.audio_chunk_len        = 0;
+                        hparams.audio_sample_rate      = 16000;
+                        hparams.audio_n_fft            = 512;
+                        hparams.audio_window_len       = 400;
+                        hparams.audio_hop_len          = 160;
+                        get_u32(KEY_A_CHUNK_SIZE,           hparams.audio_chunk_size);
+                        get_u32(KEY_A_CONV_KERNEL_SIZE,     hparams.audio_conv_kernel_size);
+                        get_u32(KEY_A_MAX_POS_EMB,          hparams.audio_max_pos_emb);
+                        get_u32(KEY_A_PROJ_WINDOW_SIZE,     hparams.audio_proj_window_size);
+                        get_u32(KEY_A_PROJ_DOWNSAMPLE_RATE, hparams.audio_proj_downsample_rate);
+                        get_u32(KEY_A_PROJ_HEAD_COUNT,      hparams.audio_proj_head_count);
+                    } break;
                 case PROJECTOR_TYPE_JANUS_PRO:
                     {
                         hparams.image_pad_color   = {127, 127, 127};
@@ -1629,13 +1721,13 @@ struct clip_model_loader {
 
         model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, prefix), false);
 
-        if (model.proj_type == PROJECTOR_TYPE_GEMMA3NV) {
-            hparams.n_layer = 0; // gemma3n does not use normal layer structure
-        }
+        const bool has_standard_layers = (
+            model.proj_type != PROJECTOR_TYPE_GEMMA3NV);
 
         // layers
-        model.layers.resize(hparams.n_layer);
-        for (int il = 0; il < hparams.n_layer; ++il) {
+        const int n_layers_to_load = has_standard_layers ? hparams.n_layer : 0;
+        model.layers.resize(n_layers_to_load);
+        for (int il = 0; il < n_layers_to_load; ++il) {
             auto & layer = model.layers[il];
             layer.k_w    = get_tensor(string_format(TN_ATTN_K,      prefix, il, "weight"), false);
             layer.q_w    = get_tensor(string_format(TN_ATTN_Q,      prefix, il, "weight"), false);
@@ -1668,6 +1760,8 @@ struct clip_model_loader {
             layer.ff_down_w = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "weight"));
             layer.ff_down_b = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "bias"),   false);
 
+            // mimovl per-head attention sink bias
+            layer.attn_sinks = get_tensor(string_format(TN_ATTN_SINKS, prefix, il), false);
 
             // qwen3vl deepstack layer
             layer.deepstack_norm_w = get_tensor(string_format(TN_DEEPSTACK_NORM, il, "weight"), false);
@@ -1694,6 +1788,7 @@ struct clip_model_loader {
                     || model.proj_type == PROJECTOR_TYPE_GEMMA3
                     || model.proj_type == PROJECTOR_TYPE_IDEFICS3
                     || model.proj_type == PROJECTOR_TYPE_MINICPMV
+                    || model.proj_type == PROJECTOR_TYPE_MINICPMV4_6
                 ) && layer.ff_up_w && layer.ff_down_w && layer.ff_down_w->ne[0] == hparams.n_embd;
             if (is_ffn_swapped) {
                 // swap up and down weights
@@ -1795,6 +1890,34 @@ struct clip_model_loader {
                     model.mm_model_ln_post_w = get_tensor(string_format(TN_MINICPMV_LN, "post", "weight"));
                     model.mm_model_ln_post_b = get_tensor(string_format(TN_MINICPMV_LN, "post", "bias"));
                 } break;
+            case PROJECTOR_TYPE_MINICPMV4_6:
+                {
+                    // ViT merger: window self-attention
+                    model.vit_merger_ln1_w     = get_tensor(string_format(TN_VIT_MERGER_LN1, "weight"));
+                    model.vit_merger_ln1_b     = get_tensor(string_format(TN_VIT_MERGER_LN1, "bias"));
+                    model.vit_merger_attn_q_w  = get_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "weight"));
+                    model.vit_merger_attn_q_b  = get_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "bias"), false);
+                    model.vit_merger_attn_k_w  = get_tensor(string_format(TN_VIT_MERGER_ATTN_K, "weight"));
+                    model.vit_merger_attn_k_b  = get_tensor(string_format(TN_VIT_MERGER_ATTN_K, "bias"), false);
+                    model.vit_merger_attn_v_w  = get_tensor(string_format(TN_VIT_MERGER_ATTN_V, "weight"));
+                    model.vit_merger_attn_v_b  = get_tensor(string_format(TN_VIT_MERGER_ATTN_V, "bias"), false);
+                    model.vit_merger_attn_o_w  = get_tensor(string_format(TN_VIT_MERGER_ATTN_O, "weight"));
+                    model.vit_merger_attn_o_b  = get_tensor(string_format(TN_VIT_MERGER_ATTN_O, "bias"), false);
+                    // ViT merger: MLP downsample
+                    model.vit_merger_ds_ln_w   = get_tensor(string_format(TN_VIT_MERGER_DS_LN, "weight"));
+                    model.vit_merger_ds_ln_b   = get_tensor(string_format(TN_VIT_MERGER_DS_LN, "bias"));
+                    model.vit_merger_ds_up_w   = get_tensor(string_format(TN_VIT_MERGER_DS_UP, "weight"));
+                    model.vit_merger_ds_up_b   = get_tensor(string_format(TN_VIT_MERGER_DS_UP, "bias"), false);
+                    model.vit_merger_ds_down_w = get_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "weight"));
+                    model.vit_merger_ds_down_b = get_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "bias"), false);
+                    // Final Merger (DownsampleMLP)
+                    model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM);
+                    model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B, false);
+                    model.mm_ffn_up_w     = get_tensor(string_format(TN_MM_UP,   "weight"));
+                    model.mm_ffn_up_b     = get_tensor(string_format(TN_MM_UP,   "bias"), false);
+                    model.mm_ffn_down_w   = get_tensor(string_format(TN_MM_DOWN, "weight"));
+                    model.mm_ffn_down_b   = get_tensor(string_format(TN_MM_DOWN, "bias"), false);
+                } break;
             case PROJECTOR_TYPE_GLM_EDGE:
                 {
                     model.mm_model_adapter_conv_w = get_tensor(string_format(TN_GLM_ADAPER_CONV, "weight"));
@@ -1823,6 +1946,13 @@ struct clip_model_loader {
                     model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
                     model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
                 } break;
+            case PROJECTOR_TYPE_MIMOVL:
+                {
+                    model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
+                    model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"), false);
+                    model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
+                    model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false);
+                } break;
             case PROJECTOR_TYPE_STEP3VL:
                 {
                     model.mm_0_w     = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
@@ -1839,6 +1969,55 @@ struct clip_model_loader {
                     model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));  // merger.mlp.2
                     model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
                 } break;
+            case PROJECTOR_TYPE_YASA2:
+                {
+                    // reuse tensors already loaded by the common section
+                    // (TN_PATCH_EMBD and TN_PATCH_BIAS have the same tensor names)
+                    GGML_ASSERT(model.patch_embeddings_0 && "yasa2 requires v.patch_embd.weight");
+                    model.yasa_patch_w = model.patch_embeddings_0;
+                    model.yasa_patch_b = model.patch_bias;
+                    model.yasa_patch_ln_w = get_tensor(TN_YASA_PATCH_LN_W, false);
+                    model.yasa_patch_ln_b = get_tensor(TN_YASA_PATCH_LN_B, false);
+                    model.yasa_backbone_ln_w = get_tensor(TN_YASA_BACKBONE_LN_W, false);
+                    model.yasa_backbone_ln_b = get_tensor(TN_YASA_BACKBONE_LN_B, false);
+                    model.yasa_vision_pos_embed = get_tensor(TN_YASA_POS_EMBD, false);
+                    model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
+                    model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"), false);
+                    model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
+                    model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false);
+
+                    model.yasa_stages.clear();
+                    for (int s = 0; ; ++s) {
+                        yasa2_stage stage;
+                        stage.down_ln_w   = get_tensor(string_format(TN_YASA_STAGE_DOWN_LN, s, "weight"), false);
+                        stage.down_ln_b   = get_tensor(string_format(TN_YASA_STAGE_DOWN_LN, s, "bias"), false);
+                        stage.down_conv_w = get_tensor(string_format(TN_YASA_STAGE_DOWN_CONV, s, "weight"), false);
+                        stage.down_conv_b = get_tensor(string_format(TN_YASA_STAGE_DOWN_CONV, s, "bias"), false);
+
+                        for (int bi = 0; ; ++bi) {
+                            yasa2_block blk;
+                            blk.dw_w = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "dw", "weight"), false);
+                            if (!blk.dw_w) {
+                                break;
+                            }
+                            blk.dw_b  = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "dw", "bias"), false);
+                            blk.ln_w  = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "ln", "weight"), false);
+                            blk.ln_b  = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "ln", "bias"), false);
+                            blk.pw1_w = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "pw1", "weight"), false);
+                            blk.pw1_b = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "pw1", "bias"), false);
+                            blk.grn_w = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "grn", "weight"), false);
+                            blk.grn_b = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "grn", "bias"), false);
+                            blk.pw2_w = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "pw2", "weight"), false);
+                            blk.pw2_b = get_tensor(string_format(TN_YASA_STAGE_BLK, s, bi, "pw2", "bias"), false);
+                            stage.blocks.push_back(blk);
+                        }
+
+                        if (!stage.down_conv_w && stage.blocks.empty()) {
+                            break;
+                        }
+                        model.yasa_stages.push_back(std::move(stage));
+                    }
+                } break;
             case PROJECTOR_TYPE_GLM4V:
                 {
                     model.mm_fc_w        = get_tensor(string_format(TN_MM_PROJECTOR, "weight"));
@@ -2159,6 +2338,7 @@ struct clip_model_loader {
                     model.mm_eoi            = get_tensor(TN_TOK_EOI);
                 } break;
             case PROJECTOR_TYPE_HUNYUANOCR:
+            case PROJECTOR_TYPE_HUNYUANVL:
                 {
                     // proj.0 -> mm.0 (conv1), proj.2 -> mm.2 (conv2), mlp -> mm.model.fc (linear)
                     model.mm_0_w            = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
@@ -2340,6 +2520,83 @@ struct clip_model_loader {
                         layer.conv_pw2_b   = get_tensor(string_format(TN_CONV_PW2,  prefix, il, "bias"));
                     }
                 } break;
+            case PROJECTOR_TYPE_GRANITE_SPEECH:
+                {
+                    model.inp_proj_w     = get_tensor(string_format(TN_INP_PROJ,    "weight"));
+                    model.inp_proj_b     = get_tensor(string_format(TN_INP_PROJ,    "bias"));
+                    model.ctc_out_w      = get_tensor(string_format(TN_CTC_OUT,     "weight"));
+                    model.ctc_out_b      = get_tensor(string_format(TN_CTC_OUT,     "bias"));
+                    model.ctc_out_mid_w  = get_tensor(string_format(TN_CTC_OUT_MID, "weight"));
+                    model.ctc_out_mid_b  = get_tensor(string_format(TN_CTC_OUT_MID, "bias"));
+
+                    // per-layer tensors not loaded by the generic loop above
+                    for (int il = 0; il < hparams.n_layer; ++il) {
+                        auto & layer = model.layers[il];
+
+                        layer.attn_rel_pos_emb = get_tensor(string_format(TN_ATTN_REL_POS_EMB, prefix, il));
+
+                        layer.ff_norm_w   = get_tensor(string_format(TN_FFN_NORM,   prefix, il, "weight"));
+                        layer.ff_norm_b   = get_tensor(string_format(TN_FFN_NORM,   prefix, il, "bias"));
+
+                        layer.ff_norm_1_w = get_tensor(string_format(TN_FFN_NORM_1, prefix, il, "weight"));
+                        layer.ff_norm_1_b = get_tensor(string_format(TN_FFN_NORM_1, prefix, il, "bias"));
+                        layer.ff_up_1_w   = get_tensor(string_format(TN_FFN_UP_1,   prefix, il, "weight"));
+                        layer.ff_up_1_b   = get_tensor(string_format(TN_FFN_UP_1,   prefix, il, "bias"));
+                        layer.ff_down_1_w = get_tensor(string_format(TN_FFN_DOWN_1, prefix, il, "weight"));
+                        layer.ff_down_1_b = get_tensor(string_format(TN_FFN_DOWN_1, prefix, il, "bias"));
+
+                        layer.norm_conv_w = get_tensor(string_format(TN_NORM_CONV, prefix, il, "weight"));
+                        layer.norm_conv_b = get_tensor(string_format(TN_NORM_CONV, prefix, il, "bias"));
+                        layer.conv_norm_w = get_tensor(string_format(TN_CONV_NORM, prefix, il, "weight"));
+                        layer.conv_norm_b = get_tensor(string_format(TN_CONV_NORM, prefix, il, "bias"));
+                        layer.conv_dw_w   = get_tensor(string_format(TN_CONV_DW,   prefix, il, "weight"));
+                        layer.conv_pw1_w  = get_tensor(string_format(TN_CONV_PW1,  prefix, il, "weight"));
+                        layer.conv_pw1_b  = get_tensor(string_format(TN_CONV_PW1,  prefix, il, "bias"));
+                        layer.conv_pw2_w  = get_tensor(string_format(TN_CONV_PW2,  prefix, il, "weight"));
+                        layer.conv_pw2_b  = get_tensor(string_format(TN_CONV_PW2,  prefix, il, "bias"));
+                    }
+
+                    model.qf_proj_query    = get_tensor(TN_QF_PROJ_QUERY);
+                    model.qf_proj_norm_w   = get_tensor(string_format(TN_QF_PROJ_NORM, "weight"));
+                    model.qf_proj_norm_b   = get_tensor(string_format(TN_QF_PROJ_NORM, "bias"));
+                    model.qf_proj_linear_w = get_tensor(string_format(TN_QF_PROJ_LINEAR, "weight"));
+                    model.qf_proj_linear_b = get_tensor(string_format(TN_QF_PROJ_LINEAR, "bias"));
+
+                    const int n_proj_layers = 2;
+                    model.qf_proj_layers.resize(n_proj_layers);
+                    for (int il = 0; il < n_proj_layers; ++il) {
+                        auto & pl = model.qf_proj_layers[il];
+
+                        pl.q_w    = get_tensor(string_format(TN_QF_SELF_ATTN_Q, il, "weight"));
+                        pl.q_b    = get_tensor(string_format(TN_QF_SELF_ATTN_Q, il, "bias"));
+                        pl.k_w    = get_tensor(string_format(TN_QF_SELF_ATTN_K, il, "weight"));
+                        pl.k_b    = get_tensor(string_format(TN_QF_SELF_ATTN_K, il, "bias"));
+                        pl.v_w    = get_tensor(string_format(TN_QF_SELF_ATTN_V, il, "weight"));
+                        pl.v_b    = get_tensor(string_format(TN_QF_SELF_ATTN_V, il, "bias"));
+                        pl.o_w    = get_tensor(string_format(TN_QF_SELF_ATTN_O, il, "weight"));
+                        pl.o_b    = get_tensor(string_format(TN_QF_SELF_ATTN_O, il, "bias"));
+                        pl.ln_1_w = get_tensor(string_format(TN_QF_SELF_ATTN_N, il, "weight"));
+                        pl.ln_1_b = get_tensor(string_format(TN_QF_SELF_ATTN_N, il, "bias"));
+
+                        pl.cross_attn_q_w    = get_tensor(string_format(TN_QF_CROSS_ATTN_Q, il, "weight"));
+                        pl.cross_attn_q_b    = get_tensor(string_format(TN_QF_CROSS_ATTN_Q, il, "bias"));
+                        pl.cross_attn_k_w    = get_tensor(string_format(TN_QF_CROSS_ATTN_K, il, "weight"));
+                        pl.cross_attn_k_b    = get_tensor(string_format(TN_QF_CROSS_ATTN_K, il, "bias"));
+                        pl.cross_attn_v_w    = get_tensor(string_format(TN_QF_CROSS_ATTN_V, il, "weight"));
+                        pl.cross_attn_v_b    = get_tensor(string_format(TN_QF_CROSS_ATTN_V, il, "bias"));
+                        pl.cross_attn_o_w    = get_tensor(string_format(TN_QF_CROSS_ATTN_O, il, "weight"));
+                        pl.cross_attn_o_b    = get_tensor(string_format(TN_QF_CROSS_ATTN_O, il, "bias"));
+                        pl.cross_attn_norm_w = get_tensor(string_format(TN_QF_CROSS_ATTN_N, il, "weight"));
+                        pl.cross_attn_norm_b = get_tensor(string_format(TN_QF_CROSS_ATTN_N, il, "bias"));
+
+                        pl.ff_up_w   = get_tensor(string_format(TN_QF_FFN_UP,   il, "weight"));
+                        pl.ff_up_b   = get_tensor(string_format(TN_QF_FFN_UP,   il, "bias"));
+                        pl.ff_down_w = get_tensor(string_format(TN_QF_FFN_DOWN, il, "weight"));
+                        pl.ff_down_b = get_tensor(string_format(TN_QF_FFN_DOWN, il, "bias"));
+                        pl.ln_2_w    = get_tensor(string_format(TN_QF_FFN_NORM, il, "weight"));
+                        pl.ln_2_b    = get_tensor(string_format(TN_QF_FFN_NORM, il, "bias"));
+                    }
+                } break;
             default:
                 GGML_ASSERT(false && "unknown projector type");
         }
@@ -2674,6 +2931,14 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
     return {ctx_vision, ctx_audio};
 }
 
+struct clip_cap clip_get_cap(const char * fname) {
+    clip_cap res;
+    clip_model_loader loader(fname, /* skip_tensors= */ true);
+    res.has_vision = loader.has_vision;
+    res.has_audio  = loader.has_audio;
+    return res;
+}
+
 struct clip_image_size * clip_image_size_init() {
     struct clip_image_size * load_image_size = new struct clip_image_size();
     load_image_size->width = 448;
@@ -2794,9 +3059,11 @@ int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 *
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:
         case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_MIMOVL:
         case PROJECTOR_TYPE_GLM4V:
         case PROJECTOR_TYPE_PADDLEOCR:
         case PROJECTOR_TYPE_HUNYUANOCR:
+        case PROJECTOR_TYPE_HUNYUANVL:
         case PROJECTOR_TYPE_YOUTUVL:
             return (img->nx / params.patch_size) / 2;
         case PROJECTOR_TYPE_STEP3VL:
@@ -2814,8 +3081,10 @@ int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 *
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:
         case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_MIMOVL:
         case PROJECTOR_TYPE_GLM4V:
         case PROJECTOR_TYPE_PADDLEOCR:
+        case PROJECTOR_TYPE_HUNYUANVL:
         case PROJECTOR_TYPE_YOUTUVL:
             return (img->ny / params.patch_size) / 2;
         case PROJECTOR_TYPE_STEP3VL:
@@ -2843,6 +3112,10 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
             {
                 // do nothing
             } break;
+        case PROJECTOR_TYPE_YASA2:
+            {
+                n_patches = 64; // adaptive average pooling to 8x8 tokens
+            } break;
         case PROJECTOR_TYPE_LDP:
         case PROJECTOR_TYPE_LDPV2:
         case PROJECTOR_TYPE_GLM_EDGE:
@@ -2879,9 +3152,15 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
                     }
                 }
             } break;
+        case PROJECTOR_TYPE_MINICPMV4_6:
+            {
+                // ViT merger 4x + final merger 4x = 16x total spatial downsample
+                n_patches = n_patches / 16;
+            } break;
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:
         case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_MIMOVL:
         case PROJECTOR_TYPE_GLM4V:
         case PROJECTOR_TYPE_YOUTUVL:
             {
@@ -2969,12 +3248,10 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
             } break;
         case PROJECTOR_TYPE_QWEN3A:
             {
-                // 3x stride-2 conv2d: each step is floor((n-1)/2)+1
-                int n = img->nx;
-                n = (n - 1) / 2 + 1;
-                n = (n - 1) / 2 + 1;
-                n = (n - 1) / 2 + 1;
-                n_patches = n;
+                // chunk_size=100 frames --> 3x stride-2 conv2d --> 13 tokens per chunk
+                const int chunk_size       = 100;
+                const int tokens_per_chunk = 13;
+                n_patches = (img->nx / chunk_size) * tokens_per_chunk;
             } break;
         case PROJECTOR_TYPE_GLMA:
             {
@@ -3003,6 +3280,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
             n_patches = h * (h + 1) + 1;
         } break;
         case PROJECTOR_TYPE_HUNYUANOCR:
+        case PROJECTOR_TYPE_HUNYUANVL:
             {
                 int merge = ctx->model.hparams.n_merge;
                 int ow = (img->nx / patch_size) / merge;
@@ -3023,6 +3301,12 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
                 }
                 n_patches = n;
             } break;
+        case PROJECTOR_TYPE_GRANITE_SPEECH:
+            {
+                const int ws = ctx->model.hparams.audio_proj_window_size;
+                const int ds = ctx->model.hparams.audio_proj_downsample_rate;
+                n_patches = ((img->nx + ws - 1) / ws) * (ws / ds);
+            } break;
         default:
             GGML_ABORT("unsupported projector type");
     }
@@ -3194,6 +3478,92 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
                 }
                 set_input_f32("omega", omega);
             } break;
+        case PROJECTOR_TYPE_MINICPMV4_6:
+            {
+                // SigLIP position buckets (same as resampler path)
+                std::vector<int32_t> positions(pos_h * pos_w);
+                int bucket_coords_h[1024];
+                int bucket_coords_w[1024];
+                for (int i = 0; i < pos_h; i++){
+                    bucket_coords_h[i] = std::floor(70.0*i/pos_h);
+                }
+                for (int i = 0; i < pos_w; i++){
+                    bucket_coords_w[i] = std::floor(70.0*i/pos_w);
+                }
+                for (int i = 0, id = 0; i < pos_h; i++){
+                    for (int j = 0; j < pos_w; j++){
+                        positions[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j];
+                    }
+                }
+                set_input_i32("positions", positions);
+
+                const int half_h = pos_h / 2;
+                const int half_w = pos_w / 2;
+
+                // window reorder indices for 2x2 windows
+                std::vector<int32_t> window_idx(n_pos);
+                std::vector<int32_t> inv_window_idx(n_pos);
+                {
+                    int k = 0;
+                    for (int wi = 0; wi < half_h; wi++) {
+                        for (int wj = 0; wj < half_w; wj++) {
+                            window_idx[k++] = (2*wi    ) * pos_w + (2*wj    );
+                            window_idx[k++] = (2*wi    ) * pos_w + (2*wj + 1);
+                            window_idx[k++] = (2*wi + 1) * pos_w + (2*wj    );
+                            window_idx[k++] = (2*wi + 1) * pos_w + (2*wj + 1);
+                        }
+                    }
+                    for (int i = 0; i < n_pos; i++) {
+                        inv_window_idx[window_idx[i]] = i;
+                    }
+                }
+                set_input_i32("vit_merger_window_idx",     window_idx);
+                set_input_i32("vit_merger_inv_window_idx", inv_window_idx);
+
+                // block-diagonal attention mask: tokens in the same 4-token
+                // window attend to each other (mask = 0), all other positions
+                // are masked out (-inf). matches the window-major reorder above.
+                std::vector<float> window_mask_data(n_pos * n_pos, std::numeric_limits<float>::lowest());
+                for (int wi = 0; wi < n_pos / 4; wi++) {
+                    for (int i = 0; i < 4; i++) {
+                        for (int j = 0; j < 4; j++) {
+                            window_mask_data[(wi*4 + i) * n_pos + (wi*4 + j)] = 0.0f;
+                        }
+                    }
+                }
+                set_input_f32("vit_merger_window_mask", window_mask_data);
+
+                // ViT merger 2x2 downsample indices
+                auto make_ds_idx = [](int off_r, int off_c, int ds_h, int ds_w, int stride_w) {
+                    std::vector<int32_t> idx(ds_h * ds_w);
+                    for (int i = 0; i < ds_h; i++) {
+                        for (int j = 0; j < ds_w; j++) {
+                            idx[i * ds_w + j] = (2*i + off_r) * stride_w + (2*j + off_c);
+                        }
+                    }
+                    return idx;
+                };
+                auto vit_merger_ds_0 = make_ds_idx(0, 0, half_h, half_w, pos_w);
+                auto vit_merger_ds_1 = make_ds_idx(0, 1, half_h, half_w, pos_w);
+                auto vit_merger_ds_2 = make_ds_idx(1, 0, half_h, half_w, pos_w);
+                auto vit_merger_ds_3 = make_ds_idx(1, 1, half_h, half_w, pos_w);
+                set_input_i32("vit_merger_ds_idx_0", vit_merger_ds_0);
+                set_input_i32("vit_merger_ds_idx_1", vit_merger_ds_1);
+                set_input_i32("vit_merger_ds_idx_2", vit_merger_ds_2);
+                set_input_i32("vit_merger_ds_idx_3", vit_merger_ds_3);
+
+                // final merger 2x2 downsample indices (operates on half_h x half_w grid)
+                const int qh = half_h / 2;
+                const int qw = half_w / 2;
+                auto m_ds_0 = make_ds_idx(0, 0, qh, qw, half_w);
+                auto m_ds_1 = make_ds_idx(0, 1, qh, qw, half_w);
+                auto m_ds_2 = make_ds_idx(1, 0, qh, qw, half_w);
+                auto m_ds_3 = make_ds_idx(1, 1, qh, qw, half_w);
+                set_input_i32("merger_ds_idx_0", m_ds_0);
+                set_input_i32("merger_ds_idx_1", m_ds_1);
+                set_input_i32("merger_ds_idx_2", m_ds_2);
+                set_input_i32("merger_ds_idx_3", m_ds_3);
+            } break;
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN3VL:
         case PROJECTOR_TYPE_GLM4V:
@@ -3360,6 +3730,89 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
 
                 set_input_i32("positions", positions);
             } break;
+        case PROJECTOR_TYPE_MIMOVL:
+            {
+                const int merge      = hparams.n_merge;        // 2
+                const int merge_unit = merge * merge;          // 4
+                const int patch      = hparams.patch_size;     // 16
+                const int H          = image_size_height / patch;
+                const int W          = image_size_width  / patch;
+                const int n_pos_full = H * W;
+                const int llm_h      = H / merge;
+                const int llm_w      = W / merge;
+                const int n_units    = llm_h * llm_w;          // n_pos / merge_unit
+
+                // Row-major merge-tile-ordered (h, w) positions
+                std::vector<int32_t> pos_h_row(n_pos_full);
+                std::vector<int32_t> pos_w_row(n_pos_full);
+                {
+                    int idx = 0;
+                    for (int ty = 0; ty < llm_h; ty++) {
+                        for (int tx = 0; tx < llm_w; tx++) {
+                            for (int dy = 0; dy < merge; dy++) {
+                                for (int dx = 0; dx < merge; dx++) {
+                                    pos_h_row[idx] = ty * merge + dy;
+                                    pos_w_row[idx] = tx * merge + dx;
+                                    idx++;
+                                }
+                            }
+                        }
+                    }
+                }
+
+                // Col-major merge-unit permutation
+                std::vector<float> idx_col(n_units);
+                for (int r = 0; r < llm_h; r++) {
+                    for (int c = 0; c < llm_w; c++) {
+                        int u_row = r * llm_w + c;
+                        int u_col = c * llm_h + r;
+                        idx_col[u_col] = (float) u_row;
+                    }
+                }
+
+                // Col-mode positions: permute pos_*_row by idx_col
+                std::vector<int32_t> pos_h_col(n_pos_full);
+                std::vector<int32_t> pos_w_col(n_pos_full);
+                for (int u = 0; u < n_units; u++) {
+                    int src = (int) idx_col[u];
+                    for (int k = 0; k < merge_unit; k++) {
+                        pos_h_col[u * merge_unit + k] = pos_h_row[src * merge_unit + k];
+                        pos_w_col[u * merge_unit + k] = pos_w_row[src * merge_unit + k];
+                    }
+                }
+
+                // Pack into ggml_rope_multi VISION-mode layout. The non-CPU kernels
+                // only read slots 0 and 1, so pack h in slot 0, w in slot 1:
+                //   positions[0..n_pos)         = h
+                //   positions[n_pos..2*n_pos)   = w
+                //   positions[2*n_pos..3*n_pos) = 0
+                //   positions[3*n_pos..4*n_pos) = 0
+                std::vector<int32_t> positions_row(static_cast<size_t>(n_pos_full) * 4, 0);
+                std::vector<int32_t> positions_col(static_cast<size_t>(n_pos_full) * 4, 0);
+                for (int i = 0; i < n_pos_full; i++) {
+                    positions_row[0 * n_pos_full + i] = pos_h_row[i];
+                    positions_row[1 * n_pos_full + i] = pos_w_row[i];
+                    positions_col[0 * n_pos_full + i] = pos_h_col[i];
+                    positions_col[1 * n_pos_full + i] = pos_w_col[i];
+                }
+
+                // Banded 1D sliding-window mask
+                const int window = hparams.attn_window_size;
+                GGML_ASSERT(window > 0);
+                std::vector<float> mask(static_cast<size_t>(n_pos_full) * n_pos_full, std::numeric_limits<float>::lowest());
+                for (int q = 0; q < n_pos_full; q++) {
+                    int lo = std::max(0, q - window);
+                    int hi = std::min(n_pos_full - 1, q + window);
+                    for (int k = lo; k <= hi; k++) {
+                        mask[static_cast<size_t>(q) * n_pos_full + k] = 0.0f;
+                    }
+                }
+
+                set_input_i32("mimovl_positions_row", positions_row);
+                set_input_i32("mimovl_positions_col", positions_col);
+                set_input_f32("mimovl_idx_col",       idx_col);
+                set_input_f32("mimovl_window_mask",   mask);
+            } break;
         case PROJECTOR_TYPE_PIXTRAL:
         case PROJECTOR_TYPE_KIMIVL:
         case PROJECTOR_TYPE_KIMIK25:
@@ -3463,9 +3916,74 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         case PROJECTOR_TYPE_PHI4:
         case PROJECTOR_TYPE_COGVLM:
         case PROJECTOR_TYPE_HUNYUANOCR:
+        case PROJECTOR_TYPE_YASA2:
             {
                 // do nothing
             } break;
+        case PROJECTOR_TYPE_HUNYUANVL:
+            {
+                // Compute the HunyuanVL 2D position embedding on CPU (with the
+                // custom sf=(target+0.1)/n_grid bilinear sampling that the
+                // reference implementation uses) and upload it to the graph
+                // input declared in clip_graph_hunyuanocr::build().
+                GGML_ASSERT(model.position_embeddings != nullptr);
+                ggml_tensor * src_t   = model.position_embeddings;
+                const int64_t n_embd  = src_t->ne[0];
+                const int64_t n_pos   = src_t->ne[1];            // = n_grid * n_grid
+                const int     n_grid  = (int)std::lround(std::sqrt((double)n_pos));
+                GGML_ASSERT((int64_t)n_grid * n_grid == n_pos);
+                const int     out_w   = pos_w;                    // pw
+                const int     out_h   = pos_h;                    // ph
+
+                // Pull weight to host.
+                std::vector<float> src(n_embd * n_pos);
+                ggml_backend_tensor_get(src_t, src.data(), 0, ggml_nbytes(src_t));
+
+                // Output layout matches ggml_new_tensor_2d(F32, n_embd, out_h*out_w):
+                //   ne[0] = n_embd (fastest), ne[1] = out_h*out_w
+                //   dst[(y*out_w + x) * n_embd + c]
+                std::vector<float> dst((size_t)n_embd * out_h * out_w);
+
+                const float sx = (float)(out_w + 0.1f) / (float)n_grid;
+                const float sy = (float)(out_h + 0.1f) / (float)n_grid;
+
+                for (int y = 0; y < out_h; ++y) {
+                    // Match ggml_compute_forward_upscale_f32 pixel-center
+                    // convention (align_corners=False): src_y = (y+0.5)/sy - 0.5.
+                    const float fy = ((float)y + 0.5f) / sy - 0.5f;
+                    int y0 = (int)std::floor(fy);
+                    int y1 = y0 + 1;
+                    y0 = std::clamp(y0, 0, n_grid - 1);
+                    y1 = std::clamp(y1, 0, n_grid - 1);
+                    float wy1 = std::clamp(fy - (float)y0, 0.0f, 1.0f);
+                    const float wy0 = 1.0f - wy1;
+                    for (int x = 0; x < out_w; ++x) {
+                        const float fx = ((float)x + 0.5f) / sx - 0.5f;
+                        int x0 = (int)std::floor(fx);
+                        int x1 = x0 + 1;
+                        x0 = std::clamp(x0, 0, n_grid - 1);
+                        x1 = std::clamp(x1, 0, n_grid - 1);
+                        float wx1 = std::clamp(fx - (float)x0, 0.0f, 1.0f);
+                        const float wx0 = 1.0f - wx1;
+
+                        const float w00 = wy0 * wx0;
+                        const float w01 = wy0 * wx1;
+                        const float w10 = wy1 * wx0;
+                        const float w11 = wy1 * wx1;
+
+                        const float * s00 = &src[((size_t)y0 * n_grid + x0) * n_embd];
+                        const float * s01 = &src[((size_t)y0 * n_grid + x1) * n_embd];
+                        const float * s10 = &src[((size_t)y1 * n_grid + x0) * n_embd];
+                        const float * s11 = &src[((size_t)y1 * n_grid + x1) * n_embd];
+                        float * d         = &dst[((size_t)y * out_w + x) * n_embd];
+                        for (int c = 0; c < n_embd; ++c) {
+                            d[c] = w00 * s00[c] + w01 * s01[c] + w10 * s10[c] + w11 * s11[c];
+                        }
+                    }
+                }
+
+                set_input_f32("hunyuanvl_pos_embd", dst);
+            } break;
         case PROJECTOR_TYPE_LLAMA4:
             {
                 // set the 2D positions
@@ -3554,6 +4072,39 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
                 }
                 set_input_f32("pos_emb", pos_emb);
             } break;
+        case PROJECTOR_TYPE_GRANITE_SPEECH:
+            {
+                const int context_size = ctx->model.hparams.audio_chunk_size;
+                const int max_pos_emb  = ctx->model.hparams.audio_max_pos_emb;
+
+                std::vector<int32_t> dists(context_size * context_size);
+                for (int i = 0; i < context_size; i++) {
+                    for (int j = 0; j < context_size; j++) {
+                        int d = i - j;
+                        if (d < -context_size) d = -context_size;
+                        if (d >  context_size) d =  context_size;
+                        dists[i * context_size + j] = d + max_pos_emb;
+                    }
+                }
+                set_input_i32("attn_dists", dists);
+
+                const int n_frames   = image_size_width;
+                const int remainder  = n_frames % context_size;
+                if (remainder > 0) {
+                    const int num_blocks = (n_frames + context_size - 1) / context_size;
+                    std::vector<float> mask(context_size * context_size * num_blocks, 0.0f);
+                    const float neg_inf = -INFINITY;
+                    const int last_block_offset = (num_blocks - 1) * context_size * context_size;
+                    for (int q = 0; q < context_size; q++) {
+                        for (int k = 0; k < context_size; k++) {
+                            if (q >= remainder || k >= remainder) {
+                                mask[last_block_offset + q * context_size + k] = neg_inf;
+                            }
+                        }
+                    }
+                    set_input_f32("attn_mask", mask);
+                }
+            } break;
         default:
             GGML_ABORT("Unknown projector type");
     }
@@ -3650,6 +4201,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return ctx->model.mm_3_b->ne[0];
         case PROJECTOR_TYPE_MINICPMV:
             return ctx->model.mm_model_proj->ne[0];
+        case PROJECTOR_TYPE_MINICPMV4_6:
+            return ctx->model.mm_ffn_down_w->ne[1];
         case PROJECTOR_TYPE_GLM_EDGE:
             return ctx->model.mm_model_mlp_3_w->ne[1];
         case PROJECTOR_TYPE_QWEN2VL:
@@ -3660,6 +4213,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
         case PROJECTOR_TYPE_QWEN3VL:
             // main path + deepstack paths
             return ctx->model.mm_1_b->ne[0] * (1 + ctx->model.n_deepstack_layers);
+        case PROJECTOR_TYPE_MIMOVL:
+            return ctx->model.mm_1_w->ne[1];
         case PROJECTOR_TYPE_STEP3VL:
             return ctx->model.mm_model_proj->ne[1];
         case PROJECTOR_TYPE_GEMMA3:
@@ -3689,8 +4244,10 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
         case PROJECTOR_TYPE_KIMIVL:
         case PROJECTOR_TYPE_PADDLEOCR:
         case PROJECTOR_TYPE_KIMIK25:
+        case PROJECTOR_TYPE_YASA2:
             return ctx->model.mm_2_w->ne[1];
         case PROJECTOR_TYPE_HUNYUANOCR:
+        case PROJECTOR_TYPE_HUNYUANVL:
             return ctx->model.mm_model_proj->ne[1];
         case PROJECTOR_TYPE_COGVLM:
             return ctx->model.mm_4h_to_h_w->ne[1];
@@ -3700,6 +4257,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return ctx->model.position_embeddings->ne[0];
         case PROJECTOR_TYPE_GEMMA4A:
             return ctx->model.hparams.projection_dim;
+        case PROJECTOR_TYPE_GRANITE_SPEECH:
+            return ctx->model.qf_proj_linear_w->ne[1];
         case PROJECTOR_TYPE_GLM4V:
             return ctx->model.mm_ffn_down_w->ne[1];
         default:
@@ -3712,6 +4271,9 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
     if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV) {
         return ctx->model.hparams.minicpmv_version;
     }
+    if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV4_6) {
+        return 46;
+    }
     return 0;
 }
 
@@ -3732,21 +4294,6 @@ bool clip_has_audio_encoder(const struct clip_ctx * ctx) {
     return ctx->model.modality == CLIP_MODALITY_AUDIO;
 }
 
-bool clip_has_whisper_encoder(const struct clip_ctx * ctx) {
-    switch (ctx->proj_type()) {
-        case PROJECTOR_TYPE_ULTRAVOX:
-        case PROJECTOR_TYPE_QWEN2A:
-        case PROJECTOR_TYPE_QWEN3A:
-        case PROJECTOR_TYPE_GLMA:
-        case PROJECTOR_TYPE_VOXTRAL:
-        case PROJECTOR_TYPE_MERALION:
-        case PROJECTOR_TYPE_MUSIC_FLAMINGO:
-            return true;
-        default:
-            return false;
-    }
-}
-
 bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) {
     clip_image_f32 clip_img;
     clip_img.buf.resize(h * w * 3);
diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h
index a859b38658d..f643ed6e979 100644
--- a/tools/mtmd/clip.h
+++ b/tools/mtmd/clip.h
@@ -115,4 +115,9 @@ void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel
 
 bool clip_has_vision_encoder(const struct clip_ctx * ctx);
 bool clip_has_audio_encoder(const struct clip_ctx * ctx);
-bool clip_has_whisper_encoder(const struct clip_ctx * ctx);
+
+struct clip_cap {
+    bool has_vision;
+    bool has_audio;
+};
+struct clip_cap clip_get_cap(const char * fname);
diff --git a/tools/mtmd/debug/mtmd-debug.cpp b/tools/mtmd/debug/mtmd-debug.cpp
index 6e32b283aa4..f19ca4cfe29 100644
--- a/tools/mtmd/debug/mtmd-debug.cpp
+++ b/tools/mtmd/debug/mtmd-debug.cpp
@@ -68,11 +68,13 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    ggml_backend_load_all();
+
     LOG_INF("%s: loading model: %s\n", __func__, params.model.path.c_str());
 
     mtmd::context_ptr ctx_mtmd;
     common_init_result_ptr llama_init;
-    base_callback_data cb_data;
+    common_debug_cb_user_data cb_data;
 
     llama_init = common_init_from_params(params);
     {
@@ -89,7 +91,7 @@ int main(int argc, char ** argv) {
         {
             // always enable debug callback
             mparams.cb_eval_user_data = &cb_data;
-            mparams.cb_eval = common_debug_cb_eval<false>;
+            mparams.cb_eval = common_debug_cb_eval;
         }
         ctx_mtmd.reset(mtmd_init_from_file(clip_path, model, mparams));
         if (!ctx_mtmd.get()) {
diff --git a/tools/mtmd/models/granite-speech.cpp b/tools/mtmd/models/granite-speech.cpp
new file mode 100644
index 00000000000..c7e3794a49e
--- /dev/null
+++ b/tools/mtmd/models/granite-speech.cpp
@@ -0,0 +1,275 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_granite_speech::build() {
+    const int n_frames     = img.nx;
+    const int context_size = hparams.audio_chunk_size;
+    const int ctc_layer    = n_layer / 2;
+    const int conv_kernel  = hparams.audio_conv_kernel_size;
+    const int conv_pad     = conv_kernel / 2;
+
+    const int num_blocks   = (n_frames + context_size - 1) / context_size;
+    const int padded_len   = num_blocks * context_size;
+    const int remainder    = n_frames % context_size;
+
+    ggml_tensor * attn_dists = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, context_size * context_size);
+    ggml_set_name(attn_dists, "attn_dists");
+    ggml_set_input(attn_dists);
+
+    ggml_tensor * attn_mask = nullptr;
+    if (remainder > 0) {
+        attn_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32,
+            context_size, context_size, 1, num_blocks);
+        ggml_set_name(attn_mask, "attn_mask");
+        ggml_set_input(attn_mask);
+    }
+
+    ggml_tensor * inp = build_inp_raw(1);
+    auto * cur = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
+    cb(cur, "inp_transposed", -1);
+
+    cur = build_mm(model.inp_proj_w, cur);
+    cur = ggml_add(ctx0, cur, model.inp_proj_b);
+    cb(cur, "inp_linear", -1);
+
+    for (int il = 0; il < n_layer; il++) {
+        const auto & layer = model.layers[il];
+        auto * residual = cur;
+
+        // ffn1 (half-step)
+        {
+            auto * ffn1 = build_norm(cur, layer.ff_norm_w, layer.ff_norm_b,
+                                     NORM_TYPE_NORMAL, eps, il);
+            cb(ffn1, "ffn1_norm", il);
+
+            ffn1 = build_ffn(ffn1,
+                layer.ff_up_w, layer.ff_up_b,
+                nullptr, nullptr,
+                layer.ff_down_w, layer.ff_down_b,
+                FFN_SILU, il);
+            cb(ffn1, "ffn1_out", il);
+
+            residual = ggml_add(ctx0, residual, ggml_scale(ctx0, ffn1, 0.5f));
+            cb(residual, "ffn1_residual", il);
+        }
+
+        // build_attn not used here: Shaw RPE needs pos_attn = mul_mat(pos_emb, Q)
+        // injected between KQ product and softmax, which build_attn doesn't support
+        {
+            auto * normed = build_norm(residual, layer.ln_1_w, layer.ln_1_b,
+                                       NORM_TYPE_NORMAL, eps, il);
+            cb(normed, "attn_norm", il);
+
+            if (n_frames < padded_len) {
+                normed = ggml_pad(ctx0, normed, 0, padded_len - n_frames, 0, 0);
+            }
+
+            ggml_tensor * Q = build_mm(layer.q_w, normed);
+            ggml_tensor * K = build_mm(layer.k_w, normed);
+            ggml_tensor * V = build_mm(layer.v_w, normed);
+
+            Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, context_size, num_blocks);
+            K = ggml_reshape_4d(ctx0, K, d_head, n_head, context_size, num_blocks);
+            V = ggml_reshape_4d(ctx0, V, d_head, n_head, context_size, num_blocks);
+
+            ggml_tensor * Q_perm = ggml_permute(ctx0, Q, 0, 2, 1, 3);
+            ggml_tensor * K_perm = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
+
+            ggml_tensor * kq = ggml_mul_mat(ctx0, K_perm, Q_perm);
+
+            // Shaw RPE: pos_emb ne[2]=1 broadcasts against Q ne[2]=num_blocks in mul_mat
+            ggml_tensor * pos_emb = ggml_get_rows(ctx0, layer.attn_rel_pos_emb, attn_dists);
+            pos_emb = ggml_reshape_3d(ctx0, pos_emb, d_head, context_size, context_size);
+            pos_emb = ggml_reshape_4d(ctx0, pos_emb, d_head, context_size, 1, context_size);
+
+            ggml_tensor * Q_shaw = ggml_permute(ctx0, Q, 0, 1, 3, 2);
+            ggml_tensor * pos_attn = ggml_mul_mat(ctx0, pos_emb, Q_shaw);
+            pos_attn = ggml_cont(ctx0, ggml_permute(ctx0, pos_attn, 0, 2, 3, 1));
+
+            ggml_tensor * scores = ggml_add(ctx0, kq, pos_attn);
+            ggml_tensor * attn_weights = ggml_soft_max_ext(ctx0, scores, attn_mask,
+                                                            kq_scale, 0.0f);
+
+            ggml_tensor * V_perm = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
+            ggml_tensor * attn_out = ggml_mul_mat(ctx0, V_perm, attn_weights);
+
+            attn_out = ggml_permute(ctx0, attn_out, 0, 2, 1, 3);
+            attn_out = ggml_cont_2d(ctx0, attn_out, n_embd, padded_len);
+
+            if (n_frames < padded_len) {
+                attn_out = ggml_view_2d(ctx0, attn_out,
+                    n_embd, n_frames, attn_out->nb[1], 0);
+            }
+
+            cur = build_mm(layer.o_w, attn_out);
+            cur = ggml_add(ctx0, cur, layer.o_b);
+            cb(cur, "attn_out", il);
+        }
+
+        residual = ggml_add(ctx0, residual, cur);
+
+        // conv module
+        {
+            cur = build_norm(residual, layer.norm_conv_w, layer.norm_conv_b,
+                             NORM_TYPE_NORMAL, eps, il);
+            cb(cur, "conv_norm", il);
+
+            auto * x = build_mm(layer.conv_pw1_w, cur);
+            x = ggml_add(ctx0, x, layer.conv_pw1_b);
+            cb(x, "conv_pw1", il);
+
+            // GLU: ggml has no fused op, manual split + sigmoid gate
+            {
+                int64_t d = x->ne[0] / 2;
+                ggml_tensor * gate = ggml_sigmoid(ctx0,
+                    ggml_view_2d(ctx0, x, d, x->ne[1], x->nb[1], d * x->nb[0]));
+                x = ggml_mul(ctx0,
+                    ggml_view_2d(ctx0, x, d, x->ne[1], x->nb[1], 0), gate);
+                x = ggml_cont(ctx0, ggml_transpose(ctx0, x));
+            }
+            cb(x, "conv_glu", il);
+
+            x = ggml_pad(ctx0, x, conv_pad, 0, 0, 0);
+            x = ggml_roll(ctx0, x, conv_pad, 0, 0, 0);
+            x = ggml_pad(ctx0, x, conv_pad, 0, 0, 0);
+            x = ggml_ssm_conv(ctx0, x, layer.conv_dw_w);
+            cb(x, "conv_dw", il);
+
+            // folded batch norm
+            x = ggml_add(ctx0, ggml_mul(ctx0, x, layer.conv_norm_w), layer.conv_norm_b);
+            x = ggml_silu(ctx0, x);
+            cb(x, "conv_bn_silu", il);
+
+            x = build_mm(layer.conv_pw2_w, x);
+            x = ggml_add(ctx0, x, layer.conv_pw2_b);
+            cb(x, "conv_pw2", il);
+
+            cur = x;
+        }
+
+        residual = ggml_add(ctx0, residual, cur);
+
+        // ffn2 (half-step)
+        {
+            auto * ffn2 = build_norm(residual, layer.ff_norm_1_w, layer.ff_norm_1_b,
+                                     NORM_TYPE_NORMAL, eps, il);
+            cb(ffn2, "ffn2_norm", il);
+
+            ffn2 = build_ffn(ffn2,
+                layer.ff_up_1_w, layer.ff_up_1_b,
+                nullptr, nullptr,
+                layer.ff_down_1_w, layer.ff_down_1_b,
+                FFN_SILU, il);
+            cb(ffn2, "ffn2_out", il);
+
+            residual = ggml_add(ctx0, residual, ggml_scale(ctx0, ffn2, 0.5f));
+        }
+
+        cur = build_norm(residual, layer.ln_2_w, layer.ln_2_b,
+                         NORM_TYPE_NORMAL, eps, il);
+        cb(cur, "layer_out", il);
+
+        // CTC branch
+        if (il + 1 == ctc_layer) {
+            auto * mid = build_mm(model.ctc_out_w, cur);
+            mid = ggml_add(ctx0, mid, model.ctc_out_b);
+            mid = ggml_soft_max(ctx0, mid);
+            mid = build_mm(model.ctc_out_mid_w, mid);
+            mid = ggml_add(ctx0, mid, model.ctc_out_mid_b);
+            cur = ggml_add(ctx0, cur, mid);
+            cb(cur, "ctc_branch", il);
+        }
+    }
+
+    cb(cur, "encoder_out", -1);
+
+    // QFormer projector
+    {
+        const int window_size     = hparams.audio_proj_window_size;
+        const int num_queries     = window_size / hparams.audio_proj_downsample_rate;
+        const int proj_n_head     = hparams.audio_proj_head_count;
+        const int proj_d_head     = n_embd / proj_n_head;
+        const float proj_kq_scale = 1.0f / sqrtf((float)proj_d_head);
+        const float proj_eps      = 1e-12f;
+        const int nblocks_proj    = (n_frames + window_size - 1) / window_size;
+        const int padded_proj     = nblocks_proj * window_size;
+
+        if (n_frames < padded_proj) {
+            cur = ggml_pad(ctx0, cur, 0, padded_proj - n_frames, 0, 0);
+        }
+
+        ggml_tensor * enc_windows = ggml_reshape_3d(ctx0, cur, n_embd, window_size, nblocks_proj);
+
+        ggml_tensor * queries = build_norm(model.qf_proj_query,
+            model.qf_proj_norm_w, model.qf_proj_norm_b,
+            NORM_TYPE_NORMAL, proj_eps, -1);
+        {
+            ggml_tensor * q_3d    = ggml_reshape_3d(ctx0, queries, n_embd, num_queries, 1);
+            ggml_tensor * q_shape = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32,
+                n_embd, num_queries, nblocks_proj);
+            queries = ggml_repeat(ctx0, q_3d, q_shape);
+        }
+
+        for (int il = 0; il < (int)model.qf_proj_layers.size(); il++) {
+            const auto & pl = model.qf_proj_layers[il];
+
+            // self-attention
+            {
+                ggml_tensor * Q = ggml_add(ctx0, build_mm(pl.q_w, queries), pl.q_b);
+                ggml_tensor * K = ggml_add(ctx0, build_mm(pl.k_w, queries), pl.k_b);
+                ggml_tensor * V = ggml_add(ctx0, build_mm(pl.v_w, queries), pl.v_b);
+
+                Q = ggml_reshape_4d(ctx0, Q, proj_d_head, proj_n_head, num_queries, nblocks_proj);
+                K = ggml_reshape_4d(ctx0, K, proj_d_head, proj_n_head, num_queries, nblocks_proj);
+                V = ggml_reshape_4d(ctx0, V, proj_d_head, proj_n_head, num_queries, nblocks_proj);
+
+                ggml_tensor * sa_out = build_attn(pl.o_w, pl.o_b,
+                    Q, K, V, nullptr, proj_kq_scale, il);
+                sa_out = ggml_reshape_3d(ctx0, sa_out, n_embd, num_queries, nblocks_proj);
+
+                queries = build_norm(ggml_add(ctx0, sa_out, queries),
+                    pl.ln_1_w, pl.ln_1_b,
+                    NORM_TYPE_NORMAL, proj_eps, il);
+            }
+
+            // cross-attention
+            {
+                ggml_tensor * Q = ggml_add(ctx0, build_mm(pl.cross_attn_q_w, queries), pl.cross_attn_q_b);
+                ggml_tensor * K = ggml_add(ctx0, build_mm(pl.cross_attn_k_w, enc_windows), pl.cross_attn_k_b);
+                ggml_tensor * V = ggml_add(ctx0, build_mm(pl.cross_attn_v_w, enc_windows), pl.cross_attn_v_b);
+
+                Q = ggml_reshape_4d(ctx0, Q, proj_d_head, proj_n_head, num_queries, nblocks_proj);
+                K = ggml_reshape_4d(ctx0, K, proj_d_head, proj_n_head, window_size, nblocks_proj);
+                V = ggml_reshape_4d(ctx0, V, proj_d_head, proj_n_head, window_size, nblocks_proj);
+
+                ggml_tensor * ca_out = build_attn(pl.cross_attn_o_w, pl.cross_attn_o_b,
+                    Q, K, V, nullptr, proj_kq_scale, il);
+                ca_out = ggml_reshape_3d(ctx0, ca_out, n_embd, num_queries, nblocks_proj);
+
+                queries = build_norm(ggml_add(ctx0, ca_out, queries),
+                    pl.cross_attn_norm_w, pl.cross_attn_norm_b,
+                    NORM_TYPE_NORMAL, proj_eps, il);
+            }
+
+            // ffn
+            {
+                ggml_tensor * ffn_out = build_ffn(queries,
+                    pl.ff_up_w, pl.ff_up_b,
+                    nullptr, nullptr,
+                    pl.ff_down_w, pl.ff_down_b,
+                    FFN_GELU, il);
+
+                queries = build_norm(ggml_add(ctx0, ffn_out, queries),
+                    pl.ln_2_w, pl.ln_2_b,
+                    NORM_TYPE_NORMAL, proj_eps, il);
+            }
+        }
+
+        cur = ggml_reshape_2d(ctx0, queries, n_embd, num_queries * nblocks_proj);
+        cur = ggml_add(ctx0, build_mm(model.qf_proj_linear_w, cur), model.qf_proj_linear_b);
+        cb(cur, "projector_out", -1);
+    }
+
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/hunyuanocr.cpp b/tools/mtmd/models/hunyuanocr.cpp
index 37d1e2b86a9..45ed684f70d 100644
--- a/tools/mtmd/models/hunyuanocr.cpp
+++ b/tools/mtmd/models/hunyuanocr.cpp
@@ -5,7 +5,21 @@ ggml_cgraph * clip_graph_hunyuanocr::build() {
     const int pw    = n_patches_x;
     const int ph    = n_patches_y;
 
-    ggml_tensor * pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BILINEAR);
+    // Position embedding interpolation.
+    // HunyuanVL needs scale factors sf=(target+0.1)/n_grid, which the standard
+    // ggml_interpolate cannot express. To avoid adding a new ggml op, the
+    // resize is computed on CPU in clip_image_batch_encode and uploaded here
+    // as a graph input (named "hunyuanvl_pos_embd").
+    // HunyuanOCR uses the same square layout and the standard ratio-based
+    // interpolation provided by resize_position_embeddings().
+    ggml_tensor * pos_embd = nullptr;
+    if (proj_type == PROJECTOR_TYPE_HUNYUANVL && model.position_embeddings) {
+        pos_embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, ph * pw);
+        ggml_set_name(pos_embd, "hunyuanvl_pos_embd");
+        ggml_set_input(pos_embd);
+    } else {
+        pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BILINEAR);
+    }
 
     ggml_tensor * inp = build_inp();
     ggml_tensor * cur = build_vit(inp, n_patches, NORM_TYPE_NORMAL, hparams.ffn_op, pos_embd, nullptr);
diff --git a/tools/mtmd/models/mimovl.cpp b/tools/mtmd/models/mimovl.cpp
new file mode 100644
index 00000000000..19db88f132a
--- /dev/null
+++ b/tools/mtmd/models/mimovl.cpp
@@ -0,0 +1,209 @@
+#include "models.h"
+
+ggml_tensor * clip_graph_mimovl::build_mm(ggml_tensor * w, ggml_tensor * x) const {
+    ggml_tensor * cur = ggml_mul_mat(ctx0, w, x);
+    ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
+    return cur;
+}
+
+// MiMoVL vision tower for MiMo-V2.5 (non-Pro). Qwen2.5-VL-shaped ViT, except:
+//   1. GQA in attention (32 Q / 8 KV heads, head_dim 64).
+//   2. Per-head attention sinks on every windowed layer. The sinks adjust
+//      the softmax denominator (equivalently, a virtual extra K column with V=0),
+//      so they decay attention weight without contributing to the output.
+//   3. Per-layer window-attention mode in hparams.wa_pattern_mode:
+//        -1 -> full,  0 -> row-window+sinks,  1 -> col-window+sinks.
+//      Col mode transposes the merge-unit grid on entry and restores
+//      it on exit. Both patch and rotary orderings are pre-computed
+//      host-side.
+//   4. 1D banded sliding window (|q-k| > window_size -> -inf) as a
+//      single 2D mask broadcast across heads.
+//   5. Per-block MLP biases.
+ggml_cgraph * clip_graph_mimovl::build() {
+    GGML_ASSERT(model.patch_embeddings_0 != nullptr);
+    GGML_ASSERT(model.patch_embeddings_1 != nullptr);
+    GGML_ASSERT(model.class_embedding == nullptr);
+    GGML_ASSERT(hparams.n_head_kv > 0);
+    GGML_ASSERT(n_head % hparams.n_head_kv == 0);
+    GGML_ASSERT((int) hparams.wa_pattern_mode.size() == n_layer);
+
+    const int batch_size = 1;
+    const int n_pos      = n_patches;
+    const int n_head_kv  = hparams.n_head_kv;
+    const int merge      = hparams.n_merge > 0 ? hparams.n_merge : 2;
+    const int merge_unit = merge * merge;
+    const int n_units    = n_pos / merge_unit;
+    GGML_ASSERT(n_units * merge_unit == n_pos);
+
+    // MiMoVL has head_dim=64 with n_embd=1280, so n_embd is NOT n_head*head_dim
+    // (the base class's d_head = n_embd/n_head = 40 is wrong here). Derive
+    // head_dim from the fused QKV projection: rows = (n_head + 2*n_head_kv)*head_dim.
+    GGML_ASSERT(model.layers[0].qkv_w != nullptr);
+    const int qkv_rows     = model.layers[0].qkv_w->ne[1];
+    const int head_dim     = qkv_rows / (n_head + 2 * n_head_kv);
+    GGML_ASSERT(head_dim * (n_head + 2 * n_head_kv) == qkv_rows);
+    const float attn_scale = 1.0f / std::sqrt((float) head_dim);
+    const int rope_n_dims = head_dim / 2;
+    int mrope_sections[4] = {rope_n_dims/2, rope_n_dims/2, 0, 0};
+
+    // Patch embed: Conv3D(kt=2) split into two Conv2D, then interleave-merge
+    // along the height axis to match the merge-tile token order.
+    ggml_tensor * inp_raw = build_inp_raw();
+    ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw,
+                                     patch_size, patch_size, 0, 0, 1, 1);
+    {
+        ggml_tensor * inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw,
+                                           patch_size, patch_size, 0, 0, 1, 1);
+        inp = ggml_add(ctx0, inp, inp_1);
+
+        GGML_ASSERT(img.nx % (patch_size * 2) == 0);
+        GGML_ASSERT(img.ny % (patch_size * 2) == 0);
+
+        inp = ggml_permute(ctx0, inp, 1, 2, 0, 3);  // [w,h,c,b] -> [c,w,h,b]
+        inp = ggml_cont_4d(ctx0, inp, n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
+        inp = ggml_reshape_4d(ctx0, inp, n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
+        inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
+        inp = ggml_cont_3d(ctx0, inp, n_embd, n_patches_x * n_patches_y, batch_size);
+    }
+    cb(inp, "patch_embed", -1);
+
+    ggml_tensor * positions_row = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos * 4);
+    ggml_set_name(positions_row, "mimovl_positions_row");
+    ggml_set_input(positions_row);
+
+    ggml_tensor * positions_col = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos * 4);
+    ggml_set_name(positions_col, "mimovl_positions_col");
+    ggml_set_input(positions_col);
+
+    // idx_col is the col-major merge-unit permutation. Take it as F32 so we can
+    // derive the inverse permutation in-graph via ggml_argsort;
+    // ggml_get_rows requires its index tensor to be I32, so cast back as well.
+    ggml_tensor * idx_col_f = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_units);
+    ggml_set_name(idx_col_f, "mimovl_idx_col");
+    ggml_set_input(idx_col_f);
+    ggml_tensor * idx_col     = ggml_cast(ctx0, idx_col_f, GGML_TYPE_I32);
+    ggml_tensor * idx_col_inv = ggml_argsort(ctx0, idx_col_f, GGML_SORT_ORDER_ASC);
+
+    ggml_tensor * window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
+    ggml_set_name(window_mask, "mimovl_window_mask");
+    ggml_set_input(window_mask);
+
+    ggml_tensor * window_mask_attn = (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED)
+        ? ggml_cast(ctx0, window_mask, GGML_TYPE_F16)
+        : window_mask;
+
+    // Reorder helper: permute patches at merge-unit granularity. The patch
+    // sequence is laid out as n_units groups of merge_unit (=4) consecutive
+    // patches; the row<->col transpose only permutes whole groups. We keep
+    // the per-group (h,w) ordering intact by reshaping to
+    // [n_embd*merge_unit, n_units] before ggml_get_rows.
+    auto reorder = [&](ggml_tensor * x, ggml_tensor * idx) {
+        ggml_tensor * y = ggml_reshape_2d(ctx0, x, n_embd * merge_unit, n_units);
+        y = ggml_get_rows(ctx0, y, idx);
+        return ggml_reshape_3d(ctx0, y, n_embd, n_pos, batch_size);
+    };
+
+    ggml_tensor * inpL = inp;
+    int prev_mode = -1;
+
+    for (int il = 0; il < n_layer; il++) {
+        const auto & layer = model.layers[il];
+        const int  mode    = hparams.wa_pattern_mode[il];
+        const bool is_full = (mode == -1);
+        const bool is_col  = (mode == 1);
+
+        // Reorder transitions on entry/exit of a col-mode run.
+        if (is_col && prev_mode != 1) {
+            inpL = reorder(inpL, idx_col);
+            cb(inpL, "reorder_to_col", il);
+        } else if (!is_col && prev_mode == 1) {
+            inpL = reorder(inpL, idx_col_inv);
+            cb(inpL, "reorder_to_row", il);
+        }
+
+        ggml_tensor * cur = inpL;
+
+        // Pre-attention RMSNorm.
+        cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_RMS, eps, il);
+        cb(cur, "ln1", il);
+
+        // Fused QKV with GQA.
+        ggml_tensor * qkv = build_mm(layer.qkv_w, cur);
+        qkv = ggml_add(ctx0, qkv, layer.qkv_b);
+
+        const size_t row    = ggml_row_size(qkv->type, head_dim);
+        const size_t off_k  = ggml_row_size(qkv->type, n_head    * head_dim);
+        const size_t off_v  = ggml_row_size(qkv->type, (n_head + n_head_kv) * head_dim);
+
+        ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, head_dim, n_head,    n_pos, row, qkv->nb[1], 0);
+        ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, head_dim, n_head_kv, n_pos, row, qkv->nb[1], off_k);
+        ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, head_dim, n_head_kv, n_pos, row, qkv->nb[1], off_v);
+
+        cb(Qcur, "Qcur", il);
+        cb(Kcur, "Kcur", il);
+        cb(Vcur, "Vcur", il);
+
+        // 2D RoPE
+        ggml_tensor * pos = is_col ? positions_col : positions_row;
+        Qcur = ggml_rope_multi(ctx0, Qcur, pos, nullptr, rope_n_dims, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000.0f, 1.0f, 0.0f, 1.0f, 32.0f, 1.0f);
+        Kcur = ggml_rope_multi(ctx0, Kcur, pos, nullptr, rope_n_dims, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000.0f, 1.0f, 0.0f, 1.0f, 32.0f, 1.0f);
+        cb(Qcur, "Qcur_rope", il);
+        cb(Kcur, "Kcur_rope", il);
+
+        // Full layers: plain attention. Windowed layers: banded mask and per-head sinks.
+        ggml_tensor * mask  = is_full ? nullptr : window_mask_attn;
+        ggml_tensor * sinks = is_full ? nullptr : layer.attn_sinks;
+        if (!is_full) {
+            GGML_ASSERT(layer.attn_sinks != nullptr);
+        }
+        ggml_tensor * attn_out = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, mask, attn_scale, il, sinks);
+        cb(attn_out, "attn_out", il);
+
+        // Residual 1.
+        cur = ggml_add(ctx0, attn_out, inpL);
+        inpL = cur;
+        cb(cur, "ffn_inp", il);
+
+        // Pre-FFN RMSNorm.
+        cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_RMS, eps, il);
+        cb(cur, "ffn_inp_normed", il);
+
+        // SwiGLU MLP with biases
+        cur = build_ffn(cur,
+            layer.ff_up_w,   layer.ff_up_b,
+            layer.ff_gate_w, layer.ff_gate_b,
+            layer.ff_down_w, layer.ff_down_b,
+            hparams.ffn_op, il);
+        cb(cur, "ffn_out", il);
+
+        // Residual 2.
+        cur = ggml_add(ctx0, inpL, cur);
+        cb(cur, "layer_out", il);
+
+        inpL = cur;
+        prev_mode = mode;
+    }
+
+    // If the last block was col-mode, undo the transpose so the merger sees patches in row order.
+    if (prev_mode == 1) {
+        inpL = reorder(inpL, idx_col_inv);
+        cb(inpL, "reorder_to_row_final", -1);
+    }
+
+    // Merger: post-LayerNorm
+    inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, 1e-6f, n_layer);
+    cb(inpL, "post_ln", -1);
+
+    // Spatial merge: pack each merge_unit (=4) of patches into a single
+    // (n_embd*merge_unit)-wide row, then run the 2-layer MLP.
+    ggml_tensor * embeddings = ggml_reshape_3d(ctx0, inpL, n_embd * merge_unit, n_units, batch_size);
+    embeddings = build_ffn(embeddings,
+        model.mm_0_w, nullptr,
+        nullptr,      nullptr,
+        model.mm_1_w, nullptr,
+        FFN_GELU, -1);
+    cb(embeddings, "vit_out", -1);
+
+    ggml_build_forward_expand(gf, embeddings);
+    return gf;
+}
diff --git a/tools/mtmd/models/minicpmv.cpp b/tools/mtmd/models/minicpmv.cpp
index 924117ab2a1..bac087ffdfc 100644
--- a/tools/mtmd/models/minicpmv.cpp
+++ b/tools/mtmd/models/minicpmv.cpp
@@ -112,3 +112,294 @@ ggml_cgraph * clip_graph_minicpmv::build() {
 
     return gf;
 }
+
+ggml_cgraph * clip_graph_minicpmv4_6::build() {
+    const int insert_lid = hparams.insert_layer_id;
+    const int n_pos      = n_patches;
+    const int half_h     = n_patches_y / 2;
+    const int half_w     = n_patches_x / 2;
+    const int n_ds       = half_h * half_w;     // after ViT merger 2x2 downsample
+    const int qh         = half_h / 2;
+    const int qw         = half_w / 2;
+    const int n_ds2      = qh * qw;             // after final merger 2x2 downsample
+
+    auto add_i32_input = [&](const char * name, int n) {
+        ggml_tensor * t = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n);
+        ggml_set_name(t, name);
+        ggml_set_input(t);
+        return t;
+    };
+
+    // position indices for ViT learned positional embeddings
+    ggml_tensor * positions = add_i32_input("positions", n_pos);
+    ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);
+
+    // ViT merger window reorder indices + block-diagonal mask
+    // (mask layout follows qwen2vl: -inf except for 4x4 blocks on the diagonal,
+    // so each window-major group of 4 tokens only attends to itself)
+    ggml_tensor * vit_merger_window_idx     = add_i32_input("vit_merger_window_idx", n_pos);
+    ggml_tensor * vit_merger_inv_window_idx = add_i32_input("vit_merger_inv_window_idx", n_pos);
+    ggml_tensor * vit_merger_window_mask    = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
+    ggml_set_name(vit_merger_window_mask, "vit_merger_window_mask");
+    ggml_set_input(vit_merger_window_mask);
+    if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
+        vit_merger_window_mask = ggml_cast(ctx0, vit_merger_window_mask, GGML_TYPE_F16);
+    }
+
+    // ViT merger 2x2 downsample gather indices
+    ggml_tensor * vit_merger_ds_idx_0 = add_i32_input("vit_merger_ds_idx_0", n_ds);
+    ggml_tensor * vit_merger_ds_idx_1 = add_i32_input("vit_merger_ds_idx_1", n_ds);
+    ggml_tensor * vit_merger_ds_idx_2 = add_i32_input("vit_merger_ds_idx_2", n_ds);
+    ggml_tensor * vit_merger_ds_idx_3 = add_i32_input("vit_merger_ds_idx_3", n_ds);
+
+    // final merger 2x2 downsample gather indices
+    ggml_tensor * merger_ds_idx_0 = add_i32_input("merger_ds_idx_0", n_ds2);
+    ggml_tensor * merger_ds_idx_1 = add_i32_input("merger_ds_idx_1", n_ds2);
+    ggml_tensor * merger_ds_idx_2 = add_i32_input("merger_ds_idx_2", n_ds2);
+    ggml_tensor * merger_ds_idx_3 = add_i32_input("merger_ds_idx_3", n_ds2);
+
+    // patch embedding + positional embedding
+    ggml_tensor * inp = build_inp();
+    inp = ggml_add(ctx0, inp, learned_pos_embd);
+    cb(inp, "pos_embed", -1);
+
+    ggml_tensor * inpL = inp;
+    if (model.pre_ln_w) {
+        inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1);
+        cb(inpL, "pre_ln", -1);
+    }
+
+    // ViT layers 0..insert_layer_id (inclusive)
+    // Mirrors the separate-qkv path of clip_graph::build_vit so the two manually
+    // unrolled segments around the ViT merger read like build_vit() expansions.
+    for (int il = 0; il <= insert_lid; il++) {
+        auto & layer = model.layers[il];
+        ggml_tensor * cur = inpL;
+
+        cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
+        cb(cur, "layer_inp_normed", il);
+
+        {
+            ggml_tensor * Qcur = build_mm(layer.q_w, cur);
+            if (layer.q_b) {
+                Qcur = ggml_add(ctx0, Qcur, layer.q_b);
+            }
+            ggml_tensor * Kcur = build_mm(layer.k_w, cur);
+            if (layer.k_b) {
+                Kcur = ggml_add(ctx0, Kcur, layer.k_b);
+            }
+            ggml_tensor * Vcur = build_mm(layer.v_w, cur);
+            if (layer.v_b) {
+                Vcur = ggml_add(ctx0, Vcur, layer.v_b);
+            }
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+
+        if (layer.ls_1_w) {
+            cur = ggml_mul(ctx0, cur, layer.ls_1_w);
+            cb(cur, "attn_out_scaled", il);
+        }
+        cur = ggml_add(ctx0, cur, inpL);
+        inpL = cur;
+        cb(cur, "ffn_inp", il);
+
+        cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
+        cb(cur, "ffn_inp_normed", il);
+
+        cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, layer.ff_gate_w, layer.ff_gate_b,
+                        layer.ff_down_w, layer.ff_down_b, hparams.ffn_op, il);
+        cb(cur, "ffn_out", il);
+
+        if (layer.ls_2_w) {
+            cur = ggml_mul(ctx0, cur, layer.ls_2_w);
+            cb(cur, "ffn_out_scaled", il);
+        }
+        cur = ggml_add(ctx0, inpL, cur);
+        cb(cur, "layer_out", il);
+
+        inpL = cur;
+    }
+
+    // ViT merger: window self-attention
+    // Tokens are reordered to window-major (4 tokens per window are contiguous),
+    // and a block-diagonal mask restricts attention to within each window. This
+    // mirrors the qwen2vl windowed-attention pattern so build_attn() can pick the
+    // flash-attention path when available.
+    {
+        ggml_tensor * residual = inpL;
+        ggml_tensor * cur = build_norm(inpL,
+            model.vit_merger_ln1_w, model.vit_merger_ln1_b,
+            NORM_TYPE_NORMAL, eps, -1);
+        cb(cur, "vit_merger_attn_inp_normed", -1);
+
+        cur = ggml_get_rows(ctx0, cur, vit_merger_window_idx);
+        cb(cur, "vit_merger_window_reorder", -1);
+
+        ggml_tensor * Qcur = build_mm(model.vit_merger_attn_q_w, cur);
+        if (model.vit_merger_attn_q_b) {
+            Qcur = ggml_add(ctx0, Qcur, model.vit_merger_attn_q_b);
+        }
+        ggml_tensor * Kcur = build_mm(model.vit_merger_attn_k_w, cur);
+        if (model.vit_merger_attn_k_b) {
+            Kcur = ggml_add(ctx0, Kcur, model.vit_merger_attn_k_b);
+        }
+        ggml_tensor * Vcur = build_mm(model.vit_merger_attn_v_w, cur);
+        if (model.vit_merger_attn_v_b) {
+            Vcur = ggml_add(ctx0, Vcur, model.vit_merger_attn_v_b);
+        }
+
+        Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
+        Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
+        Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
+        cb(Qcur, "vit_merger_Qcur", -1);
+        cb(Kcur, "vit_merger_Kcur", -1);
+        cb(Vcur, "vit_merger_Vcur", -1);
+
+        cur = build_attn(model.vit_merger_attn_o_w, model.vit_merger_attn_o_b,
+                         Qcur, Kcur, Vcur, vit_merger_window_mask, kq_scale, -1);
+        cb(cur, "vit_merger_attn_out", -1);
+
+        cur = ggml_get_rows(ctx0, cur, vit_merger_inv_window_idx);
+        inpL = ggml_add(ctx0, cur, residual);
+        cb(inpL, "vit_merger_attn_residual", -1);
+    }
+
+    // ViT merger: 2x2 spatial downsample + MLP (4 tokens -> 1)
+    {
+        ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_0);
+        ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_1);
+        ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_2);
+        ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_3);
+
+        ggml_tensor * mean_res = ggml_add(ctx0, p0, p1);
+        mean_res = ggml_add(ctx0, mean_res, p2);
+        mean_res = ggml_add(ctx0, mean_res, p3);
+        mean_res = ggml_scale(ctx0, mean_res, 0.25f);
+        cb(mean_res, "vit_merger_ds_mean_res", -1);
+
+        ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0);
+        cat = ggml_concat(ctx0, cat, p2, 0);
+        cat = ggml_concat(ctx0, cat, p3, 0);
+
+        ggml_tensor * cur = build_norm(cat,
+            model.vit_merger_ds_ln_w, model.vit_merger_ds_ln_b,
+            NORM_TYPE_NORMAL, eps, -1);
+        cb(cur, "vit_merger_ds_normed", -1);
+
+        // ViTWindowAttentionMerger downsample MLP uses gelu_pytorch_tanh (FFN_GELU)
+        cur = build_ffn(cur,
+            model.vit_merger_ds_up_w,   model.vit_merger_ds_up_b,
+            nullptr, nullptr,
+            model.vit_merger_ds_down_w, model.vit_merger_ds_down_b,
+            FFN_GELU, -1);
+        cb(cur, "vit_merger_ds_mlp_out", -1);
+
+        inpL = ggml_add(ctx0, cur, mean_res);
+        cb(inpL, "vit_merger_ds_out", -1);
+    }
+
+    // ViT layers (insert_layer_id+1)..n_layer-1, operating on the downsampled tokens
+    {
+        const int64_t n_pos_ds = n_ds;
+        for (int il = insert_lid + 1; il < n_layer; il++) {
+            auto & layer = model.layers[il];
+            ggml_tensor * cur = inpL;
+
+            cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
+            cb(cur, "layer_inp_normed", il);
+
+            {
+                ggml_tensor * Qcur = build_mm(layer.q_w, cur);
+                if (layer.q_b) {
+                    Qcur = ggml_add(ctx0, Qcur, layer.q_b);
+                }
+                ggml_tensor * Kcur = build_mm(layer.k_w, cur);
+                if (layer.k_b) {
+                    Kcur = ggml_add(ctx0, Kcur, layer.k_b);
+                }
+                ggml_tensor * Vcur = build_mm(layer.v_w, cur);
+                if (layer.v_b) {
+                    Vcur = ggml_add(ctx0, Vcur, layer.v_b);
+                }
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos_ds);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos_ds);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos_ds);
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+
+            if (layer.ls_1_w) {
+                cur = ggml_mul(ctx0, cur, layer.ls_1_w);
+                cb(cur, "attn_out_scaled", il);
+            }
+            cur = ggml_add(ctx0, cur, inpL);
+            inpL = cur;
+            cb(cur, "ffn_inp", il);
+
+            cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
+            cb(cur, "ffn_inp_normed", il);
+
+            cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, layer.ff_gate_w, layer.ff_gate_b,
+                            layer.ff_down_w, layer.ff_down_b, hparams.ffn_op, il);
+            cb(cur, "ffn_out", il);
+
+            if (layer.ls_2_w) {
+                cur = ggml_mul(ctx0, cur, layer.ls_2_w);
+                cb(cur, "ffn_out_scaled", il);
+            }
+            cur = ggml_add(ctx0, inpL, cur);
+            cb(cur, "layer_out", il);
+
+            inpL = cur;
+        }
+    }
+
+    if (model.post_ln_w) {
+        inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1);
+        cb(inpL, "post_ln", -1);
+    }
+
+    // Final Merger (DownsampleMLP): another 2x2 spatial merge -> projector embedding
+    {
+        ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, merger_ds_idx_0);
+        ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, merger_ds_idx_1);
+        ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, merger_ds_idx_2);
+        ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, merger_ds_idx_3);
+
+        ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0);
+        cat = ggml_concat(ctx0, cat, p2, 0);
+        cat = ggml_concat(ctx0, cat, p3, 0);
+
+        ggml_tensor * cur = build_norm(cat,
+            model.mm_input_norm_w, model.mm_input_norm_b,
+            NORM_TYPE_NORMAL, eps, -1);
+        cb(cur, "merger_normed", -1);
+
+        // MiniCPMV4_6DownsampleMLP uses nn.GELU() (erf-based, FFN_GELU_ERF)
+        cur = build_ffn(cur,
+            model.mm_ffn_up_w,   model.mm_ffn_up_b,
+            nullptr, nullptr,
+            model.mm_ffn_down_w, model.mm_ffn_down_b,
+            FFN_GELU_ERF, -1);
+        cb(cur, "merger_out", -1);
+
+        inpL = cur;
+    }
+
+    ggml_build_forward_expand(gf, inpL);
+    return gf;
+}
diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h
index 03d99e15b05..955daa6d6d3 100644
--- a/tools/mtmd/models/models.h
+++ b/tools/mtmd/models/models.h
@@ -33,6 +33,15 @@ struct clip_graph_qwen3vl : clip_graph {
     ggml_cgraph * build() override;
 };
 
+struct clip_graph_mimovl : clip_graph {
+    clip_graph_mimovl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+    // Force F32 mat-mul accumulation to avoid F16 overflow in the FFN down-proj
+    // when the mmproj is stored in F16 (the source weights are BF16; downcasting
+    // to F16 reduces dynamic range below the SwiGLU output magnitude on the last few layers).
+    ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
+};
+
 struct clip_graph_step3vl : clip_graph {
     clip_graph_step3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
     ggml_cgraph * build() override;
@@ -43,11 +52,24 @@ struct clip_graph_youtuvl : clip_graph {
     ggml_cgraph * build() override;
 };
 
+struct clip_graph_yasa2 : clip_graph {
+    clip_graph_yasa2(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+
+    ggml_tensor * layer_norm_channels(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b, float eps = 1e-6f);
+    ggml_tensor * convnext_grn(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b);
+};
+
 struct clip_graph_minicpmv : clip_graph {
     clip_graph_minicpmv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
     ggml_cgraph * build() override;
 };
 
+struct clip_graph_minicpmv4_6 : clip_graph {
+    clip_graph_minicpmv4_6(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
 struct clip_graph_internvl : clip_graph {
     clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
     ggml_cgraph * build() override;
@@ -103,6 +125,11 @@ struct clip_graph_conformer : clip_graph {
     ggml_cgraph * build() override;
 };
 
+struct clip_graph_granite_speech : clip_graph {
+    clip_graph_granite_speech(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
 struct clip_graph_gemma4a : clip_graph {
     clip_graph_gemma4a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
     ggml_cgraph * build() override;
diff --git a/tools/mtmd/models/qwen3a.cpp b/tools/mtmd/models/qwen3a.cpp
index 1384e5155ee..4de96955d96 100644
--- a/tools/mtmd/models/qwen3a.cpp
+++ b/tools/mtmd/models/qwen3a.cpp
@@ -1,68 +1,88 @@
 #include "models.h"
 
 ggml_cgraph * clip_graph_qwen3a::build() {
+    // Ref implementation: https://github.com/QwenLM/Qwen3-ASR/blob/main/qwen_asr/core/transformers_backend/modeling_qwen3_asr.py
+
+    // inp_raw: [n_frames, n_mel, 1]  (nx=n_frames, ny=n_mel)
     ggml_tensor * inp = build_inp_raw(1);
 
-    // conv2d block
-    // TODO: do we need to split by chunks of n_window each like on transformers impl?
-    {
-        inp = ggml_conv_2d(ctx0, model.conv2d_1_w, inp, 2, 2, 1, 1, 1, 1);
-        inp = ggml_add(ctx0, inp, model.conv2d_1_b);
-        inp = ggml_gelu_erf(ctx0, inp);
+    const int64_t n_frames   = inp->ne[0]; // total frames, padded to multiple of chunk_size
+    const int64_t n_mel      = inp->ne[1]; // 128
+    const int64_t chunk_size = 100;        // n_window * 2 (n_window=50 from model config)
+    const int64_t n_chunks   = n_frames / chunk_size;
 
-        inp = ggml_conv_2d(ctx0, model.conv2d_2_w, inp, 2, 2, 1, 1, 1, 1);
-        inp = ggml_add(ctx0, inp, model.conv2d_2_b);
-        inp = ggml_gelu_erf(ctx0, inp);
+    GGML_ASSERT(n_frames % chunk_size == 0); // preprocessor should already pad the input
+    GGML_ASSERT(inp->type == GGML_TYPE_F32);
 
-        inp = ggml_conv_2d(ctx0, model.conv2d_3_w, inp, 2, 2, 1, 1, 1, 1);
-        inp = ggml_add(ctx0, inp, model.conv2d_3_b);
-        inp = ggml_gelu_erf(ctx0, inp);
+    // View mel spectrogram as batched 100-frame chunks: [chunk_size, n_mel, 1, n_chunks]
+    inp = ggml_view_4d(ctx0, inp,
+        chunk_size, n_mel, 1, n_chunks,
+        n_frames   * (int64_t)sizeof(float), // nb[1]: stride over mel bins
+        chunk_size * (int64_t)sizeof(float), // nb[2]: stride for C=1 (unused)
+        chunk_size * (int64_t)sizeof(float), // nb[3]: stride over chunks
+        0);
+    inp = ggml_cont(ctx0, inp);
+    cb(inp, "inp_chunks", -1);
 
-        // inp [n_pos, n_mels/8, channels, 1] (W, H, C, N)
-        cb(inp, "after_conv_blocks", -1);
+    // 3 x conv2d + gelu
+    {
+        // conv output [OW, OH, C_out, n_chunks]
+        auto conv_block = [&](ggml_tensor * x, ggml_tensor * w, ggml_tensor * b) {
+            x = ggml_conv_2d(ctx0, w, x, 2, 2, 1, 1, 1, 1);
+            if (b) {
+                x = ggml_add(ctx0, x, ggml_reshape_4d(ctx0, b, 1, 1, x->ne[2], 1));
+            }
+            return ggml_gelu_erf(ctx0, x);
+        };
 
-        const int64_t n_pos_after_conv = inp->ne[0];
-        const int64_t n_mel_after_conv = inp->ne[1]; // 128/8 = 16
+        inp = conv_block(inp, model.conv2d_1_w, model.conv2d_1_b);
+        inp = conv_block(inp, model.conv2d_2_w, model.conv2d_2_b);
+        inp = conv_block(inp, model.conv2d_3_w, model.conv2d_3_b);
+        // inp: [OW=13, OH=16, OC=480, n_chunks]
+        cb(inp, "after_conv_blocks", -1);
+    }
 
-        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 3, 1));
-        inp = ggml_reshape_2d(ctx0, inp, n_pos_after_conv, n_mel_after_conv * inp->ne[3]); // [n_pos, 7680]
-        inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); // [7680, n_pos]
+    // permute [OW=25, OH=16, OC=480, n_chunks] -> [OH=16, OC=480, OW=25, n_chunks]
+    // reshape to [OH*OC=7680, OW*n_chunks]
+    // feature index h+16*c = c*16+f (matches python code)
+    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 2, 0, 1, 3));
+    inp = ggml_reshape_2d(ctx0, inp, inp->ne[0] * inp->ne[1], inp->ne[2] * inp->ne[3]);
 
-        // project to n_embd
-        inp = ggml_mul_mat(ctx0, model.conv_out_w, inp);
-        if (model.conv_out_b) {
-            inp = ggml_add(ctx0, inp, model.conv_out_b);
-        }
-        cb(inp, "after_conv_out", -1);
+    // Project to d_model: [d_model, 25*n_chunks]
+    inp = ggml_mul_mat(ctx0, model.conv_out_w, inp);
+    if (model.conv_out_b) {
+        inp = ggml_add(ctx0, inp, model.conv_out_b);
     }
+    cb(inp, "after_conv_out", -1);
 
-    auto n_pos = inp->ne[1];
+    const int64_t n_pos = inp->ne[1]; // 25 * n_chunks
 
-    ggml_tensor * pos_embd_selected = ggml_view_2d(
-        ctx0, model.position_embeddings,
-        model.position_embeddings->ne[0], n_pos,
-        model.position_embeddings->nb[1], 0
-    );
-    ggml_tensor * cur = build_vit(
-                            inp, n_pos,
-                            NORM_TYPE_NORMAL,
-                            hparams.ffn_op,
-                            pos_embd_selected,
-                            nullptr);
+    // Per-chunk positional embeddings: repeat pos[0:13] for each chunk
+    // (position indices reset 0..12 per chunk, not sequential across chunks)
+    {
+        const int64_t tokens_per_chunk = n_pos / n_chunks; // 13
+        ggml_tensor * pos_tmp = ggml_view_2d(ctx0, model.position_embeddings,
+            model.position_embeddings->ne[0], tokens_per_chunk,
+            model.position_embeddings->nb[1], 0);
+        ggml_tensor * tgt = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32,
+            model.position_embeddings->ne[0], n_pos);
+        inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, pos_tmp, tgt));
+    }
 
+    ggml_tensor * cur = build_vit(inp, n_pos,
+        NORM_TYPE_NORMAL, hparams.ffn_op,
+        nullptr,  // pos embd already added above
+        nullptr);
     cb(cur, "after_transformer", -1);
 
-    // projector
+    // MLP projector
     cur = build_ffn(cur,
         model.mm_1_w, model.mm_1_b,
         nullptr, nullptr,
         model.mm_2_w, model.mm_2_b,
-        FFN_GELU_ERF,
-        -1);
-
+        FFN_GELU_ERF, -1);
     cb(cur, "projected", -1);
 
     ggml_build_forward_expand(gf, cur);
-
     return gf;
 }
diff --git a/tools/mtmd/models/yasa2.cpp b/tools/mtmd/models/yasa2.cpp
new file mode 100644
index 00000000000..e8cd3dacbf5
--- /dev/null
+++ b/tools/mtmd/models/yasa2.cpp
@@ -0,0 +1,191 @@
+// ABOUTME: Yasa2 vision encoder graph builder for ConvNeXt-based architecture.
+// ABOUTME: Implements patch embedding, ConvNeXt stages with GRN, and adaptive pooling.
+
+#include "models.h"
+
+static ggml_tensor * add_channel_bias(
+        ggml_context * ctx0,
+        ggml_tensor * x_whcb,
+        ggml_tensor * b_c) {
+    if (!b_c) {
+        return x_whcb;
+    }
+    ggml_tensor * b4 = ggml_reshape_4d(ctx0, b_c, 1, 1, b_c->ne[0], 1);
+    return ggml_add(ctx0, x_whcb, b4);
+}
+
+static ggml_tensor * mul_channel_weight(
+        ggml_context * ctx0,
+        ggml_tensor * x_whcb,
+        ggml_tensor * w_c) {
+    if (!w_c) {
+        return x_whcb;
+    }
+    ggml_tensor * w4 = ggml_reshape_4d(ctx0, w_c, 1, 1, w_c->ne[0], 1);
+    return ggml_mul(ctx0, x_whcb, w4);
+}
+
+ggml_tensor * clip_graph_yasa2::layer_norm_channels(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b, float eps) {
+    // Match HF ConvNextLayerNorm(channels_first):
+    // u = mean_c(x), s = mean_c((x-u)^2), x = (x-u)/sqrt(s+eps)
+    // cast back to input dtype before affine.
+    ggml_tensor * cur = ggml_permute(ctx0, inp, 2, 1, 0, 3); // [W,H,C,B] -> [C,H,W,B]
+    cur = ggml_cont(ctx0, cur);
+
+    ggml_tensor * u = ggml_mean(ctx0, cur);                 // [1,H,W,B]
+    ggml_tensor * xm = ggml_sub(ctx0, cur, u);              // [C,H,W,B]
+
+    ggml_tensor * s = ggml_mul(ctx0, xm, xm);               // [C,H,W,B]
+    s = ggml_mean(ctx0, s);                                 // [1,H,W,B]
+    s = ggml_clamp(ctx0, s, eps, 1e30f);                    // avoid div-by-zero in no-alloc warmup
+    s = ggml_sqrt(ctx0, s);                                 // [1,H,W,B]
+
+    ggml_tensor * xhat = ggml_div(ctx0, xm, s);             // [C,H,W,B]
+    xhat = ggml_permute(ctx0, xhat, 2, 1, 0, 3);            // [W,H,C,B]
+    xhat = ggml_cont(ctx0, xhat);
+    xhat = mul_channel_weight(ctx0, xhat, w);
+    xhat = add_channel_bias(ctx0, xhat, b);
+    return xhat;
+}
+
+ggml_tensor * clip_graph_yasa2::convnext_grn(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b) {
+    // Exact ConvNeXtV2 GRN:
+    // Gx = ||x||_2 over spatial dims (W,H), Nx = Gx / (mean_c(Gx) + eps)
+    // y  = w * (x * Nx) + b + x
+    const int64_t wdim = inp->ne[0];
+    const int64_t hdim = inp->ne[1];
+    const int64_t cdim = inp->ne[2];
+    const int64_t bdim = inp->ne[3];
+
+    // Keep GRN math in fp32 for stability; fp16/bf16 accumulation can drift.
+    ggml_tensor * sq = ggml_mul(ctx0, inp, inp);
+    ggml_tensor * sq_flat = ggml_reshape_4d(ctx0, sq, wdim * hdim, cdim, 1, bdim);   // [WH,C,1,B]
+    ggml_tensor * gx = ggml_sum_rows(ctx0, sq_flat);                                   // [1,C,1,B]
+    gx = ggml_sqrt(ctx0, gx);                                                           // [1,C,1,B]
+
+    ggml_tensor * gx_ch_first = ggml_permute(ctx0, gx, 1, 0, 2, 3);                    // [C,1,1,B]
+    gx_ch_first = ggml_cont(ctx0, gx_ch_first);
+    ggml_tensor * gx_mean = ggml_mean(ctx0, gx_ch_first);                              // [1,1,1,B]
+
+    gx_mean = ggml_clamp(ctx0, gx_mean, 1e-6f, 1e30f);                                  // approx +eps, warmup-safe
+    ggml_tensor * nx = ggml_div(ctx0, gx, gx_mean);                                    // [1,C,1,B]
+    nx = ggml_permute(ctx0, nx, 0, 2, 1, 3);                                            // [1,1,C,B]
+    nx = ggml_cont(ctx0, nx);
+
+    ggml_tensor * xnx = ggml_mul(ctx0, inp, nx);
+    xnx = mul_channel_weight(ctx0, xnx, w);
+    xnx = add_channel_bias(ctx0, xnx, b);
+    return ggml_add(ctx0, inp, xnx);
+}
+
+ggml_cgraph * clip_graph_yasa2::build() {
+    ggml_tensor * cur = build_inp_raw();
+
+    // Patch embedding Conv2d(kernel=4, stride=4)
+    cur = ggml_conv_2d(ctx0, model.yasa_patch_w, cur, patch_size, patch_size, 0, 0, 1, 1);
+    cur = add_channel_bias(ctx0, cur, model.yasa_patch_b);
+    ggml_set_name(cur, "yasa2_patch_conv_out");
+    cb(cur, "yasa2_patch_conv_out", -1);
+    cur = layer_norm_channels(cur, model.yasa_patch_ln_w, model.yasa_patch_ln_b, eps);
+    ggml_set_name(cur, "yasa2_patch_ln_out");
+    cb(cur, "yasa2_patch_ln_out", -1);
+
+    // ConvNeXt stages
+    for (size_t s = 0; s < model.yasa_stages.size(); ++s) {
+        const auto & stage = model.yasa_stages[s];
+
+        if (stage.down_conv_w) {
+            cur = layer_norm_channels(cur, stage.down_ln_w, stage.down_ln_b, eps);
+            cur = ggml_conv_2d(ctx0, stage.down_conv_w, cur, 2, 2, 0, 0, 1, 1);
+            cur = add_channel_bias(ctx0, cur, stage.down_conv_b);
+            ggml_format_name(cur, "yasa2_stage%zu_down_out", s);
+        }
+
+        for (size_t bi = 0; bi < stage.blocks.size(); ++bi) {
+            const auto & blk = stage.blocks[bi];
+            ggml_tensor * res = cur;
+
+            ggml_tensor * x = ggml_conv_2d_dw(ctx0, blk.dw_w, cur, 1, 1, 3, 3, 1, 1);
+            x = add_channel_bias(ctx0, x, blk.dw_b);
+            x = layer_norm_channels(x, blk.ln_w, blk.ln_b, eps);
+
+            // pwconv1/pwconv2 are HF Linear layers over channels; implement via matmul on tokens.
+            const int64_t w = x->ne[0];
+            const int64_t h = x->ne[1];
+            const int64_t b = x->ne[3];
+
+            ggml_tensor * tok = ggml_reshape_3d(ctx0, x, w * h, x->ne[2], b); // [T,C,B]
+            tok = ggml_permute(ctx0, tok, 1, 0, 2, 3);                        // [C,T,B]
+            tok = ggml_cont(ctx0, tok);
+
+            tok = ggml_mul_mat(ctx0, blk.pw1_w, tok);                         // [4C,T,B]
+            if (blk.pw1_b) {
+                ggml_tensor * b1 = ggml_reshape_3d(ctx0, blk.pw1_b, blk.pw1_b->ne[0], 1, 1); // [4C,1,1]
+                tok = ggml_add(ctx0, tok, b1);
+            }
+            x = ggml_permute(ctx0, tok, 1, 0, 2, 3);                         // [T,4C,B]
+            x = ggml_cont(ctx0, x);
+            x = ggml_reshape_4d(ctx0, x, w, h, tok->ne[0], b);               // [W,H,4C,B]
+            x = ggml_gelu_erf(ctx0, x);
+            x = convnext_grn(x, blk.grn_w, blk.grn_b);
+
+            tok = ggml_reshape_3d(ctx0, x, w * h, x->ne[2], b);              // [T,4C,B]
+            tok = ggml_permute(ctx0, tok, 1, 0, 2, 3);                       // [4C,T,B]
+            tok = ggml_cont(ctx0, tok);
+
+            tok = ggml_mul_mat(ctx0, blk.pw2_w, tok);                        // [C,T,B]
+            if (blk.pw2_b) {
+                ggml_tensor * b2 = ggml_reshape_3d(ctx0, blk.pw2_b, blk.pw2_b->ne[0], 1, 1); // [C,1,1]
+                tok = ggml_add(ctx0, tok, b2);
+            }
+            x = ggml_permute(ctx0, tok, 1, 0, 2, 3);                         // [T,C,B]
+            x = ggml_cont(ctx0, x);
+            x = ggml_reshape_4d(ctx0, x, w, h, tok->ne[0], b);               // [W,H,C,B]
+
+            cur = ggml_add(ctx0, res, x);
+            ggml_format_name(cur, "yasa2_stage%zu_blk%zu_out", s, bi);
+        }
+    }
+
+    // HF path adds vision position embeddings BEFORE adaptive pooling.
+    const int64_t pre_w = cur->ne[0];
+    const int64_t pre_h = cur->ne[1];
+    ggml_tensor * tokens_pre = ggml_reshape_3d(ctx0, cur, pre_w * pre_h, cur->ne[2], cur->ne[3]); // [T,C,B]
+    tokens_pre = ggml_permute(ctx0, tokens_pre, 1, 0, 2, 3); // [C,T,B]
+    tokens_pre = ggml_cont(ctx0, tokens_pre);
+    if (model.yasa_vision_pos_embed && tokens_pre->ne[1] == model.yasa_vision_pos_embed->ne[1]) {
+        const int64_t n_ch = model.yasa_vision_pos_embed->ne[0];
+        const int64_t n_tokens = model.yasa_vision_pos_embed->ne[1];
+        ggml_tensor * pos = ggml_reshape_3d(ctx0, model.yasa_vision_pos_embed, (int) n_ch, (int) n_tokens, 1);
+        tokens_pre = ggml_add(ctx0, tokens_pre, pos);
+    }
+    cur = ggml_permute(ctx0, tokens_pre, 1, 0, 2, 3); // [T,C,B]
+    cur = ggml_cont(ctx0, cur);
+    cur = ggml_reshape_4d(ctx0, cur, pre_w, pre_h, cur->ne[1], cur->ne[2]); // [W,H,C,B]
+
+    // AdaptiveAvgPool2d target is 8x8 for real inputs, but warmup can use tiny images.
+    const int pooled_w = std::min(8, (int) cur->ne[0]);
+    const int pooled_h = std::min(8, (int) cur->ne[1]);
+    const int kw = std::max(1, (int) cur->ne[0] / pooled_w);
+    const int kh = std::max(1, (int) cur->ne[1] / pooled_h);
+    cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kw, kh, kw, kh, 0, 0);
+
+    // [W,H,C,B] -> [C,T,B]
+    ggml_tensor * tokens = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[1], cur->ne[2], cur->ne[3]);
+    tokens = ggml_permute(ctx0, tokens, 1, 0, 2, 3);
+    tokens = ggml_cont(ctx0, tokens);
+    cb(tokens, "yasa2_tokens", -1);
+
+    GGML_ASSERT(model.mm_0_w && model.mm_2_w);
+    ggml_tensor * embeddings = build_ffn(
+        tokens,
+        model.mm_0_w, model.mm_0_b,
+        nullptr, nullptr,
+        model.mm_2_w, model.mm_2_b,
+        FFN_GELU_ERF,
+        -1);
+    cb(embeddings, "yasa2_emb", -1);
+
+    ggml_build_forward_expand(gf, embeddings);
+    return gf;
+}
diff --git a/tools/mtmd/mtmd-audio.cpp b/tools/mtmd/mtmd-audio.cpp
index 38a8ce4f4a6..85352904739 100644
--- a/tools/mtmd/mtmd-audio.cpp
+++ b/tools/mtmd/mtmd-audio.cpp
@@ -403,6 +403,11 @@ static bool log_mel_spectrogram(
             return false;
         }
         std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin());
+
+        // expose the padded buffer to downstream FFT and to out.n_len computation
+        // mirrors the no_padding and center_padding branches above
+        samples   = samples_padded.data();
+        n_samples = samples_padded.size();
     }
 
     // preemphasis
@@ -604,6 +609,110 @@ bool mtmd_audio_preprocessor_whisper::preprocess(const float *                 s
     return true;
 }
 
+//
+// mtmd_audio_preprocessor_qwen3a
+//
+// Matches the Python WhisperFeatureExtractor called with truncation=False:
+//   - reflection padding of n_fft/2 samples at each end (center=True)
+//   - Whisper-style log10 + (max-8)/4 normalization applied to full audio
+//   - output split into ≤30s (3000 mel frames) windows, each padded to a
+//     multiple of 200 frames (n_window * 2) for the cgraph batch view
+//
+
+void mtmd_audio_preprocessor_qwen3a::initialize() {
+    cache.fill_sin_cos_table(hparams.audio_n_fft);
+    cache.fill_hann_window(hparams.audio_window_len, true);
+    cache.fill_mel_filterbank_matrix(hparams.n_mel_bins, hparams.audio_n_fft, hparams.audio_sample_rate);
+}
+
+bool mtmd_audio_preprocessor_qwen3a::preprocess(const float *                 samples,
+                                                 size_t                        n_samples,
+                                                 std::vector<mtmd_audio_mel> & output) {
+    if (n_samples == 0) {
+        return false;
+    }
+
+    GGML_ASSERT(!cache.sin_vals.empty());
+    GGML_ASSERT(!cache.cos_vals.empty());
+    GGML_ASSERT(!cache.filters.data.empty());
+
+    // Reflection-pad n_fft/2 samples at each end, matching WhisperFeatureExtractor center=True
+    const int pad = hparams.audio_n_fft / 2; // = 200
+
+    std::vector<float> padded(n_samples + 2 * pad, 0.0f);
+    // Reflect start: padded[0..pad-1] = samples[pad..1] (reversed)
+    for (int i = 0; i < pad; i++) {
+        int src = pad - i; // samples[pad], samples[pad-1], ..., samples[1]
+        padded[i] = (src < (int)n_samples) ? samples[src] : 0.0f;
+    }
+    std::copy(samples, samples + n_samples, padded.begin() + pad);
+    // Reflect end: padded[n+pad..n+2*pad-1] = samples[n-2..n-pad-1] (reversed)
+    for (int i = 0; i < pad; i++) {
+        int src = (int)n_samples - 2 - i; // samples[n-2], samples[n-3], ...
+        padded[n_samples + pad + i] = (src >= 0) ? samples[src] : 0.0f;
+    }
+
+    filter_params params;
+    params.n_mel            = hparams.n_mel_bins;
+    params.n_fft_bins       = 1 + (hparams.audio_n_fft / 2);
+    params.hann_window_size = hparams.audio_window_len;
+    params.hop_length       = hparams.audio_hop_len;
+    params.sample_rate      = hparams.audio_sample_rate;
+    params.no_padding       = true; // reflection padding already applied above
+    params.use_natural_log  = false; // log10
+
+    mtmd_audio_mel mel_full;
+    bool ok = log_mel_spectrogram(padded.data(), (int)padded.size(), 4, params, cache, mel_full);
+    if (!ok) {
+        return false;
+    }
+
+    // Whisper-style normalization: clamp to (max - 8), scale to [-1, 1]
+    {
+        double mmax = -1e20;
+        for (float v : mel_full.data) {
+            if (v > mmax) mmax = v;
+        }
+        mmax -= 8.0;
+        for (float & v : mel_full.data) {
+            v = (std::max((double)v, mmax) + 4.0) / 4.0;
+        }
+    }
+
+    // The effective frame count: center-padded STFT gives ~n_samples/hop_length frames.
+    // We take min(mel_full.n_len, n_samples/hop + 1) to avoid including excess frames.
+    const int n_eff = std::min(mel_full.n_len,
+                               (int)(n_samples / hparams.audio_hop_len) + 1);
+
+    // Split into inference windows matching n_window_infer=800 from model config.
+    // Each window is padded to the next multiple of chunk_size for the cgraph.
+    // The mtmd caller loops over output entries, so long audio is handled automatically.
+    const int chunk_size  = 100; // conv sub-chunk size (n_window * 2, n_window=50)
+    const int window_size = 800; // mel frames per forward pass (n_window_infer=800)
+
+    for (int off = 0; off < n_eff; off += window_size) {
+        const int win_eff    = std::min(window_size, n_eff - off);
+        const int n_chunks   = (win_eff + chunk_size - 1) / chunk_size;
+        const int n_padded   = n_chunks * chunk_size;
+
+        mtmd_audio_mel out;
+        out.n_mel     = mel_full.n_mel;
+        out.n_len     = n_padded;
+        out.n_len_org = win_eff;
+        out.data.assign(out.n_mel * out.n_len, 0.0f);
+        for (int m = 0; m < out.n_mel; m++) {
+            const int copy_len = std::min(win_eff, mel_full.n_len - off);
+            if (copy_len > 0) {
+                std::copy(mel_full.data.begin() + (size_t)m * mel_full.n_len + off,
+                          mel_full.data.begin() + (size_t)m * mel_full.n_len + off + copy_len,
+                          out.data.begin()      + (size_t)m * out.n_len);
+            }
+        }
+        output.push_back(std::move(out));
+    }
+    return true;
+}
+
 //
 // mtmd_audio_preprocessor_conformer
 //
@@ -650,6 +759,108 @@ bool mtmd_audio_preprocessor_conformer::preprocess(const float *
     return true;
 }
 
+//
+// mtmd_audio_preprocessor_granite_speech
+//
+
+void mtmd_audio_preprocessor_granite_speech::initialize() {
+    cache.fill_sin_cos_table(hparams.audio_n_fft);
+    cache.fill_hann_window(hparams.audio_window_len, true);
+    cache.fill_mel_filterbank_matrix(
+        hparams.n_mel_bins / 2, hparams.audio_n_fft, hparams.audio_sample_rate,
+        0.0f, -1.0f, false, 1.0f, true);
+}
+
+bool mtmd_audio_preprocessor_granite_speech::preprocess(const float *                 samples,
+                                                        size_t                        n_samples,
+                                                        std::vector<mtmd_audio_mel> & output) {
+    if (n_samples == 0) {
+        return false;
+    }
+
+    GGML_ASSERT(!cache.sin_vals.empty());
+    GGML_ASSERT(!cache.cos_vals.empty());
+    GGML_ASSERT(!cache.filters.data.empty());
+
+    const int n_fft = hparams.audio_n_fft;
+    const int pad   = n_fft / 2;
+
+    // reflect padding
+    const int n_padded = (int)n_samples + 2 * pad;
+    std::vector<float> padded(n_padded, 0.0f);
+    std::copy(samples, samples + n_samples, padded.data() + pad);
+    for (int i = 0; i < pad; i++) {
+        int src = i + 1;
+        if (src >= (int)n_samples) {
+            src = (int)n_samples - 1;
+        }
+        padded[pad - 1 - i] = samples[src];
+    }
+    for (int i = 0; i < pad; i++) {
+        int src = (int)n_samples - 2 - i;
+        if (src < 0) {
+            src = 0;
+        }
+        padded[pad + (int)n_samples + i] = samples[src];
+    }
+
+    filter_params params;
+    params.n_mel            = hparams.n_mel_bins / 2;
+    params.n_fft_bins       = 1 + (n_fft / 2);
+    params.hann_window_size = hparams.audio_window_len;
+    params.hop_length       = hparams.audio_hop_len;
+    params.sample_rate      = hparams.audio_sample_rate;
+    params.no_padding       = true;
+    params.center_padding   = false;
+    params.preemph          = 0.0f;
+    params.use_natural_log  = false;
+    params.norm_per_feature = false;
+    params.mel_floor        = 1e-10f;
+
+    mtmd_audio_mel mel;
+    if (!log_mel_spectrogram(padded.data(), n_padded, 4, params, cache, mel)) {
+        return false;
+    }
+
+    double mmax = -1e20;
+    for (int i = 0; i < mel.n_mel * mel.n_len; i++) {
+        if (mel.data[i] > mmax) {
+            mmax = mel.data[i];
+        }
+    }
+    mmax -= 8.0;
+
+    for (int i = 0; i < mel.n_mel * mel.n_len; i++) {
+        if (mel.data[i] < mmax) {
+            mel.data[i] = mmax;
+        }
+        mel.data[i] = (mel.data[i] + 4.0) / 4.0;
+    }
+
+    int n_frames = mel.n_len;
+    if (n_frames % 2 == 1) {
+        n_frames--;
+    }
+    const int n_mel     = mel.n_mel;
+    const int n_stacked = n_frames / 2;
+
+    mtmd_audio_mel stacked;
+    stacked.n_mel     = 2 * n_mel;
+    stacked.n_len     = n_stacked;
+    stacked.n_len_org = (int)n_samples;
+    stacked.data.resize(2 * n_mel * n_stacked);
+
+    for (int t = 0; t < n_stacked; t++) {
+        for (int m = 0; m < n_mel; m++) {
+            stacked.data[m * n_stacked + t] = mel.data[m * mel.n_len + 2 * t];
+            stacked.data[(m + n_mel) * n_stacked + t] = mel.data[m * mel.n_len + 2 * t + 1];
+        }
+    }
+
+    output.push_back(std::move(stacked));
+    return true;
+}
+
 //
 // mtmd_audio_preprocessor_gemma4a
 //
diff --git a/tools/mtmd/mtmd-audio.h b/tools/mtmd/mtmd-audio.h
index efaa14f924f..98ccb642415 100644
--- a/tools/mtmd/mtmd-audio.h
+++ b/tools/mtmd/mtmd-audio.h
@@ -78,6 +78,15 @@ struct mtmd_audio_preprocessor_conformer : mtmd_audio_preprocessor {
     mtmd_audio_cache cache;
 };
 
+struct mtmd_audio_preprocessor_granite_speech : mtmd_audio_preprocessor {
+    mtmd_audio_preprocessor_granite_speech(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
+    void initialize() override;
+    bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
+
+  private:
+    mtmd_audio_cache cache;
+};
+
 struct mtmd_audio_preprocessor_gemma4a : mtmd_audio_preprocessor {
     mtmd_audio_preprocessor_gemma4a(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
     void initialize() override;
@@ -87,6 +96,15 @@ struct mtmd_audio_preprocessor_gemma4a : mtmd_audio_preprocessor {
     mtmd_audio_cache cache;
 };
 
+struct mtmd_audio_preprocessor_qwen3a : mtmd_audio_preprocessor {
+    mtmd_audio_preprocessor_qwen3a(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
+    void initialize() override;
+    bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
+
+  private:
+    mtmd_audio_cache cache;
+};
+
 //
 // streaming ISTFT - converts spectrogram frames back to audio one frame at a time
 //
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index dd72dfb17c8..d6e551618e8 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -90,7 +90,7 @@ struct mtmd_cli_context {
     int n_threads    = 1;
     llama_pos n_past = 0;
 
-    base_callback_data cb_data;
+    common_debug_cb_user_data cb_data;
 
     mtmd_cli_context(common_params & params) : llama_init(common_init_from_params(params)) {
         model = llama_init->model();
@@ -145,7 +145,7 @@ struct mtmd_cli_context {
         mparams.image_max_tokens = params.image_max_tokens;
         if (std::getenv("MTMD_DEBUG_GRAPH") != nullptr) {
             mparams.cb_eval_user_data = &cb_data;
-            mparams.cb_eval = common_debug_cb_eval<false>;
+            mparams.cb_eval = common_debug_cb_eval;
         }
         ctx_vision.reset(mtmd_init_from_file(clip_path, model, mparams));
         if (!ctx_vision.get()) {
@@ -295,6 +295,8 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    ggml_backend_load_all();
+
     mtmd_cli_context ctx(params);
     LOG_INF("%s: loading model: %s\n", __func__, params.model.path.c_str());
 
diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp
index c1a36011788..1b058e02601 100644
--- a/tools/mtmd/mtmd-image.cpp
+++ b/tools/mtmd/mtmd-image.cpp
@@ -584,7 +584,9 @@ bool mtmd_image_preprocessor_llava_uhd::preprocess(const clip_image_u8 & img, cl
 
 mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_llava_uhd::get_slice_instructions(const clip_image_size & original_size) {
     mtmd_image_preprocessor_llava_uhd::slice_instructions res;
-    const int patch_size      = hparams.patch_size;
+    // align slices by patch_size * n_merge so an integer number of merger output tokens fits per slice
+    const int n_merge         = hparams.n_merge > 0 ? hparams.n_merge : 1;
+    const int patch_size      = hparams.patch_size * n_merge;
     const int slice_size      = hparams.image_size;
     const int original_width  = original_size.width;
     const int original_height = original_size.height;
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index 854ac81e0a5..8f12d0b43ea 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -35,15 +35,23 @@ struct mtmd_bitmap {
 
 // position indexing for decoder model
 enum mtmd_pos_type {
-    MTMD_POS_TYPE_NORMAL, // number of positions equals to number of tokens
-    MTMD_POS_TYPE_MROPE, // qwen-vl mrope style, each image takes max(t,h,w) position indexes
+    MTMD_POS_TYPE_NORMAL,    // number of positions equals to number of tokens
+    MTMD_POS_TYPE_MROPE,     // qwen-vl mrope style, each image takes max(t,h,w) position indexes
+    MTMD_POS_TYPE_HUNYUANVL, // HunyuanVL mrope + BOI/EOI/newline layout with XD-RoPE dim-3
 };
 
 struct mtmd_image_tokens {
     uint32_t nx; // number of tokens in x direction
     uint32_t ny; // number of tokens in y direction
     mtmd_pos_type pos = MTMD_POS_TYPE_NORMAL;
-    uint32_t n_tokens() const { return nx * ny; }
+    uint32_t image_idx = 0; // 0-based position of this image among image chunks in the prompt(used by pos == MTMD_POS_TYPE_HUNYUANVL)
+    uint32_t n_tokens() const {
+        if (pos == MTMD_POS_TYPE_HUNYUANVL) {
+            // [BOI] [row0 tokens + newline] ... [row(ny-1) tokens + newline] [EOI]
+            return (nx + 1) * ny + 2;
+        }
+        return nx * ny;
+    }
     clip_image_f32_batch batch_f32; // preprocessed image patches
     std::string id; // optional user-defined ID, useful for KV cache tracking
 
@@ -52,6 +60,7 @@ struct mtmd_image_tokens {
             nx,
             ny,
             pos,
+            image_idx,
             batch_f32.clone(),
             id
         };
@@ -186,6 +195,7 @@ struct mtmd_context {
 
         auto decoder_rope_type = llama_model_rope_type(text_model);
         switch (decoder_rope_type) {
+            case LLAMA_ROPE_TYPE_NONE:
             case LLAMA_ROPE_TYPE_NORM:
             case LLAMA_ROPE_TYPE_NEOX:
                 {
@@ -300,9 +310,22 @@ struct mtmd_context {
                     }
                     image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
                 } break;
+            case PROJECTOR_TYPE_MINICPMV4_6:
+                {
+                    slice_tmpl        = MTMD_SLICE_TMPL_MINICPMV_2_6;
+                    tok_ov_img_start  = {lookup_token("<image>")};
+                    tok_ov_img_end    = {lookup_token("</image>")};
+                    tok_sli_img_start = {lookup_token("<slice>")};
+                    tok_sli_img_end   = {lookup_token("</slice>")};
+                    tok_row_end       = {lookup_token("\n")};
+                    tok_row_end_trail = false; // no trailing end-of-row token
+                    ov_img_first      = true;
+                    image_preproc     = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
+                } break;
             case PROJECTOR_TYPE_QWEN2VL:
             case PROJECTOR_TYPE_QWEN25VL:
             case PROJECTOR_TYPE_QWEN3VL:
+            case PROJECTOR_TYPE_MIMOVL:
                 {
                     // <|vision_start|> ... (image embeddings) ... <|vision_end|>
                     img_beg = "<|vision_start|>";
@@ -316,6 +339,19 @@ struct mtmd_context {
                     img_end = "<|vision_end|>";
                     image_preproc = std::make_unique<mtmd_image_preprocessor_youtuvl>(ctx_v);
                 } break;
+            case PROJECTOR_TYPE_YASA2:
+                {
+                    img_beg = "<image>";
+                    img_end = "</image>";
+                    // Currently only supprots single-tile preprocessing: any input is downscaled
+                    // to one image_size x image_size tile (64 output tokens via 8x8 adaptive avg
+                    // pool).
+                    // However, the model itself supports llava-uhd multi-tile tiling for high-res
+                    // images. This will be implemented in a future PR (dispatch on has_pinpoints
+                    // - see LDP/COGVLM branch above) and emit image_grid_pinpoints in the conversion
+                    // script.
+                    image_preproc = std::make_unique<mtmd_image_preprocessor_fixed_size>(ctx_v);
+                } break;
             case PROJECTOR_TYPE_GEMMA3:
             case PROJECTOR_TYPE_GEMMA3NV:
                 {
@@ -453,6 +489,7 @@ struct mtmd_context {
                     image_preproc = std::make_unique<mtmd_image_preprocessor_deepseekocr>(ctx_v);
                 } break;
             case PROJECTOR_TYPE_HUNYUANOCR:
+            case PROJECTOR_TYPE_HUNYUANVL:
                 {
                     // note: these use fullwidth ｜ (U+FF5C) and ▁ (U+2581) to match the tokenizer vocabulary
                     img_beg = "<｜hy_place▁holder▁no▁100｜>";
@@ -478,7 +515,6 @@ struct mtmd_context {
         // set preprocessor
         switch (proj) {
             case PROJECTOR_TYPE_QWEN2A:
-            case PROJECTOR_TYPE_QWEN3A:
             case PROJECTOR_TYPE_QWEN25O:
                 {
                     // <|audio_bos|> ... (embeddings) ... <|audio_eos|>
@@ -486,6 +522,12 @@ struct mtmd_context {
                     aud_end = "<|audio_eos|>";
                     audio_preproc = std::make_unique<mtmd_audio_preprocessor_whisper>(ctx_a);
                 } break;
+            case PROJECTOR_TYPE_QWEN3A:
+                {
+                    aud_beg = "<|audio_start|>";
+                    aud_end = "<|audio_end|>";
+                    audio_preproc = std::make_unique<mtmd_audio_preprocessor_qwen3a>(ctx_a);
+                } break;
             case PROJECTOR_TYPE_VOXTRAL:
                 {
                     // [BEGIN_AUDIO] ... (embeddings) ...
@@ -508,6 +550,10 @@ struct mtmd_context {
                 {
                     audio_preproc = std::make_unique<mtmd_audio_preprocessor_conformer>(ctx_a);
                 } break;
+            case PROJECTOR_TYPE_GRANITE_SPEECH:
+                {
+                    audio_preproc = std::make_unique<mtmd_audio_preprocessor_granite_speech>(ctx_a);
+                } break;
             case PROJECTOR_TYPE_GEMMA4A:
                 {
                     aud_beg = "<|audio>";
@@ -598,6 +644,7 @@ struct mtmd_tokenizer {
     const llama_vocab * vocab;
 
     mtmd_input_chunks cur;
+    uint32_t n_images_added = 0; // 0-based index assigned to the next image chunk
 
     mtmd_tokenizer(mtmd_context * ctx,
             const mtmd_input_text * text,
@@ -806,6 +853,14 @@ struct mtmd_tokenizer {
                     image_tokens->ny = 1;
                 }
                 image_tokens->pos = ctx->pos_type;
+                // HunyuanVL wraps the image grid with BOI/EOI and adds one newline per row,
+                // and uses XD-RoPE (dim-3 = image index). Override the position type so that
+                // n_tokens() and mtmd_image_tokens_get_decoder_pos pick the HunyuanVL layout.
+                if (ctx->proj_type_v() == PROJECTOR_TYPE_HUNYUANVL) {
+                    image_tokens->pos       = MTMD_POS_TYPE_HUNYUANVL;
+                    image_tokens->image_idx = n_images_added;
+                    GGML_ASSERT(n_tokens == (size_t)image_tokens->n_tokens());
+                }
                 image_tokens->batch_f32 = std::move(batch_f32);
                 image_tokens->id = bitmap->id; // optional
 
@@ -826,6 +881,9 @@ struct mtmd_tokenizer {
                 add_text(ctx->img_end, true); // add image end token
             }
 
+            // advance image-chunk counter so the next image gets the next XD-RoPE dim-3 slot
+            n_images_added++;
+
         } else {
             // handle audio
 
@@ -1273,6 +1331,38 @@ mtmd_decoder_pos mtmd_image_tokens_get_decoder_pos(const mtmd_image_tokens * ima
                 pos.y = pos_0 + i;
                 pos.z = pos_0 + i;
             } break;
+        case MTMD_POS_TYPE_HUNYUANVL:
+            {
+                // HunyuanVL layout: [BOI] [row0 tokens + newline] ... [row(ny-1) tokens + newline] [EOI]
+                // Total = 1 + ny*(nx+1) + 1. BOI and EOI use sequential positions in every dim;
+                // content and row-newline tokens use (row, col) with XD-RoPE dim-3 = image_idx.
+                const uint32_t nx      = image_tokens->nx;
+                const uint32_t n_total = image_tokens->n_tokens();
+                if (i == 0) {
+                    // BOI
+                    pos.t = pos_0 + i;
+                    pos.x = pos_0 + i;
+                    pos.y = pos_0 + i;
+                    pos.z = pos_0 + i;
+                } else if (i == n_total - 1) {
+                    // EOI
+                    pos.t = pos_0 + i;
+                    pos.x = pos_0 + i;
+                    pos.y = pos_0 + i;
+                    pos.z = pos_0 + i;
+                } else {
+                    // content token at (row, col), or the trailing newline of a row (col == nx)
+                    //   section 0 = sequential, section 1 = w(col), section 2 = h(row), section 3 = image_count.
+                    // set_position_mrope_2d writes .y -> section 1 and .x -> section 2
+                    const uint32_t offset = (uint32_t)i - 1;
+                    const uint32_t row    = offset / (nx + 1);
+                    const uint32_t col    = offset % (nx + 1);
+                    pos.t = pos_0 + i;
+                    pos.x = row;
+                    pos.y = col;
+                    pos.z = image_tokens->image_idx;
+                }
+            } break;
         default:
             GGML_ABORT("invalid position type");
     }
@@ -1289,6 +1379,10 @@ llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens) {
             return std::max(image_tokens->nx, image_tokens->ny);
         case MTMD_POS_TYPE_NORMAL:
             return image_tokens->n_tokens();
+        case MTMD_POS_TYPE_HUNYUANVL:
+            // HunyuanVL: the sequential (dim-0) position advances by the full token count
+            // (includes BOI/EOI and row newline tokens), not by max(nx, ny)
+            return image_tokens->n_tokens();
         default:
             GGML_ABORT("invalid position type");
     }
@@ -1334,6 +1428,19 @@ void mtmd_log_set(ggml_log_callback log_callback, void * user_data) {
     g_logger_state.log_callback_user_data = user_data;
 }
 
+struct mtmd_caps mtmd_get_cap_from_file(const char * fname) {
+    try {
+        auto tmp = clip_get_cap(fname);
+        mtmd_caps cap;
+        cap.inp_audio  = tmp.has_audio;
+        cap.inp_vision = tmp.has_vision;
+        return cap;
+    } catch (const std::exception & e) {
+        LOG_ERR("%s: failed to get capabilities from file '%s': %s\n", __func__, fname, e.what());
+        return mtmd_caps{ false, false };
+    }
+}
+
 //
 // Debugging API (NOT intended for public use)
 //
diff --git a/tools/mtmd/mtmd.h b/tools/mtmd/mtmd.h
index e364174b820..54b9515a3ea 100644
--- a/tools/mtmd/mtmd.h
+++ b/tools/mtmd/mtmd.h
@@ -244,6 +244,14 @@ MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx);
 // If this is not called, or NULL is supplied, everything is output on stderr.
 MTMD_API void mtmd_log_set(ggml_log_callback log_callback, void * user_data);
 
+// EXPERIMENTAL API to get mmproj's capabilities without initializing the full context
+// This is only intended to be used by llama-server, breaking changes is expected
+struct mtmd_caps {
+    bool inp_vision;
+    bool inp_audio;
+};
+MTMD_API struct mtmd_caps mtmd_get_cap_from_file(const char * mmproj_fname);
+
 /////////////////////////////////////////
 
 // test function, to be used in test-mtmd-c-api.c
diff --git a/tools/mtmd/tests.sh b/tools/mtmd/tests.sh
index 5da48d61bfd..83416fb272b 100755
--- a/tools/mtmd/tests.sh
+++ b/tools/mtmd/tests.sh
@@ -91,6 +91,7 @@ add_test_vision "ggml-org/LightOnOCR-1B-1025-GGUF:Q8_0"
 add_test_vision "ggml-org/DeepSeek-OCR-GGUF:Q8_0" -p "Free OCR." --chat-template deepseek-ocr
 add_test_vision "ggml-org/dots.ocr-GGUF:Q8_0" -p "OCR"
 add_test_vision "ggml-org/HunyuanOCR-GGUF:Q8_0" -p "OCR"
+add_test_vision "ggml-org/HunyuanVL-4B-GGUF:Q8_0"
 add_test_vision "ggml-org/gemma-4-E2B-it-GGUF:Q8_0" --jinja
 
 add_test_audio  "ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF:Q8_0"
diff --git a/tools/rpc/rpc-server.cpp b/tools/rpc/rpc-server.cpp
index 03ab78e5f05..08e68039141 100644
--- a/tools/rpc/rpc-server.cpp
+++ b/tools/rpc/rpc-server.cpp
@@ -317,7 +317,7 @@ int main(int argc, char * argv[]) {
     const char * cache_dir = nullptr;
     std::string cache_dir_str;
     if (params.use_cache) {
-        cache_dir_str = fs_get_cache_directory() + "rpc/";
+        cache_dir_str = fs_get_cache_directory() + "rpc" + DIRECTORY_SEPARATOR;
         if (!fs_create_directory_with_parents(cache_dir_str)) {
             fprintf(stderr, "Failed to create cache directory: %s\n", cache_dir_str.c_str());
             return 1;
diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt
index 0cce99f5968..57d3e871d99 100644
--- a/tools/server/CMakeLists.txt
+++ b/tools/server/CMakeLists.txt
@@ -5,6 +5,8 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
 set(TARGET server-context)
 
 add_library(${TARGET} STATIC
+    server-chat.cpp
+    server-chat.h
     server-task.cpp
     server-task.h
     server-queue.cpp
@@ -38,36 +40,11 @@ set(TARGET_SRCS
     server-models.h
 )
 
-option(LLAMA_BUILD_WEBUI "Build the embedded Web UI" ON)
-
-if (LLAMA_BUILD_WEBUI)
-    set(PUBLIC_ASSETS
-        index.html
-        bundle.js
-        bundle.css
-        loading.html
-    )
-
-    foreach(asset ${PUBLIC_ASSETS})
-        set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
-        set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
-        list(APPEND TARGET_SRCS ${output})
-        add_custom_command(
-            DEPENDS "${input}"
-            OUTPUT "${output}"
-            COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
-        )
-        set_source_files_properties(${output} PROPERTIES GENERATED TRUE)
-    endforeach()
-    add_definitions(-DLLAMA_BUILD_WEBUI)
-else()
-endif()
-
 add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)
 
 target_include_directories(${TARGET} PRIVATE ../mtmd)
 target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
-target_link_libraries(${TARGET} PRIVATE server-context PUBLIC llama-common cpp-httplib ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE server-context llama-ui PUBLIC llama-common cpp-httplib ${CMAKE_THREAD_LIBS_INIT})
 
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md
index a9c1e7385fc..0ff334724a3 100644
--- a/tools/server/README-dev.md
+++ b/tools/server/README-dev.md
@@ -224,7 +224,7 @@ The SvelteKit-based Web UI is introduced in this PR: https://github.com/ggml-org
 
 ### Architecture
 
-The WebUI follows a layered architecture:
+The UI follows a layered architecture:
 
 ```
 Routes → Components → Hooks → Stores → Services → Storage/API
@@ -234,7 +234,7 @@ Routes → Components → Hooks → Stores → Services → Storage/API
 -   **Services** - stateless API/database communication (`ChatService`, `ModelsService`, `PropsService`, `DatabaseService`)
 -   **Hooks** - reusable logic (`useModelChangeValidation`, `useProcessingState`)
 
-For detailed architecture diagrams, see [`tools/server/webui/docs/`](webui/docs/):
+For detailed architecture diagrams, see [`tools/ui/docs/`](../ui/docs/):
 
 -   `high-level-architecture.mmd` - full architecture with all modules
 -   `high-level-architecture-simplified.mmd` - simplified overview
@@ -246,7 +246,7 @@ For detailed architecture diagrams, see [`tools/server/webui/docs/`](webui/docs/
 
 ```sh
 # make sure you have Node.js installed
-cd tools/server/webui
+cd tools/ui
 npm i
 
 # run dev server (with hot reload)
diff --git a/tools/server/README.md b/tools/server/README.md
index db1f2703904..11098af2883 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -82,7 +82,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU<br/>(env: LLAMA_ARG_CPU_MOE) |
 | `-ncmoe, --n-cpu-moe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU<br/>(env: LLAMA_ARG_N_CPU_MOE) |
 | `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
-| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
+| `-sm, --split-mode {none,layer,row,tensor}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs (pipelined)<br/>- row: split weight across GPUs by rows (parallelized)<br/>- tensor: split weights and KV across GPUs (parallelized, EXPERIMENTAL)<br/>(env: LLAMA_ARG_SPLIT_MODE) |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)<br/>(env: LLAMA_ARG_MAIN_GPU) |
 | `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')<br/>(env: LLAMA_ARG_FIT) |
@@ -100,7 +100,6 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(env: LLAMA_ARG_MODEL_URL) |
 | `-dr, --docker-repo [<repo>/]<model>[:quant]` | Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.<br/>example: gemma3<br/>(default: unused)<br/>(env: LLAMA_ARG_DOCKER_REPO) |
 | `-hf, -hfr, --hf-repo <user>/<model>[:quant]` | Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.<br/>mmproj is also downloaded automatically if available. to disable, add --no-mmproj<br/>example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M<br/>(default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
-| `-hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_HFD_REPO) |
 | `-hff, --hf-file FILE` | Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
 | `-hfv, -hfrv, --hf-repo-v <user>/<model>[:quant]` | Hugging Face model repository for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_REPO_V) |
 | `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_FILE_V) |
@@ -111,10 +110,10 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) |
 | `--offline` | Offline mode: forces use of cache, prevents network access<br/>(env: LLAMA_OFFLINE) |
 | `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:<br/> - 0: generic output<br/> - 1: error<br/> - 2: warning<br/> - 3: info<br/> - 4: debug<br/>(default: 3)<br/><br/>(env: LLAMA_LOG_VERBOSITY) |
-| `--log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_LOG_PREFIX) |
-| `--log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_LOG_TIMESTAMPS) |
-| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) |
-| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) |
+| `--log-prefix, --no-log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_ARG_LOG_PREFIX) |
+| `--log-timestamps, --no-log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_ARG_LOG_TIMESTAMPS) |
+| `--spec-draft-type-k, -ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K) |
+| `--spec-draft-type-v, -ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V) |
 
 
 ### Sampling params
@@ -182,9 +181,6 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--mmproj-offload, --no-mmproj-offload` | whether to enable GPU offloading for multimodal projector (default: enabled)<br/>(env: LLAMA_ARG_MMPROJ_OFFLOAD) |
 | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MIN_TOKENS) |
 | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MAX_TOKENS) |
-| `-otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
-| `-cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_CPU_MOE_DRAFT) |
-| `-ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_N_CPU_MOE_DRAFT) |
 | `-a, --alias STRING` | set model name aliases, comma-separated (to be used by API)<br/>(env: LLAMA_ARG_ALIAS) |
 | `--tags STRING` | set model tags, comma-separated (informational, not used for routing)<br/>(env: LLAMA_ARG_TAGS) |
 | `--host HOST` | ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
@@ -192,11 +188,15 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--reuse-port` | allow multiple sockets to bind to the same port (default: disabled)<br/>(env: LLAMA_ARG_REUSE_PORT) |
 | `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
 | `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )<br/>(env: LLAMA_ARG_API_PREFIX) |
-| `--webui-config JSON` | JSON that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG) |
-| `--webui-config-file PATH` | JSON file that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG_FILE) |
-| `--webui-mcp-proxy, --no-webui-mcp-proxy` | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)<br/>(env: LLAMA_ARG_WEBUI_MCP_PROXY) |
-| `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)<br/>specify "all" to enable all tools<br/>available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff<br/>(env: LLAMA_ARG_TOOLS) |
-| `--webui, --no-webui` | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_WEBUI) |
+| `--webui-config JSON` | [DEPRECATED: use --ui-config] JSON that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG) |
+| `--ui-config JSON` | JSON that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG) |
+| `--webui-config-file PATH` | [DEPRECATED: use --ui-config-file] JSON file that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG_FILE) |
+| `--ui-config-file PATH` | JSON file that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG_FILE) |
+| `--webui-mcp-proxy, --no-webui-mcp-proxy` | [DEPRECATED: use --ui-mcp-proxy/--no-ui-mcp-proxy] experimental: whether to enable MCP CORS proxy<br/>(env: LLAMA_ARG_WEBUI_MCP_PROXY) |
+| `--ui-mcp-proxy, --no-ui-mcp-proxy` | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)<br/>(env: LLAMA_ARG_UI_MCP_PROXY) |
+| `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)<br/>specify "all" to enable all tools<br/>available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff, get_datetime<br/>(env: LLAMA_ARG_TOOLS) |
+| `--webui, --no-webui` | [DEPRECATED: use --ui/--no-ui] whether to enable the Web UI<br/>(env: LLAMA_ARG_WEBUI) |
+| `--ui, --no-ui` | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_UI) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--rerank, --reranking` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
 | `--api-key KEY` | API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)<br/>(env: LLAMA_API_KEY) |
@@ -222,27 +222,53 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
-| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-ocr, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-ocr, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)<br/>(env: LLAMA_ARG_SKIP_CHAT_PARSING) |
 | `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled<br/><br/>(env: LLAMA_ARG_PREFILL_ASSISTANT) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled) |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
 | `--sleep-idle-seconds SECONDS` | number of seconds of idleness after which the server will sleep (default: -1; -1 = disabled) |
-| `-td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
-| `-tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |
-| `--draft, --draft-n, --draft-max N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_DRAFT_MAX) |
-| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_DRAFT_MIN) |
-| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)<br/>(env: LLAMA_ARG_DRAFT_P_MIN) |
-| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE_DRAFT) |
-| `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
-| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
-| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
-| `--spec-replace TARGET DRAFT` | translate the string in TARGET into DRAFT if the draft model and main model are not compatible |
-| `--spec-type [none\|ngram-cache\|ngram-simple\|ngram-map-k\|ngram-map-k4v\|ngram-mod]` | type of speculative decoding to use when no draft model is provided (default: none)<br/><br/>(env: LLAMA_ARG_SPEC_TYPE) |
-| `--spec-ngram-size-n N` | ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: 12) |
-| `--spec-ngram-size-m N` | ngram size M for ngram-simple/ngram-map speculative decoding, length of draft m-gram (default: 48) |
-| `--spec-ngram-min-hits N` | minimum hits for ngram-map speculative decoding (default: 1) |
+| `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) |
+| `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
+| `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |
+| `--spec-draft-cpu-mask, -Cd, --cpu-mask-draft M` | Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask) |
+| `--spec-draft-cpu-range, -Crd, --cpu-range-draft lo-hi` | Ranges of CPUs for affinity. Complements --cpu-mask-draft |
+| `--spec-draft-cpu-strict, --cpu-strict-draft <0\|1>` | Use strict CPU placement for draft model (default: same as --cpu-strict) |
+| `--spec-draft-prio, --prio-draft N` | set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) |
+| `--spec-draft-poll, --poll-draft <0\|1>` | Use polling to wait for draft model work (default: same as --poll) |
+| `--spec-draft-cpu-mask-batch, -Cbd, --cpu-mask-batch-draft M` | Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask) |
+| `--spec-draft-cpu-strict-batch, --cpu-strict-batch-draft <0\|1>` | Use strict CPU placement for draft model (default: --cpu-strict-draft) |
+| `--spec-draft-prio-batch, --prio-batch-draft N` | set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) |
+| `--spec-draft-poll-batch, --poll-batch-draft <0\|1>` | Use polling to wait for draft model work (default: --poll-draft) |
+| `--spec-draft-override-tensor, -otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
+| `--spec-draft-cpu-moe, -cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_CPU_MOE) |
+| `--spec-draft-n-cpu-moe, --spec-draft-ncmoe, -ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE) |
+| `--spec-draft-n-max N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MAX) |
+| `--spec-draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_SPEC_DRAFT_N_MIN) |
+| `--spec-draft-p-split, --draft-p-split P` | speculative decoding split probability (default: 0.10)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT) |
+| `--spec-draft-p-min, --draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)<br/>(env: LLAMA_ARG_SPEC_DRAFT_P_MIN) |
+| `--spec-draft-device, -devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
+| `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
+| `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_MODEL) |
+| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)<br/><br/>(env: LLAMA_ARG_SPEC_TYPE) |
+| `--spec-ngram-mod-n-min N` | minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48) |
+| `--spec-ngram-mod-n-max N` | maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64) |
+| `--spec-ngram-mod-n-match N` | ngram-mod lookup length (default: 24) |
+| `--spec-ngram-simple-size-n N` | ngram size N for ngram-simple speculative decoding, length of lookup n-gram (default: 12) |
+| `--spec-ngram-simple-size-m N` | ngram size M for ngram-simple speculative decoding, length of draft m-gram (default: 48) |
+| `--spec-ngram-simple-min-hits N` | minimum hits for ngram-simple speculative decoding (default: 1) |
+| `--spec-ngram-map-k-size-n N` | ngram size N for ngram-map-k speculative decoding, length of lookup n-gram (default: 12) |
+| `--spec-ngram-map-k-size-m N` | ngram size M for ngram-map-k speculative decoding, length of draft m-gram (default: 48) |
+| `--spec-ngram-map-k-min-hits N` | minimum hits for ngram-map-k speculative decoding (default: 1) |
+| `--spec-ngram-map-k4v-size-n N` | ngram size N for ngram-map-k4v speculative decoding, length of lookup n-gram (default: 12) |
+| `--spec-ngram-map-k4v-size-m N` | ngram size M for ngram-map-k4v speculative decoding, length of draft m-gram (default: 48) |
+| `--spec-ngram-map-k4v-min-hits N` | minimum hits for ngram-map-k4v speculative decoding (default: 1) |
+| `--draft, --draft-n, --draft-max N` | the argument has been removed. use --spec-draft-n-max or --spec-ngram-mod-n-max<br/>(env: LLAMA_ARG_DRAFT_MAX) |
+| `--draft-min, --draft-n-min N` | the argument has been removed. use --spec-draft-n-min or --spec-ngram-mod-n-min<br/>(env: LLAMA_ARG_DRAFT_MIN) |
+| `--spec-ngram-size-n N` | the argument has been removed. use the respective --spec-ngram-*-size-n or --spec-ngram-mod-n-match |
+| `--spec-ngram-size-m N` | the argument has been removed. use the respective --spec-ngram-*-size-m |
+| `--spec-ngram-min-hits N` | the argument has been removed. use the respective --spec-ngram-*-min-hits |
 | `-mv, --model-vocoder FNAME` | vocoder model for audio generation (default: unused) |
 | `--tts-use-guide-tokens` | Use guide tokens to improve TTS word recall |
 | `--embd-gemma-default` | use default EmbeddingGemma model (note: can download weights from the internet) |
@@ -256,6 +282,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--gpt-oss-120b-default` | use gpt-oss-120b (note: can download weights from the internet) |
 | `--vision-gemma-4b-default` | use Gemma 3 4B QAT (note: can download weights from the internet) |
 | `--vision-gemma-12b-default` | use Gemma 3 12B QAT (note: can download weights from the internet) |
+| `--spec-default` | enable default speculative decoding config |
 
 <!-- HELP_END -->
 
@@ -1020,16 +1047,23 @@ If query param `?fail_on_no_slot=1` is set, this endpoint will respond with stat
 
 This endpoint is only accessible if `--metrics` is set.
 
-Available metrics:
-- `llamacpp:prompt_tokens_total`: Number of prompt tokens processed.
-- `llamacpp:tokens_predicted_total`: Number of generation tokens processed.
-- `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s.
-- `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s.
-- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. `1` means 100 percent usage.
-- `llamacpp:kv_cache_tokens`: KV-cache tokens.
-- `llamacpp:requests_processing`: Number of requests processing.
-- `llamacpp:requests_deferred`: Number of requests deferred.
-- `llamacpp:n_tokens_max`: High watermark of the context size observed.
+In *router mode* the query param `?model={model_id}` has to be set. This endpoint will respond with status code 400 `model name is missing from the request` if not set.
+
+#### Available metrics
+
+| Metric | Type | Description |
+| ------ | ---------------------- | ----------- |
+| `llamacpp:prompt_tokens_total` | Counter | Number of prompt tokens processed. |
+| `llamacpp:prompt_seconds_total` | Counter | Prompt process time in seconds. |
+| `llamacpp:prompt_tokens_seconds` | Gauge | Average prompt throughput in tokens/s. |
+| `llamacpp:tokens_predicted_total` | Counter | Number of generation tokens processed. |
+| `llamacpp:tokens_predicted_seconds_total` | Counter | Predict process time in seconds. |
+| `llamacpp:predicted_tokens_seconds` | Gauge | Average generation throughput in tokens/s. |
+| `llamacpp:requests_processing` | Gauge | Number of requests processing. |
+| `llamacpp:requests_deferred` | Gauge | Number of requests deferred. |
+| `llamacpp:n_tokens_max` | Counter | High watermark of the context size observed. |
+| `llamacpp:n_decode_total` | Counter | Total Number of llama_decode() calls. |
+| `llamacpp:n_busy_slots_per_decode` | Gauge | Average number of busy slots per llama_decode() call. |
 
 ### POST `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.
 
@@ -1291,6 +1325,22 @@ This provides information on the performance of the server. It also allows calcu
 
 The total number of tokens in context is equal to `prompt_n + cache_n + predicted_n`
 
+The response also includes a standard `usage` object:
+
+```js
+{
+    // ...
+    "usage": {
+        "completion_tokens": 48,
+        "prompt_tokens": 44,
+        "total_tokens": 92,
+        "prompt_tokens_details": {
+            "cached_tokens": 0
+        }
+    }
+}
+```
+
 *Reasoning support*
 
 The server supports parsing and returning reasoning via the `reasoning_content` field, similar to Deepseek API.
@@ -1621,7 +1671,12 @@ Listing all models in cache. The model metadata will also include a field to ind
 }
 ```
 
-Note: For a local GGUF (stored offline in a custom directory), the model object will have `"in_cache": false`.
+Note:
+1. For a local GGUF (stored offline in a custom directory), the model object will have `"in_cache": false`.
+2. Adding `?reload=1` to the query params will refresh the list of models. The behavior is as follow:
+    - If a model is running but updated or removed from the source, it will be unloaded
+    - If a model is not running, it will be added or updated according to the source
+3. When the model is loaded, the info from `/v1/models` is forwarded to router's `/v1/models`. This includes metadata about the model and the runtime instance.
 
 The `status` object can be:
 
@@ -1779,50 +1834,12 @@ Apart from error types supported by OAI, we also have custom types that are spec
 
 ### Custom default Web UI preferences
 
-You can specify default preferences for the web UI using `--webui-config <JSON config>` or `--webui-config-file <path to JSON config>`. For example, you can disable pasting long text as attachments and enable rendering Markdown in user messages with this command:
+You can specify default preferences for the web UI using `--ui-config <JSON config>` or `--ui-config-file <path to JSON config>`. For example, you can disable pasting long text as attachments and enable rendering Markdown in user messages with this command:
 
 ```bash
-./llama-server -m model.gguf --webui-config '{"pasteLongTextToFileLen": 0, "renderUserContentAsMarkdown": true}'
+./llama-server -m model.gguf --ui-config '{"pasteLongTextToFileLen": 0, "renderUserContentAsMarkdown": true}'
 ```
 
-You may find available preferences in [settings-config.ts](webui/src/lib/constants/settings-config.ts).
-
-### Legacy completion web UI
-
-A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggml-org/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./tools/server/public_legacy`
-
-For example:
+> **Note:** The old flags `--webui-config` and `--webui-config-file` are deprecated but still work as aliases.
 
-```sh
-./llama-server -m my_model.gguf -c 8192 --path ./tools/server/public_legacy
-```
-
-### Extending or building alternative Web Front End
-
-You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
-
-Read the documentation in `/completion.js` to see convenient ways to access llama.
-
-A simple example is below:
-
-```html
-<html>
-  <body>
-    <pre>
-      <script type="module">
-        import { llama } from '/completion.js'
-
-        const prompt = `### Instruction:
-Write dad jokes, each one paragraph.
-You can use html formatting if needed.
-
-### Response:`
-
-        for await (const chunk of llama(prompt)) {
-          document.write(chunk.data.content)
-        }
-      </script>
-    </pre>
-  </body>
-</html>
-```
+You may find available preferences in [settings-config.ts](../ui/src/lib/constants/settings-config.ts).
diff --git a/tools/server/public/bundle.css b/tools/server/public/bundle.css
deleted file mode 100644
index 1d8756d82d7..00000000000
--- a/tools/server/public/bundle.css
+++ /dev/null
@@ -1 +0,0 @@
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) 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) 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) 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) 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) 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) 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) 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) 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var(--border);padding:.5rem 1rem;margin:1.5rem 0;font-style:italic;color:var(--muted-foreground);background:var(--muted);border-radius:0 .375rem .375rem 0}div.svelte-15eq738 table{width:100%;margin:1.5rem 0;border-collapse:collapse;border:1px solid var(--border);border-radius:.375rem;overflow:hidden}div.svelte-15eq738 th{background:hsl(var(--muted) / .3);border:1px solid var(--border);padding:.5rem .75rem;text-align:left;font-weight:600}div.svelte-15eq738 td{border:1px solid var(--border);padding:.5rem .75rem}div.svelte-15eq738 tr:nth-child(2n){background:hsl(var(--muted) / .1)}div.markdown-user-content.svelte-15eq738 table,div.markdown-user-content.svelte-15eq738 th,div.markdown-user-content.svelte-15eq738 td,div.markdown-user-content.svelte-15eq738 .table-wrapper{border-color:currentColor}div.svelte-15eq738 hr{border:none;border-top:1px solid var(--border);margin:1.5rem 0}div.svelte-15eq738 img{border-radius:.5rem;box-shadow:0 1px 3px #0000001a,0 1px 2px -1px #0000001a;margin:1.5rem 0;max-width:100%;height:auto}div.svelte-15eq738 .code-block-wrapper{margin:1.5rem 0;border-radius:.75rem;overflow:hidden;border:1px solid color-mix(in oklch,var(--border) 30%,transparent);background:var(--code-background);box-shadow:0 1px 2px #0000000d;min-height:var(--min-message-height);max-height:var(--max-message-height)}.dark div.svelte-15eq738 .code-block-wrapper{border-color:color-mix(in oklch,var(--border) 20%,transparent)}div.svelte-15eq738 .code-block-scroll-container,.streaming-code-scroll-container.svelte-15eq738{min-height:var(--min-message-height);max-height:var(--max-message-height);overflow-y:auto;overflow-x:auto;padding:3rem 1rem 1rem;line-height:1.3}.full-height-code-blocks.svelte-15eq738 .code-block-wrapper{max-height:none}.full-height-code-blocks.svelte-15eq738 .code-block-scroll-container,.full-height-code-blocks.svelte-15eq738 .streaming-code-scroll-container:where(.svelte-15eq738){max-height:none;overflow-y:visible}div.svelte-15eq738 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pre{background:transparent;margin:0;border-radius:0;border:none;font-size:.875rem}div.svelte-15eq738 .mention{color:hsl(var(--primary));font-weight:500;text-decoration:none}div.svelte-15eq738 .mention:hover{text-decoration:underline}div.svelte-15eq738 .hashtag{color:hsl(var(--primary));font-weight:500;text-decoration:none}div.svelte-15eq738 .hashtag:hover{text-decoration:underline}div.svelte-15eq738 table{transition:all .2s ease}div.svelte-15eq738 table:hover{box-shadow:0 4px 6px -1px #0000001a,0 2px 4px -2px #0000001a}div.svelte-15eq738 th:hover,div.svelte-15eq738 td:hover{background:var(--muted)}.markdown-user-content.svelte-15eq738 a,.markdown-user-content.svelte-15eq738 a:hover{color:inherit}.markdown-user-content.svelte-15eq738 table:hover{box-shadow:none}.markdown-user-content.svelte-15eq738 th:hover,.markdown-user-content.svelte-15eq738 td:hover{background:inherit}div.svelte-15eq738 blockquote{transition:all .2s ease;position:relative}div.svelte-15eq738 blockquote:hover{border-left-width:6px;background:var(--muted);transform:translate(2px)}div.svelte-15eq738 blockquote:before{content:'"';position:absolute;top:-.5rem;left:.5rem;font-size:3rem;color:var(--muted-foreground);font-family:serif;line-height:1}div.svelte-15eq738 img{transition:all .3s ease;cursor:pointer}div.svelte-15eq738 img:hover{transform:scale(1.02);box-shadow:0 10px 15px -3px #0000001a,0 4px 6px -4px #0000001a}div.svelte-15eq738 .image-zoom-overlay{position:fixed;inset:0;background:#000c;display:flex;align-items:center;justify-content:center;z-index:1000;cursor:pointer}div.svelte-15eq738 .image-zoom-overlay img{max-width:90vw;max-height:90vh;border-radius:.5rem;box-shadow:0 25px 50px -12px #00000040}div.svelte-15eq738 hr{border:none;height:2px;background:linear-gradient(to right,transparent,var(--border),transparent);margin:2rem 0;position:relative}div.svelte-15eq738 hr:after{content:"";position:absolute;top:50%;left:50%;transform:translate(-50%,-50%);width:1rem;height:1rem;background:var(--border);border-radius:50%}div.svelte-15eq738 .table-wrapper{overflow-x:auto;margin:1.5rem 0;border-radius:.5rem;border:1px solid var(--border)}div.svelte-15eq738 .table-wrapper table{margin:0;border:none}@media (max-width: 640px){div.svelte-15eq738 h1{font-size:1.5rem}div.svelte-15eq738 h2{font-size:1.25rem}div.svelte-15eq738 h3{font-size:1.125rem}div.svelte-15eq738 table{font-size:.875rem}div.svelte-15eq738 th,div.svelte-15eq738 td{padding:.375rem .5rem}div.svelte-15eq738 .table-wrapper{margin:.5rem -1rem;border-radius:0;border-left:none;border-right:none}}@media (prefers-color-scheme: dark){div.svelte-15eq738 blockquote:hover{background:var(--muted)}}div.svelte-15eq738 .image-load-error{display:flex;align-items:center;justify-content:center;margin:1.5rem 0;padding:1.5rem;border-radius:.5rem;background:var(--muted);border:1px dashed 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diff --git a/tools/server/public/bundle.js b/tools/server/public/bundle.js
deleted file mode 100644
index b3b5673ae95..00000000000
--- a/tools/server/public/bundle.js
+++ /dev/null
@@ -1,13088 +0,0 @@
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-resolve()}batch_values=null}#traverse_effect_tree(root2,effects,render_effects){root2.f^=CLEAN;for(var effect2=root2.first,pending_boundary=null;effect2!==null;){var flags2=effect2.f,is_branch=(flags2&(BRANCH_EFFECT|ROOT_EFFECT))!==0,is_skippable_branch=is_branch&&(flags2&CLEAN)!==0,skip=is_skippable_branch||(flags2&INERT)!==0||this.skipped_effects.has(effect2);if(!skip&&effect2.fn!==null){is_branch?effect2.f^=CLEAN:pending_boundary!==null&&(flags2&(EFFECT|RENDER_EFFECT|MANAGED_EFFECT))!==0?pending_boundary.
-b.defer_effect(effect2):(flags2&EFFECT)!==0?effects.push(effect2):is_dirty(effect2)&&((flags2&BLOCK_EFFECT)!==0&&this.#maybe_dirty_effects.add(effect2),update_effect(effect2));var child2=effect2.first;if(child2!==null){effect2=child2;continue}}var parent=effect2.parent;for(effect2=effect2.next;effect2===null&&parent!==null;)parent===pending_boundary&&(pending_boundary=null),effect2=parent.next,parent=parent.parent}}#defer_effects(effects){for(var i=0;i<effects.length;i+=1)defer_effect(effects[i],
-this.#dirty_effects,this.#maybe_dirty_effects)}capture(source2,value){value!==UNINITIALIZED&&!this.previous.has(source2)&&this.previous.set(source2,value),(source2.f&ERROR_VALUE)===0&&(this.current.set(source2,source2.v),batch_values?.set(source2,source2.v))}activate(){current_batch=this,this.apply()}deactivate(){current_batch===this&&(current_batch=null,batch_values=null)}flush(){if(this.activate(),queued_root_effects.length>0){if(flush_effects(),current_batch!==null&&current_batch!==this)return}else
-this.#pending===0&&this.process([]);this.deactivate()}discard(){for(const fn of this.#discard_callbacks)fn(this);this.#discard_callbacks.clear()}#commit(){if(batches.size>1){this.previous.clear();var previous_batch_values=batch_values,is_earlier=!0;for(const batch of batches){if(batch===this){is_earlier=!1;continue}const sources=[];for(const[source2,value]of this.current){if(batch.current.has(source2))if(is_earlier&&value!==batch.current.get(source2))batch.current.set(source2,value);else continue;
-sources.push(source2)}if(sources.length===0)continue;const others=[...batch.current.keys()].filter(s2=>!this.current.has(s2));if(others.length>0){var prev_queued_root_effects=queued_root_effects;queued_root_effects=[];const marked=new Set,checked=new Map;for(const source2 of sources)mark_effects(source2,others,marked,checked);if(queued_root_effects.length>0){current_batch=batch,batch.apply();for(const root2 of queued_root_effects)batch.#traverse_effect_tree(root2,[],[]);batch.deactivate()}queued_root_effects=
-prev_queued_root_effects}}current_batch=null,batch_values=previous_batch_values}this.committed=!0,batches.delete(this)}increment(blocking){this.#pending+=1,blocking&&(this.#blocking_pending+=1)}decrement(blocking){this.#pending-=1,blocking&&(this.#blocking_pending-=1),!this.#decrement_queued&&(this.#decrement_queued=!0,queue_micro_task(()=>{this.#decrement_queued=!1,this.is_deferred()?queued_root_effects.length>0&&this.flush():this.revive()}))}revive(){for(const e of this.#dirty_effects)this.#maybe_dirty_effects.
-delete(e),set_signal_status(e,DIRTY),schedule_effect(e);for(const e of this.#maybe_dirty_effects)set_signal_status(e,MAYBE_DIRTY),schedule_effect(e);this.flush()}oncommit(fn){this.#commit_callbacks.add(fn)}ondiscard(fn){this.#discard_callbacks.add(fn)}settled(){return(this.#deferred??=deferred()).promise}static ensure(){if(current_batch===null){const batch=current_batch=new Batch;batches.add(current_batch),is_flushing_sync||queue_micro_task(()=>{current_batch===batch&&batch.flush()})}return current_batch}apply(){}}
-function flushSync(fn){var was_flushing_sync=is_flushing_sync;is_flushing_sync=!0;try{var result;for(fn&&(current_batch!==null&&flush_effects(),result=fn());;){if(flush_tasks(),queued_root_effects.length===0&&(current_batch?.flush(),queued_root_effects.length===0))return last_scheduled_effect=null,result;flush_effects()}}finally{is_flushing_sync=was_flushing_sync}}function flush_effects(){is_flushing=!0;var source_stacks=null;try{for(var flush_count=0;queued_root_effects.length>0;){var batch=Batch.
-ensure();if(flush_count++>1e3){var updates,entry;infinite_loop_guard()}batch.process(queued_root_effects),old_values.clear()}}finally{is_flushing=!1,last_scheduled_effect=null}}function infinite_loop_guard(){try{effect_update_depth_exceeded()}catch(error2){invoke_error_boundary(error2,last_scheduled_effect)}}let eager_block_effects=null;function flush_queued_effects(effects){var length=effects.length;if(length!==0){for(var i=0;i<length;){var effect2=effects[i++];if((effect2.f&(DESTROYED|INERT))===
-0&&is_dirty(effect2)&&(eager_block_effects=new Set,update_effect(effect2),effect2.deps===null&&effect2.first===null&&effect2.nodes===null&&(effect2.teardown===null&&effect2.ac===null?unlink_effect(effect2):effect2.fn=null),eager_block_effects?.size>0)){old_values.clear();for(const e of eager_block_effects){if((e.f&(DESTROYED|INERT))!==0)continue;const ordered_effects=[e];let ancestor=e.parent;for(;ancestor!==null;)eager_block_effects.has(ancestor)&&(eager_block_effects.delete(ancestor),ordered_effects.
-push(ancestor)),ancestor=ancestor.parent;for(let j=ordered_effects.length-1;j>=0;j--){const e2=ordered_effects[j];(e2.f&(DESTROYED|INERT))===0&&update_effect(e2)}}eager_block_effects.clear()}}eager_block_effects=null}}function mark_effects(value,sources,marked,checked){if(!marked.has(value)&&(marked.add(value),value.reactions!==null))for(const reaction of value.reactions){const flags2=reaction.f;(flags2&DERIVED)!==0?mark_effects(reaction,sources,marked,checked):(flags2&(ASYNC|BLOCK_EFFECT))!==0&&
-(flags2&DIRTY)===0&&depends_on(reaction,sources,checked)&&(set_signal_status(reaction,DIRTY),schedule_effect(reaction))}}function mark_eager_effects(value,effects){if(value.reactions!==null)for(const reaction of value.reactions){const flags2=reaction.f;(flags2&DERIVED)!==0?mark_eager_effects(reaction,effects):(flags2&EAGER_EFFECT)!==0&&(set_signal_status(reaction,DIRTY),effects.add(reaction))}}function depends_on(reaction,sources,checked){const depends=checked.get(reaction);if(depends!==void 0)return depends;
-if(reaction.deps!==null)for(const dep of reaction.deps){if(includes.call(sources,dep))return!0;if((dep.f&DERIVED)!==0&&depends_on(dep,sources,checked))return checked.set(dep,!0),!0}return checked.set(reaction,!1),!1}function schedule_effect(signal){for(var effect2=last_scheduled_effect=signal;effect2.parent!==null;){effect2=effect2.parent;var flags2=effect2.f;if(is_flushing&&effect2===active_effect&&(flags2&BLOCK_EFFECT)!==0&&(flags2&HEAD_EFFECT)===0)return;if((flags2&(ROOT_EFFECT|BRANCH_EFFECT))!==
-0){if((flags2&CLEAN)===0)return;effect2.f^=CLEAN}}queued_root_effects.push(effect2)}function fork(fn){experimental_async_required(),current_batch!==null&&fork_timing();var batch=Batch.ensure();batch.is_fork=!0,batch_values=new Map;var committed=!1,settled2=batch.settled();flushSync(fn);for(var[source2,value]of batch.previous)source2.v=value;for(source2 of batch.current.keys())(source2.f&DERIVED)!==0&&set_signal_status(source2,DIRTY);return{commit:async()=>{if(committed){await settled2;return}batches.
-has(batch)||fork_discarded(),committed=!0,batch.is_fork=!1;for(var[source3,value2]of batch.current)source3.v=value2,source3.wv=increment_write_version();flushSync(()=>{var eager_effects2=new Set;for(var source4 of batch.current.keys())mark_eager_effects(source4,eager_effects2);set_eager_effects(eager_effects2),flush_eager_effects()}),batch.revive(),await settled2},discard:()=>{!committed&&batches.has(batch)&&(batches.delete(batch),batch.discard())}}}function createSubscriber(start2){let subscribers=0,
-version2=source$2(0),stop;return()=>{effect_tracking()&&(get$4(version2),render_effect(()=>(subscribers===0&&(stop=untrack$1(()=>start2(()=>increment$1(version2)))),subscribers+=1,()=>{queue_micro_task(()=>{subscribers-=1,subscribers===0&&(stop?.(),stop=void 0,increment$1(version2))})})))}}var flags=EFFECT_TRANSPARENT|EFFECT_PRESERVED|BOUNDARY_EFFECT;function boundary(node2,props,children){new Boundary(node2,props,children)}class Boundary{parent;is_pending=!1;#anchor;#hydrate_open=hydrating?hydrate_node:
-null;#props;#children;#effect;#main_effect=null;#pending_effect=null;#failed_effect=null;#offscreen_fragment=null;#pending_anchor=null;#local_pending_count=0;#pending_count=0;#pending_count_update_queued=!1;#is_creating_fallback=!1;#dirty_effects=new Set;#maybe_dirty_effects=new Set;#effect_pending=null;#effect_pending_subscriber=createSubscriber(()=>(this.#effect_pending=source$2(this.#local_pending_count),()=>{this.#effect_pending=null}));constructor(node2,props,children){this.#anchor=node2,this.#props=
-props,this.#children=children,this.parent=active_effect.b,this.is_pending=!!this.#props.pending,this.#effect=block(()=>{if(active_effect.b=this,hydrating){const comment2=this.#hydrate_open;hydrate_next(),comment2.nodeType===COMMENT_NODE&&comment2.data===HYDRATION_START_ELSE?this.#hydrate_pending_content():(this.#hydrate_resolved_content(),this.#pending_count===0&&(this.is_pending=!1))}else{var anchor=this.#get_anchor();try{this.#main_effect=branch(()=>children(anchor))}catch(error2){this.error(error2)}
-this.#pending_count>0?this.#show_pending_snippet():this.is_pending=!1}return()=>{this.#pending_anchor?.remove()}},flags),hydrating&&(this.#anchor=hydrate_node)}#hydrate_resolved_content(){try{this.#main_effect=branch(()=>this.#children(this.#anchor))}catch(error2){this.error(error2)}}#hydrate_pending_content(){const pending=this.#props.pending;pending&&(this.#pending_effect=branch(()=>pending(this.#anchor)),queue_micro_task(()=>{var anchor=this.#get_anchor();this.#main_effect=this.#run(()=>(Batch.
-ensure(),branch(()=>this.#children(anchor)))),this.#pending_count>0?this.#show_pending_snippet():(pause_effect(this.#pending_effect,()=>{this.#pending_effect=null}),this.is_pending=!1)}))}#get_anchor(){var anchor=this.#anchor;return this.is_pending&&(this.#pending_anchor=create_text(),this.#anchor.before(this.#pending_anchor),anchor=this.#pending_anchor),anchor}defer_effect(effect2){defer_effect(effect2,this.#dirty_effects,this.#maybe_dirty_effects)}is_rendered(){return!this.is_pending&&(!this.parent||
-this.parent.is_rendered())}has_pending_snippet(){return!!this.#props.pending}#run(fn){var previous_effect=active_effect,previous_reaction=active_reaction,previous_ctx=component_context;set_active_effect(this.#effect),set_active_reaction(this.#effect),set_component_context(this.#effect.ctx);try{return fn()}catch(e){return handle_error$1(e),null}finally{set_active_effect(previous_effect),set_active_reaction(previous_reaction),set_component_context(previous_ctx)}}#show_pending_snippet(){const pending=this.#props.
-pending;this.#main_effect!==null&&(this.#offscreen_fragment=document.createDocumentFragment(),this.#offscreen_fragment.append(this.#pending_anchor),move_effect(this.#main_effect,this.#offscreen_fragment)),this.#pending_effect===null&&(this.#pending_effect=branch(()=>pending(this.#anchor)))}#update_pending_count(d2){if(!this.has_pending_snippet()){this.parent&&this.parent.#update_pending_count(d2);return}if(this.#pending_count+=d2,this.#pending_count===0){this.is_pending=!1;for(const e of this.#dirty_effects)
-set_signal_status(e,DIRTY),schedule_effect(e);for(const e of this.#maybe_dirty_effects)set_signal_status(e,MAYBE_DIRTY),schedule_effect(e);this.#dirty_effects.clear(),this.#maybe_dirty_effects.clear(),this.#pending_effect&&pause_effect(this.#pending_effect,()=>{this.#pending_effect=null}),this.#offscreen_fragment&&(this.#anchor.before(this.#offscreen_fragment),this.#offscreen_fragment=null)}}update_pending_count(d2){this.#update_pending_count(d2),this.#local_pending_count+=d2,!(!this.#effect_pending||
-this.#pending_count_update_queued)&&(this.#pending_count_update_queued=!0,queue_micro_task(()=>{this.#pending_count_update_queued=!1,this.#effect_pending&&internal_set(this.#effect_pending,this.#local_pending_count)}))}get_effect_pending(){return this.#effect_pending_subscriber(),get$4(this.#effect_pending)}error(error2){var onerror=this.#props.onerror;let failed=this.#props.failed;if(this.#is_creating_fallback||!onerror&&!failed)throw error2;this.#main_effect&&(destroy_effect(this.#main_effect),
-this.#main_effect=null),this.#pending_effect&&(destroy_effect(this.#pending_effect),this.#pending_effect=null),this.#failed_effect&&(destroy_effect(this.#failed_effect),this.#failed_effect=null),hydrating&&(set_hydrate_node(this.#hydrate_open),next$1(),set_hydrate_node(skip_nodes()));var did_reset=!1,calling_on_error=!1;const reset2=()=>{if(did_reset){svelte_boundary_reset_noop();return}did_reset=!0,calling_on_error&&svelte_boundary_reset_onerror(),Batch.ensure(),this.#local_pending_count=0,this.#failed_effect!==
-null&&pause_effect(this.#failed_effect,()=>{this.#failed_effect=null}),this.is_pending=this.has_pending_snippet(),this.#main_effect=this.#run(()=>(this.#is_creating_fallback=!1,branch(()=>this.#children(this.#anchor)))),this.#pending_count>0?this.#show_pending_snippet():this.is_pending=!1};var previous_reaction=active_reaction;try{set_active_reaction(null),calling_on_error=!0,onerror?.(error2,reset2),calling_on_error=!1}catch(error3){invoke_error_boundary(error3,this.#effect&&this.#effect.parent)}finally{
-set_active_reaction(previous_reaction)}failed&&queue_micro_task(()=>{this.#failed_effect=this.#run(()=>{Batch.ensure(),this.#is_creating_fallback=!0;try{return branch(()=>{failed(this.#anchor,()=>error2,()=>reset2)})}catch(error3){return invoke_error_boundary(error3,this.#effect.parent),null}finally{this.#is_creating_fallback=!1}})})}}function flatten(blockers,sync,async,fn){const d2=is_runes()?derived:derived_safe_equal;var pending=blockers.filter(b=>!b.settled);if(async.length===0&&pending.length===
-0){fn(sync.map(d2));return}var batch=current_batch,parent=active_effect,restore=capture(),blocker_promise=pending.length===1?pending[0].promise:pending.length>1?Promise.all(pending.map(b=>b.promise)):null;function finish(values){restore();try{fn(values)}catch(error2){(parent.f&DESTROYED)===0&&invoke_error_boundary(error2,parent)}batch?.deactivate(),unset_context()}if(async.length===0){blocker_promise.then(()=>finish(sync.map(d2)));return}function run2(){restore(),Promise.all(async.map(expression=>async_derived(
-expression))).then(result=>finish([...sync.map(d2),...result])).catch(error2=>invoke_error_boundary(error2,parent))}blocker_promise?blocker_promise.then(run2):run2()}function capture(){var previous_effect=active_effect,previous_reaction=active_reaction,previous_component_context=component_context,previous_batch2=current_batch;return function(activate_batch=!0){set_active_effect(previous_effect),set_active_reaction(previous_reaction),set_component_context(previous_component_context),activate_batch&&
-previous_batch2?.activate()}}function unset_context(){set_active_effect(null),set_active_reaction(null),set_component_context(null)}function derived(fn){var flags2=DERIVED|DIRTY,parent_derived=active_reaction!==null&&(active_reaction.f&DERIVED)!==0?active_reaction:null;return active_effect!==null&&(active_effect.f|=EFFECT_PRESERVED),{ctx:component_context,deps:null,effects:null,equals,f:flags2,fn,reactions:null,rv:0,v:UNINITIALIZED,wv:0,parent:parent_derived??active_effect,ac:null}}function async_derived(fn,label,location2){
-let parent=active_effect;parent===null&&async_derived_orphan();var boundary2=parent.b,promise=void 0,signal=source$2(UNINITIALIZED),should_suspend=!active_reaction,deferreds=new Map;return async_effect(()=>{var d2=deferred();promise=d2.promise;try{Promise.resolve(fn()).then(d2.resolve,d2.reject).then(()=>{batch===current_batch&&batch.committed&&batch.deactivate(),unset_context()})}catch(error2){d2.reject(error2),unset_context()}var batch=current_batch;if(should_suspend){var blocking=boundary2.is_rendered();
-boundary2.update_pending_count(1),batch.increment(blocking),deferreds.get(batch)?.reject(STALE_REACTION),deferreds.delete(batch),deferreds.set(batch,d2)}const handler=(value,error2=void 0)=>{if(batch.activate(),error2)error2!==STALE_REACTION&&(signal.f|=ERROR_VALUE,internal_set(signal,error2));else{(signal.f&ERROR_VALUE)!==0&&(signal.f^=ERROR_VALUE),internal_set(signal,value);for(const[b,d3]of deferreds){if(deferreds.delete(b),b===batch)break;d3.reject(STALE_REACTION)}}should_suspend&&(boundary2.
-update_pending_count(-1),batch.decrement(blocking))};d2.promise.then(handler,e=>handler(null,e||"unknown"))}),teardown(()=>{for(const d2 of deferreds.values())d2.reject(STALE_REACTION)}),new Promise(fulfil=>{function next2(p2){function go2(){p2===promise?fulfil(signal):next2(promise)}p2.then(go2,go2)}next2(promise)})}function user_derived(fn){const d2=derived(fn);return push_reaction_value(d2),d2}function derived_safe_equal(fn){const signal=derived(fn);return signal.equals=safe_equals,signal}function destroy_derived_effects(derived2){
-var effects=derived2.effects;if(effects!==null){derived2.effects=null;for(var i=0;i<effects.length;i+=1)destroy_effect(effects[i])}}function get_derived_parent_effect(derived2){for(var parent=derived2.parent;parent!==null;){if((parent.f&DERIVED)===0)return(parent.f&DESTROYED)===0?parent:null;parent=parent.parent}return null}function execute_derived(derived2){var value,prev_active_effect=active_effect;set_active_effect(get_derived_parent_effect(derived2));try{derived2.f&=~WAS_MARKED,destroy_derived_effects(
-derived2),value=update_reaction(derived2)}finally{set_active_effect(prev_active_effect)}return value}function update_derived(derived2){var value=execute_derived(derived2);if(!derived2.equals(value)&&(derived2.wv=increment_write_version(),(!current_batch?.is_fork||derived2.deps===null)&&(derived2.v=value,derived2.deps===null))){set_signal_status(derived2,CLEAN);return}is_destroying_effect||(batch_values!==null?(effect_tracking()||current_batch?.is_fork)&&batch_values.set(derived2,value):update_derived_status(
-derived2))}let eager_effects=new Set;const old_values=new Map;function set_eager_effects(v){eager_effects=v}let eager_effects_deferred=!1;function source$2(v,stack){var signal={f:0,v,reactions:null,equals,rv:0,wv:0};return signal}function state$1(v,stack){const s2=source$2(v);return push_reaction_value(s2),s2}function mutable_source(initial_value,immutable=!1,trackable=!0){const s2=source$2(initial_value);return immutable||(s2.equals=safe_equals),legacy_mode_flag&&trackable&&component_context!==
-null&&component_context.l!==null&&(component_context.l.s??=[]).push(s2),s2}function set$1(source2,value,should_proxy=!1){active_reaction!==null&&(!untracking||(active_reaction.f&EAGER_EFFECT)!==0)&&is_runes()&&(active_reaction.f&(DERIVED|BLOCK_EFFECT|ASYNC|EAGER_EFFECT))!==0&&(current_sources===null||!includes.call(current_sources,source2))&&state_unsafe_mutation();let new_value=should_proxy?proxy(value):value;return internal_set(source2,new_value)}function internal_set(source2,value){if(!source2.
-equals(value)){var old_value=source2.v;is_destroying_effect?old_values.set(source2,value):old_values.set(source2,old_value),source2.v=value;var batch=Batch.ensure();if(batch.capture(source2,old_value),(source2.f&DERIVED)!==0){const derived2=source2;(source2.f&DIRTY)!==0&&execute_derived(derived2),update_derived_status(derived2)}source2.wv=increment_write_version(),mark_reactions(source2,DIRTY),is_runes()&&active_effect!==null&&(active_effect.f&CLEAN)!==0&&(active_effect.f&(BRANCH_EFFECT|ROOT_EFFECT))===
-0&&(untracked_writes===null?set_untracked_writes([source2]):untracked_writes.push(source2)),!batch.is_fork&&eager_effects.size>0&&!eager_effects_deferred&&flush_eager_effects()}return value}function flush_eager_effects(){eager_effects_deferred=!1;for(const effect2 of eager_effects)(effect2.f&CLEAN)!==0&&set_signal_status(effect2,MAYBE_DIRTY),is_dirty(effect2)&&update_effect(effect2);eager_effects.clear()}function update$1(source2,d2=1){var value=get$4(source2),result=d2===1?value++:value--;return set$1(
-source2,value),result}function increment$1(source2){set$1(source2,source2.v+1)}function mark_reactions(signal,status){var reactions=signal.reactions;if(reactions!==null)for(var runes=is_runes(),length=reactions.length,i=0;i<length;i++){var reaction=reactions[i],flags2=reaction.f;if(!(!runes&&reaction===active_effect)){var not_dirty=(flags2&DIRTY)===0;if(not_dirty&&set_signal_status(reaction,status),(flags2&DERIVED)!==0){var derived2=reaction;batch_values?.delete(derived2),(flags2&WAS_MARKED)===0&&
-(flags2&CONNECTED&&(reaction.f|=WAS_MARKED),mark_reactions(derived2,MAYBE_DIRTY))}else not_dirty&&((flags2&BLOCK_EFFECT)!==0&&eager_block_effects!==null&&eager_block_effects.add(reaction),schedule_effect(reaction))}}}function proxy(value){if(typeof value!="object"||value===null||STATE_SYMBOL in value)return value;const prototype=get_prototype_of(value);if(prototype!==object_prototype&&prototype!==array_prototype)return value;var sources=new Map,is_proxied_array=is_array(value),version2=state$1(0),
-parent_version=update_version,with_parent=fn=>{if(update_version===parent_version)return fn();var reaction=active_reaction,version3=update_version;set_active_reaction(null),set_update_version(parent_version);var result=fn();return set_active_reaction(reaction),set_update_version(version3),result};return is_proxied_array&&sources.set("length",state$1(value.length)),new Proxy(value,{defineProperty(_,prop2,descriptor){(!("value"in descriptor)||descriptor.configurable===!1||descriptor.enumerable===!1||
-descriptor.writable===!1)&&state_descriptors_fixed();var s2=sources.get(prop2);return s2===void 0?s2=with_parent(()=>{var s3=state$1(descriptor.value);return sources.set(prop2,s3),s3}):set$1(s2,descriptor.value,!0),!0},deleteProperty(target2,prop2){var s2=sources.get(prop2);if(s2===void 0){if(prop2 in target2){const s3=with_parent(()=>state$1(UNINITIALIZED));sources.set(prop2,s3),increment$1(version2)}}else set$1(s2,UNINITIALIZED),increment$1(version2);return!0},get(target2,prop2,receiver){if(prop2===
-STATE_SYMBOL)return value;var s2=sources.get(prop2),exists=prop2 in target2;if(s2===void 0&&(!exists||get_descriptor(target2,prop2)?.writable)&&(s2=with_parent(()=>{var p2=proxy(exists?target2[prop2]:UNINITIALIZED),s3=state$1(p2);return s3}),sources.set(prop2,s2)),s2!==void 0){var v=get$4(s2);return v===UNINITIALIZED?void 0:v}return Reflect.get(target2,prop2,receiver)},getOwnPropertyDescriptor(target2,prop2){var descriptor=Reflect.getOwnPropertyDescriptor(target2,prop2);if(descriptor&&"value"in descriptor){
-var s2=sources.get(prop2);s2&&(descriptor.value=get$4(s2))}else if(descriptor===void 0){var source2=sources.get(prop2),value2=source2?.v;if(source2!==void 0&&value2!==UNINITIALIZED)return{enumerable:!0,configurable:!0,value:value2,writable:!0}}return descriptor},has(target2,prop2){if(prop2===STATE_SYMBOL)return!0;var s2=sources.get(prop2),has=s2!==void 0&&s2.v!==UNINITIALIZED||Reflect.has(target2,prop2);if(s2!==void 0||active_effect!==null&&(!has||get_descriptor(target2,prop2)?.writable)){s2===void 0&&
-(s2=with_parent(()=>{var p2=has?proxy(target2[prop2]):UNINITIALIZED,s3=state$1(p2);return s3}),sources.set(prop2,s2));var value2=get$4(s2);if(value2===UNINITIALIZED)return!1}return has},set(target2,prop2,value2,receiver){var s2=sources.get(prop2),has=prop2 in target2;if(is_proxied_array&&prop2==="length")for(var i=value2;i<s2.v;i+=1){var other_s=sources.get(i+"");other_s!==void 0?set$1(other_s,UNINITIALIZED):i in target2&&(other_s=with_parent(()=>state$1(UNINITIALIZED)),sources.set(i+"",other_s))}
-if(s2===void 0)(!has||get_descriptor(target2,prop2)?.writable)&&(s2=with_parent(()=>state$1(void 0)),set$1(s2,proxy(value2)),sources.set(prop2,s2));else{has=s2.v!==UNINITIALIZED;var p2=with_parent(()=>proxy(value2));set$1(s2,p2)}var descriptor=Reflect.getOwnPropertyDescriptor(target2,prop2);if(descriptor?.set&&descriptor.set.call(receiver,value2),!has){if(is_proxied_array&&typeof prop2=="string"){var ls=sources.get("length"),n=Number(prop2);Number.isInteger(n)&&n>=ls.v&&set$1(ls,n+1)}increment$1(
-version2)}return!0},ownKeys(target2){get$4(version2);var own_keys=Reflect.ownKeys(target2).filter(key3=>{var source3=sources.get(key3);return source3===void 0||source3.v!==UNINITIALIZED});for(var[key2,source2]of sources)source2.v!==UNINITIALIZED&&!(key2 in target2)&&own_keys.push(key2);return own_keys},setPrototypeOf(){state_prototype_fixed()}})}function get_proxied_value(value){try{if(value!==null&&typeof value=="object"&&STATE_SYMBOL in value)return value[STATE_SYMBOL]}catch{}return value}function is(a,b){
-return Object.is(get_proxied_value(a),get_proxied_value(b))}var $window,$document,is_firefox,first_child_getter,next_sibling_getter;function init_operations(){if($window===void 0){$window=window,$document=document,is_firefox=/Firefox/.test(navigator.userAgent);var element_prototype=Element.prototype,node_prototype=Node.prototype,text_prototype=Text.prototype;first_child_getter=get_descriptor(node_prototype,"firstChild").get,next_sibling_getter=get_descriptor(node_prototype,"nextSibling").get,is_extensible(
-element_prototype)&&(element_prototype.__click=void 0,element_prototype.__className=void 0,element_prototype.__attributes=null,element_prototype.__style=void 0,element_prototype.__e=void 0),is_extensible(text_prototype)&&(text_prototype.__t=void 0)}}function create_text(value=""){return document.createTextNode(value)}function get_first_child(node2){return first_child_getter.call(node2)}function get_next_sibling(node2){return next_sibling_getter.call(node2)}function child(node2,is_text){if(!hydrating)
-return get_first_child(node2);var child2=get_first_child(hydrate_node);if(child2===null)child2=hydrate_node.appendChild(create_text());else if(is_text&&child2.nodeType!==TEXT_NODE){var text2=create_text();return child2?.before(text2),set_hydrate_node(text2),text2}return set_hydrate_node(child2),child2}function first_child(node2,is_text=!1){if(!hydrating){var first=get_first_child(node2);return first instanceof Comment&&first.data===""?get_next_sibling(first):first}if(is_text&&hydrate_node?.nodeType!==
-TEXT_NODE){var text2=create_text();return hydrate_node?.before(text2),set_hydrate_node(text2),text2}return hydrate_node}function sibling(node2,count=1,is_text=!1){let next_sibling=hydrating?hydrate_node:node2;for(var last_sibling;count--;)last_sibling=next_sibling,next_sibling=get_next_sibling(next_sibling);if(!hydrating)return next_sibling;if(is_text&&next_sibling?.nodeType!==TEXT_NODE){var text2=create_text();return next_sibling===null?last_sibling?.after(text2):next_sibling.before(text2),set_hydrate_node(
-text2),text2}return set_hydrate_node(next_sibling),next_sibling}function clear_text_content(node2){node2.textContent=""}function should_defer_append(){return!1}function autofocus(dom,value){if(value){const body2=document.body;dom.autofocus=!0,queue_micro_task(()=>{document.activeElement===body2&&dom.focus()})}}function remove_textarea_child(dom){hydrating&&get_first_child(dom)!==null&&clear_text_content(dom)}let listening_to_form_reset=!1;function add_form_reset_listener(){listening_to_form_reset||
-(listening_to_form_reset=!0,document.addEventListener("reset",evt=>{Promise.resolve().then(()=>{if(!evt.defaultPrevented)for(const e of evt.target.elements)e.__on_r?.()})},{capture:!0}))}function listen(target2,events,handler,call_handler_immediately=!0){call_handler_immediately&&handler();for(var name of events)target2.addEventListener(name,handler);teardown(()=>{for(var name2 of events)target2.removeEventListener(name2,handler)})}function without_reactive_context(fn){var previous_reaction=active_reaction,
-previous_effect=active_effect;set_active_reaction(null),set_active_effect(null);try{return fn()}finally{set_active_reaction(previous_reaction),set_active_effect(previous_effect)}}function listen_to_event_and_reset_event(element2,event2,handler,on_reset=handler){element2.addEventListener(event2,()=>without_reactive_context(handler));const prev2=element2.__on_r;prev2?element2.__on_r=()=>{prev2(),on_reset(!0)}:element2.__on_r=()=>on_reset(!0),add_form_reset_listener()}function validate_effect(rune){
-active_effect===null&&(active_reaction===null&&effect_orphan(),effect_in_unowned_derived()),is_destroying_effect&&effect_in_teardown()}function push_effect(effect2,parent_effect){var parent_last=parent_effect.last;parent_last===null?parent_effect.last=parent_effect.first=effect2:(parent_last.next=effect2,effect2.prev=parent_last,parent_effect.last=effect2)}function create_effect(type2,fn,sync){var parent=active_effect;parent!==null&&(parent.f&INERT)!==0&&(type2|=INERT);var effect2={ctx:component_context,
-deps:null,nodes:null,f:type2|DIRTY|CONNECTED,first:null,fn,last:null,next:null,parent,b:parent&&parent.b,prev:null,teardown:null,wv:0,ac:null};if(sync)try{update_effect(effect2),effect2.f|=EFFECT_RAN}catch(e2){throw destroy_effect(effect2),e2}else fn!==null&&schedule_effect(effect2);var e=effect2;if(sync&&e.deps===null&&e.teardown===null&&e.nodes===null&&e.first===e.last&&(e.f&EFFECT_PRESERVED)===0&&(e=e.first,(type2&BLOCK_EFFECT)!==0&&(type2&EFFECT_TRANSPARENT)!==0&&e!==null&&(e.f|=EFFECT_TRANSPARENT)),
-e!==null&&(e.parent=parent,parent!==null&&push_effect(e,parent),active_reaction!==null&&(active_reaction.f&DERIVED)!==0&&(type2&ROOT_EFFECT)===0)){var derived2=active_reaction;(derived2.effects??=[]).push(e)}return effect2}function effect_tracking(){return active_reaction!==null&&!untracking}function teardown(fn){const effect2=create_effect(RENDER_EFFECT,null,!1);return set_signal_status(effect2,CLEAN),effect2.teardown=fn,effect2}function user_effect(fn){validate_effect();var flags2=active_effect.
-f,defer=!active_reaction&&(flags2&BRANCH_EFFECT)!==0&&(flags2&EFFECT_RAN)===0;if(defer){var context=component_context;(context.e??=[]).push(fn)}else return create_user_effect(fn)}function create_user_effect(fn){return create_effect(EFFECT|USER_EFFECT,fn,!1)}function user_pre_effect(fn){return validate_effect(),create_effect(RENDER_EFFECT|USER_EFFECT,fn,!0)}function effect_root(fn){Batch.ensure();const effect2=create_effect(ROOT_EFFECT|EFFECT_PRESERVED,fn,!0);return()=>{destroy_effect(effect2)}}function component_root(fn){
-Batch.ensure();const effect2=create_effect(ROOT_EFFECT|EFFECT_PRESERVED,fn,!0);return(options={})=>new Promise(fulfil=>{options.outro?pause_effect(effect2,()=>{destroy_effect(effect2),fulfil(void 0)}):(destroy_effect(effect2),fulfil(void 0))})}function effect(fn){return create_effect(EFFECT,fn,!1)}function async_effect(fn){return create_effect(ASYNC|EFFECT_PRESERVED,fn,!0)}function render_effect(fn,flags2=0){return create_effect(RENDER_EFFECT|flags2,fn,!0)}function template_effect(fn,sync=[],async=[],blockers=[]){
-flatten(blockers,sync,async,values=>{create_effect(RENDER_EFFECT,()=>fn(...values.map(get$4)),!0)})}function deferred_template_effect(fn,sync=[],async=[],blockers=[]){var batch=current_batch,is_async=async.length>0||blockers.length>0;is_async&&batch.increment(!0),flatten(blockers,sync,async,values=>{create_effect(EFFECT,()=>fn(...values.map(get$4)),!1),is_async&&batch.decrement(!0)})}function block(fn,flags2=0){var effect2=create_effect(BLOCK_EFFECT|flags2,fn,!0);return effect2}function managed(fn,flags2=0){
-var effect2=create_effect(MANAGED_EFFECT|flags2,fn,!0);return effect2}function branch(fn){return create_effect(BRANCH_EFFECT|EFFECT_PRESERVED,fn,!0)}function execute_effect_teardown(effect2){var teardown2=effect2.teardown;if(teardown2!==null){const previously_destroying_effect=is_destroying_effect,previous_reaction=active_reaction;set_is_destroying_effect(!0),set_active_reaction(null);try{teardown2.call(null)}finally{set_is_destroying_effect(previously_destroying_effect),set_active_reaction(previous_reaction)}}}
-function destroy_effect_children(signal,remove_dom=!1){var effect2=signal.first;for(signal.first=signal.last=null;effect2!==null;){const controller=effect2.ac;controller!==null&&without_reactive_context(()=>{controller.abort(STALE_REACTION)});var next2=effect2.next;(effect2.f&ROOT_EFFECT)!==0?effect2.parent=null:destroy_effect(effect2,remove_dom),effect2=next2}}function destroy_block_effect_children(signal){for(var effect2=signal.first;effect2!==null;){var next2=effect2.next;(effect2.f&BRANCH_EFFECT)===
-0&&destroy_effect(effect2),effect2=next2}}function destroy_effect(effect2,remove_dom=!0){var removed=!1;(remove_dom||(effect2.f&HEAD_EFFECT)!==0)&&effect2.nodes!==null&&effect2.nodes.end!==null&&(remove_effect_dom(effect2.nodes.start,effect2.nodes.end),removed=!0),destroy_effect_children(effect2,remove_dom&&!removed),remove_reactions(effect2,0),set_signal_status(effect2,DESTROYED);var transitions=effect2.nodes&&effect2.nodes.t;if(transitions!==null)for(const transition2 of transitions)transition2.
-stop();execute_effect_teardown(effect2);var parent=effect2.parent;parent!==null&&parent.first!==null&&unlink_effect(effect2),effect2.next=effect2.prev=effect2.teardown=effect2.ctx=effect2.deps=effect2.fn=effect2.nodes=effect2.ac=null}function remove_effect_dom(node2,end){for(;node2!==null;){var next2=node2===end?null:get_next_sibling(node2);node2.remove(),node2=next2}}function unlink_effect(effect2){var parent=effect2.parent,prev2=effect2.prev,next2=effect2.next;prev2!==null&&(prev2.next=next2),
-next2!==null&&(next2.prev=prev2),parent!==null&&(parent.first===effect2&&(parent.first=next2),parent.last===effect2&&(parent.last=prev2))}function pause_effect(effect2,callback,destroy=!0){var transitions=[];pause_children(effect2,transitions,!0);var fn=()=>{destroy&&destroy_effect(effect2),callback&&callback()},remaining=transitions.length;if(remaining>0){var check=()=>--remaining||fn();for(var transition2 of transitions)transition2.out(check)}else fn()}function pause_children(effect2,transitions,local){
-if((effect2.f&INERT)===0){effect2.f^=INERT;var t=effect2.nodes&&effect2.nodes.t;if(t!==null)for(const transition2 of t)(transition2.is_global||local)&&transitions.push(transition2);for(var child2=effect2.first;child2!==null;){var sibling2=child2.next,transparent=(child2.f&EFFECT_TRANSPARENT)!==0||(child2.f&BRANCH_EFFECT)!==0&&(effect2.f&BLOCK_EFFECT)!==0;pause_children(child2,transitions,transparent?local:!1),child2=sibling2}}}function resume_effect(effect2){resume_children(effect2,!0)}function resume_children(effect2,local){
-if((effect2.f&INERT)!==0){effect2.f^=INERT,(effect2.f&CLEAN)===0&&(set_signal_status(effect2,DIRTY),schedule_effect(effect2));for(var child2=effect2.first;child2!==null;){var sibling2=child2.next,transparent=(child2.f&EFFECT_TRANSPARENT)!==0||(child2.f&BRANCH_EFFECT)!==0;resume_children(child2,transparent?local:!1),child2=sibling2}var t=effect2.nodes&&effect2.nodes.t;if(t!==null)for(const transition2 of t)(transition2.is_global||local)&&transition2.in()}}function move_effect(effect2,fragment){if(effect2.
-nodes)for(var node2=effect2.nodes.start,end=effect2.nodes.end;node2!==null;){var next2=node2===end?null:get_next_sibling(node2);fragment.append(node2),node2=next2}}let is_updating_effect=!1,is_destroying_effect=!1;function set_is_destroying_effect(value){is_destroying_effect=value}let active_reaction=null,untracking=!1;function set_active_reaction(reaction){active_reaction=reaction}let active_effect=null;function set_active_effect(effect2){active_effect=effect2}let current_sources=null;function push_reaction_value(value){
-active_reaction!==null&&(current_sources===null?current_sources=[value]:current_sources.push(value))}let new_deps=null,skipped_deps=0,untracked_writes=null;function set_untracked_writes(value){untracked_writes=value}let write_version=1,read_version=0,update_version=read_version;function set_update_version(value){update_version=value}function increment_write_version(){return++write_version}function is_dirty(reaction){var flags2=reaction.f;if((flags2&DIRTY)!==0)return!0;if(flags2&DERIVED&&(reaction.
-f&=~WAS_MARKED),(flags2&MAYBE_DIRTY)!==0){for(var dependencies2=reaction.deps,length=dependencies2.length,i=0;i<length;i++){var dependency=dependencies2[i];if(is_dirty(dependency)&&update_derived(dependency),dependency.wv>reaction.wv)return!0}(flags2&CONNECTED)!==0&&batch_values===null&&set_signal_status(reaction,CLEAN)}return!1}function schedule_possible_effect_self_invalidation(signal,effect2,root2=!0){var reactions=signal.reactions;if(reactions!==null&&!(current_sources!==null&&includes.call(
-current_sources,signal)))for(var i=0;i<reactions.length;i++){var reaction=reactions[i];(reaction.f&DERIVED)!==0?schedule_possible_effect_self_invalidation(reaction,effect2,!1):effect2===reaction&&(root2?set_signal_status(reaction,DIRTY):(reaction.f&CLEAN)!==0&&set_signal_status(reaction,MAYBE_DIRTY),schedule_effect(reaction))}}function update_reaction(reaction){var previous_deps=new_deps,previous_skipped_deps=skipped_deps,previous_untracked_writes=untracked_writes,previous_reaction=active_reaction,
-previous_sources=current_sources,previous_component_context=component_context,previous_untracking=untracking,previous_update_version=update_version,flags2=reaction.f;new_deps=null,skipped_deps=0,untracked_writes=null,active_reaction=(flags2&(BRANCH_EFFECT|ROOT_EFFECT))===0?reaction:null,current_sources=null,set_component_context(reaction.ctx),untracking=!1,update_version=++read_version,reaction.ac!==null&&(without_reactive_context(()=>{reaction.ac.abort(STALE_REACTION)}),reaction.ac=null);try{reaction.
-f|=REACTION_IS_UPDATING;var fn=reaction.fn,result=fn(),deps=reaction.deps;if(new_deps!==null){var i;if(remove_reactions(reaction,skipped_deps),deps!==null&&skipped_deps>0)for(deps.length=skipped_deps+new_deps.length,i=0;i<new_deps.length;i++)deps[skipped_deps+i]=new_deps[i];else reaction.deps=deps=new_deps;if(effect_tracking()&&(reaction.f&CONNECTED)!==0)for(i=skipped_deps;i<deps.length;i++)(deps[i].reactions??=[]).push(reaction)}else deps!==null&&skipped_deps<deps.length&&(remove_reactions(reaction,
-skipped_deps),deps.length=skipped_deps);if(is_runes()&&untracked_writes!==null&&!untracking&&deps!==null&&(reaction.f&(DERIVED|MAYBE_DIRTY|DIRTY))===0)for(i=0;i<untracked_writes.length;i++)schedule_possible_effect_self_invalidation(untracked_writes[i],reaction);if(previous_reaction!==null&&previous_reaction!==reaction){if(read_version++,previous_reaction.deps!==null)for(let i2=0;i2<previous_skipped_deps;i2+=1)previous_reaction.deps[i2].rv=read_version;if(previous_deps!==null)for(const dep of previous_deps)
-dep.rv=read_version;untracked_writes!==null&&(previous_untracked_writes===null?previous_untracked_writes=untracked_writes:previous_untracked_writes.push(...untracked_writes))}return(reaction.f&ERROR_VALUE)!==0&&(reaction.f^=ERROR_VALUE),result}catch(error2){return handle_error$1(error2)}finally{reaction.f^=REACTION_IS_UPDATING,new_deps=previous_deps,skipped_deps=previous_skipped_deps,untracked_writes=previous_untracked_writes,active_reaction=previous_reaction,current_sources=previous_sources,set_component_context(
-previous_component_context),untracking=previous_untracking,update_version=previous_update_version}}function remove_reaction(signal,dependency){let reactions=dependency.reactions;if(reactions!==null){var index2=index_of.call(reactions,signal);if(index2!==-1){var new_length=reactions.length-1;new_length===0?reactions=dependency.reactions=null:(reactions[index2]=reactions[new_length],reactions.pop())}}if(reactions===null&&(dependency.f&DERIVED)!==0&&(new_deps===null||!includes.call(new_deps,dependency))){
-var derived2=dependency;(derived2.f&CONNECTED)!==0&&(derived2.f^=CONNECTED,derived2.f&=~WAS_MARKED),update_derived_status(derived2),destroy_derived_effects(derived2),remove_reactions(derived2,0)}}function remove_reactions(signal,start_index){var dependencies2=signal.deps;if(dependencies2!==null)for(var i=start_index;i<dependencies2.length;i++)remove_reaction(signal,dependencies2[i])}function update_effect(effect2){var flags2=effect2.f;if((flags2&DESTROYED)===0){set_signal_status(effect2,CLEAN);var previous_effect=active_effect,
-was_updating_effect=is_updating_effect;active_effect=effect2,is_updating_effect=!0;try{(flags2&(BLOCK_EFFECT|MANAGED_EFFECT))!==0?destroy_block_effect_children(effect2):destroy_effect_children(effect2),execute_effect_teardown(effect2);var teardown2=update_reaction(effect2);effect2.teardown=typeof teardown2=="function"?teardown2:null,effect2.wv=write_version;var dep}finally{is_updating_effect=was_updating_effect,active_effect=previous_effect}}}async function tick(){await Promise.resolve(),flushSync()}
-function settled(){return Batch.ensure().settled()}function get$4(signal){var flags2=signal.f,is_derived=(flags2&DERIVED)!==0;if(active_reaction!==null&&!untracking){var destroyed=active_effect!==null&&(active_effect.f&DESTROYED)!==0;if(!destroyed&&(current_sources===null||!includes.call(current_sources,signal))){var deps=active_reaction.deps;if((active_reaction.f&REACTION_IS_UPDATING)!==0)signal.rv<read_version&&(signal.rv=read_version,new_deps===null&&deps!==null&&deps[skipped_deps]===signal?skipped_deps++:
-new_deps===null?new_deps=[signal]:new_deps.push(signal));else{(active_reaction.deps??=[]).push(signal);var reactions=signal.reactions;reactions===null?signal.reactions=[active_reaction]:includes.call(reactions,active_reaction)||reactions.push(active_reaction)}}}if(is_destroying_effect&&old_values.has(signal))return old_values.get(signal);if(is_derived){var derived2=signal;if(is_destroying_effect){var value=derived2.v;return((derived2.f&CLEAN)===0&&derived2.reactions!==null||depends_on_old_values(
-derived2))&&(value=execute_derived(derived2)),old_values.set(derived2,value),value}var should_connect=(derived2.f&CONNECTED)===0&&!untracking&&active_reaction!==null&&(is_updating_effect||(active_reaction.f&CONNECTED)!==0),is_new=derived2.deps===null;is_dirty(derived2)&&(should_connect&&(derived2.f|=CONNECTED),update_derived(derived2)),should_connect&&!is_new&&reconnect(derived2)}if(batch_values?.has(signal))return batch_values.get(signal);if((signal.f&ERROR_VALUE)!==0)throw signal.v;return signal.
-v}function reconnect(derived2){if(derived2.deps!==null){derived2.f|=CONNECTED;for(const dep of derived2.deps)(dep.reactions??=[]).push(derived2),(dep.f&DERIVED)!==0&&(dep.f&CONNECTED)===0&&reconnect(dep)}}function depends_on_old_values(derived2){if(derived2.v===UNINITIALIZED)return!0;if(derived2.deps===null)return!1;for(const dep of derived2.deps)if(old_values.has(dep)||(dep.f&DERIVED)!==0&&depends_on_old_values(dep))return!0;return!1}function untrack$1(fn){var previous_untracking=untracking;try{
-return untracking=!0,fn()}finally{untracking=previous_untracking}}function deep_read_state(value){if(!(typeof value!="object"||!value||value instanceof EventTarget)){if(STATE_SYMBOL in value)deep_read(value);else if(!Array.isArray(value))for(let key2 in value){const prop2=value[key2];typeof prop2=="object"&&prop2&&STATE_SYMBOL in prop2&&deep_read(prop2)}}}function deep_read(value,visited=new Set){if(typeof value=="object"&&value!==null&&!(value instanceof EventTarget)&&!visited.has(value)){visited.
-add(value),value instanceof Date&&value.getTime();for(let key2 in value)try{deep_read(value[key2],visited)}catch{}const proto=get_prototype_of(value);if(proto!==Object.prototype&&proto!==Array.prototype&&proto!==Map.prototype&&proto!==Set.prototype&&proto!==Date.prototype){const descriptors=get_descriptors(proto);for(let key2 in descriptors){const get2=descriptors[key2].get;if(get2)try{get2.call(value)}catch{}}}}}function createAttachmentKey(){return Symbol(ATTACHMENT_KEY)}function is_capture_event(name){
-return name.endsWith("capture")&&name!=="gotpointercapture"&&name!=="lostpointercapture"}const DELEGATED_EVENTS=["beforeinput","click","change","dblclick","contextmenu","focusin","focusout","input","keydown","keyup","mousedown","mousemove","mouseout","mouseover","mouseup","pointerdown","pointermove","pointerout","pointerover","pointerup","touchend","touchmove","touchstart"];function can_delegate_event(event_name){return DELEGATED_EVENTS.includes(event_name)}const ATTRIBUTE_ALIASES={formnovalidate:"\
-formNoValidate",ismap:"isMap",nomodule:"noModule",playsinline:"playsInline",readonly:"readOnly",defaultvalue:"defaultValue",defaultchecked:"defaultChecked",srcobject:"srcObject",novalidate:"noValidate",allowfullscreen:"allowFullscreen",disablepictureinpicture:"disablePictureInPicture",disableremoteplayback:"disableRemotePlayback"};function normalize_attribute(name){return name=name.toLowerCase(),ATTRIBUTE_ALIASES[name]??name}const PASSIVE_EVENTS=["touchstart","touchmove"];function is_passive_event(name){
-return PASSIVE_EVENTS.includes(name)}const RAW_TEXT_ELEMENTS=["textarea","script","style","title"];function is_raw_text_element(name){return RAW_TEXT_ELEMENTS.includes(name)}const all_registered_events=new Set,root_event_handles=new Set;function replay_events(dom){if(!hydrating)return;dom.removeAttribute("onload"),dom.removeAttribute("onerror");const event2=dom.__e;event2!==void 0&&(dom.__e=void 0,queueMicrotask(()=>{dom.isConnected&&dom.dispatchEvent(event2)}))}function create_event(event_name,dom,handler,options={}){
-function target_handler(event2){if(options.capture||handle_event_propagation.call(dom,event2),!event2.cancelBubble)return without_reactive_context(()=>handler?.call(this,event2))}return event_name.startsWith("pointer")||event_name.startsWith("touch")||event_name==="wheel"?queue_micro_task(()=>{dom.addEventListener(event_name,target_handler,options)}):dom.addEventListener(event_name,target_handler,options),target_handler}function on(element2,type2,handler,options={}){var target_handler=create_event(
-type2,element2,handler,options);return()=>{element2.removeEventListener(type2,target_handler,options)}}function event(event_name,dom,handler,capture2,passive){var options={capture:capture2,passive},target_handler=create_event(event_name,dom,handler,options);(dom===document.body||dom===window||dom===document||dom instanceof HTMLMediaElement)&&teardown(()=>{dom.removeEventListener(event_name,target_handler,options)})}function delegate(events){for(var i=0;i<events.length;i++)all_registered_events.add(
-events[i]);for(var fn of root_event_handles)fn(events)}let last_propagated_event=null;function handle_event_propagation(event2){var handler_element=this,owner_document=handler_element.ownerDocument,event_name=event2.type,path2=event2.composedPath?.()||[],current_target=path2[0]||event2.target;last_propagated_event=event2;var path_idx=0,handled_at=last_propagated_event===event2&&event2.__root;if(handled_at){var at_idx=path2.indexOf(handled_at);if(at_idx!==-1&&(handler_element===document||handler_element===
-window)){event2.__root=handler_element;return}var handler_idx=path2.indexOf(handler_element);if(handler_idx===-1)return;at_idx<=handler_idx&&(path_idx=at_idx)}if(current_target=path2[path_idx]||event2.target,current_target!==handler_element){define_property(event2,"currentTarget",{configurable:!0,get(){return current_target||owner_document}});var previous_reaction=active_reaction,previous_effect=active_effect;set_active_reaction(null),set_active_effect(null);try{for(var throw_error,other_errors=[];current_target!==
-null;){var parent_element2=current_target.assignedSlot||current_target.parentNode||current_target.host||null;try{var delegated=current_target["__"+event_name];delegated!=null&&(!current_target.disabled||event2.target===current_target)&&delegated.call(current_target,event2)}catch(error2){throw_error?other_errors.push(error2):throw_error=error2}if(event2.cancelBubble||parent_element2===handler_element||parent_element2===null)break;current_target=parent_element2}if(throw_error){for(let error2 of other_errors)
-queueMicrotask(()=>{throw error2});throw throw_error}}finally{event2.__root=handler_element,delete event2.currentTarget,set_active_reaction(previous_reaction),set_active_effect(previous_effect)}}}function create_fragment_from_html(html2){var elem=document.createElement("template");return elem.innerHTML=html2.replaceAll("<!>","<!---->"),elem.content}function assign_nodes(start2,end){var effect2=active_effect;effect2.nodes===null&&(effect2.nodes={start:start2,end,a:null,t:null})}function from_html(content2,flags2){
-var is_fragment=(flags2&TEMPLATE_FRAGMENT)!==0,use_import_node=(flags2&TEMPLATE_USE_IMPORT_NODE)!==0,node2,has_start=!content2.startsWith("<!>");return()=>{if(hydrating)return assign_nodes(hydrate_node,null),hydrate_node;node2===void 0&&(node2=create_fragment_from_html(has_start?content2:"<!>"+content2),is_fragment||(node2=get_first_child(node2)));var clone2=use_import_node||is_firefox?document.importNode(node2,!0):node2.cloneNode(!0);if(is_fragment){var start2=get_first_child(clone2),end=clone2.
-lastChild;assign_nodes(start2,end)}else assign_nodes(clone2,clone2);return clone2}}function from_namespace(content2,flags2,ns="svg"){var has_start=!content2.startsWith("<!>"),wrapped=`<${ns}>${has_start?content2:"<!>"+content2}</${ns}>`,node2;return()=>{if(hydrating)return assign_nodes(hydrate_node,null),hydrate_node;if(!node2){var fragment=create_fragment_from_html(wrapped),root2=get_first_child(fragment);node2=get_first_child(root2)}var clone2=node2.cloneNode(!0);return assign_nodes(clone2,clone2),
-clone2}}function from_svg(content2,flags2){return from_namespace(content2,flags2,"svg")}function text$8(value=""){if(!hydrating){var t=create_text(value+"");return assign_nodes(t,t),t}var node2=hydrate_node;return node2.nodeType!==TEXT_NODE&&(node2.before(node2=create_text()),set_hydrate_node(node2)),assign_nodes(node2,node2),node2}function comment$2(){if(hydrating)return assign_nodes(hydrate_node,null),hydrate_node;var frag=document.createDocumentFragment(),start2=document.createComment(""),anchor=create_text();
-return frag.append(start2,anchor),assign_nodes(start2,anchor),frag}function append(anchor,dom){if(hydrating){var effect2=active_effect;((effect2.f&EFFECT_RAN)===0||effect2.nodes.end===null)&&(effect2.nodes.end=hydrate_node),hydrate_next();return}anchor!==null&&anchor.before(dom)}function props_id(){if(hydrating&&hydrate_node&&hydrate_node.nodeType===COMMENT_NODE&&hydrate_node.textContent?.startsWith("$")){const id2=hydrate_node.textContent.substring(1);return hydrate_next(),id2}return(window.__svelte??=
-{}).uid??=1,`c${window.__svelte.uid++}`}let should_intro=!0;function set_should_intro(value){should_intro=value}function set_text(text2,value){var str=value==null?"":typeof value=="object"?value+"":value;str!==(text2.__t??=text2.nodeValue)&&(text2.__t=str,text2.nodeValue=str+"")}function mount(component2,options){return _mount(component2,options)}function hydrate(component2,options){init_operations(),options.intro=options.intro??!1;const target2=options.target,was_hydrating=hydrating,previous_hydrate_node=hydrate_node;
-try{for(var anchor=get_first_child(target2);anchor&&(anchor.nodeType!==COMMENT_NODE||anchor.data!==HYDRATION_START);)anchor=get_next_sibling(anchor);if(!anchor)throw HYDRATION_ERROR;set_hydrating(!0),set_hydrate_node(anchor);const instance=_mount(component2,{...options,anchor});return set_hydrating(!1),instance}catch(error2){if(error2 instanceof Error&&error2.message.split(`
-`).some(line=>line.startsWith("https://svelte.dev/e/")))throw error2;return error2!==HYDRATION_ERROR&&console.warn("Failed to hydrate: ",error2),options.recover===!1&&hydration_failed(),init_operations(),clear_text_content(target2),set_hydrating(!1),mount(component2,options)}finally{set_hydrating(was_hydrating),set_hydrate_node(previous_hydrate_node)}}const document_listeners=new Map;function _mount(Component,{target:target2,anchor,props={},events,context,intro=!0}){init_operations();var registered_events=new Set,
-event_handle=events2=>{for(var i=0;i<events2.length;i++){var event_name=events2[i];if(!registered_events.has(event_name)){registered_events.add(event_name);var passive=is_passive_event(event_name);target2.addEventListener(event_name,handle_event_propagation,{passive});var n=document_listeners.get(event_name);n===void 0?(document.addEventListener(event_name,handle_event_propagation,{passive}),document_listeners.set(event_name,1)):document_listeners.set(event_name,n+1)}}};event_handle(array_from(all_registered_events)),
-root_event_handles.add(event_handle);var component2=void 0,unmount2=component_root(()=>{var anchor_node=anchor??target2.appendChild(create_text());return boundary(anchor_node,{pending:()=>{}},anchor_node2=>{if(context){push$1({});var ctx=component_context;ctx.c=context}if(events&&(props.$$events=events),hydrating&&assign_nodes(anchor_node2,null),should_intro=intro,component2=Component(anchor_node2,props)||{},should_intro=!0,hydrating&&(active_effect.nodes.end=hydrate_node,hydrate_node===null||hydrate_node.
-nodeType!==COMMENT_NODE||hydrate_node.data!==HYDRATION_END))throw hydration_mismatch(),HYDRATION_ERROR;context&&pop()}),()=>{for(var event_name of registered_events){target2.removeEventListener(event_name,handle_event_propagation);var n=document_listeners.get(event_name);--n===0?(document.removeEventListener(event_name,handle_event_propagation),document_listeners.delete(event_name)):document_listeners.set(event_name,n)}root_event_handles.delete(event_handle),anchor_node!==anchor&&anchor_node.parentNode?.
-removeChild(anchor_node)}});return mounted_components.set(component2,unmount2),component2}let mounted_components=new WeakMap;function unmount(component2,options){const fn=mounted_components.get(component2);return fn?(mounted_components.delete(component2),fn(options)):Promise.resolve()}class BranchManager{anchor;#batches=new Map;#onscreen=new Map;#offscreen=new Map;#outroing=new Set;#transition=!0;constructor(anchor,transition2=!0){this.anchor=anchor,this.#transition=transition2}#commit=()=>{var batch=current_batch;
-if(this.#batches.has(batch)){var key2=this.#batches.get(batch),onscreen=this.#onscreen.get(key2);if(onscreen)resume_effect(onscreen),this.#outroing.delete(key2);else{var offscreen=this.#offscreen.get(key2);offscreen&&(this.#onscreen.set(key2,offscreen.effect),this.#offscreen.delete(key2),offscreen.fragment.lastChild.remove(),this.anchor.before(offscreen.fragment),onscreen=offscreen.effect)}for(const[b,k]of this.#batches){if(this.#batches.delete(b),b===batch)break;const offscreen2=this.#offscreen.
-get(k);offscreen2&&(destroy_effect(offscreen2.effect),this.#offscreen.delete(k))}for(const[k,effect2]of this.#onscreen){if(k===key2||this.#outroing.has(k))continue;const on_destroy=()=>{if(Array.from(this.#batches.values()).includes(k)){var fragment=document.createDocumentFragment();move_effect(effect2,fragment),fragment.append(create_text()),this.#offscreen.set(k,{effect:effect2,fragment})}else destroy_effect(effect2);this.#outroing.delete(k),this.#onscreen.delete(k)};this.#transition||!onscreen?
-(this.#outroing.add(k),pause_effect(effect2,on_destroy,!1)):on_destroy()}}};#discard=batch=>{this.#batches.delete(batch);const keys2=Array.from(this.#batches.values());for(const[k,branch2]of this.#offscreen)keys2.includes(k)||(destroy_effect(branch2.effect),this.#offscreen.delete(k))};ensure(key2,fn){var batch=current_batch,defer=should_defer_append();if(fn&&!this.#onscreen.has(key2)&&!this.#offscreen.has(key2))if(defer){var fragment=document.createDocumentFragment(),target2=create_text();fragment.
-append(target2),this.#offscreen.set(key2,{effect:branch(()=>fn(target2)),fragment})}else this.#onscreen.set(key2,branch(()=>fn(this.anchor)));if(this.#batches.set(batch,key2),defer){for(const[k,effect2]of this.#onscreen)k===key2?batch.skipped_effects.delete(effect2):batch.skipped_effects.add(effect2);for(const[k,branch2]of this.#offscreen)k===key2?batch.skipped_effects.delete(branch2.effect):batch.skipped_effects.add(branch2.effect);batch.oncommit(this.#commit),batch.ondiscard(this.#discard)}else
-hydrating&&(this.anchor=hydrate_node),this.#commit()}}function if_block(node2,fn,elseif=!1){hydrating&&hydrate_next();var branches=new BranchManager(node2),flags2=elseif?EFFECT_TRANSPARENT:0;function update_branch(condition,fn2){if(hydrating){const is_else=read_hydration_instruction(node2)===HYDRATION_START_ELSE;if(condition===is_else){var anchor=skip_nodes();set_hydrate_node(anchor),branches.anchor=anchor,set_hydrating(!1),branches.ensure(condition,fn2),set_hydrating(!0);return}}branches.ensure(
-condition,fn2)}block(()=>{var has_branch=!1;fn((fn2,flag=!0)=>{has_branch=!0,update_branch(flag,fn2)}),has_branch||update_branch(!1,null)},flags2)}function key$1(node2,get_key,render_fn){hydrating&&hydrate_next();var branches=new BranchManager(node2),legacy=!is_runes();block(()=>{var key2=get_key();legacy&&key2!==null&&typeof key2=="object"&&(key2={}),branches.ensure(key2,render_fn)})}function index$2(_,i){return i}function pause_effects(state2,to_destroy,controlled_anchor){for(var transitions=[],
-length=to_destroy.length,group,remaining=to_destroy.length,i=0;i<length;i++){let effect2=to_destroy[i];pause_effect(effect2,()=>{if(group){if(group.pending.delete(effect2),group.done.add(effect2),group.pending.size===0){var groups=state2.outrogroups;destroy_effects(array_from(group.done)),groups.delete(group),groups.size===0&&(state2.outrogroups=null)}}else remaining-=1},!1)}if(remaining===0){var fast_path=transitions.length===0&&controlled_anchor!==null;if(fast_path){var anchor=controlled_anchor,
-parent_node=anchor.parentNode;clear_text_content(parent_node),parent_node.append(anchor),state2.items.clear()}destroy_effects(to_destroy,!fast_path)}else group={pending:new Set(to_destroy),done:new Set},(state2.outrogroups??=new Set).add(group)}function destroy_effects(to_destroy,remove_dom=!0){for(var i=0;i<to_destroy.length;i++)destroy_effect(to_destroy[i],remove_dom)}var offscreen_anchor;function each(node2,flags2,get_collection,get_key,render_fn,fallback_fn=null){var anchor=node2,items2=new Map,
-is_controlled=(flags2&EACH_IS_CONTROLLED)!==0;if(is_controlled){var parent_node=node2;anchor=hydrating?set_hydrate_node(get_first_child(parent_node)):parent_node.appendChild(create_text())}hydrating&&hydrate_next();var fallback2=null,each_array=derived_safe_equal(()=>{var collection=get_collection();return is_array(collection)?collection:collection==null?[]:array_from(collection)}),array2,first_run=!0;function commit(){state2.fallback=fallback2,reconcile(state2,array2,anchor,flags2,get_key),fallback2!==
-null&&(array2.length===0?(fallback2.f&EFFECT_OFFSCREEN)===0?resume_effect(fallback2):(fallback2.f^=EFFECT_OFFSCREEN,move(fallback2,null,anchor)):pause_effect(fallback2,()=>{fallback2=null}))}var effect2=block(()=>{array2=get$4(each_array);var length=array2.length;let mismatch=!1;if(hydrating){var is_else=read_hydration_instruction(anchor)===HYDRATION_START_ELSE;is_else!==(length===0)&&(anchor=skip_nodes(),set_hydrate_node(anchor),set_hydrating(!1),mismatch=!0)}for(var keys2=new Set,batch=current_batch,
-defer=should_defer_append(),index2=0;index2<length;index2+=1){hydrating&&hydrate_node.nodeType===COMMENT_NODE&&hydrate_node.data===HYDRATION_END&&(anchor=hydrate_node,mismatch=!0,set_hydrating(!1));var value=array2[index2],key2=get_key(value,index2),item=first_run?null:items2.get(key2);item?(item.v&&internal_set(item.v,value),item.i&&internal_set(item.i,index2),defer&&batch.skipped_effects.delete(item.e)):(item=create_item(items2,first_run?anchor:offscreen_anchor??=create_text(),value,key2,index2,
-render_fn,flags2,get_collection),first_run||(item.e.f|=EFFECT_OFFSCREEN),items2.set(key2,item)),keys2.add(key2)}if(length===0&&fallback_fn&&!fallback2&&(first_run?fallback2=branch(()=>fallback_fn(anchor)):(fallback2=branch(()=>fallback_fn(offscreen_anchor??=create_text())),fallback2.f|=EFFECT_OFFSCREEN)),hydrating&&length>0&&set_hydrate_node(skip_nodes()),!first_run)if(defer){for(const[key3,item2]of items2)keys2.has(key3)||batch.skipped_effects.add(item2.e);batch.oncommit(commit),batch.ondiscard(
-()=>{})}else commit();mismatch&&set_hydrating(!0),get$4(each_array)}),state2={effect:effect2,items:items2,outrogroups:null,fallback:fallback2};first_run=!1,hydrating&&(anchor=hydrate_node)}function reconcile(state2,array2,anchor,flags2,get_key){var is_animated=(flags2&EACH_IS_ANIMATED)!==0,length=array2.length,items2=state2.items,current2=state2.effect.first,seen2,prev2=null,to_animate,matched=[],stashed=[],value,key2,effect2,i;if(is_animated)for(i=0;i<length;i+=1)value=array2[i],key2=get_key(value,
-i),effect2=items2.get(key2).e,(effect2.f&EFFECT_OFFSCREEN)===0&&(effect2.nodes?.a?.measure(),(to_animate??=new Set).add(effect2));for(i=0;i<length;i+=1){if(value=array2[i],key2=get_key(value,i),effect2=items2.get(key2).e,state2.outrogroups!==null)for(const group of state2.outrogroups)group.pending.delete(effect2),group.done.delete(effect2);if((effect2.f&EFFECT_OFFSCREEN)!==0)if(effect2.f^=EFFECT_OFFSCREEN,effect2===current2)move(effect2,null,anchor);else{var next2=prev2?prev2.next:current2;effect2===
-state2.effect.last&&(state2.effect.last=effect2.prev),effect2.prev&&(effect2.prev.next=effect2.next),effect2.next&&(effect2.next.prev=effect2.prev),link$2(state2,prev2,effect2),link$2(state2,effect2,next2),move(effect2,next2,anchor),prev2=effect2,matched=[],stashed=[],current2=prev2.next;continue}if((effect2.f&INERT)!==0&&(resume_effect(effect2),is_animated&&(effect2.nodes?.a?.unfix(),(to_animate??=new Set).delete(effect2))),effect2!==current2){if(seen2!==void 0&&seen2.has(effect2)){if(matched.length<
-stashed.length){var start2=stashed[0],j;prev2=start2.prev;var a=matched[0],b=matched[matched.length-1];for(j=0;j<matched.length;j+=1)move(matched[j],start2,anchor);for(j=0;j<stashed.length;j+=1)seen2.delete(stashed[j]);link$2(state2,a.prev,b.next),link$2(state2,prev2,a),link$2(state2,b,start2),current2=start2,prev2=b,i-=1,matched=[],stashed=[]}else seen2.delete(effect2),move(effect2,current2,anchor),link$2(state2,effect2.prev,effect2.next),link$2(state2,effect2,prev2===null?state2.effect.first:prev2.
-next),link$2(state2,prev2,effect2),prev2=effect2;continue}for(matched=[],stashed=[];current2!==null&&current2!==effect2;)(seen2??=new Set).add(current2),stashed.push(current2),current2=current2.next;if(current2===null)continue}(effect2.f&EFFECT_OFFSCREEN)===0&&matched.push(effect2),prev2=effect2,current2=effect2.next}if(state2.outrogroups!==null){for(const group of state2.outrogroups)group.pending.size===0&&(destroy_effects(array_from(group.done)),state2.outrogroups?.delete(group));state2.outrogroups.
-size===0&&(state2.outrogroups=null)}if(current2!==null||seen2!==void 0){var to_destroy=[];if(seen2!==void 0)for(effect2 of seen2)(effect2.f&INERT)===0&&to_destroy.push(effect2);for(;current2!==null;)(current2.f&INERT)===0&&current2!==state2.fallback&&to_destroy.push(current2),current2=current2.next;var destroy_length=to_destroy.length;if(destroy_length>0){var controlled_anchor=(flags2&EACH_IS_CONTROLLED)!==0&&length===0?anchor:null;if(is_animated){for(i=0;i<destroy_length;i+=1)to_destroy[i].nodes?.
-a?.measure();for(i=0;i<destroy_length;i+=1)to_destroy[i].nodes?.a?.fix()}pause_effects(state2,to_destroy,controlled_anchor)}}is_animated&&queue_micro_task(()=>{if(to_animate!==void 0)for(effect2 of to_animate)effect2.nodes?.a?.apply()})}function create_item(items2,anchor,value,key2,index2,render_fn,flags2,get_collection){var v=(flags2&EACH_ITEM_REACTIVE)!==0?(flags2&EACH_ITEM_IMMUTABLE)===0?mutable_source(value,!1,!1):source$2(value):null,i=(flags2&EACH_INDEX_REACTIVE)!==0?source$2(index2):null;
-return{v,i,e:branch(()=>(render_fn(anchor,v??value,i??index2,get_collection),()=>{items2.delete(key2)}))}}function move(effect2,next2,anchor){if(effect2.nodes)for(var node2=effect2.nodes.start,end=effect2.nodes.end,dest=next2&&(next2.f&EFFECT_OFFSCREEN)===0?next2.nodes.start:anchor;node2!==null;){var next_node=get_next_sibling(node2);if(dest.before(node2),node2===end)return;node2=next_node}}function link$2(state2,prev2,next2){prev2===null?state2.effect.first=next2:prev2.next=next2,next2===null?state2.
-effect.last=prev2:next2.prev=prev2}function html$6(node2,get_value,svg2=!1,mathml=!1,skip_warning=!1){var anchor=node2,value="";template_effect(()=>{var effect2=active_effect;if(value===(value=get_value()??"")){hydrating&&hydrate_next();return}if(effect2.nodes!==null&&(remove_effect_dom(effect2.nodes.start,effect2.nodes.end),effect2.nodes=null),value!==""){if(hydrating){hydrate_node.data;for(var next2=hydrate_next(),last=next2;next2!==null&&(next2.nodeType!==COMMENT_NODE||next2.data!=="");)last=
-next2,next2=get_next_sibling(next2);if(next2===null)throw hydration_mismatch(),HYDRATION_ERROR;assign_nodes(hydrate_node,last),anchor=set_hydrate_node(next2);return}var html2=value+"";svg2?html2=`<svg>${html2}</svg>`:mathml&&(html2=`<math>${html2}</math>`);var node3=create_fragment_from_html(html2);if((svg2||mathml)&&(node3=get_first_child(node3)),assign_nodes(get_first_child(node3),node3.lastChild),svg2||mathml)for(;get_first_child(node3);)anchor.before(get_first_child(node3));else anchor.before(
-node3)}})}function snippet(node2,get_snippet,...args){var branches=new BranchManager(node2);block(()=>{const snippet2=get_snippet()??null;branches.ensure(snippet2,snippet2&&(anchor=>snippet2(anchor,...args)))},EFFECT_TRANSPARENT)}function createRawSnippet(fn){return(anchor,...params)=>{var snippet2=fn(...params),element2;if(hydrating)element2=hydrate_node,hydrate_next();else{var html2=snippet2.render().trim(),fragment=create_fragment_from_html(html2);element2=get_first_child(fragment),anchor.before(
-element2)}const result=snippet2.setup?.(element2);assign_nodes(element2,element2),typeof result=="function"&&teardown(result)}}function component(node2,get_component,render_fn){hydrating&&hydrate_next();var branches=new BranchManager(node2);block(()=>{var component2=get_component()??null;branches.ensure(component2,component2&&(target2=>render_fn(target2,component2)))},EFFECT_TRANSPARENT)}const now=()=>performance.now(),raf={tick:_=>requestAnimationFrame(_),now:()=>now(),tasks:new Set};function run_tasks(){
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-value.trim()}return keyframe}const linear$1=t=>t;function transition(flags2,element2,get_fn,get_params){var is_intro=(flags2&TRANSITION_IN)!==0,is_outro=(flags2&TRANSITION_OUT)!==0,is_both=is_intro&&is_outro,is_global=(flags2&TRANSITION_GLOBAL)!==0,direction=is_both?"both":is_intro?"in":"out",current_options,inert=element2.inert,overflow=element2.style.overflow,intro,outro;function get_options(){return without_reactive_context(()=>current_options??=get_fn()(element2,get_params?.()??{},{direction}))}
-var transition2={is_global,in(){if(element2.inert=inert,!is_intro){outro?.abort(),outro?.reset?.();return}is_outro||intro?.abort(),dispatch_event(element2,"introstart"),intro=animate(element2,get_options(),outro,1,()=>{dispatch_event(element2,"introend"),intro?.abort(),intro=current_options=void 0,element2.style.overflow=overflow})},out(fn){if(!is_outro){fn?.(),current_options=void 0;return}element2.inert=!0,dispatch_event(element2,"outrostart"),outro=animate(element2,get_options(),intro,0,()=>{
-dispatch_event(element2,"outroend"),fn?.()})},stop:()=>{intro?.abort(),outro?.abort()}},e=active_effect;if((e.nodes.t??=[]).push(transition2),is_intro&&should_intro){var run2=is_global;if(!run2){for(var block2=e.parent;block2&&(block2.f&EFFECT_TRANSPARENT)!==0;)for(;(block2=block2.parent)&&(block2.f&BLOCK_EFFECT)===0;);run2=!block2||(block2.f&EFFECT_RAN)!==0}run2&&effect(()=>{untrack$1(()=>transition2.in())})}}function animate(element2,options,counterpart,t2,on_finish){var is_intro=t2===1;if(is_function(
-options)){var a,aborted2=!1;return queue_micro_task(()=>{if(!aborted2){var o=options({direction:is_intro?"in":"out"});a=animate(element2,o,counterpart,t2,on_finish)}}),{abort:()=>{aborted2=!0,a?.abort()},deactivate:()=>a.deactivate(),reset:()=>a.reset(),t:()=>a.t()}}if(counterpart?.deactivate(),!options?.duration)return on_finish(),{abort:noop$3,deactivate:noop$3,reset:noop$3,t:()=>t2};const{delay=0,css:css2,tick:tick2,easing=linear$1}=options;var keyframes=[];if(is_intro&&counterpart===void 0&&
-(tick2&&tick2(0,1),css2)){var styles2=css_to_keyframe(css2(0,1));keyframes.push(styles2,styles2)}var get_t=()=>1-t2,animation=element2.animate(keyframes,{duration:delay,fill:"forwards"});return animation.onfinish=()=>{animation.cancel();var t1=counterpart?.t()??1-t2;counterpart?.abort();var delta=t2-t1,duration2=options.duration*Math.abs(delta),keyframes2=[];if(duration2>0){var needs_overflow_hidden=!1;if(css2)for(var n=Math.ceil(duration2/16.666666666666668),i=0;i<=n;i+=1){var t=t1+delta*easing(
-i/n),styles3=css_to_keyframe(css2(t,1-t));keyframes2.push(styles3),needs_overflow_hidden||=styles3.overflow==="hidden"}needs_overflow_hidden&&(element2.style.overflow="hidden"),get_t=()=>{var time2=animation.currentTime;return t1+delta*easing(time2/duration2)},tick2&&loop(()=>{if(animation.playState!=="running")return!1;var t3=get_t();return tick2(t3,1-t3),!0})}animation=element2.animate(keyframes2,{duration:duration2,fill:"forwards"}),animation.onfinish=()=>{get_t=()=>t2,tick2?.(t2,1-t2),on_finish()}},
-{abort:()=>{animation&&(animation.cancel(),animation.effect=null,animation.onfinish=noop$3)},deactivate:()=>{on_finish=noop$3},reset:()=>{t2===0&&tick2?.(1,0)},t:()=>get_t()}}function element$4(node2,get_tag,is_svg,render_fn,get_namespace,location2){let was_hydrating=hydrating;hydrating&&hydrate_next();var element2=null;hydrating&&hydrate_node.nodeType===ELEMENT_NODE$1&&(element2=hydrate_node,hydrate_next());var anchor=hydrating?hydrate_node:node2,branches=new BranchManager(anchor,!1);block(()=>{
-const next_tag=get_tag()||null;var ns=is_svg||next_tag==="svg"?NAMESPACE_SVG:null;if(next_tag===null){branches.ensure(null,null),set_should_intro(!0);return}return branches.ensure(next_tag,anchor2=>{if(next_tag){if(element2=hydrating?element2:ns?document.createElementNS(ns,next_tag):document.createElement(next_tag),assign_nodes(element2,element2),render_fn){hydrating&&is_raw_text_element(next_tag)&&element2.append(document.createComment(""));var child_anchor=hydrating?get_first_child(element2):element2.
-appendChild(create_text());hydrating&&(child_anchor===null?set_hydrating(!1):set_hydrate_node(child_anchor)),render_fn(element2,child_anchor)}active_effect.nodes.end=element2,anchor2.before(element2)}hydrating&&set_hydrate_node(anchor2)}),set_should_intro(!0),()=>{next_tag&&set_should_intro(!1)}},EFFECT_TRANSPARENT),teardown(()=>{set_should_intro(!0)}),was_hydrating&&(set_hydrating(!0),set_hydrate_node(anchor))}function head$1(hash2,render_fn){let previous_hydrate_node=null,was_hydrating=hydrating;
-var anchor;if(hydrating){previous_hydrate_node=hydrate_node;for(var head_anchor=get_first_child(document.head);head_anchor!==null&&(head_anchor.nodeType!==COMMENT_NODE||head_anchor.data!==hash2);)head_anchor=get_next_sibling(head_anchor);if(head_anchor===null)set_hydrating(!1);else{var start2=get_next_sibling(head_anchor);head_anchor.remove(),set_hydrate_node(start2)}}hydrating||(anchor=document.head.appendChild(create_text()));try{block(()=>render_fn(anchor),HEAD_EFFECT)}finally{was_hydrating&&
-(set_hydrating(!0),set_hydrate_node(previous_hydrate_node))}}function action(dom,action2,get_value){effect(()=>{var payload=untrack$1(()=>action2(dom,get_value?.())||{});if(get_value&&payload?.update){var inited2=!1,prev2={};render_effect(()=>{var value=get_value();deep_read_state(value),inited2&&safe_not_equal(prev2,value)&&(prev2=value,payload.update(value))}),inited2=!0}if(payload?.destroy)return()=>payload.destroy()})}function attach(node2,get_fn){var fn=void 0,e;managed(()=>{fn!==(fn=get_fn())&&
-(e&&(destroy_effect(e),e=null),fn&&(e=branch(()=>{effect(()=>fn(node2))})))})}function r$1(e){var t,f,n="";if(typeof e=="string"||typeof e=="number")n+=e;else if(typeof e=="object")if(Array.isArray(e)){var o=e.length;for(t=0;t<o;t++)e[t]&&(f=r$1(e[t]))&&(n&&(n+=" "),n+=f)}else for(f in e)e[f]&&(n&&(n+=" "),n+=f);return n}function clsx$1(){for(var e,t,f=0,n="",o=arguments.length;f<o;f++)(e=arguments[f])&&(t=r$1(e))&&(n&&(n+=" "),n+=t);return n}function clsx(value){return typeof value=="object"?clsx$1(
-value):value??""}const whitespace$1=[...` 	
-\r\f \v\uFEFF`];function to_class(value,hash2,directives){var classname=value==null?"":""+value;if(hash2&&(classname=classname?classname+" "+hash2:hash2),directives){for(var key2 in directives)if(directives[key2])classname=classname?classname+" "+key2:key2;else if(classname.length)for(var len=key2.length,a=0;(a=classname.indexOf(key2,a))>=0;){var b=a+len;(a===0||whitespace$1.includes(classname[a-1]))&&(b===classname.length||whitespace$1.includes(classname[b]))?classname=(a===0?"":classname.substring(
-0,a))+classname.substring(b+1):a=b}}return classname===""?null:classname}function append_styles(styles2,important=!1){var separator=important?" !important;":";",css2="";for(var key2 in styles2){var value=styles2[key2];value!=null&&value!==""&&(css2+=" "+key2+": "+value+separator)}return css2}function to_css_name(name){return name[0]!=="-"||name[1]!=="-"?name.toLowerCase():name}function to_style(value,styles2){if(styles2){var new_style="",normal_styles,important_styles;if(Array.isArray(styles2)?(normal_styles=
-styles2[0],important_styles=styles2[1]):normal_styles=styles2,value){value=String(value).replaceAll(/\s*\/\*.*?\*\/\s*/g,"").trim();var in_str=!1,in_apo=0,in_comment=!1,reserved_names=[];normal_styles&&reserved_names.push(...Object.keys(normal_styles).map(to_css_name)),important_styles&&reserved_names.push(...Object.keys(important_styles).map(to_css_name));var start_index=0,name_index=-1;const len=value.length;for(var i=0;i<len;i++){var c2=value[i];if(in_comment?c2==="/"&&value[i-1]==="*"&&(in_comment=
-!1):in_str?in_str===c2&&(in_str=!1):c2==="/"&&value[i+1]==="*"?in_comment=!0:c2==='"'||c2==="'"?in_str=c2:c2==="("?in_apo++:c2===")"&&in_apo--,!in_comment&&in_str===!1&&in_apo===0){if(c2===":"&&name_index===-1)name_index=i;else if(c2===";"||i===len-1){if(name_index!==-1){var name=to_css_name(value.substring(start_index,name_index).trim());if(!reserved_names.includes(name)){c2!==";"&&i++;var property=value.substring(start_index,i).trim();new_style+=" "+property+";"}}start_index=i+1,name_index=-1}}}}
-return normal_styles&&(new_style+=append_styles(normal_styles)),important_styles&&(new_style+=append_styles(important_styles,!0)),new_style=new_style.trim(),new_style===""?null:new_style}return value==null?null:String(value)}function set_class(dom,is_html,value,hash2,prev_classes,next_classes){var prev2=dom.__className;if(hydrating||prev2!==value||prev2===void 0){var next_class_name=to_class(value,hash2,next_classes);(!hydrating||next_class_name!==dom.getAttribute("class"))&&(next_class_name==null?
-dom.removeAttribute("class"):is_html?dom.className=next_class_name:dom.setAttribute("class",next_class_name)),dom.__className=value}else if(next_classes&&prev_classes!==next_classes)for(var key2 in next_classes){var is_present=!!next_classes[key2];(prev_classes==null||is_present!==!!prev_classes[key2])&&dom.classList.toggle(key2,is_present)}return next_classes}function update_styles(dom,prev2={},next2,priority){for(var key2 in next2){var value=next2[key2];prev2[key2]!==value&&(next2[key2]==null?
-dom.style.removeProperty(key2):dom.style.setProperty(key2,value,priority))}}function set_style(dom,value,prev_styles,next_styles){var prev2=dom.__style;if(hydrating||prev2!==value){var next_style_attr=to_style(value,next_styles);(!hydrating||next_style_attr!==dom.getAttribute("style"))&&(next_style_attr==null?dom.removeAttribute("style"):dom.style.cssText=next_style_attr),dom.__style=value}else next_styles&&(Array.isArray(next_styles)?(update_styles(dom,prev_styles?.[0],next_styles[0]),update_styles(
-dom,prev_styles?.[1],next_styles[1],"important")):update_styles(dom,prev_styles,next_styles));return next_styles}function select_option(select,value,mounting=!1){if(select.multiple){if(value==null)return;if(!is_array(value))return select_multiple_invalid_value();for(var option2 of select.options)option2.selected=value.includes(get_option_value(option2));return}for(option2 of select.options){var option_value=get_option_value(option2);if(is(option_value,value)){option2.selected=!0;return}}(!mounting||
-value!==void 0)&&(select.selectedIndex=-1)}function init_select(select){var observer=new MutationObserver(()=>{select_option(select,select.__value)});observer.observe(select,{childList:!0,subtree:!0,attributes:!0,attributeFilter:["value"]}),teardown(()=>{observer.disconnect()})}function get_option_value(option2){return"__value"in option2?option2.__value:option2.value}const CLASS=Symbol("class"),STYLE=Symbol("style"),IS_CUSTOM_ELEMENT=Symbol("is custom element"),IS_HTML=Symbol("is html");function remove_input_defaults(input){
-if(hydrating){var already_removed=!1,remove_defaults=()=>{if(!already_removed){if(already_removed=!0,input.hasAttribute("value")){var value=input.value;set_attribute(input,"value",null),input.value=value}if(input.hasAttribute("checked")){var checked=input.checked;set_attribute(input,"checked",null),input.checked=checked}}};input.__on_r=remove_defaults,queue_micro_task(remove_defaults),add_form_reset_listener()}}function set_value(element2,value){var attributes=get_attributes(element2);attributes.
-value===(attributes.value=value??void 0)||element2.value===value&&(value!==0||element2.nodeName!=="PROGRESS")||(element2.value=value??"")}function set_selected(element2,selected){selected?element2.hasAttribute("selected")||element2.setAttribute("selected",""):element2.removeAttribute("selected")}function set_attribute(element2,attribute,value,skip_warning){var attributes=get_attributes(element2);hydrating&&(attributes[attribute]=element2.getAttribute(attribute),attribute==="src"||attribute==="sr\
-cset"||attribute==="href"&&element2.nodeName==="LINK")||attributes[attribute]!==(attributes[attribute]=value)&&(attribute==="loading"&&(element2[LOADING_ATTR_SYMBOL]=value),value==null?element2.removeAttribute(attribute):typeof value!="string"&&get_setters(element2).includes(attribute)?element2[attribute]=value:element2.setAttribute(attribute,value))}function set_attributes(element2,prev2,next2,css_hash,should_remove_defaults=!1,skip_warning=!1){if(hydrating&&should_remove_defaults&&element2.tagName===
-"INPUT"){var input=element2,attribute=input.type==="checkbox"?"defaultChecked":"defaultValue";attribute in next2||remove_input_defaults(input)}var attributes=get_attributes(element2),is_custom_element=attributes[IS_CUSTOM_ELEMENT],preserve_attribute_case=!attributes[IS_HTML];let is_hydrating_custom_element=hydrating&&is_custom_element;is_hydrating_custom_element&&set_hydrating(!1);var current2=prev2||{},is_option_element=element2.tagName==="OPTION";for(var key2 in prev2)key2 in next2||(next2[key2]=
-null);next2.class?next2.class=clsx(next2.class):(css_hash||next2[CLASS])&&(next2.class=null),next2[STYLE]&&(next2.style??=null);var setters=get_setters(element2);for(const key3 in next2){let value=next2[key3];if(is_option_element&&key3==="value"&&value==null){element2.value=element2.__value="",current2[key3]=value;continue}if(key3==="class"){var is_html=element2.namespaceURI==="http://www.w3.org/1999/xhtml";set_class(element2,is_html,value,css_hash,prev2?.[CLASS],next2[CLASS]),current2[key3]=value,
-current2[CLASS]=next2[CLASS];continue}if(key3==="style"){set_style(element2,value,prev2?.[STYLE],next2[STYLE]),current2[key3]=value,current2[STYLE]=next2[STYLE];continue}var prev_value=current2[key3];if(!(value===prev_value&&!(value===void 0&&element2.hasAttribute(key3)))){current2[key3]=value;var prefix=key3[0]+key3[1];if(prefix!=="$$")if(prefix==="on"){const opts={},event_handle_key="$$"+key3;let event_name=key3.slice(2);var delegated=can_delegate_event(event_name);if(is_capture_event(event_name)&&
-(event_name=event_name.slice(0,-7),opts.capture=!0),!delegated&&prev_value){if(value!=null)continue;element2.removeEventListener(event_name,current2[event_handle_key],opts),current2[event_handle_key]=null}if(value!=null)if(delegated)element2[`__${event_name}`]=value,delegate([event_name]);else{let handle2=function(evt){current2[key3].call(this,evt)};current2[event_handle_key]=create_event(event_name,element2,handle2,opts)}else delegated&&(element2[`__${event_name}`]=void 0)}else if(key3==="style")
-set_attribute(element2,key3,value);else if(key3==="autofocus")autofocus(element2,!!value);else if(!is_custom_element&&(key3==="__value"||key3==="value"&&value!=null))element2.value=element2.__value=value;else if(key3==="selected"&&is_option_element)set_selected(element2,value);else{var name=key3;preserve_attribute_case||(name=normalize_attribute(name));var is_default=name==="defaultValue"||name==="defaultChecked";if(value==null&&!is_custom_element&&!is_default)if(attributes[key3]=null,name==="va\
-lue"||name==="checked"){let input2=element2;const use_default=prev2===void 0;if(name==="value"){let previous2=input2.defaultValue;input2.removeAttribute(name),input2.defaultValue=previous2,input2.value=input2.__value=use_default?previous2:null}else{let previous2=input2.defaultChecked;input2.removeAttribute(name),input2.defaultChecked=previous2,input2.checked=use_default?previous2:!1}}else element2.removeAttribute(key3);else is_default||setters.includes(name)&&(is_custom_element||typeof value!="s\
-tring")?(element2[name]=value,name in attributes&&(attributes[name]=UNINITIALIZED)):typeof value!="function"&&set_attribute(element2,name,value)}}}return is_hydrating_custom_element&&set_hydrating(!0),current2}function attribute_effect(element2,fn,sync=[],async=[],blockers=[],css_hash,should_remove_defaults=!1,skip_warning=!1){flatten(blockers,sync,async,values=>{var prev2=void 0,effects={},is_select=element2.nodeName==="SELECT",inited2=!1;if(managed(()=>{var next2=fn(...values.map(get$4)),current2=set_attributes(
-element2,prev2,next2,css_hash,should_remove_defaults,skip_warning);inited2&&is_select&&"value"in next2&&select_option(element2,next2.value);for(let symbol of Object.getOwnPropertySymbols(effects))next2[symbol]||destroy_effect(effects[symbol]);for(let symbol of Object.getOwnPropertySymbols(next2)){var n=next2[symbol];symbol.description===ATTACHMENT_KEY&&(!prev2||n!==prev2[symbol])&&(effects[symbol]&&destroy_effect(effects[symbol]),effects[symbol]=branch(()=>attach(element2,()=>n))),current2[symbol]=
-n}prev2=current2}),is_select){var select=element2;effect(()=>{select_option(select,prev2.value,!0),init_select(select)})}inited2=!0})}function get_attributes(element2){return element2.__attributes??={[IS_CUSTOM_ELEMENT]:element2.nodeName.includes("-"),[IS_HTML]:element2.namespaceURI===NAMESPACE_HTML}}var setters_cache=new Map;function get_setters(element2){var cache_key=element2.getAttribute("is")||element2.nodeName,setters=setters_cache.get(cache_key);if(setters)return setters;setters_cache.set(
-cache_key,setters=[]);for(var descriptors,proto=element2,element_proto=Element.prototype;element_proto!==proto;){descriptors=get_descriptors(proto);for(var key2 in descriptors)descriptors[key2].set&&setters.push(key2);proto=get_prototype_of(proto)}return setters}function bind_value(input,get2,set2=get2){var batches2=new WeakSet;listen_to_event_and_reset_event(input,"input",async is_reset=>{var value=is_reset?input.defaultValue:input.value;if(value=is_numberlike_input(input)?to_number(value):value,
-set2(value),current_batch!==null&&batches2.add(current_batch),await tick(),value!==(value=get2())){var start2=input.selectionStart,end=input.selectionEnd,length=input.value.length;if(input.value=value??"",end!==null){var new_length=input.value.length;start2===end&&end===length&&new_length>length?(input.selectionStart=new_length,input.selectionEnd=new_length):(input.selectionStart=start2,input.selectionEnd=Math.min(end,new_length))}}}),(hydrating&&input.defaultValue!==input.value||untrack$1(get2)==
-null&&input.value)&&(set2(is_numberlike_input(input)?to_number(input.value):input.value),current_batch!==null&&batches2.add(current_batch)),render_effect(()=>{var value=get2();if(input===document.activeElement){var batch=previous_batch??current_batch;if(batches2.has(batch))return}is_numberlike_input(input)&&value===to_number(input.value)||input.type==="date"&&!value&&!input.value||value!==input.value&&(input.value=value??"")})}function is_numberlike_input(input){var type2=input.type;return type2===
-"number"||type2==="range"}function to_number(value){return value===""?null:+value}function bind_files(input,get2,set2=get2){listen_to_event_and_reset_event(input,"change",()=>{set2(input.files)}),hydrating&&input.files&&set2(input.files),render_effect(()=>{input.files=get2()})}function is_bound_this(bound_value,element_or_component){return bound_value===element_or_component||bound_value?.[STATE_SYMBOL]===element_or_component}function bind_this(element_or_component={},update2,get_value,get_parts){
-return effect(()=>{var old_parts,parts;return render_effect(()=>{old_parts=parts,parts=[],untrack$1(()=>{element_or_component!==get_value(...parts)&&(update2(element_or_component,...parts),old_parts&&is_bound_this(get_value(...old_parts),element_or_component)&&update2(null,...old_parts))})}),()=>{queue_micro_task(()=>{parts&&is_bound_this(get_value(...parts),element_or_component)&&update2(null,...parts)})}}),element_or_component}function bind_window_size(type2,set2){listen(window,["resize"],()=>without_reactive_context(
-()=>set2(window[type2])))}function init(immutable=!1){const context=component_context,callbacks=context.l.u;if(!callbacks)return;let props=()=>deep_read_state(context.s);if(immutable){let version2=0,prev2={};const d2=derived(()=>{let changed=!1;const props2=context.s;for(const key2 in props2)props2[key2]!==prev2[key2]&&(prev2[key2]=props2[key2],changed=!0);return changed&&version2++,version2});props=()=>get$4(d2)}callbacks.b.length&&user_pre_effect(()=>{observe_all(context,props),run_all(callbacks.
-b)}),user_effect(()=>{const fns=untrack$1(()=>callbacks.m.map(run));return()=>{for(const fn of fns)typeof fn=="function"&&fn()}}),callbacks.a.length&&user_effect(()=>{observe_all(context,props),run_all(callbacks.a)})}function observe_all(context,props){if(context.l.s)for(const signal of context.l.s)get$4(signal);props()}function subscribe_to_store(store,run2,invalidate){if(store==null)return run2(void 0),noop$3;const unsub=untrack$1(()=>store.subscribe(run2,invalidate));return unsub.unsubscribe?
-()=>unsub.unsubscribe():unsub}const subscriber_queue=[];function writable(value,start2=noop$3){let stop=null;const subscribers=new Set;function set2(new_value){if(safe_not_equal(value,new_value)&&(value=new_value,stop)){const run_queue=!subscriber_queue.length;for(const subscriber of subscribers)subscriber[1](),subscriber_queue.push(subscriber,value);if(run_queue){for(let i=0;i<subscriber_queue.length;i+=2)subscriber_queue[i][0](subscriber_queue[i+1]);subscriber_queue.length=0}}}function update2(fn){
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-return path2==="/"||trailing_slash==="ignore"?path2:trailing_slash==="never"?path2.endsWith("/")?path2.slice(0,-1):path2:trailing_slash==="always"&&!path2.endsWith("/")?path2+"/":path2}function decode_pathname(pathname){return pathname.split("%25").map(decodeURI).join("%25")}function decode_params(params){for(const key2 in params)params[key2]=decodeURIComponent(params[key2]);return params}function strip_hash({href}){return href.split("#")[0]}function make_trackable(url2,callback,search_params_callback,allow_hash=!1){
-const tracked=new URL(url2);Object.defineProperty(tracked,"searchParams",{value:new Proxy(tracked.searchParams,{get(obj,key2){if(key2==="get"||key2==="getAll"||key2==="has")return(param,...rest)=>(search_params_callback(param),obj[key2](param,...rest));callback();const value=Reflect.get(obj,key2);return typeof value=="function"?value.bind(obj):value}}),enumerable:!0,configurable:!0});const tracked_url_properties=["href","pathname","search","toString","toJSON"];allow_hash&&tracked_url_properties.
-push("hash");for(const property of tracked_url_properties)Object.defineProperty(tracked,property,{get(){return callback(),url2[property]},enumerable:!0,configurable:!0});return tracked}function hash$1(...values){let hash2=5381;for(const value of values)if(typeof value=="string"){let i=value.length;for(;i;)hash2=hash2*33^value.charCodeAt(--i)}else if(ArrayBuffer.isView(value)){const buffer=new Uint8Array(value.buffer,value.byteOffset,value.byteLength);let i=buffer.length;for(;i;)hash2=hash2*33^buffer[--i]}else
-throw new TypeError("value must be a string or TypedArray");return(hash2>>>0).toString(36)}new TextEncoder;new TextDecoder;function base64_decode(encoded){const binary=atob(encoded),bytes=new Uint8Array(binary.length);for(let i=0;i<binary.length;i++)bytes[i]=binary.charCodeAt(i);return bytes}const native_fetch=window.fetch;window.fetch=(input,init2)=>((input instanceof Request?input.method:init2?.method||"GET")!=="GET"&&cache.delete(build_selector(input)),native_fetch(input,init2));const cache=new Map;
-function initial_fetch(resource,opts){const selector=build_selector(resource,opts),script=document.querySelector(selector);if(script?.textContent){script.remove();let{body:body2,...init2}=JSON.parse(script.textContent);const ttl=script.getAttribute("data-ttl");return ttl&&cache.set(selector,{body:body2,init:init2,ttl:1e3*Number(ttl)}),script.getAttribute("data-b64")!==null&&(body2=base64_decode(body2)),Promise.resolve(new Response(body2,init2))}return window.fetch(resource,opts)}function subsequent_fetch(resource,resolved,opts){
-if(cache.size>0){const selector=build_selector(resource,opts),cached2=cache.get(selector);if(cached2){if(performance.now()<cached2.ttl&&["default","force-cache","only-if-cached",void 0].includes(opts?.cache))return new Response(cached2.body,cached2.init);cache.delete(selector)}}return window.fetch(resolved,opts)}function build_selector(resource,opts){let selector=`script[data-sveltekit-fetched][data-url=${JSON.stringify(resource instanceof Request?resource.url:resource)}]`;if(opts?.headers||opts?.
-body){const values=[];opts.headers&&values.push([...new Headers(opts.headers)].join(",")),opts.body&&(typeof opts.body=="string"||ArrayBuffer.isView(opts.body))&&values.push(opts.body),selector+=`[data-hash="${hash$1(...values)}"]`}return selector}const param_pattern=/^(\[)?(\.\.\.)?(\w+)(?:=(\w+))?(\])?$/;function parse_route_id(id2){const params=[];return{pattern:id2==="/"?/^\/$/:new RegExp(`^${get_route_segments(id2).map(segment=>{const rest_match=/^\[\.\.\.(\w+)(?:=(\w+))?\]$/.exec(segment);
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-""&&!/^\([^)]+\)$/.test(segment)}function get_route_segments(route){return route.slice(1).split("/").filter(affects_path)}function exec(match,params,matchers2){const result={},values=match.slice(1),values_needing_match=values.filter(value=>value!==void 0);let buffered=0;for(let i=0;i<params.length;i+=1){const param=params[i];let value=values[i-buffered];if(param.chained&&param.rest&&buffered&&(value=values.slice(i-buffered,i+1).filter(s2=>s2).join("/"),buffered=0),value===void 0)if(param.rest)value=
-"";else continue;if(!param.matcher||matchers2[param.matcher](value)){result[param.name]=value;const next_param=params[i+1],next_value=values[i+1];next_param&&!next_param.rest&&next_param.optional&&next_value&&param.chained&&(buffered=0),!next_param&&!next_value&&Object.keys(result).length===values_needing_match.length&&(buffered=0);continue}if(param.optional&&param.chained){buffered++;continue}return}if(!buffered)return result}function escape$3(str){return str.normalize().replace(/[[\]]/g,"\\$&").
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-errors:[1,...errors2||[]].map(n=>nodes2[n]),layouts:[0,...layouts||[]].map(create_layout_loader),leaf:create_leaf_loader(leaf2)};return route.errors.length=route.layouts.length=Math.max(route.errors.length,route.layouts.length),route});function create_leaf_loader(id2){const uses_server_data=id2<0;return uses_server_data&&(id2=~id2),[uses_server_data,nodes2[id2]]}function create_layout_loader(id2){return id2===void 0?id2:[layouts_with_server_load.has(id2),nodes2[id2]]}}function get$2(key2,parse2=JSON.
-parse){try{return parse2(sessionStorage[key2])}catch{}}function set(key2,value,stringify2=JSON.stringify){const data=stringify2(value);try{sessionStorage[key2]=data}catch{}}const base=globalThis.__sveltekit__?.base??"",assets=globalThis.__sveltekit__?.assets??base??"",version$3="llama-server-webui",SNAPSHOT_KEY="sveltekit:snapshot",SCROLL_KEY="sveltekit:scroll",STATES_KEY="sveltekit:states",PAGE_URL_KEY="sveltekit:pageurl",HISTORY_INDEX="sveltekit:history",NAVIGATION_INDEX="sveltekit:n\
-avigation",PRELOAD_PRIORITIES={tap:1,hover:2,viewport:3,eager:4,off:-1,false:-1},origin=location.origin;function resolve_url(url2){if(url2 instanceof URL)return url2;let baseURI=document.baseURI;if(!baseURI){const baseTags=document.getElementsByTagName("base");baseURI=baseTags.length?baseTags[0].href:document.URL}return new URL(url2,baseURI)}function scroll_state(){return{x:pageXOffset,y:pageYOffset}}function link_option(element2,name){return element2.getAttribute(`data-sveltekit-${name}`)}const levels={
-...PRELOAD_PRIORITIES,"":PRELOAD_PRIORITIES.hover};function parent_element(element2){let parent=element2.assignedSlot??element2.parentNode;return parent?.nodeType===11&&(parent=parent.host),parent}function find_anchor(element2,target2){for(;element2&&element2!==target2;){if(element2.nodeName.toUpperCase()==="A"&&element2.hasAttribute("href"))return element2;element2=parent_element(element2)}}function get_link_info(a,base2,uses_hash_router){let url2;try{if(url2=new URL(a instanceof SVGAElement?a.
-href.baseVal:a.href,document.baseURI),uses_hash_router&&url2.hash.match(/^#[^/]/)){const route=location.hash.split("#")[1]||"/";url2.hash=`#${route}${url2.hash}`}}catch{}const target2=a instanceof SVGAElement?a.target.baseVal:a.target,external=!url2||!!target2||is_external_url(url2,base2,uses_hash_router)||(a.getAttribute("rel")||"").split(/\s+/).includes("external"),download=url2?.origin===origin&&a.hasAttribute("download");return{url:url2,external,target:target2,download}}function get_router_options(element2){
-let keepfocus=null,noscroll=null,preload_code=null,preload_data=null,reload=null,replace_state=null,el=element2;for(;el&&el!==document.documentElement;)preload_code===null&&(preload_code=link_option(el,"preload-code")),preload_data===null&&(preload_data=link_option(el,"preload-data")),keepfocus===null&&(keepfocus=link_option(el,"keepfocus")),noscroll===null&&(noscroll=link_option(el,"noscroll")),reload===null&&(reload=link_option(el,"reload")),replace_state===null&&(replace_state=link_option(el,
-"replacestate")),el=parent_element(el);function get_option_state(value){switch(value){case"":case"true":return!0;case"off":case"false":return!1;default:return}}return{preload_code:levels[preload_code??"off"],preload_data:levels[preload_data??"off"],keepfocus:get_option_state(keepfocus),noscroll:get_option_state(noscroll),reload:get_option_state(reload),replace_state:get_option_state(replace_state)}}function notifiable_store(value){const store=writable(value);let ready=!0;function notify(){ready=
-!0,store.update(val=>val)}function set2(new_value){ready=!1,store.set(new_value)}function subscribe(run2){let old_value;return store.subscribe(new_value=>{(old_value===void 0||ready&&new_value!==old_value)&&run2(old_value=new_value)})}return{notify,set:set2,subscribe}}const updated_listener={v:()=>{}};function create_updated_store(){const{set:set2,subscribe}=writable(!1);let timeout;async function check(){clearTimeout(timeout);try{const res=await fetch(`${assets}/_app/version.json`,{headers:{pragma:"\
-no-cache","cache-control":"no-cache"}});if(!res.ok)return!1;const updated2=(await res.json()).version!==version$3;return updated2&&(set2(!0),updated_listener.v(),clearTimeout(timeout)),updated2}catch{return!1}}return{subscribe,check}}function is_external_url(url2,base2,hash_routing){return url2.origin!==origin||!url2.pathname.startsWith(base2)?!0:hash_routing?url2.pathname!==location.pathname:!1}const valid_layout_exports=new Set(["load","prerender","csr","ssr","trailingSlash","config"]);[...valid_layout_exports];
-const valid_layout_server_exports=new Set([...valid_layout_exports]);[...valid_layout_server_exports];function compact(arr){return arr.filter(val=>val!=null)}function get_status(error2){return error2 instanceof HttpError||error2 instanceof SvelteKitError?error2.status:500}function get_message(error2){return error2 instanceof SvelteKitError?error2.text:"Internal Error"}let page$3,navigating,updated;const is_legacy=onMount$1.toString().includes("$$")||/function \w+\(\) \{\}/.test(onMount$1.toString());
-is_legacy?(page$3={data:{},form:null,error:null,params:{},route:{id:null},state:{},status:-1,url:new URL("https://example.com")},navigating={current:null},updated={current:!1}):(page$3=new class{#data=state$1({});get data(){return get$4(this.#data)}set data(value){set$1(this.#data,value)}#form=state$1(null);get form(){return get$4(this.#form)}set form(value){set$1(this.#form,value)}#error=state$1(null);get error(){return get$4(this.#error)}set error(value){set$1(this.#error,value)}#params=state$1(
-{});get params(){return get$4(this.#params)}set params(value){set$1(this.#params,value)}#route=state$1({id:null});get route(){return get$4(this.#route)}set route(value){set$1(this.#route,value)}#state=state$1({});get state(){return get$4(this.#state)}set state(value){set$1(this.#state,value)}#status=state$1(-1);get status(){return get$4(this.#status)}set status(value){set$1(this.#status,value)}#url=state$1(new URL("https://example.com"));get url(){return get$4(this.#url)}set url(value){set$1(this.#url,
-value)}},navigating=new class{#current=state$1(null);get current(){return get$4(this.#current)}set current(value){set$1(this.#current,value)}},updated=new class{#current=state$1(!1);get current(){return get$4(this.#current)}set current(value){set$1(this.#current,value)}},updated_listener.v=()=>updated.current=!0);function update(new_page){Object.assign(page$3,new_page)}const noop_span={spanContext(){return noop_span_context},setAttribute(){return this},setAttributes(){return this},addEvent(){return this},
-setStatus(){return this},updateName(){return this},end(){return this},isRecording(){return!1},recordException(){return this},addLink(){return this},addLinks(){return this}},noop_span_context={traceId:"",spanId:"",traceFlags:0},{onMount}=svelte,untrack=untrack$1??(value=>value()),ICON_REL_ATTRIBUTES=new Set(["icon","shortcut icon","apple-touch-icon"]),scroll_positions=get$2(SCROLL_KEY)??{},snapshots=get$2(SNAPSHOT_KEY)??{},stores={url:notifiable_store({}),page:notifiable_store({}),navigating:writable(
-null),updated:create_updated_store()};function update_scroll_positions(index2){scroll_positions[index2]=scroll_state()}function clear_onward_history(current_history_index2,current_navigation_index2){let i=current_history_index2+1;for(;scroll_positions[i];)delete scroll_positions[i],i+=1;for(i=current_navigation_index2+1;snapshots[i];)delete snapshots[i],i+=1}function native_navigation(url2,replace2=!1){return replace2?location.replace(url2.href):location.href=url2.href,new Promise(()=>{})}async function update_service_worker(){
-if("serviceWorker"in navigator){const registration=await navigator.serviceWorker.getRegistration(base||"/");registration&&await registration.update()}}function noop$2(){}let routes,default_layout_loader,default_error_loader,container,target,app$1;const invalidated=[],components=[];let load_cache=null;function discard_load_cache(){load_cache?.fork?.then(f=>f?.discard()),load_cache=null}const reroute_cache=new Map,before_navigate_callbacks=new Set,on_navigate_callbacks=new Set,after_navigate_callbacks=new Set;
-let current={branch:[],error:null,url:null},hydrated=!1,started=!1,autoscroll=!0,is_navigating=!1,hash_navigating=!1,has_navigated=!1,force_invalidation=!1,root$1O,current_history_index,current_navigation_index,token;const preload_tokens=new Set,query_map=new Map;async function start$1(_app,_target,hydrate2){globalThis.__sveltekit__?.data&&globalThis.__sveltekit__.data,document.URL!==location.href&&(location.href=location.href),app$1=_app,await _app.hooks.init?.(),routes=parse$5(_app),
-container=document.documentElement,target=_target,default_layout_loader=_app.nodes[0],default_error_loader=_app.nodes[1],default_layout_loader(),default_error_loader(),current_history_index=history.state?.[HISTORY_INDEX],current_navigation_index=history.state?.[NAVIGATION_INDEX],current_history_index||(current_history_index=current_navigation_index=Date.now(),history.replaceState({...history.state,[HISTORY_INDEX]:current_history_index,[NAVIGATION_INDEX]:current_navigation_index},""));const scroll=scroll_positions[current_history_index];
-function restore_scroll(){scroll&&(history.scrollRestoration="manual",scrollTo(scroll.x,scroll.y))}hydrate2?(restore_scroll(),await _hydrate(target,hydrate2)):(await navigate({type:"enter",url:resolve_url(app$1.hash?decode_hash(new URL(location.href)):location.href),replace_state:!0}),restore_scroll()),_start_router()}function reset_invalidation(){invalidated.length=0,force_invalidation=!1}function capture_snapshot(index2){components.some(c2=>c2?.snapshot)&&(snapshots[index2]=components.map(c2=>c2?.
-snapshot?.capture()))}function restore_snapshot(index2){snapshots[index2]?.forEach((value,i)=>{components[i]?.snapshot?.restore(value)})}function persist_state(){update_scroll_positions(current_history_index),set(SCROLL_KEY,scroll_positions),capture_snapshot(current_navigation_index),set(SNAPSHOT_KEY,snapshots)}async function _goto(url2,options,redirect_count,nav_token){let query_keys;options.invalidateAll&&discard_load_cache(),await navigate({type:"goto",url:resolve_url(url2),keepfocus:options.
-keepFocus,noscroll:options.noScroll,replace_state:options.replaceState,state:options.state,redirect_count,nav_token,accept:()=>{options.invalidateAll&&(force_invalidation=!0,query_keys=[...query_map.keys()]),options.invalidate&&options.invalidate.forEach(push_invalidated)}}),options.invalidateAll&&tick().then(tick).then(()=>{query_map.forEach(({resource},key2)=>{query_keys?.includes(key2)&&resource.refresh?.()})})}async function _preload_data(intent){if(intent.id!==load_cache?.id){discard_load_cache();
-const preload2={};preload_tokens.add(preload2),load_cache={id:intent.id,token:preload2,promise:load_route({...intent,preload:preload2}).then(result=>(preload_tokens.delete(preload2),result.type==="loaded"&&result.state.error&&discard_load_cache(),result)),fork:null}}return load_cache.promise}async function _preload_code(url2){const route=(await get_navigation_intent(url2,!1))?.route;route&&await Promise.all([...route.layouts,route.leaf].map(load2=>load2?.[1]()))}async function initialize(result,target2,hydrate2){
-current=result.state;const style2=document.querySelector("style[data-sveltekit]");if(style2&&style2.remove(),Object.assign(page$3,result.props.page),root$1O=new app$1.root({target:target2,props:{...result.props,stores,components},hydrate:hydrate2,sync:!1}),await Promise.resolve(),restore_snapshot(current_navigation_index),hydrate2){const navigation={from:null,to:{params:current.params,route:{id:current.route?.id??null},url:new URL(location.href)},willUnload:!1,type:"enter",complete:Promise.resolve()};
-after_navigate_callbacks.forEach(fn=>fn(navigation))}started=!0}function get_navigation_result_from_branch({url:url2,params,branch:branch2,status,error:error2,route,form}){let slash="never";if(base&&(url2.pathname===base||url2.pathname===base+"/"))slash="always";else for(const node2 of branch2)node2?.slash!==void 0&&(slash=node2.slash);url2.pathname=normalize_path(url2.pathname,slash),url2.search=url2.search;const result={type:"loaded",state:{url:url2,params,branch:branch2,error:error2,route},props:{
-constructors:compact(branch2).map(branch_node=>branch_node.node.component),page:clone_page(page$3)}};form!==void 0&&(result.props.form=form);let data={},data_changed=!page$3,p2=0;for(let i=0;i<Math.max(branch2.length,current.branch.length);i+=1){const node2=branch2[i],prev2=current.branch[i];node2?.data!==prev2?.data&&(data_changed=!0),node2&&(data={...data,...node2.data},data_changed&&(result.props[`data_${p2}`]=data),p2+=1)}return(!current.url||url2.href!==current.url.href||current.error!==error2||
-form!==void 0&&form!==page$3.form||data_changed)&&(result.props.page={error:error2,params,route:{id:route?.id??null},state:{},status,url:new URL(url2),form:form??null,data:data_changed?data:page$3.data}),result}async function load_node({loader,parent,url:url2,params,route,server_data_node}){let data=null,is_tracking=!0;const uses={dependencies:new Set,params:new Set,parent:!1,route:!1,url:!1,search_params:new Set},node2=await loader();if(node2.universal?.load){let depends=function(...deps){for(const dep of deps){
-const{href}=new URL(dep,url2);uses.dependencies.add(href)}};const load_input={tracing:{enabled:!1,root:noop_span,current:noop_span},route:new Proxy(route,{get:(target2,key2)=>(is_tracking&&(uses.route=!0),target2[key2])}),params:new Proxy(params,{get:(target2,key2)=>(is_tracking&&uses.params.add(key2),target2[key2])}),data:server_data_node?.data??null,url:make_trackable(url2,()=>{is_tracking&&(uses.url=!0)},param=>{is_tracking&&uses.search_params.add(param)},app$1.hash),async fetch(resource,init2){
-resource instanceof Request&&(init2={body:resource.method==="GET"||resource.method==="HEAD"?void 0:await resource.blob(),cache:resource.cache,credentials:resource.credentials,headers:[...resource.headers].length>0?resource?.headers:void 0,integrity:resource.integrity,keepalive:resource.keepalive,method:resource.method,mode:resource.mode,redirect:resource.redirect,referrer:resource.referrer,referrerPolicy:resource.referrerPolicy,signal:resource.signal,...init2});const{resolved,promise}=resolve_fetch_url(
-resource,init2,url2);return is_tracking&&depends(resolved.href),promise},setHeaders:()=>{},depends,parent(){return is_tracking&&(uses.parent=!0),parent()},untrack(fn){is_tracking=!1;try{return fn()}finally{is_tracking=!0}}};data=await node2.universal.load.call(null,load_input)??null}return{node:node2,loader,server:server_data_node,universal:node2.universal?.load?{type:"data",data,uses}:null,data:data??server_data_node?.data??null,slash:node2.universal?.trailingSlash??server_data_node?.slash}}function resolve_fetch_url(input,init2,url2){
-let requested=input instanceof Request?input.url:input;const resolved=new URL(requested,url2);resolved.origin===url2.origin&&(requested=resolved.href.slice(url2.origin.length));const promise=started?subsequent_fetch(requested,resolved.href,init2):initial_fetch(requested,init2);return{resolved,promise}}function has_changed(parent_changed,route_changed,url_changed,search_params_changed,uses,params){if(force_invalidation)return!0;if(!uses)return!1;if(uses.parent&&parent_changed||uses.route&&route_changed||
-uses.url&&url_changed)return!0;for(const tracked_params of uses.search_params)if(search_params_changed.has(tracked_params))return!0;for(const param of uses.params)if(params[param]!==current.params[param])return!0;for(const href of uses.dependencies)if(invalidated.some(fn=>fn(new URL(href))))return!0;return!1}function create_data_node(node2,previous2){return node2?.type==="data"?node2:node2?.type==="skip"?previous2??null:null}function diff_search_params(old_url,new_url){if(!old_url)return new Set(
-new_url.searchParams.keys());const changed=new Set([...old_url.searchParams.keys(),...new_url.searchParams.keys()]);for(const key2 of changed){const old_values2=old_url.searchParams.getAll(key2),new_values=new_url.searchParams.getAll(key2);old_values2.every(value=>new_values.includes(value))&&new_values.every(value=>old_values2.includes(value))&&changed.delete(key2)}return changed}function preload_error({error:error2,url:url2,route,params}){return{type:"loaded",state:{error:error2,url:url2,route,
-params,branch:[]},props:{page:clone_page(page$3),constructors:[]}}}async function load_route({id:id2,invalidating,url:url2,params,route,preload:preload2}){if(load_cache?.id===id2)return preload_tokens.delete(load_cache.token),load_cache.promise;const{errors:errors2,layouts,leaf:leaf2}=route,loaders=[...layouts,leaf2];errors2.forEach(loader=>loader?.().catch(()=>{})),loaders.forEach(loader=>loader?.[1]().catch(()=>{}));const url_changed=current.url?id2!==get_page_key(current.url):!1,route_changed=current.
-route?route.id!==current.route.id:!1,search_params_changed=diff_search_params(current.url,url2);let parent_changed=!1;const branch_promises=loaders.map(async(loader,i)=>{if(!loader)return;const previous2=current.branch[i];return loader[1]===previous2?.loader&&!has_changed(parent_changed,route_changed,url_changed,search_params_changed,previous2.universal?.uses,params)?previous2:(parent_changed=!0,load_node({loader:loader[1],url:url2,params,route,parent:async()=>{const data={};for(let j=0;j<i;j+=1)
-Object.assign(data,(await branch_promises[j])?.data);return data},server_data_node:create_data_node(loader[0]?{type:"skip"}:null,loader[0]?previous2?.server:void 0)}))});for(const p2 of branch_promises)p2.catch(()=>{});const branch2=[];for(let i=0;i<loaders.length;i+=1)if(loaders[i])try{branch2.push(await branch_promises[i])}catch(err){if(err instanceof Redirect)return{type:"redirect",location:err.location};if(preload_tokens.has(preload2))return preload_error({error:await handle_error(err,{params,
-url:url2,route:{id:route.id}}),url:url2,params,route});let status=get_status(err),error2;if(err instanceof HttpError)error2=err.body;else{if(await stores.updated.check())return await update_service_worker(),await native_navigation(url2);error2=await handle_error(err,{params,url:url2,route:{id:route.id}})}const error_load=await load_nearest_error_page(i,branch2,errors2);return error_load?get_navigation_result_from_branch({url:url2,params,branch:branch2.slice(0,error_load.idx).concat(error_load.node),
-status,error:error2,route}):await server_fallback(url2,{id:route.id},error2,status)}else branch2.push(void 0);return get_navigation_result_from_branch({url:url2,params,branch:branch2,status:200,error:null,route,form:invalidating?void 0:null})}async function load_nearest_error_page(i,branch2,errors2){for(;i--;)if(errors2[i]){let j=i;for(;!branch2[j];)j-=1;try{return{idx:j+1,node:{node:await errors2[i](),loader:errors2[i],data:{},server:null,universal:null}}}catch{continue}}}async function load_root_error_page({
-status,error:error2,url:url2,route}){const params={};let server_data_node=null;try{const root_layout=await load_node({loader:default_layout_loader,url:url2,params,route,parent:()=>Promise.resolve({}),server_data_node:create_data_node(server_data_node)}),root_error={node:await default_error_loader(),loader:default_error_loader,universal:null,server:null,data:null};return get_navigation_result_from_branch({url:url2,params,branch:[root_layout,root_error],status,error:error2,route:null})}catch(error22){
-if(error22 instanceof Redirect)return _goto(new URL(error22.location,location.href),{},0);throw error22}}async function get_rerouted_url(url2){const href=url2.href;if(reroute_cache.has(href))return reroute_cache.get(href);let rerouted;try{const promise=(async()=>{let rerouted2=await app$1.hooks.reroute({url:new URL(url2),fetch:async(input,init2)=>resolve_fetch_url(input,init2,url2).promise})??url2;if(typeof rerouted2=="string"){const tmp=new URL(url2);app$1.hash?tmp.hash=rerouted2:tmp.pathname=rerouted2,
-rerouted2=tmp}return rerouted2})();reroute_cache.set(href,promise),rerouted=await promise}catch{reroute_cache.delete(href);return}return rerouted}async function get_navigation_intent(url2,invalidating){if(url2&&!is_external_url(url2,base,app$1.hash)){const rerouted=await get_rerouted_url(url2);if(!rerouted)return;const path2=get_url_path(rerouted);for(const route of routes){const params=route.exec(path2);if(params)return{id:get_page_key(url2),invalidating,route,params:decode_params(params),url:url2}}}}
-function get_url_path(url2){return decode_pathname(app$1.hash?url2.hash.replace(/^#/,"").replace(/[?#].+/,""):url2.pathname.slice(base.length))||"/"}function get_page_key(url2){return(app$1.hash?url2.hash.replace(/^#/,""):url2.pathname)+url2.search}function _before_navigate({url:url2,type:type2,intent,delta,event:event2}){let should_block=!1;const nav=create_navigation(current,intent,url2,type2);delta!==void 0&&(nav.navigation.delta=delta),event2!==void 0&&(nav.navigation.event=event2);const cancellable={
-...nav.navigation,cancel:()=>{should_block=!0,nav.reject(new Error("navigation cancelled"))}};return is_navigating||before_navigate_callbacks.forEach(fn=>fn(cancellable)),should_block?null:nav}async function navigate({type:type2,url:url2,popped,keepfocus,noscroll,replace_state,state:state2={},redirect_count=0,nav_token={},accept=noop$2,block:block2=noop$2,event:event2}){const prev_token=token;token=nav_token;const intent=await get_navigation_intent(url2,!1),nav=type2==="enter"?create_navigation(
-current,intent,url2,type2):_before_navigate({url:url2,type:type2,delta:popped?.delta,intent,event:event2});if(!nav){block2(),token===nav_token&&(token=prev_token);return}const previous_history_index=current_history_index,previous_navigation_index=current_navigation_index;accept(),is_navigating=!0,started&&nav.navigation.type!=="enter"&&stores.navigating.set(navigating.current=nav.navigation);let navigation_result=intent&&await load_route(intent);if(!navigation_result){if(is_external_url(url2,base,
-app$1.hash))return await native_navigation(url2,replace_state);navigation_result=await server_fallback(url2,{id:null},await handle_error(new SvelteKitError(404,"Not Found",`Not found: ${url2.pathname}`),{url:url2,params:{},route:{id:null}}),404,replace_state)}if(url2=intent?.url||url2,token!==nav_token)return nav.reject(new Error("navigation aborted")),!1;if(navigation_result.type==="redirect"){if(redirect_count<20){await navigate({type:type2,url:new URL(navigation_result.location,url2),popped,keepfocus,
-noscroll,replace_state,state:state2,redirect_count:redirect_count+1,nav_token}),nav.fulfil(void 0);return}navigation_result=await load_root_error_page({status:500,error:await handle_error(new Error("Redirect loop"),{url:url2,params:{},route:{id:null}}),url:url2,route:{id:null}})}else navigation_result.props.page.status>=400&&await stores.updated.check()&&(await update_service_worker(),await native_navigation(url2,replace_state));if(reset_invalidation(),update_scroll_positions(previous_history_index),
-capture_snapshot(previous_navigation_index),navigation_result.props.page.url.pathname!==url2.pathname&&(url2.pathname=navigation_result.props.page.url.pathname),state2=popped?popped.state:state2,!popped){const change=replace_state?0:1,entry={[HISTORY_INDEX]:current_history_index+=change,[NAVIGATION_INDEX]:current_navigation_index+=change,[STATES_KEY]:state2};(replace_state?history.replaceState:history.pushState).call(history,entry,"",url2),replace_state||clear_onward_history(current_history_index,
-current_navigation_index)}const load_cache_fork=intent&&load_cache?.id===intent.id?load_cache.fork:null;load_cache=null,navigation_result.props.page.state=state2;let commit_promise;if(started){const after_navigate=(await Promise.all(Array.from(on_navigate_callbacks,fn=>fn(nav.navigation)))).filter(value=>typeof value=="function");if(after_navigate.length>0){let cleanup=function(){after_navigate.forEach(fn=>{after_navigate_callbacks.delete(fn)})};after_navigate.push(cleanup),after_navigate.forEach(
-fn=>{after_navigate_callbacks.add(fn)})}current=navigation_result.state,navigation_result.props.page&&(navigation_result.props.page.url=url2);const fork2=load_cache_fork&&await load_cache_fork;fork2?commit_promise=fork2.commit():(root$1O.$set(navigation_result.props),update(navigation_result.props.page),commit_promise=settled?.()),has_navigated=!0}else await initialize(navigation_result,target,!1);const{activeElement}=document;await commit_promise,await tick(),await tick();let scroll=popped?popped.
-scroll:noscroll?scroll_state():null;if(autoscroll){const deep_linked=url2.hash&&document.getElementById(get_id(url2));if(scroll)scrollTo(scroll.x,scroll.y);else if(deep_linked){deep_linked.scrollIntoView();const{top,left}=deep_linked.getBoundingClientRect();scroll={x:pageXOffset+left,y:pageYOffset+top}}else scrollTo(0,0)}const changed_focus=document.activeElement!==activeElement&&document.activeElement!==document.body;!keepfocus&&!changed_focus&&reset_focus(url2,scroll),autoscroll=!0,navigation_result.
-props.page&&Object.assign(page$3,navigation_result.props.page),is_navigating=!1,type2==="popstate"&&restore_snapshot(current_navigation_index),nav.fulfil(void 0),after_navigate_callbacks.forEach(fn=>fn(nav.navigation)),stores.navigating.set(navigating.current=null)}async function server_fallback(url2,route,error2,status,replace_state){return url2.origin===origin&&url2.pathname===location.pathname&&!hydrated?await load_root_error_page({status,error:error2,url:url2,route}):await native_navigation(
-url2,replace_state)}function setup_preload(){let mousemove_timeout,current_a={element:void 0,href:void 0},current_priority;container.addEventListener("mousemove",event2=>{const target2=event2.target;clearTimeout(mousemove_timeout),mousemove_timeout=setTimeout(()=>{preload2(target2,PRELOAD_PRIORITIES.hover)},20)});function tap2(event2){event2.defaultPrevented||preload2(event2.composedPath()[0],PRELOAD_PRIORITIES.tap)}container.addEventListener("mousedown",tap2),container.addEventListener("touchst\
-art",tap2,{passive:!0});const observer=new IntersectionObserver(entries=>{for(const entry of entries)entry.isIntersecting&&(_preload_code(new URL(entry.target.href)),observer.unobserve(entry.target))},{threshold:0});async function preload2(element2,priority){const a=find_anchor(element2,container),interacted=a===current_a.element&&a?.href===current_a.href&&priority>=current_priority;if(!a||interacted)return;const{url:url2,external,download}=get_link_info(a,base,app$1.hash);if(external||download)
-return;const options=get_router_options(a),same_url=url2&&get_page_key(current.url)===get_page_key(url2);if(!(options.reload||same_url))if(priority<=options.preload_data){current_a={element:a,href:a.href},current_priority=PRELOAD_PRIORITIES.tap;const intent=await get_navigation_intent(url2,!1);if(!intent)return;_preload_data(intent)}else priority<=options.preload_code&&(current_a={element:a,href:a.href},current_priority=priority,_preload_code(url2))}function after_navigate(){observer.disconnect();
-for(const a of container.querySelectorAll("a")){const{url:url2,external,download}=get_link_info(a,base,app$1.hash);if(external||download)continue;const options=get_router_options(a);options.reload||(options.preload_code===PRELOAD_PRIORITIES.viewport&&observer.observe(a),options.preload_code===PRELOAD_PRIORITIES.eager&&_preload_code(url2))}}after_navigate_callbacks.add(after_navigate),after_navigate()}function handle_error(error2,event2){if(error2 instanceof HttpError)return error2.body;const status=get_status(
-error2),message=get_message(error2);return app$1.hooks.handleError({error:error2,event:event2,status,message})??{message}}function add_navigation_callback(callbacks,callback){onMount(()=>(callbacks.add(callback),()=>{callbacks.delete(callback)}))}function afterNavigate(callback){add_navigation_callback(after_navigate_callbacks,callback)}function goto(url2,opts={}){return url2=new URL(resolve_url(url2)),url2.origin!==origin?Promise.reject(new Error("goto: invalid URL")):_goto(url2,opts,0)}function push_invalidated(resource){
-if(typeof resource=="function")invalidated.push(resource);else{const{href}=new URL(resource,location.href);invalidated.push(url2=>url2.href===href)}}function replaceState(url2,state2){const opts={[HISTORY_INDEX]:current_history_index,[NAVIGATION_INDEX]:current_navigation_index,[PAGE_URL_KEY]:page$3.url.href,[STATES_KEY]:state2};history.replaceState(opts,"",resolve_url(url2)),page$3.state=state2,root$1O.$set({page:untrack(()=>clone_page(page$3))})}function _start_router(){history.scrollRestoration=
-"manual",addEventListener("beforeunload",e=>{let should_block=!1;if(persist_state(),!is_navigating){const nav=create_navigation(current,void 0,null,"leave"),navigation={...nav.navigation,cancel:()=>{should_block=!0,nav.reject(new Error("navigation cancelled"))}};before_navigate_callbacks.forEach(fn=>fn(navigation))}should_block?(e.preventDefault(),e.returnValue=""):history.scrollRestoration="auto"}),addEventListener("visibilitychange",()=>{document.visibilityState==="hidden"&&persist_state()}),navigator.
-connection?.saveData||setup_preload(),container.addEventListener("click",async event2=>{if(event2.button||event2.which!==1||event2.metaKey||event2.ctrlKey||event2.shiftKey||event2.altKey||event2.defaultPrevented)return;const a=find_anchor(event2.composedPath()[0],container);if(!a)return;const{url:url2,external,target:target2,download}=get_link_info(a,base,app$1.hash);if(!url2)return;if(target2==="_parent"||target2==="_top"){if(window.parent!==window)return}else if(target2&&target2!=="_self")return;
-const options=get_router_options(a);if(!(a instanceof SVGAElement)&&url2.protocol!==location.protocol&&!(url2.protocol==="https:"||url2.protocol==="http:")||download)return;const[nonhash,hash2]=(app$1.hash?url2.hash.replace(/^#/,""):url2.href).split("#"),same_pathname=nonhash===strip_hash(location);if(external||options.reload&&(!same_pathname||!hash2)){_before_navigate({url:url2,type:"link",event:event2})?is_navigating=!0:event2.preventDefault();return}if(hash2!==void 0&&same_pathname){const[,current_hash]=current.
-url.href.split("#");if(current_hash===hash2){if(event2.preventDefault(),hash2===""||hash2==="top"&&a.ownerDocument.getElementById("top")===null)scrollTo({top:0});else{const element2=a.ownerDocument.getElementById(decodeURIComponent(hash2));element2&&(element2.scrollIntoView(),element2.focus())}return}if(hash_navigating=!0,update_scroll_positions(current_history_index),update_url(url2),!options.replace_state)return;hash_navigating=!1}event2.preventDefault(),await new Promise(fulfil=>{requestAnimationFrame(
-()=>{setTimeout(fulfil,0)}),setTimeout(fulfil,100)}),await navigate({type:"link",url:url2,keepfocus:options.keepfocus,noscroll:options.noscroll,replace_state:options.replace_state??url2.href===location.href,event:event2})}),container.addEventListener("submit",event2=>{if(event2.defaultPrevented)return;const form=HTMLFormElement.prototype.cloneNode.call(event2.target),submitter=event2.submitter;if((submitter?.formTarget||form.target)==="_blank"||(submitter?.formMethod||form.method)!=="get")return;
-const url2=new URL(submitter?.hasAttribute("formaction")&&submitter?.formAction||form.action);if(is_external_url(url2,base,!1))return;const event_form=event2.target,options=get_router_options(event_form);if(options.reload)return;event2.preventDefault(),event2.stopPropagation();const data=new FormData(event_form,submitter);url2.search=new URLSearchParams(data).toString(),navigate({type:"form",url:url2,keepfocus:options.keepfocus,noscroll:options.noscroll,replace_state:options.replace_state??url2.
-href===location.href,event:event2})}),addEventListener("popstate",async event2=>{if(!resetting_focus){if(event2.state?.[HISTORY_INDEX]){const history_index=event2.state[HISTORY_INDEX];if(token={},history_index===current_history_index)return;const scroll=scroll_positions[history_index],state2=event2.state[STATES_KEY]??{},url2=new URL(event2.state[PAGE_URL_KEY]??location.href),navigation_index=event2.state[NAVIGATION_INDEX],is_hash_change=current.url?strip_hash(location)===strip_hash(current.url):
-!1;if(navigation_index===current_navigation_index&&(has_navigated||is_hash_change)){state2!==page$3.state&&(page$3.state=state2),update_url(url2),scroll_positions[current_history_index]=scroll_state(),scroll&&scrollTo(scroll.x,scroll.y),current_history_index=history_index;return}const delta=history_index-current_history_index;await navigate({type:"popstate",url:url2,popped:{state:state2,scroll,delta},accept:()=>{current_history_index=history_index,current_navigation_index=navigation_index},block:()=>{
-history.go(-delta)},nav_token:token,event:event2})}else if(!hash_navigating){const url2=new URL(location.href);update_url(url2),app$1.hash&&location.reload()}}}),addEventListener("hashchange",()=>{hash_navigating&&(hash_navigating=!1,history.replaceState({...history.state,[HISTORY_INDEX]:++current_history_index,[NAVIGATION_INDEX]:current_navigation_index},"",location.href))});for(const link2 of document.querySelectorAll("link"))ICON_REL_ATTRIBUTES.has(link2.rel)&&(link2.href=link2.href);addEventListener(
-"pageshow",event2=>{event2.persisted&&stores.navigating.set(navigating.current=null)});function update_url(url2){current.url=page$3.url=url2,stores.page.set(clone_page(page$3)),stores.page.notify()}}async function _hydrate(target2,{status=200,error:error2,node_ids,params,route,server_route,data:server_data_nodes,form}){hydrated=!0;const url2=new URL(location.href);let parsed_route;({params={},route={id:null}}=await get_navigation_intent(url2,!1)||{}),parsed_route=routes.find(({id:id2})=>id2===route.
-id);let result,hydrate2=!0;try{const branch_promises=node_ids.map(async(n,i)=>{const server_data_node=server_data_nodes[i];return server_data_node?.uses&&(server_data_node.uses=deserialize_uses(server_data_node.uses)),load_node({loader:app$1.nodes[n],url:url2,params,route,parent:async()=>{const data={};for(let j=0;j<i;j+=1)Object.assign(data,(await branch_promises[j]).data);return data},server_data_node:create_data_node(server_data_node)})}),branch2=await Promise.all(branch_promises);if(parsed_route){
-const layouts=parsed_route.layouts;for(let i=0;i<layouts.length;i++)layouts[i]||branch2.splice(i,0,void 0)}result=get_navigation_result_from_branch({url:url2,params,branch:branch2,status,error:error2,form,route:parsed_route??null})}catch(error22){if(error22 instanceof Redirect){await native_navigation(new URL(error22.location,location.href));return}result=await load_root_error_page({status:get_status(error22),error:await handle_error(error22,{url:url2,params,route}),url:url2,route}),target2.textContent=
-"",hydrate2=!1}result.props.page&&(result.props.page.state={}),await initialize(result,target2,hydrate2)}function deserialize_uses(uses){return{dependencies:new Set(uses?.dependencies??[]),params:new Set(uses?.params??[]),parent:!!uses?.parent,route:!!uses?.route,url:!!uses?.url,search_params:new Set(uses?.search_params??[])}}let resetting_focus=!1;function reset_focus(url2,scroll=null){const autofocus2=document.querySelector("[autofocus]");if(autofocus2)autofocus2.focus();else{const id2=get_id(
-url2);if(id2&&document.getElementById(id2)){const{x,y}=scroll??scroll_state();setTimeout(()=>{const history_state=history.state;resetting_focus=!0,location.replace(`#${id2}`),app$1.hash&&location.replace(url2.hash),history.replaceState(history_state,"",url2.hash),scrollTo(x,y),resetting_focus=!1})}else{const root2=document.body,tabindex=root2.getAttribute("tabindex");root2.tabIndex=-1,root2.focus({preventScroll:!0,focusVisible:!1}),tabindex!==null?root2.setAttribute("tabindex",tabindex):root2.removeAttribute(
-"tabindex")}const selection=getSelection();if(selection&&selection.type!=="None"){const ranges=[];for(let i=0;i<selection.rangeCount;i+=1)ranges.push(selection.getRangeAt(i));setTimeout(()=>{if(selection.rangeCount===ranges.length){for(let i=0;i<selection.rangeCount;i+=1){const a=ranges[i],b=selection.getRangeAt(i);if(a.commonAncestorContainer!==b.commonAncestorContainer||a.startContainer!==b.startContainer||a.endContainer!==b.endContainer||a.startOffset!==b.startOffset||a.endOffset!==b.endOffset)
-return}selection.removeAllRanges()}})}}}function create_navigation(current2,intent,url2,type2){let fulfil,reject;const complete=new Promise((f,r2)=>{fulfil=f,reject=r2});return complete.catch(()=>{}),{navigation:{from:{params:current2.params,route:{id:current2.route?.id??null},url:current2.url},to:url2&&{params:intent?.params??null,route:{id:intent?.route?.id??null},url:url2},willUnload:!intent,type:type2,complete},fulfil,reject}}function clone_page(page2){return{data:page2.data,error:page2.error,
-form:page2.form,params:page2.params,route:page2.route,state:page2.state,status:page2.status,url:page2.url}}function decode_hash(url2){const new_url=new URL(url2);return new_url.hash=decodeURIComponent(url2.hash),new_url}function get_id(url2){let id2;if(app$1.hash){const[,,second]=url2.hash.split("#",3);id2=second??""}else id2=url2.hash.slice(1);return decodeURIComponent(id2)}const scriptRel="modulepreload",assetsURL=function(dep,importerUrl){return new URL(dep,importerUrl).href},seen={},__vitePreload=function(baseModule,deps,importerUrl){
-let promise=Promise.resolve();if(deps&&deps.length>0){let allSettled=function(promises$2){return Promise.all(promises$2.map(p2=>Promise.resolve(p2).then(value$1=>({status:"fulfilled",value:value$1}),reason=>({status:"rejected",reason}))))};const links=document.getElementsByTagName("link"),cspNonceMeta=document.querySelector("meta[property=csp-nonce]"),cspNonce=cspNonceMeta?.nonce||cspNonceMeta?.getAttribute("nonce");promise=allSettled(deps.map(dep=>{if(dep=assetsURL(dep,importerUrl),dep in seen)
-return;seen[dep]=!0;const isCss=dep.endsWith(".css"),cssSelector=isCss?'[rel="stylesheet"]':"";if(importerUrl)for(let i$1=links.length-1;i$1>=0;i$1--){const link$12=links[i$1];if(link$12.href===dep&&(!isCss||link$12.rel==="stylesheet"))return}else if(document.querySelector(`link[href="${dep}"]${cssSelector}`))return;const link2=document.createElement("link");if(link2.rel=isCss?"stylesheet":scriptRel,isCss||(link2.as="script"),link2.crossOrigin="",link2.href=dep,cspNonce&&link2.setAttribute("nonc\
-e",cspNonce),document.head.appendChild(link2),isCss)return new Promise((res,rej)=>{link2.addEventListener("load",res),link2.addEventListener("error",()=>rej(new Error(`Unable to preload CSS for ${dep}`)))})}))}function handlePreloadError(err$2){const e$1=new Event("vite:preloadError",{cancelable:!0});if(e$1.payload=err$2,window.dispatchEvent(e$1),!e$1.defaultPrevented)throw err$2}return promise.then(res=>{for(const item of res||[])item.status==="rejected"&&handlePreloadError(item.reason);return baseModule().
-catch(handlePreloadError)})},matchers={},PUBLIC_VERSION="5";typeof window<"u"&&((window.__svelte??={}).v??=new Set).add(PUBLIC_VERSION);var root_4$G=from_html('<div id="svelte-announcer" aria-live="assertive" aria-atomic="true" style="position: absolute; left: 0; top: 0; clip: rect(0 0 0 0); clip-path: inset(50%); overflow: hidden; white-space: nowrap; width: 1px; height: 1px"><!></div>'),root$1N=from_html("<!> <!>",1);function Root$6($$anchor,$$props){push$1($$props,!0);let components2=prop($$props,
-"components",23,()=>[]),data_0=prop($$props,"data_0",3,null),data_1=prop($$props,"data_1",3,null);user_pre_effect(()=>$$props.stores.page.set($$props.page)),user_effect(()=>{$$props.stores,$$props.page,$$props.constructors,components2(),$$props.form,data_0(),data_1(),$$props.stores.page.notify()});let mounted=state$1(!1),navigated=state$1(!1),title2=state$1(null);onMount$1(()=>{const unsubscribe=$$props.stores.page.subscribe(()=>{get$4(mounted)&&(set$1(navigated,!0),tick().then(()=>{set$1(title2,
-document.title||"untitled page",!0)}))});return set$1(mounted,!0),unsubscribe});const Pyramid_1=user_derived(()=>$$props.constructors[1]);var fragment=root$1N(),node2=first_child(fragment);{var consequent=$$anchor2=>{const Pyramid_0=user_derived(()=>$$props.constructors[0]);var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>get$4(Pyramid_0),($$anchor3,Pyramid_0_1)=>{bind_this(Pyramid_0_1($$anchor3,{get data(){return data_0()},get form(){return $$props.form},get params(){
-return $$props.page.params},children:($$anchor4,$$slotProps)=>{var fragment_2=comment$2(),node_2=first_child(fragment_2);component(node_2,()=>get$4(Pyramid_1),($$anchor5,Pyramid_1_1)=>{bind_this(Pyramid_1_1($$anchor5,{get data(){return data_1()},get form(){return $$props.form},get params(){return $$props.page.params}}),$$value=>components2()[1]=$$value,()=>components2()?.[1])}),append($$anchor4,fragment_2)},$$slots:{default:!0}}),$$value=>components2()[0]=$$value,()=>components2()?.[0])}),append(
-$$anchor2,fragment_1)},alternate=$$anchor2=>{const Pyramid_0=user_derived(()=>$$props.constructors[0]);var fragment_3=comment$2(),node_3=first_child(fragment_3);component(node_3,()=>get$4(Pyramid_0),($$anchor3,Pyramid_0_2)=>{bind_this(Pyramid_0_2($$anchor3,{get data(){return data_0()},get form(){return $$props.form},get params(){return $$props.page.params}}),$$value=>components2()[0]=$$value,()=>components2()?.[0])}),append($$anchor2,fragment_3)};if_block(node2,$$render=>{$$props.constructors[1]?
-$$render(consequent):$$render(alternate,!1)})}var node_4=sibling(node2,2);{var consequent_2=$$anchor2=>{var div=root_4$G(),node_5=child(div);{var consequent_1=$$anchor3=>{var text2=text$8();template_effect(()=>set_text(text2,get$4(title2))),append($$anchor3,text2)};if_block(node_5,$$render=>{get$4(navigated)&&$$render(consequent_1)})}reset(div),append($$anchor2,div)};if_block(node_4,$$render=>{get$4(mounted)&&$$render(consequent_2)})}append($$anchor,fragment),pop()}const root$1M=asClassComponent(
-Root$6),nodes=[()=>__vitePreload(()=>Promise.resolve().then(()=>_0),void 0,import.meta.url),()=>__vitePreload(()=>Promise.resolve().then(()=>_1),void 0,import.meta.url),()=>__vitePreload(()=>Promise.resolve().then(()=>_2),void 0,import.meta.url),()=>__vitePreload(()=>Promise.resolve().then(()=>_3),void 0,import.meta.url)],server_loads=[],dictionary={"/":[2],"/chat/[id]":[3]},hooks={handleError:({error:error2})=>{console.error(error2)},reroute:()=>{},transport:{}},
-decoders=Object.fromEntries(Object.entries(hooks.transport).map(([k,v])=>[k,v.decode])),encoders=Object.fromEntries(Object.entries(hooks.transport).map(([k,v])=>[k,v.encode])),hash=!0,decode$2=(type2,value)=>decoders[type2](value),app=Object.freeze(Object.defineProperty({__proto__:null,decode:decode$2,decoders,dictionary,encoders,hash,hooks,matchers,nodes,root:root$1M,server_loads},Symbol.toStringTag,{value:"Module"}));function start(element2,options){start$1(app,element2,options)}const page$2={
-get params(){return page$3.params},get route(){return page$3.route},get status(){return page$3.status},get url(){return page$3.url}};stores.updated.check;const page$1=page$2,CLASS_PART_SEPARATOR="-",createClassGroupUtils=config2=>{const classMap=createClassMap(config2),{conflictingClassGroups,conflictingClassGroupModifiers}=config2;return{getClassGroupId:className=>{const classParts=className.split(CLASS_PART_SEPARATOR);return classParts[0]===""&&classParts.length!==1&&classParts.shift(),getGroupRecursive(
-classParts,classMap)||getGroupIdForArbitraryProperty(className)},getConflictingClassGroupIds:(classGroupId,hasPostfixModifier)=>{const conflicts=conflictingClassGroups[classGroupId]||[];return hasPostfixModifier&&conflictingClassGroupModifiers[classGroupId]?[...conflicts,...conflictingClassGroupModifiers[classGroupId]]:conflicts}}},getGroupRecursive=(classParts,classPartObject)=>{if(classParts.length===0)return classPartObject.classGroupId;const currentClassPart=classParts[0],nextClassPartObject=classPartObject.
-nextPart.get(currentClassPart),classGroupFromNextClassPart=nextClassPartObject?getGroupRecursive(classParts.slice(1),nextClassPartObject):void 0;if(classGroupFromNextClassPart)return classGroupFromNextClassPart;if(classPartObject.validators.length===0)return;const classRest=classParts.join(CLASS_PART_SEPARATOR);return classPartObject.validators.find(({validator})=>validator(classRest))?.classGroupId},arbitraryPropertyRegex=/^\[(.+)\]$/,getGroupIdForArbitraryProperty=className=>{if(arbitraryPropertyRegex.
-test(className)){const arbitraryPropertyClassName=arbitraryPropertyRegex.exec(className)[1],property=arbitraryPropertyClassName?.substring(0,arbitraryPropertyClassName.indexOf(":"));if(property)return"arbitrary.."+property}},createClassMap=config2=>{const{theme,classGroups}=config2,classMap={nextPart:new Map,validators:[]};for(const classGroupId in classGroups)processClassesRecursively(classGroups[classGroupId],classMap,classGroupId,theme);return classMap},processClassesRecursively=(classGroup,classPartObject,classGroupId,theme)=>{
-classGroup.forEach(classDefinition=>{if(typeof classDefinition=="string"){const classPartObjectToEdit=classDefinition===""?classPartObject:getPart(classPartObject,classDefinition);classPartObjectToEdit.classGroupId=classGroupId;return}if(typeof classDefinition=="function"){if(isThemeGetter(classDefinition)){processClassesRecursively(classDefinition(theme),classPartObject,classGroupId,theme);return}classPartObject.validators.push({validator:classDefinition,classGroupId});return}Object.entries(classDefinition).
-forEach(([key2,classGroup2])=>{processClassesRecursively(classGroup2,getPart(classPartObject,key2),classGroupId,theme)})})},getPart=(classPartObject,path2)=>{let currentClassPartObject=classPartObject;return path2.split(CLASS_PART_SEPARATOR).forEach(pathPart=>{currentClassPartObject.nextPart.has(pathPart)||currentClassPartObject.nextPart.set(pathPart,{nextPart:new Map,validators:[]}),currentClassPartObject=currentClassPartObject.nextPart.get(pathPart)}),currentClassPartObject},isThemeGetter=func=>func.
-isThemeGetter,createLruCache=maxCacheSize=>{if(maxCacheSize<1)return{get:()=>{},set:()=>{}};let cacheSize=0,cache2=new Map,previousCache=new Map;const update2=(key2,value)=>{cache2.set(key2,value),cacheSize++,cacheSize>maxCacheSize&&(cacheSize=0,previousCache=cache2,cache2=new Map)};return{get(key2){let value=cache2.get(key2);if(value!==void 0)return value;if((value=previousCache.get(key2))!==void 0)return update2(key2,value),value},set(key2,value){cache2.has(key2)?cache2.set(key2,value):update2(
-key2,value)}}},IMPORTANT_MODIFIER="!",MODIFIER_SEPARATOR=":",MODIFIER_SEPARATOR_LENGTH=MODIFIER_SEPARATOR.length,createParseClassName=config2=>{const{prefix,experimentalParseClassName}=config2;let parseClassName=className=>{const modifiers=[];let bracketDepth=0,parenDepth=0,modifierStart=0,postfixModifierPosition;for(let index2=0;index2<className.length;index2++){let currentCharacter=className[index2];if(bracketDepth===0&&parenDepth===0){if(currentCharacter===MODIFIER_SEPARATOR){modifiers.push(className.
-slice(modifierStart,index2)),modifierStart=index2+MODIFIER_SEPARATOR_LENGTH;continue}if(currentCharacter==="/"){postfixModifierPosition=index2;continue}}currentCharacter==="["?bracketDepth++:currentCharacter==="]"?bracketDepth--:currentCharacter==="("?parenDepth++:currentCharacter===")"&&parenDepth--}const baseClassNameWithImportantModifier=modifiers.length===0?className:className.substring(modifierStart),baseClassName=stripImportantModifier(baseClassNameWithImportantModifier),hasImportantModifier=baseClassName!==
-baseClassNameWithImportantModifier,maybePostfixModifierPosition=postfixModifierPosition&&postfixModifierPosition>modifierStart?postfixModifierPosition-modifierStart:void 0;return{modifiers,hasImportantModifier,baseClassName,maybePostfixModifierPosition}};if(prefix){const fullPrefix=prefix+MODIFIER_SEPARATOR,parseClassNameOriginal=parseClassName;parseClassName=className=>className.startsWith(fullPrefix)?parseClassNameOriginal(className.substring(fullPrefix.length)):{isExternal:!0,modifiers:[],hasImportantModifier:!1,
-baseClassName:className,maybePostfixModifierPosition:void 0}}if(experimentalParseClassName){const parseClassNameOriginal=parseClassName;parseClassName=className=>experimentalParseClassName({className,parseClassName:parseClassNameOriginal})}return parseClassName},stripImportantModifier=baseClassName=>baseClassName.endsWith(IMPORTANT_MODIFIER)?baseClassName.substring(0,baseClassName.length-1):baseClassName.startsWith(IMPORTANT_MODIFIER)?baseClassName.substring(1):baseClassName,createSortModifiers=config2=>{
-const orderSensitiveModifiers=Object.fromEntries(config2.orderSensitiveModifiers.map(modifier=>[modifier,!0]));return modifiers=>{if(modifiers.length<=1)return modifiers;const sortedModifiers=[];let unsortedModifiers=[];return modifiers.forEach(modifier=>{modifier[0]==="["||orderSensitiveModifiers[modifier]?(sortedModifiers.push(...unsortedModifiers.sort(),modifier),unsortedModifiers=[]):unsortedModifiers.push(modifier)}),sortedModifiers.push(...unsortedModifiers.sort()),sortedModifiers}},createConfigUtils=config2=>({
-cache:createLruCache(config2.cacheSize),parseClassName:createParseClassName(config2),sortModifiers:createSortModifiers(config2),...createClassGroupUtils(config2)}),SPLIT_CLASSES_REGEX=/\s+/,mergeClassList=(classList,configUtils)=>{const{parseClassName,getClassGroupId,getConflictingClassGroupIds,sortModifiers}=configUtils,classGroupsInConflict=[],classNames=classList.trim().split(SPLIT_CLASSES_REGEX);let result="";for(let index2=classNames.length-1;index2>=0;index2-=1){const originalClassName=classNames[index2],
-{isExternal,modifiers,hasImportantModifier,baseClassName,maybePostfixModifierPosition}=parseClassName(originalClassName);if(isExternal){result=originalClassName+(result.length>0?" "+result:result);continue}let hasPostfixModifier=!!maybePostfixModifierPosition,classGroupId=getClassGroupId(hasPostfixModifier?baseClassName.substring(0,maybePostfixModifierPosition):baseClassName);if(!classGroupId){if(!hasPostfixModifier){result=originalClassName+(result.length>0?" "+result:result);continue}if(classGroupId=
-getClassGroupId(baseClassName),!classGroupId){result=originalClassName+(result.length>0?" "+result:result);continue}hasPostfixModifier=!1}const variantModifier=sortModifiers(modifiers).join(":"),modifierId=hasImportantModifier?variantModifier+IMPORTANT_MODIFIER:variantModifier,classId=modifierId+classGroupId;if(classGroupsInConflict.includes(classId))continue;classGroupsInConflict.push(classId);const conflictGroups=getConflictingClassGroupIds(classGroupId,hasPostfixModifier);for(let i=0;i<conflictGroups.
-length;++i){const group=conflictGroups[i];classGroupsInConflict.push(modifierId+group)}result=originalClassName+(result.length>0?" "+result:result)}return result};function twJoin(){let index2=0,argument,resolvedValue,string2="";for(;index2<arguments.length;)(argument=arguments[index2++])&&(resolvedValue=toValue(argument))&&(string2&&(string2+=" "),string2+=resolvedValue);return string2}const toValue=mix=>{if(typeof mix=="string")return mix;let resolvedValue,string2="";for(let k=0;k<mix.length;k++)
-mix[k]&&(resolvedValue=toValue(mix[k]))&&(string2&&(string2+=" "),string2+=resolvedValue);return string2};function createTailwindMerge(createConfigFirst,...createConfigRest){let configUtils,cacheGet,cacheSet,functionToCall=initTailwindMerge;function initTailwindMerge(classList){const config2=createConfigRest.reduce((previousConfig,createConfigCurrent)=>createConfigCurrent(previousConfig),createConfigFirst());return configUtils=createConfigUtils(config2),cacheGet=configUtils.cache.get,cacheSet=configUtils.
-cache.set,functionToCall=tailwindMerge,tailwindMerge(classList)}function tailwindMerge(classList){const cachedResult=cacheGet(classList);if(cachedResult)return cachedResult;const result=mergeClassList(classList,configUtils);return cacheSet(classList,result),result}return function(){return functionToCall(twJoin.apply(null,arguments))}}const fromTheme=key2=>{const themeGetter=theme=>theme[key2]||[];return themeGetter.isThemeGetter=!0,themeGetter},arbitraryValueRegex=/^\[(?:(\w[\w-]*):)?(.+)\]$/i,arbitraryVariableRegex=/^\((?:(\w[\w-]*):)?(.+)\)$/i,
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-extend2?.slots),componentSlots=isEmptyObject(slotProps)?{}:{base:cx(options?.base,isExtendedSlotsEmpty&&extend2?.base),...slotProps},slots=isExtendedSlotsEmpty?componentSlots:joinObjects({...extend2?.slots},isEmptyObject(componentSlots)?{base:options?.base}:componentSlots),compoundVariants=isEmptyObject(extend2?.compoundVariants)?compoundVariantsProps:flatMergeArrays(extend2?.compoundVariants,compoundVariantsProps),component2=props=>{if(isEmptyObject(variants2)&&isEmptyObject(slotProps)&&isExtendedSlotsEmpty)
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-outline:"bg-background shadow-xs hover:bg-accent hover:text-accent-foreground dark:bg-input/30 dark:border-input dark:hover:bg-input/50 border",secondary:"dark:bg-secondary dark:text-secondary-foreground bg-background shadow-sm text-foreground hover:bg-muted-foreground/20",ghost:"hover:text-accent-foreground hover:bg-muted-foreground/10 backdrop-blur-sm",link:"text-primary underline-offset-4 hover:underline"},size:{default:"h-9 px-4 py-2 has-[>svg]:px-3",sm:"h-8 gap-1.5 rounded-md px-3 has-[>svg\
-]:px-2.5",lg:"h-10 rounded-md px-6 has-[>svg]:px-4","icon-lg":"size-10",icon:"size-9","icon-sm":"size-5 rounded-sm"}},defaultVariants:{variant:"default",size:"default"}});var root_1$17=from_html("<a><!></a>"),root_2$1v=from_html("<button><!></button>");function Button($$anchor,$$props){push$1($$props,!0);let variant=prop($$props,"variant",3,"default"),size2=prop($$props,"size",3,"default"),ref2=prop($$props,"ref",15,null),href=prop($$props,"href",3,void 0),type2=prop($$props,"type",3,"button"),restProps=rest_props(
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-BoxSymbol$1 in value}function isWritableBox$1(value){return isBox$1(value)&&isWritableSymbol$1 in value}function boxFrom$1(value){return isBox$1(value)?value:isFunction$2(value)?boxWith$1(value):simpleBox(value)}function boxFlatten$1(boxes){return Object.entries(boxes).reduce((acc,[key2,b])=>isBox$1(b)?(isWritableBox$1(b)?Object.defineProperty(acc,key2,{get(){return b.current},set(v){b.current=v}}):Object.defineProperty(acc,key2,{get(){return b.current}}),acc):Object.assign(acc,{[key2]:b}),{})}function toReadonlyBox$1(b){
-return isWritableBox$1(b)?{[BoxSymbol$1]:!0,get current(){return b.current}}:b}function simpleBox(initialValue){let current2=state$1(proxy(initialValue));return{[BoxSymbol$1]:!0,[isWritableSymbol$1]:!0,get current(){return get$4(current2)},set current(v){set$1(current2,v,!0)}}}function box$1(initialValue){let current2=state$1(proxy(initialValue));return{[BoxSymbol$1]:!0,[isWritableSymbol$1]:!0,get current(){return get$4(current2)},set current(v){set$1(current2,v,!0)}}}box$1.from=boxFrom$1;box$1.
-with=boxWith$1;box$1.flatten=boxFlatten$1;box$1.readonly=toReadonlyBox$1;box$1.isBox=isBox$1;box$1.isWritableBox=isWritableBox$1;function composeHandlers(...handlers2){return function(e){for(const handler of handlers2)if(handler){if(e.defaultPrevented)return;typeof handler=="function"?handler.call(this,e):handler.current?.call(this,e)}}}var commonjsGlobal=typeof globalThis<"u"?globalThis:typeof window<"u"?window:typeof global<"u"?global:typeof self<"u"?self:{};function getDefaultExportFromCjs(x){
-return x&&x.__esModule&&Object.prototype.hasOwnProperty.call(x,"default")?x.default:x}var cjs={},inlineStyleParser,hasRequiredInlineStyleParser;function requireInlineStyleParser(){if(hasRequiredInlineStyleParser)return inlineStyleParser;hasRequiredInlineStyleParser=1;var COMMENT_REGEX=/\/\*[^*]*\*+([^/*][^*]*\*+)*\//g,NEWLINE_REGEX=/\n/g,WHITESPACE_REGEX=/^\s*/,PROPERTY_REGEX=/^(\*?[-#/*\\\w]+(\[[0-9a-z_-]+\])?)\s*/,COLON_REGEX=/^:\s*/,VALUE_REGEX=/^((?:'(?:\\'|.)*?'|"(?:\\"|.)*?"|\([^)]*?\)|[^};])+)/,
-SEMICOLON_REGEX=/^[;\s]*/,TRIM_REGEX=/^\s+|\s+$/g,NEWLINE2=`
-`,FORWARD_SLASH="/",ASTERISK="*",EMPTY_STRING="",TYPE_COMMENT="comment",TYPE_DECLARATION="declaration";inlineStyleParser=function(style2,options){if(typeof style2!="string")throw new TypeError("First argument must be a string");if(!style2)return[];options=options||{};var lineno=1,column=1;function updatePosition(str){var lines=str.match(NEWLINE_REGEX);lines&&(lineno+=lines.length);var i=str.lastIndexOf(NEWLINE2);column=~i?str.length-i:column+str.length}function position2(){var start2={line:lineno,
-column};return function(node2){return node2.position=new Position(start2),whitespace2(),node2}}function Position(start2){this.start=start2,this.end={line:lineno,column},this.source=options.source}Position.prototype.content=style2;function error2(msg){var err=new Error(options.source+":"+lineno+":"+column+": "+msg);if(err.reason=msg,err.filename=options.source,err.line=lineno,err.column=column,err.source=style2,!options.silent)throw err}function match(re2){var m=re2.exec(style2);if(m){var str=m[0];
-return updatePosition(str),style2=style2.slice(str.length),m}}function whitespace2(){match(WHITESPACE_REGEX)}function comments(rules2){var c2;for(rules2=rules2||[];c2=comment2();)c2!==!1&&rules2.push(c2);return rules2}function comment2(){var pos=position2();if(!(FORWARD_SLASH!=style2.charAt(0)||ASTERISK!=style2.charAt(1))){for(var i=2;EMPTY_STRING!=style2.charAt(i)&&(ASTERISK!=style2.charAt(i)||FORWARD_SLASH!=style2.charAt(i+1));)++i;if(i+=2,EMPTY_STRING===style2.charAt(i-1))return error2("End o\
-f comment missing");var str=style2.slice(2,i-2);return column+=2,updatePosition(str),style2=style2.slice(i),column+=2,pos({type:TYPE_COMMENT,comment:str})}}function declaration(){var pos=position2(),prop2=match(PROPERTY_REGEX);if(prop2){if(comment2(),!match(COLON_REGEX))return error2("property missing ':'");var val=match(VALUE_REGEX),ret=pos({type:TYPE_DECLARATION,property:trim(prop2[0].replace(COMMENT_REGEX,EMPTY_STRING)),value:val?trim(val[0].replace(COMMENT_REGEX,EMPTY_STRING)):EMPTY_STRING});
-return match(SEMICOLON_REGEX),ret}}function declarations(){var decls=[];comments(decls);for(var decl;decl=declaration();)decl!==!1&&(decls.push(decl),comments(decls));return decls}return whitespace2(),declarations()};function trim(str){return str?str.replace(TRIM_REGEX,EMPTY_STRING):EMPTY_STRING}return inlineStyleParser}var hasRequiredCjs;function requireCjs(){if(hasRequiredCjs)return cjs;hasRequiredCjs=1;var __importDefault=cjs&&cjs.__importDefault||function(mod){return mod&&mod.__esModule?mod:
-{default:mod}};Object.defineProperty(cjs,"__esModule",{value:!0}),cjs.default=StyleToObject2;var inline_style_parser_1=__importDefault(requireInlineStyleParser());function StyleToObject2(style2,iterator){var styleObject=null;if(!style2||typeof style2!="string")return styleObject;var declarations=(0,inline_style_parser_1.default)(style2),hasIterator=typeof iterator=="function";return declarations.forEach(function(declaration){if(declaration.type==="declaration"){var property=declaration.property,
-value=declaration.value;hasIterator?iterator(property,value,declaration):value&&(styleObject=styleObject||{},styleObject[property]=value)}}),styleObject}return cjs}var cjsExports=requireCjs();const StyleToObject=getDefaultExportFromCjs(cjsExports),parse$4=StyleToObject.default||StyleToObject,NUMBER_CHAR_RE=/\d/,STR_SPLITTERS=["-","_","/","."];function isUppercase(char=""){if(!NUMBER_CHAR_RE.test(char))return char!==char.toLowerCase()}function splitByCase(str){const parts=[];let buff="",previousUpper,
-previousSplitter;for(const char of str){const isSplitter=STR_SPLITTERS.includes(char);if(isSplitter===!0){parts.push(buff),buff="",previousUpper=void 0;continue}const isUpper=isUppercase(char);if(previousSplitter===!1){if(previousUpper===!1&&isUpper===!0){parts.push(buff),buff=char,previousUpper=isUpper;continue}if(previousUpper===!0&&isUpper===!1&&buff.length>1){const lastChar=buff.at(-1);parts.push(buff.slice(0,Math.max(0,buff.length-1))),buff=lastChar+char,previousUpper=isUpper;continue}}buff+=
-char,previousUpper=isUpper,previousSplitter=isSplitter}return parts.push(buff),parts}function pascalCase(str){return str?splitByCase(str).map(p2=>upperFirst(p2)).join(""):""}function camelCase(str){return lowerFirst(pascalCase(str||""))}function upperFirst(str){return str?str[0].toUpperCase()+str.slice(1):""}function lowerFirst(str){return str?str[0].toLowerCase()+str.slice(1):""}function cssToStyleObj(css2){if(!css2)return{};const styleObj={};function iterator(name,value){if(name.startsWith("-m\
-oz-")||name.startsWith("-webkit-")||name.startsWith("-ms-")||name.startsWith("-o-")){styleObj[pascalCase(name)]=value;return}if(name.startsWith("--")){styleObj[name]=value;return}styleObj[camelCase(name)]=value}return parse$4(css2,iterator),styleObj}function executeCallbacks(...callbacks){return(...args)=>{for(const callback of callbacks)typeof callback=="function"&&callback(...args)}}function createParser$2(matcher,replacer){const regex=RegExp(matcher,"g");return str=>{if(typeof str!="string")throw new TypeError(
-`expected an argument of type string, but got ${typeof str}`);return str.match(regex)?str.replace(regex,replacer):str}}const camelToKebab$1=createParser$2(/[A-Z]/,match=>`-${match.toLowerCase()}`);function styleToCSS$1(styleObj){if(!styleObj||typeof styleObj!="object"||Array.isArray(styleObj))throw new TypeError(`expected an argument of type object, but got ${typeof styleObj}`);return Object.keys(styleObj).map(property=>`${camelToKebab$1(property)}: ${styleObj[property]};`).join(`
-`)}function styleToString$1(style2={}){return styleToCSS$1(style2).replace(`
-`," ")}const EVENT_LIST=["onabort","onanimationcancel","onanimationend","onanimationiteration","onanimationstart","onauxclick","onbeforeinput","onbeforetoggle","onblur","oncancel","oncanplay","oncanplaythrough","onchange","onclick","onclose","oncompositionend","oncompositionstart","oncompositionupdate","oncontextlost","oncontextmenu","oncontextrestored","oncopy","oncuechange","oncut","ondblclick","ondrag","ondragend","ondragenter","ondragleave","ondragover","ondragstart","ondrop","ondurationchan\
-ge","onemptied","onended","onerror","onfocus","onfocusin","onfocusout","onformdata","ongotpointercapture","oninput","oninvalid","onkeydown","onkeypress","onkeyup","onload","onloadeddata","onloadedmetadata","onloadstart","onlostpointercapture","onmousedown","onmouseenter","onmouseleave","onmousemove","onmouseout","onmouseover","onmouseup","onpaste","onpause","onplay","onplaying","onpointercancel","onpointerdown","onpointerenter","onpointerleave","onpointermove","onpointerout","onpointerover","onp\
-ointerup","onprogress","onratechange","onreset","onresize","onscroll","onscrollend","onsecuritypolicyviolation","onseeked","onseeking","onselect","onselectionchange","onselectstart","onslotchange","onstalled","onsubmit","onsuspend","ontimeupdate","ontoggle","ontouchcancel","ontouchend","ontouchmove","ontouchstart","ontransitioncancel","ontransitionend","ontransitionrun","ontransitionstart","onvolumechange","onwaiting","onwebkitanimationend","onwebkitanimationiteration","onwebkitanimationstart","\
-onwebkittransitionend","onwheel"],EVENT_LIST_SET=new Set(EVENT_LIST);function isEventHandler(key2){return EVENT_LIST_SET.has(key2)}function mergeProps(...args){const result={...args[0]};for(let i=1;i<args.length;i++){const props=args[i];if(props){for(const key2 of Object.keys(props)){const a=result[key2],b=props[key2],aIsFunction=typeof a=="function",bIsFunction=typeof b=="function";if(aIsFunction&&isEventHandler(key2)){const aHandler=a,bHandler=b;result[key2]=composeHandlers(aHandler,bHandler)}else if(aIsFunction&&
-bIsFunction)result[key2]=executeCallbacks(a,b);else if(key2==="class"){const aIsClassValue=isClassValue(a),bIsClassValue=isClassValue(b);aIsClassValue&&bIsClassValue?result[key2]=clsx$1(a,b):aIsClassValue?result[key2]=clsx$1(a):bIsClassValue&&(result[key2]=clsx$1(b))}else if(key2==="style"){const aIsObject=typeof a=="object",bIsObject=typeof b=="object",aIsString=typeof a=="string",bIsString=typeof b=="string";if(aIsObject&&bIsObject)result[key2]={...a,...b};else if(aIsObject&&bIsString){const parsedStyle=cssToStyleObj(
-b);result[key2]={...a,...parsedStyle}}else if(aIsString&&bIsObject){const parsedStyle=cssToStyleObj(a);result[key2]={...parsedStyle,...b}}else if(aIsString&&bIsString){const parsedStyleA=cssToStyleObj(a),parsedStyleB=cssToStyleObj(b);result[key2]={...parsedStyleA,...parsedStyleB}}else aIsObject?result[key2]=a:bIsObject?result[key2]=b:aIsString?result[key2]=a:bIsString&&(result[key2]=b)}else result[key2]=b!==void 0?b:a}for(const key2 of Object.getOwnPropertySymbols(props)){const a=result[key2],b=props[key2];
-result[key2]=b!==void 0?b:a}}}return typeof result.style=="object"&&(result.style=styleToString$1(result.style).replaceAll(`
-`," ")),result.hidden===!1&&(result.hidden=void 0,delete result.hidden),result.disabled===!1&&(result.disabled=void 0,delete result.disabled),result}const srOnlyStyles$1={position:"absolute",width:"1px",height:"1px",padding:"0",margin:"-1px",overflow:"hidden",clip:"rect(0, 0, 0, 0)",whiteSpace:"nowrap",borderWidth:"0",transform:"translateX(-100%)"},srOnlyStylesString=styleToString$1(srOnlyStyles$1),defaultWindow$3=typeof window<"u"?window:void 0;function getActiveElement$4(document2){let activeElement=document2.
-activeElement;for(;activeElement?.shadowRoot;){const node2=activeElement.shadowRoot.activeElement;if(node2===activeElement)break;activeElement=node2}return activeElement}var read_methods=["forEach","isDisjointFrom","isSubsetOf","isSupersetOf"],set_like_methods=["difference","intersection","symmetricDifference","union"],inited=!1;class SvelteSet extends Set{#sources=new Map;#version=state$1(0);#size=state$1(0);#update_version=update_version||-1;constructor(value){if(super(),value){for(var element2 of value)
-super.add(element2);this.#size.v=super.size}inited||this.#init()}#source(value){return update_version===this.#update_version?state$1(value):source$2(value)}#init(){inited=!0;var proto=SvelteSet.prototype,set_proto=Set.prototype;for(const method of read_methods)proto[method]=function(...v){return get$4(this.#version),set_proto[method].apply(this,v)};for(const method of set_like_methods)proto[method]=function(...v){get$4(this.#version);var set2=set_proto[method].apply(this,v);return new SvelteSet(
-set2)}}has(value){var has=super.has(value),sources=this.#sources,s2=sources.get(value);if(s2===void 0){if(!has)return get$4(this.#version),!1;s2=this.#source(!0),sources.set(value,s2)}return get$4(s2),has}add(value){return super.has(value)||(super.add(value),set$1(this.#size,super.size),increment$1(this.#version)),this}delete(value){var deleted=super.delete(value),sources=this.#sources,s2=sources.get(value);return s2!==void 0&&(sources.delete(value),set$1(s2,!1)),deleted&&(set$1(this.#size,super.
-size),increment$1(this.#version)),deleted}clear(){if(super.size!==0){super.clear();var sources=this.#sources;for(var s2 of sources.values())set$1(s2,!1);sources.clear(),set$1(this.#size,0),increment$1(this.#version)}}keys(){return this.values()}values(){return get$4(this.#version),super.values()}entries(){return get$4(this.#version),super.entries()}[Symbol.iterator](){return this.keys()}get size(){return get$4(this.#size)}}class SvelteMap extends Map{#sources=new Map;#version=state$1(0);#size=state$1(
-0);#update_version=update_version||-1;constructor(value){if(super(),value){for(var[key2,v]of value)super.set(key2,v);this.#size.v=super.size}}#source(value){return update_version===this.#update_version?state$1(value):source$2(value)}has(key2){var sources=this.#sources,s2=sources.get(key2);if(s2===void 0){var ret=super.get(key2);if(ret!==void 0)s2=this.#source(0),sources.set(key2,s2);else return get$4(this.#version),!1}return get$4(s2),!0}forEach(callbackfn,this_arg){this.#read_all(),super.forEach(
-callbackfn,this_arg)}get(key2){var sources=this.#sources,s2=sources.get(key2);if(s2===void 0){var ret=super.get(key2);if(ret!==void 0)s2=this.#source(0),sources.set(key2,s2);else{get$4(this.#version);return}}return get$4(s2),super.get(key2)}set(key2,value){var sources=this.#sources,s2=sources.get(key2),prev_res=super.get(key2),res=super.set(key2,value),version2=this.#version;if(s2===void 0)s2=this.#source(0),sources.set(key2,s2),set$1(this.#size,super.size),increment$1(version2);else if(prev_res!==
-value){increment$1(s2);var v_reactions=version2.reactions===null?null:new Set(version2.reactions),needs_version_increase=v_reactions===null||!s2.reactions?.every(r2=>v_reactions.has(r2));needs_version_increase&&increment$1(version2)}return res}delete(key2){var sources=this.#sources,s2=sources.get(key2),res=super.delete(key2);return s2!==void 0&&(sources.delete(key2),set$1(this.#size,super.size),set$1(s2,-1),increment$1(this.#version)),res}clear(){if(super.size!==0){super.clear();var sources=this.#sources;
-set$1(this.#size,0);for(var s2 of sources.values())set$1(s2,-1);increment$1(this.#version),sources.clear()}}#read_all(){get$4(this.#version);var sources=this.#sources;if(this.#size.v!==sources.size){for(var key2 of super.keys())if(!sources.has(key2)){var s2=this.#source(0);sources.set(key2,s2)}}for([,s2]of this.#sources)get$4(s2)}keys(){return get$4(this.#version),super.keys()}values(){return this.#read_all(),super.values()}entries(){return this.#read_all(),super.entries()}[Symbol.iterator](){return this.
-entries()}get size(){return get$4(this.#size),super.size}}class ReactiveValue{#fn;#subscribe;constructor(fn,onsubscribe){this.#fn=fn,this.#subscribe=createSubscriber(onsubscribe)}get current(){return this.#subscribe(),this.#fn()}}const parenthesis_regex=/\(.+\)/,non_parenthesized_keywords=new Set(["all","print","screen","and","or","not","only"]);class MediaQuery extends ReactiveValue{constructor(query,fallback2){let final_query=parenthesis_regex.test(query)||query.split(/[\s,]+/).some(keyword2=>non_parenthesized_keywords.
-has(keyword2.trim()))?query:`(${query})`;const q2=window.matchMedia(final_query);super(()=>q2.matches,update2=>on(q2,"change",update2))}}let ActiveElement$3=class{#document;#subscribe;constructor(options={}){const{window:window2=defaultWindow$3,document:document2=window2?.document}=options;window2!==void 0&&(this.#document=document2,this.#subscribe=createSubscriber(update2=>{const cleanupFocusIn=on(window2,"focusin",update2),cleanupFocusOut=on(window2,"focusout",update2);return()=>{cleanupFocusIn(),
-cleanupFocusOut()}}))}get current(){return this.#subscribe?.(),this.#document?getActiveElement$4(this.#document):null}};new ActiveElement$3;function isFunction$1(value){return typeof value=="function"}function extract(value,defaultValue){if(isFunction$1(value)){const gotten=value();return gotten===void 0?defaultValue:gotten}return value===void 0?defaultValue:value}let Context$1=class{#name;#key;constructor(name){this.#name=name,this.#key=Symbol(name)}get key(){return this.#key}exists(){return hasContext(
-this.#key)}get(){const context=getContext(this.#key);if(context===void 0)throw new Error(`Context "${this.#name}" not found`);return context}getOr(fallback2){const context=getContext(this.#key);return context===void 0?fallback2:context}set(context){return setContext(this.#key,context)}};function useDebounce(callback,wait){let context=state$1(null);const wait$=user_derived(()=>extract(wait,250));function debounced(...args){if(get$4(context))get$4(context).timeout&&clearTimeout(get$4(context).timeout);else{
-let resolve2,reject;const promise=new Promise((res,rej)=>{resolve2=res,reject=rej});set$1(context,{timeout:null,runner:null,promise,resolve:resolve2,reject},!0)}return get$4(context).runner=async()=>{if(!get$4(context))return;const ctx=get$4(context);set$1(context,null);try{ctx.resolve(await callback.apply(this,args))}catch(error2){ctx.reject(error2)}},get$4(context).timeout=setTimeout(get$4(context).runner,get$4(wait$)),get$4(context).promise}return debounced.cancel=async()=>{(!get$4(context)||
-get$4(context).timeout===null)&&(await new Promise(resolve2=>setTimeout(resolve2,0)),!get$4(context)||get$4(context).timeout===null)||(clearTimeout(get$4(context).timeout),get$4(context).reject("Cancelled"),set$1(context,null))},debounced.runScheduledNow=async()=>{(!get$4(context)||!get$4(context).timeout)&&(await new Promise(resolve2=>setTimeout(resolve2,0)),!get$4(context)||!get$4(context).timeout)||(clearTimeout(get$4(context).timeout),get$4(context).timeout=null,await get$4(context).runner?.())},
-Object.defineProperty(debounced,"pending",{enumerable:!0,get(){return!!get$4(context)?.timeout}}),debounced}function runEffect$1(flush,effect2){switch(flush){case"post":user_effect(effect2);break;case"pre":user_pre_effect(effect2);break}}function runWatcher$1(sources,flush,effect2,options={}){const{lazy=!1}=options;let active=!lazy,previousValues=Array.isArray(sources)?[]:void 0;runEffect$1(flush,()=>{const values=Array.isArray(sources)?sources.map(source2=>source2()):sources();if(!active){active=
-!0,previousValues=values;return}const cleanup=untrack$1(()=>effect2(values,previousValues));return previousValues=values,cleanup})}function watch$1(sources,effect2,options){runWatcher$1(sources,"post",effect2,options)}function watchPre$1(sources,effect2,options){runWatcher$1(sources,"pre",effect2,options)}watch$1.pre=watchPre$1;function get$1(value){return isFunction$1(value)?value():value}class ElementSize{#size={width:0,height:0};#observed=!1;#options;#node;#window;#width=user_derived(()=>(get$4(
-this.#subscribe)?.(),this.getSize().width));#height=user_derived(()=>(get$4(this.#subscribe)?.(),this.getSize().height));#subscribe=user_derived(()=>{const node$=get$1(this.#node);if(node$)return createSubscriber(update2=>{if(!this.#window)return;const observer=new this.#window.ResizeObserver(entries=>{this.#observed=!0;for(const entry of entries){const boxSize=this.#options.box==="content-box"?entry.contentBoxSize:entry.borderBoxSize,boxSizeArr=Array.isArray(boxSize)?boxSize:[boxSize];this.#size.
-width=boxSizeArr.reduce((acc,size2)=>Math.max(acc,size2.inlineSize),0),this.#size.height=boxSizeArr.reduce((acc,size2)=>Math.max(acc,size2.blockSize),0)}update2()});return observer.observe(node$),()=>{this.#observed=!1,observer.disconnect()}})});constructor(node2,options={box:"border-box"}){this.#window=options.window??defaultWindow$3,this.#options=options,this.#node=node2,this.#size={width:0,height:0}}calculateSize(){const element2=get$1(this.#node);if(!element2||!this.#window)return;const offsetWidth=element2.
-offsetWidth,offsetHeight=element2.offsetHeight;if(this.#options.box==="border-box")return{width:offsetWidth,height:offsetHeight};const style2=this.#window.getComputedStyle(element2),paddingWidth=parseFloat(style2.paddingLeft)+parseFloat(style2.paddingRight),paddingHeight=parseFloat(style2.paddingTop)+parseFloat(style2.paddingBottom),borderWidth=parseFloat(style2.borderLeftWidth)+parseFloat(style2.borderRightWidth),borderHeight=parseFloat(style2.borderTopWidth)+parseFloat(style2.borderBottomWidth),
-contentWidth=offsetWidth-paddingWidth-borderWidth,contentHeight=offsetHeight-paddingHeight-borderHeight;return{width:contentWidth,height:contentHeight}}getSize(){return this.#observed?this.#size:this.calculateSize()??this.#size}get current(){return get$4(this.#subscribe)?.(),this.getSize()}get width(){return get$4(this.#width)}get height(){return get$4(this.#height)}}class IsMounted{#isMounted=state$1(!1);constructor(){user_effect(()=>(untrack$1(()=>set$1(this.#isMounted,!0)),()=>{set$1(this.#isMounted,
-!1)}))}get current(){return get$4(this.#isMounted)}}class Previous{#previousCallback=()=>{};#previous=user_derived(()=>this.#previousCallback());constructor(getter,initialValue){let actualPrevious;initialValue!==void 0&&(actualPrevious=initialValue),this.#previousCallback=()=>{try{return actualPrevious}finally{actualPrevious=getter()}}}get current(){return get$4(this.#previous)}}function onDestroyEffect(fn){user_effect(()=>()=>{fn()})}function onMountEffect(fn){user_effect(()=>untrack$1(()=>fn()))}
-function afterSleep(ms,cb){return setTimeout(cb,ms)}function afterTick(fn){tick().then(fn)}const ELEMENT_NODE=1,DOCUMENT_NODE=9,DOCUMENT_FRAGMENT_NODE=11;function isHTMLElement$2(node2){return isObject$2(node2)&&node2.nodeType===ELEMENT_NODE&&typeof node2.nodeName=="string"}function isDocument(node2){return isObject$2(node2)&&node2.nodeType===DOCUMENT_NODE}function isWindow(node2){return isObject$2(node2)&&node2.constructor?.name==="VisualViewport"}function isNode$1(node2){return isObject$2(node2)&&
-node2.nodeType!==void 0}function isShadowRoot$1(node2){return isNode$1(node2)&&node2.nodeType===DOCUMENT_FRAGMENT_NODE&&"host"in node2}function contains$3(parent,child2){if(!parent||!child2||!isHTMLElement$2(parent)||!isHTMLElement$2(child2))return!1;const rootNode=child2.getRootNode?.();if(parent===child2||parent.contains(child2))return!0;if(rootNode&&isShadowRoot$1(rootNode)){let next2=child2;for(;next2;){if(parent===next2)return!0;next2=next2.parentNode||next2.host}}return!1}function getDocument$1(node2){
-return isDocument(node2)?node2:isWindow(node2)?node2.document:node2?.ownerDocument??document}function getWindow$1(node2){return isShadowRoot$1(node2)?getWindow$1(node2.host):isDocument(node2)?node2.defaultView??window:isHTMLElement$2(node2)?node2.ownerDocument?.defaultView??window:window}function getActiveElement$3(rootNode){let activeElement=rootNode.activeElement;for(;activeElement?.shadowRoot;){const el=activeElement.shadowRoot.activeElement;if(el===activeElement)break;activeElement=el}return activeElement}
-class DOMContext{element;#root=user_derived(()=>this.element.current?this.element.current.getRootNode()??document:document);get root(){return get$4(this.#root)}set root(value){set$1(this.#root,value)}constructor(element2){typeof element2=="function"?this.element=boxWith$1(element2):this.element=element2}getDocument=()=>getDocument$1(this.root);getWindow=()=>this.getDocument().defaultView??window;getActiveElement=()=>getActiveElement$3(this.root);isActiveElement=node2=>node2===this.getActiveElement();getElementById(id2){
-return this.root.getElementById(id2)}querySelector=selector=>this.root?this.root.querySelector(selector):null;querySelectorAll=selector=>this.root?this.root.querySelectorAll(selector):[];setTimeout=(callback,delay)=>this.getWindow().setTimeout(callback,delay);clearTimeout=timeoutId=>this.getWindow().clearTimeout(timeoutId)}function attachRef(ref2,onChange){return{[createAttachmentKey()]:node2=>isBox$1(ref2)?(ref2.current=node2,untrack$1(()=>onChange?.(node2)),()=>{"isConnected"in node2&&node2.isConnected||
-(ref2.current=null,onChange?.(null))}):(ref2(node2),untrack$1(()=>onChange?.(node2)),()=>{"isConnected"in node2&&node2.isConnected||(ref2(null),onChange?.(null))})}}function boolToStr(condition){return condition?"true":"false"}function boolToStrTrueOrUndef(condition){return condition?"true":void 0}function boolToEmptyStrOrUndef(condition){return condition?"":void 0}function boolToTrueOrUndef(condition){return condition?!0:void 0}function getDataOpenClosed(condition){return condition?"open":"clos\
-ed"}function getDataChecked(condition){return condition?"checked":"unchecked"}function getAriaChecked(checked,indeterminate){return indeterminate?"mixed":checked?"true":"false"}class BitsAttrs{#variant;#prefix;attrs;constructor(config2){this.#variant=config2.getVariant?config2.getVariant():null,this.#prefix=this.#variant?`data-${this.#variant}-`:`data-${config2.component}-`,this.getAttr=this.getAttr.bind(this),this.selector=this.selector.bind(this),this.attrs=Object.fromEntries(config2.parts.map(
-part=>[part,this.getAttr(part)]))}getAttr(part,variantOverride){return variantOverride?`data-${variantOverride}-${part}`:`${this.#prefix}${part}`}selector(part,variantOverride){return`[${this.getAttr(part,variantOverride)}]`}}function createBitsAttrs(config2){const bitsAttrs=new BitsAttrs(config2);return{...bitsAttrs.attrs,selector:bitsAttrs.selector,getAttr:bitsAttrs.getAttr}}const ARROW_DOWN="ArrowDown",ARROW_LEFT="ArrowLeft",ARROW_RIGHT="ArrowRight",ARROW_UP="ArrowUp",END="End",ENTER="Enter",
-ESCAPE="Escape",HOME="Home",PAGE_DOWN="PageDown",PAGE_UP="PageUp",SPACE=" ",TAB="Tab";function getElemDirection(elem){return window.getComputedStyle(elem).getPropertyValue("direction")}function getNextKey(dir="ltr",orientation="horizontal"){return{horizontal:dir==="rtl"?ARROW_LEFT:ARROW_RIGHT,vertical:ARROW_DOWN}[orientation]}function getPrevKey(dir="ltr",orientation="horizontal"){return{horizontal:dir==="rtl"?ARROW_RIGHT:ARROW_LEFT,vertical:ARROW_UP}[orientation]}function getDirectionalKeys(dir="\
-ltr",orientation="horizontal"){return["ltr","rtl"].includes(dir)||(dir="ltr"),["horizontal","vertical"].includes(orientation)||(orientation="horizontal"),{nextKey:getNextKey(dir,orientation),prevKey:getPrevKey(dir,orientation)}}const isBrowser$2=typeof document<"u",isIOS=getIsIOS();function getIsIOS(){return isBrowser$2&&window?.navigator?.userAgent&&(/iP(ad|hone|od)/.test(window.navigator.userAgent)||window?.navigator?.maxTouchPoints>2&&/iPad|Macintosh/.test(window?.navigator.userAgent))}function isHTMLElement$1(element2){
-return element2 instanceof HTMLElement}function isElement$1(element2){return element2 instanceof Element}function isElementOrSVGElement(element2){return element2 instanceof Element||element2 instanceof SVGElement}function isTouch(e){return e.pointerType==="touch"}function isFocusVisible(element2){return element2.matches(":focus-visible")}function isNotNull(value){return value!==null}function isSelectableInput(element2){return element2 instanceof HTMLInputElement&&"select"in element2}class RovingFocusGroup{#opts;#currentTabStopId=box$1(
-null);constructor(opts){this.#opts=opts}getCandidateNodes(){return this.#opts.rootNode.current?this.#opts.candidateSelector?Array.from(this.#opts.rootNode.current.querySelectorAll(this.#opts.candidateSelector)):this.#opts.candidateAttr?Array.from(this.#opts.rootNode.current.querySelectorAll(`[${this.#opts.candidateAttr}]:not([data-disabled])`)):[]:[]}focusFirstCandidate(){const items2=this.getCandidateNodes();items2.length&&items2[0]?.focus()}handleKeydown(node2,e,both=!1){const rootNode=this.#opts.
-rootNode.current;if(!rootNode||!node2)return;const items2=this.getCandidateNodes();if(!items2.length)return;const currentIndex=items2.indexOf(node2),dir=getElemDirection(rootNode),{nextKey,prevKey}=getDirectionalKeys(dir,this.#opts.orientation.current),loop2=this.#opts.loop.current,keyToIndex={[nextKey]:currentIndex+1,[prevKey]:currentIndex-1,[HOME]:0,[END]:items2.length-1};if(both){const altNextKey=nextKey===ARROW_DOWN?ARROW_RIGHT:ARROW_DOWN,altPrevKey=prevKey===ARROW_UP?ARROW_LEFT:ARROW_UP;keyToIndex[altNextKey]=
-currentIndex+1,keyToIndex[altPrevKey]=currentIndex-1}let itemIndex=keyToIndex[e.key];if(itemIndex===void 0)return;e.preventDefault(),itemIndex<0&&loop2?itemIndex=items2.length-1:itemIndex===items2.length&&loop2&&(itemIndex=0);const itemToFocus=items2[itemIndex];if(itemToFocus)return itemToFocus.focus(),this.#currentTabStopId.current=itemToFocus.id,this.#opts.onCandidateFocus?.(itemToFocus),itemToFocus}getTabIndex(node2){const items2=this.getCandidateNodes(),anyActive=this.#currentTabStopId.current!==
-null;return node2&&!anyActive&&items2[0]===node2?(this.#currentTabStopId.current=node2.id,0):node2?.id===this.#currentTabStopId.current?0:-1}setCurrentTabStopId(id2){this.#currentTabStopId.current=id2}focusCurrentTabStop(){const currentTabStopId=this.#currentTabStopId.current;if(!currentTabStopId)return;const currentTabStop=this.#opts.rootNode.current?.querySelector(`#${currentTabStopId}`);!currentTabStop||!isHTMLElement$1(currentTabStop)||currentTabStop.focus()}}class AnimationsComplete{#opts;#currentFrame=null;constructor(opts){
-this.#opts=opts,onDestroyEffect(()=>this.#cleanup())}#cleanup(){this.#currentFrame&&(window.cancelAnimationFrame(this.#currentFrame),this.#currentFrame=null)}run(fn){this.#cleanup();const node2=this.#opts.ref.current;if(node2){if(typeof node2.getAnimations!="function"){this.#executeCallback(fn);return}this.#currentFrame=window.requestAnimationFrame(()=>{const animations=node2.getAnimations();if(animations.length===0){this.#executeCallback(fn);return}Promise.allSettled(animations.map(animation=>animation.
-finished)).then(()=>{this.#executeCallback(fn)})})}}#executeCallback(fn){const execute=()=>{fn()};this.#opts.afterTick?afterTick(execute):execute()}}class PresenceManager{#opts;#enabled;#afterAnimations;#shouldRender=state$1(!1);constructor(opts){this.#opts=opts,set$1(this.#shouldRender,opts.open.current,!0),this.#enabled=opts.enabled??!0,this.#afterAnimations=new AnimationsComplete({ref:this.#opts.ref,afterTick:this.#opts.open}),watch$1(()=>this.#opts.open.current,isOpen=>{isOpen&&set$1(this.#shouldRender,
-!0),this.#enabled&&this.#afterAnimations.run(()=>{isOpen===this.#opts.open.current&&(this.#opts.open.current||set$1(this.#shouldRender,!1),this.#opts.onComplete?.())})})}get shouldRender(){return get$4(this.#shouldRender)}}function noop$1(){}function createId(prefixOrUid,uid){return`bits-${prefixOrUid}`}const dialogAttrs=createBitsAttrs({component:"dialog",parts:["content","trigger","overlay","title","description","close","cancel","action"]}),DialogRootContext=new Context$1("Dialog.Root | AlertD\
-ialog.Root");class DialogRootState{static create(opts){const parent=DialogRootContext.getOr(null);return DialogRootContext.set(new DialogRootState(opts,parent))}opts;#triggerNode=state$1(null);get triggerNode(){return get$4(this.#triggerNode)}set triggerNode(value){set$1(this.#triggerNode,value,!0)}#contentNode=state$1(null);get contentNode(){return get$4(this.#contentNode)}set contentNode(value){set$1(this.#contentNode,value,!0)}#overlayNode=state$1(null);get overlayNode(){return get$4(this.#overlayNode)}set overlayNode(value){
-set$1(this.#overlayNode,value,!0)}#descriptionNode=state$1(null);get descriptionNode(){return get$4(this.#descriptionNode)}set descriptionNode(value){set$1(this.#descriptionNode,value,!0)}#contentId=state$1(void 0);get contentId(){return get$4(this.#contentId)}set contentId(value){set$1(this.#contentId,value,!0)}#titleId=state$1(void 0);get titleId(){return get$4(this.#titleId)}set titleId(value){set$1(this.#titleId,value,!0)}#triggerId=state$1(void 0);get triggerId(){return get$4(this.#triggerId)}set triggerId(value){
-set$1(this.#triggerId,value,!0)}#descriptionId=state$1(void 0);get descriptionId(){return get$4(this.#descriptionId)}set descriptionId(value){set$1(this.#descriptionId,value,!0)}#cancelNode=state$1(null);get cancelNode(){return get$4(this.#cancelNode)}set cancelNode(value){set$1(this.#cancelNode,value,!0)}#nestedOpenCount=state$1(0);get nestedOpenCount(){return get$4(this.#nestedOpenCount)}set nestedOpenCount(value){set$1(this.#nestedOpenCount,value,!0)}depth;parent;contentPresence;overlayPresence;constructor(opts,parent){
-this.opts=opts,this.parent=parent,this.depth=parent?parent.depth+1:0,this.handleOpen=this.handleOpen.bind(this),this.handleClose=this.handleClose.bind(this),this.contentPresence=new PresenceManager({ref:boxWith$1(()=>this.contentNode),open:this.opts.open,enabled:!0,onComplete:()=>{this.opts.onOpenChangeComplete.current(this.opts.open.current)}}),this.overlayPresence=new PresenceManager({ref:boxWith$1(()=>this.overlayNode),open:this.opts.open,enabled:!0}),watch$1(()=>this.opts.open.current,isOpen=>{
-this.parent&&(isOpen?this.parent.incrementNested():this.parent.decrementNested())},{lazy:!0}),onDestroyEffect(()=>{this.opts.open.current&&this.parent?.decrementNested()})}handleOpen(){this.opts.open.current||(this.opts.open.current=!0)}handleClose(){this.opts.open.current&&(this.opts.open.current=!1)}getBitsAttr=part=>dialogAttrs.getAttr(part,this.opts.variant.current);incrementNested(){this.nestedOpenCount++,this.parent?.incrementNested()}decrementNested(){this.nestedOpenCount!==0&&(this.nestedOpenCount--,
-this.parent?.decrementNested())}#sharedProps=user_derived(()=>({"data-state":getDataOpenClosed(this.opts.open.current)}));get sharedProps(){return get$4(this.#sharedProps)}set sharedProps(value){set$1(this.#sharedProps,value)}}class DialogCloseState{static create(opts){return new DialogCloseState(opts,DialogRootContext.get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref),this.onclick=this.onclick.bind(this),this.onkeydown=this.
-onkeydown.bind(this)}onclick(e){this.opts.disabled.current||e.button>0||this.root.handleClose()}onkeydown(e){this.opts.disabled.current||(e.key===SPACE||e.key===ENTER)&&(e.preventDefault(),this.root.handleClose())}#props=user_derived(()=>({id:this.opts.id.current,[this.root.getBitsAttr(this.opts.variant.current)]:"",onclick:this.onclick,onkeydown:this.onkeydown,disabled:this.opts.disabled.current?!0:void 0,tabindex:0,...this.root.sharedProps,...this.attachment}));get props(){return get$4(this.#props)}set props(value){
-set$1(this.#props,value)}}class DialogActionState{static create(opts){return new DialogActionState(opts,DialogRootContext.get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref)}#props=user_derived(()=>({id:this.opts.id.current,[this.root.getBitsAttr("action")]:"",...this.root.sharedProps,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class DialogTitleState{static create(opts){
-return new DialogTitleState(opts,DialogRootContext.get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.root.titleId=this.opts.id.current,this.attachment=attachRef(this.opts.ref),watch$1.pre(()=>this.opts.id.current,id2=>{this.root.titleId=id2})}#props=user_derived(()=>({id:this.opts.id.current,role:"heading","aria-level":this.opts.level.current,[this.root.getBitsAttr("title")]:"",...this.root.sharedProps,...this.attachment}));get props(){return get$4(this.#props)}set props(value){
-set$1(this.#props,value)}}class DialogDescriptionState{static create(opts){return new DialogDescriptionState(opts,DialogRootContext.get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.root.descriptionId=this.opts.id.current,this.attachment=attachRef(this.opts.ref,v=>{this.root.descriptionNode=v}),watch$1.pre(()=>this.opts.id.current,id2=>{this.root.descriptionId=id2})}#props=user_derived(()=>({id:this.opts.id.current,[this.root.getBitsAttr("description")]:"",...this.
-root.sharedProps,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class DialogContentState{static create(opts){return new DialogContentState(opts,DialogRootContext.get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref,v=>{this.root.contentNode=v,this.root.contentId=v?.id})}#snippetProps=user_derived(()=>({open:this.root.opts.open.current}));get snippetProps(){return get$4(this.#snippetProps)}set snippetProps(value){
-set$1(this.#snippetProps,value)}#props=user_derived(()=>({id:this.opts.id.current,role:this.root.opts.variant.current==="alert-dialog"?"alertdialog":"dialog","aria-modal":"true","aria-describedby":this.root.descriptionId,"aria-labelledby":this.root.titleId,[this.root.getBitsAttr("content")]:"",style:{pointerEvents:"auto",outline:this.root.opts.variant.current==="alert-dialog"?"none":void 0,"--bits-dialog-depth":this.root.depth,"--bits-dialog-nested-count":this.root.nestedOpenCount,contain:"layou\
-t style paint"},tabindex:this.root.opts.variant.current==="alert-dialog"?-1:void 0,"data-nested-open":boolToEmptyStrOrUndef(this.root.nestedOpenCount>0),"data-nested":boolToEmptyStrOrUndef(this.root.parent!==null),...this.root.sharedProps,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}get shouldRender(){return this.root.contentPresence.shouldRender}}class DialogOverlayState{static create(opts){return new DialogOverlayState(opts,DialogRootContext.
-get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref,v=>this.root.overlayNode=v)}#snippetProps=user_derived(()=>({open:this.root.opts.open.current}));get snippetProps(){return get$4(this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}#props=user_derived(()=>({id:this.opts.id.current,[this.root.getBitsAttr("overlay")]:"",style:{pointerEvents:"auto","--bits-dialog-depth":this.root.depth,"--bits-dialog-nes\
-ted-count":this.root.nestedOpenCount},"data-nested-open":boolToEmptyStrOrUndef(this.root.nestedOpenCount>0),"data-nested":boolToEmptyStrOrUndef(this.root.parent!==null),...this.root.sharedProps,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}get shouldRender(){return this.root.overlayPresence.shouldRender}}class AlertDialogCancelState{static create(opts){return new AlertDialogCancelState(opts,DialogRootContext.get())}opts;root;attachment;constructor(opts,root2){
-this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref,v=>this.root.cancelNode=v),this.onclick=this.onclick.bind(this),this.onkeydown=this.onkeydown.bind(this)}onclick(e){this.opts.disabled.current||e.button>0||this.root.handleClose()}onkeydown(e){this.opts.disabled.current||(e.key===SPACE||e.key===ENTER)&&(e.preventDefault(),this.root.handleClose())}#props=user_derived(()=>({id:this.opts.id.current,[this.root.getBitsAttr("cancel")]:"",onclick:this.onclick,onkeydown:this.onkeydown,
-tabindex:0,...this.root.sharedProps,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}function Alert_dialog($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15,!1),onOpenChange=prop($$props,"onOpenChange",3,noop$1),onOpenChangeComplete=prop($$props,"onOpenChangeComplete",3,noop$1);DialogRootState.create({variant:boxWith$1(()=>"alert-dialog"),open:boxWith$1(()=>open2(),v=>{open2(v),onOpenChange()(v)}),onOpenChangeComplete:boxWith$1(
-()=>onOpenChangeComplete())});var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.children??noop$3),append($$anchor,fragment),pop()}var root_2$1u=from_html("<div><!></div>");function Dialog_title$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),level=prop($$props,"level",3,2),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","child","children","level"]);const titleState=DialogTitleState.
-create({id:boxWith$1(()=>id2()),level:boxWith$1(()=>level()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,titleState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1u();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(
-div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_2$1t=from_html("<button><!></button>");function Alert_dialog_action$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","children","child",
-"id","ref"]);const actionState=DialogActionState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,actionState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var button=root_2$1t();attribute_effect(button,()=>({
-...get$4(mergedProps)}));var node_2=child(button);snippet(node_2,()=>$$props.children??noop$3),reset(button),append($$anchor2,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_2$1s=from_html("<button><!></button>");function Alert_dialog_cancel$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),disabled=prop($$props,"disabl\
-ed",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","children","child","disabled"]);const cancelState=AlertDialogCancelState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),disabled:boxWith$1(()=>!!disabled())}),mergedProps=user_derived(()=>mergeProps(restProps,cancelState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.
-child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var button=root_2$1s();attribute_effect(button,()=>({...get$4(mergedProps)}));var node_2=child(button);snippet(node_2,()=>$$props.children??noop$3),reset(button),append($$anchor2,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}function Portal_consumer($$anchor,$$props){var fragment=comment$2(),node2=first_child(fragment);key$1(
-node2,()=>$$props.children,$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.children??noop$3),append($$anchor2,fragment_1)}),append($$anchor,fragment)}const BitsConfigContext=new Context$1("BitsConfig");function getBitsConfig(){const fallback2=new BitsConfigState(null,{});return BitsConfigContext.getOr(fallback2).opts}class BitsConfigState{opts;constructor(parent,opts){const resolveConfigOption=createConfigResolver(parent,opts);this.opts={defaultPortalTo:resolveConfigOption(
-config2=>config2.defaultPortalTo),defaultLocale:resolveConfigOption(config2=>config2.defaultLocale)}}}function createConfigResolver(parent,currentOpts){return getter=>boxWith$1(()=>{const value=getter(currentOpts)?.current;if(value!==void 0)return value;if(parent!==null)return getter(parent.opts)?.current})}function createPropResolver(configOption,fallback2){return getProp=>{const config2=getBitsConfig();return boxWith$1(()=>{const propValue=getProp();if(propValue!==void 0)return propValue;const option2=configOption(
-config2).current;return option2!==void 0?option2:fallback2})}}const resolvePortalToProp=createPropResolver(config2=>config2.defaultPortalTo,"body");function Portal$2($$anchor,$$props){push$1($$props,!0);const to=resolvePortalToProp(()=>$$props.to),context=getAllContexts();let target2=user_derived(getTarget);function getTarget(){if(!isBrowser$2||$$props.disabled)return null;let localTarget=null;return typeof to.current=="string"?localTarget=document.querySelector(to.current):localTarget=to.current,
-localTarget}let instance;function unmountInstance(){instance&&(unmount(instance),instance=null)}watch$1([()=>get$4(target2),()=>$$props.disabled],([target3,disabled])=>{if(!target3||disabled){unmountInstance();return}return instance=mount(Portal_consumer,{target:target3,props:{children:$$props.children},context}),()=>{unmountInstance()}});var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.
-children??noop$3),append($$anchor2,fragment_1)};if_block(node2,$$render=>{$$props.disabled&&$$render(consequent)})}append($$anchor,fragment),pop()}class CustomEventDispatcher{eventName;options;constructor(eventName,options={bubbles:!0,cancelable:!0}){this.eventName=eventName,this.options=options}createEvent(detail){return new CustomEvent(this.eventName,{...this.options,detail})}dispatch(element2,detail){const event2=this.createEvent(detail);return element2.dispatchEvent(event2),event2}listen(element2,callback,options){
-const handler=event2=>{callback(event2)};return on(element2,this.eventName,handler,options)}}function debounce$1(fn,wait=500){let timeout=null;const debounced=(...args)=>{timeout!==null&&clearTimeout(timeout),timeout=setTimeout(()=>{fn(...args)},wait)};return debounced.destroy=()=>{timeout!==null&&(clearTimeout(timeout),timeout=null)},debounced}function isOrContainsTarget(node2,target2){return node2===target2||node2.contains(target2)}function getOwnerDocument(el){return el?.ownerDocument??document}
-function isClickTrulyOutside(event2,contentNode){const{clientX,clientY}=event2,rect=contentNode.getBoundingClientRect();return clientX<rect.left||clientX>rect.right||clientY<rect.top||clientY>rect.bottom}const SELECTION_KEYS=[ENTER,SPACE],FIRST_KEYS$1=[ARROW_DOWN,PAGE_UP,HOME],LAST_KEYS$1=[ARROW_UP,PAGE_DOWN,END],FIRST_LAST_KEYS$1=[...FIRST_KEYS$1,...LAST_KEYS$1],SUB_OPEN_KEYS={ltr:[...SELECTION_KEYS,ARROW_RIGHT],rtl:[...SELECTION_KEYS,ARROW_LEFT]},SUB_CLOSE_KEYS={ltr:[ARROW_LEFT],rtl:[ARROW_RIGHT]};
-function isMouseEvent(event2){return event2.pointerType==="mouse"}function focus(element2,{select=!1}={}){if(!element2||!element2.focus)return;const doc=getDocument$1(element2);if(doc.activeElement===element2)return;const previouslyFocusedElement=doc.activeElement;element2.focus({preventScroll:!0}),element2!==previouslyFocusedElement&&isSelectableInput(element2)&&select&&element2.select()}function focusFirst(candidates,{select=!1}={},getActiveElement2){const previouslyFocusedElement=getActiveElement2();
-for(const candidate of candidates)if(focus(candidate,{select}),getActiveElement2()!==previouslyFocusedElement)return!0}let isUsingKeyboard=state$1(!1);class IsUsingKeyboard{static _refs=0;static _cleanup;constructor(){user_effect(()=>(IsUsingKeyboard._refs===0&&(IsUsingKeyboard._cleanup=effect_root(()=>{const callbacksToDispose=[],handlePointer=_=>{set$1(isUsingKeyboard,!1)},handleKeydown=_=>{set$1(isUsingKeyboard,!0)};return callbacksToDispose.push(on(document,"pointerdown",handlePointer,{capture:!0}),
-on(document,"pointermove",handlePointer,{capture:!0}),on(document,"keydown",handleKeydown,{capture:!0})),executeCallbacks(...callbacksToDispose)})),IsUsingKeyboard._refs++,()=>{IsUsingKeyboard._refs--,IsUsingKeyboard._refs===0&&(set$1(isUsingKeyboard,!1),IsUsingKeyboard._cleanup?.())}))}get current(){return get$4(isUsingKeyboard)}set current(value){set$1(isUsingKeyboard,value,!0)}}var candidateSelectors=["input:not([inert])","select:not([inert])","textarea:not([inert])","a[href]:not([inert])","b\
-utton:not([inert])","[tabindex]:not(slot):not([inert])","audio[controls]:not([inert])","video[controls]:not([inert])",'[contenteditable]:not([contenteditable="false"]):not([inert])',"details>summary:first-of-type:not([inert])","details:not([inert])"],candidateSelector=candidateSelectors.join(","),NoElement=typeof Element>"u",matches=NoElement?function(){}:Element.prototype.matches||Element.prototype.msMatchesSelector||Element.prototype.webkitMatchesSelector,getRootNode=!NoElement&&Element.prototype.
-getRootNode?function(element2){var _element$getRootNode;return element2==null||(_element$getRootNode=element2.getRootNode)===null||_element$getRootNode===void 0?void 0:_element$getRootNode.call(element2)}:function(element2){return element2?.ownerDocument},isInert=function isInert2(node2,lookUp){var _node$getAttribute;lookUp===void 0&&(lookUp=!0);var inertAtt=node2==null||(_node$getAttribute=node2.getAttribute)===null||_node$getAttribute===void 0?void 0:_node$getAttribute.call(node2,"inert"),inert=inertAtt===
-""||inertAtt==="true",result=inert||lookUp&&node2&&isInert2(node2.parentNode);return result},isContentEditable=function(node2){var _node$getAttribute2,attValue=node2==null||(_node$getAttribute2=node2.getAttribute)===null||_node$getAttribute2===void 0?void 0:_node$getAttribute2.call(node2,"contenteditable");return attValue===""||attValue==="true"},getCandidates=function(el,includeContainer,filter){if(isInert(el))return[];var candidates=Array.prototype.slice.apply(el.querySelectorAll(candidateSelector));
-return includeContainer&&matches.call(el,candidateSelector)&&candidates.unshift(el),candidates=candidates.filter(filter),candidates},getCandidatesIteratively=function getCandidatesIteratively2(elements,includeContainer,options){for(var candidates=[],elementsToCheck=Array.from(elements);elementsToCheck.length;){var element2=elementsToCheck.shift();if(!isInert(element2,!1))if(element2.tagName==="SLOT"){var assigned=element2.assignedElements(),content2=assigned.length?assigned:element2.children,nestedCandidates=getCandidatesIteratively2(
-content2,!0,options);options.flatten?candidates.push.apply(candidates,nestedCandidates):candidates.push({scopeParent:element2,candidates:nestedCandidates})}else{var validCandidate=matches.call(element2,candidateSelector);validCandidate&&options.filter(element2)&&(includeContainer||!elements.includes(element2))&&candidates.push(element2);var shadowRoot=element2.shadowRoot||typeof options.getShadowRoot=="function"&&options.getShadowRoot(element2),validShadowRoot=!isInert(shadowRoot,!1)&&(!options.
-shadowRootFilter||options.shadowRootFilter(element2));if(shadowRoot&&validShadowRoot){var _nestedCandidates=getCandidatesIteratively2(shadowRoot===!0?element2.children:shadowRoot.children,!0,options);options.flatten?candidates.push.apply(candidates,_nestedCandidates):candidates.push({scopeParent:element2,candidates:_nestedCandidates})}else elementsToCheck.unshift.apply(elementsToCheck,element2.children)}}return candidates},hasTabIndex=function(node2){return!isNaN(parseInt(node2.getAttribute("tab\
-index"),10))},getTabIndex=function(node2){if(!node2)throw new Error("No node provided");return node2.tabIndex<0&&(/^(AUDIO|VIDEO|DETAILS)$/.test(node2.tagName)||isContentEditable(node2))&&!hasTabIndex(node2)?0:node2.tabIndex},getSortOrderTabIndex=function(node2,isScope){var tabIndex=getTabIndex(node2);return tabIndex<0&&isScope&&!hasTabIndex(node2)?0:tabIndex},sortOrderedTabbables=function(a,b){return a.tabIndex===b.tabIndex?a.documentOrder-b.documentOrder:a.tabIndex-b.tabIndex},isInput=function(node2){
-return node2.tagName==="INPUT"},isHiddenInput=function(node2){return isInput(node2)&&node2.type==="hidden"},isDetailsWithSummary=function(node2){var r2=node2.tagName==="DETAILS"&&Array.prototype.slice.apply(node2.children).some(function(child2){return child2.tagName==="SUMMARY"});return r2},getCheckedRadio=function(nodes2,form){for(var i=0;i<nodes2.length;i++)if(nodes2[i].checked&&nodes2[i].form===form)return nodes2[i]},isTabbableRadio=function(node2){if(!node2.name)return!0;var radioScope=node2.
-form||getRootNode(node2),queryRadios=function(name){return radioScope.querySelectorAll('input[type="radio"][name="'+name+'"]')},radioSet;if(typeof window<"u"&&typeof window.CSS<"u"&&typeof window.CSS.escape=="function")radioSet=queryRadios(window.CSS.escape(node2.name));else try{radioSet=queryRadios(node2.name)}catch(err){return console.error("Looks like you have a radio button with a name attribute containing invalid CSS selector characters and need the CSS.escape polyfill: %s",err.message),!1}
-var checked=getCheckedRadio(radioSet,node2.form);return!checked||checked===node2},isRadio=function(node2){return isInput(node2)&&node2.type==="radio"},isNonTabbableRadio=function(node2){return isRadio(node2)&&!isTabbableRadio(node2)},isNodeAttached=function(node2){var _nodeRoot,nodeRoot=node2&&getRootNode(node2),nodeRootHost=(_nodeRoot=nodeRoot)===null||_nodeRoot===void 0?void 0:_nodeRoot.host,attached=!1;if(nodeRoot&&nodeRoot!==node2){var _nodeRootHost,_nodeRootHost$ownerDo,_node$ownerDocument;
-for(attached=!!((_nodeRootHost=nodeRootHost)!==null&&_nodeRootHost!==void 0&&(_nodeRootHost$ownerDo=_nodeRootHost.ownerDocument)!==null&&_nodeRootHost$ownerDo!==void 0&&_nodeRootHost$ownerDo.contains(nodeRootHost)||node2!=null&&(_node$ownerDocument=node2.ownerDocument)!==null&&_node$ownerDocument!==void 0&&_node$ownerDocument.contains(node2));!attached&&nodeRootHost;){var _nodeRoot2,_nodeRootHost2,_nodeRootHost2$ownerD;nodeRoot=getRootNode(nodeRootHost),nodeRootHost=(_nodeRoot2=nodeRoot)===null||
-_nodeRoot2===void 0?void 0:_nodeRoot2.host,attached=!!((_nodeRootHost2=nodeRootHost)!==null&&_nodeRootHost2!==void 0&&(_nodeRootHost2$ownerD=_nodeRootHost2.ownerDocument)!==null&&_nodeRootHost2$ownerD!==void 0&&_nodeRootHost2$ownerD.contains(nodeRootHost))}}return attached},isZeroArea=function(node2){var _node$getBoundingClie=node2.getBoundingClientRect(),width=_node$getBoundingClie.width,height=_node$getBoundingClie.height;return width===0&&height===0},isHidden=function(node2,_ref){var displayCheck=_ref.
-displayCheck,getShadowRoot=_ref.getShadowRoot;if(getComputedStyle(node2).visibility==="hidden")return!0;var isDirectSummary=matches.call(node2,"details>summary:first-of-type"),nodeUnderDetails=isDirectSummary?node2.parentElement:node2;if(matches.call(nodeUnderDetails,"details:not([open]) *"))return!0;if(!displayCheck||displayCheck==="full"||displayCheck==="legacy-full"){if(typeof getShadowRoot=="function"){for(var originalNode=node2;node2;){var parentElement=node2.parentElement,rootNode=getRootNode(
-node2);if(parentElement&&!parentElement.shadowRoot&&getShadowRoot(parentElement)===!0)return isZeroArea(node2);node2.assignedSlot?node2=node2.assignedSlot:!parentElement&&rootNode!==node2.ownerDocument?node2=rootNode.host:node2=parentElement}node2=originalNode}if(isNodeAttached(node2))return!node2.getClientRects().length;if(displayCheck!=="legacy-full")return!0}else if(displayCheck==="non-zero-area")return isZeroArea(node2);return!1},isDisabledFromFieldset=function(node2){if(/^(INPUT|BUTTON|SELECT|TEXTAREA)$/.
-test(node2.tagName))for(var parentNode=node2.parentElement;parentNode;){if(parentNode.tagName==="FIELDSET"&&parentNode.disabled){for(var i=0;i<parentNode.children.length;i++){var child2=parentNode.children.item(i);if(child2.tagName==="LEGEND")return matches.call(parentNode,"fieldset[disabled] *")?!0:!child2.contains(node2)}return!0}parentNode=parentNode.parentElement}return!1},isNodeMatchingSelectorFocusable=function(options,node2){return!(node2.disabled||isInert(node2)||isHiddenInput(node2)||isHidden(
-node2,options)||isDetailsWithSummary(node2)||isDisabledFromFieldset(node2))},isNodeMatchingSelectorTabbable=function(options,node2){return!(isNonTabbableRadio(node2)||getTabIndex(node2)<0||!isNodeMatchingSelectorFocusable(options,node2))},isValidShadowRootTabbable=function(shadowHostNode){var tabIndex=parseInt(shadowHostNode.getAttribute("tabindex"),10);return!!(isNaN(tabIndex)||tabIndex>=0)},sortByOrder=function sortByOrder2(candidates){var regularTabbables=[],orderedTabbables=[];return candidates.
-forEach(function(item,i){var isScope=!!item.scopeParent,element2=isScope?item.scopeParent:item,candidateTabindex=getSortOrderTabIndex(element2,isScope),elements=isScope?sortByOrder2(item.candidates):element2;candidateTabindex===0?isScope?regularTabbables.push.apply(regularTabbables,elements):regularTabbables.push(element2):orderedTabbables.push({documentOrder:i,tabIndex:candidateTabindex,item,isScope,content:elements})}),orderedTabbables.sort(sortOrderedTabbables).reduce(function(acc,sortable){return sortable.
-isScope?acc.push.apply(acc,sortable.content):acc.push(sortable.content),acc},[]).concat(regularTabbables)},tabbable=function(container2,options){options=options||{};var candidates;return options.getShadowRoot?candidates=getCandidatesIteratively([container2],options.includeContainer,{filter:isNodeMatchingSelectorTabbable.bind(null,options),flatten:!1,getShadowRoot:options.getShadowRoot,shadowRootFilter:isValidShadowRootTabbable}):candidates=getCandidates(container2,options.includeContainer,isNodeMatchingSelectorTabbable.
-bind(null,options)),sortByOrder(candidates)},focusable=function(container2,options){options=options||{};var candidates;return options.getShadowRoot?candidates=getCandidatesIteratively([container2],options.includeContainer,{filter:isNodeMatchingSelectorFocusable.bind(null,options),flatten:!0,getShadowRoot:options.getShadowRoot}):candidates=getCandidates(container2,options.includeContainer,isNodeMatchingSelectorFocusable.bind(null,options)),candidates},isTabbable=function(node2,options){if(options=
-options||{},!node2)throw new Error("No node provided");return matches.call(node2,candidateSelector)===!1?!1:isNodeMatchingSelectorTabbable(options,node2)},focusableCandidateSelector=candidateSelectors.concat("iframe").join(","),isFocusable=function(node2,options){if(options=options||{},!node2)throw new Error("No node provided");return matches.call(node2,focusableCandidateSelector)===!1?!1:isNodeMatchingSelectorFocusable(options,node2)};function getTabbableOptions(){return{getShadowRoot:!0,displayCheck:typeof ResizeObserver==
-"function"&&ResizeObserver.toString().includes("[native code]")?"full":"none"}}function getTabbableFrom(currentNode,direction){if(!isTabbable(currentNode,getTabbableOptions()))return getTabbableFromFocusable(currentNode,direction);const doc=getDocument$1(currentNode),allTabbable=tabbable(doc.body,getTabbableOptions());direction==="prev"&&allTabbable.reverse();const activeIndex=allTabbable.indexOf(currentNode);return activeIndex===-1?doc.body:allTabbable.slice(activeIndex+1)[0]}function getTabbableFromFocusable(currentNode,direction){
-const doc=getDocument$1(currentNode);if(!isFocusable(currentNode,getTabbableOptions()))return doc.body;const allFocusable=focusable(doc.body,getTabbableOptions());direction==="prev"&&allFocusable.reverse();const activeIndex=allFocusable.indexOf(currentNode);return activeIndex===-1?doc.body:allFocusable.slice(activeIndex+1).find(node2=>isTabbable(node2,getTabbableOptions()))??doc.body}function next(array2,index2,loop2=!0){if(!(array2.length===0||index2<0||index2>=array2.length))return array2.length===
-1&&index2===0?array2[0]:index2===array2.length-1?loop2?array2[0]:void 0:array2[index2+1]}function prev(array2,index2,loop2=!0){if(!(array2.length===0||index2<0||index2>=array2.length))return array2.length===1&&index2===0?array2[0]:index2===0?loop2?array2[array2.length-1]:void 0:array2[index2-1]}function forward(array2,index2,increment2,loop2=!0){if(array2.length===0||index2<0||index2>=array2.length)return;let targetIndex=index2+increment2;return loop2?targetIndex=(targetIndex%array2.length+array2.
-length)%array2.length:targetIndex=Math.max(0,Math.min(targetIndex,array2.length-1)),array2[targetIndex]}function backward(array2,index2,decrement,loop2=!0){if(array2.length===0||index2<0||index2>=array2.length)return;let targetIndex=index2-decrement;return loop2?targetIndex=(targetIndex%array2.length+array2.length)%array2.length:targetIndex=Math.max(0,Math.min(targetIndex,array2.length-1)),array2[targetIndex]}function getNextMatch(values,search2,currentMatch){const lowerSearch=search2.toLowerCase();
-if(lowerSearch.endsWith(" ")){const searchWithoutSpace=lowerSearch.slice(0,-1);if(values.filter(value=>value.toLowerCase().startsWith(searchWithoutSpace)).length<=1)return getNextMatch(values,searchWithoutSpace,currentMatch);const currentMatchLowercase=currentMatch?.toLowerCase();if(currentMatchLowercase&&currentMatchLowercase.startsWith(searchWithoutSpace)&&currentMatchLowercase.charAt(searchWithoutSpace.length)===" "&&search2.trim()===searchWithoutSpace)return currentMatch;const spacedMatches=values.
-filter(value=>value.toLowerCase().startsWith(lowerSearch));if(spacedMatches.length>0){const currentMatchIndex2=currentMatch?values.indexOf(currentMatch):-1;return wrapArray(spacedMatches,Math.max(currentMatchIndex2,0)).find(match=>match!==currentMatch)||currentMatch}}const normalizedSearch=search2.length>1&&Array.from(search2).every(char=>char===search2[0])?search2[0]:search2,normalizedLowerSearch=normalizedSearch.toLowerCase(),currentMatchIndex=currentMatch?values.indexOf(currentMatch):-1;let wrappedValues=wrapArray(
-values,Math.max(currentMatchIndex,0));normalizedSearch.length===1&&(wrappedValues=wrappedValues.filter(v=>v!==currentMatch));const nextMatch=wrappedValues.find(value=>value?.toLowerCase().startsWith(normalizedLowerSearch));return nextMatch!==currentMatch?nextMatch:void 0}function wrapArray(array2,startIndex){return array2.map((_,index2)=>array2[(startIndex+index2)%array2.length])}const defaultOptions={afterMs:1e4,onChange:noop$1};function boxAutoReset(defaultValue,options){const{afterMs,onChange,
-getWindow:getWindow2}={...defaultOptions,...options};let timeout=null,value=state$1(proxy(defaultValue));function resetAfter(){return getWindow2().setTimeout(()=>{set$1(value,defaultValue,!0),onChange?.(defaultValue)},afterMs)}return user_effect(()=>()=>{timeout&&getWindow2().clearTimeout(timeout)}),boxWith$1(()=>get$4(value),v=>{set$1(value,v,!0),onChange?.(v),timeout&&getWindow2().clearTimeout(timeout),timeout=resetAfter()})}class DOMTypeahead{#opts;#search;#onMatch=user_derived(()=>this.#opts.
-onMatch?this.#opts.onMatch:node2=>node2.focus());#getCurrentItem=user_derived(()=>this.#opts.getCurrentItem?this.#opts.getCurrentItem:this.#opts.getActiveElement);constructor(opts){this.#opts=opts,this.#search=boxAutoReset("",{afterMs:1e3,getWindow:opts.getWindow}),this.handleTypeaheadSearch=this.handleTypeaheadSearch.bind(this),this.resetTypeahead=this.resetTypeahead.bind(this)}handleTypeaheadSearch(key2,candidates){if(!candidates.length)return;this.#search.current=this.#search.current+key2;const currentItem=get$4(
-this.#getCurrentItem)(),currentMatch=candidates.find(item=>item===currentItem)?.textContent?.trim()??"",values=candidates.map(item=>item.textContent?.trim()??""),nextMatch=getNextMatch(values,this.#search.current,currentMatch),newItem=candidates.find(item=>item.textContent?.trim()===nextMatch);return newItem&&get$4(this.#onMatch)(newItem),newItem}resetTypeahead(){this.#search.current=""}get search(){return this.#search.current}}class GraceArea{#opts;#enabled;#isPointerInTransit;#pointerGraceArea=state$1(
-null);constructor(opts){this.#opts=opts,this.#enabled=user_derived(()=>this.#opts.enabled()),this.#isPointerInTransit=boxAutoReset(!1,{afterMs:opts.transitTimeout??300,onChange:value=>{get$4(this.#enabled)&&this.#opts.setIsPointerInTransit?.(value)},getWindow:()=>getWindow$1(this.#opts.triggerNode())}),watch$1([opts.triggerNode,opts.contentNode,opts.enabled],([triggerNode,contentNode,enabled])=>{if(!triggerNode||!contentNode||!enabled)return;const handleTriggerLeave=e=>{this.#createGraceArea(e,contentNode)},
-handleContentLeave=e=>{this.#createGraceArea(e,triggerNode)};return executeCallbacks(on(triggerNode,"pointerleave",handleTriggerLeave),on(contentNode,"pointerleave",handleContentLeave))}),watch$1(()=>get$4(this.#pointerGraceArea),()=>{const handleTrackPointerGrace=e=>{if(!get$4(this.#pointerGraceArea))return;const target2=e.target;if(!isElement$1(target2))return;const pointerPosition={x:e.clientX,y:e.clientY},hasEnteredTarget=opts.triggerNode()?.contains(target2)||opts.contentNode()?.contains(target2),
-isPointerOutsideGraceArea=!isPointInPolygon$1(pointerPosition,get$4(this.#pointerGraceArea));hasEnteredTarget?this.#removeGraceArea():isPointerOutsideGraceArea&&(this.#removeGraceArea(),opts.onPointerExit())},doc=getDocument$1(opts.triggerNode()??opts.contentNode());if(doc)return on(doc,"pointermove",handleTrackPointerGrace)})}#removeGraceArea(){set$1(this.#pointerGraceArea,null),this.#isPointerInTransit.current=!1}#createGraceArea(e,hoverTarget){const currentTarget=e.currentTarget;if(!isHTMLElement$1(
-currentTarget))return;const exitPoint={x:e.clientX,y:e.clientY},exitSide=getExitSideFromRect(exitPoint,currentTarget.getBoundingClientRect()),paddedExitPoints=getPaddedExitPoints(exitPoint,exitSide),hoverTargetPoints=getPointsFromRect(hoverTarget.getBoundingClientRect()),graceArea=getHull([...paddedExitPoints,...hoverTargetPoints]);set$1(this.#pointerGraceArea,graceArea,!0),this.#isPointerInTransit.current=!0}}function getExitSideFromRect(point2,rect){const top=Math.abs(rect.top-point2.y),bottom=Math.
-abs(rect.bottom-point2.y),right=Math.abs(rect.right-point2.x),left=Math.abs(rect.left-point2.x);switch(Math.min(top,bottom,right,left)){case left:return"left";case right:return"right";case top:return"top";case bottom:return"bottom";default:throw new Error("unreachable")}}function getPaddedExitPoints(exitPoint,exitSide,padding=5){const tipPadding=padding*1.5;switch(exitSide){case"top":return[{x:exitPoint.x-padding,y:exitPoint.y+padding},{x:exitPoint.x,y:exitPoint.y-tipPadding},{x:exitPoint.x+padding,
-y:exitPoint.y+padding}];case"bottom":return[{x:exitPoint.x-padding,y:exitPoint.y-padding},{x:exitPoint.x,y:exitPoint.y+tipPadding},{x:exitPoint.x+padding,y:exitPoint.y-padding}];case"left":return[{x:exitPoint.x+padding,y:exitPoint.y-padding},{x:exitPoint.x-tipPadding,y:exitPoint.y},{x:exitPoint.x+padding,y:exitPoint.y+padding}];case"right":return[{x:exitPoint.x-padding,y:exitPoint.y-padding},{x:exitPoint.x+tipPadding,y:exitPoint.y},{x:exitPoint.x-padding,y:exitPoint.y+padding}]}}function getPointsFromRect(rect){
-const{top,right,bottom,left}=rect;return[{x:left,y:top},{x:right,y:top},{x:right,y:bottom},{x:left,y:bottom}]}function isPointInPolygon$1(point2,polygon){const{x,y}=point2;let inside=!1;for(let i=0,j=polygon.length-1;i<polygon.length;j=i++){const xi=polygon[i].x,yi=polygon[i].y,xj=polygon[j].x,yj=polygon[j].y;yi>y!=yj>y&&x<(xj-xi)*(y-yi)/(yj-yi)+xi&&(inside=!inside)}return inside}function getHull(points){const newPoints=points.slice();return newPoints.sort((a,b)=>a.x<b.x?-1:a.x>b.x?1:a.y<b.y?-1:
-a.y>b.y?1:0),getHullPresorted(newPoints)}function getHullPresorted(points){if(points.length<=1)return points.slice();const upperHull=[];for(let i=0;i<points.length;i++){const p2=points[i];for(;upperHull.length>=2;){const q2=upperHull[upperHull.length-1],r2=upperHull[upperHull.length-2];if((q2.x-r2.x)*(p2.y-r2.y)>=(q2.y-r2.y)*(p2.x-r2.x))upperHull.pop();else break}upperHull.push(p2)}upperHull.pop();const lowerHull=[];for(let i=points.length-1;i>=0;i--){const p2=points[i];for(;lowerHull.length>=2;){
-const q2=lowerHull[lowerHull.length-1],r2=lowerHull[lowerHull.length-2];if((q2.x-r2.x)*(p2.y-r2.y)>=(q2.y-r2.y)*(p2.x-r2.x))lowerHull.pop();else break}lowerHull.push(p2)}return lowerHull.pop(),upperHull.length===1&&lowerHull.length===1&&upperHull[0].x===lowerHull[0].x&&upperHull[0].y===lowerHull[0].y?upperHull:upperHull.concat(lowerHull)}const CONTEXT_MENU_TRIGGER_ATTR="data-context-menu-trigger",CONTEXT_MENU_CONTENT_ATTR="data-context-menu-content",MenuRootContext=new Context$1("Menu.Root"),MenuMenuContext=new Context$1(
-"Menu.Root | Menu.Sub"),MenuContentContext=new Context$1("Menu.Content"),MenuOpenEvent=new CustomEventDispatcher("bitsmenuopen",{bubbles:!1,cancelable:!0}),menuAttrs=createBitsAttrs({component:"menu",parts:["trigger","content","sub-trigger","item","group","group-heading","checkbox-group","checkbox-item","radio-group","radio-item","separator","sub-content","arrow"]});class MenuRootState{static create(opts){const root2=new MenuRootState(opts);return MenuRootContext.set(root2)}opts;isUsingKeyboard=new IsUsingKeyboard;#ignoreCloseAutoFocus=state$1(
-!1);get ignoreCloseAutoFocus(){return get$4(this.#ignoreCloseAutoFocus)}set ignoreCloseAutoFocus(value){set$1(this.#ignoreCloseAutoFocus,value,!0)}#isPointerInTransit=state$1(!1);get isPointerInTransit(){return get$4(this.#isPointerInTransit)}set isPointerInTransit(value){set$1(this.#isPointerInTransit,value,!0)}constructor(opts){this.opts=opts}getBitsAttr=part=>menuAttrs.getAttr(part,this.opts.variant.current)}class MenuMenuState{static create(opts,root2){return MenuMenuContext.set(new MenuMenuState(
-opts,root2,null))}opts;root;parentMenu;contentId=boxWith$1(()=>"");#contentNode=state$1(null);get contentNode(){return get$4(this.#contentNode)}set contentNode(value){set$1(this.#contentNode,value,!0)}contentPresence;#triggerNode=state$1(null);get triggerNode(){return get$4(this.#triggerNode)}set triggerNode(value){set$1(this.#triggerNode,value,!0)}constructor(opts,root2,parentMenu){this.opts=opts,this.root=root2,this.parentMenu=parentMenu,this.contentPresence=new PresenceManager({ref:boxWith$1(
-()=>this.contentNode),open:this.opts.open,onComplete:()=>{this.opts.onOpenChangeComplete.current(this.opts.open.current)}}),parentMenu&&watch$1(()=>parentMenu.opts.open.current,()=>{parentMenu.opts.open.current||(this.opts.open.current=!1)})}toggleOpen(){this.opts.open.current=!this.opts.open.current}onOpen(){this.opts.open.current=!0}onClose(){this.opts.open.current=!1}}class MenuContentState{static create(opts){return MenuContentContext.set(new MenuContentState(opts,MenuMenuContext.get()))}opts;parentMenu;rovingFocusGroup;domContext;attachment;#search=state$1(
-"");get search(){return get$4(this.#search)}set search(value){set$1(this.#search,value,!0)}#timer=0;#handleTypeaheadSearch;#mounted=state$1(!1);get mounted(){return get$4(this.#mounted)}set mounted(value){set$1(this.#mounted,value,!0)}#isSub;constructor(opts,parentMenu){this.opts=opts,this.parentMenu=parentMenu,this.domContext=new DOMContext(opts.ref),this.attachment=attachRef(this.opts.ref,v=>{this.parentMenu.contentNode!==v&&(this.parentMenu.contentNode=v)}),parentMenu.contentId=opts.id,this.#isSub=
-opts.isSub??!1,this.onkeydown=this.onkeydown.bind(this),this.onblur=this.onblur.bind(this),this.onfocus=this.onfocus.bind(this),this.handleInteractOutside=this.handleInteractOutside.bind(this),new GraceArea({contentNode:()=>this.parentMenu.contentNode,triggerNode:()=>this.parentMenu.triggerNode,enabled:()=>this.parentMenu.opts.open.current&&!!this.parentMenu.triggerNode?.hasAttribute(this.parentMenu.root.getBitsAttr("sub-trigger")),onPointerExit:()=>{this.parentMenu.opts.open.current=!1},setIsPointerInTransit:value=>{
-this.parentMenu.root.isPointerInTransit=value}}),this.#handleTypeaheadSearch=new DOMTypeahead({getActiveElement:()=>this.domContext.getActiveElement(),getWindow:()=>this.domContext.getWindow()}).handleTypeaheadSearch,this.rovingFocusGroup=new RovingFocusGroup({rootNode:boxWith$1(()=>this.parentMenu.contentNode),candidateAttr:this.parentMenu.root.getBitsAttr("item"),loop:this.opts.loop,orientation:boxWith$1(()=>"vertical")}),watch$1(()=>this.parentMenu.contentNode,contentNode=>{if(!contentNode)return;
-const handler=()=>{afterTick(()=>{this.parentMenu.root.isUsingKeyboard.current&&this.rovingFocusGroup.focusFirstCandidate()})};return MenuOpenEvent.listen(contentNode,handler)}),user_effect(()=>{this.parentMenu.opts.open.current||this.domContext.getWindow().clearTimeout(this.#timer)})}#getCandidateNodes(){const node2=this.parentMenu.contentNode;return node2?Array.from(node2.querySelectorAll(`[${this.parentMenu.root.getBitsAttr("item")}]:not([data-disabled])`)):[]}#isPointerMovingToSubmenu(){return this.
-parentMenu.root.isPointerInTransit}onCloseAutoFocus=e=>{this.opts.onCloseAutoFocus.current?.(e),!(e.defaultPrevented||this.#isSub)&&this.parentMenu.triggerNode&&isTabbable(this.parentMenu.triggerNode)&&(e.preventDefault(),this.parentMenu.triggerNode.focus())};handleTabKeyDown(e){let rootMenu=this.parentMenu;for(;rootMenu.parentMenu!==null;)rootMenu=rootMenu.parentMenu;if(!rootMenu.triggerNode)return;e.preventDefault();const nodeToFocus=getTabbableFrom(rootMenu.triggerNode,e.shiftKey?"prev":"next");
-nodeToFocus?(this.parentMenu.root.ignoreCloseAutoFocus=!0,rootMenu.onClose(),afterTick(()=>{nodeToFocus.focus(),afterTick(()=>{this.parentMenu.root.ignoreCloseAutoFocus=!1})})):this.domContext.getDocument().body.focus()}onkeydown(e){if(e.defaultPrevented)return;if(e.key===TAB){this.handleTabKeyDown(e);return}const target2=e.target,currentTarget=e.currentTarget;if(!isHTMLElement$1(target2)||!isHTMLElement$1(currentTarget))return;const isKeydownInside=target2.closest(`[${this.parentMenu.root.getBitsAttr(
-"content")}]`)?.id===this.parentMenu.contentId.current,isModifierKey=e.ctrlKey||e.altKey||e.metaKey,isCharacterKey=e.key.length===1;if(this.rovingFocusGroup.handleKeydown(target2,e)||e.code==="Space")return;const candidateNodes=this.#getCandidateNodes();isKeydownInside&&!isModifierKey&&isCharacterKey&&this.#handleTypeaheadSearch(e.key,candidateNodes),e.target?.id===this.parentMenu.contentId.current&&FIRST_LAST_KEYS$1.includes(e.key)&&(e.preventDefault(),LAST_KEYS$1.includes(e.key)&&candidateNodes.
-reverse(),focusFirst(candidateNodes,{select:!1},()=>this.domContext.getActiveElement()))}onblur(e){isElement$1(e.currentTarget)&&isElement$1(e.target)&&(e.currentTarget.contains?.(e.target)||(this.domContext.getWindow().clearTimeout(this.#timer),this.search=""))}onfocus(_){this.parentMenu.root.isUsingKeyboard.current&&afterTick(()=>this.rovingFocusGroup.focusFirstCandidate())}onItemEnter(){return this.#isPointerMovingToSubmenu()}onItemLeave(e){if(e.currentTarget.hasAttribute(this.parentMenu.root.
-getBitsAttr("sub-trigger"))||this.#isPointerMovingToSubmenu()||this.parentMenu.root.isUsingKeyboard.current)return;this.parentMenu.contentNode?.focus(),this.rovingFocusGroup.setCurrentTabStopId("")}onTriggerLeave(){return!!this.#isPointerMovingToSubmenu()}handleInteractOutside(e){if(!isElementOrSVGElement(e.target))return;const triggerId=this.parentMenu.triggerNode?.id;if(e.target.id===triggerId){e.preventDefault();return}e.target.closest(`#${triggerId}`)&&e.preventDefault()}get shouldRender(){return this.
-parentMenu.contentPresence.shouldRender}#snippetProps=user_derived(()=>({open:this.parentMenu.opts.open.current}));get snippetProps(){return get$4(this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}#props=user_derived(()=>({id:this.opts.id.current,role:"menu","aria-orientation":"vertical",[this.parentMenu.root.getBitsAttr("content")]:"","data-state":getDataOpenClosed(this.parentMenu.opts.open.current),onkeydown:this.onkeydown,onblur:this.onblur,onfocus:this.onfocus,dir:this.
-parentMenu.root.opts.dir.current,style:{pointerEvents:"auto",contain:"layout style paint"},...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}popperProps={onCloseAutoFocus:e=>this.onCloseAutoFocus(e)}}class MenuItemSharedState{opts;content;attachment;#isFocused=state$1(!1);constructor(opts,content2){this.opts=opts,this.content=content2,this.attachment=attachRef(this.opts.ref),this.onpointermove=this.onpointermove.bind(this),this.onpointerleave=this.
-onpointerleave.bind(this),this.onfocus=this.onfocus.bind(this),this.onblur=this.onblur.bind(this)}onpointermove(e){if(!e.defaultPrevented&&isMouseEvent(e))if(this.opts.disabled.current)this.content.onItemLeave(e);else{if(this.content.onItemEnter())return;const item=e.currentTarget;if(!isHTMLElement$1(item))return;item.focus()}}onpointerleave(e){e.defaultPrevented||isMouseEvent(e)&&this.content.onItemLeave(e)}onfocus(e){afterTick(()=>{e.defaultPrevented||this.opts.disabled.current||set$1(this.#isFocused,
-!0)})}onblur(e){afterTick(()=>{e.defaultPrevented||set$1(this.#isFocused,!1)})}#props=user_derived(()=>({id:this.opts.id.current,tabindex:-1,role:"menuitem","aria-disabled":boolToStr(this.opts.disabled.current),"data-disabled":boolToEmptyStrOrUndef(this.opts.disabled.current),"data-highlighted":get$4(this.#isFocused)?"":void 0,[this.content.parentMenu.root.getBitsAttr("item")]:"",onpointermove:this.onpointermove,onpointerleave:this.onpointerleave,onfocus:this.onfocus,onblur:this.onblur,...this.attachment}));get props(){
-return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class MenuItemState{static create(opts){const item=new MenuItemSharedState(opts,MenuContentContext.get());return new MenuItemState(opts,item)}opts;item;root;#isPointerDown=!1;constructor(opts,item){this.opts=opts,this.item=item,this.root=item.content.parentMenu.root,this.onkeydown=this.onkeydown.bind(this),this.onclick=this.onclick.bind(this),this.onpointerdown=this.onpointerdown.bind(this),this.onpointerup=this.onpointerup.bind(
-this)}#handleSelect(){if(this.item.opts.disabled.current)return;const selectEvent=new CustomEvent("menuitemselect",{bubbles:!0,cancelable:!0});if(this.opts.onSelect.current(selectEvent),selectEvent.defaultPrevented){this.item.content.parentMenu.root.isUsingKeyboard.current=!1;return}this.opts.closeOnSelect.current&&this.item.content.parentMenu.root.opts.onClose()}onkeydown(e){const isTypingAhead=this.item.content.search!=="";if(!(this.item.opts.disabled.current||isTypingAhead&&e.key===SPACE)&&SELECTION_KEYS.
-includes(e.key)){if(!isHTMLElement$1(e.currentTarget))return;e.currentTarget.click(),e.preventDefault()}}onclick(_){this.item.opts.disabled.current||this.#handleSelect()}onpointerup(e){if(!e.defaultPrevented&&!this.#isPointerDown){if(!isHTMLElement$1(e.currentTarget))return;e.currentTarget?.click()}}onpointerdown(_){this.#isPointerDown=!0}#props=user_derived(()=>mergeProps(this.item.props,{onclick:this.onclick,onpointerdown:this.onpointerdown,onpointerup:this.onpointerup,onkeydown:this.onkeydown}));get props(){
-return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class MenuSubTriggerState{static create(opts){const content2=MenuContentContext.get(),item=new MenuItemSharedState(opts,content2),submenu=MenuMenuContext.get();return new MenuSubTriggerState(opts,item,content2,submenu)}opts;item;content;submenu;attachment;#openTimer=null;constructor(opts,item,content2,submenu){this.opts=opts,this.item=item,this.content=content2,this.submenu=submenu,this.attachment=attachRef(this.opts.ref,v=>this.
-submenu.triggerNode=v),this.onpointerleave=this.onpointerleave.bind(this),this.onpointermove=this.onpointermove.bind(this),this.onkeydown=this.onkeydown.bind(this),this.onclick=this.onclick.bind(this),onDestroyEffect(()=>{this.#clearOpenTimer()})}#clearOpenTimer(){this.#openTimer!==null&&(this.content.domContext.getWindow().clearTimeout(this.#openTimer),this.#openTimer=null)}onpointermove(e){isMouseEvent(e)&&!this.item.opts.disabled.current&&!this.submenu.opts.open.current&&!this.#openTimer&&!this.
-content.parentMenu.root.isPointerInTransit&&(this.#openTimer=this.content.domContext.setTimeout(()=>{this.submenu.onOpen(),this.#clearOpenTimer()},this.opts.openDelay.current))}onpointerleave(e){isMouseEvent(e)&&this.#clearOpenTimer()}onkeydown(e){const isTypingAhead=this.content.search!=="";this.item.opts.disabled.current||isTypingAhead&&e.key===SPACE||SUB_OPEN_KEYS[this.submenu.root.opts.dir.current].includes(e.key)&&(e.currentTarget.click(),e.preventDefault())}onclick(e){if(this.item.opts.disabled.
-current||!isHTMLElement$1(e.currentTarget))return;e.currentTarget.focus();const selectEvent=new CustomEvent("menusubtriggerselect",{bubbles:!0,cancelable:!0});this.opts.onSelect.current(selectEvent),this.submenu.opts.open.current||(this.submenu.onOpen(),afterTick(()=>{const contentNode=this.submenu.contentNode;contentNode&&MenuOpenEvent.dispatch(contentNode)}))}#props=user_derived(()=>mergeProps({"aria-haspopup":"menu","aria-expanded":boolToStr(this.submenu.opts.open.current),"data-state":getDataOpenClosed(
-this.submenu.opts.open.current),"aria-controls":this.submenu.opts.open.current?this.submenu.contentId.current:void 0,[this.submenu.root.getBitsAttr("sub-trigger")]:"",onclick:this.onclick,onpointermove:this.onpointermove,onpointerleave:this.onpointerleave,onkeydown:this.onkeydown,...this.attachment},this.item.props));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class MenuSeparatorState{static create(opts){return new MenuSeparatorState(opts,MenuRootContext.get())}opts;root;attachment;constructor(opts,root2){
-this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref)}#props=user_derived(()=>({id:this.opts.id.current,role:"group",[this.root.getBitsAttr("separator")]:"",...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class DropdownMenuTriggerState{static create(opts){return new DropdownMenuTriggerState(opts,MenuMenuContext.get())}opts;parentMenu;attachment;constructor(opts,parentMenu){this.opts=opts,this.parentMenu=parentMenu,this.attachment=
-attachRef(this.opts.ref,v=>this.parentMenu.triggerNode=v)}onclick=e=>{this.opts.disabled.current||e.detail!==0||(this.parentMenu.toggleOpen(),e.preventDefault())};onpointerdown=e=>{if(!this.opts.disabled.current){if(e.pointerType==="touch")return e.preventDefault();e.button===0&&e.ctrlKey===!1&&(this.parentMenu.toggleOpen(),this.parentMenu.opts.open.current||e.preventDefault())}};onpointerup=e=>{this.opts.disabled.current||e.pointerType==="touch"&&(e.preventDefault(),this.parentMenu.toggleOpen())};onkeydown=e=>{
-if(!this.opts.disabled.current){if(e.key===SPACE||e.key===ENTER){this.parentMenu.toggleOpen(),e.preventDefault();return}e.key===ARROW_DOWN&&(this.parentMenu.onOpen(),e.preventDefault())}};#ariaControls=user_derived(()=>{if(this.parentMenu.opts.open.current&&this.parentMenu.contentId.current)return this.parentMenu.contentId.current});#props=user_derived(()=>({id:this.opts.id.current,disabled:this.opts.disabled.current,"aria-haspopup":"menu","aria-expanded":boolToStr(this.parentMenu.opts.open.current),
-"aria-controls":get$4(this.#ariaControls),"data-disabled":boolToEmptyStrOrUndef(this.opts.disabled.current),"data-state":getDataOpenClosed(this.parentMenu.opts.open.current),[this.parentMenu.root.getBitsAttr("trigger")]:"",onclick:this.onclick,onpointerdown:this.onpointerdown,onpointerup:this.onpointerup,onkeydown:this.onkeydown,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class MenuSubmenuState{static create(opts){const menu=MenuMenuContext.
-get();return MenuMenuContext.set(new MenuMenuState(opts,menu.root,menu))}}globalThis.bitsDismissableLayers??=new Map;class DismissibleLayerState{static create(opts){return new DismissibleLayerState(opts)}opts;#interactOutsideProp;#behaviorType;#interceptedEvents={pointerdown:!1};#isResponsibleLayer=!1;#isFocusInsideDOMTree=!1;#documentObj=void 0;#onFocusOutside;#unsubClickListener=noop$1;constructor(opts){this.opts=opts,this.#behaviorType=opts.interactOutsideBehavior,this.#interactOutsideProp=opts.
-onInteractOutside,this.#onFocusOutside=opts.onFocusOutside,user_effect(()=>{this.#documentObj=getOwnerDocument(this.opts.ref.current)});let unsubEvents=noop$1;const cleanup=()=>{this.#resetState(),globalThis.bitsDismissableLayers.delete(this),this.#handleInteractOutside.destroy(),unsubEvents()};watch$1([()=>this.opts.enabled.current,()=>this.opts.ref.current],()=>{if(!(!this.opts.enabled.current||!this.opts.ref.current))return afterSleep(1,()=>{this.opts.ref.current&&(globalThis.bitsDismissableLayers.
-set(this,this.#behaviorType),unsubEvents(),unsubEvents=this.#addEventListeners())}),cleanup}),onDestroyEffect(()=>{this.#resetState.destroy(),globalThis.bitsDismissableLayers.delete(this),this.#handleInteractOutside.destroy(),this.#unsubClickListener(),unsubEvents()})}#handleFocus=event2=>{event2.defaultPrevented||this.opts.ref.current&&afterTick(()=>{!this.opts.ref.current||this.#isTargetWithinLayer(event2.target)||event2.target&&!this.#isFocusInsideDOMTree&&this.#onFocusOutside.current?.(event2)})};#addEventListeners(){
-return executeCallbacks(on(this.#documentObj,"pointerdown",executeCallbacks(this.#markInterceptedEvent,this.#markResponsibleLayer),{capture:!0}),on(this.#documentObj,"pointerdown",executeCallbacks(this.#markNonInterceptedEvent,this.#handleInteractOutside)),on(this.#documentObj,"focusin",this.#handleFocus))}#handleDismiss=e=>{let event2=e;event2.defaultPrevented&&(event2=createWrappedEvent(e)),this.#interactOutsideProp.current(e)};#handleInteractOutside=debounce$1(e=>{if(!this.opts.ref.current){this.#unsubClickListener();
-return}const isEventValid=this.opts.isValidEvent.current(e,this.opts.ref.current)||isValidEvent(e,this.opts.ref.current);if(!this.#isResponsibleLayer||this.#isAnyEventIntercepted()||!isEventValid){this.#unsubClickListener();return}let event2=e;if(event2.defaultPrevented&&(event2=createWrappedEvent(event2)),this.#behaviorType.current!=="close"&&this.#behaviorType.current!=="defer-otherwise-close"){this.#unsubClickListener();return}e.pointerType==="touch"?(this.#unsubClickListener(),this.#unsubClickListener=
-on(this.#documentObj,"click",this.#handleDismiss,{once:!0})):this.#interactOutsideProp.current(event2)},10);#markInterceptedEvent=e=>{this.#interceptedEvents[e.type]=!0};#markNonInterceptedEvent=e=>{this.#interceptedEvents[e.type]=!1};#markResponsibleLayer=()=>{this.opts.ref.current&&(this.#isResponsibleLayer=isResponsibleLayer(this.opts.ref.current))};#isTargetWithinLayer=target2=>this.opts.ref.current?isOrContainsTarget(this.opts.ref.current,target2):!1;#resetState=debounce$1(()=>{for(const eventType in this.#interceptedEvents)
-this.#interceptedEvents[eventType]=!1;this.#isResponsibleLayer=!1},20);#isAnyEventIntercepted(){return Object.values(this.#interceptedEvents).some(Boolean)}#onfocuscapture=()=>{this.#isFocusInsideDOMTree=!0};#onblurcapture=()=>{this.#isFocusInsideDOMTree=!1};props={onfocuscapture:this.#onfocuscapture,onblurcapture:this.#onblurcapture}}function getTopMostDismissableLayer(layersArr=[...globalThis.bitsDismissableLayers]){return layersArr.findLast(([_,{current:behaviorType}])=>behaviorType==="close"||
-behaviorType==="ignore")}function isResponsibleLayer(node2){const layersArr=[...globalThis.bitsDismissableLayers],topMostLayer=getTopMostDismissableLayer(layersArr);if(topMostLayer)return topMostLayer[0].opts.ref.current===node2;const[firstLayerNode]=layersArr[0];return firstLayerNode.opts.ref.current===node2}function isValidEvent(e,node2){const target2=e.target;if(!isElementOrSVGElement(target2))return!1;const targetIsContextMenuTrigger=!!target2.closest(`[${CONTEXT_MENU_TRIGGER_ATTR}]`);if("bu\
-tton"in e&&e.button>0&&!targetIsContextMenuTrigger)return!1;if("button"in e&&e.button===0&&targetIsContextMenuTrigger)return!0;const nodeIsContextMenu=!!node2.closest(`[${CONTEXT_MENU_CONTENT_ATTR}]`);return targetIsContextMenuTrigger&&nodeIsContextMenu?!1:getOwnerDocument(target2).documentElement.contains(target2)&&!isOrContainsTarget(node2,target2)&&isClickTrulyOutside(e,node2)}function createWrappedEvent(e){const capturedCurrentTarget=e.currentTarget,capturedTarget=e.target;let newEvent;e instanceof
-PointerEvent?newEvent=new PointerEvent(e.type,e):newEvent=new PointerEvent("pointerdown",e);let isPrevented=!1;return new Proxy(newEvent,{get:(target2,prop2)=>prop2==="currentTarget"?capturedCurrentTarget:prop2==="target"?capturedTarget:prop2==="preventDefault"?()=>{isPrevented=!0,typeof target2.preventDefault=="function"&&target2.preventDefault()}:prop2==="defaultPrevented"?isPrevented:prop2 in target2?target2[prop2]:e[prop2]})}function Dismissible_layer($$anchor,$$props){push$1($$props,!0);let interactOutsideBehavior=prop(
-$$props,"interactOutsideBehavior",3,"close"),onInteractOutside=prop($$props,"onInteractOutside",3,noop$1),onFocusOutside=prop($$props,"onFocusOutside",3,noop$1),isValidEvent2=prop($$props,"isValidEvent",3,()=>!1);const dismissibleLayerState=DismissibleLayerState.create({id:boxWith$1(()=>$$props.id),interactOutsideBehavior:boxWith$1(()=>interactOutsideBehavior()),onInteractOutside:boxWith$1(()=>onInteractOutside()),enabled:boxWith$1(()=>$$props.enabled),onFocusOutside:boxWith$1(()=>onFocusOutside()),
-isValidEvent:boxWith$1(()=>isValidEvent2()),ref:$$props.ref});var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.children??noop$3,()=>({props:dismissibleLayerState.props})),append($$anchor,fragment),pop()}globalThis.bitsEscapeLayers??=new Map;class EscapeLayerState{static create(opts){return new EscapeLayerState(opts)}opts;domContext;constructor(opts){this.opts=opts,this.domContext=new DOMContext(this.opts.ref);let unsubEvents=noop$1;watch$1(()=>opts.enabled.current,enabled=>(enabled&&
-(globalThis.bitsEscapeLayers.set(this,opts.escapeKeydownBehavior),unsubEvents=this.#addEventListener()),()=>{unsubEvents(),globalThis.bitsEscapeLayers.delete(this)}))}#addEventListener=()=>on(this.domContext.getDocument(),"keydown",this.#onkeydown,{passive:!1});#onkeydown=e=>{if(e.key!==ESCAPE||!isResponsibleEscapeLayer(this))return;const clonedEvent=new KeyboardEvent(e.type,e);e.preventDefault();const behaviorType=this.opts.escapeKeydownBehavior.current;behaviorType!=="close"&&behaviorType!=="d\
-efer-otherwise-close"||this.opts.onEscapeKeydown.current(clonedEvent)}}function isResponsibleEscapeLayer(instance){const layersArr=[...globalThis.bitsEscapeLayers],topMostLayer=layersArr.findLast(([_,{current:behaviorType}])=>behaviorType==="close"||behaviorType==="ignore");if(topMostLayer)return topMostLayer[0]===instance;const[firstLayerNode]=layersArr[0];return firstLayerNode===instance}function Escape_layer($$anchor,$$props){push$1($$props,!0);let escapeKeydownBehavior=prop($$props,"escapeKe\
-ydownBehavior",3,"close"),onEscapeKeydown=prop($$props,"onEscapeKeydown",3,noop$1);EscapeLayerState.create({escapeKeydownBehavior:boxWith$1(()=>escapeKeydownBehavior()),onEscapeKeydown:boxWith$1(()=>onEscapeKeydown()),enabled:boxWith$1(()=>$$props.enabled),ref:$$props.ref});var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.children??noop$3),append($$anchor,fragment),pop()}class FocusScopeManager{static instance;#scopeStack=simpleBox([]);#focusHistory=new WeakMap;#preFocusHistory=new WeakMap;static getInstance(){
-return this.instance||(this.instance=new FocusScopeManager),this.instance}register(scope2){const current2=this.getActive();current2&&current2!==scope2&&current2.pause();const activeElement=document.activeElement;activeElement&&activeElement!==document.body&&this.#preFocusHistory.set(scope2,activeElement),this.#scopeStack.current=this.#scopeStack.current.filter(s2=>s2!==scope2),this.#scopeStack.current.unshift(scope2)}unregister(scope2){this.#scopeStack.current=this.#scopeStack.current.filter(s2=>s2!==
-scope2);const next2=this.getActive();next2&&next2.resume()}getActive(){return this.#scopeStack.current[0]}setFocusMemory(scope2,element2){this.#focusHistory.set(scope2,element2)}getFocusMemory(scope2){return this.#focusHistory.get(scope2)}isActiveScope(scope2){return this.getActive()===scope2}setPreFocusMemory(scope2,element2){this.#preFocusHistory.set(scope2,element2)}getPreFocusMemory(scope2){return this.#preFocusHistory.get(scope2)}clearPreFocusMemory(scope2){this.#preFocusHistory.delete(scope2)}}
-class FocusScope{#paused=!1;#container=null;#manager=FocusScopeManager.getInstance();#cleanupFns=[];#opts;constructor(opts){this.#opts=opts}get paused(){return this.#paused}pause(){this.#paused=!0}resume(){this.#paused=!1}#cleanup(){for(const fn of this.#cleanupFns)fn();this.#cleanupFns=[]}mount(container2){this.#container&&this.unmount(),this.#container=container2,this.#manager.register(this),this.#setupEventListeners(),this.#handleOpenAutoFocus()}unmount(){this.#container&&(this.#cleanup(),this.#handleCloseAutoFocus(),
-this.#manager.unregister(this),this.#manager.clearPreFocusMemory(this),this.#container=null)}#handleOpenAutoFocus(){if(!this.#container)return;const event2=new CustomEvent("focusScope.onOpenAutoFocus",{bubbles:!1,cancelable:!0});this.#opts.onOpenAutoFocus.current(event2),event2.defaultPrevented||requestAnimationFrame(()=>{if(!this.#container)return;const firstTabbable=this.#getFirstTabbable();firstTabbable?(firstTabbable.focus(),this.#manager.setFocusMemory(this,firstTabbable)):this.#container.focus()})}#handleCloseAutoFocus(){
-const event2=new CustomEvent("focusScope.onCloseAutoFocus",{bubbles:!1,cancelable:!0});if(this.#opts.onCloseAutoFocus.current?.(event2),!event2.defaultPrevented){const preFocusedElement=this.#manager.getPreFocusMemory(this);if(preFocusedElement&&document.contains(preFocusedElement))try{preFocusedElement.focus()}catch{document.body.focus()}}}#setupEventListeners(){if(!this.#container||!this.#opts.trap.current)return;const container2=this.#container,doc=container2.ownerDocument,handleFocus=e=>{if(this.#paused||
-!this.#manager.isActiveScope(this))return;const target2=e.target;if(!target2)return;if(container2.contains(target2))this.#manager.setFocusMemory(this,target2);else{const lastFocused=this.#manager.getFocusMemory(this);if(lastFocused&&container2.contains(lastFocused)&&isFocusable(lastFocused))e.preventDefault(),lastFocused.focus();else{const firstTabbable=this.#getFirstTabbable(),firstFocusable=this.#getAllFocusables()[0];(firstTabbable||firstFocusable||container2).focus()}}},handleKeydown=e=>{if(!this.#opts.
-loop||this.#paused||e.key!=="Tab"||!this.#manager.isActiveScope(this))return;const tabbables=this.#getTabbables();if(tabbables.length===0)return;const first=tabbables[0],last=tabbables[tabbables.length-1];!e.shiftKey&&doc.activeElement===last?(e.preventDefault(),first.focus()):e.shiftKey&&doc.activeElement===first&&(e.preventDefault(),last.focus())};this.#cleanupFns.push(on(doc,"focusin",handleFocus,{capture:!0}),on(container2,"keydown",handleKeydown));const observer=new MutationObserver(()=>{const lastFocused=this.#manager.
-getFocusMemory(this);if(lastFocused&&!container2.contains(lastFocused)){const firstTabbable=this.#getFirstTabbable(),firstFocusable=this.#getAllFocusables()[0],elementToFocus=firstTabbable||firstFocusable;elementToFocus?(elementToFocus.focus(),this.#manager.setFocusMemory(this,elementToFocus)):container2.focus()}});observer.observe(container2,{childList:!0,subtree:!0}),this.#cleanupFns.push(()=>observer.disconnect())}#getTabbables(){return this.#container?tabbable(this.#container,{includeContainer:!1,
-getShadowRoot:!0}):[]}#getFirstTabbable(){return this.#getTabbables()[0]||null}#getAllFocusables(){return this.#container?focusable(this.#container,{includeContainer:!1,getShadowRoot:!0}):[]}static use(opts){let scope2=null;return watch$1([()=>opts.ref.current,()=>opts.enabled.current],([ref2,enabled])=>{ref2&&enabled?(scope2||(scope2=new FocusScope(opts)),scope2.mount(ref2)):scope2&&(scope2.unmount(),scope2=null)}),onDestroyEffect(()=>{scope2?.unmount()}),{get props(){return{tabindex:-1}}}}}function Focus_scope($$anchor,$$props){
-push$1($$props,!0);let enabled=prop($$props,"enabled",3,!1),trapFocus=prop($$props,"trapFocus",3,!1),loop2=prop($$props,"loop",3,!1),onCloseAutoFocus=prop($$props,"onCloseAutoFocus",3,noop$1),onOpenAutoFocus=prop($$props,"onOpenAutoFocus",3,noop$1);const focusScopeState=FocusScope.use({enabled:boxWith$1(()=>enabled()),trap:boxWith$1(()=>trapFocus()),loop:loop2(),onCloseAutoFocus:boxWith$1(()=>onCloseAutoFocus()),onOpenAutoFocus:boxWith$1(()=>onOpenAutoFocus()),ref:$$props.ref});var fragment=comment$2(),
-node2=first_child(fragment);snippet(node2,()=>$$props.focusScope??noop$3,()=>({props:focusScopeState.props})),append($$anchor,fragment),pop()}globalThis.bitsTextSelectionLayers??=new Map;class TextSelectionLayerState{static create(opts){return new TextSelectionLayerState(opts)}opts;domContext;#unsubSelectionLock=noop$1;constructor(opts){this.opts=opts,this.domContext=new DOMContext(opts.ref);let unsubEvents=noop$1;watch$1(()=>this.opts.enabled.current,isEnabled=>(isEnabled&&(globalThis.bitsTextSelectionLayers.
-set(this,this.opts.enabled),unsubEvents(),unsubEvents=this.#addEventListeners()),()=>{unsubEvents(),this.#resetSelectionLock(),globalThis.bitsTextSelectionLayers.delete(this)}))}#addEventListeners(){return executeCallbacks(on(this.domContext.getDocument(),"pointerdown",this.#pointerdown),on(this.domContext.getDocument(),"pointerup",composeHandlers(this.#resetSelectionLock,this.opts.onPointerUp.current)))}#pointerdown=e=>{const node2=this.opts.ref.current,target2=e.target;!isHTMLElement$1(node2)||
-!isHTMLElement$1(target2)||!this.opts.enabled.current||!isHighestLayer(this)||!contains$3(node2,target2)||(this.opts.onPointerDown.current(e),!e.defaultPrevented&&(this.#unsubSelectionLock=preventTextSelectionOverflow(node2,this.domContext.getDocument().body)))};#resetSelectionLock=()=>{this.#unsubSelectionLock(),this.#unsubSelectionLock=noop$1}}const getUserSelect=node2=>node2.style.userSelect||node2.style.webkitUserSelect;function preventTextSelectionOverflow(node2,body2){const originalBodyUserSelect=getUserSelect(
-body2),originalNodeUserSelect=getUserSelect(node2);return setUserSelect(body2,"none"),setUserSelect(node2,"text"),()=>{setUserSelect(body2,originalBodyUserSelect),setUserSelect(node2,originalNodeUserSelect)}}function setUserSelect(node2,value){node2.style.userSelect=value,node2.style.webkitUserSelect=value}function isHighestLayer(instance){const layersArr=[...globalThis.bitsTextSelectionLayers];if(!layersArr.length)return!1;const highestLayer=layersArr.at(-1);return highestLayer?highestLayer[0]===
-instance:!1}function Text_selection_layer($$anchor,$$props){push$1($$props,!0);let preventOverflowTextSelection=prop($$props,"preventOverflowTextSelection",3,!0),onPointerDown=prop($$props,"onPointerDown",3,noop$1),onPointerUp=prop($$props,"onPointerUp",3,noop$1);TextSelectionLayerState.create({id:boxWith$1(()=>$$props.id),onPointerDown:boxWith$1(()=>onPointerDown()),onPointerUp:boxWith$1(()=>onPointerUp()),enabled:boxWith$1(()=>$$props.enabled&&preventOverflowTextSelection()),ref:$$props.ref});
-var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.children??noop$3),append($$anchor,fragment),pop()}globalThis.bitsIdCounter??={current:0};function useId(prefix="bits"){return globalThis.bitsIdCounter.current++,`${prefix}-${globalThis.bitsIdCounter.current}`}class SharedState{#factory;#subscribers=0;#state=state$1();#scope;constructor(factory){this.#factory=factory}#dispose(){this.#subscribers-=1,this.#scope&&this.#subscribers<=0&&(this.#scope(),set$1(this.#state,void 0),
-this.#scope=void 0)}get(...args){return this.#subscribers+=1,get$4(this.#state)===void 0&&(this.#scope=effect_root(()=>{set$1(this.#state,this.#factory(...args),!0)})),user_effect(()=>()=>{this.#dispose()}),get$4(this.#state)}}const lockMap=new SvelteMap;let initialBodyStyle=state$1(null),stopTouchMoveListener=null,cleanupTimeoutId=null,isInCleanupTransition=!1;const anyLocked=boxWith$1(()=>{for(const value of lockMap.values())if(value)return!0;return!1});let cleanupScheduledAt=null;const bodyLockStackCount=new SharedState(
-()=>{function resetBodyStyle(){document.body.setAttribute("style",get$4(initialBodyStyle)??""),document.body.style.removeProperty("--scrollbar-width"),isIOS&&stopTouchMoveListener?.(),set$1(initialBodyStyle,null)}function cancelPendingCleanup(){cleanupTimeoutId!==null&&(window.clearTimeout(cleanupTimeoutId),cleanupTimeoutId=null)}function scheduleCleanupIfNoNewLocks(delay,callback){cancelPendingCleanup(),isInCleanupTransition=!0,cleanupScheduledAt=Date.now();const currentCleanupId=cleanupScheduledAt,
-cleanupFn=()=>{cleanupTimeoutId=null,cleanupScheduledAt===currentCleanupId&&(isAnyLocked(lockMap)?isInCleanupTransition=!1:(isInCleanupTransition=!1,callback()))},actualDelay=delay===null?24:delay;cleanupTimeoutId=window.setTimeout(cleanupFn,actualDelay)}function ensureInitialStyleCaptured(){get$4(initialBodyStyle)===null&&lockMap.size===0&&!isInCleanupTransition&&set$1(initialBodyStyle,document.body.getAttribute("style"),!0)}return watch$1(()=>anyLocked.current,()=>{if(!anyLocked.current)return;
-ensureInitialStyleCaptured(),isInCleanupTransition=!1;const htmlStyle=getComputedStyle(document.documentElement),bodyStyle=getComputedStyle(document.body),hasStableGutter=htmlStyle.scrollbarGutter?.includes("stable")||bodyStyle.scrollbarGutter?.includes("stable"),verticalScrollbarWidth=window.innerWidth-document.documentElement.clientWidth,config2={padding:Number.parseInt(bodyStyle.paddingRight??"0",10)+verticalScrollbarWidth,margin:Number.parseInt(bodyStyle.marginRight??"0",10)};verticalScrollbarWidth>
-0&&!hasStableGutter&&(document.body.style.paddingRight=`${config2.padding}px`,document.body.style.marginRight=`${config2.margin}px`,document.body.style.setProperty("--scrollbar-width",`${verticalScrollbarWidth}px`)),document.body.style.overflow="hidden",isIOS&&(stopTouchMoveListener=on(document,"touchmove",e=>{e.target===document.documentElement&&(e.touches.length>1||e.preventDefault())},{passive:!1})),afterTick(()=>{document.body.style.pointerEvents="none",document.body.style.overflow="hidden"})}),
-onDestroyEffect(()=>()=>{stopTouchMoveListener?.()}),{get lockMap(){return lockMap},resetBodyStyle,scheduleCleanupIfNoNewLocks,cancelPendingCleanup,ensureInitialStyleCaptured}});class BodyScrollLock{#id=useId();#initialState;#restoreScrollDelay=()=>null;#countState;locked;constructor(initialState,restoreScrollDelay=()=>null){this.#initialState=initialState,this.#restoreScrollDelay=restoreScrollDelay,this.#countState=bodyLockStackCount.get(),this.#countState&&(this.#countState.cancelPendingCleanup(),
-this.#countState.ensureInitialStyleCaptured(),this.#countState.lockMap.set(this.#id,this.#initialState??!1),this.locked=boxWith$1(()=>this.#countState.lockMap.get(this.#id)??!1,v=>this.#countState.lockMap.set(this.#id,v)),onDestroyEffect(()=>{if(this.#countState.lockMap.delete(this.#id),isAnyLocked(this.#countState.lockMap))return;const restoreScrollDelay2=this.#restoreScrollDelay();this.#countState.scheduleCleanupIfNoNewLocks(restoreScrollDelay2,()=>{this.#countState.resetBodyStyle()})}))}}function isAnyLocked(map2){
-for(const[_,value]of map2)if(value)return!0;return!1}function Scroll_lock($$anchor,$$props){push$1($$props,!0);let preventScroll=prop($$props,"preventScroll",3,!0),restoreScrollDelay=prop($$props,"restoreScrollDelay",3,null);preventScroll()&&new BodyScrollLock(preventScroll(),()=>restoreScrollDelay()),pop()}var root_6$C=from_html("<!> <!>",1),root_8$r=from_html("<!> <div><!></div>",1);function Alert_dialog_content$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"\
-id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),forceMount=prop($$props,"forceMount",3,!1),interactOutsideBehavior=prop($$props,"interactOutsideBehavior",3,"ignore"),onCloseAutoFocus=prop($$props,"onCloseAutoFocus",3,noop$1),onEscapeKeydown=prop($$props,"onEscapeKeydown",3,noop$1),onOpenAutoFocus=prop($$props,"onOpenAutoFocus",3,noop$1),onInteractOutside=prop($$props,"onInteractOutside",3,noop$1),preventScroll=prop($$props,"preventScroll",3,!0),trapFocus=prop($$props,"trapFocus",3,!0),
-restoreScrollDelay=prop($$props,"restoreScrollDelay",3,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","children","child","ref","forceMount","interactOutsideBehavior","onCloseAutoFocus","onEscapeKeydown","onOpenAutoFocus","onInteractOutside","preventScroll","trapFocus","restoreScrollDelay"]);const contentState=DialogContentState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,contentState.props));var fragment=comment$2(),
-node2=first_child(fragment);{var consequent_2=$$anchor2=>{Focus_scope($$anchor2,{get ref(){return contentState.opts.ref},loop:!0,get trapFocus(){return trapFocus()},get enabled(){return contentState.root.opts.open.current},get onCloseAutoFocus(){return onCloseAutoFocus()},onOpenAutoFocus:e=>{onOpenAutoFocus()(e),!e.defaultPrevented&&(e.preventDefault(),afterSleep(0,()=>contentState.opts.ref.current?.focus()))},focusScope:($$anchor3,$$arg0)=>{let focusScopeProps=()=>$$arg0?.().props;Escape_layer(
-$$anchor3,spread_props(()=>get$4(mergedProps),{get enabled(){return contentState.root.opts.open.current},get ref(){return contentState.opts.ref},onEscapeKeydown:e=>{onEscapeKeydown()(e),!e.defaultPrevented&&contentState.root.handleClose()},children:($$anchor4,$$slotProps)=>{Dismissible_layer($$anchor4,spread_props(()=>get$4(mergedProps),{get ref(){return contentState.opts.ref},get enabled(){return contentState.root.opts.open.current},get interactOutsideBehavior(){return interactOutsideBehavior()},
-onInteractOutside:e=>{onInteractOutside()(e),!e.defaultPrevented&&contentState.root.handleClose()},children:($$anchor5,$$slotProps2)=>{Text_selection_layer($$anchor5,spread_props(()=>get$4(mergedProps),{get ref(){return contentState.opts.ref},get enabled(){return contentState.root.opts.open.current},children:($$anchor6,$$slotProps3)=>{var fragment_5=comment$2(),node_1=first_child(fragment_5);{var consequent_1=$$anchor7=>{var fragment_6=root_6$C(),node_2=first_child(fragment_6);{var consequent=$$anchor8=>{
-Scroll_lock($$anchor8,{get preventScroll(){return preventScroll()},get restoreScrollDelay(){return restoreScrollDelay()}})};if_block(node_2,$$render=>{contentState.root.opts.open.current&&$$render(consequent)})}var node_3=sibling(node_2,2);{let $0=user_derived(()=>({props:mergeProps(get$4(mergedProps),focusScopeProps()),...contentState.snippetProps}));snippet(node_3,()=>$$props.child,()=>get$4($0))}append($$anchor7,fragment_6)},alternate=$$anchor7=>{var fragment_8=root_8$r(),node_4=first_child(fragment_8);
-Scroll_lock(node_4,{get preventScroll(){return preventScroll()}});var div=sibling(node_4,2);attribute_effect(div,$0=>({...$0}),[()=>mergeProps(get$4(mergedProps),focusScopeProps())]);var node_5=child(div);snippet(node_5,()=>$$props.children??noop$3),reset(div),append($$anchor7,fragment_8)};if_block(node_1,$$render=>{$$props.child?$$render(consequent_1):$$render(alternate,!1)})}append($$anchor6,fragment_5)},$$slots:{default:!0}}))},$$slots:{default:!0}}))},$$slots:{default:!0}}))},$$slots:{focusScope:!0}})};
-if_block(node2,$$render=>{(contentState.shouldRender||forceMount())&&$$render(consequent_2)})}append($$anchor,fragment),pop()}var root_3$10=from_html("<div><!></div>");function Dialog_overlay$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),forceMount=prop($$props,"forceMount",3,!1),ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","forceMount","child","children","ref"]);const overlayState=DialogOverlayState.
-create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,overlayState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent_1=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{var consequent=$$anchor3=>{var fragment_2=comment$2(),node_2=first_child(fragment_2);{let $0=user_derived(()=>({props:mergeProps(get$4(mergedProps)),...overlayState.snippetProps}));snippet(node_2,()=>$$props.child,()=>get$4(
-$0))}append($$anchor3,fragment_2)},alternate=$$anchor3=>{var div=root_3$10();attribute_effect(div,$0=>({...$0}),[()=>mergeProps(get$4(mergedProps))]);var node_3=child(div);snippet(node_3,()=>$$props.children??noop$3,()=>overlayState.snippetProps),reset(div),append($$anchor3,div)};if_block(node_1,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment_1)};if_block(node2,$$render=>{(overlayState.shouldRender||forceMount())&&$$render(consequent_1)})}append($$anchor,
-fragment),pop()}var root_2$1r=from_html("<div><!></div>");function Dialog_description$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","children","child","ref"]);const descriptionState=DialogDescriptionState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,descriptionState.
-props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1r();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):
-$$render(alternate,!1)})}append($$anchor,fragment),pop()}const checkboxAttrs=createBitsAttrs({component:"checkbox",parts:["root","group","group-label","input"]}),CheckboxGroupContext=new Context$1("Checkbox.Group"),CheckboxRootContext=new Context$1("Checkbox.Root");class CheckboxRootState{static create(opts,group=null){return CheckboxRootContext.set(new CheckboxRootState(opts,group))}opts;group;#trueName=user_derived(()=>this.group&&this.group.opts.name.current?this.group.opts.name.current:this.
-opts.name.current);get trueName(){return get$4(this.#trueName)}set trueName(value){set$1(this.#trueName,value)}#trueRequired=user_derived(()=>this.group&&this.group.opts.required.current?!0:this.opts.required.current);get trueRequired(){return get$4(this.#trueRequired)}set trueRequired(value){set$1(this.#trueRequired,value)}#trueDisabled=user_derived(()=>this.group&&this.group.opts.disabled.current?!0:this.opts.disabled.current);get trueDisabled(){return get$4(this.#trueDisabled)}set trueDisabled(value){
-set$1(this.#trueDisabled,value)}#trueReadonly=user_derived(()=>this.group&&this.group.opts.readonly.current?!0:this.opts.readonly.current);get trueReadonly(){return get$4(this.#trueReadonly)}set trueReadonly(value){set$1(this.#trueReadonly,value)}attachment;constructor(opts,group){this.opts=opts,this.group=group,this.attachment=attachRef(this.opts.ref),this.onkeydown=this.onkeydown.bind(this),this.onclick=this.onclick.bind(this),watch$1.pre([()=>snapshot(this.group?.opts.value.current),()=>this.
-opts.value.current],([groupValue,value])=>{!groupValue||!value||(this.opts.checked.current=groupValue.includes(value))}),watch$1.pre(()=>this.opts.checked.current,checked=>{this.group&&(checked?this.group?.addValue(this.opts.value.current):this.group?.removeValue(this.opts.value.current))})}onkeydown(e){if(!(this.trueDisabled||this.trueReadonly)){if(e.key===ENTER){e.preventDefault(),this.opts.type.current==="submit"&&e.currentTarget.closest("form")?.requestSubmit();return}e.key===SPACE&&(e.preventDefault(),
-this.#toggle())}}#toggle(){this.opts.indeterminate.current?(this.opts.indeterminate.current=!1,this.opts.checked.current=!0):this.opts.checked.current=!this.opts.checked.current}onclick(e){if(!(this.trueDisabled||this.trueReadonly)){if(this.opts.type.current==="submit"){this.#toggle();return}e.preventDefault(),this.#toggle()}}#snippetProps=user_derived(()=>({checked:this.opts.checked.current,indeterminate:this.opts.indeterminate.current}));get snippetProps(){return get$4(this.#snippetProps)}set snippetProps(value){
-set$1(this.#snippetProps,value)}#props=user_derived(()=>({id:this.opts.id.current,role:"checkbox",type:this.opts.type.current,disabled:this.trueDisabled,"aria-checked":getAriaChecked(this.opts.checked.current,this.opts.indeterminate.current),"aria-required":boolToStr(this.trueRequired),"aria-readonly":boolToStr(this.trueReadonly),"data-disabled":boolToEmptyStrOrUndef(this.trueDisabled),"data-readonly":boolToEmptyStrOrUndef(this.trueReadonly),"data-state":getCheckboxDataState(this.opts.checked.current,
-this.opts.indeterminate.current),[checkboxAttrs.root]:"",onclick:this.onclick,onkeydown:this.onkeydown,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class CheckboxInputState{static create(){return new CheckboxInputState(CheckboxRootContext.get())}root;#trueChecked=user_derived(()=>this.root.group?!!(this.root.opts.value.current!==void 0&&this.root.group.opts.value.current.includes(this.root.opts.value.current)):this.root.opts.checked.current);get trueChecked(){
-return get$4(this.#trueChecked)}set trueChecked(value){set$1(this.#trueChecked,value)}#shouldRender=user_derived(()=>!!this.root.trueName);get shouldRender(){return get$4(this.#shouldRender)}set shouldRender(value){set$1(this.#shouldRender,value)}constructor(root2){this.root=root2,this.onfocus=this.onfocus.bind(this)}onfocus(_){isHTMLElement$1(this.root.opts.ref.current)&&this.root.opts.ref.current.focus()}#props=user_derived(()=>({type:"checkbox",checked:this.root.opts.checked.current===!0,disabled:this.
-root.trueDisabled,required:this.root.trueRequired,name:this.root.trueName,value:this.root.opts.value.current,readonly:this.root.trueReadonly,onfocus:this.onfocus}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}function getCheckboxDataState(checked,indeterminate){return indeterminate?"indeterminate":checked?"checked":"unchecked"}enable_legacy_mode_flag();var root_1$16=from_html("<input/>"),root_2$1q=from_html("<input/>");function Hidden_input($$anchor,$$props){
-push$1($$props,!0);let value=prop($$props,"value",15),restProps=rest_props($$props,["$$slots","$$events","$$legacy","value"]);const mergedProps=user_derived(()=>mergeProps(restProps,{"aria-hidden":"true",tabindex:-1,style:srOnlyStylesString}));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var input=root_1$16();attribute_effect(input,()=>({...get$4(mergedProps),value:value()}),void 0,void 0,void 0,void 0,!0),append($$anchor2,input)},alternate=$$anchor2=>{var input_1=root_2$1q();
-attribute_effect(input_1,()=>({...get$4(mergedProps)}),void 0,void 0,void 0,void 0,!0),bind_value(input_1,value),append($$anchor2,input_1)};if_block(node2,$$render=>{get$4(mergedProps).type==="checkbox"?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}function Checkbox_input($$anchor,$$props){push$1($$props,!1);const inputState=CheckboxInputState.create();init();var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{Hidden_input($$anchor2,spread_props(
-()=>inputState.props))};if_block(node2,$$render=>{inputState.shouldRender&&$$render(consequent)})}append($$anchor,fragment),pop()}var root_2$1p=from_html("<button><!></button>"),root$1L=from_html("<!> <!>",1);function Checkbox$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let checked=prop($$props,"checked",15,!1),ref2=prop($$props,"ref",15,null),disabled=prop($$props,"disabled",3,!1),required2=prop($$props,"required",3,!1),name=prop($$props,"name",3,void 0),value=prop($$props,"valu\
-e",3,"on"),id2=prop($$props,"id",19,()=>createId(uid)),indeterminate=prop($$props,"indeterminate",15,!1),type2=prop($$props,"type",3,"button"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","checked","ref","onCheckedChange","children","disabled","required","name","value","id","indeterminate","onIndeterminateChange","child","type","readonly"]);const group=CheckboxGroupContext.getOr(null);group&&value()&&(group.opts.value.current.includes(value())?checked(!0):checked(!1)),watch$1.pre(
-()=>value(),()=>{group&&value()&&(group.opts.value.current.includes(value())?checked(!0):checked(!1))});const rootState=CheckboxRootState.create({checked:boxWith$1(()=>checked(),v=>{checked(v),$$props.onCheckedChange?.(v)}),disabled:boxWith$1(()=>disabled()??!1),required:boxWith$1(()=>required2()),name:boxWith$1(()=>name()),value:boxWith$1(()=>value()),id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),indeterminate:boxWith$1(()=>indeterminate(),v=>{indeterminate(v),$$props.onIndeterminateChange?.(
-v)}),type:boxWith$1(()=>type2()),readonly:boxWith$1(()=>!!$$props.readonly)},group),mergedProps=user_derived(()=>mergeProps({...restProps},rootState.props));var fragment=root$1L(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{let $0=user_derived(()=>({props:get$4(mergedProps),...rootState.snippetProps}));snippet(node_1,()=>$$props.child,()=>get$4($0))}append($$anchor2,fragment_1)},alternate=$$anchor2=>{var button=root_2$1p();attribute_effect(
-button,()=>({...get$4(mergedProps)}));var node_2=child(button);snippet(node_2,()=>$$props.children??noop$3,()=>rootState.snippetProps),reset(button),append($$anchor2,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}var node_3=sibling(node2,2);Checkbox_input(node_3,{}),append($$anchor,fragment),pop()}const collapsibleAttrs=createBitsAttrs({component:"collapsible",parts:["root","content","trigger"]}),CollapsibleRootContext=new Context$1("Collapsible.Ro\
-ot");class CollapsibleRootState{static create(opts){return CollapsibleRootContext.set(new CollapsibleRootState(opts))}opts;attachment;#contentNode=state$1(null);get contentNode(){return get$4(this.#contentNode)}set contentNode(value){set$1(this.#contentNode,value,!0)}contentPresence;#contentId=state$1(void 0);get contentId(){return get$4(this.#contentId)}set contentId(value){set$1(this.#contentId,value,!0)}constructor(opts){this.opts=opts,this.toggleOpen=this.toggleOpen.bind(this),this.attachment=
-attachRef(this.opts.ref),this.contentPresence=new PresenceManager({ref:boxWith$1(()=>this.contentNode),open:this.opts.open,onComplete:()=>{this.opts.onOpenChangeComplete.current(this.opts.open.current)}})}toggleOpen(){this.opts.open.current=!this.opts.open.current}#props=user_derived(()=>({id:this.opts.id.current,"data-state":getDataOpenClosed(this.opts.open.current),"data-disabled":boolToEmptyStrOrUndef(this.opts.disabled.current),[collapsibleAttrs.root]:"",...this.attachment}));get props(){return get$4(
-this.#props)}set props(value){set$1(this.#props,value)}}class CollapsibleContentState{static create(opts){return new CollapsibleContentState(opts,CollapsibleRootContext.get())}opts;root;attachment;#present=user_derived(()=>this.opts.hiddenUntilFound.current?this.root.opts.open.current:this.opts.forceMount.current||this.root.opts.open.current);get present(){return get$4(this.#present)}set present(value){set$1(this.#present,value)}#originalStyles;#isMountAnimationPrevented=state$1(!1);#width=state$1(
-0);#height=state$1(0);constructor(opts,root2){this.opts=opts,this.root=root2,set$1(this.#isMountAnimationPrevented,root2.opts.open.current,!0),this.root.contentId=this.opts.id.current,this.attachment=attachRef(this.opts.ref,v=>this.root.contentNode=v),watch$1.pre(()=>this.opts.id.current,id2=>{this.root.contentId=id2}),user_pre_effect(()=>{const rAF=requestAnimationFrame(()=>{set$1(this.#isMountAnimationPrevented,!1)});return()=>{cancelAnimationFrame(rAF)}}),watch$1.pre([()=>this.opts.ref.current,
-()=>this.opts.hiddenUntilFound.current],([node2,hiddenUntilFound])=>!node2||!hiddenUntilFound?void 0:on(node2,"beforematch",()=>{this.root.opts.open.current||requestAnimationFrame(()=>{this.root.opts.open.current=!0})})),watch$1([()=>this.opts.ref.current,()=>this.present],([node2])=>{node2&&afterTick(()=>{if(!this.opts.ref.current)return;this.#originalStyles=this.#originalStyles||{transitionDuration:node2.style.transitionDuration,animationName:node2.style.animationName},node2.style.transitionDuration=
-"0s",node2.style.animationName="none";const rect=node2.getBoundingClientRect();if(set$1(this.#height,rect.height,!0),set$1(this.#width,rect.width,!0),!get$4(this.#isMountAnimationPrevented)){const{animationName,transitionDuration}=this.#originalStyles;node2.style.transitionDuration=transitionDuration,node2.style.animationName=animationName}})})}get shouldRender(){return this.root.contentPresence.shouldRender}#snippetProps=user_derived(()=>({open:this.root.opts.open.current}));get snippetProps(){
-return get$4(this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}#props=user_derived(()=>({id:this.opts.id.current,style:{"--bits-collapsible-content-height":get$4(this.#height)?`${get$4(this.#height)}px`:void 0,"--bits-collapsible-content-width":get$4(this.#width)?`${get$4(this.#width)}px`:void 0},hidden:this.opts.hiddenUntilFound.current&&!this.root.opts.open.current?"until-found":void 0,"data-state":getDataOpenClosed(this.root.opts.open.current),"data-disabled":boolToEmptyStrOrUndef(
-this.root.opts.disabled.current),[collapsibleAttrs.content]:"",...this.opts.hiddenUntilFound.current&&!this.shouldRender?{}:{hidden:this.opts.hiddenUntilFound.current?!this.shouldRender:this.opts.forceMount.current?void 0:!this.shouldRender},...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class CollapsibleTriggerState{static create(opts){return new CollapsibleTriggerState(opts,CollapsibleRootContext.get())}opts;root;attachment;#isDisabled=user_derived(
-()=>this.opts.disabled.current||this.root.opts.disabled.current);constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref),this.onclick=this.onclick.bind(this),this.onkeydown=this.onkeydown.bind(this)}onclick(e){if(!get$4(this.#isDisabled)){if(e.button!==0)return e.preventDefault();this.root.toggleOpen()}}onkeydown(e){get$4(this.#isDisabled)||(e.key===SPACE||e.key===ENTER)&&(e.preventDefault(),this.root.toggleOpen())}#props=user_derived(()=>({id:this.opts.id.
-current,type:"button",disabled:get$4(this.#isDisabled),"aria-controls":this.root.contentId,"aria-expanded":boolToStr(this.root.opts.open.current),"data-state":getDataOpenClosed(this.root.opts.open.current),"data-disabled":boolToEmptyStrOrUndef(get$4(this.#isDisabled)),[collapsibleAttrs.trigger]:"",onclick:this.onclick,onkeydown:this.onkeydown,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}var root_2$1o=from_html("<div><!></div>");function Collapsible$1($$anchor,$$props){
-const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),open2=prop($$props,"open",15,!1),disabled=prop($$props,"disabled",3,!1),onOpenChange=prop($$props,"onOpenChange",3,noop$1),onOpenChangeComplete=prop($$props,"onOpenChangeComplete",3,noop$1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","children","child","id","ref","open","disabled","onOpenChange","onOpenChangeComplete"]);const rootState=CollapsibleRootState.create(
-{open:boxWith$1(()=>open2(),v=>{open2(v),onOpenChange()(v)}),disabled:boxWith$1(()=>disabled()),id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),onOpenChangeComplete:boxWith$1(()=>onOpenChangeComplete())}),mergedProps=user_derived(()=>mergeProps(restProps,rootState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,
-fragment_1)},alternate=$$anchor2=>{var div=root_2$1o();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_2$1n=from_html("<div><!></div>");function Collapsible_content$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),forceMount=prop(
-$$props,"forceMount",3,!1),hiddenUntilFound=prop($$props,"hiddenUntilFound",3,!1),id2=prop($$props,"id",19,()=>createId(uid)),restProps=rest_props($$props,["$$slots","$$events","$$legacy","child","ref","forceMount","hiddenUntilFound","children","id"]);const contentState=CollapsibleContentState.create({id:boxWith$1(()=>id2()),forceMount:boxWith$1(()=>forceMount()),hiddenUntilFound:boxWith$1(()=>hiddenUntilFound()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,
-contentState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{let $0=user_derived(()=>({...contentState.snippetProps,props:get$4(mergedProps)}));snippet(node_1,()=>$$props.child,()=>get$4($0))}append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1n();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append(
-$$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_2$1m=from_html("<button><!></button>");function Collapsible_trigger$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),disabled=prop($$props,"disabled",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","children","child","ref","id","disabled"]);const triggerState=CollapsibleTriggerState.
-create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),disabled:boxWith$1(()=>disabled())}),mergedProps=user_derived(()=>mergeProps(restProps,triggerState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var button=root_2$1m();attribute_effect(button,()=>({...get$4(mergedProps)}));
-var node_2=child(button);snippet(node_2,()=>$$props.children??noop$3),reset(button),append($$anchor2,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}const sides=["top","right","bottom","left"],min=Math.min,max=Math.max,round=Math.round,floor=Math.floor,createCoords=v=>({x:v,y:v}),oppositeSideMap={left:"right",right:"left",bottom:"top",top:"bottom"},oppositeAlignmentMap={start:"end",end:"start"};function clamp$1(start2,value,end){
-return max(start2,min(value,end))}function evaluate(value,param){return typeof value=="function"?value(param):value}function getSide$1(placement){return placement.split("-")[0]}function getAlignment(placement){return placement.split("-")[1]}function getOppositeAxis(axis){return axis==="x"?"y":"x"}function getAxisLength(axis){return axis==="y"?"height":"width"}const yAxisSides=new Set(["top","bottom"]);function getSideAxis(placement){return yAxisSides.has(getSide$1(placement))?"y":"x"}function getAlignmentAxis(placement){
-return getOppositeAxis(getSideAxis(placement))}function getAlignmentSides(placement,rects,rtl){rtl===void 0&&(rtl=!1);const alignment=getAlignment(placement),alignmentAxis=getAlignmentAxis(placement),length=getAxisLength(alignmentAxis);let mainAlignmentSide=alignmentAxis==="x"?alignment===(rtl?"end":"start")?"right":"left":alignment==="start"?"bottom":"top";return rects.reference[length]>rects.floating[length]&&(mainAlignmentSide=getOppositePlacement(mainAlignmentSide)),[mainAlignmentSide,getOppositePlacement(
-mainAlignmentSide)]}function getExpandedPlacements(placement){const oppositePlacement=getOppositePlacement(placement);return[getOppositeAlignmentPlacement(placement),oppositePlacement,getOppositeAlignmentPlacement(oppositePlacement)]}function getOppositeAlignmentPlacement(placement){return placement.replace(/start|end/g,alignment=>oppositeAlignmentMap[alignment])}const lrPlacement=["left","right"],rlPlacement=["right","left"],tbPlacement=["top","bottom"],btPlacement=["bottom","top"];function getSideList(side,isStart,rtl){
-switch(side){case"top":case"bottom":return rtl?isStart?rlPlacement:lrPlacement:isStart?lrPlacement:rlPlacement;case"left":case"right":return isStart?tbPlacement:btPlacement;default:return[]}}function getOppositeAxisPlacements(placement,flipAlignment,direction,rtl){const alignment=getAlignment(placement);let list2=getSideList(getSide$1(placement),direction==="start",rtl);return alignment&&(list2=list2.map(side=>side+"-"+alignment),flipAlignment&&(list2=list2.concat(list2.map(getOppositeAlignmentPlacement)))),
-list2}function getOppositePlacement(placement){return placement.replace(/left|right|bottom|top/g,side=>oppositeSideMap[side])}function expandPaddingObject(padding){return{top:0,right:0,bottom:0,left:0,...padding}}function getPaddingObject(padding){return typeof padding!="number"?expandPaddingObject(padding):{top:padding,right:padding,bottom:padding,left:padding}}function rectToClientRect(rect){const{x,y,width,height}=rect;return{width,height,top:y,left:x,right:x+width,bottom:y+height,x,y}}function computeCoordsFromPlacement(_ref,placement,rtl){
-let{reference,floating}=_ref;const sideAxis=getSideAxis(placement),alignmentAxis=getAlignmentAxis(placement),alignLength=getAxisLength(alignmentAxis),side=getSide$1(placement),isVertical=sideAxis==="y",commonX=reference.x+reference.width/2-floating.width/2,commonY=reference.y+reference.height/2-floating.height/2,commonAlign=reference[alignLength]/2-floating[alignLength]/2;let coords;switch(side){case"top":coords={x:commonX,y:reference.y-floating.height};break;case"bottom":coords={x:commonX,y:reference.
-y+reference.height};break;case"right":coords={x:reference.x+reference.width,y:commonY};break;case"left":coords={x:reference.x-floating.width,y:commonY};break;default:coords={x:reference.x,y:reference.y}}switch(getAlignment(placement)){case"start":coords[alignmentAxis]-=commonAlign*(rtl&&isVertical?-1:1);break;case"end":coords[alignmentAxis]+=commonAlign*(rtl&&isVertical?-1:1);break}return coords}const computePosition$1=async(reference,floating,config2)=>{const{placement="bottom",strategy="absolu\
-te",middleware=[],platform:platform2}=config2,validMiddleware=middleware.filter(Boolean),rtl=await(platform2.isRTL==null?void 0:platform2.isRTL(floating));let rects=await platform2.getElementRects({reference,floating,strategy}),{x,y}=computeCoordsFromPlacement(rects,placement,rtl),statefulPlacement=placement,middlewareData={},resetCount=0;for(let i=0;i<validMiddleware.length;i++){const{name,fn}=validMiddleware[i],{x:nextX,y:nextY,data,reset:reset2}=await fn({x,y,initialPlacement:placement,placement:statefulPlacement,
-strategy,middlewareData,rects,platform:platform2,elements:{reference,floating}});x=nextX??x,y=nextY??y,middlewareData={...middlewareData,[name]:{...middlewareData[name],...data}},reset2&&resetCount<=50&&(resetCount++,typeof reset2=="object"&&(reset2.placement&&(statefulPlacement=reset2.placement),reset2.rects&&(rects=reset2.rects===!0?await platform2.getElementRects({reference,floating,strategy}):reset2.rects),{x,y}=computeCoordsFromPlacement(rects,statefulPlacement,rtl)),i=-1)}return{x,y,placement:statefulPlacement,
-strategy,middlewareData}};async function detectOverflow(state2,options){var _await$platform$isEle;options===void 0&&(options={});const{x,y,platform:platform2,rects,elements,strategy}=state2,{boundary:boundary2="clippingAncestors",rootBoundary="viewport",elementContext="floating",altBoundary=!1,padding=0}=evaluate(options,state2),paddingObject=getPaddingObject(padding),element2=elements[altBoundary?elementContext==="floating"?"reference":"floating":elementContext],clippingClientRect=rectToClientRect(
-await platform2.getClippingRect({element:(_await$platform$isEle=await(platform2.isElement==null?void 0:platform2.isElement(element2)))==null||_await$platform$isEle?element2:element2.contextElement||await(platform2.getDocumentElement==null?void 0:platform2.getDocumentElement(elements.floating)),boundary:boundary2,rootBoundary,strategy})),rect=elementContext==="floating"?{x,y,width:rects.floating.width,height:rects.floating.height}:rects.reference,offsetParent=await(platform2.getOffsetParent==null?
-void 0:platform2.getOffsetParent(elements.floating)),offsetScale=await(platform2.isElement==null?void 0:platform2.isElement(offsetParent))?await(platform2.getScale==null?void 0:platform2.getScale(offsetParent))||{x:1,y:1}:{x:1,y:1},elementClientRect=rectToClientRect(platform2.convertOffsetParentRelativeRectToViewportRelativeRect?await platform2.convertOffsetParentRelativeRectToViewportRelativeRect({elements,rect,offsetParent,strategy}):rect);return{top:(clippingClientRect.top-elementClientRect.top+
-paddingObject.top)/offsetScale.y,bottom:(elementClientRect.bottom-clippingClientRect.bottom+paddingObject.bottom)/offsetScale.y,left:(clippingClientRect.left-elementClientRect.left+paddingObject.left)/offsetScale.x,right:(elementClientRect.right-clippingClientRect.right+paddingObject.right)/offsetScale.x}}const arrow$1=options=>({name:"arrow",options,async fn(state2){const{x,y,placement,rects,platform:platform2,elements,middlewareData}=state2,{element:element2,padding=0}=evaluate(options,state2)||
-{};if(element2==null)return{};const paddingObject=getPaddingObject(padding),coords={x,y},axis=getAlignmentAxis(placement),length=getAxisLength(axis),arrowDimensions=await platform2.getDimensions(element2),isYAxis=axis==="y",minProp=isYAxis?"top":"left",maxProp=isYAxis?"bottom":"right",clientProp=isYAxis?"clientHeight":"clientWidth",endDiff=rects.reference[length]+rects.reference[axis]-coords[axis]-rects.floating[length],startDiff=coords[axis]-rects.reference[axis],arrowOffsetParent=await(platform2.
-getOffsetParent==null?void 0:platform2.getOffsetParent(element2));let clientSize=arrowOffsetParent?arrowOffsetParent[clientProp]:0;(!clientSize||!await(platform2.isElement==null?void 0:platform2.isElement(arrowOffsetParent)))&&(clientSize=elements.floating[clientProp]||rects.floating[length]);const centerToReference=endDiff/2-startDiff/2,largestPossiblePadding=clientSize/2-arrowDimensions[length]/2-1,minPadding=min(paddingObject[minProp],largestPossiblePadding),maxPadding=min(paddingObject[maxProp],
-largestPossiblePadding),min$1=minPadding,max2=clientSize-arrowDimensions[length]-maxPadding,center=clientSize/2-arrowDimensions[length]/2+centerToReference,offset2=clamp$1(min$1,center,max2),shouldAddOffset=!middlewareData.arrow&&getAlignment(placement)!=null&&center!==offset2&&rects.reference[length]/2-(center<min$1?minPadding:maxPadding)-arrowDimensions[length]/2<0,alignmentOffset=shouldAddOffset?center<min$1?center-min$1:center-max2:0;return{[axis]:coords[axis]+alignmentOffset,data:{[axis]:offset2,
-centerOffset:center-offset2-alignmentOffset,...shouldAddOffset&&{alignmentOffset}},reset:shouldAddOffset}}}),flip$1=function(options){return options===void 0&&(options={}),{name:"flip",options,async fn(state2){var _middlewareData$arrow,_middlewareData$flip;const{placement,middlewareData,rects,initialPlacement,platform:platform2,elements}=state2,{mainAxis:checkMainAxis=!0,crossAxis:checkCrossAxis=!0,fallbackPlacements:specifiedFallbackPlacements,fallbackStrategy="bestFit",fallbackAxisSideDirection="\
-none",flipAlignment=!0,...detectOverflowOptions}=evaluate(options,state2);if((_middlewareData$arrow=middlewareData.arrow)!=null&&_middlewareData$arrow.alignmentOffset)return{};const side=getSide$1(placement),initialSideAxis=getSideAxis(initialPlacement),isBasePlacement=getSide$1(initialPlacement)===initialPlacement,rtl=await(platform2.isRTL==null?void 0:platform2.isRTL(elements.floating)),fallbackPlacements=specifiedFallbackPlacements||(isBasePlacement||!flipAlignment?[getOppositePlacement(initialPlacement)]:
-getExpandedPlacements(initialPlacement)),hasFallbackAxisSideDirection=fallbackAxisSideDirection!=="none";!specifiedFallbackPlacements&&hasFallbackAxisSideDirection&&fallbackPlacements.push(...getOppositeAxisPlacements(initialPlacement,flipAlignment,fallbackAxisSideDirection,rtl));const placements=[initialPlacement,...fallbackPlacements],overflow=await detectOverflow(state2,detectOverflowOptions),overflows=[];let overflowsData=((_middlewareData$flip=middlewareData.flip)==null?void 0:_middlewareData$flip.
-overflows)||[];if(checkMainAxis&&overflows.push(overflow[side]),checkCrossAxis){const sides2=getAlignmentSides(placement,rects,rtl);overflows.push(overflow[sides2[0]],overflow[sides2[1]])}if(overflowsData=[...overflowsData,{placement,overflows}],!overflows.every(side2=>side2<=0)){var _middlewareData$flip2,_overflowsData$filter;const nextIndex=(((_middlewareData$flip2=middlewareData.flip)==null?void 0:_middlewareData$flip2.index)||0)+1,nextPlacement=placements[nextIndex];if(nextPlacement&&(!(checkCrossAxis===
-"alignment"?initialSideAxis!==getSideAxis(nextPlacement):!1)||overflowsData.every(d2=>d2.overflows[0]>0&&getSideAxis(d2.placement)===initialSideAxis)))return{data:{index:nextIndex,overflows:overflowsData},reset:{placement:nextPlacement}};let resetPlacement=(_overflowsData$filter=overflowsData.filter(d2=>d2.overflows[0]<=0).sort((a,b)=>a.overflows[1]-b.overflows[1])[0])==null?void 0:_overflowsData$filter.placement;if(!resetPlacement)switch(fallbackStrategy){case"bestFit":{var _overflowsData$filter2;
-const placement2=(_overflowsData$filter2=overflowsData.filter(d2=>{if(hasFallbackAxisSideDirection){const currentSideAxis=getSideAxis(d2.placement);return currentSideAxis===initialSideAxis||currentSideAxis==="y"}return!0}).map(d2=>[d2.placement,d2.overflows.filter(overflow2=>overflow2>0).reduce((acc,overflow2)=>acc+overflow2,0)]).sort((a,b)=>a[1]-b[1])[0])==null?void 0:_overflowsData$filter2[0];placement2&&(resetPlacement=placement2);break}case"initialPlacement":resetPlacement=initialPlacement;break}
-if(placement!==resetPlacement)return{reset:{placement:resetPlacement}}}return{}}}};function getSideOffsets(overflow,rect){return{top:overflow.top-rect.height,right:overflow.right-rect.width,bottom:overflow.bottom-rect.height,left:overflow.left-rect.width}}function isAnySideFullyClipped(overflow){return sides.some(side=>overflow[side]>=0)}const hide$1=function(options){return options===void 0&&(options={}),{name:"hide",options,async fn(state2){const{rects}=state2,{strategy="referenceHidden",...detectOverflowOptions}=evaluate(
-options,state2);switch(strategy){case"referenceHidden":{const overflow=await detectOverflow(state2,{...detectOverflowOptions,elementContext:"reference"}),offsets=getSideOffsets(overflow,rects.reference);return{data:{referenceHiddenOffsets:offsets,referenceHidden:isAnySideFullyClipped(offsets)}}}case"escaped":{const overflow=await detectOverflow(state2,{...detectOverflowOptions,altBoundary:!0}),offsets=getSideOffsets(overflow,rects.floating);return{data:{escapedOffsets:offsets,escaped:isAnySideFullyClipped(
-offsets)}}}default:return{}}}}},originSides=new Set(["left","top"]);async function convertValueToCoords(state2,options){const{placement,platform:platform2,elements}=state2,rtl=await(platform2.isRTL==null?void 0:platform2.isRTL(elements.floating)),side=getSide$1(placement),alignment=getAlignment(placement),isVertical=getSideAxis(placement)==="y",mainAxisMulti=originSides.has(side)?-1:1,crossAxisMulti=rtl&&isVertical?-1:1,rawValue=evaluate(options,state2);let{mainAxis,crossAxis,alignmentAxis}=typeof rawValue==
-"number"?{mainAxis:rawValue,crossAxis:0,alignmentAxis:null}:{mainAxis:rawValue.mainAxis||0,crossAxis:rawValue.crossAxis||0,alignmentAxis:rawValue.alignmentAxis};return alignment&&typeof alignmentAxis=="number"&&(crossAxis=alignment==="end"?alignmentAxis*-1:alignmentAxis),isVertical?{x:crossAxis*crossAxisMulti,y:mainAxis*mainAxisMulti}:{x:mainAxis*mainAxisMulti,y:crossAxis*crossAxisMulti}}const offset$1=function(options){return options===void 0&&(options=0),{name:"offset",options,async fn(state2){
-var _middlewareData$offse,_middlewareData$arrow;const{x,y,placement,middlewareData}=state2,diffCoords=await convertValueToCoords(state2,options);return placement===((_middlewareData$offse=middlewareData.offset)==null?void 0:_middlewareData$offse.placement)&&(_middlewareData$arrow=middlewareData.arrow)!=null&&_middlewareData$arrow.alignmentOffset?{}:{x:x+diffCoords.x,y:y+diffCoords.y,data:{...diffCoords,placement}}}}},shift$1=function(options){return options===void 0&&(options={}),{name:"shift",options,
-async fn(state2){const{x,y,placement}=state2,{mainAxis:checkMainAxis=!0,crossAxis:checkCrossAxis=!1,limiter={fn:_ref=>{let{x:x2,y:y2}=_ref;return{x:x2,y:y2}}},...detectOverflowOptions}=evaluate(options,state2),coords={x,y},overflow=await detectOverflow(state2,detectOverflowOptions),crossAxis=getSideAxis(getSide$1(placement)),mainAxis=getOppositeAxis(crossAxis);let mainAxisCoord=coords[mainAxis],crossAxisCoord=coords[crossAxis];if(checkMainAxis){const minSide=mainAxis==="y"?"top":"left",maxSide=mainAxis===
-"y"?"bottom":"right",min2=mainAxisCoord+overflow[minSide],max2=mainAxisCoord-overflow[maxSide];mainAxisCoord=clamp$1(min2,mainAxisCoord,max2)}if(checkCrossAxis){const minSide=crossAxis==="y"?"top":"left",maxSide=crossAxis==="y"?"bottom":"right",min2=crossAxisCoord+overflow[minSide],max2=crossAxisCoord-overflow[maxSide];crossAxisCoord=clamp$1(min2,crossAxisCoord,max2)}const limitedCoords=limiter.fn({...state2,[mainAxis]:mainAxisCoord,[crossAxis]:crossAxisCoord});return{...limitedCoords,data:{x:limitedCoords.
-x-x,y:limitedCoords.y-y,enabled:{[mainAxis]:checkMainAxis,[crossAxis]:checkCrossAxis}}}}}},limitShift$1=function(options){return options===void 0&&(options={}),{options,fn(state2){const{x,y,placement,rects,middlewareData}=state2,{offset:offset2=0,mainAxis:checkMainAxis=!0,crossAxis:checkCrossAxis=!0}=evaluate(options,state2),coords={x,y},crossAxis=getSideAxis(placement),mainAxis=getOppositeAxis(crossAxis);let mainAxisCoord=coords[mainAxis],crossAxisCoord=coords[crossAxis];const rawOffset=evaluate(
-offset2,state2),computedOffset=typeof rawOffset=="number"?{mainAxis:rawOffset,crossAxis:0}:{mainAxis:0,crossAxis:0,...rawOffset};if(checkMainAxis){const len=mainAxis==="y"?"height":"width",limitMin=rects.reference[mainAxis]-rects.floating[len]+computedOffset.mainAxis,limitMax=rects.reference[mainAxis]+rects.reference[len]-computedOffset.mainAxis;mainAxisCoord<limitMin?mainAxisCoord=limitMin:mainAxisCoord>limitMax&&(mainAxisCoord=limitMax)}if(checkCrossAxis){var _middlewareData$offse,_middlewareData$offse2;
-const len=mainAxis==="y"?"width":"height",isOriginSide=originSides.has(getSide$1(placement)),limitMin=rects.reference[crossAxis]-rects.floating[len]+(isOriginSide&&((_middlewareData$offse=middlewareData.offset)==null?void 0:_middlewareData$offse[crossAxis])||0)+(isOriginSide?0:computedOffset.crossAxis),limitMax=rects.reference[crossAxis]+rects.reference[len]+(isOriginSide?0:((_middlewareData$offse2=middlewareData.offset)==null?void 0:_middlewareData$offse2[crossAxis])||0)-(isOriginSide?computedOffset.
-crossAxis:0);crossAxisCoord<limitMin?crossAxisCoord=limitMin:crossAxisCoord>limitMax&&(crossAxisCoord=limitMax)}return{[mainAxis]:mainAxisCoord,[crossAxis]:crossAxisCoord}}}},size$1=function(options){return options===void 0&&(options={}),{name:"size",options,async fn(state2){var _state$middlewareData,_state$middlewareData2;const{placement,rects,platform:platform2,elements}=state2,{apply=()=>{},...detectOverflowOptions}=evaluate(options,state2),overflow=await detectOverflow(state2,detectOverflowOptions),
-side=getSide$1(placement),alignment=getAlignment(placement),isYAxis=getSideAxis(placement)==="y",{width,height}=rects.floating;let heightSide,widthSide;side==="top"||side==="bottom"?(heightSide=side,widthSide=alignment===(await(platform2.isRTL==null?void 0:platform2.isRTL(elements.floating))?"start":"end")?"left":"right"):(widthSide=side,heightSide=alignment==="end"?"top":"bottom");const maximumClippingHeight=height-overflow.top-overflow.bottom,maximumClippingWidth=width-overflow.left-overflow.right,
-overflowAvailableHeight=min(height-overflow[heightSide],maximumClippingHeight),overflowAvailableWidth=min(width-overflow[widthSide],maximumClippingWidth),noShift=!state2.middlewareData.shift;let availableHeight=overflowAvailableHeight,availableWidth=overflowAvailableWidth;if((_state$middlewareData=state2.middlewareData.shift)!=null&&_state$middlewareData.enabled.x&&(availableWidth=maximumClippingWidth),(_state$middlewareData2=state2.middlewareData.shift)!=null&&_state$middlewareData2.enabled.y&&
-(availableHeight=maximumClippingHeight),noShift&&!alignment){const xMin=max(overflow.left,0),xMax=max(overflow.right,0),yMin=max(overflow.top,0),yMax=max(overflow.bottom,0);isYAxis?availableWidth=width-2*(xMin!==0||xMax!==0?xMin+xMax:max(overflow.left,overflow.right)):availableHeight=height-2*(yMin!==0||yMax!==0?yMin+yMax:max(overflow.top,overflow.bottom))}await apply({...state2,availableWidth,availableHeight});const nextDimensions=await platform2.getDimensions(elements.floating);return width!==
-nextDimensions.width||height!==nextDimensions.height?{reset:{rects:!0}}:{}}}};function hasWindow(){return typeof window<"u"}function getNodeName(node2){return isNode(node2)?(node2.nodeName||"").toLowerCase():"#document"}function getWindow(node2){var _node$ownerDocument;return(node2==null||(_node$ownerDocument=node2.ownerDocument)==null?void 0:_node$ownerDocument.defaultView)||window}function getDocumentElement(node2){var _ref;return(_ref=(isNode(node2)?node2.ownerDocument:node2.document)||window.
-document)==null?void 0:_ref.documentElement}function isNode(value){return hasWindow()?value instanceof Node||value instanceof getWindow(value).Node:!1}function isElement(value){return hasWindow()?value instanceof Element||value instanceof getWindow(value).Element:!1}function isHTMLElement(value){return hasWindow()?value instanceof HTMLElement||value instanceof getWindow(value).HTMLElement:!1}function isShadowRoot(value){return!hasWindow()||typeof ShadowRoot>"u"?!1:value instanceof ShadowRoot||value instanceof
-getWindow(value).ShadowRoot}const invalidOverflowDisplayValues=new Set(["inline","contents"]);function isOverflowElement(element2){const{overflow,overflowX,overflowY,display}=getComputedStyle$1(element2);return/auto|scroll|overlay|hidden|clip/.test(overflow+overflowY+overflowX)&&!invalidOverflowDisplayValues.has(display)}const tableElements=new Set(["table","td","th"]);function isTableElement(element2){return tableElements.has(getNodeName(element2))}const topLayerSelectors=[":popover-open",":mod\
-al"];function isTopLayer(element2){return topLayerSelectors.some(selector=>{try{return element2.matches(selector)}catch{return!1}})}const transformProperties=["transform","translate","scale","rotate","perspective"],willChangeValues=["transform","translate","scale","rotate","perspective","filter"],containValues=["paint","layout","strict","content"];function isContainingBlock(elementOrCss){const webkit=isWebKit(),css2=isElement(elementOrCss)?getComputedStyle$1(elementOrCss):elementOrCss;return transformProperties.
-some(value=>css2[value]?css2[value]!=="none":!1)||(css2.containerType?css2.containerType!=="normal":!1)||!webkit&&(css2.backdropFilter?css2.backdropFilter!=="none":!1)||!webkit&&(css2.filter?css2.filter!=="none":!1)||willChangeValues.some(value=>(css2.willChange||"").includes(value))||containValues.some(value=>(css2.contain||"").includes(value))}function getContainingBlock(element2){let currentNode=getParentNode(element2);for(;isHTMLElement(currentNode)&&!isLastTraversableNode(currentNode);){if(isContainingBlock(
-currentNode))return currentNode;if(isTopLayer(currentNode))return null;currentNode=getParentNode(currentNode)}return null}function isWebKit(){return typeof CSS>"u"||!CSS.supports?!1:CSS.supports("-webkit-backdrop-filter","none")}const lastTraversableNodeNames=new Set(["html","body","#document"]);function isLastTraversableNode(node2){return lastTraversableNodeNames.has(getNodeName(node2))}function getComputedStyle$1(element2){return getWindow(element2).getComputedStyle(element2)}function getNodeScroll(element2){
-return isElement(element2)?{scrollLeft:element2.scrollLeft,scrollTop:element2.scrollTop}:{scrollLeft:element2.scrollX,scrollTop:element2.scrollY}}function getParentNode(node2){if(getNodeName(node2)==="html")return node2;const result=node2.assignedSlot||node2.parentNode||isShadowRoot(node2)&&node2.host||getDocumentElement(node2);return isShadowRoot(result)?result.host:result}function getNearestOverflowAncestor(node2){const parentNode=getParentNode(node2);return isLastTraversableNode(parentNode)?node2.
-ownerDocument?node2.ownerDocument.body:node2.body:isHTMLElement(parentNode)&&isOverflowElement(parentNode)?parentNode:getNearestOverflowAncestor(parentNode)}function getOverflowAncestors(node2,list2,traverseIframes){var _node$ownerDocument2;list2===void 0&&(list2=[]),traverseIframes===void 0&&(traverseIframes=!0);const scrollableAncestor=getNearestOverflowAncestor(node2),isBody=scrollableAncestor===((_node$ownerDocument2=node2.ownerDocument)==null?void 0:_node$ownerDocument2.body),win=getWindow(
-scrollableAncestor);if(isBody){const frameElement=getFrameElement(win);return list2.concat(win,win.visualViewport||[],isOverflowElement(scrollableAncestor)?scrollableAncestor:[],frameElement&&traverseIframes?getOverflowAncestors(frameElement):[])}return list2.concat(scrollableAncestor,getOverflowAncestors(scrollableAncestor,[],traverseIframes))}function getFrameElement(win){return win.parent&&Object.getPrototypeOf(win.parent)?win.frameElement:null}function getCssDimensions(element2){const css2=getComputedStyle$1(
-element2);let width=parseFloat(css2.width)||0,height=parseFloat(css2.height)||0;const hasOffset=isHTMLElement(element2),offsetWidth=hasOffset?element2.offsetWidth:width,offsetHeight=hasOffset?element2.offsetHeight:height,shouldFallback=round(width)!==offsetWidth||round(height)!==offsetHeight;return shouldFallback&&(width=offsetWidth,height=offsetHeight),{width,height,$:shouldFallback}}function unwrapElement(element2){return isElement(element2)?element2:element2.contextElement}function getScale(element2){
-const domElement=unwrapElement(element2);if(!isHTMLElement(domElement))return createCoords(1);const rect=domElement.getBoundingClientRect(),{width,height,$}=getCssDimensions(domElement);let x=($?round(rect.width):rect.width)/width,y=($?round(rect.height):rect.height)/height;return(!x||!Number.isFinite(x))&&(x=1),(!y||!Number.isFinite(y))&&(y=1),{x,y}}const noOffsets=createCoords(0);function getVisualOffsets(element2){const win=getWindow(element2);return!isWebKit()||!win.visualViewport?noOffsets:
-{x:win.visualViewport.offsetLeft,y:win.visualViewport.offsetTop}}function shouldAddVisualOffsets(element2,isFixed,floatingOffsetParent){return isFixed===void 0&&(isFixed=!1),!floatingOffsetParent||isFixed&&floatingOffsetParent!==getWindow(element2)?!1:isFixed}function getBoundingClientRect(element2,includeScale,isFixedStrategy,offsetParent){includeScale===void 0&&(includeScale=!1),isFixedStrategy===void 0&&(isFixedStrategy=!1);const clientRect=element2.getBoundingClientRect(),domElement=unwrapElement(
-element2);let scale2=createCoords(1);includeScale&&(offsetParent?isElement(offsetParent)&&(scale2=getScale(offsetParent)):scale2=getScale(element2));const visualOffsets=shouldAddVisualOffsets(domElement,isFixedStrategy,offsetParent)?getVisualOffsets(domElement):createCoords(0);let x=(clientRect.left+visualOffsets.x)/scale2.x,y=(clientRect.top+visualOffsets.y)/scale2.y,width=clientRect.width/scale2.x,height=clientRect.height/scale2.y;if(domElement){const win=getWindow(domElement),offsetWin=offsetParent&&
-isElement(offsetParent)?getWindow(offsetParent):offsetParent;let currentWin=win,currentIFrame=getFrameElement(currentWin);for(;currentIFrame&&offsetParent&&offsetWin!==currentWin;){const iframeScale=getScale(currentIFrame),iframeRect=currentIFrame.getBoundingClientRect(),css2=getComputedStyle$1(currentIFrame),left=iframeRect.left+(currentIFrame.clientLeft+parseFloat(css2.paddingLeft))*iframeScale.x,top=iframeRect.top+(currentIFrame.clientTop+parseFloat(css2.paddingTop))*iframeScale.y;x*=iframeScale.
-x,y*=iframeScale.y,width*=iframeScale.x,height*=iframeScale.y,x+=left,y+=top,currentWin=getWindow(currentIFrame),currentIFrame=getFrameElement(currentWin)}}return rectToClientRect({width,height,x,y})}function getWindowScrollBarX(element2,rect){const leftScroll=getNodeScroll(element2).scrollLeft;return rect?rect.left+leftScroll:getBoundingClientRect(getDocumentElement(element2)).left+leftScroll}function getHTMLOffset(documentElement,scroll,ignoreScrollbarX){ignoreScrollbarX===void 0&&(ignoreScrollbarX=
-!1);const htmlRect=documentElement.getBoundingClientRect(),x=htmlRect.left+scroll.scrollLeft-(ignoreScrollbarX?0:getWindowScrollBarX(documentElement,htmlRect)),y=htmlRect.top+scroll.scrollTop;return{x,y}}function convertOffsetParentRelativeRectToViewportRelativeRect(_ref){let{elements,rect,offsetParent,strategy}=_ref;const isFixed=strategy==="fixed",documentElement=getDocumentElement(offsetParent),topLayer=elements?isTopLayer(elements.floating):!1;if(offsetParent===documentElement||topLayer&&isFixed)
-return rect;let scroll={scrollLeft:0,scrollTop:0},scale2=createCoords(1);const offsets=createCoords(0),isOffsetParentAnElement=isHTMLElement(offsetParent);if((isOffsetParentAnElement||!isOffsetParentAnElement&&!isFixed)&&((getNodeName(offsetParent)!=="body"||isOverflowElement(documentElement))&&(scroll=getNodeScroll(offsetParent)),isHTMLElement(offsetParent))){const offsetRect=getBoundingClientRect(offsetParent);scale2=getScale(offsetParent),offsets.x=offsetRect.x+offsetParent.clientLeft,offsets.
-y=offsetRect.y+offsetParent.clientTop}const htmlOffset=documentElement&&!isOffsetParentAnElement&&!isFixed?getHTMLOffset(documentElement,scroll,!0):createCoords(0);return{width:rect.width*scale2.x,height:rect.height*scale2.y,x:rect.x*scale2.x-scroll.scrollLeft*scale2.x+offsets.x+htmlOffset.x,y:rect.y*scale2.y-scroll.scrollTop*scale2.y+offsets.y+htmlOffset.y}}function getClientRects(element2){return Array.from(element2.getClientRects())}function getDocumentRect(element2){const html2=getDocumentElement(
-element2),scroll=getNodeScroll(element2),body2=element2.ownerDocument.body,width=max(html2.scrollWidth,html2.clientWidth,body2.scrollWidth,body2.clientWidth),height=max(html2.scrollHeight,html2.clientHeight,body2.scrollHeight,body2.clientHeight);let x=-scroll.scrollLeft+getWindowScrollBarX(element2);const y=-scroll.scrollTop;return getComputedStyle$1(body2).direction==="rtl"&&(x+=max(html2.clientWidth,body2.clientWidth)-width),{width,height,x,y}}function getViewportRect(element2,strategy){const win=getWindow(
-element2),html2=getDocumentElement(element2),visualViewport=win.visualViewport;let width=html2.clientWidth,height=html2.clientHeight,x=0,y=0;if(visualViewport){width=visualViewport.width,height=visualViewport.height;const visualViewportBased=isWebKit();(!visualViewportBased||visualViewportBased&&strategy==="fixed")&&(x=visualViewport.offsetLeft,y=visualViewport.offsetTop)}return{width,height,x,y}}const absoluteOrFixed=new Set(["absolute","fixed"]);function getInnerBoundingClientRect(element2,strategy){
-const clientRect=getBoundingClientRect(element2,!0,strategy==="fixed"),top=clientRect.top+element2.clientTop,left=clientRect.left+element2.clientLeft,scale2=isHTMLElement(element2)?getScale(element2):createCoords(1),width=element2.clientWidth*scale2.x,height=element2.clientHeight*scale2.y,x=left*scale2.x,y=top*scale2.y;return{width,height,x,y}}function getClientRectFromClippingAncestor(element2,clippingAncestor,strategy){let rect;if(clippingAncestor==="viewport")rect=getViewportRect(element2,strategy);else if(clippingAncestor===
-"document")rect=getDocumentRect(getDocumentElement(element2));else if(isElement(clippingAncestor))rect=getInnerBoundingClientRect(clippingAncestor,strategy);else{const visualOffsets=getVisualOffsets(element2);rect={x:clippingAncestor.x-visualOffsets.x,y:clippingAncestor.y-visualOffsets.y,width:clippingAncestor.width,height:clippingAncestor.height}}return rectToClientRect(rect)}function hasFixedPositionAncestor(element2,stopNode){const parentNode=getParentNode(element2);return parentNode===stopNode||
-!isElement(parentNode)||isLastTraversableNode(parentNode)?!1:getComputedStyle$1(parentNode).position==="fixed"||hasFixedPositionAncestor(parentNode,stopNode)}function getClippingElementAncestors(element2,cache2){const cachedResult=cache2.get(element2);if(cachedResult)return cachedResult;let result=getOverflowAncestors(element2,[],!1).filter(el=>isElement(el)&&getNodeName(el)!=="body"),currentContainingBlockComputedStyle=null;const elementIsFixed=getComputedStyle$1(element2).position==="fixed";let currentNode=elementIsFixed?
-getParentNode(element2):element2;for(;isElement(currentNode)&&!isLastTraversableNode(currentNode);){const computedStyle=getComputedStyle$1(currentNode),currentNodeIsContaining=isContainingBlock(currentNode);!currentNodeIsContaining&&computedStyle.position==="fixed"&&(currentContainingBlockComputedStyle=null),(elementIsFixed?!currentNodeIsContaining&&!currentContainingBlockComputedStyle:!currentNodeIsContaining&&computedStyle.position==="static"&&!!currentContainingBlockComputedStyle&&absoluteOrFixed.
-has(currentContainingBlockComputedStyle.position)||isOverflowElement(currentNode)&&!currentNodeIsContaining&&hasFixedPositionAncestor(element2,currentNode))?result=result.filter(ancestor=>ancestor!==currentNode):currentContainingBlockComputedStyle=computedStyle,currentNode=getParentNode(currentNode)}return cache2.set(element2,result),result}function getClippingRect(_ref){let{element:element2,boundary:boundary2,rootBoundary,strategy}=_ref;const clippingAncestors=[...boundary2==="clippingAncestors"?
-isTopLayer(element2)?[]:getClippingElementAncestors(element2,this._c):[].concat(boundary2),rootBoundary],firstClippingAncestor=clippingAncestors[0],clippingRect=clippingAncestors.reduce((accRect,clippingAncestor)=>{const rect=getClientRectFromClippingAncestor(element2,clippingAncestor,strategy);return accRect.top=max(rect.top,accRect.top),accRect.right=min(rect.right,accRect.right),accRect.bottom=min(rect.bottom,accRect.bottom),accRect.left=max(rect.left,accRect.left),accRect},getClientRectFromClippingAncestor(
-element2,firstClippingAncestor,strategy));return{width:clippingRect.right-clippingRect.left,height:clippingRect.bottom-clippingRect.top,x:clippingRect.left,y:clippingRect.top}}function getDimensions(element2){const{width,height}=getCssDimensions(element2);return{width,height}}function getRectRelativeToOffsetParent(element2,offsetParent,strategy){const isOffsetParentAnElement=isHTMLElement(offsetParent),documentElement=getDocumentElement(offsetParent),isFixed=strategy==="fixed",rect=getBoundingClientRect(
-element2,!0,isFixed,offsetParent);let scroll={scrollLeft:0,scrollTop:0};const offsets=createCoords(0);function setLeftRTLScrollbarOffset(){offsets.x=getWindowScrollBarX(documentElement)}if(isOffsetParentAnElement||!isOffsetParentAnElement&&!isFixed)if((getNodeName(offsetParent)!=="body"||isOverflowElement(documentElement))&&(scroll=getNodeScroll(offsetParent)),isOffsetParentAnElement){const offsetRect=getBoundingClientRect(offsetParent,!0,isFixed,offsetParent);offsets.x=offsetRect.x+offsetParent.
-clientLeft,offsets.y=offsetRect.y+offsetParent.clientTop}else documentElement&&setLeftRTLScrollbarOffset();isFixed&&!isOffsetParentAnElement&&documentElement&&setLeftRTLScrollbarOffset();const htmlOffset=documentElement&&!isOffsetParentAnElement&&!isFixed?getHTMLOffset(documentElement,scroll):createCoords(0),x=rect.left+scroll.scrollLeft-offsets.x-htmlOffset.x,y=rect.top+scroll.scrollTop-offsets.y-htmlOffset.y;return{x,y,width:rect.width,height:rect.height}}function isStaticPositioned(element2){
-return getComputedStyle$1(element2).position==="static"}function getTrueOffsetParent(element2,polyfill){if(!isHTMLElement(element2)||getComputedStyle$1(element2).position==="fixed")return null;if(polyfill)return polyfill(element2);let rawOffsetParent=element2.offsetParent;return getDocumentElement(element2)===rawOffsetParent&&(rawOffsetParent=rawOffsetParent.ownerDocument.body),rawOffsetParent}function getOffsetParent(element2,polyfill){const win=getWindow(element2);if(isTopLayer(element2))return win;
-if(!isHTMLElement(element2)){let svgOffsetParent=getParentNode(element2);for(;svgOffsetParent&&!isLastTraversableNode(svgOffsetParent);){if(isElement(svgOffsetParent)&&!isStaticPositioned(svgOffsetParent))return svgOffsetParent;svgOffsetParent=getParentNode(svgOffsetParent)}return win}let offsetParent=getTrueOffsetParent(element2,polyfill);for(;offsetParent&&isTableElement(offsetParent)&&isStaticPositioned(offsetParent);)offsetParent=getTrueOffsetParent(offsetParent,polyfill);return offsetParent&&
-isLastTraversableNode(offsetParent)&&isStaticPositioned(offsetParent)&&!isContainingBlock(offsetParent)?win:offsetParent||getContainingBlock(element2)||win}const getElementRects=async function(data){const getOffsetParentFn=this.getOffsetParent||getOffsetParent,getDimensionsFn=this.getDimensions,floatingDimensions=await getDimensionsFn(data.floating);return{reference:getRectRelativeToOffsetParent(data.reference,await getOffsetParentFn(data.floating),data.strategy),floating:{x:0,y:0,width:floatingDimensions.
-width,height:floatingDimensions.height}}};function isRTL(element2){return getComputedStyle$1(element2).direction==="rtl"}const platform={convertOffsetParentRelativeRectToViewportRelativeRect,getDocumentElement,getClippingRect,getOffsetParent,getElementRects,getClientRects,getDimensions,getScale,isElement,isRTL};function rectsAreEqual(a,b){return a.x===b.x&&a.y===b.y&&a.width===b.width&&a.height===b.height}function observeMove(element2,onMove){let io=null,timeoutId;const root2=getDocumentElement(
-element2);function cleanup(){var _io;clearTimeout(timeoutId),(_io=io)==null||_io.disconnect(),io=null}function refresh(skip,threshold){skip===void 0&&(skip=!1),threshold===void 0&&(threshold=1),cleanup();const elementRectForRootMargin=element2.getBoundingClientRect(),{left,top,width,height}=elementRectForRootMargin;if(skip||onMove(),!width||!height)return;const insetTop=floor(top),insetRight=floor(root2.clientWidth-(left+width)),insetBottom=floor(root2.clientHeight-(top+height)),insetLeft=floor(
-left),options={rootMargin:-insetTop+"px "+-insetRight+"px "+-insetBottom+"px "+-insetLeft+"px",threshold:max(0,min(1,threshold))||1};let isFirstUpdate=!0;function handleObserve(entries){const ratio=entries[0].intersectionRatio;if(ratio!==threshold){if(!isFirstUpdate)return refresh();ratio?refresh(!1,ratio):timeoutId=setTimeout(()=>{refresh(!1,1e-7)},1e3)}ratio===1&&!rectsAreEqual(elementRectForRootMargin,element2.getBoundingClientRect())&&refresh(),isFirstUpdate=!1}try{io=new IntersectionObserver(
-handleObserve,{...options,root:root2.ownerDocument})}catch{io=new IntersectionObserver(handleObserve,options)}io.observe(element2)}return refresh(!0),cleanup}function autoUpdate(reference,floating,update2,options){options===void 0&&(options={});const{ancestorScroll=!0,ancestorResize=!0,elementResize=typeof ResizeObserver=="function",layoutShift=typeof IntersectionObserver=="function",animationFrame=!1}=options,referenceEl=unwrapElement(reference),ancestors=ancestorScroll||ancestorResize?[...referenceEl?
-getOverflowAncestors(referenceEl):[],...getOverflowAncestors(floating)]:[];ancestors.forEach(ancestor=>{ancestorScroll&&ancestor.addEventListener("scroll",update2,{passive:!0}),ancestorResize&&ancestor.addEventListener("resize",update2)});const cleanupIo=referenceEl&&layoutShift?observeMove(referenceEl,update2):null;let reobserveFrame=-1,resizeObserver=null;elementResize&&(resizeObserver=new ResizeObserver(_ref=>{let[firstEntry]=_ref;firstEntry&&firstEntry.target===referenceEl&&resizeObserver&&(resizeObserver.
-unobserve(floating),cancelAnimationFrame(reobserveFrame),reobserveFrame=requestAnimationFrame(()=>{var _resizeObserver;(_resizeObserver=resizeObserver)==null||_resizeObserver.observe(floating)})),update2()}),referenceEl&&!animationFrame&&resizeObserver.observe(referenceEl),resizeObserver.observe(floating));let frameId,prevRefRect=animationFrame?getBoundingClientRect(reference):null;animationFrame&&frameLoop();function frameLoop(){const nextRefRect=getBoundingClientRect(reference);prevRefRect&&!rectsAreEqual(
-prevRefRect,nextRefRect)&&update2(),prevRefRect=nextRefRect,frameId=requestAnimationFrame(frameLoop)}return update2(),()=>{var _resizeObserver2;ancestors.forEach(ancestor=>{ancestorScroll&&ancestor.removeEventListener("scroll",update2),ancestorResize&&ancestor.removeEventListener("resize",update2)}),cleanupIo?.(),(_resizeObserver2=resizeObserver)==null||_resizeObserver2.disconnect(),resizeObserver=null,animationFrame&&cancelAnimationFrame(frameId)}}const offset=offset$1,shift=shift$1,flip=flip$1,
-size=size$1,hide=hide$1,arrow=arrow$1,limitShift=limitShift$1,computePosition=(reference,floating,options)=>{const cache2=new Map,mergedOptions={platform,...options},platformWithCache={...mergedOptions.platform,_c:cache2};return computePosition$1(reference,floating,{...mergedOptions,platform:platformWithCache})};function get(valueOrGetValue){return typeof valueOrGetValue=="function"?valueOrGetValue():valueOrGetValue}function getDPR(element2){return typeof window>"u"?1:(element2.ownerDocument.defaultView||
-window).devicePixelRatio||1}function roundByDPR(element2,value){const dpr=getDPR(element2);return Math.round(value*dpr)/dpr}function getFloatingContentCSSVars(name){return{[`--bits-${name}-content-transform-origin`]:"var(--bits-floating-transform-origin)",[`--bits-${name}-content-available-width`]:"var(--bits-floating-available-width)",[`--bits-${name}-content-available-height`]:"var(--bits-floating-available-height)",[`--bits-${name}-anchor-width`]:"var(--bits-floating-anchor-width)",[`--bits-${name}\
--anchor-height`]:"var(--bits-floating-anchor-height)"}}function useFloating(options){const whileElementsMountedOption=options.whileElementsMounted,openOption=user_derived(()=>get(options.open)??!0),middlewareOption=user_derived(()=>get(options.middleware)),transformOption=user_derived(()=>get(options.transform)??!0),placementOption=user_derived(()=>get(options.placement)??"bottom"),strategyOption=user_derived(()=>get(options.strategy)??"absolute"),sideOffsetOption=user_derived(()=>get(options.sideOffset)??
-0),alignOffsetOption=user_derived(()=>get(options.alignOffset)??0),reference=options.reference;let x=state$1(0),y=state$1(0);const floating=simpleBox(null);let strategy=state$1(proxy(get$4(strategyOption))),placement=state$1(proxy(get$4(placementOption))),middlewareData=state$1(proxy({})),isPositioned=state$1(!1);const floatingStyles=user_derived(()=>{const xVal=floating.current?roundByDPR(floating.current,get$4(x)):get$4(x),yVal=floating.current?roundByDPR(floating.current,get$4(y)):get$4(y);return get$4(
-transformOption)?{position:get$4(strategy),left:"0",top:"0",transform:`translate(${xVal}px, ${yVal}px)`,...floating.current&&getDPR(floating.current)>=1.5&&{willChange:"transform"}}:{position:get$4(strategy),left:`${xVal}px`,top:`${yVal}px`}});let whileElementsMountedCleanup;function update2(){reference.current===null||floating.current===null||computePosition(reference.current,floating.current,{middleware:get$4(middlewareOption),placement:get$4(placementOption),strategy:get$4(strategyOption)}).then(
-position2=>{if(!get$4(openOption)&&get$4(x)!==0&&get$4(y)!==0){const maxExpectedOffset=Math.max(Math.abs(get$4(sideOffsetOption)),Math.abs(get$4(alignOffsetOption)),15);if(position2.x<=maxExpectedOffset&&position2.y<=maxExpectedOffset)return}set$1(x,position2.x,!0),set$1(y,position2.y,!0),set$1(strategy,position2.strategy,!0),set$1(placement,position2.placement,!0),set$1(middlewareData,position2.middlewareData,!0),set$1(isPositioned,!0)})}function cleanup(){typeof whileElementsMountedCleanup=="f\
-unction"&&(whileElementsMountedCleanup(),whileElementsMountedCleanup=void 0)}function attach2(){if(cleanup(),whileElementsMountedOption===void 0){update2();return}reference.current===null||floating.current===null||(whileElementsMountedCleanup=whileElementsMountedOption(reference.current,floating.current,update2))}function reset2(){get$4(openOption)||set$1(isPositioned,!1)}return user_effect(update2),user_effect(attach2),user_effect(reset2),user_effect(()=>cleanup),{floating,reference,get strategy(){
-return get$4(strategy)},get placement(){return get$4(placement)},get middlewareData(){return get$4(middlewareData)},get isPositioned(){return get$4(isPositioned)},get floatingStyles(){return get$4(floatingStyles)},get update(){return update2}}}const OPPOSITE_SIDE={top:"bottom",right:"left",bottom:"top",left:"right"},FloatingRootContext=new Context$1("Floating.Root"),FloatingContentContext=new Context$1("Floating.Content"),FloatingTooltipRootContext=new Context$1("Floating.Root");class FloatingRootState{static create(tooltip=!1){
-return tooltip?FloatingTooltipRootContext.set(new FloatingRootState):FloatingRootContext.set(new FloatingRootState)}anchorNode=simpleBox(null);customAnchorNode=simpleBox(null);triggerNode=simpleBox(null);constructor(){user_effect(()=>{this.customAnchorNode.current?typeof this.customAnchorNode.current=="string"?this.anchorNode.current=document.querySelector(this.customAnchorNode.current):this.anchorNode.current=this.customAnchorNode.current:this.anchorNode.current=this.triggerNode.current})}}class FloatingContentState{static create(opts,tooltip=!1){
-return tooltip?FloatingContentContext.set(new FloatingContentState(opts,FloatingTooltipRootContext.get())):FloatingContentContext.set(new FloatingContentState(opts,FloatingRootContext.get()))}opts;root;contentRef=simpleBox(null);wrapperRef=simpleBox(null);arrowRef=simpleBox(null);contentAttachment=attachRef(this.contentRef);wrapperAttachment=attachRef(this.wrapperRef);arrowAttachment=attachRef(this.arrowRef);arrowId=simpleBox(useId());#transformedStyle=user_derived(()=>{if(typeof this.opts.style==
-"string")return cssToStyleObj(this.opts.style);if(!this.opts.style)return{}});#updatePositionStrategy=void 0;#arrowSize=new ElementSize(()=>this.arrowRef.current??void 0);#arrowWidth=user_derived(()=>this.#arrowSize?.width??0);#arrowHeight=user_derived(()=>this.#arrowSize?.height??0);#desiredPlacement=user_derived(()=>this.opts.side?.current+(this.opts.align.current!=="center"?`-${this.opts.align.current}`:""));#boundary=user_derived(()=>Array.isArray(this.opts.collisionBoundary.current)?this.opts.
-collisionBoundary.current:[this.opts.collisionBoundary.current]);#hasExplicitBoundaries=user_derived(()=>get$4(this.#boundary).length>0);get hasExplicitBoundaries(){return get$4(this.#hasExplicitBoundaries)}set hasExplicitBoundaries(value){set$1(this.#hasExplicitBoundaries,value)}#detectOverflowOptions=user_derived(()=>({padding:this.opts.collisionPadding.current,boundary:get$4(this.#boundary).filter(isNotNull),altBoundary:this.hasExplicitBoundaries}));get detectOverflowOptions(){return get$4(this.#detectOverflowOptions)}set detectOverflowOptions(value){
-set$1(this.#detectOverflowOptions,value)}#availableWidth=state$1(void 0);#availableHeight=state$1(void 0);#anchorWidth=state$1(void 0);#anchorHeight=state$1(void 0);#middleware=user_derived(()=>[offset({mainAxis:this.opts.sideOffset.current+get$4(this.#arrowHeight),alignmentAxis:this.opts.alignOffset.current}),this.opts.avoidCollisions.current&&shift({mainAxis:!0,crossAxis:!1,limiter:this.opts.sticky.current==="partial"?limitShift():void 0,...this.detectOverflowOptions}),this.opts.avoidCollisions.
-current&&flip({...this.detectOverflowOptions}),size({...this.detectOverflowOptions,apply:({rects,availableWidth,availableHeight})=>{const{width:anchorWidth,height:anchorHeight}=rects.reference;set$1(this.#availableWidth,availableWidth,!0),set$1(this.#availableHeight,availableHeight,!0),set$1(this.#anchorWidth,anchorWidth,!0),set$1(this.#anchorHeight,anchorHeight,!0)}}),this.arrowRef.current&&arrow({element:this.arrowRef.current,padding:this.opts.arrowPadding.current}),transformOrigin({arrowWidth:get$4(
-this.#arrowWidth),arrowHeight:get$4(this.#arrowHeight)}),this.opts.hideWhenDetached.current&&hide({strategy:"referenceHidden",...this.detectOverflowOptions})].filter(Boolean));get middleware(){return get$4(this.#middleware)}set middleware(value){set$1(this.#middleware,value)}floating;#placedSide=user_derived(()=>getSideFromPlacement(this.floating.placement));get placedSide(){return get$4(this.#placedSide)}set placedSide(value){set$1(this.#placedSide,value)}#placedAlign=user_derived(()=>getAlignFromPlacement(
-this.floating.placement));get placedAlign(){return get$4(this.#placedAlign)}set placedAlign(value){set$1(this.#placedAlign,value)}#arrowX=user_derived(()=>this.floating.middlewareData.arrow?.x??0);get arrowX(){return get$4(this.#arrowX)}set arrowX(value){set$1(this.#arrowX,value)}#arrowY=user_derived(()=>this.floating.middlewareData.arrow?.y??0);get arrowY(){return get$4(this.#arrowY)}set arrowY(value){set$1(this.#arrowY,value)}#cannotCenterArrow=user_derived(()=>this.floating.middlewareData.arrow?.
-centerOffset!==0);get cannotCenterArrow(){return get$4(this.#cannotCenterArrow)}set cannotCenterArrow(value){set$1(this.#cannotCenterArrow,value)}#contentZIndex=state$1();get contentZIndex(){return get$4(this.#contentZIndex)}set contentZIndex(value){set$1(this.#contentZIndex,value,!0)}#arrowBaseSide=user_derived(()=>OPPOSITE_SIDE[this.placedSide]);get arrowBaseSide(){return get$4(this.#arrowBaseSide)}set arrowBaseSide(value){set$1(this.#arrowBaseSide,value)}#wrapperProps=user_derived(()=>({id:this.
-opts.wrapperId.current,"data-bits-floating-content-wrapper":"",style:{...this.floating.floatingStyles,transform:this.floating.isPositioned?this.floating.floatingStyles.transform:"translate(0, -200%)",minWidth:"max-content",zIndex:this.contentZIndex,"--bits-floating-transform-origin":`${this.floating.middlewareData.transformOrigin?.x} ${this.floating.middlewareData.transformOrigin?.y}`,"--bits-floating-available-width":`${get$4(this.#availableWidth)}px`,"--bits-floating-available-height":`${get$4(
-this.#availableHeight)}px`,"--bits-floating-anchor-width":`${get$4(this.#anchorWidth)}px`,"--bits-floating-anchor-height":`${get$4(this.#anchorHeight)}px`,...this.floating.middlewareData.hide?.referenceHidden&&{visibility:"hidden","pointer-events":"none"},...get$4(this.#transformedStyle)},dir:this.opts.dir.current,...this.wrapperAttachment}));get wrapperProps(){return get$4(this.#wrapperProps)}set wrapperProps(value){set$1(this.#wrapperProps,value)}#props=user_derived(()=>({"data-side":this.placedSide,
-"data-align":this.placedAlign,style:styleToString$1({...get$4(this.#transformedStyle)}),...this.contentAttachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}#arrowStyle=user_derived(()=>({position:"absolute",left:this.arrowX?`${this.arrowX}px`:void 0,top:this.arrowY?`${this.arrowY}px`:void 0,[this.arrowBaseSide]:0,"transform-origin":{top:"",right:"0 0",bottom:"center 0",left:"100% 0"}[this.placedSide],transform:{top:"translateY(100%)",right:"translateY(50%\
-) rotate(90deg) translateX(-50%)",bottom:"rotate(180deg)",left:"translateY(50%) rotate(-90deg) translateX(50%)"}[this.placedSide],visibility:this.cannotCenterArrow?"hidden":void 0}));get arrowStyle(){return get$4(this.#arrowStyle)}set arrowStyle(value){set$1(this.#arrowStyle,value)}constructor(opts,root2){this.opts=opts,this.root=root2,opts.customAnchor&&(this.root.customAnchorNode.current=opts.customAnchor.current),watch$1(()=>opts.customAnchor.current,customAnchor=>{this.root.customAnchorNode.
-current=customAnchor}),this.floating=useFloating({strategy:()=>this.opts.strategy.current,placement:()=>get$4(this.#desiredPlacement),middleware:()=>this.middleware,reference:this.root.anchorNode,whileElementsMounted:(...args)=>autoUpdate(...args,{animationFrame:this.#updatePositionStrategy?.current==="always"}),open:()=>this.opts.enabled.current,sideOffset:()=>this.opts.sideOffset.current,alignOffset:()=>this.opts.alignOffset.current}),user_effect(()=>{this.floating.isPositioned&&this.opts.onPlaced?.
-current()}),watch$1(()=>this.contentRef.current,contentNode=>{if(!contentNode)return;const win=getWindow$1(contentNode);this.contentZIndex=win.getComputedStyle(contentNode).zIndex}),user_effect(()=>{this.floating.floating.current=this.wrapperRef.current})}}class FloatingArrowState{static create(opts){return new FloatingArrowState(opts,FloatingContentContext.get())}opts;content;constructor(opts,content2){this.opts=opts,this.content=content2}#props=user_derived(()=>({id:this.opts.id.current,style:this.
-content.arrowStyle,"data-side":this.content.placedSide,...this.content.arrowAttachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class FloatingAnchorState{static create(opts,tooltip=!1){return tooltip?new FloatingAnchorState(opts,FloatingTooltipRootContext.get()):new FloatingAnchorState(opts,FloatingRootContext.get())}opts;root;constructor(opts,root2){this.opts=opts,this.root=root2,opts.virtualEl&&opts.virtualEl.current?root2.triggerNode=boxFrom$1(opts.virtualEl.
-current):root2.triggerNode=opts.ref}}function transformOrigin(options){return{name:"transformOrigin",options,fn(data){const{placement,rects,middlewareData}=data,isArrowHidden=middlewareData.arrow?.centerOffset!==0,arrowWidth=isArrowHidden?0:options.arrowWidth,arrowHeight=isArrowHidden?0:options.arrowHeight,[placedSide,placedAlign]=getSideAndAlignFromPlacement(placement),noArrowAlign={start:"0%",center:"50%",end:"100%"}[placedAlign],arrowXCenter=(middlewareData.arrow?.x??0)+arrowWidth/2,arrowYCenter=(middlewareData.
-arrow?.y??0)+arrowHeight/2;let x="",y="";return placedSide==="bottom"?(x=isArrowHidden?noArrowAlign:`${arrowXCenter}px`,y=`${-arrowHeight}px`):placedSide==="top"?(x=isArrowHidden?noArrowAlign:`${arrowXCenter}px`,y=`${rects.floating.height+arrowHeight}px`):placedSide==="right"?(x=`${-arrowHeight}px`,y=isArrowHidden?noArrowAlign:`${arrowYCenter}px`):placedSide==="left"&&(x=`${rects.floating.width+arrowHeight}px`,y=isArrowHidden?noArrowAlign:`${arrowYCenter}px`),{data:{x,y}}}}}function getSideAndAlignFromPlacement(placement){
-const[side,align="center"]=placement.split("-");return[side,align]}function getSideFromPlacement(placement){return getSideAndAlignFromPlacement(placement)[0]}function getAlignFromPlacement(placement){return getSideAndAlignFromPlacement(placement)[1]}function Floating_layer($$anchor,$$props){push$1($$props,!0);let tooltip=prop($$props,"tooltip",3,!1);FloatingRootState.create(tooltip());var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.children??noop$3),append($$anchor,
-fragment),pop()}class DataTypeahead{#opts;#candidateValues=user_derived(()=>this.#opts.candidateValues());#search;constructor(opts){this.#opts=opts,this.#search=boxAutoReset("",{afterMs:1e3,getWindow:this.#opts.getWindow}),this.handleTypeaheadSearch=this.handleTypeaheadSearch.bind(this),this.resetTypeahead=this.resetTypeahead.bind(this)}handleTypeaheadSearch(key2){if(!this.#opts.enabled()||!get$4(this.#candidateValues).length)return;this.#search.current=this.#search.current+key2;const currentItem=this.#opts.
-getCurrentItem(),currentMatch=get$4(this.#candidateValues).find(item=>item===currentItem)??"",values=get$4(this.#candidateValues).map(item=>item??""),nextMatch=getNextMatch(values,this.#search.current,currentMatch),newItem=get$4(this.#candidateValues).find(item=>item===nextMatch);return newItem&&this.#opts.onMatch(newItem),newItem}resetTypeahead(){this.#search.current=""}}const FIRST_KEYS=[ARROW_DOWN,PAGE_UP,HOME],LAST_KEYS=[ARROW_UP,PAGE_DOWN,END],FIRST_LAST_KEYS=[...FIRST_KEYS,...LAST_KEYS],selectAttrs=createBitsAttrs(
-{component:"select",parts:["trigger","content","item","viewport","scroll-up-button","scroll-down-button","group","group-label","separator","arrow","input","content-wrapper","item-text","value"]}),SelectRootContext=new Context$1("Select.Root | Combobox.Root"),SelectContentContext=new Context$1("Select.Content | Combobox.Content");class SelectBaseRootState{opts;#touchedInput=state$1(!1);get touchedInput(){return get$4(this.#touchedInput)}set touchedInput(value){set$1(this.#touchedInput,value,!0)}#inputNode=state$1(
-null);get inputNode(){return get$4(this.#inputNode)}set inputNode(value){set$1(this.#inputNode,value,!0)}#contentNode=state$1(null);get contentNode(){return get$4(this.#contentNode)}set contentNode(value){set$1(this.#contentNode,value,!0)}contentPresence;#viewportNode=state$1(null);get viewportNode(){return get$4(this.#viewportNode)}set viewportNode(value){set$1(this.#viewportNode,value,!0)}#triggerNode=state$1(null);get triggerNode(){return get$4(this.#triggerNode)}set triggerNode(value){set$1(
-this.#triggerNode,value,!0)}#valueId=state$1("");get valueId(){return get$4(this.#valueId)}set valueId(value){set$1(this.#valueId,value,!0)}#highlightedNode=state$1(null);get highlightedNode(){return get$4(this.#highlightedNode)}set highlightedNode(value){set$1(this.#highlightedNode,value,!0)}#highlightedValue=user_derived(()=>this.highlightedNode?this.highlightedNode.getAttribute("data-value"):null);get highlightedValue(){return get$4(this.#highlightedValue)}set highlightedValue(value){set$1(this.#highlightedValue,
-value)}#highlightedId=user_derived(()=>{if(this.highlightedNode)return this.highlightedNode.id});get highlightedId(){return get$4(this.#highlightedId)}set highlightedId(value){set$1(this.#highlightedId,value)}#highlightedLabel=user_derived(()=>this.highlightedNode?this.highlightedNode.getAttribute("data-label"):null);get highlightedLabel(){return get$4(this.#highlightedLabel)}set highlightedLabel(value){set$1(this.#highlightedLabel,value)}isUsingKeyboard=!1;isCombobox=!1;domContext=new DOMContext(
-()=>null);constructor(opts){this.opts=opts,this.isCombobox=opts.isCombobox,this.contentPresence=new PresenceManager({ref:boxWith$1(()=>this.contentNode),open:this.opts.open,onComplete:()=>{this.opts.onOpenChangeComplete.current(this.opts.open.current)}}),user_pre_effect(()=>{this.opts.open.current||this.setHighlightedNode(null)})}setHighlightedNode(node2,initial=!1){this.highlightedNode=node2,node2&&(this.isUsingKeyboard||initial)&&node2.scrollIntoView({block:this.opts.scrollAlignment.current})}getCandidateNodes(){
-const node2=this.contentNode;return node2?Array.from(node2.querySelectorAll(`[${this.getBitsAttr("item")}]:not([data-disabled])`)):[]}setHighlightedToFirstCandidate(initial=!1){this.setHighlightedNode(null);let nodes2=this.getCandidateNodes();if(nodes2.length){if(this.viewportNode){const viewportRect=this.viewportNode.getBoundingClientRect();nodes2=nodes2.filter(node2=>{if(!this.viewportNode)return!1;const nodeRect=node2.getBoundingClientRect();return nodeRect.right<viewportRect.right&&nodeRect.
-left>viewportRect.left&&nodeRect.bottom<viewportRect.bottom&&nodeRect.top>viewportRect.top})}this.setHighlightedNode(nodes2[0],initial)}}getNodeByValue(value){return this.getCandidateNodes().find(node2=>node2.dataset.value===value)??null}setOpen(open2){this.opts.open.current=open2}toggleOpen(){this.opts.open.current=!this.opts.open.current}handleOpen(){this.setOpen(!0)}handleClose(){this.setHighlightedNode(null),this.setOpen(!1)}toggleMenu(){this.toggleOpen()}getBitsAttr=part=>selectAttrs.getAttr(
-part,this.isCombobox?"combobox":void 0)}class SelectSingleRootState extends SelectBaseRootState{opts;isMulti=!1;#hasValue=user_derived(()=>this.opts.value.current!=="");get hasValue(){return get$4(this.#hasValue)}set hasValue(value){set$1(this.#hasValue,value)}#currentLabel=user_derived(()=>this.opts.items.current.length?this.opts.items.current.find(item=>item.value===this.opts.value.current)?.label??"":"");get currentLabel(){return get$4(this.#currentLabel)}set currentLabel(value){set$1(this.#currentLabel,
-value)}#candidateLabels=user_derived(()=>this.opts.items.current.length?this.opts.items.current.filter(item=>!item.disabled).map(item=>item.label):[]);get candidateLabels(){return get$4(this.#candidateLabels)}set candidateLabels(value){set$1(this.#candidateLabels,value)}#dataTypeaheadEnabled=user_derived(()=>!(this.isMulti||this.opts.items.current.length===0));get dataTypeaheadEnabled(){return get$4(this.#dataTypeaheadEnabled)}set dataTypeaheadEnabled(value){set$1(this.#dataTypeaheadEnabled,value)}constructor(opts){
-super(opts),this.opts=opts,user_effect(()=>{!this.opts.open.current&&this.highlightedNode&&this.setHighlightedNode(null)}),watch$1(()=>this.opts.open.current,()=>{this.opts.open.current&&this.setInitialHighlightedNode()})}includesItem(itemValue){return this.opts.value.current===itemValue}toggleItem(itemValue,itemLabel=itemValue){const newValue=this.includesItem(itemValue)?"":itemValue;this.opts.value.current=newValue,newValue!==""&&(this.opts.inputValue.current=itemLabel)}setInitialHighlightedNode(){
-afterTick(()=>{if(!(this.highlightedNode&&this.domContext.getDocument().contains(this.highlightedNode))){if(this.opts.value.current!==""){const node2=this.getNodeByValue(this.opts.value.current);if(node2){this.setHighlightedNode(node2,!0);return}}this.setHighlightedToFirstCandidate(!0)}})}}class SelectMultipleRootState extends SelectBaseRootState{opts;isMulti=!0;#hasValue=user_derived(()=>this.opts.value.current.length>0);get hasValue(){return get$4(this.#hasValue)}set hasValue(value){set$1(this.#hasValue,
-value)}constructor(opts){super(opts),this.opts=opts,user_effect(()=>{!this.opts.open.current&&this.highlightedNode&&this.setHighlightedNode(null)}),watch$1(()=>this.opts.open.current,()=>{this.opts.open.current&&this.setInitialHighlightedNode()})}includesItem(itemValue){return this.opts.value.current.includes(itemValue)}toggleItem(itemValue,itemLabel=itemValue){this.includesItem(itemValue)?this.opts.value.current=this.opts.value.current.filter(v=>v!==itemValue):this.opts.value.current=[...this.opts.
-value.current,itemValue],this.opts.inputValue.current=itemLabel}setInitialHighlightedNode(){afterTick(()=>{if(this.domContext&&!(this.highlightedNode&&this.domContext.getDocument().contains(this.highlightedNode))){if(this.opts.value.current.length&&this.opts.value.current[0]!==""){const node2=this.getNodeByValue(this.opts.value.current[0]);if(node2){this.setHighlightedNode(node2,!0);return}}this.setHighlightedToFirstCandidate(!0)}})}}class SelectRootState{static create(props){const{type:type2,...rest}=props,
-rootState=type2==="single"?new SelectSingleRootState(rest):new SelectMultipleRootState(rest);return SelectRootContext.set(rootState)}}class SelectTriggerState{static create(opts){return new SelectTriggerState(opts,SelectRootContext.get())}opts;root;attachment;#domTypeahead;#dataTypeahead;constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(opts.ref,v=>this.root.triggerNode=v),this.root.domContext=new DOMContext(opts.ref),this.#domTypeahead=new DOMTypeahead({getCurrentItem:()=>this.
-root.highlightedNode,onMatch:node2=>{this.root.setHighlightedNode(node2)},getActiveElement:()=>this.root.domContext.getActiveElement(),getWindow:()=>this.root.domContext.getWindow()}),this.#dataTypeahead=new DataTypeahead({getCurrentItem:()=>this.root.isMulti?"":this.root.currentLabel,onMatch:label=>{if(this.root.isMulti||!this.root.opts.items.current)return;const matchedItem=this.root.opts.items.current.find(item=>item.label===label);matchedItem&&(this.root.opts.value.current=matchedItem.value)},
-enabled:()=>!this.root.isMulti&&this.root.dataTypeaheadEnabled,candidateValues:()=>this.root.isMulti?[]:this.root.candidateLabels,getWindow:()=>this.root.domContext.getWindow()}),this.onkeydown=this.onkeydown.bind(this),this.onpointerdown=this.onpointerdown.bind(this),this.onpointerup=this.onpointerup.bind(this),this.onclick=this.onclick.bind(this)}#handleOpen(){this.root.opts.open.current=!0,this.#dataTypeahead.resetTypeahead(),this.#domTypeahead.resetTypeahead()}#handlePointerOpen(_){this.#handleOpen()}#handleKeyboardSelection(){
-const isCurrentSelectedValue=this.root.highlightedValue===this.root.opts.value.current;return!this.root.opts.allowDeselect.current&&isCurrentSelectedValue&&!this.root.isMulti?(this.root.handleClose(),!0):(this.root.highlightedValue!==null&&this.root.toggleItem(this.root.highlightedValue,this.root.highlightedLabel??void 0),!this.root.isMulti&&!isCurrentSelectedValue?(this.root.handleClose(),!0):!1)}onkeydown(e){if(this.root.isUsingKeyboard=!0,(e.key===ARROW_UP||e.key===ARROW_DOWN)&&e.preventDefault(),
-!this.root.opts.open.current){if(e.key===ENTER||e.key===SPACE||e.key===ARROW_DOWN||e.key===ARROW_UP)e.preventDefault(),this.root.handleOpen();else if(!this.root.isMulti&&this.root.dataTypeaheadEnabled){this.#dataTypeahead.handleTypeaheadSearch(e.key);return}if(this.root.hasValue)return;const candidateNodes2=this.root.getCandidateNodes();if(!candidateNodes2.length)return;if(e.key===ARROW_DOWN){const firstCandidate=candidateNodes2[0];this.root.setHighlightedNode(firstCandidate)}else if(e.key===ARROW_UP){
-const lastCandidate=candidateNodes2[candidateNodes2.length-1];this.root.setHighlightedNode(lastCandidate)}return}if(e.key===TAB){this.root.handleClose();return}if((e.key===ENTER||e.key===SPACE&&this.#domTypeahead.search==="")&&!e.isComposing&&(e.preventDefault(),this.#handleKeyboardSelection()))return;if(e.key===ARROW_UP&&e.altKey&&this.root.handleClose(),FIRST_LAST_KEYS.includes(e.key)){e.preventDefault();const candidateNodes2=this.root.getCandidateNodes(),currHighlightedNode=this.root.highlightedNode,
-currIndex=currHighlightedNode?candidateNodes2.indexOf(currHighlightedNode):-1,loop2=this.root.opts.loop.current;let nextItem;if(e.key===ARROW_DOWN?nextItem=next(candidateNodes2,currIndex,loop2):e.key===ARROW_UP?nextItem=prev(candidateNodes2,currIndex,loop2):e.key===PAGE_DOWN?nextItem=forward(candidateNodes2,currIndex,10,loop2):e.key===PAGE_UP?nextItem=backward(candidateNodes2,currIndex,10,loop2):e.key===HOME?nextItem=candidateNodes2[0]:e.key===END&&(nextItem=candidateNodes2[candidateNodes2.length-
-1]),!nextItem)return;this.root.setHighlightedNode(nextItem);return}const isModifierKey=e.ctrlKey||e.altKey||e.metaKey,isCharacterKey=e.key.length===1,isSpaceKey=e.key===SPACE,candidateNodes=this.root.getCandidateNodes();if(e.key!==TAB){if(!isModifierKey&&(isCharacterKey||isSpaceKey)){!this.#domTypeahead.handleTypeaheadSearch(e.key,candidateNodes)&&isSpaceKey&&(e.preventDefault(),this.#handleKeyboardSelection());return}this.root.highlightedNode||this.root.setHighlightedToFirstCandidate()}}onclick(e){
-e.currentTarget.focus()}onpointerdown(e){if(this.root.opts.disabled.current)return;if(e.pointerType==="touch")return e.preventDefault();const target2=e.target;target2?.hasPointerCapture(e.pointerId)&&target2?.releasePointerCapture(e.pointerId),e.button===0&&e.ctrlKey===!1&&(this.root.opts.open.current===!1?this.#handlePointerOpen(e):this.root.handleClose())}onpointerup(e){this.root.opts.disabled.current||(e.preventDefault(),e.pointerType==="touch"&&(this.root.opts.open.current===!1?this.#handlePointerOpen(
-e):this.root.handleClose()))}#props=user_derived(()=>({id:this.opts.id.current,disabled:this.root.opts.disabled.current?!0:void 0,"aria-haspopup":"listbox","aria-expanded":boolToStr(this.root.opts.open.current),"aria-activedescendant":this.root.highlightedId,"data-state":getDataOpenClosed(this.root.opts.open.current),"data-disabled":boolToEmptyStrOrUndef(this.root.opts.disabled.current),"data-placeholder":this.root.hasValue?void 0:"",[this.root.getBitsAttr("trigger")]:"",onpointerdown:this.onpointerdown,
-onkeydown:this.onkeydown,onclick:this.onclick,onpointerup:this.onpointerup,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class SelectContentState{static create(opts){return SelectContentContext.set(new SelectContentState(opts,SelectRootContext.get()))}opts;root;attachment;#isPositioned=state$1(!1);get isPositioned(){return get$4(this.#isPositioned)}set isPositioned(value){set$1(this.#isPositioned,value,!0)}domContext;constructor(opts,root2){
-this.opts=opts,this.root=root2,this.attachment=attachRef(opts.ref,v=>this.root.contentNode=v),this.domContext=new DOMContext(this.opts.ref),this.root.domContext===null&&(this.root.domContext=this.domContext),onDestroyEffect(()=>{this.root.contentNode=null,this.isPositioned=!1}),watch$1(()=>this.root.opts.open.current,()=>{this.root.opts.open.current||(this.isPositioned=!1)}),this.onpointermove=this.onpointermove.bind(this)}onpointermove(_){this.root.isUsingKeyboard=!1}#styles=user_derived(()=>getFloatingContentCSSVars(
-this.root.isCombobox?"combobox":"select"));onInteractOutside=e=>{if(e.target===this.root.triggerNode||e.target===this.root.inputNode){e.preventDefault();return}this.opts.onInteractOutside.current(e),!e.defaultPrevented&&this.root.handleClose()};onEscapeKeydown=e=>{this.opts.onEscapeKeydown.current(e),!e.defaultPrevented&&this.root.handleClose()};onOpenAutoFocus=e=>{e.preventDefault()};onCloseAutoFocus=e=>{e.preventDefault()};get shouldRender(){return this.root.contentPresence.shouldRender}#snippetProps=user_derived(
-()=>({open:this.root.opts.open.current}));get snippetProps(){return get$4(this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}#props=user_derived(()=>({id:this.opts.id.current,role:"listbox","aria-multiselectable":this.root.isMulti?"true":void 0,"data-state":getDataOpenClosed(this.root.opts.open.current),[this.root.getBitsAttr("content")]:"",style:{display:"flex",flexDirection:"column",outline:"none",boxSizing:"border-box",pointerEvents:"auto",...get$4(this.#styles)},onpointermove:this.
-onpointermove,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}popperProps={onInteractOutside:this.onInteractOutside,onEscapeKeydown:this.onEscapeKeydown,onOpenAutoFocus:this.onOpenAutoFocus,onCloseAutoFocus:this.onCloseAutoFocus,trapFocus:!1,loop:!1,onPlaced:()=>{this.root.opts.open.current&&(this.isPositioned=!0)}}}class SelectItemState{static create(opts){return new SelectItemState(opts,SelectRootContext.get())}opts;root;attachment;#isSelected=user_derived(
-()=>this.root.includesItem(this.opts.value.current));get isSelected(){return get$4(this.#isSelected)}set isSelected(value){set$1(this.#isSelected,value)}#isHighlighted=user_derived(()=>this.root.highlightedValue===this.opts.value.current);get isHighlighted(){return get$4(this.#isHighlighted)}set isHighlighted(value){set$1(this.#isHighlighted,value)}prevHighlighted=new Previous(()=>this.isHighlighted);#mounted=state$1(!1);get mounted(){return get$4(this.#mounted)}set mounted(value){set$1(this.#mounted,
-value,!0)}constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(opts.ref),watch$1([()=>this.isHighlighted,()=>this.prevHighlighted.current],()=>{this.isHighlighted?this.opts.onHighlight.current():this.prevHighlighted.current&&this.opts.onUnhighlight.current()}),watch$1(()=>this.mounted,()=>{this.mounted&&this.root.setInitialHighlightedNode()}),this.onpointerdown=this.onpointerdown.bind(this),this.onpointerup=this.onpointerup.bind(this),this.onpointermove=this.onpointermove.
-bind(this)}handleSelect(){if(this.opts.disabled.current)return;const isCurrentSelectedValue=this.opts.value.current===this.root.opts.value.current;if(!this.root.opts.allowDeselect.current&&isCurrentSelectedValue&&!this.root.isMulti){this.root.handleClose();return}this.root.toggleItem(this.opts.value.current,this.opts.label.current),!this.root.isMulti&&!isCurrentSelectedValue&&this.root.handleClose()}#snippetProps=user_derived(()=>({selected:this.isSelected,highlighted:this.isHighlighted}));get snippetProps(){
-return get$4(this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}onpointerdown(e){e.preventDefault()}onpointerup(e){if(!(e.defaultPrevented||!this.opts.ref.current)){if(e.pointerType==="touch"&&!isIOS){on(this.opts.ref.current,"click",()=>{this.handleSelect(),this.root.setHighlightedNode(this.opts.ref.current)},{once:!0});return}e.preventDefault(),this.handleSelect(),e.pointerType==="touch"&&this.root.setHighlightedNode(this.opts.ref.current)}}onpointermove(e){e.pointerType!==
-"touch"&&this.root.highlightedNode!==this.opts.ref.current&&this.root.setHighlightedNode(this.opts.ref.current)}#props=user_derived(()=>({id:this.opts.id.current,role:"option","aria-selected":this.root.includesItem(this.opts.value.current)?"true":void 0,"data-value":this.opts.value.current,"data-disabled":boolToEmptyStrOrUndef(this.opts.disabled.current),"data-highlighted":this.root.highlightedValue===this.opts.value.current&&!this.opts.disabled.current?"":void 0,"data-selected":this.root.includesItem(
-this.opts.value.current)?"":void 0,"data-label":this.opts.label.current,[this.root.getBitsAttr("item")]:"",onpointermove:this.onpointermove,onpointerdown:this.onpointerdown,onpointerup:this.onpointerup,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class SelectHiddenInputState{static create(opts){return new SelectHiddenInputState(opts,SelectRootContext.get())}opts;root;#shouldRender=user_derived(()=>this.root.opts.name.current!=="");get shouldRender(){
-return get$4(this.#shouldRender)}set shouldRender(value){set$1(this.#shouldRender,value)}constructor(opts,root2){this.opts=opts,this.root=root2,this.onfocus=this.onfocus.bind(this)}onfocus(e){e.preventDefault(),this.root.isCombobox?this.root.inputNode?.focus():this.root.triggerNode?.focus()}#props=user_derived(()=>({disabled:boolToTrueOrUndef(this.root.opts.disabled.current),required:boolToTrueOrUndef(this.root.opts.required.current),name:this.root.opts.name.current,value:this.opts.value.current,
-onfocus:this.onfocus}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class SelectViewportState{static create(opts){return new SelectViewportState(opts,SelectContentContext.get())}opts;content;root;attachment;#prevScrollTop=state$1(0);get prevScrollTop(){return get$4(this.#prevScrollTop)}set prevScrollTop(value){set$1(this.#prevScrollTop,value,!0)}constructor(opts,content2){this.opts=opts,this.content=content2,this.root=content2.root,this.attachment=attachRef(opts.
-ref,v=>{this.root.viewportNode=v})}#props=user_derived(()=>({id:this.opts.id.current,role:"presentation",[this.root.getBitsAttr("viewport")]:"",style:{position:"relative",flex:1,overflow:"auto"},...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class SelectScrollButtonImplState{opts;content;root;attachment;autoScrollTimer=null;userScrollTimer=-1;isUserScrolling=!1;onAutoScroll=noop$1;#mounted=state$1(!1);get mounted(){return get$4(this.#mounted)}set mounted(value){
-set$1(this.#mounted,value,!0)}constructor(opts,content2){this.opts=opts,this.content=content2,this.root=content2.root,this.attachment=attachRef(opts.ref),watch$1([()=>this.mounted],()=>{if(!this.mounted){this.isUserScrolling=!1;return}this.isUserScrolling}),user_effect(()=>{this.mounted||this.clearAutoScrollInterval()}),this.onpointerdown=this.onpointerdown.bind(this),this.onpointermove=this.onpointermove.bind(this),this.onpointerleave=this.onpointerleave.bind(this)}handleUserScroll(){this.content.
-domContext.clearTimeout(this.userScrollTimer),this.isUserScrolling=!0,this.userScrollTimer=this.content.domContext.setTimeout(()=>{this.isUserScrolling=!1},200)}clearAutoScrollInterval(){this.autoScrollTimer!==null&&(this.content.domContext.clearTimeout(this.autoScrollTimer),this.autoScrollTimer=null)}onpointerdown(_){if(this.autoScrollTimer!==null)return;const autoScroll=tick2=>{this.onAutoScroll(),this.autoScrollTimer=this.content.domContext.setTimeout(()=>autoScroll(tick2+1),this.opts.delay.current(
-tick2))};this.autoScrollTimer=this.content.domContext.setTimeout(()=>autoScroll(1),this.opts.delay.current(0))}onpointermove(e){this.onpointerdown(e)}onpointerleave(_){this.clearAutoScrollInterval()}#props=user_derived(()=>({id:this.opts.id.current,"aria-hidden":boolToStrTrueOrUndef(!0),style:{flexShrink:0},onpointerdown:this.onpointerdown,onpointermove:this.onpointermove,onpointerleave:this.onpointerleave,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,
-value)}}class SelectScrollDownButtonState{static create(opts){return new SelectScrollDownButtonState(new SelectScrollButtonImplState(opts,SelectContentContext.get()))}scrollButtonState;content;root;#canScrollDown=state$1(!1);get canScrollDown(){return get$4(this.#canScrollDown)}set canScrollDown(value){set$1(this.#canScrollDown,value,!0)}scrollIntoViewTimer=null;constructor(scrollButtonState){this.scrollButtonState=scrollButtonState,this.content=scrollButtonState.content,this.root=scrollButtonState.
-root,this.scrollButtonState.onAutoScroll=this.handleAutoScroll,watch$1([()=>this.root.viewportNode,()=>this.content.isPositioned],()=>{if(!(!this.root.viewportNode||!this.content.isPositioned))return this.handleScroll(!0),on(this.root.viewportNode,"scroll",()=>this.handleScroll())}),watch$1([()=>this.root.opts.inputValue.current,()=>this.root.viewportNode,()=>this.content.isPositioned],()=>{!this.root.viewportNode||!this.content.isPositioned||this.handleScroll(!0)}),watch$1(()=>this.scrollButtonState.
-mounted,()=>{this.scrollButtonState.mounted&&(this.scrollIntoViewTimer&&clearTimeout(this.scrollIntoViewTimer),this.scrollIntoViewTimer=afterSleep(5,()=>{this.root.highlightedNode?.scrollIntoView({block:this.root.opts.scrollAlignment.current})}))})}handleScroll=(manual=!1)=>{if(manual||this.scrollButtonState.handleUserScroll(),!this.root.viewportNode)return;const maxScroll=this.root.viewportNode.scrollHeight-this.root.viewportNode.clientHeight,paddingTop=Number.parseInt(getComputedStyle(this.root.
-viewportNode).paddingTop,10);this.canScrollDown=Math.ceil(this.root.viewportNode.scrollTop)<maxScroll-paddingTop};handleAutoScroll=()=>{const viewport=this.root.viewportNode,selectedItem=this.root.highlightedNode;!viewport||!selectedItem||(viewport.scrollTop=viewport.scrollTop+selectedItem.offsetHeight)};#props=user_derived(()=>({...this.scrollButtonState.props,[this.root.getBitsAttr("scroll-down-button")]:""}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class SelectScrollUpButtonState{static create(opts){
-return new SelectScrollUpButtonState(new SelectScrollButtonImplState(opts,SelectContentContext.get()))}scrollButtonState;content;root;#canScrollUp=state$1(!1);get canScrollUp(){return get$4(this.#canScrollUp)}set canScrollUp(value){set$1(this.#canScrollUp,value,!0)}constructor(scrollButtonState){this.scrollButtonState=scrollButtonState,this.content=scrollButtonState.content,this.root=scrollButtonState.root,this.scrollButtonState.onAutoScroll=this.handleAutoScroll,watch$1([()=>this.root.viewportNode,
-()=>this.content.isPositioned],()=>{if(!(!this.root.viewportNode||!this.content.isPositioned))return this.handleScroll(!0),on(this.root.viewportNode,"scroll",()=>this.handleScroll())})}handleScroll=(manual=!1)=>{if(manual||this.scrollButtonState.handleUserScroll(),!this.root.viewportNode)return;const paddingTop=Number.parseInt(getComputedStyle(this.root.viewportNode).paddingTop,10);this.canScrollUp=this.root.viewportNode.scrollTop-paddingTop>.1};handleAutoScroll=()=>{!this.root.viewportNode||!this.
-root.highlightedNode||(this.root.viewportNode.scrollTop=this.root.viewportNode.scrollTop-this.root.highlightedNode.offsetHeight)};#props=user_derived(()=>({...this.scrollButtonState.props,[this.root.getBitsAttr("scroll-up-button")]:""}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}function Select_hidden_input($$anchor,$$props){push$1($$props,!0);let value=prop($$props,"value",15);const hiddenInputState=SelectHiddenInputState.create({value:boxWith$1(()=>value())});
-var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{Hidden_input($$anchor2,spread_props(()=>hiddenInputState.props,{get autocomplete(){return $$props.autocomplete},get value(){return value()},set value($$value){value($$value)}}))};if_block(node2,$$render=>{hiddenInputState.shouldRender&&$$render(consequent)})}append($$anchor,fragment),pop()}function Floating_layer_anchor($$anchor,$$props){push$1($$props,!0);let tooltip=prop($$props,"tooltip",3,!1);FloatingAnchorState.
-create({id:boxWith$1(()=>$$props.id),virtualEl:boxWith$1(()=>$$props.virtualEl),ref:$$props.ref},tooltip());var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.children??noop$3),append($$anchor,fragment),pop()}var root_4$F=from_svg('<svg viewBox="0 0 30 10" preserveAspectRatio="none" data-arrow=""><polygon points="0,0 30,0 15,10" fill="currentColor"></polygon></svg>'),root_2$1l=from_html("<span><!></span>");function Arrow($$anchor,$$props){push$1($$props,!0);let id2=prop(
-$$props,"id",19,useId),width=prop($$props,"width",3,10),height=prop($$props,"height",3,5),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","children","child","width","height"]);const mergedProps=user_derived(()=>mergeProps(restProps,{id:id2()}));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},
-alternate_1=$$anchor2=>{var span=root_2$1l();attribute_effect(span,()=>({...get$4(mergedProps)}));var node_2=child(span);{var consequent_1=$$anchor3=>{var fragment_2=comment$2(),node_3=first_child(fragment_2);snippet(node_3,()=>$$props.children??noop$3),append($$anchor3,fragment_2)},alternate=$$anchor3=>{var svg2=root_4$F();template_effect(()=>{set_attribute(svg2,"width",width()),set_attribute(svg2,"height",height())}),append($$anchor3,svg2)};if_block(node_2,$$render=>{$$props.children?$$render(
-consequent_1):$$render(alternate,!1)})}reset(span),append($$anchor2,span)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate_1,!1)})}append($$anchor,fragment),pop()}function Floating_layer_arrow($$anchor,$$props){push$1($$props,!0);let id2=prop($$props,"id",19,useId),ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref"]);const arrowState=FloatingArrowState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(
-v))}),mergedProps=user_derived(()=>mergeProps(restProps,arrowState.props));Arrow($$anchor,spread_props(()=>get$4(mergedProps))),pop()}function Floating_layer_content($$anchor,$$props){push$1($$props,!0);let side=prop($$props,"side",3,"bottom"),sideOffset=prop($$props,"sideOffset",3,0),align=prop($$props,"align",3,"center"),alignOffset=prop($$props,"alignOffset",3,0),arrowPadding=prop($$props,"arrowPadding",3,0),avoidCollisions=prop($$props,"avoidCollisions",3,!0),collisionBoundary=prop($$props,"\
-collisionBoundary",19,()=>[]),collisionPadding=prop($$props,"collisionPadding",3,0),hideWhenDetached=prop($$props,"hideWhenDetached",3,!1),onPlaced=prop($$props,"onPlaced",3,()=>{}),sticky=prop($$props,"sticky",3,"partial"),updatePositionStrategy=prop($$props,"updatePositionStrategy",3,"optimized"),strategy=prop($$props,"strategy",3,"fixed"),dir=prop($$props,"dir",3,"ltr"),style2=prop($$props,"style",19,()=>({})),wrapperId=prop($$props,"wrapperId",19,useId),customAnchor=prop($$props,"customAncho\
-r",3,null),tooltip=prop($$props,"tooltip",3,!1);const contentState=FloatingContentState.create({side:boxWith$1(()=>side()),sideOffset:boxWith$1(()=>sideOffset()),align:boxWith$1(()=>align()),alignOffset:boxWith$1(()=>alignOffset()),id:boxWith$1(()=>$$props.id),arrowPadding:boxWith$1(()=>arrowPadding()),avoidCollisions:boxWith$1(()=>avoidCollisions()),collisionBoundary:boxWith$1(()=>collisionBoundary()),collisionPadding:boxWith$1(()=>collisionPadding()),hideWhenDetached:boxWith$1(()=>hideWhenDetached()),
-onPlaced:boxWith$1(()=>onPlaced()),sticky:boxWith$1(()=>sticky()),updatePositionStrategy:boxWith$1(()=>updatePositionStrategy()),strategy:boxWith$1(()=>strategy()),dir:boxWith$1(()=>dir()),style:boxWith$1(()=>style2()),enabled:boxWith$1(()=>$$props.enabled),wrapperId:boxWith$1(()=>wrapperId()),customAnchor:boxWith$1(()=>customAnchor())},tooltip()),mergedProps=user_derived(()=>mergeProps(contentState.wrapperProps,{style:{pointerEvents:"auto"}}));var fragment=comment$2(),node2=first_child(fragment);
-snippet(node2,()=>$$props.content??noop$3,()=>({props:contentState.props,wrapperProps:get$4(mergedProps)})),append($$anchor,fragment),pop()}function Floating_layer_content_static($$anchor,$$props){push$1($$props,!0),onMount$1(()=>{$$props.onPlaced?.()});var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.content??noop$3,()=>({props:{},wrapperProps:{}})),append($$anchor,fragment),pop()}function Popper_content($$anchor,$$props){let isStatic=prop($$props,"isStatic",3,!1),
-restProps=rest_props($$props,["$$slots","$$events","$$legacy","content","isStatic","onPlaced"]);var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{Floating_layer_content_static($$anchor2,{get content(){return $$props.content},get onPlaced(){return $$props.onPlaced}})},alternate=$$anchor2=>{Floating_layer_content($$anchor2,spread_props({get content(){return $$props.content},get onPlaced(){return $$props.onPlaced}},()=>restProps))};if_block(node2,$$render=>{isStatic()?
-$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment)}var root_1$15=from_html("<!> <!>",1);function Popper_layer_inner($$anchor,$$props){push$1($$props,!0);let interactOutsideBehavior=prop($$props,"interactOutsideBehavior",3,"close"),trapFocus=prop($$props,"trapFocus",3,!0),isValidEvent2=prop($$props,"isValidEvent",3,()=>!1),customAnchor=prop($$props,"customAnchor",3,null),isStatic=prop($$props,"isStatic",3,!1),tooltip=prop($$props,"tooltip",3,!1),contentPointerEvents=prop($$props,
-"contentPointerEvents",3,"auto"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","popper","onEscapeKeydown","escapeKeydownBehavior","preventOverflowTextSelection","id","onPointerDown","onPointerUp","side","sideOffset","align","alignOffset","arrowPadding","avoidCollisions","collisionBoundary","collisionPadding","sticky","hideWhenDetached","updatePositionStrategy","strategy","dir","preventScroll","wrapperId","style","onPlaced","onInteractOutside","onCloseAutoFocus","onOpenAutoFocus",
-"onFocusOutside","interactOutsideBehavior","loop","trapFocus","isValidEvent","customAnchor","isStatic","enabled","ref","tooltip","contentPointerEvents"]);Popper_content($$anchor,{get isStatic(){return isStatic()},get id(){return $$props.id},get side(){return $$props.side},get sideOffset(){return $$props.sideOffset},get align(){return $$props.align},get alignOffset(){return $$props.alignOffset},get arrowPadding(){return $$props.arrowPadding},get avoidCollisions(){return $$props.avoidCollisions},get collisionBoundary(){
-return $$props.collisionBoundary},get collisionPadding(){return $$props.collisionPadding},get sticky(){return $$props.sticky},get hideWhenDetached(){return $$props.hideWhenDetached},get updatePositionStrategy(){return $$props.updatePositionStrategy},get strategy(){return $$props.strategy},get dir(){return $$props.dir},get wrapperId(){return $$props.wrapperId},get style(){return $$props.style},get onPlaced(){return $$props.onPlaced},get customAnchor(){return customAnchor()},get enabled(){return $$props.
-enabled},get tooltip(){return tooltip()},content:($$anchor2,$$arg0)=>{let floatingProps=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;var fragment_1=root_1$15(),node2=first_child(fragment_1);{var consequent=$$anchor3=>{Scroll_lock($$anchor3,{get preventScroll(){return $$props.preventScroll}})},alternate=$$anchor3=>{var fragment_3=comment$2(),node_1=first_child(fragment_3);{var consequent_1=$$anchor4=>{Scroll_lock($$anchor4,{get preventScroll(){return $$props.preventScroll}})};if_block(
-node_1,$$render=>{$$props.forceMount||$$render(consequent_1)},!0)}append($$anchor3,fragment_3)};if_block(node2,$$render=>{$$props.forceMount&&$$props.enabled?$$render(consequent):$$render(alternate,!1)})}var node_2=sibling(node2,2);Focus_scope(node_2,{get onOpenAutoFocus(){return $$props.onOpenAutoFocus},get onCloseAutoFocus(){return $$props.onCloseAutoFocus},get loop(){return $$props.loop},get enabled(){return $$props.enabled},get trapFocus(){return trapFocus()},get forceMount(){return $$props.
-forceMount},get ref(){return $$props.ref},focusScope:($$anchor3,$$arg02)=>{let focusScopeProps=()=>$$arg02?.().props;Escape_layer($$anchor3,{get onEscapeKeydown(){return $$props.onEscapeKeydown},get escapeKeydownBehavior(){return $$props.escapeKeydownBehavior},get enabled(){return $$props.enabled},get ref(){return $$props.ref},children:($$anchor4,$$slotProps)=>{Dismissible_layer($$anchor4,{get id(){return $$props.id},get onInteractOutside(){return $$props.onInteractOutside},get onFocusOutside(){
-return $$props.onFocusOutside},get interactOutsideBehavior(){return interactOutsideBehavior()},get isValidEvent(){return isValidEvent2()},get enabled(){return $$props.enabled},get ref(){return $$props.ref},children:($$anchor5,$$arg03)=>{let dismissibleProps=()=>$$arg03?.().props;Text_selection_layer($$anchor5,{get id(){return $$props.id},get preventOverflowTextSelection(){return $$props.preventOverflowTextSelection},get onPointerDown(){return $$props.onPointerDown},get onPointerUp(){return $$props.
-onPointerUp},get enabled(){return $$props.enabled},get ref(){return $$props.ref},children:($$anchor6,$$slotProps2)=>{var fragment_8=comment$2(),node_3=first_child(fragment_8);{let $0=user_derived(()=>({props:mergeProps(restProps,floatingProps(),dismissibleProps(),focusScopeProps(),{style:{pointerEvents:contentPointerEvents()}}),wrapperProps:wrapperProps()}));snippet(node_3,()=>$$props.popper??noop$3,()=>get$4($0))}append($$anchor6,fragment_8)},$$slots:{default:!0}})},$$slots:{default:!0}})},$$slots:{
-default:!0}})},$$slots:{focusScope:!0}}),append($$anchor2,fragment_1)},$$slots:{content:!0}}),pop()}function Popper_layer($$anchor,$$props){let interactOutsideBehavior=prop($$props,"interactOutsideBehavior",3,"close"),trapFocus=prop($$props,"trapFocus",3,!0),isValidEvent2=prop($$props,"isValidEvent",3,()=>!1),customAnchor=prop($$props,"customAnchor",3,null),isStatic=prop($$props,"isStatic",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","popper","open","onEscapeKeydown","esca\
-peKeydownBehavior","preventOverflowTextSelection","id","onPointerDown","onPointerUp","side","sideOffset","align","alignOffset","arrowPadding","avoidCollisions","collisionBoundary","collisionPadding","sticky","hideWhenDetached","updatePositionStrategy","strategy","dir","preventScroll","wrapperId","style","onPlaced","onInteractOutside","onCloseAutoFocus","onOpenAutoFocus","onFocusOutside","interactOutsideBehavior","loop","trapFocus","isValidEvent","customAnchor","isStatic","ref","shouldRender"]);var fragment=comment$2(),
-node2=first_child(fragment);{var consequent=$$anchor2=>{Popper_layer_inner($$anchor2,spread_props({get popper(){return $$props.popper},get onEscapeKeydown(){return $$props.onEscapeKeydown},get escapeKeydownBehavior(){return $$props.escapeKeydownBehavior},get preventOverflowTextSelection(){return $$props.preventOverflowTextSelection},get id(){return $$props.id},get onPointerDown(){return $$props.onPointerDown},get onPointerUp(){return $$props.onPointerUp},get side(){return $$props.side},get sideOffset(){
-return $$props.sideOffset},get align(){return $$props.align},get alignOffset(){return $$props.alignOffset},get arrowPadding(){return $$props.arrowPadding},get avoidCollisions(){return $$props.avoidCollisions},get collisionBoundary(){return $$props.collisionBoundary},get collisionPadding(){return $$props.collisionPadding},get sticky(){return $$props.sticky},get hideWhenDetached(){return $$props.hideWhenDetached},get updatePositionStrategy(){return $$props.updatePositionStrategy},get strategy(){return $$props.
-strategy},get dir(){return $$props.dir},get preventScroll(){return $$props.preventScroll},get wrapperId(){return $$props.wrapperId},get style(){return $$props.style},get onPlaced(){return $$props.onPlaced},get customAnchor(){return customAnchor()},get isStatic(){return isStatic()},get enabled(){return $$props.open},get onInteractOutside(){return $$props.onInteractOutside},get onCloseAutoFocus(){return $$props.onCloseAutoFocus},get onOpenAutoFocus(){return $$props.onOpenAutoFocus},get interactOutsideBehavior(){
-return interactOutsideBehavior()},get loop(){return $$props.loop},get trapFocus(){return trapFocus()},get isValidEvent(){return isValidEvent2()},get onFocusOutside(){return $$props.onFocusOutside},forceMount:!1,get ref(){return $$props.ref}},()=>restProps))};if_block(node2,$$render=>{$$props.shouldRender&&$$render(consequent)})}append($$anchor,fragment)}function Popper_layer_force_mount($$anchor,$$props){let interactOutsideBehavior=prop($$props,"interactOutsideBehavior",3,"close"),trapFocus=prop(
-$$props,"trapFocus",3,!0),isValidEvent2=prop($$props,"isValidEvent",3,()=>!1),customAnchor=prop($$props,"customAnchor",3,null),isStatic=prop($$props,"isStatic",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","popper","onEscapeKeydown","escapeKeydownBehavior","preventOverflowTextSelection","id","onPointerDown","onPointerUp","side","sideOffset","align","alignOffset","arrowPadding","avoidCollisions","collisionBoundary","collisionPadding","sticky","hideWhenDetached","updatePositi\
-onStrategy","strategy","dir","preventScroll","wrapperId","style","onPlaced","onInteractOutside","onCloseAutoFocus","onOpenAutoFocus","onFocusOutside","interactOutsideBehavior","loop","trapFocus","isValidEvent","customAnchor","isStatic","enabled"]);Popper_layer_inner($$anchor,spread_props({get popper(){return $$props.popper},get onEscapeKeydown(){return $$props.onEscapeKeydown},get escapeKeydownBehavior(){return $$props.escapeKeydownBehavior},get preventOverflowTextSelection(){return $$props.preventOverflowTextSelection},
-get id(){return $$props.id},get onPointerDown(){return $$props.onPointerDown},get onPointerUp(){return $$props.onPointerUp},get side(){return $$props.side},get sideOffset(){return $$props.sideOffset},get align(){return $$props.align},get alignOffset(){return $$props.alignOffset},get arrowPadding(){return $$props.arrowPadding},get avoidCollisions(){return $$props.avoidCollisions},get collisionBoundary(){return $$props.collisionBoundary},get collisionPadding(){return $$props.collisionPadding},get sticky(){
-return $$props.sticky},get hideWhenDetached(){return $$props.hideWhenDetached},get updatePositionStrategy(){return $$props.updatePositionStrategy},get strategy(){return $$props.strategy},get dir(){return $$props.dir},get preventScroll(){return $$props.preventScroll},get wrapperId(){return $$props.wrapperId},get style(){return $$props.style},get onPlaced(){return $$props.onPlaced},get customAnchor(){return customAnchor()},get isStatic(){return isStatic()},get enabled(){return $$props.enabled},get onInteractOutside(){
-return $$props.onInteractOutside},get onCloseAutoFocus(){return $$props.onCloseAutoFocus},get onOpenAutoFocus(){return $$props.onOpenAutoFocus},get interactOutsideBehavior(){return interactOutsideBehavior()},get loop(){return $$props.loop},get trapFocus(){return trapFocus()},get isValidEvent(){return isValidEvent2()},get onFocusOutside(){return $$props.onFocusOutside}},()=>restProps,{forceMount:!0}))}var root_4$E=from_html("<div><div><!></div></div>"),root_9$s=from_html("<div><div><!></div></div\
->");function Select_content$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),forceMount=prop($$props,"forceMount",3,!1),side=prop($$props,"side",3,"bottom"),onInteractOutside=prop($$props,"onInteractOutside",3,noop$1),onEscapeKeydown=prop($$props,"onEscapeKeydown",3,noop$1),preventScroll=prop($$props,"preventScroll",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","forceMount",
-"side","onInteractOutside","onEscapeKeydown","children","child","preventScroll","style"]);const contentState=SelectContentState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),onInteractOutside:boxWith$1(()=>onInteractOutside()),onEscapeKeydown:boxWith$1(()=>onEscapeKeydown())}),mergedProps=user_derived(()=>mergeProps(restProps,contentState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent_1=$$anchor2=>{Popper_layer_force_mount($$anchor2,spread_props(
-()=>get$4(mergedProps),()=>contentState.popperProps,{get ref(){return contentState.opts.ref},get side(){return side()},get enabled(){return contentState.root.opts.open.current},get id(){return id2()},get preventScroll(){return preventScroll()},forceMount:!0,get shouldRender(){return contentState.shouldRender},popper:($$anchor3,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),{style:contentState.props.style},{style:$$props.
-style}));var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent=$$anchor4=>{var fragment_3=comment$2(),node_2=first_child(fragment_3);{let $0=user_derived(()=>({props:get$4(finalProps),wrapperProps:wrapperProps(),...contentState.snippetProps}));snippet(node_2,()=>$$props.child,()=>get$4($0))}append($$anchor4,fragment_3)},alternate=$$anchor4=>{var div=root_4$E();attribute_effect(div,()=>({...wrapperProps()}));var div_1=child(div);attribute_effect(div_1,()=>({...get$4(finalProps)}));
-var node_3=child(div_1);snippet(node_3,()=>$$props.children??noop$3),reset(div_1),reset(div),append($$anchor4,div)};if_block(node_1,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor3,fragment_2)},$$slots:{popper:!0}}))},alternate_2=$$anchor2=>{var fragment_4=comment$2(),node_4=first_child(fragment_4);{var consequent_3=$$anchor3=>{Popper_layer($$anchor3,spread_props(()=>get$4(mergedProps),()=>contentState.popperProps,{get ref(){return contentState.opts.ref},get side(){
-return side()},get open(){return contentState.root.opts.open.current},get id(){return id2()},get preventScroll(){return preventScroll()},forceMount:!1,get shouldRender(){return contentState.shouldRender},popper:($$anchor4,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),{style:contentState.props.style},{style:$$props.style}));var fragment_6=comment$2(),node_5=first_child(fragment_6);{var consequent_2=$$anchor5=>{
-var fragment_7=comment$2(),node_6=first_child(fragment_7);{let $0=user_derived(()=>({props:get$4(finalProps),wrapperProps:wrapperProps(),...contentState.snippetProps}));snippet(node_6,()=>$$props.child,()=>get$4($0))}append($$anchor5,fragment_7)},alternate_1=$$anchor5=>{var div_2=root_9$s();attribute_effect(div_2,()=>({...wrapperProps()}));var div_3=child(div_2);attribute_effect(div_3,()=>({...get$4(finalProps)}));var node_7=child(div_3);snippet(node_7,()=>$$props.children??noop$3),reset(div_3),
-reset(div_2),append($$anchor5,div_2)};if_block(node_5,$$render=>{$$props.child?$$render(consequent_2):$$render(alternate_1,!1)})}append($$anchor4,fragment_6)},$$slots:{popper:!0}}))};if_block(node_4,$$render=>{forceMount()||$$render(consequent_3)},!0)}append($$anchor2,fragment_4)};if_block(node2,$$render=>{forceMount()?$$render(consequent_1):$$render(alternate_2,!1)})}append($$anchor,fragment),pop()}function Mounted($$anchor,$$props){push$1($$props,!0);let mounted=prop($$props,"mounted",15,!1),onMountedChange=prop(
-$$props,"onMountedChange",3,noop$1);onMountEffect(()=>(mounted(!0),onMountedChange()(!0),()=>{mounted(!1),onMountedChange()(!1)})),pop()}var root_2$1k=from_html("<div><!></div>"),root$1K=from_html("<!> <!>",1);function Select_item$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),label=prop($$props,"label",19,()=>$$props.value),disabled=prop($$props,"disabled",3,!1),onHighlight=prop($$props,"onHighlight",3,
-noop$1),onUnhighlight=prop($$props,"onUnhighlight",3,noop$1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","value","label","disabled","children","child","onHighlight","onUnhighlight"]);const itemState=SelectItemState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),value:boxWith$1(()=>$$props.value),disabled:boxWith$1(()=>disabled()),label:boxWith$1(()=>label()),onHighlight:boxWith$1(()=>onHighlight()),onUnhighlight:boxWith$1(()=>onUnhighlight())}),
-mergedProps=user_derived(()=>mergeProps(restProps,itemState.props));var fragment=root$1K(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{let $0=user_derived(()=>({props:get$4(mergedProps),...itemState.snippetProps}));snippet(node_1,()=>$$props.child,()=>get$4($0))}append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1k();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.
-children??noop$3,()=>itemState.snippetProps),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}var node_3=sibling(node2,2);Mounted(node_3,{get mounted(){return itemState.mounted},set mounted($$value){itemState.mounted=$$value}}),append($$anchor,fragment),pop()}var root_2$1j=from_html("<div><!></div>");function Select_viewport($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),
-ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","children","child"]);const viewportState=SelectViewportState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,viewportState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(
-mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1j();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_3$$=from_html("<div><!></div>"),root_1$14=from_html("<!> <!>",1);function Select_scroll_down_button$1($$anchor,$$props){const uid=props_id();
-push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),delay=prop($$props,"delay",3,()=>50),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","delay","child","children"]);const scrollButtonState=SelectScrollDownButtonState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),delay:boxWith$1(()=>delay())}),mergedProps=user_derived(()=>mergeProps(restProps,scrollButtonState.props));var fragment=comment$2(),node2=first_child(
-fragment);{var consequent_1=$$anchor2=>{var fragment_1=root_1$14(),node_1=first_child(fragment_1);Mounted(node_1,{get mounted(){return scrollButtonState.scrollButtonState.mounted},set mounted($$value){scrollButtonState.scrollButtonState.mounted=$$value}});var node_2=sibling(node_1,2);{var consequent=$$anchor3=>{var fragment_2=comment$2(),node_3=first_child(fragment_2);snippet(node_3,()=>$$props.child,()=>({props:restProps})),append($$anchor3,fragment_2)},alternate=$$anchor3=>{var div=root_3$$();
-attribute_effect(div,()=>({...get$4(mergedProps)}));var node_4=child(div);snippet(node_4,()=>$$props.children??noop$3),reset(div),append($$anchor3,div)};if_block(node_2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment_1)};if_block(node2,$$render=>{scrollButtonState.canScrollDown&&$$render(consequent_1)})}append($$anchor,fragment),pop()}var root_3$_=from_html("<div><!></div>"),root_1$13=from_html("<!> <!>",1);function Select_scroll_up_button$1($$anchor,$$props){
-const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),delay=prop($$props,"delay",3,()=>50),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","delay","child","children"]);const scrollButtonState=SelectScrollUpButtonState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),delay:boxWith$1(()=>delay())}),mergedProps=user_derived(()=>mergeProps(restProps,scrollButtonState.props));var fragment=comment$2(),
-node2=first_child(fragment);{var consequent_1=$$anchor2=>{var fragment_1=root_1$13(),node_1=first_child(fragment_1);Mounted(node_1,{get mounted(){return scrollButtonState.scrollButtonState.mounted},set mounted($$value){scrollButtonState.scrollButtonState.mounted=$$value}});var node_2=sibling(node_1,2);{var consequent=$$anchor3=>{var fragment_2=comment$2(),node_3=first_child(fragment_2);snippet(node_3,()=>$$props.child,()=>({props:restProps})),append($$anchor3,fragment_2)},alternate=$$anchor3=>{var div=root_3$_();
-attribute_effect(div,()=>({...get$4(mergedProps)}));var node_4=child(div);snippet(node_4,()=>$$props.children??noop$3),reset(div),append($$anchor3,div)};if_block(node_2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment_1)};if_block(node2,$$render=>{scrollButtonState.canScrollUp&&$$render(consequent_1)})}append($$anchor,fragment),pop()}function Menu_sub($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15,!1),onOpenChange=prop($$props,
-"onOpenChange",3,noop$1),onOpenChangeComplete=prop($$props,"onOpenChangeComplete",3,noop$1);MenuSubmenuState.create({open:boxWith$1(()=>open2(),v=>{open2(v),onOpenChange()?.(v)}),onOpenChangeComplete:boxWith$1(()=>onOpenChangeComplete())}),Floating_layer($$anchor,{children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);snippet(node2,()=>$$props.children??noop$3),append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}var root_2$1i=from_html("<div><!></div>");
-function Menu_item($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),disabled=prop($$props,"disabled",3,!1),onSelect=prop($$props,"onSelect",3,noop$1),closeOnSelect=prop($$props,"closeOnSelect",3,!0),restProps=rest_props($$props,["$$slots","$$events","$$legacy","child","children","ref","id","disabled","onSelect","closeOnSelect"]);const itemState=MenuItemState.create({id:boxWith$1(()=>id2()),disabled:boxWith$1(
-()=>disabled()),onSelect:boxWith$1(()=>onSelect()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),closeOnSelect:boxWith$1(()=>closeOnSelect())}),mergedProps=user_derived(()=>mergeProps(restProps,itemState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1i();attribute_effect(div,
-()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_2$1h=from_html("<div><!></div>");function Menu_separator($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),restProps=rest_props($$props,["$$slots","$$ev\
-ents","$$legacy","ref","id","child","children"]);const separatorState=MenuSeparatorState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,separatorState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1h();
-attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_4$D=from_html("<div><div><!></div></div>"),root_9$r=from_html("<div><div><!></div></div>");function Menu_sub_content($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),
-ref2=prop($$props,"ref",15,null),loop2=prop($$props,"loop",3,!0),onInteractOutside=prop($$props,"onInteractOutside",3,noop$1),forceMount=prop($$props,"forceMount",3,!1),onEscapeKeydown=prop($$props,"onEscapeKeydown",3,noop$1),interactOutsideBehavior=prop($$props,"interactOutsideBehavior",3,"defer-otherwise-close"),escapeKeydownBehavior=prop($$props,"escapeKeydownBehavior",3,"defer-otherwise-close"),onOpenAutoFocusProp=prop($$props,"onOpenAutoFocus",3,noop$1),onCloseAutoFocusProp=prop($$props,"on\
-CloseAutoFocus",3,noop$1),onFocusOutside=prop($$props,"onFocusOutside",3,noop$1),side=prop($$props,"side",3,"right"),trapFocus=prop($$props,"trapFocus",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","children","child","loop","onInteractOutside","forceMount","onEscapeKeydown","interactOutsideBehavior","escapeKeydownBehavior","onOpenAutoFocus","onCloseAutoFocus","onFocusOutside","side","trapFocus","style"]);const subContentState=MenuContentState.create({id:boxWith$1(
-()=>id2()),loop:boxWith$1(()=>loop2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),isSub:!0,onCloseAutoFocus:boxWith$1(()=>handleCloseAutoFocus)});function onkeydown(e){const isKeyDownInside=e.currentTarget.contains(e.target),isCloseKey=SUB_CLOSE_KEYS[subContentState.parentMenu.root.opts.dir.current].includes(e.key);isKeyDownInside&&isCloseKey&&(subContentState.parentMenu.onClose(),subContentState.parentMenu.triggerNode?.focus(),e.preventDefault())}const dataAttr=user_derived(()=>subContentState.parentMenu.
-root.getBitsAttr("sub-content")),mergedProps=user_derived(()=>mergeProps(restProps,subContentState.props,{side:side(),onkeydown,[get$4(dataAttr)]:""}));function handleOpenAutoFocus(e){onOpenAutoFocusProp()(e),!e.defaultPrevented&&(e.preventDefault(),subContentState.parentMenu.root.isUsingKeyboard&&subContentState.parentMenu.contentNode&&MenuOpenEvent.dispatch(subContentState.parentMenu.contentNode))}function handleCloseAutoFocus(e){onCloseAutoFocusProp()(e),!e.defaultPrevented&&e.preventDefault()}
-function handleInteractOutside(e){onInteractOutside()(e),!e.defaultPrevented&&subContentState.parentMenu.onClose()}function handleEscapeKeydown(e){onEscapeKeydown()(e),!e.defaultPrevented&&subContentState.parentMenu.onClose()}function handleOnFocusOutside(e){onFocusOutside()(e),!e.defaultPrevented&&isHTMLElement$1(e.target)&&e.target.id!==subContentState.parentMenu.triggerNode?.id&&subContentState.parentMenu.onClose()}var fragment=comment$2(),node2=first_child(fragment);{var consequent_1=$$anchor2=>{
-Popper_layer_force_mount($$anchor2,spread_props(()=>get$4(mergedProps),{get ref(){return subContentState.opts.ref},get interactOutsideBehavior(){return interactOutsideBehavior()},get escapeKeydownBehavior(){return escapeKeydownBehavior()},onOpenAutoFocus:handleOpenAutoFocus,get enabled(){return subContentState.parentMenu.opts.open.current},onInteractOutside:handleInteractOutside,onEscapeKeydown:handleEscapeKeydown,onFocusOutside:handleOnFocusOutside,preventScroll:!1,get loop(){return loop2()},get trapFocus(){
-return trapFocus()},get shouldRender(){return subContentState.shouldRender},popper:($$anchor3,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),get$4(mergedProps),{style:getFloatingContentCSSVars("menu")},{style:$$props.style}));var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent=$$anchor4=>{var fragment_3=comment$2(),node_2=first_child(fragment_3);{let $0=user_derived(()=>({props:get$4(finalProps),
-wrapperProps:wrapperProps(),...subContentState.snippetProps}));snippet(node_2,()=>$$props.child,()=>get$4($0))}append($$anchor4,fragment_3)},alternate=$$anchor4=>{var div=root_4$D();attribute_effect(div,()=>({...wrapperProps()}));var div_1=child(div);attribute_effect(div_1,()=>({...get$4(finalProps)}));var node_3=child(div_1);snippet(node_3,()=>$$props.children??noop$3),reset(div_1),reset(div),append($$anchor4,div)};if_block(node_1,$$render=>{$$props.child?$$render(consequent):$$render(alternate,
-!1)})}append($$anchor3,fragment_2)},$$slots:{popper:!0}}))},alternate_2=$$anchor2=>{var fragment_4=comment$2(),node_4=first_child(fragment_4);{var consequent_3=$$anchor3=>{Popper_layer($$anchor3,spread_props(()=>get$4(mergedProps),{get ref(){return subContentState.opts.ref},get interactOutsideBehavior(){return interactOutsideBehavior()},get escapeKeydownBehavior(){return escapeKeydownBehavior()},onCloseAutoFocus:handleCloseAutoFocus,onOpenAutoFocus:handleOpenAutoFocus,get open(){return subContentState.
-parentMenu.opts.open.current},onInteractOutside:handleInteractOutside,onEscapeKeydown:handleEscapeKeydown,onFocusOutside:handleOnFocusOutside,preventScroll:!1,get loop(){return loop2()},get trapFocus(){return trapFocus()},get shouldRender(){return subContentState.shouldRender},popper:($$anchor4,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),get$4(mergedProps),{style:getFloatingContentCSSVars("menu")},{style:$$props.
-style}));var fragment_6=comment$2(),node_5=first_child(fragment_6);{var consequent_2=$$anchor5=>{var fragment_7=comment$2(),node_6=first_child(fragment_7);{let $0=user_derived(()=>({props:get$4(finalProps),wrapperProps:wrapperProps(),...subContentState.snippetProps}));snippet(node_6,()=>$$props.child,()=>get$4($0))}append($$anchor5,fragment_7)},alternate_1=$$anchor5=>{var div_2=root_9$r();attribute_effect(div_2,()=>({...wrapperProps()}));var div_3=child(div_2);attribute_effect(div_3,()=>({...get$4(
-finalProps)}));var node_7=child(div_3);snippet(node_7,()=>$$props.children??noop$3),reset(div_3),reset(div_2),append($$anchor5,div_2)};if_block(node_5,$$render=>{$$props.child?$$render(consequent_2):$$render(alternate_1,!1)})}append($$anchor4,fragment_6)},$$slots:{popper:!0}}))};if_block(node_4,$$render=>{forceMount()||$$render(consequent_3)},!0)}append($$anchor2,fragment_4)};if_block(node2,$$render=>{forceMount()?$$render(consequent_1):$$render(alternate_2,!1)})}append($$anchor,fragment),pop()}
-var root_3$Z=from_html("<div><!></div>");function Menu_sub_trigger($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),disabled=prop($$props,"disabled",3,!1),ref2=prop($$props,"ref",15,null),onSelect=prop($$props,"onSelect",3,noop$1),openDelay=prop($$props,"openDelay",3,100),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","disabled","ref","children","child","onSelect","openDelay"]);const subTriggerState=MenuSubTriggerState.
-create({disabled:boxWith$1(()=>disabled()),onSelect:boxWith$1(()=>onSelect()),id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),openDelay:boxWith$1(()=>openDelay())}),mergedProps=user_derived(()=>mergeProps(restProps,subTriggerState.props));Floating_layer_anchor($$anchor,{get id(){return id2()},get ref(){return subTriggerState.opts.ref},children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);{var consequent=$$anchor3=>{var fragment_2=comment$2(),node_1=first_child(
-fragment_2);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor3,fragment_2)},alternate=$$anchor3=>{var div=root_3$Z();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor3,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}function isPointInPolygon(point2,polygon){const[x,y]=point2;
-let isInside=!1;const length=polygon.length;for(let i=0,j=length-1;i<length;j=i++){const[xi,yi]=polygon[i]??[0,0],[xj,yj]=polygon[j]??[0,0];yi>=y!=yj>=y&&x<=(xj-xi)*(y-yi)/(yj-yi)+xi&&(isInside=!isInside)}return isInside}function isInsideRect(point2,rect){return point2[0]>=rect.left&&point2[0]<=rect.right&&point2[1]>=rect.top&&point2[1]<=rect.bottom}function getSide(triggerRect,contentRect){const triggerCenterX=triggerRect.left+triggerRect.width/2,triggerCenterY=triggerRect.top+triggerRect.height/
-2,contentCenterX=contentRect.left+contentRect.width/2,contentCenterY=contentRect.top+contentRect.height/2,deltaX=contentCenterX-triggerCenterX,deltaY=contentCenterY-triggerCenterY;return Math.abs(deltaX)>Math.abs(deltaY)?deltaX>0?"right":"left":deltaY>0?"bottom":"top"}class SafePolygon{#opts;#buffer;#exitPoint=null;#exitTarget=null;constructor(opts){this.#opts=opts,this.#buffer=opts.buffer??1,watch$1([opts.triggerNode,opts.contentNode,opts.enabled],([triggerNode,contentNode,enabled])=>{if(!triggerNode||
-!contentNode||!enabled){this.#exitPoint=null,this.#exitTarget=null;return}const doc=getDocument$1(triggerNode),handlePointerMove=e=>{this.#onPointerMove(e,triggerNode,contentNode)},handleTriggerLeave=e=>{const target2=e.relatedTarget;isElement$1(target2)&&contentNode.contains(target2)||(this.#exitPoint=[e.clientX,e.clientY],this.#exitTarget="content")},handleTriggerEnter=()=>{this.#exitPoint=null,this.#exitTarget=null},handleContentEnter=()=>{this.#exitPoint=null,this.#exitTarget=null},handleContentLeave=e=>{
-const target2=e.relatedTarget;isElement$1(target2)&&triggerNode.contains(target2)||(this.#exitPoint=[e.clientX,e.clientY],this.#exitTarget="trigger")};return[on(doc,"pointermove",handlePointerMove),on(triggerNode,"pointerleave",handleTriggerLeave),on(triggerNode,"pointerenter",handleTriggerEnter),on(contentNode,"pointerenter",handleContentEnter),on(contentNode,"pointerleave",handleContentLeave)].reduce((acc,cleanup)=>()=>{acc(),cleanup()},()=>{})})}#onPointerMove(e,triggerNode,contentNode){if(!this.#exitPoint||
-!this.#exitTarget)return;const clientPoint=[e.clientX,e.clientY],triggerRect=triggerNode.getBoundingClientRect(),contentRect=contentNode.getBoundingClientRect();if(this.#exitTarget==="content"&&isInsideRect(clientPoint,contentRect)){this.#exitPoint=null,this.#exitTarget=null;return}if(this.#exitTarget==="trigger"&&isInsideRect(clientPoint,triggerRect)){this.#exitPoint=null,this.#exitTarget=null;return}const side=getSide(triggerRect,contentRect),corridorPoly=this.#getCorridorPolygon(triggerRect,contentRect,
-side);if(corridorPoly&&isPointInPolygon(clientPoint,corridorPoly))return;const targetRect=this.#exitTarget==="content"?contentRect:triggerRect,safePoly=this.#getSafePolygon(this.#exitPoint,targetRect,side,this.#exitTarget);isPointInPolygon(clientPoint,safePoly)||(this.#exitPoint=null,this.#exitTarget=null,this.#opts.onPointerExit())}#getCorridorPolygon(triggerRect,contentRect,side){const buffer=this.#buffer;switch(side){case"top":return[[Math.min(triggerRect.left,contentRect.left)-buffer,triggerRect.
-top],[Math.min(triggerRect.left,contentRect.left)-buffer,contentRect.bottom],[Math.max(triggerRect.right,contentRect.right)+buffer,contentRect.bottom],[Math.max(triggerRect.right,contentRect.right)+buffer,triggerRect.top]];case"bottom":return[[Math.min(triggerRect.left,contentRect.left)-buffer,triggerRect.bottom],[Math.min(triggerRect.left,contentRect.left)-buffer,contentRect.top],[Math.max(triggerRect.right,contentRect.right)+buffer,contentRect.top],[Math.max(triggerRect.right,contentRect.right)+
-buffer,triggerRect.bottom]];case"left":return[[triggerRect.left,Math.min(triggerRect.top,contentRect.top)-buffer],[contentRect.right,Math.min(triggerRect.top,contentRect.top)-buffer],[contentRect.right,Math.max(triggerRect.bottom,contentRect.bottom)+buffer],[triggerRect.left,Math.max(triggerRect.bottom,contentRect.bottom)+buffer]];case"right":return[[triggerRect.right,Math.min(triggerRect.top,contentRect.top)-buffer],[contentRect.left,Math.min(triggerRect.top,contentRect.top)-buffer],[contentRect.
-left,Math.max(triggerRect.bottom,contentRect.bottom)+buffer],[triggerRect.right,Math.max(triggerRect.bottom,contentRect.bottom)+buffer]]}}#getSafePolygon(exitPoint,targetRect,side,exitTarget){const buffer=this.#buffer*4,[x,y]=exitPoint;switch(exitTarget==="trigger"?this.#flipSide(side):side){case"top":return[[x-buffer,y+buffer],[x+buffer,y+buffer],[targetRect.right+buffer,targetRect.bottom],[targetRect.right+buffer,targetRect.top],[targetRect.left-buffer,targetRect.top],[targetRect.left-buffer,targetRect.
-bottom]];case"bottom":return[[x-buffer,y-buffer],[x+buffer,y-buffer],[targetRect.right+buffer,targetRect.top],[targetRect.right+buffer,targetRect.bottom],[targetRect.left-buffer,targetRect.bottom],[targetRect.left-buffer,targetRect.top]];case"left":return[[x+buffer,y-buffer],[x+buffer,y+buffer],[targetRect.right,targetRect.bottom+buffer],[targetRect.left,targetRect.bottom+buffer],[targetRect.left,targetRect.top-buffer],[targetRect.right,targetRect.top-buffer]];case"right":return[[x-buffer,y-buffer],
-[x-buffer,y+buffer],[targetRect.left,targetRect.bottom+buffer],[targetRect.right,targetRect.bottom+buffer],[targetRect.right,targetRect.top-buffer],[targetRect.left,targetRect.top-buffer]]}}#flipSide(side){switch(side){case"top":return"bottom";case"bottom":return"top";case"left":return"right";case"right":return"left"}}}const popoverAttrs=createBitsAttrs({component:"popover",parts:["root","trigger","content","close","overlay"]}),PopoverRootContext=new Context$1("Popover.Root");class PopoverRootState{static create(opts){
-return PopoverRootContext.set(new PopoverRootState(opts))}opts;#contentNode=state$1(null);get contentNode(){return get$4(this.#contentNode)}set contentNode(value){set$1(this.#contentNode,value,!0)}contentPresence;#triggerNode=state$1(null);get triggerNode(){return get$4(this.#triggerNode)}set triggerNode(value){set$1(this.#triggerNode,value,!0)}#overlayNode=state$1(null);get overlayNode(){return get$4(this.#overlayNode)}set overlayNode(value){set$1(this.#overlayNode,value,!0)}overlayPresence;#openedViaHover=state$1(
-!1);get openedViaHover(){return get$4(this.#openedViaHover)}set openedViaHover(value){set$1(this.#openedViaHover,value,!0)}#hasInteractedWithContent=state$1(!1);get hasInteractedWithContent(){return get$4(this.#hasInteractedWithContent)}set hasInteractedWithContent(value){set$1(this.#hasInteractedWithContent,value,!0)}#hoverCooldown=state$1(!1);get hoverCooldown(){return get$4(this.#hoverCooldown)}set hoverCooldown(value){set$1(this.#hoverCooldown,value,!0)}#closeDelay=state$1(0);get closeDelay(){
-return get$4(this.#closeDelay)}set closeDelay(value){set$1(this.#closeDelay,value,!0)}#closeTimeout=null;#domContext=null;constructor(opts){this.opts=opts,this.contentPresence=new PresenceManager({ref:boxWith$1(()=>this.contentNode),open:this.opts.open,onComplete:()=>{this.opts.onOpenChangeComplete.current(this.opts.open.current)}}),this.overlayPresence=new PresenceManager({ref:boxWith$1(()=>this.overlayNode),open:this.opts.open}),watch$1(()=>this.opts.open.current,isOpen=>{isOpen||(this.openedViaHover=
-!1,this.hasInteractedWithContent=!1,this.#clearCloseTimeout())})}setDomContext(ctx){this.#domContext=ctx}#clearCloseTimeout(){this.#closeTimeout!==null&&this.#domContext&&(this.#domContext.clearTimeout(this.#closeTimeout),this.#closeTimeout=null)}toggleOpen(){this.#clearCloseTimeout(),this.opts.open.current=!this.opts.open.current}handleClose(){this.#clearCloseTimeout(),this.opts.open.current&&(this.opts.open.current=!1)}handleHoverOpen(){this.#clearCloseTimeout(),!this.opts.open.current&&(this.
-openedViaHover=!0,this.opts.open.current=!0)}handleHoverClose(){this.opts.open.current&&this.openedViaHover&&!this.hasInteractedWithContent&&(this.opts.open.current=!1)}handleDelayedHoverClose(){this.opts.open.current&&(!this.openedViaHover||this.hasInteractedWithContent||(this.#clearCloseTimeout(),this.closeDelay<=0?this.opts.open.current=!1:this.#domContext&&(this.#closeTimeout=this.#domContext.setTimeout(()=>{this.openedViaHover&&!this.hasInteractedWithContent&&(this.opts.open.current=!1),this.#closeTimeout=
-null},this.closeDelay))))}cancelDelayedClose(){this.#clearCloseTimeout()}markInteraction(){this.hasInteractedWithContent=!0,this.#clearCloseTimeout()}}class PopoverTriggerState{static create(opts){return new PopoverTriggerState(opts,PopoverRootContext.get())}opts;root;attachment;domContext;#openTimeout=null;#closeTimeout=null;#isHovering=state$1(!1);constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref,v=>this.root.triggerNode=v),this.domContext=new DOMContext(
-opts.ref),this.root.setDomContext(this.domContext),this.onclick=this.onclick.bind(this),this.onkeydown=this.onkeydown.bind(this),this.onpointerenter=this.onpointerenter.bind(this),this.onpointerleave=this.onpointerleave.bind(this),watch$1(()=>this.opts.closeDelay.current,delay=>{this.root.closeDelay=delay})}#clearOpenTimeout(){this.#openTimeout!==null&&(this.domContext.clearTimeout(this.#openTimeout),this.#openTimeout=null)}#clearCloseTimeout(){this.#closeTimeout!==null&&(this.domContext.clearTimeout(
-this.#closeTimeout),this.#closeTimeout=null)}#clearAllTimeouts(){this.#clearOpenTimeout(),this.#clearCloseTimeout()}onpointerenter(e){if(this.opts.disabled.current||!this.opts.openOnHover.current||isTouch(e)||(set$1(this.#isHovering,!0),this.#clearCloseTimeout(),this.root.cancelDelayedClose(),this.root.opts.open.current||this.root.hoverCooldown))return;const delay=this.opts.openDelay.current;delay<=0?this.root.handleHoverOpen():this.#openTimeout=this.domContext.setTimeout(()=>{this.root.handleHoverOpen(),
-this.#openTimeout=null},delay)}onpointerleave(e){this.opts.disabled.current||this.opts.openOnHover.current&&(isTouch(e)||(set$1(this.#isHovering,!1),this.#clearOpenTimeout(),this.root.hoverCooldown=!1))}onclick(e){if(!this.opts.disabled.current&&e.button===0){if(this.#clearAllTimeouts(),get$4(this.#isHovering)&&this.root.opts.open.current&&this.root.openedViaHover){this.root.openedViaHover=!1,this.root.hasInteractedWithContent=!0;return}get$4(this.#isHovering)&&this.opts.openOnHover.current&&this.
-root.opts.open.current&&(this.root.hoverCooldown=!0),this.root.hoverCooldown&&!this.root.opts.open.current&&(this.root.hoverCooldown=!1),this.root.toggleOpen()}}onkeydown(e){this.opts.disabled.current||(e.key===ENTER||e.key===SPACE)&&(e.preventDefault(),this.#clearAllTimeouts(),this.root.toggleOpen())}#getAriaControls(){if(this.root.opts.open.current&&this.root.contentNode?.id)return this.root.contentNode?.id}#props=user_derived(()=>({id:this.opts.id.current,"aria-haspopup":"dialog","aria-expand\
-ed":boolToStr(this.root.opts.open.current),"data-state":getDataOpenClosed(this.root.opts.open.current),"aria-controls":this.#getAriaControls(),[popoverAttrs.trigger]:"",disabled:this.opts.disabled.current,onkeydown:this.onkeydown,onclick:this.onclick,onpointerenter:this.onpointerenter,onpointerleave:this.onpointerleave,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class PopoverContentState{static create(opts){return new PopoverContentState(
-opts,PopoverRootContext.get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref,v=>this.root.contentNode=v),this.onpointerdown=this.onpointerdown.bind(this),this.onfocusin=this.onfocusin.bind(this),this.onpointerenter=this.onpointerenter.bind(this),this.onpointerleave=this.onpointerleave.bind(this),new SafePolygon({triggerNode:()=>this.root.triggerNode,contentNode:()=>this.root.contentNode,enabled:()=>this.root.opts.open.current&&
-this.root.openedViaHover&&!this.root.hasInteractedWithContent,onPointerExit:()=>{this.root.handleDelayedHoverClose()}})}onpointerdown(_){this.root.markInteraction()}onfocusin(e){const target2=e.target;isElement$1(target2)&&isTabbable(target2)&&this.root.markInteraction()}onpointerenter(e){isTouch(e)||this.root.cancelDelayedClose()}onpointerleave(e){isTouch(e)}onInteractOutside=e=>{if(this.opts.onInteractOutside.current(e),e.defaultPrevented||!isElement$1(e.target))return;const closestTrigger=e.target.
-closest(popoverAttrs.selector("trigger"));if(!(closestTrigger&&closestTrigger===this.root.triggerNode)){if(this.opts.customAnchor.current){if(isElement$1(this.opts.customAnchor.current)){if(this.opts.customAnchor.current.contains(e.target))return}else if(typeof this.opts.customAnchor.current=="string"){const el=document.querySelector(this.opts.customAnchor.current);if(el&&el.contains(e.target))return}}this.root.handleClose()}};onEscapeKeydown=e=>{this.opts.onEscapeKeydown.current(e),!e.defaultPrevented&&
-this.root.handleClose()};get shouldRender(){return this.root.contentPresence.shouldRender}get shouldTrapFocus(){return!(this.root.openedViaHover&&!this.root.hasInteractedWithContent)}#snippetProps=user_derived(()=>({open:this.root.opts.open.current}));get snippetProps(){return get$4(this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}#props=user_derived(()=>({id:this.opts.id.current,tabindex:-1,"data-state":getDataOpenClosed(this.root.opts.open.current),[popoverAttrs.content]:"",
-style:{pointerEvents:"auto",contain:"layout style paint"},onpointerdown:this.onpointerdown,onfocusin:this.onfocusin,onpointerenter:this.onpointerenter,onpointerleave:this.onpointerleave,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}popperProps={onInteractOutside:this.onInteractOutside,onEscapeKeydown:this.onEscapeKeydown}}var root_4$C=from_html("<div><div><!></div></div>"),root_9$q=from_html("<div><div><!></div></div>");function Popover_content$1($$anchor,$$props){
-const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),forceMount=prop($$props,"forceMount",3,!1),onOpenAutoFocus=prop($$props,"onOpenAutoFocus",3,noop$1),onCloseAutoFocus=prop($$props,"onCloseAutoFocus",3,noop$1),onEscapeKeydown=prop($$props,"onEscapeKeydown",3,noop$1),onInteractOutside=prop($$props,"onInteractOutside",3,noop$1),trapFocus=prop($$props,"trapFocus",3,!0),preventScroll=prop($$props,"preventScroll",3,!1),customAnchor=prop(
-$$props,"customAnchor",3,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","child","children","ref","id","forceMount","onOpenAutoFocus","onCloseAutoFocus","onEscapeKeydown","onInteractOutside","trapFocus","preventScroll","customAnchor","style"]);const contentState=PopoverContentState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),onInteractOutside:boxWith$1(()=>onInteractOutside()),onEscapeKeydown:boxWith$1(()=>onEscapeKeydown()),customAnchor:boxWith$1(()=>customAnchor())}),
-mergedProps=user_derived(()=>mergeProps(restProps,contentState.props)),effectiveTrapFocus=user_derived(()=>trapFocus()&&contentState.shouldTrapFocus);function handleOpenAutoFocus(e){contentState.shouldTrapFocus||e.preventDefault(),onOpenAutoFocus()(e)}var fragment=comment$2(),node2=first_child(fragment);{var consequent_1=$$anchor2=>{Popper_layer_force_mount($$anchor2,spread_props(()=>get$4(mergedProps),()=>contentState.popperProps,{get ref(){return contentState.opts.ref},get enabled(){return contentState.
-root.opts.open.current},get id(){return id2()},get trapFocus(){return get$4(effectiveTrapFocus)},get preventScroll(){return preventScroll()},loop:!0,forceMount:!0,get customAnchor(){return customAnchor()},onOpenAutoFocus:handleOpenAutoFocus,get onCloseAutoFocus(){return onCloseAutoFocus()},get shouldRender(){return contentState.shouldRender},popper:($$anchor3,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),{style:getFloatingContentCSSVars(
-"popover")},{style:$$props.style}));var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent=$$anchor4=>{var fragment_3=comment$2(),node_2=first_child(fragment_3);{let $0=user_derived(()=>({props:get$4(finalProps),wrapperProps:wrapperProps(),...contentState.snippetProps}));snippet(node_2,()=>$$props.child,()=>get$4($0))}append($$anchor4,fragment_3)},alternate=$$anchor4=>{var div=root_4$C();attribute_effect(div,()=>({...wrapperProps()}));var div_1=child(div);attribute_effect(div_1,
-()=>({...get$4(finalProps)}));var node_3=child(div_1);snippet(node_3,()=>$$props.children??noop$3),reset(div_1),reset(div),append($$anchor4,div)};if_block(node_1,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor3,fragment_2)},$$slots:{popper:!0}}))},alternate_2=$$anchor2=>{var fragment_4=comment$2(),node_4=first_child(fragment_4);{var consequent_3=$$anchor3=>{Popper_layer($$anchor3,spread_props(()=>get$4(mergedProps),()=>contentState.popperProps,{get ref(){return contentState.
-opts.ref},get open(){return contentState.root.opts.open.current},get id(){return id2()},get trapFocus(){return get$4(effectiveTrapFocus)},get preventScroll(){return preventScroll()},loop:!0,forceMount:!1,get customAnchor(){return customAnchor()},onOpenAutoFocus:handleOpenAutoFocus,get onCloseAutoFocus(){return onCloseAutoFocus()},get shouldRender(){return contentState.shouldRender},popper:($$anchor4,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(
-()=>mergeProps(props(),{style:getFloatingContentCSSVars("popover")},{style:$$props.style}));var fragment_6=comment$2(),node_5=first_child(fragment_6);{var consequent_2=$$anchor5=>{var fragment_7=comment$2(),node_6=first_child(fragment_7);{let $0=user_derived(()=>({props:get$4(finalProps),wrapperProps:wrapperProps(),...contentState.snippetProps}));snippet(node_6,()=>$$props.child,()=>get$4($0))}append($$anchor5,fragment_7)},alternate_1=$$anchor5=>{var div_2=root_9$q();attribute_effect(div_2,()=>({
-...wrapperProps()}));var div_3=child(div_2);attribute_effect(div_3,()=>({...get$4(finalProps)}));var node_7=child(div_3);snippet(node_7,()=>$$props.children??noop$3),reset(div_3),reset(div_2),append($$anchor5,div_2)};if_block(node_5,$$render=>{$$props.child?$$render(consequent_2):$$render(alternate_1,!1)})}append($$anchor4,fragment_6)},$$slots:{popper:!0}}))};if_block(node_4,$$render=>{forceMount()||$$render(consequent_3)},!0)}append($$anchor2,fragment_4)};if_block(node2,$$render=>{forceMount()?
-$$render(consequent_1):$$render(alternate_2,!1)})}append($$anchor,fragment),pop()}var root_3$Y=from_html("<button><!></button>");function Popover_trigger$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),type2=prop($$props,"type",3,"button"),disabled=prop($$props,"disabled",3,!1),openOnHover=prop($$props,"openOnHover",3,!1),openDelay=prop($$props,"openDelay",3,700),closeDelay=prop($$props,"closeDelay",3,300),
-restProps=rest_props($$props,["$$slots","$$events","$$legacy","children","child","id","ref","type","disabled","openOnHover","openDelay","closeDelay"]);const triggerState=PopoverTriggerState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),disabled:boxWith$1(()=>!!disabled()),openOnHover:boxWith$1(()=>openOnHover()),openDelay:boxWith$1(()=>openDelay()),closeDelay:boxWith$1(()=>closeDelay())}),mergedProps=user_derived(()=>mergeProps(restProps,triggerState.props,{type:type2()}));
-Floating_layer_anchor($$anchor,{get id(){return id2()},get ref(){return triggerState.opts.ref},children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);{var consequent=$$anchor3=>{var fragment_2=comment$2(),node_1=first_child(fragment_2);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor3,fragment_2)},alternate=$$anchor3=>{var button=root_3$Y();attribute_effect(button,()=>({...get$4(mergedProps)}));var node_2=child(button);snippet(
-node_2,()=>$$props.children??noop$3),reset(button),append($$anchor3,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}function Dialog($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15,!1),onOpenChange=prop($$props,"onOpenChange",3,noop$1),onOpenChangeComplete=prop($$props,"onOpenChangeComplete",3,noop$1);DialogRootState.create({variant:boxWith$1(()=>"dialog"),open:boxWith$1(
-()=>open2(),v=>{open2(v),onOpenChange()(v)}),onOpenChangeComplete:boxWith$1(()=>onOpenChangeComplete())});var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.children??noop$3),append($$anchor,fragment),pop()}var root_2$1g=from_html("<button><!></button>");function Dialog_close($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),disabled=prop($$props,"disabled",3,!1),restProps=rest_props(
-$$props,["$$slots","$$events","$$legacy","children","child","id","ref","disabled"]);const closeState=DialogCloseState.create({variant:boxWith$1(()=>"close"),id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),disabled:boxWith$1(()=>!!disabled())}),mergedProps=user_derived(()=>mergeProps(restProps,closeState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({
-props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var button=root_2$1g();attribute_effect(button,()=>({...get$4(mergedProps)}));var node_2=child(button);snippet(node_2,()=>$$props.children??noop$3),reset(button),append($$anchor2,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_6$B=from_html("<!> <!>",1),root_8$q=from_html("<!> <div><!></div>",1);function Dialog_content$1($$anchor,$$props){
-const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),forceMount=prop($$props,"forceMount",3,!1),onCloseAutoFocus=prop($$props,"onCloseAutoFocus",3,noop$1),onOpenAutoFocus=prop($$props,"onOpenAutoFocus",3,noop$1),onEscapeKeydown=prop($$props,"onEscapeKeydown",3,noop$1),onInteractOutside=prop($$props,"onInteractOutside",3,noop$1),trapFocus=prop($$props,"trapFocus",3,!0),preventScroll=prop($$props,"preventScroll",3,!0),restoreScrollDelay=prop(
-$$props,"restoreScrollDelay",3,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","children","child","ref","forceMount","onCloseAutoFocus","onOpenAutoFocus","onEscapeKeydown","onInteractOutside","trapFocus","preventScroll","restoreScrollDelay"]);const contentState=DialogContentState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,contentState.props));var fragment=comment$2(),node2=first_child(fragment);
-{var consequent_2=$$anchor2=>{Focus_scope($$anchor2,{get ref(){return contentState.opts.ref},loop:!0,get trapFocus(){return trapFocus()},get enabled(){return contentState.root.opts.open.current},get onOpenAutoFocus(){return onOpenAutoFocus()},get onCloseAutoFocus(){return onCloseAutoFocus()},focusScope:($$anchor3,$$arg0)=>{let focusScopeProps=()=>$$arg0?.().props;Escape_layer($$anchor3,spread_props(()=>get$4(mergedProps),{get enabled(){return contentState.root.opts.open.current},get ref(){return contentState.
-opts.ref},onEscapeKeydown:e=>{onEscapeKeydown()(e),!e.defaultPrevented&&contentState.root.handleClose()},children:($$anchor4,$$slotProps)=>{Dismissible_layer($$anchor4,spread_props(()=>get$4(mergedProps),{get ref(){return contentState.opts.ref},get enabled(){return contentState.root.opts.open.current},onInteractOutside:e=>{onInteractOutside()(e),!e.defaultPrevented&&contentState.root.handleClose()},children:($$anchor5,$$slotProps2)=>{Text_selection_layer($$anchor5,spread_props(()=>get$4(mergedProps),
-{get ref(){return contentState.opts.ref},get enabled(){return contentState.root.opts.open.current},children:($$anchor6,$$slotProps3)=>{var fragment_5=comment$2(),node_1=first_child(fragment_5);{var consequent_1=$$anchor7=>{var fragment_6=root_6$B(),node_2=first_child(fragment_6);{var consequent=$$anchor8=>{Scroll_lock($$anchor8,{get preventScroll(){return preventScroll()},get restoreScrollDelay(){return restoreScrollDelay()}})};if_block(node_2,$$render=>{contentState.root.opts.open.current&&$$render(
-consequent)})}var node_3=sibling(node_2,2);{let $0=user_derived(()=>({props:mergeProps(get$4(mergedProps),focusScopeProps()),...contentState.snippetProps}));snippet(node_3,()=>$$props.child,()=>get$4($0))}append($$anchor7,fragment_6)},alternate=$$anchor7=>{var fragment_8=root_8$q(),node_4=first_child(fragment_8);Scroll_lock(node_4,{get preventScroll(){return preventScroll()}});var div=sibling(node_4,2);attribute_effect(div,$0=>({...$0}),[()=>mergeProps(get$4(mergedProps),focusScopeProps())]);var node_5=child(
-div);snippet(node_5,()=>$$props.children??noop$3),reset(div),append($$anchor7,fragment_8)};if_block(node_1,$$render=>{$$props.child?$$render(consequent_1):$$render(alternate,!1)})}append($$anchor6,fragment_5)},$$slots:{default:!0}}))},$$slots:{default:!0}}))},$$slots:{default:!0}}))},$$slots:{focusScope:!0}})};if_block(node2,$$render=>{(contentState.shouldRender||forceMount())&&$$render(consequent_2)})}append($$anchor,fragment),pop()}function Menu($$anchor,$$props){push$1($$props,!0);let open2=prop(
-$$props,"open",15,!1),dir=prop($$props,"dir",3,"ltr"),onOpenChange=prop($$props,"onOpenChange",3,noop$1),onOpenChangeComplete=prop($$props,"onOpenChangeComplete",3,noop$1),variant=prop($$props,"_internal_variant",3,"dropdown-menu");const root2=MenuRootState.create({variant:boxWith$1(()=>variant()),dir:boxWith$1(()=>dir()),onClose:()=>{open2(!1),onOpenChange()(!1)}});MenuMenuState.create({open:boxWith$1(()=>open2(),v=>{open2(v),onOpenChange()(v)}),onOpenChangeComplete:boxWith$1(()=>onOpenChangeComplete())},
-root2),Floating_layer($$anchor,{children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);snippet(node2,()=>$$props.children??noop$3),append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}var root_4$B=from_html("<div><div><!></div></div>"),root_9$p=from_html("<div><div><!></div></div>");function Dropdown_menu_content$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),loop2=prop(
-$$props,"loop",3,!0),onInteractOutside=prop($$props,"onInteractOutside",3,noop$1),onEscapeKeydown=prop($$props,"onEscapeKeydown",3,noop$1),onCloseAutoFocus=prop($$props,"onCloseAutoFocus",3,noop$1),forceMount=prop($$props,"forceMount",3,!1),trapFocus=prop($$props,"trapFocus",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","child","children","ref","loop","onInteractOutside","onEscapeKeydown","onCloseAutoFocus","forceMount","trapFocus","style"]);const contentState=MenuContentState.
-create({id:boxWith$1(()=>id2()),loop:boxWith$1(()=>loop2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),onCloseAutoFocus:boxWith$1(()=>onCloseAutoFocus())}),mergedProps=user_derived(()=>mergeProps(restProps,contentState.props));function handleInteractOutside(e){if(contentState.handleInteractOutside(e),!e.defaultPrevented&&(onInteractOutside()(e),!e.defaultPrevented)){if(e.target&&e.target instanceof Element){const subContentSelector=`[${contentState.parentMenu.root.getBitsAttr("sub-content")}]`;if(e.target.
-closest(subContentSelector))return}contentState.parentMenu.onClose()}}function handleEscapeKeydown(e){onEscapeKeydown()(e),!e.defaultPrevented&&contentState.parentMenu.onClose()}var fragment=comment$2(),node2=first_child(fragment);{var consequent_1=$$anchor2=>{Popper_layer_force_mount($$anchor2,spread_props(()=>get$4(mergedProps),()=>contentState.popperProps,{get ref(){return contentState.opts.ref},get enabled(){return contentState.parentMenu.opts.open.current},onInteractOutside:handleInteractOutside,
-onEscapeKeydown:handleEscapeKeydown,get trapFocus(){return trapFocus()},get loop(){return loop2()},forceMount:!0,get id(){return id2()},get shouldRender(){return contentState.shouldRender},popper:($$anchor3,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),{style:getFloatingContentCSSVars("dropdown-menu")},{style:$$props.style}));var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent=$$anchor4=>{
-var fragment_3=comment$2(),node_2=first_child(fragment_3);{let $0=user_derived(()=>({props:get$4(finalProps),wrapperProps:wrapperProps(),...contentState.snippetProps}));snippet(node_2,()=>$$props.child,()=>get$4($0))}append($$anchor4,fragment_3)},alternate=$$anchor4=>{var div=root_4$B();attribute_effect(div,()=>({...wrapperProps()}));var div_1=child(div);attribute_effect(div_1,()=>({...get$4(finalProps)}));var node_3=child(div_1);snippet(node_3,()=>$$props.children??noop$3),reset(div_1),reset(div),
-append($$anchor4,div)};if_block(node_1,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor3,fragment_2)},$$slots:{popper:!0}}))},alternate_2=$$anchor2=>{var fragment_4=comment$2(),node_4=first_child(fragment_4);{var consequent_3=$$anchor3=>{Popper_layer($$anchor3,spread_props(()=>get$4(mergedProps),()=>contentState.popperProps,{get ref(){return contentState.opts.ref},get open(){return contentState.parentMenu.opts.open.current},onInteractOutside:handleInteractOutside,
-onEscapeKeydown:handleEscapeKeydown,get trapFocus(){return trapFocus()},get loop(){return loop2()},forceMount:!1,get id(){return id2()},get shouldRender(){return contentState.shouldRender},popper:($$anchor4,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),{style:getFloatingContentCSSVars("dropdown-menu")},{style:$$props.style}));var fragment_6=comment$2(),node_5=first_child(fragment_6);{var consequent_2=$$anchor5=>{
-var fragment_7=comment$2(),node_6=first_child(fragment_7);{let $0=user_derived(()=>({props:get$4(finalProps),wrapperProps:wrapperProps(),...contentState.snippetProps}));snippet(node_6,()=>$$props.child,()=>get$4($0))}append($$anchor5,fragment_7)},alternate_1=$$anchor5=>{var div_2=root_9$p();attribute_effect(div_2,()=>({...wrapperProps()}));var div_3=child(div_2);attribute_effect(div_3,()=>({...get$4(finalProps)}));var node_7=child(div_3);snippet(node_7,()=>$$props.children??noop$3),reset(div_3),
-reset(div_2),append($$anchor5,div_2)};if_block(node_5,$$render=>{$$props.child?$$render(consequent_2):$$render(alternate_1,!1)})}append($$anchor4,fragment_6)},$$slots:{popper:!0}}))};if_block(node_4,$$render=>{forceMount()||$$render(consequent_3)},!0)}append($$anchor2,fragment_4)};if_block(node2,$$render=>{forceMount()?$$render(consequent_1):$$render(alternate_2,!1)})}append($$anchor,fragment),pop()}var root_3$X=from_html("<button><!></button>");function Menu_trigger($$anchor,$$props){const uid=props_id();
-push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),disabled=prop($$props,"disabled",3,!1),type2=prop($$props,"type",3,"button"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","child","children","disabled","type"]);const triggerState=DropdownMenuTriggerState.create({id:boxWith$1(()=>id2()),disabled:boxWith$1(()=>disabled()??!1),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,triggerState.
-props,{type:type2()}));Floating_layer_anchor($$anchor,{get id(){return id2()},get ref(){return triggerState.opts.ref},children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);{var consequent=$$anchor3=>{var fragment_2=comment$2(),node_1=first_child(fragment_2);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor3,fragment_2)},alternate=$$anchor3=>{var button=root_3$X();attribute_effect(button,()=>({...get$4(mergedProps)}));var node_2=child(
-button);snippet(node_2,()=>$$props.children??noop$3),reset(button),append($$anchor3,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}const labelAttrs=createBitsAttrs({component:"label",parts:["root"]});class LabelRootState{static create(opts){return new LabelRootState(opts)}opts;attachment;constructor(opts){this.opts=opts,this.attachment=attachRef(this.opts.ref),this.onmousedown=this.onmousedown.
-bind(this)}onmousedown(e){e.detail>1&&e.preventDefault()}#props=user_derived(()=>({id:this.opts.id.current,[labelAttrs.root]:"",onmousedown:this.onmousedown,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}var root_2$1f=from_html("<label><!></label>");function Label$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots",
-"$$events","$$legacy","children","child","id","ref","for"]);const rootState=LabelRootState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,rootState.props,{for:$$props.for}));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{
-var label=root_2$1f();attribute_effect(label,()=>({...get$4(mergedProps),for:$$props.for}));var node_2=child(label);snippet(node_2,()=>$$props.children??noop$3),reset(label),append($$anchor2,label)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}class SvelteResizeObserver{#node;#onResize;constructor(node2,onResize){this.#node=node2,this.#onResize=onResize,this.handler=this.handler.bind(this),user_effect(this.handler)}handler(){
-let rAF=0;const _node=this.#node();if(!_node)return;const resizeObserver=new ResizeObserver(()=>{cancelAnimationFrame(rAF),rAF=window.requestAnimationFrame(this.#onResize)});return resizeObserver.observe(_node),()=>{window.cancelAnimationFrame(rAF),resizeObserver.unobserve(_node)}}}class StateMachine{state;#machine;constructor(initialState,machine){this.state=simpleBox(initialState),this.#machine=machine,this.dispatch=this.dispatch.bind(this)}#reducer(event2){return this.#machine[this.state.current][event2]??
-this.state.current}dispatch(event2){this.state.current=this.#reducer(event2)}}const animationNameCache=new WeakMap,ANIMATION_NAME_CACHE_TTL_MS=16,presenceMachine={mounted:{UNMOUNT:"unmounted",ANIMATION_OUT:"unmountSuspended"},unmountSuspended:{MOUNT:"mounted",ANIMATION_END:"unmounted"},unmounted:{MOUNT:"mounted"}};class Presence{opts;#prevAnimationNameState=state$1("none");get prevAnimationNameState(){return get$4(this.#prevAnimationNameState)}set prevAnimationNameState(value){set$1(this.#prevAnimationNameState,
-value,!0)}#styles=state$1(proxy({display:"",animationName:"none"}));get styles(){return get$4(this.#styles)}set styles(value){set$1(this.#styles,value,!0)}initialStatus;previousPresent;machine;present;constructor(opts){this.opts=opts,this.present=this.opts.open,this.initialStatus=opts.open.current?"mounted":"unmounted",this.previousPresent=new Previous(()=>this.present.current),this.machine=new StateMachine(this.initialStatus,presenceMachine),this.handleAnimationEnd=this.handleAnimationEnd.bind(
-this),this.handleAnimationStart=this.handleAnimationStart.bind(this),watchPresenceChange(this),watchStatusChange(this),watchRefChange(this)}handleAnimationEnd(event2){if(!this.opts.ref.current)return;const currAnimationName=this.styles.animationName||getAnimationName(this.opts.ref.current),isCurrentAnimation=currAnimationName.includes(event2.animationName)||currAnimationName==="none";event2.target===this.opts.ref.current&&isCurrentAnimation&&this.machine.dispatch("ANIMATION_END")}handleAnimationStart(event2){
-if(this.opts.ref.current&&event2.target===this.opts.ref.current){const animationName=getAnimationName(this.opts.ref.current,!0);this.prevAnimationNameState=animationName,this.styles.animationName=animationName}}#isPresent=user_derived(()=>["mounted","unmountSuspended"].includes(this.machine.state.current));get isPresent(){return get$4(this.#isPresent)}set isPresent(value){set$1(this.#isPresent,value)}}function watchPresenceChange(state2){watch$1(()=>state2.present.current,()=>{if(!state2.opts.ref.
-current||!(state2.present.current!==state2.previousPresent.current))return;const prevAnimationName=state2.prevAnimationNameState,currAnimationName=getAnimationName(state2.opts.ref.current,!0);if(state2.styles.animationName=currAnimationName,state2.present.current)state2.machine.dispatch("MOUNT");else if(currAnimationName==="none"||state2.styles.display==="none")state2.machine.dispatch("UNMOUNT");else{const isAnimating=prevAnimationName!==currAnimationName;state2.previousPresent.current&&isAnimating?
-state2.machine.dispatch("ANIMATION_OUT"):state2.machine.dispatch("UNMOUNT")}})}function watchStatusChange(state2){watch$1(()=>state2.machine.state.current,()=>{if(!state2.opts.ref.current)return;const currAnimationName=state2.machine.state.current==="mounted"?getAnimationName(state2.opts.ref.current,!0):"none";state2.prevAnimationNameState=currAnimationName,state2.styles.animationName=currAnimationName})}function watchRefChange(state2){watch$1(()=>state2.opts.ref.current,()=>{if(!state2.opts.ref.
-current)return;const computed=getComputedStyle(state2.opts.ref.current);return state2.styles={display:computed.display,animationName:computed.animationName||"none"},executeCallbacks(on(state2.opts.ref.current,"animationstart",state2.handleAnimationStart),on(state2.opts.ref.current,"animationcancel",state2.handleAnimationEnd),on(state2.opts.ref.current,"animationend",state2.handleAnimationEnd))})}function getAnimationName(node2,forceRefresh=!1){if(!node2)return"none";const now2=performance.now(),
-cached2=animationNameCache.get(node2);if(!forceRefresh&&cached2&&now2-cached2.timestamp<ANIMATION_NAME_CACHE_TTL_MS)return cached2.value;const value=getComputedStyle(node2).animationName||"none";return animationNameCache.set(node2,{value,timestamp:now2}),value}function Presence_layer($$anchor,$$props){push$1($$props,!0);const presenceState=new Presence({open:boxWith$1(()=>$$props.open),ref:$$props.ref});var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),
-node_1=first_child(fragment_1);snippet(node_1,()=>$$props.presence??noop$3,()=>({present:presenceState.isPresent})),append($$anchor2,fragment_1)};if_block(node2,$$render=>{($$props.forceMount||$$props.open||presenceState.isPresent)&&$$render(consequent)})}append($$anchor,fragment),pop()}function Popover$1($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15,!1),onOpenChange=prop($$props,"onOpenChange",3,noop$1),onOpenChangeComplete=prop($$props,"onOpenChangeComplete",3,noop$1);PopoverRootState.
-create({open:boxWith$1(()=>open2(),v=>{open2(v),onOpenChange()(v)}),onOpenChangeComplete:boxWith$1(()=>onOpenChangeComplete())}),Floating_layer($$anchor,{children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);snippet(node2,()=>$$props.children??noop$3),append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}function clamp(n,min2,max2){return Math.min(max2,Math.max(min2,n))}const scrollAreaAttrs=createBitsAttrs({component:"scroll-area",parts:["root","viewpo\
-rt","corner","thumb","scrollbar"]}),ScrollAreaRootContext=new Context$1("ScrollArea.Root"),ScrollAreaScrollbarContext=new Context$1("ScrollArea.Scrollbar"),ScrollAreaScrollbarVisibleContext=new Context$1("ScrollArea.ScrollbarVisible"),ScrollAreaScrollbarAxisContext=new Context$1("ScrollArea.ScrollbarAxis"),ScrollAreaScrollbarSharedContext=new Context$1("ScrollArea.ScrollbarShared");class ScrollAreaRootState{static create(opts){return ScrollAreaRootContext.set(new ScrollAreaRootState(opts))}opts;attachment;#scrollAreaNode=state$1(
-null);get scrollAreaNode(){return get$4(this.#scrollAreaNode)}set scrollAreaNode(value){set$1(this.#scrollAreaNode,value,!0)}#viewportNode=state$1(null);get viewportNode(){return get$4(this.#viewportNode)}set viewportNode(value){set$1(this.#viewportNode,value,!0)}#contentNode=state$1(null);get contentNode(){return get$4(this.#contentNode)}set contentNode(value){set$1(this.#contentNode,value,!0)}#scrollbarXNode=state$1(null);get scrollbarXNode(){return get$4(this.#scrollbarXNode)}set scrollbarXNode(value){
-set$1(this.#scrollbarXNode,value,!0)}#scrollbarYNode=state$1(null);get scrollbarYNode(){return get$4(this.#scrollbarYNode)}set scrollbarYNode(value){set$1(this.#scrollbarYNode,value,!0)}#cornerWidth=state$1(0);get cornerWidth(){return get$4(this.#cornerWidth)}set cornerWidth(value){set$1(this.#cornerWidth,value,!0)}#cornerHeight=state$1(0);get cornerHeight(){return get$4(this.#cornerHeight)}set cornerHeight(value){set$1(this.#cornerHeight,value,!0)}#scrollbarXEnabled=state$1(!1);get scrollbarXEnabled(){
-return get$4(this.#scrollbarXEnabled)}set scrollbarXEnabled(value){set$1(this.#scrollbarXEnabled,value,!0)}#scrollbarYEnabled=state$1(!1);get scrollbarYEnabled(){return get$4(this.#scrollbarYEnabled)}set scrollbarYEnabled(value){set$1(this.#scrollbarYEnabled,value,!0)}domContext;constructor(opts){this.opts=opts,this.attachment=attachRef(opts.ref,v=>this.scrollAreaNode=v),this.domContext=new DOMContext(opts.ref)}#props=user_derived(()=>({id:this.opts.id.current,dir:this.opts.dir.current,style:{position:"\
-relative","--bits-scroll-area-corner-height":`${this.cornerHeight}px`,"--bits-scroll-area-corner-width":`${this.cornerWidth}px`},[scrollAreaAttrs.root]:"",...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class ScrollAreaViewportState{static create(opts){return new ScrollAreaViewportState(opts,ScrollAreaRootContext.get())}opts;root;attachment;#contentId=simpleBox(useId());#contentRef=simpleBox(null);contentAttachment=attachRef(this.#contentRef,
-v=>this.root.contentNode=v);constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(opts.ref,v=>this.root.viewportNode=v)}#props=user_derived(()=>({id:this.opts.id.current,style:{overflowX:this.root.scrollbarXEnabled?"scroll":"hidden",overflowY:this.root.scrollbarYEnabled?"scroll":"hidden"},[scrollAreaAttrs.viewport]:"",...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}#contentProps=user_derived(()=>({id:this.#contentId.
-current,"data-scroll-area-content":"",style:{minWidth:this.root.scrollbarXEnabled?"fit-content":void 0},...this.contentAttachment}));get contentProps(){return get$4(this.#contentProps)}set contentProps(value){set$1(this.#contentProps,value)}}class ScrollAreaScrollbarState{static create(opts){return ScrollAreaScrollbarContext.set(new ScrollAreaScrollbarState(opts,ScrollAreaRootContext.get()))}opts;root;#isHorizontal=user_derived(()=>this.opts.orientation.current==="horizontal");get isHorizontal(){
-return get$4(this.#isHorizontal)}set isHorizontal(value){set$1(this.#isHorizontal,value)}#hasThumb=state$1(!1);get hasThumb(){return get$4(this.#hasThumb)}set hasThumb(value){set$1(this.#hasThumb,value,!0)}constructor(opts,root2){this.opts=opts,this.root=root2,watch$1(()=>this.isHorizontal,isHorizontal=>isHorizontal?(this.root.scrollbarXEnabled=!0,()=>{this.root.scrollbarXEnabled=!1}):(this.root.scrollbarYEnabled=!0,()=>{this.root.scrollbarYEnabled=!1}))}}class ScrollAreaScrollbarHoverState{static create(){
-return new ScrollAreaScrollbarHoverState(ScrollAreaScrollbarContext.get())}scrollbar;root;#isVisible=state$1(!1);get isVisible(){return get$4(this.#isVisible)}set isVisible(value){set$1(this.#isVisible,value,!0)}constructor(scrollbar){this.scrollbar=scrollbar,this.root=scrollbar.root,user_effect(()=>{const scrollAreaNode=this.root.scrollAreaNode,hideDelay=this.root.opts.scrollHideDelay.current;let hideTimer=0;if(!scrollAreaNode)return;const handlePointerEnter=()=>{this.root.domContext.clearTimeout(
-hideTimer),untrack$1(()=>this.isVisible=!0)},handlePointerLeave=()=>{hideTimer&&this.root.domContext.clearTimeout(hideTimer),hideTimer=this.root.domContext.setTimeout(()=>{untrack$1(()=>{this.scrollbar.hasThumb=!1,this.isVisible=!1})},hideDelay)},unsubListeners=executeCallbacks(on(scrollAreaNode,"pointerenter",handlePointerEnter),on(scrollAreaNode,"pointerleave",handlePointerLeave));return()=>{this.root.domContext.getWindow().clearTimeout(hideTimer),unsubListeners()}})}#props=user_derived(()=>({
-"data-state":this.isVisible?"visible":"hidden"}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class ScrollAreaScrollbarScrollState{static create(){return new ScrollAreaScrollbarScrollState(ScrollAreaScrollbarContext.get())}scrollbar;root;machine=new StateMachine("hidden",{hidden:{SCROLL:"scrolling"},scrolling:{SCROLL_END:"idle",POINTER_ENTER:"interacting"},interacting:{SCROLL:"interacting",POINTER_LEAVE:"idle"},idle:{HIDE:"hidden",SCROLL:"scrolling",POINTER_ENTER:"\
-interacting"}});#isHidden=user_derived(()=>this.machine.state.current==="hidden");get isHidden(){return get$4(this.#isHidden)}set isHidden(value){set$1(this.#isHidden,value)}constructor(scrollbar){this.scrollbar=scrollbar,this.root=scrollbar.root;const debounceScrollend=useDebounce(()=>this.machine.dispatch("SCROLL_END"),100);user_effect(()=>{const _state=this.machine.state.current,scrollHideDelay=this.root.opts.scrollHideDelay.current;if(_state==="idle"){const hideTimer=this.root.domContext.setTimeout(
-()=>this.machine.dispatch("HIDE"),scrollHideDelay);return()=>this.root.domContext.clearTimeout(hideTimer)}}),user_effect(()=>{const viewportNode=this.root.viewportNode;if(!viewportNode)return;const scrollDirection=this.scrollbar.isHorizontal?"scrollLeft":"scrollTop";let prevScrollPos=viewportNode[scrollDirection];return on(viewportNode,"scroll",()=>{const scrollPos=viewportNode[scrollDirection];prevScrollPos!==scrollPos&&(this.machine.dispatch("SCROLL"),debounceScrollend()),prevScrollPos=scrollPos})}),
-this.onpointerenter=this.onpointerenter.bind(this),this.onpointerleave=this.onpointerleave.bind(this)}onpointerenter(_){this.machine.dispatch("POINTER_ENTER")}onpointerleave(_){this.machine.dispatch("POINTER_LEAVE")}#props=user_derived(()=>({"data-state":this.machine.state.current==="hidden"?"hidden":"visible",onpointerenter:this.onpointerenter,onpointerleave:this.onpointerleave}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class ScrollAreaScrollbarAutoState{static create(){
-return new ScrollAreaScrollbarAutoState(ScrollAreaScrollbarContext.get())}scrollbar;root;#isVisible=state$1(!1);get isVisible(){return get$4(this.#isVisible)}set isVisible(value){set$1(this.#isVisible,value,!0)}constructor(scrollbar){this.scrollbar=scrollbar,this.root=scrollbar.root;const handleResize=useDebounce(()=>{const viewportNode=this.root.viewportNode;if(!viewportNode)return;const isOverflowX=viewportNode.offsetWidth<viewportNode.scrollWidth,isOverflowY=viewportNode.offsetHeight<viewportNode.
-scrollHeight;this.isVisible=this.scrollbar.isHorizontal?isOverflowX:isOverflowY},10);new SvelteResizeObserver(()=>this.root.viewportNode,handleResize),new SvelteResizeObserver(()=>this.root.contentNode,handleResize)}#props=user_derived(()=>({"data-state":this.isVisible?"visible":"hidden"}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class ScrollAreaScrollbarVisibleState{static create(){return ScrollAreaScrollbarVisibleContext.set(new ScrollAreaScrollbarVisibleState(
-ScrollAreaScrollbarContext.get()))}scrollbar;root;#thumbNode=state$1(null);get thumbNode(){return get$4(this.#thumbNode)}set thumbNode(value){set$1(this.#thumbNode,value,!0)}#pointerOffset=state$1(0);get pointerOffset(){return get$4(this.#pointerOffset)}set pointerOffset(value){set$1(this.#pointerOffset,value,!0)}#sizes=state$1({content:0,viewport:0,scrollbar:{size:0,paddingStart:0,paddingEnd:0}});get sizes(){return get$4(this.#sizes)}set sizes(value){set$1(this.#sizes,value)}#thumbRatio=user_derived(
-()=>getThumbRatio(this.sizes.viewport,this.sizes.content));get thumbRatio(){return get$4(this.#thumbRatio)}set thumbRatio(value){set$1(this.#thumbRatio,value)}#hasThumb=user_derived(()=>this.thumbRatio>0&&this.thumbRatio<1);get hasThumb(){return get$4(this.#hasThumb)}set hasThumb(value){set$1(this.#hasThumb,value)}#prevTransformStyle=state$1("");get prevTransformStyle(){return get$4(this.#prevTransformStyle)}set prevTransformStyle(value){set$1(this.#prevTransformStyle,value,!0)}constructor(scrollbar){
-this.scrollbar=scrollbar,this.root=scrollbar.root,user_effect(()=>{this.scrollbar.hasThumb=this.hasThumb}),user_effect(()=>{!this.scrollbar.hasThumb&&this.thumbNode&&(this.prevTransformStyle=this.thumbNode.style.transform)})}setSizes(sizes){this.sizes=sizes}getScrollPosition(pointerPos,dir){return getScrollPositionFromPointer({pointerPos,pointerOffset:this.pointerOffset,sizes:this.sizes,dir})}onThumbPointerUp(){this.pointerOffset=0}onThumbPointerDown(pointerPos){this.pointerOffset=pointerPos}xOnThumbPositionChange(){
-if(!(this.root.viewportNode&&this.thumbNode))return;const scrollPos=this.root.viewportNode.scrollLeft,transformStyle=`translate3d(${getThumbOffsetFromScroll({scrollPos,sizes:this.sizes,dir:this.root.opts.dir.current})}px, 0, 0)`;this.thumbNode.style.transform=transformStyle,this.prevTransformStyle=transformStyle}xOnWheelScroll(scrollPos){this.root.viewportNode&&(this.root.viewportNode.scrollLeft=scrollPos)}xOnDragScroll(pointerPos){this.root.viewportNode&&(this.root.viewportNode.scrollLeft=this.
-getScrollPosition(pointerPos,this.root.opts.dir.current))}yOnThumbPositionChange(){if(!(this.root.viewportNode&&this.thumbNode))return;const scrollPos=this.root.viewportNode.scrollTop,transformStyle=`translate3d(0, ${getThumbOffsetFromScroll({scrollPos,sizes:this.sizes})}px, 0)`;this.thumbNode.style.transform=transformStyle,this.prevTransformStyle=transformStyle}yOnWheelScroll(scrollPos){this.root.viewportNode&&(this.root.viewportNode.scrollTop=scrollPos)}yOnDragScroll(pointerPos){this.root.viewportNode&&
-(this.root.viewportNode.scrollTop=this.getScrollPosition(pointerPos,this.root.opts.dir.current))}}class ScrollAreaScrollbarXState{static create(opts){return ScrollAreaScrollbarAxisContext.set(new ScrollAreaScrollbarXState(opts,ScrollAreaScrollbarVisibleContext.get()))}opts;scrollbarVis;root;scrollbar;attachment;#computedStyle=state$1();get computedStyle(){return get$4(this.#computedStyle)}set computedStyle(value){set$1(this.#computedStyle,value,!0)}constructor(opts,scrollbarVis){this.opts=opts,this.
-scrollbarVis=scrollbarVis,this.root=scrollbarVis.root,this.scrollbar=scrollbarVis.scrollbar,this.attachment=attachRef(this.scrollbar.opts.ref,v=>this.root.scrollbarXNode=v),user_effect(()=>{this.scrollbar.opts.ref.current&&this.opts.mounted.current&&(this.computedStyle=getComputedStyle(this.scrollbar.opts.ref.current))}),user_effect(()=>{this.onResize()})}onThumbPointerDown=pointerPos=>{this.scrollbarVis.onThumbPointerDown(pointerPos.x)};onDragScroll=pointerPos=>{this.scrollbarVis.xOnDragScroll(
-pointerPos.x)};onThumbPointerUp=()=>{this.scrollbarVis.onThumbPointerUp()};onThumbPositionChange=()=>{this.scrollbarVis.xOnThumbPositionChange()};onWheelScroll=(e,maxScrollPos)=>{if(!this.root.viewportNode)return;const scrollPos=this.root.viewportNode.scrollLeft+e.deltaX;this.scrollbarVis.xOnWheelScroll(scrollPos),isScrollingWithinScrollbarBounds(scrollPos,maxScrollPos)&&e.preventDefault()};onResize=()=>{this.scrollbar.opts.ref.current&&this.root.viewportNode&&this.computedStyle&&this.scrollbarVis.
-setSizes({content:this.root.viewportNode.scrollWidth,viewport:this.root.viewportNode.offsetWidth,scrollbar:{size:this.scrollbar.opts.ref.current.clientWidth,paddingStart:toInt(this.computedStyle.paddingLeft),paddingEnd:toInt(this.computedStyle.paddingRight)}})};#thumbSize=user_derived(()=>getThumbSize(this.scrollbarVis.sizes));get thumbSize(){return get$4(this.#thumbSize)}set thumbSize(value){set$1(this.#thumbSize,value)}#props=user_derived(()=>({id:this.scrollbar.opts.id.current,"data-orientati\
-on":"horizontal",style:{bottom:0,left:this.root.opts.dir.current==="rtl"?"var(--bits-scroll-area-corner-width)":0,right:this.root.opts.dir.current==="ltr"?"var(--bits-scroll-area-corner-width)":0,"--bits-scroll-area-thumb-width":`${this.thumbSize}px`},...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class ScrollAreaScrollbarYState{static create(opts){return ScrollAreaScrollbarAxisContext.set(new ScrollAreaScrollbarYState(opts,ScrollAreaScrollbarVisibleContext.
-get()))}opts;scrollbarVis;root;scrollbar;attachment;#computedStyle=state$1();get computedStyle(){return get$4(this.#computedStyle)}set computedStyle(value){set$1(this.#computedStyle,value,!0)}constructor(opts,scrollbarVis){this.opts=opts,this.scrollbarVis=scrollbarVis,this.root=scrollbarVis.root,this.scrollbar=scrollbarVis.scrollbar,this.attachment=attachRef(this.scrollbar.opts.ref,v=>this.root.scrollbarYNode=v),user_effect(()=>{this.scrollbar.opts.ref.current&&this.opts.mounted.current&&(this.computedStyle=
-getComputedStyle(this.scrollbar.opts.ref.current))}),user_effect(()=>{this.onResize()}),this.onThumbPointerDown=this.onThumbPointerDown.bind(this),this.onDragScroll=this.onDragScroll.bind(this),this.onThumbPointerUp=this.onThumbPointerUp.bind(this),this.onThumbPositionChange=this.onThumbPositionChange.bind(this),this.onWheelScroll=this.onWheelScroll.bind(this),this.onResize=this.onResize.bind(this)}onThumbPointerDown(pointerPos){this.scrollbarVis.onThumbPointerDown(pointerPos.y)}onDragScroll(pointerPos){
-this.scrollbarVis.yOnDragScroll(pointerPos.y)}onThumbPointerUp(){this.scrollbarVis.onThumbPointerUp()}onThumbPositionChange(){this.scrollbarVis.yOnThumbPositionChange()}onWheelScroll(e,maxScrollPos){if(!this.root.viewportNode)return;const scrollPos=this.root.viewportNode.scrollTop+e.deltaY;this.scrollbarVis.yOnWheelScroll(scrollPos),isScrollingWithinScrollbarBounds(scrollPos,maxScrollPos)&&e.preventDefault()}onResize(){this.scrollbar.opts.ref.current&&this.root.viewportNode&&this.computedStyle&&
-this.scrollbarVis.setSizes({content:this.root.viewportNode.scrollHeight,viewport:this.root.viewportNode.offsetHeight,scrollbar:{size:this.scrollbar.opts.ref.current.clientHeight,paddingStart:toInt(this.computedStyle.paddingTop),paddingEnd:toInt(this.computedStyle.paddingBottom)}})}#thumbSize=user_derived(()=>getThumbSize(this.scrollbarVis.sizes));get thumbSize(){return get$4(this.#thumbSize)}set thumbSize(value){set$1(this.#thumbSize,value)}#props=user_derived(()=>({id:this.scrollbar.opts.id.current,
-"data-orientation":"vertical",style:{top:0,right:this.root.opts.dir.current==="ltr"?0:void 0,left:this.root.opts.dir.current==="rtl"?0:void 0,bottom:"var(--bits-scroll-area-corner-height)","--bits-scroll-area-thumb-height":`${this.thumbSize}px`},...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class ScrollAreaScrollbarSharedState{static create(){return ScrollAreaScrollbarSharedContext.set(new ScrollAreaScrollbarSharedState(ScrollAreaScrollbarAxisContext.
-get()))}scrollbarState;root;scrollbarVis;scrollbar;#rect=state$1(null);get rect(){return get$4(this.#rect)}set rect(value){set$1(this.#rect,value)}#prevWebkitUserSelect=state$1("");get prevWebkitUserSelect(){return get$4(this.#prevWebkitUserSelect)}set prevWebkitUserSelect(value){set$1(this.#prevWebkitUserSelect,value,!0)}handleResize;handleThumbPositionChange;handleWheelScroll;handleThumbPointerDown;handleThumbPointerUp;#maxScrollPos=user_derived(()=>this.scrollbarVis.sizes.content-this.scrollbarVis.
-sizes.viewport);get maxScrollPos(){return get$4(this.#maxScrollPos)}set maxScrollPos(value){set$1(this.#maxScrollPos,value)}constructor(scrollbarState){this.scrollbarState=scrollbarState,this.root=scrollbarState.root,this.scrollbarVis=scrollbarState.scrollbarVis,this.scrollbar=scrollbarState.scrollbarVis.scrollbar,this.handleResize=useDebounce(()=>this.scrollbarState.onResize(),10),this.handleThumbPositionChange=this.scrollbarState.onThumbPositionChange,this.handleWheelScroll=this.scrollbarState.
-onWheelScroll,this.handleThumbPointerDown=this.scrollbarState.onThumbPointerDown,this.handleThumbPointerUp=this.scrollbarState.onThumbPointerUp,user_effect(()=>{const maxScrollPos=this.maxScrollPos,scrollbarNode=this.scrollbar.opts.ref.current;this.root.viewportNode;const handleWheel=e=>{const node2=e.target;scrollbarNode?.contains(node2)&&this.handleWheelScroll(e,maxScrollPos)};return on(this.root.domContext.getDocument(),"wheel",handleWheel,{passive:!1})}),user_pre_effect(()=>{this.scrollbarVis.
-sizes,untrack$1(()=>this.handleThumbPositionChange())}),new SvelteResizeObserver(()=>this.scrollbar.opts.ref.current,this.handleResize),new SvelteResizeObserver(()=>this.root.contentNode,this.handleResize),this.onpointerdown=this.onpointerdown.bind(this),this.onpointermove=this.onpointermove.bind(this),this.onpointerup=this.onpointerup.bind(this),this.onlostpointercapture=this.onlostpointercapture.bind(this)}handleDragScroll(e){if(!this.rect)return;const x=e.clientX-this.rect.left,y=e.clientY-this.
-rect.top;this.scrollbarState.onDragScroll({x,y})}#cleanupPointerState(){this.rect!==null&&(this.root.domContext.getDocument().body.style.webkitUserSelect=this.prevWebkitUserSelect,this.root.viewportNode&&(this.root.viewportNode.style.scrollBehavior=""),this.rect=null)}onpointerdown(e){if(e.button!==0)return;e.target.setPointerCapture(e.pointerId),this.rect=this.scrollbar.opts.ref.current?.getBoundingClientRect()??null,this.prevWebkitUserSelect=this.root.domContext.getDocument().body.style.webkitUserSelect,
-this.root.domContext.getDocument().body.style.webkitUserSelect="none",this.root.viewportNode&&(this.root.viewportNode.style.scrollBehavior="auto"),this.handleDragScroll(e)}onpointermove(e){this.handleDragScroll(e)}onpointerup(e){const target2=e.target;target2.hasPointerCapture(e.pointerId)&&target2.releasePointerCapture(e.pointerId),this.#cleanupPointerState()}onlostpointercapture(_){this.#cleanupPointerState()}#props=user_derived(()=>mergeProps({...this.scrollbarState.props,style:{position:"abs\
-olute",...this.scrollbarState.props.style},[scrollAreaAttrs.scrollbar]:"",onpointerdown:this.onpointerdown,onpointermove:this.onpointermove,onpointerup:this.onpointerup,onlostpointercapture:this.onlostpointercapture}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class ScrollAreaThumbImplState{static create(opts){return new ScrollAreaThumbImplState(opts,ScrollAreaScrollbarSharedContext.get())}opts;scrollbarState;attachment;#root;#removeUnlinkedScrollListener=state$1();#debounceScrollEnd=useDebounce(
-()=>{get$4(this.#removeUnlinkedScrollListener)&&(get$4(this.#removeUnlinkedScrollListener)(),set$1(this.#removeUnlinkedScrollListener,void 0))},100);constructor(opts,scrollbarState){this.opts=opts,this.scrollbarState=scrollbarState,this.#root=scrollbarState.root,this.attachment=attachRef(this.opts.ref,v=>this.scrollbarState.scrollbarVis.thumbNode=v),user_effect(()=>{const viewportNode=this.#root.viewportNode;if(!viewportNode)return;const handleScroll=()=>{if(this.#debounceScrollEnd(),!get$4(this.#removeUnlinkedScrollListener)){
-const listener=addUnlinkedScrollListener(viewportNode,this.scrollbarState.handleThumbPositionChange);set$1(this.#removeUnlinkedScrollListener,listener,!0),this.scrollbarState.handleThumbPositionChange()}};return untrack$1(()=>this.scrollbarState.handleThumbPositionChange()),on(viewportNode,"scroll",handleScroll)}),this.onpointerdowncapture=this.onpointerdowncapture.bind(this),this.onpointerup=this.onpointerup.bind(this)}onpointerdowncapture(e){const thumb=e.target;if(!thumb)return;const thumbRect=thumb.
-getBoundingClientRect(),x=e.clientX-thumbRect.left,y=e.clientY-thumbRect.top;this.scrollbarState.handleThumbPointerDown({x,y})}onpointerup(_){this.scrollbarState.handleThumbPointerUp()}#props=user_derived(()=>({id:this.opts.id.current,"data-state":this.scrollbarState.scrollbarVis.hasThumb?"visible":"hidden",style:{width:"var(--bits-scroll-area-thumb-width)",height:"var(--bits-scroll-area-thumb-height)",transform:this.scrollbarState.scrollbarVis.prevTransformStyle},onpointerdowncapture:this.onpointerdowncapture,
-onpointerup:this.onpointerup,[scrollAreaAttrs.thumb]:"",...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class ScrollAreaCornerImplState{static create(opts){return new ScrollAreaCornerImplState(opts,ScrollAreaRootContext.get())}opts;root;attachment;#width=state$1(0);#height=state$1(0);#hasSize=user_derived(()=>!!(get$4(this.#width)&&get$4(this.#height)));get hasSize(){return get$4(this.#hasSize)}set hasSize(value){set$1(this.#hasSize,value)}constructor(opts,root2){
-this.opts=opts,this.root=root2,this.attachment=attachRef(this.opts.ref),new SvelteResizeObserver(()=>this.root.scrollbarXNode,()=>{const height=this.root.scrollbarXNode?.offsetHeight||0;this.root.cornerHeight=height,set$1(this.#height,height,!0)}),new SvelteResizeObserver(()=>this.root.scrollbarYNode,()=>{const width=this.root.scrollbarYNode?.offsetWidth||0;this.root.cornerWidth=width,set$1(this.#width,width,!0)})}#props=user_derived(()=>({id:this.opts.id.current,style:{width:get$4(this.#width),
-height:get$4(this.#height),position:"absolute",right:this.root.opts.dir.current==="ltr"?0:void 0,left:this.root.opts.dir.current==="rtl"?0:void 0,bottom:0},[scrollAreaAttrs.corner]:"",...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}function toInt(value){return value?Number.parseInt(value,10):0}function getThumbRatio(viewportSize,contentSize){const ratio=viewportSize/contentSize;return Number.isNaN(ratio)?0:ratio}function getThumbSize(sizes){
-const ratio=getThumbRatio(sizes.viewport,sizes.content),scrollbarPadding=sizes.scrollbar.paddingStart+sizes.scrollbar.paddingEnd,thumbSize=(sizes.scrollbar.size-scrollbarPadding)*ratio;return Math.max(thumbSize,18)}function getScrollPositionFromPointer({pointerPos,pointerOffset,sizes,dir="ltr"}){const thumbSizePx=getThumbSize(sizes),thumbCenter=thumbSizePx/2,offset2=pointerOffset||thumbCenter,thumbOffsetFromEnd=thumbSizePx-offset2,minPointerPos=sizes.scrollbar.paddingStart+offset2,maxPointerPos=sizes.
-scrollbar.size-sizes.scrollbar.paddingEnd-thumbOffsetFromEnd,maxScrollPos=sizes.content-sizes.viewport,scrollRange=dir==="ltr"?[0,maxScrollPos]:[maxScrollPos*-1,0];return linearScale([minPointerPos,maxPointerPos],scrollRange)(pointerPos)}function getThumbOffsetFromScroll({scrollPos,sizes,dir="ltr"}){const thumbSizePx=getThumbSize(sizes),scrollbarPadding=sizes.scrollbar.paddingStart+sizes.scrollbar.paddingEnd,scrollbar=sizes.scrollbar.size-scrollbarPadding,maxScrollPos=sizes.content-sizes.viewport,
-maxThumbPos=scrollbar-thumbSizePx,scrollClampRange=dir==="ltr"?[0,maxScrollPos]:[maxScrollPos*-1,0],scrollWithoutMomentum=clamp(scrollPos,scrollClampRange[0],scrollClampRange[1]);return linearScale([0,maxScrollPos],[0,maxThumbPos])(scrollWithoutMomentum)}function linearScale(input,output){return value=>{if(input[0]===input[1]||output[0]===output[1])return output[0];const ratio=(output[1]-output[0])/(input[1]-input[0]);return output[0]+ratio*(value-input[0])}}function isScrollingWithinScrollbarBounds(scrollPos,maxScrollPos){
-return scrollPos>0&&scrollPos<maxScrollPos}function addUnlinkedScrollListener(node2,handler){let prevPosition={left:node2.scrollLeft,top:node2.scrollTop},rAF=0;const win=getWindow$1(node2);return function loop2(){const position2={left:node2.scrollLeft,top:node2.scrollTop},isHorizontalScroll=prevPosition.left!==position2.left,isVerticalScroll=prevPosition.top!==position2.top;(isHorizontalScroll||isVerticalScroll)&&handler(),prevPosition=position2,rAF=win.requestAnimationFrame(loop2)}(),()=>win.cancelAnimationFrame(
-rAF)}var root_2$1e=from_html("<div><!></div>");function Scroll_area$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),type2=prop($$props,"type",3,"hover"),dir=prop($$props,"dir",3,"ltr"),scrollHideDelay=prop($$props,"scrollHideDelay",3,600),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","id","type","dir","scrollHideDelay","children","child"]);const rootState=ScrollAreaRootState.create(
-{type:boxWith$1(()=>type2()),dir:boxWith$1(()=>dir()),scrollHideDelay:boxWith$1(()=>scrollHideDelay()),id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,rootState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1e();
-attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root$1J=from_html("<div><div><!></div></div>");function Scroll_area_viewport($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),restProps=rest_props(
-$$props,["$$slots","$$events","$$legacy","ref","id","children"]);const viewportState=ScrollAreaViewportState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,viewportState.props)),mergedContentProps=user_derived(()=>mergeProps({},viewportState.contentProps));var div=root$1J();attribute_effect(div,()=>({...get$4(mergedProps)}));var div_1=child(div);attribute_effect(div_1,()=>({...get$4(mergedContentProps)}));var node2=child(div_1);
-snippet(node2,()=>$$props.children??noop$3),reset(div_1),reset(div),append($$anchor,div),pop()}var root_2$1d=from_html("<div><!></div>");function Scroll_area_scrollbar_shared($$anchor,$$props){push$1($$props,!0);let restProps=rest_props($$props,["$$slots","$$events","$$legacy","child","children"]);const scrollbarSharedState=ScrollAreaScrollbarSharedState.create(),mergedProps=user_derived(()=>mergeProps(restProps,scrollbarSharedState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{
-var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1d();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}function Scroll_area_scrollbar_x($$anchor,$$props){
-push$1($$props,!0);let restProps=rest_props($$props,["$$slots","$$events","$$legacy"]);const isMounted=new IsMounted,scrollbarXState=ScrollAreaScrollbarXState.create({mounted:boxWith$1(()=>isMounted.current)}),mergedProps=user_derived(()=>mergeProps(restProps,scrollbarXState.props));Scroll_area_scrollbar_shared($$anchor,spread_props(()=>get$4(mergedProps))),pop()}function Scroll_area_scrollbar_y($$anchor,$$props){push$1($$props,!0);let restProps=rest_props($$props,["$$slots","$$events","$$legacy"]);
-const isMounted=new IsMounted,scrollbarYState=ScrollAreaScrollbarYState.create({mounted:boxWith$1(()=>isMounted.current)}),mergedProps=user_derived(()=>mergeProps(restProps,scrollbarYState.props));Scroll_area_scrollbar_shared($$anchor,spread_props(()=>get$4(mergedProps))),pop()}function Scroll_area_scrollbar_visible($$anchor,$$props){push$1($$props,!0);let restProps=rest_props($$props,["$$slots","$$events","$$legacy"]);const scrollbarVisibleState=ScrollAreaScrollbarVisibleState.create();var fragment=comment$2(),
-node2=first_child(fragment);{var consequent=$$anchor2=>{Scroll_area_scrollbar_x($$anchor2,spread_props(()=>restProps))},alternate=$$anchor2=>{Scroll_area_scrollbar_y($$anchor2,spread_props(()=>restProps))};if_block(node2,$$render=>{scrollbarVisibleState.scrollbar.opts.orientation.current==="horizontal"?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}function Scroll_area_scrollbar_auto($$anchor,$$props){push$1($$props,!0);let forceMount=prop($$props,"forceMount",3,!1),
-restProps=rest_props($$props,["$$slots","$$events","$$legacy","forceMount"]);const scrollbarAutoState=ScrollAreaScrollbarAutoState.create(),mergedProps=user_derived(()=>mergeProps(restProps,scrollbarAutoState.props));{const presence=$$anchor2=>{Scroll_area_scrollbar_visible($$anchor2,spread_props(()=>get$4(mergedProps)))};let $0=user_derived(()=>forceMount()||scrollbarAutoState.isVisible);Presence_layer($$anchor,{get open(){return get$4($0)},get ref(){return scrollbarAutoState.scrollbar.opts.ref},
-presence,$$slots:{presence:!0}})}pop()}function Scroll_area_scrollbar_scroll($$anchor,$$props){push$1($$props,!0);let forceMount=prop($$props,"forceMount",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","forceMount"]);const scrollbarScrollState=ScrollAreaScrollbarScrollState.create(),mergedProps=user_derived(()=>mergeProps(restProps,scrollbarScrollState.props));{const presence=$$anchor2=>{Scroll_area_scrollbar_visible($$anchor2,spread_props(()=>get$4(mergedProps)))};let $0=user_derived(
-()=>forceMount()||!scrollbarScrollState.isHidden);Presence_layer($$anchor,spread_props(()=>get$4(mergedProps),{get open(){return get$4($0)},get ref(){return scrollbarScrollState.scrollbar.opts.ref},presence,$$slots:{presence:!0}}))}pop()}function Scroll_area_scrollbar_hover($$anchor,$$props){push$1($$props,!0);let forceMount=prop($$props,"forceMount",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","forceMount"]);const scrollbarHoverState=ScrollAreaScrollbarHoverState.create(),
-scrollbarAutoState=ScrollAreaScrollbarAutoState.create(),mergedProps=user_derived(()=>mergeProps(restProps,scrollbarHoverState.props,scrollbarAutoState.props,{"data-state":scrollbarHoverState.isVisible?"visible":"hidden"})),open2=user_derived(()=>forceMount()||scrollbarHoverState.isVisible&&scrollbarAutoState.isVisible);Presence_layer($$anchor,{get open(){return get$4(open2)},get ref(){return scrollbarAutoState.scrollbar.opts.ref},presence:$$anchor2=>{Scroll_area_scrollbar_visible($$anchor2,spread_props(
-()=>get$4(mergedProps)))},$$slots:{presence:!0}}),pop()}function Scroll_area_scrollbar$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","id","orientation"]);const scrollbarState=ScrollAreaScrollbarState.create({orientation:boxWith$1(()=>$$props.orientation),id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),type2=user_derived(
-()=>scrollbarState.root.opts.type.current);var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{Scroll_area_scrollbar_hover($$anchor2,spread_props(()=>restProps,{get id(){return id2()}}))},alternate_2=$$anchor2=>{var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent_1=$$anchor3=>{Scroll_area_scrollbar_scroll($$anchor3,spread_props(()=>restProps,{get id(){return id2()}}))},alternate_1=$$anchor3=>{var fragment_4=comment$2(),node_2=first_child(fragment_4);
-{var consequent_2=$$anchor4=>{Scroll_area_scrollbar_auto($$anchor4,spread_props(()=>restProps,{get id(){return id2()}}))},alternate=$$anchor4=>{var fragment_6=comment$2(),node_3=first_child(fragment_6);{var consequent_3=$$anchor5=>{Scroll_area_scrollbar_visible($$anchor5,spread_props(()=>restProps,{get id(){return id2()}}))};if_block(node_3,$$render=>{get$4(type2)==="always"&&$$render(consequent_3)},!0)}append($$anchor4,fragment_6)};if_block(node_2,$$render=>{get$4(type2)==="auto"?$$render(consequent_2):
-$$render(alternate,!1)},!0)}append($$anchor3,fragment_4)};if_block(node_1,$$render=>{get$4(type2)==="scroll"?$$render(consequent_1):$$render(alternate_1,!1)},!0)}append($$anchor2,fragment_2)};if_block(node2,$$render=>{get$4(type2)==="hover"?$$render(consequent):$$render(alternate_2,!1)})}append($$anchor,fragment),pop()}var root_2$1c=from_html("<div><!></div>");function Scroll_area_thumb_impl($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["\
-$$slots","$$events","$$legacy","ref","id","child","children","present"]);const isMounted=new IsMounted,thumbState=ScrollAreaThumbImplState.create({id:boxWith$1(()=>$$props.id),ref:boxWith$1(()=>ref2(),v=>ref2(v)),mounted:boxWith$1(()=>isMounted.current)}),mergedProps=user_derived(()=>mergeProps(restProps,thumbState.props,{style:{hidden:!$$props.present}}));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(
-node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1c();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}function Scroll_area_thumb($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop(
-$$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),forceMount=prop($$props,"forceMount",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","forceMount"]);const scrollbarState=ScrollAreaScrollbarVisibleContext.get();{const presence=($$anchor2,$$arg0)=>{let present=()=>$$arg0?.().present;Scroll_area_thumb_impl($$anchor2,spread_props(()=>restProps,{get id(){return id2()},get present(){return present()},get ref(){return ref2()},set ref($$value){ref2($$value)}}))};
-let $0=user_derived(()=>forceMount()||scrollbarState.hasThumb);Presence_layer($$anchor,{get open(){return get$4($0)},get ref(){return scrollbarState.scrollbar.opts.ref},presence,$$slots:{presence:!0}})}pop()}var root_2$1b=from_html("<div><!></div>");function Scroll_area_corner_impl($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","id","children","child"]);const cornerState=ScrollAreaCornerImplState.create(
-{id:boxWith$1(()=>$$props.id),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,cornerState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$1b();attribute_effect(div,()=>({...get$4(mergedProps)}));var node_2=child(div);snippet(node_2,
-()=>$$props.children??noop$3),reset(div),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}function Scroll_area_corner($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","id"]);const scrollAreaState=ScrollAreaRootContext.get(),hasBothScrollbarsVisible=user_derived(
-()=>!!(scrollAreaState.scrollbarXNode&&scrollAreaState.scrollbarYNode)),hasCorner=user_derived(()=>scrollAreaState.opts.type.current!=="scroll"&&get$4(hasBothScrollbarsVisible));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{Scroll_area_corner_impl($$anchor2,spread_props(()=>restProps,{get id(){return id2()},get ref(){return ref2()},set ref($$value){ref2($$value)}}))};if_block(node2,$$render=>{get$4(hasCorner)&&$$render(consequent)})}append($$anchor,fragment),pop()}
-var root$1I=from_html("<!> <!>",1);function Select($$anchor,$$props){push$1($$props,!0);let value=prop($$props,"value",15),onValueChange=prop($$props,"onValueChange",3,noop$1),name=prop($$props,"name",3,""),disabled=prop($$props,"disabled",3,!1),open2=prop($$props,"open",15,!1),onOpenChange=prop($$props,"onOpenChange",3,noop$1),onOpenChangeComplete=prop($$props,"onOpenChangeComplete",3,noop$1),loop2=prop($$props,"loop",3,!1),scrollAlignment=prop($$props,"scrollAlignment",3,"nearest"),required2=prop(
-$$props,"required",3,!1),items2=prop($$props,"items",19,()=>[]),allowDeselect=prop($$props,"allowDeselect",3,!1);function handleDefaultValue(){value()===void 0&&value($$props.type==="single"?"":[])}handleDefaultValue(),watch$1.pre(()=>value(),()=>{handleDefaultValue()});let inputValue=state$1("");const rootState=SelectRootState.create({type:$$props.type,value:boxWith$1(()=>value(),v=>{value(v),onValueChange()(v)}),disabled:boxWith$1(()=>disabled()),required:boxWith$1(()=>required2()),open:boxWith$1(
-()=>open2(),v=>{open2(v),onOpenChange()(v)}),loop:boxWith$1(()=>loop2()),scrollAlignment:boxWith$1(()=>scrollAlignment()),name:boxWith$1(()=>name()),isCombobox:!1,items:boxWith$1(()=>items2()),allowDeselect:boxWith$1(()=>allowDeselect()),inputValue:boxWith$1(()=>get$4(inputValue),v=>set$1(inputValue,v,!0)),onOpenChangeComplete:boxWith$1(()=>onOpenChangeComplete())});var fragment=root$1I(),node2=first_child(fragment);Floating_layer(node2,{children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),
-node_1=first_child(fragment_1);snippet(node_1,()=>$$props.children??noop$3),append($$anchor2,fragment_1)},$$slots:{default:!0}});var node_2=sibling(node2,2);{var consequent_1=$$anchor2=>{var fragment_2=comment$2(),node_3=first_child(fragment_2);{var consequent=$$anchor3=>{Select_hidden_input($$anchor3,{get autocomplete(){return $$props.autocomplete}})},alternate=$$anchor3=>{var fragment_4=comment$2(),node_4=first_child(fragment_4);each(node_4,16,()=>rootState.opts.value.current,item=>item,($$anchor4,item)=>{
-Select_hidden_input($$anchor4,{get value(){return item},get autocomplete(){return $$props.autocomplete}})}),append($$anchor3,fragment_4)};if_block(node_3,$$render=>{rootState.opts.value.current.length===0?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment_2)},alternate_1=$$anchor2=>{Select_hidden_input($$anchor2,{get autocomplete(){return $$props.autocomplete},get value(){return rootState.opts.value.current},set value($$value){rootState.opts.value.current=$$value}})};if_block(
-node_2,$$render=>{Array.isArray(rootState.opts.value.current)?$$render(consequent_1):$$render(alternate_1,!1)})}append($$anchor,fragment),pop()}var root_3$W=from_html("<button><!></button>");function Select_trigger$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),type2=prop($$props,"type",3,"button"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","id","ref","child","children","type"]);const triggerState=SelectTriggerState.
-create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,triggerState.props,{type:type2()}));var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Floating_layer_anchor,($$anchor2,FloatingLayer_Anchor)=>{FloatingLayer_Anchor($$anchor2,{get id(){return id2()},get ref(){return triggerState.opts.ref},children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{var consequent=$$anchor4=>{
-var fragment_2=comment$2(),node_2=first_child(fragment_2);snippet(node_2,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor4,fragment_2)},alternate=$$anchor4=>{var button=root_3$W();attribute_effect(button,()=>({...get$4(mergedProps)}));var node_3=child(button);snippet(node_3,()=>$$props.children??noop$3),reset(button),append($$anchor4,button)};if_block(node_1,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor3,fragment_1)},$$slots:{default:!0}})}),
-append($$anchor,fragment),pop()}const switchAttrs=createBitsAttrs({component:"switch",parts:["root","thumb"]}),SwitchRootContext=new Context$1("Switch.Root");class SwitchRootState{static create(opts){return SwitchRootContext.set(new SwitchRootState(opts))}opts;attachment;constructor(opts){this.opts=opts,this.attachment=attachRef(opts.ref),this.onkeydown=this.onkeydown.bind(this),this.onclick=this.onclick.bind(this)}#toggle(){this.opts.checked.current=!this.opts.checked.current}onkeydown(e){!(e.key===
-ENTER||e.key===SPACE)||this.opts.disabled.current||(e.preventDefault(),this.#toggle())}onclick(_){this.opts.disabled.current||this.#toggle()}#sharedProps=user_derived(()=>({"data-disabled":boolToEmptyStrOrUndef(this.opts.disabled.current),"data-state":getDataChecked(this.opts.checked.current),"data-required":boolToEmptyStrOrUndef(this.opts.required.current)}));get sharedProps(){return get$4(this.#sharedProps)}set sharedProps(value){set$1(this.#sharedProps,value)}#snippetProps=user_derived(()=>({
-checked:this.opts.checked.current}));get snippetProps(){return get$4(this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}#props=user_derived(()=>({...this.sharedProps,id:this.opts.id.current,role:"switch",disabled:boolToTrueOrUndef(this.opts.disabled.current),"aria-checked":getAriaChecked(this.opts.checked.current,!1),"aria-required":boolToStr(this.opts.required.current),[switchAttrs.root]:"",onclick:this.onclick,onkeydown:this.onkeydown,...this.attachment}));get props(){
-return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class SwitchInputState{static create(){return new SwitchInputState(SwitchRootContext.get())}root;#shouldRender=user_derived(()=>this.root.opts.name.current!==void 0);get shouldRender(){return get$4(this.#shouldRender)}set shouldRender(value){set$1(this.#shouldRender,value)}constructor(root2){this.root=root2}#props=user_derived(()=>({type:"checkbox",name:this.root.opts.name.current,value:this.root.opts.value.current,checked:this.
-root.opts.checked.current,disabled:this.root.opts.disabled.current,required:this.root.opts.required.current}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class SwitchThumbState{static create(opts){return new SwitchThumbState(opts,SwitchRootContext.get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=attachRef(opts.ref)}#snippetProps=user_derived(()=>({checked:this.root.opts.checked.current}));get snippetProps(){return get$4(
-this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}#props=user_derived(()=>({...this.root.sharedProps,id:this.opts.id.current,[switchAttrs.thumb]:"",...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}function Switch_input($$anchor,$$props){push$1($$props,!1);const inputState=SwitchInputState.create();init();var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{Hidden_input($$anchor2,spread_props(
-()=>inputState.props))};if_block(node2,$$render=>{inputState.shouldRender&&$$render(consequent)})}append($$anchor,fragment),pop()}var root_2$1a=from_html("<button><!></button>"),root$1H=from_html("<!> <!>",1);function Switch$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),disabled=prop($$props,"disabled",3,!1),required2=prop($$props,"required",3,!1),checked=prop($$props,"checked",15,!1),value=prop($$props,
-"value",3,"on"),name=prop($$props,"name",3,void 0),type2=prop($$props,"type",3,"button"),onCheckedChange=prop($$props,"onCheckedChange",3,noop$1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","child","children","ref","id","disabled","required","checked","value","name","type","onCheckedChange"]);const rootState=SwitchRootState.create({checked:boxWith$1(()=>checked(),v=>{checked(v),onCheckedChange()?.(v)}),disabled:boxWith$1(()=>disabled()??!1),required:boxWith$1(()=>required2()),value:boxWith$1(
-()=>value()),name:boxWith$1(()=>name()),id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,rootState.props,{type:type2()}));var fragment=root$1H(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{let $0=user_derived(()=>({props:get$4(mergedProps),...rootState.snippetProps}));snippet(node_1,()=>$$props.child,()=>get$4($0))}append($$anchor2,fragment_1)},alternate=$$anchor2=>{
-var button=root_2$1a();attribute_effect(button,()=>({...get$4(mergedProps)}));var node_2=child(button);snippet(node_2,()=>$$props.children??noop$3,()=>rootState.snippetProps),reset(button),append($$anchor2,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}var node_3=sibling(node2,2);Switch_input(node_3,{}),append($$anchor,fragment),pop()}var root_2$19=from_html("<span><!></span>");function Switch_thumb($$anchor,$$props){const uid=props_id();push$1($$props,
-!0);let ref2=prop($$props,"ref",15,null),id2=prop($$props,"id",19,()=>createId(uid)),restProps=rest_props($$props,["$$slots","$$events","$$legacy","child","children","ref","id"]);const thumbState=SwitchThumbState.create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,thumbState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{let $0=user_derived(
-()=>({props:get$4(mergedProps),...thumbState.snippetProps}));snippet(node_1,()=>$$props.child,()=>get$4($0))}append($$anchor2,fragment_1)},alternate=$$anchor2=>{var span=root_2$19();attribute_effect(span,()=>({...get$4(mergedProps)}));var node_2=child(span);snippet(node_2,()=>$$props.children??noop$3,()=>thumbState.snippetProps),reset(span),append($$anchor2,span)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}class TimeoutFn{#interval;#cb;#timer=null;constructor(cb,interval){
-this.#cb=cb,this.#interval=interval,this.stop=this.stop.bind(this),this.start=this.start.bind(this),onDestroyEffect(this.stop)}#clear(){this.#timer!==null&&(window.clearTimeout(this.#timer),this.#timer=null)}stop(){this.#clear()}start(...args){this.#clear(),this.#timer=window.setTimeout(()=>{this.#timer=null,this.#cb(...args)},this.#interval)}}const tooltipAttrs=createBitsAttrs({component:"tooltip",parts:["content","trigger"]}),TooltipProviderContext=new Context$1("Tooltip.Provider"),TooltipRootContext=new Context$1(
-"Tooltip.Root");class TooltipProviderState{static create(opts){return TooltipProviderContext.set(new TooltipProviderState(opts))}opts;#isOpenDelayed=state$1(!0);get isOpenDelayed(){return get$4(this.#isOpenDelayed)}set isOpenDelayed(value){set$1(this.#isOpenDelayed,value,!0)}isPointerInTransit=simpleBox(!1);#timerFn;#openTooltip=state$1(null);constructor(opts){this.opts=opts,this.#timerFn=new TimeoutFn(()=>{this.isOpenDelayed=!0},this.opts.skipDelayDuration.current)}#startTimer=()=>{this.opts.skipDelayDuration.
-current!==0&&this.#timerFn.start()};#clearTimer=()=>{this.#timerFn.stop()};onOpen=tooltip=>{get$4(this.#openTooltip)&&get$4(this.#openTooltip)!==tooltip&&get$4(this.#openTooltip).handleClose(),this.#clearTimer(),this.isOpenDelayed=!1,set$1(this.#openTooltip,tooltip,!0)};onClose=tooltip=>{get$4(this.#openTooltip)===tooltip&&set$1(this.#openTooltip,null),this.#startTimer()};isTooltipOpen=tooltip=>get$4(this.#openTooltip)===tooltip}class TooltipRootState{static create(opts){return TooltipRootContext.
-set(new TooltipRootState(opts,TooltipProviderContext.get()))}opts;provider;#delayDuration=user_derived(()=>this.opts.delayDuration.current??this.provider.opts.delayDuration.current);get delayDuration(){return get$4(this.#delayDuration)}set delayDuration(value){set$1(this.#delayDuration,value)}#disableHoverableContent=user_derived(()=>this.opts.disableHoverableContent.current??this.provider.opts.disableHoverableContent.current);get disableHoverableContent(){return get$4(this.#disableHoverableContent)}set disableHoverableContent(value){
-set$1(this.#disableHoverableContent,value)}#disableCloseOnTriggerClick=user_derived(()=>this.opts.disableCloseOnTriggerClick.current??this.provider.opts.disableCloseOnTriggerClick.current);get disableCloseOnTriggerClick(){return get$4(this.#disableCloseOnTriggerClick)}set disableCloseOnTriggerClick(value){set$1(this.#disableCloseOnTriggerClick,value)}#disabled=user_derived(()=>this.opts.disabled.current??this.provider.opts.disabled.current);get disabled(){return get$4(this.#disabled)}set disabled(value){
-set$1(this.#disabled,value)}#ignoreNonKeyboardFocus=user_derived(()=>this.opts.ignoreNonKeyboardFocus.current??this.provider.opts.ignoreNonKeyboardFocus.current);get ignoreNonKeyboardFocus(){return get$4(this.#ignoreNonKeyboardFocus)}set ignoreNonKeyboardFocus(value){set$1(this.#ignoreNonKeyboardFocus,value)}#contentNode=state$1(null);get contentNode(){return get$4(this.#contentNode)}set contentNode(value){set$1(this.#contentNode,value,!0)}contentPresence;#triggerNode=state$1(null);get triggerNode(){
-return get$4(this.#triggerNode)}set triggerNode(value){set$1(this.#triggerNode,value,!0)}#wasOpenDelayed=state$1(!1);#timerFn;#stateAttr=user_derived(()=>this.opts.open.current?get$4(this.#wasOpenDelayed)?"delayed-open":"instant-open":"closed");get stateAttr(){return get$4(this.#stateAttr)}set stateAttr(value){set$1(this.#stateAttr,value)}constructor(opts,provider){this.opts=opts,this.provider=provider,this.#timerFn=new TimeoutFn(()=>{set$1(this.#wasOpenDelayed,!0),this.opts.open.current=!0},this.
-delayDuration??0),this.contentPresence=new PresenceManager({open:this.opts.open,ref:boxWith$1(()=>this.contentNode),onComplete:()=>{this.opts.onOpenChangeComplete.current(this.opts.open.current)}}),watch$1(()=>this.delayDuration,()=>{this.delayDuration!==void 0&&(this.#timerFn=new TimeoutFn(()=>{set$1(this.#wasOpenDelayed,!0),this.opts.open.current=!0},this.delayDuration))}),watch$1(()=>this.opts.open.current,isOpen=>{isOpen?this.provider.onOpen(this):this.provider.onClose(this)},{lazy:!0})}handleOpen=()=>{
-this.#timerFn.stop(),set$1(this.#wasOpenDelayed,!1),this.opts.open.current=!0};handleClose=()=>{this.#timerFn.stop(),this.opts.open.current=!1};#handleDelayedOpen=()=>{this.#timerFn.stop();const shouldSkipDelay=!this.provider.isOpenDelayed,delayDuration=this.delayDuration??0;shouldSkipDelay||delayDuration===0?(set$1(this.#wasOpenDelayed,delayDuration>0&&shouldSkipDelay,!0),this.opts.open.current=!0):this.#timerFn.start()};onTriggerEnter=()=>{this.#handleDelayedOpen()};onTriggerLeave=()=>{this.disableHoverableContent?
-this.handleClose():this.#timerFn.stop()}}class TooltipTriggerState{static create(opts){return new TooltipTriggerState(opts,TooltipRootContext.get())}opts;root;attachment;#isPointerDown=simpleBox(!1);#hasPointerMoveOpened=state$1(!1);#isDisabled=user_derived(()=>this.opts.disabled.current||this.root.disabled);domContext;#transitCheckTimeout=null;constructor(opts,root2){this.opts=opts,this.root=root2,this.domContext=new DOMContext(opts.ref),this.attachment=attachRef(this.opts.ref,v=>this.root.triggerNode=
-v)}#clearTransitCheck=()=>{this.#transitCheckTimeout!==null&&(clearTimeout(this.#transitCheckTimeout),this.#transitCheckTimeout=null)};handlePointerUp=()=>{this.#isPointerDown.current=!1};#onpointerup=()=>{get$4(this.#isDisabled)||(this.#isPointerDown.current=!1)};#onpointerdown=()=>{get$4(this.#isDisabled)||(this.#isPointerDown.current=!0,this.domContext.getDocument().addEventListener("pointerup",()=>{this.handlePointerUp()},{once:!0}))};#onpointerenter=e=>{if(!get$4(this.#isDisabled)&&e.pointerType!==
-"touch"){if(this.root.provider.isPointerInTransit.current){this.#clearTransitCheck(),this.#transitCheckTimeout=window.setTimeout(()=>{this.root.provider.isPointerInTransit.current&&(this.root.provider.isPointerInTransit.current=!1,this.root.onTriggerEnter(),set$1(this.#hasPointerMoveOpened,!0))},250);return}this.root.onTriggerEnter(),set$1(this.#hasPointerMoveOpened,!0)}};#onpointermove=e=>{get$4(this.#isDisabled)||e.pointerType!=="touch"&&(get$4(this.#hasPointerMoveOpened)||(this.#clearTransitCheck(),
-this.root.provider.isPointerInTransit.current=!1,this.root.onTriggerEnter(),set$1(this.#hasPointerMoveOpened,!0)))};#onpointerleave=()=>{get$4(this.#isDisabled)||(this.#clearTransitCheck(),this.root.onTriggerLeave(),set$1(this.#hasPointerMoveOpened,!1))};#onfocus=e=>{this.#isPointerDown.current||get$4(this.#isDisabled)||this.root.ignoreNonKeyboardFocus&&!isFocusVisible(e.currentTarget)||this.root.handleOpen()};#onblur=()=>{get$4(this.#isDisabled)||this.root.handleClose()};#onclick=()=>{this.root.
-disableCloseOnTriggerClick||get$4(this.#isDisabled)||this.root.handleClose()};#props=user_derived(()=>({id:this.opts.id.current,"aria-describedby":this.root.opts.open.current?this.root.contentNode?.id:void 0,"data-state":this.root.stateAttr,"data-disabled":boolToEmptyStrOrUndef(get$4(this.#isDisabled)),"data-delay-duration":`${this.root.delayDuration}`,[tooltipAttrs.trigger]:"",tabindex:get$4(this.#isDisabled)?void 0:this.opts.tabindex.current,disabled:this.opts.disabled.current,onpointerup:this.#onpointerup,
-onpointerdown:this.#onpointerdown,onpointerenter:this.#onpointerenter,onpointermove:this.#onpointermove,onpointerleave:this.#onpointerleave,onfocus:this.#onfocus,onblur:this.#onblur,onclick:this.#onclick,...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}}class TooltipContentState{static create(opts){return new TooltipContentState(opts,TooltipRootContext.get())}opts;root;attachment;constructor(opts,root2){this.opts=opts,this.root=root2,this.attachment=
-attachRef(this.opts.ref,v=>this.root.contentNode=v),new SafePolygon({triggerNode:()=>this.root.triggerNode,contentNode:()=>this.root.contentNode,enabled:()=>this.root.opts.open.current&&!this.root.disableHoverableContent,onPointerExit:()=>{this.root.provider.isTooltipOpen(this.root)&&this.root.handleClose()}}),onMountEffect(()=>on(window,"scroll",e=>{const target2=e.target;target2&&target2.contains(this.root.triggerNode)&&this.root.handleClose()}))}onInteractOutside=e=>{if(isElement$1(e.target)&&
-this.root.triggerNode?.contains(e.target)&&this.root.disableCloseOnTriggerClick){e.preventDefault();return}this.opts.onInteractOutside.current(e),!e.defaultPrevented&&this.root.handleClose()};onEscapeKeydown=e=>{this.opts.onEscapeKeydown.current?.(e),!e.defaultPrevented&&this.root.handleClose()};onOpenAutoFocus=e=>{e.preventDefault()};onCloseAutoFocus=e=>{e.preventDefault()};get shouldRender(){return this.root.contentPresence.shouldRender}#snippetProps=user_derived(()=>({open:this.root.opts.open.
-current}));get snippetProps(){return get$4(this.#snippetProps)}set snippetProps(value){set$1(this.#snippetProps,value)}#props=user_derived(()=>({id:this.opts.id.current,"data-state":this.root.stateAttr,"data-disabled":boolToEmptyStrOrUndef(this.root.disabled),style:{outline:"none"},[tooltipAttrs.content]:"",...this.attachment}));get props(){return get$4(this.#props)}set props(value){set$1(this.#props,value)}popperProps={onInteractOutside:this.onInteractOutside,onEscapeKeydown:this.onEscapeKeydown,
-onOpenAutoFocus:this.onOpenAutoFocus,onCloseAutoFocus:this.onCloseAutoFocus}}function Tooltip($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15,!1),onOpenChange=prop($$props,"onOpenChange",3,noop$1),onOpenChangeComplete=prop($$props,"onOpenChangeComplete",3,noop$1);TooltipRootState.create({open:boxWith$1(()=>open2(),v=>{open2(v),onOpenChange()(v)}),delayDuration:boxWith$1(()=>$$props.delayDuration),disableCloseOnTriggerClick:boxWith$1(()=>$$props.disableCloseOnTriggerClick),disableHoverableContent:boxWith$1(
-()=>$$props.disableHoverableContent),ignoreNonKeyboardFocus:boxWith$1(()=>$$props.ignoreNonKeyboardFocus),disabled:boxWith$1(()=>$$props.disabled),onOpenChangeComplete:boxWith$1(()=>onOpenChangeComplete())}),Floating_layer($$anchor,{tooltip:!0,children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);snippet(node2,()=>$$props.children??noop$3),append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}var root_4$A=from_html("<div><div><!></div></div>"),root_9$o=from_html(
-"<div><div><!></div></div>");function Tooltip_content$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),ref2=prop($$props,"ref",15,null),side=prop($$props,"side",3,"top"),sideOffset=prop($$props,"sideOffset",3,0),align=prop($$props,"align",3,"center"),avoidCollisions=prop($$props,"avoidCollisions",3,!0),arrowPadding=prop($$props,"arrowPadding",3,0),sticky=prop($$props,"sticky",3,"partial"),hideWhenDetached=prop($$props,"hideWhenDetached",
-3,!1),collisionPadding=prop($$props,"collisionPadding",3,0),onInteractOutside=prop($$props,"onInteractOutside",3,noop$1),onEscapeKeydown=prop($$props,"onEscapeKeydown",3,noop$1),forceMount=prop($$props,"forceMount",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","children","child","id","ref","side","sideOffset","align","avoidCollisions","arrowPadding","sticky","strategy","hideWhenDetached","collisionPadding","onInteractOutside","onEscapeKeydown","forceMount","style"]);const contentState=TooltipContentState.
-create({id:boxWith$1(()=>id2()),ref:boxWith$1(()=>ref2(),v=>ref2(v)),onInteractOutside:boxWith$1(()=>onInteractOutside()),onEscapeKeydown:boxWith$1(()=>onEscapeKeydown())}),floatingProps=user_derived(()=>({side:side(),sideOffset:sideOffset(),align:align(),avoidCollisions:avoidCollisions(),arrowPadding:arrowPadding(),sticky:sticky(),hideWhenDetached:hideWhenDetached(),collisionPadding:collisionPadding(),strategy:$$props.strategy})),mergedProps=user_derived(()=>mergeProps(restProps,get$4(floatingProps),
-contentState.props));var fragment=comment$2(),node2=first_child(fragment);{var consequent_1=$$anchor2=>{{const popper=($$anchor3,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),{style:getFloatingContentCSSVars("tooltip")},{style:$$props.style}));var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent=$$anchor4=>{var fragment_3=comment$2(),node_2=first_child(fragment_3);{let $02=user_derived(()=>({
-props:get$4(finalProps),wrapperProps:wrapperProps(),...contentState.snippetProps}));snippet(node_2,()=>$$props.child,()=>get$4($02))}append($$anchor4,fragment_3)},alternate=$$anchor4=>{var div=root_4$A();attribute_effect(div,()=>({...wrapperProps()}));var div_1=child(div);attribute_effect(div_1,()=>({...get$4(finalProps)}));var node_3=child(div_1);snippet(node_3,()=>$$props.children??noop$3),reset(div_1),reset(div),append($$anchor4,div)};if_block(node_1,$$render=>{$$props.child?$$render(consequent):
-$$render(alternate,!1)})}append($$anchor3,fragment_2)};let $0=user_derived(()=>contentState.root.disableHoverableContent?"none":"auto");Popper_layer_force_mount($$anchor2,spread_props(()=>get$4(mergedProps),()=>contentState.popperProps,{get enabled(){return contentState.root.opts.open.current},get id(){return id2()},trapFocus:!1,loop:!1,preventScroll:!1,forceMount:!0,get ref(){return contentState.opts.ref},tooltip:!0,get shouldRender(){return contentState.shouldRender},get contentPointerEvents(){
-return get$4($0)},popper,$$slots:{popper:!0}}))}},alternate_2=$$anchor2=>{var fragment_4=comment$2(),node_4=first_child(fragment_4);{var consequent_3=$$anchor3=>{{const popper=($$anchor4,$$arg0)=>{let props=()=>$$arg0?.().props,wrapperProps=()=>$$arg0?.().wrapperProps;const finalProps=user_derived(()=>mergeProps(props(),{style:getFloatingContentCSSVars("tooltip")},{style:$$props.style}));var fragment_6=comment$2(),node_5=first_child(fragment_6);{var consequent_2=$$anchor5=>{var fragment_7=comment$2(),
-node_6=first_child(fragment_7);{let $02=user_derived(()=>({props:get$4(finalProps),wrapperProps:wrapperProps(),...contentState.snippetProps}));snippet(node_6,()=>$$props.child,()=>get$4($02))}append($$anchor5,fragment_7)},alternate_1=$$anchor5=>{var div_2=root_9$o();attribute_effect(div_2,()=>({...wrapperProps()}));var div_3=child(div_2);attribute_effect(div_3,()=>({...get$4(finalProps)}));var node_7=child(div_3);snippet(node_7,()=>$$props.children??noop$3),reset(div_3),reset(div_2),append($$anchor5,
-div_2)};if_block(node_5,$$render=>{$$props.child?$$render(consequent_2):$$render(alternate_1,!1)})}append($$anchor4,fragment_6)};let $0=user_derived(()=>contentState.root.disableHoverableContent?"none":"auto");Popper_layer($$anchor3,spread_props(()=>get$4(mergedProps),()=>contentState.popperProps,{get open(){return contentState.root.opts.open.current},get id(){return id2()},trapFocus:!1,loop:!1,preventScroll:!1,forceMount:!1,get ref(){return contentState.opts.ref},tooltip:!0,get shouldRender(){return contentState.
-shouldRender},get contentPointerEvents(){return get$4($0)},popper,$$slots:{popper:!0}}))}};if_block(node_4,$$render=>{forceMount()||$$render(consequent_3)},!0)}append($$anchor2,fragment_4)};if_block(node2,$$render=>{forceMount()?$$render(consequent_1):$$render(alternate_2,!1)})}append($$anchor,fragment),pop()}var root_3$V=from_html("<button><!></button>");function Tooltip_trigger$1($$anchor,$$props){const uid=props_id();push$1($$props,!0);let id2=prop($$props,"id",19,()=>createId(uid)),disabled=prop(
-$$props,"disabled",3,!1),type2=prop($$props,"type",3,"button"),tabindex=prop($$props,"tabindex",3,0),ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","children","child","id","disabled","type","tabindex","ref"]);const triggerState=TooltipTriggerState.create({id:boxWith$1(()=>id2()),disabled:boxWith$1(()=>disabled()??!1),tabindex:boxWith$1(()=>tabindex()??0),ref:boxWith$1(()=>ref2(),v=>ref2(v))}),mergedProps=user_derived(()=>mergeProps(restProps,triggerState.
-props,{type:type2()}));Floating_layer_anchor($$anchor,{get id(){return id2()},get ref(){return triggerState.opts.ref},tooltip:!0,children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);{var consequent=$$anchor3=>{var fragment_2=comment$2(),node_1=first_child(fragment_2);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor3,fragment_2)},alternate=$$anchor3=>{var button=root_3$V();attribute_effect(button,()=>({...get$4(mergedProps)}));
-var node_2=child(button);snippet(node_2,()=>$$props.children??noop$3),reset(button),append($$anchor3,button)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}function Tooltip_arrow($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref"]);Floating_layer_arrow($$anchor,spread_props(()=>restProps,{get ref(){return ref2()},
-set ref($$value){ref2($$value)}})),pop()}function Tooltip_provider($$anchor,$$props){push$1($$props,!0);let delayDuration=prop($$props,"delayDuration",3,700),disableCloseOnTriggerClick=prop($$props,"disableCloseOnTriggerClick",3,!1),disableHoverableContent=prop($$props,"disableHoverableContent",3,!1),disabled=prop($$props,"disabled",3,!1),ignoreNonKeyboardFocus=prop($$props,"ignoreNonKeyboardFocus",3,!1),skipDelayDuration=prop($$props,"skipDelayDuration",3,300);TooltipProviderState.create({delayDuration:boxWith$1(
-()=>delayDuration()),disableCloseOnTriggerClick:boxWith$1(()=>disableCloseOnTriggerClick()),disableHoverableContent:boxWith$1(()=>disableHoverableContent()),disabled:boxWith$1(()=>disabled()),ignoreNonKeyboardFocus:boxWith$1(()=>ignoreNonKeyboardFocus()),skipDelayDuration:boxWith$1(()=>skipDelayDuration())});var fragment=comment$2(),node2=first_child(fragment);snippet(node2,()=>$$props.children??noop$3),append($$anchor,fragment),pop()}function Tooltip_trigger($$anchor,$$props){push$1($$props,!0);
-let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref"]);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Tooltip_trigger$1,($$anchor2,TooltipPrimitive_Trigger)=>{TooltipPrimitive_Trigger($$anchor2,spread_props({"data-slot":"tooltip-trigger"},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))}),append($$anchor,fragment),pop()}var root_3$U=from_html("<div></div>"),root_2$18=from_html("<!> <!>",1);function Tooltip_content($$anchor,$$props){
-push$1($$props,!0);const tooltipContent=$$anchor2=>{var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Tooltip_content$1,($$anchor3,TooltipPrimitive_Content)=>{TooltipPrimitive_Content($$anchor3,spread_props({"data-slot":"tooltip-content",get sideOffset(){return sideOffset()},get side(){return side()},get class(){return get$4(contentClass)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},children:($$anchor4,$$slotProps)=>{var fragment_1=root_2$18(),
-node_1=first_child(fragment_1);snippet(node_1,()=>$$props.children??noop$3);var node_2=sibling(node_1,2);{const child2=($$anchor5,$$arg0)=>{let props=()=>$$arg0?.().props;var div=root_3$U();attribute_effect(div,$0=>({class:$0,...props()}),[()=>cn$1("z-50 size-2.5 rotate-45 rounded-[2px] bg-primary","data-[side=top]:translate-x-1/2 data-[side=top]:translate-y-[calc(-50%_+_2px)]","data-[side=bottom]:-translate-x-1/2 data-[side=bottom]:-translate-y-[calc(-50%_+_1px)]","data-[side=right]:translate-x\
--[calc(50%_+_2px)] data-[side=right]:translate-y-1/2","data-[side=left]:-translate-y-[calc(50%_-_3px)]",$$props.arrowClasses)]),append($$anchor5,div)};component(node_2,()=>Tooltip_arrow,($$anchor5,TooltipPrimitive_Arrow)=>{TooltipPrimitive_Arrow($$anchor5,{child:child2,$$slots:{child:!0}})})}append($$anchor4,fragment_1)},$$slots:{default:!0}}))}),append($$anchor2,fragment)};let ref2=prop($$props,"ref",15,null),sideOffset=prop($$props,"sideOffset",3,0),side=prop($$props,"side",3,"top"),noPortal=prop(
-$$props,"noPortal",3,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","sideOffset","side","children","arrowClasses","noPortal"]);const contentClass=user_derived(()=>cn$1("z-50 w-fit origin-(--bits-tooltip-content-transform-origin) animate-in rounded-md bg-primary px-3 py-1.5 text-xs text-balance text-primary-foreground fade-in-0 zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side\
-=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95",$$props.class));var fragment_2=comment$2(),node_3=first_child(fragment_2);{var consequent=$$anchor2=>{tooltipContent($$anchor2)},alternate=$$anchor2=>{var fragment_4=comment$2(),node_4=first_child(fragment_4);component(node_4,()=>Portal$2,($$anchor3,TooltipPrimitive_Portal)=>{TooltipPrimitive_Portal($$anchor3,{children:($$anchor4,$$slotProps)=>{tooltipContent($$anchor4)},$$slots:{
-default:!0}})}),append($$anchor2,fragment_4)};if_block(node_3,$$render=>{noPortal()?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment_2),pop()}const Root$5=Tooltip,Provider=Tooltip_provider;var root_4$z=from_html("<p> </p>"),root_1$12=from_html("<!> <!>",1);function ActionIcon($$anchor,$$props){let variant=prop($$props,"variant",3,"ghost"),size2=prop($$props,"size",3,"sm"),className=prop($$props,"class",3,""),disabled=prop($$props,"disabled",3,!1),iconSize=prop($$props,"icon\
-Size",3,"h-3 w-3");var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$5,($$anchor2,Tooltip_Root)=>{Tooltip_Root($$anchor2,{children:($$anchor3,$$slotProps)=>{var fragment_1=root_1$12(),node_1=first_child(fragment_1);component(node_1,()=>Tooltip_trigger,($$anchor4,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor4,{children:($$anchor5,$$slotProps2)=>{{let $0=user_derived(()=>$$props["aria-label"]||$$props.tooltip);Button($$anchor5,{get variant(){return variant()},get size(){return size2()},
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-$$anchor,spread_props({name:"file"},()=>props,{get iconNode(){return iconNode},children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);snippet(node2,()=>$$props.children??noop$3),append($$anchor2,fragment_1)},$$slots:{default:!0}})),pop()}function Flask_conical($$anchor,$$props){push$1($$props,!0);let props=rest_props($$props,["$$slots","$$events","$$legacy"]);const iconNode=[["path",{d:"M14 2v6a2 2 0 0 0 .245.96l5.51 10.08A2 2 0 0 1 18 22H6a2 2 0 0 1-1.755-2.\
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-const iconNode=[["path",{d:"m6 14 1.5-2.9A2 2 0 0 1 9.24 10H20a2 2 0 0 1 1.94 2.5l-1.54 6a2 2 0 0 1-1.95 1.5H4a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h3.9a2 2 0 0 1 1.69.9l.81 1.2a2 2 0 0 0 1.67.9H18a2 2 0 0 1 2 2v2"}]];Icon($$anchor,spread_props({name:"folder-open"},()=>props,{get iconNode(){return iconNode},children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);snippet(node2,()=>$$props.children??noop$3),append($$anchor2,fragment_1)},$$slots:{default:!0}})),pop()}function Funnel($$anchor,$$props){
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-
-`,CLIPBOARD_CONTENT_QUOTE_PREFIX='"',PROMPT_TRIGGER_PREFIX="/",RESOURCE_TRIGGER_PREFIX="@",CODE_BLOCK_SCROLL_CONTAINER_CLASS="code-block-scroll-container",CODE_BLOCK_WRAPPER_CLASS="code-block-wrapper",CODE_BLOCK_HEADER_CLASS="code-block-header",CODE_BLOCK_ACTIONS_CLASS="code-block-actions",CODE_LANGUAGE_CLASS="code-language",COPY_CODE_BTN_CLASS="copy-code-btn",PREVIEW_CODE_BTN_CLASS="preview-code-btn",RELATIVE_CLASS="relative",NEWLINE=`
-`,DEFAULT_LANGUAGE="text",LANG_PATTERN=/^(\w*)\n?/,AMPERSAND_REGEX=/&/g,LT_REGEX=/</g,GT_REGEX=/>/g,FENCE_PATTERN=/^```|\n```/g,CONTEXT_KEY_MESSAGE_EDIT="chat-message-edit",CONTEXT_KEY_CHAT_ACTIONS="chat-actions",CONTEXT_KEY_CHAT_SETTINGS_DIALOG="chat-settings-dialog",BOX_BORDER="border border-border/30 focus-within:border-border  dark:border-border/20 dark:focus-within:border-border",INPUT_CLASSES=`
-    bg-muted/60 dark:bg-muted/75
-    ${BOX_BORDER}
-    shadow-sm
-    outline-none
-    text-foreground
-`,PANEL_CLASSES=`
-    bg-background
-    border border-border/30 dark:border-border/20
-    shadow-sm backdrop-blur-lg!
-    rounded-t-lg!
-`,CHAT_FORM_POPOVER_MAX_HEIGHT="max-h-80",GOOGLE_FAVICON_BASE_URL="https://www.google.com/s2/favicons",DEFAULT_FAVICON_SIZE=32,DOMAIN_SEPARATOR=".",ROOT_DOMAIN_MIN_PARTS=2,MS_PER_SECOND=1e3,SECONDS_PER_MINUTE=60,SECONDS_PER_HOUR=3600,SHORT_DURATION_THRESHOLD=1,MEDIUM_DURATION_THRESHOLD=10,DEFAULT_PERFORMANCE_TIME="0s",KEY_VALUE_PAIR_KEY_MAX_LENGTH=256,KEY_VALUE_PAIR_VALUE_MAX_LENGTH=8192,KEY_VALUE_PAIR_UNSAFE_KEY_RE=/[\x00-\x1F\x7F]/g,KEY_VALUE_PAIR_UNSAFE_VALUE_RE=/[\x00-\x08\x0A-\x0D\x0E-\x1F\x7F]/g,
-FILE_TYPE_ICONS={[FileTypeCategory.IMAGE]:Image$1,[FileTypeCategory.AUDIO]:Mic,[FileTypeCategory.TEXT]:File_text,[FileTypeCategory.PDF]:File$1},MODALITY_ICONS={[ModelModality.VISION]:Eye,[ModelModality.AUDIO]:Mic},MODALITY_LABELS={[ModelModality.VISION]:"Vision",[ModelModality.AUDIO]:"Audio"},CODE_BLOCK_REGEXP=/(```[\s\S]*?```|`[^`\n]+`)/g,LATEX_MATH_AND_CODE_PATTERN=new RegExp("(```[\\S\\s]*?```|`.*?`)|(?<!\\\\)\\\\\\[([\\S\\s]*?[^\\\\])\\\\]|(?<!\\\\)\\\\\\((.*?)\\\\\\)","g"),LATEX_LINEBREAK_REGEXP=/\$\$([\s\S]*?\\\\[\s\S]*?)\$\$/,
-LINE_BREAK=/\r?\n/,PHRASE_PARENTS=new Set(["paragraph","heading","emphasis","strong","delete","link","linkReference","tableCell"]),NBSP=" ",TAB_AS_SPACES=NBSP.repeat(4),CONFIG_LOCALSTORAGE_KEY="LlamaCppWebui.config",USER_OVERRIDES_LOCALSTORAGE_KEY="LlamaCppWebui.userOverrides",FAVORITE_MODELS_LOCALSTORAGE_KEY="LlamaCppWebui.favoriteModels",MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY="LlamaCppWebui.mcpDefaultEnabled",IMAGE_NOT_ERROR_BOUND_SELECTOR="img:not([data-error-bound])",DATA_ERROR_BOUND_ATTR="er\
-rorBound",DATA_ERROR_HANDLED_ATTR="errorHandled",BOOL_TRUE_STRING="true",DEFAULT_CLIENT_VERSION="1.0.0",DEFAULT_IMAGE_MIME_TYPE=MimeTypeImage.PNG,MCP_ALLOWED_ICON_MIME_TYPES=new Set([MimeTypeImage.PNG,MimeTypeImage.JPEG,MimeTypeImage.JPG,MimeTypeImage.SVG,MimeTypeImage.WEBP]),MCP_PROTOCOL_VERSION="2025-06-18",DEFAULT_MCP_CONFIG={protocolVersion:MCP_PROTOCOL_VERSION,capabilities:{tools:{listChanged:!0}},clientInfo:{name:"llama-webui-mcp",version:DEFAULT_CLIENT_VERSION},requestTimeoutSeconds:300,connectionTimeoutMs:1e4},
-MCP_SERVER_ID_PREFIX="LlamaCpp-WebUI-MCP-Server",MCP_RECONNECT_INITIAL_DELAY=1e3,MCP_RECONNECT_BACKOFF_MULTIPLIER=2,MCP_RECONNECT_MAX_DELAY=3e4,MCP_RECONNECT_ATTEMPT_TIMEOUT_MS=15e3,MAX_DISPLAYED_MCP_AVATARS=3,EXPECTED_THEMED_ICON_PAIR_COUNT=2,CORS_PROXY_URL_PARAM="url",MCP_SESSION_ID_VISIBLE_CHARS=5,MCP_PARTIAL_REDACT_HEADERS=new Map([["mcp-session-id",MCP_SESSION_ID_VISIBLE_CHARS]]),REDACTED_HEADERS=new Set(["authorization","api-key","cookie","mcp-session-id","proxy-authorization","set-cookie",
-"x-auth-token","x-api-key"]),MCP_TRANSPORT_LABELS={[MCPTransportType.WEBSOCKET]:"WebSocket",[MCPTransportType.STREAMABLE_HTTP]:"HTTP",[MCPTransportType.SSE]:"SSE"},MCP_TRANSPORT_ICONS={[MCPTransportType.WEBSOCKET]:Zap,[MCPTransportType.STREAMABLE_HTTP]:Globe,[MCPTransportType.SSE]:Radio},MCP_SERVER_URL_PLACEHOLDER="https://mcp.example.com/sse",MIN_AUTOCOMPLETE_INPUT_LENGTH=1,IMAGE_FILE_EXTENSION_REGEX=/\.(png|jpg|jpeg|gif|svg|webp)$/i,CODE_FILE_EXTENSION_REGEX=/\.(js|ts|json|yaml|yml|xml|html|css|py|rs|go|java|cpp|c|h|rb|sh|toml)$/i,
-TEXT_FILE_EXTENSION_REGEX=/\.(txt|md|log)$/i,PROTOCOL_PREFIX_REGEX=/^[a-z]+:\/\//,FILE_EXTENSION_REGEX=/\.[^.]+$/,DISPLAY_NAME_SEPARATOR_REGEX=/[-_]/,DATA_URI_BASE64_REGEX=/^data:([^;]+);base64,([A-Za-z0-9+/]+=*)$/,MCP_ATTACHMENT_NAME_PREFIX="mcp-attachment",MCP_RESOURCE_ATTACHMENT_ID_PREFIX="res",DEFAULT_IMAGE_EXTENSION="img",DEFAULT_RESOURCE_FILENAME="resource.txt",PATH_SEPARATOR="/",RESOURCE_TEXT_CONTENT_SEPARATOR=`
-
-`,RESOURCE_UNKNOWN_TYPE="unknown type",BINARY_CONTENT_LABEL="Binary content",IMAGE_MIME_TO_EXTENSION={[MimeTypeImage.JPEG]:"jpg",[MimeTypeImage.JPG]:"jpg",[MimeTypeImage.PNG]:"png",[MimeTypeImage.GIF]:"gif",[MimeTypeImage.WEBP]:"webp"},EXPORT_CONV_ID_TRIM_LENGTH=8,EXPORT_CONV_NAME_SUFFIX_MAX_LENGTH=20,ISO_TIMESTAMP_SLICE_LENGTH=19,NON_ALPHANUMERIC_REGEX=/[^a-z0-9]/gi,EXPORT_CONV_NONALNUM_REPLACEMENT="_",MULTIPLE_UNDERSCORE_REGEX=/_+/g,ISO_DATE_TIME_SEPARATOR="T",ISO_DATE_TIME_SEPARATOR_REPLACEMENT="\
-_",ISO_TIME_SEPARATOR=":",ISO_TIME_SEPARATOR_REPLACEMENT="-",MODEL_ID_NOT_FOUND=-1,MODEL_ID_ORG_SEPARATOR="/",MODEL_ID_SEGMENT_SEPARATOR="-",MODEL_ID_QUANTIZATION_SEPARATOR=":",MODEL_QUANTIZATION_SEGMENT_RE=/^(I?Q\d+(_[A-Z0-9]+)*|F\d+|BF\d+|MXFP\d+(_[A-Z0-9]+)*)$/i,MODEL_CUSTOM_QUANTIZATION_PREFIX_RE=/^UD$/i,MODEL_PARAMS_RE=/^\d+(\.\d+)?[BbMmKkTt]$/,MODEL_ACTIVATED_PARAMS_RE=/^[Aa]\d+(\.\d+)?[BbMmKkTt]$/,MODEL_IGNORED_SEGMENTS=new Set(["GGUF","GGML"]),PRECISION_MULTIPLIER=1e6,PROCESSING_INFO_TIMEOUT=2e3,
-STATS_UNITS={TOKENS_PER_SECOND:"t/s"},SETTING_CONFIG_DEFAULT={apiKey:"",systemMessage:"",showSystemMessage:!0,theme:ColorMode.SYSTEM,showThoughtInProgress:!1,disableReasoningParsing:!1,excludeReasoningFromContext:!1,showRawOutputSwitch:!1,keepStatsVisible:!1,showMessageStats:!0,askForTitleConfirmation:!1,titleGenerationUseFirstLine:!1,pasteLongTextToFileLen:2500,copyTextAttachmentsAsPlainText:!1,pdfAsImage:!1,disableAutoScroll:!1,renderUserContentAsMarkdown:!1,alwaysShowSidebarOnDesktop:!1,autoShowSidebarOnNewChat:!0,
-sendOnEnter:!0,autoMicOnEmpty:!1,fullHeightCodeBlocks:!1,showRawModelNames:!1,mcpServers:"[]",mcpServerUsageStats:"{}",agenticMaxTurns:10,agenticMaxToolPreviewLines:25,showToolCallInProgress:!1,alwaysShowAgenticTurns:!1,samplers:"",backend_sampling:!1,temperature:void 0,dynatemp_range:void 0,dynatemp_exponent:void 0,top_k:void 0,top_p:void 0,min_p:void 0,xtc_probability:void 0,xtc_threshold:void 0,typ_p:void 0,repeat_last_n:void 0,repeat_penalty:void 0,presence_penalty:void 0,frequency_penalty:void 0,
-dry_multiplier:void 0,dry_base:void 0,dry_allowed_length:void 0,dry_penalty_last_n:void 0,max_tokens:void 0,custom:"",preEncodeConversation:!1,pyInterpreterEnabled:!1,enableContinueGeneration:!1},SETTING_CONFIG_INFO={apiKey:"Set the API Key if you are using <code>--api-key</code> option for the server.",systemMessage:"The starting message that defines how model should behave.",showSystemMessage:"Display the system message at the top of each conversation.",theme:"Choose the color theme for the in\
-terface. You can choose between System (follows your device settings), Light, or Dark.",pasteLongTextToFileLen:"On pasting long text, it will be converted to a file. You can control the file length by setting the value of this parameter. Value 0 means disable.",copyTextAttachmentsAsPlainText:"When copying a message with text attachments, combine them into a single plain text string instead of a special format that can be pasted back as attachments.",samplers:'The order at which samplers are appl\
-ied, in simplified way. Default is "top_k;typ_p;top_p;min_p;temperature": top_k->typ_p->top_p->min_p->temperature',backend_sampling:"Enable backend-based samplers. When enabled, supported samplers run on the accelerator backend for faster sampling.",temperature:"Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.",dynatemp_range:"Addon for the temperature sampler. The added value to the range of\
- dynamic temperature, which adjusts probabilities by entropy of tokens.",dynatemp_exponent:"Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.",top_k:"Keeps only k top tokens.",top_p:"Limits tokens to those that together have a cumulative probability of at least p",min_p:"Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.",xtc_probability:"XTC sampler cuts\
- out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.",xtc_threshold:"XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.",typ_p:"Sorts and limits tokens based on the difference between log-probability and entropy.",repeat_last_n:"Last n tokens to consider for penalizing repetition",repeat_penalty:"Controls the repetition of token sequences in the generated text",presence_penalty:"Limits tokens\
- based on whether they appear in the output or not.",frequency_penalty:"Limits tokens based on how often they appear in the output.",dry_multiplier:"DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.",dry_base:"DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.",dry_allowed_length:"DRY sampling reduces repetition in generated text even across long \
-contexts. This parameter sets the allowed length for DRY sampling.",dry_penalty_last_n:"DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.",max_tokens:"The maximum number of token per output. Use -1 for infinite (no limit).",custom:"Custom JSON parameters to send to the API. Must be valid JSON format.",showThoughtInProgress:"Expand thought process by default when generating messages.",disableReasoningParsing:"Send r\
-easoning_format=none so the server returns thinking tokens inline instead of extracting them into a separate field.",excludeReasoningFromContext:"Strip thinking from previous messages before sending. When off, thinking is sent back via the reasoning_content field so the model sees its own chain-of-thought across turns.",showRawOutputSwitch:"Show toggle button to display messages as plain text instead of Markdown-formatted content",keepStatsVisible:"Keep processing statistics visible after genera\
-tion finishes.",showMessageStats:"Display generation statistics (tokens/second, token count, duration) below each assistant message.",askForTitleConfirmation:"Ask for confirmation before automatically changing conversation title when editing the first message.",titleGenerationUseFirstLine:"Use only the first non-empty line of the prompt to generate the conversation title.",pdfAsImage:"Parse PDF as image instead of text. Automatically falls back to text processing for non-vision models.",disableAutoScroll:"\
-Disable automatic scrolling while messages stream so you can control the viewport position manually.",renderUserContentAsMarkdown:"Render user messages using markdown formatting in the chat.",alwaysShowSidebarOnDesktop:"Always keep the sidebar visible on desktop instead of auto-hiding it.",autoShowSidebarOnNewChat:"Automatically show sidebar when starting a new chat. Disable to keep the sidebar hidden until you click on it.",sendOnEnter:"Use Enter to send messages and Shift + Enter for new lines\
-. When disabled, use Ctrl/Cmd + Enter.",autoMicOnEmpty:"Automatically show microphone button instead of send button when textarea is empty for models with audio modality support.",fullHeightCodeBlocks:"Always display code blocks at their full natural height, overriding any height limits.",showRawModelNames:'Display full raw model identifiers (e.g. "ggml-org/GLM-4.7-Flash-GGUF:Q8_0") instead of parsed names with badges.',mcpServers:"Configure MCP servers as a JSON list. Use the form in the MCP Cl\
-ient settings section to edit.",mcpServerUsageStats:"Usage statistics for MCP servers. Tracks how many times tools from each server have been used.",agenticMaxTurns:"Maximum number of tool execution cycles before stopping (prevents infinite loops).",agenticMaxToolPreviewLines:"Number of lines shown in tool output previews (last N lines). Only these previews and the final LLM response persist after the agentic loop completes.",showToolCallInProgress:"Automatically expand tool call details while e\
-xecuting and keep them expanded after completion.",pyInterpreterEnabled:"Enable Python interpreter using Pyodide. Allows running Python code in markdown code blocks.",preEncodeConversation:"After each response, re-submit the conversation to pre-fill the server KV cache. Makes the next turn faster since the prompt is already encoded while you read the response.",enableContinueGeneration:'Enable "Continue" button for assistant messages. Currently works only with non-reasoning models.'},SETTINGS_COLOR_MODES_CONFIG=[
-{value:ColorMode.SYSTEM,label:"System",icon:Monitor},{value:ColorMode.LIGHT,label:"Light",icon:Sun},{value:ColorMode.DARK,label:"Dark",icon:Moon}],NUMERIC_FIELDS=["temperature","top_k","top_p","min_p","max_tokens","pasteLongTextToFileLen","dynatemp_range","dynatemp_exponent","typ_p","xtc_probability","xtc_threshold","repeat_last_n","repeat_penalty","presence_penalty","frequency_penalty","dry_multiplier","dry_base","dry_allowed_length","dry_penalty_last_n","agenticMaxTurns","agenticMaxToolPreview\
-Lines"],POSITIVE_INTEGER_FIELDS=["agenticMaxTurns","agenticMaxToolPreviewLines"],SETTINGS_KEYS={THEME:"theme",API_KEY:"apiKey",SYSTEM_MESSAGE:"systemMessage",PASTE_LONG_TEXT_TO_FILE_LEN:"pasteLongTextToFileLen",COPY_TEXT_ATTACHMENTS_AS_PLAIN_TEXT:"copyTextAttachmentsAsPlainText",SEND_ON_ENTER:"sendOnEnter",ENABLE_CONTINUE_GENERATION:"enableContinueGeneration",PDF_AS_IMAGE:"pdfAsImage",ASK_FOR_TITLE_CONFIRMATION:"askForTitleConfirmation",TITLE_GENERATION_USE_FIRST_LINE:"titleGenerationUseFirstLin\
-e",SHOW_MESSAGE_STATS:"showMessageStats",SHOW_THOUGHT_IN_PROGRESS:"showThoughtInProgress",KEEP_STATS_VISIBLE:"keepStatsVisible",AUTO_MIC_ON_EMPTY:"autoMicOnEmpty",RENDER_USER_CONTENT_AS_MARKDOWN:"renderUserContentAsMarkdown",DISABLE_AUTO_SCROLL:"disableAutoScroll",ALWAYS_SHOW_SIDEBAR_ON_DESKTOP:"alwaysShowSidebarOnDesktop",AUTO_SHOW_SIDEBAR_ON_NEW_CHAT:"autoShowSidebarOnNewChat",FULL_HEIGHT_CODE_BLOCKS:"fullHeightCodeBlocks",SHOW_RAW_MODEL_NAMES:"showRawModelNames",TEMPERATURE:"temperature",DYNATEMP_RANGE:"\
-dynatemp_range",DYNATEMP_EXPONENT:"dynatemp_exponent",TOP_K:"top_k",TOP_P:"top_p",MIN_P:"min_p",XTC_PROBABILITY:"xtc_probability",XTC_THRESHOLD:"xtc_threshold",TYP_P:"typ_p",MAX_TOKENS:"max_tokens",SAMPLERS:"samplers",BACKEND_SAMPLING:"backend_sampling",REPEAT_LAST_N:"repeat_last_n",REPEAT_PENALTY:"repeat_penalty",PRESENCE_PENALTY:"presence_penalty",FREQUENCY_PENALTY:"frequency_penalty",DRY_MULTIPLIER:"dry_multiplier",DRY_BASE:"dry_base",DRY_ALLOWED_LENGTH:"dry_allowed_length",DRY_PENALTY_LAST_N:"\
-dry_penalty_last_n",AGENTIC_MAX_TURNS:"agenticMaxTurns",ALWAYS_SHOW_AGENTIC_TURNS:"alwaysShowAgenticTurns",AGENTIC_MAX_TOOL_PREVIEW_LINES:"agenticMaxToolPreviewLines",SHOW_TOOL_CALL_IN_PROGRESS:"showToolCallInProgress",PRE_ENCODE_CONVERSATION:"preEncodeConversation",DISABLE_REASONING_PARSING:"disableReasoningParsing",EXCLUDE_REASONING_FROM_CONTEXT:"excludeReasoningFromContext",SHOW_RAW_OUTPUT_SWITCH:"showRawOutputSwitch",CUSTOM:"custom"},SETTINGS_SECTION_TITLES={GENERAL:"General",DISPLAY:"Displa\
-y",SAMPLING:"Sampling",PENALTIES:"Penalties",IMPORT_EXPORT:"Import/Export",MCP:"MCP",DEVELOPER:"Developer"};FileTypeAudio.MP3+"",FileExtensionAudio.MP3,MimeTypeAudio.MP3_MPEG,MimeTypeAudio.MP3,FileTypeAudio.WAV+"",FileExtensionAudio.WAV,MimeTypeAudio.WAV;FileTypeImage.JPEG+"",FileExtensionImage.JPG,FileExtensionImage.JPEG,MimeTypeImage.JPEG,FileTypeImage.PNG+"",FileExtensionImage.PNG,MimeTypeImage.PNG,FileTypeImage.GIF+"",FileExtensionImage.GIF,MimeTypeImage.GIF,FileTypeImage.WEBP+"",FileExtensionImage.
-WEBP,MimeTypeImage.WEBP,FileTypeImage.SVG+"",FileExtensionImage.SVG,MimeTypeImage.SVG;FileTypePdf.PDF+"",FileExtensionPdf.PDF,MimeTypeApplication.PDF;FileTypeText.PLAIN_TEXT+"",FileExtensionText.TXT,MimeTypeText.PLAIN,FileTypeText.MARKDOWN+"",FileExtensionText.MD,MimeTypeText.MARKDOWN,FileTypeText.ASCIIDOC+"",FileExtensionText.ADOC,MimeTypeText.ASCIIDOC,FileTypeText.JAVASCRIPT+"",FileExtensionText.JS,MimeTypeText.JAVASCRIPT,MimeTypeText.JAVASCRIPT_APP,FileTypeText.TYPESCRIPT+"",FileExtensionText.
-TS,MimeTypeText.TYPESCRIPT,FileTypeText.JSX+"",FileExtensionText.JSX,MimeTypeText.JSX,FileTypeText.TSX+"",FileExtensionText.TSX,MimeTypeText.TSX,FileTypeText.CSS+"",FileExtensionText.CSS,MimeTypeText.CSS,FileTypeText.HTML+"",FileExtensionText.HTML,FileExtensionText.HTM,MimeTypeText.HTML,FileTypeText.JSON+"",FileExtensionText.JSON,MimeTypeText.JSON,FileTypeText.XML+"",FileExtensionText.XML,MimeTypeText.XML_TEXT,MimeTypeText.XML_APP,FileTypeText.YAML+"",FileExtensionText.YAML,FileExtensionText.YML,
-MimeTypeText.YAML_TEXT,MimeTypeText.YAML_APP,FileTypeText.CSV+"",FileExtensionText.CSV,MimeTypeText.CSV,FileTypeText.LOG+"",FileExtensionText.LOG,MimeTypeText.PLAIN,FileTypeText.PYTHON+"",FileExtensionText.PY,MimeTypeText.PYTHON,FileTypeText.JAVA+"",FileExtensionText.JAVA,MimeTypeText.JAVA,FileTypeText.CPP+"",FileExtensionText.CPP,FileExtensionText.C,FileExtensionText.H,FileExtensionText.HPP,MimeTypeText.CPP_SRC,MimeTypeText.CPP_HDR,MimeTypeText.C_SRC,MimeTypeText.C_HDR,FileTypeText.PHP+"",FileExtensionText.
-PHP,MimeTypeText.PHP,FileTypeText.RUBY+"",FileExtensionText.RB,MimeTypeText.RUBY,FileTypeText.GO+"",FileExtensionText.GO,MimeTypeText.GO,FileTypeText.RUST+"",FileExtensionText.RS,MimeTypeText.RUST,FileTypeText.SHELL+"",FileExtensionText.SH,FileExtensionText.BAT,MimeTypeText.SHELL,MimeTypeText.BAT,FileTypeText.SQL+"",FileExtensionText.SQL,MimeTypeText.SQL,FileTypeText.R+"",FileExtensionText.R,MimeTypeText.R,FileTypeText.SCALA+"",FileExtensionText.SCALA,MimeTypeText.SCALA,FileTypeText.KOTLIN+"",FileExtensionText.
-KT,MimeTypeText.KOTLIN,FileTypeText.SWIFT+"",FileExtensionText.SWIFT,MimeTypeText.SWIFT,FileTypeText.DART+"",FileExtensionText.DART,MimeTypeText.DART,FileTypeText.VUE+"",FileExtensionText.VUE,MimeTypeText.VUE,FileTypeText.SVELTE+"",FileExtensionText.SVELTE,MimeTypeText.SVELTE,FileTypeText.LATEX+"",FileExtensionText.TEX,MimeTypeText.LATEX,MimeTypeText.TEX,MimeTypeText.TEX_APP,FileTypeText.BIBTEX+"",FileExtensionText.BIB,MimeTypeText.BIBTEX,FileTypeText.CUDA+"",FileExtensionText.CU,FileExtensionText.
-CUH,MimeTypeText.CUDA,FileTypeText.VULKAN+"",FileExtensionText.COMP,MimeTypeText.PLAIN,FileTypeText.HASKELL+"",FileExtensionText.HS,MimeTypeText.HASKELL,FileTypeText.CSHARP+"",FileExtensionText.CS,MimeTypeText.CSHARP,FileTypeText.PROPERTIES+"",FileExtensionText.PROPERTIES,MimeTypeText.PROPERTIES;const BR_PATTERN=/<br\s*\/?\s*>/gi,LIST_PATTERN=/^<ul>([\s\S]*)<\/ul>$/i,LI_PATTERN=/<li>([\s\S]*?)<\/li>/gi,TOOLTIP_DELAY_DURATION=500,FORK_TREE_DEPTH_PADDING=8,SYSTEM_MESSAGE_PLACEHOLDER="System messag\
-e",URI_SCHEME_SEPARATOR="://",TEMPLATE_EXPRESSION_REGEX=/\{([+#./;?&]?)([^}]+)\}/g,URI_TEMPLATE_OPERATORS={RESERVED:"+",FRAGMENT:"#",PATH_SEGMENT:"/",LABEL:".",PATH_PARAM:";",FORM_QUERY:"?",FORM_CONTINUATION:"&"},URI_TEMPLATE_SEPARATORS={COMMA:",",SLASH:"/",PERIOD:".",SEMICOLON:";",QUERY_PREFIX:"?",QUERY_CONTINUATION:"&"},VARIABLE_EXPLODE_MODIFIER_REGEX=/[*]$/,VARIABLE_PREFIX_MODIFIER_REGEX=/:[\d]+$/,LEADING_SLASHES_REGEX=/^\/+/,DEFAULT_MOBILE_BREAKPOINT=768;class IsMobile extends MediaQuery{constructor(breakpoint=DEFAULT_MOBILE_BREAKPOINT){
-super(`max-width: ${breakpoint-1}px`)}}const SYNCABLE_PARAMETERS=[{key:"temperature",serverKey:"temperature",type:SyncableParameterType.NUMBER,canSync:!0},{key:"top_k",serverKey:"top_k",type:SyncableParameterType.NUMBER,canSync:!0},{key:"top_p",serverKey:"top_p",type:SyncableParameterType.NUMBER,canSync:!0},{key:"min_p",serverKey:"min_p",type:SyncableParameterType.NUMBER,canSync:!0},{key:"dynatemp_range",serverKey:"dynatemp_range",type:SyncableParameterType.NUMBER,canSync:!0},{key:"dynatemp_expo\
-nent",serverKey:"dynatemp_exponent",type:SyncableParameterType.NUMBER,canSync:!0},{key:"xtc_probability",serverKey:"xtc_probability",type:SyncableParameterType.NUMBER,canSync:!0},{key:"xtc_threshold",serverKey:"xtc_threshold",type:SyncableParameterType.NUMBER,canSync:!0},{key:"typ_p",serverKey:"typ_p",type:SyncableParameterType.NUMBER,canSync:!0},{key:"repeat_last_n",serverKey:"repeat_last_n",type:SyncableParameterType.NUMBER,canSync:!0},{key:"repeat_penalty",serverKey:"repeat_penalty",type:SyncableParameterType.
-NUMBER,canSync:!0},{key:"presence_penalty",serverKey:"presence_penalty",type:SyncableParameterType.NUMBER,canSync:!0},{key:"frequency_penalty",serverKey:"frequency_penalty",type:SyncableParameterType.NUMBER,canSync:!0},{key:"dry_multiplier",serverKey:"dry_multiplier",type:SyncableParameterType.NUMBER,canSync:!0},{key:"dry_base",serverKey:"dry_base",type:SyncableParameterType.NUMBER,canSync:!0},{key:"dry_allowed_length",serverKey:"dry_allowed_length",type:SyncableParameterType.NUMBER,canSync:!0},
-{key:"dry_penalty_last_n",serverKey:"dry_penalty_last_n",type:SyncableParameterType.NUMBER,canSync:!0},{key:"max_tokens",serverKey:"max_tokens",type:SyncableParameterType.NUMBER,canSync:!0},{key:"samplers",serverKey:"samplers",type:SyncableParameterType.STRING,canSync:!0},{key:"backend_sampling",serverKey:"backend_sampling",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"pasteLongTextToFileLen",serverKey:"pasteLongTextToFileLen",type:SyncableParameterType.NUMBER,canSync:!0},{key:"pdfAsImage",
-serverKey:"pdfAsImage",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"showThoughtInProgress",serverKey:"showThoughtInProgress",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"keepStatsVisible",serverKey:"keepStatsVisible",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"showMessageStats",serverKey:"showMessageStats",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"askForTitleConfirmation",serverKey:"askForTitleConfirmation",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"\
-titleGenerationUseFirstLine",serverKey:"titleGenerationUseFirstLine",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"disableAutoScroll",serverKey:"disableAutoScroll",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"renderUserContentAsMarkdown",serverKey:"renderUserContentAsMarkdown",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"autoMicOnEmpty",serverKey:"autoMicOnEmpty",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"pyInterpreterEnabled",serverKey:"pyInterpreterEnabled",type:SyncableParameterType.
-BOOLEAN,canSync:!0},{key:"enableContinueGeneration",serverKey:"enableContinueGeneration",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"fullHeightCodeBlocks",serverKey:"fullHeightCodeBlocks",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"systemMessage",serverKey:"systemMessage",type:SyncableParameterType.STRING,canSync:!0},{key:"showSystemMessage",serverKey:"showSystemMessage",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"theme",serverKey:"theme",type:SyncableParameterType.STRING,
-canSync:!0},{key:"copyTextAttachmentsAsPlainText",serverKey:"copyTextAttachmentsAsPlainText",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"showRawOutputSwitch",serverKey:"showRawOutputSwitch",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"alwaysShowSidebarOnDesktop",serverKey:"alwaysShowSidebarOnDesktop",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"autoShowSidebarOnNewChat",serverKey:"autoShowSidebarOnNewChat",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"showRawModel\
-Names",serverKey:"showRawModelNames",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"mcpServers",serverKey:"mcpServers",type:SyncableParameterType.STRING,canSync:!0},{key:"agenticMaxTurns",serverKey:"agenticMaxTurns",type:SyncableParameterType.NUMBER,canSync:!0},{key:"agenticMaxToolPreviewLines",serverKey:"agenticMaxToolPreviewLines",type:SyncableParameterType.NUMBER,canSync:!0},{key:"showToolCallInProgress",serverKey:"showToolCallInProgress",type:SyncableParameterType.BOOLEAN,canSync:!0},{
-key:"alwaysShowAgenticTurns",serverKey:"alwaysShowAgenticTurns",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"excludeReasoningFromContext",serverKey:"excludeReasoningFromContext",type:SyncableParameterType.BOOLEAN,canSync:!0},{key:"sendOnEnter",serverKey:"sendOnEnter",type:SyncableParameterType.BOOLEAN,canSync:!0}];class ParameterSyncService{static roundFloatingPoint(value){return normalizeFloatingPoint(value)}static extractServerDefaults(serverParams,webuiSettings){const extracted={};if(serverParams){
-for(const param of SYNCABLE_PARAMETERS)if(param.canSync&&param.serverKey in serverParams){const value=serverParams[param.serverKey];value!==void 0&&(extracted[param.key]=this.roundFloatingPoint(value))}serverParams.samplers&&Array.isArray(serverParams.samplers)&&(extracted.samplers=serverParams.samplers.join(";"))}if(webuiSettings){for(const param of SYNCABLE_PARAMETERS)if(param.canSync&&param.serverKey in webuiSettings){const value=webuiSettings[param.serverKey];value!==void 0&&(extracted[param.
-key]=this.roundFloatingPoint(value))}}return extracted}static mergeWithServerDefaults(currentSettings,serverDefaults,userOverrides=new Set){const merged={...currentSettings};for(const[key2,serverValue]of Object.entries(serverDefaults))userOverrides.has(key2)||(merged[key2]=this.roundFloatingPoint(serverValue));return merged}static getParameterInfo(key2,currentValue,propsDefaults,userOverrides){const hasPropsDefault=propsDefaults[key2]!==void 0,isUserOverride=userOverrides.has(key2),source2=isUserOverride?
-ParameterSource.CUSTOM:ParameterSource.DEFAULT;return{value:currentValue,source:source2,serverDefault:hasPropsDefault?propsDefaults[key2]:void 0,userOverride:isUserOverride?currentValue:void 0}}static canSyncParameter(key2){return SYNCABLE_PARAMETERS.some(param=>param.key===key2&&param.canSync)}static getSyncableParameterKeys(){return SYNCABLE_PARAMETERS.filter(param=>param.canSync).map(param=>param.key)}static validateServerParameter(key2,value){const param=SYNCABLE_PARAMETERS.find(p2=>p2.key===
-key2);if(!param)return!1;switch(param.type){case SyncableParameterType.NUMBER:return typeof value=="number"&&!isNaN(value);case SyncableParameterType.STRING:return typeof value=="string";case SyncableParameterType.BOOLEAN:return typeof value=="boolean";default:return!1}}static createParameterDiff(currentSettings,serverDefaults){const diff2={};for(const key2 of this.getSyncableParameterKeys()){const currentValue=currentSettings[key2],serverValue=serverDefaults[key2];serverValue!==void 0&&(diff2[key2]=
-{current:currentValue,server:serverValue,differs:currentValue!==serverValue})}return diff2}}class PropsService{static async fetch(autoload=!1){const params={};return autoload||(params.autoload="false"),apiFetchWithParams("./props",params,{authOnly:!0})}static async fetchForModel(modelId,autoload=!1){const params={model:modelId};return autoload||(params.autoload="false"),apiFetchWithParams("./props",params,{authOnly:!0})}}class ServerStore{#props=state$1(null);get props(){return get$4(this.#props)}set props(value){
-set$1(this.#props,value,!0)}#loading=state$1(!1);get loading(){return get$4(this.#loading)}set loading(value){set$1(this.#loading,value,!0)}#error=state$1(null);get error(){return get$4(this.#error)}set error(value){set$1(this.#error,value,!0)}#role=state$1(null);get role(){return get$4(this.#role)}set role(value){set$1(this.#role,value,!0)}fetchPromise=null;get defaultParams(){return this.props?.default_generation_settings?.params||null}get contextSize(){const nCtx=this.props?.default_generation_settings?.
-n_ctx;return typeof nCtx=="number"?nCtx:null}get webuiSettings(){return this.props?.webui_settings}get isRouterMode(){return this.role===ServerRole.ROUTER}get isModelMode(){return this.role===ServerRole.MODEL}async fetch(){if(this.fetchPromise)return this.fetchPromise;this.loading=!0,this.error=null;const fetchPromise=(async()=>{try{const props=await PropsService.fetch();this.props=props,this.error=null,this.detectRole(props)}catch(error2){this.error=this.getErrorMessage(error2),console.error("E\
-rror fetching server properties:",error2)}finally{this.loading=!1,this.fetchPromise=null}})();this.fetchPromise=fetchPromise,await fetchPromise}getErrorMessage(error2){if(error2 instanceof Error){const message=error2.message||"";if(error2.name==="TypeError"&&message.includes("fetch"))return"Server is not running or unreachable";if(message.includes("ECONNREFUSED"))return"Connection refused - server may be offline";if(message.includes("ENOTFOUND"))return"Server not found - check server address";if(message.
-includes("ETIMEDOUT"))return"Request timed out";if(message.includes("503"))return"Server temporarily unavailable";if(message.includes("500"))return"Server error - check server logs";if(message.includes("404"))return"Server endpoint not found";if(message.includes("403")||message.includes("401"))return"Access denied"}return"Failed to connect to server"}clear(){this.props=null,this.error=null,this.loading=!1,this.role=null,this.fetchPromise=null}detectRole(props){const newRole=props?.role===ServerRole.
-ROUTER?ServerRole.ROUTER:ServerRole.MODEL;this.role!==newRole&&(this.role=newRole,console.info(`Server running in ${newRole===ServerRole.ROUTER?"ROUTER":"MODEL"} mode`))}}const serverStore=new ServerStore,serverProps=()=>serverStore.props,serverLoading=()=>serverStore.loading,serverError=()=>serverStore.error,contextSize=()=>serverStore.contextSize,isRouterMode=()=>serverStore.isRouterMode;class SettingsStore{#config=state$1(proxy({...SETTING_CONFIG_DEFAULT}));get config(){return get$4(this.#config)}set config(value){
-set$1(this.#config,value,!0)}#theme=state$1("auto");get theme(){return get$4(this.#theme)}set theme(value){set$1(this.#theme,value,!0)}#isInitialized=state$1(!1);get isInitialized(){return get$4(this.#isInitialized)}set isInitialized(value){set$1(this.#isInitialized,value,!0)}#userOverrides=state$1(proxy(new Set));get userOverrides(){return get$4(this.#userOverrides)}set userOverrides(value){set$1(this.#userOverrides,value,!0)}getServerDefaults(){const serverParams=serverStore.defaultParams,webuiSettings=serverStore.
-webuiSettings;return ParameterSyncService.extractServerDefaults(serverParams,webuiSettings)}constructor(){this.initialize()}initialize(){try{this.loadConfig(),this.loadTheme(),this.isInitialized=!0}catch(error2){console.error("Failed to initialize settings store:",error2)}}loadConfig(){try{const storedConfigRaw=localStorage.getItem(CONFIG_LOCALSTORAGE_KEY),savedVal=JSON.parse(storedConfigRaw||"{}");this.config={...SETTING_CONFIG_DEFAULT,...savedVal},"sendOnEnter"in savedVal||new IsMobile().current&&
-(this.config.sendOnEnter=!1);const savedOverrides=JSON.parse(localStorage.getItem(USER_OVERRIDES_LOCALSTORAGE_KEY)||"[]");this.userOverrides=new Set(savedOverrides)}catch(error2){console.warn("Failed to parse config from localStorage, using defaults:",error2),this.config={...SETTING_CONFIG_DEFAULT},this.userOverrides=new Set}}loadTheme(){this.theme=localStorage.getItem("theme")||"auto"}updateConfig(key2,value){if(this.config[key2]=value,ParameterSyncService.canSyncParameter(key2)){const propsDefault=this.
-getServerDefaults()[key2];if(propsDefault!==void 0){const normalizedValue=normalizeFloatingPoint(value),normalizedDefault=normalizeFloatingPoint(propsDefault);normalizedValue===normalizedDefault?this.userOverrides.delete(key2):this.userOverrides.add(key2)}}this.saveConfig()}updateMultipleConfig(updates){Object.assign(this.config,updates);const propsDefaults=this.getServerDefaults();for(const[key2,value]of Object.entries(updates))if(ParameterSyncService.canSyncParameter(key2)){const propsDefault=propsDefaults[key2];
-if(propsDefault!==void 0){const normalizedValue=normalizeFloatingPoint(value),normalizedDefault=normalizeFloatingPoint(propsDefault);normalizedValue===normalizedDefault?this.userOverrides.delete(key2):this.userOverrides.add(key2)}}this.saveConfig()}saveConfig(){try{localStorage.setItem(CONFIG_LOCALSTORAGE_KEY,JSON.stringify(this.config)),localStorage.setItem(USER_OVERRIDES_LOCALSTORAGE_KEY,JSON.stringify(Array.from(this.userOverrides)))}catch(error2){console.error("Failed to save config to local\
-Storage:",error2)}}updateTheme(newTheme){this.theme=newTheme,this.saveTheme()}saveTheme(){try{this.theme==="auto"?localStorage.removeItem("theme"):localStorage.setItem("theme",this.theme)}catch(error2){console.error("Failed to save theme to localStorage:",error2)}}resetConfig(){this.config={...SETTING_CONFIG_DEFAULT},this.saveConfig()}resetTheme(){this.theme="auto",this.saveTheme()}resetAll(){this.resetConfig(),this.resetTheme()}resetParameterToServerDefault(key2){const serverDefaults=this.getServerDefaults(),
-webuiSettings=serverStore.webuiSettings;webuiSettings&&key2 in webuiSettings?setConfigValue(this.config,key2,webuiSettings[key2]):serverDefaults[key2]!==void 0?setConfigValue(this.config,key2,""):key2 in SETTING_CONFIG_DEFAULT&&setConfigValue(this.config,key2,getConfigValue(SETTING_CONFIG_DEFAULT,key2)),this.userOverrides.delete(key2),this.saveConfig()}syncWithServerDefaults(){const propsDefaults=this.getServerDefaults();if(Object.keys(propsDefaults).length===0)return;for(const[key2,propsValue]of Object.
-entries(propsDefaults)){const currentValue=getConfigValue(this.config,key2),normalizedCurrent=normalizeFloatingPoint(currentValue),normalizedDefault=normalizeFloatingPoint(propsValue);normalizedCurrent===normalizedDefault&&this.userOverrides.delete(key2)}const webuiSettings=serverStore.webuiSettings;if(webuiSettings)for(const[key2,value]of Object.entries(webuiSettings))!this.userOverrides.has(key2)&&value!==void 0&&setConfigValue(this.config,key2,value);this.saveConfig(),console.log("User overri\
-des after sync:",Array.from(this.userOverrides))}forceSyncWithServerDefaults(){const propsDefaults=this.getServerDefaults(),webuiSettings=serverStore.webuiSettings;for(const key2 of ParameterSyncService.getSyncableParameterKeys())webuiSettings&&key2 in webuiSettings?setConfigValue(this.config,key2,webuiSettings[key2]):propsDefaults[key2]!==void 0?setConfigValue(this.config,key2,""):key2 in SETTING_CONFIG_DEFAULT&&setConfigValue(this.config,key2,getConfigValue(SETTING_CONFIG_DEFAULT,key2)),this.userOverrides.
-delete(key2);this.saveConfig()}getConfig(key2){return this.config[key2]}getAllConfig(){return{...this.config}}canSyncParameter(key2){return ParameterSyncService.canSyncParameter(key2)}getParameterInfo(key2){const propsDefaults=this.getServerDefaults(),currentValue=getConfigValue(this.config,key2);return ParameterSyncService.getParameterInfo(key2,currentValue??"",propsDefaults,this.userOverrides)}getParameterDiff(){const serverDefaults=this.getServerDefaults();if(Object.keys(serverDefaults).length===
-0)return{};const configAsRecord=configToParameterRecord(this.config,ParameterSyncService.getSyncableParameterKeys());return ParameterSyncService.createParameterDiff(configAsRecord,serverDefaults)}clearAllUserOverrides(){this.userOverrides.clear(),this.saveConfig(),console.log("Cleared all user overrides")}}const settingsStore=new SettingsStore,config$1=()=>settingsStore.config;function redactValue(value,showLastChars){return showLastChars?`....${value.slice(-showLastChars)}`:"[redacted]"}function getAuthHeaders(){
-const apiKey=config$1().apiKey?.toString().trim();return apiKey?{Authorization:`Bearer ${apiKey}`}:{}}function getJsonHeaders(){return{"Content-Type":"application/json",...getAuthHeaders()}}function sanitizeHeaders(headers,extraRedactedHeaders,partialRedactHeaders){if(!headers)return{};const normalized=new Headers(headers),sanitized={},redactedHeaders=new Set(Array.from(extraRedactedHeaders??[],header=>header.toLowerCase()));for(const[key2,value]of normalized.entries()){const normalizedKey=key2.
-toLowerCase(),partialChars=partialRedactHeaders?.get(normalizedKey);partialChars!==void 0?sanitized[key2]=redactValue(value,partialChars):REDACTED_HEADERS.has(normalizedKey)||redactedHeaders.has(normalizedKey)?sanitized[key2]=redactValue(value):sanitized[key2]=value}return sanitized}async function apiFetch(path2,options={}){const{authOnly=!1,headers:customHeaders,...fetchOptions}=options,headers={...authOnly?getAuthHeaders():getJsonHeaders(),...customHeaders},url2=path2.startsWith(UrlProtocol.HTTP)||
-path2.startsWith(UrlProtocol.HTTPS)?path2:`${base}${path2}`,response=await fetch(url2,{...fetchOptions,headers});if(!response.ok){const errorMessage=await parseErrorMessage(response);throw new Error(errorMessage)}return response.json()}async function apiFetchWithParams(basePath,params,options={}){const url2=new URL(basePath,window.location.href);for(const[key2,value]of Object.entries(params))value!=null&&url2.searchParams.set(key2,value);const{authOnly=!1,headers:customHeaders,...fetchOptions}=options,
-headers={...authOnly?getAuthHeaders():getJsonHeaders(),...customHeaders},response=await fetch(url2.toString(),{...fetchOptions,headers});if(!response.ok){const errorMessage=await parseErrorMessage(response);throw new Error(errorMessage)}return response.json()}async function apiPost(path2,body2,options={}){return apiFetch(path2,{method:"POST",body:JSON.stringify(body2),...options})}async function parseErrorMessage(response){try{const errorData=await response.json();if(errorData?.error?.message)return errorData.
-error.message;if(errorData?.error&&typeof errorData.error=="string")return errorData.error;if(errorData?.message)return errorData.message}catch{}return`Request failed: ${response.status} ${response.statusText}`}function error(status,body2){throw new HttpError(status,body2)}async function validateApiKey(fetch2){try{const apiKey=config$1().apiKey,headers={"Content-Type":"application/json"};apiKey&&(headers.Authorization=`Bearer ${apiKey}`);const response=await fetch2(`${base}/props`,{headers});if(!response.
-ok){if(response.status===401||response.status===403)throw error(401,"Access denied");console.warn(`Server responded with status ${response.status} during API key validation`);return}}catch(err){if(err&&typeof err=="object"&&"status"in err)throw err;console.warn("Cannot connect to server for API key validation:",err)}}function isMcpPromptUpload(file){return file.type===SpecialFileType.MCP_PROMPT&&!!file.mcpPrompt}function isMcpPromptAttachment(attachment){return attachment.type===AttachmentType.MCP_PROMPT}
-function isMcpResourceAttachment(attachment){return attachment.type===AttachmentType.MCP_RESOURCE}function getUploadedFileCategory$1(file){const categoryByMime=getFileTypeCategory(file.type);return categoryByMime||getFileTypeCategoryByExtension(file.name)}function getAttachmentDisplayItems(options){const{uploadedFiles=[],attachments=[]}=options,items2=[];for(const file of uploadedFiles)items2.push({id:file.id,name:file.name,size:file.size,preview:file.preview,isImage:getUploadedFileCategory$1(file)===
-FileTypeCategory.IMAGE,isMcpPrompt:isMcpPromptUpload(file),isLoading:file.isLoading,loadError:file.loadError,uploadedFile:file,textContent:file.textContent});for(const[index2,attachment]of attachments.entries()){const isImage2=isImageFile(attachment),isMcpPrompt=isMcpPromptAttachment(attachment),isMcpResource=isMcpResourceAttachment(attachment);items2.push({id:`attachment-${index2}`,name:attachment.name,preview:isImage2&&"base64Url"in attachment?attachment.base64Url:void 0,isImage:isImage2,isMcpPrompt,
-isMcpResource,attachment,attachmentIndex:index2,textContent:"content"in attachment?attachment.content:void 0})}return items2.reverse()}function getUploadedFileCategory(uploadedFile){const categoryByMime=getFileTypeCategory(uploadedFile.type);return categoryByMime||getFileTypeCategoryByExtension(uploadedFile.name)}function isTextFile(attachment,uploadedFile){return uploadedFile?getUploadedFileCategory(uploadedFile)===FileTypeCategory.TEXT:attachment?attachment.type===AttachmentType.TEXT||attachment.
-type===AttachmentType.LEGACY_CONTEXT:!1}function isImageFile(attachment,uploadedFile){return uploadedFile?getUploadedFileCategory(uploadedFile)===FileTypeCategory.IMAGE:attachment?attachment.type===AttachmentType.IMAGE:!1}function isPdfFile$1(attachment,uploadedFile){return uploadedFile?getUploadedFileCategory(uploadedFile)===FileTypeCategory.PDF:attachment?attachment.type===AttachmentType.PDF:!1}function isAudioFile(attachment,uploadedFile){return uploadedFile?getUploadedFileCategory(uploadedFile)===
-FileTypeCategory.AUDIO:attachment?attachment.type===AttachmentType.AUDIO:!1}function autoResizeTextarea(textareaElement){textareaElement&&(textareaElement.style.height="1rem",textareaElement.style.height=textareaElement.scrollHeight+"px")}function findMessageById(messages,id2){if(id2)return messages.find(m=>m.id===id2)}function filterByLeafNodeId(messages,leafNodeId,includeRoot=!1){const result=[],nodeMap=new Map;for(const msg of messages)nodeMap.set(msg.id,msg);let startNode=nodeMap.get(leafNodeId);
-if(!startNode){let latestTime=-1;for(const msg of messages)msg.timestamp>latestTime&&(startNode=msg,latestTime=msg.timestamp)}let currentNode=startNode;for(;currentNode&&((currentNode.type!=="root"||includeRoot)&&result.push(currentNode),currentNode.parent!==null);)currentNode=nodeMap.get(currentNode.parent);return result.sort((a,b)=>a.role===MessageRole.SYSTEM&&b.role!==MessageRole.SYSTEM?-1:a.role!==MessageRole.SYSTEM&&b.role===MessageRole.SYSTEM?1:a.timestamp-b.timestamp),result}function findLeafNode(messages,messageId){
-const nodeMap=new Map;for(const msg of messages)nodeMap.set(msg.id,msg);let currentNode=nodeMap.get(messageId);for(;currentNode&&currentNode.children.length>0;){const lastChildId=currentNode.children[currentNode.children.length-1];currentNode=nodeMap.get(lastChildId)}return currentNode?.id??messageId}function findDescendantMessages(messages,messageId){const nodeMap=new Map;for(const msg of messages)nodeMap.set(msg.id,msg);const descendants=[],queue=[messageId];for(;queue.length>0;){const currentId=queue.
-shift(),currentNode=nodeMap.get(currentId);if(currentNode)for(const childId of currentNode.children)descendants.push(childId),queue.push(childId)}return descendants}function getMessageSiblings(messages,messageId){const nodeMap=new Map;for(const msg of messages)nodeMap.set(msg.id,msg);const message=nodeMap.get(messageId);if(!message)return null;if(message.parent===null)return{message,siblingIds:[messageId],currentIndex:0,totalSiblings:1};const parentNode=nodeMap.get(message.parent);if(!parentNode)
-return{message,siblingIds:[messageId],currentIndex:0,totalSiblings:1};const siblingIds=parentNode.children,siblingLeafIds=siblingIds.map(siblingId=>findLeafNode(messages,siblingId)),currentIndex=siblingIds.indexOf(messageId);return{message,siblingIds:siblingLeafIds,currentIndex,totalSiblings:siblingIds.length}}var core$5,hasRequiredCore$4;function requireCore$4(){if(hasRequiredCore$4)return core$5;hasRequiredCore$4=1;function deepFreeze(obj){return obj instanceof Map?obj.clear=obj.delete=obj.set=
-function(){throw new Error("map is read-only")}:obj instanceof Set&&(obj.add=obj.clear=obj.delete=function(){throw new Error("set is read-only")}),Object.freeze(obj),Object.getOwnPropertyNames(obj).forEach(name=>{const prop2=obj[name],type2=typeof prop2;(type2==="object"||type2==="function")&&!Object.isFrozen(prop2)&&deepFreeze(prop2)}),obj}class Response2{constructor(mode){mode.data===void 0&&(mode.data={}),this.data=mode.data,this.isMatchIgnored=!1}ignoreMatch(){this.isMatchIgnored=!0}}function escapeHTML(value){
-return value.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;").replace(/"/g,"&quot;").replace(/'/g,"&#x27;")}function inherit$1(original,...objects){const result=Object.create(null);for(const key2 in original)result[key2]=original[key2];return objects.forEach(function(obj){for(const key2 in obj)result[key2]=obj[key2]}),result}const SPAN_CLOSE="</span>",emitsWrappingTags=node2=>!!node2.scope,scopeToCSSClass=(name,{prefix})=>{if(name.startsWith("language:"))return name.replace("langu\
-age:","language-");if(name.includes(".")){const pieces=name.split(".");return[`${prefix}${pieces.shift()}`,...pieces.map((x,i)=>`${x}${"_".repeat(i+1)}`)].join(" ")}return`${prefix}${name}`};class HTMLRenderer{constructor(parseTree3,options){this.buffer="",this.classPrefix=options.classPrefix,parseTree3.walk(this)}addText(text2){this.buffer+=escapeHTML(text2)}openNode(node2){if(!emitsWrappingTags(node2))return;const className=scopeToCSSClass(node2.scope,{prefix:this.classPrefix});this.span(className)}closeNode(node2){
-emitsWrappingTags(node2)&&(this.buffer+=SPAN_CLOSE)}value(){return this.buffer}span(className){this.buffer+=`<span class="${className}">`}}const newNode=(opts={})=>{const result={children:[]};return Object.assign(result,opts),result};class TokenTree{constructor(){this.rootNode=newNode(),this.stack=[this.rootNode]}get top(){return this.stack[this.stack.length-1]}get root(){return this.rootNode}add(node2){this.top.children.push(node2)}openNode(scope2){const node2=newNode({scope:scope2});this.add(node2),
-this.stack.push(node2)}closeNode(){if(this.stack.length>1)return this.stack.pop()}closeAllNodes(){for(;this.closeNode(););}toJSON(){return JSON.stringify(this.rootNode,null,4)}walk(builder){return this.constructor._walk(builder,this.rootNode)}static _walk(builder,node2){return typeof node2=="string"?builder.addText(node2):node2.children&&(builder.openNode(node2),node2.children.forEach(child2=>this._walk(builder,child2)),builder.closeNode(node2)),builder}static _collapse(node2){typeof node2!="str\
-ing"&&node2.children&&(node2.children.every(el=>typeof el=="string")?node2.children=[node2.children.join("")]:node2.children.forEach(child2=>{TokenTree._collapse(child2)}))}}class TokenTreeEmitter extends TokenTree{constructor(options){super(),this.options=options}addText(text2){text2!==""&&this.add(text2)}startScope(scope2){this.openNode(scope2)}endScope(){this.closeNode()}__addSublanguage(emitter,name){const node2=emitter.root;name&&(node2.scope=`language:${name}`),this.add(node2)}toHTML(){return new HTMLRenderer(
-this,this.options).value()}finalize(){return this.closeAllNodes(),!0}}function source2(re2){return re2?typeof re2=="string"?re2:re2.source:null}function lookahead2(re2){return concat2("(?=",re2,")")}function anyNumberOfTimes(re2){return concat2("(?:",re2,")*")}function optional2(re2){return concat2("(?:",re2,")?")}function concat2(...args){return args.map(x=>source2(x)).join("")}function stripOptionsFromArgs2(args){const opts=args[args.length-1];return typeof opts=="object"&&opts.constructor===Object?
-(args.splice(args.length-1,1),opts):{}}function either2(...args){return"("+(stripOptionsFromArgs2(args).capture?"":"?:")+args.map(x=>source2(x)).join("|")+")"}function countMatchGroups(re2){return new RegExp(re2.toString()+"|").exec("").length-1}function startsWith(re2,lexeme){const match=re2&&re2.exec(lexeme);return match&&match.index===0}const BACKREF_RE=/\[(?:[^\\\]]|\\.)*\]|\(\??|\\([1-9][0-9]*)|\\./;function _rewriteBackreferences(regexps,{joinWith}){let numCaptures=0;return regexps.map(regex=>{
-numCaptures+=1;const offset2=numCaptures;let re2=source2(regex),out="";for(;re2.length>0;){const match=BACKREF_RE.exec(re2);if(!match){out+=re2;break}out+=re2.substring(0,match.index),re2=re2.substring(match.index+match[0].length),match[0][0]==="\\"&&match[1]?out+="\\"+String(Number(match[1])+offset2):(out+=match[0],match[0]==="("&&numCaptures++)}return out}).map(re2=>`(${re2})`).join(joinWith)}const MATCH_NOTHING_RE=/\b\B/,IDENT_RE2="[a-zA-Z]\\w*",UNDERSCORE_IDENT_RE="[a-zA-Z_]\\w*",NUMBER_RE="\
-\\b\\d+(\\.\\d+)?",C_NUMBER_RE="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",BINARY_NUMBER_RE="\\b(0b[01]+)",RE_STARTERS_RE="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",SHEBANG=(opts={})=>{const beginShebang=/^#![ ]*\//;return opts.binary&&(opts.begin=concat2(beginShebang,/.*\b/,opts.binary,/\b.*/)),inherit$1({scope:"meta",begin:beginShebang,end:/$/,relevance:0,"on:begin":(m,resp)=>{
-m.index!==0&&resp.ignoreMatch()}},opts)},BACKSLASH_ESCAPE={begin:"\\\\[\\s\\S]",relevance:0},APOS_STRING_MODE={scope:"string",begin:"'",end:"'",illegal:"\\n",contains:[BACKSLASH_ESCAPE]},QUOTE_STRING_MODE={scope:"string",begin:'"',end:'"',illegal:"\\n",contains:[BACKSLASH_ESCAPE]},PHRASAL_WORDS_MODE={begin:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},COMMENT=function(begin,end,modeOptions={}){const mode=inherit$1(
-{scope:"comment",begin,end,contains:[]},modeOptions);mode.contains.push({scope:"doctag",begin:"[ ]*(?=(TODO|FIXME|NOTE|BUG|OPTIMIZE|HACK|XXX):)",end:/(TODO|FIXME|NOTE|BUG|OPTIMIZE|HACK|XXX):/,excludeBegin:!0,relevance:0});const ENGLISH_WORD=either2("I","a","is","so","us","to","at","if","in","it","on",/[A-Za-z]+['](d|ve|re|ll|t|s|n)/,/[A-Za-z]+[-][a-z]+/,/[A-Za-z][a-z]{2,}/);return mode.contains.push({begin:concat2(/[ ]+/,"(",ENGLISH_WORD,/[.]?[:]?([.][ ]|[ ])/,"){3}")}),mode},C_LINE_COMMENT_MODE=COMMENT(
-"//","$"),C_BLOCK_COMMENT_MODE=COMMENT("/\\*","\\*/"),HASH_COMMENT_MODE=COMMENT("#","$"),NUMBER_MODE={scope:"number",begin:NUMBER_RE,relevance:0},C_NUMBER_MODE={scope:"number",begin:C_NUMBER_RE,relevance:0},BINARY_NUMBER_MODE={scope:"number",begin:BINARY_NUMBER_RE,relevance:0},REGEXP_MODE={scope:"regexp",begin:/\/(?=[^/\n]*\/)/,end:/\/[gimuy]*/,contains:[BACKSLASH_ESCAPE,{begin:/\[/,end:/\]/,relevance:0,contains:[BACKSLASH_ESCAPE]}]},TITLE_MODE={scope:"title",begin:IDENT_RE2,relevance:0},UNDERSCORE_TITLE_MODE={
-scope:"title",begin:UNDERSCORE_IDENT_RE,relevance:0},METHOD_GUARD={begin:"\\.\\s*"+UNDERSCORE_IDENT_RE,relevance:0};var MODES2=Object.freeze({__proto__:null,APOS_STRING_MODE,BACKSLASH_ESCAPE,BINARY_NUMBER_MODE,BINARY_NUMBER_RE,COMMENT,C_BLOCK_COMMENT_MODE,C_LINE_COMMENT_MODE,C_NUMBER_MODE,C_NUMBER_RE,END_SAME_AS_BEGIN:function(mode){return Object.assign(mode,{"on:begin":(m,resp)=>{resp.data._beginMatch=m[1]},"on:end":(m,resp)=>{resp.data._beginMatch!==m[1]&&resp.ignoreMatch()}})},HASH_COMMENT_MODE,
-IDENT_RE:IDENT_RE2,MATCH_NOTHING_RE,METHOD_GUARD,NUMBER_MODE,NUMBER_RE,PHRASAL_WORDS_MODE,QUOTE_STRING_MODE,REGEXP_MODE,RE_STARTERS_RE,SHEBANG,TITLE_MODE,UNDERSCORE_IDENT_RE,UNDERSCORE_TITLE_MODE});function skipIfHasPrecedingDot(match,response){match.input[match.index-1]==="."&&response.ignoreMatch()}function scopeClassName(mode,_parent){mode.className!==void 0&&(mode.scope=mode.className,delete mode.className)}function beginKeywords(mode,parent){parent&&mode.beginKeywords&&(mode.begin="\\b("+mode.
-beginKeywords.split(" ").join("|")+")(?!\\.)(?=\\b|\\s)",mode.__beforeBegin=skipIfHasPrecedingDot,mode.keywords=mode.keywords||mode.beginKeywords,delete mode.beginKeywords,mode.relevance===void 0&&(mode.relevance=0))}function compileIllegal(mode,_parent){Array.isArray(mode.illegal)&&(mode.illegal=either2(...mode.illegal))}function compileMatch(mode,_parent){if(mode.match){if(mode.begin||mode.end)throw new Error("begin & end are not supported with match");mode.begin=mode.match,delete mode.match}}
-function compileRelevance(mode,_parent){mode.relevance===void 0&&(mode.relevance=1)}const beforeMatchExt=(mode,parent)=>{if(!mode.beforeMatch)return;if(mode.starts)throw new Error("beforeMatch cannot be used with starts");const originalMode=Object.assign({},mode);Object.keys(mode).forEach(key2=>{delete mode[key2]}),mode.keywords=originalMode.keywords,mode.begin=concat2(originalMode.beforeMatch,lookahead2(originalMode.begin)),mode.starts={relevance:0,contains:[Object.assign(originalMode,{endsParent:!0})]},
-mode.relevance=0,delete originalMode.beforeMatch},COMMON_KEYWORDS=["of","and","for","in","not","or","if","then","parent","list","value"],DEFAULT_KEYWORD_SCOPE="keyword";function compileKeywords(rawKeywords,caseInsensitive,scopeName=DEFAULT_KEYWORD_SCOPE){const compiledKeywords=Object.create(null);return typeof rawKeywords=="string"?compileList(scopeName,rawKeywords.split(" ")):Array.isArray(rawKeywords)?compileList(scopeName,rawKeywords):Object.keys(rawKeywords).forEach(function(scopeName2){Object.
-assign(compiledKeywords,compileKeywords(rawKeywords[scopeName2],caseInsensitive,scopeName2))}),compiledKeywords;function compileList(scopeName2,keywordList){caseInsensitive&&(keywordList=keywordList.map(x=>x.toLowerCase())),keywordList.forEach(function(keyword2){const pair=keyword2.split("|");compiledKeywords[pair[0]]=[scopeName2,scoreForKeyword(pair[0],pair[1])]})}}function scoreForKeyword(keyword2,providedScore){return providedScore?Number(providedScore):commonKeyword(keyword2)?0:1}function commonKeyword(keyword2){
-return COMMON_KEYWORDS.includes(keyword2.toLowerCase())}const seenDeprecations={},error2=message=>{console.error(message)},warn2=(message,...args)=>{console.log(`WARN: ${message}`,...args)},deprecated2=(version3,message)=>{seenDeprecations[`${version3}/${message}`]||(console.log(`Deprecated as of ${version3}. ${message}`),seenDeprecations[`${version3}/${message}`]=!0)},MultiClassError=new Error;function remapScopeNames(mode,regexes,{key:key2}){let offset2=0;const scopeNames=mode[key2],emit={},positions={};
-for(let i=1;i<=regexes.length;i++)positions[i+offset2]=scopeNames[i],emit[i+offset2]=!0,offset2+=countMatchGroups(regexes[i-1]);mode[key2]=positions,mode[key2]._emit=emit,mode[key2]._multi=!0}function beginMultiClass(mode){if(Array.isArray(mode.begin)){if(mode.skip||mode.excludeBegin||mode.returnBegin)throw error2("skip, excludeBegin, returnBegin not compatible with beginScope: {}"),MultiClassError;if(typeof mode.beginScope!="object"||mode.beginScope===null)throw error2("beginScope must be objec\
-t"),MultiClassError;remapScopeNames(mode,mode.begin,{key:"beginScope"}),mode.begin=_rewriteBackreferences(mode.begin,{joinWith:""})}}function endMultiClass(mode){if(Array.isArray(mode.end)){if(mode.skip||mode.excludeEnd||mode.returnEnd)throw error2("skip, excludeEnd, returnEnd not compatible with endScope: {}"),MultiClassError;if(typeof mode.endScope!="object"||mode.endScope===null)throw error2("endScope must be object"),MultiClassError;remapScopeNames(mode,mode.end,{key:"endScope"}),mode.end=_rewriteBackreferences(
-mode.end,{joinWith:""})}}function scopeSugar(mode){mode.scope&&typeof mode.scope=="object"&&mode.scope!==null&&(mode.beginScope=mode.scope,delete mode.scope)}function MultiClass(mode){scopeSugar(mode),typeof mode.beginScope=="string"&&(mode.beginScope={_wrap:mode.beginScope}),typeof mode.endScope=="string"&&(mode.endScope={_wrap:mode.endScope}),beginMultiClass(mode),endMultiClass(mode)}function compileLanguage(language2){function langRe(value,global2){return new RegExp(source2(value),"m"+(language2.
-case_insensitive?"i":"")+(language2.unicodeRegex?"u":"")+(global2?"g":""))}class MultiRegex{constructor(){this.matchIndexes={},this.regexes=[],this.matchAt=1,this.position=0}addRule(re2,opts){opts.position=this.position++,this.matchIndexes[this.matchAt]=opts,this.regexes.push([opts,re2]),this.matchAt+=countMatchGroups(re2)+1}compile(){this.regexes.length===0&&(this.exec=()=>null);const terminators=this.regexes.map(el=>el[1]);this.matcherRe=langRe(_rewriteBackreferences(terminators,{joinWith:"|"}),
-!0),this.lastIndex=0}exec(s2){this.matcherRe.lastIndex=this.lastIndex;const match=this.matcherRe.exec(s2);if(!match)return null;const i=match.findIndex((el,i2)=>i2>0&&el!==void 0),matchData=this.matchIndexes[i];return match.splice(0,i),Object.assign(match,matchData)}}class ResumableMultiRegex{constructor(){this.rules=[],this.multiRegexes=[],this.count=0,this.lastIndex=0,this.regexIndex=0}getMatcher(index2){if(this.multiRegexes[index2])return this.multiRegexes[index2];const matcher=new MultiRegex;
-return this.rules.slice(index2).forEach(([re2,opts])=>matcher.addRule(re2,opts)),matcher.compile(),this.multiRegexes[index2]=matcher,matcher}resumingScanAtSamePosition(){return this.regexIndex!==0}considerAll(){this.regexIndex=0}addRule(re2,opts){this.rules.push([re2,opts]),opts.type==="begin"&&this.count++}exec(s2){const m=this.getMatcher(this.regexIndex);m.lastIndex=this.lastIndex;let result=m.exec(s2);if(this.resumingScanAtSamePosition()&&!(result&&result.index===this.lastIndex)){const m2=this.
-getMatcher(0);m2.lastIndex=this.lastIndex+1,result=m2.exec(s2)}return result&&(this.regexIndex+=result.position+1,this.regexIndex===this.count&&this.considerAll()),result}}function buildModeRegex(mode){const mm=new ResumableMultiRegex;return mode.contains.forEach(term=>mm.addRule(term.begin,{rule:term,type:"begin"})),mode.terminatorEnd&&mm.addRule(mode.terminatorEnd,{type:"end"}),mode.illegal&&mm.addRule(mode.illegal,{type:"illegal"}),mm}function compileMode(mode,parent){const cmode=mode;if(mode.
-isCompiled)return cmode;[scopeClassName,compileMatch,MultiClass,beforeMatchExt].forEach(ext=>ext(mode,parent)),language2.compilerExtensions.forEach(ext=>ext(mode,parent)),mode.__beforeBegin=null,[beginKeywords,compileIllegal,compileRelevance].forEach(ext=>ext(mode,parent)),mode.isCompiled=!0;let keywordPattern=null;return typeof mode.keywords=="object"&&mode.keywords.$pattern&&(mode.keywords=Object.assign({},mode.keywords),keywordPattern=mode.keywords.$pattern,delete mode.keywords.$pattern),keywordPattern=
-keywordPattern||/\w+/,mode.keywords&&(mode.keywords=compileKeywords(mode.keywords,language2.case_insensitive)),cmode.keywordPatternRe=langRe(keywordPattern,!0),parent&&(mode.begin||(mode.begin=/\B|\b/),cmode.beginRe=langRe(cmode.begin),!mode.end&&!mode.endsWithParent&&(mode.end=/\B|\b/),mode.end&&(cmode.endRe=langRe(cmode.end)),cmode.terminatorEnd=source2(cmode.end)||"",mode.endsWithParent&&parent.terminatorEnd&&(cmode.terminatorEnd+=(mode.end?"|":"")+parent.terminatorEnd)),mode.illegal&&(cmode.
-illegalRe=langRe(mode.illegal)),mode.contains||(mode.contains=[]),mode.contains=[].concat(...mode.contains.map(function(c2){return expandOrCloneMode(c2==="self"?mode:c2)})),mode.contains.forEach(function(c2){compileMode(c2,cmode)}),mode.starts&&compileMode(mode.starts,parent),cmode.matcher=buildModeRegex(cmode),cmode}if(language2.compilerExtensions||(language2.compilerExtensions=[]),language2.contains&&language2.contains.includes("self"))throw new Error("ERR: contains `self` is not supported at \
-the top-level of a language.  See documentation.");return language2.classNameAliases=inherit$1(language2.classNameAliases||{}),compileMode(language2)}function dependencyOnParent(mode){return mode?mode.endsWithParent||dependencyOnParent(mode.starts):!1}function expandOrCloneMode(mode){return mode.variants&&!mode.cachedVariants&&(mode.cachedVariants=mode.variants.map(function(variant){return inherit$1(mode,{variants:null},variant)})),mode.cachedVariants?mode.cachedVariants:dependencyOnParent(mode)?
-inherit$1(mode,{starts:mode.starts?inherit$1(mode.starts):null}):Object.isFrozen(mode)?inherit$1(mode):mode}var version2="11.11.1";class HTMLInjectionError extends Error{constructor(reason,html2){super(reason),this.name="HTMLInjectionError",this.html=html2}}const escape2=escapeHTML,inherit=inherit$1,NO_MATCH=Symbol("nomatch"),MAX_KEYWORD_HITS=7,HLJS=function(hljs){const languages=Object.create(null),aliases=Object.create(null),plugins=[];let SAFE_MODE=!0;const LANGUAGE_NOT_FOUND="Could not find \
-the language '{}', did you forget to load/include a language module?",PLAINTEXT_LANGUAGE={disableAutodetect:!0,name:"Plain text",contains:[]};let options={ignoreUnescapedHTML:!1,throwUnescapedHTML:!1,noHighlightRe:/^(no-?highlight)$/i,languageDetectRe:/\blang(?:uage)?-([\w-]+)\b/i,classPrefix:"hljs-",cssSelector:"pre code",languages:null,__emitter:TokenTreeEmitter};function shouldNotHighlight(languageName){return options.noHighlightRe.test(languageName)}function blockLanguage(block2){let classes=block2.
-className+" ";classes+=block2.parentNode?block2.parentNode.className:"";const match=options.languageDetectRe.exec(classes);if(match){const language2=getLanguage(match[1]);return language2||(warn2(LANGUAGE_NOT_FOUND.replace("{}",match[1])),warn2("Falling back to no-highlight mode for this block.",block2)),language2?match[1]:"no-highlight"}return classes.split(/\s+/).find(_class=>shouldNotHighlight(_class)||getLanguage(_class))}function highlight2(codeOrLanguageName,optionsOrCode,ignoreIllegals){let code2="",
-languageName="";typeof optionsOrCode=="object"?(code2=codeOrLanguageName,ignoreIllegals=optionsOrCode.ignoreIllegals,languageName=optionsOrCode.language):(deprecated2("10.7.0","highlight(lang, code, ...args) has been deprecated."),deprecated2("10.7.0",`Please use highlight(code, options) instead.
-https://github.com/highlightjs/highlight.js/issues/2277`),languageName=codeOrLanguageName,code2=optionsOrCode),ignoreIllegals===void 0&&(ignoreIllegals=!0);const context={code:code2,language:languageName};fire("before:highlight",context);const result=context.result?context.result:_highlight(context.language,context.code,ignoreIllegals);return result.code=context.code,fire("after:highlight",result),result}function _highlight(languageName,codeToHighlight,ignoreIllegals,continuation){const keywordHits=Object.
-create(null);function keywordData(mode,matchText){return mode.keywords[matchText]}function processKeywords(){if(!top.keywords){emitter.addText(modeBuffer);return}let lastIndex=0;top.keywordPatternRe.lastIndex=0;let match=top.keywordPatternRe.exec(modeBuffer),buf="";for(;match;){buf+=modeBuffer.substring(lastIndex,match.index);const word=language2.case_insensitive?match[0].toLowerCase():match[0],data=keywordData(top,word);if(data){const[kind,keywordRelevance]=data;if(emitter.addText(buf),buf="",keywordHits[word]=
-(keywordHits[word]||0)+1,keywordHits[word]<=MAX_KEYWORD_HITS&&(relevance+=keywordRelevance),kind.startsWith("_"))buf+=match[0];else{const cssClass=language2.classNameAliases[kind]||kind;emitKeyword(match[0],cssClass)}}else buf+=match[0];lastIndex=top.keywordPatternRe.lastIndex,match=top.keywordPatternRe.exec(modeBuffer)}buf+=modeBuffer.substring(lastIndex),emitter.addText(buf)}function processSubLanguage(){if(modeBuffer==="")return;let result2=null;if(typeof top.subLanguage=="string"){if(!languages[top.
-subLanguage]){emitter.addText(modeBuffer);return}result2=_highlight(top.subLanguage,modeBuffer,!0,continuations[top.subLanguage]),continuations[top.subLanguage]=result2._top}else result2=highlightAuto(modeBuffer,top.subLanguage.length?top.subLanguage:null);top.relevance>0&&(relevance+=result2.relevance),emitter.__addSublanguage(result2._emitter,result2.language)}function processBuffer(){top.subLanguage!=null?processSubLanguage():processKeywords(),modeBuffer=""}function emitKeyword(keyword2,scope2){
-keyword2!==""&&(emitter.startScope(scope2),emitter.addText(keyword2),emitter.endScope())}function emitMultiClass(scope2,match){let i=1;const max2=match.length-1;for(;i<=max2;){if(!scope2._emit[i]){i++;continue}const klass=language2.classNameAliases[scope2[i]]||scope2[i],text2=match[i];klass?emitKeyword(text2,klass):(modeBuffer=text2,processKeywords(),modeBuffer=""),i++}}function startNewMode(mode,match){return mode.scope&&typeof mode.scope=="string"&&emitter.openNode(language2.classNameAliases[mode.
-scope]||mode.scope),mode.beginScope&&(mode.beginScope._wrap?(emitKeyword(modeBuffer,language2.classNameAliases[mode.beginScope._wrap]||mode.beginScope._wrap),modeBuffer=""):mode.beginScope._multi&&(emitMultiClass(mode.beginScope,match),modeBuffer="")),top=Object.create(mode,{parent:{value:top}}),top}function endOfMode(mode,match,matchPlusRemainder){let matched=startsWith(mode.endRe,matchPlusRemainder);if(matched){if(mode["on:end"]){const resp=new Response2(mode);mode["on:end"](match,resp),resp.isMatchIgnored&&
-(matched=!1)}if(matched){for(;mode.endsParent&&mode.parent;)mode=mode.parent;return mode}}if(mode.endsWithParent)return endOfMode(mode.parent,match,matchPlusRemainder)}function doIgnore(lexeme){return top.matcher.regexIndex===0?(modeBuffer+=lexeme[0],1):(resumeScanAtSamePosition=!0,0)}function doBeginMatch(match){const lexeme=match[0],newMode=match.rule,resp=new Response2(newMode),beforeCallbacks=[newMode.__beforeBegin,newMode["on:begin"]];for(const cb of beforeCallbacks)if(cb&&(cb(match,resp),resp.
-isMatchIgnored))return doIgnore(lexeme);return newMode.skip?modeBuffer+=lexeme:(newMode.excludeBegin&&(modeBuffer+=lexeme),processBuffer(),!newMode.returnBegin&&!newMode.excludeBegin&&(modeBuffer=lexeme)),startNewMode(newMode,match),newMode.returnBegin?0:lexeme.length}function doEndMatch(match){const lexeme=match[0],matchPlusRemainder=codeToHighlight.substring(match.index),endMode=endOfMode(top,match,matchPlusRemainder);if(!endMode)return NO_MATCH;const origin2=top;top.endScope&&top.endScope._wrap?
-(processBuffer(),emitKeyword(lexeme,top.endScope._wrap)):top.endScope&&top.endScope._multi?(processBuffer(),emitMultiClass(top.endScope,match)):origin2.skip?modeBuffer+=lexeme:(origin2.returnEnd||origin2.excludeEnd||(modeBuffer+=lexeme),processBuffer(),origin2.excludeEnd&&(modeBuffer=lexeme));do top.scope&&emitter.closeNode(),!top.skip&&!top.subLanguage&&(relevance+=top.relevance),top=top.parent;while(top!==endMode.parent);return endMode.starts&&startNewMode(endMode.starts,match),origin2.returnEnd?
-0:lexeme.length}function processContinuations(){const list2=[];for(let current2=top;current2!==language2;current2=current2.parent)current2.scope&&list2.unshift(current2.scope);list2.forEach(item=>emitter.openNode(item))}let lastMatch={};function processLexeme(textBeforeMatch,match){const lexeme=match&&match[0];if(modeBuffer+=textBeforeMatch,lexeme==null)return processBuffer(),0;if(lastMatch.type==="begin"&&match.type==="end"&&lastMatch.index===match.index&&lexeme===""){if(modeBuffer+=codeToHighlight.
-slice(match.index,match.index+1),!SAFE_MODE){const err=new Error(`0 width match regex (${languageName})`);throw err.languageName=languageName,err.badRule=lastMatch.rule,err}return 1}if(lastMatch=match,match.type==="begin")return doBeginMatch(match);if(match.type==="illegal"&&!ignoreIllegals){const err=new Error('Illegal lexeme "'+lexeme+'" for mode "'+(top.scope||"<unnamed>")+'"');throw err.mode=top,err}else if(match.type==="end"){const processed=doEndMatch(match);if(processed!==NO_MATCH)return processed}
-if(match.type==="illegal"&&lexeme==="")return modeBuffer+=`
-`,1;if(iterations>1e5&&iterations>match.index*3)throw new Error("potential infinite loop, way more iterations than matches");return modeBuffer+=lexeme,lexeme.length}const language2=getLanguage(languageName);if(!language2)throw error2(LANGUAGE_NOT_FOUND.replace("{}",languageName)),new Error('Unknown language: "'+languageName+'"');const md=compileLanguage(language2);let result="",top=continuation||md;const continuations={},emitter=new options.__emitter(options);processContinuations();let modeBuffer="",
-relevance=0,index2=0,iterations=0,resumeScanAtSamePosition=!1;try{if(language2.__emitTokens)language2.__emitTokens(codeToHighlight,emitter);else{for(top.matcher.considerAll();;){iterations++,resumeScanAtSamePosition?resumeScanAtSamePosition=!1:top.matcher.considerAll(),top.matcher.lastIndex=index2;const match=top.matcher.exec(codeToHighlight);if(!match)break;const beforeMatch=codeToHighlight.substring(index2,match.index),processedCount=processLexeme(beforeMatch,match);index2=match.index+processedCount}
-processLexeme(codeToHighlight.substring(index2))}return emitter.finalize(),result=emitter.toHTML(),{language:languageName,value:result,relevance,illegal:!1,_emitter:emitter,_top:top}}catch(err){if(err.message&&err.message.includes("Illegal"))return{language:languageName,value:escape2(codeToHighlight),illegal:!0,relevance:0,_illegalBy:{message:err.message,index:index2,context:codeToHighlight.slice(index2-100,index2+100),mode:err.mode,resultSoFar:result},_emitter:emitter};if(SAFE_MODE)return{language:languageName,
-value:escape2(codeToHighlight),illegal:!1,relevance:0,errorRaised:err,_emitter:emitter,_top:top};throw err}}function justTextHighlightResult(code2){const result={value:escape2(code2),illegal:!1,relevance:0,_top:PLAINTEXT_LANGUAGE,_emitter:new options.__emitter(options)};return result._emitter.addText(code2),result}function highlightAuto(code2,languageSubset){languageSubset=languageSubset||options.languages||Object.keys(languages);const plaintext2=justTextHighlightResult(code2),results=languageSubset.
-filter(getLanguage).filter(autoDetection).map(name=>_highlight(name,code2,!1));results.unshift(plaintext2);const sorted=results.sort((a,b)=>{if(a.relevance!==b.relevance)return b.relevance-a.relevance;if(a.language&&b.language){if(getLanguage(a.language).supersetOf===b.language)return 1;if(getLanguage(b.language).supersetOf===a.language)return-1}return 0}),[best,secondBest]=sorted,result=best;return result.secondBest=secondBest,result}function updateClassName(element2,currentLang,resultLang){const language2=currentLang&&
-aliases[currentLang]||resultLang;element2.classList.add("hljs"),element2.classList.add(`language-${language2}`)}function highlightElement(element2){let node2=null;const language2=blockLanguage(element2);if(shouldNotHighlight(language2))return;if(fire("before:highlightElement",{el:element2,language:language2}),element2.dataset.highlighted){console.log("Element previously highlighted. To highlight again, first unset `dataset.highlighted`.",element2);return}if(element2.children.length>0&&(options.ignoreUnescapedHTML||
-(console.warn("One of your code blocks includes unescaped HTML. This is a potentially serious security risk."),console.warn("https://github.com/highlightjs/highlight.js/wiki/security"),console.warn("The element with unescaped HTML:"),console.warn(element2)),options.throwUnescapedHTML))throw new HTMLInjectionError("One of your code blocks includes unescaped HTML.",element2.innerHTML);node2=element2;const text2=node2.textContent,result=language2?highlight2(text2,{language:language2,ignoreIllegals:!0}):
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- отображениеотрицательныхзначенийпузырьковойдиаграммы отображениепанелипоиска отображениеподсказки отображениепредупрежденияприредактировании отображениеразметкиполосырегулирования отображениестраницформы отображениетаблицы отображениетекстазначениядиаграммыганта отображениеуправленияобычнойгруппы отображениефигурыкнопки палитрацветовдиаграммы поведениеобычнойгруппы поддержкамасштабадендрограммы поддержкамасштабадиаграммыганта поддержкамасштабасводнойдиаграммы поисквтаблицепривводе положениезаго\
-ловкаэлементаформы положениекартинкикнопкиформы положениекартинкиэлементаграфическойсхемы положениекоманднойпанелиформы положениекоманднойпанелиэлементаформы положениеопорнойточкиотрисовки положениеподписейкдиаграмме положениеподписейшкалызначенийизмерительнойдиаграммы положениесостоянияпросмотра положениестрокипоиска положениетекстасоединительнойлинии положениеуправленияпоиском положениешкалывремени порядокотображенияточекгоризонтальнойгистограммы порядоксерийвлегендедиаграммы размеркартинки ра\
-сположениезаголовкашкалыдиаграммы растягиваниеповертикалидиаграммыганта режимавтоотображениясостояния режимвводастроктаблицы режимвыборанезаполненного режимвыделениядаты режимвыделениястрокитаблицы режимвыделениятаблицы режимизмененияразмера режимизменениясвязанногозначения режимиспользованиядиалогапечати режимиспользованияпараметракоманды режиммасштабированияпросмотра режимосновногоокнаклиентскогоприложения режимоткрытияокнаформы режимотображениявыделения режимотображениягеографическойсхемы реж\
-имотображениязначенийсерии режимотрисовкисеткиграфическойсхемы режимполупрозрачностидиаграммы режимпробеловдиаграммы режимразмещениянастранице режимредактированияколонки режимсглаживаниядиаграммы режимсглаживанияиндикатора режимсписказадач сквозноевыравнивание сохранениеданныхформывнастройках способзаполнениятекстазаголовкашкалыдиаграммы способопределенияограничивающегозначениядиаграммы стандартнаягруппакоманд стандартноеоформление статусоповещенияпользователя стильстрелки типаппроксимациилиниит\
-рендадиаграммы типдиаграммы типединицышкалывремени типимпортасерийслоягеографическойсхемы типлиниигеографическойсхемы типлиниидиаграммы типмаркерагеографическойсхемы типмаркерадиаграммы типобластиоформления типорганизацииисточникаданныхгеографическойсхемы типотображениясериислоягеографическойсхемы типотображенияточечногообъектагеографическойсхемы типотображенияшкалыэлементалегендыгеографическойсхемы типпоискаобъектовгеографическойсхемы типпроекциигеографическойсхемы типразмещенияизмерений типраз\
-мещенияреквизитовизмерений типрамкиэлементауправления типсводнойдиаграммы типсвязидиаграммыганта типсоединениязначенийпосериямдиаграммы типсоединенияточекдиаграммы типсоединительнойлинии типстороныэлементаграфическойсхемы типформыотчета типшкалырадарнойдиаграммы факторлиниитрендадиаграммы фигуракнопки фигурыграфическойсхемы фиксациявтаблице форматдняшкалывремени форматкартинки ширинаподчиненныхэлементовформы "+
-"виддвижениябухгалтерии виддвижениянакопления видпериодарегистрарасчета видсчета видточкимаршрутабизнеспроцесса использованиеагрегатарегистранакопления использованиегруппиэлементов использованиережимапроведения использованиесреза периодичностьагрегатарегистранакопления режимавтовремя режимзаписидокумента режимпроведениядокумента "+
-"авторегистрацияизменений допустимыйномерсообщения отправкаэлементаданных получениеэлементаданных "+"использованиерасшифровкитабличногодокумента ориентациястраницы положениеитоговколоноксводнойтаблицы положениеитоговстроксводнойтаблицы положениетекстаотносительнокартинки расположениезаголовкагруппировкитабличногодокумента способчтениязначенийтабличногодокумента типдвустороннейпечати типзаполненияобластит\
-абличногодокумента типкурсоровтабличногодокумента типлиниирисункатабличногодокумента типлинииячейкитабличногодокумента типнаправленияпереходатабличногодокумента типотображениявыделениятабличногодокумента типотображениялинийсводнойтаблицы типразмещениятекстатабличногодокумента типрисункатабличногодокумента типсмещениятабличногодокумента типузоратабличногодокумента типфайлатабличногодокумента точностьпечати чередованиерасположениястраниц "+
-"отображениевремениэлементовпланировщика "+"типфайлаформатированногодокумента "+"обходрезультатазапроса типзаписизапроса "+"видзаполнениярасшифровкипостроителяотчета типдобавленияпредставлений типизмеренияпостроителяотчета типразмещенияитогов "+"доступкфайлу режимдиалогавыбо\
-рафайла режимоткрытияфайла "+"типизмеренияпостроителязапроса "+"видданныханализа методкластеризации типединицыинтервалавременианализаданных типзаполнениятаблицырезультатаанализаданных типиспользованиячисловыхзначенийанализаданных типисточникаданныхпоискаассоциаций типколонкианализаданныхдереворешений типколонкианализаданныхкластеризация типколонкианализаданныхобщаястатистика типколонкианализаданныхпоискассоциаций типколонкианализаданныхпоиск\
-последовательностей типколонкимоделипрогноза типмерырасстоянияанализаданных типотсеченияправилассоциации типполяанализаданных типстандартизациианализаданных типупорядочиванияправилассоциациианализаданных типупорядочиванияшаблоновпоследовательностейанализаданных типупрощениядереварешений "+
-"wsнаправлениепараметра вариантxpathxs вариантзаписидатыjson вариантпростоготипаxs видгруппымоделиxs видфасетаxdto действиепостроителяdom завершенностьпростоготипаxs завершенностьсоставноготипаxs завершенностьсхемыxs запрещенныеподстановкиxs исключениягруппподстановкиxs категорияиспользованияатрибутаxs категорияограниченияидентичностиxs категорияограниченияпространствименxs методнаследованияxs модельсодержимогоxs назначениетипаxml недопустимыеподстановкиxs обработкапробельныхсимволовxs обработка\
-содержимогоxs ограничениезначенияxs параметрыотбораузловdom переносстрокjson позициявдокументеdom пробельныесимволыxml типатрибутаxml типзначенияjson типканоническогоxml типкомпонентыxs типпроверкиxml типрезультатаdomxpath типузлаdom типузлаxml формаxml формапредставленияxs форматдатыjson экранированиесимволовjson "+
-"видсравнениякомпоновкиданных действиеобработкирасшифровкикомпоновкиданных направлениесортировкикомпоновкиданных расположениевложенныхэлементоврезультатакомпоновкиданных расположениеитоговкомпоновкиданных расположениегруппировкикомпоновкиданных расположениеполейгруппировкикомпоновкиданных расположениеполякомпоновкиданных расположениереквизитовкомпоновкиданных расположениересурсовкомпоновкиданных типбухгалтерскогоостаткакомпоновкиданных типвыводатекстакомпоновкиданных типгруппировкикомпоновкиданн\
-ых типгруппыэлементовотборакомпоновкиданных типдополненияпериодакомпоновкиданных типзаголовкаполейкомпоновкиданных типмакетагруппировкикомпоновкиданных типмакетаобластикомпоновкиданных типостаткакомпоновкиданных типпериодакомпоновкиданных типразмещениятекстакомпоновкиданных типсвязинаборовданныхкомпоновкиданных типэлементарезультатакомпоновкиданных расположениелегендыдиаграммыкомпоновкиданных типпримененияотборакомпоновкиданных режимотображенияэлементанастройкикомпоновкиданных режимотображенияна\
-строеккомпоновкиданных состояниеэлементанастройкикомпоновкиданных способвосстановлениянастроеккомпоновкиданных режимкомпоновкирезультата использованиепараметракомпоновкиданных автопозицияресурсовкомпоновкиданных вариантиспользованиягруппировкикомпоновкиданных расположениересурсоввдиаграммекомпоновкиданных фиксациякомпоновкиданных использованиеусловногооформлениякомпоновкиданных "+
-"важностьинтернетпочтовогосообщения обработкатекстаинтернетпочтовогосообщения способкодированияинтернетпочтовоговложения способкодированиянеasciiсимволовинтернетпочтовогосообщения типтекстапочтовогосообщения протоколинтернетпочты статусразборапочтовогосообщения "+
-"режимтранзакциизаписижурналарегистрации статустранзакциизаписижурналарегистрации уровеньжурналарегистрации "+"расположениехранилищасертификатовкриптографии режимвключениясертификатовкриптографии режимпроверкисертификатакриптографии типхранилищасертификатовкриптографии "+
-"кодировкаименфайловвzipфайле методсжатияzip методшифрованияzip режимвосстановленияпутейфайловzip режимобработкиподкаталоговzip режимсохраненияпутейzip уровеньсжатияzip "+"звуковоеоповещение направлениепереходакстроке позициявпотоке порядокбайтов режимблокировкиданных режимуправленияблокировкойданных сервисвстроенныхпокупок состояниефоновогозадания типподпис\
-чикадоставляемыхуведомлений уровеньиспользованиязащищенногосоединенияftp "+"направлениепорядкасхемызапроса типдополненияпериодамисхемызапроса типконтрольнойточкисхемызапроса типобъединениясхемызапроса типпараметрадоступнойтаблицысхемызапроса типсоединениясхемызапроса "+
-"httpметод автоиспользованиеобщегореквизита автопрефиксномеразадачи вариантвстроенногоязыка видиерархии видрегистранакопления видтаблицывнешнегоисточникаданных записьдвиженийприпроведении заполнениепоследовательностей индексирование использованиебазыпланавидоврасчета использованиебыстроговыбора использованиеобщегореквизита использованиеподчинения использованиеполнотекстовогопоиска использованиеразделяемыхданныхобщегореквизита использованиереквизита назначениеиспользованияприложения назначениерас\
-ширенияконфигурации направлениепередачи обновлениепредопределенныхданных оперативноепроведение основноепредставлениевидарасчета основноепредставлениевидахарактеристики основноепредставлениезадачи основноепредставлениепланаобмена основноепредставлениесправочника основноепредставлениесчета перемещениеграницыприпроведении периодичностьномерабизнеспроцесса периодичностьномерадокумента периодичностьрегистрарасчета периодичностьрегистрасведений повторноеиспользованиевозвращаемыхзначений полнотекстовый\
-поискпривводепостроке принадлежностьобъекта проведение разделениеаутентификацииобщегореквизита разделениеданныхобщегореквизита разделениерасширенийконфигурацииобщегореквизита режимавтонумерацииобъектов режимзаписирегистра режимиспользованиямодальности режимиспользованиясинхронныхвызововрасширенийплатформыивнешнихкомпонент режимповторногоиспользованиясеансов режимполученияданныхвыборапривводепостроке режимсовместимости режимсовместимостиинтерфейса режимуправленияблокировкойданныхпоумолчанию серии\
-кодовпланавидовхарактеристик сериикодовпланасчетов сериикодовсправочника созданиепривводе способвыбора способпоискастрокипривводепостроке способредактирования типданныхтаблицывнешнегоисточникаданных типкодапланавидоврасчета типкодасправочника типмакета типномерабизнеспроцесса типномерадокумента типномеразадачи типформы удалениедвижений "+
-"важностьпроблемыприменениярасширенияконфигурации вариантинтерфейсаклиентскогоприложения вариантмасштабаформклиентскогоприложения вариантосновногошрифтаклиентскогоприложения вариантстандартногопериода вариантстандартнойдатыначала видграницы видкартинки видотображенияполнотекстовогопоиска видрамки видсравнения видцвета видчисловогозначения видшрифта допустимаядлина допустимыйзнак использованиеbyteordermark использованиеметаданныхполнотекстовогопоиска источникрасширенийконфигурации клавиша кодвозв\
-ратадиалога кодировкаxbase кодировкатекста направлениепоиска направлениесортировки обновлениепредопределенныхданных обновлениеприизмененииданных отображениепанелиразделов проверказаполнения режимдиалогавопрос режимзапускаклиентскогоприложения режимокругления режимоткрытияформприложения режимполнотекстовогопоиска скоростьклиентскогосоединения состояниевнешнегоисточникаданных состояниеобновленияконфигурациибазыданных способвыборасертификатаwindows способкодированиястроки статуссообщения типвнешней\
-компоненты типплатформы типповеденияклавишиenter типэлементаинформацииовыполненииобновленияконфигурациибазыданных уровеньизоляциитранзакций хешфункция частидаты",TYPE="comобъект ftpсоединение httpзапрос httpсервисответ httpсоединение wsопределения wsпрокси xbase анализданных аннотацияxs блокировкаданных буфердвоичныхданных включениеxs выражениекомпо\
-новкиданных генераторслучайныхчисел географическаясхема географическиекоординаты графическаясхема группамоделиxs данныерасшифровкикомпоновкиданных двоичныеданные дендрограмма диаграмма диаграммаганта диалогвыборафайла диалогвыборацвета диалогвыборашрифта диалограсписаниярегламентногозадания диалогредактированиястандартногопериода диапазон документdom документhtml документацияxs доставляемоеуведомление записьdom записьfastinfoset записьhtml записьjson записьxml записьzipфайла записьданных записьт\
-екста записьузловdom запрос защищенноесоединениеopenssl значенияполейрасшифровкикомпоновкиданных извлечениетекста импортxs интернетпочта интернетпочтовоесообщение интернетпочтовыйпрофиль интернетпрокси интернетсоединение информациядляприложенияxs использованиеатрибутаxs использованиесобытияжурналарегистрации источникдоступныхнастроеккомпоновкиданных итераторузловdom картинка квалификаторыдаты квалификаторыдвоичныхданных квалификаторыстроки квалификаторычисла компоновщикмакетакомпоновкиданных ком\
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-пределениеxs планировщик полеанализаданных полекомпоновкиданных построительdom построительзапроса построительотчета построительотчетаанализаданных построительсхемxml поток потоквпамяти почта почтовоесообщение преобразованиеxsl преобразованиекканоническомуxml процессорвыводарезультатакомпоновкиданныхвколлекциюзначений процессорвыводарезультатакомпоновкиданныхвтабличныйдокумент процессоркомпоновкиданных разыменовательпространствименdom рамка расписаниерегламентногозадания расширенноеимяxml результ\
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-CONST_ACCESS_RIGHTS_NO_ACCESS_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_RATIFY_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_RATIFY_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW SYSRES_CONST_ACCESS_RIGHTS_VIEW_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_TYPE_CHANGE SYSRES_CONST_ACCESS_TYPE_CHANGE_CODE SYSRES_CONST_ACCESS_TYPE_EXISTS SYSRES_CONST_ACCESS_TYPE_EXISTS_CODE SY\
-SRES_CONST_ACCESS_TYPE_FULL SYSRES_CONST_ACCESS_TYPE_FULL_CODE SYSRES_CONST_ACCESS_TYPE_VIEW SYSRES_CONST_ACCESS_TYPE_VIEW_CODE SYSRES_CONST_ACTION_TYPE_ABORT SYSRES_CONST_ACTION_TYPE_ACCEPT SYSRES_CONST_ACTION_TYPE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ADD_ATTACHMENT SYSRES_CONST_ACTION_TYPE_CHANGE_CARD SYSRES_CONST_ACTION_TYPE_CHANGE_KIND SYSRES_CONST_ACTION_TYPE_CHANGE_STORAGE SYSRES_CONST_ACTION_TYPE_CONTINUE SYSRES_CONST_ACTION_TYPE_COPY SYSRES_CONST_ACTION_TYPE_CREATE SYSRES_CONST_ACTION_\
-TYPE_CREATE_VERSION SYSRES_CONST_ACTION_TYPE_DELETE SYSRES_CONST_ACTION_TYPE_DELETE_ATTACHMENT SYSRES_CONST_ACTION_TYPE_DELETE_VERSION SYSRES_CONST_ACTION_TYPE_DISABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE_AND_PASSWORD SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_PASSWORD SYSRES_CONST_ACTION_TYPE_EXPORT_WITH_LOCK SYSRES_CONST_ACTION_TYPE_EXPORT_WITHOUT_LOCK SYSR\
-ES_CONST_ACTION_TYPE_IMPORT_WITH_UNLOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITHOUT_UNLOCK SYSRES_CONST_ACTION_TYPE_LIFE_CYCLE_STAGE SYSRES_CONST_ACTION_TYPE_LOCK SYSRES_CONST_ACTION_TYPE_LOCK_FOR_SERVER SYSRES_CONST_ACTION_TYPE_LOCK_MODIFY SYSRES_CONST_ACTION_TYPE_MARK_AS_READED SYSRES_CONST_ACTION_TYPE_MARK_AS_UNREADED SYSRES_CONST_ACTION_TYPE_MODIFY SYSRES_CONST_ACTION_TYPE_MODIFY_CARD SYSRES_CONST_ACTION_TYPE_MOVE_TO_ARCHIVE SYSRES_CONST_ACTION_TYPE_OFF_ENCRYPTION SYSRES_CONST_ACTION_TYPE_PASSWO\
-RD_CHANGE SYSRES_CONST_ACTION_TYPE_PERFORM SYSRES_CONST_ACTION_TYPE_RECOVER_FROM_LOCAL_COPY SYSRES_CONST_ACTION_TYPE_RESTART SYSRES_CONST_ACTION_TYPE_RESTORE_FROM_ARCHIVE SYSRES_CONST_ACTION_TYPE_REVISION SYSRES_CONST_ACTION_TYPE_SEND_BY_MAIL SYSRES_CONST_ACTION_TYPE_SIGN SYSRES_CONST_ACTION_TYPE_START SYSRES_CONST_ACTION_TYPE_UNLOCK SYSRES_CONST_ACTION_TYPE_UNLOCK_FROM_SERVER SYSRES_CONST_ACTION_TYPE_VERSION_STATE SYSRES_CONST_ACTION_TYPE_VERSION_VISIBILITY SYSRES_CONST_ACTION_TYPE_VIEW SYSRES_\
-CONST_ACTION_TYPE_VIEW_SHADOW_COPY SYSRES_CONST_ACTION_TYPE_WORKFLOW_DESCRIPTION_MODIFY SYSRES_CONST_ACTION_TYPE_WRITE_HISTORY SYSRES_CONST_ACTIVE_VERSION_STATE_PICK_VALUE SYSRES_CONST_ADD_REFERENCE_MODE_NAME SYSRES_CONST_ADDITION_REQUISITE_CODE SYSRES_CONST_ADDITIONAL_PARAMS_REQUISITE_CODE SYSRES_CONST_ADITIONAL_JOB_END_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_READ_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_START_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_STATE_REQUISITE_NAME SYSRES_CO\
-NST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION_CODE SYSRES_CONST_ADMI\
-NISTRATION_HISTORY_DATABASE_USER_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION\
-_HISTORY_DELETION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_A\
-CTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_\
-PUBLIC_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION SYSRES_\
-CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION SYSRES_\
-CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH\
-_TYPE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION SYSRES_CONST_ALL_ACCEPT_CONDIT\
-ION_RUS SYSRES_CONST_ALL_USERS_GROUP SYSRES_CONST_ALL_USERS_GROUP_NAME SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE SYSRES_CONST_APPROVING_SIGNATURE_NAME SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE_CODE SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN SYSRES_CONST_ATTACH_TYPE_DOC SYSRES_CONST_ATTACH_TYPE_EDOC S\
-YSRES_CONST_ATTACH_TYPE_FOLDER SYSRES_CONST_ATTACH_TYPE_JOB SYSRES_CONST_ATTACH_TYPE_REFERENCE SYSRES_CONST_ATTACH_TYPE_TASK SYSRES_CONST_AUTH_ENCODED_PASSWORD SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE SYSRES_CONST_AUTH_NOVELL SYSRES_CONST_AUTH_PASSWORD SYSRES_CONST_AUTH_PASSWORD_CODE SYSRES_CONST_AUTH_WINDOWS SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME SYSRES_CONST_AUTHENTICATING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_AUTO_ENUM_METHOD_FLAG SYSRES_CONST_AUTO_NUMERATION_CODE SYSRES_CONST_AUTO_STRONG_\
-ENUM_METHOD_FLAG SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_USAGE_ALL SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE SYSRES_CONST_AUTOTEXT_USAGE_SIGN SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE SYSRES_CONST_AUTOTEXT_USAGE_WORK SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CODE SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE SYSRES_CONST_BLACK_LIFE_CYCL\
-E_STAGE_FONT_COLOR SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BTN_PART SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT SYSRES_CONST_CARD_PART SYSRES_CONST_CARD_REFERENCE_MODE_NAME SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE SYSRES_CON\
-ST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN SYSRES_CONST_CODE_COMPONENT_TYPE_DEVELOPER SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS SYSRES_\
-CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT SYSRES_CONST_CODE_COMPONENT_TYPE_URL SYSRES_CONST_CODE_REQUISITE_ACCESS SYSRES_CONST_CODE_REQUISITE_CODE SYSRES_CONST_CODE_REQUISITE_COMPONENT SYSRES_CONST_CODE_REQUISITE_DESCRIPTION SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT SYSRES_CONST_CODE_REQUISITE_RECORD SYSRES_CONST_COMMENT_REQ_COD\
-E SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE SYSRES_CONST_COMP_CODE_GRD SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DOCS SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_COMPONENT_TYPE_EDOCS SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_COMPONENT_TYPE_OTHER SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES SYSRES_CONST_COMPONENT_TYPE_REFERENCES SYSRES_CONST_\
-COMPONENT_TYPE_REPORTS SYSRES_CONST_COMPONENT_TYPE_SCRIPTS SYSRES_CONST_COMPONENT_TYPE_URL SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION SYSRES_CONST_CONST_FIRM_STATUS_COMMON SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL SYSRES_CONST_CONST_NEGATIVE_VALUE SYSRES_CONST_CONST_POSITIVE_VALUE SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE SYSRES_CONST_CONST_SERVER_STATUS_REPLICATE SYSRES_CONST_CONTENTS_REQUISITE_CODE SYSRES_CONST_DATA_TYPE_BOOLEAN SYSRES_CONST\
-_DATA_TYPE_DATE SYSRES_CONST_DATA_TYPE_FLOAT SYSRES_CONST_DATA_TYPE_INTEGER SYSRES_CONST_DATA_TYPE_PICK SYSRES_CONST_DATA_TYPE_REFERENCE SYSRES_CONST_DATA_TYPE_STRING SYSRES_CONST_DATA_TYPE_TEXT SYSRES_CONST_DATA_TYPE_VARIANT SYSRES_CONST_DATE_CLOSE_REQ_CODE SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR SYSRES_CONST_DATE_OPEN_REQ_CODE SYSRES_CONST_DATE_REQUISITE SYSRES_CONST_DATE_REQUISITE_CODE SYSRES_CONST_DATE_REQUISITE_NAME SYSRES_CONST_DATE_REQUISITE_TYPE SYSRES_CONST_DATE_TYPE_CHAR SYSRES_CONST_D\
-ATETIME_FORMAT_VALUE SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_DET1_PART SYSRES_CONST_DET2_PART SYSRES_CONST_DET3_PART SYSRES_CONST_DET4_PART SYSRES_CONST_DET5_PART SYSRES_CONST_DET6_PART SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE SYSRES_CONST_DETAIL_REQ_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME SYSRES_C\
-ONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME SYSRES_CONST_DOCUMENT_STORAGES_CODE SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME SYSRES_CONST_DOUBLE_REQUISITE_CODE SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_CODE SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_COD\
-E SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE SYSRES_CONST_EDITORS_REFERENCE_CODE SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE SYSRES_CONST_EDOC_DATE_REQUISITE_CODE SYSRES_CONST_EDOC_KIND_REFERENC\
-E_CODE SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE SYSRES_CONST_EDOC_NONE_ENCODE_CODE SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_READONLY_ACCESS_CODE SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_DATABASE_\
-NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE SYSRES_CONST_EDOC_VERSION_OBSOLETE_STAGE_CODE SYSRES_CONST_EDOC_WRITE_ACCES_CODE SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTE\
-D_REQUISITE SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_END_DATE_REQUISITE_CODE SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE SYSRES_CONST_EXIST_CONST SYSRES_CONST_EXIST_VALUE SYSRES_CONST_EXPORT_LOCK_TYPE_ASK SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK SYSRES_CONST_EXPORT_VERSION_TYPE_ASK SYSRES_CONST_EXPORT_VERSION_TYPE_LAST SYSRES_CONST_EXPORT_VERSION_TYPE_LAS\
-T_ACTIVE SYSRES_CONST_EXTENSION_REQUISITE_CODE SYSRES_CONST_FILTER_NAME_REQUISITE_CODE SYSRES_CONST_FILTER_REQUISITE_CODE SYSRES_CONST_FILTER_TYPE_COMMON_CODE SYSRES_CONST_FILTER_TYPE_COMMON_NAME SYSRES_CONST_FILTER_TYPE_USER_CODE SYSRES_CONST_FILTER_TYPE_USER_NAME SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR SYSRES_CONST_FLOAT_REQUISITE_TYPE SYSRES_CONST_FOLDER_AUTHOR_VALUE SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS SYSRES_CONST_FOLDER_KIND_COMPONENTS SYSRES_CONST_FO\
-LDER_KIND_EDOCS SYSRES_CONST_FOLDER_KIND_JOBS SYSRES_CONST_FOLDER_KIND_TASKS SYSRES_CONST_FOLDER_TYPE_COMMON SYSRES_CONST_FOLDER_TYPE_COMPONENT SYSRES_CONST_FOLDER_TYPE_FAVORITES SYSRES_CONST_FOLDER_TYPE_INBOX SYSRES_CONST_FOLDER_TYPE_OUTBOX SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH SYSRES_CONST_FOLDER_TYPE_SEARCH SYSRES_CONST_FOLDER_TYPE_SHORTCUTS SYSRES_CONST_FOLDER_TYPE_USER SYSRES_CONST_FROM_DICTIONARY_ENUM_METHOD_FLAG SYSRES_CONST_FULL_SUBSTITUTE_TYPE SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE SYSR\
-ES_CONST_FUNCTION_CANCEL_RESULT SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM SYSRES_CONST_FUNCTION_CATEGORY_USER SYSRES_CONST_FUNCTION_FAILURE_RESULT SYSRES_CONST_FUNCTION_SAVE_RESULT SYSRES_CONST_GENERATED_REQUISITE SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME SYSRES_CONST_GROUP_CATEGORY_SERVICE_CODE SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME SYSRES_CONST_GROUP_COMMON_CATEGORY_FIE\
-LD_VALUE SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_USER_REQUISITE_CODE SYSRES_CONST_GROUPS_REFERENCE_CODE SYSRES_CONST_GROUPS_REQUISITE_CODE SYSRES_CONST_HIDDEN_MODE_NAME SYSRES_CONST_HIGH_LVL_REQUISITE_CODE SYSRES_CONST_HISTORY_ACTION_CREATE_CODE SYSRES_C\
-ONST_HISTORY_ACTION_DELETE_CODE SYSRES_CONST_HISTORY_ACTION_EDIT_CODE SYSRES_CONST_HOUR_CHAR SYSRES_CONST_ID_REQUISITE_CODE SYSRES_CONST_IDSPS_REQUISITE_CODE SYSRES_CONST_IMAGE_MODE_COLOR SYSRES_CONST_IMAGE_MODE_GREYSCALE SYSRES_CONST_IMAGE_MODE_MONOCHROME SYSRES_CONST_IMPORTANCE_HIGH SYSRES_CONST_IMPORTANCE_LOW SYSRES_CONST_IMPORTANCE_NORMAL SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALUE SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE SYSRES_CONST_INT_REQUISITE SYSRES_CONST_INT_REQUISITE_TYPE SYSRES\
-_CONST_INTEGER_NUMBER_FORMAT_CHAR SYSRES_CONST_INTEGER_TYPE_CHAR SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_JOB_BLOCK_DESCRIPTION SYSRES_CONST_JOB_KIND_CONTROL_JOB SYSRES_CONST_JOB_KIND_JOB SYSRES_CONST_JOB_K\
-IND_NOTICE SYSRES_CONST_JOB_STATE_ABORTED SYSRES_CONST_JOB_STATE_COMPLETE SYSRES_CONST_JOB_STATE_WORKING SYSRES_CONST_KIND_REQUISITE_CODE SYSRES_CONST_KIND_REQUISITE_NAME SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_CHANGING_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE SYSRES_CONST_KI\
-NDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE SYSRES_CONST_KOD_INPUT_TYPE SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT SYSRES_CONST_LINK_OBJECT_KIND_DOCUMENT SYSRES_CONST_LINK_OBJECT_KIND_EDOC SYSRES_CONST_LINK_OBJECT_KIND_FOLDER\
- SYSRES_CONST_LINK_OBJECT_KIND_JOB SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE SYSRES_CONST_LINK_OBJECT_KIND_TASK SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE SYSRES_CONST_LIST_REFERENCE_MODE_NAME SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE SYSRES_CONST_MAIN_VIEW_CODE SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE SYSRES_CONST_MASTER_TABLE_REC_ID_REQUISITE_CODE SYSRES_CONST_MAXIMIZED_MODE_NAME SYSRES_CONST_ME_VALUE SYSRES_CONST_MESSAGE_ATTENTION_CAPTION SYSRES\
-_CONST_MESSAGE_CONFIRMATION_CAPTION SYSRES_CONST_MESSAGE_ERROR_CAPTION SYSRES_CONST_MESSAGE_INFORMATION_CAPTION SYSRES_CONST_MINIMIZED_MODE_NAME SYSRES_CONST_MINUTE_CHAR SYSRES_CONST_MODULE_REQUISITE_CODE SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION SYSRES_CONST_MONTH_FORMAT_VALUE SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_NAME_REQUISITE_CODE SYSRES_CONST_NAME_SINGULAR_REQUISITE_CODE SYSRES_CONST_NAMEAN_INPUT_TYPE SYSRES_CONST_NEGATIVE_PICK_VALUE SYSRES_CONST_NEGATIVE_VALUE SYSRES_CO\
-NST_NO SYSRES_CONST_NO_PICK_VALUE SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE SYSRES_CONST_NO_VALUE SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_NORMAL_MODE_NAME SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_NOTE_REQUISITE_CODE SYSRES_CONST_NOTICE_BLOC\
-K_DESCRIPTION SYSRES_CONST_NUM_REQUISITE SYSRES_CONST_NUM_STR_REQUISITE_CODE SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG SYSRES_CONST_NUMERATION_AUTO_STRONG SYSRES_CONST_NUMERATION_FROM_DICTONARY SYSRES_CONST_NUMERATION_MANUAL SYSRES_CONST_NUMERIC_TYPE_CHAR SYSRES_CONST_NUMREQ_REQUISITE_CODE SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_OPERATING_RECO\
-RD_FLAG_VALUE_MASCULINE SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_ORIGINALREF_REQUISITE_CODE SYSRES_CONST_OURFIRM_REF_CODE SYSRES_CONST_OURFIRM_REQUISITE_CODE SYSRES_CONST_OURFIRM_VAR SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE SYSRES_CONST_PICK_NEGATIVE_RESULT SYSRES_CONST_PICK_POSITIVE_RESULT SYSRES_CONST_PICK_REQUISITE SYSRES_CONST_PICK_REQUISITE_TYPE SYSRES_CONST_PICK_TYPE_CHAR SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE SYSRES_CONS\
-T_PLATFORM_VERSION_COMMENT SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_POSITIVE_PICK_VALUE SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE SYSRES_CONST_PRIORITY_REQUISITE_CODE SYSRES_CONST_QUALIFIED_TASK_TYPE SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE SYSRES_CONST_RECSTAT_REQUISITE_CODE SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_REF_REQUISITE SYSRES_CONST_REF_REQUISITE_TYPE SYSRES_CO\
-NST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE SYSRES_CONST_REFERENCE_TYPE_CHAR SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE_REQUISITE_CODE SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN SYSRES_CONST_R\
-EMOTE_SERVER_TYPE_SECONDARY SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ\
-_MODE_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_EDIT_CODE SYSRES_CONST_REQ_MODE_HIDDEN_CODE SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_VIEW_CODE SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE SYSRES_CONST_REQ_SECTION_VALUE SYSRES_CONST_REQ_TYPE_VALUE SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME SYSRES_CONST_REQUISITE_FORMAT_LEFT SYSRES_CONST_REQUISITE_FORMAT_RIGHT SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT SYSRES_CON\
-ST_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_REQUISITE_SECTION_ACTIONS SYSRES_CONST_REQUISITE_SECTION_BUTTON SYSRES_CONST_REQUISITE_SECTION_BUTTONS SYSRES_CONST_REQUISITE_SECTION_CARD SYSRES_CONST_REQUISITE_SECTION_TABLE SYSRES_CONST_REQUISITE_SECTION_TABLE10 SYSRES_CONST_REQUISITE_SECTION_TABLE11 SYSRES_CONST_REQUISITE_SECTION_TABLE12 SYSRES_CONST_REQUISITE_SECTION_TABLE13 SYSRES_CONST_REQUISITE_SECTION_TABLE14 SYSRES_CONST_REQUISITE_SECTION_TABLE15 SYSRES_CONST_REQUISITE_SECTION_TABLE16 SYS\
-RES_CONST_REQUISITE_SECTION_TABLE17 SYSRES_CONST_REQUISITE_SECTION_TABLE18 SYSRES_CONST_REQUISITE_SECTION_TABLE19 SYSRES_CONST_REQUISITE_SECTION_TABLE2 SYSRES_CONST_REQUISITE_SECTION_TABLE20 SYSRES_CONST_REQUISITE_SECTION_TABLE21 SYSRES_CONST_REQUISITE_SECTION_TABLE22 SYSRES_CONST_REQUISITE_SECTION_TABLE23 SYSRES_CONST_REQUISITE_SECTION_TABLE24 SYSRES_CONST_REQUISITE_SECTION_TABLE3 SYSRES_CONST_REQUISITE_SECTION_TABLE4 SYSRES_CONST_REQUISITE_SECTION_TABLE5 SYSRES_CONST_REQUISITE_SECTION_TABLE6 S\
-YSRES_CONST_REQUISITE_SECTION_TABLE7 SYSRES_CONST_REQUISITE_SECTION_TABLE8 SYSRES_CONST_REQUISITE_SECTION_TABLE9 SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_RIGHT_ALIGNMENT_CODE SYSRES_CONST_ROLES_REFERENCE_CODE SYSRES_CONST_ROUTE_STEP_AFTER_RUS SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS SYSRES_CONST_ROUTE_TYPE_COMPLEX SYSRES_CONST_ROUTE_TYPE_PARALLEL SYSRES_CONST_ROUTE_TYPE_SERIAL SYSRES_CONST_SBDATASETDESC_NEGATIV\
-E_VALUE SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE SYSRES_CONST_SEARCHES_COMPONENT_CONTENT SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME SYSRES_CONST_SEARCHES_EDOC_CONTENT SYSRES_CONST_SEARCHES_FOLDER_CONTENT SYSRES_CONST_SEARCHES_JOB_CONTENT SYSRES_CONST_SEARCHES_REFERENCE_CODE SYSRES_CONST_SEARCHES_TASK_CONTENT SYSRES_CONST_SECOND_CHAR SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALU\
-E SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE SYSRES_CONST_SECTION_REQUISITE_CODE SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE SYSRES_CONST_SELECT_REFERENCE_MODE_NAME SYSRES_CONST_SELECT_TYPE_SELECTABLE SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD SYSRES_CONST_SELECT_T\
-YPE_SELECTABLE_WITH_CHILD SYSRES_CONST_SELECT_TYPE_UNSLECTABLE SYSRES_CONST_SERVER_TYPE_MAIN SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE SYSRES_CON\
-ST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE SYSRES_CONST_STATE_REQ_NAME SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE SYSRES_CONST_STATE_REQUISITE_CODE SYSRES_CONST_STATIC_ROLE_TYPE_CODE SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE SYSRES_CONST_STATUS_VALUE_AUTOCLEANING SYSRES_CONST_STATUS_VALUE_BLUE_SQUARE SYSRES_CONST_STATUS_VALUE_COMPLETE SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE SYSRES_CONST_STATUS_VALUE_O\
-RANGE_SQUARE SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE SYSRES_CONST_STATUS_VALUE_RED_SQUARE SYSRES_CONST_STATUS_VALUE_SUSPEND SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE SYSRES_CONST_STORAGE_TYPE_FILE SYSRES_CONST_STORAGE_TYPE_SQL_SERVER SYSRES_CONST_STR_REQUISITE SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_STRING_FORMAT_LEFT_ALIGN_CHAR SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR SYSRES_CONST_STRING_REQUISITE_CODE SYSRES_CONST_STRI\
-NG_REQUISITE_TYPE SYSRES_CONST_STRING_TYPE_CHAR SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE SYSRES_CONST_SYSTEM_VERSION_COMMENT SYSRES_CONST_TASK_ACCESS_TYPE_ALL SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD SYSRES_CONST\
-_TASK_ENCODE_TYPE_NONE SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD SYSRES_CONST_TASK_ROUTE_ALL_CONDITION SYSRES_CONST_TASK_ROUTE_AND_CONDITION SYSRES_CONST_TASK_ROUTE_OR_CONDITION SYSRES_CONST_TASK_STATE_ABORTED SYSRES_CONST_TASK_STATE_COMPLETE SYSRES_CONST_TASK_STATE_CONTINUED SYSRES_CONST_TASK_STATE_CONTROL SYSRES_CONST_TASK_STATE_INIT SYSRES_CONST_TASK_STATE_WORKING SYSRES_CONST_TASK_TITLE SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE SYSRES_CONST_TASK_TYPES_REFERENCE_CODE SYSRES_CONST_TEMPLATES_R\
-EFERENCE_CODE SYSRES_CONST_TEST_DATE_REQUISITE_NAME SYSRES_CONST_TEST_DEV_DATABASE_NAME SYSRES_CONST_TEST_DEV_SYSTEM_CODE SYSRES_CONST_TEST_EDMS_DATABASE_NAME SYSRES_CONST_TEST_EDMS_MAIN_CODE SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME SYSRES_CONST_TEST_EDMS_SECOND_CODE SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME SYSRES_CONST_TEST_EDMS_SYSTEM_CODE SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME SYSRES_CONST_TEXT_REQUISITE SYSRES_CONST_TEXT_REQUISITE_CODE SYSRES_CONST_TEXT_REQUISITE_TYPE SYSRES_CONST_TEXT_TYPE_CHAR S\
-YSRES_CONST_TYPE_CODE_REQUISITE_CODE SYSRES_CONST_TYPE_REQUISITE_CODE SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME SYSRES_CONST_USE_ACCESS_TYPE_CODE SYSRES_CONST_USE_ACCESS_TYPE_NAME SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE\
-_CODE SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE SYSRES_CONST_USER_CATEGORY_NORMAL SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE SYSRES_CONST_USER_COMMON_CATEGORY SYSRES_CONST_USER_COMMON_CATEGORY_CODE SYSRES_CONST_USER_FULL_NAME_REQUISITE_CODE SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_USER_LOGIN\
-_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_USER_SERVICE_CATEGORY SYSRES_CONST_USER_SERVICE_CATEGORY_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME SYSRES_CONST_USER_STATUS_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_DEVELOPER_NAME SYSRES_CONST_USER_STATUS_DISABLED_CODE SYSRES_CONST_USER\
-_STATUS_DISABLED_NAME SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_USER_CODE SYSRES_CONST_USER_STATUS_USER_NAME SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER SYSRES_CONST_USER_TYPE_REQUISITE_CODE SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE SYSRES_CONST_USERS_REFERENCE_CODE SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME SYSRES_CONST_USERS_REQUISITE_CODE SYSRES_CONST_USERS\
-_SYSTEM_REQUISITE_CODE SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME SYSRES_CONST_VIEW_DEFAULT_CODE SYSRES_CONST_VIEW_DEFAULT_NAME SYSRES_CONST_VIEWER_REQUISITE_CODE SYSRES_CONST_WAITING_BLOCK_DESCRIPTION SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING  SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETA\
-LON_STRING SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORKFLOW_ROUTE_TYPR_HARD SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT SYSRES_CONST_XML_ENCODING SYSRES_CONST_XREC\
-_STAT_REQUISITE_CODE SYSRES_CONST_XRECID_FIELD_NAME SYSRES_CONST_YES SYSRES_CONST_YES_NO_2_REQUISITE_CODE SYSRES_CONST_YES_NO_REQUISITE_CODE SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_YES_PICK_VALUE SYSRES_CONST_YES_VALUE "+"CR FALSE nil NO_VALUE NULL TAB TRUE YES_VALUE "+"ADMINISTRATORS_GROUP_NAME CUSTOMIZERS_GROUP_NAME DEVELOPERS_GROUP_NAME SERVICE_USERS_GROUP_NAME "+"DECISION_BLOCK_FIRST_OPERAND_PROPERTY DECISION_BLOCK_NAME_PROPERTY DECISION_BLOCK_OPERATION_PROPERTY DECISION_B\
-LOCK_RESULT_TYPE_PROPERTY DECISION_BLOCK_SECOND_OPERAND_PROPERTY "+"ANY_FILE_EXTENTION COMPRESSED_DOCUMENT_EXTENSION EXTENDED_DOCUMENT_EXTENSION SHORT_COMPRESSED_DOCUMENT_EXTENSION SHORT_EXTENDED_DOCUMENT_EXTENSION "+"JOB_BLOCK_ABORT_DEADLINE_PROPERTY JOB_BLOCK_AFTER_FINISH_EVENT JOB_BLOCK_AFTER_QUERY_PARAMETERS_EVENT JOB_BLOCK_ATTACHMENT_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY JOB_BLOCK_BEFORE_QUERY_PARAMETERS_EVENT JOB_BLOCK_BEFORE_START_\
-EVENT JOB_BLOCK_CREATED_JOBS_PROPERTY JOB_BLOCK_DEADLINE_PROPERTY JOB_BLOCK_EXECUTION_RESULTS_PROPERTY JOB_BLOCK_IS_PARALLEL_PROPERTY JOB_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY JOB_BLOCK_JOB_TEXT_PROPERTY JOB_BLOCK_NAME_PROPERTY JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY JOB_BLOCK_PERFORMER_PROPERTY JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY JOB_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY JOB_BLOCK_SUBJECT_PROPERTY "+"ENGLISH_LANGUAGE_CODE RUSSIAN_LANGUAGE_\
-CODE "+"smHidden smMaximized smMinimized smNormal wmNo wmYes "+"COMPONENT_TOKEN_LINK_KIND DOCUMENT_LINK_KIND EDOCUMENT_LINK_KIND FOLDER_LINK_KIND JOB_LINK_KIND REFERENCE_LINK_KIND TASK_LINK_KIND "+"COMPONENT_TOKEN_LOCK_TYPE EDOCUMENT_VERSION_LOCK_TYPE "+"MONITOR_BLOCK_AFTER_FINISH_EVENT MONITOR_BLOCK_BEFORE_START_EVENT MONITOR_BLOCK_DEADLINE_PROPERTY MONITOR_BLOCK_INTERVAL_PROPERTY MONITOR_BLOCK_INTERVAL_TYPE_PROPERTY MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY MONITOR_BLOCK_NAME_PROPERTY MONITO\
-R_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY "+"NOTICE_BLOCK_AFTER_FINISH_EVENT NOTICE_BLOCK_ATTACHMENT_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY NOTICE_BLOCK_BEFORE_START_EVENT NOTICE_BLOCK_CREATED_NOTICES_PROPERTY NOTICE_BLOCK_DEADLINE_PROPERTY NOTICE_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY NOTICE_BLOCK_NAME_PROPERTY NOTICE_BLOCK_NOTICE_TEXT_PROPERTY NOTICE_BLOCK_PERFORMER_PROPERTY NOTICE_BLOCK_RELATIVE_DEA\
-DLINE_TYPE_PROPERTY NOTICE_BLOCK_SUBJECT_PROPERTY "+"dseAfterCancel dseAfterClose dseAfterDelete dseAfterDeleteOutOfTransaction dseAfterInsert dseAfterOpen dseAfterScroll dseAfterUpdate dseAfterUpdateOutOfTransaction dseBeforeCancel dseBeforeClose dseBeforeDelete dseBeforeDetailUpdate dseBeforeInsert dseBeforeOpen dseBeforeUpdate dseOnAnyRequisiteChange dseOnCloseRecord dseOnDeleteError dseOnOpenRecord dseOnPrepareUpdate dseOnUpdateError dseOnUpdateRatifiedRecord dseOnValidDelete dseOnValidUpdat\
-e reOnChange reOnChangeValues SELECTION_BEGIN_ROUTE_EVENT SELECTION_END_ROUTE_EVENT "+"CURRENT_PERIOD_IS_REQUIRED PREVIOUS_CARD_TYPE_NAME SHOW_RECORD_PROPERTIES_FORM "+"ACCESS_RIGHTS_SETTING_DIALOG_CODE ADMINISTRATOR_USER_CODE ANALYTIC_REPORT_TYPE asrtHideLocal asrtHideRemote CALCULATED_ROLE_TYPE_CODE COMPONENTS_REFERENCE_DEVELOPER_VIEW_CODE DCTS_TEST_PROTOCOLS_FOLDER_PATH E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED_BY_USER E_EDOC_VERSION_ALREDY_SIGNED E_E\
-DOC_VERSION_ALREDY_SIGNED_BY_USER EDOC_TYPES_CODE_REQUISITE_FIELD_NAME EDOCUMENTS_ALIAS_NAME FILES_FOLDER_PATH FILTER_OPERANDS_DELIMITER FILTER_OPERATIONS_DELIMITER FORMCARD_NAME FORMLIST_NAME GET_EXTENDED_DOCUMENT_EXTENSION_CREATION_MODE GET_EXTENDED_DOCUMENT_EXTENSION_IMPORT_MODE INTEGRATED_REPORT_TYPE IS_BUILDER_APPLICATION_ROLE IS_BUILDER_APPLICATION_ROLE2 IS_BUILDER_USERS ISBSYSDEV LOG_FOLDER_PATH mbCancel mbNo mbNoToAll mbOK mbYes mbYesToAll MEMORY_DATASET_DESRIPTIONS_FILENAME mrNo mrNoToA\
-ll mrYes mrYesToAll MULTIPLE_SELECT_DIALOG_CODE NONOPERATING_RECORD_FLAG_FEMININE NONOPERATING_RECORD_FLAG_MASCULINE OPERATING_RECORD_FLAG_FEMININE OPERATING_RECORD_FLAG_MASCULINE PROFILING_SETTINGS_COMMON_SETTINGS_CODE_VALUE PROGRAM_INITIATED_LOOKUP_ACTION ratDelete ratEdit ratInsert REPORT_TYPE REQUIRED_PICK_VALUES_VARIABLE rmCard rmList SBRTE_PROGID_DEV SBRTE_PROGID_RELEASE STATIC_ROLE_TYPE_CODE SUPPRESS_EMPTY_TEMPLATE_CREATION SYSTEM_USER_CODE UPDATE_DIALOG_DATASET USED_IN_OBJECT_HINT_PARAM \
-USER_INITIATED_LOOKUP_ACTION USER_NAME_FORMAT USER_SELECTION_RESTRICTIONS WORKFLOW_TEST_PROTOCOLS_FOLDER_PATH ELS_SUBTYPE_CONTROL_NAME ELS_FOLDER_KIND_CONTROL_NAME REPEAT_PROCESS_CURRENT_OBJECT_EXCEPTION_NAME "+"PRIVILEGE_COMPONENT_FULL_ACCESS PRIVILEGE_DEVELOPMENT_EXPORT PRIVILEGE_DEVELOPMENT_IMPORT PRIVILEGE_DOCUMENT_DELETE PRIVILEGE_ESD PRIVILEGE_FOLDER_DELETE PRIVILEGE_MANAGE_ACCESS_RIGHTS PRIVILEGE_MANAGE_REPLICATION PRIVILEGE_MANAGE_SESSION_SERVER PRIVILEGE_OBJECT_FULL_ACCESS PRIVILEGE_OBJ\
-ECT_VIEW PRIVILEGE_RESERVE_LICENSE PRIVILEGE_SYSTEM_CUSTOMIZE PRIVILEGE_SYSTEM_DEVELOP PRIVILEGE_SYSTEM_INSTALL PRIVILEGE_TASK_DELETE PRIVILEGE_USER_PLUGIN_SETTINGS_CUSTOMIZE PRIVILEGES_PSEUDOREFERENCE_CODE "+"ACCESS_TYPES_PSEUDOREFERENCE_CODE ALL_AVAILABLE_COMPONENTS_PSEUDOREFERENCE_CODE ALL_AVAILABLE_PRIVILEGES_PSEUDOREFERENCE_CODE ALL_REPLICATE_COMPONENTS_PSEUDOREFERENCE_CODE AVAILABLE_DEVELOPERS_COMPONENTS_PSEUDOREFERENCE_CODE COMPONENTS_PSEUDOREFERENCE_CODE FILTRATER_SETTINGS_CONFLICTS_PSEU\
-DOREFERENCE_CODE GROUPS_PSEUDOREFERENCE_CODE RECEIVE_PROTOCOL_PSEUDOREFERENCE_CODE REFERENCE_REQUISITE_PSEUDOREFERENCE_CODE REFERENCE_REQUISITES_PSEUDOREFERENCE_CODE REFTYPES_PSEUDOREFERENCE_CODE REPLICATION_SEANCES_DIARY_PSEUDOREFERENCE_CODE SEND_PROTOCOL_PSEUDOREFERENCE_CODE SUBSTITUTES_PSEUDOREFERENCE_CODE SYSTEM_SETTINGS_PSEUDOREFERENCE_CODE UNITS_PSEUDOREFERENCE_CODE USERS_PSEUDOREFERENCE_CODE VIEWERS_PSEUDOREFERENCE_CODE "+"CERTIFICATE_TYPE_ENCRYPT CERTIFICATE_TYPE_SIGN CERTIFICATE_TYPE_SI\
-GN_AND_ENCRYPT "+"STORAGE_TYPE_FILE STORAGE_TYPE_NAS_CIFS STORAGE_TYPE_SAPERION STORAGE_TYPE_SQL_SERVER "+"COMPTYPE2_REQUISITE_DOCUMENTS_VALUE COMPTYPE2_REQUISITE_TASKS_VALUE COMPTYPE2_REQUISITE_FOLDERS_VALUE COMPTYPE2_REQUISITE_REFERENCES_VALUE "+"SYSREQ_CODE SYSREQ_COMPTYPE2 SYSREQ_CONST_AVAILABLE_FOR_WEB SYSREQ_CONST_COMMON_CODE SYSREQ_CONST_COMMON_VALUE SYSREQ_CONST_FIRM_CODE SYSREQ_CONST_FIRM_STATUS SYSREQ_CONST_FIRM_VALUE SYSREQ_CONST_SERVER_STATUS SYSREQ_CONTENTS SYSREQ_DATE_OPEN SYSREQ_D\
-ATE_CLOSE SYSREQ_DESCRIPTION SYSREQ_DESCRIPTION_LOCALIZE_ID SYSREQ_DOUBLE SYSREQ_EDOC_ACCESS_TYPE SYSREQ_EDOC_AUTHOR SYSREQ_EDOC_CREATED SYSREQ_EDOC_DELEGATE_RIGHTS_REQUISITE_CODE SYSREQ_EDOC_EDITOR SYSREQ_EDOC_ENCODE_TYPE SYSREQ_EDOC_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_EXPORT_DATE SYSREQ_EDOC_EXPORTER SYSREQ_EDOC_KIND SYSREQ_EDOC_LIFE_STAGE_NAME SYSREQ_EDOC_LOCKED_FOR_SERVER_CODE SYSREQ_EDOC_MODIFIED SYSREQ_EDOC_NAME SYSREQ_EDOC_NOTE SYSREQ_EDOC_QUALIFIED_ID\
- SYSREQ_EDOC_SESSION_KEY SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_SIGNATURE_TYPE SYSREQ_EDOC_SIGNED SYSREQ_EDOC_STORAGE SYSREQ_EDOC_STORAGES_ARCHIVE_STORAGE SYSREQ_EDOC_STORAGES_CHECK_RIGHTS SYSREQ_EDOC_STORAGES_COMPUTER_NAME SYSREQ_EDOC_STORAGES_EDIT_IN_STORAGE SYSREQ_EDOC_STORAGES_EXECUTIVE_STORAGE SYSREQ_EDOC_STORAGES_FUNCTION SYSREQ_EDOC_STORAGES_INITIALIZED SYSREQ_EDOC_STORAGES_LOCAL_PATH SYSREQ_EDOC_STORAGES_SAPERION_DATAB\
-ASE_NAME SYSREQ_EDOC_STORAGES_SEARCH_BY_TEXT SYSREQ_EDOC_STORAGES_SERVER_NAME SYSREQ_EDOC_STORAGES_SHARED_SOURCE_NAME SYSREQ_EDOC_STORAGES_TYPE SYSREQ_EDOC_TEXT_MODIFIED SYSREQ_EDOC_TYPE_ACT_CODE SYSREQ_EDOC_TYPE_ACT_DESCRIPTION SYSREQ_EDOC_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_EDOC_TYPE_ACT_SECTION SYSREQ_EDOC_TYPE_ADD_PARAMS SYSREQ_EDOC_TYPE_COMMENT SYSREQ_EDOC_TYPE_EVENT_TEXT SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR SYSREQ_EDOC\
-_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_EDOC_TYPE_NAME_LOCALIZE_ID SYSREQ_EDOC_TYPE_NUMERATION_METHOD SYSREQ_EDOC_TYPE_PSEUDO_REQUISITE_CODE SYSREQ_EDOC_TYPE_REQ_CODE SYSREQ_EDOC_TYPE_REQ_DESCRIPTION SYSREQ_EDOC_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_REQ_IS_LEADING SYSREQ_EDOC_TYPE_REQ_IS_REQUIRED SYSREQ_EDOC_TYPE_REQ_NUMBER SYSREQ_EDOC_TYPE_REQ_ON_CHANGE SYSREQ_EDOC_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_EDOC_TYPE_REQ_ON_SELECT SYSREQ_EDOC_TYPE_REQ_ON_SELECT_KIND SYSREQ_EDOC_TYPE_REQ_SEC\
-TION SYSREQ_EDOC_TYPE_VIEW_CARD SYSREQ_EDOC_TYPE_VIEW_CODE SYSREQ_EDOC_TYPE_VIEW_COMMENT SYSREQ_EDOC_TYPE_VIEW_IS_MAIN SYSREQ_EDOC_TYPE_VIEW_NAME SYSREQ_EDOC_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_EDOC_VERSION_AUTHOR SYSREQ_EDOC_VERSION_CRC SYSREQ_EDOC_VERSION_DATA SYSREQ_EDOC_VERSION_EDITOR SYSREQ_EDOC_VERSION_EXPORT_DATE SYSREQ_EDOC_VERSION_EXPORTER SYSREQ_EDOC_VERSION_HIDDEN SYSREQ_EDOC_VERSION_LIFE_STAGE SYSREQ_EDOC_VERSION_MODIFIED SYSREQ_EDOC_VERSION_NOTE SYSREQ_EDOC_VERSION_SIGNATURE_TYPE SYSR\
-EQ_EDOC_VERSION_SIGNED SYSREQ_EDOC_VERSION_SIZE SYSREQ_EDOC_VERSION_SOURCE SYSREQ_EDOC_VERSION_TEXT_MODIFIED SYSREQ_EDOCKIND_DEFAULT_VERSION_STATE_CODE SYSREQ_FOLDER_KIND SYSREQ_FUNC_CATEGORY SYSREQ_FUNC_COMMENT SYSREQ_FUNC_GROUP SYSREQ_FUNC_GROUP_COMMENT SYSREQ_FUNC_GROUP_NUMBER SYSREQ_FUNC_HELP SYSREQ_FUNC_PARAM_DEF_VALUE SYSREQ_FUNC_PARAM_IDENT SYSREQ_FUNC_PARAM_NUMBER SYSREQ_FUNC_PARAM_TYPE SYSREQ_FUNC_TEXT SYSREQ_GROUP_CATEGORY SYSREQ_ID SYSREQ_LAST_UPDATE SYSREQ_LEADER_REFERENCE SYSREQ_LIN\
-E_NUMBER SYSREQ_MAIN_RECORD_ID SYSREQ_NAME SYSREQ_NAME_LOCALIZE_ID SYSREQ_NOTE SYSREQ_ORIGINAL_RECORD SYSREQ_OUR_FIRM SYSREQ_PROFILING_SETTINGS_BATCH_LOGING SYSREQ_PROFILING_SETTINGS_BATCH_SIZE SYSREQ_PROFILING_SETTINGS_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_SQL_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_START_LOGGED SYSREQ_RECORD_STATUS SYSREQ_REF_REQ_FIELD_NAME SYSREQ_REF_REQ_FORMAT SYSREQ_REF_REQ_GENERATED SYSREQ_REF_REQ_LENGTH SYSREQ_REF_REQ_PRECISION SYSREQ_REF_REQ_REFERENCE SYSREQ_RE\
-F_REQ_SECTION SYSREQ_REF_REQ_STORED SYSREQ_REF_REQ_TOKENS SYSREQ_REF_REQ_TYPE SYSREQ_REF_REQ_VIEW SYSREQ_REF_TYPE_ACT_CODE SYSREQ_REF_TYPE_ACT_DESCRIPTION SYSREQ_REF_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_ACT_ON_EXECUTE SYSREQ_REF_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_REF_TYPE_ACT_SECTION SYSREQ_REF_TYPE_ADD_PARAMS SYSREQ_REF_TYPE_COMMENT SYSREQ_REF_TYPE_COMMON_SETTINGS SYSREQ_REF_TYPE_DISPLAY_REQUISITE_NAME SYSREQ_REF_TYPE_EVENT_TEXT SYSREQ_REF_TYPE_MAIN_LEADING_REF SYSREQ_REF_TYPE_NAME_I\
-N_SINGULAR SYSREQ_REF_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_REF_TYPE_NAME_LOCALIZE_ID SYSREQ_REF_TYPE_NUMERATION_METHOD SYSREQ_REF_TYPE_REQ_CODE SYSREQ_REF_TYPE_REQ_DESCRIPTION SYSREQ_REF_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_REQ_IS_CONTROL SYSREQ_REF_TYPE_REQ_IS_FILTER SYSREQ_REF_TYPE_REQ_IS_LEADING SYSREQ_REF_TYPE_REQ_IS_REQUIRED SYSREQ_REF_TYPE_REQ_NUMBER SYSREQ_REF_TYPE_REQ_ON_CHANGE SYSREQ_REF_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_REF_TYPE_REQ_ON_SELECT SYSREQ_REF_TYPE_REQ_ON_SELEC\
-T_KIND SYSREQ_REF_TYPE_REQ_SECTION SYSREQ_REF_TYPE_VIEW_CARD SYSREQ_REF_TYPE_VIEW_CODE SYSREQ_REF_TYPE_VIEW_COMMENT SYSREQ_REF_TYPE_VIEW_IS_MAIN SYSREQ_REF_TYPE_VIEW_NAME SYSREQ_REF_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_REFERENCE_TYPE_ID SYSREQ_STATE SYSREQ_STATЕ SYSREQ_SYSTEM_SETTINGS_VALUE SYSREQ_TYPE SYSREQ_UNIT SYSREQ_UNIT_ID SYSREQ_USER_GROUPS_GROUP_FULL_NAME SYSREQ_USER_GROUPS_GROUP_NAME SYSREQ_USER_GROUPS_GROUP_SERVER_NAME SYSREQ_USERS_ACCESS_RIGHTS SYSREQ_USERS_AUTHENTICATION SYSREQ_USERS_CA\
-TEGORY SYSREQ_USERS_COMPONENT SYSREQ_USERS_COMPONENT_USER_IS_PUBLIC SYSREQ_USERS_DOMAIN SYSREQ_USERS_FULL_USER_NAME SYSREQ_USERS_GROUP SYSREQ_USERS_IS_MAIN_SERVER SYSREQ_USERS_LOGIN SYSREQ_USERS_REFERENCE_USER_IS_PUBLIC SYSREQ_USERS_STATUS SYSREQ_USERS_USER_CERTIFICATE SYSREQ_USERS_USER_CERTIFICATE_INFO SYSREQ_USERS_USER_CERTIFICATE_PLUGIN_NAME SYSREQ_USERS_USER_CERTIFICATE_PLUGIN_VERSION SYSREQ_USERS_USER_CERTIFICATE_STATE SYSREQ_USERS_USER_CERTIFICATE_SUBJECT_NAME SYSREQ_USERS_USER_CERTIFICATE\
-_THUMBPRINT SYSREQ_USERS_USER_DEFAULT_CERTIFICATE SYSREQ_USERS_USER_DESCRIPTION SYSREQ_USERS_USER_GLOBAL_NAME SYSREQ_USERS_USER_LOGIN SYSREQ_USERS_USER_MAIN_SERVER SYSREQ_USERS_USER_TYPE SYSREQ_WORK_RULES_FOLDER_ID "+"RESULT_VAR_NAME RESULT_VAR_NAME_ENG "+"AUTO_NUMERATION_RULE_ID CANT_CHANGE_ID_REQUISITE_RULE_ID CANT_CHANGE_OURFIRM_REQUISITE_RULE_ID CHECK_CHANGING_REFERENCE_RECORD_USE_RULE_ID CHECK_CODE_REQUISITE_RULE_ID CHECK_DELETING_REFERENCE_RECORD_USE_RULE_ID CHECK_FILTRATER_CHANGES_RULE_ID\
- CHECK_RECORD_INTERVAL_RULE_ID CHECK_REFERENCE_INTERVAL_RULE_ID CHECK_REQUIRED_DATA_FULLNESS_RULE_ID CHECK_REQUIRED_REQUISITES_FULLNESS_RULE_ID MAKE_RECORD_UNRATIFIED_RULE_ID RESTORE_AUTO_NUMERATION_RULE_ID SET_FIRM_CONTEXT_FROM_RECORD_RULE_ID SET_FIRST_RECORD_IN_LIST_FORM_RULE_ID SET_IDSPS_VALUE_RULE_ID SET_NEXT_CODE_VALUE_RULE_ID SET_OURFIRM_BOUNDS_RULE_ID SET_OURFIRM_REQUISITE_RULE_ID "+"SCRIPT_BLOCK_AFTER_FINISH_EVENT SCRIPT_BLOCK_BEFORE_START_EVENT SCRIPT_BLOCK_EXECUTION_RESULTS_PROPERTY SC\
-RIPT_BLOCK_NAME_PROPERTY SCRIPT_BLOCK_SCRIPT_PROPERTY "+"SUBTASK_BLOCK_ABORT_DEADLINE_PROPERTY SUBTASK_BLOCK_AFTER_FINISH_EVENT SUBTASK_BLOCK_ASSIGN_PARAMS_EVENT SUBTASK_BLOCK_ATTACHMENTS_PROPERTY SUBTASK_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY SUBTASK_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY SUBTASK_BLOCK_BEFORE_START_EVENT SUBTASK_BLOCK_CREATED_TASK_PROPERTY SUBTASK_BLOCK_CREATION_EVENT SUBTASK_BLOCK_DEADLINE_PROPERTY SUBTASK_BLOCK_IMPORTANCE_PROPERTY SUBTASK_BLOCK_INITIATOR_PROPERTY SUBTASK_BLO\
-CK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY SUBTASK_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY SUBTASK_BLOCK_JOBS_TYPE_PROPERTY SUBTASK_BLOCK_NAME_PROPERTY SUBTASK_BLOCK_PARALLEL_ROUTE_PROPERTY SUBTASK_BLOCK_PERFORMERS_PROPERTY SUBTASK_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY SUBTASK_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY SUBTASK_BLOCK_REQUIRE_SIGN_PROPERTY SUBTASK_BLOCK_STANDARD_ROUTE_PROPERTY SUBTASK_BLOCK_START_EVENT SUBTASK_BLOCK_STEP_CONTROL_PROPERTY SUBTASK_BLOCK_SUBJECT_PROPERTY SUBTASK_BLOCK_TASK_\
-CONTROL_PROPERTY SUBTASK_BLOCK_TEXT_PROPERTY SUBTASK_BLOCK_UNLOCK_ATTACHMENTS_ON_STOP_PROPERTY SUBTASK_BLOCK_USE_STANDARD_ROUTE_PROPERTY SUBTASK_BLOCK_WAIT_FOR_TASK_COMPLETE_PROPERTY "+"SYSCOMP_CONTROL_JOBS SYSCOMP_FOLDERS SYSCOMP_JOBS SYSCOMP_NOTICES SYSCOMP_TASKS "+"SYSDLG_CREATE_EDOCUMENT SYSDLG_CREATE_EDOCUMENT_VERSION SYSDLG_CURRENT_PERIOD SYSDLG_EDIT_FUNCTION_HELP SYSDLG_EDOCUMENT_KINDS_FOR_TEMPLATE SYSDLG_EXPORT_MULTIPLE_EDOCUMENTS SYSDLG_EXPORT_SINGLE_EDOCUMENT SYSDLG_IMPORT_EDOCUMENT SY\
-SDLG_MULTIPLE_SELECT SYSDLG_SETUP_ACCESS_RIGHTS SYSDLG_SETUP_DEFAULT_RIGHTS SYSDLG_SETUP_FILTER_CONDITION SYSDLG_SETUP_SIGN_RIGHTS SYSDLG_SETUP_TASK_OBSERVERS SYSDLG_SETUP_TASK_ROUTE SYSDLG_SETUP_USERS_LIST SYSDLG_SIGN_EDOCUMENT SYSDLG_SIGN_MULTIPLE_EDOCUMENTS "+"SYSREF_ACCESS_RIGHTS_TYPES SYSREF_ADMINISTRATION_HISTORY SYSREF_ALL_AVAILABLE_COMPONENTS SYSREF_ALL_AVAILABLE_PRIVILEGES SYSREF_ALL_REPLICATING_COMPONENTS SYSREF_AVAILABLE_DEVELOPERS_COMPONENTS SYSREF_CALENDAR_EVENTS SYSREF_COMPONENT_TO\
-KEN_HISTORY SYSREF_COMPONENT_TOKENS SYSREF_COMPONENTS SYSREF_CONSTANTS SYSREF_DATA_RECEIVE_PROTOCOL SYSREF_DATA_SEND_PROTOCOL SYSREF_DIALOGS SYSREF_DIALOGS_REQUISITES SYSREF_EDITORS SYSREF_EDOC_CARDS SYSREF_EDOC_TYPES SYSREF_EDOCUMENT_CARD_REQUISITES SYSREF_EDOCUMENT_CARD_TYPES SYSREF_EDOCUMENT_CARD_TYPES_REFERENCE SYSREF_EDOCUMENT_CARDS SYSREF_EDOCUMENT_HISTORY SYSREF_EDOCUMENT_KINDS SYSREF_EDOCUMENT_REQUISITES SYSREF_EDOCUMENT_SIGNATURES SYSREF_EDOCUMENT_TEMPLATES SYSREF_EDOCUMENT_TEXT_STORAGE\
-S SYSREF_EDOCUMENT_VIEWS SYSREF_FILTERER_SETUP_CONFLICTS SYSREF_FILTRATER_SETTING_CONFLICTS SYSREF_FOLDER_HISTORY SYSREF_FOLDERS SYSREF_FUNCTION_GROUPS SYSREF_FUNCTION_PARAMS SYSREF_FUNCTIONS SYSREF_JOB_HISTORY SYSREF_LINKS SYSREF_LOCALIZATION_DICTIONARY SYSREF_LOCALIZATION_LANGUAGES SYSREF_MODULES SYSREF_PRIVILEGES SYSREF_RECORD_HISTORY SYSREF_REFERENCE_REQUISITES SYSREF_REFERENCE_TYPE_VIEWS SYSREF_REFERENCE_TYPES SYSREF_REFERENCES SYSREF_REFERENCES_REQUISITES SYSREF_REMOTE_SERVERS SYSREF_REPLI\
-CATION_SESSIONS_LOG SYSREF_REPLICATION_SESSIONS_PROTOCOL SYSREF_REPORTS SYSREF_ROLES SYSREF_ROUTE_BLOCK_GROUPS SYSREF_ROUTE_BLOCKS SYSREF_SCRIPTS SYSREF_SEARCHES SYSREF_SERVER_EVENTS SYSREF_SERVER_EVENTS_HISTORY SYSREF_STANDARD_ROUTE_GROUPS SYSREF_STANDARD_ROUTES SYSREF_STATUSES SYSREF_SYSTEM_SETTINGS SYSREF_TASK_HISTORY SYSREF_TASK_KIND_GROUPS SYSREF_TASK_KINDS SYSREF_TASK_RIGHTS SYSREF_TASK_SIGNATURES SYSREF_TASKS SYSREF_UNITS SYSREF_USER_GROUPS SYSREF_USER_GROUPS_REFERENCE SYSREF_USER_SUBSTIT\
-UTION SYSREF_USERS SYSREF_USERS_REFERENCE SYSREF_VIEWERS SYSREF_WORKING_TIME_CALENDARS "+"ACCESS_RIGHTS_TABLE_NAME EDMS_ACCESS_TABLE_NAME EDOC_TYPES_TABLE_NAME "+"TEST_DEV_DB_NAME TEST_DEV_SYSTEM_CODE TEST_EDMS_DB_NAME TEST_EDMS_MAIN_CODE TEST_EDMS_MAIN_DB_NAME TEST_EDMS_SECOND_CODE TEST_EDMS_SECOND_DB_NAME TEST_EDMS_SYSTEM_CODE TEST_ISB5_MAIN_CODE TEST_ISB5_SECOND_CODE TEST_SQL_SERVER_2005_NAME TEST_SQL_SERVER_NAME "+"ATTENTION_CAPTION cbsCommandLinks cbsDefault CONFIRMATION_CAPTION ERROR_CAPTI\
-ON INFORMATION_CAPTION mrCancel mrOk "+"EDOC_VERSION_ACTIVE_STAGE_CODE EDOC_VERSION_DESIGN_STAGE_CODE EDOC_VERSION_OBSOLETE_STAGE_CODE "+"cpDataEnciphermentEnabled cpDigitalSignatureEnabled cpID cpIssuer cpPluginVersion cpSerial cpSubjectName cpSubjSimpleName cpValidFromDate cpValidToDate "+"ISBL_SYNTAX NO_SYNTAX XML_SYNTAX "+"WAIT_BLOCK_AFTER_FINISH_EVENT WAIT_BLOCK_BEFORE_START_EVENT WAIT_BLOCK_DEADLINE_PROPERTY WAIT_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY WAIT_BLOCK_NAME_PROPERTY WAIT_BLOCK_RELAT\
-IVE_DEADLINE_TYPE_PROPERTY "+"SYSRES_COMMON SYSRES_CONST SYSRES_MBFUNC SYSRES_SBDATA SYSRES_SBGUI SYSRES_SBINTF SYSRES_SBREFDSC SYSRES_SQLERRORS SYSRES_SYSCOMP ",ENUMS="atUser atGroup atRole "+"aemEnabledAlways aemDisabledAlways aemEnabledOnBrowse aemEnabledOnEdit aemDisabledOnBrowseEmpty "+"apBegin apEnd "+"alLeft alRight "+"asmNever asmNoButCustomize asmAsLastTime asmYesButCustomize asmAlways "+"cirCommon cirRevoked "+"ctSignature ctEncode ctSignatureEncode "+"clbUnchecked clbChecked clbGrayed\
- "+"ceISB ceAlways ceNever "+"ctDocument ctReference ctScript ctUnknown ctReport ctDialog ctFunction ctFolder ctEDocument ctTask ctJob ctNotice ctControlJob "+"cfInternal cfDisplay "+"ciUnspecified ciWrite ciRead "+"ckFolder ckEDocument ckTask ckJob ckComponentToken ckAny ckReference ckScript ckReport ckDialog "+"ctISBLEditor ctBevel ctButton ctCheckListBox ctComboBox ctComboEdit ctGrid ctDBCheckBox ctDBComboBox ctDBEdit ctDBEllipsis ctDBMemo ctDBNavigator ctDBRadioGroup ctDBStatusLabel ctEdit c\
-tGroupBox ctInplaceHint ctMemo ctPanel ctListBox ctRadioButton ctRichEdit ctTabSheet ctWebBrowser ctImage ctHyperLink ctLabel ctDBMultiEllipsis ctRibbon ctRichView ctInnerPanel ctPanelGroup ctBitButton "+"cctDate cctInteger cctNumeric cctPick cctReference cctString cctText "+"cltInternal cltPrimary cltGUI "+"dseBeforeOpen dseAfterOpen dseBeforeClose dseAfterClose dseOnValidDelete dseBeforeDelete dseAfterDelete dseAfterDeleteOutOfTransaction dseOnDeleteError dseBeforeInsert dseAfterInsert dseOnVa\
-lidUpdate dseBeforeUpdate dseOnUpdateRatifiedRecord dseAfterUpdate dseAfterUpdateOutOfTransaction dseOnUpdateError dseAfterScroll dseOnOpenRecord dseOnCloseRecord dseBeforeCancel dseAfterCancel dseOnUpdateDeadlockError dseBeforeDetailUpdate dseOnPrepareUpdate dseOnAnyRequisiteChange "+"dssEdit dssInsert dssBrowse dssInActive "+"dftDate dftShortDate dftDateTime dftTimeStamp "+"dotDays dotHours dotMinutes dotSeconds "+"dtkndLocal dtkndUTC "+"arNone arView arEdit arFull "+"ddaView ddaEdit "+"emLock\
- emEdit emSign emExportWithLock emImportWithUnlock emChangeVersionNote emOpenForModify emChangeLifeStage emDelete emCreateVersion emImport emUnlockExportedWithLock emStart emAbort emReInit emMarkAsReaded emMarkAsUnreaded emPerform emAccept emResume emChangeRights emEditRoute emEditObserver emRecoveryFromLocalCopy emChangeWorkAccessType emChangeEncodeTypeToCertificate emChangeEncodeTypeToPassword emChangeEncodeTypeToNone emChangeEncodeTypeToCertificatePassword emChangeStandardRoute emGetText emOp\
-enForView emMoveToStorage emCreateObject emChangeVersionHidden emDeleteVersion emChangeLifeCycleStage emApprovingSign emExport emContinue emLockFromEdit emUnLockForEdit emLockForServer emUnlockFromServer emDelegateAccessRights emReEncode "+"ecotFile ecotProcess "+"eaGet eaCopy eaCreate eaCreateStandardRoute "+"edltAll edltNothing edltQuery "+"essmText essmCard "+"esvtLast esvtLastActive esvtSpecified "+"edsfExecutive edsfArchive "+"edstSQLServer edstFile "+"edvstNone edvstEDocumentVersionCopy ed\
-vstFile edvstTemplate edvstScannedFile "+"vsDefault vsDesign vsActive vsObsolete "+"etNone etCertificate etPassword etCertificatePassword "+"ecException ecWarning ecInformation "+"estAll estApprovingOnly "+"evtLast evtLastActive evtQuery "+"fdtString fdtNumeric fdtInteger fdtDate fdtText fdtUnknown fdtWideString fdtLargeInteger "+"ftInbox ftOutbox ftFavorites ftCommonFolder ftUserFolder ftComponents ftQuickLaunch ftShortcuts ftSearch "+"grhAuto grhX1 grhX2 grhX3 "+"hltText hltRTF hltHTML "+"iffB\
-MP iffJPEG iffMultiPageTIFF iffSinglePageTIFF iffTIFF iffPNG "+"im8bGrayscale im24bRGB im1bMonochrome "+"itBMP itJPEG itWMF itPNG "+"ikhInformation ikhWarning ikhError ikhNoIcon "+"icUnknown icScript icFunction icIntegratedReport icAnalyticReport icDataSetEventHandler icActionHandler icFormEventHandler icLookUpEventHandler icRequisiteChangeEventHandler icBeforeSearchEventHandler icRoleCalculation icSelectRouteEventHandler icBlockPropertyCalculation icBlockQueryParamsEventHandler icChangeSearchRe\
-sultEventHandler icBlockEventHandler icSubTaskInitEventHandler icEDocDataSetEventHandler icEDocLookUpEventHandler icEDocActionHandler icEDocFormEventHandler icEDocRequisiteChangeEventHandler icStructuredConversionRule icStructuredConversionEventBefore icStructuredConversionEventAfter icWizardEventHandler icWizardFinishEventHandler icWizardStepEventHandler icWizardStepFinishEventHandler icWizardActionEnableEventHandler icWizardActionExecuteEventHandler icCreateJobsHandler icCreateNoticesHandler i\
-cBeforeLookUpEventHandler icAfterLookUpEventHandler icTaskAbortEventHandler icWorkflowBlockActionHandler icDialogDataSetEventHandler icDialogActionHandler icDialogLookUpEventHandler icDialogRequisiteChangeEventHandler icDialogFormEventHandler icDialogValidCloseEventHandler icBlockFormEventHandler icTaskFormEventHandler icReferenceMethod icEDocMethod icDialogMethod icProcessMessageHandler "+"isShow isHide isByUserSettings "+"jkJob jkNotice jkControlJob "+"jtInner jtLeft jtRight jtFull jtCross "+"\
-lbpAbove lbpBelow lbpLeft lbpRight "+"eltPerConnection eltPerUser "+"sfcUndefined sfcBlack sfcGreen sfcRed sfcBlue sfcOrange sfcLilac "+"sfsItalic sfsStrikeout sfsNormal "+"ldctStandardRoute ldctWizard ldctScript ldctFunction ldctRouteBlock ldctIntegratedReport ldctAnalyticReport ldctReferenceType ldctEDocumentType ldctDialog ldctServerEvents "+"mrcrtNone mrcrtUser mrcrtMaximal mrcrtCustom "+"vtEqual vtGreaterOrEqual vtLessOrEqual vtRange "+"rdYesterday rdToday rdTomorrow rdThisWeek rdThisMonth \
-rdThisYear rdNextMonth rdNextWeek rdLastWeek rdLastMonth "+"rdWindow rdFile rdPrinter "+"rdtString rdtNumeric rdtInteger rdtDate rdtReference rdtAccount rdtText rdtPick rdtUnknown rdtLargeInteger rdtDocument "+"reOnChange reOnChangeValues "+"ttGlobal ttLocal ttUser ttSystem "+"ssmBrowse ssmSelect ssmMultiSelect ssmBrowseModal "+"smSelect smLike smCard "+"stNone stAuthenticating stApproving "+"sctString sctStream "+"sstAnsiSort sstNaturalSort "+"svtEqual svtContain "+"soatString soatNumeric soatI\
-nteger soatDatetime soatReferenceRecord soatText soatPick soatBoolean soatEDocument soatAccount soatIntegerCollection soatNumericCollection soatStringCollection soatPickCollection soatDatetimeCollection soatBooleanCollection soatReferenceRecordCollection soatEDocumentCollection soatAccountCollection soatContents soatUnknown "+"tarAbortByUser tarAbortByWorkflowException "+"tvtAllWords tvtExactPhrase tvtAnyWord "+"usNone usCompleted usRedSquare usBlueSquare usYellowSquare usGreenSquare usOrangeSqu\
-are usPurpleSquare usFollowUp "+"utUnknown utUser utDeveloper utAdministrator utSystemDeveloper utDisconnected "+"btAnd btDetailAnd btOr btNotOr btOnly "+"vmView vmSelect vmNavigation "+"vsmSingle vsmMultiple vsmMultipleCheck vsmNoSelection "+"wfatPrevious wfatNext wfatCancel wfatFinish "+"wfepUndefined wfepText3 wfepText6 wfepText9 wfepSpinEdit wfepDropDown wfepRadioGroup wfepFlag wfepText12 wfepText15 wfepText18 wfepText21 wfepText24 wfepText27 wfepText30 wfepRadioGroupColumn1 wfepRadioGroupCo\
-lumn2 wfepRadioGroupColumn3 "+"wfetQueryParameter wfetText wfetDelimiter wfetLabel "+"wptString wptInteger wptNumeric wptBoolean wptDateTime wptPick wptText wptUser wptUserList wptEDocumentInfo wptEDocumentInfoList wptReferenceRecordInfo wptReferenceRecordInfoList wptFolderInfo wptTaskInfo wptContents wptFileName wptDate "+"wsrComplete wsrGoNext wsrGoPrevious wsrCustom wsrCancel wsrGoFinal "+"wstForm wstEDocument wstTaskCard wstReferenceRecordCard wstFinal "+"waAll waPerformers waManual "+"wsbSt\
-art wsbFinish wsbNotice wsbStep wsbDecision wsbWait wsbMonitor wsbScript wsbConnector wsbSubTask wsbLifeCycleStage wsbPause "+"wdtInteger wdtFloat wdtString wdtPick wdtDateTime wdtBoolean wdtTask wdtJob wdtFolder wdtEDocument wdtReferenceRecord wdtUser wdtGroup wdtRole wdtIntegerCollection wdtFloatCollection wdtStringCollection wdtPickCollection wdtDateTimeCollection wdtBooleanCollection wdtTaskCollection wdtJobCollection wdtFolderCollection wdtEDocumentCollection wdtReferenceRecordCollection wd\
-tUserCollection wdtGroupCollection wdtRoleCollection wdtContents wdtUserList wdtSearchDescription wdtDeadLine wdtPickSet wdtAccountCollection "+"wiLow wiNormal wiHigh "+"wrtSoft wrtHard "+"wsInit wsRunning wsDone wsControlled wsAborted wsContinued "+"wtmFull wtmFromCurrent wtmOnlyCurrent ",system_functions="AddSubString AdjustLineBreaks AmountInWords Analysis ArrayDimCount ArrayHighBound ArrayLowBound ArrayOf ArrayReDim Assert Assigned BeginOfMonth BeginOfPeriod BuildProfilingOperationAnalysis C\
-allProcedure CanReadFile CArrayElement CDataSetRequisite ChangeDate ChangeReferenceDataset Char CharPos CheckParam CheckParamValue CompareStrings ConstantExists ControlState ConvertDateStr Copy CopyFile CreateArray CreateCachedReference CreateConnection CreateDialog CreateDualListDialog CreateEditor CreateException CreateFile CreateFolderDialog CreateInputDialog CreateLinkFile CreateList CreateLock CreateMemoryDataSet CreateObject CreateOpenDialog CreateProgress CreateQuery CreateReference Creat\
-eReport CreateSaveDialog CreateScript CreateSQLPivotFunction CreateStringList CreateTreeListSelectDialog CSelectSQL CSQL CSubString CurrentUserID CurrentUserName CurrentVersion DataSetLocateEx DateDiff DateTimeDiff DateToStr DayOfWeek DeleteFile DirectoryExists DisableCheckAccessRights DisableCheckFullShowingRestriction DisableMassTaskSendingRestrictions DropTable DupeString EditText EnableCheckAccessRights EnableCheckFullShowingRestriction EnableMassTaskSendingRestrictions EndOfMonth EndOfPerio\
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-ot mandelbrot_set map mapatom maplist matchdeclare matchfix mat_cond mat_fullunblocker mat_function mathml_display mat_norm matrix matrixmap matrixp matrix_size mattrace mat_trace mat_unblocker max max_clique max_degree max_flow maximize_lp max_independent_set max_matching maybe md5sum mean mean_bernoulli mean_beta mean_binomial mean_chi2 mean_continuous_uniform mean_deviation mean_discrete_uniform mean_exp mean_f mean_gamma mean_general_finite_discrete mean_geometric mean_gumbel mean_hypergeome\
-tric mean_laplace mean_logistic mean_lognormal mean_negative_binomial mean_noncentral_chi2 mean_noncentral_student_t mean_normal mean_pareto mean_poisson mean_rayleigh mean_student_t mean_weibull median median_deviation member mesh metricexpandall mgf1_sha1 min min_degree min_edge_cut minfactorial minimalPoly minimize_lp minimum_spanning_tree minor minpack_lsquares minpack_solve min_vertex_cover min_vertex_cut mkdir mnewton mod mode_declare mode_identity ModeMatrix moebius mon2schur mono monomia\
-l_dimensions multibernstein_poly multi_display_for_texinfo multi_elem multinomial multinomial_coeff multi_orbit multiplot_mode multi_pui multsym multthru mycielski_graph nary natural_unit nc_degree ncexpt ncharpoly negative_picture neighbors new newcontext newdet new_graph newline newton new_variable next_prime nicedummies niceindices ninth nofix nonarray noncentral_moment nonmetricity nonnegintegerp nonscalarp nonzeroandfreeof notequal nounify nptetrad npv nroots nterms ntermst nthroot nullity \
-nullspace num numbered_boundaries numberp number_to_octets num_distinct_partitions numerval numfactor num_partitions nusum nzeta nzetai nzetar octets_to_number octets_to_oid odd_girth oddp ode2 ode_check odelin oid_to_octets op opena opena_binary openr openr_binary openw openw_binary operatorp opsubst optimize %or orbit orbits ordergreat ordergreatp orderless orderlessp orthogonal_complement orthopoly_recur orthopoly_weight outermap out_neighbors outofpois pade parabolic_cylinder_d parametric pa\
-rametric_surface parg parGosper parse_string parse_timedate part part2cont partfrac partition partition_set partpol path_digraph path_graph pathname_directory pathname_name pathname_type pdf_bernoulli pdf_beta pdf_binomial pdf_cauchy pdf_chi2 pdf_continuous_uniform pdf_discrete_uniform pdf_exp pdf_f pdf_gamma pdf_general_finite_discrete pdf_geometric pdf_gumbel pdf_hypergeometric pdf_laplace pdf_logistic pdf_lognormal pdf_negative_binomial pdf_noncentral_chi2 pdf_noncentral_student_t pdf_normal \
-pdf_pareto pdf_poisson pdf_rank_sum pdf_rayleigh pdf_signed_rank pdf_student_t pdf_weibull pearson_skewness permanent permut permutation permutations petersen_graph petrov pickapart picture_equalp picturep piechart piechart_description planar_embedding playback plog plot2d plot3d plotdf ploteq plsquares pochhammer points poisdiff poisexpt poisint poismap poisplus poissimp poissubst poistimes poistrim polar polarform polartorect polar_to_xy poly_add poly_buchberger poly_buchberger_criterion poly_\
-colon_ideal poly_content polydecomp poly_depends_p poly_elimination_ideal poly_exact_divide poly_expand poly_expt poly_gcd polygon poly_grobner poly_grobner_equal poly_grobner_member poly_grobner_subsetp poly_ideal_intersection poly_ideal_polysaturation poly_ideal_polysaturation1 poly_ideal_saturation poly_ideal_saturation1 poly_lcm poly_minimization polymod poly_multiply polynome2ele polynomialp poly_normal_form poly_normalize poly_normalize_list poly_polysaturation_extension poly_primitive_par\
-t poly_pseudo_divide poly_reduced_grobner poly_reduction poly_saturation_extension poly_s_polynomial poly_subtract polytocompanion pop postfix potential power_mod powerseries powerset prefix prev_prime primep primes principal_components print printf printfile print_graph printpois printprops prodrac product properties propvars psi psubst ptriangularize pui pui2comp pui2ele pui2polynome pui_direct puireduc push put pv qput qrange qty quad_control quad_qag quad_qagi quad_qagp quad_qags quad_qawc q\
-uad_qawf quad_qawo quad_qaws quadrilateral quantile quantile_bernoulli quantile_beta quantile_binomial quantile_cauchy quantile_chi2 quantile_continuous_uniform quantile_discrete_uniform quantile_exp quantile_f quantile_gamma quantile_general_finite_discrete quantile_geometric quantile_gumbel quantile_hypergeometric quantile_laplace quantile_logistic quantile_lognormal quantile_negative_binomial quantile_noncentral_chi2 quantile_noncentral_student_t quantile_normal quantile_pareto quantile_poiss\
-on quantile_rayleigh quantile_student_t quantile_weibull quartile_skewness quit qunit quotient racah_v racah_w radcan radius random random_bernoulli random_beta random_binomial random_bipartite_graph random_cauchy random_chi2 random_continuous_uniform random_digraph random_discrete_uniform random_exp random_f random_gamma random_general_finite_discrete random_geometric random_graph random_graph1 random_gumbel random_hypergeometric random_laplace random_logistic random_lognormal random_negative_b\
-inomial random_network random_noncentral_chi2 random_noncentral_student_t random_normal random_pareto random_permutation random_poisson random_rayleigh random_regular_graph random_student_t random_tournament random_tree random_weibull range rank rat ratcoef ratdenom ratdiff ratdisrep ratexpand ratinterpol rational rationalize ratnumer ratnump ratp ratsimp ratsubst ratvars ratweight read read_array read_binary_array read_binary_list read_binary_matrix readbyte readchar read_hashed_array readline \
-read_list read_matrix read_nested_list readonly read_xpm real_imagpart_to_conjugate realpart realroots rearray rectangle rectform rectform_log_if_constant recttopolar rediff reduce_consts reduce_order region region_boundaries region_boundaries_plus rem remainder remarray rembox remcomps remcon remcoord remfun remfunction remlet remove remove_constvalue remove_dimensions remove_edge remove_fundamental_dimensions remove_fundamental_units remove_plot_option remove_vertex rempart remrule remsym remv\
-alue rename rename_file reset reset_displays residue resolvante resolvante_alternee1 resolvante_bipartite resolvante_diedrale resolvante_klein resolvante_klein3 resolvante_produit_sym resolvante_unitaire resolvante_vierer rest resultant return reveal reverse revert revert2 rgb2level rhs ricci riemann rinvariant risch rk rmdir rncombine romberg room rootscontract round row rowop rowswap rreduce run_testsuite %s save saving scalarp scaled_bessel_i scaled_bessel_i0 scaled_bessel_i1 scalefactors sca\
-nmap scatterplot scatterplot_description scene schur2comp sconcat scopy scsimp scurvature sdowncase sec sech second sequal sequalignore set_alt_display setdifference set_draw_defaults set_edge_weight setelmx setequalp setify setp set_partitions set_plot_option set_prompt set_random_state set_tex_environment set_tex_environment_default setunits setup_autoload set_up_dot_simplifications set_vertex_label seventh sexplode sf sha1sum sha256sum shortest_path shortest_weighted_path show showcomps showr\
-atvars sierpinskiale sierpinskimap sign signum similaritytransform simp_inequality simplify_sum simplode simpmetderiv simtran sin sinh sinsert sinvertcase sixth skewness skewness_bernoulli skewness_beta skewness_binomial skewness_chi2 skewness_continuous_uniform skewness_discrete_uniform skewness_exp skewness_f skewness_gamma skewness_general_finite_discrete skewness_geometric skewness_gumbel skewness_hypergeometric skewness_laplace skewness_logistic skewness_lognormal skewness_negative_binomial\
- skewness_noncentral_chi2 skewness_noncentral_student_t skewness_normal skewness_pareto skewness_poisson skewness_rayleigh skewness_student_t skewness_weibull slength smake small_rhombicosidodecahedron_graph small_rhombicuboctahedron_graph smax smin smismatch snowmap snub_cube_graph snub_dodecahedron_graph solve solve_rec solve_rec_rat some somrac sort sparse6_decode sparse6_encode sparse6_export sparse6_import specint spherical spherical_bessel_j spherical_bessel_y spherical_hankel1 spherical_h\
-ankel2 spherical_harmonic spherical_to_xyz splice split sposition sprint sqfr sqrt sqrtdenest sremove sremovefirst sreverse ssearch ssort sstatus ssubst ssubstfirst staircase standardize standardize_inverse_trig starplot starplot_description status std std1 std_bernoulli std_beta std_binomial std_chi2 std_continuous_uniform std_discrete_uniform std_exp std_f std_gamma std_general_finite_discrete std_geometric std_gumbel std_hypergeometric std_laplace std_logistic std_lognormal std_negative_binom\
-ial std_noncentral_chi2 std_noncentral_student_t std_normal std_pareto std_poisson std_rayleigh std_student_t std_weibull stemplot stirling stirling1 stirling2 strim striml strimr string stringout stringp strong_components struve_h struve_l sublis sublist sublist_indices submatrix subsample subset subsetp subst substinpart subst_parallel substpart substring subvar subvarp sum sumcontract summand_to_rec supcase supcontext symbolp symmdifference symmetricp system take_channel take_inference tan ta\
-nh taylor taylorinfo taylorp taylor_simplifier taytorat tcl_output tcontract tellrat tellsimp tellsimpafter tentex tenth test_mean test_means_difference test_normality test_proportion test_proportions_difference test_rank_sum test_sign test_signed_rank test_variance test_variance_ratio tex tex1 tex_display texput %th third throw time timedate timer timer_info tldefint tlimit todd_coxeter toeplitz tokens to_lisp topological_sort to_poly to_poly_solve totaldisrep totalfourier totient tpartpol trac\
-e tracematrix trace_options transform_sample translate translate_file transpose treefale tree_reduce treillis treinat triangle triangularize trigexpand trigrat trigreduce trigsimp trunc truncate truncated_cube_graph truncated_dodecahedron_graph truncated_icosahedron_graph truncated_tetrahedron_graph tr_warnings_get tube tutte_graph ueivects uforget ultraspherical underlying_graph undiff union unique uniteigenvectors unitp units unit_step unitvector unorder unsum untellrat untimer untrace upperca\
-sep uricci uriemann uvect vandermonde_matrix var var1 var_bernoulli var_beta var_binomial var_chi2 var_continuous_uniform var_discrete_uniform var_exp var_f var_gamma var_general_finite_discrete var_geometric var_gumbel var_hypergeometric var_laplace var_logistic var_lognormal var_negative_binomial var_noncentral_chi2 var_noncentral_student_t var_normal var_pareto var_poisson var_rayleigh var_student_t var_weibull vector vectorpotential vectorsimp verbify vers vertex_coloring vertex_connectivity\
- vertex_degree vertex_distance vertex_eccentricity vertex_in_degree vertex_out_degree vertices vertices_to_cycle vertices_to_path %w weyl wheel_graph wiener_index wigner_3j wigner_6j wigner_9j with_stdout write_binary_data writebyte write_data writefile wronskian xreduce xthru %y Zeilberger zeroequiv zerofor zeromatrix zeromatrixp zeta zgeev zheev zlange zn_add_table zn_carmichael_lambda zn_characteristic_factors zn_determinant zn_factor_generators zn_invert_by_lu zn_log zn_mult_table absboxchar\
- activecontexts adapt_depth additive adim aform algebraic algepsilon algexact aliases allbut all_dotsimp_denoms allocation allsym alphabetic animation antisymmetric arrays askexp assume_pos assume_pos_pred assumescalar asymbol atomgrad atrig1 axes axis_3d axis_bottom axis_left axis_right axis_top azimuth background background_color backsubst berlefact bernstein_explicit besselexpand beta_args_sum_to_integer beta_expand bftorat bftrunc bindtest border boundaries_array box boxchar breakup %c cappi\
-ng cauchysum cbrange cbtics center cflength cframe_flag cnonmet_flag color color_bar color_bar_tics colorbox columns commutative complex cone context contexts contour contour_levels cosnpiflag ctaypov ctaypt ctayswitch ctayvar ct_coords ctorsion_flag ctrgsimp cube current_let_rule_package cylinder data_file_name debugmode decreasing default_let_rule_package delay dependencies derivabbrev derivsubst detout diagmetric diff dim dimensions dispflag display2d|10 display_format_internal distribute_ove\
-r doallmxops domain domxexpt domxmxops domxnctimes dontfactor doscmxops doscmxplus dot0nscsimp dot0simp dot1simp dotassoc dotconstrules dotdistrib dotexptsimp dotident dotscrules draw_graph_program draw_realpart edge_color edge_coloring edge_partition edge_type edge_width %edispflag elevation %emode endphi endtheta engineering_format_floats enhanced3d %enumer epsilon_lp erfflag erf_representation errormsg error_size error_syms error_type %e_to_numlog eval even evenfun evflag evfun ev_point expan\
-dwrt_denom expintexpand expintrep expon expop exptdispflag exptisolate exptsubst facexpand facsum_combine factlim factorflag factorial_expand factors_only fb feature features file_name file_output_append file_search_demo file_search_lisp file_search_maxima|10 file_search_tests file_search_usage file_type_lisp file_type_maxima|10 fill_color fill_density filled_func fixed_vertices flipflag float2bf font font_size fortindent fortspaces fpprec fpprintprec functions gamma_expand gammalim gdet geninde\
-x gensumnum GGFCFMAX GGFINFINITY globalsolve gnuplot_command gnuplot_curve_styles gnuplot_curve_titles gnuplot_default_term_command gnuplot_dumb_term_command gnuplot_file_args gnuplot_file_name gnuplot_out_file gnuplot_pdf_term_command gnuplot_pm3d gnuplot_png_term_command gnuplot_postamble gnuplot_preamble gnuplot_ps_term_command gnuplot_svg_term_command gnuplot_term gnuplot_view_args Gosper_in_Zeilberger gradefs grid grid2d grind halfangles head_angle head_both head_length head_type height hyp\
-ergeometric_representation %iargs ibase icc1 icc2 icounter idummyx ieqnprint ifb ifc1 ifc2 ifg ifgi ifr iframe_bracket_form ifri igeowedge_flag ikt1 ikt2 imaginary inchar increasing infeval infinity inflag infolists inm inmc1 inmc2 intanalysis integer integervalued integrate_use_rootsof integration_constant integration_constant_counter interpolate_color intfaclim ip_grid ip_grid_in irrational isolate_wrt_times iterations itr julia_parameter %k1 %k2 keepfloat key key_pos kinvariant kt label label\
-_alignment label_orientation labels lassociative lbfgs_ncorrections lbfgs_nfeval_max leftjust legend letrat let_rule_packages lfg lg lhospitallim limsubst linear linear_solver linechar linel|10 linenum line_type linewidth line_width linsolve_params linsolvewarn lispdisp listarith listconstvars listdummyvars lmxchar load_pathname loadprint logabs logarc logcb logconcoeffp logexpand lognegint logsimp logx logx_secondary logy logy_secondary logz lriem m1pbranch macroexpansion macros mainvar manual_\
-demo maperror mapprint matrix_element_add matrix_element_mult matrix_element_transpose maxapplydepth maxapplyheight maxima_tempdir|10 maxima_userdir|10 maxnegex MAX_ORD maxposex maxpsifracdenom maxpsifracnum maxpsinegint maxpsiposint maxtayorder mesh_lines_color method mod_big_prime mode_check_errorp mode_checkp mode_check_warnp mod_test mod_threshold modular_linear_solver modulus multiplicative multiplicities myoptions nary negdistrib negsumdispflag newline newtonepsilon newtonmaxiter nextlayer\
-factor niceindicespref nm nmc noeval nolabels nonegative_lp noninteger nonscalar noun noundisp nouns np npi nticks ntrig numer numer_pbranch obase odd oddfun opacity opproperties opsubst optimprefix optionset orientation origin orthopoly_returns_intervals outative outchar packagefile palette partswitch pdf_file pfeformat phiresolution %piargs piece pivot_count_sx pivot_max_sx plot_format plot_options plot_realpart png_file pochhammer_max_index points pointsize point_size points_joined point_type\
- poislim poisson poly_coefficient_ring poly_elimination_order polyfactor poly_grobner_algorithm poly_grobner_debug poly_monomial_order poly_primary_elimination_order poly_return_term_list poly_secondary_elimination_order poly_top_reduction_only posfun position powerdisp pred prederror primep_number_of_tests product_use_gamma program programmode promote_float_to_bigfloat prompt proportional_axes props psexpand ps_file radexpand radius radsubstflag rassociative ratalgdenom ratchristof ratdenomdivi\
-de rateinstein ratepsilon ratfac rational ratmx ratprint ratriemann ratsimpexpons ratvarswitch ratweights ratweyl ratwtlvl real realonly redraw refcheck resolution restart resultant ric riem rmxchar %rnum_list rombergabs rombergit rombergmin rombergtol rootsconmode rootsepsilon run_viewer same_xy same_xyz savedef savefactors scalar scalarmatrixp scale scale_lp setcheck setcheckbreak setval show_edge_color show_edges show_edge_type show_edge_width show_id show_label showtime show_vertex_color sho\
-w_vertex_size show_vertex_type show_vertices show_weight simp simplified_output simplify_products simpproduct simpsum sinnpiflag solvedecomposes solveexplicit solvefactors solvenullwarn solveradcan solvetrigwarn space sparse sphere spring_embedding_depth sqrtdispflag stardisp startphi starttheta stats_numer stringdisp structures style sublis_apply_lambda subnumsimp sumexpand sumsplitfact surface surface_hide svg_file symmetric tab taylordepth taylor_logexpand taylor_order_coefficients taylor_tru\
-ncate_polynomials tensorkill terminal testsuite_files thetaresolution timer_devalue title tlimswitch tr track transcompile transform transform_xy translate_fast_arrays transparent transrun tr_array_as_ref tr_bound_function_applyp tr_file_tty_messagesp tr_float_can_branch_complex tr_function_call_default trigexpandplus trigexpandtimes triginverses trigsign trivial_solutions tr_numer tr_optimize_max_loop tr_semicompile tr_state_vars tr_warn_bad_function_calls tr_warn_fexpr tr_warn_meval tr_warn_mo\
-de tr_warn_undeclared tr_warn_undefined_variable tstep ttyoff tube_extremes ufg ug %unitexpand unit_vectors uric uriem use_fast_arrays user_preamble usersetunits values vect_cross verbose vertex_color vertex_coloring vertex_partition vertex_size vertex_type view warnings weyl width windowname windowtitle wired_surface wireframe xaxis xaxis_color xaxis_secondary xaxis_type xaxis_width xlabel xlabel_secondary xlength xrange xrange_secondary xtics xtics_axis xtics_rotate xtics_rotate_secondary xtic\
-s_secondary xtics_secondary_axis xu_grid x_voxel xy_file xyplane xy_scale yaxis yaxis_color yaxis_secondary yaxis_type yaxis_width ylabel ylabel_secondary ylength yrange yrange_secondary ytics ytics_axis ytics_rotate ytics_rotate_secondary ytics_secondary ytics_secondary_axis yv_grid y_voxel yx_ratio zaxis zaxis_color zaxis_type zaxis_width zeroa zerob zerobern zeta%pi zlabel zlabel_rotate zlength zmin zn_primroot_limit zn_primroot_pretest",symbol:"_ __ %|0 %%|0"},contains:[{className:"comment",
-begin:"/\\*",end:"\\*/",contains:["self"]},hljs.QUOTE_STRING_MODE,{className:"number",relevance:0,variants:[{begin:"\\b(\\d+|\\d+\\.|\\.\\d+|\\d+\\.\\d+)[Ee][-+]?\\d+\\b"},{begin:"\\b(\\d+|\\d+\\.|\\.\\d+|\\d+\\.\\d+)[Bb][-+]?\\d+\\b",relevance:10},{begin:"\\b(\\.\\d+|\\d+\\.\\d+)\\b"},{begin:"\\b(\\d+|0[0-9A-Za-z]+)\\.?\\b"}]}],illegal:/@/}}return maxima_1=maxima2,maxima_1}var mel_1,hasRequiredMel;function requireMel(){if(hasRequiredMel)return mel_1;hasRequiredMel=1;function mel2(hljs){return{name:"\
-MEL",keywords:"int float string vector matrix if else switch case default while do for in break continue global proc return about abs addAttr addAttributeEditorNodeHelp addDynamic addNewShelfTab addPP addPanelCategory addPrefixToName advanceToNextDrivenKey affectedNet affects aimConstraint air alias aliasAttr align alignCtx alignCurve alignSurface allViewFit ambientLight angle angleBetween animCone animCurveEditor animDisplay animView annotate appendStringArray applicationName applyAttrPreset ap\
-plyTake arcLenDimContext arcLengthDimension arclen arrayMapper art3dPaintCtx artAttrCtx artAttrPaintVertexCtx artAttrSkinPaintCtx artAttrTool artBuildPaintMenu artFluidAttrCtx artPuttyCtx artSelectCtx artSetPaintCtx artUserPaintCtx assignCommand assignInputDevice assignViewportFactories attachCurve attachDeviceAttr attachSurface attrColorSliderGrp attrCompatibility attrControlGrp attrEnumOptionMenu attrEnumOptionMenuGrp attrFieldGrp attrFieldSliderGrp attrNavigationControlGrp attrPresetEditWin a\
-ttributeExists attributeInfo attributeMenu attributeQuery autoKeyframe autoPlace bakeClip bakeFluidShading bakePartialHistory bakeResults bakeSimulation basename basenameEx batchRender bessel bevel bevelPlus binMembership bindSkin blend2 blendShape blendShapeEditor blendShapePanel blendTwoAttr blindDataType boneLattice boundary boxDollyCtx boxZoomCtx bufferCurve buildBookmarkMenu buildKeyframeMenu button buttonManip CBG cacheFile cacheFileCombine cacheFileMerge cacheFileTrack camera cameraView c\
-anCreateManip canvas capitalizeString catch catchQuiet ceil changeSubdivComponentDisplayLevel changeSubdivRegion channelBox character characterMap characterOutlineEditor characterize chdir checkBox checkBoxGrp checkDefaultRenderGlobals choice circle circularFillet clamp clear clearCache clip clipEditor clipEditorCurrentTimeCtx clipSchedule clipSchedulerOutliner clipTrimBefore closeCurve closeSurface cluster cmdFileOutput cmdScrollFieldExecuter cmdScrollFieldReporter cmdShell coarsenSubdivSelecti\
-onList collision color colorAtPoint colorEditor colorIndex colorIndexSliderGrp colorSliderButtonGrp colorSliderGrp columnLayout commandEcho commandLine commandPort compactHairSystem componentEditor compositingInterop computePolysetVolume condition cone confirmDialog connectAttr connectControl connectDynamic connectJoint connectionInfo constrain constrainValue constructionHistory container containsMultibyte contextInfo control convertFromOldLayers convertIffToPsd convertLightmap convertSolidTx co\
-nvertTessellation convertUnit copyArray copyFlexor copyKey copySkinWeights cos cpButton cpCache cpClothSet cpCollision cpConstraint cpConvClothToMesh cpForces cpGetSolverAttr cpPanel cpProperty cpRigidCollisionFilter cpSeam cpSetEdit cpSetSolverAttr cpSolver cpSolverTypes cpTool cpUpdateClothUVs createDisplayLayer createDrawCtx createEditor createLayeredPsdFile createMotionField createNewShelf createNode createRenderLayer createSubdivRegion cross crossProduct ctxAbort ctxCompletion ctxEditMode c\
-txTraverse currentCtx currentTime currentTimeCtx currentUnit curve curveAddPtCtx curveCVCtx curveEPCtx curveEditorCtx curveIntersect curveMoveEPCtx curveOnSurface curveSketchCtx cutKey cycleCheck cylinder dagPose date defaultLightListCheckBox defaultNavigation defineDataServer defineVirtualDevice deformer deg_to_rad delete deleteAttr deleteShadingGroupsAndMaterials deleteShelfTab deleteUI deleteUnusedBrushes delrandstr detachCurve detachDeviceAttr detachSurface deviceEditor devicePanel dgInfo dg\
-dirty dgeval dgtimer dimWhen directKeyCtx directionalLight dirmap dirname disable disconnectAttr disconnectJoint diskCache displacementToPoly displayAffected displayColor displayCull displayLevelOfDetail displayPref displayRGBColor displaySmoothness displayStats displayString displaySurface distanceDimContext distanceDimension doBlur dolly dollyCtx dopeSheetEditor dot dotProduct doubleProfileBirailSurface drag dragAttrContext draggerContext dropoffLocator duplicate duplicateCurve duplicateSurfac\
-e dynCache dynControl dynExport dynExpression dynGlobals dynPaintEditor dynParticleCtx dynPref dynRelEdPanel dynRelEditor dynamicLoad editAttrLimits editDisplayLayerGlobals editDisplayLayerMembers editRenderLayerAdjustment editRenderLayerGlobals editRenderLayerMembers editor editorTemplate effector emit emitter enableDevice encodeString endString endsWith env equivalent equivalentTol erf error eval evalDeferred evalEcho event exactWorldBoundingBox exclusiveLightCheckBox exec executeForEachObject\
- exists exp expression expressionEditorListen extendCurve extendSurface extrude fcheck fclose feof fflush fgetline fgetword file fileBrowserDialog fileDialog fileExtension fileInfo filetest filletCurve filter filterCurve filterExpand filterStudioImport findAllIntersections findAnimCurves findKeyframe findMenuItem findRelatedSkinCluster finder firstParentOf fitBspline flexor floatEq floatField floatFieldGrp floatScrollBar floatSlider floatSlider2 floatSliderButtonGrp floatSliderGrp floor flow flu\
-idCacheInfo fluidEmitter fluidVoxelInfo flushUndo fmod fontDialog fopen formLayout format fprint frameLayout fread freeFormFillet frewind fromNativePath fwrite gamma gauss geometryConstraint getApplicationVersionAsFloat getAttr getClassification getDefaultBrush getFileList getFluidAttr getInputDeviceRange getMayaPanelTypes getModifiers getPanel getParticleAttr getPluginResource getenv getpid glRender glRenderEditor globalStitch gmatch goal gotoBindPose grabColor gradientControl gradientControlNo\
-Attr graphDollyCtx graphSelectContext graphTrackCtx gravity grid gridLayout group groupObjectsByName HfAddAttractorToAS HfAssignAS HfBuildEqualMap HfBuildFurFiles HfBuildFurImages HfCancelAFR HfConnectASToHF HfCreateAttractor HfDeleteAS HfEditAS HfPerformCreateAS HfRemoveAttractorFromAS HfSelectAttached HfSelectAttractors HfUnAssignAS hardenPointCurve hardware hardwareRenderPanel headsUpDisplay headsUpMessage help helpLine hermite hide hilite hitTest hotBox hotkey hotkeyCheck hsv_to_rgb hudButto\
-n hudSlider hudSliderButton hwReflectionMap hwRender hwRenderLoad hyperGraph hyperPanel hyperShade hypot iconTextButton iconTextCheckBox iconTextRadioButton iconTextRadioCollection iconTextScrollList iconTextStaticLabel ikHandle ikHandleCtx ikHandleDisplayScale ikSolver ikSplineHandleCtx ikSystem ikSystemInfo ikfkDisplayMethod illustratorCurves image imfPlugins inheritTransform insertJoint insertJointCtx insertKeyCtx insertKnotCurve insertKnotSurface instance instanceable instancer intField intF\
-ieldGrp intScrollBar intSlider intSliderGrp interToUI internalVar intersect iprEngine isAnimCurve isConnected isDirty isParentOf isSameObject isTrue isValidObjectName isValidString isValidUiName isolateSelect itemFilter itemFilterAttr itemFilterRender itemFilterType joint jointCluster jointCtx jointDisplayScale jointLattice keyTangent keyframe keyframeOutliner keyframeRegionCurrentTimeCtx keyframeRegionDirectKeyCtx keyframeRegionDollyCtx keyframeRegionInsertKeyCtx keyframeRegionMoveKeyCtx keyfra\
-meRegionScaleKeyCtx keyframeRegionSelectKeyCtx keyframeRegionSetKeyCtx keyframeRegionTrackCtx keyframeStats lassoContext lattice latticeDeformKeyCtx launch launchImageEditor layerButton layeredShaderPort layeredTexturePort layout layoutDialog lightList lightListEditor lightListPanel lightlink lineIntersection linearPrecision linstep listAnimatable listAttr listCameras listConnections listDeviceAttachments listHistory listInputDeviceAxes listInputDeviceButtons listInputDevices listMenuAnnotation \
-listNodeTypes listPanelCategories listRelatives listSets listTransforms listUnselected listerEditor loadFluid loadNewShelf loadPlugin loadPluginLanguageResources loadPrefObjects localizedPanelLabel lockNode loft log longNameOf lookThru ls lsThroughFilter lsType lsUI Mayatomr mag makeIdentity makeLive makePaintable makeRoll makeSingleSurface makeTubeOn makebot manipMoveContext manipMoveLimitsCtx manipOptions manipRotateContext manipRotateLimitsCtx manipScaleContext manipScaleLimitsCtx marker matc\
-h max memory menu menuBarLayout menuEditor menuItem menuItemToShelf menuSet menuSetPref messageLine min minimizeApp mirrorJoint modelCurrentTimeCtx modelEditor modelPanel mouse movIn movOut move moveIKtoFK moveKeyCtx moveVertexAlongDirection multiProfileBirailSurface mute nParticle nameCommand nameField namespace namespaceInfo newPanelItems newton nodeCast nodeIconButton nodeOutliner nodePreset nodeType noise nonLinear normalConstraint normalize nurbsBoolean nurbsCopyUVSet nurbsCube nurbsEditUV \
-nurbsPlane nurbsSelect nurbsSquare nurbsToPoly nurbsToPolygonsPref nurbsToSubdiv nurbsToSubdivPref nurbsUVSet nurbsViewDirectionVector objExists objectCenter objectLayer objectType objectTypeUI obsoleteProc oceanNurbsPreviewPlane offsetCurve offsetCurveOnSurface offsetSurface openGLExtension openMayaPref optionMenu optionMenuGrp optionVar orbit orbitCtx orientConstraint outlinerEditor outlinerPanel overrideModifier paintEffectsDisplay pairBlend palettePort paneLayout panel panelConfiguration pan\
-elHistory paramDimContext paramDimension paramLocator parent parentConstraint particle particleExists particleInstancer particleRenderInfo partition pasteKey pathAnimation pause pclose percent performanceOptions pfxstrokes pickWalk picture pixelMove planarSrf plane play playbackOptions playblast plugAttr plugNode pluginInfo pluginResourceUtil pointConstraint pointCurveConstraint pointLight pointMatrixMult pointOnCurve pointOnSurface pointPosition poleVectorConstraint polyAppend polyAppendFacetCt\
-x polyAppendVertex polyAutoProjection polyAverageNormal polyAverageVertex polyBevel polyBlendColor polyBlindData polyBoolOp polyBridgeEdge polyCacheMonitor polyCheck polyChipOff polyClipboard polyCloseBorder polyCollapseEdge polyCollapseFacet polyColorBlindData polyColorDel polyColorPerVertex polyColorSet polyCompare polyCone polyCopyUV polyCrease polyCreaseCtx polyCreateFacet polyCreateFacetCtx polyCube polyCut polyCutCtx polyCylinder polyCylindricalProjection polyDelEdge polyDelFacet polyDelVe\
-rtex polyDuplicateAndConnect polyDuplicateEdge polyEditUV polyEditUVShell polyEvaluate polyExtrudeEdge polyExtrudeFacet polyExtrudeVertex polyFlipEdge polyFlipUV polyForceUV polyGeoSampler polyHelix polyInfo polyInstallAction polyLayoutUV polyListComponentConversion polyMapCut polyMapDel polyMapSew polyMapSewMove polyMergeEdge polyMergeEdgeCtx polyMergeFacet polyMergeFacetCtx polyMergeUV polyMergeVertex polyMirrorFace polyMoveEdge polyMoveFacet polyMoveFacetUV polyMoveUV polyMoveVertex polyNorma\
-l polyNormalPerVertex polyNormalizeUV polyOptUvs polyOptions polyOutput polyPipe polyPlanarProjection polyPlane polyPlatonicSolid polyPoke polyPrimitive polyPrism polyProjection polyPyramid polyQuad polyQueryBlindData polyReduce polySelect polySelectConstraint polySelectConstraintMonitor polySelectCtx polySelectEditCtx polySeparate polySetToFaceNormal polySewEdge polyShortestPathCtx polySmooth polySoftEdge polySphere polySphericalProjection polySplit polySplitCtx polySplitEdge polySplitRing poly\
-SplitVertex polyStraightenUVBorder polySubdivideEdge polySubdivideFacet polyToSubdiv polyTorus polyTransfer polyTriangulate polyUVSet polyUnite polyWedgeFace popen popupMenu pose pow preloadRefEd print progressBar progressWindow projFileViewer projectCurve projectTangent projectionContext projectionManip promptDialog propModCtx propMove psdChannelOutliner psdEditTextureFile psdExport psdTextureFile putenv pwd python querySubdiv quit rad_to_deg radial radioButton radioButtonGrp radioCollection ra\
-dioMenuItemCollection rampColorPort rand randomizeFollicles randstate rangeControl readTake rebuildCurve rebuildSurface recordAttr recordDevice redo reference referenceEdit referenceQuery refineSubdivSelectionList refresh refreshAE registerPluginResource rehash reloadImage removeJoint removeMultiInstance removePanelCategory rename renameAttr renameSelectionList renameUI render renderGlobalsNode renderInfo renderLayerButton renderLayerParent renderLayerPostProcess renderLayerUnparent renderManip \
-renderPartition renderQualityNode renderSettings renderThumbnailUpdate renderWindowEditor renderWindowSelectContext renderer reorder reorderDeformers requires reroot resampleFluid resetAE resetPfxToPolyCamera resetTool resolutionNode retarget reverseCurve reverseSurface revolve rgb_to_hsv rigidBody rigidSolver roll rollCtx rootOf rot rotate rotationInterpolation roundConstantRadius rowColumnLayout rowLayout runTimeCommand runup sampleImage saveAllShelves saveAttrPreset saveFluid saveImage saveIn\
-itialState saveMenu savePrefObjects savePrefs saveShelf saveToolSettings scale scaleBrushBrightness scaleComponents scaleConstraint scaleKey scaleKeyCtx sceneEditor sceneUIReplacement scmh scriptCtx scriptEditorInfo scriptJob scriptNode scriptTable scriptToShelf scriptedPanel scriptedPanelType scrollField scrollLayout sculpt searchPathArray seed selLoadSettings select selectContext selectCurveCV selectKey selectKeyCtx selectKeyframeRegionCtx selectMode selectPref selectPriority selectType select\
-edNodes selectionConnection separator setAttr setAttrEnumResource setAttrMapping setAttrNiceNameResource setConstraintRestPosition setDefaultShadingGroup setDrivenKeyframe setDynamic setEditCtx setEditor setFluidAttr setFocus setInfinity setInputDeviceMapping setKeyCtx setKeyPath setKeyframe setKeyframeBlendshapeTargetWts setMenuMode setNodeNiceNameResource setNodeTypeFlag setParent setParticleAttr setPfxToPolyCamera setPluginResource setProject setStampDensity setStartupMessage setState setTool\
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-cvtpd2ps cvtpi2pd cvtps2dq cvtps2pd cvtsd2si cvtsd2ss cvtsi2sd cvtss2sd cvttpd2pi cvttpd2dq cvttps2dq cvttsd2si divpd divsd maxpd maxsd minpd minsd movapd movhpd movlpd movmskpd movupd mulpd mulsd orpd shufpd sqrtpd sqrtsd subpd subsd ucomisd unpckhpd unpcklpd xorpd addsubpd addsubps haddpd haddps hsubpd hsubps lddqu movddup movshdup movsldup clgi stgi vmcall vmclear vmfunc vmlaunch vmload vmmcall vmptrld vmptrst vmread vmresume vmrun vmsave vmwrite vmxoff vmxon invept invvpid pabsb pabsw pabsd \
-palignr phaddw phaddd phaddsw phsubw phsubd phsubsw pmaddubsw pmulhrsw pshufb psignb psignw psignd extrq insertq movntsd movntss lzcnt blendpd blendps blendvpd blendvps dppd dpps extractps insertps movntdqa mpsadbw packusdw pblendvb pblendw pcmpeqq pextrb pextrd pextrq phminposuw pinsrb pinsrd pinsrq pmaxsb pmaxsd pmaxud pmaxuw pminsb pminsd pminud pminuw pmovsxbw pmovsxbd pmovsxbq pmovsxwd pmovsxwq pmovsxdq pmovzxbw pmovzxbd pmovzxbq pmovzxwd pmovzxwq pmovzxdq pmuldq pmulld ptest roundpd roundp\
-s roundsd roundss crc32 pcmpestri pcmpestrm pcmpistri pcmpistrm pcmpgtq popcnt getsec pfrcpv pfrsqrtv movbe aesenc aesenclast aesdec aesdeclast aesimc aeskeygenassist vaesenc vaesenclast vaesdec vaesdeclast vaesimc vaeskeygenassist vaddpd vaddps vaddsd vaddss vaddsubpd vaddsubps vandpd vandps vandnpd vandnps vblendpd vblendps vblendvpd vblendvps vbroadcastss vbroadcastsd vbroadcastf128 vcmpeq_ospd vcmpeqpd vcmplt_ospd vcmpltpd vcmple_ospd vcmplepd vcmpunord_qpd vcmpunordpd vcmpneq_uqpd vcmpneqpd\
- vcmpnlt_uspd vcmpnltpd vcmpnle_uspd vcmpnlepd vcmpord_qpd vcmpordpd vcmpeq_uqpd vcmpnge_uspd vcmpngepd vcmpngt_uspd vcmpngtpd vcmpfalse_oqpd vcmpfalsepd vcmpneq_oqpd vcmpge_ospd vcmpgepd vcmpgt_ospd vcmpgtpd vcmptrue_uqpd vcmptruepd vcmplt_oqpd vcmple_oqpd vcmpunord_spd vcmpneq_uspd vcmpnlt_uqpd vcmpnle_uqpd vcmpord_spd vcmpeq_uspd vcmpnge_uqpd vcmpngt_uqpd vcmpfalse_ospd vcmpneq_ospd vcmpge_oqpd vcmpgt_oqpd vcmptrue_uspd vcmppd vcmpeq_osps vcmpeqps vcmplt_osps vcmpltps vcmple_osps vcmpleps vcm\
-punord_qps vcmpunordps vcmpneq_uqps vcmpneqps vcmpnlt_usps vcmpnltps vcmpnle_usps vcmpnleps vcmpord_qps vcmpordps vcmpeq_uqps vcmpnge_usps vcmpngeps vcmpngt_usps vcmpngtps vcmpfalse_oqps vcmpfalseps vcmpneq_oqps vcmpge_osps vcmpgeps vcmpgt_osps vcmpgtps vcmptrue_uqps vcmptrueps vcmplt_oqps vcmple_oqps vcmpunord_sps vcmpneq_usps vcmpnlt_uqps vcmpnle_uqps vcmpord_sps vcmpeq_usps vcmpnge_uqps vcmpngt_uqps vcmpfalse_osps vcmpneq_osps vcmpge_oqps vcmpgt_oqps vcmptrue_usps vcmpps vcmpeq_ossd vcmpeqsd \
-vcmplt_ossd vcmpltsd vcmple_ossd vcmplesd vcmpunord_qsd vcmpunordsd vcmpneq_uqsd vcmpneqsd vcmpnlt_ussd vcmpnltsd vcmpnle_ussd vcmpnlesd vcmpord_qsd vcmpordsd vcmpeq_uqsd vcmpnge_ussd vcmpngesd vcmpngt_ussd vcmpngtsd vcmpfalse_oqsd vcmpfalsesd vcmpneq_oqsd vcmpge_ossd vcmpgesd vcmpgt_ossd vcmpgtsd vcmptrue_uqsd vcmptruesd vcmplt_oqsd vcmple_oqsd vcmpunord_ssd vcmpneq_ussd vcmpnlt_uqsd vcmpnle_uqsd vcmpord_ssd vcmpeq_ussd vcmpnge_uqsd vcmpngt_uqsd vcmpfalse_ossd vcmpneq_ossd vcmpge_oqsd vcmpgt_oq\
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-false_osss vcmpneq_osss vcmpge_oqss vcmpgt_oqss vcmptrue_usss vcmpss vcomisd vcomiss vcvtdq2pd vcvtdq2ps vcvtpd2dq vcvtpd2ps vcvtps2dq vcvtps2pd vcvtsd2si vcvtsd2ss vcvtsi2sd vcvtsi2ss vcvtss2sd vcvtss2si vcvttpd2dq vcvttps2dq vcvttsd2si vcvttss2si vdivpd vdivps vdivsd vdivss vdppd vdpps vextractf128 vextractps vhaddpd vhaddps vhsubpd vhsubps vinsertf128 vinsertps vlddqu vldqqu vldmxcsr vmaskmovdqu vmaskmovps vmaskmovpd vmaxpd vmaxps vmaxsd vmaxss vminpd vminps vminsd vminss vmovapd vmovaps vmov\
-d vmovq vmovddup vmovdqa vmovqqa vmovdqu vmovqqu vmovhlps vmovhpd vmovhps vmovlhps vmovlpd vmovlps vmovmskpd vmovmskps vmovntdq vmovntqq vmovntdqa vmovntpd vmovntps vmovsd vmovshdup vmovsldup vmovss vmovupd vmovups vmpsadbw vmulpd vmulps vmulsd vmulss vorpd vorps vpabsb vpabsw vpabsd vpacksswb vpackssdw vpackuswb vpackusdw vpaddb vpaddw vpaddd vpaddq vpaddsb vpaddsw vpaddusb vpaddusw vpalignr vpand vpandn vpavgb vpavgw vpblendvb vpblendw vpcmpestri vpcmpestrm vpcmpistri vpcmpistrm vpcmpeqb vpcmp\
-eqw vpcmpeqd vpcmpeqq vpcmpgtb vpcmpgtw vpcmpgtd vpcmpgtq vpermilpd vpermilps vperm2f128 vpextrb vpextrw vpextrd vpextrq vphaddw vphaddd vphaddsw vphminposuw vphsubw vphsubd vphsubsw vpinsrb vpinsrw vpinsrd vpinsrq vpmaddwd vpmaddubsw vpmaxsb vpmaxsw vpmaxsd vpmaxub vpmaxuw vpmaxud vpminsb vpminsw vpminsd vpminub vpminuw vpminud vpmovmskb vpmovsxbw vpmovsxbd vpmovsxbq vpmovsxwd vpmovsxwq vpmovsxdq vpmovzxbw vpmovzxbd vpmovzxbq vpmovzxwd vpmovzxwq vpmovzxdq vpmulhuw vpmulhrsw vpmulhw vpmullw vpmu\
-lld vpmuludq vpmuldq vpor vpsadbw vpshufb vpshufd vpshufhw vpshuflw vpsignb vpsignw vpsignd vpslldq vpsrldq vpsllw vpslld vpsllq vpsraw vpsrad vpsrlw vpsrld vpsrlq vptest vpsubb vpsubw vpsubd vpsubq vpsubsb vpsubsw vpsubusb vpsubusw vpunpckhbw vpunpckhwd vpunpckhdq vpunpckhqdq vpunpcklbw vpunpcklwd vpunpckldq vpunpcklqdq vpxor vrcpps vrcpss vrsqrtps vrsqrtss vroundpd vroundps vroundsd vroundss vshufpd vshufps vsqrtpd vsqrtps vsqrtsd vsqrtss vstmxcsr vsubpd vsubps vsubsd vsubss vtestps vtestpd vu\
-comisd vucomiss vunpckhpd vunpckhps vunpcklpd vunpcklps vxorpd vxorps vzeroall vzeroupper pclmullqlqdq pclmulhqlqdq pclmullqhqdq pclmulhqhqdq pclmulqdq vpclmullqlqdq vpclmulhqlqdq vpclmullqhqdq vpclmulhqhqdq vpclmulqdq vfmadd132ps vfmadd132pd vfmadd312ps vfmadd312pd vfmadd213ps vfmadd213pd vfmadd123ps vfmadd123pd vfmadd231ps vfmadd231pd vfmadd321ps vfmadd321pd vfmaddsub132ps vfmaddsub132pd vfmaddsub312ps vfmaddsub312pd vfmaddsub213ps vfmaddsub213pd vfmaddsub123ps vfmaddsub123pd vfmaddsub231ps vf\
-maddsub231pd vfmaddsub321ps vfmaddsub321pd vfmsub132ps vfmsub132pd vfmsub312ps vfmsub312pd vfmsub213ps vfmsub213pd vfmsub123ps vfmsub123pd vfmsub231ps vfmsub231pd vfmsub321ps vfmsub321pd vfmsubadd132ps vfmsubadd132pd vfmsubadd312ps vfmsubadd312pd vfmsubadd213ps vfmsubadd213pd vfmsubadd123ps vfmsubadd123pd vfmsubadd231ps vfmsubadd231pd vfmsubadd321ps vfmsubadd321pd vfnmadd132ps vfnmadd132pd vfnmadd312ps vfnmadd312pd vfnmadd213ps vfnmadd213pd vfnmadd123ps vfnmadd123pd vfnmadd231ps vfnmadd231pd vfn\
-madd321ps vfnmadd321pd vfnmsub132ps vfnmsub132pd vfnmsub312ps vfnmsub312pd vfnmsub213ps vfnmsub213pd vfnmsub123ps vfnmsub123pd vfnmsub231ps vfnmsub231pd vfnmsub321ps vfnmsub321pd vfmadd132ss vfmadd132sd vfmadd312ss vfmadd312sd vfmadd213ss vfmadd213sd vfmadd123ss vfmadd123sd vfmadd231ss vfmadd231sd vfmadd321ss vfmadd321sd vfmsub132ss vfmsub132sd vfmsub312ss vfmsub312sd vfmsub213ss vfmsub213sd vfmsub123ss vfmsub123sd vfmsub231ss vfmsub231sd vfmsub321ss vfmsub321sd vfnmadd132ss vfnmadd132sd vfnmadd\
-312ss vfnmadd312sd vfnmadd213ss vfnmadd213sd vfnmadd123ss vfnmadd123sd vfnmadd231ss vfnmadd231sd vfnmadd321ss vfnmadd321sd vfnmsub132ss vfnmsub132sd vfnmsub312ss vfnmsub312sd vfnmsub213ss vfnmsub213sd vfnmsub123ss vfnmsub123sd vfnmsub231ss vfnmsub231sd vfnmsub321ss vfnmsub321sd rdfsbase rdgsbase rdrand wrfsbase wrgsbase vcvtph2ps vcvtps2ph adcx adox rdseed clac stac xstore xcryptecb xcryptcbc xcryptctr xcryptcfb xcryptofb montmul xsha1 xsha256 llwpcb slwpcb lwpval lwpins vfmaddpd vfmaddps vfmadd\
-sd vfmaddss vfmaddsubpd vfmaddsubps vfmsubaddpd vfmsubaddps vfmsubpd vfmsubps vfmsubsd vfmsubss vfnmaddpd vfnmaddps vfnmaddsd vfnmaddss vfnmsubpd vfnmsubps vfnmsubsd vfnmsubss vfrczpd vfrczps vfrczsd vfrczss vpcmov vpcomb vpcomd vpcomq vpcomub vpcomud vpcomuq vpcomuw vpcomw vphaddbd vphaddbq vphaddbw vphadddq vphaddubd vphaddubq vphaddubw vphaddudq vphadduwd vphadduwq vphaddwd vphaddwq vphsubbw vphsubdq vphsubwd vpmacsdd vpmacsdqh vpmacsdql vpmacssdd vpmacssdqh vpmacssdql vpmacsswd vpmacssww vpm\
-acswd vpmacsww vpmadcsswd vpmadcswd vpperm vprotb vprotd vprotq vprotw vpshab vpshad vpshaq vpshaw vpshlb vpshld vpshlq vpshlw vbroadcasti128 vpblendd vpbroadcastb vpbroadcastw vpbroadcastd vpbroadcastq vpermd vpermpd vpermps vpermq vperm2i128 vextracti128 vinserti128 vpmaskmovd vpmaskmovq vpsllvd vpsllvq vpsravd vpsrlvd vpsrlvq vgatherdpd vgatherqpd vgatherdps vgatherqps vpgatherdd vpgatherqd vpgatherdq vpgatherqq xabort xbegin xend xtest andn bextr blci blcic blsi blsic blcfill blsfill blcmsk \
-blsmsk blsr blcs bzhi mulx pdep pext rorx sarx shlx shrx tzcnt tzmsk t1mskc valignd valignq vblendmpd vblendmps vbroadcastf32x4 vbroadcastf64x4 vbroadcasti32x4 vbroadcasti64x4 vcompresspd vcompressps vcvtpd2udq vcvtps2udq vcvtsd2usi vcvtss2usi vcvttpd2udq vcvttps2udq vcvttsd2usi vcvttss2usi vcvtudq2pd vcvtudq2ps vcvtusi2sd vcvtusi2ss vexpandpd vexpandps vextractf32x4 vextractf64x4 vextracti32x4 vextracti64x4 vfixupimmpd vfixupimmps vfixupimmsd vfixupimmss vgetexppd vgetexpps vgetexpsd vgetexpss \
-vgetmantpd vgetmantps vgetmantsd vgetmantss vinsertf32x4 vinsertf64x4 vinserti32x4 vinserti64x4 vmovdqa32 vmovdqa64 vmovdqu32 vmovdqu64 vpabsq vpandd vpandnd vpandnq vpandq vpblendmd vpblendmq vpcmpltd vpcmpled vpcmpneqd vpcmpnltd vpcmpnled vpcmpd vpcmpltq vpcmpleq vpcmpneqq vpcmpnltq vpcmpnleq vpcmpq vpcmpequd vpcmpltud vpcmpleud vpcmpnequd vpcmpnltud vpcmpnleud vpcmpud vpcmpequq vpcmpltuq vpcmpleuq vpcmpnequq vpcmpnltuq vpcmpnleuq vpcmpuq vpcompressd vpcompressq vpermi2d vpermi2pd vpermi2ps vp\
-ermi2q vpermt2d vpermt2pd vpermt2ps vpermt2q vpexpandd vpexpandq vpmaxsq vpmaxuq vpminsq vpminuq vpmovdb vpmovdw vpmovqb vpmovqd vpmovqw vpmovsdb vpmovsdw vpmovsqb vpmovsqd vpmovsqw vpmovusdb vpmovusdw vpmovusqb vpmovusqd vpmovusqw vpord vporq vprold vprolq vprolvd vprolvq vprord vprorq vprorvd vprorvq vpscatterdd vpscatterdq vpscatterqd vpscatterqq vpsraq vpsravq vpternlogd vpternlogq vptestmd vptestmq vptestnmd vptestnmq vpxord vpxorq vrcp14pd vrcp14ps vrcp14sd vrcp14ss vrndscalepd vrndscaleps\
- vrndscalesd vrndscaless vrsqrt14pd vrsqrt14ps vrsqrt14sd vrsqrt14ss vscalefpd vscalefps vscalefsd vscalefss vscatterdpd vscatterdps vscatterqpd vscatterqps vshuff32x4 vshuff64x2 vshufi32x4 vshufi64x2 kandnw kandw kmovw knotw kortestw korw kshiftlw kshiftrw kunpckbw kxnorw kxorw vpbroadcastmb2q vpbroadcastmw2d vpconflictd vpconflictq vplzcntd vplzcntq vexp2pd vexp2ps vrcp28pd vrcp28ps vrcp28sd vrcp28ss vrsqrt28pd vrsqrt28ps vrsqrt28sd vrsqrt28ss vgatherpf0dpd vgatherpf0dps vgatherpf0qpd vgatherp\
-f0qps vgatherpf1dpd vgatherpf1dps vgatherpf1qpd vgatherpf1qps vscatterpf0dpd vscatterpf0dps vscatterpf0qpd vscatterpf0qps vscatterpf1dpd vscatterpf1dps vscatterpf1qpd vscatterpf1qps prefetchwt1 bndmk bndcl bndcu bndcn bndmov bndldx bndstx sha1rnds4 sha1nexte sha1msg1 sha1msg2 sha256rnds2 sha256msg1 sha256msg2 hint_nop0 hint_nop1 hint_nop2 hint_nop3 hint_nop4 hint_nop5 hint_nop6 hint_nop7 hint_nop8 hint_nop9 hint_nop10 hint_nop11 hint_nop12 hint_nop13 hint_nop14 hint_nop15 hint_nop16 hint_nop17 h\
-int_nop18 hint_nop19 hint_nop20 hint_nop21 hint_nop22 hint_nop23 hint_nop24 hint_nop25 hint_nop26 hint_nop27 hint_nop28 hint_nop29 hint_nop30 hint_nop31 hint_nop32 hint_nop33 hint_nop34 hint_nop35 hint_nop36 hint_nop37 hint_nop38 hint_nop39 hint_nop40 hint_nop41 hint_nop42 hint_nop43 hint_nop44 hint_nop45 hint_nop46 hint_nop47 hint_nop48 hint_nop49 hint_nop50 hint_nop51 hint_nop52 hint_nop53 hint_nop54 hint_nop55 hint_nop56 hint_nop57 hint_nop58 hint_nop59 hint_nop60 hint_nop61 hint_nop62 hint_n\
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-8 xmm19 xmm20 xmm21 xmm22 xmm23 xmm24 xmm25 xmm26 xmm27 xmm28 xmm29 xmm30 xmm31 ymm0  ymm1  ymm2  ymm3  ymm4  ymm5  ymm6  ymm7  ymm8  ymm9 ymm10  ymm11 ymm12 ymm13 ymm14 ymm15 ymm16 ymm17 ymm18 ymm19 ymm20 ymm21 ymm22 ymm23 ymm24 ymm25 ymm26 ymm27 ymm28 ymm29 ymm30 ymm31 zmm0  zmm1  zmm2  zmm3  zmm4  zmm5  zmm6  zmm7  zmm8  zmm9 zmm10  zmm11 zmm12 zmm13 zmm14 zmm15 zmm16 zmm17 zmm18 zmm19 zmm20 zmm21 zmm22 zmm23 zmm24 zmm25 zmm26 zmm27 zmm28 zmm29 zmm30 zmm31 k0 k1 k2 k3 k4 k5 k6 k7 bnd0 bnd1 bn\
-d2 bnd3 cr0 cr1 cr2 cr3 cr4 cr8 dr0 dr1 dr2 dr3 dr8 tr3 tr4 tr5 tr6 tr7 r0 r1 r2 r3 r4 r5 r6 r7 r0b r1b r2b r3b r4b r5b r6b r7b r0w r1w r2w r3w r4w r5w r6w r7w r0d r1d r2d r3d r4d r5d r6d r7d r0h r1h r2h r3h r0l r1l r2l r3l r4l r5l r6l r7l r8l r9l r10l r11l r12l r13l r14l r15l db dw dd dq dt ddq do dy dz resb resw resd resq rest resdq reso resy resz incbin equ times byte word dword qword nosplit rel abs seg wrt strict near far a32 ptr",meta:"%define %xdefine %+ %undef %defstr %deftok %assign %st\
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-data};reset=()=>{this.toasts=[],this.heights=[]}}function constructPromiseErrorMessage(response){return response&&typeof response=="object"&&"status"in response?`HTTP error! Status: ${response.status}`:`Error! ${response}`}const toastState=new ToastState;function toastFunction(message,data){return toastState.create({message,...data})}class SonnerState{#activeToasts=user_derived(()=>toastState.toasts.filter(toast2=>!toast2.dismiss));get toasts(){return get$4(this.#activeToasts)}}const basicToast=toastFunction,
-toast=Object.assign(basicToast,{success:toastState.success,info:toastState.info,warning:toastState.warning,error:toastState.error,custom:toastState.custom,message:toastState.message,promise:toastState.promise,dismiss:toastState.dismiss,loading:toastState.loading,getActiveToasts:()=>toastState.toasts.filter(toast2=>!toast2.dismiss)});function isAction(action2){return action2.label!==void 0}function useDocumentHidden(){let current2=state$1(proxy(typeof document<"u"?document.hidden:!1));return user_effect(
-()=>on(document,"visibilitychange",()=>{set$1(current2,document.hidden,!0)})),{get current(){return get$4(current2)}}}const TOAST_LIFETIME$1=4e3,GAP$1=14,SWIPE_THRESHOLD=45,TIME_BEFORE_UNMOUNT=200,SCALE_MULTIPLIER=.05,DEFAULT_TOAST_CLASSES={toast:"",title:"",description:"",loader:"",closeButton:"",cancelButton:"",actionButton:"",action:"",warning:"",error:"",success:"",default:"",info:"",loading:""};function getDefaultSwipeDirections(position2){const[y,x]=position2.split("-"),directions=[];return y&&
-directions.push(y),x&&directions.push(x),directions}function getDampening(delta){return 1/(1.5+Math.abs(delta)/20)}var root_2$17=from_html("<div><!></div>"),root_4$y=from_html('<button data-close-button=""><!></button>'),root_7$s=from_html('<div data-icon=""><!> <!></div>'),root_24$4=from_html('<div data-description=""><!></div>'),root_30$3=from_html('<button data-button="" data-cancel=""> </button>'),root_34$1=from_html('<button data-button=""> </button>'),root_6$A=from_html('<!> <div data-cont\
-ent=""><div data-title=""><!></div> <!></div> <!> <!>',1),root$1D=from_html('<li data-sonner-toast=""><!> <!></li>');function Toast($$anchor,$$props){push$1($$props,!0);const LoadingIcon=$$anchor2=>{var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor3=>{var div=root_2$17(),node_1=child(div);snippet(node_1,()=>$$props.loadingIcon),reset(div),template_effect($0=>{set_class(div,1,$0),set_attribute(div,"data-visible",get$4(toastType)==="loading")},[()=>clsx(cn(get$4(classes)?.
-loader,$$props.toast?.classes?.loader,"sonner-loader"))]),append($$anchor3,div)},alternate=$$anchor3=>{{let $0=user_derived(()=>cn(get$4(classes)?.loader,$$props.toast.classes?.loader)),$1=user_derived(()=>get$4(toastType)==="loading");Loader($$anchor3,{get class(){return get$4($0)},get visible(){return get$4($1)}})}};if_block(node2,$$render=>{$$props.loadingIcon?$$render(consequent):$$render(alternate,!1)})}append($$anchor2,fragment)};let cancelButtonStyle=prop($$props,"cancelButtonStyle",3,""),
-actionButtonStyle=prop($$props,"actionButtonStyle",3,""),descriptionClass=prop($$props,"descriptionClass",3,""),unstyled=prop($$props,"unstyled",3,!1),defaultRichColors=prop($$props,"defaultRichColors",3,!1);const defaultClasses={...DEFAULT_TOAST_CLASSES};let mounted=state$1(!1),removed=state$1(!1),swiping=state$1(!1),swipeOut=state$1(!1),isSwiped=state$1(!1),offsetBeforeRemove=state$1(0),initialHeight=state$1(0),remainingTime=$$props.toast.duration||$$props.duration||TOAST_LIFETIME$1,toastRef=state$1(
-void 0),swipeDirection=state$1(null),swipeOutDirection=state$1(null);const isFront=user_derived(()=>$$props.index===0),isVisible=user_derived(()=>$$props.index+1<=$$props.visibleToasts),toastType=user_derived(()=>$$props.toast.type),dismissable=user_derived(()=>$$props.toast.dismissable!==!1),toastClass=user_derived(()=>$$props.toast.class||""),toastDescriptionClass=user_derived(()=>$$props.toast.descriptionClass||""),heightIndex=user_derived(()=>toastState.heights.findIndex(height=>height.toastId===
-$$props.toast.id)||0),closeButton=user_derived(()=>$$props.toast.closeButton??$$props.closeButton),duration2=user_derived(()=>$$props.toast.duration??$$props.duration??TOAST_LIFETIME$1);let pointerStart=null;const coords=user_derived(()=>$$props.position.split("-")),toastsHeightBefore=user_derived(()=>toastState.heights.reduce((prev2,curr,reducerIndex)=>reducerIndex>=get$4(heightIndex)?prev2:prev2+curr.height,0)),isDocumentHidden=useDocumentHidden(),invert=user_derived(()=>$$props.toast.invert||
-$$props.invert),disabled=user_derived(()=>get$4(toastType)==="loading"),classes=user_derived(()=>({...defaultClasses,...$$props.classes})),toastTitle=user_derived(()=>$$props.toast.title),toastDescription=user_derived(()=>$$props.toast.description);let closeTimerStartTime=state$1(0),lastCloseTimerStartTime=state$1(0);const offset2=user_derived(()=>Math.round(get$4(heightIndex)*GAP$1+get$4(toastsHeightBefore)));user_effect(()=>{get$4(toastTitle),get$4(toastDescription);let scale2;$$props.expanded||
-$$props.expandByDefault?scale2=1:scale2=1-$$props.index*SCALE_MULTIPLIER;const toastEl=untrack$1(()=>get$4(toastRef));if(toastEl===void 0)return;toastEl.style.setProperty("height","auto");const offsetHeight=toastEl.offsetHeight,rectHeight=toastEl.getBoundingClientRect().height,scaledRectHeight=Math.round(rectHeight/scale2+Number.EPSILON&100)/100;toastEl.style.removeProperty("height");let finalHeight;Math.abs(scaledRectHeight-offsetHeight)<1?finalHeight=scaledRectHeight:finalHeight=offsetHeight,set$1(
-initialHeight,finalHeight,!0),untrack$1(()=>{toastState.setHeight({toastId:$$props.toast.id,height:finalHeight})})});function deleteToast(){set$1(removed,!0),set$1(offsetBeforeRemove,get$4(offset2),!0),toastState.removeHeight($$props.toast.id),setTimeout(()=>{toastState.remove($$props.toast.id)},TIME_BEFORE_UNMOUNT)}let timeoutId;const isPromiseLoadingOrInfiniteDuration=user_derived(()=>$$props.toast.promise&&get$4(toastType)==="loading"||$$props.toast.duration===Number.POSITIVE_INFINITY);function startTimer(){
-set$1(closeTimerStartTime,new Date().getTime(),!0),timeoutId=setTimeout(()=>{$$props.toast.onAutoClose?.($$props.toast),deleteToast()},remainingTime)}function pauseTimer(){if(get$4(lastCloseTimerStartTime)<get$4(closeTimerStartTime)){const elapsedTime=new Date().getTime()-get$4(closeTimerStartTime);remainingTime=remainingTime-elapsedTime}set$1(lastCloseTimerStartTime,new Date().getTime(),!0)}user_effect(()=>{$$props.toast.updated&&(clearTimeout(timeoutId),remainingTime=get$4(duration2),startTimer())}),
-user_effect(()=>(get$4(isPromiseLoadingOrInfiniteDuration)||($$props.expanded||$$props.interacting||isDocumentHidden.current?pauseTimer():startTimer()),()=>clearTimeout(timeoutId))),onMount$1(()=>{set$1(mounted,!0);const height=get$4(toastRef)?.getBoundingClientRect().height;return set$1(initialHeight,height,!0),toastState.setHeight({toastId:$$props.toast.id,height}),()=>{toastState.removeHeight($$props.toast.id)}}),user_effect(()=>{$$props.toast.delete&&untrack$1(()=>{deleteToast(),$$props.toast.
-onDismiss?.($$props.toast)})});const handlePointerDown=event2=>{if(get$4(disabled))return;set$1(offsetBeforeRemove,get$4(offset2),!0);const target2=event2.target;target2.setPointerCapture(event2.pointerId),target2.tagName!=="BUTTON"&&(set$1(swiping,!0),pointerStart={x:event2.clientX,y:event2.clientY})},handlePointerUp=()=>{if(get$4(swipeOut)||!get$4(dismissable))return;pointerStart=null;const swipeAmountX=Number(get$4(toastRef)?.style.getPropertyValue("--swipe-amount-x").replace("px","")||0),swipeAmountY=Number(
-get$4(toastRef)?.style.getPropertyValue("--swipe-amount-y").replace("px","")||0),timeTaken=new Date().getTime()-0,swipeAmount=get$4(swipeDirection)==="x"?swipeAmountX:swipeAmountY,velocity=Math.abs(swipeAmount)/timeTaken;if(Math.abs(swipeAmount)>=SWIPE_THRESHOLD||velocity>.11){set$1(offsetBeforeRemove,get$4(offset2),!0),$$props.toast.onDismiss?.($$props.toast),get$4(swipeDirection)==="x"?set$1(swipeOutDirection,swipeAmountX>0?"right":"left",!0):set$1(swipeOutDirection,swipeAmountY>0?"down":"up",
-!0),deleteToast(),set$1(swipeOut,!0);return}else get$4(toastRef)?.style.setProperty("--swipe-amount-x","0px"),get$4(toastRef)?.style.setProperty("--swipe-amount-y","0px");set$1(isSwiped,!1),set$1(swiping,!1),set$1(swipeDirection,null)},handlePointerMove=event2=>{if(!pointerStart||!get$4(dismissable)||(window.getSelection()?.toString().length??-1)>0)return;const yDelta=event2.clientY-pointerStart.y,xDelta=event2.clientX-pointerStart.x,swipeDirections=$$props.swipeDirections??getDefaultSwipeDirections(
-$$props.position);!get$4(swipeDirection)&&(Math.abs(xDelta)>1||Math.abs(yDelta)>1)&&set$1(swipeDirection,Math.abs(xDelta)>Math.abs(yDelta)?"x":"y",!0);let swipeAmount={x:0,y:0};if(get$4(swipeDirection)==="y"){if(swipeDirections.includes("top")||swipeDirections.includes("bottom"))if(swipeDirections.includes("top")&&yDelta<0||swipeDirections.includes("bottom")&&yDelta>0)swipeAmount.y=yDelta;else{const dampenedDelta=yDelta*getDampening(yDelta);swipeAmount.y=Math.abs(dampenedDelta)<Math.abs(yDelta)?
-dampenedDelta:yDelta}}else if(get$4(swipeDirection)==="x"&&(swipeDirections.includes("left")||swipeDirections.includes("right")))if(swipeDirections.includes("left")&&xDelta<0||swipeDirections.includes("right")&&xDelta>0)swipeAmount.x=xDelta;else{const dampenedDelta=xDelta*getDampening(xDelta);swipeAmount.x=Math.abs(dampenedDelta)<Math.abs(xDelta)?dampenedDelta:xDelta}(Math.abs(swipeAmount.x)>0||Math.abs(swipeAmount.y)>0)&&set$1(isSwiped,!0),get$4(toastRef)?.style.setProperty("--swipe-amount-x",`${swipeAmount.
-x}px`),get$4(toastRef)?.style.setProperty("--swipe-amount-y",`${swipeAmount.y}px`)},handleDragEnd=()=>{set$1(swiping,!1),set$1(swipeDirection,null),pointerStart=null},icon=user_derived(()=>$$props.toast.icon?$$props.toast.icon:get$4(toastType)==="success"?$$props.successIcon:get$4(toastType)==="error"?$$props.errorIcon:get$4(toastType)==="warning"?$$props.warningIcon:get$4(toastType)==="info"?$$props.infoIcon:get$4(toastType)==="loading"?$$props.loadingIcon:null);var li2=root$1D();set_attribute(
-li2,"tabindex",0);let styles2;li2.__pointermove=handlePointerMove,li2.__pointerup=handlePointerUp,li2.__pointerdown=handlePointerDown;var node_2=child(li2);{var consequent_1=$$anchor2=>{var button=root_4$y();button.__click=()=>{get$4(disabled)||!get$4(dismissable)||(deleteToast(),$$props.toast.onDismiss?.($$props.toast))};var node_3=child(button);snippet(node_3,()=>$$props.closeIcon??noop$3),reset(button),template_effect($0=>{set_attribute(button,"aria-label",$$props.closeButtonAriaLabel),set_attribute(
-button,"data-disabled",get$4(disabled)),set_class(button,1,$0)},[()=>clsx(cn(get$4(classes)?.closeButton,$$props.toast?.classes?.closeButton))]),append($$anchor2,button)};if_block(node_2,$$render=>{get$4(closeButton)&&!$$props.toast.component&&get$4(toastType)!=="loading"&&$$props.closeIcon!==null&&$$render(consequent_1)})}var node_4=sibling(node_2,2);{var consequent_2=$$anchor2=>{const Component=user_derived(()=>$$props.toast.component);var fragment_2=comment$2(),node_5=first_child(fragment_2);
-component(node_5,()=>get$4(Component),($$anchor3,Component_1)=>{Component_1($$anchor3,spread_props(()=>$$props.toast.componentProps,{closeToast:deleteToast}))}),append($$anchor2,fragment_2)},alternate_10=$$anchor2=>{var fragment_3=root_6$A(),node_6=first_child(fragment_3);{var consequent_11=$$anchor3=>{var div_1=root_7$s(),node_7=child(div_1);{var consequent_4=$$anchor4=>{var fragment_4=comment$2(),node_8=first_child(fragment_4);{var consequent_3=$$anchor5=>{var fragment_5=comment$2(),node_9=first_child(
-fragment_5);component(node_9,()=>$$props.toast.icon,($$anchor6,toast_icon)=>{toast_icon($$anchor6,{})}),append($$anchor5,fragment_5)},alternate_1=$$anchor5=>{LoadingIcon($$anchor5)};if_block(node_8,$$render=>{$$props.toast.icon?$$render(consequent_3):$$render(alternate_1,!1)})}append($$anchor4,fragment_4)};if_block(node_7,$$render=>{($$props.toast.promise||get$4(toastType)==="loading")&&$$render(consequent_4)})}var node_10=sibling(node_7,2);{var consequent_10=$$anchor4=>{var fragment_7=comment$2(),
-node_11=first_child(fragment_7);{var consequent_5=$$anchor5=>{var fragment_8=comment$2(),node_12=first_child(fragment_8);component(node_12,()=>$$props.toast.icon,($$anchor6,toast_icon_1)=>{toast_icon_1($$anchor6,{})}),append($$anchor5,fragment_8)},alternate_5=$$anchor5=>{var fragment_9=comment$2(),node_13=first_child(fragment_9);{var consequent_6=$$anchor6=>{var fragment_10=comment$2(),node_14=first_child(fragment_10);snippet(node_14,()=>$$props.successIcon??noop$3),append($$anchor6,fragment_10)},
-alternate_4=$$anchor6=>{var fragment_11=comment$2(),node_15=first_child(fragment_11);{var consequent_7=$$anchor7=>{var fragment_12=comment$2(),node_16=first_child(fragment_12);snippet(node_16,()=>$$props.errorIcon??noop$3),append($$anchor7,fragment_12)},alternate_3=$$anchor7=>{var fragment_13=comment$2(),node_17=first_child(fragment_13);{var consequent_8=$$anchor8=>{var fragment_14=comment$2(),node_18=first_child(fragment_14);snippet(node_18,()=>$$props.warningIcon??noop$3),append($$anchor8,fragment_14)},
-alternate_2=$$anchor8=>{var fragment_15=comment$2(),node_19=first_child(fragment_15);{var consequent_9=$$anchor9=>{var fragment_16=comment$2(),node_20=first_child(fragment_16);snippet(node_20,()=>$$props.infoIcon??noop$3),append($$anchor9,fragment_16)};if_block(node_19,$$render=>{get$4(toastType)==="info"&&$$render(consequent_9)},!0)}append($$anchor8,fragment_15)};if_block(node_17,$$render=>{get$4(toastType)==="warning"?$$render(consequent_8):$$render(alternate_2,!1)},!0)}append($$anchor7,fragment_13)};
-if_block(node_15,$$render=>{get$4(toastType)==="error"?$$render(consequent_7):$$render(alternate_3,!1)},!0)}append($$anchor6,fragment_11)};if_block(node_13,$$render=>{get$4(toastType)==="success"?$$render(consequent_6):$$render(alternate_4,!1)},!0)}append($$anchor5,fragment_9)};if_block(node_11,$$render=>{$$props.toast.icon?$$render(consequent_5):$$render(alternate_5,!1)})}append($$anchor4,fragment_7)};if_block(node_10,$$render=>{$$props.toast.type!=="loading"&&$$render(consequent_10)})}reset(div_1),
-template_effect($0=>set_class(div_1,1,$0),[()=>clsx(cn(get$4(classes)?.icon,$$props.toast?.classes?.icon))]),append($$anchor3,div_1)};if_block(node_6,$$render=>{(get$4(toastType)||$$props.toast.icon||$$props.toast.promise)&&$$props.toast.icon!==null&&(get$4(icon)!==null||$$props.toast.icon)&&$$render(consequent_11)})}var div_2=sibling(node_6,2),div_3=child(div_2),node_21=child(div_3);{var consequent_13=$$anchor3=>{var fragment_17=comment$2(),node_22=first_child(fragment_17);{var consequent_12=$$anchor4=>{
-const Title=user_derived(()=>$$props.toast.title);var fragment_18=comment$2(),node_23=first_child(fragment_18);component(node_23,()=>get$4(Title),($$anchor5,Title_1)=>{Title_1($$anchor5,spread_props(()=>$$props.toast.componentProps))}),append($$anchor4,fragment_18)},alternate_6=$$anchor4=>{var text2=text$8();template_effect(()=>set_text(text2,$$props.toast.title)),append($$anchor4,text2)};if_block(node_22,$$render=>{typeof $$props.toast.title!="string"?$$render(consequent_12):$$render(alternate_6,
-!1)})}append($$anchor3,fragment_17)};if_block(node_21,$$render=>{$$props.toast.title&&$$render(consequent_13)})}reset(div_3);var node_24=sibling(div_3,2);{var consequent_15=$$anchor3=>{var div_4=root_24$4(),node_25=child(div_4);{var consequent_14=$$anchor4=>{const Description=user_derived(()=>$$props.toast.description);var fragment_20=comment$2(),node_26=first_child(fragment_20);component(node_26,()=>get$4(Description),($$anchor5,Description_1)=>{Description_1($$anchor5,spread_props(()=>$$props.
-toast.componentProps))}),append($$anchor4,fragment_20)},alternate_7=$$anchor4=>{var text_1=text$8();template_effect(()=>set_text(text_1,$$props.toast.description)),append($$anchor4,text_1)};if_block(node_25,$$render=>{typeof $$props.toast.description!="string"?$$render(consequent_14):$$render(alternate_7,!1)})}reset(div_4),template_effect($0=>set_class(div_4,1,$0),[()=>clsx(cn(descriptionClass(),get$4(toastDescriptionClass),get$4(classes)?.description,$$props.toast.classes?.description))]),append(
-$$anchor3,div_4)};if_block(node_24,$$render=>{$$props.toast.description&&$$render(consequent_15)})}reset(div_2);var node_27=sibling(div_2,2);{var consequent_18=$$anchor3=>{var fragment_22=comment$2(),node_28=first_child(fragment_22);{var consequent_16=$$anchor4=>{var fragment_23=comment$2(),node_29=first_child(fragment_23);component(node_29,()=>$$props.toast.cancel,($$anchor5,toast_cancel)=>{toast_cancel($$anchor5,{})}),append($$anchor4,fragment_23)},alternate_8=$$anchor4=>{var fragment_24=comment$2(),
-node_30=first_child(fragment_24);{var consequent_17=$$anchor5=>{var button_1=root_30$3();button_1.__click=event2=>{isAction($$props.toast.cancel)&&get$4(dismissable)&&($$props.toast.cancel?.onClick?.(event2),deleteToast())};var text_2=child(button_1,!0);reset(button_1),template_effect($0=>{set_style(button_1,$$props.toast.cancelButtonStyle??cancelButtonStyle()),set_class(button_1,1,$0),set_text(text_2,$$props.toast.cancel.label)},[()=>clsx(cn(get$4(classes)?.cancelButton,$$props.toast?.classes?.
-cancelButton))]),append($$anchor5,button_1)};if_block(node_30,$$render=>{isAction($$props.toast.cancel)&&$$render(consequent_17)},!0)}append($$anchor4,fragment_24)};if_block(node_28,$$render=>{typeof $$props.toast.cancel=="function"?$$render(consequent_16):$$render(alternate_8,!1)})}append($$anchor3,fragment_22)};if_block(node_27,$$render=>{$$props.toast.cancel&&$$render(consequent_18)})}var node_31=sibling(node_27,2);{var consequent_21=$$anchor3=>{var fragment_25=comment$2(),node_32=first_child(
-fragment_25);{var consequent_19=$$anchor4=>{var fragment_26=comment$2(),node_33=first_child(fragment_26);component(node_33,()=>$$props.toast.action,($$anchor5,toast_action)=>{toast_action($$anchor5,{})}),append($$anchor4,fragment_26)},alternate_9=$$anchor4=>{var fragment_27=comment$2(),node_34=first_child(fragment_27);{var consequent_20=$$anchor5=>{var button_2=root_34$1();button_2.__click=event2=>{isAction($$props.toast.action)&&($$props.toast.action?.onClick(event2),!event2.defaultPrevented&&deleteToast())};
-var text_3=child(button_2,!0);reset(button_2),template_effect($0=>{set_style(button_2,$$props.toast.actionButtonStyle??actionButtonStyle()),set_class(button_2,1,$0),set_text(text_3,$$props.toast.action.label)},[()=>clsx(cn(get$4(classes)?.actionButton,$$props.toast?.classes?.actionButton))]),append($$anchor5,button_2)};if_block(node_34,$$render=>{isAction($$props.toast.action)&&$$render(consequent_20)},!0)}append($$anchor4,fragment_27)};if_block(node_32,$$render=>{typeof $$props.toast.action=="f\
-unction"?$$render(consequent_19):$$render(alternate_9,!1)})}append($$anchor3,fragment_25)};if_block(node_31,$$render=>{$$props.toast.action&&$$render(consequent_21)})}template_effect($0=>set_class(div_3,1,$0),[()=>clsx(cn(get$4(classes)?.title,$$props.toast?.classes?.title))]),append($$anchor2,fragment_3)};if_block(node_4,$$render=>{$$props.toast.component?$$render(consequent_2):$$render(alternate_10,!1)})}reset(li2),bind_this(li2,$$value=>set$1(toastRef,$$value),()=>get$4(toastRef)),template_effect(
-($0,$1,$2)=>{set_class(li2,1,$0),set_attribute(li2,"data-rich-colors",$$props.toast.richColors??defaultRichColors()),set_attribute(li2,"data-styled",!($$props.toast.component||$$props.toast.unstyled||unstyled())),set_attribute(li2,"data-mounted",get$4(mounted)),set_attribute(li2,"data-promise",$1),set_attribute(li2,"data-swiped",get$4(isSwiped)),set_attribute(li2,"data-removed",get$4(removed)),set_attribute(li2,"data-visible",get$4(isVisible)),set_attribute(li2,"data-y-position",get$4(coords)[0]),
-set_attribute(li2,"data-x-position",get$4(coords)[1]),set_attribute(li2,"data-index",$$props.index),set_attribute(li2,"data-front",get$4(isFront)),set_attribute(li2,"data-swiping",get$4(swiping)),set_attribute(li2,"data-dismissable",get$4(dismissable)),set_attribute(li2,"data-type",get$4(toastType)),set_attribute(li2,"data-invert",get$4(invert)),set_attribute(li2,"data-swipe-out",get$4(swipeOut)),set_attribute(li2,"data-swipe-direction",get$4(swipeOutDirection)),set_attribute(li2,"data-expanded",
-$2),styles2=set_style(li2,`${$$props.style} ${$$props.toast.style}`,styles2,{"--index":$$props.index,"--toasts-before":$$props.index,"--z-index":toastState.toasts.length-$$props.index,"--offset":`${get$4(removed)?get$4(offsetBeforeRemove):get$4(offset2)}px`,"--initial-height":$$props.expandByDefault?"auto":`${get$4(initialHeight)}px`})},[()=>clsx(cn($$props.class,get$4(toastClass),get$4(classes)?.toast,$$props.toast?.classes?.toast,get$4(classes)?.[get$4(toastType)],$$props.toast?.classes?.[get$4(
-toastType)])),()=>!!$$props.toast.promise,()=>!!($$props.expanded||$$props.expandByDefault&&get$4(mounted))]),event("dragend",li2,handleDragEnd),append($$anchor,li2),pop()}delegate(["pointermove","pointerup","pointerdown","click"]);var root$1C=from_svg('<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" height="20" width="20" data-sonner-success-icon=""><path fill-rule="evenodd" d="M10 18a8 8 0 100-16 8 8 0 000 16zm3.857-9.809a.75.75 0 00-1.214-.882l-3.483 4.79-1.88-\
-1.88a.75.75 0 10-1.06 1.061l2.5 2.5a.75.75 0 001.137-.089l4-5.5z" clip-rule="evenodd"></path></svg>');function SuccessIcon($$anchor){var svg2=root$1C();append($$anchor,svg2)}var root$1B=from_svg('<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" height="20" width="20" data-sonner-error-icon=""><path fill-rule="evenodd" d="M18 10a8 8 0 11-16 0 8 8 0 0116 0zm-8-5a.75.75 0 01.75.75v4.5a.75.75 0 01-1.5 0v-4.5A.75.75 0 0110 5zm0 10a1 1 0 100-2 1 1 0 000 2z" clip-rule="ev\
-enodd"></path></svg>');function ErrorIcon($$anchor){var svg2=root$1B();append($$anchor,svg2)}var root$1A=from_svg('<svg viewBox="0 0 64 64" fill="currentColor" height="20" width="20" data-sonner-warning-icon="" xmlns="http://www.w3.org/2000/svg"><path d="M32.427,7.987c2.183,0.124 4,1.165 5.096,3.281l17.936,36.208c1.739,3.66 -0.954,8.585 -5.373,8.656l-36.119,0c-4.022,-0.064 -7.322,-4.631 -5.352,-8.696l18.271,-36.207c0.342,-0.65 0.498,-0.838 0.793,-1.179c1.186,-1.375 2.483,-2.111 4.748,-2.063Zm-0.\
-295,3.997c-0.687,0.034 -1.316,0.419 -1.659,1.017c-6.312,11.979 -12.397,24.081 -18.301,36.267c-0.546,1.225 0.391,2.797 1.762,2.863c12.06,0.195 24.125,0.195 36.185,0c1.325,-0.064 2.321,-1.584 1.769,-2.85c-5.793,-12.184 -11.765,-24.286 -17.966,-36.267c-0.366,-0.651 -0.903,-1.042 -1.79,-1.03Z"></path><path d="M33.631,40.581l-3.348,0l-0.368,-16.449l4.1,0l-0.384,16.449Zm-3.828,5.03c0,-0.609 0.197,-1.113 0.592,-1.514c0.396,-0.4 0.935,-0.601 1.618,-0.601c0.684,0 1.223,0.201 1.618,0.601c0.395,0.401 0.593\
-,0.905 0.593,1.514c0,0.587 -0.193,1.078 -0.577,1.473c-0.385,0.395 -0.929,0.593 -1.634,0.593c-0.705,0 -1.249,-0.198 -1.634,-0.593c-0.384,-0.395 -0.576,-0.886 -0.576,-1.473Z"></path></svg>');function WarningIcon($$anchor){var svg2=root$1A();append($$anchor,svg2)}var root$1z=from_svg('<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" height="20" width="20" data-sonner-info-icon=""><path fill-rule="evenodd" d="M18 10a8 8 0 11-16 0 8 8 0 0116 0zm-7-4a1 1 0 11-2 0 1 1 0 0\
-12 0zM9 9a.75.75 0 000 1.5h.253a.25.25 0 01.244.304l-.459 2.066A1.75 1.75 0 0010.747 15H11a.75.75 0 000-1.5h-.253a.25.25 0 01-.244-.304l.459-2.066A1.75 1.75 0 009.253 9H9z" clip-rule="evenodd"></path></svg>');function InfoIcon($$anchor){var svg2=root$1z();append($$anchor,svg2)}var root$1y=from_svg('<svg xmlns="http://www.w3.org/2000/svg" width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" data-sonner-close\
--icon=""><line x1="18" y1="6" x2="6" y2="18"></line><line x1="6" y1="6" x2="18" y2="18"></line></svg>');function CloseIcon($$anchor){var svg2=root$1y();append($$anchor,svg2)}const VISIBLE_TOASTS_AMOUNT=3,VIEWPORT_OFFSET="24px",MOBILE_VIEWPORT_OFFSET="16px",TOAST_LIFETIME=4e3,TOAST_WIDTH=356,GAP=14,DARK="dark",LIGHT="light";function getOffsetObject(defaultOffset,mobileOffset){const styles2={};return[defaultOffset,mobileOffset].forEach((offset2,index2)=>{const isMobile=index2===1,prefix=isMobile?"\
---mobile-offset":"--offset",defaultValue=isMobile?MOBILE_VIEWPORT_OFFSET:VIEWPORT_OFFSET;function assignAll(offset3){["top","right","bottom","left"].forEach(key2=>{styles2[`${prefix}-${key2}`]=typeof offset3=="number"?`${offset3}px`:offset3})}typeof offset2=="number"||typeof offset2=="string"?assignAll(offset2):typeof offset2=="object"?["top","right","bottom","left"].forEach(key2=>{const value=offset2[key2];value===void 0?styles2[`${prefix}-${key2}`]=defaultValue:styles2[`${prefix}-${key2}`]=typeof value==
-"number"?`${value}px`:value}):assignAll(defaultValue)}),styles2}var root_2$16=from_html("<ol></ol>"),root$1x=from_html('<section aria-live="polite" aria-relevant="additions text" aria-atomic="false" class="svelte-nbs0zk"><!></section>');function Toaster($$anchor,$$props){push$1($$props,!0);function getInitialTheme(t){return t!=="system"?t:typeof window<"u"&&window.matchMedia&&window.matchMedia("(prefers-color-scheme: dark)").matches?DARK:LIGHT}let invert=prop($$props,"invert",3,!1),position2=prop(
-$$props,"position",3,"bottom-right"),hotkey=prop($$props,"hotkey",19,()=>["altKey","KeyT"]),expand=prop($$props,"expand",3,!1),closeButton=prop($$props,"closeButton",3,!1),offset2=prop($$props,"offset",3,VIEWPORT_OFFSET),mobileOffset=prop($$props,"mobileOffset",3,MOBILE_VIEWPORT_OFFSET),theme=prop($$props,"theme",3,"light"),richColors=prop($$props,"richColors",3,!1),duration2=prop($$props,"duration",3,TOAST_LIFETIME),visibleToasts=prop($$props,"visibleToasts",3,VISIBLE_TOASTS_AMOUNT),toastOptions=prop(
-$$props,"toastOptions",19,()=>({})),dir=prop($$props,"dir",7,"auto"),gap=prop($$props,"gap",3,GAP),containerAriaLabel=prop($$props,"containerAriaLabel",3,"Notifications"),closeButtonAriaLabel=prop($$props,"closeButtonAriaLabel",3,"Close toast"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","invert","position","hotkey","expand","closeButton","offset","mobileOffset","theme","richColors","duration","visibleToasts","toastOptions","dir","gap","loadingIcon","successIcon","errorIcon","w\
-arningIcon","closeIcon","infoIcon","containerAriaLabel","class","closeButtonAriaLabel","onblur","onfocus","onmouseenter","onmousemove","onmouseleave","ondragend","onpointerdown","onpointerup"]);function getDocumentDirection(){if(dir()!=="auto")return dir();if(typeof window>"u"||typeof document>"u")return"ltr";const dirAttribute=document.documentElement.getAttribute("dir");return dirAttribute==="auto"||!dirAttribute?(untrack$1(()=>dir(window.getComputedStyle(document.documentElement).direction??"\
-ltr")),dir()):(untrack$1(()=>dir(dirAttribute)),dirAttribute)}const possiblePositions=user_derived(()=>Array.from(new Set([position2(),...toastState.toasts.filter(toast2=>toast2.position).map(toast2=>toast2.position)].filter(Boolean))));let expanded=state$1(!1),interacting=state$1(!1),actualTheme=state$1(proxy(getInitialTheme(theme()))),listRef=state$1(void 0),lastFocusedElementRef=state$1(null),isFocusWithin=state$1(!1);const hotkeyLabel=user_derived(()=>hotkey().join("+").replace(/Key/g,"").replace(
-/Digit/g,""));user_effect(()=>{toastState.toasts.length<=1&&set$1(expanded,!1)}),user_effect(()=>{const toastsToDismiss=toastState.toasts.filter(toast2=>toast2.dismiss&&!toast2.delete);if(toastsToDismiss.length>0){const updatedToasts=toastState.toasts.map(toast2=>toastsToDismiss.find(dismissToast=>dismissToast.id===toast2.id)?{...toast2,delete:!0}:toast2);toastState.toasts=updatedToasts}}),user_effect(()=>()=>{get$4(listRef)&&get$4(lastFocusedElementRef)&&(get$4(lastFocusedElementRef).focus({preventScroll:!0}),
-set$1(lastFocusedElementRef,null),set$1(isFocusWithin,!1))}),onMount$1(()=>(toastState.reset(),on(document,"keydown",event2=>{hotkey().every(key2=>event2[key2]||event2.code===key2)&&(set$1(expanded,!0),get$4(listRef)?.focus()),event2.code==="Escape"&&(document.activeElement===get$4(listRef)||get$4(listRef)?.contains(document.activeElement))&&set$1(expanded,!1)}))),user_effect(()=>{if(theme()!=="system"&&set$1(actualTheme,theme()),typeof window<"u"){theme()==="system"&&(window.matchMedia&&window.
-matchMedia("(prefers-color-scheme: dark)").matches?set$1(actualTheme,DARK):set$1(actualTheme,LIGHT));const mediaQueryList=window.matchMedia("(prefers-color-scheme: dark)"),changeHandler=({matches:matches2})=>{set$1(actualTheme,matches2?DARK:LIGHT,!0)};"addEventListener"in mediaQueryList?mediaQueryList.addEventListener("change",changeHandler):mediaQueryList.addListener(changeHandler)}});const handleBlur=event2=>{$$props.onblur?.(event2),get$4(isFocusWithin)&&!event2.currentTarget.contains(event2.
-relatedTarget)&&(set$1(isFocusWithin,!1),get$4(lastFocusedElementRef)&&(get$4(lastFocusedElementRef).focus({preventScroll:!0}),set$1(lastFocusedElementRef,null)))},handleFocus=event2=>{$$props.onfocus?.(event2),!(event2.target instanceof HTMLElement&&event2.target.dataset.dismissable==="false")&&(get$4(isFocusWithin)||(set$1(isFocusWithin,!0),set$1(lastFocusedElementRef,event2.relatedTarget,!0)))},handlePointerDown=event2=>{$$props.onpointerdown?.(event2),!(event2.target instanceof HTMLElement&&
-event2.target.dataset.dismissable==="false")&&set$1(interacting,!0)},handleMouseEnter=event2=>{$$props.onmouseenter?.(event2),set$1(expanded,!0)},handleMouseLeave=event2=>{$$props.onmouseleave?.(event2),get$4(interacting)||set$1(expanded,!1)},handleMouseMove=event2=>{$$props.onmousemove?.(event2),set$1(expanded,!0)},handleDragEnd=event2=>{$$props.ondragend?.(event2),set$1(expanded,!1)},handlePointerUp=event2=>{$$props.onpointerup?.(event2),set$1(interacting,!1)};sonnerContext.set(new SonnerState);
-var section=root$1x();set_attribute(section,"tabindex",-1);var node2=child(section);{var consequent_10=$$anchor2=>{var fragment=comment$2(),node_1=first_child(fragment);each(node_1,18,()=>get$4(possiblePositions),position3=>position3,($$anchor3,position3,index2,$$array)=>{const computed_const=user_derived(()=>{const[y,x]=position3.split("-");return{y,x}}),offsetObject=user_derived(()=>getOffsetObject(offset2(),mobileOffset()));var ol=root_2$16();attribute_effect(ol,$0=>({tabindex:-1,dir:$0,class:$$props.
-class,"data-sonner-toaster":!0,"data-sonner-theme":get$4(actualTheme),"data-y-position":get$4(computed_const).y,"data-x-position":get$4(computed_const).x,style:$$props.style,onblur:handleBlur,onfocus:handleFocus,onmouseenter:handleMouseEnter,onmousemove:handleMouseMove,onmouseleave:handleMouseLeave,ondragend:handleDragEnd,onpointerdown:handlePointerDown,onpointerup:handlePointerUp,...restProps,[STYLE]:{"--front-toast-height":`${toastState.heights[0]?.height}px`,"--width":`${TOAST_WIDTH}px`,"--ga\
-p":`${gap()}px`,"--offset-top":get$4(offsetObject)["--offset-top"],"--offset-right":get$4(offsetObject)["--offset-right"],"--offset-bottom":get$4(offsetObject)["--offset-bottom"],"--offset-left":get$4(offsetObject)["--offset-left"],"--mobile-offset-top":get$4(offsetObject)["--mobile-offset-top"],"--mobile-offset-right":get$4(offsetObject)["--mobile-offset-right"],"--mobile-offset-bottom":get$4(offsetObject)["--mobile-offset-bottom"],"--mobile-offset-left":get$4(offsetObject)["--mobile-offset-lef\
-t"]}}),[getDocumentDirection],void 0,void 0,"svelte-nbs0zk"),each(ol,23,()=>toastState.toasts.filter(toast2=>!toast2.position&&get$4(index2)===0||toast2.position===position3),toast2=>toast2.id,($$anchor4,toast2,index3,$$array_1)=>{{const successIcon=$$anchor5=>{var fragment_2=comment$2(),node_2=first_child(fragment_2);{var consequent=$$anchor6=>{var fragment_3=comment$2(),node_3=first_child(fragment_3);snippet(node_3,()=>$$props.successIcon??noop$3),append($$anchor6,fragment_3)},alternate=$$anchor6=>{
-var fragment_4=comment$2(),node_4=first_child(fragment_4);{var consequent_1=$$anchor7=>{SuccessIcon($$anchor7)};if_block(node_4,$$render=>{$$props.successIcon!==null&&$$render(consequent_1)},!0)}append($$anchor6,fragment_4)};if_block(node_2,$$render=>{$$props.successIcon?$$render(consequent):$$render(alternate,!1)})}append($$anchor5,fragment_2)},errorIcon=$$anchor5=>{var fragment_6=comment$2(),node_5=first_child(fragment_6);{var consequent_2=$$anchor6=>{var fragment_7=comment$2(),node_6=first_child(
-fragment_7);snippet(node_6,()=>$$props.errorIcon??noop$3),append($$anchor6,fragment_7)},alternate_1=$$anchor6=>{var fragment_8=comment$2(),node_7=first_child(fragment_8);{var consequent_3=$$anchor7=>{ErrorIcon($$anchor7)};if_block(node_7,$$render=>{$$props.errorIcon!==null&&$$render(consequent_3)},!0)}append($$anchor6,fragment_8)};if_block(node_5,$$render=>{$$props.errorIcon?$$render(consequent_2):$$render(alternate_1,!1)})}append($$anchor5,fragment_6)},warningIcon=$$anchor5=>{var fragment_10=comment$2(),
-node_8=first_child(fragment_10);{var consequent_4=$$anchor6=>{var fragment_11=comment$2(),node_9=first_child(fragment_11);snippet(node_9,()=>$$props.warningIcon??noop$3),append($$anchor6,fragment_11)},alternate_2=$$anchor6=>{var fragment_12=comment$2(),node_10=first_child(fragment_12);{var consequent_5=$$anchor7=>{WarningIcon($$anchor7)};if_block(node_10,$$render=>{$$props.warningIcon!==null&&$$render(consequent_5)},!0)}append($$anchor6,fragment_12)};if_block(node_8,$$render=>{$$props.warningIcon?
-$$render(consequent_4):$$render(alternate_2,!1)})}append($$anchor5,fragment_10)},infoIcon=$$anchor5=>{var fragment_14=comment$2(),node_11=first_child(fragment_14);{var consequent_6=$$anchor6=>{var fragment_15=comment$2(),node_12=first_child(fragment_15);snippet(node_12,()=>$$props.infoIcon??noop$3),append($$anchor6,fragment_15)},alternate_3=$$anchor6=>{var fragment_16=comment$2(),node_13=first_child(fragment_16);{var consequent_7=$$anchor7=>{InfoIcon($$anchor7)};if_block(node_13,$$render=>{$$props.
-infoIcon!==null&&$$render(consequent_7)},!0)}append($$anchor6,fragment_16)};if_block(node_11,$$render=>{$$props.infoIcon?$$render(consequent_6):$$render(alternate_3,!1)})}append($$anchor5,fragment_14)},closeIcon=$$anchor5=>{var fragment_18=comment$2(),node_14=first_child(fragment_18);{var consequent_8=$$anchor6=>{var fragment_19=comment$2(),node_15=first_child(fragment_19);snippet(node_15,()=>$$props.closeIcon??noop$3),append($$anchor6,fragment_19)},alternate_4=$$anchor6=>{var fragment_20=comment$2(),
-node_16=first_child(fragment_20);{var consequent_9=$$anchor7=>{CloseIcon($$anchor7)};if_block(node_16,$$render=>{$$props.closeIcon!==null&&$$render(consequent_9)},!0)}append($$anchor6,fragment_20)};if_block(node_14,$$render=>{$$props.closeIcon?$$render(consequent_8):$$render(alternate_4,!1)})}append($$anchor5,fragment_18)};let $0=user_derived(()=>toastOptions()?.duration??duration2()),$1=user_derived(()=>toastOptions()?.class??""),$2=user_derived(()=>toastOptions()?.descriptionClass||""),$3=user_derived(
-()=>toastOptions()?.style??""),$4=user_derived(()=>toastOptions().classes||{}),$5=user_derived(()=>toastOptions().unstyled??!1),$6=user_derived(()=>toastOptions()?.cancelButtonStyle??""),$7=user_derived(()=>toastOptions()?.actionButtonStyle??""),$8=user_derived(()=>toastOptions()?.closeButtonAriaLabel??closeButtonAriaLabel());Toast($$anchor4,{get index(){return get$4(index3)},get toast(){return get$4(toast2)},get defaultRichColors(){return richColors()},get duration(){return get$4($0)},get class(){
-return get$4($1)},get descriptionClass(){return get$4($2)},get invert(){return invert()},get visibleToasts(){return visibleToasts()},get closeButton(){return closeButton()},get interacting(){return get$4(interacting)},get position(){return position3},get style(){return get$4($3)},get classes(){return get$4($4)},get unstyled(){return get$4($5)},get cancelButtonStyle(){return get$4($6)},get actionButtonStyle(){return get$4($7)},get closeButtonAriaLabel(){return get$4($8)},get expandByDefault(){return expand()},
-get expanded(){return get$4(expanded)},get loadingIcon(){return $$props.loadingIcon},successIcon,errorIcon,warningIcon,infoIcon,closeIcon,$$slots:{successIcon:!0,errorIcon:!0,warningIcon:!0,infoIcon:!0,closeIcon:!0}})}}),reset(ol),bind_this(ol,$$value=>set$1(listRef,$$value),()=>get$4(listRef)),template_effect(()=>ol.dir=ol.dir),append($$anchor3,ol)}),append($$anchor2,fragment)};if_block(node2,$$render=>{toastState.toasts.length>0&&$$render(consequent_10)})}reset(section),template_effect(()=>set_attribute(
-section,"aria-label",`${containerAriaLabel()??""} ${get$4(hotkeyLabel)??""}`)),append($$anchor,section),pop()}async function copyToClipboard(text2,successMessage="Copied to clipboard",errorMessage="Failed to copy to clipboard"){try{if(navigator.clipboard&&navigator.clipboard.writeText)return await navigator.clipboard.writeText(text2),toast.success(successMessage),!0;const textArea=document.createElement("textarea");textArea.value=text2,textArea.style.position="fixed",textArea.style.left="-999999\
-px",textArea.style.top="-999999px",document.body.appendChild(textArea),textArea.focus(),textArea.select();const successful=document.execCommand("copy");if(document.body.removeChild(textArea),successful)return toast.success(successMessage),!0;throw new Error("execCommand failed")}catch(error2){return console.error("Failed to copy to clipboard:",error2),toast.error(errorMessage),!1}}async function copyCodeToClipboard(rawCode,successMessage="Code copied to clipboard",errorMessage="Failed to copy co\
-de"){return copyToClipboard(rawCode,successMessage,errorMessage)}function formatMessageForClipboard(content2,extras,asPlainText=!1){const textAttachments=extras?.filter(extra=>extra.type===AttachmentType.TEXT||extra.type===AttachmentType.LEGACY_CONTEXT||extra.type===AttachmentType.MCP_PROMPT||extra.type===AttachmentType.MCP_RESOURCE)??[];if(textAttachments.length===0)return content2;if(asPlainText){const parts=[content2];for(const att of textAttachments)parts.push(att.content);return parts.join(
-`
-
-`)}const clipboardAttachments=textAttachments.map(att=>{if(att.type===AttachmentType.MCP_PROMPT){const mcpAtt=att;return{type:AttachmentType.MCP_PROMPT,name:mcpAtt.name,serverName:mcpAtt.serverName,promptName:mcpAtt.promptName,content:mcpAtt.content,arguments:mcpAtt.arguments}}return{type:AttachmentType.TEXT,name:att.name,content:att.content}});return`${JSON.stringify(content2)}
-${JSON.stringify(clipboardAttachments,null,2)}`}function parseClipboardContent(clipboardText){const defaultResult={message:clipboardText,textAttachments:[],mcpPromptAttachments:[]};if(!clipboardText.startsWith('"'))return defaultResult;try{let stringEndIndex=-1,escaped=!1;for(let i=1;i<clipboardText.length;i++){const char=clipboardText[i];if(escaped){escaped=!1;continue}if(char==="\\"){escaped=!0;continue}if(char==='"'){stringEndIndex=i;break}}if(stringEndIndex===-1)return defaultResult;const jsonStringPart=clipboardText.
-substring(0,stringEndIndex+1),remainingPart=clipboardText.substring(stringEndIndex+1).trim(),message=JSON.parse(jsonStringPart);if(!remainingPart||!remainingPart.startsWith("["))return{message,textAttachments:[],mcpPromptAttachments:[]};const attachments=JSON.parse(remainingPart),validTextAttachments=[],validMcpPromptAttachments=[];for(const att of attachments)isValidMcpPromptAttachment(att)?validMcpPromptAttachments.push({type:AttachmentType.MCP_PROMPT,name:att.name,serverName:att.serverName,promptName:att.
-promptName,content:att.content,arguments:att.arguments}):isValidTextAttachment(att)&&validTextAttachments.push({type:AttachmentType.TEXT,name:att.name,content:att.content});return{message,textAttachments:validTextAttachments,mcpPromptAttachments:validMcpPromptAttachments}}catch{return defaultResult}}function isValidMcpPromptAttachment(obj){if(typeof obj!="object"||obj===null)return!1;const record2=obj;return(record2.type===AttachmentType.MCP_PROMPT||record2.type==="MCP_PROMPT")&&typeof record2.name==
-"string"&&typeof record2.serverName=="string"&&typeof record2.promptName=="string"&&typeof record2.content=="string"}function isValidTextAttachment(obj){if(typeof obj!="object"||obj===null)return!1;const record2=obj;return(record2.type===AttachmentType.TEXT||record2.type==="TEXT")&&typeof record2.name=="string"&&typeof record2.content=="string"}function getFileTypeLabel(input){if(!input)return"FILE";if(input.includes("/")){const subtype=input.split("/").pop();if(subtype)return subtype.includes("\
-.")?subtype.split(".").pop()?.toUpperCase()||"FILE":subtype.toUpperCase()}if(input.includes(".")){const ext=input.split(".").pop();if(ext)return ext.toUpperCase()}return input.toUpperCase()}function getPreviewText(content2,max2=150){return content2.length>max2?content2.slice(0,max2)+"...":content2}function generateConversationTitle(content2,useFirstLine=!1){if(useFirstLine){const firstLine=content2.split(NEWLINE_SEPARATOR).find(line=>line.trim().length>0);return firstLine?firstLine.trim():content2.
-trim()}return content2.trim()}function getFileTypeCategory(mimeType){switch(mimeType){case MimeTypeImage.JPEG:case MimeTypeImage.PNG:case MimeTypeImage.GIF:case MimeTypeImage.WEBP:case MimeTypeImage.SVG:return FileTypeCategory.IMAGE;case MimeTypeAudio.MP3_MPEG:case MimeTypeAudio.MP3:case MimeTypeAudio.MP4:case MimeTypeAudio.WAV:case MimeTypeAudio.WEBM:case MimeTypeAudio.WEBM_OPUS:return FileTypeCategory.AUDIO;case MimeTypeApplication.PDF:return FileTypeCategory.PDF;case MimeTypeText.PLAIN:case MimeTypeText.
-MARKDOWN:case MimeTypeText.ASCIIDOC:case MimeTypeText.JAVASCRIPT:case MimeTypeText.JAVASCRIPT_APP:case MimeTypeText.TYPESCRIPT:case MimeTypeText.JSX:case MimeTypeText.TSX:case MimeTypeText.CSS:case MimeTypeText.HTML:case MimeTypeText.JSON:case MimeTypeText.XML_TEXT:case MimeTypeText.XML_APP:case MimeTypeText.YAML_TEXT:case MimeTypeText.YAML_APP:case MimeTypeText.CSV:case MimeTypeText.PYTHON:case MimeTypeText.JAVA:case MimeTypeText.CPP_SRC:case MimeTypeText.C_SRC:case MimeTypeText.C_HDR:case MimeTypeText.
-PHP:case MimeTypeText.RUBY:case MimeTypeText.GO:case MimeTypeText.RUST:case MimeTypeText.SHELL:case MimeTypeText.BAT:case MimeTypeText.SQL:case MimeTypeText.R:case MimeTypeText.SCALA:case MimeTypeText.KOTLIN:case MimeTypeText.SWIFT:case MimeTypeText.DART:case MimeTypeText.VUE:case MimeTypeText.SVELTE:case MimeTypeText.LATEX:case MimeTypeText.BIBTEX:case MimeTypeText.CUDA:case MimeTypeText.CPP_HDR:case MimeTypeText.CSHARP:case MimeTypeText.HASKELL:case MimeTypeText.PROPERTIES:case MimeTypeText.TEX:case MimeTypeText.
-TEX_APP:return FileTypeCategory.TEXT;default:return null}}function getFileTypeCategoryByExtension(filename){switch(filename.toLowerCase().substring(filename.lastIndexOf("."))){case FileExtensionImage.JPG:case FileExtensionImage.JPEG:case FileExtensionImage.PNG:case FileExtensionImage.GIF:case FileExtensionImage.WEBP:case FileExtensionImage.SVG:return FileTypeCategory.IMAGE;case FileExtensionAudio.MP3:case FileExtensionAudio.WAV:return FileTypeCategory.AUDIO;case FileExtensionPdf.PDF:return FileTypeCategory.
-PDF;case FileExtensionText.TXT:case FileExtensionText.MD:case FileExtensionText.ADOC:case FileExtensionText.JS:case FileExtensionText.TS:case FileExtensionText.JSX:case FileExtensionText.TSX:case FileExtensionText.CSS:case FileExtensionText.HTML:case FileExtensionText.HTM:case FileExtensionText.JSON:case FileExtensionText.XML:case FileExtensionText.YAML:case FileExtensionText.YML:case FileExtensionText.CSV:case FileExtensionText.LOG:case FileExtensionText.PY:case FileExtensionText.JAVA:case FileExtensionText.
-CPP:case FileExtensionText.C:case FileExtensionText.H:case FileExtensionText.PHP:case FileExtensionText.RB:case FileExtensionText.GO:case FileExtensionText.RS:case FileExtensionText.SH:case FileExtensionText.BAT:case FileExtensionText.SQL:case FileExtensionText.R:case FileExtensionText.SCALA:case FileExtensionText.KT:case FileExtensionText.SWIFT:case FileExtensionText.DART:case FileExtensionText.VUE:case FileExtensionText.SVELTE:case FileExtensionText.TEX:case FileExtensionText.BIB:case FileExtensionText.
-COMP:case FileExtensionText.CU:case FileExtensionText.CUH:case FileExtensionText.HPP:case FileExtensionText.HS:case FileExtensionText.PROPERTIES:return FileTypeCategory.TEXT;default:return null}}function isFileTypeSupported(filename,mimeType){if(mimeType){const category=getFileTypeCategory(mimeType);if(category===FileTypeCategory.IMAGE||category===FileTypeCategory.AUDIO||category===FileTypeCategory.PDF)return!0}const extCategory=getFileTypeCategoryByExtension(filename);return extCategory===FileTypeCategory.
-IMAGE||extCategory===FileTypeCategory.AUDIO||extCategory===FileTypeCategory.PDF,!0}function formatFileSize(bytes){if(typeof bytes!="number")return"Unknown";if(bytes===0)return"0 Bytes";const k=1024,sizes=["Bytes","KB","MB","GB"],i=Math.floor(Math.log(bytes)/Math.log(k));return parseFloat((bytes/Math.pow(k,i)).toFixed(2))+" "+sizes[i]}function formatParameters(params){return typeof params!="number"?"Unknown":params>=1e9?`${(params/1e9).toFixed(2)}B`:params>=1e6?`${(params/1e6).toFixed(2)}M`:params>=
-1e3?`${(params/1e3).toFixed(2)}K`:params.toString()}function formatNumber(num){return typeof num!="number"?"Unknown":num.toLocaleString()}function formatJsonPretty(jsonString){try{const parsed=JSON.parse(jsonString);return JSON.stringify(parsed,null,2)}catch{return jsonString}}function formatTime(date2){return date2.toLocaleTimeString("en-US",{hour12:!1,hour:"2-digit",minute:"2-digit",second:"2-digit"})}function formatPerformanceTime(ms){if(ms<0)return"0s";const totalSeconds=ms/MS_PER_SECOND;if(totalSeconds<
-SHORT_DURATION_THRESHOLD)return`${totalSeconds.toFixed(1)}s`;if(totalSeconds<MEDIUM_DURATION_THRESHOLD)return`${totalSeconds.toFixed(1)}s`;const hours=Math.floor(totalSeconds/SECONDS_PER_HOUR),minutes=Math.floor(totalSeconds%SECONDS_PER_HOUR/SECONDS_PER_MINUTE),seconds=Math.floor(totalSeconds%SECONDS_PER_MINUTE),parts=[];return hours>0&&parts.push(`${hours}h`),minutes>0&&parts.push(`${minutes}min`),(seconds>0||parts.length===0)&&parts.push(`${seconds}s`),parts.join(" ")}function formatAttachmentText(label,name,content2,extra){
-const header=extra?`${name} (${extra})`:name;return`
-
---- ${label}: ${header} ---
-${content2}`}function isIMEComposing(event2){return event2.isComposing||event2.keyCode===229}function maskInlineLaTeX(content2,latexExpressions){return content2.includes("$")?content2.split(`
-`).map(line=>{if(line.indexOf("$")==-1)return line;let processedLine="",currentPosition=0;for(;currentPosition<line.length;){const openDollarIndex=line.indexOf("$",currentPosition);if(openDollarIndex==-1){processedLine+=line.slice(currentPosition);break}const closeDollarIndex=line.indexOf("$",openDollarIndex+1);if(closeDollarIndex==-1){processedLine+=line.slice(currentPosition);break}const charBeforeOpen=openDollarIndex>0?line[openDollarIndex-1]:"",charAfterOpen=line[openDollarIndex+1],charBeforeClose=openDollarIndex+
-1<closeDollarIndex?line[closeDollarIndex-1]:"",charAfterClose=closeDollarIndex+1<line.length?line[closeDollarIndex+1]:"";let shouldSkipAsNonLatex=!1;if(closeDollarIndex==currentPosition+1&&(shouldSkipAsNonLatex=!0),/[A-Za-z0-9_$-]/.test(charBeforeOpen)&&(shouldSkipAsNonLatex=!0),/[0-9]/.test(charAfterOpen)&&(/[A-Za-z0-9_$-]/.test(charAfterClose)||charBeforeClose==" ")&&(shouldSkipAsNonLatex=!0),shouldSkipAsNonLatex){processedLine+=line.slice(currentPosition,openDollarIndex+1),currentPosition=openDollarIndex+
-1;continue}processedLine+=line.slice(currentPosition,openDollarIndex);const latexContent=line.slice(openDollarIndex,closeDollarIndex+1);latexExpressions.push(latexContent),processedLine+=`<<LATEX_${latexExpressions.length-1}>>`,currentPosition=closeDollarIndex+1}return processedLine}).join(`
-`):content2}function escapeBrackets(text2){return text2.replace(LATEX_MATH_AND_CODE_PATTERN,(match,codeBlock,squareBracket,roundBracket)=>codeBlock??(squareBracket!=null?`$$${squareBracket}$$`:roundBracket!=null?`$${roundBracket}$`:match))}function preprocessLaTeX(content2){const blockquoteMarkers=new Map;content2=content2.split(`
-`).map((line,index2)=>{const match=line.match(/^(>\s*)/);return match?(blockquoteMarkers.set(index2,match[1]),line.slice(match[1].length)):line}).join(`
-`);const codeBlocks=[];content2=content2.replace(CODE_BLOCK_REGEXP,match=>(codeBlocks.push(match),`<<CODE_BLOCK_${codeBlocks.length-1}>>`));const latexExpressions=[];return content2=content2.replace(/([\S].*?)\\\[([\s\S]*?)\\\](.*)/g,(match,group1,group2,group3)=>{if(group1.endsWith("\\"))return match;const hasSuffix=/\S/.test(group3);let optBreak;return hasSuffix?(latexExpressions.push(`\\(${group2.trim()}\\)`),optBreak=""):(latexExpressions.push(`\\[${group2}\\]`),optBreak=`
-`),`${group1}${optBreak}<<LATEX_${latexExpressions.length-1}>>${optBreak}${group3}`}),content2=content2.replace(new RegExp("(\\$\\$[\\s\\S]*?\\$\\$|(?<!\\\\)\\\\\\[[\\s\\S]*?\\\\\\]|(?<!\\\\)\\\\\\(.*?\\\\\\))","g"),match=>(latexExpressions.push(match),`<<LATEX_${latexExpressions.length-1}>>`)),content2=maskInlineLaTeX(content2,latexExpressions),content2=content2.replace(/\$(?=\d)/g,"\\$"),content2=content2.replace(/<<LATEX_(\d+)>>/g,(_,index2)=>{let expr=latexExpressions[parseInt(index2)];const match=expr.
-match(LATEX_LINEBREAK_REGEXP);if(match){const formula=match[1],prefix=formula.startsWith(`
-`)?"":`
-`,suffix=formula.endsWith(`
-`)?"":`
-`;expr="$$"+prefix+formula+suffix+"$$"}return expr}),content2=escapeBrackets(content2),content2=content2.replace(new RegExp("(?<!\\\\)\\\\\\((.+?)\\\\\\)","g"),"$$$1$").replace(new RegExp("(?<!\\\\)\\\\\\[([\\s\\S]*?)\\\\\\]","g"),(_,content22)=>`$$${content22}$$`),content2=content2.replace(/<<CODE_BLOCK_(\d+)>>/g,(_,index2)=>codeBlocks[parseInt(index2)]),blockquoteMarkers.size>0&&(content2=content2.split(`
-`).map((line,index2)=>{const marker=blockquoteMarkers.get(index2);return marker?marker+line:line}).join(`
-`)),content2}function filterFilesByModalities(files,capabilities){const supportedFiles=[],unsupportedFiles=[],modalityReasons={},{hasVision,hasAudio}=capabilities;for(const file of files){const category=getFileTypeCategory(file.type);let isSupported=!0,reason="";switch(category){case FileTypeCategory.IMAGE:hasVision||(isSupported=!1,reason="Images require a vision-capable model");break;case FileTypeCategory.AUDIO:hasAudio||(isSupported=!1,reason="Audio files require an audio-capable model");break;case FileTypeCategory.
-TEXT:case FileTypeCategory.PDF:break}isSupported?supportedFiles.push(file):(unsupportedFiles.push(file),modalityReasons[file.name]=reason)}return{supportedFiles,unsupportedFiles,modalityReasons}}function normalizeModelName(modelName){const trimmed=modelName.trim();if(!trimmed)return"";const segments=trimmed.split(/[\\/]/);if(segments.length===2){const[org,model]=segments,trimmedOrg=org?.trim(),trimmedModel=model?.trim();if(trimmedOrg&&trimmedModel)return`${trimmedOrg}/${trimmedModel}`}const normalized=segments.
-pop()?.trim();return normalized&&normalized.length>0?normalized:trimmed}function normalizeFloatingPoint(value){return typeof value=="number"?Math.round(value*PRECISION_MULTIPLIER)/PRECISION_MULTIPLIER:value}function getLanguageFromFilename(filename){switch(filename.toLowerCase().substring(filename.lastIndexOf("."))){case".js":case".mjs":case".cjs":return"javascript";case".ts":case".mts":case".cts":return"typescript";case".jsx":return"javascript";case".tsx":return"typescript";case".html":case".ht\
-m":return"html";case".css":return"css";case".scss":return"scss";case".less":return"less";case".vue":return"html";case".svelte":return"html";case".json":return"json";case".xml":return"xml";case".yaml":case".yml":return"yaml";case".toml":return"ini";case".csv":return"plaintext";case".py":return"python";case".java":return"java";case".kt":case".kts":return"kotlin";case".scala":return"scala";case".cpp":case".cc":case".cxx":case".c++":return"cpp";case".c":return"c";case".h":case".hpp":return"cpp";case"\
-.cs":return"csharp";case".go":return"go";case".rs":return"rust";case".rb":return"ruby";case".php":return"php";case".swift":return"swift";case".dart":return"dart";case".r":return"r";case".lua":return"lua";case".pl":case".pm":return"perl";case".sh":case".bash":case".zsh":return"bash";case".bat":case".cmd":return"dos";case".ps1":return"powershell";case".sql":return"sql";case".md":case".markdown":return"markdown";case".tex":case".latex":return"latex";case".adoc":case".asciidoc":return"asciidoc";case"\
-.ini":case".cfg":case".conf":return"ini";case".dockerfile":return"dockerfile";case".nginx":return"nginx";case".graphql":case".gql":return"graphql";case".proto":return"protobuf";case".diff":case".patch":return"diff";case".log":return"plaintext";case".txt":return"plaintext";default:return"plaintext"}}async function readFileAsText(file){return new Promise((resolve2,reject)=>{const reader=new FileReader;reader.onload=event2=>{event2.target?.result!==null&&event2.target?.result!==void 0?resolve2(event2.
-target.result):reject(new Error("Failed to read file"))},reader.onerror=()=>reject(new Error("File reading error")),reader.readAsText(file)})}function isLikelyTextFile(content2,options={}){if(!content2)return!0;const config2={...DEFAULT_BINARY_DETECTION_OPTIONS,...options},sample=content2.substring(0,config2.prefixLength);let nullCount=0,suspiciousControlCount=0;for(let i=0;i<sample.length;i++){const charCode=sample.charCodeAt(i);if(charCode===0){nullCount++;continue}charCode<32&&charCode!==9&&charCode!==
-10&&charCode!==13&&(charCode<8||charCode>13&&charCode<27)&&suspiciousControlCount++,charCode===65533&&suspiciousControlCount++}return!(nullCount>config2.maxAbsoluteNullBytes||suspiciousControlCount/sample.length>config2.suspiciousCharThresholdRatio)}function debounce(fn,delay){let timeoutId=null;return(...args)=>{timeoutId&&clearTimeout(timeoutId),timeoutId=setTimeout(()=>{fn(...args),timeoutId=null},delay)}}function sanitizeKeyValuePairKey(raw2){return raw2.replace(KEY_VALUE_PAIR_UNSAFE_KEY_RE,
-"").slice(0,KEY_VALUE_PAIR_KEY_MAX_LENGTH)}function sanitizeKeyValuePairValue(raw2){return raw2.replace(KEY_VALUE_PAIR_UNSAFE_VALUE_RE,"").slice(0,KEY_VALUE_PAIR_VALUE_MAX_LENGTH)}function getImageErrorFallbackHtml(src){return`<div class="image-error-content">
-		<span>Image cannot be displayed</span>
-		<a href="${src}" target="_blank" rel="noopener noreferrer">(open link)</a>
-	</div>`}function detectMcpTransportFromUrl(url2){const normalized=url2.trim().toLowerCase();return normalized.startsWith(UrlProtocol.WEBSOCKET)||normalized.startsWith(UrlProtocol.WEBSOCKET_SECURE)?MCPTransportType.WEBSOCKET:MCPTransportType.STREAMABLE_HTTP}function parseMcpServerSettings(rawServers){if(!rawServers)return[];let parsed;if(typeof rawServers=="string"){const trimmed=rawServers.trim();if(!trimmed)return[];try{parsed=JSON.parse(trimmed)}catch(error2){return console.warn("[MCP] Failed\
- to parse mcpServers JSON, ignoring value:",error2),[]}}else parsed=rawServers;return Array.isArray(parsed)?parsed.map((entry,index2)=>{const url2=typeof entry?.url=="string"?entry.url.trim():"",headers=typeof entry?.headers=="string"?entry.headers.trim():void 0;return{id:typeof entry?.id=="string"&&entry.id?.trim()?entry.id.trim():`${MCP_SERVER_ID_PREFIX}-${index2+1}`,enabled:!!entry?.enabled,url:url2,name:entry?.name,requestTimeoutSeconds:DEFAULT_MCP_CONFIG.requestTimeoutSeconds,headers:headers||
-void 0,useProxy:!!entry?.useProxy}}):[]}function getMcpLogLevelIcon(level){switch(level){case MCPLogLevel.ERROR:return Circle_x;case MCPLogLevel.WARN:return Triangle_alert;default:return Info$1}}function getMcpLogLevelClass(level){switch(level){case MCPLogLevel.ERROR:return"text-destructive";case MCPLogLevel.WARN:return"text-yellow-600 dark:text-yellow-500";default:return"text-muted-foreground"}}function isImageMimeType(mimeType){return mimeType?.startsWith(MimeTypePrefix.IMAGE)??!1}function parseResourcePath(uri2){
-try{return uri2.replace(PROTOCOL_PREFIX_REGEX,"").split(PATH_SEPARATOR).filter(p2=>p2.length>0)}catch{return[uri2]}}function getDisplayName(pathPart){return pathPart.replace(FILE_EXTENSION_REGEX,"").split(DISPLAY_NAME_SEPARATOR_REGEX).map(word=>word.charAt(0).toUpperCase()+word.slice(1)).join(" ")}function getResourceDisplayName(resource){try{const parts=parseResourcePath(resource.uri);return parts[parts.length-1]||resource.name||resource.uri}catch{return resource.name||resource.uri}}function isCodeResource(mimeType,uri2){
-const mime=mimeType?.toLowerCase()||"",u=uri2?.toLowerCase()||"";return mime.includes(MimeTypeIncludes.JSON)||mime.includes(MimeTypeIncludes.JAVASCRIPT)||mime.includes(MimeTypeIncludes.TYPESCRIPT)||CODE_FILE_EXTENSION_REGEX.test(u)}function isImageResource(mimeType,uri2){const mime=mimeType?.toLowerCase()||"",u=uri2?.toLowerCase()||"";return mime.startsWith(MimeTypePrefix.IMAGE)||IMAGE_FILE_EXTENSION_REGEX.test(u)}function getResourceIcon(mimeType,uri2){const mime=mimeType?.toLowerCase()||"",u=uri2?.
-toLowerCase()||"";return mime.startsWith(MimeTypePrefix.IMAGE)||IMAGE_FILE_EXTENSION_REGEX.test(u)?Image$1:mime.includes(MimeTypeIncludes.JSON)||mime.includes(MimeTypeIncludes.JAVASCRIPT)||mime.includes(MimeTypeIncludes.TYPESCRIPT)||CODE_FILE_EXTENSION_REGEX.test(u)?Code:mime.includes(MimeTypePrefix.TEXT)||TEXT_FILE_EXTENSION_REGEX.test(u)?File_text:u.includes(UriPattern.DATABASE_KEYWORD)||u.includes(UriPattern.DATABASE_SCHEME)?Database:File$1}function getResourceTextContent(content2){return content2?
-content2.filter(c2=>"text"in c2).map(c2=>c2.text).join(RESOURCE_TEXT_CONTENT_SEPARATOR):""}function getResourceBlobContent(content2){return content2?content2.filter(c2=>"blob"in c2):[]}function downloadResourceContent(text2,mimeType=MimeTypeText.PLAIN,filename=DEFAULT_RESOURCE_FILENAME){const blob=new Blob([text2],{type:mimeType}),url2=URL.createObjectURL(blob),a=document.createElement("a");a.href=url2,a.download=filename,document.body.appendChild(a),a.click(),document.body.removeChild(a),URL.revokeObjectURL(
-url2)}function normalizeResourceUri(uri2){const schemeEnd=uri2.indexOf(URI_SCHEME_SEPARATOR);if(schemeEnd===-1)return uri2;const scheme2=uri2.substring(0,schemeEnd),rest=uri2.substring(schemeEnd+URI_SCHEME_SEPARATOR.length).replace(LEADING_SLASHES_REGEX,"");return`${scheme2}${URI_SCHEME_SEPARATOR}${rest}`}function extractTemplateVariables(template){const variables=[],seen2=new Set;let match;for(TEMPLATE_EXPRESSION_REGEX.lastIndex=0;(match=TEMPLATE_EXPRESSION_REGEX.exec(template))!==null;){const operator2=match[1]||
-"",varList=match[2];for(const varSpec of varList.split(",")){const name=varSpec.replace(VARIABLE_EXPLODE_MODIFIER_REGEX,"").replace(VARIABLE_PREFIX_MODIFIER_REGEX,"").trim();name&&!seen2.has(name)&&(seen2.add(name),variables.push({name,operator:operator2}))}}return variables}function expandTemplate(template,values){return TEMPLATE_EXPRESSION_REGEX.lastIndex=0,template.replace(TEMPLATE_EXPRESSION_REGEX,(_match,operator2,varList)=>{const varNames=varList.split(",").map(v=>v.replace(VARIABLE_EXPLODE_MODIFIER_REGEX,
-"").replace(VARIABLE_PREFIX_MODIFIER_REGEX,"").trim()),expandedParts=varNames.map(name=>values[name]??"").filter(v=>v!=="");if(expandedParts.length===0)return"";switch(operator2){case URI_TEMPLATE_OPERATORS.RESERVED:return expandedParts.join(URI_TEMPLATE_SEPARATORS.COMMA);case URI_TEMPLATE_OPERATORS.FRAGMENT:return URI_TEMPLATE_OPERATORS.FRAGMENT+expandedParts.join(URI_TEMPLATE_SEPARATORS.COMMA);case URI_TEMPLATE_OPERATORS.PATH_SEGMENT:return URI_TEMPLATE_SEPARATORS.SLASH+expandedParts.join(URI_TEMPLATE_SEPARATORS.
-SLASH);case URI_TEMPLATE_OPERATORS.LABEL:return URI_TEMPLATE_SEPARATORS.PERIOD+expandedParts.join(URI_TEMPLATE_SEPARATORS.PERIOD);case URI_TEMPLATE_OPERATORS.PATH_PARAM:return varNames.filter((_,i)=>expandedParts[i]).map((name,i)=>`${URI_TEMPLATE_SEPARATORS.SEMICOLON}${name}=${expandedParts[i]}`).join("");case URI_TEMPLATE_OPERATORS.FORM_QUERY:return URI_TEMPLATE_SEPARATORS.QUERY_PREFIX+varNames.filter((_,i)=>expandedParts[i]).map((name,i)=>`${encodeURIComponent(name)}=${encodeURIComponent(expandedParts[i])}`).
-join(URI_TEMPLATE_SEPARATORS.COMMA);case URI_TEMPLATE_OPERATORS.FORM_CONTINUATION:return URI_TEMPLATE_SEPARATORS.QUERY_CONTINUATION+varNames.filter((_,i)=>expandedParts[i]).map((name,i)=>`${encodeURIComponent(name)}=${encodeURIComponent(expandedParts[i])}`).join(URI_TEMPLATE_SEPARATORS.COMMA);default:return expandedParts.map(v=>encodeURIComponent(v)).join(URI_TEMPLATE_SEPARATORS.COMMA)}})}function isTemplateComplete(template,values){return extractTemplateVariables(template).every(v=>(values[v.name]??
-"").trim()!=="")}function createBase64DataUrl(mimeType,base64Data){return`data:${mimeType};base64,${base64Data}`}function parseHeadersToArray(headersJson){if(!headersJson?.trim())return[];try{const parsed=JSON.parse(headersJson);if(typeof parsed=="object"&&parsed!==null&&!Array.isArray(parsed))return Object.entries(parsed).map(([key2,value])=>({key:key2,value:String(value)}))}catch{return[]}return[]}function serializeHeaders(pairs){const validPairs=pairs.filter(p2=>p2.key.trim());if(validPairs.length===
-0)return"";const obj={};for(const pair of validPairs)obj[pair.key.trim()]=pair.value;return JSON.stringify(obj)}function getFaviconUrl(urlString,useProxy=!0){try{const url2=new URL(urlString),hostnameParts=url2.hostname.split(DOMAIN_SEPARATOR),rootDomain=hostnameParts.length>=ROOT_DOMAIN_MIN_PARTS?hostnameParts.slice(-ROOT_DOMAIN_MIN_PARTS).join(DOMAIN_SEPARATOR):url2.hostname,googleFaviconUrl=`${GOOGLE_FAVICON_BASE_URL}?domain=${rootDomain}&sz=${DEFAULT_FAVICON_SIZE}`;return useProxy?getProxiedUrlString(
-googleFaviconUrl):googleFaviconUrl}catch{return null}}function deriveSingleTurnSections(message,toolMessages=[],streamingToolCalls=[],isStreaming=!1){const sections=[];if(message.reasoningContent){const toolCalls2=parseToolCalls(message.toolCalls),hasContentAfterReasoning=!!message.content?.trim()||toolCalls2.length>0||streamingToolCalls.length>0,isPending=isStreaming&&!hasContentAfterReasoning;sections.push({type:isPending?AgenticSectionType.REASONING_PENDING:AgenticSectionType.REASONING,content:message.
-reasoningContent})}message.content?.trim()&&sections.push({type:AgenticSectionType.TEXT,content:message.content});const toolCalls=parseToolCalls(message.toolCalls);for(const tc of toolCalls){const resultMsg=toolMessages.find(m=>m.toolCallId===tc.id);sections.push({type:resultMsg?AgenticSectionType.TOOL_CALL:AgenticSectionType.TOOL_CALL_PENDING,content:resultMsg?.content||"",toolName:tc.function?.name,toolArgs:tc.function?.arguments,toolResult:resultMsg?.content,toolResultExtras:resultMsg?.extra})}
-for(const tc of streamingToolCalls)tc.id&&toolCalls.find(t=>t.id===tc.id)||sections.push({type:AgenticSectionType.TOOL_CALL_STREAMING,content:"",toolName:tc.function?.name,toolArgs:tc.function?.arguments});return sections}function deriveAgenticSections(message,toolMessages=[],streamingToolCalls=[],isStreaming=!1){if(!toolMessages.some(m=>m.role===MessageRole.ASSISTANT))return deriveSingleTurnSections(message,toolMessages,streamingToolCalls,isStreaming);const sections=[],firstTurnToolMsgs=collectToolMessages(
-toolMessages,0);sections.push(...deriveSingleTurnSections(message,firstTurnToolMsgs));let i=firstTurnToolMsgs.length;for(;i<toolMessages.length;){const msg=toolMessages[i];if(msg.role===MessageRole.ASSISTANT){const turnToolMsgs=collectToolMessages(toolMessages,i+1),isLastTurn=i+1+turnToolMsgs.length>=toolMessages.length;sections.push(...deriveSingleTurnSections(msg,turnToolMsgs,isLastTurn?streamingToolCalls:[],isLastTurn&&isStreaming)),i+=1+turnToolMsgs.length}else i++}return sections}function collectToolMessages(messages,startIndex){
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-dexie_min$1)),dexie_min$1.exports}var dexie_minExports=requireDexie_min();const _Dexie=getDefaultExportFromCjs(dexie_minExports),DexieSymbol=Symbol.for("Dexie"),Dexie=globalThis[DexieSymbol]||(globalThis[DexieSymbol]=_Dexie);if(_Dexie.semVer!==Dexie.semVer)throw new Error(`Two different versions of Dexie loaded in the same app: ${_Dexie.semVer} and ${Dexie.semVer}`);const{liveQuery,mergeRanges,rangesOverlap,RangeSet,cmp,Entity,PropModification,replacePrefix,add,remove}=Dexie;class LlamacppDatabase extends Dexie{conversations;messages;constructor(){
-super("LlamacppWebui"),this.version(1).stores({conversations:"id, lastModified, currNode, name",messages:"id, convId, type, role, timestamp, parent, children"})}}const db=new LlamacppDatabase;class DatabaseService{static async createConversation(name){const conversation={id:uuid$1(),name,lastModified:Date.now(),currNode:""};return await db.conversations.add(conversation),conversation}static async createMessageBranch(message,parentId){return await db.transaction("rw",[db.conversations,db.messages],
-async()=>{if(parentId!==null&&!await db.messages.get(parentId))throw new Error(`Parent message ${parentId} not found`);const newMessage={...message,id:uuid$1(),parent:parentId,toolCalls:message.toolCalls??"",children:[]};if(await db.messages.add(newMessage),parentId!==null){const parentMessage=await db.messages.get(parentId);parentMessage&&await db.messages.update(parentId,{children:[...parentMessage.children,newMessage.id]})}return await this.updateConversation(message.convId,{currNode:newMessage.
-id}),newMessage})}static async createRootMessage(convId){const rootMessage={id:uuid$1(),convId,type:"root",timestamp:Date.now(),role:MessageRole.SYSTEM,content:"",parent:null,toolCalls:"",children:[]};return await db.messages.add(rootMessage),rootMessage.id}static async createSystemMessage(convId,systemPrompt,parentId){const trimmedPrompt=systemPrompt.trim();if(!trimmedPrompt)throw new Error("Cannot create system message with empty content");const systemMessage={id:uuid$1(),convId,type:MessageRole.
-SYSTEM,timestamp:Date.now(),role:MessageRole.SYSTEM,content:trimmedPrompt,parent:parentId,children:[]};await db.messages.add(systemMessage);const parentMessage=await db.messages.get(parentId);return parentMessage&&await db.messages.update(parentId,{children:[...parentMessage.children,systemMessage.id]}),systemMessage}static async deleteConversation(id2,options){await db.transaction("rw",[db.conversations,db.messages],async()=>{if(options?.deleteWithForks){const idsToDelete=[],queue=[id2];for(;queue.
-length>0;){const parentId=queue.pop(),children=await db.conversations.filter(c2=>c2.forkedFromConversationId===parentId).toArray();for(const child2 of children)idsToDelete.push(child2.id),queue.push(child2.id)}for(const forkId of idsToDelete)await db.conversations.delete(forkId),await db.messages.where("convId").equals(forkId).delete()}else{const newParent=(await db.conversations.get(id2))?.forkedFromConversationId,directChildren=await db.conversations.filter(c2=>c2.forkedFromConversationId===id2).
-toArray();for(const child2 of directChildren)await db.conversations.update(child2.id,{forkedFromConversationId:newParent??void 0})}await db.conversations.delete(id2),await db.messages.where("convId").equals(id2).delete()})}static async deleteMessage(messageId){await db.transaction("rw",db.messages,async()=>{const message=await db.messages.get(messageId);if(message){if(message.parent){const parent=await db.messages.get(message.parent);parent&&(parent.children=parent.children.filter(childId=>childId!==
-messageId),await db.messages.put(parent))}await db.messages.delete(messageId)}})}static async deleteMessageCascading(conversationId,messageId){return await db.transaction("rw",db.messages,async()=>{const allMessages=await db.messages.where("convId").equals(conversationId).toArray(),descendants=findDescendantMessages(allMessages,messageId),allToDelete=[messageId,...descendants],message=await db.messages.get(messageId);if(message&&message.parent){const parent=await db.messages.get(message.parent);
-parent&&(parent.children=parent.children.filter(childId=>childId!==messageId),await db.messages.put(parent))}return await db.messages.bulkDelete(allToDelete),allToDelete})}static async getAllConversations(){return await db.conversations.orderBy("lastModified").reverse().toArray()}static async getConversation(id2){return await db.conversations.get(id2)}static async getConversationMessages(convId){return await db.messages.where("convId").equals(convId).sortBy("timestamp")}static async updateConversation(id2,updates){
-await db.conversations.update(id2,{...updates,lastModified:Date.now()})}static async updateCurrentNode(convId,nodeId){await this.updateConversation(convId,{currNode:nodeId})}static async updateMessage(id2,updates){await db.messages.update(id2,updates)}static async importConversations(data){let importedCount=0,skippedCount=0;return await db.transaction("rw",[db.conversations,db.messages],async()=>{for(const item of data){const{conv,messages}=item;if(await db.conversations.get(conv.id)){console.warn(
-`Conversation "${conv.name}" already exists, skipping...`),skippedCount++;continue}await db.conversations.add(conv);for(const msg of messages)await db.messages.put(msg);importedCount++}return{imported:importedCount,skipped:skippedCount}})}static async forkConversation(sourceConvId,atMessageId,options){return await db.transaction("rw",[db.conversations,db.messages],async()=>{const sourceConv=await db.conversations.get(sourceConvId);if(!sourceConv)throw new Error(`Source conversation ${sourceConvId}\
- not found`);const allMessages=await db.messages.where("convId").equals(sourceConvId).toArray(),pathMessages=filterByLeafNodeId(allMessages,atMessageId,!0);if(pathMessages.length===0)throw new Error(`Could not resolve message path to ${atMessageId}`);const idMap=new Map;for(const msg of pathMessages)idMap.set(msg.id,uuid$1());const newConvId=uuid$1(),clonedMessages=pathMessages.map(msg=>{const newId=idMap.get(msg.id),newParent=msg.parent?idMap.get(msg.parent)??null:null,newChildren=msg.children.
-filter(childId=>idMap.has(childId)).map(childId=>idMap.get(childId));return{...msg,id:newId,convId:newConvId,parent:newParent,children:newChildren,extra:options.includeAttachments?msg.extra:void 0}}),lastClonedMessage=clonedMessages[clonedMessages.length-1],newConv={id:newConvId,name:options.name,lastModified:Date.now(),currNode:lastClonedMessage.id,forkedFromConversationId:sourceConvId,mcpServerOverrides:sourceConv.mcpServerOverrides?sourceConv.mcpServerOverrides.map(o=>({serverId:o.serverId,enabled:o.
-enabled})):void 0};await db.conversations.add(newConv);for(const msg of clonedMessages)await db.messages.add(msg);return newConv})}}const MIGRATION_DONE_KEY="llama-webui-migration-v2-done";function isMigrationNeeded(){try{return!localStorage.getItem(MIGRATION_DONE_KEY)}catch{return!1}}function markMigrationDone(){try{localStorage.setItem(MIGRATION_DONE_KEY,String(Date.now()))}catch{}}function hasLegacyMarkers(message){return message.content?LEGACY_AGENTIC_REGEX.HAS_LEGACY_MARKERS.test(message.content):
-!1}function extractLegacyReasoning(content2){let reasoning="",cleanContent=content2;const re2=new RegExp(LEGACY_AGENTIC_REGEX.REASONING_EXTRACT.source,"g");let match;for(;(match=re2.exec(content2))!==null;)reasoning+=match[1];return cleanContent=cleanContent.replace(new RegExp(LEGACY_AGENTIC_REGEX.REASONING_BLOCK.source,"g"),"").replace(LEGACY_AGENTIC_REGEX.REASONING_OPEN,""),{reasoning,cleanContent}}function parseLegacyToolCalls(content2){const turns=[],regex=new RegExp(LEGACY_AGENTIC_REGEX.COMPLETED_TOOL_CALL.
-source,"g");let lastIndex=0,currentTurn={textBefore:"",toolCalls:[]},match;for(;(match=regex.exec(content2))!==null;){const textBefore=content2.slice(lastIndex,match.index).trim();textBefore&&currentTurn.toolCalls.length>0?(turns.push(currentTurn),currentTurn={textBefore,toolCalls:[]}):textBefore&&currentTurn.toolCalls.length===0&&(currentTurn.textBefore=textBefore),currentTurn.toolCalls.push({name:match[1],args:match[2],result:match[3].replace(/^\n+|\n+$/g,"")}),lastIndex=match.index+match[0].length}
-const remainingText=content2.slice(lastIndex).trim();if(currentTurn.toolCalls.length>0&&turns.push(currentTurn),remainingText){const cleanRemaining=remainingText.replace(LEGACY_AGENTIC_REGEX.AGENTIC_TOOL_CALL_OPEN,"").trim();cleanRemaining&&turns.push({textBefore:cleanRemaining,toolCalls:[]})}return turns.length===0&&turns.push({textBefore:content2.trim(),toolCalls:[]}),turns}async function migrateConversation(convId){const allMessages=await DatabaseService.getConversationMessages(convId);let migratedCount=0;
-for(const message of allMessages){if(message.role!==MessageRole.ASSISTANT)continue;if(!hasLegacyMarkers(message)){if(message.content?.includes(LEGACY_REASONING_TAGS.START)){const{reasoning:reasoning2,cleanContent:cleanContent2}=extractLegacyReasoning(message.content);await DatabaseService.updateMessage(message.id,{content:cleanContent2.trim(),reasoningContent:reasoning2||void 0}),migratedCount++}continue}const{reasoning,cleanContent}=extractLegacyReasoning(message.content),turns=parseLegacyToolCalls(
-cleanContent);let existingToolCalls=[];if(message.toolCalls)try{existingToolCalls=JSON.parse(message.toolCalls)}catch{}const firstTurn=turns[0];if(!firstTurn)continue;const firstTurnToolCalls=firstTurn.toolCalls.map((tc,i)=>({id:(existingToolCalls.find(e=>e.function?.name===tc.name)||existingToolCalls[i])?.id||`legacy_tool_${i}`,type:"function",function:{name:tc.name,arguments:tc.args}}));await DatabaseService.updateMessage(message.id,{content:firstTurn.textBefore,reasoningContent:reasoning||void 0,
-toolCalls:firstTurnToolCalls.length>0?JSON.stringify(firstTurnToolCalls):""});let currentParentId=message.id,toolCallIdCounter=existingToolCalls.length;for(let i=0;i<firstTurn.toolCalls.length;i++){const tc=firstTurn.toolCalls[i],toolCallId=firstTurnToolCalls[i]?.id||`legacy_tool_${i}`;currentParentId=(await DatabaseService.createMessageBranch({convId,type:MessageType.TEXT,role:MessageRole.TOOL,content:tc.result,toolCallId,timestamp:message.timestamp+i+1,toolCalls:"",children:[]},currentParentId)).
-id}for(let turnIdx=1;turnIdx<turns.length;turnIdx++){const turn=turns[turnIdx],turnToolCalls=turn.toolCalls.map((tc,i)=>{const idx=toolCallIdCounter+i;return{id:existingToolCalls[idx]?.id||`legacy_tool_${idx}`,type:"function",function:{name:tc.name,arguments:tc.args}}});toolCallIdCounter+=turn.toolCalls.length,currentParentId=(await DatabaseService.createMessageBranch({convId,type:MessageType.TEXT,role:MessageRole.ASSISTANT,content:turn.textBefore,timestamp:message.timestamp+turnIdx*100,toolCalls:turnToolCalls.
-length>0?JSON.stringify(turnToolCalls):"",children:[],model:message.model},currentParentId)).id;for(let i=0;i<turn.toolCalls.length;i++){const tc=turn.toolCalls[i],toolCallId=turnToolCalls[i]?.id||`legacy_tool_${toolCallIdCounter+i}`;currentParentId=(await DatabaseService.createMessageBranch({convId,type:MessageType.TEXT,role:MessageRole.TOOL,content:tc.result,toolCallId,timestamp:message.timestamp+turnIdx*100+i+1,toolCalls:"",children:[]},currentParentId)).id}}if(message.children.length>0&&currentParentId!==
-message.id){for(const childId of message.children){const child2=allMessages.find(m=>m.id===childId);if(child2&&child2.role!==MessageRole.TOOL){await DatabaseService.updateMessage(childId,{parent:currentParentId});const newParent=await DatabaseService.getConversationMessages(convId).then(msgs=>msgs.find(m=>m.id===currentParentId));newParent&&!newParent.children.includes(childId)&&await DatabaseService.updateMessage(currentParentId,{children:[...newParent.children,childId]})}}await DatabaseService.
-updateMessage(message.id,{children:[]})}migratedCount++}return migratedCount}async function runLegacyMigration(){if(isMigrationNeeded()){console.log("[Migration] Starting legacy message format migration...");try{const conversations2=await DatabaseService.getAllConversations();let totalMigrated=0;for(const conv of conversations2){const count=await migrateConversation(conv.id);totalMigrated+=count}totalMigrated>0?console.log(`[Migration] Migrated ${totalMigrated} messages across ${conversations2.length}\
- conversations`):console.log("[Migration] No legacy messages found, marking as done"),markMigrationDone()}catch(error2){console.error("[Migration] Failed to migrate legacy messages:",error2),markMigrationDone()}}}class TTLCache{cache=new Map;ttlMs;maxEntries;onEvict;constructor(options={}){this.ttlMs=options.ttlMs??DEFAULT_CACHE_TTL_MS,this.maxEntries=options.maxEntries??DEFAULT_CACHE_MAX_ENTRIES,this.onEvict=options.onEvict}get(key2){const entry=this.cache.get(key2);return entry?Date.now()>entry.
-expiresAt?(this.delete(key2),null):(entry.lastAccessed=Date.now(),entry.value):null}set(key2,value,customTtlMs){this.cache.size>=this.maxEntries&&!this.cache.has(key2)&&this.evictOldest();const ttl=customTtlMs??this.ttlMs,now2=Date.now();this.cache.set(key2,{value,expiresAt:now2+ttl,lastAccessed:now2})}has(key2){const entry=this.cache.get(key2);return entry?Date.now()>entry.expiresAt?(this.delete(key2),!1):!0:!1}delete(key2){const entry=this.cache.get(key2);return entry&&this.onEvict&&this.onEvict(
-key2,entry.value),this.cache.delete(key2)}clear(){if(this.onEvict)for(const[key2,entry]of this.cache)this.onEvict(key2,entry.value);this.cache.clear()}get size(){return this.cache.size}prune(){const now2=Date.now();let pruned=0;for(const[key2,entry]of this.cache)now2>entry.expiresAt&&(this.delete(key2),pruned++);return pruned}keys(){const now2=Date.now(),validKeys=[];for(const[key2,entry]of this.cache)now2<=entry.expiresAt&&validKeys.push(key2);return validKeys}evictOldest(){let oldestKey=null,oldestTime=1/0;
-for(const[key2,entry]of this.cache)entry.lastAccessed<oldestTime&&(oldestTime=entry.lastAccessed,oldestKey=key2);oldestKey!==null&&this.delete(oldestKey)}touch(key2){const entry=this.cache.get(key2);if(!entry)return!1;const now2=Date.now();return now2>entry.expiresAt?(this.delete(key2),!1):(entry.expiresAt=now2+this.ttlMs,entry.lastAccessed=now2,!0)}}function getRequestUrl(input){return typeof input=="string"?input:input instanceof URL?input.href:input.url}function getRequestMethod(input,init2,baseInit){
-return init2?.method?init2.method:typeof Request<"u"&&input instanceof Request?input.method:baseInit?.method??"GET"}function getRequestBody(input,init2){if(init2?.body!==void 0)return init2.body;if(typeof Request<"u"&&input instanceof Request)return input.body}function summarizeRequestBody(body2){return body2==null?{kind:"empty"}:typeof body2=="string"?{kind:"string",size:body2.length}:body2 instanceof Blob?{kind:"blob",size:body2.size}:body2 instanceof URLSearchParams?{kind:"urlsearchparams",size:body2.
-toString().length}:body2 instanceof FormData?{kind:"formdata"}:body2 instanceof ArrayBuffer?{kind:"arraybuffer",size:body2.byteLength}:ArrayBuffer.isView(body2)?{kind:body2.constructor.name,size:body2.byteLength}:{kind:typeof body2}}function formatDiagnosticErrorMessage(error2){const message=error2 instanceof Error?error2.message:String(error2);return message.includes("Failed to fetch")?`${message} (check CORS?)`:message}function extractJsonRpcMethods(body2){if(typeof body2=="string")try{const parsed=JSON.
-parse(body2),methods=(Array.isArray(parsed)?parsed:[parsed]).map(message=>typeof message?.method=="string"?message.method:void 0).filter(method=>!!method);return methods.length>0?methods:void 0}catch{return}}function throwIfAborted(signal){if(signal?.aborted)throw new DOMException("Operation was aborted","AbortError")}function isAbortError(error2){return error2 instanceof DOMException&&error2.name==="AbortError"||error2 instanceof Error&&error2.name==="AbortError"}function uuid$1(){return globalThis.
-crypto?.randomUUID?.()??Math.random().toString(36).substring(2)}function ActionIconCopyToClipboard($$anchor,$$props){push$1($$props,!0);let ariaLabel=prop($$props,"ariaLabel",3,"Copy to clipboard"),canCopy=prop($$props,"canCopy",3,!0);{let $0=user_derived(()=>canCopy()?"pointer":"not-allowed");Copy($$anchor,{get class(){return`h-3 w-3 flex-shrink-0 cursor-${get$4($0)??""}`},get"aria-label"(){return ariaLabel()},onclick:()=>canCopy()&&copyToClipboard($$props.text)})}pop()}function ActionIconRemove($$anchor,$$props){
-push$1($$props,!0);let className=prop($$props,"class",3,""),iconSize=prop($$props,"iconSize",3,3);Button($$anchor,{type:"button",variant:"ghost",size:"icon-sm",get class(){return`bg-white/20 p-0 hover:bg-white/30 ${className()??""}`},onclick:e=>{e.stopPropagation(),$$props.onRemove?.($$props.id)},"aria-label":"Remove file",children:($$anchor2,$$slotProps)=>{X($$anchor2,{get class(){return`h-${iconSize()??""} w-${iconSize()??""}`}})},$$slots:{default:!0}}),pop()}var root_6$z=from_html("<p> </p>"),
-root_2$15=from_html("<!> <!>",1);function BadgeChatStatistic($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,"");function handleClick(){copyToClipboard(String($$props.value))}var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Root$5,($$anchor3,Tooltip_Root)=>{Tooltip_Root($$anchor3,{children:($$anchor4,$$slotProps)=>{var fragment_2=root_2$15(),node_2=first_child(
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-text2,$$props.value)),append($$anchor7,text2)},$$slots:{icon:!0,default:!0}})},$$slots:{default:!0}})});var node_4=sibling(node_2,2);component(node_4,()=>Tooltip_content,($$anchor5,Tooltip_Content)=>{Tooltip_Content($$anchor5,{children:($$anchor6,$$slotProps2)=>{var p2=root_6$z(),text_1=child(p2,!0);reset(p2),template_effect(()=>set_text(text_1,$$props.tooltipLabel)),append($$anchor6,p2)},$$slots:{default:!0}})}),append($$anchor4,fragment_2)},$$slots:{default:!0}})}),append($$anchor2,fragment_1)},
-alternate=$$anchor2=>{BadgeInfo($$anchor2,{get class(){return className()},onclick:handleClick,icon:$$anchor3=>{var fragment_7=comment$2(),node_5=first_child(fragment_7);component(node_5,()=>$$props.icon,($$anchor4,Icon_2)=>{Icon_2($$anchor4,{class:"h-3 w-3"})}),append($$anchor3,fragment_7)},children:($$anchor3,$$slotProps)=>{next$1();var text_2=text$8();template_effect(()=>set_text(text_2,$$props.value)),append($$anchor3,text_2)},$$slots:{icon:!0,default:!0}})};if_block(node2,$$render=>{$$props.
-tooltipLabel?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root$1w=from_html("<button><!> <!></button>");function BadgeInfo($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,"");var button=root$1w();button.__click=function(...$$args){$$props.onclick?.apply(this,$$args)};var node2=child(button);{var consequent=$$anchor2=>{var fragment=comment$2(),node_1=first_child(fragment);snippet(node_1,()=>$$props.icon),append($$anchor2,fragment)};if_block(
-node2,$$render=>{$$props.icon&&$$render(consequent)})}var node_2=sibling(node2,2);snippet(node_2,()=>$$props.children),reset(button),template_effect($0=>set_class(button,1,$0),[()=>clsx(cn$1("inline-flex cursor-pointer items-center gap-1 rounded-sm bg-muted-foreground/15 px-1.5 py-0.75",className()))]),append($$anchor,button),pop()}delegate(["click"]);var root_1$$=from_html("<span><!> </span>");function BadgeModality($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,"");const displayableModalities=user_derived(
-()=>$$props.modalities.filter(m=>m===ModelModality.VISION||m===ModelModality.AUDIO));var fragment=comment$2(),node2=first_child(fragment);each(node2,17,()=>get$4(displayableModalities),index$2,($$anchor2,modality)=>{const IconComponent=user_derived(()=>MODALITY_ICONS[get$4(modality)]),label=user_derived(()=>MODALITY_LABELS[get$4(modality)]);var span=root_1$$(),node_1=child(span);{var consequent=$$anchor3=>{var fragment_1=comment$2(),node_2=first_child(fragment_1);component(node_2,()=>get$4(IconComponent),
-($$anchor4,IconComponent_1)=>{IconComponent_1($$anchor4,{class:"h-3 w-3"})}),append($$anchor3,fragment_1)};if_block(node_1,$$render=>{get$4(IconComponent)&&$$render(consequent)})}var text2=sibling(node_1);reset(span),template_effect($0=>{set_class(span,1,$0),set_text(text2,` ${get$4(label)??""}`)},[()=>clsx(cn$1("inline-flex items-center gap-1 rounded-md bg-muted px-2 py-1 text-xs font-medium",className()))]),append($$anchor2,span)}),append($$anchor,fragment),pop()}var root_12$a=from_html('<div \
-class="mt-2 -mr-2 flex justify-end px-4"><!></div>'),root_2$14=from_html("<!> <!>",1),root_14$9=from_html('<div class="flex flex-wrap items-start justify-end gap-3"></div>'),root_1$_=from_html("<div><!></div>"),root$1v=from_html("<!> <!> <!> <!>",1);function ChatAttachmentsList($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),style2=prop($$props,"style",3,""),attachments=prop($$props,"attachments",19,()=>[]),readonly2=prop($$props,"readonly",3,!1),uploadedFiles=prop(
-$$props,"uploadedFiles",27,()=>proxy([])),imageClass=prop($$props,"imageClass",3,""),imageHeight=prop($$props,"imageHeight",3,"h-24"),imageWidth=prop($$props,"imageWidth",3,"w-auto"),limitToSingleRow=prop($$props,"limitToSingleRow",3,!1),displayItems=user_derived(()=>getAttachmentDisplayItems({uploadedFiles:uploadedFiles(),attachments:attachments()})),carouselRef=state$1(void 0),isScrollable=state$1(!1),previewDialogOpen=state$1(!1),previewItem=state$1(null),mcpResourcePreviewOpen=state$1(!1),mcpResourcePreviewExtra=state$1(
-null),showViewAll=user_derived(()=>limitToSingleRow()&&get$4(displayItems).length>0&&get$4(isScrollable)),viewAllDialogOpen=state$1(!1);function openPreview(item,event2){event2?.stopPropagation(),event2?.preventDefault(),set$1(previewItem,{uploadedFile:item.uploadedFile,attachment:item.attachment,preview:item.preview,name:item.name,size:item.size,textContent:item.textContent},!0),set$1(previewDialogOpen,!0)}function openMcpResourcePreview(extra){set$1(mcpResourcePreviewExtra,extra,!0),set$1(mcpResourcePreviewOpen,
-!0)}function toMcpResourceAttachment(extra,id2){return{id:id2,resource:{uri:extra.uri,name:extra.name,title:extra.name,serverName:extra.serverName}}}user_effect(()=>{get$4(carouselRef)&&get$4(displayItems).length&&get$4(carouselRef).resetScroll()});var fragment=root$1v(),node2=first_child(fragment);{var consequent_10=$$anchor2=>{var div=root_1$_(),node_1=child(div);{var consequent_5=$$anchor3=>{var fragment_1=root_2$14(),node_2=first_child(fragment_1);bind_this(HorizontalScrollCarousel(node_2,{onScrollableChange:scrollable=>set$1(
-isScrollable,scrollable,!0),children:($$anchor4,$$slotProps)=>{var fragment_2=comment$2(),node_3=first_child(fragment_2);each(node_3,17,()=>get$4(displayItems),item=>item.id,($$anchor5,item)=>{var fragment_3=comment$2(),node_4=first_child(fragment_3);{var consequent_1=$$anchor6=>{const mcpPrompt=user_derived(()=>get$4(item).attachment?.type===AttachmentType.MCP_PROMPT?get$4(item).attachment:get$4(item).uploadedFile?.mcpPrompt?{type:AttachmentType.MCP_PROMPT,name:get$4(item).name,serverName:get$4(
-item).uploadedFile.mcpPrompt.serverName,promptName:get$4(item).uploadedFile.mcpPrompt.promptName,content:get$4(item).textContent??"",arguments:get$4(item).uploadedFile.mcpPrompt.arguments}:null);var fragment_4=comment$2(),node_5=first_child(fragment_4);{var consequent=$$anchor7=>{{let $0=user_derived(()=>limitToSingleRow()?"first:ml-4 last:mr-4":""),$1=user_derived(()=>$$props.onFileRemove?()=>$$props.onFileRemove(get$4(item).id):void 0);ChatAttachmentMcpPrompt($$anchor7,{get class(){return`max-\
-w-[300px] min-w-[200px] flex-shrink-0 ${get$4($0)??""}`},get prompt(){return get$4(mcpPrompt)},get readonly(){return readonly2()},get isLoading(){return get$4(item).isLoading},get loadError(){return get$4(item).loadError},get onRemove(){return get$4($1)}})}};if_block(node_5,$$render=>{get$4(mcpPrompt)&&$$render(consequent)})}append($$anchor6,fragment_4)},alternate_2=$$anchor6=>{var fragment_6=comment$2(),node_6=first_child(fragment_6);{var consequent_2=$$anchor7=>{const mcpResource=user_derived(
-()=>get$4(item).attachment);{let $0=user_derived(()=>limitToSingleRow()?"first:ml-4 last:mr-4":""),$1=user_derived(()=>toMcpResourceAttachment(get$4(mcpResource),get$4(item).id));ChatAttachmentMcpResource($$anchor7,{get class(){return`flex-shrink-0 ${get$4($0)??""}`},get attachment(){return get$4($1)},onClick:()=>openMcpResourcePreview(get$4(mcpResource))})}},alternate_1=$$anchor7=>{var fragment_8=comment$2(),node_7=first_child(fragment_8);{var consequent_3=$$anchor8=>{{let $0=user_derived(()=>limitToSingleRow()?
-"first:ml-4 last:mr-4":"");ChatAttachmentThumbnailImage($$anchor8,{get class(){return`flex-shrink-0 cursor-pointer ${get$4($0)??""}`},get id(){return get$4(item).id},get name(){return get$4(item).name},get preview(){return get$4(item).preview},get readonly(){return readonly2()},get onRemove(){return $$props.onFileRemove},get height(){return imageHeight()},get width(){return imageWidth()},get imageClass(){return imageClass()},onClick:event2=>openPreview(get$4(item),event2)})}},alternate=$$anchor8=>{
-{let $0=user_derived(()=>limitToSingleRow()?"first:ml-4 last:mr-4":"");ChatAttachmentThumbnailFile($$anchor8,{get class(){return`flex-shrink-0 cursor-pointer ${get$4($0)??""}`},get id(){return get$4(item).id},get name(){return get$4(item).name},get size(){return get$4(item).size},get readonly(){return readonly2()},get onRemove(){return $$props.onFileRemove},get textContent(){return get$4(item).textContent},get attachment(){return get$4(item).attachment},get uploadedFile(){return get$4(item).uploadedFile},
-onClick:event2=>openPreview(get$4(item),event2)})}};if_block(node_7,$$render=>{get$4(item).isImage&&get$4(item).preview?$$render(consequent_3):$$render(alternate,!1)},!0)}append($$anchor7,fragment_8)};if_block(node_6,$$render=>{get$4(item).isMcpResource&&get$4(item).attachment?.type===AttachmentType.MCP_RESOURCE?$$render(consequent_2):$$render(alternate_1,!1)},!0)}append($$anchor6,fragment_6)};if_block(node_4,$$render=>{get$4(item).isMcpPrompt?$$render(consequent_1):$$render(alternate_2,!1)})}append(
-$$anchor5,fragment_3)}),append($$anchor4,fragment_2)},$$slots:{default:!0}}),$$value=>set$1(carouselRef,$$value,!0),()=>get$4(carouselRef));var node_8=sibling(node_2,2);{var consequent_4=$$anchor4=>{var div_1=root_12$a(),node_9=child(div_1);Button(node_9,{type:"button",variant:"ghost",size:"sm",class:"h-6 text-xs text-muted-foreground hover:text-foreground",onclick:()=>set$1(viewAllDialogOpen,!0),children:($$anchor5,$$slotProps)=>{next$1();var text2=text$8();template_effect(()=>set_text(text2,`V\
-iew all (${get$4(displayItems).length??""})`)),append($$anchor5,text2)},$$slots:{default:!0}}),reset(div_1),append($$anchor4,div_1)};if_block(node_8,$$render=>{get$4(showViewAll)&&$$render(consequent_4)})}append($$anchor3,fragment_1)},alternate_6=$$anchor3=>{var div_2=root_14$9();each(div_2,21,()=>get$4(displayItems),item=>item.id,($$anchor4,item)=>{var fragment_12=comment$2(),node_10=first_child(fragment_12);{var consequent_7=$$anchor5=>{const mcpPrompt=user_derived(()=>get$4(item).attachment?.
-type===AttachmentType.MCP_PROMPT?get$4(item).attachment:get$4(item).uploadedFile?.mcpPrompt?{type:AttachmentType.MCP_PROMPT,name:get$4(item).name,serverName:get$4(item).uploadedFile.mcpPrompt.serverName,promptName:get$4(item).uploadedFile.mcpPrompt.promptName,content:get$4(item).textContent??"",arguments:get$4(item).uploadedFile.mcpPrompt.arguments}:null);var fragment_13=comment$2(),node_11=first_child(fragment_13);{var consequent_6=$$anchor6=>{{let $0=user_derived(()=>$$props.onFileRemove?()=>$$props.
-onFileRemove(get$4(item).id):void 0);ChatAttachmentMcpPrompt($$anchor6,{class:"max-w-[300px] min-w-[200px]",get prompt(){return get$4(mcpPrompt)},get readonly(){return readonly2()},get isLoading(){return get$4(item).isLoading},get loadError(){return get$4(item).loadError},get onRemove(){return get$4($0)}})}};if_block(node_11,$$render=>{get$4(mcpPrompt)&&$$render(consequent_6)})}append($$anchor5,fragment_13)},alternate_5=$$anchor5=>{var fragment_15=comment$2(),node_12=first_child(fragment_15);{var consequent_8=$$anchor6=>{
-const mcpResource=user_derived(()=>get$4(item).attachment);{let $0=user_derived(()=>toMcpResourceAttachment(get$4(mcpResource),get$4(item).id));ChatAttachmentMcpResource($$anchor6,{get attachment(){return get$4($0)},onClick:()=>openMcpResourcePreview(get$4(mcpResource))})}},alternate_4=$$anchor6=>{var fragment_17=comment$2(),node_13=first_child(fragment_17);{var consequent_9=$$anchor7=>{ChatAttachmentThumbnailImage($$anchor7,{class:"cursor-pointer",get id(){return get$4(item).id},get name(){return get$4(
-item).name},get preview(){return get$4(item).preview},get readonly(){return readonly2()},get onRemove(){return $$props.onFileRemove},get height(){return imageHeight()},get width(){return imageWidth()},get imageClass(){return imageClass()},onClick:event2=>openPreview(get$4(item),event2)})},alternate_3=$$anchor7=>{ChatAttachmentThumbnailFile($$anchor7,{class:"cursor-pointer",get id(){return get$4(item).id},get name(){return get$4(item).name},get size(){return get$4(item).size},get readonly(){return readonly2()},
-get onRemove(){return $$props.onFileRemove},get textContent(){return get$4(item).textContent},get attachment(){return get$4(item).attachment},get uploadedFile(){return get$4(item).uploadedFile},onClick:event2=>openPreview(get$4(item),event2)})};if_block(node_13,$$render=>{get$4(item).isImage&&get$4(item).preview?$$render(consequent_9):$$render(alternate_3,!1)},!0)}append($$anchor6,fragment_17)};if_block(node_12,$$render=>{get$4(item).isMcpResource&&get$4(item).attachment?.type===AttachmentType.MCP_RESOURCE?
-$$render(consequent_8):$$render(alternate_4,!1)},!0)}append($$anchor5,fragment_15)};if_block(node_10,$$render=>{get$4(item).isMcpPrompt?$$render(consequent_7):$$render(alternate_5,!1)})}append($$anchor4,fragment_12)}),reset(div_2),append($$anchor3,div_2)};if_block(node_1,$$render=>{limitToSingleRow()?$$render(consequent_5):$$render(alternate_6,!1)})}reset(div),template_effect(()=>{set_class(div,1,clsx(className())),set_style(div,style2())}),append($$anchor2,div)};if_block(node2,$$render=>{get$4(
-displayItems).length>0&&$$render(consequent_10)})}var node_14=sibling(node2,2);{var consequent_11=$$anchor2=>{DialogChatAttachmentPreview($$anchor2,{get uploadedFile(){return get$4(previewItem).uploadedFile},get attachment(){return get$4(previewItem).attachment},get preview(){return get$4(previewItem).preview},get name(){return get$4(previewItem).name},get size(){return get$4(previewItem).size},get textContent(){return get$4(previewItem).textContent},get activeModelId(){return $$props.activeModelId},
-get open(){return get$4(previewDialogOpen)},set open($$value){set$1(previewDialogOpen,$$value,!0)}})};if_block(node_14,$$render=>{get$4(previewItem)&&$$render(consequent_11)})}var node_15=sibling(node_14,2);DialogChatAttachmentsViewAll(node_15,{get uploadedFiles(){return uploadedFiles()},get attachments(){return attachments()},get readonly(){return readonly2()},get onFileRemove(){return $$props.onFileRemove},imageHeight:"h-64",get imageClass(){return imageClass()},get activeModelId(){return $$props.
-activeModelId},get open(){return get$4(viewAllDialogOpen)},set open($$value){set$1(viewAllDialogOpen,$$value,!0)}});var node_16=sibling(node_15,2);{var consequent_12=$$anchor2=>{DialogMcpResourcePreview($$anchor2,{get extra(){return get$4(mcpResourcePreviewExtra)},get open(){return get$4(mcpResourcePreviewOpen)},set open($$value){set$1(mcpResourcePreviewOpen,$$value,!0)}})};if_block(node_16,$$render=>{get$4(mcpResourcePreviewExtra)&&$$render(consequent_12)})}append($$anchor,fragment),pop()}var root_1$Z=from_html(
-'<div class="absolute top-10 right-2 flex items-center justify-center opacity-0 transition-opacity group-hover:opacity-100"><!></div>'),root$1u=from_html("<div><!> <!></div>");function ChatAttachmentMcpPrompt($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),readonly2=prop($$props,"readonly",3,!1),isLoading2=prop($$props,"isLoading",3,!1);var div=root$1u(),node2=child(div);ChatMessageMcpPromptContent(node2,{get prompt(){return $$props.prompt},get variant(){return McpPromptVariant.
-ATTACHMENT},get isLoading(){return isLoading2()},get loadError(){return $$props.loadError}});var node_1=sibling(node2,2);{var consequent=$$anchor2=>{var div_1=root_1$Z(),node_2=child(div_1);ActionIconRemove(node_2,{get id(){return $$props.prompt.name},onRemove:()=>$$props.onRemove?.()}),reset(div_1),append($$anchor2,div_1)};if_block(node_1,$$render=>{!readonly2()&&$$props.onRemove&&$$render(consequent)})}reset(div),template_effect(()=>set_class(div,1,`group relative ${className()??""}`)),append(
-$$anchor,div),pop()}const NEVER=Object.freeze({status:"aborted"});function $constructor(name,initializer2,params){function init2(inst,def){if(inst._zod||Object.defineProperty(inst,"_zod",{value:{def,constr:_,traits:new Set},enumerable:!1}),inst._zod.traits.has(name))return;inst._zod.traits.add(name),initializer2(inst,def);const proto=_.prototype,keys2=Object.keys(proto);for(let i=0;i<keys2.length;i++){const k=keys2[i];k in inst||(inst[k]=proto[k].bind(inst))}}const Parent=params?.Parent??Object;
-class Definition extends Parent{}Object.defineProperty(Definition,"name",{value:name});function _(def){var _a2;const inst=params?.Parent?new Definition:this;init2(inst,def),(_a2=inst._zod).deferred??(_a2.deferred=[]);for(const fn of inst._zod.deferred)fn();return inst}return Object.defineProperty(_,"init",{value:init2}),Object.defineProperty(_,Symbol.hasInstance,{value:inst=>params?.Parent&&inst instanceof params.Parent?!0:inst?._zod?.traits?.has(name)}),Object.defineProperty(_,"name",{value:name}),
-_}class $ZodAsyncError extends Error{constructor(){super("Encountered Promise during synchronous parse. Use .parseAsync() instead.")}}class $ZodEncodeError extends Error{constructor(name){super(`Encountered unidirectional transform during encode: ${name}`),this.name="ZodEncodeError"}}const globalConfig={};function config(newConfig){return globalConfig}function getEnumValues(entries){const numericValues=Object.values(entries).filter(v=>typeof v=="number");return Object.entries(entries).filter(([k,
-_])=>numericValues.indexOf(+k)===-1).map(([_,v])=>v)}function jsonStringifyReplacer(_,value){return typeof value=="bigint"?value.toString():value}function cached(getter){return{get value(){{const value=getter();return Object.defineProperty(this,"value",{value}),value}}}}function nullish(input){return input==null}function cleanRegex(source2){const start2=source2.startsWith("^")?1:0,end=source2.endsWith("$")?source2.length-1:source2.length;return source2.slice(start2,end)}function floatSafeRemainder(val,step){
-const valDecCount=(val.toString().split(".")[1]||"").length,stepString=step.toString();let stepDecCount=(stepString.split(".")[1]||"").length;if(stepDecCount===0&&/\d?e-\d?/.test(stepString)){const match=stepString.match(/\d?e-(\d?)/);match?.[1]&&(stepDecCount=Number.parseInt(match[1]))}const decCount=valDecCount>stepDecCount?valDecCount:stepDecCount,valInt=Number.parseInt(val.toFixed(decCount).replace(".","")),stepInt=Number.parseInt(step.toFixed(decCount).replace(".",""));return valInt%stepInt/
-10**decCount}const EVALUATING=Symbol("evaluating");function defineLazy(object2,key2,getter){let value;Object.defineProperty(object2,key2,{get(){if(value!==EVALUATING)return value===void 0&&(value=EVALUATING,value=getter()),value},set(v){Object.defineProperty(object2,key2,{value:v})},configurable:!0})}function assignProp(target2,prop2,value){Object.defineProperty(target2,prop2,{value,writable:!0,enumerable:!0,configurable:!0})}function mergeDefs(...defs){const mergedDescriptors={};for(const def of defs){
-const descriptors=Object.getOwnPropertyDescriptors(def);Object.assign(mergedDescriptors,descriptors)}return Object.defineProperties({},mergedDescriptors)}function esc(str){return JSON.stringify(str)}function slugify(input){return input.toLowerCase().trim().replace(/[^\w\s-]/g,"").replace(/[\s_-]+/g,"-").replace(/^-+|-+$/g,"")}const captureStackTrace="captureStackTrace"in Error?Error.captureStackTrace:(..._args)=>{};function isObject$1(data){return typeof data=="object"&&data!==null&&!Array.isArray(
-data)}const allowsEval=cached(()=>{if(typeof navigator<"u"&&navigator?.userAgent?.includes("Cloudflare"))return!1;try{const F=Function;return new F(""),!0}catch{return!1}});function isPlainObject$2(o){if(isObject$1(o)===!1)return!1;const ctor=o.constructor;if(ctor===void 0||typeof ctor!="function")return!0;const prot=ctor.prototype;return!(isObject$1(prot)===!1||Object.prototype.hasOwnProperty.call(prot,"isPrototypeOf")===!1)}function shallowClone(o){return isPlainObject$2(o)?{...o}:Array.isArray(
-o)?[...o]:o}const propertyKeyTypes=new Set(["string","number","symbol"]);function escapeRegex(str){return str.replace(/[.*+?^${}()|[\]\\]/g,"\\$&")}function clone(inst,def,params){const cl=new inst._zod.constr(def??inst._zod.def);return(!def||params?.parent)&&(cl._zod.parent=inst),cl}function normalizeParams(_params){const params=_params;if(!params)return{};if(typeof params=="string")return{error:()=>params};if(params?.message!==void 0){if(params?.error!==void 0)throw new Error("Cannot specify b\
-oth `message` and `error` params");params.error=params.message}return delete params.message,typeof params.error=="string"?{...params,error:()=>params.error}:params}function optionalKeys(shape){return Object.keys(shape).filter(k=>shape[k]._zod.optin==="optional"&&shape[k]._zod.optout==="optional")}const NUMBER_FORMAT_RANGES={safeint:[Number.MIN_SAFE_INTEGER,Number.MAX_SAFE_INTEGER],int32:[-2147483648,2147483647],uint32:[0,4294967295],float32:[-34028234663852886e22,34028234663852886e22],float64:[-Number.
-MAX_VALUE,Number.MAX_VALUE]};function pick(schema,mask){const currDef=schema._zod.def,def=mergeDefs(schema._zod.def,{get shape(){const newShape={};for(const key2 in mask){if(!(key2 in currDef.shape))throw new Error(`Unrecognized key: "${key2}"`);mask[key2]&&(newShape[key2]=currDef.shape[key2])}return assignProp(this,"shape",newShape),newShape},checks:[]});return clone(schema,def)}function omit(schema,mask){const currDef=schema._zod.def,def=mergeDefs(schema._zod.def,{get shape(){const newShape={...schema.
-_zod.def.shape};for(const key2 in mask){if(!(key2 in currDef.shape))throw new Error(`Unrecognized key: "${key2}"`);mask[key2]&&delete newShape[key2]}return assignProp(this,"shape",newShape),newShape},checks:[]});return clone(schema,def)}function extend$2(schema,shape){if(!isPlainObject$2(shape))throw new Error("Invalid input to extend: expected a plain object");const checks2=schema._zod.def.checks;if(checks2&&checks2.length>0)throw new Error("Object schemas containing refinements cannot be exten\
-ded. Use `.safeExtend()` instead.");const def=mergeDefs(schema._zod.def,{get shape(){const _shape={...schema._zod.def.shape,...shape};return assignProp(this,"shape",_shape),_shape},checks:[]});return clone(schema,def)}function safeExtend(schema,shape){if(!isPlainObject$2(shape))throw new Error("Invalid input to safeExtend: expected a plain object");const def={...schema._zod.def,get shape(){const _shape={...schema._zod.def.shape,...shape};return assignProp(this,"shape",_shape),_shape},checks:schema.
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-nrecognized key: "${key2}"`);mask[key2]&&(shape[key2]=Class?new Class({type:"optional",innerType:oldShape[key2]}):oldShape[key2])}else for(const key2 in oldShape)shape[key2]=Class?new Class({type:"optional",innerType:oldShape[key2]}):oldShape[key2];return assignProp(this,"shape",shape),shape},checks:[]});return clone(schema,def)}function required$4(Class,schema,mask){const def=mergeDefs(schema._zod.def,{get shape(){const oldShape=schema._zod.def.shape,shape={...oldShape};if(mask)for(const key2 in mask){
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-_errors.push(mapper(issue2));else{let curr=fieldErrors,i=0;for(;i<issue2.path.length;){const el=issue2.path[i];i===issue2.path.length-1?(curr[el]=curr[el]||{_errors:[]},curr[el]._errors.push(mapper(issue2))):curr[el]=curr[el]||{_errors:[]},curr=curr[el],i++}}};return processError(error2),fieldErrors}const _parse=_Err=>(schema,value,_ctx,_params)=>{const ctx=_ctx?Object.assign(_ctx,{async:!1}):{async:!1},result=schema._zod.run({value,issues:[]},ctx);if(result instanceof Promise)throw new $ZodAsyncError;
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-value,_ctx),_safeEncodeAsync=_Err=>async(schema,value,_ctx)=>{const ctx=_ctx?Object.assign(_ctx,{direction:"backward"}):{direction:"backward"};return _safeParseAsync(_Err)(schema,value,ctx)},_safeDecodeAsync=_Err=>async(schema,value,_ctx)=>_safeParseAsync(_Err)(schema,value,_ctx),cuid=/^[cC][^\s-]{8,}$/,cuid2=/^[0-9a-z]+$/,ulid=/^[0-9A-HJKMNP-TV-Za-hjkmnp-tv-z]{26}$/,xid=/^[0-9a-vA-V]{20}$/,ksuid=/^[A-Za-z0-9]{27}$/,nanoid=/^[a-zA-Z0-9_-]{21}$/,duration$1=/^P(?:(\d+W)|(?!.*W)(?=\d|T\d)(\d+Y)?(\d+M)?(\d+D)?(T(?=\d)(\d+H)?(\d+M)?(\d+([.,]\d+)?S)?)?)$/,
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-^(\\p{Extended_Pictographic}|\\p{Emoji_Component})+$";function emoji(){return new RegExp(_emoji$1,"u")}const ipv4=/^(?:(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9][0-9]|[0-9])\.){3}(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9][0-9]|[0-9])$/,ipv6=/^(([0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:))$/,
-cidrv4=/^((25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9][0-9]|[0-9])\.){3}(25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9][0-9]|[0-9])\/([0-9]|[1-2][0-9]|3[0-2])$/,cidrv6=/^(([0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}|::|([0-9a-fA-F]{1,4})?::([0-9a-fA-F]{1,4}:?){0,6})\/(12[0-8]|1[01][0-9]|[1-9]?[0-9])$/,base64=/^$|^(?:[0-9a-zA-Z+/]{4})*(?:(?:[0-9a-zA-Z+/]{2}==)|(?:[0-9a-zA-Z+/]{3}=))?$/,base64url=/^[A-Za-z0-9_-]*$/,e164=/^\+(?:[0-9]){6,14}[0-9]$/,dateSource="(?:(?:\\d\\d[2468][048]|\\d\\d[13579][26]|\\d\\d0[48]|[02468][048]\
-00|[13579][26]00)-02-29|\\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\\d|30)|(?:02)-(?:0[1-9]|1\\d|2[0-8])))",date$1=new RegExp(`^${dateSource}$`);function timeSource(args){const hhmm="(?:[01]\\d|2[0-3]):[0-5]\\d";return typeof args.precision=="number"?args.precision===-1?`${hhmm}`:args.precision===0?`${hhmm}:[0-5]\\d`:`${hhmm}:[0-5]\\d\\.\\d{${args.precision}}`:`${hhmm}(?::[0-5]\\d(?:\\.\\d+)?)?`}function time$1(args){return new RegExp(`^${timeSource(args)}$`)}
-function datetime$1(args){const time2=timeSource({precision:args.precision}),opts=["Z"];args.local&&opts.push(""),args.offset&&opts.push("([+-](?:[01]\\d|2[0-3]):[0-5]\\d)");const timeRegex=`${time2}(?:${opts.join("|")})`;return new RegExp(`^${dateSource}T(?:${timeRegex})$`)}const string$3=params=>{const regex=params?`[\\s\\S]{${params?.minimum??0},${params?.maximum??""}}`:"[\\s\\S]*";return new RegExp(`^${regex}$`)},integer=/^-?\d+$/,number$3=/^-?\d+(?:\.\d+)?/,boolean$2=/^(?:true|false)$/i,_null$2=/^null$/i,
-lowercase=/^[^A-Z]*$/,uppercase$1=/^[^a-z]*$/,$ZodCheck=$constructor("$ZodCheck",(inst,def)=>{var _a2;inst._zod??(inst._zod={}),inst._zod.def=def,(_a2=inst._zod).onattach??(_a2.onattach=[])}),numericOriginMap={number:"number",bigint:"bigint",object:"date"},$ZodCheckLessThan=$constructor("$ZodCheckLessThan",(inst,def)=>{$ZodCheck.init(inst,def);const origin2=numericOriginMap[typeof def.value];inst._zod.onattach.push(inst2=>{const bag=inst2._zod.bag,curr=(def.inclusive?bag.maximum:bag.exclusiveMaximum)??
-Number.POSITIVE_INFINITY;def.value<curr&&(def.inclusive?bag.maximum=def.value:bag.exclusiveMaximum=def.value)}),inst._zod.check=payload=>{(def.inclusive?payload.value<=def.value:payload.value<def.value)||payload.issues.push({origin:origin2,code:"too_big",maximum:def.value,input:payload.value,inclusive:def.inclusive,inst,continue:!def.abort})}}),$ZodCheckGreaterThan=$constructor("$ZodCheckGreaterThan",(inst,def)=>{$ZodCheck.init(inst,def);const origin2=numericOriginMap[typeof def.value];inst._zod.
-onattach.push(inst2=>{const bag=inst2._zod.bag,curr=(def.inclusive?bag.minimum:bag.exclusiveMinimum)??Number.NEGATIVE_INFINITY;def.value>curr&&(def.inclusive?bag.minimum=def.value:bag.exclusiveMinimum=def.value)}),inst._zod.check=payload=>{(def.inclusive?payload.value>=def.value:payload.value>def.value)||payload.issues.push({origin:origin2,code:"too_small",minimum:def.value,input:payload.value,inclusive:def.inclusive,inst,continue:!def.abort})}}),$ZodCheckMultipleOf=$constructor("$ZodCheckMultip\
-leOf",(inst,def)=>{$ZodCheck.init(inst,def),inst._zod.onattach.push(inst2=>{var _a2;(_a2=inst2._zod.bag).multipleOf??(_a2.multipleOf=def.value)}),inst._zod.check=payload=>{if(typeof payload.value!=typeof def.value)throw new Error("Cannot mix number and bigint in multiple_of check.");(typeof payload.value=="bigint"?payload.value%def.value===BigInt(0):floatSafeRemainder(payload.value,def.value)===0)||payload.issues.push({origin:typeof payload.value,code:"not_multiple_of",divisor:def.value,input:payload.
-value,inst,continue:!def.abort})}}),$ZodCheckNumberFormat=$constructor("$ZodCheckNumberFormat",(inst,def)=>{$ZodCheck.init(inst,def),def.format=def.format||"float64";const isInt=def.format?.includes("int"),origin2=isInt?"int":"number",[minimum,maximum]=NUMBER_FORMAT_RANGES[def.format];inst._zod.onattach.push(inst2=>{const bag=inst2._zod.bag;bag.format=def.format,bag.minimum=minimum,bag.maximum=maximum,isInt&&(bag.pattern=integer)}),inst._zod.check=payload=>{const input=payload.value;if(isInt){if(!Number.
-isInteger(input)){payload.issues.push({expected:origin2,format:def.format,code:"invalid_type",continue:!1,input,inst});return}if(!Number.isSafeInteger(input)){input>0?payload.issues.push({input,code:"too_big",maximum:Number.MAX_SAFE_INTEGER,note:"Integers must be within the safe integer range.",inst,origin:origin2,continue:!def.abort}):payload.issues.push({input,code:"too_small",minimum:Number.MIN_SAFE_INTEGER,note:"Integers must be within the safe integer range.",inst,origin:origin2,continue:!def.
-abort});return}}input<minimum&&payload.issues.push({origin:"number",input,code:"too_small",minimum,inclusive:!0,inst,continue:!def.abort}),input>maximum&&payload.issues.push({origin:"number",input,code:"too_big",maximum,inst})}}),$ZodCheckMaxLength=$constructor("$ZodCheckMaxLength",(inst,def)=>{var _a2;$ZodCheck.init(inst,def),(_a2=inst._zod.def).when??(_a2.when=payload=>{const val=payload.value;return!nullish(val)&&val.length!==void 0}),inst._zod.onattach.push(inst2=>{const curr=inst2._zod.bag.
-maximum??Number.POSITIVE_INFINITY;def.maximum<curr&&(inst2._zod.bag.maximum=def.maximum)}),inst._zod.check=payload=>{const input=payload.value;if(input.length<=def.maximum)return;const origin2=getLengthableOrigin(input);payload.issues.push({origin:origin2,code:"too_big",maximum:def.maximum,inclusive:!0,input,inst,continue:!def.abort})}}),$ZodCheckMinLength=$constructor("$ZodCheckMinLength",(inst,def)=>{var _a2;$ZodCheck.init(inst,def),(_a2=inst._zod.def).when??(_a2.when=payload=>{const val=payload.
-value;return!nullish(val)&&val.length!==void 0}),inst._zod.onattach.push(inst2=>{const curr=inst2._zod.bag.minimum??Number.NEGATIVE_INFINITY;def.minimum>curr&&(inst2._zod.bag.minimum=def.minimum)}),inst._zod.check=payload=>{const input=payload.value;if(input.length>=def.minimum)return;const origin2=getLengthableOrigin(input);payload.issues.push({origin:origin2,code:"too_small",minimum:def.minimum,inclusive:!0,input,inst,continue:!def.abort})}}),$ZodCheckLengthEquals=$constructor("$ZodCheckLength\
-Equals",(inst,def)=>{var _a2;$ZodCheck.init(inst,def),(_a2=inst._zod.def).when??(_a2.when=payload=>{const val=payload.value;return!nullish(val)&&val.length!==void 0}),inst._zod.onattach.push(inst2=>{const bag=inst2._zod.bag;bag.minimum=def.length,bag.maximum=def.length,bag.length=def.length}),inst._zod.check=payload=>{const input=payload.value,length=input.length;if(length===def.length)return;const origin2=getLengthableOrigin(input),tooBig=length>def.length;payload.issues.push({origin:origin2,...tooBig?
-{code:"too_big",maximum:def.length}:{code:"too_small",minimum:def.length},inclusive:!0,exact:!0,input:payload.value,inst,continue:!def.abort})}}),$ZodCheckStringFormat=$constructor("$ZodCheckStringFormat",(inst,def)=>{var _a2,_b;$ZodCheck.init(inst,def),inst._zod.onattach.push(inst2=>{const bag=inst2._zod.bag;bag.format=def.format,def.pattern&&(bag.patterns??(bag.patterns=new Set),bag.patterns.add(def.pattern))}),def.pattern?(_a2=inst._zod).check??(_a2.check=payload=>{def.pattern.lastIndex=0,!def.
-pattern.test(payload.value)&&payload.issues.push({origin:"string",code:"invalid_format",format:def.format,input:payload.value,...def.pattern?{pattern:def.pattern.toString()}:{},inst,continue:!def.abort})}):(_b=inst._zod).check??(_b.check=()=>{})}),$ZodCheckRegex=$constructor("$ZodCheckRegex",(inst,def)=>{$ZodCheckStringFormat.init(inst,def),inst._zod.check=payload=>{def.pattern.lastIndex=0,!def.pattern.test(payload.value)&&payload.issues.push({origin:"string",code:"invalid_format",format:"regex",
-input:payload.value,pattern:def.pattern.toString(),inst,continue:!def.abort})}}),$ZodCheckLowerCase=$constructor("$ZodCheckLowerCase",(inst,def)=>{def.pattern??(def.pattern=lowercase),$ZodCheckStringFormat.init(inst,def)}),$ZodCheckUpperCase=$constructor("$ZodCheckUpperCase",(inst,def)=>{def.pattern??(def.pattern=uppercase$1),$ZodCheckStringFormat.init(inst,def)}),$ZodCheckIncludes=$constructor("$ZodCheckIncludes",(inst,def)=>{$ZodCheck.init(inst,def);const escapedRegex=escapeRegex(def.includes),
-pattern2=new RegExp(typeof def.position=="number"?`^.{${def.position}}${escapedRegex}`:escapedRegex);def.pattern=pattern2,inst._zod.onattach.push(inst2=>{const bag=inst2._zod.bag;bag.patterns??(bag.patterns=new Set),bag.patterns.add(pattern2)}),inst._zod.check=payload=>{payload.value.includes(def.includes,def.position)||payload.issues.push({origin:"string",code:"invalid_format",format:"includes",includes:def.includes,input:payload.value,inst,continue:!def.abort})}}),$ZodCheckStartsWith=$constructor(
-"$ZodCheckStartsWith",(inst,def)=>{$ZodCheck.init(inst,def);const pattern2=new RegExp(`^${escapeRegex(def.prefix)}.*`);def.pattern??(def.pattern=pattern2),inst._zod.onattach.push(inst2=>{const bag=inst2._zod.bag;bag.patterns??(bag.patterns=new Set),bag.patterns.add(pattern2)}),inst._zod.check=payload=>{payload.value.startsWith(def.prefix)||payload.issues.push({origin:"string",code:"invalid_format",format:"starts_with",prefix:def.prefix,input:payload.value,inst,continue:!def.abort})}}),$ZodCheckEndsWith=$constructor(
-"$ZodCheckEndsWith",(inst,def)=>{$ZodCheck.init(inst,def);const pattern2=new RegExp(`.*${escapeRegex(def.suffix)}$`);def.pattern??(def.pattern=pattern2),inst._zod.onattach.push(inst2=>{const bag=inst2._zod.bag;bag.patterns??(bag.patterns=new Set),bag.patterns.add(pattern2)}),inst._zod.check=payload=>{payload.value.endsWith(def.suffix)||payload.issues.push({origin:"string",code:"invalid_format",format:"ends_with",suffix:def.suffix,input:payload.value,inst,continue:!def.abort})}}),$ZodCheckOverwrite=$constructor(
-"$ZodCheckOverwrite",(inst,def)=>{$ZodCheck.init(inst,def),inst._zod.check=payload=>{payload.value=def.tx(payload.value)}});class Doc{constructor(args=[]){this.content=[],this.indent=0,this&&(this.args=args)}indented(fn){this.indent+=1,fn(this),this.indent-=1}write(arg){if(typeof arg=="function"){arg(this,{execution:"sync"}),arg(this,{execution:"async"});return}const lines=arg.split(`
-`).filter(x=>x),minIndent=Math.min(...lines.map(x=>x.length-x.trimStart().length)),dedented=lines.map(x=>x.slice(minIndent)).map(x=>" ".repeat(this.indent*2)+x);for(const line of dedented)this.content.push(line)}compile(){const F=Function,args=this?.args,lines=[...(this?.content??[""]).map(x=>`  ${x}`)];return new F(...args,lines.join(`
-`))}}const version$2={major:4,minor:2,patch:1},$ZodType=$constructor("$ZodType",(inst,def)=>{var _a2;inst??(inst={}),inst._zod.def=def,inst._zod.bag=inst._zod.bag||{},inst._zod.version=version$2;const checks2=[...inst._zod.def.checks??[]];inst._zod.traits.has("$ZodCheck")&&checks2.unshift(inst);for(const ch of checks2)for(const fn of ch._zod.onattach)fn(inst);if(checks2.length===0)(_a2=inst._zod).deferred??(_a2.deferred=[]),inst._zod.deferred?.push(()=>{inst._zod.run=inst._zod.parse});else{const runChecks=(payload,checks3,ctx)=>{
-let isAborted=aborted(payload),asyncResult;for(const ch of checks3){if(ch._zod.def.when){if(!ch._zod.def.when(payload))continue}else if(isAborted)continue;const currLen=payload.issues.length,_=ch._zod.check(payload);if(_ instanceof Promise&&ctx?.async===!1)throw new $ZodAsyncError;if(asyncResult||_ instanceof Promise)asyncResult=(asyncResult??Promise.resolve()).then(async()=>{await _,payload.issues.length!==currLen&&(isAborted||(isAborted=aborted(payload,currLen)))});else{if(payload.issues.length===
-currLen)continue;isAborted||(isAborted=aborted(payload,currLen))}}return asyncResult?asyncResult.then(()=>payload):payload},handleCanaryResult=(canary,payload,ctx)=>{if(aborted(canary))return canary.aborted=!0,canary;const checkResult=runChecks(payload,checks2,ctx);if(checkResult instanceof Promise){if(ctx.async===!1)throw new $ZodAsyncError;return checkResult.then(checkResult2=>inst._zod.parse(checkResult2,ctx))}return inst._zod.parse(checkResult,ctx)};inst._zod.run=(payload,ctx)=>{if(ctx.skipChecks)
-return inst._zod.parse(payload,ctx);if(ctx.direction==="backward"){const canary=inst._zod.parse({value:payload.value,issues:[]},{...ctx,skipChecks:!0});return canary instanceof Promise?canary.then(canary2=>handleCanaryResult(canary2,payload,ctx)):handleCanaryResult(canary,payload,ctx)}const result=inst._zod.parse(payload,ctx);if(result instanceof Promise){if(ctx.async===!1)throw new $ZodAsyncError;return result.then(result2=>runChecks(result2,checks2,ctx))}return runChecks(result,checks2,ctx)}}inst["\
-~standard"]={validate:value=>{try{const r2=safeParse$2(inst,value);return r2.success?{value:r2.data}:{issues:r2.error?.issues}}catch{return safeParseAsync$1(inst,value).then(r2=>r2.success?{value:r2.data}:{issues:r2.error?.issues})}},vendor:"zod",version:1}}),$ZodString=$constructor("$ZodString",(inst,def)=>{$ZodType.init(inst,def),inst._zod.pattern=[...inst?._zod.bag?.patterns??[]].pop()??string$3(inst._zod.bag),inst._zod.parse=(payload,_)=>{if(def.coerce)try{payload.value=String(payload.value)}catch{}
-return typeof payload.value=="string"||payload.issues.push({expected:"string",code:"invalid_type",input:payload.value,inst}),payload}}),$ZodStringFormat=$constructor("$ZodStringFormat",(inst,def)=>{$ZodCheckStringFormat.init(inst,def),$ZodString.init(inst,def)}),$ZodGUID=$constructor("$ZodGUID",(inst,def)=>{def.pattern??(def.pattern=guid),$ZodStringFormat.init(inst,def)}),$ZodUUID=$constructor("$ZodUUID",(inst,def)=>{if(def.version){const v={v1:1,v2:2,v3:3,v4:4,v5:5,v6:6,v7:7,v8:8}[def.version];
-if(v===void 0)throw new Error(`Invalid UUID version: "${def.version}"`);def.pattern??(def.pattern=uuid(v))}else def.pattern??(def.pattern=uuid());$ZodStringFormat.init(inst,def)}),$ZodEmail=$constructor("$ZodEmail",(inst,def)=>{def.pattern??(def.pattern=email),$ZodStringFormat.init(inst,def)}),$ZodURL=$constructor("$ZodURL",(inst,def)=>{$ZodStringFormat.init(inst,def),inst._zod.check=payload=>{try{const trimmed=payload.value.trim(),url2=new URL(trimmed);def.hostname&&(def.hostname.lastIndex=0,def.
-hostname.test(url2.hostname)||payload.issues.push({code:"invalid_format",format:"url",note:"Invalid hostname",pattern:def.hostname.source,input:payload.value,inst,continue:!def.abort})),def.protocol&&(def.protocol.lastIndex=0,def.protocol.test(url2.protocol.endsWith(":")?url2.protocol.slice(0,-1):url2.protocol)||payload.issues.push({code:"invalid_format",format:"url",note:"Invalid protocol",pattern:def.protocol.source,input:payload.value,inst,continue:!def.abort})),def.normalize?payload.value=url2.
-href:payload.value=trimmed;return}catch{payload.issues.push({code:"invalid_format",format:"url",input:payload.value,inst,continue:!def.abort})}}}),$ZodEmoji=$constructor("$ZodEmoji",(inst,def)=>{def.pattern??(def.pattern=emoji()),$ZodStringFormat.init(inst,def)}),$ZodNanoID=$constructor("$ZodNanoID",(inst,def)=>{def.pattern??(def.pattern=nanoid),$ZodStringFormat.init(inst,def)}),$ZodCUID=$constructor("$ZodCUID",(inst,def)=>{def.pattern??(def.pattern=cuid),$ZodStringFormat.init(inst,def)}),$ZodCUID2=$constructor(
-"$ZodCUID2",(inst,def)=>{def.pattern??(def.pattern=cuid2),$ZodStringFormat.init(inst,def)}),$ZodULID=$constructor("$ZodULID",(inst,def)=>{def.pattern??(def.pattern=ulid),$ZodStringFormat.init(inst,def)}),$ZodXID=$constructor("$ZodXID",(inst,def)=>{def.pattern??(def.pattern=xid),$ZodStringFormat.init(inst,def)}),$ZodKSUID=$constructor("$ZodKSUID",(inst,def)=>{def.pattern??(def.pattern=ksuid),$ZodStringFormat.init(inst,def)}),$ZodISODateTime=$constructor("$ZodISODateTime",(inst,def)=>{def.pattern??
-(def.pattern=datetime$1(def)),$ZodStringFormat.init(inst,def)}),$ZodISODate=$constructor("$ZodISODate",(inst,def)=>{def.pattern??(def.pattern=date$1),$ZodStringFormat.init(inst,def)}),$ZodISOTime=$constructor("$ZodISOTime",(inst,def)=>{def.pattern??(def.pattern=time$1(def)),$ZodStringFormat.init(inst,def)}),$ZodISODuration=$constructor("$ZodISODuration",(inst,def)=>{def.pattern??(def.pattern=duration$1),$ZodStringFormat.init(inst,def)}),$ZodIPv4=$constructor("$ZodIPv4",(inst,def)=>{def.pattern??
-(def.pattern=ipv4),$ZodStringFormat.init(inst,def),inst._zod.bag.format="ipv4"}),$ZodIPv6=$constructor("$ZodIPv6",(inst,def)=>{def.pattern??(def.pattern=ipv6),$ZodStringFormat.init(inst,def),inst._zod.bag.format="ipv6",inst._zod.check=payload=>{try{new URL(`http://[${payload.value}]`)}catch{payload.issues.push({code:"invalid_format",format:"ipv6",input:payload.value,inst,continue:!def.abort})}}}),$ZodCIDRv4=$constructor("$ZodCIDRv4",(inst,def)=>{def.pattern??(def.pattern=cidrv4),$ZodStringFormat.
-init(inst,def)}),$ZodCIDRv6=$constructor("$ZodCIDRv6",(inst,def)=>{def.pattern??(def.pattern=cidrv6),$ZodStringFormat.init(inst,def),inst._zod.check=payload=>{const parts=payload.value.split("/");try{if(parts.length!==2)throw new Error;const[address,prefix]=parts;if(!prefix)throw new Error;const prefixNum=Number(prefix);if(`${prefixNum}`!==prefix)throw new Error;if(prefixNum<0||prefixNum>128)throw new Error;new URL(`http://[${address}]`)}catch{payload.issues.push({code:"invalid_format",format:"c\
-idrv6",input:payload.value,inst,continue:!def.abort})}}});function isValidBase64(data){if(data==="")return!0;if(data.length%4!==0)return!1;try{return atob(data),!0}catch{return!1}}const $ZodBase64=$constructor("$ZodBase64",(inst,def)=>{def.pattern??(def.pattern=base64),$ZodStringFormat.init(inst,def),inst._zod.bag.contentEncoding="base64",inst._zod.check=payload=>{isValidBase64(payload.value)||payload.issues.push({code:"invalid_format",format:"base64",input:payload.value,inst,continue:!def.abort})}});
-function isValidBase64URL(data){if(!base64url.test(data))return!1;const base642=data.replace(/[-_]/g,c2=>c2==="-"?"+":"/"),padded=base642.padEnd(Math.ceil(base642.length/4)*4,"=");return isValidBase64(padded)}const $ZodBase64URL=$constructor("$ZodBase64URL",(inst,def)=>{def.pattern??(def.pattern=base64url),$ZodStringFormat.init(inst,def),inst._zod.bag.contentEncoding="base64url",inst._zod.check=payload=>{isValidBase64URL(payload.value)||payload.issues.push({code:"invalid_format",format:"base64ur\
-l",input:payload.value,inst,continue:!def.abort})}}),$ZodE164=$constructor("$ZodE164",(inst,def)=>{def.pattern??(def.pattern=e164),$ZodStringFormat.init(inst,def)});function isValidJWT(token2,algorithm=null){try{const tokensParts=token2.split(".");if(tokensParts.length!==3)return!1;const[header]=tokensParts;if(!header)return!1;const parsedHeader=JSON.parse(atob(header));return!("typ"in parsedHeader&&parsedHeader?.typ!=="JWT"||!parsedHeader.alg||algorithm&&(!("alg"in parsedHeader)||parsedHeader.alg!==
-algorithm))}catch{return!1}}const $ZodJWT=$constructor("$ZodJWT",(inst,def)=>{$ZodStringFormat.init(inst,def),inst._zod.check=payload=>{isValidJWT(payload.value,def.alg)||payload.issues.push({code:"invalid_format",format:"jwt",input:payload.value,inst,continue:!def.abort})}}),$ZodNumber=$constructor("$ZodNumber",(inst,def)=>{$ZodType.init(inst,def),inst._zod.pattern=inst._zod.bag.pattern??number$3,inst._zod.parse=(payload,_ctx)=>{if(def.coerce)try{payload.value=Number(payload.value)}catch{}const input=payload.
-value;if(typeof input=="number"&&!Number.isNaN(input)&&Number.isFinite(input))return payload;const received=typeof input=="number"?Number.isNaN(input)?"NaN":Number.isFinite(input)?void 0:"Infinity":void 0;return payload.issues.push({expected:"number",code:"invalid_type",input,inst,...received?{received}:{}}),payload}}),$ZodNumberFormat=$constructor("$ZodNumberFormat",(inst,def)=>{$ZodCheckNumberFormat.init(inst,def),$ZodNumber.init(inst,def)}),$ZodBoolean=$constructor("$ZodBoolean",(inst,def)=>{
-$ZodType.init(inst,def),inst._zod.pattern=boolean$2,inst._zod.parse=(payload,_ctx)=>{if(def.coerce)try{payload.value=!!payload.value}catch{}const input=payload.value;return typeof input=="boolean"||payload.issues.push({expected:"boolean",code:"invalid_type",input,inst}),payload}}),$ZodNull=$constructor("$ZodNull",(inst,def)=>{$ZodType.init(inst,def),inst._zod.pattern=_null$2,inst._zod.values=new Set([null]),inst._zod.parse=(payload,_ctx)=>{const input=payload.value;return input===null||payload.issues.
-push({expected:"null",code:"invalid_type",input,inst}),payload}}),$ZodAny=$constructor("$ZodAny",(inst,def)=>{$ZodType.init(inst,def),inst._zod.parse=payload=>payload}),$ZodUnknown=$constructor("$ZodUnknown",(inst,def)=>{$ZodType.init(inst,def),inst._zod.parse=payload=>payload}),$ZodNever=$constructor("$ZodNever",(inst,def)=>{$ZodType.init(inst,def),inst._zod.parse=(payload,_ctx)=>(payload.issues.push({expected:"never",code:"invalid_type",input:payload.value,inst}),payload)});function handleArrayResult(result,final,index2){
-result.issues.length&&final.issues.push(...prefixIssues(index2,result.issues)),final.value[index2]=result.value}const $ZodArray=$constructor("$ZodArray",(inst,def)=>{$ZodType.init(inst,def),inst._zod.parse=(payload,ctx)=>{const input=payload.value;if(!Array.isArray(input))return payload.issues.push({expected:"array",code:"invalid_type",input,inst}),payload;payload.value=Array(input.length);const proms=[];for(let i=0;i<input.length;i++){const item=input[i],result=def.element._zod.run({value:item,
-issues:[]},ctx);result instanceof Promise?proms.push(result.then(result2=>handleArrayResult(result2,payload,i))):handleArrayResult(result,payload,i)}return proms.length?Promise.all(proms).then(()=>payload):payload}});function handlePropertyResult(result,final,key2,input){result.issues.length&&final.issues.push(...prefixIssues(key2,result.issues)),result.value===void 0?key2 in input&&(final.value[key2]=void 0):final.value[key2]=result.value}function normalizeDef(def){const keys2=Object.keys(def.shape);
-for(const k of keys2)if(!def.shape?.[k]?._zod?.traits?.has("$ZodType"))throw new Error(`Invalid element at key "${k}": expected a Zod schema`);const okeys=optionalKeys(def.shape);return{...def,keys:keys2,keySet:new Set(keys2),numKeys:keys2.length,optionalKeys:new Set(okeys)}}function handleCatchall(proms,input,payload,ctx,def,inst){const unrecognized=[],keySet=def.keySet,_catchall=def.catchall._zod,t=_catchall.def.type;for(const key2 in input){if(keySet.has(key2))continue;if(t==="never"){unrecognized.
-push(key2);continue}const r2=_catchall.run({value:input[key2],issues:[]},ctx);r2 instanceof Promise?proms.push(r2.then(r3=>handlePropertyResult(r3,payload,key2,input))):handlePropertyResult(r2,payload,key2,input)}return unrecognized.length&&payload.issues.push({code:"unrecognized_keys",keys:unrecognized,input,inst}),proms.length?Promise.all(proms).then(()=>payload):payload}const $ZodObject=$constructor("$ZodObject",(inst,def)=>{if($ZodType.init(inst,def),!Object.getOwnPropertyDescriptor(def,"sha\
-pe")?.get){const sh=def.shape;Object.defineProperty(def,"shape",{get:()=>{const newSh={...sh};return Object.defineProperty(def,"shape",{value:newSh}),newSh}})}const _normalized=cached(()=>normalizeDef(def));defineLazy(inst._zod,"propValues",()=>{const shape=def.shape,propValues={};for(const key2 in shape){const field=shape[key2]._zod;if(field.values){propValues[key2]??(propValues[key2]=new Set);for(const v of field.values)propValues[key2].add(v)}}return propValues});const isObject2=isObject$1,catchall=def.
-catchall;let value;inst._zod.parse=(payload,ctx)=>{value??(value=_normalized.value);const input=payload.value;if(!isObject2(input))return payload.issues.push({expected:"object",code:"invalid_type",input,inst}),payload;payload.value={};const proms=[],shape=value.shape;for(const key2 of value.keys){const r2=shape[key2]._zod.run({value:input[key2],issues:[]},ctx);r2 instanceof Promise?proms.push(r2.then(r3=>handlePropertyResult(r3,payload,key2,input))):handlePropertyResult(r2,payload,key2,input)}return catchall?
-handleCatchall(proms,input,payload,ctx,_normalized.value,inst):proms.length?Promise.all(proms).then(()=>payload):payload}}),$ZodObjectJIT=$constructor("$ZodObjectJIT",(inst,def)=>{$ZodObject.init(inst,def);const superParse=inst._zod.parse,_normalized=cached(()=>normalizeDef(def)),generateFastpass=shape=>{const doc=new Doc(["shape","payload","ctx"]),normalized=_normalized.value,parseStr=key2=>{const k=esc(key2);return`shape[${k}]._zod.run({ value: input[${k}], issues: [] }, ctx)`};doc.write("cons\
-t input = payload.value;");const ids=Object.create(null);let counter=0;for(const key2 of normalized.keys)ids[key2]=`key_${counter++}`;doc.write("const newResult = {};");for(const key2 of normalized.keys){const id2=ids[key2],k=esc(key2);doc.write(`const ${id2} = ${parseStr(key2)};`),doc.write(`
-        if (${id2}.issues.length) {
-          payload.issues = payload.issues.concat(${id2}.issues.map(iss => ({
-            ...iss,
-            path: iss.path ? [${k}, ...iss.path] : [${k}]
-          })));
-        }
-        
-        
-        if (${id2}.value === undefined) {
-          if (${k} in input) {
-            newResult[${k}] = undefined;
-          }
-        } else {
-          newResult[${k}] = ${id2}.value;
-        }
-        
-      `)}doc.write("payload.value = newResult;"),doc.write("return payload;");const fn=doc.compile();return(payload,ctx)=>fn(shape,payload,ctx)};let fastpass;const isObject2=isObject$1,jit=!globalConfig.jitless,fastEnabled=jit&&allowsEval.value,catchall=def.catchall;let value;inst._zod.parse=(payload,ctx)=>{value??(value=_normalized.value);const input=payload.value;return isObject2(input)?jit&&fastEnabled&&ctx?.async===!1&&ctx.jitless!==!0?(fastpass||(fastpass=generateFastpass(def.shape)),payload=
-fastpass(payload,ctx),catchall?handleCatchall([],input,payload,ctx,value,inst):payload):superParse(payload,ctx):(payload.issues.push({expected:"object",code:"invalid_type",input,inst}),payload)}});function handleUnionResults(results,final,inst,ctx){for(const result of results)if(result.issues.length===0)return final.value=result.value,final;const nonaborted=results.filter(r2=>!aborted(r2));return nonaborted.length===1?(final.value=nonaborted[0].value,nonaborted[0]):(final.issues.push({code:"inva\
-lid_union",input:final.value,inst,errors:results.map(result=>result.issues.map(iss=>finalizeIssue(iss,ctx,config())))}),final)}const $ZodUnion=$constructor("$ZodUnion",(inst,def)=>{$ZodType.init(inst,def),defineLazy(inst._zod,"optin",()=>def.options.some(o=>o._zod.optin==="optional")?"optional":void 0),defineLazy(inst._zod,"optout",()=>def.options.some(o=>o._zod.optout==="optional")?"optional":void 0),defineLazy(inst._zod,"values",()=>{if(def.options.every(o=>o._zod.values))return new Set(def.options.
-flatMap(option2=>Array.from(option2._zod.values)))}),defineLazy(inst._zod,"pattern",()=>{if(def.options.every(o=>o._zod.pattern)){const patterns=def.options.map(o=>o._zod.pattern);return new RegExp(`^(${patterns.map(p2=>cleanRegex(p2.source)).join("|")})$`)}});const single=def.options.length===1,first=def.options[0]._zod.run;inst._zod.parse=(payload,ctx)=>{if(single)return first(payload,ctx);let async=!1;const results=[];for(const option2 of def.options){const result=option2._zod.run({value:payload.
-value,issues:[]},ctx);if(result instanceof Promise)results.push(result),async=!0;else{if(result.issues.length===0)return result;results.push(result)}}return async?Promise.all(results).then(results2=>handleUnionResults(results2,payload,inst,ctx)):handleUnionResults(results,payload,inst,ctx)}}),$ZodDiscriminatedUnion=$constructor("$ZodDiscriminatedUnion",(inst,def)=>{def.inclusive=!1,$ZodUnion.init(inst,def);const _super=inst._zod.parse;defineLazy(inst._zod,"propValues",()=>{const propValues={};for(const option2 of def.
-options){const pv=option2._zod.propValues;if(!pv||Object.keys(pv).length===0)throw new Error(`Invalid discriminated union option at index "${def.options.indexOf(option2)}"`);for(const[k,v]of Object.entries(pv)){propValues[k]||(propValues[k]=new Set);for(const val of v)propValues[k].add(val)}}return propValues});const disc=cached(()=>{const opts=def.options,map2=new Map;for(const o of opts){const values=o._zod.propValues?.[def.discriminator];if(!values||values.size===0)throw new Error(`Invalid di\
-scriminated union option at index "${def.options.indexOf(o)}"`);for(const v of values){if(map2.has(v))throw new Error(`Duplicate discriminator value "${String(v)}"`);map2.set(v,o)}}return map2});inst._zod.parse=(payload,ctx)=>{const input=payload.value;if(!isObject$1(input))return payload.issues.push({code:"invalid_type",expected:"object",input,inst}),payload;const opt=disc.value.get(input?.[def.discriminator]);return opt?opt._zod.run(payload,ctx):def.unionFallback?_super(payload,ctx):(payload.issues.
-push({code:"invalid_union",errors:[],note:"No matching discriminator",discriminator:def.discriminator,input,path:[def.discriminator],inst}),payload)}}),$ZodIntersection=$constructor("$ZodIntersection",(inst,def)=>{$ZodType.init(inst,def),inst._zod.parse=(payload,ctx)=>{const input=payload.value,left=def.left._zod.run({value:input,issues:[]},ctx),right=def.right._zod.run({value:input,issues:[]},ctx);return left instanceof Promise||right instanceof Promise?Promise.all([left,right]).then(([left2,right2])=>handleIntersectionResults(
-payload,left2,right2)):handleIntersectionResults(payload,left,right)}});function mergeValues(a,b){if(a===b)return{valid:!0,data:a};if(a instanceof Date&&b instanceof Date&&+a==+b)return{valid:!0,data:a};if(isPlainObject$2(a)&&isPlainObject$2(b)){const bKeys=Object.keys(b),sharedKeys=Object.keys(a).filter(key2=>bKeys.indexOf(key2)!==-1),newObj={...a,...b};for(const key2 of sharedKeys){const sharedValue=mergeValues(a[key2],b[key2]);if(!sharedValue.valid)return{valid:!1,mergeErrorPath:[key2,...sharedValue.
-mergeErrorPath]};newObj[key2]=sharedValue.data}return{valid:!0,data:newObj}}if(Array.isArray(a)&&Array.isArray(b)){if(a.length!==b.length)return{valid:!1,mergeErrorPath:[]};const newArray=[];for(let index2=0;index2<a.length;index2++){const itemA=a[index2],itemB=b[index2],sharedValue=mergeValues(itemA,itemB);if(!sharedValue.valid)return{valid:!1,mergeErrorPath:[index2,...sharedValue.mergeErrorPath]};newArray.push(sharedValue.data)}return{valid:!0,data:newArray}}return{valid:!1,mergeErrorPath:[]}}
-function handleIntersectionResults(result,left,right){if(left.issues.length&&result.issues.push(...left.issues),right.issues.length&&result.issues.push(...right.issues),aborted(result))return result;const merged=mergeValues(left.value,right.value);if(!merged.valid)throw new Error(`Unmergable intersection. Error path: ${JSON.stringify(merged.mergeErrorPath)}`);return result.value=merged.data,result}const $ZodRecord=$constructor("$ZodRecord",(inst,def)=>{$ZodType.init(inst,def),inst._zod.parse=(payload,ctx)=>{
-const input=payload.value;if(!isPlainObject$2(input))return payload.issues.push({expected:"record",code:"invalid_type",input,inst}),payload;const proms=[],values=def.keyType._zod.values;if(values){payload.value={};const recordKeys=new Set;for(const key2 of values)if(typeof key2=="string"||typeof key2=="number"||typeof key2=="symbol"){recordKeys.add(typeof key2=="number"?key2.toString():key2);const result=def.valueType._zod.run({value:input[key2],issues:[]},ctx);result instanceof Promise?proms.push(
-result.then(result2=>{result2.issues.length&&payload.issues.push(...prefixIssues(key2,result2.issues)),payload.value[key2]=result2.value})):(result.issues.length&&payload.issues.push(...prefixIssues(key2,result.issues)),payload.value[key2]=result.value)}let unrecognized;for(const key2 in input)recordKeys.has(key2)||(unrecognized=unrecognized??[],unrecognized.push(key2));unrecognized&&unrecognized.length>0&&payload.issues.push({code:"unrecognized_keys",input,inst,keys:unrecognized})}else{payload.
-value={};for(const key2 of Reflect.ownKeys(input)){if(key2==="__proto__")continue;const keyResult=def.keyType._zod.run({value:key2,issues:[]},ctx);if(keyResult instanceof Promise)throw new Error("Async schemas not supported in object keys currently");if(keyResult.issues.length){def.mode==="loose"?payload.value[key2]=input[key2]:payload.issues.push({code:"invalid_key",origin:"record",issues:keyResult.issues.map(iss=>finalizeIssue(iss,ctx,config())),input:key2,path:[key2],inst});continue}const result=def.
-valueType._zod.run({value:input[key2],issues:[]},ctx);result instanceof Promise?proms.push(result.then(result2=>{result2.issues.length&&payload.issues.push(...prefixIssues(key2,result2.issues)),payload.value[keyResult.value]=result2.value})):(result.issues.length&&payload.issues.push(...prefixIssues(key2,result.issues)),payload.value[keyResult.value]=result.value)}}return proms.length?Promise.all(proms).then(()=>payload):payload}}),$ZodEnum=$constructor("$ZodEnum",(inst,def)=>{$ZodType.init(inst,
-def);const values=getEnumValues(def.entries),valuesSet=new Set(values);inst._zod.values=valuesSet,inst._zod.pattern=new RegExp(`^(${values.filter(k=>propertyKeyTypes.has(typeof k)).map(o=>typeof o=="string"?escapeRegex(o):o.toString()).join("|")})$`),inst._zod.parse=(payload,_ctx)=>{const input=payload.value;return valuesSet.has(input)||payload.issues.push({code:"invalid_value",values,input,inst}),payload}}),$ZodLiteral=$constructor("$ZodLiteral",(inst,def)=>{if($ZodType.init(inst,def),def.values.
-length===0)throw new Error("Cannot create literal schema with no valid values");const values=new Set(def.values);inst._zod.values=values,inst._zod.pattern=new RegExp(`^(${def.values.map(o=>typeof o=="string"?escapeRegex(o):o?escapeRegex(o.toString()):String(o)).join("|")})$`),inst._zod.parse=(payload,_ctx)=>{const input=payload.value;return values.has(input)||payload.issues.push({code:"invalid_value",values:def.values,input,inst}),payload}}),$ZodTransform=$constructor("$ZodTransform",(inst,def)=>{
-$ZodType.init(inst,def),inst._zod.parse=(payload,ctx)=>{if(ctx.direction==="backward")throw new $ZodEncodeError(inst.constructor.name);const _out=def.transform(payload.value,payload);if(ctx.async)return(_out instanceof Promise?_out:Promise.resolve(_out)).then(output2=>(payload.value=output2,payload));if(_out instanceof Promise)throw new $ZodAsyncError;return payload.value=_out,payload}});function handleOptionalResult(result,input){return result.issues.length&&input===void 0?{issues:[],value:void 0}:
-result}const $ZodOptional=$constructor("$ZodOptional",(inst,def)=>{$ZodType.init(inst,def),inst._zod.optin="optional",inst._zod.optout="optional",defineLazy(inst._zod,"values",()=>def.innerType._zod.values?new Set([...def.innerType._zod.values,void 0]):void 0),defineLazy(inst._zod,"pattern",()=>{const pattern2=def.innerType._zod.pattern;return pattern2?new RegExp(`^(${cleanRegex(pattern2.source)})?$`):void 0}),inst._zod.parse=(payload,ctx)=>{if(def.innerType._zod.optin==="optional"){const result=def.
-innerType._zod.run(payload,ctx);return result instanceof Promise?result.then(r2=>handleOptionalResult(r2,payload.value)):handleOptionalResult(result,payload.value)}return payload.value===void 0?payload:def.innerType._zod.run(payload,ctx)}}),$ZodNullable=$constructor("$ZodNullable",(inst,def)=>{$ZodType.init(inst,def),defineLazy(inst._zod,"optin",()=>def.innerType._zod.optin),defineLazy(inst._zod,"optout",()=>def.innerType._zod.optout),defineLazy(inst._zod,"pattern",()=>{const pattern2=def.innerType.
-_zod.pattern;return pattern2?new RegExp(`^(${cleanRegex(pattern2.source)}|null)$`):void 0}),defineLazy(inst._zod,"values",()=>def.innerType._zod.values?new Set([...def.innerType._zod.values,null]):void 0),inst._zod.parse=(payload,ctx)=>payload.value===null?payload:def.innerType._zod.run(payload,ctx)}),$ZodDefault=$constructor("$ZodDefault",(inst,def)=>{$ZodType.init(inst,def),inst._zod.optin="optional",defineLazy(inst._zod,"values",()=>def.innerType._zod.values),inst._zod.parse=(payload,ctx)=>{if(ctx.
-direction==="backward")return def.innerType._zod.run(payload,ctx);if(payload.value===void 0)return payload.value=def.defaultValue,payload;const result=def.innerType._zod.run(payload,ctx);return result instanceof Promise?result.then(result2=>handleDefaultResult(result2,def)):handleDefaultResult(result,def)}});function handleDefaultResult(payload,def){return payload.value===void 0&&(payload.value=def.defaultValue),payload}const $ZodPrefault=$constructor("$ZodPrefault",(inst,def)=>{$ZodType.init(inst,
-def),inst._zod.optin="optional",defineLazy(inst._zod,"values",()=>def.innerType._zod.values),inst._zod.parse=(payload,ctx)=>(ctx.direction==="backward"||payload.value===void 0&&(payload.value=def.defaultValue),def.innerType._zod.run(payload,ctx))}),$ZodNonOptional=$constructor("$ZodNonOptional",(inst,def)=>{$ZodType.init(inst,def),defineLazy(inst._zod,"values",()=>{const v=def.innerType._zod.values;return v?new Set([...v].filter(x=>x!==void 0)):void 0}),inst._zod.parse=(payload,ctx)=>{const result=def.
-innerType._zod.run(payload,ctx);return result instanceof Promise?result.then(result2=>handleNonOptionalResult(result2,inst)):handleNonOptionalResult(result,inst)}});function handleNonOptionalResult(payload,inst){return!payload.issues.length&&payload.value===void 0&&payload.issues.push({code:"invalid_type",expected:"nonoptional",input:payload.value,inst}),payload}const $ZodCatch=$constructor("$ZodCatch",(inst,def)=>{$ZodType.init(inst,def),defineLazy(inst._zod,"optin",()=>def.innerType._zod.optin),
-defineLazy(inst._zod,"optout",()=>def.innerType._zod.optout),defineLazy(inst._zod,"values",()=>def.innerType._zod.values),inst._zod.parse=(payload,ctx)=>{if(ctx.direction==="backward")return def.innerType._zod.run(payload,ctx);const result=def.innerType._zod.run(payload,ctx);return result instanceof Promise?result.then(result2=>(payload.value=result2.value,result2.issues.length&&(payload.value=def.catchValue({...payload,error:{issues:result2.issues.map(iss=>finalizeIssue(iss,ctx,config()))},input:payload.
-value}),payload.issues=[]),payload)):(payload.value=result.value,result.issues.length&&(payload.value=def.catchValue({...payload,error:{issues:result.issues.map(iss=>finalizeIssue(iss,ctx,config()))},input:payload.value}),payload.issues=[]),payload)}}),$ZodPipe=$constructor("$ZodPipe",(inst,def)=>{$ZodType.init(inst,def),defineLazy(inst._zod,"values",()=>def.in._zod.values),defineLazy(inst._zod,"optin",()=>def.in._zod.optin),defineLazy(inst._zod,"optout",()=>def.out._zod.optout),defineLazy(inst.
-_zod,"propValues",()=>def.in._zod.propValues),inst._zod.parse=(payload,ctx)=>{if(ctx.direction==="backward"){const right=def.out._zod.run(payload,ctx);return right instanceof Promise?right.then(right2=>handlePipeResult(right2,def.in,ctx)):handlePipeResult(right,def.in,ctx)}const left=def.in._zod.run(payload,ctx);return left instanceof Promise?left.then(left2=>handlePipeResult(left2,def.out,ctx)):handlePipeResult(left,def.out,ctx)}});function handlePipeResult(left,next2,ctx){return left.issues.length?
-(left.aborted=!0,left):next2._zod.run({value:left.value,issues:left.issues},ctx)}const $ZodReadonly=$constructor("$ZodReadonly",(inst,def)=>{$ZodType.init(inst,def),defineLazy(inst._zod,"propValues",()=>def.innerType._zod.propValues),defineLazy(inst._zod,"values",()=>def.innerType._zod.values),defineLazy(inst._zod,"optin",()=>def.innerType?._zod?.optin),defineLazy(inst._zod,"optout",()=>def.innerType?._zod?.optout),inst._zod.parse=(payload,ctx)=>{if(ctx.direction==="backward")return def.innerType.
-_zod.run(payload,ctx);const result=def.innerType._zod.run(payload,ctx);return result instanceof Promise?result.then(handleReadonlyResult):handleReadonlyResult(result)}});function handleReadonlyResult(payload){return payload.value=Object.freeze(payload.value),payload}const $ZodCustom=$constructor("$ZodCustom",(inst,def)=>{$ZodCheck.init(inst,def),$ZodType.init(inst,def),inst._zod.parse=(payload,_)=>payload,inst._zod.check=payload=>{const input=payload.value,r2=def.fn(input);if(r2 instanceof Promise)
-return r2.then(r3=>handleRefineResult(r3,payload,input,inst));handleRefineResult(r2,payload,input,inst)}});function handleRefineResult(result,payload,input,inst){if(!result){const _iss={code:"custom",input,inst,path:[...inst._zod.def.path??[]],continue:!inst._zod.def.abort};inst._zod.def.params&&(_iss.params=inst._zod.def.params),payload.issues.push(issue(_iss))}}var _a;class $ZodRegistry{constructor(){this._map=new WeakMap,this._idmap=new Map}add(schema,..._meta){const meta=_meta[0];if(this._map.
-set(schema,meta),meta&&typeof meta=="object"&&"id"in meta){if(this._idmap.has(meta.id))throw new Error(`ID ${meta.id} already exists in the registry`);this._idmap.set(meta.id,schema)}return this}clear(){return this._map=new WeakMap,this._idmap=new Map,this}remove(schema){const meta=this._map.get(schema);return meta&&typeof meta=="object"&&"id"in meta&&this._idmap.delete(meta.id),this._map.delete(schema),this}get(schema){const p2=schema._zod.parent;if(p2){const pm={...this.get(p2)??{}};delete pm.
-id;const f={...pm,...this._map.get(schema)};return Object.keys(f).length?f:void 0}return this._map.get(schema)}has(schema){return this._map.has(schema)}}function registry(){return new $ZodRegistry}(_a=globalThis).__zod_globalRegistry??(_a.__zod_globalRegistry=registry());const globalRegistry=globalThis.__zod_globalRegistry;function _string(Class,params){return new Class({type:"string",...normalizeParams(params)})}function _email(Class,params){return new Class({type:"string",format:"email",check:"\
-string_format",abort:!1,...normalizeParams(params)})}function _guid(Class,params){return new Class({type:"string",format:"guid",check:"string_format",abort:!1,...normalizeParams(params)})}function _uuid(Class,params){return new Class({type:"string",format:"uuid",check:"string_format",abort:!1,...normalizeParams(params)})}function _uuidv4(Class,params){return new Class({type:"string",format:"uuid",check:"string_format",abort:!1,version:"v4",...normalizeParams(params)})}function _uuidv6(Class,params){
-return new Class({type:"string",format:"uuid",check:"string_format",abort:!1,version:"v6",...normalizeParams(params)})}function _uuidv7(Class,params){return new Class({type:"string",format:"uuid",check:"string_format",abort:!1,version:"v7",...normalizeParams(params)})}function _url$1(Class,params){return new Class({type:"string",format:"url",check:"string_format",abort:!1,...normalizeParams(params)})}function _emoji(Class,params){return new Class({type:"string",format:"emoji",check:"string_forma\
-t",abort:!1,...normalizeParams(params)})}function _nanoid(Class,params){return new Class({type:"string",format:"nanoid",check:"string_format",abort:!1,...normalizeParams(params)})}function _cuid(Class,params){return new Class({type:"string",format:"cuid",check:"string_format",abort:!1,...normalizeParams(params)})}function _cuid2(Class,params){return new Class({type:"string",format:"cuid2",check:"string_format",abort:!1,...normalizeParams(params)})}function _ulid(Class,params){return new Class({type:"\
-string",format:"ulid",check:"string_format",abort:!1,...normalizeParams(params)})}function _xid(Class,params){return new Class({type:"string",format:"xid",check:"string_format",abort:!1,...normalizeParams(params)})}function _ksuid(Class,params){return new Class({type:"string",format:"ksuid",check:"string_format",abort:!1,...normalizeParams(params)})}function _ipv4(Class,params){return new Class({type:"string",format:"ipv4",check:"string_format",abort:!1,...normalizeParams(params)})}function _ipv6(Class,params){
-return new Class({type:"string",format:"ipv6",check:"string_format",abort:!1,...normalizeParams(params)})}function _cidrv4(Class,params){return new Class({type:"string",format:"cidrv4",check:"string_format",abort:!1,...normalizeParams(params)})}function _cidrv6(Class,params){return new Class({type:"string",format:"cidrv6",check:"string_format",abort:!1,...normalizeParams(params)})}function _base64(Class,params){return new Class({type:"string",format:"base64",check:"string_format",abort:!1,...normalizeParams(
-params)})}function _base64url(Class,params){return new Class({type:"string",format:"base64url",check:"string_format",abort:!1,...normalizeParams(params)})}function _e164(Class,params){return new Class({type:"string",format:"e164",check:"string_format",abort:!1,...normalizeParams(params)})}function _jwt(Class,params){return new Class({type:"string",format:"jwt",check:"string_format",abort:!1,...normalizeParams(params)})}function _isoDateTime(Class,params){return new Class({type:"string",format:"d\
-atetime",check:"string_format",offset:!1,local:!1,precision:null,...normalizeParams(params)})}function _isoDate(Class,params){return new Class({type:"string",format:"date",check:"string_format",...normalizeParams(params)})}function _isoTime(Class,params){return new Class({type:"string",format:"time",check:"string_format",precision:null,...normalizeParams(params)})}function _isoDuration(Class,params){return new Class({type:"string",format:"duration",check:"string_format",...normalizeParams(params)})}
-function _number(Class,params){return new Class({type:"number",checks:[],...normalizeParams(params)})}function _coercedNumber(Class,params){return new Class({type:"number",coerce:!0,checks:[],...normalizeParams(params)})}function _int(Class,params){return new Class({type:"number",check:"number_format",abort:!1,format:"safeint",...normalizeParams(params)})}function _boolean(Class,params){return new Class({type:"boolean",...normalizeParams(params)})}function _null$1(Class,params){return new Class(
-{type:"null",...normalizeParams(params)})}function _any(Class){return new Class({type:"any"})}function _unknown(Class){return new Class({type:"unknown"})}function _never(Class,params){return new Class({type:"never",...normalizeParams(params)})}function _lt(value,params){return new $ZodCheckLessThan({check:"less_than",...normalizeParams(params),value,inclusive:!1})}function _lte(value,params){return new $ZodCheckLessThan({check:"less_than",...normalizeParams(params),value,inclusive:!0})}function _gt(value,params){
-return new $ZodCheckGreaterThan({check:"greater_than",...normalizeParams(params),value,inclusive:!1})}function _gte(value,params){return new $ZodCheckGreaterThan({check:"greater_than",...normalizeParams(params),value,inclusive:!0})}function _multipleOf(value,params){return new $ZodCheckMultipleOf({check:"multiple_of",...normalizeParams(params),value})}function _maxLength(maximum,params){return new $ZodCheckMaxLength({check:"max_length",...normalizeParams(params),maximum})}function _minLength(minimum,params){
-return new $ZodCheckMinLength({check:"min_length",...normalizeParams(params),minimum})}function _length(length,params){return new $ZodCheckLengthEquals({check:"length_equals",...normalizeParams(params),length})}function _regex(pattern2,params){return new $ZodCheckRegex({check:"string_format",format:"regex",...normalizeParams(params),pattern:pattern2})}function _lowercase(params){return new $ZodCheckLowerCase({check:"string_format",format:"lowercase",...normalizeParams(params)})}function _uppercase(params){
-return new $ZodCheckUpperCase({check:"string_format",format:"uppercase",...normalizeParams(params)})}function _includes(includes2,params){return new $ZodCheckIncludes({check:"string_format",format:"includes",...normalizeParams(params),includes:includes2})}function _startsWith(prefix,params){return new $ZodCheckStartsWith({check:"string_format",format:"starts_with",...normalizeParams(params),prefix})}function _endsWith(suffix,params){return new $ZodCheckEndsWith({check:"string_format",format:"end\
-s_with",...normalizeParams(params),suffix})}function _overwrite(tx){return new $ZodCheckOverwrite({check:"overwrite",tx})}function _normalize(form){return _overwrite(input=>input.normalize(form))}function _trim(){return _overwrite(input=>input.trim())}function _toLowerCase(){return _overwrite(input=>input.toLowerCase())}function _toUpperCase(){return _overwrite(input=>input.toUpperCase())}function _slugify(){return _overwrite(input=>slugify(input))}function _array(Class,element2,params){return new Class(
-{type:"array",element:element2,...normalizeParams(params)})}function _custom(Class,fn,_params){const norm=normalizeParams(_params);return norm.abort??(norm.abort=!0),new Class({type:"custom",check:"custom",fn,...norm})}function _refine(Class,fn,_params){return new Class({type:"custom",check:"custom",fn,...normalizeParams(_params)})}function _superRefine(fn){const ch=_check(payload=>(payload.addIssue=issue$1=>{if(typeof issue$1=="string")payload.issues.push(issue(issue$1,payload.value,ch._zod.def));else{
-const _issue=issue$1;_issue.fatal&&(_issue.continue=!1),_issue.code??(_issue.code="custom"),_issue.input??(_issue.input=payload.value),_issue.inst??(_issue.inst=ch),_issue.continue??(_issue.continue=!ch._zod.def.abort),payload.issues.push(issue(_issue))}},fn(payload.value,payload)));return ch}function _check(fn,params){const ch=new $ZodCheck({check:"custom",...normalizeParams(params)});return ch._zod.check=fn,ch}function initializeContext(params){let target2=params?.target??"draft-2020-12";return target2===
-"draft-4"&&(target2="draft-04"),target2==="draft-7"&&(target2="draft-07"),{processors:params.processors??{},metadataRegistry:params?.metadata??globalRegistry,target:target2,unrepresentable:params?.unrepresentable??"throw",override:params?.override??(()=>{}),io:params?.io??"output",counter:0,seen:new Map,cycles:params?.cycles??"ref",reused:params?.reused??"inline",external:params?.external??void 0}}function process$1(schema,ctx,_params={path:[],schemaPath:[]}){var _a2;const def=schema._zod.def,seen2=ctx.
-seen.get(schema);if(seen2)return seen2.count++,_params.schemaPath.includes(schema)&&(seen2.cycle=_params.path),seen2.schema;const result={schema:{},count:1,cycle:void 0,path:_params.path};ctx.seen.set(schema,result);const overrideSchema=schema._zod.toJSONSchema?.();if(overrideSchema)result.schema=overrideSchema;else{const params={..._params,schemaPath:[..._params.schemaPath,schema],path:_params.path},parent=schema._zod.parent;if(parent)result.ref=parent,process$1(parent,ctx,params),ctx.seen.get(
-parent).isParent=!0;else if(schema._zod.processJSONSchema)schema._zod.processJSONSchema(ctx,result.schema,params);else{const _json=result.schema,processor=ctx.processors[def.type];if(!processor)throw new Error(`[toJSONSchema]: Non-representable type encountered: ${def.type}`);processor(schema,ctx,_json,params)}}const meta=ctx.metadataRegistry.get(schema);return meta&&Object.assign(result.schema,meta),ctx.io==="input"&&isTransforming(schema)&&(delete result.schema.examples,delete result.schema.default),
-ctx.io==="input"&&result.schema._prefault&&((_a2=result.schema).default??(_a2.default=result.schema._prefault)),delete result.schema._prefault,ctx.seen.get(schema).schema}function extractDefs(ctx,schema){const root2=ctx.seen.get(schema);if(!root2)throw new Error("Unprocessed schema. This is a bug in Zod.");const makeURI=entry=>{const defsSegment=ctx.target==="draft-2020-12"?"$defs":"definitions";if(ctx.external){const externalId=ctx.external.registry.get(entry[0])?.id,uriGenerator=ctx.external.uri??
-(id3=>id3);if(externalId)return{ref:uriGenerator(externalId)};const id2=entry[1].defId??entry[1].schema.id??`schema${ctx.counter++}`;return entry[1].defId=id2,{defId:id2,ref:`${uriGenerator("__shared")}#/${defsSegment}/${id2}`}}if(entry[1]===root2)return{ref:"#"};const defUriPrefix=`#/${defsSegment}/`,defId=entry[1].schema.id??`__schema${ctx.counter++}`;return{defId,ref:defUriPrefix+defId}},extractToDef=entry=>{if(entry[1].schema.$ref)return;const seen2=entry[1],{ref:ref2,defId}=makeURI(entry);seen2.
-def={...seen2.schema},defId&&(seen2.defId=defId);const schema2=seen2.schema;for(const key2 in schema2)delete schema2[key2];schema2.$ref=ref2};if(ctx.cycles==="throw")for(const entry of ctx.seen.entries()){const seen2=entry[1];if(seen2.cycle)throw new Error(`Cycle detected: #/${seen2.cycle?.join("/")}/<root>
-
-Set the \`cycles\` parameter to \`"ref"\` to resolve cyclical schemas with defs.`)}for(const entry of ctx.seen.entries()){const seen2=entry[1];if(schema===entry[0]){extractToDef(entry);continue}if(ctx.external){const ext=ctx.external.registry.get(entry[0])?.id;if(schema!==entry[0]&&ext){extractToDef(entry);continue}}if(ctx.metadataRegistry.get(entry[0])?.id){extractToDef(entry);continue}if(seen2.cycle){extractToDef(entry);continue}if(seen2.count>1&&ctx.reused==="ref"){extractToDef(entry);continue}}}
-function finalize(ctx,schema){const root2=ctx.seen.get(schema);if(!root2)throw new Error("Unprocessed schema. This is a bug in Zod.");const flattenRef=zodSchema=>{const seen2=ctx.seen.get(zodSchema),schema2=seen2.def??seen2.schema,_cached={...schema2};if(seen2.ref===null)return;const ref2=seen2.ref;if(seen2.ref=null,ref2){flattenRef(ref2);const refSchema=ctx.seen.get(ref2).schema;refSchema.$ref&&(ctx.target==="draft-07"||ctx.target==="draft-04"||ctx.target==="openapi-3.0")?(schema2.allOf=schema2.
-allOf??[],schema2.allOf.push(refSchema)):(Object.assign(schema2,refSchema),Object.assign(schema2,_cached))}seen2.isParent||ctx.override({zodSchema,jsonSchema:schema2,path:seen2.path??[]})};for(const entry of[...ctx.seen.entries()].reverse())flattenRef(entry[0]);const result={};if(ctx.target==="draft-2020-12"?result.$schema="https://json-schema.org/draft/2020-12/schema":ctx.target==="draft-07"?result.$schema="http://json-schema.org/draft-07/schema#":ctx.target==="draft-04"?result.$schema="http://\
-json-schema.org/draft-04/schema#":ctx.target,ctx.external?.uri){const id2=ctx.external.registry.get(schema)?.id;if(!id2)throw new Error("Schema is missing an `id` property");result.$id=ctx.external.uri(id2)}Object.assign(result,root2.def??root2.schema);const defs=ctx.external?.defs??{};for(const entry of ctx.seen.entries()){const seen2=entry[1];seen2.def&&seen2.defId&&(defs[seen2.defId]=seen2.def)}ctx.external||Object.keys(defs).length>0&&(ctx.target==="draft-2020-12"?result.$defs=defs:result.definitions=
-defs);try{const finalized=JSON.parse(JSON.stringify(result));return Object.defineProperty(finalized,"~standard",{value:{...schema["~standard"],jsonSchema:{input:createStandardJSONSchemaMethod(schema,"input"),output:createStandardJSONSchemaMethod(schema,"output")}},enumerable:!1,writable:!1}),finalized}catch{throw new Error("Error converting schema to JSON.")}}function isTransforming(_schema,_ctx){const ctx=_ctx??{seen:new Set};if(ctx.seen.has(_schema))return!1;ctx.seen.add(_schema);const def=_schema.
-_zod.def;if(def.type==="transform")return!0;if(def.type==="array")return isTransforming(def.element,ctx);if(def.type==="set")return isTransforming(def.valueType,ctx);if(def.type==="lazy")return isTransforming(def.getter(),ctx);if(def.type==="promise"||def.type==="optional"||def.type==="nonoptional"||def.type==="nullable"||def.type==="readonly"||def.type==="default"||def.type==="prefault")return isTransforming(def.innerType,ctx);if(def.type==="intersection")return isTransforming(def.left,ctx)||isTransforming(
-def.right,ctx);if(def.type==="record"||def.type==="map")return isTransforming(def.keyType,ctx)||isTransforming(def.valueType,ctx);if(def.type==="pipe")return isTransforming(def.in,ctx)||isTransforming(def.out,ctx);if(def.type==="object"){for(const key2 in def.shape)if(isTransforming(def.shape[key2],ctx))return!0;return!1}if(def.type==="union"){for(const option2 of def.options)if(isTransforming(option2,ctx))return!0;return!1}if(def.type==="tuple"){for(const item of def.items)if(isTransforming(item,
-ctx))return!0;return!!(def.rest&&isTransforming(def.rest,ctx))}return!1}const createToJSONSchemaMethod=(schema,processors={})=>params=>{const ctx=initializeContext({...params,processors});return process$1(schema,ctx),extractDefs(ctx,schema),finalize(ctx,schema)},createStandardJSONSchemaMethod=(schema,io)=>params=>{const{libraryOptions,target:target2}=params??{},ctx=initializeContext({...libraryOptions??{},target:target2,io,processors:{}});return process$1(schema,ctx),extractDefs(ctx,schema),finalize(
-ctx,schema)},formatMap={guid:"uuid",url:"uri",datetime:"date-time",json_string:"json-string",regex:""},stringProcessor=(schema,ctx,_json,_params)=>{const json2=_json;json2.type="string";const{minimum,maximum,format:format2,patterns,contentEncoding}=schema._zod.bag;if(typeof minimum=="number"&&(json2.minLength=minimum),typeof maximum=="number"&&(json2.maxLength=maximum),format2&&(json2.format=formatMap[format2]??format2,json2.format===""&&delete json2.format),contentEncoding&&(json2.contentEncoding=
-contentEncoding),patterns&&patterns.size>0){const regexes=[...patterns];regexes.length===1?json2.pattern=regexes[0].source:regexes.length>1&&(json2.allOf=[...regexes.map(regex=>({...ctx.target==="draft-07"||ctx.target==="draft-04"||ctx.target==="openapi-3.0"?{type:"string"}:{},pattern:regex.source}))])}},numberProcessor=(schema,ctx,_json,_params)=>{const json2=_json,{minimum,maximum,format:format2,multipleOf:multipleOf2,exclusiveMaximum,exclusiveMinimum}=schema._zod.bag;typeof format2=="string"&&
-format2.includes("int")?json2.type="integer":json2.type="number",typeof exclusiveMinimum=="number"&&(ctx.target==="draft-04"||ctx.target==="openapi-3.0"?(json2.minimum=exclusiveMinimum,json2.exclusiveMinimum=!0):json2.exclusiveMinimum=exclusiveMinimum),typeof minimum=="number"&&(json2.minimum=minimum,typeof exclusiveMinimum=="number"&&ctx.target!=="draft-04"&&(exclusiveMinimum>=minimum?delete json2.minimum:delete json2.exclusiveMinimum)),typeof exclusiveMaximum=="number"&&(ctx.target==="draft-04"||
-ctx.target==="openapi-3.0"?(json2.maximum=exclusiveMaximum,json2.exclusiveMaximum=!0):json2.exclusiveMaximum=exclusiveMaximum),typeof maximum=="number"&&(json2.maximum=maximum,typeof exclusiveMaximum=="number"&&ctx.target!=="draft-04"&&(exclusiveMaximum<=maximum?delete json2.maximum:delete json2.exclusiveMaximum)),typeof multipleOf2=="number"&&(json2.multipleOf=multipleOf2)},booleanProcessor=(_schema,_ctx,json2,_params)=>{json2.type="boolean"},nullProcessor=(_schema,ctx,json2,_params)=>{ctx.target===
-"openapi-3.0"?(json2.type="string",json2.nullable=!0,json2.enum=[null]):json2.type="null"},neverProcessor=(_schema,_ctx,json2,_params)=>{json2.not={}},anyProcessor=(_schema,_ctx,_json,_params)=>{},unknownProcessor=(_schema,_ctx,_json,_params)=>{},enumProcessor=(schema,_ctx,json2,_params)=>{const def=schema._zod.def,values=getEnumValues(def.entries);values.every(v=>typeof v=="number")&&(json2.type="number"),values.every(v=>typeof v=="string")&&(json2.type="string"),json2.enum=values},literalProcessor=(schema,ctx,json2,_params)=>{
-const def=schema._zod.def,vals=[];for(const val of def.values)if(val===void 0){if(ctx.unrepresentable==="throw")throw new Error("Literal `undefined` cannot be represented in JSON Schema")}else if(typeof val=="bigint"){if(ctx.unrepresentable==="throw")throw new Error("BigInt literals cannot be represented in JSON Schema");vals.push(Number(val))}else vals.push(val);if(vals.length!==0)if(vals.length===1){const val=vals[0];json2.type=val===null?"null":typeof val,ctx.target==="draft-04"||ctx.target===
-"openapi-3.0"?json2.enum=[val]:json2.const=val}else vals.every(v=>typeof v=="number")&&(json2.type="number"),vals.every(v=>typeof v=="string")&&(json2.type="string"),vals.every(v=>typeof v=="boolean")&&(json2.type="boolean"),vals.every(v=>v===null)&&(json2.type="null"),json2.enum=vals},customProcessor=(_schema,ctx,_json,_params)=>{if(ctx.unrepresentable==="throw")throw new Error("Custom types cannot be represented in JSON Schema")},transformProcessor=(_schema,ctx,_json,_params)=>{if(ctx.unrepresentable===
-"throw")throw new Error("Transforms cannot be represented in JSON Schema")},arrayProcessor=(schema,ctx,_json,params)=>{const json2=_json,def=schema._zod.def,{minimum,maximum}=schema._zod.bag;typeof minimum=="number"&&(json2.minItems=minimum),typeof maximum=="number"&&(json2.maxItems=maximum),json2.type="array",json2.items=process$1(def.element,ctx,{...params,path:[...params.path,"items"]})},objectProcessor=(schema,ctx,_json,params)=>{const json2=_json,def=schema._zod.def;json2.type="object",json2.
-properties={};const shape=def.shape;for(const key2 in shape)json2.properties[key2]=process$1(shape[key2],ctx,{...params,path:[...params.path,"properties",key2]});const allKeys=new Set(Object.keys(shape)),requiredKeys=new Set([...allKeys].filter(key2=>{const v=def.shape[key2]._zod;return ctx.io==="input"?v.optin===void 0:v.optout===void 0}));requiredKeys.size>0&&(json2.required=Array.from(requiredKeys)),def.catchall?._zod.def.type==="never"?json2.additionalProperties=!1:def.catchall?def.catchall&&
-(json2.additionalProperties=process$1(def.catchall,ctx,{...params,path:[...params.path,"additionalProperties"]})):ctx.io==="output"&&(json2.additionalProperties=!1)},unionProcessor=(schema,ctx,json2,params)=>{const def=schema._zod.def,isExclusive=def.inclusive===!1,options=def.options.map((x,i)=>process$1(x,ctx,{...params,path:[...params.path,isExclusive?"oneOf":"anyOf",i]}));isExclusive?json2.oneOf=options:json2.anyOf=options},intersectionProcessor=(schema,ctx,json2,params)=>{const def=schema._zod.
-def,a=process$1(def.left,ctx,{...params,path:[...params.path,"allOf",0]}),b=process$1(def.right,ctx,{...params,path:[...params.path,"allOf",1]}),isSimpleIntersection=val=>"allOf"in val&&Object.keys(val).length===1,allOf2=[...isSimpleIntersection(a)?a.allOf:[a],...isSimpleIntersection(b)?b.allOf:[b]];json2.allOf=allOf2},recordProcessor=(schema,ctx,_json,params)=>{const json2=_json,def=schema._zod.def;json2.type="object",(ctx.target==="draft-07"||ctx.target==="draft-2020-12")&&(json2.propertyNames=
-process$1(def.keyType,ctx,{...params,path:[...params.path,"propertyNames"]})),json2.additionalProperties=process$1(def.valueType,ctx,{...params,path:[...params.path,"additionalProperties"]})},nullableProcessor=(schema,ctx,json2,params)=>{const def=schema._zod.def,inner2=process$1(def.innerType,ctx,params),seen2=ctx.seen.get(schema);ctx.target==="openapi-3.0"?(seen2.ref=def.innerType,json2.nullable=!0):json2.anyOf=[inner2,{type:"null"}]},nonoptionalProcessor=(schema,ctx,_json,params)=>{const def=schema.
-_zod.def;process$1(def.innerType,ctx,params);const seen2=ctx.seen.get(schema);seen2.ref=def.innerType},defaultProcessor=(schema,ctx,json2,params)=>{const def=schema._zod.def;process$1(def.innerType,ctx,params);const seen2=ctx.seen.get(schema);seen2.ref=def.innerType,json2.default=JSON.parse(JSON.stringify(def.defaultValue))},prefaultProcessor=(schema,ctx,json2,params)=>{const def=schema._zod.def;process$1(def.innerType,ctx,params);const seen2=ctx.seen.get(schema);seen2.ref=def.innerType,ctx.io===
-"input"&&(json2._prefault=JSON.parse(JSON.stringify(def.defaultValue)))},catchProcessor=(schema,ctx,json2,params)=>{const def=schema._zod.def;process$1(def.innerType,ctx,params);const seen2=ctx.seen.get(schema);seen2.ref=def.innerType;let catchValue;try{catchValue=def.catchValue(void 0)}catch{throw new Error("Dynamic catch values are not supported in JSON Schema")}json2.default=catchValue},pipeProcessor=(schema,ctx,_json,params)=>{const def=schema._zod.def,innerType=ctx.io==="input"?def.in._zod.
-def.type==="transform"?def.out:def.in:def.out;process$1(innerType,ctx,params);const seen2=ctx.seen.get(schema);seen2.ref=innerType},readonlyProcessor=(schema,ctx,json2,params)=>{const def=schema._zod.def;process$1(def.innerType,ctx,params);const seen2=ctx.seen.get(schema);seen2.ref=def.innerType,json2.readOnly=!0},optionalProcessor=(schema,ctx,_json,params)=>{const def=schema._zod.def;process$1(def.innerType,ctx,params);const seen2=ctx.seen.get(schema);seen2.ref=def.innerType};function isZ4Schema(s2){
-return!!s2._zod}function safeParse$1(schema,data){return isZ4Schema(schema)?safeParse$2(schema,data):schema.safeParse(data)}function getObjectShape(schema){if(!schema)return;let rawShape;if(isZ4Schema(schema)?rawShape=schema._zod?.def?.shape:rawShape=schema.shape,!!rawShape){if(typeof rawShape=="function")try{return rawShape()}catch{return}return rawShape}}function getLiteralValue(schema){if(isZ4Schema(schema)){const def2=schema._zod?.def;if(def2){if(def2.value!==void 0)return def2.value;if(Array.
-isArray(def2.values)&&def2.values.length>0)return def2.values[0]}}const def=schema._def;if(def){if(def.value!==void 0)return def.value;if(Array.isArray(def.values)&&def.values.length>0)return def.values[0]}const directValue=schema.value;if(directValue!==void 0)return directValue}const ZodISODateTime=$constructor("ZodISODateTime",(inst,def)=>{$ZodISODateTime.init(inst,def),ZodStringFormat.init(inst,def)});function datetime(params){return _isoDateTime(ZodISODateTime,params)}const ZodISODate=$constructor(
-"ZodISODate",(inst,def)=>{$ZodISODate.init(inst,def),ZodStringFormat.init(inst,def)});function date(params){return _isoDate(ZodISODate,params)}const ZodISOTime=$constructor("ZodISOTime",(inst,def)=>{$ZodISOTime.init(inst,def),ZodStringFormat.init(inst,def)});function time(params){return _isoTime(ZodISOTime,params)}const ZodISODuration=$constructor("ZodISODuration",(inst,def)=>{$ZodISODuration.init(inst,def),ZodStringFormat.init(inst,def)});function duration(params){return _isoDuration(ZodISODuration,
-params)}const initializer=(inst,issues)=>{$ZodError.init(inst,issues),inst.name="ZodError",Object.defineProperties(inst,{format:{value:mapper=>formatError(inst,mapper)},flatten:{value:mapper=>flattenError$1(inst,mapper)},addIssue:{value:issue2=>{inst.issues.push(issue2),inst.message=JSON.stringify(inst.issues,jsonStringifyReplacer,2)}},addIssues:{value:issues2=>{inst.issues.push(...issues2),inst.message=JSON.stringify(inst.issues,jsonStringifyReplacer,2)}},isEmpty:{get(){return inst.issues.length===
-0}}})},ZodRealError=$constructor("ZodError",initializer,{Parent:Error}),parse$3=_parse(ZodRealError),parseAsync=_parseAsync(ZodRealError),safeParse=_safeParse(ZodRealError),safeParseAsync=_safeParseAsync(ZodRealError),encode=_encode(ZodRealError),decode$1=_decode(ZodRealError),encodeAsync=_encodeAsync(ZodRealError),decodeAsync=_decodeAsync(ZodRealError),safeEncode=_safeEncode(ZodRealError),safeDecode=_safeDecode(ZodRealError),safeEncodeAsync=_safeEncodeAsync(ZodRealError),safeDecodeAsync=_safeDecodeAsync(
-ZodRealError),ZodType=$constructor("ZodType",(inst,def)=>($ZodType.init(inst,def),Object.assign(inst["~standard"],{jsonSchema:{input:createStandardJSONSchemaMethod(inst,"input"),output:createStandardJSONSchemaMethod(inst,"output")}}),inst.toJSONSchema=createToJSONSchemaMethod(inst,{}),inst.def=def,inst.type=def.type,Object.defineProperty(inst,"_def",{value:def}),inst.check=(...checks2)=>inst.clone(mergeDefs(def,{checks:[...def.checks??[],...checks2.map(ch=>typeof ch=="function"?{_zod:{check:ch,def:{
-check:"custom"},onattach:[]}}:ch)]})),inst.clone=(def2,params)=>clone(inst,def2,params),inst.brand=()=>inst,inst.register=(reg,meta)=>(reg.add(inst,meta),inst),inst.parse=(data,params)=>parse$3(inst,data,params,{callee:inst.parse}),inst.safeParse=(data,params)=>safeParse(inst,data,params),inst.parseAsync=async(data,params)=>parseAsync(inst,data,params,{callee:inst.parseAsync}),inst.safeParseAsync=async(data,params)=>safeParseAsync(inst,data,params),inst.spa=inst.safeParseAsync,inst.encode=(data,params)=>encode(
-inst,data,params),inst.decode=(data,params)=>decode$1(inst,data,params),inst.encodeAsync=async(data,params)=>encodeAsync(inst,data,params),inst.decodeAsync=async(data,params)=>decodeAsync(inst,data,params),inst.safeEncode=(data,params)=>safeEncode(inst,data,params),inst.safeDecode=(data,params)=>safeDecode(inst,data,params),inst.safeEncodeAsync=async(data,params)=>safeEncodeAsync(inst,data,params),inst.safeDecodeAsync=async(data,params)=>safeDecodeAsync(inst,data,params),inst.refine=(check,params)=>inst.
-check(refine(check,params)),inst.superRefine=refinement=>inst.check(superRefine(refinement)),inst.overwrite=fn=>inst.check(_overwrite(fn)),inst.optional=()=>optional(inst),inst.nullable=()=>nullable(inst),inst.nullish=()=>optional(nullable(inst)),inst.nonoptional=params=>nonoptional(inst,params),inst.array=()=>array(inst),inst.or=arg=>union([inst,arg]),inst.and=arg=>intersection(inst,arg),inst.transform=tx=>pipe(inst,transform$1(tx)),inst.default=def2=>_default(inst,def2),inst.prefault=def2=>prefault(
-inst,def2),inst.catch=params=>_catch(inst,params),inst.pipe=target2=>pipe(inst,target2),inst.readonly=()=>readonly(inst),inst.describe=description2=>{const cl=inst.clone();return globalRegistry.add(cl,{description:description2}),cl},Object.defineProperty(inst,"description",{get(){return globalRegistry.get(inst)?.description},configurable:!0}),inst.meta=(...args)=>{if(args.length===0)return globalRegistry.get(inst);const cl=inst.clone();return globalRegistry.add(cl,args[0]),cl},inst.isOptional=()=>inst.
-safeParse(void 0).success,inst.isNullable=()=>inst.safeParse(null).success,inst)),_ZodString=$constructor("_ZodString",(inst,def)=>{$ZodString.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>stringProcessor(inst,ctx,json2);const bag=inst._zod.bag;inst.format=bag.format??null,inst.minLength=bag.minimum??null,inst.maxLength=bag.maximum??null,inst.regex=(...args)=>inst.check(_regex(...args)),inst.includes=(...args)=>inst.check(_includes(...args)),inst.startsWith=
-(...args)=>inst.check(_startsWith(...args)),inst.endsWith=(...args)=>inst.check(_endsWith(...args)),inst.min=(...args)=>inst.check(_minLength(...args)),inst.max=(...args)=>inst.check(_maxLength(...args)),inst.length=(...args)=>inst.check(_length(...args)),inst.nonempty=(...args)=>inst.check(_minLength(1,...args)),inst.lowercase=params=>inst.check(_lowercase(params)),inst.uppercase=params=>inst.check(_uppercase(params)),inst.trim=()=>inst.check(_trim()),inst.normalize=(...args)=>inst.check(_normalize(
-...args)),inst.toLowerCase=()=>inst.check(_toLowerCase()),inst.toUpperCase=()=>inst.check(_toUpperCase()),inst.slugify=()=>inst.check(_slugify())}),ZodString=$constructor("ZodString",(inst,def)=>{$ZodString.init(inst,def),_ZodString.init(inst,def),inst.email=params=>inst.check(_email(ZodEmail,params)),inst.url=params=>inst.check(_url$1(ZodURL,params)),inst.jwt=params=>inst.check(_jwt(ZodJWT,params)),inst.emoji=params=>inst.check(_emoji(ZodEmoji,params)),inst.guid=params=>inst.check(_guid(ZodGUID,
-params)),inst.uuid=params=>inst.check(_uuid(ZodUUID,params)),inst.uuidv4=params=>inst.check(_uuidv4(ZodUUID,params)),inst.uuidv6=params=>inst.check(_uuidv6(ZodUUID,params)),inst.uuidv7=params=>inst.check(_uuidv7(ZodUUID,params)),inst.nanoid=params=>inst.check(_nanoid(ZodNanoID,params)),inst.guid=params=>inst.check(_guid(ZodGUID,params)),inst.cuid=params=>inst.check(_cuid(ZodCUID,params)),inst.cuid2=params=>inst.check(_cuid2(ZodCUID2,params)),inst.ulid=params=>inst.check(_ulid(ZodULID,params)),inst.
-base64=params=>inst.check(_base64(ZodBase64,params)),inst.base64url=params=>inst.check(_base64url(ZodBase64URL,params)),inst.xid=params=>inst.check(_xid(ZodXID,params)),inst.ksuid=params=>inst.check(_ksuid(ZodKSUID,params)),inst.ipv4=params=>inst.check(_ipv4(ZodIPv4,params)),inst.ipv6=params=>inst.check(_ipv6(ZodIPv6,params)),inst.cidrv4=params=>inst.check(_cidrv4(ZodCIDRv4,params)),inst.cidrv6=params=>inst.check(_cidrv6(ZodCIDRv6,params)),inst.e164=params=>inst.check(_e164(ZodE164,params)),inst.
-datetime=params=>inst.check(datetime(params)),inst.date=params=>inst.check(date(params)),inst.time=params=>inst.check(time(params)),inst.duration=params=>inst.check(duration(params))});function string$2(params){return _string(ZodString,params)}const ZodStringFormat=$constructor("ZodStringFormat",(inst,def)=>{$ZodStringFormat.init(inst,def),_ZodString.init(inst,def)}),ZodEmail=$constructor("ZodEmail",(inst,def)=>{$ZodEmail.init(inst,def),ZodStringFormat.init(inst,def)}),ZodGUID=$constructor("ZodG\
-UID",(inst,def)=>{$ZodGUID.init(inst,def),ZodStringFormat.init(inst,def)}),ZodUUID=$constructor("ZodUUID",(inst,def)=>{$ZodUUID.init(inst,def),ZodStringFormat.init(inst,def)}),ZodURL=$constructor("ZodURL",(inst,def)=>{$ZodURL.init(inst,def),ZodStringFormat.init(inst,def)});function url(params){return _url$1(ZodURL,params)}const ZodEmoji=$constructor("ZodEmoji",(inst,def)=>{$ZodEmoji.init(inst,def),ZodStringFormat.init(inst,def)}),ZodNanoID=$constructor("ZodNanoID",(inst,def)=>{$ZodNanoID.init(inst,
-def),ZodStringFormat.init(inst,def)}),ZodCUID=$constructor("ZodCUID",(inst,def)=>{$ZodCUID.init(inst,def),ZodStringFormat.init(inst,def)}),ZodCUID2=$constructor("ZodCUID2",(inst,def)=>{$ZodCUID2.init(inst,def),ZodStringFormat.init(inst,def)}),ZodULID=$constructor("ZodULID",(inst,def)=>{$ZodULID.init(inst,def),ZodStringFormat.init(inst,def)}),ZodXID=$constructor("ZodXID",(inst,def)=>{$ZodXID.init(inst,def),ZodStringFormat.init(inst,def)}),ZodKSUID=$constructor("ZodKSUID",(inst,def)=>{$ZodKSUID.init(
-inst,def),ZodStringFormat.init(inst,def)}),ZodIPv4=$constructor("ZodIPv4",(inst,def)=>{$ZodIPv4.init(inst,def),ZodStringFormat.init(inst,def)}),ZodIPv6=$constructor("ZodIPv6",(inst,def)=>{$ZodIPv6.init(inst,def),ZodStringFormat.init(inst,def)}),ZodCIDRv4=$constructor("ZodCIDRv4",(inst,def)=>{$ZodCIDRv4.init(inst,def),ZodStringFormat.init(inst,def)}),ZodCIDRv6=$constructor("ZodCIDRv6",(inst,def)=>{$ZodCIDRv6.init(inst,def),ZodStringFormat.init(inst,def)}),ZodBase64=$constructor("ZodBase64",(inst,def)=>{
-$ZodBase64.init(inst,def),ZodStringFormat.init(inst,def)}),ZodBase64URL=$constructor("ZodBase64URL",(inst,def)=>{$ZodBase64URL.init(inst,def),ZodStringFormat.init(inst,def)}),ZodE164=$constructor("ZodE164",(inst,def)=>{$ZodE164.init(inst,def),ZodStringFormat.init(inst,def)}),ZodJWT=$constructor("ZodJWT",(inst,def)=>{$ZodJWT.init(inst,def),ZodStringFormat.init(inst,def)}),ZodNumber=$constructor("ZodNumber",(inst,def)=>{$ZodNumber.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>numberProcessor(
-inst,ctx,json2),inst.gt=(value,params)=>inst.check(_gt(value,params)),inst.gte=(value,params)=>inst.check(_gte(value,params)),inst.min=(value,params)=>inst.check(_gte(value,params)),inst.lt=(value,params)=>inst.check(_lt(value,params)),inst.lte=(value,params)=>inst.check(_lte(value,params)),inst.max=(value,params)=>inst.check(_lte(value,params)),inst.int=params=>inst.check(int(params)),inst.safe=params=>inst.check(int(params)),inst.positive=params=>inst.check(_gt(0,params)),inst.nonnegative=params=>inst.
-check(_gte(0,params)),inst.negative=params=>inst.check(_lt(0,params)),inst.nonpositive=params=>inst.check(_lte(0,params)),inst.multipleOf=(value,params)=>inst.check(_multipleOf(value,params)),inst.step=(value,params)=>inst.check(_multipleOf(value,params)),inst.finite=()=>inst;const bag=inst._zod.bag;inst.minValue=Math.max(bag.minimum??Number.NEGATIVE_INFINITY,bag.exclusiveMinimum??Number.NEGATIVE_INFINITY)??null,inst.maxValue=Math.min(bag.maximum??Number.POSITIVE_INFINITY,bag.exclusiveMaximum??Number.
-POSITIVE_INFINITY)??null,inst.isInt=(bag.format??"").includes("int")||Number.isSafeInteger(bag.multipleOf??.5),inst.isFinite=!0,inst.format=bag.format??null});function number$2(params){return _number(ZodNumber,params)}const ZodNumberFormat=$constructor("ZodNumberFormat",(inst,def)=>{$ZodNumberFormat.init(inst,def),ZodNumber.init(inst,def)});function int(params){return _int(ZodNumberFormat,params)}const ZodBoolean=$constructor("ZodBoolean",(inst,def)=>{$ZodBoolean.init(inst,def),ZodType.init(inst,
-def),inst._zod.processJSONSchema=(ctx,json2,params)=>booleanProcessor(inst,ctx,json2)});function boolean$1(params){return _boolean(ZodBoolean,params)}const ZodNull=$constructor("ZodNull",(inst,def)=>{$ZodNull.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>nullProcessor(inst,ctx,json2)});function _null(params){return _null$1(ZodNull,params)}const ZodAny=$constructor("ZodAny",(inst,def)=>{$ZodAny.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=
-(ctx,json2,params)=>anyProcessor()});function any(){return _any(ZodAny)}const ZodUnknown=$constructor("ZodUnknown",(inst,def)=>{$ZodUnknown.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>unknownProcessor()});function unknown$2(){return _unknown(ZodUnknown)}const ZodNever=$constructor("ZodNever",(inst,def)=>{$ZodNever.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>neverProcessor(inst,ctx,json2)});function never(params){
-return _never(ZodNever,params)}const ZodArray=$constructor("ZodArray",(inst,def)=>{$ZodArray.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>arrayProcessor(inst,ctx,json2,params),inst.element=def.element,inst.min=(minLength,params)=>inst.check(_minLength(minLength,params)),inst.nonempty=params=>inst.check(_minLength(1,params)),inst.max=(maxLength,params)=>inst.check(_maxLength(maxLength,params)),inst.length=(len,params)=>inst.check(_length(len,params)),inst.
-unwrap=()=>inst.element});function array(element2,params){return _array(ZodArray,element2,params)}const ZodObject=$constructor("ZodObject",(inst,def)=>{$ZodObjectJIT.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>objectProcessor(inst,ctx,json2,params),defineLazy(inst,"shape",()=>def.shape),inst.keyof=()=>_enum$2(Object.keys(inst._zod.def.shape)),inst.catchall=catchall=>inst.clone({...inst._zod.def,catchall}),inst.passthrough=()=>inst.clone({...inst._zod.def,
-catchall:unknown$2()}),inst.loose=()=>inst.clone({...inst._zod.def,catchall:unknown$2()}),inst.strict=()=>inst.clone({...inst._zod.def,catchall:never()}),inst.strip=()=>inst.clone({...inst._zod.def,catchall:void 0}),inst.extend=incoming=>extend$2(inst,incoming),inst.safeExtend=incoming=>safeExtend(inst,incoming),inst.merge=other=>merge$1(inst,other),inst.pick=mask=>pick(inst,mask),inst.omit=mask=>omit(inst,mask),inst.partial=(...args)=>partial(ZodOptional,inst,args[0]),inst.required=(...args)=>required$4(
-ZodNonOptional,inst,args[0])});function object(shape,params){const def={type:"object",shape:shape??{},...normalizeParams(params)};return new ZodObject(def)}function looseObject(shape,params){return new ZodObject({type:"object",shape,catchall:unknown$2(),...normalizeParams(params)})}const ZodUnion=$constructor("ZodUnion",(inst,def)=>{$ZodUnion.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>unionProcessor(inst,ctx,json2,params),inst.options=def.options});function union(options,params){
-return new ZodUnion({type:"union",options,...normalizeParams(params)})}const ZodDiscriminatedUnion=$constructor("ZodDiscriminatedUnion",(inst,def)=>{ZodUnion.init(inst,def),$ZodDiscriminatedUnion.init(inst,def)});function discriminatedUnion(discriminator2,options,params){return new ZodDiscriminatedUnion({type:"union",options,discriminator:discriminator2,...normalizeParams(params)})}const ZodIntersection=$constructor("ZodIntersection",(inst,def)=>{$ZodIntersection.init(inst,def),ZodType.init(inst,
-def),inst._zod.processJSONSchema=(ctx,json2,params)=>intersectionProcessor(inst,ctx,json2,params)});function intersection(left,right){return new ZodIntersection({type:"intersection",left,right})}const ZodRecord=$constructor("ZodRecord",(inst,def)=>{$ZodRecord.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>recordProcessor(inst,ctx,json2,params),inst.keyType=def.keyType,inst.valueType=def.valueType});function record(keyType,valueType,params){return new ZodRecord(
-{type:"record",keyType,valueType,...normalizeParams(params)})}const ZodEnum=$constructor("ZodEnum",(inst,def)=>{$ZodEnum.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>enumProcessor(inst,ctx,json2),inst.enum=def.entries,inst.options=Object.values(def.entries);const keys2=new Set(Object.keys(def.entries));inst.extract=(values,params)=>{const newEntries={};for(const value of values)if(keys2.has(value))newEntries[value]=def.entries[value];else throw new Error(
-`Key ${value} not found in enum`);return new ZodEnum({...def,checks:[],...normalizeParams(params),entries:newEntries})},inst.exclude=(values,params)=>{const newEntries={...def.entries};for(const value of values)if(keys2.has(value))delete newEntries[value];else throw new Error(`Key ${value} not found in enum`);return new ZodEnum({...def,checks:[],...normalizeParams(params),entries:newEntries})}});function _enum$2(values,params){const entries=Array.isArray(values)?Object.fromEntries(values.map(v=>[
-v,v])):values;return new ZodEnum({type:"enum",entries,...normalizeParams(params)})}const ZodLiteral=$constructor("ZodLiteral",(inst,def)=>{$ZodLiteral.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>literalProcessor(inst,ctx,json2),inst.values=new Set(def.values),Object.defineProperty(inst,"value",{get(){if(def.values.length>1)throw new Error("This schema contains multiple valid literal values. Use `.values` instead.");return def.values[0]}})});function literal(value,params){
-return new ZodLiteral({type:"literal",values:Array.isArray(value)?value:[value],...normalizeParams(params)})}const ZodTransform=$constructor("ZodTransform",(inst,def)=>{$ZodTransform.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>transformProcessor(inst,ctx),inst._zod.parse=(payload,_ctx)=>{if(_ctx.direction==="backward")throw new $ZodEncodeError(inst.constructor.name);payload.addIssue=issue$1=>{if(typeof issue$1=="string")payload.issues.push(issue(issue$1,
-payload.value,def));else{const _issue=issue$1;_issue.fatal&&(_issue.continue=!1),_issue.code??(_issue.code="custom"),_issue.input??(_issue.input=payload.value),_issue.inst??(_issue.inst=inst),payload.issues.push(issue(_issue))}};const output=def.transform(payload.value,payload);return output instanceof Promise?output.then(output2=>(payload.value=output2,payload)):(payload.value=output,payload)}});function transform$1(fn){return new ZodTransform({type:"transform",transform:fn})}const ZodOptional=$constructor(
-"ZodOptional",(inst,def)=>{$ZodOptional.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>optionalProcessor(inst,ctx,json2,params),inst.unwrap=()=>inst._zod.def.innerType});function optional(innerType){return new ZodOptional({type:"optional",innerType})}const ZodNullable=$constructor("ZodNullable",(inst,def)=>{$ZodNullable.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>nullableProcessor(inst,ctx,json2,params),inst.unwrap=
-()=>inst._zod.def.innerType});function nullable(innerType){return new ZodNullable({type:"nullable",innerType})}const ZodDefault=$constructor("ZodDefault",(inst,def)=>{$ZodDefault.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>defaultProcessor(inst,ctx,json2,params),inst.unwrap=()=>inst._zod.def.innerType,inst.removeDefault=inst.unwrap});function _default(innerType,defaultValue){return new ZodDefault({type:"default",innerType,get defaultValue(){return typeof defaultValue==
-"function"?defaultValue():shallowClone(defaultValue)}})}const ZodPrefault=$constructor("ZodPrefault",(inst,def)=>{$ZodPrefault.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>prefaultProcessor(inst,ctx,json2,params),inst.unwrap=()=>inst._zod.def.innerType});function prefault(innerType,defaultValue){return new ZodPrefault({type:"prefault",innerType,get defaultValue(){return typeof defaultValue=="function"?defaultValue():shallowClone(defaultValue)}})}const ZodNonOptional=$constructor(
-"ZodNonOptional",(inst,def)=>{$ZodNonOptional.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>nonoptionalProcessor(inst,ctx,json2,params),inst.unwrap=()=>inst._zod.def.innerType});function nonoptional(innerType,params){return new ZodNonOptional({type:"nonoptional",innerType,...normalizeParams(params)})}const ZodCatch=$constructor("ZodCatch",(inst,def)=>{$ZodCatch.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>catchProcessor(
-inst,ctx,json2,params),inst.unwrap=()=>inst._zod.def.innerType,inst.removeCatch=inst.unwrap});function _catch(innerType,catchValue){return new ZodCatch({type:"catch",innerType,catchValue:typeof catchValue=="function"?catchValue:()=>catchValue})}const ZodPipe=$constructor("ZodPipe",(inst,def)=>{$ZodPipe.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>pipeProcessor(inst,ctx,json2,params),inst.in=def.in,inst.out=def.out});function pipe(in_,out){return new ZodPipe(
-{type:"pipe",in:in_,out})}const ZodReadonly=$constructor("ZodReadonly",(inst,def)=>{$ZodReadonly.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>readonlyProcessor(inst,ctx,json2,params),inst.unwrap=()=>inst._zod.def.innerType});function readonly(innerType){return new ZodReadonly({type:"readonly",innerType})}const ZodCustom=$constructor("ZodCustom",(inst,def)=>{$ZodCustom.init(inst,def),ZodType.init(inst,def),inst._zod.processJSONSchema=(ctx,json2,params)=>customProcessor(
-inst,ctx)});function custom(fn,_params){return _custom(ZodCustom,fn??(()=>!0),_params)}function refine(fn,_params={}){return _refine(ZodCustom,fn,_params)}function superRefine(fn){return _superRefine(fn)}function preprocess$1(fn,schema){return pipe(transform$1(fn),schema)}const ZodIssueCode={custom:"custom"};function number$1(params){return _coercedNumber(ZodNumber,params)}const LATEST_PROTOCOL_VERSION="2025-11-25",SUPPORTED_PROTOCOL_VERSIONS=[LATEST_PROTOCOL_VERSION,"2025-06-18","2025-03-26","2\
-024-11-05","2024-10-07"],RELATED_TASK_META_KEY="io.modelcontextprotocol/related-task",JSONRPC_VERSION="2.0",AssertObjectSchema=custom(v=>v!==null&&(typeof v=="object"||typeof v=="function")),ProgressTokenSchema=union([string$2(),number$2().int()]),CursorSchema=string$2();looseObject({ttl:union([number$2(),_null()]).optional(),pollInterval:number$2().optional()});const TaskMetadataSchema=object({ttl:number$2().optional()}),RelatedTaskMetadataSchema=object({taskId:string$2()}),RequestMetaSchema=looseObject(
-{progressToken:ProgressTokenSchema.optional(),[RELATED_TASK_META_KEY]:RelatedTaskMetadataSchema.optional()}),BaseRequestParamsSchema=object({_meta:RequestMetaSchema.optional()}),TaskAugmentedRequestParamsSchema=BaseRequestParamsSchema.extend({task:TaskMetadataSchema.optional()}),isTaskAugmentedRequestParams=value=>TaskAugmentedRequestParamsSchema.safeParse(value).success,RequestSchema=object({method:string$2(),params:BaseRequestParamsSchema.loose().optional()}),NotificationsParamsSchema=object({
-_meta:RequestMetaSchema.optional()}),NotificationSchema=object({method:string$2(),params:NotificationsParamsSchema.loose().optional()}),ResultSchema=looseObject({_meta:RequestMetaSchema.optional()}),RequestIdSchema=union([string$2(),number$2().int()]),JSONRPCRequestSchema=object({jsonrpc:literal(JSONRPC_VERSION),id:RequestIdSchema,...RequestSchema.shape}).strict(),isJSONRPCRequest=value=>JSONRPCRequestSchema.safeParse(value).success,JSONRPCNotificationSchema=object({jsonrpc:literal(JSONRPC_VERSION),
-...NotificationSchema.shape}).strict(),isJSONRPCNotification=value=>JSONRPCNotificationSchema.safeParse(value).success,JSONRPCResultResponseSchema=object({jsonrpc:literal(JSONRPC_VERSION),id:RequestIdSchema,result:ResultSchema}).strict(),isJSONRPCResultResponse=value=>JSONRPCResultResponseSchema.safeParse(value).success;var ErrorCode;(function(ErrorCode2){ErrorCode2[ErrorCode2.ConnectionClosed=-32e3]="ConnectionClosed",ErrorCode2[ErrorCode2.RequestTimeout=-32001]="RequestTimeout",ErrorCode2[ErrorCode2.
-ParseError=-32700]="ParseError",ErrorCode2[ErrorCode2.InvalidRequest=-32600]="InvalidRequest",ErrorCode2[ErrorCode2.MethodNotFound=-32601]="MethodNotFound",ErrorCode2[ErrorCode2.InvalidParams=-32602]="InvalidParams",ErrorCode2[ErrorCode2.InternalError=-32603]="InternalError",ErrorCode2[ErrorCode2.UrlElicitationRequired=-32042]="UrlElicitationRequired"})(ErrorCode||(ErrorCode={}));const JSONRPCErrorResponseSchema=object({jsonrpc:literal(JSONRPC_VERSION),id:RequestIdSchema.optional(),error:object(
-{code:number$2().int(),message:string$2(),data:unknown$2().optional()})}).strict(),isJSONRPCErrorResponse=value=>JSONRPCErrorResponseSchema.safeParse(value).success,JSONRPCMessageSchema=union([JSONRPCRequestSchema,JSONRPCNotificationSchema,JSONRPCResultResponseSchema,JSONRPCErrorResponseSchema]);union([JSONRPCResultResponseSchema,JSONRPCErrorResponseSchema]);const EmptyResultSchema=ResultSchema.strict(),CancelledNotificationParamsSchema=NotificationsParamsSchema.extend({requestId:RequestIdSchema.
-optional(),reason:string$2().optional()}),CancelledNotificationSchema=NotificationSchema.extend({method:literal("notifications/cancelled"),params:CancelledNotificationParamsSchema}),IconSchema=object({src:string$2(),mimeType:string$2().optional(),sizes:array(string$2()).optional(),theme:_enum$2(["light","dark"]).optional()}),IconsSchema=object({icons:array(IconSchema).optional()}),BaseMetadataSchema=object({name:string$2(),title:string$2().optional()}),ImplementationSchema=BaseMetadataSchema.extend(
-{...BaseMetadataSchema.shape,...IconsSchema.shape,version:string$2(),websiteUrl:string$2().optional(),description:string$2().optional()}),FormElicitationCapabilitySchema=intersection(object({applyDefaults:boolean$1().optional()}),record(string$2(),unknown$2())),ElicitationCapabilitySchema=preprocess$1(value=>value&&typeof value=="object"&&!Array.isArray(value)&&Object.keys(value).length===0?{form:{}}:value,intersection(object({form:FormElicitationCapabilitySchema.optional(),url:AssertObjectSchema.
-optional()}),record(string$2(),unknown$2()).optional())),ClientTasksCapabilitySchema=looseObject({list:AssertObjectSchema.optional(),cancel:AssertObjectSchema.optional(),requests:looseObject({sampling:looseObject({createMessage:AssertObjectSchema.optional()}).optional(),elicitation:looseObject({create:AssertObjectSchema.optional()}).optional()}).optional()}),ServerTasksCapabilitySchema=looseObject({list:AssertObjectSchema.optional(),cancel:AssertObjectSchema.optional(),requests:looseObject({tools:looseObject(
-{call:AssertObjectSchema.optional()}).optional()}).optional()}),ClientCapabilitiesSchema=object({experimental:record(string$2(),AssertObjectSchema).optional(),sampling:object({context:AssertObjectSchema.optional(),tools:AssertObjectSchema.optional()}).optional(),elicitation:ElicitationCapabilitySchema.optional(),roots:object({listChanged:boolean$1().optional()}).optional(),tasks:ClientTasksCapabilitySchema.optional()}),InitializeRequestParamsSchema=BaseRequestParamsSchema.extend({protocolVersion:string$2(),
-capabilities:ClientCapabilitiesSchema,clientInfo:ImplementationSchema}),InitializeRequestSchema=RequestSchema.extend({method:literal("initialize"),params:InitializeRequestParamsSchema}),ServerCapabilitiesSchema=object({experimental:record(string$2(),AssertObjectSchema).optional(),logging:AssertObjectSchema.optional(),completions:AssertObjectSchema.optional(),prompts:object({listChanged:boolean$1().optional()}).optional(),resources:object({subscribe:boolean$1().optional(),listChanged:boolean$1().
-optional()}).optional(),tools:object({listChanged:boolean$1().optional()}).optional(),tasks:ServerTasksCapabilitySchema.optional()}),InitializeResultSchema=ResultSchema.extend({protocolVersion:string$2(),capabilities:ServerCapabilitiesSchema,serverInfo:ImplementationSchema,instructions:string$2().optional()}),InitializedNotificationSchema=NotificationSchema.extend({method:literal("notifications/initialized"),params:NotificationsParamsSchema.optional()}),isInitializedNotification=value=>InitializedNotificationSchema.
-safeParse(value).success,PingRequestSchema=RequestSchema.extend({method:literal("ping"),params:BaseRequestParamsSchema.optional()}),ProgressSchema=object({progress:number$2(),total:optional(number$2()),message:optional(string$2())}),ProgressNotificationParamsSchema=object({...NotificationsParamsSchema.shape,...ProgressSchema.shape,progressToken:ProgressTokenSchema}),ProgressNotificationSchema=NotificationSchema.extend({method:literal("notifications/progress"),params:ProgressNotificationParamsSchema}),
-PaginatedRequestParamsSchema=BaseRequestParamsSchema.extend({cursor:CursorSchema.optional()}),PaginatedRequestSchema=RequestSchema.extend({params:PaginatedRequestParamsSchema.optional()}),PaginatedResultSchema=ResultSchema.extend({nextCursor:CursorSchema.optional()}),TaskStatusSchema=_enum$2(["working","input_required","completed","failed","cancelled"]),TaskSchema=object({taskId:string$2(),status:TaskStatusSchema,ttl:union([number$2(),_null()]),createdAt:string$2(),lastUpdatedAt:string$2(),pollInterval:optional(
-number$2()),statusMessage:optional(string$2())}),CreateTaskResultSchema=ResultSchema.extend({task:TaskSchema}),TaskStatusNotificationParamsSchema=NotificationsParamsSchema.merge(TaskSchema),TaskStatusNotificationSchema=NotificationSchema.extend({method:literal("notifications/tasks/status"),params:TaskStatusNotificationParamsSchema}),GetTaskRequestSchema=RequestSchema.extend({method:literal("tasks/get"),params:BaseRequestParamsSchema.extend({taskId:string$2()})}),GetTaskResultSchema=ResultSchema.
-merge(TaskSchema),GetTaskPayloadRequestSchema=RequestSchema.extend({method:literal("tasks/result"),params:BaseRequestParamsSchema.extend({taskId:string$2()})});ResultSchema.loose();const ListTasksRequestSchema=PaginatedRequestSchema.extend({method:literal("tasks/list")}),ListTasksResultSchema=PaginatedResultSchema.extend({tasks:array(TaskSchema)}),CancelTaskRequestSchema=RequestSchema.extend({method:literal("tasks/cancel"),params:BaseRequestParamsSchema.extend({taskId:string$2()})}),CancelTaskResultSchema=ResultSchema.
-merge(TaskSchema),ResourceContentsSchema=object({uri:string$2(),mimeType:optional(string$2()),_meta:record(string$2(),unknown$2()).optional()}),TextResourceContentsSchema=ResourceContentsSchema.extend({text:string$2()}),Base64Schema=string$2().refine(val=>{try{return atob(val),!0}catch{return!1}},{message:"Invalid Base64 string"}),BlobResourceContentsSchema=ResourceContentsSchema.extend({blob:Base64Schema}),RoleSchema=_enum$2(["user","assistant"]),AnnotationsSchema=object({audience:array(RoleSchema).
-optional(),priority:number$2().min(0).max(1).optional(),lastModified:datetime({offset:!0}).optional()}),ResourceSchema=object({...BaseMetadataSchema.shape,...IconsSchema.shape,uri:string$2(),description:optional(string$2()),mimeType:optional(string$2()),annotations:AnnotationsSchema.optional(),_meta:optional(looseObject({}))}),ResourceTemplateSchema=object({...BaseMetadataSchema.shape,...IconsSchema.shape,uriTemplate:string$2(),description:optional(string$2()),mimeType:optional(string$2()),annotations:AnnotationsSchema.
-optional(),_meta:optional(looseObject({}))}),ListResourcesRequestSchema=PaginatedRequestSchema.extend({method:literal("resources/list")}),ListResourcesResultSchema=PaginatedResultSchema.extend({resources:array(ResourceSchema)}),ListResourceTemplatesRequestSchema=PaginatedRequestSchema.extend({method:literal("resources/templates/list")}),ListResourceTemplatesResultSchema=PaginatedResultSchema.extend({resourceTemplates:array(ResourceTemplateSchema)}),ResourceRequestParamsSchema=BaseRequestParamsSchema.
-extend({uri:string$2()}),ReadResourceRequestParamsSchema=ResourceRequestParamsSchema,ReadResourceRequestSchema=RequestSchema.extend({method:literal("resources/read"),params:ReadResourceRequestParamsSchema}),ReadResourceResultSchema=ResultSchema.extend({contents:array(union([TextResourceContentsSchema,BlobResourceContentsSchema]))}),ResourceListChangedNotificationSchema=NotificationSchema.extend({method:literal("notifications/resources/list_changed"),params:NotificationsParamsSchema.optional()}),
-SubscribeRequestParamsSchema=ResourceRequestParamsSchema,SubscribeRequestSchema=RequestSchema.extend({method:literal("resources/subscribe"),params:SubscribeRequestParamsSchema}),UnsubscribeRequestParamsSchema=ResourceRequestParamsSchema,UnsubscribeRequestSchema=RequestSchema.extend({method:literal("resources/unsubscribe"),params:UnsubscribeRequestParamsSchema}),ResourceUpdatedNotificationParamsSchema=NotificationsParamsSchema.extend({uri:string$2()}),ResourceUpdatedNotificationSchema=NotificationSchema.
-extend({method:literal("notifications/resources/updated"),params:ResourceUpdatedNotificationParamsSchema}),PromptArgumentSchema=object({name:string$2(),description:optional(string$2()),required:optional(boolean$1())}),PromptSchema=object({...BaseMetadataSchema.shape,...IconsSchema.shape,description:optional(string$2()),arguments:optional(array(PromptArgumentSchema)),_meta:optional(looseObject({}))}),ListPromptsRequestSchema=PaginatedRequestSchema.extend({method:literal("prompts/list")}),ListPromptsResultSchema=PaginatedResultSchema.
-extend({prompts:array(PromptSchema)}),GetPromptRequestParamsSchema=BaseRequestParamsSchema.extend({name:string$2(),arguments:record(string$2(),string$2()).optional()}),GetPromptRequestSchema=RequestSchema.extend({method:literal("prompts/get"),params:GetPromptRequestParamsSchema}),TextContentSchema=object({type:literal("text"),text:string$2(),annotations:AnnotationsSchema.optional(),_meta:record(string$2(),unknown$2()).optional()}),ImageContentSchema=object({type:literal("image"),data:Base64Schema,
-mimeType:string$2(),annotations:AnnotationsSchema.optional(),_meta:record(string$2(),unknown$2()).optional()}),AudioContentSchema=object({type:literal("audio"),data:Base64Schema,mimeType:string$2(),annotations:AnnotationsSchema.optional(),_meta:record(string$2(),unknown$2()).optional()}),ToolUseContentSchema=object({type:literal("tool_use"),name:string$2(),id:string$2(),input:record(string$2(),unknown$2()),_meta:record(string$2(),unknown$2()).optional()}),EmbeddedResourceSchema=object({type:literal(
-"resource"),resource:union([TextResourceContentsSchema,BlobResourceContentsSchema]),annotations:AnnotationsSchema.optional(),_meta:record(string$2(),unknown$2()).optional()}),ResourceLinkSchema=ResourceSchema.extend({type:literal("resource_link")}),ContentBlockSchema=union([TextContentSchema,ImageContentSchema,AudioContentSchema,ResourceLinkSchema,EmbeddedResourceSchema]),PromptMessageSchema=object({role:RoleSchema,content:ContentBlockSchema}),GetPromptResultSchema=ResultSchema.extend({description:string$2().
-optional(),messages:array(PromptMessageSchema)}),PromptListChangedNotificationSchema=NotificationSchema.extend({method:literal("notifications/prompts/list_changed"),params:NotificationsParamsSchema.optional()}),ToolAnnotationsSchema=object({title:string$2().optional(),readOnlyHint:boolean$1().optional(),destructiveHint:boolean$1().optional(),idempotentHint:boolean$1().optional(),openWorldHint:boolean$1().optional()}),ToolExecutionSchema=object({taskSupport:_enum$2(["required","optional","forbidd\
-en"]).optional()}),ToolSchema=object({...BaseMetadataSchema.shape,...IconsSchema.shape,description:string$2().optional(),inputSchema:object({type:literal("object"),properties:record(string$2(),AssertObjectSchema).optional(),required:array(string$2()).optional()}).catchall(unknown$2()),outputSchema:object({type:literal("object"),properties:record(string$2(),AssertObjectSchema).optional(),required:array(string$2()).optional()}).catchall(unknown$2()).optional(),annotations:ToolAnnotationsSchema.optional(),
-execution:ToolExecutionSchema.optional(),_meta:record(string$2(),unknown$2()).optional()}),ListToolsRequestSchema=PaginatedRequestSchema.extend({method:literal("tools/list")}),ListToolsResultSchema=PaginatedResultSchema.extend({tools:array(ToolSchema)}),CallToolResultSchema=ResultSchema.extend({content:array(ContentBlockSchema).default([]),structuredContent:record(string$2(),unknown$2()).optional(),isError:boolean$1().optional()});CallToolResultSchema.or(ResultSchema.extend({toolResult:unknown$2()}));
-const CallToolRequestParamsSchema=TaskAugmentedRequestParamsSchema.extend({name:string$2(),arguments:record(string$2(),unknown$2()).optional()}),CallToolRequestSchema=RequestSchema.extend({method:literal("tools/call"),params:CallToolRequestParamsSchema}),ToolListChangedNotificationSchema=NotificationSchema.extend({method:literal("notifications/tools/list_changed"),params:NotificationsParamsSchema.optional()}),ListChangedOptionsBaseSchema=object({autoRefresh:boolean$1().default(!0),debounceMs:number$2().
-int().nonnegative().default(300)}),LoggingLevelSchema=_enum$2(["debug","info","notice","warning","error","critical","alert","emergency"]),SetLevelRequestParamsSchema=BaseRequestParamsSchema.extend({level:LoggingLevelSchema}),SetLevelRequestSchema=RequestSchema.extend({method:literal("logging/setLevel"),params:SetLevelRequestParamsSchema}),LoggingMessageNotificationParamsSchema=NotificationsParamsSchema.extend({level:LoggingLevelSchema,logger:string$2().optional(),data:unknown$2()}),LoggingMessageNotificationSchema=NotificationSchema.
-extend({method:literal("notifications/message"),params:LoggingMessageNotificationParamsSchema}),ModelHintSchema=object({name:string$2().optional()}),ModelPreferencesSchema=object({hints:array(ModelHintSchema).optional(),costPriority:number$2().min(0).max(1).optional(),speedPriority:number$2().min(0).max(1).optional(),intelligencePriority:number$2().min(0).max(1).optional()}),ToolChoiceSchema=object({mode:_enum$2(["auto","required","none"]).optional()}),ToolResultContentSchema=object({type:literal(
-"tool_result"),toolUseId:string$2().describe("The unique identifier for the corresponding tool call."),content:array(ContentBlockSchema).default([]),structuredContent:object({}).loose().optional(),isError:boolean$1().optional(),_meta:record(string$2(),unknown$2()).optional()}),SamplingContentSchema=discriminatedUnion("type",[TextContentSchema,ImageContentSchema,AudioContentSchema]),SamplingMessageContentBlockSchema=discriminatedUnion("type",[TextContentSchema,ImageContentSchema,AudioContentSchema,
-ToolUseContentSchema,ToolResultContentSchema]),SamplingMessageSchema=object({role:RoleSchema,content:union([SamplingMessageContentBlockSchema,array(SamplingMessageContentBlockSchema)]),_meta:record(string$2(),unknown$2()).optional()}),CreateMessageRequestParamsSchema=TaskAugmentedRequestParamsSchema.extend({messages:array(SamplingMessageSchema),modelPreferences:ModelPreferencesSchema.optional(),systemPrompt:string$2().optional(),includeContext:_enum$2(["none","thisServer","allServers"]).optional(),
-temperature:number$2().optional(),maxTokens:number$2().int(),stopSequences:array(string$2()).optional(),metadata:AssertObjectSchema.optional(),tools:array(ToolSchema).optional(),toolChoice:ToolChoiceSchema.optional()}),CreateMessageRequestSchema=RequestSchema.extend({method:literal("sampling/createMessage"),params:CreateMessageRequestParamsSchema}),CreateMessageResultSchema=ResultSchema.extend({model:string$2(),stopReason:optional(_enum$2(["endTurn","stopSequence","maxTokens"]).or(string$2())),role:RoleSchema,
-content:SamplingContentSchema}),CreateMessageResultWithToolsSchema=ResultSchema.extend({model:string$2(),stopReason:optional(_enum$2(["endTurn","stopSequence","maxTokens","toolUse"]).or(string$2())),role:RoleSchema,content:union([SamplingMessageContentBlockSchema,array(SamplingMessageContentBlockSchema)])}),BooleanSchemaSchema=object({type:literal("boolean"),title:string$2().optional(),description:string$2().optional(),default:boolean$1().optional()}),StringSchemaSchema=object({type:literal("str\
-ing"),title:string$2().optional(),description:string$2().optional(),minLength:number$2().optional(),maxLength:number$2().optional(),format:_enum$2(["email","uri","date","date-time"]).optional(),default:string$2().optional()}),NumberSchemaSchema=object({type:_enum$2(["number","integer"]),title:string$2().optional(),description:string$2().optional(),minimum:number$2().optional(),maximum:number$2().optional(),default:number$2().optional()}),UntitledSingleSelectEnumSchemaSchema=object({type:literal(
-"string"),title:string$2().optional(),description:string$2().optional(),enum:array(string$2()),default:string$2().optional()}),TitledSingleSelectEnumSchemaSchema=object({type:literal("string"),title:string$2().optional(),description:string$2().optional(),oneOf:array(object({const:string$2(),title:string$2()})),default:string$2().optional()}),LegacyTitledEnumSchemaSchema=object({type:literal("string"),title:string$2().optional(),description:string$2().optional(),enum:array(string$2()),enumNames:array(
-string$2()).optional(),default:string$2().optional()}),SingleSelectEnumSchemaSchema=union([UntitledSingleSelectEnumSchemaSchema,TitledSingleSelectEnumSchemaSchema]),UntitledMultiSelectEnumSchemaSchema=object({type:literal("array"),title:string$2().optional(),description:string$2().optional(),minItems:number$2().optional(),maxItems:number$2().optional(),items:object({type:literal("string"),enum:array(string$2())}),default:array(string$2()).optional()}),TitledMultiSelectEnumSchemaSchema=object({type:literal(
-"array"),title:string$2().optional(),description:string$2().optional(),minItems:number$2().optional(),maxItems:number$2().optional(),items:object({anyOf:array(object({const:string$2(),title:string$2()}))}),default:array(string$2()).optional()}),MultiSelectEnumSchemaSchema=union([UntitledMultiSelectEnumSchemaSchema,TitledMultiSelectEnumSchemaSchema]),EnumSchemaSchema=union([LegacyTitledEnumSchemaSchema,SingleSelectEnumSchemaSchema,MultiSelectEnumSchemaSchema]),PrimitiveSchemaDefinitionSchema=union(
-[EnumSchemaSchema,BooleanSchemaSchema,StringSchemaSchema,NumberSchemaSchema]),ElicitRequestFormParamsSchema=TaskAugmentedRequestParamsSchema.extend({mode:literal("form").optional(),message:string$2(),requestedSchema:object({type:literal("object"),properties:record(string$2(),PrimitiveSchemaDefinitionSchema),required:array(string$2()).optional()})}),ElicitRequestURLParamsSchema=TaskAugmentedRequestParamsSchema.extend({mode:literal("url"),message:string$2(),elicitationId:string$2(),url:string$2().
-url()}),ElicitRequestParamsSchema=union([ElicitRequestFormParamsSchema,ElicitRequestURLParamsSchema]),ElicitRequestSchema=RequestSchema.extend({method:literal("elicitation/create"),params:ElicitRequestParamsSchema}),ElicitationCompleteNotificationParamsSchema=NotificationsParamsSchema.extend({elicitationId:string$2()}),ElicitationCompleteNotificationSchema=NotificationSchema.extend({method:literal("notifications/elicitation/complete"),params:ElicitationCompleteNotificationParamsSchema}),ElicitResultSchema=ResultSchema.
-extend({action:_enum$2(["accept","decline","cancel"]),content:preprocess$1(val=>val===null?void 0:val,record(string$2(),union([string$2(),number$2(),boolean$1(),array(string$2())])).optional())}),ResourceTemplateReferenceSchema=object({type:literal("ref/resource"),uri:string$2()}),PromptReferenceSchema=object({type:literal("ref/prompt"),name:string$2()}),CompleteRequestParamsSchema=BaseRequestParamsSchema.extend({ref:union([PromptReferenceSchema,ResourceTemplateReferenceSchema]),argument:object(
-{name:string$2(),value:string$2()}),context:object({arguments:record(string$2(),string$2()).optional()}).optional()}),CompleteRequestSchema=RequestSchema.extend({method:literal("completion/complete"),params:CompleteRequestParamsSchema}),CompleteResultSchema=ResultSchema.extend({completion:looseObject({values:array(string$2()).max(100),total:optional(number$2().int()),hasMore:optional(boolean$1())})}),RootSchema=object({uri:string$2().startsWith("file://"),name:string$2().optional(),_meta:record(
-string$2(),unknown$2()).optional()}),ListRootsRequestSchema=RequestSchema.extend({method:literal("roots/list"),params:BaseRequestParamsSchema.optional()}),ListRootsResultSchema=ResultSchema.extend({roots:array(RootSchema)}),RootsListChangedNotificationSchema=NotificationSchema.extend({method:literal("notifications/roots/list_changed"),params:NotificationsParamsSchema.optional()});union([PingRequestSchema,InitializeRequestSchema,CompleteRequestSchema,SetLevelRequestSchema,GetPromptRequestSchema,ListPromptsRequestSchema,
-ListResourcesRequestSchema,ListResourceTemplatesRequestSchema,ReadResourceRequestSchema,SubscribeRequestSchema,UnsubscribeRequestSchema,CallToolRequestSchema,ListToolsRequestSchema,GetTaskRequestSchema,GetTaskPayloadRequestSchema,ListTasksRequestSchema,CancelTaskRequestSchema]);union([CancelledNotificationSchema,ProgressNotificationSchema,InitializedNotificationSchema,RootsListChangedNotificationSchema,TaskStatusNotificationSchema]);union([EmptyResultSchema,CreateMessageResultSchema,CreateMessageResultWithToolsSchema,
-ElicitResultSchema,ListRootsResultSchema,GetTaskResultSchema,ListTasksResultSchema,CreateTaskResultSchema]);union([PingRequestSchema,CreateMessageRequestSchema,ElicitRequestSchema,ListRootsRequestSchema,GetTaskRequestSchema,GetTaskPayloadRequestSchema,ListTasksRequestSchema,CancelTaskRequestSchema]);union([CancelledNotificationSchema,ProgressNotificationSchema,LoggingMessageNotificationSchema,ResourceUpdatedNotificationSchema,ResourceListChangedNotificationSchema,ToolListChangedNotificationSchema,
-PromptListChangedNotificationSchema,TaskStatusNotificationSchema,ElicitationCompleteNotificationSchema]);union([EmptyResultSchema,InitializeResultSchema,CompleteResultSchema,GetPromptResultSchema,ListPromptsResultSchema,ListResourcesResultSchema,ListResourceTemplatesResultSchema,ReadResourceResultSchema,CallToolResultSchema,ListToolsResultSchema,GetTaskResultSchema,ListTasksResultSchema,CreateTaskResultSchema]);class McpError extends Error{constructor(code2,message,data){super(`MCP error ${code2}\
-: ${message}`),this.code=code2,this.data=data,this.name="McpError"}static fromError(code2,message,data){if(code2===ErrorCode.UrlElicitationRequired&&data){const errorData=data;if(errorData.elicitations)return new UrlElicitationRequiredError(errorData.elicitations,message)}return new McpError(code2,message,data)}}class UrlElicitationRequiredError extends McpError{constructor(elicitations,message=`URL elicitation${elicitations.length>1?"s":""} required`){super(ErrorCode.UrlElicitationRequired,message,
-{elicitations})}get elicitations(){return this.data?.elicitations??[]}}function isTerminal(status){return status==="completed"||status==="failed"||status==="cancelled"}new Set("ABCDEFGHIJKLMNOPQRSTUVXYZabcdefghijklmnopqrstuvxyz0123456789");function getMethodLiteral(schema){const methodSchema=getObjectShape(schema)?.method;if(!methodSchema)throw new Error("Schema is missing a method literal");const value=getLiteralValue(methodSchema);if(typeof value!="string")throw new Error("Schema method litera\
-l must be a string");return value}function parseWithCompat(schema,data){const result=safeParse$1(schema,data);if(!result.success)throw result.error;return result.data}const DEFAULT_REQUEST_TIMEOUT_MSEC=6e4;class Protocol{constructor(_options){this._options=_options,this._requestMessageId=0,this._requestHandlers=new Map,this._requestHandlerAbortControllers=new Map,this._notificationHandlers=new Map,this._responseHandlers=new Map,this._progressHandlers=new Map,this._timeoutInfo=new Map,this._pendingDebouncedNotifications=
-new Set,this._taskProgressTokens=new Map,this._requestResolvers=new Map,this.setNotificationHandler(CancelledNotificationSchema,notification=>{this._oncancel(notification)}),this.setNotificationHandler(ProgressNotificationSchema,notification=>{this._onprogress(notification)}),this.setRequestHandler(PingRequestSchema,_request=>({})),this._taskStore=_options?.taskStore,this._taskMessageQueue=_options?.taskMessageQueue,this._taskStore&&(this.setRequestHandler(GetTaskRequestSchema,async(request,extra)=>{
-const task=await this._taskStore.getTask(request.params.taskId,extra.sessionId);if(!task)throw new McpError(ErrorCode.InvalidParams,"Failed to retrieve task: Task not found");return{...task}}),this.setRequestHandler(GetTaskPayloadRequestSchema,async(request,extra)=>{const handleTaskResult=async()=>{const taskId=request.params.taskId;if(this._taskMessageQueue){let queuedMessage;for(;queuedMessage=await this._taskMessageQueue.dequeue(taskId,extra.sessionId);){if(queuedMessage.type==="response"||queuedMessage.
-type==="error"){const message=queuedMessage.message,requestId=message.id,resolver2=this._requestResolvers.get(requestId);if(resolver2)if(this._requestResolvers.delete(requestId),queuedMessage.type==="response")resolver2(message);else{const errorMessage=message,error2=new McpError(errorMessage.error.code,errorMessage.error.message,errorMessage.error.data);resolver2(error2)}else{const messageType=queuedMessage.type==="response"?"Response":"Error";this._onerror(new Error(`${messageType} handler mis\
-sing for request ${requestId}`))}continue}await this._transport?.send(queuedMessage.message,{relatedRequestId:extra.requestId})}}const task=await this._taskStore.getTask(taskId,extra.sessionId);if(!task)throw new McpError(ErrorCode.InvalidParams,`Task not found: ${taskId}`);if(!isTerminal(task.status))return await this._waitForTaskUpdate(taskId,extra.signal),await handleTaskResult();if(isTerminal(task.status)){const result=await this._taskStore.getTaskResult(taskId,extra.sessionId);return this._clearTaskQueue(
-taskId),{...result,_meta:{...result._meta,[RELATED_TASK_META_KEY]:{taskId}}}}return await handleTaskResult()};return await handleTaskResult()}),this.setRequestHandler(ListTasksRequestSchema,async(request,extra)=>{try{const{tasks,nextCursor}=await this._taskStore.listTasks(request.params?.cursor,extra.sessionId);return{tasks,nextCursor,_meta:{}}}catch(error2){throw new McpError(ErrorCode.InvalidParams,`Failed to list tasks: ${error2 instanceof Error?error2.message:String(error2)}`)}}),this.setRequestHandler(
-CancelTaskRequestSchema,async(request,extra)=>{try{const task=await this._taskStore.getTask(request.params.taskId,extra.sessionId);if(!task)throw new McpError(ErrorCode.InvalidParams,`Task not found: ${request.params.taskId}`);if(isTerminal(task.status))throw new McpError(ErrorCode.InvalidParams,`Cannot cancel task in terminal status: ${task.status}`);await this._taskStore.updateTaskStatus(request.params.taskId,"cancelled","Client cancelled task execution.",extra.sessionId),this._clearTaskQueue(
-request.params.taskId);const cancelledTask=await this._taskStore.getTask(request.params.taskId,extra.sessionId);if(!cancelledTask)throw new McpError(ErrorCode.InvalidParams,`Task not found after cancellation: ${request.params.taskId}`);return{_meta:{},...cancelledTask}}catch(error2){throw error2 instanceof McpError?error2:new McpError(ErrorCode.InvalidRequest,`Failed to cancel task: ${error2 instanceof Error?error2.message:String(error2)}`)}}))}async _oncancel(notification){if(!notification.params.
-requestId)return;this._requestHandlerAbortControllers.get(notification.params.requestId)?.abort(notification.params.reason)}_setupTimeout(messageId,timeout,maxTotalTimeout,onTimeout,resetTimeoutOnProgress=!1){this._timeoutInfo.set(messageId,{timeoutId:setTimeout(onTimeout,timeout),startTime:Date.now(),timeout,maxTotalTimeout,resetTimeoutOnProgress,onTimeout})}_resetTimeout(messageId){const info2=this._timeoutInfo.get(messageId);if(!info2)return!1;const totalElapsed=Date.now()-info2.startTime;if(info2.
-maxTotalTimeout&&totalElapsed>=info2.maxTotalTimeout)throw this._timeoutInfo.delete(messageId),McpError.fromError(ErrorCode.RequestTimeout,"Maximum total timeout exceeded",{maxTotalTimeout:info2.maxTotalTimeout,totalElapsed});return clearTimeout(info2.timeoutId),info2.timeoutId=setTimeout(info2.onTimeout,info2.timeout),!0}_cleanupTimeout(messageId){const info2=this._timeoutInfo.get(messageId);info2&&(clearTimeout(info2.timeoutId),this._timeoutInfo.delete(messageId))}async connect(transport){if(this.
-_transport)throw new Error("Already connected to a transport. Call close() before connecting to a new transport, or use a separate Protocol instance per connection.");this._transport=transport;const _onclose=this.transport?.onclose;this._transport.onclose=()=>{_onclose?.(),this._onclose()};const _onerror=this.transport?.onerror;this._transport.onerror=error2=>{_onerror?.(error2),this._onerror(error2)};const _onmessage=this._transport?.onmessage;this._transport.onmessage=(message,extra)=>{_onmessage?.(
-message,extra),isJSONRPCResultResponse(message)||isJSONRPCErrorResponse(message)?this._onresponse(message):isJSONRPCRequest(message)?this._onrequest(message,extra):isJSONRPCNotification(message)?this._onnotification(message):this._onerror(new Error(`Unknown message type: ${JSON.stringify(message)}`))},await this._transport.start()}_onclose(){const responseHandlers=this._responseHandlers;this._responseHandlers=new Map,this._progressHandlers.clear(),this._taskProgressTokens.clear(),this._pendingDebouncedNotifications.
-clear();for(const controller of this._requestHandlerAbortControllers.values())controller.abort();this._requestHandlerAbortControllers.clear();const error2=McpError.fromError(ErrorCode.ConnectionClosed,"Connection closed");this._transport=void 0,this.onclose?.();for(const handler of responseHandlers.values())handler(error2)}_onerror(error2){this.onerror?.(error2)}_onnotification(notification){const handler=this._notificationHandlers.get(notification.method)??this.fallbackNotificationHandler;handler!==
-void 0&&Promise.resolve().then(()=>handler(notification)).catch(error2=>this._onerror(new Error(`Uncaught error in notification handler: ${error2}`)))}_onrequest(request,extra){const handler=this._requestHandlers.get(request.method)??this.fallbackRequestHandler,capturedTransport=this._transport,relatedTaskId=request.params?._meta?.[RELATED_TASK_META_KEY]?.taskId;if(handler===void 0){const errorResponse={jsonrpc:"2.0",id:request.id,error:{code:ErrorCode.MethodNotFound,message:"Method not found"}};
-relatedTaskId&&this._taskMessageQueue?this._enqueueTaskMessage(relatedTaskId,{type:"error",message:errorResponse,timestamp:Date.now()},capturedTransport?.sessionId).catch(error2=>this._onerror(new Error(`Failed to enqueue error response: ${error2}`))):capturedTransport?.send(errorResponse).catch(error2=>this._onerror(new Error(`Failed to send an error response: ${error2}`)));return}const abortController=new AbortController;this._requestHandlerAbortControllers.set(request.id,abortController);const taskCreationParams=isTaskAugmentedRequestParams(
-request.params)?request.params.task:void 0,taskStore=this._taskStore?this.requestTaskStore(request,capturedTransport?.sessionId):void 0,fullExtra={signal:abortController.signal,sessionId:capturedTransport?.sessionId,_meta:request.params?._meta,sendNotification:async notification=>{if(abortController.signal.aborted)return;const notificationOptions={relatedRequestId:request.id};relatedTaskId&&(notificationOptions.relatedTask={taskId:relatedTaskId}),await this.notification(notification,notificationOptions)},
-sendRequest:async(r2,resultSchema,options)=>{if(abortController.signal.aborted)throw new McpError(ErrorCode.ConnectionClosed,"Request was cancelled");const requestOptions={...options,relatedRequestId:request.id};relatedTaskId&&!requestOptions.relatedTask&&(requestOptions.relatedTask={taskId:relatedTaskId});const effectiveTaskId=requestOptions.relatedTask?.taskId??relatedTaskId;return effectiveTaskId&&taskStore&&await taskStore.updateTaskStatus(effectiveTaskId,"input_required"),await this.request(
-r2,resultSchema,requestOptions)},authInfo:extra?.authInfo,requestId:request.id,requestInfo:extra?.requestInfo,taskId:relatedTaskId,taskStore,taskRequestedTtl:taskCreationParams?.ttl,closeSSEStream:extra?.closeSSEStream,closeStandaloneSSEStream:extra?.closeStandaloneSSEStream};Promise.resolve().then(()=>{taskCreationParams&&this.assertTaskHandlerCapability(request.method)}).then(()=>handler(request,fullExtra)).then(async result=>{if(abortController.signal.aborted)return;const response={result,jsonrpc:"\
-2.0",id:request.id};relatedTaskId&&this._taskMessageQueue?await this._enqueueTaskMessage(relatedTaskId,{type:"response",message:response,timestamp:Date.now()},capturedTransport?.sessionId):await capturedTransport?.send(response)},async error2=>{if(abortController.signal.aborted)return;const errorResponse={jsonrpc:"2.0",id:request.id,error:{code:Number.isSafeInteger(error2.code)?error2.code:ErrorCode.InternalError,message:error2.message??"Internal error",...error2.data!==void 0&&{data:error2.data}}};
-relatedTaskId&&this._taskMessageQueue?await this._enqueueTaskMessage(relatedTaskId,{type:"error",message:errorResponse,timestamp:Date.now()},capturedTransport?.sessionId):await capturedTransport?.send(errorResponse)}).catch(error2=>this._onerror(new Error(`Failed to send response: ${error2}`))).finally(()=>{this._requestHandlerAbortControllers.delete(request.id)})}_onprogress(notification){const{progressToken,...params}=notification.params,messageId=Number(progressToken),handler=this._progressHandlers.
-get(messageId);if(!handler){this._onerror(new Error(`Received a progress notification for an unknown token: ${JSON.stringify(notification)}`));return}const responseHandler=this._responseHandlers.get(messageId),timeoutInfo=this._timeoutInfo.get(messageId);if(timeoutInfo&&responseHandler&&timeoutInfo.resetTimeoutOnProgress)try{this._resetTimeout(messageId)}catch(error2){this._responseHandlers.delete(messageId),this._progressHandlers.delete(messageId),this._cleanupTimeout(messageId),responseHandler(
-error2);return}handler(params)}_onresponse(response){const messageId=Number(response.id),resolver2=this._requestResolvers.get(messageId);if(resolver2){if(this._requestResolvers.delete(messageId),isJSONRPCResultResponse(response))resolver2(response);else{const error2=new McpError(response.error.code,response.error.message,response.error.data);resolver2(error2)}return}const handler=this._responseHandlers.get(messageId);if(handler===void 0){this._onerror(new Error(`Received a response for an unknow\
-n message ID: ${JSON.stringify(response)}`));return}this._responseHandlers.delete(messageId),this._cleanupTimeout(messageId);let isTaskResponse=!1;if(isJSONRPCResultResponse(response)&&response.result&&typeof response.result=="object"){const result=response.result;if(result.task&&typeof result.task=="object"){const task=result.task;typeof task.taskId=="string"&&(isTaskResponse=!0,this._taskProgressTokens.set(task.taskId,messageId))}}if(isTaskResponse||this._progressHandlers.delete(messageId),isJSONRPCResultResponse(
-response))handler(response);else{const error2=McpError.fromError(response.error.code,response.error.message,response.error.data);handler(error2)}}get transport(){return this._transport}async close(){await this._transport?.close()}async*requestStream(request,resultSchema,options){const{task}=options??{};if(!task){try{yield{type:"result",result:await this.request(request,resultSchema,options)}}catch(error2){yield{type:"error",error:error2 instanceof McpError?error2:new McpError(ErrorCode.InternalError,
-String(error2))}}return}let taskId;try{const createResult=await this.request(request,CreateTaskResultSchema,options);if(createResult.task)taskId=createResult.task.taskId,yield{type:"taskCreated",task:createResult.task};else throw new McpError(ErrorCode.InternalError,"Task creation did not return a task");for(;;){const task2=await this.getTask({taskId},options);if(yield{type:"taskStatus",task:task2},isTerminal(task2.status)){task2.status==="completed"?yield{type:"result",result:await this.getTaskResult(
-{taskId},resultSchema,options)}:task2.status==="failed"?yield{type:"error",error:new McpError(ErrorCode.InternalError,`Task ${taskId} failed`)}:task2.status==="cancelled"&&(yield{type:"error",error:new McpError(ErrorCode.InternalError,`Task ${taskId} was cancelled`)});return}if(task2.status==="input_required"){yield{type:"result",result:await this.getTaskResult({taskId},resultSchema,options)};return}const pollInterval=task2.pollInterval??this._options?.defaultTaskPollInterval??1e3;await new Promise(
-resolve2=>setTimeout(resolve2,pollInterval)),options?.signal?.throwIfAborted()}}catch(error2){yield{type:"error",error:error2 instanceof McpError?error2:new McpError(ErrorCode.InternalError,String(error2))}}}request(request,resultSchema,options){const{relatedRequestId,resumptionToken,onresumptiontoken,task,relatedTask}=options??{};return new Promise((resolve2,reject)=>{const earlyReject=error2=>{reject(error2)};if(!this._transport){earlyReject(new Error("Not connected"));return}if(this._options?.
-enforceStrictCapabilities===!0)try{this.assertCapabilityForMethod(request.method),task&&this.assertTaskCapability(request.method)}catch(e){earlyReject(e);return}options?.signal?.throwIfAborted();const messageId=this._requestMessageId++,jsonrpcRequest={...request,jsonrpc:"2.0",id:messageId};options?.onprogress&&(this._progressHandlers.set(messageId,options.onprogress),jsonrpcRequest.params={...request.params,_meta:{...request.params?._meta||{},progressToken:messageId}}),task&&(jsonrpcRequest.params=
-{...jsonrpcRequest.params,task}),relatedTask&&(jsonrpcRequest.params={...jsonrpcRequest.params,_meta:{...jsonrpcRequest.params?._meta||{},[RELATED_TASK_META_KEY]:relatedTask}});const cancel=reason=>{this._responseHandlers.delete(messageId),this._progressHandlers.delete(messageId),this._cleanupTimeout(messageId),this._transport?.send({jsonrpc:"2.0",method:"notifications/cancelled",params:{requestId:messageId,reason:String(reason)}},{relatedRequestId,resumptionToken,onresumptiontoken}).catch(error3=>this.
-_onerror(new Error(`Failed to send cancellation: ${error3}`)));const error2=reason instanceof McpError?reason:new McpError(ErrorCode.RequestTimeout,String(reason));reject(error2)};this._responseHandlers.set(messageId,response=>{if(!options?.signal?.aborted){if(response instanceof Error)return reject(response);try{const parseResult=safeParse$1(resultSchema,response.result);parseResult.success?resolve2(parseResult.data):reject(parseResult.error)}catch(error2){reject(error2)}}}),options?.signal?.addEventListener(
-"abort",()=>{cancel(options?.signal?.reason)});const timeout=options?.timeout??DEFAULT_REQUEST_TIMEOUT_MSEC,timeoutHandler=()=>cancel(McpError.fromError(ErrorCode.RequestTimeout,"Request timed out",{timeout}));this._setupTimeout(messageId,timeout,options?.maxTotalTimeout,timeoutHandler,options?.resetTimeoutOnProgress??!1);const relatedTaskId=relatedTask?.taskId;if(relatedTaskId){const responseResolver=response=>{const handler=this._responseHandlers.get(messageId);handler?handler(response):this._onerror(
-new Error(`Response handler missing for side-channeled request ${messageId}`))};this._requestResolvers.set(messageId,responseResolver),this._enqueueTaskMessage(relatedTaskId,{type:"request",message:jsonrpcRequest,timestamp:Date.now()}).catch(error2=>{this._cleanupTimeout(messageId),reject(error2)})}else this._transport.send(jsonrpcRequest,{relatedRequestId,resumptionToken,onresumptiontoken}).catch(error2=>{this._cleanupTimeout(messageId),reject(error2)})})}async getTask(params,options){return this.
-request({method:"tasks/get",params},GetTaskResultSchema,options)}async getTaskResult(params,resultSchema,options){return this.request({method:"tasks/result",params},resultSchema,options)}async listTasks(params,options){return this.request({method:"tasks/list",params},ListTasksResultSchema,options)}async cancelTask(params,options){return this.request({method:"tasks/cancel",params},CancelTaskResultSchema,options)}async notification(notification,options){if(!this._transport)throw new Error("Not con\
-nected");this.assertNotificationCapability(notification.method);const relatedTaskId=options?.relatedTask?.taskId;if(relatedTaskId){const jsonrpcNotification2={...notification,jsonrpc:"2.0",params:{...notification.params,_meta:{...notification.params?._meta||{},[RELATED_TASK_META_KEY]:options.relatedTask}}};await this._enqueueTaskMessage(relatedTaskId,{type:"notification",message:jsonrpcNotification2,timestamp:Date.now()});return}if((this._options?.debouncedNotificationMethods??[]).includes(notification.
-method)&&!notification.params&&!options?.relatedRequestId&&!options?.relatedTask){if(this._pendingDebouncedNotifications.has(notification.method))return;this._pendingDebouncedNotifications.add(notification.method),Promise.resolve().then(()=>{if(this._pendingDebouncedNotifications.delete(notification.method),!this._transport)return;let jsonrpcNotification2={...notification,jsonrpc:"2.0"};options?.relatedTask&&(jsonrpcNotification2={...jsonrpcNotification2,params:{...jsonrpcNotification2.params,_meta:{
-...jsonrpcNotification2.params?._meta||{},[RELATED_TASK_META_KEY]:options.relatedTask}}}),this._transport?.send(jsonrpcNotification2,options).catch(error2=>this._onerror(error2))});return}let jsonrpcNotification={...notification,jsonrpc:"2.0"};options?.relatedTask&&(jsonrpcNotification={...jsonrpcNotification,params:{...jsonrpcNotification.params,_meta:{...jsonrpcNotification.params?._meta||{},[RELATED_TASK_META_KEY]:options.relatedTask}}}),await this._transport.send(jsonrpcNotification,options)}setRequestHandler(requestSchema,handler){
-const method=getMethodLiteral(requestSchema);this.assertRequestHandlerCapability(method),this._requestHandlers.set(method,(request,extra)=>{const parsed=parseWithCompat(requestSchema,request);return Promise.resolve(handler(parsed,extra))})}removeRequestHandler(method){this._requestHandlers.delete(method)}assertCanSetRequestHandler(method){if(this._requestHandlers.has(method))throw new Error(`A request handler for ${method} already exists, which would be overridden`)}setNotificationHandler(notificationSchema,handler){
-const method=getMethodLiteral(notificationSchema);this._notificationHandlers.set(method,notification=>{const parsed=parseWithCompat(notificationSchema,notification);return Promise.resolve(handler(parsed))})}removeNotificationHandler(method){this._notificationHandlers.delete(method)}_cleanupTaskProgressHandler(taskId){const progressToken=this._taskProgressTokens.get(taskId);progressToken!==void 0&&(this._progressHandlers.delete(progressToken),this._taskProgressTokens.delete(taskId))}async _enqueueTaskMessage(taskId,message,sessionId){
-if(!this._taskStore||!this._taskMessageQueue)throw new Error("Cannot enqueue task message: taskStore and taskMessageQueue are not configured");const maxQueueSize=this._options?.maxTaskQueueSize;await this._taskMessageQueue.enqueue(taskId,message,sessionId,maxQueueSize)}async _clearTaskQueue(taskId,sessionId){if(this._taskMessageQueue){const messages=await this._taskMessageQueue.dequeueAll(taskId,sessionId);for(const message of messages)if(message.type==="request"&&isJSONRPCRequest(message.message)){
-const requestId=message.message.id,resolver2=this._requestResolvers.get(requestId);resolver2?(resolver2(new McpError(ErrorCode.InternalError,"Task cancelled or completed")),this._requestResolvers.delete(requestId)):this._onerror(new Error(`Resolver missing for request ${requestId} during task ${taskId} cleanup`))}}}async _waitForTaskUpdate(taskId,signal){let interval=this._options?.defaultTaskPollInterval??1e3;try{const task=await this._taskStore?.getTask(taskId);task?.pollInterval&&(interval=task.
-pollInterval)}catch{}return new Promise((resolve2,reject)=>{if(signal.aborted){reject(new McpError(ErrorCode.InvalidRequest,"Request cancelled"));return}const timeoutId=setTimeout(resolve2,interval);signal.addEventListener("abort",()=>{clearTimeout(timeoutId),reject(new McpError(ErrorCode.InvalidRequest,"Request cancelled"))},{once:!0})})}requestTaskStore(request,sessionId){const taskStore=this._taskStore;if(!taskStore)throw new Error("No task store configured");return{createTask:async taskParams=>{
-if(!request)throw new Error("No request provided");return await taskStore.createTask(taskParams,request.id,{method:request.method,params:request.params},sessionId)},getTask:async taskId=>{const task=await taskStore.getTask(taskId,sessionId);if(!task)throw new McpError(ErrorCode.InvalidParams,"Failed to retrieve task: Task not found");return task},storeTaskResult:async(taskId,status,result)=>{await taskStore.storeTaskResult(taskId,status,result,sessionId);const task=await taskStore.getTask(taskId,
-sessionId);if(task){const notification=TaskStatusNotificationSchema.parse({method:"notifications/tasks/status",params:task});await this.notification(notification),isTerminal(task.status)&&this._cleanupTaskProgressHandler(taskId)}},getTaskResult:taskId=>taskStore.getTaskResult(taskId,sessionId),updateTaskStatus:async(taskId,status,statusMessage)=>{const task=await taskStore.getTask(taskId,sessionId);if(!task)throw new McpError(ErrorCode.InvalidParams,`Task "${taskId}" not found - it may have been\
- cleaned up`);if(isTerminal(task.status))throw new McpError(ErrorCode.InvalidParams,`Cannot update task "${taskId}" from terminal status "${task.status}" to "${status}". Terminal states (completed, failed, cancelled) cannot transition to other states.`);await taskStore.updateTaskStatus(taskId,status,statusMessage,sessionId);const updatedTask=await taskStore.getTask(taskId,sessionId);if(updatedTask){const notification=TaskStatusNotificationSchema.parse({method:"notifications/tasks/status",params:updatedTask});
-await this.notification(notification),isTerminal(updatedTask.status)&&this._cleanupTaskProgressHandler(taskId)}},listTasks:cursor=>taskStore.listTasks(cursor,sessionId)}}}function isPlainObject$1(value){return value!==null&&typeof value=="object"&&!Array.isArray(value)}function mergeCapabilities(base2,additional){const result={...base2};for(const key2 in additional){const k=key2,addValue=additional[k];if(addValue===void 0)continue;const baseValue=result[k];isPlainObject$1(baseValue)&&isPlainObject$1(
-addValue)?result[k]={...baseValue,...addValue}:result[k]=addValue}return result}var ajv$1={exports:{}},core$4={},validate$1={},boolSchema$1={},errors$1={},codegen$1={},code$6={},hasRequiredCode$3;function requireCode$3(){return hasRequiredCode$3||(hasRequiredCode$3=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.regexpCode=exports.getEsmExportName=exports.getProperty=exports.safeStringify=exports.stringify=exports.strConcat=exports.addCodeArg=exports.str=exports.
-_=exports.nil=exports._Code=exports.Name=exports.IDENTIFIER=exports._CodeOrName=void 0;class _CodeOrName{}exports._CodeOrName=_CodeOrName,exports.IDENTIFIER=/^[a-z$_][a-z$_0-9]*$/i;class Name extends _CodeOrName{constructor(s2){if(super(),!exports.IDENTIFIER.test(s2))throw new Error("CodeGen: name must be a valid identifier");this.str=s2}toString(){return this.str}emptyStr(){return!1}get names(){return{[this.str]:1}}}exports.Name=Name;class _Code extends _CodeOrName{constructor(code2){super(),this.
-_items=typeof code2=="string"?[code2]:code2}toString(){return this.str}emptyStr(){if(this._items.length>1)return!1;const item=this._items[0];return item===""||item==='""'}get str(){var _a2;return(_a2=this._str)!==null&&_a2!==void 0?_a2:this._str=this._items.reduce((s2,c2)=>`${s2}${c2}`,"")}get names(){var _a2;return(_a2=this._names)!==null&&_a2!==void 0?_a2:this._names=this._items.reduce((names2,c2)=>(c2 instanceof Name&&(names2[c2.str]=(names2[c2.str]||0)+1),names2),{})}}exports._Code=_Code,exports.
-nil=new _Code("");function _(strs,...args){const code2=[strs[0]];let i=0;for(;i<args.length;)addCodeArg(code2,args[i]),code2.push(strs[++i]);return new _Code(code2)}exports._=_;const plus=new _Code("+");function str(strs,...args){const expr=[safeStringify(strs[0])];let i=0;for(;i<args.length;)expr.push(plus),addCodeArg(expr,args[i]),expr.push(plus,safeStringify(strs[++i]));return optimize(expr),new _Code(expr)}exports.str=str;function addCodeArg(code2,arg){arg instanceof _Code?code2.push(...arg.
-_items):arg instanceof Name?code2.push(arg):code2.push(interpolate(arg))}exports.addCodeArg=addCodeArg;function optimize(expr){let i=1;for(;i<expr.length-1;){if(expr[i]===plus){const res=mergeExprItems(expr[i-1],expr[i+1]);if(res!==void 0){expr.splice(i-1,3,res);continue}expr[i++]="+"}i++}}function mergeExprItems(a,b){if(b==='""')return a;if(a==='""')return b;if(typeof a=="string")return b instanceof Name||a[a.length-1]!=='"'?void 0:typeof b!="string"?`${a.slice(0,-1)}${b}"`:b[0]==='"'?a.slice(0,
--1)+b.slice(1):void 0;if(typeof b=="string"&&b[0]==='"'&&!(a instanceof Name))return`"${a}${b.slice(1)}`}function strConcat(c1,c2){return c2.emptyStr()?c1:c1.emptyStr()?c2:str`${c1}${c2}`}exports.strConcat=strConcat;function interpolate(x){return typeof x=="number"||typeof x=="boolean"||x===null?x:safeStringify(Array.isArray(x)?x.join(","):x)}function stringify2(x){return new _Code(safeStringify(x))}exports.stringify=stringify2;function safeStringify(x){return JSON.stringify(x).replace(/\u2028/g,
-"\\u2028").replace(/\u2029/g,"\\u2029")}exports.safeStringify=safeStringify;function getProperty(key2){return typeof key2=="string"&&exports.IDENTIFIER.test(key2)?new _Code(`.${key2}`):_`[${key2}]`}exports.getProperty=getProperty;function getEsmExportName(key2){if(typeof key2=="string"&&exports.IDENTIFIER.test(key2))return new _Code(`${key2}`);throw new Error(`CodeGen: invalid export name: ${key2}, use explicit $id name mapping`)}exports.getEsmExportName=getEsmExportName;function regexpCode(rx){
-return new _Code(rx.toString())}exports.regexpCode=regexpCode}(code$6)),code$6}var scope$1={},hasRequiredScope$1;function requireScope$1(){return hasRequiredScope$1||(hasRequiredScope$1=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.ValueScope=exports.ValueScopeName=exports.Scope=exports.varKinds=exports.UsedValueState=void 0;const code_1=requireCode$3();class ValueError extends Error{constructor(name){super(`CodeGen: "code" for ${name} not defined`),this.value=
-name.value}}var UsedValueState;(function(UsedValueState2){UsedValueState2[UsedValueState2.Started=0]="Started",UsedValueState2[UsedValueState2.Completed=1]="Completed"})(UsedValueState||(exports.UsedValueState=UsedValueState={})),exports.varKinds={const:new code_1.Name("const"),let:new code_1.Name("let"),var:new code_1.Name("var")};class Scope{constructor({prefixes,parent}={}){this._names={},this._prefixes=prefixes,this._parent=parent}toName(nameOrPrefix){return nameOrPrefix instanceof code_1.Name?
-nameOrPrefix:this.name(nameOrPrefix)}name(prefix){return new code_1.Name(this._newName(prefix))}_newName(prefix){const ng=this._names[prefix]||this._nameGroup(prefix);return`${prefix}${ng.index++}`}_nameGroup(prefix){var _a2,_b;if(!((_b=(_a2=this._parent)===null||_a2===void 0?void 0:_a2._prefixes)===null||_b===void 0)&&_b.has(prefix)||this._prefixes&&!this._prefixes.has(prefix))throw new Error(`CodeGen: prefix "${prefix}" is not allowed in this scope`);return this._names[prefix]={prefix,index:0}}}
-exports.Scope=Scope;class ValueScopeName extends code_1.Name{constructor(prefix,nameStr){super(nameStr),this.prefix=prefix}setValue(value,{property,itemIndex}){this.value=value,this.scopePath=(0,code_1._)`.${new code_1.Name(property)}[${itemIndex}]`}}exports.ValueScopeName=ValueScopeName;const line=(0,code_1._)`\n`;class ValueScope extends Scope{constructor(opts){super(opts),this._values={},this._scope=opts.scope,this.opts={...opts,_n:opts.lines?line:code_1.nil}}get(){return this._scope}name(prefix){
-return new ValueScopeName(prefix,this._newName(prefix))}value(nameOrPrefix,value){var _a2;if(value.ref===void 0)throw new Error("CodeGen: ref must be passed in value");const name=this.toName(nameOrPrefix),{prefix}=name,valueKey=(_a2=value.key)!==null&&_a2!==void 0?_a2:value.ref;let vs=this._values[prefix];if(vs){const _name=vs.get(valueKey);if(_name)return _name}else vs=this._values[prefix]=new Map;vs.set(valueKey,name);const s2=this._scope[prefix]||(this._scope[prefix]=[]),itemIndex=s2.length;return s2[itemIndex]=
-value.ref,name.setValue(value,{property:prefix,itemIndex}),name}getValue(prefix,keyOrRef){const vs=this._values[prefix];if(vs)return vs.get(keyOrRef)}scopeRefs(scopeName,values=this._values){return this._reduceValues(values,name=>{if(name.scopePath===void 0)throw new Error(`CodeGen: name "${name}" has no value`);return(0,code_1._)`${scopeName}${name.scopePath}`})}scopeCode(values=this._values,usedValues,getCode){return this._reduceValues(values,name=>{if(name.value===void 0)throw new Error(`Code\
-Gen: name "${name}" has no value`);return name.value.code},usedValues,getCode)}_reduceValues(values,valueCode,usedValues={},getCode){let code2=code_1.nil;for(const prefix in values){const vs=values[prefix];if(!vs)continue;const nameSet=usedValues[prefix]=usedValues[prefix]||new Map;vs.forEach(name=>{if(nameSet.has(name))return;nameSet.set(name,UsedValueState.Started);let c2=valueCode(name);if(c2){const def=this.opts.es5?exports.varKinds.var:exports.varKinds.const;code2=(0,code_1._)`${code2}${def} ${name} = ${c2};${this.
-opts._n}`}else if(c2=getCode?.(name))code2=(0,code_1._)`${code2}${c2}${this.opts._n}`;else throw new ValueError(name);nameSet.set(name,UsedValueState.Completed)})}return code2}}exports.ValueScope=ValueScope}(scope$1)),scope$1}var hasRequiredCodegen$1;function requireCodegen$1(){return hasRequiredCodegen$1||(hasRequiredCodegen$1=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.or=exports.and=exports.not=exports.CodeGen=exports.operators=exports.varKinds=exports.ValueScopeName=
-exports.ValueScope=exports.Scope=exports.Name=exports.regexpCode=exports.stringify=exports.getProperty=exports.nil=exports.strConcat=exports.str=exports._=void 0;const code_1=requireCode$3(),scope_1=requireScope$1();var code_2=requireCode$3();Object.defineProperty(exports,"_",{enumerable:!0,get:function(){return code_2._}}),Object.defineProperty(exports,"str",{enumerable:!0,get:function(){return code_2.str}}),Object.defineProperty(exports,"strConcat",{enumerable:!0,get:function(){return code_2.strConcat}}),
-Object.defineProperty(exports,"nil",{enumerable:!0,get:function(){return code_2.nil}}),Object.defineProperty(exports,"getProperty",{enumerable:!0,get:function(){return code_2.getProperty}}),Object.defineProperty(exports,"stringify",{enumerable:!0,get:function(){return code_2.stringify}}),Object.defineProperty(exports,"regexpCode",{enumerable:!0,get:function(){return code_2.regexpCode}}),Object.defineProperty(exports,"Name",{enumerable:!0,get:function(){return code_2.Name}});var scope_2=requireScope$1();
-Object.defineProperty(exports,"Scope",{enumerable:!0,get:function(){return scope_2.Scope}}),Object.defineProperty(exports,"ValueScope",{enumerable:!0,get:function(){return scope_2.ValueScope}}),Object.defineProperty(exports,"ValueScopeName",{enumerable:!0,get:function(){return scope_2.ValueScopeName}}),Object.defineProperty(exports,"varKinds",{enumerable:!0,get:function(){return scope_2.varKinds}}),exports.operators={GT:new code_1._Code(">"),GTE:new code_1._Code(">="),LT:new code_1._Code("<"),LTE:new code_1.
-_Code("<="),EQ:new code_1._Code("==="),NEQ:new code_1._Code("!=="),NOT:new code_1._Code("!"),OR:new code_1._Code("||"),AND:new code_1._Code("&&"),ADD:new code_1._Code("+")};class Node2{optimizeNodes(){return this}optimizeNames(_names,_constants){return this}}class Def extends Node2{constructor(varKind,name,rhs){super(),this.varKind=varKind,this.name=name,this.rhs=rhs}render({es5,_n}){const varKind=es5?scope_1.varKinds.var:this.varKind,rhs=this.rhs===void 0?"":` = ${this.rhs}`;return`${varKind} ${this.
-name}${rhs};`+_n}optimizeNames(names2,constants2){if(names2[this.name.str])return this.rhs&&(this.rhs=optimizeExpr(this.rhs,names2,constants2)),this}get names(){return this.rhs instanceof code_1._CodeOrName?this.rhs.names:{}}}class Assign extends Node2{constructor(lhs,rhs,sideEffects){super(),this.lhs=lhs,this.rhs=rhs,this.sideEffects=sideEffects}render({_n}){return`${this.lhs} = ${this.rhs};`+_n}optimizeNames(names2,constants2){if(!(this.lhs instanceof code_1.Name&&!names2[this.lhs.str]&&!this.
-sideEffects))return this.rhs=optimizeExpr(this.rhs,names2,constants2),this}get names(){const names2=this.lhs instanceof code_1.Name?{}:{...this.lhs.names};return addExprNames(names2,this.rhs)}}class AssignOp extends Assign{constructor(lhs,op2,rhs,sideEffects){super(lhs,rhs,sideEffects),this.op=op2}render({_n}){return`${this.lhs} ${this.op}= ${this.rhs};`+_n}}class Label2 extends Node2{constructor(label){super(),this.label=label,this.names={}}render({_n}){return`${this.label}:`+_n}}class Break extends Node2{constructor(label){
-super(),this.label=label,this.names={}}render({_n}){return`break${this.label?` ${this.label}`:""};`+_n}}class Throw extends Node2{constructor(error2){super(),this.error=error2}render({_n}){return`throw ${this.error};`+_n}get names(){return this.error.names}}class AnyCode extends Node2{constructor(code2){super(),this.code=code2}render({_n}){return`${this.code};`+_n}optimizeNodes(){return`${this.code}`?this:void 0}optimizeNames(names2,constants2){return this.code=optimizeExpr(this.code,names2,constants2),
-this}get names(){return this.code instanceof code_1._CodeOrName?this.code.names:{}}}class ParentNode extends Node2{constructor(nodes2=[]){super(),this.nodes=nodes2}render(opts){return this.nodes.reduce((code2,n)=>code2+n.render(opts),"")}optimizeNodes(){const{nodes:nodes2}=this;let i=nodes2.length;for(;i--;){const n=nodes2[i].optimizeNodes();Array.isArray(n)?nodes2.splice(i,1,...n):n?nodes2[i]=n:nodes2.splice(i,1)}return nodes2.length>0?this:void 0}optimizeNames(names2,constants2){const{nodes:nodes2}=this;
-let i=nodes2.length;for(;i--;){const n=nodes2[i];n.optimizeNames(names2,constants2)||(subtractNames(names2,n.names),nodes2.splice(i,1))}return nodes2.length>0?this:void 0}get names(){return this.nodes.reduce((names2,n)=>addNames(names2,n.names),{})}}class BlockNode extends ParentNode{render(opts){return"{"+opts._n+super.render(opts)+"}"+opts._n}}class Root2 extends ParentNode{}class Else extends BlockNode{}Else.kind="else";class If extends BlockNode{constructor(condition,nodes2){super(nodes2),this.
-condition=condition}render(opts){let code2=`if(${this.condition})`+super.render(opts);return this.else&&(code2+="else "+this.else.render(opts)),code2}optimizeNodes(){super.optimizeNodes();const cond=this.condition;if(cond===!0)return this.nodes;let e=this.else;if(e){const ns=e.optimizeNodes();e=this.else=Array.isArray(ns)?new Else(ns):ns}if(e)return cond===!1?e instanceof If?e:e.nodes:this.nodes.length?this:new If(not2(cond),e instanceof If?[e]:e.nodes);if(!(cond===!1||!this.nodes.length))return this}optimizeNames(names2,constants2){
-var _a2;if(this.else=(_a2=this.else)===null||_a2===void 0?void 0:_a2.optimizeNames(names2,constants2),!!(super.optimizeNames(names2,constants2)||this.else))return this.condition=optimizeExpr(this.condition,names2,constants2),this}get names(){const names2=super.names;return addExprNames(names2,this.condition),this.else&&addNames(names2,this.else.names),names2}}If.kind="if";class For extends BlockNode{}For.kind="for";class ForLoop extends For{constructor(iteration){super(),this.iteration=iteration}render(opts){
-return`for(${this.iteration})`+super.render(opts)}optimizeNames(names2,constants2){if(super.optimizeNames(names2,constants2))return this.iteration=optimizeExpr(this.iteration,names2,constants2),this}get names(){return addNames(super.names,this.iteration.names)}}class ForRange extends For{constructor(varKind,name,from,to){super(),this.varKind=varKind,this.name=name,this.from=from,this.to=to}render(opts){const varKind=opts.es5?scope_1.varKinds.var:this.varKind,{name,from,to}=this;return`for(${varKind}\
- ${name}=${from}; ${name}<${to}; ${name}++)`+super.render(opts)}get names(){const names2=addExprNames(super.names,this.from);return addExprNames(names2,this.to)}}class ForIter extends For{constructor(loop2,varKind,name,iterable){super(),this.loop=loop2,this.varKind=varKind,this.name=name,this.iterable=iterable}render(opts){return`for(${this.varKind} ${this.name} ${this.loop} ${this.iterable})`+super.render(opts)}optimizeNames(names2,constants2){if(super.optimizeNames(names2,constants2))return this.
-iterable=optimizeExpr(this.iterable,names2,constants2),this}get names(){return addNames(super.names,this.iterable.names)}}class Func extends BlockNode{constructor(name,args,async){super(),this.name=name,this.args=args,this.async=async}render(opts){return`${this.async?"async ":""}function ${this.name}(${this.args})`+super.render(opts)}}Func.kind="func";class Return extends ParentNode{render(opts){return"return "+super.render(opts)}}Return.kind="return";class Try extends BlockNode{render(opts){let code2="\
-try"+super.render(opts);return this.catch&&(code2+=this.catch.render(opts)),this.finally&&(code2+=this.finally.render(opts)),code2}optimizeNodes(){var _a2,_b;return super.optimizeNodes(),(_a2=this.catch)===null||_a2===void 0||_a2.optimizeNodes(),(_b=this.finally)===null||_b===void 0||_b.optimizeNodes(),this}optimizeNames(names2,constants2){var _a2,_b;return super.optimizeNames(names2,constants2),(_a2=this.catch)===null||_a2===void 0||_a2.optimizeNames(names2,constants2),(_b=this.finally)===null||
-_b===void 0||_b.optimizeNames(names2,constants2),this}get names(){const names2=super.names;return this.catch&&addNames(names2,this.catch.names),this.finally&&addNames(names2,this.finally.names),names2}}class Catch extends BlockNode{constructor(error2){super(),this.error=error2}render(opts){return`catch(${this.error})`+super.render(opts)}}Catch.kind="catch";class Finally extends BlockNode{render(opts){return"finally"+super.render(opts)}}Finally.kind="finally";class CodeGen{constructor(extScope,opts={}){
-this._values={},this._blockStarts=[],this._constants={},this.opts={...opts,_n:opts.lines?`
-`:""},this._extScope=extScope,this._scope=new scope_1.Scope({parent:extScope}),this._nodes=[new Root2]}toString(){return this._root.render(this.opts)}name(prefix){return this._scope.name(prefix)}scopeName(prefix){return this._extScope.name(prefix)}scopeValue(prefixOrName,value){const name=this._extScope.value(prefixOrName,value);return(this._values[name.prefix]||(this._values[name.prefix]=new Set)).add(name),name}getScopeValue(prefix,keyOrRef){return this._extScope.getValue(prefix,keyOrRef)}scopeRefs(scopeName){
-return this._extScope.scopeRefs(scopeName,this._values)}scopeCode(){return this._extScope.scopeCode(this._values)}_def(varKind,nameOrPrefix,rhs,constant){const name=this._scope.toName(nameOrPrefix);return rhs!==void 0&&constant&&(this._constants[name.str]=rhs),this._leafNode(new Def(varKind,name,rhs)),name}const(nameOrPrefix,rhs,_constant){return this._def(scope_1.varKinds.const,nameOrPrefix,rhs,_constant)}let(nameOrPrefix,rhs,_constant){return this._def(scope_1.varKinds.let,nameOrPrefix,rhs,_constant)}var(nameOrPrefix,rhs,_constant){
-return this._def(scope_1.varKinds.var,nameOrPrefix,rhs,_constant)}assign(lhs,rhs,sideEffects){return this._leafNode(new Assign(lhs,rhs,sideEffects))}add(lhs,rhs){return this._leafNode(new AssignOp(lhs,exports.operators.ADD,rhs))}code(c2){return typeof c2=="function"?c2():c2!==code_1.nil&&this._leafNode(new AnyCode(c2)),this}object(...keyValues){const code2=["{"];for(const[key2,value]of keyValues)code2.length>1&&code2.push(","),code2.push(key2),(key2!==value||this.opts.es5)&&(code2.push(":"),(0,code_1.
-addCodeArg)(code2,value));return code2.push("}"),new code_1._Code(code2)}if(condition,thenBody,elseBody){if(this._blockNode(new If(condition)),thenBody&&elseBody)this.code(thenBody).else().code(elseBody).endIf();else if(thenBody)this.code(thenBody).endIf();else if(elseBody)throw new Error('CodeGen: "else" body without "then" body');return this}elseIf(condition){return this._elseNode(new If(condition))}else(){return this._elseNode(new Else)}endIf(){return this._endBlockNode(If,Else)}_for(node2,forBody){
-return this._blockNode(node2),forBody&&this.code(forBody).endFor(),this}for(iteration,forBody){return this._for(new ForLoop(iteration),forBody)}forRange(nameOrPrefix,from,to,forBody,varKind=this.opts.es5?scope_1.varKinds.var:scope_1.varKinds.let){const name=this._scope.toName(nameOrPrefix);return this._for(new ForRange(varKind,name,from,to),()=>forBody(name))}forOf(nameOrPrefix,iterable,forBody,varKind=scope_1.varKinds.const){const name=this._scope.toName(nameOrPrefix);if(this.opts.es5){const arr=iterable instanceof
-code_1.Name?iterable:this.var("_arr",iterable);return this.forRange("_i",0,(0,code_1._)`${arr}.length`,i=>{this.var(name,(0,code_1._)`${arr}[${i}]`),forBody(name)})}return this._for(new ForIter("of",varKind,name,iterable),()=>forBody(name))}forIn(nameOrPrefix,obj,forBody,varKind=this.opts.es5?scope_1.varKinds.var:scope_1.varKinds.const){if(this.opts.ownProperties)return this.forOf(nameOrPrefix,(0,code_1._)`Object.keys(${obj})`,forBody);const name=this._scope.toName(nameOrPrefix);return this._for(
-new ForIter("in",varKind,name,obj),()=>forBody(name))}endFor(){return this._endBlockNode(For)}label(label){return this._leafNode(new Label2(label))}break(label){return this._leafNode(new Break(label))}return(value){const node2=new Return;if(this._blockNode(node2),this.code(value),node2.nodes.length!==1)throw new Error('CodeGen: "return" should have one node');return this._endBlockNode(Return)}try(tryBody,catchCode,finallyCode){if(!catchCode&&!finallyCode)throw new Error('CodeGen: "try" without "\
-catch" and "finally"');const node2=new Try;if(this._blockNode(node2),this.code(tryBody),catchCode){const error2=this.name("e");this._currNode=node2.catch=new Catch(error2),catchCode(error2)}return finallyCode&&(this._currNode=node2.finally=new Finally,this.code(finallyCode)),this._endBlockNode(Catch,Finally)}throw(error2){return this._leafNode(new Throw(error2))}block(body2,nodeCount){return this._blockStarts.push(this._nodes.length),body2&&this.code(body2).endBlock(nodeCount),this}endBlock(nodeCount){
-const len=this._blockStarts.pop();if(len===void 0)throw new Error("CodeGen: not in self-balancing block");const toClose=this._nodes.length-len;if(toClose<0||nodeCount!==void 0&&toClose!==nodeCount)throw new Error(`CodeGen: wrong number of nodes: ${toClose} vs ${nodeCount} expected`);return this._nodes.length=len,this}func(name,args=code_1.nil,async,funcBody){return this._blockNode(new Func(name,args,async)),funcBody&&this.code(funcBody).endFunc(),this}endFunc(){return this._endBlockNode(Func)}optimize(n=1){
-for(;n-- >0;)this._root.optimizeNodes(),this._root.optimizeNames(this._root.names,this._constants)}_leafNode(node2){return this._currNode.nodes.push(node2),this}_blockNode(node2){this._currNode.nodes.push(node2),this._nodes.push(node2)}_endBlockNode(N1,N2){const n=this._currNode;if(n instanceof N1||N2&&n instanceof N2)return this._nodes.pop(),this;throw new Error(`CodeGen: not in block "${N2?`${N1.kind}/${N2.kind}`:N1.kind}"`)}_elseNode(node2){const n=this._currNode;if(!(n instanceof If))throw new Error(
-'CodeGen: "else" without "if"');return this._currNode=n.else=node2,this}get _root(){return this._nodes[0]}get _currNode(){const ns=this._nodes;return ns[ns.length-1]}set _currNode(node2){const ns=this._nodes;ns[ns.length-1]=node2}}exports.CodeGen=CodeGen;function addNames(names2,from){for(const n in from)names2[n]=(names2[n]||0)+(from[n]||0);return names2}function addExprNames(names2,from){return from instanceof code_1._CodeOrName?addNames(names2,from.names):names2}function optimizeExpr(expr,names2,constants2){
-if(expr instanceof code_1.Name)return replaceName(expr);if(!canOptimize(expr))return expr;return new code_1._Code(expr._items.reduce((items2,c2)=>(c2 instanceof code_1.Name&&(c2=replaceName(c2)),c2 instanceof code_1._Code?items2.push(...c2._items):items2.push(c2),items2),[]));function replaceName(n){const c2=constants2[n.str];return c2===void 0||names2[n.str]!==1?n:(delete names2[n.str],c2)}function canOptimize(e){return e instanceof code_1._Code&&e._items.some(c2=>c2 instanceof code_1.Name&&names2[c2.
-str]===1&&constants2[c2.str]!==void 0)}}function subtractNames(names2,from){for(const n in from)names2[n]=(names2[n]||0)-(from[n]||0)}function not2(x){return typeof x=="boolean"||typeof x=="number"||x===null?!x:(0,code_1._)`!${par(x)}`}exports.not=not2;const andCode=mappend(exports.operators.AND);function and(...args){return args.reduce(andCode)}exports.and=and;const orCode=mappend(exports.operators.OR);function or(...args){return args.reduce(orCode)}exports.or=or;function mappend(op2){return(x,y)=>x===
-code_1.nil?y:y===code_1.nil?x:(0,code_1._)`${par(x)} ${op2} ${par(y)}`}function par(x){return x instanceof code_1.Name?x:(0,code_1._)`(${x})`}}(codegen$1)),codegen$1}var util$1={},hasRequiredUtil$1;function requireUtil$1(){if(hasRequiredUtil$1)return util$1;hasRequiredUtil$1=1,Object.defineProperty(util$1,"__esModule",{value:!0}),util$1.checkStrictMode=util$1.getErrorPath=util$1.Type=util$1.useFunc=util$1.setEvaluated=util$1.evaluatedPropsToName=util$1.mergeEvaluated=util$1.eachItem=util$1.unescapeJsonPointer=
-util$1.escapeJsonPointer=util$1.escapeFragment=util$1.unescapeFragment=util$1.schemaRefOrVal=util$1.schemaHasRulesButRef=util$1.schemaHasRules=util$1.checkUnknownRules=util$1.alwaysValidSchema=util$1.toHash=void 0;const codegen_1=requireCodegen$1(),code_1=requireCode$3();function toHash(arr){const hash2={};for(const item of arr)hash2[item]=!0;return hash2}util$1.toHash=toHash;function alwaysValidSchema(it,schema){return typeof schema=="boolean"?schema:Object.keys(schema).length===0?!0:(checkUnknownRules(
-it,schema),!schemaHasRules(schema,it.self.RULES.all))}util$1.alwaysValidSchema=alwaysValidSchema;function checkUnknownRules(it,schema=it.schema){const{opts,self:self2}=it;if(!opts.strictSchema||typeof schema=="boolean")return;const rules2=self2.RULES.keywords;for(const key2 in schema)rules2[key2]||checkStrictMode(it,`unknown keyword: "${key2}"`)}util$1.checkUnknownRules=checkUnknownRules;function schemaHasRules(schema,rules2){if(typeof schema=="boolean")return!schema;for(const key2 in schema)if(rules2[key2])
-return!0;return!1}util$1.schemaHasRules=schemaHasRules;function schemaHasRulesButRef(schema,RULES){if(typeof schema=="boolean")return!schema;for(const key2 in schema)if(key2!=="$ref"&&RULES.all[key2])return!0;return!1}util$1.schemaHasRulesButRef=schemaHasRulesButRef;function schemaRefOrVal({topSchemaRef,schemaPath},schema,keyword2,$data){if(!$data){if(typeof schema=="number"||typeof schema=="boolean")return schema;if(typeof schema=="string")return(0,codegen_1._)`${schema}`}return(0,codegen_1._)`${topSchemaRef}${schemaPath}${(0,codegen_1.
-getProperty)(keyword2)}`}util$1.schemaRefOrVal=schemaRefOrVal;function unescapeFragment(str){return unescapeJsonPointer(decodeURIComponent(str))}util$1.unescapeFragment=unescapeFragment;function escapeFragment(str){return encodeURIComponent(escapeJsonPointer(str))}util$1.escapeFragment=escapeFragment;function escapeJsonPointer(str){return typeof str=="number"?`${str}`:str.replace(/~/g,"~0").replace(/\//g,"~1")}util$1.escapeJsonPointer=escapeJsonPointer;function unescapeJsonPointer(str){return str.
-replace(/~1/g,"/").replace(/~0/g,"~")}util$1.unescapeJsonPointer=unescapeJsonPointer;function eachItem(xs,f){if(Array.isArray(xs))for(const x of xs)f(x);else f(xs)}util$1.eachItem=eachItem;function makeMergeEvaluated({mergeNames,mergeToName,mergeValues:mergeValues2,resultToName}){return(gen,from,to,toName)=>{const res=to===void 0?from:to instanceof codegen_1.Name?(from instanceof codegen_1.Name?mergeNames(gen,from,to):mergeToName(gen,from,to),to):from instanceof codegen_1.Name?(mergeToName(gen,to,
-from),from):mergeValues2(from,to);return toName===codegen_1.Name&&!(res instanceof codegen_1.Name)?resultToName(gen,res):res}}util$1.mergeEvaluated={props:makeMergeEvaluated({mergeNames:(gen,from,to)=>gen.if((0,codegen_1._)`${to} !== true && ${from} !== undefined`,()=>{gen.if((0,codegen_1._)`${from} === true`,()=>gen.assign(to,!0),()=>gen.assign(to,(0,codegen_1._)`${to} || {}`).code((0,codegen_1._)`Object.assign(${to}, ${from})`))}),mergeToName:(gen,from,to)=>gen.if((0,codegen_1._)`${to} !== true`,
-()=>{from===!0?gen.assign(to,!0):(gen.assign(to,(0,codegen_1._)`${to} || {}`),setEvaluated(gen,to,from))}),mergeValues:(from,to)=>from===!0?!0:{...from,...to},resultToName:evaluatedPropsToName}),items:makeMergeEvaluated({mergeNames:(gen,from,to)=>gen.if((0,codegen_1._)`${to} !== true && ${from} !== undefined`,()=>gen.assign(to,(0,codegen_1._)`${from} === true ? true : ${to} > ${from} ? ${to} : ${from}`)),mergeToName:(gen,from,to)=>gen.if((0,codegen_1._)`${to} !== true`,()=>gen.assign(to,from===!0?
-!0:(0,codegen_1._)`${to} > ${from} ? ${to} : ${from}`)),mergeValues:(from,to)=>from===!0?!0:Math.max(from,to),resultToName:(gen,items2)=>gen.var("items",items2)})};function evaluatedPropsToName(gen,ps){if(ps===!0)return gen.var("props",!0);const props=gen.var("props",(0,codegen_1._)`{}`);return ps!==void 0&&setEvaluated(gen,props,ps),props}util$1.evaluatedPropsToName=evaluatedPropsToName;function setEvaluated(gen,props,ps){Object.keys(ps).forEach(p2=>gen.assign((0,codegen_1._)`${props}${(0,codegen_1.
-getProperty)(p2)}`,!0))}util$1.setEvaluated=setEvaluated;const snippets={};function useFunc(gen,f){return gen.scopeValue("func",{ref:f,code:snippets[f.code]||(snippets[f.code]=new code_1._Code(f.code))})}util$1.useFunc=useFunc;var Type;(function(Type2){Type2[Type2.Num=0]="Num",Type2[Type2.Str=1]="Str"})(Type||(util$1.Type=Type={}));function getErrorPath(dataProp,dataPropType,jsPropertySyntax){if(dataProp instanceof codegen_1.Name){const isNumber2=dataPropType===Type.Num;return jsPropertySyntax?isNumber2?
-(0,codegen_1._)`"[" + ${dataProp} + "]"`:(0,codegen_1._)`"['" + ${dataProp} + "']"`:isNumber2?(0,codegen_1._)`"/" + ${dataProp}`:(0,codegen_1._)`"/" + ${dataProp}.replace(/~/g, "~0").replace(/\\//g, "~1")`}return jsPropertySyntax?(0,codegen_1.getProperty)(dataProp).toString():"/"+escapeJsonPointer(dataProp)}util$1.getErrorPath=getErrorPath;function checkStrictMode(it,msg,mode=it.opts.strictSchema){if(mode){if(msg=`strict mode: ${msg}`,mode===!0)throw new Error(msg);it.self.logger.warn(msg)}}return util$1.
-checkStrictMode=checkStrictMode,util$1}var names$1={},hasRequiredNames$1;function requireNames$1(){if(hasRequiredNames$1)return names$1;hasRequiredNames$1=1,Object.defineProperty(names$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),names2={data:new codegen_1.Name("data"),valCxt:new codegen_1.Name("valCxt"),instancePath:new codegen_1.Name("instancePath"),parentData:new codegen_1.Name("parentData"),parentDataProperty:new codegen_1.Name("parentDataProperty"),rootData:new codegen_1.Name(
-"rootData"),dynamicAnchors:new codegen_1.Name("dynamicAnchors"),vErrors:new codegen_1.Name("vErrors"),errors:new codegen_1.Name("errors"),this:new codegen_1.Name("this"),self:new codegen_1.Name("self"),scope:new codegen_1.Name("scope"),json:new codegen_1.Name("json"),jsonPos:new codegen_1.Name("jsonPos"),jsonLen:new codegen_1.Name("jsonLen"),jsonPart:new codegen_1.Name("jsonPart")};return names$1.default=names2,names$1}var hasRequiredErrors$1;function requireErrors$1(){return hasRequiredErrors$1||
-(hasRequiredErrors$1=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.extendErrors=exports.resetErrorsCount=exports.reportExtraError=exports.reportError=exports.keyword$DataError=exports.keywordError=void 0;const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),names_1=requireNames$1();exports.keywordError={message:({keyword:keyword2})=>(0,codegen_1.str)`must pass "${keyword2}" keyword validation`},exports.keyword$DataError={message:({keyword:keyword2,schemaType})=>schemaType?
-(0,codegen_1.str)`"${keyword2}" keyword must be ${schemaType} ($data)`:(0,codegen_1.str)`"${keyword2}" keyword is invalid ($data)`};function reportError(cxt,error2=exports.keywordError,errorPaths,overrideAllErrors){const{it}=cxt,{gen,compositeRule,allErrors}=it,errObj=errorObjectCode(cxt,error2,errorPaths);overrideAllErrors??(compositeRule||allErrors)?addError(gen,errObj):returnErrors(it,(0,codegen_1._)`[${errObj}]`)}exports.reportError=reportError;function reportExtraError(cxt,error2=exports.keywordError,errorPaths){
-const{it}=cxt,{gen,compositeRule,allErrors}=it,errObj=errorObjectCode(cxt,error2,errorPaths);addError(gen,errObj),compositeRule||allErrors||returnErrors(it,names_1.default.vErrors)}exports.reportExtraError=reportExtraError;function resetErrorsCount(gen,errsCount){gen.assign(names_1.default.errors,errsCount),gen.if((0,codegen_1._)`${names_1.default.vErrors} !== null`,()=>gen.if(errsCount,()=>gen.assign((0,codegen_1._)`${names_1.default.vErrors}.length`,errsCount),()=>gen.assign(names_1.default.vErrors,
-null)))}exports.resetErrorsCount=resetErrorsCount;function extendErrors({gen,keyword:keyword2,schemaValue,data,errsCount,it}){if(errsCount===void 0)throw new Error("ajv implementation error");const err=gen.name("err");gen.forRange("i",errsCount,names_1.default.errors,i=>{gen.const(err,(0,codegen_1._)`${names_1.default.vErrors}[${i}]`),gen.if((0,codegen_1._)`${err}.instancePath === undefined`,()=>gen.assign((0,codegen_1._)`${err}.instancePath`,(0,codegen_1.strConcat)(names_1.default.instancePath,
-it.errorPath))),gen.assign((0,codegen_1._)`${err}.schemaPath`,(0,codegen_1.str)`${it.errSchemaPath}/${keyword2}`),it.opts.verbose&&(gen.assign((0,codegen_1._)`${err}.schema`,schemaValue),gen.assign((0,codegen_1._)`${err}.data`,data))})}exports.extendErrors=extendErrors;function addError(gen,errObj){const err=gen.const("err",errObj);gen.if((0,codegen_1._)`${names_1.default.vErrors} === null`,()=>gen.assign(names_1.default.vErrors,(0,codegen_1._)`[${err}]`),(0,codegen_1._)`${names_1.default.vErrors}.push(${err})`),
-gen.code((0,codegen_1._)`${names_1.default.errors}++`)}function returnErrors(it,errs){const{gen,validateName,schemaEnv}=it;schemaEnv.$async?gen.throw((0,codegen_1._)`new ${it.ValidationError}(${errs})`):(gen.assign((0,codegen_1._)`${validateName}.errors`,errs),gen.return(!1))}const E={keyword:new codegen_1.Name("keyword"),schemaPath:new codegen_1.Name("schemaPath"),params:new codegen_1.Name("params"),propertyName:new codegen_1.Name("propertyName"),message:new codegen_1.Name("message"),schema:new codegen_1.
-Name("schema"),parentSchema:new codegen_1.Name("parentSchema")};function errorObjectCode(cxt,error2,errorPaths){const{createErrors}=cxt.it;return createErrors===!1?(0,codegen_1._)`{}`:errorObject(cxt,error2,errorPaths)}function errorObject(cxt,error2,errorPaths={}){const{gen,it}=cxt,keyValues=[errorInstancePath(it,errorPaths),errorSchemaPath(cxt,errorPaths)];return extraErrorProps(cxt,error2,keyValues),gen.object(...keyValues)}function errorInstancePath({errorPath},{instancePath}){const instPath=instancePath?
-(0,codegen_1.str)`${errorPath}${(0,util_1.getErrorPath)(instancePath,util_1.Type.Str)}`:errorPath;return[names_1.default.instancePath,(0,codegen_1.strConcat)(names_1.default.instancePath,instPath)]}function errorSchemaPath({keyword:keyword2,it:{errSchemaPath}},{schemaPath,parentSchema}){let schPath=parentSchema?errSchemaPath:(0,codegen_1.str)`${errSchemaPath}/${keyword2}`;return schemaPath&&(schPath=(0,codegen_1.str)`${schPath}${(0,util_1.getErrorPath)(schemaPath,util_1.Type.Str)}`),[E.schemaPath,
-schPath]}function extraErrorProps(cxt,{params,message},keyValues){const{keyword:keyword2,data,schemaValue,it}=cxt,{opts,propertyName,topSchemaRef,schemaPath}=it;keyValues.push([E.keyword,keyword2],[E.params,typeof params=="function"?params(cxt):params||(0,codegen_1._)`{}`]),opts.messages&&keyValues.push([E.message,typeof message=="function"?message(cxt):message]),opts.verbose&&keyValues.push([E.schema,schemaValue],[E.parentSchema,(0,codegen_1._)`${topSchemaRef}${schemaPath}`],[names_1.default.data,
-data]),propertyName&&keyValues.push([E.propertyName,propertyName])}}(errors$1)),errors$1}var hasRequiredBoolSchema$1;function requireBoolSchema$1(){if(hasRequiredBoolSchema$1)return boolSchema$1;hasRequiredBoolSchema$1=1,Object.defineProperty(boolSchema$1,"__esModule",{value:!0}),boolSchema$1.boolOrEmptySchema=boolSchema$1.topBoolOrEmptySchema=void 0;const errors_1=requireErrors$1(),codegen_1=requireCodegen$1(),names_1=requireNames$1(),boolError={message:"boolean schema is false"};function topBoolOrEmptySchema(it){
-const{gen,schema,validateName}=it;schema===!1?falseSchemaError(it,!1):typeof schema=="object"&&schema.$async===!0?gen.return(names_1.default.data):(gen.assign((0,codegen_1._)`${validateName}.errors`,null),gen.return(!0))}boolSchema$1.topBoolOrEmptySchema=topBoolOrEmptySchema;function boolOrEmptySchema(it,valid2){const{gen,schema}=it;schema===!1?(gen.var(valid2,!1),falseSchemaError(it)):gen.var(valid2,!0)}boolSchema$1.boolOrEmptySchema=boolOrEmptySchema;function falseSchemaError(it,overrideAllErrors){
-const{gen,data}=it,cxt={gen,keyword:"false schema",data,schema:!1,schemaCode:!1,schemaValue:!1,params:{},it};(0,errors_1.reportError)(cxt,boolError,void 0,overrideAllErrors)}return boolSchema$1}var dataType$1={},rules$1={},hasRequiredRules$1;function requireRules$1(){if(hasRequiredRules$1)return rules$1;hasRequiredRules$1=1,Object.defineProperty(rules$1,"__esModule",{value:!0}),rules$1.getRules=rules$1.isJSONType=void 0;const _jsonTypes=["string","number","integer","boolean","null","object","arr\
-ay"],jsonTypes=new Set(_jsonTypes);function isJSONType(x){return typeof x=="string"&&jsonTypes.has(x)}rules$1.isJSONType=isJSONType;function getRules(){const groups={number:{type:"number",rules:[]},string:{type:"string",rules:[]},array:{type:"array",rules:[]},object:{type:"object",rules:[]}};return{types:{...groups,integer:!0,boolean:!0,null:!0},rules:[{rules:[]},groups.number,groups.string,groups.array,groups.object],post:{rules:[]},all:{},keywords:{}}}return rules$1.getRules=getRules,rules$1}var applicability$1={},
-hasRequiredApplicability$1;function requireApplicability$1(){if(hasRequiredApplicability$1)return applicability$1;hasRequiredApplicability$1=1,Object.defineProperty(applicability$1,"__esModule",{value:!0}),applicability$1.shouldUseRule=applicability$1.shouldUseGroup=applicability$1.schemaHasRulesForType=void 0;function schemaHasRulesForType({schema,self:self2},type2){const group=self2.RULES.types[type2];return group&&group!==!0&&shouldUseGroup(schema,group)}applicability$1.schemaHasRulesForType=
-schemaHasRulesForType;function shouldUseGroup(schema,group){return group.rules.some(rule=>shouldUseRule(schema,rule))}applicability$1.shouldUseGroup=shouldUseGroup;function shouldUseRule(schema,rule){var _a2;return schema[rule.keyword]!==void 0||((_a2=rule.definition.implements)===null||_a2===void 0?void 0:_a2.some(kwd=>schema[kwd]!==void 0))}return applicability$1.shouldUseRule=shouldUseRule,applicability$1}var hasRequiredDataType$1;function requireDataType$1(){if(hasRequiredDataType$1)return dataType$1;
-hasRequiredDataType$1=1,Object.defineProperty(dataType$1,"__esModule",{value:!0}),dataType$1.reportTypeError=dataType$1.checkDataTypes=dataType$1.checkDataType=dataType$1.coerceAndCheckDataType=dataType$1.getJSONTypes=dataType$1.getSchemaTypes=dataType$1.DataType=void 0;const rules_1=requireRules$1(),applicability_1=requireApplicability$1(),errors_1=requireErrors$1(),codegen_1=requireCodegen$1(),util_1=requireUtil$1();var DataType;(function(DataType2){DataType2[DataType2.Correct=0]="Correct",DataType2[DataType2.
-Wrong=1]="Wrong"})(DataType||(dataType$1.DataType=DataType={}));function getSchemaTypes(schema){const types2=getJSONTypes(schema.type);if(types2.includes("null")){if(schema.nullable===!1)throw new Error("type: null contradicts nullable: false")}else{if(!types2.length&&schema.nullable!==void 0)throw new Error('"nullable" cannot be used without "type"');schema.nullable===!0&&types2.push("null")}return types2}dataType$1.getSchemaTypes=getSchemaTypes;function getJSONTypes(ts){const types2=Array.isArray(
-ts)?ts:ts?[ts]:[];if(types2.every(rules_1.isJSONType))return types2;throw new Error("type must be JSONType or JSONType[]: "+types2.join(","))}dataType$1.getJSONTypes=getJSONTypes;function coerceAndCheckDataType(it,types2){const{gen,data,opts}=it,coerceTo=coerceToTypes(types2,opts.coerceTypes),checkTypes=types2.length>0&&!(coerceTo.length===0&&types2.length===1&&(0,applicability_1.schemaHasRulesForType)(it,types2[0]));if(checkTypes){const wrongType=checkDataTypes(types2,data,opts.strictNumbers,DataType.
-Wrong);gen.if(wrongType,()=>{coerceTo.length?coerceData(it,types2,coerceTo):reportTypeError(it)})}return checkTypes}dataType$1.coerceAndCheckDataType=coerceAndCheckDataType;const COERCIBLE=new Set(["string","number","integer","boolean","null"]);function coerceToTypes(types2,coerceTypes){return coerceTypes?types2.filter(t=>COERCIBLE.has(t)||coerceTypes==="array"&&t==="array"):[]}function coerceData(it,types2,coerceTo){const{gen,data,opts}=it,dataType2=gen.let("dataType",(0,codegen_1._)`typeof ${data}`),
-coerced=gen.let("coerced",(0,codegen_1._)`undefined`);opts.coerceTypes==="array"&&gen.if((0,codegen_1._)`${dataType2} == 'object' && Array.isArray(${data}) && ${data}.length == 1`,()=>gen.assign(data,(0,codegen_1._)`${data}[0]`).assign(dataType2,(0,codegen_1._)`typeof ${data}`).if(checkDataTypes(types2,data,opts.strictNumbers),()=>gen.assign(coerced,data))),gen.if((0,codegen_1._)`${coerced} !== undefined`);for(const t of coerceTo)(COERCIBLE.has(t)||t==="array"&&opts.coerceTypes==="array")&&coerceSpecificType(
-t);gen.else(),reportTypeError(it),gen.endIf(),gen.if((0,codegen_1._)`${coerced} !== undefined`,()=>{gen.assign(data,coerced),assignParentData(it,coerced)});function coerceSpecificType(t){switch(t){case"string":gen.elseIf((0,codegen_1._)`${dataType2} == "number" || ${dataType2} == "boolean"`).assign(coerced,(0,codegen_1._)`"" + ${data}`).elseIf((0,codegen_1._)`${data} === null`).assign(coerced,(0,codegen_1._)`""`);return;case"number":gen.elseIf((0,codegen_1._)`${dataType2} == "boolean" || ${data} === null
-              || (${dataType2} == "string" && ${data} && ${data} == +${data})`).assign(coerced,(0,codegen_1._)`+${data}`);return;case"integer":gen.elseIf((0,codegen_1._)`${dataType2} === "boolean" || ${data} === null
-              || (${dataType2} === "string" && ${data} && ${data} == +${data} && !(${data} % 1))`).assign(coerced,(0,codegen_1._)`+${data}`);return;case"boolean":gen.elseIf((0,codegen_1._)`${data} === "false" || ${data} === 0 || ${data} === null`).assign(coerced,!1).elseIf((0,codegen_1._)`${data} === "true" || ${data} === 1`).assign(coerced,!0);return;case"null":gen.elseIf((0,codegen_1._)`${data} === "" || ${data} === 0 || ${data} === false`),gen.assign(coerced,null);return;case"array":gen.elseIf(
-(0,codegen_1._)`${dataType2} === "string" || ${dataType2} === "number"
-              || ${dataType2} === "boolean" || ${data} === null`).assign(coerced,(0,codegen_1._)`[${data}]`)}}}function assignParentData({gen,parentData,parentDataProperty},expr){gen.if((0,codegen_1._)`${parentData} !== undefined`,()=>gen.assign((0,codegen_1._)`${parentData}[${parentDataProperty}]`,expr))}function checkDataType(dataType2,data,strictNums,correct=DataType.Correct){const EQ=correct===DataType.Correct?codegen_1.operators.EQ:codegen_1.operators.NEQ;let cond;switch(dataType2){case"nul\
-l":return(0,codegen_1._)`${data} ${EQ} null`;case"array":cond=(0,codegen_1._)`Array.isArray(${data})`;break;case"object":cond=(0,codegen_1._)`${data} && typeof ${data} == "object" && !Array.isArray(${data})`;break;case"integer":cond=numCond((0,codegen_1._)`!(${data} % 1) && !isNaN(${data})`);break;case"number":cond=numCond();break;default:return(0,codegen_1._)`typeof ${data} ${EQ} ${dataType2}`}return correct===DataType.Correct?cond:(0,codegen_1.not)(cond);function numCond(_cond=codegen_1.nil){return(0,codegen_1.
-and)((0,codegen_1._)`typeof ${data} == "number"`,_cond,strictNums?(0,codegen_1._)`isFinite(${data})`:codegen_1.nil)}}dataType$1.checkDataType=checkDataType;function checkDataTypes(dataTypes,data,strictNums,correct){if(dataTypes.length===1)return checkDataType(dataTypes[0],data,strictNums,correct);let cond;const types2=(0,util_1.toHash)(dataTypes);if(types2.array&&types2.object){const notObj=(0,codegen_1._)`typeof ${data} != "object"`;cond=types2.null?notObj:(0,codegen_1._)`!${data} || ${notObj}`,
-delete types2.null,delete types2.array,delete types2.object}else cond=codegen_1.nil;types2.number&&delete types2.integer;for(const t in types2)cond=(0,codegen_1.and)(cond,checkDataType(t,data,strictNums,correct));return cond}dataType$1.checkDataTypes=checkDataTypes;const typeError={message:({schema})=>`must be ${schema}`,params:({schema,schemaValue})=>typeof schema=="string"?(0,codegen_1._)`{type: ${schema}}`:(0,codegen_1._)`{type: ${schemaValue}}`};function reportTypeError(it){const cxt=getTypeErrorContext(
-it);(0,errors_1.reportError)(cxt,typeError)}dataType$1.reportTypeError=reportTypeError;function getTypeErrorContext(it){const{gen,data,schema}=it,schemaCode=(0,util_1.schemaRefOrVal)(it,schema,"type");return{gen,keyword:"type",data,schema:schema.type,schemaCode,schemaValue:schemaCode,parentSchema:schema,params:{},it}}return dataType$1}var defaults$1={},hasRequiredDefaults$1;function requireDefaults$1(){if(hasRequiredDefaults$1)return defaults$1;hasRequiredDefaults$1=1,Object.defineProperty(defaults$1,
-"__esModule",{value:!0}),defaults$1.assignDefaults=void 0;const codegen_1=requireCodegen$1(),util_1=requireUtil$1();function assignDefaults(it,ty){const{properties:properties2,items:items2}=it.schema;if(ty==="object"&&properties2)for(const key2 in properties2)assignDefault(it,key2,properties2[key2].default);else ty==="array"&&Array.isArray(items2)&&items2.forEach((sch,i)=>assignDefault(it,i,sch.default))}defaults$1.assignDefaults=assignDefaults;function assignDefault(it,prop2,defaultValue){const{
-gen,compositeRule,data,opts}=it;if(defaultValue===void 0)return;const childData=(0,codegen_1._)`${data}${(0,codegen_1.getProperty)(prop2)}`;if(compositeRule){(0,util_1.checkStrictMode)(it,`default is ignored for: ${childData}`);return}let condition=(0,codegen_1._)`${childData} === undefined`;opts.useDefaults==="empty"&&(condition=(0,codegen_1._)`${condition} || ${childData} === null || ${childData} === ""`),gen.if(condition,(0,codegen_1._)`${childData} = ${(0,codegen_1.stringify)(defaultValue)}`)}
-return defaults$1}var keyword$1={},code$5={},hasRequiredCode$2;function requireCode$2(){if(hasRequiredCode$2)return code$5;hasRequiredCode$2=1,Object.defineProperty(code$5,"__esModule",{value:!0}),code$5.validateUnion=code$5.validateArray=code$5.usePattern=code$5.callValidateCode=code$5.schemaProperties=code$5.allSchemaProperties=code$5.noPropertyInData=code$5.propertyInData=code$5.isOwnProperty=code$5.hasPropFunc=code$5.reportMissingProp=code$5.checkMissingProp=code$5.checkReportMissingProp=void 0;
-const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),names_1=requireNames$1(),util_2=requireUtil$1();function checkReportMissingProp(cxt,prop2){const{gen,data,it}=cxt;gen.if(noPropertyInData(gen,data,prop2,it.opts.ownProperties),()=>{cxt.setParams({missingProperty:(0,codegen_1._)`${prop2}`},!0),cxt.error()})}code$5.checkReportMissingProp=checkReportMissingProp;function checkMissingProp({gen,data,it:{opts}},properties2,missing){return(0,codegen_1.or)(...properties2.map(prop2=>(0,codegen_1.and)(
-noPropertyInData(gen,data,prop2,opts.ownProperties),(0,codegen_1._)`${missing} = ${prop2}`)))}code$5.checkMissingProp=checkMissingProp;function reportMissingProp(cxt,missing){cxt.setParams({missingProperty:missing},!0),cxt.error()}code$5.reportMissingProp=reportMissingProp;function hasPropFunc(gen){return gen.scopeValue("func",{ref:Object.prototype.hasOwnProperty,code:(0,codegen_1._)`Object.prototype.hasOwnProperty`})}code$5.hasPropFunc=hasPropFunc;function isOwnProperty(gen,data,property){return(0,codegen_1.
-_)`${hasPropFunc(gen)}.call(${data}, ${property})`}code$5.isOwnProperty=isOwnProperty;function propertyInData(gen,data,property,ownProperties){const cond=(0,codegen_1._)`${data}${(0,codegen_1.getProperty)(property)} !== undefined`;return ownProperties?(0,codegen_1._)`${cond} && ${isOwnProperty(gen,data,property)}`:cond}code$5.propertyInData=propertyInData;function noPropertyInData(gen,data,property,ownProperties){const cond=(0,codegen_1._)`${data}${(0,codegen_1.getProperty)(property)} === undefined`;
-return ownProperties?(0,codegen_1.or)(cond,(0,codegen_1.not)(isOwnProperty(gen,data,property))):cond}code$5.noPropertyInData=noPropertyInData;function allSchemaProperties(schemaMap){return schemaMap?Object.keys(schemaMap).filter(p2=>p2!=="__proto__"):[]}code$5.allSchemaProperties=allSchemaProperties;function schemaProperties(it,schemaMap){return allSchemaProperties(schemaMap).filter(p2=>!(0,util_1.alwaysValidSchema)(it,schemaMap[p2]))}code$5.schemaProperties=schemaProperties;function callValidateCode({
-schemaCode,data,it:{gen,topSchemaRef,schemaPath,errorPath},it},func,context,passSchema){const dataAndSchema=passSchema?(0,codegen_1._)`${schemaCode}, ${data}, ${topSchemaRef}${schemaPath}`:data,valCxt=[[names_1.default.instancePath,(0,codegen_1.strConcat)(names_1.default.instancePath,errorPath)],[names_1.default.parentData,it.parentData],[names_1.default.parentDataProperty,it.parentDataProperty],[names_1.default.rootData,names_1.default.rootData]];it.opts.dynamicRef&&valCxt.push([names_1.default.
-dynamicAnchors,names_1.default.dynamicAnchors]);const args=(0,codegen_1._)`${dataAndSchema}, ${gen.object(...valCxt)}`;return context!==codegen_1.nil?(0,codegen_1._)`${func}.call(${context}, ${args})`:(0,codegen_1._)`${func}(${args})`}code$5.callValidateCode=callValidateCode;const newRegExp=(0,codegen_1._)`new RegExp`;function usePattern({gen,it:{opts}},pattern2){const u=opts.unicodeRegExp?"u":"",{regExp}=opts.code,rx=regExp(pattern2,u);return gen.scopeValue("pattern",{key:rx.toString(),ref:rx,code:(0,codegen_1.
-_)`${regExp.code==="new RegExp"?newRegExp:(0,util_2.useFunc)(gen,regExp)}(${pattern2}, ${u})`})}code$5.usePattern=usePattern;function validateArray(cxt){const{gen,data,keyword:keyword2,it}=cxt,valid2=gen.name("valid");if(it.allErrors){const validArr=gen.let("valid",!0);return validateItems(()=>gen.assign(validArr,!1)),validArr}return gen.var(valid2,!0),validateItems(()=>gen.break()),valid2;function validateItems(notValid){const len=gen.const("len",(0,codegen_1._)`${data}.length`);gen.forRange("i",
-0,len,i=>{cxt.subschema({keyword:keyword2,dataProp:i,dataPropType:util_1.Type.Num},valid2),gen.if((0,codegen_1.not)(valid2),notValid)})}}code$5.validateArray=validateArray;function validateUnion(cxt){const{gen,schema,keyword:keyword2,it}=cxt;if(!Array.isArray(schema))throw new Error("ajv implementation error");if(schema.some(sch=>(0,util_1.alwaysValidSchema)(it,sch))&&!it.opts.unevaluated)return;const valid2=gen.let("valid",!1),schValid=gen.name("_valid");gen.block(()=>schema.forEach((_sch,i)=>{
-const schCxt=cxt.subschema({keyword:keyword2,schemaProp:i,compositeRule:!0},schValid);gen.assign(valid2,(0,codegen_1._)`${valid2} || ${schValid}`),cxt.mergeValidEvaluated(schCxt,schValid)||gen.if((0,codegen_1.not)(valid2))})),cxt.result(valid2,()=>cxt.reset(),()=>cxt.error(!0))}return code$5.validateUnion=validateUnion,code$5}var hasRequiredKeyword$1;function requireKeyword$1(){if(hasRequiredKeyword$1)return keyword$1;hasRequiredKeyword$1=1,Object.defineProperty(keyword$1,"__esModule",{value:!0}),
-keyword$1.validateKeywordUsage=keyword$1.validSchemaType=keyword$1.funcKeywordCode=keyword$1.macroKeywordCode=void 0;const codegen_1=requireCodegen$1(),names_1=requireNames$1(),code_1=requireCode$2(),errors_1=requireErrors$1();function macroKeywordCode(cxt,def){const{gen,keyword:keyword2,schema,parentSchema,it}=cxt,macroSchema=def.macro.call(it.self,schema,parentSchema,it),schemaRef=useKeyword(gen,keyword2,macroSchema);it.opts.validateSchema!==!1&&it.self.validateSchema(macroSchema,!0);const valid2=gen.
-name("valid");cxt.subschema({schema:macroSchema,schemaPath:codegen_1.nil,errSchemaPath:`${it.errSchemaPath}/${keyword2}`,topSchemaRef:schemaRef,compositeRule:!0},valid2),cxt.pass(valid2,()=>cxt.error(!0))}keyword$1.macroKeywordCode=macroKeywordCode;function funcKeywordCode(cxt,def){var _a2;const{gen,keyword:keyword2,schema,parentSchema,$data,it}=cxt;checkAsyncKeyword(it,def);const validate2=!$data&&def.compile?def.compile.call(it.self,schema,parentSchema,it):def.validate,validateRef=useKeyword(gen,
-keyword2,validate2),valid2=gen.let("valid");cxt.block$data(valid2,validateKeyword),cxt.ok((_a2=def.valid)!==null&&_a2!==void 0?_a2:valid2);function validateKeyword(){if(def.errors===!1)assignValid(),def.modifying&&modifyData(cxt),reportErrs(()=>cxt.error());else{const ruleErrs=def.async?validateAsync():validateSync();def.modifying&&modifyData(cxt),reportErrs(()=>addErrs(cxt,ruleErrs))}}function validateAsync(){const ruleErrs=gen.let("ruleErrs",null);return gen.try(()=>assignValid((0,codegen_1._)`await `),
-e=>gen.assign(valid2,!1).if((0,codegen_1._)`${e} instanceof ${it.ValidationError}`,()=>gen.assign(ruleErrs,(0,codegen_1._)`${e}.errors`),()=>gen.throw(e))),ruleErrs}function validateSync(){const validateErrs=(0,codegen_1._)`${validateRef}.errors`;return gen.assign(validateErrs,null),assignValid(codegen_1.nil),validateErrs}function assignValid(_await=def.async?(0,codegen_1._)`await `:codegen_1.nil){const passCxt=it.opts.passContext?names_1.default.this:names_1.default.self,passSchema=!("compile"in
-def&&!$data||def.schema===!1);gen.assign(valid2,(0,codegen_1._)`${_await}${(0,code_1.callValidateCode)(cxt,validateRef,passCxt,passSchema)}`,def.modifying)}function reportErrs(errors2){var _a3;gen.if((0,codegen_1.not)((_a3=def.valid)!==null&&_a3!==void 0?_a3:valid2),errors2)}}keyword$1.funcKeywordCode=funcKeywordCode;function modifyData(cxt){const{gen,data,it}=cxt;gen.if(it.parentData,()=>gen.assign(data,(0,codegen_1._)`${it.parentData}[${it.parentDataProperty}]`))}function addErrs(cxt,errs){const{
-gen}=cxt;gen.if((0,codegen_1._)`Array.isArray(${errs})`,()=>{gen.assign(names_1.default.vErrors,(0,codegen_1._)`${names_1.default.vErrors} === null ? ${errs} : ${names_1.default.vErrors}.concat(${errs})`).assign(names_1.default.errors,(0,codegen_1._)`${names_1.default.vErrors}.length`),(0,errors_1.extendErrors)(cxt)},()=>cxt.error())}function checkAsyncKeyword({schemaEnv},def){if(def.async&&!schemaEnv.$async)throw new Error("async keyword in sync schema")}function useKeyword(gen,keyword2,result){
-if(result===void 0)throw new Error(`keyword "${keyword2}" failed to compile`);return gen.scopeValue("keyword",typeof result=="function"?{ref:result}:{ref:result,code:(0,codegen_1.stringify)(result)})}function validSchemaType(schema,schemaType,allowUndefined=!1){return!schemaType.length||schemaType.some(st=>st==="array"?Array.isArray(schema):st==="object"?schema&&typeof schema=="object"&&!Array.isArray(schema):typeof schema==st||allowUndefined&&typeof schema>"u")}keyword$1.validSchemaType=validSchemaType;
-function validateKeywordUsage({schema,opts,self:self2,errSchemaPath},def,keyword2){if(Array.isArray(def.keyword)?!def.keyword.includes(keyword2):def.keyword!==keyword2)throw new Error("ajv implementation error");const deps=def.dependencies;if(deps?.some(kwd=>!Object.prototype.hasOwnProperty.call(schema,kwd)))throw new Error(`parent schema must have dependencies of ${keyword2}: ${deps.join(",")}`);if(def.validateSchema&&!def.validateSchema(schema[keyword2])){const msg=`keyword "${keyword2}" value\
- is invalid at path "${errSchemaPath}": `+self2.errorsText(def.validateSchema.errors);if(opts.validateSchema==="log")self2.logger.error(msg);else throw new Error(msg)}}return keyword$1.validateKeywordUsage=validateKeywordUsage,keyword$1}var subschema$1={},hasRequiredSubschema$1;function requireSubschema$1(){if(hasRequiredSubschema$1)return subschema$1;hasRequiredSubschema$1=1,Object.defineProperty(subschema$1,"__esModule",{value:!0}),subschema$1.extendSubschemaMode=subschema$1.extendSubschemaData=
-subschema$1.getSubschema=void 0;const codegen_1=requireCodegen$1(),util_1=requireUtil$1();function getSubschema(it,{keyword:keyword2,schemaProp,schema,schemaPath,errSchemaPath,topSchemaRef}){if(keyword2!==void 0&&schema!==void 0)throw new Error('both "keyword" and "schema" passed, only one allowed');if(keyword2!==void 0){const sch=it.schema[keyword2];return schemaProp===void 0?{schema:sch,schemaPath:(0,codegen_1._)`${it.schemaPath}${(0,codegen_1.getProperty)(keyword2)}`,errSchemaPath:`${it.errSchemaPath}\
-/${keyword2}`}:{schema:sch[schemaProp],schemaPath:(0,codegen_1._)`${it.schemaPath}${(0,codegen_1.getProperty)(keyword2)}${(0,codegen_1.getProperty)(schemaProp)}`,errSchemaPath:`${it.errSchemaPath}/${keyword2}/${(0,util_1.escapeFragment)(schemaProp)}`}}if(schema!==void 0){if(schemaPath===void 0||errSchemaPath===void 0||topSchemaRef===void 0)throw new Error('"schemaPath", "errSchemaPath" and "topSchemaRef" are required with "schema"');return{schema,schemaPath,topSchemaRef,errSchemaPath}}throw new Error(
-'either "keyword" or "schema" must be passed')}subschema$1.getSubschema=getSubschema;function extendSubschemaData(subschema2,it,{dataProp,dataPropType:dpType,data,dataTypes,propertyName}){if(data!==void 0&&dataProp!==void 0)throw new Error('both "data" and "dataProp" passed, only one allowed');const{gen}=it;if(dataProp!==void 0){const{errorPath,dataPathArr,opts}=it,nextData=gen.let("data",(0,codegen_1._)`${it.data}${(0,codegen_1.getProperty)(dataProp)}`,!0);dataContextProps(nextData),subschema2.
-errorPath=(0,codegen_1.str)`${errorPath}${(0,util_1.getErrorPath)(dataProp,dpType,opts.jsPropertySyntax)}`,subschema2.parentDataProperty=(0,codegen_1._)`${dataProp}`,subschema2.dataPathArr=[...dataPathArr,subschema2.parentDataProperty]}if(data!==void 0){const nextData=data instanceof codegen_1.Name?data:gen.let("data",data,!0);dataContextProps(nextData),propertyName!==void 0&&(subschema2.propertyName=propertyName)}dataTypes&&(subschema2.dataTypes=dataTypes);function dataContextProps(_nextData){subschema2.
-data=_nextData,subschema2.dataLevel=it.dataLevel+1,subschema2.dataTypes=[],it.definedProperties=new Set,subschema2.parentData=it.data,subschema2.dataNames=[...it.dataNames,_nextData]}}subschema$1.extendSubschemaData=extendSubschemaData;function extendSubschemaMode(subschema2,{jtdDiscriminator,jtdMetadata,compositeRule,createErrors,allErrors}){compositeRule!==void 0&&(subschema2.compositeRule=compositeRule),createErrors!==void 0&&(subschema2.createErrors=createErrors),allErrors!==void 0&&(subschema2.
-allErrors=allErrors),subschema2.jtdDiscriminator=jtdDiscriminator,subschema2.jtdMetadata=jtdMetadata}return subschema$1.extendSubschemaMode=extendSubschemaMode,subschema$1}var resolve$1={},fastDeepEqual,hasRequiredFastDeepEqual;function requireFastDeepEqual(){return hasRequiredFastDeepEqual||(hasRequiredFastDeepEqual=1,fastDeepEqual=function equal2(a,b){if(a===b)return!0;if(a&&b&&typeof a=="object"&&typeof b=="object"){if(a.constructor!==b.constructor)return!1;var length,i,keys2;if(Array.isArray(
-a)){if(length=a.length,length!=b.length)return!1;for(i=length;i--!==0;)if(!equal2(a[i],b[i]))return!1;return!0}if(a.constructor===RegExp)return a.source===b.source&&a.flags===b.flags;if(a.valueOf!==Object.prototype.valueOf)return a.valueOf()===b.valueOf();if(a.toString!==Object.prototype.toString)return a.toString()===b.toString();if(keys2=Object.keys(a),length=keys2.length,length!==Object.keys(b).length)return!1;for(i=length;i--!==0;)if(!Object.prototype.hasOwnProperty.call(b,keys2[i]))return!1;
-for(i=length;i--!==0;){var key2=keys2[i];if(!equal2(a[key2],b[key2]))return!1}return!0}return a!==a&&b!==b}),fastDeepEqual}var jsonSchemaTraverse$1={exports:{}},hasRequiredJsonSchemaTraverse$1;function requireJsonSchemaTraverse$1(){if(hasRequiredJsonSchemaTraverse$1)return jsonSchemaTraverse$1.exports;hasRequiredJsonSchemaTraverse$1=1;var traverse=jsonSchemaTraverse$1.exports=function(schema,opts,cb){typeof opts=="function"&&(cb=opts,opts={}),cb=opts.cb||cb;var pre=typeof cb=="function"?cb:cb.pre||
-function(){},post=cb.post||function(){};_traverse(opts,pre,post,schema,"",schema)};traverse.keywords={additionalItems:!0,items:!0,contains:!0,additionalProperties:!0,propertyNames:!0,not:!0,if:!0,then:!0,else:!0},traverse.arrayKeywords={items:!0,allOf:!0,anyOf:!0,oneOf:!0},traverse.propsKeywords={$defs:!0,definitions:!0,properties:!0,patternProperties:!0,dependencies:!0},traverse.skipKeywords={default:!0,enum:!0,const:!0,required:!0,maximum:!0,minimum:!0,exclusiveMaximum:!0,exclusiveMinimum:!0,multipleOf:!0,
-maxLength:!0,minLength:!0,pattern:!0,format:!0,maxItems:!0,minItems:!0,uniqueItems:!0,maxProperties:!0,minProperties:!0};function _traverse(opts,pre,post,schema,jsonPtr,rootSchema,parentJsonPtr,parentKeyword,parentSchema,keyIndex){if(schema&&typeof schema=="object"&&!Array.isArray(schema)){pre(schema,jsonPtr,rootSchema,parentJsonPtr,parentKeyword,parentSchema,keyIndex);for(var key2 in schema){var sch=schema[key2];if(Array.isArray(sch)){if(key2 in traverse.arrayKeywords)for(var i=0;i<sch.length;i++)
-_traverse(opts,pre,post,sch[i],jsonPtr+"/"+key2+"/"+i,rootSchema,jsonPtr,key2,schema,i)}else if(key2 in traverse.propsKeywords){if(sch&&typeof sch=="object")for(var prop2 in sch)_traverse(opts,pre,post,sch[prop2],jsonPtr+"/"+key2+"/"+escapeJsonPtr(prop2),rootSchema,jsonPtr,key2,schema,prop2)}else(key2 in traverse.keywords||opts.allKeys&&!(key2 in traverse.skipKeywords))&&_traverse(opts,pre,post,sch,jsonPtr+"/"+key2,rootSchema,jsonPtr,key2,schema)}post(schema,jsonPtr,rootSchema,parentJsonPtr,parentKeyword,
-parentSchema,keyIndex)}}function escapeJsonPtr(str){return str.replace(/~/g,"~0").replace(/\//g,"~1")}return jsonSchemaTraverse$1.exports}var hasRequiredResolve$1;function requireResolve$1(){if(hasRequiredResolve$1)return resolve$1;hasRequiredResolve$1=1,Object.defineProperty(resolve$1,"__esModule",{value:!0}),resolve$1.getSchemaRefs=resolve$1.resolveUrl=resolve$1.normalizeId=resolve$1._getFullPath=resolve$1.getFullPath=resolve$1.inlineRef=void 0;const util_1=requireUtil$1(),equal2=requireFastDeepEqual(),
-traverse=requireJsonSchemaTraverse$1(),SIMPLE_INLINED=new Set(["type","format","pattern","maxLength","minLength","maxProperties","minProperties","maxItems","minItems","maximum","minimum","uniqueItems","multipleOf","required","enum","const"]);function inlineRef(schema,limit2=!0){return typeof schema=="boolean"?!0:limit2===!0?!hasRef(schema):limit2?countKeys(schema)<=limit2:!1}resolve$1.inlineRef=inlineRef;const REF_KEYWORDS=new Set(["$ref","$recursiveRef","$recursiveAnchor","$dynamicRef","$dynami\
-cAnchor"]);function hasRef(schema){for(const key2 in schema){if(REF_KEYWORDS.has(key2))return!0;const sch=schema[key2];if(Array.isArray(sch)&&sch.some(hasRef)||typeof sch=="object"&&hasRef(sch))return!0}return!1}function countKeys(schema){let count=0;for(const key2 in schema){if(key2==="$ref")return 1/0;if(count++,!SIMPLE_INLINED.has(key2)&&(typeof schema[key2]=="object"&&(0,util_1.eachItem)(schema[key2],sch=>count+=countKeys(sch)),count===1/0))return 1/0}return count}function getFullPath(resolver2,id2="",normalize2){
-normalize2!==!1&&(id2=normalizeId(id2));const p2=resolver2.parse(id2);return _getFullPath(resolver2,p2)}resolve$1.getFullPath=getFullPath;function _getFullPath(resolver2,p2){return resolver2.serialize(p2).split("#")[0]+"#"}resolve$1._getFullPath=_getFullPath;const TRAILING_SLASH_HASH=/#\/?$/;function normalizeId(id2){return id2?id2.replace(TRAILING_SLASH_HASH,""):""}resolve$1.normalizeId=normalizeId;function resolveUrl(resolver2,baseId,id2){return id2=normalizeId(id2),resolver2.resolve(baseId,id2)}
-resolve$1.resolveUrl=resolveUrl;const ANCHOR=/^[a-z_][-a-z0-9._]*$/i;function getSchemaRefs(schema,baseId){if(typeof schema=="boolean")return{};const{schemaId,uriResolver}=this.opts,schId=normalizeId(schema[schemaId]||baseId),baseIds={"":schId},pathPrefix=getFullPath(uriResolver,schId,!1),localRefs={},schemaRefs=new Set;return traverse(schema,{allKeys:!0},(sch,jsonPtr,_,parentJsonPtr)=>{if(parentJsonPtr===void 0)return;const fullPath=pathPrefix+jsonPtr;let innerBaseId=baseIds[parentJsonPtr];typeof sch[schemaId]==
-"string"&&(innerBaseId=addRef.call(this,sch[schemaId])),addAnchor.call(this,sch.$anchor),addAnchor.call(this,sch.$dynamicAnchor),baseIds[jsonPtr]=innerBaseId;function addRef(ref2){const _resolve=this.opts.uriResolver.resolve;if(ref2=normalizeId(innerBaseId?_resolve(innerBaseId,ref2):ref2),schemaRefs.has(ref2))throw ambiguos(ref2);schemaRefs.add(ref2);let schOrRef=this.refs[ref2];return typeof schOrRef=="string"&&(schOrRef=this.refs[schOrRef]),typeof schOrRef=="object"?checkAmbiguosRef(sch,schOrRef.
-schema,ref2):ref2!==normalizeId(fullPath)&&(ref2[0]==="#"?(checkAmbiguosRef(sch,localRefs[ref2],ref2),localRefs[ref2]=sch):this.refs[ref2]=fullPath),ref2}function addAnchor(anchor){if(typeof anchor=="string"){if(!ANCHOR.test(anchor))throw new Error(`invalid anchor "${anchor}"`);addRef.call(this,`#${anchor}`)}}}),localRefs;function checkAmbiguosRef(sch1,sch2,ref2){if(sch2!==void 0&&!equal2(sch1,sch2))throw ambiguos(ref2)}function ambiguos(ref2){return new Error(`reference "${ref2}" resolves to mo\
-re than one schema`)}}return resolve$1.getSchemaRefs=getSchemaRefs,resolve$1}var hasRequiredValidate$1;function requireValidate$1(){if(hasRequiredValidate$1)return validate$1;hasRequiredValidate$1=1,Object.defineProperty(validate$1,"__esModule",{value:!0}),validate$1.getData=validate$1.KeywordCxt=validate$1.validateFunctionCode=void 0;const boolSchema_1=requireBoolSchema$1(),dataType_1=requireDataType$1(),applicability_1=requireApplicability$1(),dataType_2=requireDataType$1(),defaults_1=requireDefaults$1(),
-keyword_1=requireKeyword$1(),subschema_1=requireSubschema$1(),codegen_1=requireCodegen$1(),names_1=requireNames$1(),resolve_1=requireResolve$1(),util_1=requireUtil$1(),errors_1=requireErrors$1();function validateFunctionCode(it){if(isSchemaObj(it)&&(checkKeywords(it),schemaCxtHasRules(it))){topSchemaObjCode(it);return}validateFunction(it,()=>(0,boolSchema_1.topBoolOrEmptySchema)(it))}validate$1.validateFunctionCode=validateFunctionCode;function validateFunction({gen,validateName,schema,schemaEnv,
-opts},body2){opts.code.es5?gen.func(validateName,(0,codegen_1._)`${names_1.default.data}, ${names_1.default.valCxt}`,schemaEnv.$async,()=>{gen.code((0,codegen_1._)`"use strict"; ${funcSourceUrl(schema,opts)}`),destructureValCxtES5(gen,opts),gen.code(body2)}):gen.func(validateName,(0,codegen_1._)`${names_1.default.data}, ${destructureValCxt(opts)}`,schemaEnv.$async,()=>gen.code(funcSourceUrl(schema,opts)).code(body2))}function destructureValCxt(opts){return(0,codegen_1._)`{${names_1.default.instancePath}="", ${names_1.
-default.parentData}, ${names_1.default.parentDataProperty}, ${names_1.default.rootData}=${names_1.default.data}${opts.dynamicRef?(0,codegen_1._)`, ${names_1.default.dynamicAnchors}={}`:codegen_1.nil}}={}`}function destructureValCxtES5(gen,opts){gen.if(names_1.default.valCxt,()=>{gen.var(names_1.default.instancePath,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.instancePath}`),gen.var(names_1.default.parentData,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.parentData}`),
-gen.var(names_1.default.parentDataProperty,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.parentDataProperty}`),gen.var(names_1.default.rootData,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.rootData}`),opts.dynamicRef&&gen.var(names_1.default.dynamicAnchors,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.dynamicAnchors}`)},()=>{gen.var(names_1.default.instancePath,(0,codegen_1._)`""`),gen.var(names_1.default.parentData,(0,codegen_1._)`undefined`),gen.var(names_1.
-default.parentDataProperty,(0,codegen_1._)`undefined`),gen.var(names_1.default.rootData,names_1.default.data),opts.dynamicRef&&gen.var(names_1.default.dynamicAnchors,(0,codegen_1._)`{}`)})}function topSchemaObjCode(it){const{schema,opts,gen}=it;validateFunction(it,()=>{opts.$comment&&schema.$comment&&commentKeyword(it),checkNoDefault(it),gen.let(names_1.default.vErrors,null),gen.let(names_1.default.errors,0),opts.unevaluated&&resetEvaluated(it),typeAndKeywords(it),returnResults(it)})}function resetEvaluated(it){
-const{gen,validateName}=it;it.evaluated=gen.const("evaluated",(0,codegen_1._)`${validateName}.evaluated`),gen.if((0,codegen_1._)`${it.evaluated}.dynamicProps`,()=>gen.assign((0,codegen_1._)`${it.evaluated}.props`,(0,codegen_1._)`undefined`)),gen.if((0,codegen_1._)`${it.evaluated}.dynamicItems`,()=>gen.assign((0,codegen_1._)`${it.evaluated}.items`,(0,codegen_1._)`undefined`))}function funcSourceUrl(schema,opts){const schId=typeof schema=="object"&&schema[opts.schemaId];return schId&&(opts.code.source||
-opts.code.process)?(0,codegen_1._)`/*# sourceURL=${schId} */`:codegen_1.nil}function subschemaCode(it,valid2){if(isSchemaObj(it)&&(checkKeywords(it),schemaCxtHasRules(it))){subSchemaObjCode(it,valid2);return}(0,boolSchema_1.boolOrEmptySchema)(it,valid2)}function schemaCxtHasRules({schema,self:self2}){if(typeof schema=="boolean")return!schema;for(const key2 in schema)if(self2.RULES.all[key2])return!0;return!1}function isSchemaObj(it){return typeof it.schema!="boolean"}function subSchemaObjCode(it,valid2){
-const{schema,gen,opts}=it;opts.$comment&&schema.$comment&&commentKeyword(it),updateContext(it),checkAsyncSchema(it);const errsCount=gen.const("_errs",names_1.default.errors);typeAndKeywords(it,errsCount),gen.var(valid2,(0,codegen_1._)`${errsCount} === ${names_1.default.errors}`)}function checkKeywords(it){(0,util_1.checkUnknownRules)(it),checkRefsAndKeywords(it)}function typeAndKeywords(it,errsCount){if(it.opts.jtd)return schemaKeywords(it,[],!1,errsCount);const types2=(0,dataType_1.getSchemaTypes)(
-it.schema),checkedTypes=(0,dataType_1.coerceAndCheckDataType)(it,types2);schemaKeywords(it,types2,!checkedTypes,errsCount)}function checkRefsAndKeywords(it){const{schema,errSchemaPath,opts,self:self2}=it;schema.$ref&&opts.ignoreKeywordsWithRef&&(0,util_1.schemaHasRulesButRef)(schema,self2.RULES)&&self2.logger.warn(`$ref: keywords ignored in schema at path "${errSchemaPath}"`)}function checkNoDefault(it){const{schema,opts}=it;schema.default!==void 0&&opts.useDefaults&&opts.strictSchema&&(0,util_1.
-checkStrictMode)(it,"default is ignored in the schema root")}function updateContext(it){const schId=it.schema[it.opts.schemaId];schId&&(it.baseId=(0,resolve_1.resolveUrl)(it.opts.uriResolver,it.baseId,schId))}function checkAsyncSchema(it){if(it.schema.$async&&!it.schemaEnv.$async)throw new Error("async schema in sync schema")}function commentKeyword({gen,schemaEnv,schema,errSchemaPath,opts}){const msg=schema.$comment;if(opts.$comment===!0)gen.code((0,codegen_1._)`${names_1.default.self}.logger.log(${msg})`);else if(typeof opts.
-$comment=="function"){const schemaPath=(0,codegen_1.str)`${errSchemaPath}/$comment`,rootName=gen.scopeValue("root",{ref:schemaEnv.root});gen.code((0,codegen_1._)`${names_1.default.self}.opts.$comment(${msg}, ${schemaPath}, ${rootName}.schema)`)}}function returnResults(it){const{gen,schemaEnv,validateName,ValidationError,opts}=it;schemaEnv.$async?gen.if((0,codegen_1._)`${names_1.default.errors} === 0`,()=>gen.return(names_1.default.data),()=>gen.throw((0,codegen_1._)`new ${ValidationError}(${names_1.
-default.vErrors})`)):(gen.assign((0,codegen_1._)`${validateName}.errors`,names_1.default.vErrors),opts.unevaluated&&assignEvaluated(it),gen.return((0,codegen_1._)`${names_1.default.errors} === 0`))}function assignEvaluated({gen,evaluated,props,items:items2}){props instanceof codegen_1.Name&&gen.assign((0,codegen_1._)`${evaluated}.props`,props),items2 instanceof codegen_1.Name&&gen.assign((0,codegen_1._)`${evaluated}.items`,items2)}function schemaKeywords(it,types2,typeErrors,errsCount){const{gen,
-schema,data,allErrors,opts,self:self2}=it,{RULES}=self2;if(schema.$ref&&(opts.ignoreKeywordsWithRef||!(0,util_1.schemaHasRulesButRef)(schema,RULES))){gen.block(()=>keywordCode(it,"$ref",RULES.all.$ref.definition));return}opts.jtd||checkStrictTypes(it,types2),gen.block(()=>{for(const group of RULES.rules)groupKeywords(group);groupKeywords(RULES.post)});function groupKeywords(group){(0,applicability_1.shouldUseGroup)(schema,group)&&(group.type?(gen.if((0,dataType_2.checkDataType)(group.type,data,opts.
-strictNumbers)),iterateKeywords(it,group),types2.length===1&&types2[0]===group.type&&typeErrors&&(gen.else(),(0,dataType_2.reportTypeError)(it)),gen.endIf()):iterateKeywords(it,group),allErrors||gen.if((0,codegen_1._)`${names_1.default.errors} === ${errsCount||0}`))}}function iterateKeywords(it,group){const{gen,schema,opts:{useDefaults}}=it;useDefaults&&(0,defaults_1.assignDefaults)(it,group.type),gen.block(()=>{for(const rule of group.rules)(0,applicability_1.shouldUseRule)(schema,rule)&&keywordCode(
-it,rule.keyword,rule.definition,group.type)})}function checkStrictTypes(it,types2){it.schemaEnv.meta||!it.opts.strictTypes||(checkContextTypes(it,types2),it.opts.allowUnionTypes||checkMultipleTypes(it,types2),checkKeywordTypes(it,it.dataTypes))}function checkContextTypes(it,types2){if(types2.length){if(!it.dataTypes.length){it.dataTypes=types2;return}types2.forEach(t=>{includesType(it.dataTypes,t)||strictTypesError(it,`type "${t}" not allowed by context "${it.dataTypes.join(",")}"`)}),narrowSchemaTypes(
-it,types2)}}function checkMultipleTypes(it,ts){ts.length>1&&!(ts.length===2&&ts.includes("null"))&&strictTypesError(it,"use allowUnionTypes to allow union type keyword")}function checkKeywordTypes(it,ts){const rules2=it.self.RULES.all;for(const keyword2 in rules2){const rule=rules2[keyword2];if(typeof rule=="object"&&(0,applicability_1.shouldUseRule)(it.schema,rule)){const{type:type2}=rule.definition;type2.length&&!type2.some(t=>hasApplicableType(ts,t))&&strictTypesError(it,`missing type "${type2.
-join(",")}" for keyword "${keyword2}"`)}}}function hasApplicableType(schTs,kwdT){return schTs.includes(kwdT)||kwdT==="number"&&schTs.includes("integer")}function includesType(ts,t){return ts.includes(t)||t==="integer"&&ts.includes("number")}function narrowSchemaTypes(it,withTypes){const ts=[];for(const t of it.dataTypes)includesType(withTypes,t)?ts.push(t):withTypes.includes("integer")&&t==="number"&&ts.push("integer");it.dataTypes=ts}function strictTypesError(it,msg){const schemaPath=it.schemaEnv.
-baseId+it.errSchemaPath;msg+=` at "${schemaPath}" (strictTypes)`,(0,util_1.checkStrictMode)(it,msg,it.opts.strictTypes)}class KeywordCxt{constructor(it,def,keyword2){if((0,keyword_1.validateKeywordUsage)(it,def,keyword2),this.gen=it.gen,this.allErrors=it.allErrors,this.keyword=keyword2,this.data=it.data,this.schema=it.schema[keyword2],this.$data=def.$data&&it.opts.$data&&this.schema&&this.schema.$data,this.schemaValue=(0,util_1.schemaRefOrVal)(it,this.schema,keyword2,this.$data),this.schemaType=
-def.schemaType,this.parentSchema=it.schema,this.params={},this.it=it,this.def=def,this.$data)this.schemaCode=it.gen.const("vSchema",getData(this.$data,it));else if(this.schemaCode=this.schemaValue,!(0,keyword_1.validSchemaType)(this.schema,def.schemaType,def.allowUndefined))throw new Error(`${keyword2} value must be ${JSON.stringify(def.schemaType)}`);("code"in def?def.trackErrors:def.errors!==!1)&&(this.errsCount=it.gen.const("_errs",names_1.default.errors))}result(condition,successAction,failAction){
-this.failResult((0,codegen_1.not)(condition),successAction,failAction)}failResult(condition,successAction,failAction){this.gen.if(condition),failAction?failAction():this.error(),successAction?(this.gen.else(),successAction(),this.allErrors&&this.gen.endIf()):this.allErrors?this.gen.endIf():this.gen.else()}pass(condition,failAction){this.failResult((0,codegen_1.not)(condition),void 0,failAction)}fail(condition){if(condition===void 0){this.error(),this.allErrors||this.gen.if(!1);return}this.gen.if(
-condition),this.error(),this.allErrors?this.gen.endIf():this.gen.else()}fail$data(condition){if(!this.$data)return this.fail(condition);const{schemaCode}=this;this.fail((0,codegen_1._)`${schemaCode} !== undefined && (${(0,codegen_1.or)(this.invalid$data(),condition)})`)}error(append2,errorParams,errorPaths){if(errorParams){this.setParams(errorParams),this._error(append2,errorPaths),this.setParams({});return}this._error(append2,errorPaths)}_error(append2,errorPaths){(append2?errors_1.reportExtraError:
-errors_1.reportError)(this,this.def.error,errorPaths)}$dataError(){(0,errors_1.reportError)(this,this.def.$dataError||errors_1.keyword$DataError)}reset(){if(this.errsCount===void 0)throw new Error('add "trackErrors" to keyword definition');(0,errors_1.resetErrorsCount)(this.gen,this.errsCount)}ok(cond){this.allErrors||this.gen.if(cond)}setParams(obj,assign){assign?Object.assign(this.params,obj):this.params=obj}block$data(valid2,codeBlock,$dataValid=codegen_1.nil){this.gen.block(()=>{this.check$data(
-valid2,$dataValid),codeBlock()})}check$data(valid2=codegen_1.nil,$dataValid=codegen_1.nil){if(!this.$data)return;const{gen,schemaCode,schemaType,def}=this;gen.if((0,codegen_1.or)((0,codegen_1._)`${schemaCode} === undefined`,$dataValid)),valid2!==codegen_1.nil&&gen.assign(valid2,!0),(schemaType.length||def.validateSchema)&&(gen.elseIf(this.invalid$data()),this.$dataError(),valid2!==codegen_1.nil&&gen.assign(valid2,!1)),gen.else()}invalid$data(){const{gen,schemaCode,schemaType,def,it}=this;return(0,codegen_1.
-or)(wrong$DataType(),invalid$DataSchema());function wrong$DataType(){if(schemaType.length){if(!(schemaCode instanceof codegen_1.Name))throw new Error("ajv implementation error");const st=Array.isArray(schemaType)?schemaType:[schemaType];return(0,codegen_1._)`${(0,dataType_2.checkDataTypes)(st,schemaCode,it.opts.strictNumbers,dataType_2.DataType.Wrong)}`}return codegen_1.nil}function invalid$DataSchema(){if(def.validateSchema){const validateSchemaRef=gen.scopeValue("validate$data",{ref:def.validateSchema});
-return(0,codegen_1._)`!${validateSchemaRef}(${schemaCode})`}return codegen_1.nil}}subschema(appl,valid2){const subschema2=(0,subschema_1.getSubschema)(this.it,appl);(0,subschema_1.extendSubschemaData)(subschema2,this.it,appl),(0,subschema_1.extendSubschemaMode)(subschema2,appl);const nextContext={...this.it,...subschema2,items:void 0,props:void 0};return subschemaCode(nextContext,valid2),nextContext}mergeEvaluated(schemaCxt,toName){const{it,gen}=this;it.opts.unevaluated&&(it.props!==!0&&schemaCxt.
-props!==void 0&&(it.props=util_1.mergeEvaluated.props(gen,schemaCxt.props,it.props,toName)),it.items!==!0&&schemaCxt.items!==void 0&&(it.items=util_1.mergeEvaluated.items(gen,schemaCxt.items,it.items,toName)))}mergeValidEvaluated(schemaCxt,valid2){const{it,gen}=this;if(it.opts.unevaluated&&(it.props!==!0||it.items!==!0))return gen.if(valid2,()=>this.mergeEvaluated(schemaCxt,codegen_1.Name)),!0}}validate$1.KeywordCxt=KeywordCxt;function keywordCode(it,keyword2,def,ruleType){const cxt=new KeywordCxt(
-it,def,keyword2);"code"in def?def.code(cxt,ruleType):cxt.$data&&def.validate?(0,keyword_1.funcKeywordCode)(cxt,def):"macro"in def?(0,keyword_1.macroKeywordCode)(cxt,def):(def.compile||def.validate)&&(0,keyword_1.funcKeywordCode)(cxt,def)}const JSON_POINTER=/^\/(?:[^~]|~0|~1)*$/,RELATIVE_JSON_POINTER=/^([0-9]+)(#|\/(?:[^~]|~0|~1)*)?$/;function getData($data,{dataLevel,dataNames,dataPathArr}){let jsonPointer,data;if($data==="")return names_1.default.rootData;if($data[0]==="/"){if(!JSON_POINTER.test(
-$data))throw new Error(`Invalid JSON-pointer: ${$data}`);jsonPointer=$data,data=names_1.default.rootData}else{const matches2=RELATIVE_JSON_POINTER.exec($data);if(!matches2)throw new Error(`Invalid JSON-pointer: ${$data}`);const up=+matches2[1];if(jsonPointer=matches2[2],jsonPointer==="#"){if(up>=dataLevel)throw new Error(errorMsg("property/index",up));return dataPathArr[dataLevel-up]}if(up>dataLevel)throw new Error(errorMsg("data",up));if(data=dataNames[dataLevel-up],!jsonPointer)return data}let expr=data;
-const segments=jsonPointer.split("/");for(const segment of segments)segment&&(data=(0,codegen_1._)`${data}${(0,codegen_1.getProperty)((0,util_1.unescapeJsonPointer)(segment))}`,expr=(0,codegen_1._)`${expr} && ${data}`);return expr;function errorMsg(pointerType,up){return`Cannot access ${pointerType} ${up} levels up, current level is ${dataLevel}`}}return validate$1.getData=getData,validate$1}var validation_error$1={},hasRequiredValidation_error$1;function requireValidation_error$1(){if(hasRequiredValidation_error$1)
-return validation_error$1;hasRequiredValidation_error$1=1,Object.defineProperty(validation_error$1,"__esModule",{value:!0});class ValidationError extends Error{constructor(errors2){super("validation failed"),this.errors=errors2,this.ajv=this.validation=!0}}return validation_error$1.default=ValidationError,validation_error$1}var ref_error$1={},hasRequiredRef_error$1;function requireRef_error$1(){if(hasRequiredRef_error$1)return ref_error$1;hasRequiredRef_error$1=1,Object.defineProperty(ref_error$1,
-"__esModule",{value:!0});const resolve_1=requireResolve$1();class MissingRefError extends Error{constructor(resolver2,baseId,ref2,msg){super(msg||`can't resolve reference ${ref2} from id ${baseId}`),this.missingRef=(0,resolve_1.resolveUrl)(resolver2,baseId,ref2),this.missingSchema=(0,resolve_1.normalizeId)((0,resolve_1.getFullPath)(resolver2,this.missingRef))}}return ref_error$1.default=MissingRefError,ref_error$1}var compile$1={},hasRequiredCompile$1;function requireCompile$1(){if(hasRequiredCompile$1)
-return compile$1;hasRequiredCompile$1=1,Object.defineProperty(compile$1,"__esModule",{value:!0}),compile$1.resolveSchema=compile$1.getCompilingSchema=compile$1.resolveRef=compile$1.compileSchema=compile$1.SchemaEnv=void 0;const codegen_1=requireCodegen$1(),validation_error_1=requireValidation_error$1(),names_1=requireNames$1(),resolve_1=requireResolve$1(),util_1=requireUtil$1(),validate_1=requireValidate$1();class SchemaEnv{constructor(env2){var _a2;this.refs={},this.dynamicAnchors={};let schema;
-typeof env2.schema=="object"&&(schema=env2.schema),this.schema=env2.schema,this.schemaId=env2.schemaId,this.root=env2.root||this,this.baseId=(_a2=env2.baseId)!==null&&_a2!==void 0?_a2:(0,resolve_1.normalizeId)(schema?.[env2.schemaId||"$id"]),this.schemaPath=env2.schemaPath,this.localRefs=env2.localRefs,this.meta=env2.meta,this.$async=schema?.$async,this.refs={}}}compile$1.SchemaEnv=SchemaEnv;function compileSchema(sch){const _sch=getCompilingSchema.call(this,sch);if(_sch)return _sch;const rootId=(0,resolve_1.
-getFullPath)(this.opts.uriResolver,sch.root.baseId),{es5,lines}=this.opts.code,{ownProperties}=this.opts,gen=new codegen_1.CodeGen(this.scope,{es5,lines,ownProperties});let _ValidationError;sch.$async&&(_ValidationError=gen.scopeValue("Error",{ref:validation_error_1.default,code:(0,codegen_1._)`require("ajv/dist/runtime/validation_error").default`}));const validateName=gen.scopeName("validate");sch.validateName=validateName;const schemaCxt={gen,allErrors:this.opts.allErrors,data:names_1.default.
-data,parentData:names_1.default.parentData,parentDataProperty:names_1.default.parentDataProperty,dataNames:[names_1.default.data],dataPathArr:[codegen_1.nil],dataLevel:0,dataTypes:[],definedProperties:new Set,topSchemaRef:gen.scopeValue("schema",this.opts.code.source===!0?{ref:sch.schema,code:(0,codegen_1.stringify)(sch.schema)}:{ref:sch.schema}),validateName,ValidationError:_ValidationError,schema:sch.schema,schemaEnv:sch,rootId,baseId:sch.baseId||rootId,schemaPath:codegen_1.nil,errSchemaPath:sch.
-schemaPath||(this.opts.jtd?"":"#"),errorPath:(0,codegen_1._)`""`,opts:this.opts,self:this};let sourceCode;try{this._compilations.add(sch),(0,validate_1.validateFunctionCode)(schemaCxt),gen.optimize(this.opts.code.optimize);const validateCode=gen.toString();sourceCode=`${gen.scopeRefs(names_1.default.scope)}return ${validateCode}`,this.opts.code.process&&(sourceCode=this.opts.code.process(sourceCode,sch));const validate2=new Function(`${names_1.default.self}`,`${names_1.default.scope}`,sourceCode)(
-this,this.scope.get());if(this.scope.value(validateName,{ref:validate2}),validate2.errors=null,validate2.schema=sch.schema,validate2.schemaEnv=sch,sch.$async&&(validate2.$async=!0),this.opts.code.source===!0&&(validate2.source={validateName,validateCode,scopeValues:gen._values}),this.opts.unevaluated){const{props,items:items2}=schemaCxt;validate2.evaluated={props:props instanceof codegen_1.Name?void 0:props,items:items2 instanceof codegen_1.Name?void 0:items2,dynamicProps:props instanceof codegen_1.
-Name,dynamicItems:items2 instanceof codegen_1.Name},validate2.source&&(validate2.source.evaluated=(0,codegen_1.stringify)(validate2.evaluated))}return sch.validate=validate2,sch}catch(e){throw delete sch.validate,delete sch.validateName,sourceCode&&this.logger.error("Error compiling schema, function code:",sourceCode),e}finally{this._compilations.delete(sch)}}compile$1.compileSchema=compileSchema;function resolveRef(root2,baseId,ref2){var _a2;ref2=(0,resolve_1.resolveUrl)(this.opts.uriResolver,baseId,
-ref2);const schOrFunc=root2.refs[ref2];if(schOrFunc)return schOrFunc;let _sch=resolve2.call(this,root2,ref2);if(_sch===void 0){const schema=(_a2=root2.localRefs)===null||_a2===void 0?void 0:_a2[ref2],{schemaId}=this.opts;schema&&(_sch=new SchemaEnv({schema,schemaId,root:root2,baseId}))}if(_sch!==void 0)return root2.refs[ref2]=inlineOrCompile.call(this,_sch)}compile$1.resolveRef=resolveRef;function inlineOrCompile(sch){return(0,resolve_1.inlineRef)(sch.schema,this.opts.inlineRefs)?sch.schema:sch.
-validate?sch:compileSchema.call(this,sch)}function getCompilingSchema(schEnv){for(const sch of this._compilations)if(sameSchemaEnv(sch,schEnv))return sch}compile$1.getCompilingSchema=getCompilingSchema;function sameSchemaEnv(s1,s2){return s1.schema===s2.schema&&s1.root===s2.root&&s1.baseId===s2.baseId}function resolve2(root2,ref2){let sch;for(;typeof(sch=this.refs[ref2])=="string";)ref2=sch;return sch||this.schemas[ref2]||resolveSchema.call(this,root2,ref2)}function resolveSchema(root2,ref2){const p2=this.
-opts.uriResolver.parse(ref2),refPath=(0,resolve_1._getFullPath)(this.opts.uriResolver,p2);let baseId=(0,resolve_1.getFullPath)(this.opts.uriResolver,root2.baseId,void 0);if(Object.keys(root2.schema).length>0&&refPath===baseId)return getJsonPointer.call(this,p2,root2);const id2=(0,resolve_1.normalizeId)(refPath),schOrRef=this.refs[id2]||this.schemas[id2];if(typeof schOrRef=="string"){const sch=resolveSchema.call(this,root2,schOrRef);return typeof sch?.schema!="object"?void 0:getJsonPointer.call(this,
-p2,sch)}if(typeof schOrRef?.schema=="object"){if(schOrRef.validate||compileSchema.call(this,schOrRef),id2===(0,resolve_1.normalizeId)(ref2)){const{schema}=schOrRef,{schemaId}=this.opts,schId=schema[schemaId];return schId&&(baseId=(0,resolve_1.resolveUrl)(this.opts.uriResolver,baseId,schId)),new SchemaEnv({schema,schemaId,root:root2,baseId})}return getJsonPointer.call(this,p2,schOrRef)}}compile$1.resolveSchema=resolveSchema;const PREVENT_SCOPE_CHANGE=new Set(["properties","patternProperties","enu\
-m","dependencies","definitions"]);function getJsonPointer(parsedRef,{baseId,schema,root:root2}){var _a2;if(((_a2=parsedRef.fragment)===null||_a2===void 0?void 0:_a2[0])!=="/")return;for(const part of parsedRef.fragment.slice(1).split("/")){if(typeof schema=="boolean")return;const partSchema=schema[(0,util_1.unescapeFragment)(part)];if(partSchema===void 0)return;schema=partSchema;const schId=typeof schema=="object"&&schema[this.opts.schemaId];!PREVENT_SCOPE_CHANGE.has(part)&&schId&&(baseId=(0,resolve_1.
-resolveUrl)(this.opts.uriResolver,baseId,schId))}let env2;if(typeof schema!="boolean"&&schema.$ref&&!(0,util_1.schemaHasRulesButRef)(schema,this.RULES)){const $ref=(0,resolve_1.resolveUrl)(this.opts.uriResolver,baseId,schema.$ref);env2=resolveSchema.call(this,root2,$ref)}const{schemaId}=this.opts;if(env2=env2||new SchemaEnv({schema,schemaId,root:root2,baseId}),env2.schema!==env2.root.schema)return env2}return compile$1}const $id$3="https://raw.githubusercontent.com/ajv-validator/ajv/master/lib/r\
-efs/data.json#",description$1="Meta-schema for $data reference (JSON AnySchema extension proposal)",type$3="object",required$3=["$data"],properties$6={$data:{type:"string",anyOf:[{format:"relative-json-pointer"},{format:"json-pointer"}]}},additionalProperties$3=!1,require$$9$1={$id:$id$3,description:description$1,type:type$3,required:required$3,properties:properties$6,additionalProperties:additionalProperties$3};var uri$1={},fastUri={exports:{}},utils$1,hasRequiredUtils;function requireUtils(){if(hasRequiredUtils)
-return utils$1;hasRequiredUtils=1;const isUUID=RegExp.prototype.test.bind(/^[\da-f]{8}-[\da-f]{4}-[\da-f]{4}-[\da-f]{4}-[\da-f]{12}$/iu),isIPv4=RegExp.prototype.test.bind(/^(?:(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]\d|\d)\.){3}(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]\d|\d)$/u);function stringArrayToHexStripped(input){let acc="",code2=0,i=0;for(i=0;i<input.length;i++)if(code2=input[i].charCodeAt(0),code2!==48){if(!(code2>=48&&code2<=57||code2>=65&&code2<=70||code2>=97&&code2<=102))return"";acc+=input[i];break}for(i+=
-1;i<input.length;i++){if(code2=input[i].charCodeAt(0),!(code2>=48&&code2<=57||code2>=65&&code2<=70||code2>=97&&code2<=102))return"";acc+=input[i]}return acc}const nonSimpleDomain=RegExp.prototype.test.bind(/[^!"$&'()*+,\-.;=_`a-z{}~]/u);function consumeIsZone(buffer){return buffer.length=0,!0}function consumeHextets(buffer,address,output){if(buffer.length){const hex=stringArrayToHexStripped(buffer);if(hex!=="")address.push(hex);else return output.error=!0,!1;buffer.length=0}return!0}function getIPV6(input){
-let tokenCount=0;const output={error:!1,address:"",zone:""},address=[],buffer=[];let endipv6Encountered=!1,endIpv6=!1,consume=consumeHextets;for(let i=0;i<input.length;i++){const cursor=input[i];if(!(cursor==="["||cursor==="]"))if(cursor===":"){if(endipv6Encountered===!0&&(endIpv6=!0),!consume(buffer,address,output))break;if(++tokenCount>7){output.error=!0;break}i>0&&input[i-1]===":"&&(endipv6Encountered=!0),address.push(":");continue}else if(cursor==="%"){if(!consume(buffer,address,output))break;
-consume=consumeIsZone}else{buffer.push(cursor);continue}}return buffer.length&&(consume===consumeIsZone?output.zone=buffer.join(""):endIpv6?address.push(buffer.join("")):address.push(stringArrayToHexStripped(buffer))),output.address=address.join(""),output}function normalizeIPv6(host){if(findToken(host,":")<2)return{host,isIPV6:!1};const ipv62=getIPV6(host);if(ipv62.error)return{host,isIPV6:!1};{let newHost=ipv62.address,escapedHost=ipv62.address;return ipv62.zone&&(newHost+="%"+ipv62.zone,escapedHost+=
-"%25"+ipv62.zone),{host:newHost,isIPV6:!0,escapedHost}}}function findToken(str,token2){let ind=0;for(let i=0;i<str.length;i++)str[i]===token2&&ind++;return ind}function removeDotSegments(path2){let input=path2;const output=[];let nextSlash=-1,len=0;for(;len=input.length;){if(len===1){if(input===".")break;if(input==="/"){output.push("/");break}else{output.push(input);break}}else if(len===2){if(input[0]==="."){if(input[1]===".")break;if(input[1]==="/"){input=input.slice(2);continue}}else if(input[0]===
-"/"&&(input[1]==="."||input[1]==="/")){output.push("/");break}}else if(len===3&&input==="/.."){output.length!==0&&output.pop(),output.push("/");break}if(input[0]==="."){if(input[1]==="."){if(input[2]==="/"){input=input.slice(3);continue}}else if(input[1]==="/"){input=input.slice(2);continue}}else if(input[0]==="/"&&input[1]==="."){if(input[2]==="/"){input=input.slice(2);continue}else if(input[2]==="."&&input[3]==="/"){input=input.slice(3),output.length!==0&&output.pop();continue}}if((nextSlash=input.
-indexOf("/",1))===-1){output.push(input);break}else output.push(input.slice(0,nextSlash)),input=input.slice(nextSlash)}return output.join("")}function normalizeComponentEncoding(component2,esc2){const func=esc2!==!0?escape:unescape;return component2.scheme!==void 0&&(component2.scheme=func(component2.scheme)),component2.userinfo!==void 0&&(component2.userinfo=func(component2.userinfo)),component2.host!==void 0&&(component2.host=func(component2.host)),component2.path!==void 0&&(component2.path=func(
-component2.path)),component2.query!==void 0&&(component2.query=func(component2.query)),component2.fragment!==void 0&&(component2.fragment=func(component2.fragment)),component2}function recomposeAuthority(component2){const uriTokens=[];if(component2.userinfo!==void 0&&(uriTokens.push(component2.userinfo),uriTokens.push("@")),component2.host!==void 0){let host=unescape(component2.host);if(!isIPv4(host)){const ipV6res=normalizeIPv6(host);ipV6res.isIPV6===!0?host=`[${ipV6res.escapedHost}]`:host=component2.
-host}uriTokens.push(host)}return(typeof component2.port=="number"||typeof component2.port=="string")&&(uriTokens.push(":"),uriTokens.push(String(component2.port))),uriTokens.length?uriTokens.join(""):void 0}return utils$1={nonSimpleDomain,recomposeAuthority,normalizeComponentEncoding,removeDotSegments,isIPv4,isUUID,normalizeIPv6,stringArrayToHexStripped},utils$1}var schemes,hasRequiredSchemes;function requireSchemes(){if(hasRequiredSchemes)return schemes;hasRequiredSchemes=1;const{isUUID}=requireUtils(),
-URN_REG=/([\da-z][\d\-a-z]{0,31}):((?:[\w!$'()*+,\-.:;=@]|%[\da-f]{2})+)/iu,supportedSchemeNames=["http","https","ws","wss","urn","urn:uuid"];function isValidSchemeName(name){return supportedSchemeNames.indexOf(name)!==-1}function wsIsSecure(wsComponent){return wsComponent.secure===!0?!0:wsComponent.secure===!1?!1:wsComponent.scheme?wsComponent.scheme.length===3&&(wsComponent.scheme[0]==="w"||wsComponent.scheme[0]==="W")&&(wsComponent.scheme[1]==="s"||wsComponent.scheme[1]==="S")&&(wsComponent.scheme[2]===
-"s"||wsComponent.scheme[2]==="S"):!1}function httpParse(component2){return component2.host||(component2.error=component2.error||"HTTP URIs must have a host."),component2}function httpSerialize(component2){const secure=String(component2.scheme).toLowerCase()==="https";return(component2.port===(secure?443:80)||component2.port==="")&&(component2.port=void 0),component2.path||(component2.path="/"),component2}function wsParse(wsComponent){return wsComponent.secure=wsIsSecure(wsComponent),wsComponent.
-resourceName=(wsComponent.path||"/")+(wsComponent.query?"?"+wsComponent.query:""),wsComponent.path=void 0,wsComponent.query=void 0,wsComponent}function wsSerialize(wsComponent){if((wsComponent.port===(wsIsSecure(wsComponent)?443:80)||wsComponent.port==="")&&(wsComponent.port=void 0),typeof wsComponent.secure=="boolean"&&(wsComponent.scheme=wsComponent.secure?"wss":"ws",wsComponent.secure=void 0),wsComponent.resourceName){const[path2,query]=wsComponent.resourceName.split("?");wsComponent.path=path2&&
-path2!=="/"?path2:void 0,wsComponent.query=query,wsComponent.resourceName=void 0}return wsComponent.fragment=void 0,wsComponent}function urnParse(urnComponent,options){if(!urnComponent.path)return urnComponent.error="URN can not be parsed",urnComponent;const matches2=urnComponent.path.match(URN_REG);if(matches2){const scheme2=options.scheme||urnComponent.scheme||"urn";urnComponent.nid=matches2[1].toLowerCase(),urnComponent.nss=matches2[2];const urnScheme=`${scheme2}:${options.nid||urnComponent.nid}`,
-schemeHandler=getSchemeHandler(urnScheme);urnComponent.path=void 0,schemeHandler&&(urnComponent=schemeHandler.parse(urnComponent,options))}else urnComponent.error=urnComponent.error||"URN can not be parsed.";return urnComponent}function urnSerialize(urnComponent,options){if(urnComponent.nid===void 0)throw new Error("URN without nid cannot be serialized");const scheme2=options.scheme||urnComponent.scheme||"urn",nid=urnComponent.nid.toLowerCase(),urnScheme=`${scheme2}:${options.nid||nid}`,schemeHandler=getSchemeHandler(
-urnScheme);schemeHandler&&(urnComponent=schemeHandler.serialize(urnComponent,options));const uriComponent=urnComponent,nss=urnComponent.nss;return uriComponent.path=`${nid||options.nid}:${nss}`,options.skipEscape=!0,uriComponent}function urnuuidParse(urnComponent,options){const uuidComponent=urnComponent;return uuidComponent.uuid=uuidComponent.nss,uuidComponent.nss=void 0,!options.tolerant&&(!uuidComponent.uuid||!isUUID(uuidComponent.uuid))&&(uuidComponent.error=uuidComponent.error||"UUID is not\
- valid."),uuidComponent}function urnuuidSerialize(uuidComponent){const urnComponent=uuidComponent;return urnComponent.nss=(uuidComponent.uuid||"").toLowerCase(),urnComponent}const http2={scheme:"http",domainHost:!0,parse:httpParse,serialize:httpSerialize},https={scheme:"https",domainHost:http2.domainHost,parse:httpParse,serialize:httpSerialize},ws={scheme:"ws",domainHost:!0,parse:wsParse,serialize:wsSerialize},wss={scheme:"wss",domainHost:ws.domainHost,parse:ws.parse,serialize:ws.serialize},SCHEMES={
-http:http2,https,ws,wss,urn:{scheme:"urn",parse:urnParse,serialize:urnSerialize,skipNormalize:!0},"urn:uuid":{scheme:"urn:uuid",parse:urnuuidParse,serialize:urnuuidSerialize,skipNormalize:!0}};Object.setPrototypeOf(SCHEMES,null);function getSchemeHandler(scheme2){return scheme2&&(SCHEMES[scheme2]||SCHEMES[scheme2.toLowerCase()])||void 0}return schemes={wsIsSecure,SCHEMES,isValidSchemeName,getSchemeHandler},schemes}var hasRequiredFastUri;function requireFastUri(){if(hasRequiredFastUri)return fastUri.
-exports;hasRequiredFastUri=1;const{normalizeIPv6,removeDotSegments,recomposeAuthority,normalizeComponentEncoding,isIPv4,nonSimpleDomain}=requireUtils(),{SCHEMES,getSchemeHandler}=requireSchemes();function normalize2(uri2,options){return typeof uri2=="string"?uri2=serialize2(parse2(uri2,options),options):typeof uri2=="object"&&(uri2=parse2(serialize2(uri2,options),options)),uri2}function resolve2(baseURI,relativeURI,options){const schemelessOptions=options?Object.assign({scheme:"null"},options):{
-scheme:"null"},resolved=resolveComponent(parse2(baseURI,schemelessOptions),parse2(relativeURI,schemelessOptions),schemelessOptions,!0);return schemelessOptions.skipEscape=!0,serialize2(resolved,schemelessOptions)}function resolveComponent(base2,relative,options,skipNormalization){const target2={};return skipNormalization||(base2=parse2(serialize2(base2,options),options),relative=parse2(serialize2(relative,options),options)),options=options||{},!options.tolerant&&relative.scheme?(target2.scheme=relative.
-scheme,target2.userinfo=relative.userinfo,target2.host=relative.host,target2.port=relative.port,target2.path=removeDotSegments(relative.path||""),target2.query=relative.query):(relative.userinfo!==void 0||relative.host!==void 0||relative.port!==void 0?(target2.userinfo=relative.userinfo,target2.host=relative.host,target2.port=relative.port,target2.path=removeDotSegments(relative.path||""),target2.query=relative.query):(relative.path?(relative.path[0]==="/"?target2.path=removeDotSegments(relative.
-path):((base2.userinfo!==void 0||base2.host!==void 0||base2.port!==void 0)&&!base2.path?target2.path="/"+relative.path:base2.path?target2.path=base2.path.slice(0,base2.path.lastIndexOf("/")+1)+relative.path:target2.path=relative.path,target2.path=removeDotSegments(target2.path)),target2.query=relative.query):(target2.path=base2.path,relative.query!==void 0?target2.query=relative.query:target2.query=base2.query),target2.userinfo=base2.userinfo,target2.host=base2.host,target2.port=base2.port),target2.
-scheme=base2.scheme),target2.fragment=relative.fragment,target2}function equal2(uriA,uriB,options){return typeof uriA=="string"?(uriA=unescape(uriA),uriA=serialize2(normalizeComponentEncoding(parse2(uriA,options),!0),{...options,skipEscape:!0})):typeof uriA=="object"&&(uriA=serialize2(normalizeComponentEncoding(uriA,!0),{...options,skipEscape:!0})),typeof uriB=="string"?(uriB=unescape(uriB),uriB=serialize2(normalizeComponentEncoding(parse2(uriB,options),!0),{...options,skipEscape:!0})):typeof uriB==
-"object"&&(uriB=serialize2(normalizeComponentEncoding(uriB,!0),{...options,skipEscape:!0})),uriA.toLowerCase()===uriB.toLowerCase()}function serialize2(cmpts,opts){const component2={host:cmpts.host,scheme:cmpts.scheme,userinfo:cmpts.userinfo,port:cmpts.port,path:cmpts.path,query:cmpts.query,nid:cmpts.nid,nss:cmpts.nss,uuid:cmpts.uuid,fragment:cmpts.fragment,reference:cmpts.reference,resourceName:cmpts.resourceName,secure:cmpts.secure,error:""},options=Object.assign({},opts),uriTokens=[],schemeHandler=getSchemeHandler(
-options.scheme||component2.scheme);schemeHandler&&schemeHandler.serialize&&schemeHandler.serialize(component2,options),component2.path!==void 0&&(options.skipEscape?component2.path=unescape(component2.path):(component2.path=escape(component2.path),component2.scheme!==void 0&&(component2.path=component2.path.split("%3A").join(":")))),options.reference!=="suffix"&&component2.scheme&&uriTokens.push(component2.scheme,":");const authority=recomposeAuthority(component2);if(authority!==void 0&&(options.
-reference!=="suffix"&&uriTokens.push("//"),uriTokens.push(authority),component2.path&&component2.path[0]!=="/"&&uriTokens.push("/")),component2.path!==void 0){let s2=component2.path;!options.absolutePath&&(!schemeHandler||!schemeHandler.absolutePath)&&(s2=removeDotSegments(s2)),authority===void 0&&s2[0]==="/"&&s2[1]==="/"&&(s2="/%2F"+s2.slice(2)),uriTokens.push(s2)}return component2.query!==void 0&&uriTokens.push("?",component2.query),component2.fragment!==void 0&&uriTokens.push("#",component2.fragment),
-uriTokens.join("")}const URI_PARSE=/^(?:([^#/:?]+):)?(?:\/\/((?:([^#/?@]*)@)?(\[[^#/?\]]+\]|[^#/:?]*)(?::(\d*))?))?([^#?]*)(?:\?([^#]*))?(?:#((?:.|[\n\r])*))?/u;function parse2(uri2,opts){const options=Object.assign({},opts),parsed={scheme:void 0,userinfo:void 0,host:"",port:void 0,path:"",query:void 0,fragment:void 0};let isIP=!1;options.reference==="suffix"&&(options.scheme?uri2=options.scheme+":"+uri2:uri2="//"+uri2);const matches2=uri2.match(URI_PARSE);if(matches2){if(parsed.scheme=matches2[1],
-parsed.userinfo=matches2[3],parsed.host=matches2[4],parsed.port=parseInt(matches2[5],10),parsed.path=matches2[6]||"",parsed.query=matches2[7],parsed.fragment=matches2[8],isNaN(parsed.port)&&(parsed.port=matches2[5]),parsed.host)if(isIPv4(parsed.host)===!1){const ipv6result=normalizeIPv6(parsed.host);parsed.host=ipv6result.host.toLowerCase(),isIP=ipv6result.isIPV6}else isIP=!0;parsed.scheme===void 0&&parsed.userinfo===void 0&&parsed.host===void 0&&parsed.port===void 0&&parsed.query===void 0&&!parsed.
-path?parsed.reference="same-document":parsed.scheme===void 0?parsed.reference="relative":parsed.fragment===void 0?parsed.reference="absolute":parsed.reference="uri",options.reference&&options.reference!=="suffix"&&options.reference!==parsed.reference&&(parsed.error=parsed.error||"URI is not a "+options.reference+" reference.");const schemeHandler=getSchemeHandler(options.scheme||parsed.scheme);if(!options.unicodeSupport&&(!schemeHandler||!schemeHandler.unicodeSupport)&&parsed.host&&(options.domainHost||
-schemeHandler&&schemeHandler.domainHost)&&isIP===!1&&nonSimpleDomain(parsed.host))try{parsed.host=URL.domainToASCII(parsed.host.toLowerCase())}catch(e){parsed.error=parsed.error||"Host's domain name can not be converted to ASCII: "+e}(!schemeHandler||schemeHandler&&!schemeHandler.skipNormalize)&&(uri2.indexOf("%")!==-1&&(parsed.scheme!==void 0&&(parsed.scheme=unescape(parsed.scheme)),parsed.host!==void 0&&(parsed.host=unescape(parsed.host))),parsed.path&&(parsed.path=escape(unescape(parsed.path))),
-parsed.fragment&&(parsed.fragment=encodeURI(decodeURIComponent(parsed.fragment)))),schemeHandler&&schemeHandler.parse&&schemeHandler.parse(parsed,options)}else parsed.error=parsed.error||"URI can not be parsed.";return parsed}const fastUri$1={SCHEMES,normalize:normalize2,resolve:resolve2,resolveComponent,equal:equal2,serialize:serialize2,parse:parse2};return fastUri.exports=fastUri$1,fastUri.exports.default=fastUri$1,fastUri.exports.fastUri=fastUri$1,fastUri.exports}var hasRequiredUri$1;function requireUri$1(){
-if(hasRequiredUri$1)return uri$1;hasRequiredUri$1=1,Object.defineProperty(uri$1,"__esModule",{value:!0});const uri2=requireFastUri();return uri2.code='require("ajv/dist/runtime/uri").default',uri$1.default=uri2,uri$1}var hasRequiredCore$3;function requireCore$3(){return hasRequiredCore$3||(hasRequiredCore$3=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.CodeGen=exports.Name=exports.nil=exports.stringify=exports.str=exports._=exports.KeywordCxt=void 0;var validate_1=requireValidate$1();
-Object.defineProperty(exports,"KeywordCxt",{enumerable:!0,get:function(){return validate_1.KeywordCxt}});var codegen_1=requireCodegen$1();Object.defineProperty(exports,"_",{enumerable:!0,get:function(){return codegen_1._}}),Object.defineProperty(exports,"str",{enumerable:!0,get:function(){return codegen_1.str}}),Object.defineProperty(exports,"stringify",{enumerable:!0,get:function(){return codegen_1.stringify}}),Object.defineProperty(exports,"nil",{enumerable:!0,get:function(){return codegen_1.nil}}),
-Object.defineProperty(exports,"Name",{enumerable:!0,get:function(){return codegen_1.Name}}),Object.defineProperty(exports,"CodeGen",{enumerable:!0,get:function(){return codegen_1.CodeGen}});const validation_error_1=requireValidation_error$1(),ref_error_1=requireRef_error$1(),rules_1=requireRules$1(),compile_1=requireCompile$1(),codegen_2=requireCodegen$1(),resolve_1=requireResolve$1(),dataType_1=requireDataType$1(),util_1=requireUtil$1(),$dataRefSchema=require$$9$1,uri_1=requireUri$1(),defaultRegExp=(str,flags2)=>new RegExp(
-str,flags2);defaultRegExp.code="new RegExp";const META_IGNORE_OPTIONS=["removeAdditional","useDefaults","coerceTypes"],EXT_SCOPE_NAMES=new Set(["validate","serialize","parse","wrapper","root","schema","keyword","pattern","formats","validate$data","func","obj","Error"]),removedOptions={errorDataPath:"",format:"`validateFormats: false` can be used instead.",nullable:'"nullable" keyword is supported by default.',jsonPointers:"Deprecated jsPropertySyntax can be used instead.",extendRefs:"Deprecated \
-ignoreKeywordsWithRef can be used instead.",missingRefs:"Pass empty schema with $id that should be ignored to ajv.addSchema.",processCode:"Use option `code: {process: (code, schemaEnv: object) => string}`",sourceCode:"Use option `code: {source: true}`",strictDefaults:"It is default now, see option `strict`.",strictKeywords:"It is default now, see option `strict`.",uniqueItems:'"uniqueItems" keyword is always validated.',unknownFormats:"Disable strict mode or pass `true` to `ajv.addFormat` (or `f\
-ormats` option).",cache:"Map is used as cache, schema object as key.",serialize:"Map is used as cache, schema object as key.",ajvErrors:"It is default now."},deprecatedOptions={ignoreKeywordsWithRef:"",jsPropertySyntax:"",unicode:'"minLength"/"maxLength" account for unicode characters by default.'},MAX_EXPRESSION=200;function requiredOptions(o){var _a2,_b,_c,_d,_e,_f,_g,_h,_j,_k,_l,_m,_o,_p,_q,_r,_s,_t,_u,_v,_w,_x,_y,_z,_02;const s2=o.strict,_optz=(_a2=o.code)===null||_a2===void 0?void 0:_a2.optimize,
-optimize=_optz===!0||_optz===void 0?1:_optz||0,regExp=(_c=(_b=o.code)===null||_b===void 0?void 0:_b.regExp)!==null&&_c!==void 0?_c:defaultRegExp,uriResolver=(_d=o.uriResolver)!==null&&_d!==void 0?_d:uri_1.default;return{strictSchema:(_f=(_e=o.strictSchema)!==null&&_e!==void 0?_e:s2)!==null&&_f!==void 0?_f:!0,strictNumbers:(_h=(_g=o.strictNumbers)!==null&&_g!==void 0?_g:s2)!==null&&_h!==void 0?_h:!0,strictTypes:(_k=(_j=o.strictTypes)!==null&&_j!==void 0?_j:s2)!==null&&_k!==void 0?_k:"log",strictTuples:(_m=
-(_l=o.strictTuples)!==null&&_l!==void 0?_l:s2)!==null&&_m!==void 0?_m:"log",strictRequired:(_p=(_o=o.strictRequired)!==null&&_o!==void 0?_o:s2)!==null&&_p!==void 0?_p:!1,code:o.code?{...o.code,optimize,regExp}:{optimize,regExp},loopRequired:(_q=o.loopRequired)!==null&&_q!==void 0?_q:MAX_EXPRESSION,loopEnum:(_r=o.loopEnum)!==null&&_r!==void 0?_r:MAX_EXPRESSION,meta:(_s=o.meta)!==null&&_s!==void 0?_s:!0,messages:(_t=o.messages)!==null&&_t!==void 0?_t:!0,inlineRefs:(_u=o.inlineRefs)!==null&&_u!==void 0?
-_u:!0,schemaId:(_v=o.schemaId)!==null&&_v!==void 0?_v:"$id",addUsedSchema:(_w=o.addUsedSchema)!==null&&_w!==void 0?_w:!0,validateSchema:(_x=o.validateSchema)!==null&&_x!==void 0?_x:!0,validateFormats:(_y=o.validateFormats)!==null&&_y!==void 0?_y:!0,unicodeRegExp:(_z=o.unicodeRegExp)!==null&&_z!==void 0?_z:!0,int32range:(_02=o.int32range)!==null&&_02!==void 0?_02:!0,uriResolver}}class Ajv2{constructor(opts={}){this.schemas={},this.refs={},this.formats={},this._compilations=new Set,this._loading={},
-this._cache=new Map,opts=this.opts={...opts,...requiredOptions(opts)};const{es5,lines}=this.opts.code;this.scope=new codegen_2.ValueScope({scope:{},prefixes:EXT_SCOPE_NAMES,es5,lines}),this.logger=getLogger(opts.logger);const formatOpt=opts.validateFormats;opts.validateFormats=!1,this.RULES=(0,rules_1.getRules)(),checkOptions.call(this,removedOptions,opts,"NOT SUPPORTED"),checkOptions.call(this,deprecatedOptions,opts,"DEPRECATED","warn"),this._metaOpts=getMetaSchemaOptions.call(this),opts.formats&&
-addInitialFormats.call(this),this._addVocabularies(),this._addDefaultMetaSchema(),opts.keywords&&addInitialKeywords.call(this,opts.keywords),typeof opts.meta=="object"&&this.addMetaSchema(opts.meta),addInitialSchemas.call(this),opts.validateFormats=formatOpt}_addVocabularies(){this.addKeyword("$async")}_addDefaultMetaSchema(){const{$data,meta,schemaId}=this.opts;let _dataRefSchema=$dataRefSchema;schemaId==="id"&&(_dataRefSchema={...$dataRefSchema},_dataRefSchema.id=_dataRefSchema.$id,delete _dataRefSchema.
-$id),meta&&$data&&this.addMetaSchema(_dataRefSchema,_dataRefSchema[schemaId],!1)}defaultMeta(){const{meta,schemaId}=this.opts;return this.opts.defaultMeta=typeof meta=="object"?meta[schemaId]||meta:void 0}validate(schemaKeyRef,data){let v;if(typeof schemaKeyRef=="string"){if(v=this.getSchema(schemaKeyRef),!v)throw new Error(`no schema with key or ref "${schemaKeyRef}"`)}else v=this.compile(schemaKeyRef);const valid2=v(data);return"$async"in v||(this.errors=v.errors),valid2}compile(schema,_meta){
-const sch=this._addSchema(schema,_meta);return sch.validate||this._compileSchemaEnv(sch)}compileAsync(schema,meta){if(typeof this.opts.loadSchema!="function")throw new Error("options.loadSchema should be a function");const{loadSchema}=this.opts;return runCompileAsync.call(this,schema,meta);async function runCompileAsync(_schema,_meta){await loadMetaSchema.call(this,_schema.$schema);const sch=this._addSchema(_schema,_meta);return sch.validate||_compileAsync.call(this,sch)}async function loadMetaSchema($ref){
-$ref&&!this.getSchema($ref)&&await runCompileAsync.call(this,{$ref},!0)}async function _compileAsync(sch){try{return this._compileSchemaEnv(sch)}catch(e){if(!(e instanceof ref_error_1.default))throw e;return checkLoaded.call(this,e),await loadMissingSchema.call(this,e.missingSchema),_compileAsync.call(this,sch)}}function checkLoaded({missingSchema:ref2,missingRef}){if(this.refs[ref2])throw new Error(`AnySchema ${ref2} is loaded but ${missingRef} cannot be resolved`)}async function loadMissingSchema(ref2){
-const _schema=await _loadSchema.call(this,ref2);this.refs[ref2]||await loadMetaSchema.call(this,_schema.$schema),this.refs[ref2]||this.addSchema(_schema,ref2,meta)}async function _loadSchema(ref2){const p2=this._loading[ref2];if(p2)return p2;try{return await(this._loading[ref2]=loadSchema(ref2))}finally{delete this._loading[ref2]}}}addSchema(schema,key2,_meta,_validateSchema=this.opts.validateSchema){if(Array.isArray(schema)){for(const sch of schema)this.addSchema(sch,void 0,_meta,_validateSchema);
-return this}let id2;if(typeof schema=="object"){const{schemaId}=this.opts;if(id2=schema[schemaId],id2!==void 0&&typeof id2!="string")throw new Error(`schema ${schemaId} must be string`)}return key2=(0,resolve_1.normalizeId)(key2||id2),this._checkUnique(key2),this.schemas[key2]=this._addSchema(schema,_meta,key2,_validateSchema,!0),this}addMetaSchema(schema,key2,_validateSchema=this.opts.validateSchema){return this.addSchema(schema,key2,!0,_validateSchema),this}validateSchema(schema,throwOrLogError){
-if(typeof schema=="boolean")return!0;let $schema2;if($schema2=schema.$schema,$schema2!==void 0&&typeof $schema2!="string")throw new Error("$schema must be a string");if($schema2=$schema2||this.opts.defaultMeta||this.defaultMeta(),!$schema2)return this.logger.warn("meta-schema not available"),this.errors=null,!0;const valid2=this.validate($schema2,schema);if(!valid2&&throwOrLogError){const message="schema is invalid: "+this.errorsText();if(this.opts.validateSchema==="log")this.logger.error(message);else
-throw new Error(message)}return valid2}getSchema(keyRef){let sch;for(;typeof(sch=getSchEnv.call(this,keyRef))=="string";)keyRef=sch;if(sch===void 0){const{schemaId}=this.opts,root2=new compile_1.SchemaEnv({schema:{},schemaId});if(sch=compile_1.resolveSchema.call(this,root2,keyRef),!sch)return;this.refs[keyRef]=sch}return sch.validate||this._compileSchemaEnv(sch)}removeSchema(schemaKeyRef){if(schemaKeyRef instanceof RegExp)return this._removeAllSchemas(this.schemas,schemaKeyRef),this._removeAllSchemas(
-this.refs,schemaKeyRef),this;switch(typeof schemaKeyRef){case"undefined":return this._removeAllSchemas(this.schemas),this._removeAllSchemas(this.refs),this._cache.clear(),this;case"string":{const sch=getSchEnv.call(this,schemaKeyRef);return typeof sch=="object"&&this._cache.delete(sch.schema),delete this.schemas[schemaKeyRef],delete this.refs[schemaKeyRef],this}case"object":{const cacheKey=schemaKeyRef;this._cache.delete(cacheKey);let id2=schemaKeyRef[this.opts.schemaId];return id2&&(id2=(0,resolve_1.
-normalizeId)(id2),delete this.schemas[id2],delete this.refs[id2]),this}default:throw new Error("ajv.removeSchema: invalid parameter")}}addVocabulary(definitions2){for(const def of definitions2)this.addKeyword(def);return this}addKeyword(kwdOrDef,def){let keyword2;if(typeof kwdOrDef=="string")keyword2=kwdOrDef,typeof def=="object"&&(this.logger.warn("these parameters are deprecated, see docs for addKeyword"),def.keyword=keyword2);else if(typeof kwdOrDef=="object"&&def===void 0){if(def=kwdOrDef,keyword2=
-def.keyword,Array.isArray(keyword2)&&!keyword2.length)throw new Error("addKeywords: keyword must be string or non-empty array")}else throw new Error("invalid addKeywords parameters");if(checkKeyword.call(this,keyword2,def),!def)return(0,util_1.eachItem)(keyword2,kwd=>addRule.call(this,kwd)),this;keywordMetaschema.call(this,def);const definition2={...def,type:(0,dataType_1.getJSONTypes)(def.type),schemaType:(0,dataType_1.getJSONTypes)(def.schemaType)};return(0,util_1.eachItem)(keyword2,definition2.
-type.length===0?k=>addRule.call(this,k,definition2):k=>definition2.type.forEach(t=>addRule.call(this,k,definition2,t))),this}getKeyword(keyword2){const rule=this.RULES.all[keyword2];return typeof rule=="object"?rule.definition:!!rule}removeKeyword(keyword2){const{RULES}=this;delete RULES.keywords[keyword2],delete RULES.all[keyword2];for(const group of RULES.rules){const i=group.rules.findIndex(rule=>rule.keyword===keyword2);i>=0&&group.rules.splice(i,1)}return this}addFormat(name,format2){return typeof format2==
-"string"&&(format2=new RegExp(format2)),this.formats[name]=format2,this}errorsText(errors2=this.errors,{separator=", ",dataVar="data"}={}){return!errors2||errors2.length===0?"No errors":errors2.map(e=>`${dataVar}${e.instancePath} ${e.message}`).reduce((text2,msg)=>text2+separator+msg)}$dataMetaSchema(metaSchema,keywordsJsonPointers){const rules2=this.RULES.all;metaSchema=JSON.parse(JSON.stringify(metaSchema));for(const jsonPointer of keywordsJsonPointers){const segments=jsonPointer.split("/").slice(
-1);let keywords2=metaSchema;for(const seg of segments)keywords2=keywords2[seg];for(const key2 in rules2){const rule=rules2[key2];if(typeof rule!="object")continue;const{$data}=rule.definition,schema=keywords2[key2];$data&&schema&&(keywords2[key2]=schemaOrData(schema))}}return metaSchema}_removeAllSchemas(schemas,regex){for(const keyRef in schemas){const sch=schemas[keyRef];(!regex||regex.test(keyRef))&&(typeof sch=="string"?delete schemas[keyRef]:sch&&!sch.meta&&(this._cache.delete(sch.schema),delete schemas[keyRef]))}}_addSchema(schema,meta,baseId,validateSchema=this.
-opts.validateSchema,addSchema=this.opts.addUsedSchema){let id2;const{schemaId}=this.opts;if(typeof schema=="object")id2=schema[schemaId];else{if(this.opts.jtd)throw new Error("schema must be object");if(typeof schema!="boolean")throw new Error("schema must be object or boolean")}let sch=this._cache.get(schema);if(sch!==void 0)return sch;baseId=(0,resolve_1.normalizeId)(id2||baseId);const localRefs=resolve_1.getSchemaRefs.call(this,schema,baseId);return sch=new compile_1.SchemaEnv({schema,schemaId,
-meta,baseId,localRefs}),this._cache.set(sch.schema,sch),addSchema&&!baseId.startsWith("#")&&(baseId&&this._checkUnique(baseId),this.refs[baseId]=sch),validateSchema&&this.validateSchema(schema,!0),sch}_checkUnique(id2){if(this.schemas[id2]||this.refs[id2])throw new Error(`schema with key or id "${id2}" already exists`)}_compileSchemaEnv(sch){if(sch.meta?this._compileMetaSchema(sch):compile_1.compileSchema.call(this,sch),!sch.validate)throw new Error("ajv implementation error");return sch.validate}_compileMetaSchema(sch){
-const currentOpts=this.opts;this.opts=this._metaOpts;try{compile_1.compileSchema.call(this,sch)}finally{this.opts=currentOpts}}}Ajv2.ValidationError=validation_error_1.default,Ajv2.MissingRefError=ref_error_1.default,exports.default=Ajv2;function checkOptions(checkOpts,options,msg,log="error"){for(const key2 in checkOpts){const opt=key2;opt in options&&this.logger[log](`${msg}: option ${key2}. ${checkOpts[opt]}`)}}function getSchEnv(keyRef){return keyRef=(0,resolve_1.normalizeId)(keyRef),this.schemas[keyRef]||
-this.refs[keyRef]}function addInitialSchemas(){const optsSchemas=this.opts.schemas;if(optsSchemas)if(Array.isArray(optsSchemas))this.addSchema(optsSchemas);else for(const key2 in optsSchemas)this.addSchema(optsSchemas[key2],key2)}function addInitialFormats(){for(const name in this.opts.formats){const format2=this.opts.formats[name];format2&&this.addFormat(name,format2)}}function addInitialKeywords(defs){if(Array.isArray(defs)){this.addVocabulary(defs);return}this.logger.warn("keywords option as \
-map is deprecated, pass array");for(const keyword2 in defs){const def=defs[keyword2];def.keyword||(def.keyword=keyword2),this.addKeyword(def)}}function getMetaSchemaOptions(){const metaOpts={...this.opts};for(const opt of META_IGNORE_OPTIONS)delete metaOpts[opt];return metaOpts}const noLogs={log(){},warn(){},error(){}};function getLogger(logger){if(logger===!1)return noLogs;if(logger===void 0)return console;if(logger.log&&logger.warn&&logger.error)return logger;throw new Error("logger must imple\
-ment log, warn and error methods")}const KEYWORD_NAME=/^[a-z_$][a-z0-9_$:-]*$/i;function checkKeyword(keyword2,def){const{RULES}=this;if((0,util_1.eachItem)(keyword2,kwd=>{if(RULES.keywords[kwd])throw new Error(`Keyword ${kwd} is already defined`);if(!KEYWORD_NAME.test(kwd))throw new Error(`Keyword ${kwd} has invalid name`)}),!!def&&def.$data&&!("code"in def||"validate"in def))throw new Error('$data keyword must have "code" or "validate" function')}function addRule(keyword2,definition2,dataType2){
-var _a2;const post=definition2?.post;if(dataType2&&post)throw new Error('keyword with "post" flag cannot have "type"');const{RULES}=this;let ruleGroup=post?RULES.post:RULES.rules.find(({type:t})=>t===dataType2);if(ruleGroup||(ruleGroup={type:dataType2,rules:[]},RULES.rules.push(ruleGroup)),RULES.keywords[keyword2]=!0,!definition2)return;const rule={keyword:keyword2,definition:{...definition2,type:(0,dataType_1.getJSONTypes)(definition2.type),schemaType:(0,dataType_1.getJSONTypes)(definition2.schemaType)}};
-definition2.before?addBeforeRule.call(this,ruleGroup,rule,definition2.before):ruleGroup.rules.push(rule),RULES.all[keyword2]=rule,(_a2=definition2.implements)===null||_a2===void 0||_a2.forEach(kwd=>this.addKeyword(kwd))}function addBeforeRule(ruleGroup,rule,before){const i=ruleGroup.rules.findIndex(_rule=>_rule.keyword===before);i>=0?ruleGroup.rules.splice(i,0,rule):(ruleGroup.rules.push(rule),this.logger.warn(`rule ${before} is not defined`))}function keywordMetaschema(def){let{metaSchema}=def;
-metaSchema!==void 0&&(def.$data&&this.opts.$data&&(metaSchema=schemaOrData(metaSchema)),def.validateSchema=this.compile(metaSchema,!0))}const $dataRef={$ref:"https://raw.githubusercontent.com/ajv-validator/ajv/master/lib/refs/data.json#"};function schemaOrData(schema){return{anyOf:[schema,$dataRef]}}}(core$4)),core$4}var draft7$1={},core$3={},id$1={},hasRequiredId$1;function requireId$1(){if(hasRequiredId$1)return id$1;hasRequiredId$1=1,Object.defineProperty(id$1,"__esModule",{value:!0});const def={
-keyword:"id",code(){throw new Error('NOT SUPPORTED: keyword "id", use "$id" for schema ID')}};return id$1.default=def,id$1}var ref$1={},hasRequiredRef$1;function requireRef$1(){if(hasRequiredRef$1)return ref$1;hasRequiredRef$1=1,Object.defineProperty(ref$1,"__esModule",{value:!0}),ref$1.callRef=ref$1.getValidate=void 0;const ref_error_1=requireRef_error$1(),code_1=requireCode$2(),codegen_1=requireCodegen$1(),names_1=requireNames$1(),compile_1=requireCompile$1(),util_1=requireUtil$1(),def={keyword:"\
-$ref",schemaType:"string",code(cxt){const{gen,schema:$ref,it}=cxt,{baseId,schemaEnv:env2,validateName,opts,self:self2}=it,{root:root2}=env2;if(($ref==="#"||$ref==="#/")&&baseId===root2.baseId)return callRootRef();const schOrEnv=compile_1.resolveRef.call(self2,root2,baseId,$ref);if(schOrEnv===void 0)throw new ref_error_1.default(it.opts.uriResolver,baseId,$ref);if(schOrEnv instanceof compile_1.SchemaEnv)return callValidate(schOrEnv);return inlineRefSchema(schOrEnv);function callRootRef(){if(env2===
-root2)return callRef(cxt,validateName,env2,env2.$async);const rootName=gen.scopeValue("root",{ref:root2});return callRef(cxt,(0,codegen_1._)`${rootName}.validate`,root2,root2.$async)}function callValidate(sch){const v=getValidate(cxt,sch);callRef(cxt,v,sch,sch.$async)}function inlineRefSchema(sch){const schName=gen.scopeValue("schema",opts.code.source===!0?{ref:sch,code:(0,codegen_1.stringify)(sch)}:{ref:sch}),valid2=gen.name("valid"),schCxt=cxt.subschema({schema:sch,dataTypes:[],schemaPath:codegen_1.
-nil,topSchemaRef:schName,errSchemaPath:$ref},valid2);cxt.mergeEvaluated(schCxt),cxt.ok(valid2)}}};function getValidate(cxt,sch){const{gen}=cxt;return sch.validate?gen.scopeValue("validate",{ref:sch.validate}):(0,codegen_1._)`${gen.scopeValue("wrapper",{ref:sch})}.validate`}ref$1.getValidate=getValidate;function callRef(cxt,v,sch,$async){const{gen,it}=cxt,{allErrors,schemaEnv:env2,opts}=it,passCxt=opts.passContext?names_1.default.this:codegen_1.nil;$async?callAsyncRef():callSyncRef();function callAsyncRef(){
-if(!env2.$async)throw new Error("async schema referenced by sync schema");const valid2=gen.let("valid");gen.try(()=>{gen.code((0,codegen_1._)`await ${(0,code_1.callValidateCode)(cxt,v,passCxt)}`),addEvaluatedFrom(v),allErrors||gen.assign(valid2,!0)},e=>{gen.if((0,codegen_1._)`!(${e} instanceof ${it.ValidationError})`,()=>gen.throw(e)),addErrorsFrom(e),allErrors||gen.assign(valid2,!1)}),cxt.ok(valid2)}function callSyncRef(){cxt.result((0,code_1.callValidateCode)(cxt,v,passCxt),()=>addEvaluatedFrom(
-v),()=>addErrorsFrom(v))}function addErrorsFrom(source2){const errs=(0,codegen_1._)`${source2}.errors`;gen.assign(names_1.default.vErrors,(0,codegen_1._)`${names_1.default.vErrors} === null ? ${errs} : ${names_1.default.vErrors}.concat(${errs})`),gen.assign(names_1.default.errors,(0,codegen_1._)`${names_1.default.vErrors}.length`)}function addEvaluatedFrom(source2){var _a2;if(!it.opts.unevaluated)return;const schEvaluated=(_a2=sch?.validate)===null||_a2===void 0?void 0:_a2.evaluated;if(it.props!==
-!0)if(schEvaluated&&!schEvaluated.dynamicProps)schEvaluated.props!==void 0&&(it.props=util_1.mergeEvaluated.props(gen,schEvaluated.props,it.props));else{const props=gen.var("props",(0,codegen_1._)`${source2}.evaluated.props`);it.props=util_1.mergeEvaluated.props(gen,props,it.props,codegen_1.Name)}if(it.items!==!0)if(schEvaluated&&!schEvaluated.dynamicItems)schEvaluated.items!==void 0&&(it.items=util_1.mergeEvaluated.items(gen,schEvaluated.items,it.items));else{const items2=gen.var("items",(0,codegen_1.
-_)`${source2}.evaluated.items`);it.items=util_1.mergeEvaluated.items(gen,items2,it.items,codegen_1.Name)}}}return ref$1.callRef=callRef,ref$1.default=def,ref$1}var hasRequiredCore$2;function requireCore$2(){if(hasRequiredCore$2)return core$3;hasRequiredCore$2=1,Object.defineProperty(core$3,"__esModule",{value:!0});const id_1=requireId$1(),ref_1=requireRef$1(),core2=["$schema","$id","$defs","$vocabulary",{keyword:"$comment"},"definitions",id_1.default,ref_1.default];return core$3.default=core2,core$3}
-var validation$1={},limitNumber$1={},hasRequiredLimitNumber$1;function requireLimitNumber$1(){if(hasRequiredLimitNumber$1)return limitNumber$1;hasRequiredLimitNumber$1=1,Object.defineProperty(limitNumber$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),ops=codegen_1.operators,KWDs={maximum:{okStr:"<=",ok:ops.LTE,fail:ops.GT},minimum:{okStr:">=",ok:ops.GTE,fail:ops.LT},exclusiveMaximum:{okStr:"<",ok:ops.LT,fail:ops.GTE},exclusiveMinimum:{okStr:">",ok:ops.GT,fail:ops.LTE}},error2={message:({
-keyword:keyword2,schemaCode})=>(0,codegen_1.str)`must be ${KWDs[keyword2].okStr} ${schemaCode}`,params:({keyword:keyword2,schemaCode})=>(0,codegen_1._)`{comparison: ${KWDs[keyword2].okStr}, limit: ${schemaCode}}`},def={keyword:Object.keys(KWDs),type:"number",schemaType:"number",$data:!0,error:error2,code(cxt){const{keyword:keyword2,data,schemaCode}=cxt;cxt.fail$data((0,codegen_1._)`${data} ${KWDs[keyword2].fail} ${schemaCode} || isNaN(${data})`)}};return limitNumber$1.default=def,limitNumber$1}var multipleOf$1={},
-hasRequiredMultipleOf$1;function requireMultipleOf$1(){if(hasRequiredMultipleOf$1)return multipleOf$1;hasRequiredMultipleOf$1=1,Object.defineProperty(multipleOf$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),def={keyword:"multipleOf",type:"number",schemaType:"number",$data:!0,error:{message:({schemaCode})=>(0,codegen_1.str)`must be multiple of ${schemaCode}`,params:({schemaCode})=>(0,codegen_1._)`{multipleOf: ${schemaCode}}`},code(cxt){const{gen,data,schemaCode,it}=cxt,prec=it.opts.
-multipleOfPrecision,res=gen.let("res"),invalid2=prec?(0,codegen_1._)`Math.abs(Math.round(${res}) - ${res}) > 1e-${prec}`:(0,codegen_1._)`${res} !== parseInt(${res})`;cxt.fail$data((0,codegen_1._)`(${schemaCode} === 0 || (${res} = ${data}/${schemaCode}, ${invalid2}))`)}};return multipleOf$1.default=def,multipleOf$1}var limitLength$1={},ucs2length$1={},hasRequiredUcs2length$1;function requireUcs2length$1(){if(hasRequiredUcs2length$1)return ucs2length$1;hasRequiredUcs2length$1=1,Object.defineProperty(
-ucs2length$1,"__esModule",{value:!0});function ucs2length2(str){const len=str.length;let length=0,pos=0,value;for(;pos<len;)length++,value=str.charCodeAt(pos++),value>=55296&&value<=56319&&pos<len&&(value=str.charCodeAt(pos),(value&64512)===56320&&pos++);return length}return ucs2length$1.default=ucs2length2,ucs2length2.code='require("ajv/dist/runtime/ucs2length").default',ucs2length$1}var hasRequiredLimitLength$1;function requireLimitLength$1(){if(hasRequiredLimitLength$1)return limitLength$1;hasRequiredLimitLength$1=
-1,Object.defineProperty(limitLength$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),ucs2length_1=requireUcs2length$1(),def={keyword:["maxLength","minLength"],type:"string",schemaType:"number",$data:!0,error:{message({keyword:keyword2,schemaCode}){const comp=keyword2==="maxLength"?"more":"fewer";return(0,codegen_1.str)`must NOT have ${comp} than ${schemaCode} characters`},params:({schemaCode})=>(0,codegen_1._)`{limit: ${schemaCode}}`},code(cxt){const{keyword:keyword2,
-data,schemaCode,it}=cxt,op2=keyword2==="maxLength"?codegen_1.operators.GT:codegen_1.operators.LT,len=it.opts.unicode===!1?(0,codegen_1._)`${data}.length`:(0,codegen_1._)`${(0,util_1.useFunc)(cxt.gen,ucs2length_1.default)}(${data})`;cxt.fail$data((0,codegen_1._)`${len} ${op2} ${schemaCode}`)}};return limitLength$1.default=def,limitLength$1}var pattern$1={},hasRequiredPattern$1;function requirePattern$1(){if(hasRequiredPattern$1)return pattern$1;hasRequiredPattern$1=1,Object.defineProperty(pattern$1,
-"__esModule",{value:!0});const code_1=requireCode$2(),codegen_1=requireCodegen$1(),def={keyword:"pattern",type:"string",schemaType:"string",$data:!0,error:{message:({schemaCode})=>(0,codegen_1.str)`must match pattern "${schemaCode}"`,params:({schemaCode})=>(0,codegen_1._)`{pattern: ${schemaCode}}`},code(cxt){const{data,$data,schema,schemaCode,it}=cxt,u=it.opts.unicodeRegExp?"u":"",regExp=$data?(0,codegen_1._)`(new RegExp(${schemaCode}, ${u}))`:(0,code_1.usePattern)(cxt,schema);cxt.fail$data((0,codegen_1.
-_)`!${regExp}.test(${data})`)}};return pattern$1.default=def,pattern$1}var limitProperties$1={},hasRequiredLimitProperties$1;function requireLimitProperties$1(){if(hasRequiredLimitProperties$1)return limitProperties$1;hasRequiredLimitProperties$1=1,Object.defineProperty(limitProperties$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),def={keyword:["maxProperties","minProperties"],type:"object",schemaType:"number",$data:!0,error:{message({keyword:keyword2,schemaCode}){const comp=keyword2===
-"maxProperties"?"more":"fewer";return(0,codegen_1.str)`must NOT have ${comp} than ${schemaCode} properties`},params:({schemaCode})=>(0,codegen_1._)`{limit: ${schemaCode}}`},code(cxt){const{keyword:keyword2,data,schemaCode}=cxt,op2=keyword2==="maxProperties"?codegen_1.operators.GT:codegen_1.operators.LT;cxt.fail$data((0,codegen_1._)`Object.keys(${data}).length ${op2} ${schemaCode}`)}};return limitProperties$1.default=def,limitProperties$1}var required$2={},hasRequiredRequired$1;function requireRequired$1(){
-if(hasRequiredRequired$1)return required$2;hasRequiredRequired$1=1,Object.defineProperty(required$2,"__esModule",{value:!0});const code_1=requireCode$2(),codegen_1=requireCodegen$1(),util_1=requireUtil$1(),def={keyword:"required",type:"object",schemaType:"array",$data:!0,error:{message:({params:{missingProperty}})=>(0,codegen_1.str)`must have required property '${missingProperty}'`,params:({params:{missingProperty}})=>(0,codegen_1._)`{missingProperty: ${missingProperty}}`},code(cxt){const{gen,schema,
-schemaCode,data,$data,it}=cxt,{opts}=it;if(!$data&&schema.length===0)return;const useLoop=schema.length>=opts.loopRequired;if(it.allErrors?allErrorsMode():exitOnErrorMode(),opts.strictRequired){const props=cxt.parentSchema.properties,{definedProperties}=cxt.it;for(const requiredKey of schema)if(props?.[requiredKey]===void 0&&!definedProperties.has(requiredKey)){const schemaPath=it.schemaEnv.baseId+it.errSchemaPath,msg=`required property "${requiredKey}" is not defined at "${schemaPath}" (strictR\
-equired)`;(0,util_1.checkStrictMode)(it,msg,it.opts.strictRequired)}}function allErrorsMode(){if(useLoop||$data)cxt.block$data(codegen_1.nil,loopAllRequired);else for(const prop2 of schema)(0,code_1.checkReportMissingProp)(cxt,prop2)}function exitOnErrorMode(){const missing=gen.let("missing");if(useLoop||$data){const valid2=gen.let("valid",!0);cxt.block$data(valid2,()=>loopUntilMissing(missing,valid2)),cxt.ok(valid2)}else gen.if((0,code_1.checkMissingProp)(cxt,schema,missing)),(0,code_1.reportMissingProp)(
-cxt,missing),gen.else()}function loopAllRequired(){gen.forOf("prop",schemaCode,prop2=>{cxt.setParams({missingProperty:prop2}),gen.if((0,code_1.noPropertyInData)(gen,data,prop2,opts.ownProperties),()=>cxt.error())})}function loopUntilMissing(missing,valid2){cxt.setParams({missingProperty:missing}),gen.forOf(missing,schemaCode,()=>{gen.assign(valid2,(0,code_1.propertyInData)(gen,data,missing,opts.ownProperties)),gen.if((0,codegen_1.not)(valid2),()=>{cxt.error(),gen.break()})},codegen_1.nil)}}};return required$2.
-default=def,required$2}var limitItems$1={},hasRequiredLimitItems$1;function requireLimitItems$1(){if(hasRequiredLimitItems$1)return limitItems$1;hasRequiredLimitItems$1=1,Object.defineProperty(limitItems$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),def={keyword:["maxItems","minItems"],type:"array",schemaType:"number",$data:!0,error:{message({keyword:keyword2,schemaCode}){const comp=keyword2==="maxItems"?"more":"fewer";return(0,codegen_1.str)`must NOT have ${comp} than ${schemaCode} items`},
-params:({schemaCode})=>(0,codegen_1._)`{limit: ${schemaCode}}`},code(cxt){const{keyword:keyword2,data,schemaCode}=cxt,op2=keyword2==="maxItems"?codegen_1.operators.GT:codegen_1.operators.LT;cxt.fail$data((0,codegen_1._)`${data}.length ${op2} ${schemaCode}`)}};return limitItems$1.default=def,limitItems$1}var uniqueItems$1={},equal$1={},hasRequiredEqual$1;function requireEqual$1(){if(hasRequiredEqual$1)return equal$1;hasRequiredEqual$1=1,Object.defineProperty(equal$1,"__esModule",{value:!0});const equal2=requireFastDeepEqual();
-return equal2.code='require("ajv/dist/runtime/equal").default',equal$1.default=equal2,equal$1}var hasRequiredUniqueItems$1;function requireUniqueItems$1(){if(hasRequiredUniqueItems$1)return uniqueItems$1;hasRequiredUniqueItems$1=1,Object.defineProperty(uniqueItems$1,"__esModule",{value:!0});const dataType_1=requireDataType$1(),codegen_1=requireCodegen$1(),util_1=requireUtil$1(),equal_1=requireEqual$1(),def={keyword:"uniqueItems",type:"array",schemaType:"boolean",$data:!0,error:{message:({params:{
-i,j}})=>(0,codegen_1.str)`must NOT have duplicate items (items ## ${j} and ${i} are identical)`,params:({params:{i,j}})=>(0,codegen_1._)`{i: ${i}, j: ${j}}`},code(cxt){const{gen,data,$data,schema,parentSchema,schemaCode,it}=cxt;if(!$data&&!schema)return;const valid2=gen.let("valid"),itemTypes=parentSchema.items?(0,dataType_1.getSchemaTypes)(parentSchema.items):[];cxt.block$data(valid2,validateUniqueItems,(0,codegen_1._)`${schemaCode} === false`),cxt.ok(valid2);function validateUniqueItems(){const i=gen.
-let("i",(0,codegen_1._)`${data}.length`),j=gen.let("j");cxt.setParams({i,j}),gen.assign(valid2,!0),gen.if((0,codegen_1._)`${i} > 1`,()=>(canOptimize()?loopN:loopN2)(i,j))}function canOptimize(){return itemTypes.length>0&&!itemTypes.some(t=>t==="object"||t==="array")}function loopN(i,j){const item=gen.name("item"),wrongType=(0,dataType_1.checkDataTypes)(itemTypes,item,it.opts.strictNumbers,dataType_1.DataType.Wrong),indices=gen.const("indices",(0,codegen_1._)`{}`);gen.for((0,codegen_1._)`;${i}--;`,
-()=>{gen.let(item,(0,codegen_1._)`${data}[${i}]`),gen.if(wrongType,(0,codegen_1._)`continue`),itemTypes.length>1&&gen.if((0,codegen_1._)`typeof ${item} == "string"`,(0,codegen_1._)`${item} += "_"`),gen.if((0,codegen_1._)`typeof ${indices}[${item}] == "number"`,()=>{gen.assign(j,(0,codegen_1._)`${indices}[${item}]`),cxt.error(),gen.assign(valid2,!1).break()}).code((0,codegen_1._)`${indices}[${item}] = ${i}`)})}function loopN2(i,j){const eql=(0,util_1.useFunc)(gen,equal_1.default),outer=gen.name("\
-outer");gen.label(outer).for((0,codegen_1._)`;${i}--;`,()=>gen.for((0,codegen_1._)`${j} = ${i}; ${j}--;`,()=>gen.if((0,codegen_1._)`${eql}(${data}[${i}], ${data}[${j}])`,()=>{cxt.error(),gen.assign(valid2,!1).break(outer)})))}}};return uniqueItems$1.default=def,uniqueItems$1}var _const$1={},hasRequired_const$1;function require_const$1(){if(hasRequired_const$1)return _const$1;hasRequired_const$1=1,Object.defineProperty(_const$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),
-equal_1=requireEqual$1(),def={keyword:"const",$data:!0,error:{message:"must be equal to constant",params:({schemaCode})=>(0,codegen_1._)`{allowedValue: ${schemaCode}}`},code(cxt){const{gen,data,$data,schemaCode,schema}=cxt;$data||schema&&typeof schema=="object"?cxt.fail$data((0,codegen_1._)`!${(0,util_1.useFunc)(gen,equal_1.default)}(${data}, ${schemaCode})`):cxt.fail((0,codegen_1._)`${schema} !== ${data}`)}};return _const$1.default=def,_const$1}var _enum$1={},hasRequired_enum$1;function require_enum$1(){
-if(hasRequired_enum$1)return _enum$1;hasRequired_enum$1=1,Object.defineProperty(_enum$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),equal_1=requireEqual$1(),def={keyword:"enum",schemaType:"array",$data:!0,error:{message:"must be equal to one of the allowed values",params:({schemaCode})=>(0,codegen_1._)`{allowedValues: ${schemaCode}}`},code(cxt){const{gen,data,$data,schema,schemaCode,it}=cxt;if(!$data&&schema.length===0)throw new Error("enum must have non-em\
-pty array");const useLoop=schema.length>=it.opts.loopEnum;let eql;const getEql=()=>eql??(eql=(0,util_1.useFunc)(gen,equal_1.default));let valid2;if(useLoop||$data)valid2=gen.let("valid"),cxt.block$data(valid2,loopEnum);else{if(!Array.isArray(schema))throw new Error("ajv implementation error");const vSchema=gen.const("vSchema",schemaCode);valid2=(0,codegen_1.or)(...schema.map((_x,i)=>equalCode(vSchema,i)))}cxt.pass(valid2);function loopEnum(){gen.assign(valid2,!1),gen.forOf("v",schemaCode,v=>gen.
-if((0,codegen_1._)`${getEql()}(${data}, ${v})`,()=>gen.assign(valid2,!0).break()))}function equalCode(vSchema,i){const sch=schema[i];return typeof sch=="object"&&sch!==null?(0,codegen_1._)`${getEql()}(${data}, ${vSchema}[${i}])`:(0,codegen_1._)`${data} === ${sch}`}}};return _enum$1.default=def,_enum$1}var hasRequiredValidation$1;function requireValidation$1(){if(hasRequiredValidation$1)return validation$1;hasRequiredValidation$1=1,Object.defineProperty(validation$1,"__esModule",{value:!0});const limitNumber_1=requireLimitNumber$1(),
-multipleOf_1=requireMultipleOf$1(),limitLength_1=requireLimitLength$1(),pattern_1=requirePattern$1(),limitProperties_1=requireLimitProperties$1(),required_1=requireRequired$1(),limitItems_1=requireLimitItems$1(),uniqueItems_1=requireUniqueItems$1(),const_1=require_const$1(),enum_1=require_enum$1(),validation2=[limitNumber_1.default,multipleOf_1.default,limitLength_1.default,pattern_1.default,limitProperties_1.default,required_1.default,limitItems_1.default,uniqueItems_1.default,{keyword:"type",schemaType:[
-"string","array"]},{keyword:"nullable",schemaType:"boolean"},const_1.default,enum_1.default];return validation$1.default=validation2,validation$1}var applicator$1={},additionalItems$1={},hasRequiredAdditionalItems$1;function requireAdditionalItems$1(){if(hasRequiredAdditionalItems$1)return additionalItems$1;hasRequiredAdditionalItems$1=1,Object.defineProperty(additionalItems$1,"__esModule",{value:!0}),additionalItems$1.validateAdditionalItems=void 0;const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),
-def={keyword:"additionalItems",type:"array",schemaType:["boolean","object"],before:"uniqueItems",error:{message:({params:{len}})=>(0,codegen_1.str)`must NOT have more than ${len} items`,params:({params:{len}})=>(0,codegen_1._)`{limit: ${len}}`},code(cxt){const{parentSchema,it}=cxt,{items:items2}=parentSchema;if(!Array.isArray(items2)){(0,util_1.checkStrictMode)(it,'"additionalItems" is ignored when "items" is not an array of schemas');return}validateAdditionalItems(cxt,items2)}};function validateAdditionalItems(cxt,items2){
-const{gen,schema,data,keyword:keyword2,it}=cxt;it.items=!0;const len=gen.const("len",(0,codegen_1._)`${data}.length`);if(schema===!1)cxt.setParams({len:items2.length}),cxt.pass((0,codegen_1._)`${len} <= ${items2.length}`);else if(typeof schema=="object"&&!(0,util_1.alwaysValidSchema)(it,schema)){const valid2=gen.var("valid",(0,codegen_1._)`${len} <= ${items2.length}`);gen.if((0,codegen_1.not)(valid2),()=>validateItems(valid2)),cxt.ok(valid2)}function validateItems(valid2){gen.forRange("i",items2.
-length,len,i=>{cxt.subschema({keyword:keyword2,dataProp:i,dataPropType:util_1.Type.Num},valid2),it.allErrors||gen.if((0,codegen_1.not)(valid2),()=>gen.break())})}}return additionalItems$1.validateAdditionalItems=validateAdditionalItems,additionalItems$1.default=def,additionalItems$1}var prefixItems$1={},items$1={},hasRequiredItems$1;function requireItems$1(){if(hasRequiredItems$1)return items$1;hasRequiredItems$1=1,Object.defineProperty(items$1,"__esModule",{value:!0}),items$1.validateTuple=void 0;
-const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),code_1=requireCode$2(),def={keyword:"items",type:"array",schemaType:["object","array","boolean"],before:"uniqueItems",code(cxt){const{schema,it}=cxt;if(Array.isArray(schema))return validateTuple(cxt,"additionalItems",schema);it.items=!0,!(0,util_1.alwaysValidSchema)(it,schema)&&cxt.ok((0,code_1.validateArray)(cxt))}};function validateTuple(cxt,extraItems,schArr=cxt.schema){const{gen,parentSchema,data,keyword:keyword2,it}=cxt;checkStrictTuple(
-parentSchema),it.opts.unevaluated&&schArr.length&&it.items!==!0&&(it.items=util_1.mergeEvaluated.items(gen,schArr.length,it.items));const valid2=gen.name("valid"),len=gen.const("len",(0,codegen_1._)`${data}.length`);schArr.forEach((sch,i)=>{(0,util_1.alwaysValidSchema)(it,sch)||(gen.if((0,codegen_1._)`${len} > ${i}`,()=>cxt.subschema({keyword:keyword2,schemaProp:i,dataProp:i},valid2)),cxt.ok(valid2))});function checkStrictTuple(sch){const{opts,errSchemaPath}=it,l=schArr.length,fullTuple=l===sch.
-minItems&&(l===sch.maxItems||sch[extraItems]===!1);if(opts.strictTuples&&!fullTuple){const msg=`"${keyword2}" is ${l}-tuple, but minItems or maxItems/${extraItems} are not specified or different at path "${errSchemaPath}"`;(0,util_1.checkStrictMode)(it,msg,opts.strictTuples)}}}return items$1.validateTuple=validateTuple,items$1.default=def,items$1}var hasRequiredPrefixItems$1;function requirePrefixItems$1(){if(hasRequiredPrefixItems$1)return prefixItems$1;hasRequiredPrefixItems$1=1,Object.defineProperty(
-prefixItems$1,"__esModule",{value:!0});const items_1=requireItems$1(),def={keyword:"prefixItems",type:"array",schemaType:["array"],before:"uniqueItems",code:cxt=>(0,items_1.validateTuple)(cxt,"items")};return prefixItems$1.default=def,prefixItems$1}var items2020$1={},hasRequiredItems2020$1;function requireItems2020$1(){if(hasRequiredItems2020$1)return items2020$1;hasRequiredItems2020$1=1,Object.defineProperty(items2020$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),
-code_1=requireCode$2(),additionalItems_1=requireAdditionalItems$1(),def={keyword:"items",type:"array",schemaType:["object","boolean"],before:"uniqueItems",error:{message:({params:{len}})=>(0,codegen_1.str)`must NOT have more than ${len} items`,params:({params:{len}})=>(0,codegen_1._)`{limit: ${len}}`},code(cxt){const{schema,parentSchema,it}=cxt,{prefixItems:prefixItems2}=parentSchema;it.items=!0,!(0,util_1.alwaysValidSchema)(it,schema)&&(prefixItems2?(0,additionalItems_1.validateAdditionalItems)(
-cxt,prefixItems2):cxt.ok((0,code_1.validateArray)(cxt)))}};return items2020$1.default=def,items2020$1}var contains$2={},hasRequiredContains$1;function requireContains$1(){if(hasRequiredContains$1)return contains$2;hasRequiredContains$1=1,Object.defineProperty(contains$2,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),def={keyword:"contains",type:"array",schemaType:["object","boolean"],before:"uniqueItems",trackErrors:!0,error:{message:({params:{min:min2,max:max2}})=>max2===
-void 0?(0,codegen_1.str)`must contain at least ${min2} valid item(s)`:(0,codegen_1.str)`must contain at least ${min2} and no more than ${max2} valid item(s)`,params:({params:{min:min2,max:max2}})=>max2===void 0?(0,codegen_1._)`{minContains: ${min2}}`:(0,codegen_1._)`{minContains: ${min2}, maxContains: ${max2}}`},code(cxt){const{gen,schema,parentSchema,data,it}=cxt;let min2,max2;const{minContains,maxContains}=parentSchema;it.opts.next?(min2=minContains===void 0?1:minContains,max2=maxContains):min2=
-1;const len=gen.const("len",(0,codegen_1._)`${data}.length`);if(cxt.setParams({min:min2,max:max2}),max2===void 0&&min2===0){(0,util_1.checkStrictMode)(it,'"minContains" == 0 without "maxContains": "contains" keyword ignored');return}if(max2!==void 0&&min2>max2){(0,util_1.checkStrictMode)(it,'"minContains" > "maxContains" is always invalid'),cxt.fail();return}if((0,util_1.alwaysValidSchema)(it,schema)){let cond=(0,codegen_1._)`${len} >= ${min2}`;max2!==void 0&&(cond=(0,codegen_1._)`${cond} && ${len} <= ${max2}`),
-cxt.pass(cond);return}it.items=!0;const valid2=gen.name("valid");max2===void 0&&min2===1?validateItems(valid2,()=>gen.if(valid2,()=>gen.break())):min2===0?(gen.let(valid2,!0),max2!==void 0&&gen.if((0,codegen_1._)`${data}.length > 0`,validateItemsWithCount)):(gen.let(valid2,!1),validateItemsWithCount()),cxt.result(valid2,()=>cxt.reset());function validateItemsWithCount(){const schValid=gen.name("_valid"),count=gen.let("count",0);validateItems(schValid,()=>gen.if(schValid,()=>checkLimits(count)))}
-function validateItems(_valid,block2){gen.forRange("i",0,len,i=>{cxt.subschema({keyword:"contains",dataProp:i,dataPropType:util_1.Type.Num,compositeRule:!0},_valid),block2()})}function checkLimits(count){gen.code((0,codegen_1._)`${count}++`),max2===void 0?gen.if((0,codegen_1._)`${count} >= ${min2}`,()=>gen.assign(valid2,!0).break()):(gen.if((0,codegen_1._)`${count} > ${max2}`,()=>gen.assign(valid2,!1).break()),min2===1?gen.assign(valid2,!0):gen.if((0,codegen_1._)`${count} >= ${min2}`,()=>gen.assign(
-valid2,!0)))}}};return contains$2.default=def,contains$2}var dependencies$1={},hasRequiredDependencies$1;function requireDependencies$1(){return hasRequiredDependencies$1||(hasRequiredDependencies$1=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.validateSchemaDeps=exports.validatePropertyDeps=exports.error=void 0;const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),code_1=requireCode$2();exports.error={message:({params:{property,depsCount,deps}})=>{const property_ies=depsCount===
-1?"property":"properties";return(0,codegen_1.str)`must have ${property_ies} ${deps} when property ${property} is present`},params:({params:{property,depsCount,deps,missingProperty}})=>(0,codegen_1._)`{property: ${property},
-    missingProperty: ${missingProperty},
-    depsCount: ${depsCount},
-    deps: ${deps}}`};const def={keyword:"dependencies",type:"object",schemaType:"object",error:exports.error,code(cxt){const[propDeps,schDeps]=splitDependencies(cxt);validatePropertyDeps(cxt,propDeps),validateSchemaDeps(cxt,schDeps)}};function splitDependencies({schema}){const propertyDeps={},schemaDeps={};for(const key2 in schema){if(key2==="__proto__")continue;const deps=Array.isArray(schema[key2])?propertyDeps:schemaDeps;deps[key2]=schema[key2]}return[propertyDeps,schemaDeps]}function validatePropertyDeps(cxt,propertyDeps=cxt.
-schema){const{gen,data,it}=cxt;if(Object.keys(propertyDeps).length===0)return;const missing=gen.let("missing");for(const prop2 in propertyDeps){const deps=propertyDeps[prop2];if(deps.length===0)continue;const hasProperty=(0,code_1.propertyInData)(gen,data,prop2,it.opts.ownProperties);cxt.setParams({property:prop2,depsCount:deps.length,deps:deps.join(", ")}),it.allErrors?gen.if(hasProperty,()=>{for(const depProp of deps)(0,code_1.checkReportMissingProp)(cxt,depProp)}):(gen.if((0,codegen_1._)`${hasProperty} && (${(0,code_1.
-checkMissingProp)(cxt,deps,missing)})`),(0,code_1.reportMissingProp)(cxt,missing),gen.else())}}exports.validatePropertyDeps=validatePropertyDeps;function validateSchemaDeps(cxt,schemaDeps=cxt.schema){const{gen,data,keyword:keyword2,it}=cxt,valid2=gen.name("valid");for(const prop2 in schemaDeps)(0,util_1.alwaysValidSchema)(it,schemaDeps[prop2])||(gen.if((0,code_1.propertyInData)(gen,data,prop2,it.opts.ownProperties),()=>{const schCxt=cxt.subschema({keyword:keyword2,schemaProp:prop2},valid2);cxt.mergeValidEvaluated(
-schCxt,valid2)},()=>gen.var(valid2,!0)),cxt.ok(valid2))}exports.validateSchemaDeps=validateSchemaDeps,exports.default=def}(dependencies$1)),dependencies$1}var propertyNames$1={},hasRequiredPropertyNames$1;function requirePropertyNames$1(){if(hasRequiredPropertyNames$1)return propertyNames$1;hasRequiredPropertyNames$1=1,Object.defineProperty(propertyNames$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),def={keyword:"propertyNames",type:"object",schemaType:["ob\
-ject","boolean"],error:{message:"property name must be valid",params:({params})=>(0,codegen_1._)`{propertyName: ${params.propertyName}}`},code(cxt){const{gen,schema,data,it}=cxt;if((0,util_1.alwaysValidSchema)(it,schema))return;const valid2=gen.name("valid");gen.forIn("key",data,key2=>{cxt.setParams({propertyName:key2}),cxt.subschema({keyword:"propertyNames",data:key2,dataTypes:["string"],propertyName:key2,compositeRule:!0},valid2),gen.if((0,codegen_1.not)(valid2),()=>{cxt.error(!0),it.allErrors||
-gen.break()})}),cxt.ok(valid2)}};return propertyNames$1.default=def,propertyNames$1}var additionalProperties$2={},hasRequiredAdditionalProperties$1;function requireAdditionalProperties$1(){if(hasRequiredAdditionalProperties$1)return additionalProperties$2;hasRequiredAdditionalProperties$1=1,Object.defineProperty(additionalProperties$2,"__esModule",{value:!0});const code_1=requireCode$2(),codegen_1=requireCodegen$1(),names_1=requireNames$1(),util_1=requireUtil$1(),def={keyword:"additionalProperti\
-es",type:["object"],schemaType:["boolean","object"],allowUndefined:!0,trackErrors:!0,error:{message:"must NOT have additional properties",params:({params})=>(0,codegen_1._)`{additionalProperty: ${params.additionalProperty}}`},code(cxt){const{gen,schema,parentSchema,data,errsCount,it}=cxt;if(!errsCount)throw new Error("ajv implementation error");const{allErrors,opts}=it;if(it.props=!0,opts.removeAdditional!=="all"&&(0,util_1.alwaysValidSchema)(it,schema))return;const props=(0,code_1.allSchemaProperties)(
-parentSchema.properties),patProps=(0,code_1.allSchemaProperties)(parentSchema.patternProperties);checkAdditionalProperties(),cxt.ok((0,codegen_1._)`${errsCount} === ${names_1.default.errors}`);function checkAdditionalProperties(){gen.forIn("key",data,key2=>{!props.length&&!patProps.length?additionalPropertyCode(key2):gen.if(isAdditional(key2),()=>additionalPropertyCode(key2))})}function isAdditional(key2){let definedProp;if(props.length>8){const propsSchema=(0,util_1.schemaRefOrVal)(it,parentSchema.
-properties,"properties");definedProp=(0,code_1.isOwnProperty)(gen,propsSchema,key2)}else props.length?definedProp=(0,codegen_1.or)(...props.map(p2=>(0,codegen_1._)`${key2} === ${p2}`)):definedProp=codegen_1.nil;return patProps.length&&(definedProp=(0,codegen_1.or)(definedProp,...patProps.map(p2=>(0,codegen_1._)`${(0,code_1.usePattern)(cxt,p2)}.test(${key2})`))),(0,codegen_1.not)(definedProp)}function deleteAdditional(key2){gen.code((0,codegen_1._)`delete ${data}[${key2}]`)}function additionalPropertyCode(key2){
-if(opts.removeAdditional==="all"||opts.removeAdditional&&schema===!1){deleteAdditional(key2);return}if(schema===!1){cxt.setParams({additionalProperty:key2}),cxt.error(),allErrors||gen.break();return}if(typeof schema=="object"&&!(0,util_1.alwaysValidSchema)(it,schema)){const valid2=gen.name("valid");opts.removeAdditional==="failing"?(applyAdditionalSchema(key2,valid2,!1),gen.if((0,codegen_1.not)(valid2),()=>{cxt.reset(),deleteAdditional(key2)})):(applyAdditionalSchema(key2,valid2),allErrors||gen.
-if((0,codegen_1.not)(valid2),()=>gen.break()))}}function applyAdditionalSchema(key2,valid2,errors2){const subschema2={keyword:"additionalProperties",dataProp:key2,dataPropType:util_1.Type.Str};errors2===!1&&Object.assign(subschema2,{compositeRule:!0,createErrors:!1,allErrors:!1}),cxt.subschema(subschema2,valid2)}}};return additionalProperties$2.default=def,additionalProperties$2}var properties$5={},hasRequiredProperties$1;function requireProperties$1(){if(hasRequiredProperties$1)return properties$5;
-hasRequiredProperties$1=1,Object.defineProperty(properties$5,"__esModule",{value:!0});const validate_1=requireValidate$1(),code_1=requireCode$2(),util_1=requireUtil$1(),additionalProperties_1=requireAdditionalProperties$1(),def={keyword:"properties",type:"object",schemaType:"object",code(cxt){const{gen,schema,parentSchema,data,it}=cxt;it.opts.removeAdditional==="all"&&parentSchema.additionalProperties===void 0&&additionalProperties_1.default.code(new validate_1.KeywordCxt(it,additionalProperties_1.
-default,"additionalProperties"));const allProps=(0,code_1.allSchemaProperties)(schema);for(const prop2 of allProps)it.definedProperties.add(prop2);it.opts.unevaluated&&allProps.length&&it.props!==!0&&(it.props=util_1.mergeEvaluated.props(gen,(0,util_1.toHash)(allProps),it.props));const properties2=allProps.filter(p2=>!(0,util_1.alwaysValidSchema)(it,schema[p2]));if(properties2.length===0)return;const valid2=gen.name("valid");for(const prop2 of properties2)hasDefault(prop2)?applyPropertySchema(prop2):
-(gen.if((0,code_1.propertyInData)(gen,data,prop2,it.opts.ownProperties)),applyPropertySchema(prop2),it.allErrors||gen.else().var(valid2,!0),gen.endIf()),cxt.it.definedProperties.add(prop2),cxt.ok(valid2);function hasDefault(prop2){return it.opts.useDefaults&&!it.compositeRule&&schema[prop2].default!==void 0}function applyPropertySchema(prop2){cxt.subschema({keyword:"properties",schemaProp:prop2,dataProp:prop2},valid2)}}};return properties$5.default=def,properties$5}var patternProperties$1={},hasRequiredPatternProperties$1;
-function requirePatternProperties$1(){if(hasRequiredPatternProperties$1)return patternProperties$1;hasRequiredPatternProperties$1=1,Object.defineProperty(patternProperties$1,"__esModule",{value:!0});const code_1=requireCode$2(),codegen_1=requireCodegen$1(),util_1=requireUtil$1(),util_2=requireUtil$1(),def={keyword:"patternProperties",type:"object",schemaType:"object",code(cxt){const{gen,schema,data,parentSchema,it}=cxt,{opts}=it,patterns=(0,code_1.allSchemaProperties)(schema),alwaysValidPatterns=patterns.
-filter(p2=>(0,util_1.alwaysValidSchema)(it,schema[p2]));if(patterns.length===0||alwaysValidPatterns.length===patterns.length&&(!it.opts.unevaluated||it.props===!0))return;const checkProperties=opts.strictSchema&&!opts.allowMatchingProperties&&parentSchema.properties,valid2=gen.name("valid");it.props!==!0&&!(it.props instanceof codegen_1.Name)&&(it.props=(0,util_2.evaluatedPropsToName)(gen,it.props));const{props}=it;validatePatternProperties();function validatePatternProperties(){for(const pat of patterns)
-checkProperties&&checkMatchingProperties(pat),it.allErrors?validateProperties(pat):(gen.var(valid2,!0),validateProperties(pat),gen.if(valid2))}function checkMatchingProperties(pat){for(const prop2 in checkProperties)new RegExp(pat).test(prop2)&&(0,util_1.checkStrictMode)(it,`property ${prop2} matches pattern ${pat} (use allowMatchingProperties)`)}function validateProperties(pat){gen.forIn("key",data,key2=>{gen.if((0,codegen_1._)`${(0,code_1.usePattern)(cxt,pat)}.test(${key2})`,()=>{const alwaysValid=alwaysValidPatterns.
-includes(pat);alwaysValid||cxt.subschema({keyword:"patternProperties",schemaProp:pat,dataProp:key2,dataPropType:util_2.Type.Str},valid2),it.opts.unevaluated&&props!==!0?gen.assign((0,codegen_1._)`${props}[${key2}]`,!0):!alwaysValid&&!it.allErrors&&gen.if((0,codegen_1.not)(valid2),()=>gen.break())})})}}};return patternProperties$1.default=def,patternProperties$1}var not$1={},hasRequiredNot$1;function requireNot$1(){if(hasRequiredNot$1)return not$1;hasRequiredNot$1=1,Object.defineProperty(not$1,"_\
-_esModule",{value:!0});const util_1=requireUtil$1(),def={keyword:"not",schemaType:["object","boolean"],trackErrors:!0,code(cxt){const{gen,schema,it}=cxt;if((0,util_1.alwaysValidSchema)(it,schema)){cxt.fail();return}const valid2=gen.name("valid");cxt.subschema({keyword:"not",compositeRule:!0,createErrors:!1,allErrors:!1},valid2),cxt.failResult(valid2,()=>cxt.reset(),()=>cxt.error())},error:{message:"must NOT be valid"}};return not$1.default=def,not$1}var anyOf$1={},hasRequiredAnyOf$1;function requireAnyOf$1(){
-if(hasRequiredAnyOf$1)return anyOf$1;hasRequiredAnyOf$1=1,Object.defineProperty(anyOf$1,"__esModule",{value:!0});const def={keyword:"anyOf",schemaType:"array",trackErrors:!0,code:requireCode$2().validateUnion,error:{message:"must match a schema in anyOf"}};return anyOf$1.default=def,anyOf$1}var oneOf$1={},hasRequiredOneOf$1;function requireOneOf$1(){if(hasRequiredOneOf$1)return oneOf$1;hasRequiredOneOf$1=1,Object.defineProperty(oneOf$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),
-util_1=requireUtil$1(),def={keyword:"oneOf",schemaType:"array",trackErrors:!0,error:{message:"must match exactly one schema in oneOf",params:({params})=>(0,codegen_1._)`{passingSchemas: ${params.passing}}`},code(cxt){const{gen,schema,parentSchema,it}=cxt;if(!Array.isArray(schema))throw new Error("ajv implementation error");if(it.opts.discriminator&&parentSchema.discriminator)return;const schArr=schema,valid2=gen.let("valid",!1),passing=gen.let("passing",null),schValid=gen.name("_valid");cxt.setParams(
-{passing}),gen.block(validateOneOf),cxt.result(valid2,()=>cxt.reset(),()=>cxt.error(!0));function validateOneOf(){schArr.forEach((sch,i)=>{let schCxt;(0,util_1.alwaysValidSchema)(it,sch)?gen.var(schValid,!0):schCxt=cxt.subschema({keyword:"oneOf",schemaProp:i,compositeRule:!0},schValid),i>0&&gen.if((0,codegen_1._)`${schValid} && ${valid2}`).assign(valid2,!1).assign(passing,(0,codegen_1._)`[${passing}, ${i}]`).else(),gen.if(schValid,()=>{gen.assign(valid2,!0),gen.assign(passing,i),schCxt&&cxt.mergeEvaluated(
-schCxt,codegen_1.Name)})})}}};return oneOf$1.default=def,oneOf$1}var allOf$1={},hasRequiredAllOf$1;function requireAllOf$1(){if(hasRequiredAllOf$1)return allOf$1;hasRequiredAllOf$1=1,Object.defineProperty(allOf$1,"__esModule",{value:!0});const util_1=requireUtil$1(),def={keyword:"allOf",schemaType:"array",code(cxt){const{gen,schema,it}=cxt;if(!Array.isArray(schema))throw new Error("ajv implementation error");const valid2=gen.name("valid");schema.forEach((sch,i)=>{if((0,util_1.alwaysValidSchema)(
-it,sch))return;const schCxt=cxt.subschema({keyword:"allOf",schemaProp:i},valid2);cxt.ok(valid2),cxt.mergeEvaluated(schCxt)})}};return allOf$1.default=def,allOf$1}var _if$1={},hasRequired_if$1;function require_if$1(){if(hasRequired_if$1)return _if$1;hasRequired_if$1=1,Object.defineProperty(_if$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),util_1=requireUtil$1(),def={keyword:"if",schemaType:["object","boolean"],trackErrors:!0,error:{message:({params})=>(0,codegen_1.str)`must match "${params.
-ifClause}" schema`,params:({params})=>(0,codegen_1._)`{failingKeyword: ${params.ifClause}}`},code(cxt){const{gen,parentSchema,it}=cxt;parentSchema.then===void 0&&parentSchema.else===void 0&&(0,util_1.checkStrictMode)(it,'"if" without "then" and "else" is ignored');const hasThen=hasSchema(it,"then"),hasElse=hasSchema(it,"else");if(!hasThen&&!hasElse)return;const valid2=gen.let("valid",!0),schValid=gen.name("_valid");if(validateIf(),cxt.reset(),hasThen&&hasElse){const ifClause=gen.let("ifClause");
-cxt.setParams({ifClause}),gen.if(schValid,validateClause("then",ifClause),validateClause("else",ifClause))}else hasThen?gen.if(schValid,validateClause("then")):gen.if((0,codegen_1.not)(schValid),validateClause("else"));cxt.pass(valid2,()=>cxt.error(!0));function validateIf(){const schCxt=cxt.subschema({keyword:"if",compositeRule:!0,createErrors:!1,allErrors:!1},schValid);cxt.mergeEvaluated(schCxt)}function validateClause(keyword2,ifClause){return()=>{const schCxt=cxt.subschema({keyword:keyword2},
-schValid);gen.assign(valid2,schValid),cxt.mergeValidEvaluated(schCxt,valid2),ifClause?gen.assign(ifClause,(0,codegen_1._)`${keyword2}`):cxt.setParams({ifClause:keyword2})}}}};function hasSchema(it,keyword2){const schema=it.schema[keyword2];return schema!==void 0&&!(0,util_1.alwaysValidSchema)(it,schema)}return _if$1.default=def,_if$1}var thenElse$1={},hasRequiredThenElse$1;function requireThenElse$1(){if(hasRequiredThenElse$1)return thenElse$1;hasRequiredThenElse$1=1,Object.defineProperty(thenElse$1,
-"__esModule",{value:!0});const util_1=requireUtil$1(),def={keyword:["then","else"],schemaType:["object","boolean"],code({keyword:keyword2,parentSchema,it}){parentSchema.if===void 0&&(0,util_1.checkStrictMode)(it,`"${keyword2}" without "if" is ignored`)}};return thenElse$1.default=def,thenElse$1}var hasRequiredApplicator$1;function requireApplicator$1(){if(hasRequiredApplicator$1)return applicator$1;hasRequiredApplicator$1=1,Object.defineProperty(applicator$1,"__esModule",{value:!0});const additionalItems_1=requireAdditionalItems$1(),
-prefixItems_1=requirePrefixItems$1(),items_1=requireItems$1(),items2020_1=requireItems2020$1(),contains_1=requireContains$1(),dependencies_1=requireDependencies$1(),propertyNames_1=requirePropertyNames$1(),additionalProperties_1=requireAdditionalProperties$1(),properties_12=requireProperties$1(),patternProperties_1=requirePatternProperties$1(),not_1=requireNot$1(),anyOf_1=requireAnyOf$1(),oneOf_1=requireOneOf$1(),allOf_1=requireAllOf$1(),if_1=require_if$1(),thenElse_1=requireThenElse$1();function getApplicator(draft2020=!1){
-const applicator2=[not_1.default,anyOf_1.default,oneOf_1.default,allOf_1.default,if_1.default,thenElse_1.default,propertyNames_1.default,additionalProperties_1.default,dependencies_1.default,properties_12.default,patternProperties_1.default];return draft2020?applicator2.push(prefixItems_1.default,items2020_1.default):applicator2.push(additionalItems_1.default,items_1.default),applicator2.push(contains_1.default),applicator2}return applicator$1.default=getApplicator,applicator$1}var format$3={},format$2={},
-hasRequiredFormat$3;function requireFormat$3(){if(hasRequiredFormat$3)return format$2;hasRequiredFormat$3=1,Object.defineProperty(format$2,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),def={keyword:"format",type:["number","string"],schemaType:"string",$data:!0,error:{message:({schemaCode})=>(0,codegen_1.str)`must match format "${schemaCode}"`,params:({schemaCode})=>(0,codegen_1._)`{format: ${schemaCode}}`},code(cxt,ruleType){const{gen,data,$data,schema,schemaCode,it}=cxt,{opts,errSchemaPath,
-schemaEnv,self:self2}=it;if(!opts.validateFormats)return;$data?validate$DataFormat():validateFormat();function validate$DataFormat(){const fmts=gen.scopeValue("formats",{ref:self2.formats,code:opts.code.formats}),fDef=gen.const("fDef",(0,codegen_1._)`${fmts}[${schemaCode}]`),fType=gen.let("fType"),format2=gen.let("format");gen.if((0,codegen_1._)`typeof ${fDef} == "object" && !(${fDef} instanceof RegExp)`,()=>gen.assign(fType,(0,codegen_1._)`${fDef}.type || "string"`).assign(format2,(0,codegen_1.
-_)`${fDef}.validate`),()=>gen.assign(fType,(0,codegen_1._)`"string"`).assign(format2,fDef)),cxt.fail$data((0,codegen_1.or)(unknownFmt(),invalidFmt()));function unknownFmt(){return opts.strictSchema===!1?codegen_1.nil:(0,codegen_1._)`${schemaCode} && !${format2}`}function invalidFmt(){const callFormat=schemaEnv.$async?(0,codegen_1._)`(${fDef}.async ? await ${format2}(${data}) : ${format2}(${data}))`:(0,codegen_1._)`${format2}(${data})`,validData=(0,codegen_1._)`(typeof ${format2} == "function" ? ${callFormat} : ${format2}.test(${data}))`;
-return(0,codegen_1._)`${format2} && ${format2} !== true && ${fType} === ${ruleType} && !${validData}`}}function validateFormat(){const formatDef=self2.formats[schema];if(!formatDef){unknownFormat();return}if(formatDef===!0)return;const[fmtType,format2,fmtRef]=getFormat(formatDef);fmtType===ruleType&&cxt.pass(validCondition());function unknownFormat(){if(opts.strictSchema===!1){self2.logger.warn(unknownMsg());return}throw new Error(unknownMsg());function unknownMsg(){return`unknown format "${schema}\
-" ignored in schema at path "${errSchemaPath}"`}}function getFormat(fmtDef){const code2=fmtDef instanceof RegExp?(0,codegen_1.regexpCode)(fmtDef):opts.code.formats?(0,codegen_1._)`${opts.code.formats}${(0,codegen_1.getProperty)(schema)}`:void 0,fmt=gen.scopeValue("formats",{key:schema,ref:fmtDef,code:code2});return typeof fmtDef=="object"&&!(fmtDef instanceof RegExp)?[fmtDef.type||"string",fmtDef.validate,(0,codegen_1._)`${fmt}.validate`]:["string",fmtDef,fmt]}function validCondition(){if(typeof formatDef==
-"object"&&!(formatDef instanceof RegExp)&&formatDef.async){if(!schemaEnv.$async)throw new Error("async format in sync schema");return(0,codegen_1._)`await ${fmtRef}(${data})`}return typeof format2=="function"?(0,codegen_1._)`${fmtRef}(${data})`:(0,codegen_1._)`${fmtRef}.test(${data})`}}}};return format$2.default=def,format$2}var hasRequiredFormat$2;function requireFormat$2(){if(hasRequiredFormat$2)return format$3;hasRequiredFormat$2=1,Object.defineProperty(format$3,"__esModule",{value:!0});const format2=[
-requireFormat$3().default];return format$3.default=format2,format$3}var metadata$1={},hasRequiredMetadata$1;function requireMetadata$1(){return hasRequiredMetadata$1||(hasRequiredMetadata$1=1,Object.defineProperty(metadata$1,"__esModule",{value:!0}),metadata$1.contentVocabulary=metadata$1.metadataVocabulary=void 0,metadata$1.metadataVocabulary=["title","description","default","deprecated","readOnly","writeOnly","examples"],metadata$1.contentVocabulary=["contentMediaType","contentEncoding","conte\
-ntSchema"]),metadata$1}var hasRequiredDraft7$1;function requireDraft7$1(){if(hasRequiredDraft7$1)return draft7$1;hasRequiredDraft7$1=1,Object.defineProperty(draft7$1,"__esModule",{value:!0});const core_1=requireCore$2(),validation_1=requireValidation$1(),applicator_1=requireApplicator$1(),format_1=requireFormat$2(),metadata_1=requireMetadata$1(),draft7Vocabularies=[core_1.default,validation_1.default,(0,applicator_1.default)(),format_1.default,metadata_1.metadataVocabulary,metadata_1.contentVocabulary];
-return draft7$1.default=draft7Vocabularies,draft7$1}var discriminator$1={},types$2={},hasRequiredTypes$1;function requireTypes$1(){if(hasRequiredTypes$1)return types$2;hasRequiredTypes$1=1,Object.defineProperty(types$2,"__esModule",{value:!0}),types$2.DiscrError=void 0;var DiscrError;return function(DiscrError2){DiscrError2.Tag="tag",DiscrError2.Mapping="mapping"}(DiscrError||(types$2.DiscrError=DiscrError={})),types$2}var hasRequiredDiscriminator$1;function requireDiscriminator$1(){if(hasRequiredDiscriminator$1)
-return discriminator$1;hasRequiredDiscriminator$1=1,Object.defineProperty(discriminator$1,"__esModule",{value:!0});const codegen_1=requireCodegen$1(),types_1=requireTypes$1(),compile_1=requireCompile$1(),ref_error_1=requireRef_error$1(),util_1=requireUtil$1(),def={keyword:"discriminator",type:"object",schemaType:"object",error:{message:({params:{discrError,tagName}})=>discrError===types_1.DiscrError.Tag?`tag "${tagName}" must be string`:`value of tag "${tagName}" must be in oneOf`,params:({params:{
-discrError,tag,tagName}})=>(0,codegen_1._)`{error: ${discrError}, tag: ${tagName}, tagValue: ${tag}}`},code(cxt){const{gen,data,schema,parentSchema,it}=cxt,{oneOf:oneOf2}=parentSchema;if(!it.opts.discriminator)throw new Error("discriminator: requires discriminator option");const tagName=schema.propertyName;if(typeof tagName!="string")throw new Error("discriminator: requires propertyName");if(schema.mapping)throw new Error("discriminator: mapping is not supported");if(!oneOf2)throw new Error("dis\
-criminator: requires oneOf keyword");const valid2=gen.let("valid",!1),tag=gen.const("tag",(0,codegen_1._)`${data}${(0,codegen_1.getProperty)(tagName)}`);gen.if((0,codegen_1._)`typeof ${tag} == "string"`,()=>validateMapping(),()=>cxt.error(!1,{discrError:types_1.DiscrError.Tag,tag,tagName})),cxt.ok(valid2);function validateMapping(){const mapping=getMapping();gen.if(!1);for(const tagValue in mapping)gen.elseIf((0,codegen_1._)`${tag} === ${tagValue}`),gen.assign(valid2,applyTagSchema(mapping[tagValue]));
-gen.else(),cxt.error(!1,{discrError:types_1.DiscrError.Mapping,tag,tagName}),gen.endIf()}function applyTagSchema(schemaProp){const _valid=gen.name("valid"),schCxt=cxt.subschema({keyword:"oneOf",schemaProp},_valid);return cxt.mergeEvaluated(schCxt,codegen_1.Name),_valid}function getMapping(){var _a2;const oneOfMapping={},topRequired=hasRequired(parentSchema);let tagRequired=!0;for(let i=0;i<oneOf2.length;i++){let sch=oneOf2[i];if(sch?.$ref&&!(0,util_1.schemaHasRulesButRef)(sch,it.self.RULES)){const ref2=sch.
-$ref;if(sch=compile_1.resolveRef.call(it.self,it.schemaEnv.root,it.baseId,ref2),sch instanceof compile_1.SchemaEnv&&(sch=sch.schema),sch===void 0)throw new ref_error_1.default(it.opts.uriResolver,it.baseId,ref2)}const propSch=(_a2=sch?.properties)===null||_a2===void 0?void 0:_a2[tagName];if(typeof propSch!="object")throw new Error(`discriminator: oneOf subschemas (or referenced schemas) must have "properties/${tagName}"`);tagRequired=tagRequired&&(topRequired||hasRequired(sch)),addMappings(propSch,
-i)}if(!tagRequired)throw new Error(`discriminator: "${tagName}" must be required`);return oneOfMapping;function hasRequired({required:required2}){return Array.isArray(required2)&&required2.includes(tagName)}function addMappings(sch,i){if(sch.const)addMapping(sch.const,i);else if(sch.enum)for(const tagValue of sch.enum)addMapping(tagValue,i);else throw new Error(`discriminator: "properties/${tagName}" must have "const" or "enum"`)}function addMapping(tagValue,i){if(typeof tagValue!="string"||tagValue in
-oneOfMapping)throw new Error(`discriminator: "${tagName}" values must be unique strings`);oneOfMapping[tagValue]=i}}}};return discriminator$1.default=def,discriminator$1}const $schema$1="http://json-schema.org/draft-07/schema#",$id$2="http://json-schema.org/draft-07/schema#",title$1="Core schema meta-schema",definitions$1={schemaArray:{type:"array",minItems:1,items:{$ref:"#"}},nonNegativeInteger:{type:"integer",minimum:0},nonNegativeIntegerDefault0:{allOf:[{$ref:"#/definitions/nonNegativeInteger"},
-{default:0}]},simpleTypes:{enum:["array","boolean","integer","null","number","object","string"]},stringArray:{type:"array",items:{type:"string"},uniqueItems:!0,default:[]}},type$2=["object","boolean"],properties$4={$id:{type:"string",format:"uri-reference"},$schema:{type:"string",format:"uri"},$ref:{type:"string",format:"uri-reference"},$comment:{type:"string"},title:{type:"string"},description:{type:"string"},default:!0,readOnly:{type:"boolean",default:!1},examples:{type:"array",items:!0},multipleOf:{
-type:"number",exclusiveMinimum:0},maximum:{type:"number"},exclusiveMaximum:{type:"number"},minimum:{type:"number"},exclusiveMinimum:{type:"number"},maxLength:{$ref:"#/definitions/nonNegativeInteger"},minLength:{$ref:"#/definitions/nonNegativeIntegerDefault0"},pattern:{type:"string",format:"regex"},additionalItems:{$ref:"#"},items:{anyOf:[{$ref:"#"},{$ref:"#/definitions/schemaArray"}],default:!0},maxItems:{$ref:"#/definitions/nonNegativeInteger"},minItems:{$ref:"#/definitions/nonNegativeIntegerDe\
-fault0"},uniqueItems:{type:"boolean",default:!1},contains:{$ref:"#"},maxProperties:{$ref:"#/definitions/nonNegativeInteger"},minProperties:{$ref:"#/definitions/nonNegativeIntegerDefault0"},required:{$ref:"#/definitions/stringArray"},additionalProperties:{$ref:"#"},definitions:{type:"object",additionalProperties:{$ref:"#"},default:{}},properties:{type:"object",additionalProperties:{$ref:"#"},default:{}},patternProperties:{type:"object",additionalProperties:{$ref:"#"},propertyNames:{format:"regex"},
-default:{}},dependencies:{type:"object",additionalProperties:{anyOf:[{$ref:"#"},{$ref:"#/definitions/stringArray"}]}},propertyNames:{$ref:"#"},const:!0,enum:{type:"array",items:!0,minItems:1,uniqueItems:!0},type:{anyOf:[{$ref:"#/definitions/simpleTypes"},{type:"array",items:{$ref:"#/definitions/simpleTypes"},minItems:1,uniqueItems:!0}]},format:{type:"string"},contentMediaType:{type:"string"},contentEncoding:{type:"string"},if:{$ref:"#"},then:{$ref:"#"},else:{$ref:"#"},allOf:{$ref:"#/definitions/\
-schemaArray"},anyOf:{$ref:"#/definitions/schemaArray"},oneOf:{$ref:"#/definitions/schemaArray"},not:{$ref:"#"}},require$$3$1={$schema:$schema$1,$id:$id$2,title:title$1,definitions:definitions$1,type:type$2,properties:properties$4,default:!0};var hasRequiredAjv$1;function requireAjv$1(){return hasRequiredAjv$1||(hasRequiredAjv$1=1,function(module,exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.MissingRefError=exports.ValidationError=exports.CodeGen=exports.Name=exports.nil=
-exports.stringify=exports.str=exports._=exports.KeywordCxt=exports.Ajv=void 0;const core_1=requireCore$3(),draft7_1=requireDraft7$1(),discriminator_1=requireDiscriminator$1(),draft7MetaSchema=require$$3$1,META_SUPPORT_DATA=["/properties"],META_SCHEMA_ID="http://json-schema.org/draft-07/schema";class Ajv2 extends core_1.default{_addVocabularies(){super._addVocabularies(),draft7_1.default.forEach(v=>this.addVocabulary(v)),this.opts.discriminator&&this.addKeyword(discriminator_1.default)}_addDefaultMetaSchema(){
-if(super._addDefaultMetaSchema(),!this.opts.meta)return;const metaSchema=this.opts.$data?this.$dataMetaSchema(draft7MetaSchema,META_SUPPORT_DATA):draft7MetaSchema;this.addMetaSchema(metaSchema,META_SCHEMA_ID,!1),this.refs["http://json-schema.org/schema"]=META_SCHEMA_ID}defaultMeta(){return this.opts.defaultMeta=super.defaultMeta()||(this.getSchema(META_SCHEMA_ID)?META_SCHEMA_ID:void 0)}}exports.Ajv=Ajv2,module.exports=exports=Ajv2,module.exports.Ajv=Ajv2,Object.defineProperty(exports,"__esModule",
-{value:!0}),exports.default=Ajv2;var validate_1=requireValidate$1();Object.defineProperty(exports,"KeywordCxt",{enumerable:!0,get:function(){return validate_1.KeywordCxt}});var codegen_1=requireCodegen$1();Object.defineProperty(exports,"_",{enumerable:!0,get:function(){return codegen_1._}}),Object.defineProperty(exports,"str",{enumerable:!0,get:function(){return codegen_1.str}}),Object.defineProperty(exports,"stringify",{enumerable:!0,get:function(){return codegen_1.stringify}}),Object.defineProperty(
-exports,"nil",{enumerable:!0,get:function(){return codegen_1.nil}}),Object.defineProperty(exports,"Name",{enumerable:!0,get:function(){return codegen_1.Name}}),Object.defineProperty(exports,"CodeGen",{enumerable:!0,get:function(){return codegen_1.CodeGen}});var validation_error_1=requireValidation_error$1();Object.defineProperty(exports,"ValidationError",{enumerable:!0,get:function(){return validation_error_1.default}});var ref_error_1=requireRef_error$1();Object.defineProperty(exports,"MissingR\
-efError",{enumerable:!0,get:function(){return ref_error_1.default}})}(ajv$1,ajv$1.exports)),ajv$1.exports}var ajvExports=requireAjv$1();const Ajv=getDefaultExportFromCjs(ajvExports);var dist={exports:{}},formats={},hasRequiredFormats;function requireFormats(){return hasRequiredFormats||(hasRequiredFormats=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.formatNames=exports.fastFormats=exports.fullFormats=void 0;function fmtDef(validate2,compare){return{validate:validate2,
-compare}}exports.fullFormats={date:fmtDef(date2,compareDate),time:fmtDef(getTime(!0),compareTime),"date-time":fmtDef(getDateTime(!0),compareDateTime),"iso-time":fmtDef(getTime(),compareIsoTime),"iso-date-time":fmtDef(getDateTime(),compareIsoDateTime),duration:/^P(?!$)((\d+Y)?(\d+M)?(\d+D)?(T(?=\d)(\d+H)?(\d+M)?(\d+S)?)?|(\d+W)?)$/,uri:uri2,"uri-reference":/^(?:[a-z][a-z0-9+\-.]*:)?(?:\/?\/(?:(?:[a-z0-9\-._~!$&'()*+,;=:]|%[0-9a-f]{2})*@)?(?:\[(?:(?:(?:(?:[0-9a-f]{1,4}:){6}|::(?:[0-9a-f]{1,4}:){5}|(?:[0-9a-f]{1,4})?::(?:[0-9a-f]{1,4}:){4}|(?:(?:[0-9a-f]{1,4}:){0,1}[0-9a-f]{1,4})?::(?:[0-9a-f]{1,4}:){3}|(?:(?:[0-9a-f]{1,4}:){0,2}[0-9a-f]{1,4})?::(?:[0-9a-f]{1,4}:){2}|(?:(?:[0-9a-f]{1,4}:){0,3}[0-9a-f]{1,4})?::[0-9a-f]{1,4}:|(?:(?:[0-9a-f]{1,4}:){0,4}[0-9a-f]{1,4})?::)(?:[0-9a-f]{1,4}:[0-9a-f]{1,4}|(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?))|(?:(?:[0-9a-f]{1,4}:){0,5}[0-9a-f]{1,4})?::[0-9a-f]{1,4}|(?:(?:[0-9a-f]{1,4}:){0,6}[0-9a-f]{1,4})?::)|[Vv][0-9a-f]+\.[a-z0-9\-._~!$&'()*+,;=:]+)\]|(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?)|(?:[a-z0-9\-._~!$&'"()*+,;=]|%[0-9a-f]{2})*)(?::\d*)?(?:\/(?:[a-z0-9\-._~!$&'"()*+,;=:@]|%[0-9a-f]{2})*)*|\/(?:(?:[a-z0-9\-._~!$&'"()*+,;=:@]|%[0-9a-f]{2})+(?:\/(?:[a-z0-9\-._~!$&'"()*+,;=:@]|%[0-9a-f]{2})*)*)?|(?:[a-z0-9\-._~!$&'"()*+,;=:@]|%[0-9a-f]{2})+(?:\/(?:[a-z0-9\-._~!$&'"()*+,;=:@]|%[0-9a-f]{2})*)*)?(?:\?(?:[a-z0-9\-._~!$&'"()*+,;=:@/?]|%[0-9a-f]{2})*)?(?:#(?:[a-z0-9\-._~!$&'"()*+,;=:@/?]|%[0-9a-f]{2})*)?$/i,
-"uri-template":/^(?:(?:[^\x00-\x20"'<>%\\^`{|}]|%[0-9a-f]{2})|\{[+#./;?&=,!@|]?(?:[a-z0-9_]|%[0-9a-f]{2})+(?::[1-9][0-9]{0,3}|\*)?(?:,(?:[a-z0-9_]|%[0-9a-f]{2})+(?::[1-9][0-9]{0,3}|\*)?)*\})*$/i,url:/^(?:https?|ftp):\/\/(?:\S+(?::\S*)?@)?(?:(?!(?:10|127)(?:\.\d{1,3}){3})(?!(?:169\.254|192\.168)(?:\.\d{1,3}){2})(?!172\.(?:1[6-9]|2\d|3[0-1])(?:\.\d{1,3}){2})(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))|(?:(?:[a-z0-9\u{00a1}-\u{ffff}]+-)*[a-z0-9\u{00a1}-\u{ffff}]+)(?:\.(?:[a-z0-9\u{00a1}-\u{ffff}]+-)*[a-z0-9\u{00a1}-\u{ffff}]+)*(?:\.(?:[a-z\u{00a1}-\u{ffff}]{2,})))(?::\d{2,5})?(?:\/[^\s]*)?$/iu,
-email:/^[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?$/i,hostname:/^(?=.{1,253}\.?$)[a-z0-9](?:[a-z0-9-]{0,61}[a-z0-9])?(?:\.[a-z0-9](?:[-0-9a-z]{0,61}[0-9a-z])?)*\.?$/i,ipv4:/^(?:(?:25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)\.){3}(?:25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)$/,ipv6:/^((([0-9a-f]{1,4}:){7}([0-9a-f]{1,4}|:))|(([0-9a-f]{1,4}:){6}(:[0-9a-f]{1,4}|((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3})|:))|(([0-9a-f]{1,4}:){5}(((:[0-9a-f]{1,4}){1,2})|:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3})|:))|(([0-9a-f]{1,4}:){4}(((:[0-9a-f]{1,4}){1,3})|((:[0-9a-f]{1,4})?:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9a-f]{1,4}:){3}(((:[0-9a-f]{1,4}){1,4})|((:[0-9a-f]{1,4}){0,2}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9a-f]{1,4}:){2}(((:[0-9a-f]{1,4}){1,5})|((:[0-9a-f]{1,4}){0,3}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9a-f]{1,4}:){1}(((:[0-9a-f]{1,4}){1,6})|((:[0-9a-f]{1,4}){0,4}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(:(((:[0-9a-f]{1,4}){1,7})|((:[0-9a-f]{1,4}){0,5}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:)))$/i,
-regex,uuid:/^(?:urn:uuid:)?[0-9a-f]{8}-(?:[0-9a-f]{4}-){3}[0-9a-f]{12}$/i,"json-pointer":/^(?:\/(?:[^~/]|~0|~1)*)*$/,"json-pointer-uri-fragment":/^#(?:\/(?:[a-z0-9_\-.!$&'()*+,;:=@]|%[0-9a-f]{2}|~0|~1)*)*$/i,"relative-json-pointer":/^(?:0|[1-9][0-9]*)(?:#|(?:\/(?:[^~/]|~0|~1)*)*)$/,byte,int32:{type:"number",validate:validateInt32},int64:{type:"number",validate:validateInt64},float:{type:"number",validate:validateNumber},double:{type:"number",validate:validateNumber},password:!0,binary:!0},exports.
-fastFormats={...exports.fullFormats,date:fmtDef(/^\d\d\d\d-[0-1]\d-[0-3]\d$/,compareDate),time:fmtDef(/^(?:[0-2]\d:[0-5]\d:[0-5]\d|23:59:60)(?:\.\d+)?(?:z|[+-]\d\d(?::?\d\d)?)$/i,compareTime),"date-time":fmtDef(/^\d\d\d\d-[0-1]\d-[0-3]\dt(?:[0-2]\d:[0-5]\d:[0-5]\d|23:59:60)(?:\.\d+)?(?:z|[+-]\d\d(?::?\d\d)?)$/i,compareDateTime),"iso-time":fmtDef(/^(?:[0-2]\d:[0-5]\d:[0-5]\d|23:59:60)(?:\.\d+)?(?:z|[+-]\d\d(?::?\d\d)?)?$/i,compareIsoTime),"iso-date-time":fmtDef(/^\d\d\d\d-[0-1]\d-[0-3]\d[t\s](?:[0-2]\d:[0-5]\d:[0-5]\d|23:59:60)(?:\.\d+)?(?:z|[+-]\d\d(?::?\d\d)?)?$/i,
-compareIsoDateTime),uri:/^(?:[a-z][a-z0-9+\-.]*:)(?:\/?\/)?[^\s]*$/i,"uri-reference":/^(?:(?:[a-z][a-z0-9+\-.]*:)?\/?\/)?(?:[^\\\s#][^\s#]*)?(?:#[^\\\s]*)?$/i,email:/^[a-z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-z0-9](?:[a-z0-9-]{0,61}[a-z0-9])?(?:\.[a-z0-9](?:[a-z0-9-]{0,61}[a-z0-9])?)*$/i},exports.formatNames=Object.keys(exports.fullFormats);function isLeapYear(year){return year%4===0&&(year%100!==0||year%400===0)}const DATE2=/^(\d\d\d\d)-(\d\d)-(\d\d)$/,DAYS=[0,31,28,31,30,31,30,31,31,30,31,30,31];function date2(str){
-const matches2=DATE2.exec(str);if(!matches2)return!1;const year=+matches2[1],month=+matches2[2],day=+matches2[3];return month>=1&&month<=12&&day>=1&&day<=(month===2&&isLeapYear(year)?29:DAYS[month])}function compareDate(d1,d2){if(d1&&d2)return d1>d2?1:d1<d2?-1:0}const TIME=/^(\d\d):(\d\d):(\d\d(?:\.\d+)?)(z|([+-])(\d\d)(?::?(\d\d))?)?$/i;function getTime(strictTimeZone){return function(str){const matches2=TIME.exec(str);if(!matches2)return!1;const hr=+matches2[1],min2=+matches2[2],sec=+matches2[3],
-tz=matches2[4],tzSign=matches2[5]==="-"?-1:1,tzH=+(matches2[6]||0),tzM=+(matches2[7]||0);if(tzH>23||tzM>59||strictTimeZone&&!tz)return!1;if(hr<=23&&min2<=59&&sec<60)return!0;const utcMin=min2-tzM*tzSign,utcHr=hr-tzH*tzSign-(utcMin<0?1:0);return(utcHr===23||utcHr===-1)&&(utcMin===59||utcMin===-1)&&sec<61}}function compareTime(s1,s2){if(!(s1&&s2))return;const t1=new Date("2020-01-01T"+s1).valueOf(),t2=new Date("2020-01-01T"+s2).valueOf();if(t1&&t2)return t1-t2}function compareIsoTime(t1,t2){if(!(t1&&
-t2))return;const a1=TIME.exec(t1),a2=TIME.exec(t2);if(a1&&a2)return t1=a1[1]+a1[2]+a1[3],t2=a2[1]+a2[2]+a2[3],t1>t2?1:t1<t2?-1:0}const DATE_TIME_SEPARATOR=/t|\s/i;function getDateTime(strictTimeZone){const time2=getTime(strictTimeZone);return function(str){const dateTime=str.split(DATE_TIME_SEPARATOR);return dateTime.length===2&&date2(dateTime[0])&&time2(dateTime[1])}}function compareDateTime(dt1,dt2){if(!(dt1&&dt2))return;const d1=new Date(dt1).valueOf(),d2=new Date(dt2).valueOf();if(d1&&d2)return d1-
-d2}function compareIsoDateTime(dt1,dt2){if(!(dt1&&dt2))return;const[d1,t1]=dt1.split(DATE_TIME_SEPARATOR),[d2,t2]=dt2.split(DATE_TIME_SEPARATOR),res=compareDate(d1,d2);if(res!==void 0)return res||compareTime(t1,t2)}const NOT_URI_FRAGMENT=/\/|:/,URI=/^(?:[a-z][a-z0-9+\-.]*:)(?:\/?\/(?:(?:[a-z0-9\-._~!$&'()*+,;=:]|%[0-9a-f]{2})*@)?(?:\[(?:(?:(?:(?:[0-9a-f]{1,4}:){6}|::(?:[0-9a-f]{1,4}:){5}|(?:[0-9a-f]{1,4})?::(?:[0-9a-f]{1,4}:){4}|(?:(?:[0-9a-f]{1,4}:){0,1}[0-9a-f]{1,4})?::(?:[0-9a-f]{1,4}:){3}|(?:(?:[0-9a-f]{1,4}:){0,2}[0-9a-f]{1,4})?::(?:[0-9a-f]{1,4}:){2}|(?:(?:[0-9a-f]{1,4}:){0,3}[0-9a-f]{1,4})?::[0-9a-f]{1,4}:|(?:(?:[0-9a-f]{1,4}:){0,4}[0-9a-f]{1,4})?::)(?:[0-9a-f]{1,4}:[0-9a-f]{1,4}|(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?))|(?:(?:[0-9a-f]{1,4}:){0,5}[0-9a-f]{1,4})?::[0-9a-f]{1,4}|(?:(?:[0-9a-f]{1,4}:){0,6}[0-9a-f]{1,4})?::)|[Vv][0-9a-f]+\.[a-z0-9\-._~!$&'()*+,;=:]+)\]|(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?)|(?:[a-z0-9\-._~!$&'()*+,;=]|%[0-9a-f]{2})*)(?::\d*)?(?:\/(?:[a-z0-9\-._~!$&'()*+,;=:@]|%[0-9a-f]{2})*)*|\/(?:(?:[a-z0-9\-._~!$&'()*+,;=:@]|%[0-9a-f]{2})+(?:\/(?:[a-z0-9\-._~!$&'()*+,;=:@]|%[0-9a-f]{2})*)*)?|(?:[a-z0-9\-._~!$&'()*+,;=:@]|%[0-9a-f]{2})+(?:\/(?:[a-z0-9\-._~!$&'()*+,;=:@]|%[0-9a-f]{2})*)*)(?:\?(?:[a-z0-9\-._~!$&'()*+,;=:@/?]|%[0-9a-f]{2})*)?(?:#(?:[a-z0-9\-._~!$&'()*+,;=:@/?]|%[0-9a-f]{2})*)?$/i;
-function uri2(str){return NOT_URI_FRAGMENT.test(str)&&URI.test(str)}const BYTE=/^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?$/gm;function byte(str){return BYTE.lastIndex=0,BYTE.test(str)}const MIN_INT32=-2147483648,MAX_INT32=2**31-1;function validateInt32(value){return Number.isInteger(value)&&value<=MAX_INT32&&value>=MIN_INT32}function validateInt64(value){return Number.isInteger(value)}function validateNumber(){return!0}const Z_ANCHOR=/[^\\]\\Z/;function regex(str){if(Z_ANCHOR.
-test(str))return!1;try{return new RegExp(str),!0}catch{return!1}}}(formats)),formats}var limit={},ajv={exports:{}},core$2={},validate={},boolSchema={},errors={},codegen={},code$4={},hasRequiredCode$1;function requireCode$1(){return hasRequiredCode$1||(hasRequiredCode$1=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.regexpCode=exports.getEsmExportName=exports.getProperty=exports.safeStringify=exports.stringify=exports.strConcat=exports.addCodeArg=exports.str=exports.
-_=exports.nil=exports._Code=exports.Name=exports.IDENTIFIER=exports._CodeOrName=void 0;class _CodeOrName{}exports._CodeOrName=_CodeOrName,exports.IDENTIFIER=/^[a-z$_][a-z$_0-9]*$/i;class Name extends _CodeOrName{constructor(s2){if(super(),!exports.IDENTIFIER.test(s2))throw new Error("CodeGen: name must be a valid identifier");this.str=s2}toString(){return this.str}emptyStr(){return!1}get names(){return{[this.str]:1}}}exports.Name=Name;class _Code extends _CodeOrName{constructor(code2){super(),this.
-_items=typeof code2=="string"?[code2]:code2}toString(){return this.str}emptyStr(){if(this._items.length>1)return!1;const item=this._items[0];return item===""||item==='""'}get str(){var _a2;return(_a2=this._str)!==null&&_a2!==void 0?_a2:this._str=this._items.reduce((s2,c2)=>`${s2}${c2}`,"")}get names(){var _a2;return(_a2=this._names)!==null&&_a2!==void 0?_a2:this._names=this._items.reduce((names2,c2)=>(c2 instanceof Name&&(names2[c2.str]=(names2[c2.str]||0)+1),names2),{})}}exports._Code=_Code,exports.
-nil=new _Code("");function _(strs,...args){const code2=[strs[0]];let i=0;for(;i<args.length;)addCodeArg(code2,args[i]),code2.push(strs[++i]);return new _Code(code2)}exports._=_;const plus=new _Code("+");function str(strs,...args){const expr=[safeStringify(strs[0])];let i=0;for(;i<args.length;)expr.push(plus),addCodeArg(expr,args[i]),expr.push(plus,safeStringify(strs[++i]));return optimize(expr),new _Code(expr)}exports.str=str;function addCodeArg(code2,arg){arg instanceof _Code?code2.push(...arg.
-_items):arg instanceof Name?code2.push(arg):code2.push(interpolate(arg))}exports.addCodeArg=addCodeArg;function optimize(expr){let i=1;for(;i<expr.length-1;){if(expr[i]===plus){const res=mergeExprItems(expr[i-1],expr[i+1]);if(res!==void 0){expr.splice(i-1,3,res);continue}expr[i++]="+"}i++}}function mergeExprItems(a,b){if(b==='""')return a;if(a==='""')return b;if(typeof a=="string")return b instanceof Name||a[a.length-1]!=='"'?void 0:typeof b!="string"?`${a.slice(0,-1)}${b}"`:b[0]==='"'?a.slice(0,
--1)+b.slice(1):void 0;if(typeof b=="string"&&b[0]==='"'&&!(a instanceof Name))return`"${a}${b.slice(1)}`}function strConcat(c1,c2){return c2.emptyStr()?c1:c1.emptyStr()?c2:str`${c1}${c2}`}exports.strConcat=strConcat;function interpolate(x){return typeof x=="number"||typeof x=="boolean"||x===null?x:safeStringify(Array.isArray(x)?x.join(","):x)}function stringify2(x){return new _Code(safeStringify(x))}exports.stringify=stringify2;function safeStringify(x){return JSON.stringify(x).replace(/\u2028/g,
-"\\u2028").replace(/\u2029/g,"\\u2029")}exports.safeStringify=safeStringify;function getProperty(key2){return typeof key2=="string"&&exports.IDENTIFIER.test(key2)?new _Code(`.${key2}`):_`[${key2}]`}exports.getProperty=getProperty;function getEsmExportName(key2){if(typeof key2=="string"&&exports.IDENTIFIER.test(key2))return new _Code(`${key2}`);throw new Error(`CodeGen: invalid export name: ${key2}, use explicit $id name mapping`)}exports.getEsmExportName=getEsmExportName;function regexpCode(rx){
-return new _Code(rx.toString())}exports.regexpCode=regexpCode}(code$4)),code$4}var scope={},hasRequiredScope;function requireScope(){return hasRequiredScope||(hasRequiredScope=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.ValueScope=exports.ValueScopeName=exports.Scope=exports.varKinds=exports.UsedValueState=void 0;const code_1=requireCode$1();class ValueError extends Error{constructor(name){super(`CodeGen: "code" for ${name} not defined`),this.value=name.value}}
-var UsedValueState;(function(UsedValueState2){UsedValueState2[UsedValueState2.Started=0]="Started",UsedValueState2[UsedValueState2.Completed=1]="Completed"})(UsedValueState||(exports.UsedValueState=UsedValueState={})),exports.varKinds={const:new code_1.Name("const"),let:new code_1.Name("let"),var:new code_1.Name("var")};class Scope{constructor({prefixes,parent}={}){this._names={},this._prefixes=prefixes,this._parent=parent}toName(nameOrPrefix){return nameOrPrefix instanceof code_1.Name?nameOrPrefix:
-this.name(nameOrPrefix)}name(prefix){return new code_1.Name(this._newName(prefix))}_newName(prefix){const ng=this._names[prefix]||this._nameGroup(prefix);return`${prefix}${ng.index++}`}_nameGroup(prefix){var _a2,_b;if(!((_b=(_a2=this._parent)===null||_a2===void 0?void 0:_a2._prefixes)===null||_b===void 0)&&_b.has(prefix)||this._prefixes&&!this._prefixes.has(prefix))throw new Error(`CodeGen: prefix "${prefix}" is not allowed in this scope`);return this._names[prefix]={prefix,index:0}}}exports.Scope=
-Scope;class ValueScopeName extends code_1.Name{constructor(prefix,nameStr){super(nameStr),this.prefix=prefix}setValue(value,{property,itemIndex}){this.value=value,this.scopePath=(0,code_1._)`.${new code_1.Name(property)}[${itemIndex}]`}}exports.ValueScopeName=ValueScopeName;const line=(0,code_1._)`\n`;class ValueScope extends Scope{constructor(opts){super(opts),this._values={},this._scope=opts.scope,this.opts={...opts,_n:opts.lines?line:code_1.nil}}get(){return this._scope}name(prefix){return new ValueScopeName(
-prefix,this._newName(prefix))}value(nameOrPrefix,value){var _a2;if(value.ref===void 0)throw new Error("CodeGen: ref must be passed in value");const name=this.toName(nameOrPrefix),{prefix}=name,valueKey=(_a2=value.key)!==null&&_a2!==void 0?_a2:value.ref;let vs=this._values[prefix];if(vs){const _name=vs.get(valueKey);if(_name)return _name}else vs=this._values[prefix]=new Map;vs.set(valueKey,name);const s2=this._scope[prefix]||(this._scope[prefix]=[]),itemIndex=s2.length;return s2[itemIndex]=value.
-ref,name.setValue(value,{property:prefix,itemIndex}),name}getValue(prefix,keyOrRef){const vs=this._values[prefix];if(vs)return vs.get(keyOrRef)}scopeRefs(scopeName,values=this._values){return this._reduceValues(values,name=>{if(name.scopePath===void 0)throw new Error(`CodeGen: name "${name}" has no value`);return(0,code_1._)`${scopeName}${name.scopePath}`})}scopeCode(values=this._values,usedValues,getCode){return this._reduceValues(values,name=>{if(name.value===void 0)throw new Error(`CodeGen: n\
-ame "${name}" has no value`);return name.value.code},usedValues,getCode)}_reduceValues(values,valueCode,usedValues={},getCode){let code2=code_1.nil;for(const prefix in values){const vs=values[prefix];if(!vs)continue;const nameSet=usedValues[prefix]=usedValues[prefix]||new Map;vs.forEach(name=>{if(nameSet.has(name))return;nameSet.set(name,UsedValueState.Started);let c2=valueCode(name);if(c2){const def=this.opts.es5?exports.varKinds.var:exports.varKinds.const;code2=(0,code_1._)`${code2}${def} ${name} = ${c2};${this.
-opts._n}`}else if(c2=getCode?.(name))code2=(0,code_1._)`${code2}${c2}${this.opts._n}`;else throw new ValueError(name);nameSet.set(name,UsedValueState.Completed)})}return code2}}exports.ValueScope=ValueScope}(scope)),scope}var hasRequiredCodegen;function requireCodegen(){return hasRequiredCodegen||(hasRequiredCodegen=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.or=exports.and=exports.not=exports.CodeGen=exports.operators=exports.varKinds=exports.ValueScopeName=
-exports.ValueScope=exports.Scope=exports.Name=exports.regexpCode=exports.stringify=exports.getProperty=exports.nil=exports.strConcat=exports.str=exports._=void 0;const code_1=requireCode$1(),scope_1=requireScope();var code_2=requireCode$1();Object.defineProperty(exports,"_",{enumerable:!0,get:function(){return code_2._}}),Object.defineProperty(exports,"str",{enumerable:!0,get:function(){return code_2.str}}),Object.defineProperty(exports,"strConcat",{enumerable:!0,get:function(){return code_2.strConcat}}),
-Object.defineProperty(exports,"nil",{enumerable:!0,get:function(){return code_2.nil}}),Object.defineProperty(exports,"getProperty",{enumerable:!0,get:function(){return code_2.getProperty}}),Object.defineProperty(exports,"stringify",{enumerable:!0,get:function(){return code_2.stringify}}),Object.defineProperty(exports,"regexpCode",{enumerable:!0,get:function(){return code_2.regexpCode}}),Object.defineProperty(exports,"Name",{enumerable:!0,get:function(){return code_2.Name}});var scope_2=requireScope();
-Object.defineProperty(exports,"Scope",{enumerable:!0,get:function(){return scope_2.Scope}}),Object.defineProperty(exports,"ValueScope",{enumerable:!0,get:function(){return scope_2.ValueScope}}),Object.defineProperty(exports,"ValueScopeName",{enumerable:!0,get:function(){return scope_2.ValueScopeName}}),Object.defineProperty(exports,"varKinds",{enumerable:!0,get:function(){return scope_2.varKinds}}),exports.operators={GT:new code_1._Code(">"),GTE:new code_1._Code(">="),LT:new code_1._Code("<"),LTE:new code_1.
-_Code("<="),EQ:new code_1._Code("==="),NEQ:new code_1._Code("!=="),NOT:new code_1._Code("!"),OR:new code_1._Code("||"),AND:new code_1._Code("&&"),ADD:new code_1._Code("+")};class Node2{optimizeNodes(){return this}optimizeNames(_names,_constants){return this}}class Def extends Node2{constructor(varKind,name,rhs){super(),this.varKind=varKind,this.name=name,this.rhs=rhs}render({es5,_n}){const varKind=es5?scope_1.varKinds.var:this.varKind,rhs=this.rhs===void 0?"":` = ${this.rhs}`;return`${varKind} ${this.
-name}${rhs};`+_n}optimizeNames(names2,constants2){if(names2[this.name.str])return this.rhs&&(this.rhs=optimizeExpr(this.rhs,names2,constants2)),this}get names(){return this.rhs instanceof code_1._CodeOrName?this.rhs.names:{}}}class Assign extends Node2{constructor(lhs,rhs,sideEffects){super(),this.lhs=lhs,this.rhs=rhs,this.sideEffects=sideEffects}render({_n}){return`${this.lhs} = ${this.rhs};`+_n}optimizeNames(names2,constants2){if(!(this.lhs instanceof code_1.Name&&!names2[this.lhs.str]&&!this.
-sideEffects))return this.rhs=optimizeExpr(this.rhs,names2,constants2),this}get names(){const names2=this.lhs instanceof code_1.Name?{}:{...this.lhs.names};return addExprNames(names2,this.rhs)}}class AssignOp extends Assign{constructor(lhs,op2,rhs,sideEffects){super(lhs,rhs,sideEffects),this.op=op2}render({_n}){return`${this.lhs} ${this.op}= ${this.rhs};`+_n}}class Label2 extends Node2{constructor(label){super(),this.label=label,this.names={}}render({_n}){return`${this.label}:`+_n}}class Break extends Node2{constructor(label){
-super(),this.label=label,this.names={}}render({_n}){return`break${this.label?` ${this.label}`:""};`+_n}}class Throw extends Node2{constructor(error2){super(),this.error=error2}render({_n}){return`throw ${this.error};`+_n}get names(){return this.error.names}}class AnyCode extends Node2{constructor(code2){super(),this.code=code2}render({_n}){return`${this.code};`+_n}optimizeNodes(){return`${this.code}`?this:void 0}optimizeNames(names2,constants2){return this.code=optimizeExpr(this.code,names2,constants2),
-this}get names(){return this.code instanceof code_1._CodeOrName?this.code.names:{}}}class ParentNode extends Node2{constructor(nodes2=[]){super(),this.nodes=nodes2}render(opts){return this.nodes.reduce((code2,n)=>code2+n.render(opts),"")}optimizeNodes(){const{nodes:nodes2}=this;let i=nodes2.length;for(;i--;){const n=nodes2[i].optimizeNodes();Array.isArray(n)?nodes2.splice(i,1,...n):n?nodes2[i]=n:nodes2.splice(i,1)}return nodes2.length>0?this:void 0}optimizeNames(names2,constants2){const{nodes:nodes2}=this;
-let i=nodes2.length;for(;i--;){const n=nodes2[i];n.optimizeNames(names2,constants2)||(subtractNames(names2,n.names),nodes2.splice(i,1))}return nodes2.length>0?this:void 0}get names(){return this.nodes.reduce((names2,n)=>addNames(names2,n.names),{})}}class BlockNode extends ParentNode{render(opts){return"{"+opts._n+super.render(opts)+"}"+opts._n}}class Root2 extends ParentNode{}class Else extends BlockNode{}Else.kind="else";class If extends BlockNode{constructor(condition,nodes2){super(nodes2),this.
-condition=condition}render(opts){let code2=`if(${this.condition})`+super.render(opts);return this.else&&(code2+="else "+this.else.render(opts)),code2}optimizeNodes(){super.optimizeNodes();const cond=this.condition;if(cond===!0)return this.nodes;let e=this.else;if(e){const ns=e.optimizeNodes();e=this.else=Array.isArray(ns)?new Else(ns):ns}if(e)return cond===!1?e instanceof If?e:e.nodes:this.nodes.length?this:new If(not2(cond),e instanceof If?[e]:e.nodes);if(!(cond===!1||!this.nodes.length))return this}optimizeNames(names2,constants2){
-var _a2;if(this.else=(_a2=this.else)===null||_a2===void 0?void 0:_a2.optimizeNames(names2,constants2),!!(super.optimizeNames(names2,constants2)||this.else))return this.condition=optimizeExpr(this.condition,names2,constants2),this}get names(){const names2=super.names;return addExprNames(names2,this.condition),this.else&&addNames(names2,this.else.names),names2}}If.kind="if";class For extends BlockNode{}For.kind="for";class ForLoop extends For{constructor(iteration){super(),this.iteration=iteration}render(opts){
-return`for(${this.iteration})`+super.render(opts)}optimizeNames(names2,constants2){if(super.optimizeNames(names2,constants2))return this.iteration=optimizeExpr(this.iteration,names2,constants2),this}get names(){return addNames(super.names,this.iteration.names)}}class ForRange extends For{constructor(varKind,name,from,to){super(),this.varKind=varKind,this.name=name,this.from=from,this.to=to}render(opts){const varKind=opts.es5?scope_1.varKinds.var:this.varKind,{name,from,to}=this;return`for(${varKind}\
- ${name}=${from}; ${name}<${to}; ${name}++)`+super.render(opts)}get names(){const names2=addExprNames(super.names,this.from);return addExprNames(names2,this.to)}}class ForIter extends For{constructor(loop2,varKind,name,iterable){super(),this.loop=loop2,this.varKind=varKind,this.name=name,this.iterable=iterable}render(opts){return`for(${this.varKind} ${this.name} ${this.loop} ${this.iterable})`+super.render(opts)}optimizeNames(names2,constants2){if(super.optimizeNames(names2,constants2))return this.
-iterable=optimizeExpr(this.iterable,names2,constants2),this}get names(){return addNames(super.names,this.iterable.names)}}class Func extends BlockNode{constructor(name,args,async){super(),this.name=name,this.args=args,this.async=async}render(opts){return`${this.async?"async ":""}function ${this.name}(${this.args})`+super.render(opts)}}Func.kind="func";class Return extends ParentNode{render(opts){return"return "+super.render(opts)}}Return.kind="return";class Try extends BlockNode{render(opts){let code2="\
-try"+super.render(opts);return this.catch&&(code2+=this.catch.render(opts)),this.finally&&(code2+=this.finally.render(opts)),code2}optimizeNodes(){var _a2,_b;return super.optimizeNodes(),(_a2=this.catch)===null||_a2===void 0||_a2.optimizeNodes(),(_b=this.finally)===null||_b===void 0||_b.optimizeNodes(),this}optimizeNames(names2,constants2){var _a2,_b;return super.optimizeNames(names2,constants2),(_a2=this.catch)===null||_a2===void 0||_a2.optimizeNames(names2,constants2),(_b=this.finally)===null||
-_b===void 0||_b.optimizeNames(names2,constants2),this}get names(){const names2=super.names;return this.catch&&addNames(names2,this.catch.names),this.finally&&addNames(names2,this.finally.names),names2}}class Catch extends BlockNode{constructor(error2){super(),this.error=error2}render(opts){return`catch(${this.error})`+super.render(opts)}}Catch.kind="catch";class Finally extends BlockNode{render(opts){return"finally"+super.render(opts)}}Finally.kind="finally";class CodeGen{constructor(extScope,opts={}){
-this._values={},this._blockStarts=[],this._constants={},this.opts={...opts,_n:opts.lines?`
-`:""},this._extScope=extScope,this._scope=new scope_1.Scope({parent:extScope}),this._nodes=[new Root2]}toString(){return this._root.render(this.opts)}name(prefix){return this._scope.name(prefix)}scopeName(prefix){return this._extScope.name(prefix)}scopeValue(prefixOrName,value){const name=this._extScope.value(prefixOrName,value);return(this._values[name.prefix]||(this._values[name.prefix]=new Set)).add(name),name}getScopeValue(prefix,keyOrRef){return this._extScope.getValue(prefix,keyOrRef)}scopeRefs(scopeName){
-return this._extScope.scopeRefs(scopeName,this._values)}scopeCode(){return this._extScope.scopeCode(this._values)}_def(varKind,nameOrPrefix,rhs,constant){const name=this._scope.toName(nameOrPrefix);return rhs!==void 0&&constant&&(this._constants[name.str]=rhs),this._leafNode(new Def(varKind,name,rhs)),name}const(nameOrPrefix,rhs,_constant){return this._def(scope_1.varKinds.const,nameOrPrefix,rhs,_constant)}let(nameOrPrefix,rhs,_constant){return this._def(scope_1.varKinds.let,nameOrPrefix,rhs,_constant)}var(nameOrPrefix,rhs,_constant){
-return this._def(scope_1.varKinds.var,nameOrPrefix,rhs,_constant)}assign(lhs,rhs,sideEffects){return this._leafNode(new Assign(lhs,rhs,sideEffects))}add(lhs,rhs){return this._leafNode(new AssignOp(lhs,exports.operators.ADD,rhs))}code(c2){return typeof c2=="function"?c2():c2!==code_1.nil&&this._leafNode(new AnyCode(c2)),this}object(...keyValues){const code2=["{"];for(const[key2,value]of keyValues)code2.length>1&&code2.push(","),code2.push(key2),(key2!==value||this.opts.es5)&&(code2.push(":"),(0,code_1.
-addCodeArg)(code2,value));return code2.push("}"),new code_1._Code(code2)}if(condition,thenBody,elseBody){if(this._blockNode(new If(condition)),thenBody&&elseBody)this.code(thenBody).else().code(elseBody).endIf();else if(thenBody)this.code(thenBody).endIf();else if(elseBody)throw new Error('CodeGen: "else" body without "then" body');return this}elseIf(condition){return this._elseNode(new If(condition))}else(){return this._elseNode(new Else)}endIf(){return this._endBlockNode(If,Else)}_for(node2,forBody){
-return this._blockNode(node2),forBody&&this.code(forBody).endFor(),this}for(iteration,forBody){return this._for(new ForLoop(iteration),forBody)}forRange(nameOrPrefix,from,to,forBody,varKind=this.opts.es5?scope_1.varKinds.var:scope_1.varKinds.let){const name=this._scope.toName(nameOrPrefix);return this._for(new ForRange(varKind,name,from,to),()=>forBody(name))}forOf(nameOrPrefix,iterable,forBody,varKind=scope_1.varKinds.const){const name=this._scope.toName(nameOrPrefix);if(this.opts.es5){const arr=iterable instanceof
-code_1.Name?iterable:this.var("_arr",iterable);return this.forRange("_i",0,(0,code_1._)`${arr}.length`,i=>{this.var(name,(0,code_1._)`${arr}[${i}]`),forBody(name)})}return this._for(new ForIter("of",varKind,name,iterable),()=>forBody(name))}forIn(nameOrPrefix,obj,forBody,varKind=this.opts.es5?scope_1.varKinds.var:scope_1.varKinds.const){if(this.opts.ownProperties)return this.forOf(nameOrPrefix,(0,code_1._)`Object.keys(${obj})`,forBody);const name=this._scope.toName(nameOrPrefix);return this._for(
-new ForIter("in",varKind,name,obj),()=>forBody(name))}endFor(){return this._endBlockNode(For)}label(label){return this._leafNode(new Label2(label))}break(label){return this._leafNode(new Break(label))}return(value){const node2=new Return;if(this._blockNode(node2),this.code(value),node2.nodes.length!==1)throw new Error('CodeGen: "return" should have one node');return this._endBlockNode(Return)}try(tryBody,catchCode,finallyCode){if(!catchCode&&!finallyCode)throw new Error('CodeGen: "try" without "\
-catch" and "finally"');const node2=new Try;if(this._blockNode(node2),this.code(tryBody),catchCode){const error2=this.name("e");this._currNode=node2.catch=new Catch(error2),catchCode(error2)}return finallyCode&&(this._currNode=node2.finally=new Finally,this.code(finallyCode)),this._endBlockNode(Catch,Finally)}throw(error2){return this._leafNode(new Throw(error2))}block(body2,nodeCount){return this._blockStarts.push(this._nodes.length),body2&&this.code(body2).endBlock(nodeCount),this}endBlock(nodeCount){
-const len=this._blockStarts.pop();if(len===void 0)throw new Error("CodeGen: not in self-balancing block");const toClose=this._nodes.length-len;if(toClose<0||nodeCount!==void 0&&toClose!==nodeCount)throw new Error(`CodeGen: wrong number of nodes: ${toClose} vs ${nodeCount} expected`);return this._nodes.length=len,this}func(name,args=code_1.nil,async,funcBody){return this._blockNode(new Func(name,args,async)),funcBody&&this.code(funcBody).endFunc(),this}endFunc(){return this._endBlockNode(Func)}optimize(n=1){
-for(;n-- >0;)this._root.optimizeNodes(),this._root.optimizeNames(this._root.names,this._constants)}_leafNode(node2){return this._currNode.nodes.push(node2),this}_blockNode(node2){this._currNode.nodes.push(node2),this._nodes.push(node2)}_endBlockNode(N1,N2){const n=this._currNode;if(n instanceof N1||N2&&n instanceof N2)return this._nodes.pop(),this;throw new Error(`CodeGen: not in block "${N2?`${N1.kind}/${N2.kind}`:N1.kind}"`)}_elseNode(node2){const n=this._currNode;if(!(n instanceof If))throw new Error(
-'CodeGen: "else" without "if"');return this._currNode=n.else=node2,this}get _root(){return this._nodes[0]}get _currNode(){const ns=this._nodes;return ns[ns.length-1]}set _currNode(node2){const ns=this._nodes;ns[ns.length-1]=node2}}exports.CodeGen=CodeGen;function addNames(names2,from){for(const n in from)names2[n]=(names2[n]||0)+(from[n]||0);return names2}function addExprNames(names2,from){return from instanceof code_1._CodeOrName?addNames(names2,from.names):names2}function optimizeExpr(expr,names2,constants2){
-if(expr instanceof code_1.Name)return replaceName(expr);if(!canOptimize(expr))return expr;return new code_1._Code(expr._items.reduce((items2,c2)=>(c2 instanceof code_1.Name&&(c2=replaceName(c2)),c2 instanceof code_1._Code?items2.push(...c2._items):items2.push(c2),items2),[]));function replaceName(n){const c2=constants2[n.str];return c2===void 0||names2[n.str]!==1?n:(delete names2[n.str],c2)}function canOptimize(e){return e instanceof code_1._Code&&e._items.some(c2=>c2 instanceof code_1.Name&&names2[c2.
-str]===1&&constants2[c2.str]!==void 0)}}function subtractNames(names2,from){for(const n in from)names2[n]=(names2[n]||0)-(from[n]||0)}function not2(x){return typeof x=="boolean"||typeof x=="number"||x===null?!x:(0,code_1._)`!${par(x)}`}exports.not=not2;const andCode=mappend(exports.operators.AND);function and(...args){return args.reduce(andCode)}exports.and=and;const orCode=mappend(exports.operators.OR);function or(...args){return args.reduce(orCode)}exports.or=or;function mappend(op2){return(x,y)=>x===
-code_1.nil?y:y===code_1.nil?x:(0,code_1._)`${par(x)} ${op2} ${par(y)}`}function par(x){return x instanceof code_1.Name?x:(0,code_1._)`(${x})`}}(codegen)),codegen}var util={},hasRequiredUtil;function requireUtil(){if(hasRequiredUtil)return util;hasRequiredUtil=1,Object.defineProperty(util,"__esModule",{value:!0}),util.checkStrictMode=util.getErrorPath=util.Type=util.useFunc=util.setEvaluated=util.evaluatedPropsToName=util.mergeEvaluated=util.eachItem=util.unescapeJsonPointer=util.escapeJsonPointer=
-util.escapeFragment=util.unescapeFragment=util.schemaRefOrVal=util.schemaHasRulesButRef=util.schemaHasRules=util.checkUnknownRules=util.alwaysValidSchema=util.toHash=void 0;const codegen_1=requireCodegen(),code_1=requireCode$1();function toHash(arr){const hash2={};for(const item of arr)hash2[item]=!0;return hash2}util.toHash=toHash;function alwaysValidSchema(it,schema){return typeof schema=="boolean"?schema:Object.keys(schema).length===0?!0:(checkUnknownRules(it,schema),!schemaHasRules(schema,it.
-self.RULES.all))}util.alwaysValidSchema=alwaysValidSchema;function checkUnknownRules(it,schema=it.schema){const{opts,self:self2}=it;if(!opts.strictSchema||typeof schema=="boolean")return;const rules2=self2.RULES.keywords;for(const key2 in schema)rules2[key2]||checkStrictMode(it,`unknown keyword: "${key2}"`)}util.checkUnknownRules=checkUnknownRules;function schemaHasRules(schema,rules2){if(typeof schema=="boolean")return!schema;for(const key2 in schema)if(rules2[key2])return!0;return!1}util.schemaHasRules=
-schemaHasRules;function schemaHasRulesButRef(schema,RULES){if(typeof schema=="boolean")return!schema;for(const key2 in schema)if(key2!=="$ref"&&RULES.all[key2])return!0;return!1}util.schemaHasRulesButRef=schemaHasRulesButRef;function schemaRefOrVal({topSchemaRef,schemaPath},schema,keyword2,$data){if(!$data){if(typeof schema=="number"||typeof schema=="boolean")return schema;if(typeof schema=="string")return(0,codegen_1._)`${schema}`}return(0,codegen_1._)`${topSchemaRef}${schemaPath}${(0,codegen_1.
-getProperty)(keyword2)}`}util.schemaRefOrVal=schemaRefOrVal;function unescapeFragment(str){return unescapeJsonPointer(decodeURIComponent(str))}util.unescapeFragment=unescapeFragment;function escapeFragment(str){return encodeURIComponent(escapeJsonPointer(str))}util.escapeFragment=escapeFragment;function escapeJsonPointer(str){return typeof str=="number"?`${str}`:str.replace(/~/g,"~0").replace(/\//g,"~1")}util.escapeJsonPointer=escapeJsonPointer;function unescapeJsonPointer(str){return str.replace(
-/~1/g,"/").replace(/~0/g,"~")}util.unescapeJsonPointer=unescapeJsonPointer;function eachItem(xs,f){if(Array.isArray(xs))for(const x of xs)f(x);else f(xs)}util.eachItem=eachItem;function makeMergeEvaluated({mergeNames,mergeToName,mergeValues:mergeValues2,resultToName}){return(gen,from,to,toName)=>{const res=to===void 0?from:to instanceof codegen_1.Name?(from instanceof codegen_1.Name?mergeNames(gen,from,to):mergeToName(gen,from,to),to):from instanceof codegen_1.Name?(mergeToName(gen,to,from),from):
-mergeValues2(from,to);return toName===codegen_1.Name&&!(res instanceof codegen_1.Name)?resultToName(gen,res):res}}util.mergeEvaluated={props:makeMergeEvaluated({mergeNames:(gen,from,to)=>gen.if((0,codegen_1._)`${to} !== true && ${from} !== undefined`,()=>{gen.if((0,codegen_1._)`${from} === true`,()=>gen.assign(to,!0),()=>gen.assign(to,(0,codegen_1._)`${to} || {}`).code((0,codegen_1._)`Object.assign(${to}, ${from})`))}),mergeToName:(gen,from,to)=>gen.if((0,codegen_1._)`${to} !== true`,()=>{from===
-!0?gen.assign(to,!0):(gen.assign(to,(0,codegen_1._)`${to} || {}`),setEvaluated(gen,to,from))}),mergeValues:(from,to)=>from===!0?!0:{...from,...to},resultToName:evaluatedPropsToName}),items:makeMergeEvaluated({mergeNames:(gen,from,to)=>gen.if((0,codegen_1._)`${to} !== true && ${from} !== undefined`,()=>gen.assign(to,(0,codegen_1._)`${from} === true ? true : ${to} > ${from} ? ${to} : ${from}`)),mergeToName:(gen,from,to)=>gen.if((0,codegen_1._)`${to} !== true`,()=>gen.assign(to,from===!0?!0:(0,codegen_1.
-_)`${to} > ${from} ? ${to} : ${from}`)),mergeValues:(from,to)=>from===!0?!0:Math.max(from,to),resultToName:(gen,items2)=>gen.var("items",items2)})};function evaluatedPropsToName(gen,ps){if(ps===!0)return gen.var("props",!0);const props=gen.var("props",(0,codegen_1._)`{}`);return ps!==void 0&&setEvaluated(gen,props,ps),props}util.evaluatedPropsToName=evaluatedPropsToName;function setEvaluated(gen,props,ps){Object.keys(ps).forEach(p2=>gen.assign((0,codegen_1._)`${props}${(0,codegen_1.getProperty)(
-p2)}`,!0))}util.setEvaluated=setEvaluated;const snippets={};function useFunc(gen,f){return gen.scopeValue("func",{ref:f,code:snippets[f.code]||(snippets[f.code]=new code_1._Code(f.code))})}util.useFunc=useFunc;var Type;(function(Type2){Type2[Type2.Num=0]="Num",Type2[Type2.Str=1]="Str"})(Type||(util.Type=Type={}));function getErrorPath(dataProp,dataPropType,jsPropertySyntax){if(dataProp instanceof codegen_1.Name){const isNumber2=dataPropType===Type.Num;return jsPropertySyntax?isNumber2?(0,codegen_1.
-_)`"[" + ${dataProp} + "]"`:(0,codegen_1._)`"['" + ${dataProp} + "']"`:isNumber2?(0,codegen_1._)`"/" + ${dataProp}`:(0,codegen_1._)`"/" + ${dataProp}.replace(/~/g, "~0").replace(/\\//g, "~1")`}return jsPropertySyntax?(0,codegen_1.getProperty)(dataProp).toString():"/"+escapeJsonPointer(dataProp)}util.getErrorPath=getErrorPath;function checkStrictMode(it,msg,mode=it.opts.strictSchema){if(mode){if(msg=`strict mode: ${msg}`,mode===!0)throw new Error(msg);it.self.logger.warn(msg)}}return util.checkStrictMode=
-checkStrictMode,util}var names={},hasRequiredNames;function requireNames(){if(hasRequiredNames)return names;hasRequiredNames=1,Object.defineProperty(names,"__esModule",{value:!0});const codegen_1=requireCodegen(),names$12={data:new codegen_1.Name("data"),valCxt:new codegen_1.Name("valCxt"),instancePath:new codegen_1.Name("instancePath"),parentData:new codegen_1.Name("parentData"),parentDataProperty:new codegen_1.Name("parentDataProperty"),rootData:new codegen_1.Name("rootData"),dynamicAnchors:new codegen_1.
-Name("dynamicAnchors"),vErrors:new codegen_1.Name("vErrors"),errors:new codegen_1.Name("errors"),this:new codegen_1.Name("this"),self:new codegen_1.Name("self"),scope:new codegen_1.Name("scope"),json:new codegen_1.Name("json"),jsonPos:new codegen_1.Name("jsonPos"),jsonLen:new codegen_1.Name("jsonLen"),jsonPart:new codegen_1.Name("jsonPart")};return names.default=names$12,names}var hasRequiredErrors;function requireErrors(){return hasRequiredErrors||(hasRequiredErrors=1,function(exports){Object.defineProperty(
-exports,"__esModule",{value:!0}),exports.extendErrors=exports.resetErrorsCount=exports.reportExtraError=exports.reportError=exports.keyword$DataError=exports.keywordError=void 0;const codegen_1=requireCodegen(),util_1=requireUtil(),names_1=requireNames();exports.keywordError={message:({keyword:keyword2})=>(0,codegen_1.str)`must pass "${keyword2}" keyword validation`},exports.keyword$DataError={message:({keyword:keyword2,schemaType})=>schemaType?(0,codegen_1.str)`"${keyword2}" keyword must be ${schemaType} ($data)`:
-(0,codegen_1.str)`"${keyword2}" keyword is invalid ($data)`};function reportError(cxt,error2=exports.keywordError,errorPaths,overrideAllErrors){const{it}=cxt,{gen,compositeRule,allErrors}=it,errObj=errorObjectCode(cxt,error2,errorPaths);overrideAllErrors??(compositeRule||allErrors)?addError(gen,errObj):returnErrors(it,(0,codegen_1._)`[${errObj}]`)}exports.reportError=reportError;function reportExtraError(cxt,error2=exports.keywordError,errorPaths){const{it}=cxt,{gen,compositeRule,allErrors}=it,errObj=errorObjectCode(
-cxt,error2,errorPaths);addError(gen,errObj),compositeRule||allErrors||returnErrors(it,names_1.default.vErrors)}exports.reportExtraError=reportExtraError;function resetErrorsCount(gen,errsCount){gen.assign(names_1.default.errors,errsCount),gen.if((0,codegen_1._)`${names_1.default.vErrors} !== null`,()=>gen.if(errsCount,()=>gen.assign((0,codegen_1._)`${names_1.default.vErrors}.length`,errsCount),()=>gen.assign(names_1.default.vErrors,null)))}exports.resetErrorsCount=resetErrorsCount;function extendErrors({
-gen,keyword:keyword2,schemaValue,data,errsCount,it}){if(errsCount===void 0)throw new Error("ajv implementation error");const err=gen.name("err");gen.forRange("i",errsCount,names_1.default.errors,i=>{gen.const(err,(0,codegen_1._)`${names_1.default.vErrors}[${i}]`),gen.if((0,codegen_1._)`${err}.instancePath === undefined`,()=>gen.assign((0,codegen_1._)`${err}.instancePath`,(0,codegen_1.strConcat)(names_1.default.instancePath,it.errorPath))),gen.assign((0,codegen_1._)`${err}.schemaPath`,(0,codegen_1.
-str)`${it.errSchemaPath}/${keyword2}`),it.opts.verbose&&(gen.assign((0,codegen_1._)`${err}.schema`,schemaValue),gen.assign((0,codegen_1._)`${err}.data`,data))})}exports.extendErrors=extendErrors;function addError(gen,errObj){const err=gen.const("err",errObj);gen.if((0,codegen_1._)`${names_1.default.vErrors} === null`,()=>gen.assign(names_1.default.vErrors,(0,codegen_1._)`[${err}]`),(0,codegen_1._)`${names_1.default.vErrors}.push(${err})`),gen.code((0,codegen_1._)`${names_1.default.errors}++`)}function returnErrors(it,errs){
-const{gen,validateName,schemaEnv}=it;schemaEnv.$async?gen.throw((0,codegen_1._)`new ${it.ValidationError}(${errs})`):(gen.assign((0,codegen_1._)`${validateName}.errors`,errs),gen.return(!1))}const E={keyword:new codegen_1.Name("keyword"),schemaPath:new codegen_1.Name("schemaPath"),params:new codegen_1.Name("params"),propertyName:new codegen_1.Name("propertyName"),message:new codegen_1.Name("message"),schema:new codegen_1.Name("schema"),parentSchema:new codegen_1.Name("parentSchema")};function errorObjectCode(cxt,error2,errorPaths){
-const{createErrors}=cxt.it;return createErrors===!1?(0,codegen_1._)`{}`:errorObject(cxt,error2,errorPaths)}function errorObject(cxt,error2,errorPaths={}){const{gen,it}=cxt,keyValues=[errorInstancePath(it,errorPaths),errorSchemaPath(cxt,errorPaths)];return extraErrorProps(cxt,error2,keyValues),gen.object(...keyValues)}function errorInstancePath({errorPath},{instancePath}){const instPath=instancePath?(0,codegen_1.str)`${errorPath}${(0,util_1.getErrorPath)(instancePath,util_1.Type.Str)}`:errorPath;
-return[names_1.default.instancePath,(0,codegen_1.strConcat)(names_1.default.instancePath,instPath)]}function errorSchemaPath({keyword:keyword2,it:{errSchemaPath}},{schemaPath,parentSchema}){let schPath=parentSchema?errSchemaPath:(0,codegen_1.str)`${errSchemaPath}/${keyword2}`;return schemaPath&&(schPath=(0,codegen_1.str)`${schPath}${(0,util_1.getErrorPath)(schemaPath,util_1.Type.Str)}`),[E.schemaPath,schPath]}function extraErrorProps(cxt,{params,message},keyValues){const{keyword:keyword2,data,schemaValue,
-it}=cxt,{opts,propertyName,topSchemaRef,schemaPath}=it;keyValues.push([E.keyword,keyword2],[E.params,typeof params=="function"?params(cxt):params||(0,codegen_1._)`{}`]),opts.messages&&keyValues.push([E.message,typeof message=="function"?message(cxt):message]),opts.verbose&&keyValues.push([E.schema,schemaValue],[E.parentSchema,(0,codegen_1._)`${topSchemaRef}${schemaPath}`],[names_1.default.data,data]),propertyName&&keyValues.push([E.propertyName,propertyName])}}(errors)),errors}var hasRequiredBoolSchema;
-function requireBoolSchema(){if(hasRequiredBoolSchema)return boolSchema;hasRequiredBoolSchema=1,Object.defineProperty(boolSchema,"__esModule",{value:!0}),boolSchema.boolOrEmptySchema=boolSchema.topBoolOrEmptySchema=void 0;const errors_1=requireErrors(),codegen_1=requireCodegen(),names_1=requireNames(),boolError={message:"boolean schema is false"};function topBoolOrEmptySchema(it){const{gen,schema,validateName}=it;schema===!1?falseSchemaError(it,!1):typeof schema=="object"&&schema.$async===!0?gen.
-return(names_1.default.data):(gen.assign((0,codegen_1._)`${validateName}.errors`,null),gen.return(!0))}boolSchema.topBoolOrEmptySchema=topBoolOrEmptySchema;function boolOrEmptySchema(it,valid2){const{gen,schema}=it;schema===!1?(gen.var(valid2,!1),falseSchemaError(it)):gen.var(valid2,!0)}boolSchema.boolOrEmptySchema=boolOrEmptySchema;function falseSchemaError(it,overrideAllErrors){const{gen,data}=it,cxt={gen,keyword:"false schema",data,schema:!1,schemaCode:!1,schemaValue:!1,params:{},it};(0,errors_1.
-reportError)(cxt,boolError,void 0,overrideAllErrors)}return boolSchema}var dataType={},rules={},hasRequiredRules;function requireRules(){if(hasRequiredRules)return rules;hasRequiredRules=1,Object.defineProperty(rules,"__esModule",{value:!0}),rules.getRules=rules.isJSONType=void 0;const _jsonTypes=["string","number","integer","boolean","null","object","array"],jsonTypes=new Set(_jsonTypes);function isJSONType(x){return typeof x=="string"&&jsonTypes.has(x)}rules.isJSONType=isJSONType;function getRules(){
-const groups={number:{type:"number",rules:[]},string:{type:"string",rules:[]},array:{type:"array",rules:[]},object:{type:"object",rules:[]}};return{types:{...groups,integer:!0,boolean:!0,null:!0},rules:[{rules:[]},groups.number,groups.string,groups.array,groups.object],post:{rules:[]},all:{},keywords:{}}}return rules.getRules=getRules,rules}var applicability={},hasRequiredApplicability;function requireApplicability(){if(hasRequiredApplicability)return applicability;hasRequiredApplicability=1,Object.
-defineProperty(applicability,"__esModule",{value:!0}),applicability.shouldUseRule=applicability.shouldUseGroup=applicability.schemaHasRulesForType=void 0;function schemaHasRulesForType({schema,self:self2},type2){const group=self2.RULES.types[type2];return group&&group!==!0&&shouldUseGroup(schema,group)}applicability.schemaHasRulesForType=schemaHasRulesForType;function shouldUseGroup(schema,group){return group.rules.some(rule=>shouldUseRule(schema,rule))}applicability.shouldUseGroup=shouldUseGroup;
-function shouldUseRule(schema,rule){var _a2;return schema[rule.keyword]!==void 0||((_a2=rule.definition.implements)===null||_a2===void 0?void 0:_a2.some(kwd=>schema[kwd]!==void 0))}return applicability.shouldUseRule=shouldUseRule,applicability}var hasRequiredDataType;function requireDataType(){if(hasRequiredDataType)return dataType;hasRequiredDataType=1,Object.defineProperty(dataType,"__esModule",{value:!0}),dataType.reportTypeError=dataType.checkDataTypes=dataType.checkDataType=dataType.coerceAndCheckDataType=
-dataType.getJSONTypes=dataType.getSchemaTypes=dataType.DataType=void 0;const rules_1=requireRules(),applicability_1=requireApplicability(),errors_1=requireErrors(),codegen_1=requireCodegen(),util_1=requireUtil();var DataType;(function(DataType2){DataType2[DataType2.Correct=0]="Correct",DataType2[DataType2.Wrong=1]="Wrong"})(DataType||(dataType.DataType=DataType={}));function getSchemaTypes(schema){const types2=getJSONTypes(schema.type);if(types2.includes("null")){if(schema.nullable===!1)throw new Error(
-"type: null contradicts nullable: false")}else{if(!types2.length&&schema.nullable!==void 0)throw new Error('"nullable" cannot be used without "type"');schema.nullable===!0&&types2.push("null")}return types2}dataType.getSchemaTypes=getSchemaTypes;function getJSONTypes(ts){const types2=Array.isArray(ts)?ts:ts?[ts]:[];if(types2.every(rules_1.isJSONType))return types2;throw new Error("type must be JSONType or JSONType[]: "+types2.join(","))}dataType.getJSONTypes=getJSONTypes;function coerceAndCheckDataType(it,types2){
-const{gen,data,opts}=it,coerceTo=coerceToTypes(types2,opts.coerceTypes),checkTypes=types2.length>0&&!(coerceTo.length===0&&types2.length===1&&(0,applicability_1.schemaHasRulesForType)(it,types2[0]));if(checkTypes){const wrongType=checkDataTypes(types2,data,opts.strictNumbers,DataType.Wrong);gen.if(wrongType,()=>{coerceTo.length?coerceData(it,types2,coerceTo):reportTypeError(it)})}return checkTypes}dataType.coerceAndCheckDataType=coerceAndCheckDataType;const COERCIBLE=new Set(["string","number","\
-integer","boolean","null"]);function coerceToTypes(types2,coerceTypes){return coerceTypes?types2.filter(t=>COERCIBLE.has(t)||coerceTypes==="array"&&t==="array"):[]}function coerceData(it,types2,coerceTo){const{gen,data,opts}=it,dataType2=gen.let("dataType",(0,codegen_1._)`typeof ${data}`),coerced=gen.let("coerced",(0,codegen_1._)`undefined`);opts.coerceTypes==="array"&&gen.if((0,codegen_1._)`${dataType2} == 'object' && Array.isArray(${data}) && ${data}.length == 1`,()=>gen.assign(data,(0,codegen_1.
-_)`${data}[0]`).assign(dataType2,(0,codegen_1._)`typeof ${data}`).if(checkDataTypes(types2,data,opts.strictNumbers),()=>gen.assign(coerced,data))),gen.if((0,codegen_1._)`${coerced} !== undefined`);for(const t of coerceTo)(COERCIBLE.has(t)||t==="array"&&opts.coerceTypes==="array")&&coerceSpecificType(t);gen.else(),reportTypeError(it),gen.endIf(),gen.if((0,codegen_1._)`${coerced} !== undefined`,()=>{gen.assign(data,coerced),assignParentData(it,coerced)});function coerceSpecificType(t){switch(t){case"\
-string":gen.elseIf((0,codegen_1._)`${dataType2} == "number" || ${dataType2} == "boolean"`).assign(coerced,(0,codegen_1._)`"" + ${data}`).elseIf((0,codegen_1._)`${data} === null`).assign(coerced,(0,codegen_1._)`""`);return;case"number":gen.elseIf((0,codegen_1._)`${dataType2} == "boolean" || ${data} === null
-              || (${dataType2} == "string" && ${data} && ${data} == +${data})`).assign(coerced,(0,codegen_1._)`+${data}`);return;case"integer":gen.elseIf((0,codegen_1._)`${dataType2} === "boolean" || ${data} === null
-              || (${dataType2} === "string" && ${data} && ${data} == +${data} && !(${data} % 1))`).assign(coerced,(0,codegen_1._)`+${data}`);return;case"boolean":gen.elseIf((0,codegen_1._)`${data} === "false" || ${data} === 0 || ${data} === null`).assign(coerced,!1).elseIf((0,codegen_1._)`${data} === "true" || ${data} === 1`).assign(coerced,!0);return;case"null":gen.elseIf((0,codegen_1._)`${data} === "" || ${data} === 0 || ${data} === false`),gen.assign(coerced,null);return;case"array":gen.elseIf(
-(0,codegen_1._)`${dataType2} === "string" || ${dataType2} === "number"
-              || ${dataType2} === "boolean" || ${data} === null`).assign(coerced,(0,codegen_1._)`[${data}]`)}}}function assignParentData({gen,parentData,parentDataProperty},expr){gen.if((0,codegen_1._)`${parentData} !== undefined`,()=>gen.assign((0,codegen_1._)`${parentData}[${parentDataProperty}]`,expr))}function checkDataType(dataType2,data,strictNums,correct=DataType.Correct){const EQ=correct===DataType.Correct?codegen_1.operators.EQ:codegen_1.operators.NEQ;let cond;switch(dataType2){case"nul\
-l":return(0,codegen_1._)`${data} ${EQ} null`;case"array":cond=(0,codegen_1._)`Array.isArray(${data})`;break;case"object":cond=(0,codegen_1._)`${data} && typeof ${data} == "object" && !Array.isArray(${data})`;break;case"integer":cond=numCond((0,codegen_1._)`!(${data} % 1) && !isNaN(${data})`);break;case"number":cond=numCond();break;default:return(0,codegen_1._)`typeof ${data} ${EQ} ${dataType2}`}return correct===DataType.Correct?cond:(0,codegen_1.not)(cond);function numCond(_cond=codegen_1.nil){return(0,codegen_1.
-and)((0,codegen_1._)`typeof ${data} == "number"`,_cond,strictNums?(0,codegen_1._)`isFinite(${data})`:codegen_1.nil)}}dataType.checkDataType=checkDataType;function checkDataTypes(dataTypes,data,strictNums,correct){if(dataTypes.length===1)return checkDataType(dataTypes[0],data,strictNums,correct);let cond;const types2=(0,util_1.toHash)(dataTypes);if(types2.array&&types2.object){const notObj=(0,codegen_1._)`typeof ${data} != "object"`;cond=types2.null?notObj:(0,codegen_1._)`!${data} || ${notObj}`,delete types2.
-null,delete types2.array,delete types2.object}else cond=codegen_1.nil;types2.number&&delete types2.integer;for(const t in types2)cond=(0,codegen_1.and)(cond,checkDataType(t,data,strictNums,correct));return cond}dataType.checkDataTypes=checkDataTypes;const typeError={message:({schema})=>`must be ${schema}`,params:({schema,schemaValue})=>typeof schema=="string"?(0,codegen_1._)`{type: ${schema}}`:(0,codegen_1._)`{type: ${schemaValue}}`};function reportTypeError(it){const cxt=getTypeErrorContext(it);
-(0,errors_1.reportError)(cxt,typeError)}dataType.reportTypeError=reportTypeError;function getTypeErrorContext(it){const{gen,data,schema}=it,schemaCode=(0,util_1.schemaRefOrVal)(it,schema,"type");return{gen,keyword:"type",data,schema:schema.type,schemaCode,schemaValue:schemaCode,parentSchema:schema,params:{},it}}return dataType}var defaults={},hasRequiredDefaults;function requireDefaults(){if(hasRequiredDefaults)return defaults;hasRequiredDefaults=1,Object.defineProperty(defaults,"__esModule",{value:!0}),
-defaults.assignDefaults=void 0;const codegen_1=requireCodegen(),util_1=requireUtil();function assignDefaults(it,ty){const{properties:properties2,items:items2}=it.schema;if(ty==="object"&&properties2)for(const key2 in properties2)assignDefault(it,key2,properties2[key2].default);else ty==="array"&&Array.isArray(items2)&&items2.forEach((sch,i)=>assignDefault(it,i,sch.default))}defaults.assignDefaults=assignDefaults;function assignDefault(it,prop2,defaultValue){const{gen,compositeRule,data,opts}=it;
-if(defaultValue===void 0)return;const childData=(0,codegen_1._)`${data}${(0,codegen_1.getProperty)(prop2)}`;if(compositeRule){(0,util_1.checkStrictMode)(it,`default is ignored for: ${childData}`);return}let condition=(0,codegen_1._)`${childData} === undefined`;opts.useDefaults==="empty"&&(condition=(0,codegen_1._)`${condition} || ${childData} === null || ${childData} === ""`),gen.if(condition,(0,codegen_1._)`${childData} = ${(0,codegen_1.stringify)(defaultValue)}`)}return defaults}var keyword={},
-code$3={},hasRequiredCode;function requireCode(){if(hasRequiredCode)return code$3;hasRequiredCode=1,Object.defineProperty(code$3,"__esModule",{value:!0}),code$3.validateUnion=code$3.validateArray=code$3.usePattern=code$3.callValidateCode=code$3.schemaProperties=code$3.allSchemaProperties=code$3.noPropertyInData=code$3.propertyInData=code$3.isOwnProperty=code$3.hasPropFunc=code$3.reportMissingProp=code$3.checkMissingProp=code$3.checkReportMissingProp=void 0;const codegen_1=requireCodegen(),util_1=requireUtil(),
-names_1=requireNames(),util_2=requireUtil();function checkReportMissingProp(cxt,prop2){const{gen,data,it}=cxt;gen.if(noPropertyInData(gen,data,prop2,it.opts.ownProperties),()=>{cxt.setParams({missingProperty:(0,codegen_1._)`${prop2}`},!0),cxt.error()})}code$3.checkReportMissingProp=checkReportMissingProp;function checkMissingProp({gen,data,it:{opts}},properties2,missing){return(0,codegen_1.or)(...properties2.map(prop2=>(0,codegen_1.and)(noPropertyInData(gen,data,prop2,opts.ownProperties),(0,codegen_1.
-_)`${missing} = ${prop2}`)))}code$3.checkMissingProp=checkMissingProp;function reportMissingProp(cxt,missing){cxt.setParams({missingProperty:missing},!0),cxt.error()}code$3.reportMissingProp=reportMissingProp;function hasPropFunc(gen){return gen.scopeValue("func",{ref:Object.prototype.hasOwnProperty,code:(0,codegen_1._)`Object.prototype.hasOwnProperty`})}code$3.hasPropFunc=hasPropFunc;function isOwnProperty(gen,data,property){return(0,codegen_1._)`${hasPropFunc(gen)}.call(${data}, ${property})`}
-code$3.isOwnProperty=isOwnProperty;function propertyInData(gen,data,property,ownProperties){const cond=(0,codegen_1._)`${data}${(0,codegen_1.getProperty)(property)} !== undefined`;return ownProperties?(0,codegen_1._)`${cond} && ${isOwnProperty(gen,data,property)}`:cond}code$3.propertyInData=propertyInData;function noPropertyInData(gen,data,property,ownProperties){const cond=(0,codegen_1._)`${data}${(0,codegen_1.getProperty)(property)} === undefined`;return ownProperties?(0,codegen_1.or)(cond,(0,codegen_1.
-not)(isOwnProperty(gen,data,property))):cond}code$3.noPropertyInData=noPropertyInData;function allSchemaProperties(schemaMap){return schemaMap?Object.keys(schemaMap).filter(p2=>p2!=="__proto__"):[]}code$3.allSchemaProperties=allSchemaProperties;function schemaProperties(it,schemaMap){return allSchemaProperties(schemaMap).filter(p2=>!(0,util_1.alwaysValidSchema)(it,schemaMap[p2]))}code$3.schemaProperties=schemaProperties;function callValidateCode({schemaCode,data,it:{gen,topSchemaRef,schemaPath,errorPath},
-it},func,context,passSchema){const dataAndSchema=passSchema?(0,codegen_1._)`${schemaCode}, ${data}, ${topSchemaRef}${schemaPath}`:data,valCxt=[[names_1.default.instancePath,(0,codegen_1.strConcat)(names_1.default.instancePath,errorPath)],[names_1.default.parentData,it.parentData],[names_1.default.parentDataProperty,it.parentDataProperty],[names_1.default.rootData,names_1.default.rootData]];it.opts.dynamicRef&&valCxt.push([names_1.default.dynamicAnchors,names_1.default.dynamicAnchors]);const args=(0,codegen_1.
-_)`${dataAndSchema}, ${gen.object(...valCxt)}`;return context!==codegen_1.nil?(0,codegen_1._)`${func}.call(${context}, ${args})`:(0,codegen_1._)`${func}(${args})`}code$3.callValidateCode=callValidateCode;const newRegExp=(0,codegen_1._)`new RegExp`;function usePattern({gen,it:{opts}},pattern2){const u=opts.unicodeRegExp?"u":"",{regExp}=opts.code,rx=regExp(pattern2,u);return gen.scopeValue("pattern",{key:rx.toString(),ref:rx,code:(0,codegen_1._)`${regExp.code==="new RegExp"?newRegExp:(0,util_2.useFunc)(
-gen,regExp)}(${pattern2}, ${u})`})}code$3.usePattern=usePattern;function validateArray(cxt){const{gen,data,keyword:keyword2,it}=cxt,valid2=gen.name("valid");if(it.allErrors){const validArr=gen.let("valid",!0);return validateItems(()=>gen.assign(validArr,!1)),validArr}return gen.var(valid2,!0),validateItems(()=>gen.break()),valid2;function validateItems(notValid){const len=gen.const("len",(0,codegen_1._)`${data}.length`);gen.forRange("i",0,len,i=>{cxt.subschema({keyword:keyword2,dataProp:i,dataPropType:util_1.
-Type.Num},valid2),gen.if((0,codegen_1.not)(valid2),notValid)})}}code$3.validateArray=validateArray;function validateUnion(cxt){const{gen,schema,keyword:keyword2,it}=cxt;if(!Array.isArray(schema))throw new Error("ajv implementation error");if(schema.some(sch=>(0,util_1.alwaysValidSchema)(it,sch))&&!it.opts.unevaluated)return;const valid2=gen.let("valid",!1),schValid=gen.name("_valid");gen.block(()=>schema.forEach((_sch,i)=>{const schCxt=cxt.subschema({keyword:keyword2,schemaProp:i,compositeRule:!0},
-schValid);gen.assign(valid2,(0,codegen_1._)`${valid2} || ${schValid}`),cxt.mergeValidEvaluated(schCxt,schValid)||gen.if((0,codegen_1.not)(valid2))})),cxt.result(valid2,()=>cxt.reset(),()=>cxt.error(!0))}return code$3.validateUnion=validateUnion,code$3}var hasRequiredKeyword;function requireKeyword(){if(hasRequiredKeyword)return keyword;hasRequiredKeyword=1,Object.defineProperty(keyword,"__esModule",{value:!0}),keyword.validateKeywordUsage=keyword.validSchemaType=keyword.funcKeywordCode=keyword.macroKeywordCode=
-void 0;const codegen_1=requireCodegen(),names_1=requireNames(),code_1=requireCode(),errors_1=requireErrors();function macroKeywordCode(cxt,def){const{gen,keyword:keyword2,schema,parentSchema,it}=cxt,macroSchema=def.macro.call(it.self,schema,parentSchema,it),schemaRef=useKeyword(gen,keyword2,macroSchema);it.opts.validateSchema!==!1&&it.self.validateSchema(macroSchema,!0);const valid2=gen.name("valid");cxt.subschema({schema:macroSchema,schemaPath:codegen_1.nil,errSchemaPath:`${it.errSchemaPath}/${keyword2}`,
-topSchemaRef:schemaRef,compositeRule:!0},valid2),cxt.pass(valid2,()=>cxt.error(!0))}keyword.macroKeywordCode=macroKeywordCode;function funcKeywordCode(cxt,def){var _a2;const{gen,keyword:keyword2,schema,parentSchema,$data,it}=cxt;checkAsyncKeyword(it,def);const validate2=!$data&&def.compile?def.compile.call(it.self,schema,parentSchema,it):def.validate,validateRef=useKeyword(gen,keyword2,validate2),valid2=gen.let("valid");cxt.block$data(valid2,validateKeyword),cxt.ok((_a2=def.valid)!==null&&_a2!==
-void 0?_a2:valid2);function validateKeyword(){if(def.errors===!1)assignValid(),def.modifying&&modifyData(cxt),reportErrs(()=>cxt.error());else{const ruleErrs=def.async?validateAsync():validateSync();def.modifying&&modifyData(cxt),reportErrs(()=>addErrs(cxt,ruleErrs))}}function validateAsync(){const ruleErrs=gen.let("ruleErrs",null);return gen.try(()=>assignValid((0,codegen_1._)`await `),e=>gen.assign(valid2,!1).if((0,codegen_1._)`${e} instanceof ${it.ValidationError}`,()=>gen.assign(ruleErrs,(0,codegen_1.
-_)`${e}.errors`),()=>gen.throw(e))),ruleErrs}function validateSync(){const validateErrs=(0,codegen_1._)`${validateRef}.errors`;return gen.assign(validateErrs,null),assignValid(codegen_1.nil),validateErrs}function assignValid(_await=def.async?(0,codegen_1._)`await `:codegen_1.nil){const passCxt=it.opts.passContext?names_1.default.this:names_1.default.self,passSchema=!("compile"in def&&!$data||def.schema===!1);gen.assign(valid2,(0,codegen_1._)`${_await}${(0,code_1.callValidateCode)(cxt,validateRef,
-passCxt,passSchema)}`,def.modifying)}function reportErrs(errors2){var _a3;gen.if((0,codegen_1.not)((_a3=def.valid)!==null&&_a3!==void 0?_a3:valid2),errors2)}}keyword.funcKeywordCode=funcKeywordCode;function modifyData(cxt){const{gen,data,it}=cxt;gen.if(it.parentData,()=>gen.assign(data,(0,codegen_1._)`${it.parentData}[${it.parentDataProperty}]`))}function addErrs(cxt,errs){const{gen}=cxt;gen.if((0,codegen_1._)`Array.isArray(${errs})`,()=>{gen.assign(names_1.default.vErrors,(0,codegen_1._)`${names_1.
-default.vErrors} === null ? ${errs} : ${names_1.default.vErrors}.concat(${errs})`).assign(names_1.default.errors,(0,codegen_1._)`${names_1.default.vErrors}.length`),(0,errors_1.extendErrors)(cxt)},()=>cxt.error())}function checkAsyncKeyword({schemaEnv},def){if(def.async&&!schemaEnv.$async)throw new Error("async keyword in sync schema")}function useKeyword(gen,keyword2,result){if(result===void 0)throw new Error(`keyword "${keyword2}" failed to compile`);return gen.scopeValue("keyword",typeof result==
-"function"?{ref:result}:{ref:result,code:(0,codegen_1.stringify)(result)})}function validSchemaType(schema,schemaType,allowUndefined=!1){return!schemaType.length||schemaType.some(st=>st==="array"?Array.isArray(schema):st==="object"?schema&&typeof schema=="object"&&!Array.isArray(schema):typeof schema==st||allowUndefined&&typeof schema>"u")}keyword.validSchemaType=validSchemaType;function validateKeywordUsage({schema,opts,self:self2,errSchemaPath},def,keyword2){if(Array.isArray(def.keyword)?!def.
-keyword.includes(keyword2):def.keyword!==keyword2)throw new Error("ajv implementation error");const deps=def.dependencies;if(deps?.some(kwd=>!Object.prototype.hasOwnProperty.call(schema,kwd)))throw new Error(`parent schema must have dependencies of ${keyword2}: ${deps.join(",")}`);if(def.validateSchema&&!def.validateSchema(schema[keyword2])){const msg=`keyword "${keyword2}" value is invalid at path "${errSchemaPath}": `+self2.errorsText(def.validateSchema.errors);if(opts.validateSchema==="log")self2.
-logger.error(msg);else throw new Error(msg)}}return keyword.validateKeywordUsage=validateKeywordUsage,keyword}var subschema={},hasRequiredSubschema;function requireSubschema(){if(hasRequiredSubschema)return subschema;hasRequiredSubschema=1,Object.defineProperty(subschema,"__esModule",{value:!0}),subschema.extendSubschemaMode=subschema.extendSubschemaData=subschema.getSubschema=void 0;const codegen_1=requireCodegen(),util_1=requireUtil();function getSubschema(it,{keyword:keyword2,schemaProp,schema,
-schemaPath,errSchemaPath,topSchemaRef}){if(keyword2!==void 0&&schema!==void 0)throw new Error('both "keyword" and "schema" passed, only one allowed');if(keyword2!==void 0){const sch=it.schema[keyword2];return schemaProp===void 0?{schema:sch,schemaPath:(0,codegen_1._)`${it.schemaPath}${(0,codegen_1.getProperty)(keyword2)}`,errSchemaPath:`${it.errSchemaPath}/${keyword2}`}:{schema:sch[schemaProp],schemaPath:(0,codegen_1._)`${it.schemaPath}${(0,codegen_1.getProperty)(keyword2)}${(0,codegen_1.getProperty)(
-schemaProp)}`,errSchemaPath:`${it.errSchemaPath}/${keyword2}/${(0,util_1.escapeFragment)(schemaProp)}`}}if(schema!==void 0){if(schemaPath===void 0||errSchemaPath===void 0||topSchemaRef===void 0)throw new Error('"schemaPath", "errSchemaPath" and "topSchemaRef" are required with "schema"');return{schema,schemaPath,topSchemaRef,errSchemaPath}}throw new Error('either "keyword" or "schema" must be passed')}subschema.getSubschema=getSubschema;function extendSubschemaData(subschema2,it,{dataProp,dataPropType:dpType,
-data,dataTypes,propertyName}){if(data!==void 0&&dataProp!==void 0)throw new Error('both "data" and "dataProp" passed, only one allowed');const{gen}=it;if(dataProp!==void 0){const{errorPath,dataPathArr,opts}=it,nextData=gen.let("data",(0,codegen_1._)`${it.data}${(0,codegen_1.getProperty)(dataProp)}`,!0);dataContextProps(nextData),subschema2.errorPath=(0,codegen_1.str)`${errorPath}${(0,util_1.getErrorPath)(dataProp,dpType,opts.jsPropertySyntax)}`,subschema2.parentDataProperty=(0,codegen_1._)`${dataProp}`,
-subschema2.dataPathArr=[...dataPathArr,subschema2.parentDataProperty]}if(data!==void 0){const nextData=data instanceof codegen_1.Name?data:gen.let("data",data,!0);dataContextProps(nextData),propertyName!==void 0&&(subschema2.propertyName=propertyName)}dataTypes&&(subschema2.dataTypes=dataTypes);function dataContextProps(_nextData){subschema2.data=_nextData,subschema2.dataLevel=it.dataLevel+1,subschema2.dataTypes=[],it.definedProperties=new Set,subschema2.parentData=it.data,subschema2.dataNames=[
-...it.dataNames,_nextData]}}subschema.extendSubschemaData=extendSubschemaData;function extendSubschemaMode(subschema2,{jtdDiscriminator,jtdMetadata,compositeRule,createErrors,allErrors}){compositeRule!==void 0&&(subschema2.compositeRule=compositeRule),createErrors!==void 0&&(subschema2.createErrors=createErrors),allErrors!==void 0&&(subschema2.allErrors=allErrors),subschema2.jtdDiscriminator=jtdDiscriminator,subschema2.jtdMetadata=jtdMetadata}return subschema.extendSubschemaMode=extendSubschemaMode,
-subschema}var resolve={},jsonSchemaTraverse={exports:{}},hasRequiredJsonSchemaTraverse;function requireJsonSchemaTraverse(){if(hasRequiredJsonSchemaTraverse)return jsonSchemaTraverse.exports;hasRequiredJsonSchemaTraverse=1;var traverse=jsonSchemaTraverse.exports=function(schema,opts,cb){typeof opts=="function"&&(cb=opts,opts={}),cb=opts.cb||cb;var pre=typeof cb=="function"?cb:cb.pre||function(){},post=cb.post||function(){};_traverse(opts,pre,post,schema,"",schema)};traverse.keywords={additionalItems:!0,
-items:!0,contains:!0,additionalProperties:!0,propertyNames:!0,not:!0,if:!0,then:!0,else:!0},traverse.arrayKeywords={items:!0,allOf:!0,anyOf:!0,oneOf:!0},traverse.propsKeywords={$defs:!0,definitions:!0,properties:!0,patternProperties:!0,dependencies:!0},traverse.skipKeywords={default:!0,enum:!0,const:!0,required:!0,maximum:!0,minimum:!0,exclusiveMaximum:!0,exclusiveMinimum:!0,multipleOf:!0,maxLength:!0,minLength:!0,pattern:!0,format:!0,maxItems:!0,minItems:!0,uniqueItems:!0,maxProperties:!0,minProperties:!0};
-function _traverse(opts,pre,post,schema,jsonPtr,rootSchema,parentJsonPtr,parentKeyword,parentSchema,keyIndex){if(schema&&typeof schema=="object"&&!Array.isArray(schema)){pre(schema,jsonPtr,rootSchema,parentJsonPtr,parentKeyword,parentSchema,keyIndex);for(var key2 in schema){var sch=schema[key2];if(Array.isArray(sch)){if(key2 in traverse.arrayKeywords)for(var i=0;i<sch.length;i++)_traverse(opts,pre,post,sch[i],jsonPtr+"/"+key2+"/"+i,rootSchema,jsonPtr,key2,schema,i)}else if(key2 in traverse.propsKeywords){
-if(sch&&typeof sch=="object")for(var prop2 in sch)_traverse(opts,pre,post,sch[prop2],jsonPtr+"/"+key2+"/"+escapeJsonPtr(prop2),rootSchema,jsonPtr,key2,schema,prop2)}else(key2 in traverse.keywords||opts.allKeys&&!(key2 in traverse.skipKeywords))&&_traverse(opts,pre,post,sch,jsonPtr+"/"+key2,rootSchema,jsonPtr,key2,schema)}post(schema,jsonPtr,rootSchema,parentJsonPtr,parentKeyword,parentSchema,keyIndex)}}function escapeJsonPtr(str){return str.replace(/~/g,"~0").replace(/\//g,"~1")}return jsonSchemaTraverse.
-exports}var hasRequiredResolve;function requireResolve(){if(hasRequiredResolve)return resolve;hasRequiredResolve=1,Object.defineProperty(resolve,"__esModule",{value:!0}),resolve.getSchemaRefs=resolve.resolveUrl=resolve.normalizeId=resolve._getFullPath=resolve.getFullPath=resolve.inlineRef=void 0;const util_1=requireUtil(),equal2=requireFastDeepEqual(),traverse=requireJsonSchemaTraverse(),SIMPLE_INLINED=new Set(["type","format","pattern","maxLength","minLength","maxProperties","minProperties","ma\
-xItems","minItems","maximum","minimum","uniqueItems","multipleOf","required","enum","const"]);function inlineRef(schema,limit2=!0){return typeof schema=="boolean"?!0:limit2===!0?!hasRef(schema):limit2?countKeys(schema)<=limit2:!1}resolve.inlineRef=inlineRef;const REF_KEYWORDS=new Set(["$ref","$recursiveRef","$recursiveAnchor","$dynamicRef","$dynamicAnchor"]);function hasRef(schema){for(const key2 in schema){if(REF_KEYWORDS.has(key2))return!0;const sch=schema[key2];if(Array.isArray(sch)&&sch.some(
-hasRef)||typeof sch=="object"&&hasRef(sch))return!0}return!1}function countKeys(schema){let count=0;for(const key2 in schema){if(key2==="$ref")return 1/0;if(count++,!SIMPLE_INLINED.has(key2)&&(typeof schema[key2]=="object"&&(0,util_1.eachItem)(schema[key2],sch=>count+=countKeys(sch)),count===1/0))return 1/0}return count}function getFullPath(resolver2,id2="",normalize2){normalize2!==!1&&(id2=normalizeId(id2));const p2=resolver2.parse(id2);return _getFullPath(resolver2,p2)}resolve.getFullPath=getFullPath;
-function _getFullPath(resolver2,p2){return resolver2.serialize(p2).split("#")[0]+"#"}resolve._getFullPath=_getFullPath;const TRAILING_SLASH_HASH=/#\/?$/;function normalizeId(id2){return id2?id2.replace(TRAILING_SLASH_HASH,""):""}resolve.normalizeId=normalizeId;function resolveUrl(resolver2,baseId,id2){return id2=normalizeId(id2),resolver2.resolve(baseId,id2)}resolve.resolveUrl=resolveUrl;const ANCHOR=/^[a-z_][-a-z0-9._]*$/i;function getSchemaRefs(schema,baseId){if(typeof schema=="boolean")return{};
-const{schemaId,uriResolver}=this.opts,schId=normalizeId(schema[schemaId]||baseId),baseIds={"":schId},pathPrefix=getFullPath(uriResolver,schId,!1),localRefs={},schemaRefs=new Set;return traverse(schema,{allKeys:!0},(sch,jsonPtr,_,parentJsonPtr)=>{if(parentJsonPtr===void 0)return;const fullPath=pathPrefix+jsonPtr;let innerBaseId=baseIds[parentJsonPtr];typeof sch[schemaId]=="string"&&(innerBaseId=addRef.call(this,sch[schemaId])),addAnchor.call(this,sch.$anchor),addAnchor.call(this,sch.$dynamicAnchor),
-baseIds[jsonPtr]=innerBaseId;function addRef(ref2){const _resolve=this.opts.uriResolver.resolve;if(ref2=normalizeId(innerBaseId?_resolve(innerBaseId,ref2):ref2),schemaRefs.has(ref2))throw ambiguos(ref2);schemaRefs.add(ref2);let schOrRef=this.refs[ref2];return typeof schOrRef=="string"&&(schOrRef=this.refs[schOrRef]),typeof schOrRef=="object"?checkAmbiguosRef(sch,schOrRef.schema,ref2):ref2!==normalizeId(fullPath)&&(ref2[0]==="#"?(checkAmbiguosRef(sch,localRefs[ref2],ref2),localRefs[ref2]=sch):this.
-refs[ref2]=fullPath),ref2}function addAnchor(anchor){if(typeof anchor=="string"){if(!ANCHOR.test(anchor))throw new Error(`invalid anchor "${anchor}"`);addRef.call(this,`#${anchor}`)}}}),localRefs;function checkAmbiguosRef(sch1,sch2,ref2){if(sch2!==void 0&&!equal2(sch1,sch2))throw ambiguos(ref2)}function ambiguos(ref2){return new Error(`reference "${ref2}" resolves to more than one schema`)}}return resolve.getSchemaRefs=getSchemaRefs,resolve}var hasRequiredValidate;function requireValidate(){if(hasRequiredValidate)
-return validate;hasRequiredValidate=1,Object.defineProperty(validate,"__esModule",{value:!0}),validate.getData=validate.KeywordCxt=validate.validateFunctionCode=void 0;const boolSchema_1=requireBoolSchema(),dataType_1=requireDataType(),applicability_1=requireApplicability(),dataType_2=requireDataType(),defaults_1=requireDefaults(),keyword_1=requireKeyword(),subschema_1=requireSubschema(),codegen_1=requireCodegen(),names_1=requireNames(),resolve_1=requireResolve(),util_1=requireUtil(),errors_1=requireErrors();
-function validateFunctionCode(it){if(isSchemaObj(it)&&(checkKeywords(it),schemaCxtHasRules(it))){topSchemaObjCode(it);return}validateFunction(it,()=>(0,boolSchema_1.topBoolOrEmptySchema)(it))}validate.validateFunctionCode=validateFunctionCode;function validateFunction({gen,validateName,schema,schemaEnv,opts},body2){opts.code.es5?gen.func(validateName,(0,codegen_1._)`${names_1.default.data}, ${names_1.default.valCxt}`,schemaEnv.$async,()=>{gen.code((0,codegen_1._)`"use strict"; ${funcSourceUrl(schema,
-opts)}`),destructureValCxtES5(gen,opts),gen.code(body2)}):gen.func(validateName,(0,codegen_1._)`${names_1.default.data}, ${destructureValCxt(opts)}`,schemaEnv.$async,()=>gen.code(funcSourceUrl(schema,opts)).code(body2))}function destructureValCxt(opts){return(0,codegen_1._)`{${names_1.default.instancePath}="", ${names_1.default.parentData}, ${names_1.default.parentDataProperty}, ${names_1.default.rootData}=${names_1.default.data}${opts.dynamicRef?(0,codegen_1._)`, ${names_1.default.dynamicAnchors}={}`:
-codegen_1.nil}}={}`}function destructureValCxtES5(gen,opts){gen.if(names_1.default.valCxt,()=>{gen.var(names_1.default.instancePath,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.instancePath}`),gen.var(names_1.default.parentData,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.parentData}`),gen.var(names_1.default.parentDataProperty,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.parentDataProperty}`),gen.var(names_1.default.rootData,(0,codegen_1._)`${names_1.
-default.valCxt}.${names_1.default.rootData}`),opts.dynamicRef&&gen.var(names_1.default.dynamicAnchors,(0,codegen_1._)`${names_1.default.valCxt}.${names_1.default.dynamicAnchors}`)},()=>{gen.var(names_1.default.instancePath,(0,codegen_1._)`""`),gen.var(names_1.default.parentData,(0,codegen_1._)`undefined`),gen.var(names_1.default.parentDataProperty,(0,codegen_1._)`undefined`),gen.var(names_1.default.rootData,names_1.default.data),opts.dynamicRef&&gen.var(names_1.default.dynamicAnchors,(0,codegen_1.
-_)`{}`)})}function topSchemaObjCode(it){const{schema,opts,gen}=it;validateFunction(it,()=>{opts.$comment&&schema.$comment&&commentKeyword(it),checkNoDefault(it),gen.let(names_1.default.vErrors,null),gen.let(names_1.default.errors,0),opts.unevaluated&&resetEvaluated(it),typeAndKeywords(it),returnResults(it)})}function resetEvaluated(it){const{gen,validateName}=it;it.evaluated=gen.const("evaluated",(0,codegen_1._)`${validateName}.evaluated`),gen.if((0,codegen_1._)`${it.evaluated}.dynamicProps`,()=>gen.
-assign((0,codegen_1._)`${it.evaluated}.props`,(0,codegen_1._)`undefined`)),gen.if((0,codegen_1._)`${it.evaluated}.dynamicItems`,()=>gen.assign((0,codegen_1._)`${it.evaluated}.items`,(0,codegen_1._)`undefined`))}function funcSourceUrl(schema,opts){const schId=typeof schema=="object"&&schema[opts.schemaId];return schId&&(opts.code.source||opts.code.process)?(0,codegen_1._)`/*# sourceURL=${schId} */`:codegen_1.nil}function subschemaCode(it,valid2){if(isSchemaObj(it)&&(checkKeywords(it),schemaCxtHasRules(
-it))){subSchemaObjCode(it,valid2);return}(0,boolSchema_1.boolOrEmptySchema)(it,valid2)}function schemaCxtHasRules({schema,self:self2}){if(typeof schema=="boolean")return!schema;for(const key2 in schema)if(self2.RULES.all[key2])return!0;return!1}function isSchemaObj(it){return typeof it.schema!="boolean"}function subSchemaObjCode(it,valid2){const{schema,gen,opts}=it;opts.$comment&&schema.$comment&&commentKeyword(it),updateContext(it),checkAsyncSchema(it);const errsCount=gen.const("_errs",names_1.
-default.errors);typeAndKeywords(it,errsCount),gen.var(valid2,(0,codegen_1._)`${errsCount} === ${names_1.default.errors}`)}function checkKeywords(it){(0,util_1.checkUnknownRules)(it),checkRefsAndKeywords(it)}function typeAndKeywords(it,errsCount){if(it.opts.jtd)return schemaKeywords(it,[],!1,errsCount);const types2=(0,dataType_1.getSchemaTypes)(it.schema),checkedTypes=(0,dataType_1.coerceAndCheckDataType)(it,types2);schemaKeywords(it,types2,!checkedTypes,errsCount)}function checkRefsAndKeywords(it){
-const{schema,errSchemaPath,opts,self:self2}=it;schema.$ref&&opts.ignoreKeywordsWithRef&&(0,util_1.schemaHasRulesButRef)(schema,self2.RULES)&&self2.logger.warn(`$ref: keywords ignored in schema at path "${errSchemaPath}"`)}function checkNoDefault(it){const{schema,opts}=it;schema.default!==void 0&&opts.useDefaults&&opts.strictSchema&&(0,util_1.checkStrictMode)(it,"default is ignored in the schema root")}function updateContext(it){const schId=it.schema[it.opts.schemaId];schId&&(it.baseId=(0,resolve_1.
-resolveUrl)(it.opts.uriResolver,it.baseId,schId))}function checkAsyncSchema(it){if(it.schema.$async&&!it.schemaEnv.$async)throw new Error("async schema in sync schema")}function commentKeyword({gen,schemaEnv,schema,errSchemaPath,opts}){const msg=schema.$comment;if(opts.$comment===!0)gen.code((0,codegen_1._)`${names_1.default.self}.logger.log(${msg})`);else if(typeof opts.$comment=="function"){const schemaPath=(0,codegen_1.str)`${errSchemaPath}/$comment`,rootName=gen.scopeValue("root",{ref:schemaEnv.
-root});gen.code((0,codegen_1._)`${names_1.default.self}.opts.$comment(${msg}, ${schemaPath}, ${rootName}.schema)`)}}function returnResults(it){const{gen,schemaEnv,validateName,ValidationError,opts}=it;schemaEnv.$async?gen.if((0,codegen_1._)`${names_1.default.errors} === 0`,()=>gen.return(names_1.default.data),()=>gen.throw((0,codegen_1._)`new ${ValidationError}(${names_1.default.vErrors})`)):(gen.assign((0,codegen_1._)`${validateName}.errors`,names_1.default.vErrors),opts.unevaluated&&assignEvaluated(
-it),gen.return((0,codegen_1._)`${names_1.default.errors} === 0`))}function assignEvaluated({gen,evaluated,props,items:items2}){props instanceof codegen_1.Name&&gen.assign((0,codegen_1._)`${evaluated}.props`,props),items2 instanceof codegen_1.Name&&gen.assign((0,codegen_1._)`${evaluated}.items`,items2)}function schemaKeywords(it,types2,typeErrors,errsCount){const{gen,schema,data,allErrors,opts,self:self2}=it,{RULES}=self2;if(schema.$ref&&(opts.ignoreKeywordsWithRef||!(0,util_1.schemaHasRulesButRef)(
-schema,RULES))){gen.block(()=>keywordCode(it,"$ref",RULES.all.$ref.definition));return}opts.jtd||checkStrictTypes(it,types2),gen.block(()=>{for(const group of RULES.rules)groupKeywords(group);groupKeywords(RULES.post)});function groupKeywords(group){(0,applicability_1.shouldUseGroup)(schema,group)&&(group.type?(gen.if((0,dataType_2.checkDataType)(group.type,data,opts.strictNumbers)),iterateKeywords(it,group),types2.length===1&&types2[0]===group.type&&typeErrors&&(gen.else(),(0,dataType_2.reportTypeError)(
-it)),gen.endIf()):iterateKeywords(it,group),allErrors||gen.if((0,codegen_1._)`${names_1.default.errors} === ${errsCount||0}`))}}function iterateKeywords(it,group){const{gen,schema,opts:{useDefaults}}=it;useDefaults&&(0,defaults_1.assignDefaults)(it,group.type),gen.block(()=>{for(const rule of group.rules)(0,applicability_1.shouldUseRule)(schema,rule)&&keywordCode(it,rule.keyword,rule.definition,group.type)})}function checkStrictTypes(it,types2){it.schemaEnv.meta||!it.opts.strictTypes||(checkContextTypes(
-it,types2),it.opts.allowUnionTypes||checkMultipleTypes(it,types2),checkKeywordTypes(it,it.dataTypes))}function checkContextTypes(it,types2){if(types2.length){if(!it.dataTypes.length){it.dataTypes=types2;return}types2.forEach(t=>{includesType(it.dataTypes,t)||strictTypesError(it,`type "${t}" not allowed by context "${it.dataTypes.join(",")}"`)}),narrowSchemaTypes(it,types2)}}function checkMultipleTypes(it,ts){ts.length>1&&!(ts.length===2&&ts.includes("null"))&&strictTypesError(it,"use allowUnionT\
-ypes to allow union type keyword")}function checkKeywordTypes(it,ts){const rules2=it.self.RULES.all;for(const keyword2 in rules2){const rule=rules2[keyword2];if(typeof rule=="object"&&(0,applicability_1.shouldUseRule)(it.schema,rule)){const{type:type2}=rule.definition;type2.length&&!type2.some(t=>hasApplicableType(ts,t))&&strictTypesError(it,`missing type "${type2.join(",")}" for keyword "${keyword2}"`)}}}function hasApplicableType(schTs,kwdT){return schTs.includes(kwdT)||kwdT==="number"&&schTs.
-includes("integer")}function includesType(ts,t){return ts.includes(t)||t==="integer"&&ts.includes("number")}function narrowSchemaTypes(it,withTypes){const ts=[];for(const t of it.dataTypes)includesType(withTypes,t)?ts.push(t):withTypes.includes("integer")&&t==="number"&&ts.push("integer");it.dataTypes=ts}function strictTypesError(it,msg){const schemaPath=it.schemaEnv.baseId+it.errSchemaPath;msg+=` at "${schemaPath}" (strictTypes)`,(0,util_1.checkStrictMode)(it,msg,it.opts.strictTypes)}class KeywordCxt{constructor(it,def,keyword2){
-if((0,keyword_1.validateKeywordUsage)(it,def,keyword2),this.gen=it.gen,this.allErrors=it.allErrors,this.keyword=keyword2,this.data=it.data,this.schema=it.schema[keyword2],this.$data=def.$data&&it.opts.$data&&this.schema&&this.schema.$data,this.schemaValue=(0,util_1.schemaRefOrVal)(it,this.schema,keyword2,this.$data),this.schemaType=def.schemaType,this.parentSchema=it.schema,this.params={},this.it=it,this.def=def,this.$data)this.schemaCode=it.gen.const("vSchema",getData(this.$data,it));else if(this.
-schemaCode=this.schemaValue,!(0,keyword_1.validSchemaType)(this.schema,def.schemaType,def.allowUndefined))throw new Error(`${keyword2} value must be ${JSON.stringify(def.schemaType)}`);("code"in def?def.trackErrors:def.errors!==!1)&&(this.errsCount=it.gen.const("_errs",names_1.default.errors))}result(condition,successAction,failAction){this.failResult((0,codegen_1.not)(condition),successAction,failAction)}failResult(condition,successAction,failAction){this.gen.if(condition),failAction?failAction():
-this.error(),successAction?(this.gen.else(),successAction(),this.allErrors&&this.gen.endIf()):this.allErrors?this.gen.endIf():this.gen.else()}pass(condition,failAction){this.failResult((0,codegen_1.not)(condition),void 0,failAction)}fail(condition){if(condition===void 0){this.error(),this.allErrors||this.gen.if(!1);return}this.gen.if(condition),this.error(),this.allErrors?this.gen.endIf():this.gen.else()}fail$data(condition){if(!this.$data)return this.fail(condition);const{schemaCode}=this;this.
-fail((0,codegen_1._)`${schemaCode} !== undefined && (${(0,codegen_1.or)(this.invalid$data(),condition)})`)}error(append2,errorParams,errorPaths){if(errorParams){this.setParams(errorParams),this._error(append2,errorPaths),this.setParams({});return}this._error(append2,errorPaths)}_error(append2,errorPaths){(append2?errors_1.reportExtraError:errors_1.reportError)(this,this.def.error,errorPaths)}$dataError(){(0,errors_1.reportError)(this,this.def.$dataError||errors_1.keyword$DataError)}reset(){if(this.
-errsCount===void 0)throw new Error('add "trackErrors" to keyword definition');(0,errors_1.resetErrorsCount)(this.gen,this.errsCount)}ok(cond){this.allErrors||this.gen.if(cond)}setParams(obj,assign){assign?Object.assign(this.params,obj):this.params=obj}block$data(valid2,codeBlock,$dataValid=codegen_1.nil){this.gen.block(()=>{this.check$data(valid2,$dataValid),codeBlock()})}check$data(valid2=codegen_1.nil,$dataValid=codegen_1.nil){if(!this.$data)return;const{gen,schemaCode,schemaType,def}=this;gen.
-if((0,codegen_1.or)((0,codegen_1._)`${schemaCode} === undefined`,$dataValid)),valid2!==codegen_1.nil&&gen.assign(valid2,!0),(schemaType.length||def.validateSchema)&&(gen.elseIf(this.invalid$data()),this.$dataError(),valid2!==codegen_1.nil&&gen.assign(valid2,!1)),gen.else()}invalid$data(){const{gen,schemaCode,schemaType,def,it}=this;return(0,codegen_1.or)(wrong$DataType(),invalid$DataSchema());function wrong$DataType(){if(schemaType.length){if(!(schemaCode instanceof codegen_1.Name))throw new Error(
-"ajv implementation error");const st=Array.isArray(schemaType)?schemaType:[schemaType];return(0,codegen_1._)`${(0,dataType_2.checkDataTypes)(st,schemaCode,it.opts.strictNumbers,dataType_2.DataType.Wrong)}`}return codegen_1.nil}function invalid$DataSchema(){if(def.validateSchema){const validateSchemaRef=gen.scopeValue("validate$data",{ref:def.validateSchema});return(0,codegen_1._)`!${validateSchemaRef}(${schemaCode})`}return codegen_1.nil}}subschema(appl,valid2){const subschema2=(0,subschema_1.getSubschema)(
-this.it,appl);(0,subschema_1.extendSubschemaData)(subschema2,this.it,appl),(0,subschema_1.extendSubschemaMode)(subschema2,appl);const nextContext={...this.it,...subschema2,items:void 0,props:void 0};return subschemaCode(nextContext,valid2),nextContext}mergeEvaluated(schemaCxt,toName){const{it,gen}=this;it.opts.unevaluated&&(it.props!==!0&&schemaCxt.props!==void 0&&(it.props=util_1.mergeEvaluated.props(gen,schemaCxt.props,it.props,toName)),it.items!==!0&&schemaCxt.items!==void 0&&(it.items=util_1.
-mergeEvaluated.items(gen,schemaCxt.items,it.items,toName)))}mergeValidEvaluated(schemaCxt,valid2){const{it,gen}=this;if(it.opts.unevaluated&&(it.props!==!0||it.items!==!0))return gen.if(valid2,()=>this.mergeEvaluated(schemaCxt,codegen_1.Name)),!0}}validate.KeywordCxt=KeywordCxt;function keywordCode(it,keyword2,def,ruleType){const cxt=new KeywordCxt(it,def,keyword2);"code"in def?def.code(cxt,ruleType):cxt.$data&&def.validate?(0,keyword_1.funcKeywordCode)(cxt,def):"macro"in def?(0,keyword_1.macroKeywordCode)(
-cxt,def):(def.compile||def.validate)&&(0,keyword_1.funcKeywordCode)(cxt,def)}const JSON_POINTER=/^\/(?:[^~]|~0|~1)*$/,RELATIVE_JSON_POINTER=/^([0-9]+)(#|\/(?:[^~]|~0|~1)*)?$/;function getData($data,{dataLevel,dataNames,dataPathArr}){let jsonPointer,data;if($data==="")return names_1.default.rootData;if($data[0]==="/"){if(!JSON_POINTER.test($data))throw new Error(`Invalid JSON-pointer: ${$data}`);jsonPointer=$data,data=names_1.default.rootData}else{const matches2=RELATIVE_JSON_POINTER.exec($data);
-if(!matches2)throw new Error(`Invalid JSON-pointer: ${$data}`);const up=+matches2[1];if(jsonPointer=matches2[2],jsonPointer==="#"){if(up>=dataLevel)throw new Error(errorMsg("property/index",up));return dataPathArr[dataLevel-up]}if(up>dataLevel)throw new Error(errorMsg("data",up));if(data=dataNames[dataLevel-up],!jsonPointer)return data}let expr=data;const segments=jsonPointer.split("/");for(const segment of segments)segment&&(data=(0,codegen_1._)`${data}${(0,codegen_1.getProperty)((0,util_1.unescapeJsonPointer)(
-segment))}`,expr=(0,codegen_1._)`${expr} && ${data}`);return expr;function errorMsg(pointerType,up){return`Cannot access ${pointerType} ${up} levels up, current level is ${dataLevel}`}}return validate.getData=getData,validate}var validation_error={},hasRequiredValidation_error;function requireValidation_error(){if(hasRequiredValidation_error)return validation_error;hasRequiredValidation_error=1,Object.defineProperty(validation_error,"__esModule",{value:!0});class ValidationError extends Error{constructor(errors2){
-super("validation failed"),this.errors=errors2,this.ajv=this.validation=!0}}return validation_error.default=ValidationError,validation_error}var ref_error={},hasRequiredRef_error;function requireRef_error(){if(hasRequiredRef_error)return ref_error;hasRequiredRef_error=1,Object.defineProperty(ref_error,"__esModule",{value:!0});const resolve_1=requireResolve();class MissingRefError extends Error{constructor(resolver2,baseId,ref2,msg){super(msg||`can't resolve reference ${ref2} from id ${baseId}`),
-this.missingRef=(0,resolve_1.resolveUrl)(resolver2,baseId,ref2),this.missingSchema=(0,resolve_1.normalizeId)((0,resolve_1.getFullPath)(resolver2,this.missingRef))}}return ref_error.default=MissingRefError,ref_error}var compile={},hasRequiredCompile;function requireCompile(){if(hasRequiredCompile)return compile;hasRequiredCompile=1,Object.defineProperty(compile,"__esModule",{value:!0}),compile.resolveSchema=compile.getCompilingSchema=compile.resolveRef=compile.compileSchema=compile.SchemaEnv=void 0;
-const codegen_1=requireCodegen(),validation_error_1=requireValidation_error(),names_1=requireNames(),resolve_1=requireResolve(),util_1=requireUtil(),validate_1=requireValidate();class SchemaEnv{constructor(env2){var _a2;this.refs={},this.dynamicAnchors={};let schema;typeof env2.schema=="object"&&(schema=env2.schema),this.schema=env2.schema,this.schemaId=env2.schemaId,this.root=env2.root||this,this.baseId=(_a2=env2.baseId)!==null&&_a2!==void 0?_a2:(0,resolve_1.normalizeId)(schema?.[env2.schemaId||
-"$id"]),this.schemaPath=env2.schemaPath,this.localRefs=env2.localRefs,this.meta=env2.meta,this.$async=schema?.$async,this.refs={}}}compile.SchemaEnv=SchemaEnv;function compileSchema(sch){const _sch=getCompilingSchema.call(this,sch);if(_sch)return _sch;const rootId=(0,resolve_1.getFullPath)(this.opts.uriResolver,sch.root.baseId),{es5,lines}=this.opts.code,{ownProperties}=this.opts,gen=new codegen_1.CodeGen(this.scope,{es5,lines,ownProperties});let _ValidationError;sch.$async&&(_ValidationError=gen.
-scopeValue("Error",{ref:validation_error_1.default,code:(0,codegen_1._)`require("ajv/dist/runtime/validation_error").default`}));const validateName=gen.scopeName("validate");sch.validateName=validateName;const schemaCxt={gen,allErrors:this.opts.allErrors,data:names_1.default.data,parentData:names_1.default.parentData,parentDataProperty:names_1.default.parentDataProperty,dataNames:[names_1.default.data],dataPathArr:[codegen_1.nil],dataLevel:0,dataTypes:[],definedProperties:new Set,topSchemaRef:gen.
-scopeValue("schema",this.opts.code.source===!0?{ref:sch.schema,code:(0,codegen_1.stringify)(sch.schema)}:{ref:sch.schema}),validateName,ValidationError:_ValidationError,schema:sch.schema,schemaEnv:sch,rootId,baseId:sch.baseId||rootId,schemaPath:codegen_1.nil,errSchemaPath:sch.schemaPath||(this.opts.jtd?"":"#"),errorPath:(0,codegen_1._)`""`,opts:this.opts,self:this};let sourceCode;try{this._compilations.add(sch),(0,validate_1.validateFunctionCode)(schemaCxt),gen.optimize(this.opts.code.optimize);
-const validateCode=gen.toString();sourceCode=`${gen.scopeRefs(names_1.default.scope)}return ${validateCode}`,this.opts.code.process&&(sourceCode=this.opts.code.process(sourceCode,sch));const validate2=new Function(`${names_1.default.self}`,`${names_1.default.scope}`,sourceCode)(this,this.scope.get());if(this.scope.value(validateName,{ref:validate2}),validate2.errors=null,validate2.schema=sch.schema,validate2.schemaEnv=sch,sch.$async&&(validate2.$async=!0),this.opts.code.source===!0&&(validate2.source=
-{validateName,validateCode,scopeValues:gen._values}),this.opts.unevaluated){const{props,items:items2}=schemaCxt;validate2.evaluated={props:props instanceof codegen_1.Name?void 0:props,items:items2 instanceof codegen_1.Name?void 0:items2,dynamicProps:props instanceof codegen_1.Name,dynamicItems:items2 instanceof codegen_1.Name},validate2.source&&(validate2.source.evaluated=(0,codegen_1.stringify)(validate2.evaluated))}return sch.validate=validate2,sch}catch(e){throw delete sch.validate,delete sch.
-validateName,sourceCode&&this.logger.error("Error compiling schema, function code:",sourceCode),e}finally{this._compilations.delete(sch)}}compile.compileSchema=compileSchema;function resolveRef(root2,baseId,ref2){var _a2;ref2=(0,resolve_1.resolveUrl)(this.opts.uriResolver,baseId,ref2);const schOrFunc=root2.refs[ref2];if(schOrFunc)return schOrFunc;let _sch=resolve2.call(this,root2,ref2);if(_sch===void 0){const schema=(_a2=root2.localRefs)===null||_a2===void 0?void 0:_a2[ref2],{schemaId}=this.opts;
-schema&&(_sch=new SchemaEnv({schema,schemaId,root:root2,baseId}))}if(_sch!==void 0)return root2.refs[ref2]=inlineOrCompile.call(this,_sch)}compile.resolveRef=resolveRef;function inlineOrCompile(sch){return(0,resolve_1.inlineRef)(sch.schema,this.opts.inlineRefs)?sch.schema:sch.validate?sch:compileSchema.call(this,sch)}function getCompilingSchema(schEnv){for(const sch of this._compilations)if(sameSchemaEnv(sch,schEnv))return sch}compile.getCompilingSchema=getCompilingSchema;function sameSchemaEnv(s1,s2){
-return s1.schema===s2.schema&&s1.root===s2.root&&s1.baseId===s2.baseId}function resolve2(root2,ref2){let sch;for(;typeof(sch=this.refs[ref2])=="string";)ref2=sch;return sch||this.schemas[ref2]||resolveSchema.call(this,root2,ref2)}function resolveSchema(root2,ref2){const p2=this.opts.uriResolver.parse(ref2),refPath=(0,resolve_1._getFullPath)(this.opts.uriResolver,p2);let baseId=(0,resolve_1.getFullPath)(this.opts.uriResolver,root2.baseId,void 0);if(Object.keys(root2.schema).length>0&&refPath===baseId)
-return getJsonPointer.call(this,p2,root2);const id2=(0,resolve_1.normalizeId)(refPath),schOrRef=this.refs[id2]||this.schemas[id2];if(typeof schOrRef=="string"){const sch=resolveSchema.call(this,root2,schOrRef);return typeof sch?.schema!="object"?void 0:getJsonPointer.call(this,p2,sch)}if(typeof schOrRef?.schema=="object"){if(schOrRef.validate||compileSchema.call(this,schOrRef),id2===(0,resolve_1.normalizeId)(ref2)){const{schema}=schOrRef,{schemaId}=this.opts,schId=schema[schemaId];return schId&&
-(baseId=(0,resolve_1.resolveUrl)(this.opts.uriResolver,baseId,schId)),new SchemaEnv({schema,schemaId,root:root2,baseId})}return getJsonPointer.call(this,p2,schOrRef)}}compile.resolveSchema=resolveSchema;const PREVENT_SCOPE_CHANGE=new Set(["properties","patternProperties","enum","dependencies","definitions"]);function getJsonPointer(parsedRef,{baseId,schema,root:root2}){var _a2;if(((_a2=parsedRef.fragment)===null||_a2===void 0?void 0:_a2[0])!=="/")return;for(const part of parsedRef.fragment.slice(
-1).split("/")){if(typeof schema=="boolean")return;const partSchema=schema[(0,util_1.unescapeFragment)(part)];if(partSchema===void 0)return;schema=partSchema;const schId=typeof schema=="object"&&schema[this.opts.schemaId];!PREVENT_SCOPE_CHANGE.has(part)&&schId&&(baseId=(0,resolve_1.resolveUrl)(this.opts.uriResolver,baseId,schId))}let env2;if(typeof schema!="boolean"&&schema.$ref&&!(0,util_1.schemaHasRulesButRef)(schema,this.RULES)){const $ref=(0,resolve_1.resolveUrl)(this.opts.uriResolver,baseId,
-schema.$ref);env2=resolveSchema.call(this,root2,$ref)}const{schemaId}=this.opts;if(env2=env2||new SchemaEnv({schema,schemaId,root:root2,baseId}),env2.schema!==env2.root.schema)return env2}return compile}const $id$1="https://raw.githubusercontent.com/ajv-validator/ajv/master/lib/refs/data.json#",description="Meta-schema for $data reference (JSON AnySchema extension proposal)",type$1="object",required$1=["$data"],properties$3={$data:{type:"string",anyOf:[{format:"relative-json-pointer"},{format:"j\
-son-pointer"}]}},additionalProperties$1=!1,require$$9={$id:$id$1,description,type:type$1,required:required$1,properties:properties$3,additionalProperties:additionalProperties$1};var uri={},hasRequiredUri;function requireUri(){if(hasRequiredUri)return uri;hasRequiredUri=1,Object.defineProperty(uri,"__esModule",{value:!0});const uri$12=requireFastUri();return uri$12.code='require("ajv/dist/runtime/uri").default',uri.default=uri$12,uri}var hasRequiredCore$1;function requireCore$1(){return hasRequiredCore$1||
-(hasRequiredCore$1=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.CodeGen=exports.Name=exports.nil=exports.stringify=exports.str=exports._=exports.KeywordCxt=void 0;var validate_1=requireValidate();Object.defineProperty(exports,"KeywordCxt",{enumerable:!0,get:function(){return validate_1.KeywordCxt}});var codegen_1=requireCodegen();Object.defineProperty(exports,"_",{enumerable:!0,get:function(){return codegen_1._}}),Object.defineProperty(exports,"str",{enumerable:!0,
-get:function(){return codegen_1.str}}),Object.defineProperty(exports,"stringify",{enumerable:!0,get:function(){return codegen_1.stringify}}),Object.defineProperty(exports,"nil",{enumerable:!0,get:function(){return codegen_1.nil}}),Object.defineProperty(exports,"Name",{enumerable:!0,get:function(){return codegen_1.Name}}),Object.defineProperty(exports,"CodeGen",{enumerable:!0,get:function(){return codegen_1.CodeGen}});const validation_error_1=requireValidation_error(),ref_error_1=requireRef_error(),
-rules_1=requireRules(),compile_1=requireCompile(),codegen_2=requireCodegen(),resolve_1=requireResolve(),dataType_1=requireDataType(),util_1=requireUtil(),$dataRefSchema=require$$9,uri_1=requireUri(),defaultRegExp=(str,flags2)=>new RegExp(str,flags2);defaultRegExp.code="new RegExp";const META_IGNORE_OPTIONS=["removeAdditional","useDefaults","coerceTypes"],EXT_SCOPE_NAMES=new Set(["validate","serialize","parse","wrapper","root","schema","keyword","pattern","formats","validate$data","func","obj","E\
-rror"]),removedOptions={errorDataPath:"",format:"`validateFormats: false` can be used instead.",nullable:'"nullable" keyword is supported by default.',jsonPointers:"Deprecated jsPropertySyntax can be used instead.",extendRefs:"Deprecated ignoreKeywordsWithRef can be used instead.",missingRefs:"Pass empty schema with $id that should be ignored to ajv.addSchema.",processCode:"Use option `code: {process: (code, schemaEnv: object) => string}`",sourceCode:"Use option `code: {source: true}`",strictDefaults:"\
-It is default now, see option `strict`.",strictKeywords:"It is default now, see option `strict`.",uniqueItems:'"uniqueItems" keyword is always validated.',unknownFormats:"Disable strict mode or pass `true` to `ajv.addFormat` (or `formats` option).",cache:"Map is used as cache, schema object as key.",serialize:"Map is used as cache, schema object as key.",ajvErrors:"It is default now."},deprecatedOptions={ignoreKeywordsWithRef:"",jsPropertySyntax:"",unicode:'"minLength"/"maxLength" account for un\
-icode characters by default.'},MAX_EXPRESSION=200;function requiredOptions(o){var _a2,_b,_c,_d,_e,_f,_g,_h,_j,_k,_l,_m,_o,_p,_q,_r,_s,_t,_u,_v,_w,_x,_y,_z,_02;const s2=o.strict,_optz=(_a2=o.code)===null||_a2===void 0?void 0:_a2.optimize,optimize=_optz===!0||_optz===void 0?1:_optz||0,regExp=(_c=(_b=o.code)===null||_b===void 0?void 0:_b.regExp)!==null&&_c!==void 0?_c:defaultRegExp,uriResolver=(_d=o.uriResolver)!==null&&_d!==void 0?_d:uri_1.default;return{strictSchema:(_f=(_e=o.strictSchema)!==null&&
-_e!==void 0?_e:s2)!==null&&_f!==void 0?_f:!0,strictNumbers:(_h=(_g=o.strictNumbers)!==null&&_g!==void 0?_g:s2)!==null&&_h!==void 0?_h:!0,strictTypes:(_k=(_j=o.strictTypes)!==null&&_j!==void 0?_j:s2)!==null&&_k!==void 0?_k:"log",strictTuples:(_m=(_l=o.strictTuples)!==null&&_l!==void 0?_l:s2)!==null&&_m!==void 0?_m:"log",strictRequired:(_p=(_o=o.strictRequired)!==null&&_o!==void 0?_o:s2)!==null&&_p!==void 0?_p:!1,code:o.code?{...o.code,optimize,regExp}:{optimize,regExp},loopRequired:(_q=o.loopRequired)!==
-null&&_q!==void 0?_q:MAX_EXPRESSION,loopEnum:(_r=o.loopEnum)!==null&&_r!==void 0?_r:MAX_EXPRESSION,meta:(_s=o.meta)!==null&&_s!==void 0?_s:!0,messages:(_t=o.messages)!==null&&_t!==void 0?_t:!0,inlineRefs:(_u=o.inlineRefs)!==null&&_u!==void 0?_u:!0,schemaId:(_v=o.schemaId)!==null&&_v!==void 0?_v:"$id",addUsedSchema:(_w=o.addUsedSchema)!==null&&_w!==void 0?_w:!0,validateSchema:(_x=o.validateSchema)!==null&&_x!==void 0?_x:!0,validateFormats:(_y=o.validateFormats)!==null&&_y!==void 0?_y:!0,unicodeRegExp:(_z=
-o.unicodeRegExp)!==null&&_z!==void 0?_z:!0,int32range:(_02=o.int32range)!==null&&_02!==void 0?_02:!0,uriResolver}}class Ajv2{constructor(opts={}){this.schemas={},this.refs={},this.formats={},this._compilations=new Set,this._loading={},this._cache=new Map,opts=this.opts={...opts,...requiredOptions(opts)};const{es5,lines}=this.opts.code;this.scope=new codegen_2.ValueScope({scope:{},prefixes:EXT_SCOPE_NAMES,es5,lines}),this.logger=getLogger(opts.logger);const formatOpt=opts.validateFormats;opts.validateFormats=
-!1,this.RULES=(0,rules_1.getRules)(),checkOptions.call(this,removedOptions,opts,"NOT SUPPORTED"),checkOptions.call(this,deprecatedOptions,opts,"DEPRECATED","warn"),this._metaOpts=getMetaSchemaOptions.call(this),opts.formats&&addInitialFormats.call(this),this._addVocabularies(),this._addDefaultMetaSchema(),opts.keywords&&addInitialKeywords.call(this,opts.keywords),typeof opts.meta=="object"&&this.addMetaSchema(opts.meta),addInitialSchemas.call(this),opts.validateFormats=formatOpt}_addVocabularies(){
-this.addKeyword("$async")}_addDefaultMetaSchema(){const{$data,meta,schemaId}=this.opts;let _dataRefSchema=$dataRefSchema;schemaId==="id"&&(_dataRefSchema={...$dataRefSchema},_dataRefSchema.id=_dataRefSchema.$id,delete _dataRefSchema.$id),meta&&$data&&this.addMetaSchema(_dataRefSchema,_dataRefSchema[schemaId],!1)}defaultMeta(){const{meta,schemaId}=this.opts;return this.opts.defaultMeta=typeof meta=="object"?meta[schemaId]||meta:void 0}validate(schemaKeyRef,data){let v;if(typeof schemaKeyRef=="str\
-ing"){if(v=this.getSchema(schemaKeyRef),!v)throw new Error(`no schema with key or ref "${schemaKeyRef}"`)}else v=this.compile(schemaKeyRef);const valid2=v(data);return"$async"in v||(this.errors=v.errors),valid2}compile(schema,_meta){const sch=this._addSchema(schema,_meta);return sch.validate||this._compileSchemaEnv(sch)}compileAsync(schema,meta){if(typeof this.opts.loadSchema!="function")throw new Error("options.loadSchema should be a function");const{loadSchema}=this.opts;return runCompileAsync.
-call(this,schema,meta);async function runCompileAsync(_schema,_meta){await loadMetaSchema.call(this,_schema.$schema);const sch=this._addSchema(_schema,_meta);return sch.validate||_compileAsync.call(this,sch)}async function loadMetaSchema($ref){$ref&&!this.getSchema($ref)&&await runCompileAsync.call(this,{$ref},!0)}async function _compileAsync(sch){try{return this._compileSchemaEnv(sch)}catch(e){if(!(e instanceof ref_error_1.default))throw e;return checkLoaded.call(this,e),await loadMissingSchema.
-call(this,e.missingSchema),_compileAsync.call(this,sch)}}function checkLoaded({missingSchema:ref2,missingRef}){if(this.refs[ref2])throw new Error(`AnySchema ${ref2} is loaded but ${missingRef} cannot be resolved`)}async function loadMissingSchema(ref2){const _schema=await _loadSchema.call(this,ref2);this.refs[ref2]||await loadMetaSchema.call(this,_schema.$schema),this.refs[ref2]||this.addSchema(_schema,ref2,meta)}async function _loadSchema(ref2){const p2=this._loading[ref2];if(p2)return p2;try{return await(this.
-_loading[ref2]=loadSchema(ref2))}finally{delete this._loading[ref2]}}}addSchema(schema,key2,_meta,_validateSchema=this.opts.validateSchema){if(Array.isArray(schema)){for(const sch of schema)this.addSchema(sch,void 0,_meta,_validateSchema);return this}let id2;if(typeof schema=="object"){const{schemaId}=this.opts;if(id2=schema[schemaId],id2!==void 0&&typeof id2!="string")throw new Error(`schema ${schemaId} must be string`)}return key2=(0,resolve_1.normalizeId)(key2||id2),this._checkUnique(key2),this.
-schemas[key2]=this._addSchema(schema,_meta,key2,_validateSchema,!0),this}addMetaSchema(schema,key2,_validateSchema=this.opts.validateSchema){return this.addSchema(schema,key2,!0,_validateSchema),this}validateSchema(schema,throwOrLogError){if(typeof schema=="boolean")return!0;let $schema2;if($schema2=schema.$schema,$schema2!==void 0&&typeof $schema2!="string")throw new Error("$schema must be a string");if($schema2=$schema2||this.opts.defaultMeta||this.defaultMeta(),!$schema2)return this.logger.warn(
-"meta-schema not available"),this.errors=null,!0;const valid2=this.validate($schema2,schema);if(!valid2&&throwOrLogError){const message="schema is invalid: "+this.errorsText();if(this.opts.validateSchema==="log")this.logger.error(message);else throw new Error(message)}return valid2}getSchema(keyRef){let sch;for(;typeof(sch=getSchEnv.call(this,keyRef))=="string";)keyRef=sch;if(sch===void 0){const{schemaId}=this.opts,root2=new compile_1.SchemaEnv({schema:{},schemaId});if(sch=compile_1.resolveSchema.
-call(this,root2,keyRef),!sch)return;this.refs[keyRef]=sch}return sch.validate||this._compileSchemaEnv(sch)}removeSchema(schemaKeyRef){if(schemaKeyRef instanceof RegExp)return this._removeAllSchemas(this.schemas,schemaKeyRef),this._removeAllSchemas(this.refs,schemaKeyRef),this;switch(typeof schemaKeyRef){case"undefined":return this._removeAllSchemas(this.schemas),this._removeAllSchemas(this.refs),this._cache.clear(),this;case"string":{const sch=getSchEnv.call(this,schemaKeyRef);return typeof sch==
-"object"&&this._cache.delete(sch.schema),delete this.schemas[schemaKeyRef],delete this.refs[schemaKeyRef],this}case"object":{const cacheKey=schemaKeyRef;this._cache.delete(cacheKey);let id2=schemaKeyRef[this.opts.schemaId];return id2&&(id2=(0,resolve_1.normalizeId)(id2),delete this.schemas[id2],delete this.refs[id2]),this}default:throw new Error("ajv.removeSchema: invalid parameter")}}addVocabulary(definitions2){for(const def of definitions2)this.addKeyword(def);return this}addKeyword(kwdOrDef,def){
-let keyword2;if(typeof kwdOrDef=="string")keyword2=kwdOrDef,typeof def=="object"&&(this.logger.warn("these parameters are deprecated, see docs for addKeyword"),def.keyword=keyword2);else if(typeof kwdOrDef=="object"&&def===void 0){if(def=kwdOrDef,keyword2=def.keyword,Array.isArray(keyword2)&&!keyword2.length)throw new Error("addKeywords: keyword must be string or non-empty array")}else throw new Error("invalid addKeywords parameters");if(checkKeyword.call(this,keyword2,def),!def)return(0,util_1.
-eachItem)(keyword2,kwd=>addRule.call(this,kwd)),this;keywordMetaschema.call(this,def);const definition2={...def,type:(0,dataType_1.getJSONTypes)(def.type),schemaType:(0,dataType_1.getJSONTypes)(def.schemaType)};return(0,util_1.eachItem)(keyword2,definition2.type.length===0?k=>addRule.call(this,k,definition2):k=>definition2.type.forEach(t=>addRule.call(this,k,definition2,t))),this}getKeyword(keyword2){const rule=this.RULES.all[keyword2];return typeof rule=="object"?rule.definition:!!rule}removeKeyword(keyword2){
-const{RULES}=this;delete RULES.keywords[keyword2],delete RULES.all[keyword2];for(const group of RULES.rules){const i=group.rules.findIndex(rule=>rule.keyword===keyword2);i>=0&&group.rules.splice(i,1)}return this}addFormat(name,format2){return typeof format2=="string"&&(format2=new RegExp(format2)),this.formats[name]=format2,this}errorsText(errors2=this.errors,{separator=", ",dataVar="data"}={}){return!errors2||errors2.length===0?"No errors":errors2.map(e=>`${dataVar}${e.instancePath} ${e.message}`).
-reduce((text2,msg)=>text2+separator+msg)}$dataMetaSchema(metaSchema,keywordsJsonPointers){const rules2=this.RULES.all;metaSchema=JSON.parse(JSON.stringify(metaSchema));for(const jsonPointer of keywordsJsonPointers){const segments=jsonPointer.split("/").slice(1);let keywords2=metaSchema;for(const seg of segments)keywords2=keywords2[seg];for(const key2 in rules2){const rule=rules2[key2];if(typeof rule!="object")continue;const{$data}=rule.definition,schema=keywords2[key2];$data&&schema&&(keywords2[key2]=
-schemaOrData(schema))}}return metaSchema}_removeAllSchemas(schemas,regex){for(const keyRef in schemas){const sch=schemas[keyRef];(!regex||regex.test(keyRef))&&(typeof sch=="string"?delete schemas[keyRef]:sch&&!sch.meta&&(this._cache.delete(sch.schema),delete schemas[keyRef]))}}_addSchema(schema,meta,baseId,validateSchema=this.opts.validateSchema,addSchema=this.opts.addUsedSchema){let id2;const{schemaId}=this.opts;if(typeof schema=="object")id2=schema[schemaId];else{if(this.opts.jtd)throw new Error(
-"schema must be object");if(typeof schema!="boolean")throw new Error("schema must be object or boolean")}let sch=this._cache.get(schema);if(sch!==void 0)return sch;baseId=(0,resolve_1.normalizeId)(id2||baseId);const localRefs=resolve_1.getSchemaRefs.call(this,schema,baseId);return sch=new compile_1.SchemaEnv({schema,schemaId,meta,baseId,localRefs}),this._cache.set(sch.schema,sch),addSchema&&!baseId.startsWith("#")&&(baseId&&this._checkUnique(baseId),this.refs[baseId]=sch),validateSchema&&this.validateSchema(
-schema,!0),sch}_checkUnique(id2){if(this.schemas[id2]||this.refs[id2])throw new Error(`schema with key or id "${id2}" already exists`)}_compileSchemaEnv(sch){if(sch.meta?this._compileMetaSchema(sch):compile_1.compileSchema.call(this,sch),!sch.validate)throw new Error("ajv implementation error");return sch.validate}_compileMetaSchema(sch){const currentOpts=this.opts;this.opts=this._metaOpts;try{compile_1.compileSchema.call(this,sch)}finally{this.opts=currentOpts}}}Ajv2.ValidationError=validation_error_1.
-default,Ajv2.MissingRefError=ref_error_1.default,exports.default=Ajv2;function checkOptions(checkOpts,options,msg,log="error"){for(const key2 in checkOpts){const opt=key2;opt in options&&this.logger[log](`${msg}: option ${key2}. ${checkOpts[opt]}`)}}function getSchEnv(keyRef){return keyRef=(0,resolve_1.normalizeId)(keyRef),this.schemas[keyRef]||this.refs[keyRef]}function addInitialSchemas(){const optsSchemas=this.opts.schemas;if(optsSchemas)if(Array.isArray(optsSchemas))this.addSchema(optsSchemas);else
-for(const key2 in optsSchemas)this.addSchema(optsSchemas[key2],key2)}function addInitialFormats(){for(const name in this.opts.formats){const format2=this.opts.formats[name];format2&&this.addFormat(name,format2)}}function addInitialKeywords(defs){if(Array.isArray(defs)){this.addVocabulary(defs);return}this.logger.warn("keywords option as map is deprecated, pass array");for(const keyword2 in defs){const def=defs[keyword2];def.keyword||(def.keyword=keyword2),this.addKeyword(def)}}function getMetaSchemaOptions(){
-const metaOpts={...this.opts};for(const opt of META_IGNORE_OPTIONS)delete metaOpts[opt];return metaOpts}const noLogs={log(){},warn(){},error(){}};function getLogger(logger){if(logger===!1)return noLogs;if(logger===void 0)return console;if(logger.log&&logger.warn&&logger.error)return logger;throw new Error("logger must implement log, warn and error methods")}const KEYWORD_NAME=/^[a-z_$][a-z0-9_$:-]*$/i;function checkKeyword(keyword2,def){const{RULES}=this;if((0,util_1.eachItem)(keyword2,kwd=>{if(RULES.
-keywords[kwd])throw new Error(`Keyword ${kwd} is already defined`);if(!KEYWORD_NAME.test(kwd))throw new Error(`Keyword ${kwd} has invalid name`)}),!!def&&def.$data&&!("code"in def||"validate"in def))throw new Error('$data keyword must have "code" or "validate" function')}function addRule(keyword2,definition2,dataType2){var _a2;const post=definition2?.post;if(dataType2&&post)throw new Error('keyword with "post" flag cannot have "type"');const{RULES}=this;let ruleGroup=post?RULES.post:RULES.rules.
-find(({type:t})=>t===dataType2);if(ruleGroup||(ruleGroup={type:dataType2,rules:[]},RULES.rules.push(ruleGroup)),RULES.keywords[keyword2]=!0,!definition2)return;const rule={keyword:keyword2,definition:{...definition2,type:(0,dataType_1.getJSONTypes)(definition2.type),schemaType:(0,dataType_1.getJSONTypes)(definition2.schemaType)}};definition2.before?addBeforeRule.call(this,ruleGroup,rule,definition2.before):ruleGroup.rules.push(rule),RULES.all[keyword2]=rule,(_a2=definition2.implements)===null||_a2===
-void 0||_a2.forEach(kwd=>this.addKeyword(kwd))}function addBeforeRule(ruleGroup,rule,before){const i=ruleGroup.rules.findIndex(_rule=>_rule.keyword===before);i>=0?ruleGroup.rules.splice(i,0,rule):(ruleGroup.rules.push(rule),this.logger.warn(`rule ${before} is not defined`))}function keywordMetaschema(def){let{metaSchema}=def;metaSchema!==void 0&&(def.$data&&this.opts.$data&&(metaSchema=schemaOrData(metaSchema)),def.validateSchema=this.compile(metaSchema,!0))}const $dataRef={$ref:"https://raw.git\
-hubusercontent.com/ajv-validator/ajv/master/lib/refs/data.json#"};function schemaOrData(schema){return{anyOf:[schema,$dataRef]}}}(core$2)),core$2}var draft7={},core$1={},id={},hasRequiredId;function requireId(){if(hasRequiredId)return id;hasRequiredId=1,Object.defineProperty(id,"__esModule",{value:!0});const def={keyword:"id",code(){throw new Error('NOT SUPPORTED: keyword "id", use "$id" for schema ID')}};return id.default=def,id}var ref={},hasRequiredRef;function requireRef(){if(hasRequiredRef)
-return ref;hasRequiredRef=1,Object.defineProperty(ref,"__esModule",{value:!0}),ref.callRef=ref.getValidate=void 0;const ref_error_1=requireRef_error(),code_1=requireCode(),codegen_1=requireCodegen(),names_1=requireNames(),compile_1=requireCompile(),util_1=requireUtil(),def={keyword:"$ref",schemaType:"string",code(cxt){const{gen,schema:$ref,it}=cxt,{baseId,schemaEnv:env2,validateName,opts,self:self2}=it,{root:root2}=env2;if(($ref==="#"||$ref==="#/")&&baseId===root2.baseId)return callRootRef();const schOrEnv=compile_1.
-resolveRef.call(self2,root2,baseId,$ref);if(schOrEnv===void 0)throw new ref_error_1.default(it.opts.uriResolver,baseId,$ref);if(schOrEnv instanceof compile_1.SchemaEnv)return callValidate(schOrEnv);return inlineRefSchema(schOrEnv);function callRootRef(){if(env2===root2)return callRef(cxt,validateName,env2,env2.$async);const rootName=gen.scopeValue("root",{ref:root2});return callRef(cxt,(0,codegen_1._)`${rootName}.validate`,root2,root2.$async)}function callValidate(sch){const v=getValidate(cxt,sch);
-callRef(cxt,v,sch,sch.$async)}function inlineRefSchema(sch){const schName=gen.scopeValue("schema",opts.code.source===!0?{ref:sch,code:(0,codegen_1.stringify)(sch)}:{ref:sch}),valid2=gen.name("valid"),schCxt=cxt.subschema({schema:sch,dataTypes:[],schemaPath:codegen_1.nil,topSchemaRef:schName,errSchemaPath:$ref},valid2);cxt.mergeEvaluated(schCxt),cxt.ok(valid2)}}};function getValidate(cxt,sch){const{gen}=cxt;return sch.validate?gen.scopeValue("validate",{ref:sch.validate}):(0,codegen_1._)`${gen.scopeValue(
-"wrapper",{ref:sch})}.validate`}ref.getValidate=getValidate;function callRef(cxt,v,sch,$async){const{gen,it}=cxt,{allErrors,schemaEnv:env2,opts}=it,passCxt=opts.passContext?names_1.default.this:codegen_1.nil;$async?callAsyncRef():callSyncRef();function callAsyncRef(){if(!env2.$async)throw new Error("async schema referenced by sync schema");const valid2=gen.let("valid");gen.try(()=>{gen.code((0,codegen_1._)`await ${(0,code_1.callValidateCode)(cxt,v,passCxt)}`),addEvaluatedFrom(v),allErrors||gen.assign(
-valid2,!0)},e=>{gen.if((0,codegen_1._)`!(${e} instanceof ${it.ValidationError})`,()=>gen.throw(e)),addErrorsFrom(e),allErrors||gen.assign(valid2,!1)}),cxt.ok(valid2)}function callSyncRef(){cxt.result((0,code_1.callValidateCode)(cxt,v,passCxt),()=>addEvaluatedFrom(v),()=>addErrorsFrom(v))}function addErrorsFrom(source2){const errs=(0,codegen_1._)`${source2}.errors`;gen.assign(names_1.default.vErrors,(0,codegen_1._)`${names_1.default.vErrors} === null ? ${errs} : ${names_1.default.vErrors}.concat(${errs})`),
-gen.assign(names_1.default.errors,(0,codegen_1._)`${names_1.default.vErrors}.length`)}function addEvaluatedFrom(source2){var _a2;if(!it.opts.unevaluated)return;const schEvaluated=(_a2=sch?.validate)===null||_a2===void 0?void 0:_a2.evaluated;if(it.props!==!0)if(schEvaluated&&!schEvaluated.dynamicProps)schEvaluated.props!==void 0&&(it.props=util_1.mergeEvaluated.props(gen,schEvaluated.props,it.props));else{const props=gen.var("props",(0,codegen_1._)`${source2}.evaluated.props`);it.props=util_1.mergeEvaluated.
-props(gen,props,it.props,codegen_1.Name)}if(it.items!==!0)if(schEvaluated&&!schEvaluated.dynamicItems)schEvaluated.items!==void 0&&(it.items=util_1.mergeEvaluated.items(gen,schEvaluated.items,it.items));else{const items2=gen.var("items",(0,codegen_1._)`${source2}.evaluated.items`);it.items=util_1.mergeEvaluated.items(gen,items2,it.items,codegen_1.Name)}}}return ref.callRef=callRef,ref.default=def,ref}var hasRequiredCore;function requireCore(){if(hasRequiredCore)return core$1;hasRequiredCore=1,Object.
-defineProperty(core$1,"__esModule",{value:!0});const id_1=requireId(),ref_1=requireRef(),core2=["$schema","$id","$defs","$vocabulary",{keyword:"$comment"},"definitions",id_1.default,ref_1.default];return core$1.default=core2,core$1}var validation={},limitNumber={},hasRequiredLimitNumber;function requireLimitNumber(){if(hasRequiredLimitNumber)return limitNumber;hasRequiredLimitNumber=1,Object.defineProperty(limitNumber,"__esModule",{value:!0});const codegen_1=requireCodegen(),ops=codegen_1.operators,
-KWDs={maximum:{okStr:"<=",ok:ops.LTE,fail:ops.GT},minimum:{okStr:">=",ok:ops.GTE,fail:ops.LT},exclusiveMaximum:{okStr:"<",ok:ops.LT,fail:ops.GTE},exclusiveMinimum:{okStr:">",ok:ops.GT,fail:ops.LTE}},error2={message:({keyword:keyword2,schemaCode})=>(0,codegen_1.str)`must be ${KWDs[keyword2].okStr} ${schemaCode}`,params:({keyword:keyword2,schemaCode})=>(0,codegen_1._)`{comparison: ${KWDs[keyword2].okStr}, limit: ${schemaCode}}`},def={keyword:Object.keys(KWDs),type:"number",schemaType:"number",$data:!0,
-error:error2,code(cxt){const{keyword:keyword2,data,schemaCode}=cxt;cxt.fail$data((0,codegen_1._)`${data} ${KWDs[keyword2].fail} ${schemaCode} || isNaN(${data})`)}};return limitNumber.default=def,limitNumber}var multipleOf={},hasRequiredMultipleOf;function requireMultipleOf(){if(hasRequiredMultipleOf)return multipleOf;hasRequiredMultipleOf=1,Object.defineProperty(multipleOf,"__esModule",{value:!0});const codegen_1=requireCodegen(),def={keyword:"multipleOf",type:"number",schemaType:"number",$data:!0,
-error:{message:({schemaCode})=>(0,codegen_1.str)`must be multiple of ${schemaCode}`,params:({schemaCode})=>(0,codegen_1._)`{multipleOf: ${schemaCode}}`},code(cxt){const{gen,data,schemaCode,it}=cxt,prec=it.opts.multipleOfPrecision,res=gen.let("res"),invalid2=prec?(0,codegen_1._)`Math.abs(Math.round(${res}) - ${res}) > 1e-${prec}`:(0,codegen_1._)`${res} !== parseInt(${res})`;cxt.fail$data((0,codegen_1._)`(${schemaCode} === 0 || (${res} = ${data}/${schemaCode}, ${invalid2}))`)}};return multipleOf.default=
-def,multipleOf}var limitLength={},ucs2length={},hasRequiredUcs2length;function requireUcs2length(){if(hasRequiredUcs2length)return ucs2length;hasRequiredUcs2length=1,Object.defineProperty(ucs2length,"__esModule",{value:!0});function ucs2length$12(str){const len=str.length;let length=0,pos=0,value;for(;pos<len;)length++,value=str.charCodeAt(pos++),value>=55296&&value<=56319&&pos<len&&(value=str.charCodeAt(pos),(value&64512)===56320&&pos++);return length}return ucs2length.default=ucs2length$12,ucs2length$12.
-code='require("ajv/dist/runtime/ucs2length").default',ucs2length}var hasRequiredLimitLength;function requireLimitLength(){if(hasRequiredLimitLength)return limitLength;hasRequiredLimitLength=1,Object.defineProperty(limitLength,"__esModule",{value:!0});const codegen_1=requireCodegen(),util_1=requireUtil(),ucs2length_1=requireUcs2length(),def={keyword:["maxLength","minLength"],type:"string",schemaType:"number",$data:!0,error:{message({keyword:keyword2,schemaCode}){const comp=keyword2==="maxLength"?
-"more":"fewer";return(0,codegen_1.str)`must NOT have ${comp} than ${schemaCode} characters`},params:({schemaCode})=>(0,codegen_1._)`{limit: ${schemaCode}}`},code(cxt){const{keyword:keyword2,data,schemaCode,it}=cxt,op2=keyword2==="maxLength"?codegen_1.operators.GT:codegen_1.operators.LT,len=it.opts.unicode===!1?(0,codegen_1._)`${data}.length`:(0,codegen_1._)`${(0,util_1.useFunc)(cxt.gen,ucs2length_1.default)}(${data})`;cxt.fail$data((0,codegen_1._)`${len} ${op2} ${schemaCode}`)}};return limitLength.
-default=def,limitLength}var pattern={},hasRequiredPattern;function requirePattern(){if(hasRequiredPattern)return pattern;hasRequiredPattern=1,Object.defineProperty(pattern,"__esModule",{value:!0});const code_1=requireCode(),codegen_1=requireCodegen(),def={keyword:"pattern",type:"string",schemaType:"string",$data:!0,error:{message:({schemaCode})=>(0,codegen_1.str)`must match pattern "${schemaCode}"`,params:({schemaCode})=>(0,codegen_1._)`{pattern: ${schemaCode}}`},code(cxt){const{data,$data,schema,
-schemaCode,it}=cxt,u=it.opts.unicodeRegExp?"u":"",regExp=$data?(0,codegen_1._)`(new RegExp(${schemaCode}, ${u}))`:(0,code_1.usePattern)(cxt,schema);cxt.fail$data((0,codegen_1._)`!${regExp}.test(${data})`)}};return pattern.default=def,pattern}var limitProperties={},hasRequiredLimitProperties;function requireLimitProperties(){if(hasRequiredLimitProperties)return limitProperties;hasRequiredLimitProperties=1,Object.defineProperty(limitProperties,"__esModule",{value:!0});const codegen_1=requireCodegen(),
-def={keyword:["maxProperties","minProperties"],type:"object",schemaType:"number",$data:!0,error:{message({keyword:keyword2,schemaCode}){const comp=keyword2==="maxProperties"?"more":"fewer";return(0,codegen_1.str)`must NOT have ${comp} than ${schemaCode} properties`},params:({schemaCode})=>(0,codegen_1._)`{limit: ${schemaCode}}`},code(cxt){const{keyword:keyword2,data,schemaCode}=cxt,op2=keyword2==="maxProperties"?codegen_1.operators.GT:codegen_1.operators.LT;cxt.fail$data((0,codegen_1._)`Object.keys(${data}).length ${op2} ${schemaCode}`)}};
-return limitProperties.default=def,limitProperties}var required={},hasRequiredRequired;function requireRequired(){if(hasRequiredRequired)return required;hasRequiredRequired=1,Object.defineProperty(required,"__esModule",{value:!0});const code_1=requireCode(),codegen_1=requireCodegen(),util_1=requireUtil(),def={keyword:"required",type:"object",schemaType:"array",$data:!0,error:{message:({params:{missingProperty}})=>(0,codegen_1.str)`must have required property '${missingProperty}'`,params:({params:{
-missingProperty}})=>(0,codegen_1._)`{missingProperty: ${missingProperty}}`},code(cxt){const{gen,schema,schemaCode,data,$data,it}=cxt,{opts}=it;if(!$data&&schema.length===0)return;const useLoop=schema.length>=opts.loopRequired;if(it.allErrors?allErrorsMode():exitOnErrorMode(),opts.strictRequired){const props=cxt.parentSchema.properties,{definedProperties}=cxt.it;for(const requiredKey of schema)if(props?.[requiredKey]===void 0&&!definedProperties.has(requiredKey)){const schemaPath=it.schemaEnv.baseId+
-it.errSchemaPath,msg=`required property "${requiredKey}" is not defined at "${schemaPath}" (strictRequired)`;(0,util_1.checkStrictMode)(it,msg,it.opts.strictRequired)}}function allErrorsMode(){if(useLoop||$data)cxt.block$data(codegen_1.nil,loopAllRequired);else for(const prop2 of schema)(0,code_1.checkReportMissingProp)(cxt,prop2)}function exitOnErrorMode(){const missing=gen.let("missing");if(useLoop||$data){const valid2=gen.let("valid",!0);cxt.block$data(valid2,()=>loopUntilMissing(missing,valid2)),
-cxt.ok(valid2)}else gen.if((0,code_1.checkMissingProp)(cxt,schema,missing)),(0,code_1.reportMissingProp)(cxt,missing),gen.else()}function loopAllRequired(){gen.forOf("prop",schemaCode,prop2=>{cxt.setParams({missingProperty:prop2}),gen.if((0,code_1.noPropertyInData)(gen,data,prop2,opts.ownProperties),()=>cxt.error())})}function loopUntilMissing(missing,valid2){cxt.setParams({missingProperty:missing}),gen.forOf(missing,schemaCode,()=>{gen.assign(valid2,(0,code_1.propertyInData)(gen,data,missing,opts.
-ownProperties)),gen.if((0,codegen_1.not)(valid2),()=>{cxt.error(),gen.break()})},codegen_1.nil)}}};return required.default=def,required}var limitItems={},hasRequiredLimitItems;function requireLimitItems(){if(hasRequiredLimitItems)return limitItems;hasRequiredLimitItems=1,Object.defineProperty(limitItems,"__esModule",{value:!0});const codegen_1=requireCodegen(),def={keyword:["maxItems","minItems"],type:"array",schemaType:"number",$data:!0,error:{message({keyword:keyword2,schemaCode}){const comp=keyword2===
-"maxItems"?"more":"fewer";return(0,codegen_1.str)`must NOT have ${comp} than ${schemaCode} items`},params:({schemaCode})=>(0,codegen_1._)`{limit: ${schemaCode}}`},code(cxt){const{keyword:keyword2,data,schemaCode}=cxt,op2=keyword2==="maxItems"?codegen_1.operators.GT:codegen_1.operators.LT;cxt.fail$data((0,codegen_1._)`${data}.length ${op2} ${schemaCode}`)}};return limitItems.default=def,limitItems}var uniqueItems={},equal={},hasRequiredEqual;function requireEqual(){if(hasRequiredEqual)return equal;
-hasRequiredEqual=1,Object.defineProperty(equal,"__esModule",{value:!0});const equal$12=requireFastDeepEqual();return equal$12.code='require("ajv/dist/runtime/equal").default',equal.default=equal$12,equal}var hasRequiredUniqueItems;function requireUniqueItems(){if(hasRequiredUniqueItems)return uniqueItems;hasRequiredUniqueItems=1,Object.defineProperty(uniqueItems,"__esModule",{value:!0});const dataType_1=requireDataType(),codegen_1=requireCodegen(),util_1=requireUtil(),equal_1=requireEqual(),def={
-keyword:"uniqueItems",type:"array",schemaType:"boolean",$data:!0,error:{message:({params:{i,j}})=>(0,codegen_1.str)`must NOT have duplicate items (items ## ${j} and ${i} are identical)`,params:({params:{i,j}})=>(0,codegen_1._)`{i: ${i}, j: ${j}}`},code(cxt){const{gen,data,$data,schema,parentSchema,schemaCode,it}=cxt;if(!$data&&!schema)return;const valid2=gen.let("valid"),itemTypes=parentSchema.items?(0,dataType_1.getSchemaTypes)(parentSchema.items):[];cxt.block$data(valid2,validateUniqueItems,(0,codegen_1.
-_)`${schemaCode} === false`),cxt.ok(valid2);function validateUniqueItems(){const i=gen.let("i",(0,codegen_1._)`${data}.length`),j=gen.let("j");cxt.setParams({i,j}),gen.assign(valid2,!0),gen.if((0,codegen_1._)`${i} > 1`,()=>(canOptimize()?loopN:loopN2)(i,j))}function canOptimize(){return itemTypes.length>0&&!itemTypes.some(t=>t==="object"||t==="array")}function loopN(i,j){const item=gen.name("item"),wrongType=(0,dataType_1.checkDataTypes)(itemTypes,item,it.opts.strictNumbers,dataType_1.DataType.Wrong),
-indices=gen.const("indices",(0,codegen_1._)`{}`);gen.for((0,codegen_1._)`;${i}--;`,()=>{gen.let(item,(0,codegen_1._)`${data}[${i}]`),gen.if(wrongType,(0,codegen_1._)`continue`),itemTypes.length>1&&gen.if((0,codegen_1._)`typeof ${item} == "string"`,(0,codegen_1._)`${item} += "_"`),gen.if((0,codegen_1._)`typeof ${indices}[${item}] == "number"`,()=>{gen.assign(j,(0,codegen_1._)`${indices}[${item}]`),cxt.error(),gen.assign(valid2,!1).break()}).code((0,codegen_1._)`${indices}[${item}] = ${i}`)})}function loopN2(i,j){
-const eql=(0,util_1.useFunc)(gen,equal_1.default),outer=gen.name("outer");gen.label(outer).for((0,codegen_1._)`;${i}--;`,()=>gen.for((0,codegen_1._)`${j} = ${i}; ${j}--;`,()=>gen.if((0,codegen_1._)`${eql}(${data}[${i}], ${data}[${j}])`,()=>{cxt.error(),gen.assign(valid2,!1).break(outer)})))}}};return uniqueItems.default=def,uniqueItems}var _const={},hasRequired_const;function require_const(){if(hasRequired_const)return _const;hasRequired_const=1,Object.defineProperty(_const,"__esModule",{value:!0});
-const codegen_1=requireCodegen(),util_1=requireUtil(),equal_1=requireEqual(),def={keyword:"const",$data:!0,error:{message:"must be equal to constant",params:({schemaCode})=>(0,codegen_1._)`{allowedValue: ${schemaCode}}`},code(cxt){const{gen,data,$data,schemaCode,schema}=cxt;$data||schema&&typeof schema=="object"?cxt.fail$data((0,codegen_1._)`!${(0,util_1.useFunc)(gen,equal_1.default)}(${data}, ${schemaCode})`):cxt.fail((0,codegen_1._)`${schema} !== ${data}`)}};return _const.default=def,_const}var _enum={},
-hasRequired_enum;function require_enum(){if(hasRequired_enum)return _enum;hasRequired_enum=1,Object.defineProperty(_enum,"__esModule",{value:!0});const codegen_1=requireCodegen(),util_1=requireUtil(),equal_1=requireEqual(),def={keyword:"enum",schemaType:"array",$data:!0,error:{message:"must be equal to one of the allowed values",params:({schemaCode})=>(0,codegen_1._)`{allowedValues: ${schemaCode}}`},code(cxt){const{gen,data,$data,schema,schemaCode,it}=cxt;if(!$data&&schema.length===0)throw new Error(
-"enum must have non-empty array");const useLoop=schema.length>=it.opts.loopEnum;let eql;const getEql=()=>eql??(eql=(0,util_1.useFunc)(gen,equal_1.default));let valid2;if(useLoop||$data)valid2=gen.let("valid"),cxt.block$data(valid2,loopEnum);else{if(!Array.isArray(schema))throw new Error("ajv implementation error");const vSchema=gen.const("vSchema",schemaCode);valid2=(0,codegen_1.or)(...schema.map((_x,i)=>equalCode(vSchema,i)))}cxt.pass(valid2);function loopEnum(){gen.assign(valid2,!1),gen.forOf(
-"v",schemaCode,v=>gen.if((0,codegen_1._)`${getEql()}(${data}, ${v})`,()=>gen.assign(valid2,!0).break()))}function equalCode(vSchema,i){const sch=schema[i];return typeof sch=="object"&&sch!==null?(0,codegen_1._)`${getEql()}(${data}, ${vSchema}[${i}])`:(0,codegen_1._)`${data} === ${sch}`}}};return _enum.default=def,_enum}var hasRequiredValidation;function requireValidation(){if(hasRequiredValidation)return validation;hasRequiredValidation=1,Object.defineProperty(validation,"__esModule",{value:!0});
-const limitNumber_1=requireLimitNumber(),multipleOf_1=requireMultipleOf(),limitLength_1=requireLimitLength(),pattern_1=requirePattern(),limitProperties_1=requireLimitProperties(),required_1=requireRequired(),limitItems_1=requireLimitItems(),uniqueItems_1=requireUniqueItems(),const_1=require_const(),enum_1=require_enum(),validation$12=[limitNumber_1.default,multipleOf_1.default,limitLength_1.default,pattern_1.default,limitProperties_1.default,required_1.default,limitItems_1.default,uniqueItems_1.
-default,{keyword:"type",schemaType:["string","array"]},{keyword:"nullable",schemaType:"boolean"},const_1.default,enum_1.default];return validation.default=validation$12,validation}var applicator={},additionalItems={},hasRequiredAdditionalItems;function requireAdditionalItems(){if(hasRequiredAdditionalItems)return additionalItems;hasRequiredAdditionalItems=1,Object.defineProperty(additionalItems,"__esModule",{value:!0}),additionalItems.validateAdditionalItems=void 0;const codegen_1=requireCodegen(),
-util_1=requireUtil(),def={keyword:"additionalItems",type:"array",schemaType:["boolean","object"],before:"uniqueItems",error:{message:({params:{len}})=>(0,codegen_1.str)`must NOT have more than ${len} items`,params:({params:{len}})=>(0,codegen_1._)`{limit: ${len}}`},code(cxt){const{parentSchema,it}=cxt,{items:items2}=parentSchema;if(!Array.isArray(items2)){(0,util_1.checkStrictMode)(it,'"additionalItems" is ignored when "items" is not an array of schemas');return}validateAdditionalItems(cxt,items2)}};
-function validateAdditionalItems(cxt,items2){const{gen,schema,data,keyword:keyword2,it}=cxt;it.items=!0;const len=gen.const("len",(0,codegen_1._)`${data}.length`);if(schema===!1)cxt.setParams({len:items2.length}),cxt.pass((0,codegen_1._)`${len} <= ${items2.length}`);else if(typeof schema=="object"&&!(0,util_1.alwaysValidSchema)(it,schema)){const valid2=gen.var("valid",(0,codegen_1._)`${len} <= ${items2.length}`);gen.if((0,codegen_1.not)(valid2),()=>validateItems(valid2)),cxt.ok(valid2)}function validateItems(valid2){
-gen.forRange("i",items2.length,len,i=>{cxt.subschema({keyword:keyword2,dataProp:i,dataPropType:util_1.Type.Num},valid2),it.allErrors||gen.if((0,codegen_1.not)(valid2),()=>gen.break())})}}return additionalItems.validateAdditionalItems=validateAdditionalItems,additionalItems.default=def,additionalItems}var prefixItems={},items={},hasRequiredItems;function requireItems(){if(hasRequiredItems)return items;hasRequiredItems=1,Object.defineProperty(items,"__esModule",{value:!0}),items.validateTuple=void 0;
-const codegen_1=requireCodegen(),util_1=requireUtil(),code_1=requireCode(),def={keyword:"items",type:"array",schemaType:["object","array","boolean"],before:"uniqueItems",code(cxt){const{schema,it}=cxt;if(Array.isArray(schema))return validateTuple(cxt,"additionalItems",schema);it.items=!0,!(0,util_1.alwaysValidSchema)(it,schema)&&cxt.ok((0,code_1.validateArray)(cxt))}};function validateTuple(cxt,extraItems,schArr=cxt.schema){const{gen,parentSchema,data,keyword:keyword2,it}=cxt;checkStrictTuple(parentSchema),
-it.opts.unevaluated&&schArr.length&&it.items!==!0&&(it.items=util_1.mergeEvaluated.items(gen,schArr.length,it.items));const valid2=gen.name("valid"),len=gen.const("len",(0,codegen_1._)`${data}.length`);schArr.forEach((sch,i)=>{(0,util_1.alwaysValidSchema)(it,sch)||(gen.if((0,codegen_1._)`${len} > ${i}`,()=>cxt.subschema({keyword:keyword2,schemaProp:i,dataProp:i},valid2)),cxt.ok(valid2))});function checkStrictTuple(sch){const{opts,errSchemaPath}=it,l=schArr.length,fullTuple=l===sch.minItems&&(l===
-sch.maxItems||sch[extraItems]===!1);if(opts.strictTuples&&!fullTuple){const msg=`"${keyword2}" is ${l}-tuple, but minItems or maxItems/${extraItems} are not specified or different at path "${errSchemaPath}"`;(0,util_1.checkStrictMode)(it,msg,opts.strictTuples)}}}return items.validateTuple=validateTuple,items.default=def,items}var hasRequiredPrefixItems;function requirePrefixItems(){if(hasRequiredPrefixItems)return prefixItems;hasRequiredPrefixItems=1,Object.defineProperty(prefixItems,"__esModule",
-{value:!0});const items_1=requireItems(),def={keyword:"prefixItems",type:"array",schemaType:["array"],before:"uniqueItems",code:cxt=>(0,items_1.validateTuple)(cxt,"items")};return prefixItems.default=def,prefixItems}var items2020={},hasRequiredItems2020;function requireItems2020(){if(hasRequiredItems2020)return items2020;hasRequiredItems2020=1,Object.defineProperty(items2020,"__esModule",{value:!0});const codegen_1=requireCodegen(),util_1=requireUtil(),code_1=requireCode(),additionalItems_1=requireAdditionalItems(),
-def={keyword:"items",type:"array",schemaType:["object","boolean"],before:"uniqueItems",error:{message:({params:{len}})=>(0,codegen_1.str)`must NOT have more than ${len} items`,params:({params:{len}})=>(0,codegen_1._)`{limit: ${len}}`},code(cxt){const{schema,parentSchema,it}=cxt,{prefixItems:prefixItems2}=parentSchema;it.items=!0,!(0,util_1.alwaysValidSchema)(it,schema)&&(prefixItems2?(0,additionalItems_1.validateAdditionalItems)(cxt,prefixItems2):cxt.ok((0,code_1.validateArray)(cxt)))}};return items2020.
-default=def,items2020}var contains$1={},hasRequiredContains;function requireContains(){if(hasRequiredContains)return contains$1;hasRequiredContains=1,Object.defineProperty(contains$1,"__esModule",{value:!0});const codegen_1=requireCodegen(),util_1=requireUtil(),def={keyword:"contains",type:"array",schemaType:["object","boolean"],before:"uniqueItems",trackErrors:!0,error:{message:({params:{min:min2,max:max2}})=>max2===void 0?(0,codegen_1.str)`must contain at least ${min2} valid item(s)`:(0,codegen_1.
-str)`must contain at least ${min2} and no more than ${max2} valid item(s)`,params:({params:{min:min2,max:max2}})=>max2===void 0?(0,codegen_1._)`{minContains: ${min2}}`:(0,codegen_1._)`{minContains: ${min2}, maxContains: ${max2}}`},code(cxt){const{gen,schema,parentSchema,data,it}=cxt;let min2,max2;const{minContains,maxContains}=parentSchema;it.opts.next?(min2=minContains===void 0?1:minContains,max2=maxContains):min2=1;const len=gen.const("len",(0,codegen_1._)`${data}.length`);if(cxt.setParams({min:min2,
-max:max2}),max2===void 0&&min2===0){(0,util_1.checkStrictMode)(it,'"minContains" == 0 without "maxContains": "contains" keyword ignored');return}if(max2!==void 0&&min2>max2){(0,util_1.checkStrictMode)(it,'"minContains" > "maxContains" is always invalid'),cxt.fail();return}if((0,util_1.alwaysValidSchema)(it,schema)){let cond=(0,codegen_1._)`${len} >= ${min2}`;max2!==void 0&&(cond=(0,codegen_1._)`${cond} && ${len} <= ${max2}`),cxt.pass(cond);return}it.items=!0;const valid2=gen.name("valid");max2===
-void 0&&min2===1?validateItems(valid2,()=>gen.if(valid2,()=>gen.break())):min2===0?(gen.let(valid2,!0),max2!==void 0&&gen.if((0,codegen_1._)`${data}.length > 0`,validateItemsWithCount)):(gen.let(valid2,!1),validateItemsWithCount()),cxt.result(valid2,()=>cxt.reset());function validateItemsWithCount(){const schValid=gen.name("_valid"),count=gen.let("count",0);validateItems(schValid,()=>gen.if(schValid,()=>checkLimits(count)))}function validateItems(_valid,block2){gen.forRange("i",0,len,i=>{cxt.subschema(
-{keyword:"contains",dataProp:i,dataPropType:util_1.Type.Num,compositeRule:!0},_valid),block2()})}function checkLimits(count){gen.code((0,codegen_1._)`${count}++`),max2===void 0?gen.if((0,codegen_1._)`${count} >= ${min2}`,()=>gen.assign(valid2,!0).break()):(gen.if((0,codegen_1._)`${count} > ${max2}`,()=>gen.assign(valid2,!1).break()),min2===1?gen.assign(valid2,!0):gen.if((0,codegen_1._)`${count} >= ${min2}`,()=>gen.assign(valid2,!0)))}}};return contains$1.default=def,contains$1}var dependencies={},
-hasRequiredDependencies;function requireDependencies(){return hasRequiredDependencies||(hasRequiredDependencies=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.validateSchemaDeps=exports.validatePropertyDeps=exports.error=void 0;const codegen_1=requireCodegen(),util_1=requireUtil(),code_1=requireCode();exports.error={message:({params:{property,depsCount,deps}})=>{const property_ies=depsCount===1?"property":"properties";return(0,codegen_1.str)`must have ${property_ies} ${deps} when property ${property} is present`},
-params:({params:{property,depsCount,deps,missingProperty}})=>(0,codegen_1._)`{property: ${property},
-    missingProperty: ${missingProperty},
-    depsCount: ${depsCount},
-    deps: ${deps}}`};const def={keyword:"dependencies",type:"object",schemaType:"object",error:exports.error,code(cxt){const[propDeps,schDeps]=splitDependencies(cxt);validatePropertyDeps(cxt,propDeps),validateSchemaDeps(cxt,schDeps)}};function splitDependencies({schema}){const propertyDeps={},schemaDeps={};for(const key2 in schema){if(key2==="__proto__")continue;const deps=Array.isArray(schema[key2])?propertyDeps:schemaDeps;deps[key2]=schema[key2]}return[propertyDeps,schemaDeps]}function validatePropertyDeps(cxt,propertyDeps=cxt.
-schema){const{gen,data,it}=cxt;if(Object.keys(propertyDeps).length===0)return;const missing=gen.let("missing");for(const prop2 in propertyDeps){const deps=propertyDeps[prop2];if(deps.length===0)continue;const hasProperty=(0,code_1.propertyInData)(gen,data,prop2,it.opts.ownProperties);cxt.setParams({property:prop2,depsCount:deps.length,deps:deps.join(", ")}),it.allErrors?gen.if(hasProperty,()=>{for(const depProp of deps)(0,code_1.checkReportMissingProp)(cxt,depProp)}):(gen.if((0,codegen_1._)`${hasProperty} && (${(0,code_1.
-checkMissingProp)(cxt,deps,missing)})`),(0,code_1.reportMissingProp)(cxt,missing),gen.else())}}exports.validatePropertyDeps=validatePropertyDeps;function validateSchemaDeps(cxt,schemaDeps=cxt.schema){const{gen,data,keyword:keyword2,it}=cxt,valid2=gen.name("valid");for(const prop2 in schemaDeps)(0,util_1.alwaysValidSchema)(it,schemaDeps[prop2])||(gen.if((0,code_1.propertyInData)(gen,data,prop2,it.opts.ownProperties),()=>{const schCxt=cxt.subschema({keyword:keyword2,schemaProp:prop2},valid2);cxt.mergeValidEvaluated(
-schCxt,valid2)},()=>gen.var(valid2,!0)),cxt.ok(valid2))}exports.validateSchemaDeps=validateSchemaDeps,exports.default=def}(dependencies)),dependencies}var propertyNames={},hasRequiredPropertyNames;function requirePropertyNames(){if(hasRequiredPropertyNames)return propertyNames;hasRequiredPropertyNames=1,Object.defineProperty(propertyNames,"__esModule",{value:!0});const codegen_1=requireCodegen(),util_1=requireUtil(),def={keyword:"propertyNames",type:"object",schemaType:["object","boolean"],error:{
-message:"property name must be valid",params:({params})=>(0,codegen_1._)`{propertyName: ${params.propertyName}}`},code(cxt){const{gen,schema,data,it}=cxt;if((0,util_1.alwaysValidSchema)(it,schema))return;const valid2=gen.name("valid");gen.forIn("key",data,key2=>{cxt.setParams({propertyName:key2}),cxt.subschema({keyword:"propertyNames",data:key2,dataTypes:["string"],propertyName:key2,compositeRule:!0},valid2),gen.if((0,codegen_1.not)(valid2),()=>{cxt.error(!0),it.allErrors||gen.break()})}),cxt.ok(
-valid2)}};return propertyNames.default=def,propertyNames}var additionalProperties={},hasRequiredAdditionalProperties;function requireAdditionalProperties(){if(hasRequiredAdditionalProperties)return additionalProperties;hasRequiredAdditionalProperties=1,Object.defineProperty(additionalProperties,"__esModule",{value:!0});const code_1=requireCode(),codegen_1=requireCodegen(),names_1=requireNames(),util_1=requireUtil(),def={keyword:"additionalProperties",type:["object"],schemaType:["boolean","object"],
-allowUndefined:!0,trackErrors:!0,error:{message:"must NOT have additional properties",params:({params})=>(0,codegen_1._)`{additionalProperty: ${params.additionalProperty}}`},code(cxt){const{gen,schema,parentSchema,data,errsCount,it}=cxt;if(!errsCount)throw new Error("ajv implementation error");const{allErrors,opts}=it;if(it.props=!0,opts.removeAdditional!=="all"&&(0,util_1.alwaysValidSchema)(it,schema))return;const props=(0,code_1.allSchemaProperties)(parentSchema.properties),patProps=(0,code_1.
-allSchemaProperties)(parentSchema.patternProperties);checkAdditionalProperties(),cxt.ok((0,codegen_1._)`${errsCount} === ${names_1.default.errors}`);function checkAdditionalProperties(){gen.forIn("key",data,key2=>{!props.length&&!patProps.length?additionalPropertyCode(key2):gen.if(isAdditional(key2),()=>additionalPropertyCode(key2))})}function isAdditional(key2){let definedProp;if(props.length>8){const propsSchema=(0,util_1.schemaRefOrVal)(it,parentSchema.properties,"properties");definedProp=(0,code_1.
-isOwnProperty)(gen,propsSchema,key2)}else props.length?definedProp=(0,codegen_1.or)(...props.map(p2=>(0,codegen_1._)`${key2} === ${p2}`)):definedProp=codegen_1.nil;return patProps.length&&(definedProp=(0,codegen_1.or)(definedProp,...patProps.map(p2=>(0,codegen_1._)`${(0,code_1.usePattern)(cxt,p2)}.test(${key2})`))),(0,codegen_1.not)(definedProp)}function deleteAdditional(key2){gen.code((0,codegen_1._)`delete ${data}[${key2}]`)}function additionalPropertyCode(key2){if(opts.removeAdditional==="all"||
-opts.removeAdditional&&schema===!1){deleteAdditional(key2);return}if(schema===!1){cxt.setParams({additionalProperty:key2}),cxt.error(),allErrors||gen.break();return}if(typeof schema=="object"&&!(0,util_1.alwaysValidSchema)(it,schema)){const valid2=gen.name("valid");opts.removeAdditional==="failing"?(applyAdditionalSchema(key2,valid2,!1),gen.if((0,codegen_1.not)(valid2),()=>{cxt.reset(),deleteAdditional(key2)})):(applyAdditionalSchema(key2,valid2),allErrors||gen.if((0,codegen_1.not)(valid2),()=>gen.
-break()))}}function applyAdditionalSchema(key2,valid2,errors2){const subschema2={keyword:"additionalProperties",dataProp:key2,dataPropType:util_1.Type.Str};errors2===!1&&Object.assign(subschema2,{compositeRule:!0,createErrors:!1,allErrors:!1}),cxt.subschema(subschema2,valid2)}}};return additionalProperties.default=def,additionalProperties}var properties$2={},hasRequiredProperties;function requireProperties(){if(hasRequiredProperties)return properties$2;hasRequiredProperties=1,Object.defineProperty(
-properties$2,"__esModule",{value:!0});const validate_1=requireValidate(),code_1=requireCode(),util_1=requireUtil(),additionalProperties_1=requireAdditionalProperties(),def={keyword:"properties",type:"object",schemaType:"object",code(cxt){const{gen,schema,parentSchema,data,it}=cxt;it.opts.removeAdditional==="all"&&parentSchema.additionalProperties===void 0&&additionalProperties_1.default.code(new validate_1.KeywordCxt(it,additionalProperties_1.default,"additionalProperties"));const allProps=(0,code_1.
-allSchemaProperties)(schema);for(const prop2 of allProps)it.definedProperties.add(prop2);it.opts.unevaluated&&allProps.length&&it.props!==!0&&(it.props=util_1.mergeEvaluated.props(gen,(0,util_1.toHash)(allProps),it.props));const properties2=allProps.filter(p2=>!(0,util_1.alwaysValidSchema)(it,schema[p2]));if(properties2.length===0)return;const valid2=gen.name("valid");for(const prop2 of properties2)hasDefault(prop2)?applyPropertySchema(prop2):(gen.if((0,code_1.propertyInData)(gen,data,prop2,it.opts.
-ownProperties)),applyPropertySchema(prop2),it.allErrors||gen.else().var(valid2,!0),gen.endIf()),cxt.it.definedProperties.add(prop2),cxt.ok(valid2);function hasDefault(prop2){return it.opts.useDefaults&&!it.compositeRule&&schema[prop2].default!==void 0}function applyPropertySchema(prop2){cxt.subschema({keyword:"properties",schemaProp:prop2,dataProp:prop2},valid2)}}};return properties$2.default=def,properties$2}var patternProperties={},hasRequiredPatternProperties;function requirePatternProperties(){
-if(hasRequiredPatternProperties)return patternProperties;hasRequiredPatternProperties=1,Object.defineProperty(patternProperties,"__esModule",{value:!0});const code_1=requireCode(),codegen_1=requireCodegen(),util_1=requireUtil(),util_2=requireUtil(),def={keyword:"patternProperties",type:"object",schemaType:"object",code(cxt){const{gen,schema,data,parentSchema,it}=cxt,{opts}=it,patterns=(0,code_1.allSchemaProperties)(schema),alwaysValidPatterns=patterns.filter(p2=>(0,util_1.alwaysValidSchema)(it,schema[p2]));
-if(patterns.length===0||alwaysValidPatterns.length===patterns.length&&(!it.opts.unevaluated||it.props===!0))return;const checkProperties=opts.strictSchema&&!opts.allowMatchingProperties&&parentSchema.properties,valid2=gen.name("valid");it.props!==!0&&!(it.props instanceof codegen_1.Name)&&(it.props=(0,util_2.evaluatedPropsToName)(gen,it.props));const{props}=it;validatePatternProperties();function validatePatternProperties(){for(const pat of patterns)checkProperties&&checkMatchingProperties(pat),
-it.allErrors?validateProperties(pat):(gen.var(valid2,!0),validateProperties(pat),gen.if(valid2))}function checkMatchingProperties(pat){for(const prop2 in checkProperties)new RegExp(pat).test(prop2)&&(0,util_1.checkStrictMode)(it,`property ${prop2} matches pattern ${pat} (use allowMatchingProperties)`)}function validateProperties(pat){gen.forIn("key",data,key2=>{gen.if((0,codegen_1._)`${(0,code_1.usePattern)(cxt,pat)}.test(${key2})`,()=>{const alwaysValid=alwaysValidPatterns.includes(pat);alwaysValid||
-cxt.subschema({keyword:"patternProperties",schemaProp:pat,dataProp:key2,dataPropType:util_2.Type.Str},valid2),it.opts.unevaluated&&props!==!0?gen.assign((0,codegen_1._)`${props}[${key2}]`,!0):!alwaysValid&&!it.allErrors&&gen.if((0,codegen_1.not)(valid2),()=>gen.break())})})}}};return patternProperties.default=def,patternProperties}var not={},hasRequiredNot;function requireNot(){if(hasRequiredNot)return not;hasRequiredNot=1,Object.defineProperty(not,"__esModule",{value:!0});const util_1=requireUtil(),
-def={keyword:"not",schemaType:["object","boolean"],trackErrors:!0,code(cxt){const{gen,schema,it}=cxt;if((0,util_1.alwaysValidSchema)(it,schema)){cxt.fail();return}const valid2=gen.name("valid");cxt.subschema({keyword:"not",compositeRule:!0,createErrors:!1,allErrors:!1},valid2),cxt.failResult(valid2,()=>cxt.reset(),()=>cxt.error())},error:{message:"must NOT be valid"}};return not.default=def,not}var anyOf={},hasRequiredAnyOf;function requireAnyOf(){if(hasRequiredAnyOf)return anyOf;hasRequiredAnyOf=
-1,Object.defineProperty(anyOf,"__esModule",{value:!0});const def={keyword:"anyOf",schemaType:"array",trackErrors:!0,code:requireCode().validateUnion,error:{message:"must match a schema in anyOf"}};return anyOf.default=def,anyOf}var oneOf={},hasRequiredOneOf;function requireOneOf(){if(hasRequiredOneOf)return oneOf;hasRequiredOneOf=1,Object.defineProperty(oneOf,"__esModule",{value:!0});const codegen_1=requireCodegen(),util_1=requireUtil(),def={keyword:"oneOf",schemaType:"array",trackErrors:!0,error:{
-message:"must match exactly one schema in oneOf",params:({params})=>(0,codegen_1._)`{passingSchemas: ${params.passing}}`},code(cxt){const{gen,schema,parentSchema,it}=cxt;if(!Array.isArray(schema))throw new Error("ajv implementation error");if(it.opts.discriminator&&parentSchema.discriminator)return;const schArr=schema,valid2=gen.let("valid",!1),passing=gen.let("passing",null),schValid=gen.name("_valid");cxt.setParams({passing}),gen.block(validateOneOf),cxt.result(valid2,()=>cxt.reset(),()=>cxt.error(
-!0));function validateOneOf(){schArr.forEach((sch,i)=>{let schCxt;(0,util_1.alwaysValidSchema)(it,sch)?gen.var(schValid,!0):schCxt=cxt.subschema({keyword:"oneOf",schemaProp:i,compositeRule:!0},schValid),i>0&&gen.if((0,codegen_1._)`${schValid} && ${valid2}`).assign(valid2,!1).assign(passing,(0,codegen_1._)`[${passing}, ${i}]`).else(),gen.if(schValid,()=>{gen.assign(valid2,!0),gen.assign(passing,i),schCxt&&cxt.mergeEvaluated(schCxt,codegen_1.Name)})})}}};return oneOf.default=def,oneOf}var allOf={},
-hasRequiredAllOf;function requireAllOf(){if(hasRequiredAllOf)return allOf;hasRequiredAllOf=1,Object.defineProperty(allOf,"__esModule",{value:!0});const util_1=requireUtil(),def={keyword:"allOf",schemaType:"array",code(cxt){const{gen,schema,it}=cxt;if(!Array.isArray(schema))throw new Error("ajv implementation error");const valid2=gen.name("valid");schema.forEach((sch,i)=>{if((0,util_1.alwaysValidSchema)(it,sch))return;const schCxt=cxt.subschema({keyword:"allOf",schemaProp:i},valid2);cxt.ok(valid2),
-cxt.mergeEvaluated(schCxt)})}};return allOf.default=def,allOf}var _if={},hasRequired_if;function require_if(){if(hasRequired_if)return _if;hasRequired_if=1,Object.defineProperty(_if,"__esModule",{value:!0});const codegen_1=requireCodegen(),util_1=requireUtil(),def={keyword:"if",schemaType:["object","boolean"],trackErrors:!0,error:{message:({params})=>(0,codegen_1.str)`must match "${params.ifClause}" schema`,params:({params})=>(0,codegen_1._)`{failingKeyword: ${params.ifClause}}`},code(cxt){const{
-gen,parentSchema,it}=cxt;parentSchema.then===void 0&&parentSchema.else===void 0&&(0,util_1.checkStrictMode)(it,'"if" without "then" and "else" is ignored');const hasThen=hasSchema(it,"then"),hasElse=hasSchema(it,"else");if(!hasThen&&!hasElse)return;const valid2=gen.let("valid",!0),schValid=gen.name("_valid");if(validateIf(),cxt.reset(),hasThen&&hasElse){const ifClause=gen.let("ifClause");cxt.setParams({ifClause}),gen.if(schValid,validateClause("then",ifClause),validateClause("else",ifClause))}else
-hasThen?gen.if(schValid,validateClause("then")):gen.if((0,codegen_1.not)(schValid),validateClause("else"));cxt.pass(valid2,()=>cxt.error(!0));function validateIf(){const schCxt=cxt.subschema({keyword:"if",compositeRule:!0,createErrors:!1,allErrors:!1},schValid);cxt.mergeEvaluated(schCxt)}function validateClause(keyword2,ifClause){return()=>{const schCxt=cxt.subschema({keyword:keyword2},schValid);gen.assign(valid2,schValid),cxt.mergeValidEvaluated(schCxt,valid2),ifClause?gen.assign(ifClause,(0,codegen_1.
-_)`${keyword2}`):cxt.setParams({ifClause:keyword2})}}}};function hasSchema(it,keyword2){const schema=it.schema[keyword2];return schema!==void 0&&!(0,util_1.alwaysValidSchema)(it,schema)}return _if.default=def,_if}var thenElse={},hasRequiredThenElse;function requireThenElse(){if(hasRequiredThenElse)return thenElse;hasRequiredThenElse=1,Object.defineProperty(thenElse,"__esModule",{value:!0});const util_1=requireUtil(),def={keyword:["then","else"],schemaType:["object","boolean"],code({keyword:keyword2,
-parentSchema,it}){parentSchema.if===void 0&&(0,util_1.checkStrictMode)(it,`"${keyword2}" without "if" is ignored`)}};return thenElse.default=def,thenElse}var hasRequiredApplicator;function requireApplicator(){if(hasRequiredApplicator)return applicator;hasRequiredApplicator=1,Object.defineProperty(applicator,"__esModule",{value:!0});const additionalItems_1=requireAdditionalItems(),prefixItems_1=requirePrefixItems(),items_1=requireItems(),items2020_1=requireItems2020(),contains_1=requireContains(),
-dependencies_1=requireDependencies(),propertyNames_1=requirePropertyNames(),additionalProperties_1=requireAdditionalProperties(),properties_12=requireProperties(),patternProperties_1=requirePatternProperties(),not_1=requireNot(),anyOf_1=requireAnyOf(),oneOf_1=requireOneOf(),allOf_1=requireAllOf(),if_1=require_if(),thenElse_1=requireThenElse();function getApplicator(draft2020=!1){const applicator2=[not_1.default,anyOf_1.default,oneOf_1.default,allOf_1.default,if_1.default,thenElse_1.default,propertyNames_1.
-default,additionalProperties_1.default,dependencies_1.default,properties_12.default,patternProperties_1.default];return draft2020?applicator2.push(prefixItems_1.default,items2020_1.default):applicator2.push(additionalItems_1.default,items_1.default),applicator2.push(contains_1.default),applicator2}return applicator.default=getApplicator,applicator}var format$1={},format={},hasRequiredFormat$1;function requireFormat$1(){if(hasRequiredFormat$1)return format;hasRequiredFormat$1=1,Object.defineProperty(
-format,"__esModule",{value:!0});const codegen_1=requireCodegen(),def={keyword:"format",type:["number","string"],schemaType:"string",$data:!0,error:{message:({schemaCode})=>(0,codegen_1.str)`must match format "${schemaCode}"`,params:({schemaCode})=>(0,codegen_1._)`{format: ${schemaCode}}`},code(cxt,ruleType){const{gen,data,$data,schema,schemaCode,it}=cxt,{opts,errSchemaPath,schemaEnv,self:self2}=it;if(!opts.validateFormats)return;$data?validate$DataFormat():validateFormat();function validate$DataFormat(){
-const fmts=gen.scopeValue("formats",{ref:self2.formats,code:opts.code.formats}),fDef=gen.const("fDef",(0,codegen_1._)`${fmts}[${schemaCode}]`),fType=gen.let("fType"),format2=gen.let("format");gen.if((0,codegen_1._)`typeof ${fDef} == "object" && !(${fDef} instanceof RegExp)`,()=>gen.assign(fType,(0,codegen_1._)`${fDef}.type || "string"`).assign(format2,(0,codegen_1._)`${fDef}.validate`),()=>gen.assign(fType,(0,codegen_1._)`"string"`).assign(format2,fDef)),cxt.fail$data((0,codegen_1.or)(unknownFmt(),
-invalidFmt()));function unknownFmt(){return opts.strictSchema===!1?codegen_1.nil:(0,codegen_1._)`${schemaCode} && !${format2}`}function invalidFmt(){const callFormat=schemaEnv.$async?(0,codegen_1._)`(${fDef}.async ? await ${format2}(${data}) : ${format2}(${data}))`:(0,codegen_1._)`${format2}(${data})`,validData=(0,codegen_1._)`(typeof ${format2} == "function" ? ${callFormat} : ${format2}.test(${data}))`;return(0,codegen_1._)`${format2} && ${format2} !== true && ${fType} === ${ruleType} && !${validData}`}}
-function validateFormat(){const formatDef=self2.formats[schema];if(!formatDef){unknownFormat();return}if(formatDef===!0)return;const[fmtType,format2,fmtRef]=getFormat(formatDef);fmtType===ruleType&&cxt.pass(validCondition());function unknownFormat(){if(opts.strictSchema===!1){self2.logger.warn(unknownMsg());return}throw new Error(unknownMsg());function unknownMsg(){return`unknown format "${schema}" ignored in schema at path "${errSchemaPath}"`}}function getFormat(fmtDef){const code2=fmtDef instanceof
-RegExp?(0,codegen_1.regexpCode)(fmtDef):opts.code.formats?(0,codegen_1._)`${opts.code.formats}${(0,codegen_1.getProperty)(schema)}`:void 0,fmt=gen.scopeValue("formats",{key:schema,ref:fmtDef,code:code2});return typeof fmtDef=="object"&&!(fmtDef instanceof RegExp)?[fmtDef.type||"string",fmtDef.validate,(0,codegen_1._)`${fmt}.validate`]:["string",fmtDef,fmt]}function validCondition(){if(typeof formatDef=="object"&&!(formatDef instanceof RegExp)&&formatDef.async){if(!schemaEnv.$async)throw new Error(
-"async format in sync schema");return(0,codegen_1._)`await ${fmtRef}(${data})`}return typeof format2=="function"?(0,codegen_1._)`${fmtRef}(${data})`:(0,codegen_1._)`${fmtRef}.test(${data})`}}}};return format.default=def,format}var hasRequiredFormat;function requireFormat(){if(hasRequiredFormat)return format$1;hasRequiredFormat=1,Object.defineProperty(format$1,"__esModule",{value:!0});const format2=[requireFormat$1().default];return format$1.default=format2,format$1}var metadata={},hasRequiredMetadata;
-function requireMetadata(){return hasRequiredMetadata||(hasRequiredMetadata=1,Object.defineProperty(metadata,"__esModule",{value:!0}),metadata.contentVocabulary=metadata.metadataVocabulary=void 0,metadata.metadataVocabulary=["title","description","default","deprecated","readOnly","writeOnly","examples"],metadata.contentVocabulary=["contentMediaType","contentEncoding","contentSchema"]),metadata}var hasRequiredDraft7;function requireDraft7(){if(hasRequiredDraft7)return draft7;hasRequiredDraft7=1,Object.
-defineProperty(draft7,"__esModule",{value:!0});const core_1=requireCore(),validation_1=requireValidation(),applicator_1=requireApplicator(),format_1=requireFormat(),metadata_1=requireMetadata(),draft7Vocabularies=[core_1.default,validation_1.default,(0,applicator_1.default)(),format_1.default,metadata_1.metadataVocabulary,metadata_1.contentVocabulary];return draft7.default=draft7Vocabularies,draft7}var discriminator={},types$1={},hasRequiredTypes;function requireTypes(){if(hasRequiredTypes)return types$1;
-hasRequiredTypes=1,Object.defineProperty(types$1,"__esModule",{value:!0}),types$1.DiscrError=void 0;var DiscrError;return function(DiscrError2){DiscrError2.Tag="tag",DiscrError2.Mapping="mapping"}(DiscrError||(types$1.DiscrError=DiscrError={})),types$1}var hasRequiredDiscriminator;function requireDiscriminator(){if(hasRequiredDiscriminator)return discriminator;hasRequiredDiscriminator=1,Object.defineProperty(discriminator,"__esModule",{value:!0});const codegen_1=requireCodegen(),types_1=requireTypes(),
-compile_1=requireCompile(),ref_error_1=requireRef_error(),util_1=requireUtil(),def={keyword:"discriminator",type:"object",schemaType:"object",error:{message:({params:{discrError,tagName}})=>discrError===types_1.DiscrError.Tag?`tag "${tagName}" must be string`:`value of tag "${tagName}" must be in oneOf`,params:({params:{discrError,tag,tagName}})=>(0,codegen_1._)`{error: ${discrError}, tag: ${tagName}, tagValue: ${tag}}`},code(cxt){const{gen,data,schema,parentSchema,it}=cxt,{oneOf:oneOf2}=parentSchema;
-if(!it.opts.discriminator)throw new Error("discriminator: requires discriminator option");const tagName=schema.propertyName;if(typeof tagName!="string")throw new Error("discriminator: requires propertyName");if(schema.mapping)throw new Error("discriminator: mapping is not supported");if(!oneOf2)throw new Error("discriminator: requires oneOf keyword");const valid2=gen.let("valid",!1),tag=gen.const("tag",(0,codegen_1._)`${data}${(0,codegen_1.getProperty)(tagName)}`);gen.if((0,codegen_1._)`typeof ${tag} == "string"`,
-()=>validateMapping(),()=>cxt.error(!1,{discrError:types_1.DiscrError.Tag,tag,tagName})),cxt.ok(valid2);function validateMapping(){const mapping=getMapping();gen.if(!1);for(const tagValue in mapping)gen.elseIf((0,codegen_1._)`${tag} === ${tagValue}`),gen.assign(valid2,applyTagSchema(mapping[tagValue]));gen.else(),cxt.error(!1,{discrError:types_1.DiscrError.Mapping,tag,tagName}),gen.endIf()}function applyTagSchema(schemaProp){const _valid=gen.name("valid"),schCxt=cxt.subschema({keyword:"oneOf",schemaProp},
-_valid);return cxt.mergeEvaluated(schCxt,codegen_1.Name),_valid}function getMapping(){var _a2;const oneOfMapping={},topRequired=hasRequired(parentSchema);let tagRequired=!0;for(let i=0;i<oneOf2.length;i++){let sch=oneOf2[i];if(sch?.$ref&&!(0,util_1.schemaHasRulesButRef)(sch,it.self.RULES)){const ref2=sch.$ref;if(sch=compile_1.resolveRef.call(it.self,it.schemaEnv.root,it.baseId,ref2),sch instanceof compile_1.SchemaEnv&&(sch=sch.schema),sch===void 0)throw new ref_error_1.default(it.opts.uriResolver,
-it.baseId,ref2)}const propSch=(_a2=sch?.properties)===null||_a2===void 0?void 0:_a2[tagName];if(typeof propSch!="object")throw new Error(`discriminator: oneOf subschemas (or referenced schemas) must have "properties/${tagName}"`);tagRequired=tagRequired&&(topRequired||hasRequired(sch)),addMappings(propSch,i)}if(!tagRequired)throw new Error(`discriminator: "${tagName}" must be required`);return oneOfMapping;function hasRequired({required:required2}){return Array.isArray(required2)&&required2.includes(
-tagName)}function addMappings(sch,i){if(sch.const)addMapping(sch.const,i);else if(sch.enum)for(const tagValue of sch.enum)addMapping(tagValue,i);else throw new Error(`discriminator: "properties/${tagName}" must have "const" or "enum"`)}function addMapping(tagValue,i){if(typeof tagValue!="string"||tagValue in oneOfMapping)throw new Error(`discriminator: "${tagName}" values must be unique strings`);oneOfMapping[tagValue]=i}}}};return discriminator.default=def,discriminator}const $schema="http://js\
-on-schema.org/draft-07/schema#",$id="http://json-schema.org/draft-07/schema#",title="Core schema meta-schema",definitions={schemaArray:{type:"array",minItems:1,items:{$ref:"#"}},nonNegativeInteger:{type:"integer",minimum:0},nonNegativeIntegerDefault0:{allOf:[{$ref:"#/definitions/nonNegativeInteger"},{default:0}]},simpleTypes:{enum:["array","boolean","integer","null","number","object","string"]},stringArray:{type:"array",items:{type:"string"},uniqueItems:!0,default:[]}},type=["object","boolean"],
-properties$1={$id:{type:"string",format:"uri-reference"},$schema:{type:"string",format:"uri"},$ref:{type:"string",format:"uri-reference"},$comment:{type:"string"},title:{type:"string"},description:{type:"string"},default:!0,readOnly:{type:"boolean",default:!1},examples:{type:"array",items:!0},multipleOf:{type:"number",exclusiveMinimum:0},maximum:{type:"number"},exclusiveMaximum:{type:"number"},minimum:{type:"number"},exclusiveMinimum:{type:"number"},maxLength:{$ref:"#/definitions/nonNegativeInte\
-ger"},minLength:{$ref:"#/definitions/nonNegativeIntegerDefault0"},pattern:{type:"string",format:"regex"},additionalItems:{$ref:"#"},items:{anyOf:[{$ref:"#"},{$ref:"#/definitions/schemaArray"}],default:!0},maxItems:{$ref:"#/definitions/nonNegativeInteger"},minItems:{$ref:"#/definitions/nonNegativeIntegerDefault0"},uniqueItems:{type:"boolean",default:!1},contains:{$ref:"#"},maxProperties:{$ref:"#/definitions/nonNegativeInteger"},minProperties:{$ref:"#/definitions/nonNegativeIntegerDefault0"},required:{
-$ref:"#/definitions/stringArray"},additionalProperties:{$ref:"#"},definitions:{type:"object",additionalProperties:{$ref:"#"},default:{}},properties:{type:"object",additionalProperties:{$ref:"#"},default:{}},patternProperties:{type:"object",additionalProperties:{$ref:"#"},propertyNames:{format:"regex"},default:{}},dependencies:{type:"object",additionalProperties:{anyOf:[{$ref:"#"},{$ref:"#/definitions/stringArray"}]}},propertyNames:{$ref:"#"},const:!0,enum:{type:"array",items:!0,minItems:1,uniqueItems:!0},
-type:{anyOf:[{$ref:"#/definitions/simpleTypes"},{type:"array",items:{$ref:"#/definitions/simpleTypes"},minItems:1,uniqueItems:!0}]},format:{type:"string"},contentMediaType:{type:"string"},contentEncoding:{type:"string"},if:{$ref:"#"},then:{$ref:"#"},else:{$ref:"#"},allOf:{$ref:"#/definitions/schemaArray"},anyOf:{$ref:"#/definitions/schemaArray"},oneOf:{$ref:"#/definitions/schemaArray"},not:{$ref:"#"}},require$$3={$schema,$id,title,definitions,type,properties:properties$1,default:!0};var hasRequiredAjv;
-function requireAjv(){return hasRequiredAjv||(hasRequiredAjv=1,function(module,exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.MissingRefError=exports.ValidationError=exports.CodeGen=exports.Name=exports.nil=exports.stringify=exports.str=exports._=exports.KeywordCxt=exports.Ajv=void 0;const core_1=requireCore$1(),draft7_1=requireDraft7(),discriminator_1=requireDiscriminator(),draft7MetaSchema=require$$3,META_SUPPORT_DATA=["/properties"],META_SCHEMA_ID="http://json-schema.\
-org/draft-07/schema";class Ajv2 extends core_1.default{_addVocabularies(){super._addVocabularies(),draft7_1.default.forEach(v=>this.addVocabulary(v)),this.opts.discriminator&&this.addKeyword(discriminator_1.default)}_addDefaultMetaSchema(){if(super._addDefaultMetaSchema(),!this.opts.meta)return;const metaSchema=this.opts.$data?this.$dataMetaSchema(draft7MetaSchema,META_SUPPORT_DATA):draft7MetaSchema;this.addMetaSchema(metaSchema,META_SCHEMA_ID,!1),this.refs["http://json-schema.org/schema"]=META_SCHEMA_ID}defaultMeta(){
-return this.opts.defaultMeta=super.defaultMeta()||(this.getSchema(META_SCHEMA_ID)?META_SCHEMA_ID:void 0)}}exports.Ajv=Ajv2,module.exports=exports=Ajv2,module.exports.Ajv=Ajv2,Object.defineProperty(exports,"__esModule",{value:!0}),exports.default=Ajv2;var validate_1=requireValidate();Object.defineProperty(exports,"KeywordCxt",{enumerable:!0,get:function(){return validate_1.KeywordCxt}});var codegen_1=requireCodegen();Object.defineProperty(exports,"_",{enumerable:!0,get:function(){return codegen_1.
-_}}),Object.defineProperty(exports,"str",{enumerable:!0,get:function(){return codegen_1.str}}),Object.defineProperty(exports,"stringify",{enumerable:!0,get:function(){return codegen_1.stringify}}),Object.defineProperty(exports,"nil",{enumerable:!0,get:function(){return codegen_1.nil}}),Object.defineProperty(exports,"Name",{enumerable:!0,get:function(){return codegen_1.Name}}),Object.defineProperty(exports,"CodeGen",{enumerable:!0,get:function(){return codegen_1.CodeGen}});var validation_error_1=requireValidation_error();
-Object.defineProperty(exports,"ValidationError",{enumerable:!0,get:function(){return validation_error_1.default}});var ref_error_1=requireRef_error();Object.defineProperty(exports,"MissingRefError",{enumerable:!0,get:function(){return ref_error_1.default}})}(ajv,ajv.exports)),ajv.exports}var hasRequiredLimit;function requireLimit(){return hasRequiredLimit||(hasRequiredLimit=1,function(exports){Object.defineProperty(exports,"__esModule",{value:!0}),exports.formatLimitDefinition=void 0;const ajv_1=requireAjv(),
-codegen_1=requireCodegen(),ops=codegen_1.operators,KWDs={formatMaximum:{okStr:"<=",ok:ops.LTE,fail:ops.GT},formatMinimum:{okStr:">=",ok:ops.GTE,fail:ops.LT},formatExclusiveMaximum:{okStr:"<",ok:ops.LT,fail:ops.GTE},formatExclusiveMinimum:{okStr:">",ok:ops.GT,fail:ops.LTE}},error2={message:({keyword:keyword2,schemaCode})=>(0,codegen_1.str)`should be ${KWDs[keyword2].okStr} ${schemaCode}`,params:({keyword:keyword2,schemaCode})=>(0,codegen_1._)`{comparison: ${KWDs[keyword2].okStr}, limit: ${schemaCode}}`};
-exports.formatLimitDefinition={keyword:Object.keys(KWDs),type:"string",schemaType:"string",$data:!0,error:error2,code(cxt){const{gen,data,schemaCode,keyword:keyword2,it}=cxt,{opts,self:self2}=it;if(!opts.validateFormats)return;const fCxt=new ajv_1.KeywordCxt(it,self2.RULES.all.format.definition,"format");fCxt.$data?validate$DataFormat():validateFormat();function validate$DataFormat(){const fmts=gen.scopeValue("formats",{ref:self2.formats,code:opts.code.formats}),fmt=gen.const("fmt",(0,codegen_1.
-_)`${fmts}[${fCxt.schemaCode}]`);cxt.fail$data((0,codegen_1.or)((0,codegen_1._)`typeof ${fmt} != "object"`,(0,codegen_1._)`${fmt} instanceof RegExp`,(0,codegen_1._)`typeof ${fmt}.compare != "function"`,compareCode(fmt)))}function validateFormat(){const format2=fCxt.schema,fmtDef=self2.formats[format2];if(!fmtDef||fmtDef===!0)return;if(typeof fmtDef!="object"||fmtDef instanceof RegExp||typeof fmtDef.compare!="function")throw new Error(`"${keyword2}": format "${format2}" does not define "compare" \
-function`);const fmt=gen.scopeValue("formats",{key:format2,ref:fmtDef,code:opts.code.formats?(0,codegen_1._)`${opts.code.formats}${(0,codegen_1.getProperty)(format2)}`:void 0});cxt.fail$data(compareCode(fmt))}function compareCode(fmt){return(0,codegen_1._)`${fmt}.compare(${data}, ${schemaCode}) ${KWDs[keyword2].fail} 0`}},dependencies:["format"]};const formatLimitPlugin=ajv2=>(ajv2.addKeyword(exports.formatLimitDefinition),ajv2);exports.default=formatLimitPlugin}(limit)),limit}var hasRequiredDist;
-function requireDist(){return hasRequiredDist||(hasRequiredDist=1,function(module,exports){Object.defineProperty(exports,"__esModule",{value:!0});const formats_1=requireFormats(),limit_1=requireLimit(),codegen_1=requireCodegen(),fullName=new codegen_1.Name("fullFormats"),fastName=new codegen_1.Name("fastFormats"),formatsPlugin=(ajv2,opts={keywords:!0})=>{if(Array.isArray(opts))return addFormats(ajv2,opts,formats_1.fullFormats,fullName),ajv2;const[formats2,exportName]=opts.mode==="fast"?[formats_1.
-fastFormats,fastName]:[formats_1.fullFormats,fullName],list2=opts.formats||formats_1.formatNames;return addFormats(ajv2,list2,formats2,exportName),opts.keywords&&(0,limit_1.default)(ajv2),ajv2};formatsPlugin.get=(name,mode="full")=>{const f=(mode==="fast"?formats_1.fastFormats:formats_1.fullFormats)[name];if(!f)throw new Error(`Unknown format "${name}"`);return f};function addFormats(ajv2,list2,fs,exportName){var _a2,_b;(_a2=(_b=ajv2.opts.code).formats)!==null&&_a2!==void 0||(_b.formats=(0,codegen_1.
-_)`require("ajv-formats/dist/formats").${exportName}`);for(const f of list2)ajv2.addFormat(f,fs[f])}module.exports=exports=formatsPlugin,Object.defineProperty(exports,"__esModule",{value:!0}),exports.default=formatsPlugin}(dist,dist.exports)),dist.exports}var distExports=requireDist();const _addFormats=getDefaultExportFromCjs(distExports);function createDefaultAjvInstance(){const ajv2=new Ajv({strict:!1,validateFormats:!0,validateSchema:!1,allErrors:!0});return _addFormats(ajv2),ajv2}class AjvJsonSchemaValidator{constructor(ajv2){
-this._ajv=ajv2??createDefaultAjvInstance()}getValidator(schema){const ajvValidator="$id"in schema&&typeof schema.$id=="string"?this._ajv.getSchema(schema.$id)??this._ajv.compile(schema):this._ajv.compile(schema);return input=>ajvValidator(input)?{valid:!0,data:input,errorMessage:void 0}:{valid:!1,data:void 0,errorMessage:this._ajv.errorsText(ajvValidator.errors)}}}class ExperimentalClientTasks{constructor(_client){this._client=_client}async*callToolStream(params,resultSchema=CallToolResultSchema,options){
-const clientInternal=this._client,optionsWithTask={...options,task:options?.task??(clientInternal.isToolTask(params.name)?{}:void 0)},stream=clientInternal.requestStream({method:"tools/call",params},resultSchema,optionsWithTask),validator=clientInternal.getToolOutputValidator(params.name);for await(const message of stream){if(message.type==="result"&&validator){const result=message.result;if(!result.structuredContent&&!result.isError){yield{type:"error",error:new McpError(ErrorCode.InvalidRequest,
-`Tool ${params.name} has an output schema but did not return structured content`)};return}if(result.structuredContent)try{const validationResult=validator(result.structuredContent);if(!validationResult.valid){yield{type:"error",error:new McpError(ErrorCode.InvalidParams,`Structured content does not match the tool's output schema: ${validationResult.errorMessage}`)};return}}catch(error2){if(error2 instanceof McpError){yield{type:"error",error:error2};return}yield{type:"error",error:new McpError(ErrorCode.
-InvalidParams,`Failed to validate structured content: ${error2 instanceof Error?error2.message:String(error2)}`)};return}}yield message}}async getTask(taskId,options){return this._client.getTask({taskId},options)}async getTaskResult(taskId,resultSchema,options){return this._client.getTaskResult({taskId},resultSchema,options)}async listTasks(cursor,options){return this._client.listTasks(cursor?{cursor}:void 0,options)}async cancelTask(taskId,options){return this._client.cancelTask({taskId},options)}requestStream(request,resultSchema,options){
-return this._client.requestStream(request,resultSchema,options)}}function assertToolsCallTaskCapability(requests,method,entityName){if(!requests)throw new Error(`${entityName} does not support task creation (required for ${method})`);switch(method){case"tools/call":if(!requests.tools?.call)throw new Error(`${entityName} does not support task creation for tools/call (required for ${method})`);break}}function assertClientRequestTaskCapability(requests,method,entityName){if(!requests)throw new Error(
-`${entityName} does not support task creation (required for ${method})`);switch(method){case"sampling/createMessage":if(!requests.sampling?.createMessage)throw new Error(`${entityName} does not support task creation for sampling/createMessage (required for ${method})`);break;case"elicitation/create":if(!requests.elicitation?.create)throw new Error(`${entityName} does not support task creation for elicitation/create (required for ${method})`);break}}function applyElicitationDefaults(schema,data){
-if(!(!schema||data===null||typeof data!="object")){if(schema.type==="object"&&schema.properties&&typeof schema.properties=="object"){const obj=data,props=schema.properties;for(const key2 of Object.keys(props)){const propSchema=props[key2];obj[key2]===void 0&&Object.prototype.hasOwnProperty.call(propSchema,"default")&&(obj[key2]=propSchema.default),obj[key2]!==void 0&&applyElicitationDefaults(propSchema,obj[key2])}}if(Array.isArray(schema.anyOf))for(const sub2 of schema.anyOf)typeof sub2!="boolea\
-n"&&applyElicitationDefaults(sub2,data);if(Array.isArray(schema.oneOf))for(const sub2 of schema.oneOf)typeof sub2!="boolean"&&applyElicitationDefaults(sub2,data)}}function getSupportedElicitationModes(capabilities){if(!capabilities)return{supportsFormMode:!1,supportsUrlMode:!1};const hasFormCapability=capabilities.form!==void 0,hasUrlCapability=capabilities.url!==void 0;return{supportsFormMode:hasFormCapability||!hasFormCapability&&!hasUrlCapability,supportsUrlMode:hasUrlCapability}}class Client extends Protocol{constructor(_clientInfo,options){
-super(options),this._clientInfo=_clientInfo,this._cachedToolOutputValidators=new Map,this._cachedKnownTaskTools=new Set,this._cachedRequiredTaskTools=new Set,this._listChangedDebounceTimers=new Map,this._capabilities=options?.capabilities??{},this._jsonSchemaValidator=options?.jsonSchemaValidator??new AjvJsonSchemaValidator,options?.listChanged&&(this._pendingListChangedConfig=options.listChanged)}_setupListChangedHandlers(config2){config2.tools&&this._serverCapabilities?.tools?.listChanged&&this.
-_setupListChangedHandler("tools",ToolListChangedNotificationSchema,config2.tools,async()=>(await this.listTools()).tools),config2.prompts&&this._serverCapabilities?.prompts?.listChanged&&this._setupListChangedHandler("prompts",PromptListChangedNotificationSchema,config2.prompts,async()=>(await this.listPrompts()).prompts),config2.resources&&this._serverCapabilities?.resources?.listChanged&&this._setupListChangedHandler("resources",ResourceListChangedNotificationSchema,config2.resources,async()=>(await this.
-listResources()).resources)}get experimental(){return this._experimental||(this._experimental={tasks:new ExperimentalClientTasks(this)}),this._experimental}registerCapabilities(capabilities){if(this.transport)throw new Error("Cannot register capabilities after connecting to transport");this._capabilities=mergeCapabilities(this._capabilities,capabilities)}setRequestHandler(requestSchema,handler){const methodSchema=getObjectShape(requestSchema)?.method;if(!methodSchema)throw new Error("Schema is m\
-issing a method literal");let methodValue;if(isZ4Schema(methodSchema)){const v4Schema=methodSchema;methodValue=v4Schema._zod?.def?.value??v4Schema.value}else{const v3Schema=methodSchema;methodValue=v3Schema._def?.value??v3Schema.value}if(typeof methodValue!="string")throw new Error("Schema method literal must be a string");const method=methodValue;if(method==="elicitation/create"){const wrappedHandler=async(request,extra)=>{const validatedRequest=safeParse$1(ElicitRequestSchema,request);if(!validatedRequest.
-success){const errorMessage=validatedRequest.error instanceof Error?validatedRequest.error.message:String(validatedRequest.error);throw new McpError(ErrorCode.InvalidParams,`Invalid elicitation request: ${errorMessage}`)}const{params}=validatedRequest.data;params.mode=params.mode??"form";const{supportsFormMode,supportsUrlMode}=getSupportedElicitationModes(this._capabilities.elicitation);if(params.mode==="form"&&!supportsFormMode)throw new McpError(ErrorCode.InvalidParams,"Client does not support\
- form-mode elicitation requests");if(params.mode==="url"&&!supportsUrlMode)throw new McpError(ErrorCode.InvalidParams,"Client does not support URL-mode elicitation requests");const result=await Promise.resolve(handler(request,extra));if(params.task){const taskValidationResult=safeParse$1(CreateTaskResultSchema,result);if(!taskValidationResult.success){const errorMessage=taskValidationResult.error instanceof Error?taskValidationResult.error.message:String(taskValidationResult.error);throw new McpError(
-ErrorCode.InvalidParams,`Invalid task creation result: ${errorMessage}`)}return taskValidationResult.data}const validationResult=safeParse$1(ElicitResultSchema,result);if(!validationResult.success){const errorMessage=validationResult.error instanceof Error?validationResult.error.message:String(validationResult.error);throw new McpError(ErrorCode.InvalidParams,`Invalid elicitation result: ${errorMessage}`)}const validatedResult=validationResult.data,requestedSchema=params.mode==="form"?params.requestedSchema:
-void 0;if(params.mode==="form"&&validatedResult.action==="accept"&&validatedResult.content&&requestedSchema&&this._capabilities.elicitation?.form?.applyDefaults)try{applyElicitationDefaults(requestedSchema,validatedResult.content)}catch{}return validatedResult};return super.setRequestHandler(requestSchema,wrappedHandler)}if(method==="sampling/createMessage"){const wrappedHandler=async(request,extra)=>{const validatedRequest=safeParse$1(CreateMessageRequestSchema,request);if(!validatedRequest.success){
-const errorMessage=validatedRequest.error instanceof Error?validatedRequest.error.message:String(validatedRequest.error);throw new McpError(ErrorCode.InvalidParams,`Invalid sampling request: ${errorMessage}`)}const{params}=validatedRequest.data,result=await Promise.resolve(handler(request,extra));if(params.task){const taskValidationResult=safeParse$1(CreateTaskResultSchema,result);if(!taskValidationResult.success){const errorMessage=taskValidationResult.error instanceof Error?taskValidationResult.
-error.message:String(taskValidationResult.error);throw new McpError(ErrorCode.InvalidParams,`Invalid task creation result: ${errorMessage}`)}return taskValidationResult.data}const resultSchema=params.tools||params.toolChoice?CreateMessageResultWithToolsSchema:CreateMessageResultSchema,validationResult=safeParse$1(resultSchema,result);if(!validationResult.success){const errorMessage=validationResult.error instanceof Error?validationResult.error.message:String(validationResult.error);throw new McpError(
-ErrorCode.InvalidParams,`Invalid sampling result: ${errorMessage}`)}return validationResult.data};return super.setRequestHandler(requestSchema,wrappedHandler)}return super.setRequestHandler(requestSchema,handler)}assertCapability(capability,method){if(!this._serverCapabilities?.[capability])throw new Error(`Server does not support ${capability} (required for ${method})`)}async connect(transport,options){if(await super.connect(transport),transport.sessionId===void 0)try{const result=await this.request(
-{method:"initialize",params:{protocolVersion:LATEST_PROTOCOL_VERSION,capabilities:this._capabilities,clientInfo:this._clientInfo}},InitializeResultSchema,options);if(result===void 0)throw new Error(`Server sent invalid initialize result: ${result}`);if(!SUPPORTED_PROTOCOL_VERSIONS.includes(result.protocolVersion))throw new Error(`Server's protocol version is not supported: ${result.protocolVersion}`);this._serverCapabilities=result.capabilities,this._serverVersion=result.serverInfo,transport.setProtocolVersion&&
-transport.setProtocolVersion(result.protocolVersion),this._instructions=result.instructions,await this.notification({method:"notifications/initialized"}),this._pendingListChangedConfig&&(this._setupListChangedHandlers(this._pendingListChangedConfig),this._pendingListChangedConfig=void 0)}catch(error2){throw this.close(),error2}}getServerCapabilities(){return this._serverCapabilities}getServerVersion(){return this._serverVersion}getInstructions(){return this._instructions}assertCapabilityForMethod(method){
-switch(method){case"logging/setLevel":if(!this._serverCapabilities?.logging)throw new Error(`Server does not support logging (required for ${method})`);break;case"prompts/get":case"prompts/list":if(!this._serverCapabilities?.prompts)throw new Error(`Server does not support prompts (required for ${method})`);break;case"resources/list":case"resources/templates/list":case"resources/read":case"resources/subscribe":case"resources/unsubscribe":if(!this._serverCapabilities?.resources)throw new Error(`S\
-erver does not support resources (required for ${method})`);if(method==="resources/subscribe"&&!this._serverCapabilities.resources.subscribe)throw new Error(`Server does not support resource subscriptions (required for ${method})`);break;case"tools/call":case"tools/list":if(!this._serverCapabilities?.tools)throw new Error(`Server does not support tools (required for ${method})`);break;case"completion/complete":if(!this._serverCapabilities?.completions)throw new Error(`Server does not support com\
-pletions (required for ${method})`);break}}assertNotificationCapability(method){switch(method){case"notifications/roots/list_changed":if(!this._capabilities.roots?.listChanged)throw new Error(`Client does not support roots list changed notifications (required for ${method})`);break}}assertRequestHandlerCapability(method){if(this._capabilities)switch(method){case"sampling/createMessage":if(!this._capabilities.sampling)throw new Error(`Client does not support sampling capability (required for ${method}\
-)`);break;case"elicitation/create":if(!this._capabilities.elicitation)throw new Error(`Client does not support elicitation capability (required for ${method})`);break;case"roots/list":if(!this._capabilities.roots)throw new Error(`Client does not support roots capability (required for ${method})`);break;case"tasks/get":case"tasks/list":case"tasks/result":case"tasks/cancel":if(!this._capabilities.tasks)throw new Error(`Client does not support tasks capability (required for ${method})`);break}}assertTaskCapability(method){
-assertToolsCallTaskCapability(this._serverCapabilities?.tasks?.requests,method,"Server")}assertTaskHandlerCapability(method){this._capabilities&&assertClientRequestTaskCapability(this._capabilities.tasks?.requests,method,"Client")}async ping(options){return this.request({method:"ping"},EmptyResultSchema,options)}async complete(params,options){return this.request({method:"completion/complete",params},CompleteResultSchema,options)}async setLoggingLevel(level,options){return this.request({method:"l\
-ogging/setLevel",params:{level}},EmptyResultSchema,options)}async getPrompt(params,options){return this.request({method:"prompts/get",params},GetPromptResultSchema,options)}async listPrompts(params,options){return this.request({method:"prompts/list",params},ListPromptsResultSchema,options)}async listResources(params,options){return this.request({method:"resources/list",params},ListResourcesResultSchema,options)}async listResourceTemplates(params,options){return this.request({method:"resources/te\
-mplates/list",params},ListResourceTemplatesResultSchema,options)}async readResource(params,options){return this.request({method:"resources/read",params},ReadResourceResultSchema,options)}async subscribeResource(params,options){return this.request({method:"resources/subscribe",params},EmptyResultSchema,options)}async unsubscribeResource(params,options){return this.request({method:"resources/unsubscribe",params},EmptyResultSchema,options)}async callTool(params,resultSchema=CallToolResultSchema,options){
-if(this.isToolTaskRequired(params.name))throw new McpError(ErrorCode.InvalidRequest,`Tool "${params.name}" requires task-based execution. Use client.experimental.tasks.callToolStream() instead.`);const result=await this.request({method:"tools/call",params},resultSchema,options),validator=this.getToolOutputValidator(params.name);if(validator){if(!result.structuredContent&&!result.isError)throw new McpError(ErrorCode.InvalidRequest,`Tool ${params.name} has an output schema but did not return struc\
-tured content`);if(result.structuredContent)try{const validationResult=validator(result.structuredContent);if(!validationResult.valid)throw new McpError(ErrorCode.InvalidParams,`Structured content does not match the tool's output schema: ${validationResult.errorMessage}`)}catch(error2){throw error2 instanceof McpError?error2:new McpError(ErrorCode.InvalidParams,`Failed to validate structured content: ${error2 instanceof Error?error2.message:String(error2)}`)}}return result}isToolTask(toolName){return this.
-_serverCapabilities?.tasks?.requests?.tools?.call?this._cachedKnownTaskTools.has(toolName):!1}isToolTaskRequired(toolName){return this._cachedRequiredTaskTools.has(toolName)}cacheToolMetadata(tools){this._cachedToolOutputValidators.clear(),this._cachedKnownTaskTools.clear(),this._cachedRequiredTaskTools.clear();for(const tool of tools){if(tool.outputSchema){const toolValidator=this._jsonSchemaValidator.getValidator(tool.outputSchema);this._cachedToolOutputValidators.set(tool.name,toolValidator)}
-const taskSupport=tool.execution?.taskSupport;(taskSupport==="required"||taskSupport==="optional")&&this._cachedKnownTaskTools.add(tool.name),taskSupport==="required"&&this._cachedRequiredTaskTools.add(tool.name)}}getToolOutputValidator(toolName){return this._cachedToolOutputValidators.get(toolName)}async listTools(params,options){const result=await this.request({method:"tools/list",params},ListToolsResultSchema,options);return this.cacheToolMetadata(result.tools),result}_setupListChangedHandler(listType,notificationSchema,options,fetcher){
-const parseResult=ListChangedOptionsBaseSchema.safeParse(options);if(!parseResult.success)throw new Error(`Invalid ${listType} listChanged options: ${parseResult.error.message}`);if(typeof options.onChanged!="function")throw new Error(`Invalid ${listType} listChanged options: onChanged must be a function`);const{autoRefresh,debounceMs}=parseResult.data,{onChanged}=options,refresh=async()=>{if(!autoRefresh){onChanged(null,null);return}try{const items2=await fetcher();onChanged(null,items2)}catch(e){
-const error2=e instanceof Error?e:new Error(String(e));onChanged(error2,null)}},handler=()=>{if(debounceMs){const existingTimer=this._listChangedDebounceTimers.get(listType);existingTimer&&clearTimeout(existingTimer);const timer=setTimeout(refresh,debounceMs);this._listChangedDebounceTimers.set(listType,timer)}else refresh()};this.setNotificationHandler(notificationSchema,handler)}async sendRootsListChanged(){return this.notification({method:"notifications/roots/list_changed"})}}function normalizeHeaders(headers){
-return headers?headers instanceof Headers?Object.fromEntries(headers.entries()):Array.isArray(headers)?Object.fromEntries(headers):{...headers}:{}}function createFetchWithInit(baseFetch=fetch,baseInit){return baseInit?async(url2,init2)=>{const mergedInit={...baseInit,...init2,headers:init2?.headers?{...normalizeHeaders(baseInit.headers),...normalizeHeaders(init2.headers)}:baseInit.headers};return baseFetch(url2,mergedInit)}:baseFetch}let crypto$1;crypto$1=globalThis.crypto;async function getRandomValues(size2){
-return(await crypto$1).getRandomValues(new Uint8Array(size2))}async function random(size2){const mask="abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-._~",evenDistCutoff=Math.pow(2,8)-Math.pow(2,8)%mask.length;let result="";for(;result.length<size2;){const randomBytes=await getRandomValues(size2-result.length);for(const randomByte of randomBytes)randomByte<evenDistCutoff&&(result+=mask[randomByte%mask.length])}return result}async function generateVerifier(length){return await random(
-length)}async function generateChallenge(code_verifier){const buffer=await(await crypto$1).subtle.digest("SHA-256",new TextEncoder().encode(code_verifier));return btoa(String.fromCharCode(...new Uint8Array(buffer))).replace(/\//g,"_").replace(/\+/g,"-").replace(/=/g,"")}async function pkceChallenge(length){if(length||(length=43),length<43||length>128)throw`Expected a length between 43 and 128. Received ${length}.`;const verifier=await generateVerifier(length),challenge=await generateChallenge(verifier);
-return{code_verifier:verifier,code_challenge:challenge}}const SafeUrlSchema=url().superRefine((val,ctx)=>{if(!URL.canParse(val))return ctx.addIssue({code:ZodIssueCode.custom,message:"URL must be parseable",fatal:!0}),NEVER}).refine(url2=>{const u=new URL(url2);return u.protocol!=="javascript:"&&u.protocol!=="data:"&&u.protocol!=="vbscript:"},{message:"URL cannot use javascript:, data:, or vbscript: scheme"}),OAuthProtectedResourceMetadataSchema=looseObject({resource:string$2().url(),authorization_servers:array(
-SafeUrlSchema).optional(),jwks_uri:string$2().url().optional(),scopes_supported:array(string$2()).optional(),bearer_methods_supported:array(string$2()).optional(),resource_signing_alg_values_supported:array(string$2()).optional(),resource_name:string$2().optional(),resource_documentation:string$2().optional(),resource_policy_uri:string$2().url().optional(),resource_tos_uri:string$2().url().optional(),tls_client_certificate_bound_access_tokens:boolean$1().optional(),authorization_details_types_supported:array(
-string$2()).optional(),dpop_signing_alg_values_supported:array(string$2()).optional(),dpop_bound_access_tokens_required:boolean$1().optional()}),OAuthMetadataSchema=looseObject({issuer:string$2(),authorization_endpoint:SafeUrlSchema,token_endpoint:SafeUrlSchema,registration_endpoint:SafeUrlSchema.optional(),scopes_supported:array(string$2()).optional(),response_types_supported:array(string$2()),response_modes_supported:array(string$2()).optional(),grant_types_supported:array(string$2()).optional(),
-token_endpoint_auth_methods_supported:array(string$2()).optional(),token_endpoint_auth_signing_alg_values_supported:array(string$2()).optional(),service_documentation:SafeUrlSchema.optional(),revocation_endpoint:SafeUrlSchema.optional(),revocation_endpoint_auth_methods_supported:array(string$2()).optional(),revocation_endpoint_auth_signing_alg_values_supported:array(string$2()).optional(),introspection_endpoint:string$2().optional(),introspection_endpoint_auth_methods_supported:array(string$2()).
-optional(),introspection_endpoint_auth_signing_alg_values_supported:array(string$2()).optional(),code_challenge_methods_supported:array(string$2()).optional(),client_id_metadata_document_supported:boolean$1().optional()}),OpenIdProviderMetadataSchema=looseObject({issuer:string$2(),authorization_endpoint:SafeUrlSchema,token_endpoint:SafeUrlSchema,userinfo_endpoint:SafeUrlSchema.optional(),jwks_uri:SafeUrlSchema,registration_endpoint:SafeUrlSchema.optional(),scopes_supported:array(string$2()).optional(),
-response_types_supported:array(string$2()),response_modes_supported:array(string$2()).optional(),grant_types_supported:array(string$2()).optional(),acr_values_supported:array(string$2()).optional(),subject_types_supported:array(string$2()),id_token_signing_alg_values_supported:array(string$2()),id_token_encryption_alg_values_supported:array(string$2()).optional(),id_token_encryption_enc_values_supported:array(string$2()).optional(),userinfo_signing_alg_values_supported:array(string$2()).optional(),
-userinfo_encryption_alg_values_supported:array(string$2()).optional(),userinfo_encryption_enc_values_supported:array(string$2()).optional(),request_object_signing_alg_values_supported:array(string$2()).optional(),request_object_encryption_alg_values_supported:array(string$2()).optional(),request_object_encryption_enc_values_supported:array(string$2()).optional(),token_endpoint_auth_methods_supported:array(string$2()).optional(),token_endpoint_auth_signing_alg_values_supported:array(string$2()).optional(),
-display_values_supported:array(string$2()).optional(),claim_types_supported:array(string$2()).optional(),claims_supported:array(string$2()).optional(),service_documentation:string$2().optional(),claims_locales_supported:array(string$2()).optional(),ui_locales_supported:array(string$2()).optional(),claims_parameter_supported:boolean$1().optional(),request_parameter_supported:boolean$1().optional(),request_uri_parameter_supported:boolean$1().optional(),require_request_uri_registration:boolean$1().
-optional(),op_policy_uri:SafeUrlSchema.optional(),op_tos_uri:SafeUrlSchema.optional(),client_id_metadata_document_supported:boolean$1().optional()}),OpenIdProviderDiscoveryMetadataSchema=object({...OpenIdProviderMetadataSchema.shape,...OAuthMetadataSchema.pick({code_challenge_methods_supported:!0}).shape}),OAuthTokensSchema=object({access_token:string$2(),id_token:string$2().optional(),token_type:string$2(),expires_in:number$1().optional(),scope:string$2().optional(),refresh_token:string$2().optional()}).
-strip(),OAuthErrorResponseSchema=object({error:string$2(),error_description:string$2().optional(),error_uri:string$2().optional()}),OptionalSafeUrlSchema=SafeUrlSchema.optional().or(literal("").transform(()=>{})),OAuthClientMetadataSchema=object({redirect_uris:array(SafeUrlSchema),token_endpoint_auth_method:string$2().optional(),grant_types:array(string$2()).optional(),response_types:array(string$2()).optional(),client_name:string$2().optional(),client_uri:SafeUrlSchema.optional(),logo_uri:OptionalSafeUrlSchema,
-scope:string$2().optional(),contacts:array(string$2()).optional(),tos_uri:OptionalSafeUrlSchema,policy_uri:string$2().optional(),jwks_uri:SafeUrlSchema.optional(),jwks:any().optional(),software_id:string$2().optional(),software_version:string$2().optional(),software_statement:string$2().optional()}).strip(),OAuthClientInformationSchema=object({client_id:string$2(),client_secret:string$2().optional(),client_id_issued_at:number$2().optional(),client_secret_expires_at:number$2().optional()}).strip(),
-OAuthClientInformationFullSchema=OAuthClientMetadataSchema.merge(OAuthClientInformationSchema);object({error:string$2(),error_description:string$2().optional()}).strip();object({token:string$2(),token_type_hint:string$2().optional()}).strip();function resourceUrlFromServerUrl(url2){const resourceURL=typeof url2=="string"?new URL(url2):new URL(url2.href);return resourceURL.hash="",resourceURL}function checkResourceAllowed({requestedResource,configuredResource}){const requested=typeof requestedResource==
-"string"?new URL(requestedResource):new URL(requestedResource.href),configured=typeof configuredResource=="string"?new URL(configuredResource):new URL(configuredResource.href);if(requested.origin!==configured.origin||requested.pathname.length<configured.pathname.length)return!1;const requestedPath=requested.pathname.endsWith("/")?requested.pathname:requested.pathname+"/",configuredPath=configured.pathname.endsWith("/")?configured.pathname:configured.pathname+"/";return requestedPath.startsWith(configuredPath)}
-class OAuthError extends Error{constructor(message,errorUri){super(message),this.errorUri=errorUri,this.name=this.constructor.name}toResponseObject(){const response={error:this.errorCode,error_description:this.message};return this.errorUri&&(response.error_uri=this.errorUri),response}get errorCode(){return this.constructor.errorCode}}class InvalidRequestError extends OAuthError{}InvalidRequestError.errorCode="invalid_request";class InvalidClientError extends OAuthError{}InvalidClientError.errorCode=
-"invalid_client";class InvalidGrantError extends OAuthError{}InvalidGrantError.errorCode="invalid_grant";class UnauthorizedClientError extends OAuthError{}UnauthorizedClientError.errorCode="unauthorized_client";class UnsupportedGrantTypeError extends OAuthError{}UnsupportedGrantTypeError.errorCode="unsupported_grant_type";class InvalidScopeError extends OAuthError{}InvalidScopeError.errorCode="invalid_scope";class AccessDeniedError extends OAuthError{}AccessDeniedError.errorCode="access_denied";
-class ServerError extends OAuthError{}ServerError.errorCode="server_error";class TemporarilyUnavailableError extends OAuthError{}TemporarilyUnavailableError.errorCode="temporarily_unavailable";class UnsupportedResponseTypeError extends OAuthError{}UnsupportedResponseTypeError.errorCode="unsupported_response_type";class UnsupportedTokenTypeError extends OAuthError{}UnsupportedTokenTypeError.errorCode="unsupported_token_type";class InvalidTokenError extends OAuthError{}InvalidTokenError.errorCode=
-"invalid_token";class MethodNotAllowedError extends OAuthError{}MethodNotAllowedError.errorCode="method_not_allowed";class TooManyRequestsError extends OAuthError{}TooManyRequestsError.errorCode="too_many_requests";class InvalidClientMetadataError extends OAuthError{}InvalidClientMetadataError.errorCode="invalid_client_metadata";class InsufficientScopeError extends OAuthError{}InsufficientScopeError.errorCode="insufficient_scope";class InvalidTargetError extends OAuthError{}InvalidTargetError.errorCode=
-"invalid_target";const OAUTH_ERRORS={[InvalidRequestError.errorCode]:InvalidRequestError,[InvalidClientError.errorCode]:InvalidClientError,[InvalidGrantError.errorCode]:InvalidGrantError,[UnauthorizedClientError.errorCode]:UnauthorizedClientError,[UnsupportedGrantTypeError.errorCode]:UnsupportedGrantTypeError,[InvalidScopeError.errorCode]:InvalidScopeError,[AccessDeniedError.errorCode]:AccessDeniedError,[ServerError.errorCode]:ServerError,[TemporarilyUnavailableError.errorCode]:TemporarilyUnavailableError,
-[UnsupportedResponseTypeError.errorCode]:UnsupportedResponseTypeError,[UnsupportedTokenTypeError.errorCode]:UnsupportedTokenTypeError,[InvalidTokenError.errorCode]:InvalidTokenError,[MethodNotAllowedError.errorCode]:MethodNotAllowedError,[TooManyRequestsError.errorCode]:TooManyRequestsError,[InvalidClientMetadataError.errorCode]:InvalidClientMetadataError,[InsufficientScopeError.errorCode]:InsufficientScopeError,[InvalidTargetError.errorCode]:InvalidTargetError};class UnauthorizedError extends Error{constructor(message){
-super(message??"Unauthorized")}}function isClientAuthMethod(method){return["client_secret_basic","client_secret_post","none"].includes(method)}const AUTHORIZATION_CODE_RESPONSE_TYPE="code",AUTHORIZATION_CODE_CHALLENGE_METHOD="S256";function selectClientAuthMethod(clientInformation,supportedMethods){const hasClientSecret=clientInformation.client_secret!==void 0;return supportedMethods.length===0?hasClientSecret?"client_secret_post":"none":"token_endpoint_auth_method"in clientInformation&&clientInformation.
-token_endpoint_auth_method&&isClientAuthMethod(clientInformation.token_endpoint_auth_method)&&supportedMethods.includes(clientInformation.token_endpoint_auth_method)?clientInformation.token_endpoint_auth_method:hasClientSecret&&supportedMethods.includes("client_secret_basic")?"client_secret_basic":hasClientSecret&&supportedMethods.includes("client_secret_post")?"client_secret_post":supportedMethods.includes("none")?"none":hasClientSecret?"client_secret_post":"none"}function applyClientAuthentication(method,clientInformation,headers,params){
-const{client_id,client_secret}=clientInformation;switch(method){case"client_secret_basic":applyBasicAuth(client_id,client_secret,headers);return;case"client_secret_post":applyPostAuth(client_id,client_secret,params);return;case"none":applyPublicAuth(client_id,params);return;default:throw new Error(`Unsupported client authentication method: ${method}`)}}function applyBasicAuth(clientId,clientSecret,headers){if(!clientSecret)throw new Error("client_secret_basic authentication requires a client_sec\
-ret");const credentials=btoa(`${clientId}:${clientSecret}`);headers.set("Authorization",`Basic ${credentials}`)}function applyPostAuth(clientId,clientSecret,params){params.set("client_id",clientId),clientSecret&&params.set("client_secret",clientSecret)}function applyPublicAuth(clientId,params){params.set("client_id",clientId)}async function parseErrorResponse(input){const statusCode=input instanceof Response?input.status:void 0,body2=input instanceof Response?await input.text():input;try{const result=OAuthErrorResponseSchema.
-parse(JSON.parse(body2)),{error:error2,error_description,error_uri}=result,errorClass=OAUTH_ERRORS[error2]||ServerError;return new errorClass(error_description||"",error_uri)}catch(error2){const errorMessage=`${statusCode?`HTTP ${statusCode}: `:""}Invalid OAuth error response: ${error2}. Raw body: ${body2}`;return new ServerError(errorMessage)}}async function auth(provider,options){try{return await authInternal(provider,options)}catch(error2){if(error2 instanceof InvalidClientError||error2 instanceof
-UnauthorizedClientError)return await provider.invalidateCredentials?.("all"),await authInternal(provider,options);if(error2 instanceof InvalidGrantError)return await provider.invalidateCredentials?.("tokens"),await authInternal(provider,options);throw error2}}async function authInternal(provider,{serverUrl,authorizationCode,scope:scope2,resourceMetadataUrl,fetchFn}){let resourceMetadata,authorizationServerUrl;try{resourceMetadata=await discoverOAuthProtectedResourceMetadata(serverUrl,{resourceMetadataUrl},
-fetchFn),resourceMetadata.authorization_servers&&resourceMetadata.authorization_servers.length>0&&(authorizationServerUrl=resourceMetadata.authorization_servers[0])}catch{}authorizationServerUrl||(authorizationServerUrl=new URL("/",serverUrl));const resource=await selectResourceURL(serverUrl,provider,resourceMetadata),metadata2=await discoverAuthorizationServerMetadata(authorizationServerUrl,{fetchFn});let clientInformation=await Promise.resolve(provider.clientInformation());if(!clientInformation){
-if(authorizationCode!==void 0)throw new Error("Existing OAuth client information is required when exchanging an authorization code");const supportsUrlBasedClientId=metadata2?.client_id_metadata_document_supported===!0,clientMetadataUrl=provider.clientMetadataUrl;if(clientMetadataUrl&&!isHttpsUrl(clientMetadataUrl))throw new InvalidClientMetadataError(`clientMetadataUrl must be a valid HTTPS URL with a non-root pathname, got: ${clientMetadataUrl}`);if(supportsUrlBasedClientId&&clientMetadataUrl)clientInformation=
-{client_id:clientMetadataUrl},await provider.saveClientInformation?.(clientInformation);else{if(!provider.saveClientInformation)throw new Error("OAuth client information must be saveable for dynamic registration");const fullInformation=await registerClient(authorizationServerUrl,{metadata:metadata2,clientMetadata:provider.clientMetadata,fetchFn});await provider.saveClientInformation(fullInformation),clientInformation=fullInformation}}const nonInteractiveFlow=!provider.redirectUrl;if(authorizationCode!==
-void 0||nonInteractiveFlow){const tokens2=await fetchToken(provider,authorizationServerUrl,{metadata:metadata2,resource,authorizationCode,fetchFn});return await provider.saveTokens(tokens2),"AUTHORIZED"}const tokens=await provider.tokens();if(tokens?.refresh_token)try{const newTokens=await refreshAuthorization(authorizationServerUrl,{metadata:metadata2,clientInformation,refreshToken:tokens.refresh_token,resource,addClientAuthentication:provider.addClientAuthentication,fetchFn});return await provider.
-saveTokens(newTokens),"AUTHORIZED"}catch(error2){if(!(!(error2 instanceof OAuthError)||error2 instanceof ServerError))throw error2}const state2=provider.state?await provider.state():void 0,{authorizationUrl,codeVerifier}=await startAuthorization(authorizationServerUrl,{metadata:metadata2,clientInformation,state:state2,redirectUrl:provider.redirectUrl,scope:scope2||resourceMetadata?.scopes_supported?.join(" ")||provider.clientMetadata.scope,resource});return await provider.saveCodeVerifier(codeVerifier),
-await provider.redirectToAuthorization(authorizationUrl),"REDIRECT"}function isHttpsUrl(value){if(!value)return!1;try{const url2=new URL(value);return url2.protocol==="https:"&&url2.pathname!=="/"}catch{return!1}}async function selectResourceURL(serverUrl,provider,resourceMetadata){const defaultResource=resourceUrlFromServerUrl(serverUrl);if(provider.validateResourceURL)return await provider.validateResourceURL(defaultResource,resourceMetadata?.resource);if(resourceMetadata){if(!checkResourceAllowed(
-{requestedResource:defaultResource,configuredResource:resourceMetadata.resource}))throw new Error(`Protected resource ${resourceMetadata.resource} does not match expected ${defaultResource} (or origin)`);return new URL(resourceMetadata.resource)}}function extractWWWAuthenticateParams(res){const authenticateHeader=res.headers.get("WWW-Authenticate");if(!authenticateHeader)return{};const[type2,scheme2]=authenticateHeader.split(" ");if(type2.toLowerCase()!=="bearer"||!scheme2)return{};const resourceMetadataMatch=extractFieldFromWwwAuth(
-res,"resource_metadata")||void 0;let resourceMetadataUrl;if(resourceMetadataMatch)try{resourceMetadataUrl=new URL(resourceMetadataMatch)}catch{}const scope2=extractFieldFromWwwAuth(res,"scope")||void 0,error2=extractFieldFromWwwAuth(res,"error")||void 0;return{resourceMetadataUrl,scope:scope2,error:error2}}function extractFieldFromWwwAuth(response,fieldName){const wwwAuthHeader=response.headers.get("WWW-Authenticate");if(!wwwAuthHeader)return null;const pattern2=new RegExp(`${fieldName}=(?:"([^"\
-]+)"|([^\\s,]+))`),match=wwwAuthHeader.match(pattern2);return match?match[1]||match[2]:null}async function discoverOAuthProtectedResourceMetadata(serverUrl,opts,fetchFn=fetch){const response=await discoverMetadataWithFallback(serverUrl,"oauth-protected-resource",fetchFn,{protocolVersion:opts?.protocolVersion,metadataUrl:opts?.resourceMetadataUrl});if(!response||response.status===404)throw await response?.body?.cancel(),new Error("Resource server does not implement OAuth 2.0 Protected Resource Me\
-tadata.");if(!response.ok)throw await response.body?.cancel(),new Error(`HTTP ${response.status} trying to load well-known OAuth protected resource metadata.`);return OAuthProtectedResourceMetadataSchema.parse(await response.json())}async function fetchWithCorsRetry(url2,headers,fetchFn=fetch){try{return await fetchFn(url2,{headers})}catch(error2){if(error2 instanceof TypeError)return headers?fetchWithCorsRetry(url2,void 0,fetchFn):void 0;throw error2}}function buildWellKnownPath(wellKnownPrefix,pathname="",options={}){
-return pathname.endsWith("/")&&(pathname=pathname.slice(0,-1)),options.prependPathname?`${pathname}/.well-known/${wellKnownPrefix}`:`/.well-known/${wellKnownPrefix}${pathname}`}async function tryMetadataDiscovery(url2,protocolVersion,fetchFn=fetch){return await fetchWithCorsRetry(url2,{"MCP-Protocol-Version":protocolVersion},fetchFn)}function shouldAttemptFallback(response,pathname){return!response||response.status>=400&&response.status<500&&pathname!=="/"}async function discoverMetadataWithFallback(serverUrl,wellKnownType,fetchFn,opts){
-const issuer=new URL(serverUrl),protocolVersion=opts?.protocolVersion??LATEST_PROTOCOL_VERSION;let url2;if(opts?.metadataUrl)url2=new URL(opts.metadataUrl);else{const wellKnownPath=buildWellKnownPath(wellKnownType,issuer.pathname);url2=new URL(wellKnownPath,opts?.metadataServerUrl??issuer),url2.search=issuer.search}let response=await tryMetadataDiscovery(url2,protocolVersion,fetchFn);if(!opts?.metadataUrl&&shouldAttemptFallback(response,issuer.pathname)){const rootUrl=new URL(`/.well-known/${wellKnownType}`,
-issuer);response=await tryMetadataDiscovery(rootUrl,protocolVersion,fetchFn)}return response}function buildDiscoveryUrls(authorizationServerUrl){const url2=typeof authorizationServerUrl=="string"?new URL(authorizationServerUrl):authorizationServerUrl,hasPath=url2.pathname!=="/",urlsToTry=[];if(!hasPath)return urlsToTry.push({url:new URL("/.well-known/oauth-authorization-server",url2.origin),type:"oauth"}),urlsToTry.push({url:new URL("/.well-known/openid-configuration",url2.origin),type:"oidc"}),
-urlsToTry;let pathname=url2.pathname;return pathname.endsWith("/")&&(pathname=pathname.slice(0,-1)),urlsToTry.push({url:new URL(`/.well-known/oauth-authorization-server${pathname}`,url2.origin),type:"oauth"}),urlsToTry.push({url:new URL(`/.well-known/openid-configuration${pathname}`,url2.origin),type:"oidc"}),urlsToTry.push({url:new URL(`${pathname}/.well-known/openid-configuration`,url2.origin),type:"oidc"}),urlsToTry}async function discoverAuthorizationServerMetadata(authorizationServerUrl,{fetchFn=fetch,
-protocolVersion=LATEST_PROTOCOL_VERSION}={}){const headers={"MCP-Protocol-Version":protocolVersion,Accept:"application/json"},urlsToTry=buildDiscoveryUrls(authorizationServerUrl);for(const{url:endpointUrl,type:type2}of urlsToTry){const response=await fetchWithCorsRetry(endpointUrl,headers,fetchFn);if(response){if(!response.ok){if(await response.body?.cancel(),response.status>=400&&response.status<500)continue;throw new Error(`HTTP ${response.status} trying to load ${type2==="oauth"?"OAuth":"Open\
-ID provider"} metadata from ${endpointUrl}`)}return type2==="oauth"?OAuthMetadataSchema.parse(await response.json()):OpenIdProviderDiscoveryMetadataSchema.parse(await response.json())}}}async function startAuthorization(authorizationServerUrl,{metadata:metadata2,clientInformation,redirectUrl,scope:scope2,state:state2,resource}){let authorizationUrl;if(metadata2){if(authorizationUrl=new URL(metadata2.authorization_endpoint),!metadata2.response_types_supported.includes(AUTHORIZATION_CODE_RESPONSE_TYPE))
-throw new Error(`Incompatible auth server: does not support response type ${AUTHORIZATION_CODE_RESPONSE_TYPE}`);if(metadata2.code_challenge_methods_supported&&!metadata2.code_challenge_methods_supported.includes(AUTHORIZATION_CODE_CHALLENGE_METHOD))throw new Error(`Incompatible auth server: does not support code challenge method ${AUTHORIZATION_CODE_CHALLENGE_METHOD}`)}else authorizationUrl=new URL("/authorize",authorizationServerUrl);const challenge=await pkceChallenge(),codeVerifier=challenge.
-code_verifier,codeChallenge=challenge.code_challenge;return authorizationUrl.searchParams.set("response_type",AUTHORIZATION_CODE_RESPONSE_TYPE),authorizationUrl.searchParams.set("client_id",clientInformation.client_id),authorizationUrl.searchParams.set("code_challenge",codeChallenge),authorizationUrl.searchParams.set("code_challenge_method",AUTHORIZATION_CODE_CHALLENGE_METHOD),authorizationUrl.searchParams.set("redirect_uri",String(redirectUrl)),state2&&authorizationUrl.searchParams.set("state",
-state2),scope2&&authorizationUrl.searchParams.set("scope",scope2),scope2?.includes("offline_access")&&authorizationUrl.searchParams.append("prompt","consent"),resource&&authorizationUrl.searchParams.set("resource",resource.href),{authorizationUrl,codeVerifier}}function prepareAuthorizationCodeRequest(authorizationCode,codeVerifier,redirectUri){return new URLSearchParams({grant_type:"authorization_code",code:authorizationCode,code_verifier:codeVerifier,redirect_uri:String(redirectUri)})}async function executeTokenRequest(authorizationServerUrl,{
-metadata:metadata2,tokenRequestParams,clientInformation,addClientAuthentication,resource,fetchFn}){const tokenUrl=metadata2?.token_endpoint?new URL(metadata2.token_endpoint):new URL("/token",authorizationServerUrl),headers=new Headers({"Content-Type":"application/x-www-form-urlencoded",Accept:"application/json"});if(resource&&tokenRequestParams.set("resource",resource.href),addClientAuthentication)await addClientAuthentication(headers,tokenRequestParams,tokenUrl,metadata2);else if(clientInformation){
-const supportedMethods=metadata2?.token_endpoint_auth_methods_supported??[],authMethod=selectClientAuthMethod(clientInformation,supportedMethods);applyClientAuthentication(authMethod,clientInformation,headers,tokenRequestParams)}const response=await(fetchFn??fetch)(tokenUrl,{method:"POST",headers,body:tokenRequestParams});if(!response.ok)throw await parseErrorResponse(response);return OAuthTokensSchema.parse(await response.json())}async function refreshAuthorization(authorizationServerUrl,{metadata:metadata2,
-clientInformation,refreshToken,resource,addClientAuthentication,fetchFn}){const tokenRequestParams=new URLSearchParams({grant_type:"refresh_token",refresh_token:refreshToken}),tokens=await executeTokenRequest(authorizationServerUrl,{metadata:metadata2,tokenRequestParams,clientInformation,addClientAuthentication,resource,fetchFn});return{refresh_token:refreshToken,...tokens}}async function fetchToken(provider,authorizationServerUrl,{metadata:metadata2,resource,authorizationCode,fetchFn}={}){const scope2=provider.
-clientMetadata.scope;let tokenRequestParams;if(provider.prepareTokenRequest&&(tokenRequestParams=await provider.prepareTokenRequest(scope2)),!tokenRequestParams){if(!authorizationCode)throw new Error("Either provider.prepareTokenRequest() or authorizationCode is required");if(!provider.redirectUrl)throw new Error("redirectUrl is required for authorization_code flow");const codeVerifier=await provider.codeVerifier();tokenRequestParams=prepareAuthorizationCodeRequest(authorizationCode,codeVerifier,
-provider.redirectUrl)}const clientInformation=await provider.clientInformation();return executeTokenRequest(authorizationServerUrl,{metadata:metadata2,tokenRequestParams,clientInformation:clientInformation??void 0,addClientAuthentication:provider.addClientAuthentication,resource,fetchFn})}async function registerClient(authorizationServerUrl,{metadata:metadata2,clientMetadata,fetchFn}){let registrationUrl;if(metadata2){if(!metadata2.registration_endpoint)throw new Error("Incompatible auth server:\
- does not support dynamic client registration");registrationUrl=new URL(metadata2.registration_endpoint)}else registrationUrl=new URL("/register",authorizationServerUrl);const response=await(fetchFn??fetch)(registrationUrl,{method:"POST",headers:{"Content-Type":"application/json"},body:JSON.stringify(clientMetadata)});if(!response.ok)throw await parseErrorResponse(response);return OAuthClientInformationFullSchema.parse(await response.json())}let ParseError$1=class extends Error{constructor(message,options){
-super(message),this.name="ParseError",this.type=options.type,this.field=options.field,this.value=options.value,this.line=options.line}};function noop(_arg){}function createParser$1(callbacks){if(typeof callbacks=="function")throw new TypeError("`callbacks` must be an object, got a function instead. Did you mean `{onEvent: fn}`?");const{onEvent=noop,onError=noop,onRetry=noop,onComment}=callbacks;let incompleteLine="",isFirstChunk=!0,id2,data="",eventType="";function feed(newChunk){const chunk=isFirstChunk?
-newChunk.replace(/^\xEF\xBB\xBF/,""):newChunk,[complete,incomplete]=splitLines(`${incompleteLine}${chunk}`);for(const line of complete)parseLine(line);incompleteLine=incomplete,isFirstChunk=!1}function parseLine(line){if(line===""){dispatchEvent2();return}if(line.startsWith(":")){onComment&&onComment(line.slice(line.startsWith(": ")?2:1));return}const fieldSeparatorIndex=line.indexOf(":");if(fieldSeparatorIndex!==-1){const field=line.slice(0,fieldSeparatorIndex),offset2=line[fieldSeparatorIndex+
-1]===" "?2:1,value=line.slice(fieldSeparatorIndex+offset2);processField(field,value,line);return}processField(line,"",line)}function processField(field,value,line){switch(field){case"event":eventType=value;break;case"data":data=`${data}${value}
-`;break;case"id":id2=value.includes("\0")?void 0:value;break;case"retry":/^\d+$/.test(value)?onRetry(parseInt(value,10)):onError(new ParseError$1(`Invalid \`retry\` value: "${value}"`,{type:"invalid-retry",value,line}));break;default:onError(new ParseError$1(`Unknown field "${field.length>20?`${field.slice(0,20)}…`:field}"`,{type:"unknown-field",field,value,line}));break}}function dispatchEvent2(){data.length>0&&onEvent({id:id2,event:eventType||void 0,data:data.endsWith(`
-`)?data.slice(0,-1):data}),id2=void 0,data="",eventType=""}function reset2(options={}){incompleteLine&&options.consume&&parseLine(incompleteLine),isFirstChunk=!0,id2=void 0,data="",eventType="",incompleteLine=""}return{feed,reset:reset2}}function splitLines(chunk){const lines=[];let incompleteLine="",searchIndex=0;for(;searchIndex<chunk.length;){const crIndex=chunk.indexOf("\r",searchIndex),lfIndex=chunk.indexOf(`
-`,searchIndex);let lineEnd=-1;if(crIndex!==-1&&lfIndex!==-1?lineEnd=Math.min(crIndex,lfIndex):crIndex!==-1?crIndex===chunk.length-1?lineEnd=-1:lineEnd=crIndex:lfIndex!==-1&&(lineEnd=lfIndex),lineEnd===-1){incompleteLine=chunk.slice(searchIndex);break}else{const line=chunk.slice(searchIndex,lineEnd);lines.push(line),searchIndex=lineEnd+1,chunk[searchIndex-1]==="\r"&&chunk[searchIndex]===`
-`&&searchIndex++}}return[lines,incompleteLine]}class EventSourceParserStream extends TransformStream{constructor({onError,onRetry,onComment}={}){let parser;super({start(controller){parser=createParser$1({onEvent:event2=>{controller.enqueue(event2)},onError(error2){onError==="terminate"?controller.error(error2):typeof onError=="function"&&onError(error2)},onRetry,onComment})},transform(chunk){parser.feed(chunk)}})}}const DEFAULT_STREAMABLE_HTTP_RECONNECTION_OPTIONS={initialReconnectionDelay:1e3,maxReconnectionDelay:3e4,
-reconnectionDelayGrowFactor:1.5,maxRetries:2};class StreamableHTTPError extends Error{constructor(code2,message){super(`Streamable HTTP error: ${message}`),this.code=code2}}class StreamableHTTPClientTransport{constructor(url2,opts){this._hasCompletedAuthFlow=!1,this._url=url2,this._resourceMetadataUrl=void 0,this._scope=void 0,this._requestInit=opts?.requestInit,this._authProvider=opts?.authProvider,this._fetch=opts?.fetch,this._fetchWithInit=createFetchWithInit(opts?.fetch,opts?.requestInit),this.
-_sessionId=opts?.sessionId,this._reconnectionOptions=opts?.reconnectionOptions??DEFAULT_STREAMABLE_HTTP_RECONNECTION_OPTIONS}async _authThenStart(){if(!this._authProvider)throw new UnauthorizedError("No auth provider");let result;try{result=await auth(this._authProvider,{serverUrl:this._url,resourceMetadataUrl:this._resourceMetadataUrl,scope:this._scope,fetchFn:this._fetchWithInit})}catch(error2){throw this.onerror?.(error2),error2}if(result!=="AUTHORIZED")throw new UnauthorizedError;return await this.
-_startOrAuthSse({resumptionToken:void 0})}async _commonHeaders(){const headers={};if(this._authProvider){const tokens=await this._authProvider.tokens();tokens&&(headers.Authorization=`Bearer ${tokens.access_token}`)}this._sessionId&&(headers["mcp-session-id"]=this._sessionId),this._protocolVersion&&(headers["mcp-protocol-version"]=this._protocolVersion);const extraHeaders=normalizeHeaders(this._requestInit?.headers);return new Headers({...headers,...extraHeaders})}async _startOrAuthSse(options){
-const{resumptionToken}=options;try{const headers=await this._commonHeaders();headers.set("Accept","text/event-stream"),resumptionToken&&headers.set("last-event-id",resumptionToken);const response=await(this._fetch??fetch)(this._url,{method:"GET",headers,signal:this._abortController?.signal});if(!response.ok){if(await response.body?.cancel(),response.status===401&&this._authProvider)return await this._authThenStart();if(response.status===405)return;throw new StreamableHTTPError(response.status,`F\
-ailed to open SSE stream: ${response.statusText}`)}this._handleSseStream(response.body,options,!0)}catch(error2){throw this.onerror?.(error2),error2}}_getNextReconnectionDelay(attempt){if(this._serverRetryMs!==void 0)return this._serverRetryMs;const initialDelay=this._reconnectionOptions.initialReconnectionDelay,growFactor=this._reconnectionOptions.reconnectionDelayGrowFactor,maxDelay=this._reconnectionOptions.maxReconnectionDelay;return Math.min(initialDelay*Math.pow(growFactor,attempt),maxDelay)}_scheduleReconnection(options,attemptCount=0){
-const maxRetries=this._reconnectionOptions.maxRetries;if(attemptCount>=maxRetries){this.onerror?.(new Error(`Maximum reconnection attempts (${maxRetries}) exceeded.`));return}const delay=this._getNextReconnectionDelay(attemptCount);this._reconnectionTimeout=setTimeout(()=>{this._startOrAuthSse(options).catch(error2=>{this.onerror?.(new Error(`Failed to reconnect SSE stream: ${error2 instanceof Error?error2.message:String(error2)}`)),this._scheduleReconnection(options,attemptCount+1)})},delay)}_handleSseStream(stream,options,isReconnectable){
-if(!stream)return;const{onresumptiontoken,replayMessageId}=options;let lastEventId,hasPrimingEvent=!1,receivedResponse=!1;(async()=>{try{const reader=stream.pipeThrough(new TextDecoderStream).pipeThrough(new EventSourceParserStream({onRetry:retryMs=>{this._serverRetryMs=retryMs}})).getReader();for(;;){const{value:event2,done}=await reader.read();if(done)break;if(event2.id&&(lastEventId=event2.id,hasPrimingEvent=!0,onresumptiontoken?.(event2.id)),!!event2.data&&(!event2.event||event2.event==="mes\
-sage"))try{const message=JSONRPCMessageSchema.parse(JSON.parse(event2.data));isJSONRPCResultResponse(message)&&(receivedResponse=!0,replayMessageId!==void 0&&(message.id=replayMessageId)),this.onmessage?.(message)}catch(error2){this.onerror?.(error2)}}(isReconnectable||hasPrimingEvent)&&!receivedResponse&&this._abortController&&!this._abortController.signal.aborted&&this._scheduleReconnection({resumptionToken:lastEventId,onresumptiontoken,replayMessageId},0)}catch(error2){if(this.onerror?.(new Error(
-`SSE stream disconnected: ${error2}`)),(isReconnectable||hasPrimingEvent)&&!receivedResponse&&this._abortController&&!this._abortController.signal.aborted)try{this._scheduleReconnection({resumptionToken:lastEventId,onresumptiontoken,replayMessageId},0)}catch(error3){this.onerror?.(new Error(`Failed to reconnect: ${error3 instanceof Error?error3.message:String(error3)}`))}}})()}async start(){if(this._abortController)throw new Error("StreamableHTTPClientTransport already started! If using Client c\
-lass, note that connect() calls start() automatically.");this._abortController=new AbortController}async finishAuth(authorizationCode){if(!this._authProvider)throw new UnauthorizedError("No auth provider");if(await auth(this._authProvider,{serverUrl:this._url,authorizationCode,resourceMetadataUrl:this._resourceMetadataUrl,scope:this._scope,fetchFn:this._fetchWithInit})!=="AUTHORIZED")throw new UnauthorizedError("Failed to authorize")}async close(){this._reconnectionTimeout&&(clearTimeout(this._reconnectionTimeout),
-this._reconnectionTimeout=void 0),this._abortController?.abort(),this.onclose?.()}async send(message,options){try{const{resumptionToken,onresumptiontoken}=options||{};if(resumptionToken){this._startOrAuthSse({resumptionToken,replayMessageId:isJSONRPCRequest(message)?message.id:void 0}).catch(err=>this.onerror?.(err));return}const headers=await this._commonHeaders();headers.set("content-type","application/json"),headers.set("accept","application/json, text/event-stream");const init2={...this._requestInit,
-method:"POST",headers,body:JSON.stringify(message),signal:this._abortController?.signal},response=await(this._fetch??fetch)(this._url,init2),sessionId=response.headers.get("mcp-session-id");if(sessionId&&(this._sessionId=sessionId),!response.ok){const text2=await response.text().catch(()=>null);if(response.status===401&&this._authProvider){if(this._hasCompletedAuthFlow)throw new StreamableHTTPError(401,"Server returned 401 after successful authentication");const{resourceMetadataUrl,scope:scope2}=extractWWWAuthenticateParams(
-response);if(this._resourceMetadataUrl=resourceMetadataUrl,this._scope=scope2,await auth(this._authProvider,{serverUrl:this._url,resourceMetadataUrl:this._resourceMetadataUrl,scope:this._scope,fetchFn:this._fetchWithInit})!=="AUTHORIZED")throw new UnauthorizedError;return this._hasCompletedAuthFlow=!0,this.send(message)}if(response.status===403&&this._authProvider){const{resourceMetadataUrl,scope:scope2,error:error2}=extractWWWAuthenticateParams(response);if(error2==="insufficient_scope"){const wwwAuthHeader=response.
-headers.get("WWW-Authenticate");if(this._lastUpscopingHeader===wwwAuthHeader)throw new StreamableHTTPError(403,"Server returned 403 after trying upscoping");if(scope2&&(this._scope=scope2),resourceMetadataUrl&&(this._resourceMetadataUrl=resourceMetadataUrl),this._lastUpscopingHeader=wwwAuthHeader??void 0,await auth(this._authProvider,{serverUrl:this._url,resourceMetadataUrl:this._resourceMetadataUrl,scope:this._scope,fetchFn:this._fetch})!=="AUTHORIZED")throw new UnauthorizedError;return this.send(
-message)}}throw new StreamableHTTPError(response.status,`Error POSTing to endpoint: ${text2}`)}if(this._hasCompletedAuthFlow=!1,this._lastUpscopingHeader=void 0,response.status===202){await response.body?.cancel(),isInitializedNotification(message)&&this._startOrAuthSse({resumptionToken:void 0}).catch(err=>this.onerror?.(err));return}const hasRequests=(Array.isArray(message)?message:[message]).filter(msg=>"method"in msg&&"id"in msg&&msg.id!==void 0).length>0,contentType=response.headers.get("con\
-tent-type");if(hasRequests)if(contentType?.includes("text/event-stream"))this._handleSseStream(response.body,{onresumptiontoken},!1);else if(contentType?.includes("application/json")){const data=await response.json(),responseMessages=Array.isArray(data)?data.map(msg=>JSONRPCMessageSchema.parse(msg)):[JSONRPCMessageSchema.parse(data)];for(const msg of responseMessages)this.onmessage?.(msg)}else throw await response.body?.cancel(),new StreamableHTTPError(-1,`Unexpected content type: ${contentType}`);else
-await response.body?.cancel()}catch(error2){throw this.onerror?.(error2),error2}}get sessionId(){return this._sessionId}async terminateSession(){if(this._sessionId)try{const headers=await this._commonHeaders(),init2={...this._requestInit,method:"DELETE",headers,signal:this._abortController?.signal},response=await(this._fetch??fetch)(this._url,init2);if(await response.body?.cancel(),!response.ok&&response.status!==405)throw new StreamableHTTPError(response.status,`Failed to terminate session: ${response.
-statusText}`);this._sessionId=void 0}catch(error2){throw this.onerror?.(error2),error2}}setProtocolVersion(version2){this._protocolVersion=version2}get protocolVersion(){return this._protocolVersion}async resumeStream(lastEventId,options){await this._startOrAuthSse({resumptionToken:lastEventId,onresumptiontoken:options?.onresumptiontoken})}}class ErrorEvent extends Event{constructor(type2,errorEventInitDict){var _a2,_b;super(type2),this.code=(_a2=errorEventInitDict?.code)!=null?_a2:void 0,this.message=
-(_b=errorEventInitDict?.message)!=null?_b:void 0}[Symbol.for("nodejs.util.inspect.custom")](_depth,options,inspect){return inspect(inspectableError(this),options)}[Symbol.for("Deno.customInspect")](inspect,options){return inspect(inspectableError(this),options)}}function syntaxError(message){const DomException=globalThis.DOMException;return typeof DomException=="function"?new DomException(message,"SyntaxError"):new SyntaxError(message)}function flattenError(err){return err instanceof Error?"erro\
-rs"in err&&Array.isArray(err.errors)?err.errors.map(flattenError).join(", "):"cause"in err&&err.cause instanceof Error?`${err}: ${flattenError(err.cause)}`:err.message:`${err}`}function inspectableError(err){return{type:err.type,message:err.message,code:err.code,defaultPrevented:err.defaultPrevented,cancelable:err.cancelable,timeStamp:err.timeStamp}}var __typeError2=msg=>{throw TypeError(msg)},__accessCheck2=(obj,member,msg)=>member.has(obj)||__typeError2("Cannot "+msg),__privateGet2=(obj,member,getter)=>(__accessCheck2(
-obj,member,"read from private field"),getter?getter.call(obj):member.get(obj)),__privateAdd2=(obj,member,value)=>member.has(obj)?__typeError2("Cannot add the same private member more than once"):member instanceof WeakSet?member.add(obj):member.set(obj,value),__privateSet2=(obj,member,value,setter)=>(__accessCheck2(obj,member,"write to private field"),member.set(obj,value),value),__privateMethod2=(obj,member,method)=>(__accessCheck2(obj,member,"access private method"),method),_readyState,_url,_redirectUrl,
-_withCredentials,_fetch,_reconnectInterval,_reconnectTimer,_lastEventId,_controller,_parser,_onError,_onMessage,_onOpen,_EventSource_instances,connect_fn,_onFetchResponse,_onFetchError,getRequestOptions_fn,_onEvent,_onRetryChange,failConnection_fn,scheduleReconnect_fn,_reconnect;class EventSource extends EventTarget{constructor(url2,eventSourceInitDict){var _a2,_b;super(),__privateAdd2(this,_EventSource_instances),this.CONNECTING=0,this.OPEN=1,this.CLOSED=2,__privateAdd2(this,_readyState),__privateAdd2(
-this,_url),__privateAdd2(this,_redirectUrl),__privateAdd2(this,_withCredentials),__privateAdd2(this,_fetch),__privateAdd2(this,_reconnectInterval),__privateAdd2(this,_reconnectTimer),__privateAdd2(this,_lastEventId,null),__privateAdd2(this,_controller),__privateAdd2(this,_parser),__privateAdd2(this,_onError,null),__privateAdd2(this,_onMessage,null),__privateAdd2(this,_onOpen,null),__privateAdd2(this,_onFetchResponse,async response=>{var _a22;__privateGet2(this,_parser).reset();const{body:body2,redirected,
-status,headers}=response;if(status===204){__privateMethod2(this,_EventSource_instances,failConnection_fn).call(this,"Server sent HTTP 204, not reconnecting",204),this.close();return}if(redirected?__privateSet2(this,_redirectUrl,new URL(response.url)):__privateSet2(this,_redirectUrl,void 0),status!==200){__privateMethod2(this,_EventSource_instances,failConnection_fn).call(this,`Non-200 status code (${status})`,status);return}if(!(headers.get("content-type")||"").startsWith("text/event-stream")){__privateMethod2(
-this,_EventSource_instances,failConnection_fn).call(this,'Invalid content type, expected "text/event-stream"',status);return}if(__privateGet2(this,_readyState)===this.CLOSED)return;__privateSet2(this,_readyState,this.OPEN);const openEvent=new Event("open");if((_a22=__privateGet2(this,_onOpen))==null||_a22.call(this,openEvent),this.dispatchEvent(openEvent),typeof body2!="object"||!body2||!("getReader"in body2)){__privateMethod2(this,_EventSource_instances,failConnection_fn).call(this,"Invalid res\
-ponse body, expected a web ReadableStream",status),this.close();return}const decoder=new TextDecoder,reader=body2.getReader();let open2=!0;do{const{done,value}=await reader.read();value&&__privateGet2(this,_parser).feed(decoder.decode(value,{stream:!done})),done&&(open2=!1,__privateGet2(this,_parser).reset(),__privateMethod2(this,_EventSource_instances,scheduleReconnect_fn).call(this))}while(open2)}),__privateAdd2(this,_onFetchError,err=>{__privateSet2(this,_controller,void 0),!(err.name==="Abor\
-tError"||err.type==="aborted")&&__privateMethod2(this,_EventSource_instances,scheduleReconnect_fn).call(this,flattenError(err))}),__privateAdd2(this,_onEvent,event2=>{typeof event2.id=="string"&&__privateSet2(this,_lastEventId,event2.id);const messageEvent=new MessageEvent(event2.event||"message",{data:event2.data,origin:__privateGet2(this,_redirectUrl)?__privateGet2(this,_redirectUrl).origin:__privateGet2(this,_url).origin,lastEventId:event2.id||""});__privateGet2(this,_onMessage)&&(!event2.event||
-event2.event==="message")&&__privateGet2(this,_onMessage).call(this,messageEvent),this.dispatchEvent(messageEvent)}),__privateAdd2(this,_onRetryChange,value=>{__privateSet2(this,_reconnectInterval,value)}),__privateAdd2(this,_reconnect,()=>{__privateSet2(this,_reconnectTimer,void 0),__privateGet2(this,_readyState)===this.CONNECTING&&__privateMethod2(this,_EventSource_instances,connect_fn).call(this)});try{if(url2 instanceof URL)__privateSet2(this,_url,url2);else if(typeof url2=="string")__privateSet2(
-this,_url,new URL(url2,getBaseURL()));else throw new Error("Invalid URL")}catch{throw syntaxError("An invalid or illegal string was specified")}__privateSet2(this,_parser,createParser$1({onEvent:__privateGet2(this,_onEvent),onRetry:__privateGet2(this,_onRetryChange)})),__privateSet2(this,_readyState,this.CONNECTING),__privateSet2(this,_reconnectInterval,3e3),__privateSet2(this,_fetch,(_a2=eventSourceInitDict?.fetch)!=null?_a2:globalThis.fetch),__privateSet2(this,_withCredentials,(_b=eventSourceInitDict?.
-withCredentials)!=null?_b:!1),__privateMethod2(this,_EventSource_instances,connect_fn).call(this)}get readyState(){return __privateGet2(this,_readyState)}get url(){return __privateGet2(this,_url).href}get withCredentials(){return __privateGet2(this,_withCredentials)}get onerror(){return __privateGet2(this,_onError)}set onerror(value){__privateSet2(this,_onError,value)}get onmessage(){return __privateGet2(this,_onMessage)}set onmessage(value){__privateSet2(this,_onMessage,value)}get onopen(){return __privateGet2(
-this,_onOpen)}set onopen(value){__privateSet2(this,_onOpen,value)}addEventListener(type2,listener,options){const listen2=listener;super.addEventListener(type2,listen2,options)}removeEventListener(type2,listener,options){const listen2=listener;super.removeEventListener(type2,listen2,options)}close(){__privateGet2(this,_reconnectTimer)&&clearTimeout(__privateGet2(this,_reconnectTimer)),__privateGet2(this,_readyState)!==this.CLOSED&&(__privateGet2(this,_controller)&&__privateGet2(this,_controller).
-abort(),__privateSet2(this,_readyState,this.CLOSED),__privateSet2(this,_controller,void 0))}}_readyState=new WeakMap,_url=new WeakMap,_redirectUrl=new WeakMap,_withCredentials=new WeakMap,_fetch=new WeakMap,_reconnectInterval=new WeakMap,_reconnectTimer=new WeakMap,_lastEventId=new WeakMap,_controller=new WeakMap,_parser=new WeakMap,_onError=new WeakMap,_onMessage=new WeakMap,_onOpen=new WeakMap,_EventSource_instances=new WeakSet,connect_fn=function(){__privateSet2(this,_readyState,this.CONNECTING),
-__privateSet2(this,_controller,new AbortController),__privateGet2(this,_fetch)(__privateGet2(this,_url),__privateMethod2(this,_EventSource_instances,getRequestOptions_fn).call(this)).then(__privateGet2(this,_onFetchResponse)).catch(__privateGet2(this,_onFetchError))},_onFetchResponse=new WeakMap,_onFetchError=new WeakMap,getRequestOptions_fn=function(){var _a2;const init2={mode:"cors",redirect:"follow",headers:{Accept:"text/event-stream",...__privateGet2(this,_lastEventId)?{"Last-Event-ID":__privateGet2(
-this,_lastEventId)}:void 0},cache:"no-store",signal:(_a2=__privateGet2(this,_controller))==null?void 0:_a2.signal};return"window"in globalThis&&(init2.credentials=this.withCredentials?"include":"same-origin"),init2},_onEvent=new WeakMap,_onRetryChange=new WeakMap,failConnection_fn=function(message,code2){var _a2;__privateGet2(this,_readyState)!==this.CLOSED&&__privateSet2(this,_readyState,this.CLOSED);const errorEvent=new ErrorEvent("error",{code:code2,message});(_a2=__privateGet2(this,_onError))==
-null||_a2.call(this,errorEvent),this.dispatchEvent(errorEvent)},scheduleReconnect_fn=function(message,code2){var _a2;if(__privateGet2(this,_readyState)===this.CLOSED)return;__privateSet2(this,_readyState,this.CONNECTING);const errorEvent=new ErrorEvent("error",{code:code2,message});(_a2=__privateGet2(this,_onError))==null||_a2.call(this,errorEvent),this.dispatchEvent(errorEvent),__privateSet2(this,_reconnectTimer,setTimeout(__privateGet2(this,_reconnect),__privateGet2(this,_reconnectInterval)))},
-_reconnect=new WeakMap,EventSource.CONNECTING=0,EventSource.OPEN=1,EventSource.CLOSED=2;function getBaseURL(){const doc="document"in globalThis?globalThis.document:void 0;return doc&&typeof doc=="object"&&"baseURI"in doc&&typeof doc.baseURI=="string"?doc.baseURI:void 0}class SseError extends Error{constructor(code2,message,event2){super(`SSE error: ${message}`),this.code=code2,this.event=event2}}class SSEClientTransport{constructor(url2,opts){this._url=url2,this._resourceMetadataUrl=void 0,this.
-_scope=void 0,this._eventSourceInit=opts?.eventSourceInit,this._requestInit=opts?.requestInit,this._authProvider=opts?.authProvider,this._fetch=opts?.fetch,this._fetchWithInit=createFetchWithInit(opts?.fetch,opts?.requestInit)}async _authThenStart(){if(!this._authProvider)throw new UnauthorizedError("No auth provider");let result;try{result=await auth(this._authProvider,{serverUrl:this._url,resourceMetadataUrl:this._resourceMetadataUrl,scope:this._scope,fetchFn:this._fetchWithInit})}catch(error2){
-throw this.onerror?.(error2),error2}if(result!=="AUTHORIZED")throw new UnauthorizedError;return await this._startOrAuth()}async _commonHeaders(){const headers={};if(this._authProvider){const tokens=await this._authProvider.tokens();tokens&&(headers.Authorization=`Bearer ${tokens.access_token}`)}this._protocolVersion&&(headers["mcp-protocol-version"]=this._protocolVersion);const extraHeaders=normalizeHeaders(this._requestInit?.headers);return new Headers({...headers,...extraHeaders})}_startOrAuth(){
-const fetchImpl=this?._eventSourceInit?.fetch??this._fetch??fetch;return new Promise((resolve2,reject)=>{this._eventSource=new EventSource(this._url.href,{...this._eventSourceInit,fetch:async(url2,init2)=>{const headers=await this._commonHeaders();headers.set("Accept","text/event-stream");const response=await fetchImpl(url2,{...init2,headers});if(response.status===401&&response.headers.has("www-authenticate")){const{resourceMetadataUrl,scope:scope2}=extractWWWAuthenticateParams(response);this._resourceMetadataUrl=
-resourceMetadataUrl,this._scope=scope2}return response}}),this._abortController=new AbortController,this._eventSource.onerror=event2=>{if(event2.code===401&&this._authProvider){this._authThenStart().then(resolve2,reject);return}const error2=new SseError(event2.code,event2.message,event2);reject(error2),this.onerror?.(error2)},this._eventSource.onopen=()=>{},this._eventSource.addEventListener("endpoint",event2=>{const messageEvent=event2;try{if(this._endpoint=new URL(messageEvent.data,this._url),
-this._endpoint.origin!==this._url.origin)throw new Error(`Endpoint origin does not match connection origin: ${this._endpoint.origin}`)}catch(error2){reject(error2),this.onerror?.(error2),this.close();return}resolve2()}),this._eventSource.onmessage=event2=>{const messageEvent=event2;let message;try{message=JSONRPCMessageSchema.parse(JSON.parse(messageEvent.data))}catch(error2){this.onerror?.(error2);return}this.onmessage?.(message)}})}async start(){if(this._eventSource)throw new Error("SSEClientT\
-ransport already started! If using Client class, note that connect() calls start() automatically.");return await this._startOrAuth()}async finishAuth(authorizationCode){if(!this._authProvider)throw new UnauthorizedError("No auth provider");if(await auth(this._authProvider,{serverUrl:this._url,authorizationCode,resourceMetadataUrl:this._resourceMetadataUrl,scope:this._scope,fetchFn:this._fetchWithInit})!=="AUTHORIZED")throw new UnauthorizedError("Failed to authorize")}async close(){this._abortController?.
-abort(),this._eventSource?.close(),this.onclose?.()}async send(message){if(!this._endpoint)throw new Error("Not connected");try{const headers=await this._commonHeaders();headers.set("content-type","application/json");const init2={...this._requestInit,method:"POST",headers,body:JSON.stringify(message),signal:this._abortController?.signal},response=await(this._fetch??fetch)(this._endpoint,init2);if(!response.ok){const text2=await response.text().catch(()=>null);if(response.status===401&&this._authProvider){
-const{resourceMetadataUrl,scope:scope2}=extractWWWAuthenticateParams(response);if(this._resourceMetadataUrl=resourceMetadataUrl,this._scope=scope2,await auth(this._authProvider,{serverUrl:this._url,resourceMetadataUrl:this._resourceMetadataUrl,scope:this._scope,fetchFn:this._fetchWithInit})!=="AUTHORIZED")throw new UnauthorizedError;return this.send(message)}throw new Error(`Error POSTing to endpoint (HTTP ${response.status}): ${text2}`)}await response.body?.cancel()}catch(error2){throw this.onerror?.(
-error2),error2}}setProtocolVersion(version2){this._protocolVersion=version2}}const SUBPROTOCOL="mcp";class WebSocketClientTransport{constructor(url2){this._url=url2}start(){if(this._socket)throw new Error("WebSocketClientTransport already started! If using Client class, note that connect() calls start() automatically.");return new Promise((resolve2,reject)=>{this._socket=new WebSocket(this._url,SUBPROTOCOL),this._socket.onerror=event2=>{const error2="error"in event2?event2.error:new Error(`WebSo\
-cket error: ${JSON.stringify(event2)}`);reject(error2),this.onerror?.(error2)},this._socket.onopen=()=>{resolve2()},this._socket.onclose=()=>{this.onclose?.()},this._socket.onmessage=event2=>{let message;try{message=JSONRPCMessageSchema.parse(JSON.parse(event2.data))}catch(error2){this.onerror?.(error2);return}this.onmessage?.(message)}})}async close(){this._socket?.close()}send(message){return new Promise((resolve2,reject)=>{if(!this._socket){reject(new Error("Not connected"));return}this._socket?.
-send(JSON.stringify(message)),resolve2()})}}class MCPService{static createLog(phase,message,level=MCPLogLevel.INFO,details){return{timestamp:new Date,phase,message,level,details}}static createDiagnosticRequestDetails(input,init2,baseInit,requestHeaders,extraRedactedHeaders){const body2=getRequestBody(input,init2),details={url:getRequestUrl(input),method:getRequestMethod(input,init2,baseInit).toUpperCase(),credentials:init2?.credentials??baseInit.credentials,mode:init2?.mode??baseInit.mode,headers:sanitizeHeaders(
-requestHeaders,extraRedactedHeaders,MCP_PARTIAL_REDACT_HEADERS),body:summarizeRequestBody(body2)},jsonRpcMethods=extractJsonRpcMethods(body2);return jsonRpcMethods&&(details.jsonRpcMethods=jsonRpcMethods),details}static summarizeError(error2){return error2 instanceof Error?{name:error2.name,message:error2.message,cause:error2.cause instanceof Error?{name:error2.cause.name,message:error2.cause.message}:error2.cause,stack:error2.stack?.split(`
-`).slice(0,6).join(`
-`)}:{value:String(error2)}}static getBrowserContext(targetUrl,useProxy){if(!(typeof window>"u"))return{location:window.location.href,origin:window.location.origin,protocol:window.location.protocol,isSecureContext:window.isSecureContext,targetOrigin:targetUrl.origin,targetProtocol:targetUrl.protocol,sameOrigin:window.location.origin===targetUrl.origin,useProxy}}static getConnectionHints(targetUrl,config2,error2){const hints=[],message=error2 instanceof Error?error2.message:String(error2),headerNames=Object.
-keys(config2.headers??{});return typeof window<"u"&&(window.location.protocol==="https:"&&targetUrl.protocol==="http:"&&!config2.useProxy&&hints.push("The page is running over HTTPS but the MCP server is HTTP. Browsers often block this as mixed content; enable the proxy or use HTTPS/WSS for the MCP server."),window.location.origin!==targetUrl.origin&&!config2.useProxy&&hints.push("This is a cross-origin browser request. If the server is reachable from curl or Node but not from the browser, missi\
-ng CORS headers are the most likely cause.")),headerNames.length>0&&hints.push(`Custom request headers are configured (${headerNames.join(", ")}). That triggers a CORS preflight, so the server must allow OPTIONS and include the matching Access-Control-Allow-Headers response.`),config2.credentials&&config2.credentials!=="omit"&&hints.push("Credentials are enabled for this connection. Cross-origin credentialed requests need Access-Control-Allow-Credentials: true and cannot use a wildcard Access-Co\
-ntrol-Allow-Origin."),message.includes("Failed to fetch")&&hints.push('"Failed to fetch" is a browser-level network failure. Common causes are CORS rejection, mixed-content blocking, certificate/TLS errors, DNS failures, or nothing listening on the target port.'),hints}static createDiagnosticFetch(serverName,config2,baseInit,targetUrl,useProxy,onLog){let enabled=!0;const logIfEnabled=log=>{enabled&&onLog?.(log)};return{fetch:async(input,init2)=>{const startedAt=performance.now(),requestHeaders=new Headers(
-baseInit.headers);if(typeof Request<"u"&&input instanceof Request)for(const[key2,value]of input.headers.entries())requestHeaders.set(key2,value);if(init2?.headers)for(const[key2,value]of new Headers(init2.headers).entries())requestHeaders.set(key2,value);const request=this.createDiagnosticRequestDetails(input,init2,baseInit,requestHeaders,Object.keys(config2.headers??{})),{method,url:url2}=request;logIfEnabled(this.createLog(MCPConnectionPhase.INITIALIZING,`HTTP ${method} ${url2}`,MCPLogLevel.INFO,
-{serverName,request}));try{const response=await fetch(input,{...baseInit,...init2,headers:requestHeaders}),durationMs=Math.round(performance.now()-startedAt);return logIfEnabled(this.createLog(MCPConnectionPhase.INITIALIZING,`HTTP ${response.status} ${method} ${url2} (${durationMs}ms)`,response.ok?MCPLogLevel.INFO:MCPLogLevel.WARN,{response:{url:url2,status:response.status,statusText:response.statusText,headers:sanitizeHeaders(response.headers,void 0,MCP_PARTIAL_REDACT_HEADERS),durationMs}})),response}catch(error2){
-const durationMs=Math.round(performance.now()-startedAt);throw logIfEnabled(this.createLog(MCPConnectionPhase.ERROR,`HTTP ${method} ${url2} failed: ${formatDiagnosticErrorMessage(error2)}`,MCPLogLevel.ERROR,{serverName,request,error:this.summarizeError(error2),browser:this.getBrowserContext(targetUrl,useProxy),hints:this.getConnectionHints(targetUrl,config2,error2),durationMs})),error2}},disable:()=>{enabled=!1}}}static isSessionExpiredError(error2){return error2 instanceof StreamableHTTPError&&
-error2.code===404}static createTransport(serverName,config2,onLog){if(!config2.url)throw new Error("MCP server configuration is missing url");const useProxy=config2.useProxy??!1,requestInit={};if(config2.headers&&(requestInit.headers=config2.useProxy?buildProxiedHeaders(config2.headers):config2.headers),useProxy&&(requestInit.headers={...getAuthHeaders(),...requestInit.headers}),config2.credentials&&(requestInit.credentials=config2.credentials),config2.transport===MCPTransportType.WEBSOCKET){if(useProxy)
-throw new Error("WebSocket transport is not supported when using CORS proxy. Use HTTP transport instead.");const url22=new URL(config2.url);return{transport:new WebSocketClientTransport(url22),type:MCPTransportType.WEBSOCKET,stopPhaseLogging:()=>{}}}const url2=useProxy?buildProxiedUrl(config2.url):new URL(config2.url),{fetch:diagnosticFetch,disable:stopPhaseLogging}=this.createDiagnosticFetch(serverName,config2,requestInit,url2,useProxy,onLog);try{return{transport:new StreamableHTTPClientTransport(
-url2,{requestInit,fetch:diagnosticFetch}),type:MCPTransportType.STREAMABLE_HTTP,stopPhaseLogging}}catch(httpError){console.warn("[MCPService] StreamableHTTP failed, trying SSE transport...",httpError);try{return{transport:new SSEClientTransport(url2,{requestInit,fetch:diagnosticFetch,eventSourceInit:{fetch:diagnosticFetch}}),type:MCPTransportType.SSE,stopPhaseLogging}}catch(sseError){const httpMsg=httpError instanceof Error?httpError.message:String(httpError),sseMsg=sseError instanceof Error?sseError.
-message:String(sseError);throw new Error(`Failed to create transport. StreamableHTTP: ${httpMsg}; SSE: ${sseMsg}`)}}}static extractServerInfo(impl){if(impl)return{name:impl.name,version:impl.version,title:impl.title,description:impl.description,websiteUrl:impl.websiteUrl,icons:impl.icons?.map(icon=>({src:icon.src,mimeType:icon.mimeType,sizes:icon.sizes}))}}static async connect(serverName,serverConfig,clientInfo,capabilities,onPhase,listChangedHandlers){const startTime=performance.now(),effectiveClientInfo=clientInfo??
-DEFAULT_MCP_CONFIG.clientInfo,effectiveCapabilities=capabilities??DEFAULT_MCP_CONFIG.capabilities;onPhase?.(MCPConnectionPhase.TRANSPORT_CREATING,this.createLog(MCPConnectionPhase.TRANSPORT_CREATING,`Creating transport for ${serverConfig.url}`));const{transport,type:transportType,stopPhaseLogging}=this.createTransport(serverName,serverConfig,log=>onPhase?.(log.phase,log));transportType===MCPTransportType.WEBSOCKET&&(transport.onclose=()=>{console.log(`[MCPService][${serverName}] WebSocket closed\
-, notifying for reconnection`),onPhase?.(MCPConnectionPhase.DISCONNECTED,this.createLog(MCPConnectionPhase.DISCONNECTED,"WebSocket connection closed"))}),onPhase?.(MCPConnectionPhase.TRANSPORT_READY,this.createLog(MCPConnectionPhase.TRANSPORT_READY,`Transport ready (${transportType})`),{transportType});const client=new Client({name:effectiveClientInfo.name,version:effectiveClientInfo.version??DEFAULT_CLIENT_VERSION},{capabilities:effectiveCapabilities,listChanged:listChangedHandlers}),runtimeErrorHandler=error2=>{
-console.error(`[MCPService][${serverName}] Protocol error after initialize:`,error2)};client.onerror=error2=>{onPhase?.(MCPConnectionPhase.ERROR,this.createLog(MCPConnectionPhase.ERROR,`Protocol error: ${error2.message}`,MCPLogLevel.ERROR,{error:this.summarizeError(error2)}))},onPhase?.(MCPConnectionPhase.INITIALIZING,this.createLog(MCPConnectionPhase.INITIALIZING,"Sending initialize request...")),console.log(`[MCPService][${serverName}] Connecting to server...`);try{await client.connect(transport),
-stopPhaseLogging(),client.onerror=runtimeErrorHandler}catch(error2){client.onerror=runtimeErrorHandler;const url2=serverConfig.useProxy??!1?buildProxiedUrl(serverConfig.url):new URL(serverConfig.url);throw onPhase?.(MCPConnectionPhase.ERROR,this.createLog(MCPConnectionPhase.ERROR,`Connection failed during initialize: ${error2 instanceof Error?error2.message:String(error2)}`,MCPLogLevel.ERROR,{error:this.summarizeError(error2),config:{serverName,configuredUrl:serverConfig.url,effectiveUrl:url2.href,
-transportType,useProxy:serverConfig.useProxy??!1,headers:sanitizeHeaders(serverConfig.headers,Object.keys(serverConfig.headers??{}),MCP_PARTIAL_REDACT_HEADERS),credentials:serverConfig.credentials},browser:this.getBrowserContext(url2,serverConfig.useProxy??!1),hints:this.getConnectionHints(url2,serverConfig,error2)})),error2}const serverVersion=client.getServerVersion(),serverCapabilities=client.getServerCapabilities(),instructions=client.getInstructions(),serverInfo=this.extractServerInfo(serverVersion);
-onPhase?.(MCPConnectionPhase.CAPABILITIES_EXCHANGED,this.createLog(MCPConnectionPhase.CAPABILITIES_EXCHANGED,"Capabilities exchanged successfully",MCPLogLevel.INFO,{serverCapabilities,serverInfo}),{serverInfo,serverCapabilities,clientCapabilities:effectiveCapabilities,instructions}),onPhase?.(MCPConnectionPhase.LISTING_TOOLS,this.createLog(MCPConnectionPhase.LISTING_TOOLS,"Listing available tools...")),console.log(`[MCPService][${serverName}] Connected, listing tools...`);const tools=await this.
-listTools({client,transport,tools:[],serverName,transportType,connectionTimeMs:0}),connectionTimeMs=Math.round(performance.now()-startTime);return onPhase?.(MCPConnectionPhase.CONNECTED,this.createLog(MCPConnectionPhase.CONNECTED,`Connection established with ${tools.length} tools (${connectionTimeMs}ms)`)),console.log(`[MCPService][${serverName}] Initialization complete with ${tools.length} tools in ${connectionTimeMs}ms`),{client,transport,tools,serverName,transportType,serverInfo,serverCapabilities,
-clientCapabilities:effectiveCapabilities,protocolVersion:DEFAULT_MCP_CONFIG.protocolVersion,instructions,connectionTimeMs}}static async disconnect(connection){console.log(`[MCPService][${connection.serverName}] Disconnecting...`);try{connection.transport.onclose&&(connection.transport.onclose=void 0),await connection.client.close()}catch(error2){console.warn(`[MCPService][${connection.serverName}] Error during disconnect:`,error2)}}static async listTools(connection){try{return(await connection.client.
-listTools()).tools??[]}catch(error2){if(this.isSessionExpiredError(error2))throw error2;return console.warn(`[MCPService][${connection.serverName}] Failed to list tools:`,error2),[]}}static async listPrompts(connection){try{return(await connection.client.listPrompts()).prompts??[]}catch(error2){if(this.isSessionExpiredError(error2))throw error2;return console.warn(`[MCPService][${connection.serverName}] Failed to list prompts:`,error2),[]}}static async getPrompt(connection,name,args){try{return await connection.
-client.getPrompt({name,arguments:args})}catch(error2){throw console.error(`[MCPService][${connection.serverName}] Failed to get prompt:`,error2),error2}}static async callTool(connection,params,signal){throwIfAborted(signal);try{const result=await connection.client.callTool({name:params.name,arguments:params.arguments},void 0,{signal});return{content:this.formatToolResult(result),isError:result.isError??!1}}catch(error2){if(isAbortError(error2)||this.isSessionExpiredError(error2))throw error2;const message=error2 instanceof
-Error?error2.message:String(error2);throw new Error(`Tool "${params.name}" execution failed on server "${connection.serverName}": ${message}`,{cause:error2 instanceof Error?error2:void 0})}}static formatToolResult(result){const content2=result.content;return Array.isArray(content2)?content2.map(item=>this.formatSingleContent(item)).filter(Boolean).join(`
-`):""}static formatSingleContent(content2){if(content2.type===MCPContentType.TEXT&&content2.text)return content2.text;if(content2.type===MCPContentType.IMAGE&&content2.data)return createBase64DataUrl(content2.mimeType??DEFAULT_IMAGE_MIME_TYPE,content2.data);if(content2.type===MCPContentType.RESOURCE&&content2.resource){const resource=content2.resource;return resource.text?resource.text:resource.blob?resource.blob:JSON.stringify(resource)}return content2.data&&content2.mimeType?createBase64DataUrl(
-content2.mimeType,content2.data):JSON.stringify(content2)}static async complete(connection,ref2,argument){try{return(await connection.client.complete({ref:ref2,argument})).completion}catch(error2){return console.error("[MCPService] Failed to get completions:",error2),null}}static async listResources(connection,cursor){try{const result=await connection.client.listResources(cursor?{cursor}:void 0);return{resources:result.resources??[],nextCursor:result.nextCursor}}catch(error2){if(this.isSessionExpiredError(
-error2))throw error2;return console.warn(`[MCPService][${connection.serverName}] Failed to list resources:`,error2),{resources:[]}}}static async listAllResources(connection){const allResources=[];let cursor;do{const result=await this.listResources(connection,cursor);allResources.push(...result.resources),cursor=result.nextCursor}while(cursor);return allResources}static async listResourceTemplates(connection,cursor){try{const result=await connection.client.listResourceTemplates(cursor?{cursor}:void 0);
-return{resourceTemplates:result.resourceTemplates??[],nextCursor:result.nextCursor}}catch(error2){if(this.isSessionExpiredError(error2))throw error2;return console.warn(`[MCPService][${connection.serverName}] Failed to list resource templates:`,error2),{resourceTemplates:[]}}}static async listAllResourceTemplates(connection){const allTemplates=[];let cursor;do{const result=await this.listResourceTemplates(connection,cursor);allTemplates.push(...result.resourceTemplates),cursor=result.nextCursor}while(cursor);
-return allTemplates}static async readResource(connection,uri2){try{const result=await connection.client.readResource({uri:uri2});return{contents:result.contents??[],_meta:result._meta}}catch(error2){throw console.error(`[MCPService][${connection.serverName}] Failed to read resource:`,error2),error2}}static async subscribeResource(connection,uri2){try{await connection.client.subscribeResource({uri:uri2}),console.log(`[MCPService][${connection.serverName}] Subscribed to resource: ${uri2}`)}catch(error2){
-throw console.error(`[MCPService][${connection.serverName}] Failed to subscribe to resource:`,error2),error2}}static async unsubscribeResource(connection,uri2){try{await connection.client.unsubscribeResource({uri:uri2}),console.log(`[MCPService][${connection.serverName}] Unsubscribed from resource: ${uri2}`)}catch(error2){throw console.error(`[MCPService][${connection.serverName}] Failed to unsubscribe from resource:`,error2),error2}}static supportsResources(connection){return connection.serverCapabilities?.
-resources!==void 0}static supportsResourceSubscriptions(connection){return!!connection.serverCapabilities?.resources?.subscribe}}function generateAttachmentId(){return`${MCP_RESOURCE_ATTACHMENT_ID_PREFIX}-${Date.now()}-${Math.random().toString(36).substring(2,9)}`}class MCPResourceStore{#_serverResources=state$1(proxy(new SvelteMap));get _serverResources(){return get$4(this.#_serverResources)}set _serverResources(value){set$1(this.#_serverResources,value,!0)}#_cachedResources=state$1(proxy(new SvelteMap));get _cachedResources(){
-return get$4(this.#_cachedResources)}set _cachedResources(value){set$1(this.#_cachedResources,value,!0)}#_subscriptions=state$1(proxy(new SvelteMap));get _subscriptions(){return get$4(this.#_subscriptions)}set _subscriptions(value){set$1(this.#_subscriptions,value,!0)}#_attachments=state$1(proxy([]));get _attachments(){return get$4(this.#_attachments)}set _attachments(value){set$1(this.#_attachments,value,!0)}#_isLoading=state$1(!1);get _isLoading(){return get$4(this.#_isLoading)}set _isLoading(value){
-set$1(this.#_isLoading,value,!0)}get serverResources(){return this._serverResources}get cachedResources(){return this._cachedResources}get subscriptions(){return this._subscriptions}get attachments(){return this._attachments}get isLoading(){return this._isLoading}get totalResourceCount(){let count=0;for(const serverRes of this._serverResources.values())count+=serverRes.resources.length;return count}get totalTemplateCount(){let count=0;for(const serverRes of this._serverResources.values())count+=
-serverRes.templates.length;return count}get attachmentCount(){return this._attachments.length}get hasAttachments(){return this._attachments.length>0}setServerResources(serverName,resources,templates){this._serverResources.set(serverName,{serverName,resources,templates,lastFetched:new Date,loading:!1,error:void 0}),console.log(`[MCPResources][${serverName}] Set ${resources.length} resources, ${templates.length} templates`)}setServerLoading(serverName,loading){const existing=this._serverResources.
-get(serverName);existing?this._serverResources.set(serverName,{...existing,loading}):this._serverResources.set(serverName,{serverName,resources:[],templates:[],loading,error:void 0})}setServerError(serverName,error2){const existing=this._serverResources.get(serverName);existing?this._serverResources.set(serverName,{...existing,loading:!1,error:error2}):this._serverResources.set(serverName,{serverName,resources:[],templates:[],loading:!1,error:error2})}getServerResources(serverName){return this._serverResources.
-get(serverName)}getAllResourceInfos(){const result=[];for(const[serverName,serverRes]of this._serverResources)for(const resource of serverRes.resources)result.push({uri:resource.uri,name:resource.name,title:resource.title,description:resource.description,mimeType:resource.mimeType,serverName,annotations:resource.annotations,icons:resource.icons});return result}getAllTemplateInfos(){const result=[];for(const[serverName,serverRes]of this._serverResources)for(const template of serverRes.templates)result.
-push({uriTemplate:template.uriTemplate,name:template.name,title:template.title,description:template.description,mimeType:template.mimeType,serverName,annotations:template.annotations,icons:template.icons});return result}clearServerResources(serverName){this._serverResources.delete(serverName);for(const[uri2,cached2]of this._cachedResources)cached2.resource.serverName===serverName&&this._cachedResources.delete(uri2);for(const[uri2,sub2]of this._subscriptions)sub2.serverName===serverName&&this._subscriptions.
-delete(uri2);console.log(`[MCPResources][${serverName}] Cleared all resources`)}cacheResourceContent(resource,content2){if(this._cachedResources.size>=MCP_RESOURCE_CACHE_MAX_ENTRIES){const oldestKey=this._cachedResources.keys().next().value;oldestKey&&this._cachedResources.delete(oldestKey)}this._cachedResources.set(resource.uri,{resource,content:content2,fetchedAt:new Date,subscribed:this._subscriptions.has(resource.uri)}),console.log(`[MCPResources] Cached content for: ${resource.uri}`)}getCachedContent(uri2){
-const cached2=this._cachedResources.get(uri2);if(!cached2)return;if(Date.now()-cached2.fetchedAt.getTime()>MCP_RESOURCE_CACHE_TTL_MS&&!cached2.subscribed){this._cachedResources.delete(uri2);return}return cached2}invalidateCache(uri2){this._cachedResources.delete(uri2),console.log(`[MCPResources] Invalidated cache for: ${uri2}`)}clearCache(){this._cachedResources.clear(),console.log("[MCPResources] Cleared all cached content")}addSubscription(uri2,serverName){this._subscriptions.set(uri2,{uri:uri2,
-serverName,subscribedAt:new Date});const cached2=this._cachedResources.get(uri2);cached2&&this._cachedResources.set(uri2,{...cached2,subscribed:!0}),console.log(`[MCPResources] Added subscription: ${uri2}`)}removeSubscription(uri2){this._subscriptions.delete(uri2);const cached2=this._cachedResources.get(uri2);cached2&&this._cachedResources.set(uri2,{...cached2,subscribed:!1}),console.log(`[MCPResources] Removed subscription: ${uri2}`)}isSubscribed(uri2){return this._subscriptions.has(uri2)}handleResourceUpdate(uri2){
-this.invalidateCache(uri2);const sub2=this._subscriptions.get(uri2);sub2&&this._subscriptions.set(uri2,{...sub2,lastUpdate:new Date}),console.log(`[MCPResources] Resource updated: ${uri2}`)}handleResourcesListChanged(serverName){const existing=this._serverResources.get(serverName);existing&&this._serverResources.set(serverName,{...existing,lastFetched:void 0}),console.log(`[MCPResources][${serverName}] Resources list changed, needs refresh`)}addAttachment(resource){const attachment={id:generateAttachmentId(),
-resource,loading:!0};return this._attachments=[...this._attachments,attachment],console.log(`[MCPResources] Added attachment: ${resource.uri}`),attachment}updateAttachmentContent(attachmentId,content2){this._attachments=this._attachments.map(att=>att.id===attachmentId?{...att,content:content2,loading:!1,error:void 0}:att)}updateAttachmentError(attachmentId,error2){this._attachments=this._attachments.map(att=>att.id===attachmentId?{...att,loading:!1,error:error2}:att)}removeAttachment(attachmentId){
-this._attachments=this._attachments.filter(att=>att.id!==attachmentId),console.log(`[MCPResources] Removed attachment: ${attachmentId}`)}clearAttachments(){this._attachments=[],console.log("[MCPResources] Cleared all attachments")}getAttachment(attachmentId){return this._attachments.find(att=>att.id===attachmentId)}isAttached(uri2){const normalizedUri=normalizeResourceUri(uri2);return this._attachments.some(att=>att.resource.uri===uri2||normalizeResourceUri(att.resource.uri)===normalizedUri)}setLoading(loading){
-this._isLoading=loading}findResourceByUri(uri2){const normalizedUri=normalizeResourceUri(uri2);for(const[serverName,serverRes]of this._serverResources){const resource=serverRes.resources.find(r2=>r2.uri===uri2)??serverRes.resources.find(r2=>normalizeResourceUri(r2.uri)===normalizedUri);if(resource)return{uri:resource.uri,name:resource.name,title:resource.title,description:resource.description,mimeType:resource.mimeType,serverName,annotations:resource.annotations,icons:resource.icons}}}findServerForUri(uri2){
-for(const[serverName,serverRes]of this._serverResources)if(serverRes.resources.some(r2=>r2.uri===uri2))return serverName}clear(){this._serverResources.clear(),this._cachedResources.clear(),this._subscriptions.clear(),this._attachments=[],this._isLoading=!1,console.log("[MCPResources] Cleared all state")}formatAttachmentsForContext(){if(this._attachments.length===0)return"";const parts=[];for(const attachment of this._attachments){if(attachment.error||!attachment.content||attachment.content.length===
-0)continue;const resourceName=attachment.resource.title||attachment.resource.name,serverName=attachment.resource.serverName;for(const content2 of attachment.content)"text"in content2&&content2.text?parts.push(`
-
---- Resource: ${resourceName} (from ${serverName}) ---
-${content2.text}`):"blob"in content2&&content2.blob&&parts.push(`
-
---- Resource: ${resourceName} (from ${serverName}) ---
-[${BINARY_CONTENT_LABEL}: ${content2.mimeType||RESOURCE_UNKNOWN_TYPE}]`)}return parts.join("")}toMessageExtras(){const extras=[];for(const attachment of this._attachments){if(attachment.error||!attachment.content||attachment.content.length===0)continue;const resourceName=attachment.resource.title||attachment.resource.name,contentParts=[];for(const content2 of attachment.content)"text"in content2&&content2.text?contentParts.push(content2.text):"blob"in content2&&content2.blob&&contentParts.push(`\
-[${BINARY_CONTENT_LABEL}: ${content2.mimeType||RESOURCE_UNKNOWN_TYPE}]`);contentParts.length>0&&extras.push({type:AttachmentType.MCP_RESOURCE,name:resourceName,uri:attachment.resource.uri,serverName:attachment.resource.serverName,content:contentParts.join(NEWLINE_SEPARATOR),mimeType:attachment.resource.mimeType})}return extras}}const mcpResourceStore=new MCPResourceStore,mcpResources=()=>mcpResourceStore.serverResources,mcpResourceAttachments=()=>mcpResourceStore.attachments,mcpHasResourceAttachments=()=>mcpResourceStore.
-hasAttachments,mcpTotalResourceCount=()=>mcpResourceStore.totalResourceCount,mcpResourcesLoading=()=>mcpResourceStore.isLoading,defaultWindow$1=typeof window<"u"?window:void 0;function getActiveElement$1(document2){let activeElement=document2.activeElement;for(;activeElement?.shadowRoot;){const node2=activeElement.shadowRoot.activeElement;if(node2===activeElement)break;activeElement=node2}return activeElement}let ActiveElement$1=class{#document;#subscribe;constructor(options={}){const{window:window2=defaultWindow$1,
-document:document2=window2?.document}=options;window2!==void 0&&(this.#document=document2,this.#subscribe=createSubscriber(update2=>{const cleanupFocusIn=on(window2,"focusin",update2),cleanupFocusOut=on(window2,"focusout",update2);return()=>{cleanupFocusIn(),cleanupFocusOut()}}))}get current(){return this.#subscribe?.(),this.#document?getActiveElement$1(this.#document):null}};new ActiveElement$1;function runEffect(flush,effect2){switch(flush){case"post":user_effect(effect2);break;case"pre":user_pre_effect(
-effect2);break}}function runWatcher(sources,flush,effect2,options={}){const{lazy=!1}=options;let active=!lazy,previousValues=Array.isArray(sources)?[]:void 0;runEffect(flush,()=>{const values=Array.isArray(sources)?sources.map(source2=>source2()):sources();if(!active){active=!0,previousValues=values;return}const cleanup=untrack$1(()=>effect2(values,previousValues));return previousValues=values,cleanup})}function watch(sources,effect2,options){runWatcher(sources,"post",effect2,options)}function watchPre(sources,effect2,options){
-runWatcher(sources,"pre",effect2,options)}watch.pre=watchPre;function getStorage(storageType,window2){switch(storageType){case"local":return window2.localStorage;case"session":return window2.sessionStorage}}class PersistedState{#current;#key;#serializer;#storage;#subscribe;#version=state$1(0);constructor(key2,initialValue,options={}){const{storage:storageType="local",serializer:serializer2={serialize:JSON.stringify,deserialize:JSON.parse},syncTabs=!0,window:window2=defaultWindow$1}=options;if(this.#current=
-initialValue,this.#key=key2,this.#serializer=serializer2,window2===void 0)return;const storage=getStorage(storageType,window2);this.#storage=storage;const existingValue=storage.getItem(key2);existingValue!==null?this.#current=this.#deserialize(existingValue):this.#serialize(initialValue),syncTabs&&storageType==="local"&&(this.#subscribe=createSubscriber(()=>on(window2,"storage",this.#handleStorageEvent)))}get current(){this.#subscribe?.(),get$4(this.#version);const root2=this.#deserialize(this.#storage?.
-getItem(this.#key))??this.#current,proxies=new WeakMap,proxy2=value=>{if(value===null||value?.constructor.name==="Date"||typeof value!="object")return value;let p2=proxies.get(value);return p2||(p2=new Proxy(value,{get:(target2,property)=>(get$4(this.#version),proxy2(Reflect.get(target2,property))),set:(target2,property,value2)=>(set$1(this.#version,get$4(this.#version)+1),Reflect.set(target2,property,value2),this.#serialize(root2),!0)}),proxies.set(value,p2)),p2};return proxy2(root2)}set current(newValue){
-this.#serialize(newValue),set$1(this.#version,get$4(this.#version)+1)}#handleStorageEvent=event2=>{event2.key!==this.#key||event2.newValue===null||(this.#current=this.#deserialize(event2.newValue),set$1(this.#version,get$4(this.#version)+1))};#deserialize(value){try{return this.#serializer.deserialize(value)}catch(error2){console.error(`Error when parsing "${value}" from persisted store "${this.#key}"`,error2);return}}#serialize(value){try{value!=null&&this.#storage?.setItem(this.#key,this.#serializer.
-serialize(value))}catch(error2){console.error(`Error when writing value from persisted store "${this.#key}" to ${this.#storage}`,error2)}}}function sanitizeClassNames(classNames){return classNames.filter(className=>className.length>0)}const noopStorage={getItem:_key=>null,setItem:(_key,_value)=>{}},isBrowser=typeof document<"u";function isFunction(value){return typeof value=="function"}function isObject(value){return value!==null&&typeof value=="object"}const BoxSymbol=Symbol("box"),isWritableSymbol=Symbol(
-"is-writable");function isBox(value){return isObject(value)&&BoxSymbol in value}function isWritableBox(value){return box.isBox(value)&&isWritableSymbol in value}function box(initialValue){let current2=state$1(proxy(initialValue));return{[BoxSymbol]:!0,[isWritableSymbol]:!0,get current(){return get$4(current2)},set current(v){set$1(current2,v,!0)}}}function boxWith(getter,setter){const derived2=user_derived(getter);return setter?{[BoxSymbol]:!0,[isWritableSymbol]:!0,get current(){return get$4(derived2)},
-set current(v){setter(v)}}:{[BoxSymbol]:!0,get current(){return getter()}}}function boxFrom(value){return box.isBox(value)?value:isFunction(value)?box.with(value):box(value)}function boxFlatten(boxes){return Object.entries(boxes).reduce((acc,[key2,b])=>box.isBox(b)?(box.isWritableBox(b)?Object.defineProperty(acc,key2,{get(){return b.current},set(v){b.current=v}}):Object.defineProperty(acc,key2,{get(){return b.current}}),acc):Object.assign(acc,{[key2]:b}),{})}function toReadonlyBox(b){return box.
-isWritableBox(b)?{[BoxSymbol]:!0,get current(){return b.current}}:b}box.from=boxFrom;box.with=boxWith;box.flatten=boxFlatten;box.readonly=toReadonlyBox;box.isBox=isBox;box.isWritableBox=isWritableBox;function createParser(matcher,replacer){const regex=RegExp(matcher,"g");return str=>{if(typeof str!="string")throw new TypeError(`expected an argument of type string, but got ${typeof str}`);return str.match(regex)?str.replace(regex,replacer):str}}const camelToKebab=createParser(/[A-Z]/,match=>`-${match.
-toLowerCase()}`);function styleToCSS(styleObj){if(!styleObj||typeof styleObj!="object"||Array.isArray(styleObj))throw new TypeError(`expected an argument of type object, but got ${typeof styleObj}`);return Object.keys(styleObj).map(property=>`${camelToKebab(property)}: ${styleObj[property]};`).join(`
-`)}function styleToString(style2={}){return styleToCSS(style2).replace(`
-`," ")}const srOnlyStyles={position:"absolute",width:"1px",height:"1px",padding:"0",margin:"-1px",overflow:"hidden",clip:"rect(0, 0, 0, 0)",whiteSpace:"nowrap",borderWidth:"0",transform:"translateX(-100%)"};styleToString(srOnlyStyles);const defaultWindow=typeof window<"u"?window:void 0;function getActiveElement(document2){let activeElement=document2.activeElement;for(;activeElement?.shadowRoot;){const node2=activeElement.shadowRoot.activeElement;if(node2===activeElement)break;activeElement=node2}
-return activeElement}class ActiveElement4{#document;#subscribe;constructor(options={}){const{window:window2=defaultWindow,document:document2=window2?.document}=options;window2!==void 0&&(this.#document=document2,this.#subscribe=createSubscriber(update2=>{const cleanupFocusIn=on(window2,"focusin",update2),cleanupFocusOut=on(window2,"focusout",update2);return()=>{cleanupFocusIn(),cleanupFocusOut()}}))}get current(){return this.#subscribe?.(),this.#document?getActiveElement(this.#document):null}}new ActiveElement4;
-const modeStorageKey=box("mode-watcher-mode"),themeStorageKey=box("mode-watcher-theme"),modes=["dark","light","system"];function isValidMode(value){return typeof value!="string"?!1:modes.includes(value)}class UserPrefersMode{#defaultValue="system";#storage=isBrowser?localStorage:noopStorage;#initialValue=this.#storage.getItem(modeStorageKey.current);#value=isValidMode(this.#initialValue)?this.#initialValue:this.#defaultValue;#persisted=state$1(proxy(this.#makePersisted()));#makePersisted(value=this.#value){
-return new PersistedState(modeStorageKey.current,value,{serializer:{serialize:v=>v,deserialize:v=>isValidMode(v)?v:this.#defaultValue}})}constructor(){effect_root(()=>watch.pre(()=>modeStorageKey.current,(_,prevStorageKey)=>{const currModeValue=get$4(this.#persisted).current;set$1(this.#persisted,this.#makePersisted(currModeValue),!0),prevStorageKey&&localStorage.removeItem(prevStorageKey)}))}get current(){return get$4(this.#persisted).current}set current(newValue){get$4(this.#persisted).current=
-newValue}}class SystemPrefersMode{#defaultValue=void 0;#track=!0;#current=state$1(proxy(this.#defaultValue));#mediaQueryState=typeof window<"u"&&typeof window.matchMedia=="function"?new MediaQuery("prefers-color-scheme: light"):{current:!1};query(){isBrowser&&set$1(this.#current,this.#mediaQueryState.current?"light":"dark",!0)}tracking(active){this.#track=active}constructor(){effect_root(()=>{user_pre_effect(()=>{this.#track&&this.query()})}),this.query=this.query.bind(this),this.tracking=this.tracking.
-bind(this)}get current(){return get$4(this.#current)}}const userPrefersMode=new UserPrefersMode,systemPrefersMode=new SystemPrefersMode;class CustomTheme{#storage=isBrowser?localStorage:noopStorage;#initialValue=this.#storage.getItem(themeStorageKey.current);#value=this.#initialValue===null||this.#initialValue===void 0?"":this.#initialValue;#persisted=state$1(proxy(this.#makePersisted()));#makePersisted(value=this.#value){return new PersistedState(themeStorageKey.current,value,{serializer:{serialize:v=>typeof v!=
-"string"?"":v,deserialize:v=>v}})}constructor(){effect_root(()=>watch.pre(()=>themeStorageKey.current,(_,prevStorageKey)=>{const currModeValue=get$4(this.#persisted).current;set$1(this.#persisted,this.#makePersisted(currModeValue),!0),prevStorageKey&&localStorage.removeItem(prevStorageKey)}))}get current(){return get$4(this.#persisted).current}set current(newValue){get$4(this.#persisted).current=newValue}}const customTheme=new CustomTheme;let timeoutAction,timeoutEnable,hasLoaded=!1,styleElement=null;
-function getStyleElement(){return styleElement||(styleElement=document.createElement("style"),styleElement.appendChild(document.createTextNode(`* {
-		-webkit-transition: none !important;
-		-moz-transition: none !important;
-		-o-transition: none !important;
-		-ms-transition: none !important;
-		transition: none !important;
-	}`)),styleElement)}function withoutTransition(action2,synchronous=!1){if(typeof document>"u")return;if(!hasLoaded){hasLoaded=!0,action2();return}if(typeof window<"u"&&window.__vitest_worker__){action2();return}clearTimeout(timeoutAction),clearTimeout(timeoutEnable);const style2=getStyleElement(),disable2=()=>document.head.appendChild(style2),enable=()=>{style2.parentNode&&document.head.removeChild(style2)};function executeAction(){action2(),window.requestAnimationFrame(enable)}if(typeof window.
-requestAnimationFrame<"u"){disable2(),synchronous?executeAction():window.requestAnimationFrame(()=>{executeAction()});return}disable2(),timeoutAction=window.setTimeout(()=>{action2(),timeoutEnable=window.setTimeout(enable,16)},16)}const themeColors=box(void 0),disableTransitions=box(!0),synchronousModeChanges=box(!1),darkClassNames=box([]),lightClassNames=box([]);function createDerivedMode(){const current2=user_derived(()=>{if(!isBrowser)return;const derivedMode2=userPrefersMode.current==="syste\
-m"?systemPrefersMode.current:userPrefersMode.current,sanitizedDarkClassNames=sanitizeClassNames(darkClassNames.current),sanitizedLightClassNames=sanitizeClassNames(lightClassNames.current);function update2(){const htmlEl=document.documentElement,themeColorEl=document.querySelector('meta[name="theme-color"]');derivedMode2==="light"?(sanitizedDarkClassNames.length&&htmlEl.classList.remove(...sanitizedDarkClassNames),sanitizedLightClassNames.length&&htmlEl.classList.add(...sanitizedLightClassNames),
-htmlEl.style.colorScheme="light",themeColorEl&&themeColors.current&&themeColorEl.setAttribute("content",themeColors.current.light)):(sanitizedLightClassNames.length&&htmlEl.classList.remove(...sanitizedLightClassNames),sanitizedDarkClassNames.length&&htmlEl.classList.add(...sanitizedDarkClassNames),htmlEl.style.colorScheme="dark",themeColorEl&&themeColors.current&&themeColorEl.setAttribute("content",themeColors.current.dark))}return disableTransitions.current?withoutTransition(update2,synchronousModeChanges.
-current):update2(),derivedMode2});return{get current(){return get$4(current2)}}}function createDerivedTheme(){const current2=user_derived(()=>{if(customTheme.current,!isBrowser)return;function update2(){document.documentElement.setAttribute("data-theme",customTheme.current)}return disableTransitions.current?withoutTransition(update2,untrack$1(()=>synchronousModeChanges.current)):update2(),customTheme.current});return{get current(){return get$4(current2)}}}const derivedMode=createDerivedMode(),derivedTheme=createDerivedTheme();
-function setMode(mode){userPrefersMode.current=mode}function setTheme(newTheme){customTheme.current=newTheme}function setInitialMode({defaultMode="system",themeColors:themeColors2,darkClassNames:darkClassNames2=["dark"],lightClassNames:lightClassNames2=[],defaultTheme="",modeStorageKey:modeStorageKey2="mode-watcher-mode",themeStorageKey:themeStorageKey2="mode-watcher-theme"}){const rootEl=document.documentElement,mode=localStorage.getItem(modeStorageKey2)??defaultMode,theme=localStorage.getItem(themeStorageKey2)??defaultTheme,light=mode==="light"||mode==="system"&&window.matchMedia("(prefers-color-sche\
-me: light)").matches;if(light?(darkClassNames2.length&&rootEl.classList.remove(...darkClassNames2.filter(Boolean)),lightClassNames2.length&&rootEl.classList.add(...lightClassNames2.filter(Boolean))):(lightClassNames2.length&&rootEl.classList.remove(...lightClassNames2.filter(Boolean)),darkClassNames2.length&&rootEl.classList.add(...darkClassNames2.filter(Boolean))),rootEl.style.colorScheme=light?"light":"dark",themeColors2){const themeMetaEl=document.querySelector('meta[name="theme-color"]');themeMetaEl&&
-themeMetaEl.setAttribute("content",mode==="light"?themeColors2.light:themeColors2.dark)}theme&&(rootEl.setAttribute("data-theme",theme),localStorage.setItem(themeStorageKey2,theme)),localStorage.setItem(modeStorageKey2,mode)}var root_1$Y=from_html('<meta name="theme-color"/>');function Mode_watcher_lite($$anchor,$$props){push$1($$props,!0);var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var meta=root_1$Y();template_effect(()=>set_attribute(meta,"content",$$props.
-themeColors.dark)),append($$anchor2,meta)};if_block(node2,$$render=>{$$props.themeColors&&$$render(consequent)})}append($$anchor,fragment),pop()}var root_2$13=from_html('<meta name="theme-color"/>'),root_1$X=from_html("<!> <!>",1);function Mode_watcher_full($$anchor,$$props){push$1($$props,!0);let trueNonce=prop($$props,"trueNonce",3,"");head$1("1funsus",$$anchor2=>{var fragment=root_1$X(),node2=first_child(fragment);{var consequent=$$anchor3=>{var meta=root_2$13();template_effect(()=>set_attribute(
-meta,"content",$$props.themeColors.dark)),append($$anchor3,meta)};if_block(node2,$$render=>{$$props.themeColors&&$$render(consequent)})}var node_1=sibling(node2,2);html$6(node_1,()=>`<script${trueNonce()?` nonce=${trueNonce()}`:""}>(`+setInitialMode.toString()+")("+JSON.stringify($$props.initConfig)+");<\/script>"),append($$anchor2,fragment)}),pop()}function Mode_watcher($$anchor,$$props){push$1($$props,!0);let track2=prop($$props,"track",3,!0),defaultMode=prop($$props,"defaultMode",3,"system"),
-disableTransitionsProp=prop($$props,"disableTransitions",3,!0),darkClassNamesProp=prop($$props,"darkClassNames",19,()=>["dark"]),lightClassNamesProp=prop($$props,"lightClassNames",19,()=>[]),defaultTheme=prop($$props,"defaultTheme",3,""),nonce=prop($$props,"nonce",3,""),themeStorageKeyProp=prop($$props,"themeStorageKey",3,"mode-watcher-theme"),modeStorageKeyProp=prop($$props,"modeStorageKey",3,"mode-watcher-mode"),disableHeadScriptInjection=prop($$props,"disableHeadScriptInjection",3,!1),synchronousModeChangesProp=prop(
-$$props,"synchronousModeChanges",3,!1);modeStorageKey.current=modeStorageKeyProp(),themeStorageKey.current=themeStorageKeyProp(),darkClassNames.current=darkClassNamesProp(),lightClassNames.current=lightClassNamesProp(),disableTransitions.current=disableTransitionsProp(),themeColors.current=$$props.themeColors,synchronousModeChanges.current=synchronousModeChangesProp(),user_pre_effect(()=>{synchronousModeChanges.current=synchronousModeChangesProp()}),user_pre_effect(()=>{disableTransitions.current=
-disableTransitionsProp()}),user_pre_effect(()=>{themeColors.current=$$props.themeColors}),user_pre_effect(()=>{darkClassNames.current=darkClassNamesProp()}),user_pre_effect(()=>{lightClassNames.current=lightClassNamesProp()}),user_pre_effect(()=>{modeStorageKey.current=modeStorageKeyProp()}),user_pre_effect(()=>{themeStorageKey.current=themeStorageKeyProp()}),user_pre_effect(()=>{derivedMode.current,modeStorageKey.current,themeStorageKey.current,derivedTheme.current}),onMount$1(()=>{systemPrefersMode.
-tracking(track2()),systemPrefersMode.query();const localStorageMode=localStorage.getItem(modeStorageKey.current);setMode(isValidMode(localStorageMode)?localStorageMode:defaultMode());const localStorageTheme=localStorage.getItem(themeStorageKey.current);setTheme(localStorageTheme||defaultTheme())});const initConfig={defaultMode:defaultMode(),themeColors:$$props.themeColors,darkClassNames:darkClassNamesProp(),lightClassNames:lightClassNamesProp(),defaultTheme:defaultTheme(),modeStorageKey:modeStorageKeyProp(),
-themeStorageKey:themeStorageKeyProp()},trueNonce=user_derived(()=>typeof window>"u"?nonce():"");var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{Mode_watcher_lite($$anchor2,{get themeColors(){return themeColors.current}})},alternate=$$anchor2=>{Mode_watcher_full($$anchor2,{get trueNonce(){return get$4(trueNonce)},get initConfig(){return initConfig},get themeColors(){return themeColors.current}})};if_block(node2,$$render=>{disableHeadScriptInjection()?$$render(consequent):
-$$render(alternate,!1)})}append($$anchor,fragment),pop()}class MCPStore{#_isInitializing=state$1(!1);get _isInitializing(){return get$4(this.#_isInitializing)}set _isInitializing(value){set$1(this.#_isInitializing,value,!0)}#_error=state$1(null);get _error(){return get$4(this.#_error)}set _error(value){set$1(this.#_error,value,!0)}#_toolCount=state$1(0);get _toolCount(){return get$4(this.#_toolCount)}set _toolCount(value){set$1(this.#_toolCount,value,!0)}#_connectedServers=state$1(proxy([]));get _connectedServers(){
-return get$4(this.#_connectedServers)}set _connectedServers(value){set$1(this.#_connectedServers,value,!0)}#_healthChecks=state$1(proxy({}));get _healthChecks(){return get$4(this.#_healthChecks)}set _healthChecks(value){set$1(this.#_healthChecks,value,!0)}#_proxyAvailable=state$1(!1);get _proxyAvailable(){return get$4(this.#_proxyAvailable)}set _proxyAvailable(value){set$1(this.#_proxyAvailable,value,!0)}connections=new Map;toolsIndex=new Map;serverConfigs=new Map;reconnectingServers=new Set;configSignature=null;initPromise=null;activeFlowCount=0;constructor(){
-this.probeProxy()}async probeProxy(){try{const response=await fetch(`${base}${CORS_PROXY_ENDPOINT}`,{method:"HEAD"});this._proxyAvailable=response.status!==404}catch{this._proxyAvailable=!1}}get isProxyAvailable(){return this._proxyAvailable}#generateServerId(id2,index2){return typeof id2=="string"&&id2.trim()?id2.trim():`${MCP_SERVER_ID_PREFIX}-${index2+1}`}#parseServerSettings(rawServers){if(!rawServers)return[];let parsed;if(typeof rawServers=="string"){const trimmed=rawServers.trim();if(!trimmed)
-return[];try{parsed=JSON.parse(trimmed)}catch(error2){return console.warn("[MCP] Failed to parse mcpServers JSON:",error2),[]}}else parsed=rawServers;return Array.isArray(parsed)?parsed.map((entry,index2)=>{const url2=typeof entry?.url=="string"?entry.url.trim():"",headers=typeof entry?.headers=="string"?entry.headers.trim():void 0;return{id:this.#generateServerId(entry?.id,index2),enabled:!!entry?.enabled,url:url2,name:entry?.name,requestTimeoutSeconds:DEFAULT_MCP_CONFIG.requestTimeoutSeconds,headers:headers||
-void 0,useProxy:!!entry?.useProxy}}):[]}#buildServerConfig(entry,connectionTimeoutMs=DEFAULT_MCP_CONFIG.connectionTimeoutMs){if(!entry?.url)return;let headers;if(entry.headers)try{const parsed=JSON.parse(entry.headers);typeof parsed=="object"&&parsed!==null&&!Array.isArray(parsed)&&(headers=parsed)}catch{console.warn("[MCP] Failed to parse custom headers JSON:",entry.headers)}return{url:entry.url,transport:detectMcpTransportFromUrl(entry.url),handshakeTimeoutMs:connectionTimeoutMs,requestTimeoutMs:Math.
-round(entry.requestTimeoutSeconds*1e3),headers,useProxy:entry.useProxy}}#checkServerEnabled(server,perChatOverrides){return perChatOverrides?.find(o=>o.serverId===server.id)?.enabled??!1}#buildMcpClientConfig(cfg,perChatOverrides){const rawServers=this.#parseServerSettings(cfg.mcpServers);if(!rawServers.length)return;const servers={};for(const[index2,entry]of rawServers.entries()){if(!this.#checkServerEnabled(entry,perChatOverrides))continue;const normalized=this.#buildServerConfig(entry);normalized&&
-(servers[this.#generateServerId(entry.id,index2)]=normalized)}if(Object.keys(servers).length!==0)return{protocolVersion:DEFAULT_MCP_CONFIG.protocolVersion,capabilities:DEFAULT_MCP_CONFIG.capabilities,clientInfo:DEFAULT_MCP_CONFIG.clientInfo,requestTimeoutMs:Math.round(DEFAULT_MCP_CONFIG.requestTimeoutSeconds*1e3),servers}}#buildCapabilitiesInfo(serverCaps,clientCaps){return{server:{tools:serverCaps?.tools?{listChanged:serverCaps.tools.listChanged}:void 0,prompts:serverCaps?.prompts?{listChanged:serverCaps.
-prompts.listChanged}:void 0,resources:serverCaps?.resources?{subscribe:serverCaps.resources.subscribe,listChanged:serverCaps.resources.listChanged}:void 0,logging:!!serverCaps?.logging,completions:!!serverCaps?.completions,tasks:!!serverCaps?.tasks},client:{roots:clientCaps?.roots?{listChanged:clientCaps.roots.listChanged}:void 0,sampling:!!clientCaps?.sampling,elicitation:clientCaps?.elicitation?{form:!!clientCaps.elicitation.form,url:!!clientCaps.elicitation.url}:void 0,tasks:!!clientCaps?.tasks}}}get isInitializing(){
-return this._isInitializing}get isInitialized(){return this.connections.size>0}get error(){return this._error}get toolCount(){return this._toolCount}get connectedServerCount(){return this._connectedServers.length}get connectedServerNames(){return this._connectedServers}get isEnabled(){const mcpConfig=this.#buildMcpClientConfig(config$1());return mcpConfig!=null&&Object.keys(mcpConfig.servers).length>0}get availableTools(){return Array.from(this.toolsIndex.keys())}updateState(state2){state2.isInitializing!==
-void 0&&(this._isInitializing=state2.isInitializing),state2.error!==void 0&&(this._error=state2.error),state2.toolCount!==void 0&&(this._toolCount=state2.toolCount),state2.connectedServers!==void 0&&(this._connectedServers=state2.connectedServers)}updateHealthCheck(serverId,state2){this._healthChecks={...this._healthChecks,[serverId]:state2}}getHealthCheckState(serverId){return this._healthChecks[serverId]??{status:HealthCheckStatus.IDLE}}hasHealthCheck(serverId){return serverId in this._healthChecks&&
-this._healthChecks[serverId].status!==HealthCheckStatus.IDLE}clearHealthCheck(serverId){const{[serverId]:_removed,...rest}=this._healthChecks;this._healthChecks=rest}clearAllHealthChecks(){this._healthChecks={}}clearError(){this._error=null}getServers(){return parseMcpServerSettings(config$1().mcpServers)}getConnections(){return this.connections}getServerLabel(server){const healthState=this.getHealthCheckState(server.id);return healthState?.status===HealthCheckStatus.SUCCESS&&(healthState.serverInfo?.
-title||healthState.serverInfo?.name||server.name)||server.url}getServerById(serverId){return this.getServers().find(s2=>s2.id===serverId)}getServerDisplayName(serverId){const server=this.getServerById(serverId);return server?this.getServerLabel(server):serverId}#isValidIconUri(src){try{return src.startsWith(UrlProtocol.DATA)?!0:new URL(src).protocol===UrlProtocol.HTTPS}catch{return!1}}#getMcpIconUrl(icons,isDark=!1){if(!icons?.length)return null;const validIcons=icons.filter(icon=>!(!icon.src||!this.#isValidIconUri(
-icon.src)||icon.mimeType&&!MCP_ALLOWED_ICON_MIME_TYPES.has(icon.mimeType)));if(validIcons.length===0)return null;const preferredTheme=isDark?ColorMode.DARK:ColorMode.LIGHT,themedIcon=validIcons.find(icon=>icon.theme===preferredTheme);if(themedIcon)return this.#proxyIconSrc(themedIcon.src);const universalIcons=validIcons.filter(icon=>!icon.theme);return universalIcons.length===EXPECTED_THEMED_ICON_PAIR_COUNT?this.#proxyIconSrc(universalIcons[isDark?1:0].src):universalIcons.length>0?this.#proxyIconSrc(
-universalIcons[0].src):this.#proxyIconSrc(validIcons[0].src)}#proxyIconSrc(src){return src.startsWith("data:")||!this._proxyAvailable?src:getProxiedUrlString(src)}getServerFavicon(serverId){const server=this.getServerById(serverId);if(!server)return null;const isDark=derivedMode.current===ColorMode.DARK,healthState=this.getHealthCheckState(serverId);if(healthState.status===HealthCheckStatus.SUCCESS&&healthState.serverInfo?.icons){const mcpIconUrl=this.#getMcpIconUrl(healthState.serverInfo.icons,
-isDark);if(mcpIconUrl)return mcpIconUrl}return getFaviconUrl(server.url,this._proxyAvailable)}isAnyServerLoading(){return this.getServers().some(s2=>{const state2=this.getHealthCheckState(s2.id);return state2.status===HealthCheckStatus.IDLE||state2.status===HealthCheckStatus.CONNECTING})}getServersSorted(){const servers=this.getServers();return this.isAnyServerLoading()?servers:[...servers].sort((a,b)=>this.getServerLabel(a).localeCompare(this.getServerLabel(b)))}addServer(serverData){const servers=this.
-getServers(),newServer={id:serverData.id||(uuid$1()??`server-${Date.now()}`),enabled:serverData.enabled,url:serverData.url.trim(),name:serverData.name,headers:serverData.headers?.trim()||void 0,requestTimeoutSeconds:DEFAULT_MCP_CONFIG.requestTimeoutSeconds,useProxy:serverData.useProxy};settingsStore.updateConfig("mcpServers",JSON.stringify([...servers,newServer]))}updateServer(id2,updates){const servers=this.getServers();settingsStore.updateConfig("mcpServers",JSON.stringify(servers.map(server=>server.
-id===id2?{...server,...updates}:server)))}removeServer(id2){const servers=this.getServers();settingsStore.updateConfig("mcpServers",JSON.stringify(servers.filter(s2=>s2.id!==id2))),this.clearHealthCheck(id2)}hasAvailableServers(){return parseMcpServerSettings(config$1().mcpServers).some(s2=>s2.enabled&&s2.url.trim())}hasEnabledServers(perChatOverrides){return!!this.#buildMcpClientConfig(config$1(),perChatOverrides)}getEnabledServersForConversation(perChatOverrides){return this.getServers().filter(
-server=>this.#checkServerEnabled(server,perChatOverrides))}async ensureInitialized(perChatOverrides){const mcpConfig=this.#buildMcpClientConfig(config$1(),perChatOverrides),signature=mcpConfig?JSON.stringify(mcpConfig):null;return signature?this.isInitialized&&this.configSignature===signature?!0:this.initPromise&&this.configSignature===signature?this.initPromise:((this.connections.size>0||this.initPromise)&&await this.shutdown(),this.initialize(signature,mcpConfig)):(await this.shutdown(),!1)}async initialize(signature,mcpConfig){
-this.updateState({isInitializing:!0,error:null}),this.configSignature=signature;const serverEntries=Object.entries(mcpConfig.servers);return serverEntries.length===0?(this.updateState({isInitializing:!1,toolCount:0,connectedServers:[]}),!1):(this.initPromise=this.doInitialize(signature,mcpConfig,serverEntries),this.initPromise)}async doInitialize(signature,mcpConfig,serverEntries){const clientInfo=mcpConfig.clientInfo??DEFAULT_MCP_CONFIG.clientInfo,capabilities=mcpConfig.capabilities??DEFAULT_MCP_CONFIG.
-capabilities,results=await Promise.allSettled(serverEntries.map(async([name,serverConfig])=>{this.serverConfigs.set(name,serverConfig);const listChangedHandlers=this.createListChangedHandlers(name),connection=await MCPService.connect(name,serverConfig,clientInfo,capabilities,phase=>{phase===MCPConnectionPhase.DISCONNECTED&&(console.log(`[MCPStore][${name}] Connection lost, starting auto-reconnect`),this.autoReconnect(name))},listChangedHandlers);return{name,connection}}));if(this.configSignature!==
-signature){for(const result of results)result.status==="fulfilled"&&await MCPService.disconnect(result.value.connection).catch(console.warn);return!1}for(const result of results)if(result.status==="fulfilled"){const{name,connection}=result.value;this.connections.set(name,connection);for(const tool of connection.tools)this.toolsIndex.has(tool.name)&&console.warn(`[MCPStore] Tool name conflict: "${tool.name}" exists in "${this.toolsIndex.get(tool.name)}" and "${name}". Using tool from "${name}".`),
-this.toolsIndex.set(tool.name,name)}else console.error("[MCPStore] Failed to connect:",result.reason);return this.connections.size===0&&serverEntries.length>0?(this.updateState({isInitializing:!1,error:"All MCP server connections failed",toolCount:0,connectedServers:[]}),this.initPromise=null,!1):(this.updateState({isInitializing:!1,error:null,toolCount:this.toolsIndex.size,connectedServers:Array.from(this.connections.keys())}),this.initPromise=null,!0)}createListChangedHandlers(serverName){return{
-tools:{onChanged:(error2,tools)=>{if(error2){console.warn(`[MCPStore][${serverName}] Tools list changed error:`,error2);return}this.handleToolsListChanged(serverName,tools??[])}},prompts:{onChanged:error2=>{if(error2){console.warn(`[MCPStore][${serverName}] Prompts list changed error:`,error2);return}}}}}handleToolsListChanged(serverName,tools){const connection=this.connections.get(serverName);if(connection){for(const[toolName,ownerServer]of this.toolsIndex.entries())ownerServer===serverName&&this.
-toolsIndex.delete(toolName);connection.tools=tools;for(const tool of tools)this.toolsIndex.has(tool.name)&&console.warn(`[MCPStore] Tool name conflict after list change: "${tool.name}" exists in "${this.toolsIndex.get(tool.name)}" and "${serverName}". Using tool from "${serverName}".`),this.toolsIndex.set(tool.name,serverName);this.updateState({toolCount:this.toolsIndex.size})}}acquireConnection(){this.activeFlowCount++}async releaseConnection(shutdownIfUnused=!1){this.activeFlowCount=Math.max(0,
-this.activeFlowCount-1),shutdownIfUnused&&this.activeFlowCount===0&&await this.shutdown()}getActiveFlowCount(){return this.activeFlowCount}async shutdown(){this.initPromise&&(await this.initPromise.catch(()=>{}),this.initPromise=null),this.connections.size!==0&&(await Promise.all(Array.from(this.connections.values()).map(conn=>MCPService.disconnect(conn).catch(error2=>console.warn(`[MCPStore] Error disconnecting ${conn.serverName}:`,error2)))),this.connections.clear(),this.toolsIndex.clear(),this.
-serverConfigs.clear(),this.configSignature=null,this.updateState({isInitializing:!1,error:null,toolCount:0,connectedServers:[]}))}async reconnectServer(serverName){const serverConfig=this.serverConfigs.get(serverName);if(!serverConfig)throw new Error(`[MCPStore] No config found for ${serverName}, cannot reconnect`);const oldConnection=this.connections.get(serverName);oldConnection&&(await MCPService.disconnect(oldConnection).catch(console.warn),this.connections.delete(serverName)),console.log(`[\
-MCPStore][${serverName}] Session expired, reconnecting with fresh session...`);const listChangedHandlers=this.createListChangedHandlers(serverName),connection=await MCPService.connect(serverName,serverConfig,DEFAULT_MCP_CONFIG.clientInfo,DEFAULT_MCP_CONFIG.capabilities,phase=>{phase===MCPConnectionPhase.DISCONNECTED&&(console.log(`[MCPStore][${serverName}] Connection lost, starting auto-reconnect`),this.autoReconnect(serverName))},listChangedHandlers);this.connections.set(serverName,connection);
-for(const tool of connection.tools)this.toolsIndex.set(tool.name,serverName);console.log(`[MCPStore][${serverName}] Session recovered successfully`)}async autoReconnect(serverName){if(this.reconnectingServers.has(serverName)){console.log(`[MCPStore][${serverName}] Reconnection already in progress, skipping`);return}const serverConfig=this.serverConfigs.get(serverName);if(!serverConfig){console.error(`[MCPStore] No config found for ${serverName}, cannot reconnect`);return}this.reconnectingServers.
-add(serverName);let backoff=MCP_RECONNECT_INITIAL_DELAY,needsReconnect=!1;try{for(;;){await new Promise(resolve2=>setTimeout(resolve2,backoff)),console.log(`[MCPStore][${serverName}] Auto-reconnecting...`);try{const timeoutPromise=new Promise((_,reject)=>setTimeout(()=>reject(new Error(`Reconnect attempt timed out after ${MCP_RECONNECT_ATTEMPT_TIMEOUT_MS}ms`)),MCP_RECONNECT_ATTEMPT_TIMEOUT_MS));needsReconnect=!1;const listChangedHandlers=this.createListChangedHandlers(serverName),connectPromise=MCPService.
-connect(serverName,serverConfig,DEFAULT_MCP_CONFIG.clientInfo,DEFAULT_MCP_CONFIG.capabilities,phase=>{phase===MCPConnectionPhase.DISCONNECTED&&(this.reconnectingServers.has(serverName)?needsReconnect=!0:(console.log(`[MCPStore][${serverName}] Connection lost, restarting auto-reconnect`),this.autoReconnect(serverName)))},listChangedHandlers),connection=await Promise.race([connectPromise,timeoutPromise]);this.connections.set(serverName,connection);for(const tool of connection.tools)this.toolsIndex.
-set(tool.name,serverName);console.log(`[MCPStore][${serverName}] Reconnected successfully`);break}catch(error2){console.warn(`[MCPStore][${serverName}] Reconnection failed:`,error2),backoff=Math.min(backoff*MCP_RECONNECT_BACKOFF_MULTIPLIER,MCP_RECONNECT_MAX_DELAY)}}}finally{this.reconnectingServers.delete(serverName),needsReconnect&&(console.log(`[MCPStore][${serverName}] Deferred disconnect detected, restarting auto-reconnect`),this.autoReconnect(serverName))}}getToolDefinitionsForLLM(){const tools=[];
-for(const connection of this.connections.values())for(const tool of connection.tools){const rawSchema=tool.inputSchema??{type:JsonSchemaType.OBJECT,properties:{},required:[]};tools.push({type:ToolCallType.FUNCTION,function:{name:tool.name,description:tool.description,parameters:this.normalizeSchemaProperties(rawSchema)}})}return tools}normalizeSchemaProperties(schema){if(!schema||typeof schema!="object")return schema;const normalized={...schema};if(normalized.properties&&typeof normalized.properties==
-"object"){const props=normalized.properties,normalizedProps={};for(const[key2,prop2]of Object.entries(props)){if(!prop2||typeof prop2!="object"){normalizedProps[key2]=prop2;continue}const normalizedProp={...prop2};if(!normalizedProp.type&&normalizedProp.default!==void 0){const defaultVal=normalizedProp.default;typeof defaultVal=="string"?normalizedProp.type="string":typeof defaultVal=="number"?normalizedProp.type=Number.isInteger(defaultVal)?"integer":"number":typeof defaultVal=="boolean"?normalizedProp.
-type="boolean":Array.isArray(defaultVal)?normalizedProp.type="array":typeof defaultVal=="object"&&defaultVal!==null&&(normalizedProp.type="object")}normalizedProp.properties&&Object.assign(normalizedProp,this.normalizeSchemaProperties(normalizedProp)),normalizedProp.items&&typeof normalizedProp.items=="object"&&(normalizedProp.items=this.normalizeSchemaProperties(normalizedProp.items)),normalizedProps[key2]=normalizedProp}normalized.properties=normalizedProps}return normalized}getToolNames(){return Array.
-from(this.toolsIndex.keys())}hasTool(toolName){return this.toolsIndex.has(toolName)}getToolServer(toolName){return this.toolsIndex.get(toolName)}hasPromptsSupport(){for(const connection of this.connections.values())if(connection.serverCapabilities?.prompts)return!0;return!1}hasPromptsCapability(perChatOverrides){if(perChatOverrides!==void 0){const enabledServerIds=new Set(perChatOverrides.filter(o=>o.enabled).map(o=>o.serverId));if(enabledServerIds.size===0)return!1;for(const[serverId,state2]of Object.
-entries(this._healthChecks))if(enabledServerIds.has(serverId)&&state2.status===HealthCheckStatus.SUCCESS&&state2.capabilities?.server?.prompts!==void 0)return!0;for(const[serverName,connection]of this.connections)if(enabledServerIds.has(serverName)&&connection.serverCapabilities?.prompts)return!0;return!1}for(const state2 of Object.values(this._healthChecks))if(state2.status===HealthCheckStatus.SUCCESS&&state2.capabilities?.server?.prompts!==void 0)return!0;for(const connection of this.connections.
-values())if(connection.serverCapabilities?.prompts)return!0;return!1}async getAllPrompts(){const results=[];for(const[serverName,connection]of this.connections){if(!connection.serverCapabilities?.prompts)continue;const prompts=await MCPService.listPrompts(connection);for(const prompt of prompts)results.push({name:prompt.name,description:prompt.description,title:prompt.title,serverName,arguments:prompt.arguments?.map(arg=>({name:arg.name,description:arg.description,required:arg.required}))})}return results}async getPrompt(serverName,promptName,args){
-const connection=this.connections.get(serverName);if(!connection)throw new Error(`Server "${serverName}" not found for prompt "${promptName}"`);return MCPService.getPrompt(connection,promptName,args)}async executeTool(toolCall,signal){const toolName=toolCall.function.name,serverName=this.toolsIndex.get(toolName);if(!serverName)throw new Error(`Unknown tool: ${toolName}`);const connection=this.connections.get(serverName);if(!connection)throw new Error(`Server "${serverName}" is not connected`);const args=this.
-parseToolArguments(toolCall.function.arguments);try{return await MCPService.callTool(connection,{name:toolName,arguments:args},signal)}catch(error2){if(MCPService.isSessionExpiredError(error2)){await this.reconnectServer(serverName);const newConnection=this.connections.get(serverName);if(!newConnection)throw new Error(`Failed to reconnect to "${serverName}"`);return MCPService.callTool(newConnection,{name:toolName,arguments:args},signal)}throw error2}}async executeToolByName(toolName,args,signal){
-const serverName=this.toolsIndex.get(toolName);if(!serverName)throw new Error(`Unknown tool: ${toolName}`);const connection=this.connections.get(serverName);if(!connection)throw new Error(`Server "${serverName}" is not connected`);try{return await MCPService.callTool(connection,{name:toolName,arguments:args},signal)}catch(error2){if(MCPService.isSessionExpiredError(error2)){await this.reconnectServer(serverName);const newConnection=this.connections.get(serverName);if(!newConnection)throw new Error(
-`Failed to reconnect to "${serverName}"`);return MCPService.callTool(newConnection,{name:toolName,arguments:args},signal)}throw error2}}parseToolArguments(args){if(typeof args=="string"){const trimmed=args.trim();if(trimmed==="")return{};try{const parsed=JSON.parse(trimmed);if(typeof parsed!="object"||parsed===null||Array.isArray(parsed))throw new Error(`Tool arguments must be an object, got ${Array.isArray(parsed)?"array":typeof parsed}`);return parsed}catch(error2){throw new Error(`Failed to p\
-arse tool arguments as JSON: ${error2.message}`)}}if(typeof args=="object"&&args!==null&&!Array.isArray(args))return args;throw new Error(`Invalid tool arguments type: ${typeof args}`)}async getPromptCompletions(serverName,promptName,argumentName,argumentValue){const connection=this.connections.get(serverName);return connection?connection.serverCapabilities?.completions?MCPService.complete(connection,{type:MCPRefType.PROMPT,name:promptName},{name:argumentName,value:argumentValue}):null:(console.
-warn(`[MCPStore] Server "${serverName}" is not connected`),null)}async getResourceCompletions(serverName,uriTemplate,argumentName,argumentValue){const connection=this.connections.get(serverName);return connection?connection.serverCapabilities?.completions?MCPService.complete(connection,{type:MCPRefType.RESOURCE,uri:uriTemplate},{name:argumentName,value:argumentValue}):null:(console.warn(`[MCPStore] Server "${serverName}" is not connected`),null)}async readResourceByUri(serverName,uri2){const connection=this.
-connections.get(serverName);if(!connection)return console.error(`[MCPStore] No connection found for server: ${serverName}`),null;try{return(await MCPService.readResource(connection,uri2)).contents}catch(error2){return console.error(`[MCPStore] Failed to read resource ${uri2}:`,error2),null}}parseHeaders(headersJson){if(headersJson?.trim())try{const parsed=JSON.parse(headersJson);if(typeof parsed=="object"&&parsed!==null&&!Array.isArray(parsed))return parsed}catch{console.warn("[MCPStore] Failed \
-to parse custom headers JSON:",headersJson)}}async runHealthChecksForServers(servers,skipIfChecked=!0,promoteToActive=!1){const serversToCheck=skipIfChecked?servers.filter(s2=>!this.hasHealthCheck(s2.id)&&s2.url.trim()):servers.filter(s2=>s2.url.trim());if(serversToCheck.length===0)return;const BATCH_SIZE=5;for(let i=0;i<serversToCheck.length;i+=BATCH_SIZE){const batch=serversToCheck.slice(i,i+BATCH_SIZE);await Promise.allSettled(batch.map(server=>this.runHealthCheck(server,promoteToActive)))}}getExistingConnection(serverId){
-return this.connections.get(serverId)}async runHealthCheck(server,promoteToActive=!1){const existingConnection=this.connections.get(server.id);if(existingConnection)try{const tools=await MCPService.listTools(existingConnection),capabilities=this.#buildCapabilitiesInfo(existingConnection.serverCapabilities,existingConnection.clientCapabilities);this.updateHealthCheck(server.id,{status:HealthCheckStatus.SUCCESS,tools:tools.map(tool=>({name:tool.name,description:tool.description,title:tool.title})),
-serverInfo:existingConnection.serverInfo,capabilities,transportType:existingConnection.transportType,protocolVersion:existingConnection.protocolVersion,instructions:existingConnection.instructions,connectionTimeMs:existingConnection.connectionTimeMs,logs:[]});return}catch(error2){console.warn(`[MCPStore] Failed to reuse connection for ${server.id}, creating new one:`,error2),this.connections.delete(server.id)}const trimmedUrl=server.url.trim(),logs=[];let currentPhase=MCPConnectionPhase.IDLE;if(!trimmedUrl){
-this.updateHealthCheck(server.id,{status:HealthCheckStatus.ERROR,message:"Please enter a server URL first.",logs:[]});return}this.updateHealthCheck(server.id,{status:HealthCheckStatus.CONNECTING,phase:MCPConnectionPhase.TRANSPORT_CREATING,logs:[]});const timeoutMs=Math.round(server.requestTimeoutSeconds*1e3),headers=this.parseHeaders(server.headers);try{const serverConfig={url:trimmedUrl,transport:detectMcpTransportFromUrl(trimmedUrl),handshakeTimeoutMs:DEFAULT_MCP_CONFIG.connectionTimeoutMs,requestTimeoutMs:timeoutMs,
-headers,useProxy:server.useProxy};this.serverConfigs.set(server.id,serverConfig);const connection=await MCPService.connect(server.id,serverConfig,DEFAULT_MCP_CONFIG.clientInfo,DEFAULT_MCP_CONFIG.capabilities,(phase,log)=>{currentPhase=phase,logs.push(log),this.updateHealthCheck(server.id,{status:HealthCheckStatus.CONNECTING,phase,logs:[...logs]}),phase===MCPConnectionPhase.DISCONNECTED&&promoteToActive&&(console.log(`[MCPStore][${server.id}] Connection lost during health check, starting auto-rec\
-onnect`),this.autoReconnect(server.id))}),tools=connection.tools.map(tool=>({name:tool.name,description:tool.description,title:tool.title})),capabilities=this.#buildCapabilitiesInfo(connection.serverCapabilities,connection.clientCapabilities);this.updateHealthCheck(server.id,{status:HealthCheckStatus.SUCCESS,tools,serverInfo:connection.serverInfo,capabilities,transportType:connection.transportType,protocolVersion:connection.protocolVersion,instructions:connection.instructions,connectionTimeMs:connection.
-connectionTimeMs,logs}),promoteToActive&&server.enabled?this.promoteHealthCheckToConnection(server.id,connection):await MCPService.disconnect(connection)}catch(error2){const message=error2 instanceof Error?error2.message:"Unknown error occurred";logs.at(-1)?.phase!==MCPConnectionPhase.ERROR&&logs.push({timestamp:new Date,phase:MCPConnectionPhase.ERROR,message:`Connection failed: ${message}`,level:MCPLogLevel.ERROR}),this.updateHealthCheck(server.id,{status:HealthCheckStatus.ERROR,message,phase:currentPhase,
-logs})}}promoteHealthCheckToConnection(serverId,connection){for(const tool of connection.tools)this.toolsIndex.has(tool.name)&&console.warn(`[MCPStore] Tool name conflict during promotion: "${tool.name}" exists in "${this.toolsIndex.get(tool.name)}" and "${serverId}". Using tool from "${serverId}".`),this.toolsIndex.set(tool.name,serverId);this.connections.set(serverId,connection),this.updateState({toolCount:this.toolsIndex.size,connectedServers:Array.from(this.connections.keys())})}getServersStatus(){
-const statuses=[];for(const[name,connection]of this.connections)statuses.push({name,isConnected:!0,toolCount:connection.tools.length,error:void 0});return statuses}getServerInstructions(){const results=[];for(const[serverName,connection]of this.connections)connection.instructions&&results.push({serverName,serverTitle:connection.serverInfo?.title||connection.serverInfo?.name,instructions:connection.instructions});return results}getHealthCheckInstructions(){const results=[];for(const[serverId,state2]of Object.
-entries(this._healthChecks))state2.status===HealthCheckStatus.SUCCESS&&state2.instructions&&results.push({serverId,serverTitle:state2.serverInfo?.title||state2.serverInfo?.name,instructions:state2.instructions});return results}hasServerInstructions(){for(const connection of this.connections.values())if(connection.instructions)return!0;return!1}hasResourcesCapability(perChatOverrides){if(perChatOverrides!==void 0){const enabledServerIds=new Set(perChatOverrides.filter(o=>o.enabled).map(o=>o.serverId));
-if(enabledServerIds.size===0)return!1;for(const[serverId,state2]of Object.entries(this._healthChecks))if(enabledServerIds.has(serverId)&&state2.status===HealthCheckStatus.SUCCESS&&state2.capabilities?.server?.resources!==void 0)return!0;for(const[serverName,connection]of this.connections)if(enabledServerIds.has(serverName)&&MCPService.supportsResources(connection))return!0;return!1}for(const state2 of Object.values(this._healthChecks))if(state2.status===HealthCheckStatus.SUCCESS&&state2.capabilities?.
-server?.resources!==void 0)return!0;for(const connection of this.connections.values())if(MCPService.supportsResources(connection))return!0;return!1}getServersWithResources(){const servers=[];for(const[name,connection]of this.connections)MCPService.supportsResources(connection)&&!servers.includes(name)&&servers.push(name);for(const[serverId,state2]of Object.entries(this._healthChecks))!servers.includes(serverId)&&state2.status===HealthCheckStatus.SUCCESS&&state2.capabilities?.server?.resources!==
-void 0&&servers.push(serverId);return servers}async fetchAllResources(forceRefresh=!1){const serversWithResources=this.getServersWithResources();if(serversWithResources.length!==0){if(!forceRefresh&&serversWithResources.every(serverName=>{const serverRes=mcpResourceStore.getServerResources(serverName);return!serverRes||!serverRes.lastFetched?!1:Date.now()-serverRes.lastFetched.getTime()<DEFAULT_CACHE_TTL_MS})){console.log("[MCPStore] Using cached resources");return}mcpResourceStore.setLoading(!0);
-try{await Promise.all(serversWithResources.map(serverName=>this.fetchServerResources(serverName)))}finally{mcpResourceStore.setLoading(!1)}}}async fetchServerResources(serverName){const connection=this.connections.get(serverName);if(!connection){console.warn(`[MCPStore] No connection found for server: ${serverName}`);return}if(MCPService.supportsResources(connection)){mcpResourceStore.setServerLoading(serverName,!0);try{const[resources,templates]=await Promise.all([MCPService.listAllResources(connection),
-MCPService.listAllResourceTemplates(connection)]);mcpResourceStore.setServerResources(serverName,resources,templates)}catch(error2){const message=error2 instanceof Error?error2.message:String(error2);mcpResourceStore.setServerError(serverName,message),console.error(`[MCPStore][${serverName}] Failed to fetch resources:`,error2)}}}async readResource(uri2){const cached2=mcpResourceStore.getCachedContent(uri2);if(cached2)return cached2.content;const serverName=mcpResourceStore.findServerForUri(uri2);
-if(!serverName)return console.error(`[MCPStore] No server found for resource URI: ${uri2}`),null;const connection=this.connections.get(serverName);if(!connection)return console.error(`[MCPStore] No connection found for server: ${serverName}`),null;try{const result=await MCPService.readResource(connection,uri2),resourceInfo=mcpResourceStore.findResourceByUri(uri2);return resourceInfo&&mcpResourceStore.cacheResourceContent(resourceInfo,result.contents),result.contents}catch(error2){return console.
-error(`[MCPStore] Failed to read resource ${uri2}:`,error2),null}}async subscribeToResource(uri2){const serverName=mcpResourceStore.findServerForUri(uri2);if(!serverName)return console.error(`[MCPStore] No server found for resource URI: ${uri2}`),!1;const connection=this.connections.get(serverName);if(!connection)return console.error(`[MCPStore] No connection found for server: ${serverName}`),!1;if(!MCPService.supportsResourceSubscriptions(connection))return!1;try{return await MCPService.subscribeResource(
-connection,uri2),mcpResourceStore.addSubscription(uri2,serverName),!0}catch(error2){return console.error(`[MCPStore] Failed to subscribe to resource ${uri2}:`,error2),!1}}async unsubscribeFromResource(uri2){const serverName=mcpResourceStore.findServerForUri(uri2);if(!serverName)return console.error(`[MCPStore] No server found for resource URI: ${uri2}`),!1;const connection=this.connections.get(serverName);if(!connection)return console.error(`[MCPStore] No connection found for server: ${serverName}`),
-!1;try{return await MCPService.unsubscribeResource(connection,uri2),mcpResourceStore.removeSubscription(uri2),!0}catch(error2){return console.error(`[MCPStore] Failed to unsubscribe from resource ${uri2}:`,error2),!1}}async attachResource(uri2){const resourceInfo=mcpResourceStore.findResourceByUri(uri2);if(!resourceInfo)return console.error(`[MCPStore] Resource not found: ${uri2}`),null;if(mcpResourceStore.isAttached(uri2))return null;const attachment=mcpResourceStore.addAttachment(resourceInfo);
-try{const content2=await this.readResource(uri2);content2?mcpResourceStore.updateAttachmentContent(attachment.id,content2):mcpResourceStore.updateAttachmentError(attachment.id,"Failed to read resource")}catch(error2){const message=error2 instanceof Error?error2.message:String(error2);mcpResourceStore.updateAttachmentError(attachment.id,message)}return mcpResourceStore.getAttachment(attachment.id)??null}removeResourceAttachment(attachmentId){mcpResourceStore.removeAttachment(attachmentId)}clearResourceAttachments(){
-mcpResourceStore.clearAttachments()}getResourceContextForChat(){return mcpResourceStore.formatAttachmentsForContext()}consumeResourceAttachmentsAsExtras(){const extras=mcpResourceStore.toMessageExtras();return extras.length>0&&mcpResourceStore.clearAttachments(),extras}}const mcpStore=new MCPStore;var root_2$12=from_html('<button type="button"><!> <span class="max-w-[150px] truncate text-xs"> </span> <!></button>'),root_9$n=from_html('<img alt="" class="h-3 w-3 shrink-0 rounded-sm"/>'),root_8$p=from_html(
-'<div class="flex items-center gap-1 text-xs"><!> <span class="truncate"> </span></div>'),root_1$W=from_html("<!> <!>",1);function ChatAttachmentMcpResource($$anchor,$$props){push$1($$props,!0);function getStatusClass(attachment){return attachment.error?"border-red-500/50 bg-red-500/10":(attachment.loading,"border-border/50 bg-muted/30")}const ResourceIcon=user_derived(()=>getResourceIcon($$props.attachment.resource.mimeType,$$props.attachment.resource.uri)),serverName=user_derived(()=>mcpStore.
-getServerDisplayName($$props.attachment.resource.serverName)),favicon=user_derived(()=>mcpStore.getServerFavicon($$props.attachment.resource.serverName));var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$5,($$anchor2,Tooltip_Root)=>{Tooltip_Root($$anchor2,{children:($$anchor3,$$slotProps)=>{var fragment_1=root_1$W(),node_1=first_child(fragment_1);component(node_1,()=>Tooltip_trigger,($$anchor4,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor4,{children:($$anchor5,$$slotProps2)=>{
-var button=root_2$12();button.__click=function(...$$args){$$props.onClick?.apply(this,$$args)};var node_2=child(button);{var consequent=$$anchor6=>{Loader_circle($$anchor6,{class:"h-3 w-3 animate-spin text-muted-foreground"})},alternate_1=$$anchor6=>{var fragment_3=comment$2(),node_3=first_child(fragment_3);{var consequent_1=$$anchor7=>{Circle_alert($$anchor7,{class:"h-3 w-3 text-red-500"})},alternate=$$anchor7=>{var fragment_5=comment$2(),node_4=first_child(fragment_5);component(node_4,()=>get$4(
-ResourceIcon),($$anchor8,ResourceIcon_1)=>{ResourceIcon_1($$anchor8,{class:"h-3 w-3 text-muted-foreground"})}),append($$anchor7,fragment_5)};if_block(node_3,$$render=>{$$props.attachment.error?$$render(consequent_1):$$render(alternate,!1)},!0)}append($$anchor6,fragment_3)};if_block(node_2,$$render=>{$$props.attachment.loading?$$render(consequent):$$render(alternate_1,!1)})}var span=sibling(node_2,2),text2=child(span,!0);reset(span);var node_5=sibling(span,2);{var consequent_2=$$anchor6=>{ActionIconRemove(
-$$anchor6,{class:"-my-2 -mr-1.5 bg-transparent",iconSize:2,get id(){return $$props.attachment.id},get onRemove(){return $$props.onRemove}})};if_block(node_5,$$render=>{$$props.onRemove&&$$render(consequent_2)})}reset(button),template_effect(($0,$1)=>{set_class(button,1,$0),button.disabled=!$$props.onClick,set_text(text2,$1)},[()=>clsx(cn$1("flex flex-shrink-0 items-center gap-1.5 rounded-md border px-2 py-0.75 text-sm transition-colors",getStatusClass($$props.attachment),$$props.onClick&&"cursor\
--pointer hover:bg-muted/50",$$props.class)),()=>getResourceDisplayName($$props.attachment.resource)]),append($$anchor5,button)},$$slots:{default:!0}})});var node_6=sibling(node_1,2);component(node_6,()=>Tooltip_content,($$anchor4,Tooltip_Content)=>{Tooltip_Content($$anchor4,{children:($$anchor5,$$slotProps2)=>{var div=root_8$p(),node_7=child(div);{var consequent_3=$$anchor6=>{var img=root_9$n();template_effect(()=>set_attribute(img,"src",get$4(favicon))),event("error",img,e=>{e.currentTarget.style.
-display="none"}),replay_events(img),append($$anchor6,img)};if_block(node_7,$$render=>{get$4(favicon)&&$$render(consequent_3)})}var span_1=sibling(node_7,2),text_1=child(span_1,!0);reset(span_1),reset(div),template_effect(()=>set_text(text_1,get$4(serverName))),append($$anchor5,div)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}delegate(["click"]);const alertVariants=tv({base:"relative grid w-full grid-cols-[0_1fr] items-start gap\
--y-0.5 rounded-lg border px-4 py-3 text-sm has-[>svg]:grid-cols-[calc(var(--spacing)*4)_1fr] has-[>svg]:gap-x-3 [&>svg]:size-4 [&>svg]:translate-y-0.5 [&>svg]:text-current",variants:{variant:{default:"bg-card text-card-foreground",destructive:"text-destructive bg-card *:data-[slot=alert-description]:text-destructive/90 [&>svg]:text-current"}},defaultVariants:{variant:"default"}});var root$1t=from_html("<div><!></div>");function Alert($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"re\
-f",15,null),variant=prop($$props,"variant",3,"default"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","variant","children"]);var div=root$1t();attribute_effect(div,$0=>({"data-slot":"alert",class:$0,...restProps,role:"alert"}),[()=>cn$1(alertVariants({variant:variant()}),$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root$1s=from_html("<div><\
-!></div>");function Alert_description($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$1s();attribute_effect(div,$0=>({"data-slot":"alert-description",class:$0,...restProps}),[()=>cn$1("col-start-2 grid justify-items-start gap-1 text-sm text-muted-foreground [&_p]:leading-relaxed",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),
-bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root$1r=from_html("<div><!></div>");function Alert_title($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$1r();attribute_effect(div,$0=>({"data-slot":"alert-title",class:$0,...restProps}),[()=>cn$1("col-start-2 line-clamp-1 min-h-4 font-medium tracking-tight",$$props.class)]);var node2=child(
-div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}class AudioRecorder{mediaRecorder=null;audioChunks=[];stream=null;recordingState=!1;async startRecording(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:{echoCancellation:!0,noiseSuppression:!0,autoGainControl:!0}}),this.initializeRecorder(this.stream),this.audioChunks=[],this.mediaRecorder.start(100),this.recordingState=!0}catch(error2){throw console.
-error("Failed to start recording:",error2),new Error("Failed to access microphone. Please check permissions.")}}async stopRecording(){return new Promise((resolve2,reject)=>{if(!this.mediaRecorder||this.mediaRecorder.state==="inactive"){reject(new Error("No active recording to stop"));return}this.mediaRecorder.onstop=()=>{const mimeType=this.mediaRecorder?.mimeType||MimeTypeAudio.WAV,audioBlob=new Blob(this.audioChunks,{type:mimeType});this.cleanup(),resolve2(audioBlob)},this.mediaRecorder.onerror=
-event2=>{console.error("Recording error:",event2),this.cleanup(),reject(new Error("Recording failed"))},this.mediaRecorder.stop()})}isRecording(){return this.recordingState}cancelRecording(){this.mediaRecorder&&this.mediaRecorder.state!=="inactive"&&this.mediaRecorder.stop(),this.cleanup()}initializeRecorder(stream){const options={};MediaRecorder.isTypeSupported(MimeTypeAudio.WAV)?options.mimeType=MimeTypeAudio.WAV:MediaRecorder.isTypeSupported(MimeTypeAudio.WEBM_OPUS)?options.mimeType=MimeTypeAudio.
-WEBM_OPUS:MediaRecorder.isTypeSupported(MimeTypeAudio.WEBM)?options.mimeType=MimeTypeAudio.WEBM:MediaRecorder.isTypeSupported(MimeTypeAudio.MP4)?options.mimeType=MimeTypeAudio.MP4:console.warn("No preferred audio format supported, using default"),this.mediaRecorder=new MediaRecorder(stream,options),this.mediaRecorder.ondataavailable=event2=>{event2.data.size>0&&this.audioChunks.push(event2.data)},this.mediaRecorder.onstop=()=>{this.recordingState=!1},this.mediaRecorder.onerror=event2=>{console.error(
-"MediaRecorder error:",event2),this.recordingState=!1}}cleanup(){if(this.stream){for(const track2 of this.stream.getTracks())track2.stop();this.stream=null}this.mediaRecorder=null,this.audioChunks=[],this.recordingState=!1}}async function convertToWav(audioBlob){try{if(audioBlob.type.includes("wav"))return audioBlob;const arrayBuffer=await audioBlob.arrayBuffer(),audioContext=new(window.AudioContext||window.webkitAudioContext),audioBuffer=await audioContext.decodeAudioData(arrayBuffer),wavBlob=audioBufferToWav(
-audioBuffer);return audioContext.close(),wavBlob}catch(error2){return console.error("Failed to convert audio to WAV:",error2),audioBlob}}function audioBufferToWav(buffer){const length=buffer.length,numberOfChannels=buffer.numberOfChannels,sampleRate=buffer.sampleRate,blockAlign=numberOfChannels*2,byteRate=sampleRate*blockAlign,dataSize=length*blockAlign,bufferSize=44+dataSize,arrayBuffer=new ArrayBuffer(bufferSize),view=new DataView(arrayBuffer),writeString=(offset22,string2)=>{for(let i=0;i<string2.
-length;i++)view.setUint8(offset22+i,string2.charCodeAt(i))};writeString(0,"RIFF"),view.setUint32(4,bufferSize-8,!0),writeString(8,"WAVE"),writeString(12,"fmt "),view.setUint32(16,16,!0),view.setUint16(20,1,!0),view.setUint16(22,numberOfChannels,!0),view.setUint32(24,sampleRate,!0),view.setUint32(28,byteRate,!0),view.setUint16(32,blockAlign,!0),view.setUint16(34,16,!0),writeString(36,"data"),view.setUint32(40,dataSize,!0);let offset2=44;for(let i=0;i<length;i++)for(let channel=0;channel<numberOfChannels;channel++){
-const sample=Math.max(-1,Math.min(1,buffer.getChannelData(channel)[i]));view.setInt16(offset2,sample*32767,!0),offset2+=2}return new Blob([arrayBuffer],{type:MimeTypeAudio.WAV})}function createAudioFile(audioBlob,filename){const timestamp=new Date().toISOString().replace(/[:.]/g,"-"),extension2=audioBlob.type.includes("wav")?"wav":"mp3",defaultFilename=`recording-${timestamp}.${extension2}`;return new File([audioBlob],defaultFilename,{type:audioBlob.type,lastModified:Date.now()})}function isAudioRecordingSupported(){
-return!!(typeof navigator<"u"&&navigator.mediaDevices&&typeof navigator.mediaDevices.getUserMedia=="function"&&typeof window<"u"&&window.MediaRecorder)}const isNodeJS=typeof process=="object"&&process+""=="[object process]"&&!process.versions.nw&&!(process.versions.electron&&process.type&&process.type!=="browser"),FONT_IDENTITY_MATRIX=[.001,0,0,.001,0,0],LINE_FACTOR=1.35,RenderingIntentFlag={ANY:1,DISPLAY:2,PRINT:4,ANNOTATIONS_FORMS:16,ANNOTATIONS_STORAGE:32,ANNOTATIONS_DISABLE:64,IS_EDITING:128,
-OPLIST:256},AnnotationMode={DISABLE:0,ENABLE:1,ENABLE_FORMS:2,ENABLE_STORAGE:3},AnnotationEditorPrefix="pdfjs_internal_editor_",AnnotationEditorType={DISABLE:-1,NONE:0,FREETEXT:3,HIGHLIGHT:9,STAMP:13,INK:15,SIGNATURE:101,COMMENT:102},AnnotationEditorParamsType={RESIZE:1,CREATE:2,FREETEXT_SIZE:11,FREETEXT_COLOR:12,FREETEXT_OPACITY:13,INK_COLOR:21,INK_THICKNESS:22,INK_OPACITY:23,HIGHLIGHT_COLOR:31,HIGHLIGHT_THICKNESS:32,HIGHLIGHT_FREE:33,HIGHLIGHT_SHOW_ALL:34,DRAW_STEP:41},PermissionFlag={PRINT:4,
-MODIFY_CONTENTS:8,COPY:16,MODIFY_ANNOTATIONS:32,FILL_INTERACTIVE_FORMS:256,COPY_FOR_ACCESSIBILITY:512,ASSEMBLE:1024,PRINT_HIGH_QUALITY:2048},TextRenderingMode={FILL:0,STROKE:1,FILL_STROKE:2,INVISIBLE:3,FILL_STROKE_MASK:3,ADD_TO_PATH_FLAG:4},util_ImageKind={GRAYSCALE_1BPP:1,RGB_24BPP:2,RGBA_32BPP:3},AnnotationType={TEXT:1,LINK:2,FREETEXT:3,LINE:4,SQUARE:5,CIRCLE:6,POLYGON:7,POLYLINE:8,HIGHLIGHT:9,UNDERLINE:10,SQUIGGLY:11,STRIKEOUT:12,STAMP:13,CARET:14,INK:15,POPUP:16,FILEATTACHMENT:17,SOUND:18,MOVIE:19,
-WIDGET:20,SCREEN:21,PRINTERMARK:22,TRAPNET:23,WATERMARK:24,THREED:25,REDACT:26},AnnotationBorderStyleType={SOLID:1,DASHED:2,BEVELED:3,INSET:4,UNDERLINE:5},VerbosityLevel={ERRORS:0,WARNINGS:1,INFOS:5},OPS={dependency:1,setLineWidth:2,setLineCap:3,setLineJoin:4,setMiterLimit:5,setDash:6,setRenderingIntent:7,setFlatness:8,setGState:9,save:10,restore:11,transform:12,moveTo:13,lineTo:14,curveTo:15,curveTo2:16,curveTo3:17,closePath:18,rectangle:19,stroke:20,closeStroke:21,fill:22,eoFill:23,fillStroke:24,
-eoFillStroke:25,closeFillStroke:26,closeEOFillStroke:27,endPath:28,clip:29,eoClip:30,beginText:31,endText:32,setCharSpacing:33,setWordSpacing:34,setHScale:35,setLeading:36,setFont:37,setTextRenderingMode:38,setTextRise:39,moveText:40,setLeadingMoveText:41,setTextMatrix:42,nextLine:43,showText:44,showSpacedText:45,nextLineShowText:46,nextLineSetSpacingShowText:47,setCharWidth:48,setCharWidthAndBounds:49,setStrokeColorSpace:50,setFillColorSpace:51,setStrokeColor:52,setStrokeColorN:53,setFillColor:54,
-setFillColorN:55,setStrokeGray:56,setFillGray:57,setStrokeRGBColor:58,setFillRGBColor:59,setStrokeCMYKColor:60,setFillCMYKColor:61,shadingFill:62,beginInlineImage:63,beginImageData:64,endInlineImage:65,paintXObject:66,markPoint:67,markPointProps:68,beginMarkedContent:69,beginMarkedContentProps:70,endMarkedContent:71,beginCompat:72,endCompat:73,paintFormXObjectBegin:74,paintFormXObjectEnd:75,beginGroup:76,endGroup:77,beginAnnotation:80,endAnnotation:81,paintImageMaskXObject:83,paintImageMaskXObjectGroup:84,
-paintImageXObject:85,paintInlineImageXObject:86,paintInlineImageXObjectGroup:87,paintImageXObjectRepeat:88,paintImageMaskXObjectRepeat:89,paintSolidColorImageMask:90,constructPath:91,setStrokeTransparent:92,setFillTransparent:93,rawFillPath:94},DrawOPS={moveTo:0,lineTo:1,curveTo:2,closePath:3},PasswordResponses={NEED_PASSWORD:1,INCORRECT_PASSWORD:2};let verbosity=VerbosityLevel.WARNINGS;function setVerbosityLevel(level){Number.isInteger(level)&&(verbosity=level)}function getVerbosityLevel(){return verbosity}
-function info(msg){verbosity>=VerbosityLevel.INFOS&&console.log(`Info: ${msg}`)}function warn(msg){verbosity>=VerbosityLevel.WARNINGS&&console.log(`Warning: ${msg}`)}function unreachable(msg){throw new Error(msg)}function assert$1(cond,msg){cond||unreachable(msg)}function _isValidProtocol(url2){switch(url2?.protocol){case"http:":case"https:":case"ftp:":case"mailto:":case"tel:":return!0;default:return!1}}function createValidAbsoluteUrl(url2,baseUrl=null,options=null){if(!url2)return null;if(options&&
-typeof url2=="string"&&(options.addDefaultProtocol&&url2.startsWith("www.")&&url2.match(/\./g)?.length>=2&&(url2=`http://${url2}`),options.tryConvertEncoding))try{url2=stringToUTF8String(url2)}catch{}const absoluteUrl=baseUrl?URL.parse(url2,baseUrl):URL.parse(url2);return _isValidProtocol(absoluteUrl)?absoluteUrl:null}function updateUrlHash(url2,hash2,allowRel=!1){const res=URL.parse(url2);return res?(res.hash=hash2,res.href):allowRel&&createValidAbsoluteUrl(url2,"http://example.com")?url2.split(
-"#",1)[0]+`${hash2?`#${hash2}`:""}`:""}function shadow(obj,prop2,value,nonSerializable=!1){return Object.defineProperty(obj,prop2,{value,enumerable:!nonSerializable,configurable:!0,writable:!1}),value}const BaseException=function(){function BaseException2(message,name){this.message=message,this.name=name}return BaseException2.prototype=new Error,BaseException2.constructor=BaseException2,BaseException2}();class PasswordException extends BaseException{constructor(msg,code2){super(msg,"PasswordExce\
-ption"),this.code=code2}}class UnknownErrorException extends BaseException{constructor(msg,details){super(msg,"UnknownErrorException"),this.details=details}}class InvalidPDFException extends BaseException{constructor(msg){super(msg,"InvalidPDFException")}}class ResponseException extends BaseException{constructor(msg,status,missing){super(msg,"ResponseException"),this.status=status,this.missing=missing}}class FormatError extends BaseException{constructor(msg){super(msg,"FormatError")}}class AbortException extends BaseException{constructor(msg){
-super(msg,"AbortException")}}function bytesToString(bytes){(typeof bytes!="object"||bytes?.length===void 0)&&unreachable("Invalid argument for bytesToString");const length=bytes.length,MAX_ARGUMENT_COUNT=8192;if(length<MAX_ARGUMENT_COUNT)return String.fromCharCode.apply(null,bytes);const strBuf=[];for(let i=0;i<length;i+=MAX_ARGUMENT_COUNT){const chunkEnd=Math.min(i+MAX_ARGUMENT_COUNT,length),chunk=bytes.subarray(i,chunkEnd);strBuf.push(String.fromCharCode.apply(null,chunk))}return strBuf.join("")}
-function stringToBytes(str){typeof str!="string"&&unreachable("Invalid argument for stringToBytes");const length=str.length,bytes=new Uint8Array(length);for(let i=0;i<length;++i)bytes[i]=str.charCodeAt(i)&255;return bytes}function string32(value){return String.fromCharCode(value>>24&255,value>>16&255,value>>8&255,value&255)}function isLittleEndian(){const buffer8=new Uint8Array(4);return buffer8[0]=1,new Uint32Array(buffer8.buffer,0,1)[0]===1}function isEvalSupported(){try{return new Function(""),
-!0}catch{return!1}}class util_FeatureTest{static get isLittleEndian(){return shadow(this,"isLittleEndian",isLittleEndian())}static get isEvalSupported(){return shadow(this,"isEvalSupported",isEvalSupported())}static get isOffscreenCanvasSupported(){return shadow(this,"isOffscreenCanvasSupported",typeof OffscreenCanvas<"u")}static get isImageDecoderSupported(){return shadow(this,"isImageDecoderSupported",typeof ImageDecoder<"u")}static get platform(){const{platform:platform2,userAgent}=navigator;
-return shadow(this,"platform",{isAndroid:userAgent.includes("Android"),isLinux:platform2.includes("Linux"),isMac:platform2.includes("Mac"),isWindows:platform2.includes("Win"),isFirefox:userAgent.includes("Firefox")})}static get isCSSRoundSupported(){return shadow(this,"isCSSRoundSupported",globalThis.CSS?.supports?.("width: round(1.5px, 1px)"))}}const hexNumbers=Array.from(Array(256).keys(),n=>n.toString(16).padStart(2,"0"));class Util{static makeHexColor(r2,g,b){return`#${hexNumbers[r2]}${hexNumbers[g]}${hexNumbers[b]}`}static scaleMinMax(transform2,minMax){
-let temp;transform2[0]?(transform2[0]<0&&(temp=minMax[0],minMax[0]=minMax[2],minMax[2]=temp),minMax[0]*=transform2[0],minMax[2]*=transform2[0],transform2[3]<0&&(temp=minMax[1],minMax[1]=minMax[3],minMax[3]=temp),minMax[1]*=transform2[3],minMax[3]*=transform2[3]):(temp=minMax[0],minMax[0]=minMax[1],minMax[1]=temp,temp=minMax[2],minMax[2]=minMax[3],minMax[3]=temp,transform2[1]<0&&(temp=minMax[1],minMax[1]=minMax[3],minMax[3]=temp),minMax[1]*=transform2[1],minMax[3]*=transform2[1],transform2[2]<0&&
-(temp=minMax[0],minMax[0]=minMax[2],minMax[2]=temp),minMax[0]*=transform2[2],minMax[2]*=transform2[2]),minMax[0]+=transform2[4],minMax[1]+=transform2[5],minMax[2]+=transform2[4],minMax[3]+=transform2[5]}static transform(m1,m2){return[m1[0]*m2[0]+m1[2]*m2[1],m1[1]*m2[0]+m1[3]*m2[1],m1[0]*m2[2]+m1[2]*m2[3],m1[1]*m2[2]+m1[3]*m2[3],m1[0]*m2[4]+m1[2]*m2[5]+m1[4],m1[1]*m2[4]+m1[3]*m2[5]+m1[5]]}static applyTransform(p2,m,pos=0){const p0=p2[pos],p1=p2[pos+1];p2[pos]=p0*m[0]+p1*m[2]+m[4],p2[pos+1]=p0*m[1]+
-p1*m[3]+m[5]}static applyTransformToBezier(p2,transform2,pos=0){const m0=transform2[0],m1=transform2[1],m2=transform2[2],m3=transform2[3],m4=transform2[4],m5=transform2[5];for(let i=0;i<6;i+=2){const pI=p2[pos+i],pI1=p2[pos+i+1];p2[pos+i]=pI*m0+pI1*m2+m4,p2[pos+i+1]=pI*m1+pI1*m3+m5}}static applyInverseTransform(p2,m){const p0=p2[0],p1=p2[1],d2=m[0]*m[3]-m[1]*m[2];p2[0]=(p0*m[3]-p1*m[2]+m[2]*m[5]-m[4]*m[3])/d2,p2[1]=(-p0*m[1]+p1*m[0]+m[4]*m[1]-m[5]*m[0])/d2}static axialAlignedBoundingBox(rect,transform2,output){
-const m0=transform2[0],m1=transform2[1],m2=transform2[2],m3=transform2[3],m4=transform2[4],m5=transform2[5],r0=rect[0],r1=rect[1],r2=rect[2],r3=rect[3];let a0=m0*r0+m4,a2=a0,a1=m0*r2+m4,a3=a1,b0=m3*r1+m5,b2=b0,b1=m3*r3+m5,b3=b1;if(m1!==0||m2!==0){const m1r0=m1*r0,m1r2=m1*r2,m2r1=m2*r1,m2r3=m2*r3;a0+=m2r1,a3+=m2r1,a1+=m2r3,a2+=m2r3,b0+=m1r0,b3+=m1r0,b1+=m1r2,b2+=m1r2}output[0]=Math.min(output[0],a0,a1,a2,a3),output[1]=Math.min(output[1],b0,b1,b2,b3),output[2]=Math.max(output[2],a0,a1,a2,a3),output[3]=
-Math.max(output[3],b0,b1,b2,b3)}static inverseTransform(m){const d2=m[0]*m[3]-m[1]*m[2];return[m[3]/d2,-m[1]/d2,-m[2]/d2,m[0]/d2,(m[2]*m[5]-m[4]*m[3])/d2,(m[4]*m[1]-m[5]*m[0])/d2]}static singularValueDecompose2dScale(matrix,output){const m0=matrix[0],m1=matrix[1],m2=matrix[2],m3=matrix[3],a=m0**2+m1**2,b=m0*m2+m1*m3,c2=m2**2+m3**2,first=(a+c2)/2,second=Math.sqrt(first**2-(a*c2-b**2));output[0]=Math.sqrt(first+second||1),output[1]=Math.sqrt(first-second||1)}static normalizeRect(rect){const r2=rect.
-slice(0);return rect[0]>rect[2]&&(r2[0]=rect[2],r2[2]=rect[0]),rect[1]>rect[3]&&(r2[1]=rect[3],r2[3]=rect[1]),r2}static intersect(rect1,rect2){const xLow=Math.max(Math.min(rect1[0],rect1[2]),Math.min(rect2[0],rect2[2])),xHigh=Math.min(Math.max(rect1[0],rect1[2]),Math.max(rect2[0],rect2[2]));if(xLow>xHigh)return null;const yLow=Math.max(Math.min(rect1[1],rect1[3]),Math.min(rect2[1],rect2[3])),yHigh=Math.min(Math.max(rect1[1],rect1[3]),Math.max(rect2[1],rect2[3]));return yLow>yHigh?null:[xLow,yLow,
-xHigh,yHigh]}static pointBoundingBox(x,y,minMax){minMax[0]=Math.min(minMax[0],x),minMax[1]=Math.min(minMax[1],y),minMax[2]=Math.max(minMax[2],x),minMax[3]=Math.max(minMax[3],y)}static rectBoundingBox(x0,y0,x1,y1,minMax){minMax[0]=Math.min(minMax[0],x0,x1),minMax[1]=Math.min(minMax[1],y0,y1),minMax[2]=Math.max(minMax[2],x0,x1),minMax[3]=Math.max(minMax[3],y0,y1)}static#getExtremumOnCurve(x0,x1,x2,x3,y0,y1,y2,y3,t,minMax){if(t<=0||t>=1)return;const mt=1-t,tt=t*t,ttt=tt*t,x=mt*(mt*(mt*x0+3*t*x1)+3*
-tt*x2)+ttt*x3,y=mt*(mt*(mt*y0+3*t*y1)+3*tt*y2)+ttt*y3;minMax[0]=Math.min(minMax[0],x),minMax[1]=Math.min(minMax[1],y),minMax[2]=Math.max(minMax[2],x),minMax[3]=Math.max(minMax[3],y)}static#getExtremum(x0,x1,x2,x3,y0,y1,y2,y3,a,b,c2,minMax){if(Math.abs(a)<1e-12){Math.abs(b)>=1e-12&&this.#getExtremumOnCurve(x0,x1,x2,x3,y0,y1,y2,y3,-c2/b,minMax);return}const delta=b**2-4*c2*a;if(delta<0)return;const sqrtDelta=Math.sqrt(delta),a2=2*a;this.#getExtremumOnCurve(x0,x1,x2,x3,y0,y1,y2,y3,(-b+sqrtDelta)/a2,
-minMax),this.#getExtremumOnCurve(x0,x1,x2,x3,y0,y1,y2,y3,(-b-sqrtDelta)/a2,minMax)}static bezierBoundingBox(x0,y0,x1,y1,x2,y2,x3,y3,minMax){minMax[0]=Math.min(minMax[0],x0,x3),minMax[1]=Math.min(minMax[1],y0,y3),minMax[2]=Math.max(minMax[2],x0,x3),minMax[3]=Math.max(minMax[3],y0,y3),this.#getExtremum(x0,x1,x2,x3,y0,y1,y2,y3,3*(-x0+3*(x1-x2)+x3),6*(x0-2*x1+x2),3*(x1-x0),minMax),this.#getExtremum(x0,x1,x2,x3,y0,y1,y2,y3,3*(-y0+3*(y1-y2)+y3),6*(y0-2*y1+y2),3*(y1-y0),minMax)}}function stringToUTF8String(str){
-return decodeURIComponent(escape(str))}let NormalizeRegex=null,NormalizationMap=null;function normalizeUnicode(str){return NormalizeRegex||(NormalizeRegex=/([\u00a0\u00b5\u037e\u0eb3\u2000-\u200a\u202f\u2126\ufb00-\ufb04\ufb06\ufb20-\ufb36\ufb38-\ufb3c\ufb3e\ufb40-\ufb41\ufb43-\ufb44\ufb46-\ufba1\ufba4-\ufba9\ufbae-\ufbb1\ufbd3-\ufbdc\ufbde-\ufbe7\ufbea-\ufbf8\ufbfc-\ufbfd\ufc00-\ufc5d\ufc64-\ufcf1\ufcf5-\ufd3d\ufd88\ufdf4\ufdfa-\ufdfb\ufe71\ufe77\ufe79\ufe7b\ufe7d]+)|(\ufb05+)/gu,NormalizationMap=
-new Map([["ſt","ſt"]])),str.replaceAll(NormalizeRegex,(_,p1,p2)=>p1?p1.normalize("NFKC"):NormalizationMap.get(p2))}function getUuid(){if(typeof crypto.randomUUID=="function")return crypto.randomUUID();const buf=new Uint8Array(32);return crypto.getRandomValues(buf),bytesToString(buf)}const AnnotationPrefix="pdfjs_internal_id_";function _isValidExplicitDest(validRef,validName,dest){if(!Array.isArray(dest)||dest.length<2)return!1;const[page2,zoom,...args]=dest;if(!validRef(page2)&&!Number.isInteger(
-page2)||!validName(zoom))return!1;const argsLen=args.length;let allowNull=!0;switch(zoom.name){case"XYZ":if(argsLen<2||argsLen>3)return!1;break;case"Fit":case"FitB":return argsLen===0;case"FitH":case"FitBH":case"FitV":case"FitBV":if(argsLen>1)return!1;break;case"FitR":if(argsLen!==4)return!1;allowNull=!1;break;default:return!1}for(const arg of args)if(!(typeof arg=="number"||allowNull&&arg===null))return!1;return!0}function MathClamp(v,min2,max2){return Math.min(Math.max(v,min2),max2)}function toBase64Util(arr){
-return Uint8Array.prototype.toBase64?arr.toBase64():btoa(bytesToString(arr))}function fromBase64Util(str){return Uint8Array.fromBase64?Uint8Array.fromBase64(str):stringToBytes(atob(str))}typeof Promise.try!="function"&&(Promise.try=function(fn,...args){return new Promise(resolve2=>{resolve2(fn(...args))})});typeof Math.sumPrecise!="function"&&(Math.sumPrecise=function(numbers){return numbers.reduce((a,b)=>a+b,0)});const SVG_NS="http://www.w3.org/2000/svg";class PixelsPerInch{static CSS=96;static PDF=72;static PDF_TO_CSS_UNITS=this.
-CSS/this.PDF}async function fetchData(url2,type2="text"){if(isValidFetchUrl(url2,document.baseURI)){const response=await fetch(url2);if(!response.ok)throw new Error(response.statusText);switch(type2){case"arraybuffer":return response.arrayBuffer();case"blob":return response.blob();case"json":return response.json()}return response.text()}return new Promise((resolve2,reject)=>{const request=new XMLHttpRequest;request.open("GET",url2,!0),request.responseType=type2,request.onreadystatechange=()=>{if(request.
-readyState===XMLHttpRequest.DONE){if(request.status===200||request.status===0){switch(type2){case"arraybuffer":case"blob":case"json":resolve2(request.response);return}resolve2(request.responseText);return}reject(new Error(request.statusText))}},request.send(null)})}class PageViewport{constructor({viewBox,userUnit,scale:scale2,rotation,offsetX=0,offsetY=0,dontFlip=!1}){this.viewBox=viewBox,this.userUnit=userUnit,this.scale=scale2,this.rotation=rotation,this.offsetX=offsetX,this.offsetY=offsetY,scale2*=
-userUnit;const centerX=(viewBox[2]+viewBox[0])/2,centerY=(viewBox[3]+viewBox[1])/2;let rotateA,rotateB,rotateC,rotateD;switch(rotation%=360,rotation<0&&(rotation+=360),rotation){case 180:rotateA=-1,rotateB=0,rotateC=0,rotateD=1;break;case 90:rotateA=0,rotateB=1,rotateC=1,rotateD=0;break;case 270:rotateA=0,rotateB=-1,rotateC=-1,rotateD=0;break;case 0:rotateA=1,rotateB=0,rotateC=0,rotateD=-1;break;default:throw new Error("PageViewport: Invalid rotation, must be a multiple of 90 degrees.")}dontFlip&&
-(rotateC=-rotateC,rotateD=-rotateD);let offsetCanvasX,offsetCanvasY,width,height;rotateA===0?(offsetCanvasX=Math.abs(centerY-viewBox[1])*scale2+offsetX,offsetCanvasY=Math.abs(centerX-viewBox[0])*scale2+offsetY,width=(viewBox[3]-viewBox[1])*scale2,height=(viewBox[2]-viewBox[0])*scale2):(offsetCanvasX=Math.abs(centerX-viewBox[0])*scale2+offsetX,offsetCanvasY=Math.abs(centerY-viewBox[1])*scale2+offsetY,width=(viewBox[2]-viewBox[0])*scale2,height=(viewBox[3]-viewBox[1])*scale2),this.transform=[rotateA*
-scale2,rotateB*scale2,rotateC*scale2,rotateD*scale2,offsetCanvasX-rotateA*scale2*centerX-rotateC*scale2*centerY,offsetCanvasY-rotateB*scale2*centerX-rotateD*scale2*centerY],this.width=width,this.height=height}get rawDims(){const dims=this.viewBox;return shadow(this,"rawDims",{pageWidth:dims[2]-dims[0],pageHeight:dims[3]-dims[1],pageX:dims[0],pageY:dims[1]})}clone({scale:scale2=this.scale,rotation=this.rotation,offsetX=this.offsetX,offsetY=this.offsetY,dontFlip=!1}={}){return new PageViewport({viewBox:this.
-viewBox.slice(),userUnit:this.userUnit,scale:scale2,rotation,offsetX,offsetY,dontFlip})}convertToViewportPoint(x,y){const p2=[x,y];return Util.applyTransform(p2,this.transform),p2}convertToViewportRectangle(rect){const topLeft=[rect[0],rect[1]];Util.applyTransform(topLeft,this.transform);const bottomRight=[rect[2],rect[3]];return Util.applyTransform(bottomRight,this.transform),[topLeft[0],topLeft[1],bottomRight[0],bottomRight[1]]}convertToPdfPoint(x,y){const p2=[x,y];return Util.applyInverseTransform(
-p2,this.transform),p2}}class RenderingCancelledException extends BaseException{constructor(msg,extraDelay=0){super(msg,"RenderingCancelledException"),this.extraDelay=extraDelay}}function isDataScheme(url2){const ii=url2.length;let i=0;for(;i<ii&&url2[i].trim()==="";)i++;return url2.substring(i,i+5).toLowerCase()==="data:"}function isPdfFile(filename){return typeof filename=="string"&&/\.pdf$/i.test(filename)}function getFilenameFromUrl(url2){return[url2]=url2.split(/[#?]/,1),url2.substring(url2.
-lastIndexOf("/")+1)}function getPdfFilenameFromUrl(url2,defaultFilename="document.pdf"){if(typeof url2!="string")return defaultFilename;if(isDataScheme(url2))return warn('getPdfFilenameFromUrl: ignore "data:"-URL for performance reasons.'),defaultFilename;const newURL=(urlString=>{try{return new URL(urlString)}catch{try{return new URL(decodeURIComponent(urlString))}catch{try{return new URL(urlString,"https://foo.bar")}catch{try{return new URL(decodeURIComponent(urlString),"https://foo.bar")}catch{
-return null}}}}})(url2);if(!newURL)return defaultFilename;const decode2=name=>{try{let decoded=decodeURIComponent(name);return decoded.includes("/")?(decoded=decoded.split("/").at(-1),decoded.test(/^\.pdf$/i)?decoded:name):decoded}catch{return name}},pdfRegex=/\.pdf$/i,filename=newURL.pathname.split("/").at(-1);if(pdfRegex.test(filename))return decode2(filename);if(newURL.searchParams.size>0){const values=Array.from(newURL.searchParams.values()).reverse();for(const value of values)if(pdfRegex.test(
-value))return decode2(value);const keys2=Array.from(newURL.searchParams.keys()).reverse();for(const key2 of keys2)if(pdfRegex.test(key2))return decode2(key2)}if(newURL.hash){const hashFilename=/[^/?#=]+\.pdf\b(?!.*\.pdf\b)/i.exec(newURL.hash);if(hashFilename)return decode2(hashFilename[0])}return defaultFilename}class StatTimer{started=Object.create(null);times=[];time(name){name in this.started&&warn(`Timer is already running for ${name}`),this.started[name]=Date.now()}timeEnd(name){name in this.
-started||warn(`Timer has not been started for ${name}`),this.times.push({name,start:this.started[name],end:Date.now()}),delete this.started[name]}toString(){const outBuf=[];let longest=0;for(const{name}of this.times)longest=Math.max(name.length,longest);for(const{name,start:start2,end}of this.times)outBuf.push(`${name.padEnd(longest)} ${end-start2}ms
-`);return outBuf.join("")}}function isValidFetchUrl(url2,baseUrl){const res=baseUrl?URL.parse(url2,baseUrl):URL.parse(url2);return res?.protocol==="http:"||res?.protocol==="https:"}function noContextMenu(e){e.preventDefault()}function stopEvent(e){e.preventDefault(),e.stopPropagation()}function deprecated(details){console.log("Deprecated API usage: "+details)}class PDFDateString{static#regex;static toDateObject(input){if(input instanceof Date)return input;if(!input||typeof input!="string")return null;
-this.#regex||=new RegExp("^D:(\\d{4})(\\d{2})?(\\d{2})?(\\d{2})?(\\d{2})?(\\d{2})?([Z|+|-])?(\\d{2})?'?(\\d{2})?'?");const matches2=this.#regex.exec(input);if(!matches2)return null;const year=parseInt(matches2[1],10);let month=parseInt(matches2[2],10);month=month>=1&&month<=12?month-1:0;let day=parseInt(matches2[3],10);day=day>=1&&day<=31?day:1;let hour=parseInt(matches2[4],10);hour=hour>=0&&hour<=23?hour:0;let minute=parseInt(matches2[5],10);minute=minute>=0&&minute<=59?minute:0;let second=parseInt(
-matches2[6],10);second=second>=0&&second<=59?second:0;const universalTimeRelation=matches2[7]||"Z";let offsetHour=parseInt(matches2[8],10);offsetHour=offsetHour>=0&&offsetHour<=23?offsetHour:0;let offsetMinute=parseInt(matches2[9],10)||0;return offsetMinute=offsetMinute>=0&&offsetMinute<=59?offsetMinute:0,universalTimeRelation==="-"?(hour+=offsetHour,minute+=offsetMinute):universalTimeRelation==="+"&&(hour-=offsetHour,minute-=offsetMinute),new Date(Date.UTC(year,month,day,hour,minute,second))}}function getXfaPageViewport(xfaPage,{
-scale:scale2=1,rotation=0}){const{width,height}=xfaPage.attributes.style,viewBox=[0,0,parseInt(width),parseInt(height)];return new PageViewport({viewBox,userUnit:1,scale:scale2,rotation})}function getRGB(color){if(color.startsWith("#")){const colorRGB=parseInt(color.slice(1),16);return[(colorRGB&16711680)>>16,(colorRGB&65280)>>8,colorRGB&255]}return color.startsWith("rgb(")?color.slice(4,-1).split(",").map(x=>parseInt(x)):color.startsWith("rgba(")?color.slice(5,-1).split(",").map(x=>parseInt(x)).
-slice(0,3):(warn(`Not a valid color format: "${color}"`),[0,0,0])}function getColorValues(colors){const span=document.createElement("span");span.style.visibility="hidden",span.style.colorScheme="only light",document.body.append(span);for(const name of colors.keys()){span.style.color=name;const computedColor=window.getComputedStyle(span).color;colors.set(name,getRGB(computedColor))}span.remove()}function getCurrentTransform(ctx){const{a,b,c:c2,d:d2,e,f}=ctx.getTransform();return[a,b,c2,d2,e,f]}function getCurrentTransformInverse(ctx){
-const{a,b,c:c2,d:d2,e,f}=ctx.getTransform().invertSelf();return[a,b,c2,d2,e,f]}function setLayerDimensions(div,viewport,mustFlip=!1,mustRotate=!0){if(viewport instanceof PageViewport){const{pageWidth,pageHeight}=viewport.rawDims,{style:style2}=div,useRound=util_FeatureTest.isCSSRoundSupported,w=`var(--total-scale-factor) * ${pageWidth}px`,h2=`var(--total-scale-factor) * ${pageHeight}px`,widthStr=useRound?`round(down, ${w}, var(--scale-round-x))`:`calc(${w})`,heightStr=useRound?`round(down, ${h2}\
-, var(--scale-round-y))`:`calc(${h2})`;!mustFlip||viewport.rotation%180===0?(style2.width=widthStr,style2.height=heightStr):(style2.width=heightStr,style2.height=widthStr)}mustRotate&&div.setAttribute("data-main-rotation",viewport.rotation)}class OutputScale{constructor(){const{pixelRatio}=OutputScale;this.sx=pixelRatio,this.sy=pixelRatio}get scaled(){return this.sx!==1||this.sy!==1}get symmetric(){return this.sx===this.sy}limitCanvas(width,height,maxPixels,maxDim,capAreaFactor=-1){let maxAreaScale=1/0,
-maxWidthScale=1/0,maxHeightScale=1/0;maxPixels=OutputScale.capPixels(maxPixels,capAreaFactor),maxPixels>0&&(maxAreaScale=Math.sqrt(maxPixels/(width*height))),maxDim!==-1&&(maxWidthScale=maxDim/width,maxHeightScale=maxDim/height);const maxScale=Math.min(maxAreaScale,maxWidthScale,maxHeightScale);return this.sx>maxScale||this.sy>maxScale?(this.sx=maxScale,this.sy=maxScale,!0):!1}static get pixelRatio(){return globalThis.devicePixelRatio||1}static capPixels(maxPixels,capAreaFactor){if(capAreaFactor>=
-0){const winPixels=Math.ceil(window.screen.availWidth*window.screen.availHeight*this.pixelRatio**2*(1+capAreaFactor/100));return maxPixels>0?Math.min(maxPixels,winPixels):winPixels}return maxPixels}}const SupportedImageMimeTypes=["image/apng","image/avif","image/bmp","image/gif","image/jpeg","image/png","image/svg+xml","image/webp","image/x-icon"];class EditorToolbar{#toolbar=null;#colorPicker=null;#editor;#buttons=null;#altText=null;#comment=null;#signatureDescriptionButton=null;static#l10nRemove=null;constructor(editor){
-this.#editor=editor,EditorToolbar.#l10nRemove||=Object.freeze({freetext:"pdfjs-editor-remove-freetext-button",highlight:"pdfjs-editor-remove-highlight-button",ink:"pdfjs-editor-remove-ink-button",stamp:"pdfjs-editor-remove-stamp-button",signature:"pdfjs-editor-remove-signature-button"})}render(){const editToolbar=this.#toolbar=document.createElement("div");editToolbar.classList.add("editToolbar","hidden"),editToolbar.setAttribute("role","toolbar");const signal=this.#editor._uiManager._signal;editToolbar.
-addEventListener("contextmenu",noContextMenu,{signal}),editToolbar.addEventListener("pointerdown",EditorToolbar.#pointerDown,{signal});const buttons=this.#buttons=document.createElement("div");buttons.className="buttons",editToolbar.append(buttons);const position2=this.#editor.toolbarPosition;if(position2){const{style:style2}=editToolbar,x=this.#editor._uiManager.direction==="ltr"?1-position2[0]:position2[0];style2.insetInlineEnd=`${100*x}%`,style2.top=`calc(${100*position2[1]}% + var(--editor-t\
-oolbar-vert-offset))`}return editToolbar}get div(){return this.#toolbar}static#pointerDown(e){e.stopPropagation()}#focusIn(e){this.#editor._focusEventsAllowed=!1,stopEvent(e)}#focusOut(e){this.#editor._focusEventsAllowed=!0,stopEvent(e)}#addListenersToElement(element2){const signal=this.#editor._uiManager._signal;element2.addEventListener("focusin",this.#focusIn.bind(this),{capture:!0,signal}),element2.addEventListener("focusout",this.#focusOut.bind(this),{capture:!0,signal}),element2.addEventListener(
-"contextmenu",noContextMenu,{signal})}hide(){this.#toolbar.classList.add("hidden"),this.#colorPicker?.hideDropdown()}show(){this.#toolbar.classList.remove("hidden"),this.#altText?.shown(),this.#comment?.shown()}addDeleteButton(){const{editorType,_uiManager}=this.#editor,button=document.createElement("button");button.className="delete",button.tabIndex=0,button.setAttribute("data-l10n-id",EditorToolbar.#l10nRemove[editorType]),this.#addListenersToElement(button),button.addEventListener("click",e=>{
-_uiManager.delete()},{signal:_uiManager._signal}),this.#buttons.append(button)}get#divider(){const divider=document.createElement("div");return divider.className="divider",divider}async addAltText(altText){const button=await altText.render();this.#addListenersToElement(button),this.#buttons.append(button,this.#divider),this.#altText=altText}addComment(comment2){if(this.#comment)return;const button=comment2.render();button&&(this.#addListenersToElement(button),this.#buttons.prepend(button,this.#divider),
-this.#comment=comment2,comment2.toolbar=this)}addColorPicker(colorPicker){if(this.#colorPicker)return;this.#colorPicker=colorPicker;const button=colorPicker.renderButton();this.#addListenersToElement(button),this.#buttons.append(button,this.#divider)}async addEditSignatureButton(signatureManager){const button=this.#signatureDescriptionButton=await signatureManager.renderEditButton(this.#editor);this.#addListenersToElement(button),this.#buttons.append(button,this.#divider)}async addButton(name,tool){
-switch(name){case"colorPicker":this.addColorPicker(tool);break;case"altText":await this.addAltText(tool);break;case"editSignature":await this.addEditSignatureButton(tool);break;case"delete":this.addDeleteButton();break;case"comment":this.addComment(tool);break}}updateEditSignatureButton(description2){this.#signatureDescriptionButton&&(this.#signatureDescriptionButton.title=description2)}remove(){this.#toolbar.remove(),this.#colorPicker?.destroy(),this.#colorPicker=null}}class HighlightToolbar{#buttons=null;#toolbar=null;#uiManager;constructor(uiManager){
-this.#uiManager=uiManager}#render(){const editToolbar=this.#toolbar=document.createElement("div");editToolbar.className="editToolbar",editToolbar.setAttribute("role","toolbar"),editToolbar.addEventListener("contextmenu",noContextMenu,{signal:this.#uiManager._signal});const buttons=this.#buttons=document.createElement("div");return buttons.className="buttons",editToolbar.append(buttons),this.#addHighlightButton(),editToolbar}#getLastPoint(boxes,isLTR){let lastY=0,lastX=0;for(const box2 of boxes){
-const y=box2.y+box2.height;if(y<lastY)continue;const x=box2.x+(isLTR?box2.width:0);if(y>lastY){lastX=x,lastY=y;continue}isLTR?x>lastX&&(lastX=x):x<lastX&&(lastX=x)}return[isLTR?1-lastX:lastX,lastY]}show(parent,boxes,isLTR){const[x,y]=this.#getLastPoint(boxes,isLTR),{style:style2}=this.#toolbar||=this.#render();parent.append(this.#toolbar),style2.insetInlineEnd=`${100*x}%`,style2.top=`calc(${100*y}% + var(--editor-toolbar-vert-offset))`}hide(){this.#toolbar.remove()}#addHighlightButton(){const button=document.
-createElement("button");button.className="highlightButton",button.tabIndex=0,button.setAttribute("data-l10n-id","pdfjs-highlight-floating-button1");const span=document.createElement("span");button.append(span),span.className="visuallyHidden",span.setAttribute("data-l10n-id","pdfjs-highlight-floating-button-label");const signal=this.#uiManager._signal;button.addEventListener("contextmenu",noContextMenu,{signal}),button.addEventListener("click",()=>{this.#uiManager.highlightSelection("floating_but\
-ton")},{signal}),this.#buttons.append(button)}}function bindEvents(obj,element2,names2){for(const name of names2)element2.addEventListener(name,obj[name].bind(obj))}class IdManager{#id=0;get id(){return`${AnnotationEditorPrefix}${this.#id++}`}}class ImageManager{#baseId=getUuid();#id=0;#cache=null;static get _isSVGFittingCanvas(){const svg2='data:image/svg+xml;charset=UTF-8,<svg viewBox="0 0 1 1" width="1" height="1" xmlns="http://www.w3.org/2000/svg"><rect width="1" height="1" style="fill:red;"\
-/></svg>',ctx=new OffscreenCanvas(1,3).getContext("2d",{willReadFrequently:!0}),image2=new Image;image2.src=svg2;const promise=image2.decode().then(()=>(ctx.drawImage(image2,0,0,1,1,0,0,1,3),new Uint32Array(ctx.getImageData(0,0,1,1).data.buffer)[0]===0));return shadow(this,"_isSVGFittingCanvas",promise)}async#get(key2,rawData){this.#cache||=new Map;let data=this.#cache.get(key2);if(data===null)return null;if(data?.bitmap)return data.refCounter+=1,data;try{data||={bitmap:null,id:`image_${this.#baseId}\
-_${this.#id++}`,refCounter:0,isSvg:!1};let image2;if(typeof rawData=="string"?(data.url=rawData,image2=await fetchData(rawData,"blob")):rawData instanceof File?image2=data.file=rawData:rawData instanceof Blob&&(image2=rawData),image2.type==="image/svg+xml"){const mustRemoveAspectRatioPromise=ImageManager._isSVGFittingCanvas,fileReader=new FileReader,imageElement=new Image,imagePromise=new Promise((resolve2,reject)=>{imageElement.onload=()=>{data.bitmap=imageElement,data.isSvg=!0,resolve2()},fileReader.
-onload=async()=>{const url2=data.svgUrl=fileReader.result;imageElement.src=await mustRemoveAspectRatioPromise?`${url2}#svgView(preserveAspectRatio(none))`:url2},imageElement.onerror=fileReader.onerror=reject});fileReader.readAsDataURL(image2),await imagePromise}else data.bitmap=await createImageBitmap(image2);data.refCounter=1}catch(e){warn(e),data=null}return this.#cache.set(key2,data),data&&this.#cache.set(data.id,data),data}async getFromFile(file){const{lastModified,name,size:size2,type:type2}=file;
-return this.#get(`${lastModified}_${name}_${size2}_${type2}`,file)}async getFromUrl(url2){return this.#get(url2,url2)}async getFromBlob(id2,blobPromise){const blob=await blobPromise;return this.#get(id2,blob)}async getFromId(id2){this.#cache||=new Map;const data=this.#cache.get(id2);if(!data)return null;if(data.bitmap)return data.refCounter+=1,data;if(data.file)return this.getFromFile(data.file);if(data.blobPromise){const{blobPromise}=data;return delete data.blobPromise,this.getFromBlob(data.id,
-blobPromise)}return this.getFromUrl(data.url)}getFromCanvas(id2,canvas){this.#cache||=new Map;let data=this.#cache.get(id2);if(data?.bitmap)return data.refCounter+=1,data;const offscreen=new OffscreenCanvas(canvas.width,canvas.height);return offscreen.getContext("2d").drawImage(canvas,0,0),data={bitmap:offscreen.transferToImageBitmap(),id:`image_${this.#baseId}_${this.#id++}`,refCounter:1,isSvg:!1},this.#cache.set(id2,data),this.#cache.set(data.id,data),data}getSvgUrl(id2){const data=this.#cache.
-get(id2);return data?.isSvg?data.svgUrl:null}deleteId(id2){this.#cache||=new Map;const data=this.#cache.get(id2);if(!data||(data.refCounter-=1,data.refCounter!==0))return;const{bitmap}=data;if(!data.url&&!data.file){const canvas=new OffscreenCanvas(bitmap.width,bitmap.height);canvas.getContext("bitmaprenderer").transferFromImageBitmap(bitmap),data.blobPromise=canvas.convertToBlob()}bitmap.close?.(),data.bitmap=null}isValidId(id2){return id2.startsWith(`image_${this.#baseId}_`)}}class CommandManager{#commands=[];#locked=!1;#maxSize;#position=-1;constructor(maxSize=128){
-this.#maxSize=maxSize}add({cmd,undo,post,mustExec,type:type2=NaN,overwriteIfSameType=!1,keepUndo=!1}){if(mustExec&&cmd(),this.#locked)return;const save={cmd,undo,post,type:type2};if(this.#position===-1){this.#commands.length>0&&(this.#commands.length=0),this.#position=0,this.#commands.push(save);return}if(overwriteIfSameType&&this.#commands[this.#position].type===type2){keepUndo&&(save.undo=this.#commands[this.#position].undo),this.#commands[this.#position]=save;return}const next2=this.#position+
-1;next2===this.#maxSize?this.#commands.splice(0,1):(this.#position=next2,next2<this.#commands.length&&this.#commands.splice(next2)),this.#commands.push(save)}undo(){if(this.#position===-1)return;this.#locked=!0;const{undo,post}=this.#commands[this.#position];undo(),post?.(),this.#locked=!1,this.#position-=1}redo(){if(this.#position<this.#commands.length-1){this.#position+=1,this.#locked=!0;const{cmd,post}=this.#commands[this.#position];cmd(),post?.(),this.#locked=!1}}hasSomethingToUndo(){return this.#position!==
--1}hasSomethingToRedo(){return this.#position<this.#commands.length-1}cleanType(type2){if(this.#position!==-1){for(let i=this.#position;i>=0;i--)if(this.#commands[i].type!==type2){this.#commands.splice(i+1,this.#position-i),this.#position=i;return}this.#commands.length=0,this.#position=-1}}destroy(){this.#commands=null}}class KeyboardManager{constructor(callbacks){this.buffer=[],this.callbacks=new Map,this.allKeys=new Set;const{isMac}=util_FeatureTest.platform;for(const[keys2,callback,options={}]of callbacks)
-for(const key2 of keys2){const isMacKey=key2.startsWith("mac+");isMac&&isMacKey?(this.callbacks.set(key2.slice(4),{callback,options}),this.allKeys.add(key2.split("+").at(-1))):!isMac&&!isMacKey&&(this.callbacks.set(key2,{callback,options}),this.allKeys.add(key2.split("+").at(-1)))}}#serialize(event2){event2.altKey&&this.buffer.push("alt"),event2.ctrlKey&&this.buffer.push("ctrl"),event2.metaKey&&this.buffer.push("meta"),event2.shiftKey&&this.buffer.push("shift"),this.buffer.push(event2.key);const str=this.
-buffer.join("+");return this.buffer.length=0,str}exec(self2,event2){if(!this.allKeys.has(event2.key))return;const info2=this.callbacks.get(this.#serialize(event2));if(!info2)return;const{callback,options:{bubbles=!1,args=[],checker=null}}=info2;checker&&!checker(self2,event2)||(callback.bind(self2,...args,event2)(),bubbles||stopEvent(event2))}}class ColorManager{static _colorsMapping=new Map([["CanvasText",[0,0,0]],["Canvas",[255,255,255]]]);get _colors(){const colors=new Map([["CanvasText",null],
-["Canvas",null]]);return getColorValues(colors),shadow(this,"_colors",colors)}convert(color){const rgb=getRGB(color);if(!window.matchMedia("(forced-colors: active)").matches)return rgb;for(const[name,RGB]of this._colors)if(RGB.every((x,i)=>x===rgb[i]))return ColorManager._colorsMapping.get(name);return rgb}getHexCode(name){const rgb=this._colors.get(name);return rgb?Util.makeHexColor(...rgb):name}}class AnnotationEditorUIManager{#abortController=new AbortController;#activeEditor=null;#allEditors=new Map;#allLayers=new Map;#altTextManager=null;#annotationStorage=null;#changedExistingAnnotations=null;#commandManager=new CommandManager;#commentManager=null;#copyPasteAC=null;#currentDrawingSession=null;#currentPageIndex=0;#deletedAnnotationsElementIds=new Set;#draggingEditors=null;#editorTypes=null;#editorsToRescale=new Set;_editorUndoBar=null;#enableHighlightFloatingButton=!1;#enableUpdatedAddImage=!1;#enableNewAltTextWhenAddingImage=!1;#filterFactory=null;#focusMainContainerTimeoutId=null;#focusManagerAC=null;#highlightColors=null;#highlightWhenShiftUp=!1;#highlightToolbar=null;#idManager=new IdManager;#isEnabled=!1;#isWaiting=!1;#keyboardManagerAC=null;#lastActiveElement=null;#mainHighlightColorPicker=null;#missingCanvases=null;#mlManager=null;#mode=AnnotationEditorType.
-NONE;#selectedEditors=new Set;#selectedTextNode=null;#signatureManager=null;#pageColors=null;#showAllStates=null;#previousStates={isEditing:!1,isEmpty:!0,hasSomethingToUndo:!1,hasSomethingToRedo:!1,hasSelectedEditor:!1,hasSelectedText:!1};#translation=[0,0];#translationTimeoutId=null;#container=null;#viewer=null;#viewerAlert=null;#updateModeCapability=null;static TRANSLATE_SMALL=1;static TRANSLATE_BIG=10;static get _keyboardManager(){const proto=AnnotationEditorUIManager.prototype,arrowChecker=self2=>self2.#container.
-contains(document.activeElement)&&document.activeElement.tagName!=="BUTTON"&&self2.hasSomethingToControl(),textInputChecker=(_self,{target:el})=>{if(el instanceof HTMLInputElement){const{type:type2}=el;return type2!=="text"&&type2!=="number"}return!0},small=this.TRANSLATE_SMALL,big=this.TRANSLATE_BIG;return shadow(this,"_keyboardManager",new KeyboardManager([[["ctrl+a","mac+meta+a"],proto.selectAll,{checker:textInputChecker}],[["ctrl+z","mac+meta+z"],proto.undo,{checker:textInputChecker}],[["ctr\
-l+y","ctrl+shift+z","mac+meta+shift+z","ctrl+shift+Z","mac+meta+shift+Z"],proto.redo,{checker:textInputChecker}],[["Backspace","alt+Backspace","ctrl+Backspace","shift+Backspace","mac+Backspace","mac+alt+Backspace","mac+ctrl+Backspace","Delete","ctrl+Delete","shift+Delete","mac+Delete"],proto.delete,{checker:textInputChecker}],[["Enter","mac+Enter"],proto.addNewEditorFromKeyboard,{checker:(self2,{target:el})=>!(el instanceof HTMLButtonElement)&&self2.#container.contains(el)&&!self2.isEnterHandled}],
-[[" ","mac+ "],proto.addNewEditorFromKeyboard,{checker:(self2,{target:el})=>!(el instanceof HTMLButtonElement)&&self2.#container.contains(document.activeElement)}],[["Escape","mac+Escape"],proto.unselectAll],[["ArrowLeft","mac+ArrowLeft"],proto.translateSelectedEditors,{args:[-small,0],checker:arrowChecker}],[["ctrl+ArrowLeft","mac+shift+ArrowLeft"],proto.translateSelectedEditors,{args:[-big,0],checker:arrowChecker}],[["ArrowRight","mac+ArrowRight"],proto.translateSelectedEditors,{args:[small,0],
-checker:arrowChecker}],[["ctrl+ArrowRight","mac+shift+ArrowRight"],proto.translateSelectedEditors,{args:[big,0],checker:arrowChecker}],[["ArrowUp","mac+ArrowUp"],proto.translateSelectedEditors,{args:[0,-small],checker:arrowChecker}],[["ctrl+ArrowUp","mac+shift+ArrowUp"],proto.translateSelectedEditors,{args:[0,-big],checker:arrowChecker}],[["ArrowDown","mac+ArrowDown"],proto.translateSelectedEditors,{args:[0,small],checker:arrowChecker}],[["ctrl+ArrowDown","mac+shift+ArrowDown"],proto.translateSelectedEditors,
-{args:[0,big],checker:arrowChecker}]]))}constructor(container2,viewer,viewerAlert,altTextManager,commentManager,signatureManager,eventBus,pdfDocument,pageColors,highlightColors,enableHighlightFloatingButton,enableUpdatedAddImage,enableNewAltTextWhenAddingImage,mlManager,editorUndoBar,supportsPinchToZoom){const signal=this._signal=this.#abortController.signal;this.#container=container2,this.#viewer=viewer,this.#viewerAlert=viewerAlert,this.#altTextManager=altTextManager,this.#commentManager=commentManager,
-this.#signatureManager=signatureManager,this._eventBus=eventBus,eventBus._on("editingaction",this.onEditingAction.bind(this),{signal}),eventBus._on("pagechanging",this.onPageChanging.bind(this),{signal}),eventBus._on("scalechanging",this.onScaleChanging.bind(this),{signal}),eventBus._on("rotationchanging",this.onRotationChanging.bind(this),{signal}),eventBus._on("setpreference",this.onSetPreference.bind(this),{signal}),eventBus._on("switchannotationeditorparams",evt=>this.updateParams(evt.type,evt.
-value),{signal}),this.#addSelectionListener(),this.#addDragAndDropListeners(),this.#addKeyboardManager(),this.#annotationStorage=pdfDocument.annotationStorage,this.#filterFactory=pdfDocument.filterFactory,this.#pageColors=pageColors,this.#highlightColors=highlightColors||null,this.#enableHighlightFloatingButton=enableHighlightFloatingButton,this.#enableUpdatedAddImage=enableUpdatedAddImage,this.#enableNewAltTextWhenAddingImage=enableNewAltTextWhenAddingImage,this.#mlManager=mlManager||null,this.
-viewParameters={realScale:PixelsPerInch.PDF_TO_CSS_UNITS,rotation:0},this.isShiftKeyDown=!1,this._editorUndoBar=editorUndoBar||null,this._supportsPinchToZoom=supportsPinchToZoom!==!1}destroy(){this.#updateModeCapability?.resolve(),this.#updateModeCapability=null,this.#abortController?.abort(),this.#abortController=null,this._signal=null;for(const layer of this.#allLayers.values())layer.destroy();this.#allLayers.clear(),this.#allEditors.clear(),this.#editorsToRescale.clear(),this.#missingCanvases?.
-clear(),this.#activeEditor=null,this.#selectedEditors.clear(),this.#commandManager.destroy(),this.#altTextManager?.destroy(),this.#commentManager?.destroy(),this.#signatureManager?.destroy(),this.#highlightToolbar?.hide(),this.#highlightToolbar=null,this.#mainHighlightColorPicker?.destroy(),this.#mainHighlightColorPicker=null,this.#focusMainContainerTimeoutId&&(clearTimeout(this.#focusMainContainerTimeoutId),this.#focusMainContainerTimeoutId=null),this.#translationTimeoutId&&(clearTimeout(this.#translationTimeoutId),
-this.#translationTimeoutId=null),this._editorUndoBar?.destroy()}combinedSignal(ac){return AbortSignal.any([this._signal,ac.signal])}get mlManager(){return this.#mlManager}get useNewAltTextFlow(){return this.#enableUpdatedAddImage}get useNewAltTextWhenAddingImage(){return this.#enableNewAltTextWhenAddingImage}get hcmFilter(){return shadow(this,"hcmFilter",this.#pageColors?this.#filterFactory.addHCMFilter(this.#pageColors.foreground,this.#pageColors.background):"none")}get direction(){return shadow(
-this,"direction",getComputedStyle(this.#container).direction)}get _highlightColors(){return shadow(this,"_highlightColors",this.#highlightColors?new Map(this.#highlightColors.split(",").map(pair=>(pair=pair.split("=").map(x=>x.trim()),pair[1]=pair[1].toUpperCase(),pair))):null)}get highlightColors(){const{_highlightColors}=this;if(!_highlightColors)return shadow(this,"highlightColors",null);const map2=new Map,hasHCM=!!this.#pageColors;for(const[name,color]of _highlightColors){const isNameForHCM=name.
-endsWith("_HCM");if(hasHCM&&isNameForHCM){map2.set(name.replace("_HCM",""),color);continue}!hasHCM&&!isNameForHCM&&map2.set(name,color)}return shadow(this,"highlightColors",map2)}get highlightColorNames(){return shadow(this,"highlightColorNames",this.highlightColors?new Map(Array.from(this.highlightColors,e=>e.reverse())):null)}getNonHCMColor(color){if(!this._highlightColors)return color;const colorName=this.highlightColorNames.get(color);return this._highlightColors.get(colorName)||color}getNonHCMColorName(color){
-return this.highlightColorNames.get(color)||color}setCurrentDrawingSession(layer){layer?(this.unselectAll(),this.disableUserSelect(!0)):this.disableUserSelect(!1),this.#currentDrawingSession=layer}setMainHighlightColorPicker(colorPicker){this.#mainHighlightColorPicker=colorPicker}editAltText(editor,firstTime=!1){this.#altTextManager?.editAltText(this,editor,firstTime)}hasCommentManager(){return!!this.#commentManager}editComment(editor,position2){this.#commentManager?.open(this,editor,position2)}getSignature(editor){
-this.#signatureManager?.getSignature({uiManager:this,editor})}get signatureManager(){return this.#signatureManager}switchToMode(mode,callback){this._eventBus.on("annotationeditormodechanged",callback,{once:!0,signal:this._signal}),this._eventBus.dispatch("showannotationeditorui",{source:this,mode})}setPreference(name,value){this._eventBus.dispatch("setpreference",{source:this,name,value})}onSetPreference({name,value}){switch(name){case"enableNewAltTextWhenAddingImage":this.#enableNewAltTextWhenAddingImage=
-value;break}}onPageChanging({pageNumber}){this.#currentPageIndex=pageNumber-1}focusMainContainer(){this.#container.focus()}findParent(x,y){for(const layer of this.#allLayers.values()){const{x:layerX,y:layerY,width,height}=layer.div.getBoundingClientRect();if(x>=layerX&&x<=layerX+width&&y>=layerY&&y<=layerY+height)return layer}return null}disableUserSelect(value=!1){this.#viewer.classList.toggle("noUserSelect",value)}addShouldRescale(editor){this.#editorsToRescale.add(editor)}removeShouldRescale(editor){
-this.#editorsToRescale.delete(editor)}onScaleChanging({scale:scale2}){this.commitOrRemove(),this.viewParameters.realScale=scale2*PixelsPerInch.PDF_TO_CSS_UNITS;for(const editor of this.#editorsToRescale)editor.onScaleChanging();this.#currentDrawingSession?.onScaleChanging()}onRotationChanging({pagesRotation}){this.commitOrRemove(),this.viewParameters.rotation=pagesRotation}#getAnchorElementForSelection({anchorNode}){return anchorNode.nodeType===Node.TEXT_NODE?anchorNode.parentElement:anchorNode}#getLayerForTextLayer(textLayer){
-const{currentLayer}=this;if(currentLayer.hasTextLayer(textLayer))return currentLayer;for(const layer of this.#allLayers.values())if(layer.hasTextLayer(textLayer))return layer;return null}highlightSelection(methodOfCreation=""){const selection=document.getSelection();if(!selection||selection.isCollapsed)return;const{anchorNode,anchorOffset,focusNode,focusOffset}=selection,text2=selection.toString(),textLayer=this.#getAnchorElementForSelection(selection).closest(".textLayer"),boxes=this.getSelectionBoxes(
-textLayer);if(!boxes)return;selection.empty();const layer=this.#getLayerForTextLayer(textLayer),isNoneMode=this.#mode===AnnotationEditorType.NONE,callback=()=>{layer?.createAndAddNewEditor({x:0,y:0},!1,{methodOfCreation,boxes,anchorNode,anchorOffset,focusNode,focusOffset,text:text2}),isNoneMode&&this.showAllEditors("highlight",!0,!0)};if(isNoneMode){this.switchToMode(AnnotationEditorType.HIGHLIGHT,callback);return}callback()}#displayHighlightToolbar(){const selection=document.getSelection();if(!selection||
-selection.isCollapsed)return;const textLayer=this.#getAnchorElementForSelection(selection).closest(".textLayer"),boxes=this.getSelectionBoxes(textLayer);boxes&&(this.#highlightToolbar||=new HighlightToolbar(this),this.#highlightToolbar.show(textLayer,boxes,this.direction==="ltr"))}addToAnnotationStorage(editor){!editor.isEmpty()&&this.#annotationStorage&&!this.#annotationStorage.has(editor.id)&&this.#annotationStorage.setValue(editor.id,editor)}a11yAlert(messageId,args=null){const viewerAlert=this.#viewerAlert;
-viewerAlert&&(viewerAlert.setAttribute("data-l10n-id",messageId),args?viewerAlert.setAttribute("data-l10n-args",JSON.stringify(args)):viewerAlert.removeAttribute("data-l10n-args"))}#selectionChange(){const selection=document.getSelection();if(!selection||selection.isCollapsed){this.#selectedTextNode&&(this.#highlightToolbar?.hide(),this.#selectedTextNode=null,this.#dispatchUpdateStates({hasSelectedText:!1}));return}const{anchorNode}=selection;if(anchorNode===this.#selectedTextNode)return;const textLayer=this.#getAnchorElementForSelection(
-selection).closest(".textLayer");if(!textLayer){this.#selectedTextNode&&(this.#highlightToolbar?.hide(),this.#selectedTextNode=null,this.#dispatchUpdateStates({hasSelectedText:!1}));return}if(this.#highlightToolbar?.hide(),this.#selectedTextNode=anchorNode,this.#dispatchUpdateStates({hasSelectedText:!0}),!(this.#mode!==AnnotationEditorType.HIGHLIGHT&&this.#mode!==AnnotationEditorType.NONE)&&(this.#mode===AnnotationEditorType.HIGHLIGHT&&this.showAllEditors("highlight",!0,!0),this.#highlightWhenShiftUp=
-this.isShiftKeyDown,!this.isShiftKeyDown)){const activeLayer=this.#mode===AnnotationEditorType.HIGHLIGHT?this.#getLayerForTextLayer(textLayer):null;activeLayer?.toggleDrawing();const ac=new AbortController,signal=this.combinedSignal(ac),pointerup=e=>{e.type==="pointerup"&&e.button!==0||(ac.abort(),activeLayer?.toggleDrawing(!0),e.type==="pointerup"&&this.#onSelectEnd("main_toolbar"))};window.addEventListener("pointerup",pointerup,{signal}),window.addEventListener("blur",pointerup,{signal})}}#onSelectEnd(methodOfCreation=""){
-this.#mode===AnnotationEditorType.HIGHLIGHT?this.highlightSelection(methodOfCreation):this.#enableHighlightFloatingButton&&this.#displayHighlightToolbar()}#addSelectionListener(){document.addEventListener("selectionchange",this.#selectionChange.bind(this),{signal:this._signal})}#addFocusManager(){if(this.#focusManagerAC)return;this.#focusManagerAC=new AbortController;const signal=this.combinedSignal(this.#focusManagerAC);window.addEventListener("focus",this.focus.bind(this),{signal}),window.addEventListener(
-"blur",this.blur.bind(this),{signal})}#removeFocusManager(){this.#focusManagerAC?.abort(),this.#focusManagerAC=null}blur(){if(this.isShiftKeyDown=!1,this.#highlightWhenShiftUp&&(this.#highlightWhenShiftUp=!1,this.#onSelectEnd("main_toolbar")),!this.hasSelection)return;const{activeElement}=document;for(const editor of this.#selectedEditors)if(editor.div.contains(activeElement)){this.#lastActiveElement=[editor,activeElement],editor._focusEventsAllowed=!1;break}}focus(){if(!this.#lastActiveElement)
-return;const[lastEditor,lastActiveElement]=this.#lastActiveElement;this.#lastActiveElement=null,lastActiveElement.addEventListener("focusin",()=>{lastEditor._focusEventsAllowed=!0},{once:!0,signal:this._signal}),lastActiveElement.focus()}#addKeyboardManager(){if(this.#keyboardManagerAC)return;this.#keyboardManagerAC=new AbortController;const signal=this.combinedSignal(this.#keyboardManagerAC);window.addEventListener("keydown",this.keydown.bind(this),{signal}),window.addEventListener("keyup",this.
-keyup.bind(this),{signal})}#removeKeyboardManager(){this.#keyboardManagerAC?.abort(),this.#keyboardManagerAC=null}#addCopyPasteListeners(){if(this.#copyPasteAC)return;this.#copyPasteAC=new AbortController;const signal=this.combinedSignal(this.#copyPasteAC);document.addEventListener("copy",this.copy.bind(this),{signal}),document.addEventListener("cut",this.cut.bind(this),{signal}),document.addEventListener("paste",this.paste.bind(this),{signal})}#removeCopyPasteListeners(){this.#copyPasteAC?.abort(),
-this.#copyPasteAC=null}#addDragAndDropListeners(){const signal=this._signal;document.addEventListener("dragover",this.dragOver.bind(this),{signal}),document.addEventListener("drop",this.drop.bind(this),{signal})}addEditListeners(){this.#addKeyboardManager(),this.#addCopyPasteListeners()}removeEditListeners(){this.#removeKeyboardManager(),this.#removeCopyPasteListeners()}dragOver(event2){for(const{type:type2}of event2.dataTransfer.items)for(const editorType of this.#editorTypes)if(editorType.isHandlingMimeForPasting(
-type2)){event2.dataTransfer.dropEffect="copy",event2.preventDefault();return}}drop(event2){for(const item of event2.dataTransfer.items)for(const editorType of this.#editorTypes)if(editorType.isHandlingMimeForPasting(item.type)){editorType.paste(item,this.currentLayer),event2.preventDefault();return}}copy(event2){if(event2.preventDefault(),this.#activeEditor?.commitOrRemove(),!this.hasSelection)return;const editors=[];for(const editor of this.#selectedEditors){const serialized=editor.serialize(!0);
-serialized&&editors.push(serialized)}editors.length!==0&&event2.clipboardData.setData("application/pdfjs",JSON.stringify(editors))}cut(event2){this.copy(event2),this.delete()}async paste(event2){event2.preventDefault();const{clipboardData}=event2;for(const item of clipboardData.items)for(const editorType of this.#editorTypes)if(editorType.isHandlingMimeForPasting(item.type)){editorType.paste(item,this.currentLayer);return}let data=clipboardData.getData("application/pdfjs");if(!data)return;try{data=
-JSON.parse(data)}catch(ex){warn(`paste: "${ex.message}".`);return}if(!Array.isArray(data))return;this.unselectAll();const layer=this.currentLayer;try{const newEditors=[];for(const editor of data){const deserializedEditor=await layer.deserialize(editor);if(!deserializedEditor)return;newEditors.push(deserializedEditor)}const cmd=()=>{for(const editor of newEditors)this.#addEditorToLayer(editor);this.#selectEditors(newEditors)},undo=()=>{for(const editor of newEditors)editor.remove()};this.addCommands(
-{cmd,undo,mustExec:!0})}catch(ex){warn(`paste: "${ex.message}".`)}}keydown(event2){!this.isShiftKeyDown&&event2.key==="Shift"&&(this.isShiftKeyDown=!0),this.#mode!==AnnotationEditorType.NONE&&!this.isEditorHandlingKeyboard&&AnnotationEditorUIManager._keyboardManager.exec(this,event2)}keyup(event2){this.isShiftKeyDown&&event2.key==="Shift"&&(this.isShiftKeyDown=!1,this.#highlightWhenShiftUp&&(this.#highlightWhenShiftUp=!1,this.#onSelectEnd("main_toolbar")))}onEditingAction({name}){switch(name){case"\
-undo":case"redo":case"delete":case"selectAll":this[name]();break;case"highlightSelection":this.highlightSelection("context_menu");break}}#dispatchUpdateStates(details){Object.entries(details).some(([key2,value])=>this.#previousStates[key2]!==value)&&(this._eventBus.dispatch("annotationeditorstateschanged",{source:this,details:Object.assign(this.#previousStates,details)}),this.#mode===AnnotationEditorType.HIGHLIGHT&&details.hasSelectedEditor===!1&&this.#dispatchUpdateUI([[AnnotationEditorParamsType.
-HIGHLIGHT_FREE,!0]]))}#dispatchUpdateUI(details){this._eventBus.dispatch("annotationeditorparamschanged",{source:this,details})}setEditingState(isEditing2){isEditing2?(this.#addFocusManager(),this.#addCopyPasteListeners(),this.#dispatchUpdateStates({isEditing:this.#mode!==AnnotationEditorType.NONE,isEmpty:this.#isEmpty(),hasSomethingToUndo:this.#commandManager.hasSomethingToUndo(),hasSomethingToRedo:this.#commandManager.hasSomethingToRedo(),hasSelectedEditor:!1})):(this.#removeFocusManager(),this.#removeCopyPasteListeners(),
-this.#dispatchUpdateStates({isEditing:!1}),this.disableUserSelect(!1))}registerEditorTypes(types2){if(!this.#editorTypes){this.#editorTypes=types2;for(const editorType of this.#editorTypes)this.#dispatchUpdateUI(editorType.defaultPropertiesToUpdate)}}getId(){return this.#idManager.id}get currentLayer(){return this.#allLayers.get(this.#currentPageIndex)}getLayer(pageIndex){return this.#allLayers.get(pageIndex)}get currentPageIndex(){return this.#currentPageIndex}addLayer(layer){this.#allLayers.set(
-layer.pageIndex,layer),this.#isEnabled?layer.enable():layer.disable()}removeLayer(layer){this.#allLayers.delete(layer.pageIndex)}async updateMode(mode,editId=null,isFromKeyboard=!1,mustEnterInEditMode=!1,editComment=!1){if(this.#mode!==mode&&!(this.#updateModeCapability&&(await this.#updateModeCapability.promise,!this.#updateModeCapability))){if(this.#updateModeCapability=Promise.withResolvers(),this.#currentDrawingSession?.commitOrRemove(),this.#mode=mode,mode===AnnotationEditorType.NONE){this.
-setEditingState(!1),this.#disableAll(),this._editorUndoBar?.hide(),this.#updateModeCapability.resolve();return}mode===AnnotationEditorType.SIGNATURE&&await this.#signatureManager?.loadSignatures(),this.setEditingState(!0),await this.#enableAll(),this.unselectAll();for(const layer of this.#allLayers.values())layer.updateMode(mode);if(!editId){isFromKeyboard&&this.addNewEditorFromKeyboard(),this.#updateModeCapability.resolve();return}for(const editor of this.#allEditors.values())editor.annotationElementId===
-editId||editor.id===editId?(this.setSelected(editor),editComment?editor.editComment():mustEnterInEditMode&&editor.enterInEditMode()):editor.unselect();this.#updateModeCapability.resolve()}}addNewEditorFromKeyboard(){this.currentLayer.canCreateNewEmptyEditor()&&this.currentLayer.addNewEditor()}updateToolbar(options){options.mode!==this.#mode&&this._eventBus.dispatch("switchannotationeditormode",{source:this,...options})}updateParams(type2,value){if(this.#editorTypes){switch(type2){case AnnotationEditorParamsType.
-CREATE:this.currentLayer.addNewEditor(value);return;case AnnotationEditorParamsType.HIGHLIGHT_SHOW_ALL:this._eventBus.dispatch("reporttelemetry",{source:this,details:{type:"editing",data:{type:"highlight",action:"toggle_visibility"}}}),(this.#showAllStates||=new Map).set(type2,value),this.showAllEditors("highlight",value);break}if(this.hasSelection)for(const editor of this.#selectedEditors)editor.updateParams(type2,value);else for(const editorType of this.#editorTypes)editorType.updateDefaultParams(
-type2,value)}}showAllEditors(type2,visible,updateButton=!1){for(const editor of this.#allEditors.values())editor.editorType===type2&&editor.show(visible);(this.#showAllStates?.get(AnnotationEditorParamsType.HIGHLIGHT_SHOW_ALL)??!0)!==visible&&this.#dispatchUpdateUI([[AnnotationEditorParamsType.HIGHLIGHT_SHOW_ALL,visible]])}enableWaiting(mustWait=!1){if(this.#isWaiting!==mustWait){this.#isWaiting=mustWait;for(const layer of this.#allLayers.values())mustWait?layer.disableClick():layer.enableClick(),
-layer.div.classList.toggle("waiting",mustWait)}}async#enableAll(){if(!this.#isEnabled){this.#isEnabled=!0;const promises=[];for(const layer of this.#allLayers.values())promises.push(layer.enable());await Promise.all(promises);for(const editor of this.#allEditors.values())editor.enable()}}#disableAll(){if(this.unselectAll(),this.#isEnabled){this.#isEnabled=!1;for(const layer of this.#allLayers.values())layer.disable();for(const editor of this.#allEditors.values())editor.disable()}}getEditors(pageIndex){
-const editors=[];for(const editor of this.#allEditors.values())editor.pageIndex===pageIndex&&editors.push(editor);return editors}getEditor(id2){return this.#allEditors.get(id2)}addEditor(editor){this.#allEditors.set(editor.id,editor)}removeEditor(editor){editor.div.contains(document.activeElement)&&(this.#focusMainContainerTimeoutId&&clearTimeout(this.#focusMainContainerTimeoutId),this.#focusMainContainerTimeoutId=setTimeout(()=>{this.focusMainContainer(),this.#focusMainContainerTimeoutId=null},
-0)),this.#allEditors.delete(editor.id),editor.annotationElementId&&this.#missingCanvases?.delete(editor.annotationElementId),this.unselect(editor),(!editor.annotationElementId||!this.#deletedAnnotationsElementIds.has(editor.annotationElementId))&&this.#annotationStorage?.remove(editor.id)}addDeletedAnnotationElement(editor){this.#deletedAnnotationsElementIds.add(editor.annotationElementId),this.addChangedExistingAnnotation(editor),editor.deleted=!0}isDeletedAnnotationElement(annotationElementId){
-return this.#deletedAnnotationsElementIds.has(annotationElementId)}removeDeletedAnnotationElement(editor){this.#deletedAnnotationsElementIds.delete(editor.annotationElementId),this.removeChangedExistingAnnotation(editor),editor.deleted=!1}#addEditorToLayer(editor){const layer=this.#allLayers.get(editor.pageIndex);layer?layer.addOrRebuild(editor):(this.addEditor(editor),this.addToAnnotationStorage(editor))}setActiveEditor(editor){this.#activeEditor!==editor&&(this.#activeEditor=editor,editor&&this.#dispatchUpdateUI(
-editor.propertiesToUpdate))}get#lastSelectedEditor(){let ed=null;for(ed of this.#selectedEditors);return ed}updateUI(editor){this.#lastSelectedEditor===editor&&this.#dispatchUpdateUI(editor.propertiesToUpdate)}updateUIForDefaultProperties(editorType){this.#dispatchUpdateUI(editorType.defaultPropertiesToUpdate)}toggleSelected(editor){if(this.#selectedEditors.has(editor)){this.#selectedEditors.delete(editor),editor.unselect(),this.#dispatchUpdateStates({hasSelectedEditor:this.hasSelection});return}
-this.#selectedEditors.add(editor),editor.select(),this.#dispatchUpdateUI(editor.propertiesToUpdate),this.#dispatchUpdateStates({hasSelectedEditor:!0})}setSelected(editor){this.updateToolbar({mode:editor.mode,editId:editor.id}),this.#currentDrawingSession?.commitOrRemove();for(const ed of this.#selectedEditors)ed!==editor&&ed.unselect();this.#selectedEditors.clear(),this.#selectedEditors.add(editor),editor.select(),this.#dispatchUpdateUI(editor.propertiesToUpdate),this.#dispatchUpdateStates({hasSelectedEditor:!0})}isSelected(editor){
-return this.#selectedEditors.has(editor)}get firstSelectedEditor(){return this.#selectedEditors.values().next().value}unselect(editor){editor.unselect(),this.#selectedEditors.delete(editor),this.#dispatchUpdateStates({hasSelectedEditor:this.hasSelection})}get hasSelection(){return this.#selectedEditors.size!==0}get isEnterHandled(){return this.#selectedEditors.size===1&&this.firstSelectedEditor.isEnterHandled}undo(){this.#commandManager.undo(),this.#dispatchUpdateStates({hasSomethingToUndo:this.#commandManager.
-hasSomethingToUndo(),hasSomethingToRedo:!0,isEmpty:this.#isEmpty()}),this._editorUndoBar?.hide()}redo(){this.#commandManager.redo(),this.#dispatchUpdateStates({hasSomethingToUndo:!0,hasSomethingToRedo:this.#commandManager.hasSomethingToRedo(),isEmpty:this.#isEmpty()})}addCommands(params){this.#commandManager.add(params),this.#dispatchUpdateStates({hasSomethingToUndo:!0,hasSomethingToRedo:!1,isEmpty:this.#isEmpty()})}cleanUndoStack(type2){this.#commandManager.cleanType(type2)}#isEmpty(){if(this.#allEditors.
-size===0)return!0;if(this.#allEditors.size===1)for(const editor of this.#allEditors.values())return editor.isEmpty();return!1}delete(){this.commitOrRemove();const drawingEditor=this.currentLayer?.endDrawingSession(!0);if(!this.hasSelection&&!drawingEditor)return;const editors=drawingEditor?[drawingEditor]:[...this.#selectedEditors],cmd=()=>{this._editorUndoBar?.show(undo,editors.length===1?editors[0].editorType:editors.length);for(const editor of editors)editor.remove()},undo=()=>{for(const editor of editors)
-this.#addEditorToLayer(editor)};this.addCommands({cmd,undo,mustExec:!0})}commitOrRemove(){this.#activeEditor?.commitOrRemove()}hasSomethingToControl(){return this.#activeEditor||this.hasSelection}#selectEditors(editors){for(const editor of this.#selectedEditors)editor.unselect();this.#selectedEditors.clear();for(const editor of editors)editor.isEmpty()||(this.#selectedEditors.add(editor),editor.select());this.#dispatchUpdateStates({hasSelectedEditor:this.hasSelection})}selectAll(){for(const editor of this.#selectedEditors)
-editor.commit();this.#selectEditors(this.#allEditors.values())}unselectAll(){if(!(this.#activeEditor&&(this.#activeEditor.commitOrRemove(),this.#mode!==AnnotationEditorType.NONE))&&!this.#currentDrawingSession?.commitOrRemove()&&this.hasSelection){for(const editor of this.#selectedEditors)editor.unselect();this.#selectedEditors.clear(),this.#dispatchUpdateStates({hasSelectedEditor:!1})}}translateSelectedEditors(x,y,noCommit=!1){if(noCommit||this.commitOrRemove(),!this.hasSelection)return;this.#translation[0]+=
-x,this.#translation[1]+=y;const[totalX,totalY]=this.#translation,editors=[...this.#selectedEditors],TIME_TO_WAIT=1e3;this.#translationTimeoutId&&clearTimeout(this.#translationTimeoutId),this.#translationTimeoutId=setTimeout(()=>{this.#translationTimeoutId=null,this.#translation[0]=this.#translation[1]=0,this.addCommands({cmd:()=>{for(const editor of editors)this.#allEditors.has(editor.id)&&(editor.translateInPage(totalX,totalY),editor.translationDone())},undo:()=>{for(const editor of editors)this.#allEditors.
-has(editor.id)&&(editor.translateInPage(-totalX,-totalY),editor.translationDone())},mustExec:!1})},TIME_TO_WAIT);for(const editor of editors)editor.translateInPage(x,y),editor.translationDone()}setUpDragSession(){if(this.hasSelection){this.disableUserSelect(!0),this.#draggingEditors=new Map;for(const editor of this.#selectedEditors)this.#draggingEditors.set(editor,{savedX:editor.x,savedY:editor.y,savedPageIndex:editor.pageIndex,newX:0,newY:0,newPageIndex:-1})}}endDragSession(){if(!this.#draggingEditors)
-return!1;this.disableUserSelect(!1);const map2=this.#draggingEditors;this.#draggingEditors=null;let mustBeAddedInUndoStack=!1;for(const[{x,y,pageIndex},value]of map2)value.newX=x,value.newY=y,value.newPageIndex=pageIndex,mustBeAddedInUndoStack||=x!==value.savedX||y!==value.savedY||pageIndex!==value.savedPageIndex;if(!mustBeAddedInUndoStack)return!1;const move2=(editor,x,y,pageIndex)=>{if(this.#allEditors.has(editor.id)){const parent=this.#allLayers.get(pageIndex);parent?editor._setParentAndPosition(
-parent,x,y):(editor.pageIndex=pageIndex,editor.x=x,editor.y=y)}};return this.addCommands({cmd:()=>{for(const[editor,{newX,newY,newPageIndex}]of map2)move2(editor,newX,newY,newPageIndex)},undo:()=>{for(const[editor,{savedX,savedY,savedPageIndex}]of map2)move2(editor,savedX,savedY,savedPageIndex)},mustExec:!0}),!0}dragSelectedEditors(tx,ty){if(this.#draggingEditors)for(const editor of this.#draggingEditors.keys())editor.drag(tx,ty)}rebuild(editor){if(editor.parent===null){const parent=this.getLayer(
-editor.pageIndex);parent?(parent.changeParent(editor),parent.addOrRebuild(editor)):(this.addEditor(editor),this.addToAnnotationStorage(editor),editor.rebuild())}else editor.parent.addOrRebuild(editor)}get isEditorHandlingKeyboard(){return this.getActive()?.shouldGetKeyboardEvents()||this.#selectedEditors.size===1&&this.firstSelectedEditor.shouldGetKeyboardEvents()}isActive(editor){return this.#activeEditor===editor}getActive(){return this.#activeEditor}getMode(){return this.#mode}get imageManager(){
-return shadow(this,"imageManager",new ImageManager)}getSelectionBoxes(textLayer){if(!textLayer)return null;const selection=document.getSelection();for(let i=0,ii=selection.rangeCount;i<ii;i++)if(!textLayer.contains(selection.getRangeAt(i).commonAncestorContainer))return null;const{x:layerX,y:layerY,width:parentWidth,height:parentHeight}=textLayer.getBoundingClientRect();let rotator;switch(textLayer.getAttribute("data-main-rotation")){case"90":rotator=(x,y,w,h2)=>({x:(y-layerY)/parentHeight,y:1-(x+
-w-layerX)/parentWidth,width:h2/parentHeight,height:w/parentWidth});break;case"180":rotator=(x,y,w,h2)=>({x:1-(x+w-layerX)/parentWidth,y:1-(y+h2-layerY)/parentHeight,width:w/parentWidth,height:h2/parentHeight});break;case"270":rotator=(x,y,w,h2)=>({x:1-(y+h2-layerY)/parentHeight,y:(x-layerX)/parentWidth,width:h2/parentHeight,height:w/parentWidth});break;default:rotator=(x,y,w,h2)=>({x:(x-layerX)/parentWidth,y:(y-layerY)/parentHeight,width:w/parentWidth,height:h2/parentHeight});break}const boxes=[];
-for(let i=0,ii=selection.rangeCount;i<ii;i++){const range=selection.getRangeAt(i);if(!range.collapsed)for(const{x,y,width,height}of range.getClientRects())width===0||height===0||boxes.push(rotator(x,y,width,height))}return boxes.length===0?null:boxes}addChangedExistingAnnotation({annotationElementId,id:id2}){(this.#changedExistingAnnotations||=new Map).set(annotationElementId,id2)}removeChangedExistingAnnotation({annotationElementId}){this.#changedExistingAnnotations?.delete(annotationElementId)}renderAnnotationElement(annotation){
-const editorId=this.#changedExistingAnnotations?.get(annotation.data.id);if(!editorId)return;const editor=this.#annotationStorage.getRawValue(editorId);editor&&(this.#mode===AnnotationEditorType.NONE&&!editor.hasBeenModified||editor.renderAnnotationElement(annotation))}setMissingCanvas(annotationId,annotationElementId,canvas){const editor=this.#missingCanvases?.get(annotationId);editor&&(editor.setCanvas(annotationElementId,canvas),this.#missingCanvases.delete(annotationId))}addMissingCanvas(annotationId,editor){
-(this.#missingCanvases||=new Map).set(annotationId,editor)}}class AltText{#altText=null;#altTextDecorative=!1;#altTextButton=null;#altTextButtonLabel=null;#altTextTooltip=null;#altTextTooltipTimeout=null;#altTextWasFromKeyBoard=!1;#badge=null;#editor=null;#guessedText=null;#textWithDisclaimer=null;#useNewAltTextFlow=!1;static#l10nNewButton=null;static _l10n=null;constructor(editor){this.#editor=editor,this.#useNewAltTextFlow=editor._uiManager.useNewAltTextFlow,AltText.#l10nNewButton||=Object.freeze(
-{added:"pdfjs-editor-new-alt-text-added-button","added-label":"pdfjs-editor-new-alt-text-added-button-label",missing:"pdfjs-editor-new-alt-text-missing-button","missing-label":"pdfjs-editor-new-alt-text-missing-button-label",review:"pdfjs-editor-new-alt-text-to-review-button","review-label":"pdfjs-editor-new-alt-text-to-review-button-label"})}static initialize(l10n){AltText._l10n??=l10n}async render(){const altText=this.#altTextButton=document.createElement("button");altText.className="altText",
-altText.tabIndex="0";const label=this.#altTextButtonLabel=document.createElement("span");altText.append(label),this.#useNewAltTextFlow?(altText.classList.add("new"),altText.setAttribute("data-l10n-id",AltText.#l10nNewButton.missing),label.setAttribute("data-l10n-id",AltText.#l10nNewButton["missing-label"])):(altText.setAttribute("data-l10n-id","pdfjs-editor-alt-text-button"),label.setAttribute("data-l10n-id","pdfjs-editor-alt-text-button-label"));const signal=this.#editor._uiManager._signal;altText.
-addEventListener("contextmenu",noContextMenu,{signal}),altText.addEventListener("pointerdown",event2=>event2.stopPropagation(),{signal});const onClick=event2=>{event2.preventDefault(),this.#editor._uiManager.editAltText(this.#editor),this.#useNewAltTextFlow&&this.#editor._reportTelemetry({action:"pdfjs.image.alt_text.image_status_label_clicked",data:{label:this.#label}})};return altText.addEventListener("click",onClick,{capture:!0,signal}),altText.addEventListener("keydown",event2=>{event2.target===
-altText&&event2.key==="Enter"&&(this.#altTextWasFromKeyBoard=!0,onClick(event2))},{signal}),await this.#setState(),altText}get#label(){return this.#altText&&"added"||this.#altText===null&&this.guessedText&&"review"||"missing"}finish(){this.#altTextButton&&(this.#altTextButton.focus({focusVisible:this.#altTextWasFromKeyBoard}),this.#altTextWasFromKeyBoard=!1)}isEmpty(){return this.#useNewAltTextFlow?this.#altText===null:!this.#altText&&!this.#altTextDecorative}hasData(){return this.#useNewAltTextFlow?
-this.#altText!==null||!!this.#guessedText:this.isEmpty()}get guessedText(){return this.#guessedText}async setGuessedText(guessedText){this.#altText===null&&(this.#guessedText=guessedText,this.#textWithDisclaimer=await AltText._l10n.get("pdfjs-editor-new-alt-text-generated-alt-text-with-disclaimer",{generatedAltText:guessedText}),this.#setState())}toggleAltTextBadge(visibility=!1){if(!this.#useNewAltTextFlow||this.#altText){this.#badge?.remove(),this.#badge=null;return}if(!this.#badge){const badge=this.#badge=
-document.createElement("div");badge.className="noAltTextBadge",this.#editor.div.append(badge)}this.#badge.classList.toggle("hidden",!visibility)}serialize(isForCopying){let altText=this.#altText;return!isForCopying&&this.#guessedText===altText&&(altText=this.#textWithDisclaimer),{altText,decorative:this.#altTextDecorative,guessedText:this.#guessedText,textWithDisclaimer:this.#textWithDisclaimer}}get data(){return{altText:this.#altText,decorative:this.#altTextDecorative}}set data({altText,decorative,
-guessedText,textWithDisclaimer,cancel=!1}){guessedText&&(this.#guessedText=guessedText,this.#textWithDisclaimer=textWithDisclaimer),!(this.#altText===altText&&this.#altTextDecorative===decorative)&&(cancel||(this.#altText=altText,this.#altTextDecorative=decorative),this.#setState())}toggle(enabled=!1){this.#altTextButton&&(!enabled&&this.#altTextTooltipTimeout&&(clearTimeout(this.#altTextTooltipTimeout),this.#altTextTooltipTimeout=null),this.#altTextButton.disabled=!enabled)}shown(){this.#editor.
-_reportTelemetry({action:"pdfjs.image.alt_text.image_status_label_displayed",data:{label:this.#label}})}destroy(){this.#altTextButton?.remove(),this.#altTextButton=null,this.#altTextButtonLabel=null,this.#altTextTooltip=null,this.#badge?.remove(),this.#badge=null}async#setState(){const button=this.#altTextButton;if(!button)return;if(this.#useNewAltTextFlow){if(button.classList.toggle("done",!!this.#altText),button.setAttribute("data-l10n-id",AltText.#l10nNewButton[this.#label]),this.#altTextButtonLabel?.
-setAttribute("data-l10n-id",AltText.#l10nNewButton[`${this.#label}-label`]),!this.#altText){this.#altTextTooltip?.remove();return}}else{if(!this.#altText&&!this.#altTextDecorative){button.classList.remove("done"),this.#altTextTooltip?.remove();return}button.classList.add("done"),button.setAttribute("data-l10n-id","pdfjs-editor-alt-text-edit-button")}let tooltip=this.#altTextTooltip;if(!tooltip){this.#altTextTooltip=tooltip=document.createElement("span"),tooltip.className="tooltip",tooltip.setAttribute(
-"role","tooltip"),tooltip.id=`alt-text-tooltip-${this.#editor.id}`;const DELAY_TO_SHOW_TOOLTIP=100,signal=this.#editor._uiManager._signal;signal.addEventListener("abort",()=>{clearTimeout(this.#altTextTooltipTimeout),this.#altTextTooltipTimeout=null},{once:!0}),button.addEventListener("mouseenter",()=>{this.#altTextTooltipTimeout=setTimeout(()=>{this.#altTextTooltipTimeout=null,this.#altTextTooltip.classList.add("show"),this.#editor._reportTelemetry({action:"alt_text_tooltip"})},DELAY_TO_SHOW_TOOLTIP)},
-{signal}),button.addEventListener("mouseleave",()=>{this.#altTextTooltipTimeout&&(clearTimeout(this.#altTextTooltipTimeout),this.#altTextTooltipTimeout=null),this.#altTextTooltip?.classList.remove("show")},{signal})}this.#altTextDecorative?tooltip.setAttribute("data-l10n-id","pdfjs-editor-alt-text-decorative-tooltip"):(tooltip.removeAttribute("data-l10n-id"),tooltip.textContent=this.#altText),tooltip.parentNode||button.append(tooltip),this.#editor.getElementForAltText()?.setAttribute("aria-descr\
-ibedby",tooltip.id)}}let Comment$1=class{#commentButton=null;#commentWasFromKeyBoard=!1;#editor=null;#initialText=null;#text=null;#date=null;#deleted=!1;constructor(editor){this.#editor=editor,this.toolbar=null}render(){if(!this.#editor._uiManager.hasCommentManager())return null;const comment2=this.#commentButton=document.createElement("button");comment2.className="comment",comment2.tabIndex="0",comment2.setAttribute("data-l10n-id","pdfjs-editor-edit-comment-button");const signal=this.#editor._uiManager.
-_signal;comment2.addEventListener("contextmenu",noContextMenu,{signal}),comment2.addEventListener("pointerdown",event2=>event2.stopPropagation(),{signal});const onClick=event2=>{event2.preventDefault(),this.edit()};return comment2.addEventListener("click",onClick,{capture:!0,signal}),comment2.addEventListener("keydown",event2=>{event2.target===comment2&&event2.key==="Enter"&&(this.#commentWasFromKeyBoard=!0,onClick(event2))},{signal}),comment2}edit(){const{bottom,left,right}=this.#editor.getClientDimensions(),
-position2={top:bottom};this.#editor._uiManager.direction==="ltr"?position2.right=right:position2.left=left,this.#editor._uiManager.editComment(this.#editor,position2)}finish(){this.#commentButton&&(this.#commentButton.focus({focusVisible:this.#commentWasFromKeyBoard}),this.#commentWasFromKeyBoard=!1)}isDeleted(){return this.#deleted||this.#text===""}hasBeenEdited(){return this.isDeleted()||this.#text!==this.#initialText}serialize(){return this.data}get data(){return{text:this.#text,date:this.#date,
-deleted:this.#deleted}}set data(text2){if(text2===null){this.#text="",this.#deleted=!0;return}this.#text=text2,this.#date=new Date,this.#deleted=!1}setInitialText(text2){this.#initialText=text2,this.data=text2}toggle(enabled=!1){this.#commentButton&&(this.#commentButton.disabled=!enabled)}shown(){}destroy(){this.#commentButton?.remove(),this.#commentButton=null,this.#text="",this.#date=null,this.#editor=null,this.#commentWasFromKeyBoard=!1,this.#deleted=!1}};class TouchManager{#container;#isPinching=!1;#isPinchingStopped=null;#isPinchingDisabled;#onPinchStart;#onPinching;#onPinchEnd;#pointerDownAC=null;#signal;#touchInfo=null;#touchManagerAC;#touchMoveAC=null;constructor({
-container:container2,isPinchingDisabled=null,isPinchingStopped=null,onPinchStart=null,onPinching=null,onPinchEnd=null,signal}){this.#container=container2,this.#isPinchingStopped=isPinchingStopped,this.#isPinchingDisabled=isPinchingDisabled,this.#onPinchStart=onPinchStart,this.#onPinching=onPinching,this.#onPinchEnd=onPinchEnd,this.#touchManagerAC=new AbortController,this.#signal=AbortSignal.any([signal,this.#touchManagerAC.signal]),container2.addEventListener("touchstart",this.#onTouchStart.bind(
-this),{passive:!1,signal:this.#signal})}get MIN_TOUCH_DISTANCE_TO_PINCH(){return 35/OutputScale.pixelRatio}#onTouchStart(evt){if(this.#isPinchingDisabled?.())return;if(evt.touches.length===1){if(this.#pointerDownAC)return;const pointerDownAC=this.#pointerDownAC=new AbortController,signal=AbortSignal.any([this.#signal,pointerDownAC.signal]),container2=this.#container,opts={capture:!0,signal,passive:!1},cancelPointerDown=e=>{e.pointerType==="touch"&&(this.#pointerDownAC?.abort(),this.#pointerDownAC=
-null)};container2.addEventListener("pointerdown",e=>{e.pointerType==="touch"&&(stopEvent(e),cancelPointerDown(e))},opts),container2.addEventListener("pointerup",cancelPointerDown,opts),container2.addEventListener("pointercancel",cancelPointerDown,opts);return}if(!this.#touchMoveAC){this.#touchMoveAC=new AbortController;const signal=AbortSignal.any([this.#signal,this.#touchMoveAC.signal]),container2=this.#container,opt={signal,capture:!1,passive:!1};container2.addEventListener("touchmove",this.#onTouchMove.
-bind(this),opt);const onTouchEnd=this.#onTouchEnd.bind(this);container2.addEventListener("touchend",onTouchEnd,opt),container2.addEventListener("touchcancel",onTouchEnd,opt),opt.capture=!0,container2.addEventListener("pointerdown",stopEvent,opt),container2.addEventListener("pointermove",stopEvent,opt),container2.addEventListener("pointercancel",stopEvent,opt),container2.addEventListener("pointerup",stopEvent,opt),this.#onPinchStart?.()}if(stopEvent(evt),evt.touches.length!==2||this.#isPinchingStopped?.()){
-this.#touchInfo=null;return}let[touch0,touch1]=evt.touches;touch0.identifier>touch1.identifier&&([touch0,touch1]=[touch1,touch0]),this.#touchInfo={touch0X:touch0.screenX,touch0Y:touch0.screenY,touch1X:touch1.screenX,touch1Y:touch1.screenY}}#onTouchMove(evt){if(!this.#touchInfo||evt.touches.length!==2)return;stopEvent(evt);let[touch0,touch1]=evt.touches;touch0.identifier>touch1.identifier&&([touch0,touch1]=[touch1,touch0]);const{screenX:screen0X,screenY:screen0Y}=touch0,{screenX:screen1X,screenY:screen1Y}=touch1,
-touchInfo=this.#touchInfo,{touch0X:pTouch0X,touch0Y:pTouch0Y,touch1X:pTouch1X,touch1Y:pTouch1Y}=touchInfo,prevGapX=pTouch1X-pTouch0X,prevGapY=pTouch1Y-pTouch0Y,currGapX=screen1X-screen0X,currGapY=screen1Y-screen0Y,distance=Math.hypot(currGapX,currGapY)||1,pDistance=Math.hypot(prevGapX,prevGapY)||1;if(!this.#isPinching&&Math.abs(pDistance-distance)<=TouchManager.MIN_TOUCH_DISTANCE_TO_PINCH)return;if(touchInfo.touch0X=screen0X,touchInfo.touch0Y=screen0Y,touchInfo.touch1X=screen1X,touchInfo.touch1Y=
-screen1Y,!this.#isPinching){this.#isPinching=!0;return}const origin2=[(screen0X+screen1X)/2,(screen0Y+screen1Y)/2];this.#onPinching?.(origin2,pDistance,distance)}#onTouchEnd(evt){evt.touches.length>=2||(this.#touchMoveAC&&(this.#touchMoveAC.abort(),this.#touchMoveAC=null,this.#onPinchEnd?.()),this.#touchInfo&&(stopEvent(evt),this.#touchInfo=null,this.#isPinching=!1))}destroy(){this.#touchManagerAC?.abort(),this.#touchManagerAC=null,this.#pointerDownAC?.abort(),this.#pointerDownAC=null}}class AnnotationEditor{#accessibilityData=null;#allResizerDivs=null;#altText=null;#comment=null;#disabled=!1;#dragPointerId=null;#dragPointerType="";#keepAspectRatio=!1;#resizersDiv=null;#lastPointerCoords=null;#savedDimensions=null;#focusAC=null;#focusedResizerName="";#hasBeenClicked=!1;#initialRect=null;#isEditing=!1;#isInEditMode=!1;#isResizerEnabledForKeyboard=!1;#moveInDOMTimeout=null;#prevDragX=0;#prevDragY=0;#telemetryTimeouts=null;#touchManager=null;isSelected=!1;_isCopy=!1;_editToolbar=null;_initialOptions=Object.
-create(null);_initialData=null;_isVisible=!0;_uiManager=null;_focusEventsAllowed=!0;static _l10n=null;static _l10nResizer=null;#isDraggable=!1;#zIndex=AnnotationEditor._zIndex++;static _borderLineWidth=-1;static _colorManager=new ColorManager;static _zIndex=1;static _telemetryTimeout=1e3;static get _resizerKeyboardManager(){const resize=AnnotationEditor.prototype._resizeWithKeyboard,small=AnnotationEditorUIManager.TRANSLATE_SMALL,big=AnnotationEditorUIManager.TRANSLATE_BIG;return shadow(this,"_r\
-esizerKeyboardManager",new KeyboardManager([[["ArrowLeft","mac+ArrowLeft"],resize,{args:[-small,0]}],[["ctrl+ArrowLeft","mac+shift+ArrowLeft"],resize,{args:[-big,0]}],[["ArrowRight","mac+ArrowRight"],resize,{args:[small,0]}],[["ctrl+ArrowRight","mac+shift+ArrowRight"],resize,{args:[big,0]}],[["ArrowUp","mac+ArrowUp"],resize,{args:[0,-small]}],[["ctrl+ArrowUp","mac+shift+ArrowUp"],resize,{args:[0,-big]}],[["ArrowDown","mac+ArrowDown"],resize,{args:[0,small]}],[["ctrl+ArrowDown","mac+shift+ArrowDo\
-wn"],resize,{args:[0,big]}],[["Escape","mac+Escape"],AnnotationEditor.prototype._stopResizingWithKeyboard]]))}constructor(parameters){this.parent=parameters.parent,this.id=parameters.id,this.width=this.height=null,this.pageIndex=parameters.parent.pageIndex,this.name=parameters.name,this.div=null,this._uiManager=parameters.uiManager,this.annotationElementId=null,this._willKeepAspectRatio=!1,this._initialOptions.isCentered=parameters.isCentered,this._structTreeParentId=null,this.annotationElementId=
-parameters.annotationElementId||null;const{rotation,rawDims:{pageWidth,pageHeight,pageX,pageY}}=this.parent.viewport;this.rotation=rotation,this.pageRotation=(360+rotation-this._uiManager.viewParameters.rotation)%360,this.pageDimensions=[pageWidth,pageHeight],this.pageTranslation=[pageX,pageY];const[width,height]=this.parentDimensions;this.x=parameters.x/width,this.y=parameters.y/height,this.isAttachedToDOM=!1,this.deleted=!1}get editorType(){return Object.getPrototypeOf(this).constructor._type}get mode(){
-return Object.getPrototypeOf(this).constructor._editorType}static get isDrawer(){return!1}static get _defaultLineColor(){return shadow(this,"_defaultLineColor",this._colorManager.getHexCode("CanvasText"))}static deleteAnnotationElement(editor){const fakeEditor=new FakeEditor({id:editor.parent.getNextId(),parent:editor.parent,uiManager:editor._uiManager});fakeEditor.annotationElementId=editor.annotationElementId,fakeEditor.deleted=!0,fakeEditor._uiManager.addToAnnotationStorage(fakeEditor)}static initialize(l10n,_uiManager){
-if(AnnotationEditor._l10n??=l10n,AnnotationEditor._l10nResizer||=Object.freeze({topLeft:"pdfjs-editor-resizer-top-left",topMiddle:"pdfjs-editor-resizer-top-middle",topRight:"pdfjs-editor-resizer-top-right",middleRight:"pdfjs-editor-resizer-middle-right",bottomRight:"pdfjs-editor-resizer-bottom-right",bottomMiddle:"pdfjs-editor-resizer-bottom-middle",bottomLeft:"pdfjs-editor-resizer-bottom-left",middleLeft:"pdfjs-editor-resizer-middle-left"}),AnnotationEditor._borderLineWidth!==-1)return;const style2=getComputedStyle(
-document.documentElement);AnnotationEditor._borderLineWidth=parseFloat(style2.getPropertyValue("--outline-width"))||0}static updateDefaultParams(_type,_value){}static get defaultPropertiesToUpdate(){return[]}static isHandlingMimeForPasting(mime){return!1}static paste(item,parent){unreachable("Not implemented")}get propertiesToUpdate(){return[]}get _isDraggable(){return this.#isDraggable}set _isDraggable(value){this.#isDraggable=value,this.div?.classList.toggle("draggable",value)}get isEnterHandled(){
-return!0}center(){const[pageWidth,pageHeight]=this.pageDimensions;switch(this.parentRotation){case 90:this.x-=this.height*pageHeight/(pageWidth*2),this.y+=this.width*pageWidth/(pageHeight*2);break;case 180:this.x+=this.width/2,this.y+=this.height/2;break;case 270:this.x+=this.height*pageHeight/(pageWidth*2),this.y-=this.width*pageWidth/(pageHeight*2);break;default:this.x-=this.width/2,this.y-=this.height/2;break}this.fixAndSetPosition()}addCommands(params){this._uiManager.addCommands(params)}get currentLayer(){
-return this._uiManager.currentLayer}setInBackground(){this.div.style.zIndex=0}setInForeground(){this.div.style.zIndex=this.#zIndex}setParent(parent){parent!==null?(this.pageIndex=parent.pageIndex,this.pageDimensions=parent.pageDimensions):this.#stopResizing(),this.parent=parent}focusin(event2){this._focusEventsAllowed&&(this.#hasBeenClicked?this.#hasBeenClicked=!1:this.parent.setSelected(this))}focusout(event2){!this._focusEventsAllowed||!this.isAttachedToDOM||event2.relatedTarget?.closest(`#${this.
-id}`)||(event2.preventDefault(),this.parent?.isMultipleSelection||this.commitOrRemove())}commitOrRemove(){this.isEmpty()?this.remove():this.commit()}commit(){this.isInEditMode()&&this.addToAnnotationStorage()}addToAnnotationStorage(){this._uiManager.addToAnnotationStorage(this)}setAt(x,y,tx,ty){const[width,height]=this.parentDimensions;[tx,ty]=this.screenToPageTranslation(tx,ty),this.x=(x+tx)/width,this.y=(y+ty)/height,this.fixAndSetPosition()}_moveAfterPaste(baseX,baseY){const[parentWidth,parentHeight]=this.
-parentDimensions;this.setAt(baseX*parentWidth,baseY*parentHeight,this.width*parentWidth,this.height*parentHeight),this._onTranslated()}#translate([width,height],x,y){[x,y]=this.screenToPageTranslation(x,y),this.x+=x/width,this.y+=y/height,this._onTranslating(this.x,this.y),this.fixAndSetPosition()}translate(x,y){this.#translate(this.parentDimensions,x,y)}translateInPage(x,y){this.#initialRect||=[this.x,this.y,this.width,this.height],this.#translate(this.pageDimensions,x,y),this.div.scrollIntoView(
-{block:"nearest"})}translationDone(){this._onTranslated(this.x,this.y)}drag(tx,ty){this.#initialRect||=[this.x,this.y,this.width,this.height];const{div,parentDimensions:[parentWidth,parentHeight]}=this;if(this.x+=tx/parentWidth,this.y+=ty/parentHeight,this.parent&&(this.x<0||this.x>1||this.y<0||this.y>1)){const{x:x2,y:y2}=this.div.getBoundingClientRect();this.parent.findNewParent(this,x2,y2)&&(this.x-=Math.floor(this.x),this.y-=Math.floor(this.y))}let{x,y}=this;const[bx,by]=this.getBaseTranslation();
-x+=bx,y+=by;const{style:style2}=div;style2.left=`${(100*x).toFixed(2)}%`,style2.top=`${(100*y).toFixed(2)}%`,this._onTranslating(x,y),div.scrollIntoView({block:"nearest"})}_onTranslating(x,y){}_onTranslated(x,y){}get _hasBeenMoved(){return!!this.#initialRect&&(this.#initialRect[0]!==this.x||this.#initialRect[1]!==this.y)}get _hasBeenResized(){return!!this.#initialRect&&(this.#initialRect[2]!==this.width||this.#initialRect[3]!==this.height)}getBaseTranslation(){const[parentWidth,parentHeight]=this.
-parentDimensions,{_borderLineWidth}=AnnotationEditor,x=_borderLineWidth/parentWidth,y=_borderLineWidth/parentHeight;switch(this.rotation){case 90:return[-x,y];case 180:return[x,y];case 270:return[x,-y];default:return[-x,-y]}}get _mustFixPosition(){return!0}fixAndSetPosition(rotation=this.rotation){const{div:{style:style2},pageDimensions:[pageWidth,pageHeight]}=this;let{x,y,width,height}=this;if(width*=pageWidth,height*=pageHeight,x*=pageWidth,y*=pageHeight,this._mustFixPosition)switch(rotation){case 0:
-x=MathClamp(x,0,pageWidth-width),y=MathClamp(y,0,pageHeight-height);break;case 90:x=MathClamp(x,0,pageWidth-height),y=MathClamp(y,width,pageHeight);break;case 180:x=MathClamp(x,width,pageWidth),y=MathClamp(y,height,pageHeight);break;case 270:x=MathClamp(x,height,pageWidth),y=MathClamp(y,0,pageHeight-width);break}this.x=x/=pageWidth,this.y=y/=pageHeight;const[bx,by]=this.getBaseTranslation();x+=bx,y+=by,style2.left=`${(100*x).toFixed(2)}%`,style2.top=`${(100*y).toFixed(2)}%`,this.moveInDOM()}static#rotatePoint(x,y,angle){
-switch(angle){case 90:return[y,-x];case 180:return[-x,-y];case 270:return[-y,x];default:return[x,y]}}screenToPageTranslation(x,y){return AnnotationEditor.#rotatePoint(x,y,this.parentRotation)}pageTranslationToScreen(x,y){return AnnotationEditor.#rotatePoint(x,y,360-this.parentRotation)}#getRotationMatrix(rotation){switch(rotation){case 90:{const[pageWidth,pageHeight]=this.pageDimensions;return[0,-pageWidth/pageHeight,pageHeight/pageWidth,0]}case 180:return[-1,0,0,-1];case 270:{const[pageWidth,pageHeight]=this.
-pageDimensions;return[0,pageWidth/pageHeight,-pageHeight/pageWidth,0]}default:return[1,0,0,1]}}get parentScale(){return this._uiManager.viewParameters.realScale}get parentRotation(){return(this._uiManager.viewParameters.rotation+this.pageRotation)%360}get parentDimensions(){const{parentScale,pageDimensions:[pageWidth,pageHeight]}=this;return[pageWidth*parentScale,pageHeight*parentScale]}setDims(width,height){const[parentWidth,parentHeight]=this.parentDimensions,{style:style2}=this.div;style2.width=
-`${(100*width/parentWidth).toFixed(2)}%`,this.#keepAspectRatio||(style2.height=`${(100*height/parentHeight).toFixed(2)}%`)}fixDims(){const{style:style2}=this.div,{height,width}=style2,widthPercent=width.endsWith("%"),heightPercent=!this.#keepAspectRatio&&height.endsWith("%");if(widthPercent&&heightPercent)return;const[parentWidth,parentHeight]=this.parentDimensions;widthPercent||(style2.width=`${(100*parseFloat(width)/parentWidth).toFixed(2)}%`),!this.#keepAspectRatio&&!heightPercent&&(style2.height=
-`${(100*parseFloat(height)/parentHeight).toFixed(2)}%`)}getInitialTranslation(){return[0,0]}#createResizers(){if(this.#resizersDiv)return;this.#resizersDiv=document.createElement("div"),this.#resizersDiv.classList.add("resizers");const classes=this._willKeepAspectRatio?["topLeft","topRight","bottomRight","bottomLeft"]:["topLeft","topMiddle","topRight","middleRight","bottomRight","bottomMiddle","bottomLeft","middleLeft"],signal=this._uiManager._signal;for(const name of classes){const div=document.
-createElement("div");this.#resizersDiv.append(div),div.classList.add("resizer",name),div.setAttribute("data-resizer-name",name),div.addEventListener("pointerdown",this.#resizerPointerdown.bind(this,name),{signal}),div.addEventListener("contextmenu",noContextMenu,{signal}),div.tabIndex=-1}this.div.prepend(this.#resizersDiv)}#resizerPointerdown(name,event2){event2.preventDefault();const{isMac}=util_FeatureTest.platform;if(event2.button!==0||event2.ctrlKey&&isMac)return;this.#altText?.toggle(!1);const savedDraggable=this.
-_isDraggable;this._isDraggable=!1,this.#lastPointerCoords=[event2.screenX,event2.screenY];const ac=new AbortController,signal=this._uiManager.combinedSignal(ac);this.parent.togglePointerEvents(!1),window.addEventListener("pointermove",this.#resizerPointermove.bind(this,name),{passive:!0,capture:!0,signal}),window.addEventListener("touchmove",stopEvent,{passive:!1,signal}),window.addEventListener("contextmenu",noContextMenu,{signal}),this.#savedDimensions={savedX:this.x,savedY:this.y,savedWidth:this.
-width,savedHeight:this.height};const savedParentCursor=this.parent.div.style.cursor,savedCursor=this.div.style.cursor;this.div.style.cursor=this.parent.div.style.cursor=window.getComputedStyle(event2.target).cursor;const pointerUpCallback=()=>{ac.abort(),this.parent.togglePointerEvents(!0),this.#altText?.toggle(!0),this._isDraggable=savedDraggable,this.parent.div.style.cursor=savedParentCursor,this.div.style.cursor=savedCursor,this.#addResizeToUndoStack()};window.addEventListener("pointerup",pointerUpCallback,
-{signal}),window.addEventListener("blur",pointerUpCallback,{signal})}#resize(x,y,width,height){this.width=width,this.height=height,this.x=x,this.y=y;const[parentWidth,parentHeight]=this.parentDimensions;this.setDims(parentWidth*width,parentHeight*height),this.fixAndSetPosition(),this._onResized()}_onResized(){}#addResizeToUndoStack(){if(!this.#savedDimensions)return;const{savedX,savedY,savedWidth,savedHeight}=this.#savedDimensions;this.#savedDimensions=null;const newX=this.x,newY=this.y,newWidth=this.
-width,newHeight=this.height;newX===savedX&&newY===savedY&&newWidth===savedWidth&&newHeight===savedHeight||this.addCommands({cmd:this.#resize.bind(this,newX,newY,newWidth,newHeight),undo:this.#resize.bind(this,savedX,savedY,savedWidth,savedHeight),mustExec:!0})}static _round(x){return Math.round(x*1e4)/1e4}#resizerPointermove(name,event2){const[parentWidth,parentHeight]=this.parentDimensions,savedX=this.x,savedY=this.y,savedWidth=this.width,savedHeight=this.height,minWidth=AnnotationEditor.MIN_SIZE/
-parentWidth,minHeight=AnnotationEditor.MIN_SIZE/parentHeight,rotationMatrix=this.#getRotationMatrix(this.rotation),transf=(x,y)=>[rotationMatrix[0]*x+rotationMatrix[2]*y,rotationMatrix[1]*x+rotationMatrix[3]*y],invRotationMatrix=this.#getRotationMatrix(360-this.rotation),invTransf=(x,y)=>[invRotationMatrix[0]*x+invRotationMatrix[2]*y,invRotationMatrix[1]*x+invRotationMatrix[3]*y];let getPoint2,getOpposite,isDiagonal=!1,isHorizontal=!1;switch(name){case"topLeft":isDiagonal=!0,getPoint2=(w,h2)=>[0,
-0],getOpposite=(w,h2)=>[w,h2];break;case"topMiddle":getPoint2=(w,h2)=>[w/2,0],getOpposite=(w,h2)=>[w/2,h2];break;case"topRight":isDiagonal=!0,getPoint2=(w,h2)=>[w,0],getOpposite=(w,h2)=>[0,h2];break;case"middleRight":isHorizontal=!0,getPoint2=(w,h2)=>[w,h2/2],getOpposite=(w,h2)=>[0,h2/2];break;case"bottomRight":isDiagonal=!0,getPoint2=(w,h2)=>[w,h2],getOpposite=(w,h2)=>[0,0];break;case"bottomMiddle":getPoint2=(w,h2)=>[w/2,h2],getOpposite=(w,h2)=>[w/2,0];break;case"bottomLeft":isDiagonal=!0,getPoint2=
-(w,h2)=>[0,h2],getOpposite=(w,h2)=>[w,0];break;case"middleLeft":isHorizontal=!0,getPoint2=(w,h2)=>[0,h2/2],getOpposite=(w,h2)=>[w,h2/2];break}const point2=getPoint2(savedWidth,savedHeight),oppositePoint=getOpposite(savedWidth,savedHeight);let transfOppositePoint=transf(...oppositePoint);const oppositeX=AnnotationEditor._round(savedX+transfOppositePoint[0]),oppositeY=AnnotationEditor._round(savedY+transfOppositePoint[1]);let ratioX=1,ratioY=1,deltaX,deltaY;if(event2.fromKeyboard)({deltaX,deltaY}=
-event2);else{const{screenX,screenY}=event2,[lastScreenX,lastScreenY]=this.#lastPointerCoords;[deltaX,deltaY]=this.screenToPageTranslation(screenX-lastScreenX,screenY-lastScreenY),this.#lastPointerCoords[0]=screenX,this.#lastPointerCoords[1]=screenY}if([deltaX,deltaY]=invTransf(deltaX/parentWidth,deltaY/parentHeight),isDiagonal){const oldDiag=Math.hypot(savedWidth,savedHeight);ratioX=ratioY=Math.max(Math.min(Math.hypot(oppositePoint[0]-point2[0]-deltaX,oppositePoint[1]-point2[1]-deltaY)/oldDiag,1/
-savedWidth,1/savedHeight),minWidth/savedWidth,minHeight/savedHeight)}else isHorizontal?ratioX=MathClamp(Math.abs(oppositePoint[0]-point2[0]-deltaX),minWidth,1)/savedWidth:ratioY=MathClamp(Math.abs(oppositePoint[1]-point2[1]-deltaY),minHeight,1)/savedHeight;const newWidth=AnnotationEditor._round(savedWidth*ratioX),newHeight=AnnotationEditor._round(savedHeight*ratioY);transfOppositePoint=transf(...getOpposite(newWidth,newHeight));const newX=oppositeX-transfOppositePoint[0],newY=oppositeY-transfOppositePoint[1];
-this.#initialRect||=[this.x,this.y,this.width,this.height],this.width=newWidth,this.height=newHeight,this.x=newX,this.y=newY,this.setDims(parentWidth*newWidth,parentHeight*newHeight),this.fixAndSetPosition(),this._onResizing()}_onResizing(){}altTextFinish(){this.#altText?.finish()}get toolbarButtons(){return null}async addEditToolbar(){if(this._editToolbar||this.#isInEditMode)return this._editToolbar;this._editToolbar=new EditorToolbar(this),this.div.append(this._editToolbar.render()),this._editToolbar.
-addButton("comment",this.addCommentButton());const{toolbarButtons}=this;if(toolbarButtons)for(const[name,tool]of toolbarButtons)await this._editToolbar.addButton(name,tool);return this._editToolbar.addButton("delete"),this._editToolbar}removeEditToolbar(){this._editToolbar&&(this._editToolbar.remove(),this._editToolbar=null,this.#altText?.destroy())}addContainer(container2){const editToolbarDiv=this._editToolbar?.div;editToolbarDiv?editToolbarDiv.before(container2):this.div.append(container2)}getClientDimensions(){
-return this.div.getBoundingClientRect()}createAltText(){return this.#altText||(AltText.initialize(AnnotationEditor._l10n),this.#altText=new AltText(this),this.#accessibilityData&&(this.#altText.data=this.#accessibilityData,this.#accessibilityData=null)),this.#altText}get altTextData(){return this.#altText?.data}set altTextData(data){this.#altText&&(this.#altText.data=data)}get guessedAltText(){return this.#altText?.guessedText}async setGuessedAltText(text2){await this.#altText?.setGuessedText(text2)}serializeAltText(isForCopying){
-return this.#altText?.serialize(isForCopying)}hasAltText(){return!!this.#altText&&!this.#altText.isEmpty()}hasAltTextData(){return this.#altText?.hasData()??!1}addCommentButton(){return this.#comment?this.#comment:this.#comment=new Comment$1(this)}get commentColor(){return null}get comment(){const comment2=this.#comment;return{text:comment2.data.text,date:comment2.data.date,deleted:comment2.isDeleted(),color:this.commentColor}}set comment(text2){this.#comment||(this.#comment=new Comment$1(this)),
-this.#comment.data=text2}setCommentData(text2){this.#comment||(this.#comment=new Comment$1(this)),this.#comment.setInitialText(text2)}get hasEditedComment(){return this.#comment?.hasBeenEdited()}async editComment(){this.#comment||(this.#comment=new Comment$1(this)),this.#comment.edit()}addComment(serialized){this.hasEditedComment&&(serialized.popup={contents:this.comment.text,deleted:this.comment.deleted})}render(){const div=this.div=document.createElement("div");div.setAttribute("data-editor-ro\
-tation",(360-this.rotation)%360),div.className=this.name,div.setAttribute("id",this.id),div.tabIndex=this.#disabled?-1:0,div.setAttribute("role","application"),this.defaultL10nId&&div.setAttribute("data-l10n-id",this.defaultL10nId),this._isVisible||div.classList.add("hidden"),this.setInForeground(),this.#addFocusListeners();const[parentWidth,parentHeight]=this.parentDimensions;this.parentRotation%180!==0&&(div.style.maxWidth=`${(100*parentHeight/parentWidth).toFixed(2)}%`,div.style.maxHeight=`${(100*
-parentWidth/parentHeight).toFixed(2)}%`);const[tx,ty]=this.getInitialTranslation();return this.translate(tx,ty),bindEvents(this,div,["keydown","pointerdown","dblclick"]),this.isResizable&&this._uiManager._supportsPinchToZoom&&(this.#touchManager||=new TouchManager({container:div,isPinchingDisabled:()=>!this.isSelected,onPinchStart:this.#touchPinchStartCallback.bind(this),onPinching:this.#touchPinchCallback.bind(this),onPinchEnd:this.#touchPinchEndCallback.bind(this),signal:this._uiManager._signal})),
-this._uiManager._editorUndoBar?.hide(),div}#touchPinchStartCallback(){this.#savedDimensions={savedX:this.x,savedY:this.y,savedWidth:this.width,savedHeight:this.height},this.#altText?.toggle(!1),this.parent.togglePointerEvents(!1)}#touchPinchCallback(_origin,prevDistance,distance){let factor=.7*(distance/prevDistance)+1-.7;if(factor===1)return;const rotationMatrix=this.#getRotationMatrix(this.rotation),transf=(x,y)=>[rotationMatrix[0]*x+rotationMatrix[2]*y,rotationMatrix[1]*x+rotationMatrix[3]*y],
-[parentWidth,parentHeight]=this.parentDimensions,savedX=this.x,savedY=this.y,savedWidth=this.width,savedHeight=this.height,minWidth=AnnotationEditor.MIN_SIZE/parentWidth,minHeight=AnnotationEditor.MIN_SIZE/parentHeight;factor=Math.max(Math.min(factor,1/savedWidth,1/savedHeight),minWidth/savedWidth,minHeight/savedHeight);const newWidth=AnnotationEditor._round(savedWidth*factor),newHeight=AnnotationEditor._round(savedHeight*factor);if(newWidth===savedWidth&&newHeight===savedHeight)return;this.#initialRect||=
-[savedX,savedY,savedWidth,savedHeight];const transfCenterPoint=transf(savedWidth/2,savedHeight/2),centerX=AnnotationEditor._round(savedX+transfCenterPoint[0]),centerY=AnnotationEditor._round(savedY+transfCenterPoint[1]),newTransfCenterPoint=transf(newWidth/2,newHeight/2);this.x=centerX-newTransfCenterPoint[0],this.y=centerY-newTransfCenterPoint[1],this.width=newWidth,this.height=newHeight,this.setDims(parentWidth*newWidth,parentHeight*newHeight),this.fixAndSetPosition(),this._onResizing()}#touchPinchEndCallback(){
-this.#altText?.toggle(!0),this.parent.togglePointerEvents(!0),this.#addResizeToUndoStack()}pointerdown(event2){const{isMac}=util_FeatureTest.platform;if(event2.button!==0||event2.ctrlKey&&isMac){event2.preventDefault();return}if(this.#hasBeenClicked=!0,this._isDraggable){this.#setUpDragSession(event2);return}this.#selectOnPointerEvent(event2)}#selectOnPointerEvent(event2){const{isMac}=util_FeatureTest.platform;event2.ctrlKey&&!isMac||event2.shiftKey||event2.metaKey&&isMac?this.parent.toggleSelected(
-this):this.parent.setSelected(this)}#setUpDragSession(event2){const{isSelected}=this;this._uiManager.setUpDragSession();let hasDraggingStarted=!1;const ac=new AbortController,signal=this._uiManager.combinedSignal(ac),opts={capture:!0,passive:!1,signal},cancelDrag=e=>{ac.abort(),this.#dragPointerId=null,this.#hasBeenClicked=!1,this._uiManager.endDragSession()||this.#selectOnPointerEvent(e),hasDraggingStarted&&this._onStopDragging()};isSelected&&(this.#prevDragX=event2.clientX,this.#prevDragY=event2.
-clientY,this.#dragPointerId=event2.pointerId,this.#dragPointerType=event2.pointerType,window.addEventListener("pointermove",e=>{hasDraggingStarted||(hasDraggingStarted=!0,this._onStartDragging());const{clientX:x,clientY:y,pointerId}=e;if(pointerId!==this.#dragPointerId){stopEvent(e);return}const[tx,ty]=this.screenToPageTranslation(x-this.#prevDragX,y-this.#prevDragY);this.#prevDragX=x,this.#prevDragY=y,this._uiManager.dragSelectedEditors(tx,ty)},opts),window.addEventListener("touchmove",stopEvent,
-opts),window.addEventListener("pointerdown",e=>{e.pointerType===this.#dragPointerType&&(this.#touchManager||e.isPrimary)&&cancelDrag(e),stopEvent(e)},opts));const pointerUpCallback=e=>{if(!this.#dragPointerId||this.#dragPointerId===e.pointerId){cancelDrag(e);return}stopEvent(e)};window.addEventListener("pointerup",pointerUpCallback,{signal}),window.addEventListener("blur",pointerUpCallback,{signal})}_onStartDragging(){}_onStopDragging(){}moveInDOM(){this.#moveInDOMTimeout&&clearTimeout(this.#moveInDOMTimeout),
-this.#moveInDOMTimeout=setTimeout(()=>{this.#moveInDOMTimeout=null,this.parent?.moveEditorInDOM(this)},0)}_setParentAndPosition(parent,x,y){parent.changeParent(this),this.x=x,this.y=y,this.fixAndSetPosition(),this._onTranslated()}getRect(tx,ty,rotation=this.rotation){const scale2=this.parentScale,[pageWidth,pageHeight]=this.pageDimensions,[pageX,pageY]=this.pageTranslation,shiftX=tx/scale2,shiftY=ty/scale2,x=this.x*pageWidth,y=this.y*pageHeight,width=this.width*pageWidth,height=this.height*pageHeight;
-switch(rotation){case 0:return[x+shiftX+pageX,pageHeight-y-shiftY-height+pageY,x+shiftX+width+pageX,pageHeight-y-shiftY+pageY];case 90:return[x+shiftY+pageX,pageHeight-y+shiftX+pageY,x+shiftY+height+pageX,pageHeight-y+shiftX+width+pageY];case 180:return[x-shiftX-width+pageX,pageHeight-y+shiftY+pageY,x-shiftX+pageX,pageHeight-y+shiftY+height+pageY];case 270:return[x-shiftY-height+pageX,pageHeight-y-shiftX-width+pageY,x-shiftY+pageX,pageHeight-y-shiftX+pageY];default:throw new Error("Invalid rotat\
-ion")}}getRectInCurrentCoords(rect,pageHeight){const[x1,y1,x2,y2]=rect,width=x2-x1,height=y2-y1;switch(this.rotation){case 0:return[x1,pageHeight-y2,width,height];case 90:return[x1,pageHeight-y1,height,width];case 180:return[x2,pageHeight-y1,width,height];case 270:return[x2,pageHeight-y2,height,width];default:throw new Error("Invalid rotation")}}onceAdded(focus2){}isEmpty(){return!1}enableEditMode(){return this.isInEditMode()?!1:(this.parent.setEditingState(!1),this.#isInEditMode=!0,!0)}disableEditMode(){
-return this.isInEditMode()?(this.parent.setEditingState(!0),this.#isInEditMode=!1,!0):!1}isInEditMode(){return this.#isInEditMode}shouldGetKeyboardEvents(){return this.#isResizerEnabledForKeyboard}needsToBeRebuilt(){return this.div&&!this.isAttachedToDOM}get isOnScreen(){const{top,left,bottom,right}=this.getClientDimensions(),{innerHeight,innerWidth}=window;return left<innerWidth&&right>0&&top<innerHeight&&bottom>0}#addFocusListeners(){if(this.#focusAC||!this.div)return;this.#focusAC=new AbortController;
-const signal=this._uiManager.combinedSignal(this.#focusAC);this.div.addEventListener("focusin",this.focusin.bind(this),{signal}),this.div.addEventListener("focusout",this.focusout.bind(this),{signal})}rebuild(){this.#addFocusListeners()}rotate(_angle){}resize(){}serializeDeleted(){return{id:this.annotationElementId,deleted:!0,pageIndex:this.pageIndex,popupRef:this._initialData?.popupRef||""}}serialize(isForCopying=!1,context=null){unreachable("An editor must be serializable")}static async deserialize(data,parent,uiManager){
-const editor=new this.prototype.constructor({parent,id:parent.getNextId(),uiManager,annotationElementId:data.annotationElementId});editor.rotation=data.rotation,editor.#accessibilityData=data.accessibilityData,editor._isCopy=data.isCopy||!1;const[pageWidth,pageHeight]=editor.pageDimensions,[x,y,width,height]=editor.getRectInCurrentCoords(data.rect,pageHeight);return editor.x=x/pageWidth,editor.y=y/pageHeight,editor.width=width/pageWidth,editor.height=height/pageHeight,editor}get hasBeenModified(){
-return!!this.annotationElementId&&(this.deleted||this.serialize()!==null)}remove(){if(this.#focusAC?.abort(),this.#focusAC=null,this.isEmpty()||this.commit(),this.parent?this.parent.remove(this):this._uiManager.removeEditor(this),this.#moveInDOMTimeout&&(clearTimeout(this.#moveInDOMTimeout),this.#moveInDOMTimeout=null),this.#stopResizing(),this.removeEditToolbar(),this.#telemetryTimeouts){for(const timeout of this.#telemetryTimeouts.values())clearTimeout(timeout);this.#telemetryTimeouts=null}this.
-parent=null,this.#touchManager?.destroy(),this.#touchManager=null}get isResizable(){return!1}makeResizable(){this.isResizable&&(this.#createResizers(),this.#resizersDiv.classList.remove("hidden"))}get toolbarPosition(){return null}keydown(event2){if(!this.isResizable||event2.target!==this.div||event2.key!=="Enter")return;this._uiManager.setSelected(this),this.#savedDimensions={savedX:this.x,savedY:this.y,savedWidth:this.width,savedHeight:this.height};const children=this.#resizersDiv.children;if(!this.#allResizerDivs){
-this.#allResizerDivs=Array.from(children);const boundResizerKeydown=this.#resizerKeydown.bind(this),boundResizerBlur=this.#resizerBlur.bind(this),signal=this._uiManager._signal;for(const div of this.#allResizerDivs){const name=div.getAttribute("data-resizer-name");div.setAttribute("role","spinbutton"),div.addEventListener("keydown",boundResizerKeydown,{signal}),div.addEventListener("blur",boundResizerBlur,{signal}),div.addEventListener("focus",this.#resizerFocus.bind(this,name),{signal}),div.setAttribute(
-"data-l10n-id",AnnotationEditor._l10nResizer[name])}}const first=this.#allResizerDivs[0];let firstPosition=0;for(const div of children){if(div===first)break;firstPosition++}const nextFirstPosition=(360-this.rotation+this.parentRotation)%360/90*(this.#allResizerDivs.length/4);if(nextFirstPosition!==firstPosition){if(nextFirstPosition<firstPosition)for(let i2=0;i2<firstPosition-nextFirstPosition;i2++)this.#resizersDiv.append(this.#resizersDiv.firstChild);else if(nextFirstPosition>firstPosition)for(let i2=0;i2<
-nextFirstPosition-firstPosition;i2++)this.#resizersDiv.firstChild.before(this.#resizersDiv.lastChild);let i=0;for(const child2 of children){const name=this.#allResizerDivs[i++].getAttribute("data-resizer-name");child2.setAttribute("data-l10n-id",AnnotationEditor._l10nResizer[name])}}this.#setResizerTabIndex(0),this.#isResizerEnabledForKeyboard=!0,this.#resizersDiv.firstChild.focus({focusVisible:!0}),event2.preventDefault(),event2.stopImmediatePropagation()}#resizerKeydown(event2){AnnotationEditor.
-_resizerKeyboardManager.exec(this,event2)}#resizerBlur(event2){this.#isResizerEnabledForKeyboard&&event2.relatedTarget?.parentNode!==this.#resizersDiv&&this.#stopResizing()}#resizerFocus(name){this.#focusedResizerName=this.#isResizerEnabledForKeyboard?name:""}#setResizerTabIndex(value){if(this.#allResizerDivs)for(const div of this.#allResizerDivs)div.tabIndex=value}_resizeWithKeyboard(x,y){this.#isResizerEnabledForKeyboard&&this.#resizerPointermove(this.#focusedResizerName,{deltaX:x,deltaY:y,fromKeyboard:!0})}#stopResizing(){
-this.#isResizerEnabledForKeyboard=!1,this.#setResizerTabIndex(-1),this.#addResizeToUndoStack()}_stopResizingWithKeyboard(){this.#stopResizing(),this.div.focus()}select(){if(!(this.isSelected&&this._editToolbar)){if(this.isSelected=!0,this.makeResizable(),this.div?.classList.add("selectedEditor"),!this._editToolbar){this.addEditToolbar().then(()=>{this.div?.classList.contains("selectedEditor")&&this._editToolbar?.show()});return}this._editToolbar?.show(),this.#altText?.toggleAltTextBadge(!1)}}unselect(){
-this.isSelected&&(this.isSelected=!1,this.#resizersDiv?.classList.add("hidden"),this.div?.classList.remove("selectedEditor"),this.div?.contains(document.activeElement)&&this._uiManager.currentLayer.div.focus({preventScroll:!0}),this._editToolbar?.hide(),this.#altText?.toggleAltTextBadge(!0))}updateParams(type2,value){}disableEditing(){}enableEditing(){}get canChangeContent(){return!1}enterInEditMode(){this.canChangeContent&&(this.enableEditMode(),this.div.focus())}dblclick(event2){this.enterInEditMode(),
-this.parent.updateToolbar({mode:this.constructor._editorType,editId:this.id})}getElementForAltText(){return this.div}get contentDiv(){return this.div}get isEditing(){return this.#isEditing}set isEditing(value){this.#isEditing=value,this.parent&&(value?(this.parent.setSelected(this),this.parent.setActiveEditor(this)):this.parent.setActiveEditor(null))}setAspectRatio(width,height){this.#keepAspectRatio=!0;const aspectRatio=width/height,{style:style2}=this.div;style2.aspectRatio=aspectRatio,style2.
-height="auto"}static get MIN_SIZE(){return 16}static canCreateNewEmptyEditor(){return!0}get telemetryInitialData(){return{action:"added"}}get telemetryFinalData(){return null}_reportTelemetry(data,mustWait=!1){if(mustWait){this.#telemetryTimeouts||=new Map;const{action:action2}=data;let timeout=this.#telemetryTimeouts.get(action2);timeout&&clearTimeout(timeout),timeout=setTimeout(()=>{this._reportTelemetry(data),this.#telemetryTimeouts.delete(action2),this.#telemetryTimeouts.size===0&&(this.#telemetryTimeouts=
-null)},AnnotationEditor._telemetryTimeout),this.#telemetryTimeouts.set(action2,timeout);return}data.type||=this.editorType,this._uiManager._eventBus.dispatch("reporttelemetry",{source:this,details:{type:"editing",data}})}show(visible=this._isVisible){this.div.classList.toggle("hidden",!visible),this._isVisible=visible}enable(){this.div&&(this.div.tabIndex=0),this.#disabled=!1}disable(){this.div&&(this.div.tabIndex=-1),this.#disabled=!0}renderAnnotationElement(annotation){let content2=annotation.
-container.querySelector(".annotationContent");if(!content2)content2=document.createElement("div"),content2.classList.add("annotationContent",this.editorType),annotation.container.prepend(content2);else if(content2.nodeName==="CANVAS"){const canvas=content2;content2=document.createElement("div"),content2.classList.add("annotationContent",this.editorType),canvas.before(content2)}return content2}resetAnnotationElement(annotation){const{firstChild}=annotation.container;firstChild?.nodeName==="DIV"&&
-firstChild.classList.contains("annotationContent")&&firstChild.remove()}}class FakeEditor extends AnnotationEditor{constructor(params){super(params),this.annotationElementId=params.annotationElementId,this.deleted=!0}serialize(){return this.serializeDeleted()}}const SEED=3285377520,MASK_HIGH=4294901760,MASK_LOW=65535;class MurmurHash3_64{constructor(seed){this.h1=seed?seed&4294967295:SEED,this.h2=seed?seed&4294967295:SEED}update(input){let data,length;if(typeof input=="string"){data=new Uint8Array(
-input.length*2),length=0;for(let i=0,ii=input.length;i<ii;i++){const code2=input.charCodeAt(i);code2<=255?data[length++]=code2:(data[length++]=code2>>>8,data[length++]=code2&255)}}else if(ArrayBuffer.isView(input))data=input.slice(),length=data.byteLength;else throw new Error("Invalid data format, must be a string or TypedArray.");const blockCounts=length>>2,tailLength=length-blockCounts*4,dataUint32=new Uint32Array(data.buffer,0,blockCounts);let k1=0,k2=0,h1=this.h1,h2=this.h2;const C1=3432918353,
-C2=461845907,C1_LOW=C1&MASK_LOW,C2_LOW=C2&MASK_LOW;for(let i=0;i<blockCounts;i++)i&1?(k1=dataUint32[i],k1=k1*C1&MASK_HIGH|k1*C1_LOW&MASK_LOW,k1=k1<<15|k1>>>17,k1=k1*C2&MASK_HIGH|k1*C2_LOW&MASK_LOW,h1^=k1,h1=h1<<13|h1>>>19,h1=h1*5+3864292196):(k2=dataUint32[i],k2=k2*C1&MASK_HIGH|k2*C1_LOW&MASK_LOW,k2=k2<<15|k2>>>17,k2=k2*C2&MASK_HIGH|k2*C2_LOW&MASK_LOW,h2^=k2,h2=h2<<13|h2>>>19,h2=h2*5+3864292196);switch(k1=0,tailLength){case 3:k1^=data[blockCounts*4+2]<<16;case 2:k1^=data[blockCounts*4+1]<<8;case 1:
-k1^=data[blockCounts*4],k1=k1*C1&MASK_HIGH|k1*C1_LOW&MASK_LOW,k1=k1<<15|k1>>>17,k1=k1*C2&MASK_HIGH|k1*C2_LOW&MASK_LOW,blockCounts&1?h1^=k1:h2^=k1}this.h1=h1,this.h2=h2}hexdigest(){let h1=this.h1,h2=this.h2;return h1^=h2>>>1,h1=h1*3981806797&MASK_HIGH|h1*36045&MASK_LOW,h2=h2*4283543511&MASK_HIGH|((h2<<16|h1>>>16)*2950163797&MASK_HIGH)>>>16,h1^=h2>>>1,h1=h1*444984403&MASK_HIGH|h1*60499&MASK_LOW,h2=h2*3301882366&MASK_HIGH|((h2<<16|h1>>>16)*3120437893&MASK_HIGH)>>>16,h1^=h2>>>1,(h1>>>0).toString(16).
-padStart(8,"0")+(h2>>>0).toString(16).padStart(8,"0")}}const SerializableEmpty=Object.freeze({map:null,hash:"",transfer:void 0});class AnnotationStorage{#modified=!1;#modifiedIds=null;#storage=new Map;constructor(){this.onSetModified=null,this.onResetModified=null,this.onAnnotationEditor=null}getValue(key2,defaultValue){const value=this.#storage.get(key2);return value===void 0?defaultValue:Object.assign(defaultValue,value)}getRawValue(key2){return this.#storage.get(key2)}remove(key2){if(this.#storage.
-delete(key2),this.#storage.size===0&&this.resetModified(),typeof this.onAnnotationEditor=="function"){for(const value of this.#storage.values())if(value instanceof AnnotationEditor)return;this.onAnnotationEditor(null)}}setValue(key2,value){const obj=this.#storage.get(key2);let modified=!1;if(obj!==void 0)for(const[entry,val]of Object.entries(value))obj[entry]!==val&&(modified=!0,obj[entry]=val);else modified=!0,this.#storage.set(key2,value);modified&&this.#setModified(),value instanceof AnnotationEditor&&
-typeof this.onAnnotationEditor=="function"&&this.onAnnotationEditor(value.constructor._type)}has(key2){return this.#storage.has(key2)}get size(){return this.#storage.size}#setModified(){this.#modified||(this.#modified=!0,typeof this.onSetModified=="function"&&this.onSetModified())}resetModified(){this.#modified&&(this.#modified=!1,typeof this.onResetModified=="function"&&this.onResetModified())}get print(){return new PrintAnnotationStorage(this)}get serializable(){if(this.#storage.size===0)return SerializableEmpty;
-const map2=new Map,hash2=new MurmurHash3_64,transfer=[],context=Object.create(null);let hasBitmap=!1;for(const[key2,val]of this.#storage){const serialized=val instanceof AnnotationEditor?val.serialize(!1,context):val;serialized&&(map2.set(key2,serialized),hash2.update(`${key2}:${JSON.stringify(serialized)}`),hasBitmap||=!!serialized.bitmap)}if(hasBitmap)for(const value of map2.values())value.bitmap&&transfer.push(value.bitmap);return map2.size>0?{map:map2,hash:hash2.hexdigest(),transfer}:SerializableEmpty}get editorStats(){
-let stats=null;const typeToEditor=new Map;for(const value of this.#storage.values()){if(!(value instanceof AnnotationEditor))continue;const editorStats=value.telemetryFinalData;if(!editorStats)continue;const{type:type2}=editorStats;typeToEditor.has(type2)||typeToEditor.set(type2,Object.getPrototypeOf(value).constructor),stats||=Object.create(null);const map2=stats[type2]||=new Map;for(const[key2,val]of Object.entries(editorStats)){if(key2==="type")continue;let counters=map2.get(key2);counters||(counters=
-new Map,map2.set(key2,counters));const count=counters.get(val)??0;counters.set(val,count+1)}}for(const[type2,editor]of typeToEditor)stats[type2]=editor.computeTelemetryFinalData(stats[type2]);return stats}resetModifiedIds(){this.#modifiedIds=null}get modifiedIds(){if(this.#modifiedIds)return this.#modifiedIds;const ids=[];for(const value of this.#storage.values())!(value instanceof AnnotationEditor)||!value.annotationElementId||!value.serialize()||ids.push(value.annotationElementId);return this.#modifiedIds=
-{ids:new Set(ids),hash:ids.join(",")}}[Symbol.iterator](){return this.#storage.entries()}}class PrintAnnotationStorage extends AnnotationStorage{#serializable;constructor(parent){super();const{map:map2,hash:hash2,transfer}=parent.serializable,clone2=structuredClone(map2,transfer?{transfer}:null);this.#serializable={map:clone2,hash:hash2,transfer}}get print(){unreachable("Should not call PrintAnnotationStorage.print")}get serializable(){return this.#serializable}get modifiedIds(){return shadow(this,
-"modifiedIds",{ids:new Set,hash:""})}}class FontLoader{#systemFonts=new Set;constructor({ownerDocument=globalThis.document,styleElement:styleElement2=null}){this._document=ownerDocument,this.nativeFontFaces=new Set,this.styleElement=null,this.loadingRequests=[],this.loadTestFontId=0}addNativeFontFace(nativeFontFace){this.nativeFontFaces.add(nativeFontFace),this._document.fonts.add(nativeFontFace)}removeNativeFontFace(nativeFontFace){this.nativeFontFaces.delete(nativeFontFace),this._document.fonts.
-delete(nativeFontFace)}insertRule(rule){this.styleElement||(this.styleElement=this._document.createElement("style"),this._document.documentElement.getElementsByTagName("head")[0].append(this.styleElement));const styleSheet=this.styleElement.sheet;styleSheet.insertRule(rule,styleSheet.cssRules.length)}clear(){for(const nativeFontFace of this.nativeFontFaces)this._document.fonts.delete(nativeFontFace);this.nativeFontFaces.clear(),this.#systemFonts.clear(),this.styleElement&&(this.styleElement.remove(),
-this.styleElement=null)}async loadSystemFont({systemFontInfo:info2,disableFontFace,_inspectFont}){if(!(!info2||this.#systemFonts.has(info2.loadedName))){if(assert$1(!disableFontFace,"loadSystemFont shouldn't be called when `disableFontFace` is set."),this.isFontLoadingAPISupported){const{loadedName,src,style:style2}=info2,fontFace=new FontFace(loadedName,src,style2);this.addNativeFontFace(fontFace);try{await fontFace.load(),this.#systemFonts.add(loadedName),_inspectFont?.(info2)}catch{warn(`Cann\
-ot load system font: ${info2.baseFontName}, installing it could help to improve PDF rendering.`),this.removeNativeFontFace(fontFace)}return}unreachable("Not implemented: loadSystemFont without the Font Loading API.")}}async bind(font){if(font.attached||font.missingFile&&!font.systemFontInfo)return;if(font.attached=!0,font.systemFontInfo){await this.loadSystemFont(font);return}if(this.isFontLoadingAPISupported){const nativeFontFace=font.createNativeFontFace();if(nativeFontFace){this.addNativeFontFace(
-nativeFontFace);try{await nativeFontFace.loaded}catch(ex){throw warn(`Failed to load font '${nativeFontFace.family}': '${ex}'.`),font.disableFontFace=!0,ex}}return}const rule=font.createFontFaceRule();if(rule){if(this.insertRule(rule),this.isSyncFontLoadingSupported)return;await new Promise(resolve2=>{const request=this._queueLoadingCallback(resolve2);this._prepareFontLoadEvent(font,request)})}}get isFontLoadingAPISupported(){const hasFonts=!!this._document?.fonts;return shadow(this,"isFontLoadi\
-ngAPISupported",hasFonts)}get isSyncFontLoadingSupported(){return shadow(this,"isSyncFontLoadingSupported",isNodeJS||util_FeatureTest.platform.isFirefox)}_queueLoadingCallback(callback){function completeRequest(){for(assert$1(!request.done,"completeRequest() cannot be called twice."),request.done=!0;loadingRequests.length>0&&loadingRequests[0].done;){const otherRequest=loadingRequests.shift();setTimeout(otherRequest.callback,0)}}const{loadingRequests}=this,request={done:!1,complete:completeRequest,
-callback};return loadingRequests.push(request),request}get _loadTestFont(){const testFont=atob("T1RUTwALAIAAAwAwQ0ZGIDHtZg4AAAOYAAAAgUZGVE1lkzZwAAAEHAAAABxHREVGABQAFQAABDgAAAAeT1MvMlYNYwkAAAEgAAAAYGNtYXABDQLUAAACNAAAAUJoZWFk/xVFDQAAALwAAAA2aGhlYQdkA+oAAAD0AAAAJGhtdHgD6AAAAAAEWAAAAAZtYXhwAAJQAAAAARgAAAAGbmFtZVjmdH4AAAGAAAAAsXBvc3T/hgAzAAADeAAAACAAAQAAAAEAALZRFsRfDzz1AAsD6AAAAADOBOTLAAAAAM4KHDwAAAAAA+gDIQAAAAgAAgAAAAAAAAABAAADIQAAAFoD6AAAAAAD6AABAAAAAAAAAAAAAAAAAAAAAQAAUAAAAgAAAAQD6AH0AAUAAAKKArwA\
-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\
-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\
-AAABAAAAAMmJbzEAAAAAzgTjFQAAAADOBOQpAAEAAAAAAAAADAAUAAQAAAABAAAAAgABAAAAAAAAAAAD6AAAAAAAAA==");return shadow(this,"_loadTestFont",testFont)}_prepareFontLoadEvent(font,request){function int32(data2,offset2){return data2.charCodeAt(offset2)<<24|data2.charCodeAt(offset2+1)<<16|data2.charCodeAt(offset2+2)<<8|data2.charCodeAt(offset2+3)&255}function spliceString(s2,offset2,remove2,insert){const chunk1=s2.substring(0,offset2),chunk2=s2.substring(offset2+remove2);return chunk1+insert+chunk2}let i,ii;const canvas=this.
-_document.createElement("canvas");canvas.width=1,canvas.height=1;const ctx=canvas.getContext("2d");let called=0;function isFontReady(name,callback){if(++called>30){warn("Load test font never loaded."),callback();return}if(ctx.font="30px "+name,ctx.fillText(".",0,20),ctx.getImageData(0,0,1,1).data[3]>0){callback();return}setTimeout(isFontReady.bind(null,name,callback))}const loadTestFontId=`lt${Date.now()}${this.loadTestFontId++}`;let data=this._loadTestFont;data=spliceString(data,976,loadTestFontId.
-length,loadTestFontId);const CFF_CHECKSUM_OFFSET=16,XXXX_VALUE=1482184792;let checksum=int32(data,CFF_CHECKSUM_OFFSET);for(i=0,ii=loadTestFontId.length-3;i<ii;i+=4)checksum=checksum-XXXX_VALUE+int32(loadTestFontId,i)|0;i<loadTestFontId.length&&(checksum=checksum-XXXX_VALUE+int32(loadTestFontId+"XXX",i)|0),data=spliceString(data,CFF_CHECKSUM_OFFSET,4,string32(checksum));const url2=`url(data:font/opentype;base64,${btoa(data)});`,rule=`@font-face {font-family:"${loadTestFontId}";src:${url2}}`;this.
-insertRule(rule);const div=this._document.createElement("div");div.style.visibility="hidden",div.style.width=div.style.height="10px",div.style.position="absolute",div.style.top=div.style.left="0px";for(const name of[font.loadedName,loadTestFontId]){const span=this._document.createElement("span");span.textContent="Hi",span.style.fontFamily=name,div.append(span)}this._document.body.append(div),isFontReady(loadTestFontId,()=>{div.remove(),request.complete()})}}class FontFaceObject{constructor(translatedData,inspectFont=null){
-this.compiledGlyphs=Object.create(null);for(const i in translatedData)this[i]=translatedData[i];this._inspectFont=inspectFont}createNativeFontFace(){if(!this.data||this.disableFontFace)return null;let nativeFontFace;if(!this.cssFontInfo)nativeFontFace=new FontFace(this.loadedName,this.data,{});else{const css2={weight:this.cssFontInfo.fontWeight};this.cssFontInfo.italicAngle&&(css2.style=`oblique ${this.cssFontInfo.italicAngle}deg`),nativeFontFace=new FontFace(this.cssFontInfo.fontFamily,this.data,
-css2)}return this._inspectFont?.(this),nativeFontFace}createFontFaceRule(){if(!this.data||this.disableFontFace)return null;const url2=`url(data:${this.mimetype};base64,${toBase64Util(this.data)});`;let rule;if(!this.cssFontInfo)rule=`@font-face {font-family:"${this.loadedName}";src:${url2}}`;else{let css2=`font-weight: ${this.cssFontInfo.fontWeight};`;this.cssFontInfo.italicAngle&&(css2+=`font-style: oblique ${this.cssFontInfo.italicAngle}deg;`),rule=`@font-face {font-family:"${this.cssFontInfo.
-fontFamily}";${css2}src:${url2}}`}return this._inspectFont?.(this,url2),rule}getPathGenerator(objs,character){if(this.compiledGlyphs[character]!==void 0)return this.compiledGlyphs[character];const objId=this.loadedName+"_path_"+character;let cmds;try{cmds=objs.get(objId)}catch(ex){warn(`getPathGenerator - ignoring character: "${ex}".`)}const path2=new Path2D(cmds||"");return this.fontExtraProperties||objs.delete(objId),this.compiledGlyphs[character]=path2}}function getUrlProp(val){if(val instanceof
-URL)return val.href;if(typeof val=="string"){if(isNodeJS)return val;const url2=URL.parse(val,window.location);if(url2)return url2.href}throw new Error("Invalid PDF url data: either string or URL-object is expected in the url property.")}function getDataProp(val){if(isNodeJS&&typeof Buffer<"u"&&val instanceof Buffer)throw new Error("Please provide binary data as `Uint8Array`, rather than `Buffer`.");if(val instanceof Uint8Array&&val.byteLength===val.buffer.byteLength)return val;if(typeof val=="st\
-ring")return stringToBytes(val);if(val instanceof ArrayBuffer||ArrayBuffer.isView(val)||typeof val=="object"&&!isNaN(val?.length))return new Uint8Array(val);throw new Error("Invalid PDF binary data: either TypedArray, string, or array-like object is expected in the data property.")}function getFactoryUrlProp(val){if(typeof val!="string")return null;if(val.endsWith("/"))return val;throw new Error(`Invalid factory url: "${val}" must include trailing slash.`)}const isRefProxy=v=>typeof v=="object"&&
-Number.isInteger(v?.num)&&v.num>=0&&Number.isInteger(v?.gen)&&v.gen>=0,isNameProxy=v=>typeof v=="object"&&typeof v?.name=="string",isValidExplicitDest=_isValidExplicitDest.bind(null,isRefProxy,isNameProxy);class LoopbackPort{#listeners=new Map;#deferred=Promise.resolve();postMessage(obj,transfer){const event2={data:structuredClone(obj,transfer?{transfer}:null)};this.#deferred.then(()=>{for(const[listener]of this.#listeners)listener.call(this,event2)})}addEventListener(name,listener,options=null){
-let rmAbort=null;if(options?.signal instanceof AbortSignal){const{signal}=options;if(signal.aborted){warn("LoopbackPort - cannot use an `aborted` signal.");return}const onAbort=()=>this.removeEventListener(name,listener);rmAbort=()=>signal.removeEventListener("abort",onAbort),signal.addEventListener("abort",onAbort)}this.#listeners.set(listener,rmAbort)}removeEventListener(name,listener){this.#listeners.get(listener)?.(),this.#listeners.delete(listener)}terminate(){for(const[,rmAbort]of this.#listeners)
-rmAbort?.();this.#listeners.clear()}}const CallbackKind={DATA:1,ERROR:2},StreamKind={CANCEL:1,CANCEL_COMPLETE:2,CLOSE:3,ENQUEUE:4,ERROR:5,PULL:6,PULL_COMPLETE:7,START_COMPLETE:8};function onFn(){}function wrapReason(ex){if(ex instanceof AbortException||ex instanceof InvalidPDFException||ex instanceof PasswordException||ex instanceof ResponseException||ex instanceof UnknownErrorException)return ex;switch(ex instanceof Error||typeof ex=="object"&&ex!==null||unreachable('wrapReason: Expected "reaso\
-n" to be a (possibly cloned) Error.'),ex.name){case"AbortException":return new AbortException(ex.message);case"InvalidPDFException":return new InvalidPDFException(ex.message);case"PasswordException":return new PasswordException(ex.message,ex.code);case"ResponseException":return new ResponseException(ex.message,ex.status,ex.missing);case"UnknownErrorException":return new UnknownErrorException(ex.message,ex.details)}return new UnknownErrorException(ex.message,ex.toString())}class MessageHandler{#messageAC=new AbortController;constructor(sourceName,targetName,comObj){
-this.sourceName=sourceName,this.targetName=targetName,this.comObj=comObj,this.callbackId=1,this.streamId=1,this.streamSinks=Object.create(null),this.streamControllers=Object.create(null),this.callbackCapabilities=Object.create(null),this.actionHandler=Object.create(null),comObj.addEventListener("message",this.#onMessage.bind(this),{signal:this.#messageAC.signal})}#onMessage({data}){if(data.targetName!==this.sourceName)return;if(data.stream){this.#processStreamMessage(data);return}if(data.callback){
-const callbackId=data.callbackId,capability=this.callbackCapabilities[callbackId];if(!capability)throw new Error(`Cannot resolve callback ${callbackId}`);if(delete this.callbackCapabilities[callbackId],data.callback===CallbackKind.DATA)capability.resolve(data.data);else if(data.callback===CallbackKind.ERROR)capability.reject(wrapReason(data.reason));else throw new Error("Unexpected callback case");return}const action2=this.actionHandler[data.action];if(!action2)throw new Error(`Unknown action fr\
-om worker: ${data.action}`);if(data.callbackId){const sourceName=this.sourceName,targetName=data.sourceName,comObj=this.comObj;Promise.try(action2,data.data).then(function(result){comObj.postMessage({sourceName,targetName,callback:CallbackKind.DATA,callbackId:data.callbackId,data:result})},function(reason){comObj.postMessage({sourceName,targetName,callback:CallbackKind.ERROR,callbackId:data.callbackId,reason:wrapReason(reason)})});return}if(data.streamId){this.#createStreamSink(data);return}action2(
-data.data)}on(actionName,handler){const ah=this.actionHandler;if(ah[actionName])throw new Error(`There is already an actionName called "${actionName}"`);ah[actionName]=handler}send(actionName,data,transfers){this.comObj.postMessage({sourceName:this.sourceName,targetName:this.targetName,action:actionName,data},transfers)}sendWithPromise(actionName,data,transfers){const callbackId=this.callbackId++,capability=Promise.withResolvers();this.callbackCapabilities[callbackId]=capability;try{this.comObj.
-postMessage({sourceName:this.sourceName,targetName:this.targetName,action:actionName,callbackId,data},transfers)}catch(ex){capability.reject(ex)}return capability.promise}sendWithStream(actionName,data,queueingStrategy,transfers){const streamId=this.streamId++,sourceName=this.sourceName,targetName=this.targetName,comObj=this.comObj;return new ReadableStream({start:controller=>{const startCapability=Promise.withResolvers();return this.streamControllers[streamId]={controller,startCall:startCapability,
-pullCall:null,cancelCall:null,isClosed:!1},comObj.postMessage({sourceName,targetName,action:actionName,streamId,data,desiredSize:controller.desiredSize},transfers),startCapability.promise},pull:controller=>{const pullCapability=Promise.withResolvers();return this.streamControllers[streamId].pullCall=pullCapability,comObj.postMessage({sourceName,targetName,stream:StreamKind.PULL,streamId,desiredSize:controller.desiredSize}),pullCapability.promise},cancel:reason=>{assert$1(reason instanceof Error,
-"cancel must have a valid reason");const cancelCapability=Promise.withResolvers();return this.streamControllers[streamId].cancelCall=cancelCapability,this.streamControllers[streamId].isClosed=!0,comObj.postMessage({sourceName,targetName,stream:StreamKind.CANCEL,streamId,reason:wrapReason(reason)}),cancelCapability.promise}},queueingStrategy)}#createStreamSink(data){const streamId=data.streamId,sourceName=this.sourceName,targetName=data.sourceName,comObj=this.comObj,self2=this,action2=this.actionHandler[data.
-action],streamSink={enqueue(chunk,size2=1,transfers){if(this.isCancelled)return;const lastDesiredSize=this.desiredSize;this.desiredSize-=size2,lastDesiredSize>0&&this.desiredSize<=0&&(this.sinkCapability=Promise.withResolvers(),this.ready=this.sinkCapability.promise),comObj.postMessage({sourceName,targetName,stream:StreamKind.ENQUEUE,streamId,chunk},transfers)},close(){this.isCancelled||(this.isCancelled=!0,comObj.postMessage({sourceName,targetName,stream:StreamKind.CLOSE,streamId}),delete self2.
-streamSinks[streamId])},error(reason){assert$1(reason instanceof Error,"error must have a valid reason"),!this.isCancelled&&(this.isCancelled=!0,comObj.postMessage({sourceName,targetName,stream:StreamKind.ERROR,streamId,reason:wrapReason(reason)}))},sinkCapability:Promise.withResolvers(),onPull:null,onCancel:null,isCancelled:!1,desiredSize:data.desiredSize,ready:null};streamSink.sinkCapability.resolve(),streamSink.ready=streamSink.sinkCapability.promise,this.streamSinks[streamId]=streamSink,Promise.
-try(action2,data.data,streamSink).then(function(){comObj.postMessage({sourceName,targetName,stream:StreamKind.START_COMPLETE,streamId,success:!0})},function(reason){comObj.postMessage({sourceName,targetName,stream:StreamKind.START_COMPLETE,streamId,reason:wrapReason(reason)})})}#processStreamMessage(data){const streamId=data.streamId,sourceName=this.sourceName,targetName=data.sourceName,comObj=this.comObj,streamController=this.streamControllers[streamId],streamSink=this.streamSinks[streamId];switch(data.
-stream){case StreamKind.START_COMPLETE:data.success?streamController.startCall.resolve():streamController.startCall.reject(wrapReason(data.reason));break;case StreamKind.PULL_COMPLETE:data.success?streamController.pullCall.resolve():streamController.pullCall.reject(wrapReason(data.reason));break;case StreamKind.PULL:if(!streamSink){comObj.postMessage({sourceName,targetName,stream:StreamKind.PULL_COMPLETE,streamId,success:!0});break}streamSink.desiredSize<=0&&data.desiredSize>0&&streamSink.sinkCapability.
-resolve(),streamSink.desiredSize=data.desiredSize,Promise.try(streamSink.onPull||onFn).then(function(){comObj.postMessage({sourceName,targetName,stream:StreamKind.PULL_COMPLETE,streamId,success:!0})},function(reason){comObj.postMessage({sourceName,targetName,stream:StreamKind.PULL_COMPLETE,streamId,reason:wrapReason(reason)})});break;case StreamKind.ENQUEUE:if(assert$1(streamController,"enqueue should have stream controller"),streamController.isClosed)break;streamController.controller.enqueue(data.
-chunk);break;case StreamKind.CLOSE:if(assert$1(streamController,"close should have stream controller"),streamController.isClosed)break;streamController.isClosed=!0,streamController.controller.close(),this.#deleteStreamController(streamController,streamId);break;case StreamKind.ERROR:assert$1(streamController,"error should have stream controller"),streamController.controller.error(wrapReason(data.reason)),this.#deleteStreamController(streamController,streamId);break;case StreamKind.CANCEL_COMPLETE:
-data.success?streamController.cancelCall.resolve():streamController.cancelCall.reject(wrapReason(data.reason)),this.#deleteStreamController(streamController,streamId);break;case StreamKind.CANCEL:if(!streamSink)break;const dataReason=wrapReason(data.reason);Promise.try(streamSink.onCancel||onFn,dataReason).then(function(){comObj.postMessage({sourceName,targetName,stream:StreamKind.CANCEL_COMPLETE,streamId,success:!0})},function(reason){comObj.postMessage({sourceName,targetName,stream:StreamKind.
-CANCEL_COMPLETE,streamId,reason:wrapReason(reason)})}),streamSink.sinkCapability.reject(dataReason),streamSink.isCancelled=!0,delete this.streamSinks[streamId];break;default:throw new Error("Unexpected stream case")}}async#deleteStreamController(streamController,streamId){await Promise.allSettled([streamController.startCall?.promise,streamController.pullCall?.promise,streamController.cancelCall?.promise]),delete this.streamControllers[streamId]}destroy(){this.#messageAC?.abort(),this.#messageAC=
-null}}class BaseCanvasFactory{#enableHWA=!1;constructor({enableHWA=!1}){this.#enableHWA=enableHWA}create(width,height){if(width<=0||height<=0)throw new Error("Invalid canvas size");const canvas=this._createCanvas(width,height);return{canvas,context:canvas.getContext("2d",{willReadFrequently:!this.#enableHWA})}}reset(canvasAndContext,width,height){if(!canvasAndContext.canvas)throw new Error("Canvas is not specified");if(width<=0||height<=0)throw new Error("Invalid canvas size");canvasAndContext.canvas.
-width=width,canvasAndContext.canvas.height=height}destroy(canvasAndContext){if(!canvasAndContext.canvas)throw new Error("Canvas is not specified");canvasAndContext.canvas.width=0,canvasAndContext.canvas.height=0,canvasAndContext.canvas=null,canvasAndContext.context=null}_createCanvas(width,height){unreachable("Abstract method `_createCanvas` called.")}}class DOMCanvasFactory extends BaseCanvasFactory{constructor({ownerDocument=globalThis.document,enableHWA=!1}){super({enableHWA}),this._document=
-ownerDocument}_createCanvas(width,height){const canvas=this._document.createElement("canvas");return canvas.width=width,canvas.height=height,canvas}}class BaseCMapReaderFactory{constructor({baseUrl=null,isCompressed=!0}){this.baseUrl=baseUrl,this.isCompressed=isCompressed}async fetch({name}){if(!this.baseUrl)throw new Error("Ensure that the `cMapUrl` and `cMapPacked` API parameters are provided.");if(!name)throw new Error("CMap name must be specified.");const url2=this.baseUrl+name+(this.isCompressed?
-".bcmap":"");return this._fetch(url2).then(cMapData=>({cMapData,isCompressed:this.isCompressed})).catch(reason=>{throw new Error(`Unable to load ${this.isCompressed?"binary ":""}CMap at: ${url2}`)})}async _fetch(url2){unreachable("Abstract method `_fetch` called.")}}class DOMCMapReaderFactory extends BaseCMapReaderFactory{async _fetch(url2){const data=await fetchData(url2,this.isCompressed?"arraybuffer":"text");return data instanceof ArrayBuffer?new Uint8Array(data):stringToBytes(data)}}class BaseFilterFactory{addFilter(maps){
-return"none"}addHCMFilter(fgColor,bgColor){return"none"}addAlphaFilter(map2){return"none"}addLuminosityFilter(map2){return"none"}addHighlightHCMFilter(filterName,fgColor,bgColor,newFgColor,newBgColor){return"none"}destroy(keepHCM=!1){}}class DOMFilterFactory extends BaseFilterFactory{#baseUrl;#_cache;#_defs;#docId;#document;#_hcmCache;#id=0;constructor({docId,ownerDocument=globalThis.document}){super(),this.#docId=docId,this.#document=ownerDocument}get#cache(){return this.#_cache||=new Map}get#hcmCache(){
-return this.#_hcmCache||=new Map}get#defs(){if(!this.#_defs){const div=this.#document.createElement("div"),{style:style2}=div;style2.visibility="hidden",style2.contain="strict",style2.width=style2.height=0,style2.position="absolute",style2.top=style2.left=0,style2.zIndex=-1;const svg2=this.#document.createElementNS(SVG_NS,"svg");svg2.setAttribute("width",0),svg2.setAttribute("height",0),this.#_defs=this.#document.createElementNS(SVG_NS,"defs"),div.append(svg2),svg2.append(this.#_defs),this.#document.
-body.append(div)}return this.#_defs}#createTables(maps){if(maps.length===1){const mapR2=maps[0],buffer=new Array(256);for(let i=0;i<256;i++)buffer[i]=mapR2[i]/255;const table2=buffer.join(",");return[table2,table2,table2]}const[mapR,mapG,mapB]=maps,bufferR=new Array(256),bufferG=new Array(256),bufferB=new Array(256);for(let i=0;i<256;i++)bufferR[i]=mapR[i]/255,bufferG[i]=mapG[i]/255,bufferB[i]=mapB[i]/255;return[bufferR.join(","),bufferG.join(","),bufferB.join(",")]}#createUrl(id2){if(this.#baseUrl===
-void 0){this.#baseUrl="";const url2=this.#document.URL;url2!==this.#document.baseURI&&(isDataScheme(url2)?warn('#createUrl: ignore "data:"-URL for performance reasons.'):this.#baseUrl=updateUrlHash(url2,""))}return`url(${this.#baseUrl}#${id2})`}addFilter(maps){if(!maps)return"none";let value=this.#cache.get(maps);if(value)return value;const[tableR,tableG,tableB]=this.#createTables(maps),key2=maps.length===1?tableR:`${tableR}${tableG}${tableB}`;if(value=this.#cache.get(key2),value)return this.#cache.
-set(maps,value),value;const id2=`g_${this.#docId}_transfer_map_${this.#id++}`,url2=this.#createUrl(id2);this.#cache.set(maps,url2),this.#cache.set(key2,url2);const filter=this.#createFilter(id2);return this.#addTransferMapConversion(tableR,tableG,tableB,filter),url2}addHCMFilter(fgColor,bgColor){const key2=`${fgColor}-${bgColor}`,filterName="base";let info2=this.#hcmCache.get(filterName);if(info2?.key===key2||(info2?(info2.filter?.remove(),info2.key=key2,info2.url="none",info2.filter=null):(info2=
-{key:key2,url:"none",filter:null},this.#hcmCache.set(filterName,info2)),!fgColor||!bgColor))return info2.url;const fgRGB=this.#getRGB(fgColor);fgColor=Util.makeHexColor(...fgRGB);const bgRGB=this.#getRGB(bgColor);if(bgColor=Util.makeHexColor(...bgRGB),this.#defs.style.color="",fgColor==="#000000"&&bgColor==="#ffffff"||fgColor===bgColor)return info2.url;const map2=new Array(256);for(let i=0;i<=255;i++){const x=i/255;map2[i]=x<=.03928?x/12.92:((x+.055)/1.055)**2.4}const table2=map2.join(","),id2=`\
-g_${this.#docId}_hcm_filter`,filter=info2.filter=this.#createFilter(id2);this.#addTransferMapConversion(table2,table2,table2,filter),this.#addGrayConversion(filter);const getSteps=(c2,n)=>{const start2=fgRGB[c2]/255,end=bgRGB[c2]/255,arr=new Array(n+1);for(let i=0;i<=n;i++)arr[i]=start2+i/n*(end-start2);return arr.join(",")};return this.#addTransferMapConversion(getSteps(0,5),getSteps(1,5),getSteps(2,5),filter),info2.url=this.#createUrl(id2),info2.url}addAlphaFilter(map2){let value=this.#cache.get(
-map2);if(value)return value;const[tableA]=this.#createTables([map2]),key2=`alpha_${tableA}`;if(value=this.#cache.get(key2),value)return this.#cache.set(map2,value),value;const id2=`g_${this.#docId}_alpha_map_${this.#id++}`,url2=this.#createUrl(id2);this.#cache.set(map2,url2),this.#cache.set(key2,url2);const filter=this.#createFilter(id2);return this.#addTransferMapAlphaConversion(tableA,filter),url2}addLuminosityFilter(map2){let value=this.#cache.get(map2||"luminosity");if(value)return value;let tableA,
-key2;if(map2?([tableA]=this.#createTables([map2]),key2=`luminosity_${tableA}`):key2="luminosity",value=this.#cache.get(key2),value)return this.#cache.set(map2,value),value;const id2=`g_${this.#docId}_luminosity_map_${this.#id++}`,url2=this.#createUrl(id2);this.#cache.set(map2,url2),this.#cache.set(key2,url2);const filter=this.#createFilter(id2);return this.#addLuminosityConversion(filter),map2&&this.#addTransferMapAlphaConversion(tableA,filter),url2}addHighlightHCMFilter(filterName,fgColor,bgColor,newFgColor,newBgColor){
-const key2=`${fgColor}-${bgColor}-${newFgColor}-${newBgColor}`;let info2=this.#hcmCache.get(filterName);if(info2?.key===key2||(info2?(info2.filter?.remove(),info2.key=key2,info2.url="none",info2.filter=null):(info2={key:key2,url:"none",filter:null},this.#hcmCache.set(filterName,info2)),!fgColor||!bgColor))return info2.url;const[fgRGB,bgRGB]=[fgColor,bgColor].map(this.#getRGB.bind(this));let fgGray=Math.round(.2126*fgRGB[0]+.7152*fgRGB[1]+.0722*fgRGB[2]),bgGray=Math.round(.2126*bgRGB[0]+.7152*bgRGB[1]+
-.0722*bgRGB[2]),[newFgRGB,newBgRGB]=[newFgColor,newBgColor].map(this.#getRGB.bind(this));bgGray<fgGray&&([fgGray,bgGray,newFgRGB,newBgRGB]=[bgGray,fgGray,newBgRGB,newFgRGB]),this.#defs.style.color="";const getSteps=(fg,bg,n)=>{const arr=new Array(256),step=(bgGray-fgGray)/n,newStart=fg/255,newStep=(bg-fg)/(255*n);let prev2=0;for(let i=0;i<=n;i++){const k=Math.round(fgGray+i*step),value=newStart+i*newStep;for(let j=prev2;j<=k;j++)arr[j]=value;prev2=k+1}for(let i=prev2;i<256;i++)arr[i]=arr[prev2-1];
-return arr.join(",")},id2=`g_${this.#docId}_hcm_${filterName}_filter`,filter=info2.filter=this.#createFilter(id2);return this.#addGrayConversion(filter),this.#addTransferMapConversion(getSteps(newFgRGB[0],newBgRGB[0],5),getSteps(newFgRGB[1],newBgRGB[1],5),getSteps(newFgRGB[2],newBgRGB[2],5),filter),info2.url=this.#createUrl(id2),info2.url}destroy(keepHCM=!1){keepHCM&&this.#_hcmCache?.size||(this.#_defs?.parentNode.parentNode.remove(),this.#_defs=null,this.#_cache?.clear(),this.#_cache=null,this.#_hcmCache?.
-clear(),this.#_hcmCache=null,this.#id=0)}#addLuminosityConversion(filter){const feColorMatrix=this.#document.createElementNS(SVG_NS,"feColorMatrix");feColorMatrix.setAttribute("type","matrix"),feColorMatrix.setAttribute("values","0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.59 0.11 0 0"),filter.append(feColorMatrix)}#addGrayConversion(filter){const feColorMatrix=this.#document.createElementNS(SVG_NS,"feColorMatrix");feColorMatrix.setAttribute("type","matrix"),feColorMatrix.setAttribute("values","0.2126 0\
-.7152 0.0722 0 0 0.2126 0.7152 0.0722 0 0 0.2126 0.7152 0.0722 0 0 0 0 0 1 0"),filter.append(feColorMatrix)}#createFilter(id2){const filter=this.#document.createElementNS(SVG_NS,"filter");return filter.setAttribute("color-interpolation-filters","sRGB"),filter.setAttribute("id",id2),this.#defs.append(filter),filter}#appendFeFunc(feComponentTransfer,func,table2){const feFunc=this.#document.createElementNS(SVG_NS,func);feFunc.setAttribute("type","discrete"),feFunc.setAttribute("tableValues",table2),
-feComponentTransfer.append(feFunc)}#addTransferMapConversion(rTable,gTable,bTable,filter){const feComponentTransfer=this.#document.createElementNS(SVG_NS,"feComponentTransfer");filter.append(feComponentTransfer),this.#appendFeFunc(feComponentTransfer,"feFuncR",rTable),this.#appendFeFunc(feComponentTransfer,"feFuncG",gTable),this.#appendFeFunc(feComponentTransfer,"feFuncB",bTable)}#addTransferMapAlphaConversion(aTable,filter){const feComponentTransfer=this.#document.createElementNS(SVG_NS,"feComp\
-onentTransfer");filter.append(feComponentTransfer),this.#appendFeFunc(feComponentTransfer,"feFuncA",aTable)}#getRGB(color){return this.#defs.style.color=color,getRGB(getComputedStyle(this.#defs).getPropertyValue("color"))}}class BaseStandardFontDataFactory{constructor({baseUrl=null}){this.baseUrl=baseUrl}async fetch({filename}){if(!this.baseUrl)throw new Error("Ensure that the `standardFontDataUrl` API parameter is provided.");if(!filename)throw new Error("Font filename must be specified.");const url2=`${this.
-baseUrl}${filename}`;return this._fetch(url2).catch(reason=>{throw new Error(`Unable to load font data at: ${url2}`)})}async _fetch(url2){unreachable("Abstract method `_fetch` called.")}}class DOMStandardFontDataFactory extends BaseStandardFontDataFactory{async _fetch(url2){const data=await fetchData(url2,"arraybuffer");return new Uint8Array(data)}}class BaseWasmFactory{constructor({baseUrl=null}){this.baseUrl=baseUrl}async fetch({filename}){if(!this.baseUrl)throw new Error("Ensure that the `was\
-mUrl` API parameter is provided.");if(!filename)throw new Error("Wasm filename must be specified.");const url2=`${this.baseUrl}${filename}`;return this._fetch(url2).catch(reason=>{throw new Error(`Unable to load wasm data at: ${url2}`)})}async _fetch(url2){unreachable("Abstract method `_fetch` called.")}}class DOMWasmFactory extends BaseWasmFactory{async _fetch(url2){const data=await fetchData(url2,"arraybuffer");return new Uint8Array(data)}}isNodeJS&&warn("Please use the `legacy` build in Node.\
-js environments.");async function node_utils_fetchData(url2){const data=await process.getBuiltinModule("fs").promises.readFile(url2);return new Uint8Array(data)}class NodeFilterFactory extends BaseFilterFactory{}class NodeCanvasFactory extends BaseCanvasFactory{_createCanvas(width,height){return process.getBuiltinModule("module").createRequire(import.meta.url)("@napi-rs/canvas").createCanvas(width,height)}}class NodeCMapReaderFactory extends BaseCMapReaderFactory{async _fetch(url2){return node_utils_fetchData(
-url2)}}class NodeStandardFontDataFactory extends BaseStandardFontDataFactory{async _fetch(url2){return node_utils_fetchData(url2)}}class NodeWasmFactory extends BaseWasmFactory{async _fetch(url2){return node_utils_fetchData(url2)}}const PathType={FILL:"Fill",STROKE:"Stroke",SHADING:"Shading"};function applyBoundingBox(ctx,bbox){if(!bbox)return;const width=bbox[2]-bbox[0],height=bbox[3]-bbox[1],region=new Path2D;region.rect(bbox[0],bbox[1],width,height),ctx.clip(region)}class BaseShadingPattern{isModifyingCurrentTransform(){
-return!1}getPattern(){unreachable("Abstract method `getPattern` called.")}}class RadialAxialShadingPattern extends BaseShadingPattern{constructor(IR){super(),this._type=IR[1],this._bbox=IR[2],this._colorStops=IR[3],this._p0=IR[4],this._p1=IR[5],this._r0=IR[6],this._r1=IR[7],this.matrix=null}_createGradient(ctx){let grad;this._type==="axial"?grad=ctx.createLinearGradient(this._p0[0],this._p0[1],this._p1[0],this._p1[1]):this._type==="radial"&&(grad=ctx.createRadialGradient(this._p0[0],this._p0[1],
-this._r0,this._p1[0],this._p1[1],this._r1));for(const colorStop of this._colorStops)grad.addColorStop(colorStop[0],colorStop[1]);return grad}getPattern(ctx,owner,inverse,pathType){let pattern2;if(pathType===PathType.STROKE||pathType===PathType.FILL){const ownerBBox=owner.current.getClippedPathBoundingBox(pathType,getCurrentTransform(ctx))||[0,0,0,0],width=Math.ceil(ownerBBox[2]-ownerBBox[0])||1,height=Math.ceil(ownerBBox[3]-ownerBBox[1])||1,tmpCanvas=owner.cachedCanvases.getCanvas("pattern",width,
-height),tmpCtx=tmpCanvas.context;tmpCtx.clearRect(0,0,tmpCtx.canvas.width,tmpCtx.canvas.height),tmpCtx.beginPath(),tmpCtx.rect(0,0,tmpCtx.canvas.width,tmpCtx.canvas.height),tmpCtx.translate(-ownerBBox[0],-ownerBBox[1]),inverse=Util.transform(inverse,[1,0,0,1,ownerBBox[0],ownerBBox[1]]),tmpCtx.transform(...owner.baseTransform),this.matrix&&tmpCtx.transform(...this.matrix),applyBoundingBox(tmpCtx,this._bbox),tmpCtx.fillStyle=this._createGradient(tmpCtx),tmpCtx.fill(),pattern2=ctx.createPattern(tmpCanvas.
-canvas,"no-repeat");const domMatrix=new DOMMatrix(inverse);pattern2.setTransform(domMatrix)}else applyBoundingBox(ctx,this._bbox),pattern2=this._createGradient(ctx);return pattern2}}function drawTriangle(data,context,p1,p2,p3,c1,c2,c3){const coords=context.coords,colors=context.colors,bytes=data.data,rowSize=data.width*4;let tmp;coords[p1+1]>coords[p2+1]&&(tmp=p1,p1=p2,p2=tmp,tmp=c1,c1=c2,c2=tmp),coords[p2+1]>coords[p3+1]&&(tmp=p2,p2=p3,p3=tmp,tmp=c2,c2=c3,c3=tmp),coords[p1+1]>coords[p2+1]&&(tmp=
-p1,p1=p2,p2=tmp,tmp=c1,c1=c2,c2=tmp);const x1=(coords[p1]+context.offsetX)*context.scaleX,y1=(coords[p1+1]+context.offsetY)*context.scaleY,x2=(coords[p2]+context.offsetX)*context.scaleX,y2=(coords[p2+1]+context.offsetY)*context.scaleY,x3=(coords[p3]+context.offsetX)*context.scaleX,y3=(coords[p3+1]+context.offsetY)*context.scaleY;if(y1>=y3)return;const c1r=colors[c1],c1g=colors[c1+1],c1b=colors[c1+2],c2r=colors[c2],c2g=colors[c2+1],c2b=colors[c2+2],c3r=colors[c3],c3g=colors[c3+1],c3b=colors[c3+2],
-minY=Math.round(y1),maxY=Math.round(y3);let xa,car,cag,cab,xb,cbr,cbg,cbb;for(let y=minY;y<=maxY;y++){if(y<y2){const k2=y<y1?0:(y1-y)/(y1-y2);xa=x1-(x1-x2)*k2,car=c1r-(c1r-c2r)*k2,cag=c1g-(c1g-c2g)*k2,cab=c1b-(c1b-c2b)*k2}else{let k2;y>y3?k2=1:y2===y3?k2=0:k2=(y2-y)/(y2-y3),xa=x2-(x2-x3)*k2,car=c2r-(c2r-c3r)*k2,cag=c2g-(c2g-c3g)*k2,cab=c2b-(c2b-c3b)*k2}let k;y<y1?k=0:y>y3?k=1:k=(y1-y)/(y1-y3),xb=x1-(x1-x3)*k,cbr=c1r-(c1r-c3r)*k,cbg=c1g-(c1g-c3g)*k,cbb=c1b-(c1b-c3b)*k;const x1_=Math.round(Math.min(
-xa,xb)),x2_=Math.round(Math.max(xa,xb));let j=rowSize*y+x1_*4;for(let x=x1_;x<=x2_;x++)k=(xa-x)/(xa-xb),k<0?k=0:k>1&&(k=1),bytes[j++]=car-(car-cbr)*k|0,bytes[j++]=cag-(cag-cbg)*k|0,bytes[j++]=cab-(cab-cbb)*k|0,bytes[j++]=255}}function drawFigure(data,figure,context){const ps=figure.coords,cs=figure.colors;let i,ii;switch(figure.type){case"lattice":const verticesPerRow=figure.verticesPerRow,rows=Math.floor(ps.length/verticesPerRow)-1,cols=verticesPerRow-1;for(i=0;i<rows;i++){let q2=i*verticesPerRow;
-for(let j=0;j<cols;j++,q2++)drawTriangle(data,context,ps[q2],ps[q2+1],ps[q2+verticesPerRow],cs[q2],cs[q2+1],cs[q2+verticesPerRow]),drawTriangle(data,context,ps[q2+verticesPerRow+1],ps[q2+1],ps[q2+verticesPerRow],cs[q2+verticesPerRow+1],cs[q2+1],cs[q2+verticesPerRow])}break;case"triangles":for(i=0,ii=ps.length;i<ii;i+=3)drawTriangle(data,context,ps[i],ps[i+1],ps[i+2],cs[i],cs[i+1],cs[i+2]);break;default:throw new Error("illegal figure")}}class MeshShadingPattern extends BaseShadingPattern{constructor(IR){
-super(),this._coords=IR[2],this._colors=IR[3],this._figures=IR[4],this._bounds=IR[5],this._bbox=IR[6],this._background=IR[7],this.matrix=null}_createMeshCanvas(combinedScale,backgroundColor,cachedCanvases){const offsetX=Math.floor(this._bounds[0]),offsetY=Math.floor(this._bounds[1]),boundsWidth=Math.ceil(this._bounds[2])-offsetX,boundsHeight=Math.ceil(this._bounds[3])-offsetY,width=Math.min(Math.ceil(Math.abs(boundsWidth*combinedScale[0]*1.1)),3e3),height=Math.min(Math.ceil(Math.abs(boundsHeight*
-combinedScale[1]*1.1)),3e3),scaleX=boundsWidth/width,scaleY=boundsHeight/height,context={coords:this._coords,colors:this._colors,offsetX:-offsetX,offsetY:-offsetY,scaleX:1/scaleX,scaleY:1/scaleY},paddedWidth=width+4,paddedHeight=height+4,tmpCanvas=cachedCanvases.getCanvas("mesh",paddedWidth,paddedHeight),tmpCtx=tmpCanvas.context,data=tmpCtx.createImageData(width,height);if(backgroundColor){const bytes=data.data;for(let i=0,ii=bytes.length;i<ii;i+=4)bytes[i]=backgroundColor[0],bytes[i+1]=backgroundColor[1],
-bytes[i+2]=backgroundColor[2],bytes[i+3]=255}for(const figure of this._figures)drawFigure(data,figure,context);return tmpCtx.putImageData(data,2,2),{canvas:tmpCanvas.canvas,offsetX:offsetX-2*scaleX,offsetY:offsetY-2*scaleY,scaleX,scaleY}}isModifyingCurrentTransform(){return!0}getPattern(ctx,owner,inverse,pathType){applyBoundingBox(ctx,this._bbox);const scale2=new Float32Array(2);if(pathType===PathType.SHADING)Util.singularValueDecompose2dScale(getCurrentTransform(ctx),scale2);else if(this.matrix){
-Util.singularValueDecompose2dScale(this.matrix,scale2);const[matrixScaleX,matrixScaleY]=scale2;Util.singularValueDecompose2dScale(owner.baseTransform,scale2),scale2[0]*=matrixScaleX,scale2[1]*=matrixScaleY}else Util.singularValueDecompose2dScale(owner.baseTransform,scale2);const temporaryPatternCanvas=this._createMeshCanvas(scale2,pathType===PathType.SHADING?null:this._background,owner.cachedCanvases);return pathType!==PathType.SHADING&&(ctx.setTransform(...owner.baseTransform),this.matrix&&ctx.
-transform(...this.matrix)),ctx.translate(temporaryPatternCanvas.offsetX,temporaryPatternCanvas.offsetY),ctx.scale(temporaryPatternCanvas.scaleX,temporaryPatternCanvas.scaleY),ctx.createPattern(temporaryPatternCanvas.canvas,"no-repeat")}}class DummyShadingPattern extends BaseShadingPattern{getPattern(){return"hotpink"}}function getShadingPattern(IR){switch(IR[0]){case"RadialAxial":return new RadialAxialShadingPattern(IR);case"Mesh":return new MeshShadingPattern(IR);case"Dummy":return new DummyShadingPattern}
-throw new Error(`Unknown IR type: ${IR[0]}`)}const PaintType={COLORED:1,UNCOLORED:2};class TilingPattern{static MAX_PATTERN_SIZE=3e3;constructor(IR,ctx,canvasGraphicsFactory,baseTransform){this.color=IR[1],this.operatorList=IR[2],this.matrix=IR[3],this.bbox=IR[4],this.xstep=IR[5],this.ystep=IR[6],this.paintType=IR[7],this.tilingType=IR[8],this.ctx=ctx,this.canvasGraphicsFactory=canvasGraphicsFactory,this.baseTransform=baseTransform}createPatternCanvas(owner){const{bbox,operatorList,paintType,tilingType,
-color,canvasGraphicsFactory}=this;let{xstep,ystep}=this;xstep=Math.abs(xstep),ystep=Math.abs(ystep),info("TilingType: "+tilingType);const x0=bbox[0],y0=bbox[1],x1=bbox[2],y1=bbox[3],width=x1-x0,height=y1-y0,scale2=new Float32Array(2);Util.singularValueDecompose2dScale(this.matrix,scale2);const[matrixScaleX,matrixScaleY]=scale2;Util.singularValueDecompose2dScale(this.baseTransform,scale2);const combinedScaleX=matrixScaleX*scale2[0],combinedScaleY=matrixScaleY*scale2[1];let canvasWidth=width,canvasHeight=height,
-redrawHorizontally=!1,redrawVertically=!1;const xScaledStep=Math.ceil(xstep*combinedScaleX),yScaledStep=Math.ceil(ystep*combinedScaleY),xScaledWidth=Math.ceil(width*combinedScaleX),yScaledHeight=Math.ceil(height*combinedScaleY);xScaledStep>=xScaledWidth?canvasWidth=xstep:redrawHorizontally=!0,yScaledStep>=yScaledHeight?canvasHeight=ystep:redrawVertically=!0;const dimx=this.getSizeAndScale(canvasWidth,this.ctx.canvas.width,combinedScaleX),dimy=this.getSizeAndScale(canvasHeight,this.ctx.canvas.height,
-combinedScaleY),tmpCanvas=owner.cachedCanvases.getCanvas("pattern",dimx.size,dimy.size),tmpCtx=tmpCanvas.context,graphics=canvasGraphicsFactory.createCanvasGraphics(tmpCtx);if(graphics.groupLevel=owner.groupLevel,this.setFillAndStrokeStyleToContext(graphics,paintType,color),tmpCtx.translate(-dimx.scale*x0,-dimy.scale*y0),graphics.transform(dimx.scale,0,0,dimy.scale,0,0),tmpCtx.save(),this.clipBbox(graphics,x0,y0,x1,y1),graphics.baseTransform=getCurrentTransform(graphics.ctx),graphics.executeOperatorList(
-operatorList),graphics.endDrawing(),tmpCtx.restore(),redrawHorizontally||redrawVertically){const image2=tmpCanvas.canvas;redrawHorizontally&&(canvasWidth=xstep),redrawVertically&&(canvasHeight=ystep);const dimx2=this.getSizeAndScale(canvasWidth,this.ctx.canvas.width,combinedScaleX),dimy2=this.getSizeAndScale(canvasHeight,this.ctx.canvas.height,combinedScaleY),xSize=dimx2.size,ySize=dimy2.size,tmpCanvas2=owner.cachedCanvases.getCanvas("pattern-workaround",xSize,ySize),tmpCtx2=tmpCanvas2.context,ii=redrawHorizontally?
-Math.floor(width/xstep):0,jj=redrawVertically?Math.floor(height/ystep):0;for(let i=0;i<=ii;i++)for(let j=0;j<=jj;j++)tmpCtx2.drawImage(image2,xSize*i,ySize*j,xSize,ySize,0,0,xSize,ySize);return{canvas:tmpCanvas2.canvas,scaleX:dimx2.scale,scaleY:dimy2.scale,offsetX:x0,offsetY:y0}}return{canvas:tmpCanvas.canvas,scaleX:dimx.scale,scaleY:dimy.scale,offsetX:x0,offsetY:y0}}getSizeAndScale(step,realOutputSize,scale2){const maxSize=Math.max(TilingPattern.MAX_PATTERN_SIZE,realOutputSize);let size2=Math.ceil(
-step*scale2);return size2>=maxSize?size2=maxSize:scale2=size2/step,{scale:scale2,size:size2}}clipBbox(graphics,x0,y0,x1,y1){const bboxWidth=x1-x0,bboxHeight=y1-y0;graphics.ctx.rect(x0,y0,bboxWidth,bboxHeight),Util.axialAlignedBoundingBox([x0,y0,x1,y1],getCurrentTransform(graphics.ctx),graphics.current.minMax),graphics.clip(),graphics.endPath()}setFillAndStrokeStyleToContext(graphics,paintType,color){const context=graphics.ctx,current2=graphics.current;switch(paintType){case PaintType.COLORED:const{
-fillStyle,strokeStyle}=this.ctx;context.fillStyle=current2.fillColor=fillStyle,context.strokeStyle=current2.strokeColor=strokeStyle;break;case PaintType.UNCOLORED:context.fillStyle=context.strokeStyle=color,current2.fillColor=current2.strokeColor=color;break;default:throw new FormatError(`Unsupported paint type: ${paintType}`)}}isModifyingCurrentTransform(){return!1}getPattern(ctx,owner,inverse,pathType){let matrix=inverse;pathType!==PathType.SHADING&&(matrix=Util.transform(matrix,owner.baseTransform),
-this.matrix&&(matrix=Util.transform(matrix,this.matrix)));const temporaryPatternCanvas=this.createPatternCanvas(owner);let domMatrix=new DOMMatrix(matrix);domMatrix=domMatrix.translate(temporaryPatternCanvas.offsetX,temporaryPatternCanvas.offsetY),domMatrix=domMatrix.scale(1/temporaryPatternCanvas.scaleX,1/temporaryPatternCanvas.scaleY);const pattern2=ctx.createPattern(temporaryPatternCanvas.canvas,"repeat");return pattern2.setTransform(domMatrix),pattern2}}function convertBlackAndWhiteToRGBA({src,
-srcPos=0,dest,width,height,nonBlackColor=4294967295,inverseDecode=!1}){const black=util_FeatureTest.isLittleEndian?4278190080:255,[zeroMapping,oneMapping]=inverseDecode?[nonBlackColor,black]:[black,nonBlackColor],widthInSource=width>>3,widthRemainder=width&7,srcLength=src.length;dest=new Uint32Array(dest.buffer);let destPos=0;for(let i=0;i<height;i++){for(const max2=srcPos+widthInSource;srcPos<max2;srcPos++){const elem2=srcPos<srcLength?src[srcPos]:255;dest[destPos++]=elem2&128?oneMapping:zeroMapping,
-dest[destPos++]=elem2&64?oneMapping:zeroMapping,dest[destPos++]=elem2&32?oneMapping:zeroMapping,dest[destPos++]=elem2&16?oneMapping:zeroMapping,dest[destPos++]=elem2&8?oneMapping:zeroMapping,dest[destPos++]=elem2&4?oneMapping:zeroMapping,dest[destPos++]=elem2&2?oneMapping:zeroMapping,dest[destPos++]=elem2&1?oneMapping:zeroMapping}if(widthRemainder===0)continue;const elem=srcPos<srcLength?src[srcPos++]:255;for(let j=0;j<widthRemainder;j++)dest[destPos++]=elem&1<<7-j?oneMapping:zeroMapping}return{
-srcPos,destPos}}const MIN_FONT_SIZE=16,MAX_FONT_SIZE=100,EXECUTION_TIME=15,EXECUTION_STEPS=10,FULL_CHUNK_HEIGHT=16,SCALE_MATRIX=new DOMMatrix,XY=new Float32Array(2),MIN_MAX_INIT=new Float32Array([1/0,1/0,-1/0,-1/0]);function mirrorContextOperations(ctx,destCtx){if(ctx._removeMirroring)throw new Error("Context is already forwarding operations.");ctx.__originalSave=ctx.save,ctx.__originalRestore=ctx.restore,ctx.__originalRotate=ctx.rotate,ctx.__originalScale=ctx.scale,ctx.__originalTranslate=ctx.translate,
-ctx.__originalTransform=ctx.transform,ctx.__originalSetTransform=ctx.setTransform,ctx.__originalResetTransform=ctx.resetTransform,ctx.__originalClip=ctx.clip,ctx.__originalMoveTo=ctx.moveTo,ctx.__originalLineTo=ctx.lineTo,ctx.__originalBezierCurveTo=ctx.bezierCurveTo,ctx.__originalRect=ctx.rect,ctx.__originalClosePath=ctx.closePath,ctx.__originalBeginPath=ctx.beginPath,ctx._removeMirroring=()=>{ctx.save=ctx.__originalSave,ctx.restore=ctx.__originalRestore,ctx.rotate=ctx.__originalRotate,ctx.scale=
-ctx.__originalScale,ctx.translate=ctx.__originalTranslate,ctx.transform=ctx.__originalTransform,ctx.setTransform=ctx.__originalSetTransform,ctx.resetTransform=ctx.__originalResetTransform,ctx.clip=ctx.__originalClip,ctx.moveTo=ctx.__originalMoveTo,ctx.lineTo=ctx.__originalLineTo,ctx.bezierCurveTo=ctx.__originalBezierCurveTo,ctx.rect=ctx.__originalRect,ctx.closePath=ctx.__originalClosePath,ctx.beginPath=ctx.__originalBeginPath,delete ctx._removeMirroring},ctx.save=function(){destCtx.save(),this.__originalSave()},
-ctx.restore=function(){destCtx.restore(),this.__originalRestore()},ctx.translate=function(x,y){destCtx.translate(x,y),this.__originalTranslate(x,y)},ctx.scale=function(x,y){destCtx.scale(x,y),this.__originalScale(x,y)},ctx.transform=function(a,b,c2,d2,e,f){destCtx.transform(a,b,c2,d2,e,f),this.__originalTransform(a,b,c2,d2,e,f)},ctx.setTransform=function(a,b,c2,d2,e,f){destCtx.setTransform(a,b,c2,d2,e,f),this.__originalSetTransform(a,b,c2,d2,e,f)},ctx.resetTransform=function(){destCtx.resetTransform(),
-this.__originalResetTransform()},ctx.rotate=function(angle){destCtx.rotate(angle),this.__originalRotate(angle)},ctx.clip=function(rule){destCtx.clip(rule),this.__originalClip(rule)},ctx.moveTo=function(x,y){destCtx.moveTo(x,y),this.__originalMoveTo(x,y)},ctx.lineTo=function(x,y){destCtx.lineTo(x,y),this.__originalLineTo(x,y)},ctx.bezierCurveTo=function(cp1x,cp1y,cp2x,cp2y,x,y){destCtx.bezierCurveTo(cp1x,cp1y,cp2x,cp2y,x,y),this.__originalBezierCurveTo(cp1x,cp1y,cp2x,cp2y,x,y)},ctx.rect=function(x,y,width,height){
-destCtx.rect(x,y,width,height),this.__originalRect(x,y,width,height)},ctx.closePath=function(){destCtx.closePath(),this.__originalClosePath()},ctx.beginPath=function(){destCtx.beginPath(),this.__originalBeginPath()}}class CachedCanvases{constructor(canvasFactory){this.canvasFactory=canvasFactory,this.cache=Object.create(null)}getCanvas(id2,width,height){let canvasEntry;return this.cache[id2]!==void 0?(canvasEntry=this.cache[id2],this.canvasFactory.reset(canvasEntry,width,height)):(canvasEntry=this.
-canvasFactory.create(width,height),this.cache[id2]=canvasEntry),canvasEntry}delete(id2){delete this.cache[id2]}clear(){for(const id2 in this.cache){const canvasEntry=this.cache[id2];this.canvasFactory.destroy(canvasEntry),delete this.cache[id2]}}}function drawImageAtIntegerCoords(ctx,srcImg,srcX,srcY,srcW,srcH,destX,destY,destW,destH){const[a,b,c2,d2,tx,ty]=getCurrentTransform(ctx);if(b===0&&c2===0){const tlX=destX*a+tx,rTlX=Math.round(tlX),tlY=destY*d2+ty,rTlY=Math.round(tlY),brX=(destX+destW)*
-a+tx,rWidth=Math.abs(Math.round(brX)-rTlX)||1,brY=(destY+destH)*d2+ty,rHeight=Math.abs(Math.round(brY)-rTlY)||1;return ctx.setTransform(Math.sign(a),0,0,Math.sign(d2),rTlX,rTlY),ctx.drawImage(srcImg,srcX,srcY,srcW,srcH,0,0,rWidth,rHeight),ctx.setTransform(a,b,c2,d2,tx,ty),[rWidth,rHeight]}if(a===0&&d2===0){const tlX=destY*c2+tx,rTlX=Math.round(tlX),tlY=destX*b+ty,rTlY=Math.round(tlY),brX=(destY+destH)*c2+tx,rWidth=Math.abs(Math.round(brX)-rTlX)||1,brY=(destX+destW)*b+ty,rHeight=Math.abs(Math.round(
-brY)-rTlY)||1;return ctx.setTransform(0,Math.sign(b),Math.sign(c2),0,rTlX,rTlY),ctx.drawImage(srcImg,srcX,srcY,srcW,srcH,0,0,rHeight,rWidth),ctx.setTransform(a,b,c2,d2,tx,ty),[rHeight,rWidth]}ctx.drawImage(srcImg,srcX,srcY,srcW,srcH,destX,destY,destW,destH);const scaleX=Math.hypot(a,b),scaleY=Math.hypot(c2,d2);return[scaleX*destW,scaleY*destH]}class CanvasExtraState{alphaIsShape=!1;fontSize=0;fontSizeScale=1;textMatrix=null;textMatrixScale=1;fontMatrix=FONT_IDENTITY_MATRIX;leading=0;x=0;y=0;lineX=0;lineY=0;charSpacing=0;wordSpacing=0;textHScale=1;textRenderingMode=TextRenderingMode.
-FILL;textRise=0;fillColor="#000000";strokeColor="#000000";patternFill=!1;patternStroke=!1;fillAlpha=1;strokeAlpha=1;lineWidth=1;activeSMask=null;transferMaps="none";constructor(width,height){this.clipBox=new Float32Array([0,0,width,height]),this.minMax=MIN_MAX_INIT.slice()}clone(){const clone2=Object.create(this);return clone2.clipBox=this.clipBox.slice(),clone2.minMax=this.minMax.slice(),clone2}getPathBoundingBox(pathType=PathType.FILL,transform2=null){const box2=this.minMax.slice();if(pathType===
-PathType.STROKE){transform2||unreachable("Stroke bounding box must include transform."),Util.singularValueDecompose2dScale(transform2,XY);const xStrokePad=XY[0]*this.lineWidth/2,yStrokePad=XY[1]*this.lineWidth/2;box2[0]-=xStrokePad,box2[1]-=yStrokePad,box2[2]+=xStrokePad,box2[3]+=yStrokePad}return box2}updateClipFromPath(){const intersect=Util.intersect(this.clipBox,this.getPathBoundingBox());this.startNewPathAndClipBox(intersect||[0,0,0,0])}isEmptyClip(){return this.minMax[0]===1/0}startNewPathAndClipBox(box2){
-this.clipBox.set(box2,0),this.minMax.set(MIN_MAX_INIT,0)}getClippedPathBoundingBox(pathType=PathType.FILL,transform2=null){return Util.intersect(this.clipBox,this.getPathBoundingBox(pathType,transform2))}}function putBinaryImageData(ctx,imgData){if(imgData instanceof ImageData){ctx.putImageData(imgData,0,0);return}const height=imgData.height,width=imgData.width,partialChunkHeight=height%FULL_CHUNK_HEIGHT,fullChunks=(height-partialChunkHeight)/FULL_CHUNK_HEIGHT,totalChunks=partialChunkHeight===0?
-fullChunks:fullChunks+1,chunkImgData=ctx.createImageData(width,FULL_CHUNK_HEIGHT);let srcPos=0,destPos;const src=imgData.data,dest=chunkImgData.data;let i,j,thisChunkHeight,elemsInThisChunk;if(imgData.kind===util_ImageKind.GRAYSCALE_1BPP){const srcLength=src.byteLength,dest32=new Uint32Array(dest.buffer,0,dest.byteLength>>2),dest32DataLength=dest32.length,fullSrcDiff=width+7>>3,white=4294967295,black=util_FeatureTest.isLittleEndian?4278190080:255;for(i=0;i<totalChunks;i++){for(thisChunkHeight=i<
-fullChunks?FULL_CHUNK_HEIGHT:partialChunkHeight,destPos=0,j=0;j<thisChunkHeight;j++){const srcDiff=srcLength-srcPos;let k=0;const kEnd=srcDiff>fullSrcDiff?width:srcDiff*8-7,kEndUnrolled=kEnd&-8;let mask=0,srcByte=0;for(;k<kEndUnrolled;k+=8)srcByte=src[srcPos++],dest32[destPos++]=srcByte&128?white:black,dest32[destPos++]=srcByte&64?white:black,dest32[destPos++]=srcByte&32?white:black,dest32[destPos++]=srcByte&16?white:black,dest32[destPos++]=srcByte&8?white:black,dest32[destPos++]=srcByte&4?white:
-black,dest32[destPos++]=srcByte&2?white:black,dest32[destPos++]=srcByte&1?white:black;for(;k<kEnd;k++)mask===0&&(srcByte=src[srcPos++],mask=128),dest32[destPos++]=srcByte&mask?white:black,mask>>=1}for(;destPos<dest32DataLength;)dest32[destPos++]=0;ctx.putImageData(chunkImgData,0,i*FULL_CHUNK_HEIGHT)}}else if(imgData.kind===util_ImageKind.RGBA_32BPP){for(j=0,elemsInThisChunk=width*FULL_CHUNK_HEIGHT*4,i=0;i<fullChunks;i++)dest.set(src.subarray(srcPos,srcPos+elemsInThisChunk)),srcPos+=elemsInThisChunk,
-ctx.putImageData(chunkImgData,0,j),j+=FULL_CHUNK_HEIGHT;i<totalChunks&&(elemsInThisChunk=width*partialChunkHeight*4,dest.set(src.subarray(srcPos,srcPos+elemsInThisChunk)),ctx.putImageData(chunkImgData,0,j))}else if(imgData.kind===util_ImageKind.RGB_24BPP)for(thisChunkHeight=FULL_CHUNK_HEIGHT,elemsInThisChunk=width*thisChunkHeight,i=0;i<totalChunks;i++){for(i>=fullChunks&&(thisChunkHeight=partialChunkHeight,elemsInThisChunk=width*thisChunkHeight),destPos=0,j=elemsInThisChunk;j--;)dest[destPos++]=
-src[srcPos++],dest[destPos++]=src[srcPos++],dest[destPos++]=src[srcPos++],dest[destPos++]=255;ctx.putImageData(chunkImgData,0,i*FULL_CHUNK_HEIGHT)}else throw new Error(`bad image kind: ${imgData.kind}`)}function putBinaryImageMask(ctx,imgData){if(imgData.bitmap){ctx.drawImage(imgData.bitmap,0,0);return}const height=imgData.height,width=imgData.width,partialChunkHeight=height%FULL_CHUNK_HEIGHT,fullChunks=(height-partialChunkHeight)/FULL_CHUNK_HEIGHT,totalChunks=partialChunkHeight===0?fullChunks:fullChunks+
-1,chunkImgData=ctx.createImageData(width,FULL_CHUNK_HEIGHT);let srcPos=0;const src=imgData.data,dest=chunkImgData.data;for(let i=0;i<totalChunks;i++){const thisChunkHeight=i<fullChunks?FULL_CHUNK_HEIGHT:partialChunkHeight;({srcPos}=convertBlackAndWhiteToRGBA({src,srcPos,dest,width,height:thisChunkHeight,nonBlackColor:0})),ctx.putImageData(chunkImgData,0,i*FULL_CHUNK_HEIGHT)}}function copyCtxState(sourceCtx,destCtx){const properties2=["strokeStyle","fillStyle","fillRule","globalAlpha","lineWidth",
-"lineCap","lineJoin","miterLimit","globalCompositeOperation","font","filter"];for(const property of properties2)sourceCtx[property]!==void 0&&(destCtx[property]=sourceCtx[property]);sourceCtx.setLineDash!==void 0&&(destCtx.setLineDash(sourceCtx.getLineDash()),destCtx.lineDashOffset=sourceCtx.lineDashOffset)}function resetCtxToDefault(ctx){ctx.strokeStyle=ctx.fillStyle="#000000",ctx.fillRule="nonzero",ctx.globalAlpha=1,ctx.lineWidth=1,ctx.lineCap="butt",ctx.lineJoin="miter",ctx.miterLimit=10,ctx.
-globalCompositeOperation="source-over",ctx.font="10px sans-serif",ctx.setLineDash!==void 0&&(ctx.setLineDash([]),ctx.lineDashOffset=0);const{filter}=ctx;filter!=="none"&&filter!==""&&(ctx.filter="none")}function getImageSmoothingEnabled(transform2,interpolate){if(interpolate)return!0;Util.singularValueDecompose2dScale(transform2,XY);const actualScale=Math.fround(OutputScale.pixelRatio*PixelsPerInch.PDF_TO_CSS_UNITS);return XY[0]<=actualScale&&XY[1]<=actualScale}const LINE_CAP_STYLES=["butt","rou\
-nd","square"],LINE_JOIN_STYLES=["miter","round","bevel"],NORMAL_CLIP={},EO_CLIP={};class CanvasGraphics{constructor(canvasCtx,commonObjs,objs,canvasFactory,filterFactory,{optionalContentConfig,markedContentStack=null},annotationCanvasMap,pageColors){this.ctx=canvasCtx,this.current=new CanvasExtraState(this.ctx.canvas.width,this.ctx.canvas.height),this.stateStack=[],this.pendingClip=null,this.pendingEOFill=!1,this.res=null,this.xobjs=null,this.commonObjs=commonObjs,this.objs=objs,this.canvasFactory=
-canvasFactory,this.filterFactory=filterFactory,this.groupStack=[],this.baseTransform=null,this.baseTransformStack=[],this.groupLevel=0,this.smaskStack=[],this.smaskCounter=0,this.tempSMask=null,this.suspendedCtx=null,this.contentVisible=!0,this.markedContentStack=markedContentStack||[],this.optionalContentConfig=optionalContentConfig,this.cachedCanvases=new CachedCanvases(this.canvasFactory),this.cachedPatterns=new Map,this.annotationCanvasMap=annotationCanvasMap,this.viewportScale=1,this.outputScaleX=
-1,this.outputScaleY=1,this.pageColors=pageColors,this._cachedScaleForStroking=[-1,0],this._cachedGetSinglePixelWidth=null,this._cachedBitmapsMap=new Map}getObject(data,fallback2=null){return typeof data=="string"?data.startsWith("g_")?this.commonObjs.get(data):this.objs.get(data):fallback2}beginDrawing({transform:transform2,viewport,transparency=!1,background=null}){const width=this.ctx.canvas.width,height=this.ctx.canvas.height,savedFillStyle=this.ctx.fillStyle;if(this.ctx.fillStyle=background||
-"#ffffff",this.ctx.fillRect(0,0,width,height),this.ctx.fillStyle=savedFillStyle,transparency){const transparentCanvas=this.cachedCanvases.getCanvas("transparent",width,height);this.compositeCtx=this.ctx,this.transparentCanvas=transparentCanvas.canvas,this.ctx=transparentCanvas.context,this.ctx.save(),this.ctx.transform(...getCurrentTransform(this.compositeCtx))}this.ctx.save(),resetCtxToDefault(this.ctx),transform2&&(this.ctx.transform(...transform2),this.outputScaleX=transform2[0],this.outputScaleY=
-transform2[0]),this.ctx.transform(...viewport.transform),this.viewportScale=viewport.scale,this.baseTransform=getCurrentTransform(this.ctx)}executeOperatorList(operatorList,executionStartIdx,continueCallback,stepper){const argsArray=operatorList.argsArray,fnArray=operatorList.fnArray;let i=executionStartIdx||0;const argsArrayLen=argsArray.length;if(argsArrayLen===i)return i;const chunkOperations=argsArrayLen-i>EXECUTION_STEPS&&typeof continueCallback=="function",endTime=chunkOperations?Date.now()+
-EXECUTION_TIME:0;let steps=0;const commonObjs=this.commonObjs,objs=this.objs;let fnId;for(;;){if(stepper!==void 0&&i===stepper.nextBreakPoint)return stepper.breakIt(i,continueCallback),i;if(fnId=fnArray[i],fnId!==OPS.dependency)this[fnId].apply(this,argsArray[i]);else for(const depObjId of argsArray[i]){const objsPool=depObjId.startsWith("g_")?commonObjs:objs;if(!objsPool.has(depObjId))return objsPool.get(depObjId,continueCallback),i}if(i++,i===argsArrayLen)return i;if(chunkOperations&&++steps>EXECUTION_STEPS){
-if(Date.now()>endTime)return continueCallback(),i;steps=0}}}#restoreInitialState(){for(;this.stateStack.length||this.inSMaskMode;)this.restore();this.current.activeSMask=null,this.ctx.restore(),this.transparentCanvas&&(this.ctx=this.compositeCtx,this.ctx.save(),this.ctx.setTransform(1,0,0,1,0,0),this.ctx.drawImage(this.transparentCanvas,0,0),this.ctx.restore(),this.transparentCanvas=null)}endDrawing(){this.#restoreInitialState(),this.cachedCanvases.clear(),this.cachedPatterns.clear();for(const cache2 of this.
-_cachedBitmapsMap.values()){for(const canvas of cache2.values())typeof HTMLCanvasElement<"u"&&canvas instanceof HTMLCanvasElement&&(canvas.width=canvas.height=0);cache2.clear()}this._cachedBitmapsMap.clear(),this.#drawFilter()}#drawFilter(){if(this.pageColors){const hcmFilterId=this.filterFactory.addHCMFilter(this.pageColors.foreground,this.pageColors.background);if(hcmFilterId!=="none"){const savedFilter=this.ctx.filter;this.ctx.filter=hcmFilterId,this.ctx.drawImage(this.ctx.canvas,0,0),this.ctx.
-filter=savedFilter}}}_scaleImage(img,inverseTransform){const width=img.width??img.displayWidth,height=img.height??img.displayHeight;let widthScale=Math.max(Math.hypot(inverseTransform[0],inverseTransform[1]),1),heightScale=Math.max(Math.hypot(inverseTransform[2],inverseTransform[3]),1),paintWidth=width,paintHeight=height,tmpCanvasId="prescale1",tmpCanvas,tmpCtx;for(;widthScale>2&&paintWidth>1||heightScale>2&&paintHeight>1;){let newWidth=paintWidth,newHeight=paintHeight;widthScale>2&&paintWidth>1&&
-(newWidth=paintWidth>=16384?Math.floor(paintWidth/2)-1||1:Math.ceil(paintWidth/2),widthScale/=paintWidth/newWidth),heightScale>2&&paintHeight>1&&(newHeight=paintHeight>=16384?Math.floor(paintHeight/2)-1||1:Math.ceil(paintHeight)/2,heightScale/=paintHeight/newHeight),tmpCanvas=this.cachedCanvases.getCanvas(tmpCanvasId,newWidth,newHeight),tmpCtx=tmpCanvas.context,tmpCtx.clearRect(0,0,newWidth,newHeight),tmpCtx.drawImage(img,0,0,paintWidth,paintHeight,0,0,newWidth,newHeight),img=tmpCanvas.canvas,paintWidth=
-newWidth,paintHeight=newHeight,tmpCanvasId=tmpCanvasId==="prescale1"?"prescale2":"prescale1"}return{img,paintWidth,paintHeight}}_createMaskCanvas(img){const ctx=this.ctx,{width,height}=img,fillColor=this.current.fillColor,isPatternFill=this.current.patternFill,currentTransform=getCurrentTransform(ctx);let cache2,cacheKey,scaled,maskCanvas;if((img.bitmap||img.data)&&img.count>1){const mainKey=img.bitmap||img.data.buffer;cacheKey=JSON.stringify(isPatternFill?currentTransform:[currentTransform.slice(
-0,4),fillColor]),cache2=this._cachedBitmapsMap.get(mainKey),cache2||(cache2=new Map,this._cachedBitmapsMap.set(mainKey,cache2));const cachedImage=cache2.get(cacheKey);if(cachedImage&&!isPatternFill){const offsetX2=Math.round(Math.min(currentTransform[0],currentTransform[2])+currentTransform[4]),offsetY2=Math.round(Math.min(currentTransform[1],currentTransform[3])+currentTransform[5]);return{canvas:cachedImage,offsetX:offsetX2,offsetY:offsetY2}}scaled=cachedImage}scaled||(maskCanvas=this.cachedCanvases.
-getCanvas("maskCanvas",width,height),putBinaryImageMask(maskCanvas.context,img));let maskToCanvas=Util.transform(currentTransform,[1/width,0,0,-1/height,0,0]);maskToCanvas=Util.transform(maskToCanvas,[1,0,0,1,0,-height]);const minMax=MIN_MAX_INIT.slice();Util.axialAlignedBoundingBox([0,0,width,height],maskToCanvas,minMax);const[minX,minY,maxX,maxY]=minMax,drawnWidth=Math.round(maxX-minX)||1,drawnHeight=Math.round(maxY-minY)||1,fillCanvas=this.cachedCanvases.getCanvas("fillCanvas",drawnWidth,drawnHeight),
-fillCtx=fillCanvas.context,offsetX=minX,offsetY=minY;fillCtx.translate(-offsetX,-offsetY),fillCtx.transform(...maskToCanvas),scaled||(scaled=this._scaleImage(maskCanvas.canvas,getCurrentTransformInverse(fillCtx)),scaled=scaled.img,cache2&&isPatternFill&&cache2.set(cacheKey,scaled)),fillCtx.imageSmoothingEnabled=getImageSmoothingEnabled(getCurrentTransform(fillCtx),img.interpolate),drawImageAtIntegerCoords(fillCtx,scaled,0,0,scaled.width,scaled.height,0,0,width,height),fillCtx.globalCompositeOperation=
-"source-in";const inverse=Util.transform(getCurrentTransformInverse(fillCtx),[1,0,0,1,-offsetX,-offsetY]);return fillCtx.fillStyle=isPatternFill?fillColor.getPattern(ctx,this,inverse,PathType.FILL):fillColor,fillCtx.fillRect(0,0,width,height),cache2&&!isPatternFill&&(this.cachedCanvases.delete("fillCanvas"),cache2.set(cacheKey,fillCanvas.canvas)),{canvas:fillCanvas.canvas,offsetX:Math.round(offsetX),offsetY:Math.round(offsetY)}}setLineWidth(width){width!==this.current.lineWidth&&(this._cachedScaleForStroking[0]=
--1),this.current.lineWidth=width,this.ctx.lineWidth=width}setLineCap(style2){this.ctx.lineCap=LINE_CAP_STYLES[style2]}setLineJoin(style2){this.ctx.lineJoin=LINE_JOIN_STYLES[style2]}setMiterLimit(limit2){this.ctx.miterLimit=limit2}setDash(dashArray,dashPhase){const ctx=this.ctx;ctx.setLineDash!==void 0&&(ctx.setLineDash(dashArray),ctx.lineDashOffset=dashPhase)}setRenderingIntent(intent){}setFlatness(flatness){}setGState(states){for(const[key2,value]of states)switch(key2){case"LW":this.setLineWidth(
-value);break;case"LC":this.setLineCap(value);break;case"LJ":this.setLineJoin(value);break;case"ML":this.setMiterLimit(value);break;case"D":this.setDash(value[0],value[1]);break;case"RI":this.setRenderingIntent(value);break;case"FL":this.setFlatness(value);break;case"Font":this.setFont(value[0],value[1]);break;case"CA":this.current.strokeAlpha=value;break;case"ca":this.ctx.globalAlpha=this.current.fillAlpha=value;break;case"BM":this.ctx.globalCompositeOperation=value;break;case"SMask":this.current.
-activeSMask=value?this.tempSMask:null,this.tempSMask=null,this.checkSMaskState();break;case"TR":this.ctx.filter=this.current.transferMaps=this.filterFactory.addFilter(value);break}}get inSMaskMode(){return!!this.suspendedCtx}checkSMaskState(){const inSMaskMode=this.inSMaskMode;this.current.activeSMask&&!inSMaskMode?this.beginSMaskMode():!this.current.activeSMask&&inSMaskMode&&this.endSMaskMode()}beginSMaskMode(){if(this.inSMaskMode)throw new Error("beginSMaskMode called while already in smask mo\
-de");const drawnWidth=this.ctx.canvas.width,drawnHeight=this.ctx.canvas.height,cacheId="smaskGroupAt"+this.groupLevel,scratchCanvas=this.cachedCanvases.getCanvas(cacheId,drawnWidth,drawnHeight);this.suspendedCtx=this.ctx;const ctx=this.ctx=scratchCanvas.context;ctx.setTransform(this.suspendedCtx.getTransform()),copyCtxState(this.suspendedCtx,ctx),mirrorContextOperations(ctx,this.suspendedCtx),this.setGState([["BM","source-over"]])}endSMaskMode(){if(!this.inSMaskMode)throw new Error("endSMaskMode\
- called while not in smask mode");this.ctx._removeMirroring(),copyCtxState(this.ctx,this.suspendedCtx),this.ctx=this.suspendedCtx,this.suspendedCtx=null}compose(dirtyBox){if(!this.current.activeSMask)return;dirtyBox?(dirtyBox[0]=Math.floor(dirtyBox[0]),dirtyBox[1]=Math.floor(dirtyBox[1]),dirtyBox[2]=Math.ceil(dirtyBox[2]),dirtyBox[3]=Math.ceil(dirtyBox[3])):dirtyBox=[0,0,this.ctx.canvas.width,this.ctx.canvas.height];const smask=this.current.activeSMask,suspendedCtx=this.suspendedCtx;this.composeSMask(
-suspendedCtx,smask,this.ctx,dirtyBox),this.ctx.save(),this.ctx.setTransform(1,0,0,1,0,0),this.ctx.clearRect(0,0,this.ctx.canvas.width,this.ctx.canvas.height),this.ctx.restore()}composeSMask(ctx,smask,layerCtx,layerBox){const layerOffsetX=layerBox[0],layerOffsetY=layerBox[1],layerWidth=layerBox[2]-layerOffsetX,layerHeight=layerBox[3]-layerOffsetY;layerWidth===0||layerHeight===0||(this.genericComposeSMask(smask.context,layerCtx,layerWidth,layerHeight,smask.subtype,smask.backdrop,smask.transferMap,
-layerOffsetX,layerOffsetY,smask.offsetX,smask.offsetY),ctx.save(),ctx.globalAlpha=1,ctx.globalCompositeOperation="source-over",ctx.setTransform(1,0,0,1,0,0),ctx.drawImage(layerCtx.canvas,0,0),ctx.restore())}genericComposeSMask(maskCtx,layerCtx,width,height,subtype,backdrop,transferMap,layerOffsetX,layerOffsetY,maskOffsetX,maskOffsetY){let maskCanvas=maskCtx.canvas,maskX=layerOffsetX-maskOffsetX,maskY=layerOffsetY-maskOffsetY;if(backdrop)if(maskX<0||maskY<0||maskX+width>maskCanvas.width||maskY+height>
-maskCanvas.height){const canvas=this.cachedCanvases.getCanvas("maskExtension",width,height),ctx=canvas.context;ctx.drawImage(maskCanvas,-maskX,-maskY),ctx.globalCompositeOperation="destination-atop",ctx.fillStyle=backdrop,ctx.fillRect(0,0,width,height),ctx.globalCompositeOperation="source-over",maskCanvas=canvas.canvas,maskX=maskY=0}else{maskCtx.save(),maskCtx.globalAlpha=1,maskCtx.setTransform(1,0,0,1,0,0);const clip2=new Path2D;clip2.rect(maskX,maskY,width,height),maskCtx.clip(clip2),maskCtx.globalCompositeOperation=
-"destination-atop",maskCtx.fillStyle=backdrop,maskCtx.fillRect(maskX,maskY,width,height),maskCtx.restore()}layerCtx.save(),layerCtx.globalAlpha=1,layerCtx.setTransform(1,0,0,1,0,0),subtype==="Alpha"&&transferMap?layerCtx.filter=this.filterFactory.addAlphaFilter(transferMap):subtype==="Luminosity"&&(layerCtx.filter=this.filterFactory.addLuminosityFilter(transferMap));const clip=new Path2D;clip.rect(layerOffsetX,layerOffsetY,width,height),layerCtx.clip(clip),layerCtx.globalCompositeOperation="dest\
-ination-in",layerCtx.drawImage(maskCanvas,maskX,maskY,width,height,layerOffsetX,layerOffsetY,width,height),layerCtx.restore()}save(){this.inSMaskMode&&copyCtxState(this.ctx,this.suspendedCtx),this.ctx.save();const old=this.current;this.stateStack.push(old),this.current=old.clone()}restore(){if(this.stateStack.length===0){this.inSMaskMode&&this.endSMaskMode();return}this.current=this.stateStack.pop(),this.ctx.restore(),this.inSMaskMode&&copyCtxState(this.suspendedCtx,this.ctx),this.checkSMaskState(),
-this.pendingClip=null,this._cachedScaleForStroking[0]=-1,this._cachedGetSinglePixelWidth=null}transform(a,b,c2,d2,e,f){this.ctx.transform(a,b,c2,d2,e,f),this._cachedScaleForStroking[0]=-1,this._cachedGetSinglePixelWidth=null}constructPath(op2,data,minMax){let[path2]=data;if(!minMax){path2||=data[0]=new Path2D,this[op2](path2);return}if(!(path2 instanceof Path2D)){const path2d=data[0]=new Path2D;for(let i=0,ii=path2.length;i<ii;)switch(path2[i++]){case DrawOPS.moveTo:path2d.moveTo(path2[i++],path2[i++]);
-break;case DrawOPS.lineTo:path2d.lineTo(path2[i++],path2[i++]);break;case DrawOPS.curveTo:path2d.bezierCurveTo(path2[i++],path2[i++],path2[i++],path2[i++],path2[i++],path2[i++]);break;case DrawOPS.closePath:path2d.closePath();break;default:warn(`Unrecognized drawing path operator: ${path2[i-1]}`);break}path2=path2d}Util.axialAlignedBoundingBox(minMax,getCurrentTransform(this.ctx),this.current.minMax),this[op2](path2)}closePath(){this.ctx.closePath()}stroke(path2,consumePath=!0){const ctx=this.ctx,
-strokeColor=this.current.strokeColor;if(ctx.globalAlpha=this.current.strokeAlpha,this.contentVisible)if(typeof strokeColor=="object"&&strokeColor?.getPattern){const baseTransform=strokeColor.isModifyingCurrentTransform()?ctx.getTransform():null;if(ctx.save(),ctx.strokeStyle=strokeColor.getPattern(ctx,this,getCurrentTransformInverse(ctx),PathType.STROKE),baseTransform){const newPath=new Path2D;newPath.addPath(path2,ctx.getTransform().invertSelf().multiplySelf(baseTransform)),path2=newPath}this.rescaleAndStroke(
-path2,!1),ctx.restore()}else this.rescaleAndStroke(path2,!0);consumePath&&this.consumePath(path2,this.current.getClippedPathBoundingBox(PathType.STROKE,getCurrentTransform(this.ctx))),ctx.globalAlpha=this.current.fillAlpha}closeStroke(path2){this.stroke(path2)}fill(path2,consumePath=!0){const ctx=this.ctx,fillColor=this.current.fillColor,isPatternFill=this.current.patternFill;let needRestore=!1;if(isPatternFill){const baseTransform=fillColor.isModifyingCurrentTransform()?ctx.getTransform():null;
-if(ctx.save(),ctx.fillStyle=fillColor.getPattern(ctx,this,getCurrentTransformInverse(ctx),PathType.FILL),baseTransform){const newPath=new Path2D;newPath.addPath(path2,ctx.getTransform().invertSelf().multiplySelf(baseTransform)),path2=newPath}needRestore=!0}const intersect=this.current.getClippedPathBoundingBox();this.contentVisible&&intersect!==null&&(this.pendingEOFill?(ctx.fill(path2,"evenodd"),this.pendingEOFill=!1):ctx.fill(path2)),needRestore&&ctx.restore(),consumePath&&this.consumePath(path2,
-intersect)}eoFill(path2){this.pendingEOFill=!0,this.fill(path2)}fillStroke(path2){this.fill(path2,!1),this.stroke(path2,!1),this.consumePath(path2)}eoFillStroke(path2){this.pendingEOFill=!0,this.fillStroke(path2)}closeFillStroke(path2){this.fillStroke(path2)}closeEOFillStroke(path2){this.pendingEOFill=!0,this.fillStroke(path2)}endPath(path2){this.consumePath(path2)}rawFillPath(path2){this.ctx.fill(path2)}clip(){this.pendingClip=NORMAL_CLIP}eoClip(){this.pendingClip=EO_CLIP}beginText(){this.current.
-textMatrix=null,this.current.textMatrixScale=1,this.current.x=this.current.lineX=0,this.current.y=this.current.lineY=0}endText(){const paths=this.pendingTextPaths,ctx=this.ctx;if(paths===void 0)return;const newPath=new Path2D,invTransf=ctx.getTransform().invertSelf();for(const{transform:transform2,x,y,fontSize,path:path2}of paths)path2&&newPath.addPath(path2,new DOMMatrix(transform2).preMultiplySelf(invTransf).translate(x,y).scale(fontSize,-fontSize));ctx.clip(newPath),delete this.pendingTextPaths}setCharSpacing(spacing2){
-this.current.charSpacing=spacing2}setWordSpacing(spacing2){this.current.wordSpacing=spacing2}setHScale(scale2){this.current.textHScale=scale2/100}setLeading(leading){this.current.leading=-leading}setFont(fontRefName,size2){const fontObj=this.commonObjs.get(fontRefName),current2=this.current;if(!fontObj)throw new Error(`Can't find font for ${fontRefName}`);if(current2.fontMatrix=fontObj.fontMatrix||FONT_IDENTITY_MATRIX,(current2.fontMatrix[0]===0||current2.fontMatrix[3]===0)&&warn("Invalid font m\
-atrix for font "+fontRefName),size2<0?(size2=-size2,current2.fontDirection=-1):current2.fontDirection=1,this.current.font=fontObj,this.current.fontSize=size2,fontObj.isType3Font)return;const name=fontObj.loadedName||"sans-serif",typeface=fontObj.systemFontInfo?.css||`"${name}", ${fontObj.fallbackName}`;let bold="normal";fontObj.black?bold="900":fontObj.bold&&(bold="bold");const italic=fontObj.italic?"italic":"normal";let browserFontSize=size2;size2<MIN_FONT_SIZE?browserFontSize=MIN_FONT_SIZE:size2>
-MAX_FONT_SIZE&&(browserFontSize=MAX_FONT_SIZE),this.current.fontSizeScale=size2/browserFontSize,this.ctx.font=`${italic} ${bold} ${browserFontSize}px ${typeface}`}setTextRenderingMode(mode){this.current.textRenderingMode=mode}setTextRise(rise){this.current.textRise=rise}moveText(x,y){this.current.x=this.current.lineX+=x,this.current.y=this.current.lineY+=y}setLeadingMoveText(x,y){this.setLeading(-y),this.moveText(x,y)}setTextMatrix(matrix){const{current:current2}=this;current2.textMatrix=matrix,
-current2.textMatrixScale=Math.hypot(matrix[0],matrix[1]),current2.x=current2.lineX=0,current2.y=current2.lineY=0}nextLine(){this.moveText(0,this.current.leading)}#getScaledPath(path2,currentTransform,transform2){const newPath=new Path2D;return newPath.addPath(path2,new DOMMatrix(transform2).invertSelf().multiplySelf(currentTransform)),newPath}paintChar(character,x,y,patternFillTransform,patternStrokeTransform){const ctx=this.ctx,current2=this.current,font=current2.font,textRenderingMode=current2.
-textRenderingMode,fontSize=current2.fontSize/current2.fontSizeScale,fillStrokeMode=textRenderingMode&TextRenderingMode.FILL_STROKE_MASK,isAddToPathSet=!!(textRenderingMode&TextRenderingMode.ADD_TO_PATH_FLAG),patternFill=current2.patternFill&&!font.missingFile,patternStroke=current2.patternStroke&&!font.missingFile;let path2;if((font.disableFontFace||isAddToPathSet||patternFill||patternStroke)&&!font.missingFile&&(path2=font.getPathGenerator(this.commonObjs,character)),path2&&(font.disableFontFace||
-patternFill||patternStroke)){ctx.save(),ctx.translate(x,y),ctx.scale(fontSize,-fontSize);let currentTransform;if((fillStrokeMode===TextRenderingMode.FILL||fillStrokeMode===TextRenderingMode.FILL_STROKE)&&(patternFillTransform?(currentTransform=ctx.getTransform(),ctx.setTransform(...patternFillTransform),ctx.fill(this.#getScaledPath(path2,currentTransform,patternFillTransform))):ctx.fill(path2)),fillStrokeMode===TextRenderingMode.STROKE||fillStrokeMode===TextRenderingMode.FILL_STROKE)if(patternStrokeTransform){
-currentTransform||=ctx.getTransform(),ctx.setTransform(...patternStrokeTransform);const{a,b,c:c2,d:d2}=currentTransform,invPatternTransform=Util.inverseTransform(patternStrokeTransform),transf=Util.transform([a,b,c2,d2,0,0],invPatternTransform);Util.singularValueDecompose2dScale(transf,XY),ctx.lineWidth*=Math.max(XY[0],XY[1])/fontSize,ctx.stroke(this.#getScaledPath(path2,currentTransform,patternStrokeTransform))}else ctx.lineWidth/=fontSize,ctx.stroke(path2);ctx.restore()}else(fillStrokeMode===TextRenderingMode.
-FILL||fillStrokeMode===TextRenderingMode.FILL_STROKE)&&ctx.fillText(character,x,y),(fillStrokeMode===TextRenderingMode.STROKE||fillStrokeMode===TextRenderingMode.FILL_STROKE)&&ctx.strokeText(character,x,y);isAddToPathSet&&(this.pendingTextPaths||=[]).push({transform:getCurrentTransform(ctx),x,y,fontSize,path:path2})}get isFontSubpixelAAEnabled(){const{context:ctx}=this.cachedCanvases.getCanvas("isFontSubpixelAAEnabled",10,10);ctx.scale(1.5,1),ctx.fillText("I",0,10);const data=ctx.getImageData(0,
-0,10,10).data;let enabled=!1;for(let i=3;i<data.length;i+=4)if(data[i]>0&&data[i]<255){enabled=!0;break}return shadow(this,"isFontSubpixelAAEnabled",enabled)}showText(glyphs){const current2=this.current,font=current2.font;if(font.isType3Font)return this.showType3Text(glyphs);const fontSize=current2.fontSize;if(fontSize===0)return;const ctx=this.ctx,fontSizeScale=current2.fontSizeScale,charSpacing=current2.charSpacing,wordSpacing=current2.wordSpacing,fontDirection=current2.fontDirection,textHScale=current2.
-textHScale*fontDirection,glyphsLength=glyphs.length,vertical=font.vertical,spacingDir=vertical?1:-1,defaultVMetrics=font.defaultVMetrics,widthAdvanceScale=fontSize*current2.fontMatrix[0],simpleFillText=current2.textRenderingMode===TextRenderingMode.FILL&&!font.disableFontFace&&!current2.patternFill;ctx.save(),current2.textMatrix&&ctx.transform(...current2.textMatrix),ctx.translate(current2.x,current2.y+current2.textRise),fontDirection>0?ctx.scale(textHScale,-1):ctx.scale(textHScale,1);let patternFillTransform,
-patternStrokeTransform;if(current2.patternFill){ctx.save();const pattern2=current2.fillColor.getPattern(ctx,this,getCurrentTransformInverse(ctx),PathType.FILL);patternFillTransform=getCurrentTransform(ctx),ctx.restore(),ctx.fillStyle=pattern2}if(current2.patternStroke){ctx.save();const pattern2=current2.strokeColor.getPattern(ctx,this,getCurrentTransformInverse(ctx),PathType.STROKE);patternStrokeTransform=getCurrentTransform(ctx),ctx.restore(),ctx.strokeStyle=pattern2}let lineWidth=current2.lineWidth;
-const scale2=current2.textMatrixScale;if(scale2===0||lineWidth===0){const fillStrokeMode=current2.textRenderingMode&TextRenderingMode.FILL_STROKE_MASK;(fillStrokeMode===TextRenderingMode.STROKE||fillStrokeMode===TextRenderingMode.FILL_STROKE)&&(lineWidth=this.getSinglePixelWidth())}else lineWidth/=scale2;if(fontSizeScale!==1&&(ctx.scale(fontSizeScale,fontSizeScale),lineWidth/=fontSizeScale),ctx.lineWidth=lineWidth,font.isInvalidPDFjsFont){const chars=[];let width=0;for(const glyph of glyphs)chars.
-push(glyph.unicode),width+=glyph.width;ctx.fillText(chars.join(""),0,0),current2.x+=width*widthAdvanceScale*textHScale,ctx.restore(),this.compose();return}let x=0,i;for(i=0;i<glyphsLength;++i){const glyph=glyphs[i];if(typeof glyph=="number"){x+=spacingDir*glyph*fontSize/1e3;continue}let restoreNeeded=!1;const spacing2=(glyph.isSpace?wordSpacing:0)+charSpacing,character=glyph.fontChar,accent2=glyph.accent;let scaledX,scaledY,width=glyph.width;if(vertical){const vmetric=glyph.vmetric||defaultVMetrics,
-vx=-(glyph.vmetric?vmetric[1]:width*.5)*widthAdvanceScale,vy=vmetric[2]*widthAdvanceScale;width=vmetric?-vmetric[0]:width,scaledX=vx/fontSizeScale,scaledY=(x+vy)/fontSizeScale}else scaledX=x/fontSizeScale,scaledY=0;if(font.remeasure&&width>0){const measuredWidth=ctx.measureText(character).width*1e3/fontSize*fontSizeScale;if(width<measuredWidth&&this.isFontSubpixelAAEnabled){const characterScaleX=width/measuredWidth;restoreNeeded=!0,ctx.save(),ctx.scale(characterScaleX,1),scaledX/=characterScaleX}else
-width!==measuredWidth&&(scaledX+=(width-measuredWidth)/2e3*fontSize/fontSizeScale)}if(this.contentVisible&&(glyph.isInFont||font.missingFile)){if(simpleFillText&&!accent2)ctx.fillText(character,scaledX,scaledY);else if(this.paintChar(character,scaledX,scaledY,patternFillTransform,patternStrokeTransform),accent2){const scaledAccentX=scaledX+fontSize*accent2.offset.x/fontSizeScale,scaledAccentY=scaledY-fontSize*accent2.offset.y/fontSizeScale;this.paintChar(accent2.fontChar,scaledAccentX,scaledAccentY,
-patternFillTransform,patternStrokeTransform)}}const charWidth=vertical?width*widthAdvanceScale-spacing2*fontDirection:width*widthAdvanceScale+spacing2*fontDirection;x+=charWidth,restoreNeeded&&ctx.restore()}vertical?current2.y-=x:current2.x+=x*textHScale,ctx.restore(),this.compose()}showType3Text(glyphs){const ctx=this.ctx,current2=this.current,font=current2.font,fontSize=current2.fontSize,fontDirection=current2.fontDirection,spacingDir=font.vertical?1:-1,charSpacing=current2.charSpacing,wordSpacing=current2.
-wordSpacing,textHScale=current2.textHScale*fontDirection,fontMatrix=current2.fontMatrix||FONT_IDENTITY_MATRIX,glyphsLength=glyphs.length,isTextInvisible=current2.textRenderingMode===TextRenderingMode.INVISIBLE;let i,glyph,width,spacingLength;if(!(isTextInvisible||fontSize===0)){for(this._cachedScaleForStroking[0]=-1,this._cachedGetSinglePixelWidth=null,ctx.save(),current2.textMatrix&&ctx.transform(...current2.textMatrix),ctx.translate(current2.x,current2.y+current2.textRise),ctx.scale(textHScale,
-fontDirection),i=0;i<glyphsLength;++i){if(glyph=glyphs[i],typeof glyph=="number"){spacingLength=spacingDir*glyph*fontSize/1e3,this.ctx.translate(spacingLength,0),current2.x+=spacingLength*textHScale;continue}const spacing2=(glyph.isSpace?wordSpacing:0)+charSpacing,operatorList=font.charProcOperatorList[glyph.operatorListId];operatorList?this.contentVisible&&(this.save(),ctx.scale(fontSize,fontSize),ctx.transform(...fontMatrix),this.executeOperatorList(operatorList),this.restore()):warn(`Type3 ch\
-aracter "${glyph.operatorListId}" is not available.`);const p2=[glyph.width,0];Util.applyTransform(p2,fontMatrix),width=p2[0]*fontSize+spacing2,ctx.translate(width,0),current2.x+=width*textHScale}ctx.restore()}}setCharWidth(xWidth,yWidth){}setCharWidthAndBounds(xWidth,yWidth,llx,lly,urx,ury){const clip=new Path2D;clip.rect(llx,lly,urx-llx,ury-lly),this.ctx.clip(clip),this.endPath()}getColorN_Pattern(IR){let pattern2;if(IR[0]==="TilingPattern"){const baseTransform=this.baseTransform||getCurrentTransform(
-this.ctx),canvasGraphicsFactory={createCanvasGraphics:ctx=>new CanvasGraphics(ctx,this.commonObjs,this.objs,this.canvasFactory,this.filterFactory,{optionalContentConfig:this.optionalContentConfig,markedContentStack:this.markedContentStack})};pattern2=new TilingPattern(IR,this.ctx,canvasGraphicsFactory,baseTransform)}else pattern2=this._getPattern(IR[1],IR[2]);return pattern2}setStrokeColorN(){this.current.strokeColor=this.getColorN_Pattern(arguments),this.current.patternStroke=!0}setFillColorN(){
-this.current.fillColor=this.getColorN_Pattern(arguments),this.current.patternFill=!0}setStrokeRGBColor(color){this.ctx.strokeStyle=this.current.strokeColor=color,this.current.patternStroke=!1}setStrokeTransparent(){this.ctx.strokeStyle=this.current.strokeColor="transparent",this.current.patternStroke=!1}setFillRGBColor(color){this.ctx.fillStyle=this.current.fillColor=color,this.current.patternFill=!1}setFillTransparent(){this.ctx.fillStyle=this.current.fillColor="transparent",this.current.patternFill=
-!1}_getPattern(objId,matrix=null){let pattern2;return this.cachedPatterns.has(objId)?pattern2=this.cachedPatterns.get(objId):(pattern2=getShadingPattern(this.getObject(objId)),this.cachedPatterns.set(objId,pattern2)),matrix&&(pattern2.matrix=matrix),pattern2}shadingFill(objId){if(!this.contentVisible)return;const ctx=this.ctx;this.save();const pattern2=this._getPattern(objId);ctx.fillStyle=pattern2.getPattern(ctx,this,getCurrentTransformInverse(ctx),PathType.SHADING);const inv=getCurrentTransformInverse(
-ctx);if(inv){const{width,height}=ctx.canvas,minMax=MIN_MAX_INIT.slice();Util.axialAlignedBoundingBox([0,0,width,height],inv,minMax);const[x0,y0,x1,y1]=minMax;this.ctx.fillRect(x0,y0,x1-x0,y1-y0)}else this.ctx.fillRect(-1e10,-1e10,2e10,2e10);this.compose(this.current.getClippedPathBoundingBox()),this.restore()}beginInlineImage(){unreachable("Should not call beginInlineImage")}beginImageData(){unreachable("Should not call beginImageData")}paintFormXObjectBegin(matrix,bbox){if(this.contentVisible&&
-(this.save(),this.baseTransformStack.push(this.baseTransform),matrix&&this.transform(...matrix),this.baseTransform=getCurrentTransform(this.ctx),bbox)){Util.axialAlignedBoundingBox(bbox,this.baseTransform,this.current.minMax);const[x0,y0,x1,y1]=bbox,clip=new Path2D;clip.rect(x0,y0,x1-x0,y1-y0),this.ctx.clip(clip),this.endPath()}}paintFormXObjectEnd(){this.contentVisible&&(this.restore(),this.baseTransform=this.baseTransformStack.pop())}beginGroup(group){if(!this.contentVisible)return;this.save(),
-this.inSMaskMode&&(this.endSMaskMode(),this.current.activeSMask=null);const currentCtx=this.ctx;group.isolated||info("TODO: Support non-isolated groups."),group.knockout&&warn("Knockout groups not supported.");const currentTransform=getCurrentTransform(currentCtx);if(group.matrix&&currentCtx.transform(...group.matrix),!group.bbox)throw new Error("Bounding box is required.");let bounds=MIN_MAX_INIT.slice();Util.axialAlignedBoundingBox(group.bbox,getCurrentTransform(currentCtx),bounds);const canvasBounds=[
-0,0,currentCtx.canvas.width,currentCtx.canvas.height];bounds=Util.intersect(bounds,canvasBounds)||[0,0,0,0];const offsetX=Math.floor(bounds[0]),offsetY=Math.floor(bounds[1]),drawnWidth=Math.max(Math.ceil(bounds[2])-offsetX,1),drawnHeight=Math.max(Math.ceil(bounds[3])-offsetY,1);this.current.startNewPathAndClipBox([0,0,drawnWidth,drawnHeight]);let cacheId="groupAt"+this.groupLevel;group.smask&&(cacheId+="_smask_"+this.smaskCounter++%2);const scratchCanvas=this.cachedCanvases.getCanvas(cacheId,drawnWidth,
-drawnHeight),groupCtx=scratchCanvas.context;groupCtx.translate(-offsetX,-offsetY),groupCtx.transform(...currentTransform);let clip=new Path2D;const[x0,y0,x1,y1]=group.bbox;if(clip.rect(x0,y0,x1-x0,y1-y0),group.matrix){const path2=new Path2D;path2.addPath(clip,new DOMMatrix(group.matrix)),clip=path2}groupCtx.clip(clip),group.smask?this.smaskStack.push({canvas:scratchCanvas.canvas,context:groupCtx,offsetX,offsetY,subtype:group.smask.subtype,backdrop:group.smask.backdrop,transferMap:group.smask.transferMap||
-null,startTransformInverse:null}):(currentCtx.setTransform(1,0,0,1,0,0),currentCtx.translate(offsetX,offsetY),currentCtx.save()),copyCtxState(currentCtx,groupCtx),this.ctx=groupCtx,this.setGState([["BM","source-over"],["ca",1],["CA",1]]),this.groupStack.push(currentCtx),this.groupLevel++}endGroup(group){if(!this.contentVisible)return;this.groupLevel--;const groupCtx=this.ctx,ctx=this.groupStack.pop();if(this.ctx=ctx,this.ctx.imageSmoothingEnabled=!1,group.smask)this.tempSMask=this.smaskStack.pop(),
-this.restore();else{this.ctx.restore();const currentMtx=getCurrentTransform(this.ctx);this.restore(),this.ctx.save(),this.ctx.setTransform(...currentMtx);const dirtyBox=MIN_MAX_INIT.slice();Util.axialAlignedBoundingBox([0,0,groupCtx.canvas.width,groupCtx.canvas.height],currentMtx,dirtyBox),this.ctx.drawImage(groupCtx.canvas,0,0),this.ctx.restore(),this.compose(dirtyBox)}}beginAnnotation(id2,rect,transform2,matrix,hasOwnCanvas){if(this.#restoreInitialState(),resetCtxToDefault(this.ctx),this.ctx.save(),
-this.save(),this.baseTransform&&this.ctx.setTransform(...this.baseTransform),rect){const width=rect[2]-rect[0],height=rect[3]-rect[1];if(hasOwnCanvas&&this.annotationCanvasMap){transform2=transform2.slice(),transform2[4]-=rect[0],transform2[5]-=rect[1],rect=rect.slice(),rect[0]=rect[1]=0,rect[2]=width,rect[3]=height,Util.singularValueDecompose2dScale(getCurrentTransform(this.ctx),XY);const{viewportScale}=this,canvasWidth=Math.ceil(width*this.outputScaleX*viewportScale),canvasHeight=Math.ceil(height*
-this.outputScaleY*viewportScale);this.annotationCanvas=this.canvasFactory.create(canvasWidth,canvasHeight);const{canvas,context}=this.annotationCanvas;this.annotationCanvasMap.set(id2,canvas),this.annotationCanvas.savedCtx=this.ctx,this.ctx=context,this.ctx.save(),this.ctx.setTransform(XY[0],0,0,-XY[1],0,height*XY[1]),resetCtxToDefault(this.ctx)}else{resetCtxToDefault(this.ctx),this.endPath();const clip=new Path2D;clip.rect(rect[0],rect[1],width,height),this.ctx.clip(clip)}}this.current=new CanvasExtraState(
-this.ctx.canvas.width,this.ctx.canvas.height),this.transform(...transform2),this.transform(...matrix)}endAnnotation(){this.annotationCanvas&&(this.ctx.restore(),this.#drawFilter(),this.ctx=this.annotationCanvas.savedCtx,delete this.annotationCanvas.savedCtx,delete this.annotationCanvas)}paintImageMaskXObject(img){if(!this.contentVisible)return;const count=img.count;img=this.getObject(img.data,img),img.count=count;const ctx=this.ctx,mask=this._createMaskCanvas(img),maskCanvas=mask.canvas;ctx.save(),
-ctx.setTransform(1,0,0,1,0,0),ctx.drawImage(maskCanvas,mask.offsetX,mask.offsetY),ctx.restore(),this.compose()}paintImageMaskXObjectRepeat(img,scaleX,skewX=0,skewY=0,scaleY,positions){if(!this.contentVisible)return;img=this.getObject(img.data,img);const ctx=this.ctx;ctx.save();const currentTransform=getCurrentTransform(ctx);ctx.transform(scaleX,skewX,skewY,scaleY,0,0);const mask=this._createMaskCanvas(img);ctx.setTransform(1,0,0,1,mask.offsetX-currentTransform[4],mask.offsetY-currentTransform[5]);
-for(let i=0,ii=positions.length;i<ii;i+=2){const trans=Util.transform(currentTransform,[scaleX,skewX,skewY,scaleY,positions[i],positions[i+1]]);ctx.drawImage(mask.canvas,trans[4],trans[5])}ctx.restore(),this.compose()}paintImageMaskXObjectGroup(images){if(!this.contentVisible)return;const ctx=this.ctx,fillColor=this.current.fillColor,isPatternFill=this.current.patternFill;for(const image2 of images){const{data,width,height,transform:transform2}=image2,maskCanvas=this.cachedCanvases.getCanvas("ma\
-skCanvas",width,height),maskCtx=maskCanvas.context;maskCtx.save();const img=this.getObject(data,image2);putBinaryImageMask(maskCtx,img),maskCtx.globalCompositeOperation="source-in",maskCtx.fillStyle=isPatternFill?fillColor.getPattern(maskCtx,this,getCurrentTransformInverse(ctx),PathType.FILL):fillColor,maskCtx.fillRect(0,0,width,height),maskCtx.restore(),ctx.save(),ctx.transform(...transform2),ctx.scale(1,-1),drawImageAtIntegerCoords(ctx,maskCanvas.canvas,0,0,width,height,0,-1,1,1),ctx.restore()}
-this.compose()}paintImageXObject(objId){if(!this.contentVisible)return;const imgData=this.getObject(objId);if(!imgData){warn("Dependent image isn't ready yet");return}this.paintInlineImageXObject(imgData)}paintImageXObjectRepeat(objId,scaleX,scaleY,positions){if(!this.contentVisible)return;const imgData=this.getObject(objId);if(!imgData){warn("Dependent image isn't ready yet");return}const width=imgData.width,height=imgData.height,map2=[];for(let i=0,ii=positions.length;i<ii;i+=2)map2.push({transform:[
-scaleX,0,0,scaleY,positions[i],positions[i+1]],x:0,y:0,w:width,h:height});this.paintInlineImageXObjectGroup(imgData,map2)}applyTransferMapsToCanvas(ctx){return this.current.transferMaps!=="none"&&(ctx.filter=this.current.transferMaps,ctx.drawImage(ctx.canvas,0,0),ctx.filter="none"),ctx.canvas}applyTransferMapsToBitmap(imgData){if(this.current.transferMaps==="none")return imgData.bitmap;const{bitmap,width,height}=imgData,tmpCanvas=this.cachedCanvases.getCanvas("inlineImage",width,height),tmpCtx=tmpCanvas.
-context;return tmpCtx.filter=this.current.transferMaps,tmpCtx.drawImage(bitmap,0,0),tmpCtx.filter="none",tmpCanvas.canvas}paintInlineImageXObject(imgData){if(!this.contentVisible)return;const width=imgData.width,height=imgData.height,ctx=this.ctx;this.save();const{filter}=ctx;filter!=="none"&&filter!==""&&(ctx.filter="none"),ctx.scale(1/width,-1/height);let imgToPaint;if(imgData.bitmap)imgToPaint=this.applyTransferMapsToBitmap(imgData);else if(typeof HTMLElement=="function"&&imgData instanceof HTMLElement||
-!imgData.data)imgToPaint=imgData;else{const tmpCtx=this.cachedCanvases.getCanvas("inlineImage",width,height).context;putBinaryImageData(tmpCtx,imgData),imgToPaint=this.applyTransferMapsToCanvas(tmpCtx)}const scaled=this._scaleImage(imgToPaint,getCurrentTransformInverse(ctx));ctx.imageSmoothingEnabled=getImageSmoothingEnabled(getCurrentTransform(ctx),imgData.interpolate),drawImageAtIntegerCoords(ctx,scaled.img,0,0,scaled.paintWidth,scaled.paintHeight,0,-height,width,height),this.compose(),this.restore()}paintInlineImageXObjectGroup(imgData,map2){
-if(!this.contentVisible)return;const ctx=this.ctx;let imgToPaint;if(imgData.bitmap)imgToPaint=imgData.bitmap;else{const w=imgData.width,h2=imgData.height,tmpCtx=this.cachedCanvases.getCanvas("inlineImage",w,h2).context;putBinaryImageData(tmpCtx,imgData),imgToPaint=this.applyTransferMapsToCanvas(tmpCtx)}for(const entry of map2)ctx.save(),ctx.transform(...entry.transform),ctx.scale(1,-1),drawImageAtIntegerCoords(ctx,imgToPaint,entry.x,entry.y,entry.w,entry.h,0,-1,1,1),ctx.restore();this.compose()}paintSolidColorImageMask(){
-this.contentVisible&&(this.ctx.fillRect(0,0,1,1),this.compose())}markPoint(tag){}markPointProps(tag,properties2){}beginMarkedContent(tag){this.markedContentStack.push({visible:!0})}beginMarkedContentProps(tag,properties2){tag==="OC"?this.markedContentStack.push({visible:this.optionalContentConfig.isVisible(properties2)}):this.markedContentStack.push({visible:!0}),this.contentVisible=this.isContentVisible()}endMarkedContent(){this.markedContentStack.pop(),this.contentVisible=this.isContentVisible()}beginCompat(){}endCompat(){}consumePath(path2,clipBox){
-const isEmpty=this.current.isEmptyClip();this.pendingClip&&this.current.updateClipFromPath(),this.pendingClip||this.compose(clipBox);const ctx=this.ctx;this.pendingClip&&(isEmpty||(this.pendingClip===EO_CLIP?ctx.clip(path2,"evenodd"):ctx.clip(path2)),this.pendingClip=null),this.current.startNewPathAndClipBox(this.current.clipBox)}getSinglePixelWidth(){if(!this._cachedGetSinglePixelWidth){const m=getCurrentTransform(this.ctx);if(m[1]===0&&m[2]===0)this._cachedGetSinglePixelWidth=1/Math.min(Math.abs(
-m[0]),Math.abs(m[3]));else{const absDet=Math.abs(m[0]*m[3]-m[2]*m[1]),normX=Math.hypot(m[0],m[2]),normY=Math.hypot(m[1],m[3]);this._cachedGetSinglePixelWidth=Math.max(normX,normY)/absDet}}return this._cachedGetSinglePixelWidth}getScaleForStroking(){if(this._cachedScaleForStroking[0]===-1){const{lineWidth}=this.current,{a,b,c:c2,d:d2}=this.ctx.getTransform();let scaleX,scaleY;if(b===0&&c2===0){const normX=Math.abs(a),normY=Math.abs(d2);if(normX===normY)if(lineWidth===0)scaleX=scaleY=1/normX;else{
-const scaledLineWidth=normX*lineWidth;scaleX=scaleY=scaledLineWidth<1?1/scaledLineWidth:1}else if(lineWidth===0)scaleX=1/normX,scaleY=1/normY;else{const scaledXLineWidth=normX*lineWidth,scaledYLineWidth=normY*lineWidth;scaleX=scaledXLineWidth<1?1/scaledXLineWidth:1,scaleY=scaledYLineWidth<1?1/scaledYLineWidth:1}}else{const absDet=Math.abs(a*d2-b*c2),normX=Math.hypot(a,b),normY=Math.hypot(c2,d2);if(lineWidth===0)scaleX=normY/absDet,scaleY=normX/absDet;else{const baseArea=lineWidth*absDet;scaleX=normY>
-baseArea?normY/baseArea:1,scaleY=normX>baseArea?normX/baseArea:1}}this._cachedScaleForStroking[0]=scaleX,this._cachedScaleForStroking[1]=scaleY}return this._cachedScaleForStroking}rescaleAndStroke(path2,saveRestore){const{ctx,current:{lineWidth}}=this,[scaleX,scaleY]=this.getScaleForStroking();if(scaleX===scaleY){ctx.lineWidth=(lineWidth||1)*scaleX,ctx.stroke(path2);return}const dashes=ctx.getLineDash();saveRestore&&ctx.save(),ctx.scale(scaleX,scaleY),SCALE_MATRIX.a=1/scaleX,SCALE_MATRIX.d=1/scaleY;
-const newPath=new Path2D;if(newPath.addPath(path2,SCALE_MATRIX),dashes.length>0){const scale2=Math.max(scaleX,scaleY);ctx.setLineDash(dashes.map(x=>x/scale2)),ctx.lineDashOffset/=scale2}ctx.lineWidth=lineWidth||1,ctx.stroke(newPath),saveRestore&&ctx.restore()}isContentVisible(){for(let i=this.markedContentStack.length-1;i>=0;i--)if(!this.markedContentStack[i].visible)return!1;return!0}}for(const op2 in OPS)CanvasGraphics.prototype[op2]!==void 0&&(CanvasGraphics.prototype[OPS[op2]]=CanvasGraphics.
-prototype[op2]);class GlobalWorkerOptions{static#port=null;static#src="";static get workerPort(){return this.#port}static set workerPort(val){if(!(typeof Worker<"u"&&val instanceof Worker)&&val!==null)throw new Error("Invalid `workerPort` type.");this.#port=val}static get workerSrc(){return this.#src}static set workerSrc(val){if(typeof val!="string")throw new Error("Invalid `workerSrc` type.");this.#src=val}}class Metadata{#map;#data;constructor({parsedData,rawData}){this.#map=parsedData,this.#data=
-rawData}getRaw(){return this.#data}get(name){return this.#map.get(name)??null}[Symbol.iterator](){return this.#map.entries()}}const INTERNAL=Symbol("INTERNAL");class OptionalContentGroup{#isDisplay=!1;#isPrint=!1;#userSet=!1;#visible=!0;constructor(renderingIntent,{name,intent,usage,rbGroups}){this.#isDisplay=!!(renderingIntent&RenderingIntentFlag.DISPLAY),this.#isPrint=!!(renderingIntent&RenderingIntentFlag.PRINT),this.name=name,this.intent=intent,this.usage=usage,this.rbGroups=rbGroups}get visible(){
-if(this.#userSet)return this.#visible;if(!this.#visible)return!1;const{print,view}=this.usage;return this.#isDisplay?view?.viewState!=="OFF":this.#isPrint?print?.printState!=="OFF":!0}_setVisible(internal,visible,userSet=!1){internal!==INTERNAL&&unreachable("Internal method `_setVisible` called."),this.#userSet=userSet,this.#visible=visible}}class OptionalContentConfig{#cachedGetHash=null;#groups=new Map;#initialHash=null;#order=null;constructor(data,renderingIntent=RenderingIntentFlag.DISPLAY){
-if(this.renderingIntent=renderingIntent,this.name=null,this.creator=null,data!==null){this.name=data.name,this.creator=data.creator,this.#order=data.order;for(const group of data.groups)this.#groups.set(group.id,new OptionalContentGroup(renderingIntent,group));if(data.baseState==="OFF")for(const group of this.#groups.values())group._setVisible(INTERNAL,!1);for(const on2 of data.on)this.#groups.get(on2)._setVisible(INTERNAL,!0);for(const off of data.off)this.#groups.get(off)._setVisible(INTERNAL,
-!1);this.#initialHash=this.getHash()}}#evaluateVisibilityExpression(array2){const length=array2.length;if(length<2)return!0;const operator2=array2[0];for(let i=1;i<length;i++){const element2=array2[i];let state2;if(Array.isArray(element2))state2=this.#evaluateVisibilityExpression(element2);else if(this.#groups.has(element2))state2=this.#groups.get(element2).visible;else return warn(`Optional content group not found: ${element2}`),!0;switch(operator2){case"And":if(!state2)return!1;break;case"Or":
-if(state2)return!0;break;case"Not":return!state2;default:return!0}}return operator2==="And"}isVisible(group){if(this.#groups.size===0)return!0;if(!group)return info("Optional content group not defined."),!0;if(group.type==="OCG")return this.#groups.has(group.id)?this.#groups.get(group.id).visible:(warn(`Optional content group not found: ${group.id}`),!0);if(group.type==="OCMD"){if(group.expression)return this.#evaluateVisibilityExpression(group.expression);if(!group.policy||group.policy==="AnyOn"){
-for(const id2 of group.ids){if(!this.#groups.has(id2))return warn(`Optional content group not found: ${id2}`),!0;if(this.#groups.get(id2).visible)return!0}return!1}else if(group.policy==="AllOn"){for(const id2 of group.ids){if(!this.#groups.has(id2))return warn(`Optional content group not found: ${id2}`),!0;if(!this.#groups.get(id2).visible)return!1}return!0}else if(group.policy==="AnyOff"){for(const id2 of group.ids){if(!this.#groups.has(id2))return warn(`Optional content group not found: ${id2}`),
-!0;if(!this.#groups.get(id2).visible)return!0}return!1}else if(group.policy==="AllOff"){for(const id2 of group.ids){if(!this.#groups.has(id2))return warn(`Optional content group not found: ${id2}`),!0;if(this.#groups.get(id2).visible)return!1}return!0}return warn(`Unknown optional content policy ${group.policy}.`),!0}return warn(`Unknown group type ${group.type}.`),!0}setVisibility(id2,visible=!0,preserveRB=!0){const group=this.#groups.get(id2);if(!group){warn(`Optional content group not found: ${id2}`);
-return}if(preserveRB&&visible&&group.rbGroups.length)for(const rbGroup of group.rbGroups)for(const otherId of rbGroup)otherId!==id2&&this.#groups.get(otherId)?._setVisible(INTERNAL,!1,!0);group._setVisible(INTERNAL,!!visible,!0),this.#cachedGetHash=null}setOCGState({state:state2,preserveRB}){let operator2;for(const elem of state2){switch(elem){case"ON":case"OFF":case"Toggle":operator2=elem;continue}const group=this.#groups.get(elem);if(group)switch(operator2){case"ON":this.setVisibility(elem,!0,
-preserveRB);break;case"OFF":this.setVisibility(elem,!1,preserveRB);break;case"Toggle":this.setVisibility(elem,!group.visible,preserveRB);break}}this.#cachedGetHash=null}get hasInitialVisibility(){return this.#initialHash===null||this.getHash()===this.#initialHash}getOrder(){return this.#groups.size?this.#order?this.#order.slice():[...this.#groups.keys()]:null}getGroup(id2){return this.#groups.get(id2)||null}getHash(){if(this.#cachedGetHash!==null)return this.#cachedGetHash;const hash2=new MurmurHash3_64;
-for(const[id2,group]of this.#groups)hash2.update(`${id2}:${group.visible}`);return this.#cachedGetHash=hash2.hexdigest()}[Symbol.iterator](){return this.#groups.entries()}}class PDFDataTransportStream{constructor(pdfDataRangeTransport,{disableRange=!1,disableStream=!1}){assert$1(pdfDataRangeTransport,'PDFDataTransportStream - missing required "pdfDataRangeTransport" argument.');const{length,initialData,progressiveDone,contentDispositionFilename}=pdfDataRangeTransport;if(this._queuedChunks=[],this.
-_progressiveDone=progressiveDone,this._contentDispositionFilename=contentDispositionFilename,initialData?.length>0){const buffer=initialData instanceof Uint8Array&&initialData.byteLength===initialData.buffer.byteLength?initialData.buffer:new Uint8Array(initialData).buffer;this._queuedChunks.push(buffer)}this._pdfDataRangeTransport=pdfDataRangeTransport,this._isStreamingSupported=!disableStream,this._isRangeSupported=!disableRange,this._contentLength=length,this._fullRequestReader=null,this._rangeReaders=
-[],pdfDataRangeTransport.addRangeListener((begin,chunk)=>{this._onReceiveData({begin,chunk})}),pdfDataRangeTransport.addProgressListener((loaded,total)=>{this._onProgress({loaded,total})}),pdfDataRangeTransport.addProgressiveReadListener(chunk=>{this._onReceiveData({chunk})}),pdfDataRangeTransport.addProgressiveDoneListener(()=>{this._onProgressiveDone()}),pdfDataRangeTransport.transportReady()}_onReceiveData({begin,chunk}){const buffer=chunk instanceof Uint8Array&&chunk.byteLength===chunk.buffer.
-byteLength?chunk.buffer:new Uint8Array(chunk).buffer;if(begin===void 0)this._fullRequestReader?this._fullRequestReader._enqueue(buffer):this._queuedChunks.push(buffer);else{const found=this._rangeReaders.some(function(rangeReader){return rangeReader._begin!==begin?!1:(rangeReader._enqueue(buffer),!0)});assert$1(found,"_onReceiveData - no `PDFDataTransportStreamRangeReader` instance found.")}}get _progressiveDataLength(){return this._fullRequestReader?._loaded??0}_onProgress(evt){evt.total===void 0?
-this._rangeReaders[0]?.onProgress?.({loaded:evt.loaded}):this._fullRequestReader?.onProgress?.({loaded:evt.loaded,total:evt.total})}_onProgressiveDone(){this._fullRequestReader?.progressiveDone(),this._progressiveDone=!0}_removeRangeReader(reader){const i=this._rangeReaders.indexOf(reader);i>=0&&this._rangeReaders.splice(i,1)}getFullReader(){assert$1(!this._fullRequestReader,"PDFDataTransportStream.getFullReader can only be called once.");const queuedChunks=this._queuedChunks;return this._queuedChunks=
-null,new PDFDataTransportStreamReader(this,queuedChunks,this._progressiveDone,this._contentDispositionFilename)}getRangeReader(begin,end){if(end<=this._progressiveDataLength)return null;const reader=new PDFDataTransportStreamRangeReader(this,begin,end);return this._pdfDataRangeTransport.requestDataRange(begin,end),this._rangeReaders.push(reader),reader}cancelAllRequests(reason){this._fullRequestReader?.cancel(reason);for(const reader of this._rangeReaders.slice(0))reader.cancel(reason);this._pdfDataRangeTransport.
-abort()}}class PDFDataTransportStreamReader{constructor(stream,queuedChunks,progressiveDone=!1,contentDispositionFilename=null){this._stream=stream,this._done=progressiveDone||!1,this._filename=isPdfFile(contentDispositionFilename)?contentDispositionFilename:null,this._queuedChunks=queuedChunks||[],this._loaded=0;for(const chunk of this._queuedChunks)this._loaded+=chunk.byteLength;this._requests=[],this._headersReady=Promise.resolve(),stream._fullRequestReader=this,this.onProgress=null}_enqueue(chunk){
-this._done||(this._requests.length>0?this._requests.shift().resolve({value:chunk,done:!1}):this._queuedChunks.push(chunk),this._loaded+=chunk.byteLength)}get headersReady(){return this._headersReady}get filename(){return this._filename}get isRangeSupported(){return this._stream._isRangeSupported}get isStreamingSupported(){return this._stream._isStreamingSupported}get contentLength(){return this._stream._contentLength}async read(){if(this._queuedChunks.length>0)return{value:this._queuedChunks.shift(),
-done:!1};if(this._done)return{value:void 0,done:!0};const requestCapability=Promise.withResolvers();return this._requests.push(requestCapability),requestCapability.promise}cancel(reason){this._done=!0;for(const requestCapability of this._requests)requestCapability.resolve({value:void 0,done:!0});this._requests.length=0}progressiveDone(){this._done||(this._done=!0)}}class PDFDataTransportStreamRangeReader{constructor(stream,begin,end){this._stream=stream,this._begin=begin,this._end=end,this._queuedChunk=
-null,this._requests=[],this._done=!1,this.onProgress=null}_enqueue(chunk){if(!this._done){if(this._requests.length===0)this._queuedChunk=chunk;else{this._requests.shift().resolve({value:chunk,done:!1});for(const requestCapability of this._requests)requestCapability.resolve({value:void 0,done:!0});this._requests.length=0}this._done=!0,this._stream._removeRangeReader(this)}}get isStreamingSupported(){return!1}async read(){if(this._queuedChunk){const chunk=this._queuedChunk;return this._queuedChunk=
-null,{value:chunk,done:!1}}if(this._done)return{value:void 0,done:!0};const requestCapability=Promise.withResolvers();return this._requests.push(requestCapability),requestCapability.promise}cancel(reason){this._done=!0;for(const requestCapability of this._requests)requestCapability.resolve({value:void 0,done:!0});this._requests.length=0,this._stream._removeRangeReader(this)}}function getFilenameFromContentDispositionHeader(contentDisposition){let needsEncodingFixup=!0,tmp=toParamRegExp("filename\
-\\*","i").exec(contentDisposition);if(tmp){tmp=tmp[1];let filename=rfc2616unquote(tmp);return filename=unescape(filename),filename=rfc5987decode(filename),filename=rfc2047decode(filename),fixupEncoding(filename)}if(tmp=rfc2231getparam(contentDisposition),tmp){const filename=rfc2047decode(tmp);return fixupEncoding(filename)}if(tmp=toParamRegExp("filename","i").exec(contentDisposition),tmp){tmp=tmp[1];let filename=rfc2616unquote(tmp);return filename=rfc2047decode(filename),fixupEncoding(filename)}
-function toParamRegExp(attributePattern,flags2){return new RegExp("(?:^|;)\\s*"+attributePattern+'\\s*=\\s*([^";\\s][^;\\s]*|"(?:[^"\\\\]|\\\\"?)+"?)',flags2)}function textdecode(encoding,value){if(encoding){if(!/^[\x00-\xFF]+$/.test(value))return value;try{const decoder=new TextDecoder(encoding,{fatal:!0}),buffer=stringToBytes(value);value=decoder.decode(buffer),needsEncodingFixup=!1}catch{}}return value}function fixupEncoding(value){return needsEncodingFixup&&/[\x80-\xff]/.test(value)&&(value=
-textdecode("utf-8",value),needsEncodingFixup&&(value=textdecode("iso-8859-1",value))),value}function rfc2231getparam(contentDispositionStr){const matches2=[];let match;const iter=toParamRegExp("filename\\*((?!0\\d)\\d+)(\\*?)","ig");for(;(match=iter.exec(contentDispositionStr))!==null;){let[,n,quot,part]=match;if(n=parseInt(n,10),n in matches2){if(n===0)break;continue}matches2[n]=[quot,part]}const parts=[];for(let n=0;n<matches2.length&&n in matches2;++n){let[quot,part]=matches2[n];part=rfc2616unquote(
-part),quot&&(part=unescape(part),n===0&&(part=rfc5987decode(part))),parts.push(part)}return parts.join("")}function rfc2616unquote(value){if(value.startsWith('"')){const parts=value.slice(1).split('\\"');for(let i=0;i<parts.length;++i){const quotindex=parts[i].indexOf('"');quotindex!==-1&&(parts[i]=parts[i].slice(0,quotindex),parts.length=i+1),parts[i]=parts[i].replaceAll(/\\(.)/g,"$1")}value=parts.join('"')}return value}function rfc5987decode(extvalue){const encodingend=extvalue.indexOf("'");if(encodingend===
--1)return extvalue;const encoding=extvalue.slice(0,encodingend),value=extvalue.slice(encodingend+1).replace(/^[^']*'/,"");return textdecode(encoding,value)}function rfc2047decode(value){return!value.startsWith("=?")||/[\x00-\x19\x80-\xff]/.test(value)?value:value.replaceAll(/=\?([\w-]*)\?([QqBb])\?((?:[^?]|\?(?!=))*)\?=/g,function(matches2,charset,encoding,text2){if(encoding==="q"||encoding==="Q")return text2=text2.replaceAll("_"," "),text2=text2.replaceAll(/=([0-9a-fA-F]{2})/g,function(match,hex){
-return String.fromCharCode(parseInt(hex,16))}),textdecode(charset,text2);try{text2=atob(text2)}catch{}return textdecode(charset,text2)})}return""}function createHeaders(isHttp,httpHeaders){const headers=new Headers;if(!isHttp||!httpHeaders||typeof httpHeaders!="object")return headers;for(const key2 in httpHeaders){const val=httpHeaders[key2];val!==void 0&&headers.append(key2,val)}return headers}function getResponseOrigin(url2){return URL.parse(url2)?.origin??null}function validateRangeRequestCapabilities({
-responseHeaders,isHttp,rangeChunkSize,disableRange}){const returnValues={allowRangeRequests:!1,suggestedLength:void 0},length=parseInt(responseHeaders.get("Content-Length"),10);return!Number.isInteger(length)||(returnValues.suggestedLength=length,length<=2*rangeChunkSize)||disableRange||!isHttp||responseHeaders.get("Accept-Ranges")!=="bytes"||(responseHeaders.get("Content-Encoding")||"identity")!=="identity"||(returnValues.allowRangeRequests=!0),returnValues}function extractFilenameFromHeader(responseHeaders){
-const contentDisposition=responseHeaders.get("Content-Disposition");if(contentDisposition){let filename=getFilenameFromContentDispositionHeader(contentDisposition);if(filename.includes("%"))try{filename=decodeURIComponent(filename)}catch{}if(isPdfFile(filename))return filename}return null}function createResponseError(status,url2){return new ResponseException(`Unexpected server response (${status}) while retrieving PDF "${url2}".`,status,status===404||status===0&&url2.startsWith("file:"))}function validateResponseStatus(status){
-return status===200||status===206}function createFetchOptions(headers,withCredentials,abortController){return{method:"GET",headers,signal:abortController.signal,mode:"cors",credentials:withCredentials?"include":"same-origin",redirect:"follow"}}function getArrayBuffer(val){return val instanceof Uint8Array?val.buffer:val instanceof ArrayBuffer?val:(warn(`getArrayBuffer - unexpected data format: ${val}`),new Uint8Array(val).buffer)}class PDFFetchStream{_responseOrigin=null;constructor(source2){this.
-source=source2,this.isHttp=/^https?:/i.test(source2.url),this.headers=createHeaders(this.isHttp,source2.httpHeaders),this._fullRequestReader=null,this._rangeRequestReaders=[]}get _progressiveDataLength(){return this._fullRequestReader?._loaded??0}getFullReader(){return assert$1(!this._fullRequestReader,"PDFFetchStream.getFullReader can only be called once."),this._fullRequestReader=new PDFFetchStreamReader(this),this._fullRequestReader}getRangeReader(begin,end){if(end<=this._progressiveDataLength)
-return null;const reader=new PDFFetchStreamRangeReader(this,begin,end);return this._rangeRequestReaders.push(reader),reader}cancelAllRequests(reason){this._fullRequestReader?.cancel(reason);for(const reader of this._rangeRequestReaders.slice(0))reader.cancel(reason)}}class PDFFetchStreamReader{constructor(stream){this._stream=stream,this._reader=null,this._loaded=0,this._filename=null;const source2=stream.source;this._withCredentials=source2.withCredentials||!1,this._contentLength=source2.length,
-this._headersCapability=Promise.withResolvers(),this._disableRange=source2.disableRange||!1,this._rangeChunkSize=source2.rangeChunkSize,!this._rangeChunkSize&&!this._disableRange&&(this._disableRange=!0),this._abortController=new AbortController,this._isStreamingSupported=!source2.disableStream,this._isRangeSupported=!source2.disableRange;const headers=new Headers(stream.headers),url2=source2.url;fetch(url2,createFetchOptions(headers,this._withCredentials,this._abortController)).then(response=>{
-if(stream._responseOrigin=getResponseOrigin(response.url),!validateResponseStatus(response.status))throw createResponseError(response.status,url2);this._reader=response.body.getReader(),this._headersCapability.resolve();const responseHeaders=response.headers,{allowRangeRequests,suggestedLength}=validateRangeRequestCapabilities({responseHeaders,isHttp:stream.isHttp,rangeChunkSize:this._rangeChunkSize,disableRange:this._disableRange});this._isRangeSupported=allowRangeRequests,this._contentLength=suggestedLength||
-this._contentLength,this._filename=extractFilenameFromHeader(responseHeaders),!this._isStreamingSupported&&this._isRangeSupported&&this.cancel(new AbortException("Streaming is disabled."))}).catch(this._headersCapability.reject),this.onProgress=null}get headersReady(){return this._headersCapability.promise}get filename(){return this._filename}get contentLength(){return this._contentLength}get isRangeSupported(){return this._isRangeSupported}get isStreamingSupported(){return this._isStreamingSupported}async read(){
-await this._headersCapability.promise;const{value,done}=await this._reader.read();return done?{value,done}:(this._loaded+=value.byteLength,this.onProgress?.({loaded:this._loaded,total:this._contentLength}),{value:getArrayBuffer(value),done:!1})}cancel(reason){this._reader?.cancel(reason),this._abortController.abort()}}class PDFFetchStreamRangeReader{constructor(stream,begin,end){this._stream=stream,this._reader=null,this._loaded=0;const source2=stream.source;this._withCredentials=source2.withCredentials||
-!1,this._readCapability=Promise.withResolvers(),this._isStreamingSupported=!source2.disableStream,this._abortController=new AbortController;const headers=new Headers(stream.headers);headers.append("Range",`bytes=${begin}-${end-1}`);const url2=source2.url;fetch(url2,createFetchOptions(headers,this._withCredentials,this._abortController)).then(response=>{const responseOrigin=getResponseOrigin(response.url);if(responseOrigin!==stream._responseOrigin)throw new Error(`Expected range response-origin "${responseOrigin}\
-" to match "${stream._responseOrigin}".`);if(!validateResponseStatus(response.status))throw createResponseError(response.status,url2);this._readCapability.resolve(),this._reader=response.body.getReader()}).catch(this._readCapability.reject),this.onProgress=null}get isStreamingSupported(){return this._isStreamingSupported}async read(){await this._readCapability.promise;const{value,done}=await this._reader.read();return done?{value,done}:(this._loaded+=value.byteLength,this.onProgress?.({loaded:this.
-_loaded}),{value:getArrayBuffer(value),done:!1})}cancel(reason){this._reader?.cancel(reason),this._abortController.abort()}}const OK_RESPONSE=200,PARTIAL_CONTENT_RESPONSE=206;function network_getArrayBuffer(xhr){const data=xhr.response;return typeof data!="string"?data:stringToBytes(data).buffer}class NetworkManager{_responseOrigin=null;constructor({url:url2,httpHeaders,withCredentials}){this.url=url2,this.isHttp=/^https?:/i.test(url2),this.headers=createHeaders(this.isHttp,httpHeaders),this.withCredentials=
-withCredentials||!1,this.currXhrId=0,this.pendingRequests=Object.create(null)}request(args){const xhr=new XMLHttpRequest,xhrId=this.currXhrId++,pendingRequest=this.pendingRequests[xhrId]={xhr};xhr.open("GET",this.url),xhr.withCredentials=this.withCredentials;for(const[key2,val]of this.headers)xhr.setRequestHeader(key2,val);return this.isHttp&&"begin"in args&&"end"in args?(xhr.setRequestHeader("Range",`bytes=${args.begin}-${args.end-1}`),pendingRequest.expectedStatus=PARTIAL_CONTENT_RESPONSE):pendingRequest.
-expectedStatus=OK_RESPONSE,xhr.responseType="arraybuffer",assert$1(args.onError,"Expected `onError` callback to be provided."),xhr.onerror=()=>{args.onError(xhr.status)},xhr.onreadystatechange=this.onStateChange.bind(this,xhrId),xhr.onprogress=this.onProgress.bind(this,xhrId),pendingRequest.onHeadersReceived=args.onHeadersReceived,pendingRequest.onDone=args.onDone,pendingRequest.onError=args.onError,pendingRequest.onProgress=args.onProgress,xhr.send(null),xhrId}onProgress(xhrId,evt){const pendingRequest=this.
-pendingRequests[xhrId];pendingRequest&&pendingRequest.onProgress?.(evt)}onStateChange(xhrId,evt){const pendingRequest=this.pendingRequests[xhrId];if(!pendingRequest)return;const xhr=pendingRequest.xhr;if(xhr.readyState>=2&&pendingRequest.onHeadersReceived&&(pendingRequest.onHeadersReceived(),delete pendingRequest.onHeadersReceived),xhr.readyState!==4||!(xhrId in this.pendingRequests))return;if(delete this.pendingRequests[xhrId],xhr.status===0&&this.isHttp){pendingRequest.onError(xhr.status);return}
-const xhrStatus=xhr.status||OK_RESPONSE;if(!(xhrStatus===OK_RESPONSE&&pendingRequest.expectedStatus===PARTIAL_CONTENT_RESPONSE)&&xhrStatus!==pendingRequest.expectedStatus){pendingRequest.onError(xhr.status);return}const chunk=network_getArrayBuffer(xhr);if(xhrStatus===PARTIAL_CONTENT_RESPONSE){const rangeHeader=xhr.getResponseHeader("Content-Range"),matches2=/bytes (\d+)-(\d+)\/(\d+)/.exec(rangeHeader);matches2?pendingRequest.onDone({begin:parseInt(matches2[1],10),chunk}):(warn('Missing or inval\
-id "Content-Range" header.'),pendingRequest.onError(0))}else chunk?pendingRequest.onDone({begin:0,chunk}):pendingRequest.onError(xhr.status)}getRequestXhr(xhrId){return this.pendingRequests[xhrId].xhr}isPendingRequest(xhrId){return xhrId in this.pendingRequests}abortRequest(xhrId){const xhr=this.pendingRequests[xhrId].xhr;delete this.pendingRequests[xhrId],xhr.abort()}}class PDFNetworkStream{constructor(source2){this._source=source2,this._manager=new NetworkManager(source2),this._rangeChunkSize=
-source2.rangeChunkSize,this._fullRequestReader=null,this._rangeRequestReaders=[]}_onRangeRequestReaderClosed(reader){const i=this._rangeRequestReaders.indexOf(reader);i>=0&&this._rangeRequestReaders.splice(i,1)}getFullReader(){return assert$1(!this._fullRequestReader,"PDFNetworkStream.getFullReader can only be called once."),this._fullRequestReader=new PDFNetworkStreamFullRequestReader(this._manager,this._source),this._fullRequestReader}getRangeReader(begin,end){const reader=new PDFNetworkStreamRangeRequestReader(
-this._manager,begin,end);return reader.onClosed=this._onRangeRequestReaderClosed.bind(this),this._rangeRequestReaders.push(reader),reader}cancelAllRequests(reason){this._fullRequestReader?.cancel(reason);for(const reader of this._rangeRequestReaders.slice(0))reader.cancel(reason)}}class PDFNetworkStreamFullRequestReader{constructor(manager,source2){this._manager=manager,this._url=source2.url,this._fullRequestId=manager.request({onHeadersReceived:this._onHeadersReceived.bind(this),onDone:this._onDone.
-bind(this),onError:this._onError.bind(this),onProgress:this._onProgress.bind(this)}),this._headersCapability=Promise.withResolvers(),this._disableRange=source2.disableRange||!1,this._contentLength=source2.length,this._rangeChunkSize=source2.rangeChunkSize,!this._rangeChunkSize&&!this._disableRange&&(this._disableRange=!0),this._isStreamingSupported=!1,this._isRangeSupported=!1,this._cachedChunks=[],this._requests=[],this._done=!1,this._storedError=void 0,this._filename=null,this.onProgress=null}_onHeadersReceived(){
-const fullRequestXhrId=this._fullRequestId,fullRequestXhr=this._manager.getRequestXhr(fullRequestXhrId);this._manager._responseOrigin=getResponseOrigin(fullRequestXhr.responseURL);const rawResponseHeaders=fullRequestXhr.getAllResponseHeaders(),responseHeaders=new Headers(rawResponseHeaders?rawResponseHeaders.trimStart().replace(/[^\S ]+$/,"").split(/[\r\n]+/).map(x=>{const[key2,...val]=x.split(": ");return[key2,val.join(": ")]}):[]),{allowRangeRequests,suggestedLength}=validateRangeRequestCapabilities(
-{responseHeaders,isHttp:this._manager.isHttp,rangeChunkSize:this._rangeChunkSize,disableRange:this._disableRange});allowRangeRequests&&(this._isRangeSupported=!0),this._contentLength=suggestedLength||this._contentLength,this._filename=extractFilenameFromHeader(responseHeaders),this._isRangeSupported&&this._manager.abortRequest(fullRequestXhrId),this._headersCapability.resolve()}_onDone(data){if(data&&(this._requests.length>0?this._requests.shift().resolve({value:data.chunk,done:!1}):this._cachedChunks.
-push(data.chunk)),this._done=!0,!(this._cachedChunks.length>0)){for(const requestCapability of this._requests)requestCapability.resolve({value:void 0,done:!0});this._requests.length=0}}_onError(status){this._storedError=createResponseError(status,this._url),this._headersCapability.reject(this._storedError);for(const requestCapability of this._requests)requestCapability.reject(this._storedError);this._requests.length=0,this._cachedChunks.length=0}_onProgress(evt){this.onProgress?.({loaded:evt.loaded,
-total:evt.lengthComputable?evt.total:this._contentLength})}get filename(){return this._filename}get isRangeSupported(){return this._isRangeSupported}get isStreamingSupported(){return this._isStreamingSupported}get contentLength(){return this._contentLength}get headersReady(){return this._headersCapability.promise}async read(){if(await this._headersCapability.promise,this._storedError)throw this._storedError;if(this._cachedChunks.length>0)return{value:this._cachedChunks.shift(),done:!1};if(this._done)
-return{value:void 0,done:!0};const requestCapability=Promise.withResolvers();return this._requests.push(requestCapability),requestCapability.promise}cancel(reason){this._done=!0,this._headersCapability.reject(reason);for(const requestCapability of this._requests)requestCapability.resolve({value:void 0,done:!0});this._requests.length=0,this._manager.isPendingRequest(this._fullRequestId)&&this._manager.abortRequest(this._fullRequestId),this._fullRequestReader=null}}class PDFNetworkStreamRangeRequestReader{constructor(manager,begin,end){
-this._manager=manager,this._url=manager.url,this._requestId=manager.request({begin,end,onHeadersReceived:this._onHeadersReceived.bind(this),onDone:this._onDone.bind(this),onError:this._onError.bind(this),onProgress:this._onProgress.bind(this)}),this._requests=[],this._queuedChunk=null,this._done=!1,this._storedError=void 0,this.onProgress=null,this.onClosed=null}_onHeadersReceived(){const responseOrigin=getResponseOrigin(this._manager.getRequestXhr(this._requestId)?.responseURL);responseOrigin!==
-this._manager._responseOrigin&&(this._storedError=new Error(`Expected range response-origin "${responseOrigin}" to match "${this._manager._responseOrigin}".`),this._onError(0))}_close(){this.onClosed?.(this)}_onDone(data){const chunk=data.chunk;this._requests.length>0?this._requests.shift().resolve({value:chunk,done:!1}):this._queuedChunk=chunk,this._done=!0;for(const requestCapability of this._requests)requestCapability.resolve({value:void 0,done:!0});this._requests.length=0,this._close()}_onError(status){
-this._storedError??=createResponseError(status,this._url);for(const requestCapability of this._requests)requestCapability.reject(this._storedError);this._requests.length=0,this._queuedChunk=null}_onProgress(evt){this.isStreamingSupported||this.onProgress?.({loaded:evt.loaded})}get isStreamingSupported(){return!1}async read(){if(this._storedError)throw this._storedError;if(this._queuedChunk!==null){const chunk=this._queuedChunk;return this._queuedChunk=null,{value:chunk,done:!1}}if(this._done)return{
-value:void 0,done:!0};const requestCapability=Promise.withResolvers();return this._requests.push(requestCapability),requestCapability.promise}cancel(reason){this._done=!0;for(const requestCapability of this._requests)requestCapability.resolve({value:void 0,done:!0});this._requests.length=0,this._manager.isPendingRequest(this._requestId)&&this._manager.abortRequest(this._requestId),this._close()}}const urlRegex=/^[a-z][a-z0-9\-+.]+:/i;function parseUrlOrPath(sourceUrl){if(urlRegex.test(sourceUrl))
-return new URL(sourceUrl);const url2=process.getBuiltinModule("url");return new URL(url2.pathToFileURL(sourceUrl))}class PDFNodeStream{constructor(source2){this.source=source2,this.url=parseUrlOrPath(source2.url),assert$1(this.url.protocol==="file:","PDFNodeStream only supports file:// URLs."),this._fullRequestReader=null,this._rangeRequestReaders=[]}get _progressiveDataLength(){return this._fullRequestReader?._loaded??0}getFullReader(){return assert$1(!this._fullRequestReader,"PDFNodeStream.get\
-FullReader can only be called once."),this._fullRequestReader=new PDFNodeStreamFsFullReader(this),this._fullRequestReader}getRangeReader(start2,end){if(end<=this._progressiveDataLength)return null;const rangeReader=new PDFNodeStreamFsRangeReader(this,start2,end);return this._rangeRequestReaders.push(rangeReader),rangeReader}cancelAllRequests(reason){this._fullRequestReader?.cancel(reason);for(const reader of this._rangeRequestReaders.slice(0))reader.cancel(reason)}}class PDFNodeStreamFsFullReader{constructor(stream){
-this._url=stream.url,this._done=!1,this._storedError=null,this.onProgress=null;const source2=stream.source;this._contentLength=source2.length,this._loaded=0,this._filename=null,this._disableRange=source2.disableRange||!1,this._rangeChunkSize=source2.rangeChunkSize,!this._rangeChunkSize&&!this._disableRange&&(this._disableRange=!0),this._isStreamingSupported=!source2.disableStream,this._isRangeSupported=!source2.disableRange,this._readableStream=null,this._readCapability=Promise.withResolvers(),this.
-_headersCapability=Promise.withResolvers();const fs=process.getBuiltinModule("fs");fs.promises.lstat(this._url).then(stat=>{this._contentLength=stat.size,this._setReadableStream(fs.createReadStream(this._url)),this._headersCapability.resolve()},error2=>{error2.code==="ENOENT"&&(error2=createResponseError(0,this._url.href)),this._storedError=error2,this._headersCapability.reject(error2)})}get headersReady(){return this._headersCapability.promise}get filename(){return this._filename}get contentLength(){
-return this._contentLength}get isRangeSupported(){return this._isRangeSupported}get isStreamingSupported(){return this._isStreamingSupported}async read(){if(await this._readCapability.promise,this._done)return{value:void 0,done:!0};if(this._storedError)throw this._storedError;const chunk=this._readableStream.read();return chunk===null?(this._readCapability=Promise.withResolvers(),this.read()):(this._loaded+=chunk.length,this.onProgress?.({loaded:this._loaded,total:this._contentLength}),{value:new Uint8Array(
-chunk).buffer,done:!1})}cancel(reason){if(!this._readableStream){this._error(reason);return}this._readableStream.destroy(reason)}_error(reason){this._storedError=reason,this._readCapability.resolve()}_setReadableStream(readableStream){this._readableStream=readableStream,readableStream.on("readable",()=>{this._readCapability.resolve()}),readableStream.on("end",()=>{readableStream.destroy(),this._done=!0,this._readCapability.resolve()}),readableStream.on("error",reason=>{this._error(reason)}),!this.
-_isStreamingSupported&&this._isRangeSupported&&this._error(new AbortException("streaming is disabled")),this._storedError&&this._readableStream.destroy(this._storedError)}}class PDFNodeStreamFsRangeReader{constructor(stream,start2,end){this._url=stream.url,this._done=!1,this._storedError=null,this.onProgress=null,this._loaded=0,this._readableStream=null,this._readCapability=Promise.withResolvers();const source2=stream.source;this._isStreamingSupported=!source2.disableStream;const fs=process.getBuiltinModule(
-"fs");this._setReadableStream(fs.createReadStream(this._url,{start:start2,end:end-1}))}get isStreamingSupported(){return this._isStreamingSupported}async read(){if(await this._readCapability.promise,this._done)return{value:void 0,done:!0};if(this._storedError)throw this._storedError;const chunk=this._readableStream.read();return chunk===null?(this._readCapability=Promise.withResolvers(),this.read()):(this._loaded+=chunk.length,this.onProgress?.({loaded:this._loaded}),{value:new Uint8Array(chunk).
-buffer,done:!1})}cancel(reason){if(!this._readableStream){this._error(reason);return}this._readableStream.destroy(reason)}_error(reason){this._storedError=reason,this._readCapability.resolve()}_setReadableStream(readableStream){this._readableStream=readableStream,readableStream.on("readable",()=>{this._readCapability.resolve()}),readableStream.on("end",()=>{readableStream.destroy(),this._done=!0,this._readCapability.resolve()}),readableStream.on("error",reason=>{this._error(reason)}),this._storedError&&
-this._readableStream.destroy(this._storedError)}}const INITIAL_DATA=Symbol("INITIAL_DATA");class PDFObjects{#objs=Object.create(null);#ensureObj(objId){return this.#objs[objId]||={...Promise.withResolvers(),data:INITIAL_DATA}}get(objId,callback=null){if(callback){const obj2=this.#ensureObj(objId);return obj2.promise.then(()=>callback(obj2.data)),null}const obj=this.#objs[objId];if(!obj||obj.data===INITIAL_DATA)throw new Error(`Requesting object that isn't resolved yet ${objId}.`);return obj.data}has(objId){
-const obj=this.#objs[objId];return!!obj&&obj.data!==INITIAL_DATA}delete(objId){const obj=this.#objs[objId];return!obj||obj.data===INITIAL_DATA?!1:(delete this.#objs[objId],!0)}resolve(objId,data=null){const obj=this.#ensureObj(objId);obj.data=data,obj.resolve()}clear(){for(const objId in this.#objs){const{data}=this.#objs[objId];data?.bitmap?.close()}this.#objs=Object.create(null)}*[Symbol.iterator](){for(const objId in this.#objs){const{data}=this.#objs[objId];data!==INITIAL_DATA&&(yield[objId,
-data])}}}const MAX_TEXT_DIVS_TO_RENDER=1e5,DEFAULT_FONT_SIZE=30;class TextLayer{#capability=Promise.withResolvers();#container=null;#disableProcessItems=!1;#fontInspectorEnabled=!!globalThis.FontInspector?.enabled;#lang=null;#layoutTextParams=null;#pageHeight=0;#pageWidth=0;#reader=null;#rootContainer=null;#rotation=0;#scale=0;#styleCache=Object.create(null);#textContentItemsStr=[];#textContentSource=null;#textDivs=[];#textDivProperties=new WeakMap;#transform=null;static#ascentCache=new Map;static#canvasContexts=new Map;static#canvasCtxFonts=new WeakMap;static#minFontSize=null;static#pendingTextLayers=new Set;constructor({
-textContentSource,container:container2,viewport}){if(textContentSource instanceof ReadableStream)this.#textContentSource=textContentSource;else if(typeof textContentSource=="object")this.#textContentSource=new ReadableStream({start(controller){controller.enqueue(textContentSource),controller.close()}});else throw new Error('No "textContentSource" parameter specified.');this.#container=this.#rootContainer=container2,this.#scale=viewport.scale*OutputScale.pixelRatio,this.#rotation=viewport.rotation,
-this.#layoutTextParams={div:null,properties:null,ctx:null};const{pageWidth,pageHeight,pageX,pageY}=viewport.rawDims;this.#transform=[1,0,0,-1,-pageX,pageY+pageHeight],this.#pageWidth=pageWidth,this.#pageHeight=pageHeight,TextLayer.#ensureMinFontSizeComputed(),setLayerDimensions(container2,viewport),this.#capability.promise.finally(()=>{TextLayer.#pendingTextLayers.delete(this),this.#layoutTextParams=null,this.#styleCache=null}).catch(()=>{})}static get fontFamilyMap(){const{isWindows,isFirefox}=util_FeatureTest.
-platform;return shadow(this,"fontFamilyMap",new Map([["sans-serif",`${isWindows&&isFirefox?"Calibri, ":""}sans-serif`],["monospace",`${isWindows&&isFirefox?"Lucida Console, ":""}monospace`]]))}render(){const pump=()=>{this.#reader.read().then(({value,done})=>{if(done){this.#capability.resolve();return}this.#lang??=value.lang,Object.assign(this.#styleCache,value.styles),this.#processItems(value.items),pump()},this.#capability.reject)};return this.#reader=this.#textContentSource.getReader(),TextLayer.#pendingTextLayers.
-add(this),pump(),this.#capability.promise}update({viewport,onBefore=null}){const scale2=viewport.scale*OutputScale.pixelRatio,rotation=viewport.rotation;if(rotation!==this.#rotation&&(onBefore?.(),this.#rotation=rotation,setLayerDimensions(this.#rootContainer,{rotation})),scale2!==this.#scale){onBefore?.(),this.#scale=scale2;const params={div:null,properties:null,ctx:TextLayer.#getCtx(this.#lang)};for(const div of this.#textDivs)params.properties=this.#textDivProperties.get(div),params.div=div,this.#layout(
-params)}}cancel(){const abortEx=new AbortException("TextLayer task cancelled.");this.#reader?.cancel(abortEx).catch(()=>{}),this.#reader=null,this.#capability.reject(abortEx)}get textDivs(){return this.#textDivs}get textContentItemsStr(){return this.#textContentItemsStr}#processItems(items2){if(this.#disableProcessItems)return;this.#layoutTextParams.ctx??=TextLayer.#getCtx(this.#lang);const textDivs=this.#textDivs,textContentItemsStr=this.#textContentItemsStr;for(const item of items2){if(textDivs.
-length>MAX_TEXT_DIVS_TO_RENDER){warn("Ignoring additional textDivs for performance reasons."),this.#disableProcessItems=!0;return}if(item.str===void 0){if(item.type==="beginMarkedContentProps"||item.type==="beginMarkedContent"){const parent=this.#container;this.#container=document.createElement("span"),this.#container.classList.add("markedContent"),item.id&&this.#container.setAttribute("id",`${item.id}`),parent.append(this.#container)}else item.type==="endMarkedContent"&&(this.#container=this.#container.
-parentNode);continue}textContentItemsStr.push(item.str),this.#appendText(item)}}#appendText(geom){const textDiv=document.createElement("span"),textDivProperties={angle:0,canvasWidth:0,hasText:geom.str!=="",hasEOL:geom.hasEOL,fontSize:0};this.#textDivs.push(textDiv);const tx=Util.transform(this.#transform,geom.transform);let angle=Math.atan2(tx[1],tx[0]);const style2=this.#styleCache[geom.fontName];style2.vertical&&(angle+=Math.PI/2);let fontFamily=this.#fontInspectorEnabled&&style2.fontSubstitution||
-style2.fontFamily;fontFamily=TextLayer.fontFamilyMap.get(fontFamily)||fontFamily;const fontHeight=Math.hypot(tx[2],tx[3]),fontAscent=fontHeight*TextLayer.#getAscent(fontFamily,style2,this.#lang);let left,top;angle===0?(left=tx[4],top=tx[5]-fontAscent):(left=tx[4]+fontAscent*Math.sin(angle),top=tx[5]-fontAscent*Math.cos(angle));const scaleFactorStr="calc(var(--total-scale-factor) *",divStyle=textDiv.style;this.#container===this.#rootContainer?(divStyle.left=`${(100*left/this.#pageWidth).toFixed(2)}\
-%`,divStyle.top=`${(100*top/this.#pageHeight).toFixed(2)}%`):(divStyle.left=`${scaleFactorStr}${left.toFixed(2)}px)`,divStyle.top=`${scaleFactorStr}${top.toFixed(2)}px)`),divStyle.fontSize=`${scaleFactorStr}${(TextLayer.#minFontSize*fontHeight).toFixed(2)}px)`,divStyle.fontFamily=fontFamily,textDivProperties.fontSize=fontHeight,textDiv.setAttribute("role","presentation"),textDiv.textContent=geom.str,textDiv.dir=geom.dir,this.#fontInspectorEnabled&&(textDiv.dataset.fontName=style2.fontSubstitutionLoadedName||
-geom.fontName),angle!==0&&(textDivProperties.angle=angle*(180/Math.PI));let shouldScaleText=!1;if(geom.str.length>1)shouldScaleText=!0;else if(geom.str!==" "&&geom.transform[0]!==geom.transform[3]){const absScaleX=Math.abs(geom.transform[0]),absScaleY=Math.abs(geom.transform[3]);absScaleX!==absScaleY&&Math.max(absScaleX,absScaleY)/Math.min(absScaleX,absScaleY)>1.5&&(shouldScaleText=!0)}if(shouldScaleText&&(textDivProperties.canvasWidth=style2.vertical?geom.height:geom.width),this.#textDivProperties.
-set(textDiv,textDivProperties),this.#layoutTextParams.div=textDiv,this.#layoutTextParams.properties=textDivProperties,this.#layout(this.#layoutTextParams),textDivProperties.hasText&&this.#container.append(textDiv),textDivProperties.hasEOL){const br2=document.createElement("br");br2.setAttribute("role","presentation"),this.#container.append(br2)}}#layout(params){const{div,properties:properties2,ctx}=params,{style:style2}=div;let transform2="";if(TextLayer.#minFontSize>1&&(transform2=`scale(${1/TextLayer.#minFontSize}\
-)`),properties2.canvasWidth!==0&&properties2.hasText){const{fontFamily}=style2,{canvasWidth,fontSize}=properties2;TextLayer.#ensureCtxFont(ctx,fontSize*this.#scale,fontFamily);const{width}=ctx.measureText(div.textContent);width>0&&(transform2=`scaleX(${canvasWidth*this.#scale/width}) ${transform2}`)}properties2.angle!==0&&(transform2=`rotate(${properties2.angle}deg) ${transform2}`),transform2.length>0&&(style2.transform=transform2)}static cleanup(){if(!(this.#pendingTextLayers.size>0)){this.#ascentCache.
-clear();for(const{canvas}of this.#canvasContexts.values())canvas.remove();this.#canvasContexts.clear()}}static#getCtx(lang=null){let ctx=this.#canvasContexts.get(lang||="");if(!ctx){const canvas=document.createElement("canvas");canvas.className="hiddenCanvasElement",canvas.lang=lang,document.body.append(canvas),ctx=canvas.getContext("2d",{alpha:!1,willReadFrequently:!0}),this.#canvasContexts.set(lang,ctx),this.#canvasCtxFonts.set(ctx,{size:0,family:""})}return ctx}static#ensureCtxFont(ctx,size2,family){
-const cached2=this.#canvasCtxFonts.get(ctx);size2===cached2.size&&family===cached2.family||(ctx.font=`${size2}px ${family}`,cached2.size=size2,cached2.family=family)}static#ensureMinFontSizeComputed(){if(this.#minFontSize!==null)return;const div=document.createElement("div");div.style.opacity=0,div.style.lineHeight=1,div.style.fontSize="1px",div.style.position="absolute",div.textContent="X",document.body.append(div),this.#minFontSize=div.getBoundingClientRect().height,div.remove()}static#getAscent(fontFamily,style2,lang){
-const cachedAscent=this.#ascentCache.get(fontFamily);if(cachedAscent)return cachedAscent;const ctx=this.#getCtx(lang);ctx.canvas.width=ctx.canvas.height=DEFAULT_FONT_SIZE,this.#ensureCtxFont(ctx,DEFAULT_FONT_SIZE,fontFamily);const metrics=ctx.measureText(""),ascent=metrics.fontBoundingBoxAscent,descent=Math.abs(metrics.fontBoundingBoxDescent);ctx.canvas.width=ctx.canvas.height=0;let ratio=.8;return ascent?ratio=ascent/(ascent+descent):(util_FeatureTest.platform.isFirefox&&warn("Enable the `dom.t\
-extMetrics.fontBoundingBox.enabled` preference in `about:config` to improve TextLayer rendering."),style2.ascent?ratio=style2.ascent:style2.descent&&(ratio=1+style2.descent)),this.#ascentCache.set(fontFamily,ratio),ratio}}class XfaText{static textContent(xfa){const items2=[],output={items:items2,styles:Object.create(null)};function walk(node2){if(!node2)return;let str=null;const name=node2.name;if(name==="#text")str=node2.value;else if(XfaText.shouldBuildText(name))node2?.attributes?.textContent?
-str=node2.attributes.textContent:node2.value&&(str=node2.value);else return;if(str!==null&&items2.push({str}),!!node2.children)for(const child2 of node2.children)walk(child2)}return walk(xfa),output}static shouldBuildText(name){return!(name==="textarea"||name==="input"||name==="option"||name==="select")}}const RENDERING_CANCELLED_TIMEOUT=100;function getDocument(src={}){typeof src=="string"||src instanceof URL?src={url:src}:(src instanceof ArrayBuffer||ArrayBuffer.isView(src))&&(src={data:src});
-const task=new PDFDocumentLoadingTask,{docId}=task,url2=src.url?getUrlProp(src.url):null,data=src.data?getDataProp(src.data):null,httpHeaders=src.httpHeaders||null,withCredentials=src.withCredentials===!0,password=src.password??null,rangeTransport=src.range instanceof PDFDataRangeTransport?src.range:null,rangeChunkSize=Number.isInteger(src.rangeChunkSize)&&src.rangeChunkSize>0?src.rangeChunkSize:2**16;let worker=src.worker instanceof PDFWorker?src.worker:null;const verbosity2=src.verbosity,docBaseUrl=typeof src.
-docBaseUrl=="string"&&!isDataScheme(src.docBaseUrl)?src.docBaseUrl:null,cMapUrl=getFactoryUrlProp(src.cMapUrl),cMapPacked=src.cMapPacked!==!1,CMapReaderFactory=src.CMapReaderFactory||(isNodeJS?NodeCMapReaderFactory:DOMCMapReaderFactory),iccUrl=getFactoryUrlProp(src.iccUrl),standardFontDataUrl=getFactoryUrlProp(src.standardFontDataUrl),StandardFontDataFactory=src.StandardFontDataFactory||(isNodeJS?NodeStandardFontDataFactory:DOMStandardFontDataFactory),wasmUrl=getFactoryUrlProp(src.wasmUrl),WasmFactory=src.
-WasmFactory||(isNodeJS?NodeWasmFactory:DOMWasmFactory),ignoreErrors=src.stopAtErrors!==!0,maxImageSize=Number.isInteger(src.maxImageSize)&&src.maxImageSize>-1?src.maxImageSize:-1,isEvalSupported2=src.isEvalSupported!==!1,isOffscreenCanvasSupported=typeof src.isOffscreenCanvasSupported=="boolean"?src.isOffscreenCanvasSupported:!isNodeJS,isImageDecoderSupported=typeof src.isImageDecoderSupported=="boolean"?src.isImageDecoderSupported:!isNodeJS&&(util_FeatureTest.platform.isFirefox||!globalThis.chrome),
-canvasMaxAreaInBytes=Number.isInteger(src.canvasMaxAreaInBytes)?src.canvasMaxAreaInBytes:-1,disableFontFace=typeof src.disableFontFace=="boolean"?src.disableFontFace:isNodeJS,fontExtraProperties=src.fontExtraProperties===!0,enableXfa=src.enableXfa===!0,ownerDocument=src.ownerDocument||globalThis.document,disableRange=src.disableRange===!0,disableStream=src.disableStream===!0,disableAutoFetch=src.disableAutoFetch===!0,pdfBug=src.pdfBug===!0,CanvasFactory=src.CanvasFactory||(isNodeJS?NodeCanvasFactory:
-DOMCanvasFactory),FilterFactory=src.FilterFactory||(isNodeJS?NodeFilterFactory:DOMFilterFactory),enableHWA=src.enableHWA===!0,useWasm=src.useWasm!==!1,length=rangeTransport?rangeTransport.length:src.length??NaN,useSystemFonts=typeof src.useSystemFonts=="boolean"?src.useSystemFonts:!isNodeJS&&!disableFontFace,useWorkerFetch=typeof src.useWorkerFetch=="boolean"?src.useWorkerFetch:!!(CMapReaderFactory===DOMCMapReaderFactory&&StandardFontDataFactory===DOMStandardFontDataFactory&&WasmFactory===DOMWasmFactory&&
-cMapUrl&&standardFontDataUrl&&wasmUrl&&isValidFetchUrl(cMapUrl,document.baseURI)&&isValidFetchUrl(standardFontDataUrl,document.baseURI)&&isValidFetchUrl(wasmUrl,document.baseURI)),styleElement2=null;setVerbosityLevel(verbosity2);const transportFactory={canvasFactory:new CanvasFactory({ownerDocument,enableHWA}),filterFactory:new FilterFactory({docId,ownerDocument}),cMapReaderFactory:useWorkerFetch?null:new CMapReaderFactory({baseUrl:cMapUrl,isCompressed:cMapPacked}),standardFontDataFactory:useWorkerFetch?
-null:new StandardFontDataFactory({baseUrl:standardFontDataUrl}),wasmFactory:useWorkerFetch?null:new WasmFactory({baseUrl:wasmUrl})};worker||(worker=PDFWorker.create({verbosity:verbosity2,port:GlobalWorkerOptions.workerPort}),task._worker=worker);const docParams={docId,apiVersion:"5.4.54",data,password,disableAutoFetch,rangeChunkSize,length,docBaseUrl,enableXfa,evaluatorOptions:{maxImageSize,disableFontFace,ignoreErrors,isEvalSupported:isEvalSupported2,isOffscreenCanvasSupported,isImageDecoderSupported,
-canvasMaxAreaInBytes,fontExtraProperties,useSystemFonts,useWasm,useWorkerFetch,cMapUrl,iccUrl,standardFontDataUrl,wasmUrl}},transportParams={ownerDocument,pdfBug,styleElement:styleElement2,loadingParams:{disableAutoFetch,enableXfa}};return worker.promise.then(function(){if(task.destroyed)throw new Error("Loading aborted");if(worker.destroyed)throw new Error("Worker was destroyed");const workerIdPromise=worker.messageHandler.sendWithPromise("GetDocRequest",docParams,data?[data.buffer]:null);let networkStream;
-if(rangeTransport)networkStream=new PDFDataTransportStream(rangeTransport,{disableRange,disableStream});else if(!data){if(!url2)throw new Error("getDocument - no `url` parameter provided.");const NetworkStream=isValidFetchUrl(url2)?PDFFetchStream:isNodeJS?PDFNodeStream:PDFNetworkStream;networkStream=new NetworkStream({url:url2,length,httpHeaders,withCredentials,rangeChunkSize,disableRange,disableStream})}return workerIdPromise.then(workerId=>{if(task.destroyed)throw new Error("Loading aborted");
-if(worker.destroyed)throw new Error("Worker was destroyed");const messageHandler=new MessageHandler(docId,workerId,worker.port),transport=new WorkerTransport(messageHandler,task,networkStream,transportParams,transportFactory,enableHWA);task._transport=transport,messageHandler.send("Ready",null)})}).catch(task._capability.reject),task}class PDFDocumentLoadingTask{static#docId=0;_capability=Promise.withResolvers();_transport=null;_worker=null;docId=`d${PDFDocumentLoadingTask.#docId++}`;destroyed=!1;onPassword=null;onProgress=null;get promise(){
-return this._capability.promise}async destroy(){this.destroyed=!0;try{this._worker?.port&&(this._worker._pendingDestroy=!0),await this._transport?.destroy()}catch(ex){throw this._worker?.port&&delete this._worker._pendingDestroy,ex}this._transport=null,this._worker?.destroy(),this._worker=null}async getData(){return this._transport.getData()}}class PDFDataRangeTransport{#capability=Promise.withResolvers();#progressiveDoneListeners=[];#progressiveReadListeners=[];#progressListeners=[];#rangeListeners=[];constructor(length,initialData,progressiveDone=!1,contentDispositionFilename=null){
-this.length=length,this.initialData=initialData,this.progressiveDone=progressiveDone,this.contentDispositionFilename=contentDispositionFilename}addRangeListener(listener){this.#rangeListeners.push(listener)}addProgressListener(listener){this.#progressListeners.push(listener)}addProgressiveReadListener(listener){this.#progressiveReadListeners.push(listener)}addProgressiveDoneListener(listener){this.#progressiveDoneListeners.push(listener)}onDataRange(begin,chunk){for(const listener of this.#rangeListeners)
-listener(begin,chunk)}onDataProgress(loaded,total){this.#capability.promise.then(()=>{for(const listener of this.#progressListeners)listener(loaded,total)})}onDataProgressiveRead(chunk){this.#capability.promise.then(()=>{for(const listener of this.#progressiveReadListeners)listener(chunk)})}onDataProgressiveDone(){this.#capability.promise.then(()=>{for(const listener of this.#progressiveDoneListeners)listener()})}transportReady(){this.#capability.resolve()}requestDataRange(begin,end){unreachable(
-"Abstract method PDFDataRangeTransport.requestDataRange")}abort(){}}class PDFDocumentProxy{constructor(pdfInfo,transport){this._pdfInfo=pdfInfo,this._transport=transport}get annotationStorage(){return this._transport.annotationStorage}get canvasFactory(){return this._transport.canvasFactory}get filterFactory(){return this._transport.filterFactory}get numPages(){return this._pdfInfo.numPages}get fingerprints(){return this._pdfInfo.fingerprints}get isPureXfa(){return shadow(this,"isPureXfa",!!this.
-_transport._htmlForXfa)}get allXfaHtml(){return this._transport._htmlForXfa}getPage(pageNumber){return this._transport.getPage(pageNumber)}getPageIndex(ref2){return this._transport.getPageIndex(ref2)}getDestinations(){return this._transport.getDestinations()}getDestination(id2){return this._transport.getDestination(id2)}getPageLabels(){return this._transport.getPageLabels()}getPageLayout(){return this._transport.getPageLayout()}getPageMode(){return this._transport.getPageMode()}getViewerPreferences(){
-return this._transport.getViewerPreferences()}getOpenAction(){return this._transport.getOpenAction()}getAttachments(){return this._transport.getAttachments()}getJSActions(){return this._transport.getDocJSActions()}getOutline(){return this._transport.getOutline()}getOptionalContentConfig({intent="display"}={}){const{renderingIntent}=this._transport.getRenderingIntent(intent);return this._transport.getOptionalContentConfig(renderingIntent)}getPermissions(){return this._transport.getPermissions()}getMetadata(){
-return this._transport.getMetadata()}getMarkInfo(){return this._transport.getMarkInfo()}getData(){return this._transport.getData()}saveDocument(){return this._transport.saveDocument()}getDownloadInfo(){return this._transport.downloadInfoCapability.promise}cleanup(keepLoadedFonts=!1){return this._transport.startCleanup(keepLoadedFonts||this.isPureXfa)}destroy(){return this.loadingTask.destroy()}cachedPageNumber(ref2){return this._transport.cachedPageNumber(ref2)}get loadingParams(){return this._transport.
-loadingParams}get loadingTask(){return this._transport.loadingTask}getFieldObjects(){return this._transport.getFieldObjects()}hasJSActions(){return this._transport.hasJSActions()}getCalculationOrderIds(){return this._transport.getCalculationOrderIds()}}class PDFPageProxy{#pendingCleanup=!1;constructor(pageIndex,pageInfo,transport,pdfBug=!1){this._pageIndex=pageIndex,this._pageInfo=pageInfo,this._transport=transport,this._stats=pdfBug?new StatTimer:null,this._pdfBug=pdfBug,this.commonObjs=transport.
-commonObjs,this.objs=new PDFObjects,this._intentStates=new Map,this.destroyed=!1}get pageNumber(){return this._pageIndex+1}get rotate(){return this._pageInfo.rotate}get ref(){return this._pageInfo.ref}get userUnit(){return this._pageInfo.userUnit}get view(){return this._pageInfo.view}getViewport({scale:scale2,rotation=this.rotate,offsetX=0,offsetY=0,dontFlip=!1}={}){return new PageViewport({viewBox:this.view,userUnit:this.userUnit,scale:scale2,rotation,offsetX,offsetY,dontFlip})}getAnnotations({
-intent="display"}={}){const{renderingIntent}=this._transport.getRenderingIntent(intent);return this._transport.getAnnotations(this._pageIndex,renderingIntent)}getJSActions(){return this._transport.getPageJSActions(this._pageIndex)}get filterFactory(){return this._transport.filterFactory}get isPureXfa(){return shadow(this,"isPureXfa",!!this._transport._htmlForXfa)}async getXfa(){return this._transport._htmlForXfa?.children[this._pageIndex]||null}render({canvasContext,canvas=canvasContext.canvas,viewport,
-intent="display",annotationMode=AnnotationMode.ENABLE,transform:transform2=null,background=null,optionalContentConfigPromise=null,annotationCanvasMap=null,pageColors=null,printAnnotationStorage=null,isEditing:isEditing2=!1}){this._stats?.time("Overall");const intentArgs=this._transport.getRenderingIntent(intent,annotationMode,printAnnotationStorage,isEditing2),{renderingIntent,cacheKey}=intentArgs;this.#pendingCleanup=!1,optionalContentConfigPromise||=this._transport.getOptionalContentConfig(renderingIntent);
-let intentState=this._intentStates.get(cacheKey);intentState||(intentState=Object.create(null),this._intentStates.set(cacheKey,intentState)),intentState.streamReaderCancelTimeout&&(clearTimeout(intentState.streamReaderCancelTimeout),intentState.streamReaderCancelTimeout=null);const intentPrint=!!(renderingIntent&RenderingIntentFlag.PRINT);intentState.displayReadyCapability||(intentState.displayReadyCapability=Promise.withResolvers(),intentState.operatorList={fnArray:[],argsArray:[],lastChunk:!1,
-separateAnnots:null},this._stats?.time("Page Request"),this._pumpOperatorList(intentArgs));const complete=error2=>{intentState.renderTasks.delete(internalRenderTask),intentPrint&&(this.#pendingCleanup=!0),this.#tryCleanup(),error2?(internalRenderTask.capability.reject(error2),this._abortOperatorList({intentState,reason:error2 instanceof Error?error2:new Error(error2)})):internalRenderTask.capability.resolve(),this._stats&&(this._stats.timeEnd("Rendering"),this._stats.timeEnd("Overall"),globalThis.
-Stats?.enabled&&globalThis.Stats.add(this.pageNumber,this._stats))},internalRenderTask=new InternalRenderTask({callback:complete,params:{canvas,canvasContext,viewport,transform:transform2,background},objs:this.objs,commonObjs:this.commonObjs,annotationCanvasMap,operatorList:intentState.operatorList,pageIndex:this._pageIndex,canvasFactory:this._transport.canvasFactory,filterFactory:this._transport.filterFactory,useRequestAnimationFrame:!intentPrint,pdfBug:this._pdfBug,pageColors,enableHWA:this._transport.
-enableHWA});(intentState.renderTasks||=new Set).add(internalRenderTask);const renderTask=internalRenderTask.task;return Promise.all([intentState.displayReadyCapability.promise,optionalContentConfigPromise]).then(([transparency,optionalContentConfig])=>{if(this.destroyed){complete();return}if(this._stats?.time("Rendering"),!(optionalContentConfig.renderingIntent&renderingIntent))throw new Error("Must use the same `intent`-argument when calling the `PDFPageProxy.render` and `PDFDocumentProxy.getOp\
-tionalContentConfig` methods.");internalRenderTask.initializeGraphics({transparency,optionalContentConfig}),internalRenderTask.operatorListChanged()}).catch(complete),renderTask}getOperatorList({intent="display",annotationMode=AnnotationMode.ENABLE,printAnnotationStorage=null,isEditing:isEditing2=!1}={}){function operatorListChanged(){intentState.operatorList.lastChunk&&(intentState.opListReadCapability.resolve(intentState.operatorList),intentState.renderTasks.delete(opListTask))}const intentArgs=this.
-_transport.getRenderingIntent(intent,annotationMode,printAnnotationStorage,isEditing2,!0);let intentState=this._intentStates.get(intentArgs.cacheKey);intentState||(intentState=Object.create(null),this._intentStates.set(intentArgs.cacheKey,intentState));let opListTask;return intentState.opListReadCapability||(opListTask=Object.create(null),opListTask.operatorListChanged=operatorListChanged,intentState.opListReadCapability=Promise.withResolvers(),(intentState.renderTasks||=new Set).add(opListTask),
-intentState.operatorList={fnArray:[],argsArray:[],lastChunk:!1,separateAnnots:null},this._stats?.time("Page Request"),this._pumpOperatorList(intentArgs)),intentState.opListReadCapability.promise}streamTextContent({includeMarkedContent=!1,disableNormalization=!1}={}){return this._transport.messageHandler.sendWithStream("GetTextContent",{pageIndex:this._pageIndex,includeMarkedContent:includeMarkedContent===!0,disableNormalization:disableNormalization===!0},{highWaterMark:100,size(textContent){return textContent.
-items.length}})}getTextContent(params={}){if(this._transport._htmlForXfa)return this.getXfa().then(xfa=>XfaText.textContent(xfa));const readableStream=this.streamTextContent(params);return new Promise(function(resolve2,reject){function pump(){reader.read().then(function({value,done}){if(done){resolve2(textContent);return}textContent.lang??=value.lang,Object.assign(textContent.styles,value.styles),textContent.items.push(...value.items),pump()},reject)}const reader=readableStream.getReader(),textContent={
-items:[],styles:Object.create(null),lang:null};pump()})}getStructTree(){return this._transport.getStructTree(this._pageIndex)}_destroy(){this.destroyed=!0;const waitOn=[];for(const intentState of this._intentStates.values())if(this._abortOperatorList({intentState,reason:new Error("Page was destroyed."),force:!0}),!intentState.opListReadCapability)for(const internalRenderTask of intentState.renderTasks)waitOn.push(internalRenderTask.completed),internalRenderTask.cancel();return this.objs.clear(),
-this.#pendingCleanup=!1,Promise.all(waitOn)}cleanup(resetStats=!1){this.#pendingCleanup=!0;const success=this.#tryCleanup();return resetStats&&success&&(this._stats&&=new StatTimer),success}#tryCleanup(){if(!this.#pendingCleanup||this.destroyed)return!1;for(const{renderTasks,operatorList}of this._intentStates.values())if(renderTasks.size>0||!operatorList.lastChunk)return!1;return this._intentStates.clear(),this.objs.clear(),this.#pendingCleanup=!1,!0}_startRenderPage(transparency,cacheKey){const intentState=this.
-_intentStates.get(cacheKey);intentState&&(this._stats?.timeEnd("Page Request"),intentState.displayReadyCapability?.resolve(transparency))}_renderPageChunk(operatorListChunk,intentState){for(let i=0,ii=operatorListChunk.length;i<ii;i++)intentState.operatorList.fnArray.push(operatorListChunk.fnArray[i]),intentState.operatorList.argsArray.push(operatorListChunk.argsArray[i]);intentState.operatorList.lastChunk=operatorListChunk.lastChunk,intentState.operatorList.separateAnnots=operatorListChunk.separateAnnots;
-for(const internalRenderTask of intentState.renderTasks)internalRenderTask.operatorListChanged();operatorListChunk.lastChunk&&this.#tryCleanup()}_pumpOperatorList({renderingIntent,cacheKey,annotationStorageSerializable,modifiedIds}){const{map:map2,transfer}=annotationStorageSerializable,reader=this._transport.messageHandler.sendWithStream("GetOperatorList",{pageIndex:this._pageIndex,intent:renderingIntent,cacheKey,annotationStorage:map2,modifiedIds},transfer).getReader(),intentState=this._intentStates.
-get(cacheKey);intentState.streamReader=reader;const pump=()=>{reader.read().then(({value,done})=>{if(done){intentState.streamReader=null;return}this._transport.destroyed||(this._renderPageChunk(value,intentState),pump())},reason=>{if(intentState.streamReader=null,!this._transport.destroyed){if(intentState.operatorList){intentState.operatorList.lastChunk=!0;for(const internalRenderTask of intentState.renderTasks)internalRenderTask.operatorListChanged();this.#tryCleanup()}if(intentState.displayReadyCapability)
-intentState.displayReadyCapability.reject(reason);else if(intentState.opListReadCapability)intentState.opListReadCapability.reject(reason);else throw reason}})};pump()}_abortOperatorList({intentState,reason,force=!1}){if(intentState.streamReader){if(intentState.streamReaderCancelTimeout&&(clearTimeout(intentState.streamReaderCancelTimeout),intentState.streamReaderCancelTimeout=null),!force){if(intentState.renderTasks.size>0)return;if(reason instanceof RenderingCancelledException){let delay=RENDERING_CANCELLED_TIMEOUT;
-reason.extraDelay>0&&reason.extraDelay<1e3&&(delay+=reason.extraDelay),intentState.streamReaderCancelTimeout=setTimeout(()=>{intentState.streamReaderCancelTimeout=null,this._abortOperatorList({intentState,reason,force:!0})},delay);return}}if(intentState.streamReader.cancel(new AbortException(reason.message)).catch(()=>{}),intentState.streamReader=null,!this._transport.destroyed){for(const[curCacheKey,curIntentState]of this._intentStates)if(curIntentState===intentState){this._intentStates.delete(
-curCacheKey);break}this.cleanup()}}}get stats(){return this._stats}}var _capability,_messageHandler,_port,_webWorker,_fakeWorkerId,_isWorkerDisabled,_workerPorts,_PDFWorker_instances,resolve_fn,initializeFromPort_fn,initialize_fn,setupFakeWorker_fn,_PDFWorker_static,mainThreadWorkerMessageHandler_get;const _PDFWorker=class _PDFWorker{constructor({name=null,port=null,verbosity:verbosity2=getVerbosityLevel()}={}){__privateAdd(this,_PDFWorker_instances);__privateAdd(this,_capability,Promise.withResolvers());
-__privateAdd(this,_messageHandler,null);__privateAdd(this,_port,null);__privateAdd(this,_webWorker,null);if(this.name=name,this.destroyed=!1,this.verbosity=verbosity2,port){if(__privateGet(_PDFWorker,_workerPorts).has(port))throw new Error("Cannot use more than one PDFWorker per port.");__privateGet(_PDFWorker,_workerPorts).set(port,this),__privateMethod(this,_PDFWorker_instances,initializeFromPort_fn).call(this,port)}else __privateMethod(this,_PDFWorker_instances,initialize_fn).call(this)}get promise(){
-return __privateGet(this,_capability).promise}get port(){return __privateGet(this,_port)}get messageHandler(){return __privateGet(this,_messageHandler)}destroy(){this.destroyed=!0,__privateGet(this,_webWorker)?.terminate(),__privateSet(this,_webWorker,null),__privateGet(_PDFWorker,_workerPorts).delete(__privateGet(this,_port)),__privateSet(this,_port,null),__privateGet(this,_messageHandler)?.destroy(),__privateSet(this,_messageHandler,null)}static create(params){const cachedPort=__privateGet(this,
-_workerPorts).get(params?.port);if(cachedPort){if(cachedPort._pendingDestroy)throw new Error("PDFWorker.create - the worker is being destroyed.\nPlease remember to await `PDFDocumentLoadingTask.destroy()`-calls.");return cachedPort}return new _PDFWorker(params)}static get workerSrc(){if(GlobalWorkerOptions.workerSrc)return GlobalWorkerOptions.workerSrc;throw new Error('No "GlobalWorkerOptions.workerSrc" specified.')}static get _setupFakeWorkerGlobal(){return shadow(this,"_setupFakeWorkerGlobal",
-(async()=>__privateGet(this,_PDFWorker_static,mainThreadWorkerMessageHandler_get)?__privateGet(this,_PDFWorker_static,mainThreadWorkerMessageHandler_get):(await import(this.workerSrc)).WorkerMessageHandler)())}};_capability=new WeakMap,_messageHandler=new WeakMap,_port=new WeakMap,_webWorker=new WeakMap,_fakeWorkerId=new WeakMap,_isWorkerDisabled=new WeakMap,_workerPorts=new WeakMap,_PDFWorker_instances=new WeakSet,resolve_fn=function(){__privateGet(this,_capability).resolve(),__privateGet(this,
-_messageHandler).send("configure",{verbosity:this.verbosity})},initializeFromPort_fn=function(port){__privateSet(this,_port,port),__privateSet(this,_messageHandler,new MessageHandler("main","worker",port)),__privateGet(this,_messageHandler).on("ready",()=>{}),__privateMethod(this,_PDFWorker_instances,resolve_fn).call(this)},initialize_fn=function(){if(__privateGet(_PDFWorker,_isWorkerDisabled)||__privateGet(_PDFWorker,_PDFWorker_static,mainThreadWorkerMessageHandler_get)){__privateMethod(this,_PDFWorker_instances,
-setupFakeWorker_fn).call(this);return}let{workerSrc}=_PDFWorker;try{_PDFWorker._isSameOrigin(window.location,workerSrc)||(workerSrc=_PDFWorker._createCDNWrapper(new URL(workerSrc,window.location).href));const worker=new Worker(workerSrc,{type:"module"}),messageHandler=new MessageHandler("main","worker",worker),terminateEarly=()=>{ac.abort(),messageHandler.destroy(),worker.terminate(),this.destroyed?__privateGet(this,_capability).reject(new Error("Worker was destroyed")):__privateMethod(this,_PDFWorker_instances,
-setupFakeWorker_fn).call(this)},ac=new AbortController;worker.addEventListener("error",()=>{__privateGet(this,_webWorker)||terminateEarly()},{signal:ac.signal}),messageHandler.on("test",data=>{if(ac.abort(),this.destroyed||!data){terminateEarly();return}__privateSet(this,_messageHandler,messageHandler),__privateSet(this,_port,worker),__privateSet(this,_webWorker,worker),__privateMethod(this,_PDFWorker_instances,resolve_fn).call(this)}),messageHandler.on("ready",data=>{if(ac.abort(),this.destroyed){
-terminateEarly();return}try{sendTest()}catch{__privateMethod(this,_PDFWorker_instances,setupFakeWorker_fn).call(this)}});const sendTest=()=>{const testObj=new Uint8Array;messageHandler.send("test",testObj,[testObj.buffer])};sendTest();return}catch{info("The worker has been disabled.")}__privateMethod(this,_PDFWorker_instances,setupFakeWorker_fn).call(this)},setupFakeWorker_fn=function(){__privateGet(_PDFWorker,_isWorkerDisabled)||(warn("Setting up fake worker."),__privateSet(_PDFWorker,_isWorkerDisabled,
-!0)),_PDFWorker._setupFakeWorkerGlobal.then(WorkerMessageHandler=>{if(this.destroyed){__privateGet(this,_capability).reject(new Error("Worker was destroyed"));return}const port=new LoopbackPort;__privateSet(this,_port,port);const id2=`fake${__privateWrapper(_PDFWorker,_fakeWorkerId)._++}`,workerHandler=new MessageHandler(id2+"_worker",id2,port);WorkerMessageHandler.setup(workerHandler,port),__privateSet(this,_messageHandler,new MessageHandler(id2,id2+"_worker",port)),__privateMethod(this,_PDFWorker_instances,
-resolve_fn).call(this)}).catch(reason=>{__privateGet(this,_capability).reject(new Error(`Setting up fake worker failed: "${reason.message}".`))})},_PDFWorker_static=new WeakSet,mainThreadWorkerMessageHandler_get=function(){try{return globalThis.pdfjsWorker?.WorkerMessageHandler||null}catch{return null}},__privateAdd(_PDFWorker,_PDFWorker_static),__privateAdd(_PDFWorker,_fakeWorkerId,0),__privateAdd(_PDFWorker,_isWorkerDisabled,!1),__privateAdd(_PDFWorker,_workerPorts,new WeakMap),isNodeJS&&(__privateSet(
-_PDFWorker,_isWorkerDisabled,!0),GlobalWorkerOptions.workerSrc||="./pdf.worker.mjs"),_PDFWorker._isSameOrigin=(baseUrl,otherUrl)=>{const base2=URL.parse(baseUrl);if(!base2?.origin||base2.origin==="null")return!1;const other=new URL(otherUrl,base2);return base2.origin===other.origin},_PDFWorker._createCDNWrapper=url2=>{const wrapper=`await import("${url2}");`;return URL.createObjectURL(new Blob([wrapper],{type:"text/javascript"}))},_PDFWorker.fromPort=params=>{if(deprecated("`PDFWorker.fromPort` \
-- please use `PDFWorker.create` instead."),!params?.port)throw new Error("PDFWorker.fromPort - invalid method signature.");return _PDFWorker.create(params)};let PDFWorker=_PDFWorker;class WorkerTransport{#methodPromises=new Map;#pageCache=new Map;#pagePromises=new Map;#pageRefCache=new Map;#passwordCapability=null;constructor(messageHandler,loadingTask,networkStream,params,factory,enableHWA){this.messageHandler=messageHandler,this.loadingTask=loadingTask,this.commonObjs=new PDFObjects,this.fontLoader=
-new FontLoader({ownerDocument:params.ownerDocument,styleElement:params.styleElement}),this.loadingParams=params.loadingParams,this._params=params,this.canvasFactory=factory.canvasFactory,this.filterFactory=factory.filterFactory,this.cMapReaderFactory=factory.cMapReaderFactory,this.standardFontDataFactory=factory.standardFontDataFactory,this.wasmFactory=factory.wasmFactory,this.destroyed=!1,this.destroyCapability=null,this._networkStream=networkStream,this._fullReader=null,this._lastProgress=null,
-this.downloadInfoCapability=Promise.withResolvers(),this.enableHWA=enableHWA,this.setupMessageHandler()}#cacheSimpleMethod(name,data=null){const cachedPromise=this.#methodPromises.get(name);if(cachedPromise)return cachedPromise;const promise=this.messageHandler.sendWithPromise(name,data);return this.#methodPromises.set(name,promise),promise}get annotationStorage(){return shadow(this,"annotationStorage",new AnnotationStorage)}getRenderingIntent(intent,annotationMode=AnnotationMode.ENABLE,printAnnotationStorage=null,isEditing2=!1,isOpList=!1){
-let renderingIntent=RenderingIntentFlag.DISPLAY,annotationStorageSerializable=SerializableEmpty;switch(intent){case"any":renderingIntent=RenderingIntentFlag.ANY;break;case"display":break;case"print":renderingIntent=RenderingIntentFlag.PRINT;break;default:warn(`getRenderingIntent - invalid intent: ${intent}`)}const annotationStorage=renderingIntent&RenderingIntentFlag.PRINT&&printAnnotationStorage instanceof PrintAnnotationStorage?printAnnotationStorage:this.annotationStorage;switch(annotationMode){case AnnotationMode.
-DISABLE:renderingIntent+=RenderingIntentFlag.ANNOTATIONS_DISABLE;break;case AnnotationMode.ENABLE:break;case AnnotationMode.ENABLE_FORMS:renderingIntent+=RenderingIntentFlag.ANNOTATIONS_FORMS;break;case AnnotationMode.ENABLE_STORAGE:renderingIntent+=RenderingIntentFlag.ANNOTATIONS_STORAGE,annotationStorageSerializable=annotationStorage.serializable;break;default:warn(`getRenderingIntent - invalid annotationMode: ${annotationMode}`)}isEditing2&&(renderingIntent+=RenderingIntentFlag.IS_EDITING),isOpList&&
-(renderingIntent+=RenderingIntentFlag.OPLIST);const{ids:modifiedIds,hash:modifiedIdsHash}=annotationStorage.modifiedIds,cacheKeyBuf=[renderingIntent,annotationStorageSerializable.hash,modifiedIdsHash];return{renderingIntent,cacheKey:cacheKeyBuf.join("_"),annotationStorageSerializable,modifiedIds}}destroy(){if(this.destroyCapability)return this.destroyCapability.promise;this.destroyed=!0,this.destroyCapability=Promise.withResolvers(),this.#passwordCapability?.reject(new Error("Worker was destroye\
-d during onPassword callback"));const waitOn=[];for(const page2 of this.#pageCache.values())waitOn.push(page2._destroy());this.#pageCache.clear(),this.#pagePromises.clear(),this.#pageRefCache.clear(),this.hasOwnProperty("annotationStorage")&&this.annotationStorage.resetModified();const terminated=this.messageHandler.sendWithPromise("Terminate",null);return waitOn.push(terminated),Promise.all(waitOn).then(()=>{this.commonObjs.clear(),this.fontLoader.clear(),this.#methodPromises.clear(),this.filterFactory.
-destroy(),TextLayer.cleanup(),this._networkStream?.cancelAllRequests(new AbortException("Worker was terminated.")),this.messageHandler?.destroy(),this.messageHandler=null,this.destroyCapability.resolve()},this.destroyCapability.reject),this.destroyCapability.promise}setupMessageHandler(){const{messageHandler,loadingTask}=this;messageHandler.on("GetReader",(data,sink)=>{assert$1(this._networkStream,"GetReader - no `IPDFStream` instance available."),this._fullReader=this._networkStream.getFullReader(),
-this._fullReader.onProgress=evt=>{this._lastProgress={loaded:evt.loaded,total:evt.total}},sink.onPull=()=>{this._fullReader.read().then(function({value,done}){if(done){sink.close();return}assert$1(value instanceof ArrayBuffer,"GetReader - expected an ArrayBuffer."),sink.enqueue(new Uint8Array(value),1,[value])}).catch(reason=>{sink.error(reason)})},sink.onCancel=reason=>{this._fullReader.cancel(reason),sink.ready.catch(readyReason=>{if(!this.destroyed)throw readyReason})}}),messageHandler.on("Re\
-aderHeadersReady",async data=>{await this._fullReader.headersReady;const{isStreamingSupported,isRangeSupported,contentLength}=this._fullReader;return(!isStreamingSupported||!isRangeSupported)&&(this._lastProgress&&loadingTask.onProgress?.(this._lastProgress),this._fullReader.onProgress=evt=>{loadingTask.onProgress?.({loaded:evt.loaded,total:evt.total})}),{isStreamingSupported,isRangeSupported,contentLength}}),messageHandler.on("GetRangeReader",(data,sink)=>{assert$1(this._networkStream,"GetRange\
-Reader - no `IPDFStream` instance available.");const rangeReader=this._networkStream.getRangeReader(data.begin,data.end);if(!rangeReader){sink.close();return}sink.onPull=()=>{rangeReader.read().then(function({value,done}){if(done){sink.close();return}assert$1(value instanceof ArrayBuffer,"GetRangeReader - expected an ArrayBuffer."),sink.enqueue(new Uint8Array(value),1,[value])}).catch(reason=>{sink.error(reason)})},sink.onCancel=reason=>{rangeReader.cancel(reason),sink.ready.catch(readyReason=>{
-if(!this.destroyed)throw readyReason})}}),messageHandler.on("GetDoc",({pdfInfo})=>{this._numPages=pdfInfo.numPages,this._htmlForXfa=pdfInfo.htmlForXfa,delete pdfInfo.htmlForXfa,loadingTask._capability.resolve(new PDFDocumentProxy(pdfInfo,this))}),messageHandler.on("DocException",ex=>{loadingTask._capability.reject(wrapReason(ex))}),messageHandler.on("PasswordRequest",ex=>{this.#passwordCapability=Promise.withResolvers();try{if(!loadingTask.onPassword)throw wrapReason(ex);const updatePassword=password=>{
-password instanceof Error?this.#passwordCapability.reject(password):this.#passwordCapability.resolve({password})};loadingTask.onPassword(updatePassword,ex.code)}catch(err){this.#passwordCapability.reject(err)}return this.#passwordCapability.promise}),messageHandler.on("DataLoaded",data=>{loadingTask.onProgress?.({loaded:data.length,total:data.length}),this.downloadInfoCapability.resolve(data)}),messageHandler.on("StartRenderPage",data=>{if(this.destroyed)return;this.#pageCache.get(data.pageIndex).
-_startRenderPage(data.transparency,data.cacheKey)}),messageHandler.on("commonobj",([id2,type2,exportedData])=>{if(this.destroyed||this.commonObjs.has(id2))return null;switch(type2){case"Font":if("error"in exportedData){const exportedError=exportedData.error;warn(`Error during font loading: ${exportedError}`),this.commonObjs.resolve(id2,exportedError);break}const inspectFont=this._params.pdfBug&&globalThis.FontInspector?.enabled?(font2,url2)=>globalThis.FontInspector.fontAdded(font2,url2):null,font=new FontFaceObject(
-exportedData,inspectFont);this.fontLoader.bind(font).catch(()=>messageHandler.sendWithPromise("FontFallback",{id:id2})).finally(()=>{!font.fontExtraProperties&&font.data&&(font.data=null),this.commonObjs.resolve(id2,font)});break;case"CopyLocalImage":const{imageRef}=exportedData;assert$1(imageRef,"The imageRef must be defined.");for(const pageProxy of this.#pageCache.values())for(const[,data]of pageProxy.objs)if(data?.ref===imageRef)return data.dataLen?(this.commonObjs.resolve(id2,structuredClone(
-data)),data.dataLen):null;break;case"FontPath":case"Image":case"Pattern":this.commonObjs.resolve(id2,exportedData);break;default:throw new Error(`Got unknown common object type ${type2}`)}return null}),messageHandler.on("obj",([id2,pageIndex,type2,imageData])=>{if(this.destroyed)return;const pageProxy=this.#pageCache.get(pageIndex);if(!pageProxy.objs.has(id2)){if(pageProxy._intentStates.size===0){imageData?.bitmap?.close();return}switch(type2){case"Image":case"Pattern":pageProxy.objs.resolve(id2,
-imageData);break;default:throw new Error(`Got unknown object type ${type2}`)}}}),messageHandler.on("DocProgress",data=>{this.destroyed||loadingTask.onProgress?.({loaded:data.loaded,total:data.total})}),messageHandler.on("FetchBinaryData",async data=>{if(this.destroyed)throw new Error("Worker was destroyed.");const factory=this[data.type];if(!factory)throw new Error(`${data.type} not initialized, see the \`useWorkerFetch\` parameter.`);return factory.fetch(data)})}getData(){return this.messageHandler.
-sendWithPromise("GetData",null)}saveDocument(){this.annotationStorage.size<=0&&warn("saveDocument called while `annotationStorage` is empty, please use the getData-method instead.");const{map:map2,transfer}=this.annotationStorage.serializable;return this.messageHandler.sendWithPromise("SaveDocument",{isPureXfa:!!this._htmlForXfa,numPages:this._numPages,annotationStorage:map2,filename:this._fullReader?.filename??null},transfer).finally(()=>{this.annotationStorage.resetModified()})}getPage(pageNumber){
-if(!Number.isInteger(pageNumber)||pageNumber<=0||pageNumber>this._numPages)return Promise.reject(new Error("Invalid page request."));const pageIndex=pageNumber-1,cachedPromise=this.#pagePromises.get(pageIndex);if(cachedPromise)return cachedPromise;const promise=this.messageHandler.sendWithPromise("GetPage",{pageIndex}).then(pageInfo=>{if(this.destroyed)throw new Error("Transport destroyed");pageInfo.refStr&&this.#pageRefCache.set(pageInfo.refStr,pageNumber);const page2=new PDFPageProxy(pageIndex,
-pageInfo,this,this._params.pdfBug);return this.#pageCache.set(pageIndex,page2),page2});return this.#pagePromises.set(pageIndex,promise),promise}getPageIndex(ref2){return isRefProxy(ref2)?this.messageHandler.sendWithPromise("GetPageIndex",{num:ref2.num,gen:ref2.gen}):Promise.reject(new Error("Invalid pageIndex request."))}getAnnotations(pageIndex,intent){return this.messageHandler.sendWithPromise("GetAnnotations",{pageIndex,intent})}getFieldObjects(){return this.#cacheSimpleMethod("GetFieldObject\
-s")}hasJSActions(){return this.#cacheSimpleMethod("HasJSActions")}getCalculationOrderIds(){return this.messageHandler.sendWithPromise("GetCalculationOrderIds",null)}getDestinations(){return this.messageHandler.sendWithPromise("GetDestinations",null)}getDestination(id2){return typeof id2!="string"?Promise.reject(new Error("Invalid destination request.")):this.messageHandler.sendWithPromise("GetDestination",{id:id2})}getPageLabels(){return this.messageHandler.sendWithPromise("GetPageLabels",null)}getPageLayout(){
-return this.messageHandler.sendWithPromise("GetPageLayout",null)}getPageMode(){return this.messageHandler.sendWithPromise("GetPageMode",null)}getViewerPreferences(){return this.messageHandler.sendWithPromise("GetViewerPreferences",null)}getOpenAction(){return this.messageHandler.sendWithPromise("GetOpenAction",null)}getAttachments(){return this.messageHandler.sendWithPromise("GetAttachments",null)}getDocJSActions(){return this.#cacheSimpleMethod("GetDocJSActions")}getPageJSActions(pageIndex){return this.
-messageHandler.sendWithPromise("GetPageJSActions",{pageIndex})}getStructTree(pageIndex){return this.messageHandler.sendWithPromise("GetStructTree",{pageIndex})}getOutline(){return this.messageHandler.sendWithPromise("GetOutline",null)}getOptionalContentConfig(renderingIntent){return this.#cacheSimpleMethod("GetOptionalContentConfig").then(data=>new OptionalContentConfig(data,renderingIntent))}getPermissions(){return this.messageHandler.sendWithPromise("GetPermissions",null)}getMetadata(){const name="\
-GetMetadata",cachedPromise=this.#methodPromises.get(name);if(cachedPromise)return cachedPromise;const promise=this.messageHandler.sendWithPromise(name,null).then(results=>({info:results[0],metadata:results[1]?new Metadata(results[1]):null,contentDispositionFilename:this._fullReader?.filename??null,contentLength:this._fullReader?.contentLength??null}));return this.#methodPromises.set(name,promise),promise}getMarkInfo(){return this.messageHandler.sendWithPromise("GetMarkInfo",null)}async startCleanup(keepLoadedFonts=!1){
-if(!this.destroyed){await this.messageHandler.sendWithPromise("Cleanup",null);for(const page2 of this.#pageCache.values())if(!page2.cleanup())throw new Error(`startCleanup: Page ${page2.pageNumber} is currently rendering.`);this.commonObjs.clear(),keepLoadedFonts||this.fontLoader.clear(),this.#methodPromises.clear(),this.filterFactory.destroy(!0),TextLayer.cleanup()}}cachedPageNumber(ref2){if(!isRefProxy(ref2))return null;const refStr=ref2.gen===0?`${ref2.num}R`:`${ref2.num}R${ref2.gen}`;return this.#pageRefCache.
-get(refStr)??null}}class RenderTask{#internalRenderTask=null;onContinue=null;onError=null;constructor(internalRenderTask){this.#internalRenderTask=internalRenderTask}get promise(){return this.#internalRenderTask.capability.promise}cancel(extraDelay=0){this.#internalRenderTask.cancel(null,extraDelay)}get separateAnnots(){const{separateAnnots}=this.#internalRenderTask.operatorList;if(!separateAnnots)return!1;const{annotationCanvasMap}=this.#internalRenderTask;return separateAnnots.form||separateAnnots.
-canvas&&annotationCanvasMap?.size>0}}class InternalRenderTask{#rAF=null;static#canvasInUse=new WeakSet;constructor({callback,params,objs,commonObjs,annotationCanvasMap,operatorList,pageIndex,canvasFactory,filterFactory,useRequestAnimationFrame=!1,pdfBug=!1,pageColors=null,enableHWA=!1}){this.callback=callback,this.params=params,this.objs=objs,this.commonObjs=commonObjs,this.annotationCanvasMap=annotationCanvasMap,this.operatorListIdx=null,this.operatorList=operatorList,this._pageIndex=pageIndex,
-this.canvasFactory=canvasFactory,this.filterFactory=filterFactory,this._pdfBug=pdfBug,this.pageColors=pageColors,this.running=!1,this.graphicsReadyCallback=null,this.graphicsReady=!1,this._useRequestAnimationFrame=useRequestAnimationFrame===!0&&typeof window<"u",this.cancelled=!1,this.capability=Promise.withResolvers(),this.task=new RenderTask(this),this._cancelBound=this.cancel.bind(this),this._continueBound=this._continue.bind(this),this._scheduleNextBound=this._scheduleNext.bind(this),this._nextBound=
-this._next.bind(this),this._canvas=params.canvas,this._canvasContext=params.canvas?null:params.canvasContext,this._enableHWA=enableHWA}get completed(){return this.capability.promise.catch(function(){})}initializeGraphics({transparency=!1,optionalContentConfig}){if(this.cancelled)return;if(this._canvas){if(InternalRenderTask.#canvasInUse.has(this._canvas))throw new Error("Cannot use the same canvas during multiple render() operations. Use different canvas or ensure previous operations were cancel\
-led or completed.");InternalRenderTask.#canvasInUse.add(this._canvas)}this._pdfBug&&globalThis.StepperManager?.enabled&&(this.stepper=globalThis.StepperManager.create(this._pageIndex),this.stepper.init(this.operatorList),this.stepper.nextBreakPoint=this.stepper.getNextBreakPoint());const{viewport,transform:transform2,background}=this.params,canvasContext=this._canvasContext||this._canvas.getContext("2d",{alpha:!1,willReadFrequently:!this._enableHWA});this.gfx=new CanvasGraphics(canvasContext,this.
-commonObjs,this.objs,this.canvasFactory,this.filterFactory,{optionalContentConfig},this.annotationCanvasMap,this.pageColors),this.gfx.beginDrawing({transform:transform2,viewport,transparency,background}),this.operatorListIdx=0,this.graphicsReady=!0,this.graphicsReadyCallback?.()}cancel(error2=null,extraDelay=0){this.running=!1,this.cancelled=!0,this.gfx?.endDrawing(),this.#rAF&&(window.cancelAnimationFrame(this.#rAF),this.#rAF=null),InternalRenderTask.#canvasInUse.delete(this._canvas),error2||=new RenderingCancelledException(
-`Rendering cancelled, page ${this._pageIndex+1}`,extraDelay),this.callback(error2),this.task.onError?.(error2)}operatorListChanged(){if(!this.graphicsReady){this.graphicsReadyCallback||=this._continueBound;return}this.stepper?.updateOperatorList(this.operatorList),!this.running&&this._continue()}_continue(){this.running=!0,!this.cancelled&&(this.task.onContinue?this.task.onContinue(this._scheduleNextBound):this._scheduleNext())}_scheduleNext(){this._useRequestAnimationFrame?this.#rAF=window.requestAnimationFrame(
-()=>{this.#rAF=null,this._nextBound().catch(this._cancelBound)}):Promise.resolve().then(this._nextBound).catch(this._cancelBound)}async _next(){this.cancelled||(this.operatorListIdx=this.gfx.executeOperatorList(this.operatorList,this.operatorListIdx,this._continueBound,this.stepper),this.operatorListIdx===this.operatorList.argsArray.length&&(this.running=!1,this.operatorList.lastChunk&&(this.gfx.endDrawing(),InternalRenderTask.#canvasInUse.delete(this._canvas),this.callback())))}}const version$1="\
-5.4.54",build="295fb3ec4";class ColorPicker{#button=null;#buttonSwatch=null;#defaultColor;#dropdown=null;#dropdownWasFromKeyboard=!1;#isMainColorPicker=!1;#editor=null;#eventBus;#openDropdownAC=null;#uiManager=null;static#l10nColor=null;static get _keyboardManager(){return shadow(this,"_keyboardManager",new KeyboardManager([[["Escape","mac+Escape"],ColorPicker.prototype._hideDropdownFromKeyboard],[[" ","mac+ "],ColorPicker.prototype._colorSelectFromKeyboard],[["ArrowDown","ArrowRight","mac+Arrow\
-Down","mac+ArrowRight"],ColorPicker.prototype._moveToNext],[["ArrowUp","ArrowLeft","mac+ArrowUp","mac+ArrowLeft"],ColorPicker.prototype._moveToPrevious],[["Home","mac+Home"],ColorPicker.prototype._moveToBeginning],[["End","mac+End"],ColorPicker.prototype._moveToEnd]]))}constructor({editor=null,uiManager=null}){editor?(this.#isMainColorPicker=!1,this.#editor=editor):this.#isMainColorPicker=!0,this.#uiManager=editor?._uiManager||uiManager,this.#eventBus=this.#uiManager._eventBus,this.#defaultColor=
-editor?.color?.toUpperCase()||this.#uiManager?.highlightColors.values().next().value||"#FFFF98",ColorPicker.#l10nColor||=Object.freeze({blue:"pdfjs-editor-colorpicker-blue",green:"pdfjs-editor-colorpicker-green",pink:"pdfjs-editor-colorpicker-pink",red:"pdfjs-editor-colorpicker-red",yellow:"pdfjs-editor-colorpicker-yellow"})}renderButton(){const button=this.#button=document.createElement("button");button.className="colorPicker",button.tabIndex="0",button.setAttribute("data-l10n-id","pdfjs-editor\
--colorpicker-button"),button.ariaHasPopup="true",this.#editor&&(button.ariaControls=`${this.#editor.id}_colorpicker_dropdown`);const signal=this.#uiManager._signal;button.addEventListener("click",this.#openDropdown.bind(this),{signal}),button.addEventListener("keydown",this.#keyDown.bind(this),{signal});const swatch=this.#buttonSwatch=document.createElement("span");return swatch.className="swatch",swatch.ariaHidden="true",swatch.style.backgroundColor=this.#defaultColor,button.append(swatch),button}renderMainDropdown(){
-const dropdown=this.#dropdown=this.#getDropdownRoot();return dropdown.ariaOrientation="horizontal",dropdown.ariaLabelledBy="highlightColorPickerLabel",dropdown}#getDropdownRoot(){const div=document.createElement("div"),signal=this.#uiManager._signal;div.addEventListener("contextmenu",noContextMenu,{signal}),div.className="dropdown",div.role="listbox",div.ariaMultiSelectable="false",div.ariaOrientation="vertical",div.setAttribute("data-l10n-id","pdfjs-editor-colorpicker-dropdown"),this.#editor&&(div.
-id=`${this.#editor.id}_colorpicker_dropdown`);for(const[name,color]of this.#uiManager.highlightColors){const button=document.createElement("button");button.tabIndex="0",button.role="option",button.setAttribute("data-color",color),button.title=name,button.setAttribute("data-l10n-id",ColorPicker.#l10nColor[name]);const swatch=document.createElement("span");button.append(swatch),swatch.className="swatch",swatch.style.backgroundColor=color,button.ariaSelected=color===this.#defaultColor,button.addEventListener(
-"click",this.#colorSelect.bind(this,color),{signal}),div.append(button)}return div.addEventListener("keydown",this.#keyDown.bind(this),{signal}),div}#colorSelect(color,event2){event2.stopPropagation(),this.#eventBus.dispatch("switchannotationeditorparams",{source:this,type:AnnotationEditorParamsType.HIGHLIGHT_COLOR,value:color}),this.updateColor(color)}_colorSelectFromKeyboard(event2){if(event2.target===this.#button){this.#openDropdown(event2);return}const color=event2.target.getAttribute("data-\
-color");color&&this.#colorSelect(color,event2)}_moveToNext(event2){if(!this.#isDropdownVisible){this.#openDropdown(event2);return}if(event2.target===this.#button){this.#dropdown.firstChild?.focus();return}event2.target.nextSibling?.focus()}_moveToPrevious(event2){if(event2.target===this.#dropdown?.firstChild||event2.target===this.#button){this.#isDropdownVisible&&this._hideDropdownFromKeyboard();return}this.#isDropdownVisible||this.#openDropdown(event2),event2.target.previousSibling?.focus()}_moveToBeginning(event2){
-if(!this.#isDropdownVisible){this.#openDropdown(event2);return}this.#dropdown.firstChild?.focus()}_moveToEnd(event2){if(!this.#isDropdownVisible){this.#openDropdown(event2);return}this.#dropdown.lastChild?.focus()}#keyDown(event2){ColorPicker._keyboardManager.exec(this,event2)}#openDropdown(event2){if(this.#isDropdownVisible){this.hideDropdown();return}if(this.#dropdownWasFromKeyboard=event2.detail===0,this.#openDropdownAC||(this.#openDropdownAC=new AbortController,window.addEventListener("point\
-erdown",this.#pointerDown.bind(this),{signal:this.#uiManager.combinedSignal(this.#openDropdownAC)})),this.#button.ariaExpanded="true",this.#dropdown){this.#dropdown.classList.remove("hidden");return}const root2=this.#dropdown=this.#getDropdownRoot();this.#button.append(root2)}#pointerDown(event2){this.#dropdown?.contains(event2.target)||this.hideDropdown()}hideDropdown(){this.#dropdown?.classList.add("hidden"),this.#button.ariaExpanded="false",this.#openDropdownAC?.abort(),this.#openDropdownAC=null}get#isDropdownVisible(){
-return this.#dropdown&&!this.#dropdown.classList.contains("hidden")}_hideDropdownFromKeyboard(){if(!this.#isMainColorPicker){if(!this.#isDropdownVisible){this.#editor?.unselect();return}this.hideDropdown(),this.#button.focus({preventScroll:!0,focusVisible:this.#dropdownWasFromKeyboard})}}updateColor(color){if(this.#buttonSwatch&&(this.#buttonSwatch.style.backgroundColor=color),!this.#dropdown)return;const i=this.#uiManager.highlightColors.values();for(const child2 of this.#dropdown.children)child2.
-ariaSelected=i.next().value===color.toUpperCase()}destroy(){this.#button?.remove(),this.#button=null,this.#buttonSwatch=null,this.#dropdown?.remove(),this.#dropdown=null}}class BasicColorPicker{#input=null;#editor=null;#uiManager=null;static#l10nColor=null;constructor(editor){this.#editor=editor,this.#uiManager=editor._uiManager,BasicColorPicker.#l10nColor||=Object.freeze({freetext:"pdfjs-editor-color-picker-free-text-input",ink:"pdfjs-editor-color-picker-ink-input"})}renderButton(){if(this.#input)
-return this.#input;const{editorType,colorType,colorValue}=this.#editor,input=this.#input=document.createElement("input");return input.type="color",input.value=colorValue||"#000000",input.className="basicColorPicker",input.tabIndex=0,input.setAttribute("data-l10n-id",BasicColorPicker.#l10nColor[editorType]),input.addEventListener("input",()=>{this.#uiManager.updateParams(colorType,input.value)},{signal:this.#uiManager._signal}),input}update(value){this.#input&&(this.#input.value=value)}destroy(){
-this.#input?.remove(),this.#input=null}hideDropdown(){}}function makeColorComp(n){return Math.floor(Math.max(0,Math.min(1,n))*255).toString(16).padStart(2,"0")}function scaleAndClamp(x){return Math.max(0,Math.min(255,255*x))}class ColorConverters{static CMYK_G([c2,y,m,k]){return["G",1-Math.min(1,.3*c2+.59*m+.11*y+k)]}static G_CMYK([g]){return["CMYK",0,0,0,1-g]}static G_RGB([g]){return["RGB",g,g,g]}static G_rgb([g]){return g=scaleAndClamp(g),[g,g,g]}static G_HTML([g]){const G=makeColorComp(g);return`\
-#${G}${G}${G}`}static RGB_G([r2,g,b]){return["G",.3*r2+.59*g+.11*b]}static RGB_rgb(color){return color.map(scaleAndClamp)}static RGB_HTML(color){return`#${color.map(makeColorComp).join("")}`}static T_HTML(){return"#00000000"}static T_rgb(){return[null]}static CMYK_RGB([c2,y,m,k]){return["RGB",1-Math.min(1,c2+k),1-Math.min(1,m+k),1-Math.min(1,y+k)]}static CMYK_rgb([c2,y,m,k]){return[scaleAndClamp(1-Math.min(1,c2+k)),scaleAndClamp(1-Math.min(1,m+k)),scaleAndClamp(1-Math.min(1,y+k))]}static CMYK_HTML(components2){
-const rgb=this.CMYK_RGB(components2).slice(1);return this.RGB_HTML(rgb)}static RGB_CMYK([r2,g,b]){const c2=1-r2,m=1-g,y=1-b,k=Math.min(c2,m,y);return["CMYK",c2,m,y,k]}}class BaseSVGFactory{create(width,height,skipDimensions=!1){if(width<=0||height<=0)throw new Error("Invalid SVG dimensions");const svg2=this._createSVG("svg:svg");return svg2.setAttribute("version","1.1"),skipDimensions||(svg2.setAttribute("width",`${width}px`),svg2.setAttribute("height",`${height}px`)),svg2.setAttribute("preserve\
-AspectRatio","none"),svg2.setAttribute("viewBox",`0 0 ${width} ${height}`),svg2}createElement(type2){if(typeof type2!="string")throw new Error("Invalid SVG element type");return this._createSVG(type2)}_createSVG(type2){unreachable("Abstract method `_createSVG` called.")}}class DOMSVGFactory extends BaseSVGFactory{_createSVG(type2){return document.createElementNS(SVG_NS,type2)}}class XfaLayer{static setupStorage(html2,id2,element2,storage,intent){const storedData=storage.getValue(id2,{value:null});
-switch(element2.name){case"textarea":if(storedData.value!==null&&(html2.textContent=storedData.value),intent==="print")break;html2.addEventListener("input",event2=>{storage.setValue(id2,{value:event2.target.value})});break;case"input":if(element2.attributes.type==="radio"||element2.attributes.type==="checkbox"){if(storedData.value===element2.attributes.xfaOn?html2.setAttribute("checked",!0):storedData.value===element2.attributes.xfaOff&&html2.removeAttribute("checked"),intent==="print")break;html2.
-addEventListener("change",event2=>{storage.setValue(id2,{value:event2.target.checked?event2.target.getAttribute("xfaOn"):event2.target.getAttribute("xfaOff")})})}else{if(storedData.value!==null&&html2.setAttribute("value",storedData.value),intent==="print")break;html2.addEventListener("input",event2=>{storage.setValue(id2,{value:event2.target.value})})}break;case"select":if(storedData.value!==null){html2.setAttribute("value",storedData.value);for(const option2 of element2.children)option2.attributes.
-value===storedData.value?option2.attributes.selected=!0:option2.attributes.hasOwnProperty("selected")&&delete option2.attributes.selected}html2.addEventListener("input",event2=>{const options=event2.target.options,value=options.selectedIndex===-1?"":options[options.selectedIndex].value;storage.setValue(id2,{value})});break}}static setAttributes({html:html2,element:element2,storage=null,intent,linkService}){const{attributes}=element2,isHTMLAnchorElement=html2 instanceof HTMLAnchorElement;attributes.
-type==="radio"&&(attributes.name=`${attributes.name}-${intent}`);for(const[key2,value]of Object.entries(attributes))if(value!=null)switch(key2){case"class":value.length&&html2.setAttribute(key2,value.join(" "));break;case"dataId":break;case"id":html2.setAttribute("data-element-id",value);break;case"style":Object.assign(html2.style,value);break;case"textContent":html2.textContent=value;break;default:(!isHTMLAnchorElement||key2!=="href"&&key2!=="newWindow")&&html2.setAttribute(key2,value)}isHTMLAnchorElement&&
-linkService.addLinkAttributes(html2,attributes.href,attributes.newWindow),storage&&attributes.dataId&&this.setupStorage(html2,attributes.dataId,element2,storage)}static render(parameters){const storage=parameters.annotationStorage,linkService=parameters.linkService,root2=parameters.xfaHtml,intent=parameters.intent||"display",rootHtml=document.createElement(root2.name);root2.attributes&&this.setAttributes({html:rootHtml,element:root2,intent,linkService});const isNotForRichText=intent!=="richText",
-rootDiv=parameters.div;if(rootDiv.append(rootHtml),parameters.viewport){const transform2=`matrix(${parameters.viewport.transform.join(",")})`;rootDiv.style.transform=transform2}isNotForRichText&&rootDiv.setAttribute("class","xfaLayer xfaFont");const textDivs=[];if(root2.children.length===0){if(root2.value){const node2=document.createTextNode(root2.value);rootHtml.append(node2),isNotForRichText&&XfaText.shouldBuildText(root2.name)&&textDivs.push(node2)}return{textDivs}}const stack=[[root2,-1,rootHtml]];
-for(;stack.length>0;){const[parent,i,html2]=stack.at(-1);if(i+1===parent.children.length){stack.pop();continue}const child2=parent.children[++stack.at(-1)[1]];if(child2===null)continue;const{name}=child2;if(name==="#text"){const node2=document.createTextNode(child2.value);textDivs.push(node2),html2.append(node2);continue}const childHtml=child2?.attributes?.xmlns?document.createElementNS(child2.attributes.xmlns,name):document.createElement(name);if(html2.append(childHtml),child2.attributes&&this.
-setAttributes({html:childHtml,element:child2,storage,intent,linkService}),child2.children?.length>0)stack.push([child2,-1,childHtml]);else if(child2.value){const node2=document.createTextNode(child2.value);isNotForRichText&&XfaText.shouldBuildText(name)&&textDivs.push(node2),childHtml.append(node2)}}for(const el of rootDiv.querySelectorAll(".xfaNonInteractive input, .xfaNonInteractive textarea"))el.setAttribute("readOnly",!0);return{textDivs}}static update(parameters){const transform2=`matrix(${parameters.
-viewport.transform.join(",")})`;parameters.div.style.transform=transform2,parameters.div.hidden=!1}}const annotation_layer_DEFAULT_FONT_SIZE=9,GetElementsByNameSet=new WeakSet,TIMEZONE_OFFSET=new Date().getTimezoneOffset()*60*1e3;class AnnotationElementFactory{static create(parameters){switch(parameters.data.annotationType){case AnnotationType.LINK:return new LinkAnnotationElement(parameters);case AnnotationType.TEXT:return new TextAnnotationElement(parameters);case AnnotationType.WIDGET:switch(parameters.
-data.fieldType){case"Tx":return new TextWidgetAnnotationElement(parameters);case"Btn":return parameters.data.radioButton?new RadioButtonWidgetAnnotationElement(parameters):parameters.data.checkBox?new CheckboxWidgetAnnotationElement(parameters):new PushButtonWidgetAnnotationElement(parameters);case"Ch":return new ChoiceWidgetAnnotationElement(parameters);case"Sig":return new SignatureWidgetAnnotationElement(parameters)}return new WidgetAnnotationElement(parameters);case AnnotationType.POPUP:return new PopupAnnotationElement(
-parameters);case AnnotationType.FREETEXT:return new FreeTextAnnotationElement(parameters);case AnnotationType.LINE:return new LineAnnotationElement(parameters);case AnnotationType.SQUARE:return new SquareAnnotationElement(parameters);case AnnotationType.CIRCLE:return new CircleAnnotationElement(parameters);case AnnotationType.POLYLINE:return new PolylineAnnotationElement(parameters);case AnnotationType.CARET:return new CaretAnnotationElement(parameters);case AnnotationType.INK:return new InkAnnotationElement(
-parameters);case AnnotationType.POLYGON:return new PolygonAnnotationElement(parameters);case AnnotationType.HIGHLIGHT:return new HighlightAnnotationElement(parameters);case AnnotationType.UNDERLINE:return new UnderlineAnnotationElement(parameters);case AnnotationType.SQUIGGLY:return new SquigglyAnnotationElement(parameters);case AnnotationType.STRIKEOUT:return new StrikeOutAnnotationElement(parameters);case AnnotationType.STAMP:return new StampAnnotationElement(parameters);case AnnotationType.FILEATTACHMENT:
-return new FileAttachmentAnnotationElement(parameters);default:return new AnnotationElement(parameters)}}}class AnnotationElement{#updates=null;#hasBorder=!1;#popupElement=null;constructor(parameters,{isRenderable=!1,ignoreBorder=!1,createQuadrilaterals=!1}={}){this.isRenderable=isRenderable,this.data=parameters.data,this.layer=parameters.layer,this.linkService=parameters.linkService,this.downloadManager=parameters.downloadManager,this.imageResourcesPath=parameters.imageResourcesPath,this.renderForms=
-parameters.renderForms,this.svgFactory=parameters.svgFactory,this.annotationStorage=parameters.annotationStorage,this.enableScripting=parameters.enableScripting,this.hasJSActions=parameters.hasJSActions,this._fieldObjects=parameters.fieldObjects,this.parent=parameters.parent,isRenderable&&(this.container=this._createContainer(ignoreBorder)),createQuadrilaterals&&this._createQuadrilaterals()}static _hasPopupData({contentsObj,richText}){return!!(contentsObj?.str||richText?.str)}get _isEditable(){return this.
-data.isEditable}get hasPopupData(){return AnnotationElement._hasPopupData(this.data)}updateEdited(params){if(!this.container)return;params.rect&&(this.#updates||={rect:this.data.rect.slice(0)});const{rect,popup:newPopup}=params;rect&&this.#setRectEdited(rect);let popup=this.#popupElement?.popup||this.popup;!popup&&newPopup?.text&&(this._createPopup(newPopup),popup=this.#popupElement.popup),popup&&(popup.updateEdited(params),newPopup?.deleted&&(popup.remove(),this.#popupElement=null,this.popup=null))}resetEdited(){
-this.#updates&&(this.#setRectEdited(this.#updates.rect),this.#popupElement?.popup.resetEdited(),this.#updates=null)}#setRectEdited(rect){const{container:{style:style2},data:{rect:currentRect,rotation},parent:{viewport:{rawDims:{pageWidth,pageHeight,pageX,pageY}}}}=this;currentRect?.splice(0,4,...rect),style2.left=`${100*(rect[0]-pageX)/pageWidth}%`,style2.top=`${100*(pageHeight-rect[3]+pageY)/pageHeight}%`,rotation===0?(style2.width=`${100*(rect[2]-rect[0])/pageWidth}%`,style2.height=`${100*(rect[3]-
-rect[1])/pageHeight}%`):this.setRotation(rotation)}_createContainer(ignoreBorder){const{data,parent:{page:page2,viewport}}=this,container2=document.createElement("section");container2.setAttribute("data-annotation-id",data.id),!(this instanceof WidgetAnnotationElement)&&!(this instanceof LinkAnnotationElement)&&(container2.tabIndex=0);const{style:style2}=container2;if(style2.zIndex=this.parent.zIndex++,data.alternativeText&&(container2.title=data.alternativeText),data.noRotate&&container2.classList.
-add("norotate"),!data.rect||this instanceof PopupAnnotationElement){const{rotation:rotation2}=data;return!data.hasOwnCanvas&&rotation2!==0&&this.setRotation(rotation2,container2),container2}const{width,height}=this;if(!ignoreBorder&&data.borderStyle.width>0){style2.borderWidth=`${data.borderStyle.width}px`;const horizontalRadius=data.borderStyle.horizontalCornerRadius,verticalRadius=data.borderStyle.verticalCornerRadius;if(horizontalRadius>0||verticalRadius>0){const radius=`calc(${horizontalRadius}\
-px * var(--total-scale-factor)) / calc(${verticalRadius}px * var(--total-scale-factor))`;style2.borderRadius=radius}else if(this instanceof RadioButtonWidgetAnnotationElement){const radius=`calc(${width}px * var(--total-scale-factor)) / calc(${height}px * var(--total-scale-factor))`;style2.borderRadius=radius}switch(data.borderStyle.style){case AnnotationBorderStyleType.SOLID:style2.borderStyle="solid";break;case AnnotationBorderStyleType.DASHED:style2.borderStyle="dashed";break;case AnnotationBorderStyleType.
-BEVELED:warn("Unimplemented border style: beveled");break;case AnnotationBorderStyleType.INSET:warn("Unimplemented border style: inset");break;case AnnotationBorderStyleType.UNDERLINE:style2.borderBottomStyle="solid";break}const borderColor=data.borderColor||null;borderColor?(this.#hasBorder=!0,style2.borderColor=Util.makeHexColor(borderColor[0]|0,borderColor[1]|0,borderColor[2]|0)):style2.borderWidth=0}const rect=Util.normalizeRect([data.rect[0],page2.view[3]-data.rect[1]+page2.view[1],data.rect[2],
-page2.view[3]-data.rect[3]+page2.view[1]]),{pageWidth,pageHeight,pageX,pageY}=viewport.rawDims;style2.left=`${100*(rect[0]-pageX)/pageWidth}%`,style2.top=`${100*(rect[1]-pageY)/pageHeight}%`;const{rotation}=data;return data.hasOwnCanvas||rotation===0?(style2.width=`${100*width/pageWidth}%`,style2.height=`${100*height/pageHeight}%`):this.setRotation(rotation,container2),container2}setRotation(angle,container2=this.container){if(!this.data.rect)return;const{pageWidth,pageHeight}=this.parent.viewport.
-rawDims;let{width,height}=this;angle%180!==0&&([width,height]=[height,width]),container2.style.width=`${100*width/pageWidth}%`,container2.style.height=`${100*height/pageHeight}%`,container2.setAttribute("data-main-rotation",(360-angle)%360)}get _commonActions(){const setColor=(jsName,styleName,event2)=>{const color=event2.detail[jsName],colorType=color[0],colorArray=color.slice(1);event2.target.style[styleName]=ColorConverters[`${colorType}_HTML`](colorArray),this.annotationStorage.setValue(this.
-data.id,{[styleName]:ColorConverters[`${colorType}_rgb`](colorArray)})};return shadow(this,"_commonActions",{display:event2=>{const{display}=event2.detail,hidden2=display%2===1;this.container.style.visibility=hidden2?"hidden":"visible",this.annotationStorage.setValue(this.data.id,{noView:hidden2,noPrint:display===1||display===2})},print:event2=>{this.annotationStorage.setValue(this.data.id,{noPrint:!event2.detail.print})},hidden:event2=>{const{hidden:hidden2}=event2.detail;this.container.style.visibility=
-hidden2?"hidden":"visible",this.annotationStorage.setValue(this.data.id,{noPrint:hidden2,noView:hidden2})},focus:event2=>{setTimeout(()=>event2.target.focus({preventScroll:!1}),0)},userName:event2=>{event2.target.title=event2.detail.userName},readonly:event2=>{event2.target.disabled=event2.detail.readonly},required:event2=>{this._setRequired(event2.target,event2.detail.required)},bgColor:event2=>{setColor("bgColor","backgroundColor",event2)},fillColor:event2=>{setColor("fillColor","backgroundCol\
-or",event2)},fgColor:event2=>{setColor("fgColor","color",event2)},textColor:event2=>{setColor("textColor","color",event2)},borderColor:event2=>{setColor("borderColor","borderColor",event2)},strokeColor:event2=>{setColor("strokeColor","borderColor",event2)},rotation:event2=>{const angle=event2.detail.rotation;this.setRotation(angle),this.annotationStorage.setValue(this.data.id,{rotation:angle})}})}_dispatchEventFromSandbox(actions,jsEvent){const commonActions=this._commonActions;for(const name of Object.
-keys(jsEvent.detail))(actions[name]||commonActions[name])?.(jsEvent)}_setDefaultPropertiesFromJS(element2){if(!this.enableScripting)return;const storedData=this.annotationStorage.getRawValue(this.data.id);if(!storedData)return;const commonActions=this._commonActions;for(const[actionName,detail]of Object.entries(storedData)){const action2=commonActions[actionName];if(action2){const eventProxy={detail:{[actionName]:detail},target:element2};action2(eventProxy),delete storedData[actionName]}}}_createQuadrilaterals(){
-if(!this.container)return;const{quadPoints}=this.data;if(!quadPoints)return;const[rectBlX,rectBlY,rectTrX,rectTrY]=this.data.rect.map(x=>Math.fround(x));if(quadPoints.length===8){const[trX,trY,blX,blY]=quadPoints.subarray(2,6);if(rectTrX===trX&&rectTrY===trY&&rectBlX===blX&&rectBlY===blY)return}const{style:style2}=this.container;let svgBuffer;if(this.#hasBorder){const{borderColor,borderWidth}=style2;style2.borderWidth=0,svgBuffer=["url('data:image/svg+xml;utf8,",'<svg xmlns="http://www.w3.org/20\
-00/svg"',' preserveAspectRatio="none" viewBox="0 0 1 1">',`<g fill="transparent" stroke="${borderColor}" stroke-width="${borderWidth}">`],this.container.classList.add("hasBorder")}const width=rectTrX-rectBlX,height=rectTrY-rectBlY,{svgFactory}=this,svg2=svgFactory.createElement("svg");svg2.classList.add("quadrilateralsContainer"),svg2.setAttribute("width",0),svg2.setAttribute("height",0),svg2.role="none";const defs=svgFactory.createElement("defs");svg2.append(defs);const clipPath=svgFactory.createElement(
-"clipPath"),id2=`clippath_${this.data.id}`;clipPath.setAttribute("id",id2),clipPath.setAttribute("clipPathUnits","objectBoundingBox"),defs.append(clipPath);for(let i=2,ii=quadPoints.length;i<ii;i+=8){const trX=quadPoints[i],trY=quadPoints[i+1],blX=quadPoints[i+2],blY=quadPoints[i+3],rect=svgFactory.createElement("rect"),x=(blX-rectBlX)/width,y=(rectTrY-trY)/height,rectWidth=(trX-blX)/width,rectHeight=(trY-blY)/height;rect.setAttribute("x",x),rect.setAttribute("y",y),rect.setAttribute("width",rectWidth),
-rect.setAttribute("height",rectHeight),clipPath.append(rect),svgBuffer?.push(`<rect vector-effect="non-scaling-stroke" x="${x}" y="${y}" width="${rectWidth}" height="${rectHeight}"/>`)}this.#hasBorder&&(svgBuffer.push("</g></svg>')"),style2.backgroundImage=svgBuffer.join("")),this.container.append(svg2),this.container.style.clipPath=`url(#${id2})`}_createPopup(popupData=null){const{data}=this;let contentsObj,modificationDate;popupData?(contentsObj={str:popupData.text},modificationDate=popupData.
-date):(contentsObj=data.contentsObj,modificationDate=data.modificationDate);const popup=this.#popupElement=new PopupAnnotationElement({data:{color:data.color,titleObj:data.titleObj,modificationDate,contentsObj,richText:data.richText,parentRect:data.rect,borderStyle:0,id:`popup_${data.id}`,rotation:data.rotation,noRotate:!0},linkService:this.linkService,parent:this.parent,elements:[this]});this.parent.div.append(popup.render())}get hasPopupElement(){return!!(this.#popupElement||this.popup||this.data.
-popupRef)}render(){unreachable("Abstract method `AnnotationElement.render` called")}_getElementsByName(name,skipId=null){const fields=[];if(this._fieldObjects){const fieldObj=this._fieldObjects[name];if(fieldObj)for(const{page:page2,id:id2,exportValues}of fieldObj){if(page2===-1||id2===skipId)continue;const exportValue=typeof exportValues=="string"?exportValues:null,domElement=document.querySelector(`[data-element-id="${id2}"]`);if(domElement&&!GetElementsByNameSet.has(domElement)){warn(`_getEle\
-mentsByName - element not allowed: ${id2}`);continue}fields.push({id:id2,exportValue,domElement})}return fields}for(const domElement of document.getElementsByName(name)){const{exportValue}=domElement,id2=domElement.getAttribute("data-element-id");id2!==skipId&&GetElementsByNameSet.has(domElement)&&fields.push({id:id2,exportValue,domElement})}return fields}show(){this.container&&(this.container.hidden=!1),this.popup?.maybeShow()}hide(){this.container&&(this.container.hidden=!0),this.popup?.forceHide()}getElementsToTriggerPopup(){
-return this.container}addHighlightArea(){const triggers=this.getElementsToTriggerPopup();if(Array.isArray(triggers))for(const element2 of triggers)element2.classList.add("highlightArea");else triggers.classList.add("highlightArea")}_editOnDoubleClick(){if(!this._isEditable)return;const{annotationEditorType:mode,data:{id:editId}}=this;this.container.addEventListener("dblclick",()=>{this.linkService.eventBus?.dispatch("switchannotationeditormode",{source:this,mode,editId,mustEnterInEditMode:!0})})}get width(){
-return this.data.rect[2]-this.data.rect[0]}get height(){return this.data.rect[3]-this.data.rect[1]}}class LinkAnnotationElement extends AnnotationElement{constructor(parameters,options=null){super(parameters,{isRenderable:!0,ignoreBorder:!!options?.ignoreBorder,createQuadrilaterals:!0}),this.isTooltipOnly=parameters.data.isTooltipOnly}render(){const{data,linkService}=this,link2=document.createElement("a");link2.setAttribute("data-element-id",data.id);let isBound=!1;return data.url?(linkService.addLinkAttributes(
-link2,data.url,data.newWindow),isBound=!0):data.action?(this._bindNamedAction(link2,data.action,data.overlaidText),isBound=!0):data.attachment?(this.#bindAttachment(link2,data.attachment,data.overlaidText,data.attachmentDest),isBound=!0):data.setOCGState?(this.#bindSetOCGState(link2,data.setOCGState,data.overlaidText),isBound=!0):data.dest?(this._bindLink(link2,data.dest,data.overlaidText),isBound=!0):(data.actions&&(data.actions.Action||data.actions["Mouse Up"]||data.actions["Mouse Down"])&&this.
-enableScripting&&this.hasJSActions&&(this._bindJSAction(link2,data),isBound=!0),data.resetForm?(this._bindResetFormAction(link2,data.resetForm),isBound=!0):this.isTooltipOnly&&!isBound&&(this._bindLink(link2,""),isBound=!0)),this.container.classList.add("linkAnnotation"),isBound&&this.container.append(link2),this.container}#setInternalLink(){this.container.setAttribute("data-internal-link","")}_bindLink(link2,destination,overlaidText=""){link2.href=this.linkService.getDestinationHash(destination),
-link2.onclick=()=>(destination&&this.linkService.goToDestination(destination),!1),(destination||destination==="")&&this.#setInternalLink(),overlaidText&&(link2.title=overlaidText)}_bindNamedAction(link2,action2,overlaidText=""){link2.href=this.linkService.getAnchorUrl(""),link2.onclick=()=>(this.linkService.executeNamedAction(action2),!1),overlaidText&&(link2.title=overlaidText),this.#setInternalLink()}#bindAttachment(link2,attachment,overlaidText="",dest=null){link2.href=this.linkService.getAnchorUrl(
-""),attachment.description?link2.title=attachment.description:overlaidText&&(link2.title=overlaidText),link2.onclick=()=>(this.downloadManager?.openOrDownloadData(attachment.content,attachment.filename,dest),!1),this.#setInternalLink()}#bindSetOCGState(link2,action2,overlaidText=""){link2.href=this.linkService.getAnchorUrl(""),link2.onclick=()=>(this.linkService.executeSetOCGState(action2),!1),overlaidText&&(link2.title=overlaidText),this.#setInternalLink()}_bindJSAction(link2,data){link2.href=this.
-linkService.getAnchorUrl("");const map2=new Map([["Action","onclick"],["Mouse Up","onmouseup"],["Mouse Down","onmousedown"]]);for(const name of Object.keys(data.actions)){const jsName=map2.get(name);jsName&&(link2[jsName]=()=>(this.linkService.eventBus?.dispatch("dispatcheventinsandbox",{source:this,detail:{id:data.id,name}}),!1))}data.overlaidText&&(link2.title=data.overlaidText),link2.onclick||(link2.onclick=()=>!1),this.#setInternalLink()}_bindResetFormAction(link2,resetForm){const otherClickAction=link2.
-onclick;if(otherClickAction||(link2.href=this.linkService.getAnchorUrl("")),this.#setInternalLink(),!this._fieldObjects){warn('_bindResetFormAction - "resetForm" action not supported, ensure that the `fieldObjects` parameter is provided.'),otherClickAction||(link2.onclick=()=>!1);return}link2.onclick=()=>{otherClickAction?.();const{fields:resetFormFields,refs:resetFormRefs,include}=resetForm,allFields=[];if(resetFormFields.length!==0||resetFormRefs.length!==0){const fieldIds=new Set(resetFormRefs);
-for(const fieldName of resetFormFields){const fields=this._fieldObjects[fieldName]||[];for(const{id:id2}of fields)fieldIds.add(id2)}for(const fields of Object.values(this._fieldObjects))for(const field of fields)fieldIds.has(field.id)===include&&allFields.push(field)}else for(const fields of Object.values(this._fieldObjects))allFields.push(...fields);const storage=this.annotationStorage,allIds=[];for(const field of allFields){const{id:id2}=field;switch(allIds.push(id2),field.type){case"text":{const value=field.
-defaultValue||"";storage.setValue(id2,{value});break}case"checkbox":case"radiobutton":{const value=field.defaultValue===field.exportValues;storage.setValue(id2,{value});break}case"combobox":case"listbox":{const value=field.defaultValue||"";storage.setValue(id2,{value});break}default:continue}const domElement=document.querySelector(`[data-element-id="${id2}"]`);if(domElement){if(!GetElementsByNameSet.has(domElement)){warn(`_bindResetFormAction - element not allowed: ${id2}`);continue}}else continue;
-domElement.dispatchEvent(new Event("resetform"))}return this.enableScripting&&this.linkService.eventBus?.dispatch("dispatcheventinsandbox",{source:this,detail:{id:"app",ids:allIds,name:"ResetForm"}}),!1}}}class TextAnnotationElement extends AnnotationElement{constructor(parameters){super(parameters,{isRenderable:!0})}render(){this.container.classList.add("textAnnotation");const image2=document.createElement("img");return image2.src=this.imageResourcesPath+"annotation-"+this.data.name.toLowerCase()+
-".svg",image2.setAttribute("data-l10n-id","pdfjs-text-annotation-type"),image2.setAttribute("data-l10n-args",JSON.stringify({type:this.data.name})),!this.data.popupRef&&this.hasPopupData&&this._createPopup(),this.container.append(image2),this.container}}class WidgetAnnotationElement extends AnnotationElement{render(){return this.container}showElementAndHideCanvas(element2){this.data.hasOwnCanvas&&(element2.previousSibling?.nodeName==="CANVAS"&&(element2.previousSibling.hidden=!0),element2.hidden=
-!1)}_getKeyModifier(event2){return util_FeatureTest.platform.isMac?event2.metaKey:event2.ctrlKey}_setEventListener(element2,elementData,baseName,eventName,valueGetter){baseName.includes("mouse")?element2.addEventListener(baseName,event2=>{this.linkService.eventBus?.dispatch("dispatcheventinsandbox",{source:this,detail:{id:this.data.id,name:eventName,value:valueGetter(event2),shift:event2.shiftKey,modifier:this._getKeyModifier(event2)}})}):element2.addEventListener(baseName,event2=>{if(baseName===
-"blur"){if(!elementData.focused||!event2.relatedTarget)return;elementData.focused=!1}else if(baseName==="focus"){if(elementData.focused)return;elementData.focused=!0}valueGetter&&this.linkService.eventBus?.dispatch("dispatcheventinsandbox",{source:this,detail:{id:this.data.id,name:eventName,value:valueGetter(event2)}})})}_setEventListeners(element2,elementData,names2,getter){for(const[baseName,eventName]of names2)(eventName==="Action"||this.data.actions?.[eventName])&&((eventName==="Focus"||eventName===
-"Blur")&&(elementData||={focused:!1}),this._setEventListener(element2,elementData,baseName,eventName,getter),eventName==="Focus"&&!this.data.actions?.Blur?this._setEventListener(element2,elementData,"blur","Blur",null):eventName==="Blur"&&!this.data.actions?.Focus&&this._setEventListener(element2,elementData,"focus","Focus",null))}_setBackgroundColor(element2){const color=this.data.backgroundColor||null;element2.style.backgroundColor=color===null?"transparent":Util.makeHexColor(color[0],color[1],
-color[2])}_setTextStyle(element2){const TEXT_ALIGNMENT=["left","center","right"],{fontColor}=this.data.defaultAppearanceData,fontSize=this.data.defaultAppearanceData.fontSize||annotation_layer_DEFAULT_FONT_SIZE,style2=element2.style;let computedFontSize;const BORDER_SIZE=2,roundToOneDecimal=x=>Math.round(10*x)/10;if(this.data.multiLine){const height=Math.abs(this.data.rect[3]-this.data.rect[1]-BORDER_SIZE),numberOfLines=Math.round(height/(LINE_FACTOR*fontSize))||1,lineHeight=height/numberOfLines;
-computedFontSize=Math.min(fontSize,roundToOneDecimal(lineHeight/LINE_FACTOR))}else{const height=Math.abs(this.data.rect[3]-this.data.rect[1]-BORDER_SIZE);computedFontSize=Math.min(fontSize,roundToOneDecimal(height/LINE_FACTOR))}style2.fontSize=`calc(${computedFontSize}px * var(--total-scale-factor))`,style2.color=Util.makeHexColor(fontColor[0],fontColor[1],fontColor[2]),this.data.textAlignment!==null&&(style2.textAlign=TEXT_ALIGNMENT[this.data.textAlignment])}_setRequired(element2,isRequired){isRequired?
-element2.setAttribute("required",!0):element2.removeAttribute("required"),element2.setAttribute("aria-required",isRequired)}}class TextWidgetAnnotationElement extends WidgetAnnotationElement{constructor(parameters){const isRenderable=parameters.renderForms||parameters.data.hasOwnCanvas||!parameters.data.hasAppearance&&!!parameters.data.fieldValue;super(parameters,{isRenderable})}setPropertyOnSiblings(base2,key2,value,keyInStorage){const storage=this.annotationStorage;for(const element2 of this._getElementsByName(
-base2.name,base2.id))element2.domElement&&(element2.domElement[key2]=value),storage.setValue(element2.id,{[keyInStorage]:value})}render(){const storage=this.annotationStorage,id2=this.data.id;this.container.classList.add("textWidgetAnnotation");let element2=null;if(this.renderForms){const storedData=storage.getValue(id2,{value:this.data.fieldValue});let textContent=storedData.value||"";const maxLen=storage.getValue(id2,{charLimit:this.data.maxLen}).charLimit;maxLen&&textContent.length>maxLen&&(textContent=
-textContent.slice(0,maxLen));let fieldFormattedValues=storedData.formattedValue||this.data.textContent?.join(`
-`)||null;fieldFormattedValues&&this.data.comb&&(fieldFormattedValues=fieldFormattedValues.replaceAll(/\s+/g,""));const elementData={userValue:textContent,formattedValue:fieldFormattedValues,lastCommittedValue:null,commitKey:1,focused:!1};this.data.multiLine?(element2=document.createElement("textarea"),element2.textContent=fieldFormattedValues??textContent,this.data.doNotScroll&&(element2.style.overflowY="hidden")):(element2=document.createElement("input"),element2.type=this.data.password?"passwo\
-rd":"text",element2.setAttribute("value",fieldFormattedValues??textContent),this.data.doNotScroll&&(element2.style.overflowX="hidden")),this.data.hasOwnCanvas&&(element2.hidden=!0),GetElementsByNameSet.add(element2),element2.setAttribute("data-element-id",id2),element2.disabled=this.data.readOnly,element2.name=this.data.fieldName,element2.tabIndex=0;const{datetimeFormat,datetimeType,timeStep}=this.data,hasDateOrTime=!!datetimeType&&this.enableScripting;datetimeFormat&&(element2.title=datetimeFormat),
-this._setRequired(element2,this.data.required),maxLen&&(element2.maxLength=maxLen),element2.addEventListener("input",event2=>{storage.setValue(id2,{value:event2.target.value}),this.setPropertyOnSiblings(element2,"value",event2.target.value,"value"),elementData.formattedValue=null}),element2.addEventListener("resetform",event2=>{const defaultValue=this.data.defaultFieldValue??"";element2.value=elementData.userValue=defaultValue,elementData.formattedValue=null});let blurListener=event2=>{const{formattedValue}=elementData;
-formattedValue!=null&&(event2.target.value=formattedValue),event2.target.scrollLeft=0};if(this.enableScripting&&this.hasJSActions){element2.addEventListener("focus",event2=>{if(elementData.focused)return;const{target:target2}=event2;if(hasDateOrTime&&(target2.type=datetimeType,timeStep&&(target2.step=timeStep)),elementData.userValue){const value=elementData.userValue;if(hasDateOrTime)if(datetimeType==="time"){const date2=new Date(value),parts=[date2.getHours(),date2.getMinutes(),date2.getSeconds()];
-target2.value=parts.map(v=>v.toString().padStart(2,"0")).join(":")}else target2.value=new Date(value-TIMEZONE_OFFSET).toISOString().split(datetimeType==="date"?"T":".",1)[0];else target2.value=value}elementData.lastCommittedValue=target2.value,elementData.commitKey=1,this.data.actions?.Focus||(elementData.focused=!0)}),element2.addEventListener("updatefromsandbox",jsEvent=>{this.showElementAndHideCanvas(jsEvent.target);const actions={value(event2){elementData.userValue=event2.detail.value??"",hasDateOrTime||
-storage.setValue(id2,{value:elementData.userValue.toString()}),event2.target.value=elementData.userValue},formattedValue(event2){const{formattedValue}=event2.detail;elementData.formattedValue=formattedValue,formattedValue!=null&&event2.target!==document.activeElement&&(event2.target.value=formattedValue);const data={formattedValue};hasDateOrTime&&(data.value=formattedValue),storage.setValue(id2,data)},selRange(event2){event2.target.setSelectionRange(...event2.detail.selRange)},charLimit:event2=>{
-const{charLimit}=event2.detail,{target:target2}=event2;if(charLimit===0){target2.removeAttribute("maxLength");return}target2.setAttribute("maxLength",charLimit);let value=elementData.userValue;!value||value.length<=charLimit||(value=value.slice(0,charLimit),target2.value=elementData.userValue=value,storage.setValue(id2,{value}),this.linkService.eventBus?.dispatch("dispatcheventinsandbox",{source:this,detail:{id:id2,name:"Keystroke",value,willCommit:!0,commitKey:1,selStart:target2.selectionStart,
-selEnd:target2.selectionEnd}}))}};this._dispatchEventFromSandbox(actions,jsEvent)}),element2.addEventListener("keydown",event2=>{elementData.commitKey=1;let commitKey=-1;if(event2.key==="Escape"?commitKey=0:event2.key==="Enter"&&!this.data.multiLine?commitKey=2:event2.key==="Tab"&&(elementData.commitKey=3),commitKey===-1)return;const{value}=event2.target;elementData.lastCommittedValue!==value&&(elementData.lastCommittedValue=value,elementData.userValue=value,this.linkService.eventBus?.dispatch("\
-dispatcheventinsandbox",{source:this,detail:{id:id2,name:"Keystroke",value,willCommit:!0,commitKey,selStart:event2.target.selectionStart,selEnd:event2.target.selectionEnd}}))});const _blurListener=blurListener;blurListener=null,element2.addEventListener("blur",event2=>{if(!elementData.focused||!event2.relatedTarget)return;this.data.actions?.Blur||(elementData.focused=!1);const{target:target2}=event2;let{value}=target2;if(hasDateOrTime){if(value&&datetimeType==="time"){const parts=value.split(":").
-map(v=>parseInt(v,10));value=new Date(2e3,0,1,parts[0],parts[1],parts[2]||0).valueOf(),target2.step=""}else value=new Date(value).valueOf();target2.type="text"}elementData.userValue=value,elementData.lastCommittedValue!==value&&this.linkService.eventBus?.dispatch("dispatcheventinsandbox",{source:this,detail:{id:id2,name:"Keystroke",value,willCommit:!0,commitKey:elementData.commitKey,selStart:event2.target.selectionStart,selEnd:event2.target.selectionEnd}}),_blurListener(event2)}),this.data.actions?.
-Keystroke&&element2.addEventListener("beforeinput",event2=>{elementData.lastCommittedValue=null;const{data,target:target2}=event2,{value,selectionStart,selectionEnd}=target2;let selStart=selectionStart,selEnd=selectionEnd;switch(event2.inputType){case"deleteWordBackward":{const match=value.substring(0,selectionStart).match(/\w*[^\w]*$/);match&&(selStart-=match[0].length);break}case"deleteWordForward":{const match=value.substring(selectionStart).match(/^[^\w]*\w*/);match&&(selEnd+=match[0].length);
-break}case"deleteContentBackward":selectionStart===selectionEnd&&(selStart-=1);break;case"deleteContentForward":selectionStart===selectionEnd&&(selEnd+=1);break}event2.preventDefault(),this.linkService.eventBus?.dispatch("dispatcheventinsandbox",{source:this,detail:{id:id2,name:"Keystroke",value,change:data||"",willCommit:!1,selStart,selEnd}})}),this._setEventListeners(element2,elementData,[["focus","Focus"],["blur","Blur"],["mousedown","Mouse Down"],["mouseenter","Mouse Enter"],["mouseleave","M\
-ouse Exit"],["mouseup","Mouse Up"]],event2=>event2.target.value)}if(blurListener&&element2.addEventListener("blur",blurListener),this.data.comb){const combWidth=(this.data.rect[2]-this.data.rect[0])/maxLen;element2.classList.add("comb"),element2.style.letterSpacing=`calc(${combWidth}px * var(--total-scale-factor) - 1ch)`}}else element2=document.createElement("div"),element2.textContent=this.data.fieldValue,element2.style.verticalAlign="middle",element2.style.display="table-cell",this.data.hasOwnCanvas&&
-(element2.hidden=!0);return this._setTextStyle(element2),this._setBackgroundColor(element2),this._setDefaultPropertiesFromJS(element2),this.container.append(element2),this.container}}class SignatureWidgetAnnotationElement extends WidgetAnnotationElement{constructor(parameters){super(parameters,{isRenderable:!!parameters.data.hasOwnCanvas})}}class CheckboxWidgetAnnotationElement extends WidgetAnnotationElement{constructor(parameters){super(parameters,{isRenderable:parameters.renderForms})}render(){
-const storage=this.annotationStorage,data=this.data,id2=data.id;let value=storage.getValue(id2,{value:data.exportValue===data.fieldValue}).value;typeof value=="string"&&(value=value!=="Off",storage.setValue(id2,{value})),this.container.classList.add("buttonWidgetAnnotation","checkBox");const element2=document.createElement("input");return GetElementsByNameSet.add(element2),element2.setAttribute("data-element-id",id2),element2.disabled=data.readOnly,this._setRequired(element2,this.data.required),
-element2.type="checkbox",element2.name=data.fieldName,value&&element2.setAttribute("checked",!0),element2.setAttribute("exportValue",data.exportValue),element2.tabIndex=0,element2.addEventListener("change",event2=>{const{name,checked}=event2.target;for(const checkbox of this._getElementsByName(name,id2)){const curChecked=checked&&checkbox.exportValue===data.exportValue;checkbox.domElement&&(checkbox.domElement.checked=curChecked),storage.setValue(checkbox.id,{value:curChecked})}storage.setValue(
-id2,{value:checked})}),element2.addEventListener("resetform",event2=>{const defaultValue=data.defaultFieldValue||"Off";event2.target.checked=defaultValue===data.exportValue}),this.enableScripting&&this.hasJSActions&&(element2.addEventListener("updatefromsandbox",jsEvent=>{const actions={value(event2){event2.target.checked=event2.detail.value!=="Off",storage.setValue(id2,{value:event2.target.checked})}};this._dispatchEventFromSandbox(actions,jsEvent)}),this._setEventListeners(element2,null,[["cha\
-nge","Validate"],["change","Action"],["focus","Focus"],["blur","Blur"],["mousedown","Mouse Down"],["mouseenter","Mouse Enter"],["mouseleave","Mouse Exit"],["mouseup","Mouse Up"]],event2=>event2.target.checked)),this._setBackgroundColor(element2),this._setDefaultPropertiesFromJS(element2),this.container.append(element2),this.container}}class RadioButtonWidgetAnnotationElement extends WidgetAnnotationElement{constructor(parameters){super(parameters,{isRenderable:parameters.renderForms})}render(){this.
-container.classList.add("buttonWidgetAnnotation","radioButton");const storage=this.annotationStorage,data=this.data,id2=data.id;let value=storage.getValue(id2,{value:data.fieldValue===data.buttonValue}).value;if(typeof value=="string"&&(value=value!==data.buttonValue,storage.setValue(id2,{value})),value)for(const radio of this._getElementsByName(data.fieldName,id2))storage.setValue(radio.id,{value:!1});const element2=document.createElement("input");if(GetElementsByNameSet.add(element2),element2.
-setAttribute("data-element-id",id2),element2.disabled=data.readOnly,this._setRequired(element2,this.data.required),element2.type="radio",element2.name=data.fieldName,value&&element2.setAttribute("checked",!0),element2.tabIndex=0,element2.addEventListener("change",event2=>{const{name,checked}=event2.target;for(const radio of this._getElementsByName(name,id2))storage.setValue(radio.id,{value:!1});storage.setValue(id2,{value:checked})}),element2.addEventListener("resetform",event2=>{const defaultValue=data.
-defaultFieldValue;event2.target.checked=defaultValue!=null&&defaultValue===data.buttonValue}),this.enableScripting&&this.hasJSActions){const pdfButtonValue=data.buttonValue;element2.addEventListener("updatefromsandbox",jsEvent=>{const actions={value:event2=>{const checked=pdfButtonValue===event2.detail.value;for(const radio of this._getElementsByName(event2.target.name)){const curChecked=checked&&radio.id===id2;radio.domElement&&(radio.domElement.checked=curChecked),storage.setValue(radio.id,{value:curChecked})}}};
-this._dispatchEventFromSandbox(actions,jsEvent)}),this._setEventListeners(element2,null,[["change","Validate"],["change","Action"],["focus","Focus"],["blur","Blur"],["mousedown","Mouse Down"],["mouseenter","Mouse Enter"],["mouseleave","Mouse Exit"],["mouseup","Mouse Up"]],event2=>event2.target.checked)}return this._setBackgroundColor(element2),this._setDefaultPropertiesFromJS(element2),this.container.append(element2),this.container}}class PushButtonWidgetAnnotationElement extends LinkAnnotationElement{constructor(parameters){
-super(parameters,{ignoreBorder:parameters.data.hasAppearance})}render(){const container2=super.render();container2.classList.add("buttonWidgetAnnotation","pushButton");const linkElement=container2.lastChild;return this.enableScripting&&this.hasJSActions&&linkElement&&(this._setDefaultPropertiesFromJS(linkElement),linkElement.addEventListener("updatefromsandbox",jsEvent=>{this._dispatchEventFromSandbox({},jsEvent)})),container2}}class ChoiceWidgetAnnotationElement extends WidgetAnnotationElement{constructor(parameters){
-super(parameters,{isRenderable:parameters.renderForms})}render(){this.container.classList.add("choiceWidgetAnnotation");const storage=this.annotationStorage,id2=this.data.id,storedData=storage.getValue(id2,{value:this.data.fieldValue}),selectElement=document.createElement("select");GetElementsByNameSet.add(selectElement),selectElement.setAttribute("data-element-id",id2),selectElement.disabled=this.data.readOnly,this._setRequired(selectElement,this.data.required),selectElement.name=this.data.fieldName,
-selectElement.tabIndex=0;let addAnEmptyEntry=this.data.combo&&this.data.options.length>0;this.data.combo||(selectElement.size=this.data.options.length,this.data.multiSelect&&(selectElement.multiple=!0)),selectElement.addEventListener("resetform",event2=>{const defaultValue=this.data.defaultFieldValue;for(const option2 of selectElement.options)option2.selected=option2.value===defaultValue});for(const option2 of this.data.options){const optionElement=document.createElement("option");optionElement.
-textContent=option2.displayValue,optionElement.value=option2.exportValue,storedData.value.includes(option2.exportValue)&&(optionElement.setAttribute("selected",!0),addAnEmptyEntry=!1),selectElement.append(optionElement)}let removeEmptyEntry=null;if(addAnEmptyEntry){const noneOptionElement=document.createElement("option");noneOptionElement.value=" ",noneOptionElement.setAttribute("hidden",!0),noneOptionElement.setAttribute("selected",!0),selectElement.prepend(noneOptionElement),removeEmptyEntry=()=>{
-noneOptionElement.remove(),selectElement.removeEventListener("input",removeEmptyEntry),removeEmptyEntry=null},selectElement.addEventListener("input",removeEmptyEntry)}const getValue=isExport=>{const name=isExport?"value":"textContent",{options,multiple}=selectElement;return multiple?Array.prototype.filter.call(options,option2=>option2.selected).map(option2=>option2[name]):options.selectedIndex===-1?null:options[options.selectedIndex][name]};let selectedValues=getValue(!1);const getItems=event2=>{
-const options=event2.target.options;return Array.prototype.map.call(options,option2=>({displayValue:option2.textContent,exportValue:option2.value}))};return this.enableScripting&&this.hasJSActions?(selectElement.addEventListener("updatefromsandbox",jsEvent=>{const actions={value(event2){removeEmptyEntry?.();const value=event2.detail.value,values=new Set(Array.isArray(value)?value:[value]);for(const option2 of selectElement.options)option2.selected=values.has(option2.value);storage.setValue(id2,{
-value:getValue(!0)}),selectedValues=getValue(!1)},multipleSelection(event2){selectElement.multiple=!0},remove(event2){const options=selectElement.options,index2=event2.detail.remove;options[index2].selected=!1,selectElement.remove(index2),options.length>0&&Array.prototype.findIndex.call(options,option2=>option2.selected)===-1&&(options[0].selected=!0),storage.setValue(id2,{value:getValue(!0),items:getItems(event2)}),selectedValues=getValue(!1)},clear(event2){for(;selectElement.length!==0;)selectElement.
-remove(0);storage.setValue(id2,{value:null,items:[]}),selectedValues=getValue(!1)},insert(event2){const{index:index2,displayValue,exportValue}=event2.detail.insert,selectChild=selectElement.children[index2],optionElement=document.createElement("option");optionElement.textContent=displayValue,optionElement.value=exportValue,selectChild?selectChild.before(optionElement):selectElement.append(optionElement),storage.setValue(id2,{value:getValue(!0),items:getItems(event2)}),selectedValues=getValue(!1)},
-items(event2){const{items:items2}=event2.detail;for(;selectElement.length!==0;)selectElement.remove(0);for(const item of items2){const{displayValue,exportValue}=item,optionElement=document.createElement("option");optionElement.textContent=displayValue,optionElement.value=exportValue,selectElement.append(optionElement)}selectElement.options.length>0&&(selectElement.options[0].selected=!0),storage.setValue(id2,{value:getValue(!0),items:getItems(event2)}),selectedValues=getValue(!1)},indices(event2){
-const indices=new Set(event2.detail.indices);for(const option2 of event2.target.options)option2.selected=indices.has(option2.index);storage.setValue(id2,{value:getValue(!0)}),selectedValues=getValue(!1)},editable(event2){event2.target.disabled=!event2.detail.editable}};this._dispatchEventFromSandbox(actions,jsEvent)}),selectElement.addEventListener("input",event2=>{const exportValue=getValue(!0),change=getValue(!1);storage.setValue(id2,{value:exportValue}),event2.preventDefault(),this.linkService.
-eventBus?.dispatch("dispatcheventinsandbox",{source:this,detail:{id:id2,name:"Keystroke",value:selectedValues,change,changeEx:exportValue,willCommit:!1,commitKey:1,keyDown:!1}})}),this._setEventListeners(selectElement,null,[["focus","Focus"],["blur","Blur"],["mousedown","Mouse Down"],["mouseenter","Mouse Enter"],["mouseleave","Mouse Exit"],["mouseup","Mouse Up"],["input","Action"],["input","Validate"]],event2=>event2.target.value)):selectElement.addEventListener("input",function(event2){storage.
-setValue(id2,{value:getValue(!0)})}),this.data.combo&&this._setTextStyle(selectElement),this._setBackgroundColor(selectElement),this._setDefaultPropertiesFromJS(selectElement),this.container.append(selectElement),this.container}}class PopupAnnotationElement extends AnnotationElement{constructor(parameters){const{data,elements}=parameters;super(parameters,{isRenderable:AnnotationElement._hasPopupData(data)}),this.elements=elements,this.popup=null}render(){const{container:container2}=this;container2.
-classList.add("popupAnnotation"),container2.role="comment";const popup=this.popup=new PopupElement({container:this.container,color:this.data.color,titleObj:this.data.titleObj,modificationDate:this.data.modificationDate||this.data.creationDate,contentsObj:this.data.contentsObj,richText:this.data.richText,rect:this.data.rect,parentRect:this.data.parentRect||null,parent:this.parent,elements:this.elements,open:this.data.open}),elementIds=[];for(const element2 of this.elements)element2.popup=popup,element2.
-container.ariaHasPopup="dialog",elementIds.push(element2.data.id),element2.addHighlightArea();return this.container.setAttribute("aria-controls",elementIds.map(id2=>`${AnnotationPrefix}${id2}`).join(",")),this.container}}class PopupElement{#boundKeyDown=this.#keyDown.bind(this);#boundHide=this.#hide.bind(this);#boundShow=this.#show.bind(this);#boundToggle=this.#toggle.bind(this);#color=null;#container=null;#contentsObj=null;#dateObj=null;#elements=null;#parent=null;#parentRect=null;#pinned=!1;#popup=null;#popupAbortController=null;#position=null;#rect=null;#richText=null;#titleObj=null;#updates=null;#wasVisible=!1;constructor({
-container:container2,color,elements,titleObj,modificationDate,contentsObj,richText,parent,rect,parentRect,open:open2}){this.#container=container2,this.#titleObj=titleObj,this.#contentsObj=contentsObj,this.#richText=richText,this.#parent=parent,this.#color=color,this.#rect=rect,this.#parentRect=parentRect,this.#elements=elements,this.#dateObj=PDFDateString.toDateObject(modificationDate),this.trigger=elements.flatMap(e=>e.getElementsToTriggerPopup()),this.#addEventListeners(),this.#container.hidden=
-!0,open2&&this.#toggle()}#addEventListeners(){if(this.#popupAbortController)return;this.#popupAbortController=new AbortController;const{signal}=this.#popupAbortController;for(const element2 of this.trigger)element2.addEventListener("click",this.#boundToggle,{signal}),element2.addEventListener("mouseenter",this.#boundShow,{signal}),element2.addEventListener("mouseleave",this.#boundHide,{signal}),element2.classList.add("popupTriggerArea");for(const element2 of this.#elements)element2.container?.addEventListener(
-"keydown",this.#boundKeyDown,{signal})}render(){if(this.#popup)return;const popup=this.#popup=document.createElement("div");if(popup.className="popup",this.#color){const baseColor=popup.style.outlineColor=Util.makeHexColor(...this.#color);popup.style.backgroundColor=`color-mix(in srgb, ${baseColor} 30%, white)`}const header=document.createElement("span");if(header.className="header",this.#titleObj?.str){const title2=document.createElement("span");title2.className="title",header.append(title2),{dir:title2.
-dir,str:title2.textContent}=this.#titleObj}if(popup.append(header),this.#dateObj){const modificationDate=document.createElement("time");modificationDate.className="popupDate",modificationDate.setAttribute("data-l10n-id","pdfjs-annotation-date-time-string"),modificationDate.setAttribute("data-l10n-args",JSON.stringify({dateObj:this.#dateObj.valueOf()})),modificationDate.dateTime=this.#dateObj.toISOString(),header.append(modificationDate)}const html2=this.#html;if(html2)XfaLayer.render({xfaHtml:html2,
-intent:"richText",div:popup}),popup.lastChild.classList.add("richText","popupContent");else{const contents=this._formatContents(this.#contentsObj);popup.append(contents)}this.#container.append(popup)}get#html(){const richText=this.#richText,contentsObj=this.#contentsObj;return richText?.str&&(!contentsObj?.str||contentsObj.str===richText.str)&&this.#richText.html||null}get#fontSize(){return this.#html?.attributes?.style?.fontSize||0}get#fontColor(){return this.#html?.attributes?.style?.color||null}#makePopupContent(text2){
-const popupLines=[],popupContent={str:text2,html:{name:"div",attributes:{dir:"auto"},children:[{name:"p",children:popupLines}]}},lineAttributes={style:{color:this.#fontColor,fontSize:this.#fontSize?`calc(${this.#fontSize}px * var(--total-scale-factor))`:""}};for(const line of text2.split(`
-`))popupLines.push({name:"span",value:line,attributes:lineAttributes});return popupContent}_formatContents({str,dir}){const p2=document.createElement("p");p2.classList.add("popupContent"),p2.dir=dir;const lines=str.split(/(?:\r\n?|\n)/);for(let i=0,ii=lines.length;i<ii;++i){const line=lines[i];p2.append(document.createTextNode(line)),i<ii-1&&p2.append(document.createElement("br"))}return p2}#keyDown(event2){event2.altKey||event2.shiftKey||event2.ctrlKey||event2.metaKey||(event2.key==="Enter"||event2.
-key==="Escape"&&this.#pinned)&&this.#toggle()}updateEdited({rect,popup,deleted}){if(deleted||popup?.deleted){this.remove();return}this.#addEventListeners(),this.#updates||={contentsObj:this.#contentsObj,richText:this.#richText},rect&&(this.#position=null),popup&&(this.#richText=this.#makePopupContent(popup.text),this.#dateObj=PDFDateString.toDateObject(popup.date),this.#contentsObj=null),this.#popup?.remove(),this.#popup=null}resetEdited(){this.#updates&&({contentsObj:this.#contentsObj,richText:this.#richText}=
-this.#updates,this.#updates=null,this.#popup?.remove(),this.#popup=null,this.#position=null)}remove(){this.#popupAbortController?.abort(),this.#popupAbortController=null,this.#popup?.remove(),this.#popup=null,this.#wasVisible=!1,this.#pinned=!1;for(const element2 of this.trigger)element2.classList.remove("popupTriggerArea")}#setPosition(){if(this.#position!==null)return;const{page:{view},viewport:{rawDims:{pageWidth,pageHeight,pageX,pageY}}}=this.#parent;let useParentRect=!!this.#parentRect,rect=useParentRect?
-this.#parentRect:this.#rect;for(const element2 of this.#elements)if(!rect||Util.intersect(element2.data.rect,rect)!==null){rect=element2.data.rect,useParentRect=!0;break}const normalizedRect=Util.normalizeRect([rect[0],view[3]-rect[1]+view[1],rect[2],view[3]-rect[3]+view[1]]),parentWidth=useParentRect?rect[2]-rect[0]+5:0,popupLeft=normalizedRect[0]+parentWidth,popupTop=normalizedRect[1];this.#position=[100*(popupLeft-pageX)/pageWidth,100*(popupTop-pageY)/pageHeight];const{style:style2}=this.#container;
-style2.left=`${this.#position[0]}%`,style2.top=`${this.#position[1]}%`}#toggle(){this.#pinned=!this.#pinned,this.#pinned?(this.#show(),this.#container.addEventListener("click",this.#boundToggle),this.#container.addEventListener("keydown",this.#boundKeyDown)):(this.#hide(),this.#container.removeEventListener("click",this.#boundToggle),this.#container.removeEventListener("keydown",this.#boundKeyDown))}#show(){this.#popup||this.render(),this.isVisible?this.#pinned&&this.#container.classList.add("fo\
-cused"):(this.#setPosition(),this.#container.hidden=!1,this.#container.style.zIndex=parseInt(this.#container.style.zIndex)+1e3)}#hide(){this.#container.classList.remove("focused"),!(this.#pinned||!this.isVisible)&&(this.#container.hidden=!0,this.#container.style.zIndex=parseInt(this.#container.style.zIndex)-1e3)}forceHide(){this.#wasVisible=this.isVisible,this.#wasVisible&&(this.#container.hidden=!0)}maybeShow(){this.#addEventListeners(),this.#wasVisible&&(this.#popup||this.#show(),this.#wasVisible=
-!1,this.#container.hidden=!1)}get isVisible(){return this.#container.hidden===!1}}class FreeTextAnnotationElement extends AnnotationElement{constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0}),this.textContent=parameters.data.textContent,this.textPosition=parameters.data.textPosition,this.annotationEditorType=AnnotationEditorType.FREETEXT}render(){if(this.container.classList.add("freeTextAnnotation"),this.textContent){const content2=document.createElement("div");content2.classList.
-add("annotationTextContent"),content2.setAttribute("role","comment");for(const line of this.textContent){const lineSpan=document.createElement("span");lineSpan.textContent=line,content2.append(lineSpan)}this.container.append(content2)}return!this.data.popupRef&&this.hasPopupData&&this._createPopup(),this._editOnDoubleClick(),this.container}}class LineAnnotationElement extends AnnotationElement{#line=null;constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0})}render(){this.container.
-classList.add("lineAnnotation");const{data,width,height}=this,svg2=this.svgFactory.create(width,height,!0),line=this.#line=this.svgFactory.createElement("svg:line");return line.setAttribute("x1",data.rect[2]-data.lineCoordinates[0]),line.setAttribute("y1",data.rect[3]-data.lineCoordinates[1]),line.setAttribute("x2",data.rect[2]-data.lineCoordinates[2]),line.setAttribute("y2",data.rect[3]-data.lineCoordinates[3]),line.setAttribute("stroke-width",data.borderStyle.width||1),line.setAttribute("strok\
-e","transparent"),line.setAttribute("fill","transparent"),svg2.append(line),this.container.append(svg2),!data.popupRef&&this.hasPopupData&&this._createPopup(),this.container}getElementsToTriggerPopup(){return this.#line}addHighlightArea(){this.container.classList.add("highlightArea")}}class SquareAnnotationElement extends AnnotationElement{#square=null;constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0})}render(){this.container.classList.add("squareAnnotation");const{data,
-width,height}=this,svg2=this.svgFactory.create(width,height,!0),borderWidth=data.borderStyle.width,square=this.#square=this.svgFactory.createElement("svg:rect");return square.setAttribute("x",borderWidth/2),square.setAttribute("y",borderWidth/2),square.setAttribute("width",width-borderWidth),square.setAttribute("height",height-borderWidth),square.setAttribute("stroke-width",borderWidth||1),square.setAttribute("stroke","transparent"),square.setAttribute("fill","transparent"),svg2.append(square),this.
-container.append(svg2),!data.popupRef&&this.hasPopupData&&this._createPopup(),this.container}getElementsToTriggerPopup(){return this.#square}addHighlightArea(){this.container.classList.add("highlightArea")}}class CircleAnnotationElement extends AnnotationElement{#circle=null;constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0})}render(){this.container.classList.add("circleAnnotation");const{data,width,height}=this,svg2=this.svgFactory.create(width,height,!0),borderWidth=data.
-borderStyle.width,circle=this.#circle=this.svgFactory.createElement("svg:ellipse");return circle.setAttribute("cx",width/2),circle.setAttribute("cy",height/2),circle.setAttribute("rx",width/2-borderWidth/2),circle.setAttribute("ry",height/2-borderWidth/2),circle.setAttribute("stroke-width",borderWidth||1),circle.setAttribute("stroke","transparent"),circle.setAttribute("fill","transparent"),svg2.append(circle),this.container.append(svg2),!data.popupRef&&this.hasPopupData&&this._createPopup(),this.
-container}getElementsToTriggerPopup(){return this.#circle}addHighlightArea(){this.container.classList.add("highlightArea")}}class PolylineAnnotationElement extends AnnotationElement{#polyline=null;constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0}),this.containerClassName="polylineAnnotation",this.svgElementName="svg:polyline"}render(){this.container.classList.add(this.containerClassName);const{data:{rect,vertices,borderStyle,popupRef},width,height}=this;if(!vertices)return this.
-container;const svg2=this.svgFactory.create(width,height,!0);let points=[];for(let i=0,ii=vertices.length;i<ii;i+=2){const x=vertices[i]-rect[0],y=rect[3]-vertices[i+1];points.push(`${x},${y}`)}points=points.join(" ");const polyline=this.#polyline=this.svgFactory.createElement(this.svgElementName);return polyline.setAttribute("points",points),polyline.setAttribute("stroke-width",borderStyle.width||1),polyline.setAttribute("stroke","transparent"),polyline.setAttribute("fill","transparent"),svg2.append(
-polyline),this.container.append(svg2),!popupRef&&this.hasPopupData&&this._createPopup(),this.container}getElementsToTriggerPopup(){return this.#polyline}addHighlightArea(){this.container.classList.add("highlightArea")}}class PolygonAnnotationElement extends PolylineAnnotationElement{constructor(parameters){super(parameters),this.containerClassName="polygonAnnotation",this.svgElementName="svg:polygon"}}class CaretAnnotationElement extends AnnotationElement{constructor(parameters){super(parameters,
-{isRenderable:!0,ignoreBorder:!0})}render(){return this.container.classList.add("caretAnnotation"),!this.data.popupRef&&this.hasPopupData&&this._createPopup(),this.container}}class InkAnnotationElement extends AnnotationElement{#polylinesGroupElement=null;#polylines=[];constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0}),this.containerClassName="inkAnnotation",this.svgElementName="svg:polyline",this.annotationEditorType=this.data.it==="InkHighlight"?AnnotationEditorType.HIGHLIGHT:
-AnnotationEditorType.INK}#getTransform(rotation,rect){switch(rotation){case 90:return{transform:`rotate(90) translate(${-rect[0]},${rect[1]}) scale(1,-1)`,width:rect[3]-rect[1],height:rect[2]-rect[0]};case 180:return{transform:`rotate(180) translate(${-rect[2]},${rect[1]}) scale(1,-1)`,width:rect[2]-rect[0],height:rect[3]-rect[1]};case 270:return{transform:`rotate(270) translate(${-rect[2]},${rect[3]}) scale(1,-1)`,width:rect[3]-rect[1],height:rect[2]-rect[0]};default:return{transform:`translate\
-(${-rect[0]},${rect[3]}) scale(1,-1)`,width:rect[2]-rect[0],height:rect[3]-rect[1]}}}render(){this.container.classList.add(this.containerClassName);const{data:{rect,rotation,inkLists,borderStyle,popupRef}}=this,{transform:transform2,width,height}=this.#getTransform(rotation,rect),svg2=this.svgFactory.create(width,height,!0),g=this.#polylinesGroupElement=this.svgFactory.createElement("svg:g");svg2.append(g),g.setAttribute("stroke-width",borderStyle.width||1),g.setAttribute("stroke-linecap","round"),
-g.setAttribute("stroke-linejoin","round"),g.setAttribute("stroke-miterlimit",10),g.setAttribute("stroke","transparent"),g.setAttribute("fill","transparent"),g.setAttribute("transform",transform2);for(let i=0,ii=inkLists.length;i<ii;i++){const polyline=this.svgFactory.createElement(this.svgElementName);this.#polylines.push(polyline),polyline.setAttribute("points",inkLists[i].join(",")),g.append(polyline)}return!popupRef&&this.hasPopupData&&this._createPopup(),this.container.append(svg2),this._editOnDoubleClick(),
-this.container}updateEdited(params){super.updateEdited(params);const{thickness,points,rect}=params,g=this.#polylinesGroupElement;if(thickness>=0&&g.setAttribute("stroke-width",thickness||1),points)for(let i=0,ii=this.#polylines.length;i<ii;i++)this.#polylines[i].setAttribute("points",points[i].join(","));if(rect){const{transform:transform2,width,height}=this.#getTransform(this.data.rotation,rect);g.parentElement.setAttribute("viewBox",`0 0 ${width} ${height}`),g.setAttribute("transform",transform2)}}getElementsToTriggerPopup(){
-return this.#polylines}addHighlightArea(){this.container.classList.add("highlightArea")}}class HighlightAnnotationElement extends AnnotationElement{constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0,createQuadrilaterals:!0}),this.annotationEditorType=AnnotationEditorType.HIGHLIGHT}render(){const{data:{overlaidText,popupRef}}=this;if(!popupRef&&this.hasPopupData&&this._createPopup(),this.container.classList.add("highlightAnnotation"),this._editOnDoubleClick(),overlaidText){
-const mark2=document.createElement("mark");mark2.classList.add("overlaidText"),mark2.textContent=overlaidText,this.container.append(mark2)}return this.container}}class UnderlineAnnotationElement extends AnnotationElement{constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0,createQuadrilaterals:!0})}render(){const{data:{overlaidText,popupRef}}=this;if(!popupRef&&this.hasPopupData&&this._createPopup(),this.container.classList.add("underlineAnnotation"),overlaidText){const underline=document.
-createElement("u");underline.classList.add("overlaidText"),underline.textContent=overlaidText,this.container.append(underline)}return this.container}}class SquigglyAnnotationElement extends AnnotationElement{constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0,createQuadrilaterals:!0})}render(){const{data:{overlaidText,popupRef}}=this;if(!popupRef&&this.hasPopupData&&this._createPopup(),this.container.classList.add("squigglyAnnotation"),overlaidText){const underline=document.
-createElement("u");underline.classList.add("overlaidText"),underline.textContent=overlaidText,this.container.append(underline)}return this.container}}class StrikeOutAnnotationElement extends AnnotationElement{constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0,createQuadrilaterals:!0})}render(){const{data:{overlaidText,popupRef}}=this;if(!popupRef&&this.hasPopupData&&this._createPopup(),this.container.classList.add("strikeoutAnnotation"),overlaidText){const strikeout=document.
-createElement("s");strikeout.classList.add("overlaidText"),strikeout.textContent=overlaidText,this.container.append(strikeout)}return this.container}}class StampAnnotationElement extends AnnotationElement{constructor(parameters){super(parameters,{isRenderable:!0,ignoreBorder:!0}),this.annotationEditorType=AnnotationEditorType.STAMP}render(){return this.container.classList.add("stampAnnotation"),this.container.setAttribute("role","img"),!this.data.popupRef&&this.hasPopupData&&this._createPopup(),
-this._editOnDoubleClick(),this.container}}class FileAttachmentAnnotationElement extends AnnotationElement{#trigger=null;constructor(parameters){super(parameters,{isRenderable:!0});const{file}=this.data;this.filename=file.filename,this.content=file.content,this.linkService.eventBus?.dispatch("fileattachmentannotation",{source:this,...file})}render(){this.container.classList.add("fileAttachmentAnnotation");const{container:container2,data}=this;let trigger;data.hasAppearance||data.fillAlpha===0?trigger=
-document.createElement("div"):(trigger=document.createElement("img"),trigger.src=`${this.imageResourcesPath}annotation-${/paperclip/i.test(data.name)?"paperclip":"pushpin"}.svg`,data.fillAlpha&&data.fillAlpha<1&&(trigger.style=`filter: opacity(${Math.round(data.fillAlpha*100)}%);`)),trigger.addEventListener("dblclick",this.#download.bind(this)),this.#trigger=trigger;const{isMac}=util_FeatureTest.platform;return container2.addEventListener("keydown",evt=>{evt.key==="Enter"&&(isMac?evt.metaKey:evt.
-ctrlKey)&&this.#download()}),!data.popupRef&&this.hasPopupData?this._createPopup():trigger.classList.add("popupTriggerArea"),container2.append(trigger),container2}getElementsToTriggerPopup(){return this.#trigger}addHighlightArea(){this.container.classList.add("highlightArea")}#download(){this.downloadManager?.openOrDownloadData(this.content,this.filename)}}class AnnotationLayer{#accessibilityManager=null;#annotationCanvasMap=null;#editableAnnotations=new Map;#structTreeLayer=null;constructor({div,
-accessibilityManager,annotationCanvasMap,annotationEditorUIManager,page:page2,viewport,structTreeLayer}){this.div=div,this.#accessibilityManager=accessibilityManager,this.#annotationCanvasMap=annotationCanvasMap,this.#structTreeLayer=structTreeLayer||null,this.page=page2,this.viewport=viewport,this.zIndex=0,this._annotationEditorUIManager=annotationEditorUIManager}hasEditableAnnotations(){return this.#editableAnnotations.size>0}async#appendElement(element2,id2,popupElements){const contentElement=element2.
-firstChild||element2,annotationId=contentElement.id=`${AnnotationPrefix}${id2}`,ariaAttributes=await this.#structTreeLayer?.getAriaAttributes(annotationId);if(ariaAttributes)for(const[key2,value]of ariaAttributes)contentElement.setAttribute(key2,value);popupElements?popupElements.at(-1).container.after(element2):(this.div.append(element2),this.#accessibilityManager?.moveElementInDOM(this.div,element2,contentElement,!1))}async render(params){const{annotations}=params,layer=this.div;setLayerDimensions(
-layer,this.viewport);const popupToElements=new Map,elementParams={data:null,layer,linkService:params.linkService,downloadManager:params.downloadManager,imageResourcesPath:params.imageResourcesPath||"",renderForms:params.renderForms!==!1,svgFactory:new DOMSVGFactory,annotationStorage:params.annotationStorage||new AnnotationStorage,enableScripting:params.enableScripting===!0,hasJSActions:params.hasJSActions,fieldObjects:params.fieldObjects,parent:this,elements:null};for(const data of annotations){
-if(data.noHTML)continue;const isPopupAnnotation=data.annotationType===AnnotationType.POPUP;if(isPopupAnnotation){const elements=popupToElements.get(data.id);if(!elements)continue;elementParams.elements=elements}else if(data.rect[2]===data.rect[0]||data.rect[3]===data.rect[1])continue;elementParams.data=data;const element2=AnnotationElementFactory.create(elementParams);if(!element2.isRenderable)continue;if(!isPopupAnnotation&&data.popupRef){const elements=popupToElements.get(data.popupRef);elements?
-elements.push(element2):popupToElements.set(data.popupRef,[element2])}const rendered=element2.render();data.hidden&&(rendered.style.visibility="hidden"),await this.#appendElement(rendered,data.id,elementParams.elements),element2._isEditable&&(this.#editableAnnotations.set(element2.data.id,element2),this._annotationEditorUIManager?.renderAnnotationElement(element2))}this.#setAnnotationCanvasMap()}async addLinkAnnotations(annotations,linkService){const elementParams={data:null,layer:this.div,linkService,
-svgFactory:new DOMSVGFactory,parent:this};for(const data of annotations){data.borderStyle||=AnnotationLayer._defaultBorderStyle,elementParams.data=data;const element2=AnnotationElementFactory.create(elementParams);if(!element2.isRenderable)continue;const rendered=element2.render();await this.#appendElement(rendered,data.id,null)}}update({viewport}){const layer=this.div;this.viewport=viewport,setLayerDimensions(layer,{rotation:viewport.rotation}),this.#setAnnotationCanvasMap(),layer.hidden=!1}#setAnnotationCanvasMap(){
-if(!this.#annotationCanvasMap)return;const layer=this.div;for(const[id2,canvas]of this.#annotationCanvasMap){const element2=layer.querySelector(`[data-annotation-id="${id2}"]`);if(!element2)continue;canvas.className="annotationContent";const{firstChild}=element2;firstChild?firstChild.nodeName==="CANVAS"?firstChild.replaceWith(canvas):firstChild.classList.contains("annotationContent")?firstChild.after(canvas):firstChild.before(canvas):element2.append(canvas);const editableAnnotation=this.#editableAnnotations.
-get(id2);editableAnnotation&&(editableAnnotation._hasNoCanvas?(this._annotationEditorUIManager?.setMissingCanvas(id2,element2.id,canvas),editableAnnotation._hasNoCanvas=!1):editableAnnotation.canvas=canvas)}this.#annotationCanvasMap.clear()}getEditableAnnotations(){return Array.from(this.#editableAnnotations.values())}getEditableAnnotation(id2){return this.#editableAnnotations.get(id2)}static get _defaultBorderStyle(){return shadow(this,"_defaultBorderStyle",Object.freeze({width:1,rawWidth:1,style:AnnotationBorderStyleType.
-SOLID,dashArray:[3],horizontalCornerRadius:0,verticalCornerRadius:0}))}}const EOL_PATTERN=/\r\n?|\n/g;class FreeTextEditor extends AnnotationEditor{#color;#content="";#editorDivId=`${this.id}-editor`;#editModeAC=null;#fontSize;_colorPicker=null;static _freeTextDefaultContent="";static _internalPadding=0;static _defaultColor=null;static _defaultFontSize=10;static get _keyboardManager(){const proto=FreeTextEditor.prototype,arrowChecker=self2=>self2.isEmpty(),small=AnnotationEditorUIManager.TRANSLATE_SMALL,
-big=AnnotationEditorUIManager.TRANSLATE_BIG;return shadow(this,"_keyboardManager",new KeyboardManager([[["ctrl+s","mac+meta+s","ctrl+p","mac+meta+p"],proto.commitOrRemove,{bubbles:!0}],[["ctrl+Enter","mac+meta+Enter","Escape","mac+Escape"],proto.commitOrRemove],[["ArrowLeft","mac+ArrowLeft"],proto._translateEmpty,{args:[-small,0],checker:arrowChecker}],[["ctrl+ArrowLeft","mac+shift+ArrowLeft"],proto._translateEmpty,{args:[-big,0],checker:arrowChecker}],[["ArrowRight","mac+ArrowRight"],proto._translateEmpty,
-{args:[small,0],checker:arrowChecker}],[["ctrl+ArrowRight","mac+shift+ArrowRight"],proto._translateEmpty,{args:[big,0],checker:arrowChecker}],[["ArrowUp","mac+ArrowUp"],proto._translateEmpty,{args:[0,-small],checker:arrowChecker}],[["ctrl+ArrowUp","mac+shift+ArrowUp"],proto._translateEmpty,{args:[0,-big],checker:arrowChecker}],[["ArrowDown","mac+ArrowDown"],proto._translateEmpty,{args:[0,small],checker:arrowChecker}],[["ctrl+ArrowDown","mac+shift+ArrowDown"],proto._translateEmpty,{args:[0,big],checker:arrowChecker}]]))}static _type="\
-freetext";static _editorType=AnnotationEditorType.FREETEXT;constructor(params){super({...params,name:"freeTextEditor"}),this.#color=params.color||FreeTextEditor._defaultColor||AnnotationEditor._defaultLineColor,this.#fontSize=params.fontSize||FreeTextEditor._defaultFontSize,this.annotationElementId||this._uiManager.a11yAlert("pdfjs-editor-freetext-added-alert")}static initialize(l10n,uiManager){AnnotationEditor.initialize(l10n,uiManager);const style2=getComputedStyle(document.documentElement);this.
-_internalPadding=parseFloat(style2.getPropertyValue("--freetext-padding"))}static updateDefaultParams(type2,value){switch(type2){case AnnotationEditorParamsType.FREETEXT_SIZE:FreeTextEditor._defaultFontSize=value;break;case AnnotationEditorParamsType.FREETEXT_COLOR:FreeTextEditor._defaultColor=value;break}}updateParams(type2,value){switch(type2){case AnnotationEditorParamsType.FREETEXT_SIZE:this.#updateFontSize(value);break;case AnnotationEditorParamsType.FREETEXT_COLOR:this.#updateColor(value);
-break}}static get defaultPropertiesToUpdate(){return[[AnnotationEditorParamsType.FREETEXT_SIZE,FreeTextEditor._defaultFontSize],[AnnotationEditorParamsType.FREETEXT_COLOR,FreeTextEditor._defaultColor||AnnotationEditor._defaultLineColor]]}get propertiesToUpdate(){return[[AnnotationEditorParamsType.FREETEXT_SIZE,this.#fontSize],[AnnotationEditorParamsType.FREETEXT_COLOR,this.#color]]}get toolbarButtons(){return this._colorPicker||=new BasicColorPicker(this),[["colorPicker",this._colorPicker]]}get colorType(){
-return AnnotationEditorParamsType.FREETEXT_COLOR}get colorValue(){return this.#color}#updateFontSize(fontSize){const setFontsize=size2=>{this.editorDiv.style.fontSize=`calc(${size2}px * var(--total-scale-factor))`,this.translate(0,-(size2-this.#fontSize)*this.parentScale),this.#fontSize=size2,this.#setEditorDimensions()},savedFontsize=this.#fontSize;this.addCommands({cmd:setFontsize.bind(this,fontSize),undo:setFontsize.bind(this,savedFontsize),post:this._uiManager.updateUI.bind(this._uiManager,this),
-mustExec:!0,type:AnnotationEditorParamsType.FREETEXT_SIZE,overwriteIfSameType:!0,keepUndo:!0})}#updateColor(color){const setColor=col=>{this.#color=this.editorDiv.style.color=col,this._colorPicker?.update(col)},savedColor=this.#color;this.addCommands({cmd:setColor.bind(this,color),undo:setColor.bind(this,savedColor),post:this._uiManager.updateUI.bind(this._uiManager,this),mustExec:!0,type:AnnotationEditorParamsType.FREETEXT_COLOR,overwriteIfSameType:!0,keepUndo:!0})}_translateEmpty(x,y){this._uiManager.
-translateSelectedEditors(x,y,!0)}getInitialTranslation(){const scale2=this.parentScale;return[-FreeTextEditor._internalPadding*scale2,-(FreeTextEditor._internalPadding+this.#fontSize)*scale2]}rebuild(){this.parent&&(super.rebuild(),this.div!==null&&(this.isAttachedToDOM||this.parent.add(this)))}enableEditMode(){if(!super.enableEditMode())return!1;this.overlayDiv.classList.remove("enabled"),this.editorDiv.contentEditable=!0,this._isDraggable=!1,this.div.removeAttribute("aria-activedescendant"),this.#editModeAC=
-new AbortController;const signal=this._uiManager.combinedSignal(this.#editModeAC);return this.editorDiv.addEventListener("keydown",this.editorDivKeydown.bind(this),{signal}),this.editorDiv.addEventListener("focus",this.editorDivFocus.bind(this),{signal}),this.editorDiv.addEventListener("blur",this.editorDivBlur.bind(this),{signal}),this.editorDiv.addEventListener("input",this.editorDivInput.bind(this),{signal}),this.editorDiv.addEventListener("paste",this.editorDivPaste.bind(this),{signal}),!0}disableEditMode(){
-return super.disableEditMode()?(this.overlayDiv.classList.add("enabled"),this.editorDiv.contentEditable=!1,this.div.setAttribute("aria-activedescendant",this.#editorDivId),this._isDraggable=!0,this.#editModeAC?.abort(),this.#editModeAC=null,this.div.focus({preventScroll:!0}),this.isEditing=!1,this.parent.div.classList.add("freetextEditing"),!0):!1}focusin(event2){this._focusEventsAllowed&&(super.focusin(event2),event2.target!==this.editorDiv&&this.editorDiv.focus())}onceAdded(focus2){this.width||
-(this.enableEditMode(),focus2&&this.editorDiv.focus(),this._initialOptions?.isCentered&&this.center(),this._initialOptions=null)}isEmpty(){return!this.editorDiv||this.editorDiv.innerText.trim()===""}remove(){this.isEditing=!1,this.parent&&(this.parent.setEditingState(!0),this.parent.div.classList.add("freetextEditing")),super.remove()}#extractText(){const buffer=[];this.editorDiv.normalize();let prevChild=null;for(const child2 of this.editorDiv.childNodes)prevChild?.nodeType===Node.TEXT_NODE&&child2.
-nodeName==="BR"||(buffer.push(FreeTextEditor.#getNodeContent(child2)),prevChild=child2);return buffer.join(`
-`)}#setEditorDimensions(){const[parentWidth,parentHeight]=this.parentDimensions;let rect;if(this.isAttachedToDOM)rect=this.div.getBoundingClientRect();else{const{currentLayer,div}=this,savedDisplay=div.style.display,savedVisibility=div.classList.contains("hidden");div.classList.remove("hidden"),div.style.display="hidden",currentLayer.div.append(this.div),rect=div.getBoundingClientRect(),div.remove(),div.style.display=savedDisplay,div.classList.toggle("hidden",savedVisibility)}this.rotation%180===
-this.parentRotation%180?(this.width=rect.width/parentWidth,this.height=rect.height/parentHeight):(this.width=rect.height/parentWidth,this.height=rect.width/parentHeight),this.fixAndSetPosition()}commit(){if(!this.isInEditMode())return;super.commit(),this.disableEditMode();const savedText=this.#content,newText=this.#content=this.#extractText().trimEnd();if(savedText===newText)return;const setText=text2=>{if(this.#content=text2,!text2){this.remove();return}this.#setContent(),this._uiManager.rebuild(
-this),this.#setEditorDimensions()};this.addCommands({cmd:()=>{setText(newText)},undo:()=>{setText(savedText)},mustExec:!1}),this.#setEditorDimensions()}shouldGetKeyboardEvents(){return this.isInEditMode()}enterInEditMode(){this.enableEditMode(),this.editorDiv.focus()}keydown(event2){event2.target===this.div&&event2.key==="Enter"&&(this.enterInEditMode(),event2.preventDefault())}editorDivKeydown(event2){FreeTextEditor._keyboardManager.exec(this,event2)}editorDivFocus(event2){this.isEditing=!0}editorDivBlur(event2){
-this.isEditing=!1}editorDivInput(event2){this.parent.div.classList.toggle("freetextEditing",this.isEmpty())}disableEditing(){this.editorDiv.setAttribute("role","comment"),this.editorDiv.removeAttribute("aria-multiline")}enableEditing(){this.editorDiv.setAttribute("role","textbox"),this.editorDiv.setAttribute("aria-multiline",!0)}get canChangeContent(){return!0}render(){if(this.div)return this.div;let baseX,baseY;(this._isCopy||this.annotationElementId)&&(baseX=this.x,baseY=this.y),super.render(),
-this.editorDiv=document.createElement("div"),this.editorDiv.className="internal",this.editorDiv.setAttribute("id",this.#editorDivId),this.editorDiv.setAttribute("data-l10n-id","pdfjs-free-text2"),this.editorDiv.setAttribute("data-l10n-attrs","default-content"),this.enableEditing(),this.editorDiv.contentEditable=!0;const{style:style2}=this.editorDiv;if(style2.fontSize=`calc(${this.#fontSize}px * var(--total-scale-factor))`,style2.color=this.#color,this.div.append(this.editorDiv),this.overlayDiv=document.
-createElement("div"),this.overlayDiv.classList.add("overlay","enabled"),this.div.append(this.overlayDiv),this._isCopy||this.annotationElementId){const[parentWidth,parentHeight]=this.parentDimensions;if(this.annotationElementId){const{position:position2}=this._initialData;let[tx,ty]=this.getInitialTranslation();[tx,ty]=this.pageTranslationToScreen(tx,ty);const[pageWidth,pageHeight]=this.pageDimensions,[pageX,pageY]=this.pageTranslation;let posX,posY;switch(this.rotation){case 0:posX=baseX+(position2[0]-
-pageX)/pageWidth,posY=baseY+this.height-(position2[1]-pageY)/pageHeight;break;case 90:posX=baseX+(position2[0]-pageX)/pageWidth,posY=baseY-(position2[1]-pageY)/pageHeight,[tx,ty]=[ty,-tx];break;case 180:posX=baseX-this.width+(position2[0]-pageX)/pageWidth,posY=baseY-(position2[1]-pageY)/pageHeight,[tx,ty]=[-tx,-ty];break;case 270:posX=baseX+(position2[0]-pageX-this.height*pageHeight)/pageWidth,posY=baseY+(position2[1]-pageY-this.width*pageWidth)/pageHeight,[tx,ty]=[-ty,tx];break}this.setAt(posX*
-parentWidth,posY*parentHeight,tx,ty)}else this._moveAfterPaste(baseX,baseY);this.#setContent(),this._isDraggable=!0,this.editorDiv.contentEditable=!1}else this._isDraggable=!1,this.editorDiv.contentEditable=!0;return this.div}static#getNodeContent(node2){return(node2.nodeType===Node.TEXT_NODE?node2.nodeValue:node2.innerText).replaceAll(EOL_PATTERN,"")}editorDivPaste(event2){const clipboardData=event2.clipboardData||window.clipboardData,{types:types2}=clipboardData;if(types2.length===1&&types2[0]===
-"text/plain")return;event2.preventDefault();const paste=FreeTextEditor.#deserializeContent(clipboardData.getData("text")||"").replaceAll(EOL_PATTERN,`
-`);if(!paste)return;const selection=window.getSelection();if(!selection.rangeCount)return;this.editorDiv.normalize(),selection.deleteFromDocument();const range=selection.getRangeAt(0);if(!paste.includes(`
-`)){range.insertNode(document.createTextNode(paste)),this.editorDiv.normalize(),selection.collapseToStart();return}const{startContainer,startOffset}=range,bufferBefore=[],bufferAfter=[];if(startContainer.nodeType===Node.TEXT_NODE){const parent=startContainer.parentElement;if(bufferAfter.push(startContainer.nodeValue.slice(startOffset).replaceAll(EOL_PATTERN,"")),parent!==this.editorDiv){let buffer=bufferBefore;for(const child2 of this.editorDiv.childNodes){if(child2===parent){buffer=bufferAfter;
-continue}buffer.push(FreeTextEditor.#getNodeContent(child2))}}bufferBefore.push(startContainer.nodeValue.slice(0,startOffset).replaceAll(EOL_PATTERN,""))}else if(startContainer===this.editorDiv){let buffer=bufferBefore,i=0;for(const child2 of this.editorDiv.childNodes)i++===startOffset&&(buffer=bufferAfter),buffer.push(FreeTextEditor.#getNodeContent(child2))}this.#content=`${bufferBefore.join(`
-`)}${paste}${bufferAfter.join(`
-`)}`,this.#setContent();const newRange=new Range;let beforeLength=Math.sumPrecise(bufferBefore.map(line=>line.length));for(const{firstChild}of this.editorDiv.childNodes)if(firstChild.nodeType===Node.TEXT_NODE){const length=firstChild.nodeValue.length;if(beforeLength<=length){newRange.setStart(firstChild,beforeLength),newRange.setEnd(firstChild,beforeLength);break}beforeLength-=length}selection.removeAllRanges(),selection.addRange(newRange)}#setContent(){if(this.editorDiv.replaceChildren(),!!this.#content)
-for(const line of this.#content.split(`
-`)){const div=document.createElement("div");div.append(line?document.createTextNode(line):document.createElement("br")),this.editorDiv.append(div)}}#serializeContent(){return this.#content.replaceAll(" "," ")}static#deserializeContent(content2){return content2.replaceAll(" "," ")}get contentDiv(){return this.editorDiv}static async deserialize(data,parent,uiManager){let initialData=null;if(data instanceof FreeTextAnnotationElement){const{data:{defaultAppearanceData:{fontSize,fontColor},rect,rotation,
-id:id2,popupRef,contentsObj},textContent,textPosition,parent:{page:{pageNumber}}}=data;if(!textContent||textContent.length===0)return null;initialData=data={annotationType:AnnotationEditorType.FREETEXT,color:Array.from(fontColor),fontSize,value:textContent.join(`
-`),position:textPosition,pageIndex:pageNumber-1,rect:rect.slice(0),rotation,annotationElementId:id2,id:id2,deleted:!1,popupRef,comment:contentsObj?.str||null}}const editor=await super.deserialize(data,parent,uiManager);return editor.#fontSize=data.fontSize,editor.#color=Util.makeHexColor(...data.color),editor.#content=FreeTextEditor.#deserializeContent(data.value),editor._initialData=initialData,data.comment&&editor.setCommentData(data.comment),editor}serialize(isForCopying=!1){if(this.isEmpty())
-return null;if(this.deleted)return this.serializeDeleted();const padding=FreeTextEditor._internalPadding*this.parentScale,rect=this.getRect(padding,padding),color=AnnotationEditor._colorManager.convert(this.isAttachedToDOM?getComputedStyle(this.editorDiv).color:this.#color),serialized={annotationType:AnnotationEditorType.FREETEXT,color,fontSize:this.#fontSize,value:this.#serializeContent(),pageIndex:this.pageIndex,rect,rotation:this.rotation,structTreeParentId:this._structTreeParentId};return this.
-addComment(serialized),isForCopying?(serialized.isCopy=!0,serialized):this.annotationElementId&&!this.#hasElementChanged(serialized)?null:(serialized.id=this.annotationElementId,serialized)}#hasElementChanged(serialized){const{value,fontSize,color,pageIndex}=this._initialData;return this.hasEditedComment||this._hasBeenMoved||serialized.value!==value||serialized.fontSize!==fontSize||serialized.color.some((c2,i)=>c2!==color[i])||serialized.pageIndex!==pageIndex}renderAnnotationElement(annotation){
-const content2=super.renderAnnotationElement(annotation),{style:style2}=content2;style2.fontSize=`calc(${this.#fontSize}px * var(--total-scale-factor))`,style2.color=this.#color,content2.replaceChildren();for(const line of this.#content.split(`
-`)){const div=document.createElement("div");div.append(line?document.createTextNode(line):document.createElement("br")),content2.append(div)}const padding=FreeTextEditor._internalPadding*this.parentScale,params={rect:this.getRect(padding,padding)};return params.popup=this.hasEditedComment?this.comment:{text:this.#content},annotation.updateEdited(params),content2}resetAnnotationElement(annotation){super.resetAnnotationElement(annotation),annotation.resetEdited()}}class Outline{static PRECISION=1e-4;toSVGPath(){
-unreachable("Abstract method `toSVGPath` must be implemented.")}get box(){unreachable("Abstract getter `box` must be implemented.")}serialize(_bbox,_rotation){unreachable("Abstract method `serialize` must be implemented.")}static _rescale(src,tx,ty,sx,sy,dest){dest||=new Float32Array(src.length);for(let i=0,ii=src.length;i<ii;i+=2)dest[i]=tx+src[i]*sx,dest[i+1]=ty+src[i+1]*sy;return dest}static _rescaleAndSwap(src,tx,ty,sx,sy,dest){dest||=new Float32Array(src.length);for(let i=0,ii=src.length;i<
-ii;i+=2)dest[i]=tx+src[i+1]*sx,dest[i+1]=ty+src[i]*sy;return dest}static _translate(src,tx,ty,dest){dest||=new Float32Array(src.length);for(let i=0,ii=src.length;i<ii;i+=2)dest[i]=tx+src[i],dest[i+1]=ty+src[i+1];return dest}static svgRound(x){return Math.round(x*1e4)}static _normalizePoint(x,y,parentWidth,parentHeight,rotation){switch(rotation){case 90:return[1-y/parentWidth,x/parentHeight];case 180:return[1-x/parentWidth,1-y/parentHeight];case 270:return[y/parentWidth,1-x/parentHeight];default:
-return[x/parentWidth,y/parentHeight]}}static _normalizePagePoint(x,y,rotation){switch(rotation){case 90:return[1-y,x];case 180:return[1-x,1-y];case 270:return[y,1-x];default:return[x,y]}}static createBezierPoints(x1,y1,x2,y2,x3,y3){return[(x1+5*x2)/6,(y1+5*y2)/6,(5*x2+x3)/6,(5*y2+y3)/6,(x2+x3)/2,(y2+y3)/2]}}class FreeDrawOutliner{#box;#bottom=[];#innerMargin;#isLTR;#top=[];#last=new Float32Array(18);#lastX;#lastY;#min;#min_dist;#scaleFactor;#thickness;#points=[];static#MIN_DIST=8;static#MIN_DIFF=2;static#MIN=FreeDrawOutliner.#MIN_DIST+
-FreeDrawOutliner.#MIN_DIFF;constructor({x,y},box2,scaleFactor,thickness,isLTR,innerMargin=0){this.#box=box2,this.#thickness=thickness*scaleFactor,this.#isLTR=isLTR,this.#last.set([NaN,NaN,NaN,NaN,x,y],6),this.#innerMargin=innerMargin,this.#min_dist=FreeDrawOutliner.#MIN_DIST*scaleFactor,this.#min=FreeDrawOutliner.#MIN*scaleFactor,this.#scaleFactor=scaleFactor,this.#points.push(x,y)}isEmpty(){return isNaN(this.#last[8])}#getLastCoords(){const lastTop=this.#last.subarray(4,6),lastBottom=this.#last.
-subarray(16,18),[x,y,width,height]=this.#box;return[(this.#lastX+(lastTop[0]-lastBottom[0])/2-x)/width,(this.#lastY+(lastTop[1]-lastBottom[1])/2-y)/height,(this.#lastX+(lastBottom[0]-lastTop[0])/2-x)/width,(this.#lastY+(lastBottom[1]-lastTop[1])/2-y)/height]}add({x,y}){this.#lastX=x,this.#lastY=y;const[layerX,layerY,layerWidth,layerHeight]=this.#box;let[x1,y1,x2,y2]=this.#last.subarray(8,12);const diffX=x-x2,diffY=y-y2,d2=Math.hypot(diffX,diffY);if(d2<this.#min)return!1;const diffD=d2-this.#min_dist,
-K=diffD/d2,shiftX=K*diffX,shiftY=K*diffY;let x0=x1,y0=y1;x1=x2,y1=y2,x2+=shiftX,y2+=shiftY,this.#points?.push(x,y);const nX=-shiftY/diffD,nY=shiftX/diffD,thX=nX*this.#thickness,thY=nY*this.#thickness;return this.#last.set(this.#last.subarray(2,8),0),this.#last.set([x2+thX,y2+thY],4),this.#last.set(this.#last.subarray(14,18),12),this.#last.set([x2-thX,y2-thY],16),isNaN(this.#last[6])?(this.#top.length===0&&(this.#last.set([x1+thX,y1+thY],2),this.#top.push(NaN,NaN,NaN,NaN,(x1+thX-layerX)/layerWidth,
-(y1+thY-layerY)/layerHeight),this.#last.set([x1-thX,y1-thY],14),this.#bottom.push(NaN,NaN,NaN,NaN,(x1-thX-layerX)/layerWidth,(y1-thY-layerY)/layerHeight)),this.#last.set([x0,y0,x1,y1,x2,y2],6),!this.isEmpty()):(this.#last.set([x0,y0,x1,y1,x2,y2],6),Math.abs(Math.atan2(y0-y1,x0-x1)-Math.atan2(shiftY,shiftX))<Math.PI/2?([x1,y1,x2,y2]=this.#last.subarray(2,6),this.#top.push(NaN,NaN,NaN,NaN,((x1+x2)/2-layerX)/layerWidth,((y1+y2)/2-layerY)/layerHeight),[x1,y1,x0,y0]=this.#last.subarray(14,18),this.#bottom.
-push(NaN,NaN,NaN,NaN,((x0+x1)/2-layerX)/layerWidth,((y0+y1)/2-layerY)/layerHeight),!0):([x0,y0,x1,y1,x2,y2]=this.#last.subarray(0,6),this.#top.push(((x0+5*x1)/6-layerX)/layerWidth,((y0+5*y1)/6-layerY)/layerHeight,((5*x1+x2)/6-layerX)/layerWidth,((5*y1+y2)/6-layerY)/layerHeight,((x1+x2)/2-layerX)/layerWidth,((y1+y2)/2-layerY)/layerHeight),[x2,y2,x1,y1,x0,y0]=this.#last.subarray(12,18),this.#bottom.push(((x0+5*x1)/6-layerX)/layerWidth,((y0+5*y1)/6-layerY)/layerHeight,((5*x1+x2)/6-layerX)/layerWidth,
-((5*y1+y2)/6-layerY)/layerHeight,((x1+x2)/2-layerX)/layerWidth,((y1+y2)/2-layerY)/layerHeight),!0))}toSVGPath(){if(this.isEmpty())return"";const top=this.#top,bottom=this.#bottom;if(isNaN(this.#last[6])&&!this.isEmpty())return this.#toSVGPathTwoPoints();const buffer=[];buffer.push(`M${top[4]} ${top[5]}`);for(let i=6;i<top.length;i+=6)isNaN(top[i])?buffer.push(`L${top[i+4]} ${top[i+5]}`):buffer.push(`C${top[i]} ${top[i+1]} ${top[i+2]} ${top[i+3]} ${top[i+4]} ${top[i+5]}`);this.#toSVGPathEnd(buffer);
-for(let i=bottom.length-6;i>=6;i-=6)isNaN(bottom[i])?buffer.push(`L${bottom[i+4]} ${bottom[i+5]}`):buffer.push(`C${bottom[i]} ${bottom[i+1]} ${bottom[i+2]} ${bottom[i+3]} ${bottom[i+4]} ${bottom[i+5]}`);return this.#toSVGPathStart(buffer),buffer.join(" ")}#toSVGPathTwoPoints(){const[x,y,width,height]=this.#box,[lastTopX,lastTopY,lastBottomX,lastBottomY]=this.#getLastCoords();return`M${(this.#last[2]-x)/width} ${(this.#last[3]-y)/height} L${(this.#last[4]-x)/width} ${(this.#last[5]-y)/height} L${lastTopX}\
- ${lastTopY} L${lastBottomX} ${lastBottomY} L${(this.#last[16]-x)/width} ${(this.#last[17]-y)/height} L${(this.#last[14]-x)/width} ${(this.#last[15]-y)/height} Z`}#toSVGPathStart(buffer){const bottom=this.#bottom;buffer.push(`L${bottom[4]} ${bottom[5]} Z`)}#toSVGPathEnd(buffer){const[x,y,width,height]=this.#box,lastTop=this.#last.subarray(4,6),lastBottom=this.#last.subarray(16,18),[lastTopX,lastTopY,lastBottomX,lastBottomY]=this.#getLastCoords();buffer.push(`L${(lastTop[0]-x)/width} ${(lastTop[1]-
-y)/height} L${lastTopX} ${lastTopY} L${lastBottomX} ${lastBottomY} L${(lastBottom[0]-x)/width} ${(lastBottom[1]-y)/height}`)}newFreeDrawOutline(outline,points,box2,scaleFactor,innerMargin,isLTR){return new FreeDrawOutline(outline,points,box2,scaleFactor,innerMargin,isLTR)}getOutlines(){const top=this.#top,bottom=this.#bottom,last=this.#last,[layerX,layerY,layerWidth,layerHeight]=this.#box,points=new Float32Array((this.#points?.length??0)+2);for(let i=0,ii=points.length-2;i<ii;i+=2)points[i]=(this.#points[i]-
-layerX)/layerWidth,points[i+1]=(this.#points[i+1]-layerY)/layerHeight;if(points[points.length-2]=(this.#lastX-layerX)/layerWidth,points[points.length-1]=(this.#lastY-layerY)/layerHeight,isNaN(last[6])&&!this.isEmpty())return this.#getOutlineTwoPoints(points);const outline=new Float32Array(this.#top.length+24+this.#bottom.length);let N=top.length;for(let i=0;i<N;i+=2){if(isNaN(top[i])){outline[i]=outline[i+1]=NaN;continue}outline[i]=top[i],outline[i+1]=top[i+1]}N=this.#getOutlineEnd(outline,N);for(let i=bottom.
-length-6;i>=6;i-=6)for(let j=0;j<6;j+=2){if(isNaN(bottom[i+j])){outline[N]=outline[N+1]=NaN,N+=2;continue}outline[N]=bottom[i+j],outline[N+1]=bottom[i+j+1],N+=2}return this.#getOutlineStart(outline,N),this.newFreeDrawOutline(outline,points,this.#box,this.#scaleFactor,this.#innerMargin,this.#isLTR)}#getOutlineTwoPoints(points){const last=this.#last,[layerX,layerY,layerWidth,layerHeight]=this.#box,[lastTopX,lastTopY,lastBottomX,lastBottomY]=this.#getLastCoords(),outline=new Float32Array(36);return outline.
-set([NaN,NaN,NaN,NaN,(last[2]-layerX)/layerWidth,(last[3]-layerY)/layerHeight,NaN,NaN,NaN,NaN,(last[4]-layerX)/layerWidth,(last[5]-layerY)/layerHeight,NaN,NaN,NaN,NaN,lastTopX,lastTopY,NaN,NaN,NaN,NaN,lastBottomX,lastBottomY,NaN,NaN,NaN,NaN,(last[16]-layerX)/layerWidth,(last[17]-layerY)/layerHeight,NaN,NaN,NaN,NaN,(last[14]-layerX)/layerWidth,(last[15]-layerY)/layerHeight],0),this.newFreeDrawOutline(outline,points,this.#box,this.#scaleFactor,this.#innerMargin,this.#isLTR)}#getOutlineStart(outline,pos){
-const bottom=this.#bottom;return outline.set([NaN,NaN,NaN,NaN,bottom[4],bottom[5]],pos),pos+=6}#getOutlineEnd(outline,pos){const lastTop=this.#last.subarray(4,6),lastBottom=this.#last.subarray(16,18),[layerX,layerY,layerWidth,layerHeight]=this.#box,[lastTopX,lastTopY,lastBottomX,lastBottomY]=this.#getLastCoords();return outline.set([NaN,NaN,NaN,NaN,(lastTop[0]-layerX)/layerWidth,(lastTop[1]-layerY)/layerHeight,NaN,NaN,NaN,NaN,lastTopX,lastTopY,NaN,NaN,NaN,NaN,lastBottomX,lastBottomY,NaN,NaN,NaN,
-NaN,(lastBottom[0]-layerX)/layerWidth,(lastBottom[1]-layerY)/layerHeight],pos),pos+=24}}class FreeDrawOutline extends Outline{#box;#bbox=new Float32Array(4);#innerMargin;#isLTR;#points;#scaleFactor;#outline;constructor(outline,points,box2,scaleFactor,innerMargin,isLTR){super(),this.#outline=outline,this.#points=points,this.#box=box2,this.#scaleFactor=scaleFactor,this.#innerMargin=innerMargin,this.#isLTR=isLTR,this.lastPoint=[NaN,NaN],this.#computeMinMax(isLTR);const[x,y,width,height]=this.#bbox;
-for(let i=0,ii=outline.length;i<ii;i+=2)outline[i]=(outline[i]-x)/width,outline[i+1]=(outline[i+1]-y)/height;for(let i=0,ii=points.length;i<ii;i+=2)points[i]=(points[i]-x)/width,points[i+1]=(points[i+1]-y)/height}toSVGPath(){const buffer=[`M${this.#outline[4]} ${this.#outline[5]}`];for(let i=6,ii=this.#outline.length;i<ii;i+=6){if(isNaN(this.#outline[i])){buffer.push(`L${this.#outline[i+4]} ${this.#outline[i+5]}`);continue}buffer.push(`C${this.#outline[i]} ${this.#outline[i+1]} ${this.#outline[i+
-2]} ${this.#outline[i+3]} ${this.#outline[i+4]} ${this.#outline[i+5]}`)}return buffer.push("Z"),buffer.join(" ")}serialize([blX,blY,trX,trY],rotation){const width=trX-blX,height=trY-blY;let outline,points;switch(rotation){case 0:outline=Outline._rescale(this.#outline,blX,trY,width,-height),points=Outline._rescale(this.#points,blX,trY,width,-height);break;case 90:outline=Outline._rescaleAndSwap(this.#outline,blX,blY,width,height),points=Outline._rescaleAndSwap(this.#points,blX,blY,width,height);break;case 180:
-outline=Outline._rescale(this.#outline,trX,blY,-width,height),points=Outline._rescale(this.#points,trX,blY,-width,height);break;case 270:outline=Outline._rescaleAndSwap(this.#outline,trX,trY,-width,-height),points=Outline._rescaleAndSwap(this.#points,trX,trY,-width,-height);break}return{outline:Array.from(outline),points:[Array.from(points)]}}#computeMinMax(isLTR){const outline=this.#outline;let lastX=outline[4],lastY=outline[5];const minMax=[lastX,lastY,lastX,lastY];let lastPointX=lastX,lastPointY=lastY;
-const ltrCallback=isLTR?Math.max:Math.min;for(let i=6,ii=outline.length;i<ii;i+=6){const x=outline[i+4],y=outline[i+5];if(isNaN(outline[i]))Util.pointBoundingBox(x,y,minMax),lastPointY<y?(lastPointX=x,lastPointY=y):lastPointY===y&&(lastPointX=ltrCallback(lastPointX,x));else{const bbox2=[1/0,1/0,-1/0,-1/0];Util.bezierBoundingBox(lastX,lastY,...outline.slice(i,i+6),bbox2),Util.rectBoundingBox(...bbox2,minMax),lastPointY<bbox2[3]?(lastPointX=bbox2[2],lastPointY=bbox2[3]):lastPointY===bbox2[3]&&(lastPointX=
-ltrCallback(lastPointX,bbox2[2]))}lastX=x,lastY=y}const bbox=this.#bbox;bbox[0]=minMax[0]-this.#innerMargin,bbox[1]=minMax[1]-this.#innerMargin,bbox[2]=minMax[2]-minMax[0]+2*this.#innerMargin,bbox[3]=minMax[3]-minMax[1]+2*this.#innerMargin,this.lastPoint=[lastPointX,lastPointY]}get box(){return this.#bbox}newOutliner(point2,box2,scaleFactor,thickness,isLTR,innerMargin=0){return new FreeDrawOutliner(point2,box2,scaleFactor,thickness,isLTR,innerMargin)}getNewOutline(thickness,innerMargin){const[x,
-y,width,height]=this.#bbox,[layerX,layerY,layerWidth,layerHeight]=this.#box,sx=width*layerWidth,sy=height*layerHeight,tx=x*layerWidth+layerX,ty=y*layerHeight+layerY,outliner=this.newOutliner({x:this.#points[0]*sx+tx,y:this.#points[1]*sy+ty},this.#box,this.#scaleFactor,thickness,this.#isLTR,innerMargin??this.#innerMargin);for(let i=2;i<this.#points.length;i+=2)outliner.add({x:this.#points[i]*sx+tx,y:this.#points[i+1]*sy+ty});return outliner.getOutlines()}}class HighlightOutliner{#box;#lastPoint;#verticalEdges=[];#intervals=[];constructor(boxes,borderWidth=0,innerMargin=0,isLTR=!0){
-const minMax=[1/0,1/0,-1/0,-1/0],EPSILON=10**-4;for(const{x,y,width,height}of boxes){const x1=Math.floor((x-borderWidth)/EPSILON)*EPSILON,x2=Math.ceil((x+width+borderWidth)/EPSILON)*EPSILON,y1=Math.floor((y-borderWidth)/EPSILON)*EPSILON,y2=Math.ceil((y+height+borderWidth)/EPSILON)*EPSILON,left=[x1,y1,y2,!0],right=[x2,y1,y2,!1];this.#verticalEdges.push(left,right),Util.rectBoundingBox(x1,y1,x2,y2,minMax)}const bboxWidth=minMax[2]-minMax[0]+2*innerMargin,bboxHeight=minMax[3]-minMax[1]+2*innerMargin,
-shiftedMinX=minMax[0]-innerMargin,shiftedMinY=minMax[1]-innerMargin,lastEdge=this.#verticalEdges.at(isLTR?-1:-2),lastPoint=[lastEdge[0],lastEdge[2]];for(const edge of this.#verticalEdges){const[x,y1,y2]=edge;edge[0]=(x-shiftedMinX)/bboxWidth,edge[1]=(y1-shiftedMinY)/bboxHeight,edge[2]=(y2-shiftedMinY)/bboxHeight}this.#box=new Float32Array([shiftedMinX,shiftedMinY,bboxWidth,bboxHeight]),this.#lastPoint=lastPoint}getOutlines(){this.#verticalEdges.sort((a,b)=>a[0]-b[0]||a[1]-b[1]||a[2]-b[2]);const outlineVerticalEdges=[];
-for(const edge of this.#verticalEdges)edge[3]?(outlineVerticalEdges.push(...this.#breakEdge(edge)),this.#insert(edge)):(this.#remove(edge),outlineVerticalEdges.push(...this.#breakEdge(edge)));return this.#getOutlines(outlineVerticalEdges)}#getOutlines(outlineVerticalEdges){const edges=[],allEdges=new Set;for(const edge of outlineVerticalEdges){const[x,y1,y2]=edge;edges.push([x,y1,edge],[x,y2,edge])}edges.sort((a,b)=>a[1]-b[1]||a[0]-b[0]);for(let i=0,ii=edges.length;i<ii;i+=2){const edge1=edges[i][2],
-edge2=edges[i+1][2];edge1.push(edge2),edge2.push(edge1),allEdges.add(edge1),allEdges.add(edge2)}const outlines=[];let outline;for(;allEdges.size>0;){const edge=allEdges.values().next().value;let[x,y1,y2,edge1,edge2]=edge;allEdges.delete(edge);let lastPointX=x,lastPointY=y1;for(outline=[x,y2],outlines.push(outline);;){let e;if(allEdges.has(edge1))e=edge1;else if(allEdges.has(edge2))e=edge2;else break;allEdges.delete(e),[x,y1,y2,edge1,edge2]=e,lastPointX!==x&&(outline.push(lastPointX,lastPointY,x,
-lastPointY===y1?y1:y2),lastPointX=x),lastPointY=lastPointY===y1?y2:y1}outline.push(lastPointX,lastPointY)}return new HighlightOutline(outlines,this.#box,this.#lastPoint)}#binarySearch(y){const array2=this.#intervals;let start2=0,end=array2.length-1;for(;start2<=end;){const middle=start2+end>>1,y1=array2[middle][0];if(y1===y)return middle;y1<y?start2=middle+1:end=middle-1}return end+1}#insert([,y1,y2]){const index2=this.#binarySearch(y1);this.#intervals.splice(index2,0,[y1,y2])}#remove([,y1,y2]){
-const index2=this.#binarySearch(y1);for(let i=index2;i<this.#intervals.length;i++){const[start2,end]=this.#intervals[i];if(start2!==y1)break;if(start2===y1&&end===y2){this.#intervals.splice(i,1);return}}for(let i=index2-1;i>=0;i--){const[start2,end]=this.#intervals[i];if(start2!==y1)break;if(start2===y1&&end===y2){this.#intervals.splice(i,1);return}}}#breakEdge(edge){const[x,y1,y2]=edge,results=[[x,y1,y2]],index2=this.#binarySearch(y2);for(let i=0;i<index2;i++){const[start2,end]=this.#intervals[i];
-for(let j=0,jj=results.length;j<jj;j++){const[,y3,y4]=results[j];if(!(end<=y3||y4<=start2)){if(y3>=start2){if(y4>end)results[j][1]=end;else{if(jj===1)return[];results.splice(j,1),j--,jj--}continue}results[j][2]=start2,y4>end&&results.push([x,end,y4])}}}return results}}class HighlightOutline extends Outline{#box;#outlines;constructor(outlines,box2,lastPoint){super(),this.#outlines=outlines,this.#box=box2,this.lastPoint=lastPoint}toSVGPath(){const buffer=[];for(const polygon of this.#outlines){let[
-prevX,prevY]=polygon;buffer.push(`M${prevX} ${prevY}`);for(let i=2;i<polygon.length;i+=2){const x=polygon[i],y=polygon[i+1];x===prevX?(buffer.push(`V${y}`),prevY=y):y===prevY&&(buffer.push(`H${x}`),prevX=x)}buffer.push("Z")}return buffer.join(" ")}serialize([blX,blY,trX,trY],_rotation){const outlines=[],width=trX-blX,height=trY-blY;for(const outline of this.#outlines){const points=new Array(outline.length);for(let i=0;i<outline.length;i+=2)points[i]=blX+outline[i]*width,points[i+1]=trY-outline[i+
-1]*height;outlines.push(points)}return outlines}get box(){return this.#box}get classNamesForOutlining(){return["highlightOutline"]}}class FreeHighlightOutliner extends FreeDrawOutliner{newFreeDrawOutline(outline,points,box2,scaleFactor,innerMargin,isLTR){return new FreeHighlightOutline(outline,points,box2,scaleFactor,innerMargin,isLTR)}}class FreeHighlightOutline extends FreeDrawOutline{newOutliner(point2,box2,scaleFactor,thickness,isLTR,innerMargin=0){return new FreeHighlightOutliner(point2,box2,
-scaleFactor,thickness,isLTR,innerMargin)}}class HighlightEditor extends AnnotationEditor{#anchorNode=null;#anchorOffset=0;#boxes;#clipPathId=null;#colorPicker=null;#focusOutlines=null;#focusNode=null;#focusOffset=0;#highlightDiv=null;#highlightOutlines=null;#id=null;#isFreeHighlight=!1;#lastPoint=null;#opacity;#outlineId=null;#text="";#thickness;#methodOfCreation="";static _defaultColor=null;static _defaultOpacity=1;static _defaultThickness=12;static _type="highlight";static _editorType=AnnotationEditorType.
-HIGHLIGHT;static _freeHighlightId=-1;static _freeHighlight=null;static _freeHighlightClipId="";static get _keyboardManager(){const proto=HighlightEditor.prototype;return shadow(this,"_keyboardManager",new KeyboardManager([[["ArrowLeft","mac+ArrowLeft"],proto._moveCaret,{args:[0]}],[["ArrowRight","mac+ArrowRight"],proto._moveCaret,{args:[1]}],[["ArrowUp","mac+ArrowUp"],proto._moveCaret,{args:[2]}],[["ArrowDown","mac+ArrowDown"],proto._moveCaret,{args:[3]}]]))}constructor(params){super({...params,
-name:"highlightEditor"}),this.color=params.color||HighlightEditor._defaultColor,this.#thickness=params.thickness||HighlightEditor._defaultThickness,this.#opacity=params.opacity||HighlightEditor._defaultOpacity,this.#boxes=params.boxes||null,this.#methodOfCreation=params.methodOfCreation||"",this.#text=params.text||"",this._isDraggable=!1,this.defaultL10nId="pdfjs-editor-highlight-editor",params.highlightId>-1?(this.#isFreeHighlight=!0,this.#createFreeOutlines(params),this.#addToDrawLayer()):this.#boxes&&
-(this.#anchorNode=params.anchorNode,this.#anchorOffset=params.anchorOffset,this.#focusNode=params.focusNode,this.#focusOffset=params.focusOffset,this.#createOutlines(),this.#addToDrawLayer(),this.rotate(this.rotation)),this.annotationElementId||this._uiManager.a11yAlert("pdfjs-editor-highlight-added-alert")}get telemetryInitialData(){return{action:"added",type:this.#isFreeHighlight?"free_highlight":"highlight",color:this._uiManager.getNonHCMColorName(this.color),thickness:this.#thickness,methodOfCreation:this.#methodOfCreation}}get telemetryFinalData(){
-return{type:"highlight",color:this._uiManager.getNonHCMColorName(this.color)}}get commentColor(){return this.color}static computeTelemetryFinalData(data){return{numberOfColors:data.get("color").size}}#createOutlines(){const outliner=new HighlightOutliner(this.#boxes,.001);this.#highlightOutlines=outliner.getOutlines(),[this.x,this.y,this.width,this.height]=this.#highlightOutlines.box;const outlinerForOutline=new HighlightOutliner(this.#boxes,.0025,.001,this._uiManager.direction==="ltr");this.#focusOutlines=
-outlinerForOutline.getOutlines();const{lastPoint}=this.#focusOutlines;this.#lastPoint=[(lastPoint[0]-this.x)/this.width,(lastPoint[1]-this.y)/this.height]}#createFreeOutlines({highlightOutlines,highlightId,clipPathId}){this.#highlightOutlines=highlightOutlines;const extraThickness=1.5;if(this.#focusOutlines=highlightOutlines.getNewOutline(this.#thickness/2+extraThickness,.0025),highlightId>=0)this.#id=highlightId,this.#clipPathId=clipPathId,this.parent.drawLayer.finalizeDraw(highlightId,{bbox:highlightOutlines.
-box,path:{d:highlightOutlines.toSVGPath()}}),this.#outlineId=this.parent.drawLayer.drawOutline({rootClass:{highlightOutline:!0,free:!0},bbox:this.#focusOutlines.box,path:{d:this.#focusOutlines.toSVGPath()}},!0);else if(this.parent){const angle=this.parent.viewport.rotation;this.parent.drawLayer.updateProperties(this.#id,{bbox:HighlightEditor.#rotateBbox(this.#highlightOutlines.box,(angle-this.rotation+360)%360),path:{d:highlightOutlines.toSVGPath()}}),this.parent.drawLayer.updateProperties(this.#outlineId,
-{bbox:HighlightEditor.#rotateBbox(this.#focusOutlines.box,angle),path:{d:this.#focusOutlines.toSVGPath()}})}const[x,y,width,height]=highlightOutlines.box;switch(this.rotation){case 0:this.x=x,this.y=y,this.width=width,this.height=height;break;case 90:{const[pageWidth,pageHeight]=this.parentDimensions;this.x=y,this.y=1-x,this.width=width*pageHeight/pageWidth,this.height=height*pageWidth/pageHeight;break}case 180:this.x=1-x,this.y=1-y,this.width=width,this.height=height;break;case 270:{const[pageWidth,
-pageHeight]=this.parentDimensions;this.x=1-y,this.y=x,this.width=width*pageHeight/pageWidth,this.height=height*pageWidth/pageHeight;break}}const{lastPoint}=this.#focusOutlines;this.#lastPoint=[(lastPoint[0]-x)/width,(lastPoint[1]-y)/height]}static initialize(l10n,uiManager){AnnotationEditor.initialize(l10n,uiManager),HighlightEditor._defaultColor||=uiManager.highlightColors?.values().next().value||"#fff066"}static updateDefaultParams(type2,value){switch(type2){case AnnotationEditorParamsType.HIGHLIGHT_COLOR:
-HighlightEditor._defaultColor=value;break;case AnnotationEditorParamsType.HIGHLIGHT_THICKNESS:HighlightEditor._defaultThickness=value;break}}translateInPage(x,y){}get toolbarPosition(){return this.#lastPoint}updateParams(type2,value){switch(type2){case AnnotationEditorParamsType.HIGHLIGHT_COLOR:this.#updateColor(value);break;case AnnotationEditorParamsType.HIGHLIGHT_THICKNESS:this.#updateThickness(value);break}}static get defaultPropertiesToUpdate(){return[[AnnotationEditorParamsType.HIGHLIGHT_COLOR,
-HighlightEditor._defaultColor],[AnnotationEditorParamsType.HIGHLIGHT_THICKNESS,HighlightEditor._defaultThickness]]}get propertiesToUpdate(){return[[AnnotationEditorParamsType.HIGHLIGHT_COLOR,this.color||HighlightEditor._defaultColor],[AnnotationEditorParamsType.HIGHLIGHT_THICKNESS,this.#thickness||HighlightEditor._defaultThickness],[AnnotationEditorParamsType.HIGHLIGHT_FREE,this.#isFreeHighlight]]}#updateColor(color){const setColorAndOpacity=(col,opa)=>{this.color=col,this.#opacity=opa,this.parent?.
-drawLayer.updateProperties(this.#id,{root:{fill:col,"fill-opacity":opa}}),this.#colorPicker?.updateColor(col)},savedColor=this.color,savedOpacity=this.#opacity;this.addCommands({cmd:setColorAndOpacity.bind(this,color,HighlightEditor._defaultOpacity),undo:setColorAndOpacity.bind(this,savedColor,savedOpacity),post:this._uiManager.updateUI.bind(this._uiManager,this),mustExec:!0,type:AnnotationEditorParamsType.HIGHLIGHT_COLOR,overwriteIfSameType:!0,keepUndo:!0}),this._reportTelemetry({action:"color_\
-changed",color:this._uiManager.getNonHCMColorName(color)},!0)}#updateThickness(thickness){const savedThickness=this.#thickness,setThickness=th=>{this.#thickness=th,this.#changeThickness(th)};this.addCommands({cmd:setThickness.bind(this,thickness),undo:setThickness.bind(this,savedThickness),post:this._uiManager.updateUI.bind(this._uiManager,this),mustExec:!0,type:AnnotationEditorParamsType.INK_THICKNESS,overwriteIfSameType:!0,keepUndo:!0}),this._reportTelemetry({action:"thickness_changed",thickness},
-!0)}get toolbarButtons(){return this._uiManager.highlightColors?[["colorPicker",this.#colorPicker=new ColorPicker({editor:this})]]:super.toolbarButtons}disableEditing(){super.disableEditing(),this.div.classList.toggle("disabled",!0)}enableEditing(){super.enableEditing(),this.div.classList.toggle("disabled",!1)}fixAndSetPosition(){return super.fixAndSetPosition(this.#getRotation())}getBaseTranslation(){return[0,0]}getRect(tx,ty){return super.getRect(tx,ty,this.#getRotation())}onceAdded(focus2){this.
-annotationElementId||this.parent.addUndoableEditor(this),focus2&&this.div.focus()}remove(){this.#cleanDrawLayer(),this._reportTelemetry({action:"deleted"}),super.remove()}rebuild(){this.parent&&(super.rebuild(),this.div!==null&&(this.#addToDrawLayer(),this.isAttachedToDOM||this.parent.add(this)))}setParent(parent){let mustBeSelected=!1;this.parent&&!parent?this.#cleanDrawLayer():parent&&(this.#addToDrawLayer(parent),mustBeSelected=!this.parent&&this.div?.classList.contains("selectedEditor")),super.
-setParent(parent),this.show(this._isVisible),mustBeSelected&&this.select()}#changeThickness(thickness){if(!this.#isFreeHighlight)return;this.#createFreeOutlines({highlightOutlines:this.#highlightOutlines.getNewOutline(thickness/2)}),this.fixAndSetPosition();const[parentWidth,parentHeight]=this.parentDimensions;this.setDims(this.width*parentWidth,this.height*parentHeight)}#cleanDrawLayer(){this.#id===null||!this.parent||(this.parent.drawLayer.remove(this.#id),this.#id=null,this.parent.drawLayer.remove(
-this.#outlineId),this.#outlineId=null)}#addToDrawLayer(parent=this.parent){this.#id===null&&({id:this.#id,clipPathId:this.#clipPathId}=parent.drawLayer.draw({bbox:this.#highlightOutlines.box,root:{viewBox:"0 0 1 1",fill:this.color,"fill-opacity":this.#opacity},rootClass:{highlight:!0,free:this.#isFreeHighlight},path:{d:this.#highlightOutlines.toSVGPath()}},!1,!0),this.#outlineId=parent.drawLayer.drawOutline({rootClass:{highlightOutline:!0,free:this.#isFreeHighlight},bbox:this.#focusOutlines.box,
-path:{d:this.#focusOutlines.toSVGPath()}},this.#isFreeHighlight),this.#highlightDiv&&(this.#highlightDiv.style.clipPath=this.#clipPathId))}static#rotateBbox([x,y,width,height],angle){switch(angle){case 90:return[1-y-height,x,height,width];case 180:return[1-x-width,1-y-height,width,height];case 270:return[y,1-x-width,height,width]}return[x,y,width,height]}rotate(angle){const{drawLayer}=this.parent;let box2;this.#isFreeHighlight?(angle=(angle-this.rotation+360)%360,box2=HighlightEditor.#rotateBbox(
-this.#highlightOutlines.box,angle)):box2=HighlightEditor.#rotateBbox([this.x,this.y,this.width,this.height],angle),drawLayer.updateProperties(this.#id,{bbox:box2,root:{"data-main-rotation":angle}}),drawLayer.updateProperties(this.#outlineId,{bbox:HighlightEditor.#rotateBbox(this.#focusOutlines.box,angle),root:{"data-main-rotation":angle}})}render(){if(this.div)return this.div;const div=super.render();this.#text&&(div.setAttribute("aria-label",this.#text),div.setAttribute("role","mark")),this.#isFreeHighlight?
-div.classList.add("free"):this.div.addEventListener("keydown",this.#keydown.bind(this),{signal:this._uiManager._signal});const highlightDiv=this.#highlightDiv=document.createElement("div");div.append(highlightDiv),highlightDiv.setAttribute("aria-hidden","true"),highlightDiv.className="internal",highlightDiv.style.clipPath=this.#clipPathId;const[parentWidth,parentHeight]=this.parentDimensions;return this.setDims(this.width*parentWidth,this.height*parentHeight),bindEvents(this,this.#highlightDiv,[
-"pointerover","pointerleave"]),this.enableEditing(),div}pointerover(){this.isSelected||this.parent?.drawLayer.updateProperties(this.#outlineId,{rootClass:{hovered:!0}})}pointerleave(){this.isSelected||this.parent?.drawLayer.updateProperties(this.#outlineId,{rootClass:{hovered:!1}})}#keydown(event2){HighlightEditor._keyboardManager.exec(this,event2)}_moveCaret(direction){switch(this.parent.unselect(this),direction){case 0:case 2:this.#setCaret(!0);break;case 1:case 3:this.#setCaret(!1);break}}#setCaret(start2){
-if(!this.#anchorNode)return;const selection=window.getSelection();start2?selection.setPosition(this.#anchorNode,this.#anchorOffset):selection.setPosition(this.#focusNode,this.#focusOffset)}select(){super.select(),this.#outlineId&&this.parent?.drawLayer.updateProperties(this.#outlineId,{rootClass:{hovered:!1,selected:!0}})}unselect(){super.unselect(),this.#outlineId&&(this.parent?.drawLayer.updateProperties(this.#outlineId,{rootClass:{selected:!1}}),this.#isFreeHighlight||this.#setCaret(!1))}get _mustFixPosition(){
-return!this.#isFreeHighlight}show(visible=this._isVisible){super.show(visible),this.parent&&(this.parent.drawLayer.updateProperties(this.#id,{rootClass:{hidden:!visible}}),this.parent.drawLayer.updateProperties(this.#outlineId,{rootClass:{hidden:!visible}}))}#getRotation(){return this.#isFreeHighlight?this.rotation:0}#serializeBoxes(){if(this.#isFreeHighlight)return null;const[pageWidth,pageHeight]=this.pageDimensions,[pageX,pageY]=this.pageTranslation,boxes=this.#boxes,quadPoints=new Float32Array(
-boxes.length*8);let i=0;for(const{x,y,width,height}of boxes){const sx=x*pageWidth+pageX,sy=(1-y)*pageHeight+pageY;quadPoints[i]=quadPoints[i+4]=sx,quadPoints[i+1]=quadPoints[i+3]=sy,quadPoints[i+2]=quadPoints[i+6]=sx+width*pageWidth,quadPoints[i+5]=quadPoints[i+7]=sy-height*pageHeight,i+=8}return quadPoints}#serializeOutlines(rect){return this.#highlightOutlines.serialize(rect,this.#getRotation())}static startHighlighting(parent,isLTR,{target:textLayer,x,y}){const{x:layerX,y:layerY,width:parentWidth,
-height:parentHeight}=textLayer.getBoundingClientRect(),ac=new AbortController,signal=parent.combinedSignal(ac),pointerUpCallback=e=>{ac.abort(),this.#endHighlight(parent,e)};window.addEventListener("blur",pointerUpCallback,{signal}),window.addEventListener("pointerup",pointerUpCallback,{signal}),window.addEventListener("pointerdown",stopEvent,{capture:!0,passive:!1,signal}),window.addEventListener("contextmenu",noContextMenu,{signal}),textLayer.addEventListener("pointermove",this.#highlightMove.
-bind(this,parent),{signal}),this._freeHighlight=new FreeHighlightOutliner({x,y},[layerX,layerY,parentWidth,parentHeight],parent.scale,this._defaultThickness/2,isLTR,.001),{id:this._freeHighlightId,clipPathId:this._freeHighlightClipId}=parent.drawLayer.draw({bbox:[0,0,1,1],root:{viewBox:"0 0 1 1",fill:this._defaultColor,"fill-opacity":this._defaultOpacity},rootClass:{highlight:!0,free:!0},path:{d:this._freeHighlight.toSVGPath()}},!0,!0)}static#highlightMove(parent,event2){this._freeHighlight.add(
-event2)&&parent.drawLayer.updateProperties(this._freeHighlightId,{path:{d:this._freeHighlight.toSVGPath()}})}static#endHighlight(parent,event2){this._freeHighlight.isEmpty()?parent.drawLayer.remove(this._freeHighlightId):parent.createAndAddNewEditor(event2,!1,{highlightId:this._freeHighlightId,highlightOutlines:this._freeHighlight.getOutlines(),clipPathId:this._freeHighlightClipId,methodOfCreation:"main_toolbar"}),this._freeHighlightId=-1,this._freeHighlight=null,this._freeHighlightClipId=""}static async deserialize(data,parent,uiManager){
-let initialData=null;if(data instanceof HighlightAnnotationElement){const{data:{quadPoints:quadPoints2,rect,rotation,id:id2,color:color2,opacity:opacity2,popupRef,contentsObj},parent:{page:{pageNumber}}}=data;initialData=data={annotationType:AnnotationEditorType.HIGHLIGHT,color:Array.from(color2),opacity:opacity2,quadPoints:quadPoints2,boxes:null,pageIndex:pageNumber-1,rect:rect.slice(0),rotation,annotationElementId:id2,id:id2,deleted:!1,popupRef,comment:contentsObj?.str||null}}else if(data instanceof
-InkAnnotationElement){const{data:{inkLists:inkLists2,rect,rotation,id:id2,color:color2,borderStyle:{rawWidth:thickness},popupRef,contentsObj},parent:{page:{pageNumber}}}=data;initialData=data={annotationType:AnnotationEditorType.HIGHLIGHT,color:Array.from(color2),thickness,inkLists:inkLists2,boxes:null,pageIndex:pageNumber-1,rect:rect.slice(0),rotation,annotationElementId:id2,id:id2,deleted:!1,popupRef,comment:contentsObj?.str||null}}const{color,quadPoints,inkLists,opacity}=data,editor=await super.
-deserialize(data,parent,uiManager);editor.color=Util.makeHexColor(...color),editor.#opacity=opacity||1,inkLists&&(editor.#thickness=data.thickness),editor._initialData=initialData,data.comment&&editor.setCommentData(data.comment);const[pageWidth,pageHeight]=editor.pageDimensions,[pageX,pageY]=editor.pageTranslation;if(quadPoints){const boxes=editor.#boxes=[];for(let i=0;i<quadPoints.length;i+=8)boxes.push({x:(quadPoints[i]-pageX)/pageWidth,y:1-(quadPoints[i+1]-pageY)/pageHeight,width:(quadPoints[i+
-2]-quadPoints[i])/pageWidth,height:(quadPoints[i+1]-quadPoints[i+5])/pageHeight});editor.#createOutlines(),editor.#addToDrawLayer(),editor.rotate(editor.rotation)}else if(inkLists){editor.#isFreeHighlight=!0;const points=inkLists[0],point2={x:points[0]-pageX,y:pageHeight-(points[1]-pageY)},outliner=new FreeHighlightOutliner(point2,[0,0,pageWidth,pageHeight],1,editor.#thickness/2,!0,.001);for(let i=0,ii=points.length;i<ii;i+=2)point2.x=points[i]-pageX,point2.y=pageHeight-(points[i+1]-pageY),outliner.
-add(point2);const{id:id2,clipPathId}=parent.drawLayer.draw({bbox:[0,0,1,1],root:{viewBox:"0 0 1 1",fill:editor.color,"fill-opacity":editor._defaultOpacity},rootClass:{highlight:!0,free:!0},path:{d:outliner.toSVGPath()}},!0,!0);editor.#createFreeOutlines({highlightOutlines:outliner.getOutlines(),highlightId:id2,clipPathId}),editor.#addToDrawLayer(),editor.rotate(editor.parentRotation)}return editor}serialize(isForCopying=!1){if(this.isEmpty()||isForCopying)return null;if(this.deleted)return this.
-serializeDeleted();const rect=this.getRect(0,0),color=AnnotationEditor._colorManager.convert(this._uiManager.getNonHCMColor(this.color)),serialized={annotationType:AnnotationEditorType.HIGHLIGHT,color,opacity:this.#opacity,thickness:this.#thickness,quadPoints:this.#serializeBoxes(),outlines:this.#serializeOutlines(rect),pageIndex:this.pageIndex,rect,rotation:this.#getRotation(),structTreeParentId:this._structTreeParentId};return this.addComment(serialized),this.annotationElementId&&!this.#hasElementChanged(
-serialized)?null:(serialized.id=this.annotationElementId,serialized)}#hasElementChanged(serialized){const{color}=this._initialData;return this.hasEditedComment||serialized.color.some((c2,i)=>c2!==color[i])}renderAnnotationElement(annotation){const params={rect:this.getRect(0,0)};return this.hasEditedComment&&(params.popup=this.comment),annotation.updateEdited(params),null}static canCreateNewEmptyEditor(){return!1}}class DrawingOptions{#svgProperties=Object.create(null);updateProperty(name,value){
-this[name]=value,this.updateSVGProperty(name,value)}updateProperties(properties2){if(properties2)for(const[name,value]of Object.entries(properties2))name.startsWith("_")||this.updateProperty(name,value)}updateSVGProperty(name,value){this.#svgProperties[name]=value}toSVGProperties(){const root2=this.#svgProperties;return this.#svgProperties=Object.create(null),{root:root2}}reset(){this.#svgProperties=Object.create(null)}updateAll(options=this){this.updateProperties(options)}clone(){unreachable("N\
-ot implemented")}}class DrawingEditor extends AnnotationEditor{#drawOutlines=null;#mustBeCommitted;_colorPicker=null;_drawId=null;static _currentDrawId=-1;static _currentParent=null;static#currentDraw=null;static#currentDrawingAC=null;static#currentDrawingOptions=null;static#currentPointerId=NaN;static#currentPointerType=null;static#currentPointerIds=null;static#currentMoveTimestamp=NaN;static _INNER_MARGIN=3;constructor(params){super(params),this.#mustBeCommitted=params.mustBeCommitted||!1,this.
-_addOutlines(params)}_addOutlines(params){params.drawOutlines&&(this.#createDrawOutlines(params),this.#addToDrawLayer())}#createDrawOutlines({drawOutlines,drawId,drawingOptions}){this.#drawOutlines=drawOutlines,this._drawingOptions||=drawingOptions,this.annotationElementId||this._uiManager.a11yAlert(`pdfjs-editor-${this.editorType}-added-alert`),drawId>=0?(this._drawId=drawId,this.parent.drawLayer.finalizeDraw(drawId,drawOutlines.defaultProperties)):this._drawId=this.#createDrawing(drawOutlines,
-this.parent),this.#updateBbox(drawOutlines.box)}#createDrawing(drawOutlines,parent){const{id:id2}=parent.drawLayer.draw(DrawingEditor._mergeSVGProperties(this._drawingOptions.toSVGProperties(),drawOutlines.defaultSVGProperties),!1,!1);return id2}static _mergeSVGProperties(p1,p2){const p1Keys=new Set(Object.keys(p1));for(const[key2,value]of Object.entries(p2))p1Keys.has(key2)?Object.assign(p1[key2],value):p1[key2]=value;return p1}static getDefaultDrawingOptions(_options){unreachable("Not implemen\
-ted")}static get typesMap(){unreachable("Not implemented")}static get isDrawer(){return!0}static get supportMultipleDrawings(){return!1}static updateDefaultParams(type2,value){const propertyName=this.typesMap.get(type2);propertyName&&this._defaultDrawingOptions.updateProperty(propertyName,value),this._currentParent&&(DrawingEditor.#currentDraw.updateProperty(propertyName,value),this._currentParent.drawLayer.updateProperties(this._currentDrawId,this._defaultDrawingOptions.toSVGProperties()))}updateParams(type2,value){
-const propertyName=this.constructor.typesMap.get(type2);propertyName&&this._updateProperty(type2,propertyName,value)}static get defaultPropertiesToUpdate(){const properties2=[],options=this._defaultDrawingOptions;for(const[type2,name]of this.typesMap)properties2.push([type2,options[name]]);return properties2}get propertiesToUpdate(){const properties2=[],{_drawingOptions}=this;for(const[type2,name]of this.constructor.typesMap)properties2.push([type2,_drawingOptions[name]]);return properties2}_updateProperty(type2,name,value){
-const options=this._drawingOptions,savedValue=options[name],setter=val=>{options.updateProperty(name,val);const bbox=this.#drawOutlines.updateProperty(name,val);bbox&&this.#updateBbox(bbox),this.parent?.drawLayer.updateProperties(this._drawId,options.toSVGProperties()),type2===this.colorType&&this._colorPicker?.update(val)};this.addCommands({cmd:setter.bind(this,value),undo:setter.bind(this,savedValue),post:this._uiManager.updateUI.bind(this._uiManager,this),mustExec:!0,type:type2,overwriteIfSameType:!0,
-keepUndo:!0})}_onResizing(){this.parent?.drawLayer.updateProperties(this._drawId,DrawingEditor._mergeSVGProperties(this.#drawOutlines.getPathResizingSVGProperties(this.#convertToDrawSpace()),{bbox:this.#rotateBox()}))}_onResized(){this.parent?.drawLayer.updateProperties(this._drawId,DrawingEditor._mergeSVGProperties(this.#drawOutlines.getPathResizedSVGProperties(this.#convertToDrawSpace()),{bbox:this.#rotateBox()}))}_onTranslating(_x,_y){this.parent?.drawLayer.updateProperties(this._drawId,{bbox:this.#rotateBox()})}_onTranslated(){
-this.parent?.drawLayer.updateProperties(this._drawId,DrawingEditor._mergeSVGProperties(this.#drawOutlines.getPathTranslatedSVGProperties(this.#convertToDrawSpace(),this.parentDimensions),{bbox:this.#rotateBox()}))}_onStartDragging(){this.parent?.drawLayer.updateProperties(this._drawId,{rootClass:{moving:!0}})}_onStopDragging(){this.parent?.drawLayer.updateProperties(this._drawId,{rootClass:{moving:!1}})}commit(){super.commit(),this.disableEditMode(),this.disableEditing()}disableEditing(){super.disableEditing(),
-this.div.classList.toggle("disabled",!0)}enableEditing(){super.enableEditing(),this.div.classList.toggle("disabled",!1)}getBaseTranslation(){return[0,0]}get isResizable(){return!0}onceAdded(focus2){this.annotationElementId||this.parent.addUndoableEditor(this),this._isDraggable=!0,this.#mustBeCommitted&&(this.#mustBeCommitted=!1,this.commit(),this.parent.setSelected(this),focus2&&this.isOnScreen&&this.div.focus())}remove(){this.#cleanDrawLayer(),super.remove()}rebuild(){this.parent&&(super.rebuild(),
-this.div!==null&&(this.#addToDrawLayer(),this.#updateBbox(this.#drawOutlines.box),this.isAttachedToDOM||this.parent.add(this)))}setParent(parent){let mustBeSelected=!1;this.parent&&!parent?(this._uiManager.removeShouldRescale(this),this.#cleanDrawLayer()):parent&&(this._uiManager.addShouldRescale(this),this.#addToDrawLayer(parent),mustBeSelected=!this.parent&&this.div?.classList.contains("selectedEditor")),super.setParent(parent),mustBeSelected&&this.select()}#cleanDrawLayer(){this._drawId===null||
-!this.parent||(this.parent.drawLayer.remove(this._drawId),this._drawId=null,this._drawingOptions.reset())}#addToDrawLayer(parent=this.parent){if(!(this._drawId!==null&&this.parent===parent)){if(this._drawId!==null){this.parent.drawLayer.updateParent(this._drawId,parent.drawLayer);return}this._drawingOptions.updateAll(),this._drawId=this.#createDrawing(this.#drawOutlines,parent)}}#convertToParentSpace([x,y,width,height]){const{parentDimensions:[pW,pH],rotation}=this;switch(rotation){case 90:return[
-y,1-x,width*(pH/pW),height*(pW/pH)];case 180:return[1-x,1-y,width,height];case 270:return[1-y,x,width*(pH/pW),height*(pW/pH)];default:return[x,y,width,height]}}#convertToDrawSpace(){const{x,y,width,height,parentDimensions:[pW,pH],rotation}=this;switch(rotation){case 90:return[1-y,x,width*(pW/pH),height*(pH/pW)];case 180:return[1-x,1-y,width,height];case 270:return[y,1-x,width*(pW/pH),height*(pH/pW)];default:return[x,y,width,height]}}#updateBbox(bbox){if([this.x,this.y,this.width,this.height]=this.#convertToParentSpace(
-bbox),this.div){this.fixAndSetPosition();const[parentWidth,parentHeight]=this.parentDimensions;this.setDims(this.width*parentWidth,this.height*parentHeight)}this._onResized()}#rotateBox(){const{x,y,width,height,rotation,parentRotation,parentDimensions:[pW,pH]}=this;switch((rotation*4+parentRotation)/90){case 1:return[1-y-height,x,height,width];case 2:return[1-x-width,1-y-height,width,height];case 3:return[y,1-x-width,height,width];case 4:return[x,y-width*(pW/pH),height*(pH/pW),width*(pW/pH)];case 5:
-return[1-y,x,width*(pW/pH),height*(pH/pW)];case 6:return[1-x-height*(pH/pW),1-y,height*(pH/pW),width*(pW/pH)];case 7:return[y-width*(pW/pH),1-x-height*(pH/pW),width*(pW/pH),height*(pH/pW)];case 8:return[x-width,y-height,width,height];case 9:return[1-y,x-width,height,width];case 10:return[1-x,1-y,width,height];case 11:return[y-height,1-x,height,width];case 12:return[x-height*(pH/pW),y,height*(pH/pW),width*(pW/pH)];case 13:return[1-y-width*(pW/pH),x-height*(pH/pW),width*(pW/pH),height*(pH/pW)];case 14:
-return[1-x,1-y-width*(pW/pH),height*(pH/pW),width*(pW/pH)];case 15:return[y,1-x,width*(pW/pH),height*(pH/pW)];default:return[x,y,width,height]}}rotate(){this.parent&&this.parent.drawLayer.updateProperties(this._drawId,DrawingEditor._mergeSVGProperties({bbox:this.#rotateBox()},this.#drawOutlines.updateRotation((this.parentRotation-this.rotation+360)%360)))}onScaleChanging(){this.parent&&this.#updateBbox(this.#drawOutlines.updateParentDimensions(this.parentDimensions,this.parent.scale))}static onScaleChangingWhenDrawing(){}render(){
-if(this.div)return this.div;let baseX,baseY;this._isCopy&&(baseX=this.x,baseY=this.y);const div=super.render();div.classList.add("draw");const drawDiv=document.createElement("div");div.append(drawDiv),drawDiv.setAttribute("aria-hidden","true"),drawDiv.className="internal";const[parentWidth,parentHeight]=this.parentDimensions;return this.setDims(this.width*parentWidth,this.height*parentHeight),this._uiManager.addShouldRescale(this),this.disableEditing(),this._isCopy&&this._moveAfterPaste(baseX,baseY),
-div}static createDrawerInstance(_x,_y,_parentWidth,_parentHeight,_rotation){unreachable("Not implemented")}static startDrawing(parent,uiManager,_isLTR,event2){const{target:target2,offsetX:x,offsetY:y,pointerId,pointerType}=event2;if(DrawingEditor.#currentPointerType&&DrawingEditor.#currentPointerType!==pointerType)return;const{viewport:{rotation}}=parent,{width:parentWidth,height:parentHeight}=target2.getBoundingClientRect(),ac=DrawingEditor.#currentDrawingAC=new AbortController,signal=parent.combinedSignal(
-ac);if(DrawingEditor.#currentPointerId||=pointerId,DrawingEditor.#currentPointerType??=pointerType,window.addEventListener("pointerup",e=>{DrawingEditor.#currentPointerId===e.pointerId?this._endDraw(e):DrawingEditor.#currentPointerIds?.delete(e.pointerId)},{signal}),window.addEventListener("pointercancel",e=>{DrawingEditor.#currentPointerId===e.pointerId?this._currentParent.endDrawingSession():DrawingEditor.#currentPointerIds?.delete(e.pointerId)},{signal}),window.addEventListener("pointerdown",
-e=>{DrawingEditor.#currentPointerType===e.pointerType&&((DrawingEditor.#currentPointerIds||=new Set).add(e.pointerId),DrawingEditor.#currentDraw.isCancellable()&&(DrawingEditor.#currentDraw.removeLastElement(),DrawingEditor.#currentDraw.isEmpty()?this._currentParent.endDrawingSession(!0):this._endDraw(null)))},{capture:!0,passive:!1,signal}),window.addEventListener("contextmenu",noContextMenu,{signal}),target2.addEventListener("pointermove",this._drawMove.bind(this),{signal}),target2.addEventListener(
-"touchmove",e=>{e.timeStamp===DrawingEditor.#currentMoveTimestamp&&stopEvent(e)},{signal}),parent.toggleDrawing(),uiManager._editorUndoBar?.hide(),DrawingEditor.#currentDraw){parent.drawLayer.updateProperties(this._currentDrawId,DrawingEditor.#currentDraw.startNew(x,y,parentWidth,parentHeight,rotation));return}uiManager.updateUIForDefaultProperties(this),DrawingEditor.#currentDraw=this.createDrawerInstance(x,y,parentWidth,parentHeight,rotation),DrawingEditor.#currentDrawingOptions=this.getDefaultDrawingOptions(),
-this._currentParent=parent,{id:this._currentDrawId}=parent.drawLayer.draw(this._mergeSVGProperties(DrawingEditor.#currentDrawingOptions.toSVGProperties(),DrawingEditor.#currentDraw.defaultSVGProperties),!0,!1)}static _drawMove(event2){if(DrawingEditor.#currentMoveTimestamp=-1,!DrawingEditor.#currentDraw)return;const{offsetX,offsetY,pointerId}=event2;if(DrawingEditor.#currentPointerId===pointerId){if(DrawingEditor.#currentPointerIds?.size>=1){this._endDraw(event2);return}this._currentParent.drawLayer.
-updateProperties(this._currentDrawId,DrawingEditor.#currentDraw.add(offsetX,offsetY)),DrawingEditor.#currentMoveTimestamp=event2.timeStamp,stopEvent(event2)}}static _cleanup(all2){all2&&(this._currentDrawId=-1,this._currentParent=null,DrawingEditor.#currentDraw=null,DrawingEditor.#currentDrawingOptions=null,DrawingEditor.#currentPointerType=null,DrawingEditor.#currentMoveTimestamp=NaN),DrawingEditor.#currentDrawingAC&&(DrawingEditor.#currentDrawingAC.abort(),DrawingEditor.#currentDrawingAC=null,
-DrawingEditor.#currentPointerId=NaN,DrawingEditor.#currentPointerIds=null)}static _endDraw(event2){const parent=this._currentParent;if(parent){if(parent.toggleDrawing(!0),this._cleanup(!1),event2?.target===parent.div&&parent.drawLayer.updateProperties(this._currentDrawId,DrawingEditor.#currentDraw.end(event2.offsetX,event2.offsetY)),this.supportMultipleDrawings){const draw=DrawingEditor.#currentDraw,drawId=this._currentDrawId,lastElement=draw.getLastElement();parent.addCommands({cmd:()=>{parent.
-drawLayer.updateProperties(drawId,draw.setLastElement(lastElement))},undo:()=>{parent.drawLayer.updateProperties(drawId,draw.removeLastElement())},mustExec:!1,type:AnnotationEditorParamsType.DRAW_STEP});return}this.endDrawing(!1)}}static endDrawing(isAborted){const parent=this._currentParent;if(!parent)return null;if(parent.toggleDrawing(!0),parent.cleanUndoStack(AnnotationEditorParamsType.DRAW_STEP),!DrawingEditor.#currentDraw.isEmpty()){const{pageDimensions:[pageWidth,pageHeight],scale:scale2}=parent,
-editor=parent.createAndAddNewEditor({offsetX:0,offsetY:0},!1,{drawId:this._currentDrawId,drawOutlines:DrawingEditor.#currentDraw.getOutlines(pageWidth*scale2,pageHeight*scale2,scale2,this._INNER_MARGIN),drawingOptions:DrawingEditor.#currentDrawingOptions,mustBeCommitted:!isAborted});return this._cleanup(!0),editor}return parent.drawLayer.remove(this._currentDrawId),this._cleanup(!0),null}createDrawingOptions(_data){}static deserializeDraw(_pageX,_pageY,_pageWidth,_pageHeight,_innerWidth,_data){unreachable(
-"Not implemented")}static async deserialize(data,parent,uiManager){const{rawDims:{pageWidth,pageHeight,pageX,pageY}}=parent.viewport,drawOutlines=this.deserializeDraw(pageX,pageY,pageWidth,pageHeight,this._INNER_MARGIN,data),editor=await super.deserialize(data,parent,uiManager);return editor.createDrawingOptions(data),editor.#createDrawOutlines({drawOutlines}),editor.#addToDrawLayer(),editor.onScaleChanging(),editor.rotate(),editor}serializeDraw(isForCopying){const[pageX,pageY]=this.pageTranslation,
-[pageWidth,pageHeight]=this.pageDimensions;return this.#drawOutlines.serialize([pageX,pageY,pageWidth,pageHeight],isForCopying)}renderAnnotationElement(annotation){return annotation.updateEdited({rect:this.getRect(0,0)}),null}static canCreateNewEmptyEditor(){return!1}}class InkDrawOutliner{#last=new Float64Array(6);#line;#lines;#rotation;#thickness;#points;#lastSVGPath="";#lastIndex=0;#outlines=new InkDrawOutline;#parentWidth;#parentHeight;constructor(x,y,parentWidth,parentHeight,rotation,thickness){
-this.#parentWidth=parentWidth,this.#parentHeight=parentHeight,this.#rotation=rotation,this.#thickness=thickness,[x,y]=this.#normalizePoint(x,y);const line=this.#line=[NaN,NaN,NaN,NaN,x,y];this.#points=[x,y],this.#lines=[{line,points:this.#points}],this.#last.set(line,0)}updateProperty(name,value){name==="stroke-width"&&(this.#thickness=value)}#normalizePoint(x,y){return Outline._normalizePoint(x,y,this.#parentWidth,this.#parentHeight,this.#rotation)}isEmpty(){return!this.#lines||this.#lines.length===
-0}isCancellable(){return this.#points.length<=10}add(x,y){[x,y]=this.#normalizePoint(x,y);const[x1,y1,x2,y2]=this.#last.subarray(2,6),diffX=x-x2,diffY=y-y2;return Math.hypot(this.#parentWidth*diffX,this.#parentHeight*diffY)<=2?null:(this.#points.push(x,y),isNaN(x1)?(this.#last.set([x2,y2,x,y],2),this.#line.push(NaN,NaN,NaN,NaN,x,y),{path:{d:this.toSVGPath()}}):(isNaN(this.#last[0])&&this.#line.splice(6,6),this.#last.set([x1,y1,x2,y2,x,y],0),this.#line.push(...Outline.createBezierPoints(x1,y1,x2,
-y2,x,y)),{path:{d:this.toSVGPath()}}))}end(x,y){const change=this.add(x,y);return change||(this.#points.length===2?{path:{d:this.toSVGPath()}}:null)}startNew(x,y,parentWidth,parentHeight,rotation){this.#parentWidth=parentWidth,this.#parentHeight=parentHeight,this.#rotation=rotation,[x,y]=this.#normalizePoint(x,y);const line=this.#line=[NaN,NaN,NaN,NaN,x,y];this.#points=[x,y];const last=this.#lines.at(-1);return last&&(last.line=new Float32Array(last.line),last.points=new Float32Array(last.points)),
-this.#lines.push({line,points:this.#points}),this.#last.set(line,0),this.#lastIndex=0,this.toSVGPath(),null}getLastElement(){return this.#lines.at(-1)}setLastElement(element2){return this.#lines?(this.#lines.push(element2),this.#line=element2.line,this.#points=element2.points,this.#lastIndex=0,{path:{d:this.toSVGPath()}}):this.#outlines.setLastElement(element2)}removeLastElement(){if(!this.#lines)return this.#outlines.removeLastElement();this.#lines.pop(),this.#lastSVGPath="";for(let i=0,ii=this.#lines.
-length;i<ii;i++){const{line,points}=this.#lines[i];this.#line=line,this.#points=points,this.#lastIndex=0,this.toSVGPath()}return{path:{d:this.#lastSVGPath}}}toSVGPath(){const firstX=Outline.svgRound(this.#line[4]),firstY=Outline.svgRound(this.#line[5]);if(this.#points.length===2)return this.#lastSVGPath=`${this.#lastSVGPath} M ${firstX} ${firstY} Z`,this.#lastSVGPath;if(this.#points.length<=6){const i=this.#lastSVGPath.lastIndexOf("M");this.#lastSVGPath=`${this.#lastSVGPath.slice(0,i)} M ${firstX}\
- ${firstY}`,this.#lastIndex=6}if(this.#points.length===4){const secondX=Outline.svgRound(this.#line[10]),secondY=Outline.svgRound(this.#line[11]);return this.#lastSVGPath=`${this.#lastSVGPath} L ${secondX} ${secondY}`,this.#lastIndex=12,this.#lastSVGPath}const buffer=[];this.#lastIndex===0&&(buffer.push(`M ${firstX} ${firstY}`),this.#lastIndex=6);for(let i=this.#lastIndex,ii=this.#line.length;i<ii;i+=6){const[c1x,c1y,c2x,c2y,x,y]=this.#line.slice(i,i+6).map(Outline.svgRound);buffer.push(`C${c1x}\
- ${c1y} ${c2x} ${c2y} ${x} ${y}`)}return this.#lastSVGPath+=buffer.join(" "),this.#lastIndex=this.#line.length,this.#lastSVGPath}getOutlines(parentWidth,parentHeight,scale2,innerMargin){const last=this.#lines.at(-1);return last.line=new Float32Array(last.line),last.points=new Float32Array(last.points),this.#outlines.build(this.#lines,parentWidth,parentHeight,scale2,this.#rotation,this.#thickness,innerMargin),this.#last=null,this.#line=null,this.#lines=null,this.#lastSVGPath=null,this.#outlines}get defaultSVGProperties(){
-return{root:{viewBox:"0 0 10000 10000"},rootClass:{draw:!0},bbox:[0,0,1,1]}}}class InkDrawOutline extends Outline{#bbox;#currentRotation=0;#innerMargin;#lines;#parentWidth;#parentHeight;#parentScale;#rotation;#thickness;build(lines,parentWidth,parentHeight,parentScale,rotation,thickness,innerMargin){this.#parentWidth=parentWidth,this.#parentHeight=parentHeight,this.#parentScale=parentScale,this.#rotation=rotation,this.#thickness=thickness,this.#innerMargin=innerMargin??0,this.#lines=lines,this.#computeBbox()}get thickness(){
-return this.#thickness}setLastElement(element2){return this.#lines.push(element2),{path:{d:this.toSVGPath()}}}removeLastElement(){return this.#lines.pop(),{path:{d:this.toSVGPath()}}}toSVGPath(){const buffer=[];for(const{line}of this.#lines){if(buffer.push(`M${Outline.svgRound(line[4])} ${Outline.svgRound(line[5])}`),line.length===6){buffer.push("Z");continue}if(line.length===12&&isNaN(line[6])){buffer.push(`L${Outline.svgRound(line[10])} ${Outline.svgRound(line[11])}`);continue}for(let i=6,ii=line.
-length;i<ii;i+=6){const[c1x,c1y,c2x,c2y,x,y]=line.subarray(i,i+6).map(Outline.svgRound);buffer.push(`C${c1x} ${c1y} ${c2x} ${c2y} ${x} ${y}`)}}return buffer.join("")}serialize([pageX,pageY,pageWidth,pageHeight],isForCopying){const serializedLines=[],serializedPoints=[],[x,y,width,height]=this.#getBBoxWithNoMargin();let tx,ty,sx,sy,x1,y1,x2,y2,rescaleFn;switch(this.#rotation){case 0:rescaleFn=Outline._rescale,tx=pageX,ty=pageY+pageHeight,sx=pageWidth,sy=-pageHeight,x1=pageX+x*pageWidth,y1=pageY+(1-
-y-height)*pageHeight,x2=pageX+(x+width)*pageWidth,y2=pageY+(1-y)*pageHeight;break;case 90:rescaleFn=Outline._rescaleAndSwap,tx=pageX,ty=pageY,sx=pageWidth,sy=pageHeight,x1=pageX+y*pageWidth,y1=pageY+x*pageHeight,x2=pageX+(y+height)*pageWidth,y2=pageY+(x+width)*pageHeight;break;case 180:rescaleFn=Outline._rescale,tx=pageX+pageWidth,ty=pageY,sx=-pageWidth,sy=pageHeight,x1=pageX+(1-x-width)*pageWidth,y1=pageY+y*pageHeight,x2=pageX+(1-x)*pageWidth,y2=pageY+(y+height)*pageHeight;break;case 270:rescaleFn=
-Outline._rescaleAndSwap,tx=pageX+pageWidth,ty=pageY+pageHeight,sx=-pageWidth,sy=-pageHeight,x1=pageX+(1-y-height)*pageWidth,y1=pageY+(1-x-width)*pageHeight,x2=pageX+(1-y)*pageWidth,y2=pageY+(1-x)*pageHeight;break}for(const{line,points}of this.#lines)serializedLines.push(rescaleFn(line,tx,ty,sx,sy,isForCopying?new Array(line.length):null)),serializedPoints.push(rescaleFn(points,tx,ty,sx,sy,isForCopying?new Array(points.length):null));return{lines:serializedLines,points:serializedPoints,rect:[x1,y1,
-x2,y2]}}static deserialize(pageX,pageY,pageWidth,pageHeight,innerMargin,{paths:{lines,points},rotation,thickness}){const newLines=[];let tx,ty,sx,sy,rescaleFn;switch(rotation){case 0:rescaleFn=Outline._rescale,tx=-pageX/pageWidth,ty=pageY/pageHeight+1,sx=1/pageWidth,sy=-1/pageHeight;break;case 90:rescaleFn=Outline._rescaleAndSwap,tx=-pageY/pageHeight,ty=-pageX/pageWidth,sx=1/pageHeight,sy=1/pageWidth;break;case 180:rescaleFn=Outline._rescale,tx=pageX/pageWidth+1,ty=-pageY/pageHeight,sx=-1/pageWidth,
-sy=1/pageHeight;break;case 270:rescaleFn=Outline._rescaleAndSwap,tx=pageY/pageHeight+1,ty=pageX/pageWidth+1,sx=-1/pageHeight,sy=-1/pageWidth;break}if(!lines){lines=[];for(const point2 of points){const len=point2.length;if(len===2){lines.push(new Float32Array([NaN,NaN,NaN,NaN,point2[0],point2[1]]));continue}if(len===4){lines.push(new Float32Array([NaN,NaN,NaN,NaN,point2[0],point2[1],NaN,NaN,NaN,NaN,point2[2],point2[3]]));continue}const line=new Float32Array(3*(len-2));lines.push(line);let[x1,y1,x2,
-y2]=point2.subarray(0,4);line.set([NaN,NaN,NaN,NaN,x1,y1],0);for(let i=4;i<len;i+=2){const x=point2[i],y=point2[i+1];line.set(Outline.createBezierPoints(x1,y1,x2,y2,x,y),(i-2)*3),[x1,y1,x2,y2]=[x2,y2,x,y]}}}for(let i=0,ii=lines.length;i<ii;i++)newLines.push({line:rescaleFn(lines[i].map(x=>x??NaN),tx,ty,sx,sy),points:rescaleFn(points[i].map(x=>x??NaN),tx,ty,sx,sy)});const outlines=new this.prototype.constructor;return outlines.build(newLines,pageWidth,pageHeight,1,rotation,thickness,innerMargin),
-outlines}#getMarginComponents(thickness=this.#thickness){const margin=this.#innerMargin+thickness/2*this.#parentScale;return this.#rotation%180===0?[margin/this.#parentWidth,margin/this.#parentHeight]:[margin/this.#parentHeight,margin/this.#parentWidth]}#getBBoxWithNoMargin(){const[x,y,width,height]=this.#bbox,[marginX,marginY]=this.#getMarginComponents(0);return[x+marginX,y+marginY,width-2*marginX,height-2*marginY]}#computeBbox(){const bbox=this.#bbox=new Float32Array([1/0,1/0,-1/0,-1/0]);for(const{
-line}of this.#lines){if(line.length<=12){for(let i=4,ii=line.length;i<ii;i+=6)Util.pointBoundingBox(line[i],line[i+1],bbox);continue}let lastX=line[4],lastY=line[5];for(let i=6,ii=line.length;i<ii;i+=6){const[c1x,c1y,c2x,c2y,x,y]=line.subarray(i,i+6);Util.bezierBoundingBox(lastX,lastY,c1x,c1y,c2x,c2y,x,y,bbox),lastX=x,lastY=y}}const[marginX,marginY]=this.#getMarginComponents();bbox[0]=MathClamp(bbox[0]-marginX,0,1),bbox[1]=MathClamp(bbox[1]-marginY,0,1),bbox[2]=MathClamp(bbox[2]+marginX,0,1),bbox[3]=
-MathClamp(bbox[3]+marginY,0,1),bbox[2]-=bbox[0],bbox[3]-=bbox[1]}get box(){return this.#bbox}updateProperty(name,value){return name==="stroke-width"?this.#updateThickness(value):null}#updateThickness(thickness){const[oldMarginX,oldMarginY]=this.#getMarginComponents();this.#thickness=thickness;const[newMarginX,newMarginY]=this.#getMarginComponents(),[diffMarginX,diffMarginY]=[newMarginX-oldMarginX,newMarginY-oldMarginY],bbox=this.#bbox;return bbox[0]-=diffMarginX,bbox[1]-=diffMarginY,bbox[2]+=2*diffMarginX,
-bbox[3]+=2*diffMarginY,bbox}updateParentDimensions([width,height],scale2){const[oldMarginX,oldMarginY]=this.#getMarginComponents();this.#parentWidth=width,this.#parentHeight=height,this.#parentScale=scale2;const[newMarginX,newMarginY]=this.#getMarginComponents(),diffMarginX=newMarginX-oldMarginX,diffMarginY=newMarginY-oldMarginY,bbox=this.#bbox;return bbox[0]-=diffMarginX,bbox[1]-=diffMarginY,bbox[2]+=2*diffMarginX,bbox[3]+=2*diffMarginY,bbox}updateRotation(rotation){return this.#currentRotation=
-rotation,{path:{transform:this.rotationTransform}}}get viewBox(){return this.#bbox.map(Outline.svgRound).join(" ")}get defaultProperties(){const[x,y]=this.#bbox;return{root:{viewBox:this.viewBox},path:{"transform-origin":`${Outline.svgRound(x)} ${Outline.svgRound(y)}`}}}get rotationTransform(){const[,,width,height]=this.#bbox;let a=0,b=0,c2=0,d2=0,e=0,f=0;switch(this.#currentRotation){case 90:b=height/width,c2=-width/height,e=width;break;case 180:a=-1,d2=-1,e=width,f=height;break;case 270:b=-height/
-width,c2=width/height,f=height;break;default:return""}return`matrix(${a} ${b} ${c2} ${d2} ${Outline.svgRound(e)} ${Outline.svgRound(f)})`}getPathResizingSVGProperties([newX,newY,newWidth,newHeight]){const[marginX,marginY]=this.#getMarginComponents(),[x,y,width,height]=this.#bbox;if(Math.abs(width-marginX)<=Outline.PRECISION||Math.abs(height-marginY)<=Outline.PRECISION){const tx=newX+newWidth/2-(x+width/2),ty=newY+newHeight/2-(y+height/2);return{path:{"transform-origin":`${Outline.svgRound(newX)}\
- ${Outline.svgRound(newY)}`,transform:`${this.rotationTransform} translate(${tx} ${ty})`}}}const s1x=(newWidth-2*marginX)/(width-2*marginX),s1y=(newHeight-2*marginY)/(height-2*marginY),s2x=width/newWidth,s2y=height/newHeight;return{path:{"transform-origin":`${Outline.svgRound(x)} ${Outline.svgRound(y)}`,transform:`${this.rotationTransform} scale(${s2x} ${s2y}) translate(${Outline.svgRound(marginX)} ${Outline.svgRound(marginY)}) scale(${s1x} ${s1y}) translate(${Outline.svgRound(-marginX)} ${Outline.
-svgRound(-marginY)})`}}}getPathResizedSVGProperties([newX,newY,newWidth,newHeight]){const[marginX,marginY]=this.#getMarginComponents(),bbox=this.#bbox,[x,y,width,height]=bbox;if(bbox[0]=newX,bbox[1]=newY,bbox[2]=newWidth,bbox[3]=newHeight,Math.abs(width-marginX)<=Outline.PRECISION||Math.abs(height-marginY)<=Outline.PRECISION){const tx2=newX+newWidth/2-(x+width/2),ty2=newY+newHeight/2-(y+height/2);for(const{line,points}of this.#lines)Outline._translate(line,tx2,ty2,line),Outline._translate(points,
-tx2,ty2,points);return{root:{viewBox:this.viewBox},path:{"transform-origin":`${Outline.svgRound(newX)} ${Outline.svgRound(newY)}`,transform:this.rotationTransform||null,d:this.toSVGPath()}}}const s1x=(newWidth-2*marginX)/(width-2*marginX),s1y=(newHeight-2*marginY)/(height-2*marginY),tx=-s1x*(x+marginX)+newX+marginX,ty=-s1y*(y+marginY)+newY+marginY;if(s1x!==1||s1y!==1||tx!==0||ty!==0)for(const{line,points}of this.#lines)Outline._rescale(line,tx,ty,s1x,s1y,line),Outline._rescale(points,tx,ty,s1x,s1y,
-points);return{root:{viewBox:this.viewBox},path:{"transform-origin":`${Outline.svgRound(newX)} ${Outline.svgRound(newY)}`,transform:this.rotationTransform||null,d:this.toSVGPath()}}}getPathTranslatedSVGProperties([newX,newY],parentDimensions){const[newParentWidth,newParentHeight]=parentDimensions,bbox=this.#bbox,tx=newX-bbox[0],ty=newY-bbox[1];if(this.#parentWidth===newParentWidth&&this.#parentHeight===newParentHeight)for(const{line,points}of this.#lines)Outline._translate(line,tx,ty,line),Outline.
-_translate(points,tx,ty,points);else{const sx=this.#parentWidth/newParentWidth,sy=this.#parentHeight/newParentHeight;this.#parentWidth=newParentWidth,this.#parentHeight=newParentHeight;for(const{line,points}of this.#lines)Outline._rescale(line,tx,ty,sx,sy,line),Outline._rescale(points,tx,ty,sx,sy,points);bbox[2]*=sx,bbox[3]*=sy}return bbox[0]=newX,bbox[1]=newY,{root:{viewBox:this.viewBox},path:{d:this.toSVGPath(),"transform-origin":`${Outline.svgRound(newX)} ${Outline.svgRound(newY)}`}}}get defaultSVGProperties(){
-const bbox=this.#bbox;return{root:{viewBox:this.viewBox},rootClass:{draw:!0},path:{d:this.toSVGPath(),"transform-origin":`${Outline.svgRound(bbox[0])} ${Outline.svgRound(bbox[1])}`,transform:this.rotationTransform||null},bbox}}}class InkDrawingOptions extends DrawingOptions{constructor(viewerParameters){super(),this._viewParameters=viewerParameters,super.updateProperties({fill:"none",stroke:AnnotationEditor._defaultLineColor,"stroke-opacity":1,"stroke-width":1,"stroke-linecap":"round","stroke-li\
-nejoin":"round","stroke-miterlimit":10})}updateSVGProperty(name,value){name==="stroke-width"&&(value??=this["stroke-width"],value*=this._viewParameters.realScale),super.updateSVGProperty(name,value)}clone(){const clone2=new InkDrawingOptions(this._viewParameters);return clone2.updateAll(this),clone2}}class InkEditor extends DrawingEditor{static _type="ink";static _editorType=AnnotationEditorType.INK;static _defaultDrawingOptions=null;constructor(params){super({...params,name:"inkEditor"}),this._willKeepAspectRatio=
-!0,this.defaultL10nId="pdfjs-editor-ink-editor"}static initialize(l10n,uiManager){AnnotationEditor.initialize(l10n,uiManager),this._defaultDrawingOptions=new InkDrawingOptions(uiManager.viewParameters)}static getDefaultDrawingOptions(options){const clone2=this._defaultDrawingOptions.clone();return clone2.updateProperties(options),clone2}static get supportMultipleDrawings(){return!0}static get typesMap(){return shadow(this,"typesMap",new Map([[AnnotationEditorParamsType.INK_THICKNESS,"stroke-widt\
-h"],[AnnotationEditorParamsType.INK_COLOR,"stroke"],[AnnotationEditorParamsType.INK_OPACITY,"stroke-opacity"]]))}static createDrawerInstance(x,y,parentWidth,parentHeight,rotation){return new InkDrawOutliner(x,y,parentWidth,parentHeight,rotation,this._defaultDrawingOptions["stroke-width"])}static deserializeDraw(pageX,pageY,pageWidth,pageHeight,innerMargin,data){return InkDrawOutline.deserialize(pageX,pageY,pageWidth,pageHeight,innerMargin,data)}static async deserialize(data,parent,uiManager){let initialData=null;
-if(data instanceof InkAnnotationElement){const{data:{inkLists,rect,rotation,id:id2,color,opacity,borderStyle:{rawWidth:thickness},popupRef,contentsObj},parent:{page:{pageNumber}}}=data;initialData=data={annotationType:AnnotationEditorType.INK,color:Array.from(color),thickness,opacity,paths:{points:inkLists},boxes:null,pageIndex:pageNumber-1,rect:rect.slice(0),rotation,annotationElementId:id2,id:id2,deleted:!1,popupRef,comment:contentsObj?.str||null}}const editor=await super.deserialize(data,parent,
-uiManager);return editor._initialData=initialData,data.comment&&editor.setCommentData(data.comment),editor}get toolbarButtons(){return this._colorPicker||=new BasicColorPicker(this),[["colorPicker",this._colorPicker]]}get colorType(){return AnnotationEditorParamsType.INK_COLOR}get colorValue(){return this._drawingOptions.stroke}onScaleChanging(){if(!this.parent)return;super.onScaleChanging();const{_drawId,_drawingOptions,parent}=this;_drawingOptions.updateSVGProperty("stroke-width"),parent.drawLayer.
-updateProperties(_drawId,_drawingOptions.toSVGProperties())}static onScaleChangingWhenDrawing(){const parent=this._currentParent;parent&&(super.onScaleChangingWhenDrawing(),this._defaultDrawingOptions.updateSVGProperty("stroke-width"),parent.drawLayer.updateProperties(this._currentDrawId,this._defaultDrawingOptions.toSVGProperties()))}createDrawingOptions({color,thickness,opacity}){this._drawingOptions=InkEditor.getDefaultDrawingOptions({stroke:Util.makeHexColor(...color),"stroke-width":thickness,
-"stroke-opacity":opacity})}serialize(isForCopying=!1){if(this.isEmpty())return null;if(this.deleted)return this.serializeDeleted();const{lines,points,rect}=this.serializeDraw(isForCopying),{_drawingOptions:{stroke,"stroke-opacity":opacity,"stroke-width":thickness}}=this,serialized={annotationType:AnnotationEditorType.INK,color:AnnotationEditor._colorManager.convert(stroke),opacity,thickness,paths:{lines,points},pageIndex:this.pageIndex,rect,rotation:this.rotation,structTreeParentId:this._structTreeParentId};
-return this.addComment(serialized),isForCopying?(serialized.isCopy=!0,serialized):this.annotationElementId&&!this.#hasElementChanged(serialized)?null:(serialized.id=this.annotationElementId,serialized)}#hasElementChanged(serialized){const{color,thickness,opacity,pageIndex}=this._initialData;return this.hasEditedComment||this._hasBeenMoved||this._hasBeenResized||serialized.color.some((c2,i)=>c2!==color[i])||serialized.thickness!==thickness||serialized.opacity!==opacity||serialized.pageIndex!==pageIndex}renderAnnotationElement(annotation){
-const{points,rect}=this.serializeDraw(!1),params={rect,thickness:this._drawingOptions["stroke-width"],points};return this.hasEditedComment&&(params.popup=this.comment),annotation.updateEdited(params),null}}class ContourDrawOutline extends InkDrawOutline{toSVGPath(){let path2=super.toSVGPath();return path2.endsWith("Z")||(path2+="Z"),path2}}const BASE_HEADER_LENGTH=8,POINTS_PROPERTIES_NUMBER=3;class SignatureExtractor{static#PARAMETERS={maxDim:512,sigmaSFactor:.02,sigmaR:25,kernelSize:16};static#neighborIndexToId(i0,j0,i,j){
-return i-=i0,j-=j0,i===0?j>0?0:4:i===1?j+6:2-j}static#neighborIdToIndex=new Int32Array([0,1,-1,1,-1,0,-1,-1,0,-1,1,-1,1,0,1,1]);static#clockwiseNonZero(buf,width,i0,j0,i,j,offset2){const id2=this.#neighborIndexToId(i0,j0,i,j);for(let k=0;k<8;k++){const kk=(-k+id2-offset2+16)%8,shiftI=this.#neighborIdToIndex[2*kk],shiftJ=this.#neighborIdToIndex[2*kk+1];if(buf[(i0+shiftI)*width+(j0+shiftJ)]!==0)return kk}return-1}static#counterClockwiseNonZero(buf,width,i0,j0,i,j,offset2){const id2=this.#neighborIndexToId(
-i0,j0,i,j);for(let k=0;k<8;k++){const kk=(k+id2+offset2+16)%8,shiftI=this.#neighborIdToIndex[2*kk],shiftJ=this.#neighborIdToIndex[2*kk+1];if(buf[(i0+shiftI)*width+(j0+shiftJ)]!==0)return kk}return-1}static#findContours(buf,width,height,threshold){const N=buf.length,types2=new Int32Array(N);for(let i=0;i<N;i++)types2[i]=buf[i]<=threshold?1:0;for(let i=1;i<height-1;i++)types2[i*width]=types2[i*width+width-1]=0;for(let i=0;i<width;i++)types2[i]=types2[width*height-1-i]=0;let nbd=1,lnbd;const contours=[];
-for(let i=1;i<height-1;i++){lnbd=1;for(let j=1;j<width-1;j++){const ij=i*width+j,pix=types2[ij];if(pix===0)continue;let i2=i,j2=j;if(pix===1&&types2[ij-1]===0)nbd+=1,j2-=1;else if(pix>=1&&types2[ij+1]===0)nbd+=1,j2+=1,pix>1&&(lnbd=pix);else{pix!==1&&(lnbd=Math.abs(pix));continue}const points=[j,i],isHole=j2===j+1,contour={isHole,points,id:nbd,parent:0};contours.push(contour);let contour0;for(const c2 of contours)if(c2.id===lnbd){contour0=c2;break}contour0?contour0.isHole?contour.parent=isHole?contour0.
-parent:lnbd:contour.parent=isHole?lnbd:contour0.parent:contour.parent=isHole?lnbd:0;const k=this.#clockwiseNonZero(types2,width,i,j,i2,j2,0);if(k===-1){types2[ij]=-nbd,types2[ij]!==1&&(lnbd=Math.abs(types2[ij]));continue}let shiftI=this.#neighborIdToIndex[2*k],shiftJ=this.#neighborIdToIndex[2*k+1];const i1=i+shiftI,j1=j+shiftJ;i2=i1,j2=j1;let i3=i,j3=j;for(;;){const kk=this.#counterClockwiseNonZero(types2,width,i3,j3,i2,j2,1);shiftI=this.#neighborIdToIndex[2*kk],shiftJ=this.#neighborIdToIndex[2*
-kk+1];const i4=i3+shiftI,j4=j3+shiftJ;points.push(j4,i4);const ij3=i3*width+j3;if(types2[ij3+1]===0?types2[ij3]=-nbd:types2[ij3]===1&&(types2[ij3]=nbd),i4===i&&j4===j&&i3===i1&&j3===j1){types2[ij]!==1&&(lnbd=Math.abs(types2[ij]));break}else i2=i3,j2=j3,i3=i4,j3=j4}}}return contours}static#douglasPeuckerHelper(points,start2,end,output){if(end-start2<=4){for(let i=start2;i<end-2;i+=2)output.push(points[i],points[i+1]);return}const ax=points[start2],ay=points[start2+1],abx=points[end-4]-ax,aby=points[end-
-3]-ay,dist2=Math.hypot(abx,aby),nabx=abx/dist2,naby=aby/dist2,aa=nabx*ay-naby*ax,m=aby/abx,invS=1/dist2,phi=Math.atan(m),cosPhi=Math.cos(phi),sinPhi=Math.sin(phi),tmax=invS*(Math.abs(cosPhi)+Math.abs(sinPhi)),poly=invS*(1-tmax+tmax**2),partialPhi=Math.max(Math.atan(Math.abs(sinPhi+cosPhi)*poly),Math.atan(Math.abs(sinPhi-cosPhi)*poly));let dmax=0,index2=start2;for(let i=start2+2;i<end-2;i+=2){const d2=Math.abs(aa-nabx*points[i+1]+naby*points[i]);d2>dmax&&(index2=i,dmax=d2)}dmax>(dist2*partialPhi)**
-2?(this.#douglasPeuckerHelper(points,start2,index2+2,output),this.#douglasPeuckerHelper(points,index2,end,output)):output.push(ax,ay)}static#douglasPeucker(points){const output=[],len=points.length;return this.#douglasPeuckerHelper(points,0,len,output),output.push(points[len-2],points[len-1]),output.length<=4?null:output}static#bilateralFilter(buf,width,height,sigmaS,sigmaR,kernelSize){const kernel=new Float32Array(kernelSize**2),sigmaS2=-2*sigmaS**2,halfSize=kernelSize>>1;for(let i=0;i<kernelSize;i++){
-const x=(i-halfSize)**2;for(let j=0;j<kernelSize;j++)kernel[i*kernelSize+j]=Math.exp((x+(j-halfSize)**2)/sigmaS2)}const rangeValues=new Float32Array(256),sigmaR2=-2*sigmaR**2;for(let i=0;i<256;i++)rangeValues[i]=Math.exp(i**2/sigmaR2);const N=buf.length,out=new Uint8Array(N),histogram=new Uint32Array(256);for(let i=0;i<height;i++)for(let j=0;j<width;j++){const ij=i*width+j,center=buf[ij];let sum=0,norm=0;for(let k=0;k<kernelSize;k++){const y=i+k-halfSize;if(!(y<0||y>=height))for(let l=0;l<kernelSize;l++){
-const x=j+l-halfSize;if(x<0||x>=width)continue;const neighbour=buf[y*width+x],w=kernel[k*kernelSize+l]*rangeValues[Math.abs(neighbour-center)];sum+=neighbour*w,norm+=w}}const pix=out[ij]=Math.round(sum/norm);histogram[pix]++}return[out,histogram]}static#getHistogram(buf){const histogram=new Uint32Array(256);for(const g of buf)histogram[g]++;return histogram}static#toUint8(buf){const N=buf.length,out=new Uint8ClampedArray(N>>2);let max2=-1/0,min2=1/0;for(let i=0,ii=out.length;i<ii;i++){const pix=out[i]=
-buf[i<<2];max2=Math.max(max2,pix),min2=Math.min(min2,pix)}const ratio=255/(max2-min2);for(let i=0,ii=out.length;i<ii;i++)out[i]=(out[i]-min2)*ratio;return out}static#guessThreshold(histogram){let i,M=-1/0,L=-1/0;const min2=histogram.findIndex(v=>v!==0);let pos=min2,spos=min2;for(i=min2;i<256;i++){const v=histogram[i];v>M&&(i-pos>L&&(L=i-pos,spos=i-1),M=v,pos=i)}for(i=spos-1;i>=0&&!(histogram[i]>histogram[i+1]);i--);return i}static#getGrayPixels(bitmap){const originalBitmap=bitmap,{width,height}=bitmap,
-{maxDim}=this.#PARAMETERS;let newWidth=width,newHeight=height;if(width>maxDim||height>maxDim){let prevWidth=width,prevHeight=height,steps=Math.log2(Math.max(width,height)/maxDim);const isteps=Math.floor(steps);steps=steps===isteps?isteps-1:isteps;for(let i=0;i<steps;i++){newWidth=Math.ceil(prevWidth/2),newHeight=Math.ceil(prevHeight/2);const offscreen2=new OffscreenCanvas(newWidth,newHeight);offscreen2.getContext("2d").drawImage(bitmap,0,0,prevWidth,prevHeight,0,0,newWidth,newHeight),prevWidth=newWidth,
-prevHeight=newHeight,bitmap!==originalBitmap&&bitmap.close(),bitmap=offscreen2.transferToImageBitmap()}const ratio=Math.min(maxDim/newWidth,maxDim/newHeight);newWidth=Math.round(newWidth*ratio),newHeight=Math.round(newHeight*ratio)}const ctx=new OffscreenCanvas(newWidth,newHeight).getContext("2d",{willReadFrequently:!0});ctx.fillStyle="white",ctx.fillRect(0,0,newWidth,newHeight),ctx.filter="grayscale(1)",ctx.drawImage(bitmap,0,0,bitmap.width,bitmap.height,0,0,newWidth,newHeight);const grayImage=ctx.
-getImageData(0,0,newWidth,newHeight).data;return[this.#toUint8(grayImage),newWidth,newHeight]}static extractContoursFromText(text2,{fontFamily,fontStyle,fontWeight},pageWidth,pageHeight,rotation,innerMargin){let canvas=new OffscreenCanvas(1,1),ctx=canvas.getContext("2d",{alpha:!1});const fontSize=200,font=ctx.font=`${fontStyle} ${fontWeight} ${fontSize}px ${fontFamily}`,{actualBoundingBoxLeft,actualBoundingBoxRight,actualBoundingBoxAscent,actualBoundingBoxDescent,fontBoundingBoxAscent,fontBoundingBoxDescent,
-width}=ctx.measureText(text2),SCALE=1.5,canvasWidth=Math.ceil(Math.max(Math.abs(actualBoundingBoxLeft)+Math.abs(actualBoundingBoxRight)||0,width)*SCALE),canvasHeight=Math.ceil(Math.max(Math.abs(actualBoundingBoxAscent)+Math.abs(actualBoundingBoxDescent)||fontSize,Math.abs(fontBoundingBoxAscent)+Math.abs(fontBoundingBoxDescent)||fontSize)*SCALE);canvas=new OffscreenCanvas(canvasWidth,canvasHeight),ctx=canvas.getContext("2d",{alpha:!0,willReadFrequently:!0}),ctx.font=font,ctx.filter="grayscale(1)",
-ctx.fillStyle="white",ctx.fillRect(0,0,canvasWidth,canvasHeight),ctx.fillStyle="black",ctx.fillText(text2,canvasWidth*(SCALE-1)/2,canvasHeight*(3-SCALE)/2);const uint8Buf=this.#toUint8(ctx.getImageData(0,0,canvasWidth,canvasHeight).data),histogram=this.#getHistogram(uint8Buf),threshold=this.#guessThreshold(histogram),contourList=this.#findContours(uint8Buf,canvasWidth,canvasHeight,threshold);return this.processDrawnLines({lines:{curves:contourList,width:canvasWidth,height:canvasHeight},pageWidth,
-pageHeight,rotation,innerMargin,mustSmooth:!0,areContours:!0})}static process(bitmap,pageWidth,pageHeight,rotation,innerMargin){const[uint8Buf,width,height]=this.#getGrayPixels(bitmap),[buffer,histogram]=this.#bilateralFilter(uint8Buf,width,height,Math.hypot(width,height)*this.#PARAMETERS.sigmaSFactor,this.#PARAMETERS.sigmaR,this.#PARAMETERS.kernelSize),threshold=this.#guessThreshold(histogram),contourList=this.#findContours(buffer,width,height,threshold);return this.processDrawnLines({lines:{curves:contourList,
-width,height},pageWidth,pageHeight,rotation,innerMargin,mustSmooth:!0,areContours:!0})}static processDrawnLines({lines,pageWidth,pageHeight,rotation,innerMargin,mustSmooth,areContours}){rotation%180!==0&&([pageWidth,pageHeight]=[pageHeight,pageWidth]);const{curves,width,height}=lines,thickness=lines.thickness??0,linesAndPoints=[],ratio=Math.min(pageWidth/width,pageHeight/height),xScale=ratio/pageWidth,yScale=ratio/pageHeight,newCurves=[];for(const{points}of curves){const reducedPoints=mustSmooth?
-this.#douglasPeucker(points):points;if(!reducedPoints)continue;newCurves.push(reducedPoints);const len=reducedPoints.length,newPoints=new Float32Array(len),line=new Float32Array(3*(len===2?2:len-2));if(linesAndPoints.push({line,points:newPoints}),len===2){newPoints[0]=reducedPoints[0]*xScale,newPoints[1]=reducedPoints[1]*yScale,line.set([NaN,NaN,NaN,NaN,newPoints[0],newPoints[1]],0);continue}let[x1,y1,x2,y2]=reducedPoints;x1*=xScale,y1*=yScale,x2*=xScale,y2*=yScale,newPoints.set([x1,y1,x2,y2],0),
-line.set([NaN,NaN,NaN,NaN,x1,y1],0);for(let i=4;i<len;i+=2){const x=newPoints[i]=reducedPoints[i]*xScale,y=newPoints[i+1]=reducedPoints[i+1]*yScale;line.set(Outline.createBezierPoints(x1,y1,x2,y2,x,y),(i-2)*3),[x1,y1,x2,y2]=[x2,y2,x,y]}}if(linesAndPoints.length===0)return null;const outline=areContours?new ContourDrawOutline:new InkDrawOutline;return outline.build(linesAndPoints,pageWidth,pageHeight,1,rotation,areContours?0:thickness,innerMargin),{outline,newCurves,areContours,thickness,width,height}}static async compressSignature({
-outlines,areContours,thickness,width,height}){let minDiff=1/0,maxDiff=-1/0,outlinesLength=0;for(const points of outlines){outlinesLength+=points.length;for(let i=2,ii=points.length;i<ii;i++){const dx=points[i]-points[i-2];minDiff=Math.min(minDiff,dx),maxDiff=Math.max(maxDiff,dx)}}let bufferType;minDiff>=-128&&maxDiff<=127?bufferType=Int8Array:minDiff>=-32768&&maxDiff<=32767?bufferType=Int16Array:bufferType=Int32Array;const len=outlines.length,headerLength=BASE_HEADER_LENGTH+POINTS_PROPERTIES_NUMBER*
-len,header=new Uint32Array(headerLength);let offset2=0;header[offset2++]=headerLength*Uint32Array.BYTES_PER_ELEMENT+(outlinesLength-2*len)*bufferType.BYTES_PER_ELEMENT,header[offset2++]=0,header[offset2++]=width,header[offset2++]=height,header[offset2++]=areContours?0:1,header[offset2++]=Math.max(0,Math.floor(thickness??0)),header[offset2++]=len,header[offset2++]=bufferType.BYTES_PER_ELEMENT;for(const points of outlines)header[offset2++]=points.length-2,header[offset2++]=points[0],header[offset2++]=
-points[1];const cs=new CompressionStream("deflate-raw"),writer=cs.writable.getWriter();await writer.ready,writer.write(header);const BufferCtor=bufferType.prototype.constructor;for(const points of outlines){const diffs=new BufferCtor(points.length-2);for(let i=2,ii=points.length;i<ii;i++)diffs[i-2]=points[i]-points[i-2];writer.write(diffs)}writer.close();const buf=await new Response(cs.readable).arrayBuffer(),bytes=new Uint8Array(buf);return toBase64Util(bytes)}static async decompressSignature(signatureData){
-try{const bytes=fromBase64Util(signatureData),{readable,writable:writable2}=new DecompressionStream("deflate-raw"),writer=writable2.getWriter();await writer.ready,writer.write(bytes).then(async()=>{await writer.ready,await writer.close()}).catch(()=>{});let data=null,offset2=0;for await(const chunk of readable)data||=new Uint8Array(new Uint32Array(chunk.buffer,0,4)[0]),data.set(chunk,offset2),offset2+=chunk.length;const header=new Uint32Array(data.buffer,0,data.length>>2),version2=header[1];if(version2!==
-0)throw new Error(`Invalid version: ${version2}`);const width=header[2],height=header[3],areContours=header[4]===0,thickness=header[5],numberOfDrawings=header[6],bufferType=header[7],outlines=[],diffsOffset=(BASE_HEADER_LENGTH+POINTS_PROPERTIES_NUMBER*numberOfDrawings)*Uint32Array.BYTES_PER_ELEMENT;let diffs;switch(bufferType){case Int8Array.BYTES_PER_ELEMENT:diffs=new Int8Array(data.buffer,diffsOffset);break;case Int16Array.BYTES_PER_ELEMENT:diffs=new Int16Array(data.buffer,diffsOffset);break;case Int32Array.
-BYTES_PER_ELEMENT:diffs=new Int32Array(data.buffer,diffsOffset);break}offset2=0;for(let i=0;i<numberOfDrawings;i++){const len=header[POINTS_PROPERTIES_NUMBER*i+BASE_HEADER_LENGTH],points=new Float32Array(len+2);outlines.push(points);for(let j=0;j<POINTS_PROPERTIES_NUMBER-1;j++)points[j]=header[POINTS_PROPERTIES_NUMBER*i+BASE_HEADER_LENGTH+j+1];for(let j=0;j<len;j++)points[j+2]=points[j]+diffs[offset2++]}return{areContours,thickness,outlines,width,height}}catch(e){return warn(`decompressSignature\
-: ${e}`),null}}}class SignatureOptions extends DrawingOptions{constructor(){super(),super.updateProperties({fill:AnnotationEditor._defaultLineColor,"stroke-width":0})}clone(){const clone2=new SignatureOptions;return clone2.updateAll(this),clone2}}class DrawnSignatureOptions extends InkDrawingOptions{constructor(viewerParameters){super(viewerParameters),super.updateProperties({stroke:AnnotationEditor._defaultLineColor,"stroke-width":1})}clone(){const clone2=new DrawnSignatureOptions(this._viewParameters);
-return clone2.updateAll(this),clone2}}class SignatureEditor extends DrawingEditor{#isExtracted=!1;#description=null;#signatureData=null;#signatureUUID=null;static _type="signature";static _editorType=AnnotationEditorType.SIGNATURE;static _defaultDrawingOptions=null;constructor(params){super({...params,mustBeCommitted:!0,name:"signatureEditor"}),this._willKeepAspectRatio=!0,this.#signatureData=params.signatureData||null,this.#description=null,this.defaultL10nId="pdfjs-editor-signature-editor1"}static initialize(l10n,uiManager){
-AnnotationEditor.initialize(l10n,uiManager),this._defaultDrawingOptions=new SignatureOptions,this._defaultDrawnSignatureOptions=new DrawnSignatureOptions(uiManager.viewParameters)}static getDefaultDrawingOptions(options){const clone2=this._defaultDrawingOptions.clone();return clone2.updateProperties(options),clone2}static get supportMultipleDrawings(){return!1}static get typesMap(){return shadow(this,"typesMap",new Map)}static get isDrawer(){return!1}get telemetryFinalData(){return{type:"signatu\
-re",hasDescription:!!this.#description}}static computeTelemetryFinalData(data){const hasDescriptionStats=data.get("hasDescription");return{hasAltText:hasDescriptionStats.get(!0)??0,hasNoAltText:hasDescriptionStats.get(!1)??0}}get isResizable(){return!0}onScaleChanging(){this._drawId!==null&&super.onScaleChanging()}render(){if(this.div)return this.div;let baseX,baseY;const{_isCopy}=this;if(_isCopy&&(this._isCopy=!1,baseX=this.x,baseY=this.y),super.render(),this._drawId===null)if(this.#signatureData){
-const{lines,mustSmooth,areContours,description:description2,uuid:uuid2,heightInPage}=this.#signatureData,{rawDims:{pageWidth,pageHeight},rotation}=this.parent.viewport,outline=SignatureExtractor.processDrawnLines({lines,pageWidth,pageHeight,rotation,innerMargin:SignatureEditor._INNER_MARGIN,mustSmooth,areContours});this.addSignature(outline,heightInPage,description2,uuid2)}else this.div.setAttribute("data-l10n-args",JSON.stringify({description:""})),this.div.hidden=!0,this._uiManager.getSignature(
-this);else this.div.setAttribute("data-l10n-args",JSON.stringify({description:this.#description||""}));return _isCopy&&(this._isCopy=!0,this._moveAfterPaste(baseX,baseY)),this.div}setUuid(uuid2){this.#signatureUUID=uuid2,this.addEditToolbar()}getUuid(){return this.#signatureUUID}get description(){return this.#description}set description(description2){this.#description=description2,this.div&&(this.div.setAttribute("data-l10n-args",JSON.stringify({description:description2})),super.addEditToolbar().
-then(toolbar=>{toolbar?.updateEditSignatureButton(description2)}))}getSignaturePreview(){const{newCurves,areContours,thickness,width,height}=this.#signatureData,maxDim=Math.max(width,height),outlineData=SignatureExtractor.processDrawnLines({lines:{curves:newCurves.map(points=>({points})),thickness,width,height},pageWidth:maxDim,pageHeight:maxDim,rotation:0,innerMargin:0,mustSmooth:!1,areContours});return{areContours,outline:outlineData.outline}}get toolbarButtons(){return this._uiManager.signatureManager?
-[["editSignature",this._uiManager.signatureManager]]:super.toolbarButtons}addSignature(data,heightInPage,description2,uuid2){const{x:savedX,y:savedY}=this,{outline}=this.#signatureData=data;this.#isExtracted=outline instanceof ContourDrawOutline,this.description=description2;let drawingOptions;this.#isExtracted?drawingOptions=SignatureEditor.getDefaultDrawingOptions():(drawingOptions=SignatureEditor._defaultDrawnSignatureOptions.clone(),drawingOptions.updateProperties({"stroke-width":outline.thickness})),
-this._addOutlines({drawOutlines:outline,drawingOptions});const[parentWidth,parentHeight]=this.parentDimensions,[,pageHeight]=this.pageDimensions;let newHeight=heightInPage/pageHeight;newHeight=newHeight>=1?.5:newHeight,this.width*=newHeight/this.height,this.width>=1&&(newHeight*=.9/this.width,this.width=.9),this.height=newHeight,this.setDims(parentWidth*this.width,parentHeight*this.height),this.x=savedX,this.y=savedY,this.center(),this._onResized(),this.onScaleChanging(),this.rotate(),this._uiManager.
-addToAnnotationStorage(this),this.setUuid(uuid2),this._reportTelemetry({action:"pdfjs.signature.inserted",data:{hasBeenSaved:!!uuid2,hasDescription:!!description2}}),this.div.hidden=!1}getFromImage(bitmap){const{rawDims:{pageWidth,pageHeight},rotation}=this.parent.viewport;return SignatureExtractor.process(bitmap,pageWidth,pageHeight,rotation,SignatureEditor._INNER_MARGIN)}getFromText(text2,fontInfo){const{rawDims:{pageWidth,pageHeight},rotation}=this.parent.viewport;return SignatureExtractor.extractContoursFromText(
-text2,fontInfo,pageWidth,pageHeight,rotation,SignatureEditor._INNER_MARGIN)}getDrawnSignature(curves){const{rawDims:{pageWidth,pageHeight},rotation}=this.parent.viewport;return SignatureExtractor.processDrawnLines({lines:curves,pageWidth,pageHeight,rotation,innerMargin:SignatureEditor._INNER_MARGIN,mustSmooth:!1,areContours:!1})}createDrawingOptions({areContours,thickness}){areContours?this._drawingOptions=SignatureEditor.getDefaultDrawingOptions():(this._drawingOptions=SignatureEditor._defaultDrawnSignatureOptions.
-clone(),this._drawingOptions.updateProperties({"stroke-width":thickness}))}serialize(isForCopying=!1){if(this.isEmpty())return null;const{lines,points,rect}=this.serializeDraw(isForCopying),{_drawingOptions:{"stroke-width":thickness}}=this,serialized={annotationType:AnnotationEditorType.SIGNATURE,isSignature:!0,areContours:this.#isExtracted,color:[0,0,0],thickness:this.#isExtracted?0:thickness,pageIndex:this.pageIndex,rect,rotation:this.rotation,structTreeParentId:this._structTreeParentId};return this.
-addComment(serialized),isForCopying?(serialized.paths={lines,points},serialized.uuid=this.#signatureUUID,serialized.isCopy=!0):serialized.lines=lines,this.#description&&(serialized.accessibilityData={type:"Figure",alt:this.#description}),serialized}static deserializeDraw(pageX,pageY,pageWidth,pageHeight,innerMargin,data){return data.areContours?ContourDrawOutline.deserialize(pageX,pageY,pageWidth,pageHeight,innerMargin,data):InkDrawOutline.deserialize(pageX,pageY,pageWidth,pageHeight,innerMargin,
-data)}static async deserialize(data,parent,uiManager){const editor=await super.deserialize(data,parent,uiManager);return editor.#isExtracted=data.areContours,editor.description=data.accessibilityData?.alt||"",editor.#signatureUUID=data.uuid,editor}}class StampEditor extends AnnotationEditor{#bitmap=null;#bitmapId=null;#bitmapPromise=null;#bitmapUrl=null;#bitmapFile=null;#bitmapFileName="";#canvas=null;#missingCanvas=!1;#resizeTimeoutId=null;#isSvg=!1;#hasBeenAddedInUndoStack=!1;static _type="sta\
-mp";static _editorType=AnnotationEditorType.STAMP;constructor(params){super({...params,name:"stampEditor"}),this.#bitmapUrl=params.bitmapUrl,this.#bitmapFile=params.bitmapFile,this.defaultL10nId="pdfjs-editor-stamp-editor"}static initialize(l10n,uiManager){AnnotationEditor.initialize(l10n,uiManager)}static isHandlingMimeForPasting(mime){return SupportedImageMimeTypes.includes(mime)}static paste(item,parent){parent.pasteEditor({mode:AnnotationEditorType.STAMP},{bitmapFile:item.getAsFile()})}altTextFinish(){
-this._uiManager.useNewAltTextFlow&&(this.div.hidden=!1),super.altTextFinish()}get telemetryFinalData(){return{type:"stamp",hasAltText:!!this.altTextData?.altText}}static computeTelemetryFinalData(data){const hasAltTextStats=data.get("hasAltText");return{hasAltText:hasAltTextStats.get(!0)??0,hasNoAltText:hasAltTextStats.get(!1)??0}}#getBitmapFetched(data,fromId=!1){if(!data){this.remove();return}this.#bitmap=data.bitmap,fromId||(this.#bitmapId=data.id,this.#isSvg=data.isSvg),data.file&&(this.#bitmapFileName=
-data.file.name),this.#createCanvas()}#getBitmapDone(){if(this.#bitmapPromise=null,this._uiManager.enableWaiting(!1),!!this.#canvas){if(this._uiManager.useNewAltTextWhenAddingImage&&this._uiManager.useNewAltTextFlow&&this.#bitmap){this.addEditToolbar().then(()=>{this._editToolbar.hide(),this._uiManager.editAltText(this,!0)});return}if(!this._uiManager.useNewAltTextWhenAddingImage&&this._uiManager.useNewAltTextFlow&&this.#bitmap){this._reportTelemetry({action:"pdfjs.image.image_added",data:{alt_text_modal:!1,
-alt_text_type:"empty"}});try{this.mlGuessAltText()}catch{}}this.div.focus()}}async mlGuessAltText(imageData=null,updateAltTextData=!0){if(this.hasAltTextData())return null;const{mlManager}=this._uiManager;if(!mlManager)throw new Error("No ML.");if(!await mlManager.isEnabledFor("altText"))throw new Error("ML isn't enabled for alt text.");const{data,width,height}=imageData||this.copyCanvas(null,null,!0).imageData,response=await mlManager.guess({name:"altText",request:{data,width,height,channels:data.
-length/(width*height)}});if(!response)throw new Error("No response from the AI service.");if(response.error)throw new Error("Error from the AI service.");if(response.cancel)return null;if(!response.output)throw new Error("No valid response from the AI service.");const altText=response.output;return await this.setGuessedAltText(altText),updateAltTextData&&!this.hasAltTextData()&&(this.altTextData={alt:altText,decorative:!1}),altText}#getBitmap(){if(this.#bitmapId){this._uiManager.enableWaiting(!0),
-this._uiManager.imageManager.getFromId(this.#bitmapId).then(data=>this.#getBitmapFetched(data,!0)).finally(()=>this.#getBitmapDone());return}if(this.#bitmapUrl){const url2=this.#bitmapUrl;this.#bitmapUrl=null,this._uiManager.enableWaiting(!0),this.#bitmapPromise=this._uiManager.imageManager.getFromUrl(url2).then(data=>this.#getBitmapFetched(data)).finally(()=>this.#getBitmapDone());return}if(this.#bitmapFile){const file=this.#bitmapFile;this.#bitmapFile=null,this._uiManager.enableWaiting(!0),this.#bitmapPromise=
-this._uiManager.imageManager.getFromFile(file).then(data=>this.#getBitmapFetched(data)).finally(()=>this.#getBitmapDone());return}const input=document.createElement("input");input.type="file",input.accept=SupportedImageMimeTypes.join(",");const signal=this._uiManager._signal;this.#bitmapPromise=new Promise(resolve2=>{input.addEventListener("change",async()=>{if(!input.files||input.files.length===0)this.remove();else{this._uiManager.enableWaiting(!0);const data=await this._uiManager.imageManager.
-getFromFile(input.files[0]);this._reportTelemetry({action:"pdfjs.image.image_selected",data:{alt_text_modal:this._uiManager.useNewAltTextFlow}}),this.#getBitmapFetched(data)}resolve2()},{signal}),input.addEventListener("cancel",()=>{this.remove(),resolve2()},{signal})}).finally(()=>this.#getBitmapDone()),input.click()}remove(){this.#bitmapId&&(this.#bitmap=null,this._uiManager.imageManager.deleteId(this.#bitmapId),this.#canvas?.remove(),this.#canvas=null,this.#resizeTimeoutId&&(clearTimeout(this.#resizeTimeoutId),
-this.#resizeTimeoutId=null)),super.remove()}rebuild(){if(!this.parent){this.#bitmapId&&this.#getBitmap();return}super.rebuild(),this.div!==null&&(this.#bitmapId&&this.#canvas===null&&this.#getBitmap(),this.isAttachedToDOM||this.parent.add(this))}onceAdded(focus2){this._isDraggable=!0,focus2&&this.div.focus()}isEmpty(){return!(this.#bitmapPromise||this.#bitmap||this.#bitmapUrl||this.#bitmapFile||this.#bitmapId||this.#missingCanvas)}get toolbarButtons(){return[["altText",this.createAltText()]]}get isResizable(){
-return!0}render(){if(this.div)return this.div;let baseX,baseY;return this._isCopy&&(baseX=this.x,baseY=this.y),super.render(),this.div.hidden=!0,this.createAltText(),this.#missingCanvas||(this.#bitmap?this.#createCanvas():this.#getBitmap()),this._isCopy&&this._moveAfterPaste(baseX,baseY),this._uiManager.addShouldRescale(this),this.div}setCanvas(annotationElementId,canvas){const{id:bitmapId,bitmap}=this._uiManager.imageManager.getFromCanvas(annotationElementId,canvas);canvas.remove(),bitmapId&&this.
-_uiManager.imageManager.isValidId(bitmapId)&&(this.#bitmapId=bitmapId,bitmap&&(this.#bitmap=bitmap),this.#missingCanvas=!1,this.#createCanvas())}_onResized(){this.onScaleChanging()}onScaleChanging(){if(!this.parent)return;this.#resizeTimeoutId!==null&&clearTimeout(this.#resizeTimeoutId);const TIME_TO_WAIT=200;this.#resizeTimeoutId=setTimeout(()=>{this.#resizeTimeoutId=null,this.#drawBitmap()},TIME_TO_WAIT)}#createCanvas(){const{div}=this;let{width,height}=this.#bitmap;const[pageWidth,pageHeight]=this.
-pageDimensions,MAX_RATIO=.75;if(this.width)width=this.width*pageWidth,height=this.height*pageHeight;else if(width>MAX_RATIO*pageWidth||height>MAX_RATIO*pageHeight){const factor=Math.min(MAX_RATIO*pageWidth/width,MAX_RATIO*pageHeight/height);width*=factor,height*=factor}const[parentWidth,parentHeight]=this.parentDimensions;this.setDims(width*parentWidth/pageWidth,height*parentHeight/pageHeight),this._uiManager.enableWaiting(!1);const canvas=this.#canvas=document.createElement("canvas");canvas.setAttribute(
-"role","img"),this.addContainer(canvas),this.width=width/pageWidth,this.height=height/pageHeight,this._initialOptions?.isCentered?this.center():this.fixAndSetPosition(),this._initialOptions=null,(!this._uiManager.useNewAltTextWhenAddingImage||!this._uiManager.useNewAltTextFlow||this.annotationElementId)&&(div.hidden=!1),this.#drawBitmap(),this.#hasBeenAddedInUndoStack||(this.parent.addUndoableEditor(this),this.#hasBeenAddedInUndoStack=!0),this._reportTelemetry({action:"inserted_image"}),this.#bitmapFileName&&
-this.div.setAttribute("aria-description",this.#bitmapFileName),this.annotationElementId||this._uiManager.a11yAlert("pdfjs-editor-stamp-added-alert")}copyCanvas(maxDataDimension,maxPreviewDimension,createImageData=!1){maxDataDimension||(maxDataDimension=224);const{width:bitmapWidth,height:bitmapHeight}=this.#bitmap,outputScale=new OutputScale;let bitmap=this.#bitmap,width=bitmapWidth,height=bitmapHeight,canvas=null;if(maxPreviewDimension){if(bitmapWidth>maxPreviewDimension||bitmapHeight>maxPreviewDimension){
-const ratio=Math.min(maxPreviewDimension/bitmapWidth,maxPreviewDimension/bitmapHeight);width=Math.floor(bitmapWidth*ratio),height=Math.floor(bitmapHeight*ratio)}canvas=document.createElement("canvas");const scaledWidth=canvas.width=Math.ceil(width*outputScale.sx),scaledHeight=canvas.height=Math.ceil(height*outputScale.sy);this.#isSvg||(bitmap=this.#scaleBitmap(scaledWidth,scaledHeight));const ctx=canvas.getContext("2d");ctx.filter=this._uiManager.hcmFilter;let white="white",black="#cfcfd8";this.
-_uiManager.hcmFilter!=="none"?black="black":window.matchMedia?.("(prefers-color-scheme: dark)").matches&&(white="#8f8f9d",black="#42414d");const boxDim=15,boxDimWidth=boxDim*outputScale.sx,boxDimHeight=boxDim*outputScale.sy,pattern2=new OffscreenCanvas(boxDimWidth*2,boxDimHeight*2),patternCtx=pattern2.getContext("2d");patternCtx.fillStyle=white,patternCtx.fillRect(0,0,boxDimWidth*2,boxDimHeight*2),patternCtx.fillStyle=black,patternCtx.fillRect(0,0,boxDimWidth,boxDimHeight),patternCtx.fillRect(boxDimWidth,
-boxDimHeight,boxDimWidth,boxDimHeight),ctx.fillStyle=ctx.createPattern(pattern2,"repeat"),ctx.fillRect(0,0,scaledWidth,scaledHeight),ctx.drawImage(bitmap,0,0,bitmap.width,bitmap.height,0,0,scaledWidth,scaledHeight)}let imageData=null;if(createImageData){let dataWidth,dataHeight;if(outputScale.symmetric&&bitmap.width<maxDataDimension&&bitmap.height<maxDataDimension)dataWidth=bitmap.width,dataHeight=bitmap.height;else if(bitmap=this.#bitmap,bitmapWidth>maxDataDimension||bitmapHeight>maxDataDimension){
-const ratio=Math.min(maxDataDimension/bitmapWidth,maxDataDimension/bitmapHeight);dataWidth=Math.floor(bitmapWidth*ratio),dataHeight=Math.floor(bitmapHeight*ratio),this.#isSvg||(bitmap=this.#scaleBitmap(dataWidth,dataHeight))}const offscreenCtx=new OffscreenCanvas(dataWidth,dataHeight).getContext("2d",{willReadFrequently:!0});offscreenCtx.drawImage(bitmap,0,0,bitmap.width,bitmap.height,0,0,dataWidth,dataHeight),imageData={width:dataWidth,height:dataHeight,data:offscreenCtx.getImageData(0,0,dataWidth,
-dataHeight).data}}return{canvas,width,height,imageData}}#scaleBitmap(width,height){const{width:bitmapWidth,height:bitmapHeight}=this.#bitmap;let newWidth=bitmapWidth,newHeight=bitmapHeight,bitmap=this.#bitmap;for(;newWidth>2*width||newHeight>2*height;){const prevWidth=newWidth,prevHeight=newHeight;newWidth>2*width&&(newWidth=newWidth>=16384?Math.floor(newWidth/2)-1:Math.ceil(newWidth/2)),newHeight>2*height&&(newHeight=newHeight>=16384?Math.floor(newHeight/2)-1:Math.ceil(newHeight/2));const offscreen=new OffscreenCanvas(
-newWidth,newHeight);offscreen.getContext("2d").drawImage(bitmap,0,0,prevWidth,prevHeight,0,0,newWidth,newHeight),bitmap=offscreen.transferToImageBitmap()}return bitmap}#drawBitmap(){const[parentWidth,parentHeight]=this.parentDimensions,{width,height}=this,outputScale=new OutputScale,scaledWidth=Math.ceil(width*parentWidth*outputScale.sx),scaledHeight=Math.ceil(height*parentHeight*outputScale.sy),canvas=this.#canvas;if(!canvas||canvas.width===scaledWidth&&canvas.height===scaledHeight)return;canvas.
-width=scaledWidth,canvas.height=scaledHeight;const bitmap=this.#isSvg?this.#bitmap:this.#scaleBitmap(scaledWidth,scaledHeight),ctx=canvas.getContext("2d");ctx.filter=this._uiManager.hcmFilter,ctx.drawImage(bitmap,0,0,bitmap.width,bitmap.height,0,0,scaledWidth,scaledHeight)}#serializeBitmap(toUrl){if(toUrl){if(this.#isSvg){const url2=this._uiManager.imageManager.getSvgUrl(this.#bitmapId);if(url2)return url2}const canvas=document.createElement("canvas");return{width:canvas.width,height:canvas.height}=
-this.#bitmap,canvas.getContext("2d").drawImage(this.#bitmap,0,0),canvas.toDataURL()}if(this.#isSvg){const[pageWidth,pageHeight]=this.pageDimensions,width=Math.round(this.width*pageWidth*PixelsPerInch.PDF_TO_CSS_UNITS),height=Math.round(this.height*pageHeight*PixelsPerInch.PDF_TO_CSS_UNITS),offscreen=new OffscreenCanvas(width,height);return offscreen.getContext("2d").drawImage(this.#bitmap,0,0,this.#bitmap.width,this.#bitmap.height,0,0,width,height),offscreen.transferToImageBitmap()}return structuredClone(
-this.#bitmap)}static async deserialize(data,parent,uiManager){let initialData=null,missingCanvas=!1;if(data instanceof StampAnnotationElement){const{data:{rect:rect2,rotation,id:id2,structParent,popupRef,contentsObj},container:container2,parent:{page:{pageNumber}},canvas}=data;let bitmapId2,bitmap2;canvas?(delete data.canvas,{id:bitmapId2,bitmap:bitmap2}=uiManager.imageManager.getFromCanvas(container2.id,canvas),canvas.remove()):(missingCanvas=!0,data._hasNoCanvas=!0);const altText=(await parent.
-_structTree.getAriaAttributes(`${AnnotationPrefix}${id2}`))?.get("aria-label")||"";initialData=data={annotationType:AnnotationEditorType.STAMP,bitmapId:bitmapId2,bitmap:bitmap2,pageIndex:pageNumber-1,rect:rect2.slice(0),rotation,annotationElementId:id2,id:id2,deleted:!1,accessibilityData:{decorative:!1,altText},isSvg:!1,structParent,popupRef,comment:contentsObj?.str||null}}const editor=await super.deserialize(data,parent,uiManager),{rect,bitmap,bitmapUrl,bitmapId,isSvg,accessibilityData}=data;missingCanvas?
-(uiManager.addMissingCanvas(data.id,editor),editor.#missingCanvas=!0):bitmapId&&uiManager.imageManager.isValidId(bitmapId)?(editor.#bitmapId=bitmapId,bitmap&&(editor.#bitmap=bitmap)):editor.#bitmapUrl=bitmapUrl,editor.#isSvg=isSvg;const[parentWidth,parentHeight]=editor.pageDimensions;return editor.width=(rect[2]-rect[0])/parentWidth,editor.height=(rect[3]-rect[1])/parentHeight,accessibilityData&&(editor.altTextData=accessibilityData),editor._initialData=initialData,data.comment&&editor.setCommentData(
-data.comment),editor.#hasBeenAddedInUndoStack=!!initialData,editor}serialize(isForCopying=!1,context=null){if(this.isEmpty())return null;if(this.deleted)return this.serializeDeleted();const serialized={annotationType:AnnotationEditorType.STAMP,bitmapId:this.#bitmapId,pageIndex:this.pageIndex,rect:this.getRect(0,0),rotation:this.rotation,isSvg:this.#isSvg,structTreeParentId:this._structTreeParentId};if(this.addComment(serialized),isForCopying)return serialized.bitmapUrl=this.#serializeBitmap(!0),
-serialized.accessibilityData=this.serializeAltText(!0),serialized.isCopy=!0,serialized;const{decorative,altText}=this.serializeAltText(!1);if(!decorative&&altText&&(serialized.accessibilityData={type:"Figure",alt:altText}),this.annotationElementId){const changes=this.#hasElementChanged(serialized);if(changes.isSame)return null;changes.isSameAltText?delete serialized.accessibilityData:serialized.accessibilityData.structParent=this._initialData.structParent??-1}if(serialized.id=this.annotationElementId,
-context===null)return serialized;context.stamps||=new Map;const area=this.#isSvg?(serialized.rect[2]-serialized.rect[0])*(serialized.rect[3]-serialized.rect[1]):null;if(!context.stamps.has(this.#bitmapId))context.stamps.set(this.#bitmapId,{area,serialized}),serialized.bitmap=this.#serializeBitmap(!1);else if(this.#isSvg){const prevData=context.stamps.get(this.#bitmapId);area>prevData.area&&(prevData.area=area,prevData.serialized.bitmap.close(),prevData.serialized.bitmap=this.#serializeBitmap(!1))}
-return serialized}#hasElementChanged(serialized){const{pageIndex,accessibilityData:{altText}}=this._initialData,isSamePageIndex=serialized.pageIndex===pageIndex,isSameAltText=(serialized.accessibilityData?.alt||"")===altText;return{isSame:!this.hasEditedComment&&!this._hasBeenMoved&&!this._hasBeenResized&&isSamePageIndex&&isSameAltText,isSameAltText}}renderAnnotationElement(annotation){const params={rect:this.getRect(0,0)};return this.hasEditedComment&&(params.popup=this.comment),annotation.updateEdited(
-params),null}}class AnnotationEditorLayer{#accessibilityManager;#allowClick=!1;#annotationLayer=null;#clickAC=null;#editorFocusTimeoutId=null;#editors=new Map;#hadPointerDown=!1;#isDisabling=!1;#isEnabling=!1;#drawingAC=null;#focusedElement=null;#textLayer=null;#textSelectionAC=null;#textLayerDblClickAC=null;#lastPointerDownTimestamp=-1;#uiManager;static _initialized=!1;static#editorTypes=new Map([FreeTextEditor,InkEditor,StampEditor,HighlightEditor,SignatureEditor].map(type2=>[type2._editorType,
-type2]));constructor({uiManager,pageIndex,div,structTreeLayer,accessibilityManager,annotationLayer,drawLayer,textLayer,viewport,l10n}){const editorTypes=[...AnnotationEditorLayer.#editorTypes.values()];if(!AnnotationEditorLayer._initialized){AnnotationEditorLayer._initialized=!0;for(const editorType of editorTypes)editorType.initialize(l10n,uiManager)}uiManager.registerEditorTypes(editorTypes),this.#uiManager=uiManager,this.pageIndex=pageIndex,this.div=div,this.#accessibilityManager=accessibilityManager,
-this.#annotationLayer=annotationLayer,this.viewport=viewport,this.#textLayer=textLayer,this.drawLayer=drawLayer,this._structTree=structTreeLayer,this.#uiManager.addLayer(this)}get isEmpty(){return this.#editors.size===0}get isInvisible(){return this.isEmpty&&this.#uiManager.getMode()===AnnotationEditorType.NONE}updateToolbar(options){this.#uiManager.updateToolbar(options)}updateMode(mode=this.#uiManager.getMode()){switch(this.#cleanup(),mode){case AnnotationEditorType.NONE:this.disableTextSelection(),
-this.togglePointerEvents(!1),this.toggleAnnotationLayerPointerEvents(!0),this.disableClick();return;case AnnotationEditorType.INK:this.disableTextSelection(),this.togglePointerEvents(!0),this.enableClick();break;case AnnotationEditorType.HIGHLIGHT:this.enableTextSelection(),this.togglePointerEvents(!1),this.disableClick();break;default:this.disableTextSelection(),this.togglePointerEvents(!0),this.enableClick()}this.toggleAnnotationLayerPointerEvents(!1);const{classList}=this.div;for(const editorType of AnnotationEditorLayer.#editorTypes.
-values())classList.toggle(`${editorType._type}Editing`,mode===editorType._editorType);this.div.hidden=!1}hasTextLayer(textLayer){return textLayer===this.#textLayer?.div}setEditingState(isEditing2){this.#uiManager.setEditingState(isEditing2)}addCommands(params){this.#uiManager.addCommands(params)}cleanUndoStack(type2){this.#uiManager.cleanUndoStack(type2)}toggleDrawing(enabled=!1){this.div.classList.toggle("drawing",!enabled)}togglePointerEvents(enabled=!1){this.div.classList.toggle("disabled",!enabled)}toggleAnnotationLayerPointerEvents(enabled=!1){
-this.#annotationLayer?.div.classList.toggle("disabled",!enabled)}async enable(){this.#isEnabling=!0,this.div.tabIndex=0,this.togglePointerEvents(!0),this.#textLayerDblClickAC?.abort(),this.#textLayerDblClickAC=null;const annotationElementIds=new Set;for(const editor of this.#editors.values())editor.enableEditing(),editor.show(!0),editor.annotationElementId&&(this.#uiManager.removeChangedExistingAnnotation(editor),annotationElementIds.add(editor.annotationElementId));if(!this.#annotationLayer){this.#isEnabling=
-!1;return}const editables=this.#annotationLayer.getEditableAnnotations();for(const editable of editables){if(editable.hide(),this.#uiManager.isDeletedAnnotationElement(editable.data.id)||annotationElementIds.has(editable.data.id))continue;const editor=await this.deserialize(editable);editor&&(this.addOrRebuild(editor),editor.enableEditing())}this.#isEnabling=!1}disable(){if(this.#isDisabling=!0,this.div.tabIndex=-1,this.togglePointerEvents(!1),this.#textLayer&&!this.#textLayerDblClickAC){this.#textLayerDblClickAC=
-new AbortController;const signal=this.#uiManager.combinedSignal(this.#textLayerDblClickAC);this.#textLayer.div.addEventListener("pointerdown",e=>{const{clientX,clientY,timeStamp}=e,lastPointerDownTimestamp=this.#lastPointerDownTimestamp;if(timeStamp-lastPointerDownTimestamp>500){this.#lastPointerDownTimestamp=timeStamp;return}this.#lastPointerDownTimestamp=-1;const{classList:classList2}=this.div;classList2.toggle("getElements",!0);const elements=document.elementsFromPoint(clientX,clientY);if(classList2.
-toggle("getElements",!1),!this.div.contains(elements[0]))return;let id2;const regex=new RegExp(`^${AnnotationEditorPrefix}[0-9]+$`);for(const element2 of elements)if(regex.test(element2.id)){id2=element2.id;break}if(!id2)return;const editor=this.#editors.get(id2);editor?.annotationElementId===null&&(e.stopPropagation(),e.preventDefault(),editor.dblclick())},{signal,capture:!0})}const changedAnnotations=new Map,resetAnnotations=new Map;for(const editor of this.#editors.values())if(editor.disableEditing(),
-!!editor.annotationElementId){if(editor.serialize()!==null){changedAnnotations.set(editor.annotationElementId,editor);continue}else resetAnnotations.set(editor.annotationElementId,editor);this.getEditableAnnotation(editor.annotationElementId)?.show(),editor.remove()}if(this.#annotationLayer){const editables=this.#annotationLayer.getEditableAnnotations();for(const editable of editables){const{id:id2}=editable.data;if(this.#uiManager.isDeletedAnnotationElement(id2)){editable.updateEdited({deleted:!0});
-continue}let editor=resetAnnotations.get(id2);if(editor){editor.resetAnnotationElement(editable),editor.show(!1),editable.show();continue}editor=changedAnnotations.get(id2),editor&&(this.#uiManager.addChangedExistingAnnotation(editor),editor.renderAnnotationElement(editable)&&editor.show(!1)),editable.show()}}this.#cleanup(),this.isEmpty&&(this.div.hidden=!0);const{classList}=this.div;for(const editorType of AnnotationEditorLayer.#editorTypes.values())classList.remove(`${editorType._type}Editing`);
-this.disableTextSelection(),this.toggleAnnotationLayerPointerEvents(!0),this.#isDisabling=!1}getEditableAnnotation(id2){return this.#annotationLayer?.getEditableAnnotation(id2)||null}setActiveEditor(editor){this.#uiManager.getActive()!==editor&&this.#uiManager.setActiveEditor(editor)}enableTextSelection(){if(this.div.tabIndex=-1,this.#textLayer?.div&&!this.#textSelectionAC){this.#textSelectionAC=new AbortController;const signal=this.#uiManager.combinedSignal(this.#textSelectionAC);this.#textLayer.
-div.addEventListener("pointerdown",this.#textLayerPointerDown.bind(this),{signal}),this.#textLayer.div.classList.add("highlighting")}}disableTextSelection(){this.div.tabIndex=0,this.#textLayer?.div&&this.#textSelectionAC&&(this.#textSelectionAC.abort(),this.#textSelectionAC=null,this.#textLayer.div.classList.remove("highlighting"))}#textLayerPointerDown(event2){this.#uiManager.unselectAll();const{target:target2}=event2;if(target2===this.#textLayer.div||(target2.getAttribute("role")==="img"||target2.
-classList.contains("endOfContent"))&&this.#textLayer.div.contains(target2)){const{isMac}=util_FeatureTest.platform;if(event2.button!==0||event2.ctrlKey&&isMac)return;this.#uiManager.showAllEditors("highlight",!0,!0),this.#textLayer.div.classList.add("free"),this.toggleDrawing(),HighlightEditor.startHighlighting(this,this.#uiManager.direction==="ltr",{target:this.#textLayer.div,x:event2.x,y:event2.y}),this.#textLayer.div.addEventListener("pointerup",()=>{this.#textLayer.div.classList.remove("free"),
-this.toggleDrawing(!0)},{once:!0,signal:this.#uiManager._signal}),event2.preventDefault()}}enableClick(){if(this.#clickAC)return;this.#clickAC=new AbortController;const signal=this.#uiManager.combinedSignal(this.#clickAC);this.div.addEventListener("pointerdown",this.pointerdown.bind(this),{signal});const pointerup=this.pointerup.bind(this);this.div.addEventListener("pointerup",pointerup,{signal}),this.div.addEventListener("pointercancel",pointerup,{signal})}disableClick(){this.#clickAC?.abort(),
-this.#clickAC=null}attach(editor){this.#editors.set(editor.id,editor);const{annotationElementId}=editor;annotationElementId&&this.#uiManager.isDeletedAnnotationElement(annotationElementId)&&this.#uiManager.removeDeletedAnnotationElement(editor)}detach(editor){this.#editors.delete(editor.id),this.#accessibilityManager?.removePointerInTextLayer(editor.contentDiv),!this.#isDisabling&&editor.annotationElementId&&this.#uiManager.addDeletedAnnotationElement(editor)}remove(editor){this.detach(editor),this.#uiManager.
-removeEditor(editor),editor.div.remove(),editor.isAttachedToDOM=!1}changeParent(editor){editor.parent!==this&&(editor.parent&&editor.annotationElementId&&(this.#uiManager.addDeletedAnnotationElement(editor.annotationElementId),AnnotationEditor.deleteAnnotationElement(editor),editor.annotationElementId=null),this.attach(editor),editor.parent?.detach(editor),editor.setParent(this),editor.div&&editor.isAttachedToDOM&&(editor.div.remove(),this.div.append(editor.div)))}add(editor){if(!(editor.parent===
-this&&editor.isAttachedToDOM)){if(this.changeParent(editor),this.#uiManager.addEditor(editor),this.attach(editor),!editor.isAttachedToDOM){const div=editor.render();this.div.append(div),editor.isAttachedToDOM=!0}editor.fixAndSetPosition(),editor.onceAdded(!this.#isEnabling),this.#uiManager.addToAnnotationStorage(editor),editor._reportTelemetry(editor.telemetryInitialData)}}moveEditorInDOM(editor){if(!editor.isAttachedToDOM)return;const{activeElement}=document;editor.div.contains(activeElement)&&
-!this.#editorFocusTimeoutId&&(editor._focusEventsAllowed=!1,this.#editorFocusTimeoutId=setTimeout(()=>{this.#editorFocusTimeoutId=null,editor.div.contains(document.activeElement)?editor._focusEventsAllowed=!0:(editor.div.addEventListener("focusin",()=>{editor._focusEventsAllowed=!0},{once:!0,signal:this.#uiManager._signal}),activeElement.focus())},0)),editor._structTreeParentId=this.#accessibilityManager?.moveElementInDOM(this.div,editor.div,editor.contentDiv,!0)}addOrRebuild(editor){editor.needsToBeRebuilt()?
-(editor.parent||=this,editor.rebuild(),editor.show()):this.add(editor)}addUndoableEditor(editor){const cmd=()=>editor._uiManager.rebuild(editor),undo=()=>{editor.remove()};this.addCommands({cmd,undo,mustExec:!1})}getNextId(){return this.#uiManager.getId()}get#currentEditorType(){return AnnotationEditorLayer.#editorTypes.get(this.#uiManager.getMode())}combinedSignal(ac){return this.#uiManager.combinedSignal(ac)}#createNewEditor(params){const editorType=this.#currentEditorType;return editorType?new editorType.
-prototype.constructor(params):null}canCreateNewEmptyEditor(){return this.#currentEditorType?.canCreateNewEmptyEditor()}async pasteEditor(options,params){this.updateToolbar(options),await this.#uiManager.updateMode(options.mode);const{offsetX,offsetY}=this.#getCenterPoint(),id2=this.getNextId(),editor=this.#createNewEditor({parent:this,id:id2,x:offsetX,y:offsetY,uiManager:this.#uiManager,isCentered:!0,...params});editor&&this.add(editor)}async deserialize(data){return await AnnotationEditorLayer.#editorTypes.
-get(data.annotationType??data.annotationEditorType)?.deserialize(data,this,this.#uiManager)||null}createAndAddNewEditor(event2,isCentered,data={}){const id2=this.getNextId(),editor=this.#createNewEditor({parent:this,id:id2,x:event2.offsetX,y:event2.offsetY,uiManager:this.#uiManager,isCentered,...data});return editor&&this.add(editor),editor}#getCenterPoint(){const{x,y,width,height}=this.div.getBoundingClientRect(),tlX=Math.max(0,x),tlY=Math.max(0,y),brX=Math.min(window.innerWidth,x+width),brY=Math.
-min(window.innerHeight,y+height),centerX=(tlX+brX)/2-x,centerY=(tlY+brY)/2-y,[offsetX,offsetY]=this.viewport.rotation%180===0?[centerX,centerY]:[centerY,centerX];return{offsetX,offsetY}}addNewEditor(data={}){this.createAndAddNewEditor(this.#getCenterPoint(),!0,data)}setSelected(editor){this.#uiManager.setSelected(editor)}toggleSelected(editor){this.#uiManager.toggleSelected(editor)}unselect(editor){this.#uiManager.unselect(editor)}pointerup(event2){const{isMac}=util_FeatureTest.platform;if(event2.
-button!==0||event2.ctrlKey&&isMac||event2.target!==this.div||!this.#hadPointerDown||(this.#hadPointerDown=!1,this.#currentEditorType?.isDrawer&&this.#currentEditorType.supportMultipleDrawings))return;if(!this.#allowClick){this.#allowClick=!0;return}const currentMode=this.#uiManager.getMode();if(currentMode===AnnotationEditorType.STAMP||currentMode===AnnotationEditorType.SIGNATURE){this.#uiManager.unselectAll();return}this.createAndAddNewEditor(event2,!1)}pointerdown(event2){if(this.#uiManager.getMode()===
-AnnotationEditorType.HIGHLIGHT&&this.enableTextSelection(),this.#hadPointerDown){this.#hadPointerDown=!1;return}const{isMac}=util_FeatureTest.platform;if(event2.button!==0||event2.ctrlKey&&isMac||event2.target!==this.div)return;if(this.#hadPointerDown=!0,this.#currentEditorType?.isDrawer){this.startDrawingSession(event2);return}const editor=this.#uiManager.getActive();this.#allowClick=!editor||editor.isEmpty()}startDrawingSession(event2){if(this.div.focus({preventScroll:!0}),this.#drawingAC){this.#currentEditorType.
-startDrawing(this,this.#uiManager,!1,event2);return}this.#uiManager.setCurrentDrawingSession(this),this.#drawingAC=new AbortController;const signal=this.#uiManager.combinedSignal(this.#drawingAC);this.div.addEventListener("blur",({relatedTarget})=>{relatedTarget&&!this.div.contains(relatedTarget)&&(this.#focusedElement=null,this.commitOrRemove())},{signal}),this.#currentEditorType.startDrawing(this,this.#uiManager,!1,event2)}pause(on2){if(on2){const{activeElement}=document;this.div.contains(activeElement)&&
-(this.#focusedElement=activeElement);return}this.#focusedElement&&setTimeout(()=>{this.#focusedElement?.focus(),this.#focusedElement=null},0)}endDrawingSession(isAborted=!1){return this.#drawingAC?(this.#uiManager.setCurrentDrawingSession(null),this.#drawingAC.abort(),this.#drawingAC=null,this.#focusedElement=null,this.#currentEditorType.endDrawing(isAborted)):null}findNewParent(editor,x,y){const layer=this.#uiManager.findParent(x,y);return layer===null||layer===this?!1:(layer.changeParent(editor),
-!0)}commitOrRemove(){return this.#drawingAC?(this.endDrawingSession(),!0):!1}onScaleChanging(){this.#drawingAC&&this.#currentEditorType.onScaleChangingWhenDrawing(this)}destroy(){this.commitOrRemove(),this.#uiManager.getActive()?.parent===this&&(this.#uiManager.commitOrRemove(),this.#uiManager.setActiveEditor(null)),this.#editorFocusTimeoutId&&(clearTimeout(this.#editorFocusTimeoutId),this.#editorFocusTimeoutId=null);for(const editor of this.#editors.values())this.#accessibilityManager?.removePointerInTextLayer(
-editor.contentDiv),editor.setParent(null),editor.isAttachedToDOM=!1,editor.div.remove();this.div=null,this.#editors.clear(),this.#uiManager.removeLayer(this)}#cleanup(){for(const editor of this.#editors.values())editor.isEmpty()&&editor.remove()}render({viewport}){this.viewport=viewport,setLayerDimensions(this.div,viewport);for(const editor of this.#uiManager.getEditors(this.pageIndex))this.add(editor),editor.rebuild();this.updateMode()}update({viewport}){this.#uiManager.commitOrRemove(),this.#cleanup();
-const oldRotation=this.viewport.rotation,rotation=viewport.rotation;if(this.viewport=viewport,setLayerDimensions(this.div,{rotation}),oldRotation!==rotation)for(const editor of this.#editors.values())editor.rotate(rotation)}get pageDimensions(){const{pageWidth,pageHeight}=this.viewport.rawDims;return[pageWidth,pageHeight]}get scale(){return this.#uiManager.viewParameters.realScale}}class DrawLayer{#parent=null;#mapping=new Map;#toUpdate=new Map;static#id=0;constructor({pageIndex}){this.pageIndex=
-pageIndex}setParent(parent){if(!this.#parent){this.#parent=parent;return}if(this.#parent!==parent){if(this.#mapping.size>0)for(const root2 of this.#mapping.values())root2.remove(),parent.append(root2);this.#parent=parent}}static get _svgFactory(){return shadow(this,"_svgFactory",new DOMSVGFactory)}static#setBox(element2,[x,y,width,height]){const{style:style2}=element2;style2.top=`${100*y}%`,style2.left=`${100*x}%`,style2.width=`${100*width}%`,style2.height=`${100*height}%`}#createSVG(){const svg2=DrawLayer.
-_svgFactory.create(1,1,!0);return this.#parent.append(svg2),svg2.setAttribute("aria-hidden",!0),svg2}#createClipPath(defs,pathId){const clipPath=DrawLayer._svgFactory.createElement("clipPath");defs.append(clipPath);const clipPathId=`clip_${pathId}`;clipPath.setAttribute("id",clipPathId),clipPath.setAttribute("clipPathUnits","objectBoundingBox");const clipPathUse=DrawLayer._svgFactory.createElement("use");return clipPath.append(clipPathUse),clipPathUse.setAttribute("href",`#${pathId}`),clipPathUse.
-classList.add("clip"),clipPathId}#updateProperties(element2,properties2){for(const[key2,value]of Object.entries(properties2))value===null?element2.removeAttribute(key2):element2.setAttribute(key2,value)}draw(properties2,isPathUpdatable=!1,hasClip=!1){const id2=DrawLayer.#id++,root2=this.#createSVG(),defs=DrawLayer._svgFactory.createElement("defs");root2.append(defs);const path2=DrawLayer._svgFactory.createElement("path");defs.append(path2);const pathId=`path_p${this.pageIndex}_${id2}`;path2.setAttribute(
-"id",pathId),path2.setAttribute("vector-effect","non-scaling-stroke"),isPathUpdatable&&this.#toUpdate.set(id2,path2);const clipPathId=hasClip?this.#createClipPath(defs,pathId):null,use=DrawLayer._svgFactory.createElement("use");return root2.append(use),use.setAttribute("href",`#${pathId}`),this.updateProperties(root2,properties2),this.#mapping.set(id2,root2),{id:id2,clipPathId:`url(#${clipPathId})`}}drawOutline(properties2,mustRemoveSelfIntersections){const id2=DrawLayer.#id++,root2=this.#createSVG(),
-defs=DrawLayer._svgFactory.createElement("defs");root2.append(defs);const path2=DrawLayer._svgFactory.createElement("path");defs.append(path2);const pathId=`path_p${this.pageIndex}_${id2}`;path2.setAttribute("id",pathId),path2.setAttribute("vector-effect","non-scaling-stroke");let maskId;if(mustRemoveSelfIntersections){const mask=DrawLayer._svgFactory.createElement("mask");defs.append(mask),maskId=`mask_p${this.pageIndex}_${id2}`,mask.setAttribute("id",maskId),mask.setAttribute("maskUnits","obje\
-ctBoundingBox");const rect=DrawLayer._svgFactory.createElement("rect");mask.append(rect),rect.setAttribute("width","1"),rect.setAttribute("height","1"),rect.setAttribute("fill","white");const use=DrawLayer._svgFactory.createElement("use");mask.append(use),use.setAttribute("href",`#${pathId}`),use.setAttribute("stroke","none"),use.setAttribute("fill","black"),use.setAttribute("fill-rule","nonzero"),use.classList.add("mask")}const use1=DrawLayer._svgFactory.createElement("use");root2.append(use1),
-use1.setAttribute("href",`#${pathId}`),maskId&&use1.setAttribute("mask",`url(#${maskId})`);const use2=use1.cloneNode();return root2.append(use2),use1.classList.add("mainOutline"),use2.classList.add("secondaryOutline"),this.updateProperties(root2,properties2),this.#mapping.set(id2,root2),id2}finalizeDraw(id2,properties2){this.#toUpdate.delete(id2),this.updateProperties(id2,properties2)}updateProperties(elementOrId,properties2){if(!properties2)return;const{root:root2,bbox,rootClass,path:path2}=properties2,
-element2=typeof elementOrId=="number"?this.#mapping.get(elementOrId):elementOrId;if(element2){if(root2&&this.#updateProperties(element2,root2),bbox&&DrawLayer.#setBox(element2,bbox),rootClass){const{classList}=element2;for(const[className,value]of Object.entries(rootClass))classList.toggle(className,value)}if(path2){const pathElement=element2.firstChild.firstChild;this.#updateProperties(pathElement,path2)}}}updateParent(id2,layer){if(layer===this)return;const root2=this.#mapping.get(id2);root2&&
-(layer.#parent.append(root2),this.#mapping.delete(id2),layer.#mapping.set(id2,root2))}remove(id2){this.#toUpdate.delete(id2),this.#parent!==null&&(this.#mapping.get(id2).remove(),this.#mapping.delete(id2))}destroy(){this.#parent=null;for(const root2 of this.#mapping.values())root2.remove();this.#mapping.clear(),this.#toUpdate.clear()}}globalThis._pdfjsTestingUtils={HighlightOutliner};globalThis.pdfjsLib={AbortException,AnnotationEditorLayer,AnnotationEditorParamsType,AnnotationEditorType,AnnotationEditorUIManager,
-AnnotationLayer,AnnotationMode,AnnotationType,build,ColorPicker,createValidAbsoluteUrl,DOMSVGFactory,DrawLayer,FeatureTest:util_FeatureTest,fetchData,getDocument,getFilenameFromUrl,getPdfFilenameFromUrl,getRGB,getUuid,getXfaPageViewport,GlobalWorkerOptions,ImageKind:util_ImageKind,InvalidPDFException,isDataScheme,isPdfFile,isValidExplicitDest,MathClamp,noContextMenu,normalizeUnicode,OPS,OutputScale,PasswordResponses,PDFDataRangeTransport,PDFDateString,PDFWorker,PermissionFlag,PixelsPerInch,RenderingCancelledException,
-ResponseException,setLayerDimensions,shadow,SignatureExtractor,stopEvent,SupportedImageMimeTypes,TextLayer,TouchManager,updateUrlHash,Util,VerbosityLevel,version:version$1,XfaLayer};__vitePreload(()=>Promise.resolve().then(()=>pdf_worker_min$1),[],import.meta.url).then(workerModule=>{const workerBlob=new Blob([workerModule.default],{type:"application/javascript"});GlobalWorkerOptions.workerSrc=URL.createObjectURL(workerBlob)}).catch(()=>{console.warn("Failed to load PDF.js worker, \
-PDF processing may not work")});async function getFileAsBuffer(file){return new Promise((resolve2,reject)=>{const reader=new FileReader;reader.onload=event2=>{event2.target?.result?resolve2(event2.target.result):reject(new Error("Failed to read file."))},reader.onerror=()=>{reject(new Error("Failed to read file."))},reader.readAsArrayBuffer(file)})}async function convertPDFToText(file){try{const buffer=await getFileAsBuffer(file),pdf=await getDocument(buffer).promise,numPages=pdf.numPages,textContentPromises=[];
-for(let i=1;i<=numPages;i++)textContentPromises.push(pdf.getPage(i).then(page2=>page2.getTextContent()));return(await Promise.all(textContentPromises)).flatMap(textContent=>textContent.items.map(item=>item.str??"")).join(`
-`)}catch(error2){throw console.error("Error converting PDF to text:",error2),new Error(`Failed to convert PDF to text: ${error2 instanceof Error?error2.message:"Unknown error"}`)}}async function convertPDFToImage(file,scale2=1.5){try{const buffer=await getFileAsBuffer(file),doc=await getDocument(buffer).promise,pages=[];for(let i=1;i<=doc.numPages;i++){const page2=await doc.getPage(i),viewport=page2.getViewport({scale:scale2}),canvas=document.createElement("canvas"),ctx=canvas.getContext("2d");if(canvas.
-width=viewport.width,canvas.height=viewport.height,!ctx)throw new Error("Failed to get 2D context from canvas");const task=page2.render({canvasContext:ctx,viewport,canvas});pages.push(task.promise.then(()=>canvas.toDataURL(MimeTypeImage.PNG)))}return await Promise.all(pages)}catch(error2){throw console.error("Error converting PDF to images:",error2),new Error(`Failed to convert PDF to images: ${error2 instanceof Error?error2.message:"Unknown error"}`)}}function svgBase64UrlToPngDataURL(base64UrlSvg,backgroundColor="\
-white"){return new Promise((resolve2,reject)=>{try{const img=new Image;img.onload=()=>{const canvas=document.createElement("canvas"),ctx=canvas.getContext("2d");if(!ctx){reject(new Error("Failed to get 2D canvas context."));return}const targetWidth=img.naturalWidth||300,targetHeight=img.naturalHeight||300;canvas.width=targetWidth,canvas.height=targetHeight,backgroundColor&&(ctx.fillStyle=backgroundColor,ctx.fillRect(0,0,canvas.width,canvas.height)),ctx.drawImage(img,0,0,targetWidth,targetHeight),
-resolve2(canvas.toDataURL(MimeTypeImage.PNG))},img.onerror=()=>{reject(new Error("Failed to load SVG image. Ensure the SVG data is valid."))},img.src=base64UrlSvg}catch(error2){const errorMessage=`Error converting SVG to PNG: ${error2 instanceof Error?error2.message:String(error2)}`;console.error(errorMessage,error2),reject(new Error(errorMessage))}})}function isSvgMimeType(mimeType){return mimeType===MimeTypeImage.SVG}function webpBase64UrlToPngDataURL(base64UrlWebp,backgroundColor="white"){return new Promise(
-(resolve2,reject)=>{try{const img=new Image;img.onload=()=>{const canvas=document.createElement("canvas"),ctx=canvas.getContext("2d");if(!ctx){reject(new Error("Failed to get 2D canvas context."));return}const targetWidth=img.naturalWidth||300,targetHeight=img.naturalHeight||300;canvas.width=targetWidth,canvas.height=targetHeight,backgroundColor&&(ctx.fillStyle=backgroundColor,ctx.fillRect(0,0,canvas.width,canvas.height)),ctx.drawImage(img,0,0,targetWidth,targetHeight),resolve2(canvas.toDataURL(
-MimeTypeImage.PNG))},img.onerror=()=>{reject(new Error("Failed to load WebP image. Ensure the WebP data is valid."))},img.src=base64UrlWebp}catch(error2){const errorMessage=`Error converting WebP to PNG: ${error2 instanceof Error?error2.message:String(error2)}`;console.error(errorMessage,error2),reject(new Error(errorMessage))}})}function isWebpMimeType(mimeType){return mimeType===MimeTypeImage.WEBP}class ChatService{static async sendMessage(messages,options={},conversationId,signal){const{stream,
-onChunk,onComplete,onError,onReasoningChunk,onToolCallChunk,onModel,onTimings,tools,temperature,max_tokens,dynatemp_range,dynatemp_exponent,top_k,top_p,min_p,xtc_probability,xtc_threshold,typ_p,repeat_last_n,repeat_penalty,presence_penalty,frequency_penalty,dry_multiplier,dry_base,dry_allowed_length,dry_penalty_last_n,samplers,backend_sampling,custom:custom2,timings_per_token,disableReasoningParsing,excludeReasoningFromContext}=options,normalizedMessages=messages.map(msg=>{if("id"in msg&&"convId"in
-msg&&"timestamp"in msg){const dbMsg=msg;return ChatService.convertDbMessageToApiChatMessageData(dbMsg)}else return msg}).filter(msg=>msg.role===MessageRole.SYSTEM?(typeof msg.content=="string"?msg.content:"").trim().length>0:!0);options.model&&!modelsStore.modelSupportsVision(options.model)&&normalizedMessages.forEach(msg=>{Array.isArray(msg.content)&&(msg.content=msg.content.filter(part=>part.type===ContentPartType.IMAGE_URL?(console.info(`[ChatService] Skipping image attachment in message hist\
-ory (model "${options.model}" does not support vision)`),!1):!0),msg.content.length===1&&msg.content[0].type===ContentPartType.TEXT&&(msg.content=msg.content[0].text))});const requestBody={messages:normalizedMessages.map(msg=>{const mapped={role:msg.role,content:msg.content,tool_calls:msg.tool_calls,tool_call_id:msg.tool_call_id};return!excludeReasoningFromContext&&msg.reasoning_content&&(mapped.reasoning_content=msg.reasoning_content),mapped}),stream,return_progress:stream?!0:void 0,tools:tools&&
-tools.length>0?tools:void 0};if(options.model&&(requestBody.model=options.model),requestBody.reasoning_format=disableReasoningParsing?ReasoningFormat.NONE:ReasoningFormat.AUTO,temperature!==void 0&&(requestBody.temperature=temperature),max_tokens!==void 0&&(requestBody.max_tokens=max_tokens!==null&&max_tokens!==0?max_tokens:-1),dynatemp_range!==void 0&&(requestBody.dynatemp_range=dynatemp_range),dynatemp_exponent!==void 0&&(requestBody.dynatemp_exponent=dynatemp_exponent),top_k!==void 0&&(requestBody.
-top_k=top_k),top_p!==void 0&&(requestBody.top_p=top_p),min_p!==void 0&&(requestBody.min_p=min_p),xtc_probability!==void 0&&(requestBody.xtc_probability=xtc_probability),xtc_threshold!==void 0&&(requestBody.xtc_threshold=xtc_threshold),typ_p!==void 0&&(requestBody.typ_p=typ_p),repeat_last_n!==void 0&&(requestBody.repeat_last_n=repeat_last_n),repeat_penalty!==void 0&&(requestBody.repeat_penalty=repeat_penalty),presence_penalty!==void 0&&(requestBody.presence_penalty=presence_penalty),frequency_penalty!==
-void 0&&(requestBody.frequency_penalty=frequency_penalty),dry_multiplier!==void 0&&(requestBody.dry_multiplier=dry_multiplier),dry_base!==void 0&&(requestBody.dry_base=dry_base),dry_allowed_length!==void 0&&(requestBody.dry_allowed_length=dry_allowed_length),dry_penalty_last_n!==void 0&&(requestBody.dry_penalty_last_n=dry_penalty_last_n),samplers!==void 0&&(requestBody.samplers=typeof samplers=="string"?samplers.split(";").filter(s2=>s2.trim()):samplers),backend_sampling!==void 0&&(requestBody.backend_sampling=
-backend_sampling),timings_per_token!==void 0&&(requestBody.timings_per_token=timings_per_token),custom2)try{const customParams=typeof custom2=="string"?JSON.parse(custom2):custom2;Object.assign(requestBody,customParams)}catch(error2){console.warn("Failed to parse custom parameters:",error2)}try{const response=await fetch("./v1/chat/completions",{method:"POST",headers:getJsonHeaders(),body:JSON.stringify(requestBody),signal});if(!response.ok){const error2=await ChatService.parseErrorResponse(response);
-throw onError&&onError(error2),error2}if(stream){await ChatService.handleStreamResponse(response,onChunk,onComplete,onError,onReasoningChunk,onToolCallChunk,onModel,onTimings,conversationId,signal);return}else return ChatService.handleNonStreamResponse(response,onComplete,onError,onToolCallChunk,onModel)}catch(error2){if(isAbortError(error2)){console.log("Chat completion request was aborted");return}let userFriendlyError;throw error2 instanceof Error?error2.name==="TypeError"&&error2.message.includes(
-"fetch")?(userFriendlyError=new Error("Unable to connect to server - please check if the server is running"),userFriendlyError.name="NetworkError"):error2.message.includes("ECONNREFUSED")?(userFriendlyError=new Error("Connection refused - server may be offline"),userFriendlyError.name="NetworkError"):error2.message.includes("ETIMEDOUT")?(userFriendlyError=new Error("Request timed out - the server took too long to respond"),userFriendlyError.name="TimeoutError"):userFriendlyError=error2:userFriendlyError=
-new Error("Unknown error occurred while sending message"),console.error("Error in sendMessage:",error2),onError&&onError(userFriendlyError),userFriendlyError}}static async areAllSlotsIdle(model,signal){try{const url2=model?`./slots?model=${encodeURIComponent(model)}`:"./slots",res=await fetch(url2,{signal});return res.ok?(await res.json()).every(s2=>!s2.is_processing):!0}catch{return!0}}static async preEncode(messages,model,excludeReasoning,signal){const requestBody={messages:messages.map(msg=>"\
-id"in msg&&"convId"in msg&&"timestamp"in msg?ChatService.convertDbMessageToApiChatMessageData(msg):msg).filter(msg=>msg.role===MessageRole.SYSTEM?(typeof msg.content=="string"?msg.content:"").trim().length>0:!0).map(msg=>{const mapped={role:msg.role,content:excludeReasoning?ChatService.stripReasoningContent(msg.content):msg.content,tool_calls:msg.tool_calls,tool_call_id:msg.tool_call_id};return!excludeReasoning&&msg.reasoning_content&&(mapped.reasoning_content=msg.reasoning_content),mapped}),stream:!1,
-n_predict:0};model&&(requestBody.model=model);try{await fetch("./v1/chat/completions",{method:"POST",headers:getJsonHeaders(),body:JSON.stringify(requestBody),signal})}catch(error2){isAbortError(error2)||console.warn("[ChatService] Pre-encode request failed:",error2)}}static async handleStreamResponse(response,onChunk,onComplete,onError,onReasoningChunk,onToolCallChunk,onModel,onTimings,conversationId,abortSignal){const reader=response.body?.getReader();if(!reader)throw new Error("No response bo\
-dy");const decoder=new TextDecoder;let aggregatedContent="",fullReasoningContent="",aggregatedToolCalls=[],lastTimings,streamFinished=!1,modelEmitted=!1,toolCallIndexOffset=0,hasOpenToolCallBatch=!1;const finalizeOpenToolCallBatch=()=>{hasOpenToolCallBatch&&(toolCallIndexOffset=aggregatedToolCalls.length,hasOpenToolCallBatch=!1)},processToolCallDelta=toolCalls=>{if(!toolCalls||toolCalls.length===0||(aggregatedToolCalls=ChatService.mergeToolCallDeltas(aggregatedToolCalls,toolCalls,toolCallIndexOffset),
-aggregatedToolCalls.length===0))return;hasOpenToolCallBatch=!0;const serializedToolCalls=JSON.stringify(aggregatedToolCalls);serializedToolCalls&&(abortSignal?.aborted||onToolCallChunk?.(serializedToolCalls))};try{let chunk="";for(;!abortSignal?.aborted;){const{done,value}=await reader.read();if(done||abortSignal?.aborted)break;chunk+=decoder.decode(value,{stream:!0});const lines=chunk.split(`
-`);chunk=lines.pop()||"";for(const line of lines){if(abortSignal?.aborted)break;if(line.startsWith(UrlProtocol.DATA)){const data=line.slice(6);if(data==="[DONE]"){streamFinished=!0;continue}try{const parsed=JSON.parse(data),content2=parsed.choices[0]?.delta?.content,reasoningContent=parsed.choices[0]?.delta?.reasoning_content,toolCalls=parsed.choices[0]?.delta?.tool_calls,timings=parsed.timings,promptProgress=parsed.prompt_progress,chunkModel=ChatService.extractModelName(parsed);chunkModel&&!modelEmitted&&
-(modelEmitted=!0,onModel?.(chunkModel)),promptProgress&&ChatService.notifyTimings(void 0,promptProgress,onTimings),timings&&(ChatService.notifyTimings(timings,promptProgress,onTimings),lastTimings=timings),content2&&(finalizeOpenToolCallBatch(),aggregatedContent+=content2,abortSignal?.aborted||onChunk?.(content2)),reasoningContent&&(finalizeOpenToolCallBatch(),fullReasoningContent+=reasoningContent,abortSignal?.aborted||onReasoningChunk?.(reasoningContent)),processToolCallDelta(toolCalls)}catch(e){
-console.error("Error parsing JSON chunk:",e)}}}if(abortSignal?.aborted)break}if(abortSignal?.aborted)return;if(streamFinished){finalizeOpenToolCallBatch();const finalToolCalls=aggregatedToolCalls.length>0?JSON.stringify(aggregatedToolCalls):void 0;onComplete?.(aggregatedContent,fullReasoningContent||void 0,lastTimings,finalToolCalls)}}catch(error2){const err=error2 instanceof Error?error2:new Error("Stream error");throw onError?.(err),err}finally{reader.releaseLock()}}static async handleNonStreamResponse(response,onComplete,onError,onToolCallChunk,onModel){
-try{const responseText=await response.text();if(!responseText.trim())throw new Error("No response received from server. Please try again.");const data=JSON.parse(responseText),responseModel=ChatService.extractModelName(data);responseModel&&onModel?.(responseModel);const content2=data.choices[0]?.message?.content||"",reasoningContent=data.choices[0]?.message?.reasoning_content,toolCalls=data.choices[0]?.message?.tool_calls;let serializedToolCalls;if(toolCalls&&toolCalls.length>0){const mergedToolCalls=ChatService.
-mergeToolCallDeltas([],toolCalls);mergedToolCalls.length>0&&(serializedToolCalls=JSON.stringify(mergedToolCalls),serializedToolCalls&&onToolCallChunk?.(serializedToolCalls))}if(!content2.trim()&&!serializedToolCalls)throw new Error("No response received from server. Please try again.");return onComplete?.(content2,reasoningContent,void 0,serializedToolCalls),content2}catch(error2){const err=error2 instanceof Error?error2:new Error("Parse error");throw onError?.(err),err}}static mergeToolCallDeltas(existing,deltas,indexOffset=0){
-const result=existing.map(call=>({...call,function:call.function?{...call.function}:void 0}));for(const delta of deltas){const index2=typeof delta.index=="number"&&delta.index>=0?delta.index+indexOffset:result.length;for(;result.length<=index2;)result.push({function:void 0});const target2=result[index2];if(delta.id&&(target2.id=delta.id),delta.type&&(target2.type=delta.type),delta.function){const fn=target2.function?{...target2.function}:{};delta.function.name&&(fn.name=delta.function.name),delta.
-function.arguments&&(fn.arguments=(fn.arguments??"")+delta.function.arguments),target2.function=fn}}return result}static convertDbMessageToApiChatMessageData(message){if(message.role===MessageRole.TOOL&&message.toolCallId)return{role:MessageRole.TOOL,content:message.content,tool_call_id:message.toolCallId};let toolCalls;if(message.toolCalls)try{toolCalls=JSON.parse(message.toolCalls)}catch{}if(!message.extra||message.extra.length===0){const result2={role:message.role,content:message.content};return message.
-reasoningContent&&(result2.reasoning_content=message.reasoningContent),toolCalls&&toolCalls.length>0&&(result2.tool_calls=toolCalls),result2}const contentParts=[];message.content&&contentParts.push({type:ContentPartType.TEXT,text:message.content});const imageFiles=message.extra.filter(extra=>extra.type===AttachmentType.IMAGE);for(const image2 of imageFiles)contentParts.push({type:ContentPartType.IMAGE_URL,image_url:{url:image2.base64Url}});const textFiles=message.extra.filter(extra=>extra.type===
-AttachmentType.TEXT);for(const textFile of textFiles)contentParts.push({type:ContentPartType.TEXT,text:formatAttachmentText("File",textFile.name,textFile.content)});const legacyContextFiles=message.extra.filter(extra=>extra.type===AttachmentType.LEGACY_CONTEXT);for(const legacyContextFile of legacyContextFiles)contentParts.push({type:ContentPartType.TEXT,text:formatAttachmentText("File",legacyContextFile.name,legacyContextFile.content)});const audioFiles=message.extra.filter(extra=>extra.type===
-AttachmentType.AUDIO);for(const audio of audioFiles)contentParts.push({type:ContentPartType.INPUT_AUDIO,input_audio:{data:audio.base64Data,format:audio.mimeType.includes("wav")?"wav":"mp3"}});const pdfFiles=message.extra.filter(extra=>extra.type===AttachmentType.PDF);for(const pdfFile of pdfFiles)if(pdfFile.processedAsImages&&pdfFile.images)for(let i=0;i<pdfFile.images.length;i++)contentParts.push({type:ContentPartType.IMAGE_URL,image_url:{url:pdfFile.images[i]}});else contentParts.push({type:ContentPartType.
-TEXT,text:formatAttachmentText(ATTACHMENT_LABEL_PDF_FILE,pdfFile.name,pdfFile.content)});const mcpPrompts=message.extra.filter(extra=>extra.type===AttachmentType.MCP_PROMPT);for(const mcpPrompt of mcpPrompts)contentParts.push({type:ContentPartType.TEXT,text:formatAttachmentText(ATTACHMENT_LABEL_MCP_PROMPT,mcpPrompt.name,mcpPrompt.content,mcpPrompt.serverName)});const mcpResources2=message.extra.filter(extra=>extra.type===AttachmentType.MCP_RESOURCE);for(const mcpResource of mcpResources2)contentParts.
-push({type:ContentPartType.TEXT,text:formatAttachmentText(ATTACHMENT_LABEL_MCP_RESOURCE,mcpResource.name,mcpResource.content,mcpResource.serverName)});const result={role:message.role,content:contentParts};return message.reasoningContent&&(result.reasoning_content=message.reasoningContent),toolCalls&&toolCalls.length>0&&(result.tool_calls=toolCalls),result}static stripReasoningContent(content2){const stripFromString=text2=>text2.replace(LEGACY_AGENTIC_REGEX.REASONING_BLOCK,"").trim();return typeof content2==
-"string"?stripFromString(content2):content2.map(part=>part.type===ContentPartType.TEXT&&part.text?{...part,text:stripFromString(part.text)}:part)}static async parseErrorResponse(response){try{const errorText=await response.text(),errorData=JSON.parse(errorText),message=errorData.error?.message||"Unknown server error",error2=new Error(message);return error2.name=response.status===400?"ServerError":"HttpError",errorData.error&&"n_prompt_tokens"in errorData.error&&"n_ctx"in errorData.error&&(error2.
-contextInfo={n_prompt_tokens:errorData.error.n_prompt_tokens,n_ctx:errorData.error.n_ctx}),error2}catch{const fallback2=new Error(`Server error (${response.status}): ${response.statusText}`);return fallback2.name="HttpError",fallback2}}static extractModelName(data){const asRecord=value=>typeof value=="object"&&value!==null?value:void 0,getTrimmedString=value=>typeof value=="string"&&value.trim()?value.trim():void 0,root2=asRecord(data);if(!root2)return;const rootModel=getTrimmedString(root2.model);
-if(rootModel)return rootModel;const firstChoice=Array.isArray(root2.choices)?asRecord(root2.choices[0]):void 0;if(!firstChoice)return;const deltaModel=getTrimmedString(asRecord(firstChoice.delta)?.model);if(deltaModel)return deltaModel;const messageModel=getTrimmedString(asRecord(firstChoice.message)?.model);if(messageModel)return messageModel}static notifyTimings(timings,promptProgress,onTimingsCallback){!onTimingsCallback||!timings&&!promptProgress||onTimingsCallback(timings,promptProgress)}}class ModelsService{static async list(){
-return apiFetch(API_MODELS.LIST)}static async listRouter(){return apiFetch(API_MODELS.LIST)}static async load(modelId,extraArgs){const payload={model:modelId};return extraArgs&&extraArgs.length>0&&(payload.extra_args=extraArgs),apiPost(API_MODELS.LOAD,payload)}static async unload(modelId){return apiPost(API_MODELS.UNLOAD,{model:modelId})}static isModelLoaded(model){return model.status.value===ServerModelStatus.LOADED}static isModelLoading(model){return model.status.value===ServerModelStatus.LOADING}static parseModelId(modelId){
-const result={raw:modelId,orgName:null,modelName:null,params:null,activatedParams:null,quantization:null,tags:[]},colonIdx=modelId.indexOf(MODEL_ID_QUANTIZATION_SEPARATOR);let modelPath;colonIdx!==MODEL_ID_NOT_FOUND?(result.quantization=modelId.slice(colonIdx+1)||null,modelPath=modelId.slice(0,colonIdx)):modelPath=modelId;const slashIdx=modelPath.indexOf(MODEL_ID_ORG_SEPARATOR);let modelStr;slashIdx!==MODEL_ID_NOT_FOUND?(result.orgName=modelPath.slice(0,slashIdx),modelStr=modelPath.slice(slashIdx+
-1)):modelStr=modelPath;const dotIdx=modelStr.lastIndexOf(".");if(dotIdx!==MODEL_ID_NOT_FOUND&&!result.quantization){const afterDot=modelStr.slice(dotIdx+1);MODEL_QUANTIZATION_SEGMENT_RE.test(afterDot)&&(result.quantization=afterDot,modelStr=modelStr.slice(0,dotIdx))}const segments=modelStr.split(MODEL_ID_SEGMENT_SEPARATOR);if(!result.quantization&&segments.length>1){const last=segments[segments.length-1],secondLast=segments.length>2?segments[segments.length-2]:null;MODEL_QUANTIZATION_SEGMENT_RE.
-test(last)&&(secondLast&&MODEL_CUSTOM_QUANTIZATION_PREFIX_RE.test(secondLast)?(result.quantization=`${secondLast}-${last}`,segments.splice(segments.length-2,2)):(result.quantization=last,segments.pop()))}let paramsIdx=MODEL_ID_NOT_FOUND,activatedParamsIdx=MODEL_ID_NOT_FOUND;for(let i=0;i<segments.length;i++){const seg=segments[i];paramsIdx===MODEL_ID_NOT_FOUND&&MODEL_PARAMS_RE.test(seg)?(paramsIdx=i,result.params=seg.toUpperCase()):paramsIdx!==MODEL_ID_NOT_FOUND&&MODEL_ACTIVATED_PARAMS_RE.test(seg)&&
-(activatedParamsIdx=i,result.activatedParams=seg.toUpperCase())}const pivotIdx=paramsIdx!==MODEL_ID_NOT_FOUND?paramsIdx:segments.length;return result.modelName=segments.slice(0,pivotIdx).join(MODEL_ID_SEGMENT_SEPARATOR)||null,paramsIdx!==MODEL_ID_NOT_FOUND&&(result.tags=segments.slice(paramsIdx+1).filter((_,relIdx)=>{const absIdx=paramsIdx+1+relIdx;return absIdx===activatedParamsIdx?!1:!MODEL_IGNORED_SEGMENTS.has(segments[absIdx].toUpperCase())})),result}}class ModelsStore{#models=state$1(proxy(
-[]));get models(){return get$4(this.#models)}set models(value){set$1(this.#models,value,!0)}#routerModels=state$1(proxy([]));get routerModels(){return get$4(this.#routerModels)}set routerModels(value){set$1(this.#routerModels,value,!0)}#loading=state$1(!1);get loading(){return get$4(this.#loading)}set loading(value){set$1(this.#loading,value,!0)}#updating=state$1(!1);get updating(){return get$4(this.#updating)}set updating(value){set$1(this.#updating,value,!0)}#error=state$1(null);get error(){return get$4(
-this.#error)}set error(value){set$1(this.#error,value,!0)}#selectedModelId=state$1(null);get selectedModelId(){return get$4(this.#selectedModelId)}set selectedModelId(value){set$1(this.#selectedModelId,value,!0)}#selectedModelName=state$1(null);get selectedModelName(){return get$4(this.#selectedModelName)}set selectedModelName(value){set$1(this.#selectedModelName,value,!0)}#modelUsage=state$1(proxy(new Map));get modelUsage(){return get$4(this.#modelUsage)}set modelUsage(value){set$1(this.#modelUsage,
-value,!0)}modelLoadingStates=new SvelteMap;#favoriteModelIds=state$1(proxy(this.loadFavoritesFromStorage()));get favoriteModelIds(){return get$4(this.#favoriteModelIds)}set favoriteModelIds(value){set$1(this.#favoriteModelIds,value,!0)}modelPropsCache=new TTLCache({ttlMs:MODEL_PROPS_CACHE_TTL_MS,maxEntries:MODEL_PROPS_CACHE_MAX_ENTRIES});#modelPropsFetching=state$1(proxy(new Set));get modelPropsFetching(){return get$4(this.#modelPropsFetching)}set modelPropsFetching(value){set$1(this.#modelPropsFetching,
-value,!0)}#propsCacheVersion=state$1(0);get propsCacheVersion(){return get$4(this.#propsCacheVersion)}set propsCacheVersion(value){set$1(this.#propsCacheVersion,value,!0)}get selectedModel(){return this.selectedModelId?this.models.find(model=>model.id===this.selectedModelId)??null:null}get loadedModelIds(){return this.routerModels.filter(m=>m.status.value===ServerModelStatus.LOADED||m.status.value===ServerModelStatus.SLEEPING).map(m=>m.id)}get loadingModelIds(){return Array.from(this.modelLoadingStates.
-entries()).filter(([,loading])=>loading).map(([id2])=>id2)}get singleModelName(){if(serverStore.isRouterMode)return null;const props=serverStore.props;return props?.model_alias?props.model_alias:props?.model_path&&props.model_path.split(/(\\|\/)/).pop()||null}getModelModalities(modelId){const model=this.models.find(m=>m.model===modelId||m.id===modelId);if(model?.modalities)return model.modalities;const props=this.modelPropsCache.get(modelId);return props?.modalities?{vision:props.modalities.vision??
-!1,audio:props.modalities.audio??!1}:null}modelSupportsVision(modelId){return this.getModelModalities(modelId)?.vision??!1}modelSupportsAudio(modelId){return this.getModelModalities(modelId)?.audio??!1}getModelModalitiesArray(modelId){const modalities=this.getModelModalities(modelId);if(!modalities)return[];const result=[];return modalities.vision&&result.push(ModelModality.VISION),modalities.audio&&result.push(ModelModality.AUDIO),result}getModelProps(modelId){return this.modelPropsCache.get(modelId)}getModelContextSize(modelId){
-const nCtx=this.getModelProps(modelId)?.default_generation_settings?.n_ctx;return typeof nCtx=="number"?nCtx:null}get selectedModelContextSize(){return this.selectedModelName?this.getModelContextSize(this.selectedModelName):null}isModelPropsFetching(modelId){return this.modelPropsFetching.has(modelId)}isModelLoaded(modelId){const model=this.routerModels.find(m=>m.id===modelId);return model?.status.value===ServerModelStatus.LOADED||model?.status.value===ServerModelStatus.SLEEPING||!1}isModelOperationInProgress(modelId){
-return this.modelLoadingStates.get(modelId)??!1}getModelStatus(modelId){return this.routerModels.find(m=>m.id===modelId)?.status.value??null}getModelUsage(modelId){return this.modelUsage.get(modelId)??new SvelteSet}isModelInUse(modelId){const usage=this.modelUsage.get(modelId);return usage!==void 0&&usage.size>0}async fetch(force=!1){if(!this.loading&&!(this.models.length>0&&!force)){this.loading=!0,this.error=null;try{serverStore.props||await serverStore.fetch();const response=await ModelsService.
-list(),models=response.data.map((item,index2)=>{const details=response.models?.[index2],rawCapabilities=Array.isArray(details?.capabilities)?details?.capabilities:[],displayNameSource=details?.name&&details.name.trim().length>0?details.name:item.id,displayName=this.toDisplayName(displayNameSource),modelId=details?.model||item.id;return{id:item.id,name:displayName,model:modelId,description:details?.description,capabilities:rawCapabilities.filter(value=>!!value),details:details?.details,meta:item.
-meta??null,parsedId:ModelsService.parseModelId(modelId),aliases:item.aliases??[],tags:item.tags??[]}});this.models=models;const serverProps2=serverStore.props;if(serverStore.isModelMode&&this.models.length>0&&serverProps2?.modalities){const modalities={vision:serverProps2.modalities.vision??!1,audio:serverProps2.modalities.audio??!1};this.modelPropsCache.set(this.models[0].model,serverProps2),this.models=this.models.map((model,index2)=>index2===0?{...model,modalities}:model)}}catch(error2){throw this.
-models=[],this.error=error2 instanceof Error?error2.message:"Failed to load models",error2}finally{this.loading=!1}}}async fetchRouterModels(){try{const response=await ModelsService.listRouter();this.routerModels=response.data,await this.fetchModalitiesForLoadedModels();const o=this.models.filter(option2=>this.getModelProps(option2.model)?.webui!==!1);o.length===1&&this.isModelLoaded(o[0].model)&&this.selectModelById(o[0].id)}catch(error2){console.warn("Failed to fetch router models:",error2),this.
-routerModels=[]}}async fetchModelProps(modelId){const cached2=this.modelPropsCache.get(modelId);if(cached2)return cached2;if(serverStore.isRouterMode&&!this.isModelLoaded(modelId)||this.modelPropsFetching.has(modelId))return null;this.modelPropsFetching.add(modelId);try{const props=await PropsService.fetchForModel(modelId);return this.modelPropsCache.set(modelId,props),props}catch(error2){return console.warn(`Failed to fetch props for model ${modelId}:`,error2),null}finally{this.modelPropsFetching.
-delete(modelId)}}async fetchModalitiesForLoadedModels(){const loadedModelIds2=this.loadedModelIds;if(loadedModelIds2.length===0)return;const propsPromises=loadedModelIds2.map(modelId=>this.fetchModelProps(modelId));try{const results=await Promise.all(propsPromises);this.models=this.models.map(model=>{const modelIndex=loadedModelIds2.indexOf(model.model);if(modelIndex===-1)return model;const props=results[modelIndex];if(!props?.modalities)return model;const modalities={vision:props.modalities.vision??
-!1,audio:props.modalities.audio??!1};return{...model,modalities}}),this.propsCacheVersion++}catch(error2){console.warn("Failed to fetch modalities for loaded models:",error2)}}async updateModelModalities(modelId){try{const props=await this.fetchModelProps(modelId);if(!props?.modalities)return;const modalities={vision:props.modalities.vision??!1,audio:props.modalities.audio??!1};this.models=this.models.map(model=>model.model===modelId?{...model,modalities}:model),this.propsCacheVersion++}catch(error2){
-console.warn(`Failed to update modalities for model ${modelId}:`,error2)}}async selectModelById(modelId){if(!modelId||this.updating||this.selectedModelId===modelId)return;const option2=this.models.find(model=>model.id===modelId);if(!option2)throw new Error("Selected model is not available");this.updating=!0,this.error=null;try{this.selectedModelId=option2.id,this.selectedModelName=option2.model}finally{this.updating=!1}}selectModelByName(modelName){const option2=this.models.find(model=>model.model===
-modelName);option2&&(this.selectedModelId=option2.id,this.selectedModelName=option2.model)}clearSelection(){this.selectedModelId=null,this.selectedModelName=null}findModelByName(modelName){return this.models.find(model=>model.model===modelName)??null}findModelById(modelId){return this.models.find(model=>model.id===modelId)??null}hasModel(modelName){return this.models.some(model=>model.model===modelName)}static STATUS_POLL_INTERVAL=500;async pollForModelStatus(modelId,expectedStatus){let attempt=0;
-for(;;){await this.fetchRouterModels();const currentStatus=this.getModelStatus(modelId);if(currentStatus===expectedStatus)return;if(currentStatus===ServerModelStatus.FAILED)throw new Error(`Model failed to ${expectedStatus===ServerModelStatus.LOADED?"load":"unload"}`);if(expectedStatus===ServerModelStatus.LOADED&&currentStatus===ServerModelStatus.UNLOADED&&attempt>2)throw new Error("Model was unloaded unexpectedly during loading");attempt++,await new Promise(resolve2=>setTimeout(resolve2,ModelsStore.
-STATUS_POLL_INTERVAL))}}async loadModel(modelId){if(!this.isModelLoaded(modelId)&&!this.modelLoadingStates.get(modelId)){this.modelLoadingStates.set(modelId,!0),this.error=null;try{await ModelsService.load(modelId),await this.pollForModelStatus(modelId,ServerModelStatus.LOADED),await this.updateModelModalities(modelId),toast.success(`Model loaded: ${this.toDisplayName(modelId)}`)}catch(error2){throw this.error=error2 instanceof Error?error2.message:"Failed to load model",toast.error(`Failed to l\
-oad model: ${this.toDisplayName(modelId)}`),error2}finally{this.modelLoadingStates.set(modelId,!1)}}}async unloadModel(modelId){if(this.isModelLoaded(modelId)&&!this.modelLoadingStates.get(modelId)){this.modelLoadingStates.set(modelId,!0),this.error=null;try{await ModelsService.unload(modelId),await this.pollForModelStatus(modelId,ServerModelStatus.UNLOADED),toast.info(`Model unloaded: ${this.toDisplayName(modelId)}`)}catch(error2){throw this.error=error2 instanceof Error?error2.message:"Failed \
-to unload model",toast.error(`Failed to unload model: ${this.toDisplayName(modelId)}`),error2}finally{this.modelLoadingStates.set(modelId,!1)}}}async ensureModelLoaded(modelId){this.isModelLoaded(modelId)||await this.loadModel(modelId)}isFavorite(modelId){return this.favoriteModelIds.has(modelId)}toggleFavorite(modelId){const next2=new SvelteSet(this.favoriteModelIds);next2.has(modelId)?next2.delete(modelId):next2.add(modelId),this.favoriteModelIds=next2;try{localStorage.setItem(FAVORITE_MODELS_LOCALSTORAGE_KEY,
-JSON.stringify([...next2]))}catch{toast.error("Failed to save favorite models to local storage")}}loadFavoritesFromStorage(){try{const raw2=localStorage.getItem(FAVORITE_MODELS_LOCALSTORAGE_KEY);return raw2?new Set(JSON.parse(raw2)):new Set}catch{return toast.error("Failed to load favorite models from local storage"),new Set}}toDisplayName(id2){const candidate=id2.split(/\\|\//).pop();return candidate&&candidate.trim().length>0?candidate:id2}clear(){this.models=[],this.routerModels=[],this.loading=
-!1,this.updating=!1,this.error=null,this.selectedModelId=null,this.selectedModelName=null,this.modelUsage.clear(),this.modelLoadingStates.clear(),this.modelPropsCache.clear(),this.modelPropsFetching.clear()}pruneExpiredCache(){return this.modelPropsCache.prune()}}const modelsStore=new ModelsStore,modelOptions=()=>modelsStore.models,routerModels=()=>modelsStore.routerModels,modelsLoading=()=>modelsStore.loading,modelsUpdating=()=>modelsStore.updating,selectedModelId=()=>modelsStore.selectedModelId,
-selectedModelName=()=>modelsStore.selectedModelName,singleModelName=()=>modelsStore.singleModelName,selectedModelContextSize=()=>modelsStore.selectedModelContextSize;function readFileAsBase64(file){return new Promise((resolve2,reject)=>{const reader=new FileReader;reader.onload=()=>{const base642=reader.result.split(",")[1];resolve2(base642)},reader.onerror=()=>reject(reader.error),reader.readAsDataURL(file)})}async function parseFilesToMessageExtras(files,activeModelId){const extras=[],emptyFiles=[];
-for(const file of files){if(file.type===SpecialFileType.MCP_PROMPT&&file.mcpPrompt){extras.push({type:AttachmentType.MCP_PROMPT,name:file.name,serverName:file.mcpPrompt.serverName,promptName:file.mcpPrompt.promptName,content:file.textContent??"",arguments:file.mcpPrompt.arguments});continue}if(getFileTypeCategory(file.type)===FileTypeCategory.IMAGE){if(file.preview){let base64Url=file.preview;if(isSvgMimeType(file.type))try{base64Url=await svgBase64UrlToPngDataURL(base64Url)}catch(error2){console.
-error("Failed to convert SVG to PNG for database storage:",error2)}else if(isWebpMimeType(file.type))try{base64Url=await webpBase64UrlToPngDataURL(base64Url)}catch(error2){console.error("Failed to convert WebP to PNG for database storage:",error2)}extras.push({type:AttachmentType.IMAGE,name:file.name,base64Url})}}else if(getFileTypeCategory(file.type)===FileTypeCategory.AUDIO)try{const base64Data=await readFileAsBase64(file.file);extras.push({type:AttachmentType.AUDIO,name:file.name,base64Data,mimeType:file.
-type})}catch(error2){console.error(`Failed to process audio file ${file.name}:`,error2)}else if(getFileTypeCategory(file.type)===FileTypeCategory.PDF)try{const base64Data=await readFileAsBase64(file.file),currentConfig=config$1(),hasVisionSupport=activeModelId?modelsStore.modelSupportsVision(activeModelId):!1;let shouldProcessAsImages=!!currentConfig.pdfAsImage&&hasVisionSupport;if(currentConfig.pdfAsImage&&!hasVisionSupport&&(console.log("Non-vision model detected: forcing PDF-to-text mode and \
-updating settings"),settingsStore.updateConfig("pdfAsImage",!1),toast.warning("PDF setting changed: Non-vision model detected, PDFs will be processed as text instead of images.",{duration:5e3}),shouldProcessAsImages=!1),shouldProcessAsImages)try{const images=await convertPDFToImage(file.file);toast.success(`PDF "${file.name}" processed as ${images.length} images for vision model.`,{duration:3e3}),extras.push({type:AttachmentType.PDF,name:file.name,content:`PDF file with ${images.length} pages`,images,
-processedAsImages:!0,base64Data})}catch(imageError){console.warn(`Failed to process PDF ${file.name} as images, falling back to text:`,imageError);const content2=await convertPDFToText(file.file);extras.push({type:AttachmentType.PDF,name:file.name,content:content2,processedAsImages:!1,base64Data})}else{const content2=await convertPDFToText(file.file);toast.success(`PDF "${file.name}" processed as text content.`,{duration:3e3}),extras.push({type:AttachmentType.PDF,name:file.name,content:content2,
-processedAsImages:!1,base64Data})}}catch(error2){console.error(`Failed to process PDF file ${file.name}:`,error2)}else try{const content2=await readFileAsText(file.file);content2.trim()===""?(console.warn(`File ${file.name} is empty and will be skipped`),emptyFiles.push(file.name)):isLikelyTextFile(content2)?extras.push({type:AttachmentType.TEXT,name:file.name,content:content2}):console.warn(`File ${file.name} appears to be binary and will be skipped`)}catch(error2){console.error(`Failed to read\
- file ${file.name}:`,error2)}}return{extras,emptyFiles}}function readFileAsDataURL(file){return new Promise((resolve2,reject)=>{const reader=new FileReader;reader.onload=()=>resolve2(reader.result),reader.onerror=()=>reject(reader.error),reader.readAsDataURL(file)})}function readFileAsUTF8(file){return new Promise((resolve2,reject)=>{const reader=new FileReader;reader.onload=()=>resolve2(reader.result),reader.onerror=()=>reject(reader.error),reader.readAsText(file)})}async function processFilesToChatUploaded(files,activeModelId){
-const results=[];for(const file of files){const base2={id:Date.now().toString()+Math.random().toString(36).substr(2,9),name:file.name,size:file.size,type:file.type,file};try{if(getFileTypeCategory(file.type)===FileTypeCategory.IMAGE){let preview=await readFileAsDataURL(file);if(isSvgMimeType(file.type))try{preview=await svgBase64UrlToPngDataURL(preview)}catch(err){console.error("Failed to convert SVG to PNG:",err)}else if(isWebpMimeType(file.type))try{preview=await webpBase64UrlToPngDataURL(preview)}catch(err){
-console.error("Failed to convert WebP to PNG:",err)}results.push({...base2,preview})}else if(getFileTypeCategory(file.type)===FileTypeCategory.PDF){try{const textContent=await convertPDFToText(file);results.push({...base2,textContent})}catch(err){console.warn("Failed to extract text from PDF, adding without content:",err),results.push(base2)}const hasVisionSupport=activeModelId?modelsStore.modelSupportsVision(activeModelId):!1,currentConfig=settingsStore.config;hasVisionSupport&&!currentConfig.pdfAsImage&&
-toast.info("You can enable parsing PDF as images with vision models.",{duration:8e3,action:{label:"Enable PDF as Images",onClick:()=>{settingsStore.updateConfig("pdfAsImage",!0),toast.success("PDF parsing as images enabled!",{duration:3e3})}}})}else if(getFileTypeCategory(file.type)===FileTypeCategory.AUDIO){const preview=await readFileAsDataURL(file);results.push({...base2,preview})}else try{const textContent=await readFileAsUTF8(file);results.push({...base2,textContent})}catch(err){console.warn(
-"Failed to read file as text, adding without content:",err),results.push(base2)}}catch(error2){console.error("Error processing file",file.name,error2),results.push(base2)}}return results}var root_2$11=from_html("<!> Text",1),root_4$x=from_html('<div class="mr-1 h-4 w-4 animate-spin rounded-full border-2 border-current border-t-transparent"></div>'),root_3$T=from_html("<!> Pages",1),root_1$V=from_html('<div class="flex items-center gap-2"><!> <!></div>'),root_6$y=from_html('<div class="flex items\
--center justify-center"><img class="max-h-full rounded-lg object-contain shadow-lg"/></div>'),root_12$9=from_html('<span class="inline-flex">The selected model does not support vision. Only the extracted  <span class="mx-1 cursor-pointer underline">text</span> will be sent to the model.</span>'),root_10$i=from_html("<!> <!> <!>",1),root_13$d=from_html('<div class="flex items-center justify-center p-8"><div class="text-center"><div class="mx-auto mb-4 h-8 w-8 animate-spin rounded-full border-4 bo\
-rder-primary border-t-transparent"></div> <p class="text-muted-foreground">Converting PDF to images...</p></div></div>'),root_15$6=from_html('<div class="flex items-center justify-center p-8"><div class="text-center"><!> <p class="mb-4 text-muted-foreground">Failed to load PDF images</p> <p class="text-sm text-muted-foreground"> </p> <!></div></div>'),root_19$3=from_html('<div class="text-center"><p class="mb-2 text-sm text-muted-foreground"> </p> <img class="mx-auto max-w-full rounded-lg shadow\
--lg"/></div>'),root_18$6=from_html('<div class="max-h-[70vh] space-y-4 overflow-auto"></div>'),root_20$a=from_html('<div class="flex items-center justify-center p-8"><div class="text-center"><!> <p class="mb-4 text-muted-foreground">No PDF pages available</p></div></div>'),root_8$o=from_html("<!> <!>",1),root_25=from_html('<audio controls class="mb-4 w-full">Your browser does not support the audio element.</audio>'),root_27$2=from_html('<audio controls class="mb-4 w-full">Your browser does not s\
-upport the audio element.</audio>'),root_28$4=from_html('<p class="mb-4 text-muted-foreground">Audio preview not available</p>'),root_24$3=from_html('<div class="flex items-center justify-center p-8"><div class="w-full max-w-md text-center"><!> <!> <p class="text-sm text-muted-foreground"> </p></div></div>'),root_29$2=from_html('<div class="flex items-center justify-center p-8"><div class="text-center"><!> <p class="mb-4 text-muted-foreground">Preview not available for this file type</p></div></\
-div>'),root$1q=from_html('<div class="space-y-4"><div class="flex items-center justify-end gap-6"><!></div> <div class="flex-1 overflow-auto"><!></div></div>');function ChatAttachmentPreview($$anchor,$$props){push$1($$props,!0);let hasVisionModality=user_derived(()=>$$props.activeModelId?modelsStore.modelSupportsVision($$props.activeModelId):!1),displayName=user_derived(()=>$$props.uploadedFile?.name||$$props.attachment?.name||$$props.name||"Unknown File"),isAudio=user_derived(()=>isAudioFile($$props.
-attachment,$$props.uploadedFile)),isImage2=user_derived(()=>isImageFile($$props.attachment,$$props.uploadedFile)),isPdf=user_derived(()=>isPdfFile$1($$props.attachment,$$props.uploadedFile)),isText=user_derived(()=>isTextFile($$props.attachment,$$props.uploadedFile)),displayPreview=user_derived(()=>$$props.uploadedFile?.preview||(get$4(isImage2)&&$$props.attachment&&"base64Url"in $$props.attachment?$$props.attachment.base64Url:$$props.preview)),displayTextContent=user_derived(()=>$$props.uploadedFile?.
-textContent||($$props.attachment&&"content"in $$props.attachment?$$props.attachment.content:$$props.textContent)),language2=user_derived(()=>getLanguageFromFilename(get$4(displayName))),IconComponent=user_derived(()=>()=>get$4(isImage2)?Image$1:get$4(isText)||get$4(isPdf)?File_text:get$4(isAudio)?Music:File$1),pdfViewMode=state$1("pages"),pdfImages=state$1(proxy([])),pdfImagesLoading=state$1(!1),pdfImagesError=state$1(null);async function loadPdfImages(){if(!(!get$4(isPdf)||get$4(pdfImages).length>
-0||get$4(pdfImagesLoading))){set$1(pdfImagesLoading,!0),set$1(pdfImagesError,null);try{let file=null;if($$props.uploadedFile?.file)file=$$props.uploadedFile.file;else if(get$4(isPdf)&&$$props.attachment){if("images"in $$props.attachment&&$$props.attachment.images&&Array.isArray($$props.attachment.images)&&$$props.attachment.images.length>0){set$1(pdfImages,$$props.attachment.images,!0);return}if("base64Data"in $$props.attachment&&$$props.attachment.base64Data){const base64Data=$$props.attachment.
-base64Data,byteCharacters=atob(base64Data),byteNumbers=new Array(byteCharacters.length);for(let i=0;i<byteCharacters.length;i++)byteNumbers[i]=byteCharacters.charCodeAt(i);const byteArray=new Uint8Array(byteNumbers);file=new File([byteArray],get$4(displayName),{type:"application/pdf"})}}if(file)set$1(pdfImages,await convertPDFToImage(file),!0);else throw new Error("No PDF file available for conversion")}catch(error2){set$1(pdfImagesError,error2 instanceof Error?error2.message:"Failed to load PDF\
- images",!0)}finally{set$1(pdfImagesLoading,!1)}}}function reset$1(){set$1(pdfImages,[],!0),set$1(pdfImagesLoading,!1),set$1(pdfImagesError,null),set$1(pdfViewMode,"pages")}user_effect(()=>{get$4(isPdf)&&get$4(pdfViewMode)==="pages"&&loadPdfImages()});var $$exports={reset:reset$1},div=root$1q(),div_1=child(div),node2=child(div_1);{var consequent_1=$$anchor2=>{var div_2=root_1$V(),node_1=child(div_2);{let $0=user_derived(()=>get$4(pdfViewMode)==="text"?"default":"outline");Button(node_1,{get variant(){
-return get$4($0)},size:"sm",onclick:()=>set$1(pdfViewMode,"text"),get disabled(){return get$4(pdfImagesLoading)},children:($$anchor3,$$slotProps)=>{var fragment=root_2$11(),node_2=first_child(fragment);File_text(node_2,{class:"mr-1 h-4 w-4"}),next$1(),append($$anchor3,fragment)},$$slots:{default:!0}})}var node_3=sibling(node_1,2);{let $0=user_derived(()=>get$4(pdfViewMode)==="pages"?"default":"outline");Button(node_3,{get variant(){return get$4($0)},size:"sm",onclick:()=>{set$1(pdfViewMode,"page\
-s"),loadPdfImages()},get disabled(){return get$4(pdfImagesLoading)},children:($$anchor3,$$slotProps)=>{var fragment_1=root_3$T(),node_4=first_child(fragment_1);{var consequent=$$anchor4=>{var div_3=root_4$x();append($$anchor4,div_3)},alternate=$$anchor4=>{Eye($$anchor4,{class:"mr-1 h-4 w-4"})};if_block(node_4,$$render=>{get$4(pdfImagesLoading)?$$render(consequent):$$render(alternate,!1)})}next$1(),append($$anchor3,fragment_1)},$$slots:{default:!0}})}reset(div_2),append($$anchor2,div_2)};if_block(
-node2,$$render=>{get$4(isPdf)&&$$render(consequent_1)})}reset(div_1);var div_4=sibling(div_1,2),node_5=child(div_4);{var consequent_2=$$anchor2=>{var div_5=root_6$y(),img=child(div_5);reset(div_5),template_effect(()=>{set_attribute(img,"src",get$4(displayPreview)),set_attribute(img,"alt",get$4(displayName))}),append($$anchor2,div_5)},alternate_9=$$anchor2=>{var fragment_3=comment$2(),node_6=first_child(fragment_3);{var consequent_7=$$anchor3=>{var fragment_4=root_8$o(),node_7=first_child(fragment_4);
-{var consequent_3=$$anchor4=>{var fragment_5=comment$2(),node_8=first_child(fragment_5);component(node_8,()=>Alert,($$anchor5,Alert_Root)=>{Alert_Root($$anchor5,{class:"mb-4",children:($$anchor6,$$slotProps)=>{var fragment_6=root_10$i(),node_9=first_child(fragment_6);Info$1(node_9,{class:"h-4 w-4"});var node_10=sibling(node_9,2);component(node_10,()=>Alert_title,($$anchor7,Alert_Title)=>{Alert_Title($$anchor7,{children:($$anchor8,$$slotProps2)=>{next$1();var text2=text$8("Preview only");append($$anchor8,
-text2)},$$slots:{default:!0}})});var node_11=sibling(node_10,2);component(node_11,()=>Alert_description,($$anchor7,Alert_Description)=>{Alert_Description($$anchor7,{children:($$anchor8,$$slotProps2)=>{var span=root_12$9(),span_1=sibling(child(span));span_1.__click=()=>set$1(pdfViewMode,"text"),next$1(),reset(span),append($$anchor8,span)},$$slots:{default:!0}})}),append($$anchor6,fragment_6)},$$slots:{default:!0}})}),append($$anchor4,fragment_5)};if_block(node_7,$$render=>{!get$4(hasVisionModality)&&
-$$props.activeModelId&&$$render(consequent_3)})}var node_12=sibling(node_7,2);{var consequent_4=$$anchor4=>{var div_6=root_13$d();append($$anchor4,div_6)},alternate_3=$$anchor4=>{var fragment_7=comment$2(),node_13=first_child(fragment_7);{var consequent_5=$$anchor5=>{var div_7=root_15$6(),div_8=child(div_7),node_14=child(div_8);File_text(node_14,{class:"mx-auto mb-4 h-16 w-16 text-muted-foreground"});var p2=sibling(node_14,4),text_1=child(p2,!0);reset(p2);var node_15=sibling(p2,2);Button(node_15,
-{class:"mt-4",onclick:()=>set$1(pdfViewMode,"text"),children:($$anchor6,$$slotProps)=>{next$1();var text_2=text$8("View as Text");append($$anchor6,text_2)},$$slots:{default:!0}}),reset(div_8),reset(div_7),template_effect(()=>set_text(text_1,get$4(pdfImagesError))),append($$anchor5,div_7)},alternate_2=$$anchor5=>{var fragment_8=comment$2(),node_16=first_child(fragment_8);{var consequent_6=$$anchor6=>{var div_9=root_18$6();each(div_9,22,()=>get$4(pdfImages),image2=>image2,($$anchor7,image2,index2)=>{
-var div_10=root_19$3(),p_1=child(div_10),text_3=child(p_1);reset(p_1);var img_1=sibling(p_1,2);reset(div_10),template_effect(()=>{set_text(text_3,`Page ${get$4(index2)+1}`),set_attribute(img_1,"src",image2),set_attribute(img_1,"alt",`PDF Page ${get$4(index2)+1}`)}),append($$anchor7,div_10)}),reset(div_9),append($$anchor6,div_9)},alternate_1=$$anchor6=>{var div_11=root_20$a(),div_12=child(div_11),node_17=child(div_12);File_text(node_17,{class:"mx-auto mb-4 h-16 w-16 text-muted-foreground"}),next$1(
-2),reset(div_12),reset(div_11),append($$anchor6,div_11)};if_block(node_16,$$render=>{get$4(pdfImages).length>0?$$render(consequent_6):$$render(alternate_1,!1)},!0)}append($$anchor5,fragment_8)};if_block(node_13,$$render=>{get$4(pdfImagesError)?$$render(consequent_5):$$render(alternate_2,!1)},!0)}append($$anchor4,fragment_7)};if_block(node_12,$$render=>{get$4(pdfImagesLoading)?$$render(consequent_4):$$render(alternate_3,!1)})}append($$anchor3,fragment_4)},alternate_8=$$anchor3=>{var fragment_9=comment$2(),
-node_18=first_child(fragment_9);{var consequent_8=$$anchor4=>{SyntaxHighlightedCode($$anchor4,{get code(){return get$4(displayTextContent)},get language(){return get$4(language2)},maxWidth:"calc(69rem - 2rem)"})},alternate_7=$$anchor4=>{var fragment_11=comment$2(),node_19=first_child(fragment_11);{var consequent_11=$$anchor5=>{var div_13=root_24$3(),div_14=child(div_13),node_20=child(div_14);Music(node_20,{class:"mx-auto mb-4 h-16 w-16 text-muted-foreground"});var node_21=sibling(node_20,2);{var consequent_9=$$anchor6=>{
-var audio=root_25();template_effect(()=>set_attribute(audio,"src",$$props.uploadedFile.preview)),append($$anchor6,audio)},alternate_5=$$anchor6=>{var fragment_12=comment$2(),node_22=first_child(fragment_12);{var consequent_10=$$anchor7=>{var audio_1=root_27$2();template_effect($0=>set_attribute(audio_1,"src",$0),[()=>createBase64DataUrl($$props.attachment.mimeType,$$props.attachment.base64Data)]),append($$anchor7,audio_1)},alternate_4=$$anchor7=>{var p_2=root_28$4();append($$anchor7,p_2)};if_block(
-node_22,$$render=>{get$4(isAudio)&&$$props.attachment&&"mimeType"in $$props.attachment&&"base64Data"in $$props.attachment?$$render(consequent_10):$$render(alternate_4,!1)},!0)}append($$anchor6,fragment_12)};if_block(node_21,$$render=>{$$props.uploadedFile?.preview?$$render(consequent_9):$$render(alternate_5,!1)})}var p_3=sibling(node_21,2),text_4=child(p_3,!0);reset(p_3),reset(div_14),reset(div_13),template_effect(()=>set_text(text_4,get$4(displayName))),append($$anchor5,div_13)},alternate_6=$$anchor5=>{
-var div_15=root_29$2(),div_16=child(div_15),node_23=child(div_16);{var consequent_12=$$anchor6=>{var fragment_13=comment$2(),node_24=first_child(fragment_13);component(node_24,()=>get$4(IconComponent),($$anchor7,IconComponent_1)=>{IconComponent_1($$anchor7,{class:"mx-auto mb-4 h-16 w-16 text-muted-foreground"})}),append($$anchor6,fragment_13)};if_block(node_23,$$render=>{get$4(IconComponent)&&$$render(consequent_12)})}next$1(2),reset(div_16),reset(div_15),append($$anchor5,div_15)};if_block(node_19,
-$$render=>{get$4(isAudio)?$$render(consequent_11):$$render(alternate_6,!1)},!0)}append($$anchor4,fragment_11)};if_block(node_18,$$render=>{(get$4(isText)||get$4(isPdf)&&get$4(pdfViewMode)==="text")&&get$4(displayTextContent)?$$render(consequent_8):$$render(alternate_7,!1)},!0)}append($$anchor3,fragment_9)};if_block(node_6,$$render=>{get$4(isPdf)&&get$4(pdfViewMode)==="pages"?$$render(consequent_7):$$render(alternate_8,!1)},!0)}append($$anchor2,fragment_3)};if_block(node_5,$$render=>{get$4(isImage2)&&
-get$4(displayPreview)?$$render(consequent_2):$$render(alternate_9,!1)})}return reset(div_4),reset(div),append($$anchor,div),pop($$exports)}delegate(["click"]);var root_1$U=from_html("<div><!></div>");function ChatAttachmentMcpResources($$anchor,$$props){push$1($$props,!0);const attachments=user_derived(mcpResourceAttachments),hasAttachments=user_derived(mcpHasResourceAttachments);function handleRemove(attachmentId){mcpStore.removeResourceAttachment(attachmentId)}function handleResourceClick(uri2){
-$$props.onResourceClick?.(uri2)}var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var div=root_1$U(),node_1=child(div);HorizontalScrollCarousel(node_1,{gapSize:"2",children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_2=first_child(fragment_1);each(node_2,19,()=>get$4(attachments),attachment=>attachment.id,($$anchor4,attachment,i)=>{{let $0=user_derived(()=>get$4(i)===0?"ml-3":"");ChatAttachmentMcpResource($$anchor4,{get class(){return get$4($0)},get attachment(){
-return get$4(attachment)},onRemove:handleRemove,onClick:()=>handleResourceClick(get$4(attachment).resource.uri)})}}),append($$anchor3,fragment_1)},$$slots:{default:!0}}),reset(div),template_effect(()=>set_class(div,1,clsx($$props.class))),append($$anchor2,div)};if_block(node2,$$render=>{get$4(hasAttachments)&&$$render(consequent)})}append($$anchor,fragment),pop()}var root_3$S=from_html('<span class="text-xs text-muted-foreground"> </span>'),root_5$u=from_html('<div class="pointer-events-none abs\
-olute right-0 bottom-0 left-0 h-6 bg-gradient-to-t from-muted to-transparent"></div>'),root_4$w=from_html('<div class="relative mt-2 w-full"><div class="overflow-hidden font-mono text-xs leading-relaxed break-words whitespace-pre-wrap text-muted-foreground"> </div> <!></div>'),root_2$10=from_html('<button type="button"><div class="flex items-start gap-3"><div class="flex min-w-0 flex-1 flex-col items-start text-left"><span class="w-full truncate text-sm font-medium text-foreground"> </span> <!> \
-<!></div></div></button>'),root_8$n=from_html('<div class="pointer-events-none absolute right-0 bottom-0 left-0 h-4 bg-gradient-to-t from-muted to-transparent"></div>'),root_7$r=from_html('<div class="relative"><div class="overflow-hidden font-mono text-xs leading-relaxed break-words whitespace-pre-wrap text-muted-foreground" style="max-height: 3rem; line-height: 1.2em;"> </div> <!></div>'),root_6$x=from_html('<button><div class="absolute top-2 right-2 opacity-0 transition-opacity group-hover:op\
-acity-100"><!></div> <div class="pr-8"><span class="mb-3 block truncate text-sm font-medium text-foreground"> </span> <!></div></button>'),root_10$h=from_html('<span class="text-left text-xs text-muted-foreground"> </span>'),root_12$8=from_html('<span class="text-left text-xs text-muted-foreground"> </span>'),root_13$c=from_html('<div class="absolute top-2 right-2 opacity-0 transition-opacity group-hover:opacity-100"><!></div>'),root_9$m=from_html('<button><div class="flex h-8 w-8 items-center j\
-ustify-center rounded bg-primary/10 text-xs font-medium text-primary"> </div> <div class="flex flex-col gap-0.5"><span> </span> <!></div> <!></button>');function ChatAttachmentThumbnailFile($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),readonly2=prop($$props,"readonly",3,!1),isText=user_derived(()=>isTextFile($$props.attachment,$$props.uploadedFile)),fileTypeLabel=user_derived(()=>{if($$props.uploadedFile?.type)return getFileTypeLabel($$props.uploadedFile.type);if($$props.
-attachment){if("mimeType"in $$props.attachment&&$$props.attachment.mimeType)return getFileTypeLabel($$props.attachment.mimeType);if($$props.attachment.type)return getFileTypeLabel($$props.attachment.type)}return getFileTypeLabel($$props.name)}),pdfProcessingMode=user_derived(()=>$$props.attachment?.type===AttachmentType.PDF?$$props.attachment.processedAsImages?"Sent as Image":"Sent as Text":null);var fragment=comment$2(),node2=first_child(fragment);{var consequent_6=$$anchor2=>{var fragment_1=comment$2(),
-node_1=first_child(fragment_1);{var consequent_3=$$anchor3=>{var button=root_2$10();button.__click=function(...$$args){$$props.onClick?.apply(this,$$args)};var div=child(button),div_1=child(div),span=child(div_1),text2=child(span,!0);reset(span);var node_2=sibling(span,2);{var consequent=$$anchor4=>{var span_1=root_3$S(),text_1=child(span_1,!0);reset(span_1),template_effect($0=>set_text(text_1,$0),[()=>formatFileSize($$props.size)]),append($$anchor4,span_1)};if_block(node_2,$$render=>{$$props.size&&
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-button_1),node_5=child(div_5);ActionIconRemove(node_5,{get id(){return $$props.id},get onRemove(){return $$props.onRemove}}),reset(div_5);var div_6=sibling(div_5,2),span_2=child(div_6),text_3=child(span_2,!0);reset(span_2);var node_6=sibling(span_2,2);{var consequent_5=$$anchor4=>{var div_7=root_7$r(),div_8=child(div_7),text_4=child(div_8,!0);reset(div_8);var node_7=sibling(div_8,2);{var consequent_4=$$anchor5=>{var div_9=root_8$n();append($$anchor5,div_9)};if_block(node_7,$$render=>{$$props.textContent.
-length>150&&$$render(consequent_4)})}reset(div_7),template_effect($0=>set_text(text_4,$0),[()=>getPreviewText($$props.textContent)]),append($$anchor4,div_7)};if_block(node_6,$$render=>{$$props.textContent&&$$render(consequent_5)})}reset(div_6),reset(button_1),template_effect(()=>{set_class(button_1,1,`group relative rounded-lg border border-border bg-muted p-3 ${className()??""} ${$$props.textContent?"max-h-24 max-w-72":"max-w-36"} cursor-pointer text-left`),set_text(text_3,$$props.name)}),append(
-$$anchor3,button_1)};if_block(node_1,$$render=>{readonly2()?$$render(consequent_3):$$render(alternate,!1)})}append($$anchor2,fragment_1)},alternate_2=$$anchor2=>{var button_2=root_9$m();button_2.__click=function(...$$args){$$props.onClick?.apply(this,$$args)};var div_10=child(button_2),text_5=child(div_10,!0);reset(div_10);var div_11=sibling(div_10,2),span_3=child(div_11),text_6=child(span_3,!0);reset(span_3);var node_8=sibling(span_3,2);{var consequent_7=$$anchor3=>{var span_4=root_10$h(),text_7=child(
-span_4,!0);reset(span_4),template_effect(()=>set_text(text_7,get$4(pdfProcessingMode))),append($$anchor3,span_4)},alternate_1=$$anchor3=>{var fragment_2=comment$2(),node_9=first_child(fragment_2);{var consequent_8=$$anchor4=>{var span_5=root_12$8(),text_8=child(span_5,!0);reset(span_5),template_effect($0=>set_text(text_8,$0),[()=>formatFileSize($$props.size)]),append($$anchor4,span_5)};if_block(node_9,$$render=>{$$props.size&&$$render(consequent_8)},!0)}append($$anchor3,fragment_2)};if_block(node_8,
-$$render=>{get$4(pdfProcessingMode)?$$render(consequent_7):$$render(alternate_1,!1)})}reset(div_11);var node_10=sibling(div_11,2);{var consequent_9=$$anchor3=>{var div_12=root_13$c(),node_11=child(div_12);ActionIconRemove(node_11,{get id(){return $$props.id},get onRemove(){return $$props.onRemove}}),reset(div_12),append($$anchor3,div_12)};if_block(node_10,$$render=>{readonly2()||$$render(consequent_9)})}reset(button_2),template_effect(()=>{set_class(button_2,1,`group flex items-center gap-3 roun\
-ded-lg border border-border bg-muted p-3 ${className()??""} relative`),set_text(text_5,get$4(fileTypeLabel)),set_class(span_3,1,`max-w-24 truncate text-sm font-medium text-foreground ${readonly2()?"":"group-hover:pr-6"} md:max-w-32`),set_text(text_6,$$props.name)}),append($$anchor2,button_2)};if_block(node2,$$render=>{get$4(isText)?$$render(consequent_6):$$render(alternate_2,!1)})}append($$anchor,fragment),pop()}delegate(["click"]);var root_1$T=from_html('<button type="button" class="block h-ful\
-l w-full rounded-lg focus:ring-2 focus:ring-primary focus:ring-offset-2 focus:outline-none"><img/></button>'),root_2$$=from_html("<img/>"),root_3$R=from_html('<div class="absolute top-1 right-1 flex items-center justify-center opacity-0 transition-opacity group-hover:opacity-100"><!></div>'),root$1p=from_html("<div><!> <!></div>");function ChatAttachmentThumbnailImage($$anchor,$$props){let readonly2=prop($$props,"readonly",3,!1),className=prop($$props,"class",3,""),width=prop($$props,"width",3,"\
-w-auto"),height=prop($$props,"height",3,"h-16"),imageClass=prop($$props,"imageClass",3,"");var div=root$1p(),node2=child(div);{var consequent=$$anchor2=>{var button=root_1$T();button.__click=function(...$$args){$$props.onClick?.apply(this,$$args)};var img=child(button);reset(button),template_effect(()=>{set_attribute(button,"aria-label",`Preview ${$$props.name??""}`),set_attribute(img,"src",$$props.preview),set_attribute(img,"alt",$$props.name),set_class(img,1,`${height()??""} ${width()??""} cur\
-sor-pointer object-cover ${imageClass()??""}`)}),append($$anchor2,button)},alternate=$$anchor2=>{var img_1=root_2$$();template_effect(()=>{set_attribute(img_1,"src",$$props.preview),set_attribute(img_1,"alt",$$props.name),set_class(img_1,1,`${height()??""} ${width()??""} cursor-pointer object-cover ${imageClass()??""}`)}),append($$anchor2,img_1)};if_block(node2,$$render=>{$$props.onClick?$$render(consequent):$$render(alternate,!1)})}var node_1=sibling(node2,2);{var consequent_1=$$anchor2=>{var div_1=root_3$R(),
-node_2=child(div_1);ActionIconRemove(node_2,{get id(){return $$props.id},get onRemove(){return $$props.onRemove},class:"text-white"}),reset(div_1),append($$anchor2,div_1)};if_block(node_1,$$render=>{readonly2()||$$render(consequent_1)})}reset(div),template_effect(()=>set_class(div,1,`group relative overflow-hidden rounded-lg bg-muted shadow-lg dark:border dark:border-muted ${className()??""}`)),append($$anchor,div)}delegate(["click"]);var root_1$S=from_html('<div><h3 class="mb-3 text-sm font-med\
-ium text-foreground"> </h3> <div class="flex flex-wrap items-start gap-3"></div></div>'),root_3$Q=from_html('<div><h3 class="mb-3 text-sm font-medium text-foreground"> </h3> <div class="flex flex-wrap items-start gap-3"></div></div>'),root$1o=from_html('<div class="space-y-4"><div class="min-h-0 flex-1 space-y-6 overflow-y-auto px-1"><!> <!></div></div> <!>',1);function ChatAttachmentsViewAll($$anchor,$$props){push$1($$props,!0);let uploadedFiles=prop($$props,"uploadedFiles",19,()=>[]),attachments=prop(
-$$props,"attachments",19,()=>[]),readonly2=prop($$props,"readonly",3,!1),imageHeight=prop($$props,"imageHeight",3,"h-24"),imageWidth=prop($$props,"imageWidth",3,"w-auto"),imageClass=prop($$props,"imageClass",3,""),previewDialogOpen=state$1(!1),previewItem=state$1(null),displayItems=user_derived(()=>getAttachmentDisplayItems({uploadedFiles:uploadedFiles(),attachments:attachments()})),imageItems=user_derived(()=>get$4(displayItems).filter(item=>item.isImage)),fileItems=user_derived(()=>get$4(displayItems).
-filter(item=>!item.isImage));function openPreview(item,event2){event2&&(event2.preventDefault(),event2.stopPropagation()),set$1(previewItem,{uploadedFile:item.uploadedFile,attachment:item.attachment,preview:item.preview,name:item.name,size:item.size,textContent:item.textContent},!0),set$1(previewDialogOpen,!0)}var fragment=root$1o(),div=first_child(fragment),div_1=child(div),node2=child(div_1);{var consequent=$$anchor2=>{var div_2=root_1$S(),h3=child(div_2),text2=child(h3);reset(h3);var div_3=sibling(
-h3,2);each(div_3,21,()=>get$4(fileItems),item=>item.id,($$anchor3,item)=>{ChatAttachmentThumbnailFile($$anchor3,{class:"cursor-pointer",get id(){return get$4(item).id},get name(){return get$4(item).name},get size(){return get$4(item).size},get readonly(){return readonly2()},get onRemove(){return $$props.onFileRemove},get textContent(){return get$4(item).textContent},get attachment(){return get$4(item).attachment},get uploadedFile(){return get$4(item).uploadedFile},onClick:event2=>openPreview(get$4(
-item),event2)})}),reset(div_3),reset(div_2),template_effect(()=>set_text(text2,`Files (${get$4(fileItems).length??""})`)),append($$anchor2,div_2)};if_block(node2,$$render=>{get$4(fileItems).length>0&&$$render(consequent)})}var node_1=sibling(node2,2);{var consequent_2=$$anchor2=>{var div_4=root_3$Q(),h3_1=child(div_4),text_1=child(h3_1);reset(h3_1);var div_5=sibling(h3_1,2);each(div_5,21,()=>get$4(imageItems),item=>item.id,($$anchor3,item)=>{var fragment_2=comment$2(),node_2=first_child(fragment_2);
-{var consequent_1=$$anchor4=>{ChatAttachmentThumbnailImage($$anchor4,{class:"cursor-pointer",get id(){return get$4(item).id},get name(){return get$4(item).name},get preview(){return get$4(item).preview},get readonly(){return readonly2()},get onRemove(){return $$props.onFileRemove},get height(){return imageHeight()},get width(){return imageWidth()},get imageClass(){return imageClass()},onClick:event2=>openPreview(get$4(item),event2)})};if_block(node_2,$$render=>{get$4(item).preview&&$$render(consequent_1)})}
-append($$anchor3,fragment_2)}),reset(div_5),reset(div_4),template_effect(()=>set_text(text_1,`Images (${get$4(imageItems).length??""})`)),append($$anchor2,div_4)};if_block(node_1,$$render=>{get$4(imageItems).length>0&&$$render(consequent_2)})}reset(div_1),reset(div);var node_3=sibling(div,2);{var consequent_3=$$anchor2=>{DialogChatAttachmentPreview($$anchor2,{get uploadedFile(){return get$4(previewItem).uploadedFile},get attachment(){return get$4(previewItem).attachment},get preview(){return get$4(
-previewItem).preview},get name(){return get$4(previewItem).name},get size(){return get$4(previewItem).size},get textContent(){return get$4(previewItem).textContent},get activeModelId(){return $$props.activeModelId},get open(){return get$4(previewDialogOpen)},set open($$value){set$1(previewDialogOpen,$$value,!0)}})};if_block(node_3,$$render=>{get$4(previewItem)&&$$render(consequent_3)})}append($$anchor,fragment),pop()}function Dialog_title($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,
-"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("text-lg leading-none font-semibold",$$props.class));component(node2,()=>Dialog_title$1,($$anchor2,DialogPrimitive_Title)=>{DialogPrimitive_Title($$anchor2,spread_props({"data-slot":"dialog-title",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor,fragment),
-pop()}var root$1n=from_html("<div><!></div>");function Dialog_footer($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$1n();attribute_effect(div,$0=>({"data-slot":"dialog-footer",class:$0,...restProps}),[()=>cn$1("flex flex-col-reverse gap-2 sm:flex-row sm:justify-end",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,
-$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root$1m=from_html("<div><!></div>");function Dialog_header($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$1m();attribute_effect(div,$0=>({"data-slot":"dialog-header",class:$0,...restProps}),[()=>cn$1("flex flex-col gap-2 text-center sm:text-left",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.
-children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}function Dialog_overlay($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:animate-in data-[state=\
-open]:fade-in-0",$$props.class));component(node2,()=>Dialog_overlay$1,($$anchor2,DialogPrimitive_Overlay)=>{DialogPrimitive_Overlay($$anchor2,spread_props({"data-slot":"dialog-overlay",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor,fragment),pop()}var root_4$v=from_html('<!> <span class="sr-only">Close</span>',1),root_2$_=from_html("<!> <!>",1),root_1$R=from_html("<!> <!>",1);function Dialog_content($$anchor,$$props){push$1(
-$$props,!0);let ref2=prop($$props,"ref",15,null),showCloseButton=prop($$props,"showCloseButton",3,!0),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","portalProps","children","showCloseButton"]);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Portal$1,($$anchor2,Dialog_Portal)=>{Dialog_Portal($$anchor2,spread_props(()=>$$props.portalProps,{children:($$anchor3,$$slotProps)=>{var fragment_1=root_1$R(),node_1=first_child(fragment_1);component(node_1,
-()=>Dialog_overlay,($$anchor4,Dialog_Overlay)=>{Dialog_Overlay($$anchor4,{})});var node_2=sibling(node_1,2);{let $0=user_derived(()=>cn$1("fixed top-[50%] left-[50%] z-50 grid max-h-[100dvh] w-full max-w-[calc(100%-2rem)] translate-x-[-50%] translate-y-[-50%] gap-4 overflow-y-auto rounded-lg border border-border/30 bg-background p-6 shadow-lg duration-200 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:\
-fade-in-0 data-[state=open]:zoom-in-95 sm:max-w-lg md:max-h-[100vh]",$$props.class));component(node_2,()=>Dialog_content$1,($$anchor4,DialogPrimitive_Content)=>{DialogPrimitive_Content($$anchor4,spread_props({"data-slot":"dialog-content",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},children:($$anchor5,$$slotProps2)=>{var fragment_2=root_2$_(),node_3=first_child(fragment_2);snippet(node_3,()=>$$props.children??noop$3);var node_4=sibling(node_3,
-2);{var consequent=$$anchor6=>{var fragment_3=comment$2(),node_5=first_child(fragment_3);component(node_5,()=>Dialog_close,($$anchor7,DialogPrimitive_Close)=>{DialogPrimitive_Close($$anchor7,{class:"absolute top-4 right-4 rounded-xs opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:ring-2 focus:ring-ring focus:ring-offset-2 focus:outline-hidden disabled:pointer-events-none [&_svg]:pointer-events-none [&_svg]:shrink-0 [&_svg:not([class*='size-'])]:size-4",children:($$anchor8,$$slotProps3)=>{
-var fragment_4=root_4$v(),node_6=first_child(fragment_4);X(node_6,{}),next$1(2),append($$anchor8,fragment_4)},$$slots:{default:!0}})}),append($$anchor6,fragment_3)};if_block(node_4,$$render=>{showCloseButton()&&$$render(consequent)})}append($$anchor5,fragment_2)},$$slots:{default:!0}}))})}append($$anchor3,fragment_1)},$$slots:{default:!0}}))}),append($$anchor,fragment),pop()}function Dialog_description($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props(
-$$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("text-sm text-muted-foreground",$$props.class));component(node2,()=>Dialog_description$1,($$anchor2,DialogPrimitive_Description)=>{DialogPrimitive_Description($$anchor2,spread_props({"data-slot":"dialog-description",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor,fragment),pop()}const Root$4=Dialog,
-Portal$1=Portal$2;function DialogChatSettings($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",3,!1),chatSettingsRef=state$1(void 0);function handleClose(){$$props.onOpenChange?.(!1)}function handleSave(){$$props.onOpenChange?.(!1)}user_effect(()=>{open2()&&get$4(chatSettingsRef)&&get$4(chatSettingsRef).reset()});var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$4,($$anchor2,Dialog_Root)=>{Dialog_Root($$anchor2,{get open(){return open2()},onOpenChange:handleClose,
-children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Dialog_content,($$anchor4,Dialog_Content)=>{Dialog_Content($$anchor4,{class:`z-999999 flex h-[100dvh] max-h-[100dvh] min-h-[100dvh] max-w-4xl! flex-col gap-0 rounded-none
-			p-0 md:h-[64vh] md:max-h-[64vh] md:min-h-0 md:rounded-lg`,children:($$anchor5,$$slotProps2)=>{bind_this(ChatSettings($$anchor5,{onSave:handleSave,get initialSection(){return $$props.initialSection}}),$$value=>set$1(chatSettingsRef,$$value,!0),()=>get$4(chatSettingsRef))},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}function Alert_dialog_title($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props(
-$$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("text-lg font-semibold",$$props.class));component(node2,()=>Dialog_title$1,($$anchor2,AlertDialogPrimitive_Title)=>{AlertDialogPrimitive_Title($$anchor2,spread_props({"data-slot":"alert-dialog-title",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor,fragment),pop()}function Alert_dialog_action($$anchor,$$props){
-push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1(buttonVariants(),$$props.class));component(node2,()=>Alert_dialog_action$1,($$anchor2,AlertDialogPrimitive_Action)=>{AlertDialogPrimitive_Action($$anchor2,spread_props({"data-slot":"alert-dialog-action",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){
-ref2($$value)}}))})}append($$anchor,fragment),pop()}function Alert_dialog_cancel($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1(buttonVariants({variant:"outline"}),$$props.class));component(node2,()=>Alert_dialog_cancel$1,($$anchor2,AlertDialogPrimitive_Cancel)=>{AlertDialogPrimitive_Cancel($$anchor2,spread_props(
-{"data-slot":"alert-dialog-cancel",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor,fragment),pop()}var root$1l=from_html("<div><!></div>");function Alert_dialog_footer($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$1l();attribute_effect(div,$0=>({"data-slot":"alert-dialog-footer",class:$0,...restProps}),
-[()=>cn$1("mt-6 flex flex-row gap-2 sm:mt-0 sm:justify-end [&>*]:flex-1 sm:[&>*]:flex-none",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root$1k=from_html("<div><!></div>");function Alert_dialog_header($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$1k();
-attribute_effect(div,$0=>({"data-slot":"alert-dialog-header",class:$0,...restProps}),[()=>cn$1("flex flex-col gap-2 text-center sm:text-left",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}function Alert_dialog_overlay($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),
-node2=first_child(fragment);{let $0=user_derived(()=>cn$1("fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:animate-in data-[state=open]:fade-in-0",$$props.class));component(node2,()=>Dialog_overlay$1,($$anchor2,AlertDialogPrimitive_Overlay)=>{AlertDialogPrimitive_Overlay($$anchor2,spread_props({"data-slot":"alert-dialog-overlay",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}
-append($$anchor,fragment),pop()}var root_1$Q=from_html("<!> <!>",1);function Alert_dialog_content($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","portalProps"]);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Portal$2,($$anchor2,AlertDialogPrimitive_Portal)=>{AlertDialogPrimitive_Portal($$anchor2,spread_props(()=>$$props.portalProps,{children:($$anchor3,$$slotProps)=>{var fragment_1=root_1$Q(),
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-from-bottom-full","sm:top-[50%] sm:right-auto sm:bottom-auto sm:left-[50%] sm:max-h-[100vh] sm:max-w-lg sm:translate-x-[-50%] sm:translate-y-[-50%] sm:rounded-lg","sm:data-[state=closed]:slide-out-to-bottom-0 sm:data-[state=closed]:zoom-out-95","sm:data-[state=open]:slide-in-from-bottom-0 sm:data-[state=open]:zoom-in-95",$$props.class));component(node_2,()=>Alert_dialog_content$1,($$anchor4,AlertDialogPrimitive_Content)=>{AlertDialogPrimitive_Content($$anchor4,spread_props({"data-slot":"alert-di\
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-"<!> <!> <!>",1);function DialogConfirmation($$anchor,$$props){push$1($$props,!0);let confirmText=prop($$props,"confirmText",3,"Confirm"),cancelText=prop($$props,"cancelText",3,"Cancel"),variant=prop($$props,"variant",3,"default");function handleKeydown(event2){event2.key===KeyboardKey.ENTER&&(event2.preventDefault(),$$props.onConfirm()),$$props.onKeydown?.(event2)}function handleOpenChange(newOpen){newOpen||$$props.onCancel()}var fragment=comment$2(),node2=first_child(fragment);component(node2,
-()=>Root$3,($$anchor2,AlertDialog_Root)=>{AlertDialog_Root($$anchor2,{get open(){return $$props.open},onOpenChange:handleOpenChange,children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Alert_dialog_content,($$anchor4,AlertDialog_Content)=>{AlertDialog_Content($$anchor4,{onkeydown:handleKeydown,children:($$anchor5,$$slotProps2)=>{var fragment_2=root_2$Z(),node_2=first_child(fragment_2);component(node_2,()=>Alert_dialog_header,($$anchor6,AlertDialog_Header)=>{
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-confirmText())),append($$anchor9,text_3)},$$slots:{default:!0}})})}append($$anchor7,fragment_8)},$$slots:{default:!0}})}),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}var root_3$O=from_html("<!> <!>",1),root_6$w=from_html("<!> <!>",1),root_2$Y=from_html('<!> <div class="space-y-4 pt-2 pb-6"><div class="space-y-2"><p class="text-sm font-medium text-muted-foreground">Current title:</p> <p class="rounde\
-d-md bg-muted/50 p-3 text-sm font-medium"> </p></div> <div class="space-y-2"><p class="text-sm font-medium text-muted-foreground">New title would be:</p> <p class="rounded-md bg-muted/50 p-3 text-sm font-medium"> </p></div></div> <!>',1);function DialogConversationTitleUpdate($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$3,($$anchor2,AlertDialog_Root)=>{AlertDialog_Root($$anchor2,{get open(){return open2()},
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-{children:($$anchor7,$$slotProps3)=>{var fragment_4=root_6$w(),node_6=first_child(fragment_4);Button(node_6,{variant:"outline",get onclick(){return $$props.onCancel},children:($$anchor8,$$slotProps4)=>{next$1();var text_4=text$8("Keep Current Title");append($$anchor8,text_4)},$$slots:{default:!0}});var node_7=sibling(node_6,2);Button(node_7,{get onclick(){return $$props.onConfirm},children:($$anchor8,$$slotProps4)=>{next$1();var text_5=text$8("Update Title");append($$anchor8,text_5)},$$slots:{default:!0}}),
-append($$anchor7,fragment_4)},$$slots:{default:!0}})}),template_effect(()=>{set_text(text_2,$$props.currentTitle),set_text(text_3,$$props.newTitle)}),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}var root_4$t=from_html('<!> <span class="sr-only">Close preview</span>',1),root_3$N=from_html('<iframe sandbox="allow-scripts" class="code-preview-iframe"></iframe> <!>',1),root_2$X=from_html("<!> <!>",1);function DialogCodePreview($$anchor,$$props){
-push$1($$props,!0);let open2=prop($$props,"open",15),iframeRef=state$1(null);user_effect(()=>{get$4(iframeRef)&&(open2()?get$4(iframeRef).srcdoc=$$props.code:get$4(iframeRef).srcdoc="")});function handleOpenChange(nextOpen){open2(nextOpen),$$props.onOpenChange?.(nextOpen)}var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Dialog,($$anchor2,DialogPrimitive_Root)=>{DialogPrimitive_Root($$anchor2,{get open(){return open2()},onOpenChange:handleOpenChange,children:($$anchor3,$$slotProps)=>{
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-wn File"),displaySize=user_derived(()=>$$props.uploadedFile?.size||$$props.size);user_effect(()=>{open2()&&get$4(chatAttachmentPreviewRef)&&get$4(chatAttachmentPreviewRef).reset()});var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$4,($$anchor2,Dialog_Root)=>{Dialog_Root($$anchor2,{get onOpenChange(){return $$props.onOpenChange},get open(){return open2()},set open($$value){open2($$value)},children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(
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-$$props,"open",15,!1),uploadedFiles=prop($$props,"uploadedFiles",19,()=>[]),attachments=prop($$props,"attachments",19,()=>[]),readonly2=prop($$props,"readonly",3,!1),imageHeight=prop($$props,"imageHeight",3,"h-24"),imageWidth=prop($$props,"imageWidth",3,"w-auto"),imageClass=prop($$props,"imageClass",3,""),totalCount=user_derived(()=>uploadedFiles().length+attachments().length);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$4,($$anchor2,Dialog_Root)=>{Dialog_Root($$anchor2,
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-node_7,{get uploadedFiles(){return uploadedFiles()},get attachments(){return attachments()},get readonly(){return readonly2()},get onFileRemove(){return $$props.onFileRemove},get imageHeight(){return imageHeight()},get imageWidth(){return imageWidth()},get imageClass(){return imageClass()},get activeModelId(){return $$props.activeModelId}}),append($$anchor7,fragment_3)},$$slots:{default:!0}})}),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),
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-()=>$$props.type===ErrorDialogType.TIMEOUT),title2=user_derived(()=>get$4(isTimeout)?"TCP Timeout":"Server Error"),description2=user_derived(()=>get$4(isTimeout)?"The request did not receive a response from the server before timing out.":"The server responded with an error message. Review the details below."),iconClass=user_derived(()=>get$4(isTimeout)?"text-destructive":"text-amber-500"),badgeClass=user_derived(()=>get$4(isTimeout)?"border-destructive/40 bg-destructive/10 text-destructive":"bor\
-der-amber-500/40 bg-amber-500/10 text-amber-600 dark:text-amber-400");function handleOpenChange(newOpen){open2(newOpen),$$props.onOpenChange?.(newOpen)}var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$3,($$anchor2,AlertDialog_Root)=>{AlertDialog_Root($$anchor2,{get open(){return open2()},onOpenChange:handleOpenChange,children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Alert_dialog_content,($$anchor4,AlertDialog_Content)=>{
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-p2,2);{var consequent_2=$$anchor6=>{var div_1=root_8$l(),p_1=child(div_1),text_3=sibling(child(p_1));reset(p_1);var node_7=sibling(p_1,2);{var consequent_1=$$anchor7=>{var p_2=root_9$l(),text_4=sibling(child(p_2));reset(p_2),template_effect($0=>set_text(text_4,` ${$0??""}`),[()=>$$props.contextInfo.n_ctx.toLocaleString()]),append($$anchor7,p_2)};if_block(node_7,$$render=>{$$props.contextInfo.n_ctx&&$$render(consequent_1)})}reset(div_1),template_effect($0=>set_text(text_3,` ${$0??""}`),[()=>$$props.
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-				.resizable-text-container {
-					max-width: calc(100vw - var(--threshold));
-				}
-			}</style> <!> <div class="space-y-6 py-4"><!></div>`,1);function DialogModelInformation($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15),modelId=prop($$props,"modelId",3,null),isRouter=user_derived(()=>serverStore.isRouterMode),routerModelProps=state$1(null),isLoadingRouterProps=state$1(!1),serverProps2=user_derived(()=>get$4(isRouter)&&modelId()?get$4(routerModelProps):serverStore.props),modelName=user_derived(()=>get$4(isRouter)&&modelId()?modelId():modelsStore.singleModelName),
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-return get$4(serverProps2).model_path},ariaLabel:"Copy model path to clipboard"}),template_effect(()=>set_text(text_5,get$4(serverProps2).model_path)),append($$anchor16,fragment_12)},$$slots:{default:!0}})}),append($$anchor14,fragment_11)},$$slots:{default:!0}})});var node_19=sibling(node_15,2);{var consequent_1=$$anchor13=>{var fragment_13=comment$2(),node_20=first_child(fragment_13);component(node_20,()=>Table_row,($$anchor14,Table_Row_2)=>{Table_Row_2($$anchor14,{children:($$anchor15,$$slotProps5)=>{
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-"Not available");append($$anchor17,text_9)},$$slots:{default:!0}})}),append($$anchor15,fragment_17)},$$slots:{default:!0}})}),append($$anchor13,fragment_16)};if_block(node_19,$$render=>{get$4(serverProps2)?.default_generation_settings?.n_ctx?$$render(consequent_1):$$render(alternate,!1)})}var node_26=sibling(node_19,2);{var consequent_2=$$anchor13=>{var fragment_18=comment$2(),node_27=first_child(fragment_18);component(node_27,()=>Table_row,($$anchor14,Table_Row_4)=>{Table_Row_4($$anchor14,{children:($$anchor15,$$slotProps5)=>{
-var fragment_19=root_28$3(),node_28=first_child(fragment_19);component(node_28,()=>Table_cell,($$anchor16,Table_Cell_6)=>{Table_Cell_6($$anchor16,{class:"h-10 align-middle font-medium",children:($$anchor17,$$slotProps6)=>{next$1();var text_10=text$8("Training Context");append($$anchor17,text_10)},$$slots:{default:!0}})});var node_29=sibling(node_28,2);component(node_29,()=>Table_cell,($$anchor16,Table_Cell_7)=>{Table_Cell_7($$anchor16,{children:($$anchor17,$$slotProps6)=>{next$1();var text_11=text$8();
-template_effect($0=>set_text(text_11,`${$0??""} tokens`),[()=>formatNumber(get$4(modelMeta).n_ctx_train)]),append($$anchor17,text_11)},$$slots:{default:!0}})}),append($$anchor15,fragment_19)},$$slots:{default:!0}})}),append($$anchor13,fragment_18)};if_block(node_26,$$render=>{get$4(modelMeta)?.n_ctx_train&&$$render(consequent_2)})}var node_30=sibling(node_26,2);{var consequent_3=$$anchor13=>{var fragment_21=comment$2(),node_31=first_child(fragment_21);component(node_31,()=>Table_row,($$anchor14,Table_Row_5)=>{
-Table_Row_5($$anchor14,{children:($$anchor15,$$slotProps5)=>{var fragment_22=root_32$1(),node_32=first_child(fragment_22);component(node_32,()=>Table_cell,($$anchor16,Table_Cell_8)=>{Table_Cell_8($$anchor16,{class:"h-10 align-middle font-medium",children:($$anchor17,$$slotProps6)=>{next$1();var text_12=text$8("Model Size");append($$anchor17,text_12)},$$slots:{default:!0}})});var node_33=sibling(node_32,2);component(node_33,()=>Table_cell,($$anchor16,Table_Cell_9)=>{Table_Cell_9($$anchor16,{children:($$anchor17,$$slotProps6)=>{
-next$1();var text_13=text$8();template_effect($0=>set_text(text_13,$0),[()=>formatFileSize(get$4(modelMeta).size)]),append($$anchor17,text_13)},$$slots:{default:!0}})}),append($$anchor15,fragment_22)},$$slots:{default:!0}})}),append($$anchor13,fragment_21)};if_block(node_30,$$render=>{get$4(modelMeta)?.size&&$$render(consequent_3)})}var node_34=sibling(node_30,2);{var consequent_4=$$anchor13=>{var fragment_24=comment$2(),node_35=first_child(fragment_24);component(node_35,()=>Table_row,($$anchor14,Table_Row_6)=>{
-Table_Row_6($$anchor14,{children:($$anchor15,$$slotProps5)=>{var fragment_25=root_36(),node_36=first_child(fragment_25);component(node_36,()=>Table_cell,($$anchor16,Table_Cell_10)=>{Table_Cell_10($$anchor16,{class:"h-10 align-middle font-medium",children:($$anchor17,$$slotProps6)=>{next$1();var text_14=text$8("Parameters");append($$anchor17,text_14)},$$slots:{default:!0}})});var node_37=sibling(node_36,2);component(node_37,()=>Table_cell,($$anchor16,Table_Cell_11)=>{Table_Cell_11($$anchor16,{children:($$anchor17,$$slotProps6)=>{
-next$1();var text_15=text$8();template_effect($0=>set_text(text_15,$0),[()=>formatParameters(get$4(modelMeta).n_params)]),append($$anchor17,text_15)},$$slots:{default:!0}})}),append($$anchor15,fragment_25)},$$slots:{default:!0}})}),append($$anchor13,fragment_24)};if_block(node_34,$$render=>{get$4(modelMeta)?.n_params&&$$render(consequent_4)})}var node_38=sibling(node_34,2);{var consequent_5=$$anchor13=>{var fragment_27=comment$2(),node_39=first_child(fragment_27);component(node_39,()=>Table_row,
-($$anchor14,Table_Row_7)=>{Table_Row_7($$anchor14,{children:($$anchor15,$$slotProps5)=>{var fragment_28=root_40$1(),node_40=first_child(fragment_28);component(node_40,()=>Table_cell,($$anchor16,Table_Cell_12)=>{Table_Cell_12($$anchor16,{class:"align-middle font-medium",children:($$anchor17,$$slotProps6)=>{next$1();var text_16=text$8("Embedding Size");append($$anchor17,text_16)},$$slots:{default:!0}})});var node_41=sibling(node_40,2);component(node_41,()=>Table_cell,($$anchor16,Table_Cell_13)=>{Table_Cell_13(
-$$anchor16,{children:($$anchor17,$$slotProps6)=>{next$1();var text_17=text$8();template_effect($0=>set_text(text_17,$0),[()=>formatNumber(get$4(modelMeta).n_embd)]),append($$anchor17,text_17)},$$slots:{default:!0}})}),append($$anchor15,fragment_28)},$$slots:{default:!0}})}),append($$anchor13,fragment_27)};if_block(node_38,$$render=>{get$4(modelMeta)?.n_embd&&$$render(consequent_5)})}var node_42=sibling(node_38,2);{var consequent_6=$$anchor13=>{var fragment_30=comment$2(),node_43=first_child(fragment_30);
-component(node_43,()=>Table_row,($$anchor14,Table_Row_8)=>{Table_Row_8($$anchor14,{children:($$anchor15,$$slotProps5)=>{var fragment_31=root_44$1(),node_44=first_child(fragment_31);component(node_44,()=>Table_cell,($$anchor16,Table_Cell_14)=>{Table_Cell_14($$anchor16,{class:"align-middle font-medium",children:($$anchor17,$$slotProps6)=>{next$1();var text_18=text$8("Vocabulary Size");append($$anchor17,text_18)},$$slots:{default:!0}})});var node_45=sibling(node_44,2);component(node_45,()=>Table_cell,
-($$anchor16,Table_Cell_15)=>{Table_Cell_15($$anchor16,{children:($$anchor17,$$slotProps6)=>{next$1();var text_19=text$8();template_effect($0=>set_text(text_19,`${$0??""} tokens`),[()=>formatNumber(get$4(modelMeta).n_vocab)]),append($$anchor17,text_19)},$$slots:{default:!0}})}),append($$anchor15,fragment_31)},$$slots:{default:!0}})}),append($$anchor13,fragment_30)};if_block(node_42,$$render=>{get$4(modelMeta)?.n_vocab&&$$render(consequent_6)})}var node_46=sibling(node_42,2);{var consequent_7=$$anchor13=>{
-var fragment_33=comment$2(),node_47=first_child(fragment_33);component(node_47,()=>Table_row,($$anchor14,Table_Row_9)=>{Table_Row_9($$anchor14,{children:($$anchor15,$$slotProps5)=>{var fragment_34=root_48(),node_48=first_child(fragment_34);component(node_48,()=>Table_cell,($$anchor16,Table_Cell_16)=>{Table_Cell_16($$anchor16,{class:"align-middle font-medium",children:($$anchor17,$$slotProps6)=>{next$1();var text_20=text$8("Vocabulary Type");append($$anchor17,text_20)},$$slots:{default:!0}})});var node_49=sibling(
-node_48,2);component(node_49,()=>Table_cell,($$anchor16,Table_Cell_17)=>{Table_Cell_17($$anchor16,{class:"align-middle capitalize",children:($$anchor17,$$slotProps6)=>{next$1();var text_21=text$8();template_effect(()=>set_text(text_21,get$4(modelMeta).vocab_type)),append($$anchor17,text_21)},$$slots:{default:!0}})}),append($$anchor15,fragment_34)},$$slots:{default:!0}})}),append($$anchor13,fragment_33)};if_block(node_46,$$render=>{get$4(modelMeta)?.vocab_type&&$$render(consequent_7)})}var node_50=sibling(
-node_46,2);component(node_50,()=>Table_row,($$anchor13,Table_Row_10)=>{Table_Row_10($$anchor13,{children:($$anchor14,$$slotProps5)=>{var fragment_36=root_51(),node_51=first_child(fragment_36);component(node_51,()=>Table_cell,($$anchor15,Table_Cell_18)=>{Table_Cell_18($$anchor15,{class:"align-middle font-medium",children:($$anchor16,$$slotProps6)=>{next$1();var text_22=text$8("Parallel Slots");append($$anchor16,text_22)},$$slots:{default:!0}})});var node_52=sibling(node_51,2);component(node_52,()=>Table_cell,
-($$anchor15,Table_Cell_19)=>{Table_Cell_19($$anchor15,{children:($$anchor16,$$slotProps6)=>{next$1();var text_23=text$8();template_effect(()=>set_text(text_23,get$4(serverProps2).total_slots)),append($$anchor16,text_23)},$$slots:{default:!0}})}),append($$anchor14,fragment_36)},$$slots:{default:!0}})});var node_53=sibling(node_50,2);{var consequent_8=$$anchor13=>{var fragment_38=comment$2(),node_54=first_child(fragment_38);component(node_54,()=>Table_row,($$anchor14,Table_Row_11)=>{Table_Row_11($$anchor14,
-{children:($$anchor15,$$slotProps5)=>{var fragment_39=root_55(),node_55=first_child(fragment_39);component(node_55,()=>Table_cell,($$anchor16,Table_Cell_20)=>{Table_Cell_20($$anchor16,{class:"align-middle font-medium",children:($$anchor17,$$slotProps6)=>{next$1();var text_24=text$8("Modalities");append($$anchor17,text_24)},$$slots:{default:!0}})});var node_56=sibling(node_55,2);component(node_56,()=>Table_cell,($$anchor16,Table_Cell_21)=>{Table_Cell_21($$anchor16,{children:($$anchor17,$$slotProps6)=>{
-var div_3=root_57(),node_57=child(div_3);BadgeModality(node_57,{get modalities(){return get$4(modalities)}}),reset(div_3),append($$anchor17,div_3)},$$slots:{default:!0}})}),append($$anchor15,fragment_39)},$$slots:{default:!0}})}),append($$anchor13,fragment_38)};if_block(node_53,$$render=>{get$4(modalities).length>0&&$$render(consequent_8)})}var node_58=sibling(node_53,2);component(node_58,()=>Table_row,($$anchor13,Table_Row_12)=>{Table_Row_12($$anchor13,{children:($$anchor14,$$slotProps5)=>{var fragment_40=root_58(),
-node_59=first_child(fragment_40);component(node_59,()=>Table_cell,($$anchor15,Table_Cell_22)=>{Table_Cell_22($$anchor15,{class:"align-middle font-medium",children:($$anchor16,$$slotProps6)=>{next$1();var text_25=text$8("Build Info");append($$anchor16,text_25)},$$slots:{default:!0}})});var node_60=sibling(node_59,2);component(node_60,()=>Table_cell,($$anchor15,Table_Cell_23)=>{Table_Cell_23($$anchor15,{class:"align-middle font-mono text-xs",children:($$anchor16,$$slotProps6)=>{next$1();var text_26=text$8();
-template_effect(()=>set_text(text_26,get$4(serverProps2).build_info)),append($$anchor16,text_26)},$$slots:{default:!0}})}),append($$anchor14,fragment_40)},$$slots:{default:!0}})});var node_61=sibling(node_58,2);{var consequent_9=$$anchor13=>{var fragment_42=comment$2(),node_62=first_child(fragment_42);component(node_62,()=>Table_row,($$anchor14,Table_Row_13)=>{Table_Row_13($$anchor14,{children:($$anchor15,$$slotProps5)=>{var fragment_43=root_62(),node_63=first_child(fragment_43);component(node_63,
-()=>Table_cell,($$anchor16,Table_Cell_24)=>{Table_Cell_24($$anchor16,{class:"align-middle font-medium",children:($$anchor17,$$slotProps6)=>{next$1();var text_27=text$8("Chat Template");append($$anchor17,text_27)},$$slots:{default:!0}})});var node_64=sibling(node_63,2);component(node_64,()=>Table_cell,($$anchor16,Table_Cell_25)=>{Table_Cell_25($$anchor16,{class:"py-10",children:($$anchor17,$$slotProps6)=>{var div_4=root_64(),pre=child(div_4),text_28=child(pre,!0);reset(pre),reset(div_4),template_effect(
-()=>set_text(text_28,get$4(serverProps2).chat_template)),append($$anchor17,div_4)},$$slots:{default:!0}})}),append($$anchor15,fragment_43)},$$slots:{default:!0}})}),append($$anchor13,fragment_42)};if_block(node_61,$$render=>{get$4(serverProps2).chat_template&&$$render(consequent_9)})}append($$anchor12,fragment_10)},$$slots:{default:!0}})}),append($$anchor10,fragment_7)},$$slots:{default:!0}})}),append($$anchor8,fragment_6)};if_block(node_7,$$render=>{get$4(serverProps2)&&$$render(consequent_10)})}
-append($$anchor7,fragment_5)},alternate_1=$$anchor7=>{var fragment_44=comment$2(),node_65=first_child(fragment_44);{var consequent_12=$$anchor8=>{var div_5=root_66();append($$anchor8,div_5)};if_block(node_65,$$render=>{get$4(isLoadingModels)||$$render(consequent_12)},!0)}append($$anchor7,fragment_44)};if_block(node_6,$$render=>{get$4(firstModel)?$$render(consequent_11):$$render(alternate_1,!1)},!0)}append($$anchor6,fragment_4)};if_block(node_5,$$render=>{get$4(isLoadingModels)||get$4(isLoadingRouterProps)?
-$$render(consequent):$$render(alternate_2,!1)})}reset(div),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}class ConversationsStore{#conversations=state$1(proxy([]));get conversations(){return get$4(this.#conversations)}set conversations(value){set$1(this.#conversations,value,!0)}#activeConversation=state$1(null);get activeConversation(){return get$4(this.#activeConversation)}set activeConversation(value){
-set$1(this.#activeConversation,value,!0)}#activeMessages=state$1(proxy([]));get activeMessages(){return get$4(this.#activeMessages)}set activeMessages(value){set$1(this.#activeMessages,value,!0)}#isInitialized=state$1(!1);get isInitialized(){return get$4(this.#isInitialized)}set isInitialized(value){set$1(this.#isInitialized,value,!0)}#pendingMcpServerOverrides=state$1(proxy(ConversationsStore.loadMcpDefaults()));get pendingMcpServerOverrides(){return get$4(this.#pendingMcpServerOverrides)}set pendingMcpServerOverrides(value){
-set$1(this.#pendingMcpServerOverrides,value,!0)}static loadMcpDefaults(){if(typeof globalThis.localStorage>"u")return[];try{const raw2=localStorage.getItem(MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY);if(!raw2)return[];const parsed=JSON.parse(raw2);return Array.isArray(parsed)?parsed.filter(o=>typeof o=="object"&&o!==null&&"serverId"in o&&"enabled"in o):[]}catch{return[]}}saveMcpDefaults(){if(typeof globalThis.localStorage>"u")return;const plain=this.pendingMcpServerOverrides.map(o=>({serverId:o.serverId,
-enabled:o.enabled}));plain.length>0?localStorage.setItem(MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY,JSON.stringify(plain)):localStorage.removeItem(MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY)}titleUpdateConfirmationCallback;messageUpdateCallback=null;async init(){if(!this.isInitialized)try{await runLegacyMigration(),await this.loadConversations(),this.isInitialized=!0}catch(error2){console.error("Failed to initialize conversations:",error2)}}async initialize(){return this.init()}registerMessageUpdateCallback(callback){
-this.messageUpdateCallback=callback}addMessageToActive(message){this.activeMessages.push(message)}updateMessageAtIndex(index2,updates){index2!==-1&&this.activeMessages[index2]&&(this.activeMessages[index2]={...this.activeMessages[index2],...updates})}findMessageIndex(messageId){return this.activeMessages.findIndex(m=>m.id===messageId)}sliceActiveMessages(startIndex){this.activeMessages=this.activeMessages.slice(0,startIndex)}removeMessageAtIndex(index2){if(index2!==-1)return this.activeMessages.
-splice(index2,1)[0]}setTitleUpdateConfirmationCallback(callback){this.titleUpdateConfirmationCallback=callback}async loadConversations(){const conversations2=await DatabaseService.getAllConversations();this.conversations=conversations2}async createConversation(name){const conversationName=name||`Chat ${new Date().toLocaleString()}`,conversation=await DatabaseService.createConversation(conversationName);if(this.pendingMcpServerOverrides.length>0){const plainOverrides=this.pendingMcpServerOverrides.
-map(o=>({serverId:o.serverId,enabled:o.enabled}));conversation.mcpServerOverrides=plainOverrides,await DatabaseService.updateConversation(conversation.id,{mcpServerOverrides:plainOverrides}),this.pendingMcpServerOverrides=[]}return this.conversations=[conversation,...this.conversations],this.activeConversation=conversation,this.activeMessages=[],await goto(`#/chat/${conversation.id}`),conversation.id}async loadConversation(convId){try{const conversation=await DatabaseService.getConversation(convId);
-if(!conversation)return!1;if(this.pendingMcpServerOverrides=[],this.activeConversation=conversation,conversation.currNode){const allMessages=await DatabaseService.getConversationMessages(convId),filteredMessages=filterByLeafNodeId(allMessages,conversation.currNode,!1);this.activeMessages=filteredMessages}else{const messages=await DatabaseService.getConversationMessages(convId);this.activeMessages=messages}return!0}catch(error2){return console.error("Failed to load conversation:",error2),!1}}clearActiveConversation(){
-this.activeConversation=null,this.activeMessages=[],this.pendingMcpServerOverrides=ConversationsStore.loadMcpDefaults()}async deleteConversation(convId,options){try{if(await DatabaseService.deleteConversation(convId,options),options?.deleteWithForks){const idsToRemove=new SvelteSet([convId]),queue=[convId];for(;queue.length>0;){const parentId=queue.pop();for(const c2 of this.conversations)c2.forkedFromConversationId===parentId&&!idsToRemove.has(c2.id)&&(idsToRemove.add(c2.id),queue.push(c2.id))}
-this.conversations=this.conversations.filter(c2=>!idsToRemove.has(c2.id)),this.activeConversation&&idsToRemove.has(this.activeConversation.id)&&(this.clearActiveConversation(),await goto("?new_chat=true#/"))}else{const newParent=this.conversations.find(c2=>c2.id===convId)?.forkedFromConversationId;this.conversations=this.conversations.filter(c2=>c2.id!==convId).map(c2=>c2.forkedFromConversationId===convId?{...c2,forkedFromConversationId:newParent}:c2),this.activeConversation?.id===convId&&(this.
-clearActiveConversation(),await goto("?new_chat=true#/"))}}catch(error2){console.error("Failed to delete conversation:",error2)}}async deleteAll(){try{const allConversations=await DatabaseService.getAllConversations();for(const conv of allConversations)await DatabaseService.deleteConversation(conv.id);this.clearActiveConversation(),this.conversations=[],toast.success("All conversations deleted"),await goto("?new_chat=true#/")}catch(error2){console.error("Failed to delete all conversations:",error2),
-toast.error("Failed to delete conversations")}}async refreshActiveMessages(){if(!this.activeConversation)return;const allMessages=await DatabaseService.getConversationMessages(this.activeConversation.id);if(allMessages.length===0){this.activeMessages=[];return}const leafNodeId=this.activeConversation.currNode||allMessages.reduce((latest,msg)=>msg.timestamp>latest.timestamp?msg:latest).id,currentPath=filterByLeafNodeId(allMessages,leafNodeId,!1);this.activeMessages=currentPath}async getConversationMessages(convId){
-return await DatabaseService.getConversationMessages(convId)}async updateConversationName(convId,name){try{await DatabaseService.updateConversation(convId,{name});const convIndex=this.conversations.findIndex(c2=>c2.id===convId);convIndex!==-1&&(this.conversations[convIndex].name=name,this.conversations=[...this.conversations]),this.activeConversation?.id===convId&&(this.activeConversation={...this.activeConversation,name})}catch(error2){console.error("Failed to update conversation name:",error2)}}async updateConversationTitleWithConfirmation(convId,newTitle){
-try{if(config$1().askForTitleConfirmation&&this.titleUpdateConfirmationCallback){const conversation=await DatabaseService.getConversation(convId);if(!conversation||!await this.titleUpdateConfirmationCallback(conversation.name,newTitle))return!1}return await this.updateConversationName(convId,newTitle),!0}catch(error2){return console.error("Failed to update conversation title with confirmation:",error2),!1}}updateConversationTimestamp(){if(!this.activeConversation)return;const chatIndex=this.conversations.
-findIndex(c2=>c2.id===this.activeConversation.id);if(chatIndex!==-1){this.conversations[chatIndex].lastModified=Date.now();const updatedConv=this.conversations.splice(chatIndex,1)[0];this.conversations=[updatedConv,...this.conversations]}}async updateCurrentNode(nodeId){this.activeConversation&&(await DatabaseService.updateCurrentNode(this.activeConversation.id,nodeId),this.activeConversation={...this.activeConversation,currNode:nodeId})}async navigateToSibling(siblingId){if(!this.activeConversation)
-return;const allMessages=await DatabaseService.getConversationMessages(this.activeConversation.id),rootMessage=allMessages.find(m=>m.type==="root"&&m.parent===null),currentFirstUserMessage=this.activeMessages.find(m=>m.role===MessageRole.USER&&m.parent===rootMessage?.id),currentLeafNodeId=findLeafNode(allMessages,siblingId);if(await DatabaseService.updateCurrentNode(this.activeConversation.id,currentLeafNodeId),this.activeConversation={...this.activeConversation,currNode:currentLeafNodeId},await this.
-refreshActiveMessages(),rootMessage&&this.activeMessages.length>0){const newFirstUserMessage=this.activeMessages.find(m=>m.role===MessageRole.USER&&m.parent===rootMessage.id);newFirstUserMessage&&newFirstUserMessage.content.trim()&&(!currentFirstUserMessage||newFirstUserMessage.id!==currentFirstUserMessage.id||newFirstUserMessage.content.trim()!==currentFirstUserMessage.content.trim())&&await this.updateConversationTitleWithConfirmation(this.activeConversation.id,generateConversationTitle(newFirstUserMessage.
-content,!!config$1().titleGenerationUseFirstLine))}}getMcpServerOverride(serverId){return this.activeConversation?this.activeConversation.mcpServerOverrides?.find(o=>o.serverId===serverId):this.pendingMcpServerOverrides.find(o=>o.serverId===serverId)}getAllMcpServerOverrides(){return this.activeConversation?.mcpServerOverrides?this.activeConversation.mcpServerOverrides:this.pendingMcpServerOverrides}isMcpServerEnabledForChat(serverId){return this.getMcpServerOverride(serverId)?.enabled??!1}async setMcpServerOverride(serverId,enabled){
-if(!this.activeConversation){this.setPendingMcpServerOverride(serverId,enabled);return}const currentOverrides=(this.activeConversation.mcpServerOverrides||[]).map(o=>({serverId:o.serverId,enabled:o.enabled}));let newOverrides;if(enabled===void 0)newOverrides=currentOverrides.filter(o=>o.serverId!==serverId);else{const existingIndex=currentOverrides.findIndex(o=>o.serverId===serverId);existingIndex>=0?(newOverrides=[...currentOverrides],newOverrides[existingIndex]={serverId,enabled}):newOverrides=
-[...currentOverrides,{serverId,enabled}]}await DatabaseService.updateConversation(this.activeConversation.id,{mcpServerOverrides:newOverrides.length>0?newOverrides:void 0}),this.activeConversation={...this.activeConversation,mcpServerOverrides:newOverrides.length>0?newOverrides:void 0};const convIndex=this.conversations.findIndex(c2=>c2.id===this.activeConversation.id);convIndex!==-1&&(this.conversations[convIndex].mcpServerOverrides=newOverrides.length>0?newOverrides:void 0,this.conversations=[
-...this.conversations])}setPendingMcpServerOverride(serverId,enabled){if(enabled===void 0)this.pendingMcpServerOverrides=this.pendingMcpServerOverrides.filter(o=>o.serverId!==serverId);else{const existingIndex=this.pendingMcpServerOverrides.findIndex(o=>o.serverId===serverId);if(existingIndex>=0){const newOverrides=[...this.pendingMcpServerOverrides];newOverrides[existingIndex]={serverId,enabled},this.pendingMcpServerOverrides=newOverrides}else this.pendingMcpServerOverrides=[...this.pendingMcpServerOverrides,
-{serverId,enabled}]}this.saveMcpDefaults()}async toggleMcpServerForChat(serverId){const currentEnabled=this.isMcpServerEnabledForChat(serverId);await this.setMcpServerOverride(serverId,!currentEnabled)}async removeMcpServerOverride(serverId){await this.setMcpServerOverride(serverId,void 0)}clearPendingMcpServerOverrides(){this.pendingMcpServerOverrides=[],this.saveMcpDefaults()}async forkConversation(messageId,options){if(!this.activeConversation)return null;try{const newConv=await DatabaseService.
-forkConversation(this.activeConversation.id,messageId,options);return this.conversations=[newConv,...this.conversations],await goto(`#/chat/${newConv.id}`),toast.success("Conversation forked"),newConv.id}catch(error2){return console.error("Failed to fork conversation:",error2),toast.error("Failed to fork conversation"),null}}generateConversationFilename(conversation,msgs){const sanitizedName=(conversation.name??"").trim().toLowerCase().replace(NON_ALPHANUMERIC_REGEX,EXPORT_CONV_NONALNUM_REPLACEMENT).
-replace(MULTIPLE_UNDERSCORE_REGEX,"_").substring(0,EXPORT_CONV_NAME_SUFFIX_MAX_LENGTH),formattedDate=(msgs?.length?new Date(Math.max(...msgs.map(m=>m.timestamp))):new Date).toISOString().slice(0,ISO_TIMESTAMP_SLICE_LENGTH).replace(ISO_DATE_TIME_SEPARATOR,ISO_DATE_TIME_SEPARATOR_REPLACEMENT).replaceAll(ISO_TIME_SEPARATOR,ISO_TIME_SEPARATOR_REPLACEMENT),trimmedConvId=conversation.id?.slice(0,EXPORT_CONV_ID_TRIM_LENGTH)??"";return`${formattedDate}_conv_${trimmedConvId}_${sanitizedName}.json`}downloadConversationFile(data,filename){
-const conversation="conv"in data?data.conv:Array.isArray(data)?data[0]?.conv:void 0,msgs="messages"in data?data.messages:Array.isArray(data)?data[0]?.messages:void 0;if(!conversation){console.error("Invalid data: missing conversation");return}const downloadFilename=filename??this.generateConversationFilename(conversation,msgs),blob=new Blob([JSON.stringify(data,null,2)],{type:"application/json"}),url2=URL.createObjectURL(blob),a=document.createElement("a");a.href=url2,a.download=downloadFilename,
-document.body.appendChild(a),a.click(),document.body.removeChild(a),URL.revokeObjectURL(url2)}async downloadConversation(convId){let conversation,messages;if(this.activeConversation?.id===convId)conversation=this.activeConversation,messages=this.activeMessages;else{if(conversation=await DatabaseService.getConversation(convId),!conversation)return;messages=await DatabaseService.getConversationMessages(convId)}this.downloadConversationFile({conv:conversation,messages})}async importConversations(){
-return new Promise((resolve2,reject)=>{const input=document.createElement("input");input.type="file",input.accept=".json",input.onchange=async e=>{const file=e.target?.files?.[0];if(!file){reject(new Error("No file selected"));return}try{const text2=await file.text(),parsedData=JSON.parse(text2);let importedData;if(Array.isArray(parsedData))importedData=parsedData;else if(parsedData&&typeof parsedData=="object"&&"conv"in parsedData&&"messages"in parsedData)importedData=[parsedData];else throw new Error(
-"Invalid file format");const result=await DatabaseService.importConversations(importedData);toast.success(`Imported ${result.imported} conversation(s), skipped ${result.skipped}`),await this.loadConversations();const importedConversations=(Array.isArray(importedData)?importedData:[importedData]).map(item=>item.conv);resolve2(importedConversations)}catch(err){const message=err instanceof Error?err.message:"Unknown error";console.error("Failed to import conversations:",err),toast.error("Import fai\
-led",{description:message}),reject(new Error(`Import failed: ${message}`))}},input.click()})}async importConversationsData(data){const result=await DatabaseService.importConversations(data);return await this.loadConversations(),result}}const conversationsStore=new ConversationsStore;conversationsStore.init();const conversations=()=>conversationsStore.conversations,activeConversation=()=>conversationsStore.activeConversation,activeMessages=()=>conversationsStore.activeMessages,isConversationsInitialized=()=>conversationsStore.
-isInitialized;function buildConversationTree(convs){const childrenByParent=new SvelteMap,forkIds=new SvelteSet;for(const conv of convs)if(conv.forkedFromConversationId){forkIds.add(conv.id);const siblings2=childrenByParent.get(conv.forkedFromConversationId)||[];siblings2.push(conv),childrenByParent.set(conv.forkedFromConversationId,siblings2)}const result=[],visited=new SvelteSet;function walk(conv,depth){visited.add(conv.id),result.push({conversation:conv,depth});const children=childrenByParent.
-get(conv.id);if(children){children.sort((a,b)=>b.lastModified-a.lastModified);for(const child2 of children)walk(child2,depth+1)}}const roots=convs.filter(c2=>!forkIds.has(c2.id));for(const root2 of roots)walk(root2,0);for(const conv of convs)visited.has(conv.id)||walk(conv,1);return result}var root_5$t=from_html('<span class="text-sm font-normal text-muted-foreground"> </span>'),root_4$n=from_html("<!> <span>MCP Resources</span> <!>",1),root_3$F=from_html("<!> <!>",1),root_8$k=from_html('<p clas\
-s="mb-4 text-xs text-muted-foreground"> </p>'),root_9$k=from_html('<div class="flex flex-1 items-center justify-center"><!></div>'),root_11$b=from_html('<div class="flex flex-1 flex-col items-center justify-center gap-2 text-red-500"><span class="text-sm"> </span> <!></div>'),root_7$p=from_html('<div class="flex h-full flex-col"><div class="mb-3 flex items-center gap-2"><!> <span class="text-sm font-medium"> </span></div> <!> <div class="mb-4 rounded-md border border-border/50 bg-muted/30 px-3 p\
-y-2"><p class="font-mono text-xs break-all text-muted-foreground"> </p></div> <!></div>'),root_19$2=from_html('<div class="flex flex-col gap-10"></div>'),root_22$6=from_html('<div class="flex h-full items-center justify-center text-sm text-muted-foreground">Select a resource to preview</div>'),root_26$4=from_html("<!> Attach Resource",1),root_30$2=from_html("<!> ",1),root_23$4=from_html("<!> <!>",1),root_2$P=from_html('<!> <div class="flex h-[500px] min-w-0"><div class="w-72 shrink-0 overflow-y-\
-auto border-r border-border/30 p-4"><!></div> <div class="min-w-0 flex-1 overflow-auto p-4"><!></div></div> <!>',1);function DialogMcpResources($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15,!1),selectedResources=new SvelteSet,lastSelectedUri=state$1(null),isAttaching=state$1(!1),selectedTemplate=state$1(null),templatePreviewUri=state$1(null),templatePreviewContent=state$1(null),templatePreviewLoading=state$1(!1),templatePreviewError=state$1(null);const totalCount=user_derived(
-mcpTotalResourceCount);user_effect(()=>{open2()&&(loadResources(),$$props.preSelectedUri&&(selectedResources.clear(),selectedResources.add($$props.preSelectedUri),set$1(lastSelectedUri,$$props.preSelectedUri,!0)))});async function loadResources(){const perChatOverrides=conversationsStore.getAllMcpServerOverrides();await mcpStore.ensureInitialized(perChatOverrides)&&await mcpStore.fetchAllResources()}function handleOpenChange(newOpen){open2(newOpen),$$props.onOpenChange?.(newOpen),newOpen||(selectedResources.
-clear(),set$1(lastSelectedUri,null),clearTemplateState())}function clearTemplateState(){set$1(selectedTemplate,null),set$1(templatePreviewUri,null),set$1(templatePreviewContent,null),set$1(templatePreviewLoading,!1),set$1(templatePreviewError,null)}function handleTemplateSelect(template){if(selectedResources.clear(),set$1(lastSelectedUri,null),get$4(selectedTemplate)?.uriTemplate===template.uriTemplate&&get$4(selectedTemplate)?.serverName===template.serverName){clearTemplateState();return}set$1(
-selectedTemplate,template,!0),set$1(templatePreviewUri,null),set$1(templatePreviewContent,null),set$1(templatePreviewLoading,!1),set$1(templatePreviewError,null)}async function handleTemplateResolve(uri2,serverName){set$1(templatePreviewUri,uri2,!0),set$1(templatePreviewContent,null),set$1(templatePreviewLoading,!0),set$1(templatePreviewError,null);try{const content2=await mcpStore.readResourceByUri(serverName,uri2);content2?set$1(templatePreviewContent,content2,!0):set$1(templatePreviewError,"F\
-ailed to read resource")}catch(error2){set$1(templatePreviewError,error2 instanceof Error?error2.message:"Unknown error",!0)}finally{set$1(templatePreviewLoading,!1)}}function handleTemplateCancelForm(){clearTemplateState()}async function handleAttachTemplateResource(){if(!(!get$4(templatePreviewUri)||!get$4(selectedTemplate)||!get$4(templatePreviewContent))){set$1(isAttaching,!0);try{const knownResource=mcpResourceStore.findResourceByUri(get$4(templatePreviewUri));if(knownResource)mcpResourceStore.
-isAttached(knownResource.uri)||await mcpStore.attachResource(knownResource.uri),toast.success(`Resource attached: ${knownResource.title||knownResource.name}`);else{if(mcpResourceStore.isAttached(get$4(templatePreviewUri))){toast.info("Resource already attached"),handleOpenChange(!1);return}const resourceInfo={uri:get$4(templatePreviewUri),name:get$4(templatePreviewUri).split("/").pop()||get$4(templatePreviewUri),serverName:get$4(selectedTemplate).serverName},attachment=mcpResourceStore.addAttachment(
-resourceInfo);mcpResourceStore.updateAttachmentContent(attachment.id,get$4(templatePreviewContent)),toast.success(`Resource attached: ${resourceInfo.name}`)}handleOpenChange(!1)}catch(error2){console.error("Failed to attach template resource:",error2)}finally{set$1(isAttaching,!1)}}}function handleResourceSelect(resource,shiftKey=!1){if(clearTemplateState(),shiftKey&&get$4(lastSelectedUri)){const allResources=getAllResourcesFlatInTreeOrder(),lastIndex=allResources.findIndex(r2=>r2.uri===get$4(lastSelectedUri)),
-currentIndex=allResources.findIndex(r2=>r2.uri===resource.uri);if(lastIndex!==-1&&currentIndex!==-1){const start2=Math.min(lastIndex,currentIndex),end=Math.max(lastIndex,currentIndex);for(let i=start2;i<=end;i++)selectedResources.add(allResources[i].uri)}}else selectedResources.clear(),selectedResources.add(resource.uri),set$1(lastSelectedUri,resource.uri,!0)}function handleResourceToggle(resource,checked){clearTemplateState(),checked?selectedResources.add(resource.uri):selectedResources.delete(
-resource.uri),set$1(lastSelectedUri,resource.uri,!0)}function getAllResourcesFlatInTreeOrder(){const allResources=[],resourcesMap=mcpResources();for(const[serverName,serverRes]of resourcesMap.entries())for(const resource of serverRes.resources)allResources.push({...resource,serverName});return allResources.sort((a,b)=>{const aName=getResourceDisplayName(a),bName=getResourceDisplayName(b);return aName.localeCompare(bName)})}async function handleAttach(){if(selectedResources.size!==0){set$1(isAttaching,
-!0);try{const resourcesToAttach=getAllResourcesFlatInTreeOrder().filter(r2=>selectedResources.has(r2.uri));for(const resource of resourcesToAttach)await mcpStore.attachResource(resource.uri),$$props.onAttach?.(resource);const count=resourcesToAttach.length;toast.success(count===1?`Resource attached: ${resourcesToAttach[0].name}`:`${count} resources attached`),handleOpenChange(!1)}catch(error2){console.error("Failed to attach resources:",error2)}finally{set$1(isAttaching,!1)}}}const selectedTemplateUri=user_derived(
-()=>get$4(selectedTemplate)?.uriTemplate??null),hasTemplateResult=user_derived(()=>!!get$4(selectedTemplate)&&!!get$4(templatePreviewContent)&&!!get$4(templatePreviewUri));var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$4,($$anchor2,Dialog_Root)=>{Dialog_Root($$anchor2,{get open(){return open2()},onOpenChange:handleOpenChange,children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Dialog_content,($$anchor4,Dialog_Content)=>{
-Dialog_Content($$anchor4,{class:"max-h-[80vh] !max-w-4xl overflow-hidden p-0",children:($$anchor5,$$slotProps2)=>{var fragment_2=root_2$P(),node_2=first_child(fragment_2);component(node_2,()=>Dialog_header,($$anchor6,Dialog_Header)=>{Dialog_Header($$anchor6,{class:"border-b border-border/30 px-6 py-4",children:($$anchor7,$$slotProps3)=>{var fragment_3=root_3$F(),node_3=first_child(fragment_3);component(node_3,()=>Dialog_title,($$anchor8,Dialog_Title)=>{Dialog_Title($$anchor8,{class:"flex items-c\
-enter gap-2",children:($$anchor9,$$slotProps4)=>{var fragment_4=root_4$n(),node_4=first_child(fragment_4);Folder_open(node_4,{class:"h-5 w-5"});var node_5=sibling(node_4,4);{var consequent=$$anchor10=>{var span=root_5$t(),text2=child(span);reset(span),template_effect(()=>set_text(text2,`(${get$4(totalCount)??""})`)),append($$anchor10,span)};if_block(node_5,$$render=>{get$4(totalCount)>0&&$$render(consequent)})}append($$anchor9,fragment_4)},$$slots:{default:!0}})});var node_6=sibling(node_3,2);component(
-node_6,()=>Dialog_description,($$anchor8,Dialog_Description)=>{Dialog_Description($$anchor8,{children:($$anchor9,$$slotProps4)=>{next$1();var text_1=text$8("Browse and attach resources from connected MCP servers to your chat context.");append($$anchor9,text_1)},$$slots:{default:!0}})}),append($$anchor7,fragment_3)},$$slots:{default:!0}})});var div=sibling(node_2,2),div_1=child(div),node_7=child(div_1);McpResourceBrowser(node_7,{onSelect:handleResourceSelect,onToggle:handleResourceToggle,onTemplateSelect:handleTemplateSelect,
-get selectedUris(){return selectedResources},get selectedTemplateUri(){return get$4(selectedTemplateUri)},get expandToUri(){return $$props.preSelectedUri}}),reset(div_1);var div_2=sibling(div_1,2),node_8=child(div_2);{var consequent_4=$$anchor6=>{var div_3=root_7$p(),div_4=child(div_3),node_9=child(div_4);Braces(node_9,{class:"h-4 w-4 text-muted-foreground"});var span_1=sibling(node_9,2),text_2=child(span_1,!0);reset(span_1),reset(div_4);var node_10=sibling(div_4,2);{var consequent_1=$$anchor7=>{
-var p2=root_8$k(),text_3=child(p2,!0);reset(p2),template_effect(()=>set_text(text_3,get$4(selectedTemplate).description)),append($$anchor7,p2)};if_block(node_10,$$render=>{get$4(selectedTemplate).description&&$$render(consequent_1)})}var div_5=sibling(node_10,2),p_1=child(div_5),text_4=child(p_1,!0);reset(p_1),reset(div_5);var node_11=sibling(div_5,2);{var consequent_2=$$anchor7=>{var div_6=root_9$k(),node_12=child(div_6);Loader_circle(node_12,{class:"h-6 w-6 animate-spin text-muted-foreground"}),
-reset(div_6),append($$anchor7,div_6)},alternate_1=$$anchor7=>{var fragment_5=comment$2(),node_13=first_child(fragment_5);{var consequent_3=$$anchor8=>{var div_7=root_11$b(),span_2=child(div_7),text_5=child(span_2,!0);reset(span_2);var node_14=sibling(span_2,2);Button(node_14,{size:"sm",variant:"outline",onclick:()=>{set$1(templatePreviewError,null)},children:($$anchor9,$$slotProps3)=>{next$1();var text_6=text$8("Try again");append($$anchor9,text_6)},$$slots:{default:!0}}),reset(div_7),template_effect(
-()=>set_text(text_5,get$4(templatePreviewError))),append($$anchor8,div_7)},alternate=$$anchor8=>{McpResourceTemplateForm($$anchor8,{get template(){return get$4(selectedTemplate)},onResolve:handleTemplateResolve,onCancel:handleTemplateCancelForm})};if_block(node_13,$$render=>{get$4(templatePreviewError)?$$render(consequent_3):$$render(alternate,!1)},!0)}append($$anchor7,fragment_5)};if_block(node_11,$$render=>{get$4(templatePreviewLoading)?$$render(consequent_2):$$render(alternate_1,!1)})}reset(div_3),
-template_effect(()=>{set_text(text_2,get$4(selectedTemplate).title||get$4(selectedTemplate).name),set_text(text_4,get$4(selectedTemplate).uriTemplate)}),append($$anchor6,div_3)},alternate_5=$$anchor6=>{var fragment_7=comment$2(),node_15=first_child(fragment_7);{var consequent_5=$$anchor7=>{{let $0=user_derived(()=>({uri:get$4(templatePreviewUri)??"",name:get$4(templatePreviewUri)?.split("/").pop()||(get$4(templatePreviewUri)??""),serverName:get$4(selectedTemplate)?.serverName||""}));McpResourcePreview(
-$$anchor7,{get resource(){return get$4($0)},get preloadedContent(){return get$4(templatePreviewContent)}})}},alternate_4=$$anchor7=>{var fragment_9=comment$2(),node_16=first_child(fragment_9);{var consequent_6=$$anchor8=>{const allResources=user_derived(getAllResourcesFlatInTreeOrder),selectedResource=user_derived(()=>get$4(allResources).find(r2=>selectedResources.has(r2.uri)));{let $0=user_derived(()=>get$4(selectedResource)??null);McpResourcePreview($$anchor8,{get resource(){return get$4($0)}})}},
-alternate_3=$$anchor8=>{var fragment_11=comment$2(),node_17=first_child(fragment_11);{var consequent_8=$$anchor9=>{var div_8=root_19$2();each(div_8,21,getAllResourcesFlatInTreeOrder,resource=>resource.uri,($$anchor10,resource)=>{var fragment_12=comment$2(),node_18=first_child(fragment_12);{var consequent_7=$$anchor11=>{McpResourcePreview($$anchor11,{get resource(){return get$4(resource)}})};if_block(node_18,$$render=>{selectedResources.has(get$4(resource).uri)&&$$render(consequent_7)})}append($$anchor10,
-fragment_12)}),reset(div_8),append($$anchor9,div_8)},alternate_2=$$anchor9=>{var div_9=root_22$6();append($$anchor9,div_9)};if_block(node_17,$$render=>{selectedResources.size>1?$$render(consequent_8):$$render(alternate_2,!1)},!0)}append($$anchor8,fragment_11)};if_block(node_16,$$render=>{selectedResources.size===1?$$render(consequent_6):$$render(alternate_3,!1)},!0)}append($$anchor7,fragment_9)};if_block(node_15,$$render=>{get$4(hasTemplateResult)?$$render(consequent_5):$$render(alternate_4,!1)},
-!0)}append($$anchor6,fragment_7)};if_block(node_8,$$render=>{get$4(selectedTemplate)&&!get$4(templatePreviewContent)?$$render(consequent_4):$$render(alternate_5,!1)})}reset(div_2),reset(div);var node_19=sibling(div,2);component(node_19,()=>Dialog_footer,($$anchor6,Dialog_Footer)=>{Dialog_Footer($$anchor6,{class:"border-t border-border/30 px-6 py-4",children:($$anchor7,$$slotProps3)=>{var fragment_14=root_23$4(),node_20=first_child(fragment_14);Button(node_20,{variant:"outline",onclick:()=>handleOpenChange(
-!1),children:($$anchor8,$$slotProps4)=>{next$1();var text_7=text$8("Cancel");append($$anchor8,text_7)},$$slots:{default:!0}});var node_21=sibling(node_20,2);{var consequent_10=$$anchor8=>{Button($$anchor8,{onclick:handleAttachTemplateResource,get disabled(){return get$4(isAttaching)},children:($$anchor9,$$slotProps4)=>{var fragment_16=root_26$4(),node_22=first_child(fragment_16);{var consequent_9=$$anchor10=>{Loader_circle($$anchor10,{class:"mr-2 h-4 w-4 animate-spin"})},alternate_6=$$anchor10=>{
-Plus($$anchor10,{class:"mr-2 h-4 w-4"})};if_block(node_22,$$render=>{get$4(isAttaching)?$$render(consequent_9):$$render(alternate_6,!1)})}next$1(),append($$anchor9,fragment_16)},$$slots:{default:!0}})},alternate_8=$$anchor8=>{{let $0=user_derived(()=>selectedResources.size===0||get$4(isAttaching));Button($$anchor8,{onclick:handleAttach,get disabled(){return get$4($0)},children:($$anchor9,$$slotProps4)=>{var fragment_20=root_30$2(),node_23=first_child(fragment_20);{var consequent_11=$$anchor10=>{
-Loader_circle($$anchor10,{class:"mr-2 h-4 w-4 animate-spin"})},alternate_7=$$anchor10=>{Plus($$anchor10,{class:"mr-2 h-4 w-4"})};if_block(node_23,$$render=>{get$4(isAttaching)?$$render(consequent_11):$$render(alternate_7,!1)})}var text_8=sibling(node_23);template_effect(()=>set_text(text_8,` Attach ${selectedResources.size>0?`(${selectedResources.size})`:"Resource"}`)),append($$anchor9,fragment_20)},$$slots:{default:!0}})}};if_block(node_21,$$render=>{get$4(hasTemplateResult)?$$render(consequent_10):
-$$render(alternate_8,!1)})}append($$anchor7,fragment_14)},$$slots:{default:!0}})}),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}var root_7$o=from_html('<img alt="" class="h-3 w-3 shrink-0 rounded-sm"/>'),root_6$s=from_html('<span class="flex items-center gap-1 text-xs text-muted-foreground">· <!> </span>'),root_8$j=from_html('<span class="rounded bg-muted px-1.5 py-0.5 text-xs"> </span>'),root_5$s=from_html(
-'<div class="flex items-center gap-2"><span class="text-xs text-muted-foreground"> </span> <!> <!></div>'),root_3$E=from_html("<!> <!>",1),root_10$f=from_html('<div class="flex items-center justify-center"><img class="max-h-[70vh] max-w-full rounded object-contain"/></div>'),root_14$7=from_html('<pre class="max-h-[70vh] overflow-auto rounded-md border bg-muted/30 p-4 font-mono text-sm break-words whitespace-pre-wrap"> </pre>'),root_15$4=from_html('<div class="py-8 text-center text-sm text-muted-\
-foreground">No content available</div>'),root_2$O=from_html('<!> <div class="flex items-center justify-end gap-1"><!> <!></div> <div class="overflow-auto"><!></div>',1);function DialogMcpResourcePreview($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15);const serverName=user_derived(()=>mcpStore.getServerDisplayName($$props.extra.serverName)),favicon=user_derived(()=>mcpStore.getServerFavicon($$props.extra.serverName));function getLanguage(){if($$props.extra.mimeType?.includes(
-MimeTypeIncludes.JSON))return MimeTypeIncludes.JSON;if($$props.extra.mimeType?.includes(MimeTypeIncludes.JAVASCRIPT))return MimeTypeIncludes.JAVASCRIPT;if($$props.extra.mimeType?.includes(MimeTypeIncludes.TYPESCRIPT))return MimeTypeIncludes.TYPESCRIPT;const name=$$props.extra.name||$$props.extra.uri||"";return getLanguageFromFilename(name)||"plaintext"}function handleDownload(){$$props.extra.content&&downloadResourceContent($$props.extra.content,$$props.extra.mimeType||MimeTypeText.PLAIN,$$props.
-extra.name||DEFAULT_RESOURCE_FILENAME)}var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$4,($$anchor2,Dialog_Root)=>{Dialog_Root($$anchor2,{get onOpenChange(){return $$props.onOpenChange},get open(){return open2()},set open($$value){open2($$value)},children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Dialog_content,($$anchor4,Dialog_Content)=>{Dialog_Content($$anchor4,{class:"grid max-h-[90vh] max-w-5xl over\
-flow-hidden sm:w-auto sm:max-w-6xl",children:($$anchor5,$$slotProps2)=>{var fragment_2=root_2$O(),node_2=first_child(fragment_2);component(node_2,()=>Dialog_header,($$anchor6,Dialog_Header)=>{Dialog_Header($$anchor6,{children:($$anchor7,$$slotProps3)=>{var fragment_3=root_3$E(),node_3=first_child(fragment_3);component(node_3,()=>Dialog_title,($$anchor8,Dialog_Title)=>{Dialog_Title($$anchor8,{class:"pr-8",children:($$anchor9,$$slotProps4)=>{next$1();var text2=text$8();template_effect(()=>set_text(
-text2,$$props.extra.name)),append($$anchor9,text2)},$$slots:{default:!0}})});var node_4=sibling(node_3,2);component(node_4,()=>Dialog_description,($$anchor8,Dialog_Description)=>{Dialog_Description($$anchor8,{children:($$anchor9,$$slotProps4)=>{var div=root_5$s(),span=child(div),text_1=child(span,!0);reset(span);var node_5=sibling(span,2);{var consequent_1=$$anchor10=>{var span_1=root_6$s(),node_6=sibling(child(span_1));{var consequent=$$anchor11=>{var img=root_7$o();template_effect(()=>set_attribute(
-img,"src",get$4(favicon))),event("error",img,e=>{e.currentTarget.style.display="none"}),replay_events(img),append($$anchor11,img)};if_block(node_6,$$render=>{get$4(favicon)&&$$render(consequent)})}var text_2=sibling(node_6);reset(span_1),template_effect(()=>set_text(text_2,` ${get$4(serverName)??""}`)),append($$anchor10,span_1)};if_block(node_5,$$render=>{get$4(serverName)&&$$render(consequent_1)})}var node_7=sibling(node_5,2);{var consequent_2=$$anchor10=>{var span_2=root_8$j(),text_3=child(span_2,
-!0);reset(span_2),template_effect(()=>set_text(text_3,$$props.extra.mimeType)),append($$anchor10,span_2)};if_block(node_7,$$render=>{$$props.extra.mimeType&&$$render(consequent_2)})}reset(div),template_effect(()=>set_text(text_1,$$props.extra.uri)),append($$anchor9,div)},$$slots:{default:!0}})}),append($$anchor7,fragment_3)},$$slots:{default:!0}})});var div_1=sibling(node_2,2),node_8=child(div_1);{let $0=user_derived(()=>!!$$props.extra.content);ActionIconCopyToClipboard(node_8,{get text(){return $$props.
-extra.content},get canCopy(){return get$4($0)},ariaLabel:"Copy content"})}var node_9=sibling(node_8,2);{let $0=user_derived(()=>!$$props.extra.content);Button(node_9,{variant:"ghost",size:"sm",class:"h-7 w-7 p-0",onclick:handleDownload,get disabled(){return get$4($0)},title:"Download content",children:($$anchor6,$$slotProps3)=>{Download($$anchor6,{class:"h-3.5 w-3.5"})},$$slots:{default:!0}})}reset(div_1);var div_2=sibling(div_1,2),node_10=child(div_2);{var consequent_3=$$anchor6=>{var div_3=root_10$f(),
-img_1=child(div_3);reset(div_3),template_effect($0=>{set_attribute(img_1,"src",$0),set_attribute(img_1,"alt",$$props.extra.name)},[()=>$$props.extra.content.startsWith("data:")?$$props.extra.content:`data:${$$props.extra.mimeType||"image/png"};base64,${$$props.extra.content}`]),append($$anchor6,div_3)},alternate_2=$$anchor6=>{var fragment_6=comment$2(),node_11=first_child(fragment_6);{var consequent_4=$$anchor7=>{{let $0=user_derived(getLanguage);SyntaxHighlightedCode($$anchor7,{get code(){return $$props.
-extra.content},get language(){return get$4($0)},maxHeight:"70vh"})}},alternate_1=$$anchor7=>{var fragment_8=comment$2(),node_12=first_child(fragment_8);{var consequent_5=$$anchor8=>{var pre=root_14$7(),text_4=child(pre,!0);reset(pre),template_effect(()=>set_text(text_4,$$props.extra.content)),append($$anchor8,pre)},alternate=$$anchor8=>{var div_4=root_15$4();append($$anchor8,div_4)};if_block(node_12,$$render=>{$$props.extra.content?$$render(consequent_5):$$render(alternate,!1)},!0)}append($$anchor7,
-fragment_8)};if_block(node_11,$$render=>{isCodeResource($$props.extra.mimeType,$$props.extra.uri)&&$$props.extra.content?$$render(consequent_4):$$render(alternate_1,!1)},!0)}append($$anchor6,fragment_6)};if_block(node_10,$$render=>{isImageResource($$props.extra.mimeType,$$props.extra.uri)&&$$props.extra.content?$$render(consequent_3):$$render(alternate_2,!1)})}reset(div_2),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,
-fragment),pop()}function createDefaultSession(){return{isRunning:!1,currentTurn:0,totalToolCalls:0,lastError:null,streamingToolCall:null}}function toAgenticMessages(messages){return messages.map(message=>message.role===MessageRole.ASSISTANT&&message.tool_calls&&message.tool_calls.length>0?{role:MessageRole.ASSISTANT,content:message.content,tool_calls:message.tool_calls.map((call,index2)=>({id:call.id??`call_${index2}`,type:call.type??ToolCallType.FUNCTION,function:{name:call.function?.name??"",arguments:call.
-function?.arguments??""}}))}:message.role===MessageRole.TOOL&&message.tool_call_id?{role:MessageRole.TOOL,tool_call_id:message.tool_call_id,content:typeof message.content=="string"?message.content:""}:{role:message.role,content:message.content})}class AgenticStore{#_sessions=state$1(proxy(new Map));get _sessions(){return get$4(this.#_sessions)}set _sessions(value){set$1(this.#_sessions,value,!0)}get isReady(){return!0}get isAnyRunning(){for(const session of this._sessions.values())if(session.isRunning)
-return!0;return!1}getSession(conversationId){let session=this._sessions.get(conversationId);return session||(session=createDefaultSession(),this._sessions.set(conversationId,session)),session}updateSession(conversationId,update2){const session=this.getSession(conversationId);this._sessions.set(conversationId,{...session,...update2})}clearSession(conversationId){this._sessions.delete(conversationId)}getActiveSessions(){const active=[];for(const[conversationId,session]of this._sessions.entries())session.
-isRunning&&active.push({conversationId,session});return active}isRunning(conversationId){return this.getSession(conversationId).isRunning}currentTurn(conversationId){return this.getSession(conversationId).currentTurn}totalToolCalls(conversationId){return this.getSession(conversationId).totalToolCalls}lastError(conversationId){return this.getSession(conversationId).lastError}streamingToolCall(conversationId){return this.getSession(conversationId).streamingToolCall}clearError(conversationId){this.
-updateSession(conversationId,{lastError:null})}getConfig(settings,perChatOverrides){const maxTurns=Number(settings.agenticMaxTurns)||DEFAULT_AGENTIC_CONFIG.maxTurns,maxToolPreviewLines=Number(settings.agenticMaxToolPreviewLines)||DEFAULT_AGENTIC_CONFIG.maxToolPreviewLines;return{enabled:mcpStore.hasEnabledServers(perChatOverrides)&&DEFAULT_AGENTIC_CONFIG.enabled,maxTurns,maxToolPreviewLines}}async runAgenticFlow(params){const{conversationId,messages,options={},callbacks,signal,perChatOverrides}=params,
-agenticConfig=this.getConfig(config$1(),perChatOverrides);if(!agenticConfig.enabled)return{handled:!1};if(!await mcpStore.ensureInitialized(perChatOverrides))return console.log("[AgenticStore] MCP not initialized, falling back to standard chat"),{handled:!1};const tools=mcpStore.getToolDefinitionsForLLM();if(tools.length===0)return console.log("[AgenticStore] No tools available, falling back to standard chat"),{handled:!1};console.log(`[AgenticStore] Starting agentic flow with ${tools.length} to\
-ols`);const normalizedMessages=messages.map(msg=>"id"in msg&&"convId"in msg&&"timestamp"in msg?ChatService.convertDbMessageToApiChatMessageData(msg):msg).filter(msg=>msg.role===MessageRole.SYSTEM?(typeof msg.content=="string"?msg.content:"").trim().length>0:!0);this.updateSession(conversationId,{isRunning:!0,currentTurn:0,totalToolCalls:0,lastError:null}),mcpStore.acquireConnection();try{return await this.executeAgenticLoop({conversationId,messages:normalizedMessages,options,tools,agenticConfig,
-callbacks,signal}),{handled:!0}}catch(error2){const normalizedError=error2 instanceof Error?error2:new Error(String(error2));return this.updateSession(conversationId,{lastError:normalizedError}),callbacks.onError?.(normalizedError),{handled:!0,error:normalizedError}}finally{this.updateSession(conversationId,{isRunning:!1}),await mcpStore.releaseConnection().catch(err=>console.warn("[AgenticStore] Failed to release MCP connection:",err))}}async executeAgenticLoop(params){const{conversationId,messages,
-options,tools,agenticConfig,callbacks,signal}=params,{onChunk,onReasoningChunk,onToolCallsStreaming,onAttachments,onModel,onAssistantTurnComplete,createToolResultMessage,createAssistantMessage,onFlowComplete,onTimings,onTurnComplete}=callbacks,sessionMessages=toAgenticMessages(messages);let capturedTimings,totalToolCallCount=0;const agenticTimings={turns:0,toolCallsCount:0,toolsMs:0,toolCalls:[],perTurn:[],llm:{predicted_n:0,predicted_ms:0,prompt_n:0,prompt_ms:0}},maxTurns=agenticConfig.maxTurns,
-effectiveModel=options.model||modelsStore.models[0]?.model||"";for(let turn=0;turn<maxTurns;turn++){if(this.updateSession(conversationId,{currentTurn:turn+1}),agenticTimings.turns=turn+1,signal?.aborted){onFlowComplete?.(this.buildFinalTimings(capturedTimings,agenticTimings));return}turn>0&&createAssistantMessage&&await createAssistantMessage();let turnContent="",turnReasoningContent="",turnToolCalls=[],lastStreamingToolCallName="",lastStreamingToolCallArgsLength=0,turnTimings;const turnStats={turn:turn+
-1,llm:{predicted_n:0,predicted_ms:0,prompt_n:0,prompt_ms:0},toolCalls:[],toolsMs:0};try{await ChatService.sendMessage(sessionMessages,{...options,stream:!0,tools:tools.length>0?tools:void 0,onChunk:chunk=>{turnContent+=chunk,onChunk?.(chunk)},onReasoningChunk:chunk=>{turnReasoningContent+=chunk,onReasoningChunk?.(chunk)},onToolCallChunk:serialized=>{try{if(turnToolCalls=JSON.parse(serialized),onToolCallsStreaming?.(turnToolCalls),turnToolCalls.length>0&&turnToolCalls[0]?.function){const name=turnToolCalls[0].
-function.name||"",args=turnToolCalls[0].function.arguments||"",argsLengthBucket=Math.floor(args.length/100);(name!==lastStreamingToolCallName||argsLengthBucket!==lastStreamingToolCallArgsLength)&&(lastStreamingToolCallName=name,lastStreamingToolCallArgsLength=argsLengthBucket,this.updateSession(conversationId,{streamingToolCall:{name,arguments:args}}))}}catch{}},onModel,onTimings:(timings,progress)=>{onTimings?.(timings,progress),timings&&(capturedTimings=timings,turnTimings=timings)},onComplete:()=>{},
-onError:error2=>{throw error2}},void 0,signal),this.updateSession(conversationId,{streamingToolCall:null}),turnTimings&&(agenticTimings.llm.predicted_n+=turnTimings.predicted_n||0,agenticTimings.llm.predicted_ms+=turnTimings.predicted_ms||0,agenticTimings.llm.prompt_n+=turnTimings.prompt_n||0,agenticTimings.llm.prompt_ms+=turnTimings.prompt_ms||0,turnStats.llm.predicted_n=turnTimings.predicted_n||0,turnStats.llm.predicted_ms=turnTimings.predicted_ms||0,turnStats.llm.prompt_n=turnTimings.prompt_n||
-0,turnStats.llm.prompt_ms=turnTimings.prompt_ms||0)}catch(error2){if(signal?.aborted){await onAssistantTurnComplete?.(turnContent,turnReasoningContent||void 0,this.buildFinalTimings(capturedTimings,agenticTimings),void 0),onFlowComplete?.(this.buildFinalTimings(capturedTimings,agenticTimings));return}const normalizedError=error2 instanceof Error?error2:new Error("LLM stream error");throw onChunk?.(`${LLM_ERROR_BLOCK_START}${normalizedError.message}${LLM_ERROR_BLOCK_END}`),await onAssistantTurnComplete?.(
-turnContent+`${LLM_ERROR_BLOCK_START}${normalizedError.message}${LLM_ERROR_BLOCK_END}`,turnReasoningContent||void 0,this.buildFinalTimings(capturedTimings,agenticTimings),void 0),onFlowComplete?.(this.buildFinalTimings(capturedTimings,agenticTimings)),normalizedError}if(turnToolCalls.length===0){agenticTimings.perTurn.push(turnStats);const finalTimings=this.buildFinalTimings(capturedTimings,agenticTimings);await onAssistantTurnComplete?.(turnContent,turnReasoningContent||void 0,finalTimings,void 0),
-finalTimings&&onTurnComplete?.(finalTimings),onFlowComplete?.(finalTimings);return}const normalizedCalls=this.normalizeToolCalls(turnToolCalls);if(normalizedCalls.length===0){await onAssistantTurnComplete?.(turnContent,turnReasoningContent||void 0,this.buildFinalTimings(capturedTimings,agenticTimings),void 0),onFlowComplete?.(this.buildFinalTimings(capturedTimings,agenticTimings));return}totalToolCallCount+=normalizedCalls.length,this.updateSession(conversationId,{totalToolCalls:totalToolCallCount}),
-await onAssistantTurnComplete?.(turnContent,turnReasoningContent||void 0,turnTimings,normalizedCalls),sessionMessages.push({role:MessageRole.ASSISTANT,content:turnContent||void 0,reasoning_content:turnReasoningContent||void 0,tool_calls:normalizedCalls});for(const toolCall of normalizedCalls){if(signal?.aborted){onFlowComplete?.(this.buildFinalTimings(capturedTimings,agenticTimings));return}const toolStartTime=performance.now(),mcpCall={id:toolCall.id,function:{name:toolCall.function.name,arguments:toolCall.
-function.arguments}};let result,toolSuccess=!0;try{result=(await mcpStore.executeTool(mcpCall,signal)).content}catch(error2){if(isAbortError(error2)){onFlowComplete?.(this.buildFinalTimings(capturedTimings,agenticTimings));return}result=`Error: ${error2 instanceof Error?error2.message:String(error2)}`,toolSuccess=!1}const toolDurationMs=performance.now()-toolStartTime,toolTiming={name:toolCall.function.name,duration_ms:Math.round(toolDurationMs),success:toolSuccess};if(agenticTimings.toolCalls.push(
-toolTiming),agenticTimings.toolCallsCount++,agenticTimings.toolsMs+=Math.round(toolDurationMs),turnStats.toolCalls.push(toolTiming),turnStats.toolsMs+=Math.round(toolDurationMs),signal?.aborted){onFlowComplete?.(this.buildFinalTimings(capturedTimings,agenticTimings));return}const{cleanedResult,attachments}=this.extractBase64Attachments(result);let toolResultMessage;createToolResultMessage&&(toolResultMessage=await createToolResultMessage(toolCall.id,cleanedResult,attachments.length>0?attachments:
-void 0)),attachments.length>0&&toolResultMessage&&onAttachments?.(toolResultMessage.id,attachments);const contentParts=[{type:ContentPartType.TEXT,text:cleanedResult}];for(const attachment of attachments)attachment.type===AttachmentType.IMAGE&&(modelsStore.modelSupportsVision(effectiveModel)?contentParts.push({type:ContentPartType.IMAGE_URL,image_url:{url:attachment.base64Url}}):console.info(`[AgenticStore] Skipping image attachment (model "${effectiveModel}" does not support vision)`));sessionMessages.
-push({role:MessageRole.TOOL,tool_call_id:toolCall.id,content:contentParts.length===1?cleanedResult:contentParts})}if(turnStats.toolCalls.length>0){agenticTimings.perTurn.push(turnStats);const intermediateTimings=this.buildFinalTimings(capturedTimings,agenticTimings);intermediateTimings&&onTurnComplete?.(intermediateTimings)}}onChunk?.(TURN_LIMIT_MESSAGE),await onAssistantTurnComplete?.(TURN_LIMIT_MESSAGE,void 0,this.buildFinalTimings(capturedTimings,agenticTimings),void 0),onFlowComplete?.(this.
-buildFinalTimings(capturedTimings,agenticTimings))}buildFinalTimings(capturedTimings,agenticTimings){return agenticTimings.toolCallsCount===0?capturedTimings:{predicted_n:capturedTimings?.predicted_n,predicted_ms:capturedTimings?.predicted_ms,prompt_n:capturedTimings?.prompt_n,prompt_ms:capturedTimings?.prompt_ms,cache_n:capturedTimings?.cache_n,agentic:agenticTimings}}normalizeToolCalls(toolCalls){return toolCalls?toolCalls.map((call,index2)=>({id:call?.id??`tool_${index2}`,type:call?.type??ToolCallType.
-FUNCTION,function:{name:call?.function?.name??"",arguments:call?.function?.arguments??""}})):[]}extractBase64Attachments(result){if(!result.trim())return{cleanedResult:result,attachments:[]};const lines=result.split(NEWLINE_SEPARATOR),attachments=[];let attachmentIndex=0;return{cleanedResult:lines.map(line=>{const trimmedLine=line.trim(),match=trimmedLine.match(DATA_URI_BASE64_REGEX);if(!match)return line;const mimeType=match[1].toLowerCase();if(!match[2])return line;attachmentIndex+=1;const name=this.
-buildAttachmentName(mimeType,attachmentIndex);return mimeType.startsWith(MimeTypePrefix.IMAGE)?(attachments.push({type:AttachmentType.IMAGE,name,base64Url:trimmedLine}),`[Attachment saved: ${name}]`):line}).join(NEWLINE_SEPARATOR),attachments}}buildAttachmentName(mimeType,index2){const extension2=IMAGE_MIME_TO_EXTENSION[mimeType]??DEFAULT_IMAGE_EXTENSION;return`${MCP_ATTACHMENT_NAME_PREFIX}-${Date.now()}-${index2}.${extension2}`}}const agenticStore=new AgenticStore;class ChatStore{#activeProcessingState=state$1(
-null);get activeProcessingState(){return get$4(this.#activeProcessingState)}set activeProcessingState(value){set$1(this.#activeProcessingState,value,!0)}#currentResponse=state$1("");get currentResponse(){return get$4(this.#currentResponse)}set currentResponse(value){set$1(this.#currentResponse,value,!0)}#errorDialogState=state$1(null);get errorDialogState(){return get$4(this.#errorDialogState)}set errorDialogState(value){set$1(this.#errorDialogState,value,!0)}#isLoading=state$1(!1);get isLoading(){
-return get$4(this.#isLoading)}set isLoading(value){set$1(this.#isLoading,value,!0)}chatLoadingStates=new SvelteMap;chatStreamingStates=new SvelteMap;abortControllers=new SvelteMap;preEncodeAbortController=null;processingStates=new SvelteMap;conversationStateTimestamps=new SvelteMap;#activeConversationId=state$1(null);get activeConversationId(){return get$4(this.#activeConversationId)}set activeConversationId(value){set$1(this.#activeConversationId,value,!0)}#isStreamingActive=state$1(!1);get isStreamingActive(){
-return get$4(this.#isStreamingActive)}set isStreamingActive(value){set$1(this.#isStreamingActive,value,!0)}#isEditModeActive=state$1(!1);get isEditModeActive(){return get$4(this.#isEditModeActive)}set isEditModeActive(value){set$1(this.#isEditModeActive,value,!0)}#addFilesHandler=state$1(null);get addFilesHandler(){return get$4(this.#addFilesHandler)}set addFilesHandler(value){set$1(this.#addFilesHandler,value,!0)}#pendingEditMessageId=state$1(null);get pendingEditMessageId(){return get$4(this.#pendingEditMessageId)}set pendingEditMessageId(value){
-set$1(this.#pendingEditMessageId,value,!0)}messageUpdateCallback=null;#_pendingDraftMessage=state$1("");get _pendingDraftMessage(){return get$4(this.#_pendingDraftMessage)}set _pendingDraftMessage(value){set$1(this.#_pendingDraftMessage,value,!0)}#_pendingDraftFiles=state$1(proxy([]));get _pendingDraftFiles(){return get$4(this.#_pendingDraftFiles)}set _pendingDraftFiles(value){set$1(this.#_pendingDraftFiles,value,!0)}setChatLoading(convId,loading){this.touchConversationState(convId),loading?(this.
-chatLoadingStates.set(convId,!0),convId===conversationsStore.activeConversation?.id&&(this.isLoading=!0)):(this.chatLoadingStates.delete(convId),convId===conversationsStore.activeConversation?.id&&(this.isLoading=!1))}setChatStreaming(convId,response,messageId){this.touchConversationState(convId),this.chatStreamingStates.set(convId,{response,messageId}),convId===conversationsStore.activeConversation?.id&&(this.currentResponse=response)}clearChatStreaming(convId){this.chatStreamingStates.delete(convId),
-convId===conversationsStore.activeConversation?.id&&(this.currentResponse="")}getChatStreaming(convId){return this.chatStreamingStates.get(convId)}syncLoadingStateForChat(convId){this.isLoading=this.chatLoadingStates.get(convId)||!1;const s2=this.chatStreamingStates.get(convId);if(this.currentResponse=s2?.response||"",this.isStreamingActive=s2!==void 0,this.setActiveProcessingConversation(convId),s2?.response&&s2?.messageId){const idx=conversationsStore.findMessageIndex(s2.messageId);idx!==-1&&conversationsStore.
-updateMessageAtIndex(idx,{content:s2.response})}}clearUIState(){this.isLoading=!1,this.currentResponse="",this.isStreamingActive=!1}setActiveProcessingConversation(conversationId){this.activeConversationId=conversationId,this.activeProcessingState=conversationId&&this.processingStates.get(conversationId)||null}getProcessingState(conversationId){return this.processingStates.get(conversationId)||null}setProcessingState(conversationId,state2){state2===null?this.processingStates.delete(conversationId):
-this.processingStates.set(conversationId,state2),conversationId===this.activeConversationId&&(this.activeProcessingState=state2)}clearProcessingState(conversationId){this.processingStates.delete(conversationId),conversationId===this.activeConversationId&&(this.activeProcessingState=null)}getActiveProcessingState(){return this.activeProcessingState}getCurrentProcessingStateSync(){return this.activeProcessingState}setStreamingActive(active){this.isStreamingActive=active}isStreaming(){return this.isStreamingActive}getOrCreateAbortController(convId){
-let c2=this.abortControllers.get(convId);return(!c2||c2.signal.aborted)&&(c2=new AbortController,this.abortControllers.set(convId,c2)),c2}abortRequest(convId){if(convId){const c2=this.abortControllers.get(convId);c2&&(c2.abort(),this.abortControllers.delete(convId))}else{for(const c2 of this.abortControllers.values())c2.abort();this.abortControllers.clear()}}showErrorDialog(state2){this.errorDialogState=state2}dismissErrorDialog(){this.errorDialogState=null}clearEditMode(){this.isEditModeActive=
-!1,this.addFilesHandler=null}isEditing(){return this.isEditModeActive}setEditModeActive(handler){this.isEditModeActive=!0,this.addFilesHandler=handler}getAddFilesHandler(){return this.addFilesHandler}clearPendingEditMessageId(){this.pendingEditMessageId=null}savePendingDraft(message,files){this._pendingDraftMessage=message,this._pendingDraftFiles=[...files]}consumePendingDraft(){if(!this._pendingDraftMessage&&this._pendingDraftFiles.length===0)return null;const d2={message:this._pendingDraftMessage,
-files:[...this._pendingDraftFiles]};return this._pendingDraftMessage="",this._pendingDraftFiles=[],d2}hasPendingDraft(){return!!this._pendingDraftMessage||this._pendingDraftFiles.length>0}getAllLoadingChats(){return Array.from(this.chatLoadingStates.keys())}getAllStreamingChats(){return Array.from(this.chatStreamingStates.keys())}getChatStreamingPublic(convId){return this.getChatStreaming(convId)}isChatLoadingPublic(convId){return this.chatLoadingStates.get(convId)||!1}isChatLoadingInternal(convId){
-return this.chatStreamingStates.has(convId)}touchConversationState(convId){this.conversationStateTimestamps.set(convId,{lastAccessed:Date.now()})}cleanupOldConversationStates(activeConversationIds){const now2=Date.now(),activeIdsList=activeConversationIds??[],preserveIds=this.activeConversationId?[...activeIdsList,this.activeConversationId]:activeIdsList,allConvIds=[...new Set([...this.chatLoadingStates.keys(),...this.chatStreamingStates.keys(),...this.abortControllers.keys(),...this.processingStates.
-keys(),...this.conversationStateTimestamps.keys()])],cleanupCandidates=[];for(const convId of allConvIds){if(preserveIds.includes(convId)||this.chatLoadingStates.get(convId)||this.chatStreamingStates.has(convId))continue;const ts=this.conversationStateTimestamps.get(convId);cleanupCandidates.push({convId,lastAccessed:ts?.lastAccessed??0})}cleanupCandidates.sort((a,b)=>a.lastAccessed-b.lastAccessed);let cleanedUp=0;for(const{convId,lastAccessed}of cleanupCandidates)(cleanupCandidates.length-cleanedUp>
-MAX_INACTIVE_CONVERSATION_STATES||now2-lastAccessed>INACTIVE_CONVERSATION_STATE_MAX_AGE_MS)&&(this.cleanupConversationState(convId),cleanedUp++);return cleanedUp}cleanupConversationState(convId){const c2=this.abortControllers.get(convId);c2&&!c2.signal.aborted&&c2.abort(),this.chatLoadingStates.delete(convId),this.chatStreamingStates.delete(convId),this.abortControllers.delete(convId),this.processingStates.delete(convId),this.conversationStateTimestamps.delete(convId)}getTrackedConversationCount(){
-return new Set([...this.chatLoadingStates.keys(),...this.chatStreamingStates.keys(),...this.abortControllers.keys(),...this.processingStates.keys()]).size}getMessageByIdWithRole(messageId,expectedRole){const index2=conversationsStore.findMessageIndex(messageId);if(index2===-1)return null;const message=conversationsStore.activeMessages[index2];return expectedRole&&message.role!==expectedRole?null:{message,index:index2}}async addMessage(role,content2,type2=MessageType.TEXT,parent="-1",extras){const activeConv=conversationsStore.
-activeConversation;if(!activeConv)throw new Error("No active conversation");let parentId=null;if(parent==="-1"){const am=conversationsStore.activeMessages;if(am.length>0)parentId=am[am.length-1].id;else{const r2=(await conversationsStore.getConversationMessages(activeConv.id)).find(m=>m.parent===null&&m.type==="root");parentId=r2?r2.id:await DatabaseService.createRootMessage(activeConv.id)}}else parentId=parent;const message=await DatabaseService.createMessageBranch({convId:activeConv.id,role,content:content2,
-type:type2,timestamp:Date.now(),toolCalls:"",children:[],extra:extras},parentId);return conversationsStore.addMessageToActive(message),await conversationsStore.updateCurrentNode(message.id),conversationsStore.updateConversationTimestamp(),message}async addSystemPrompt(){let activeConv=conversationsStore.activeConversation;if(activeConv||(await conversationsStore.createConversation(),activeConv=conversationsStore.activeConversation),!!activeConv)try{const allMessages=await conversationsStore.getConversationMessages(
-activeConv.id),rootMessage=allMessages.find(m=>m.type==="root"&&m.parent===null),rootId=rootMessage?rootMessage.id:await DatabaseService.createRootMessage(activeConv.id),existingSystemMessage=allMessages.find(m=>m.role===MessageRole.SYSTEM&&m.parent===rootId);if(existingSystemMessage){this.pendingEditMessageId=existingSystemMessage.id,conversationsStore.activeMessages.some(m=>m.id===existingSystemMessage.id)||conversationsStore.activeMessages.unshift(existingSystemMessage);return}const firstActiveMessage=conversationsStore.
-activeMessages.find(m=>m.parent===rootId),systemMessage=await DatabaseService.createSystemMessage(activeConv.id,SYSTEM_MESSAGE_PLACEHOLDER,rootId);if(firstActiveMessage){await DatabaseService.updateMessage(firstActiveMessage.id,{parent:systemMessage.id}),await DatabaseService.updateMessage(systemMessage.id,{children:[firstActiveMessage.id]});const updatedRootChildren=rootMessage?rootMessage.children.filter(id2=>id2!==firstActiveMessage.id):[];await DatabaseService.updateMessage(rootId,{children:[
-...updatedRootChildren.filter(id2=>id2!==systemMessage.id),systemMessage.id]});const firstMsgIndex=conversationsStore.findMessageIndex(firstActiveMessage.id);firstMsgIndex!==-1&&conversationsStore.updateMessageAtIndex(firstMsgIndex,{parent:systemMessage.id})}conversationsStore.activeMessages.unshift(systemMessage),this.pendingEditMessageId=systemMessage.id,conversationsStore.updateConversationTimestamp()}catch(error2){console.error("Failed to add system prompt:",error2)}}async removeSystemPromptPlaceholder(messageId){
-const activeConv=conversationsStore.activeConversation;if(!activeConv)return!1;try{const allMessages=await conversationsStore.getConversationMessages(activeConv.id),systemMessage=findMessageById(allMessages,messageId);if(!systemMessage||systemMessage.role!==MessageRole.SYSTEM)return!1;const rootMessage=allMessages.find(m=>m.type==="root"&&m.parent===null);if(!rootMessage)return!1;if(allMessages.length===2&&systemMessage.children.length===0)return await conversationsStore.deleteConversation(activeConv.
-id),!0;for(const childId of systemMessage.children){await DatabaseService.updateMessage(childId,{parent:rootMessage.id});const childIndex=conversationsStore.findMessageIndex(childId);childIndex!==-1&&conversationsStore.updateMessageAtIndex(childIndex,{parent:rootMessage.id})}await DatabaseService.updateMessage(rootMessage.id,{children:[...rootMessage.children.filter(id2=>id2!==messageId),...systemMessage.children]}),await DatabaseService.deleteMessage(messageId);const systemIndex=conversationsStore.
-findMessageIndex(messageId);return systemIndex!==-1&&conversationsStore.activeMessages.splice(systemIndex,1),conversationsStore.updateConversationTimestamp(),!1}catch(error2){return console.error("Failed to remove system prompt placeholder:",error2),!1}}async createAssistantMessage(parentId){const activeConv=conversationsStore.activeConversation;if(!activeConv)throw new Error("No active conversation");return await DatabaseService.createMessageBranch({convId:activeConv.id,type:MessageType.TEXT,role:MessageRole.
-ASSISTANT,content:"",timestamp:Date.now(),toolCalls:"",children:[],model:null},parentId||null)}async sendMessage(content2,extras){if(!content2.trim()&&(!extras||extras.length===0))return;const activeConv=conversationsStore.activeConversation;if(activeConv&&this.isChatLoadingInternal(activeConv.id))return;this.cancelPreEncode();const resourceExtras=mcpStore.consumeResourceAttachmentsAsExtras(),allExtras=resourceExtras.length>0?[...extras||[],...resourceExtras]:extras;let isNewConversation=!1;activeConv||
-(await conversationsStore.createConversation(),isNewConversation=!0);const currentConv=conversationsStore.activeConversation;if(currentConv){this.showErrorDialog(null),this.setChatLoading(currentConv.id,!0),this.clearChatStreaming(currentConv.id);try{let parentIdForUserMessage;if(isNewConversation){const rootId=await DatabaseService.createRootMessage(currentConv.id),systemPrompt=config$1().systemMessage?.toString().trim();if(systemPrompt){const systemMessage=await DatabaseService.createSystemMessage(
-currentConv.id,systemPrompt,rootId);conversationsStore.addMessageToActive(systemMessage),parentIdForUserMessage=systemMessage.id}else parentIdForUserMessage=rootId}const userMessage=await this.addMessage(MessageRole.USER,content2,MessageType.TEXT,parentIdForUserMessage??"-1",allExtras);isNewConversation&&content2&&await conversationsStore.updateConversationName(currentConv.id,generateConversationTitle(content2,!!config$1().titleGenerationUseFirstLine));const assistantMessage=await this.createAssistantMessage(
-userMessage.id);conversationsStore.addMessageToActive(assistantMessage),await this.streamChatCompletion(conversationsStore.activeMessages.slice(0,-1),assistantMessage)}catch(error2){if(isAbortError(error2)){this.setChatLoading(currentConv.id,!1);return}console.error("Failed to send message:",error2),this.setChatLoading(currentConv.id,!1);const dialogType=error2 instanceof Error&&error2.name==="TimeoutError"?ErrorDialogType.TIMEOUT:ErrorDialogType.SERVER,contextInfo=error2.contextInfo;this.showErrorDialog(
-{type:dialogType,message:error2 instanceof Error?error2.message:"Unknown error",contextInfo})}}}async streamChatCompletion(allMessages,assistantMessage,onComplete,onError,modelOverride){let effectiveModel=modelOverride;if(isRouterMode()&&!effectiveModel){const conversationModel=this.getConversationModel(allMessages);effectiveModel=selectedModelName()||conversationModel}isRouterMode()&&effectiveModel&&(modelsStore.getModelProps(effectiveModel)||await modelsStore.fetchModelProps(effectiveModel));let currentMessageId=assistantMessage.
-id,streamedContent="",streamedReasoningContent="",resolvedModel=null,modelPersisted=!1;const convId=assistantMessage.convId,recordModel=(modelName,persistImmediately=!0)=>{if(!modelName)return;const n=normalizeModelName(modelName);if(!n||n===resolvedModel)return;resolvedModel=n;const idx=conversationsStore.findMessageIndex(currentMessageId);conversationsStore.updateMessageAtIndex(idx,{model:n}),persistImmediately&&!modelPersisted&&(modelPersisted=!0,DatabaseService.updateMessage(currentMessageId,
-{model:n}).catch(()=>{modelPersisted=!1,resolvedModel=null}))},updateStreamingUI=()=>{this.setChatStreaming(convId,streamedContent,currentMessageId);const idx=conversationsStore.findMessageIndex(currentMessageId);conversationsStore.updateMessageAtIndex(idx,{content:streamedContent})},cleanupStreamingState=()=>{this.setStreamingActive(!1),this.setChatLoading(convId,!1),this.clearChatStreaming(convId),this.setProcessingState(convId,null)};this.setStreamingActive(!0),this.setActiveProcessingConversation(
-convId);const abortController=this.getOrCreateAbortController(convId),streamCallbacks={onChunk:chunk=>{streamedContent+=chunk,updateStreamingUI()},onReasoningChunk:chunk=>{streamedReasoningContent+=chunk;const idx=conversationsStore.findMessageIndex(currentMessageId);conversationsStore.updateMessageAtIndex(idx,{reasoningContent:streamedReasoningContent})},onToolCallsStreaming:toolCalls=>{const idx=conversationsStore.findMessageIndex(currentMessageId);conversationsStore.updateMessageAtIndex(idx,{
-toolCalls:JSON.stringify(toolCalls)})},onAttachments:(messageId,extras)=>{if(!extras.length)return;const idx=conversationsStore.findMessageIndex(messageId);if(idx===-1)return;const updatedExtras=[...conversationsStore.activeMessages[idx].extra||[],...extras];conversationsStore.updateMessageAtIndex(idx,{extra:updatedExtras}),DatabaseService.updateMessage(messageId,{extra:updatedExtras}).catch(console.error)},onModel:modelName=>recordModel(modelName),onTurnComplete:intermediateTimings=>{const idx=conversationsStore.
-findMessageIndex(assistantMessage.id);conversationsStore.updateMessageAtIndex(idx,{timings:intermediateTimings})},onTimings:(timings,promptProgress)=>{const tokensPerSecond=timings?.predicted_ms&&timings?.predicted_n?timings.predicted_n/timings.predicted_ms*1e3:0;this.updateProcessingStateFromTimings({prompt_n:timings?.prompt_n||0,prompt_ms:timings?.prompt_ms,predicted_n:timings?.predicted_n||0,predicted_per_second:tokensPerSecond,cache_n:timings?.cache_n||0,prompt_progress:promptProgress},convId)},
-onAssistantTurnComplete:async(content2,reasoningContent,timings,toolCalls)=>{const updateData={content:content2,reasoningContent:reasoningContent||void 0,toolCalls:toolCalls?JSON.stringify(toolCalls):"",timings};resolvedModel&&!modelPersisted&&(updateData.model=resolvedModel),await DatabaseService.updateMessage(currentMessageId,updateData);const idx=conversationsStore.findMessageIndex(currentMessageId),uiUpdate={content:content2,reasoningContent:reasoningContent||void 0,toolCalls:toolCalls?JSON.
-stringify(toolCalls):""};timings&&(uiUpdate.timings=timings),resolvedModel&&(uiUpdate.model=resolvedModel),conversationsStore.updateMessageAtIndex(idx,uiUpdate),await conversationsStore.updateCurrentNode(currentMessageId)},createToolResultMessage:async(toolCallId,content2,extras)=>{const msg=await DatabaseService.createMessageBranch({convId,type:MessageType.TEXT,role:MessageRole.TOOL,content:content2,toolCallId,timestamp:Date.now(),toolCalls:"",children:[],extra:extras},currentMessageId);return conversationsStore.
-addMessageToActive(msg),await conversationsStore.updateCurrentNode(msg.id),msg},createAssistantMessage:async()=>{streamedContent="",streamedReasoningContent="";const lastMsg=conversationsStore.activeMessages[conversationsStore.activeMessages.length-1],msg=await DatabaseService.createMessageBranch({convId,type:MessageType.TEXT,role:MessageRole.ASSISTANT,content:"",timestamp:Date.now(),toolCalls:"",children:[],model:resolvedModel},lastMsg.id);return conversationsStore.addMessageToActive(msg),currentMessageId=
-msg.id,msg},onFlowComplete:finalTimings=>{if(finalTimings){const idx=conversationsStore.findMessageIndex(assistantMessage.id);conversationsStore.updateMessageAtIndex(idx,{timings:finalTimings}),DatabaseService.updateMessage(assistantMessage.id,{timings:finalTimings}).catch(console.error)}cleanupStreamingState(),onComplete&&onComplete(streamedContent),isRouterMode()&&modelsStore.fetchRouterModels().catch(console.error),config$1().preEncodeConversation&&this.triggerPreEncode(allMessages,assistantMessage,
-streamedContent,effectiveModel,!!config$1().excludeReasoningFromContext)},onError:error2=>{if(this.setStreamingActive(!1),isAbortError(error2)){cleanupStreamingState();return}console.error("Streaming error:",error2),cleanupStreamingState();const idx=conversationsStore.findMessageIndex(assistantMessage.id);if(idx!==-1){const failedMessage=conversationsStore.removeMessageAtIndex(idx);failedMessage&&DatabaseService.deleteMessage(failedMessage.id).catch(console.error)}const contextInfo=error2.contextInfo;
-this.showErrorDialog({type:error2.name==="TimeoutError"?ErrorDialogType.TIMEOUT:ErrorDialogType.SERVER,message:error2.message,contextInfo}),onError&&onError(error2)}},perChatOverrides=conversationsStore.activeConversation?.mcpServerOverrides;agenticStore.getConfig(config$1(),perChatOverrides).enabled&&(await agenticStore.runAgenticFlow({conversationId:convId,messages:allMessages,options:{...this.getApiOptions(),...effectiveModel?{model:effectiveModel}:{}},callbacks:streamCallbacks,signal:abortController.
-signal,perChatOverrides})).handled||await ChatService.sendMessage(allMessages,{...this.getApiOptions(),...effectiveModel?{model:effectiveModel}:{},stream:!0,onChunk:streamCallbacks.onChunk,onReasoningChunk:streamCallbacks.onReasoningChunk,onModel:streamCallbacks.onModel,onTimings:streamCallbacks.onTimings,onComplete:async(finalContent,reasoningContent,timings,toolCalls)=>{const content2=streamedContent||finalContent||"",reasoning=streamedReasoningContent||reasoningContent,updateData={content:content2,
-reasoningContent:reasoning||void 0,toolCalls:toolCalls||"",timings};resolvedModel&&!modelPersisted&&(updateData.model=resolvedModel),await DatabaseService.updateMessage(currentMessageId,updateData);const idx=conversationsStore.findMessageIndex(currentMessageId),uiUpdate={content:content2,reasoningContent:reasoning||void 0,toolCalls:toolCalls||""};timings&&(uiUpdate.timings=timings),resolvedModel&&(uiUpdate.model=resolvedModel),conversationsStore.updateMessageAtIndex(idx,uiUpdate),await conversationsStore.
-updateCurrentNode(currentMessageId),cleanupStreamingState(),onComplete&&await onComplete(content2),isRouterMode()&&modelsStore.fetchRouterModels().catch(console.error)},onError:streamCallbacks.onError},convId,abortController.signal)}async stopGeneration(){const activeConv=conversationsStore.activeConversation;activeConv&&await this.stopGenerationForChat(activeConv.id)}async stopGenerationForChat(convId){await this.savePartialResponseIfNeeded(convId),this.setStreamingActive(!1),this.abortRequest(
-convId),this.setChatLoading(convId,!1),this.clearChatStreaming(convId),this.setProcessingState(convId,null)}async savePartialResponseIfNeeded(convId){const conversationId=convId||conversationsStore.activeConversation?.id;if(!conversationId)return;const streamingState=this.getChatStreaming(conversationId);if(!streamingState||!streamingState.response.trim())return;const messages=conversationId===conversationsStore.activeConversation?.id?conversationsStore.activeMessages:await conversationsStore.getConversationMessages(
-conversationId);if(!messages.length)return;const lastMessage=messages[messages.length-1];if(lastMessage?.role===MessageRole.ASSISTANT)try{const updateData={content:streamingState.response},lastKnownState=this.getProcessingState(conversationId);lastKnownState&&(updateData.timings={prompt_n:lastKnownState.promptTokens||0,prompt_ms:lastKnownState.promptMs,predicted_n:lastKnownState.tokensDecoded||0,cache_n:lastKnownState.cacheTokens||0,predicted_ms:lastKnownState.tokensPerSecond&&lastKnownState.tokensDecoded?
-lastKnownState.tokensDecoded/lastKnownState.tokensPerSecond*1e3:void 0}),await DatabaseService.updateMessage(lastMessage.id,updateData),lastMessage.content=streamingState.response,updateData.timings&&(lastMessage.timings=updateData.timings)}catch(error2){lastMessage.content=streamingState.response,console.error("Failed to save partial response:",error2)}}async updateMessage(messageId,newContent){const activeConv=conversationsStore.activeConversation;if(!activeConv)return;this.isChatLoadingInternal(
-activeConv.id)&&await this.stopGeneration();const result=this.getMessageByIdWithRole(messageId,MessageRole.USER);if(!result)return;const{message:messageToUpdate,index:messageIndex}=result,originalContent=messageToUpdate.content;try{const rootMessage=(await conversationsStore.getConversationMessages(activeConv.id)).find(m=>m.type==="root"&&m.parent===null),isFirstUserMessage=rootMessage&&messageToUpdate.parent===rootMessage.id;conversationsStore.updateMessageAtIndex(messageIndex,{content:newContent}),
-await DatabaseService.updateMessage(messageId,{content:newContent}),isFirstUserMessage&&newContent.trim()&&await conversationsStore.updateConversationTitleWithConfirmation(activeConv.id,generateConversationTitle(newContent,!!config$1().titleGenerationUseFirstLine));const messagesToRemove=conversationsStore.activeMessages.slice(messageIndex+1);for(const message of messagesToRemove)await DatabaseService.deleteMessage(message.id);conversationsStore.sliceActiveMessages(messageIndex+1),conversationsStore.
-updateConversationTimestamp(),this.setChatLoading(activeConv.id,!0),this.clearChatStreaming(activeConv.id);const assistantMessage=await this.createAssistantMessage();conversationsStore.addMessageToActive(assistantMessage),await conversationsStore.updateCurrentNode(assistantMessage.id),await this.streamChatCompletion(conversationsStore.activeMessages.slice(0,-1),assistantMessage,void 0,()=>{conversationsStore.updateMessageAtIndex(conversationsStore.findMessageIndex(messageId),{content:originalContent})})}catch(error2){
-isAbortError(error2)||console.error("Failed to update message:",error2)}}async regenerateMessage(messageId){const activeConv=conversationsStore.activeConversation;if(!activeConv||this.isChatLoadingInternal(activeConv.id))return;this.cancelPreEncode();const result=this.getMessageByIdWithRole(messageId,MessageRole.ASSISTANT);if(!result)return;const{index:messageIndex}=result;try{const messagesToRemove=conversationsStore.activeMessages.slice(messageIndex);for(const message of messagesToRemove)await DatabaseService.
-deleteMessage(message.id);conversationsStore.sliceActiveMessages(messageIndex),conversationsStore.updateConversationTimestamp(),this.setChatLoading(activeConv.id,!0),this.clearChatStreaming(activeConv.id);const parentMessageId=conversationsStore.activeMessages.length>0?conversationsStore.activeMessages[conversationsStore.activeMessages.length-1].id:void 0,assistantMessage=await this.createAssistantMessage(parentMessageId);conversationsStore.addMessageToActive(assistantMessage),await this.streamChatCompletion(
-conversationsStore.activeMessages.slice(0,-1),assistantMessage)}catch(error2){isAbortError(error2)||console.error("Failed to regenerate message:",error2),this.setChatLoading(activeConv?.id||"",!1)}}async regenerateMessageWithBranching(messageId,modelOverride){const activeConv=conversationsStore.activeConversation;if(!(!activeConv||this.isChatLoadingInternal(activeConv.id))){this.cancelPreEncode();try{const idx=conversationsStore.findMessageIndex(messageId);if(idx===-1)return;const msg=conversationsStore.
-activeMessages[idx];if(msg.role!==MessageRole.ASSISTANT)return;const allMessages=await conversationsStore.getConversationMessages(activeConv.id),parentMessage=findMessageById(allMessages,msg.parent);if(!parentMessage)return;this.setChatLoading(activeConv.id,!0),this.clearChatStreaming(activeConv.id);const newAssistantMessage=await DatabaseService.createMessageBranch({convId:msg.convId,type:msg.type,timestamp:Date.now(),role:msg.role,content:"",toolCalls:"",children:[],model:null},parentMessage.id);
-await conversationsStore.updateCurrentNode(newAssistantMessage.id),conversationsStore.updateConversationTimestamp(),await conversationsStore.refreshActiveMessages();const conversationPath=filterByLeafNodeId(allMessages,parentMessage.id,!1),modelToUse=modelOverride||msg.model||void 0;await this.streamChatCompletion(conversationPath,newAssistantMessage,void 0,void 0,modelToUse)}catch(error2){isAbortError(error2)||console.error("Failed to regenerate message with branching:",error2),this.setChatLoading(
-activeConv?.id||"",!1)}}}async getDeletionInfo(messageId){const activeConv=conversationsStore.activeConversation;if(!activeConv)return{totalCount:0,userMessages:0,assistantMessages:0,messageTypes:[]};const allMessages=await conversationsStore.getConversationMessages(activeConv.id);if(findMessageById(allMessages,messageId)?.role===MessageRole.SYSTEM){const messagesToDelete2=allMessages.filter(m=>m.id===messageId);let userMessages2=0,assistantMessages2=0;const messageTypes2=[];for(const msg of messagesToDelete2)
-msg.role===MessageRole.USER?(userMessages2++,messageTypes2.includes("user message")||messageTypes2.push("user message")):msg.role===MessageRole.ASSISTANT&&(assistantMessages2++,messageTypes2.includes("assistant response")||messageTypes2.push("assistant response"));return{totalCount:1,userMessages:userMessages2,assistantMessages:assistantMessages2,messageTypes:messageTypes2}}const descendants=findDescendantMessages(allMessages,messageId),allToDelete=[messageId,...descendants],messagesToDelete=allMessages.
-filter(m=>allToDelete.includes(m.id));let userMessages=0,assistantMessages=0;const messageTypes=[];for(const msg of messagesToDelete)msg.role===MessageRole.USER?(userMessages++,messageTypes.includes("user message")||messageTypes.push("user message")):msg.role===MessageRole.ASSISTANT&&(assistantMessages++,messageTypes.includes("assistant response")||messageTypes.push("assistant response"));return{totalCount:allToDelete.length,userMessages,assistantMessages,messageTypes}}async deleteMessage(messageId){
-const activeConv=conversationsStore.activeConversation;if(activeConv)try{const allMessages=await conversationsStore.getConversationMessages(activeConv.id),messageToDelete=findMessageById(allMessages,messageId);if(!messageToDelete)return;if(filterByLeafNodeId(allMessages,activeConv.currNode||"",!1).some(m=>m.id===messageId)&&messageToDelete.parent){const siblings2=allMessages.filter(m=>m.parent===messageToDelete.parent&&m.id!==messageId);if(siblings2.length>0){const latestSibling=siblings2.reduce(
-(latest,sibling2)=>sibling2.timestamp>latest.timestamp?sibling2:latest);await conversationsStore.updateCurrentNode(findLeafNode(allMessages,latestSibling.id))}else messageToDelete.parent&&await conversationsStore.updateCurrentNode(findLeafNode(allMessages,messageToDelete.parent))}await DatabaseService.deleteMessageCascading(activeConv.id,messageId),await conversationsStore.refreshActiveMessages(),conversationsStore.updateConversationTimestamp()}catch(error2){console.error("Failed to delete messa\
-ge:",error2)}}async continueAssistantMessage(messageId){const activeConv=conversationsStore.activeConversation;if(!activeConv||this.isChatLoadingInternal(activeConv.id))return;const result=this.getMessageByIdWithRole(messageId,MessageRole.ASSISTANT);if(!result)return;const{message:msg,index:idx}=result;try{this.showErrorDialog(null),this.setChatLoading(activeConv.id,!0),this.clearChatStreaming(activeConv.id);const allMessages=await conversationsStore.getConversationMessages(activeConv.id),dbMessage=findMessageById(
-allMessages,messageId);if(!dbMessage){this.setChatLoading(activeConv.id,!1);return}const originalContent=dbMessage.content,originalReasoning=dbMessage.reasoningContent||"",contextWithContinue=[...conversationsStore.activeMessages.slice(0,idx),{role:MessageRole.ASSISTANT,content:originalContent}];let appendedContent="",appendedReasoning="",hasReceivedContent=!1;const updateStreamingContent=fullContent=>{this.setChatStreaming(msg.convId,fullContent,msg.id),conversationsStore.updateMessageAtIndex(idx,
-{content:fullContent})},abortController=this.getOrCreateAbortController(msg.convId);await ChatService.sendMessage(contextWithContinue,{...this.getApiOptions(),onChunk:chunk=>{appendedContent+=chunk,hasReceivedContent=!0,updateStreamingContent(originalContent+appendedContent)},onReasoningChunk:chunk=>{appendedReasoning+=chunk,hasReceivedContent=!0,conversationsStore.updateMessageAtIndex(idx,{reasoningContent:originalReasoning+appendedReasoning})},onTimings:(timings,promptProgress)=>{const tokensPerSecond=timings?.
-predicted_ms&&timings?.predicted_n?timings.predicted_n/timings.predicted_ms*1e3:0;this.updateProcessingStateFromTimings({prompt_n:timings?.prompt_n||0,prompt_ms:timings?.prompt_ms,predicted_n:timings?.predicted_n||0,predicted_per_second:tokensPerSecond,cache_n:timings?.cache_n||0,prompt_progress:promptProgress},msg.convId)},onComplete:async(finalContent,reasoningContent,timings)=>{const finalAppendedContent=hasReceivedContent?appendedContent:finalContent||"",finalAppendedReasoning=hasReceivedContent?
-appendedReasoning:reasoningContent||"",fullContent=originalContent+finalAppendedContent,fullReasoning=originalReasoning+finalAppendedReasoning||void 0;await DatabaseService.updateMessage(msg.id,{content:fullContent,reasoningContent:fullReasoning,timestamp:Date.now(),timings}),conversationsStore.updateMessageAtIndex(idx,{content:fullContent,reasoningContent:fullReasoning,timestamp:Date.now(),timings}),conversationsStore.updateConversationTimestamp(),this.setChatLoading(msg.convId,!1),this.clearChatStreaming(
-msg.convId),this.setProcessingState(msg.convId,null)},onError:async error2=>{if(isAbortError(error2)){hasReceivedContent&&appendedContent&&(await DatabaseService.updateMessage(msg.id,{content:originalContent+appendedContent,reasoningContent:originalReasoning+appendedReasoning||void 0,timestamp:Date.now()}),conversationsStore.updateMessageAtIndex(idx,{content:originalContent+appendedContent,reasoningContent:originalReasoning+appendedReasoning||void 0,timestamp:Date.now()})),this.setChatLoading(msg.
-convId,!1),this.clearChatStreaming(msg.convId),this.setProcessingState(msg.convId,null);return}console.error("Continue generation error:",error2),conversationsStore.updateMessageAtIndex(idx,{content:originalContent}),await DatabaseService.updateMessage(msg.id,{content:originalContent}),this.setChatLoading(msg.convId,!1),this.clearChatStreaming(msg.convId),this.setProcessingState(msg.convId,null),this.showErrorDialog({type:error2.name==="TimeoutError"?ErrorDialogType.TIMEOUT:ErrorDialogType.SERVER,
-message:error2.message})}},msg.convId,abortController.signal)}catch(error2){isAbortError(error2)||console.error("Failed to continue message:",error2),activeConv&&this.setChatLoading(activeConv.id,!1)}}async editAssistantMessage(messageId,newContent,shouldBranch){const activeConv=conversationsStore.activeConversation;if(!activeConv||this.isChatLoadingInternal(activeConv.id))return;const result=this.getMessageByIdWithRole(messageId,MessageRole.ASSISTANT);if(!result)return;const{message:msg,index:idx}=result;
-try{if(shouldBranch){const newMessage=await DatabaseService.createMessageBranch({convId:msg.convId,type:msg.type,timestamp:Date.now(),role:msg.role,content:newContent,toolCalls:msg.toolCalls||"",children:[],model:msg.model},msg.parent);await conversationsStore.updateCurrentNode(newMessage.id)}else await DatabaseService.updateMessage(msg.id,{content:newContent}),conversationsStore.updateMessageAtIndex(idx,{content:newContent});conversationsStore.updateConversationTimestamp(),await conversationsStore.
-refreshActiveMessages()}catch(error2){console.error("Failed to edit assistant message:",error2)}}async editUserMessagePreserveResponses(messageId,newContent,newExtras){const activeConv=conversationsStore.activeConversation;if(!activeConv)return;const result=this.getMessageByIdWithRole(messageId,MessageRole.USER);if(!result)return;const{message:msg,index:idx}=result;try{const updateData={content:newContent};newExtras!==void 0&&(updateData.extra=JSON.parse(JSON.stringify(newExtras))),await DatabaseService.
-updateMessage(messageId,updateData),conversationsStore.updateMessageAtIndex(idx,updateData);const rootMessage=(await conversationsStore.getConversationMessages(activeConv.id)).find(m=>m.type==="root"&&m.parent===null);rootMessage&&msg.parent===rootMessage.id&&newContent.trim()&&await conversationsStore.updateConversationTitleWithConfirmation(activeConv.id,generateConversationTitle(newContent,!!config$1().titleGenerationUseFirstLine)),conversationsStore.updateConversationTimestamp()}catch(error2){
-console.error("Failed to edit user message:",error2)}}async editMessageWithBranching(messageId,newContent,newExtras){const activeConv=conversationsStore.activeConversation;if(!activeConv||this.isChatLoadingInternal(activeConv.id))return;let result=this.getMessageByIdWithRole(messageId,MessageRole.USER);if(result||(result=this.getMessageByIdWithRole(messageId,MessageRole.SYSTEM)),!result)return;const{message:msg,index:idx}=result;try{const allMessages=await conversationsStore.getConversationMessages(
-activeConv.id),rootMessage=allMessages.find(m=>m.type==="root"&&m.parent===null),isFirstUserMessage=msg.role===MessageRole.USER&&rootMessage&&msg.parent===rootMessage.id,extrasToUse=newExtras!==void 0?JSON.parse(JSON.stringify(newExtras)):msg.extra?JSON.parse(JSON.stringify(msg.extra)):void 0;let messageIdForResponse;const dbMsg=findMessageById(allMessages,msg.id);if(dbMsg?dbMsg.children.length>0:msg.children.length>0){const parentId=msg.parent||rootMessage?.id;if(!parentId)return;const newMessage=await DatabaseService.
-createMessageBranch({convId:msg.convId,type:msg.type,timestamp:Date.now(),role:msg.role,content:newContent,toolCalls:msg.toolCalls||"",children:[],extra:extrasToUse,model:msg.model},parentId);await conversationsStore.updateCurrentNode(newMessage.id),messageIdForResponse=newMessage.id}else{const updates={content:newContent,timestamp:Date.now(),extra:extrasToUse};await DatabaseService.updateMessage(msg.id,updates),conversationsStore.updateMessageAtIndex(idx,updates),messageIdForResponse=msg.id}conversationsStore.
-updateConversationTimestamp(),isFirstUserMessage&&newContent.trim()&&await conversationsStore.updateConversationTitleWithConfirmation(activeConv.id,generateConversationTitle(newContent,!!config$1().titleGenerationUseFirstLine)),await conversationsStore.refreshActiveMessages(),msg.role===MessageRole.USER&&await this.generateResponseForMessage(messageIdForResponse)}catch(error2){console.error("Failed to edit message with branching:",error2)}}async generateResponseForMessage(userMessageId){const activeConv=conversationsStore.
-activeConversation;if(activeConv){this.showErrorDialog(null),this.setChatLoading(activeConv.id,!0),this.clearChatStreaming(activeConv.id);try{const allMessages=await conversationsStore.getConversationMessages(activeConv.id),conversationPath=filterByLeafNodeId(allMessages,userMessageId,!1),assistantMessage=await DatabaseService.createMessageBranch({convId:activeConv.id,type:MessageType.TEXT,timestamp:Date.now(),role:MessageRole.ASSISTANT,content:"",toolCalls:"",children:[],model:null},userMessageId);
-conversationsStore.addMessageToActive(assistantMessage),await this.streamChatCompletion(conversationPath,assistantMessage)}catch(error2){console.error("Failed to generate response:",error2),this.setChatLoading(activeConv.id,!1)}}}getContextTotal(){const activeConvId=this.activeConversationId,activeState=activeConvId?this.getProcessingState(activeConvId):null;if(activeState&&typeof activeState.contextTotal=="number"&&activeState.contextTotal>0)return activeState.contextTotal;if(isRouterMode()){const modelContextSize=selectedModelContextSize();
-if(typeof modelContextSize=="number"&&modelContextSize>0)return modelContextSize}else{const propsContextSize=contextSize();if(typeof propsContextSize=="number"&&propsContextSize>0)return propsContextSize}return null}updateProcessingStateFromTimings(timingData,conversationId){const processingState=this.parseTimingData(timingData);if(processingState===null){console.warn("Failed to parse timing data - skipping update");return}const targetId=conversationId||this.activeConversationId;targetId&&this.setProcessingState(
-targetId,processingState)}parseTimingData(timingData){const promptTokens=timingData.prompt_n||0,promptMs=timingData.prompt_ms||void 0,predictedTokens=timingData.predicted_n||0,tokensPerSecond=timingData.predicted_per_second||0,cacheTokens=timingData.cache_n||0,promptProgress=timingData.prompt_progress,contextTotal=this.getContextTotal(),currentConfig=config$1(),outputTokensMax=currentConfig.max_tokens||-1,contextUsed=promptTokens+cacheTokens+predictedTokens,outputTokensUsed=predictedTokens,progressCache=promptProgress?.
-cache||0,progressActualDone=(promptProgress?.processed??0)-progressCache,progressActualTotal=(promptProgress?.total??0)-progressCache,progressPercent=promptProgress?Math.round(progressActualDone/progressActualTotal*100):void 0;return{status:predictedTokens>0?"generating":promptProgress?"preparing":"idle",tokensDecoded:predictedTokens,tokensRemaining:outputTokensMax-predictedTokens,contextUsed,contextTotal,outputTokensUsed,outputTokensMax,hasNextToken:predictedTokens>0,tokensPerSecond,temperature:currentConfig.
-temperature??.8,topP:currentConfig.top_p??.95,speculative:!1,progressPercent,promptProgress,promptTokens,promptMs,cacheTokens}}restoreProcessingStateFromMessages(messages,conversationId){for(let i=messages.length-1;i>=0;i--){const message=messages[i];if(message.role===MessageRole.ASSISTANT&&message.timings){const restoredState=this.parseTimingData({prompt_n:message.timings.prompt_n||0,prompt_ms:message.timings.prompt_ms,predicted_n:message.timings.predicted_n||0,predicted_per_second:message.timings.
-predicted_n&&message.timings.predicted_ms?message.timings.predicted_n/message.timings.predicted_ms*1e3:0,cache_n:message.timings.cache_n||0});if(restoredState){this.setProcessingState(conversationId,restoredState);return}}}}getConversationModel(messages){for(let i=messages.length-1;i>=0;i--){const message=messages[i];if(message.role===MessageRole.ASSISTANT&&message.model)return message.model}return null}getApiOptions(){const currentConfig=config$1(),hasValue=value=>value!=null&&value!=="",apiOptions={
-stream:!0,timings_per_token:!0};if(isRouterMode()){const modelName=selectedModelName();modelName&&(apiOptions.model=modelName)}return currentConfig.systemMessage&&(apiOptions.systemMessage=currentConfig.systemMessage),currentConfig.disableReasoningParsing&&(apiOptions.disableReasoningParsing=!0),currentConfig.excludeReasoningFromContext&&(apiOptions.excludeReasoningFromContext=!0),hasValue(currentConfig.temperature)&&(apiOptions.temperature=Number(currentConfig.temperature)),hasValue(currentConfig.
-max_tokens)&&(apiOptions.max_tokens=Number(currentConfig.max_tokens)),hasValue(currentConfig.dynatemp_range)&&(apiOptions.dynatemp_range=Number(currentConfig.dynatemp_range)),hasValue(currentConfig.dynatemp_exponent)&&(apiOptions.dynatemp_exponent=Number(currentConfig.dynatemp_exponent)),hasValue(currentConfig.top_k)&&(apiOptions.top_k=Number(currentConfig.top_k)),hasValue(currentConfig.top_p)&&(apiOptions.top_p=Number(currentConfig.top_p)),hasValue(currentConfig.min_p)&&(apiOptions.min_p=Number(
-currentConfig.min_p)),hasValue(currentConfig.xtc_probability)&&(apiOptions.xtc_probability=Number(currentConfig.xtc_probability)),hasValue(currentConfig.xtc_threshold)&&(apiOptions.xtc_threshold=Number(currentConfig.xtc_threshold)),hasValue(currentConfig.typ_p)&&(apiOptions.typ_p=Number(currentConfig.typ_p)),hasValue(currentConfig.repeat_last_n)&&(apiOptions.repeat_last_n=Number(currentConfig.repeat_last_n)),hasValue(currentConfig.repeat_penalty)&&(apiOptions.repeat_penalty=Number(currentConfig.
-repeat_penalty)),hasValue(currentConfig.presence_penalty)&&(apiOptions.presence_penalty=Number(currentConfig.presence_penalty)),hasValue(currentConfig.frequency_penalty)&&(apiOptions.frequency_penalty=Number(currentConfig.frequency_penalty)),hasValue(currentConfig.dry_multiplier)&&(apiOptions.dry_multiplier=Number(currentConfig.dry_multiplier)),hasValue(currentConfig.dry_base)&&(apiOptions.dry_base=Number(currentConfig.dry_base)),hasValue(currentConfig.dry_allowed_length)&&(apiOptions.dry_allowed_length=
-Number(currentConfig.dry_allowed_length)),hasValue(currentConfig.dry_penalty_last_n)&&(apiOptions.dry_penalty_last_n=Number(currentConfig.dry_penalty_last_n)),currentConfig.samplers&&(apiOptions.samplers=currentConfig.samplers),apiOptions.backend_sampling=currentConfig.backend_sampling,currentConfig.custom&&(apiOptions.custom=currentConfig.custom),apiOptions}cancelPreEncode(){this.preEncodeAbortController&&(this.preEncodeAbortController.abort(),this.preEncodeAbortController=null)}async triggerPreEncode(allMessages,assistantMessage,assistantContent,model,excludeReasoning){
-this.cancelPreEncode(),this.preEncodeAbortController=new AbortController;const signal=this.preEncodeAbortController.signal;try{if(!await ChatService.areAllSlotsIdle(model,signal)||signal.aborted)return;const messagesWithAssistant=[...allMessages,{...assistantMessage,content:assistantContent}];await ChatService.preEncode(messagesWithAssistant,model,excludeReasoning,signal)}catch(err){isAbortError(err)||console.warn("[ChatStore] Pre-encode failed:",err)}}}const chatStore=new ChatStore,activeProcessingState=()=>chatStore.
-activeProcessingState,errorDialog=()=>chatStore.errorDialogState,getAddFilesHandler=()=>chatStore.getAddFilesHandler(),getAllLoadingChats=()=>chatStore.getAllLoadingChats(),isChatStreaming=()=>chatStore.isStreaming(),isEditing=()=>chatStore.isEditing(),isLoading=()=>chatStore.isLoading,pendingEditMessageId=()=>chatStore.pendingEditMessageId;var root$1d=from_html('<!> <form><!> <!> <div data-slot="input-area"><!> <div class="flex-column relative min-h-[48px] items-center rounded-3xl py-2 pb-2.25 \
-shadow-sm transition-all focus-within:shadow-md md:!py-3"><!> <!> <!></div></div></form> <!>',1);function ChatForm($$anchor,$$props){push$1($$props,!0);let attachments=prop($$props,"attachments",19,()=>[]),className=prop($$props,"class",3,""),disabled=prop($$props,"disabled",3,!1),isLoading2=prop($$props,"isLoading",3,!1),placeholder=prop($$props,"placeholder",3,"Type a message..."),showMcpPromptButton=prop($$props,"showMcpPromptButton",3,!1),uploadedFiles=prop($$props,"uploadedFiles",31,()=>proxy(
-[])),value=prop($$props,"value",15,""),audioRecorder,chatFormActionsRef=state$1(void 0),fileInputRef=state$1(void 0),promptPickerRef=state$1(void 0),resourcePickerRef=state$1(void 0),textareaRef=state$1(void 0),isRecording=state$1(!1),recordingSupported=state$1(!1),isPromptPickerOpen=state$1(!1),promptSearchQuery=state$1(""),isInlineResourcePickerOpen=state$1(!1),resourceSearchQuery=state$1(""),isResourceDialogOpen=state$1(!1),preSelectedResourceUri=state$1(void 0),currentConfig=user_derived(config$1),
-pasteLongTextToFileLength=user_derived(()=>{const n=Number(get$4(currentConfig).pasteLongTextToFileLen);return Number.isNaN(n)?Number(SETTING_CONFIG_DEFAULT.pasteLongTextToFileLen):n}),isRouter=user_derived(isRouterMode),conversationModel=user_derived(()=>chatStore.getConversationModel(activeMessages())),activeModelId=user_derived(()=>{const options=modelOptions();if(!get$4(isRouter))return options.length>0?options[0].model:null;const selectedId=selectedModelId();if(selectedId){const model=options.
-find(m=>m.id===selectedId);if(model)return model.model}if(get$4(conversationModel)){const model=options.find(m=>m.model===get$4(conversationModel));if(model)return model.model}return null}),hasModelSelected=user_derived(()=>!get$4(isRouter)||!!get$4(conversationModel)||!!selectedModelId()),hasLoadingAttachments=user_derived(()=>uploadedFiles().some(f=>f.isLoading)),hasAttachments=user_derived(()=>attachments()&&attachments().length>0||uploadedFiles()&&uploadedFiles().length>0),canSubmit=user_derived(
-()=>value().trim().length>0||get$4(hasAttachments));onMount$1(()=>{set$1(recordingSupported,isAudioRecordingSupported(),!0),audioRecorder=new AudioRecorder});function focus2(){get$4(textareaRef)?.focus()}function resetTextareaHeight(){get$4(textareaRef)?.resetHeight()}function openModelSelector(){get$4(chatFormActionsRef)?.openModelSelector()}function checkModelSelected(){return get$4(hasModelSelected)?!0:(get$4(chatFormActionsRef)?.openModelSelector(),!1)}function handleFileSelect(files){$$props.
-onFilesAdd?.(files)}function handleFileUpload(){get$4(fileInputRef)?.click()}function handleFileRemove(fileId){if(fileId.startsWith("attachment-")){const index2=parseInt(fileId.replace("attachment-",""),10);!isNaN(index2)&&index2>=0&&index2<attachments().length&&$$props.onAttachmentRemove?.(index2)}else $$props.onUploadedFileRemove?.(fileId)}function handleInput(){const perChatOverrides=conversationsStore.getAllMcpServerOverrides(),hasServers=mcpStore.hasEnabledServers(perChatOverrides);value().
-startsWith(PROMPT_TRIGGER_PREFIX)&&hasServers?(set$1(isPromptPickerOpen,!0),set$1(promptSearchQuery,value().slice(1),!0),set$1(isInlineResourcePickerOpen,!1),set$1(resourceSearchQuery,"")):value().startsWith(RESOURCE_TRIGGER_PREFIX)&&hasServers&&mcpStore.hasResourcesCapability(perChatOverrides)?(set$1(isInlineResourcePickerOpen,!0),set$1(resourceSearchQuery,value().slice(1),!0),set$1(isPromptPickerOpen,!1),set$1(promptSearchQuery,"")):(set$1(isPromptPickerOpen,!1),set$1(promptSearchQuery,""),set$1(
-isInlineResourcePickerOpen,!1),set$1(resourceSearchQuery,""))}function handleKeydown(event2){if(!(get$4(isPromptPickerOpen)&&get$4(promptPickerRef)?.handleKeydown(event2))&&!(get$4(isInlineResourcePickerOpen)&&get$4(resourcePickerRef)?.handleKeydown(event2))){if(event2.key===KeyboardKey.ESCAPE&&get$4(isPromptPickerOpen)){set$1(isPromptPickerOpen,!1),set$1(promptSearchQuery,"");return}if(event2.key===KeyboardKey.ESCAPE&&get$4(isInlineResourcePickerOpen)){set$1(isInlineResourcePickerOpen,!1),set$1(
-resourceSearchQuery,"");return}if(event2.key===KeyboardKey.ENTER&&!event2.shiftKey&&!isIMEComposing(event2)){const isModifier=event2.ctrlKey||event2.metaKey;if(get$4(currentConfig).sendOnEnter!==!1||isModifier){if(event2.preventDefault(),!get$4(canSubmit)||disabled()||isLoading2()||get$4(hasLoadingAttachments))return;$$props.onSubmit?.()}}}}function handlePaste(event2){if(!event2.clipboardData)return;const files=Array.from(event2.clipboardData.items).filter(item=>item.kind==="file").map(item=>item.
-getAsFile()).filter(file=>file!==null);if(files.length>0){event2.preventDefault(),$$props.onFilesAdd?.(files);return}const text2=event2.clipboardData.getData(MimeTypeText.PLAIN);if(text2.startsWith(CLIPBOARD_CONTENT_QUOTE_PREFIX)){const parsed=parseClipboardContent(text2);if(parsed.textAttachments.length>0||parsed.mcpPromptAttachments.length>0){if(event2.preventDefault(),value(parsed.message),$$props.onValueChange?.(parsed.message),parsed.textAttachments.length>0){const attachmentFiles=parsed.textAttachments.
-map(att=>new File([att.content],att.name,{type:MimeTypeText.PLAIN}));$$props.onFilesAdd?.(attachmentFiles)}if(parsed.mcpPromptAttachments.length>0){const mcpPromptFiles=parsed.mcpPromptAttachments.map(att=>({id:uuid$1(),name:att.name,size:att.content.length,type:SpecialFileType.MCP_PROMPT,file:new File([att.content],`${att.name}${FileExtensionText.TXT}`,{type:MimeTypeText.PLAIN}),isLoading:!1,textContent:att.content,mcpPrompt:{serverName:att.serverName,promptName:att.promptName,arguments:att.arguments}}));
-uploadedFiles([...uploadedFiles(),...mcpPromptFiles]),$$props.onUploadedFilesChange?.(uploadedFiles())}setTimeout(()=>{get$4(textareaRef)?.focus()},10);return}}if(text2.length>0&&get$4(pasteLongTextToFileLength)>0&&text2.length>get$4(pasteLongTextToFileLength)){event2.preventDefault();const textFile=new File([text2],"Pasted",{type:MimeTypeText.PLAIN});$$props.onFilesAdd?.([textFile])}}function handlePromptLoadStart(placeholderId,promptInfo,args){value().startsWith(PROMPT_TRIGGER_PREFIX)&&(value(
-""),$$props.onValueChange?.("")),set$1(isPromptPickerOpen,!1),set$1(promptSearchQuery,"");const promptName=promptInfo.title||promptInfo.name,placeholder2={id:placeholderId,name:promptName,size:INITIAL_FILE_SIZE,type:SpecialFileType.MCP_PROMPT,file:new File([],"loading"),isLoading:!0,mcpPrompt:{serverName:promptInfo.serverName,promptName:promptInfo.name,arguments:args?{...args}:void 0}};uploadedFiles([...uploadedFiles(),placeholder2]),$$props.onUploadedFilesChange?.(uploadedFiles()),get$4(textareaRef)?.
-focus()}function handlePromptLoadComplete(placeholderId,result){const promptText=result.messages?.map(msg=>typeof msg.content=="string"?msg.content:msg.content.type===ContentPartType.TEXT?msg.content.text:"").filter(Boolean).join(PROMPT_CONTENT_SEPARATOR);uploadedFiles(uploadedFiles().map(f=>f.id===placeholderId?{...f,isLoading:!1,textContent:promptText,size:promptText.length,file:new File([promptText],`${f.name}${FileExtensionText.TXT}`,{type:MimeTypeText.PLAIN})}:f)),$$props.onUploadedFilesChange?.(
-uploadedFiles())}function handlePromptLoadError(placeholderId,error2){uploadedFiles(uploadedFiles().map(f=>f.id===placeholderId?{...f,isLoading:!1,loadError:error2}:f)),$$props.onUploadedFilesChange?.(uploadedFiles())}function handlePromptPickerClose(){set$1(isPromptPickerOpen,!1),set$1(promptSearchQuery,""),get$4(textareaRef)?.focus()}function handleInlineResourcePickerClose(){set$1(isInlineResourcePickerOpen,!1),set$1(resourceSearchQuery,""),get$4(textareaRef)?.focus()}function handleInlineResourceSelect(){
-value().startsWith(RESOURCE_TRIGGER_PREFIX)&&(value(""),$$props.onValueChange?.("")),set$1(isInlineResourcePickerOpen,!1),set$1(resourceSearchQuery,""),get$4(textareaRef)?.focus()}function handleBrowseResources(){set$1(isInlineResourcePickerOpen,!1),set$1(resourceSearchQuery,""),value().startsWith(RESOURCE_TRIGGER_PREFIX)&&(value(""),$$props.onValueChange?.("")),set$1(isResourceDialogOpen,!0)}async function handleMicClick(){if(!audioRecorder||!get$4(recordingSupported)){console.warn("Audio recor\
-ding not supported");return}if(get$4(isRecording))try{const audioBlob=await audioRecorder.stopRecording(),wavBlob=await convertToWav(audioBlob),audioFile=createAudioFile(wavBlob);$$props.onFilesAdd?.([audioFile]),set$1(isRecording,!1)}catch(error2){console.error("Failed to stop recording:",error2),set$1(isRecording,!1)}else try{await audioRecorder.startRecording(),set$1(isRecording,!0)}catch(error2){console.error("Failed to start recording:",error2)}}var $$exports={focus:focus2,resetTextareaHeight,
-openModelSelector,checkModelSelected},fragment=root$1d(),node2=first_child(fragment);bind_this(ChatFormFileInputInvisible(node2,{onFileSelect:handleFileSelect}),$$value=>set$1(fileInputRef,$$value,!0),()=>get$4(fileInputRef));var form=sibling(node2,2),node_1=child(form);bind_this(ChatFormPromptPicker(node_1,{get isOpen(){return get$4(isPromptPickerOpen)},get searchQuery(){return get$4(promptSearchQuery)},onClose:handlePromptPickerClose,onPromptLoadStart:handlePromptLoadStart,onPromptLoadComplete:handlePromptLoadComplete,
-onPromptLoadError:handlePromptLoadError}),$$value=>set$1(promptPickerRef,$$value,!0),()=>get$4(promptPickerRef));var node_2=sibling(node_1,2);bind_this(ChatFormResourcePicker(node_2,{get isOpen(){return get$4(isInlineResourcePickerOpen)},get searchQuery(){return get$4(resourceSearchQuery)},onClose:handleInlineResourcePickerClose,onResourceSelect:handleInlineResourceSelect,onBrowse:handleBrowseResources}),$$value=>set$1(resourcePickerRef,$$value,!0),()=>get$4(resourcePickerRef));var div=sibling(node_2,
-2),node_3=child(div);{let $0=user_derived(()=>get$4(activeModelId)??void 0);ChatAttachmentsList(node_3,{get attachments(){return attachments()},onFileRemove:handleFileRemove,limitToSingleRow:!0,class:"py-5",style:"scroll-padding: 1rem;",get activeModelId(){return get$4($0)},get uploadedFiles(){return uploadedFiles()},set uploadedFiles($$value){uploadedFiles($$value)}})}var div_1=sibling(node_3,2),node_4=child(div_1);bind_this(ChatFormTextarea(node_4,{class:"px-5 py-1.5 md:pt-0",onKeydown:handleKeydown,
-onInput:()=>{handleInput(),$$props.onValueChange?.(value())},get disabled(){return disabled()},get placeholder(){return placeholder()},get value(){return value()},set value($$value){value($$value)}}),$$value=>set$1(textareaRef,$$value,!0),()=>get$4(textareaRef));var node_5=sibling(node_4,2);{var consequent=$$anchor2=>{ChatAttachmentMcpResources($$anchor2,{class:"mb-3",onResourceClick:uri2=>{set$1(preSelectedResourceUri,uri2,!0),set$1(isResourceDialogOpen,!0)}})};if_block(node_5,$$render=>{mcpHasResourceAttachments()&&
-$$render(consequent)})}var node_6=sibling(node_5,2);{let $0=user_derived(()=>value().trim().length>0),$1=user_derived(()=>showMcpPromptButton()?()=>set$1(isPromptPickerOpen,!0):void 0);bind_this(ChatFormActions(node_6,{class:"px-3",get canSend(){return get$4(canSubmit)},get hasText(){return get$4($0)},get disabled(){return disabled()},get isLoading(){return isLoading2()},get isRecording(){return get$4(isRecording)},get uploadedFiles(){return uploadedFiles()},onFileUpload:handleFileUpload,onMicClick:handleMicClick,
-get onStop(){return $$props.onStop},onSystemPromptClick:()=>$$props.onSystemPromptClick?.({message:value(),files:uploadedFiles()}),get onMcpPromptClick(){return get$4($1)},onMcpResourcesClick:()=>set$1(isResourceDialogOpen,!0)}),$$value=>set$1(chatFormActionsRef,$$value,!0),()=>get$4(chatFormActionsRef))}reset(div_1),reset(div),reset(form);var node_7=sibling(form,2);return DialogMcpResources(node_7,{get preSelectedUri(){return get$4(preSelectedResourceUri)},onAttach:resource=>{mcpStore.attachResource(
-resource.uri)},onOpenChange:newOpen=>{newOpen||set$1(preSelectedResourceUri,void 0)},get open(){return get$4(isResourceDialogOpen)},set open($$value){set$1(isResourceDialogOpen,$$value,!0)}}),template_effect(()=>{set_class(form,1,`relative ${className()??""}`),set_class(div,1,`${INPUT_CLASSES??""} overflow-hidden rounded-3xl backdrop-blur-md ${disabled()?"cursor-not-allowed opacity-60":""}`)}),event("submit",form,e=>{e.preventDefault(),!(!get$4(canSubmit)||disabled()||isLoading2()||get$4(hasLoadingAttachments))&&
-$$props.onSubmit?.()}),event("paste",div_1,handlePaste),append($$anchor,fragment),pop($$exports)}function Dropdown_menu_content($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),sideOffset=prop($$props,"sideOffset",3,4),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","sideOffset","portalProps","class"]);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Portal$2,($$anchor2,DropdownMenuPrimitive_Portal)=>{DropdownMenuPrimitive_Portal(
-$$anchor2,spread_props(()=>$$props.portalProps,{children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{let $0=user_derived(()=>cn$1("z-50 max-h-(--bits-dropdown-menu-content-available-height) min-w-[8rem] origin-(--bits-dropdown-menu-content-transform-origin) overflow-x-hidden overflow-y-auto rounded-md border border-border bg-popover p-1.5 text-popover-foreground shadow-md outline-none data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-r\
-ight-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95 dark:border-border/20",$$props.class));component(node_1,()=>Dropdown_menu_content$1,($$anchor4,DropdownMenuPrimitive_Content)=>{DropdownMenuPrimitive_Content($$anchor4,spread_props({"data-slot":"dropdown-menu-content",get sideOffset(){
-return sideOffset()},get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor3,fragment_1)},$$slots:{default:!0}}))}),append($$anchor,fragment),pop()}function Dropdown_menu_item($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),variant=prop($$props,"variant",3,"default"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","inset","variant"]);var fragment=comment$2(),node2=first_child(
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-push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:animate-in data-[state=open]:fade-in-0",$$props.class));component(node2,()=>Dialog_overlay$1,($$anchor2,SheetPrimitive_Overlay)=>{SheetPrimitive_Overlay($$anchor2,spread_props(
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-op data-[state=open]:slide-in-from-top inset-x-0 top-0 h-auto border-b",bottom:"data-[state=closed]:slide-out-to-bottom data-[state=open]:slide-in-from-bottom inset-x-0 bottom-0 h-auto border-t",left:"data-[state=closed]:slide-out-to-left data-[state=open]:slide-in-from-left inset-y-0 left-0 h-full w-3/4 border-r sm:max-w-sm",right:"data-[state=closed]:slide-out-to-right data-[state=open]:slide-in-from-right inset-y-0 right-0 h-full w-3/4 border-l sm:max-w-sm"}},defaultVariants:{side:"right"}});
-var root_3$C=from_html('<!> <span class="sr-only">Close</span>',1),root_2$N=from_html("<!> <!>",1),root_1$N=from_html("<!> <!>",1);function Sheet_content($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),side=prop($$props,"side",3,"right"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","side","portalProps","children"]);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Portal$2,($$anchor2,SheetPrimitive_Portal)=>{SheetPrimitive_Portal(
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-push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("text-sm text-muted-foreground",$$props.class));component(node2,()=>Dialog_description$1,($$anchor2,SheetPrimitive_Description)=>{SheetPrimitive_Description($$anchor2,spread_props({"data-slot":"sheet-description",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},
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-500 text-white hover:bg-red-600":""),$1=user_derived(()=>disabled()||isLoading2()||!hasAudioModality());Button($$anchor5,{get class(){return`h-8 w-8 rounded-full p-0 ${get$4($0)??""}`},get disabled(){return get$4($1)},get onclick(){return $$props.onMicClick},type:"button",children:($$anchor6,$$slotProps3)=>{var fragment_2=root_3$A(),span=first_child(fragment_2),text2=child(span,!0);reset(span);var node_2=sibling(span,2);{var consequent=$$anchor7=>{Square($$anchor7,{class:"h-4 w-4 animate-pulse f\
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-return getContext(CHAT_SETTINGS_DIALOG_KEY)}var root_6$q=from_html('<span class="sr-only">Stop</span> <!>',1),root$18=from_html('<div style="container-type: inline-size"><div class="mr-auto flex items-center gap-2"><!> <!></div> <div class="ml-auto flex items-center gap-1.5"><!></div> <!></div>');function ChatFormActions($$anchor,$$props){push$1($$props,!0);let canSend=prop($$props,"canSend",3,!1),className=prop($$props,"class",3,""),disabled=prop($$props,"disabled",3,!1),isLoading2=prop($$props,
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-get$4(conversationModel),modelsStore.selectModelByName(get$4(conversationModel));else if(get$4(isRouter)&&!modelsStore.selectedModelId&&modelsStore.loadedModelIds.length>0){lastSyncedConversationModel=null;const first=modelOptions().find(m=>modelsStore.loadedModelIds.includes(m.model));first&&modelsStore.selectModelById(first.id)}});let activeModelId=user_derived(()=>{const options=modelOptions();if(!get$4(isRouter))return options.length>0?options[0].model:null;const selectedId=selectedModelId();
-if(selectedId){const model=options.find(m=>m.id===selectedId);if(model)return model.model}if(get$4(conversationModel)){const model=options.find(m=>m.model===get$4(conversationModel));if(model)return model.model}return null}),modelPropsVersion=state$1(0);user_effect(()=>{get$4(activeModelId)&&(modelsStore.getModelProps(get$4(activeModelId))||modelsStore.fetchModelProps(get$4(activeModelId)).then(()=>{update$1(modelPropsVersion)}))});let hasAudioModality=user_derived(()=>get$4(activeModelId)?(get$4(
-modelPropsVersion),modelsStore.modelSupportsAudio(get$4(activeModelId))):!1),hasVisionModality=user_derived(()=>get$4(activeModelId)?(get$4(modelPropsVersion),modelsStore.modelSupportsVision(get$4(activeModelId))):!1),hasAudioAttachments=user_derived(()=>uploadedFiles().some(file=>getFileTypeCategory(file.type)===FileTypeCategory.AUDIO)),shouldShowRecordButton=user_derived(()=>get$4(hasAudioModality)&&!hasText()&&!get$4(hasAudioAttachments)&&get$4(currentConfig).autoMicOnEmpty),hasModelSelected=user_derived(
-()=>!get$4(isRouter)||!!get$4(conversationModel)||!!selectedModelId()),isSelectedModelInCache=user_derived(()=>{if(!get$4(isRouter))return!0;if(get$4(conversationModel))return modelOptions().some(option2=>option2.model===get$4(conversationModel));const currentModelId=selectedModelId();return currentModelId?modelOptions().some(option2=>option2.id===currentModelId):!1}),submitTooltip=user_derived(()=>get$4(hasModelSelected)?get$4(isSelectedModelInCache)?"":"Selected model is not available, please \
-select another":"Please select a model first"),selectorModelRef=state$1(void 0),isMobile=new IsMobile;function openModelSelector(){get$4(selectorModelRef)?.open()}const chatSettingsDialog=getChatSettingsDialogContext();let hasMcpPromptsSupport=user_derived(()=>{const perChatOverrides=conversationsStore.getAllMcpServerOverrides();return mcpStore.hasPromptsCapability(perChatOverrides)}),hasMcpResourcesSupport=user_derived(()=>{const perChatOverrides=conversationsStore.getAllMcpServerOverrides();return mcpStore.
-hasResourcesCapability(perChatOverrides)});var $$exports={openModelSelector},div=root$18(),div_1=child(div),node2=child(div_1);{var consequent=$$anchor2=>{ChatFormActionAttachmentsSheet($$anchor2,{get disabled(){return disabled()},get hasAudioModality(){return get$4(hasAudioModality)},get hasVisionModality(){return get$4(hasVisionModality)},get hasMcpPromptsSupport(){return get$4(hasMcpPromptsSupport)},get hasMcpResourcesSupport(){return get$4(hasMcpResourcesSupport)},get onFileUpload(){return $$props.
-onFileUpload},get onSystemPromptClick(){return $$props.onSystemPromptClick},get onMcpPromptClick(){return $$props.onMcpPromptClick},get onMcpResourcesClick(){return $$props.onMcpResourcesClick},onMcpSettingsClick:()=>chatSettingsDialog.open(SETTINGS_SECTION_TITLES.MCP)})},alternate=$$anchor2=>{ChatFormActionAttachmentsDropdown($$anchor2,{get disabled(){return disabled()},get hasAudioModality(){return get$4(hasAudioModality)},get hasVisionModality(){return get$4(hasVisionModality)},get hasMcpPromptsSupport(){
-return get$4(hasMcpPromptsSupport)},get hasMcpResourcesSupport(){return get$4(hasMcpResourcesSupport)},get onFileUpload(){return $$props.onFileUpload},get onSystemPromptClick(){return $$props.onSystemPromptClick},get onMcpPromptClick(){return $$props.onMcpPromptClick},get onMcpResourcesClick(){return $$props.onMcpResourcesClick},onMcpSettingsClick:()=>chatSettingsDialog.open(SETTINGS_SECTION_TITLES.MCP)})};if_block(node2,$$render=>{isMobile.current?$$render(consequent):$$render(alternate,!1)})}var node_1=sibling(
-node2,2);McpServersSelector(node_1,{get disabled(){return disabled()},onSettingsClick:()=>chatSettingsDialog.open(SETTINGS_SECTION_TITLES.MCP)}),reset(div_1);var div_2=sibling(div_1,2),node_2=child(div_2);{var consequent_1=$$anchor2=>{{let $0=user_derived(()=>disabled()||get$4(isOffline));bind_this(ModelsSelectorSheet($$anchor2,{get disabled(){return get$4($0)},get currentModel(){return get$4(conversationModel)},forceForegroundText:!0,useGlobalSelection:!0}),$$value=>set$1(selectorModelRef,$$value,
-!0),()=>get$4(selectorModelRef))}},alternate_1=$$anchor2=>{{let $0=user_derived(()=>disabled()||get$4(isOffline));bind_this(ModelsSelector($$anchor2,{get disabled(){return get$4($0)},get currentModel(){return get$4(conversationModel)},forceForegroundText:!0,useGlobalSelection:!0}),$$value=>set$1(selectorModelRef,$$value,!0),()=>get$4(selectorModelRef))}};if_block(node_2,$$render=>{isMobile.current?$$render(consequent_1):$$render(alternate_1,!1)})}reset(div_2);var node_3=sibling(div_2,2);{var consequent_2=$$anchor2=>{
-Button($$anchor2,{type:"button",variant:"secondary",get onclick(){return $$props.onStop},class:"group h-8 w-8 rounded-full p-0 hover:bg-destructive/10!",children:($$anchor3,$$slotProps)=>{var fragment_5=root_6$q(),node_4=sibling(first_child(fragment_5),2);Square(node_4,{class:"h-8 w-8 fill-muted-foreground stroke-muted-foreground group-hover:fill-destructive group-hover:stroke-destructive hover:fill-destructive hover:stroke-destructive"}),append($$anchor3,fragment_5)},$$slots:{default:!0}})},alternate_3=$$anchor2=>{
-var fragment_6=comment$2(),node_5=first_child(fragment_6);{var consequent_3=$$anchor3=>{ChatFormActionRecord($$anchor3,{get disabled(){return disabled()},get hasAudioModality(){return get$4(hasAudioModality)},get isLoading(){return isLoading2()},get isRecording(){return isRecording()},get onMicClick(){return $$props.onMicClick}})},alternate_2=$$anchor3=>{{let $0=user_derived(()=>canSend()&&get$4(hasModelSelected)&&get$4(isSelectedModelInCache)),$1=user_derived(()=>get$4(hasModelSelected)&&!get$4(
-isSelectedModelInCache));ChatFormActionSubmit($$anchor3,{get canSend(){return get$4($0)},get disabled(){return disabled()},get isLoading(){return isLoading2()},get tooltipLabel(){return get$4(submitTooltip)},get showErrorState(){return get$4($1)}})}};if_block(node_5,$$render=>{get$4(shouldShowRecordButton)?$$render(consequent_3):$$render(alternate_2,!1)},!0)}append($$anchor2,fragment_6)};if_block(node_3,$$render=>{isLoading2()?$$render(consequent_2):$$render(alternate_3,!1)})}return reset(div),template_effect(
-()=>set_class(div,1,`flex w-full items-center gap-3 ${className()??""}`)),append($$anchor,div),pop($$exports)}var root_2$L=from_html('<span class="sr-only">Send</span> <!>',1),root_6$p=from_html("<p> </p>"),root_4$l=from_html("<!> <!>",1);function ChatFormActionSubmit($$anchor,$$props){push$1($$props,!0);const submitButton=($$anchor2,$$arg0)=>{let props=derived_safe_equal(()=>fallback($$arg0?.(),()=>({}),!0));{let $0=user_derived(()=>cn$1("h-8 w-8 rounded-full p-0",showErrorState()?"bg-red-400/1\
-0 text-red-400 hover:bg-red-400/20 hover:text-red-400 disabled:opacity-100":""));Button($$anchor2,spread_props({type:"submit",get disabled(){return get$4(isDisabled)},get class(){return get$4($0)}},()=>get$4(props),{children:($$anchor3,$$slotProps)=>{var fragment_1=root_2$L(),node2=sibling(first_child(fragment_1),2);Arrow_up(node2,{class:"h-12 w-12"}),append($$anchor3,fragment_1)},$$slots:{default:!0}}))}};let canSend=prop($$props,"canSend",3,!1),disabled=prop($$props,"disabled",3,!1),isLoading2=prop(
-$$props,"isLoading",3,!1),showErrorState=prop($$props,"showErrorState",3,!1),isDisabled=user_derived(()=>!canSend()||disabled()||isLoading2());var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent=$$anchor2=>{var fragment_3=comment$2(),node_2=first_child(fragment_3);component(node_2,()=>Root$5,($$anchor3,Tooltip_Root)=>{Tooltip_Root($$anchor3,{children:($$anchor4,$$slotProps)=>{var fragment_4=root_4$l(),node_3=first_child(fragment_4);component(node_3,()=>Tooltip_trigger,($$anchor5,Tooltip_Trigger)=>{
-Tooltip_Trigger($$anchor5,{children:($$anchor6,$$slotProps2)=>{submitButton($$anchor6)},$$slots:{default:!0}})});var node_4=sibling(node_3,2);component(node_4,()=>Tooltip_content,($$anchor5,Tooltip_Content)=>{Tooltip_Content($$anchor5,{children:($$anchor6,$$slotProps2)=>{var p2=root_6$p(),text2=child(p2,!0);reset(p2),template_effect(()=>set_text(text2,$$props.tooltipLabel)),append($$anchor6,p2)},$$slots:{default:!0}})}),append($$anchor4,fragment_4)},$$slots:{default:!0}})}),append($$anchor2,fragment_3)},
-alternate=$$anchor2=>{submitButton($$anchor2)};if_block(node_1,$$render=>{$$props.tooltipLabel?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment_2),pop()}var root$17=from_html('<input type="file"/>');function ChatFormFileInputInvisible($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),multiple=prop($$props,"multiple",3,!0),fileInputElement;function click(){fileInputElement?.click()}function handleFileSelect(event2){const input=event2.target;input.
-files&&$$props.onFileSelect?.(Array.from(input.files))}var $$exports={click},input_1=root$17();return input_1.__change=handleFileSelect,bind_this(input_1,$$value=>fileInputElement=$$value,()=>fileInputElement),template_effect(()=>{input_1.multiple=multiple(),set_class(input_1,1,`hidden ${className()??""}`)}),append($$anchor,input_1),pop($$exports)}delegate(["change"]);var root_2$K=from_html('<p class="text-xs text-muted-foreground">Press <kbd class="rounded bg-muted px-1 py-0.5 font-mono text-xs\
-">Enter</kbd> to send, <kbd class="rounded bg-muted px-1 py-0.5 font-mono text-xs">Shift + Enter</kbd> for new line</p>'),root_3$z=from_html(`<p class="text-xs text-muted-foreground">Press <kbd class="rounded bg-muted px-1 py-0.5 font-mono text-xs"> </kbd> to
-				send, <kbd class="rounded bg-muted px-1 py-0.5 font-mono text-xs">Enter</kbd> for new line</p>`),root_1$K=from_html("<div><!></div>");function ChatFormHelperText($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),show=prop($$props,"show",3,!0),sendOnEnter=user_derived(()=>config$1().sendOnEnter!==!1),modKey=/Mac|iPhone|iPad|iPod/.test(navigator.platform)?"Cmd":"Ctrl";var fragment=comment$2(),node2=first_child(fragment);{var consequent_1=$$anchor2=>{var div=root_1$K(),
-node_1=child(div);{var consequent=$$anchor3=>{var p2=root_2$K();append($$anchor3,p2)},alternate=$$anchor3=>{var p_1=root_3$z(),kbd=sibling(child(p_1)),text2=child(kbd);reset(kbd),next$1(3),reset(p_1),template_effect(()=>set_text(text2,`${modKey} + Enter`)),append($$anchor3,p_1)};if_block(node_1,$$render=>{get$4(sendOnEnter)?$$render(consequent):$$render(alternate,!1)})}reset(div),template_effect(()=>set_class(div,1,`mt-6 items-center justify-center ${className()??""} hidden md:flex`)),append($$anchor2,
-div)};if_block(node2,$$render=>{show()&&$$render(consequent_1)})}append($$anchor,fragment),pop()}var root$16=from_html('<div><textarea style="max-height: var(--max-message-height);"></textarea></div>');function ChatFormTextarea($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),disabled=prop($$props,"disabled",3,!1),placeholder=prop($$props,"placeholder",3,"Ask anything..."),value=prop($$props,"value",15,""),textareaElement;onMount$1(()=>{textareaElement&&(autoResizeTextarea(
-textareaElement),textareaElement.focus())});function getElement(){return textareaElement}function focus2(){textareaElement?.focus()}function resetHeight(){textareaElement&&(textareaElement.style.height="1rem")}var $$exports={getElement,focus:focus2,resetHeight},div=root$16(),textarea=child(div);remove_textarea_child(textarea);let classes;return textarea.__keydown=function(...$$args){$$props.onKeydown?.apply(this,$$args)},textarea.__input=event2=>{autoResizeTextarea(event2.currentTarget),$$props.
-onInput?.()},bind_this(textarea,$$value=>textareaElement=$$value,()=>textareaElement),reset(div),template_effect(()=>{set_class(div,1,`flex-1 ${className()??""}`),classes=set_class(textarea,1,"text-md min-h-12 w-full resize-none border-0 bg-transparent p-0 leading-6 outline-none placeholder:text-muted-foreground focus-visible:ring-0 focus-visible:ring-offset-0",null,classes,{"cursor-not-allowed":disabled()}),textarea.disabled=disabled(),set_attribute(textarea,"placeholder",placeholder())}),event(
-"paste",textarea,function(...$$args){$$props.onPaste?.apply(this,$$args)}),bind_value(textarea,value),append($$anchor,div),pop($$exports)}delegate(["keydown","input"]);const badgeVariants=tv({base:"focus-visible:border-ring focus-visible:ring-ring/50 aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 aria-invalid:border-destructive inline-flex w-fit shrink-0 items-center justify-center gap-1 overflow-hidden whitespace-nowrap rounded-md border px-2 py-0.5 text-xs font-medium t\
-ransition-[color,box-shadow] focus-visible:ring-[3px] [&>svg]:pointer-events-none [&>svg]:size-3",variants:{variant:{default:"bg-primary text-primary-foreground [a&]:hover:bg-primary/90 border-transparent",secondary:"bg-secondary text-secondary-foreground [a&]:hover:bg-secondary/90 border-transparent",tertiary:"bg-foreground/15 dark:bg-foreground/10 text-foreground [a&]:hover:bg-foreground/25 border-transparent",destructive:"bg-destructive [a&]:hover:bg-destructive/90 focus-visible:ring-destruct\
-ive/20 dark:focus-visible:ring-destructive/40 dark:bg-destructive/70 border-transparent text-white",outline:"text-foreground [a&]:hover:bg-accent [a&]:hover:text-accent-foreground"}},defaultVariants:{variant:"default"}});function Badge($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),variant=prop($$props,"variant",3,"default"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","href","class","variant","children"]);var fragment=comment$2(),node2=first_child(
-fragment);element$4(node2,()=>$$props.href?"a":"span",!1,($$element,$$anchor2)=>{bind_this($$element,$$value=>ref2($$value),()=>ref2()),attribute_effect($$element,$0=>({"data-slot":"badge",href:$$props.href,class:$0,...restProps}),[()=>cn$1(badgeVariants({variant:variant()}),$$props.class,"backdrop-blur-sm")]);var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.children??noop$3),append($$anchor2,fragment_1)}),append($$anchor,fragment),pop()}var root_2$J=from_html(
-'<div class="p-4"><!> <!></div>');function ChatFormPromptPicker($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),isOpen=prop($$props,"isOpen",7,!1),searchQuery=prop($$props,"searchQuery",3,""),prompts=state$1(proxy([])),isLoading2=state$1(!1),selectedPrompt=state$1(null),promptArgs=state$1(proxy({})),selectedIndex=state$1(0),internalSearchQuery=state$1(""),promptError=state$1(null),selectedIndexBeforeArgumentForm=state$1(null),suggestions=proxy({}),loadingSuggestions=proxy(
-{}),activeAutocomplete=state$1(null),autocompleteIndex=state$1(0),serverSettingsMap=user_derived(()=>{const servers=mcpStore.getServers(),map2=new SvelteMap;for(const server of servers)map2.set(server.id,server);return map2});user_effect(()=>{isOpen()?(loadPrompts(),set$1(selectedIndex,0)):(set$1(selectedPrompt,null),set$1(promptArgs,{},!0),set$1(promptError,null))}),user_effect(()=>{get$4(filteredPrompts).length>0&&get$4(selectedIndex)>=get$4(filteredPrompts).length&&set$1(selectedIndex,0)});async function loadPrompts(){
-set$1(isLoading2,!0);try{const perChatOverrides=conversationsStore.getAllMcpServerOverrides();if(!await mcpStore.ensureInitialized(perChatOverrides)){set$1(prompts,[],!0);return}set$1(prompts,await mcpStore.getAllPrompts(),!0)}catch(error2){console.error("[ChatFormPromptPicker] Failed to load prompts:",error2),set$1(prompts,[],!0)}finally{set$1(isLoading2,!1)}}function handlePromptClick(prompt){const args=prompt.arguments??[];args.length>0?(set$1(selectedIndexBeforeArgumentForm,get$4(selectedIndex),
-!0),set$1(selectedPrompt,prompt,!0),set$1(promptArgs,{},!0),set$1(promptError,null),requestAnimationFrame(()=>{const firstInput=document.querySelector(`#arg-${args[0].name}`);firstInput&&firstInput.focus()})):executePrompt(prompt,{})}async function executePrompt(prompt,args){set$1(promptError,null);const placeholderId=uuid$1(),nonEmptyArgs=Object.fromEntries(Object.entries(args).filter(([,value])=>value.trim()!=="")),argsToPass=Object.keys(nonEmptyArgs).length>0?nonEmptyArgs:void 0;$$props.onPromptLoadStart?.(
-placeholderId,prompt,argsToPass),$$props.onClose?.();try{const result=await mcpStore.getPrompt(prompt.serverName,prompt.name,args);$$props.onPromptLoadComplete?.(placeholderId,result)}catch(error2){const errorMessage=error2 instanceof Error?error2.message:"Unknown error executing prompt";$$props.onPromptLoadError?.(placeholderId,errorMessage)}}function handleArgumentSubmit(event2){event2.preventDefault(),get$4(selectedPrompt)&&executePrompt(get$4(selectedPrompt),get$4(promptArgs))}const fetchCompletions=debounce(
-async(argName,value)=>{if(!get$4(selectedPrompt)||value.length<1){suggestions[argName]=[];return}loadingSuggestions[argName]=!0;try{const result=await mcpStore.getPromptCompletions(get$4(selectedPrompt).serverName,get$4(selectedPrompt).name,argName,value);if(result&&result.values.length>0){const filteredValues=result.values.filter(v=>v.trim()!=="");filteredValues.length>0?(suggestions[argName]=filteredValues,set$1(activeAutocomplete,argName,!0),set$1(autocompleteIndex,0)):suggestions[argName]=[]}else
-suggestions[argName]=[]}catch(error2){console.error("[ChatFormPromptPicker] Failed to fetch completions:",error2),suggestions[argName]=[]}finally{loadingSuggestions[argName]=!1}},200);function handleArgInput(argName,value){get$4(promptArgs)[argName]=value,fetchCompletions(argName,value)}function selectSuggestion(argName,value){get$4(promptArgs)[argName]=value,suggestions[argName]=[],set$1(activeAutocomplete,null)}function handleArgKeydown(event2,argName){const argSuggestions=suggestions[argName]??
-[];if(event2.key===KeyboardKey.ESCAPE){event2.preventDefault(),event2.stopPropagation(),handleCancelArgumentForm();return}argSuggestions.length===0||get$4(activeAutocomplete)!==argName||(event2.key===KeyboardKey.ARROW_DOWN?(event2.preventDefault(),set$1(autocompleteIndex,Math.min(get$4(autocompleteIndex)+1,argSuggestions.length-1),!0)):event2.key===KeyboardKey.ARROW_UP?(event2.preventDefault(),set$1(autocompleteIndex,Math.max(get$4(autocompleteIndex)-1,0),!0)):event2.key===KeyboardKey.ENTER&&argSuggestions[get$4(
-autocompleteIndex)]&&(event2.preventDefault(),event2.stopPropagation(),selectSuggestion(argName,argSuggestions[get$4(autocompleteIndex)])))}function handleArgBlur(argName){setTimeout(()=>{get$4(activeAutocomplete)===argName&&(suggestions[argName]=[],set$1(activeAutocomplete,null))},150)}function handleArgFocus(argName){(suggestions[argName]?.length??0)>0&&set$1(activeAutocomplete,argName,!0)}function handleCancelArgumentForm(){get$4(selectedIndexBeforeArgumentForm)!==null&&(set$1(selectedIndex,get$4(
-selectedIndexBeforeArgumentForm),!0),set$1(selectedIndexBeforeArgumentForm,null)),set$1(selectedPrompt,null),set$1(promptArgs,{},!0),set$1(promptError,null)}function handleKeydown(event2){return isOpen()?event2.key===KeyboardKey.ESCAPE?(event2.preventDefault(),get$4(selectedPrompt)?handleCancelArgumentForm():$$props.onClose?.(),!0):event2.key===KeyboardKey.ARROW_DOWN?(event2.preventDefault(),get$4(filteredPrompts).length>0&&set$1(selectedIndex,(get$4(selectedIndex)+1)%get$4(filteredPrompts).length),
-!0):event2.key===KeyboardKey.ARROW_UP?(event2.preventDefault(),get$4(filteredPrompts).length>0&&set$1(selectedIndex,get$4(selectedIndex)===0?get$4(filteredPrompts).length-1:get$4(selectedIndex)-1,!0),!0):event2.key===KeyboardKey.ENTER&&!get$4(selectedPrompt)?(event2.preventDefault(),get$4(filteredPrompts)[get$4(selectedIndex)]&&handlePromptClick(get$4(filteredPrompts)[get$4(selectedIndex)]),!0):!1:!1}let filteredPrompts=user_derived(()=>{const sortedServers=mcpStore.getServersSorted(),serverOrderMap=new Map(
-sortedServers.map((server,index2)=>[server.id,index2])),sortedPrompts=[...get$4(prompts)].sort((a,b)=>{const orderA=serverOrderMap.get(a.serverName)??Number.MAX_SAFE_INTEGER,orderB=serverOrderMap.get(b.serverName)??Number.MAX_SAFE_INTEGER;return orderA-orderB}),query=(searchQuery()||get$4(internalSearchQuery)).toLowerCase();return query?sortedPrompts.filter(prompt=>prompt.name.toLowerCase().includes(query)||prompt.title?.toLowerCase().includes(query)||prompt.description?.toLowerCase().includes(query)):
-sortedPrompts}),showSearchInput=user_derived(()=>get$4(prompts).length>3);var $$exports={handleKeydown};return ChatFormPickerPopover($$anchor,{get class(){return className()},srLabel:"Open prompt picker",get onClose(){return $$props.onClose},onKeydown:handleKeydown,get isOpen(){return isOpen()},set isOpen($$value){isOpen($$value)},children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);{var consequent_1=$$anchor3=>{const prompt=user_derived(()=>get$4(selectedPrompt)),
-server=user_derived(()=>get$4(serverSettingsMap).get(get$4(prompt).serverName)),serverLabel=user_derived(()=>get$4(server)?mcpStore.getServerLabel(get$4(server)):get$4(prompt).serverName);var div=root_2$J(),node_1=child(div);{const titleExtra=$$anchor4=>{var fragment_2=comment$2(),node_2=first_child(fragment_2);{var consequent=$$anchor5=>{Badge($$anchor5,{variant:"secondary",children:($$anchor6,$$slotProps2)=>{next$1();var text2=text$8();template_effect(()=>set_text(text2,`${get$4(prompt).arguments.
-length??""} arg${get$4(prompt).arguments.length>1?"s":""}`)),append($$anchor6,text2)},$$slots:{default:!0}})};if_block(node_2,$$render=>{get$4(prompt).arguments?.length&&$$render(consequent)})}append($$anchor4,fragment_2)};let $0=user_derived(()=>get$4(prompt).title||get$4(prompt).name);ChatFormPickerItemHeader(node_1,{get server(){return get$4(server)},get serverLabel(){return get$4(serverLabel)},get title(){return get$4($0)},get description(){return get$4(prompt).description},titleExtra,$$slots:{
-titleExtra:!0}})}var node_3=sibling(node_1,2);ChatFormPromptPickerArgumentForm(node_3,{get prompt(){return get$4(selectedPrompt)},get promptArgs(){return get$4(promptArgs)},get suggestions(){return suggestions},get loadingSuggestions(){return loadingSuggestions},get activeAutocomplete(){return get$4(activeAutocomplete)},get autocompleteIndex(){return get$4(autocompleteIndex)},get promptError(){return get$4(promptError)},onArgInput:handleArgInput,onArgKeydown:handleArgKeydown,onArgBlur:handleArgBlur,
-onArgFocus:handleArgFocus,onSelectSuggestion:selectSuggestion,onSubmit:handleArgumentSubmit,onCancel:handleCancelArgumentForm}),reset(div),append($$anchor3,div)},alternate=$$anchor3=>{ChatFormPickerList($$anchor3,{get items(){return get$4(filteredPrompts)},get isLoading(){return get$4(isLoading2)},get selectedIndex(){return get$4(selectedIndex)},get showSearchInput(){return get$4(showSearchInput)},searchPlaceholder:"Search prompts...",emptyMessage:"No MCP prompts available",itemKey:prompt=>prompt.
-serverName+":"+prompt.name,get searchQuery(){return get$4(internalSearchQuery)},set searchQuery($$value){set$1(internalSearchQuery,$$value,!0)},item:($$anchor4,prompt=noop$3,index2=noop$3,isSelected=noop$3)=>{const server=user_derived(()=>get$4(serverSettingsMap).get(prompt().serverName)),serverLabel=user_derived(()=>get$4(server)?mcpStore.getServerLabel(get$4(server)):prompt().serverName);ChatFormPickerListItem($$anchor4,{get dataIndex(){return index2()},get isSelected(){return isSelected()},onClick:()=>handlePromptClick(
-prompt()),children:($$anchor5,$$slotProps2)=>{{const titleExtra=$$anchor6=>{var fragment_8=comment$2(),node_4=first_child(fragment_8);{var consequent_2=$$anchor7=>{Badge($$anchor7,{variant:"secondary",children:($$anchor8,$$slotProps3)=>{next$1();var text_1=text$8();template_effect(()=>set_text(text_1,`${prompt().arguments.length??""} arg${prompt().arguments.length>1?"s":""}`)),append($$anchor8,text_1)},$$slots:{default:!0}})};if_block(node_4,$$render=>{prompt().arguments?.length&&$$render(consequent_2)})}
-append($$anchor6,fragment_8)};let $0=user_derived(()=>prompt().title||prompt().name);ChatFormPickerItemHeader($$anchor5,{get server(){return get$4(server)},get serverLabel(){return get$4(serverLabel)},get title(){return get$4($0)},get description(){return prompt().description},titleExtra,$$slots:{titleExtra:!0}})}},$$slots:{default:!0}})},skeleton:$$anchor4=>{ChatFormPickerListItemSkeleton($$anchor4,{titleWidth:"w-32",showBadge:!0})},$$slots:{item:!0,skeleton:!0}})};if_block(node2,$$render=>{get$4(
-selectedPrompt)?$$render(consequent_1):$$render(alternate,!1)})}append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop($$exports)}const linear=x=>x;function cubic_out(t){const f=t-1;return f*f*f+1}function split_css_unit(value){const split=typeof value=="string"&&value.match(/^\s*(-?[\d.]+)([^\s]*)\s*$/);return split?[parseFloat(split[1]),split[2]||"px"]:[value,"px"]}function fade(node2,{delay=0,duration:duration2=400,easing=linear}={}){const o=+getComputedStyle(node2).opacity;return{delay,duration:duration2,
-easing,css:t=>`opacity: ${t*o}`}}function fly(node2,{delay=0,duration:duration2=400,easing=cubic_out,x=0,y=0,opacity=0}={}){const style2=getComputedStyle(node2),target_opacity=+style2.opacity,transform2=style2.transform==="none"?"":style2.transform,od=target_opacity*(1-opacity),[x_value,x_unit]=split_css_unit(x),[y_value,y_unit]=split_css_unit(y);return{delay,duration:duration2,easing,css:(t,u)=>`
-			transform: ${transform2} translate(${(1-t)*x_value}${x_unit}, ${(1-t)*y_value}${y_unit});
-			opacity: ${target_opacity-od*u}`}}function slide(node2,{delay=0,duration:duration2=400,easing=cubic_out,axis="y"}={}){const style2=getComputedStyle(node2),opacity=+style2.opacity,primary_property=axis==="y"?"height":"width",primary_property_value=parseFloat(style2[primary_property]),secondary_properties=axis==="y"?["top","bottom"]:["left","right"],capitalized_secondary_properties=secondary_properties.map(e=>`${e[0].toUpperCase()}${e.slice(1)}`),padding_start_value=parseFloat(style2[`padding${capitalized_secondary_properties[0]}`]),
-padding_end_value=parseFloat(style2[`padding${capitalized_secondary_properties[1]}`]),margin_start_value=parseFloat(style2[`margin${capitalized_secondary_properties[0]}`]),margin_end_value=parseFloat(style2[`margin${capitalized_secondary_properties[1]}`]),border_width_start_value=parseFloat(style2[`border${capitalized_secondary_properties[0]}Width`]),border_width_end_value=parseFloat(style2[`border${capitalized_secondary_properties[1]}Width`]);return{delay,duration:duration2,easing,css:t=>`overf\
-low: hidden;opacity: ${Math.min(t*20,1)*opacity};${primary_property}: ${t*primary_property_value}px;padding-${secondary_properties[0]}: ${t*padding_start_value}px;padding-${secondary_properties[1]}: ${t*padding_end_value}px;margin-${secondary_properties[0]}: ${t*margin_start_value}px;margin-${secondary_properties[1]}: ${t*margin_end_value}px;border-${secondary_properties[0]}-width: ${t*border_width_start_value}px;border-${secondary_properties[1]}-width: ${t*border_width_end_value}px;min-${primary_property}\
-: 0`}}function scale(node2,{delay=0,duration:duration2=400,easing=cubic_out,start:start2=0,opacity=0}={}){const style2=getComputedStyle(node2),target_opacity=+style2.opacity,transform2=style2.transform==="none"?"":style2.transform,sd=1-start2,od=target_opacity*(1-opacity);return{delay,duration:duration2,easing,css:(_t,u)=>`
-			transform: ${transform2} scale(${1-sd*u});
-			opacity: ${target_opacity-od*u}
-		`}}var root_1$J=from_html("<input/>"),root_2$I=from_html("<input/>");function Input($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),value=prop($$props,"value",15),files=prop($$props,"files",15),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","value","type","files","class"]);var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var input=root_1$J();attribute_effect(input,$0=>({"data-slot":"input",class:$0,type:"file",...restProps}),
-[()=>cn$1("flex h-9 w-full min-w-0 rounded-md border border-input bg-transparent px-3 pt-1.5 text-sm font-medium shadow-xs ring-offset-background transition-[color,box-shadow] outline-none selection:bg-primary selection:text-primary-foreground placeholder:text-muted-foreground disabled:cursor-not-allowed disabled:opacity-50 md:text-sm dark:bg-input/30","focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50","aria-invalid:border-destructive aria-invalid:ring-destructive/20\
- dark:aria-invalid:ring-destructive/40",$$props.class)],void 0,void 0,void 0,!0),bind_this(input,$$value=>ref2($$value),()=>ref2()),bind_files(input,files),bind_value(input,value),append($$anchor2,input)},alternate=$$anchor2=>{var input_1=root_2$I();attribute_effect(input_1,$0=>({"data-slot":"input",class:$0,style:"backdrop-filter: blur(0.5rem);",type:$$props.type,...restProps}),[()=>cn$1("flex h-9 w-full min-w-0 rounded-md border border-input bg-background px-3 py-1 text-base shadow-xs ring-off\
-set-background transition-[color,box-shadow] outline-none selection:bg-primary selection:text-primary-foreground placeholder:text-muted-foreground disabled:cursor-not-allowed disabled:opacity-50 md:text-sm dark:bg-input/30","focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50","aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40",$$props.class)],void 0,void 0,void 0,!0),bind_this(input_1,$$value=>ref2($$value),()=>ref2()),
-bind_value(input_1,value),append($$anchor2,input_1)};if_block(node2,$$render=>{$$props.type==="file"?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}function Label($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("flex items-center gap-2 text-sm leading-none font-medium select-none g\
-roup-data-[disabled=true]:pointer-events-none group-data-[disabled=true]:opacity-50 peer-disabled:cursor-not-allowed peer-disabled:opacity-50",$$props.class));component(node2,()=>Label$1,($$anchor2,LabelPrimitive_Root)=>{LabelPrimitive_Root($$anchor2,spread_props({"data-slot":"label",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor,fragment),pop()}var root_2$H=from_html('<span class="text-destructive">*</span>'),root_3$y=from_html(
-'<span class="text-xs text-muted-foreground/50">...</span>'),root_1$I=from_html("<span> <!></span> <!>",1),root_5$q=from_html('<button type="button"> </button>'),root_4$k=from_html('<div class="absolute top-full right-0 left-0 z-10 mt-1 max-h-32 overflow-y-auto rounded-lg border border-border/50 bg-background shadow-lg"></div>'),root$15=from_html('<div class="relative grid gap-1"><!> <!> <!></div>');function ChatFormPromptPickerArgumentInput($$anchor,$$props){push$1($$props,!0);let value=prop($$props,
-"value",3,""),suggestions=prop($$props,"suggestions",19,()=>[]),isLoadingSuggestions=prop($$props,"isLoadingSuggestions",3,!1),isAutocompleteActive=prop($$props,"isAutocompleteActive",3,!1),autocompleteIndex=prop($$props,"autocompleteIndex",3,0);var div=root$15(),node2=child(div);Label(node2,{get for(){return`arg-${$$props.argument.name??""}`},class:"mb-1 text-muted-foreground",children:($$anchor2,$$slotProps)=>{var fragment=root_1$I(),span=first_child(fragment),text2=child(span),node_1=sibling(
-text2);{var consequent=$$anchor3=>{var span_1=root_2$H();append($$anchor3,span_1)};if_block(node_1,$$render=>{$$props.argument.required&&$$render(consequent)})}reset(span);var node_2=sibling(span,2);{var consequent_1=$$anchor3=>{var span_2=root_3$y();append($$anchor3,span_2)};if_block(node_2,$$render=>{isLoadingSuggestions()&&$$render(consequent_1)})}template_effect(()=>set_text(text2,`${$$props.argument.name??""} `)),append($$anchor2,fragment)},$$slots:{default:!0}});var node_3=sibling(node2,2);
-{let $0=user_derived(()=>$$props.argument.description||$$props.argument.name);Input(node_3,{get id(){return`arg-${$$props.argument.name??""}`},type:"text",get value(){return value()},oninput:e=>$$props.onInput(e.currentTarget.value),get onkeydown(){return $$props.onKeydown},get onblur(){return $$props.onBlur},get onfocus(){return $$props.onFocus},get placeholder(){return get$4($0)},get required(){return $$props.argument.required},autocomplete:"off"})}var node_4=sibling(node_3,2);{var consequent_2=$$anchor2=>{
-var div_1=root_4$k();each(div_1,22,suggestions,suggestion=>suggestion,($$anchor3,suggestion,i)=>{var button=root_5$q();button.__mousedown=()=>$$props.onSelectSuggestion(suggestion);var text_1=child(button,!0);reset(button),template_effect(()=>{set_class(button,1,`w-full px-3 py-1.5 text-left text-sm hover:bg-accent ${get$4(i)===autocompleteIndex()?"bg-accent":""}`),set_text(text_1,suggestion)}),append($$anchor3,button)}),reset(div_1),transition(3,div_1,()=>fly,()=>({y:-5,duration:100})),append($$anchor2,
-div_1)};if_block(node_4,$$render=>{isAutocompleteActive()&&suggestions().length>0&&$$render(consequent_2)})}reset(div),append($$anchor,div),pop()}delegate(["mousedown"]);var root_2$G=from_html('<div class="flex items-start gap-2 rounded-lg border border-destructive/50 bg-destructive/10 px-3 py-2 text-sm text-destructive" role="alert"><span class="shrink-0">⚠</span> <span> </span></div>'),root$14=from_html('<form class="space-y-3 pt-4"><!> <!> <div class="mt-8 flex justify-end gap-2"><!> <!></d\
-iv></form>');function ChatFormPromptPickerArgumentForm($$anchor,$$props){push$1($$props,!0);var form=root$14(),node2=child(form);each(node2,17,()=>$$props.prompt.arguments??[],arg=>arg.name,($$anchor2,arg)=>{{let $0=user_derived(()=>$$props.promptArgs[get$4(arg).name]??""),$1=user_derived(()=>$$props.suggestions[get$4(arg).name]??[]),$2=user_derived(()=>$$props.loadingSuggestions[get$4(arg).name]??!1),$3=user_derived(()=>$$props.activeAutocomplete===get$4(arg).name),$4=user_derived(()=>$$props.activeAutocomplete===
-get$4(arg).name?$$props.autocompleteIndex:0);ChatFormPromptPickerArgumentInput($$anchor2,{get argument(){return get$4(arg)},get value(){return get$4($0)},get suggestions(){return get$4($1)},get isLoadingSuggestions(){return get$4($2)},get isAutocompleteActive(){return get$4($3)},get autocompleteIndex(){return get$4($4)},onInput:value=>$$props.onArgInput(get$4(arg).name,value),onKeydown:e=>$$props.onArgKeydown(e,get$4(arg).name),onBlur:()=>$$props.onArgBlur(get$4(arg).name),onFocus:()=>$$props.onArgFocus(
-get$4(arg).name),onSelectSuggestion:value=>$$props.onSelectSuggestion(get$4(arg).name,value)})}});var node_1=sibling(node2,2);{var consequent=$$anchor2=>{var div=root_2$G(),span=sibling(child(div),2),text2=child(span,!0);reset(span),reset(div),template_effect(()=>set_text(text2,$$props.promptError)),append($$anchor2,div)};if_block(node_1,$$render=>{$$props.promptError&&$$render(consequent)})}var div_1=sibling(node_1,2),node_2=child(div_1);Button(node_2,{type:"button",size:"sm",get onclick(){return $$props.
-onCancel},variant:"secondary",children:($$anchor2,$$slotProps)=>{next$1();var text_1=text$8("Cancel");append($$anchor2,text_1)},$$slots:{default:!0}});var node_3=sibling(node_2,2);Button(node_3,{size:"sm",type:"submit",children:($$anchor2,$$slotProps)=>{next$1();var text_2=text$8("Use Prompt");append($$anchor2,text_2)},$$slots:{default:!0}}),reset(div_1),reset(form),event("submit",form,function(...$$args){$$props.onSubmit?.apply(this,$$args)}),append($$anchor,form),pop()}function Popover($$anchor,$$props){
-push$1($$props,!0);let open2=prop($$props,"open",15,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","open"]);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Popover$1,($$anchor2,PopoverPrimitive_Root)=>{PopoverPrimitive_Root($$anchor2,spread_props(()=>restProps,{get open(){return open2()},set open($$value){open2($$value)}}))}),append($$anchor,fragment),pop()}function Popover_portal($$anchor,$$props){let restProps=rest_props($$props,["$$slots","$$events",
-"$$legacy"]);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Portal$2,($$anchor2,PopoverPrimitive_Portal)=>{PopoverPrimitive_Portal($$anchor2,spread_props(()=>restProps))}),append($$anchor,fragment)}function Popover_content($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),sideOffset=prop($$props,"sideOffset",3,4),align=prop($$props,"align",3,"center"),collisionPadding=prop($$props,"collisionPadding",3,8),avoidCollisions=prop($$props,"avoidCollisi\
-ons",3,!0),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","sideOffset","side","align","collisionPadding","avoidCollisions","portalProps"]);Popover_portal($$anchor,spread_props(()=>$$props.portalProps,{children:($$anchor2,$$slotProps)=>{var fragment_1=comment$2(),node2=first_child(fragment_1);{let $0=user_derived(()=>cn$1("z-50 w-72 origin-(--bits-popover-content-transform-origin) rounded-md border bg-popover p-4 text-popover-foreground shadow-md outline-hidden data-[\
-side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-end-2 data-[side=right]:slide-in-from-start-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95",$$props.class));component(node2,()=>Popover_content$1,($$anchor3,PopoverPrimitive_Content)=>{PopoverPrimitive_Content($$anchor3,spread_props({"data-slot":"popover-conten\
-t",get sideOffset(){return sideOffset()},get side(){return $$props.side},get align(){return align()},get collisionPadding(){return collisionPadding()},get avoidCollisions(){return avoidCollisions()},get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor2,fragment_1)},$$slots:{default:!0}})),pop()}function Popover_trigger($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$s\
-lots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("",$$props.class));component(node2,()=>Popover_trigger$1,($$anchor2,PopoverPrimitive_Trigger)=>{PopoverPrimitive_Trigger($$anchor2,spread_props({"data-slot":"popover-trigger",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)}}))})}append($$anchor,fragment),pop()}var root_2$F=from_html('<span class="sr-only"> </span>'),root_1$H=from_html(
-"<!> <!>",1);function ChatFormPickerPopover($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),isOpen=prop($$props,"isOpen",15,!1),srLabel=prop($$props,"srLabel",3,"Open picker");var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Popover,($$anchor2,Popover_Root)=>{Popover_Root($$anchor2,{onOpenChange:open2=>{open2||$$props.onClose?.()},get open(){return isOpen()},set open($$value){isOpen($$value)},children:($$anchor3,$$slotProps)=>{var fragment_1=root_1$H(),
-node_1=first_child(fragment_1);component(node_1,()=>Popover_trigger,($$anchor4,Popover_Trigger)=>{Popover_Trigger($$anchor4,{class:"pointer-events-none absolute inset-0 opacity-0",children:($$anchor5,$$slotProps2)=>{var span=root_2$F(),text2=child(span,!0);reset(span),template_effect(()=>set_text(text2,srLabel())),append($$anchor5,span)},$$slots:{default:!0}})});var node_2=sibling(node_1,2);component(node_2,()=>Popover_content,($$anchor4,Popover_Content)=>{Popover_Content($$anchor4,{side:"top",align:"\
-start",sideOffset:12,get class(){return`w-[var(--bits-popover-anchor-width)] max-w-none rounded-xl border-border/50 p-0 shadow-xl ${className()??""}`},get onkeydown(){return $$props.onKeydown},onOpenAutoFocus:e=>e.preventDefault(),children:($$anchor5,$$slotProps2)=>{var fragment_2=comment$2(),node_3=first_child(fragment_2);snippet(node_3,()=>$$props.children),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}
-var root_1$G=from_html("<!> <!>",1);function Scroll_area_scrollbar($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),orientation=prop($$props,"orientation",3,"vertical"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","orientation","children"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("flex touch-none p-px transition-colors select-none",orientation()==="vertical"&&"h-full w-2.5 border-l border-l-transpa\
-rent",orientation()==="horizontal"&&"h-2.5 flex-col border-t border-t-transparent",$$props.class));component(node2,()=>Scroll_area_scrollbar$1,($$anchor2,ScrollAreaPrimitive_Scrollbar)=>{ScrollAreaPrimitive_Scrollbar($$anchor2,spread_props({"data-slot":"scroll-area-scrollbar",get orientation(){return orientation()},get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},children:($$anchor3,$$slotProps)=>{var fragment_1=root_1$G(),node_1=first_child(
-fragment_1);snippet(node_1,()=>$$props.children??noop$3);var node_2=sibling(node_1,2);component(node_2,()=>Scroll_area_thumb,($$anchor4,ScrollAreaPrimitive_Thumb)=>{ScrollAreaPrimitive_Thumb($$anchor4,{"data-slot":"scroll-area-thumb",class:"relative flex-1 rounded-full bg-border"})}),append($$anchor3,fragment_1)},$$slots:{default:!0}}))})}append($$anchor,fragment),pop()}var root_1$F=from_html("<!> <!> <!> <!>",1);function Scroll_area($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"re\
-f",15,null),orientation=prop($$props,"orientation",3,"vertical"),scrollbarXClasses=prop($$props,"scrollbarXClasses",3,""),scrollbarYClasses=prop($$props,"scrollbarYClasses",3,""),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","orientation","scrollbarXClasses","scrollbarYClasses","children"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("relative",$$props.class));component(node2,()=>Scroll_area$1,($$anchor2,ScrollAreaPrimitive_Root)=>{
-ScrollAreaPrimitive_Root($$anchor2,spread_props({"data-slot":"scroll-area",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},children:($$anchor3,$$slotProps)=>{var fragment_1=root_1$F(),node_1=first_child(fragment_1);component(node_1,()=>Scroll_area_viewport,($$anchor4,ScrollAreaPrimitive_Viewport)=>{ScrollAreaPrimitive_Viewport($$anchor4,{"data-slot":"scroll-area-viewport",class:"size-full rounded-[inherit] ring-ring/10 outline-ring/50 transi\
-tion-[color,box-shadow] focus-visible:ring-4 focus-visible:outline-1 dark:ring-ring/20 dark:outline-ring/40",children:($$anchor5,$$slotProps2)=>{var fragment_2=comment$2(),node_2=first_child(fragment_2);snippet(node_2,()=>$$props.children??noop$3),append($$anchor5,fragment_2)},$$slots:{default:!0}})});var node_3=sibling(node_1,2);{var consequent=$$anchor4=>{Scroll_area_scrollbar($$anchor4,{orientation:"vertical",get class(){return scrollbarYClasses()}})};if_block(node_3,$$render=>{(orientation()===
-"vertical"||orientation()==="both")&&$$render(consequent)})}var node_4=sibling(node_3,2);{var consequent_1=$$anchor4=>{Scroll_area_scrollbar($$anchor4,{orientation:"horizontal",get class(){return scrollbarXClasses()}})};if_block(node_4,$$render=>{(orientation()==="horizontal"||orientation()==="both")&&$$render(consequent_1)})}var node_5=sibling(node_4,2);component(node_5,()=>Scroll_area_corner,($$anchor4,ScrollAreaPrimitive_Corner)=>{ScrollAreaPrimitive_Corner($$anchor4,{})}),append($$anchor3,fragment_1)},
-$$slots:{default:!0}}))})}append($$anchor,fragment),pop()}var root_2$E=from_html('<div class="absolute top-0 right-0 left-0 z-10 p-2 pb-0"><!></div>'),root_6$o=from_html('<div class="py-6 text-center text-sm text-muted-foreground"> </div>'),root_1$E=from_html("<!> <div><!></div> <!>",1);function ChatFormPickerList($$anchor,$$props){push$1($$props,!0);let searchQuery=prop($$props,"searchQuery",15),searchPlaceholder=prop($$props,"searchPlaceholder",3,"Search..."),emptyMessage=prop($$props,"emptyMe\
-ssage",3,"No items available"),listContainer=state$1(null);user_effect(()=>{if(get$4(listContainer)&&$$props.selectedIndex>=0&&$$props.selectedIndex<$$props.items.length){const selectedElement=get$4(listContainer).querySelector(`[data-picker-index="${$$props.selectedIndex}"]`);selectedElement&&selectedElement.scrollIntoView({behavior:"smooth",block:"center",inline:"nearest"})}}),Scroll_area($$anchor,{children:($$anchor2,$$slotProps)=>{var fragment_1=root_1$E(),node2=first_child(fragment_1);{var consequent=$$anchor3=>{
-var div=root_2$E(),node_1=child(div);SearchInput(node_1,{get placeholder(){return searchPlaceholder()},get value(){return searchQuery()},set value($$value){searchQuery($$value)}}),reset(div),append($$anchor3,div)};if_block(node2,$$render=>{$$props.showSearchInput&&$$render(consequent)})}var div_1=sibling(node2,2);let classes;var node_2=child(div_1);{var consequent_2=$$anchor3=>{var fragment_2=comment$2(),node_3=first_child(fragment_2);{var consequent_1=$$anchor4=>{var fragment_3=comment$2(),node_4=first_child(
-fragment_3);snippet(node_4,()=>$$props.skeleton),append($$anchor4,fragment_3)};if_block(node_3,$$render=>{$$props.skeleton&&$$render(consequent_1)})}append($$anchor3,fragment_2)},alternate_1=$$anchor3=>{var fragment_4=comment$2(),node_5=first_child(fragment_4);{var consequent_3=$$anchor4=>{var div_2=root_6$o(),text2=child(div_2,!0);reset(div_2),template_effect(()=>set_text(text2,emptyMessage())),append($$anchor4,div_2)},alternate=$$anchor4=>{var fragment_5=comment$2(),node_6=first_child(fragment_5);
-each(node_6,19,()=>$$props.items,(itemData,index2)=>$$props.itemKey(itemData,index2),($$anchor5,itemData,index2)=>{var fragment_6=comment$2(),node_7=first_child(fragment_6);snippet(node_7,()=>$$props.item,()=>get$4(itemData),()=>get$4(index2),()=>get$4(index2)===$$props.selectedIndex),append($$anchor5,fragment_6)}),append($$anchor4,fragment_5)};if_block(node_5,$$render=>{$$props.items.length===0?$$render(consequent_3):$$render(alternate,!1)},!0)}append($$anchor3,fragment_4)};if_block(node_2,$$render=>{
-$$props.isLoading?$$render(consequent_2):$$render(alternate_1,!1)})}reset(div_1),bind_this(div_1,$$value=>set$1(listContainer,$$value),()=>get$4(listContainer));var node_8=sibling(div_1,2);{var consequent_4=$$anchor3=>{var fragment_7=comment$2(),node_9=first_child(fragment_7);snippet(node_9,()=>$$props.footer),append($$anchor3,fragment_7)};if_block(node_8,$$render=>{$$props.footer&&$$render(consequent_4)})}template_effect(()=>classes=set_class(div_1,1,`${CHAT_FORM_POPOVER_MAX_HEIGHT} p-2`,null,classes,
-{"pt-13":$$props.showSearchInput})),append($$anchor2,fragment_1)},$$slots:{default:!0}}),pop()}var root$13=from_html('<button type="button"><!></button>');function ChatFormPickerListItem($$anchor,$$props){let isSelected=prop($$props,"isSelected",3,!1);var button=root$13();button.__click=function(...$$args){$$props.onClick?.apply(this,$$args)};var node2=child(button);snippet(node2,()=>$$props.children),reset(button),template_effect(()=>{set_attribute(button,"data-picker-index",$$props.dataIndex),
-set_class(button,1,`flex w-full cursor-pointer items-start gap-3 rounded-lg px-3 py-2 text-left hover:bg-accent/50 ${isSelected()?"bg-accent/50":""}`)}),append($$anchor,button)}delegate(["click"]);var root_1$D=from_html('<img alt="" class="h-3 w-3 shrink-0 rounded-sm"/>'),root_3$x=from_html('<p class="mt-0.5 truncate text-sm text-muted-foreground"> </p>'),root$12=from_html('<div class="min-w-0 flex-1"><div class="mb-0.5 flex items-center gap-1.5 text-xs text-muted-foreground"><!> <span> </span><\
-/div> <div class="flex items-center gap-2"><span class="font-medium"> </span> <!></div> <!> <!></div>');function ChatFormPickerItemHeader($$anchor,$$props){push$1($$props,!0);let faviconUrl=user_derived(()=>$$props.server?mcpStore.getServerFavicon($$props.server.id):null);var div=root$12(),div_1=child(div),node2=child(div_1);{var consequent=$$anchor2=>{var img=root_1$D();template_effect(()=>set_attribute(img,"src",get$4(faviconUrl))),event("error",img,e=>{e.currentTarget.style.display="none"}),replay_events(
-img),append($$anchor2,img)};if_block(node2,$$render=>{get$4(faviconUrl)&&$$render(consequent)})}var span=sibling(node2,2),text2=child(span,!0);reset(span),reset(div_1);var div_2=sibling(div_1,2),span_1=child(div_2),text_1=child(span_1,!0);reset(span_1);var node_1=sibling(span_1,2);{var consequent_1=$$anchor2=>{var fragment=comment$2(),node_2=first_child(fragment);snippet(node_2,()=>$$props.titleExtra),append($$anchor2,fragment)};if_block(node_1,$$render=>{$$props.titleExtra&&$$render(consequent_1)})}
-reset(div_2);var node_3=sibling(div_2,2);{var consequent_2=$$anchor2=>{var p2=root_3$x(),text_2=child(p2,!0);reset(p2),template_effect(()=>set_text(text_2,$$props.description)),append($$anchor2,p2)};if_block(node_3,$$render=>{$$props.description&&$$render(consequent_2)})}var node_4=sibling(node_3,2);{var consequent_3=$$anchor2=>{var fragment_1=comment$2(),node_5=first_child(fragment_1);snippet(node_5,()=>$$props.subtitle),append($$anchor2,fragment_1)};if_block(node_4,$$render=>{$$props.subtitle&&
-$$render(consequent_3)})}reset(div),template_effect(()=>{set_text(text2,$$props.serverLabel),set_text(text_1,$$props.title)}),append($$anchor,div),pop()}var root_1$C=from_html('<div class="h-4 w-12 animate-pulse rounded-full bg-muted"></div>'),root$11=from_html('<div class="flex w-full items-start gap-3 rounded-lg px-3 py-2"><div class="min-w-0 flex-1 space-y-2"><div class="mb-2 flex items-center gap-1.5"><div class="h-3 w-3 shrink-0 animate-pulse rounded-sm bg-muted"></div> <div class="h-3 w-24\
- animate-pulse rounded bg-muted"></div></div> <div class="flex items-center gap-2"><div></div> <!></div> <div class="h-3 w-full animate-pulse rounded bg-muted"></div></div></div>');function ChatFormPickerListItemSkeleton($$anchor,$$props){let titleWidth=prop($$props,"titleWidth",3,"w-48"),showBadge=prop($$props,"showBadge",3,!1);var div=root$11(),div_1=child(div),div_2=sibling(child(div_1),2),div_3=child(div_2),node2=sibling(div_3,2);{var consequent=$$anchor2=>{var div_4=root_1$C();append($$anchor2,
-div_4)};if_block(node2,$$render=>{showBadge()&&$$render(consequent)})}reset(div_2),next$1(2),reset(div_1),reset(div),template_effect(()=>set_class(div_3,1,`h-4 ${titleWidth()??""} animate-pulse rounded bg-muted`)),append($$anchor,div)}var root_5$p=from_html('<span class="inline-flex items-center rounded-full bg-primary/10 px-1.5 py-0.5 text-[10px] font-medium text-primary">attached</span>'),root_6$n=from_html('<p class="mt-0.5 truncate text-xs text-muted-foreground/60"> </p>'),root_10$d=from_html(
-"<!> Browse all",1);function ChatFormResourcePicker($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),isOpen=prop($$props,"isOpen",7,!1),searchQuery=prop($$props,"searchQuery",3,""),resources=state$1(proxy([])),isLoading2=state$1(!1),selectedIndex=state$1(0),internalSearchQuery=state$1(""),serverSettingsMap=user_derived(()=>{const servers=mcpStore.getServers(),map2=new SvelteMap;for(const server of servers)map2.set(server.id,server);return map2});user_effect(()=>{isOpen()&&
-(loadResources(),set$1(selectedIndex,0))}),user_effect(()=>{get$4(filteredResources).length>0&&get$4(selectedIndex)>=get$4(filteredResources).length&&set$1(selectedIndex,0)});async function loadResources(){set$1(isLoading2,!0);try{const perChatOverrides=conversationsStore.getAllMcpServerOverrides();if(!await mcpStore.ensureInitialized(perChatOverrides)){set$1(resources,[],!0);return}await mcpStore.fetchAllResources(),set$1(resources,mcpResourceStore.getAllResourceInfos(),!0)}catch(error2){console.
-error("[ChatFormResourcePicker] Failed to load resources:",error2),set$1(resources,[],!0)}finally{set$1(isLoading2,!1)}}function handleResourceClick(resource){mcpStore.attachResource(resource.uri),$$props.onResourceSelect?.(resource),$$props.onClose?.()}function isResourceAttached(uri2){return mcpResourceStore.isAttached(uri2)}function handleKeydown(event2){return isOpen()?event2.key===KeyboardKey.ESCAPE?(event2.preventDefault(),$$props.onClose?.(),!0):event2.key===KeyboardKey.ARROW_DOWN?(event2.
-preventDefault(),get$4(filteredResources).length>0&&set$1(selectedIndex,(get$4(selectedIndex)+1)%get$4(filteredResources).length),!0):event2.key===KeyboardKey.ARROW_UP?(event2.preventDefault(),get$4(filteredResources).length>0&&set$1(selectedIndex,get$4(selectedIndex)===0?get$4(filteredResources).length-1:get$4(selectedIndex)-1,!0),!0):event2.key===KeyboardKey.ENTER?(event2.preventDefault(),get$4(filteredResources)[get$4(selectedIndex)]&&handleResourceClick(get$4(filteredResources)[get$4(selectedIndex)]),
-!0):!1:!1}let filteredResources=user_derived(()=>{const sortedServers=mcpStore.getServersSorted(),serverOrderMap=new Map(sortedServers.map((server,index2)=>[server.id,index2])),sortedResources=[...get$4(resources)].sort((a,b)=>{const orderA=serverOrderMap.get(a.serverName)??Number.MAX_SAFE_INTEGER,orderB=serverOrderMap.get(b.serverName)??Number.MAX_SAFE_INTEGER;return orderA-orderB}),query=(searchQuery()||get$4(internalSearchQuery)).toLowerCase();return query?sortedResources.filter(resource=>resource.
-name.toLowerCase().includes(query)||resource.title?.toLowerCase().includes(query)||resource.description?.toLowerCase().includes(query)||resource.uri.toLowerCase().includes(query)):sortedResources}),showSearchInput=user_derived(()=>get$4(resources).length>3);var $$exports={handleKeydown};return ChatFormPickerPopover($$anchor,{get class(){return className()},srLabel:"Open resource picker",get onClose(){return $$props.onClose},onKeydown:handleKeydown,get isOpen(){return isOpen()},set isOpen($$value){
-isOpen($$value)},children:($$anchor2,$$slotProps)=>{ChatFormPickerList($$anchor2,{get items(){return get$4(filteredResources)},get isLoading(){return get$4(isLoading2)},get selectedIndex(){return get$4(selectedIndex)},get showSearchInput(){return get$4(showSearchInput)},searchPlaceholder:"Search resources...",emptyMessage:"No MCP resources available",itemKey:resource=>resource.serverName+":"+resource.uri,get searchQuery(){return get$4(internalSearchQuery)},set searchQuery($$value){set$1(internalSearchQuery,
-$$value,!0)},item:($$anchor3,resource=noop$3,index2=noop$3,isSelected=noop$3)=>{const server=user_derived(()=>get$4(serverSettingsMap).get(resource().serverName)),serverLabel=user_derived(()=>get$4(server)?mcpStore.getServerLabel(get$4(server)):resource().serverName);ChatFormPickerListItem($$anchor3,{get dataIndex(){return index2()},get isSelected(){return isSelected()},onClick:()=>handleResourceClick(resource()),children:($$anchor4,$$slotProps2)=>{{const titleExtra=$$anchor5=>{var fragment_4=comment$2(),
-node2=first_child(fragment_4);{var consequent=$$anchor6=>{var span=root_5$p();append($$anchor6,span)};if_block(node2,$$render=>{isResourceAttached(resource().uri)&&$$render(consequent)})}append($$anchor5,fragment_4)},subtitle=$$anchor5=>{var p2=root_6$n(),text2=child(p2,!0);reset(p2),template_effect(()=>set_text(text2,resource().uri)),append($$anchor5,p2)};let $0=user_derived(()=>resource().title||resource().name);ChatFormPickerItemHeader($$anchor4,{get server(){return get$4(server)},get serverLabel(){
-return get$4(serverLabel)},get title(){return get$4($0)},get description(){return resource().description},titleExtra,subtitle,$$slots:{titleExtra:!0,subtitle:!0}})}},$$slots:{default:!0}})},skeleton:$$anchor3=>{ChatFormPickerListItemSkeleton($$anchor3,{})},footer:$$anchor3=>{var fragment_6=comment$2(),node_1=first_child(fragment_6);{var consequent_1=$$anchor4=>{Button($$anchor4,{class:"fixed right-3 bottom-3",type:"button",get onclick(){return $$props.onBrowse},variant:"secondary",size:"sm",children:($$anchor5,$$slotProps2)=>{
-var fragment_8=root_10$d(),node_2=first_child(fragment_8);Folder_open(node_2,{class:"h-3 w-3"}),next$1(),append($$anchor5,fragment_8)},$$slots:{default:!0}})};if_block(node_1,$$render=>{$$props.onBrowse&&get$4(resources).length>3&&$$render(consequent_1)})}append($$anchor3,fragment_6)},$$slots:{item:!0,skeleton:!0,footer:!0}})},$$slots:{default:!0}}),pop($$exports)}function fadeInView(node2,options={}){const{duration:duration2=300,y=0,skipIfVisible=!1}=options;if(skipIfVisible){const rect=node2.getBoundingClientRect();
-if(rect.top<window.innerHeight&&rect.bottom>0&&rect.left<window.innerWidth&&rect.right>0)return}node2.style.opacity="0",node2.style.transform=`translateY(${y}px)`,node2.style.transition=`opacity ${duration2}ms ease-out, transform ${duration2}ms ease-out`,user_effect(()=>{const observer=new IntersectionObserver(entries=>{for(const entry of entries)entry.isIntersecting&&(requestAnimationFrame(()=>{node2.style.opacity="1",node2.style.transform="translateY(0)"}),observer.disconnect())},{threshold:.05});
-return observer.observe(node2),()=>{observer.disconnect()}})}var root_1$B=from_html("<div><!></div>"),root$10=from_html('<div style="height: auto; min-height: calc(100dvh - 14rem);"></div>');function ChatMessages($$anchor,$$props){push$1($$props,!0);let messages=prop($$props,"messages",19,()=>[]),allConversationMessages=state$1(proxy([]));const currentConfig=config$1();setChatActionsContext({copy:async message=>{const asPlainText=!!currentConfig.copyTextAttachmentsAsPlainText,clipboardContent=formatMessageForClipboard(
-message.content,message.extra,asPlainText);await copyToClipboard(clipboardContent,"Message copied to clipboard")},delete:async message=>{await chatStore.deleteMessage(message.id),refreshAllMessages()},navigateToSibling:async siblingId=>{await conversationsStore.navigateToSibling(siblingId)},editWithBranching:async(message,newContent,newExtras)=>{$$props.onUserAction?.(),await chatStore.editMessageWithBranching(message.id,newContent,newExtras),refreshAllMessages()},editWithReplacement:async(message,newContent,shouldBranch)=>{
-$$props.onUserAction?.(),await chatStore.editAssistantMessage(message.id,newContent,shouldBranch),refreshAllMessages()},editUserMessagePreserveResponses:async(message,newContent,newExtras)=>{$$props.onUserAction?.(),await chatStore.editUserMessagePreserveResponses(message.id,newContent,newExtras),refreshAllMessages()},regenerateWithBranching:async(message,modelOverride)=>{$$props.onUserAction?.(),await chatStore.regenerateMessageWithBranching(message.id,modelOverride),refreshAllMessages()},continueAssistantMessage:async message=>{
-$$props.onUserAction?.(),await chatStore.continueAssistantMessage(message.id),refreshAllMessages()},forkConversation:async(message,options)=>{await conversationsStore.forkConversation(message.id,options)}});function refreshAllMessages(){const conversation=activeConversation();conversation?conversationsStore.getConversationMessages(conversation.id).then(messages2=>{set$1(allConversationMessages,messages2,!0)}):set$1(allConversationMessages,[],!0)}user_effect(()=>{activeConversation()&&refreshAllMessages()});
-let displayMessages=user_derived(()=>{if(!messages().length)return[];const filteredMessages=currentConfig.showSystemMessage?messages():messages().filter(msg=>msg.type!==MessageRole.SYSTEM),result=[];for(let i=0;i<filteredMessages.length;i++){const msg=filteredMessages[i];if(msg.role===MessageRole.TOOL)continue;const toolMessages=[];if(msg.role===MessageRole.ASSISTANT&&hasAgenticContent(msg)){let j=i+1;for(;j<filteredMessages.length;){const next2=filteredMessages[j];if(next2.role===MessageRole.TOOL)
-toolMessages.push(next2),j++;else if(next2.role===MessageRole.ASSISTANT)toolMessages.push(next2),j++;else break}i=j-1}else if(msg.role===MessageRole.ASSISTANT){let j=i+1;for(;j<filteredMessages.length&&filteredMessages[j].role===MessageRole.TOOL;)toolMessages.push(filteredMessages[j]),j++}const siblingInfo=getMessageSiblings(get$4(allConversationMessages),msg.id);result.push({message:msg,toolMessages,isLastAssistantMessage:!1,siblingInfo:siblingInfo||{message:msg,siblingIds:[msg.id],currentIndex:0,
-totalSiblings:1}})}for(let i=result.length-1;i>=0;i--)if(result[i].message.role===MessageRole.ASSISTANT){result[i].isLastAssistantMessage=!0;break}return result});var div=root$10();each(div,21,()=>get$4(displayMessages),({message,toolMessages,isLastAssistantMessage,siblingInfo})=>message.id,($$anchor2,$$item)=>{let message=()=>get$4($$item).message,toolMessages=()=>get$4($$item).toolMessages,isLastAssistantMessage=()=>get$4($$item).isLastAssistantMessage,siblingInfo=()=>get$4($$item).siblingInfo;
-var div_1=root_1$B(),node2=child(div_1);ChatMessage(node2,{class:"mx-auto w-full max-w-[48rem]",get message(){return message()},get toolMessages(){return toolMessages()},get isLastAssistantMessage(){return isLastAssistantMessage()},get siblingInfo(){return siblingInfo()}}),reset(div_1),action(div_1,$$node=>fadeInView?.($$node)),append($$anchor2,div_1)}),reset(div),template_effect(()=>set_class(div,1,`flex h-full flex-col space-y-10 pt-24 ${$$props.class??""}`)),append($$anchor,div),pop()}function ChatMessage($$anchor,$$props){
-push$1($$props,!0);let className=prop($$props,"class",3,""),toolMessages=prop($$props,"toolMessages",19,()=>[]),isLastAssistantMessage=prop($$props,"isLastAssistantMessage",3,!1),siblingInfo=prop($$props,"siblingInfo",3,null);const chatActions=getChatActionsContext();let deletionInfo=state$1(null),editedContent=user_derived(()=>$$props.message.content),editedExtras=user_derived(()=>$$props.message.extra?[...$$props.message.extra]:[]),editedUploadedFiles=state$1(proxy([])),isEditing2=state$1(!1),
-showDeleteDialog=state$1(!1),shouldBranchAfterEdit=state$1(!1),textareaElement=state$1(void 0),showSaveOnlyOption=user_derived(()=>$$props.message.role===MessageRole.USER);setMessageEditContext({get isEditing(){return get$4(isEditing2)},get editedContent(){return get$4(editedContent)},get editedExtras(){return get$4(editedExtras)},get editedUploadedFiles(){return get$4(editedUploadedFiles)},get originalContent(){return $$props.message.content},get originalExtras(){return $$props.message.extra||[]},
-get showSaveOnlyOption(){return get$4(showSaveOnlyOption)},setContent:content2=>{set$1(editedContent,content2)},setExtras:extras=>{set$1(editedExtras,extras)},setUploadedFiles:files=>{set$1(editedUploadedFiles,files,!0)},save:handleSaveEdit,saveOnly:handleSaveEditOnly,cancel:handleCancelEdit,startEdit:handleEdit});let mcpPromptExtra=user_derived(()=>{if($$props.message.role!==MessageRole.USER||$$props.message.content.trim()||!$$props.message.extra||$$props.message.extra.length!==1)return null;const extra=$$props.
-message.extra[0];return extra.type===AttachmentType.MCP_PROMPT?extra:null});user_effect(()=>{const pendingId=pendingEditMessageId();pendingId&&pendingId===$$props.message.id&&!get$4(isEditing2)&&(handleEdit(),chatStore.clearPendingEditMessageId())});async function handleCancelEdit(){if(set$1(isEditing2,!1),$$props.message.role===MessageRole.SYSTEM){await chatStore.removeSystemPromptPlaceholder($$props.message.id)&&goto(`${base}/`);return}set$1(editedContent,$$props.message.content),set$1(editedExtras,
-$$props.message.extra?[...$$props.message.extra]:[]),set$1(editedUploadedFiles,[],!0)}function handleCopy(){chatActions.copy($$props.message)}async function handleConfirmDelete(){$$props.message.role===MessageRole.SYSTEM?await chatStore.removeSystemPromptPlaceholder($$props.message.id)&&goto(`${base}/`):chatActions.delete($$props.message),set$1(showDeleteDialog,!1)}async function handleDelete2(){set$1(deletionInfo,await chatStore.getDeletionInfo($$props.message.id),!0),set$1(showDeleteDialog,!0)}
-function handleEdit(){set$1(isEditing2,!0),set$1(editedContent,$$props.message.role===MessageRole.SYSTEM&&$$props.message.content===SYSTEM_MESSAGE_PLACEHOLDER?"":$$props.message.content),get$4(textareaElement)?.focus(),set$1(editedExtras,$$props.message.extra?[...$$props.message.extra]:[]),set$1(editedUploadedFiles,[],!0),setTimeout(()=>{get$4(textareaElement)&&(get$4(textareaElement).focus(),get$4(textareaElement).setSelectionRange(get$4(textareaElement).value.length,get$4(textareaElement).value.
-length))},0)}function handleRegenerate(modelOverride){chatActions.regenerateWithBranching($$props.message,modelOverride)}function handleContinue(){chatActions.continueAssistantMessage($$props.message)}function handleForkConversation(options){chatActions.forkConversation($$props.message,options)}function handleNavigateToSibling(siblingId){chatActions.navigateToSibling(siblingId)}async function handleSaveEdit(){if($$props.message.role===MessageRole.SYSTEM){const newContent=get$4(editedContent).trim();
-if(!newContent){const conversationDeleted=await chatStore.removeSystemPromptPlaceholder($$props.message.id);set$1(isEditing2,!1),conversationDeleted&&goto(`${base}/`);return}await DatabaseService.updateMessage($$props.message.id,{content:newContent});const index2=conversationsStore.findMessageIndex($$props.message.id);index2!==-1&&conversationsStore.updateMessageAtIndex(index2,{content:newContent})}else if($$props.message.role===MessageRole.USER){const finalExtras=await getMergedExtras();chatActions.
-editWithBranching($$props.message,get$4(editedContent).trim(),finalExtras)}else chatActions.editWithReplacement($$props.message,get$4(editedContent),get$4(shouldBranchAfterEdit));set$1(isEditing2,!1),set$1(shouldBranchAfterEdit,!1),set$1(editedUploadedFiles,[],!0)}async function handleSaveEditOnly(){if($$props.message.role===MessageRole.USER){const finalExtras=await getMergedExtras();chatActions.editUserMessagePreserveResponses($$props.message,get$4(editedContent).trim(),finalExtras)}set$1(isEditing2,
-!1),set$1(editedUploadedFiles,[],!0)}async function getMergedExtras(){if(get$4(editedUploadedFiles).length===0)return get$4(editedExtras);const plainFiles=snapshot(get$4(editedUploadedFiles)),newExtras=(await parseFilesToMessageExtras(plainFiles))?.extras||[];return[...get$4(editedExtras),...newExtras]}function handleShowDeleteDialogChange(show){set$1(showDeleteDialog,show,!0)}var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{ChatMessageSystem($$anchor2,{get class(){
-return className()},get deletionInfo(){return get$4(deletionInfo)},get message(){return $$props.message},onConfirmDelete:handleConfirmDelete,onCopy:handleCopy,onDelete:handleDelete2,onEdit:handleEdit,onNavigateToSibling:handleNavigateToSibling,onShowDeleteDialogChange:handleShowDeleteDialogChange,get showDeleteDialog(){return get$4(showDeleteDialog)},get siblingInfo(){return siblingInfo()},get textareaElement(){return get$4(textareaElement)},set textareaElement($$value){set$1(textareaElement,$$value,
-!0)}})},alternate_2=$$anchor2=>{var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent_1=$$anchor3=>{ChatMessageMcpPrompt($$anchor3,{get class(){return className()},get deletionInfo(){return get$4(deletionInfo)},get message(){return $$props.message},get mcpPrompt(){return get$4(mcpPromptExtra)},onConfirmDelete:handleConfirmDelete,onCopy:handleCopy,onDelete:handleDelete2,onEdit:handleEdit,onNavigateToSibling:handleNavigateToSibling,onShowDeleteDialogChange:handleShowDeleteDialogChange,
-get showDeleteDialog(){return get$4(showDeleteDialog)},get siblingInfo(){return siblingInfo()}})},alternate_1=$$anchor3=>{var fragment_4=comment$2(),node_2=first_child(fragment_4);{var consequent_2=$$anchor4=>{ChatMessageUser($$anchor4,{get class(){return className()},get deletionInfo(){return get$4(deletionInfo)},get message(){return $$props.message},onConfirmDelete:handleConfirmDelete,onCopy:handleCopy,onDelete:handleDelete2,onEdit:handleEdit,onForkConversation:handleForkConversation,onNavigateToSibling:handleNavigateToSibling,
-onShowDeleteDialogChange:handleShowDeleteDialogChange,get showDeleteDialog(){return get$4(showDeleteDialog)},get siblingInfo(){return siblingInfo()}})},alternate=$$anchor4=>{ChatMessageAssistant($$anchor4,{get class(){return className()},get deletionInfo(){return get$4(deletionInfo)},get isLastAssistantMessage(){return isLastAssistantMessage()},get message(){return $$props.message},get toolMessages(){return toolMessages()},get messageContent(){return $$props.message.content},onConfirmDelete:handleConfirmDelete,
-onContinue:handleContinue,onCopy:handleCopy,onDelete:handleDelete2,onEdit:handleEdit,onForkConversation:handleForkConversation,onNavigateToSibling:handleNavigateToSibling,onRegenerate:handleRegenerate,onShowDeleteDialogChange:handleShowDeleteDialogChange,get showDeleteDialog(){return get$4(showDeleteDialog)},get siblingInfo(){return siblingInfo()},get textareaElement(){return get$4(textareaElement)},set textareaElement($$value){set$1(textareaElement,$$value,!0)}})};if_block(node_2,$$render=>{$$props.
-message.role===MessageRole.USER?$$render(consequent_2):$$render(alternate,!1)},!0)}append($$anchor3,fragment_4)};if_block(node_1,$$render=>{get$4(mcpPromptExtra)?$$render(consequent_1):$$render(alternate_1,!1)},!0)}append($$anchor2,fragment_2)};if_block(node2,$$render=>{$$props.message.role===MessageRole.SYSTEM?$$render(consequent):$$render(alternate_2,!1)})}append($$anchor,fragment),pop()}var root_2$D=from_html('<div class="agentic-text svelte-1uhcmx5"><!></div>'),root_9$h=from_html('<div class\
-="rounded bg-muted/30 p-2 text-xs text-muted-foreground italic">Receiving arguments...</div>'),root_10$c=from_html('<div class="rounded bg-yellow-500/10 p-2 text-xs text-yellow-600 italic dark:text-yellow-400">Response was truncated</div>'),root_5$o=from_html('<div class="pt-3"><div class="my-3 flex items-center gap-2 text-xs text-muted-foreground"><span>Arguments:</span> <!></div> <!></div>'),root_14$5=from_html('<div class="pt-3"><div class="my-3 text-xs text-muted-foreground">Arguments:</div>\
- <!></div>'),root_18$3=from_html('<img class="mt-2 mb-2 h-auto max-w-full rounded-lg" loading="lazy"/>'),root_17$4=from_html('<div class="font-mono text-xs leading-relaxed whitespace-pre-wrap"> </div> <!>',1),root_16$5=from_html('<div class="overflow-auto rounded-lg border border-border bg-muted p-4"></div>'),root_20$7=from_html('<div class="rounded bg-muted/30 p-2 text-xs text-muted-foreground italic">Waiting for result...</div>'),root_13$a=from_html('<!> <div class="pt-3"><div class="my-3 flex\
- items-center gap-2 text-xs text-muted-foreground"><span>Result:</span> <!></div> <!></div>',1),root_23$3=from_html('<div class="pt-3"><div class="text-xs leading-relaxed break-words whitespace-pre-wrap"> </div></div>'),root_26$2=from_html('<div class="pt-3"><div class="text-xs leading-relaxed break-words whitespace-pre-wrap"> </div></div>'),root_30=from_html('<div class="turn-stats svelte-1uhcmx5"><!></div>'),root_28$1=from_html('<div class="agentic-turn my-2 hover:bg-muted/80 dark:hover:bg-mut\
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-()=>set_text(text_1,section().content)),append($$anchor7,div_11)},$$slots:{default:!0}})}},alternate_3=$$anchor6=>{var fragment_14=comment$2(),node_14=first_child(fragment_14);{var consequent_12=$$anchor7=>{const reasoningTitle=user_derived(()=>isStreaming()?"Reasoning...":"Reasoning"),reasoningSubtitle=user_derived(()=>isStreaming()?"":"incomplete");{let $0=user_derived(()=>isExpanded(index2(),section()));CollapsibleContentBlock($$anchor7,{get open(){return get$4($0)},class:"my-2",get icon(){return Brain},
-get title(){return get$4(reasoningTitle)},get subtitle(){return get$4(reasoningSubtitle)},get isStreaming(){return isStreaming()},onToggle:()=>toggleExpanded(index2(),section()),children:($$anchor8,$$slotProps)=>{var div_13=root_26$2(),div_14=child(div_13),text_2=child(div_14,!0);reset(div_14),reset(div_13),template_effect(()=>set_text(text_2,section().content)),append($$anchor8,div_13)},$$slots:{default:!0}})}};if_block(node_14,$$render=>{section().type===AgenticSectionType.REASONING_PENDING&&$$render(
-consequent_12)},!0)}append($$anchor6,fragment_14)};if_block(node_13,$$render=>{section().type===AgenticSectionType.REASONING?$$render(consequent_11):$$render(alternate_3,!1)},!0)}append($$anchor5,fragment_12)};if_block(node_6,$$render=>{section().type===AgenticSectionType.TOOL_CALL||section().type===AgenticSectionType.TOOL_CALL_PENDING?$$render(consequent_10):$$render(alternate_4,!1)},!0)}append($$anchor4,fragment_6)};if_block(node_2,$$render=>{section().type===AgenticSectionType.TOOL_CALL_STREAMING?
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-()=>config$1().showThoughtInProgress),sections=user_derived(()=>deriveAgenticSections($$props.message,toolMessages(),[],isStreaming())),sectionsParsed=user_derived(()=>get$4(sections).map(section=>({...section,parsedLines:section.toolResult?parseToolResultWithImages(section.toolResult,section.toolResultExtras||$$props.message?.extra):[]}))),turnGroups=user_derived(()=>{const turns=[];let currentTurn=[],currentIndices=[],prevWasTool=!1;for(let i=0;i<get$4(sectionsParsed).length;i++){const section=get$4(
-sectionsParsed)[i],isTool=section.type===AgenticSectionType.TOOL_CALL||section.type===AgenticSectionType.TOOL_CALL_PENDING||section.type===AgenticSectionType.TOOL_CALL_STREAMING;!isTool&&prevWasTool&&currentTurn.length>0&&(turns.push({sections:currentTurn,flatIndices:currentIndices}),currentTurn=[],currentIndices=[]),currentTurn.push(section),currentIndices.push(i),prevWasTool=isTool}return currentTurn.length>0&&turns.push({sections:currentTurn,flatIndices:currentIndices}),turns});function getDefaultExpanded(section){
-return section.type===AgenticSectionType.TOOL_CALL_PENDING||section.type===AgenticSectionType.TOOL_CALL_STREAMING?get$4(showToolCallInProgress):section.type===AgenticSectionType.REASONING_PENDING?get$4(showThoughtInProgress):!1}function isExpanded(index2,section){return expandedStates[index2]!==void 0?expandedStates[index2]:getDefaultExpanded(section)}function toggleExpanded(index2,section){const currentState=isExpanded(index2,section);expandedStates[index2]=!currentState}function buildTurnAgenticTimings(stats){
-return{turns:1,toolCallsCount:stats.toolCalls.length,toolsMs:stats.toolsMs,toolCalls:stats.toolCalls,llm:stats.llm}}var div_15=root$$(),node_15=child(div_15);{var consequent_14=$$anchor2=>{var fragment_16=comment$2(),node_16=first_child(fragment_16);each(node_16,17,()=>get$4(turnGroups),index$2,($$anchor3,turn,turnIndex)=>{const turnStats=user_derived(()=>$$props.message?.timings?.agentic?.perTurn?.[turnIndex]);var div_16=root_28$1(),span=child(div_16);span.textContent=`Turn ${turnIndex+1}`;var node_17=sibling(
-span,2);each(node_17,19,()=>get$4(turn).sections,(section,sIdx)=>get$4(turn).flatIndices[sIdx],($$anchor4,section,sIdx)=>{renderSection($$anchor4,()=>get$4(section),()=>get$4(turn).flatIndices[get$4(sIdx)])});var node_18=sibling(node_17,2);{var consequent_13=$$anchor4=>{var div_17=root_30(),node_19=child(div_17);{let $0=user_derived(()=>get$4(turnStats).toolCalls.length>0?buildTurnAgenticTimings(get$4(turnStats)):void 0);ChatMessageStatistics(node_19,{get promptTokens(){return get$4(turnStats).llm.
-prompt_n},get promptMs(){return get$4(turnStats).llm.prompt_ms},get predictedTokens(){return get$4(turnStats).llm.predicted_n},get predictedMs(){return get$4(turnStats).llm.predicted_ms},get agenticTimings(){return get$4($0)},get initialView(){return ChatMessageStatsView.GENERATION},hideSummary:!0})}reset(div_17),append($$anchor4,div_17)};if_block(node_18,$$render=>{get$4(turnStats)&&$$render(consequent_13)})}reset(div_16),append($$anchor3,div_16)}),append($$anchor2,fragment_16)},alternate_7=$$anchor2=>{
-var fragment_18=comment$2(),node_20=first_child(fragment_18);each(node_20,17,()=>get$4(sectionsParsed),index$2,($$anchor3,section,index2)=>{renderSection($$anchor3,()=>get$4(section),()=>index2)}),append($$anchor2,fragment_18)};if_block(node_15,$$render=>{highlightTurns()&&get$4(turnGroups).length>1?$$render(consequent_14):$$render(alternate_7,!1)})}reset(div_15),append($$anchor,div_15),pop()}var root_1$A=from_html('<div data-slot="checkbox-indicator" class="text-current transition-none"><!></di\
-v>');function Checkbox($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),checked=prop($$props,"checked",15,!1),indeterminate=prop($$props,"indeterminate",15,!1),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","checked","indeterminate","class"]);var fragment=comment$2(),node2=first_child(fragment);{const children=($$anchor2,$$arg0)=>{let checked2=()=>$$arg0?.().checked,indeterminate2=()=>$$arg0?.().indeterminate;var div=root_1$A(),node_1=child(div);{var consequent=$$anchor3=>{
-Check($$anchor3,{class:"size-3.5"})},alternate=$$anchor3=>{var fragment_2=comment$2(),node_2=first_child(fragment_2);{var consequent_1=$$anchor4=>{Minus($$anchor4,{class:"size-3.5"})};if_block(node_2,$$render=>{indeterminate2()&&$$render(consequent_1)},!0)}append($$anchor3,fragment_2)};if_block(node_1,$$render=>{checked2()?$$render(consequent):$$render(alternate,!1)})}reset(div),append($$anchor2,div)};let $0=user_derived(()=>cn$1("peer flex size-4 shrink-0 items-center justify-center rounded-[4p\
-x] border border-input shadow-xs transition-shadow outline-none focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 disabled:cursor-not-allowed disabled:opacity-50 aria-invalid:border-destructive aria-invalid:ring-destructive/20 data-[state=checked]:border-primary data-[state=checked]:bg-primary data-[state=checked]:text-primary-foreground dark:bg-input/30 dark:aria-invalid:ring-destructive/40 dark:data-[state=checked]:bg-primary",$$props.class));component(node2,()=>Checkbox$1,
-($$anchor2,CheckboxPrimitive_Root)=>{CheckboxPrimitive_Root($$anchor2,spread_props({"data-slot":"checkbox",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},get checked(){return checked()},set checked($$value){checked($$value)},get indeterminate(){return indeterminate()},set indeterminate($$value){indeterminate($$value)},children,$$slots:{default:!0}}))})}append($$anchor,fragment),pop()}var root_6$m=from_html('<div class="flex items-center gap\
--2"><span class="text-xs text-muted-foreground">Show raw output</span> <!></div>'),root_7$k=from_html('<div class="flex flex-col gap-4 py-2"><div class="flex flex-col gap-2"><!> <!></div> <div class="flex items-center gap-2"><!> <!></div></div>'),root$_=from_html('<div><div><!> <div class="pointer-events-auto inset-0 flex items-center gap-1 opacity-100 transition-all duration-150"><!> <!> <!> <!> <!> <!></div></div> <!></div> <!> <!>',1);function ChatMessageActions($$anchor,$$props){push$1($$props,
-!0);let siblingInfo=prop($$props,"siblingInfo",3,null),showDeleteDialog=prop($$props,"showDeleteDialog",7),showRawOutputSwitch=prop($$props,"showRawOutputSwitch",3,!1),rawOutputEnabled=prop($$props,"rawOutputEnabled",3,!1),showForkDialog=state$1(!1),forkName=state$1(""),forkIncludeAttachments=state$1(!0);function handleConfirmDelete(){$$props.onConfirmDelete(),$$props.onShowDeleteDialogChange(!1)}function handleOpenForkDialog(){const conv=activeConversation();set$1(forkName,`Fork of ${conv?.name??
-"Conversation"}`),set$1(forkIncludeAttachments,!0),set$1(showForkDialog,!0)}function handleConfirmFork(){$$props.onForkConversation?.({name:get$4(forkName).trim(),includeAttachments:get$4(forkIncludeAttachments)}),set$1(showForkDialog,!1)}var fragment=root$_(),div=first_child(fragment),div_1=child(div),node2=child(div_1);{var consequent=$$anchor2=>{ChatMessageBranchingControls($$anchor2,{get siblingInfo(){return siblingInfo()},get onNavigateToSibling(){return $$props.onNavigateToSibling}})};if_block(
-node2,$$render=>{siblingInfo()&&siblingInfo().totalSiblings>1&&$$render(consequent)})}var div_2=sibling(node2,2),node_1=child(div_2);ActionIcon(node_1,{get icon(){return Copy},tooltip:"Copy",get onclick(){return $$props.onCopy}});var node_2=sibling(node_1,2);{var consequent_1=$$anchor2=>{ActionIcon($$anchor2,{get icon(){return Square_pen},tooltip:"Edit",get onclick(){return $$props.onEdit}})};if_block(node_2,$$render=>{$$props.onEdit&&$$render(consequent_1)})}var node_3=sibling(node_2,2);{var consequent_2=$$anchor2=>{
-ActionIcon($$anchor2,{get icon(){return Refresh_cw},tooltip:"Regenerate",onclick:()=>$$props.onRegenerate()})};if_block(node_3,$$render=>{$$props.role===MessageRole.ASSISTANT&&$$props.onRegenerate&&$$render(consequent_2)})}var node_4=sibling(node_3,2);{var consequent_3=$$anchor2=>{ActionIcon($$anchor2,{get icon(){return Arrow_right},tooltip:"Continue",get onclick(){return $$props.onContinue}})};if_block(node_4,$$render=>{$$props.role===MessageRole.ASSISTANT&&$$props.onContinue&&$$render(consequent_3)})}
-var node_5=sibling(node_4,2);{var consequent_4=$$anchor2=>{ActionIcon($$anchor2,{get icon(){return Git_branch},tooltip:"Fork conversation",onclick:handleOpenForkDialog})};if_block(node_5,$$render=>{$$props.onForkConversation&&$$render(consequent_4)})}var node_6=sibling(node_5,2);ActionIcon(node_6,{get icon(){return Trash_2},tooltip:"Delete",get onclick(){return $$props.onDelete}}),reset(div_2),reset(div_1);var node_7=sibling(div_1,2);{var consequent_5=$$anchor2=>{var div_3=root_6$m(),node_8=sibling(
-child(div_3),2);Switch(node_8,{get checked(){return rawOutputEnabled()},onCheckedChange:checked=>$$props.onRawOutputToggle?.(checked)}),reset(div_3),append($$anchor2,div_3)};if_block(node_7,$$render=>{showRawOutputSwitch()&&$$render(consequent_5)})}reset(div);var node_9=sibling(div,2);{let $0=user_derived(()=>$$props.deletionInfo&&$$props.deletionInfo.totalCount>1?`This will delete ${$$props.deletionInfo.totalCount} messages including: ${$$props.deletionInfo.userMessages} user message${$$props.deletionInfo.
-userMessages>1?"s":""} and ${$$props.deletionInfo.assistantMessages} assistant response${$$props.deletionInfo.assistantMessages>1?"s":""}. All messages in this branch and their responses will be permanently removed. This action cannot be undone.`:"Are you sure you want to delete this message? This action cannot be undone."),$1=user_derived(()=>$$props.deletionInfo&&$$props.deletionInfo.totalCount>1?`Delete ${$$props.deletionInfo.totalCount} Messages`:"Delete");DialogConfirmation(node_9,{title:"D\
-elete Message",get description(){return get$4($0)},get confirmText(){return get$4($1)},cancelText:"Cancel",variant:"destructive",get icon(){return Trash_2},onConfirm:handleConfirmDelete,onCancel:()=>$$props.onShowDeleteDialogChange(!1),get open(){return showDeleteDialog()},set open($$value){showDeleteDialog($$value)}})}var node_10=sibling(node_9,2);DialogConfirmation(node_10,{title:"Fork Conversation",description:"Create a new conversation branching from this message.",confirmText:"Fork",cancelText:"\
-Cancel",get icon(){return Git_branch},onConfirm:handleConfirmFork,onCancel:()=>set$1(showForkDialog,!1),get open(){return get$4(showForkDialog)},set open($$value){set$1(showForkDialog,$$value,!0)},children:($$anchor2,$$slotProps)=>{var div_4=root_7$k(),div_5=child(div_4),node_11=child(div_5);Label(node_11,{for:"fork-name",children:($$anchor3,$$slotProps2)=>{next$1();var text2=text$8("Title");append($$anchor3,text2)},$$slots:{default:!0}});var node_12=sibling(node_11,2);Input(node_12,{id:"fork-na\
-me",class:"text-foreground",placeholder:"Enter fork name",type:"text",get value(){return get$4(forkName)},set value($$value){set$1(forkName,$$value,!0)}}),reset(div_5);var div_6=sibling(div_5,2),node_13=child(div_6);Checkbox(node_13,{id:"fork-attachments",get checked(){return get$4(forkIncludeAttachments)},onCheckedChange:checked=>{set$1(forkIncludeAttachments,checked===!0)}});var node_14=sibling(node_13,2);Label(node_14,{for:"fork-attachments",class:"cursor-pointer text-sm font-normal",children:($$anchor3,$$slotProps2)=>{
-next$1();var text_1=text$8("Include all attachments");append($$anchor3,text_1)},$$slots:{default:!0}}),reset(div_6),reset(div_4),append($$anchor2,div_4)},$$slots:{default:!0}}),template_effect(()=>{set_class(div,1,`relative ${$$props.justify==="start"?"mt-2":""} flex h-6 items-center justify-between`),set_class(div_1,1,`${$$props.actionsPosition==="left"?"left-0":"right-0"} flex items-center gap-2 opacity-100 transition-opacity`)}),append($$anchor,fragment),pop()}var root_5$n=from_html("<p>Previ\
-ous version</p>"),root_2$C=from_html("<!> <!>",1),root_9$g=from_html("<p>Next version</p>"),root_6$l=from_html("<!> <!>",1),root_1$z=from_html('<div role="navigation"><!> <span class="px-1 font-mono text-xs"> </span> <!></div>');function ChatMessageBranchingControls($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),hasPrevious=user_derived(()=>$$props.siblingInfo&&$$props.siblingInfo.currentIndex>0),hasNext=user_derived(()=>$$props.siblingInfo&&$$props.siblingInfo.currentIndex<
-$$props.siblingInfo.totalSiblings-1),nextSiblingId=user_derived(()=>get$4(hasNext)?$$props.siblingInfo.siblingIds[$$props.siblingInfo.currentIndex+1]:null),previousSiblingId=user_derived(()=>get$4(hasPrevious)?$$props.siblingInfo.siblingIds[$$props.siblingInfo.currentIndex-1]:null);function handleNext(){get$4(nextSiblingId)&&$$props.onNavigateToSibling?.(get$4(nextSiblingId))}function handlePrevious(){get$4(previousSiblingId)&&$$props.onNavigateToSibling?.(get$4(previousSiblingId))}var fragment=comment$2(),
-node2=first_child(fragment);{var consequent=$$anchor2=>{var div=root_1$z(),node_1=child(div);component(node_1,()=>Root$5,($$anchor3,Tooltip_Root)=>{Tooltip_Root($$anchor3,{children:($$anchor4,$$slotProps)=>{var fragment_1=root_2$C(),node_2=first_child(fragment_1);component(node_2,()=>Tooltip_trigger,($$anchor5,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor5,{children:($$anchor6,$$slotProps2)=>{{let $0=user_derived(()=>get$4(hasPrevious)?"":"cursor-not-allowed opacity-30"),$1=user_derived(()=>!get$4(
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-append($$anchor6,p2)},$$slots:{default:!0}})}),append($$anchor4,fragment_1)},$$slots:{default:!0}})});var span=sibling(node_1,2),text2=child(span);reset(span);var node_4=sibling(span,2);component(node_4,()=>Root$5,($$anchor3,Tooltip_Root_1)=>{Tooltip_Root_1($$anchor3,{children:($$anchor4,$$slotProps)=>{var fragment_4=root_6$l(),node_5=first_child(fragment_4);component(node_5,()=>Tooltip_trigger,($$anchor5,Tooltip_Trigger_1)=>{Tooltip_Trigger_1($$anchor5,{children:($$anchor6,$$slotProps2)=>{{let $0=user_derived(
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-Tooltip_Content_1($$anchor5,{children:($$anchor6,$$slotProps2)=>{var p_1=root_9$g();append($$anchor6,p_1)},$$slots:{default:!0}})}),append($$anchor4,fragment_4)},$$slots:{default:!0}})}),reset(div),template_effect(()=>{set_attribute(div,"aria-label",`Message version ${$$props.siblingInfo.currentIndex+1} of ${$$props.siblingInfo.totalSiblings??""}`),set_class(div,1,`flex items-center gap-1 text-xs text-muted-foreground ${className()??""}`),set_text(text2,`${$$props.siblingInfo.currentIndex+1}/${$$props.
-siblingInfo.totalSiblings??""}`)}),append($$anchor2,div)};if_block(node2,$$render=>{$$props.siblingInfo&&$$props.siblingInfo.totalSiblings>1&&$$render(consequent)})}append($$anchor,fragment),pop()}var root_3$w=from_html('<button type="button"><!> <span class="sr-only">Reading</span></button>'),root_4$j=from_html("<p>Reading (prompt processing)</p>"),root_2$B=from_html("<!> <!>",1),root_6$k=from_html('<button type="button"><!> <span class="sr-only">Generation</span></button>'),root_7$j=from_html(
-"<p> </p>"),root_5$m=from_html("<!> <!>",1),root_10$b=from_html('<button type="button"><!> <span class="sr-only">Tools</span></button>'),root_11$8=from_html("<p>Tool calls</p>"),root_9$f=from_html("<!> <!>",1),root_14$4=from_html('<button type="button"><!> <span class="sr-only">Summary</span></button>'),root_15$3=from_html("<p>Agentic summary</p>"),root_13$9=from_html("<!> <!>",1),root_8$h=from_html("<!> <!>",1),root_16$4=from_html("<!> <!> <!>",1),root_18$2=from_html("<!> <!> <!>",1),root_20$6=from_html(
-"<!> <!> <!>",1),root_22$4=from_html("<!> <!> <!>",1),root$Z=from_html('<div class="inline-flex items-center text-xs text-muted-foreground"><div class="inline-flex items-center rounded-sm bg-muted-foreground/15 p-0.5"><!> <!> <!></div> <div class="flex items-center gap-1 px-2"><!></div></div>');function ChatMessageStatistics($$anchor,$$props){push$1($$props,!0);let isLive=prop($$props,"isLive",3,!1),isProcessingPrompt=prop($$props,"isProcessingPrompt",3,!1),initialView=prop($$props,"initialView",
-19,()=>ChatMessageStatsView.GENERATION),hideSummary=prop($$props,"hideSummary",3,!1),activeView=user_derived(initialView),hasAutoSwitchedToGeneration=state$1(!1);user_effect(()=>{$$props.onActiveViewChange?.(get$4(activeView))}),user_effect(()=>{isLive()&&(!get$4(hasAutoSwitchedToGeneration)&&!isProcessingPrompt()&&$$props.predictedTokens&&$$props.predictedTokens>0?(set$1(activeView,ChatMessageStatsView.GENERATION),set$1(hasAutoSwitchedToGeneration,!0)):get$4(hasAutoSwitchedToGeneration)||set$1(
-activeView,ChatMessageStatsView.READING))});let hasGenerationStats=user_derived(()=>$$props.predictedTokens!==void 0&&$$props.predictedTokens>0&&$$props.predictedMs!==void 0&&$$props.predictedMs>0),tokensPerSecond=user_derived(()=>get$4(hasGenerationStats)?$$props.predictedTokens/$$props.predictedMs*MS_PER_SECOND:0),formattedTime=user_derived(()=>$$props.predictedMs!==void 0?formatPerformanceTime($$props.predictedMs):DEFAULT_PERFORMANCE_TIME),promptTokensPerSecond=user_derived(()=>$$props.promptTokens!==
-void 0&&$$props.promptMs!==void 0&&$$props.promptMs>0?$$props.promptTokens/$$props.promptMs*MS_PER_SECOND:void 0),formattedPromptTime=user_derived(()=>$$props.promptMs!==void 0?formatPerformanceTime($$props.promptMs):void 0),hasPromptStats=user_derived(()=>$$props.promptTokens!==void 0&&$$props.promptMs!==void 0&&get$4(promptTokensPerSecond)!==void 0&&get$4(formattedPromptTime)!==void 0),isGenerationDisabled=user_derived(()=>isLive()&&!get$4(hasGenerationStats)),hasAgenticStats=user_derived(()=>$$props.
-agenticTimings!==void 0&&$$props.agenticTimings.toolCallsCount>0),agenticToolsPerSecond=user_derived(()=>get$4(hasAgenticStats)&&$$props.agenticTimings.toolsMs>0?$$props.agenticTimings.toolCallsCount/$$props.agenticTimings.toolsMs*MS_PER_SECOND:0),formattedAgenticToolsTime=user_derived(()=>get$4(hasAgenticStats)?formatPerformanceTime($$props.agenticTimings.toolsMs):DEFAULT_PERFORMANCE_TIME),agenticTotalTimeMs=user_derived(()=>get$4(hasAgenticStats)?$$props.agenticTimings.toolsMs+$$props.agenticTimings.
-llm.predicted_ms+$$props.agenticTimings.llm.prompt_ms:0),formattedAgenticTotalTime=user_derived(()=>formatPerformanceTime(get$4(agenticTotalTimeMs)));var div=root$Z(),div_1=child(div),node2=child(div_1);{var consequent=$$anchor2=>{var fragment=comment$2(),node_1=first_child(fragment);component(node_1,()=>Root$5,($$anchor3,Tooltip_Root)=>{Tooltip_Root($$anchor3,{children:($$anchor4,$$slotProps)=>{var fragment_1=root_2$B(),node_2=first_child(fragment_1);component(node_2,()=>Tooltip_trigger,($$anchor5,Tooltip_Trigger)=>{
-Tooltip_Trigger($$anchor5,{children:($$anchor6,$$slotProps2)=>{var button=root_3$w();button.__click=()=>set$1(activeView,ChatMessageStatsView.READING);var node_3=child(button);Book_open_text(node_3,{class:"h-3 w-3"}),next$1(2),reset(button),template_effect(()=>set_class(button,1,`inline-flex h-5 w-5 items-center justify-center rounded-sm transition-colors ${get$4(activeView)===ChatMessageStatsView.READING?"bg-background text-foreground shadow-sm":"hover:text-foreground"}`)),append($$anchor6,button)},
-$$slots:{default:!0}})});var node_4=sibling(node_2,2);component(node_4,()=>Tooltip_content,($$anchor5,Tooltip_Content)=>{Tooltip_Content($$anchor5,{children:($$anchor6,$$slotProps2)=>{var p2=root_4$j();append($$anchor6,p2)},$$slots:{default:!0}})}),append($$anchor4,fragment_1)},$$slots:{default:!0}})}),append($$anchor2,fragment)};if_block(node2,$$render=>{(get$4(hasPromptStats)||isLive())&&$$render(consequent)})}var node_5=sibling(node2,2);component(node_5,()=>Root$5,($$anchor2,Tooltip_Root_1)=>{
-Tooltip_Root_1($$anchor2,{children:($$anchor3,$$slotProps)=>{var fragment_2=root_5$m(),node_6=first_child(fragment_2);component(node_6,()=>Tooltip_trigger,($$anchor4,Tooltip_Trigger_1)=>{Tooltip_Trigger_1($$anchor4,{children:($$anchor5,$$slotProps2)=>{var button_1=root_6$k();button_1.__click=()=>!get$4(isGenerationDisabled)&&set$1(activeView,ChatMessageStatsView.GENERATION);var node_7=child(button_1);Sparkles(node_7,{class:"h-3 w-3"}),next$1(2),reset(button_1),template_effect(()=>{set_class(button_1,
-1,`inline-flex h-5 w-5 items-center justify-center rounded-sm transition-colors ${get$4(activeView)===ChatMessageStatsView.GENERATION?"bg-background text-foreground shadow-sm":get$4(isGenerationDisabled)?"cursor-not-allowed opacity-40":"hover:text-foreground"}`),button_1.disabled=get$4(isGenerationDisabled)}),append($$anchor5,button_1)},$$slots:{default:!0}})});var node_8=sibling(node_6,2);component(node_8,()=>Tooltip_content,($$anchor4,Tooltip_Content_1)=>{Tooltip_Content_1($$anchor4,{children:($$anchor5,$$slotProps2)=>{
-var p_1=root_7$j(),text2=child(p_1,!0);reset(p_1),template_effect(()=>set_text(text2,get$4(isGenerationDisabled)?"Generation (waiting for tokens...)":"Generation (token output)")),append($$anchor5,p_1)},$$slots:{default:!0}})}),append($$anchor3,fragment_2)},$$slots:{default:!0}})});var node_9=sibling(node_5,2);{var consequent_2=$$anchor2=>{var fragment_3=root_8$h(),node_10=first_child(fragment_3);component(node_10,()=>Root$5,($$anchor3,Tooltip_Root_2)=>{Tooltip_Root_2($$anchor3,{children:($$anchor4,$$slotProps)=>{
-var fragment_4=root_9$f(),node_11=first_child(fragment_4);component(node_11,()=>Tooltip_trigger,($$anchor5,Tooltip_Trigger_2)=>{Tooltip_Trigger_2($$anchor5,{children:($$anchor6,$$slotProps2)=>{var button_2=root_10$b();button_2.__click=()=>set$1(activeView,ChatMessageStatsView.TOOLS);var node_12=child(button_2);Wrench(node_12,{class:"h-3 w-3"}),next$1(2),reset(button_2),template_effect(()=>set_class(button_2,1,`inline-flex h-5 w-5 items-center justify-center rounded-sm transition-colors ${get$4(activeView)===
-ChatMessageStatsView.TOOLS?"bg-background text-foreground shadow-sm":"hover:text-foreground"}`)),append($$anchor6,button_2)},$$slots:{default:!0}})});var node_13=sibling(node_11,2);component(node_13,()=>Tooltip_content,($$anchor5,Tooltip_Content_2)=>{Tooltip_Content_2($$anchor5,{children:($$anchor6,$$slotProps2)=>{var p_2=root_11$8();append($$anchor6,p_2)},$$slots:{default:!0}})}),append($$anchor4,fragment_4)},$$slots:{default:!0}})});var node_14=sibling(node_10,2);{var consequent_1=$$anchor3=>{
-var fragment_5=comment$2(),node_15=first_child(fragment_5);component(node_15,()=>Root$5,($$anchor4,Tooltip_Root_3)=>{Tooltip_Root_3($$anchor4,{children:($$anchor5,$$slotProps)=>{var fragment_6=root_13$9(),node_16=first_child(fragment_6);component(node_16,()=>Tooltip_trigger,($$anchor6,Tooltip_Trigger_3)=>{Tooltip_Trigger_3($$anchor6,{children:($$anchor7,$$slotProps2)=>{var button_3=root_14$4();button_3.__click=()=>set$1(activeView,ChatMessageStatsView.SUMMARY);var node_17=child(button_3);Layers(
-node_17,{class:"h-3 w-3"}),next$1(2),reset(button_3),template_effect(()=>set_class(button_3,1,`inline-flex h-5 w-5 items-center justify-center rounded-sm transition-colors ${get$4(activeView)===ChatMessageStatsView.SUMMARY?"bg-background text-foreground shadow-sm":"hover:text-foreground"}`)),append($$anchor7,button_3)},$$slots:{default:!0}})});var node_18=sibling(node_16,2);component(node_18,()=>Tooltip_content,($$anchor6,Tooltip_Content_3)=>{Tooltip_Content_3($$anchor6,{children:($$anchor7,$$slotProps2)=>{
-var p_3=root_15$3();append($$anchor7,p_3)},$$slots:{default:!0}})}),append($$anchor5,fragment_6)},$$slots:{default:!0}})}),append($$anchor3,fragment_5)};if_block(node_14,$$render=>{hideSummary()||$$render(consequent_1)})}append($$anchor2,fragment_3)};if_block(node_9,$$render=>{get$4(hasAgenticStats)&&$$render(consequent_2)})}reset(div_1);var div_2=sibling(div_1,2),node_19=child(div_2);{var consequent_3=$$anchor2=>{var fragment_7=root_16$4(),node_20=first_child(fragment_7);{let $0=user_derived(()=>$$props.
-predictedTokens?.toLocaleString());BadgeChatStatistic(node_20,{class:"bg-transparent",get icon(){return Whole_word},get value(){return`${get$4($0)??""} tokens`},tooltipLabel:"Generated tokens"})}var node_21=sibling(node_20,2);BadgeChatStatistic(node_21,{class:"bg-transparent",get icon(){return Clock},get value(){return get$4(formattedTime)},tooltipLabel:"Generation time"});var node_22=sibling(node_21,2);{let $0=user_derived(()=>get$4(tokensPerSecond).toFixed(2));BadgeChatStatistic(node_22,{class:"\
-bg-transparent",get icon(){return Gauge},get value(){return`${get$4($0)??""} t/s`},tooltipLabel:"Generation speed"})}append($$anchor2,fragment_7)},alternate_2=$$anchor2=>{var fragment_8=comment$2(),node_23=first_child(fragment_8);{var consequent_4=$$anchor3=>{var fragment_9=root_18$2(),node_24=first_child(fragment_9);BadgeChatStatistic(node_24,{class:"bg-transparent",get icon(){return Wrench},get value(){return`${$$props.agenticTimings.toolCallsCount??""} calls`},tooltipLabel:"Tool calls execute\
-d"});var node_25=sibling(node_24,2);BadgeChatStatistic(node_25,{class:"bg-transparent",get icon(){return Clock},get value(){return get$4(formattedAgenticToolsTime)},tooltipLabel:"Tool execution time"});var node_26=sibling(node_25,2);{let $0=user_derived(()=>get$4(agenticToolsPerSecond).toFixed(2));BadgeChatStatistic(node_26,{class:"bg-transparent",get icon(){return Gauge},get value(){return`${get$4($0)??""} calls/s`},tooltipLabel:"Tool execution rate"})}append($$anchor3,fragment_9)},alternate_1=$$anchor3=>{
-var fragment_10=comment$2(),node_27=first_child(fragment_10);{var consequent_5=$$anchor4=>{var fragment_11=root_20$6(),node_28=first_child(fragment_11);BadgeChatStatistic(node_28,{class:"bg-transparent",get icon(){return Layers},get value(){return`${$$props.agenticTimings.turns??""} turns`},tooltipLabel:"Agentic turns (LLM calls)"});var node_29=sibling(node_28,2);{let $0=user_derived(()=>$$props.agenticTimings.llm.predicted_n.toLocaleString());BadgeChatStatistic(node_29,{class:"bg-transparent",get icon(){
-return Whole_word},get value(){return`${get$4($0)??""} tokens`},tooltipLabel:"Total tokens generated"})}var node_30=sibling(node_29,2);BadgeChatStatistic(node_30,{class:"bg-transparent",get icon(){return Clock},get value(){return get$4(formattedAgenticTotalTime)},tooltipLabel:"Total time (LLM + tools)"}),append($$anchor4,fragment_11)},alternate=$$anchor4=>{var fragment_12=comment$2(),node_31=first_child(fragment_12);{var consequent_6=$$anchor5=>{var fragment_13=root_22$4(),node_32=first_child(fragment_13);
-BadgeChatStatistic(node_32,{class:"bg-transparent",get icon(){return Whole_word},get value(){return`${$$props.promptTokens??""} tokens`},tooltipLabel:"Prompt tokens"});var node_33=sibling(node_32,2);{let $0=user_derived(()=>get$4(formattedPromptTime)??"0s");BadgeChatStatistic(node_33,{class:"bg-transparent",get icon(){return Clock},get value(){return get$4($0)},tooltipLabel:"Prompt processing time"})}var node_34=sibling(node_33,2);{let $0=user_derived(()=>get$4(promptTokensPerSecond).toFixed(2));
-BadgeChatStatistic(node_34,{class:"bg-transparent",get icon(){return Gauge},get value(){return`${get$4($0)??""} tokens/s`},tooltipLabel:"Prompt processing speed"})}append($$anchor5,fragment_13)};if_block(node_31,$$render=>{get$4(hasPromptStats)&&$$render(consequent_6)},!0)}append($$anchor4,fragment_12)};if_block(node_27,$$render=>{get$4(activeView)===ChatMessageStatsView.SUMMARY&&get$4(hasAgenticStats)?$$render(consequent_5):$$render(alternate,!1)},!0)}append($$anchor3,fragment_10)};if_block(node_23,
-$$render=>{get$4(activeView)===ChatMessageStatsView.TOOLS&&get$4(hasAgenticStats)?$$render(consequent_4):$$render(alternate_1,!1)},!0)}append($$anchor2,fragment_8)};if_block(node_19,$$render=>{get$4(activeView)===ChatMessageStatsView.GENERATION&&get$4(hasGenerationStats)?$$render(consequent_3):$$render(alternate_2,!1)})}reset(div_2),reset(div),append($$anchor,div),pop()}delegate(["click"]);var root_3$v=from_html('<div class="max-w-[80%]"><!></div>'),root_2$A=from_html("<!> <!>",1),root$Y=from_html(
-'<div aria-label="MCP Prompt message with actions" role="group"><!></div>');function ChatMessageMcpPrompt($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),siblingInfo=prop($$props,"siblingInfo",3,null);const editCtx=getMessageEditContext();var div=root$Y(),node2=child(div);{var consequent=$$anchor2=>{ChatMessageEditForm($$anchor2,{})},alternate=$$anchor2=>{var fragment_1=root_2$A(),node_1=first_child(fragment_1);ChatMessageMcpPromptContent(node_1,{get prompt(){return $$props.
-mcpPrompt},get variant(){return McpPromptVariant.MESSAGE},class:"w-full max-w-[80%]"});var node_2=sibling(node_1,2);{var consequent_1=$$anchor3=>{var div_1=root_3$v(),node_3=child(div_1);ChatMessageActions(node_3,{actionsPosition:"right",get deletionInfo(){return $$props.deletionInfo},justify:"end",get onConfirmDelete(){return $$props.onConfirmDelete},get onCopy(){return $$props.onCopy},get onDelete(){return $$props.onDelete},get onEdit(){return $$props.onEdit},get onNavigateToSibling(){return $$props.
-onNavigateToSibling},get onShowDeleteDialogChange(){return $$props.onShowDeleteDialogChange},get siblingInfo(){return siblingInfo()},get showDeleteDialog(){return $$props.showDeleteDialog},get role(){return MessageRole.USER}}),reset(div_1),append($$anchor3,div_1)};if_block(node_2,$$render=>{$$props.message.timestamp&&$$render(consequent_1)})}append($$anchor2,fragment_1)};if_block(node2,$$render=>{editCtx.isEditing?$$render(consequent):$$render(alternate,!1)})}reset(div),template_effect(()=>set_class(
-div,1,`group flex flex-col items-end gap-3 md:gap-2 ${className()??""}`)),append($$anchor,div),pop()}var root$X=from_html("<div><!></div>");function Card($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$X();attribute_effect(div,$0=>({"data-slot":"card",class:$0,...restProps}),[()=>cn$1("flex flex-col gap-6 rounded-xl bg-card py-6 text-card-foreground shadow-sm",BOX_BORDER,
-$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root_1$y=from_html('<button class="absolute top-1/2 right-3 -translate-y-1/2 text-muted-foreground hover:text-foreground" type="button"><!></button>'),root_4$i=from_html('<tr><td colspan="3" class="p-8 text-center text-sm text-muted-foreground"><!></td></tr>'),root_8$g=from_html('<tr class="cursor-pointer border-b transition-\
-colors hover:bg-muted/50"><td class="p-3"><!></td><td class="p-3 text-sm"><div class="max-w-[17rem] truncate"> </div></td><td class="p-3 text-sm text-muted-foreground"> </td></tr>'),root_3$u=from_html('<table class="w-full"><thead class="sticky top-0 z-10 bg-muted"><tr class="border-b"><th class="w-12 p-3 text-left"><!></th><th class="p-3 text-left text-sm font-medium">Conversation Name</th><th class="w-32 p-3 text-left text-sm font-medium">Messages</th></tr></thead><tbody><!></tbody></table>'),
-root$W=from_html('<div class="space-y-4"><div class="relative"><!> <!> <!></div> <div class="flex items-center justify-between text-sm text-muted-foreground"><span> <!></span></div> <div class="overflow-hidden rounded-md border"><!></div> <div class="flex justify-end gap-2"><!> <!></div></div>');function ConversationSelection($$anchor,$$props){push$1($$props,!0);let messageCountMap=prop($$props,"messageCountMap",19,()=>new Map),searchQuery=state$1(""),selectedIds=state$1(getInitialSelectedIds()),
-lastClickedId=state$1(null);function getInitialSelectedIds(){return new SvelteSet($$props.conversations.map(c2=>c2.id))}let filteredConversations=user_derived(()=>$$props.conversations.filter(conv=>(conv.name||"Untitled conversation").toLowerCase().includes(get$4(searchQuery).toLowerCase()))),allSelected=user_derived(()=>get$4(filteredConversations).length>0&&get$4(filteredConversations).every(conv=>get$4(selectedIds).has(conv.id))),someSelected=user_derived(()=>get$4(filteredConversations).some(
-conv=>get$4(selectedIds).has(conv.id))&&!get$4(allSelected));function toggleConversation(id2,shiftKey=!1){const newSet=new SvelteSet(get$4(selectedIds));if(shiftKey&&get$4(lastClickedId)!==null){const lastIndex=get$4(filteredConversations).findIndex(c2=>c2.id===get$4(lastClickedId)),currentIndex=get$4(filteredConversations).findIndex(c2=>c2.id===id2);if(lastIndex!==-1&&currentIndex!==-1){const start2=Math.min(lastIndex,currentIndex),end=Math.max(lastIndex,currentIndex),shouldSelect=!newSet.has(id2);
-for(let i=start2;i<=end;i++)shouldSelect?newSet.add(get$4(filteredConversations)[i].id):newSet.delete(get$4(filteredConversations)[i].id);set$1(selectedIds,newSet);return}}newSet.has(id2)?newSet.delete(id2):newSet.add(id2),set$1(selectedIds,newSet),set$1(lastClickedId,id2,!0)}function toggleAll(){if(get$4(allSelected)){const newSet=new SvelteSet(get$4(selectedIds));get$4(filteredConversations).forEach(conv=>newSet.delete(conv.id)),set$1(selectedIds,newSet)}else{const newSet=new SvelteSet(get$4(selectedIds));
-get$4(filteredConversations).forEach(conv=>newSet.add(conv.id)),set$1(selectedIds,newSet)}}function handleConfirm(){const selected=$$props.conversations.filter(conv=>get$4(selectedIds).has(conv.id));$$props.onConfirm(selected)}function handleCancel(){set$1(selectedIds,getInitialSelectedIds()),set$1(searchQuery,""),set$1(lastClickedId,null),$$props.onCancel()}function reset$1(){set$1(selectedIds,getInitialSelectedIds()),set$1(searchQuery,""),set$1(lastClickedId,null)}var $$exports={reset:reset$1},
-div=root$W(),div_1=child(div),node2=child(div_1);Search(node2,{class:"absolute top-1/2 left-3 h-4 w-4 -translate-y-1/2 text-muted-foreground"});var node_1=sibling(node2,2);Input(node_1,{placeholder:"Search conversations...",class:"pr-9 pl-9",get value(){return get$4(searchQuery)},set value($$value){set$1(searchQuery,$$value,!0)}});var node_2=sibling(node_1,2);{var consequent=$$anchor2=>{var button=root_1$y();button.__click=()=>set$1(searchQuery,"");var node_3=child(button);X(node_3,{class:"h-4 w\
--4"}),reset(button),append($$anchor2,button)};if_block(node_2,$$render=>{get$4(searchQuery)&&$$render(consequent)})}reset(div_1);var div_2=sibling(div_1,2),span=child(div_2),text2=child(span),node_4=sibling(text2);{var consequent_1=$$anchor2=>{var text_1=text$8();template_effect(()=>set_text(text_1,`(${get$4(filteredConversations).length??""} shown)`)),append($$anchor2,text_1)};if_block(node_4,$$render=>{get$4(searchQuery)&&$$render(consequent_1)})}reset(span),reset(div_2);var div_3=sibling(div_2,
-2),node_5=child(div_3);Scroll_area(node_5,{class:"h-[400px]",children:($$anchor2,$$slotProps)=>{var table2=root_3$u(),thead2=child(table2),tr2=child(thead2),th=child(tr2),node_6=child(th);Checkbox(node_6,{get checked(){return get$4(allSelected)},get indeterminate(){return get$4(someSelected)},onCheckedChange:toggleAll}),reset(th),next$1(2),reset(tr2),reset(thead2);var tbody2=sibling(thead2),node_7=child(tbody2);{var consequent_3=$$anchor3=>{var tr_1=root_4$i(),td=child(tr_1),node_8=child(td);{var consequent_2=$$anchor4=>{
-var text_2=text$8();template_effect(()=>set_text(text_2,`No conversations found matching "${get$4(searchQuery)??""}"`)),append($$anchor4,text_2)},alternate=$$anchor4=>{var text_3=text$8("No conversations available");append($$anchor4,text_3)};if_block(node_8,$$render=>{get$4(searchQuery)?$$render(consequent_2):$$render(alternate,!1)})}reset(td),reset(tr_1),append($$anchor3,tr_1)},alternate_1=$$anchor3=>{var fragment_2=comment$2(),node_9=first_child(fragment_2);each(node_9,17,()=>get$4(filteredConversations),
-conv=>conv.id,($$anchor4,conv)=>{var tr_2=root_8$g();tr_2.__click=e=>toggleConversation(get$4(conv).id,e.shiftKey);var td_1=child(tr_2),node_10=child(td_1);{let $0=user_derived(()=>get$4(selectedIds).has(get$4(conv).id));Checkbox(node_10,{get checked(){return get$4($0)},onclick:e=>{e.preventDefault(),e.stopPropagation(),toggleConversation(get$4(conv).id,e.shiftKey)}})}reset(td_1);var td_2=sibling(td_1),div_4=child(td_2),text_4=child(div_4,!0);reset(div_4),reset(td_2);var td_3=sibling(td_2),text_5=child(
-td_3,!0);reset(td_3),reset(tr_2),template_effect($0=>{set_attribute(div_4,"title",get$4(conv).name||"Untitled conversation"),set_text(text_4,get$4(conv).name||"Untitled conversation"),set_text(text_5,$0)},[()=>messageCountMap().get(get$4(conv).id)??0]),append($$anchor4,tr_2)}),append($$anchor3,fragment_2)};if_block(node_7,$$render=>{get$4(filteredConversations).length===0?$$render(consequent_3):$$render(alternate_1,!1)})}reset(tbody2),reset(table2),append($$anchor2,table2)},$$slots:{default:!0}}),
-reset(div_3);var div_5=sibling(div_3,2),node_11=child(div_5);Button(node_11,{variant:"outline",onclick:handleCancel,children:($$anchor2,$$slotProps)=>{next$1();var text_6=text$8("Cancel");append($$anchor2,text_6)},$$slots:{default:!0}});var node_12=sibling(node_11,2);{let $0=user_derived(()=>get$4(selectedIds).size===0);Button(node_12,{onclick:handleConfirm,get disabled(){return get$4($0)},children:($$anchor2,$$slotProps)=>{next$1();var text_7=text$8();template_effect(()=>set_text(text_7,`${$$props.
-mode==="export"?"Export":"Import"} (${get$4(selectedIds).size??""})`)),append($$anchor2,text_7)},$$slots:{default:!0}})}return reset(div_5),reset(div),template_effect(()=>set_text(text2,`${get$4(selectedIds).size??""} of ${$$props.conversations.length??""} selected `)),append($$anchor,div),pop($$exports)}delegate(["click"]);var root$V=from_html('<div><button aria-label="Scroll left"><!></button> <div><!></div> <button aria-label="Scroll right"><!></button></div>');function HorizontalScrollCarousel($$anchor,$$props){
-push$1($$props,!0);let className=prop($$props,"class",3,""),gapSize=prop($$props,"gapSize",3,"3"),canScrollLeft=state$1(!1),canScrollRight=state$1(!1),scrollContainer=state$1(void 0);function scrollLeft(event2){event2?.stopPropagation(),event2?.preventDefault(),get$4(scrollContainer)&&get$4(scrollContainer).scrollBy({left:get$4(scrollContainer).clientWidth*-.67,behavior:"smooth"})}function scrollRight(event2){event2?.stopPropagation(),event2?.preventDefault(),get$4(scrollContainer)&&get$4(scrollContainer).
-scrollBy({left:get$4(scrollContainer).clientWidth*.67,behavior:"smooth"})}function updateScrollButtons(){if(!get$4(scrollContainer))return;const{scrollLeft:scrollLeft2,scrollWidth,clientWidth}=get$4(scrollContainer);set$1(canScrollLeft,scrollLeft2>0),set$1(canScrollRight,scrollLeft2<scrollWidth-clientWidth-1);const isScrollable=scrollWidth>clientWidth;$$props.onScrollableChange?.(isScrollable)}function resetScroll(){get$4(scrollContainer)&&(get$4(scrollContainer).scrollLeft=0,setTimeout(()=>{updateScrollButtons()},
-0))}user_effect(()=>{get$4(scrollContainer)&&setTimeout(()=>{updateScrollButtons()},0)});var $$exports={resetScroll},div=root$V(),button=child(div);button.__click=scrollLeft;var node2=child(button);Chevron_left(node2,{class:"h-4 w-4"}),reset(button);var div_1=sibling(button,2),node_1=child(div_1);snippet(node_1,()=>$$props.children??noop$3),reset(div_1),bind_this(div_1,$$value=>set$1(scrollContainer,$$value),()=>get$4(scrollContainer));var button_1=sibling(div_1,2);button_1.__click=scrollRight;var node_2=child(
-button_1);return Chevron_right(node_2,{class:"h-4 w-4"}),reset(button_1),reset(div),template_effect(()=>{set_class(div,1,`relative ${className()??""}`),set_class(button,1,`absolute top-1/2 left-4 z-10 flex h-6 w-6 -translate-y-1/2 items-center justify-center rounded-full bg-foreground/15 shadow-md backdrop-blur-xs transition-opacity hover:bg-foreground/35 ${get$4(canScrollLeft)?"opacity-100":"pointer-events-none opacity-0"}`),set_class(div_1,1,`scrollbar-hide flex items-start gap-${gapSize()??""}\
- overflow-x-auto`),set_class(button_1,1,`absolute top-1/2 right-4 z-10 flex h-6 w-6 -translate-y-1/2 items-center justify-center rounded-full bg-foreground/15 shadow-md backdrop-blur-xs transition-opacity hover:bg-foreground/35 ${get$4(canScrollRight)?"opacity-100":"pointer-events-none opacity-0"}`)}),event("scroll",div_1,updateScrollButtons),append($$anchor,div),pop($$exports)}delegate(["click"]);var root_3$t=from_html('<span class="block truncate"> </span>'),root_4$h=from_html("<p> </p>"),root_2$z=from_html(
-"<!> <!>",1),root_5$l=from_html("<span> </span>");function TruncatedText($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),showTooltip=prop($$props,"showTooltip",3,!0),textElement=state$1(void 0),isTruncated=state$1(!1);function checkTruncation(){get$4(textElement)&&set$1(isTruncated,get$4(textElement).scrollWidth>get$4(textElement).clientWidth)}user_effect(()=>{if(get$4(textElement)){checkTruncation();const observer=new ResizeObserver(checkTruncation);return observer.
-observe(get$4(textElement)),()=>observer.disconnect()}});var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Root$5,($$anchor3,Tooltip_Root)=>{Tooltip_Root($$anchor3,{children:($$anchor4,$$slotProps)=>{var fragment_2=root_2$z(),node_2=first_child(fragment_2);component(node_2,()=>Tooltip_trigger,($$anchor5,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor5,{get class(){return className()},children:($$anchor6,$$slotProps2)=>{
-var span=root_3$t(),text_1=child(span,!0);reset(span),bind_this(span,$$value=>set$1(textElement,$$value),()=>get$4(textElement)),template_effect(()=>set_text(text_1,$$props.text)),append($$anchor6,span)},$$slots:{default:!0}})});var node_3=sibling(node_2,2);component(node_3,()=>Tooltip_content,($$anchor5,Tooltip_Content)=>{Tooltip_Content($$anchor5,{class:"z-[9999]",children:($$anchor6,$$slotProps2)=>{var p2=root_4$h(),text_2=child(p2,!0);reset(p2),template_effect(()=>set_text(text_2,$$props.text)),
-append($$anchor6,p2)},$$slots:{default:!0}})}),append($$anchor4,fragment_2)},$$slots:{default:!0}})}),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var span_1=root_5$l(),text_3=child(span_1,!0);reset(span_1),bind_this(span_1,$$value=>set$1(textElement,$$value),()=>get$4(textElement)),template_effect(()=>{set_class(span_1,1,`${className()??""} block truncate`),set_text(text_3,$$props.text)}),append($$anchor2,span_1)};if_block(node2,$$render=>{get$4(isTruncated)&&showTooltip()?$$render(consequent):
-$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_4$g=from_html("<span>⌘</span>"),root_6$j=from_html("<span></span>"),root_1$x=from_html("<!> <!>",1),root$U=from_html("<kbd></kbd>");function KeyboardShortcutInfo($$anchor,$$props){push$1($$props,!0);let variant=prop($$props,"variant",3,"default"),className=prop($$props,"class",3,""),variantClasses=user_derived(()=>variant()==="destructive"?"text-destructive":"text-muted-foreground");var kbd=root$U();each(kbd,21,()=>$$props.keys,
-index$2,($$anchor2,key2,index2)=>{var fragment=root_1$x(),node2=first_child(fragment);{var consequent=$$anchor3=>{{let $0=user_derived(()=>variant()==="destructive"?"text-destructive":"");Arrow_big_up($$anchor3,{get class(){return`h-1 w-1 ${get$4($0)??""} -mr-1`}})}},alternate_1=$$anchor3=>{var fragment_2=comment$2(),node_1=first_child(fragment_2);{var consequent_1=$$anchor4=>{var span=root_4$g();template_effect(()=>set_class(span,1,clsx(variant()==="destructive"?"text-destructive":""))),append(
-$$anchor4,span)},alternate=$$anchor4=>{var text2=text$8();template_effect($0=>set_text(text2,$0),[()=>get$4(key2).toUpperCase()]),append($$anchor4,text2)};if_block(node_1,$$render=>{get$4(key2)==="cmd"?$$render(consequent_1):$$render(alternate,!1)},!0)}append($$anchor3,fragment_2)};if_block(node2,$$render=>{get$4(key2)==="shift"?$$render(consequent):$$render(alternate_1,!1)})}var node_2=sibling(node2,2);{var consequent_2=$$anchor3=>{var span_1=root_6$j();append($$anchor3,span_1)};if_block(node_2,
-$$render=>{index2<$$props.keys.length-1&&$$render(consequent_2)})}append($$anchor2,fragment)}),reset(kbd),template_effect(()=>set_class(kbd,1,`px-1 pointer-events-none inline-flex select-none items-center gap-0.5 font-sans text-md font-medium opacity-0 transition-opacity -my-1 ${get$4(variantClasses)??""} ${className()??""}`)),append($$anchor,kbd),pop()}var root_3$s=from_html('<img alt="" class="h-3.5 w-3.5 shrink-0 rounded-sm"/>'),root_4$f=from_html("<span> </span>"),root_1$w=from_html("<!> <!>",
-1),root_8$f=from_html("<span> </span>"),root_9$e=from_html('<span class="max-w-xs break-all"> </span>'),root_7$i=from_html("<!> <!>",1),root_5$k=from_html('<div class="flex flex-wrap justify-end gap-1"></div>'),root_11$7=from_html("<div><span> </span></div>"),root_14$3=from_html('<div><div class="space-y-2"><div class="h-3 w-3/4 animate-pulse rounded bg-foreground/20"></div> <div class="h-3 w-full animate-pulse rounded bg-foreground/20"></div> <div class="h-3 w-5/6 animate-pulse rounded bg-foreg\
-round/20"></div></div></div>'),root_19$1=from_html("<span> </span>"),root_20$5=from_html("<span> </span>"),root_17$3=from_html("<div><span></span></div>"),root$T=from_html('<div><div class="flex items-center justify-between gap-2"><div class="inline-flex flex-wrap items-center gap-1.25 text-xs text-muted-foreground"><!> <!></div> <!></div> <!></div>');function ChatMessageMcpPromptContent($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),variant=prop($$props,"variant",
-19,()=>McpPromptVariant.MESSAGE),isLoading2=prop($$props,"isLoading",3,!1),hoveredArgKey=state$1(null),argumentEntries=user_derived(()=>Object.entries($$props.prompt.arguments??{})),hasArguments=user_derived(()=>$$props.prompt.arguments&&Object.keys($$props.prompt.arguments).length>0),hasContent=user_derived(()=>$$props.prompt.content&&$$props.prompt.content.trim().length>0),contentParts=user_derived(()=>{if(!$$props.prompt.content||!get$4(hasArguments))return[{text:$$props.prompt.content||"",argKey:null}];
-const parts=[];let remaining=$$props.prompt.content;const valueToKey=new SvelteMap;for(const[key2,value]of get$4(argumentEntries))value&&value.trim()&&valueToKey.set(value,key2);const sortedValues=[...valueToKey.keys()].sort((a,b)=>b.length-a.length);for(;remaining.length>0;){let earliestMatch=null;for(const value of sortedValues){const index2=remaining.indexOf(value);index2!==-1&&(earliestMatch===null||index2<earliestMatch.index)&&(earliestMatch={index:index2,value,key:valueToKey.get(value)})}if(earliestMatch)
-earliestMatch.index>0&&parts.push({text:remaining.slice(0,earliestMatch.index),argKey:null}),parts.push({text:earliestMatch.value,argKey:earliestMatch.key}),remaining=remaining.slice(earliestMatch.index+earliestMatch.value.length);else{parts.push({text:remaining,argKey:null});break}}return parts}),showArgBadges=user_derived(()=>get$4(hasArguments)&&!isLoading2()&&!$$props.loadError),isAttachment=user_derived(()=>variant()===McpPromptVariant.ATTACHMENT),textSizeClass=user_derived(()=>get$4(isAttachment)?
-"text-xs":"text-md"),paddingClass=user_derived(()=>get$4(isAttachment)?"px-3 py-2":"px-3.75 py-2.5"),maxHeightStyle=user_derived(()=>get$4(isAttachment)?"max-height: 6rem;":"max-height: var(--max-message-height);");const serverFavicon=user_derived(()=>mcpStore.getServerFavicon($$props.prompt.serverName)),serverDisplayName=user_derived(()=>mcpStore.getServerDisplayName($$props.prompt.serverName));var div=root$T(),div_1=child(div),div_2=child(div_1),node2=child(div_2);component(node2,()=>Root$5,($$anchor2,Tooltip_Root)=>{
-Tooltip_Root($$anchor2,{children:($$anchor3,$$slotProps)=>{var fragment=root_1$w(),node_1=first_child(fragment);component(node_1,()=>Tooltip_trigger,($$anchor4,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor4,{children:($$anchor5,$$slotProps2)=>{var fragment_1=comment$2(),node_2=first_child(fragment_1);{var consequent=$$anchor6=>{var img=root_3$s();template_effect(()=>set_attribute(img,"src",get$4(serverFavicon))),event("error",img,e=>{e.currentTarget.style.display="none"}),replay_events(img),append(
-$$anchor6,img)};if_block(node_2,$$render=>{get$4(serverFavicon)&&$$render(consequent)})}append($$anchor5,fragment_1)},$$slots:{default:!0}})});var node_3=sibling(node_1,2);component(node_3,()=>Tooltip_content,($$anchor4,Tooltip_Content)=>{Tooltip_Content($$anchor4,{children:($$anchor5,$$slotProps2)=>{var span=root_4$f(),text2=child(span,!0);reset(span),template_effect(()=>set_text(text2,get$4(serverDisplayName))),append($$anchor5,span)},$$slots:{default:!0}})}),append($$anchor3,fragment)},$$slots:{
-default:!0}})});var node_4=sibling(node2,2);TruncatedText(node_4,{get text(){return $$props.prompt.name}}),reset(div_2);var node_5=sibling(div_2,2);{var consequent_1=$$anchor2=>{var div_3=root_5$k();each(div_3,21,()=>get$4(argumentEntries),([key2,value])=>key2,($$anchor3,$$item)=>{var $$array=user_derived(()=>to_array(get$4($$item),2));let key2=()=>get$4($$array)[0],value=()=>get$4($$array)[1];var fragment_2=comment$2(),node_6=first_child(fragment_2);component(node_6,()=>Root$5,($$anchor4,Tooltip_Root_1)=>{
-Tooltip_Root_1($$anchor4,{children:($$anchor5,$$slotProps)=>{var fragment_3=root_7$i(),node_7=first_child(fragment_3);component(node_7,()=>Tooltip_trigger,($$anchor6,Tooltip_Trigger_1)=>{Tooltip_Trigger_1($$anchor6,{children:($$anchor7,$$slotProps2)=>{var span_1=root_8$f(),text_1=child(span_1,!0);reset(span_1),template_effect(()=>{set_class(span_1,1,`rounded-sm bg-purple-200/60 px-1.5 py-0.5 text-[10px] leading-none text-purple-700 transition-opacity dark:bg-purple-800/40 dark:text-purple-300 ${get$4(
-hoveredArgKey)&&get$4(hoveredArgKey)!==key2()?"opacity-30":""}`),set_text(text_1,key2())}),event("mouseenter",span_1,()=>set$1(hoveredArgKey,key2(),!0)),event("mouseleave",span_1,()=>set$1(hoveredArgKey,null)),append($$anchor7,span_1)},$$slots:{default:!0}})});var node_8=sibling(node_7,2);component(node_8,()=>Tooltip_content,($$anchor6,Tooltip_Content_1)=>{Tooltip_Content_1($$anchor6,{children:($$anchor7,$$slotProps2)=>{var span_2=root_9$e(),text_2=child(span_2,!0);reset(span_2),template_effect(
-()=>set_text(text_2,value())),append($$anchor7,span_2)},$$slots:{default:!0}})}),append($$anchor5,fragment_3)},$$slots:{default:!0}})}),append($$anchor3,fragment_2)}),reset(div_3),append($$anchor2,div_3)};if_block(node_5,$$render=>{get$4(showArgBadges)&&$$render(consequent_1)})}reset(div_1);var node_9=sibling(div_1,2);{var consequent_2=$$anchor2=>{Card($$anchor2,{class:"relative overflow-hidden rounded-[1.125rem] border border-destructive/50 bg-destructive/10 backdrop-blur-md",children:($$anchor3,$$slotProps)=>{
-var div_4=root_11$7(),span_3=child(div_4),text_3=child(span_3,!0);reset(span_3),reset(div_4),template_effect(()=>{set_class(div_4,1,`overflow-y-auto ${get$4(paddingClass)??""}`),set_style(div_4,`${get$4(maxHeightStyle)??""} overflow-wrap: anywhere; word-break: break-word;`),set_class(span_3,1,`${get$4(textSizeClass)??""} text-destructive`),set_text(text_3,$$props.loadError)}),append($$anchor3,div_4)},$$slots:{default:!0}})},alternate_2=$$anchor2=>{var fragment_5=comment$2(),node_10=first_child(fragment_5);
-{var consequent_3=$$anchor3=>{Card($$anchor3,{class:"relative overflow-hidden rounded-[1.125rem] border border-purple-200 bg-purple-500/10 px-1 py-2 backdrop-blur-md dark:border-purple-800 dark:bg-purple-500/20",children:($$anchor4,$$slotProps)=>{var div_5=root_14$3();template_effect(()=>{set_class(div_5,1,`overflow-y-auto ${get$4(paddingClass)??""}`),set_style(div_5,`${get$4(maxHeightStyle)??""} overflow-wrap: anywhere; word-break: break-word;`)}),append($$anchor4,div_5)},$$slots:{default:!0}})},
-alternate_1=$$anchor3=>{var fragment_7=comment$2(),node_11=first_child(fragment_7);{var consequent_5=$$anchor4=>{Card($$anchor4,{class:"relative overflow-hidden rounded-[1.125rem] border border-purple-200 bg-purple-500/10 py-0 text-foreground backdrop-blur-md dark:border-purple-800 dark:bg-purple-500/20",children:($$anchor5,$$slotProps)=>{var div_6=root_17$3(),span_4=child(div_6);each(span_4,21,()=>get$4(contentParts),index$2,($$anchor6,part)=>{var fragment_9=comment$2(),node_12=first_child(fragment_9);
-{var consequent_4=$$anchor7=>{var span_5=root_19$1(),text_4=child(span_5,!0);reset(span_5),template_effect(()=>{set_class(span_5,1,`rounded-sm bg-purple-300/50 px-0.5 text-purple-900 transition-opacity dark:bg-purple-700/50 dark:text-purple-100 ${get$4(hoveredArgKey)&&get$4(hoveredArgKey)!==get$4(part).argKey?"opacity-30":""}`),set_text(text_4,get$4(part).text)}),event("mouseenter",span_5,()=>set$1(hoveredArgKey,get$4(part).argKey,!0)),event("mouseleave",span_5,()=>set$1(hoveredArgKey,null)),append(
-$$anchor7,span_5)},alternate=$$anchor7=>{var span_6=root_20$5(),text_5=child(span_6,!0);reset(span_6),template_effect(()=>{set_class(span_6,1,`transition-opacity ${get$4(hoveredArgKey)?"opacity-30":""}`),set_text(text_5,get$4(part).text)}),append($$anchor7,span_6)};if_block(node_12,$$render=>{get$4(part).argKey?$$render(consequent_4):$$render(alternate,!1)})}append($$anchor6,fragment_9)}),reset(span_4),reset(div_6),template_effect(()=>{set_class(div_6,1,`overflow-y-auto ${get$4(paddingClass)??""}`),
-set_style(div_6,`${get$4(maxHeightStyle)??""} overflow-wrap: anywhere; word-break: break-word;`),set_class(span_4,1,`${get$4(textSizeClass)??""} whitespace-pre-wrap`)}),append($$anchor5,div_6)},$$slots:{default:!0}})};if_block(node_11,$$render=>{get$4(hasContent)&&$$render(consequent_5)},!0)}append($$anchor3,fragment_7)};if_block(node_10,$$render=>{isLoading2()?$$render(consequent_3):$$render(alternate_1,!1)},!0)}append($$anchor2,fragment_5)};if_block(node_9,$$render=>{$$props.loadError?$$render(
-consequent_2):$$render(alternate_2,!1)})}reset(div),template_effect(()=>set_class(div,1,`flex flex-col gap-2 ${className()??""}`)),append($$anchor,div),pop()}var root_2$y=from_html("<!> Cancel",1),root_3$r=from_html("<!> Save",1),root_1$v=from_html('<div class="w-full max-w-[80%]"><textarea placeholder="Edit system message..."></textarea> <div class="mt-2 flex justify-end gap-2"><!> <!></div></div>'),root_7$h=from_html("<div><!></div>"),root_8$e=from_html("<span> </span>"),root_9$d=from_html('<d\
-iv class="pointer-events-none absolute right-0 bottom-0 left-0 h-48 bg-gradient-to-t from-muted to-transparent"></div> <div class="pointer-events-none absolute right-0 bottom-4 left-0 flex justify-center opacity-0 transition-opacity group-hover/expand:opacity-100"><!></div>',1),root_11$6=from_html('<div class="mb-2 flex justify-center"><!></div>'),root_6$i=from_html("<div><!> <!></div> <!>",1),root_5$j=from_html('<div class="relative max-w-[80%]"><button type="button"><!></button></div>'),root_13$8=from_html(
-'<div class="max-w-[80%]"><!></div>'),root_4$e=from_html("<!> <!>",1),root$S=from_html('<div aria-label="System message with actions" role="group"><!></div>');function ChatMessageSystem($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),siblingInfo=prop($$props,"siblingInfo",3,null),textareaElement=prop($$props,"textareaElement",15);const editCtx=getMessageEditContext();function handleEditKeydown(event2){event2.key===KeyboardKey.ENTER&&!event2.shiftKey&&!isIMEComposing(
-event2)?(event2.preventDefault(),editCtx.save()):event2.key===KeyboardKey.ESCAPE&&(event2.preventDefault(),editCtx.cancel())}let isMultiline=state$1(!1),messageElement=state$1(void 0),isExpanded=state$1(!1),contentHeight=state$1(0);const MAX_HEIGHT=200,currentConfig=config$1();let showExpandButton=user_derived(()=>get$4(contentHeight)>MAX_HEIGHT);user_effect(()=>{if(!get$4(messageElement)||!$$props.message.content.trim())return;$$props.message.content.includes(`
-`)&&set$1(isMultiline,!0);const resizeObserver=new ResizeObserver(entries=>{for(const entry of entries){const element2=entry.target;set$1(isMultiline,element2.offsetHeight>24*1.5),set$1(contentHeight,element2.scrollHeight,!0)}});return resizeObserver.observe(get$4(messageElement)),()=>{resizeObserver.disconnect()}});function toggleExpand(){set$1(isExpanded,!get$4(isExpanded))}var div=root$S(),node2=child(div);{var consequent=$$anchor2=>{var div_1=root_1$v(),textarea=child(div_1);remove_textarea_child(
-textarea),textarea.__keydown=handleEditKeydown,textarea.__input=e=>editCtx.setContent(e.currentTarget.value),bind_this(textarea,$$value=>textareaElement($$value),()=>textareaElement());var div_2=sibling(textarea,2),node_1=child(div_2);Button(node_1,{class:"h-8 px-3",get onclick(){return editCtx.cancel},size:"sm",variant:"outline",children:($$anchor3,$$slotProps)=>{var fragment=root_2$y(),node_2=first_child(fragment);X(node_2,{class:"mr-1 h-3 w-3"}),next$1(),append($$anchor3,fragment)},$$slots:{default:!0}});
-var node_3=sibling(node_1,2);{let $0=user_derived(()=>!editCtx.editedContent.trim());Button(node_3,{class:"h-8 px-3",get onclick(){return editCtx.save},get disabled(){return get$4($0)},size:"sm",children:($$anchor3,$$slotProps)=>{var fragment_1=root_3$r(),node_4=first_child(fragment_1);Check(node_4,{class:"mr-1 h-3 w-3"}),next$1(),append($$anchor3,fragment_1)},$$slots:{default:!0}})}reset(div_2),reset(div_1),template_effect(()=>{set_value(textarea,editCtx.editedContent),set_class(textarea,1,`min\
--h-[60px] w-full resize-none rounded-2xl px-3 py-2 text-sm ${INPUT_CLASSES??""}`)}),append($$anchor2,div_1)},alternate_1=$$anchor2=>{var fragment_2=root_4$e(),node_5=first_child(fragment_2);{var consequent_4=$$anchor3=>{var div_3=root_5$j(),button=child(div_3);button.__click=function(...$$args){(get$4(showExpandButton)&&!get$4(isExpanded)?toggleExpand:void 0)?.apply(this,$$args)};var node_6=child(button);{let $0=user_derived(()=>get$4(isMultiline)?"":void 0);Card(node_6,{class:"overflow-y-auto r\
-ounded-[1.125rem] !border-2 !border-dashed !border-border/50 bg-muted px-3.75 py-1.5 data-[multiline]:py-2.5",get"data-multiline"(){return get$4($0)},style:"border: 2px dashed hsl(var(--border)); max-height: var(--max-message-height); overflow-wrap: anywhere; word-break: break-word;",children:($$anchor4,$$slotProps)=>{var fragment_3=root_6$i(),div_4=first_child(fragment_3),node_7=child(div_4);{var consequent_1=$$anchor5=>{var div_5=root_7$h(),node_8=child(div_5);MarkdownContent(node_8,{class:"ma\
-rkdown-system-content -my-4",get content(){return $$props.message.content}}),reset(div_5),bind_this(div_5,$$value=>set$1(messageElement,$$value),()=>get$4(messageElement)),template_effect(()=>set_class(div_5,1,clsx(get$4(isExpanded)?"cursor-text":""))),append($$anchor5,div_5)},alternate=$$anchor5=>{var span=root_8$e(),text2=child(span,!0);reset(span),bind_this(span,$$value=>set$1(messageElement,$$value),()=>get$4(messageElement)),template_effect(()=>{set_class(span,1,`text-md whitespace-pre-wrap\
- ${get$4(isExpanded)?"cursor-text":""}`),set_text(text2,$$props.message.content)}),append($$anchor5,span)};if_block(node_7,$$render=>{currentConfig.renderUserContentAsMarkdown?$$render(consequent_1):$$render(alternate,!1)})}var node_9=sibling(node_7,2);{var consequent_2=$$anchor5=>{var fragment_4=root_9$d(),div_6=sibling(first_child(fragment_4),2),node_10=child(div_6);Button(node_10,{class:"rounded-full px-4 py-1.5 text-xs shadow-md",size:"sm",variant:"outline",children:($$anchor6,$$slotProps2)=>{
-next$1();var text_1=text$8("Show full system message");append($$anchor6,text_1)},$$slots:{default:!0}}),reset(div_6),append($$anchor5,fragment_4)};if_block(node_9,$$render=>{!get$4(isExpanded)&&get$4(showExpandButton)&&$$render(consequent_2)})}reset(div_4);var node_11=sibling(div_4,2);{var consequent_3=$$anchor5=>{var div_7=root_11$6(),node_12=child(div_7);Button(node_12,{class:"rounded-full px-4 py-1.5 text-xs",onclick:e=>{e.stopPropagation(),toggleExpand()},size:"sm",variant:"outline",children:($$anchor6,$$slotProps2)=>{
-next$1();var text_2=text$8("Collapse System Message");append($$anchor6,text_2)},$$slots:{default:!0}}),reset(div_7),append($$anchor5,div_7)};if_block(node_11,$$render=>{get$4(isExpanded)&&get$4(showExpandButton)&&$$render(consequent_3)})}template_effect(()=>{set_class(div_4,1,`relative transition-all duration-300 ${get$4(isExpanded)?"cursor-text select-text":"select-none"}`),set_style(div_4,!get$4(isExpanded)&&get$4(showExpandButton)?`max-height: ${MAX_HEIGHT}px;`:"max-height: none;")}),append($$anchor4,
-fragment_3)},$$slots:{default:!0}})}reset(button),reset(div_3),template_effect(()=>set_class(button,1,`group/expand w-full text-left ${!get$4(isExpanded)&&get$4(showExpandButton)?"cursor-pointer":"cursor-auto"}`)),append($$anchor3,div_3)};if_block(node_5,$$render=>{$$props.message.content.trim()&&$$render(consequent_4)})}var node_13=sibling(node_5,2);{var consequent_5=$$anchor3=>{var div_8=root_13$8(),node_14=child(div_8);ChatMessageActions(node_14,{actionsPosition:"right",get deletionInfo(){return $$props.
-deletionInfo},justify:"end",get onConfirmDelete(){return $$props.onConfirmDelete},get onCopy(){return $$props.onCopy},get onDelete(){return $$props.onDelete},get onEdit(){return $$props.onEdit},get onNavigateToSibling(){return $$props.onNavigateToSibling},get onShowDeleteDialogChange(){return $$props.onShowDeleteDialogChange},get siblingInfo(){return siblingInfo()},get showDeleteDialog(){return $$props.showDeleteDialog},get role(){return MessageRole.USER}}),reset(div_8),append($$anchor3,div_8)};
-if_block(node_13,$$render=>{$$props.message.timestamp&&$$render(consequent_5)})}append($$anchor2,fragment_2)};if_block(node2,$$render=>{editCtx.isEditing?$$render(consequent):$$render(alternate_1,!1)})}reset(div),template_effect(()=>set_class(div,1,`group flex flex-col items-end gap-3 md:gap-2 ${className()??""}`)),append($$anchor,div),pop()}delegate(["keydown","input","click"]);var root_1$u=from_html('<div class="flex items-center gap-2"><!> <label for="save-only-switch" class="cursor-pointer t\
-ext-xs text-muted-foreground">Update without re-sending</label></div>'),root_2$x=from_html("<div></div>"),root_3$q=from_html("<!> Cancel",1),root$R=from_html('<div class="relative w-full max-w-[80%]"><!></div> <div class="mt-2 flex w-full max-w-[80%] items-center justify-between"><!> <!></div> <!>',1);function ChatMessageEditForm($$anchor,$$props){push$1($$props,!0);const editCtx=getMessageEditContext();let inputAreaRef=state$1(void 0),saveWithoutRegenerate=state$1(!1),showDiscardDialog=state$1(
-!1),hasUnsavedChanges=user_derived(()=>!!(editCtx.editedContent!==editCtx.originalContent||editCtx.editedUploadedFiles.length>0||editCtx.editedExtras.length!==editCtx.originalExtras.length||editCtx.editedExtras.some((extra,i)=>extra!==editCtx.originalExtras[i]))),hasAttachments=user_derived(()=>editCtx.editedExtras&&editCtx.editedExtras.length>0||editCtx.editedUploadedFiles&&editCtx.editedUploadedFiles.length>0),canSubmit=user_derived(()=>editCtx.editedContent.trim().length>0||get$4(hasAttachments));
-function handleGlobalKeydown(event2){event2.key===KeyboardKey.ESCAPE&&(event2.preventDefault(),attemptCancel())}function attemptCancel(){get$4(hasUnsavedChanges)?set$1(showDiscardDialog,!0):editCtx.cancel()}function handleSubmit(){get$4(canSubmit)&&(get$4(saveWithoutRegenerate)&&editCtx.showSaveOnlyOption?editCtx.saveOnly():editCtx.save(),set$1(saveWithoutRegenerate,!1))}function handleAttachmentRemove(index2){const newExtras=[...editCtx.editedExtras];newExtras.splice(index2,1),editCtx.setExtras(
-newExtras)}function handleUploadedFileRemove(fileId){const newFiles=editCtx.editedUploadedFiles.filter(f=>f.id!==fileId);editCtx.setUploadedFiles(newFiles)}async function handleFilesAdd(files){const processed=await processFilesToChatUploaded(files);editCtx.setUploadedFiles([...editCtx.editedUploadedFiles,...processed])}function handleUploadedFilesChange(files){editCtx.setUploadedFiles(files)}user_effect(()=>(chatStore.setEditModeActive(handleFilesAdd),()=>{chatStore.clearEditMode()}));var fragment=root$R();
-event("keydown",$window,handleGlobalKeydown);var div=first_child(fragment),node2=child(div);bind_this(ChatForm(node2,{get value(){return editCtx.editedContent},get attachments(){return editCtx.editedExtras},get uploadedFiles(){return editCtx.editedUploadedFiles},placeholder:"Edit your message...",showMcpPromptButton:!0,get onValueChange(){return editCtx.setContent},onAttachmentRemove:handleAttachmentRemove,onUploadedFileRemove:handleUploadedFileRemove,onUploadedFilesChange:handleUploadedFilesChange,
-onFilesAdd:handleFilesAdd,onSubmit:handleSubmit}),$$value=>set$1(inputAreaRef,$$value,!0),()=>get$4(inputAreaRef)),reset(div);var div_1=sibling(div,2),node_1=child(div_1);{var consequent=$$anchor2=>{var div_2=root_1$u(),node_2=child(div_2);Switch(node_2,{id:"save-only-switch",class:"scale-75",get checked(){return get$4(saveWithoutRegenerate)},set checked($$value){set$1(saveWithoutRegenerate,$$value,!0)}}),next$1(2),reset(div_2),append($$anchor2,div_2)},alternate=$$anchor2=>{var div_3=root_2$x();
-append($$anchor2,div_3)};if_block(node_1,$$render=>{editCtx.showSaveOnlyOption?$$render(consequent):$$render(alternate,!1)})}var node_3=sibling(node_1,2);Button(node_3,{class:"h-7 px-3 text-xs",onclick:attemptCancel,size:"sm",variant:"ghost",children:($$anchor2,$$slotProps)=>{var fragment_1=root_3$q(),node_4=first_child(fragment_1);X(node_4,{class:"mr-1 h-3 w-3"}),next$1(),append($$anchor2,fragment_1)},$$slots:{default:!0}}),reset(div_1);var node_5=sibling(div_1,2);DialogConfirmation(node_5,{title:"\
-Discard changes?",description:"You have unsaved changes. Are you sure you want to discard them?",confirmText:"Discard",cancelText:"Keep editing",variant:"destructive",get icon(){return Triangle_alert},get onConfirm(){return editCtx.cancel},onCancel:()=>set$1(showDiscardDialog,!1),get open(){return get$4(showDiscardDialog)},set open($$value){set$1(showDiscardDialog,$$value,!0)}}),append($$anchor,fragment),pop()}var root_3$p=from_html('<div class="mb-2 max-w-[80%]"><!></div>'),root_6$h=from_html("\
-<div><!></div>"),root_7$g=from_html('<span class="text-md whitespace-pre-wrap"> </span>'),root_8$d=from_html('<div class="max-w-[80%]"><!></div>'),root_2$w=from_html("<!> <!> <!>",1),root$Q=from_html('<div aria-label="User message with actions" role="group"><!></div>');function ChatMessageUser($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),siblingInfo=prop($$props,"siblingInfo",3,null);const editCtx=getMessageEditContext();let isMultiline=state$1(!1),messageElement=state$1(
-void 0);const currentConfig=config$1();user_effect(()=>{if(!get$4(messageElement)||!$$props.message.content.trim())return;if($$props.message.content.includes(`
-`)){set$1(isMultiline,!0);return}const resizeObserver=new ResizeObserver(entries=>{for(const entry of entries){const element2=entry.target;set$1(isMultiline,element2.offsetHeight>24*1.5)}});return resizeObserver.observe(get$4(messageElement)),()=>{resizeObserver.disconnect()}});var div=root$Q(),node2=child(div);{var consequent=$$anchor2=>{ChatMessageEditForm($$anchor2,{})},alternate_1=$$anchor2=>{var fragment_1=root_2$w(),node_1=first_child(fragment_1);{var consequent_1=$$anchor3=>{var div_1=root_3$p(),
-node_2=child(div_1);ChatAttachmentsList(node_2,{get attachments(){return $$props.message.extra},readonly:!0,imageHeight:"h-80"}),reset(div_1),append($$anchor3,div_1)};if_block(node_1,$$render=>{$$props.message.extra&&$$props.message.extra.length>0&&$$render(consequent_1)})}var node_3=sibling(node_1,2);{var consequent_3=$$anchor3=>{{let $0=user_derived(()=>get$4(isMultiline)?"":void 0);Card($$anchor3,{class:"max-w-[80%] overflow-y-auto rounded-[1.125rem] border-none bg-primary/5 px-3.75 py-1.5 te\
-xt-foreground backdrop-blur-md data-[multiline]:py-2.5 dark:bg-primary/15",get"data-multiline"(){return get$4($0)},style:"max-height: var(--max-message-height); overflow-wrap: anywhere; word-break: break-word;",children:($$anchor4,$$slotProps)=>{var fragment_3=comment$2(),node_4=first_child(fragment_3);{var consequent_2=$$anchor5=>{var div_2=root_6$h(),node_5=child(div_2);MarkdownContent(node_5,{class:"markdown-user-content -my-4",get content(){return $$props.message.content}}),reset(div_2),bind_this(
-div_2,$$value=>set$1(messageElement,$$value),()=>get$4(messageElement)),append($$anchor5,div_2)},alternate=$$anchor5=>{var span=root_7$g(),text2=child(span,!0);reset(span),bind_this(span,$$value=>set$1(messageElement,$$value),()=>get$4(messageElement)),template_effect(()=>set_text(text2,$$props.message.content)),append($$anchor5,span)};if_block(node_4,$$render=>{currentConfig.renderUserContentAsMarkdown?$$render(consequent_2):$$render(alternate,!1)})}append($$anchor4,fragment_3)},$$slots:{default:!0}})}};
-if_block(node_3,$$render=>{$$props.message.content.trim()&&$$render(consequent_3)})}var node_6=sibling(node_3,2);{var consequent_4=$$anchor3=>{var div_3=root_8$d(),node_7=child(div_3);ChatMessageActions(node_7,{actionsPosition:"right",get deletionInfo(){return $$props.deletionInfo},justify:"end",get onConfirmDelete(){return $$props.onConfirmDelete},get onCopy(){return $$props.onCopy},get onDelete(){return $$props.onDelete},get onEdit(){return $$props.onEdit},get onForkConversation(){return $$props.
-onForkConversation},get onNavigateToSibling(){return $$props.onNavigateToSibling},get onShowDeleteDialogChange(){return $$props.onShowDeleteDialogChange},get siblingInfo(){return siblingInfo()},get showDeleteDialog(){return $$props.showDeleteDialog},get role(){return MessageRole.USER}}),reset(div_3),append($$anchor3,div_3)};if_block(node_6,$$render=>{$$props.message.timestamp&&$$render(consequent_4)})}append($$anchor2,fragment_1)};if_block(node2,$$render=>{editCtx.isEditing?$$render(consequent):
-$$render(alternate_1,!1)})}reset(div),template_effect(()=>set_class(div,1,`group flex flex-col items-end gap-3 md:gap-2 ${className()??""}`)),append($$anchor,div),pop()}function useProcessingState(){let isMonitoring=state$1(!1),lastKnownState=state$1(null),lastKnownProcessingStats=state$1(null);const processingState=user_derived(()=>get$4(isMonitoring)?activeProcessingState():get$4(lastKnownState));user_effect(()=>{get$4(processingState)&&get$4(isMonitoring)&&set$1(lastKnownState,get$4(processingState),
-!0)}),user_effect(()=>{if(get$4(processingState)?.promptProgress){const{processed,total,time_ms,cache:cache2}=get$4(processingState).promptProgress,actualProcessed=processed-cache2,actualTotal=total-cache2;if(actualProcessed>0&&time_ms>0){const tokensPerSecond=actualProcessed/(time_ms/1e3);set$1(lastKnownProcessingStats,{tokensProcessed:actualProcessed,totalTokens:actualTotal,timeMs:time_ms,tokensPerSecond},!0)}}});function getETASecs(done,total,elapsedMs){const elapsedSecs=elapsedMs/1e3;return done===
-0||elapsedSecs<.5?void 0:elapsedSecs*(total/done-1)}function startMonitoring(){get$4(isMonitoring)||set$1(isMonitoring,!0)}function stopMonitoring(){if(!get$4(isMonitoring))return;set$1(isMonitoring,!1),config$1().keepStatsVisible||(set$1(lastKnownState,null),set$1(lastKnownProcessingStats,null))}function getProcessingMessage(){if(!get$4(processingState))return"Processing...";switch(get$4(processingState).status){case"initializing":return"Initializing...";case"preparing":return get$4(processingState).
-progressPercent!==void 0?`Processing (${get$4(processingState).progressPercent}%)`:"Preparing response...";case"generating":return"";default:return"Processing..."}}function getProcessingDetails(){const stateToUse=get$4(processingState)||get$4(lastKnownState);if(!stateToUse)return[];const details=[];if(stateToUse.promptProgress){const{processed,total,time_ms,cache:cache2}=stateToUse.promptProgress,actualProcessed=processed-cache2,actualTotal=total-cache2;if(actualProcessed<actualTotal&&actualProcessed>
-0){const percent=Math.round(actualProcessed/actualTotal*100),eta=getETASecs(actualProcessed,actualTotal,time_ms);if(eta!==void 0){const etaSecs=Math.ceil(eta);details.push(`Processing ${percent}% (ETA: ${etaSecs}s)`)}else details.push(`Processing ${percent}%`)}}if(typeof stateToUse.contextTotal=="number"&&stateToUse.contextUsed>=0&&stateToUse.contextTotal>0){const contextPercent=Math.round(stateToUse.contextUsed/stateToUse.contextTotal*100);details.push(`Context: ${stateToUse.contextUsed}/${stateToUse.
-contextTotal} (${contextPercent}%)`)}if(stateToUse.outputTokensUsed>0)if(stateToUse.outputTokensMax<=0)details.push(`Output: ${stateToUse.outputTokensUsed}/∞`);else{const outputPercent=Math.round(stateToUse.outputTokensUsed/stateToUse.outputTokensMax*100);details.push(`Output: ${stateToUse.outputTokensUsed}/${stateToUse.outputTokensMax} (${outputPercent}%)`)}return stateToUse.tokensPerSecond&&stateToUse.tokensPerSecond>0&&details.push(`${stateToUse.tokensPerSecond.toFixed(1)} ${STATS_UNITS.TOKENS_PER_SECOND}`),
-stateToUse.speculative&&details.push("Speculative decoding enabled"),details}function getTechnicalDetails(){const stateToUse=get$4(processingState)||get$4(lastKnownState);if(!stateToUse)return[];const details=[];if(typeof stateToUse.contextTotal=="number"&&stateToUse.contextUsed>=0&&stateToUse.contextTotal>0){const contextPercent=Math.round(stateToUse.contextUsed/stateToUse.contextTotal*100);details.push(`Context: ${stateToUse.contextUsed}/${stateToUse.contextTotal} (${contextPercent}%)`)}if(stateToUse.
-outputTokensUsed>0)if(stateToUse.outputTokensMax<=0)details.push(`Output: ${stateToUse.outputTokensUsed}/∞`);else{const outputPercent=Math.round(stateToUse.outputTokensUsed/stateToUse.outputTokensMax*100);details.push(`Output: ${stateToUse.outputTokensUsed}/${stateToUse.outputTokensMax} (${outputPercent}%)`)}return stateToUse.tokensPerSecond&&stateToUse.tokensPerSecond>0&&details.push(`${stateToUse.tokensPerSecond.toFixed(1)} ${STATS_UNITS.TOKENS_PER_SECOND}`),stateToUse.speculative&&details.push(
-"Speculative decoding enabled"),details}function shouldShowDetails(){return get$4(processingState)!==null&&get$4(processingState).status!=="idle"}function getPromptProgressText(){if(!get$4(processingState)?.promptProgress)return null;const{processed,total,cache:cache2}=get$4(processingState).promptProgress,actualProcessed=processed-cache2,actualTotal=total-cache2,percent=Math.round(actualProcessed/actualTotal*100),eta=getETASecs(actualProcessed,actualTotal,get$4(processingState).promptProgress.time_ms);
-if(eta!==void 0){const etaSecs=Math.ceil(eta);return`Processing ${percent}% (ETA: ${etaSecs}s)`}return`Processing ${percent}%`}function getLiveProcessingStats(){if(get$4(processingState)?.promptProgress){const{processed,total,time_ms,cache:cache2}=get$4(processingState).promptProgress,actualProcessed=processed-cache2,actualTotal=total-cache2;if(actualProcessed>0&&time_ms>0){const tokensPerSecond=actualProcessed/(time_ms/1e3);return{tokensProcessed:actualProcessed,totalTokens:actualTotal,timeMs:time_ms,
-tokensPerSecond}}}return get$4(lastKnownProcessingStats)}function getLiveGenerationStats(){if(!get$4(processingState))return null;const{tokensDecoded,tokensPerSecond}=get$4(processingState);if(tokensDecoded<=0)return null;const timeMs=tokensPerSecond&&tokensPerSecond>0?tokensDecoded/tokensPerSecond*1e3:0;return{tokensGenerated:tokensDecoded,timeMs,tokensPerSecond:tokensPerSecond||0}}return{get processingState(){return get$4(processingState)},getProcessingDetails,getTechnicalDetails,getProcessingMessage,
-getPromptProgressText,getLiveProcessingStats,getLiveGenerationStats,shouldShowDetails,startMonitoring,stopMonitoring}}var root_1$t=from_html('<div class="mt-6 w-full max-w-[48rem]"><div class="processing-container svelte-14103tf"><span class="processing-text svelte-14103tf"> </span></div></div>'),root_4$d=from_html("<!> Cancel",1),root_5$i=from_html("<!> Save",1),root_2$v=from_html('<div class="w-full"><textarea placeholder="Edit assistant message..."></textarea> <div class="mt-2 flex items-cent\
-er justify-between"><div class="flex items-center space-x-2"><!> <!></div> <div class="flex gap-2"><!> <!></div></div></div>'),root_8$c=from_html('<pre class="raw-output svelte-14103tf"> </pre>'),root_10$a=from_html('<div class="text-sm whitespace-pre-wrap"> </div>'),root_11$5=from_html('<div class="mt-4 w-full max-w-[48rem]"><div class="processing-container svelte-14103tf"><span class="processing-text svelte-14103tf"> </span></div></div>'),root_12$6=from_html('<div class="inline-flex flex-wrap \
-items-start gap-2 text-xs text-muted-foreground"><!> <!></div>'),root$P=from_html('<div role="group" aria-label="Assistant message with actions"><!> <!> <!> <div class="info my-6 grid gap-4 tabular-nums"><!></div> <!></div>');function ChatMessageAssistant($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),isLastAssistantMessage=prop($$props,"isLastAssistantMessage",3,!1),toolMessages=prop($$props,"toolMessages",19,()=>[]),siblingInfo=prop($$props,"siblingInfo",3,null),
-textareaElement=prop($$props,"textareaElement",15);const editCtx=getMessageEditContext();let shouldBranchAfterEdit=state$1(!1);function handleEditKeydown(event2){event2.key===KeyboardKey.ENTER&&!event2.shiftKey&&!isIMEComposing(event2)?(event2.preventDefault(),editCtx.save()):event2.key===KeyboardKey.ESCAPE&&(event2.preventDefault(),editCtx.cancel())}const isAgentic=user_derived(()=>hasAgenticContent($$props.message,toolMessages())),hasReasoning=user_derived(()=>!!$$props.message.reasoningContent),
-processingState=useProcessingState();let currentConfig=user_derived(config$1),isRouter=user_derived(isRouterMode),showRawOutput=state$1(!1),rawOutputContent=user_derived(()=>{const sections=deriveAgenticSections($$props.message,toolMessages(),[],!1),parts=[];for(const section of sections)switch(section.type){case AgenticSectionType.REASONING:case AgenticSectionType.REASONING_PENDING:parts.push(`${REASONING_TAGS.START}
-${section.content}
-${REASONING_TAGS.END}`);break;case AgenticSectionType.TEXT:parts.push(section.content);break;case AgenticSectionType.TOOL_CALL:case AgenticSectionType.TOOL_CALL_PENDING:case AgenticSectionType.TOOL_CALL_STREAMING:{const callObj={name:section.toolName};if(section.toolArgs)try{callObj.arguments=JSON.parse(section.toolArgs)}catch{callObj.arguments=section.toolArgs}parts.push(JSON.stringify(callObj,null,2)),section.toolResult&&parts.push(`[Tool Result]
-${section.toolResult}`);break}}return parts.join(`
-
-
-`)}),activeStatsView=state$1(proxy(ChatMessageStatsView.GENERATION)),statsContainerEl=state$1(void 0);function getScrollParent(el){let parent=el.parentElement;for(;parent;){const style2=getComputedStyle(parent);if(/(auto|scroll)/.test(style2.overflowY))return parent;parent=parent.parentElement}return null}async function handleStatsViewChange(view){const el=get$4(statsContainerEl);if(!el){set$1(activeStatsView,view,!0);return}const scrollParent=getScrollParent(el);if(!scrollParent){set$1(activeStatsView,
-view,!0);return}const yBefore=el.getBoundingClientRect().top;set$1(activeStatsView,view,!0),await tick();const delta=el.getBoundingClientRect().top-yBefore;delta!==0&&(scrollParent.scrollTop+=delta),requestAnimationFrame(()=>{const drift=el.getBoundingClientRect().top-yBefore;Math.abs(drift)>1&&(scrollParent.scrollTop+=drift)})}let highlightAgenticTurns=user_derived(()=>get$4(isAgentic)&&(get$4(currentConfig).alwaysShowAgenticTurns||get$4(activeStatsView)===ChatMessageStatsView.SUMMARY)),displayedModel=user_derived(
-()=>$$props.message.model??null),isCurrentlyLoading=user_derived(isLoading),isStreaming=user_derived(isChatStreaming),hasNoContent=user_derived(()=>!$$props.message?.content?.trim()),isActivelyProcessing=user_derived(()=>get$4(isCurrentlyLoading)||get$4(isStreaming)),showProcessingInfoTop=user_derived(()=>$$props.message?.role===MessageRole.ASSISTANT&&get$4(isActivelyProcessing)&&get$4(hasNoContent)&&!get$4(isAgentic)&&isLastAssistantMessage()),showProcessingInfoBottom=user_derived(()=>$$props.message?.
-role===MessageRole.ASSISTANT&&get$4(isActivelyProcessing)&&(!get$4(hasNoContent)||get$4(isAgentic))&&isLastAssistantMessage());function handleCopyModel(){copyToClipboard(get$4(displayedModel)??"")}user_effect(()=>{editCtx.isEditing&&textareaElement()&&autoResizeTextarea(textareaElement())}),user_effect(()=>{(get$4(showProcessingInfoTop)||get$4(showProcessingInfoBottom))&&processingState.startMonitoring()});var div=root$P(),node2=child(div);{var consequent=$$anchor2=>{var div_1=root_1$t(),div_2=child(
-div_1),span=child(div_2),text2=child(span,!0);reset(span),reset(div_2),reset(div_1),template_effect($0=>set_text(text2,$0),[()=>processingState.getPromptProgressText()??processingState.getProcessingMessage()??"Processing..."]),transition(1,div_1,()=>fade),append($$anchor2,div_1)};if_block(node2,$$render=>{get$4(showProcessingInfoTop)&&$$render(consequent)})}var node_1=sibling(node2,2);{var consequent_1=$$anchor2=>{var div_3=root_2$v(),textarea=child(div_3);remove_textarea_child(textarea),textarea.
-__keydown=handleEditKeydown,textarea.__input=e=>{autoResizeTextarea(e.currentTarget),editCtx.setContent(e.currentTarget.value)},bind_this(textarea,$$value=>textareaElement($$value),()=>textareaElement());var div_4=sibling(textarea,2),div_5=child(div_4),node_2=child(div_5);Checkbox(node_2,{id:"branch-after-edit",onCheckedChange:checked=>set$1(shouldBranchAfterEdit,checked===!0),get checked(){return get$4(shouldBranchAfterEdit)},set checked($$value){set$1(shouldBranchAfterEdit,$$value,!0)}});var node_3=sibling(
-node_2,2);Label(node_3,{for:"branch-after-edit",class:"cursor-pointer text-sm text-muted-foreground",children:($$anchor3,$$slotProps)=>{next$1();var text_1=text$8("Branch conversation after edit");append($$anchor3,text_1)},$$slots:{default:!0}}),reset(div_5);var div_6=sibling(div_5,2),node_4=child(div_6);Button(node_4,{class:"h-8 px-3",get onclick(){return editCtx.cancel},size:"sm",variant:"outline",children:($$anchor3,$$slotProps)=>{var fragment=root_4$d(),node_5=first_child(fragment);X(node_5,
-{class:"mr-1 h-3 w-3"}),next$1(),append($$anchor3,fragment)},$$slots:{default:!0}});var node_6=sibling(node_4,2);{let $0=user_derived(()=>!editCtx.editedContent?.trim());Button(node_6,{class:"h-8 px-3",get onclick(){return editCtx.save},get disabled(){return get$4($0)},size:"sm",children:($$anchor3,$$slotProps)=>{var fragment_1=root_5$i(),node_7=first_child(fragment_1);Check(node_7,{class:"mr-1 h-3 w-3"}),next$1(),append($$anchor3,fragment_1)},$$slots:{default:!0}})}reset(div_6),reset(div_4),reset(
-div_3),template_effect(()=>{set_value(textarea,editCtx.editedContent),set_class(textarea,1,`min-h-[50vh] w-full resize-y rounded-2xl px-3 py-2 text-sm ${INPUT_CLASSES??""}`,"svelte-14103tf")}),append($$anchor2,div_3)},alternate_2=$$anchor2=>{var fragment_2=comment$2(),node_8=first_child(fragment_2);{var consequent_3=$$anchor3=>{var fragment_3=comment$2(),node_9=first_child(fragment_3);{var consequent_2=$$anchor4=>{var pre=root_8$c(),text_2=child(pre,!0);reset(pre),template_effect(()=>set_text(text_2,
-get$4(rawOutputContent)||"")),append($$anchor4,pre)},alternate=$$anchor4=>{{let $0=user_derived(isChatStreaming);ChatMessageAgenticContent($$anchor4,{get message(){return $$props.message},get toolMessages(){return toolMessages()},get isStreaming(){return get$4($0)},get highlightTurns(){return get$4(highlightAgenticTurns)}})}};if_block(node_9,$$render=>{get$4(showRawOutput)?$$render(consequent_2):$$render(alternate,!1)})}append($$anchor3,fragment_3)},alternate_1=$$anchor3=>{var div_7=root_10$a(),
-text_3=child(div_7,!0);reset(div_7),template_effect(()=>set_text(text_3,$$props.messageContent)),append($$anchor3,div_7)};if_block(node_8,$$render=>{$$props.message.role===MessageRole.ASSISTANT?$$render(consequent_3):$$render(alternate_1,!1)},!0)}append($$anchor2,fragment_2)};if_block(node_1,$$render=>{editCtx.isEditing?$$render(consequent_1):$$render(alternate_2,!1)})}var node_10=sibling(node_1,2);{var consequent_4=$$anchor2=>{var div_8=root_11$5(),div_9=child(div_8),span_1=child(div_9),text_4=child(
-span_1,!0);reset(span_1),reset(div_9),reset(div_8),template_effect($0=>set_text(text_4,$0),[()=>processingState.getPromptProgressText()??processingState.getProcessingMessage()??"Processing..."]),transition(1,div_8,()=>fade),append($$anchor2,div_8)};if_block(node_10,$$render=>{get$4(showProcessingInfoBottom)&&$$render(consequent_4)})}var div_10=sibling(node_10,2),node_11=child(div_10);{var consequent_9=$$anchor2=>{var div_11=root_12$6(),node_12=child(div_11);{var consequent_5=$$anchor3=>{{let $0=user_derived(
-isLoading);ModelsSelector($$anchor3,{get currentModel(){return get$4(displayedModel)},get disabled(){return get$4($0)},onModelChange:async(modelId,modelName)=>(modelsStore.getModelStatus(modelId)!==ServerModelStatus.LOADED&&await modelsStore.loadModel(modelId),$$props.onRegenerate(modelName),!0)})}},alternate_3=$$anchor3=>{{let $0=user_derived(()=>get$4(displayedModel)||void 0);ModelBadge($$anchor3,{get model(){return get$4($0)},onclick:handleCopyModel})}};if_block(node_12,$$render=>{get$4(isRouter)?
-$$render(consequent_5):$$render(alternate_3,!1)})}var node_13=sibling(node_12,2);{var consequent_6=$$anchor3=>{const agentic=user_derived(()=>$$props.message.timings.agentic);{let $0=user_derived(()=>get$4(agentic)?get$4(agentic).llm.prompt_n:$$props.message.timings.prompt_n),$1=user_derived(()=>get$4(agentic)?get$4(agentic).llm.prompt_ms:$$props.message.timings.prompt_ms),$2=user_derived(()=>get$4(agentic)?get$4(agentic).llm.predicted_n:$$props.message.timings.predicted_n),$3=user_derived(()=>get$4(
-agentic)?get$4(agentic).llm.predicted_ms:$$props.message.timings.predicted_ms);ChatMessageStatistics($$anchor3,{get promptTokens(){return get$4($0)},get promptMs(){return get$4($1)},get predictedTokens(){return get$4($2)},get predictedMs(){return get$4($3)},get agenticTimings(){return get$4(agentic)},onActiveViewChange:handleStatsViewChange})}},alternate_4=$$anchor3=>{var fragment_8=comment$2(),node_14=first_child(fragment_8);{var consequent_8=$$anchor4=>{const liveStats=user_derived(()=>processingState.
-getLiveProcessingStats()),genStats=user_derived(()=>processingState.getLiveGenerationStats()),promptProgress=user_derived(()=>processingState.processingState?.promptProgress),isStillProcessingPrompt=user_derived(()=>get$4(promptProgress)&&get$4(promptProgress).processed<get$4(promptProgress).total);var fragment_9=comment$2(),node_15=first_child(fragment_9);{var consequent_7=$$anchor5=>{{let $0=user_derived(()=>!!get$4(isStillProcessingPrompt)),$1=user_derived(()=>get$4(liveStats)?.tokensProcessed),
-$2=user_derived(()=>get$4(liveStats)?.timeMs),$3=user_derived(()=>get$4(genStats)?.tokensGenerated),$4=user_derived(()=>get$4(genStats)?.timeMs);ChatMessageStatistics($$anchor5,{isLive:!0,get isProcessingPrompt(){return get$4($0)},get promptTokens(){return get$4($1)},get promptMs(){return get$4($2)},get predictedTokens(){return get$4($3)},get predictedMs(){return get$4($4)}})}};if_block(node_15,$$render=>{(get$4(liveStats)||get$4(genStats))&&$$render(consequent_7)})}append($$anchor4,fragment_9)};
-if_block(node_14,$$render=>{isLoading()&&get$4(currentConfig).showMessageStats&&$$render(consequent_8)},!0)}append($$anchor3,fragment_8)};if_block(node_13,$$render=>{get$4(currentConfig).showMessageStats&&$$props.message.timings&&$$props.message.timings.predicted_n&&$$props.message.timings.predicted_ms?$$render(consequent_6):$$render(alternate_4,!1)})}reset(div_11),bind_this(div_11,$$value=>set$1(statsContainerEl,$$value),()=>get$4(statsContainerEl)),append($$anchor2,div_11)};if_block(node_11,$$render=>{
-get$4(displayedModel)&&$$render(consequent_9)})}reset(div_10);var node_16=sibling(div_10,2);{var consequent_10=$$anchor2=>{{let $0=user_derived(()=>get$4(currentConfig).enableContinueGeneration&&!get$4(hasReasoning)?$$props.onContinue:void 0);ChatMessageActions($$anchor2,{get role(){return MessageRole.ASSISTANT},justify:"start",actionsPosition:"left",get siblingInfo(){return siblingInfo()},get showDeleteDialog(){return $$props.showDeleteDialog},get deletionInfo(){return $$props.deletionInfo},get onCopy(){
-return $$props.onCopy},get onEdit(){return $$props.onEdit},get onRegenerate(){return $$props.onRegenerate},get onContinue(){return get$4($0)},get onForkConversation(){return $$props.onForkConversation},get onDelete(){return $$props.onDelete},get onConfirmDelete(){return $$props.onConfirmDelete},get onNavigateToSibling(){return $$props.onNavigateToSibling},get onShowDeleteDialogChange(){return $$props.onShowDeleteDialogChange},get showRawOutputSwitch(){return get$4(currentConfig).showRawOutputSwitch},
-get rawOutputEnabled(){return get$4(showRawOutput)},onRawOutputToggle:enabled=>set$1(showRawOutput,enabled,!0)})}};if_block(node_16,$$render=>{$$props.message.timestamp&&!editCtx.isEditing&&$$render(consequent_10)})}reset(div),template_effect(()=>set_class(div,1,`text-md group w-full leading-7.5 ${className()??""}`,"svelte-14103tf")),append($$anchor,div),pop()}delegate(["keydown","input"]);class AutoScrollController{#_autoScrollEnabled=state$1(!0);get _autoScrollEnabled(){return get$4(this.#_autoScrollEnabled)}set _autoScrollEnabled(value){
-set$1(this.#_autoScrollEnabled,value,!0)}#_userScrolledUp=state$1(!1);get _userScrolledUp(){return get$4(this.#_userScrolledUp)}set _userScrolledUp(value){set$1(this.#_userScrolledUp,value,!0)}#_lastScrollTop=state$1(0);get _lastScrollTop(){return get$4(this.#_lastScrollTop)}set _lastScrollTop(value){set$1(this.#_lastScrollTop,value,!0)}_scrollInterval;_scrollTimeout;_container;_disabled;_isColumnReverse;_mutationObserver=null;_rafPending=!1;_observerEnabled=!1;constructor(options={}){this._disabled=
-options.disabled??!1,this._isColumnReverse=options.isColumnReverse??!1}get autoScrollEnabled(){return this._autoScrollEnabled}get userScrolledUp(){return this._userScrolledUp}setContainer(container2){this._doStopObserving(),this._container=container2,this._observerEnabled&&container2&&!this._disabled&&this._doStartObserving()}setDisabled(disabled){this._disabled=disabled,disabled?(this._autoScrollEnabled=!1,this.stopInterval(),this._doStopObserving()):this._observerEnabled&&this._container&&!this.
-_mutationObserver&&this._doStartObserving()}handleScroll(){if(this._disabled||!this._container)return;const{scrollTop,scrollHeight,clientHeight}=this._container;let distanceFromBottom,isScrollingUp;this._isColumnReverse?(distanceFromBottom=Math.abs(scrollTop),isScrollingUp=scrollTop<this._lastScrollTop):(distanceFromBottom=scrollHeight-clientHeight-scrollTop,isScrollingUp=scrollTop<this._lastScrollTop);const isAtBottom=distanceFromBottom<AUTO_SCROLL_AT_BOTTOM_THRESHOLD;isScrollingUp&&!isAtBottom?
-(this._userScrolledUp=!0,this._autoScrollEnabled=!1):isAtBottom&&this._userScrolledUp&&(this._userScrolledUp=!1,this._autoScrollEnabled=!0),this._scrollTimeout&&clearTimeout(this._scrollTimeout),this._scrollTimeout=setTimeout(()=>{isAtBottom&&(this._userScrolledUp=!1,this._autoScrollEnabled=!0)},AUTO_SCROLL_INTERVAL),this._lastScrollTop=scrollTop}scrollToBottom(behavior="smooth"){this._disabled||!this._container||(this._isColumnReverse?this._container.scrollTo({top:0,behavior}):this._container.scrollTo(
-{top:this._container.scrollHeight,behavior}))}enable(){this._disabled||(this._userScrolledUp=!1,this._autoScrollEnabled=!0)}startInterval(){this._disabled||this._scrollInterval||(this._scrollInterval=setInterval(()=>{this.scrollToBottom()},AUTO_SCROLL_INTERVAL))}stopInterval(){this._scrollInterval&&(clearInterval(this._scrollInterval),this._scrollInterval=void 0)}updateInterval(isStreaming){if(this._disabled){this.stopInterval();return}isStreaming&&this._autoScrollEnabled?this._scrollInterval||this.
-startInterval():this.stopInterval()}destroy(){this.stopInterval(),this._doStopObserving(),this._scrollTimeout&&(clearTimeout(this._scrollTimeout),this._scrollTimeout=void 0)}startObserving(){this._observerEnabled=!0,this._container&&!this._disabled&&!this._mutationObserver&&this._doStartObserving()}stopObserving(){this._observerEnabled=!1,this._doStopObserving()}_doStartObserving(){if(!this._container||this._mutationObserver)return;const isReverse=this._isColumnReverse;this._mutationObserver=new MutationObserver(
-()=>{!this._autoScrollEnabled||this._rafPending||(this._rafPending=!0,requestAnimationFrame(()=>{this._rafPending=!1,this._autoScrollEnabled&&this._container&&(isReverse?this._container.scrollTop=0:this._container.scrollTop=this._container.scrollHeight)}))}),this._mutationObserver.observe(this._container,{childList:!0,subtree:!0,characterData:!0})}_doStopObserving(){this._mutationObserver&&(this._mutationObserver.disconnect(),this._mutationObserver=null),this._rafPending=!1}}function createAutoScrollController(options={}){
-return new AutoScrollController(options)}var root$O=from_html('<div class="pointer-events-none fixed inset-0 z-50 flex items-center justify-center bg-black/50 backdrop-blur-sm"><div class="flex flex-col items-center justify-center rounded-2xl border-2 border-dashed border-border bg-background p-12 shadow-lg"><!> <p class="text-lg font-medium text-foreground">Attach a file</p> <p class="text-sm text-muted-foreground">Drop your files here to upload</p></div></div>');function ChatScreenDragOverlay($$anchor){
-var div=root$O(),div_1=child(div),node2=child(div_1);Upload(node2,{class:"mb-4 h-12 w-12 text-muted-foreground"}),next$1(4),reset(div_1),reset(div),append($$anchor,div)}var root_5$h=from_html('<span>Server unavailable</span> <button class="flex items-center gap-1.5 rounded-lg bg-destructive/20 px-2 py-1 text-xs font-medium hover:bg-destructive/30 disabled:opacity-50"><!> </button>',1),root_4$c=from_html("<!> <!> <!>",1),root_3$o=from_html('<div class="pointer-events-auto mx-auto mb-4 max-w-[48re\
-m] px-1"><!></div>'),root_2$u=from_html('<div aria-label="Chat interface with file drop zone" class="flex h-full flex-col-reverse overflow-y-auto px-4 md:px-6" role="main"><div class="flex flex-col"><!> <div class="pointer-events-none sticky right-0 bottom-4 left-0 mt-auto"><!> <!> <div class="conversation-chat-form pointer-events-auto rounded-t-3xl svelte-lwk0qk"><!></div></div></div></div>'),root_12$5=from_html('<span>Server unavailable</span> <button class="flex items-center gap-1.5 rounded-l\
-g bg-destructive/20 px-2 py-1 text-xs font-medium hover:bg-destructive/30 disabled:opacity-50"><!> </button>',1),root_11$4=from_html("<!> <!> <!>",1),root_10$9=from_html('<div class="mb-4"><!></div>'),root_9$c=from_html('<div aria-label="Welcome screen with file drop zone" class="flex h-full items-center justify-center" role="main"><div class="w-full max-w-[48rem] px-4"><div class="mb-10 text-center"><h1 class="mb-2 text-2xl font-semibold tracking-tight md:text-3xl">llama.cpp</h1> <p class="text\
--muted-foreground md:text-lg"> </p></div> <!> <div><!></div></div></div>'),root_17$2=from_html("<!> <!>",1),root_21$3=from_html('<div class="rounded-md bg-destructive/10 px-3 py-2"><p class="font-mono text-sm break-all text-destructive"> </p> <p class="mt-1 text-xs text-muted-foreground">File type not supported</p></div>'),root_20$4=from_html('<div class="space-y-2"><h4 class="text-sm font-medium text-destructive">Unsupported File Types</h4> <div class="space-y-1"></div></div>'),root_23$2=from_html(
-'<div class="rounded-md bg-destructive/10 px-3 py-2"><p class="font-mono text-sm break-all text-destructive"> </p> <p class="mt-1 text-xs text-muted-foreground"> </p></div>'),root_22$3=from_html('<div class="space-y-2"><div class="space-y-1"></div></div>'),root_16$3=from_html('<!> <div class="!max-h-[50vh] min-h-0 flex-1 space-y-4 overflow-y-auto"><!> <!></div> <div class="rounded-md bg-muted/50 p-3"><h4 class="mb-2 text-sm font-medium">This model supports:</h4> <p class="text-sm text-muted-fore\
-ground"> </p></div> <!>',1),root_15$2=from_html("<!> <!>",1),root$N=from_html("<!> <!> <!> <!> <!> <!> <!>",1);function ChatScreen($$anchor,$$props){push$1($$props,!0);let showCenteredEmpty=prop($$props,"showCenteredEmpty",3,!1),disableAutoScroll=user_derived(()=>!!config$1().disableAutoScroll),chatScrollContainer=state$1(void 0),dragCounter=state$1(0),isDragOver=state$1(!1),showFileErrorDialog=state$1(!1),uploadedFiles=state$1(proxy([]));const autoScroll=createAutoScrollController({isColumnReverse:!0});
-let fileErrorData=state$1(proxy({generallyUnsupported:[],modalityUnsupported:[],modalityReasons:{},supportedTypes:[]})),showDeleteDialog=state$1(!1),showEmptyFileDialog=state$1(!1),emptyFileNames=state$1(proxy([])),initialMessage=state$1(""),isEmpty=user_derived(()=>showCenteredEmpty()&&!activeConversation()&&activeMessages().length===0&&!isLoading()),activeErrorDialog=user_derived(errorDialog),isServerLoading=user_derived(serverLoading),hasPropsError=user_derived(()=>!!serverError()),isCurrentConversationLoading=user_derived(
-()=>isLoading()||isChatStreaming()),isRouter=user_derived(isRouterMode),conversationModel=user_derived(()=>chatStore.getConversationModel(activeMessages())),activeModelId=user_derived(()=>{const options=modelOptions();if(!get$4(isRouter))return options.length>0?options[0].model:null;const selectedId=selectedModelId();if(selectedId){const model=options.find(m=>m.id===selectedId);if(model)return model.model}if(get$4(conversationModel)){const model=options.find(m=>m.model===get$4(conversationModel));
-if(model)return model.model}return null}),modelPropsVersion=state$1(0);user_effect(()=>{get$4(activeModelId)&&(modelsStore.getModelProps(get$4(activeModelId))||modelsStore.fetchModelProps(get$4(activeModelId)).then(()=>{update$1(modelPropsVersion)}))});let hasAudioModality=user_derived(()=>get$4(activeModelId)?(get$4(modelPropsVersion),modelsStore.modelSupportsAudio(get$4(activeModelId))):!1),hasVisionModality=user_derived(()=>get$4(activeModelId)?(get$4(modelPropsVersion),modelsStore.modelSupportsVision(
-get$4(activeModelId))):!1);async function handleDeleteConfirm(){const conversation=activeConversation();conversation&&await conversationsStore.deleteConversation(conversation.id),set$1(showDeleteDialog,!1)}function handleDragEnter(event2){event2.preventDefault(),update$1(dragCounter),event2.dataTransfer?.types.includes("Files")&&set$1(isDragOver,!0)}function handleDragLeave(event2){event2.preventDefault(),update$1(dragCounter,-1),get$4(dragCounter)===0&&set$1(isDragOver,!1)}function handleErrorDialogOpenChange(open2){
-open2||chatStore.dismissErrorDialog()}function handleDragOver(event2){event2.preventDefault()}function handleDrop(event2){if(event2.preventDefault(),set$1(isDragOver,!1),set$1(dragCounter,0),event2.dataTransfer?.files){const files=Array.from(event2.dataTransfer.files);if(isEditing()){const handler=getAddFilesHandler();if(handler){handler(files);return}}processFiles(files)}}function handleFileRemove(fileId){set$1(uploadedFiles,get$4(uploadedFiles).filter(f=>f.id!==fileId),!0)}function handleFileUpload(files){
-processFiles(files)}function handleKeydown(event2){(event2.ctrlKey||event2.metaKey)&&event2.shiftKey&&(event2.key===KeyboardKey.D_LOWER||event2.key===KeyboardKey.D_UPPER)&&(event2.preventDefault(),activeConversation()&&set$1(showDeleteDialog,!0))}async function handleSystemPromptAdd(draft){(draft.message||draft.files.length>0)&&chatStore.savePendingDraft(draft.message,draft.files),await chatStore.addSystemPrompt()}function handleScroll(){autoScroll.handleScroll()}async function handleSendMessage(message,files){
-const plainFiles=files?snapshot(files):void 0,result=plainFiles?await parseFilesToMessageExtras(plainFiles,get$4(activeModelId)??void 0):void 0;if(result?.emptyFiles&&result.emptyFiles.length>0){if(set$1(emptyFileNames,result.emptyFiles,!0),set$1(showEmptyFileDialog,!0),files){const emptyFileNamesSet=new Set(result.emptyFiles);set$1(uploadedFiles,get$4(uploadedFiles).filter(file=>!emptyFileNamesSet.has(file.name)),!0)}return!1}const extras=result?.extras;return autoScroll.enable(),await chatStore.
-sendMessage(message,extras),autoScroll.scrollToBottom(),!0}async function processFiles(files){const generallySupported=[],generallyUnsupported=[];for(const file of files)isFileTypeSupported(file.name,file.type)?generallySupported.push(file):generallyUnsupported.push(file);const capabilities={hasVision:get$4(hasVisionModality),hasAudio:get$4(hasAudioModality)},{supportedFiles,unsupportedFiles,modalityReasons}=filterFilesByModalities(generallySupported,capabilities);if([...generallyUnsupported,...unsupportedFiles].
-length>0){const supportedTypes=["text files","PDFs"];get$4(hasVisionModality)&&supportedTypes.push("images"),get$4(hasAudioModality)&&supportedTypes.push("audio files"),set$1(fileErrorData,{generallyUnsupported,modalityUnsupported:unsupportedFiles,modalityReasons,supportedTypes},!0),set$1(showFileErrorDialog,!0)}if(supportedFiles.length>0){const processed=await processFilesToChatUploaded(supportedFiles,get$4(activeModelId)??void 0);set$1(uploadedFiles,[...get$4(uploadedFiles),...processed],!0)}}
-afterNavigate(()=>{get$4(disableAutoScroll)||autoScroll.enable()}),onMount$1(()=>{autoScroll.startObserving(),get$4(disableAutoScroll)||autoScroll.enable();const pendingDraft=chatStore.consumePendingDraft();pendingDraft&&(set$1(initialMessage,pendingDraft.message,!0),set$1(uploadedFiles,pendingDraft.files,!0))}),user_effect(()=>{autoScroll.setContainer(get$4(chatScrollContainer))}),user_effect(()=>{autoScroll.setDisabled(get$4(disableAutoScroll))});var fragment=root$N();event("keydown",$window,handleKeydown);
-var node2=first_child(fragment);{var consequent=$$anchor2=>{ChatScreenDragOverlay($$anchor2)};if_block(node2,$$render=>{get$4(isDragOver)&&$$render(consequent)})}var node_1=sibling(node2,2);ChatScreenHeader(node_1,{});var node_2=sibling(node_1,2);{var consequent_2=$$anchor2=>{var div=root_2$u(),div_1=child(div),node_3=child(div_1);{let $0=user_derived(activeMessages);ChatMessages(node_3,{class:"mb-16 md:mb-24",get messages(){return get$4($0)},onUserAction:()=>{autoScroll.enable(),autoScroll.scrollToBottom()}})}
-var div_2=sibling(node_3,2),node_4=child(div_2);ChatScreenProcessingInfo(node_4,{});var node_5=sibling(node_4,2);{var consequent_1=$$anchor3=>{var div_3=root_3$o(),node_6=child(div_3);component(node_6,()=>Alert,($$anchor4,Alert_Root)=>{Alert_Root($$anchor4,{variant:"destructive",children:($$anchor5,$$slotProps)=>{var fragment_2=root_4$c(),node_7=first_child(fragment_2);Triangle_alert(node_7,{class:"h-4 w-4"});var node_8=sibling(node_7,2);component(node_8,()=>Alert_title,($$anchor6,Alert_Title)=>{
-Alert_Title($$anchor6,{class:"flex items-center justify-between",children:($$anchor7,$$slotProps2)=>{var fragment_3=root_5$h(),button=sibling(first_child(fragment_3),2);button.__click=()=>serverStore.fetch();var node_9=child(button);{let $0=user_derived(()=>get$4(isServerLoading)?"animate-spin":"");Refresh_cw(node_9,{get class(){return`h-3 w-3 ${get$4($0)??""}`}})}var text2=sibling(node_9);reset(button),template_effect(()=>{button.disabled=get$4(isServerLoading),set_text(text2,` ${get$4(isServerLoading)?
-"Retrying...":"Retry"}`)}),append($$anchor7,fragment_3)},$$slots:{default:!0}})});var node_10=sibling(node_8,2);component(node_10,()=>Alert_description,($$anchor6,Alert_Description)=>{Alert_Description($$anchor6,{children:($$anchor7,$$slotProps2)=>{next$1();var text_1=text$8();template_effect($0=>set_text(text_1,$0),[serverError]),append($$anchor7,text_1)},$$slots:{default:!0}})}),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),reset(div_3),transition(1,div_3,()=>fly,()=>({y:10,duration:250})),
-append($$anchor3,div_3)};if_block(node_5,$$render=>{get$4(hasPropsError)&&$$render(consequent_1)})}var div_4=sibling(node_5,2),node_11=child(div_4);{let $0=user_derived(()=>get$4(hasPropsError)||isEditing());ChatScreenForm(node_11,{get disabled(){return get$4($0)},get initialMessage(){return get$4(initialMessage)},get isLoading(){return get$4(isCurrentConversationLoading)},onFileRemove:handleFileRemove,onFileUpload:handleFileUpload,onSend:handleSendMessage,onStop:()=>chatStore.stopGeneration(),onSystemPromptAdd:handleSystemPromptAdd,
-showHelperText:!1,get uploadedFiles(){return get$4(uploadedFiles)},set uploadedFiles($$value){set$1(uploadedFiles,$$value,!0)}})}reset(div_4),reset(div_2),reset(div_1),reset(div),bind_this(div,$$value=>set$1(chatScrollContainer,$$value),()=>get$4(chatScrollContainer)),event("dragenter",div,handleDragEnter),event("dragleave",div,handleDragLeave),event("dragover",div,handleDragOver),event("drop",div,handleDrop),event("scroll",div,handleScroll),transition(1,div_2,()=>slide,()=>({duration:150,axis:"\
-y"})),append($$anchor2,div)},alternate_1=$$anchor2=>{var fragment_5=comment$2(),node_12=first_child(fragment_5);{var consequent_3=$$anchor3=>{ServerLoadingSplash($$anchor3,{})},alternate=$$anchor3=>{var div_5=root_9$c(),div_6=child(div_5),div_7=child(div_6),p2=sibling(child(div_7),2),text_2=child(p2);reset(p2),reset(div_7);var node_13=sibling(div_7,2);{var consequent_4=$$anchor4=>{var div_8=root_10$9(),node_14=child(div_8);component(node_14,()=>Alert,($$anchor5,Alert_Root_1)=>{Alert_Root_1($$anchor5,
-{variant:"destructive",children:($$anchor6,$$slotProps)=>{var fragment_7=root_11$4(),node_15=first_child(fragment_7);Triangle_alert(node_15,{class:"h-4 w-4"});var node_16=sibling(node_15,2);component(node_16,()=>Alert_title,($$anchor7,Alert_Title_1)=>{Alert_Title_1($$anchor7,{class:"flex items-center justify-between",children:($$anchor8,$$slotProps2)=>{var fragment_8=root_12$5(),button_1=sibling(first_child(fragment_8),2);button_1.__click=()=>serverStore.fetch();var node_17=child(button_1);{let $0=user_derived(
-()=>get$4(isServerLoading)?"animate-spin":"");Refresh_cw(node_17,{get class(){return`h-3 w-3 ${get$4($0)??""}`}})}var text_3=sibling(node_17);reset(button_1),template_effect(()=>{button_1.disabled=get$4(isServerLoading),set_text(text_3,` ${get$4(isServerLoading)?"Retrying...":"Retry"}`)}),append($$anchor8,fragment_8)},$$slots:{default:!0}})});var node_18=sibling(node_16,2);component(node_18,()=>Alert_description,($$anchor7,Alert_Description_1)=>{Alert_Description_1($$anchor7,{children:($$anchor8,$$slotProps2)=>{
-next$1();var text_4=text$8();template_effect($0=>set_text(text_4,$0),[serverError]),append($$anchor8,text_4)},$$slots:{default:!0}})}),append($$anchor6,fragment_7)},$$slots:{default:!0}})}),reset(div_8),transition(1,div_8,()=>fly,()=>({y:10,duration:250})),append($$anchor4,div_8)};if_block(node_13,$$render=>{get$4(hasPropsError)&&$$render(consequent_4)})}var div_9=sibling(node_13,2),node_19=child(div_9);ChatScreenForm(node_19,{get disabled(){return get$4(hasPropsError)},get initialMessage(){return get$4(
-initialMessage)},get isLoading(){return get$4(isCurrentConversationLoading)},onFileRemove:handleFileRemove,onFileUpload:handleFileUpload,onSend:handleSendMessage,onStop:()=>chatStore.stopGeneration(),onSystemPromptAdd:handleSystemPromptAdd,showHelperText:!0,get uploadedFiles(){return get$4(uploadedFiles)},set uploadedFiles($$value){set$1(uploadedFiles,$$value,!0)}}),reset(div_9),reset(div_6),reset(div_5),template_effect(()=>set_text(text_2,`${serverStore.props?.modalities?.audio?"Record audio, t\
-ype a message ":"Type a message"} or upload files to get started`)),event("dragenter",div_5,handleDragEnter),event("dragleave",div_5,handleDragLeave),event("dragover",div_5,handleDragOver),event("drop",div_5,handleDrop),transition(1,div_7,()=>fade,()=>({duration:300})),transition(1,div_9,()=>fly,()=>({y:10,duration:250,delay:get$4(hasPropsError)?0:300})),append($$anchor3,div_5)};if_block(node_12,$$render=>{get$4(isServerLoading)?$$render(consequent_3):$$render(alternate,!1)},!0)}append($$anchor2,
-fragment_5)};if_block(node_2,$$render=>{get$4(isEmpty)?$$render(alternate_1,!1):$$render(consequent_2)})}var node_20=sibling(node_2,2);component(node_20,()=>Root$3,($$anchor2,AlertDialog_Root)=>{AlertDialog_Root($$anchor2,{get open(){return get$4(showFileErrorDialog)},set open($$value){set$1(showFileErrorDialog,$$value,!0)},children:($$anchor3,$$slotProps)=>{var fragment_10=comment$2(),node_21=first_child(fragment_10);component(node_21,()=>Portal,($$anchor4,AlertDialog_Portal)=>{AlertDialog_Portal(
-$$anchor4,{children:($$anchor5,$$slotProps2)=>{var fragment_11=root_15$2(),node_22=first_child(fragment_11);component(node_22,()=>Alert_dialog_overlay,($$anchor6,AlertDialog_Overlay)=>{AlertDialog_Overlay($$anchor6,{})});var node_23=sibling(node_22,2);component(node_23,()=>Alert_dialog_content,($$anchor6,AlertDialog_Content)=>{AlertDialog_Content($$anchor6,{class:"flex max-w-md flex-col",children:($$anchor7,$$slotProps3)=>{var fragment_12=root_16$3(),node_24=first_child(fragment_12);component(node_24,
-()=>Alert_dialog_header,($$anchor8,AlertDialog_Header)=>{AlertDialog_Header($$anchor8,{children:($$anchor9,$$slotProps4)=>{var fragment_13=root_17$2(),node_25=first_child(fragment_13);component(node_25,()=>Alert_dialog_title,($$anchor10,AlertDialog_Title)=>{AlertDialog_Title($$anchor10,{children:($$anchor11,$$slotProps5)=>{next$1();var text_5=text$8("File Upload Error");append($$anchor11,text_5)},$$slots:{default:!0}})});var node_26=sibling(node_25,2);component(node_26,()=>Alert_dialog_description,
-($$anchor10,AlertDialog_Description)=>{AlertDialog_Description($$anchor10,{class:"text-sm text-muted-foreground",children:($$anchor11,$$slotProps5)=>{next$1();var text_6=text$8("Some files cannot be uploaded with the current model.");append($$anchor11,text_6)},$$slots:{default:!0}})}),append($$anchor9,fragment_13)},$$slots:{default:!0}})});var div_10=sibling(node_24,2),node_27=child(div_10);{var consequent_5=$$anchor8=>{var div_11=root_20$4(),div_12=sibling(child(div_11),2);each(div_12,21,()=>get$4(
-fileErrorData).generallyUnsupported,file=>file.name,($$anchor9,file)=>{var div_13=root_21$3(),p_1=child(div_13),text_7=child(p_1,!0);reset(p_1),next$1(2),reset(div_13),template_effect(()=>set_text(text_7,get$4(file).name)),append($$anchor9,div_13)}),reset(div_12),reset(div_11),append($$anchor8,div_11)};if_block(node_27,$$render=>{get$4(fileErrorData).generallyUnsupported.length>0&&$$render(consequent_5)})}var node_28=sibling(node_27,2);{var consequent_6=$$anchor8=>{var div_14=root_22$3(),div_15=child(
-div_14);each(div_15,21,()=>get$4(fileErrorData).modalityUnsupported,file=>file.name,($$anchor9,file)=>{var div_16=root_23$2(),p_2=child(div_16),text_8=child(p_2,!0);reset(p_2);var p_3=sibling(p_2,2),text_9=child(p_3,!0);reset(p_3),reset(div_16),template_effect(()=>{set_text(text_8,get$4(file).name),set_text(text_9,get$4(fileErrorData).modalityReasons[get$4(file).name]||"Not supported by current model")}),append($$anchor9,div_16)}),reset(div_15),reset(div_14),append($$anchor8,div_14)};if_block(node_28,
-$$render=>{get$4(fileErrorData).modalityUnsupported.length>0&&$$render(consequent_6)})}reset(div_10);var div_17=sibling(div_10,2),p_4=sibling(child(div_17),2),text_10=child(p_4,!0);reset(p_4),reset(div_17);var node_29=sibling(div_17,2);component(node_29,()=>Alert_dialog_footer,($$anchor8,AlertDialog_Footer)=>{AlertDialog_Footer($$anchor8,{children:($$anchor9,$$slotProps4)=>{var fragment_14=comment$2(),node_30=first_child(fragment_14);component(node_30,()=>Alert_dialog_action,($$anchor10,AlertDialog_Action)=>{
-AlertDialog_Action($$anchor10,{onclick:()=>set$1(showFileErrorDialog,!1),children:($$anchor11,$$slotProps5)=>{next$1();var text_11=text$8("Got it");append($$anchor11,text_11)},$$slots:{default:!0}})}),append($$anchor9,fragment_14)},$$slots:{default:!0}})}),template_effect($0=>set_text(text_10,$0),[()=>get$4(fileErrorData).supportedTypes.join(", ")]),append($$anchor7,fragment_12)},$$slots:{default:!0}})}),append($$anchor5,fragment_11)},$$slots:{default:!0}})}),append($$anchor3,fragment_10)},$$slots:{
-default:!0}})});var node_31=sibling(node_20,2);DialogConfirmation(node_31,{title:"Delete Conversation",description:"Are you sure you want to delete this conversation? This action cannot be undone and will permanently remove all messages in this conversation.",confirmText:"Delete",cancelText:"Cancel",variant:"destructive",get icon(){return Trash_2},onConfirm:handleDeleteConfirm,onCancel:()=>set$1(showDeleteDialog,!1),get open(){return get$4(showDeleteDialog)},set open($$value){set$1(showDeleteDialog,
-$$value,!0)}});var node_32=sibling(node_31,2);DialogEmptyFileAlert(node_32,{get emptyFiles(){return get$4(emptyFileNames)},onOpenChange:open2=>{open2||set$1(emptyFileNames,[],!0)},get open(){return get$4(showEmptyFileDialog)},set open($$value){set$1(showEmptyFileDialog,$$value,!0)}});var node_33=sibling(node_32,2);{let $0=user_derived(()=>get$4(activeErrorDialog)?.message??""),$1=user_derived(()=>get$4(activeErrorDialog)?.contextInfo),$2=user_derived(()=>!!get$4(activeErrorDialog)),$3=user_derived(
-()=>get$4(activeErrorDialog)?.type??ErrorDialogType.SERVER);DialogChatError(node_33,{get message(){return get$4($0)},get contextInfo(){return get$4($1)},onOpenChange:handleErrorDialogOpenChange,get open(){return get$4($2)},get type(){return get$4($3)}})}append($$anchor,fragment),pop()}delegate(["click"]);var root$M=from_html('<div class="relative mx-auto max-w-[48rem]"><!></div> <!>',1);function ChatScreenForm($$anchor,$$props){push$1($$props,!0);let disabled=prop($$props,"disabled",3,!1),initialMessage=prop(
-$$props,"initialMessage",3,""),isLoading2=prop($$props,"isLoading",3,!1),showHelperText=prop($$props,"showHelperText",3,!0),uploadedFiles=prop($$props,"uploadedFiles",31,()=>proxy([])),chatFormRef=state$1(void 0),message=user_derived(initialMessage),previousIsLoading=user_derived(isLoading2),previousInitialMessage=user_derived(initialMessage);user_effect(()=>{initialMessage()!==get$4(previousInitialMessage)&&(set$1(message,initialMessage()),set$1(previousInitialMessage,initialMessage()))});function handleSystemPromptClick(){
-$$props.onSystemPromptAdd?.({message:get$4(message),files:uploadedFiles()})}let hasLoadingAttachments=user_derived(()=>uploadedFiles().some(f=>f.isLoading));async function handleSubmit(){if(!get$4(message).trim()&&uploadedFiles().length===0||disabled()||isLoading2()||get$4(hasLoadingAttachments)||!get$4(chatFormRef)?.checkModelSelected())return;const messageToSend=get$4(message).trim(),filesToSend=[...uploadedFiles()];set$1(message,""),uploadedFiles([]),get$4(chatFormRef)?.resetTextareaHeight(),
-await $$props.onSend?.(messageToSend,filesToSend)||(set$1(message,messageToSend),uploadedFiles(filesToSend))}function handleFilesAdd(files){$$props.onFileUpload?.(files)}function handleUploadedFileRemove(fileId){$$props.onFileRemove?.(fileId)}onMount$1(()=>{setTimeout(()=>get$4(chatFormRef)?.focus(),10)}),afterNavigate(()=>{setTimeout(()=>get$4(chatFormRef)?.focus(),10)}),user_effect(()=>{get$4(previousIsLoading)&&!isLoading2()&&setTimeout(()=>get$4(chatFormRef)?.focus(),10),set$1(previousIsLoading,
-isLoading2())});var fragment=root$M(),div=first_child(fragment),node2=child(div);bind_this(ChatForm(node2,{get class(){return $$props.class},get disabled(){return disabled()},get isLoading(){return isLoading2()},showMcpPromptButton:!0,onFilesAdd:handleFilesAdd,get onStop(){return $$props.onStop},onSubmit:handleSubmit,onSystemPromptClick:handleSystemPromptClick,onUploadedFileRemove:handleUploadedFileRemove,get value(){return get$4(message)},set value($$value){set$1(message,$$value)},get uploadedFiles(){
-return uploadedFiles()},set uploadedFiles($$value){uploadedFiles($$value)}}),$$value=>set$1(chatFormRef,$$value,!0),()=>get$4(chatFormRef)),reset(div);var node_1=sibling(div,2);ChatFormHelperText(node_1,{get show(){return showHelperText()}}),append($$anchor,fragment),pop()}const SIDEBAR_COOKIE_NAME="sidebar:state",SIDEBAR_COOKIE_MAX_AGE=3600*24*7,SIDEBAR_WIDTH="18rem",SIDEBAR_WIDTH_MOBILE="18rem",SIDEBAR_WIDTH_ICON="3rem",SIDEBAR_KEYBOARD_SHORTCUT="b";class SidebarState{props;#open=user_derived(
-()=>this.props.open());get open(){return get$4(this.#open)}set open(value){set$1(this.#open,value)}#openMobile=state$1(!1);get openMobile(){return get$4(this.#openMobile)}set openMobile(value){set$1(this.#openMobile,value,!0)}setOpen;#isMobile;#state=user_derived(()=>this.open?"expanded":"collapsed");get state(){return get$4(this.#state)}set state(value){set$1(this.#state,value)}constructor(props){this.setOpen=props.setOpen,this.#isMobile=new IsMobile,this.props=props}get isMobile(){return this.#isMobile.
-current}handleShortcutKeydown=e=>{e.key===SIDEBAR_KEYBOARD_SHORTCUT&&(e.metaKey||e.ctrlKey)&&(e.preventDefault(),this.toggle())};setOpenMobile=value=>{this.openMobile=value};toggle=()=>this.#isMobile.current?this.openMobile=!this.openMobile:this.setOpen(!this.open)}const SYMBOL_KEY="scn-sidebar";function setSidebar(props){return setContext(Symbol.for(SYMBOL_KEY),new SidebarState(props))}function useSidebar(){return getContext(Symbol.for(SYMBOL_KEY))}var root$L=from_html("<div><!></div>");function Sidebar_group_content($$anchor,$$props){
-push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$L();attribute_effect(div,$0=>({"data-slot":"sidebar-group-content","data-sidebar":"group-content",class:$0,...restProps}),[()=>cn$1("w-full text-sm",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root_2$t=from_html("\
-<div><!></div>");function Sidebar_group_label($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","children","child","class"]);const mergedProps=user_derived(()=>({class:cn$1("text-sidebar-foreground/70 ring-sidebar-ring outline-hidden flex h-8 shrink-0 items-center rounded-md px-2 text-xs font-medium transition-[margin,opacity] duration-200 ease-linear focus-visible:ring-2 [&>svg]:size-4 [&>svg]:shrink-0",
-"group-data-[collapsible=icon]:-mt-8 group-data-[collapsible=icon]:opacity-0",$$props.class),"data-slot":"sidebar-group-label","data-sidebar":"group-label",...restProps}));var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.child,()=>({props:get$4(mergedProps)})),append($$anchor2,fragment_1)},alternate=$$anchor2=>{var div=root_2$t();attribute_effect(div,()=>({...get$4(mergedProps)}));
-var node_2=child(div);snippet(node_2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor2,div)};if_block(node2,$$render=>{$$props.child?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root$K=from_html("<div><!></div>");function Sidebar_group($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var div=root$K();
-attribute_effect(div,$0=>({"data-slot":"sidebar-group","data-sidebar":"group",class:$0,...restProps}),[()=>cn$1("relative flex w-full min-w-0 flex-col p-2",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root$J=from_html("<div><!></div>");function Sidebar_header($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots",
-"$$events","$$legacy","ref","class","children"]);var div=root$J();attribute_effect(div,$0=>({"data-slot":"sidebar-header","data-sidebar":"header",class:$0,...restProps}),[()=>cn$1("flex flex-col gap-2 p-2",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root$I=from_html("<main><!></main>");function Sidebar_inset($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"r\
-ef",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var main2=root$I();attribute_effect(main2,$0=>({"data-slot":"sidebar-inset",class:$0,...restProps}),[()=>cn$1("relative flex w-full flex-1 flex-col","md:peer-data-[variant=inset]:m-2 md:peer-data-[variant=inset]:ml-0 md:peer-data-[variant=inset]:rounded-xl md:peer-data-[variant=inset]:shadow-sm md:peer-data-[variant=inset]:peer-data-[state=collapsed]:ml-2",$$props.class)]);var node2=child(main2);
-snippet(node2,()=>$$props.children??noop$3),reset(main2),bind_this(main2,$$value=>ref2($$value),()=>ref2()),append($$anchor,main2),pop()}tv({base:"peer/menu-button outline-hidden ring-sidebar-ring hover:bg-sidebar-accent hover:text-sidebar-accent-foreground active:bg-sidebar-accent active:text-sidebar-accent-foreground group-has-data-[sidebar=menu-action]/menu-item:pr-8 data-[active=true]:bg-sidebar-accent data-[active=true]:text-sidebar-accent-foreground data-[state=open]:hover:bg-sidebar-accen\
-t data-[state=open]:hover:text-sidebar-accent-foreground group-data-[collapsible=icon]:size-8! group-data-[collapsible=icon]:p-2! flex w-full items-center gap-2 overflow-hidden rounded-md p-2 text-left text-sm transition-[width,height,padding] focus-visible:ring-2 disabled:pointer-events-none disabled:opacity-50 aria-disabled:pointer-events-none aria-disabled:opacity-50 data-[active=true]:font-medium [&>span:last-child]:truncate [&>svg]:size-4 [&>svg]:shrink-0",variants:{variant:{default:"hover:\
-bg-sidebar-accent hover:text-sidebar-accent-foreground",outline:"bg-background hover:bg-sidebar-accent hover:text-sidebar-accent-foreground shadow-[0_0_0_1px_var(--sidebar-border)] hover:shadow-[0_0_0_1px_var(--sidebar-accent)]"},size:{default:"h-8 text-sm",sm:"h-7 text-xs",lg:"group-data-[collapsible=icon]:p-0! h-12 text-sm"}},defaultVariants:{variant:"default",size:"default"}});var root$H=from_html("<li><!></li>");function Sidebar_menu_item($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,
-"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","children"]);var li2=root$H();attribute_effect(li2,$0=>({"data-slot":"sidebar-menu-item","data-sidebar":"menu-item",class:$0,...restProps}),[()=>cn$1("group/menu-item relative",$$props.class)]);var node2=child(li2);snippet(node2,()=>$$props.children??noop$3),reset(li2),bind_this(li2,$$value=>ref2($$value),()=>ref2()),append($$anchor,li2),pop()}var root$G=from_html("<div></div>");function Skeleton($$anchor,$$props){
-push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var div=root$G();attribute_effect(div,$0=>({"data-slot":"skeleton",class:$0,...restProps}),[()=>cn$1("animate-pulse rounded-md bg-accent",$$props.class)]),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root$F=from_html("<ul><!></ul>");function Sidebar_menu($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props(
-$$props,["$$slots","$$events","$$legacy","ref","class","children"]);var ul=root$F();attribute_effect(ul,$0=>({"data-slot":"sidebar-menu","data-sidebar":"menu",class:$0,...restProps}),[()=>cn$1("flex w-full min-w-0 flex-col gap-1",$$props.class)]);var node2=child(ul);snippet(node2,()=>$$props.children??noop$3),reset(ul),bind_this(ul,$$value=>ref2($$value),()=>ref2()),append($$anchor,ul),pop()}var root$E=from_html("<div><!></div>");function Sidebar_provider($$anchor,$$props){push$1($$props,!0);let ref2=prop(
-$$props,"ref",15,null),open2=prop($$props,"open",15,!0),onOpenChange=prop($$props,"onOpenChange",3,()=>{}),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","open","onOpenChange","class","style","children"]);const sidebar=setSidebar({open:()=>open2(),setOpen:value=>{open2(value),onOpenChange()(value),document.cookie=`${SIDEBAR_COOKIE_NAME}=${open2()}; path=/; max-age=${SIDEBAR_COOKIE_MAX_AGE}`}});var div=root$E();event("keydown",$window,function(...$$args){sidebar.handleShortcutKeydown?.
-apply(this,$$args)}),attribute_effect(div,$0=>({"data-slot":"sidebar-wrapper",style:`--sidebar-width: ${SIDEBAR_WIDTH}; --sidebar-width-icon: ${SIDEBAR_WIDTH_ICON}; ${$$props.style??""}`,class:$0,...restProps}),[()=>cn$1("group/sidebar-wrapper flex min-h-svh w-full has-data-[variant=inset]:bg-sidebar",$$props.class)]);var node2=child(div);snippet(node2,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor,div),pop()}var root_1$s=from_html('<!> \
-<span class="sr-only">Toggle Sidebar</span>',1);function Sidebar_trigger($$anchor,$$props){push$1($$props,!0),prop($$props,"ref",11,null);let restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","onclick"]);const sidebar=useSidebar();{let $0=user_derived(()=>$$props.class),$1=user_derived(()=>sidebar.open?"unset":"2");Button($$anchor,spread_props({"data-sidebar":"trigger","data-slot":"sidebar-trigger",variant:"ghost",size:"icon-lg",get class(){return`rounded-full backdrop-\
-blur-lg ${get$4($0)??""} md:left-${get$4($1)??""} -top-2 -left-2 md:top-0`},type:"button",onclick:e=>{$$props.onclick?.(e),sidebar.toggle()}},()=>restProps,{children:($$anchor2,$$slotProps)=>{var fragment_1=root_1$s(),node2=first_child(fragment_1);Panel_left(node2,{}),next$1(2),append($$anchor2,fragment_1)},$$slots:{default:!0}}))}pop()}var root_1$r=from_html("<div><!></div>"),root_6$g=from_html("<!> <!>",1),root_5$g=from_html('<!> <div class="flex h-full w-full flex-col"><!></div>',1),root_9$b=from_html(
-'<div class="group peer hidden text-sidebar-foreground md:block" data-slot="sidebar"><div data-slot="sidebar-gap"></div> <div><div data-sidebar="sidebar" data-slot="sidebar-inner" class="flex h-full w-full flex-col bg-sidebar group-data-[variant=floating]:rounded-lg group-data-[variant=floating]:border group-data-[variant=floating]:border-sidebar-border group-data-[variant=floating]:shadow-sm"><!></div></div></div>');function Sidebar($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"re\
-f",15,null),side=prop($$props,"side",3,"left"),variant=prop($$props,"variant",3,"sidebar"),collapsible=prop($$props,"collapsible",3,"offcanvas"),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","side","variant","collapsible","class","children"]);const sidebar=useSidebar();var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{var div=root_1$r();attribute_effect(div,$0=>({class:$0,...restProps}),[()=>cn$1("flex h-full w-(--sidebar-width) flex-col bg-si\
-debar text-sidebar-foreground",$$props.class)]);var node_1=child(div);snippet(node_1,()=>$$props.children??noop$3),reset(div),bind_this(div,$$value=>ref2($$value),()=>ref2()),append($$anchor2,div)},alternate_1=$$anchor2=>{var fragment_1=comment$2(),node_2=first_child(fragment_1);{var consequent_1=$$anchor3=>{var fragment_2=comment$2(),node_3=first_child(fragment_2),bind_get=()=>sidebar.openMobile,bind_set=v=>sidebar.setOpenMobile(v);component(node_3,()=>Root$1,($$anchor4,Sheet_Root)=>{Sheet_Root(
-$$anchor4,spread_props({get open(){return bind_get()},set open($$value){bind_set($$value)}},()=>restProps,{children:($$anchor5,$$slotProps)=>{var fragment_3=comment$2(),node_4=first_child(fragment_3);component(node_4,()=>Sheet_content,($$anchor6,Sheet_Content)=>{Sheet_Content($$anchor6,{"data-sidebar":"sidebar","data-slot":"sidebar","data-mobile":"true",class:"z-99999 w-(--sidebar-width) bg-sidebar p-0 text-sidebar-foreground sm:z-99 [&>button]:hidden",get style(){return`--sidebar-width: ${SIDEBAR_WIDTH_MOBILE}\
-;`},get side(){return side()},children:($$anchor7,$$slotProps2)=>{var fragment_4=root_5$g(),node_5=first_child(fragment_4);component(node_5,()=>Sheet_header,($$anchor8,Sheet_Header)=>{Sheet_Header($$anchor8,{class:"sr-only",children:($$anchor9,$$slotProps3)=>{var fragment_5=root_6$g(),node_6=first_child(fragment_5);component(node_6,()=>Sheet_title,($$anchor10,Sheet_Title)=>{Sheet_Title($$anchor10,{children:($$anchor11,$$slotProps4)=>{next$1();var text2=text$8("Sidebar");append($$anchor11,text2)},
-$$slots:{default:!0}})});var node_7=sibling(node_6,2);component(node_7,()=>Sheet_description,($$anchor10,Sheet_Description)=>{Sheet_Description($$anchor10,{children:($$anchor11,$$slotProps4)=>{next$1();var text_1=text$8("Displays the mobile sidebar.");append($$anchor11,text_1)},$$slots:{default:!0}})}),append($$anchor9,fragment_5)},$$slots:{default:!0}})});var div_1=sibling(node_5,2),node_8=child(div_1);snippet(node_8,()=>$$props.children??noop$3),reset(div_1),append($$anchor7,fragment_4)},$$slots:{
-default:!0}})}),append($$anchor5,fragment_3)},$$slots:{default:!0}}))}),append($$anchor3,fragment_2)},alternate=$$anchor3=>{var div_2=root_9$b(),div_3=child(div_2),div_4=sibling(div_3,2);attribute_effect(div_4,$0=>({"data-slot":"sidebar-container",class:$0,...restProps}),[()=>cn$1("fixed inset-y-0 z-999 hidden h-svh w-(--sidebar-width) transition-[left,right,width] duration-200 ease-linear md:z-0 md:flex",side()==="left"?"left-0 group-data-[collapsible=offcanvas]:left-[calc(var(--sidebar-width)*\
--1)]":"right-0 group-data-[collapsible=offcanvas]:right-[calc(var(--sidebar-width)*-1)]",variant()==="floating"||variant()==="inset"?"p-2 group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]":"group-data-[collapsible=icon]:w-(--sidebar-width-icon)",$$props.class)]);var div_5=child(div_4),node_9=child(div_5);snippet(node_9,()=>$$props.children??noop$3),reset(div_5),reset(div_4),reset(div_2),bind_this(div_2,$$value=>ref2($$value),()=>ref2()),template_effect($0=>{set_attribute(
-div_2,"data-state",sidebar.state),set_attribute(div_2,"data-collapsible",sidebar.state==="collapsed"?collapsible():""),set_attribute(div_2,"data-variant",variant()),set_attribute(div_2,"data-side",side()),set_class(div_3,1,$0)},[()=>clsx(cn$1("relative w-(--sidebar-width) bg-transparent transition-[width] duration-200 ease-linear","group-data-[collapsible=offcanvas]:w-0","group-data-[side=right]:rotate-180",variant()==="floating"||variant()==="inset"?"group-data-[collapsible=icon]:w-[calc(var(--\
-sidebar-width-icon)+(--spacing(4))+2px)]":"group-data-[collapsible=icon]:w-(--sidebar-width-icon)"))]),append($$anchor3,div_2)};if_block(node_2,$$render=>{sidebar.isMobile?$$render(consequent_1):$$render(alternate,!1)},!0)}append($$anchor2,fragment_1)};if_block(node2,$$render=>{collapsible()==="none"?$$render(consequent):$$render(alternate_1,!1)})}append($$anchor,fragment),pop()}var root$D=from_html('<header><div class="pointer-events-auto flex items-center space-x-2"><!></div></header>');function ChatScreenHeader($$anchor,$$props){
-push$1($$props,!1);const sidebar=useSidebar(),chatSettingsDialog=getChatSettingsDialogContext();init();var header=root$D(),div=child(header),node2=child(div);Button(node2,{variant:"ghost",size:"icon-lg",onclick:()=>chatSettingsDialog.open(),class:"rounded-full backdrop-blur-lg",children:($$anchor2,$$slotProps)=>{Settings$1($$anchor2,{class:"h-4 w-4"})},$$slots:{default:!0}}),reset(div),reset(header),template_effect(()=>set_class(header,1,`pointer-events-none fixed top-0 right-0 left-0 z-50 flex \
-items-center justify-end p-2 duration-200 ease-linear md:p-4 ${sidebar.open?"md:left-[var(--sidebar-width)]":""}`)),append($$anchor,header),pop()}var root_1$q=from_html('<span class="chat-processing-info-detail pointer-events-auto backdrop-blur-sm svelte-1ktvj8d"> </span>'),root$C=from_html('<div><div class="chat-processing-info-content svelte-1ktvj8d"></div></div>');function ChatScreenProcessingInfo($$anchor,$$props){push$1($$props,!0);const processingState=useProcessingState();let isCurrentConversationLoading=user_derived(
-isLoading),isStreaming=user_derived(isChatStreaming),hasProcessingData=user_derived(()=>processingState.processingState!==null),processingDetails=user_derived(()=>processingState.getTechnicalDetails()),showProcessingInfo=user_derived(()=>get$4(isCurrentConversationLoading)||get$4(isStreaming)||config$1().keepStatsVisible||get$4(hasProcessingData));user_effect(()=>{const conversation=activeConversation();untrack$1(()=>chatStore.setActiveProcessingConversation(conversation?.id??null))}),user_effect(
-()=>{const keepStatsVisible=config$1().keepStatsVisible;if((keepStatsVisible||get$4(isCurrentConversationLoading)||get$4(isStreaming))&&processingState.startMonitoring(),!get$4(isCurrentConversationLoading)&&!get$4(isStreaming)&&!keepStatsVisible){const timeout=setTimeout(()=>{!config$1().keepStatsVisible&&!isChatStreaming()&&processingState.stopMonitoring()},PROCESSING_INFO_TIMEOUT);return()=>clearTimeout(timeout)}}),user_effect(()=>{const conversation=activeConversation(),messages=activeMessages();
-if(config$1().keepStatsVisible&&conversation){if(messages.length===0){untrack$1(()=>chatStore.clearProcessingState(conversation.id));return}!get$4(isCurrentConversationLoading)&&!get$4(isStreaming)&&untrack$1(()=>chatStore.restoreProcessingStateFromMessages(messages,conversation.id))}});var div=root$C();let classes;var div_1=child(div);each(div_1,20,()=>get$4(processingDetails),detail=>detail,($$anchor2,detail)=>{var span=root_1$q(),text2=child(span,!0);reset(span),template_effect(()=>set_text(text2,
-detail)),append($$anchor2,span)}),reset(div_1),reset(div),template_effect(()=>classes=set_class(div,1,"chat-processing-info-container pointer-events-none svelte-1ktvj8d",null,classes,{visible:get$4(showProcessingInfo)})),append($$anchor,div),pop()}var root_1$p=from_html('<button><!> <span class="ml-2"> </span></button>'),root_2$s=from_html("<button><!> <span> </span></button>"),root_6$f=from_html('<div class="space-y-6"><!> <div class="border-t border-border/30 pt-6"><!></div></div>'),root_7$f=from_html(
-'<div class="space-y-6"><!></div>'),root_3$n=from_html(`<div class="space-y-6 p-4 md:p-6"><div class="grid"><div class="mb-6 flex hidden items-center gap-2 border-b border-border/30 pb-6 md:flex"><!> <h3 class="text-lg font-semibold"> </h3></div> <!></div> <div class="mt-8 border-t pt-6"><p class="text-xs text-muted-foreground">Settings are saved in browser's localStorage</p></div></div>`),root$B=from_html('<div class="flex h-full flex-col overflow-hidden md:flex-row"><div class="hidden w-64 bor\
-der-r border-border/30 p-6 md:block"><nav class="space-y-1 py-2"></nav></div> <div class="flex flex-col pt-6 md:hidden"><div class="border-b border-border/30 pt-4 md:py-4"><div class="relative flex items-center" style="scroll-padding: 1rem;"><button aria-label="Scroll left"><!></button> <div class="scrollbar-hide overflow-x-auto py-2"><div class="flex min-w-max gap-2"></div></div> <button aria-label="Scroll right"><!></button></div></div></div> <!></div> <!>',1);function ChatSettings($$anchor,$$props){
-push$1($$props,!0);const settingSections=[{title:SETTINGS_SECTION_TITLES.GENERAL,icon:Settings$1,fields:[{key:SETTINGS_KEYS.THEME,label:"Theme",type:SettingsFieldType.SELECT,options:SETTINGS_COLOR_MODES_CONFIG},{key:SETTINGS_KEYS.API_KEY,label:"API Key",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.SYSTEM_MESSAGE,label:"System Message",type:SettingsFieldType.TEXTAREA},{key:SETTINGS_KEYS.PASTE_LONG_TEXT_TO_FILE_LEN,label:"Paste long text to file length",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.
-SEND_ON_ENTER,label:"Send message on Enter",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.COPY_TEXT_ATTACHMENTS_AS_PLAIN_TEXT,label:"Copy text attachments as plain text",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.ENABLE_CONTINUE_GENERATION,label:'Enable "Continue" button',type:SettingsFieldType.CHECKBOX,isExperimental:!0},{key:SETTINGS_KEYS.PDF_AS_IMAGE,label:"Parse PDF as image",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.ASK_FOR_TITLE_CONFIRMATION,label:"Ask for confirma\
-tion before changing conversation title",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.TITLE_GENERATION_USE_FIRST_LINE,label:"Use first non-empty line for conversation title",type:SettingsFieldType.CHECKBOX}]},{title:SETTINGS_SECTION_TITLES.DISPLAY,icon:Monitor,fields:[{key:SETTINGS_KEYS.SHOW_MESSAGE_STATS,label:"Show message generation statistics",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.SHOW_THOUGHT_IN_PROGRESS,label:"Show thought in progress",type:SettingsFieldType.CHECKBOX},
-{key:SETTINGS_KEYS.KEEP_STATS_VISIBLE,label:"Keep stats visible after generation",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.AUTO_MIC_ON_EMPTY,label:"Show microphone on empty input",type:SettingsFieldType.CHECKBOX,isExperimental:!0},{key:SETTINGS_KEYS.RENDER_USER_CONTENT_AS_MARKDOWN,label:"Render user content as Markdown",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.FULL_HEIGHT_CODE_BLOCKS,label:"Use full height code blocks",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.DISABLE_AUTO_SCROLL,
-label:"Disable automatic scroll",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.ALWAYS_SHOW_SIDEBAR_ON_DESKTOP,label:"Always show sidebar on desktop",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.AUTO_SHOW_SIDEBAR_ON_NEW_CHAT,label:"Auto-show sidebar on new chat",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.SHOW_RAW_MODEL_NAMES,label:"Show raw model names",type:SettingsFieldType.CHECKBOX}]},{title:SETTINGS_SECTION_TITLES.SAMPLING,icon:Funnel,fields:[{key:SETTINGS_KEYS.TEMPERATURE,
-label:"Temperature",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.DYNATEMP_RANGE,label:"Dynamic temperature range",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.DYNATEMP_EXPONENT,label:"Dynamic temperature exponent",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.TOP_K,label:"Top K",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.TOP_P,label:"Top P",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.MIN_P,label:"Min P",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.XTC_PROBABILITY,label:"\
-XTC probability",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.XTC_THRESHOLD,label:"XTC threshold",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.TYP_P,label:"Typical P",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.MAX_TOKENS,label:"Max tokens",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.SAMPLERS,label:"Samplers",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.BACKEND_SAMPLING,label:"Backend sampling",type:SettingsFieldType.CHECKBOX}]},{title:SETTINGS_SECTION_TITLES.PENALTIES,icon:Triangle_alert,
-fields:[{key:SETTINGS_KEYS.REPEAT_LAST_N,label:"Repeat last N",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.REPEAT_PENALTY,label:"Repeat penalty",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.PRESENCE_PENALTY,label:"Presence penalty",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.FREQUENCY_PENALTY,label:"Frequency penalty",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.DRY_MULTIPLIER,label:"DRY multiplier",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.DRY_BASE,label:"DRY base",type:SettingsFieldType.
-INPUT},{key:SETTINGS_KEYS.DRY_ALLOWED_LENGTH,label:"DRY allowed length",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.DRY_PENALTY_LAST_N,label:"DRY penalty last N",type:SettingsFieldType.INPUT}]},{title:SETTINGS_SECTION_TITLES.IMPORT_EXPORT,icon:Database,fields:[]},{title:SETTINGS_SECTION_TITLES.MCP,icon:McpLogo,fields:[{key:SETTINGS_KEYS.AGENTIC_MAX_TURNS,label:"Agentic loop max turns",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.ALWAYS_SHOW_AGENTIC_TURNS,label:"Always show agentic turns\
- in conversation",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.AGENTIC_MAX_TOOL_PREVIEW_LINES,label:"Max lines per tool preview",type:SettingsFieldType.INPUT},{key:SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS,label:"Show tool call in progress",type:SettingsFieldType.CHECKBOX}]},{title:SETTINGS_SECTION_TITLES.DEVELOPER,icon:Code,fields:[{key:SETTINGS_KEYS.PRE_ENCODE_CONVERSATION,label:"Pre-fill KV cache after response",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.DISABLE_REASONING_PARSING,
-label:"Disable server-side thinking extraction",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.EXCLUDE_REASONING_FROM_CONTEXT,label:"Strip thinking from message history",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.SHOW_RAW_OUTPUT_SWITCH,label:"Enable raw output toggle",type:SettingsFieldType.CHECKBOX},{key:SETTINGS_KEYS.CUSTOM,label:"Custom JSON",type:SettingsFieldType.TEXTAREA}]}];let activeSection=user_derived(()=>$$props.initialSection??SETTINGS_SECTION_TITLES.GENERAL),currentSection=user_derived(
-()=>settingSections.find(section=>section.title===get$4(activeSection))||settingSections[0]),localConfig=state$1(proxy({...config$1()})),canScrollLeft=state$1(!1),canScrollRight=state$1(!1),scrollContainer=state$1(void 0);user_effect(()=>{$$props.initialSection&&set$1(activeSection,$$props.initialSection)});function handleThemeChange(newTheme){get$4(localConfig).theme=newTheme,setMode(newTheme)}function handleConfigChange(key2,value){get$4(localConfig)[key2]=value}function handleReset(){set$1(localConfig,
-{...config$1()},!0),setMode(get$4(localConfig).theme)}function handleSave(){if(get$4(localConfig).custom&&typeof get$4(localConfig).custom=="string"&&get$4(localConfig).custom.trim())try{JSON.parse(get$4(localConfig).custom)}catch(error2){alert("Invalid JSON in custom parameters. Please check the format and try again."),console.error(error2);return}const processedConfig={...get$4(localConfig)};for(const field of NUMERIC_FIELDS)if(processedConfig[field]!==void 0&&processedConfig[field]!==""){const numValue=Number(
-processedConfig[field]);if(!isNaN(numValue))POSITIVE_INTEGER_FIELDS.includes(field)?processedConfig[field]=Math.max(1,Math.round(numValue)):processedConfig[field]=numValue;else{alert(`Invalid numeric value for ${field}. Please enter a valid number.`);return}}settingsStore.updateMultipleConfig(processedConfig),$$props.onSave?.()}function scrollToCenter(element2){if(!get$4(scrollContainer))return;const containerRect=get$4(scrollContainer).getBoundingClientRect(),elementRect=element2.getBoundingClientRect(),
-elementCenter=elementRect.left+elementRect.width/2,containerCenter=containerRect.left+containerRect.width/2,scrollOffset=elementCenter-containerCenter;get$4(scrollContainer).scrollBy({left:scrollOffset,behavior:"smooth"})}function scrollLeft(){get$4(scrollContainer)&&get$4(scrollContainer).scrollBy({left:-250,behavior:"smooth"})}function scrollRight(){get$4(scrollContainer)&&get$4(scrollContainer).scrollBy({left:250,behavior:"smooth"})}function updateScrollButtons(){if(!get$4(scrollContainer))return;
-const{scrollLeft:scrollLeft2,scrollWidth,clientWidth}=get$4(scrollContainer);set$1(canScrollLeft,scrollLeft2>0),set$1(canScrollRight,scrollLeft2<scrollWidth-clientWidth-1)}function reset$1(){set$1(localConfig,{...config$1()},!0),setTimeout(updateScrollButtons,100)}user_effect(()=>{get$4(scrollContainer)&&updateScrollButtons()});var $$exports={reset:reset$1},fragment=root$B(),div=first_child(fragment),div_1=child(div),nav=child(div_1);each(nav,21,()=>settingSections,section=>section.title,($$anchor2,section)=>{
-var button=root_1$p();button.__click=()=>set$1(activeSection,get$4(section).title);var node2=child(button);component(node2,()=>get$4(section).icon,($$anchor3,section_icon)=>{section_icon($$anchor3,{class:"h-4 w-4"})});var span=sibling(node2,2),text2=child(span,!0);reset(span),reset(button),template_effect(()=>{set_class(button,1,`flex w-full cursor-pointer items-center gap-3 rounded-lg px-3 py-2 text-left text-sm transition-colors hover:bg-accent ${get$4(activeSection)===get$4(section).title?"bg\
--accent text-accent-foreground":"text-muted-foreground"}`),set_text(text2,get$4(section).title)}),append($$anchor2,button)}),reset(nav),reset(div_1);var div_2=sibling(div_1,2),div_3=child(div_2),div_4=child(div_3),button_1=child(div_4);button_1.__click=scrollLeft;var node_1=child(button_1);Chevron_left(node_1,{class:"h-4 w-4"}),reset(button_1);var div_5=sibling(button_1,2),div_6=child(div_5);each(div_6,21,()=>settingSections,section=>section.title,($$anchor2,section)=>{var button_2=root_2$s();button_2.
-__click=e=>{set$1(activeSection,get$4(section).title),scrollToCenter(e.currentTarget)};var node_2=child(button_2);component(node_2,()=>get$4(section).icon,($$anchor3,section_icon_1)=>{section_icon_1($$anchor3,{class:"h-4 w-4 flex-shrink-0"})});var span_1=sibling(node_2,2),text_1=child(span_1,!0);reset(span_1),reset(button_2),template_effect(()=>{set_class(button_2,1,`flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm whitespace-nowrap transition-colors first:ml-4 last:mr-4 hover\
-:bg-accent ${get$4(activeSection)===get$4(section).title?"bg-accent text-accent-foreground":"text-muted-foreground"}`),set_text(text_1,get$4(section).title)}),append($$anchor2,button_2)}),reset(div_6),reset(div_5),bind_this(div_5,$$value=>set$1(scrollContainer,$$value),()=>get$4(scrollContainer));var button_3=sibling(div_5,2);button_3.__click=scrollRight;var node_3=child(button_3);Chevron_right(node_3,{class:"h-4 w-4"}),reset(button_3),reset(div_4),reset(div_3),reset(div_2);var node_4=sibling(div_2,
-2);Scroll_area(node_4,{class:"max-h-[calc(100dvh-13.5rem)] flex-1 md:max-h-[calc(100vh-13.5rem)]",children:($$anchor2,$$slotProps)=>{var div_7=root_3$n(),div_8=child(div_7),div_9=child(div_8),node_5=child(div_9);component(node_5,()=>get$4(currentSection).icon,($$anchor3,currentSection_icon)=>{currentSection_icon($$anchor3,{class:"h-5 w-5"})});var h3=sibling(node_5,2),text_2=child(h3,!0);reset(h3),reset(div_9);var node_6=sibling(div_9,2);{var consequent=$$anchor3=>{ChatSettingsImportExportTab($$anchor3,
-{})},alternate_1=$$anchor3=>{var fragment_2=comment$2(),node_7=first_child(fragment_2);{var consequent_1=$$anchor4=>{var div_10=root_6$f(),node_8=child(div_10);ChatSettingsFields(node_8,{get fields(){return get$4(currentSection).fields},get localConfig(){return get$4(localConfig)},onConfigChange:handleConfigChange,onThemeChange:handleThemeChange});var div_11=sibling(node_8,2),node_9=child(div_11);McpServersSettings(node_9,{}),reset(div_11),reset(div_10),append($$anchor4,div_10)},alternate=$$anchor4=>{
-var div_12=root_7$f(),node_10=child(div_12);ChatSettingsFields(node_10,{get fields(){return get$4(currentSection).fields},get localConfig(){return get$4(localConfig)},onConfigChange:handleConfigChange,onThemeChange:handleThemeChange}),reset(div_12),append($$anchor4,div_12)};if_block(node_7,$$render=>{get$4(currentSection).title===SETTINGS_SECTION_TITLES.MCP?$$render(consequent_1):$$render(alternate,!1)},!0)}append($$anchor3,fragment_2)};if_block(node_6,$$render=>{get$4(currentSection).title===SETTINGS_SECTION_TITLES.
-IMPORT_EXPORT?$$render(consequent):$$render(alternate_1,!1)})}reset(div_8),next$1(2),reset(div_7),template_effect(()=>set_text(text_2,get$4(currentSection).title)),append($$anchor2,div_7)},$$slots:{default:!0}}),reset(div);var node_11=sibling(div,2);return ChatSettingsFooter(node_11,{onReset:handleReset,onSave:handleSave}),template_effect(()=>{set_class(button_1,1,`absolute left-2 z-10 flex h-6 w-6 items-center justify-center rounded-full bg-muted shadow-md backdrop-blur-sm transition-opacity ho\
-ver:bg-accent ${get$4(canScrollLeft)?"opacity-100":"pointer-events-none opacity-0"}`),set_class(button_3,1,`absolute right-2 z-10 flex h-6 w-6 items-center justify-center rounded-full bg-muted shadow-md backdrop-blur-sm transition-opacity hover:bg-accent ${get$4(canScrollRight)?"opacity-100":"pointer-events-none opacity-0"}`)}),event("scroll",div_5,updateScrollButtons),append($$anchor,fragment),pop($$exports)}delegate(["click"]);var root_1$o=from_html("<!> Reset to default",1),root_5$f=from_html(
-"<!> <!>",1),root_8$b=from_html("<!> <!>",1),root_4$b=from_html("<!> <!>",1),root$A=from_html('<div class="flex justify-between border-t border-border/30 p-6"><div class="flex gap-2"><!></div> <!></div> <!>',1);function ChatSettingsFooter($$anchor,$$props){push$1($$props,!0);let showResetDialog=state$1(!1);function handleResetClick(){set$1(showResetDialog,!0)}function handleConfirmReset(){settingsStore.forceSyncWithServerDefaults(),$$props.onReset?.(),set$1(showResetDialog,!1)}function handleSave(){
-$$props.onSave?.()}var fragment=root$A(),div=first_child(fragment),div_1=child(div),node2=child(div_1);Button(node2,{variant:"outline",onclick:handleResetClick,children:($$anchor2,$$slotProps)=>{var fragment_1=root_1$o(),node_1=first_child(fragment_1);Rotate_ccw(node_1,{class:"h-3 w-3"}),next$1(),append($$anchor2,fragment_1)},$$slots:{default:!0}}),reset(div_1);var node_2=sibling(div_1,2);Button(node_2,{onclick:handleSave,children:($$anchor2,$$slotProps)=>{next$1();var text2=text$8("Save setting\
-s");append($$anchor2,text2)},$$slots:{default:!0}}),reset(div);var node_3=sibling(div,2);component(node_3,()=>Root$3,($$anchor2,AlertDialog_Root)=>{AlertDialog_Root($$anchor2,{get open(){return get$4(showResetDialog)},set open($$value){set$1(showResetDialog,$$value,!0)},children:($$anchor3,$$slotProps)=>{var fragment_2=comment$2(),node_4=first_child(fragment_2);component(node_4,()=>Alert_dialog_content,($$anchor4,AlertDialog_Content)=>{AlertDialog_Content($$anchor4,{children:($$anchor5,$$slotProps2)=>{
-var fragment_3=root_4$b(),node_5=first_child(fragment_3);component(node_5,()=>Alert_dialog_header,($$anchor6,AlertDialog_Header)=>{AlertDialog_Header($$anchor6,{children:($$anchor7,$$slotProps3)=>{var fragment_4=root_5$f(),node_6=first_child(fragment_4);component(node_6,()=>Alert_dialog_title,($$anchor8,AlertDialog_Title)=>{AlertDialog_Title($$anchor8,{children:($$anchor9,$$slotProps4)=>{next$1();var text_1=text$8("Reset Settings to Default");append($$anchor9,text_1)},$$slots:{default:!0}})});var node_7=sibling(
-node_6,2);component(node_7,()=>Alert_dialog_description,($$anchor8,AlertDialog_Description)=>{AlertDialog_Description($$anchor8,{children:($$anchor9,$$slotProps4)=>{next$1();var text_2=text$8(`Are you sure you want to reset all settings to their default values? This will reset all
-				parameters to the values provided by the server's /props endpoint and remove all your custom
-				configurations.`);append($$anchor9,text_2)},$$slots:{default:!0}})}),append($$anchor7,fragment_4)},$$slots:{default:!0}})});var node_8=sibling(node_5,2);component(node_8,()=>Alert_dialog_footer,($$anchor6,AlertDialog_Footer)=>{AlertDialog_Footer($$anchor6,{children:($$anchor7,$$slotProps3)=>{var fragment_5=root_8$b(),node_9=first_child(fragment_5);component(node_9,()=>Alert_dialog_cancel,($$anchor8,AlertDialog_Cancel)=>{AlertDialog_Cancel($$anchor8,{children:($$anchor9,$$slotProps4)=>{next$1();
-var text_3=text$8("Cancel");append($$anchor9,text_3)},$$slots:{default:!0}})});var node_10=sibling(node_9,2);component(node_10,()=>Alert_dialog_action,($$anchor8,AlertDialog_Action)=>{AlertDialog_Action($$anchor8,{onclick:handleConfirmReset,children:($$anchor9,$$slotProps4)=>{next$1();var text_4=text$8("Reset to Default");append($$anchor9,text_4)},$$slots:{default:!0}})}),append($$anchor7,fragment_5)},$$slots:{default:!0}})}),append($$anchor5,fragment_3)},$$slots:{default:!0}})}),append($$anchor3,
-fragment_2)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}var root_1$n=from_html('<span class="absolute right-2 flex size-3.5 items-center justify-center"><!></span> <!>',1);function Select_item($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","value","label","children"]);var fragment=comment$2(),node2=first_child(fragment);{const children=($$anchor2,$$arg0)=>{let selected=()=>$$arg0?.().
-selected,highlighted=()=>$$arg0?.().highlighted;var fragment_1=root_1$n(),span=first_child(fragment_1),node_1=child(span);{var consequent=$$anchor3=>{Check($$anchor3,{class:"size-4"})};if_block(node_1,$$render=>{selected()&&$$render(consequent)})}reset(span);var node_2=sibling(span,2);{var consequent_1=$$anchor3=>{var fragment_3=comment$2(),node_3=first_child(fragment_3);snippet(node_3,()=>$$props.children,()=>({selected:selected(),highlighted:highlighted()})),append($$anchor3,fragment_3)},alternate=$$anchor3=>{
-var text2=text$8();template_effect(()=>set_text(text2,$$props.label||$$props.value)),append($$anchor3,text2)};if_block(node_2,$$render=>{$$props.children?$$render(consequent_1):$$render(alternate,!1)})}append($$anchor2,fragment_1)};let $0=user_derived(()=>cn$1("relative flex w-full cursor-default items-center gap-2 rounded-sm py-1.5 pr-8 pl-2 text-sm outline-hidden select-none data-[disabled]:pointer-events-none data-[disabled]:opacity-50 data-[highlighted]:bg-accent data-[highlighted]:text-acce\
-nt-foreground [&_svg]:pointer-events-none [&_svg]:shrink-0 [&_svg:not([class*='size-'])]:size-4 [&_svg:not([class*='text-'])]:text-muted-foreground *:[span]:last:flex *:[span]:last:items-center *:[span]:last:gap-2",$$props.class));component(node2,()=>Select_item$1,($$anchor2,SelectPrimitive_Item)=>{SelectPrimitive_Item($$anchor2,spread_props({get value(){return $$props.value},"data-slot":"select-item",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},
-children,$$slots:{default:!0}}))})}append($$anchor,fragment),pop()}function Select_scroll_up_button($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1("flex cursor-default items-center justify-center py-1",$$props.class));component(node2,()=>Select_scroll_up_button$1,($$anchor2,SelectPrimitive_ScrollUpButton)=>{SelectPrimitive_ScrollUpButton(
-$$anchor2,spread_props({"data-slot":"select-scroll-up-button",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},children:($$anchor3,$$slotProps)=>{Chevron_up($$anchor3,{class:"size-4"})},$$slots:{default:!0}}))})}append($$anchor,fragment),pop()}function Select_scroll_down_button($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class"]);var fragment=comment$2(),
-node2=first_child(fragment);{let $0=user_derived(()=>cn$1("flex cursor-default items-center justify-center py-1",$$props.class));component(node2,()=>Select_scroll_down_button$1,($$anchor2,SelectPrimitive_ScrollDownButton)=>{SelectPrimitive_ScrollDownButton($$anchor2,spread_props({"data-slot":"select-scroll-down-button",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},children:($$anchor3,$$slotProps)=>{Chevron_down($$anchor3,{class:"size-4"})},
-$$slots:{default:!0}}))})}append($$anchor,fragment),pop()}var root_2$r=from_html("<!> <!> <!>",1);function Select_content($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),sideOffset=prop($$props,"sideOffset",3,4),restProps=rest_props($$props,["$$slots","$$events","$$legacy","ref","class","sideOffset","portalProps","children"]),cleanupInternalListeners;onMount$1(()=>{const listenerOptions={passive:!1},blockOutsideWheel=event2=>{if(!ref2())return;const target2=event2.target;
-(!target2||!ref2().contains(target2))&&(event2.preventDefault(),event2.stopPropagation())},blockOutsideTouchMove=event2=>{if(!ref2())return;const target2=event2.target;(!target2||!ref2().contains(target2))&&(event2.preventDefault(),event2.stopPropagation())};return document.addEventListener("wheel",blockOutsideWheel,listenerOptions),document.addEventListener("touchmove",blockOutsideTouchMove,listenerOptions),()=>{document.removeEventListener("wheel",blockOutsideWheel,listenerOptions),document.removeEventListener(
-"touchmove",blockOutsideTouchMove,listenerOptions)}}),user_effect(()=>{const element2=ref2();if(cleanupInternalListeners?.(),!element2)return;const stopWheelPropagation=event2=>{event2.stopPropagation()},stopTouchPropagation=event2=>{event2.stopPropagation()};element2.addEventListener("wheel",stopWheelPropagation),element2.addEventListener("touchmove",stopTouchPropagation),cleanupInternalListeners=()=>{element2.removeEventListener("wheel",stopWheelPropagation),element2.removeEventListener("touch\
-move",stopTouchPropagation)}}),onDestroy(()=>{cleanupInternalListeners?.()});var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Portal$2,($$anchor2,SelectPrimitive_Portal)=>{SelectPrimitive_Portal($$anchor2,spread_props(()=>$$props.portalProps,{children:($$anchor3,$$slotProps)=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);{let $0=user_derived(()=>cn$1("relative z-[var(--layer-popover,1000000)] max-h-(--bits-select-content-available-height) min-w-[8rem] origin-\
-(--bits-select-content-transform-origin) overflow-x-hidden overflow-y-auto rounded-md border bg-popover text-popover-foreground shadow-md data-[side=bottom]:translate-y-1 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:-translate-x-1 data-[side=left]:slide-in-from-right-2 data-[side=right]:translate-x-1 data-[side=right]:slide-in-from-left-2 data-[side=top]:-translate-y-1 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=clo\
-sed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95",$$props.class));component(node_1,()=>Select_content$1,($$anchor4,SelectPrimitive_Content)=>{SelectPrimitive_Content($$anchor4,spread_props({get sideOffset(){return sideOffset()},"data-slot":"select-content",get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},children:($$anchor5,$$slotProps2)=>{var fragment_2=root_2$r(),node_2=first_child(fragment_2);
-Select_scroll_up_button(node_2,{});var node_3=sibling(node_2,2);{let $02=user_derived(()=>cn$1("h-(--bits-select-anchor-height) w-full min-w-(--bits-select-anchor-width) scroll-my-1 p-1"));component(node_3,()=>Select_viewport,($$anchor6,SelectPrimitive_Viewport)=>{SelectPrimitive_Viewport($$anchor6,{get class(){return get$4($02)},children:($$anchor7,$$slotProps3)=>{var fragment_3=comment$2(),node_4=first_child(fragment_3);snippet(node_4,()=>$$props.children??noop$3),append($$anchor7,fragment_3)},
-$$slots:{default:!0}})})}var node_5=sibling(node_3,2);Select_scroll_down_button(node_5,{}),append($$anchor5,fragment_2)},$$slots:{default:!0}}))})}append($$anchor3,fragment_1)},$$slots:{default:!0}}))}),append($$anchor,fragment),pop()}var root_1$m=from_html("<!> <!>",1);function Select_trigger($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),size2=prop($$props,"size",3,"default"),variant=prop($$props,"variant",3,"default"),restProps=rest_props($$props,["$$slots","$$event\
-s","$$legacy","ref","class","children","size","variant"]);const baseClasses=user_derived(()=>variant()==="plain"?"group inline-flex w-full items-center justify-end gap-2 whitespace-nowrap px-0 py-0 text-sm font-medium text-muted-foreground transition-colors focus-visible:outline-none focus-visible:ring-0 focus-visible:ring-offset-0 disabled:cursor-not-allowed disabled:opacity-50 data-[placeholder]:text-muted-foreground data-[size=default]:h-9 data-[size=sm]:h-8 [&_svg]:pointer-events-none [&_svg\
-]:shrink-0 [&_svg:not([class*='size-'])]:size-3 [&_svg:not([class*='text-'])]:text-muted-foreground":"flex w-fit items-center justify-between gap-2 rounded-md border border-input bg-transparent px-3 py-2 text-sm whitespace-nowrap shadow-xs transition-[color,box-shadow] outline-none select-none focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 disabled:cursor-not-allowed disabled:opacity-50 aria-invalid:border-destructive aria-invalid:ring-destructive/20 data-[placehol\
-der]:text-muted-foreground data-[size=default]:h-9 data-[size=sm]:h-8 *:data-[slot=select-value]:line-clamp-1 *:data-[slot=select-value]:flex *:data-[slot=select-value]:items-center *:data-[slot=select-value]:gap-2 dark:bg-input/30 dark:hover:bg-input/50 dark:aria-invalid:ring-destructive/40 [&_svg]:pointer-events-none [&_svg]:shrink-0 [&_svg:not([class*='size-'])]:size-4 [&_svg:not([class*='text-'])]:text-muted-foreground"),chevronClasses=user_derived(()=>variant()==="plain"?"size-3 opacity-60 \
-transition-transform group-data-[state=open]:-rotate-180":"size-4 opacity-50");var fragment=comment$2(),node2=first_child(fragment);{let $0=user_derived(()=>cn$1(get$4(baseClasses),$$props.class));component(node2,()=>Select_trigger$1,($$anchor2,SelectPrimitive_Trigger)=>{SelectPrimitive_Trigger($$anchor2,spread_props({"data-slot":"select-trigger",get"data-size"(){return size2()},get class(){return get$4($0)}},()=>restProps,{get ref(){return ref2()},set ref($$value){ref2($$value)},children:($$anchor3,$$slotProps)=>{
-var fragment_1=root_1$m(),node_1=first_child(fragment_1);snippet(node_1,()=>$$props.children??noop$3);var node_2=sibling(node_1,2);Chevron_down(node_2,{get class(){return get$4(chevronClasses)}}),append($$anchor3,fragment_1)},$$slots:{default:!0}}))})}append($$anchor,fragment),pop()}const Root=Select;var root$z=from_html("<textarea></textarea>");function Textarea($$anchor,$$props){push$1($$props,!0);let ref2=prop($$props,"ref",15,null),value=prop($$props,"value",15),restProps=rest_props($$props,
-["$$slots","$$events","$$legacy","ref","value","class"]);var textarea=root$z();remove_textarea_child(textarea),attribute_effect(textarea,$0=>({"data-slot":"textarea",class:$0,...restProps}),[()=>cn$1("flex field-sizing-content min-h-16 w-full rounded-md border border-input bg-transparent px-3 py-2 text-base shadow-xs transition-[color,box-shadow] outline-none placeholder:text-muted-foreground focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 disabled:cursor-not-allowe\
-d disabled:opacity-50 aria-invalid:border-destructive aria-invalid:ring-destructive/20 md:text-sm dark:bg-input/30 dark:aria-invalid:ring-destructive/40",$$props.class)]),bind_this(textarea,$$value=>ref2($$value),()=>ref2()),bind_value(textarea,value),append($$anchor,textarea),pop()}var root_3$m=from_html(" <!>",1),root_6$e=from_html('<button type="button" class="absolute top-1/2 right-2 inline-flex h-5 w-5 -translate-y-1/2 items-center justify-center rounded transition-colors hover:bg-muted" ar\
-ia-label="Reset to default" title="Reset to default"><!></button>'),root_7$e=from_html('<p class="mt-1 text-xs text-muted-foreground"><!></p>'),root_2$q=from_html('<div class="flex items-center gap-2"><!> <!></div> <div class="relative w-full md:max-w-md"><!> <!></div> <!>',1),root_10$8=from_html(" <!>",1),root_12$4=from_html('<p class="mt-1 text-xs text-muted-foreground"> </p>'),root_13$7=from_html('<div class="mt-3 flex items-center gap-2"><!> <!></div>'),root_9$a=from_html("<!> <!> <!> <!>",1),
-root_17$1=from_html(" <!>",1),root_21$2=from_html('<div class="flex items-center gap-2"><!> </div>'),root_23$1=from_html('<button type="button" class="absolute top-1/2 right-8 inline-flex h-5 w-5 -translate-y-1/2 items-center justify-center rounded transition-colors hover:bg-muted" aria-label="Reset to default" title="Reset to default"><!></button>'),root_27$1=from_html('<div class="flex items-center gap-2"><!> </div>'),root_20$3=from_html('<div class="relative w-full md:w-auto md:max-w-md"><!> \
-<!></div> <!>',1),root_29=from_html('<p class="mt-1 text-xs text-muted-foreground"> </p>'),root_16$2=from_html('<div class="flex items-center gap-2"><!> <!></div> <!> <!>',1),root_33=from_html('<p class="text-xs text-muted-foreground"> </p>'),root_31=from_html('<div class="flex items-start space-x-3"><!> <div class="space-y-1"><label class="flex cursor-pointer items-center gap-1.5 pt-1 pb-0.5 text-sm leading-none font-medium"> <!></label> <!></div></div>'),root_1$l=from_html('<div class="space-y\
--2"><!></div>');function ChatSettingsFields($$anchor,$$props){push$1($$props,!0);let sp=user_derived(()=>{if(serverStore.isRouterMode){const m=selectedModelName();if(m)return modelsStore.getModelProps(m)?.default_generation_settings?.params??{}}return serverStore.defaultParams??{}});var fragment=comment$2(),node2=first_child(fragment);each(node2,17,()=>$$props.fields,field=>field.key,($$anchor2,field)=>{var div=root_1$l(),node_1=child(div);{var consequent_4=$$anchor3=>{const currentValue=user_derived(
-()=>String($$props.localConfig[get$4(field).key]??"")),serverDefault=user_derived(()=>get$4(sp)[get$4(field).key]),isCustomRealTime=user_derived(()=>(()=>{if(get$4(serverDefault)==null||get$4(currentValue)==="")return!1;const numericInput=parseFloat(get$4(currentValue)),normalizedInput=isNaN(numericInput)?get$4(currentValue):Math.round(numericInput*1e6)/1e6,normalizedDefault=typeof get$4(serverDefault)=="number"?Math.round(get$4(serverDefault)*1e6)/1e6:get$4(serverDefault);return normalizedInput!==
-normalizedDefault})());var fragment_1=root_2$q(),div_1=first_child(fragment_1),node_2=child(div_1);Label(node_2,{get for(){return get$4(field).key},class:"flex items-center gap-1.5 text-sm font-medium",children:($$anchor4,$$slotProps)=>{next$1();var fragment_2=root_3$m(),text2=first_child(fragment_2),node_3=sibling(text2);{var consequent=$$anchor5=>{Flask_conical($$anchor5,{class:"h-3.5 w-3.5 text-muted-foreground"})};if_block(node_3,$$render=>{get$4(field).isExperimental&&$$render(consequent)})}
-template_effect(()=>set_text(text2,`${get$4(field).label??""} `)),append($$anchor4,fragment_2)},$$slots:{default:!0}});var node_4=sibling(node_2,2);{var consequent_1=$$anchor4=>{ChatSettingsParameterSourceIndicator($$anchor4,{})};if_block(node_4,$$render=>{get$4(isCustomRealTime)&&$$render(consequent_1)})}reset(div_1);var div_2=sibling(div_1,2),node_5=child(div_2);{let $0=user_derived(()=>get$4(sp)[get$4(field).key]!=null?`Default: ${normalizeFloatingPoint(get$4(sp)[get$4(field).key])}`:""),$1=user_derived(
-()=>get$4(isCustomRealTime)?"pr-8":"");Input(node_5,{get id(){return get$4(field).key},get value(){return get$4(currentValue)},oninput:e=>{$$props.onConfigChange(get$4(field).key,e.currentTarget.value)},get placeholder(){return get$4($0)},get class(){return`w-full ${get$4($1)??""}`}})}var node_6=sibling(node_5,2);{var consequent_2=$$anchor4=>{var button=root_6$e();button.__click=()=>{settingsStore.resetParameterToServerDefault(get$4(field).key),$$props.onConfigChange(get$4(field).key,"")};var node_7=child(
-button);Rotate_ccw(node_7,{class:"h-3 w-3"}),reset(button),append($$anchor4,button)};if_block(node_6,$$render=>{get$4(isCustomRealTime)&&$$render(consequent_2)})}reset(div_2);var node_8=sibling(div_2,2);{var consequent_3=$$anchor4=>{var p_1=root_7$e(),node_9=child(p_1);html$6(node_9,()=>get$4(field).help||SETTING_CONFIG_INFO[get$4(field).key]),reset(p_1),append($$anchor4,p_1)};if_block(node_8,$$render=>{(get$4(field).help||SETTING_CONFIG_INFO[get$4(field).key])&&$$render(consequent_3)})}append($$anchor3,
-fragment_1)},alternate_2=$$anchor3=>{var fragment_5=comment$2(),node_10=first_child(fragment_5);{var consequent_8=$$anchor4=>{var fragment_6=root_9$a(),node_11=first_child(fragment_6);Label(node_11,{get for(){return get$4(field).key},class:"block flex items-center gap-1.5 text-sm font-medium",children:($$anchor5,$$slotProps)=>{next$1();var fragment_7=root_10$8(),text_1=first_child(fragment_7),node_12=sibling(text_1);{var consequent_5=$$anchor6=>{Flask_conical($$anchor6,{class:"h-3.5 w-3.5 text-m\
-uted-foreground"})};if_block(node_12,$$render=>{get$4(field).isExperimental&&$$render(consequent_5)})}template_effect(()=>set_text(text_1,`${get$4(field).label??""} `)),append($$anchor5,fragment_7)},$$slots:{default:!0}});var node_13=sibling(node_11,2);{let $0=user_derived(()=>String($$props.localConfig[get$4(field).key]??""));Textarea(node_13,{get id(){return get$4(field).key},get value(){return get$4($0)},onchange:e=>$$props.onConfigChange(get$4(field).key,e.currentTarget.value),placeholder:"",
-class:"min-h-[10rem] w-full md:max-w-2xl"})}var node_14=sibling(node_13,2);{var consequent_6=$$anchor5=>{var p_2=root_12$4(),text_2=child(p_2,!0);reset(p_2),template_effect(()=>set_text(text_2,get$4(field).help||SETTING_CONFIG_INFO[get$4(field).key])),append($$anchor5,p_2)};if_block(node_14,$$render=>{(get$4(field).help||SETTING_CONFIG_INFO[get$4(field).key])&&$$render(consequent_6)})}var node_15=sibling(node_14,2);{var consequent_7=$$anchor5=>{var div_3=root_13$7(),node_16=child(div_3);{let $0=user_derived(
-()=>!!($$props.localConfig.showSystemMessage??!0));Checkbox(node_16,{id:"showSystemMessage",get checked(){return get$4($0)},onCheckedChange:checked=>$$props.onConfigChange("showSystemMessage",!!checked)})}var node_17=sibling(node_16,2);Label(node_17,{for:"showSystemMessage",class:"cursor-pointer text-sm font-normal",children:($$anchor6,$$slotProps)=>{next$1();var text_3=text$8("Show system message in conversations");append($$anchor6,text_3)},$$slots:{default:!0}}),reset(div_3),append($$anchor5,div_3)};
-if_block(node_15,$$render=>{get$4(field).key===SETTINGS_KEYS.SYSTEM_MESSAGE&&$$render(consequent_7)})}append($$anchor4,fragment_6)},alternate_1=$$anchor4=>{var fragment_9=comment$2(),node_18=first_child(fragment_9);{var consequent_16=$$anchor5=>{const selectedOption=user_derived(()=>get$4(field).options?.find(opt=>opt.value===$$props.localConfig[get$4(field).key])),currentValue=user_derived(()=>$$props.localConfig[get$4(field).key]),serverDefault=user_derived(()=>get$4(sp)[get$4(field).key]),isCustomRealTime=user_derived(
-()=>get$4(serverDefault)==null||get$4(currentValue)===""||get$4(currentValue)===void 0?!1:get$4(currentValue)!==get$4(serverDefault));var fragment_10=root_16$2(),div_4=first_child(fragment_10),node_19=child(div_4);Label(node_19,{get for(){return get$4(field).key},class:"flex items-center gap-1.5 text-sm font-medium",children:($$anchor6,$$slotProps)=>{next$1();var fragment_11=root_17$1(),text_4=first_child(fragment_11),node_20=sibling(text_4);{var consequent_9=$$anchor7=>{Flask_conical($$anchor7,
-{class:"h-3.5 w-3.5 text-muted-foreground"})};if_block(node_20,$$render=>{get$4(field).isExperimental&&$$render(consequent_9)})}template_effect(()=>set_text(text_4,`${get$4(field).label??""} `)),append($$anchor6,fragment_11)},$$slots:{default:!0}});var node_21=sibling(node_19,2);{var consequent_10=$$anchor6=>{ChatSettingsParameterSourceIndicator($$anchor6,{})};if_block(node_21,$$render=>{get$4(isCustomRealTime)&&$$render(consequent_10)})}reset(div_4);var node_22=sibling(div_4,2);component(node_22,
-()=>Root,($$anchor6,Select_Root)=>{Select_Root($$anchor6,{type:"single",get value(){return get$4(currentValue)},onValueChange:value=>{get$4(field).key===SETTINGS_KEYS.THEME&&value&&$$props.onThemeChange?$$props.onThemeChange(value):$$props.onConfigChange(get$4(field).key,value)},children:($$anchor7,$$slotProps)=>{var fragment_14=root_20$3(),div_5=first_child(fragment_14),node_23=child(div_5);component(node_23,()=>Select_trigger,($$anchor8,Select_Trigger)=>{Select_Trigger($$anchor8,{class:"w-full",
-children:($$anchor9,$$slotProps2)=>{var div_6=root_21$2(),node_24=child(div_6);{var consequent_11=$$anchor10=>{const IconComponent=user_derived(()=>get$4(selectedOption).icon);var fragment_15=comment$2(),node_25=first_child(fragment_15);component(node_25,()=>get$4(IconComponent),($$anchor11,IconComponent_1)=>{IconComponent_1($$anchor11,{class:"h-4 w-4"})}),append($$anchor10,fragment_15)};if_block(node_24,$$render=>{get$4(selectedOption)?.icon&&$$render(consequent_11)})}var text_5=sibling(node_24);
-reset(div_6),template_effect($0=>set_text(text_5,` ${$0??""}`),[()=>get$4(selectedOption)?.label||`Select ${get$4(field).label.toLowerCase()}`]),append($$anchor9,div_6)},$$slots:{default:!0}})});var node_26=sibling(node_23,2);{var consequent_12=$$anchor8=>{var button_1=root_23$1();button_1.__click=()=>{settingsStore.resetParameterToServerDefault(get$4(field).key),$$props.onConfigChange(get$4(field).key,"")};var node_27=child(button_1);Rotate_ccw(node_27,{class:"h-3 w-3"}),reset(button_1),append(
-$$anchor8,button_1)};if_block(node_26,$$render=>{get$4(isCustomRealTime)&&$$render(consequent_12)})}reset(div_5);var node_28=sibling(div_5,2);component(node_28,()=>Select_content,($$anchor8,Select_Content)=>{Select_Content($$anchor8,{children:($$anchor9,$$slotProps2)=>{var fragment_16=comment$2(),node_29=first_child(fragment_16);{var consequent_14=$$anchor10=>{var fragment_17=comment$2(),node_30=first_child(fragment_17);each(node_30,17,()=>get$4(field).options,option2=>option2.value,($$anchor11,option2)=>{
-var fragment_18=comment$2(),node_31=first_child(fragment_18);component(node_31,()=>Select_item,($$anchor12,Select_Item)=>{Select_Item($$anchor12,{get value(){return get$4(option2).value},get label(){return get$4(option2).label},children:($$anchor13,$$slotProps3)=>{var div_7=root_27$1(),node_32=child(div_7);{var consequent_13=$$anchor14=>{const IconComponent=user_derived(()=>get$4(option2).icon);var fragment_19=comment$2(),node_33=first_child(fragment_19);component(node_33,()=>get$4(IconComponent),
-($$anchor15,IconComponent_2)=>{IconComponent_2($$anchor15,{class:"h-4 w-4"})}),append($$anchor14,fragment_19)};if_block(node_32,$$render=>{get$4(option2).icon&&$$render(consequent_13)})}var text_6=sibling(node_32);reset(div_7),template_effect(()=>set_text(text_6,` ${get$4(option2).label??""}`)),append($$anchor13,div_7)},$$slots:{default:!0}})}),append($$anchor11,fragment_18)}),append($$anchor10,fragment_17)};if_block(node_29,$$render=>{get$4(field).options&&$$render(consequent_14)})}append($$anchor9,
-fragment_16)},$$slots:{default:!0}})}),append($$anchor7,fragment_14)},$$slots:{default:!0}})});var node_34=sibling(node_22,2);{var consequent_15=$$anchor6=>{var p_3=root_29(),text_7=child(p_3,!0);reset(p_3),template_effect(()=>set_text(text_7,get$4(field).help||SETTING_CONFIG_INFO[get$4(field).key])),append($$anchor6,p_3)};if_block(node_34,$$render=>{(get$4(field).help||SETTING_CONFIG_INFO[get$4(field).key])&&$$render(consequent_15)})}append($$anchor5,fragment_10)},alternate=$$anchor5=>{var fragment_20=comment$2(),
-node_35=first_child(fragment_20);{var consequent_19=$$anchor6=>{var div_8=root_31(),node_36=child(div_8);{let $0=user_derived(()=>!!$$props.localConfig[get$4(field).key]);Checkbox(node_36,{get id(){return get$4(field).key},get checked(){return get$4($0)},onCheckedChange:checked=>$$props.onConfigChange(get$4(field).key,checked),class:"mt-1"})}var div_9=sibling(node_36,2),label=child(div_9),text_8=child(label),node_37=sibling(text_8);{var consequent_17=$$anchor7=>{Flask_conical($$anchor7,{class:"h\
--3.5 w-3.5 text-muted-foreground"})};if_block(node_37,$$render=>{get$4(field).isExperimental&&$$render(consequent_17)})}reset(label);var node_38=sibling(label,2);{var consequent_18=$$anchor7=>{var p_4=root_33(),text_9=child(p_4,!0);reset(p_4),template_effect(()=>set_text(text_9,get$4(field).help||SETTING_CONFIG_INFO[get$4(field).key])),append($$anchor7,p_4)};if_block(node_38,$$render=>{(get$4(field).help||SETTING_CONFIG_INFO[get$4(field).key])&&$$render(consequent_18)})}reset(div_9),reset(div_8),
-template_effect(()=>{set_attribute(label,"for",get$4(field).key),set_text(text_8,`${get$4(field).label??""} `)}),append($$anchor6,div_8)};if_block(node_35,$$render=>{get$4(field).type===SettingsFieldType.CHECKBOX&&$$render(consequent_19)},!0)}append($$anchor5,fragment_20)};if_block(node_18,$$render=>{get$4(field).type===SettingsFieldType.SELECT?$$render(consequent_16):$$render(alternate,!1)},!0)}append($$anchor4,fragment_9)};if_block(node_10,$$render=>{get$4(field).type===SettingsFieldType.TEXTAREA?
-$$render(consequent_8):$$render(alternate_1,!1)},!0)}append($$anchor3,fragment_5)};if_block(node_1,$$render=>{get$4(field).type===SettingsFieldType.INPUT?$$render(consequent_4):$$render(alternate_2,!1)})}reset(div),append($$anchor2,div)}),append($$anchor,fragment),pop()}delegate(["click"]);var root_1$k=from_html("<!> Export conversations",1),root_3$l=from_html('<li class="truncate"> </li>'),root_4$a=from_html('<li class="italic"> </li>'),root_2$p=from_html('<div class="mt-4 grid overflow-x-auto \
-rounded-lg border border-border/50 bg-muted/30 p-4"><h5 class="mb-2 text-sm font-medium"> </h5> <ul class="space-y-1 text-sm text-muted-foreground"><!> <!></ul></div>'),root_5$e=from_html("<!> Import conversations",1),root_7$d=from_html('<li class="truncate"> </li>'),root_8$a=from_html('<li class="italic"> </li>'),root_6$d=from_html('<div class="mt-4 grid overflow-x-auto rounded-lg border border-border/50 bg-muted/30 p-4"><h5 class="mb-2 text-sm font-medium"> </h5> <ul class="space-y-1 text-sm t\
-ext-muted-foreground"><!> <!></ul></div>'),root_9$9=from_html("<!> Delete all conversations",1),root$y=from_html(`<div class="space-y-6"><div class="space-y-4"><div class="grid"><h4 class="mb-2 text-sm font-medium">Export Conversations</h4> <p class="mb-4 text-sm text-muted-foreground">Download all your conversations as a JSON file. This includes all messages, attachments, and
-				conversation history.</p> <!> <!></div> <div class="grid border-t border-border/30 pt-4"><h4 class="mb-2 text-sm font-medium">Import Conversations</h4> <p class="mb-4 text-sm text-muted-foreground">Import one or more conversations from a previously exported JSON file. This will merge with
-				your existing conversations.</p> <!> <!></div> <div class="grid border-t border-border/30 pt-4"><h4 class="mb-2 text-sm font-medium text-destructive">Delete All Conversations</h4> <p class="mb-4 text-sm text-muted-foreground">Permanently delete all conversations and their messages. This action cannot be undone.
-				Consider exporting your conversations first if you want to keep a backup.</p> <!></div></div></div> <!> <!> <!>`,1);function ChatSettingsImportExportTab($$anchor,$$props){push$1($$props,!0);let exportedConversations=state$1(proxy([])),importedConversations=state$1(proxy([])),showExportSummary=state$1(!1),showImportSummary=state$1(!1),showExportDialog=state$1(!1),showImportDialog=state$1(!1),availableConversations=state$1(proxy([])),messageCountMap=state$1(proxy(new Map)),fullImportData=state$1(
-proxy([])),showDeleteDialog=state$1(!1);async function handleExportClick(){try{const allConversations=conversations();if(allConversations.length===0){toast.info("No conversations to export");return}const conversationsWithMessages=await Promise.all(allConversations.map(async conv=>{const messages=await conversationsStore.getConversationMessages(conv.id);return{conv,messages}}));set$1(messageCountMap,createMessageCountMap(conversationsWithMessages),!0),set$1(availableConversations,allConversations,
-!0),set$1(showExportDialog,!0)}catch(err){console.error("Failed to load conversations:",err),alert("Failed to load conversations")}}async function handleExportConfirm(selectedConversations){try{const allData=await Promise.all(selectedConversations.map(async conv=>{const messages=await conversationsStore.getConversationMessages(conv.id);return{conv:snapshot(conv),messages:snapshot(messages)}}));conversationsStore.downloadConversationFile(allData,`${new Date().toISOString().split(ISO_DATE_TIME_SEPARATOR)[0]}\
-_conversations.json`),set$1(exportedConversations,selectedConversations,!0),set$1(showExportSummary,!0),set$1(showImportSummary,!1),set$1(showExportDialog,!1)}catch(err){console.error("Export failed:",err),alert("Failed to export conversations")}}async function handleImportClick(){try{const input=document.createElement("input");input.type="file",input.accept=".json",input.onchange=async e=>{const file=e.target?.files?.[0];if(file)try{const text2=await file.text(),parsedData=JSON.parse(text2);let importedData;
-if(Array.isArray(parsedData))importedData=parsedData;else if(parsedData&&typeof parsedData=="object"&&"conv"in parsedData&&"messages"in parsedData)importedData=[parsedData];else throw new Error("Invalid file format: expected array of conversations or single conversation object");set$1(fullImportData,importedData,!0),set$1(availableConversations,importedData.map(item=>item.conv),!0),set$1(messageCountMap,createMessageCountMap(importedData),!0),set$1(showImportDialog,!0)}catch(err){const message=err instanceof
-Error?err.message:"Unknown error";console.error("Failed to parse file:",err),alert(`Failed to parse file: ${message}`)}},input.click()}catch(err){console.error("Import failed:",err),alert("Failed to import conversations")}}async function handleImportConfirm(selectedConversations){try{const selectedIds=new Set(selectedConversations.map(c2=>c2.id)),selectedData=snapshot(get$4(fullImportData)).filter(item=>selectedIds.has(item.conv.id));await conversationsStore.importConversationsData(selectedData),
-set$1(importedConversations,selectedConversations,!0),set$1(showImportSummary,!0),set$1(showExportSummary,!1),set$1(showImportDialog,!1)}catch(err){console.error("Import failed:",err),alert("Failed to import conversations. Please check the file format.")}}async function handleDeleteAllClick(){try{if(conversations().length===0){toast.info("No conversations to delete");return}set$1(showDeleteDialog,!0)}catch(err){console.error("Failed to load conversations for deletion:",err),toast.error("Failed t\
-o load conversations")}}async function handleDeleteAllConfirm(){try{await conversationsStore.deleteAll(),set$1(showDeleteDialog,!1)}catch(err){console.error("Failed to delete conversations:",err)}}function handleDeleteAllCancel(){set$1(showDeleteDialog,!1)}var fragment=root$y(),div=first_child(fragment),div_1=child(div),div_2=child(div_1),node2=sibling(child(div_2),4);Button(node2,{class:"w-full justify-start justify-self-start md:w-auto",onclick:handleExportClick,variant:"outline",children:($$anchor2,$$slotProps)=>{
-var fragment_1=root_1$k(),node_1=first_child(fragment_1);Download(node_1,{class:"mr-2 h-4 w-4"}),next$1(),append($$anchor2,fragment_1)},$$slots:{default:!0}});var node_2=sibling(node2,2);{var consequent_1=$$anchor2=>{var div_3=root_2$p(),h5=child(div_3),text_1=child(h5);reset(h5);var ul=sibling(h5,2),node_3=child(ul);each(node_3,17,()=>get$4(exportedConversations).slice(0,10),conv=>conv.id,($$anchor3,conv)=>{var li2=root_3$l(),text_2=child(li2);reset(li2),template_effect(()=>set_text(text_2,`• ${get$4(
-conv).name||"Untitled conversation"}`)),append($$anchor3,li2)});var node_4=sibling(node_3,2);{var consequent=$$anchor3=>{var li_1=root_4$a(),text_3=child(li_1);reset(li_1),template_effect(()=>set_text(text_3,`... and ${get$4(exportedConversations).length-10} more`)),append($$anchor3,li_1)};if_block(node_4,$$render=>{get$4(exportedConversations).length>10&&$$render(consequent)})}reset(ul),reset(div_3),template_effect(()=>set_text(text_1,`Exported ${get$4(exportedConversations).length??""} convers\
-ation${get$4(exportedConversations).length===1?"":"s"}`)),append($$anchor2,div_3)};if_block(node_2,$$render=>{get$4(showExportSummary)&&get$4(exportedConversations).length>0&&$$render(consequent_1)})}reset(div_2);var div_4=sibling(div_2,2),node_5=sibling(child(div_4),4);Button(node_5,{class:"w-full justify-start justify-self-start md:w-auto",onclick:handleImportClick,variant:"outline",children:($$anchor2,$$slotProps)=>{var fragment_2=root_5$e(),node_6=first_child(fragment_2);Upload(node_6,{class:"\
-mr-2 h-4 w-4"}),next$1(),append($$anchor2,fragment_2)},$$slots:{default:!0}});var node_7=sibling(node_5,2);{var consequent_3=$$anchor2=>{var div_5=root_6$d(),h5_1=child(div_5),text_4=child(h5_1);reset(h5_1);var ul_1=sibling(h5_1,2),node_8=child(ul_1);each(node_8,17,()=>get$4(importedConversations).slice(0,10),conv=>conv.id,($$anchor3,conv)=>{var li_2=root_7$d(),text_5=child(li_2);reset(li_2),template_effect(()=>set_text(text_5,`• ${get$4(conv).name||"Untitled conversation"}`)),append($$anchor3,
-li_2)});var node_9=sibling(node_8,2);{var consequent_2=$$anchor3=>{var li_3=root_8$a(),text_6=child(li_3);reset(li_3),template_effect(()=>set_text(text_6,`... and ${get$4(importedConversations).length-10} more`)),append($$anchor3,li_3)};if_block(node_9,$$render=>{get$4(importedConversations).length>10&&$$render(consequent_2)})}reset(ul_1),reset(div_5),template_effect(()=>set_text(text_4,`Imported ${get$4(importedConversations).length??""} conversation${get$4(importedConversations).length===1?"":
-"s"}`)),append($$anchor2,div_5)};if_block(node_7,$$render=>{get$4(showImportSummary)&&get$4(importedConversations).length>0&&$$render(consequent_3)})}reset(div_4);var div_6=sibling(div_4,2),node_10=sibling(child(div_6),4);Button(node_10,{class:"text-destructive-foreground w-full justify-start justify-self-start bg-destructive hover:bg-destructive/80 md:w-auto",onclick:handleDeleteAllClick,variant:"destructive",children:($$anchor2,$$slotProps)=>{var fragment_3=root_9$9(),node_11=first_child(fragment_3);
-Trash_2(node_11,{class:"mr-2 h-4 w-4"}),next$1(),append($$anchor2,fragment_3)},$$slots:{default:!0}}),reset(div_6),reset(div_1),reset(div);var node_12=sibling(div,2);DialogConversationSelection(node_12,{get conversations(){return get$4(availableConversations)},get messageCountMap(){return get$4(messageCountMap)},mode:"export",onCancel:()=>set$1(showExportDialog,!1),onConfirm:handleExportConfirm,get open(){return get$4(showExportDialog)},set open($$value){set$1(showExportDialog,$$value,!0)}});var node_13=sibling(
-node_12,2);DialogConversationSelection(node_13,{get conversations(){return get$4(availableConversations)},get messageCountMap(){return get$4(messageCountMap)},mode:"import",onCancel:()=>set$1(showImportDialog,!1),onConfirm:handleImportConfirm,get open(){return get$4(showImportDialog)},set open($$value){set$1(showImportDialog,$$value,!0)}});var node_14=sibling(node_13,2);DialogConfirmation(node_14,{title:"Delete all conversations",description:"Are you sure you want to delete all conversations? Th\
-is action cannot be undone and will permanently remove all your conversations and messages.",confirmText:"Delete All",cancelText:"Cancel",variant:"destructive",get icon(){return Trash_2},onConfirm:handleDeleteAllConfirm,onCancel:handleDeleteAllCancel,get open(){return get$4(showDeleteDialog)},set open($$value){set$1(showDeleteDialog,$$value,!0)}}),append($$anchor,fragment),pop()}var root_1$j=from_html("<!> Custom",1);function ChatSettingsParameterSourceIndicator($$anchor,$$props){let className=prop(
-$$props,"class",3,"");Badge($$anchor,{variant:"secondary",get class(){return`h-5 bg-orange-100 px-1.5 py-0.5 text-xs text-orange-800 dark:bg-orange-900 dark:text-orange-200 ${className()??""}`},children:($$anchor2,$$slotProps)=>{var fragment_1=root_1$j(),node2=first_child(fragment_1);Wrench(node2,{class:"mr-1 h-3 w-3"}),next$1(),append($$anchor2,fragment_1)},$$slots:{default:!0}})}var root_1$i=from_html('<div class="relative"><!> <!> <!></div>'),root_3$k=from_html('<div class="flex items-center \
-gap-2"><!> New chat</div> <!>',1),root_4$9=from_html('<div class="flex items-center gap-2"><!> Search</div> <!>',1),root_5$d=from_html('<div class="flex items-center gap-2"><!> MCP Servers</div>'),root_2$o=from_html("<!> <!> <!>",1),root$x=from_html('<div class="my-1 space-y-1"><!></div>');function ChatSidebarActions($$anchor,$$props){push$1($$props,!0);let isSearchModeActive=prop($$props,"isSearchModeActive",15),searchQuery=prop($$props,"searchQuery",15),searchInput=state$1(null);const chatSettingsDialog=getChatSettingsDialogContext();
-function handleSearchModeDeactivate(){isSearchModeActive(!1),searchQuery("")}user_effect(()=>{isSearchModeActive()&&get$4(searchInput)?.focus()});var div=root$x(),node2=child(div);{var consequent=$$anchor2=>{var div_1=root_1$i(),node_1=child(div_1);Search(node_1,{class:"absolute top-2.5 left-2 h-4 w-4 text-muted-foreground"});var node_2=sibling(node_1,2);Input(node_2,{onkeydown:e=>e.key==="Escape"&&handleSearchModeDeactivate(),placeholder:"Search conversations...",class:"pl-8",get ref(){return get$4(
-searchInput)},set ref($$value){set$1(searchInput,$$value,!0)},get value(){return searchQuery()},set value($$value){searchQuery($$value)}});var node_3=sibling(node_2,2);X(node_3,{class:"cursor-pointertext-muted-foreground absolute top-2.5 right-2 h-4 w-4",onclick:handleSearchModeDeactivate}),reset(div_1),append($$anchor2,div_1)},alternate=$$anchor2=>{var fragment=root_2$o(),node_4=first_child(fragment);Button(node_4,{class:"w-full justify-between backdrop-blur-none! hover:[&>kbd]:opacity-100",href:"\
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-[&>kbd]:opacity-100",onclick:()=>{isSearchModeActive(!0)},variant:"ghost",children:($$anchor3,$$slotProps)=>{var fragment_2=root_4$9(),div_3=first_child(fragment_2),node_8=child(div_3);Search(node_8,{class:"h-4 w-4"}),next$1(),reset(div_3);var node_9=sibling(div_3,2);KeyboardShortcutInfo(node_9,{keys:["cmd","k"]}),append($$anchor3,fragment_2)},$$slots:{default:!0}});var node_10=sibling(node_7,2);Button(node_10,{class:"w-full justify-between backdrop-blur-none! hover:[&>kbd]:opacity-100",onclick:()=>{
-chatSettingsDialog.open(SETTINGS_SECTION_TITLES.MCP)},variant:"ghost",children:($$anchor3,$$slotProps)=>{var div_4=root_5$d(),node_11=child(div_4);McpLogo(node_11,{class:"h-4 w-4"}),next$1(),reset(div_4),append($$anchor3,div_4)},$$slots:{default:!0}}),append($$anchor2,fragment)};if_block(node2,$$render=>{isSearchModeActive()?$$render(consequent):$$render(alternate,!1)})}reset(div),append($$anchor,div),pop()}var root_2$n=from_html('<a href="#/"><h1 class="inline-flex items-center gap-1 px-2 text-\
-xl font-semibold">llama.cpp</h1></a> <!>',1),root_10$7=from_html('<div class="px-2 py-4 text-center"><p class="mb-4 p-4 text-sm text-muted-foreground"> </p></div>'),root_7$c=from_html("<!> <!>",1),root_3$j=from_html("<!> <!>",1),root_1$h=from_html("<!> <!>",1),root_12$3=from_html('<div class="flex items-center gap-2 py-2"><!> <!></div>'),root$w=from_html("<!> <!> <!>",1);function ChatSidebar($$anchor,$$props){push$1($$props,!0);const sidebar=useSidebar();let currentChatId=user_derived(()=>page$1.
-params.id),isSearchModeActive=state$1(!1),searchQuery=state$1(""),showDeleteDialog=state$1(!1),deleteWithForks=state$1(!1),showEditDialog=state$1(!1),selectedConversation=state$1(null),editedName=state$1(""),selectedConversationNamePreview=user_derived(()=>get$4(selectedConversation)?getPreviewText(get$4(selectedConversation).name):""),filteredConversations=user_derived(()=>get$4(searchQuery).trim().length>0?conversations().filter(conversation=>conversation.name.toLowerCase().includes(get$4(searchQuery).
-toLowerCase())):conversations()),conversationTree=user_derived(()=>buildConversationTree(get$4(filteredConversations))),selectedConversationHasDescendants=user_derived(()=>{if(!get$4(selectedConversation))return!1;const allConvs=conversations(),queue=[get$4(selectedConversation).id];for(;queue.length>0;){const parentId=queue.pop();for(const c2 of allConvs)if(c2.forkedFromConversationId===parentId)return!0}return!1});async function handleDeleteConversation(id2){const conversation=conversations().
-find(conv=>conv.id===id2);conversation&&(set$1(selectedConversation,conversation,!0),set$1(deleteWithForks,!1),set$1(showDeleteDialog,!0))}async function handleEditConversation(id2){const conversation=conversations().find(conv=>conv.id===id2);conversation&&(set$1(selectedConversation,conversation,!0),set$1(editedName,conversation.name,!0),set$1(showEditDialog,!0))}function handleConfirmDelete(){if(get$4(selectedConversation)){const convId=get$4(selectedConversation).id,withForks=get$4(deleteWithForks);
-set$1(showDeleteDialog,!1),setTimeout(()=>{conversationsStore.deleteConversation(convId,{deleteWithForks:withForks})},100)}}function handleConfirmEdit(){!get$4(editedName).trim()||!get$4(selectedConversation)||(set$1(showEditDialog,!1),conversationsStore.updateConversationName(get$4(selectedConversation).id,get$4(editedName)),set$1(selectedConversation,null))}function handleMobileSidebarItemClick(){sidebar.isMobile&&sidebar.toggle()}function activateSearchMode(){set$1(isSearchModeActive,!0)}function editActiveConversation(){
-if(get$4(currentChatId)&&get$4(filteredConversations).find(conv=>conv.id===get$4(currentChatId))){const event2=new CustomEvent("edit-active-conversation",{detail:{conversationId:get$4(currentChatId)}});document.dispatchEvent(event2)}}async function selectConversation(id2){get$4(isSearchModeActive)&&(set$1(isSearchModeActive,!1),set$1(searchQuery,"")),await goto(`#/chat/${id2}`)}function handleStopGeneration(id2){chatStore.stopGenerationForChat(id2)}var $$exports={handleMobileSidebarItemClick,activateSearchMode,
-editActiveConversation},fragment=root$w(),node2=first_child(fragment);Scroll_area(node2,{class:"h-[100vh]",children:($$anchor2,$$slotProps)=>{var fragment_1=root_1$h(),node_1=first_child(fragment_1);component(node_1,()=>Sidebar_header,($$anchor3,Sidebar_Header)=>{Sidebar_Header($$anchor3,{class:" top-0 z-10 gap-4 bg-sidebar/50 p-4 pb-2 backdrop-blur-lg md:sticky",children:($$anchor4,$$slotProps2)=>{var fragment_2=root_2$n(),a=first_child(fragment_2);a.__click=handleMobileSidebarItemClick;var node_2=sibling(
-a,2);ChatSidebarActions(node_2,{handleMobileSidebarItemClick,get isSearchModeActive(){return get$4(isSearchModeActive)},set isSearchModeActive($$value){set$1(isSearchModeActive,$$value,!0)},get searchQuery(){return get$4(searchQuery)},set searchQuery($$value){set$1(searchQuery,$$value,!0)}}),append($$anchor4,fragment_2)},$$slots:{default:!0}})});var node_3=sibling(node_1,2);component(node_3,()=>Sidebar_group,($$anchor3,Sidebar_Group)=>{Sidebar_Group($$anchor3,{class:"mt-2 space-y-2 p-0 px-4",children:($$anchor4,$$slotProps2)=>{
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-$$render=>{(get$4(filteredConversations).length>0&&get$4(isSearchModeActive)||!get$4(isSearchModeActive))&&$$render(consequent)})}var node_6=sibling(node_4,2);component(node_6,()=>Sidebar_group_content,($$anchor5,Sidebar_GroupContent)=>{Sidebar_GroupContent($$anchor5,{children:($$anchor6,$$slotProps3)=>{var fragment_6=comment$2(),node_7=first_child(fragment_6);component(node_7,()=>Sidebar_menu,($$anchor7,Sidebar_Menu)=>{Sidebar_Menu($$anchor7,{children:($$anchor8,$$slotProps4)=>{var fragment_7=root_7$c(),
-node_8=first_child(fragment_7);each(node_8,17,()=>get$4(conversationTree),({conversation,depth})=>conversation.id,($$anchor9,$$item)=>{let conversation=()=>get$4($$item).conversation,depth=()=>get$4($$item).depth;var fragment_8=comment$2(),node_9=first_child(fragment_8);component(node_9,()=>Sidebar_menu_item,($$anchor10,Sidebar_MenuItem)=>{Sidebar_MenuItem($$anchor10,{class:"mb-1 p-0",children:($$anchor11,$$slotProps5)=>{{let $0=user_derived(()=>({id:conversation().id,name:conversation().name,lastModified:conversation().
-lastModified,currNode:conversation().currNode,forkedFromConversationId:conversation().forkedFromConversationId})),$1=user_derived(()=>get$4(currentChatId)===conversation().id);ChatSidebarConversationItem($$anchor11,{get conversation(){return get$4($0)},get depth(){return depth()},handleMobileSidebarItemClick,get isActive(){return get$4($1)},onSelect:selectConversation,onEdit:handleEditConversation,onDelete:handleDeleteConversation,onStop:handleStopGeneration})}},$$slots:{default:!0}})}),append($$anchor9,
-fragment_8)});var node_10=sibling(node_8,2);{var consequent_1=$$anchor9=>{var div=root_10$7(),p2=child(div),text_1=child(p2,!0);reset(p2),reset(div),template_effect(()=>set_text(text_1,get$4(searchQuery).length>0?"No results found":get$4(isSearchModeActive)?"Start typing to see results":"No conversations yet")),append($$anchor9,div)};if_block(node_10,$$render=>{get$4(conversationTree).length===0&&$$render(consequent_1)})}append($$anchor8,fragment_7)},$$slots:{default:!0}})}),append($$anchor6,fragment_6)},
-$$slots:{default:!0}})}),append($$anchor4,fragment_3)},$$slots:{default:!0}})}),append($$anchor2,fragment_1)},$$slots:{default:!0}});var node_11=sibling(node2,2);{let $0=user_derived(()=>get$4(selectedConversation)?`Are you sure you want to delete "${get$4(selectedConversationNamePreview)}"? This action cannot be undone and will permanently remove all messages in this conversation.`:"");DialogConfirmation(node_11,{title:"Delete Conversation",get description(){return get$4($0)},confirmText:"Delet\
-e",cancelText:"Cancel",variant:"destructive",get icon(){return Trash_2},onConfirm:handleConfirmDelete,onCancel:()=>{set$1(showDeleteDialog,!1),set$1(selectedConversation,null)},get open(){return get$4(showDeleteDialog)},set open($$value){set$1(showDeleteDialog,$$value,!0)},children:($$anchor2,$$slotProps)=>{var fragment_10=comment$2(),node_12=first_child(fragment_10);{var consequent_2=$$anchor3=>{var div_1=root_12$3(),node_13=child(div_1);Checkbox(node_13,{id:"delete-with-forks",get checked(){return get$4(
-deleteWithForks)},set checked($$value){set$1(deleteWithForks,$$value,!0)}});var node_14=sibling(node_13,2);Label(node_14,{for:"delete-with-forks",class:"text-sm",children:($$anchor4,$$slotProps2)=>{next$1();var text_2=text$8("Also delete all forked conversations");append($$anchor4,text_2)},$$slots:{default:!0}}),reset(div_1),append($$anchor3,div_1)};if_block(node_12,$$render=>{get$4(selectedConversationHasDescendants)&&$$render(consequent_2)})}append($$anchor2,fragment_10)},$$slots:{default:!0}})}
-var node_15=sibling(node_11,2);return DialogConfirmation(node_15,{title:"Edit Conversation Name",description:"",confirmText:"Save",cancelText:"Cancel",get icon(){return Pencil},onConfirm:handleConfirmEdit,onCancel:()=>{set$1(showEditDialog,!1),set$1(selectedConversation,null)},onKeydown:e=>{e.key==="Enter"&&(e.preventDefault(),e.stopImmediatePropagation(),handleConfirmEdit())},get open(){return get$4(showEditDialog)},set open($$value){set$1(showEditDialog,$$value,!0)},children:($$anchor2,$$slotProps)=>{
-Input($$anchor2,{class:"text-foreground",placeholder:"Enter a new name",type:"text",get value(){return get$4(editedName)},set value($$value){set$1(editedName,$$value,!0)}})},$$slots:{default:!0}}),append($$anchor,fragment),pop($$exports)}delegate(["click"]);var root_3$i=from_html('<a class="flex shrink-0 items-center text-muted-foreground transition-colors hover:text-foreground"><!></a>'),root_4$8=from_html("<p>See parent conversation</p>"),root_2$m=from_html("<!> <!>",1),root_7$b=from_html('<di\
-v class="stop-button flex h-4 w-4 shrink-0 cursor-pointer items-center justify-center rounded text-muted-foreground transition-colors hover:text-foreground svelte-76ksb2" role="button" tabindex="0" aria-label="Stop generation"><!> <!></div>'),root_8$9=from_html("<p>Stop generation</p>"),root_6$c=from_html("<!> <!>",1),root_9$8=from_html('<div class="actions flex items-center"><!></div>'),root$v=from_html('<button><div class="flex min-w-0 flex-1 items-center gap-2"><!> <!>  <span class="truncate \
-text-sm font-medium"> </span></div> <!></button>');function ChatSidebarConversationItem($$anchor,$$props){push$1($$props,!0);let isActive=prop($$props,"isActive",3,!1),depth=prop($$props,"depth",3,0),renderActionsDropdown=state$1(!1),dropdownOpen=state$1(!1),isLoading2=user_derived(()=>getAllLoadingChats().includes($$props.conversation.id));function handleEdit(event2){event2.stopPropagation(),$$props.onEdit?.($$props.conversation.id)}function handleDelete2(event2){event2.stopPropagation(),$$props.
-onDelete?.($$props.conversation.id)}function handleStop(event2){event2.stopPropagation(),$$props.onStop?.($$props.conversation.id)}function handleGlobalEditEvent(event2){event2.detail.conversationId===$$props.conversation.id&&isActive()&&handleEdit(event2)}function handleMouseLeave(){get$4(dropdownOpen)||set$1(renderActionsDropdown,!1)}function handleMouseOver(){set$1(renderActionsDropdown,!0)}function handleSelect(){$$props.onSelect?.($$props.conversation.id)}user_effect(()=>{get$4(dropdownOpen)||
-set$1(renderActionsDropdown,!1)}),onMount$1(()=>(document.addEventListener("edit-active-conversation",handleGlobalEditEvent),()=>{document.removeEventListener("edit-active-conversation",handleGlobalEditEvent)}));var button=root$v();button.__click=handleSelect,button.__mouseover=handleMouseOver;var div=child(button);let styles2;var node2=child(div);{var consequent=$$anchor2=>{var fragment=comment$2(),node_1=first_child(fragment);component(node_1,()=>Root$5,($$anchor3,Tooltip_Root)=>{Tooltip_Root(
-$$anchor3,{children:($$anchor4,$$slotProps)=>{var fragment_1=root_2$m(),node_2=first_child(fragment_1);component(node_2,()=>Tooltip_trigger,($$anchor5,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor5,{children:($$anchor6,$$slotProps2)=>{var a=root_3$i(),node_3=child(a);Git_branch(node_3,{class:"h-3.5 w-3.5"}),reset(a),template_effect(()=>set_attribute(a,"href",`#/chat/${$$props.conversation.forkedFromConversationId??""}`)),append($$anchor6,a)},$$slots:{default:!0}})});var node_4=sibling(node_2,2);component(
-node_4,()=>Tooltip_content,($$anchor5,Tooltip_Content)=>{Tooltip_Content($$anchor5,{children:($$anchor6,$$slotProps2)=>{var p2=root_4$8();append($$anchor6,p2)},$$slots:{default:!0}})}),append($$anchor4,fragment_1)},$$slots:{default:!0}})}),append($$anchor2,fragment)};if_block(node2,$$render=>{depth()>0&&$$render(consequent)})}var node_5=sibling(node2,2);{var consequent_1=$$anchor2=>{var fragment_2=comment$2(),node_6=first_child(fragment_2);component(node_6,()=>Root$5,($$anchor3,Tooltip_Root_1)=>{
-Tooltip_Root_1($$anchor3,{children:($$anchor4,$$slotProps)=>{var fragment_3=root_6$c(),node_7=first_child(fragment_3);component(node_7,()=>Tooltip_trigger,($$anchor5,Tooltip_Trigger_1)=>{Tooltip_Trigger_1($$anchor5,{children:($$anchor6,$$slotProps2)=>{var div_1=root_7$b();div_1.__click=handleStop,div_1.__keydown=e=>e.key==="Enter"&&handleStop(e);var node_8=child(div_1);Loader_circle(node_8,{class:"loading-icon h-3.5 w-3.5 animate-spin"});var node_9=sibling(node_8,2);Square(node_9,{class:"stop-ic\
-on hidden h-3 w-3 fill-current text-destructive"}),reset(div_1),append($$anchor6,div_1)},$$slots:{default:!0}})});var node_10=sibling(node_7,2);component(node_10,()=>Tooltip_content,($$anchor5,Tooltip_Content_1)=>{Tooltip_Content_1($$anchor5,{children:($$anchor6,$$slotProps2)=>{var p_1=root_8$9();append($$anchor6,p_1)},$$slots:{default:!0}})}),append($$anchor4,fragment_3)},$$slots:{default:!0}})}),append($$anchor2,fragment_2)};if_block(node_5,$$render=>{get$4(isLoading2)&&$$render(consequent_1)})}
-var span=sibling(node_5,2);span.__click=function(...$$args){$$props.handleMobileSidebarItemClick?.apply(this,$$args)};var text2=child(span,!0);reset(span),reset(div);var node_11=sibling(div,2);{var consequent_2=$$anchor2=>{var div_2=root_9$8(),node_12=child(div_2);{let $0=user_derived(()=>[{icon:Pencil,label:"Edit",onclick:handleEdit,shortcut:["shift","cmd","e"]},{icon:Download,label:"Export",onclick:e=>{e.stopPropagation(),conversationsStore.downloadConversation($$props.conversation.id)},shortcut:[
-"shift","cmd","s"]},{icon:Trash_2,label:"Delete",onclick:handleDelete2,variant:"destructive",shortcut:["shift","cmd","d"],separator:!0}]);DropdownMenuActions(node_12,{get triggerIcon(){return Ellipsis},triggerTooltip:"More actions",get actions(){return get$4($0)},get open(){return get$4(dropdownOpen)},set open($$value){set$1(dropdownOpen,$$value,!0)}})}reset(div_2),append($$anchor2,div_2)};if_block(node_11,$$render=>{get$4(renderActionsDropdown)&&$$render(consequent_2)})}reset(button),template_effect(
-()=>{set_class(button,1,`group flex min-h-9 w-full cursor-pointer items-center justify-between space-x-3 rounded-lg py-1.5 text-left transition-colors hover:bg-foreground/10 ${isActive()?"bg-foreground/5 text-accent-foreground":""} px-3`,"svelte-76ksb2"),styles2=set_style(div,"",styles2,{"padding-left":`${depth()*FORK_TREE_DEPTH_PADDING}px`}),set_text(text2,$$props.conversation.name)}),event("mouseleave",button,handleMouseLeave),append($$anchor,button),pop()}delegate(["click","mouseover","keydow\
-n"]);const emptyOptions$7={};function toString$1(value,options){const settings=emptyOptions$7,includeImageAlt=typeof settings.includeImageAlt=="boolean"?settings.includeImageAlt:!0,includeHtml=typeof settings.includeHtml=="boolean"?settings.includeHtml:!0;return one$2(value,includeImageAlt,includeHtml)}function one$2(value,includeImageAlt,includeHtml){if(node(value)){if("value"in value)return value.type==="html"&&!includeHtml?"":value.value;if(includeImageAlt&&"alt"in value&&value.alt)return value.
-alt;if("children"in value)return all$2(value.children,includeImageAlt,includeHtml)}return Array.isArray(value)?all$2(value,includeImageAlt,includeHtml):""}function all$2(values,includeImageAlt,includeHtml){const result=[];let index2=-1;for(;++index2<values.length;)result[index2]=one$2(values[index2],includeImageAlt,includeHtml);return result.join("")}function node(value){return!!(value&&typeof value=="object")}const element$3=document.createElement("i");function decodeNamedCharacterReference(value){
-const characterReference2="&"+value+";";element$3.innerHTML=characterReference2;const character=element$3.textContent;return character.charCodeAt(character.length-1)===59&&value!=="semi"||character===characterReference2?!1:character}function splice(list2,start2,remove2,items2){const end=list2.length;let chunkStart=0,parameters;if(start2<0?start2=-start2>end?0:end+start2:start2=start2>end?end:start2,remove2=remove2>0?remove2:0,items2.length<1e4)parameters=Array.from(items2),parameters.unshift(start2,
-remove2),list2.splice(...parameters);else for(remove2&&list2.splice(start2,remove2);chunkStart<items2.length;)parameters=items2.slice(chunkStart,chunkStart+1e4),parameters.unshift(start2,0),list2.splice(...parameters),chunkStart+=1e4,start2+=1e4}function push(list2,items2){return list2.length>0?(splice(list2,list2.length,0,items2),list2):items2}const hasOwnProperty={}.hasOwnProperty;function combineExtensions(extensions){const all2={};let index2=-1;for(;++index2<extensions.length;)syntaxExtension(
-all2,extensions[index2]);return all2}function syntaxExtension(all2,extension2){let hook;for(hook in extension2){const left=(hasOwnProperty.call(all2,hook)?all2[hook]:void 0)||(all2[hook]={}),right=extension2[hook];let code2;if(right)for(code2 in right){hasOwnProperty.call(left,code2)||(left[code2]=[]);const value=right[code2];constructs(left[code2],Array.isArray(value)?value:value?[value]:[])}}}function constructs(existing,list2){let index2=-1;const before=[];for(;++index2<list2.length;)(list2[index2].
-add==="after"?existing:before).push(list2[index2]);splice(existing,0,0,before)}function decodeNumericCharacterReference(value,base2){const code2=Number.parseInt(value,base2);return code2<9||code2===11||code2>13&&code2<32||code2>126&&code2<160||code2>55295&&code2<57344||code2>64975&&code2<65008||(code2&65535)===65535||(code2&65535)===65534||code2>1114111?"�":String.fromCodePoint(code2)}function normalizeIdentifier(value){return value.replace(/[\t\n\r ]+/g," ").replace(/^ | $/g,"").toLowerCase().
-toUpperCase()}const asciiAlpha=regexCheck(/[A-Za-z]/),asciiAlphanumeric=regexCheck(/[\dA-Za-z]/),asciiAtext=regexCheck(/[#-'*+\--9=?A-Z^-~]/);function asciiControl(code2){return code2!==null&&(code2<32||code2===127)}const asciiDigit=regexCheck(/\d/),asciiHexDigit=regexCheck(/[\dA-Fa-f]/),asciiPunctuation=regexCheck(/[!-/:-@[-`{-~]/);function markdownLineEnding(code2){return code2!==null&&code2<-2}function markdownLineEndingOrSpace(code2){return code2!==null&&(code2<0||code2===32)}function markdownSpace(code2){
-return code2===-2||code2===-1||code2===32}const unicodePunctuation=regexCheck(new RegExp("\\p{P}|\\p{S}","u")),unicodeWhitespace=regexCheck(/\s/);function regexCheck(regex){return check;function check(code2){return code2!==null&&code2>-1&&regex.test(String.fromCharCode(code2))}}function normalizeUri(value){const result=[];let index2=-1,start2=0,skip=0;for(;++index2<value.length;){const code2=value.charCodeAt(index2);let replace2="";if(code2===37&&asciiAlphanumeric(value.charCodeAt(index2+1))&&asciiAlphanumeric(
-value.charCodeAt(index2+2)))skip=2;else if(code2<128)/[!#$&-;=?-Z_a-z~]/.test(String.fromCharCode(code2))||(replace2=String.fromCharCode(code2));else if(code2>55295&&code2<57344){const next2=value.charCodeAt(index2+1);code2<56320&&next2>56319&&next2<57344?(replace2=String.fromCharCode(code2,next2),skip=1):replace2="�"}else replace2=String.fromCharCode(code2);replace2&&(result.push(value.slice(start2,index2),encodeURIComponent(replace2)),start2=index2+skip+1,replace2=""),skip&&(index2+=skip,skip=
-0)}return result.join("")+value.slice(start2)}function factorySpace(effects,ok,type2,max2){const limit2=max2?max2-1:Number.POSITIVE_INFINITY;let size2=0;return start2;function start2(code2){return markdownSpace(code2)?(effects.enter(type2),prefix(code2)):ok(code2)}function prefix(code2){return markdownSpace(code2)&&size2++<limit2?(effects.consume(code2),prefix):(effects.exit(type2),ok(code2))}}const content$1={tokenize:initializeContent};function initializeContent(effects){const contentStart=effects.
-attempt(this.parser.constructs.contentInitial,afterContentStartConstruct,paragraphInitial);let previous2;return contentStart;function afterContentStartConstruct(code2){if(code2===null){effects.consume(code2);return}return effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),factorySpace(effects,contentStart,"linePrefix")}function paragraphInitial(code2){return effects.enter("paragraph"),lineStart(code2)}function lineStart(code2){const token2=effects.enter("chunkText",{contentType:"\
-text",previous:previous2});return previous2&&(previous2.next=token2),previous2=token2,data(code2)}function data(code2){if(code2===null){effects.exit("chunkText"),effects.exit("paragraph"),effects.consume(code2);return}return markdownLineEnding(code2)?(effects.consume(code2),effects.exit("chunkText"),lineStart):(effects.consume(code2),data)}}const document$2={tokenize:initializeDocument},containerConstruct={tokenize:tokenizeContainer};function initializeDocument(effects){const self2=this,stack=[];
-let continued=0,childFlow,childToken,lineStartOffset;return start2;function start2(code2){if(continued<stack.length){const item=stack[continued];return self2.containerState=item[1],effects.attempt(item[0].continuation,documentContinue,checkNewContainers)(code2)}return checkNewContainers(code2)}function documentContinue(code2){if(continued++,self2.containerState._closeFlow){self2.containerState._closeFlow=void 0,childFlow&&closeFlow();const indexBeforeExits=self2.events.length;let indexBeforeFlow=indexBeforeExits,
-point2;for(;indexBeforeFlow--;)if(self2.events[indexBeforeFlow][0]==="exit"&&self2.events[indexBeforeFlow][1].type==="chunkFlow"){point2=self2.events[indexBeforeFlow][1].end;break}exitContainers(continued);let index2=indexBeforeExits;for(;index2<self2.events.length;)self2.events[index2][1].end={...point2},index2++;return splice(self2.events,indexBeforeFlow+1,0,self2.events.slice(indexBeforeExits)),self2.events.length=index2,checkNewContainers(code2)}return start2(code2)}function checkNewContainers(code2){
-if(continued===stack.length){if(!childFlow)return documentContinued(code2);if(childFlow.currentConstruct&&childFlow.currentConstruct.concrete)return flowStart(code2);self2.interrupt=!!(childFlow.currentConstruct&&!childFlow._gfmTableDynamicInterruptHack)}return self2.containerState={},effects.check(containerConstruct,thereIsANewContainer,thereIsNoNewContainer)(code2)}function thereIsANewContainer(code2){return childFlow&&closeFlow(),exitContainers(continued),documentContinued(code2)}function thereIsNoNewContainer(code2){
-return self2.parser.lazy[self2.now().line]=continued!==stack.length,lineStartOffset=self2.now().offset,flowStart(code2)}function documentContinued(code2){return self2.containerState={},effects.attempt(containerConstruct,containerContinue,flowStart)(code2)}function containerContinue(code2){return continued++,stack.push([self2.currentConstruct,self2.containerState]),documentContinued(code2)}function flowStart(code2){if(code2===null){childFlow&&closeFlow(),exitContainers(0),effects.consume(code2);return}
-return childFlow=childFlow||self2.parser.flow(self2.now()),effects.enter("chunkFlow",{_tokenizer:childFlow,contentType:"flow",previous:childToken}),flowContinue(code2)}function flowContinue(code2){if(code2===null){writeToChild(effects.exit("chunkFlow"),!0),exitContainers(0),effects.consume(code2);return}return markdownLineEnding(code2)?(effects.consume(code2),writeToChild(effects.exit("chunkFlow")),continued=0,self2.interrupt=void 0,start2):(effects.consume(code2),flowContinue)}function writeToChild(token2,endOfFile){
-const stream=self2.sliceStream(token2);if(endOfFile&&stream.push(null),token2.previous=childToken,childToken&&(childToken.next=token2),childToken=token2,childFlow.defineSkip(token2.start),childFlow.write(stream),self2.parser.lazy[token2.start.line]){let index2=childFlow.events.length;for(;index2--;)if(childFlow.events[index2][1].start.offset<lineStartOffset&&(!childFlow.events[index2][1].end||childFlow.events[index2][1].end.offset>lineStartOffset))return;const indexBeforeExits=self2.events.length;
-let indexBeforeFlow=indexBeforeExits,seen2,point2;for(;indexBeforeFlow--;)if(self2.events[indexBeforeFlow][0]==="exit"&&self2.events[indexBeforeFlow][1].type==="chunkFlow"){if(seen2){point2=self2.events[indexBeforeFlow][1].end;break}seen2=!0}for(exitContainers(continued),index2=indexBeforeExits;index2<self2.events.length;)self2.events[index2][1].end={...point2},index2++;splice(self2.events,indexBeforeFlow+1,0,self2.events.slice(indexBeforeExits)),self2.events.length=index2}}function exitContainers(size2){
-let index2=stack.length;for(;index2-- >size2;){const entry=stack[index2];self2.containerState=entry[1],entry[0].exit.call(self2,effects)}stack.length=size2}function closeFlow(){childFlow.write([null]),childToken=void 0,childFlow=void 0,self2.containerState._closeFlow=void 0}}function tokenizeContainer(effects,ok,nok){return factorySpace(effects,effects.attempt(this.parser.constructs.document,ok,nok),"linePrefix",this.parser.constructs.disable.null.includes("codeIndented")?void 0:4)}function classifyCharacter(code2){
-if(code2===null||markdownLineEndingOrSpace(code2)||unicodeWhitespace(code2))return 1;if(unicodePunctuation(code2))return 2}function resolveAll(constructs2,events,context){const called=[];let index2=-1;for(;++index2<constructs2.length;){const resolve2=constructs2[index2].resolveAll;resolve2&&!called.includes(resolve2)&&(events=resolve2(events,context),called.push(resolve2))}return events}const attention={name:"attention",resolveAll:resolveAllAttention,tokenize:tokenizeAttention};function resolveAllAttention(events,context){
-let index2=-1,open2,group,text2,openingSequence,closingSequence,use,nextEvents,offset2;for(;++index2<events.length;)if(events[index2][0]==="enter"&&events[index2][1].type==="attentionSequence"&&events[index2][1]._close){for(open2=index2;open2--;)if(events[open2][0]==="exit"&&events[open2][1].type==="attentionSequence"&&events[open2][1]._open&&context.sliceSerialize(events[open2][1]).charCodeAt(0)===context.sliceSerialize(events[index2][1]).charCodeAt(0)){if((events[open2][1]._close||events[index2][1].
-_open)&&(events[index2][1].end.offset-events[index2][1].start.offset)%3&&!((events[open2][1].end.offset-events[open2][1].start.offset+events[index2][1].end.offset-events[index2][1].start.offset)%3))continue;use=events[open2][1].end.offset-events[open2][1].start.offset>1&&events[index2][1].end.offset-events[index2][1].start.offset>1?2:1;const start2={...events[open2][1].end},end={...events[index2][1].start};movePoint(start2,-use),movePoint(end,use),openingSequence={type:use>1?"strongSequence":"em\
-phasisSequence",start:start2,end:{...events[open2][1].end}},closingSequence={type:use>1?"strongSequence":"emphasisSequence",start:{...events[index2][1].start},end},text2={type:use>1?"strongText":"emphasisText",start:{...events[open2][1].end},end:{...events[index2][1].start}},group={type:use>1?"strong":"emphasis",start:{...openingSequence.start},end:{...closingSequence.end}},events[open2][1].end={...openingSequence.start},events[index2][1].start={...closingSequence.end},nextEvents=[],events[open2][1].
-end.offset-events[open2][1].start.offset&&(nextEvents=push(nextEvents,[["enter",events[open2][1],context],["exit",events[open2][1],context]])),nextEvents=push(nextEvents,[["enter",group,context],["enter",openingSequence,context],["exit",openingSequence,context],["enter",text2,context]]),nextEvents=push(nextEvents,resolveAll(context.parser.constructs.insideSpan.null,events.slice(open2+1,index2),context)),nextEvents=push(nextEvents,[["exit",text2,context],["enter",closingSequence,context],["exit",
-closingSequence,context],["exit",group,context]]),events[index2][1].end.offset-events[index2][1].start.offset?(offset2=2,nextEvents=push(nextEvents,[["enter",events[index2][1],context],["exit",events[index2][1],context]])):offset2=0,splice(events,open2-1,index2-open2+3,nextEvents),index2=open2+nextEvents.length-offset2-2;break}}for(index2=-1;++index2<events.length;)events[index2][1].type==="attentionSequence"&&(events[index2][1].type="data");return events}function tokenizeAttention(effects,ok){const attentionMarkers2=this.
-parser.constructs.attentionMarkers.null,previous2=this.previous,before=classifyCharacter(previous2);let marker;return start2;function start2(code2){return marker=code2,effects.enter("attentionSequence"),inside(code2)}function inside(code2){if(code2===marker)return effects.consume(code2),inside;const token2=effects.exit("attentionSequence"),after=classifyCharacter(code2),open2=!after||after===2&&before||attentionMarkers2.includes(code2),close2=!before||before===2&&after||attentionMarkers2.includes(
-previous2);return token2._open=!!(marker===42?open2:open2&&(before||!close2)),token2._close=!!(marker===42?close2:close2&&(after||!open2)),ok(code2)}}function movePoint(point2,offset2){point2.column+=offset2,point2.offset+=offset2,point2._bufferIndex+=offset2}const autolink={name:"autolink",tokenize:tokenizeAutolink};function tokenizeAutolink(effects,ok,nok){let size2=0;return start2;function start2(code2){return effects.enter("autolink"),effects.enter("autolinkMarker"),effects.consume(code2),effects.
-exit("autolinkMarker"),effects.enter("autolinkProtocol"),open2}function open2(code2){return asciiAlpha(code2)?(effects.consume(code2),schemeOrEmailAtext):code2===64?nok(code2):emailAtext(code2)}function schemeOrEmailAtext(code2){return code2===43||code2===45||code2===46||asciiAlphanumeric(code2)?(size2=1,schemeInsideOrEmailAtext(code2)):emailAtext(code2)}function schemeInsideOrEmailAtext(code2){return code2===58?(effects.consume(code2),size2=0,urlInside):(code2===43||code2===45||code2===46||asciiAlphanumeric(
-code2))&&size2++<32?(effects.consume(code2),schemeInsideOrEmailAtext):(size2=0,emailAtext(code2))}function urlInside(code2){return code2===62?(effects.exit("autolinkProtocol"),effects.enter("autolinkMarker"),effects.consume(code2),effects.exit("autolinkMarker"),effects.exit("autolink"),ok):code2===null||code2===32||code2===60||asciiControl(code2)?nok(code2):(effects.consume(code2),urlInside)}function emailAtext(code2){return code2===64?(effects.consume(code2),emailAtSignOrDot):asciiAtext(code2)?
-(effects.consume(code2),emailAtext):nok(code2)}function emailAtSignOrDot(code2){return asciiAlphanumeric(code2)?emailLabel(code2):nok(code2)}function emailLabel(code2){return code2===46?(effects.consume(code2),size2=0,emailAtSignOrDot):code2===62?(effects.exit("autolinkProtocol").type="autolinkEmail",effects.enter("autolinkMarker"),effects.consume(code2),effects.exit("autolinkMarker"),effects.exit("autolink"),ok):emailValue(code2)}function emailValue(code2){if((code2===45||asciiAlphanumeric(code2))&&
-size2++<63){const next2=code2===45?emailValue:emailLabel;return effects.consume(code2),next2}return nok(code2)}}const blankLine={partial:!0,tokenize:tokenizeBlankLine};function tokenizeBlankLine(effects,ok,nok){return start2;function start2(code2){return markdownSpace(code2)?factorySpace(effects,after,"linePrefix")(code2):after(code2)}function after(code2){return code2===null||markdownLineEnding(code2)?ok(code2):nok(code2)}}const blockQuote={continuation:{tokenize:tokenizeBlockQuoteContinuation},
-exit:exit$1,name:"blockQuote",tokenize:tokenizeBlockQuoteStart};function tokenizeBlockQuoteStart(effects,ok,nok){const self2=this;return start2;function start2(code2){if(code2===62){const state2=self2.containerState;return state2.open||(effects.enter("blockQuote",{_container:!0}),state2.open=!0),effects.enter("blockQuotePrefix"),effects.enter("blockQuoteMarker"),effects.consume(code2),effects.exit("blockQuoteMarker"),after}return nok(code2)}function after(code2){return markdownSpace(code2)?(effects.
-enter("blockQuotePrefixWhitespace"),effects.consume(code2),effects.exit("blockQuotePrefixWhitespace"),effects.exit("blockQuotePrefix"),ok):(effects.exit("blockQuotePrefix"),ok(code2))}}function tokenizeBlockQuoteContinuation(effects,ok,nok){const self2=this;return contStart;function contStart(code2){return markdownSpace(code2)?factorySpace(effects,contBefore,"linePrefix",self2.parser.constructs.disable.null.includes("codeIndented")?void 0:4)(code2):contBefore(code2)}function contBefore(code2){return effects.
-attempt(blockQuote,ok,nok)(code2)}}function exit$1(effects){effects.exit("blockQuote")}const characterEscape={name:"characterEscape",tokenize:tokenizeCharacterEscape};function tokenizeCharacterEscape(effects,ok,nok){return start2;function start2(code2){return effects.enter("characterEscape"),effects.enter("escapeMarker"),effects.consume(code2),effects.exit("escapeMarker"),inside}function inside(code2){return asciiPunctuation(code2)?(effects.enter("characterEscapeValue"),effects.consume(code2),effects.
-exit("characterEscapeValue"),effects.exit("characterEscape"),ok):nok(code2)}}const characterReference={name:"characterReference",tokenize:tokenizeCharacterReference};function tokenizeCharacterReference(effects,ok,nok){const self2=this;let size2=0,max2,test;return start2;function start2(code2){return effects.enter("characterReference"),effects.enter("characterReferenceMarker"),effects.consume(code2),effects.exit("characterReferenceMarker"),open2}function open2(code2){return code2===35?(effects.enter(
-"characterReferenceMarkerNumeric"),effects.consume(code2),effects.exit("characterReferenceMarkerNumeric"),numeric):(effects.enter("characterReferenceValue"),max2=31,test=asciiAlphanumeric,value(code2))}function numeric(code2){return code2===88||code2===120?(effects.enter("characterReferenceMarkerHexadecimal"),effects.consume(code2),effects.exit("characterReferenceMarkerHexadecimal"),effects.enter("characterReferenceValue"),max2=6,test=asciiHexDigit,value):(effects.enter("characterReferenceValue"),
-max2=7,test=asciiDigit,value(code2))}function value(code2){if(code2===59&&size2){const token2=effects.exit("characterReferenceValue");return test===asciiAlphanumeric&&!decodeNamedCharacterReference(self2.sliceSerialize(token2))?nok(code2):(effects.enter("characterReferenceMarker"),effects.consume(code2),effects.exit("characterReferenceMarker"),effects.exit("characterReference"),ok)}return test(code2)&&size2++<max2?(effects.consume(code2),value):nok(code2)}}const nonLazyContinuation$1={partial:!0,
-tokenize:tokenizeNonLazyContinuation$1},codeFenced={concrete:!0,name:"codeFenced",tokenize:tokenizeCodeFenced};function tokenizeCodeFenced(effects,ok,nok){const self2=this,closeStart={partial:!0,tokenize:tokenizeCloseStart};let initialPrefix=0,sizeOpen=0,marker;return start2;function start2(code2){return beforeSequenceOpen(code2)}function beforeSequenceOpen(code2){const tail=self2.events[self2.events.length-1];return initialPrefix=tail&&tail[1].type==="linePrefix"?tail[2].sliceSerialize(tail[1],
-!0).length:0,marker=code2,effects.enter("codeFenced"),effects.enter("codeFencedFence"),effects.enter("codeFencedFenceSequence"),sequenceOpen(code2)}function sequenceOpen(code2){return code2===marker?(sizeOpen++,effects.consume(code2),sequenceOpen):sizeOpen<3?nok(code2):(effects.exit("codeFencedFenceSequence"),markdownSpace(code2)?factorySpace(effects,infoBefore,"whitespace")(code2):infoBefore(code2))}function infoBefore(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("codeFen\
-cedFence"),self2.interrupt?ok(code2):effects.check(nonLazyContinuation$1,atNonLazyBreak,after)(code2)):(effects.enter("codeFencedFenceInfo"),effects.enter("chunkString",{contentType:"string"}),info2(code2))}function info2(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("chunkString"),effects.exit("codeFencedFenceInfo"),infoBefore(code2)):markdownSpace(code2)?(effects.exit("chunkString"),effects.exit("codeFencedFenceInfo"),factorySpace(effects,metaBefore,"whitespace")(code2)):
-code2===96&&code2===marker?nok(code2):(effects.consume(code2),info2)}function metaBefore(code2){return code2===null||markdownLineEnding(code2)?infoBefore(code2):(effects.enter("codeFencedFenceMeta"),effects.enter("chunkString",{contentType:"string"}),meta(code2))}function meta(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("chunkString"),effects.exit("codeFencedFenceMeta"),infoBefore(code2)):code2===96&&code2===marker?nok(code2):(effects.consume(code2),meta)}function atNonLazyBreak(code2){
-return effects.attempt(closeStart,after,contentBefore)(code2)}function contentBefore(code2){return effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),contentStart}function contentStart(code2){return initialPrefix>0&&markdownSpace(code2)?factorySpace(effects,beforeContentChunk,"linePrefix",initialPrefix+1)(code2):beforeContentChunk(code2)}function beforeContentChunk(code2){return code2===null||markdownLineEnding(code2)?effects.check(nonLazyContinuation$1,atNonLazyBreak,
-after)(code2):(effects.enter("codeFlowValue"),contentChunk(code2))}function contentChunk(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("codeFlowValue"),beforeContentChunk(code2)):(effects.consume(code2),contentChunk)}function after(code2){return effects.exit("codeFenced"),ok(code2)}function tokenizeCloseStart(effects2,ok2,nok2){let size2=0;return startBefore;function startBefore(code2){return effects2.enter("lineEnding"),effects2.consume(code2),effects2.exit("lineEnding"),start3}
-function start3(code2){return effects2.enter("codeFencedFence"),markdownSpace(code2)?factorySpace(effects2,beforeSequenceClose,"linePrefix",self2.parser.constructs.disable.null.includes("codeIndented")?void 0:4)(code2):beforeSequenceClose(code2)}function beforeSequenceClose(code2){return code2===marker?(effects2.enter("codeFencedFenceSequence"),sequenceClose(code2)):nok2(code2)}function sequenceClose(code2){return code2===marker?(size2++,effects2.consume(code2),sequenceClose):size2>=sizeOpen?(effects2.
-exit("codeFencedFenceSequence"),markdownSpace(code2)?factorySpace(effects2,sequenceCloseAfter,"whitespace")(code2):sequenceCloseAfter(code2)):nok2(code2)}function sequenceCloseAfter(code2){return code2===null||markdownLineEnding(code2)?(effects2.exit("codeFencedFence"),ok2(code2)):nok2(code2)}}}function tokenizeNonLazyContinuation$1(effects,ok,nok){const self2=this;return start2;function start2(code2){return code2===null?nok(code2):(effects.enter("lineEnding"),effects.consume(code2),effects.exit(
-"lineEnding"),lineStart)}function lineStart(code2){return self2.parser.lazy[self2.now().line]?nok(code2):ok(code2)}}const codeIndented={name:"codeIndented",tokenize:tokenizeCodeIndented},furtherStart={partial:!0,tokenize:tokenizeFurtherStart};function tokenizeCodeIndented(effects,ok,nok){const self2=this;return start2;function start2(code2){return effects.enter("codeIndented"),factorySpace(effects,afterPrefix,"linePrefix",5)(code2)}function afterPrefix(code2){const tail=self2.events[self2.events.
-length-1];return tail&&tail[1].type==="linePrefix"&&tail[2].sliceSerialize(tail[1],!0).length>=4?atBreak(code2):nok(code2)}function atBreak(code2){return code2===null?after(code2):markdownLineEnding(code2)?effects.attempt(furtherStart,atBreak,after)(code2):(effects.enter("codeFlowValue"),inside(code2))}function inside(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("codeFlowValue"),atBreak(code2)):(effects.consume(code2),inside)}function after(code2){return effects.exit("code\
-Indented"),ok(code2)}}function tokenizeFurtherStart(effects,ok,nok){const self2=this;return furtherStart2;function furtherStart2(code2){return self2.parser.lazy[self2.now().line]?nok(code2):markdownLineEnding(code2)?(effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),furtherStart2):factorySpace(effects,afterPrefix,"linePrefix",5)(code2)}function afterPrefix(code2){const tail=self2.events[self2.events.length-1];return tail&&tail[1].type==="linePrefix"&&tail[2].sliceSerialize(
-tail[1],!0).length>=4?ok(code2):markdownLineEnding(code2)?furtherStart2(code2):nok(code2)}}const codeText={name:"codeText",previous:previous$2,resolve:resolveCodeText,tokenize:tokenizeCodeText};function resolveCodeText(events){let tailExitIndex=events.length-4,headEnterIndex=3,index2,enter;if((events[headEnterIndex][1].type==="lineEnding"||events[headEnterIndex][1].type==="space")&&(events[tailExitIndex][1].type==="lineEnding"||events[tailExitIndex][1].type==="space")){for(index2=headEnterIndex;++index2<
-tailExitIndex;)if(events[index2][1].type==="codeTextData"){events[headEnterIndex][1].type="codeTextPadding",events[tailExitIndex][1].type="codeTextPadding",headEnterIndex+=2,tailExitIndex-=2;break}}for(index2=headEnterIndex-1,tailExitIndex++;++index2<=tailExitIndex;)enter===void 0?index2!==tailExitIndex&&events[index2][1].type!=="lineEnding"&&(enter=index2):(index2===tailExitIndex||events[index2][1].type==="lineEnding")&&(events[enter][1].type="codeTextData",index2!==enter+2&&(events[enter][1].end=
-events[index2-1][1].end,events.splice(enter+2,index2-enter-2),tailExitIndex-=index2-enter-2,index2=enter+2),enter=void 0);return events}function previous$2(code2){return code2!==96||this.events[this.events.length-1][1].type==="characterEscape"}function tokenizeCodeText(effects,ok,nok){let sizeOpen=0,size2,token2;return start2;function start2(code2){return effects.enter("codeText"),effects.enter("codeTextSequence"),sequenceOpen(code2)}function sequenceOpen(code2){return code2===96?(effects.consume(
-code2),sizeOpen++,sequenceOpen):(effects.exit("codeTextSequence"),between2(code2))}function between2(code2){return code2===null?nok(code2):code2===32?(effects.enter("space"),effects.consume(code2),effects.exit("space"),between2):code2===96?(token2=effects.enter("codeTextSequence"),size2=0,sequenceClose(code2)):markdownLineEnding(code2)?(effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),between2):(effects.enter("codeTextData"),data(code2))}function data(code2){return code2===
-null||code2===32||code2===96||markdownLineEnding(code2)?(effects.exit("codeTextData"),between2(code2)):(effects.consume(code2),data)}function sequenceClose(code2){return code2===96?(effects.consume(code2),size2++,sequenceClose):size2===sizeOpen?(effects.exit("codeTextSequence"),effects.exit("codeText"),ok(code2)):(token2.type="codeTextData",data(code2))}}class SpliceBuffer{constructor(initial){this.left=initial?[...initial]:[],this.right=[]}get(index2){if(index2<0||index2>=this.left.length+this.
-right.length)throw new RangeError("Cannot access index `"+index2+"` in a splice buffer of size `"+(this.left.length+this.right.length)+"`");return index2<this.left.length?this.left[index2]:this.right[this.right.length-index2+this.left.length-1]}get length(){return this.left.length+this.right.length}shift(){return this.setCursor(0),this.right.pop()}slice(start2,end){const stop=end??Number.POSITIVE_INFINITY;return stop<this.left.length?this.left.slice(start2,stop):start2>this.left.length?this.right.
-slice(this.right.length-stop+this.left.length,this.right.length-start2+this.left.length).reverse():this.left.slice(start2).concat(this.right.slice(this.right.length-stop+this.left.length).reverse())}splice(start2,deleteCount,items2){const count=deleteCount||0;this.setCursor(Math.trunc(start2));const removed=this.right.splice(this.right.length-count,Number.POSITIVE_INFINITY);return items2&&chunkedPush(this.left,items2),removed.reverse()}pop(){return this.setCursor(Number.POSITIVE_INFINITY),this.left.
-pop()}push(item){this.setCursor(Number.POSITIVE_INFINITY),this.left.push(item)}pushMany(items2){this.setCursor(Number.POSITIVE_INFINITY),chunkedPush(this.left,items2)}unshift(item){this.setCursor(0),this.right.push(item)}unshiftMany(items2){this.setCursor(0),chunkedPush(this.right,items2.reverse())}setCursor(n){if(!(n===this.left.length||n>this.left.length&&this.right.length===0||n<0&&this.left.length===0))if(n<this.left.length){const removed=this.left.splice(n,Number.POSITIVE_INFINITY);chunkedPush(
-this.right,removed.reverse())}else{const removed=this.right.splice(this.left.length+this.right.length-n,Number.POSITIVE_INFINITY);chunkedPush(this.left,removed.reverse())}}}function chunkedPush(list2,right){let chunkStart=0;if(right.length<1e4)list2.push(...right);else for(;chunkStart<right.length;)list2.push(...right.slice(chunkStart,chunkStart+1e4)),chunkStart+=1e4}function subtokenize(eventsArray){const jumps={};let index2=-1,event2,lineIndex,otherIndex,otherEvent,parameters,subevents,more;const events=new SpliceBuffer(
-eventsArray);for(;++index2<events.length;){for(;index2 in jumps;)index2=jumps[index2];if(event2=events.get(index2),index2&&event2[1].type==="chunkFlow"&&events.get(index2-1)[1].type==="listItemPrefix"&&(subevents=event2[1]._tokenizer.events,otherIndex=0,otherIndex<subevents.length&&subevents[otherIndex][1].type==="lineEndingBlank"&&(otherIndex+=2),otherIndex<subevents.length&&subevents[otherIndex][1].type==="content"))for(;++otherIndex<subevents.length&&subevents[otherIndex][1].type!=="content";)
-subevents[otherIndex][1].type==="chunkText"&&(subevents[otherIndex][1]._isInFirstContentOfListItem=!0,otherIndex++);if(event2[0]==="enter")event2[1].contentType&&(Object.assign(jumps,subcontent(events,index2)),index2=jumps[index2],more=!0);else if(event2[1]._container){for(otherIndex=index2,lineIndex=void 0;otherIndex--;)if(otherEvent=events.get(otherIndex),otherEvent[1].type==="lineEnding"||otherEvent[1].type==="lineEndingBlank")otherEvent[0]==="enter"&&(lineIndex&&(events.get(lineIndex)[1].type=
-"lineEndingBlank"),otherEvent[1].type="lineEnding",lineIndex=otherIndex);else if(!(otherEvent[1].type==="linePrefix"||otherEvent[1].type==="listItemIndent"))break;lineIndex&&(event2[1].end={...events.get(lineIndex)[1].start},parameters=events.slice(lineIndex,index2),parameters.unshift(event2),events.splice(lineIndex,index2-lineIndex+1,parameters))}}return splice(eventsArray,0,Number.POSITIVE_INFINITY,events.slice(0)),!more}function subcontent(events,eventIndex){const token2=events.get(eventIndex)[1],
-context=events.get(eventIndex)[2];let startPosition=eventIndex-1;const startPositions=[];let tokenizer=token2._tokenizer;tokenizer||(tokenizer=context.parser[token2.contentType](token2.start),token2._contentTypeTextTrailing&&(tokenizer._contentTypeTextTrailing=!0));const childEvents=tokenizer.events,jumps=[],gaps={};let stream,previous2,index2=-1,current2=token2,adjust=0,start2=0;const breaks=[start2];for(;current2;){for(;events.get(++startPosition)[1]!==current2;);startPositions.push(startPosition),
-current2._tokenizer||(stream=context.sliceStream(current2),current2.next||stream.push(null),previous2&&tokenizer.defineSkip(current2.start),current2._isInFirstContentOfListItem&&(tokenizer._gfmTasklistFirstContentOfListItem=!0),tokenizer.write(stream),current2._isInFirstContentOfListItem&&(tokenizer._gfmTasklistFirstContentOfListItem=void 0)),previous2=current2,current2=current2.next}for(current2=token2;++index2<childEvents.length;)childEvents[index2][0]==="exit"&&childEvents[index2-1][0]==="ent\
-er"&&childEvents[index2][1].type===childEvents[index2-1][1].type&&childEvents[index2][1].start.line!==childEvents[index2][1].end.line&&(start2=index2+1,breaks.push(start2),current2._tokenizer=void 0,current2.previous=void 0,current2=current2.next);for(tokenizer.events=[],current2?(current2._tokenizer=void 0,current2.previous=void 0):breaks.pop(),index2=breaks.length;index2--;){const slice=childEvents.slice(breaks[index2],breaks[index2+1]),start3=startPositions.pop();jumps.push([start3,start3+slice.
-length-1]),events.splice(start3,2,slice)}for(jumps.reverse(),index2=-1;++index2<jumps.length;)gaps[adjust+jumps[index2][0]]=adjust+jumps[index2][1],adjust+=jumps[index2][1]-jumps[index2][0]-1;return gaps}const content={resolve:resolveContent,tokenize:tokenizeContent},continuationConstruct={partial:!0,tokenize:tokenizeContinuation};function resolveContent(events){return subtokenize(events),events}function tokenizeContent(effects,ok){let previous2;return chunkStart;function chunkStart(code2){return effects.
-enter("content"),previous2=effects.enter("chunkContent",{contentType:"content"}),chunkInside(code2)}function chunkInside(code2){return code2===null?contentEnd(code2):markdownLineEnding(code2)?effects.check(continuationConstruct,contentContinue,contentEnd)(code2):(effects.consume(code2),chunkInside)}function contentEnd(code2){return effects.exit("chunkContent"),effects.exit("content"),ok(code2)}function contentContinue(code2){return effects.consume(code2),effects.exit("chunkContent"),previous2.next=
-effects.enter("chunkContent",{contentType:"content",previous:previous2}),previous2=previous2.next,chunkInside}}function tokenizeContinuation(effects,ok,nok){const self2=this;return startLookahead;function startLookahead(code2){return effects.exit("chunkContent"),effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),factorySpace(effects,prefixed,"linePrefix")}function prefixed(code2){if(code2===null||markdownLineEnding(code2))return nok(code2);const tail=self2.events[self2.
-events.length-1];return!self2.parser.constructs.disable.null.includes("codeIndented")&&tail&&tail[1].type==="linePrefix"&&tail[2].sliceSerialize(tail[1],!0).length>=4?ok(code2):effects.interrupt(self2.parser.constructs.flow,nok,ok)(code2)}}function factoryDestination(effects,ok,nok,type2,literalType,literalMarkerType,rawType,stringType,max2){const limit2=max2||Number.POSITIVE_INFINITY;let balance=0;return start2;function start2(code2){return code2===60?(effects.enter(type2),effects.enter(literalType),
-effects.enter(literalMarkerType),effects.consume(code2),effects.exit(literalMarkerType),enclosedBefore):code2===null||code2===32||code2===41||asciiControl(code2)?nok(code2):(effects.enter(type2),effects.enter(rawType),effects.enter(stringType),effects.enter("chunkString",{contentType:"string"}),raw2(code2))}function enclosedBefore(code2){return code2===62?(effects.enter(literalMarkerType),effects.consume(code2),effects.exit(literalMarkerType),effects.exit(literalType),effects.exit(type2),ok):(effects.
-enter(stringType),effects.enter("chunkString",{contentType:"string"}),enclosed(code2))}function enclosed(code2){return code2===62?(effects.exit("chunkString"),effects.exit(stringType),enclosedBefore(code2)):code2===null||code2===60||markdownLineEnding(code2)?nok(code2):(effects.consume(code2),code2===92?enclosedEscape:enclosed)}function enclosedEscape(code2){return code2===60||code2===62||code2===92?(effects.consume(code2),enclosed):enclosed(code2)}function raw2(code2){return!balance&&(code2===null||
-code2===41||markdownLineEndingOrSpace(code2))?(effects.exit("chunkString"),effects.exit(stringType),effects.exit(rawType),effects.exit(type2),ok(code2)):balance<limit2&&code2===40?(effects.consume(code2),balance++,raw2):code2===41?(effects.consume(code2),balance--,raw2):code2===null||code2===32||code2===40||asciiControl(code2)?nok(code2):(effects.consume(code2),code2===92?rawEscape:raw2)}function rawEscape(code2){return code2===40||code2===41||code2===92?(effects.consume(code2),raw2):raw2(code2)}}
-function factoryLabel(effects,ok,nok,type2,markerType,stringType){const self2=this;let size2=0,seen2;return start2;function start2(code2){return effects.enter(type2),effects.enter(markerType),effects.consume(code2),effects.exit(markerType),effects.enter(stringType),atBreak}function atBreak(code2){return size2>999||code2===null||code2===91||code2===93&&!seen2||code2===94&&!size2&&"_hiddenFootnoteSupport"in self2.parser.constructs?nok(code2):code2===93?(effects.exit(stringType),effects.enter(markerType),
-effects.consume(code2),effects.exit(markerType),effects.exit(type2),ok):markdownLineEnding(code2)?(effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),atBreak):(effects.enter("chunkString",{contentType:"string"}),labelInside(code2))}function labelInside(code2){return code2===null||code2===91||code2===93||markdownLineEnding(code2)||size2++>999?(effects.exit("chunkString"),atBreak(code2)):(effects.consume(code2),seen2||(seen2=!markdownSpace(code2)),code2===92?labelEscape:
-labelInside)}function labelEscape(code2){return code2===91||code2===92||code2===93?(effects.consume(code2),size2++,labelInside):labelInside(code2)}}function factoryTitle(effects,ok,nok,type2,markerType,stringType){let marker;return start2;function start2(code2){return code2===34||code2===39||code2===40?(effects.enter(type2),effects.enter(markerType),effects.consume(code2),effects.exit(markerType),marker=code2===40?41:code2,begin):nok(code2)}function begin(code2){return code2===marker?(effects.enter(
-markerType),effects.consume(code2),effects.exit(markerType),effects.exit(type2),ok):(effects.enter(stringType),atBreak(code2))}function atBreak(code2){return code2===marker?(effects.exit(stringType),begin(marker)):code2===null?nok(code2):markdownLineEnding(code2)?(effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),factorySpace(effects,atBreak,"linePrefix")):(effects.enter("chunkString",{contentType:"string"}),inside(code2))}function inside(code2){return code2===marker||
-code2===null||markdownLineEnding(code2)?(effects.exit("chunkString"),atBreak(code2)):(effects.consume(code2),code2===92?escape2:inside)}function escape2(code2){return code2===marker||code2===92?(effects.consume(code2),inside):inside(code2)}}function factoryWhitespace(effects,ok){let seen2;return start2;function start2(code2){return markdownLineEnding(code2)?(effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),seen2=!0,start2):markdownSpace(code2)?factorySpace(effects,start2,
-seen2?"linePrefix":"lineSuffix")(code2):ok(code2)}}const definition$1={name:"definition",tokenize:tokenizeDefinition},titleBefore={partial:!0,tokenize:tokenizeTitleBefore};function tokenizeDefinition(effects,ok,nok){const self2=this;let identifier2;return start2;function start2(code2){return effects.enter("definition"),before(code2)}function before(code2){return factoryLabel.call(self2,effects,labelAfter,nok,"definitionLabel","definitionLabelMarker","definitionLabelString")(code2)}function labelAfter(code2){
-return identifier2=normalizeIdentifier(self2.sliceSerialize(self2.events[self2.events.length-1][1]).slice(1,-1)),code2===58?(effects.enter("definitionMarker"),effects.consume(code2),effects.exit("definitionMarker"),markerAfter):nok(code2)}function markerAfter(code2){return markdownLineEndingOrSpace(code2)?factoryWhitespace(effects,destinationBefore)(code2):destinationBefore(code2)}function destinationBefore(code2){return factoryDestination(effects,destinationAfter,nok,"definitionDestination","de\
-finitionDestinationLiteral","definitionDestinationLiteralMarker","definitionDestinationRaw","definitionDestinationString")(code2)}function destinationAfter(code2){return effects.attempt(titleBefore,after,after)(code2)}function after(code2){return markdownSpace(code2)?factorySpace(effects,afterWhitespace,"whitespace")(code2):afterWhitespace(code2)}function afterWhitespace(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("definition"),self2.parser.defined.push(identifier2),ok(code2)):
-nok(code2)}}function tokenizeTitleBefore(effects,ok,nok){return titleBefore2;function titleBefore2(code2){return markdownLineEndingOrSpace(code2)?factoryWhitespace(effects,beforeMarker)(code2):nok(code2)}function beforeMarker(code2){return factoryTitle(effects,titleAfter,nok,"definitionTitle","definitionTitleMarker","definitionTitleString")(code2)}function titleAfter(code2){return markdownSpace(code2)?factorySpace(effects,titleAfterOptionalWhitespace,"whitespace")(code2):titleAfterOptionalWhitespace(
-code2)}function titleAfterOptionalWhitespace(code2){return code2===null||markdownLineEnding(code2)?ok(code2):nok(code2)}}const hardBreakEscape={name:"hardBreakEscape",tokenize:tokenizeHardBreakEscape};function tokenizeHardBreakEscape(effects,ok,nok){return start2;function start2(code2){return effects.enter("hardBreakEscape"),effects.consume(code2),after}function after(code2){return markdownLineEnding(code2)?(effects.exit("hardBreakEscape"),ok(code2)):nok(code2)}}const headingAtx={name:"headingAt\
-x",resolve:resolveHeadingAtx,tokenize:tokenizeHeadingAtx};function resolveHeadingAtx(events,context){let contentEnd=events.length-2,contentStart=3,content2,text2;return events[contentStart][1].type==="whitespace"&&(contentStart+=2),contentEnd-2>contentStart&&events[contentEnd][1].type==="whitespace"&&(contentEnd-=2),events[contentEnd][1].type==="atxHeadingSequence"&&(contentStart===contentEnd-1||contentEnd-4>contentStart&&events[contentEnd-2][1].type==="whitespace")&&(contentEnd-=contentStart+1===
-contentEnd?2:4),contentEnd>contentStart&&(content2={type:"atxHeadingText",start:events[contentStart][1].start,end:events[contentEnd][1].end},text2={type:"chunkText",start:events[contentStart][1].start,end:events[contentEnd][1].end,contentType:"text"},splice(events,contentStart,contentEnd-contentStart+1,[["enter",content2,context],["enter",text2,context],["exit",text2,context],["exit",content2,context]])),events}function tokenizeHeadingAtx(effects,ok,nok){let size2=0;return start2;function start2(code2){
-return effects.enter("atxHeading"),before(code2)}function before(code2){return effects.enter("atxHeadingSequence"),sequenceOpen(code2)}function sequenceOpen(code2){return code2===35&&size2++<6?(effects.consume(code2),sequenceOpen):code2===null||markdownLineEndingOrSpace(code2)?(effects.exit("atxHeadingSequence"),atBreak(code2)):nok(code2)}function atBreak(code2){return code2===35?(effects.enter("atxHeadingSequence"),sequenceFurther(code2)):code2===null||markdownLineEnding(code2)?(effects.exit("a\
-txHeading"),ok(code2)):markdownSpace(code2)?factorySpace(effects,atBreak,"whitespace")(code2):(effects.enter("atxHeadingText"),data(code2))}function sequenceFurther(code2){return code2===35?(effects.consume(code2),sequenceFurther):(effects.exit("atxHeadingSequence"),atBreak(code2))}function data(code2){return code2===null||code2===35||markdownLineEndingOrSpace(code2)?(effects.exit("atxHeadingText"),atBreak(code2)):(effects.consume(code2),data)}}const htmlBlockNames=["address","article","aside","\
-base","basefont","blockquote","body","caption","center","col","colgroup","dd","details","dialog","dir","div","dl","dt","fieldset","figcaption","figure","footer","form","frame","frameset","h1","h2","h3","h4","h5","h6","head","header","hr","html","iframe","legend","li","link","main","menu","menuitem","nav","noframes","ol","optgroup","option","p","param","search","section","summary","table","tbody","td","tfoot","th","thead","title","tr","track","ul"],htmlRawNames=["pre","script","style","textarea"],
-htmlFlow={concrete:!0,name:"htmlFlow",resolveTo:resolveToHtmlFlow,tokenize:tokenizeHtmlFlow},blankLineBefore={partial:!0,tokenize:tokenizeBlankLineBefore},nonLazyContinuationStart={partial:!0,tokenize:tokenizeNonLazyContinuationStart};function resolveToHtmlFlow(events){let index2=events.length;for(;index2--&&!(events[index2][0]==="enter"&&events[index2][1].type==="htmlFlow"););return index2>1&&events[index2-2][1].type==="linePrefix"&&(events[index2][1].start=events[index2-2][1].start,events[index2+
-1][1].start=events[index2-2][1].start,events.splice(index2-2,2)),events}function tokenizeHtmlFlow(effects,ok,nok){const self2=this;let marker,closingTag,buffer,index2,markerB;return start2;function start2(code2){return before(code2)}function before(code2){return effects.enter("htmlFlow"),effects.enter("htmlFlowData"),effects.consume(code2),open2}function open2(code2){return code2===33?(effects.consume(code2),declarationOpen):code2===47?(effects.consume(code2),closingTag=!0,tagCloseStart):code2===
-63?(effects.consume(code2),marker=3,self2.interrupt?ok:continuationDeclarationInside):asciiAlpha(code2)?(effects.consume(code2),buffer=String.fromCharCode(code2),tagName):nok(code2)}function declarationOpen(code2){return code2===45?(effects.consume(code2),marker=2,commentOpenInside):code2===91?(effects.consume(code2),marker=5,index2=0,cdataOpenInside):asciiAlpha(code2)?(effects.consume(code2),marker=4,self2.interrupt?ok:continuationDeclarationInside):nok(code2)}function commentOpenInside(code2){
-return code2===45?(effects.consume(code2),self2.interrupt?ok:continuationDeclarationInside):nok(code2)}function cdataOpenInside(code2){const value="CDATA[";return code2===value.charCodeAt(index2++)?(effects.consume(code2),index2===value.length?self2.interrupt?ok:continuation:cdataOpenInside):nok(code2)}function tagCloseStart(code2){return asciiAlpha(code2)?(effects.consume(code2),buffer=String.fromCharCode(code2),tagName):nok(code2)}function tagName(code2){if(code2===null||code2===47||code2===62||
-markdownLineEndingOrSpace(code2)){const slash=code2===47,name=buffer.toLowerCase();return!slash&&!closingTag&&htmlRawNames.includes(name)?(marker=1,self2.interrupt?ok(code2):continuation(code2)):htmlBlockNames.includes(buffer.toLowerCase())?(marker=6,slash?(effects.consume(code2),basicSelfClosing):self2.interrupt?ok(code2):continuation(code2)):(marker=7,self2.interrupt&&!self2.parser.lazy[self2.now().line]?nok(code2):closingTag?completeClosingTagAfter(code2):completeAttributeNameBefore(code2))}return code2===
-45||asciiAlphanumeric(code2)?(effects.consume(code2),buffer+=String.fromCharCode(code2),tagName):nok(code2)}function basicSelfClosing(code2){return code2===62?(effects.consume(code2),self2.interrupt?ok:continuation):nok(code2)}function completeClosingTagAfter(code2){return markdownSpace(code2)?(effects.consume(code2),completeClosingTagAfter):completeEnd(code2)}function completeAttributeNameBefore(code2){return code2===47?(effects.consume(code2),completeEnd):code2===58||code2===95||asciiAlpha(code2)?
-(effects.consume(code2),completeAttributeName):markdownSpace(code2)?(effects.consume(code2),completeAttributeNameBefore):completeEnd(code2)}function completeAttributeName(code2){return code2===45||code2===46||code2===58||code2===95||asciiAlphanumeric(code2)?(effects.consume(code2),completeAttributeName):completeAttributeNameAfter(code2)}function completeAttributeNameAfter(code2){return code2===61?(effects.consume(code2),completeAttributeValueBefore):markdownSpace(code2)?(effects.consume(code2),completeAttributeNameAfter):
-completeAttributeNameBefore(code2)}function completeAttributeValueBefore(code2){return code2===null||code2===60||code2===61||code2===62||code2===96?nok(code2):code2===34||code2===39?(effects.consume(code2),markerB=code2,completeAttributeValueQuoted):markdownSpace(code2)?(effects.consume(code2),completeAttributeValueBefore):completeAttributeValueUnquoted(code2)}function completeAttributeValueQuoted(code2){return code2===markerB?(effects.consume(code2),markerB=null,completeAttributeValueQuotedAfter):
-code2===null||markdownLineEnding(code2)?nok(code2):(effects.consume(code2),completeAttributeValueQuoted)}function completeAttributeValueUnquoted(code2){return code2===null||code2===34||code2===39||code2===47||code2===60||code2===61||code2===62||code2===96||markdownLineEndingOrSpace(code2)?completeAttributeNameAfter(code2):(effects.consume(code2),completeAttributeValueUnquoted)}function completeAttributeValueQuotedAfter(code2){return code2===47||code2===62||markdownSpace(code2)?completeAttributeNameBefore(
-code2):nok(code2)}function completeEnd(code2){return code2===62?(effects.consume(code2),completeAfter):nok(code2)}function completeAfter(code2){return code2===null||markdownLineEnding(code2)?continuation(code2):markdownSpace(code2)?(effects.consume(code2),completeAfter):nok(code2)}function continuation(code2){return code2===45&&marker===2?(effects.consume(code2),continuationCommentInside):code2===60&&marker===1?(effects.consume(code2),continuationRawTagOpen):code2===62&&marker===4?(effects.consume(
-code2),continuationClose):code2===63&&marker===3?(effects.consume(code2),continuationDeclarationInside):code2===93&&marker===5?(effects.consume(code2),continuationCdataInside):markdownLineEnding(code2)&&(marker===6||marker===7)?(effects.exit("htmlFlowData"),effects.check(blankLineBefore,continuationAfter,continuationStart)(code2)):code2===null||markdownLineEnding(code2)?(effects.exit("htmlFlowData"),continuationStart(code2)):(effects.consume(code2),continuation)}function continuationStart(code2){
-return effects.check(nonLazyContinuationStart,continuationStartNonLazy,continuationAfter)(code2)}function continuationStartNonLazy(code2){return effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),continuationBefore}function continuationBefore(code2){return code2===null||markdownLineEnding(code2)?continuationStart(code2):(effects.enter("htmlFlowData"),continuation(code2))}function continuationCommentInside(code2){return code2===45?(effects.consume(code2),continuationDeclarationInside):
-continuation(code2)}function continuationRawTagOpen(code2){return code2===47?(effects.consume(code2),buffer="",continuationRawEndTag):continuation(code2)}function continuationRawEndTag(code2){if(code2===62){const name=buffer.toLowerCase();return htmlRawNames.includes(name)?(effects.consume(code2),continuationClose):continuation(code2)}return asciiAlpha(code2)&&buffer.length<8?(effects.consume(code2),buffer+=String.fromCharCode(code2),continuationRawEndTag):continuation(code2)}function continuationCdataInside(code2){
-return code2===93?(effects.consume(code2),continuationDeclarationInside):continuation(code2)}function continuationDeclarationInside(code2){return code2===62?(effects.consume(code2),continuationClose):code2===45&&marker===2?(effects.consume(code2),continuationDeclarationInside):continuation(code2)}function continuationClose(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("htmlFlowData"),continuationAfter(code2)):(effects.consume(code2),continuationClose)}function continuationAfter(code2){
-return effects.exit("htmlFlow"),ok(code2)}}function tokenizeNonLazyContinuationStart(effects,ok,nok){const self2=this;return start2;function start2(code2){return markdownLineEnding(code2)?(effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),after):nok(code2)}function after(code2){return self2.parser.lazy[self2.now().line]?nok(code2):ok(code2)}}function tokenizeBlankLineBefore(effects,ok,nok){return start2;function start2(code2){return effects.enter("lineEnding"),effects.
-consume(code2),effects.exit("lineEnding"),effects.attempt(blankLine,ok,nok)}}const htmlText={name:"htmlText",tokenize:tokenizeHtmlText};function tokenizeHtmlText(effects,ok,nok){const self2=this;let marker,index2,returnState;return start2;function start2(code2){return effects.enter("htmlText"),effects.enter("htmlTextData"),effects.consume(code2),open2}function open2(code2){return code2===33?(effects.consume(code2),declarationOpen):code2===47?(effects.consume(code2),tagCloseStart):code2===63?(effects.
-consume(code2),instruction):asciiAlpha(code2)?(effects.consume(code2),tagOpen):nok(code2)}function declarationOpen(code2){return code2===45?(effects.consume(code2),commentOpenInside):code2===91?(effects.consume(code2),index2=0,cdataOpenInside):asciiAlpha(code2)?(effects.consume(code2),declaration):nok(code2)}function commentOpenInside(code2){return code2===45?(effects.consume(code2),commentEnd):nok(code2)}function comment2(code2){return code2===null?nok(code2):code2===45?(effects.consume(code2),
-commentClose):markdownLineEnding(code2)?(returnState=comment2,lineEndingBefore(code2)):(effects.consume(code2),comment2)}function commentClose(code2){return code2===45?(effects.consume(code2),commentEnd):comment2(code2)}function commentEnd(code2){return code2===62?end(code2):code2===45?commentClose(code2):comment2(code2)}function cdataOpenInside(code2){const value="CDATA[";return code2===value.charCodeAt(index2++)?(effects.consume(code2),index2===value.length?cdata:cdataOpenInside):nok(code2)}function cdata(code2){
-return code2===null?nok(code2):code2===93?(effects.consume(code2),cdataClose):markdownLineEnding(code2)?(returnState=cdata,lineEndingBefore(code2)):(effects.consume(code2),cdata)}function cdataClose(code2){return code2===93?(effects.consume(code2),cdataEnd):cdata(code2)}function cdataEnd(code2){return code2===62?end(code2):code2===93?(effects.consume(code2),cdataEnd):cdata(code2)}function declaration(code2){return code2===null||code2===62?end(code2):markdownLineEnding(code2)?(returnState=declaration,
-lineEndingBefore(code2)):(effects.consume(code2),declaration)}function instruction(code2){return code2===null?nok(code2):code2===63?(effects.consume(code2),instructionClose):markdownLineEnding(code2)?(returnState=instruction,lineEndingBefore(code2)):(effects.consume(code2),instruction)}function instructionClose(code2){return code2===62?end(code2):instruction(code2)}function tagCloseStart(code2){return asciiAlpha(code2)?(effects.consume(code2),tagClose):nok(code2)}function tagClose(code2){return code2===
-45||asciiAlphanumeric(code2)?(effects.consume(code2),tagClose):tagCloseBetween(code2)}function tagCloseBetween(code2){return markdownLineEnding(code2)?(returnState=tagCloseBetween,lineEndingBefore(code2)):markdownSpace(code2)?(effects.consume(code2),tagCloseBetween):end(code2)}function tagOpen(code2){return code2===45||asciiAlphanumeric(code2)?(effects.consume(code2),tagOpen):code2===47||code2===62||markdownLineEndingOrSpace(code2)?tagOpenBetween(code2):nok(code2)}function tagOpenBetween(code2){
-return code2===47?(effects.consume(code2),end):code2===58||code2===95||asciiAlpha(code2)?(effects.consume(code2),tagOpenAttributeName):markdownLineEnding(code2)?(returnState=tagOpenBetween,lineEndingBefore(code2)):markdownSpace(code2)?(effects.consume(code2),tagOpenBetween):end(code2)}function tagOpenAttributeName(code2){return code2===45||code2===46||code2===58||code2===95||asciiAlphanumeric(code2)?(effects.consume(code2),tagOpenAttributeName):tagOpenAttributeNameAfter(code2)}function tagOpenAttributeNameAfter(code2){
-return code2===61?(effects.consume(code2),tagOpenAttributeValueBefore):markdownLineEnding(code2)?(returnState=tagOpenAttributeNameAfter,lineEndingBefore(code2)):markdownSpace(code2)?(effects.consume(code2),tagOpenAttributeNameAfter):tagOpenBetween(code2)}function tagOpenAttributeValueBefore(code2){return code2===null||code2===60||code2===61||code2===62||code2===96?nok(code2):code2===34||code2===39?(effects.consume(code2),marker=code2,tagOpenAttributeValueQuoted):markdownLineEnding(code2)?(returnState=
-tagOpenAttributeValueBefore,lineEndingBefore(code2)):markdownSpace(code2)?(effects.consume(code2),tagOpenAttributeValueBefore):(effects.consume(code2),tagOpenAttributeValueUnquoted)}function tagOpenAttributeValueQuoted(code2){return code2===marker?(effects.consume(code2),marker=void 0,tagOpenAttributeValueQuotedAfter):code2===null?nok(code2):markdownLineEnding(code2)?(returnState=tagOpenAttributeValueQuoted,lineEndingBefore(code2)):(effects.consume(code2),tagOpenAttributeValueQuoted)}function tagOpenAttributeValueUnquoted(code2){
-return code2===null||code2===34||code2===39||code2===60||code2===61||code2===96?nok(code2):code2===47||code2===62||markdownLineEndingOrSpace(code2)?tagOpenBetween(code2):(effects.consume(code2),tagOpenAttributeValueUnquoted)}function tagOpenAttributeValueQuotedAfter(code2){return code2===47||code2===62||markdownLineEndingOrSpace(code2)?tagOpenBetween(code2):nok(code2)}function end(code2){return code2===62?(effects.consume(code2),effects.exit("htmlTextData"),effects.exit("htmlText"),ok):nok(code2)}
-function lineEndingBefore(code2){return effects.exit("htmlTextData"),effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),lineEndingAfter}function lineEndingAfter(code2){return markdownSpace(code2)?factorySpace(effects,lineEndingAfterPrefix,"linePrefix",self2.parser.constructs.disable.null.includes("codeIndented")?void 0:4)(code2):lineEndingAfterPrefix(code2)}function lineEndingAfterPrefix(code2){return effects.enter("htmlTextData"),returnState(code2)}}const labelEnd={name:"\
-labelEnd",resolveAll:resolveAllLabelEnd,resolveTo:resolveToLabelEnd,tokenize:tokenizeLabelEnd},resourceConstruct={tokenize:tokenizeResource},referenceFullConstruct={tokenize:tokenizeReferenceFull},referenceCollapsedConstruct={tokenize:tokenizeReferenceCollapsed};function resolveAllLabelEnd(events){let index2=-1;const newEvents=[];for(;++index2<events.length;){const token2=events[index2][1];if(newEvents.push(events[index2]),token2.type==="labelImage"||token2.type==="labelLink"||token2.type==="lab\
-elEnd"){const offset2=token2.type==="labelImage"?4:2;token2.type="data",index2+=offset2}}return events.length!==newEvents.length&&splice(events,0,events.length,newEvents),events}function resolveToLabelEnd(events,context){let index2=events.length,offset2=0,token2,open2,close2,media;for(;index2--;)if(token2=events[index2][1],open2){if(token2.type==="link"||token2.type==="labelLink"&&token2._inactive)break;events[index2][0]==="enter"&&token2.type==="labelLink"&&(token2._inactive=!0)}else if(close2){
-if(events[index2][0]==="enter"&&(token2.type==="labelImage"||token2.type==="labelLink")&&!token2._balanced&&(open2=index2,token2.type!=="labelLink")){offset2=2;break}}else token2.type==="labelEnd"&&(close2=index2);const group={type:events[open2][1].type==="labelLink"?"link":"image",start:{...events[open2][1].start},end:{...events[events.length-1][1].end}},label={type:"label",start:{...events[open2][1].start},end:{...events[close2][1].end}},text2={type:"labelText",start:{...events[open2+offset2+2][1].
-end},end:{...events[close2-2][1].start}};return media=[["enter",group,context],["enter",label,context]],media=push(media,events.slice(open2+1,open2+offset2+3)),media=push(media,[["enter",text2,context]]),media=push(media,resolveAll(context.parser.constructs.insideSpan.null,events.slice(open2+offset2+4,close2-3),context)),media=push(media,[["exit",text2,context],events[close2-2],events[close2-1],["exit",label,context]]),media=push(media,events.slice(close2+1)),media=push(media,[["exit",group,context]]),
-splice(events,open2,events.length,media),events}function tokenizeLabelEnd(effects,ok,nok){const self2=this;let index2=self2.events.length,labelStart,defined;for(;index2--;)if((self2.events[index2][1].type==="labelImage"||self2.events[index2][1].type==="labelLink")&&!self2.events[index2][1]._balanced){labelStart=self2.events[index2][1];break}return start2;function start2(code2){return labelStart?labelStart._inactive?labelEndNok(code2):(defined=self2.parser.defined.includes(normalizeIdentifier(self2.
-sliceSerialize({start:labelStart.end,end:self2.now()}))),effects.enter("labelEnd"),effects.enter("labelMarker"),effects.consume(code2),effects.exit("labelMarker"),effects.exit("labelEnd"),after):nok(code2)}function after(code2){return code2===40?effects.attempt(resourceConstruct,labelEndOk,defined?labelEndOk:labelEndNok)(code2):code2===91?effects.attempt(referenceFullConstruct,labelEndOk,defined?referenceNotFull:labelEndNok)(code2):defined?labelEndOk(code2):labelEndNok(code2)}function referenceNotFull(code2){
-return effects.attempt(referenceCollapsedConstruct,labelEndOk,labelEndNok)(code2)}function labelEndOk(code2){return ok(code2)}function labelEndNok(code2){return labelStart._balanced=!0,nok(code2)}}function tokenizeResource(effects,ok,nok){return resourceStart;function resourceStart(code2){return effects.enter("resource"),effects.enter("resourceMarker"),effects.consume(code2),effects.exit("resourceMarker"),resourceBefore}function resourceBefore(code2){return markdownLineEndingOrSpace(code2)?factoryWhitespace(
-effects,resourceOpen)(code2):resourceOpen(code2)}function resourceOpen(code2){return code2===41?resourceEnd(code2):factoryDestination(effects,resourceDestinationAfter,resourceDestinationMissing,"resourceDestination","resourceDestinationLiteral","resourceDestinationLiteralMarker","resourceDestinationRaw","resourceDestinationString",32)(code2)}function resourceDestinationAfter(code2){return markdownLineEndingOrSpace(code2)?factoryWhitespace(effects,resourceBetween)(code2):resourceEnd(code2)}function resourceDestinationMissing(code2){
-return nok(code2)}function resourceBetween(code2){return code2===34||code2===39||code2===40?factoryTitle(effects,resourceTitleAfter,nok,"resourceTitle","resourceTitleMarker","resourceTitleString")(code2):resourceEnd(code2)}function resourceTitleAfter(code2){return markdownLineEndingOrSpace(code2)?factoryWhitespace(effects,resourceEnd)(code2):resourceEnd(code2)}function resourceEnd(code2){return code2===41?(effects.enter("resourceMarker"),effects.consume(code2),effects.exit("resourceMarker"),effects.
-exit("resource"),ok):nok(code2)}}function tokenizeReferenceFull(effects,ok,nok){const self2=this;return referenceFull;function referenceFull(code2){return factoryLabel.call(self2,effects,referenceFullAfter,referenceFullMissing,"reference","referenceMarker","referenceString")(code2)}function referenceFullAfter(code2){return self2.parser.defined.includes(normalizeIdentifier(self2.sliceSerialize(self2.events[self2.events.length-1][1]).slice(1,-1)))?ok(code2):nok(code2)}function referenceFullMissing(code2){
-return nok(code2)}}function tokenizeReferenceCollapsed(effects,ok,nok){return referenceCollapsedStart;function referenceCollapsedStart(code2){return effects.enter("reference"),effects.enter("referenceMarker"),effects.consume(code2),effects.exit("referenceMarker"),referenceCollapsedOpen}function referenceCollapsedOpen(code2){return code2===93?(effects.enter("referenceMarker"),effects.consume(code2),effects.exit("referenceMarker"),effects.exit("reference"),ok):nok(code2)}}const labelStartImage={name:"\
-labelStartImage",resolveAll:labelEnd.resolveAll,tokenize:tokenizeLabelStartImage};function tokenizeLabelStartImage(effects,ok,nok){const self2=this;return start2;function start2(code2){return effects.enter("labelImage"),effects.enter("labelImageMarker"),effects.consume(code2),effects.exit("labelImageMarker"),open2}function open2(code2){return code2===91?(effects.enter("labelMarker"),effects.consume(code2),effects.exit("labelMarker"),effects.exit("labelImage"),after):nok(code2)}function after(code2){
-return code2===94&&"_hiddenFootnoteSupport"in self2.parser.constructs?nok(code2):ok(code2)}}const labelStartLink={name:"labelStartLink",resolveAll:labelEnd.resolveAll,tokenize:tokenizeLabelStartLink};function tokenizeLabelStartLink(effects,ok,nok){const self2=this;return start2;function start2(code2){return effects.enter("labelLink"),effects.enter("labelMarker"),effects.consume(code2),effects.exit("labelMarker"),effects.exit("labelLink"),after}function after(code2){return code2===94&&"_hiddenFoo\
-tnoteSupport"in self2.parser.constructs?nok(code2):ok(code2)}}const lineEnding={name:"lineEnding",tokenize:tokenizeLineEnding};function tokenizeLineEnding(effects,ok){return start2;function start2(code2){return effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),factorySpace(effects,ok,"linePrefix")}}const thematicBreak$2={name:"thematicBreak",tokenize:tokenizeThematicBreak};function tokenizeThematicBreak(effects,ok,nok){let size2=0,marker;return start2;function start2(code2){
-return effects.enter("thematicBreak"),before(code2)}function before(code2){return marker=code2,atBreak(code2)}function atBreak(code2){return code2===marker?(effects.enter("thematicBreakSequence"),sequence(code2)):size2>=3&&(code2===null||markdownLineEnding(code2))?(effects.exit("thematicBreak"),ok(code2)):nok(code2)}function sequence(code2){return code2===marker?(effects.consume(code2),size2++,sequence):(effects.exit("thematicBreakSequence"),markdownSpace(code2)?factorySpace(effects,atBreak,"whi\
-tespace")(code2):atBreak(code2))}}const list$3={continuation:{tokenize:tokenizeListContinuation},exit:tokenizeListEnd,name:"list",tokenize:tokenizeListStart},listItemPrefixWhitespaceConstruct={partial:!0,tokenize:tokenizeListItemPrefixWhitespace},indentConstruct={partial:!0,tokenize:tokenizeIndent$1};function tokenizeListStart(effects,ok,nok){const self2=this,tail=self2.events[self2.events.length-1];let initialSize=tail&&tail[1].type==="linePrefix"?tail[2].sliceSerialize(tail[1],!0).length:0,size2=0;
-return start2;function start2(code2){const kind=self2.containerState.type||(code2===42||code2===43||code2===45?"listUnordered":"listOrdered");if(kind==="listUnordered"?!self2.containerState.marker||code2===self2.containerState.marker:asciiDigit(code2)){if(self2.containerState.type||(self2.containerState.type=kind,effects.enter(kind,{_container:!0})),kind==="listUnordered")return effects.enter("listItemPrefix"),code2===42||code2===45?effects.check(thematicBreak$2,nok,atMarker)(code2):atMarker(code2);
-if(!self2.interrupt||code2===49)return effects.enter("listItemPrefix"),effects.enter("listItemValue"),inside(code2)}return nok(code2)}function inside(code2){return asciiDigit(code2)&&++size2<10?(effects.consume(code2),inside):(!self2.interrupt||size2<2)&&(self2.containerState.marker?code2===self2.containerState.marker:code2===41||code2===46)?(effects.exit("listItemValue"),atMarker(code2)):nok(code2)}function atMarker(code2){return effects.enter("listItemMarker"),effects.consume(code2),effects.exit(
-"listItemMarker"),self2.containerState.marker=self2.containerState.marker||code2,effects.check(blankLine,self2.interrupt?nok:onBlank,effects.attempt(listItemPrefixWhitespaceConstruct,endOfPrefix,otherPrefix))}function onBlank(code2){return self2.containerState.initialBlankLine=!0,initialSize++,endOfPrefix(code2)}function otherPrefix(code2){return markdownSpace(code2)?(effects.enter("listItemPrefixWhitespace"),effects.consume(code2),effects.exit("listItemPrefixWhitespace"),endOfPrefix):nok(code2)}
-function endOfPrefix(code2){return self2.containerState.size=initialSize+self2.sliceSerialize(effects.exit("listItemPrefix"),!0).length,ok(code2)}}function tokenizeListContinuation(effects,ok,nok){const self2=this;return self2.containerState._closeFlow=void 0,effects.check(blankLine,onBlank,notBlank);function onBlank(code2){return self2.containerState.furtherBlankLines=self2.containerState.furtherBlankLines||self2.containerState.initialBlankLine,factorySpace(effects,ok,"listItemIndent",self2.containerState.
-size+1)(code2)}function notBlank(code2){return self2.containerState.furtherBlankLines||!markdownSpace(code2)?(self2.containerState.furtherBlankLines=void 0,self2.containerState.initialBlankLine=void 0,notInCurrentItem(code2)):(self2.containerState.furtherBlankLines=void 0,self2.containerState.initialBlankLine=void 0,effects.attempt(indentConstruct,ok,notInCurrentItem)(code2))}function notInCurrentItem(code2){return self2.containerState._closeFlow=!0,self2.interrupt=void 0,factorySpace(effects,effects.
-attempt(list$3,ok,nok),"linePrefix",self2.parser.constructs.disable.null.includes("codeIndented")?void 0:4)(code2)}}function tokenizeIndent$1(effects,ok,nok){const self2=this;return factorySpace(effects,afterPrefix,"listItemIndent",self2.containerState.size+1);function afterPrefix(code2){const tail=self2.events[self2.events.length-1];return tail&&tail[1].type==="listItemIndent"&&tail[2].sliceSerialize(tail[1],!0).length===self2.containerState.size?ok(code2):nok(code2)}}function tokenizeListEnd(effects){
-effects.exit(this.containerState.type)}function tokenizeListItemPrefixWhitespace(effects,ok,nok){const self2=this;return factorySpace(effects,afterPrefix,"listItemPrefixWhitespace",self2.parser.constructs.disable.null.includes("codeIndented")?void 0:5);function afterPrefix(code2){const tail=self2.events[self2.events.length-1];return!markdownSpace(code2)&&tail&&tail[1].type==="listItemPrefixWhitespace"?ok(code2):nok(code2)}}const setextUnderline={name:"setextUnderline",resolveTo:resolveToSetextUnderline,
-tokenize:tokenizeSetextUnderline};function resolveToSetextUnderline(events,context){let index2=events.length,content2,text2,definition2;for(;index2--;)if(events[index2][0]==="enter"){if(events[index2][1].type==="content"){content2=index2;break}events[index2][1].type==="paragraph"&&(text2=index2)}else events[index2][1].type==="content"&&events.splice(index2,1),!definition2&&events[index2][1].type==="definition"&&(definition2=index2);const heading2={type:"setextHeading",start:{...events[content2][1].
-start},end:{...events[events.length-1][1].end}};return events[text2][1].type="setextHeadingText",definition2?(events.splice(text2,0,["enter",heading2,context]),events.splice(definition2+1,0,["exit",events[content2][1],context]),events[content2][1].end={...events[definition2][1].end}):events[content2][1]=heading2,events.push(["exit",heading2,context]),events}function tokenizeSetextUnderline(effects,ok,nok){const self2=this;let marker;return start2;function start2(code2){let index2=self2.events.length,
-paragraph2;for(;index2--;)if(self2.events[index2][1].type!=="lineEnding"&&self2.events[index2][1].type!=="linePrefix"&&self2.events[index2][1].type!=="content"){paragraph2=self2.events[index2][1].type==="paragraph";break}return!self2.parser.lazy[self2.now().line]&&(self2.interrupt||paragraph2)?(effects.enter("setextHeadingLine"),marker=code2,before(code2)):nok(code2)}function before(code2){return effects.enter("setextHeadingLineSequence"),inside(code2)}function inside(code2){return code2===marker?
-(effects.consume(code2),inside):(effects.exit("setextHeadingLineSequence"),markdownSpace(code2)?factorySpace(effects,after,"lineSuffix")(code2):after(code2))}function after(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("setextHeadingLine"),ok(code2)):nok(code2)}}const flow$1={tokenize:initializeFlow};function initializeFlow(effects){const self2=this,initial=effects.attempt(blankLine,atBlankEnding,effects.attempt(this.parser.constructs.flowInitial,afterConstruct,factorySpace(
-effects,effects.attempt(this.parser.constructs.flow,afterConstruct,effects.attempt(content,afterConstruct)),"linePrefix")));return initial;function atBlankEnding(code2){if(code2===null){effects.consume(code2);return}return effects.enter("lineEndingBlank"),effects.consume(code2),effects.exit("lineEndingBlank"),self2.currentConstruct=void 0,initial}function afterConstruct(code2){if(code2===null){effects.consume(code2);return}return effects.enter("lineEnding"),effects.consume(code2),effects.exit("l\
-ineEnding"),self2.currentConstruct=void 0,initial}}const resolver={resolveAll:createResolver()},string$1=initializeFactory("string"),text$7=initializeFactory("text");function initializeFactory(field){return{resolveAll:createResolver(field==="text"?resolveAllLineSuffixes:void 0),tokenize:initializeText};function initializeText(effects){const self2=this,constructs2=this.parser.constructs[field],text2=effects.attempt(constructs2,start2,notText);return start2;function start2(code2){return atBreak(code2)?
-text2(code2):notText(code2)}function notText(code2){if(code2===null){effects.consume(code2);return}return effects.enter("data"),effects.consume(code2),data}function data(code2){return atBreak(code2)?(effects.exit("data"),text2(code2)):(effects.consume(code2),data)}function atBreak(code2){if(code2===null)return!0;const list2=constructs2[code2];let index2=-1;if(list2)for(;++index2<list2.length;){const item=list2[index2];if(!item.previous||item.previous.call(self2,self2.previous))return!0}return!1}}}
-function createResolver(extraResolver){return resolveAllText;function resolveAllText(events,context){let index2=-1,enter;for(;++index2<=events.length;)enter===void 0?events[index2]&&events[index2][1].type==="data"&&(enter=index2,index2++):(!events[index2]||events[index2][1].type!=="data")&&(index2!==enter+2&&(events[enter][1].end=events[index2-1][1].end,events.splice(enter+2,index2-enter-2),index2=enter+2),enter=void 0);return extraResolver?extraResolver(events,context):events}}function resolveAllLineSuffixes(events,context){
-let eventIndex=0;for(;++eventIndex<=events.length;)if((eventIndex===events.length||events[eventIndex][1].type==="lineEnding")&&events[eventIndex-1][1].type==="data"){const data=events[eventIndex-1][1],chunks=context.sliceStream(data);let index2=chunks.length,bufferIndex=-1,size2=0,tabs;for(;index2--;){const chunk=chunks[index2];if(typeof chunk=="string"){for(bufferIndex=chunk.length;chunk.charCodeAt(bufferIndex-1)===32;)size2++,bufferIndex--;if(bufferIndex)break;bufferIndex=-1}else if(chunk===-2)
-tabs=!0,size2++;else if(chunk!==-1){index2++;break}}if(context._contentTypeTextTrailing&&eventIndex===events.length&&(size2=0),size2){const token2={type:eventIndex===events.length||tabs||size2<2?"lineSuffix":"hardBreakTrailing",start:{_bufferIndex:index2?bufferIndex:data.start._bufferIndex+bufferIndex,_index:data.start._index+index2,line:data.end.line,column:data.end.column-size2,offset:data.end.offset-size2},end:{...data.end}};data.end={...token2.start},data.start.offset===data.end.offset?Object.
-assign(data,token2):(events.splice(eventIndex,0,["enter",token2,context],["exit",token2,context]),eventIndex+=2)}eventIndex++}return events}const document$1={42:list$3,43:list$3,45:list$3,48:list$3,49:list$3,50:list$3,51:list$3,52:list$3,53:list$3,54:list$3,55:list$3,56:list$3,57:list$3,62:blockQuote},contentInitial={91:definition$1},flowInitial={[-2]:codeIndented,[-1]:codeIndented,32:codeIndented},flow={35:headingAtx,42:thematicBreak$2,45:[setextUnderline,thematicBreak$2],60:htmlFlow,61:setextUnderline,
-95:thematicBreak$2,96:codeFenced,126:codeFenced},string={38:characterReference,92:characterEscape},text$6={[-5]:lineEnding,[-4]:lineEnding,[-3]:lineEnding,33:labelStartImage,38:characterReference,42:attention,60:[autolink,htmlText],91:labelStartLink,92:[hardBreakEscape,characterEscape],93:labelEnd,95:attention,96:codeText},insideSpan={null:[attention,resolver]},attentionMarkers={null:[42,95]},disable={null:[]},defaultConstructs=Object.freeze(Object.defineProperty({__proto__:null,attentionMarkers,
-contentInitial,disable,document:document$1,flow,flowInitial,insideSpan,string,text:text$6},Symbol.toStringTag,{value:"Module"}));function createTokenizer(parser,initialize2,from){let point2={_bufferIndex:-1,_index:0,line:from&&from.line||1,column:from&&from.column||1,offset:from&&from.offset||0};const columnStart={},resolveAllConstructs=[];let chunks=[],stack=[];const effects={attempt:constructFactory(onsuccessfulconstruct),check:constructFactory(onsuccessfulcheck),consume,enter,exit:exit2,interrupt:constructFactory(
-onsuccessfulcheck,{interrupt:!0})},context={code:null,containerState:{},defineSkip,events:[],now:now2,parser,previous:null,sliceSerialize,sliceStream,write};let state2=initialize2.tokenize.call(context,effects);return initialize2.resolveAll&&resolveAllConstructs.push(initialize2),context;function write(slice){return chunks=push(chunks,slice),main2(),chunks[chunks.length-1]!==null?[]:(addResult(initialize2,0),context.events=resolveAll(resolveAllConstructs,context.events,context),context.events)}function sliceSerialize(token2,expandTabs){
-return serializeChunks(sliceStream(token2),expandTabs)}function sliceStream(token2){return sliceChunks(chunks,token2)}function now2(){const{_bufferIndex,_index,line,column,offset:offset2}=point2;return{_bufferIndex,_index,line,column,offset:offset2}}function defineSkip(value){columnStart[value.line]=value.column,accountForPotentialSkip()}function main2(){let chunkIndex;for(;point2._index<chunks.length;){const chunk=chunks[point2._index];if(typeof chunk=="string")for(chunkIndex=point2._index,point2.
-_bufferIndex<0&&(point2._bufferIndex=0);point2._index===chunkIndex&&point2._bufferIndex<chunk.length;)go2(chunk.charCodeAt(point2._bufferIndex));else go2(chunk)}}function go2(code2){state2=state2(code2)}function consume(code2){markdownLineEnding(code2)?(point2.line++,point2.column=1,point2.offset+=code2===-3?2:1,accountForPotentialSkip()):code2!==-1&&(point2.column++,point2.offset++),point2._bufferIndex<0?point2._index++:(point2._bufferIndex++,point2._bufferIndex===chunks[point2._index].length&&
-(point2._bufferIndex=-1,point2._index++)),context.previous=code2}function enter(type2,fields){const token2=fields||{};return token2.type=type2,token2.start=now2(),context.events.push(["enter",token2,context]),stack.push(token2),token2}function exit2(type2){const token2=stack.pop();return token2.end=now2(),context.events.push(["exit",token2,context]),token2}function onsuccessfulconstruct(construct,info2){addResult(construct,info2.from)}function onsuccessfulcheck(_,info2){info2.restore()}function constructFactory(onreturn,fields){
-return hook;function hook(constructs2,returnState,bogusState){let listOfConstructs,constructIndex,currentConstruct,info2;return Array.isArray(constructs2)?handleListOfConstructs(constructs2):"tokenize"in constructs2?handleListOfConstructs([constructs2]):handleMapOfConstructs(constructs2);function handleMapOfConstructs(map2){return start2;function start2(code2){const left=code2!==null&&map2[code2],all2=code2!==null&&map2.null,list2=[...Array.isArray(left)?left:left?[left]:[],...Array.isArray(all2)?
-all2:all2?[all2]:[]];return handleListOfConstructs(list2)(code2)}}function handleListOfConstructs(list2){return listOfConstructs=list2,constructIndex=0,list2.length===0?bogusState:handleConstruct(list2[constructIndex])}function handleConstruct(construct){return start2;function start2(code2){return info2=store(),currentConstruct=construct,construct.partial||(context.currentConstruct=construct),construct.name&&context.parser.constructs.disable.null.includes(construct.name)?nok():construct.tokenize.
-call(fields?Object.assign(Object.create(context),fields):context,effects,ok,nok)(code2)}}function ok(code2){return onreturn(currentConstruct,info2),returnState}function nok(code2){return info2.restore(),++constructIndex<listOfConstructs.length?handleConstruct(listOfConstructs[constructIndex]):bogusState}}}function addResult(construct,from2){construct.resolveAll&&!resolveAllConstructs.includes(construct)&&resolveAllConstructs.push(construct),construct.resolve&&splice(context.events,from2,context.
-events.length-from2,construct.resolve(context.events.slice(from2),context)),construct.resolveTo&&(context.events=construct.resolveTo(context.events,context))}function store(){const startPoint=now2(),startPrevious=context.previous,startCurrentConstruct=context.currentConstruct,startEventsIndex=context.events.length,startStack=Array.from(stack);return{from:startEventsIndex,restore};function restore(){point2=startPoint,context.previous=startPrevious,context.currentConstruct=startCurrentConstruct,context.
-events.length=startEventsIndex,stack=startStack,accountForPotentialSkip()}}function accountForPotentialSkip(){point2.line in columnStart&&point2.column<2&&(point2.column=columnStart[point2.line],point2.offset+=columnStart[point2.line]-1)}}function sliceChunks(chunks,token2){const startIndex=token2.start._index,startBufferIndex=token2.start._bufferIndex,endIndex=token2.end._index,endBufferIndex=token2.end._bufferIndex;let view;if(startIndex===endIndex)view=[chunks[startIndex].slice(startBufferIndex,
-endBufferIndex)];else{if(view=chunks.slice(startIndex,endIndex),startBufferIndex>-1){const head2=view[0];typeof head2=="string"?view[0]=head2.slice(startBufferIndex):view.shift()}endBufferIndex>0&&view.push(chunks[endIndex].slice(0,endBufferIndex))}return view}function serializeChunks(chunks,expandTabs){let index2=-1;const result=[];let atTab;for(;++index2<chunks.length;){const chunk=chunks[index2];let value;if(typeof chunk=="string")value=chunk;else switch(chunk){case-5:{value="\r";break}case-4:{
-value=`
-`;break}case-3:{value=`\r
-`;break}case-2:{value=expandTabs?" ":"	";break}case-1:{if(!expandTabs&&atTab)continue;value=" ";break}default:value=String.fromCharCode(chunk)}atTab=chunk===-2,result.push(value)}return result.join("")}function parse$2(options){const parser={constructs:combineExtensions([defaultConstructs,...(options||{}).extensions||[]]),content:create2(content$1),defined:[],document:create2(document$2),flow:create2(flow$1),lazy:{},string:create2(string$1),text:create2(text$7)};return parser;function create2(initial){
-return creator;function creator(from){return createTokenizer(parser,initial,from)}}}function postprocess(events){for(;!subtokenize(events););return events}const search$1=/[\0\t\n\r]/g;function preprocess(){let column=1,buffer="",start2=!0,atCarriageReturn;return preprocessor;function preprocessor(value,encoding,end){const chunks=[];let match,next2,startPosition,endPosition,code2;for(value=buffer+(typeof value=="string"?value.toString():new TextDecoder(encoding||void 0).decode(value)),startPosition=
-0,buffer="",start2&&(value.charCodeAt(0)===65279&&startPosition++,start2=void 0);startPosition<value.length;){if(search$1.lastIndex=startPosition,match=search$1.exec(value),endPosition=match&&match.index!==void 0?match.index:value.length,code2=value.charCodeAt(endPosition),!match){buffer=value.slice(startPosition);break}if(code2===10&&startPosition===endPosition&&atCarriageReturn)chunks.push(-3),atCarriageReturn=void 0;else switch(atCarriageReturn&&(chunks.push(-5),atCarriageReturn=void 0),startPosition<
-endPosition&&(chunks.push(value.slice(startPosition,endPosition)),column+=endPosition-startPosition),code2){case 0:{chunks.push(65533),column++;break}case 9:{for(next2=Math.ceil(column/4)*4,chunks.push(-2);column++<next2;)chunks.push(-1);break}case 10:{chunks.push(-4),column=1;break}default:atCarriageReturn=!0,column=1}startPosition=endPosition+1}return end&&(atCarriageReturn&&chunks.push(-5),buffer&&chunks.push(buffer),chunks.push(null)),chunks}}const characterEscapeOrReference=/\\([!-/:-@[-`{-~])|&(#(?:\d{1,7}|x[\da-f]{1,6})|[\da-z]{1,31});/gi;
-function decodeString(value){return value.replace(characterEscapeOrReference,decode)}function decode($0,$1,$2){if($1)return $1;if($2.charCodeAt(0)===35){const head3=$2.charCodeAt(1),hex=head3===120||head3===88;return decodeNumericCharacterReference($2.slice(hex?2:1),hex?16:10)}return decodeNamedCharacterReference($2)||$0}function stringifyPosition$1(value){return!value||typeof value!="object"?"":"position"in value||"type"in value?position$2(value.position):"start"in value||"end"in value?position$2(
-value):"line"in value||"column"in value?point$3(value):""}function point$3(point2){return index$1(point2&&point2.line)+":"+index$1(point2&&point2.column)}function position$2(pos){return point$3(pos&&pos.start)+"-"+point$3(pos&&pos.end)}function index$1(value){return value&&typeof value=="number"?value:1}const own$6={}.hasOwnProperty;function fromMarkdown(value,encoding,options){return typeof encoding!="string"&&(options=encoding,encoding=void 0),compiler(options)(postprocess(parse$2(options).document().
-write(preprocess()(value,encoding,!0))))}function compiler(options){const config2={transforms:[],canContainEols:["emphasis","fragment","heading","paragraph","strong"],enter:{autolink:opener(link2),autolinkProtocol:onenterdata,autolinkEmail:onenterdata,atxHeading:opener(heading2),blockQuote:opener(blockQuote2),characterEscape:onenterdata,characterReference:onenterdata,codeFenced:opener(codeFlow),codeFencedFenceInfo:buffer,codeFencedFenceMeta:buffer,codeIndented:opener(codeFlow,buffer),codeText:opener(
-codeText2,buffer),codeTextData:onenterdata,data:onenterdata,codeFlowValue:onenterdata,definition:opener(definition2),definitionDestinationString:buffer,definitionLabelString:buffer,definitionTitleString:buffer,emphasis:opener(emphasis2),hardBreakEscape:opener(hardBreak2),hardBreakTrailing:opener(hardBreak2),htmlFlow:opener(html2,buffer),htmlFlowData:onenterdata,htmlText:opener(html2,buffer),htmlTextData:onenterdata,image:opener(image2),label:buffer,link:opener(link2),listItem:opener(listItem2),listItemValue:onenterlistitemvalue,
-listOrdered:opener(list2,onenterlistordered),listUnordered:opener(list2),paragraph:opener(paragraph2),reference:onenterreference,referenceString:buffer,resourceDestinationString:buffer,resourceTitleString:buffer,setextHeading:opener(heading2),strong:opener(strong2),thematicBreak:opener(thematicBreak2)},exit:{atxHeading:closer(),atxHeadingSequence:onexitatxheadingsequence,autolink:closer(),autolinkEmail:onexitautolinkemail,autolinkProtocol:onexitautolinkprotocol,blockQuote:closer(),characterEscapeValue:onexitdata,
-characterReferenceMarkerHexadecimal:onexitcharacterreferencemarker,characterReferenceMarkerNumeric:onexitcharacterreferencemarker,characterReferenceValue:onexitcharacterreferencevalue,characterReference:onexitcharacterreference,codeFenced:closer(onexitcodefenced),codeFencedFence:onexitcodefencedfence,codeFencedFenceInfo:onexitcodefencedfenceinfo,codeFencedFenceMeta:onexitcodefencedfencemeta,codeFlowValue:onexitdata,codeIndented:closer(onexitcodeindented),codeText:closer(onexitcodetext),codeTextData:onexitdata,
-data:onexitdata,definition:closer(),definitionDestinationString:onexitdefinitiondestinationstring,definitionLabelString:onexitdefinitionlabelstring,definitionTitleString:onexitdefinitiontitlestring,emphasis:closer(),hardBreakEscape:closer(onexithardbreak),hardBreakTrailing:closer(onexithardbreak),htmlFlow:closer(onexithtmlflow),htmlFlowData:onexitdata,htmlText:closer(onexithtmltext),htmlTextData:onexitdata,image:closer(onexitimage),label:onexitlabel,labelText:onexitlabeltext,lineEnding:onexitlineending,
-link:closer(onexitlink),listItem:closer(),listOrdered:closer(),listUnordered:closer(),paragraph:closer(),referenceString:onexitreferencestring,resourceDestinationString:onexitresourcedestinationstring,resourceTitleString:onexitresourcetitlestring,resource:onexitresource,setextHeading:closer(onexitsetextheading),setextHeadingLineSequence:onexitsetextheadinglinesequence,setextHeadingText:onexitsetextheadingtext,strong:closer(),thematicBreak:closer()}};configure$1(config2,(options||{}).mdastExtensions||
-[]);const data={};return compile2;function compile2(events){let tree={type:"root",children:[]};const context={stack:[tree],tokenStack:[],config:config2,enter,exit:exit2,buffer,resume,data},listStack=[];let index2=-1;for(;++index2<events.length;)if(events[index2][1].type==="listOrdered"||events[index2][1].type==="listUnordered")if(events[index2][0]==="enter")listStack.push(index2);else{const tail=listStack.pop();index2=prepareList(events,tail,index2)}for(index2=-1;++index2<events.length;){const handler=config2[events[index2][0]];
-own$6.call(handler,events[index2][1].type)&&handler[events[index2][1].type].call(Object.assign({sliceSerialize:events[index2][2].sliceSerialize},context),events[index2][1])}if(context.tokenStack.length>0){const tail=context.tokenStack[context.tokenStack.length-1];(tail[1]||defaultOnError).call(context,void 0,tail[0])}for(tree.position={start:point$2(events.length>0?events[0][1].start:{line:1,column:1,offset:0}),end:point$2(events.length>0?events[events.length-2][1].end:{line:1,column:1,offset:0})},
-index2=-1;++index2<config2.transforms.length;)tree=config2.transforms[index2](tree)||tree;return tree}function prepareList(events,start2,length){let index2=start2-1,containerBalance=-1,listSpread=!1,listItem3,lineIndex,firstBlankLineIndex,atMarker;for(;++index2<=length;){const event2=events[index2];switch(event2[1].type){case"listUnordered":case"listOrdered":case"blockQuote":{event2[0]==="enter"?containerBalance++:containerBalance--,atMarker=void 0;break}case"lineEndingBlank":{event2[0]==="enter"&&
-(listItem3&&!atMarker&&!containerBalance&&!firstBlankLineIndex&&(firstBlankLineIndex=index2),atMarker=void 0);break}case"linePrefix":case"listItemValue":case"listItemMarker":case"listItemPrefix":case"listItemPrefixWhitespace":break;default:atMarker=void 0}if(!containerBalance&&event2[0]==="enter"&&event2[1].type==="listItemPrefix"||containerBalance===-1&&event2[0]==="exit"&&(event2[1].type==="listUnordered"||event2[1].type==="listOrdered")){if(listItem3){let tailIndex=index2;for(lineIndex=void 0;tailIndex--;){
-const tailEvent=events[tailIndex];if(tailEvent[1].type==="lineEnding"||tailEvent[1].type==="lineEndingBlank"){if(tailEvent[0]==="exit")continue;lineIndex&&(events[lineIndex][1].type="lineEndingBlank",listSpread=!0),tailEvent[1].type="lineEnding",lineIndex=tailIndex}else if(!(tailEvent[1].type==="linePrefix"||tailEvent[1].type==="blockQuotePrefix"||tailEvent[1].type==="blockQuotePrefixWhitespace"||tailEvent[1].type==="blockQuoteMarker"||tailEvent[1].type==="listItemIndent"))break}firstBlankLineIndex&&
-(!lineIndex||firstBlankLineIndex<lineIndex)&&(listItem3._spread=!0),listItem3.end=Object.assign({},lineIndex?events[lineIndex][1].start:event2[1].end),events.splice(lineIndex||index2,0,["exit",listItem3,event2[2]]),index2++,length++}if(event2[1].type==="listItemPrefix"){const item={type:"listItem",_spread:!1,start:Object.assign({},event2[1].start),end:void 0};listItem3=item,events.splice(index2,0,["enter",item,event2[2]]),index2++,length++,firstBlankLineIndex=void 0,atMarker=!0}}}return events[start2][1].
-_spread=listSpread,length}function opener(create2,and){return open2;function open2(token2){enter.call(this,create2(token2),token2),and&&and.call(this,token2)}}function buffer(){this.stack.push({type:"fragment",children:[]})}function enter(node2,token2,errorHandler){this.stack[this.stack.length-1].children.push(node2),this.stack.push(node2),this.tokenStack.push([token2,errorHandler||void 0]),node2.position={start:point$2(token2.start),end:void 0}}function closer(and){return close2;function close2(token2){
-and&&and.call(this,token2),exit2.call(this,token2)}}function exit2(token2,onExitError){const node2=this.stack.pop(),open2=this.tokenStack.pop();if(open2)open2[0].type!==token2.type&&(onExitError?onExitError.call(this,token2,open2[0]):(open2[1]||defaultOnError).call(this,token2,open2[0]));else throw new Error("Cannot close `"+token2.type+"` ("+stringifyPosition$1({start:token2.start,end:token2.end})+"): it’s not open");node2.position.end=point$2(token2.end)}function resume(){return toString$1(this.
-stack.pop())}function onenterlistordered(){this.data.expectingFirstListItemValue=!0}function onenterlistitemvalue(token2){if(this.data.expectingFirstListItemValue){const ancestor=this.stack[this.stack.length-2];ancestor.start=Number.parseInt(this.sliceSerialize(token2),10),this.data.expectingFirstListItemValue=void 0}}function onexitcodefencedfenceinfo(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.lang=data2}function onexitcodefencedfencemeta(){const data2=this.resume(),
-node2=this.stack[this.stack.length-1];node2.meta=data2}function onexitcodefencedfence(){this.data.flowCodeInside||(this.buffer(),this.data.flowCodeInside=!0)}function onexitcodefenced(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.value=data2.replace(/^(\r?\n|\r)|(\r?\n|\r)$/g,""),this.data.flowCodeInside=void 0}function onexitcodeindented(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.value=data2.replace(/(\r?\n|\r)$/g,"")}function onexitdefinitionlabelstring(token2){
-const label=this.resume(),node2=this.stack[this.stack.length-1];node2.label=label,node2.identifier=normalizeIdentifier(this.sliceSerialize(token2)).toLowerCase()}function onexitdefinitiontitlestring(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.title=data2}function onexitdefinitiondestinationstring(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.url=data2}function onexitatxheadingsequence(token2){const node2=this.stack[this.stack.length-1];if(!node2.
-depth){const depth=this.sliceSerialize(token2).length;node2.depth=depth}}function onexitsetextheadingtext(){this.data.setextHeadingSlurpLineEnding=!0}function onexitsetextheadinglinesequence(token2){const node2=this.stack[this.stack.length-1];node2.depth=this.sliceSerialize(token2).codePointAt(0)===61?1:2}function onexitsetextheading(){this.data.setextHeadingSlurpLineEnding=void 0}function onenterdata(token2){const siblings2=this.stack[this.stack.length-1].children;let tail=siblings2[siblings2.length-
-1];(!tail||tail.type!=="text")&&(tail=text2(),tail.position={start:point$2(token2.start),end:void 0},siblings2.push(tail)),this.stack.push(tail)}function onexitdata(token2){const tail=this.stack.pop();tail.value+=this.sliceSerialize(token2),tail.position.end=point$2(token2.end)}function onexitlineending(token2){const context=this.stack[this.stack.length-1];if(this.data.atHardBreak){const tail=context.children[context.children.length-1];tail.position.end=point$2(token2.end),this.data.atHardBreak=
-void 0;return}!this.data.setextHeadingSlurpLineEnding&&config2.canContainEols.includes(context.type)&&(onenterdata.call(this,token2),onexitdata.call(this,token2))}function onexithardbreak(){this.data.atHardBreak=!0}function onexithtmlflow(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.value=data2}function onexithtmltext(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.value=data2}function onexitcodetext(){const data2=this.resume(),node2=this.stack[this.
-stack.length-1];node2.value=data2}function onexitlink(){const node2=this.stack[this.stack.length-1];if(this.data.inReference){const referenceType=this.data.referenceType||"shortcut";node2.type+="Reference",node2.referenceType=referenceType,delete node2.url,delete node2.title}else delete node2.identifier,delete node2.label;this.data.referenceType=void 0}function onexitimage(){const node2=this.stack[this.stack.length-1];if(this.data.inReference){const referenceType=this.data.referenceType||"shortc\
-ut";node2.type+="Reference",node2.referenceType=referenceType,delete node2.url,delete node2.title}else delete node2.identifier,delete node2.label;this.data.referenceType=void 0}function onexitlabeltext(token2){const string2=this.sliceSerialize(token2),ancestor=this.stack[this.stack.length-2];ancestor.label=decodeString(string2),ancestor.identifier=normalizeIdentifier(string2).toLowerCase()}function onexitlabel(){const fragment=this.stack[this.stack.length-1],value=this.resume(),node2=this.stack[this.
-stack.length-1];if(this.data.inReference=!0,node2.type==="link"){const children=fragment.children;node2.children=children}else node2.alt=value}function onexitresourcedestinationstring(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.url=data2}function onexitresourcetitlestring(){const data2=this.resume(),node2=this.stack[this.stack.length-1];node2.title=data2}function onexitresource(){this.data.inReference=void 0}function onenterreference(){this.data.referenceType="collaps\
-ed"}function onexitreferencestring(token2){const label=this.resume(),node2=this.stack[this.stack.length-1];node2.label=label,node2.identifier=normalizeIdentifier(this.sliceSerialize(token2)).toLowerCase(),this.data.referenceType="full"}function onexitcharacterreferencemarker(token2){this.data.characterReferenceType=token2.type}function onexitcharacterreferencevalue(token2){const data2=this.sliceSerialize(token2),type2=this.data.characterReferenceType;let value;type2?(value=decodeNumericCharacterReference(
-data2,type2==="characterReferenceMarkerNumeric"?10:16),this.data.characterReferenceType=void 0):value=decodeNamedCharacterReference(data2);const tail=this.stack[this.stack.length-1];tail.value+=value}function onexitcharacterreference(token2){const tail=this.stack.pop();tail.position.end=point$2(token2.end)}function onexitautolinkprotocol(token2){onexitdata.call(this,token2);const node2=this.stack[this.stack.length-1];node2.url=this.sliceSerialize(token2)}function onexitautolinkemail(token2){onexitdata.
-call(this,token2);const node2=this.stack[this.stack.length-1];node2.url="mailto:"+this.sliceSerialize(token2)}function blockQuote2(){return{type:"blockquote",children:[]}}function codeFlow(){return{type:"code",lang:null,meta:null,value:""}}function codeText2(){return{type:"inlineCode",value:""}}function definition2(){return{type:"definition",identifier:"",label:null,title:null,url:""}}function emphasis2(){return{type:"emphasis",children:[]}}function heading2(){return{type:"heading",depth:0,children:[]}}
-function hardBreak2(){return{type:"break"}}function html2(){return{type:"html",value:""}}function image2(){return{type:"image",title:null,url:"",alt:null}}function link2(){return{type:"link",title:null,url:"",children:[]}}function list2(token2){return{type:"list",ordered:token2.type==="listOrdered",start:null,spread:token2._spread,children:[]}}function listItem2(token2){return{type:"listItem",spread:token2._spread,checked:null,children:[]}}function paragraph2(){return{type:"paragraph",children:[]}}
-function strong2(){return{type:"strong",children:[]}}function text2(){return{type:"text",value:""}}function thematicBreak2(){return{type:"thematicBreak"}}}function point$2(d2){return{line:d2.line,column:d2.column,offset:d2.offset}}function configure$1(combined,extensions){let index2=-1;for(;++index2<extensions.length;){const value=extensions[index2];Array.isArray(value)?configure$1(combined,value):extension(combined,value)}}function extension(combined,extension2){let key2;for(key2 in extension2)
-if(own$6.call(extension2,key2))switch(key2){case"canContainEols":{const right=extension2[key2];right&&combined[key2].push(...right);break}case"transforms":{const right=extension2[key2];right&&combined[key2].push(...right);break}case"enter":case"exit":{const right=extension2[key2];right&&Object.assign(combined[key2],right);break}}}function defaultOnError(left,right){throw left?new Error("Cannot close `"+left.type+"` ("+stringifyPosition$1({start:left.start,end:left.end})+"): a different token (`"+
-right.type+"`, "+stringifyPosition$1({start:right.start,end:right.end})+") is open"):new Error("Cannot close document, a token (`"+right.type+"`, "+stringifyPosition$1({start:right.start,end:right.end})+") is still open")}function remarkParse(options){const self2=this;self2.parser=parser;function parser(doc){return fromMarkdown(doc,{...self2.data("settings"),...options,extensions:self2.data("micromarkExtensions")||[],mdastExtensions:self2.data("fromMarkdownExtensions")||[]})}}const own$5={}.hasOwnProperty;
-function zwitch(key2,options){const settings=options||{};function one2(value,...parameters){let fn=one2.invalid;const handlers2=one2.handlers;if(value&&own$5.call(value,key2)){const id2=String(value[key2]);fn=own$5.call(handlers2,id2)?handlers2[id2]:one2.unknown}if(fn)return fn.call(this,value,...parameters)}return one2.handlers=settings.handlers||{},one2.invalid=settings.invalid,one2.unknown=settings.unknown,one2}const own$4={}.hasOwnProperty;function configure(base2,extension2){let index2=-1,key2;
-if(extension2.extensions)for(;++index2<extension2.extensions.length;)configure(base2,extension2.extensions[index2]);for(key2 in extension2)if(own$4.call(extension2,key2))switch(key2){case"extensions":break;case"unsafe":{list$2(base2[key2],extension2[key2]);break}case"join":{list$2(base2[key2],extension2[key2]);break}case"handlers":{map$2(base2[key2],extension2[key2]);break}default:base2.options[key2]=extension2[key2]}return base2}function list$2(left,right){right&&left.push(...right)}function map$2(left,right){
-right&&Object.assign(left,right)}function blockquote$1(node2,_,state2,info2){const exit2=state2.enter("blockquote"),tracker=state2.createTracker(info2);tracker.move("> "),tracker.shift(2);const value=state2.indentLines(state2.containerFlow(node2,tracker.current()),map$1);return exit2(),value}function map$1(line,_,blank){return">"+(blank?"":" ")+line}function patternInScope(stack,pattern2){return listInScope(stack,pattern2.inConstruct,!0)&&!listInScope(stack,pattern2.notInConstruct,!1)}function listInScope(stack,list2,none){
-if(typeof list2=="string"&&(list2=[list2]),!list2||list2.length===0)return none;let index2=-1;for(;++index2<list2.length;)if(stack.includes(list2[index2]))return!0;return!1}function hardBreak$1(_,_12,state2,info2){let index2=-1;for(;++index2<state2.unsafe.length;)if(state2.unsafe[index2].character===`
-`&&patternInScope(state2.stack,state2.unsafe[index2]))return/[ \t]/.test(info2.before)?"":" ";return`\\
-`}function longestStreak(value,substring){const source2=String(value);let index2=source2.indexOf(substring),expected=index2,count=0,max2=0;if(typeof substring!="string")throw new TypeError("Expected substring");for(;index2!==-1;)index2===expected?++count>max2&&(max2=count):count=1,expected=index2+substring.length,index2=source2.indexOf(substring,expected);return max2}function formatCodeAsIndented(node2,state2){return!!(state2.options.fences===!1&&node2.value&&!node2.lang&&/[^ \r\n]/.test(node2.value)&&
-!/^[\t ]*(?:[\r\n]|$)|(?:^|[\r\n])[\t ]*$/.test(node2.value))}function checkFence(state2){const marker=state2.options.fence||"`";if(marker!=="`"&&marker!=="~")throw new Error("Cannot serialize code with `"+marker+"` for `options.fence`, expected `` ` `` or `~`");return marker}function code$2(node2,_,state2,info2){const marker=checkFence(state2),raw2=node2.value||"",suffix=marker==="`"?"GraveAccent":"Tilde";if(formatCodeAsIndented(node2,state2)){const exit3=state2.enter("codeIndented"),value2=state2.
-indentLines(raw2,map);return exit3(),value2}const tracker=state2.createTracker(info2),sequence=marker.repeat(Math.max(longestStreak(raw2,marker)+1,3)),exit2=state2.enter("codeFenced");let value=tracker.move(sequence);if(node2.lang){const subexit=state2.enter(`codeFencedLang${suffix}`);value+=tracker.move(state2.safe(node2.lang,{before:value,after:" ",encode:["`"],...tracker.current()})),subexit()}if(node2.lang&&node2.meta){const subexit=state2.enter(`codeFencedMeta${suffix}`);value+=tracker.move(
-" "),value+=tracker.move(state2.safe(node2.meta,{before:value,after:`
-`,encode:["`"],...tracker.current()})),subexit()}return value+=tracker.move(`
-`),raw2&&(value+=tracker.move(raw2+`
-`)),value+=tracker.move(sequence),exit2(),value}function map(line,_,blank){return(blank?"":"    ")+line}function checkQuote(state2){const marker=state2.options.quote||'"';if(marker!=='"'&&marker!=="'")throw new Error("Cannot serialize title with `"+marker+"` for `options.quote`, expected `\"`, or `'`");return marker}function definition(node2,_,state2,info2){const quote=checkQuote(state2),suffix=quote==='"'?"Quote":"Apostrophe",exit2=state2.enter("definition");let subexit=state2.enter("label");const tracker=state2.
-createTracker(info2);let value=tracker.move("[");return value+=tracker.move(state2.safe(state2.associationId(node2),{before:value,after:"]",...tracker.current()})),value+=tracker.move("]: "),subexit(),!node2.url||/[\0- \u007F]/.test(node2.url)?(subexit=state2.enter("destinationLiteral"),value+=tracker.move("<"),value+=tracker.move(state2.safe(node2.url,{before:value,after:">",...tracker.current()})),value+=tracker.move(">")):(subexit=state2.enter("destinationRaw"),value+=tracker.move(state2.safe(
-node2.url,{before:value,after:node2.title?" ":`
-`,...tracker.current()}))),subexit(),node2.title&&(subexit=state2.enter(`title${suffix}`),value+=tracker.move(" "+quote),value+=tracker.move(state2.safe(node2.title,{before:value,after:quote,...tracker.current()})),value+=tracker.move(quote),subexit()),exit2(),value}function checkEmphasis(state2){const marker=state2.options.emphasis||"*";if(marker!=="*"&&marker!=="_")throw new Error("Cannot serialize emphasis with `"+marker+"` for `options.emphasis`, expected `*`, or `_`");return marker}function encodeCharacterReference(code2){
-return"&#x"+code2.toString(16).toUpperCase()+";"}function encodeInfo(outside,inside,marker){const outsideKind=classifyCharacter(outside),insideKind=classifyCharacter(inside);return outsideKind===void 0?insideKind===void 0?marker==="_"?{inside:!0,outside:!0}:{inside:!1,outside:!1}:insideKind===1?{inside:!0,outside:!0}:{inside:!1,outside:!0}:outsideKind===1?insideKind===void 0?{inside:!1,outside:!1}:insideKind===1?{inside:!0,outside:!0}:{inside:!1,outside:!1}:insideKind===void 0?{inside:!1,outside:!1}:
-insideKind===1?{inside:!0,outside:!1}:{inside:!1,outside:!1}}emphasis$1.peek=emphasisPeek;function emphasis$1(node2,_,state2,info2){const marker=checkEmphasis(state2),exit2=state2.enter("emphasis"),tracker=state2.createTracker(info2),before=tracker.move(marker);let between2=tracker.move(state2.containerPhrasing(node2,{after:marker,before,...tracker.current()}));const betweenHead=between2.charCodeAt(0),open2=encodeInfo(info2.before.charCodeAt(info2.before.length-1),betweenHead,marker);open2.inside&&
-(between2=encodeCharacterReference(betweenHead)+between2.slice(1));const betweenTail=between2.charCodeAt(between2.length-1),close2=encodeInfo(info2.after.charCodeAt(0),betweenTail,marker);close2.inside&&(between2=between2.slice(0,-1)+encodeCharacterReference(betweenTail));const after=tracker.move(marker);return exit2(),state2.attentionEncodeSurroundingInfo={after:close2.outside,before:open2.outside},before+between2+after}function emphasisPeek(_,_12,state2){return state2.options.emphasis||"*"}const convert=function(test){
-if(test==null)return ok$1;if(typeof test=="function")return castFactory$1(test);if(typeof test=="object")return Array.isArray(test)?anyFactory$1(test):propsFactory(test);if(typeof test=="string")return typeFactory(test);throw new Error("Expected function, string, or object as test")};function anyFactory$1(tests){const checks2=[];let index2=-1;for(;++index2<tests.length;)checks2[index2]=convert(tests[index2]);return castFactory$1(any2);function any2(...parameters){let index3=-1;for(;++index3<checks2.
-length;)if(checks2[index3].apply(this,parameters))return!0;return!1}}function propsFactory(check){const checkAsRecord=check;return castFactory$1(all2);function all2(node2){const nodeAsRecord=node2;let key2;for(key2 in check)if(nodeAsRecord[key2]!==checkAsRecord[key2])return!1;return!0}}function typeFactory(check){return castFactory$1(type2);function type2(node2){return node2&&node2.type===check}}function castFactory$1(testFunction){return check;function check(value,index2,parent){return!!(looksLikeANode(
-value)&&testFunction.call(this,value,typeof index2=="number"?index2:void 0,parent||void 0))}}function ok$1(){return!0}function looksLikeANode(value){return value!==null&&typeof value=="object"&&"type"in value}const empty$1=[],CONTINUE=!0,EXIT=!1,SKIP="skip";function visitParents(tree,test,visitor,reverse){let check;typeof test=="function"&&typeof visitor!="function"?(reverse=visitor,visitor=test):check=test;const is2=convert(check),step=reverse?-1:1;factory(tree,void 0,[])();function factory(node2,index2,parents){const value=node2&&typeof node2=="object"?node2:{};if(typeof value.type=="string"){const name=typeof value.tagName=="string"?value.tagName:typeof value.name=="string"?value.name:void 0;Object.
-defineProperty(visit2,"name",{value:"node ("+(node2.type+(name?"<"+name+">":""))+")"})}return visit2;function visit2(){let result=empty$1,subresult,offset2,grandparents;if((!test||is2(node2,index2,parents[parents.length-1]||void 0))&&(result=toResult(visitor(node2,parents)),result[0]===EXIT))return result;if("children"in node2&&node2.children){const nodeAsParent=node2;if(nodeAsParent.children&&result[0]!==SKIP)for(offset2=(reverse?nodeAsParent.children.length:-1)+step,grandparents=parents.concat(
-nodeAsParent);offset2>-1&&offset2<nodeAsParent.children.length;){const child2=nodeAsParent.children[offset2];if(subresult=factory(child2,offset2,grandparents)(),subresult[0]===EXIT)return subresult;offset2=typeof subresult[1]=="number"?subresult[1]:offset2+step}}return result}}}function toResult(value){return Array.isArray(value)?value:typeof value=="number"?[CONTINUE,value]:value==null?empty$1:[value]}function visit(tree,testOrVisitor,visitorOrReverse,maybeReverse){let reverse,test,visitor;typeof testOrVisitor==
-"function"&&typeof visitorOrReverse!="function"?(test=void 0,visitor=testOrVisitor,reverse=visitorOrReverse):(test=testOrVisitor,visitor=visitorOrReverse,reverse=maybeReverse),visitParents(tree,test,overload,reverse);function overload(node2,parents){const parent=parents[parents.length-1],index2=parent?parent.children.indexOf(node2):void 0;return visitor(node2,index2,parent)}}function formatHeadingAsSetext(node2,state2){let literalWithBreak=!1;return visit(node2,function(node3){if("value"in node3&&
-/\r?\n|\r/.test(node3.value)||node3.type==="break")return literalWithBreak=!0,EXIT}),!!((!node2.depth||node2.depth<3)&&toString$1(node2)&&(state2.options.setext||literalWithBreak))}function heading$1(node2,_,state2,info2){const rank=Math.max(Math.min(6,node2.depth||1),1),tracker=state2.createTracker(info2);if(formatHeadingAsSetext(node2,state2)){const exit3=state2.enter("headingSetext"),subexit2=state2.enter("phrasing"),value2=state2.containerPhrasing(node2,{...tracker.current(),before:`
-`,after:`
-`});return subexit2(),exit3(),value2+`
-`+(rank===1?"=":"-").repeat(value2.length-(Math.max(value2.lastIndexOf("\r"),value2.lastIndexOf(`
-`))+1))}const sequence="#".repeat(rank),exit2=state2.enter("headingAtx"),subexit=state2.enter("phrasing");tracker.move(sequence+" ");let value=state2.containerPhrasing(node2,{before:"# ",after:`
-`,...tracker.current()});return/^[\t ]/.test(value)&&(value=encodeCharacterReference(value.charCodeAt(0))+value.slice(1)),value=value?sequence+" "+value:sequence,state2.options.closeAtx&&(value+=" "+sequence),subexit(),exit2(),value}html$5.peek=htmlPeek;function html$5(node2){return node2.value||""}function htmlPeek(){return"<"}image$1.peek=imagePeek;function image$1(node2,_,state2,info2){const quote=checkQuote(state2),suffix=quote==='"'?"Quote":"Apostrophe",exit2=state2.enter("image");let subexit=state2.
-enter("label");const tracker=state2.createTracker(info2);let value=tracker.move("![");return value+=tracker.move(state2.safe(node2.alt,{before:value,after:"]",...tracker.current()})),value+=tracker.move("]("),subexit(),!node2.url&&node2.title||/[\0- \u007F]/.test(node2.url)?(subexit=state2.enter("destinationLiteral"),value+=tracker.move("<"),value+=tracker.move(state2.safe(node2.url,{before:value,after:">",...tracker.current()})),value+=tracker.move(">")):(subexit=state2.enter("destinationRaw"),
-value+=tracker.move(state2.safe(node2.url,{before:value,after:node2.title?" ":")",...tracker.current()}))),subexit(),node2.title&&(subexit=state2.enter(`title${suffix}`),value+=tracker.move(" "+quote),value+=tracker.move(state2.safe(node2.title,{before:value,after:quote,...tracker.current()})),value+=tracker.move(quote),subexit()),value+=tracker.move(")"),exit2(),value}function imagePeek(){return"!"}imageReference$1.peek=imageReferencePeek;function imageReference$1(node2,_,state2,info2){const type2=node2.
-referenceType,exit2=state2.enter("imageReference");let subexit=state2.enter("label");const tracker=state2.createTracker(info2);let value=tracker.move("![");const alt=state2.safe(node2.alt,{before:value,after:"]",...tracker.current()});value+=tracker.move(alt+"]["),subexit();const stack=state2.stack;state2.stack=[],subexit=state2.enter("reference");const reference=state2.safe(state2.associationId(node2),{before:value,after:"]",...tracker.current()});return subexit(),state2.stack=stack,exit2(),type2===
-"full"||!alt||alt!==reference?value+=tracker.move(reference+"]"):type2==="shortcut"?value=value.slice(0,-1):value+=tracker.move("]"),value}function imageReferencePeek(){return"!"}inlineCode$1.peek=inlineCodePeek;function inlineCode$1(node2,_,state2){let value=node2.value||"",sequence="`",index2=-1;for(;new RegExp("(^|[^`])"+sequence+"([^`]|$)").test(value);)sequence+="`";for(/[^ \r\n]/.test(value)&&(/^[ \r\n]/.test(value)&&/[ \r\n]$/.test(value)||/^`|`$/.test(value))&&(value=" "+value+" ");++index2<
-state2.unsafe.length;){const pattern2=state2.unsafe[index2],expression=state2.compilePattern(pattern2);let match;if(pattern2.atBreak)for(;match=expression.exec(value);){let position2=match.index;value.charCodeAt(position2)===10&&value.charCodeAt(position2-1)===13&&position2--,value=value.slice(0,position2)+" "+value.slice(match.index+1)}}return sequence+value+sequence}function inlineCodePeek(){return"`"}function formatLinkAsAutolink(node2,state2){const raw2=toString$1(node2);return!!(!state2.options.
-resourceLink&&node2.url&&!node2.title&&node2.children&&node2.children.length===1&&node2.children[0].type==="text"&&(raw2===node2.url||"mailto:"+raw2===node2.url)&&/^[a-z][a-z+.-]+:/i.test(node2.url)&&!/[\0- <>\u007F]/.test(node2.url))}link$1.peek=linkPeek;function link$1(node2,_,state2,info2){const quote=checkQuote(state2),suffix=quote==='"'?"Quote":"Apostrophe",tracker=state2.createTracker(info2);let exit2,subexit;if(formatLinkAsAutolink(node2,state2)){const stack=state2.stack;state2.stack=[],exit2=
-state2.enter("autolink");let value2=tracker.move("<");return value2+=tracker.move(state2.containerPhrasing(node2,{before:value2,after:">",...tracker.current()})),value2+=tracker.move(">"),exit2(),state2.stack=stack,value2}exit2=state2.enter("link"),subexit=state2.enter("label");let value=tracker.move("[");return value+=tracker.move(state2.containerPhrasing(node2,{before:value,after:"](",...tracker.current()})),value+=tracker.move("]("),subexit(),!node2.url&&node2.title||/[\0- \u007F]/.test(node2.
-url)?(subexit=state2.enter("destinationLiteral"),value+=tracker.move("<"),value+=tracker.move(state2.safe(node2.url,{before:value,after:">",...tracker.current()})),value+=tracker.move(">")):(subexit=state2.enter("destinationRaw"),value+=tracker.move(state2.safe(node2.url,{before:value,after:node2.title?" ":")",...tracker.current()}))),subexit(),node2.title&&(subexit=state2.enter(`title${suffix}`),value+=tracker.move(" "+quote),value+=tracker.move(state2.safe(node2.title,{before:value,after:quote,
-...tracker.current()})),value+=tracker.move(quote),subexit()),value+=tracker.move(")"),exit2(),value}function linkPeek(node2,_,state2){return formatLinkAsAutolink(node2,state2)?"<":"["}linkReference$1.peek=linkReferencePeek;function linkReference$1(node2,_,state2,info2){const type2=node2.referenceType,exit2=state2.enter("linkReference");let subexit=state2.enter("label");const tracker=state2.createTracker(info2);let value=tracker.move("[");const text2=state2.containerPhrasing(node2,{before:value,
-after:"]",...tracker.current()});value+=tracker.move(text2+"]["),subexit();const stack=state2.stack;state2.stack=[],subexit=state2.enter("reference");const reference=state2.safe(state2.associationId(node2),{before:value,after:"]",...tracker.current()});return subexit(),state2.stack=stack,exit2(),type2==="full"||!text2||text2!==reference?value+=tracker.move(reference+"]"):type2==="shortcut"?value=value.slice(0,-1):value+=tracker.move("]"),value}function linkReferencePeek(){return"["}function checkBullet(state2){
-const marker=state2.options.bullet||"*";if(marker!=="*"&&marker!=="+"&&marker!=="-")throw new Error("Cannot serialize items with `"+marker+"` for `options.bullet`, expected `*`, `+`, or `-`");return marker}function checkBulletOther(state2){const bullet=checkBullet(state2),bulletOther=state2.options.bulletOther;if(!bulletOther)return bullet==="*"?"-":"*";if(bulletOther!=="*"&&bulletOther!=="+"&&bulletOther!=="-")throw new Error("Cannot serialize items with `"+bulletOther+"` for `options.bulletOth\
-er`, expected `*`, `+`, or `-`");if(bulletOther===bullet)throw new Error("Expected `bullet` (`"+bullet+"`) and `bulletOther` (`"+bulletOther+"`) to be different");return bulletOther}function checkBulletOrdered(state2){const marker=state2.options.bulletOrdered||".";if(marker!=="."&&marker!==")")throw new Error("Cannot serialize items with `"+marker+"` for `options.bulletOrdered`, expected `.` or `)`");return marker}function checkRule(state2){const marker=state2.options.rule||"*";if(marker!=="*"&&
-marker!=="-"&&marker!=="_")throw new Error("Cannot serialize rules with `"+marker+"` for `options.rule`, expected `*`, `-`, or `_`");return marker}function list$1(node2,parent,state2,info2){const exit2=state2.enter("list"),bulletCurrent=state2.bulletCurrent;let bullet=node2.ordered?checkBulletOrdered(state2):checkBullet(state2);const bulletOther=node2.ordered?bullet==="."?")":".":checkBulletOther(state2);let useDifferentMarker=parent&&state2.bulletLastUsed?bullet===state2.bulletLastUsed:!1;if(!node2.
-ordered){const firstListItem=node2.children?node2.children[0]:void 0;if((bullet==="*"||bullet==="-")&&firstListItem&&(!firstListItem.children||!firstListItem.children[0])&&state2.stack[state2.stack.length-1]==="list"&&state2.stack[state2.stack.length-2]==="listItem"&&state2.stack[state2.stack.length-3]==="list"&&state2.stack[state2.stack.length-4]==="listItem"&&state2.indexStack[state2.indexStack.length-1]===0&&state2.indexStack[state2.indexStack.length-2]===0&&state2.indexStack[state2.indexStack.
-length-3]===0&&(useDifferentMarker=!0),checkRule(state2)===bullet&&firstListItem){let index2=-1;for(;++index2<node2.children.length;){const item=node2.children[index2];if(item&&item.type==="listItem"&&item.children&&item.children[0]&&item.children[0].type==="thematicBreak"){useDifferentMarker=!0;break}}}}useDifferentMarker&&(bullet=bulletOther),state2.bulletCurrent=bullet;const value=state2.containerFlow(node2,info2);return state2.bulletLastUsed=bullet,state2.bulletCurrent=bulletCurrent,exit2(),
-value}function checkListItemIndent(state2){const style2=state2.options.listItemIndent||"one";if(style2!=="tab"&&style2!=="one"&&style2!=="mixed")throw new Error("Cannot serialize items with `"+style2+"` for `options.listItemIndent`, expected `tab`, `one`, or `mixed`");return style2}function listItem$1(node2,parent,state2,info2){const listItemIndent=checkListItemIndent(state2);let bullet=state2.bulletCurrent||checkBullet(state2);parent&&parent.type==="list"&&parent.ordered&&(bullet=(typeof parent.
-start=="number"&&parent.start>-1?parent.start:1)+(state2.options.incrementListMarker===!1?0:parent.children.indexOf(node2))+bullet);let size2=bullet.length+1;(listItemIndent==="tab"||listItemIndent==="mixed"&&(parent&&parent.type==="list"&&parent.spread||node2.spread))&&(size2=Math.ceil(size2/4)*4);const tracker=state2.createTracker(info2);tracker.move(bullet+" ".repeat(size2-bullet.length)),tracker.shift(size2);const exit2=state2.enter("listItem"),value=state2.indentLines(state2.containerFlow(node2,
-tracker.current()),map2);return exit2(),value;function map2(line,index2,blank){return index2?(blank?"":" ".repeat(size2))+line:(blank?bullet:bullet+" ".repeat(size2-bullet.length))+line}}function paragraph$1(node2,_,state2,info2){const exit2=state2.enter("paragraph"),subexit=state2.enter("phrasing"),value=state2.containerPhrasing(node2,info2);return subexit(),exit2(),value}const phrasing=convert(["break","delete","emphasis","footnote","footnoteReference","image","imageReference","inlineCode","in\
-lineMath","link","linkReference","mdxJsxTextElement","mdxTextExpression","strong","text","textDirective"]);function root$u(node2,_,state2,info2){return(node2.children.some(function(d2){return phrasing(d2)})?state2.containerPhrasing:state2.containerFlow).call(state2,node2,info2)}function checkStrong(state2){const marker=state2.options.strong||"*";if(marker!=="*"&&marker!=="_")throw new Error("Cannot serialize strong with `"+marker+"` for `options.strong`, expected `*`, or `_`");return marker}strong$1.
-peek=strongPeek;function strong$1(node2,_,state2,info2){const marker=checkStrong(state2),exit2=state2.enter("strong"),tracker=state2.createTracker(info2),before=tracker.move(marker+marker);let between2=tracker.move(state2.containerPhrasing(node2,{after:marker,before,...tracker.current()}));const betweenHead=between2.charCodeAt(0),open2=encodeInfo(info2.before.charCodeAt(info2.before.length-1),betweenHead,marker);open2.inside&&(between2=encodeCharacterReference(betweenHead)+between2.slice(1));const betweenTail=between2.
-charCodeAt(between2.length-1),close2=encodeInfo(info2.after.charCodeAt(0),betweenTail,marker);close2.inside&&(between2=between2.slice(0,-1)+encodeCharacterReference(betweenTail));const after=tracker.move(marker+marker);return exit2(),state2.attentionEncodeSurroundingInfo={after:close2.outside,before:open2.outside},before+between2+after}function strongPeek(_,_12,state2){return state2.options.strong||"*"}function text$5(node2,_,state2,info2){return state2.safe(node2.value,info2)}function checkRuleRepetition(state2){
-const repetition=state2.options.ruleRepetition||3;if(repetition<3)throw new Error("Cannot serialize rules with repetition `"+repetition+"` for `options.ruleRepetition`, expected `3` or more");return repetition}function thematicBreak$1(_,_12,state2){const value=(checkRule(state2)+(state2.options.ruleSpaces?" ":"")).repeat(checkRuleRepetition(state2));return state2.options.ruleSpaces?value.slice(0,-1):value}const handle$1={blockquote:blockquote$1,break:hardBreak$1,code:code$2,definition,emphasis:emphasis$1,
-hardBreak:hardBreak$1,heading:heading$1,html:html$5,image:image$1,imageReference:imageReference$1,inlineCode:inlineCode$1,link:link$1,linkReference:linkReference$1,list:list$1,listItem:listItem$1,paragraph:paragraph$1,root:root$u,strong:strong$1,text:text$5,thematicBreak:thematicBreak$1},join$1=[joinDefaults];function joinDefaults(left,right,parent,state2){if(right.type==="code"&&formatCodeAsIndented(right,state2)&&(left.type==="list"||left.type===right.type&&formatCodeAsIndented(left,state2)))return!1;
-if("spread"in parent&&typeof parent.spread=="boolean")return left.type==="paragraph"&&(left.type===right.type||right.type==="definition"||right.type==="heading"&&formatHeadingAsSetext(right,state2))?void 0:parent.spread?1:0}const fullPhrasingSpans=["autolink","destinationLiteral","destinationRaw","reference","titleQuote","titleApostrophe"],unsafe=[{character:"	",after:"[\\r\\n]",inConstruct:"phrasing"},{character:"	",before:"[\\r\\n]",inConstruct:"phrasing"},{character:"	",inConstruct:["codeFenc\
-edLangGraveAccent","codeFencedLangTilde"]},{character:"\r",inConstruct:["codeFencedLangGraveAccent","codeFencedLangTilde","codeFencedMetaGraveAccent","codeFencedMetaTilde","destinationLiteral","headingAtx"]},{character:`
-`,inConstruct:["codeFencedLangGraveAccent","codeFencedLangTilde","codeFencedMetaGraveAccent","codeFencedMetaTilde","destinationLiteral","headingAtx"]},{character:" ",after:"[\\r\\n]",inConstruct:"phrasing"},{character:" ",before:"[\\r\\n]",inConstruct:"phrasing"},{character:" ",inConstruct:["codeFencedLangGraveAccent","codeFencedLangTilde"]},{character:"!",after:"\\[",inConstruct:"phrasing",notInConstruct:fullPhrasingSpans},{character:'"',inConstruct:"titleQuote"},{atBreak:!0,character:"#"},{character:"\
-#",inConstruct:"headingAtx",after:`(?:[\r
-]|$)`},{character:"&",after:"[#A-Za-z]",inConstruct:"phrasing"},{character:"'",inConstruct:"titleApostrophe"},{character:"(",inConstruct:"destinationRaw"},{before:"\\]",character:"(",inConstruct:"phrasing",notInConstruct:fullPhrasingSpans},{atBreak:!0,before:"\\d+",character:")"},{character:")",inConstruct:"destinationRaw"},{atBreak:!0,character:"*",after:`(?:[ 	\r
-*])`},{character:"*",inConstruct:"phrasing",notInConstruct:fullPhrasingSpans},{atBreak:!0,character:"+",after:`(?:[ 	\r
-])`},{atBreak:!0,character:"-",after:`(?:[ 	\r
--])`},{atBreak:!0,before:"\\d+",character:".",after:`(?:[ 	\r
-]|$)`},{atBreak:!0,character:"<",after:"[!/?A-Za-z]"},{character:"<",after:"[!/?A-Za-z]",inConstruct:"phrasing",notInConstruct:fullPhrasingSpans},{character:"<",inConstruct:"destinationLiteral"},{atBreak:!0,character:"="},{atBreak:!0,character:">"},{character:">",inConstruct:"destinationLiteral"},{atBreak:!0,character:"["},{character:"[",inConstruct:"phrasing",notInConstruct:fullPhrasingSpans},{character:"[",inConstruct:["label","reference"]},{character:"\\",after:"[\\r\\n]",inConstruct:"phrasin\
-g"},{character:"]",inConstruct:["label","reference"]},{atBreak:!0,character:"_"},{character:"_",inConstruct:"phrasing",notInConstruct:fullPhrasingSpans},{atBreak:!0,character:"`"},{character:"`",inConstruct:["codeFencedLangGraveAccent","codeFencedMetaGraveAccent"]},{character:"`",inConstruct:"phrasing",notInConstruct:fullPhrasingSpans},{atBreak:!0,character:"~"}];function association(node2){return node2.label||!node2.identifier?node2.label||"":decodeString(node2.identifier)}function compilePattern(pattern2){
-if(!pattern2._compiled){const before=(pattern2.atBreak?"[\\r\\n][\\t ]*":"")+(pattern2.before?"(?:"+pattern2.before+")":"");pattern2._compiled=new RegExp((before?"("+before+")":"")+(/[|\\{}()[\]^$+*?.-]/.test(pattern2.character)?"\\":"")+pattern2.character+(pattern2.after?"(?:"+pattern2.after+")":""),"g")}return pattern2._compiled}function containerPhrasing(parent,state2,info2){const indexStack=state2.indexStack,children=parent.children||[],results=[];let index2=-1,before=info2.before,encodeAfter;
-indexStack.push(-1);let tracker=state2.createTracker(info2);for(;++index2<children.length;){const child2=children[index2];let after;if(indexStack[indexStack.length-1]=index2,index2+1<children.length){let handle2=state2.handle.handlers[children[index2+1].type];handle2&&handle2.peek&&(handle2=handle2.peek),after=handle2?handle2(children[index2+1],parent,state2,{before:"",after:"",...tracker.current()}).charAt(0):""}else after=info2.after;results.length>0&&(before==="\r"||before===`
-`)&&child2.type==="html"&&(results[results.length-1]=results[results.length-1].replace(/(\r?\n|\r)$/," "),before=" ",tracker=state2.createTracker(info2),tracker.move(results.join("")));let value=state2.handle(child2,parent,state2,{...tracker.current(),after,before});encodeAfter&&encodeAfter===value.slice(0,1)&&(value=encodeCharacterReference(encodeAfter.charCodeAt(0))+value.slice(1));const encodingInfo=state2.attentionEncodeSurroundingInfo;state2.attentionEncodeSurroundingInfo=void 0,encodeAfter=
-void 0,encodingInfo&&(results.length>0&&encodingInfo.before&&before===results[results.length-1].slice(-1)&&(results[results.length-1]=results[results.length-1].slice(0,-1)+encodeCharacterReference(before.charCodeAt(0))),encodingInfo.after&&(encodeAfter=after)),tracker.move(value),results.push(value),before=value.slice(-1)}return indexStack.pop(),results.join("")}function containerFlow(parent,state2,info2){const indexStack=state2.indexStack,children=parent.children||[],tracker=state2.createTracker(
-info2),results=[];let index2=-1;for(indexStack.push(-1);++index2<children.length;){const child2=children[index2];indexStack[indexStack.length-1]=index2,results.push(tracker.move(state2.handle(child2,parent,state2,{before:`
-`,after:`
-`,...tracker.current()}))),child2.type!=="list"&&(state2.bulletLastUsed=void 0),index2<children.length-1&&results.push(tracker.move(between(child2,children[index2+1],parent,state2)))}return indexStack.pop(),results.join("")}function between(left,right,parent,state2){let index2=state2.join.length;for(;index2--;){const result=state2.join[index2](left,right,parent,state2);if(result===!0||result===1)break;if(typeof result=="number")return`
-`.repeat(1+result);if(result===!1)return`
-
-<!---->
-
-`}return`
-
-`}const eol=/\r?\n|\r/g;function indentLines(value,map2){const result=[];let start2=0,line=0,match;for(;match=eol.exec(value);)one2(value.slice(start2,match.index)),result.push(match[0]),start2=match.index+match[0].length,line++;return one2(value.slice(start2)),result.join("");function one2(value2){result.push(map2(value2,line,!value2))}}function safe(state2,input,config2){const value=(config2.before||"")+(input||"")+(config2.after||""),positions=[],result=[],infos={};let index2=-1;for(;++index2<
-state2.unsafe.length;){const pattern2=state2.unsafe[index2];if(!patternInScope(state2.stack,pattern2))continue;const expression=state2.compilePattern(pattern2);let match;for(;match=expression.exec(value);){const before="before"in pattern2||!!pattern2.atBreak,after="after"in pattern2,position2=match.index+(before?match[1].length:0);positions.includes(position2)?(infos[position2].before&&!before&&(infos[position2].before=!1),infos[position2].after&&!after&&(infos[position2].after=!1)):(positions.push(
-position2),infos[position2]={before,after})}}positions.sort(numerical);let start2=config2.before?config2.before.length:0;const end=value.length-(config2.after?config2.after.length:0);for(index2=-1;++index2<positions.length;){const position2=positions[index2];position2<start2||position2>=end||position2+1<end&&positions[index2+1]===position2+1&&infos[position2].after&&!infos[position2+1].before&&!infos[position2+1].after||positions[index2-1]===position2-1&&infos[position2].before&&!infos[position2-
-1].before&&!infos[position2-1].after||(start2!==position2&&result.push(escapeBackslashes(value.slice(start2,position2),"\\")),start2=position2,/[!-/:-@[-`{-~]/.test(value.charAt(position2))&&(!config2.encode||!config2.encode.includes(value.charAt(position2)))?result.push("\\"):(result.push(encodeCharacterReference(value.charCodeAt(position2))),start2++))}return result.push(escapeBackslashes(value.slice(start2,end),config2.after)),result.join("")}function numerical(a,b){return a-b}function escapeBackslashes(value,after){
-const expression=/\\(?=[!-/:-@[-`{-~])/g,positions=[],results=[],whole=value+after;let index2=-1,start2=0,match;for(;match=expression.exec(whole);)positions.push(match.index);for(;++index2<positions.length;)start2!==positions[index2]&&results.push(value.slice(start2,positions[index2])),results.push("\\"),start2=positions[index2];return results.push(value.slice(start2)),results.join("")}function track(config2){const options=config2||{},now2=options.now||{};let lineShift=options.lineShift||0,line=now2.
-line||1,column=now2.column||1;return{move:move2,current:current2,shift:shift2};function current2(){return{now:{line,column},lineShift}}function shift2(value){lineShift+=value}function move2(input){const value=input||"",chunks=value.split(/\r?\n|\r/g),tail=chunks[chunks.length-1];return line+=chunks.length-1,column=chunks.length===1?column+tail.length:1+tail.length+lineShift,value}}function toMarkdown(tree,options){const settings=options||{},state2={associationId:association,containerPhrasing:containerPhrasingBound,
-containerFlow:containerFlowBound,createTracker:track,compilePattern,enter,handlers:{...handle$1},handle:void 0,indentLines,indexStack:[],join:[...join$1],options:{},safe:safeBound,stack:[],unsafe:[...unsafe]};configure(state2,settings),state2.options.tightDefinitions&&state2.join.push(joinDefinition),state2.handle=zwitch("type",{invalid:invalid$1,unknown:unknown$1,handlers:state2.handlers});let result=state2.handle(tree,void 0,state2,{before:`
-`,after:`
-`,now:{line:1,column:1},lineShift:0});return result&&result.charCodeAt(result.length-1)!==10&&result.charCodeAt(result.length-1)!==13&&(result+=`
-`),result;function enter(name){return state2.stack.push(name),exit2;function exit2(){state2.stack.pop()}}}function invalid$1(value){throw new Error("Cannot handle value `"+value+"`, expected node")}function unknown$1(value){const node2=value;throw new Error("Cannot handle unknown node `"+node2.type+"`")}function joinDefinition(left,right){if(left.type==="definition"&&left.type===right.type)return 0}function containerPhrasingBound(parent,info2){return containerPhrasing(parent,this,info2)}function containerFlowBound(parent,info2){
-return containerFlow(parent,this,info2)}function safeBound(value,config2){return safe(this,value,config2)}function remarkStringify(options){const self2=this;self2.compiler=compiler2;function compiler2(tree){return toMarkdown(tree,{...self2.data("settings"),...options,extensions:self2.data("toMarkdownExtensions")||[]})}}function bail(error2){if(error2)throw error2}var extend$1,hasRequiredExtend;function requireExtend(){if(hasRequiredExtend)return extend$1;hasRequiredExtend=1;var hasOwn=Object.prototype.
-hasOwnProperty,toStr=Object.prototype.toString,defineProperty=Object.defineProperty,gOPD=Object.getOwnPropertyDescriptor,isArray=function(arr){return typeof Array.isArray=="function"?Array.isArray(arr):toStr.call(arr)==="[object Array]"},isPlainObject2=function(obj){if(!obj||toStr.call(obj)!=="[object Object]")return!1;var hasOwnConstructor=hasOwn.call(obj,"constructor"),hasIsPrototypeOf=obj.constructor&&obj.constructor.prototype&&hasOwn.call(obj.constructor.prototype,"isPrototypeOf");if(obj.constructor&&
-!hasOwnConstructor&&!hasIsPrototypeOf)return!1;var key2;for(key2 in obj);return typeof key2>"u"||hasOwn.call(obj,key2)},setProperty=function(target2,options){defineProperty&&options.name==="__proto__"?defineProperty(target2,options.name,{enumerable:!0,configurable:!0,value:options.newValue,writable:!0}):target2[options.name]=options.newValue},getProperty=function(obj,name){if(name==="__proto__")if(hasOwn.call(obj,name)){if(gOPD)return gOPD(obj,name).value}else return;return obj[name]};return extend$1=
-function extend2(){var options,name,src,copy,copyIsArray,clone2,target2=arguments[0],i=1,length=arguments.length,deep=!1;for(typeof target2=="boolean"&&(deep=target2,target2=arguments[1]||{},i=2),(target2==null||typeof target2!="object"&&typeof target2!="function")&&(target2={});i<length;++i)if(options=arguments[i],options!=null)for(name in options)src=getProperty(target2,name),copy=getProperty(options,name),target2!==copy&&(deep&&copy&&(isPlainObject2(copy)||(copyIsArray=isArray(copy)))?(copyIsArray?
-(copyIsArray=!1,clone2=src&&isArray(src)?src:[]):clone2=src&&isPlainObject2(src)?src:{},setProperty(target2,{name,newValue:extend2(deep,clone2,copy)})):typeof copy<"u"&&setProperty(target2,{name,newValue:copy}));return target2},extend$1}var extendExports=requireExtend();const extend=getDefaultExportFromCjs(extendExports);function isPlainObject(value){if(typeof value!="object"||value===null)return!1;const prototype=Object.getPrototypeOf(value);return(prototype===null||prototype===Object.prototype||Object.getPrototypeOf(prototype)===null)&&!(Symbol.toStringTag in value)&&!(Symbol.iterator in value)}function trough(){const fns=[],pipeline={run:run2,use};return pipeline;function run2(...values){let middlewareIndex=-1;const callback=values.pop();if(typeof callback!="function")throw new TypeError("Expected function as\
- last argument, not "+callback);next2(null,...values);function next2(error2,...output){const fn=fns[++middlewareIndex];let index2=-1;if(error2){callback(error2);return}for(;++index2<values.length;)(output[index2]===null||output[index2]===void 0)&&(output[index2]=values[index2]);values=output,fn?wrap$1(fn,next2)(...output):callback(null,...output)}}function use(middelware){if(typeof middelware!="function")throw new TypeError("Expected `middelware` to be a function, not "+middelware);return fns.push(
-middelware),pipeline}}function wrap$1(middleware,callback){let called;return wrapped;function wrapped(...parameters){const fnExpectsCallback=middleware.length>parameters.length;let result;fnExpectsCallback&&parameters.push(done);try{result=middleware.apply(this,parameters)}catch(error2){const exception=error2;if(fnExpectsCallback&&called)throw exception;return done(exception)}fnExpectsCallback||(result&&result.then&&typeof result.then=="function"?result.then(then,done):result instanceof Error?done(
-result):then(result))}function done(error2,...output){called||(called=!0,callback(error2,...output))}function then(value){done(null,value)}}function stringifyPosition(value){return!value||typeof value!="object"?"":"position"in value||"type"in value?position$1(value.position):"start"in value||"end"in value?position$1(value):"line"in value||"column"in value?point$1(value):""}function point$1(point2){return index(point2&&point2.line)+":"+index(point2&&point2.column)}function position$1(pos){return point$1(
-pos&&pos.start)+"-"+point$1(pos&&pos.end)}function index(value){return value&&typeof value=="number"?value:1}class VFileMessage extends Error{constructor(causeOrReason,optionsOrParentOrPlace,origin2){super(),typeof optionsOrParentOrPlace=="string"&&(origin2=optionsOrParentOrPlace,optionsOrParentOrPlace=void 0);let reason="",options={},legacyCause=!1;if(optionsOrParentOrPlace&&("line"in optionsOrParentOrPlace&&"column"in optionsOrParentOrPlace?options={place:optionsOrParentOrPlace}:"start"in optionsOrParentOrPlace&&
-"end"in optionsOrParentOrPlace?options={place:optionsOrParentOrPlace}:"type"in optionsOrParentOrPlace?options={ancestors:[optionsOrParentOrPlace],place:optionsOrParentOrPlace.position}:options={...optionsOrParentOrPlace}),typeof causeOrReason=="string"?reason=causeOrReason:!options.cause&&causeOrReason&&(legacyCause=!0,reason=causeOrReason.message,options.cause=causeOrReason),!options.ruleId&&!options.source&&typeof origin2=="string"){const index2=origin2.indexOf(":");index2===-1?options.ruleId=
-origin2:(options.source=origin2.slice(0,index2),options.ruleId=origin2.slice(index2+1))}if(!options.place&&options.ancestors&&options.ancestors){const parent=options.ancestors[options.ancestors.length-1];parent&&(options.place=parent.position)}const start2=options.place&&"start"in options.place?options.place.start:options.place;this.ancestors=options.ancestors||void 0,this.cause=options.cause||void 0,this.column=start2?start2.column:void 0,this.fatal=void 0,this.file,this.message=reason,this.line=
-start2?start2.line:void 0,this.name=stringifyPosition(options.place)||"1:1",this.place=options.place||void 0,this.reason=this.message,this.ruleId=options.ruleId||void 0,this.source=options.source||void 0,this.stack=legacyCause&&options.cause&&typeof options.cause.stack=="string"?options.cause.stack:"",this.actual,this.expected,this.note,this.url}}VFileMessage.prototype.file="";VFileMessage.prototype.name="";VFileMessage.prototype.reason="";VFileMessage.prototype.message="";VFileMessage.prototype.
-stack="";VFileMessage.prototype.column=void 0;VFileMessage.prototype.line=void 0;VFileMessage.prototype.ancestors=void 0;VFileMessage.prototype.cause=void 0;VFileMessage.prototype.fatal=void 0;VFileMessage.prototype.place=void 0;VFileMessage.prototype.ruleId=void 0;VFileMessage.prototype.source=void 0;const minpath={basename,dirname,extname,join,sep:"/"};function basename(path2,extname2){if(extname2!==void 0&&typeof extname2!="string")throw new TypeError('"ext" argument must be a string');assertPath$1(
-path2);let start2=0,end=-1,index2=path2.length,seenNonSlash;if(extname2===void 0||extname2.length===0||extname2.length>path2.length){for(;index2--;)if(path2.codePointAt(index2)===47){if(seenNonSlash){start2=index2+1;break}}else end<0&&(seenNonSlash=!0,end=index2+1);return end<0?"":path2.slice(start2,end)}if(extname2===path2)return"";let firstNonSlashEnd=-1,extnameIndex=extname2.length-1;for(;index2--;)if(path2.codePointAt(index2)===47){if(seenNonSlash){start2=index2+1;break}}else firstNonSlashEnd<
-0&&(seenNonSlash=!0,firstNonSlashEnd=index2+1),extnameIndex>-1&&(path2.codePointAt(index2)===extname2.codePointAt(extnameIndex--)?extnameIndex<0&&(end=index2):(extnameIndex=-1,end=firstNonSlashEnd));return start2===end?end=firstNonSlashEnd:end<0&&(end=path2.length),path2.slice(start2,end)}function dirname(path2){if(assertPath$1(path2),path2.length===0)return".";let end=-1,index2=path2.length,unmatchedSlash;for(;--index2;)if(path2.codePointAt(index2)===47){if(unmatchedSlash){end=index2;break}}else
-unmatchedSlash||(unmatchedSlash=!0);return end<0?path2.codePointAt(0)===47?"/":".":end===1&&path2.codePointAt(0)===47?"//":path2.slice(0,end)}function extname(path2){assertPath$1(path2);let index2=path2.length,end=-1,startPart=0,startDot=-1,preDotState=0,unmatchedSlash;for(;index2--;){const code2=path2.codePointAt(index2);if(code2===47){if(unmatchedSlash){startPart=index2+1;break}continue}end<0&&(unmatchedSlash=!0,end=index2+1),code2===46?startDot<0?startDot=index2:preDotState!==1&&(preDotState=
-1):startDot>-1&&(preDotState=-1)}return startDot<0||end<0||preDotState===0||preDotState===1&&startDot===end-1&&startDot===startPart+1?"":path2.slice(startDot,end)}function join(...segments){let index2=-1,joined;for(;++index2<segments.length;)assertPath$1(segments[index2]),segments[index2]&&(joined=joined===void 0?segments[index2]:joined+"/"+segments[index2]);return joined===void 0?".":normalize$1(joined)}function normalize$1(path2){assertPath$1(path2);const absolute=path2.codePointAt(0)===47;let value=normalizeString(
-path2,!absolute);return value.length===0&&!absolute&&(value="."),value.length>0&&path2.codePointAt(path2.length-1)===47&&(value+="/"),absolute?"/"+value:value}function normalizeString(path2,allowAboveRoot){let result="",lastSegmentLength=0,lastSlash=-1,dots=0,index2=-1,code2,lastSlashIndex;for(;++index2<=path2.length;){if(index2<path2.length)code2=path2.codePointAt(index2);else{if(code2===47)break;code2=47}if(code2===47){if(!(lastSlash===index2-1||dots===1))if(lastSlash!==index2-1&&dots===2){if(result.
-length<2||lastSegmentLength!==2||result.codePointAt(result.length-1)!==46||result.codePointAt(result.length-2)!==46){if(result.length>2){if(lastSlashIndex=result.lastIndexOf("/"),lastSlashIndex!==result.length-1){lastSlashIndex<0?(result="",lastSegmentLength=0):(result=result.slice(0,lastSlashIndex),lastSegmentLength=result.length-1-result.lastIndexOf("/")),lastSlash=index2,dots=0;continue}}else if(result.length>0){result="",lastSegmentLength=0,lastSlash=index2,dots=0;continue}}allowAboveRoot&&(result=
-result.length>0?result+"/..":"..",lastSegmentLength=2)}else result.length>0?result+="/"+path2.slice(lastSlash+1,index2):result=path2.slice(lastSlash+1,index2),lastSegmentLength=index2-lastSlash-1;lastSlash=index2,dots=0}else code2===46&&dots>-1?dots++:dots=-1}return result}function assertPath$1(path2){if(typeof path2!="string")throw new TypeError("Path must be a string. Received "+JSON.stringify(path2))}const minproc={cwd};function cwd(){return"/"}function isUrl(fileUrlOrPath){return!!(fileUrlOrPath!==
-null&&typeof fileUrlOrPath=="object"&&"href"in fileUrlOrPath&&fileUrlOrPath.href&&"protocol"in fileUrlOrPath&&fileUrlOrPath.protocol&&fileUrlOrPath.auth===void 0)}function urlToPath(path2){if(typeof path2=="string")path2=new URL(path2);else if(!isUrl(path2)){const error2=new TypeError('The "path" argument must be of type string or an instance of URL. Received `'+path2+"`");throw error2.code="ERR_INVALID_ARG_TYPE",error2}if(path2.protocol!=="file:"){const error2=new TypeError("The URL must be of \
-scheme file");throw error2.code="ERR_INVALID_URL_SCHEME",error2}return getPathFromURLPosix(path2)}function getPathFromURLPosix(url2){if(url2.hostname!==""){const error2=new TypeError('File URL host must be "localhost" or empty on darwin');throw error2.code="ERR_INVALID_FILE_URL_HOST",error2}const pathname=url2.pathname;let index2=-1;for(;++index2<pathname.length;)if(pathname.codePointAt(index2)===37&&pathname.codePointAt(index2+1)===50){const third=pathname.codePointAt(index2+2);if(third===70||third===
-102){const error2=new TypeError("File URL path must not include encoded / characters");throw error2.code="ERR_INVALID_FILE_URL_PATH",error2}}return decodeURIComponent(pathname)}const order=["history","path","basename","stem","extname","dirname"];class VFile{constructor(value){let options;value?isUrl(value)?options={path:value}:typeof value=="string"||isUint8Array$1(value)?options={value}:options=value:options={},this.cwd="cwd"in options?"":minproc.cwd(),this.data={},this.history=[],this.messages=
-[],this.value,this.map,this.result,this.stored;let index2=-1;for(;++index2<order.length;){const field2=order[index2];field2 in options&&options[field2]!==void 0&&options[field2]!==null&&(this[field2]=field2==="history"?[...options[field2]]:options[field2])}let field;for(field in options)order.includes(field)||(this[field]=options[field])}get basename(){return typeof this.path=="string"?minpath.basename(this.path):void 0}set basename(basename2){assertNonEmpty(basename2,"basename"),assertPart(basename2,
-"basename"),this.path=minpath.join(this.dirname||"",basename2)}get dirname(){return typeof this.path=="string"?minpath.dirname(this.path):void 0}set dirname(dirname2){assertPath(this.basename,"dirname"),this.path=minpath.join(dirname2||"",this.basename)}get extname(){return typeof this.path=="string"?minpath.extname(this.path):void 0}set extname(extname2){if(assertPart(extname2,"extname"),assertPath(this.dirname,"extname"),extname2){if(extname2.codePointAt(0)!==46)throw new Error("`extname` must\
- start with `.`");if(extname2.includes(".",1))throw new Error("`extname` cannot contain multiple dots")}this.path=minpath.join(this.dirname,this.stem+(extname2||""))}get path(){return this.history[this.history.length-1]}set path(path2){isUrl(path2)&&(path2=urlToPath(path2)),assertNonEmpty(path2,"path"),this.path!==path2&&this.history.push(path2)}get stem(){return typeof this.path=="string"?minpath.basename(this.path,this.extname):void 0}set stem(stem){assertNonEmpty(stem,"stem"),assertPart(stem,
-"stem"),this.path=minpath.join(this.dirname||"",stem+(this.extname||""))}fail(causeOrReason,optionsOrParentOrPlace,origin2){const message=this.message(causeOrReason,optionsOrParentOrPlace,origin2);throw message.fatal=!0,message}info(causeOrReason,optionsOrParentOrPlace,origin2){const message=this.message(causeOrReason,optionsOrParentOrPlace,origin2);return message.fatal=void 0,message}message(causeOrReason,optionsOrParentOrPlace,origin2){const message=new VFileMessage(causeOrReason,optionsOrParentOrPlace,
-origin2);return this.path&&(message.name=this.path+":"+message.name,message.file=this.path),message.fatal=!1,this.messages.push(message),message}toString(encoding){return this.value===void 0?"":typeof this.value=="string"?this.value:new TextDecoder(encoding||void 0).decode(this.value)}}function assertPart(part,name){if(part&&part.includes(minpath.sep))throw new Error("`"+name+"` cannot be a path: did not expect `"+minpath.sep+"`")}function assertNonEmpty(part,name){if(!part)throw new Error("`"+name+
-"` cannot be empty")}function assertPath(path2,name){if(!path2)throw new Error("Setting `"+name+"` requires `path` to be set too")}function isUint8Array$1(value){return!!(value&&typeof value=="object"&&"byteLength"in value&&"byteOffset"in value)}const CallableInstance=function(property){const proto=this.constructor.prototype,value=proto[property],apply=function(){return value.apply(apply,arguments)};return Object.setPrototypeOf(apply,proto),apply},own$3={}.hasOwnProperty;class Processor extends CallableInstance{constructor(){
-super("copy"),this.Compiler=void 0,this.Parser=void 0,this.attachers=[],this.compiler=void 0,this.freezeIndex=-1,this.frozen=void 0,this.namespace={},this.parser=void 0,this.transformers=trough()}copy(){const destination=new Processor;let index2=-1;for(;++index2<this.attachers.length;){const attacher=this.attachers[index2];destination.use(...attacher)}return destination.data(extend(!0,{},this.namespace)),destination}data(key2,value){return typeof key2=="string"?arguments.length===2?(assertUnfrozen(
-"data",this.frozen),this.namespace[key2]=value,this):own$3.call(this.namespace,key2)&&this.namespace[key2]||void 0:key2?(assertUnfrozen("data",this.frozen),this.namespace=key2,this):this.namespace}freeze(){if(this.frozen)return this;const self2=this;for(;++this.freezeIndex<this.attachers.length;){const[attacher,...options]=this.attachers[this.freezeIndex];if(options[0]===!1)continue;options[0]===!0&&(options[0]=void 0);const transformer=attacher.call(self2,...options);typeof transformer=="functi\
-on"&&this.transformers.use(transformer)}return this.frozen=!0,this.freezeIndex=Number.POSITIVE_INFINITY,this}parse(file){this.freeze();const realFile=vfile(file),parser=this.parser||this.Parser;return assertParser("parse",parser),parser(String(realFile),realFile)}process(file,done){const self2=this;return this.freeze(),assertParser("process",this.parser||this.Parser),assertCompiler("process",this.compiler||this.Compiler),done?executor(void 0,done):new Promise(executor);function executor(resolve2,reject){
-const realFile=vfile(file),parseTree3=self2.parse(realFile);self2.run(parseTree3,realFile,function(error2,tree,file2){if(error2||!tree||!file2)return realDone(error2);const compileTree=tree,compileResult=self2.stringify(compileTree,file2);looksLikeAValue(compileResult)?file2.value=compileResult:file2.result=compileResult,realDone(error2,file2)});function realDone(error2,file2){error2||!file2?reject(error2):resolve2?resolve2(file2):done(void 0,file2)}}}processSync(file){let complete=!1,result;return this.
-freeze(),assertParser("processSync",this.parser||this.Parser),assertCompiler("processSync",this.compiler||this.Compiler),this.process(file,realDone),assertDone("processSync","process",complete),result;function realDone(error2,file2){complete=!0,bail(error2),result=file2}}run(tree,file,done){assertNode(tree),this.freeze();const transformers=this.transformers;return!done&&typeof file=="function"&&(done=file,file=void 0),done?executor(void 0,done):new Promise(executor);function executor(resolve2,reject){
-const realFile=vfile(file);transformers.run(tree,realFile,realDone);function realDone(error2,outputTree,file2){const resultingTree=outputTree||tree;error2?reject(error2):resolve2?resolve2(resultingTree):done(void 0,resultingTree,file2)}}}runSync(tree,file){let complete=!1,result;return this.run(tree,file,realDone),assertDone("runSync","run",complete),result;function realDone(error2,tree2){bail(error2),result=tree2,complete=!0}}stringify(tree,file){this.freeze();const realFile=vfile(file),compiler2=this.
-compiler||this.Compiler;return assertCompiler("stringify",compiler2),assertNode(tree),compiler2(tree,realFile)}use(value,...parameters){const attachers=this.attachers,namespace=this.namespace;if(assertUnfrozen("use",this.frozen),value!=null)if(typeof value=="function")addPlugin(value,parameters);else if(typeof value=="object")Array.isArray(value)?addList(value):addPreset(value);else throw new TypeError("Expected usable value, not `"+value+"`");return this;function add2(value2){if(typeof value2==
-"function")addPlugin(value2,[]);else if(typeof value2=="object")if(Array.isArray(value2)){const[plugin,...parameters2]=value2;addPlugin(plugin,parameters2)}else addPreset(value2);else throw new TypeError("Expected usable value, not `"+value2+"`")}function addPreset(result){if(!("plugins"in result)&&!("settings"in result))throw new Error("Expected usable value but received an empty preset, which is probably a mistake: presets typically come with `plugins` and sometimes with `settings`, but this h\
-as neither");addList(result.plugins),result.settings&&(namespace.settings=extend(!0,namespace.settings,result.settings))}function addList(plugins){let index2=-1;if(plugins!=null)if(Array.isArray(plugins))for(;++index2<plugins.length;){const thing=plugins[index2];add2(thing)}else throw new TypeError("Expected a list of plugins, not `"+plugins+"`")}function addPlugin(plugin,parameters2){let index2=-1,entryIndex=-1;for(;++index2<attachers.length;)if(attachers[index2][0]===plugin){entryIndex=index2;
-break}if(entryIndex===-1)attachers.push([plugin,...parameters2]);else if(parameters2.length>0){let[primary,...rest]=parameters2;const currentPrimary=attachers[entryIndex][1];isPlainObject(currentPrimary)&&isPlainObject(primary)&&(primary=extend(!0,currentPrimary,primary)),attachers[entryIndex]=[plugin,primary,...rest]}}}}const unified=new Processor().freeze();function assertParser(name,value){if(typeof value!="function")throw new TypeError("Cannot `"+name+"` without `parser`")}function assertCompiler(name,value){
-if(typeof value!="function")throw new TypeError("Cannot `"+name+"` without `compiler`")}function assertUnfrozen(name,frozen){if(frozen)throw new Error("Cannot call `"+name+"` on a frozen processor.\nCreate a new processor first, by calling it: use `processor()` instead of `processor`.")}function assertNode(node2){if(!isPlainObject(node2)||typeof node2.type!="string")throw new TypeError("Expected node, got `"+node2+"`")}function assertDone(name,asyncName,complete){if(!complete)throw new Error("`"+
-name+"` finished async. Use `"+asyncName+"` instead")}function vfile(value){return looksLikeAVFile(value)?value:new VFile(value)}function looksLikeAVFile(value){return!!(value&&typeof value=="object"&&"message"in value&&"messages"in value)}function looksLikeAValue(value){return typeof value=="string"||isUint8Array(value)}function isUint8Array(value){return!!(value&&typeof value=="object"&&"byteLength"in value&&"byteOffset"in value)}const remark=unified().use(remarkParse).use(remarkStringify).freeze();
-function escapeStringRegexp(string2){if(typeof string2!="string")throw new TypeError("Expected a string");return string2.replace(/[|\\{}()[\]^$+*?.]/g,"\\$&").replace(/-/g,"\\x2d")}function findAndReplace(tree,list2,options){const ignored=convert((options||{}).ignore||[]),pairs=toPairs(list2);let pairIndex=-1;for(;++pairIndex<pairs.length;)visitParents(tree,"text",visitor);function visitor(node2,parents){let index2=-1,grandparent;for(;++index2<parents.length;){const parent=parents[index2],siblings2=grandparent?
-grandparent.children:void 0;if(ignored(parent,siblings2?siblings2.indexOf(parent):void 0,grandparent))return;grandparent=parent}if(grandparent)return handler(node2,parents)}function handler(node2,parents){const parent=parents[parents.length-1],find2=pairs[pairIndex][0],replace2=pairs[pairIndex][1];let start2=0;const index2=parent.children.indexOf(node2);let change=!1,nodes2=[];find2.lastIndex=0;let match=find2.exec(node2.value);for(;match;){const position2=match.index,matchObject={index:match.index,
-input:match.input,stack:[...parents,node2]};let value=replace2(...match,matchObject);if(typeof value=="string"&&(value=value.length>0?{type:"text",value}:void 0),value===!1?find2.lastIndex=position2+1:(start2!==position2&&nodes2.push({type:"text",value:node2.value.slice(start2,position2)}),Array.isArray(value)?nodes2.push(...value):value&&nodes2.push(value),start2=position2+match[0].length,change=!0),!find2.global)break;match=find2.exec(node2.value)}return change?(start2<node2.value.length&&nodes2.
-push({type:"text",value:node2.value.slice(start2)}),parent.children.splice(index2,1,...nodes2)):nodes2=[node2],index2+nodes2.length}}function toPairs(tupleOrList){const result=[];if(!Array.isArray(tupleOrList))throw new TypeError("Expected find and replace tuple or list of tuples");const list2=!tupleOrList[0]||Array.isArray(tupleOrList[0])?tupleOrList:[tupleOrList];let index2=-1;for(;++index2<list2.length;){const tuple=list2[index2];result.push([toExpression(tuple[0]),toFunction(tuple[1])])}return result}
-function toExpression(find2){return typeof find2=="string"?new RegExp(escapeStringRegexp(find2),"g"):find2}function toFunction(replace2){return typeof replace2=="function"?replace2:function(){return replace2}}function newlineToBreak(tree){findAndReplace(tree,[/\r?\n|\r/g,replace$1])}function replace$1(){return{type:"break"}}function remarkBreaks(){return function(tree){newlineToBreak(tree)}}function ccount(value,character){const source2=String(value);if(typeof character!="string")throw new TypeError(
-"Expected character");let count=0,index2=source2.indexOf(character);for(;index2!==-1;)count++,index2=source2.indexOf(character,index2+character.length);return count}const inConstruct="phrasing",notInConstruct=["autolink","link","image","label"];function gfmAutolinkLiteralFromMarkdown(){return{transforms:[transformGfmAutolinkLiterals],enter:{literalAutolink:enterLiteralAutolink,literalAutolinkEmail:enterLiteralAutolinkValue,literalAutolinkHttp:enterLiteralAutolinkValue,literalAutolinkWww:enterLiteralAutolinkValue},
-exit:{literalAutolink:exitLiteralAutolink,literalAutolinkEmail:exitLiteralAutolinkEmail,literalAutolinkHttp:exitLiteralAutolinkHttp,literalAutolinkWww:exitLiteralAutolinkWww}}}function gfmAutolinkLiteralToMarkdown(){return{unsafe:[{character:"@",before:"[+\\-.\\w]",after:"[\\-.\\w]",inConstruct,notInConstruct},{character:".",before:"[Ww]",after:"[\\-.\\w]",inConstruct,notInConstruct},{character:":",before:"[ps]",after:"\\/",inConstruct,notInConstruct}]}}function enterLiteralAutolink(token2){this.
-enter({type:"link",title:null,url:"",children:[]},token2)}function enterLiteralAutolinkValue(token2){this.config.enter.autolinkProtocol.call(this,token2)}function exitLiteralAutolinkHttp(token2){this.config.exit.autolinkProtocol.call(this,token2)}function exitLiteralAutolinkWww(token2){this.config.exit.data.call(this,token2);const node2=this.stack[this.stack.length-1];node2.type,node2.url="http://"+this.sliceSerialize(token2)}function exitLiteralAutolinkEmail(token2){this.config.exit.autolinkEmail.
-call(this,token2)}function exitLiteralAutolink(token2){this.exit(token2)}function transformGfmAutolinkLiterals(tree){findAndReplace(tree,[[/(https?:\/\/|www(?=\.))([-.\w]+)([^ \t\r\n]*)/gi,findUrl],[new RegExp("(?<=^|\\s|\\p{P}|\\p{S})([-.\\w+]+)@([-\\w]+(?:\\.[-\\w]+)+)","gu"),findEmail]],{ignore:["link","linkReference"]})}function findUrl(_,protocol,domain2,path2,match){let prefix="";if(!previous$1(match)||(/^w/i.test(protocol)&&(domain2=protocol+domain2,protocol="",prefix="http://"),!isCorrectDomain(
-domain2)))return!1;const parts=splitUrl(domain2+path2);if(!parts[0])return!1;const result={type:"link",title:null,url:prefix+protocol+parts[0],children:[{type:"text",value:protocol+parts[0]}]};return parts[1]?[result,{type:"text",value:parts[1]}]:result}function findEmail(_,atext,label,match){return!previous$1(match,!0)||/[-\d_]$/.test(label)?!1:{type:"link",title:null,url:"mailto:"+atext+"@"+label,children:[{type:"text",value:atext+"@"+label}]}}function isCorrectDomain(domain2){const parts=domain2.
-split(".");return!(parts.length<2||parts[parts.length-1]&&(/_/.test(parts[parts.length-1])||!/[a-zA-Z\d]/.test(parts[parts.length-1]))||parts[parts.length-2]&&(/_/.test(parts[parts.length-2])||!/[a-zA-Z\d]/.test(parts[parts.length-2])))}function splitUrl(url2){const trailExec=/[!"&'),.:;<>?\]}]+$/.exec(url2);if(!trailExec)return[url2,void 0];url2=url2.slice(0,trailExec.index);let trail2=trailExec[0],closingParenIndex=trail2.indexOf(")");const openingParens=ccount(url2,"(");let closingParens=ccount(
-url2,")");for(;closingParenIndex!==-1&&openingParens>closingParens;)url2+=trail2.slice(0,closingParenIndex+1),trail2=trail2.slice(closingParenIndex+1),closingParenIndex=trail2.indexOf(")"),closingParens++;return[url2,trail2]}function previous$1(match,email2){const code2=match.input.charCodeAt(match.index-1);return(match.index===0||unicodeWhitespace(code2)||unicodePunctuation(code2))&&(!email2||code2!==47)}footnoteReference$1.peek=footnoteReferencePeek;function enterFootnoteCallString(){this.buffer()}
-function enterFootnoteCall(token2){this.enter({type:"footnoteReference",identifier:"",label:""},token2)}function enterFootnoteDefinitionLabelString(){this.buffer()}function enterFootnoteDefinition(token2){this.enter({type:"footnoteDefinition",identifier:"",label:"",children:[]},token2)}function exitFootnoteCallString(token2){const label=this.resume(),node2=this.stack[this.stack.length-1];node2.type,node2.identifier=normalizeIdentifier(this.sliceSerialize(token2)).toLowerCase(),node2.label=label}
-function exitFootnoteCall(token2){this.exit(token2)}function exitFootnoteDefinitionLabelString(token2){const label=this.resume(),node2=this.stack[this.stack.length-1];node2.type,node2.identifier=normalizeIdentifier(this.sliceSerialize(token2)).toLowerCase(),node2.label=label}function exitFootnoteDefinition(token2){this.exit(token2)}function footnoteReferencePeek(){return"["}function footnoteReference$1(node2,_,state2,info2){const tracker=state2.createTracker(info2);let value=tracker.move("[^");const exit2=state2.
-enter("footnoteReference"),subexit=state2.enter("reference");return value+=tracker.move(state2.safe(state2.associationId(node2),{after:"]",before:value})),subexit(),exit2(),value+=tracker.move("]"),value}function gfmFootnoteFromMarkdown(){return{enter:{gfmFootnoteCallString:enterFootnoteCallString,gfmFootnoteCall:enterFootnoteCall,gfmFootnoteDefinitionLabelString:enterFootnoteDefinitionLabelString,gfmFootnoteDefinition:enterFootnoteDefinition},exit:{gfmFootnoteCallString:exitFootnoteCallString,gfmFootnoteCall:exitFootnoteCall,
-gfmFootnoteDefinitionLabelString:exitFootnoteDefinitionLabelString,gfmFootnoteDefinition:exitFootnoteDefinition}}}function gfmFootnoteToMarkdown(options){let firstLineBlank=!1;return options&&options.firstLineBlank&&(firstLineBlank=!0),{handlers:{footnoteDefinition,footnoteReference:footnoteReference$1},unsafe:[{character:"[",inConstruct:["label","phrasing","reference"]}]};function footnoteDefinition(node2,_,state2,info2){const tracker=state2.createTracker(info2);let value=tracker.move("[^");const exit2=state2.
-enter("footnoteDefinition"),subexit=state2.enter("label");return value+=tracker.move(state2.safe(state2.associationId(node2),{before:value,after:"]"})),subexit(),value+=tracker.move("]:"),node2.children&&node2.children.length>0&&(tracker.shift(4),value+=tracker.move((firstLineBlank?`
-`:" ")+state2.indentLines(state2.containerFlow(node2,tracker.current()),firstLineBlank?mapAll:mapExceptFirst))),exit2(),value}}function mapExceptFirst(line,index2,blank){return index2===0?line:mapAll(line,index2,blank)}function mapAll(line,index2,blank){return(blank?"":"    ")+line}const constructsWithoutStrikethrough=["autolink","destinationLiteral","destinationRaw","reference","titleQuote","titleApostrophe"];handleDelete.peek=peekDelete;function gfmStrikethroughFromMarkdown(){return{canContainEols:[
-"delete"],enter:{strikethrough:enterStrikethrough},exit:{strikethrough:exitStrikethrough}}}function gfmStrikethroughToMarkdown(){return{unsafe:[{character:"~",inConstruct:"phrasing",notInConstruct:constructsWithoutStrikethrough}],handlers:{delete:handleDelete}}}function enterStrikethrough(token2){this.enter({type:"delete",children:[]},token2)}function exitStrikethrough(token2){this.exit(token2)}function handleDelete(node2,_,state2,info2){const tracker=state2.createTracker(info2),exit2=state2.enter(
-"strikethrough");let value=tracker.move("~~");return value+=state2.containerPhrasing(node2,{...tracker.current(),before:value,after:"~"}),value+=tracker.move("~~"),exit2(),value}function peekDelete(){return"~"}function defaultStringLength(value){return value.length}function markdownTable(table2,options){const settings=options||{},align=(settings.align||[]).concat(),stringLength=settings.stringLength||defaultStringLength,alignments=[],cellMatrix=[],sizeMatrix=[],longestCellByColumn=[];let mostCellsPerRow=0,
-rowIndex=-1;for(;++rowIndex<table2.length;){const row3=[],sizes2=[];let columnIndex2=-1;for(table2[rowIndex].length>mostCellsPerRow&&(mostCellsPerRow=table2[rowIndex].length);++columnIndex2<table2[rowIndex].length;){const cell2=serialize$1(table2[rowIndex][columnIndex2]);if(settings.alignDelimiters!==!1){const size2=stringLength(cell2);sizes2[columnIndex2]=size2,(longestCellByColumn[columnIndex2]===void 0||size2>longestCellByColumn[columnIndex2])&&(longestCellByColumn[columnIndex2]=size2)}row3.push(
-cell2)}cellMatrix[rowIndex]=row3,sizeMatrix[rowIndex]=sizes2}let columnIndex=-1;if(typeof align=="object"&&"length"in align)for(;++columnIndex<mostCellsPerRow;)alignments[columnIndex]=toAlignment(align[columnIndex]);else{const code2=toAlignment(align);for(;++columnIndex<mostCellsPerRow;)alignments[columnIndex]=code2}columnIndex=-1;const row2=[],sizes=[];for(;++columnIndex<mostCellsPerRow;){const code2=alignments[columnIndex];let before="",after="";code2===99?(before=":",after=":"):code2===108?before=
-":":code2===114&&(after=":");let size2=settings.alignDelimiters===!1?1:Math.max(1,longestCellByColumn[columnIndex]-before.length-after.length);const cell2=before+"-".repeat(size2)+after;settings.alignDelimiters!==!1&&(size2=before.length+size2+after.length,size2>longestCellByColumn[columnIndex]&&(longestCellByColumn[columnIndex]=size2),sizes[columnIndex]=size2),row2[columnIndex]=cell2}cellMatrix.splice(1,0,row2),sizeMatrix.splice(1,0,sizes),rowIndex=-1;const lines=[];for(;++rowIndex<cellMatrix.length;){
-const row3=cellMatrix[rowIndex],sizes2=sizeMatrix[rowIndex];columnIndex=-1;const line=[];for(;++columnIndex<mostCellsPerRow;){const cell2=row3[columnIndex]||"";let before="",after="";if(settings.alignDelimiters!==!1){const size2=longestCellByColumn[columnIndex]-(sizes2[columnIndex]||0),code2=alignments[columnIndex];code2===114?before=" ".repeat(size2):code2===99?size2%2?(before=" ".repeat(size2/2+.5),after=" ".repeat(size2/2-.5)):(before=" ".repeat(size2/2),after=before):after=" ".repeat(size2)}
-settings.delimiterStart!==!1&&!columnIndex&&line.push("|"),settings.padding!==!1&&!(settings.alignDelimiters===!1&&cell2==="")&&(settings.delimiterStart!==!1||columnIndex)&&line.push(" "),settings.alignDelimiters!==!1&&line.push(before),line.push(cell2),settings.alignDelimiters!==!1&&line.push(after),settings.padding!==!1&&line.push(" "),(settings.delimiterEnd!==!1||columnIndex!==mostCellsPerRow-1)&&line.push("|")}lines.push(settings.delimiterEnd===!1?line.join("").replace(/ +$/,""):line.join(""))}
-return lines.join(`
-`)}function serialize$1(value){return value==null?"":String(value)}function toAlignment(value){const code2=typeof value=="string"?value.codePointAt(0):0;return code2===67||code2===99?99:code2===76||code2===108?108:code2===82||code2===114?114:0}function gfmTableFromMarkdown(){return{enter:{table:enterTable,tableData:enterCell,tableHeader:enterCell,tableRow:enterRow},exit:{codeText:exitCodeText,table:exitTable,tableData:exit,tableHeader:exit,tableRow:exit}}}function enterTable(token2){const align=token2.
-_align;this.enter({type:"table",align:align.map(function(d2){return d2==="none"?null:d2}),children:[]},token2),this.data.inTable=!0}function exitTable(token2){this.exit(token2),this.data.inTable=void 0}function enterRow(token2){this.enter({type:"tableRow",children:[]},token2)}function exit(token2){this.exit(token2)}function enterCell(token2){this.enter({type:"tableCell",children:[]},token2)}function exitCodeText(token2){let value=this.resume();this.data.inTable&&(value=value.replace(/\\([\\|])/g,
-replace));const node2=this.stack[this.stack.length-1];node2.type,node2.value=value,this.exit(token2)}function replace($0,$1){return $1==="|"?$1:$0}function gfmTableToMarkdown(options){const settings=options||{},padding=settings.tableCellPadding,alignDelimiters=settings.tablePipeAlign,stringLength=settings.stringLength,around=padding?" ":"|";return{unsafe:[{character:"\r",inConstruct:"tableCell"},{character:`
-`,inConstruct:"tableCell"},{atBreak:!0,character:"|",after:"[	 :-]"},{character:"|",inConstruct:"tableCell"},{atBreak:!0,character:":",after:"-"},{atBreak:!0,character:"-",after:"[:|-]"}],handlers:{inlineCode:inlineCodeWithTable,table:handleTable,tableCell:handleTableCell,tableRow:handleTableRow}};function handleTable(node2,_,state2,info2){return serializeData(handleTableAsData(node2,state2,info2),node2.align)}function handleTableRow(node2,_,state2,info2){const row2=handleTableRowAsData(node2,state2,
-info2),value=serializeData([row2]);return value.slice(0,value.indexOf(`
-`))}function handleTableCell(node2,_,state2,info2){const exit2=state2.enter("tableCell"),subexit=state2.enter("phrasing"),value=state2.containerPhrasing(node2,{...info2,before:around,after:around});return subexit(),exit2(),value}function serializeData(matrix,align){return markdownTable(matrix,{align,alignDelimiters,padding,stringLength})}function handleTableAsData(node2,state2,info2){const children=node2.children;let index2=-1;const result=[],subexit=state2.enter("table");for(;++index2<children.
-length;)result[index2]=handleTableRowAsData(children[index2],state2,info2);return subexit(),result}function handleTableRowAsData(node2,state2,info2){const children=node2.children;let index2=-1;const result=[],subexit=state2.enter("tableRow");for(;++index2<children.length;)result[index2]=handleTableCell(children[index2],node2,state2,info2);return subexit(),result}function inlineCodeWithTable(node2,parent,state2){let value=handle$1.inlineCode(node2,parent,state2);return state2.stack.includes("tabl\
-eCell")&&(value=value.replace(/\|/g,"\\$&")),value}}function gfmTaskListItemFromMarkdown(){return{exit:{taskListCheckValueChecked:exitCheck,taskListCheckValueUnchecked:exitCheck,paragraph:exitParagraphWithTaskListItem}}}function gfmTaskListItemToMarkdown(){return{unsafe:[{atBreak:!0,character:"-",after:"[:|-]"}],handlers:{listItem:listItemWithTaskListItem}}}function exitCheck(token2){const node2=this.stack[this.stack.length-2];node2.type,node2.checked=token2.type==="taskListCheckValueChecked"}function exitParagraphWithTaskListItem(token2){
-const parent=this.stack[this.stack.length-2];if(parent&&parent.type==="listItem"&&typeof parent.checked=="boolean"){const node2=this.stack[this.stack.length-1];node2.type;const head2=node2.children[0];if(head2&&head2.type==="text"){const siblings2=parent.children;let index2=-1,firstParaghraph;for(;++index2<siblings2.length;){const sibling2=siblings2[index2];if(sibling2.type==="paragraph"){firstParaghraph=sibling2;break}}firstParaghraph===node2&&(head2.value=head2.value.slice(1),head2.value.length===
-0?node2.children.shift():node2.position&&head2.position&&typeof head2.position.start.offset=="number"&&(head2.position.start.column++,head2.position.start.offset++,node2.position.start=Object.assign({},head2.position.start)))}}this.exit(token2)}function listItemWithTaskListItem(node2,parent,state2,info2){const head2=node2.children[0],checkable=typeof node2.checked=="boolean"&&head2&&head2.type==="paragraph",checkbox="["+(node2.checked?"x":" ")+"] ",tracker=state2.createTracker(info2);checkable&&
-tracker.move(checkbox);let value=handle$1.listItem(node2,parent,state2,{...info2,...tracker.current()});return checkable&&(value=value.replace(/^(?:[*+-]|\d+\.)([\r\n]| {1,3})/,check)),value;function check($0){return $0+checkbox}}function gfmFromMarkdown(){return[gfmAutolinkLiteralFromMarkdown(),gfmFootnoteFromMarkdown(),gfmStrikethroughFromMarkdown(),gfmTableFromMarkdown(),gfmTaskListItemFromMarkdown()]}function gfmToMarkdown(options){return{extensions:[gfmAutolinkLiteralToMarkdown(),gfmFootnoteToMarkdown(
-options),gfmStrikethroughToMarkdown(),gfmTableToMarkdown(options),gfmTaskListItemToMarkdown()]}}const wwwPrefix={tokenize:tokenizeWwwPrefix,partial:!0},domain={tokenize:tokenizeDomain,partial:!0},path$1={tokenize:tokenizePath,partial:!0},trail={tokenize:tokenizeTrail,partial:!0},emailDomainDotTrail={tokenize:tokenizeEmailDomainDotTrail,partial:!0},wwwAutolink={name:"wwwAutolink",tokenize:tokenizeWwwAutolink,previous:previousWww},protocolAutolink={name:"protocolAutolink",tokenize:tokenizeProtocolAutolink,
-previous:previousProtocol},emailAutolink={name:"emailAutolink",tokenize:tokenizeEmailAutolink,previous:previousEmail},text$4={};function gfmAutolinkLiteral(){return{text:text$4}}let code$1=48;for(;code$1<123;)text$4[code$1]=emailAutolink,code$1++,code$1===58?code$1=65:code$1===91&&(code$1=97);text$4[43]=emailAutolink;text$4[45]=emailAutolink;text$4[46]=emailAutolink;text$4[95]=emailAutolink;text$4[72]=[emailAutolink,protocolAutolink];text$4[104]=[emailAutolink,protocolAutolink];text$4[87]=[emailAutolink,
-wwwAutolink];text$4[119]=[emailAutolink,wwwAutolink];function tokenizeEmailAutolink(effects,ok,nok){const self2=this;let dot,data;return start2;function start2(code2){return!gfmAtext(code2)||!previousEmail.call(self2,self2.previous)||previousUnbalanced(self2.events)?nok(code2):(effects.enter("literalAutolink"),effects.enter("literalAutolinkEmail"),atext(code2))}function atext(code2){return gfmAtext(code2)?(effects.consume(code2),atext):code2===64?(effects.consume(code2),emailDomain):nok(code2)}function emailDomain(code2){
-return code2===46?effects.check(emailDomainDotTrail,emailDomainAfter,emailDomainDot)(code2):code2===45||code2===95||asciiAlphanumeric(code2)?(data=!0,effects.consume(code2),emailDomain):emailDomainAfter(code2)}function emailDomainDot(code2){return effects.consume(code2),dot=!0,emailDomain}function emailDomainAfter(code2){return data&&dot&&asciiAlpha(self2.previous)?(effects.exit("literalAutolinkEmail"),effects.exit("literalAutolink"),ok(code2)):nok(code2)}}function tokenizeWwwAutolink(effects,ok,nok){
-const self2=this;return wwwStart;function wwwStart(code2){return code2!==87&&code2!==119||!previousWww.call(self2,self2.previous)||previousUnbalanced(self2.events)?nok(code2):(effects.enter("literalAutolink"),effects.enter("literalAutolinkWww"),effects.check(wwwPrefix,effects.attempt(domain,effects.attempt(path$1,wwwAfter),nok),nok)(code2))}function wwwAfter(code2){return effects.exit("literalAutolinkWww"),effects.exit("literalAutolink"),ok(code2)}}function tokenizeProtocolAutolink(effects,ok,nok){
-const self2=this;let buffer="",seen2=!1;return protocolStart;function protocolStart(code2){return(code2===72||code2===104)&&previousProtocol.call(self2,self2.previous)&&!previousUnbalanced(self2.events)?(effects.enter("literalAutolink"),effects.enter("literalAutolinkHttp"),buffer+=String.fromCodePoint(code2),effects.consume(code2),protocolPrefixInside):nok(code2)}function protocolPrefixInside(code2){if(asciiAlpha(code2)&&buffer.length<5)return buffer+=String.fromCodePoint(code2),effects.consume(
-code2),protocolPrefixInside;if(code2===58){const protocol=buffer.toLowerCase();if(protocol==="http"||protocol==="https")return effects.consume(code2),protocolSlashesInside}return nok(code2)}function protocolSlashesInside(code2){return code2===47?(effects.consume(code2),seen2?afterProtocol:(seen2=!0,protocolSlashesInside)):nok(code2)}function afterProtocol(code2){return code2===null||asciiControl(code2)||markdownLineEndingOrSpace(code2)||unicodeWhitespace(code2)||unicodePunctuation(code2)?nok(code2):
-effects.attempt(domain,effects.attempt(path$1,protocolAfter),nok)(code2)}function protocolAfter(code2){return effects.exit("literalAutolinkHttp"),effects.exit("literalAutolink"),ok(code2)}}function tokenizeWwwPrefix(effects,ok,nok){let size2=0;return wwwPrefixInside;function wwwPrefixInside(code2){return(code2===87||code2===119)&&size2<3?(size2++,effects.consume(code2),wwwPrefixInside):code2===46&&size2===3?(effects.consume(code2),wwwPrefixAfter):nok(code2)}function wwwPrefixAfter(code2){return code2===
-null?nok(code2):ok(code2)}}function tokenizeDomain(effects,ok,nok){let underscoreInLastSegment,underscoreInLastLastSegment,seen2;return domainInside;function domainInside(code2){return code2===46||code2===95?effects.check(trail,domainAfter,domainAtPunctuation)(code2):code2===null||markdownLineEndingOrSpace(code2)||unicodeWhitespace(code2)||code2!==45&&unicodePunctuation(code2)?domainAfter(code2):(seen2=!0,effects.consume(code2),domainInside)}function domainAtPunctuation(code2){return code2===95?
-underscoreInLastSegment=!0:(underscoreInLastLastSegment=underscoreInLastSegment,underscoreInLastSegment=void 0),effects.consume(code2),domainInside}function domainAfter(code2){return underscoreInLastLastSegment||underscoreInLastSegment||!seen2?nok(code2):ok(code2)}}function tokenizePath(effects,ok){let sizeOpen=0,sizeClose=0;return pathInside;function pathInside(code2){return code2===40?(sizeOpen++,effects.consume(code2),pathInside):code2===41&&sizeClose<sizeOpen?pathAtPunctuation(code2):code2===
-33||code2===34||code2===38||code2===39||code2===41||code2===42||code2===44||code2===46||code2===58||code2===59||code2===60||code2===63||code2===93||code2===95||code2===126?effects.check(trail,ok,pathAtPunctuation)(code2):code2===null||markdownLineEndingOrSpace(code2)||unicodeWhitespace(code2)?ok(code2):(effects.consume(code2),pathInside)}function pathAtPunctuation(code2){return code2===41&&sizeClose++,effects.consume(code2),pathInside}}function tokenizeTrail(effects,ok,nok){return trail2;function trail2(code2){
-return code2===33||code2===34||code2===39||code2===41||code2===42||code2===44||code2===46||code2===58||code2===59||code2===63||code2===95||code2===126?(effects.consume(code2),trail2):code2===38?(effects.consume(code2),trailCharacterReferenceStart):code2===93?(effects.consume(code2),trailBracketAfter):code2===60||code2===null||markdownLineEndingOrSpace(code2)||unicodeWhitespace(code2)?ok(code2):nok(code2)}function trailBracketAfter(code2){return code2===null||code2===40||code2===91||markdownLineEndingOrSpace(
-code2)||unicodeWhitespace(code2)?ok(code2):trail2(code2)}function trailCharacterReferenceStart(code2){return asciiAlpha(code2)?trailCharacterReferenceInside(code2):nok(code2)}function trailCharacterReferenceInside(code2){return code2===59?(effects.consume(code2),trail2):asciiAlpha(code2)?(effects.consume(code2),trailCharacterReferenceInside):nok(code2)}}function tokenizeEmailDomainDotTrail(effects,ok,nok){return start2;function start2(code2){return effects.consume(code2),after}function after(code2){
-return asciiAlphanumeric(code2)?nok(code2):ok(code2)}}function previousWww(code2){return code2===null||code2===40||code2===42||code2===95||code2===91||code2===93||code2===126||markdownLineEndingOrSpace(code2)}function previousProtocol(code2){return!asciiAlpha(code2)}function previousEmail(code2){return!(code2===47||gfmAtext(code2))}function gfmAtext(code2){return code2===43||code2===45||code2===46||code2===95||asciiAlphanumeric(code2)}function previousUnbalanced(events){let index2=events.length,
-result=!1;for(;index2--;){const token2=events[index2][1];if((token2.type==="labelLink"||token2.type==="labelImage")&&!token2._balanced){result=!0;break}if(token2._gfmAutolinkLiteralWalkedInto){result=!1;break}}return events.length>0&&!result&&(events[events.length-1][1]._gfmAutolinkLiteralWalkedInto=!0),result}const indent={tokenize:tokenizeIndent,partial:!0};function gfmFootnote(){return{document:{91:{name:"gfmFootnoteDefinition",tokenize:tokenizeDefinitionStart,continuation:{tokenize:tokenizeDefinitionContinuation},
-exit:gfmFootnoteDefinitionEnd}},text:{91:{name:"gfmFootnoteCall",tokenize:tokenizeGfmFootnoteCall},93:{name:"gfmPotentialFootnoteCall",add:"after",tokenize:tokenizePotentialGfmFootnoteCall,resolveTo:resolveToPotentialGfmFootnoteCall}}}}function tokenizePotentialGfmFootnoteCall(effects,ok,nok){const self2=this;let index2=self2.events.length;const defined=self2.parser.gfmFootnotes||(self2.parser.gfmFootnotes=[]);let labelStart;for(;index2--;){const token2=self2.events[index2][1];if(token2.type==="\
-labelImage"){labelStart=token2;break}if(token2.type==="gfmFootnoteCall"||token2.type==="labelLink"||token2.type==="label"||token2.type==="image"||token2.type==="link")break}return start2;function start2(code2){if(!labelStart||!labelStart._balanced)return nok(code2);const id2=normalizeIdentifier(self2.sliceSerialize({start:labelStart.end,end:self2.now()}));return id2.codePointAt(0)!==94||!defined.includes(id2.slice(1))?nok(code2):(effects.enter("gfmFootnoteCallLabelMarker"),effects.consume(code2),
-effects.exit("gfmFootnoteCallLabelMarker"),ok(code2))}}function resolveToPotentialGfmFootnoteCall(events,context){let index2=events.length;for(;index2--;)if(events[index2][1].type==="labelImage"&&events[index2][0]==="enter"){events[index2][1];break}events[index2+1][1].type="data",events[index2+3][1].type="gfmFootnoteCallLabelMarker";const call={type:"gfmFootnoteCall",start:Object.assign({},events[index2+3][1].start),end:Object.assign({},events[events.length-1][1].end)},marker={type:"gfmFootnoteC\
-allMarker",start:Object.assign({},events[index2+3][1].end),end:Object.assign({},events[index2+3][1].end)};marker.end.column++,marker.end.offset++,marker.end._bufferIndex++;const string2={type:"gfmFootnoteCallString",start:Object.assign({},marker.end),end:Object.assign({},events[events.length-1][1].start)},chunk={type:"chunkString",contentType:"string",start:Object.assign({},string2.start),end:Object.assign({},string2.end)},replacement=[events[index2+1],events[index2+2],["enter",call,context],events[index2+
-3],events[index2+4],["enter",marker,context],["exit",marker,context],["enter",string2,context],["enter",chunk,context],["exit",chunk,context],["exit",string2,context],events[events.length-2],events[events.length-1],["exit",call,context]];return events.splice(index2,events.length-index2+1,...replacement),events}function tokenizeGfmFootnoteCall(effects,ok,nok){const self2=this,defined=self2.parser.gfmFootnotes||(self2.parser.gfmFootnotes=[]);let size2=0,data;return start2;function start2(code2){return effects.
-enter("gfmFootnoteCall"),effects.enter("gfmFootnoteCallLabelMarker"),effects.consume(code2),effects.exit("gfmFootnoteCallLabelMarker"),callStart}function callStart(code2){return code2!==94?nok(code2):(effects.enter("gfmFootnoteCallMarker"),effects.consume(code2),effects.exit("gfmFootnoteCallMarker"),effects.enter("gfmFootnoteCallString"),effects.enter("chunkString").contentType="string",callData)}function callData(code2){if(size2>999||code2===93&&!data||code2===null||code2===91||markdownLineEndingOrSpace(
-code2))return nok(code2);if(code2===93){effects.exit("chunkString");const token2=effects.exit("gfmFootnoteCallString");return defined.includes(normalizeIdentifier(self2.sliceSerialize(token2)))?(effects.enter("gfmFootnoteCallLabelMarker"),effects.consume(code2),effects.exit("gfmFootnoteCallLabelMarker"),effects.exit("gfmFootnoteCall"),ok):nok(code2)}return markdownLineEndingOrSpace(code2)||(data=!0),size2++,effects.consume(code2),code2===92?callEscape:callData}function callEscape(code2){return code2===
-91||code2===92||code2===93?(effects.consume(code2),size2++,callData):callData(code2)}}function tokenizeDefinitionStart(effects,ok,nok){const self2=this,defined=self2.parser.gfmFootnotes||(self2.parser.gfmFootnotes=[]);let identifier2,size2=0,data;return start2;function start2(code2){return effects.enter("gfmFootnoteDefinition")._container=!0,effects.enter("gfmFootnoteDefinitionLabel"),effects.enter("gfmFootnoteDefinitionLabelMarker"),effects.consume(code2),effects.exit("gfmFootnoteDefinitionLabe\
-lMarker"),labelAtMarker}function labelAtMarker(code2){return code2===94?(effects.enter("gfmFootnoteDefinitionMarker"),effects.consume(code2),effects.exit("gfmFootnoteDefinitionMarker"),effects.enter("gfmFootnoteDefinitionLabelString"),effects.enter("chunkString").contentType="string",labelInside):nok(code2)}function labelInside(code2){if(size2>999||code2===93&&!data||code2===null||code2===91||markdownLineEndingOrSpace(code2))return nok(code2);if(code2===93){effects.exit("chunkString");const token2=effects.
-exit("gfmFootnoteDefinitionLabelString");return identifier2=normalizeIdentifier(self2.sliceSerialize(token2)),effects.enter("gfmFootnoteDefinitionLabelMarker"),effects.consume(code2),effects.exit("gfmFootnoteDefinitionLabelMarker"),effects.exit("gfmFootnoteDefinitionLabel"),labelAfter}return markdownLineEndingOrSpace(code2)||(data=!0),size2++,effects.consume(code2),code2===92?labelEscape:labelInside}function labelEscape(code2){return code2===91||code2===92||code2===93?(effects.consume(code2),size2++,
-labelInside):labelInside(code2)}function labelAfter(code2){return code2===58?(effects.enter("definitionMarker"),effects.consume(code2),effects.exit("definitionMarker"),defined.includes(identifier2)||defined.push(identifier2),factorySpace(effects,whitespaceAfter,"gfmFootnoteDefinitionWhitespace")):nok(code2)}function whitespaceAfter(code2){return ok(code2)}}function tokenizeDefinitionContinuation(effects,ok,nok){return effects.check(blankLine,ok,effects.attempt(indent,ok,nok))}function gfmFootnoteDefinitionEnd(effects){
-effects.exit("gfmFootnoteDefinition")}function tokenizeIndent(effects,ok,nok){const self2=this;return factorySpace(effects,afterPrefix,"gfmFootnoteDefinitionIndent",5);function afterPrefix(code2){const tail=self2.events[self2.events.length-1];return tail&&tail[1].type==="gfmFootnoteDefinitionIndent"&&tail[2].sliceSerialize(tail[1],!0).length===4?ok(code2):nok(code2)}}function gfmStrikethrough(options){let single=(options||{}).singleTilde;const tokenizer={name:"strikethrough",tokenize:tokenizeStrikethrough,
-resolveAll:resolveAllStrikethrough};return single==null&&(single=!0),{text:{126:tokenizer},insideSpan:{null:[tokenizer]},attentionMarkers:{null:[126]}};function resolveAllStrikethrough(events,context){let index2=-1;for(;++index2<events.length;)if(events[index2][0]==="enter"&&events[index2][1].type==="strikethroughSequenceTemporary"&&events[index2][1]._close){let open2=index2;for(;open2--;)if(events[open2][0]==="exit"&&events[open2][1].type==="strikethroughSequenceTemporary"&&events[open2][1]._open&&
-events[index2][1].end.offset-events[index2][1].start.offset===events[open2][1].end.offset-events[open2][1].start.offset){events[index2][1].type="strikethroughSequence",events[open2][1].type="strikethroughSequence";const strikethrough2={type:"strikethrough",start:Object.assign({},events[open2][1].start),end:Object.assign({},events[index2][1].end)},text2={type:"strikethroughText",start:Object.assign({},events[open2][1].end),end:Object.assign({},events[index2][1].start)},nextEvents=[["enter",strikethrough2,
-context],["enter",events[open2][1],context],["exit",events[open2][1],context],["enter",text2,context]],insideSpan2=context.parser.constructs.insideSpan.null;insideSpan2&&splice(nextEvents,nextEvents.length,0,resolveAll(insideSpan2,events.slice(open2+1,index2),context)),splice(nextEvents,nextEvents.length,0,[["exit",text2,context],["enter",events[index2][1],context],["exit",events[index2][1],context],["exit",strikethrough2,context]]),splice(events,open2-1,index2-open2+3,nextEvents),index2=open2+nextEvents.
-length-2;break}}for(index2=-1;++index2<events.length;)events[index2][1].type==="strikethroughSequenceTemporary"&&(events[index2][1].type="data");return events}function tokenizeStrikethrough(effects,ok,nok){const previous2=this.previous,events=this.events;let size2=0;return start2;function start2(code2){return previous2===126&&events[events.length-1][1].type!=="characterEscape"?nok(code2):(effects.enter("strikethroughSequenceTemporary"),more(code2))}function more(code2){const before=classifyCharacter(
-previous2);if(code2===126)return size2>1?nok(code2):(effects.consume(code2),size2++,more);if(size2<2&&!single)return nok(code2);const token2=effects.exit("strikethroughSequenceTemporary"),after=classifyCharacter(code2);return token2._open=!after||after===2&&!!before,token2._close=!before||before===2&&!!after,ok(code2)}}}class EditMap{constructor(){this.map=[]}add(index2,remove2,add2){addImplementation(this,index2,remove2,add2)}consume(events){if(this.map.sort(function(a,b){return a[0]-b[0]}),this.
-map.length===0)return;let index2=this.map.length;const vecs=[];for(;index2>0;)index2-=1,vecs.push(events.slice(this.map[index2][0]+this.map[index2][1]),this.map[index2][2]),events.length=this.map[index2][0];vecs.push(events.slice()),events.length=0;let slice=vecs.pop();for(;slice;){for(const element2 of slice)events.push(element2);slice=vecs.pop()}this.map.length=0}}function addImplementation(editMap,at,remove2,add2){let index2=0;if(!(remove2===0&&add2.length===0)){for(;index2<editMap.map.length;){
-if(editMap.map[index2][0]===at){editMap.map[index2][1]+=remove2,editMap.map[index2][2].push(...add2);return}index2+=1}editMap.map.push([at,remove2,add2])}}function gfmTableAlign(events,index2){let inDelimiterRow=!1;const align=[];for(;index2<events.length;){const event2=events[index2];if(inDelimiterRow){if(event2[0]==="enter")event2[1].type==="tableContent"&&align.push(events[index2+1][1].type==="tableDelimiterMarker"?"left":"none");else if(event2[1].type==="tableContent"){if(events[index2-1][1].
-type==="tableDelimiterMarker"){const alignIndex=align.length-1;align[alignIndex]=align[alignIndex]==="left"?"center":"right"}}else if(event2[1].type==="tableDelimiterRow")break}else event2[0]==="enter"&&event2[1].type==="tableDelimiterRow"&&(inDelimiterRow=!0);index2+=1}return align}function gfmTable(){return{flow:{null:{name:"table",tokenize:tokenizeTable,resolveAll:resolveTable}}}}function tokenizeTable(effects,ok,nok){const self2=this;let size2=0,sizeB=0,seen2;return start2;function start2(code2){
-let index2=self2.events.length-1;for(;index2>-1;){const type2=self2.events[index2][1].type;if(type2==="lineEnding"||type2==="linePrefix")index2--;else break}const tail=index2>-1?self2.events[index2][1].type:null,next2=tail==="tableHead"||tail==="tableRow"?bodyRowStart:headRowBefore;return next2===bodyRowStart&&self2.parser.lazy[self2.now().line]?nok(code2):next2(code2)}function headRowBefore(code2){return effects.enter("tableHead"),effects.enter("tableRow"),headRowStart(code2)}function headRowStart(code2){
-return code2===124||(seen2=!0,sizeB+=1),headRowBreak(code2)}function headRowBreak(code2){return code2===null?nok(code2):markdownLineEnding(code2)?sizeB>1?(sizeB=0,self2.interrupt=!0,effects.exit("tableRow"),effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),headDelimiterStart):nok(code2):markdownSpace(code2)?factorySpace(effects,headRowBreak,"whitespace")(code2):(sizeB+=1,seen2&&(seen2=!1,size2+=1),code2===124?(effects.enter("tableCellDivider"),effects.consume(code2),effects.
-exit("tableCellDivider"),seen2=!0,headRowBreak):(effects.enter("data"),headRowData(code2)))}function headRowData(code2){return code2===null||code2===124||markdownLineEndingOrSpace(code2)?(effects.exit("data"),headRowBreak(code2)):(effects.consume(code2),code2===92?headRowEscape:headRowData)}function headRowEscape(code2){return code2===92||code2===124?(effects.consume(code2),headRowData):headRowData(code2)}function headDelimiterStart(code2){return self2.interrupt=!1,self2.parser.lazy[self2.now().
-line]?nok(code2):(effects.enter("tableDelimiterRow"),seen2=!1,markdownSpace(code2)?factorySpace(effects,headDelimiterBefore,"linePrefix",self2.parser.constructs.disable.null.includes("codeIndented")?void 0:4)(code2):headDelimiterBefore(code2))}function headDelimiterBefore(code2){return code2===45||code2===58?headDelimiterValueBefore(code2):code2===124?(seen2=!0,effects.enter("tableCellDivider"),effects.consume(code2),effects.exit("tableCellDivider"),headDelimiterCellBefore):headDelimiterNok(code2)}
-function headDelimiterCellBefore(code2){return markdownSpace(code2)?factorySpace(effects,headDelimiterValueBefore,"whitespace")(code2):headDelimiterValueBefore(code2)}function headDelimiterValueBefore(code2){return code2===58?(sizeB+=1,seen2=!0,effects.enter("tableDelimiterMarker"),effects.consume(code2),effects.exit("tableDelimiterMarker"),headDelimiterLeftAlignmentAfter):code2===45?(sizeB+=1,headDelimiterLeftAlignmentAfter(code2)):code2===null||markdownLineEnding(code2)?headDelimiterCellAfter(
-code2):headDelimiterNok(code2)}function headDelimiterLeftAlignmentAfter(code2){return code2===45?(effects.enter("tableDelimiterFiller"),headDelimiterFiller(code2)):headDelimiterNok(code2)}function headDelimiterFiller(code2){return code2===45?(effects.consume(code2),headDelimiterFiller):code2===58?(seen2=!0,effects.exit("tableDelimiterFiller"),effects.enter("tableDelimiterMarker"),effects.consume(code2),effects.exit("tableDelimiterMarker"),headDelimiterRightAlignmentAfter):(effects.exit("tableDel\
-imiterFiller"),headDelimiterRightAlignmentAfter(code2))}function headDelimiterRightAlignmentAfter(code2){return markdownSpace(code2)?factorySpace(effects,headDelimiterCellAfter,"whitespace")(code2):headDelimiterCellAfter(code2)}function headDelimiterCellAfter(code2){return code2===124?headDelimiterBefore(code2):code2===null||markdownLineEnding(code2)?!seen2||size2!==sizeB?headDelimiterNok(code2):(effects.exit("tableDelimiterRow"),effects.exit("tableHead"),ok(code2)):headDelimiterNok(code2)}function headDelimiterNok(code2){
-return nok(code2)}function bodyRowStart(code2){return effects.enter("tableRow"),bodyRowBreak(code2)}function bodyRowBreak(code2){return code2===124?(effects.enter("tableCellDivider"),effects.consume(code2),effects.exit("tableCellDivider"),bodyRowBreak):code2===null||markdownLineEnding(code2)?(effects.exit("tableRow"),ok(code2)):markdownSpace(code2)?factorySpace(effects,bodyRowBreak,"whitespace")(code2):(effects.enter("data"),bodyRowData(code2))}function bodyRowData(code2){return code2===null||code2===
-124||markdownLineEndingOrSpace(code2)?(effects.exit("data"),bodyRowBreak(code2)):(effects.consume(code2),code2===92?bodyRowEscape:bodyRowData)}function bodyRowEscape(code2){return code2===92||code2===124?(effects.consume(code2),bodyRowData):bodyRowData(code2)}}function resolveTable(events,context){let index2=-1,inFirstCellAwaitingPipe=!0,rowKind=0,lastCell=[0,0,0,0],cell2=[0,0,0,0],afterHeadAwaitingFirstBodyRow=!1,lastTableEnd=0,currentTable,currentBody,currentCell;const map2=new EditMap;for(;++index2<
-events.length;){const event2=events[index2],token2=event2[1];event2[0]==="enter"?token2.type==="tableHead"?(afterHeadAwaitingFirstBodyRow=!1,lastTableEnd!==0&&(flushTableEnd(map2,context,lastTableEnd,currentTable,currentBody),currentBody=void 0,lastTableEnd=0),currentTable={type:"table",start:Object.assign({},token2.start),end:Object.assign({},token2.end)},map2.add(index2,0,[["enter",currentTable,context]])):token2.type==="tableRow"||token2.type==="tableDelimiterRow"?(inFirstCellAwaitingPipe=!0,
-currentCell=void 0,lastCell=[0,0,0,0],cell2=[0,index2+1,0,0],afterHeadAwaitingFirstBodyRow&&(afterHeadAwaitingFirstBodyRow=!1,currentBody={type:"tableBody",start:Object.assign({},token2.start),end:Object.assign({},token2.end)},map2.add(index2,0,[["enter",currentBody,context]])),rowKind=token2.type==="tableDelimiterRow"?2:currentBody?3:1):rowKind&&(token2.type==="data"||token2.type==="tableDelimiterMarker"||token2.type==="tableDelimiterFiller")?(inFirstCellAwaitingPipe=!1,cell2[2]===0&&(lastCell[1]!==
-0&&(cell2[0]=cell2[1],currentCell=flushCell(map2,context,lastCell,rowKind,void 0,currentCell),lastCell=[0,0,0,0]),cell2[2]=index2)):token2.type==="tableCellDivider"&&(inFirstCellAwaitingPipe?inFirstCellAwaitingPipe=!1:(lastCell[1]!==0&&(cell2[0]=cell2[1],currentCell=flushCell(map2,context,lastCell,rowKind,void 0,currentCell)),lastCell=cell2,cell2=[lastCell[1],index2,0,0])):token2.type==="tableHead"?(afterHeadAwaitingFirstBodyRow=!0,lastTableEnd=index2):token2.type==="tableRow"||token2.type==="ta\
-bleDelimiterRow"?(lastTableEnd=index2,lastCell[1]!==0?(cell2[0]=cell2[1],currentCell=flushCell(map2,context,lastCell,rowKind,index2,currentCell)):cell2[1]!==0&&(currentCell=flushCell(map2,context,cell2,rowKind,index2,currentCell)),rowKind=0):rowKind&&(token2.type==="data"||token2.type==="tableDelimiterMarker"||token2.type==="tableDelimiterFiller")&&(cell2[3]=index2)}for(lastTableEnd!==0&&flushTableEnd(map2,context,lastTableEnd,currentTable,currentBody),map2.consume(context.events),index2=-1;++index2<
-context.events.length;){const event2=context.events[index2];event2[0]==="enter"&&event2[1].type==="table"&&(event2[1]._align=gfmTableAlign(context.events,index2))}return events}function flushCell(map2,context,range,rowKind,rowEnd,previousCell){const groupName=rowKind===1?"tableHeader":rowKind===2?"tableDelimiter":"tableData",valueName="tableContent";range[0]!==0&&(previousCell.end=Object.assign({},getPoint(context.events,range[0])),map2.add(range[0],0,[["exit",previousCell,context]]));const now2=getPoint(
-context.events,range[1]);if(previousCell={type:groupName,start:Object.assign({},now2),end:Object.assign({},now2)},map2.add(range[1],0,[["enter",previousCell,context]]),range[2]!==0){const relatedStart=getPoint(context.events,range[2]),relatedEnd=getPoint(context.events,range[3]),valueToken={type:valueName,start:Object.assign({},relatedStart),end:Object.assign({},relatedEnd)};if(map2.add(range[2],0,[["enter",valueToken,context]]),rowKind!==2){const start2=context.events[range[2]],end=context.events[range[3]];
-if(start2[1].end=Object.assign({},end[1].end),start2[1].type="chunkText",start2[1].contentType="text",range[3]>range[2]+1){const a=range[2]+1,b=range[3]-range[2]-1;map2.add(a,b,[])}}map2.add(range[3]+1,0,[["exit",valueToken,context]])}return rowEnd!==void 0&&(previousCell.end=Object.assign({},getPoint(context.events,rowEnd)),map2.add(rowEnd,0,[["exit",previousCell,context]]),previousCell=void 0),previousCell}function flushTableEnd(map2,context,index2,table2,tableBody){const exits=[],related=getPoint(
-context.events,index2);tableBody&&(tableBody.end=Object.assign({},related),exits.push(["exit",tableBody,context])),table2.end=Object.assign({},related),exits.push(["exit",table2,context]),map2.add(index2+1,0,exits)}function getPoint(events,index2){const event2=events[index2],side=event2[0]==="enter"?"start":"end";return event2[1][side]}const tasklistCheck={name:"tasklistCheck",tokenize:tokenizeTasklistCheck};function gfmTaskListItem(){return{text:{91:tasklistCheck}}}function tokenizeTasklistCheck(effects,ok,nok){
-const self2=this;return open2;function open2(code2){return self2.previous!==null||!self2._gfmTasklistFirstContentOfListItem?nok(code2):(effects.enter("taskListCheck"),effects.enter("taskListCheckMarker"),effects.consume(code2),effects.exit("taskListCheckMarker"),inside)}function inside(code2){return markdownLineEndingOrSpace(code2)?(effects.enter("taskListCheckValueUnchecked"),effects.consume(code2),effects.exit("taskListCheckValueUnchecked"),close2):code2===88||code2===120?(effects.enter("taskL\
-istCheckValueChecked"),effects.consume(code2),effects.exit("taskListCheckValueChecked"),close2):nok(code2)}function close2(code2){return code2===93?(effects.enter("taskListCheckMarker"),effects.consume(code2),effects.exit("taskListCheckMarker"),effects.exit("taskListCheck"),after):nok(code2)}function after(code2){return markdownLineEnding(code2)?ok(code2):markdownSpace(code2)?effects.check({tokenize:spaceThenNonSpace},ok,nok)(code2):nok(code2)}}function spaceThenNonSpace(effects,ok,nok){return factorySpace(
-effects,after,"whitespace");function after(code2){return code2===null?nok(code2):ok(code2)}}function gfm(options){return combineExtensions([gfmAutolinkLiteral(),gfmFootnote(),gfmStrikethrough(options),gfmTable(),gfmTaskListItem()])}const emptyOptions$6={};function remarkGfm(options){const self2=this,settings=options||emptyOptions$6,data=self2.data(),micromarkExtensions=data.micromarkExtensions||(data.micromarkExtensions=[]),fromMarkdownExtensions=data.fromMarkdownExtensions||(data.fromMarkdownExtensions=
-[]),toMarkdownExtensions=data.toMarkdownExtensions||(data.toMarkdownExtensions=[]);micromarkExtensions.push(gfm(settings)),fromMarkdownExtensions.push(gfmFromMarkdown()),toMarkdownExtensions.push(gfmToMarkdown(settings))}function mathFromMarkdown(){return{enter:{mathFlow:enterMathFlow,mathFlowFenceMeta:enterMathFlowMeta,mathText:enterMathText},exit:{mathFlow:exitMathFlow,mathFlowFence:exitMathFlowFence,mathFlowFenceMeta:exitMathFlowMeta,mathFlowValue:exitMathData,mathText:exitMathText,mathTextData:exitMathData}};
-function enterMathFlow(token2){const code2={type:"element",tagName:"code",properties:{className:["language-math","math-display"]},children:[]};this.enter({type:"math",meta:null,value:"",data:{hName:"pre",hChildren:[code2]}},token2)}function enterMathFlowMeta(){this.buffer()}function exitMathFlowMeta(){const data=this.resume(),node2=this.stack[this.stack.length-1];node2.type,node2.meta=data}function exitMathFlowFence(){this.data.mathFlowInside||(this.buffer(),this.data.mathFlowInside=!0)}function exitMathFlow(token2){
-const data=this.resume().replace(/^(\r?\n|\r)|(\r?\n|\r)$/g,""),node2=this.stack[this.stack.length-1];node2.type,this.exit(token2),node2.value=data;const code2=node2.data.hChildren[0];code2.type,code2.tagName,code2.children.push({type:"text",value:data}),this.data.mathFlowInside=void 0}function enterMathText(token2){this.enter({type:"inlineMath",value:"",data:{hName:"code",hProperties:{className:["language-math","math-inline"]},hChildren:[]}},token2),this.buffer()}function exitMathText(token2){const data=this.
-resume(),node2=this.stack[this.stack.length-1];node2.type,this.exit(token2),node2.value=data,node2.data.hChildren.push({type:"text",value:data})}function exitMathData(token2){this.config.enter.data.call(this,token2),this.config.exit.data.call(this,token2)}}function mathToMarkdown(options){let single=(options||{}).singleDollarTextMath;return single==null&&(single=!0),inlineMath.peek=inlineMathPeek,{unsafe:[{character:"\r",inConstruct:"mathFlowMeta"},{character:`
-`,inConstruct:"mathFlowMeta"},{character:"$",after:single?void 0:"\\$",inConstruct:"phrasing"},{character:"$",inConstruct:"mathFlowMeta"},{atBreak:!0,character:"$",after:"\\$"}],handlers:{math:math2,inlineMath}};function math2(node2,_,state2,info2){const raw2=node2.value||"",tracker=state2.createTracker(info2),sequence="$".repeat(Math.max(longestStreak(raw2,"$")+1,2)),exit2=state2.enter("mathFlow");let value=tracker.move(sequence);if(node2.meta){const subexit=state2.enter("mathFlowMeta");value+=
-tracker.move(state2.safe(node2.meta,{after:`
-`,before:value,encode:["$"],...tracker.current()})),subexit()}return value+=tracker.move(`
-`),raw2&&(value+=tracker.move(raw2+`
-`)),value+=tracker.move(sequence),exit2(),value}function inlineMath(node2,_,state2){let value=node2.value||"",size2=1;for(single||size2++;new RegExp("(^|[^$])"+"\\$".repeat(size2)+"([^$]|$)").test(value);)size2++;const sequence="$".repeat(size2);/[^ \r\n]/.test(value)&&(/^[ \r\n]/.test(value)&&/[ \r\n]$/.test(value)||/^\$|\$$/.test(value))&&(value=" "+value+" ");let index2=-1;for(;++index2<state2.unsafe.length;){const pattern2=state2.unsafe[index2];if(!pattern2.atBreak)continue;const expression=state2.
-compilePattern(pattern2);let match;for(;match=expression.exec(value);){let position2=match.index;value.codePointAt(position2)===10&&value.codePointAt(position2-1)===13&&position2--,value=value.slice(0,position2)+" "+value.slice(match.index+1)}}return sequence+value+sequence}function inlineMathPeek(){return"$"}}const mathFlow={tokenize:tokenizeMathFenced,concrete:!0,name:"mathFlow"},nonLazyContinuation={tokenize:tokenizeNonLazyContinuation,partial:!0};function tokenizeMathFenced(effects,ok,nok){const self2=this,
-tail=self2.events[self2.events.length-1],initialSize=tail&&tail[1].type==="linePrefix"?tail[2].sliceSerialize(tail[1],!0).length:0;let sizeOpen=0;return start2;function start2(code2){return effects.enter("mathFlow"),effects.enter("mathFlowFence"),effects.enter("mathFlowFenceSequence"),sequenceOpen(code2)}function sequenceOpen(code2){return code2===36?(effects.consume(code2),sizeOpen++,sequenceOpen):sizeOpen<2?nok(code2):(effects.exit("mathFlowFenceSequence"),factorySpace(effects,metaBefore,"whit\
-espace")(code2))}function metaBefore(code2){return code2===null||markdownLineEnding(code2)?metaAfter(code2):(effects.enter("mathFlowFenceMeta"),effects.enter("chunkString",{contentType:"string"}),meta(code2))}function meta(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("chunkString"),effects.exit("mathFlowFenceMeta"),metaAfter(code2)):code2===36?nok(code2):(effects.consume(code2),meta)}function metaAfter(code2){return effects.exit("mathFlowFence"),self2.interrupt?ok(code2):effects.
-attempt(nonLazyContinuation,beforeNonLazyContinuation,after)(code2)}function beforeNonLazyContinuation(code2){return effects.attempt({tokenize:tokenizeClosingFence,partial:!0},after,contentStart)(code2)}function contentStart(code2){return(initialSize?factorySpace(effects,beforeContentChunk,"linePrefix",initialSize+1):beforeContentChunk)(code2)}function beforeContentChunk(code2){return code2===null?after(code2):markdownLineEnding(code2)?effects.attempt(nonLazyContinuation,beforeNonLazyContinuation,
-after)(code2):(effects.enter("mathFlowValue"),contentChunk(code2))}function contentChunk(code2){return code2===null||markdownLineEnding(code2)?(effects.exit("mathFlowValue"),beforeContentChunk(code2)):(effects.consume(code2),contentChunk)}function after(code2){return effects.exit("mathFlow"),ok(code2)}function tokenizeClosingFence(effects2,ok2,nok2){let size2=0;return factorySpace(effects2,beforeSequenceClose,"linePrefix",self2.parser.constructs.disable.null.includes("codeIndented")?void 0:4);function beforeSequenceClose(code2){
-return effects2.enter("mathFlowFence"),effects2.enter("mathFlowFenceSequence"),sequenceClose(code2)}function sequenceClose(code2){return code2===36?(size2++,effects2.consume(code2),sequenceClose):size2<sizeOpen?nok2(code2):(effects2.exit("mathFlowFenceSequence"),factorySpace(effects2,afterSequenceClose,"whitespace")(code2))}function afterSequenceClose(code2){return code2===null||markdownLineEnding(code2)?(effects2.exit("mathFlowFence"),ok2(code2)):nok2(code2)}}}function tokenizeNonLazyContinuation(effects,ok,nok){
-const self2=this;return start2;function start2(code2){return code2===null?ok(code2):(effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),lineStart)}function lineStart(code2){return self2.parser.lazy[self2.now().line]?nok(code2):ok(code2)}}function mathText(options){let single=(options||{}).singleDollarTextMath;return single==null&&(single=!0),{tokenize:tokenizeMathText,resolve:resolveMathText,previous,name:"mathText"};function tokenizeMathText(effects,ok,nok){let sizeOpen=0,
-size2,token2;return start2;function start2(code2){return effects.enter("mathText"),effects.enter("mathTextSequence"),sequenceOpen(code2)}function sequenceOpen(code2){return code2===36?(effects.consume(code2),sizeOpen++,sequenceOpen):sizeOpen<2&&!single?nok(code2):(effects.exit("mathTextSequence"),between2(code2))}function between2(code2){return code2===null?nok(code2):code2===36?(token2=effects.enter("mathTextSequence"),size2=0,sequenceClose(code2)):code2===32?(effects.enter("space"),effects.consume(
-code2),effects.exit("space"),between2):markdownLineEnding(code2)?(effects.enter("lineEnding"),effects.consume(code2),effects.exit("lineEnding"),between2):(effects.enter("mathTextData"),data(code2))}function data(code2){return code2===null||code2===32||code2===36||markdownLineEnding(code2)?(effects.exit("mathTextData"),between2(code2)):(effects.consume(code2),data)}function sequenceClose(code2){return code2===36?(effects.consume(code2),size2++,sequenceClose):size2===sizeOpen?(effects.exit("mathTe\
-xtSequence"),effects.exit("mathText"),ok(code2)):(token2.type="mathTextData",data(code2))}}}function resolveMathText(events){let tailExitIndex=events.length-4,headEnterIndex=3,index2,enter;if((events[headEnterIndex][1].type==="lineEnding"||events[headEnterIndex][1].type==="space")&&(events[tailExitIndex][1].type==="lineEnding"||events[tailExitIndex][1].type==="space")){for(index2=headEnterIndex;++index2<tailExitIndex;)if(events[index2][1].type==="mathTextData"){events[tailExitIndex][1].type="mat\
-hTextPadding",events[headEnterIndex][1].type="mathTextPadding",headEnterIndex+=2,tailExitIndex-=2;break}}for(index2=headEnterIndex-1,tailExitIndex++;++index2<=tailExitIndex;)enter===void 0?index2!==tailExitIndex&&events[index2][1].type!=="lineEnding"&&(enter=index2):(index2===tailExitIndex||events[index2][1].type==="lineEnding")&&(events[enter][1].type="mathTextData",index2!==enter+2&&(events[enter][1].end=events[index2-1][1].end,events.splice(enter+2,index2-enter-2),tailExitIndex-=index2-enter-
-2,index2=enter+2),enter=void 0);return events}function previous(code2){return code2!==36||this.events[this.events.length-1][1].type==="characterEscape"}function math$1(options){return{flow:{36:mathFlow},text:{36:mathText(options)}}}class SourceLocation{constructor(lexer,start2,end){this.lexer=void 0,this.start=void 0,this.end=void 0,this.lexer=lexer,this.start=start2,this.end=end}static range(first,second){return second?!first||!first.loc||!second.loc||first.loc.lexer!==second.loc.lexer?null:new SourceLocation(
-first.loc.lexer,first.loc.start,second.loc.end):first&&first.loc}}class Token{constructor(text2,loc){this.text=void 0,this.loc=void 0,this.noexpand=void 0,this.treatAsRelax=void 0,this.text=text2,this.loc=loc}range(endToken,text2){return new Token(text2,SourceLocation.range(this,endToken))}}class ParseError2{constructor(message,token2){this.name=void 0,this.position=void 0,this.length=void 0,this.rawMessage=void 0;var error2="KaTeX parse error: "+message,start2,end,loc=token2&&token2.loc;if(loc&&
-loc.start<=loc.end){var input=loc.lexer.input;start2=loc.start,end=loc.end,start2===input.length?error2+=" at end of input: ":error2+=" at position "+(start2+1)+": ";var underlined=input.slice(start2,end).replace(/[^]/g,"$&̲"),left;start2>15?left="…"+input.slice(start2-15,start2):left=input.slice(0,start2);var right;end+15<input.length?right=input.slice(end,end+15)+"…":right=input.slice(end),error2+=left+underlined+right}var self2=new Error(error2);return self2.name="ParseError",self2.__proto__=
-ParseError2.prototype,self2.position=start2,start2!=null&&end!=null&&(self2.length=end-start2),self2.rawMessage=message,self2}}ParseError2.prototype.__proto__=Error.prototype;var contains=function(list2,elem){return list2.indexOf(elem)!==-1},deflt=function(setting,defaultIfUndefined){return setting===void 0?defaultIfUndefined:setting},uppercase=/([A-Z])/g,hyphenate=function(str){return str.replace(uppercase,"-$1").toLowerCase()},ESCAPE_LOOKUP={"&":"&amp;",">":"&gt;","<":"&lt;",'"':"&quot;","'":"\
-&#x27;"},ESCAPE_REGEX=/[&><"']/g;function escape$2(text2){return String(text2).replace(ESCAPE_REGEX,match=>ESCAPE_LOOKUP[match])}var getBaseElem=function getBaseElem2(group){return group.type==="ordgroup"||group.type==="color"?group.body.length===1?getBaseElem2(group.body[0]):group:group.type==="font"?getBaseElem2(group.body):group},isCharacterBox=function(group){var baseElem=getBaseElem(group);return baseElem.type==="mathord"||baseElem.type==="textord"||baseElem.type==="atom"},assert=function(value){
-if(!value)throw new Error("Expected non-null, but got "+String(value));return value},protocolFromUrl=function(url2){var protocol=/^[\x00-\x20]*([^\\/#?]*?)(:|&#0*58|&#x0*3a|&colon)/i.exec(url2);return protocol?protocol[2]!==":"||!/^[a-zA-Z][a-zA-Z0-9+\-.]*$/.test(protocol[1])?null:protocol[1].toLowerCase():"_relative"},utils={contains,deflt,escape:escape$2,hyphenate,getBaseElem,isCharacterBox,protocolFromUrl},SETTINGS_SCHEMA={displayMode:{type:"boolean",description:"Render math in display mode, \
-which puts the math in display style (so \\int and \\sum are large, for example), and centers the math on the page on its own line.",cli:"-d, --display-mode"},output:{type:{enum:["htmlAndMathml","html","mathml"]},description:"Determines the markup language of the output.",cli:"-F, --format <type>"},leqno:{type:"boolean",description:"Render display math in leqno style (left-justified tags)."},fleqn:{type:"boolean",description:"Render display math flush left."},throwOnError:{type:"boolean",default:!0,
-cli:"-t, --no-throw-on-error",cliDescription:"Render errors (in the color given by --error-color) instead of throwing a ParseError exception when encountering an error."},errorColor:{type:"string",default:"#cc0000",cli:"-c, --error-color <color>",cliDescription:"A color string given in the format 'rgb' or 'rrggbb' (no #). This option determines the color of errors rendered by the -t option.",cliProcessor:color=>"#"+color},macros:{type:"object",cli:"-m, --macro <def>",cliDescription:"Define custo\
-m macro of the form '\\foo:expansion' (use multiple -m arguments for multiple macros).",cliDefault:[],cliProcessor:(def,defs)=>(defs.push(def),defs)},minRuleThickness:{type:"number",description:"Specifies a minimum thickness, in ems, for fraction lines, `\\sqrt` top lines, `{array}` vertical lines, `\\hline`, `\\hdashline`, `\\underline`, `\\overline`, and the borders of `\\fbox`, `\\boxed`, and `\\fcolorbox`.",processor:t=>Math.max(0,t),cli:"--min-rule-thickness <size>",cliProcessor:parseFloat},
-colorIsTextColor:{type:"boolean",description:"Makes \\color behave like LaTeX's 2-argument \\textcolor, instead of LaTeX's one-argument \\color mode change.",cli:"-b, --color-is-text-color"},strict:{type:[{enum:["warn","ignore","error"]},"boolean","function"],description:"Turn on strict / LaTeX faithfulness mode, which throws an error if the input uses features that are not supported by LaTeX.",cli:"-S, --strict",cliDefault:!1},trust:{type:["boolean","function"],description:"Trust the input, ena\
-bling all HTML features such as \\url.",cli:"-T, --trust"},maxSize:{type:"number",default:1/0,description:"If non-zero, all user-specified sizes, e.g. in \\rule{500em}{500em}, will be capped to maxSize ems. Otherwise, elements and spaces can be arbitrarily large",processor:s2=>Math.max(0,s2),cli:"-s, --max-size <n>",cliProcessor:parseInt},maxExpand:{type:"number",default:1e3,description:"Limit the number of macro expansions to the specified number, to prevent e.g. infinite macro loops. If set to\
- Infinity, the macro expander will try to fully expand as in LaTeX.",processor:n=>Math.max(0,n),cli:"-e, --max-expand <n>",cliProcessor:n=>n==="Infinity"?1/0:parseInt(n)},globalGroup:{type:"boolean",cli:!1}};function getDefaultValue(schema){if(schema.default)return schema.default;var type2=schema.type,defaultType=Array.isArray(type2)?type2[0]:type2;if(typeof defaultType!="string")return defaultType.enum[0];switch(defaultType){case"boolean":return!1;case"string":return"";case"number":return 0;case"\
-object":return{}}}class Settings{constructor(options){this.displayMode=void 0,this.output=void 0,this.leqno=void 0,this.fleqn=void 0,this.throwOnError=void 0,this.errorColor=void 0,this.macros=void 0,this.minRuleThickness=void 0,this.colorIsTextColor=void 0,this.strict=void 0,this.trust=void 0,this.maxSize=void 0,this.maxExpand=void 0,this.globalGroup=void 0,options=options||{};for(var prop2 in SETTINGS_SCHEMA)if(SETTINGS_SCHEMA.hasOwnProperty(prop2)){var schema=SETTINGS_SCHEMA[prop2];this[prop2]=
-options[prop2]!==void 0?schema.processor?schema.processor(options[prop2]):options[prop2]:getDefaultValue(schema)}}reportNonstrict(errorCode,errorMsg,token2){var strict=this.strict;if(typeof strict=="function"&&(strict=strict(errorCode,errorMsg,token2)),!(!strict||strict==="ignore")){if(strict===!0||strict==="error")throw new ParseError2("LaTeX-incompatible input and strict mode is set to 'error': "+(errorMsg+" ["+errorCode+"]"),token2);strict==="warn"?typeof console<"u"&&console.warn("LaTeX-inco\
-mpatible input and strict mode is set to 'warn': "+(errorMsg+" ["+errorCode+"]")):typeof console<"u"&&console.warn("LaTeX-incompatible input and strict mode is set to "+("unrecognized '"+strict+"': "+errorMsg+" ["+errorCode+"]"))}}useStrictBehavior(errorCode,errorMsg,token2){var strict=this.strict;if(typeof strict=="function")try{strict=strict(errorCode,errorMsg,token2)}catch{strict="error"}return!strict||strict==="ignore"?!1:strict===!0||strict==="error"?!0:strict==="warn"?(typeof console<"u"&&
-console.warn("LaTeX-incompatible input and strict mode is set to 'warn': "+(errorMsg+" ["+errorCode+"]")),!1):(typeof console<"u"&&console.warn("LaTeX-incompatible input and strict mode is set to "+("unrecognized '"+strict+"': "+errorMsg+" ["+errorCode+"]")),!1)}isTrusted(context){if(context.url&&!context.protocol){var protocol=utils.protocolFromUrl(context.url);if(protocol==null)return!1;context.protocol=protocol}var trust=typeof this.trust=="function"?this.trust(context):this.trust;return!!trust}}
-class Style{constructor(id2,size2,cramped){this.id=void 0,this.size=void 0,this.cramped=void 0,this.id=id2,this.size=size2,this.cramped=cramped}sup(){return styles[sup[this.id]]}sub(){return styles[sub[this.id]]}fracNum(){return styles[fracNum[this.id]]}fracDen(){return styles[fracDen[this.id]]}cramp(){return styles[cramp[this.id]]}text(){return styles[text$1$1[this.id]]}isTight(){return this.size>=2}}var D=0,Dc=1,T=2,Tc=3,S=4,Sc=5,SS=6,SSc=7,styles=[new Style(D,0,!1),new Style(Dc,0,!0),new Style(
-T,1,!1),new Style(Tc,1,!0),new Style(S,2,!1),new Style(Sc,2,!0),new Style(SS,3,!1),new Style(SSc,3,!0)],sup=[S,Sc,S,Sc,SS,SSc,SS,SSc],sub=[Sc,Sc,Sc,Sc,SSc,SSc,SSc,SSc],fracNum=[T,Tc,S,Sc,SS,SSc,SS,SSc],fracDen=[Tc,Tc,Sc,Sc,SSc,SSc,SSc,SSc],cramp=[Dc,Dc,Tc,Tc,Sc,Sc,SSc,SSc],text$1$1=[D,Dc,T,Tc,T,Tc,T,Tc],Style$1={DISPLAY:styles[D],TEXT:styles[T],SCRIPT:styles[S],SCRIPTSCRIPT:styles[SS]},scriptData=[{name:"latin",blocks:[[256,591],[768,879]]},{name:"cyrillic",blocks:[[1024,1279]]},{name:"armenian",
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-math,main,accent,"ˋ","\\grave");defineSymbol(math,main,accent,"¨","\\ddot");defineSymbol(math,main,accent,"~","\\tilde");defineSymbol(math,main,accent,"ˉ","\\bar");defineSymbol(math,main,accent,"˘","\\breve");defineSymbol(math,main,accent,"ˇ","\\check");defineSymbol(math,main,accent,"^","\\hat");defineSymbol(math,main,accent,"⃗","\\vec");defineSymbol(math,main,accent,"˙","\\dot");defineSymbol(math,main,accent,"˚","\\mathring");defineSymbol(math,main,mathord,"","\\@imath");defineSymbol(
-math,main,mathord,"","\\@jmath");defineSymbol(math,main,textord,"ı","ı");defineSymbol(math,main,textord,"ȷ","ȷ");defineSymbol(text$3,main,textord,"ı","\\i",!0);defineSymbol(text$3,main,textord,"ȷ","\\j",!0);defineSymbol(text$3,main,textord,"ß","\\ss",!0);defineSymbol(text$3,main,textord,"æ","\\ae",!0);defineSymbol(text$3,main,textord,"œ","\\oe",!0);defineSymbol(text$3,main,textord,"ø","\\o",!0);defineSymbol(text$3,main,textord,"Æ","\\AE",!0);defineSymbol(text$3,main,textord,"Œ","\\\
-OE",!0);defineSymbol(text$3,main,textord,"Ø","\\O",!0);defineSymbol(text$3,main,accent,"ˊ","\\'");defineSymbol(text$3,main,accent,"ˋ","\\`");defineSymbol(text$3,main,accent,"ˆ","\\^");defineSymbol(text$3,main,accent,"˜","\\~");defineSymbol(text$3,main,accent,"ˉ","\\=");defineSymbol(text$3,main,accent,"˘","\\u");defineSymbol(text$3,main,accent,"˙","\\.");defineSymbol(text$3,main,accent,"¸","\\c");defineSymbol(text$3,main,accent,"˚","\\r");defineSymbol(text$3,main,accent,"ˇ","\\v");defineSymbol(
-text$3,main,accent,"¨",'\\"');defineSymbol(text$3,main,accent,"˝","\\H");defineSymbol(text$3,main,accent,"◯","\\textcircled");var ligatures={"--":!0,"---":!0,"``":!0,"''":!0};defineSymbol(text$3,main,textord,"–","--",!0);defineSymbol(text$3,main,textord,"–","\\textendash");defineSymbol(text$3,main,textord,"—","---",!0);defineSymbol(text$3,main,textord,"—","\\textemdash");defineSymbol(text$3,main,textord,"‘","`",!0);defineSymbol(text$3,main,textord,"‘","\\textquoteleft");defineSymbol(
-text$3,main,textord,"’","'",!0);defineSymbol(text$3,main,textord,"’","\\textquoteright");defineSymbol(text$3,main,textord,"“","``",!0);defineSymbol(text$3,main,textord,"“","\\textquotedblleft");defineSymbol(text$3,main,textord,"”","''",!0);defineSymbol(text$3,main,textord,"”","\\textquotedblright");defineSymbol(math,main,textord,"°","\\degree",!0);defineSymbol(text$3,main,textord,"°","\\degree");defineSymbol(text$3,main,textord,"°","\\textdegree",!0);defineSymbol(math,main,textord,
-"£","\\pounds");defineSymbol(math,main,textord,"£","\\mathsterling",!0);defineSymbol(text$3,main,textord,"£","\\pounds");defineSymbol(text$3,main,textord,"£","\\textsterling",!0);defineSymbol(math,ams,textord,"✠","\\maltese");defineSymbol(text$3,ams,textord,"✠","\\maltese");var mathTextSymbols='0123456789/@."';for(var i=0;i<mathTextSymbols.length;i++){var ch=mathTextSymbols.charAt(i);defineSymbol(math,main,textord,ch,ch)}var textSymbols='0123456789!@*()-=+";:?/.,';for(var _i=0;_i<textSymbols.
-length;_i++){var _ch=textSymbols.charAt(_i);defineSymbol(text$3,main,textord,_ch,_ch)}var letters="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";for(var _i2=0;_i2<letters.length;_i2++){var _ch2=letters.charAt(_i2);defineSymbol(math,main,mathord,_ch2,_ch2),defineSymbol(text$3,main,textord,_ch2,_ch2)}defineSymbol(math,ams,textord,"C","ℂ");defineSymbol(text$3,ams,textord,"C","ℂ");defineSymbol(math,ams,textord,"H","ℍ");defineSymbol(text$3,ams,textord,"H","ℍ");defineSymbol(math,ams,textord,
-"N","ℕ");defineSymbol(text$3,ams,textord,"N","ℕ");defineSymbol(math,ams,textord,"P","ℙ");defineSymbol(text$3,ams,textord,"P","ℙ");defineSymbol(math,ams,textord,"Q","ℚ");defineSymbol(text$3,ams,textord,"Q","ℚ");defineSymbol(math,ams,textord,"R","ℝ");defineSymbol(text$3,ams,textord,"R","ℝ");defineSymbol(math,ams,textord,"Z","ℤ");defineSymbol(text$3,ams,textord,"Z","ℤ");defineSymbol(math,main,mathord,"h","ℎ");defineSymbol(text$3,main,mathord,"h","ℎ");var wideChar="";for(var _i3=0;_i3<
-letters.length;_i3++){var _ch3=letters.charAt(_i3);wideChar=String.fromCharCode(55349,56320+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),wideChar=String.fromCharCode(55349,56372+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),wideChar=String.fromCharCode(55349,56424+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),wideChar=String.fromCharCode(
-55349,56580+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),wideChar=String.fromCharCode(55349,56684+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),wideChar=String.fromCharCode(55349,56736+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),wideChar=String.fromCharCode(55349,56788+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,
-main,textord,_ch3,wideChar),wideChar=String.fromCharCode(55349,56840+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),wideChar=String.fromCharCode(55349,56944+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),_i3<26&&(wideChar=String.fromCharCode(55349,56632+_i3),defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar),wideChar=String.fromCharCode(55349,56476+_i3),
-defineSymbol(math,main,mathord,_ch3,wideChar),defineSymbol(text$3,main,textord,_ch3,wideChar))}wideChar="𝕜";defineSymbol(math,main,mathord,"k",wideChar);defineSymbol(text$3,main,textord,"k",wideChar);for(var _i4=0;_i4<10;_i4++){var _ch4=_i4.toString();wideChar=String.fromCharCode(55349,57294+_i4),defineSymbol(math,main,mathord,_ch4,wideChar),defineSymbol(text$3,main,textord,_ch4,wideChar),wideChar=String.fromCharCode(55349,57314+_i4),defineSymbol(math,main,mathord,_ch4,wideChar),defineSymbol(
-text$3,main,textord,_ch4,wideChar),wideChar=String.fromCharCode(55349,57324+_i4),defineSymbol(math,main,mathord,_ch4,wideChar),defineSymbol(text$3,main,textord,_ch4,wideChar),wideChar=String.fromCharCode(55349,57334+_i4),defineSymbol(math,main,mathord,_ch4,wideChar),defineSymbol(text$3,main,textord,_ch4,wideChar)}var extraLatin="ÐÞþ";for(var _i5=0;_i5<extraLatin.length;_i5++){var _ch5=extraLatin.charAt(_i5);defineSymbol(math,main,mathord,_ch5,_ch5),defineSymbol(text$3,main,textord,_ch5,_ch5)}
-var wideLatinLetterData=[["mathbf","textbf","Main-Bold"],["mathbf","textbf","Main-Bold"],["mathnormal","textit","Math-Italic"],["mathnormal","textit","Math-Italic"],["boldsymbol","boldsymbol","Main-BoldItalic"],["boldsymbol","boldsymbol","Main-BoldItalic"],["mathscr","textscr","Script-Regular"],["","",""],["","",""],["","",""],["mathfrak","textfrak","Fraktur-Regular"],["mathfrak","textfrak","Fraktur-Regular"],["mathbb","textbb","AMS-Regular"],["mathbb","textbb","AMS-Regular"],["mathboldfrak","te\
-xtboldfrak","Fraktur-Regular"],["mathboldfrak","textboldfrak","Fraktur-Regular"],["mathsf","textsf","SansSerif-Regular"],["mathsf","textsf","SansSerif-Regular"],["mathboldsf","textboldsf","SansSerif-Bold"],["mathboldsf","textboldsf","SansSerif-Bold"],["mathitsf","textitsf","SansSerif-Italic"],["mathitsf","textitsf","SansSerif-Italic"],["","",""],["","",""],["mathtt","texttt","Typewriter-Regular"],["mathtt","texttt","Typewriter-Regular"]],wideNumeralData=[["mathbf","textbf","Main-Bold"],["","",""],
-["mathsf","textsf","SansSerif-Regular"],["mathboldsf","textboldsf","SansSerif-Bold"],["mathtt","texttt","Typewriter-Regular"]],wideCharacterFont=function(wideChar2,mode){var H=wideChar2.charCodeAt(0),L=wideChar2.charCodeAt(1),codePoint=(H-55296)*1024+(L-56320)+65536,j=mode==="math"?0:1;if(119808<=codePoint&&codePoint<120484){var i=Math.floor((codePoint-119808)/26);return[wideLatinLetterData[i][2],wideLatinLetterData[i][j]]}else if(120782<=codePoint&&codePoint<=120831){var _i=Math.floor((codePoint-
-120782)/10);return[wideNumeralData[_i][2],wideNumeralData[_i][j]]}else{if(codePoint===120485||codePoint===120486)return[wideLatinLetterData[0][2],wideLatinLetterData[0][j]];if(120486<codePoint&&codePoint<120782)return["",""];throw new ParseError2("Unsupported character: "+wideChar2)}},lookupSymbol=function(value,fontName,mode){return symbols[mode][value]&&symbols[mode][value].replace&&(value=symbols[mode][value].replace),{value,metrics:getCharacterMetrics(value,fontName,mode)}},makeSymbol=function(value,fontName,mode,options,classes){
-var lookup=lookupSymbol(value,fontName,mode),metrics=lookup.metrics;value=lookup.value;var symbolNode;if(metrics){var italic=metrics.italic;(mode==="text"||options&&options.font==="mathit")&&(italic=0),symbolNode=new SymbolNode(value,metrics.height,metrics.depth,italic,metrics.skew,metrics.width,classes)}else typeof console<"u"&&console.warn("No character metrics "+("for '"+value+"' in style '"+fontName+"' and mode '"+mode+"'")),symbolNode=new SymbolNode(value,0,0,0,0,0,classes);if(options){symbolNode.
-maxFontSize=options.sizeMultiplier,options.style.isTight()&&symbolNode.classes.push("mtight");var color=options.getColor();color&&(symbolNode.style.color=color)}return symbolNode},mathsym=function(value,mode,options,classes){return classes===void 0&&(classes=[]),options.font==="boldsymbol"&&lookupSymbol(value,"Main-Bold",mode).metrics?makeSymbol(value,"Main-Bold",mode,options,classes.concat(["mathbf"])):value==="\\"||symbols[mode][value].font==="main"?makeSymbol(value,"Main-Regular",mode,options,
-classes):makeSymbol(value,"AMS-Regular",mode,options,classes.concat(["amsrm"]))},boldsymbol=function(value,mode,options,classes,type2){return type2!=="textord"&&lookupSymbol(value,"Math-BoldItalic",mode).metrics?{fontName:"Math-BoldItalic",fontClass:"boldsymbol"}:{fontName:"Main-Bold",fontClass:"mathbf"}},makeOrd=function(group,options,type2){var mode=group.mode,text2=group.text,classes=["mord"],isFont=mode==="math"||mode==="text"&&options.font,fontOrFamily=isFont?options.font:options.fontFamily,
-wideFontName="",wideFontClass="";if(text2.charCodeAt(0)===55349&&([wideFontName,wideFontClass]=wideCharacterFont(text2,mode)),wideFontName.length>0)return makeSymbol(text2,wideFontName,mode,options,classes.concat(wideFontClass));if(fontOrFamily){var fontName,fontClasses;if(fontOrFamily==="boldsymbol"){var fontData=boldsymbol(text2,mode,options,classes,type2);fontName=fontData.fontName,fontClasses=[fontData.fontClass]}else isFont?(fontName=fontMap[fontOrFamily].fontName,fontClasses=[fontOrFamily]):
-(fontName=retrieveTextFontName(fontOrFamily,options.fontWeight,options.fontShape),fontClasses=[fontOrFamily,options.fontWeight,options.fontShape]);if(lookupSymbol(text2,fontName,mode).metrics)return makeSymbol(text2,fontName,mode,options,classes.concat(fontClasses));if(ligatures.hasOwnProperty(text2)&&fontName.slice(0,10)==="Typewriter"){for(var parts=[],i=0;i<text2.length;i++)parts.push(makeSymbol(text2[i],fontName,mode,options,classes.concat(fontClasses)));return makeFragment(parts)}}if(type2===
-"mathord")return makeSymbol(text2,"Math-Italic",mode,options,classes.concat(["mathnormal"]));if(type2==="textord"){var font=symbols[mode][text2]&&symbols[mode][text2].font;if(font==="ams"){var _fontName=retrieveTextFontName("amsrm",options.fontWeight,options.fontShape);return makeSymbol(text2,_fontName,mode,options,classes.concat("amsrm",options.fontWeight,options.fontShape))}else if(font==="main"||!font){var _fontName2=retrieveTextFontName("textrm",options.fontWeight,options.fontShape);return makeSymbol(
-text2,_fontName2,mode,options,classes.concat(options.fontWeight,options.fontShape))}else{var _fontName3=retrieveTextFontName(font,options.fontWeight,options.fontShape);return makeSymbol(text2,_fontName3,mode,options,classes.concat(_fontName3,options.fontWeight,options.fontShape))}}else throw new Error("unexpected type: "+type2+" in makeOrd")},canCombine=(prev2,next2)=>{if(createClass(prev2.classes)!==createClass(next2.classes)||prev2.skew!==next2.skew||prev2.maxFontSize!==next2.maxFontSize)return!1;
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-elem.depth=depth,elem.maxFontSize=maxFontSize},makeSpan$2=function(classes,children,options,style2){var span=new Span(classes,children,options,style2);return sizeElementFromChildren(span),span},makeSvgSpan=(classes,children,options,style2)=>new Span(classes,children,options,style2),makeLineSpan=function(className,options,thickness){var line=makeSpan$2([className],[],options);return line.height=Math.max(thickness||options.fontMetrics().defaultRuleThickness,options.minRuleThickness),line.style.borderBottomWidth=
-makeEm(line.height),line.maxFontSize=1,line},makeAnchor=function(href,classes,children,options){var anchor=new Anchor(href,classes,children,options);return sizeElementFromChildren(anchor),anchor},makeFragment=function(children){var fragment=new DocumentFragment(children);return sizeElementFromChildren(fragment),fragment},wrapFragment=function(group,options){return group instanceof DocumentFragment?makeSpan$2([],[group],options):group},getVListChildrenAndDepth=function(params){if(params.positionType===
-"individualShift"){for(var oldChildren=params.children,children=[oldChildren[0]],_depth=-oldChildren[0].shift-oldChildren[0].elem.depth,currPos=_depth,i=1;i<oldChildren.length;i++){var diff2=-oldChildren[i].shift-currPos-oldChildren[i].elem.depth,size2=diff2-(oldChildren[i-1].elem.height+oldChildren[i-1].elem.depth);currPos=currPos+diff2,children.push({type:"kern",size:size2}),children.push(oldChildren[i])}return{children,depth:_depth}}var depth;if(params.positionType==="top"){for(var bottom=params.
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-minner:thinspace}},tightSpacings={mord:{mop:thinspace},mop:{mord:thinspace,mop:thinspace},mbin:{},mrel:{},mopen:{},mclose:{mop:thinspace},mpunct:{},minner:{mop:thinspace}},_functions={},_htmlGroupBuilders={},_mathmlGroupBuilders={};function defineFunction(_ref){for(var{type:type2,names:names2,props,handler,htmlBuilder:htmlBuilder3,mathmlBuilder:mathmlBuilder3}=_ref,data={type:type2,numArgs:props.numArgs,argTypes:props.argTypes,allowedInArgument:!!props.allowedInArgument,allowedInText:!!props.allowedInText,
-allowedInMath:props.allowedInMath===void 0?!0:props.allowedInMath,numOptionalArgs:props.numOptionalArgs||0,infix:!!props.infix,primitive:!!props.primitive,handler},i=0;i<names2.length;++i)_functions[names2[i]]=data;type2&&(htmlBuilder3&&(_htmlGroupBuilders[type2]=htmlBuilder3),mathmlBuilder3&&(_mathmlGroupBuilders[type2]=mathmlBuilder3))}function defineFunctionBuilders(_ref2){var{type:type2,htmlBuilder:htmlBuilder3,mathmlBuilder:mathmlBuilder3}=_ref2;defineFunction({type:type2,names:[],props:{numArgs:0},
-handler(){throw new Error("Should never be called.")},htmlBuilder:htmlBuilder3,mathmlBuilder:mathmlBuilder3})}var normalizeArgument=function(arg){return arg.type==="ordgroup"&&arg.body.length===1?arg.body[0]:arg},ordargument=function(arg){return arg.type==="ordgroup"?arg.body:[arg]},makeSpan$1=buildCommon.makeSpan,binLeftCanceller=["leftmost","mbin","mopen","mrel","mop","mpunct"],binRightCanceller=["rightmost","mrel","mclose","mpunct"],styleMap$1={display:Style$1.DISPLAY,text:Style$1.TEXT,script:Style$1.
-SCRIPT,scriptscript:Style$1.SCRIPTSCRIPT},DomEnum={mord:"mord",mop:"mop",mbin:"mbin",mrel:"mrel",mopen:"mopen",mclose:"mclose",mpunct:"mpunct",minner:"minner"},buildExpression$1=function(expression,options,isRealGroup,surrounding){surrounding===void 0&&(surrounding=[null,null]);for(var groups=[],i=0;i<expression.length;i++){var output=buildGroup$1(expression[i],options);if(output instanceof DocumentFragment){var children=output.children;groups.push(...children)}else groups.push(output)}if(buildCommon.
-tryCombineChars(groups),!isRealGroup)return groups;var glueOptions=options;if(expression.length===1){var node2=expression[0];node2.type==="sizing"?glueOptions=options.havingSize(node2.size):node2.type==="styling"&&(glueOptions=options.havingStyle(styleMap$1[node2.style]))}var dummyPrev=makeSpan$1([surrounding[0]||"leftmost"],[],options),dummyNext=makeSpan$1([surrounding[1]||"rightmost"],[],options),isRoot=isRealGroup==="root";return traverseNonSpaceNodes(groups,(node3,prev2)=>{var prevType=prev2.
-classes[0],type2=node3.classes[0];prevType==="mbin"&&utils.contains(binRightCanceller,type2)?prev2.classes[0]="mord":type2==="mbin"&&utils.contains(binLeftCanceller,prevType)&&(node3.classes[0]="mord")},{node:dummyPrev},dummyNext,isRoot),traverseNonSpaceNodes(groups,(node3,prev2)=>{var prevType=getTypeOfDomTree(prev2),type2=getTypeOfDomTree(node3),space2=prevType&&type2?node3.hasClass("mtight")?tightSpacings[prevType][type2]:spacings[prevType][type2]:null;if(space2)return buildCommon.makeGlue(space2,
-glueOptions)},{node:dummyPrev},dummyNext,isRoot),groups},traverseNonSpaceNodes=function traverseNonSpaceNodes2(nodes2,callback,prev2,next2,isRoot){next2&&nodes2.push(next2);for(var i=0;i<nodes2.length;i++){var node2=nodes2[i],partialGroup=checkPartialGroup(node2);if(partialGroup){traverseNonSpaceNodes2(partialGroup.children,callback,prev2,null,isRoot);continue}var nonspace=!node2.hasClass("mspace");if(nonspace){var result=callback(node2,prev2.node);result&&(prev2.insertAfter?prev2.insertAfter(result):
-(nodes2.unshift(result),i++))}nonspace?prev2.node=node2:isRoot&&node2.hasClass("newline")&&(prev2.node=makeSpan$1(["leftmost"])),prev2.insertAfter=(index2=>n=>{nodes2.splice(index2+1,0,n),i++})(i)}next2&&nodes2.pop()},checkPartialGroup=function(node2){return node2 instanceof DocumentFragment||node2 instanceof Anchor||node2 instanceof Span&&node2.hasClass("enclosing")?node2:null},getOutermostNode=function getOutermostNode2(node2,side){var partialGroup=checkPartialGroup(node2);if(partialGroup){var children=partialGroup.
-children;if(children.length){if(side==="right")return getOutermostNode2(children[children.length-1],"right");if(side==="left")return getOutermostNode2(children[0],"left")}}return node2},getTypeOfDomTree=function(node2,side){return node2?(side&&(node2=getOutermostNode(node2,side)),DomEnum[node2.classes[0]]||null):null},makeNullDelimiter=function(options,classes){var moreClasses=["nulldelimiter"].concat(options.baseSizingClasses());return makeSpan$1(classes.concat(moreClasses))},buildGroup$1=function(group,options,baseOptions){
-if(!group)return makeSpan$1();if(_htmlGroupBuilders[group.type]){var groupNode=_htmlGroupBuilders[group.type](group,options);if(baseOptions&&options.size!==baseOptions.size){groupNode=makeSpan$1(options.sizingClasses(baseOptions),[groupNode],options);var multiplier=options.sizeMultiplier/baseOptions.sizeMultiplier;groupNode.height*=multiplier,groupNode.depth*=multiplier}return groupNode}else throw new ParseError2("Got group of unknown type: '"+group.type+"'")};function buildHTMLUnbreakable(children,options){
-var body2=makeSpan$1(["base"],children,options),strut=makeSpan$1(["strut"]);return strut.style.height=makeEm(body2.height+body2.depth),body2.depth&&(strut.style.verticalAlign=makeEm(-body2.depth)),body2.children.unshift(strut),body2}function buildHTML(tree,options){var tag=null;tree.length===1&&tree[0].type==="tag"&&(tag=tree[0].tag,tree=tree[0].body);var expression=buildExpression$1(tree,options,"root"),eqnNum;expression.length===2&&expression[1].hasClass("tag")&&(eqnNum=expression.pop());for(var children=[],
-parts=[],i=0;i<expression.length;i++)if(parts.push(expression[i]),expression[i].hasClass("mbin")||expression[i].hasClass("mrel")||expression[i].hasClass("allowbreak")){for(var nobreak=!1;i<expression.length-1&&expression[i+1].hasClass("mspace")&&!expression[i+1].hasClass("newline");)i++,parts.push(expression[i]),expression[i].hasClass("nobreak")&&(nobreak=!0);nobreak||(children.push(buildHTMLUnbreakable(parts,options)),parts=[])}else expression[i].hasClass("newline")&&(parts.pop(),parts.length>0&&
-(children.push(buildHTMLUnbreakable(parts,options)),parts=[]),children.push(expression[i]));parts.length>0&&children.push(buildHTMLUnbreakable(parts,options));var tagChild;tag?(tagChild=buildHTMLUnbreakable(buildExpression$1(tag,options,!0)),tagChild.classes=["tag"],children.push(tagChild)):eqnNum&&children.push(eqnNum);var htmlNode=makeSpan$1(["katex-html"],children);if(htmlNode.setAttribute("aria-hidden","true"),tagChild){var strut=tagChild.children[0];strut.style.height=makeEm(htmlNode.height+
-htmlNode.depth),htmlNode.depth&&(strut.style.verticalAlign=makeEm(-htmlNode.depth))}return htmlNode}function newDocumentFragment(children){return new DocumentFragment(children)}class MathNode{constructor(type2,children,classes){this.type=void 0,this.attributes=void 0,this.children=void 0,this.classes=void 0,this.type=type2,this.attributes={},this.children=children||[],this.classes=classes||[]}setAttribute(name,value){this.attributes[name]=value}getAttribute(name){return this.attributes[name]}toNode(){
-var node2=document.createElementNS("http://www.w3.org/1998/Math/MathML",this.type);for(var attr in this.attributes)Object.prototype.hasOwnProperty.call(this.attributes,attr)&&node2.setAttribute(attr,this.attributes[attr]);this.classes.length>0&&(node2.className=createClass(this.classes));for(var i=0;i<this.children.length;i++)if(this.children[i]instanceof TextNode&&this.children[i+1]instanceof TextNode){for(var text2=this.children[i].toText()+this.children[++i].toText();this.children[i+1]instanceof
-TextNode;)text2+=this.children[++i].toText();node2.appendChild(new TextNode(text2).toNode())}else node2.appendChild(this.children[i].toNode());return node2}toMarkup(){var markup="<"+this.type;for(var attr in this.attributes)Object.prototype.hasOwnProperty.call(this.attributes,attr)&&(markup+=" "+attr+'="',markup+=utils.escape(this.attributes[attr]),markup+='"');this.classes.length>0&&(markup+=' class ="'+utils.escape(createClass(this.classes))+'"'),markup+=">";for(var i=0;i<this.children.length;i++)
-markup+=this.children[i].toMarkup();return markup+="</"+this.type+">",markup}toText(){return this.children.map(child2=>child2.toText()).join("")}}class TextNode{constructor(text2){this.text=void 0,this.text=text2}toNode(){return document.createTextNode(this.text)}toMarkup(){return utils.escape(this.toText())}toText(){return this.text}}class SpaceNode{constructor(width){this.width=void 0,this.character=void 0,this.width=width,width>=.05555&&width<=.05556?this.character=" ":width>=.1666&&width<=
-.1667?this.character=" ":width>=.2222&&width<=.2223?this.character=" ":width>=.2777&&width<=.2778?this.character="  ":width>=-.05556&&width<=-.05555?this.character=" ⁣":width>=-.1667&&width<=-.1666?this.character=" ⁣":width>=-.2223&&width<=-.2222?this.character=" ⁣":width>=-.2778&&width<=-.2777?this.character=" ⁣":this.character=null}toNode(){if(this.character)return document.createTextNode(this.character);var node2=document.createElementNS("http://www.w3.org/1998/Math/Ma\
-thML","mspace");return node2.setAttribute("width",makeEm(this.width)),node2}toMarkup(){return this.character?"<mtext>"+this.character+"</mtext>":'<mspace width="'+makeEm(this.width)+'"/>'}toText(){return this.character?this.character:" "}}var mathMLTree={MathNode,TextNode,SpaceNode,newDocumentFragment},makeText=function(text2,mode,options){return symbols[mode][text2]&&symbols[mode][text2].replace&&text2.charCodeAt(0)!==55349&&!(ligatures.hasOwnProperty(text2)&&options&&(options.fontFamily&&options.
-fontFamily.slice(4,6)==="tt"||options.font&&options.font.slice(4,6)==="tt"))&&(text2=symbols[mode][text2].replace),new mathMLTree.TextNode(text2)},makeRow=function(body2){return body2.length===1?body2[0]:new mathMLTree.MathNode("mrow",body2)},getVariant=function(group,options){if(options.fontFamily==="texttt")return"monospace";if(options.fontFamily==="textsf")return options.fontShape==="textit"&&options.fontWeight==="textbf"?"sans-serif-bold-italic":options.fontShape==="textit"?"sans-serif-itali\
-c":options.fontWeight==="textbf"?"bold-sans-serif":"sans-serif";if(options.fontShape==="textit"&&options.fontWeight==="textbf")return"bold-italic";if(options.fontShape==="textit")return"italic";if(options.fontWeight==="textbf")return"bold";var font=options.font;if(!font||font==="mathnormal")return null;var mode=group.mode;if(font==="mathit")return"italic";if(font==="boldsymbol")return group.type==="textord"?"bold":"bold-italic";if(font==="mathbf")return"bold";if(font==="mathbb")return"double-str\
-uck";if(font==="mathsfit")return"sans-serif-italic";if(font==="mathfrak")return"fraktur";if(font==="mathscr"||font==="mathcal")return"script";if(font==="mathsf")return"sans-serif";if(font==="mathtt")return"monospace";var text2=group.text;if(utils.contains(["\\imath","\\jmath"],text2))return null;symbols[mode][text2]&&symbols[mode][text2].replace&&(text2=symbols[mode][text2].replace);var fontName=buildCommon.fontMap[font].fontName;return getCharacterMetrics(text2,fontName,mode)?buildCommon.fontMap[font].
-variant:null};function isNumberPunctuation(group){if(!group)return!1;if(group.type==="mi"&&group.children.length===1){var child2=group.children[0];return child2 instanceof TextNode&&child2.text==="."}else if(group.type==="mo"&&group.children.length===1&&group.getAttribute("separator")==="true"&&group.getAttribute("lspace")==="0em"&&group.getAttribute("rspace")==="0em"){var _child=group.children[0];return _child instanceof TextNode&&_child.text===","}else return!1}var buildExpression2=function(expression,options,isOrdgroup){
-if(expression.length===1){var group=buildGroup2(expression[0],options);return isOrdgroup&&group instanceof MathNode&&group.type==="mo"&&(group.setAttribute("lspace","0em"),group.setAttribute("rspace","0em")),[group]}for(var groups=[],lastGroup,i=0;i<expression.length;i++){var _group=buildGroup2(expression[i],options);if(_group instanceof MathNode&&lastGroup instanceof MathNode){if(_group.type==="mtext"&&lastGroup.type==="mtext"&&_group.getAttribute("mathvariant")===lastGroup.getAttribute("mathva\
-riant")){lastGroup.children.push(..._group.children);continue}else if(_group.type==="mn"&&lastGroup.type==="mn"){lastGroup.children.push(..._group.children);continue}else if(isNumberPunctuation(_group)&&lastGroup.type==="mn"){lastGroup.children.push(..._group.children);continue}else if(_group.type==="mn"&&isNumberPunctuation(lastGroup))_group.children=[...lastGroup.children,..._group.children],groups.pop();else if((_group.type==="msup"||_group.type==="msub")&&_group.children.length>=1&&(lastGroup.
-type==="mn"||isNumberPunctuation(lastGroup))){var base2=_group.children[0];base2 instanceof MathNode&&base2.type==="mn"&&(base2.children=[...lastGroup.children,...base2.children],groups.pop())}else if(lastGroup.type==="mi"&&lastGroup.children.length===1){var lastChild=lastGroup.children[0];if(lastChild instanceof TextNode&&lastChild.text==="̸"&&(_group.type==="mo"||_group.type==="mi"||_group.type==="mn")){var child2=_group.children[0];child2 instanceof TextNode&&child2.text.length>0&&(child2.text=
-child2.text.slice(0,1)+"̸"+child2.text.slice(1),groups.pop())}}}groups.push(_group),lastGroup=_group}return groups},buildExpressionRow=function(expression,options,isOrdgroup){return makeRow(buildExpression2(expression,options,isOrdgroup))},buildGroup2=function(group,options){if(!group)return new mathMLTree.MathNode("mrow");if(_mathmlGroupBuilders[group.type]){var result=_mathmlGroupBuilders[group.type](group,options);return result}else throw new ParseError2("Got group of unknown type: '"+group.
-type+"'")};function buildMathML(tree,texExpression,options,isDisplayMode,forMathmlOnly){var expression=buildExpression2(tree,options),wrapper;expression.length===1&&expression[0]instanceof MathNode&&utils.contains(["mrow","mtable"],expression[0].type)?wrapper=expression[0]:wrapper=new mathMLTree.MathNode("mrow",expression);var annotation=new mathMLTree.MathNode("annotation",[new mathMLTree.TextNode(texExpression)]);annotation.setAttribute("encoding","application/x-tex");var semantics=new mathMLTree.
-MathNode("semantics",[wrapper,annotation]),math2=new mathMLTree.MathNode("math",[semantics]);math2.setAttribute("xmlns","http://www.w3.org/1998/Math/MathML"),isDisplayMode&&math2.setAttribute("display","block");var wrapperClass=forMathmlOnly?"katex":"katex-mathml";return buildCommon.makeSpan([wrapperClass],[math2])}var optionsFromSettings=function(settings){return new Options({style:settings.displayMode?Style$1.DISPLAY:Style$1.TEXT,maxSize:settings.maxSize,minRuleThickness:settings.minRuleThickness})},
-displayWrap=function(node2,settings){if(settings.displayMode){var classes=["katex-display"];settings.leqno&&classes.push("leqno"),settings.fleqn&&classes.push("fleqn"),node2=buildCommon.makeSpan(classes,[node2])}return node2},buildTree=function(tree,expression,settings){var options=optionsFromSettings(settings),katexNode;if(settings.output==="mathml")return buildMathML(tree,expression,options,settings.displayMode,!0);if(settings.output==="html"){var htmlNode=buildHTML(tree,options);katexNode=buildCommon.
-makeSpan(["katex"],[htmlNode])}else{var mathMLNode=buildMathML(tree,expression,options,settings.displayMode,!1),_htmlNode=buildHTML(tree,options);katexNode=buildCommon.makeSpan(["katex"],[mathMLNode,_htmlNode])}return displayWrap(katexNode,settings)},buildHTMLTree=function(tree,expression,settings){var options=optionsFromSettings(settings),htmlNode=buildHTML(tree,options),katexNode=buildCommon.makeSpan(["katex"],[htmlNode]);return displayWrap(katexNode,settings)},stretchyCodePoint={widehat:"^",widecheck:"\
-ˇ",widetilde:"~",utilde:"~",overleftarrow:"←",underleftarrow:"←",xleftarrow:"←",overrightarrow:"→",underrightarrow:"→",xrightarrow:"→",underbrace:"⏟",overbrace:"⏞",overgroup:"⏠",undergroup:"⏡",overleftrightarrow:"↔",underleftrightarrow:"↔",xleftrightarrow:"↔",Overrightarrow:"⇒",xRightarrow:"⇒",overleftharpoon:"↼",xleftharpoonup:"↼",overrightharpoon:"⇀",xrightharpoonup:"⇀",xLeftarrow:"⇐",xLeftrightarrow:"⇔",xhookleftarrow:"↩",xhookrightarrow:"↪",xmapsto:"\
-↦",xrightharpoondown:"⇁",xleftharpoondown:"↽",xrightleftharpoons:"⇌",xleftrightharpoons:"⇋",xtwoheadleftarrow:"↞",xtwoheadrightarrow:"↠",xlongequal:"=",xtofrom:"⇄",xrightleftarrows:"⇄",xrightequilibrium:"⇌",xleftequilibrium:"⇋","\\cdrightarrow":"→","\\cdleftarrow":"←","\\cdlongequal":"="},mathMLnode=function(label){var node2=new mathMLTree.MathNode("mo",[new mathMLTree.TextNode(stretchyCodePoint[label.replace(/^\\/,"")])]);return node2.setAttribute("stretchy","true"),node2},
-katexImagesData={overrightarrow:[["rightarrow"],.888,522,"xMaxYMin"],overleftarrow:[["leftarrow"],.888,522,"xMinYMin"],underrightarrow:[["rightarrow"],.888,522,"xMaxYMin"],underleftarrow:[["leftarrow"],.888,522,"xMinYMin"],xrightarrow:[["rightarrow"],1.469,522,"xMaxYMin"],"\\cdrightarrow":[["rightarrow"],3,522,"xMaxYMin"],xleftarrow:[["leftarrow"],1.469,522,"xMinYMin"],"\\cdleftarrow":[["leftarrow"],3,522,"xMinYMin"],Overrightarrow:[["doublerightarrow"],.888,560,"xMaxYMin"],xRightarrow:[["double\
-rightarrow"],1.526,560,"xMaxYMin"],xLeftarrow:[["doubleleftarrow"],1.526,560,"xMinYMin"],overleftharpoon:[["leftharpoon"],.888,522,"xMinYMin"],xleftharpoonup:[["leftharpoon"],.888,522,"xMinYMin"],xleftharpoondown:[["leftharpoondown"],.888,522,"xMinYMin"],overrightharpoon:[["rightharpoon"],.888,522,"xMaxYMin"],xrightharpoonup:[["rightharpoon"],.888,522,"xMaxYMin"],xrightharpoondown:[["rightharpoondown"],.888,522,"xMaxYMin"],xlongequal:[["longequal"],.888,334,"xMinYMin"],"\\cdlongequal":[["longequ\
-al"],3,334,"xMinYMin"],xtwoheadleftarrow:[["twoheadleftarrow"],.888,334,"xMinYMin"],xtwoheadrightarrow:[["twoheadrightarrow"],.888,334,"xMaxYMin"],overleftrightarrow:[["leftarrow","rightarrow"],.888,522],overbrace:[["leftbrace","midbrace","rightbrace"],1.6,548],underbrace:[["leftbraceunder","midbraceunder","rightbraceunder"],1.6,548],underleftrightarrow:[["leftarrow","rightarrow"],.888,522],xleftrightarrow:[["leftarrow","rightarrow"],1.75,522],xLeftrightarrow:[["doubleleftarrow","doublerightarro\
-w"],1.75,560],xrightleftharpoons:[["leftharpoondownplus","rightharpoonplus"],1.75,716],xleftrightharpoons:[["leftharpoonplus","rightharpoondownplus"],1.75,716],xhookleftarrow:[["leftarrow","righthook"],1.08,522],xhookrightarrow:[["lefthook","rightarrow"],1.08,522],overlinesegment:[["leftlinesegment","rightlinesegment"],.888,522],underlinesegment:[["leftlinesegment","rightlinesegment"],.888,522],overgroup:[["leftgroup","rightgroup"],.888,342],undergroup:[["leftgroupunder","rightgroupunder"],.888,
-342],xmapsto:[["leftmapsto","rightarrow"],1.5,522],xtofrom:[["leftToFrom","rightToFrom"],1.75,528],xrightleftarrows:[["baraboveleftarrow","rightarrowabovebar"],1.75,901],xrightequilibrium:[["baraboveshortleftharpoon","rightharpoonaboveshortbar"],1.75,716],xleftequilibrium:[["shortbaraboveleftharpoon","shortrightharpoonabovebar"],1.75,716]},groupLength=function(arg){return arg.type==="ordgroup"?arg.body.length:1},svgSpan=function(group,options){function buildSvgSpan_(){var viewBoxWidth=4e5,label=group.
-label.slice(1);if(utils.contains(["widehat","widecheck","widetilde","utilde"],label)){var grp=group,numChars=groupLength(grp.base),viewBoxHeight,pathName,_height;if(numChars>5)label==="widehat"||label==="widecheck"?(viewBoxHeight=420,viewBoxWidth=2364,_height=.42,pathName=label+"4"):(viewBoxHeight=312,viewBoxWidth=2340,_height=.34,pathName="tilde4");else{var imgIndex=[1,1,2,2,3,3][numChars];label==="widehat"||label==="widecheck"?(viewBoxWidth=[0,1062,2364,2364,2364][imgIndex],viewBoxHeight=[0,239,
-300,360,420][imgIndex],_height=[0,.24,.3,.3,.36,.42][imgIndex],pathName=label+imgIndex):(viewBoxWidth=[0,600,1033,2339,2340][imgIndex],viewBoxHeight=[0,260,286,306,312][imgIndex],_height=[0,.26,.286,.3,.306,.34][imgIndex],pathName="tilde"+imgIndex)}var path2=new PathNode(pathName),svgNode=new SvgNode([path2],{width:"100%",height:makeEm(_height),viewBox:"0 0 "+viewBoxWidth+" "+viewBoxHeight,preserveAspectRatio:"none"});return{span:buildCommon.makeSvgSpan([],[svgNode],options),minWidth:0,height:_height}}else{
-var spans=[],data=katexImagesData[label],[paths,_minWidth,_viewBoxHeight]=data,_height2=_viewBoxHeight/1e3,numSvgChildren=paths.length,widthClasses,aligns;if(numSvgChildren===1){var align1=data[3];widthClasses=["hide-tail"],aligns=[align1]}else if(numSvgChildren===2)widthClasses=["halfarrow-left","halfarrow-right"],aligns=["xMinYMin","xMaxYMin"];else if(numSvgChildren===3)widthClasses=["brace-left","brace-center","brace-right"],aligns=["xMinYMin","xMidYMin","xMaxYMin"];else throw new Error(`Corr\
-ect katexImagesData or update code here to support
-                    `+numSvgChildren+" children.");for(var i=0;i<numSvgChildren;i++){var _path=new PathNode(paths[i]),_svgNode=new SvgNode([_path],{width:"400em",height:makeEm(_height2),viewBox:"0 0 "+viewBoxWidth+" "+_viewBoxHeight,preserveAspectRatio:aligns[i]+" slice"}),_span=buildCommon.makeSvgSpan([widthClasses[i]],[_svgNode],options);if(numSvgChildren===1)return{span:_span,minWidth:_minWidth,height:_height2};_span.style.height=makeEm(_height2),spans.push(_span)}return{span:buildCommon.makeSpan(
-["stretchy"],spans,options),minWidth:_minWidth,height:_height2}}}var{span,minWidth,height}=buildSvgSpan_();return span.height=height,span.style.height=makeEm(height),minWidth>0&&(span.style.minWidth=makeEm(minWidth)),span},encloseSpan=function(inner2,label,topPad,bottomPad,options){var img,totalHeight=inner2.height+inner2.depth+topPad+bottomPad;if(/fbox|color|angl/.test(label)){if(img=buildCommon.makeSpan(["stretchy",label],[],options),label==="fbox"){var color=options.color&&options.getColor();
-color&&(img.style.borderColor=color)}}else{var lines=[];/^[bx]cancel$/.test(label)&&lines.push(new LineNode({x1:"0",y1:"0",x2:"100%",y2:"100%","stroke-width":"0.046em"})),/^x?cancel$/.test(label)&&lines.push(new LineNode({x1:"0",y1:"100%",x2:"100%",y2:"0","stroke-width":"0.046em"}));var svgNode=new SvgNode(lines,{width:"100%",height:makeEm(totalHeight)});img=buildCommon.makeSvgSpan([],[svgNode],options)}return img.height=totalHeight,img.style.height=makeEm(totalHeight),img},stretchy={encloseSpan,
-mathMLnode,svgSpan};function assertNodeType(node2,type2){if(!node2||node2.type!==type2)throw new Error("Expected node of type "+type2+", but got "+(node2?"node of type "+node2.type:String(node2)));return node2}function assertSymbolNodeType(node2){var typedNode=checkSymbolNodeType(node2);if(!typedNode)throw new Error("Expected node of symbol group type, but got "+(node2?"node of type "+node2.type:String(node2)));return typedNode}function checkSymbolNodeType(node2){return node2&&(node2.type==="ato\
-m"||NON_ATOMS.hasOwnProperty(node2.type))?node2:null}var htmlBuilder$a=(grp,options)=>{var base2,group,supSubGroup;grp&&grp.type==="supsub"?(group=assertNodeType(grp.base,"accent"),base2=group.base,grp.base=base2,supSubGroup=assertSpan(buildGroup$1(grp,options)),grp.base=group):(group=assertNodeType(grp,"accent"),base2=group.base);var body2=buildGroup$1(base2,options.havingCrampedStyle()),mustShift=group.isShifty&&utils.isCharacterBox(base2),skew=0;if(mustShift){var baseChar=utils.getBaseElem(base2),
-baseGroup=buildGroup$1(baseChar,options.havingCrampedStyle());skew=assertSymbolDomNode(baseGroup).skew}var accentBelow=group.label==="\\c",clearance=accentBelow?body2.height+body2.depth:Math.min(body2.height,options.fontMetrics().xHeight),accentBody;if(group.isStretchy)accentBody=stretchy.svgSpan(group,options),accentBody=buildCommon.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:body2},{type:"elem",elem:accentBody,wrapperClasses:["svg-align"],wrapperStyle:skew>0?{width:"cal\
-c(100% - "+makeEm(2*skew)+")",marginLeft:makeEm(2*skew)}:void 0}]},options);else{var accent2,width;group.label==="\\vec"?(accent2=buildCommon.staticSvg("vec",options),width=buildCommon.svgData.vec[1]):(accent2=buildCommon.makeOrd({mode:group.mode,text:group.label},options,"textord"),accent2=assertSymbolDomNode(accent2),accent2.italic=0,width=accent2.width,accentBelow&&(clearance+=accent2.depth)),accentBody=buildCommon.makeSpan(["accent-body"],[accent2]);var accentFull=group.label==="\\textcircled";
-accentFull&&(accentBody.classes.push("accent-full"),clearance=body2.height);var left=skew;accentFull||(left-=width/2),accentBody.style.left=makeEm(left),group.label==="\\textcircled"&&(accentBody.style.top=".2em"),accentBody=buildCommon.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:body2},{type:"kern",size:-clearance},{type:"elem",elem:accentBody}]},options)}var accentWrap=buildCommon.makeSpan(["mord","accent"],[accentBody],options);return supSubGroup?(supSubGroup.children[0]=
-accentWrap,supSubGroup.height=Math.max(accentWrap.height,supSubGroup.height),supSubGroup.classes[0]="mord",supSubGroup):accentWrap},mathmlBuilder$9=(group,options)=>{var accentNode=group.isStretchy?stretchy.mathMLnode(group.label):new mathMLTree.MathNode("mo",[makeText(group.label,group.mode)]),node2=new mathMLTree.MathNode("mover",[buildGroup2(group.base,options),accentNode]);return node2.setAttribute("accent","true"),node2},NON_STRETCHY_ACCENT_REGEX=new RegExp(["\\acute","\\grave","\\ddot","\\t\
-ilde","\\bar","\\breve","\\check","\\hat","\\vec","\\dot","\\mathring"].map(accent2=>"\\"+accent2).join("|"));defineFunction({type:"accent",names:["\\acute","\\grave","\\ddot","\\tilde","\\bar","\\breve","\\check","\\hat","\\vec","\\dot","\\mathring","\\widecheck","\\widehat","\\widetilde","\\overrightarrow","\\overleftarrow","\\Overrightarrow","\\overleftrightarrow","\\overgroup","\\overlinesegment","\\overleftharpoon","\\overrightharpoon"],props:{numArgs:1},handler:(context,args)=>{var base2=normalizeArgument(
-args[0]),isStretchy=!NON_STRETCHY_ACCENT_REGEX.test(context.funcName),isShifty=!isStretchy||context.funcName==="\\widehat"||context.funcName==="\\widetilde"||context.funcName==="\\widecheck";return{type:"accent",mode:context.parser.mode,label:context.funcName,isStretchy,isShifty,base:base2}},htmlBuilder:htmlBuilder$a,mathmlBuilder:mathmlBuilder$9});defineFunction({type:"accent",names:["\\'","\\`","\\^","\\~","\\=","\\u","\\.",'\\"',"\\c","\\r","\\H","\\v","\\textcircled"],props:{numArgs:1,allowedInText:!0,
-allowedInMath:!0,argTypes:["primitive"]},handler:(context,args)=>{var base2=args[0],mode=context.parser.mode;return mode==="math"&&(context.parser.settings.reportNonstrict("mathVsTextAccents","LaTeX's accent "+context.funcName+" works only in text mode"),mode="text"),{type:"accent",mode,label:context.funcName,isStretchy:!1,isShifty:!0,base:base2}},htmlBuilder:htmlBuilder$a,mathmlBuilder:mathmlBuilder$9});defineFunction({type:"accentUnder",names:["\\underleftarrow","\\underrightarrow","\\underleft\
-rightarrow","\\undergroup","\\underlinesegment","\\utilde"],props:{numArgs:1},handler:(_ref,args)=>{var{parser,funcName}=_ref,base2=args[0];return{type:"accentUnder",mode:parser.mode,label:funcName,base:base2}},htmlBuilder:(group,options)=>{var innerGroup=buildGroup$1(group.base,options),accentBody=stretchy.svgSpan(group,options),kern=group.label==="\\utilde"?.12:0,vlist=buildCommon.makeVList({positionType:"top",positionData:innerGroup.height,children:[{type:"elem",elem:accentBody,wrapperClasses:[
-"svg-align"]},{type:"kern",size:kern},{type:"elem",elem:innerGroup}]},options);return buildCommon.makeSpan(["mord","accentunder"],[vlist],options)},mathmlBuilder:(group,options)=>{var accentNode=stretchy.mathMLnode(group.label),node2=new mathMLTree.MathNode("munder",[buildGroup2(group.base,options),accentNode]);return node2.setAttribute("accentunder","true"),node2}});var paddedNode=group=>{var node2=new mathMLTree.MathNode("mpadded",group?[group]:[]);return node2.setAttribute("width","+0.6em"),node2.
-setAttribute("lspace","0.3em"),node2};defineFunction({type:"xArrow",names:["\\xleftarrow","\\xrightarrow","\\xLeftarrow","\\xRightarrow","\\xleftrightarrow","\\xLeftrightarrow","\\xhookleftarrow","\\xhookrightarrow","\\xmapsto","\\xrightharpoondown","\\xrightharpoonup","\\xleftharpoondown","\\xleftharpoonup","\\xrightleftharpoons","\\xleftrightharpoons","\\xlongequal","\\xtwoheadrightarrow","\\xtwoheadleftarrow","\\xtofrom","\\xrightleftarrows","\\xrightequilibrium","\\xleftequilibrium","\\\\cdrig\
-htarrow","\\\\cdleftarrow","\\\\cdlongequal"],props:{numArgs:1,numOptionalArgs:1},handler(_ref,args,optArgs){var{parser,funcName}=_ref;return{type:"xArrow",mode:parser.mode,label:funcName,body:args[0],below:optArgs[0]}},htmlBuilder(group,options){var style2=options.style,newOptions=options.havingStyle(style2.sup()),upperGroup=buildCommon.wrapFragment(buildGroup$1(group.body,newOptions,options),options),arrowPrefix=group.label.slice(0,2)==="\\x"?"x":"cd";upperGroup.classes.push(arrowPrefix+"-arro\
-w-pad");var lowerGroup;group.below&&(newOptions=options.havingStyle(style2.sub()),lowerGroup=buildCommon.wrapFragment(buildGroup$1(group.below,newOptions,options),options),lowerGroup.classes.push(arrowPrefix+"-arrow-pad"));var arrowBody=stretchy.svgSpan(group,options),arrowShift=-options.fontMetrics().axisHeight+.5*arrowBody.height,upperShift=-options.fontMetrics().axisHeight-.5*arrowBody.height-.111;(upperGroup.depth>.25||group.label==="\\xleftequilibrium")&&(upperShift-=upperGroup.depth);var vlist;
-if(lowerGroup){var lowerShift=-options.fontMetrics().axisHeight+lowerGroup.height+.5*arrowBody.height+.111;vlist=buildCommon.makeVList({positionType:"individualShift",children:[{type:"elem",elem:upperGroup,shift:upperShift},{type:"elem",elem:arrowBody,shift:arrowShift},{type:"elem",elem:lowerGroup,shift:lowerShift}]},options)}else vlist=buildCommon.makeVList({positionType:"individualShift",children:[{type:"elem",elem:upperGroup,shift:upperShift},{type:"elem",elem:arrowBody,shift:arrowShift}]},options);
-return vlist.children[0].children[0].children[1].classes.push("svg-align"),buildCommon.makeSpan(["mrel","x-arrow"],[vlist],options)},mathmlBuilder(group,options){var arrowNode=stretchy.mathMLnode(group.label);arrowNode.setAttribute("minsize",group.label.charAt(0)==="x"?"1.75em":"3.0em");var node2;if(group.body){var upperNode=paddedNode(buildGroup2(group.body,options));if(group.below){var lowerNode=paddedNode(buildGroup2(group.below,options));node2=new mathMLTree.MathNode("munderover",[arrowNode,
-lowerNode,upperNode])}else node2=new mathMLTree.MathNode("mover",[arrowNode,upperNode])}else if(group.below){var _lowerNode=paddedNode(buildGroup2(group.below,options));node2=new mathMLTree.MathNode("munder",[arrowNode,_lowerNode])}else node2=paddedNode(),node2=new mathMLTree.MathNode("mover",[arrowNode,node2]);return node2}});var makeSpan2=buildCommon.makeSpan;function htmlBuilder$9(group,options){var elements=buildExpression$1(group.body,options,!0);return makeSpan2([group.mclass],elements,options)}
-function mathmlBuilder$8(group,options){var node2,inner2=buildExpression2(group.body,options);return group.mclass==="minner"?node2=new mathMLTree.MathNode("mpadded",inner2):group.mclass==="mord"?group.isCharacterBox?(node2=inner2[0],node2.type="mi"):node2=new mathMLTree.MathNode("mi",inner2):(group.isCharacterBox?(node2=inner2[0],node2.type="mo"):node2=new mathMLTree.MathNode("mo",inner2),group.mclass==="mbin"?(node2.attributes.lspace="0.22em",node2.attributes.rspace="0.22em"):group.mclass==="mp\
-unct"?(node2.attributes.lspace="0em",node2.attributes.rspace="0.17em"):group.mclass==="mopen"||group.mclass==="mclose"?(node2.attributes.lspace="0em",node2.attributes.rspace="0em"):group.mclass==="minner"&&(node2.attributes.lspace="0.0556em",node2.attributes.width="+0.1111em")),node2}defineFunction({type:"mclass",names:["\\mathord","\\mathbin","\\mathrel","\\mathopen","\\mathclose","\\mathpunct","\\mathinner"],props:{numArgs:1,primitive:!0},handler(_ref,args){var{parser,funcName}=_ref,body2=args[0];
-return{type:"mclass",mode:parser.mode,mclass:"m"+funcName.slice(5),body:ordargument(body2),isCharacterBox:utils.isCharacterBox(body2)}},htmlBuilder:htmlBuilder$9,mathmlBuilder:mathmlBuilder$8});var binrelClass=arg=>{var atom=arg.type==="ordgroup"&&arg.body.length?arg.body[0]:arg;return atom.type==="atom"&&(atom.family==="bin"||atom.family==="rel")?"m"+atom.family:"mord"};defineFunction({type:"mclass",names:["\\@binrel"],props:{numArgs:2},handler(_ref2,args){var{parser}=_ref2;return{type:"mclass",
-mode:parser.mode,mclass:binrelClass(args[0]),body:ordargument(args[1]),isCharacterBox:utils.isCharacterBox(args[1])}}});defineFunction({type:"mclass",names:["\\stackrel","\\overset","\\underset"],props:{numArgs:2},handler(_ref3,args){var{parser,funcName}=_ref3,baseArg=args[1],shiftedArg=args[0],mclass;funcName!=="\\stackrel"?mclass=binrelClass(baseArg):mclass="mrel";var baseOp={type:"op",mode:baseArg.mode,limits:!0,alwaysHandleSupSub:!0,parentIsSupSub:!1,symbol:!1,suppressBaseShift:funcName!=="\\\
-stackrel",body:ordargument(baseArg)},supsub={type:"supsub",mode:shiftedArg.mode,base:baseOp,sup:funcName==="\\underset"?null:shiftedArg,sub:funcName==="\\underset"?shiftedArg:null};return{type:"mclass",mode:parser.mode,mclass,body:[supsub],isCharacterBox:utils.isCharacterBox(supsub)}},htmlBuilder:htmlBuilder$9,mathmlBuilder:mathmlBuilder$8});defineFunction({type:"pmb",names:["\\pmb"],props:{numArgs:1,allowedInText:!0},handler(_ref,args){var{parser}=_ref;return{type:"pmb",mode:parser.mode,mclass:binrelClass(
-args[0]),body:ordargument(args[0])}},htmlBuilder(group,options){var elements=buildExpression$1(group.body,options,!0),node2=buildCommon.makeSpan([group.mclass],elements,options);return node2.style.textShadow="0.02em 0.01em 0.04px",node2},mathmlBuilder(group,style2){var inner2=buildExpression2(group.body,style2),node2=new mathMLTree.MathNode("mstyle",inner2);return node2.setAttribute("style","text-shadow: 0.02em 0.01em 0.04px"),node2}});var cdArrowFunctionName={">":"\\\\cdrightarrow","<":"\\\\cdlef\
-tarrow","=":"\\\\cdlongequal",A:"\\uparrow",V:"\\downarrow","|":"\\Vert",".":"no arrow"},newCell=()=>({type:"styling",body:[],mode:"math",style:"display"}),isStartOfArrow=node2=>node2.type==="textord"&&node2.text==="@",isLabelEnd=(node2,endChar)=>(node2.type==="mathord"||node2.type==="atom")&&node2.text===endChar;function cdArrow(arrowChar,labels,parser){var funcName=cdArrowFunctionName[arrowChar];switch(funcName){case"\\\\cdrightarrow":case"\\\\cdleftarrow":return parser.callFunction(funcName,[
-labels[0]],[labels[1]]);case"\\uparrow":case"\\downarrow":{var leftLabel=parser.callFunction("\\\\cdleft",[labels[0]],[]),bareArrow={type:"atom",text:funcName,mode:"math",family:"rel"},sizedArrow=parser.callFunction("\\Big",[bareArrow],[]),rightLabel=parser.callFunction("\\\\cdright",[labels[1]],[]),arrowGroup={type:"ordgroup",mode:"math",body:[leftLabel,sizedArrow,rightLabel]};return parser.callFunction("\\\\cdparent",[arrowGroup],[])}case"\\\\cdlongequal":return parser.callFunction("\\\\cdlongeq\
-ual",[],[]);case"\\Vert":{var arrow2={type:"textord",text:"\\Vert",mode:"math"};return parser.callFunction("\\Big",[arrow2],[])}default:return{type:"textord",text:" ",mode:"math"}}}function parseCD(parser){var parsedRows=[];for(parser.gullet.beginGroup(),parser.gullet.macros.set("\\cr","\\\\\\relax"),parser.gullet.beginGroup();;){parsedRows.push(parser.parseExpression(!1,"\\\\")),parser.gullet.endGroup(),parser.gullet.beginGroup();var next2=parser.fetch().text;if(next2==="&"||next2==="\\\\")parser.
-consume();else if(next2==="\\end"){parsedRows[parsedRows.length-1].length===0&&parsedRows.pop();break}else throw new ParseError2("Expected \\\\ or \\cr or \\end",parser.nextToken)}for(var row2=[],body2=[row2],i=0;i<parsedRows.length;i++){for(var rowNodes=parsedRows[i],cell2=newCell(),j=0;j<rowNodes.length;j++)if(!isStartOfArrow(rowNodes[j]))cell2.body.push(rowNodes[j]);else{row2.push(cell2),j+=1;var arrowChar=assertSymbolNodeType(rowNodes[j]).text,labels=new Array(2);if(labels[0]={type:"ordgroup",
-mode:"math",body:[]},labels[1]={type:"ordgroup",mode:"math",body:[]},!("=|.".indexOf(arrowChar)>-1))if("<>AV".indexOf(arrowChar)>-1)for(var labelNum=0;labelNum<2;labelNum++){for(var inLabel=!0,k=j+1;k<rowNodes.length;k++){if(isLabelEnd(rowNodes[k],arrowChar)){inLabel=!1,j=k;break}if(isStartOfArrow(rowNodes[k]))throw new ParseError2("Missing a "+arrowChar+" character to complete a CD arrow.",rowNodes[k]);labels[labelNum].body.push(rowNodes[k])}if(inLabel)throw new ParseError2("Missing a "+arrowChar+
-" character to complete a CD arrow.",rowNodes[j])}else throw new ParseError2('Expected one of "<>AV=|." after @',rowNodes[j]);var arrow2=cdArrow(arrowChar,labels,parser),wrappedArrow={type:"styling",body:[arrow2],mode:"math",style:"display"};row2.push(wrappedArrow),cell2=newCell()}i%2===0?row2.push(cell2):row2.shift(),row2=[],body2.push(row2)}parser.gullet.endGroup(),parser.gullet.endGroup();var cols=new Array(body2[0].length).fill({type:"align",align:"c",pregap:.25,postgap:.25});return{type:"ar\
-ray",mode:"math",body:body2,arraystretch:1,addJot:!0,rowGaps:[null],cols,colSeparationType:"CD",hLinesBeforeRow:new Array(body2.length+1).fill([])}}defineFunction({type:"cdlabel",names:["\\\\cdleft","\\\\cdright"],props:{numArgs:1},handler(_ref,args){var{parser,funcName}=_ref;return{type:"cdlabel",mode:parser.mode,side:funcName.slice(4),label:args[0]}},htmlBuilder(group,options){var newOptions=options.havingStyle(options.style.sup()),label=buildCommon.wrapFragment(buildGroup$1(group.label,newOptions,
-options),options);return label.classes.push("cd-label-"+group.side),label.style.bottom=makeEm(.8-label.depth),label.height=0,label.depth=0,label},mathmlBuilder(group,options){var label=new mathMLTree.MathNode("mrow",[buildGroup2(group.label,options)]);return label=new mathMLTree.MathNode("mpadded",[label]),label.setAttribute("width","0"),group.side==="left"&&label.setAttribute("lspace","-1width"),label.setAttribute("voffset","0.7em"),label=new mathMLTree.MathNode("mstyle",[label]),label.setAttribute(
-"displaystyle","false"),label.setAttribute("scriptlevel","1"),label}});defineFunction({type:"cdlabelparent",names:["\\\\cdparent"],props:{numArgs:1},handler(_ref2,args){var{parser}=_ref2;return{type:"cdlabelparent",mode:parser.mode,fragment:args[0]}},htmlBuilder(group,options){var parent=buildCommon.wrapFragment(buildGroup$1(group.fragment,options),options);return parent.classes.push("cd-vert-arrow"),parent},mathmlBuilder(group,options){return new mathMLTree.MathNode("mrow",[buildGroup2(group.fragment,
-options)])}});defineFunction({type:"textord",names:["\\@char"],props:{numArgs:1,allowedInText:!0},handler(_ref,args){for(var{parser}=_ref,arg=assertNodeType(args[0],"ordgroup"),group=arg.body,number2="",i=0;i<group.length;i++){var node2=assertNodeType(group[i],"textord");number2+=node2.text}var code2=parseInt(number2),text2;if(isNaN(code2))throw new ParseError2("\\@char has non-numeric argument "+number2);if(code2<0||code2>=1114111)throw new ParseError2("\\@char with invalid code point "+number2);
-return code2<=65535?text2=String.fromCharCode(code2):(code2-=65536,text2=String.fromCharCode((code2>>10)+55296,(code2&1023)+56320)),{type:"textord",mode:parser.mode,text:text2}}});var htmlBuilder$8=(group,options)=>{var elements=buildExpression$1(group.body,options.withColor(group.color),!1);return buildCommon.makeFragment(elements)},mathmlBuilder$7=(group,options)=>{var inner2=buildExpression2(group.body,options.withColor(group.color)),node2=new mathMLTree.MathNode("mstyle",inner2);return node2.
-setAttribute("mathcolor",group.color),node2};defineFunction({type:"color",names:["\\textcolor"],props:{numArgs:2,allowedInText:!0,argTypes:["color","original"]},handler(_ref,args){var{parser}=_ref,color=assertNodeType(args[0],"color-token").color,body2=args[1];return{type:"color",mode:parser.mode,color,body:ordargument(body2)}},htmlBuilder:htmlBuilder$8,mathmlBuilder:mathmlBuilder$7});defineFunction({type:"color",names:["\\color"],props:{numArgs:1,allowedInText:!0,argTypes:["color"]},handler(_ref2,args){
-var{parser,breakOnTokenText}=_ref2,color=assertNodeType(args[0],"color-token").color;parser.gullet.macros.set("\\current@color",color);var body2=parser.parseExpression(!0,breakOnTokenText);return{type:"color",mode:parser.mode,color,body:body2}},htmlBuilder:htmlBuilder$8,mathmlBuilder:mathmlBuilder$7});defineFunction({type:"cr",names:["\\\\"],props:{numArgs:0,numOptionalArgs:0,allowedInText:!0},handler(_ref,args,optArgs){var{parser}=_ref,size2=parser.gullet.future().text==="["?parser.parseSizeGroup(
-!0):null,newLine=!parser.settings.displayMode||!parser.settings.useStrictBehavior("newLineInDisplayMode","In LaTeX, \\\\ or \\newline does nothing in display mode");return{type:"cr",mode:parser.mode,newLine,size:size2&&assertNodeType(size2,"size").value}},htmlBuilder(group,options){var span=buildCommon.makeSpan(["mspace"],[],options);return group.newLine&&(span.classes.push("newline"),group.size&&(span.style.marginTop=makeEm(calculateSize(group.size,options)))),span},mathmlBuilder(group,options){
-var node2=new mathMLTree.MathNode("mspace");return group.newLine&&(node2.setAttribute("linebreak","newline"),group.size&&node2.setAttribute("height",makeEm(calculateSize(group.size,options)))),node2}});var globalMap={"\\global":"\\global","\\long":"\\\\globallong","\\\\globallong":"\\\\globallong","\\def":"\\gdef","\\gdef":"\\gdef","\\edef":"\\xdef","\\xdef":"\\xdef","\\let":"\\\\globallet","\\futurelet":"\\\\globalfuture"},checkControlSequence=tok=>{var name=tok.text;if(/^(?:[\\{}$&#^_]|EOF)$/.
-test(name))throw new ParseError2("Expected a control sequence",tok);return name},getRHS=parser=>{var tok=parser.gullet.popToken();return tok.text==="="&&(tok=parser.gullet.popToken(),tok.text===" "&&(tok=parser.gullet.popToken())),tok},letCommand=(parser,name,tok,global2)=>{var macro=parser.gullet.macros.get(tok.text);macro==null&&(tok.noexpand=!0,macro={tokens:[tok],numArgs:0,unexpandable:!parser.gullet.isExpandable(tok.text)}),parser.gullet.macros.set(name,macro,global2)};defineFunction({type:"\
-internal",names:["\\global","\\long","\\\\globallong"],props:{numArgs:0,allowedInText:!0},handler(_ref){var{parser,funcName}=_ref;parser.consumeSpaces();var token2=parser.fetch();if(globalMap[token2.text])return(funcName==="\\global"||funcName==="\\\\globallong")&&(token2.text=globalMap[token2.text]),assertNodeType(parser.parseFunction(),"internal");throw new ParseError2("Invalid token after macro prefix",token2)}});defineFunction({type:"internal",names:["\\def","\\gdef","\\edef","\\xdef"],props:{
-numArgs:0,allowedInText:!0,primitive:!0},handler(_ref2){var{parser,funcName}=_ref2,tok=parser.gullet.popToken(),name=tok.text;if(/^(?:[\\{}$&#^_]|EOF)$/.test(name))throw new ParseError2("Expected a control sequence",tok);for(var numArgs=0,insert,delimiters2=[[]];parser.gullet.future().text!=="{";)if(tok=parser.gullet.popToken(),tok.text==="#"){if(parser.gullet.future().text==="{"){insert=parser.gullet.future(),delimiters2[numArgs].push("{");break}if(tok=parser.gullet.popToken(),!/^[1-9]$/.test(tok.
-text))throw new ParseError2('Invalid argument number "'+tok.text+'"');if(parseInt(tok.text)!==numArgs+1)throw new ParseError2('Argument number "'+tok.text+'" out of order');numArgs++,delimiters2.push([])}else{if(tok.text==="EOF")throw new ParseError2("Expected a macro definition");delimiters2[numArgs].push(tok.text)}var{tokens}=parser.gullet.consumeArg();return insert&&tokens.unshift(insert),(funcName==="\\edef"||funcName==="\\xdef")&&(tokens=parser.gullet.expandTokens(tokens),tokens.reverse()),
-parser.gullet.macros.set(name,{tokens,numArgs,delimiters:delimiters2},funcName===globalMap[funcName]),{type:"internal",mode:parser.mode}}});defineFunction({type:"internal",names:["\\let","\\\\globallet"],props:{numArgs:0,allowedInText:!0,primitive:!0},handler(_ref3){var{parser,funcName}=_ref3,name=checkControlSequence(parser.gullet.popToken());parser.gullet.consumeSpaces();var tok=getRHS(parser);return letCommand(parser,name,tok,funcName==="\\\\globallet"),{type:"internal",mode:parser.mode}}});defineFunction(
-{type:"internal",names:["\\futurelet","\\\\globalfuture"],props:{numArgs:0,allowedInText:!0,primitive:!0},handler(_ref4){var{parser,funcName}=_ref4,name=checkControlSequence(parser.gullet.popToken()),middle=parser.gullet.popToken(),tok=parser.gullet.popToken();return letCommand(parser,name,tok,funcName==="\\\\globalfuture"),parser.gullet.pushToken(tok),parser.gullet.pushToken(middle),{type:"internal",mode:parser.mode}}});var getMetrics=function(symbol,font,mode){var replace2=symbols.math[symbol]&&
-symbols.math[symbol].replace,metrics=getCharacterMetrics(replace2||symbol,font,mode);if(!metrics)throw new Error("Unsupported symbol "+symbol+" and font size "+font+".");return metrics},styleWrap=function(delim,toStyle,options,classes){var newOptions=options.havingBaseStyle(toStyle),span=buildCommon.makeSpan(classes.concat(newOptions.sizingClasses(options)),[delim],options),delimSizeMultiplier=newOptions.sizeMultiplier/options.sizeMultiplier;return span.height*=delimSizeMultiplier,span.depth*=delimSizeMultiplier,
-span.maxFontSize=newOptions.sizeMultiplier,span},centerSpan=function(span,options,style2){var newOptions=options.havingBaseStyle(style2),shift2=(1-options.sizeMultiplier/newOptions.sizeMultiplier)*options.fontMetrics().axisHeight;span.classes.push("delimcenter"),span.style.top=makeEm(shift2),span.height-=shift2,span.depth+=shift2},makeSmallDelim=function(delim,style2,center,options,mode,classes){var text2=buildCommon.makeSymbol(delim,"Main-Regular",mode,options),span=styleWrap(text2,style2,options,
-classes);return center&&centerSpan(span,options,style2),span},mathrmSize=function(value,size2,mode,options){return buildCommon.makeSymbol(value,"Size"+size2+"-Regular",mode,options)},makeLargeDelim=function(delim,size2,center,options,mode,classes){var inner2=mathrmSize(delim,size2,mode,options),span=styleWrap(buildCommon.makeSpan(["delimsizing","size"+size2],[inner2],options),Style$1.TEXT,options,classes);return center&&centerSpan(span,options,Style$1.TEXT),span},makeGlyphSpan=function(symbol,font,mode){
-var sizeClass;font==="Size1-Regular"?sizeClass="delim-size1":sizeClass="delim-size4";var corner=buildCommon.makeSpan(["delimsizinginner",sizeClass],[buildCommon.makeSpan([],[buildCommon.makeSymbol(symbol,font,mode)])]);return{type:"elem",elem:corner}},makeInner=function(ch,height,options){var width=fontMetricsData["Size4-Regular"][ch.charCodeAt(0)]?fontMetricsData["Size4-Regular"][ch.charCodeAt(0)][4]:fontMetricsData["Size1-Regular"][ch.charCodeAt(0)][4],path2=new PathNode("inner",innerPath(ch,Math.
-round(1e3*height))),svgNode=new SvgNode([path2],{width:makeEm(width),height:makeEm(height),style:"width:"+makeEm(width),viewBox:"0 0 "+1e3*width+" "+Math.round(1e3*height),preserveAspectRatio:"xMinYMin"}),span=buildCommon.makeSvgSpan([],[svgNode],options);return span.height=height,span.style.height=makeEm(height),span.style.width=makeEm(width),{type:"elem",elem:span}},lapInEms=.008,lap={type:"kern",size:-1*lapInEms},verts=["|","\\lvert","\\rvert","\\vert"],doubleVerts=["\\|","\\lVert","\\rVert",
-"\\Vert"],makeStackedDelim=function(delim,heightTotal,center,options,mode,classes){var top,middle,repeat,bottom,svgLabel="",viewBoxWidth=0;top=repeat=bottom=delim,middle=null;var font="Size1-Regular";delim==="\\uparrow"?repeat=bottom="⏐":delim==="\\Uparrow"?repeat=bottom="‖":delim==="\\downarrow"?top=repeat="⏐":delim==="\\Downarrow"?top=repeat="‖":delim==="\\updownarrow"?(top="\\uparrow",repeat="⏐",bottom="\\downarrow"):delim==="\\Updownarrow"?(top="\\Uparrow",repeat="‖",bottom="\\Dow\
-narrow"):utils.contains(verts,delim)?(repeat="∣",svgLabel="vert",viewBoxWidth=333):utils.contains(doubleVerts,delim)?(repeat="∥",svgLabel="doublevert",viewBoxWidth=556):delim==="["||delim==="\\lbrack"?(top="⎡",repeat="⎢",bottom="⎣",font="Size4-Regular",svgLabel="lbrack",viewBoxWidth=667):delim==="]"||delim==="\\rbrack"?(top="⎤",repeat="⎥",bottom="⎦",font="Size4-Regular",svgLabel="rbrack",viewBoxWidth=667):delim==="\\lfloor"||delim==="⌊"?(repeat=top="⎢",bottom="⎣",font="Size\
-4-Regular",svgLabel="lfloor",viewBoxWidth=667):delim==="\\lceil"||delim==="⌈"?(top="⎡",repeat=bottom="⎢",font="Size4-Regular",svgLabel="lceil",viewBoxWidth=667):delim==="\\rfloor"||delim==="⌋"?(repeat=top="⎥",bottom="⎦",font="Size4-Regular",svgLabel="rfloor",viewBoxWidth=667):delim==="\\rceil"||delim==="⌉"?(top="⎤",repeat=bottom="⎥",font="Size4-Regular",svgLabel="rceil",viewBoxWidth=667):delim==="("||delim==="\\lparen"?(top="⎛",repeat="⎜",bottom="⎝",font="Size4-Regular",svgLabel=
-"lparen",viewBoxWidth=875):delim===")"||delim==="\\rparen"?(top="⎞",repeat="⎟",bottom="⎠",font="Size4-Regular",svgLabel="rparen",viewBoxWidth=875):delim==="\\{"||delim==="\\lbrace"?(top="⎧",middle="⎨",bottom="⎩",repeat="⎪",font="Size4-Regular"):delim==="\\}"||delim==="\\rbrace"?(top="⎫",middle="⎬",bottom="⎭",repeat="⎪",font="Size4-Regular"):delim==="\\lgroup"||delim==="⟮"?(top="⎧",bottom="⎩",repeat="⎪",font="Size4-Regular"):delim==="\\rgroup"||delim==="⟯"?(top="⎫",
-bottom="⎭",repeat="⎪",font="Size4-Regular"):delim==="\\lmoustache"||delim==="⎰"?(top="⎧",bottom="⎭",repeat="⎪",font="Size4-Regular"):(delim==="\\rmoustache"||delim==="⎱")&&(top="⎫",bottom="⎩",repeat="⎪",font="Size4-Regular");var topMetrics=getMetrics(top,font,mode),topHeightTotal=topMetrics.height+topMetrics.depth,repeatMetrics=getMetrics(repeat,font,mode),repeatHeightTotal=repeatMetrics.height+repeatMetrics.depth,bottomMetrics=getMetrics(bottom,font,mode),bottomHeightTotal=bottomMetrics.
-height+bottomMetrics.depth,middleHeightTotal=0,middleFactor=1;if(middle!==null){var middleMetrics=getMetrics(middle,font,mode);middleHeightTotal=middleMetrics.height+middleMetrics.depth,middleFactor=2}var minHeight=topHeightTotal+bottomHeightTotal+middleHeightTotal,repeatCount=Math.max(0,Math.ceil((heightTotal-minHeight)/(middleFactor*repeatHeightTotal))),realHeightTotal=minHeight+repeatCount*middleFactor*repeatHeightTotal,axisHeight=options.fontMetrics().axisHeight;center&&(axisHeight*=options.
-sizeMultiplier);var depth=realHeightTotal/2-axisHeight,stack=[];if(svgLabel.length>0){var midHeight=realHeightTotal-topHeightTotal-bottomHeightTotal,viewBoxHeight=Math.round(realHeightTotal*1e3),pathStr=tallDelim(svgLabel,Math.round(midHeight*1e3)),path2=new PathNode(svgLabel,pathStr),width=(viewBoxWidth/1e3).toFixed(3)+"em",height=(viewBoxHeight/1e3).toFixed(3)+"em",svg2=new SvgNode([path2],{width,height,viewBox:"0 0 "+viewBoxWidth+" "+viewBoxHeight}),wrapper=buildCommon.makeSvgSpan([],[svg2],options);
-wrapper.height=viewBoxHeight/1e3,wrapper.style.width=width,wrapper.style.height=height,stack.push({type:"elem",elem:wrapper})}else{if(stack.push(makeGlyphSpan(bottom,font,mode)),stack.push(lap),middle===null){var innerHeight=realHeightTotal-topHeightTotal-bottomHeightTotal+2*lapInEms;stack.push(makeInner(repeat,innerHeight,options))}else{var _innerHeight=(realHeightTotal-topHeightTotal-bottomHeightTotal-middleHeightTotal)/2+2*lapInEms;stack.push(makeInner(repeat,_innerHeight,options)),stack.push(
-lap),stack.push(makeGlyphSpan(middle,font,mode)),stack.push(lap),stack.push(makeInner(repeat,_innerHeight,options))}stack.push(lap),stack.push(makeGlyphSpan(top,font,mode))}var newOptions=options.havingBaseStyle(Style$1.TEXT),inner2=buildCommon.makeVList({positionType:"bottom",positionData:depth,children:stack},newOptions);return styleWrap(buildCommon.makeSpan(["delimsizing","mult"],[inner2],newOptions),Style$1.TEXT,options,classes)},vbPad=80,emPad=.08,sqrtSvg=function(sqrtName,height,viewBoxHeight,extraVinculum,options){
-var path2=sqrtPath(sqrtName,extraVinculum,viewBoxHeight),pathNode=new PathNode(sqrtName,path2),svg2=new SvgNode([pathNode],{width:"400em",height:makeEm(height),viewBox:"0 0 400000 "+viewBoxHeight,preserveAspectRatio:"xMinYMin slice"});return buildCommon.makeSvgSpan(["hide-tail"],[svg2],options)},makeSqrtImage=function(height,options){var newOptions=options.havingBaseSizing(),delim=traverseSequence("\\surd",height*newOptions.sizeMultiplier,stackLargeDelimiterSequence,newOptions),sizeMultiplier=newOptions.
-sizeMultiplier,extraVinculum=Math.max(0,options.minRuleThickness-options.fontMetrics().sqrtRuleThickness),span,spanHeight=0,texHeight=0,viewBoxHeight=0,advanceWidth;return delim.type==="small"?(viewBoxHeight=1e3+1e3*extraVinculum+vbPad,height<1?sizeMultiplier=1:height<1.4&&(sizeMultiplier=.7),spanHeight=(1+extraVinculum+emPad)/sizeMultiplier,texHeight=(1+extraVinculum)/sizeMultiplier,span=sqrtSvg("sqrtMain",spanHeight,viewBoxHeight,extraVinculum,options),span.style.minWidth="0.853em",advanceWidth=
-.833/sizeMultiplier):delim.type==="large"?(viewBoxHeight=(1e3+vbPad)*sizeToMaxHeight[delim.size],texHeight=(sizeToMaxHeight[delim.size]+extraVinculum)/sizeMultiplier,spanHeight=(sizeToMaxHeight[delim.size]+extraVinculum+emPad)/sizeMultiplier,span=sqrtSvg("sqrtSize"+delim.size,spanHeight,viewBoxHeight,extraVinculum,options),span.style.minWidth="1.02em",advanceWidth=1/sizeMultiplier):(spanHeight=height+extraVinculum+emPad,texHeight=height+extraVinculum,viewBoxHeight=Math.floor(1e3*height+extraVinculum)+
-vbPad,span=sqrtSvg("sqrtTall",spanHeight,viewBoxHeight,extraVinculum,options),span.style.minWidth="0.742em",advanceWidth=1.056),span.height=texHeight,span.style.height=makeEm(spanHeight),{span,advanceWidth,ruleWidth:(options.fontMetrics().sqrtRuleThickness+extraVinculum)*sizeMultiplier}},stackLargeDelimiters=["(","\\lparen",")","\\rparen","[","\\lbrack","]","\\rbrack","\\{","\\lbrace","\\}","\\rbrace","\\lfloor","\\rfloor","⌊","⌋","\\lceil","\\rceil","⌈","⌉","\\surd"],stackAlwaysDelimiters=[
-"\\uparrow","\\downarrow","\\updownarrow","\\Uparrow","\\Downarrow","\\Updownarrow","|","\\|","\\vert","\\Vert","\\lvert","\\rvert","\\lVert","\\rVert","\\lgroup","\\rgroup","⟮","⟯","\\lmoustache","\\rmoustache","⎰","⎱"],stackNeverDelimiters=["<",">","\\langle","\\rangle","/","\\backslash","\\lt","\\gt"],sizeToMaxHeight=[0,1.2,1.8,2.4,3],makeSizedDelim=function(delim,size2,options,mode,classes){if(delim==="<"||delim==="\\lt"||delim==="⟨"?delim="\\langle":(delim===">"||delim==="\\gt"||delim===
-"⟩")&&(delim="\\rangle"),utils.contains(stackLargeDelimiters,delim)||utils.contains(stackNeverDelimiters,delim))return makeLargeDelim(delim,size2,!1,options,mode,classes);if(utils.contains(stackAlwaysDelimiters,delim))return makeStackedDelim(delim,sizeToMaxHeight[size2],!1,options,mode,classes);throw new ParseError2("Illegal delimiter: '"+delim+"'")},stackNeverDelimiterSequence=[{type:"small",style:Style$1.SCRIPTSCRIPT},{type:"small",style:Style$1.SCRIPT},{type:"small",style:Style$1.TEXT},{type:"\
-large",size:1},{type:"large",size:2},{type:"large",size:3},{type:"large",size:4}],stackAlwaysDelimiterSequence=[{type:"small",style:Style$1.SCRIPTSCRIPT},{type:"small",style:Style$1.SCRIPT},{type:"small",style:Style$1.TEXT},{type:"stack"}],stackLargeDelimiterSequence=[{type:"small",style:Style$1.SCRIPTSCRIPT},{type:"small",style:Style$1.SCRIPT},{type:"small",style:Style$1.TEXT},{type:"large",size:1},{type:"large",size:2},{type:"large",size:3},{type:"large",size:4},{type:"stack"}],delimTypeToFont=function(type2){
-if(type2.type==="small")return"Main-Regular";if(type2.type==="large")return"Size"+type2.size+"-Regular";if(type2.type==="stack")return"Size4-Regular";throw new Error("Add support for delim type '"+type2.type+"' here.")},traverseSequence=function(delim,height,sequence,options){for(var start2=Math.min(2,3-options.style.size),i=start2;i<sequence.length&&sequence[i].type!=="stack";i++){var metrics=getMetrics(delim,delimTypeToFont(sequence[i]),"math"),heightDepth=metrics.height+metrics.depth;if(sequence[i].
-type==="small"){var newOptions=options.havingBaseStyle(sequence[i].style);heightDepth*=newOptions.sizeMultiplier}if(heightDepth>height)return sequence[i]}return sequence[sequence.length-1]},makeCustomSizedDelim=function(delim,height,center,options,mode,classes){delim==="<"||delim==="\\lt"||delim==="⟨"?delim="\\langle":(delim===">"||delim==="\\gt"||delim==="⟩")&&(delim="\\rangle");var sequence;utils.contains(stackNeverDelimiters,delim)?sequence=stackNeverDelimiterSequence:utils.contains(stackLargeDelimiters,
-delim)?sequence=stackLargeDelimiterSequence:sequence=stackAlwaysDelimiterSequence;var delimType=traverseSequence(delim,height,sequence,options);return delimType.type==="small"?makeSmallDelim(delim,delimType.style,center,options,mode,classes):delimType.type==="large"?makeLargeDelim(delim,delimType.size,center,options,mode,classes):makeStackedDelim(delim,height,center,options,mode,classes)},makeLeftRightDelim=function(delim,height,depth,options,mode,classes){var axisHeight=options.fontMetrics().axisHeight*
-options.sizeMultiplier,delimiterFactor=901,delimiterExtend=5/options.fontMetrics().ptPerEm,maxDistFromAxis=Math.max(height-axisHeight,depth+axisHeight),totalHeight=Math.max(maxDistFromAxis/500*delimiterFactor,2*maxDistFromAxis-delimiterExtend);return makeCustomSizedDelim(delim,totalHeight,!0,options,mode,classes)},delimiter={sqrtImage:makeSqrtImage,sizedDelim:makeSizedDelim,sizeToMaxHeight,customSizedDelim:makeCustomSizedDelim,leftRightDelim:makeLeftRightDelim},delimiterSizes={"\\bigl":{mclass:"\
-mopen",size:1},"\\Bigl":{mclass:"mopen",size:2},"\\biggl":{mclass:"mopen",size:3},"\\Biggl":{mclass:"mopen",size:4},"\\bigr":{mclass:"mclose",size:1},"\\Bigr":{mclass:"mclose",size:2},"\\biggr":{mclass:"mclose",size:3},"\\Biggr":{mclass:"mclose",size:4},"\\bigm":{mclass:"mrel",size:1},"\\Bigm":{mclass:"mrel",size:2},"\\biggm":{mclass:"mrel",size:3},"\\Biggm":{mclass:"mrel",size:4},"\\big":{mclass:"mord",size:1},"\\Big":{mclass:"mord",size:2},"\\bigg":{mclass:"mord",size:3},"\\Bigg":{mclass:"mord",
-size:4}},delimiters=["(","\\lparen",")","\\rparen","[","\\lbrack","]","\\rbrack","\\{","\\lbrace","\\}","\\rbrace","\\lfloor","\\rfloor","⌊","⌋","\\lceil","\\rceil","⌈","⌉","<",">","\\langle","⟨","\\rangle","⟩","\\lt","\\gt","\\lvert","\\rvert","\\lVert","\\rVert","\\lgroup","\\rgroup","⟮","⟯","\\lmoustache","\\rmoustache","⎰","⎱","/","\\backslash","|","\\vert","\\|","\\Vert","\\uparrow","\\Uparrow","\\downarrow","\\Downarrow","\\updownarrow","\\Updownarrow","."];function checkDelimiter(delim,context){
-var symDelim=checkSymbolNodeType(delim);if(symDelim&&utils.contains(delimiters,symDelim.text))return symDelim;throw symDelim?new ParseError2("Invalid delimiter '"+symDelim.text+"' after '"+context.funcName+"'",delim):new ParseError2("Invalid delimiter type '"+delim.type+"'",delim)}defineFunction({type:"delimsizing",names:["\\bigl","\\Bigl","\\biggl","\\Biggl","\\bigr","\\Bigr","\\biggr","\\Biggr","\\bigm","\\Bigm","\\biggm","\\Biggm","\\big","\\Big","\\bigg","\\Bigg"],props:{numArgs:1,argTypes:[
-"primitive"]},handler:(context,args)=>{var delim=checkDelimiter(args[0],context);return{type:"delimsizing",mode:context.parser.mode,size:delimiterSizes[context.funcName].size,mclass:delimiterSizes[context.funcName].mclass,delim:delim.text}},htmlBuilder:(group,options)=>group.delim==="."?buildCommon.makeSpan([group.mclass]):delimiter.sizedDelim(group.delim,group.size,options,group.mode,[group.mclass]),mathmlBuilder:group=>{var children=[];group.delim!=="."&&children.push(makeText(group.delim,group.
-mode));var node2=new mathMLTree.MathNode("mo",children);group.mclass==="mopen"||group.mclass==="mclose"?node2.setAttribute("fence","true"):node2.setAttribute("fence","false"),node2.setAttribute("stretchy","true");var size2=makeEm(delimiter.sizeToMaxHeight[group.size]);return node2.setAttribute("minsize",size2),node2.setAttribute("maxsize",size2),node2}});function assertParsed(group){if(!group.body)throw new Error("Bug: The leftright ParseNode wasn't fully parsed.")}defineFunction({type:"leftrigh\
-t-right",names:["\\right"],props:{numArgs:1,primitive:!0},handler:(context,args)=>{var color=context.parser.gullet.macros.get("\\current@color");if(color&&typeof color!="string")throw new ParseError2("\\current@color set to non-string in \\right");return{type:"leftright-right",mode:context.parser.mode,delim:checkDelimiter(args[0],context).text,color}}});defineFunction({type:"leftright",names:["\\left"],props:{numArgs:1,primitive:!0},handler:(context,args)=>{var delim=checkDelimiter(args[0],context),
-parser=context.parser;++parser.leftrightDepth;var body2=parser.parseExpression(!1);--parser.leftrightDepth,parser.expect("\\right",!1);var right=assertNodeType(parser.parseFunction(),"leftright-right");return{type:"leftright",mode:parser.mode,body:body2,left:delim.text,right:right.delim,rightColor:right.color}},htmlBuilder:(group,options)=>{assertParsed(group);for(var inner2=buildExpression$1(group.body,options,!0,["mopen","mclose"]),innerHeight=0,innerDepth=0,hadMiddle=!1,i=0;i<inner2.length;i++)
-inner2[i].isMiddle?hadMiddle=!0:(innerHeight=Math.max(inner2[i].height,innerHeight),innerDepth=Math.max(inner2[i].depth,innerDepth));innerHeight*=options.sizeMultiplier,innerDepth*=options.sizeMultiplier;var leftDelim;if(group.left==="."?leftDelim=makeNullDelimiter(options,["mopen"]):leftDelim=delimiter.leftRightDelim(group.left,innerHeight,innerDepth,options,group.mode,["mopen"]),inner2.unshift(leftDelim),hadMiddle)for(var _i=1;_i<inner2.length;_i++){var middleDelim=inner2[_i],isMiddle=middleDelim.
-isMiddle;isMiddle&&(inner2[_i]=delimiter.leftRightDelim(isMiddle.delim,innerHeight,innerDepth,isMiddle.options,group.mode,[]))}var rightDelim;if(group.right===".")rightDelim=makeNullDelimiter(options,["mclose"]);else{var colorOptions=group.rightColor?options.withColor(group.rightColor):options;rightDelim=delimiter.leftRightDelim(group.right,innerHeight,innerDepth,colorOptions,group.mode,["mclose"])}return inner2.push(rightDelim),buildCommon.makeSpan(["minner"],inner2,options)},mathmlBuilder:(group,options)=>{
-assertParsed(group);var inner2=buildExpression2(group.body,options);if(group.left!=="."){var leftNode=new mathMLTree.MathNode("mo",[makeText(group.left,group.mode)]);leftNode.setAttribute("fence","true"),inner2.unshift(leftNode)}if(group.right!=="."){var rightNode=new mathMLTree.MathNode("mo",[makeText(group.right,group.mode)]);rightNode.setAttribute("fence","true"),group.rightColor&&rightNode.setAttribute("mathcolor",group.rightColor),inner2.push(rightNode)}return makeRow(inner2)}});defineFunction(
-{type:"middle",names:["\\middle"],props:{numArgs:1,primitive:!0},handler:(context,args)=>{var delim=checkDelimiter(args[0],context);if(!context.parser.leftrightDepth)throw new ParseError2("\\middle without preceding \\left",delim);return{type:"middle",mode:context.parser.mode,delim:delim.text}},htmlBuilder:(group,options)=>{var middleDelim;if(group.delim===".")middleDelim=makeNullDelimiter(options,[]);else{middleDelim=delimiter.sizedDelim(group.delim,1,options,group.mode,[]);var isMiddle={delim:group.
-delim,options};middleDelim.isMiddle=isMiddle}return middleDelim},mathmlBuilder:(group,options)=>{var textNode=group.delim==="\\vert"||group.delim==="|"?makeText("|","text"):makeText(group.delim,group.mode),middleNode=new mathMLTree.MathNode("mo",[textNode]);return middleNode.setAttribute("fence","true"),middleNode.setAttribute("lspace","0.05em"),middleNode.setAttribute("rspace","0.05em"),middleNode}});var htmlBuilder$7=(group,options)=>{var inner2=buildCommon.wrapFragment(buildGroup$1(group.body,
-options),options),label=group.label.slice(1),scale2=options.sizeMultiplier,img,imgShift=0,isSingleChar=utils.isCharacterBox(group.body);if(label==="sout")img=buildCommon.makeSpan(["stretchy","sout"]),img.height=options.fontMetrics().defaultRuleThickness/scale2,imgShift=-.5*options.fontMetrics().xHeight;else if(label==="phase"){var lineWeight=calculateSize({number:.6,unit:"pt"},options),clearance=calculateSize({number:.35,unit:"ex"},options),newOptions=options.havingBaseSizing();scale2=scale2/newOptions.
-sizeMultiplier;var angleHeight=inner2.height+inner2.depth+lineWeight+clearance;inner2.style.paddingLeft=makeEm(angleHeight/2+lineWeight);var viewBoxHeight=Math.floor(1e3*angleHeight*scale2),path2=phasePath(viewBoxHeight),svgNode=new SvgNode([new PathNode("phase",path2)],{width:"400em",height:makeEm(viewBoxHeight/1e3),viewBox:"0 0 400000 "+viewBoxHeight,preserveAspectRatio:"xMinYMin slice"});img=buildCommon.makeSvgSpan(["hide-tail"],[svgNode],options),img.style.height=makeEm(angleHeight),imgShift=
-inner2.depth+lineWeight+clearance}else{/cancel/.test(label)?isSingleChar||inner2.classes.push("cancel-pad"):label==="angl"?inner2.classes.push("anglpad"):inner2.classes.push("boxpad");var topPad=0,bottomPad=0,ruleThickness=0;/box/.test(label)?(ruleThickness=Math.max(options.fontMetrics().fboxrule,options.minRuleThickness),topPad=options.fontMetrics().fboxsep+(label==="colorbox"?0:ruleThickness),bottomPad=topPad):label==="angl"?(ruleThickness=Math.max(options.fontMetrics().defaultRuleThickness,options.
-minRuleThickness),topPad=4*ruleThickness,bottomPad=Math.max(0,.25-inner2.depth)):(topPad=isSingleChar?.2:0,bottomPad=topPad),img=stretchy.encloseSpan(inner2,label,topPad,bottomPad,options),/fbox|boxed|fcolorbox/.test(label)?(img.style.borderStyle="solid",img.style.borderWidth=makeEm(ruleThickness)):label==="angl"&&ruleThickness!==.049&&(img.style.borderTopWidth=makeEm(ruleThickness),img.style.borderRightWidth=makeEm(ruleThickness)),imgShift=inner2.depth+bottomPad,group.backgroundColor&&(img.style.
-backgroundColor=group.backgroundColor,group.borderColor&&(img.style.borderColor=group.borderColor))}var vlist;if(group.backgroundColor)vlist=buildCommon.makeVList({positionType:"individualShift",children:[{type:"elem",elem:img,shift:imgShift},{type:"elem",elem:inner2,shift:0}]},options);else{var classes=/cancel|phase/.test(label)?["svg-align"]:[];vlist=buildCommon.makeVList({positionType:"individualShift",children:[{type:"elem",elem:inner2,shift:0},{type:"elem",elem:img,shift:imgShift,wrapperClasses:classes}]},
-options)}return/cancel/.test(label)&&(vlist.height=inner2.height,vlist.depth=inner2.depth),/cancel/.test(label)&&!isSingleChar?buildCommon.makeSpan(["mord","cancel-lap"],[vlist],options):buildCommon.makeSpan(["mord"],[vlist],options)},mathmlBuilder$6=(group,options)=>{var fboxsep=0,node2=new mathMLTree.MathNode(group.label.indexOf("colorbox")>-1?"mpadded":"menclose",[buildGroup2(group.body,options)]);switch(group.label){case"\\cancel":node2.setAttribute("notation","updiagonalstrike");break;case"\
-\\bcancel":node2.setAttribute("notation","downdiagonalstrike");break;case"\\phase":node2.setAttribute("notation","phasorangle");break;case"\\sout":node2.setAttribute("notation","horizontalstrike");break;case"\\fbox":node2.setAttribute("notation","box");break;case"\\angl":node2.setAttribute("notation","actuarial");break;case"\\fcolorbox":case"\\colorbox":if(fboxsep=options.fontMetrics().fboxsep*options.fontMetrics().ptPerEm,node2.setAttribute("width","+"+2*fboxsep+"pt"),node2.setAttribute("height",
-"+"+2*fboxsep+"pt"),node2.setAttribute("lspace",fboxsep+"pt"),node2.setAttribute("voffset",fboxsep+"pt"),group.label==="\\fcolorbox"){var thk=Math.max(options.fontMetrics().fboxrule,options.minRuleThickness);node2.setAttribute("style","border: "+thk+"em solid "+String(group.borderColor))}break;case"\\xcancel":node2.setAttribute("notation","updiagonalstrike downdiagonalstrike");break}return group.backgroundColor&&node2.setAttribute("mathbackground",group.backgroundColor),node2};defineFunction({type:"\
-enclose",names:["\\colorbox"],props:{numArgs:2,allowedInText:!0,argTypes:["color","text"]},handler(_ref,args,optArgs){var{parser,funcName}=_ref,color=assertNodeType(args[0],"color-token").color,body2=args[1];return{type:"enclose",mode:parser.mode,label:funcName,backgroundColor:color,body:body2}},htmlBuilder:htmlBuilder$7,mathmlBuilder:mathmlBuilder$6});defineFunction({type:"enclose",names:["\\fcolorbox"],props:{numArgs:3,allowedInText:!0,argTypes:["color","color","text"]},handler(_ref2,args,optArgs){
-var{parser,funcName}=_ref2,borderColor=assertNodeType(args[0],"color-token").color,backgroundColor=assertNodeType(args[1],"color-token").color,body2=args[2];return{type:"enclose",mode:parser.mode,label:funcName,backgroundColor,borderColor,body:body2}},htmlBuilder:htmlBuilder$7,mathmlBuilder:mathmlBuilder$6});defineFunction({type:"enclose",names:["\\fbox"],props:{numArgs:1,argTypes:["hbox"],allowedInText:!0},handler(_ref3,args){var{parser}=_ref3;return{type:"enclose",mode:parser.mode,label:"\\fbox",
-body:args[0]}}});defineFunction({type:"enclose",names:["\\cancel","\\bcancel","\\xcancel","\\sout","\\phase"],props:{numArgs:1},handler(_ref4,args){var{parser,funcName}=_ref4,body2=args[0];return{type:"enclose",mode:parser.mode,label:funcName,body:body2}},htmlBuilder:htmlBuilder$7,mathmlBuilder:mathmlBuilder$6});defineFunction({type:"enclose",names:["\\angl"],props:{numArgs:1,argTypes:["hbox"],allowedInText:!1},handler(_ref5,args){var{parser}=_ref5;return{type:"enclose",mode:parser.mode,label:"\\\
-angl",body:args[0]}}});var _environments={};function defineEnvironment(_ref){for(var{type:type2,names:names2,props,handler,htmlBuilder:htmlBuilder3,mathmlBuilder:mathmlBuilder3}=_ref,data={type:type2,numArgs:props.numArgs||0,allowedInText:!1,numOptionalArgs:0,handler},i=0;i<names2.length;++i)_environments[names2[i]]=data;htmlBuilder3&&(_htmlGroupBuilders[type2]=htmlBuilder3),mathmlBuilder3&&(_mathmlGroupBuilders[type2]=mathmlBuilder3)}var _macros={};function defineMacro(name,body2){_macros[name]=
-body2}function getHLines(parser){var hlineInfo=[];parser.consumeSpaces();var nxt=parser.fetch().text;for(nxt==="\\relax"&&(parser.consume(),parser.consumeSpaces(),nxt=parser.fetch().text);nxt==="\\hline"||nxt==="\\hdashline";)parser.consume(),hlineInfo.push(nxt==="\\hdashline"),parser.consumeSpaces(),nxt=parser.fetch().text;return hlineInfo}var validateAmsEnvironmentContext=context=>{var settings=context.parser.settings;if(!settings.displayMode)throw new ParseError2("{"+context.envName+"} can be\
- used only in display mode.")};function getAutoTag(name){if(name.indexOf("ed")===-1)return name.indexOf("*")===-1}function parseArray(parser,_ref,style2){var{hskipBeforeAndAfter,addJot,cols,arraystretch,colSeparationType,autoTag,singleRow,emptySingleRow,maxNumCols,leqno}=_ref;if(parser.gullet.beginGroup(),singleRow||parser.gullet.macros.set("\\cr","\\\\\\relax"),!arraystretch){var stretch=parser.gullet.expandMacroAsText("\\arraystretch");if(stretch==null)arraystretch=1;else if(arraystretch=parseFloat(
-stretch),!arraystretch||arraystretch<0)throw new ParseError2("Invalid \\arraystretch: "+stretch)}parser.gullet.beginGroup();var row2=[],body2=[row2],rowGaps=[],hLinesBeforeRow=[],tags=autoTag!=null?[]:void 0;function beginRow(){autoTag&&parser.gullet.macros.set("\\@eqnsw","1",!0)}function endRow(){tags&&(parser.gullet.macros.get("\\df@tag")?(tags.push(parser.subparse([new Token("\\df@tag")])),parser.gullet.macros.set("\\df@tag",void 0,!0)):tags.push(!!autoTag&&parser.gullet.macros.get("\\@eqnsw")===
-"1"))}for(beginRow(),hLinesBeforeRow.push(getHLines(parser));;){var cell2=parser.parseExpression(!1,singleRow?"\\end":"\\\\");parser.gullet.endGroup(),parser.gullet.beginGroup(),cell2={type:"ordgroup",mode:parser.mode,body:cell2},style2&&(cell2={type:"styling",mode:parser.mode,style:style2,body:[cell2]}),row2.push(cell2);var next2=parser.fetch().text;if(next2==="&"){if(maxNumCols&&row2.length===maxNumCols){if(singleRow||colSeparationType)throw new ParseError2("Too many tab characters: &",parser.
-nextToken);parser.settings.reportNonstrict("textEnv","Too few columns specified in the {array} column argument.")}parser.consume()}else if(next2==="\\end"){endRow(),row2.length===1&&cell2.type==="styling"&&cell2.body[0].body.length===0&&(body2.length>1||!emptySingleRow)&&body2.pop(),hLinesBeforeRow.length<body2.length+1&&hLinesBeforeRow.push([]);break}else if(next2==="\\\\"){parser.consume();var size2=void 0;parser.gullet.future().text!==" "&&(size2=parser.parseSizeGroup(!0)),rowGaps.push(size2?
-size2.value:null),endRow(),hLinesBeforeRow.push(getHLines(parser)),row2=[],body2.push(row2),beginRow()}else throw new ParseError2("Expected & or \\\\ or \\cr or \\end",parser.nextToken)}return parser.gullet.endGroup(),parser.gullet.endGroup(),{type:"array",mode:parser.mode,addJot,arraystretch,body:body2,cols,rowGaps,hskipBeforeAndAfter,hLinesBeforeRow,colSeparationType,tags,leqno}}function dCellStyle(envName){return envName.slice(0,1)==="d"?"display":"text"}var htmlBuilder$6=function(group,options){
-var r2,c2,nr=group.body.length,hLinesBeforeRow=group.hLinesBeforeRow,nc=0,body2=new Array(nr),hlines=[],ruleThickness=Math.max(options.fontMetrics().arrayRuleWidth,options.minRuleThickness),pt=1/options.fontMetrics().ptPerEm,arraycolsep=5*pt;if(group.colSeparationType&&group.colSeparationType==="small"){var localMultiplier=options.havingStyle(Style$1.SCRIPT).sizeMultiplier;arraycolsep=.2778*(localMultiplier/options.sizeMultiplier)}var baselineskip=group.colSeparationType==="CD"?calculateSize({number:3,
-unit:"ex"},options):12*pt,jot=3*pt,arrayskip=group.arraystretch*baselineskip,arstrutHeight=.7*arrayskip,arstrutDepth=.3*arrayskip,totalHeight=0;function setHLinePos(hlinesInGap){for(var i=0;i<hlinesInGap.length;++i)i>0&&(totalHeight+=.25),hlines.push({pos:totalHeight,isDashed:hlinesInGap[i]})}for(setHLinePos(hLinesBeforeRow[0]),r2=0;r2<group.body.length;++r2){var inrow=group.body[r2],height=arstrutHeight,depth=arstrutDepth;nc<inrow.length&&(nc=inrow.length);var outrow=new Array(inrow.length);for(c2=
-0;c2<inrow.length;++c2){var elt=buildGroup$1(inrow[c2],options);depth<elt.depth&&(depth=elt.depth),height<elt.height&&(height=elt.height),outrow[c2]=elt}var rowGap=group.rowGaps[r2],gap=0;rowGap&&(gap=calculateSize(rowGap,options),gap>0&&(gap+=arstrutDepth,depth<gap&&(depth=gap),gap=0)),group.addJot&&(depth+=jot),outrow.height=height,outrow.depth=depth,totalHeight+=height,outrow.pos=totalHeight,totalHeight+=depth+gap,body2[r2]=outrow,setHLinePos(hLinesBeforeRow[r2+1])}var offset2=totalHeight/2+options.
-fontMetrics().axisHeight,colDescriptions=group.cols||[],cols=[],colSep,colDescrNum,tagSpans=[];if(group.tags&&group.tags.some(tag2=>tag2))for(r2=0;r2<nr;++r2){var rw=body2[r2],shift2=rw.pos-offset2,tag=group.tags[r2],tagSpan=void 0;tag===!0?tagSpan=buildCommon.makeSpan(["eqn-num"],[],options):tag===!1?tagSpan=buildCommon.makeSpan([],[],options):tagSpan=buildCommon.makeSpan([],buildExpression$1(tag,options,!0),options),tagSpan.depth=rw.depth,tagSpan.height=rw.height,tagSpans.push({type:"elem",elem:tagSpan,
-shift:shift2})}for(c2=0,colDescrNum=0;c2<nc||colDescrNum<colDescriptions.length;++c2,++colDescrNum){for(var colDescr=colDescriptions[colDescrNum]||{},firstSeparator=!0;colDescr.type==="separator";){if(firstSeparator||(colSep=buildCommon.makeSpan(["arraycolsep"],[]),colSep.style.width=makeEm(options.fontMetrics().doubleRuleSep),cols.push(colSep)),colDescr.separator==="|"||colDescr.separator===":"){var lineType=colDescr.separator==="|"?"solid":"dashed",separator=buildCommon.makeSpan(["vertical-sep\
-arator"],[],options);separator.style.height=makeEm(totalHeight),separator.style.borderRightWidth=makeEm(ruleThickness),separator.style.borderRightStyle=lineType,separator.style.margin="0 "+makeEm(-ruleThickness/2);var _shift=totalHeight-offset2;_shift&&(separator.style.verticalAlign=makeEm(-_shift)),cols.push(separator)}else throw new ParseError2("Invalid separator type: "+colDescr.separator);colDescrNum++,colDescr=colDescriptions[colDescrNum]||{},firstSeparator=!1}if(!(c2>=nc)){var sepwidth=void 0;
-(c2>0||group.hskipBeforeAndAfter)&&(sepwidth=utils.deflt(colDescr.pregap,arraycolsep),sepwidth!==0&&(colSep=buildCommon.makeSpan(["arraycolsep"],[]),colSep.style.width=makeEm(sepwidth),cols.push(colSep)));var col=[];for(r2=0;r2<nr;++r2){var row2=body2[r2],elem=row2[c2];if(elem){var _shift2=row2.pos-offset2;elem.depth=row2.depth,elem.height=row2.height,col.push({type:"elem",elem,shift:_shift2})}}col=buildCommon.makeVList({positionType:"individualShift",children:col},options),col=buildCommon.makeSpan(
-["col-align-"+(colDescr.align||"c")],[col]),cols.push(col),(c2<nc-1||group.hskipBeforeAndAfter)&&(sepwidth=utils.deflt(colDescr.postgap,arraycolsep),sepwidth!==0&&(colSep=buildCommon.makeSpan(["arraycolsep"],[]),colSep.style.width=makeEm(sepwidth),cols.push(colSep)))}}if(body2=buildCommon.makeSpan(["mtable"],cols),hlines.length>0){for(var line=buildCommon.makeLineSpan("hline",options,ruleThickness),dashes=buildCommon.makeLineSpan("hdashline",options,ruleThickness),vListElems=[{type:"elem",elem:body2,
-shift:0}];hlines.length>0;){var hline=hlines.pop(),lineShift=hline.pos-offset2;hline.isDashed?vListElems.push({type:"elem",elem:dashes,shift:lineShift}):vListElems.push({type:"elem",elem:line,shift:lineShift})}body2=buildCommon.makeVList({positionType:"individualShift",children:vListElems},options)}if(tagSpans.length===0)return buildCommon.makeSpan(["mord"],[body2],options);var eqnNumCol=buildCommon.makeVList({positionType:"individualShift",children:tagSpans},options);return eqnNumCol=buildCommon.
-makeSpan(["tag"],[eqnNumCol],options),buildCommon.makeFragment([body2,eqnNumCol])},alignMap={c:"center ",l:"left ",r:"right "},mathmlBuilder$5=function(group,options){for(var tbl=[],glue=new mathMLTree.MathNode("mtd",[],["mtr-glue"]),tag=new mathMLTree.MathNode("mtd",[],["mml-eqn-num"]),i=0;i<group.body.length;i++){for(var rw=group.body[i],row2=[],j=0;j<rw.length;j++)row2.push(new mathMLTree.MathNode("mtd",[buildGroup2(rw[j],options)]));group.tags&&group.tags[i]&&(row2.unshift(glue),row2.push(glue),
-group.leqno?row2.unshift(tag):row2.push(tag)),tbl.push(new mathMLTree.MathNode("mtr",row2))}var table2=new mathMLTree.MathNode("mtable",tbl),gap=group.arraystretch===.5?.1:.16+group.arraystretch-1+(group.addJot?.09:0);table2.setAttribute("rowspacing",makeEm(gap));var menclose="",align="";if(group.cols&&group.cols.length>0){var cols=group.cols,columnLines="",prevTypeWasAlign=!1,iStart=0,iEnd=cols.length;cols[0].type==="separator"&&(menclose+="top ",iStart=1),cols[cols.length-1].type==="separator"&&
-(menclose+="bottom ",iEnd-=1);for(var _i=iStart;_i<iEnd;_i++)cols[_i].type==="align"?(align+=alignMap[cols[_i].align],prevTypeWasAlign&&(columnLines+="none "),prevTypeWasAlign=!0):cols[_i].type==="separator"&&prevTypeWasAlign&&(columnLines+=cols[_i].separator==="|"?"solid ":"dashed ",prevTypeWasAlign=!1);table2.setAttribute("columnalign",align.trim()),/[sd]/.test(columnLines)&&table2.setAttribute("columnlines",columnLines.trim())}if(group.colSeparationType==="align"){for(var _cols=group.cols||[],
-spacing2="",_i2=1;_i2<_cols.length;_i2++)spacing2+=_i2%2?"0em ":"1em ";table2.setAttribute("columnspacing",spacing2.trim())}else group.colSeparationType==="alignat"||group.colSeparationType==="gather"?table2.setAttribute("columnspacing","0em"):group.colSeparationType==="small"?table2.setAttribute("columnspacing","0.2778em"):group.colSeparationType==="CD"?table2.setAttribute("columnspacing","0.5em"):table2.setAttribute("columnspacing","1em");var rowLines="",hlines=group.hLinesBeforeRow;menclose+=
-hlines[0].length>0?"left ":"",menclose+=hlines[hlines.length-1].length>0?"right ":"";for(var _i3=1;_i3<hlines.length-1;_i3++)rowLines+=hlines[_i3].length===0?"none ":hlines[_i3][0]?"dashed ":"solid ";return/[sd]/.test(rowLines)&&table2.setAttribute("rowlines",rowLines.trim()),menclose!==""&&(table2=new mathMLTree.MathNode("menclose",[table2]),table2.setAttribute("notation",menclose.trim())),group.arraystretch&&group.arraystretch<1&&(table2=new mathMLTree.MathNode("mstyle",[table2]),table2.setAttribute(
-"scriptlevel","1")),table2},alignedHandler=function(context,args){context.envName.indexOf("ed")===-1&&validateAmsEnvironmentContext(context);var cols=[],separationType=context.envName.indexOf("at")>-1?"alignat":"align",isSplit=context.envName==="split",res=parseArray(context.parser,{cols,addJot:!0,autoTag:isSplit?void 0:getAutoTag(context.envName),emptySingleRow:!0,colSeparationType:separationType,maxNumCols:isSplit?2:void 0,leqno:context.parser.settings.leqno},"display"),numMaths,numCols=0,emptyGroup={
-type:"ordgroup",mode:context.mode,body:[]};if(args[0]&&args[0].type==="ordgroup"){for(var arg0="",i=0;i<args[0].body.length;i++){var textord2=assertNodeType(args[0].body[i],"textord");arg0+=textord2.text}numMaths=Number(arg0),numCols=numMaths*2}var isAligned=!numCols;res.body.forEach(function(row2){for(var _i4=1;_i4<row2.length;_i4+=2){var styling=assertNodeType(row2[_i4],"styling"),ordgroup=assertNodeType(styling.body[0],"ordgroup");ordgroup.body.unshift(emptyGroup)}if(isAligned)numCols<row2.length&&
-(numCols=row2.length);else{var curMaths=row2.length/2;if(numMaths<curMaths)throw new ParseError2("Too many math in a row: "+("expected "+numMaths+", but got "+curMaths),row2[0])}});for(var _i5=0;_i5<numCols;++_i5){var align="r",pregap=0;_i5%2===1?align="l":_i5>0&&isAligned&&(pregap=1),cols[_i5]={type:"align",align,pregap,postgap:0}}return res.colSeparationType=isAligned?"align":"alignat",res};defineEnvironment({type:"array",names:["array","darray"],props:{numArgs:1},handler(context,args){var symNode=checkSymbolNodeType(
-args[0]),colalign=symNode?[args[0]]:assertNodeType(args[0],"ordgroup").body,cols=colalign.map(function(nde){var node2=assertSymbolNodeType(nde),ca=node2.text;if("lcr".indexOf(ca)!==-1)return{type:"align",align:ca};if(ca==="|")return{type:"separator",separator:"|"};if(ca===":")return{type:"separator",separator:":"};throw new ParseError2("Unknown column alignment: "+ca,nde)}),res={cols,hskipBeforeAndAfter:!0,maxNumCols:cols.length};return parseArray(context.parser,res,dCellStyle(context.envName))},
-htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});defineEnvironment({type:"array",names:["matrix","pmatrix","bmatrix","Bmatrix","vmatrix","Vmatrix","matrix*","pmatrix*","bmatrix*","Bmatrix*","vmatrix*","Vmatrix*"],props:{numArgs:0},handler(context){var delimiters2={matrix:null,pmatrix:["(",")"],bmatrix:["[","]"],Bmatrix:["\\{","\\}"],vmatrix:["|","|"],Vmatrix:["\\Vert","\\Vert"]}[context.envName.replace("*","")],colAlign="c",payload={hskipBeforeAndAfter:!1,cols:[{type:"align",align:colAlign}]};
-if(context.envName.charAt(context.envName.length-1)==="*"){var parser=context.parser;if(parser.consumeSpaces(),parser.fetch().text==="["){if(parser.consume(),parser.consumeSpaces(),colAlign=parser.fetch().text,"lcr".indexOf(colAlign)===-1)throw new ParseError2("Expected l or c or r",parser.nextToken);parser.consume(),parser.consumeSpaces(),parser.expect("]"),parser.consume(),payload.cols=[{type:"align",align:colAlign}]}}var res=parseArray(context.parser,payload,dCellStyle(context.envName)),numCols=Math.
-max(0,...res.body.map(row2=>row2.length));return res.cols=new Array(numCols).fill({type:"align",align:colAlign}),delimiters2?{type:"leftright",mode:context.mode,body:[res],left:delimiters2[0],right:delimiters2[1],rightColor:void 0}:res},htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});defineEnvironment({type:"array",names:["smallmatrix"],props:{numArgs:0},handler(context){var payload={arraystretch:.5},res=parseArray(context.parser,payload,"script");return res.colSeparationType="small",res},
-htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});defineEnvironment({type:"array",names:["subarray"],props:{numArgs:1},handler(context,args){var symNode=checkSymbolNodeType(args[0]),colalign=symNode?[args[0]]:assertNodeType(args[0],"ordgroup").body,cols=colalign.map(function(nde){var node2=assertSymbolNodeType(nde),ca=node2.text;if("lc".indexOf(ca)!==-1)return{type:"align",align:ca};throw new ParseError2("Unknown column alignment: "+ca,nde)});if(cols.length>1)throw new ParseError2("{suba\
-rray} can contain only one column");var res={cols,hskipBeforeAndAfter:!1,arraystretch:.5};if(res=parseArray(context.parser,res,"script"),res.body.length>0&&res.body[0].length>1)throw new ParseError2("{subarray} can contain only one column");return res},htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});defineEnvironment({type:"array",names:["cases","dcases","rcases","drcases"],props:{numArgs:0},handler(context){var payload={arraystretch:1.2,cols:[{type:"align",align:"l",pregap:0,postgap:1},
-{type:"align",align:"l",pregap:0,postgap:0}]},res=parseArray(context.parser,payload,dCellStyle(context.envName));return{type:"leftright",mode:context.mode,body:[res],left:context.envName.indexOf("r")>-1?".":"\\{",right:context.envName.indexOf("r")>-1?"\\}":".",rightColor:void 0}},htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});defineEnvironment({type:"array",names:["align","align*","aligned","split"],props:{numArgs:0},handler:alignedHandler,htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});
-defineEnvironment({type:"array",names:["gathered","gather","gather*"],props:{numArgs:0},handler(context){utils.contains(["gather","gather*"],context.envName)&&validateAmsEnvironmentContext(context);var res={cols:[{type:"align",align:"c"}],addJot:!0,colSeparationType:"gather",autoTag:getAutoTag(context.envName),emptySingleRow:!0,leqno:context.parser.settings.leqno};return parseArray(context.parser,res,"display")},htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});defineEnvironment({type:"a\
-rray",names:["alignat","alignat*","alignedat"],props:{numArgs:1},handler:alignedHandler,htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});defineEnvironment({type:"array",names:["equation","equation*"],props:{numArgs:0},handler(context){validateAmsEnvironmentContext(context);var res={autoTag:getAutoTag(context.envName),emptySingleRow:!0,singleRow:!0,maxNumCols:1,leqno:context.parser.settings.leqno};return parseArray(context.parser,res,"display")},htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});
-defineEnvironment({type:"array",names:["CD"],props:{numArgs:0},handler(context){return validateAmsEnvironmentContext(context),parseCD(context.parser)},htmlBuilder:htmlBuilder$6,mathmlBuilder:mathmlBuilder$5});defineMacro("\\nonumber","\\gdef\\@eqnsw{0}");defineMacro("\\notag","\\nonumber");defineFunction({type:"text",names:["\\hline","\\hdashline"],props:{numArgs:0,allowedInText:!0,allowedInMath:!0},handler(context,args){throw new ParseError2(context.funcName+" valid only within array environmen\
-t")}});var environments=_environments;defineFunction({type:"environment",names:["\\begin","\\end"],props:{numArgs:1,argTypes:["text"]},handler(_ref,args){var{parser,funcName}=_ref,nameGroup=args[0];if(nameGroup.type!=="ordgroup")throw new ParseError2("Invalid environment name",nameGroup);for(var envName="",i=0;i<nameGroup.body.length;++i)envName+=assertNodeType(nameGroup.body[i],"textord").text;if(funcName==="\\begin"){if(!environments.hasOwnProperty(envName))throw new ParseError2("No such envir\
-onment: "+envName,nameGroup);var env2=environments[envName],{args:_args,optArgs}=parser.parseArguments("\\begin{"+envName+"}",env2),context={mode:parser.mode,envName,parser},result=env2.handler(context,_args,optArgs);parser.expect("\\end",!1);var endNameToken=parser.nextToken,end=assertNodeType(parser.parseFunction(),"environment");if(end.name!==envName)throw new ParseError2("Mismatch: \\begin{"+envName+"} matched by \\end{"+end.name+"}",endNameToken);return result}return{type:"environment",mode:parser.
-mode,name:envName,nameGroup}}});var htmlBuilder$5=(group,options)=>{var font=group.font,newOptions=options.withFont(font);return buildGroup$1(group.body,newOptions)},mathmlBuilder$4=(group,options)=>{var font=group.font,newOptions=options.withFont(font);return buildGroup2(group.body,newOptions)},fontAliases={"\\Bbb":"\\mathbb","\\bold":"\\mathbf","\\frak":"\\mathfrak","\\bm":"\\boldsymbol"};defineFunction({type:"font",names:["\\mathrm","\\mathit","\\mathbf","\\mathnormal","\\mathsfit","\\mathbb",
-"\\mathcal","\\mathfrak","\\mathscr","\\mathsf","\\mathtt","\\Bbb","\\bold","\\frak"],props:{numArgs:1,allowedInArgument:!0},handler:(_ref,args)=>{var{parser,funcName}=_ref,body2=normalizeArgument(args[0]),func=funcName;return func in fontAliases&&(func=fontAliases[func]),{type:"font",mode:parser.mode,font:func.slice(1),body:body2}},htmlBuilder:htmlBuilder$5,mathmlBuilder:mathmlBuilder$4});defineFunction({type:"mclass",names:["\\boldsymbol","\\bm"],props:{numArgs:1},handler:(_ref2,args)=>{var{parser}=_ref2,
-body2=args[0],isCharacterBox3=utils.isCharacterBox(body2);return{type:"mclass",mode:parser.mode,mclass:binrelClass(body2),body:[{type:"font",mode:parser.mode,font:"boldsymbol",body:body2}],isCharacterBox:isCharacterBox3}}});defineFunction({type:"font",names:["\\rm","\\sf","\\tt","\\bf","\\it","\\cal"],props:{numArgs:0,allowedInText:!0},handler:(_ref3,args)=>{var{parser,funcName,breakOnTokenText}=_ref3,{mode}=parser,body2=parser.parseExpression(!0,breakOnTokenText),style2="math"+funcName.slice(1);
-return{type:"font",mode,font:style2,body:{type:"ordgroup",mode:parser.mode,body:body2}}},htmlBuilder:htmlBuilder$5,mathmlBuilder:mathmlBuilder$4});var adjustStyle=(size2,originalStyle)=>{var style2=originalStyle;return size2==="display"?style2=style2.id>=Style$1.SCRIPT.id?style2.text():Style$1.DISPLAY:size2==="text"&&style2.size===Style$1.DISPLAY.size?style2=Style$1.TEXT:size2==="script"?style2=Style$1.SCRIPT:size2==="scriptscript"&&(style2=Style$1.SCRIPTSCRIPT),style2},htmlBuilder$4=(group,options)=>{
-var style2=adjustStyle(group.size,options.style),nstyle=style2.fracNum(),dstyle=style2.fracDen(),newOptions;newOptions=options.havingStyle(nstyle);var numerm=buildGroup$1(group.numer,newOptions,options);if(group.continued){var hStrut=8.5/options.fontMetrics().ptPerEm,dStrut=3.5/options.fontMetrics().ptPerEm;numerm.height=numerm.height<hStrut?hStrut:numerm.height,numerm.depth=numerm.depth<dStrut?dStrut:numerm.depth}newOptions=options.havingStyle(dstyle);var denomm=buildGroup$1(group.denom,newOptions,
-options),rule,ruleWidth,ruleSpacing;group.hasBarLine?(group.barSize?(ruleWidth=calculateSize(group.barSize,options),rule=buildCommon.makeLineSpan("frac-line",options,ruleWidth)):rule=buildCommon.makeLineSpan("frac-line",options),ruleWidth=rule.height,ruleSpacing=rule.height):(rule=null,ruleWidth=0,ruleSpacing=options.fontMetrics().defaultRuleThickness);var numShift,clearance,denomShift;style2.size===Style$1.DISPLAY.size||group.size==="display"?(numShift=options.fontMetrics().num1,ruleWidth>0?clearance=
-3*ruleSpacing:clearance=7*ruleSpacing,denomShift=options.fontMetrics().denom1):(ruleWidth>0?(numShift=options.fontMetrics().num2,clearance=ruleSpacing):(numShift=options.fontMetrics().num3,clearance=3*ruleSpacing),denomShift=options.fontMetrics().denom2);var frac2;if(rule){var axisHeight=options.fontMetrics().axisHeight;numShift-numerm.depth-(axisHeight+.5*ruleWidth)<clearance&&(numShift+=clearance-(numShift-numerm.depth-(axisHeight+.5*ruleWidth))),axisHeight-.5*ruleWidth-(denomm.height-denomShift)<
-clearance&&(denomShift+=clearance-(axisHeight-.5*ruleWidth-(denomm.height-denomShift)));var midShift=-(axisHeight-.5*ruleWidth);frac2=buildCommon.makeVList({positionType:"individualShift",children:[{type:"elem",elem:denomm,shift:denomShift},{type:"elem",elem:rule,shift:midShift},{type:"elem",elem:numerm,shift:-numShift}]},options)}else{var candidateClearance=numShift-numerm.depth-(denomm.height-denomShift);candidateClearance<clearance&&(numShift+=.5*(clearance-candidateClearance),denomShift+=.5*
-(clearance-candidateClearance)),frac2=buildCommon.makeVList({positionType:"individualShift",children:[{type:"elem",elem:denomm,shift:denomShift},{type:"elem",elem:numerm,shift:-numShift}]},options)}newOptions=options.havingStyle(style2),frac2.height*=newOptions.sizeMultiplier/options.sizeMultiplier,frac2.depth*=newOptions.sizeMultiplier/options.sizeMultiplier;var delimSize;style2.size===Style$1.DISPLAY.size?delimSize=options.fontMetrics().delim1:style2.size===Style$1.SCRIPTSCRIPT.size?delimSize=
-options.havingStyle(Style$1.SCRIPT).fontMetrics().delim2:delimSize=options.fontMetrics().delim2;var leftDelim,rightDelim;return group.leftDelim==null?leftDelim=makeNullDelimiter(options,["mopen"]):leftDelim=delimiter.customSizedDelim(group.leftDelim,delimSize,!0,options.havingStyle(style2),group.mode,["mopen"]),group.continued?rightDelim=buildCommon.makeSpan([]):group.rightDelim==null?rightDelim=makeNullDelimiter(options,["mclose"]):rightDelim=delimiter.customSizedDelim(group.rightDelim,delimSize,
-!0,options.havingStyle(style2),group.mode,["mclose"]),buildCommon.makeSpan(["mord"].concat(newOptions.sizingClasses(options)),[leftDelim,buildCommon.makeSpan(["mfrac"],[frac2]),rightDelim],options)},mathmlBuilder$3=(group,options)=>{var node2=new mathMLTree.MathNode("mfrac",[buildGroup2(group.numer,options),buildGroup2(group.denom,options)]);if(!group.hasBarLine)node2.setAttribute("linethickness","0px");else if(group.barSize){var ruleWidth=calculateSize(group.barSize,options);node2.setAttribute(
-"linethickness",makeEm(ruleWidth))}var style2=adjustStyle(group.size,options.style);if(style2.size!==options.style.size){node2=new mathMLTree.MathNode("mstyle",[node2]);var isDisplay=style2.size===Style$1.DISPLAY.size?"true":"false";node2.setAttribute("displaystyle",isDisplay),node2.setAttribute("scriptlevel","0")}if(group.leftDelim!=null||group.rightDelim!=null){var withDelims=[];if(group.leftDelim!=null){var leftOp=new mathMLTree.MathNode("mo",[new mathMLTree.TextNode(group.leftDelim.replace("\
-\\",""))]);leftOp.setAttribute("fence","true"),withDelims.push(leftOp)}if(withDelims.push(node2),group.rightDelim!=null){var rightOp=new mathMLTree.MathNode("mo",[new mathMLTree.TextNode(group.rightDelim.replace("\\",""))]);rightOp.setAttribute("fence","true"),withDelims.push(rightOp)}return makeRow(withDelims)}return node2};defineFunction({type:"genfrac",names:["\\dfrac","\\frac","\\tfrac","\\dbinom","\\binom","\\tbinom","\\\\atopfrac","\\\\bracefrac","\\\\brackfrac"],props:{numArgs:2,allowedInArgument:!0},
-handler:(_ref,args)=>{var{parser,funcName}=_ref,numer=args[0],denom=args[1],hasBarLine,leftDelim=null,rightDelim=null,size2="auto";switch(funcName){case"\\dfrac":case"\\frac":case"\\tfrac":hasBarLine=!0;break;case"\\\\atopfrac":hasBarLine=!1;break;case"\\dbinom":case"\\binom":case"\\tbinom":hasBarLine=!1,leftDelim="(",rightDelim=")";break;case"\\\\bracefrac":hasBarLine=!1,leftDelim="\\{",rightDelim="\\}";break;case"\\\\brackfrac":hasBarLine=!1,leftDelim="[",rightDelim="]";break;default:throw new Error(
-"Unrecognized genfrac command")}switch(funcName){case"\\dfrac":case"\\dbinom":size2="display";break;case"\\tfrac":case"\\tbinom":size2="text";break}return{type:"genfrac",mode:parser.mode,continued:!1,numer,denom,hasBarLine,leftDelim,rightDelim,size:size2,barSize:null}},htmlBuilder:htmlBuilder$4,mathmlBuilder:mathmlBuilder$3});defineFunction({type:"genfrac",names:["\\cfrac"],props:{numArgs:2},handler:(_ref2,args)=>{var{parser,funcName}=_ref2,numer=args[0],denom=args[1];return{type:"genfrac",mode:parser.
-mode,continued:!0,numer,denom,hasBarLine:!0,leftDelim:null,rightDelim:null,size:"display",barSize:null}}});defineFunction({type:"infix",names:["\\over","\\choose","\\atop","\\brace","\\brack"],props:{numArgs:0,infix:!0},handler(_ref3){var{parser,funcName,token:token2}=_ref3,replaceWith;switch(funcName){case"\\over":replaceWith="\\frac";break;case"\\choose":replaceWith="\\binom";break;case"\\atop":replaceWith="\\\\atopfrac";break;case"\\brace":replaceWith="\\\\bracefrac";break;case"\\brack":replaceWith=
-"\\\\brackfrac";break;default:throw new Error("Unrecognized infix genfrac command")}return{type:"infix",mode:parser.mode,replaceWith,token:token2}}});var stylArray=["display","text","script","scriptscript"],delimFromValue=function(delimString){var delim=null;return delimString.length>0&&(delim=delimString,delim=delim==="."?null:delim),delim};defineFunction({type:"genfrac",names:["\\genfrac"],props:{numArgs:6,allowedInArgument:!0,argTypes:["math","math","size","text","math","math"]},handler(_ref4,args){
-var{parser}=_ref4,numer=args[4],denom=args[5],leftNode=normalizeArgument(args[0]),leftDelim=leftNode.type==="atom"&&leftNode.family==="open"?delimFromValue(leftNode.text):null,rightNode=normalizeArgument(args[1]),rightDelim=rightNode.type==="atom"&&rightNode.family==="close"?delimFromValue(rightNode.text):null,barNode=assertNodeType(args[2],"size"),hasBarLine,barSize=null;barNode.isBlank?hasBarLine=!0:(barSize=barNode.value,hasBarLine=barSize.number>0);var size2="auto",styl=args[3];if(styl.type===
-"ordgroup"){if(styl.body.length>0){var textOrd=assertNodeType(styl.body[0],"textord");size2=stylArray[Number(textOrd.text)]}}else styl=assertNodeType(styl,"textord"),size2=stylArray[Number(styl.text)];return{type:"genfrac",mode:parser.mode,numer,denom,continued:!1,hasBarLine,barSize,leftDelim,rightDelim,size:size2}},htmlBuilder:htmlBuilder$4,mathmlBuilder:mathmlBuilder$3});defineFunction({type:"infix",names:["\\above"],props:{numArgs:1,argTypes:["size"],infix:!0},handler(_ref5,args){var{parser,funcName,
-token:token2}=_ref5;return{type:"infix",mode:parser.mode,replaceWith:"\\\\abovefrac",size:assertNodeType(args[0],"size").value,token:token2}}});defineFunction({type:"genfrac",names:["\\\\abovefrac"],props:{numArgs:3,argTypes:["math","size","math"]},handler:(_ref6,args)=>{var{parser,funcName}=_ref6,numer=args[0],barSize=assert(assertNodeType(args[1],"infix").size),denom=args[2],hasBarLine=barSize.number>0;return{type:"genfrac",mode:parser.mode,numer,denom,continued:!1,hasBarLine,barSize,leftDelim:null,
-rightDelim:null,size:"auto"}},htmlBuilder:htmlBuilder$4,mathmlBuilder:mathmlBuilder$3});var htmlBuilder$3=(grp,options)=>{var style2=options.style,supSubGroup,group;grp.type==="supsub"?(supSubGroup=grp.sup?buildGroup$1(grp.sup,options.havingStyle(style2.sup()),options):buildGroup$1(grp.sub,options.havingStyle(style2.sub()),options),group=assertNodeType(grp.base,"horizBrace")):group=assertNodeType(grp,"horizBrace");var body2=buildGroup$1(group.base,options.havingBaseStyle(Style$1.DISPLAY)),braceBody=stretchy.
-svgSpan(group,options),vlist;if(group.isOver?(vlist=buildCommon.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:body2},{type:"kern",size:.1},{type:"elem",elem:braceBody}]},options),vlist.children[0].children[0].children[1].classes.push("svg-align")):(vlist=buildCommon.makeVList({positionType:"bottom",positionData:body2.depth+.1+braceBody.height,children:[{type:"elem",elem:braceBody},{type:"kern",size:.1},{type:"elem",elem:body2}]},options),vlist.children[0].children[0].children[0].
-classes.push("svg-align")),supSubGroup){var vSpan=buildCommon.makeSpan(["mord",group.isOver?"mover":"munder"],[vlist],options);group.isOver?vlist=buildCommon.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:vSpan},{type:"kern",size:.2},{type:"elem",elem:supSubGroup}]},options):vlist=buildCommon.makeVList({positionType:"bottom",positionData:vSpan.depth+.2+supSubGroup.height+supSubGroup.depth,children:[{type:"elem",elem:supSubGroup},{type:"kern",size:.2},{type:"elem",elem:vSpan}]},
-options)}return buildCommon.makeSpan(["mord",group.isOver?"mover":"munder"],[vlist],options)},mathmlBuilder$2=(group,options)=>{var accentNode=stretchy.mathMLnode(group.label);return new mathMLTree.MathNode(group.isOver?"mover":"munder",[buildGroup2(group.base,options),accentNode])};defineFunction({type:"horizBrace",names:["\\overbrace","\\underbrace"],props:{numArgs:1},handler(_ref,args){var{parser,funcName}=_ref;return{type:"horizBrace",mode:parser.mode,label:funcName,isOver:/^\\over/.test(funcName),
-base:args[0]}},htmlBuilder:htmlBuilder$3,mathmlBuilder:mathmlBuilder$2});defineFunction({type:"href",names:["\\href"],props:{numArgs:2,argTypes:["url","original"],allowedInText:!0},handler:(_ref,args)=>{var{parser}=_ref,body2=args[1],href=assertNodeType(args[0],"url").url;return parser.settings.isTrusted({command:"\\href",url:href})?{type:"href",mode:parser.mode,href,body:ordargument(body2)}:parser.formatUnsupportedCmd("\\href")},htmlBuilder:(group,options)=>{var elements=buildExpression$1(group.
-body,options,!1);return buildCommon.makeAnchor(group.href,[],elements,options)},mathmlBuilder:(group,options)=>{var math2=buildExpressionRow(group.body,options);return math2 instanceof MathNode||(math2=new MathNode("mrow",[math2])),math2.setAttribute("href",group.href),math2}});defineFunction({type:"href",names:["\\url"],props:{numArgs:1,argTypes:["url"],allowedInText:!0},handler:(_ref2,args)=>{var{parser}=_ref2,href=assertNodeType(args[0],"url").url;if(!parser.settings.isTrusted({command:"\\url",
-url:href}))return parser.formatUnsupportedCmd("\\url");for(var chars=[],i=0;i<href.length;i++){var c2=href[i];c2==="~"&&(c2="\\textasciitilde"),chars.push({type:"textord",mode:"text",text:c2})}var body2={type:"text",mode:parser.mode,font:"\\texttt",body:chars};return{type:"href",mode:parser.mode,href,body:ordargument(body2)}}});defineFunction({type:"hbox",names:["\\hbox"],props:{numArgs:1,argTypes:["text"],allowedInText:!0,primitive:!0},handler(_ref,args){var{parser}=_ref;return{type:"hbox",mode:parser.
-mode,body:ordargument(args[0])}},htmlBuilder(group,options){var elements=buildExpression$1(group.body,options,!1);return buildCommon.makeFragment(elements)},mathmlBuilder(group,options){return new mathMLTree.MathNode("mrow",buildExpression2(group.body,options))}});defineFunction({type:"html",names:["\\htmlClass","\\htmlId","\\htmlStyle","\\htmlData"],props:{numArgs:2,argTypes:["raw","original"],allowedInText:!0},handler:(_ref,args)=>{var{parser,funcName,token:token2}=_ref,value=assertNodeType(args[0],
-"raw").string,body2=args[1];parser.settings.strict&&parser.settings.reportNonstrict("htmlExtension","HTML extension is disabled on strict mode");var trustContext,attributes={};switch(funcName){case"\\htmlClass":attributes.class=value,trustContext={command:"\\htmlClass",class:value};break;case"\\htmlId":attributes.id=value,trustContext={command:"\\htmlId",id:value};break;case"\\htmlStyle":attributes.style=value,trustContext={command:"\\htmlStyle",style:value};break;case"\\htmlData":{for(var data=value.
-split(","),i=0;i<data.length;i++){var keyVal=data[i].split("=");if(keyVal.length!==2)throw new ParseError2("Error parsing key-value for \\htmlData");attributes["data-"+keyVal[0].trim()]=keyVal[1].trim()}trustContext={command:"\\htmlData",attributes};break}default:throw new Error("Unrecognized html command")}return parser.settings.isTrusted(trustContext)?{type:"html",mode:parser.mode,attributes,body:ordargument(body2)}:parser.formatUnsupportedCmd(funcName)},htmlBuilder:(group,options)=>{var elements=buildExpression$1(
-group.body,options,!1),classes=["enclosing"];group.attributes.class&&classes.push(...group.attributes.class.trim().split(/\s+/));var span=buildCommon.makeSpan(classes,elements,options);for(var attr in group.attributes)attr!=="class"&&group.attributes.hasOwnProperty(attr)&&span.setAttribute(attr,group.attributes[attr]);return span},mathmlBuilder:(group,options)=>buildExpressionRow(group.body,options)});defineFunction({type:"htmlmathml",names:["\\html@mathml"],props:{numArgs:2,allowedInText:!0},handler:(_ref,args)=>{
-var{parser}=_ref;return{type:"htmlmathml",mode:parser.mode,html:ordargument(args[0]),mathml:ordargument(args[1])}},htmlBuilder:(group,options)=>{var elements=buildExpression$1(group.html,options,!1);return buildCommon.makeFragment(elements)},mathmlBuilder:(group,options)=>buildExpressionRow(group.mathml,options)});var sizeData=function(str){if(/^[-+]? *(\d+(\.\d*)?|\.\d+)$/.test(str))return{number:+str,unit:"bp"};var match=/([-+]?) *(\d+(?:\.\d*)?|\.\d+) *([a-z]{2})/.exec(str);if(!match)throw new ParseError2(
-"Invalid size: '"+str+"' in \\includegraphics");var data={number:+(match[1]+match[2]),unit:match[3]};if(!validUnit(data))throw new ParseError2("Invalid unit: '"+data.unit+"' in \\includegraphics.");return data};defineFunction({type:"includegraphics",names:["\\includegraphics"],props:{numArgs:1,numOptionalArgs:1,argTypes:["raw","url"],allowedInText:!1},handler:(_ref,args,optArgs)=>{var{parser}=_ref,width={number:0,unit:"em"},height={number:.9,unit:"em"},totalheight={number:0,unit:"em"},alt="";if(optArgs[0])
-for(var attributeStr=assertNodeType(optArgs[0],"raw").string,attributes=attributeStr.split(","),i=0;i<attributes.length;i++){var keyVal=attributes[i].split("=");if(keyVal.length===2){var str=keyVal[1].trim();switch(keyVal[0].trim()){case"alt":alt=str;break;case"width":width=sizeData(str);break;case"height":height=sizeData(str);break;case"totalheight":totalheight=sizeData(str);break;default:throw new ParseError2("Invalid key: '"+keyVal[0]+"' in \\includegraphics.")}}}var src=assertNodeType(args[0],
-"url").url;return alt===""&&(alt=src,alt=alt.replace(/^.*[\\/]/,""),alt=alt.substring(0,alt.lastIndexOf("."))),parser.settings.isTrusted({command:"\\includegraphics",url:src})?{type:"includegraphics",mode:parser.mode,alt,width,height,totalheight,src}:parser.formatUnsupportedCmd("\\includegraphics")},htmlBuilder:(group,options)=>{var height=calculateSize(group.height,options),depth=0;group.totalheight.number>0&&(depth=calculateSize(group.totalheight,options)-height);var width=0;group.width.number>
-0&&(width=calculateSize(group.width,options));var style2={height:makeEm(height+depth)};width>0&&(style2.width=makeEm(width)),depth>0&&(style2.verticalAlign=makeEm(-depth));var node2=new Img(group.src,group.alt,style2);return node2.height=height,node2.depth=depth,node2},mathmlBuilder:(group,options)=>{var node2=new mathMLTree.MathNode("mglyph",[]);node2.setAttribute("alt",group.alt);var height=calculateSize(group.height,options),depth=0;if(group.totalheight.number>0&&(depth=calculateSize(group.totalheight,
-options)-height,node2.setAttribute("valign",makeEm(-depth))),node2.setAttribute("height",makeEm(height+depth)),group.width.number>0){var width=calculateSize(group.width,options);node2.setAttribute("width",makeEm(width))}return node2.setAttribute("src",group.src),node2}});defineFunction({type:"kern",names:["\\kern","\\mkern","\\hskip","\\mskip"],props:{numArgs:1,argTypes:["size"],primitive:!0,allowedInText:!0},handler(_ref,args){var{parser,funcName}=_ref,size2=assertNodeType(args[0],"size");if(parser.
-settings.strict){var mathFunction=funcName[1]==="m",muUnit=size2.value.unit==="mu";mathFunction?(muUnit||parser.settings.reportNonstrict("mathVsTextUnits","LaTeX's "+funcName+" supports only mu units, "+("not "+size2.value.unit+" units")),parser.mode!=="math"&&parser.settings.reportNonstrict("mathVsTextUnits","LaTeX's "+funcName+" works only in math mode")):muUnit&&parser.settings.reportNonstrict("mathVsTextUnits","LaTeX's "+funcName+" doesn't support mu units")}return{type:"kern",mode:parser.mode,
-dimension:size2.value}},htmlBuilder(group,options){return buildCommon.makeGlue(group.dimension,options)},mathmlBuilder(group,options){var dimension=calculateSize(group.dimension,options);return new mathMLTree.SpaceNode(dimension)}});defineFunction({type:"lap",names:["\\mathllap","\\mathrlap","\\mathclap"],props:{numArgs:1,allowedInText:!0},handler:(_ref,args)=>{var{parser,funcName}=_ref,body2=args[0];return{type:"lap",mode:parser.mode,alignment:funcName.slice(5),body:body2}},htmlBuilder:(group,options)=>{
-var inner2;group.alignment==="clap"?(inner2=buildCommon.makeSpan([],[buildGroup$1(group.body,options)]),inner2=buildCommon.makeSpan(["inner"],[inner2],options)):inner2=buildCommon.makeSpan(["inner"],[buildGroup$1(group.body,options)]);var fix2=buildCommon.makeSpan(["fix"],[]),node2=buildCommon.makeSpan([group.alignment],[inner2,fix2],options),strut=buildCommon.makeSpan(["strut"]);return strut.style.height=makeEm(node2.height+node2.depth),node2.depth&&(strut.style.verticalAlign=makeEm(-node2.depth)),
-node2.children.unshift(strut),node2=buildCommon.makeSpan(["thinbox"],[node2],options),buildCommon.makeSpan(["mord","vbox"],[node2],options)},mathmlBuilder:(group,options)=>{var node2=new mathMLTree.MathNode("mpadded",[buildGroup2(group.body,options)]);if(group.alignment!=="rlap"){var offset2=group.alignment==="llap"?"-1":"-0.5";node2.setAttribute("lspace",offset2+"width")}return node2.setAttribute("width","0px"),node2}});defineFunction({type:"styling",names:["\\(","$"],props:{numArgs:0,allowedInText:!0,
-allowedInMath:!1},handler(_ref,args){var{funcName,parser}=_ref,outerMode=parser.mode;parser.switchMode("math");var close2=funcName==="\\("?"\\)":"$",body2=parser.parseExpression(!1,close2);return parser.expect(close2),parser.switchMode(outerMode),{type:"styling",mode:parser.mode,style:"text",body:body2}}});defineFunction({type:"text",names:["\\)","\\]"],props:{numArgs:0,allowedInText:!0,allowedInMath:!1},handler(context,args){throw new ParseError2("Mismatched "+context.funcName)}});var chooseMathStyle=(group,options)=>{
-switch(options.style.size){case Style$1.DISPLAY.size:return group.display;case Style$1.TEXT.size:return group.text;case Style$1.SCRIPT.size:return group.script;case Style$1.SCRIPTSCRIPT.size:return group.scriptscript;default:return group.text}};defineFunction({type:"mathchoice",names:["\\mathchoice"],props:{numArgs:4,primitive:!0},handler:(_ref,args)=>{var{parser}=_ref;return{type:"mathchoice",mode:parser.mode,display:ordargument(args[0]),text:ordargument(args[1]),script:ordargument(args[2]),scriptscript:ordargument(
-args[3])}},htmlBuilder:(group,options)=>{var body2=chooseMathStyle(group,options),elements=buildExpression$1(body2,options,!1);return buildCommon.makeFragment(elements)},mathmlBuilder:(group,options)=>{var body2=chooseMathStyle(group,options);return buildExpressionRow(body2,options)}});var assembleSupSub=(base2,supGroup,subGroup,options,style2,slant,baseShift)=>{base2=buildCommon.makeSpan([],[base2]);var subIsSingleCharacter=subGroup&&utils.isCharacterBox(subGroup),sub2,sup2;if(supGroup){var elem=buildGroup$1(
-supGroup,options.havingStyle(style2.sup()),options);sup2={elem,kern:Math.max(options.fontMetrics().bigOpSpacing1,options.fontMetrics().bigOpSpacing3-elem.depth)}}if(subGroup){var _elem=buildGroup$1(subGroup,options.havingStyle(style2.sub()),options);sub2={elem:_elem,kern:Math.max(options.fontMetrics().bigOpSpacing2,options.fontMetrics().bigOpSpacing4-_elem.height)}}var finalGroup;if(sup2&&sub2){var bottom=options.fontMetrics().bigOpSpacing5+sub2.elem.height+sub2.elem.depth+sub2.kern+base2.depth+
-baseShift;finalGroup=buildCommon.makeVList({positionType:"bottom",positionData:bottom,children:[{type:"kern",size:options.fontMetrics().bigOpSpacing5},{type:"elem",elem:sub2.elem,marginLeft:makeEm(-slant)},{type:"kern",size:sub2.kern},{type:"elem",elem:base2},{type:"kern",size:sup2.kern},{type:"elem",elem:sup2.elem,marginLeft:makeEm(slant)},{type:"kern",size:options.fontMetrics().bigOpSpacing5}]},options)}else if(sub2){var top=base2.height-baseShift;finalGroup=buildCommon.makeVList({positionType:"\
-top",positionData:top,children:[{type:"kern",size:options.fontMetrics().bigOpSpacing5},{type:"elem",elem:sub2.elem,marginLeft:makeEm(-slant)},{type:"kern",size:sub2.kern},{type:"elem",elem:base2}]},options)}else if(sup2){var _bottom=base2.depth+baseShift;finalGroup=buildCommon.makeVList({positionType:"bottom",positionData:_bottom,children:[{type:"elem",elem:base2},{type:"kern",size:sup2.kern},{type:"elem",elem:sup2.elem,marginLeft:makeEm(slant)},{type:"kern",size:options.fontMetrics().bigOpSpacing5}]},
-options)}else return base2;var parts=[finalGroup];if(sub2&&slant!==0&&!subIsSingleCharacter){var spacer=buildCommon.makeSpan(["mspace"],[],options);spacer.style.marginRight=makeEm(slant),parts.unshift(spacer)}return buildCommon.makeSpan(["mop","op-limits"],parts,options)},noSuccessor=["\\smallint"],htmlBuilder$2=(grp,options)=>{var supGroup,subGroup,hasLimits=!1,group;grp.type==="supsub"?(supGroup=grp.sup,subGroup=grp.sub,group=assertNodeType(grp.base,"op"),hasLimits=!0):group=assertNodeType(grp,
-"op");var style2=options.style,large=!1;style2.size===Style$1.DISPLAY.size&&group.symbol&&!utils.contains(noSuccessor,group.name)&&(large=!0);var base2;if(group.symbol){var fontName=large?"Size2-Regular":"Size1-Regular",stash="";if((group.name==="\\oiint"||group.name==="\\oiiint")&&(stash=group.name.slice(1),group.name=stash==="oiint"?"\\iint":"\\iiint"),base2=buildCommon.makeSymbol(group.name,fontName,"math",options,["mop","op-symbol",large?"large-op":"small-op"]),stash.length>0){var italic=base2.
-italic,oval=buildCommon.staticSvg(stash+"Size"+(large?"2":"1"),options);base2=buildCommon.makeVList({positionType:"individualShift",children:[{type:"elem",elem:base2,shift:0},{type:"elem",elem:oval,shift:large?.08:0}]},options),group.name="\\"+stash,base2.classes.unshift("mop"),base2.italic=italic}}else if(group.body){var inner2=buildExpression$1(group.body,options,!0);inner2.length===1&&inner2[0]instanceof SymbolNode?(base2=inner2[0],base2.classes[0]="mop"):base2=buildCommon.makeSpan(["mop"],inner2,
-options)}else{for(var output=[],i=1;i<group.name.length;i++)output.push(buildCommon.mathsym(group.name[i],group.mode,options));base2=buildCommon.makeSpan(["mop"],output,options)}var baseShift=0,slant=0;return(base2 instanceof SymbolNode||group.name==="\\oiint"||group.name==="\\oiiint")&&!group.suppressBaseShift&&(baseShift=(base2.height-base2.depth)/2-options.fontMetrics().axisHeight,slant=base2.italic),hasLimits?assembleSupSub(base2,supGroup,subGroup,options,style2,slant,baseShift):(baseShift&&
-(base2.style.position="relative",base2.style.top=makeEm(baseShift)),base2)},mathmlBuilder$1=(group,options)=>{var node2;if(group.symbol)node2=new MathNode("mo",[makeText(group.name,group.mode)]),utils.contains(noSuccessor,group.name)&&node2.setAttribute("largeop","false");else if(group.body)node2=new MathNode("mo",buildExpression2(group.body,options));else{node2=new MathNode("mi",[new TextNode(group.name.slice(1))]);var operator2=new MathNode("mo",[makeText("⁡","text")]);group.parentIsSupSub?node2=
-new MathNode("mrow",[node2,operator2]):node2=newDocumentFragment([node2,operator2])}return node2},singleCharBigOps={"∏":"\\prod","∐":"\\coprod","∑":"\\sum","⋀":"\\bigwedge","⋁":"\\bigvee","⋂":"\\bigcap","⋃":"\\bigcup","⨀":"\\bigodot","⨁":"\\bigoplus","⨂":"\\bigotimes","⨄":"\\biguplus","⨆":"\\bigsqcup"};defineFunction({type:"op",names:["\\coprod","\\bigvee","\\bigwedge","\\biguplus","\\bigcap","\\bigcup","\\intop","\\prod","\\sum","\\bigotimes","\\bigoplus","\\bigodot","\\b\
-igsqcup","\\smallint","∏","∐","∑","⋀","⋁","⋂","⋃","⨀","⨁","⨂","⨄","⨆"],props:{numArgs:0},handler:(_ref,args)=>{var{parser,funcName}=_ref,fName=funcName;return fName.length===1&&(fName=singleCharBigOps[fName]),{type:"op",mode:parser.mode,limits:!0,parentIsSupSub:!1,symbol:!0,name:fName}},htmlBuilder:htmlBuilder$2,mathmlBuilder:mathmlBuilder$1});defineFunction({type:"op",names:["\\mathop"],props:{numArgs:1,primitive:!0},handler:(_ref2,args)=>{var{parser}=_ref2,body2=args[0];
-return{type:"op",mode:parser.mode,limits:!1,parentIsSupSub:!1,symbol:!1,body:ordargument(body2)}},htmlBuilder:htmlBuilder$2,mathmlBuilder:mathmlBuilder$1});var singleCharIntegrals={"∫":"\\int","∬":"\\iint","∭":"\\iiint","∮":"\\oint","∯":"\\oiint","∰":"\\oiiint"};defineFunction({type:"op",names:["\\arcsin","\\arccos","\\arctan","\\arctg","\\arcctg","\\arg","\\ch","\\cos","\\cosec","\\cosh","\\cot","\\cotg","\\coth","\\csc","\\ctg","\\cth","\\deg","\\dim","\\exp","\\hom","\\ker","\\lg",
-"\\ln","\\log","\\sec","\\sin","\\sinh","\\sh","\\tan","\\tanh","\\tg","\\th"],props:{numArgs:0},handler(_ref3){var{parser,funcName}=_ref3;return{type:"op",mode:parser.mode,limits:!1,parentIsSupSub:!1,symbol:!1,name:funcName}},htmlBuilder:htmlBuilder$2,mathmlBuilder:mathmlBuilder$1});defineFunction({type:"op",names:["\\det","\\gcd","\\inf","\\lim","\\max","\\min","\\Pr","\\sup"],props:{numArgs:0},handler(_ref4){var{parser,funcName}=_ref4;return{type:"op",mode:parser.mode,limits:!0,parentIsSupSub:!1,
-symbol:!1,name:funcName}},htmlBuilder:htmlBuilder$2,mathmlBuilder:mathmlBuilder$1});defineFunction({type:"op",names:["\\int","\\iint","\\iiint","\\oint","\\oiint","\\oiiint","∫","∬","∭","∮","∯","∰"],props:{numArgs:0},handler(_ref5){var{parser,funcName}=_ref5,fName=funcName;return fName.length===1&&(fName=singleCharIntegrals[fName]),{type:"op",mode:parser.mode,limits:!1,parentIsSupSub:!1,symbol:!0,name:fName}},htmlBuilder:htmlBuilder$2,mathmlBuilder:mathmlBuilder$1});var htmlBuilder$1=(grp,options)=>{
-var supGroup,subGroup,hasLimits=!1,group;grp.type==="supsub"?(supGroup=grp.sup,subGroup=grp.sub,group=assertNodeType(grp.base,"operatorname"),hasLimits=!0):group=assertNodeType(grp,"operatorname");var base2;if(group.body.length>0){for(var body2=group.body.map(child3=>{var childText=child3.text;return typeof childText=="string"?{type:"textord",mode:child3.mode,text:childText}:child3}),expression=buildExpression$1(body2,options.withFont("mathrm"),!0),i=0;i<expression.length;i++){var child2=expression[i];
-child2 instanceof SymbolNode&&(child2.text=child2.text.replace(/\u2212/,"-").replace(/\u2217/,"*"))}base2=buildCommon.makeSpan(["mop"],expression,options)}else base2=buildCommon.makeSpan(["mop"],[],options);return hasLimits?assembleSupSub(base2,supGroup,subGroup,options,options.style,0,0):base2},mathmlBuilder2=(group,options)=>{for(var expression=buildExpression2(group.body,options.withFont("mathrm")),isAllString=!0,i=0;i<expression.length;i++){var node2=expression[i];if(!(node2 instanceof mathMLTree.
-SpaceNode))if(node2 instanceof mathMLTree.MathNode)switch(node2.type){case"mi":case"mn":case"ms":case"mspace":case"mtext":break;case"mo":{var child2=node2.children[0];node2.children.length===1&&child2 instanceof mathMLTree.TextNode?child2.text=child2.text.replace(/\u2212/,"-").replace(/\u2217/,"*"):isAllString=!1;break}default:isAllString=!1}else isAllString=!1}if(isAllString){var word=expression.map(node3=>node3.toText()).join("");expression=[new mathMLTree.TextNode(word)]}var identifier2=new mathMLTree.
-MathNode("mi",expression);identifier2.setAttribute("mathvariant","normal");var operator2=new mathMLTree.MathNode("mo",[makeText("⁡","text")]);return group.parentIsSupSub?new mathMLTree.MathNode("mrow",[identifier2,operator2]):mathMLTree.newDocumentFragment([identifier2,operator2])};defineFunction({type:"operatorname",names:["\\operatorname@","\\operatornamewithlimits"],props:{numArgs:1},handler:(_ref,args)=>{var{parser,funcName}=_ref,body2=args[0];return{type:"operatorname",mode:parser.mode,body:ordargument(
-body2),alwaysHandleSupSub:funcName==="\\operatornamewithlimits",limits:!1,parentIsSupSub:!1}},htmlBuilder:htmlBuilder$1,mathmlBuilder:mathmlBuilder2});defineMacro("\\operatorname","\\@ifstar\\operatornamewithlimits\\operatorname@");defineFunctionBuilders({type:"ordgroup",htmlBuilder(group,options){return group.semisimple?buildCommon.makeFragment(buildExpression$1(group.body,options,!1)):buildCommon.makeSpan(["mord"],buildExpression$1(group.body,options,!0),options)},mathmlBuilder(group,options){
-return buildExpressionRow(group.body,options,!0)}});defineFunction({type:"overline",names:["\\overline"],props:{numArgs:1},handler(_ref,args){var{parser}=_ref,body2=args[0];return{type:"overline",mode:parser.mode,body:body2}},htmlBuilder(group,options){var innerGroup=buildGroup$1(group.body,options.havingCrampedStyle()),line=buildCommon.makeLineSpan("overline-line",options),defaultRuleThickness=options.fontMetrics().defaultRuleThickness,vlist=buildCommon.makeVList({positionType:"firstBaseline",children:[
-{type:"elem",elem:innerGroup},{type:"kern",size:3*defaultRuleThickness},{type:"elem",elem:line},{type:"kern",size:defaultRuleThickness}]},options);return buildCommon.makeSpan(["mord","overline"],[vlist],options)},mathmlBuilder(group,options){var operator2=new mathMLTree.MathNode("mo",[new mathMLTree.TextNode("‾")]);operator2.setAttribute("stretchy","true");var node2=new mathMLTree.MathNode("mover",[buildGroup2(group.body,options),operator2]);return node2.setAttribute("accent","true"),node2}});
-defineFunction({type:"phantom",names:["\\phantom"],props:{numArgs:1,allowedInText:!0},handler:(_ref,args)=>{var{parser}=_ref,body2=args[0];return{type:"phantom",mode:parser.mode,body:ordargument(body2)}},htmlBuilder:(group,options)=>{var elements=buildExpression$1(group.body,options.withPhantom(),!1);return buildCommon.makeFragment(elements)},mathmlBuilder:(group,options)=>{var inner2=buildExpression2(group.body,options);return new mathMLTree.MathNode("mphantom",inner2)}});defineFunction({type:"\
-hphantom",names:["\\hphantom"],props:{numArgs:1,allowedInText:!0},handler:(_ref2,args)=>{var{parser}=_ref2,body2=args[0];return{type:"hphantom",mode:parser.mode,body:body2}},htmlBuilder:(group,options)=>{var node2=buildCommon.makeSpan([],[buildGroup$1(group.body,options.withPhantom())]);if(node2.height=0,node2.depth=0,node2.children)for(var i=0;i<node2.children.length;i++)node2.children[i].height=0,node2.children[i].depth=0;return node2=buildCommon.makeVList({positionType:"firstBaseline",children:[
-{type:"elem",elem:node2}]},options),buildCommon.makeSpan(["mord"],[node2],options)},mathmlBuilder:(group,options)=>{var inner2=buildExpression2(ordargument(group.body),options),phantom=new mathMLTree.MathNode("mphantom",inner2),node2=new mathMLTree.MathNode("mpadded",[phantom]);return node2.setAttribute("height","0px"),node2.setAttribute("depth","0px"),node2}});defineFunction({type:"vphantom",names:["\\vphantom"],props:{numArgs:1,allowedInText:!0},handler:(_ref3,args)=>{var{parser}=_ref3,body2=args[0];
-return{type:"vphantom",mode:parser.mode,body:body2}},htmlBuilder:(group,options)=>{var inner2=buildCommon.makeSpan(["inner"],[buildGroup$1(group.body,options.withPhantom())]),fix2=buildCommon.makeSpan(["fix"],[]);return buildCommon.makeSpan(["mord","rlap"],[inner2,fix2],options)},mathmlBuilder:(group,options)=>{var inner2=buildExpression2(ordargument(group.body),options),phantom=new mathMLTree.MathNode("mphantom",inner2),node2=new mathMLTree.MathNode("mpadded",[phantom]);return node2.setAttribute(
-"width","0px"),node2}});defineFunction({type:"raisebox",names:["\\raisebox"],props:{numArgs:2,argTypes:["size","hbox"],allowedInText:!0},handler(_ref,args){var{parser}=_ref,amount=assertNodeType(args[0],"size").value,body2=args[1];return{type:"raisebox",mode:parser.mode,dy:amount,body:body2}},htmlBuilder(group,options){var body2=buildGroup$1(group.body,options),dy=calculateSize(group.dy,options);return buildCommon.makeVList({positionType:"shift",positionData:-dy,children:[{type:"elem",elem:body2}]},
-options)},mathmlBuilder(group,options){var node2=new mathMLTree.MathNode("mpadded",[buildGroup2(group.body,options)]),dy=group.dy.number+group.dy.unit;return node2.setAttribute("voffset",dy),node2}});defineFunction({type:"internal",names:["\\relax"],props:{numArgs:0,allowedInText:!0,allowedInArgument:!0},handler(_ref){var{parser}=_ref;return{type:"internal",mode:parser.mode}}});defineFunction({type:"rule",names:["\\rule"],props:{numArgs:2,numOptionalArgs:1,allowedInText:!0,allowedInMath:!0,argTypes:[
-"size","size","size"]},handler(_ref,args,optArgs){var{parser}=_ref,shift2=optArgs[0],width=assertNodeType(args[0],"size"),height=assertNodeType(args[1],"size");return{type:"rule",mode:parser.mode,shift:shift2&&assertNodeType(shift2,"size").value,width:width.value,height:height.value}},htmlBuilder(group,options){var rule=buildCommon.makeSpan(["mord","rule"],[],options),width=calculateSize(group.width,options),height=calculateSize(group.height,options),shift2=group.shift?calculateSize(group.shift,
-options):0;return rule.style.borderRightWidth=makeEm(width),rule.style.borderTopWidth=makeEm(height),rule.style.bottom=makeEm(shift2),rule.width=width,rule.height=height+shift2,rule.depth=-shift2,rule.maxFontSize=height*1.125*options.sizeMultiplier,rule},mathmlBuilder(group,options){var width=calculateSize(group.width,options),height=calculateSize(group.height,options),shift2=group.shift?calculateSize(group.shift,options):0,color=options.color&&options.getColor()||"black",rule=new mathMLTree.MathNode(
-"mspace");rule.setAttribute("mathbackground",color),rule.setAttribute("width",makeEm(width)),rule.setAttribute("height",makeEm(height));var wrapper=new mathMLTree.MathNode("mpadded",[rule]);return shift2>=0?wrapper.setAttribute("height",makeEm(shift2)):(wrapper.setAttribute("height",makeEm(shift2)),wrapper.setAttribute("depth",makeEm(-shift2))),wrapper.setAttribute("voffset",makeEm(shift2)),wrapper}});function sizingGroup(value,options,baseOptions){for(var inner2=buildExpression$1(value,options,
-!1),multiplier=options.sizeMultiplier/baseOptions.sizeMultiplier,i=0;i<inner2.length;i++){var pos=inner2[i].classes.indexOf("sizing");pos<0?Array.prototype.push.apply(inner2[i].classes,options.sizingClasses(baseOptions)):inner2[i].classes[pos+1]==="reset-size"+options.size&&(inner2[i].classes[pos+1]="reset-size"+baseOptions.size),inner2[i].height*=multiplier,inner2[i].depth*=multiplier}return buildCommon.makeFragment(inner2)}var sizeFuncs=["\\tiny","\\sixptsize","\\scriptsize","\\footnotesize","\
-\\small","\\normalsize","\\large","\\Large","\\LARGE","\\huge","\\Huge"],htmlBuilder2=(group,options)=>{var newOptions=options.havingSize(group.size);return sizingGroup(group.body,newOptions,options)};defineFunction({type:"sizing",names:sizeFuncs,props:{numArgs:0,allowedInText:!0},handler:(_ref,args)=>{var{breakOnTokenText,funcName,parser}=_ref,body2=parser.parseExpression(!1,breakOnTokenText);return{type:"sizing",mode:parser.mode,size:sizeFuncs.indexOf(funcName)+1,body:body2}},htmlBuilder:htmlBuilder2,
-mathmlBuilder:(group,options)=>{var newOptions=options.havingSize(group.size),inner2=buildExpression2(group.body,newOptions),node2=new mathMLTree.MathNode("mstyle",inner2);return node2.setAttribute("mathsize",makeEm(newOptions.sizeMultiplier)),node2}});defineFunction({type:"smash",names:["\\smash"],props:{numArgs:1,numOptionalArgs:1,allowedInText:!0},handler:(_ref,args,optArgs)=>{var{parser}=_ref,smashHeight=!1,smashDepth=!1,tbArg=optArgs[0]&&assertNodeType(optArgs[0],"ordgroup");if(tbArg)for(var letter="",
-i=0;i<tbArg.body.length;++i){var node2=tbArg.body[i];if(letter=node2.text,letter==="t")smashHeight=!0;else if(letter==="b")smashDepth=!0;else{smashHeight=!1,smashDepth=!1;break}}else smashHeight=!0,smashDepth=!0;var body2=args[0];return{type:"smash",mode:parser.mode,body:body2,smashHeight,smashDepth}},htmlBuilder:(group,options)=>{var node2=buildCommon.makeSpan([],[buildGroup$1(group.body,options)]);if(!group.smashHeight&&!group.smashDepth)return node2;if(group.smashHeight&&(node2.height=0,node2.
-children))for(var i=0;i<node2.children.length;i++)node2.children[i].height=0;if(group.smashDepth&&(node2.depth=0,node2.children))for(var _i=0;_i<node2.children.length;_i++)node2.children[_i].depth=0;var smashedNode=buildCommon.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:node2}]},options);return buildCommon.makeSpan(["mord"],[smashedNode],options)},mathmlBuilder:(group,options)=>{var node2=new mathMLTree.MathNode("mpadded",[buildGroup2(group.body,options)]);return group.smashHeight&&
-node2.setAttribute("height","0px"),group.smashDepth&&node2.setAttribute("depth","0px"),node2}});defineFunction({type:"sqrt",names:["\\sqrt"],props:{numArgs:1,numOptionalArgs:1},handler(_ref,args,optArgs){var{parser}=_ref,index2=optArgs[0],body2=args[0];return{type:"sqrt",mode:parser.mode,body:body2,index:index2}},htmlBuilder(group,options){var inner2=buildGroup$1(group.body,options.havingCrampedStyle());inner2.height===0&&(inner2.height=options.fontMetrics().xHeight),inner2=buildCommon.wrapFragment(
-inner2,options);var metrics=options.fontMetrics(),theta=metrics.defaultRuleThickness,phi=theta;options.style.id<Style$1.TEXT.id&&(phi=options.fontMetrics().xHeight);var lineClearance=theta+phi/4,minDelimiterHeight=inner2.height+inner2.depth+lineClearance+theta,{span:img,ruleWidth,advanceWidth}=delimiter.sqrtImage(minDelimiterHeight,options),delimDepth=img.height-ruleWidth;delimDepth>inner2.height+inner2.depth+lineClearance&&(lineClearance=(lineClearance+delimDepth-inner2.height-inner2.depth)/2);
-var imgShift=img.height-inner2.height-lineClearance-ruleWidth;inner2.style.paddingLeft=makeEm(advanceWidth);var body2=buildCommon.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:inner2,wrapperClasses:["svg-align"]},{type:"kern",size:-(inner2.height+imgShift)},{type:"elem",elem:img},{type:"kern",size:ruleWidth}]},options);if(group.index){var newOptions=options.havingStyle(Style$1.SCRIPTSCRIPT),rootm=buildGroup$1(group.index,newOptions,options),toShift=.6*(body2.height-body2.depth),
-rootVList=buildCommon.makeVList({positionType:"shift",positionData:-toShift,children:[{type:"elem",elem:rootm}]},options),rootVListWrap=buildCommon.makeSpan(["root"],[rootVList]);return buildCommon.makeSpan(["mord","sqrt"],[rootVListWrap,body2],options)}else return buildCommon.makeSpan(["mord","sqrt"],[body2],options)},mathmlBuilder(group,options){var{body:body2,index:index2}=group;return index2?new mathMLTree.MathNode("mroot",[buildGroup2(body2,options),buildGroup2(index2,options)]):new mathMLTree.
-MathNode("msqrt",[buildGroup2(body2,options)])}});var styleMap={display:Style$1.DISPLAY,text:Style$1.TEXT,script:Style$1.SCRIPT,scriptscript:Style$1.SCRIPTSCRIPT};defineFunction({type:"styling",names:["\\displaystyle","\\textstyle","\\scriptstyle","\\scriptscriptstyle"],props:{numArgs:0,allowedInText:!0,primitive:!0},handler(_ref,args){var{breakOnTokenText,funcName,parser}=_ref,body2=parser.parseExpression(!0,breakOnTokenText),style2=funcName.slice(1,funcName.length-5);return{type:"styling",mode:parser.
-mode,style:style2,body:body2}},htmlBuilder(group,options){var newStyle=styleMap[group.style],newOptions=options.havingStyle(newStyle).withFont("");return sizingGroup(group.body,newOptions,options)},mathmlBuilder(group,options){var newStyle=styleMap[group.style],newOptions=options.havingStyle(newStyle),inner2=buildExpression2(group.body,newOptions),node2=new mathMLTree.MathNode("mstyle",inner2),styleAttributes={display:["0","true"],text:["0","false"],script:["1","false"],scriptscript:["2","false"]},
-attr=styleAttributes[group.style];return node2.setAttribute("scriptlevel",attr[0]),node2.setAttribute("displaystyle",attr[1]),node2}});var htmlBuilderDelegate=function(group,options){var base2=group.base;if(base2)if(base2.type==="op"){var delegate2=base2.limits&&(options.style.size===Style$1.DISPLAY.size||base2.alwaysHandleSupSub);return delegate2?htmlBuilder$2:null}else if(base2.type==="operatorname"){var _delegate=base2.alwaysHandleSupSub&&(options.style.size===Style$1.DISPLAY.size||base2.limits);
-return _delegate?htmlBuilder$1:null}else{if(base2.type==="accent")return utils.isCharacterBox(base2.base)?htmlBuilder$a:null;if(base2.type==="horizBrace"){var isSup=!group.sub;return isSup===base2.isOver?htmlBuilder$3:null}else return null}else return null};defineFunctionBuilders({type:"supsub",htmlBuilder(group,options){var builderDelegate=htmlBuilderDelegate(group,options);if(builderDelegate)return builderDelegate(group,options);var{base:valueBase,sup:valueSup,sub:valueSub}=group,base2=buildGroup$1(
-valueBase,options),supm,subm,metrics=options.fontMetrics(),supShift=0,subShift=0,isCharacterBox3=valueBase&&utils.isCharacterBox(valueBase);if(valueSup){var newOptions=options.havingStyle(options.style.sup());supm=buildGroup$1(valueSup,newOptions,options),isCharacterBox3||(supShift=base2.height-newOptions.fontMetrics().supDrop*newOptions.sizeMultiplier/options.sizeMultiplier)}if(valueSub){var _newOptions=options.havingStyle(options.style.sub());subm=buildGroup$1(valueSub,_newOptions,options),isCharacterBox3||
-(subShift=base2.depth+_newOptions.fontMetrics().subDrop*_newOptions.sizeMultiplier/options.sizeMultiplier)}var minSupShift;options.style===Style$1.DISPLAY?minSupShift=metrics.sup1:options.style.cramped?minSupShift=metrics.sup3:minSupShift=metrics.sup2;var multiplier=options.sizeMultiplier,marginRight=makeEm(.5/metrics.ptPerEm/multiplier),marginLeft=null;if(subm){var isOiint=group.base&&group.base.type==="op"&&group.base.name&&(group.base.name==="\\oiint"||group.base.name==="\\oiiint");(base2 instanceof
-SymbolNode||isOiint)&&(marginLeft=makeEm(-base2.italic))}var supsub;if(supm&&subm){supShift=Math.max(supShift,minSupShift,supm.depth+.25*metrics.xHeight),subShift=Math.max(subShift,metrics.sub2);var ruleWidth=metrics.defaultRuleThickness,maxWidth=4*ruleWidth;if(supShift-supm.depth-(subm.height-subShift)<maxWidth){subShift=maxWidth-(supShift-supm.depth)+subm.height;var psi=.8*metrics.xHeight-(supShift-supm.depth);psi>0&&(supShift+=psi,subShift-=psi)}var vlistElem=[{type:"elem",elem:subm,shift:subShift,
-marginRight,marginLeft},{type:"elem",elem:supm,shift:-supShift,marginRight}];supsub=buildCommon.makeVList({positionType:"individualShift",children:vlistElem},options)}else if(subm){subShift=Math.max(subShift,metrics.sub1,subm.height-.8*metrics.xHeight);var _vlistElem=[{type:"elem",elem:subm,marginLeft,marginRight}];supsub=buildCommon.makeVList({positionType:"shift",positionData:subShift,children:_vlistElem},options)}else if(supm)supShift=Math.max(supShift,minSupShift,supm.depth+.25*metrics.xHeight),
-supsub=buildCommon.makeVList({positionType:"shift",positionData:-supShift,children:[{type:"elem",elem:supm,marginRight}]},options);else throw new Error("supsub must have either sup or sub.");var mclass=getTypeOfDomTree(base2,"right")||"mord";return buildCommon.makeSpan([mclass],[base2,buildCommon.makeSpan(["msupsub"],[supsub])],options)},mathmlBuilder(group,options){var isBrace=!1,isOver,isSup;group.base&&group.base.type==="horizBrace"&&(isSup=!!group.sup,isSup===group.base.isOver&&(isBrace=!0,isOver=
-group.base.isOver)),group.base&&(group.base.type==="op"||group.base.type==="operatorname")&&(group.base.parentIsSupSub=!0);var children=[buildGroup2(group.base,options)];group.sub&&children.push(buildGroup2(group.sub,options)),group.sup&&children.push(buildGroup2(group.sup,options));var nodeType;if(isBrace)nodeType=isOver?"mover":"munder";else if(group.sub)if(group.sup){var _base2=group.base;_base2&&_base2.type==="op"&&_base2.limits&&options.style===Style$1.DISPLAY||_base2&&_base2.type==="operat\
-orname"&&_base2.alwaysHandleSupSub&&(options.style===Style$1.DISPLAY||_base2.limits)?nodeType="munderover":nodeType="msubsup"}else{var _base=group.base;_base&&_base.type==="op"&&_base.limits&&(options.style===Style$1.DISPLAY||_base.alwaysHandleSupSub)||_base&&_base.type==="operatorname"&&_base.alwaysHandleSupSub&&(_base.limits||options.style===Style$1.DISPLAY)?nodeType="munder":nodeType="msub"}else{var base2=group.base;base2&&base2.type==="op"&&base2.limits&&(options.style===Style$1.DISPLAY||base2.
-alwaysHandleSupSub)||base2&&base2.type==="operatorname"&&base2.alwaysHandleSupSub&&(base2.limits||options.style===Style$1.DISPLAY)?nodeType="mover":nodeType="msup"}return new mathMLTree.MathNode(nodeType,children)}});defineFunctionBuilders({type:"atom",htmlBuilder(group,options){return buildCommon.mathsym(group.text,group.mode,options,["m"+group.family])},mathmlBuilder(group,options){var node2=new mathMLTree.MathNode("mo",[makeText(group.text,group.mode)]);if(group.family==="bin"){var variant=getVariant(
-group,options);variant==="bold-italic"&&node2.setAttribute("mathvariant",variant)}else group.family==="punct"?node2.setAttribute("separator","true"):(group.family==="open"||group.family==="close")&&node2.setAttribute("stretchy","false");return node2}});var defaultVariant={mi:"italic",mn:"normal",mtext:"normal"};defineFunctionBuilders({type:"mathord",htmlBuilder(group,options){return buildCommon.makeOrd(group,options,"mathord")},mathmlBuilder(group,options){var node2=new mathMLTree.MathNode("mi",
-[makeText(group.text,group.mode,options)]),variant=getVariant(group,options)||"italic";return variant!==defaultVariant[node2.type]&&node2.setAttribute("mathvariant",variant),node2}});defineFunctionBuilders({type:"textord",htmlBuilder(group,options){return buildCommon.makeOrd(group,options,"textord")},mathmlBuilder(group,options){var text2=makeText(group.text,group.mode,options),variant=getVariant(group,options)||"normal",node2;return group.mode==="text"?node2=new mathMLTree.MathNode("mtext",[text2]):
-/[0-9]/.test(group.text)?node2=new mathMLTree.MathNode("mn",[text2]):group.text==="\\prime"?node2=new mathMLTree.MathNode("mo",[text2]):node2=new mathMLTree.MathNode("mi",[text2]),variant!==defaultVariant[node2.type]&&node2.setAttribute("mathvariant",variant),node2}});var cssSpace={"\\nobreak":"nobreak","\\allowbreak":"allowbreak"},regularSpace={" ":{},"\\ ":{},"~":{className:"nobreak"},"\\space":{},"\\nobreakspace":{className:"nobreak"}};defineFunctionBuilders({type:"spacing",htmlBuilder(group,options){
-if(regularSpace.hasOwnProperty(group.text)){var className=regularSpace[group.text].className||"";if(group.mode==="text"){var ord=buildCommon.makeOrd(group,options,"textord");return ord.classes.push(className),ord}else return buildCommon.makeSpan(["mspace",className],[buildCommon.mathsym(group.text,group.mode,options)],options)}else{if(cssSpace.hasOwnProperty(group.text))return buildCommon.makeSpan(["mspace",cssSpace[group.text]],[],options);throw new ParseError2('Unknown type of space "'+group.text+
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-"\\textrm","\\textsf","\\texttt","\\textnormal","\\textbf","\\textmd","\\textit","\\textup","\\emph"],props:{numArgs:1,argTypes:["text"],allowedInArgument:!0,allowedInText:!0},handler(_ref,args){var{parser,funcName}=_ref,body2=args[0];return{type:"text",mode:parser.mode,body:ordargument(body2),font:funcName}},htmlBuilder(group,options){var newOptions=optionsWithFont(group,options),inner2=buildExpression$1(group.body,newOptions,!0);return buildCommon.makeSpan(["mord","text"],inner2,newOptions)},mathmlBuilder(group,options){
-var newOptions=optionsWithFont(group,options);return buildExpressionRow(group.body,newOptions)}});defineFunction({type:"underline",names:["\\underline"],props:{numArgs:1,allowedInText:!0},handler(_ref,args){var{parser}=_ref;return{type:"underline",mode:parser.mode,body:args[0]}},htmlBuilder(group,options){var innerGroup=buildGroup$1(group.body,options),line=buildCommon.makeLineSpan("underline-line",options),defaultRuleThickness=options.fontMetrics().defaultRuleThickness,vlist=buildCommon.makeVList(
-{positionType:"top",positionData:innerGroup.height,children:[{type:"kern",size:defaultRuleThickness},{type:"elem",elem:line},{type:"kern",size:3*defaultRuleThickness},{type:"elem",elem:innerGroup}]},options);return buildCommon.makeSpan(["mord","underline"],[vlist],options)},mathmlBuilder(group,options){var operator2=new mathMLTree.MathNode("mo",[new mathMLTree.TextNode("‾")]);operator2.setAttribute("stretchy","true");var node2=new mathMLTree.MathNode("munder",[buildGroup2(group.body,options),operator2]);
-return node2.setAttribute("accentunder","true"),node2}});defineFunction({type:"vcenter",names:["\\vcenter"],props:{numArgs:1,argTypes:["original"],allowedInText:!1},handler(_ref,args){var{parser}=_ref;return{type:"vcenter",mode:parser.mode,body:args[0]}},htmlBuilder(group,options){var body2=buildGroup$1(group.body,options),axisHeight=options.fontMetrics().axisHeight,dy=.5*(body2.height-axisHeight-(body2.depth+axisHeight));return buildCommon.makeVList({positionType:"shift",positionData:dy,children:[
-{type:"elem",elem:body2}]},options)},mathmlBuilder(group,options){return new mathMLTree.MathNode("mpadded",[buildGroup2(group.body,options)],["vcenter"])}});defineFunction({type:"verb",names:["\\verb"],props:{numArgs:0,allowedInText:!0},handler(context,args,optArgs){throw new ParseError2("\\verb ended by end of line instead of matching delimiter")},htmlBuilder(group,options){for(var text2=makeVerb(group),body2=[],newOptions=options.havingStyle(options.style.text()),i=0;i<text2.length;i++){var c2=text2[i];
-c2==="~"&&(c2="\\textasciitilde"),body2.push(buildCommon.makeSymbol(c2,"Typewriter-Regular",group.mode,newOptions,["mord","texttt"]))}return buildCommon.makeSpan(["mord","text"].concat(newOptions.sizingClasses(options)),buildCommon.tryCombineChars(body2),newOptions)},mathmlBuilder(group,options){var text2=new mathMLTree.TextNode(makeVerb(group)),node2=new mathMLTree.MathNode("mtext",[text2]);return node2.setAttribute("mathvariant","monospace"),node2}});var makeVerb=group=>group.body.replace(/ /g,
-group.star?"␣":" "),functions=_functions,spaceRegexString=`[ \r
-	]`,controlWordRegexString="\\\\[a-zA-Z@]+",controlSymbolRegexString="\\\\[^\uD800-\uDFFF]",controlWordWhitespaceRegexString="("+controlWordRegexString+")"+spaceRegexString+"*",controlSpaceRegexString=`\\\\(
-|[ \r	]+
-?)[ \r	]*`,combiningDiacriticalMarkString="[̀-ͯ]",combiningDiacriticalMarksEndRegex=new RegExp(combiningDiacriticalMarkString+"+$"),tokenRegexString="("+spaceRegexString+"+)|"+(controlSpaceRegexString+"|")+"([!-\\[\\]-‧‪-퟿豈-￿]"+(combiningDiacriticalMarkString+"*")+"|[\uD800-\uDBFF][\uDC00-\uDFFF]"+(combiningDiacriticalMarkString+"*")+"|\\\\verb\\*([^]).*?\\4|\\\\verb([^*a-zA-Z]).*?\\5"+("|"+controlWordWhitespaceRegexString)+("|"+controlSymbolRegexString+")");class Lexer{constructor(input,settings){
-this.input=void 0,this.settings=void 0,this.tokenRegex=void 0,this.catcodes=void 0,this.input=input,this.settings=settings,this.tokenRegex=new RegExp(tokenRegexString,"g"),this.catcodes={"%":14,"~":13}}setCatcode(char,code2){this.catcodes[char]=code2}lex(){var input=this.input,pos=this.tokenRegex.lastIndex;if(pos===input.length)return new Token("EOF",new SourceLocation(this,pos,pos));var match=this.tokenRegex.exec(input);if(match===null||match.index!==pos)throw new ParseError2("Unexpected charac\
-ter: '"+input[pos]+"'",new Token(input[pos],new SourceLocation(this,pos,pos+1)));var text2=match[6]||match[3]||(match[2]?"\\ ":" ");if(this.catcodes[text2]===14){var nlIndex=input.indexOf(`
-`,this.tokenRegex.lastIndex);return nlIndex===-1?(this.tokenRegex.lastIndex=input.length,this.settings.reportNonstrict("commentAtEnd","% comment has no terminating newline; LaTeX would fail because of commenting the end of math mode (e.g. $)")):this.tokenRegex.lastIndex=nlIndex+1,this.lex()}return new Token(text2,new SourceLocation(this,pos,this.tokenRegex.lastIndex))}}class Namespace{constructor(builtins,globalMacros){builtins===void 0&&(builtins={}),globalMacros===void 0&&(globalMacros={}),this.
-current=void 0,this.builtins=void 0,this.undefStack=void 0,this.current=globalMacros,this.builtins=builtins,this.undefStack=[]}beginGroup(){this.undefStack.push({})}endGroup(){if(this.undefStack.length===0)throw new ParseError2("Unbalanced namespace destruction: attempt to pop global namespace; please report this as a bug");var undefs=this.undefStack.pop();for(var undef in undefs)undefs.hasOwnProperty(undef)&&(undefs[undef]==null?delete this.current[undef]:this.current[undef]=undefs[undef])}endGroups(){
-for(;this.undefStack.length>0;)this.endGroup()}has(name){return this.current.hasOwnProperty(name)||this.builtins.hasOwnProperty(name)}get(name){return this.current.hasOwnProperty(name)?this.current[name]:this.builtins[name]}set(name,value,global2){if(global2===void 0&&(global2=!1),global2){for(var i=0;i<this.undefStack.length;i++)delete this.undefStack[i][name];this.undefStack.length>0&&(this.undefStack[this.undefStack.length-1][name]=value)}else{var top=this.undefStack[this.undefStack.length-1];
-top&&!top.hasOwnProperty(name)&&(top[name]=this.current[name])}value==null?delete this.current[name]:this.current[name]=value}}var macros=_macros;defineMacro("\\noexpand",function(context){var t=context.popToken();return context.isExpandable(t.text)&&(t.noexpand=!0,t.treatAsRelax=!0),{tokens:[t],numArgs:0}});defineMacro("\\expandafter",function(context){var t=context.popToken();return context.expandOnce(!0),{tokens:[t],numArgs:0}});defineMacro("\\@firstoftwo",function(context){var args=context.consumeArgs(
-2);return{tokens:args[0],numArgs:0}});defineMacro("\\@secondoftwo",function(context){var args=context.consumeArgs(2);return{tokens:args[1],numArgs:0}});defineMacro("\\@ifnextchar",function(context){var args=context.consumeArgs(3);context.consumeSpaces();var nextToken=context.future();return args[0].length===1&&args[0][0].text===nextToken.text?{tokens:args[1],numArgs:0}:{tokens:args[2],numArgs:0}});defineMacro("\\@ifstar","\\@ifnextchar *{\\@firstoftwo{#1}}");defineMacro("\\TextOrMath",function(context){
-var args=context.consumeArgs(2);return context.mode==="text"?{tokens:args[0],numArgs:0}:{tokens:args[1],numArgs:0}});var digitToNumber={0:0,1:1,2:2,3:3,4:4,5:5,6:6,7:7,8:8,9:9,a:10,A:10,b:11,B:11,c:12,C:12,d:13,D:13,e:14,E:14,f:15,F:15};defineMacro("\\char",function(context){var token2=context.popToken(),base2,number2="";if(token2.text==="'")base2=8,token2=context.popToken();else if(token2.text==='"')base2=16,token2=context.popToken();else if(token2.text==="`")if(token2=context.popToken(),token2.
-text[0]==="\\")number2=token2.text.charCodeAt(1);else{if(token2.text==="EOF")throw new ParseError2("\\char` missing argument");number2=token2.text.charCodeAt(0)}else base2=10;if(base2){if(number2=digitToNumber[token2.text],number2==null||number2>=base2)throw new ParseError2("Invalid base-"+base2+" digit "+token2.text);for(var digit;(digit=digitToNumber[context.future().text])!=null&&digit<base2;)number2*=base2,number2+=digit,context.popToken()}return"\\@char{"+number2+"}"});var newcommand=(context,existsOK,nonexistsOK,skipIfExists)=>{
-var arg=context.consumeArg().tokens;if(arg.length!==1)throw new ParseError2("\\newcommand's first argument must be a macro name");var name=arg[0].text,exists=context.isDefined(name);if(exists&&!existsOK)throw new ParseError2("\\newcommand{"+name+"} attempting to redefine "+(name+"; use \\renewcommand"));if(!exists&&!nonexistsOK)throw new ParseError2("\\renewcommand{"+name+"} when command "+name+" does not yet exist; use \\newcommand");var numArgs=0;if(arg=context.consumeArg().tokens,arg.length===
-1&&arg[0].text==="["){for(var argText="",token2=context.expandNextToken();token2.text!=="]"&&token2.text!=="EOF";)argText+=token2.text,token2=context.expandNextToken();if(!argText.match(/^\s*[0-9]+\s*$/))throw new ParseError2("Invalid number of arguments: "+argText);numArgs=parseInt(argText),arg=context.consumeArg().tokens}return exists&&skipIfExists||context.macros.set(name,{tokens:arg,numArgs}),""};defineMacro("\\newcommand",context=>newcommand(context,!1,!0,!1));defineMacro("\\renewcommand",context=>newcommand(
-context,!0,!1,!1));defineMacro("\\providecommand",context=>newcommand(context,!0,!0,!0));defineMacro("\\message",context=>{var arg=context.consumeArgs(1)[0];return console.log(arg.reverse().map(token2=>token2.text).join("")),""});defineMacro("\\errmessage",context=>{var arg=context.consumeArgs(1)[0];return console.error(arg.reverse().map(token2=>token2.text).join("")),""});defineMacro("\\show",context=>{var tok=context.popToken(),name=tok.text;return console.log(tok,context.macros.get(name),functions[name],
-symbols.math[name],symbols.text[name]),""});defineMacro("\\bgroup","{");defineMacro("\\egroup","}");defineMacro("~","\\nobreakspace");defineMacro("\\lq","`");defineMacro("\\rq","'");defineMacro("\\aa","\\r a");defineMacro("\\AA","\\r A");defineMacro("\\textcopyright","\\html@mathml{\\textcircled{c}}{\\char`©}");defineMacro("\\copyright","\\TextOrMath{\\textcopyright}{\\text{\\textcopyright}}");defineMacro("\\textregistered","\\html@mathml{\\textcircled{\\scriptsize R}}{\\char`®}");defineMacro(
-"ℬ","\\mathscr{B}");defineMacro("ℰ","\\mathscr{E}");defineMacro("ℱ","\\mathscr{F}");defineMacro("ℋ","\\mathscr{H}");defineMacro("ℐ","\\mathscr{I}");defineMacro("ℒ","\\mathscr{L}");defineMacro("ℳ","\\mathscr{M}");defineMacro("ℛ","\\mathscr{R}");defineMacro("ℭ","\\mathfrak{C}");defineMacro("ℌ","\\mathfrak{H}");defineMacro("ℨ","\\mathfrak{Z}");defineMacro("\\Bbbk","\\Bbb{k}");defineMacro("·","\\cdotp");defineMacro("\\llap","\\mathllap{\\textrm{#1}}");defineMacro("\\rlap","\\ma\
-thrlap{\\textrm{#1}}");defineMacro("\\clap","\\mathclap{\\textrm{#1}}");defineMacro("\\mathstrut","\\vphantom{(}");defineMacro("\\underbar","\\underline{\\text{#1}}");defineMacro("\\not",'\\html@mathml{\\mathrel{\\mathrlap\\@not}}{\\char"338}');defineMacro("\\neq","\\html@mathml{\\mathrel{\\not=}}{\\mathrel{\\char`≠}}");defineMacro("\\ne","\\neq");defineMacro("≠","\\neq");defineMacro("\\notin","\\html@mathml{\\mathrel{{\\in}\\mathllap{/\\mskip1mu}}}{\\mathrel{\\char`∉}}");defineMacro("∉",
-"\\notin");defineMacro("≘","\\html@mathml{\\mathrel{=\\kern{-1em}\\raisebox{0.4em}{$\\scriptsize\\frown$}}}{\\mathrel{\\char`≘}}");defineMacro("≙","\\html@mathml{\\stackrel{\\tiny\\wedge}{=}}{\\mathrel{\\char`≘}}");defineMacro("≚","\\html@mathml{\\stackrel{\\tiny\\vee}{=}}{\\mathrel{\\char`≚}}");defineMacro("≛","\\html@mathml{\\stackrel{\\scriptsize\\star}{=}}{\\mathrel{\\char`≛}}");defineMacro("≝","\\html@mathml{\\stackrel{\\tiny\\mathrm{def}}{=}}{\\mathrel{\\char`≝}}");defineMacro(
-"≞","\\html@mathml{\\stackrel{\\tiny\\mathrm{m}}{=}}{\\mathrel{\\char`≞}}");defineMacro("≟","\\html@mathml{\\stackrel{\\tiny?}{=}}{\\mathrel{\\char`≟}}");defineMacro("⟂","\\perp");defineMacro("‼","\\mathclose{!\\mkern-0.8mu!}");defineMacro("∌","\\notni");defineMacro("⌜","\\ulcorner");defineMacro("⌝","\\urcorner");defineMacro("⌞","\\llcorner");defineMacro("⌟","\\lrcorner");defineMacro("©","\\copyright");defineMacro("®","\\textregistered");defineMacro("️","\\textregistered");
-defineMacro("\\ulcorner",'\\html@mathml{\\@ulcorner}{\\mathop{\\char"231c}}');defineMacro("\\urcorner",'\\html@mathml{\\@urcorner}{\\mathop{\\char"231d}}');defineMacro("\\llcorner",'\\html@mathml{\\@llcorner}{\\mathop{\\char"231e}}');defineMacro("\\lrcorner",'\\html@mathml{\\@lrcorner}{\\mathop{\\char"231f}}');defineMacro("\\vdots","{\\varvdots\\rule{0pt}{15pt}}");defineMacro("⋮","\\vdots");defineMacro("\\varGamma","\\mathit{\\Gamma}");defineMacro("\\varDelta","\\mathit{\\Delta}");defineMacro(
-"\\varTheta","\\mathit{\\Theta}");defineMacro("\\varLambda","\\mathit{\\Lambda}");defineMacro("\\varXi","\\mathit{\\Xi}");defineMacro("\\varPi","\\mathit{\\Pi}");defineMacro("\\varSigma","\\mathit{\\Sigma}");defineMacro("\\varUpsilon","\\mathit{\\Upsilon}");defineMacro("\\varPhi","\\mathit{\\Phi}");defineMacro("\\varPsi","\\mathit{\\Psi}");defineMacro("\\varOmega","\\mathit{\\Omega}");defineMacro("\\substack","\\begin{subarray}{c}#1\\end{subarray}");defineMacro("\\colon","\\nobreak\\mskip2mu\\mathp\
-unct{}\\mathchoice{\\mkern-3mu}{\\mkern-3mu}{}{}{:}\\mskip6mu\\relax");defineMacro("\\boxed","\\fbox{$\\displaystyle{#1}$}");defineMacro("\\iff","\\DOTSB\\;\\Longleftrightarrow\\;");defineMacro("\\implies","\\DOTSB\\;\\Longrightarrow\\;");defineMacro("\\impliedby","\\DOTSB\\;\\Longleftarrow\\;");defineMacro("\\dddot","{\\overset{\\raisebox{-0.1ex}{\\normalsize ...}}{#1}}");defineMacro("\\ddddot","{\\overset{\\raisebox{-0.1ex}{\\normalsize ....}}{#1}}");var dotsByToken={",":"\\dotsc","\\not":"\\do\
-tsb","+":"\\dotsb","=":"\\dotsb","<":"\\dotsb",">":"\\dotsb","-":"\\dotsb","*":"\\dotsb",":":"\\dotsb","\\DOTSB":"\\dotsb","\\coprod":"\\dotsb","\\bigvee":"\\dotsb","\\bigwedge":"\\dotsb","\\biguplus":"\\dotsb","\\bigcap":"\\dotsb","\\bigcup":"\\dotsb","\\prod":"\\dotsb","\\sum":"\\dotsb","\\bigotimes":"\\dotsb","\\bigoplus":"\\dotsb","\\bigodot":"\\dotsb","\\bigsqcup":"\\dotsb","\\And":"\\dotsb","\\longrightarrow":"\\dotsb","\\Longrightarrow":"\\dotsb","\\longleftarrow":"\\dotsb","\\Longleftarro\
-w":"\\dotsb","\\longleftrightarrow":"\\dotsb","\\Longleftrightarrow":"\\dotsb","\\mapsto":"\\dotsb","\\longmapsto":"\\dotsb","\\hookrightarrow":"\\dotsb","\\doteq":"\\dotsb","\\mathbin":"\\dotsb","\\mathrel":"\\dotsb","\\relbar":"\\dotsb","\\Relbar":"\\dotsb","\\xrightarrow":"\\dotsb","\\xleftarrow":"\\dotsb","\\DOTSI":"\\dotsi","\\int":"\\dotsi","\\oint":"\\dotsi","\\iint":"\\dotsi","\\iiint":"\\dotsi","\\iiiint":"\\dotsi","\\idotsint":"\\dotsi","\\DOTSX":"\\dotsx"};defineMacro("\\dots",function(context){
-var thedots="\\dotso",next2=context.expandAfterFuture().text;return next2 in dotsByToken?thedots=dotsByToken[next2]:(next2.slice(0,4)==="\\not"||next2 in symbols.math&&utils.contains(["bin","rel"],symbols.math[next2].group))&&(thedots="\\dotsb"),thedots});var spaceAfterDots={")":!0,"]":!0,"\\rbrack":!0,"\\}":!0,"\\rbrace":!0,"\\rangle":!0,"\\rceil":!0,"\\rfloor":!0,"\\rgroup":!0,"\\rmoustache":!0,"\\right":!0,"\\bigr":!0,"\\biggr":!0,"\\Bigr":!0,"\\Biggr":!0,$:!0,";":!0,".":!0,",":!0};defineMacro(
-"\\dotso",function(context){var next2=context.future().text;return next2 in spaceAfterDots?"\\ldots\\,":"\\ldots"});defineMacro("\\dotsc",function(context){var next2=context.future().text;return next2 in spaceAfterDots&&next2!==","?"\\ldots\\,":"\\ldots"});defineMacro("\\cdots",function(context){var next2=context.future().text;return next2 in spaceAfterDots?"\\@cdots\\,":"\\@cdots"});defineMacro("\\dotsb","\\cdots");defineMacro("\\dotsm","\\cdots");defineMacro("\\dotsi","\\!\\cdots");defineMacro(
-"\\dotsx","\\ldots\\,");defineMacro("\\DOTSI","\\relax");defineMacro("\\DOTSB","\\relax");defineMacro("\\DOTSX","\\relax");defineMacro("\\tmspace","\\TextOrMath{\\kern#1#3}{\\mskip#1#2}\\relax");defineMacro("\\,","\\tmspace+{3mu}{.1667em}");defineMacro("\\thinspace","\\,");defineMacro("\\>","\\mskip{4mu}");defineMacro("\\:","\\tmspace+{4mu}{.2222em}");defineMacro("\\medspace","\\:");defineMacro("\\;","\\tmspace+{5mu}{.2777em}");defineMacro("\\thickspace","\\;");defineMacro("\\!","\\tmspace-{3mu}{\
-.1667em}");defineMacro("\\negthinspace","\\!");defineMacro("\\negmedspace","\\tmspace-{4mu}{.2222em}");defineMacro("\\negthickspace","\\tmspace-{5mu}{.277em}");defineMacro("\\enspace","\\kern.5em ");defineMacro("\\enskip","\\hskip.5em\\relax");defineMacro("\\quad","\\hskip1em\\relax");defineMacro("\\qquad","\\hskip2em\\relax");defineMacro("\\tag","\\@ifstar\\tag@literal\\tag@paren");defineMacro("\\tag@paren","\\tag@literal{({#1})}");defineMacro("\\tag@literal",context=>{if(context.macros.get("\\d\
-f@tag"))throw new ParseError2("Multiple \\tag");return"\\gdef\\df@tag{\\text{#1}}"});defineMacro("\\bmod","\\mathchoice{\\mskip1mu}{\\mskip1mu}{\\mskip5mu}{\\mskip5mu}\\mathbin{\\rm mod}\\mathchoice{\\mskip1mu}{\\mskip1mu}{\\mskip5mu}{\\mskip5mu}");defineMacro("\\pod","\\allowbreak\\mathchoice{\\mkern18mu}{\\mkern8mu}{\\mkern8mu}{\\mkern8mu}(#1)");defineMacro("\\pmod","\\pod{{\\rm mod}\\mkern6mu#1}");defineMacro("\\mod","\\allowbreak\\mathchoice{\\mkern18mu}{\\mkern12mu}{\\mkern12mu}{\\mkern12mu}{\\rm \
-mod}\\,\\,#1");defineMacro("\\newline","\\\\\\relax");defineMacro("\\TeX","\\textrm{\\html@mathml{T\\kern-.1667em\\raisebox{-.5ex}{E}\\kern-.125emX}{TeX}}");var latexRaiseA=makeEm(fontMetricsData["Main-Regular"][84][1]-.7*fontMetricsData["Main-Regular"][65][1]);defineMacro("\\LaTeX","\\textrm{\\html@mathml{"+("L\\kern-.36em\\raisebox{"+latexRaiseA+"}{\\scriptstyle A}")+"\\kern-.15em\\TeX}{LaTeX}}");defineMacro("\\KaTeX","\\textrm{\\html@mathml{"+("K\\kern-.17em\\raisebox{"+latexRaiseA+"}{\\script\
-style A}")+"\\kern-.15em\\TeX}{KaTeX}}");defineMacro("\\hspace","\\@ifstar\\@hspacer\\@hspace");defineMacro("\\@hspace","\\hskip #1\\relax");defineMacro("\\@hspacer","\\rule{0pt}{0pt}\\hskip #1\\relax");defineMacro("\\ordinarycolon",":");defineMacro("\\vcentcolon","\\mathrel{\\mathop\\ordinarycolon}");defineMacro("\\dblcolon",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-.9mu}\\vcentcolon}}{\\mathop{\\char"2237}}');defineMacro("\\coloneqq",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mke\
-rn-1.2mu}=}}{\\mathop{\\char"2254}}');defineMacro("\\Coloneqq",'\\html@mathml{\\mathrel{\\dblcolon\\mathrel{\\mkern-1.2mu}=}}{\\mathop{\\char"2237\\char"3d}}');defineMacro("\\coloneq",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-1.2mu}\\mathrel{-}}}{\\mathop{\\char"3a\\char"2212}}');defineMacro("\\Coloneq",'\\html@mathml{\\mathrel{\\dblcolon\\mathrel{\\mkern-1.2mu}\\mathrel{-}}}{\\mathop{\\char"2237\\char"2212}}');defineMacro("\\eqqcolon",'\\html@mathml{\\mathrel{=\\mathrel{\\mkern-1.2mu}\\\
-vcentcolon}}{\\mathop{\\char"2255}}');defineMacro("\\Eqqcolon",'\\html@mathml{\\mathrel{=\\mathrel{\\mkern-1.2mu}\\dblcolon}}{\\mathop{\\char"3d\\char"2237}}');defineMacro("\\eqcolon",'\\html@mathml{\\mathrel{\\mathrel{-}\\mathrel{\\mkern-1.2mu}\\vcentcolon}}{\\mathop{\\char"2239}}');defineMacro("\\Eqcolon",'\\html@mathml{\\mathrel{\\mathrel{-}\\mathrel{\\mkern-1.2mu}\\dblcolon}}{\\mathop{\\char"2212\\char"2237}}');defineMacro("\\colonapprox",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-1.2\
-mu}\\approx}}{\\mathop{\\char"3a\\char"2248}}');defineMacro("\\Colonapprox",'\\html@mathml{\\mathrel{\\dblcolon\\mathrel{\\mkern-1.2mu}\\approx}}{\\mathop{\\char"2237\\char"2248}}');defineMacro("\\colonsim",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-1.2mu}\\sim}}{\\mathop{\\char"3a\\char"223c}}');defineMacro("\\Colonsim",'\\html@mathml{\\mathrel{\\dblcolon\\mathrel{\\mkern-1.2mu}\\sim}}{\\mathop{\\char"2237\\char"223c}}');defineMacro("∷","\\dblcolon");defineMacro("∹","\\eqcolon");
-defineMacro("≔","\\coloneqq");defineMacro("≕","\\eqqcolon");defineMacro("⩴","\\Coloneqq");defineMacro("\\ratio","\\vcentcolon");defineMacro("\\coloncolon","\\dblcolon");defineMacro("\\colonequals","\\coloneqq");defineMacro("\\coloncolonequals","\\Coloneqq");defineMacro("\\equalscolon","\\eqqcolon");defineMacro("\\equalscoloncolon","\\Eqqcolon");defineMacro("\\colonminus","\\coloneq");defineMacro("\\coloncolonminus","\\Coloneq");defineMacro("\\minuscolon","\\eqcolon");defineMacro("\\minuscol\
-oncolon","\\Eqcolon");defineMacro("\\coloncolonapprox","\\Colonapprox");defineMacro("\\coloncolonsim","\\Colonsim");defineMacro("\\simcolon","\\mathrel{\\sim\\mathrel{\\mkern-1.2mu}\\vcentcolon}");defineMacro("\\simcoloncolon","\\mathrel{\\sim\\mathrel{\\mkern-1.2mu}\\dblcolon}");defineMacro("\\approxcolon","\\mathrel{\\approx\\mathrel{\\mkern-1.2mu}\\vcentcolon}");defineMacro("\\approxcoloncolon","\\mathrel{\\approx\\mathrel{\\mkern-1.2mu}\\dblcolon}");defineMacro("\\notni","\\html@mathml{\\not\\n\
-i}{\\mathrel{\\char`∌}}");defineMacro("\\limsup","\\DOTSB\\operatorname*{lim\\,sup}");defineMacro("\\liminf","\\DOTSB\\operatorname*{lim\\,inf}");defineMacro("\\injlim","\\DOTSB\\operatorname*{inj\\,lim}");defineMacro("\\projlim","\\DOTSB\\operatorname*{proj\\,lim}");defineMacro("\\varlimsup","\\DOTSB\\operatorname*{\\overline{lim}}");defineMacro("\\varliminf","\\DOTSB\\operatorname*{\\underline{lim}}");defineMacro("\\varinjlim","\\DOTSB\\operatorname*{\\underrightarrow{lim}}");defineMacro("\\v\
-arprojlim","\\DOTSB\\operatorname*{\\underleftarrow{lim}}");defineMacro("\\gvertneqq","\\html@mathml{\\@gvertneqq}{≩}");defineMacro("\\lvertneqq","\\html@mathml{\\@lvertneqq}{≨}");defineMacro("\\ngeqq","\\html@mathml{\\@ngeqq}{≱}");defineMacro("\\ngeqslant","\\html@mathml{\\@ngeqslant}{≱}");defineMacro("\\nleqq","\\html@mathml{\\@nleqq}{≰}");defineMacro("\\nleqslant","\\html@mathml{\\@nleqslant}{≰}");defineMacro("\\nshortmid","\\html@mathml{\\@nshortmid}{∤}");defineMacro("\\nshortpa\
-rallel","\\html@mathml{\\@nshortparallel}{∦}");defineMacro("\\nsubseteqq","\\html@mathml{\\@nsubseteqq}{⊈}");defineMacro("\\nsupseteqq","\\html@mathml{\\@nsupseteqq}{⊉}");defineMacro("\\varsubsetneq","\\html@mathml{\\@varsubsetneq}{⊊}");defineMacro("\\varsubsetneqq","\\html@mathml{\\@varsubsetneqq}{⫋}");defineMacro("\\varsupsetneq","\\html@mathml{\\@varsupsetneq}{⊋}");defineMacro("\\varsupsetneqq","\\html@mathml{\\@varsupsetneqq}{⫌}");defineMacro("\\imath","\\html@mathml{\\@imath}{ı}");
-defineMacro("\\jmath","\\html@mathml{\\@jmath}{ȷ}");defineMacro("\\llbracket","\\html@mathml{\\mathopen{[\\mkern-3.2mu[}}{\\mathopen{\\char`⟦}}");defineMacro("\\rrbracket","\\html@mathml{\\mathclose{]\\mkern-3.2mu]}}{\\mathclose{\\char`⟧}}");defineMacro("⟦","\\llbracket");defineMacro("⟧","\\rrbracket");defineMacro("\\lBrace","\\html@mathml{\\mathopen{\\{\\mkern-3.2mu[}}{\\mathopen{\\char`⦃}}");defineMacro("\\rBrace","\\html@mathml{\\mathclose{]\\mkern-3.2mu\\}}}{\\mathclose{\\char`⦄}}");
-defineMacro("⦃","\\lBrace");defineMacro("⦄","\\rBrace");defineMacro("\\minuso","\\mathbin{\\html@mathml{{\\mathrlap{\\mathchoice{\\kern{0.145em}}{\\kern{0.145em}}{\\kern{0.1015em}}{\\kern{0.0725em}}\\circ}{-}}}{\\char`⦵}}");defineMacro("⦵","\\minuso");defineMacro("\\darr","\\downarrow");defineMacro("\\dArr","\\Downarrow");defineMacro("\\Darr","\\Downarrow");defineMacro("\\lang","\\langle");defineMacro("\\rang","\\rangle");defineMacro("\\uarr","\\uparrow");defineMacro("\\uArr","\\Uparrow");
-defineMacro("\\Uarr","\\Uparrow");defineMacro("\\N","\\mathbb{N}");defineMacro("\\R","\\mathbb{R}");defineMacro("\\Z","\\mathbb{Z}");defineMacro("\\alef","\\aleph");defineMacro("\\alefsym","\\aleph");defineMacro("\\Alpha","\\mathrm{A}");defineMacro("\\Beta","\\mathrm{B}");defineMacro("\\bull","\\bullet");defineMacro("\\Chi","\\mathrm{X}");defineMacro("\\clubs","\\clubsuit");defineMacro("\\cnums","\\mathbb{C}");defineMacro("\\Complex","\\mathbb{C}");defineMacro("\\Dagger","\\ddagger");defineMacro(
-"\\diamonds","\\diamondsuit");defineMacro("\\empty","\\emptyset");defineMacro("\\Epsilon","\\mathrm{E}");defineMacro("\\Eta","\\mathrm{H}");defineMacro("\\exist","\\exists");defineMacro("\\harr","\\leftrightarrow");defineMacro("\\hArr","\\Leftrightarrow");defineMacro("\\Harr","\\Leftrightarrow");defineMacro("\\hearts","\\heartsuit");defineMacro("\\image","\\Im");defineMacro("\\infin","\\infty");defineMacro("\\Iota","\\mathrm{I}");defineMacro("\\isin","\\in");defineMacro("\\Kappa","\\mathrm{K}");
-defineMacro("\\larr","\\leftarrow");defineMacro("\\lArr","\\Leftarrow");defineMacro("\\Larr","\\Leftarrow");defineMacro("\\lrarr","\\leftrightarrow");defineMacro("\\lrArr","\\Leftrightarrow");defineMacro("\\Lrarr","\\Leftrightarrow");defineMacro("\\Mu","\\mathrm{M}");defineMacro("\\natnums","\\mathbb{N}");defineMacro("\\Nu","\\mathrm{N}");defineMacro("\\Omicron","\\mathrm{O}");defineMacro("\\plusmn","\\pm");defineMacro("\\rarr","\\rightarrow");defineMacro("\\rArr","\\Rightarrow");defineMacro("\\R\
-arr","\\Rightarrow");defineMacro("\\real","\\Re");defineMacro("\\reals","\\mathbb{R}");defineMacro("\\Reals","\\mathbb{R}");defineMacro("\\Rho","\\mathrm{P}");defineMacro("\\sdot","\\cdot");defineMacro("\\sect","\\S");defineMacro("\\spades","\\spadesuit");defineMacro("\\sub","\\subset");defineMacro("\\sube","\\subseteq");defineMacro("\\supe","\\supseteq");defineMacro("\\Tau","\\mathrm{T}");defineMacro("\\thetasym","\\vartheta");defineMacro("\\weierp","\\wp");defineMacro("\\Zeta","\\mathrm{Z}");defineMacro(
-"\\argmin","\\DOTSB\\operatorname*{arg\\,min}");defineMacro("\\argmax","\\DOTSB\\operatorname*{arg\\,max}");defineMacro("\\plim","\\DOTSB\\mathop{\\operatorname{plim}}\\limits");defineMacro("\\bra","\\mathinner{\\langle{#1}|}");defineMacro("\\ket","\\mathinner{|{#1}\\rangle}");defineMacro("\\braket","\\mathinner{\\langle{#1}\\rangle}");defineMacro("\\Bra","\\left\\langle#1\\right|");defineMacro("\\Ket","\\left|#1\\right\\rangle");var braketHelper=one2=>context=>{var left=context.consumeArg().tokens,
-middle=context.consumeArg().tokens,middleDouble=context.consumeArg().tokens,right=context.consumeArg().tokens,oldMiddle=context.macros.get("|"),oldMiddleDouble=context.macros.get("\\|");context.macros.beginGroup();var midMacro=double=>context2=>{one2&&(context2.macros.set("|",oldMiddle),middleDouble.length&&context2.macros.set("\\|",oldMiddleDouble));var doubled=double;if(!double&&middleDouble.length){var nextToken=context2.future();nextToken.text==="|"&&(context2.popToken(),doubled=!0)}return{tokens:doubled?
-middleDouble:middle,numArgs:0}};context.macros.set("|",midMacro(!1)),middleDouble.length&&context.macros.set("\\|",midMacro(!0));var arg=context.consumeArg().tokens,expanded=context.expandTokens([...right,...arg,...left]);return context.macros.endGroup(),{tokens:expanded.reverse(),numArgs:0}};defineMacro("\\bra@ket",braketHelper(!1));defineMacro("\\bra@set",braketHelper(!0));defineMacro("\\Braket","\\bra@ket{\\left\\langle}{\\,\\middle\\vert\\,}{\\,\\middle\\vert\\,}{\\right\\rangle}");defineMacro(
-"\\Set","\\bra@set{\\left\\{\\:}{\\;\\middle\\vert\\;}{\\;\\middle\\Vert\\;}{\\:\\right\\}}");defineMacro("\\set","\\bra@set{\\{\\,}{\\mid}{}{\\,\\}}");defineMacro("\\angln","{\\angl n}");defineMacro("\\blue","\\textcolor{##6495ed}{#1}");defineMacro("\\orange","\\textcolor{##ffa500}{#1}");defineMacro("\\pink","\\textcolor{##ff00af}{#1}");defineMacro("\\red","\\textcolor{##df0030}{#1}");defineMacro("\\green","\\textcolor{##28ae7b}{#1}");defineMacro("\\gray","\\textcolor{gray}{#1}");defineMacro("\\\
-purple","\\textcolor{##9d38bd}{#1}");defineMacro("\\blueA","\\textcolor{##ccfaff}{#1}");defineMacro("\\blueB","\\textcolor{##80f6ff}{#1}");defineMacro("\\blueC","\\textcolor{##63d9ea}{#1}");defineMacro("\\blueD","\\textcolor{##11accd}{#1}");defineMacro("\\blueE","\\textcolor{##0c7f99}{#1}");defineMacro("\\tealA","\\textcolor{##94fff5}{#1}");defineMacro("\\tealB","\\textcolor{##26edd5}{#1}");defineMacro("\\tealC","\\textcolor{##01d1c1}{#1}");defineMacro("\\tealD","\\textcolor{##01a995}{#1}");defineMacro(
-"\\tealE","\\textcolor{##208170}{#1}");defineMacro("\\greenA","\\textcolor{##b6ffb0}{#1}");defineMacro("\\greenB","\\textcolor{##8af281}{#1}");defineMacro("\\greenC","\\textcolor{##74cf70}{#1}");defineMacro("\\greenD","\\textcolor{##1fab54}{#1}");defineMacro("\\greenE","\\textcolor{##0d923f}{#1}");defineMacro("\\goldA","\\textcolor{##ffd0a9}{#1}");defineMacro("\\goldB","\\textcolor{##ffbb71}{#1}");defineMacro("\\goldC","\\textcolor{##ff9c39}{#1}");defineMacro("\\goldD","\\textcolor{##e07d10}{#1}");
-defineMacro("\\goldE","\\textcolor{##a75a05}{#1}");defineMacro("\\redA","\\textcolor{##fca9a9}{#1}");defineMacro("\\redB","\\textcolor{##ff8482}{#1}");defineMacro("\\redC","\\textcolor{##f9685d}{#1}");defineMacro("\\redD","\\textcolor{##e84d39}{#1}");defineMacro("\\redE","\\textcolor{##bc2612}{#1}");defineMacro("\\maroonA","\\textcolor{##ffbde0}{#1}");defineMacro("\\maroonB","\\textcolor{##ff92c6}{#1}");defineMacro("\\maroonC","\\textcolor{##ed5fa6}{#1}");defineMacro("\\maroonD","\\textcolor{##ca\
-337c}{#1}");defineMacro("\\maroonE","\\textcolor{##9e034e}{#1}");defineMacro("\\purpleA","\\textcolor{##ddd7ff}{#1}");defineMacro("\\purpleB","\\textcolor{##c6b9fc}{#1}");defineMacro("\\purpleC","\\textcolor{##aa87ff}{#1}");defineMacro("\\purpleD","\\textcolor{##7854ab}{#1}");defineMacro("\\purpleE","\\textcolor{##543b78}{#1}");defineMacro("\\mintA","\\textcolor{##f5f9e8}{#1}");defineMacro("\\mintB","\\textcolor{##edf2df}{#1}");defineMacro("\\mintC","\\textcolor{##e0e5cc}{#1}");defineMacro("\\gra\
-yA","\\textcolor{##f6f7f7}{#1}");defineMacro("\\grayB","\\textcolor{##f0f1f2}{#1}");defineMacro("\\grayC","\\textcolor{##e3e5e6}{#1}");defineMacro("\\grayD","\\textcolor{##d6d8da}{#1}");defineMacro("\\grayE","\\textcolor{##babec2}{#1}");defineMacro("\\grayF","\\textcolor{##888d93}{#1}");defineMacro("\\grayG","\\textcolor{##626569}{#1}");defineMacro("\\grayH","\\textcolor{##3b3e40}{#1}");defineMacro("\\grayI","\\textcolor{##21242c}{#1}");defineMacro("\\kaBlue","\\textcolor{##314453}{#1}");defineMacro(
-"\\kaGreen","\\textcolor{##71B307}{#1}");var implicitCommands={"^":!0,_:!0,"\\limits":!0,"\\nolimits":!0};class MacroExpander{constructor(input,settings,mode){this.settings=void 0,this.expansionCount=void 0,this.lexer=void 0,this.macros=void 0,this.stack=void 0,this.mode=void 0,this.settings=settings,this.expansionCount=0,this.feed(input),this.macros=new Namespace(macros,settings.macros),this.mode=mode,this.stack=[]}feed(input){this.lexer=new Lexer(input,this.settings)}switchMode(newMode){this.mode=
-newMode}beginGroup(){this.macros.beginGroup()}endGroup(){this.macros.endGroup()}endGroups(){this.macros.endGroups()}future(){return this.stack.length===0&&this.pushToken(this.lexer.lex()),this.stack[this.stack.length-1]}popToken(){return this.future(),this.stack.pop()}pushToken(token2){this.stack.push(token2)}pushTokens(tokens){this.stack.push(...tokens)}scanArgument(isOptional){var start2,end,tokens;if(isOptional){if(this.consumeSpaces(),this.future().text!=="[")return null;start2=this.popToken(),
-{tokens,end}=this.consumeArg(["]"])}else({tokens,start:start2,end}=this.consumeArg());return this.pushToken(new Token("EOF",end.loc)),this.pushTokens(tokens),start2.range(end,"")}consumeSpaces(){for(;;){var token2=this.future();if(token2.text===" ")this.stack.pop();else break}}consumeArg(delims){var tokens=[],isDelimited=delims&&delims.length>0;isDelimited||this.consumeSpaces();var start2=this.future(),tok,depth=0,match=0;do{if(tok=this.popToken(),tokens.push(tok),tok.text==="{")++depth;else if(tok.
-text==="}"){if(--depth,depth===-1)throw new ParseError2("Extra }",tok)}else if(tok.text==="EOF")throw new ParseError2("Unexpected end of input in a macro argument, expected '"+(delims&&isDelimited?delims[match]:"}")+"'",tok);if(delims&&isDelimited)if((depth===0||depth===1&&delims[match]==="{")&&tok.text===delims[match]){if(++match,match===delims.length){tokens.splice(-match,match);break}}else match=0}while(depth!==0||isDelimited);return start2.text==="{"&&tokens[tokens.length-1].text==="}"&&(tokens.
-pop(),tokens.shift()),tokens.reverse(),{tokens,start:start2,end:tok}}consumeArgs(numArgs,delimiters2){if(delimiters2){if(delimiters2.length!==numArgs+1)throw new ParseError2("The length of delimiters doesn't match the number of args!");for(var delims=delimiters2[0],i=0;i<delims.length;i++){var tok=this.popToken();if(delims[i]!==tok.text)throw new ParseError2("Use of the macro doesn't match its definition",tok)}}for(var args=[],_i=0;_i<numArgs;_i++)args.push(this.consumeArg(delimiters2&&delimiters2[_i+
-1]).tokens);return args}countExpansion(amount){if(this.expansionCount+=amount,this.expansionCount>this.settings.maxExpand)throw new ParseError2("Too many expansions: infinite loop or need to increase maxExpand setting")}expandOnce(expandableOnly){var topToken=this.popToken(),name=topToken.text,expansion=topToken.noexpand?null:this._getExpansion(name);if(expansion==null||expandableOnly&&expansion.unexpandable){if(expandableOnly&&expansion==null&&name[0]==="\\"&&!this.isDefined(name))throw new ParseError2(
-"Undefined control sequence: "+name);return this.pushToken(topToken),!1}this.countExpansion(1);var tokens=expansion.tokens,args=this.consumeArgs(expansion.numArgs,expansion.delimiters);if(expansion.numArgs){tokens=tokens.slice();for(var i=tokens.length-1;i>=0;--i){var tok=tokens[i];if(tok.text==="#"){if(i===0)throw new ParseError2("Incomplete placeholder at end of macro body",tok);if(tok=tokens[--i],tok.text==="#")tokens.splice(i+1,1);else if(/^[1-9]$/.test(tok.text))tokens.splice(i,2,...args[+tok.
-text-1]);else throw new ParseError2("Not a valid argument number",tok)}}}return this.pushTokens(tokens),tokens.length}expandAfterFuture(){return this.expandOnce(),this.future()}expandNextToken(){for(;;)if(this.expandOnce()===!1){var token2=this.stack.pop();return token2.treatAsRelax&&(token2.text="\\relax"),token2}throw new Error}expandMacro(name){return this.macros.has(name)?this.expandTokens([new Token(name)]):void 0}expandTokens(tokens){var output=[],oldStackLength=this.stack.length;for(this.
-pushTokens(tokens);this.stack.length>oldStackLength;)if(this.expandOnce(!0)===!1){var token2=this.stack.pop();token2.treatAsRelax&&(token2.noexpand=!1,token2.treatAsRelax=!1),output.push(token2)}return this.countExpansion(output.length),output}expandMacroAsText(name){var tokens=this.expandMacro(name);return tokens&&tokens.map(token2=>token2.text).join("")}_getExpansion(name){var definition2=this.macros.get(name);if(definition2==null)return definition2;if(name.length===1){var catcode=this.lexer.catcodes[name];
-if(catcode!=null&&catcode!==13)return}var expansion=typeof definition2=="function"?definition2(this):definition2;if(typeof expansion=="string"){var numArgs=0;if(expansion.indexOf("#")!==-1)for(var stripped=expansion.replace(/##/g,"");stripped.indexOf("#"+(numArgs+1))!==-1;)++numArgs;for(var bodyLexer=new Lexer(expansion,this.settings),tokens=[],tok=bodyLexer.lex();tok.text!=="EOF";)tokens.push(tok),tok=bodyLexer.lex();tokens.reverse();var expanded={tokens,numArgs};return expanded}return expansion}isDefined(name){
-return this.macros.has(name)||functions.hasOwnProperty(name)||symbols.math.hasOwnProperty(name)||symbols.text.hasOwnProperty(name)||implicitCommands.hasOwnProperty(name)}isExpandable(name){var macro=this.macros.get(name);return macro!=null?typeof macro=="string"||typeof macro=="function"||!macro.unexpandable:functions.hasOwnProperty(name)&&!functions[name].primitive}}var unicodeSubRegEx=/^[₊₋₌₍₎₀₁₂₃₄₅₆₇₈₉ₐₑₕᵢⱼₖₗₘₙₒₚᵣₛₜᵤᵥₓᵦᵧᵨᵩᵪ]/,
-uSubsAndSups=Object.freeze({"₊":"+","₋":"-","₌":"=","₍":"(","₎":")","₀":"0","₁":"1","₂":"2","₃":"3","₄":"4","₅":"5","₆":"6","₇":"7","₈":"8","₉":"9","ₐ":"a","ₑ":"e","ₕ":"h","ᵢ":"i","ⱼ":"j","ₖ":"k","ₗ":"l","ₘ":"m","ₙ":"n","ₒ":"o","ₚ":"p","ᵣ":"r","ₛ":"s","ₜ":"t","ᵤ":"u","ᵥ":"v","ₓ":"x","ᵦ":"β","ᵧ":"γ","ᵨ":"ρ","ᵩ":"ϕ","ᵪ":"χ","⁺":"+","⁻":"-","⁼":"=","⁽":"(","⁾":")","⁰":"0","¹":"1","²":"2","³":"3","⁴":"4",
-"⁵":"5","⁶":"6","⁷":"7","⁸":"8","⁹":"9","ᴬ":"A","ᴮ":"B","ᴰ":"D","ᴱ":"E","ᴳ":"G","ᴴ":"H","ᴵ":"I","ᴶ":"J","ᴷ":"K","ᴸ":"L","ᴹ":"M","ᴺ":"N","ᴼ":"O","ᴾ":"P","ᴿ":"R","ᵀ":"T","ᵁ":"U","ⱽ":"V","ᵂ":"W","ᵃ":"a","ᵇ":"b","ᶜ":"c","ᵈ":"d","ᵉ":"e","ᶠ":"f","ᵍ":"g",ʰ:"h","ⁱ":"i",ʲ:"j","ᵏ":"k",ˡ:"l","ᵐ":"m",ⁿ:"n","ᵒ":"o","ᵖ":"p",ʳ:"r",ˢ:"s","ᵗ":"t","ᵘ":"u","ᵛ":"v",ʷ:"w",ˣ:"x",ʸ:"y","ᶻ":"z","ᵝ":"β","ᵞ":"γ","ᵟ":"δ","ᵠ":"\
-ϕ","ᵡ":"χ","ᶿ":"θ"}),unicodeAccents={"́":{text:"\\'",math:"\\acute"},"̀":{text:"\\`",math:"\\grave"},"̈":{text:'\\"',math:"\\ddot"},"̃":{text:"\\~",math:"\\tilde"},"̄":{text:"\\=",math:"\\bar"},"̆":{text:"\\u",math:"\\breve"},"̌":{text:"\\v",math:"\\check"},"̂":{text:"\\^",math:"\\hat"},"̇":{text:"\\.",math:"\\dot"},"̊":{text:"\\r",math:"\\mathring"},"̋":{text:"\\H"},"̧":{text:"\\c"}},unicodeSymbols={á:"á",à:"à",ä:"ä",ǟ:"ǟ",ã:"ã",ā:"ā",ă:"ă",ắ:"ắ",
-ằ:"ằ",ẵ:"ẵ",ǎ:"ǎ",â:"â",ấ:"ấ",ầ:"ầ",ẫ:"ẫ",ȧ:"ȧ",ǡ:"ǡ",å:"å",ǻ:"ǻ",ḃ:"ḃ",ć:"ć",ḉ:"ḉ",č:"č",ĉ:"ĉ",ċ:"ċ",ç:"ç",ď:"ď",ḋ:"ḋ",ḑ:"ḑ",é:"é",è:"è",ë:"ë",ẽ:"ẽ",ē:"ē",ḗ:"ḗ",ḕ:"ḕ",ĕ:"ĕ",ḝ:"ḝ",ě:"ě",ê:"ê",ế:"ế",ề:"ề",ễ:"ễ",ė:"ė",ȩ:"ȩ",ḟ:"ḟ",ǵ:"ǵ",ḡ:"ḡ",ğ:"ğ",ǧ:"ǧ",ĝ:"ĝ",ġ:"ġ",ģ:"ģ",ḧ:"ḧ",ȟ:"ȟ",ĥ:"ĥ",ḣ:"ḣ",ḩ:"ḩ",í:"\
-í",ì:"ì",ï:"ï",ḯ:"ḯ",ĩ:"ĩ",ī:"ī",ĭ:"ĭ",ǐ:"ǐ",î:"î",ǰ:"ǰ",ĵ:"ĵ",ḱ:"ḱ",ǩ:"ǩ",ķ:"ķ",ĺ:"ĺ",ľ:"ľ",ļ:"ļ",ḿ:"ḿ",ṁ:"ṁ",ń:"ń",ǹ:"ǹ",ñ:"ñ",ň:"ň",ṅ:"ṅ",ņ:"ņ",ó:"ó",ò:"ò",ö:"ö",ȫ:"ȫ",õ:"õ",ṍ:"ṍ",ṏ:"ṏ",ȭ:"ȭ",ō:"ō",ṓ:"ṓ",ṑ:"ṑ",ŏ:"ŏ",ǒ:"ǒ",ô:"ô",ố:"ố",ồ:"ồ",ỗ:"ỗ",ȯ:"ȯ",ȱ:"ȱ",ő:"ő",ṕ:"ṕ",ṗ:"ṗ",ŕ:"ŕ",ř:"ř",ṙ:"ṙ",ŗ:"ŗ",ś:"ś",
-ṥ:"ṥ",š:"š",ṧ:"ṧ",ŝ:"ŝ",ṡ:"ṡ",ş:"ş",ẗ:"ẗ",ť:"ť",ṫ:"ṫ",ţ:"ţ",ú:"ú",ù:"ù",ü:"ü",ǘ:"ǘ",ǜ:"ǜ",ǖ:"ǖ",ǚ:"ǚ",ũ:"ũ",ṹ:"ṹ",ū:"ū",ṻ:"ṻ",ŭ:"ŭ",ǔ:"ǔ",û:"û",ů:"ů",ű:"ű",ṽ:"ṽ",ẃ:"ẃ",ẁ:"ẁ",ẅ:"ẅ",ŵ:"ŵ",ẇ:"ẇ",ẘ:"ẘ",ẍ:"ẍ",ẋ:"ẋ",ý:"ý",ỳ:"ỳ",ÿ:"ÿ",ỹ:"ỹ",ȳ:"ȳ",ŷ:"ŷ",ẏ:"ẏ",ẙ:"ẙ",ź:"ź",ž:"ž",ẑ:"ẑ",ż:"ż",Á:"Á",À:"À",Ä:"Ä",Ǟ:"Ǟ",Ã:"\
-Ã",Ā:"Ā",Ă:"Ă",Ắ:"Ắ",Ằ:"Ằ",Ẵ:"Ẵ",Ǎ:"Ǎ",Â:"Â",Ấ:"Ấ",Ầ:"Ầ",Ẫ:"Ẫ",Ȧ:"Ȧ",Ǡ:"Ǡ",Å:"Å",Ǻ:"Ǻ",Ḃ:"Ḃ",Ć:"Ć",Ḉ:"Ḉ",Č:"Č",Ĉ:"Ĉ",Ċ:"Ċ",Ç:"Ç",Ď:"Ď",Ḋ:"Ḋ",Ḑ:"Ḑ",É:"É",È:"È",Ë:"Ë",Ẽ:"Ẽ",Ē:"Ē",Ḗ:"Ḗ",Ḕ:"Ḕ",Ĕ:"Ĕ",Ḝ:"Ḝ",Ě:"Ě",Ê:"Ê",Ế:"Ế",Ề:"Ề",Ễ:"Ễ",Ė:"Ė",Ȩ:"Ȩ",Ḟ:"Ḟ",Ǵ:"Ǵ",Ḡ:"Ḡ",Ğ:"Ğ",Ǧ:"Ǧ",Ĝ:"Ĝ",Ġ:"Ġ",Ģ:"Ģ",Ḧ:"Ḧ",Ȟ:"\
-Ȟ",Ĥ:"Ĥ",Ḣ:"Ḣ",Ḩ:"Ḩ",Í:"Í",Ì:"Ì",Ï:"Ï",Ḯ:"Ḯ",Ĩ:"Ĩ",Ī:"Ī",Ĭ:"Ĭ",Ǐ:"Ǐ",Î:"Î",İ:"İ",Ĵ:"Ĵ",Ḱ:"Ḱ",Ǩ:"Ǩ",Ķ:"Ķ",Ĺ:"Ĺ",Ľ:"Ľ",Ļ:"Ļ",Ḿ:"Ḿ",Ṁ:"Ṁ",Ń:"Ń",Ǹ:"Ǹ",Ñ:"Ñ",Ň:"Ň",Ṅ:"Ṅ",Ņ:"Ņ",Ó:"Ó",Ò:"Ò",Ö:"Ö",Ȫ:"Ȫ",Õ:"Õ",Ṍ:"Ṍ",Ṏ:"Ṏ",Ȭ:"Ȭ",Ō:"Ō",Ṓ:"Ṓ",Ṑ:"Ṑ",Ŏ:"Ŏ",Ǒ:"Ǒ",Ô:"Ô",Ố:"Ố",Ồ:"Ồ",Ỗ:"Ỗ",Ȯ:"Ȯ",Ȱ:"Ȱ",Ő:"Ő",Ṕ:"Ṕ",Ṗ:"Ṗ",Ŕ:"Ŕ",
-Ř:"Ř",Ṙ:"Ṙ",Ŗ:"Ŗ",Ś:"Ś",Ṥ:"Ṥ",Š:"Š",Ṧ:"Ṧ",Ŝ:"Ŝ",Ṡ:"Ṡ",Ş:"Ş",Ť:"Ť",Ṫ:"Ṫ",Ţ:"Ţ",Ú:"Ú",Ù:"Ù",Ü:"Ü",Ǘ:"Ǘ",Ǜ:"Ǜ",Ǖ:"Ǖ",Ǚ:"Ǚ",Ũ:"Ũ",Ṹ:"Ṹ",Ū:"Ū",Ṻ:"Ṻ",Ŭ:"Ŭ",Ǔ:"Ǔ",Û:"Û",Ů:"Ů",Ű:"Ű",Ṽ:"Ṽ",Ẃ:"Ẃ",Ẁ:"Ẁ",Ẅ:"Ẅ",Ŵ:"Ŵ",Ẇ:"Ẇ",Ẍ:"Ẍ",Ẋ:"Ẋ",Ý:"Ý",Ỳ:"Ỳ",Ÿ:"Ÿ",Ỹ:"Ỹ",Ȳ:"Ȳ",Ŷ:"Ŷ",Ẏ:"Ẏ",Ź:"Ź",Ž:"Ž",Ẑ:"Ẑ",Ż:"Ż",ά:"ά",ὰ:"ὰ",ᾱ:"ᾱ",ᾰ:"\
-ᾰ",έ:"έ",ὲ:"ὲ",ή:"ή",ὴ:"ὴ",ί:"ί",ὶ:"ὶ",ϊ:"ϊ",ΐ:"ΐ",ῒ:"ῒ",ῑ:"ῑ",ῐ:"ῐ",ό:"ό",ὸ:"ὸ",ύ:"ύ",ὺ:"ὺ",ϋ:"ϋ",ΰ:"ΰ",ῢ:"ῢ",ῡ:"ῡ",ῠ:"ῠ",ώ:"ώ",ὼ:"ὼ",Ύ:"Ύ",Ὺ:"Ὺ",Ϋ:"Ϋ",Ῡ:"Ῡ",Ῠ:"Ῠ",Ώ:"Ώ",Ὼ:"Ὼ"};class Parser{constructor(input,settings){this.mode=void 0,this.gullet=void 0,this.settings=void 0,this.leftrightDepth=void 0,this.nextToken=void 0,this.mode="math",this.gullet=new MacroExpander(
-input,settings,this.mode),this.settings=settings,this.leftrightDepth=0}expect(text2,consume){if(consume===void 0&&(consume=!0),this.fetch().text!==text2)throw new ParseError2("Expected '"+text2+"', got '"+this.fetch().text+"'",this.fetch());consume&&this.consume()}consume(){this.nextToken=null}fetch(){return this.nextToken==null&&(this.nextToken=this.gullet.expandNextToken()),this.nextToken}switchMode(newMode){this.mode=newMode,this.gullet.switchMode(newMode)}parse(){this.settings.globalGroup||this.
-gullet.beginGroup(),this.settings.colorIsTextColor&&this.gullet.macros.set("\\color","\\textcolor");try{var parse2=this.parseExpression(!1);return this.expect("EOF"),this.settings.globalGroup||this.gullet.endGroup(),parse2}finally{this.gullet.endGroups()}}subparse(tokens){var oldToken=this.nextToken;this.consume(),this.gullet.pushToken(new Token("}")),this.gullet.pushTokens(tokens);var parse2=this.parseExpression(!1);return this.expect("}"),this.nextToken=oldToken,parse2}parseExpression(breakOnInfix,breakOnTokenText){
-for(var body2=[];;){this.mode==="math"&&this.consumeSpaces();var lex=this.fetch();if(Parser.endOfExpression.indexOf(lex.text)!==-1||breakOnTokenText&&lex.text===breakOnTokenText||breakOnInfix&&functions[lex.text]&&functions[lex.text].infix)break;var atom=this.parseAtom(breakOnTokenText);if(atom){if(atom.type==="internal")continue}else break;body2.push(atom)}return this.mode==="text"&&this.formLigatures(body2),this.handleInfixNodes(body2)}handleInfixNodes(body2){for(var overIndex=-1,funcName,i=0;i<
-body2.length;i++)if(body2[i].type==="infix"){if(overIndex!==-1)throw new ParseError2("only one infix operator per group",body2[i].token);overIndex=i,funcName=body2[i].replaceWith}if(overIndex!==-1&&funcName){var numerNode,denomNode,numerBody=body2.slice(0,overIndex),denomBody=body2.slice(overIndex+1);numerBody.length===1&&numerBody[0].type==="ordgroup"?numerNode=numerBody[0]:numerNode={type:"ordgroup",mode:this.mode,body:numerBody},denomBody.length===1&&denomBody[0].type==="ordgroup"?denomNode=denomBody[0]:
-denomNode={type:"ordgroup",mode:this.mode,body:denomBody};var node2;return funcName==="\\\\abovefrac"?node2=this.callFunction(funcName,[numerNode,body2[overIndex],denomNode],[]):node2=this.callFunction(funcName,[numerNode,denomNode],[]),[node2]}else return body2}handleSupSubscript(name){var symbolToken=this.fetch(),symbol=symbolToken.text;this.consume(),this.consumeSpaces();var group;do{var _group;group=this.parseGroup(name)}while(((_group=group)==null?void 0:_group.type)==="internal");if(!group)
-throw new ParseError2("Expected group after '"+symbol+"'",symbolToken);return group}formatUnsupportedCmd(text2){for(var textordArray=[],i=0;i<text2.length;i++)textordArray.push({type:"textord",mode:"text",text:text2[i]});var textNode={type:"text",mode:this.mode,body:textordArray},colorNode={type:"color",mode:this.mode,color:this.settings.errorColor,body:[textNode]};return colorNode}parseAtom(breakOnTokenText){var base2=this.parseGroup("atom",breakOnTokenText);if(base2?.type==="internal"||this.mode===
-"text")return base2;for(var superscript,subscript;;){this.consumeSpaces();var lex=this.fetch();if(lex.text==="\\limits"||lex.text==="\\nolimits"){if(base2&&base2.type==="op"){var limits=lex.text==="\\limits";base2.limits=limits,base2.alwaysHandleSupSub=!0}else if(base2&&base2.type==="operatorname")base2.alwaysHandleSupSub&&(base2.limits=lex.text==="\\limits");else throw new ParseError2("Limit controls must follow a math operator",lex);this.consume()}else if(lex.text==="^"){if(superscript)throw new ParseError2(
-"Double superscript",lex);superscript=this.handleSupSubscript("superscript")}else if(lex.text==="_"){if(subscript)throw new ParseError2("Double subscript",lex);subscript=this.handleSupSubscript("subscript")}else if(lex.text==="'"){if(superscript)throw new ParseError2("Double superscript",lex);var prime={type:"textord",mode:this.mode,text:"\\prime"},primes=[prime];for(this.consume();this.fetch().text==="'";)primes.push(prime),this.consume();this.fetch().text==="^"&&primes.push(this.handleSupSubscript(
-"superscript")),superscript={type:"ordgroup",mode:this.mode,body:primes}}else if(uSubsAndSups[lex.text]){var isSub=unicodeSubRegEx.test(lex.text),subsupTokens=[];for(subsupTokens.push(new Token(uSubsAndSups[lex.text])),this.consume();;){var token2=this.fetch().text;if(!uSubsAndSups[token2]||unicodeSubRegEx.test(token2)!==isSub)break;subsupTokens.unshift(new Token(uSubsAndSups[token2])),this.consume()}var body2=this.subparse(subsupTokens);isSub?subscript={type:"ordgroup",mode:"math",body:body2}:superscript=
-{type:"ordgroup",mode:"math",body:body2}}else break}return superscript||subscript?{type:"supsub",mode:this.mode,base:base2,sup:superscript,sub:subscript}:base2}parseFunction(breakOnTokenText,name){var token2=this.fetch(),func=token2.text,funcData=functions[func];if(!funcData)return null;if(this.consume(),name&&name!=="atom"&&!funcData.allowedInArgument)throw new ParseError2("Got function '"+func+"' with no arguments"+(name?" as "+name:""),token2);if(this.mode==="text"&&!funcData.allowedInText)throw new ParseError2(
-"Can't use function '"+func+"' in text mode",token2);if(this.mode==="math"&&funcData.allowedInMath===!1)throw new ParseError2("Can't use function '"+func+"' in math mode",token2);var{args,optArgs}=this.parseArguments(func,funcData);return this.callFunction(func,args,optArgs,token2,breakOnTokenText)}callFunction(name,args,optArgs,token2,breakOnTokenText){var context={funcName:name,parser:this,token:token2,breakOnTokenText},func=functions[name];if(func&&func.handler)return func.handler(context,args,
-optArgs);throw new ParseError2("No function handler for "+name)}parseArguments(func,funcData){var totalArgs=funcData.numArgs+funcData.numOptionalArgs;if(totalArgs===0)return{args:[],optArgs:[]};for(var args=[],optArgs=[],i=0;i<totalArgs;i++){var argType=funcData.argTypes&&funcData.argTypes[i],isOptional=i<funcData.numOptionalArgs;(funcData.primitive&&argType==null||funcData.type==="sqrt"&&i===1&&optArgs[0]==null)&&(argType="primitive");var arg=this.parseGroupOfType("argument to '"+func+"'",argType,
-isOptional);if(isOptional)optArgs.push(arg);else if(arg!=null)args.push(arg);else throw new ParseError2("Null argument, please report this as a bug")}return{args,optArgs}}parseGroupOfType(name,type2,optional2){switch(type2){case"color":return this.parseColorGroup(optional2);case"size":return this.parseSizeGroup(optional2);case"url":return this.parseUrlGroup(optional2);case"math":case"text":return this.parseArgumentGroup(optional2,type2);case"hbox":{var group=this.parseArgumentGroup(optional2,"te\
-xt");return group!=null?{type:"styling",mode:group.mode,body:[group],style:"text"}:null}case"raw":{var token2=this.parseStringGroup("raw",optional2);return token2!=null?{type:"raw",mode:"text",string:token2.text}:null}case"primitive":{if(optional2)throw new ParseError2("A primitive argument cannot be optional");var _group2=this.parseGroup(name);if(_group2==null)throw new ParseError2("Expected group as "+name,this.fetch());return _group2}case"original":case null:case void 0:return this.parseArgumentGroup(
-optional2);default:throw new ParseError2("Unknown group type as "+name,this.fetch())}}consumeSpaces(){for(;this.fetch().text===" ";)this.consume()}parseStringGroup(modeName,optional2){var argToken=this.gullet.scanArgument(optional2);if(argToken==null)return null;for(var str="",nextToken;(nextToken=this.fetch()).text!=="EOF";)str+=nextToken.text,this.consume();return this.consume(),argToken.text=str,argToken}parseRegexGroup(regex,modeName){for(var firstToken=this.fetch(),lastToken=firstToken,str="",
-nextToken;(nextToken=this.fetch()).text!=="EOF"&&regex.test(str+nextToken.text);)lastToken=nextToken,str+=lastToken.text,this.consume();if(str==="")throw new ParseError2("Invalid "+modeName+": '"+firstToken.text+"'",firstToken);return firstToken.range(lastToken,str)}parseColorGroup(optional2){var res=this.parseStringGroup("color",optional2);if(res==null)return null;var match=/^(#[a-f0-9]{3}|#?[a-f0-9]{6}|[a-z]+)$/i.exec(res.text);if(!match)throw new ParseError2("Invalid color: '"+res.text+"'",res);
-var color=match[0];return/^[0-9a-f]{6}$/i.test(color)&&(color="#"+color),{type:"color-token",mode:this.mode,color}}parseSizeGroup(optional2){var res,isBlank=!1;if(this.gullet.consumeSpaces(),!optional2&&this.gullet.future().text!=="{"?res=this.parseRegexGroup(/^[-+]? *(?:$|\d+|\d+\.\d*|\.\d*) *[a-z]{0,2} *$/,"size"):res=this.parseStringGroup("size",optional2),!res)return null;!optional2&&res.text.length===0&&(res.text="0pt",isBlank=!0);var match=/([-+]?) *(\d+(?:\.\d*)?|\.\d+) *([a-z]{2})/.exec(
-res.text);if(!match)throw new ParseError2("Invalid size: '"+res.text+"'",res);var data={number:+(match[1]+match[2]),unit:match[3]};if(!validUnit(data))throw new ParseError2("Invalid unit: '"+data.unit+"'",res);return{type:"size",mode:this.mode,value:data,isBlank}}parseUrlGroup(optional2){this.gullet.lexer.setCatcode("%",13),this.gullet.lexer.setCatcode("~",12);var res=this.parseStringGroup("url",optional2);if(this.gullet.lexer.setCatcode("%",14),this.gullet.lexer.setCatcode("~",13),res==null)return null;
-var url2=res.text.replace(/\\([#$%&~_^{}])/g,"$1");return{type:"url",mode:this.mode,url:url2}}parseArgumentGroup(optional2,mode){var argToken=this.gullet.scanArgument(optional2);if(argToken==null)return null;var outerMode=this.mode;mode&&this.switchMode(mode),this.gullet.beginGroup();var expression=this.parseExpression(!1,"EOF");this.expect("EOF"),this.gullet.endGroup();var result={type:"ordgroup",mode:this.mode,loc:argToken.loc,body:expression};return mode&&this.switchMode(outerMode),result}parseGroup(name,breakOnTokenText){
-var firstToken=this.fetch(),text2=firstToken.text,result;if(text2==="{"||text2==="\\begingroup"){this.consume();var groupEnd=text2==="{"?"}":"\\endgroup";this.gullet.beginGroup();var expression=this.parseExpression(!1,groupEnd),lastToken=this.fetch();this.expect(groupEnd),this.gullet.endGroup(),result={type:"ordgroup",mode:this.mode,loc:SourceLocation.range(firstToken,lastToken),body:expression,semisimple:text2==="\\begingroup"||void 0}}else if(result=this.parseFunction(breakOnTokenText,name)||this.
-parseSymbol(),result==null&&text2[0]==="\\"&&!implicitCommands.hasOwnProperty(text2)){if(this.settings.throwOnError)throw new ParseError2("Undefined control sequence: "+text2,firstToken);result=this.formatUnsupportedCmd(text2),this.consume()}return result}formLigatures(group){for(var n=group.length-1,i=0;i<n;++i){var a=group[i],v=a.text;v==="-"&&group[i+1].text==="-"&&(i+1<n&&group[i+2].text==="-"?(group.splice(i,3,{type:"textord",mode:"text",loc:SourceLocation.range(a,group[i+2]),text:"---"}),n-=
-2):(group.splice(i,2,{type:"textord",mode:"text",loc:SourceLocation.range(a,group[i+1]),text:"--"}),n-=1)),(v==="'"||v==="`")&&group[i+1].text===v&&(group.splice(i,2,{type:"textord",mode:"text",loc:SourceLocation.range(a,group[i+1]),text:v+v}),n-=1)}}parseSymbol(){var nucleus=this.fetch(),text2=nucleus.text;if(/^\\verb[^a-zA-Z]/.test(text2)){this.consume();var arg=text2.slice(5),star=arg.charAt(0)==="*";if(star&&(arg=arg.slice(1)),arg.length<2||arg.charAt(0)!==arg.slice(-1))throw new ParseError2(
-`\\verb assertion failed --
-                    please report what input caused this bug`);return arg=arg.slice(1,-1),{type:"verb",mode:"text",body:arg,star}}unicodeSymbols.hasOwnProperty(text2[0])&&!symbols[this.mode][text2[0]]&&(this.settings.strict&&this.mode==="math"&&this.settings.reportNonstrict("unicodeTextInMathMode",'Accented Unicode text character "'+text2[0]+'" used in math mode',nucleus),text2=unicodeSymbols[text2[0]]+text2.slice(1));var match=combiningDiacriticalMarksEndRegex.exec(text2);match&&(text2=text2.substring(
-0,match.index),text2==="i"?text2="ı":text2==="j"&&(text2="ȷ"));var symbol;if(symbols[this.mode][text2]){this.settings.strict&&this.mode==="math"&&extraLatin.indexOf(text2)>=0&&this.settings.reportNonstrict("unicodeTextInMathMode",'Latin-1/Unicode text character "'+text2[0]+'" used in math mode',nucleus);var group=symbols[this.mode][text2].group,loc=SourceLocation.range(nucleus),s2;if(ATOMS.hasOwnProperty(group)){var family=group;s2={type:"atom",mode:this.mode,family,loc,text:text2}}else s2={type:group,
-mode:this.mode,loc,text:text2};symbol=s2}else if(text2.charCodeAt(0)>=128)this.settings.strict&&(supportedCodepoint(text2.charCodeAt(0))?this.mode==="math"&&this.settings.reportNonstrict("unicodeTextInMathMode",'Unicode text character "'+text2[0]+'" used in math mode',nucleus):this.settings.reportNonstrict("unknownSymbol",'Unrecognized Unicode character "'+text2[0]+'"'+(" ("+text2.charCodeAt(0)+")"),nucleus)),symbol={type:"textord",mode:"text",loc:SourceLocation.range(nucleus),text:text2};else return null;
-if(this.consume(),match)for(var i=0;i<match[0].length;i++){var accent2=match[0][i];if(!unicodeAccents[accent2])throw new ParseError2("Unknown accent ' "+accent2+"'",nucleus);var command=unicodeAccents[accent2][this.mode]||unicodeAccents[accent2].text;if(!command)throw new ParseError2("Accent "+accent2+" unsupported in "+this.mode+" mode",nucleus);symbol={type:"accent",mode:this.mode,loc:SourceLocation.range(nucleus),label:command,isStretchy:!1,isShifty:!0,base:symbol}}return symbol}}Parser.endOfExpression=
-["}","\\endgroup","\\end","\\right","&"];var parseTree=function(toParse,settings){if(!(typeof toParse=="string"||toParse instanceof String))throw new TypeError("KaTeX can only parse string typed expression");var parser=new Parser(toParse,settings);delete parser.gullet.macros.current["\\df@tag"];var tree=parser.parse();if(delete parser.gullet.macros.current["\\current@color"],delete parser.gullet.macros.current["\\color"],parser.gullet.macros.get("\\df@tag")){if(!settings.displayMode)throw new ParseError2(
-"\\tag works only in display equations");tree=[{type:"tag",mode:"text",body:tree,tag:parser.subparse([new Token("\\df@tag")])}]}return tree},render=function(expression,baseNode,options){baseNode.textContent="";var node2=renderToDomTree(expression,options).toNode();baseNode.appendChild(node2)};typeof document<"u"&&document.compatMode!=="CSS1Compat"&&(typeof console<"u"&&console.warn("Warning: KaTeX doesn't work in quirks mode. Make sure your website has a suitable doctype."),render=function(){throw new ParseError2(
-"KaTeX doesn't work in quirks mode.")});var renderToString=function(expression,options){var markup=renderToDomTree(expression,options).toMarkup();return markup},generateParseTree=function(expression,options){var settings=new Settings(options);return parseTree(expression,settings)},renderError=function(error2,expression,options){if(options.throwOnError||!(error2 instanceof ParseError2))throw error2;var node2=buildCommon.makeSpan(["katex-error"],[new SymbolNode(expression)]);return node2.setAttribute(
-"title",error2.toString()),node2.setAttribute("style","color:"+options.errorColor),node2},renderToDomTree=function(expression,options){var settings=new Settings(options);try{var tree=parseTree(expression,settings);return buildTree(tree,expression,settings)}catch(error2){return renderError(error2,expression,settings)}},renderToHTMLTree=function(expression,options){var settings=new Settings(options);try{var tree=parseTree(expression,settings);return buildHTMLTree(tree,expression,settings)}catch(error2){
-return renderError(error2,expression,settings)}},version="0.16.22",__domTree={Span,Anchor,SymbolNode,SvgNode,PathNode,LineNode},katex={version,render,renderToString,ParseError:ParseError2,SETTINGS_SCHEMA,__parse:generateParseTree,__renderToDomTree:renderToDomTree,__renderToHTMLTree:renderToHTMLTree,__setFontMetrics:setFontMetrics,__defineSymbol:defineSymbol,__defineFunction:defineFunction,__defineMacro:defineMacro,__domTree};const emptyOptions$5={};function remarkMath(options){const self2=this,settings=options||
-emptyOptions$5,data=self2.data(),micromarkExtensions=data.micromarkExtensions||(data.micromarkExtensions=[]),fromMarkdownExtensions=data.fromMarkdownExtensions||(data.fromMarkdownExtensions=[]),toMarkdownExtensions=data.toMarkdownExtensions||(data.toMarkdownExtensions=[]);micromarkExtensions.push(math$1(settings)),fromMarkdownExtensions.push(mathFromMarkdown()),toMarkdownExtensions.push(mathToMarkdown(settings))}const findAfter=function(parent,index2,test){const is2=convert(test);if(!parent||!parent.
-type||!parent.children)throw new Error("Expected parent node");if(typeof index2=="number"){if(index2<0||index2===Number.POSITIVE_INFINITY)throw new Error("Expected positive finite number as index")}else if(index2=parent.children.indexOf(index2),index2<0)throw new Error("Expected child node or index");for(;++index2<parent.children.length;)if(is2(parent.children[index2],index2,parent))return parent.children[index2]},convertElement=function(test){if(test==null)return element$2;if(typeof test=="stri\
-ng")return tagNameFactory(test);if(typeof test=="object")return anyFactory(test);if(typeof test=="function")return castFactory(test);throw new Error("Expected function, string, or array as `test`")};function anyFactory(tests){const checks2=[];let index2=-1;for(;++index2<tests.length;)checks2[index2]=convertElement(tests[index2]);return castFactory(any2);function any2(...parameters){let index3=-1;for(;++index3<checks2.length;)if(checks2[index3].apply(this,parameters))return!0;return!1}}function tagNameFactory(check){
-return castFactory(tagName);function tagName(element2){return element2.tagName===check}}function castFactory(testFunction){return check;function check(value,index2,parent){return!!(looksLikeAnElement(value)&&testFunction.call(this,value,typeof index2=="number"?index2:void 0,parent||void 0))}}function element$2(element2){return!!(element2&&typeof element2=="object"&&"type"in element2&&element2.type==="element"&&"tagName"in element2&&typeof element2.tagName=="string")}function looksLikeAnElement(value){
-return value!==null&&typeof value=="object"&&"type"in value&&"tagName"in value}const searchLineFeeds=/\n/g,searchTabOrSpaces=/[\t ]+/g,br=convertElement("br"),cell=convertElement(isCell),p$1=convertElement("p"),row=convertElement("tr"),notRendered=convertElement(["datalist","head","noembed","noframes","noscript","rp","script","style","template","title",hidden,closedDialog]),blockOrCaption=convertElement(["address","article","aside","blockquote","body","caption","center","dd","dialog","dir","dl",
-"dt","div","figure","figcaption","footer","form,","h1","h2","h3","h4","h5","h6","header","hgroup","hr","html","legend","li","listing","main","menu","nav","ol","p","plaintext","pre","section","ul","xmp"]);function toText(tree,options){const options_=options||{},children="children"in tree?tree.children:[],block2=blockOrCaption(tree),whitespace2=inferWhitespace(tree,{whitespace:options_.whitespace||"normal"}),results=[];(tree.type==="text"||tree.type==="comment")&&results.push(...collectText(tree,{
-breakBefore:!0,breakAfter:!0}));let index2=-1;for(;++index2<children.length;)results.push(...renderedTextCollection(children[index2],tree,{whitespace:whitespace2,breakBefore:index2?void 0:block2,breakAfter:index2<children.length-1?br(children[index2+1]):block2}));const result=[];let count;for(index2=-1;++index2<results.length;){const value=results[index2];typeof value=="number"?count!==void 0&&value>count&&(count=value):value&&(count!==void 0&&count>-1&&result.push(`
-`.repeat(count)||" "),count=-1,result.push(value))}return result.join("")}function renderedTextCollection(node2,parent,info2){return node2.type==="element"?collectElement(node2,parent,info2):node2.type==="text"?info2.whitespace==="normal"?collectText(node2,info2):collectPreText(node2):[]}function collectElement(node2,parent,info2){const whitespace2=inferWhitespace(node2,info2),children=node2.children||[];let index2=-1,items2=[];if(notRendered(node2))return items2;let prefix,suffix;for(br(node2)||
-row(node2)&&findAfter(parent,node2,row)?suffix=`
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-у получитьминимальнуюдлинупаролейпользователей получитьнавигационнуюссылку получитьнавигационнуюссылкуинформационнойбазы получитьобновлениеконфигурациибазыданных получитьобновлениепредопределенныхданныхинформационнойбазы получитьобщиймакет получитьобщуюформу получитьокна получитьоперативнуюотметкувремени получитьотключениебезопасногорежима получитьпараметрыфункциональныхопцийинтерфейса получитьполноеимяпредопределенногозначения получитьпредставлениянавигационныхссылок получитьпроверкусложностипа\
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-нныедляредактирования разделитьфайл разорватьсоединениесвнешнимисточникомданных раскодироватьстроку рольдоступна секунда сигнал символ скопироватьжурналрегистрации смещениелетнеговремени смещениестандартноговремени соединитьбуферыдвоичныхданных создатькаталог создатьфабрикуxdto сокрл сокрлп сокрп сообщить состояние сохранитьзначение сохранитьнастройкипользователя сред стрдлина стрзаканчиваетсяна стрзаменить стрнайти стрначинаетсяс строка строкасоединенияинформационнойбазы стрполучитьстроку стрра\
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-ырасчета регистрысведений регламентныезадания сериализаторxdto справочники средствагеопозиционирования средствакриптографии средствамультимедиа средстваотображениярекламы средствапочты средствателефонии фабрикаxdto файловыепотоки фоновыезадания хранилищанастроек хранилищевариантовотчетов хранилищенастроекданныхформ хранилищеобщихнастроек хранилищепользовательскихнастроекдинамическихсписков хранилищепользовательскихнастроекотчетов хранилищесистемныхнастроек ",
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-ниеинтерваладиаграммыганта отображениекнопки отображениекнопкивыбора отображениеобсужденийформы отображениеобычнойгруппы отображениеотрицательныхзначенийпузырьковойдиаграммы отображениепанелипоиска отображениеподсказки отображениепредупрежденияприредактировании отображениеразметкиполосырегулирования отображениестраницформы отображениетаблицы отображениетекстазначениядиаграммыганта отображениеуправленияобычнойгруппы отображениефигурыкнопки палитрацветовдиаграммы поведениеобычнойгруппы поддержкама\
-сштабадендрограммы поддержкамасштабадиаграммыганта поддержкамасштабасводнойдиаграммы поисквтаблицепривводе положениезаголовкаэлементаформы положениекартинкикнопкиформы положениекартинкиэлементаграфическойсхемы положениекоманднойпанелиформы положениекоманднойпанелиэлементаформы положениеопорнойточкиотрисовки положениеподписейкдиаграмме положениеподписейшкалызначенийизмерительнойдиаграммы положениесостоянияпросмотра положениестрокипоиска положениетекстасоединительнойлинии положениеуправленияпоиско\
-м положениешкалывремени порядокотображенияточекгоризонтальнойгистограммы порядоксерийвлегендедиаграммы размеркартинки расположениезаголовкашкалыдиаграммы растягиваниеповертикалидиаграммыганта режимавтоотображениясостояния режимвводастроктаблицы режимвыборанезаполненного режимвыделениядаты режимвыделениястрокитаблицы режимвыделениятаблицы режимизмененияразмера режимизменениясвязанногозначения режимиспользованиядиалогапечати режимиспользованияпараметракоманды режиммасштабированияпросмотра режимосн\
-овногоокнаклиентскогоприложения режимоткрытияокнаформы режимотображениявыделения режимотображениягеографическойсхемы режимотображениязначенийсерии режимотрисовкисеткиграфическойсхемы режимполупрозрачностидиаграммы режимпробеловдиаграммы режимразмещениянастранице режимредактированияколонки режимсглаживаниядиаграммы режимсглаживанияиндикатора режимсписказадач сквозноевыравнивание сохранениеданныхформывнастройках способзаполнениятекстазаголовкашкалыдиаграммы способопределенияограничивающегозначения\
-диаграммы стандартнаягруппакоманд стандартноеоформление статусоповещенияпользователя стильстрелки типаппроксимациилиниитрендадиаграммы типдиаграммы типединицышкалывремени типимпортасерийслоягеографическойсхемы типлиниигеографическойсхемы типлиниидиаграммы типмаркерагеографическойсхемы типмаркерадиаграммы типобластиоформления типорганизацииисточникаданныхгеографическойсхемы типотображениясериислоягеографическойсхемы типотображенияточечногообъектагеографическойсхемы типотображенияшкалыэлементалеге\
-ндыгеографическойсхемы типпоискаобъектовгеографическойсхемы типпроекциигеографическойсхемы типразмещенияизмерений типразмещенияреквизитовизмерений типрамкиэлементауправления типсводнойдиаграммы типсвязидиаграммыганта типсоединениязначенийпосериямдиаграммы типсоединенияточекдиаграммы типсоединительнойлинии типстороныэлементаграфическойсхемы типформыотчета типшкалырадарнойдиаграммы факторлиниитрендадиаграммы фигуракнопки фигурыграфическойсхемы фиксациявтаблице форматдняшкалывремени форматкартинки \
-ширинаподчиненныхэлементовформы "+"виддвижениябухгалтерии виддвижениянакопления видпериодарегистрарасчета видсчета видточкимаршрутабизнеспроцесса использованиеагрегатарегистранакопления использованиегруппиэлементов использованиережимапроведения использованиесреза периодичностьагрегатарегистранакопления режимавтовремя режимзаписидокумента режимпроведениядокумента "+
-"авторегистрацияизменений допустимыйномерсообщения отправкаэлементаданных получениеэлементаданных "+"использованиерасшифровкитабличногодокумента ориентациястраницы положениеитоговколоноксводнойтаблицы положениеитоговстроксводнойтаблицы положениетекстаотносительнокартинки расположениезаголовкагруппировкитабличногодокумента способчтениязначенийтабличногодокумента типдвустороннейпечати типзаполненияобластит\
-абличногодокумента типкурсоровтабличногодокумента типлиниирисункатабличногодокумента типлинииячейкитабличногодокумента типнаправленияпереходатабличногодокумента типотображениявыделениятабличногодокумента типотображениялинийсводнойтаблицы типразмещениятекстатабличногодокумента типрисункатабличногодокумента типсмещениятабличногодокумента типузоратабличногодокумента типфайлатабличногодокумента точностьпечати чередованиерасположениястраниц "+
-"отображениевремениэлементовпланировщика "+"типфайлаформатированногодокумента "+"обходрезультатазапроса типзаписизапроса "+"видзаполнениярасшифровкипостроителяотчета типдобавленияпредставлений типизмеренияпостроителяотчета типразмещенияитогов "+"доступкфайлу режимдиалогавыбо\
-рафайла режимоткрытияфайла "+"типизмеренияпостроителязапроса "+"видданныханализа методкластеризации типединицыинтервалавременианализаданных типзаполнениятаблицырезультатаанализаданных типиспользованиячисловыхзначенийанализаданных типисточникаданныхпоискаассоциаций типколонкианализаданныхдереворешений типколонкианализаданныхкластеризация типколонкианализаданныхобщаястатистика типколонкианализаданныхпоискассоциаций типколонкианализаданныхпоиск\
-последовательностей типколонкимоделипрогноза типмерырасстоянияанализаданных типотсеченияправилассоциации типполяанализаданных типстандартизациианализаданных типупорядочиванияправилассоциациианализаданных типупорядочиванияшаблоновпоследовательностейанализаданных типупрощениядереварешений "+
-"wsнаправлениепараметра вариантxpathxs вариантзаписидатыjson вариантпростоготипаxs видгруппымоделиxs видфасетаxdto действиепостроителяdom завершенностьпростоготипаxs завершенностьсоставноготипаxs завершенностьсхемыxs запрещенныеподстановкиxs исключениягруппподстановкиxs категорияиспользованияатрибутаxs категорияограниченияидентичностиxs категорияограниченияпространствименxs методнаследованияxs модельсодержимогоxs назначениетипаxml недопустимыеподстановкиxs обработкапробельныхсимволовxs обработка\
-содержимогоxs ограничениезначенияxs параметрыотбораузловdom переносстрокjson позициявдокументеdom пробельныесимволыxml типатрибутаxml типзначенияjson типканоническогоxml типкомпонентыxs типпроверкиxml типрезультатаdomxpath типузлаdom типузлаxml формаxml формапредставленияxs форматдатыjson экранированиесимволовjson "+
-"видсравнениякомпоновкиданных действиеобработкирасшифровкикомпоновкиданных направлениесортировкикомпоновкиданных расположениевложенныхэлементоврезультатакомпоновкиданных расположениеитоговкомпоновкиданных расположениегруппировкикомпоновкиданных расположениеполейгруппировкикомпоновкиданных расположениеполякомпоновкиданных расположениереквизитовкомпоновкиданных расположениересурсовкомпоновкиданных типбухгалтерскогоостаткакомпоновкиданных типвыводатекстакомпоновкиданных типгруппировкикомпоновкиданн\
-ых типгруппыэлементовотборакомпоновкиданных типдополненияпериодакомпоновкиданных типзаголовкаполейкомпоновкиданных типмакетагруппировкикомпоновкиданных типмакетаобластикомпоновкиданных типостаткакомпоновкиданных типпериодакомпоновкиданных типразмещениятекстакомпоновкиданных типсвязинаборовданныхкомпоновкиданных типэлементарезультатакомпоновкиданных расположениелегендыдиаграммыкомпоновкиданных типпримененияотборакомпоновкиданных режимотображенияэлементанастройкикомпоновкиданных режимотображенияна\
-строеккомпоновкиданных состояниеэлементанастройкикомпоновкиданных способвосстановлениянастроеккомпоновкиданных режимкомпоновкирезультата использованиепараметракомпоновкиданных автопозицияресурсовкомпоновкиданных вариантиспользованиягруппировкикомпоновкиданных расположениересурсоввдиаграммекомпоновкиданных фиксациякомпоновкиданных использованиеусловногооформлениякомпоновкиданных "+
-"важностьинтернетпочтовогосообщения обработкатекстаинтернетпочтовогосообщения способкодированияинтернетпочтовоговложения способкодированиянеasciiсимволовинтернетпочтовогосообщения типтекстапочтовогосообщения протоколинтернетпочты статусразборапочтовогосообщения "+
-"режимтранзакциизаписижурналарегистрации статустранзакциизаписижурналарегистрации уровеньжурналарегистрации "+"расположениехранилищасертификатовкриптографии режимвключениясертификатовкриптографии режимпроверкисертификатакриптографии типхранилищасертификатовкриптографии "+
-"кодировкаименфайловвzipфайле методсжатияzip методшифрованияzip режимвосстановленияпутейфайловzip режимобработкиподкаталоговzip режимсохраненияпутейzip уровеньсжатияzip "+"звуковоеоповещение направлениепереходакстроке позициявпотоке порядокбайтов режимблокировкиданных режимуправленияблокировкойданных сервисвстроенныхпокупок состояниефоновогозадания типподпис\
-чикадоставляемыхуведомлений уровеньиспользованиязащищенногосоединенияftp "+"направлениепорядкасхемызапроса типдополненияпериодамисхемызапроса типконтрольнойточкисхемызапроса типобъединениясхемызапроса типпараметрадоступнойтаблицысхемызапроса типсоединениясхемызапроса "+
-"httpметод автоиспользованиеобщегореквизита автопрефиксномеразадачи вариантвстроенногоязыка видиерархии видрегистранакопления видтаблицывнешнегоисточникаданных записьдвиженийприпроведении заполнениепоследовательностей индексирование использованиебазыпланавидоврасчета использованиебыстроговыбора использованиеобщегореквизита использованиеподчинения использованиеполнотекстовогопоиска использованиеразделяемыхданныхобщегореквизита использованиереквизита назначениеиспользованияприложения назначениерас\
-ширенияконфигурации направлениепередачи обновлениепредопределенныхданных оперативноепроведение основноепредставлениевидарасчета основноепредставлениевидахарактеристики основноепредставлениезадачи основноепредставлениепланаобмена основноепредставлениесправочника основноепредставлениесчета перемещениеграницыприпроведении периодичностьномерабизнеспроцесса периодичностьномерадокумента периодичностьрегистрарасчета периодичностьрегистрасведений повторноеиспользованиевозвращаемыхзначений полнотекстовый\
-поискпривводепостроке принадлежностьобъекта проведение разделениеаутентификацииобщегореквизита разделениеданныхобщегореквизита разделениерасширенийконфигурацииобщегореквизита режимавтонумерацииобъектов режимзаписирегистра режимиспользованиямодальности режимиспользованиясинхронныхвызововрасширенийплатформыивнешнихкомпонент режимповторногоиспользованиясеансов режимполученияданныхвыборапривводепостроке режимсовместимости режимсовместимостиинтерфейса режимуправленияблокировкойданныхпоумолчанию серии\
-кодовпланавидовхарактеристик сериикодовпланасчетов сериикодовсправочника созданиепривводе способвыбора способпоискастрокипривводепостроке способредактирования типданныхтаблицывнешнегоисточникаданных типкодапланавидоврасчета типкодасправочника типмакета типномерабизнеспроцесса типномерадокумента типномеразадачи типформы удалениедвижений "+
-"важностьпроблемыприменениярасширенияконфигурации вариантинтерфейсаклиентскогоприложения вариантмасштабаформклиентскогоприложения вариантосновногошрифтаклиентскогоприложения вариантстандартногопериода вариантстандартнойдатыначала видграницы видкартинки видотображенияполнотекстовогопоиска видрамки видсравнения видцвета видчисловогозначения видшрифта допустимаядлина допустимыйзнак использованиеbyteordermark использованиеметаданныхполнотекстовогопоиска источникрасширенийконфигурации клавиша кодвозв\
-ратадиалога кодировкаxbase кодировкатекста направлениепоиска направлениесортировки обновлениепредопределенныхданных обновлениеприизмененииданных отображениепанелиразделов проверказаполнения режимдиалогавопрос режимзапускаклиентскогоприложения режимокругления режимоткрытияформприложения режимполнотекстовогопоиска скоростьклиентскогосоединения состояниевнешнегоисточникаданных состояниеобновленияконфигурациибазыданных способвыборасертификатаwindows способкодированиястроки статуссообщения типвнешней\
-компоненты типплатформы типповеденияклавишиenter типэлементаинформацииовыполненииобновленияконфигурациибазыданных уровеньизоляциитранзакций хешфункция частидаты",TYPE="comобъект ftpсоединение httpзапрос httpсервисответ httpсоединение wsопределения wsпрокси xbase анализданных аннотацияxs блокировкаданных буфердвоичныхданных включениеxs выражениекомпо\
-новкиданных генераторслучайныхчисел географическаясхема географическиекоординаты графическаясхема группамоделиxs данныерасшифровкикомпоновкиданных двоичныеданные дендрограмма диаграмма диаграммаганта диалогвыборафайла диалогвыборацвета диалогвыборашрифта диалограсписаниярегламентногозадания диалогредактированиястандартногопериода диапазон документdom документhtml документацияxs доставляемоеуведомление записьdom записьfastinfoset записьhtml записьjson записьxml записьzipфайла записьданных записьт\
-екста записьузловdom запрос защищенноесоединениеopenssl значенияполейрасшифровкикомпоновкиданных извлечениетекста импортxs интернетпочта интернетпочтовоесообщение интернетпочтовыйпрофиль интернетпрокси интернетсоединение информациядляприложенияxs использованиеатрибутаxs использованиесобытияжурналарегистрации источникдоступныхнастроеккомпоновкиданных итераторузловdom картинка квалификаторыдаты квалификаторыдвоичныхданных квалификаторыстроки квалификаторычисла компоновщикмакетакомпоновкиданных ком\
-поновщикнастроеккомпоновкиданных конструктормакетаоформлениякомпоновкиданных конструкторнастроеккомпоновкиданных конструкторформатнойстроки линия макеткомпоновкиданных макетобластикомпоновкиданных макетоформлениякомпоновкиданных маскаxs менеджеркриптографии наборсхемxml настройкикомпоновкиданных настройкисериализацииjson обработкакартинок обработкарасшифровкикомпоновкиданных обходдереваdom объявлениеатрибутаxs объявлениенотацииxs объявлениеэлементаxs описаниеиспользованиясобытиядоступжурналареги\
-страции описаниеиспользованиясобытияотказвдоступежурналарегистрации описаниеобработкирасшифровкикомпоновкиданных описаниепередаваемогофайла описаниетипов определениегруппыатрибутовxs определениегруппымоделиxs определениеограниченияидентичностиxs определениепростоготипаxs определениесоставноготипаxs определениетипадокументаdom определенияxpathxs отборкомпоновкиданных пакетотображаемыхдокументов параметрвыбора параметркомпоновкиданных параметрызаписиjson параметрызаписиxml параметрычтенияxml перео\
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-_HISTORY_UPDATING_CATEGORY_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION SYSRES_CONST_AD\
-MINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_P\
-ASSWORD_CHANGE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION SYSRES_CONST_ALL_ACCEPT_CONDITION_RUS SYSRES_CONST_ALL_USERS_GROUP SYSRES_CONST_ALL_USERS_GROUP_NAME SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE SYSRES_CONST_APPROVING_SIGNATURE_NAME SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE SYSRES_CONST_ASSISTANT_SUBSTITUE_TY\
-PE_CODE SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN SYSRES_CONST_ATTACH_TYPE_DOC SYSRES_CONST_ATTACH_TYPE_EDOC SYSRES_CONST_ATTACH_TYPE_FOLDER SYSRES_CONST_ATTACH_TYPE_JOB SYSRES_CONST_ATTACH_TYPE_REFERENCE SYSRES_CONST_ATTACH_TYPE_TASK SYSRES_CONST_AUTH_ENCODED_PASSWORD SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE SYSRES_CONST_AUTH_NOVELL SYSRES_CONST_AUTH_PASSWORD SYSRES_CONST_AUTH_PASSWORD_CODE SYSRES_CONST_AUTH_WINDOWS SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME SYSRES_CONST_AUTHENTICATING_SIGNATURE_\
-REQUISITE_CODE SYSRES_CONST_AUTO_ENUM_METHOD_FLAG SYSRES_CONST_AUTO_NUMERATION_CODE SYSRES_CONST_AUTO_STRONG_ENUM_METHOD_FLAG SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_USAGE_ALL SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE SYSRES_CONST_AUTOTEXT_USAGE_SIGN SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE SYSRES_CONST_AUTOTEXT_USAGE_WORK SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CO\
-DE SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE SYSRES_CONST_BLACK_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BTN_PART SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT SYSRES_CONST_CARD_PART SYSRES_CONST_CARD_REFERENCE_MODE_NAME SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE SYSRES_CONST_CE\
-RTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE SYSRES_CONST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN SYSRES_CONST_CODE_COMPO\
-NENT_TYPE_DEVELOPER SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT SYSRES_CONST_CODE_COMPONENT_TYPE_URL SYSRES_CONST_CODE_REQUISITE_ACCESS SYSRES_CONST_CODE_REQUISITE_CODE SYSRES_CONST_CODE_REQUISITE_COMPONENT SYSRES_CONST_CODE_REQUISITE_DESCRIPTION \
-SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT SYSRES_CONST_CODE_REQUISITE_RECORD SYSRES_CONST_COMMENT_REQ_CODE SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE SYSRES_CONST_COMP_CODE_GRD SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DOCS SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_COMPONENT_TYPE_EDOCS SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_COMPONEN\
-T_TYPE_OTHER SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES SYSRES_CONST_COMPONENT_TYPE_REFERENCES SYSRES_CONST_COMPONENT_TYPE_REPORTS SYSRES_CONST_COMPONENT_TYPE_SCRIPTS SYSRES_CONST_COMPONENT_TYPE_URL SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION SYSRES_CONST_CONST_FIRM_STATUS_COMMON SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL SYSRES_CONST_CONST_NEGATIVE_VALUE SYSRES_CONST_CONST_POSITIVE_VALUE SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE SYSRES_CONST_C\
-ONST_SERVER_STATUS_REPLICATE SYSRES_CONST_CONTENTS_REQUISITE_CODE SYSRES_CONST_DATA_TYPE_BOOLEAN SYSRES_CONST_DATA_TYPE_DATE SYSRES_CONST_DATA_TYPE_FLOAT SYSRES_CONST_DATA_TYPE_INTEGER SYSRES_CONST_DATA_TYPE_PICK SYSRES_CONST_DATA_TYPE_REFERENCE SYSRES_CONST_DATA_TYPE_STRING SYSRES_CONST_DATA_TYPE_TEXT SYSRES_CONST_DATA_TYPE_VARIANT SYSRES_CONST_DATE_CLOSE_REQ_CODE SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR SYSRES_CONST_DATE_OPEN_REQ_CODE SYSRES_CONST_DATE_REQUISITE SYSRES_CONST_DATE_REQUISITE_CODE\
- SYSRES_CONST_DATE_REQUISITE_NAME SYSRES_CONST_DATE_REQUISITE_TYPE SYSRES_CONST_DATE_TYPE_CHAR SYSRES_CONST_DATETIME_FORMAT_VALUE SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_DET1_PART SYSRES_CONST_DET2_PART SYSRES_CONST_DET3_PART SYSRES_CONST_DET4_PART SYSRES_CONST_DET5_PART SYSRES_CONST_DET6_PART SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE SYSRES_CON\
-ST_DETAIL_REQ_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME SYSRES_CONST_DOCUMENT_STORAGES_CODE SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME SYSRES_CONST_DOUBLE_REQUISITE_CODE SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_C\
-ODE SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_CODE SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE SYSRES_CONST_EDITORS_REFERENCE_CODE SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE SY\
-SRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE SYSRES_CONST_EDOC_DATE_REQUISITE_CODE SYSRES_CONST_EDOC_KIND_REFERENCE_CODE SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE SYSRES_CONST_EDOC_NONE_ENCODE_CODE SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_READONLY_ACCESS_CODE SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIG\
-HTS_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_DATABASE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE SYSRES_CONST_EDOC_VERSION_OBSOLET\
-E_STAGE_CODE SYSRES_CONST_EDOC_WRITE_ACCES_CODE SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_END_DATE_REQUISITE_CODE SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE SYSRES_CONST_EXIST_CONST SYSRES_CONST_EXIST_VALUE SYSRES_CONST_EXPORT_LOCK_TYPE_ASK SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK SY\
-SRES_CONST_EXPORT_VERSION_TYPE_ASK SYSRES_CONST_EXPORT_VERSION_TYPE_LAST SYSRES_CONST_EXPORT_VERSION_TYPE_LAST_ACTIVE SYSRES_CONST_EXTENSION_REQUISITE_CODE SYSRES_CONST_FILTER_NAME_REQUISITE_CODE SYSRES_CONST_FILTER_REQUISITE_CODE SYSRES_CONST_FILTER_TYPE_COMMON_CODE SYSRES_CONST_FILTER_TYPE_COMMON_NAME SYSRES_CONST_FILTER_TYPE_USER_CODE SYSRES_CONST_FILTER_TYPE_USER_NAME SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR SYSRES_CONST_FLOAT_REQUISITE_TYPE SYSRES_CONST\
-_FOLDER_AUTHOR_VALUE SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS SYSRES_CONST_FOLDER_KIND_COMPONENTS SYSRES_CONST_FOLDER_KIND_EDOCS SYSRES_CONST_FOLDER_KIND_JOBS SYSRES_CONST_FOLDER_KIND_TASKS SYSRES_CONST_FOLDER_TYPE_COMMON SYSRES_CONST_FOLDER_TYPE_COMPONENT SYSRES_CONST_FOLDER_TYPE_FAVORITES SYSRES_CONST_FOLDER_TYPE_INBOX SYSRES_CONST_FOLDER_TYPE_OUTBOX SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH SYSRES_CONST_FOLDER_TYPE_SEARCH SYSRES_CONST_FOLDER_TYPE_SHORTCUTS SYSRES_CONST_FOLDER_TYPE_USER SYSRES_CONST_F\
-ROM_DICTIONARY_ENUM_METHOD_FLAG SYSRES_CONST_FULL_SUBSTITUTE_TYPE SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE SYSRES_CONST_FUNCTION_CANCEL_RESULT SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM SYSRES_CONST_FUNCTION_CATEGORY_USER SYSRES_CONST_FUNCTION_FAILURE_RESULT SYSRES_CONST_FUNCTION_SAVE_RESULT SYSRES_CONST_GENERATED_REQUISITE SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME SYSRES_CONS\
-T_GROUP_CATEGORY_SERVICE_CODE SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME SYSRES_CONST_GROUP_COMMON_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_USER_REQUISITE_CODE SYSRES_CONST_GROUPS_REFERENCE_CODE SYSRES_CONST_GROUPS_REQUISITE_CODE SYSRES\
-_CONST_HIDDEN_MODE_NAME SYSRES_CONST_HIGH_LVL_REQUISITE_CODE SYSRES_CONST_HISTORY_ACTION_CREATE_CODE SYSRES_CONST_HISTORY_ACTION_DELETE_CODE SYSRES_CONST_HISTORY_ACTION_EDIT_CODE SYSRES_CONST_HOUR_CHAR SYSRES_CONST_ID_REQUISITE_CODE SYSRES_CONST_IDSPS_REQUISITE_CODE SYSRES_CONST_IMAGE_MODE_COLOR SYSRES_CONST_IMAGE_MODE_GREYSCALE SYSRES_CONST_IMAGE_MODE_MONOCHROME SYSRES_CONST_IMPORTANCE_HIGH SYSRES_CONST_IMPORTANCE_LOW SYSRES_CONST_IMPORTANCE_NORMAL SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALU\
-E SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE SYSRES_CONST_INT_REQUISITE SYSRES_CONST_INT_REQUISITE_TYPE SYSRES_CONST_INTEGER_NUMBER_FORMAT_CHAR SYSRES_CONST_INTEGER_TYPE_CHAR SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE SYSR\
-ES_CONST_JOB_BLOCK_DESCRIPTION SYSRES_CONST_JOB_KIND_CONTROL_JOB SYSRES_CONST_JOB_KIND_JOB SYSRES_CONST_JOB_KIND_NOTICE SYSRES_CONST_JOB_STATE_ABORTED SYSRES_CONST_JOB_STATE_COMPLETE SYSRES_CONST_JOB_STATE_WORKING SYSRES_CONST_KIND_REQUISITE_CODE SYSRES_CONST_KIND_REQUISITE_NAME SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_\
-CHANGING_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE SYSRES_CONST_KOD_INPUT_TYPE SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT S\
-YSRES_CONST_LINK_OBJECT_KIND_DOCUMENT SYSRES_CONST_LINK_OBJECT_KIND_EDOC SYSRES_CONST_LINK_OBJECT_KIND_FOLDER SYSRES_CONST_LINK_OBJECT_KIND_JOB SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE SYSRES_CONST_LINK_OBJECT_KIND_TASK SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE SYSRES_CONST_LIST_REFERENCE_MODE_NAME SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE SYSRES_CONST_MAIN_VIEW_CODE SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE SYSRES_CONST_MASTER_TABLE_REC_ID_REQUIS\
-ITE_CODE SYSRES_CONST_MAXIMIZED_MODE_NAME SYSRES_CONST_ME_VALUE SYSRES_CONST_MESSAGE_ATTENTION_CAPTION SYSRES_CONST_MESSAGE_CONFIRMATION_CAPTION SYSRES_CONST_MESSAGE_ERROR_CAPTION SYSRES_CONST_MESSAGE_INFORMATION_CAPTION SYSRES_CONST_MINIMIZED_MODE_NAME SYSRES_CONST_MINUTE_CHAR SYSRES_CONST_MODULE_REQUISITE_CODE SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION SYSRES_CONST_MONTH_FORMAT_VALUE SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_NAME_REQUISITE_CODE SYSRES_CONST_NAME_SINGULAR_REQUISI\
-TE_CODE SYSRES_CONST_NAMEAN_INPUT_TYPE SYSRES_CONST_NEGATIVE_PICK_VALUE SYSRES_CONST_NEGATIVE_VALUE SYSRES_CONST_NO SYSRES_CONST_NO_PICK_VALUE SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE SYSRES_CONST_NO_VALUE SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_NORMAL_MODE_NAME SYSRES_CONST_NOT_ALLOWED_ACCESS_\
-TYPE_CODE SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_NOTE_REQUISITE_CODE SYSRES_CONST_NOTICE_BLOCK_DESCRIPTION SYSRES_CONST_NUM_REQUISITE SYSRES_CONST_NUM_STR_REQUISITE_CODE SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG SYSRES_CONST_NUMERATION_AUTO_STRONG SYSRES_CONST_NUMERATION_FROM_DICTONARY SYSRES_CONST_NUMERATION_MANUAL SYSRES_CONST_NUMERIC_TYPE_CHAR SYSRES_CONST_NUMREQ_REQUISITE_CODE SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE SYSRES_CONST_O\
-PERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_ORIGINALREF_REQUISITE_CODE SYSRES_CONST_OURFIRM_REF_CODE SYSRES_CONST_OURFIRM_REQUISITE_CODE SYSRES_CONST_OURFIRM_VAR SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE SYSRES_CONST_PICK_NEGATIVE_RESULT SYSRES_CONST_PICK_POSITIVE_RESULT SYSRES_CONST_PICK_REQUISITE SYS\
-RES_CONST_PICK_REQUISITE_TYPE SYSRES_CONST_PICK_TYPE_CHAR SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE SYSRES_CONST_PLATFORM_VERSION_COMMENT SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_POSITIVE_PICK_VALUE SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE SYSRES_CONST_PRIORITY_REQUISITE_CODE SYSRES_CONST_QUALIFIED_TASK_TYPE SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE SYSRES_CONST_RECSTAT_REQUISITE_CODE SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRE\
-S_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_REF_REQUISITE SYSRES_CONST_REF_REQUISITE_TYPE SYSRES_CONST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE SYSRES_CONST_REFERENCE_TYPE_CHAR SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE\
-_REQUISITE_CODE SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN SYSRES_CONST_REMOTE_SERVER_TYPE_SECONDARY SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE SYSRE\
-S_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_MODE_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_EDIT_CODE SYSRES_CONST_REQ_MODE_HIDDEN_CODE SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_VIEW_CODE SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE SYSRES_CONST_REQ_SECTION_VALUE SYSRES_CONST_REQ_TYPE_VALUE SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME SYSRES_CONST_RE\
-QUISITE_FORMAT_LEFT SYSRES_CONST_REQUISITE_FORMAT_RIGHT SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT SYSRES_CONST_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_REQUISITE_SECTION_ACTIONS SYSRES_CONST_REQUISITE_SECTION_BUTTON SYSRES_CONST_REQUISITE_SECTION_BUTTONS SYSRES_CONST_REQUISITE_SECTION_CARD SYSRES_CONST_REQUISITE_SECTION_TABLE SYSRES_CONST_REQUISITE_SECTION_TABLE10 SYSRES_CONST_REQUISITE_SECTION_TABLE11 SYSRES_CONST_REQUISITE_SECTION_TABLE12 SYSRES_CONST_REQUISITE_SECTION_TABLE13 SYSRES_CONS\
-T_REQUISITE_SECTION_TABLE14 SYSRES_CONST_REQUISITE_SECTION_TABLE15 SYSRES_CONST_REQUISITE_SECTION_TABLE16 SYSRES_CONST_REQUISITE_SECTION_TABLE17 SYSRES_CONST_REQUISITE_SECTION_TABLE18 SYSRES_CONST_REQUISITE_SECTION_TABLE19 SYSRES_CONST_REQUISITE_SECTION_TABLE2 SYSRES_CONST_REQUISITE_SECTION_TABLE20 SYSRES_CONST_REQUISITE_SECTION_TABLE21 SYSRES_CONST_REQUISITE_SECTION_TABLE22 SYSRES_CONST_REQUISITE_SECTION_TABLE23 SYSRES_CONST_REQUISITE_SECTION_TABLE24 SYSRES_CONST_REQUISITE_SECTION_TABLE3 SYSRES\
-_CONST_REQUISITE_SECTION_TABLE4 SYSRES_CONST_REQUISITE_SECTION_TABLE5 SYSRES_CONST_REQUISITE_SECTION_TABLE6 SYSRES_CONST_REQUISITE_SECTION_TABLE7 SYSRES_CONST_REQUISITE_SECTION_TABLE8 SYSRES_CONST_REQUISITE_SECTION_TABLE9 SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_RIGHT_ALIGNMENT_CODE SYSRES_CONST_ROLES_REFERENCE_CODE SYSRES_CONST_ROUTE_STEP_AFTER_RUS SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS SYSRES_CONST_ROUTE_TY\
-PE_COMPLEX SYSRES_CONST_ROUTE_TYPE_PARALLEL SYSRES_CONST_ROUTE_TYPE_SERIAL SYSRES_CONST_SBDATASETDESC_NEGATIVE_VALUE SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE SYSRES_CONST_SEARCHES_COMPONENT_CONTENT SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME SYSRES_CONST_SEARCHES_EDOC_CONTENT SYSRES_CONST_SEARCHES_FOLDER_CONTENT SYSRES_CONST_SEARCHES_JOB_CONTENT SYSRES_CONST_SEARCHES_REFERENCE\
-_CODE SYSRES_CONST_SEARCHES_TASK_CONTENT SYSRES_CONST_SECOND_CHAR SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALUE SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE SYSRES_CONST_SECTION_REQUISITE_CODE SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE SYSRES_CONST_SELECT_REFERENCE_MODE_\
-NAME SYSRES_CONST_SELECT_TYPE_SELECTABLE SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD SYSRES_CONST_SELECT_TYPE_SELECTABLE_WITH_CHILD SYSRES_CONST_SELECT_TYPE_UNSLECTABLE SYSRES_CONST_SERVER_TYPE_MAIN SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_C\
-ONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE SYSRES_CONST_STATE_REQ_NAME SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE SYSRES_CONST_STATE_REQUISITE_CODE SYSRES_CONST_STATIC_ROLE_TYPE_CODE SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE SYSRES_CONST_STATUS_VALUE_AUTOCLEANING SYSRES_CONST_STATUS_VALUE_BLUE\
-_SQUARE SYSRES_CONST_STATUS_VALUE_COMPLETE SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE SYSRES_CONST_STATUS_VALUE_ORANGE_SQUARE SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE SYSRES_CONST_STATUS_VALUE_RED_SQUARE SYSRES_CONST_STATUS_VALUE_SUSPEND SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE SYSRES_CONST_STORAGE_TYPE_FILE SYSRES_CONST_STORAGE_TYPE_SQL_SERVER SYSRES_CONST_STR_REQUISITE SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_STRING_FORMAT_LEF\
-T_ALIGN_CHAR SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR SYSRES_CONST_STRING_REQUISITE_CODE SYSRES_CONST_STRING_REQUISITE_TYPE SYSRES_CONST_STRING_TYPE_CHAR SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE SYSRES_CONST_SYSTEM_VERSION_COMMENT SYSRES_CONST_TASK_ACCESS_TYPE_ALL SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL SYSR\
-ES_CONST_TASK_ENCODE_TYPE_CERTIFICATION SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD SYSRES_CONST_TASK_ENCODE_TYPE_NONE SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD SYSRES_CONST_TASK_ROUTE_ALL_CONDITION SYSRES_CONST_TASK_ROUTE_AND_CONDITION SYSRES_CONST_TASK_ROUTE_OR_CONDITION SYSRES_CONST_TASK_STATE_ABORTED SYSRES_CONST_TASK_STATE_COMPLETE SYSRES_CONST_TASK_STATE_CONTINUED SYSRES_CONST_TASK_STATE_CONTROL SYSRES_CONST_TASK_STATE_INIT SYSRES_CONST_TASK_STATE_WORKING SYSRES_CONST_TASK_TITLE \
-SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE SYSRES_CONST_TASK_TYPES_REFERENCE_CODE SYSRES_CONST_TEMPLATES_REFERENCE_CODE SYSRES_CONST_TEST_DATE_REQUISITE_NAME SYSRES_CONST_TEST_DEV_DATABASE_NAME SYSRES_CONST_TEST_DEV_SYSTEM_CODE SYSRES_CONST_TEST_EDMS_DATABASE_NAME SYSRES_CONST_TEST_EDMS_MAIN_CODE SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME SYSRES_CONST_TEST_EDMS_SECOND_CODE SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME SYSRES_CONST_TEST_EDMS_SYSTEM_CODE SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME SYSRES_CONST_T\
-EXT_REQUISITE SYSRES_CONST_TEXT_REQUISITE_CODE SYSRES_CONST_TEXT_REQUISITE_TYPE SYSRES_CONST_TEXT_TYPE_CHAR SYSRES_CONST_TYPE_CODE_REQUISITE_CODE SYSRES_CONST_TYPE_REQUISITE_CODE SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME SYSRES_CONST_USE_ACCESS_TYPE_CODE SYSRES_CONST_USE_\
-ACCESS_TYPE_NAME SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE_CODE SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE SYSRES_CONST_USER_CATEGORY_NORMAL SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE SYSRES_CONST_USER_COMMON_CATEGORY SYSRES_CONST_USER_COMMON_CATEGORY_CODE S\
-YSRES_CONST_USER_FULL_NAME_REQUISITE_CODE SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_USER_LOGIN_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_USER_SERVICE_CATEGORY SYSRES_CONST_USER_SERVICE_CATEGORY_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME SYSRES_CONST_USER_STATUS_DE\
-VELOPER_CODE SYSRES_CONST_USER_STATUS_DEVELOPER_NAME SYSRES_CONST_USER_STATUS_DISABLED_CODE SYSRES_CONST_USER_STATUS_DISABLED_NAME SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_USER_CODE SYSRES_CONST_USER_STATUS_USER_NAME SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER SYSRES_CONST_USER_TYPE_REQUISITE_CODE SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE SYSRES_CONST_USERS_REFERENCE_CODE \
-SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME SYSRES_CONST_USERS_REQUISITE_CODE SYSRES_CONST_USERS_SYSTEM_REQUISITE_CODE SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME SYSRES_CONST_VIEW_DEFAULT_CODE SYSRES_CONST_VIEW_DEFAULT_NAME SYSRES_CONST_VIEWER_REQUISITE_CODE SYSRES_CONST_WAI\
-TING_BLOCK_DESCRIPTION SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING  SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETALON_STRING SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CON\
-ST_WORKFLOW_ROUTE_TYPR_HARD SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT SYSRES_CONST_XML_ENCODING SYSRES_CONST_XREC_STAT_REQUISITE_CODE SYSRES_CONST_XRECID_FIELD_NAME SYSRES_CONST_YES SYSRES_CONST_YES_NO_2_REQUISITE_CODE SYSRES_CONST_YES_NO_REQUISITE_CODE SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_YES_PICK_VALUE SYSRES_CONST_YES_VALUE "+"CR FALSE nil NO_VALUE NULL TAB TRUE YES_VALUE "+"ADMINISTRATORS_GROUP_NAME CUSTOMIZERS_GROUP_NAME DEVELOPERS_GROUP_NAME SERVICE_USERS_GROUP_NAME "+"DE\
-CISION_BLOCK_FIRST_OPERAND_PROPERTY DECISION_BLOCK_NAME_PROPERTY DECISION_BLOCK_OPERATION_PROPERTY DECISION_BLOCK_RESULT_TYPE_PROPERTY DECISION_BLOCK_SECOND_OPERAND_PROPERTY "+"ANY_FILE_EXTENTION COMPRESSED_DOCUMENT_EXTENSION EXTENDED_DOCUMENT_EXTENSION SHORT_COMPRESSED_DOCUMENT_EXTENSION SHORT_EXTENDED_DOCUMENT_EXTENSION "+"JOB_BLOCK_ABORT_DEADLINE_PROPERTY JOB_BLOCK_AFTER_FINISH_EVENT JOB_BLOCK_AFTER_QUERY_PARAMETERS_EVENT JOB_BLOCK_ATTACHMENT_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPER\
-TY JOB_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY JOB_BLOCK_BEFORE_QUERY_PARAMETERS_EVENT JOB_BLOCK_BEFORE_START_EVENT JOB_BLOCK_CREATED_JOBS_PROPERTY JOB_BLOCK_DEADLINE_PROPERTY JOB_BLOCK_EXECUTION_RESULTS_PROPERTY JOB_BLOCK_IS_PARALLEL_PROPERTY JOB_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY JOB_BLOCK_JOB_TEXT_PROPERTY JOB_BLOCK_NAME_PROPERTY JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY JOB_BLOCK_PERFORMER_PROPERTY JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY JO\
-B_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY JOB_BLOCK_SUBJECT_PROPERTY "+"ENGLISH_LANGUAGE_CODE RUSSIAN_LANGUAGE_CODE "+"smHidden smMaximized smMinimized smNormal wmNo wmYes "+"COMPONENT_TOKEN_LINK_KIND DOCUMENT_LINK_KIND EDOCUMENT_LINK_KIND FOLDER_LINK_KIND JOB_LINK_KIND REFERENCE_LINK_KIND TASK_LINK_KIND "+"COMPONENT_TOKEN_LOCK_TYPE EDOCUMENT_VERSION_LOCK_TYPE "+"MONITOR_BLOCK_AFTER_FINISH_EVENT MONITOR_BLOCK_BEFORE_START_EVENT MONITOR_BLOCK_DEADLINE_PROPERTY MONITOR_BLOCK_INTERVAL_PROPERTY MONITO\
-R_BLOCK_INTERVAL_TYPE_PROPERTY MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY MONITOR_BLOCK_NAME_PROPERTY MONITOR_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY "+"NOTICE_BLOCK_AFTER_FINISH_EVENT NOTICE_BLOCK_ATTACHMENT_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY NOTICE_BLOCK_BEFORE_START_EVENT NOTICE_BLOCK_CREATED_NOTICES_PROPERTY NOTICE_BLOCK_DEADLINE_PROPERTY NOTICE_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY NOTICE_BL\
-OCK_NAME_PROPERTY NOTICE_BLOCK_NOTICE_TEXT_PROPERTY NOTICE_BLOCK_PERFORMER_PROPERTY NOTICE_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY NOTICE_BLOCK_SUBJECT_PROPERTY "+"dseAfterCancel dseAfterClose dseAfterDelete dseAfterDeleteOutOfTransaction dseAfterInsert dseAfterOpen dseAfterScroll dseAfterUpdate dseAfterUpdateOutOfTransaction dseBeforeCancel dseBeforeClose dseBeforeDelete dseBeforeDetailUpdate dseBeforeInsert dseBeforeOpen dseBeforeUpdate dseOnAnyRequisiteChange dseOnCloseRecord dseOnDeleteError d\
-seOnOpenRecord dseOnPrepareUpdate dseOnUpdateError dseOnUpdateRatifiedRecord dseOnValidDelete dseOnValidUpdate reOnChange reOnChangeValues SELECTION_BEGIN_ROUTE_EVENT SELECTION_END_ROUTE_EVENT "+"CURRENT_PERIOD_IS_REQUIRED PREVIOUS_CARD_TYPE_NAME SHOW_RECORD_PROPERTIES_FORM "+"ACCESS_RIGHTS_SETTING_DIALOG_CODE ADMINISTRATOR_USER_CODE ANALYTIC_REPORT_TYPE asrtHideLocal asrtHideRemote CALCULATED_ROLE_TYPE_CODE COMPONENTS_REFERENCE_DEVELOPER_VIEW_CODE DCTS_TEST_PROTOCOLS_FOLDER_PATH E_EDOC_VERSION_\
-ALREADY_APPROVINGLY_SIGNED E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED_BY_USER E_EDOC_VERSION_ALREDY_SIGNED E_EDOC_VERSION_ALREDY_SIGNED_BY_USER EDOC_TYPES_CODE_REQUISITE_FIELD_NAME EDOCUMENTS_ALIAS_NAME FILES_FOLDER_PATH FILTER_OPERANDS_DELIMITER FILTER_OPERATIONS_DELIMITER FORMCARD_NAME FORMLIST_NAME GET_EXTENDED_DOCUMENT_EXTENSION_CREATION_MODE GET_EXTENDED_DOCUMENT_EXTENSION_IMPORT_MODE INTEGRATED_REPORT_TYPE IS_BUILDER_APPLICATION_ROLE IS_BUILDER_APPLICATION_ROLE2 IS_BUILDER_USERS ISBSYSDEV L\
-OG_FOLDER_PATH mbCancel mbNo mbNoToAll mbOK mbYes mbYesToAll MEMORY_DATASET_DESRIPTIONS_FILENAME mrNo mrNoToAll mrYes mrYesToAll MULTIPLE_SELECT_DIALOG_CODE NONOPERATING_RECORD_FLAG_FEMININE NONOPERATING_RECORD_FLAG_MASCULINE OPERATING_RECORD_FLAG_FEMININE OPERATING_RECORD_FLAG_MASCULINE PROFILING_SETTINGS_COMMON_SETTINGS_CODE_VALUE PROGRAM_INITIATED_LOOKUP_ACTION ratDelete ratEdit ratInsert REPORT_TYPE REQUIRED_PICK_VALUES_VARIABLE rmCard rmList SBRTE_PROGID_DEV SBRTE_PROGID_RELEASE STATIC_ROLE\
-_TYPE_CODE SUPPRESS_EMPTY_TEMPLATE_CREATION SYSTEM_USER_CODE UPDATE_DIALOG_DATASET USED_IN_OBJECT_HINT_PARAM USER_INITIATED_LOOKUP_ACTION USER_NAME_FORMAT USER_SELECTION_RESTRICTIONS WORKFLOW_TEST_PROTOCOLS_FOLDER_PATH ELS_SUBTYPE_CONTROL_NAME ELS_FOLDER_KIND_CONTROL_NAME REPEAT_PROCESS_CURRENT_OBJECT_EXCEPTION_NAME "+"PRIVILEGE_COMPONENT_FULL_ACCESS PRIVILEGE_DEVELOPMENT_EXPORT PRIVILEGE_DEVELOPMENT_IMPORT PRIVILEGE_DOCUMENT_DELETE PRIVILEGE_ESD PRIVILEGE_FOLDER_DELETE PRIVILEGE_MANAGE_ACCESS_R\
-IGHTS PRIVILEGE_MANAGE_REPLICATION PRIVILEGE_MANAGE_SESSION_SERVER PRIVILEGE_OBJECT_FULL_ACCESS PRIVILEGE_OBJECT_VIEW PRIVILEGE_RESERVE_LICENSE PRIVILEGE_SYSTEM_CUSTOMIZE PRIVILEGE_SYSTEM_DEVELOP PRIVILEGE_SYSTEM_INSTALL PRIVILEGE_TASK_DELETE PRIVILEGE_USER_PLUGIN_SETTINGS_CUSTOMIZE PRIVILEGES_PSEUDOREFERENCE_CODE "+"ACCESS_TYPES_PSEUDOREFERENCE_CODE ALL_AVAILABLE_COMPONENTS_PSEUDOREFERENCE_CODE ALL_AVAILABLE_PRIVILEGES_PSEUDOREFERENCE_CODE ALL_REPLICATE_COMPONENTS_PSEUDOREFERENCE_CODE AVAILABLE\
-_DEVELOPERS_COMPONENTS_PSEUDOREFERENCE_CODE COMPONENTS_PSEUDOREFERENCE_CODE FILTRATER_SETTINGS_CONFLICTS_PSEUDOREFERENCE_CODE GROUPS_PSEUDOREFERENCE_CODE RECEIVE_PROTOCOL_PSEUDOREFERENCE_CODE REFERENCE_REQUISITE_PSEUDOREFERENCE_CODE REFERENCE_REQUISITES_PSEUDOREFERENCE_CODE REFTYPES_PSEUDOREFERENCE_CODE REPLICATION_SEANCES_DIARY_PSEUDOREFERENCE_CODE SEND_PROTOCOL_PSEUDOREFERENCE_CODE SUBSTITUTES_PSEUDOREFERENCE_CODE SYSTEM_SETTINGS_PSEUDOREFERENCE_CODE UNITS_PSEUDOREFERENCE_CODE USERS_PSEUDOREFE\
-RENCE_CODE VIEWERS_PSEUDOREFERENCE_CODE "+"CERTIFICATE_TYPE_ENCRYPT CERTIFICATE_TYPE_SIGN CERTIFICATE_TYPE_SIGN_AND_ENCRYPT "+"STORAGE_TYPE_FILE STORAGE_TYPE_NAS_CIFS STORAGE_TYPE_SAPERION STORAGE_TYPE_SQL_SERVER "+"COMPTYPE2_REQUISITE_DOCUMENTS_VALUE COMPTYPE2_REQUISITE_TASKS_VALUE COMPTYPE2_REQUISITE_FOLDERS_VALUE COMPTYPE2_REQUISITE_REFERENCES_VALUE "+"SYSREQ_CODE SYSREQ_COMPTYPE2 SYSREQ_CONST_AVAILABLE_FOR_WEB SYSREQ_CONST_COMMON_CODE SYSREQ_CONST_COMMON_VALUE SYSREQ_CONST_FIRM_CODE SYSREQ_C\
-ONST_FIRM_STATUS SYSREQ_CONST_FIRM_VALUE SYSREQ_CONST_SERVER_STATUS SYSREQ_CONTENTS SYSREQ_DATE_OPEN SYSREQ_DATE_CLOSE SYSREQ_DESCRIPTION SYSREQ_DESCRIPTION_LOCALIZE_ID SYSREQ_DOUBLE SYSREQ_EDOC_ACCESS_TYPE SYSREQ_EDOC_AUTHOR SYSREQ_EDOC_CREATED SYSREQ_EDOC_DELEGATE_RIGHTS_REQUISITE_CODE SYSREQ_EDOC_EDITOR SYSREQ_EDOC_ENCODE_TYPE SYSREQ_EDOC_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_EXPORT_DATE SYSREQ_EDOC_EXPORTER SYSREQ_EDOC_KIND SYSREQ_EDOC_LIFE_STAGE_NAME SYSRE\
-Q_EDOC_LOCKED_FOR_SERVER_CODE SYSREQ_EDOC_MODIFIED SYSREQ_EDOC_NAME SYSREQ_EDOC_NOTE SYSREQ_EDOC_QUALIFIED_ID SYSREQ_EDOC_SESSION_KEY SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_SIGNATURE_TYPE SYSREQ_EDOC_SIGNED SYSREQ_EDOC_STORAGE SYSREQ_EDOC_STORAGES_ARCHIVE_STORAGE SYSREQ_EDOC_STORAGES_CHECK_RIGHTS SYSREQ_EDOC_STORAGES_COMPUTER_NAME SYSREQ_EDOC_STORAGES_EDIT_IN_STORAGE SYSREQ_EDOC_STORAGES_EXECUTIVE_STORAGE SYSREQ_EDOC_STORAGES_\
-FUNCTION SYSREQ_EDOC_STORAGES_INITIALIZED SYSREQ_EDOC_STORAGES_LOCAL_PATH SYSREQ_EDOC_STORAGES_SAPERION_DATABASE_NAME SYSREQ_EDOC_STORAGES_SEARCH_BY_TEXT SYSREQ_EDOC_STORAGES_SERVER_NAME SYSREQ_EDOC_STORAGES_SHARED_SOURCE_NAME SYSREQ_EDOC_STORAGES_TYPE SYSREQ_EDOC_TEXT_MODIFIED SYSREQ_EDOC_TYPE_ACT_CODE SYSREQ_EDOC_TYPE_ACT_DESCRIPTION SYSREQ_EDOC_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_EDOC_TYPE_ACT_SECTION SYSREQ_EDOC_TYPE_\
-ADD_PARAMS SYSREQ_EDOC_TYPE_COMMENT SYSREQ_EDOC_TYPE_EVENT_TEXT SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_EDOC_TYPE_NAME_LOCALIZE_ID SYSREQ_EDOC_TYPE_NUMERATION_METHOD SYSREQ_EDOC_TYPE_PSEUDO_REQUISITE_CODE SYSREQ_EDOC_TYPE_REQ_CODE SYSREQ_EDOC_TYPE_REQ_DESCRIPTION SYSREQ_EDOC_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_REQ_IS_LEADING SYSREQ_EDOC_TYPE_REQ_IS_REQUIRED SYSREQ_EDOC_TYPE_REQ_NUMBER SYSREQ_EDOC_TYPE_REQ_ON_CHANGE SYSREQ_EDOC_TYPE_REQ\
-_ON_CHANGE_EXISTS SYSREQ_EDOC_TYPE_REQ_ON_SELECT SYSREQ_EDOC_TYPE_REQ_ON_SELECT_KIND SYSREQ_EDOC_TYPE_REQ_SECTION SYSREQ_EDOC_TYPE_VIEW_CARD SYSREQ_EDOC_TYPE_VIEW_CODE SYSREQ_EDOC_TYPE_VIEW_COMMENT SYSREQ_EDOC_TYPE_VIEW_IS_MAIN SYSREQ_EDOC_TYPE_VIEW_NAME SYSREQ_EDOC_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_EDOC_VERSION_AUTHOR SYSREQ_EDOC_VERSION_CRC SYSREQ_EDOC_VERSION_DATA SYSREQ_EDOC_VERSION_EDITOR SYSREQ_EDOC_VERSION_EXPORT_DATE SYSREQ_EDOC_VERSION_EXPORTER SYSREQ_EDOC_VERSION_HIDDEN SYSREQ_EDOC_VER\
-SION_LIFE_STAGE SYSREQ_EDOC_VERSION_MODIFIED SYSREQ_EDOC_VERSION_NOTE SYSREQ_EDOC_VERSION_SIGNATURE_TYPE SYSREQ_EDOC_VERSION_SIGNED SYSREQ_EDOC_VERSION_SIZE SYSREQ_EDOC_VERSION_SOURCE SYSREQ_EDOC_VERSION_TEXT_MODIFIED SYSREQ_EDOCKIND_DEFAULT_VERSION_STATE_CODE SYSREQ_FOLDER_KIND SYSREQ_FUNC_CATEGORY SYSREQ_FUNC_COMMENT SYSREQ_FUNC_GROUP SYSREQ_FUNC_GROUP_COMMENT SYSREQ_FUNC_GROUP_NUMBER SYSREQ_FUNC_HELP SYSREQ_FUNC_PARAM_DEF_VALUE SYSREQ_FUNC_PARAM_IDENT SYSREQ_FUNC_PARAM_NUMBER SYSREQ_FUNC_PARA\
-M_TYPE SYSREQ_FUNC_TEXT SYSREQ_GROUP_CATEGORY SYSREQ_ID SYSREQ_LAST_UPDATE SYSREQ_LEADER_REFERENCE SYSREQ_LINE_NUMBER SYSREQ_MAIN_RECORD_ID SYSREQ_NAME SYSREQ_NAME_LOCALIZE_ID SYSREQ_NOTE SYSREQ_ORIGINAL_RECORD SYSREQ_OUR_FIRM SYSREQ_PROFILING_SETTINGS_BATCH_LOGING SYSREQ_PROFILING_SETTINGS_BATCH_SIZE SYSREQ_PROFILING_SETTINGS_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_SQL_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_START_LOGGED SYSREQ_RECORD_STATUS SYSREQ_REF_REQ_FIELD_NAME SYSREQ_REF_REQ_FORM\
-AT SYSREQ_REF_REQ_GENERATED SYSREQ_REF_REQ_LENGTH SYSREQ_REF_REQ_PRECISION SYSREQ_REF_REQ_REFERENCE SYSREQ_REF_REQ_SECTION SYSREQ_REF_REQ_STORED SYSREQ_REF_REQ_TOKENS SYSREQ_REF_REQ_TYPE SYSREQ_REF_REQ_VIEW SYSREQ_REF_TYPE_ACT_CODE SYSREQ_REF_TYPE_ACT_DESCRIPTION SYSREQ_REF_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_ACT_ON_EXECUTE SYSREQ_REF_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_REF_TYPE_ACT_SECTION SYSREQ_REF_TYPE_ADD_PARAMS SYSREQ_REF_TYPE_COMMENT SYSREQ_REF_TYPE_COMMON_SETTINGS SYSREQ_REF_T\
-YPE_DISPLAY_REQUISITE_NAME SYSREQ_REF_TYPE_EVENT_TEXT SYSREQ_REF_TYPE_MAIN_LEADING_REF SYSREQ_REF_TYPE_NAME_IN_SINGULAR SYSREQ_REF_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_REF_TYPE_NAME_LOCALIZE_ID SYSREQ_REF_TYPE_NUMERATION_METHOD SYSREQ_REF_TYPE_REQ_CODE SYSREQ_REF_TYPE_REQ_DESCRIPTION SYSREQ_REF_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_REQ_IS_CONTROL SYSREQ_REF_TYPE_REQ_IS_FILTER SYSREQ_REF_TYPE_REQ_IS_LEADING SYSREQ_REF_TYPE_REQ_IS_REQUIRED SYSREQ_REF_TYPE_REQ_NUMBER SYSREQ_REF_TYPE_\
-REQ_ON_CHANGE SYSREQ_REF_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_REF_TYPE_REQ_ON_SELECT SYSREQ_REF_TYPE_REQ_ON_SELECT_KIND SYSREQ_REF_TYPE_REQ_SECTION SYSREQ_REF_TYPE_VIEW_CARD SYSREQ_REF_TYPE_VIEW_CODE SYSREQ_REF_TYPE_VIEW_COMMENT SYSREQ_REF_TYPE_VIEW_IS_MAIN SYSREQ_REF_TYPE_VIEW_NAME SYSREQ_REF_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_REFERENCE_TYPE_ID SYSREQ_STATE SYSREQ_STATЕ SYSREQ_SYSTEM_SETTINGS_VALUE SYSREQ_TYPE SYSREQ_UNIT SYSREQ_UNIT_ID SYSREQ_USER_GROUPS_GROUP_FULL_NAME SYSREQ_USER_GROUPS_GROUP_NAM\
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-gion|Owner)?Say|Shout|Listen(Control|Remove)?|Sensor(Repeat|Remove)?|Detected(Name|Key|Owner|Type|Pos|Vel|Grab|Rot|Group|LinkNumber)|Die|Ground|Wind|([GS]et)(AnimationOverride|MemoryLimit|PrimMediaParams|ParcelMusicURL|Object(Desc|Name)|PhysicsMaterial|Status|Scale|Color|Alpha|Texture|Pos|Rot|Force|Torque)|ResetAnimationOverride|(Scale|Offset|Rotate)Texture|(Rot)?Target(Remove)?|(Stop)?MoveToTarget|Apply(Rotational)?Impulse|Set(KeyframedMotion|ContentType|RegionPos|(Angular)?Velocity|Buoyancy|Ho\
-verHeight|ForceAndTorque|TimerEvent|ScriptState|Damage|TextureAnim|Sound(Queueing|Radius)|Vehicle(Type|(Float|Vector|Rotation)Param)|(Touch|Sit)?Text|Camera(Eye|At)Offset|PrimitiveParams|ClickAction|Link(Alpha|Color|PrimitiveParams(Fast)?|Texture(Anim)?|Camera|Media)|RemoteScriptAccessPin|PayPrice|LocalRot)|ScaleByFactor|Get((Max|Min)ScaleFactor|ClosestNavPoint|StaticPath|SimStats|Env|PrimitiveParams|Link(PrimitiveParams|Number(OfSides)?|Key|Name|Media)|HTTPHeader|FreeURLs|Object(Details|PermMas\
-k|PrimCount)|Parcel(MaxPrims|Details|Prim(Count|Owners))|Attached(List)?|(SPMax|Free|Used)Memory|Region(Name|TimeDilation|FPS|Corner|AgentCount)|Root(Position|Rotation)|UnixTime|(Parcel|Region)Flags|(Wall|GMT)clock|SimulatorHostname|BoundingBox|GeometricCenter|Creator|NumberOf(Prims|NotecardLines|Sides)|Animation(List)?|(Camera|Local)(Pos|Rot)|Vel|Accel|Omega|Time(stamp|OfDay)|(Object|CenterOf)?Mass|MassMKS|Energy|Owner|(Owner)?Key|SunDirection|Texture(Offset|Scale|Rot)|Inventory(Number|Name|Key\
-|Type|Creator|PermMask)|Permissions(Key)?|StartParameter|List(Length|EntryType)|Date|Agent(Size|Info|Language|List)|LandOwnerAt|NotecardLine|Script(Name|State))|(Get|Reset|GetAndReset)Time|PlaySound(Slave)?|LoopSound(Master|Slave)?|(Trigger|Stop|Preload)Sound|((Get|Delete)Sub|Insert)String|To(Upper|Lower)|Give(InventoryList|Money)|RezObject|(Stop)?LookAt|Sleep|CollisionFilter|(Take|Release)Controls|DetachFromAvatar|AttachToAvatar(Temp)?|InstantMessage|(GetNext)?Email|StopHover|MinEventDelay|RotL\
-ookAt|String(Length|Trim)|(Start|Stop)Animation|TargetOmega|Request(Experience)?Permissions|(Create|Break)Link|BreakAllLinks|(Give|Remove)Inventory|Water|PassTouches|Request(Agent|Inventory)Data|TeleportAgent(Home|GlobalCoords)?|ModifyLand|CollisionSound|ResetScript|MessageLinked|PushObject|PassCollisions|AxisAngle2Rot|Rot2(Axis|Angle)|A(cos|sin)|AngleBetween|AllowInventoryDrop|SubStringIndex|List2(CSV|Integer|Json|Float|String|Key|Vector|Rot|List(Strided)?)|DeleteSubList|List(Statistics|Sort|Ra\
-ndomize|(Insert|Find|Replace)List)|EdgeOfWorld|AdjustSoundVolume|Key2Name|TriggerSoundLimited|EjectFromLand|(CSV|ParseString)2List|OverMyLand|SameGroup|UnSit|Ground(Slope|Normal|Contour)|GroundRepel|(Set|Remove)VehicleFlags|SitOnLink|(AvatarOn)?(Link)?SitTarget|Script(Danger|Profiler)|Dialog|VolumeDetect|ResetOtherScript|RemoteLoadScriptPin|(Open|Close)RemoteDataChannel|SendRemoteData|RemoteDataReply|(Integer|String)ToBase64|XorBase64|Log(10)?|Base64To(String|Integer)|ParseStringKeepNulls|RezAtR\
-oot|RequestSimulatorData|ForceMouselook|(Load|Release|(E|Une)scape)URL|ParcelMedia(CommandList|Query)|ModPow|MapDestination|(RemoveFrom|AddTo|Reset)Land(Pass|Ban)List|(Set|Clear)CameraParams|HTTP(Request|Response)|TextBox|DetectedTouch(UV|Face|Pos|(N|Bin)ormal|ST)|(MD5|SHA1|DumpList2)String|Request(Secure)?URL|Clear(Prim|Link)Media|(Link)?ParticleSystem|(Get|Request)(Username|DisplayName)|RegionSayTo|CastRay|GenerateKey|TransferLindenDollars|ManageEstateAccess|(Create|Delete)Character|ExecCharac\
-terCmd|Evade|FleeFrom|NavigateTo|PatrolPoints|Pursue|UpdateCharacter|WanderWithin))\\b"};return{name:"LSL (Linden Scripting Language)",illegal:":",contains:[LSL_STRINGS,{className:"comment",variants:[hljs.COMMENT("//","$"),hljs.COMMENT("/\\*","\\*/")],relevance:0},LSL_NUMBERS,{className:"section",variants:[{begin:"\\b(state|default)\\b"},{begin:"\\b(state_(entry|exit)|touch(_(start|end))?|(land_)?collision(_(start|end))?|timer|listen|(no_)?sensor|control|(not_)?at_(rot_)?target|money|email|experi\
-ence_permissions(_denied)?|run_time_permissions|changed|attach|dataserver|moving_(start|end)|link_message|(on|object)_rez|remote_data|http_re(sponse|quest)|path_update|transaction_result)\\b"}]},LSL_FUNCTIONS,LSL_CONSTANTS,{className:"type",begin:"\\b(integer|float|string|key|vector|quaternion|rotation|list)\\b"}]}}const SYSTEM_SYMBOLS=["AASTriangle","AbelianGroup","Abort","AbortKernels","AbortProtect","AbortScheduledTask","Above","Abs","AbsArg","AbsArgPlot","Absolute","AbsoluteCorrelation","Abs\
-oluteCorrelationFunction","AbsoluteCurrentValue","AbsoluteDashing","AbsoluteFileName","AbsoluteOptions","AbsolutePointSize","AbsoluteThickness","AbsoluteTime","AbsoluteTiming","AcceptanceThreshold","AccountingForm","Accumulate","Accuracy","AccuracyGoal","AcousticAbsorbingValue","AcousticImpedanceValue","AcousticNormalVelocityValue","AcousticPDEComponent","AcousticPressureCondition","AcousticRadiationValue","AcousticSoundHardValue","AcousticSoundSoftCondition","ActionDelay","ActionMenu","ActionMe\
-nuBox","ActionMenuBoxOptions","Activate","Active","ActiveClassification","ActiveClassificationObject","ActiveItem","ActivePrediction","ActivePredictionObject","ActiveStyle","AcyclicGraphQ","AddOnHelpPath","AddSides","AddTo","AddToSearchIndex","AddUsers","AdjacencyGraph","AdjacencyList","AdjacencyMatrix","AdjacentMeshCells","Adjugate","AdjustmentBox","AdjustmentBoxOptions","AdjustTimeSeriesForecast","AdministrativeDivisionData","AffineHalfSpace","AffineSpace","AffineStateSpaceModel","AffineTransf\
-orm","After","AggregatedEntityClass","AggregationLayer","AircraftData","AirportData","AirPressureData","AirSoundAttenuation","AirTemperatureData","AiryAi","AiryAiPrime","AiryAiZero","AiryBi","AiryBiPrime","AiryBiZero","AlgebraicIntegerQ","AlgebraicNumber","AlgebraicNumberDenominator","AlgebraicNumberNorm","AlgebraicNumberPolynomial","AlgebraicNumberTrace","AlgebraicRules","AlgebraicRulesData","Algebraics","AlgebraicUnitQ","Alignment","AlignmentMarker","AlignmentPoint","All","AllowAdultContent","\
-AllowChatServices","AllowedCloudExtraParameters","AllowedCloudParameterExtensions","AllowedDimensions","AllowedFrequencyRange","AllowedHeads","AllowGroupClose","AllowIncomplete","AllowInlineCells","AllowKernelInitialization","AllowLooseGrammar","AllowReverseGroupClose","AllowScriptLevelChange","AllowVersionUpdate","AllTrue","Alphabet","AlphabeticOrder","AlphabeticSort","AlphaChannel","AlternateImage","AlternatingFactorial","AlternatingGroup","AlternativeHypothesis","Alternatives","AltitudeMethod",
-"AmbientLight","AmbiguityFunction","AmbiguityList","Analytic","AnatomyData","AnatomyForm","AnatomyPlot3D","AnatomySkinStyle","AnatomyStyling","AnchoredSearch","And","AndersonDarlingTest","AngerJ","AngleBisector","AngleBracket","AnglePath","AnglePath3D","AngleVector","AngularGauge","Animate","AnimatedImage","AnimationCycleOffset","AnimationCycleRepetitions","AnimationDirection","AnimationDisplayTime","AnimationRate","AnimationRepetitions","AnimationRunning","AnimationRunTime","AnimationTimeIndex",
-"AnimationVideo","Animator","AnimatorBox","AnimatorBoxOptions","AnimatorElements","Annotate","Annotation","AnnotationDelete","AnnotationKeys","AnnotationRules","AnnotationValue","Annuity","AnnuityDue","Annulus","AnomalyDetection","AnomalyDetector","AnomalyDetectorFunction","Anonymous","Antialiasing","Antihermitian","AntihermitianMatrixQ","Antisymmetric","AntisymmetricMatrixQ","Antonyms","AnyOrder","AnySubset","AnyTrue","Apart","ApartSquareFree","APIFunction","Appearance","AppearanceElements","Ap\
-pearanceRules","AppellF1","Append","AppendCheck","AppendLayer","AppendTo","Application","Apply","ApplyReaction","ApplySides","ApplyTo","ArcCos","ArcCosh","ArcCot","ArcCoth","ArcCsc","ArcCsch","ArcCurvature","ARCHProcess","ArcLength","ArcSec","ArcSech","ArcSin","ArcSinDistribution","ArcSinh","ArcTan","ArcTanh","Area","Arg","ArgMax","ArgMin","ArgumentCountQ","ArgumentsOptions","ARIMAProcess","ArithmeticGeometricMean","ARMAProcess","Around","AroundReplace","ARProcess","Array","ArrayComponents","Arr\
-ayDepth","ArrayFilter","ArrayFlatten","ArrayMesh","ArrayPad","ArrayPlot","ArrayPlot3D","ArrayQ","ArrayReduce","ArrayResample","ArrayReshape","ArrayRules","Arrays","Arrow","Arrow3DBox","ArrowBox","Arrowheads","ASATriangle","Ask","AskAppend","AskConfirm","AskDisplay","AskedQ","AskedValue","AskFunction","AskState","AskTemplateDisplay","AspectRatio","AspectRatioFixed","Assert","AssessmentFunction","AssessmentResultObject","AssociateTo","Association","AssociationFormat","AssociationMap","AssociationQ",
-"AssociationThread","AssumeDeterministic","Assuming","Assumptions","AstroAngularSeparation","AstroBackground","AstroCenter","AstroDistance","AstroGraphics","AstroGridLines","AstroGridLinesStyle","AstronomicalData","AstroPosition","AstroProjection","AstroRange","AstroRangePadding","AstroReferenceFrame","AstroStyling","AstroZoomLevel","Asymptotic","AsymptoticDSolveValue","AsymptoticEqual","AsymptoticEquivalent","AsymptoticExpectation","AsymptoticGreater","AsymptoticGreaterEqual","AsymptoticIntegra\
-te","AsymptoticLess","AsymptoticLessEqual","AsymptoticOutputTracker","AsymptoticProbability","AsymptoticProduct","AsymptoticRSolveValue","AsymptoticSolve","AsymptoticSum","Asynchronous","AsynchronousTaskObject","AsynchronousTasks","Atom","AtomCoordinates","AtomCount","AtomDiagramCoordinates","AtomLabels","AtomLabelStyle","AtomList","AtomQ","AttachCell","AttachedCell","AttentionLayer","Attributes","Audio","AudioAmplify","AudioAnnotate","AudioAnnotationLookup","AudioBlockMap","AudioCapture","Audio\
-ChannelAssignment","AudioChannelCombine","AudioChannelMix","AudioChannels","AudioChannelSeparate","AudioData","AudioDelay","AudioDelete","AudioDevice","AudioDistance","AudioEncoding","AudioFade","AudioFrequencyShift","AudioGenerator","AudioIdentify","AudioInputDevice","AudioInsert","AudioInstanceQ","AudioIntervals","AudioJoin","AudioLabel","AudioLength","AudioLocalMeasurements","AudioLooping","AudioLoudness","AudioMeasurements","AudioNormalize","AudioOutputDevice","AudioOverlay","AudioPad","Audi\
-oPan","AudioPartition","AudioPause","AudioPitchShift","AudioPlay","AudioPlot","AudioQ","AudioRecord","AudioReplace","AudioResample","AudioReverb","AudioReverse","AudioSampleRate","AudioSpectralMap","AudioSpectralTransformation","AudioSplit","AudioStop","AudioStream","AudioStreams","AudioTimeStretch","AudioTrackApply","AudioTrackSelection","AudioTrim","AudioType","AugmentedPolyhedron","AugmentedSymmetricPolynomial","Authenticate","Authentication","AuthenticationDialog","AutoAction","Autocomplete",
-"AutocompletionFunction","AutoCopy","AutocorrelationTest","AutoDelete","AutoEvaluateEvents","AutoGeneratedPackage","AutoIndent","AutoIndentSpacings","AutoItalicWords","AutoloadPath","AutoMatch","Automatic","AutomaticImageSize","AutoMultiplicationSymbol","AutoNumberFormatting","AutoOpenNotebooks","AutoOpenPalettes","AutoOperatorRenderings","AutoQuoteCharacters","AutoRefreshed","AutoRemove","AutorunSequencing","AutoScaling","AutoScroll","AutoSpacing","AutoStyleOptions","AutoStyleWords","AutoSubmit\
-ting","Axes","AxesEdge","AxesLabel","AxesOrigin","AxesStyle","AxiomaticTheory","Axis","Axis3DBox","Axis3DBoxOptions","AxisBox","AxisBoxOptions","AxisLabel","AxisObject","AxisStyle","BabyMonsterGroupB","Back","BackFaceColor","BackFaceGlowColor","BackFaceOpacity","BackFaceSpecularColor","BackFaceSpecularExponent","BackFaceSurfaceAppearance","BackFaceTexture","Background","BackgroundAppearance","BackgroundTasksSettings","Backslash","Backsubstitution","Backward","Ball","Band","BandpassFilter","Bands\
-topFilter","BarabasiAlbertGraphDistribution","BarChart","BarChart3D","BarcodeImage","BarcodeRecognize","BaringhausHenzeTest","BarLegend","BarlowProschanImportance","BarnesG","BarOrigin","BarSpacing","BartlettHannWindow","BartlettWindow","BaseDecode","BaseEncode","BaseForm","Baseline","BaselinePosition","BaseStyle","BasicRecurrentLayer","BatchNormalizationLayer","BatchSize","BatesDistribution","BattleLemarieWavelet","BayesianMaximization","BayesianMaximizationObject","BayesianMinimization","Bayes\
-ianMinimizationObject","Because","BeckmannDistribution","Beep","Before","Begin","BeginDialogPacket","BeginPackage","BellB","BellY","Below","BenfordDistribution","BeniniDistribution","BenktanderGibratDistribution","BenktanderWeibullDistribution","BernoulliB","BernoulliDistribution","BernoulliGraphDistribution","BernoulliProcess","BernsteinBasis","BesagL","BesselFilterModel","BesselI","BesselJ","BesselJZero","BesselK","BesselY","BesselYZero","Beta","BetaBinomialDistribution","BetaDistribution","Be\
-taNegativeBinomialDistribution","BetaPrimeDistribution","BetaRegularized","Between","BetweennessCentrality","Beveled","BeveledPolyhedron","BezierCurve","BezierCurve3DBox","BezierCurve3DBoxOptions","BezierCurveBox","BezierCurveBoxOptions","BezierFunction","BilateralFilter","BilateralLaplaceTransform","BilateralZTransform","Binarize","BinaryDeserialize","BinaryDistance","BinaryFormat","BinaryImageQ","BinaryRead","BinaryReadList","BinarySerialize","BinaryWrite","BinCounts","BinLists","BinnedVariogr\
-amList","Binomial","BinomialDistribution","BinomialPointProcess","BinomialProcess","BinormalDistribution","BiorthogonalSplineWavelet","BioSequence","BioSequenceBackTranslateList","BioSequenceComplement","BioSequenceInstances","BioSequenceModify","BioSequencePlot","BioSequenceQ","BioSequenceReverseComplement","BioSequenceTranscribe","BioSequenceTranslate","BipartiteGraphQ","BiquadraticFilterModel","BirnbaumImportance","BirnbaumSaundersDistribution","BitAnd","BitClear","BitGet","BitLength","BitNot",
-"BitOr","BitRate","BitSet","BitShiftLeft","BitShiftRight","BitXor","BiweightLocation","BiweightMidvariance","Black","BlackmanHarrisWindow","BlackmanNuttallWindow","BlackmanWindow","Blank","BlankForm","BlankNullSequence","BlankSequence","Blend","Block","BlockchainAddressData","BlockchainBase","BlockchainBlockData","BlockchainContractValue","BlockchainData","BlockchainGet","BlockchainKeyEncode","BlockchainPut","BlockchainTokenData","BlockchainTransaction","BlockchainTransactionData","BlockchainTra\
-nsactionSign","BlockchainTransactionSubmit","BlockDiagonalMatrix","BlockLowerTriangularMatrix","BlockMap","BlockRandom","BlockUpperTriangularMatrix","BlomqvistBeta","BlomqvistBetaTest","Blue","Blur","Blurring","BodePlot","BohmanWindow","Bold","Bond","BondCount","BondLabels","BondLabelStyle","BondList","BondQ","Bookmarks","Boole","BooleanConsecutiveFunction","BooleanConvert","BooleanCountingFunction","BooleanFunction","BooleanGraph","BooleanMaxterms","BooleanMinimize","BooleanMinterms","BooleanQ",
-"BooleanRegion","Booleans","BooleanStrings","BooleanTable","BooleanVariables","BorderDimensions","BorelTannerDistribution","Bottom","BottomHatTransform","BoundaryDiscretizeGraphics","BoundaryDiscretizeRegion","BoundaryMesh","BoundaryMeshRegion","BoundaryMeshRegionQ","BoundaryStyle","BoundedRegionQ","BoundingRegion","Bounds","Box","BoxBaselineShift","BoxData","BoxDimensions","Boxed","Boxes","BoxForm","BoxFormFormatTypes","BoxFrame","BoxID","BoxMargins","BoxMatrix","BoxObject","BoxRatios","BoxRota\
-tion","BoxRotationPoint","BoxStyle","BoxWhiskerChart","Bra","BracketingBar","BraKet","BrayCurtisDistance","BreadthFirstScan","Break","BridgeData","BrightnessEqualize","BroadcastStationData","Brown","BrownForsytheTest","BrownianBridgeProcess","BrowserCategory","BSplineBasis","BSplineCurve","BSplineCurve3DBox","BSplineCurve3DBoxOptions","BSplineCurveBox","BSplineCurveBoxOptions","BSplineFunction","BSplineSurface","BSplineSurface3DBox","BSplineSurface3DBoxOptions","BubbleChart","BubbleChart3D","Bub\
-bleScale","BubbleSizes","BuckyballGraph","BuildCompiledComponent","BuildingData","BulletGauge","BusinessDayQ","ButterflyGraph","ButterworthFilterModel","Button","ButtonBar","ButtonBox","ButtonBoxOptions","ButtonCell","ButtonContents","ButtonData","ButtonEvaluator","ButtonExpandable","ButtonFrame","ButtonFunction","ButtonMargins","ButtonMinHeight","ButtonNote","ButtonNotebook","ButtonSource","ButtonStyle","ButtonStyleMenuListing","Byte","ByteArray","ByteArrayFormat","ByteArrayFormatQ","ByteArrayQ",
-"ByteArrayToString","ByteCount","ByteOrdering","C","CachedValue","CacheGraphics","CachePersistence","CalendarConvert","CalendarData","CalendarType","Callout","CalloutMarker","CalloutStyle","CallPacket","CanberraDistance","Cancel","CancelButton","CandlestickChart","CanonicalGraph","CanonicalizePolygon","CanonicalizePolyhedron","CanonicalizeRegion","CanonicalName","CanonicalWarpingCorrespondence","CanonicalWarpingDistance","CantorMesh","CantorStaircase","Canvas","Cap","CapForm","CapitalDifferentia\
-lD","Capitalize","CapsuleShape","CaptureRunning","CaputoD","CardinalBSplineBasis","CarlemanLinearize","CarlsonRC","CarlsonRD","CarlsonRE","CarlsonRF","CarlsonRG","CarlsonRJ","CarlsonRK","CarlsonRM","CarmichaelLambda","CaseOrdering","Cases","CaseSensitive","Cashflow","Casoratian","Cast","Catalan","CatalanNumber","Catch","CategoricalDistribution","Catenate","CatenateLayer","CauchyDistribution","CauchyMatrix","CauchyPointProcess","CauchyWindow","CayleyGraph","CDF","CDFDeploy","CDFInformation","CDFW\
-avelet","Ceiling","CelestialSystem","Cell","CellAutoOverwrite","CellBaseline","CellBoundingBox","CellBracketOptions","CellChangeTimes","CellContents","CellContext","CellDingbat","CellDingbatMargin","CellDynamicExpression","CellEditDuplicate","CellElementsBoundingBox","CellElementSpacings","CellEpilog","CellEvaluationDuplicate","CellEvaluationFunction","CellEvaluationLanguage","CellEventActions","CellFrame","CellFrameColor","CellFrameLabelMargins","CellFrameLabels","CellFrameMargins","CellFrameSt\
-yle","CellGroup","CellGroupData","CellGrouping","CellGroupingRules","CellHorizontalScrolling","CellID","CellInsertionPointCell","CellLabel","CellLabelAutoDelete","CellLabelMargins","CellLabelPositioning","CellLabelStyle","CellLabelTemplate","CellMargins","CellObject","CellOpen","CellPrint","CellProlog","Cells","CellSize","CellStyle","CellTags","CellTrayPosition","CellTrayWidgets","CellularAutomaton","CensoredDistribution","Censoring","Center","CenterArray","CenterDot","CenteredInterval","Central\
-Feature","CentralMoment","CentralMomentGeneratingFunction","Cepstrogram","CepstrogramArray","CepstrumArray","CForm","ChampernowneNumber","ChangeOptions","ChannelBase","ChannelBrokerAction","ChannelDatabin","ChannelHistoryLength","ChannelListen","ChannelListener","ChannelListeners","ChannelListenerWait","ChannelObject","ChannelPreSendFunction","ChannelReceiverFunction","ChannelSend","ChannelSubscribers","ChanVeseBinarize","Character","CharacterCounts","CharacterEncoding","CharacterEncodingsPath",
-"CharacteristicFunction","CharacteristicPolynomial","CharacterName","CharacterNormalize","CharacterRange","Characters","ChartBaseStyle","ChartElementData","ChartElementDataFunction","ChartElementFunction","ChartElements","ChartLabels","ChartLayout","ChartLegends","ChartStyle","Chebyshev1FilterModel","Chebyshev2FilterModel","ChebyshevDistance","ChebyshevT","ChebyshevU","Check","CheckAbort","CheckAll","CheckArguments","Checkbox","CheckboxBar","CheckboxBox","CheckboxBoxOptions","ChemicalConvert","C\
-hemicalData","ChemicalFormula","ChemicalInstance","ChemicalReaction","ChessboardDistance","ChiDistribution","ChineseRemainder","ChiSquareDistribution","ChoiceButtons","ChoiceDialog","CholeskyDecomposition","Chop","ChromaticityPlot","ChromaticityPlot3D","ChromaticPolynomial","Circle","CircleBox","CircleDot","CircleMinus","CirclePlus","CirclePoints","CircleThrough","CircleTimes","CirculantGraph","CircularArcThrough","CircularOrthogonalMatrixDistribution","CircularQuaternionMatrixDistribution","Cir\
-cularRealMatrixDistribution","CircularSymplecticMatrixDistribution","CircularUnitaryMatrixDistribution","Circumsphere","CityData","ClassifierFunction","ClassifierInformation","ClassifierMeasurements","ClassifierMeasurementsObject","Classify","ClassPriors","Clear","ClearAll","ClearAttributes","ClearCookies","ClearPermissions","ClearSystemCache","ClebschGordan","ClickPane","ClickToCopy","ClickToCopyEnabled","Clip","ClipboardNotebook","ClipFill","ClippingStyle","ClipPlanes","ClipPlanesStyle","ClipR\
-ange","Clock","ClockGauge","ClockwiseContourIntegral","Close","Closed","CloseKernels","ClosenessCentrality","Closing","ClosingAutoSave","ClosingEvent","CloudAccountData","CloudBase","CloudConnect","CloudConnections","CloudDeploy","CloudDirectory","CloudDisconnect","CloudEvaluate","CloudExport","CloudExpression","CloudExpressions","CloudFunction","CloudGet","CloudImport","CloudLoggingData","CloudObject","CloudObjectInformation","CloudObjectInformationData","CloudObjectNameFormat","CloudObjects","\
-CloudObjectURLType","CloudPublish","CloudPut","CloudRenderingMethod","CloudSave","CloudShare","CloudSubmit","CloudSymbol","CloudUnshare","CloudUserID","ClusterClassify","ClusterDissimilarityFunction","ClusteringComponents","ClusteringMeasurements","ClusteringTree","CMYKColor","Coarse","CodeAssistOptions","Coefficient","CoefficientArrays","CoefficientDomain","CoefficientList","CoefficientRules","CoifletWavelet","Collect","CollinearPoints","Colon","ColonForm","ColorBalance","ColorCombine","ColorCo\
-nvert","ColorCoverage","ColorData","ColorDataFunction","ColorDetect","ColorDistance","ColorFunction","ColorFunctionBinning","ColorFunctionScaling","Colorize","ColorNegate","ColorOutput","ColorProfileData","ColorQ","ColorQuantize","ColorReplace","ColorRules","ColorSelectorSettings","ColorSeparate","ColorSetter","ColorSetterBox","ColorSetterBoxOptions","ColorSlider","ColorsNear","ColorSpace","ColorToneMapping","Column","ColumnAlignments","ColumnBackgrounds","ColumnForm","ColumnLines","ColumnsEqual",
-"ColumnSpacings","ColumnWidths","CombinatorB","CombinatorC","CombinatorI","CombinatorK","CombinatorS","CombinatorW","CombinatorY","CombinedEntityClass","CombinerFunction","CometData","CommonDefaultFormatTypes","Commonest","CommonestFilter","CommonName","CommonUnits","CommunityBoundaryStyle","CommunityGraphPlot","CommunityLabels","CommunityRegionStyle","CompanyData","CompatibleUnitQ","CompilationOptions","CompilationTarget","Compile","Compiled","CompiledCodeFunction","CompiledComponent","Compiled\
-ExpressionDeclaration","CompiledFunction","CompiledLayer","CompilerCallback","CompilerEnvironment","CompilerEnvironmentAppend","CompilerEnvironmentAppendTo","CompilerEnvironmentObject","CompilerOptions","Complement","ComplementedEntityClass","CompleteGraph","CompleteGraphQ","CompleteIntegral","CompleteKaryTree","CompletionsListPacket","Complex","ComplexArrayPlot","ComplexContourPlot","Complexes","ComplexExpand","ComplexInfinity","ComplexityFunction","ComplexListPlot","ComplexPlot","ComplexPlot3D",
-"ComplexRegionPlot","ComplexStreamPlot","ComplexVectorPlot","ComponentMeasurements","ComponentwiseContextMenu","Compose","ComposeList","ComposeSeries","CompositeQ","Composition","CompoundElement","CompoundExpression","CompoundPoissonDistribution","CompoundPoissonProcess","CompoundRenewalProcess","Compress","CompressedData","CompressionLevel","ComputeUncertainty","ConcaveHullMesh","Condition","ConditionalExpression","Conditioned","Cone","ConeBox","ConfidenceLevel","ConfidenceRange","ConfidenceTra\
-nsform","ConfigurationPath","Confirm","ConfirmAssert","ConfirmBy","ConfirmMatch","ConfirmQuiet","ConformationMethod","ConformAudio","ConformImages","Congruent","ConicGradientFilling","ConicHullRegion","ConicHullRegion3DBox","ConicHullRegion3DBoxOptions","ConicHullRegionBox","ConicHullRegionBoxOptions","ConicOptimization","Conjugate","ConjugateTranspose","Conjunction","Connect","ConnectedComponents","ConnectedGraphComponents","ConnectedGraphQ","ConnectedMeshComponents","ConnectedMoleculeComponent\
-s","ConnectedMoleculeQ","ConnectionSettings","ConnectLibraryCallbackFunction","ConnectSystemModelComponents","ConnectSystemModelController","ConnesWindow","ConoverTest","ConservativeConvectionPDETerm","ConsoleMessage","Constant","ConstantArray","ConstantArrayLayer","ConstantImage","ConstantPlusLayer","ConstantRegionQ","Constants","ConstantTimesLayer","ConstellationData","ConstrainedMax","ConstrainedMin","Construct","Containing","ContainsAll","ContainsAny","ContainsExactly","ContainsNone","Contai\
-nsOnly","ContentDetectorFunction","ContentFieldOptions","ContentLocationFunction","ContentObject","ContentPadding","ContentsBoundingBox","ContentSelectable","ContentSize","Context","ContextMenu","Contexts","ContextToFileName","Continuation","Continue","ContinuedFraction","ContinuedFractionK","ContinuousAction","ContinuousMarkovProcess","ContinuousTask","ContinuousTimeModelQ","ContinuousWaveletData","ContinuousWaveletTransform","ContourDetect","ContourGraphics","ContourIntegral","ContourLabels","\
-ContourLines","ContourPlot","ContourPlot3D","Contours","ContourShading","ContourSmoothing","ContourStyle","ContraharmonicMean","ContrastiveLossLayer","Control","ControlActive","ControlAlignment","ControlGroupContentsBox","ControllabilityGramian","ControllabilityMatrix","ControllableDecomposition","ControllableModelQ","ControllerDuration","ControllerInformation","ControllerInformationData","ControllerLinking","ControllerManipulate","ControllerMethod","ControllerPath","ControllerState","ControlPla\
-cement","ControlsRendering","ControlType","ConvectionPDETerm","Convergents","ConversionOptions","ConversionRules","ConvertToPostScript","ConvertToPostScriptPacket","ConvexHullMesh","ConvexHullRegion","ConvexOptimization","ConvexPolygonQ","ConvexPolyhedronQ","ConvexRegionQ","ConvolutionLayer","Convolve","ConwayGroupCo1","ConwayGroupCo2","ConwayGroupCo3","CookieFunction","Cookies","CoordinateBoundingBox","CoordinateBoundingBoxArray","CoordinateBounds","CoordinateBoundsArray","CoordinateChartData",
-"CoordinatesToolOptions","CoordinateTransform","CoordinateTransformData","CoplanarPoints","CoprimeQ","Coproduct","CopulaDistribution","Copyable","CopyDatabin","CopyDirectory","CopyFile","CopyFunction","CopyTag","CopyToClipboard","CoreNilpotentDecomposition","CornerFilter","CornerNeighbors","Correlation","CorrelationDistance","CorrelationFunction","CorrelationTest","Cos","Cosh","CoshIntegral","CosineDistance","CosineWindow","CosIntegral","Cot","Coth","CoulombF","CoulombG","CoulombH1","CoulombH2",
-"Count","CountDistinct","CountDistinctBy","CounterAssignments","CounterBox","CounterBoxOptions","CounterClockwiseContourIntegral","CounterEvaluator","CounterFunction","CounterIncrements","CounterStyle","CounterStyleMenuListing","CountRoots","CountryData","Counts","CountsBy","Covariance","CovarianceEstimatorFunction","CovarianceFunction","CoxianDistribution","CoxIngersollRossProcess","CoxModel","CoxModelFit","CramerVonMisesTest","CreateArchive","CreateCellID","CreateChannel","CreateCloudExpressio\
-n","CreateCompilerEnvironment","CreateDatabin","CreateDataStructure","CreateDataSystemModel","CreateDialog","CreateDirectory","CreateDocument","CreateFile","CreateIntermediateDirectories","CreateLicenseEntitlement","CreateManagedLibraryExpression","CreateNotebook","CreatePacletArchive","CreatePalette","CreatePermissionsGroup","CreateScheduledTask","CreateSearchIndex","CreateSystemModel","CreateTemporary","CreateTypeInstance","CreateUUID","CreateWindow","CriterionFunction","CriticalityFailureImpo\
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-rthogonal_complement orthopoly_recur orthopoly_weight outermap out_neighbors outofpois pade parabolic_cylinder_d parametric parametric_surface parg parGosper parse_string parse_timedate part part2cont partfrac partition partition_set partpol path_digraph path_graph pathname_directory pathname_name pathname_type pdf_bernoulli pdf_beta pdf_binomial pdf_cauchy pdf_chi2 pdf_continuous_uniform pdf_discrete_uniform pdf_exp pdf_f pdf_gamma pdf_general_finite_discrete pdf_geometric pdf_gumbel pdf_hyperg\
-eometric pdf_laplace pdf_logistic pdf_lognormal pdf_negative_binomial pdf_noncentral_chi2 pdf_noncentral_student_t pdf_normal pdf_pareto pdf_poisson pdf_rank_sum pdf_rayleigh pdf_signed_rank pdf_student_t pdf_weibull pearson_skewness permanent permut permutation permutations petersen_graph petrov pickapart picture_equalp picturep piechart piechart_description planar_embedding playback plog plot2d plot3d plotdf ploteq plsquares pochhammer points poisdiff poisexpt poisint poismap poisplus poissimp\
- poissubst poistimes poistrim polar polarform polartorect polar_to_xy poly_add poly_buchberger poly_buchberger_criterion poly_colon_ideal poly_content polydecomp poly_depends_p poly_elimination_ideal poly_exact_divide poly_expand poly_expt poly_gcd polygon poly_grobner poly_grobner_equal poly_grobner_member poly_grobner_subsetp poly_ideal_intersection poly_ideal_polysaturation poly_ideal_polysaturation1 poly_ideal_saturation poly_ideal_saturation1 poly_lcm poly_minimization polymod poly_multiply\
- polynome2ele polynomialp poly_normal_form poly_normalize poly_normalize_list poly_polysaturation_extension poly_primitive_part poly_pseudo_divide poly_reduced_grobner poly_reduction poly_saturation_extension poly_s_polynomial poly_subtract polytocompanion pop postfix potential power_mod powerseries powerset prefix prev_prime primep primes principal_components print printf printfile print_graph printpois printprops prodrac product properties propvars psi psubst ptriangularize pui pui2comp pui2el\
-e pui2polynome pui_direct puireduc push put pv qput qrange qty quad_control quad_qag quad_qagi quad_qagp quad_qags quad_qawc quad_qawf quad_qawo quad_qaws quadrilateral quantile quantile_bernoulli quantile_beta quantile_binomial quantile_cauchy quantile_chi2 quantile_continuous_uniform quantile_discrete_uniform quantile_exp quantile_f quantile_gamma quantile_general_finite_discrete quantile_geometric quantile_gumbel quantile_hypergeometric quantile_laplace quantile_logistic quantile_lognormal qu\
-antile_negative_binomial quantile_noncentral_chi2 quantile_noncentral_student_t quantile_normal quantile_pareto quantile_poisson quantile_rayleigh quantile_student_t quantile_weibull quartile_skewness quit qunit quotient racah_v racah_w radcan radius random random_bernoulli random_beta random_binomial random_bipartite_graph random_cauchy random_chi2 random_continuous_uniform random_digraph random_discrete_uniform random_exp random_f random_gamma random_general_finite_discrete random_geometric ra\
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-_general_finite_discrete std_geometric std_gumbel std_hypergeometric std_laplace std_logistic std_lognormal std_negative_binomial std_noncentral_chi2 std_noncentral_student_t std_normal std_pareto std_poisson std_rayleigh std_student_t std_weibull stemplot stirling stirling1 stirling2 strim striml strimr string stringout stringp strong_components struve_h struve_l sublis sublist sublist_indices submatrix subsample subset subsetp subst substinpart subst_parallel substpart substring subvar subvarp\
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-!0),match[0]),last=match.index+match[0].length,match=search2.exec(source2);return lines.push(trimLine(source2.slice(last),last>0,!1)),lines.join("")}function trimLine(value,start2,end){let startIndex=0,endIndex=value.length;if(start2){let code2=value.codePointAt(startIndex);for(;code2===tab||code2===space;)startIndex++,code2=value.codePointAt(startIndex)}if(end){let code2=value.codePointAt(endIndex-1);for(;code2===tab||code2===space;)endIndex--,code2=value.codePointAt(endIndex-1)}return endIndex>
-startIndex?value.slice(startIndex,endIndex):""}function text$2(state2,node2){const result={type:"text",value:trimLines(String(node2.value))};return state2.patch(node2,result),state2.applyData(node2,result)}function thematicBreak(state2,node2){const result={type:"element",tagName:"hr",properties:{},children:[]};return state2.patch(node2,result),state2.applyData(node2,result)}const handlers={blockquote,break:hardBreak,code,delete:strikethrough,emphasis,footnoteReference,heading,html:html$4,imageReference,
-image,inlineCode,linkReference,link,listItem,list,paragraph,root:root$t,strong,table,tableCell,tableRow,text:text$2,thematicBreak,toml:ignore,yaml:ignore,definition:ignore,footnoteDefinition:ignore};function ignore(){}const VOID=-1,PRIMITIVE=0,ARRAY=1,OBJECT=2,DATE=3,REGEXP=4,MAP=5,SET=6,ERROR=7,BIGINT=8,env=typeof self=="object"?self:globalThis,deserializer=($,_)=>{const as=(out,index2)=>($.set(index2,out),out),unpair=index2=>{if($.has(index2))return $.get(index2);const[type2,value]=_[index2];switch(type2){case PRIMITIVE:case VOID:
-return as(value,index2);case ARRAY:{const arr=as([],index2);for(const index3 of value)arr.push(unpair(index3));return arr}case OBJECT:{const object2=as({},index2);for(const[key2,index3]of value)object2[unpair(key2)]=unpair(index3);return object2}case DATE:return as(new Date(value),index2);case REGEXP:{const{source:source2,flags:flags2}=value;return as(new RegExp(source2,flags2),index2)}case MAP:{const map2=as(new Map,index2);for(const[key2,index3]of value)map2.set(unpair(key2),unpair(index3));return map2}case SET:{
-const set2=as(new Set,index2);for(const index3 of value)set2.add(unpair(index3));return set2}case ERROR:{const{name,message}=value;return as(new env[name](message),index2)}case BIGINT:return as(BigInt(value),index2);case"BigInt":return as(Object(BigInt(value)),index2);case"ArrayBuffer":return as(new Uint8Array(value).buffer,value);case"DataView":{const{buffer}=new Uint8Array(value);return as(new DataView(buffer),value)}}return as(new env[type2](value),index2)};return unpair},deserialize=serialized=>deserializer(
-new Map,serialized)(0),EMPTY="",{toString}={},{keys}=Object,typeOf=value=>{const type2=typeof value;if(type2!=="object"||!value)return[PRIMITIVE,type2];const asString=toString.call(value).slice(8,-1);switch(asString){case"Array":return[ARRAY,EMPTY];case"Object":return[OBJECT,EMPTY];case"Date":return[DATE,EMPTY];case"RegExp":return[REGEXP,EMPTY];case"Map":return[MAP,EMPTY];case"Set":return[SET,EMPTY];case"DataView":return[ARRAY,asString]}return asString.includes("Array")?[ARRAY,asString]:asString.
-includes("Error")?[ERROR,asString]:[OBJECT,asString]},shouldSkip=([TYPE,type2])=>TYPE===PRIMITIVE&&(type2==="function"||type2==="symbol"),serializer=(strict,json2,$,_)=>{const as=(out,value)=>{const index2=_.push(out)-1;return $.set(value,index2),index2},pair=value=>{if($.has(value))return $.get(value);let[TYPE,type2]=typeOf(value);switch(TYPE){case PRIMITIVE:{let entry=value;switch(type2){case"bigint":TYPE=BIGINT,entry=value.toString();break;case"function":case"symbol":if(strict)throw new TypeError(
-"unable to serialize "+type2);entry=null;break;case"undefined":return as([VOID],value)}return as([TYPE,entry],value)}case ARRAY:{if(type2){let spread=value;return type2==="DataView"?spread=new Uint8Array(value.buffer):type2==="ArrayBuffer"&&(spread=new Uint8Array(value)),as([type2,[...spread]],value)}const arr=[],index2=as([TYPE,arr],value);for(const entry of value)arr.push(pair(entry));return index2}case OBJECT:{if(type2)switch(type2){case"BigInt":return as([type2,value.toString()],value);case"\
-Boolean":case"Number":case"String":return as([type2,value.valueOf()],value)}if(json2&&"toJSON"in value)return pair(value.toJSON());const entries=[],index2=as([TYPE,entries],value);for(const key2 of keys(value))(strict||!shouldSkip(typeOf(value[key2])))&&entries.push([pair(key2),pair(value[key2])]);return index2}case DATE:return as([TYPE,value.toISOString()],value);case REGEXP:{const{source:source2,flags:flags2}=value;return as([TYPE,{source:source2,flags:flags2}],value)}case MAP:{const entries=[],
-index2=as([TYPE,entries],value);for(const[key2,entry]of value)(strict||!(shouldSkip(typeOf(key2))||shouldSkip(typeOf(entry))))&&entries.push([pair(key2),pair(entry)]);return index2}case SET:{const entries=[],index2=as([TYPE,entries],value);for(const entry of value)(strict||!shouldSkip(typeOf(entry)))&&entries.push(pair(entry));return index2}}const{message}=value;return as([TYPE,{name:type2,message}],value)};return pair},serialize=(value,{json:json2,lossy}={})=>{const _=[];return serializer(!(json2||
-lossy),!!json2,new Map,_)(value),_},structuredClone$1=typeof structuredClone=="function"?(any2,options)=>options&&("json"in options||"lossy"in options)?deserialize(serialize(any2,options)):structuredClone(any2):(any2,options)=>deserialize(serialize(any2,options));function defaultFootnoteBackContent(_,rereferenceIndex){const result=[{type:"text",value:"↩"}];return rereferenceIndex>1&&result.push({type:"element",tagName:"sup",properties:{},children:[{type:"text",value:String(rereferenceIndex)}]}),
-result}function defaultFootnoteBackLabel(referenceIndex,rereferenceIndex){return"Back to reference "+(referenceIndex+1)+(rereferenceIndex>1?"-"+rereferenceIndex:"")}function footer(state2){const clobberPrefix=typeof state2.options.clobberPrefix=="string"?state2.options.clobberPrefix:"user-content-",footnoteBackContent=state2.options.footnoteBackContent||defaultFootnoteBackContent,footnoteBackLabel=state2.options.footnoteBackLabel||defaultFootnoteBackLabel,footnoteLabel=state2.options.footnoteLabel||
-"Footnotes",footnoteLabelTagName=state2.options.footnoteLabelTagName||"h2",footnoteLabelProperties=state2.options.footnoteLabelProperties||{className:["sr-only"]},listItems=[];let referenceIndex=-1;for(;++referenceIndex<state2.footnoteOrder.length;){const definition2=state2.footnoteById.get(state2.footnoteOrder[referenceIndex]);if(!definition2)continue;const content2=state2.all(definition2),id2=String(definition2.identifier).toUpperCase(),safeId=normalizeUri(id2.toLowerCase());let rereferenceIndex=0;
-const backReferences=[],counts=state2.footnoteCounts.get(id2);for(;counts!==void 0&&++rereferenceIndex<=counts;){backReferences.length>0&&backReferences.push({type:"text",value:" "});let children=typeof footnoteBackContent=="string"?footnoteBackContent:footnoteBackContent(referenceIndex,rereferenceIndex);typeof children=="string"&&(children={type:"text",value:children}),backReferences.push({type:"element",tagName:"a",properties:{href:"#"+clobberPrefix+"fnref-"+safeId+(rereferenceIndex>1?"-"+rereferenceIndex:
-""),dataFootnoteBackref:"",ariaLabel:typeof footnoteBackLabel=="string"?footnoteBackLabel:footnoteBackLabel(referenceIndex,rereferenceIndex),className:["data-footnote-backref"]},children:Array.isArray(children)?children:[children]})}const tail=content2[content2.length-1];if(tail&&tail.type==="element"&&tail.tagName==="p"){const tailTail=tail.children[tail.children.length-1];tailTail&&tailTail.type==="text"?tailTail.value+=" ":tail.children.push({type:"text",value:" "}),tail.children.push(...backReferences)}else
-content2.push(...backReferences);const listItem2={type:"element",tagName:"li",properties:{id:clobberPrefix+"fn-"+safeId},children:state2.wrap(content2,!0)};state2.patch(definition2,listItem2),listItems.push(listItem2)}if(listItems.length!==0)return{type:"element",tagName:"section",properties:{dataFootnotes:!0,className:["footnotes"]},children:[{type:"element",tagName:footnoteLabelTagName,properties:{...structuredClone$1(footnoteLabelProperties),id:"footnote-label"},children:[{type:"text",value:footnoteLabel}]},
-{type:"text",value:`
-`},{type:"element",tagName:"ol",properties:{},children:state2.wrap(listItems,!0)},{type:"text",value:`
-`}]}}const own$2={}.hasOwnProperty,emptyOptions$2={};function createState(tree,options){const settings=options||emptyOptions$2,definitionById=new Map,footnoteById=new Map,footnoteCounts=new Map,handlers$1={...handlers,...settings.handlers},state2={all:all2,applyData,definitionById,footnoteById,footnoteCounts,footnoteOrder:[],handlers:handlers$1,one:one2,options:settings,patch,wrap};return visit(tree,function(node2){if(node2.type==="definition"||node2.type==="footnoteDefinition"){const map2=node2.
-type==="definition"?definitionById:footnoteById,id2=String(node2.identifier).toUpperCase();map2.has(id2)||map2.set(id2,node2)}}),state2;function one2(node2,parent){const type2=node2.type,handle2=state2.handlers[type2];if(own$2.call(state2.handlers,type2)&&handle2)return handle2(state2,node2,parent);if(state2.options.passThrough&&state2.options.passThrough.includes(type2)){if("children"in node2){const{children,...shallow}=node2,result=structuredClone$1(shallow);return result.children=state2.all(node2),
-result}return structuredClone$1(node2)}return(state2.options.unknownHandler||defaultUnknownHandler)(state2,node2,parent)}function all2(parent){const values=[];if("children"in parent){const nodes2=parent.children;let index2=-1;for(;++index2<nodes2.length;){const result=state2.one(nodes2[index2],parent);if(result){if(index2&&nodes2[index2-1].type==="break"&&(!Array.isArray(result)&&result.type==="text"&&(result.value=trimMarkdownSpaceStart(result.value)),!Array.isArray(result)&&result.type==="elem\
-ent")){const head2=result.children[0];head2&&head2.type==="text"&&(head2.value=trimMarkdownSpaceStart(head2.value))}Array.isArray(result)?values.push(...result):values.push(result)}}}return values}}function patch(from,to){from.position&&(to.position=position(from))}function applyData(from,to){let result=to;if(from&&from.data){const hName=from.data.hName,hChildren=from.data.hChildren,hProperties=from.data.hProperties;if(typeof hName=="string")if(result.type==="element")result.tagName=hName;else{const children="\
-children"in result?result.children:[result];result={type:"element",tagName:hName,properties:{},children}}result.type==="element"&&hProperties&&Object.assign(result.properties,structuredClone$1(hProperties)),"children"in result&&result.children&&hChildren!==null&&hChildren!==void 0&&(result.children=hChildren)}return result}function defaultUnknownHandler(state2,node2){const data=node2.data||{},result="value"in node2&&!(own$2.call(data,"hProperties")||own$2.call(data,"hChildren"))?{type:"text",value:node2.
-value}:{type:"element",tagName:"div",properties:{},children:state2.all(node2)};return state2.patch(node2,result),state2.applyData(node2,result)}function wrap(nodes2,loose){const result=[];let index2=-1;for(loose&&result.push({type:"text",value:`
-`});++index2<nodes2.length;)index2&&result.push({type:"text",value:`
-`}),result.push(nodes2[index2]);return loose&&nodes2.length>0&&result.push({type:"text",value:`
-`}),result}function trimMarkdownSpaceStart(value){let index2=0,code2=value.charCodeAt(index2);for(;code2===9||code2===32;)index2++,code2=value.charCodeAt(index2);return value.slice(index2)}function toHast(tree,options){const state2=createState(tree,options),node2=state2.one(tree,void 0),foot=footer(state2),result=Array.isArray(node2)?{type:"root",children:node2}:node2||{type:"root",children:[]};return foot&&result.children.push({type:"text",value:`
-`},foot),result}function remarkRehype(destination,options){return destination&&"run"in destination?async function(tree,file){const hastTree=toHast(tree,{file,...options});await destination.run(hastTree,file)}:function(tree,file){return toHast(tree,{file,...destination||options})}}class Schema{constructor(property,normal,space2){this.normal=normal,this.property=property,space2&&(this.space=space2)}}Schema.prototype.normal={};Schema.prototype.property={};Schema.prototype.space=void 0;function merge(definitions2,space2){
-const property={},normal={};for(const definition2 of definitions2)Object.assign(property,definition2.property),Object.assign(normal,definition2.normal);return new Schema(property,normal,space2)}function normalize(value){return value.toLowerCase()}class Info{constructor(property,attribute){this.attribute=attribute,this.property=property}}Info.prototype.attribute="";Info.prototype.booleanish=!1;Info.prototype.boolean=!1;Info.prototype.commaOrSpaceSeparated=!1;Info.prototype.commaSeparated=!1;Info.
-prototype.defined=!1;Info.prototype.mustUseProperty=!1;Info.prototype.number=!1;Info.prototype.overloadedBoolean=!1;Info.prototype.property="";Info.prototype.spaceSeparated=!1;Info.prototype.space=void 0;let powers=0;const boolean=increment(),booleanish=increment(),overloadedBoolean=increment(),number=increment(),spaceSeparated=increment(),commaSeparated=increment(),commaOrSpaceSeparated=increment();function increment(){return 2**++powers}const types=Object.freeze(Object.defineProperty({__proto__:null,
-boolean,booleanish,commaOrSpaceSeparated,commaSeparated,number,overloadedBoolean,spaceSeparated},Symbol.toStringTag,{value:"Module"})),checks=Object.keys(types);class DefinedInfo extends Info{constructor(property,attribute,mask,space2){let index2=-1;if(super(property,attribute),mark(this,"space",space2),typeof mask=="number")for(;++index2<checks.length;){const check=checks[index2];mark(this,checks[index2],(mask&types[check])===types[check])}}}DefinedInfo.prototype.defined=!0;function mark(values,key2,value){
-value&&(values[key2]=value)}function create(definition2){const properties2={},normals={};for(const[property,value]of Object.entries(definition2.properties)){const info2=new DefinedInfo(property,definition2.transform(definition2.attributes||{},property),value,definition2.space);definition2.mustUseProperty&&definition2.mustUseProperty.includes(property)&&(info2.mustUseProperty=!0),properties2[property]=info2,normals[normalize(property)]=property,normals[normalize(info2.attribute)]=property}return new Schema(
-properties2,normals,definition2.space)}const aria=create({properties:{ariaActiveDescendant:null,ariaAtomic:booleanish,ariaAutoComplete:null,ariaBusy:booleanish,ariaChecked:booleanish,ariaColCount:number,ariaColIndex:number,ariaColSpan:number,ariaControls:spaceSeparated,ariaCurrent:null,ariaDescribedBy:spaceSeparated,ariaDetails:null,ariaDisabled:booleanish,ariaDropEffect:spaceSeparated,ariaErrorMessage:null,ariaExpanded:booleanish,ariaFlowTo:spaceSeparated,ariaGrabbed:booleanish,ariaHasPopup:null,
-ariaHidden:booleanish,ariaInvalid:null,ariaKeyShortcuts:null,ariaLabel:null,ariaLabelledBy:spaceSeparated,ariaLevel:number,ariaLive:null,ariaModal:booleanish,ariaMultiLine:booleanish,ariaMultiSelectable:booleanish,ariaOrientation:null,ariaOwns:spaceSeparated,ariaPlaceholder:null,ariaPosInSet:number,ariaPressed:booleanish,ariaReadOnly:booleanish,ariaRelevant:null,ariaRequired:booleanish,ariaRoleDescription:spaceSeparated,ariaRowCount:number,ariaRowIndex:number,ariaRowSpan:number,ariaSelected:booleanish,
-ariaSetSize:number,ariaSort:null,ariaValueMax:number,ariaValueMin:number,ariaValueNow:number,ariaValueText:null,role:null},transform(_,property){return property==="role"?property:"aria-"+property.slice(4).toLowerCase()}});function caseSensitiveTransform(attributes,attribute){return attribute in attributes?attributes[attribute]:attribute}function caseInsensitiveTransform(attributes,property){return caseSensitiveTransform(attributes,property.toLowerCase())}const html$3=create({attributes:{acceptcharset:"\
-accept-charset",classname:"class",htmlfor:"for",httpequiv:"http-equiv"},mustUseProperty:["checked","multiple","muted","selected"],properties:{abbr:null,accept:commaSeparated,acceptCharset:spaceSeparated,accessKey:spaceSeparated,action:null,allow:null,allowFullScreen:boolean,allowPaymentRequest:boolean,allowUserMedia:boolean,alt:null,as:null,async:boolean,autoCapitalize:null,autoComplete:spaceSeparated,autoFocus:boolean,autoPlay:boolean,blocking:spaceSeparated,capture:null,charSet:null,checked:boolean,
-cite:null,className:spaceSeparated,cols:number,colSpan:null,content:null,contentEditable:booleanish,controls:boolean,controlsList:spaceSeparated,coords:number|commaSeparated,crossOrigin:null,data:null,dateTime:null,decoding:null,default:boolean,defer:boolean,dir:null,dirName:null,disabled:boolean,download:overloadedBoolean,draggable:booleanish,encType:null,enterKeyHint:null,fetchPriority:null,form:null,formAction:null,formEncType:null,formMethod:null,formNoValidate:boolean,formTarget:null,headers:spaceSeparated,
-height:number,hidden:overloadedBoolean,high:number,href:null,hrefLang:null,htmlFor:spaceSeparated,httpEquiv:spaceSeparated,id:null,imageSizes:null,imageSrcSet:null,inert:boolean,inputMode:null,integrity:null,is:null,isMap:boolean,itemId:null,itemProp:spaceSeparated,itemRef:spaceSeparated,itemScope:boolean,itemType:spaceSeparated,kind:null,label:null,lang:null,language:null,list:null,loading:null,loop:boolean,low:number,manifest:null,max:null,maxLength:number,media:null,method:null,min:null,minLength:number,
-multiple:boolean,muted:boolean,name:null,nonce:null,noModule:boolean,noValidate:boolean,onAbort:null,onAfterPrint:null,onAuxClick:null,onBeforeMatch:null,onBeforePrint:null,onBeforeToggle:null,onBeforeUnload:null,onBlur:null,onCancel:null,onCanPlay:null,onCanPlayThrough:null,onChange:null,onClick:null,onClose:null,onContextLost:null,onContextMenu:null,onContextRestored:null,onCopy:null,onCueChange:null,onCut:null,onDblClick:null,onDrag:null,onDragEnd:null,onDragEnter:null,onDragExit:null,onDragLeave:null,
-onDragOver:null,onDragStart:null,onDrop:null,onDurationChange:null,onEmptied:null,onEnded:null,onError:null,onFocus:null,onFormData:null,onHashChange:null,onInput:null,onInvalid:null,onKeyDown:null,onKeyPress:null,onKeyUp:null,onLanguageChange:null,onLoad:null,onLoadedData:null,onLoadedMetadata:null,onLoadEnd:null,onLoadStart:null,onMessage:null,onMessageError:null,onMouseDown:null,onMouseEnter:null,onMouseLeave:null,onMouseMove:null,onMouseOut:null,onMouseOver:null,onMouseUp:null,onOffline:null,
-onOnline:null,onPageHide:null,onPageShow:null,onPaste:null,onPause:null,onPlay:null,onPlaying:null,onPopState:null,onProgress:null,onRateChange:null,onRejectionHandled:null,onReset:null,onResize:null,onScroll:null,onScrollEnd:null,onSecurityPolicyViolation:null,onSeeked:null,onSeeking:null,onSelect:null,onSlotChange:null,onStalled:null,onStorage:null,onSubmit:null,onSuspend:null,onTimeUpdate:null,onToggle:null,onUnhandledRejection:null,onUnload:null,onVolumeChange:null,onWaiting:null,onWheel:null,
-open:boolean,optimum:number,pattern:null,ping:spaceSeparated,placeholder:null,playsInline:boolean,popover:null,popoverTarget:null,popoverTargetAction:null,poster:null,preload:null,readOnly:boolean,referrerPolicy:null,rel:spaceSeparated,required:boolean,reversed:boolean,rows:number,rowSpan:number,sandbox:spaceSeparated,scope:null,scoped:boolean,seamless:boolean,selected:boolean,shadowRootClonable:boolean,shadowRootDelegatesFocus:boolean,shadowRootMode:null,shape:null,size:number,sizes:null,slot:null,
-span:number,spellCheck:booleanish,src:null,srcDoc:null,srcLang:null,srcSet:null,start:number,step:null,style:null,tabIndex:number,target:null,title:null,translate:null,type:null,typeMustMatch:boolean,useMap:null,value:booleanish,width:number,wrap:null,writingSuggestions:null,align:null,aLink:null,archive:spaceSeparated,axis:null,background:null,bgColor:null,border:number,borderColor:null,bottomMargin:number,cellPadding:null,cellSpacing:null,char:null,charOff:null,classId:null,clear:null,code:null,
-codeBase:null,codeType:null,color:null,compact:boolean,declare:boolean,event:null,face:null,frame:null,frameBorder:null,hSpace:number,leftMargin:number,link:null,longDesc:null,lowSrc:null,marginHeight:number,marginWidth:number,noResize:boolean,noHref:boolean,noShade:boolean,noWrap:boolean,object:null,profile:null,prompt:null,rev:null,rightMargin:number,rules:null,scheme:null,scrolling:booleanish,standby:null,summary:null,text:null,topMargin:number,valueType:null,version:null,vAlign:null,vLink:null,
-vSpace:number,allowTransparency:null,autoCorrect:null,autoSave:null,disablePictureInPicture:boolean,disableRemotePlayback:boolean,prefix:null,property:null,results:number,security:null,unselectable:null},space:"html",transform:caseInsensitiveTransform}),svg$1=create({attributes:{accentHeight:"accent-height",alignmentBaseline:"alignment-baseline",arabicForm:"arabic-form",baselineShift:"baseline-shift",capHeight:"cap-height",className:"class",clipPath:"clip-path",clipRule:"clip-rule",colorInterpolation:"\
-color-interpolation",colorInterpolationFilters:"color-interpolation-filters",colorProfile:"color-profile",colorRendering:"color-rendering",crossOrigin:"crossorigin",dataType:"datatype",dominantBaseline:"dominant-baseline",enableBackground:"enable-background",fillOpacity:"fill-opacity",fillRule:"fill-rule",floodColor:"flood-color",floodOpacity:"flood-opacity",fontFamily:"font-family",fontSize:"font-size",fontSizeAdjust:"font-size-adjust",fontStretch:"font-stretch",fontStyle:"font-style",fontVariant:"\
-font-variant",fontWeight:"font-weight",glyphName:"glyph-name",glyphOrientationHorizontal:"glyph-orientation-horizontal",glyphOrientationVertical:"glyph-orientation-vertical",hrefLang:"hreflang",horizAdvX:"horiz-adv-x",horizOriginX:"horiz-origin-x",horizOriginY:"horiz-origin-y",imageRendering:"image-rendering",letterSpacing:"letter-spacing",lightingColor:"lighting-color",markerEnd:"marker-end",markerMid:"marker-mid",markerStart:"marker-start",navDown:"nav-down",navDownLeft:"nav-down-left",navDownRight:"\
-nav-down-right",navLeft:"nav-left",navNext:"nav-next",navPrev:"nav-prev",navRight:"nav-right",navUp:"nav-up",navUpLeft:"nav-up-left",navUpRight:"nav-up-right",onAbort:"onabort",onActivate:"onactivate",onAfterPrint:"onafterprint",onBeforePrint:"onbeforeprint",onBegin:"onbegin",onCancel:"oncancel",onCanPlay:"oncanplay",onCanPlayThrough:"oncanplaythrough",onChange:"onchange",onClick:"onclick",onClose:"onclose",onCopy:"oncopy",onCueChange:"oncuechange",onCut:"oncut",onDblClick:"ondblclick",onDrag:"o\
-ndrag",onDragEnd:"ondragend",onDragEnter:"ondragenter",onDragExit:"ondragexit",onDragLeave:"ondragleave",onDragOver:"ondragover",onDragStart:"ondragstart",onDrop:"ondrop",onDurationChange:"ondurationchange",onEmptied:"onemptied",onEnd:"onend",onEnded:"onended",onError:"onerror",onFocus:"onfocus",onFocusIn:"onfocusin",onFocusOut:"onfocusout",onHashChange:"onhashchange",onInput:"oninput",onInvalid:"oninvalid",onKeyDown:"onkeydown",onKeyPress:"onkeypress",onKeyUp:"onkeyup",onLoad:"onload",onLoadedData:"\
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-˜",ensp:" ",emsp:" ",thinsp:" ",zwnj:"‌",zwj:"‍",lrm:"‎",rlm:"‏",ndash:"–",mdash:"—",lsquo:"‘",rsquo:"’",sbquo:"‚",ldquo:"“",rdquo:"”",bdquo:"„",dagger:"†",Dagger:"‡",permil:"‰",lsaquo:"‹",rsaquo:"›",euro:"€"},dangerous=["cent","copy","divide","gt","lt","not","para","times"],own$1={}.hasOwnProperty,characters={};let key;for(key in characterEntitiesHtml4)own$1.call(characterEntitiesHtml4,key)&&(characters[characterEntitiesHtml4[key]]=key);const notAlphanumericRegex=/[^\dA-Za-z]/;
-function toNamed(code2,next2,omit2,attribute){const character=String.fromCharCode(code2);if(own$1.call(characters,character)){const name=characters[character],value="&"+name;return omit2&&characterEntitiesLegacy.includes(name)&&!dangerous.includes(name)&&(!attribute||next2&&next2!==61&&notAlphanumericRegex.test(String.fromCharCode(next2)))?value:value+";"}return""}function formatSmart(code2,next2,options){let numeric=toHexadecimal(code2,next2,options.omitOptionalSemicolons),named;if((options.useNamedReferences||
-options.useShortestReferences)&&(named=toNamed(code2,next2,options.omitOptionalSemicolons,options.attribute)),(options.useShortestReferences||!named)&&options.useShortestReferences){const decimal=toDecimal(code2,next2,options.omitOptionalSemicolons);decimal.length<numeric.length&&(numeric=decimal)}return named&&(!options.useShortestReferences||named.length<numeric.length)?named:numeric}function stringifyEntities(value,options){return core(value,Object.assign({format:formatSmart},options))}const htmlCommentRegex=/^>|^->|<!--|-->|--!>|<!-$/g,
-bogusCommentEntitySubset=[">"],commentEntitySubset=["<",">"];function comment(node2,_12,_22,state2){return state2.settings.bogusComments?"<?"+stringifyEntities(node2.value,Object.assign({},state2.settings.characterReferences,{subset:bogusCommentEntitySubset}))+">":"<!--"+node2.value.replace(htmlCommentRegex,encode2)+"-->";function encode2($0){return stringifyEntities($0,Object.assign({},state2.settings.characterReferences,{subset:commentEntitySubset}))}}function doctype(_12,_22,_32,state2){return"\
-<!"+(state2.settings.upperDoctype?"DOCTYPE":"doctype")+(state2.settings.tightDoctype?"":" ")+"html>"}const re=/[ \t\n\f\r]/g;function whitespace(thing){return typeof thing=="object"?thing.type==="text"?empty(thing.value):!1:empty(thing)}function empty(value){return value.replace(re,"")===""}const siblingAfter=siblings(1),siblingBefore=siblings(-1),emptyChildren$1=[];function siblings(increment2){return sibling2;function sibling2(parent,index2,includeWhitespace){const siblings2=parent?parent.children:
-emptyChildren$1;let offset2=(index2||0)+increment2,next2=siblings2[offset2];if(!includeWhitespace)for(;next2&&whitespace(next2);)offset2+=increment2,next2=siblings2[offset2];return next2}}const own={}.hasOwnProperty;function omission(handlers2){return omit2;function omit2(node2,index2,parent){return own.call(handlers2,node2.tagName)&&handlers2[node2.tagName](node2,index2,parent)}}const closing=omission({body:body$1,caption:headOrColgroupOrCaption,colgroup:headOrColgroupOrCaption,dd,dt,head:headOrColgroupOrCaption,
-html:html$1,li,optgroup,option,p,rp:rubyElement,rt:rubyElement,tbody:tbody$1,td:cells,tfoot,th:cells,thead,tr});function headOrColgroupOrCaption(_,index2,parent){const next2=siblingAfter(parent,index2,!0);return!next2||next2.type!=="comment"&&!(next2.type==="text"&&whitespace(next2.value.charAt(0)))}function html$1(_,index2,parent){const next2=siblingAfter(parent,index2);return!next2||next2.type!=="comment"}function body$1(_,index2,parent){const next2=siblingAfter(parent,index2);return!next2||next2.
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-3"||next2.tagName==="h4"||next2.tagName==="h5"||next2.tagName==="h6"||next2.tagName==="header"||next2.tagName==="hgroup"||next2.tagName==="hr"||next2.tagName==="main"||next2.tagName==="menu"||next2.tagName==="nav"||next2.tagName==="ol"||next2.tagName==="p"||next2.tagName==="pre"||next2.tagName==="section"||next2.tagName==="table"||next2.tagName==="ul"):!parent||!(parent.type==="element"&&(parent.tagName==="a"||parent.tagName==="audio"||parent.tagName==="del"||parent.tagName==="ins"||parent.tagName===
-"map"||parent.tagName==="noscript"||parent.tagName==="video"))}function li(_,index2,parent){const next2=siblingAfter(parent,index2);return!next2||next2.type==="element"&&next2.tagName==="li"}function dt(_,index2,parent){const next2=siblingAfter(parent,index2);return!!(next2&&next2.type==="element"&&(next2.tagName==="dt"||next2.tagName==="dd"))}function dd(_,index2,parent){const next2=siblingAfter(parent,index2);return!next2||next2.type==="element"&&(next2.tagName==="dt"||next2.tagName==="dd")}function rubyElement(_,index2,parent){
-const next2=siblingAfter(parent,index2);return!next2||next2.type==="element"&&(next2.tagName==="rp"||next2.tagName==="rt")}function optgroup(_,index2,parent){const next2=siblingAfter(parent,index2);return!next2||next2.type==="element"&&next2.tagName==="optgroup"}function option(_,index2,parent){const next2=siblingAfter(parent,index2);return!next2||next2.type==="element"&&(next2.tagName==="option"||next2.tagName==="optgroup")}function thead(_,index2,parent){const next2=siblingAfter(parent,index2);
-return!!(next2&&next2.type==="element"&&(next2.tagName==="tbody"||next2.tagName==="tfoot"))}function tbody$1(_,index2,parent){const next2=siblingAfter(parent,index2);return!next2||next2.type==="element"&&(next2.tagName==="tbody"||next2.tagName==="tfoot")}function tfoot(_,index2,parent){return!siblingAfter(parent,index2)}function tr(_,index2,parent){const next2=siblingAfter(parent,index2);return!next2||next2.type==="element"&&next2.tagName==="tr"}function cells(_,index2,parent){const next2=siblingAfter(
-parent,index2);return!next2||next2.type==="element"&&(next2.tagName==="td"||next2.tagName==="th")}const opening=omission({body,colgroup,head,html,tbody});function html(node2){const head2=siblingAfter(node2,-1);return!head2||head2.type!=="comment"}function head(node2){const seen2=new Set;for(const child3 of node2.children)if(child3.type==="element"&&(child3.tagName==="base"||child3.tagName==="title")){if(seen2.has(child3.tagName))return!1;seen2.add(child3.tagName)}const child2=node2.children[0];return!child2||
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-nt"&&previous2.tagName==="colgroup"&&closing(previous2,parent.children.indexOf(previous2),parent)?!1:!!(head2&&head2.type==="element"&&head2.tagName==="col")}function tbody(node2,index2,parent){const previous2=siblingBefore(parent,index2),head2=siblingAfter(node2,-1);return parent&&previous2&&previous2.type==="element"&&(previous2.tagName==="thead"||previous2.tagName==="tbody")&&closing(previous2,parent.children.indexOf(previous2),parent)?!1:!!(head2&&head2.type==="element"&&head2.tagName==="tr")}
-const constants={name:[[`	
-\f\r &/=>`.split(""),`	
-\f\r "&'/=>\``.split("")],[`\0	
-\f\r "&'/<=>`.split(""),`\0	
-\f\r "&'/<=>\``.split("")]],unquoted:[[`	
-\f\r &>`.split(""),`\0	
-\f\r "&'<=>\``.split("")],[`\0	
-\f\r "&'<=>\``.split(""),`\0	
-\f\r "&'<=>\``.split("")]],single:[["&'".split(""),"\"&'`".split("")],["\0&'".split(""),"\0\"&'`".split("")]],double:[['"&'.split(""),"\"&'`".split("")],['\0"&'.split(""),"\0\"&'`".split("")]]};function element(node2,index2,parent,state2){const schema=state2.schema,omit2=schema.space==="svg"?!1:state2.settings.omitOptionalTags;let selfClosing=schema.space==="svg"?state2.settings.closeEmptyElements:state2.settings.voids.includes(node2.tagName.toLowerCase());const parts=[];let last;schema.space===
-"html"&&node2.tagName==="svg"&&(state2.schema=svg);const attributes=serializeAttributes(state2,node2.properties),content2=state2.all(schema.space==="html"&&node2.tagName==="template"?node2.content:node2);return state2.schema=schema,content2&&(selfClosing=!1),(attributes||!omit2||!opening(node2,index2,parent))&&(parts.push("<",node2.tagName,attributes?" "+attributes:""),selfClosing&&(schema.space==="svg"||state2.settings.closeSelfClosing)&&(last=attributes.charAt(attributes.length-1),(!state2.settings.
-tightSelfClosing||last==="/"||last&&last!=='"'&&last!=="'")&&parts.push(" "),parts.push("/")),parts.push(">")),parts.push(content2),!selfClosing&&(!omit2||!closing(node2,index2,parent))&&parts.push("</"+node2.tagName+">"),parts.join("")}function serializeAttributes(state2,properties2){const values=[];let index2=-1,key2;if(properties2){for(key2 in properties2)if(properties2[key2]!==null&&properties2[key2]!==void 0){const value=serializeAttribute(state2,key2,properties2[key2]);value&&values.push(value)}}
-for(;++index2<values.length;){const last=state2.settings.tightAttributes?values[index2].charAt(values[index2].length-1):void 0;index2!==values.length-1&&last!=='"'&&last!=="'"&&(values[index2]+=" ")}return values.join("")}function serializeAttribute(state2,key2,value){const info2=find(state2.schema,key2),x=state2.settings.allowParseErrors&&state2.schema.space==="html"?0:1,y=state2.settings.allowDangerousCharacters?0:1;let quote=state2.quote,result;if(info2.overloadedBoolean&&(value===info2.attribute||
-value==="")?value=!0:(info2.boolean||info2.overloadedBoolean)&&(typeof value!="string"||value===info2.attribute||value==="")&&(value=!!value),value==null||value===!1||typeof value=="number"&&Number.isNaN(value))return"";const name=stringifyEntities(info2.attribute,Object.assign({},state2.settings.characterReferences,{subset:constants.name[x][y]}));return value===!0||(value=Array.isArray(value)?(info2.commaSeparated?stringify$1:stringify)(value,{padLeft:!state2.settings.tightCommaSeparatedLists}):
-String(value),state2.settings.collapseEmptyAttributes&&!value)?name:(state2.settings.preferUnquoted&&(result=stringifyEntities(value,Object.assign({},state2.settings.characterReferences,{attribute:!0,subset:constants.unquoted[x][y]}))),result!==value&&(state2.settings.quoteSmart&&ccount(value,quote)>ccount(value,state2.alternative)&&(quote=state2.alternative),result=quote+stringifyEntities(value,Object.assign({},state2.settings.characterReferences,{subset:(quote==="'"?constants.single:constants.
-double)[x][y],attribute:!0}))+quote),name+(result&&"="+result))}const textEntitySubset=["<","&"];function text(node2,_,parent,state2){return parent&&parent.type==="element"&&(parent.tagName==="script"||parent.tagName==="style")?node2.value:stringifyEntities(node2.value,Object.assign({},state2.settings.characterReferences,{subset:textEntitySubset}))}function raw(node2,index2,parent,state2){return state2.settings.allowDangerousHtml?node2.value:text(node2,index2,parent,state2)}function root$r(node2,_12,_22,state2){
-return state2.all(node2)}const handle=zwitch("type",{invalid,unknown,handlers:{comment,doctype,element,raw,root:root$r,text}});function invalid(node2){throw new Error("Expected node, not `"+node2+"`")}function unknown(node_){const node2=node_;throw new Error("Cannot compile unknown node `"+node2.type+"`")}const emptyOptions={},emptyCharacterReferences={},emptyChildren=[];function toHtml(tree,options){const options_=options||emptyOptions,quote=options_.quote||'"',alternative=quote==='"'?"'":'"';if(quote!==
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-!1,tightCommaSeparatedLists:options_.tightCommaSeparatedLists||!1,tightSelfClosing:options_.tightSelfClosing||!1,collapseEmptyAttributes:options_.collapseEmptyAttributes||!1,allowDangerousHtml:options_.allowDangerousHtml||!1,voids:options_.voids||htmlVoidElements,characterReferences:options_.characterReferences||emptyCharacterReferences,closeSelfClosing:options_.closeSelfClosing||!1,closeEmptyElements:options_.closeEmptyElements||!1},schema:options_.space==="svg"?svg:html$2,quote,alternative}.one(
-Array.isArray(tree)?{type:"root",children:tree}:tree,void 0,void 0)}function one(node2,index2,parent){return handle(node2,index2,parent,this)}function all(parent){const results=[],children=parent&&parent.children||emptyChildren;let index2=-1;for(;++index2<children.length;)results[index2]=this.one(children[index2],index2,parent);return results.join("")}function rehypeStringify(options){const self2=this,settings={...self2.data("settings"),...options};self2.compiler=compiler2;function compiler2(tree){
-return toHtml(tree,settings)}}function expandBrTags(value){const matches2=[...value.matchAll(BR_PATTERN)];if(!matches2.length)return[{type:"text",value}];const result=[];let cursor=0;for(const m of matches2)m.index>cursor&&result.push({type:"text",value:value.slice(cursor,m.index)}),result.push({type:"element",tagName:"br",properties:{},children:[]}),cursor=m.index+m[0].length;return cursor<value.length&&result.push({type:"text",value:value.slice(cursor)}),result}function parseList(value){const match=value.
-trim().match(LIST_PATTERN);if(!match)return null;const body2=match[1],items2=[];let cursor=0;for(const liMatch of body2.matchAll(LI_PATTERN)){if(body2.slice(cursor,liMatch.index).trim())return null;items2.push({type:"element",tagName:"li",properties:{},children:expandBrTags(liMatch[1]??"")}),cursor=liMatch.index+liMatch[0].length}return!items2.length||body2.slice(cursor).trim()?null:{type:"element",tagName:"ul",properties:{},children:items2}}function processCell(cell2){visitParents(cell2,"text",
-(textNode,ancestors)=>{const parent=ancestors[ancestors.length-1];if(!parent||parent.type!=="element")return;const siblings2=parent.children,startIndex=siblings2.indexOf(textNode);if(startIndex===-1)return;let combined="",endIndex=startIndex;for(let i=startIndex;i<siblings2.length;i++){const sib=siblings2[i];if(sib.type==="text")combined+=sib.value,endIndex=i;else if(sib.type==="element"&&sib.tagName==="br")combined+=`
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-properties??{};props.href&&(props.target="_blank",props.rel="noopener noreferrer",node2.properties=props)})},COPY_ICON_SVG='<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-copy-icon lucide-copy"><rect width="14" height="14" x="8" y="8" rx="2" ry="2"/><path d="M4 16c-1.1 0-2-.9-2-2V4c0-1.1.9-2 2-2h10c1.1 0 2 .9 2 2"/></svg>',PREVIEW_ICON_SVG='<s\
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-{type:"raw",value:html2}]}}function createCopyButton(codeId){return{type:"element",tagName:"button",properties:{className:[COPY_CODE_BTN_CLASS],"data-code-id":codeId,title:"Copy code",type:"button"},children:[createRawHtmlElement(COPY_ICON_SVG)]}}function createPreviewButton(codeId){return{type:"element",tagName:"button",properties:{className:[PREVIEW_CODE_BTN_CLASS],"data-code-id":codeId,title:"Preview code",type:"button"},children:[createRawHtmlElement(PREVIEW_ICON_SVG)]}}function createHeader(language2,codeId){
-const actions=[createCopyButton(codeId)];return language2.toLowerCase()==="html"&&actions.push(createPreviewButton(codeId)),{type:"element",tagName:"div",properties:{className:[CODE_BLOCK_HEADER_CLASS]},children:[{type:"element",tagName:"span",properties:{className:[CODE_LANGUAGE_CLASS]},children:[{type:"text",value:language2}]},{type:"element",tagName:"div",properties:{className:[CODE_BLOCK_ACTIONS_CLASS]},children:actions}]}}function createScrollContainer(preElement){return{type:"element",tagName:"\
-div",properties:{className:[CODE_BLOCK_SCROLL_CONTAINER_CLASS]},children:[preElement]}}function createWrapper(header,preElement){return{type:"element",tagName:"div",properties:{className:[CODE_BLOCK_WRAPPER_CLASS,RELATIVE_CLASS]},children:[header,createScrollContainer(preElement)]}}function extractLanguage(codeElement){const className=codeElement.properties?.className;if(!Array.isArray(className))return"text";for(const cls of className)if(typeof cls=="string"&&cls.startsWith("language-"))return cls.
-replace("language-","");return"text"}function generateCodeId(){return typeof window<"u"?`code-${window.idxCodeBlock=(window.idxCodeBlock??0)+1}`:`code-${Date.now()}-${Math.random().toString(36).slice(2,7)}`}const rehypeEnhanceCodeBlocks=()=>tree=>{visit(tree,"element",(node2,index2,parent)=>{if(node2.tagName!=="pre"||!parent||index2===void 0)return;const codeElement=node2.children.find(child2=>child2.type==="element"&&child2.tagName==="code");if(!codeElement)return;const language2=extractLanguage(
-codeElement),codeId=generateCodeId();codeElement.properties={...codeElement.properties,"data-code-id":codeId};const header=createHeader(language2,codeId),wrapper=createWrapper(header,node2);parent.children[index2]=wrapper})};function rehypeResolveAttachmentImages(options){return tree=>{visit(tree,"element",node2=>{if(node2.tagName==="img"&&node2.properties?.src){const src=String(node2.properties.src);if(src.startsWith(UrlProtocol.DATA)||src.startsWith(UrlProtocol.HTTP))return;const attachment=options.
-attachments?.find(a=>a.type===AttachmentType.IMAGE&&a.name===src);attachment?.base64Url&&(node2.properties.src=attachment.base64Url)}})}}const rehypeRtlSupport=()=>tree=>{visit(tree,"element",node2=>{node2.children&&node2.children.length>0&&(node2.properties={...node2.properties,dir:"auto"})})};function preserveIndent(line){let index2=0,output="";for(;index2<line.length;){const char=line[index2];if(char===" "){output+=NBSP,index2+=1;continue}if(char==="	"){output+=TAB_AS_SPACES,index2+=1;continue}
-break}return output+line.slice(index2)}function createLiteralChildren(value){const lines=value.split(LINE_BREAK),nodes2=[];for(const[lineIndex,rawLine]of lines.entries())lineIndex>0&&nodes2.push({type:"break"}),nodes2.push({type:"text",value:preserveIndent(rawLine)});return nodes2.length||nodes2.push({type:"text",value:""}),nodes2}const remarkLiteralHtml=()=>tree=>{visit(tree,"html",(node2,index2,parent)=>{if(!parent||typeof index2!="number")return;const replacement=createLiteralChildren(node2.value);
-if(!PHRASE_PARENTS.has(parent.type)){const paragraph2={type:"paragraph",children:replacement,data:{literalHtml:!0}},siblings2=parent.children;if(siblings2.splice(index2,1,paragraph2),index2>0){const previous2=siblings2[index2-1];if(previous2?.type==="paragraph"&&previous2.data?.literalHtml){const prevChildren=previous2.children;return prevChildren.length&&prevChildren[prevChildren.length-1].type!=="break"&&prevChildren.push({type:"break"}),prevChildren.push(...paragraph2.children),siblings2.splice(
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-newPairs)}function updatePairValue(index2,rawValue){const value=sanitizeKeyValuePairValue(rawValue),newPairs=[...$$props.pairs];newPairs[index2]={...newPairs[index2],value},$$props.onPairsChange(newPairs)}function trimPairValue(index2,value){const trimmed=value.trim();if(trimmed===value)return;const newPairs=[...$$props.pairs];newPairs[index2]={...newPairs[index2],value:trimmed},$$props.onPairsChange(newPairs)}var div=root$n(),div_1=child(div),node2=child(div_1);{var consequent_1=$$anchor2=>{var span=root_1$e(),
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-<!> <!></div></div>'),root_7$a=from_html('<div class="rounded-md border border-dashed p-4 text-sm text-muted-foreground">No MCP Servers configured yet. Add one to enable agentic features.</div>'),root_8$8=from_html('<div class="space-y-3"></div>'),root$l=from_html('<div class="space-y-5 md:space-y-4"><div class="flex items-start justify-between gap-4"><div><h4 class="text-base font-semibold">Manage Servers</h4></div> <!></div> <!> <!> <!></div>');function McpServersSettings($$anchor,$$props){push$1(
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-$$slots:{default:!0}});var node_6=sibling(node_5,2);{let $0=user_derived(()=>!!get$4(newServerUrlError));Button(node_6,{variant:"default",size:"sm",onclick:saveNewServer,get disabled(){return get$4($0)},"aria-label":"Save",children:($$anchor5,$$slotProps2)=>{next$1();var text_1=text$8("Add");append($$anchor5,text_1)},$$slots:{default:!0}})}reset(div_3),reset(div_2),append($$anchor4,div_2)},$$slots:{default:!0}})}),append($$anchor2,fragment_2)};if_block(node_2,$$render=>{get$4(isAddingServer)&&$$render(
-consequent_1)})}var node_7=sibling(node_2,2);{var consequent_2=$$anchor2=>{var div_4=root_7$a();append($$anchor2,div_4)};if_block(node_7,$$render=>{get$4(servers).length===0&&!get$4(isAddingServer)&&$$render(consequent_2)})}var node_8=sibling(node_7,2);{var consequent_4=$$anchor2=>{var div_5=root_8$8();each(div_5,21,()=>get$4(servers),server=>server.id,($$anchor3,server)=>{var fragment_3=comment$2(),node_9=first_child(fragment_3);{var consequent_3=$$anchor4=>{McpServerCardSkeleton($$anchor4)},alternate=$$anchor4=>{
-{let $0=user_derived(()=>mcpStore.getServerFavicon(get$4(server).id)),$1=user_derived(()=>conversationsStore.isMcpServerEnabledForChat(get$4(server).id));McpServerCard($$anchor4,{get server(){return get$4(server)},get faviconUrl(){return get$4($0)},get enabled(){return get$4($1)},onToggle:async()=>await conversationsStore.toggleMcpServerForChat(get$4(server).id),onUpdate:updates=>mcpStore.updateServer(get$4(server).id,updates),onDelete:()=>mcpStore.removeServer(get$4(server).id)})}};if_block(node_9,
-$$render=>{get$4(initialLoadComplete)?$$render(alternate,!1):$$render(consequent_3)})}append($$anchor3,fragment_3)}),reset(div_5),append($$anchor2,div_5)};if_block(node_8,$$render=>{get$4(servers).length>0&&$$render(consequent_4)})}reset(div),append($$anchor,div),pop()}var root_2$g=from_html('<div class="box-shadow-lg overflow-hidden rounded-full bg-muted ring-1 ring-muted"><img alt="" class="h-4 w-4"/></div>'),root_3$d=from_html('<span class="text-xs text-muted-foreground"> </span>'),root_1$c=from_html(
-'<div><div class="flex -space-x-1"></div> <!></div>');function McpActiveServersAvatars($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),mcpServers=user_derived(()=>mcpStore.getServersSorted().filter(s2=>s2.enabled)),enabledMcpServersForChat=user_derived(()=>get$4(mcpServers).filter(s2=>conversationsStore.isMcpServerEnabledForChat(s2.id)&&s2.url.trim())),healthyEnabledMcpServers=user_derived(()=>get$4(enabledMcpServersForChat).filter(s2=>mcpStore.getHealthCheckState(s2.
-id).status!==HealthCheckStatus.ERROR)),hasEnabledMcpServers=user_derived(()=>get$4(enabledMcpServersForChat).length>0),extraServersCount=user_derived(()=>Math.max(0,get$4(healthyEnabledMcpServers).length-MAX_DISPLAYED_MCP_AVATARS)),mcpFavicons=user_derived(()=>get$4(healthyEnabledMcpServers).slice(0,MAX_DISPLAYED_MCP_AVATARS).map(s2=>({id:s2.id,url:mcpStore.getServerFavicon(s2.id)})).filter(f=>f.url!==null));var fragment=comment$2(),node2=first_child(fragment);{var consequent_1=$$anchor2=>{var div=root_1$c(),
-div_1=child(div);each(div_1,21,()=>get$4(mcpFavicons),favicon=>favicon.id,($$anchor3,favicon)=>{var div_2=root_2$g(),img=child(div_2);reset(div_2),template_effect(()=>set_attribute(img,"src",get$4(favicon).url)),event("error",img,e=>{e.currentTarget.style.display="none"}),replay_events(img),append($$anchor3,div_2)}),reset(div_1);var node_1=sibling(div_1,2);{var consequent=$$anchor3=>{var span=root_3$d(),text2=child(span);reset(span),template_effect(()=>set_text(text2,`+${get$4(extraServersCount)??
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-isabled:cursor-not-allowed disabled:opacity-60" aria-label="MCP Servers"><!></button>'),root_6$a=from_html("<!> <span>Manage MCP Servers</span>",1),root_9$7=from_html('<img alt="" class="h-4 w-4 shrink-0 rounded-sm"/>'),root_10$6=from_html('<span class="shrink-0 rounded bg-destructive/15 px-1.5 py-0.5 text-xs text-destructive">Error</span>'),root_8$7=from_html('<button type="button" class="flex w-full items-center justify-between gap-2 px-2 py-2 text-left transition-colors hover:bg-accent disabl\
-ed:cursor-not-allowed disabled:opacity-50"><div class="flex min-w-0 flex-1 items-center gap-2"><!> <span class="truncate text-sm"> </span> <!></div> <!></button>'),root_7$9=from_html('<div class="max-h-64 overflow-y-auto"></div>'),root_2$f=from_html("<!> <!>",1);function McpServersSelector($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),disabled=prop($$props,"disabled",3,!1),searchQuery=state$1(""),mcpServers=user_derived(()=>mcpStore.getServersSorted().filter(s2=>s2.
-enabled)),hasMcpServers=user_derived(()=>get$4(mcpServers).length>0),enabledMcpServersForChat=user_derived(()=>get$4(mcpServers).filter(s2=>conversationsStore.isMcpServerEnabledForChat(s2.id)&&s2.url.trim())),healthyEnabledMcpServers=user_derived(()=>get$4(enabledMcpServersForChat).filter(s2=>mcpStore.getHealthCheckState(s2.id).status!==HealthCheckStatus.ERROR)),hasEnabledMcpServers=user_derived(()=>get$4(enabledMcpServersForChat).length>0),mcpFavicons=user_derived(()=>get$4(healthyEnabledMcpServers).
-slice(0,3).map(s2=>({id:s2.id,url:mcpStore.getServerFavicon(s2.id)})).filter(f=>f.url!==null)),filteredMcpServers=user_derived(()=>{const query=get$4(searchQuery).toLowerCase().trim();return query?get$4(mcpServers).filter(s2=>{const name=getServerLabel(s2).toLowerCase(),url2=s2.url.toLowerCase();return name.includes(query)||url2.includes(query)}):get$4(mcpServers)});function getServerLabel(server){return mcpStore.getServerLabel(server)}function handleDropdownOpen(open2){open2&&mcpStore.runHealthChecksForServers(
-get$4(mcpServers))}function isServerEnabledForChat(serverId){return conversationsStore.isMcpServerEnabledForChat(serverId)}async function toggleServerForChat(serverId){await conversationsStore.toggleMcpServerForChat(serverId)}var fragment=comment$2(),node2=first_child(fragment);{var consequent_2=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Root$2,($$anchor3,DropdownMenu_Root)=>{DropdownMenu_Root($$anchor3,{onOpenChange:open2=>{open2||set$1(searchQuery,
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-disabled=disabled()),append($$anchor6,button)},$$slots:{default:!0}})});var node_4=sibling(node_2,2);component(node_4,()=>Dropdown_menu_content,($$anchor5,DropdownMenu_Content)=>{DropdownMenu_Content($$anchor5,{align:"start",class:"w-72 pt-0",children:($$anchor6,$$slotProps2)=>{{const footer2=$$anchor7=>{var fragment_4=comment$2(),node_5=first_child(fragment_4);component(node_5,()=>Dropdown_menu_item,($$anchor8,DropdownMenu_Item)=>{DropdownMenu_Item($$anchor8,{class:"flex cursor-pointer items-ce\
-nter gap-2",get onclick(){return $$props.onSettingsClick},children:($$anchor9,$$slotProps3)=>{var fragment_5=root_6$a(),node_6=first_child(fragment_5);Settings$1(node_6,{class:"h-4 w-4"}),next$1(2),append($$anchor9,fragment_5)},$$slots:{default:!0}})}),append($$anchor7,fragment_4)};let $0=user_derived(()=>get$4(filteredMcpServers).length===0);DropdownMenuSearchable($$anchor6,{placeholder:"Search servers...",emptyMessage:"No servers found",get isEmpty(){return get$4($0)},get searchValue(){return get$4(
-searchQuery)},set searchValue($$value){set$1(searchQuery,$$value,!0)},footer:footer2,children:($$anchor7,$$slotProps3)=>{var div=root_7$9();each(div,21,()=>get$4(filteredMcpServers),server=>server.id,($$anchor8,server)=>{const healthState=user_derived(()=>mcpStore.getHealthCheckState(get$4(server).id)),hasError=user_derived(()=>get$4(healthState).status===HealthCheckStatus.ERROR),isEnabledForChat=user_derived(()=>isServerEnabledForChat(get$4(server).id));var button_1=root_8$7();button_1.__click=
-()=>!get$4(hasError)&&toggleServerForChat(get$4(server).id);var div_1=child(button_1),node_7=child(div_1);{var consequent=$$anchor9=>{var img=root_9$7();template_effect($02=>set_attribute(img,"src",$02),[()=>mcpStore.getServerFavicon(get$4(server).id)]),event("error",img,e=>{e.currentTarget.style.display="none"}),replay_events(img),append($$anchor9,img)};if_block(node_7,$$render=>{mcpStore.getServerFavicon(get$4(server).id)&&$$render(consequent)})}var span=sibling(node_7,2),text2=child(span,!0);
-reset(span);var node_8=sibling(span,2);{var consequent_1=$$anchor9=>{var span_1=root_10$6();append($$anchor9,span_1)};if_block(node_8,$$render=>{get$4(hasError)&&$$render(consequent_1)})}reset(div_1);var node_9=sibling(div_1,2);Switch(node_9,{get checked(){return get$4(isEnabledForChat)},get disabled(){return get$4(hasError)},onclick:e=>e.stopPropagation(),onCheckedChange:()=>toggleServerForChat(get$4(server).id)}),reset(button_1),template_effect($02=>{button_1.disabled=get$4(hasError),set_text(
-text2,$02)},[()=>getServerLabel(get$4(server))]),append($$anchor8,button_1)}),reset(div),append($$anchor7,div)},$$slots:{footer:!0,default:!0}})}},$$slots:{default:!0}})}),append($$anchor4,fragment_2)},$$slots:{default:!0}})}),append($$anchor2,fragment_1)};if_block(node2,$$render=>{get$4(hasMcpServers)&&get$4(hasEnabledMcpServers)&&get$4(mcpFavicons).length>0&&$$render(consequent_2)})}append($$anchor,fragment),pop()}delegate(["click"]);var root_3$b=from_html("<!> Tools",1),root_5$a=from_html("<!\
-> Resources",1),root_7$8=from_html("<!> Prompts",1),root_9$6=from_html("<!> Logging",1),root_11$3=from_html("<!> Completions",1),root_13$6=from_html("<!> Tasks",1),root_1$b=from_html("<!> <!> <!> <!> <!> <!>",1);function McpCapabilitiesBadges($$anchor,$$props){push$1($$props,!0);var fragment=comment$2(),node2=first_child(fragment);{var consequent_6=$$anchor2=>{var fragment_1=root_1$b(),node_1=first_child(fragment_1);{var consequent=$$anchor3=>{Badge($$anchor3,{variant:"outline",class:"h-5 gap-1 \
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-div_1=first_child(fragment_4),div_2=child(div_1),node_6=child(div_2);Skeleton(node_6,{class:"h-4 w-4 rounded"});var node_7=sibling(node_6,2);Skeleton(node_7,{class:"h-3 w-24"}),reset(div_2);var div_3=sibling(div_2,2),node_8=child(div_3);Skeleton(node_8,{class:"h-5 w-16 rounded-full"});var node_9=sibling(node_8,2);Skeleton(node_9,{class:"h-5 w-20 rounded-full"});var node_10=sibling(node_9,2);Skeleton(node_10,{class:"h-5 w-14 rounded-full"}),reset(div_3),reset(div_1);var div_4=sibling(div_1,2),div_5=child(
-div_4),node_11=child(div_5);Skeleton(node_11,{class:"h-4 w-4 rounded"});var node_12=sibling(node_11,2);Skeleton(node_12,{class:"h-3 w-32"}),reset(div_5),reset(div_4),append($$anchor5,fragment_4)},alternate=$$anchor5=>{var fragment_5=root_7$6(),node_13=first_child(fragment_5);{var consequent_4=$$anchor6=>{McpServerInfo($$anchor6,{get instructions(){return get$4(instructions)}})};if_block(node_13,$$render=>{get$4(isConnected)&&get$4(instructions)&&$$render(consequent_4)})}var node_14=sibling(node_13,
-2);{var consequent_5=$$anchor6=>{McpServerCardToolsList($$anchor6,{get tools(){return get$4(tools)}})};if_block(node_14,$$render=>{get$4(tools).length>0&&$$render(consequent_5)})}var node_15=sibling(node_14,2);{var consequent_6=$$anchor6=>{McpConnectionLogs($$anchor6,{get logs(){return get$4(connectionLogs)},get connectionTimeMs(){return get$4(connectionTimeMs)}})};if_block(node_15,$$render=>{get$4(connectionLogs).length>0&&$$render(consequent_6)})}append($$anchor5,fragment_5)};if_block(node_5,$$render=>{
-get$4(showSkeleton)?$$render(consequent_3):$$render(alternate,!1)})}reset(div);var div_6=sibling(div,2),node_16=child(div_6);{var consequent_7=$$anchor5=>{Skeleton($$anchor5,{class:"h-3 w-28"})},alternate_1=$$anchor5=>{var fragment_10=comment$2(),node_17=first_child(fragment_10);{var consequent_8=$$anchor6=>{var div_7=root_13$5(),span=child(div_7),text_2=child(span);reset(span),reset(div_7),template_effect(()=>set_text(text_2,`Protocol version: ${get$4(protocolVersion)??""}`)),append($$anchor6,div_7)};
-if_block(node_17,$$render=>{get$4(protocolVersion)&&$$render(consequent_8)},!0)}append($$anchor5,fragment_10)};if_block(node_16,$$render=>{get$4(showSkeleton)?$$render(consequent_7):$$render(alternate_1,!1)})}var node_18=sibling(node_16,2);McpServerCardActions(node_18,{get isHealthChecking(){return get$4(isHealthChecking)},onEdit:startEditing,onRefresh:handleHealthCheck,onDelete:handleDeleteClick}),reset(div_6),append($$anchor4,fragment_3)};if_block(node_1,$$render=>{get$4(isEditing2)?$$render(consequent):
-$$render(alternate_2,!1)})}append($$anchor3,fragment_1)},$$slots:{default:!0}})});var node_19=sibling(node2,2);McpServerCardDeleteDialog(node_19,{get displayName(){return get$4(displayName)},onOpenChange:open2=>set$1(showDeleteDialog,open2,!0),get onConfirm(){return $$props.onDelete},get open(){return get$4(showDeleteDialog)},set open($$value){set$1(showDeleteDialog,$$value,!0)}}),append($$anchor,fragment),pop()}var root_1$9=from_html('<img alt="" class="h-5 w-5 shrink-0 rounded"/>'),root_2$c=from_html(
-'<div class="flex h-5 w-5 shrink-0 items-center justify-center rounded bg-muted"><!></div>'),root_5$8=from_html('<a target="_blank" rel="noopener noreferrer" class="shrink-0 text-muted-foreground hover:text-foreground" aria-label="Open website"><!></a>'),root_8$5=from_html("<!> ",1),root_6$7=from_html('<div class="flex flex-wrap items-center gap-1"><!> <!></div>'),root$h=from_html('<div class="space-y-3"><div class="flex items-start justify-between gap-3"><div class="grid min-w-0 gap-3"><div cla\
-ss="flex items-center gap-2 overflow-hidden"><!> <p class="min-w-0 shrink-0 truncate leading-none font-medium"> </p> <!> <!></div> <!></div> <div class="flex shrink-0 items-center pl-2"><!></div></div></div>');function McpServerCardHeader($$anchor,$$props){push$1($$props,!0);let disabled=prop($$props,"disabled",3,!1);var div=root$h(),div_1=child(div),div_2=child(div_1),div_3=child(div_2),node2=child(div_3);{var consequent=$$anchor2=>{var img=root_1$9();template_effect(()=>set_attribute(img,"src",
-$$props.faviconUrl)),event("error",img,e=>{e.currentTarget.style.display="none"}),replay_events(img),append($$anchor2,img)},alternate=$$anchor2=>{var div_4=root_2$c(),node_1=child(div_4);Cable(node_1,{class:"h-3 w-3 text-muted-foreground"}),reset(div_4),append($$anchor2,div_4)};if_block(node2,$$render=>{$$props.faviconUrl?$$render(consequent):$$render(alternate,!1)})}var p2=sibling(node2,2),text2=child(p2,!0);reset(p2);var node_2=sibling(p2,2);{var consequent_1=$$anchor2=>{Badge($$anchor2,{variant:"\
-secondary",class:"h-4 min-w-0 truncate px-1 text-[10px]",children:($$anchor3,$$slotProps)=>{next$1();var text_1=text$8();template_effect(()=>set_text(text_1,`v${$$props.serverInfo.version??""}`)),append($$anchor3,text_1)},$$slots:{default:!0}})};if_block(node_2,$$render=>{$$props.serverInfo?.version&&$$render(consequent_1)})}var node_3=sibling(node_2,2);{var consequent_2=$$anchor2=>{var a=root_5$8(),node_4=child(a);External_link(node_4,{class:"h-3 w-3"}),reset(a),template_effect(()=>set_attribute(
-a,"href",$$props.serverInfo.websiteUrl)),append($$anchor2,a)};if_block(node_3,$$render=>{$$props.serverInfo?.websiteUrl&&$$render(consequent_2)})}reset(div_3);var node_5=sibling(div_3,2);{var consequent_6=$$anchor2=>{var div_5=root_6$7(),node_6=child(div_5);{var consequent_4=$$anchor3=>{const TransportIcon=user_derived(()=>MCP_TRANSPORT_ICONS[$$props.transportType]);Badge($$anchor3,{variant:"outline",class:"h-5 gap-1 px-1.5 text-[10px]",children:($$anchor4,$$slotProps)=>{var fragment_3=root_8$5(),
-node_7=first_child(fragment_3);{var consequent_3=$$anchor5=>{var fragment_4=comment$2(),node_8=first_child(fragment_4);component(node_8,()=>get$4(TransportIcon),($$anchor6,TransportIcon_1)=>{TransportIcon_1($$anchor6,{class:"h-3 w-3"})}),append($$anchor5,fragment_4)};if_block(node_7,$$render=>{get$4(TransportIcon)&&$$render(consequent_3)})}var text_2=sibling(node_7);template_effect(()=>set_text(text_2,` ${(MCP_TRANSPORT_LABELS[$$props.transportType]||$$props.transportType)??""}`)),append($$anchor4,
-fragment_3)},$$slots:{default:!0}})};if_block(node_6,$$render=>{$$props.transportType&&$$render(consequent_4)})}var node_9=sibling(node_6,2);{var consequent_5=$$anchor3=>{McpCapabilitiesBadges($$anchor3,{get capabilities(){return $$props.capabilities}})};if_block(node_9,$$render=>{$$props.capabilities&&$$render(consequent_5)})}reset(div_5),append($$anchor2,div_5)};if_block(node_5,$$render=>{($$props.capabilities||$$props.transportType)&&$$render(consequent_6)})}reset(div_2);var div_6=sibling(div_2,
-2),node_10=child(div_6);Switch(node_10,{get checked(){return $$props.enabled},get disabled(){return disabled()},get onCheckedChange(){return $$props.onToggle}}),reset(div_6),reset(div_1),reset(div),template_effect(()=>set_text(text2,$$props.displayName)),append($$anchor,div),pop()}var root$g=from_html('<div class="flex shrink-0 items-center gap-1"><!> <!> <!></div>');function McpServerCardActions($$anchor,$$props){var div=root$g(),node2=child(div);Button(node2,{variant:"ghost",size:"icon",class:"\
-h-7 w-7",get onclick(){return $$props.onEdit},"aria-label":"Edit",children:($$anchor2,$$slotProps)=>{Pencil($$anchor2,{class:"h-3.5 w-3.5"})},$$slots:{default:!0}});var node_1=sibling(node2,2);Button(node_1,{variant:"ghost",size:"icon",class:"h-7 w-7",get onclick(){return $$props.onRefresh},get disabled(){return $$props.isHealthChecking},"aria-label":"Refresh",children:($$anchor2,$$slotProps)=>{Refresh_cw($$anchor2,{class:"h-3.5 w-3.5"})},$$slots:{default:!0}});var node_2=sibling(node_1,2);Button(
-node_2,{variant:"ghost",size:"icon",class:"hover:text-destructive-foreground h-7 w-7 text-destructive hover:bg-destructive/10",get onclick(){return $$props.onDelete},"aria-label":"Delete",children:($$anchor2,$$slotProps)=>{Trash_2($$anchor2,{class:"h-3.5 w-3.5"})},$$slots:{default:!0}}),reset(div),append($$anchor,div)}var root_2$b=from_html("<!> <span> </span>",1),root_8$4=from_html('<p class="mt-1 text-xs text-muted-foreground"> </p>'),root_6$6=from_html("<div><!> <!></div>"),root_5$7=from_html(
-'<div class="max-h-64 space-y-3 overflow-y-auto"></div>'),root_1$8=from_html("<!> <!>",1);function McpServerCardToolsList($$anchor,$$props){push$1($$props,!0);let isExpanded=state$1(!1),toolsCount=user_derived(()=>$$props.tools.length);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Collapsible,($$anchor2,Collapsible_Root)=>{Collapsible_Root($$anchor2,{get open(){return get$4(isExpanded)},set open($$value){set$1(isExpanded,$$value,!0)},children:($$anchor3,$$slotProps)=>{
-var fragment_1=root_1$8(),node_1=first_child(fragment_1);component(node_1,()=>Collapsible_trigger,($$anchor4,Collapsible_Trigger)=>{Collapsible_Trigger($$anchor4,{class:"flex w-full items-center gap-1 text-xs text-muted-foreground hover:text-foreground",children:($$anchor5,$$slotProps2)=>{var fragment_2=root_2$b(),node_2=first_child(fragment_2);{var consequent=$$anchor6=>{Chevron_down($$anchor6,{class:"h-3.5 w-3.5"})},alternate=$$anchor6=>{Chevron_right($$anchor6,{class:"h-3.5 w-3.5"})};if_block(
-node_2,$$render=>{get$4(isExpanded)?$$render(consequent):$$render(alternate,!1)})}var span=sibling(node_2,2),text2=child(span);reset(span),template_effect(()=>set_text(text2,`${get$4(toolsCount)??""} tools available · Show details`)),append($$anchor5,fragment_2)},$$slots:{default:!0}})});var node_3=sibling(node_1,2);component(node_3,()=>Collapsible_content,($$anchor4,Collapsible_Content)=>{Collapsible_Content($$anchor4,{class:"mt-2",children:($$anchor5,$$slotProps2)=>{var div=root_5$7();each(div,
-21,()=>$$props.tools,tool=>tool.name,($$anchor6,tool)=>{var div_1=root_6$6(),node_4=child(div_1);Badge(node_4,{variant:"secondary",children:($$anchor7,$$slotProps3)=>{next$1();var text_1=text$8();template_effect(()=>set_text(text_1,get$4(tool).name)),append($$anchor7,text_1)},$$slots:{default:!0}});var node_5=sibling(node_4,2);{var consequent_1=$$anchor7=>{var p2=root_8$4(),text_2=child(p2,!0);reset(p2),template_effect(()=>set_text(text_2,get$4(tool).description)),append($$anchor7,p2)};if_block(
-node_5,$$render=>{get$4(tool).description&&$$render(consequent_1)})}reset(div_1),append($$anchor6,div_1)}),reset(div),append($$anchor5,div)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}var root$f=from_html('<div class="space-y-4"><p class="font-medium">Configure Server</p> <!> <div class="flex items-center justify-end gap-2"><!> <!></div></div>');function McpServerCardEditForm($$anchor,$$props){push$1($$props,!0);let serverUseProxy=prop(
-$$props,"serverUseProxy",3,!1),editUrl=user_derived(()=>$$props.serverUrl),editHeaders=state$1(""),editUseProxy=user_derived(serverUseProxy),urlError=user_derived(()=>{if(!get$4(editUrl).trim())return"URL is required";try{return new URL(get$4(editUrl)),null}catch{return"Invalid URL format"}}),canSave=user_derived(()=>!get$4(urlError));function handleSave(){get$4(canSave)&&$$props.onSave(get$4(editUrl).trim(),get$4(editHeaders).trim(),get$4(editUseProxy))}function setInitialValues(url2,headers,useProxy){
-set$1(editUrl,url2),set$1(editHeaders,headers,!0),set$1(editUseProxy,useProxy)}var $$exports={setInitialValues},div=root$f(),node2=sibling(child(div),2);{let $0=user_derived(()=>get$4(editUrl)?get$4(urlError):null);McpServerForm(node2,{get url(){return get$4(editUrl)},get headers(){return get$4(editHeaders)},get useProxy(){return get$4(editUseProxy)},onUrlChange:v=>set$1(editUrl,v),onHeadersChange:v=>set$1(editHeaders,v,!0),onUseProxyChange:v=>set$1(editUseProxy,v),get urlError(){return get$4($0)},
-get id(){return $$props.serverId}})}var div_1=sibling(node2,2),node_1=child(div_1);Button(node_1,{variant:"secondary",size:"sm",get onclick(){return $$props.onCancel},children:($$anchor2,$$slotProps)=>{next$1();var text2=text$8("Cancel");append($$anchor2,text2)},$$slots:{default:!0}});var node_2=sibling(node_1,2);{let $0=user_derived(()=>!get$4(canSave));Button(node_2,{size:"sm",onclick:handleSave,get disabled(){return get$4($0)},children:($$anchor2,$$slotProps)=>{next$1();var text_1=text$8();template_effect(
-$02=>set_text(text_1,$02),[()=>$$props.serverUrl.trim()?"Update":"Add"]),append($$anchor2,text_1)},$$slots:{default:!0}})}return reset(div_1),reset(div),append($$anchor,div),pop($$exports)}var root_5$6=from_html(`Are you sure you want to delete <strong> </strong>? This action cannot be
-				undone.`,1),root_3$7=from_html("<!> <!>",1),root_6$5=from_html("<!> <!>",1),root_2$a=from_html("<!> <!>",1);function McpServerCardDeleteDialog($$anchor,$$props){push$1($$props,!0);let open2=prop($$props,"open",15);var fragment=comment$2(),node2=first_child(fragment);component(node2,()=>Root$3,($$anchor2,AlertDialog_Root)=>{AlertDialog_Root($$anchor2,{get onOpenChange(){return $$props.onOpenChange},get open(){return open2()},set open($$value){open2($$value)},children:($$anchor3,$$slotProps)=>{
-var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Alert_dialog_content,($$anchor4,AlertDialog_Content)=>{AlertDialog_Content($$anchor4,{children:($$anchor5,$$slotProps2)=>{var fragment_2=root_2$a(),node_2=first_child(fragment_2);component(node_2,()=>Alert_dialog_header,($$anchor6,AlertDialog_Header)=>{AlertDialog_Header($$anchor6,{children:($$anchor7,$$slotProps3)=>{var fragment_3=root_3$7(),node_3=first_child(fragment_3);component(node_3,()=>Alert_dialog_title,($$anchor8,AlertDialog_Title)=>{
-AlertDialog_Title($$anchor8,{children:($$anchor9,$$slotProps4)=>{next$1();var text2=text$8("Delete Server");append($$anchor9,text2)},$$slots:{default:!0}})});var node_4=sibling(node_3,2);component(node_4,()=>Alert_dialog_description,($$anchor8,AlertDialog_Description)=>{AlertDialog_Description($$anchor8,{children:($$anchor9,$$slotProps4)=>{next$1();var fragment_4=root_5$6(),strong2=sibling(first_child(fragment_4)),text_1=child(strong2,!0);reset(strong2),next$1(),template_effect(()=>set_text(text_1,
-$$props.displayName)),append($$anchor9,fragment_4)},$$slots:{default:!0}})}),append($$anchor7,fragment_3)},$$slots:{default:!0}})});var node_5=sibling(node_2,2);component(node_5,()=>Alert_dialog_footer,($$anchor6,AlertDialog_Footer)=>{AlertDialog_Footer($$anchor6,{children:($$anchor7,$$slotProps3)=>{var fragment_5=root_6$5(),node_6=first_child(fragment_5);component(node_6,()=>Alert_dialog_cancel,($$anchor8,AlertDialog_Cancel)=>{AlertDialog_Cancel($$anchor8,{children:($$anchor9,$$slotProps4)=>{next$1();
-var text_2=text$8("Cancel");append($$anchor9,text_2)},$$slots:{default:!0}})});var node_7=sibling(node_6,2);component(node_7,()=>Alert_dialog_action,($$anchor8,AlertDialog_Action)=>{AlertDialog_Action($$anchor8,{class:"text-destructive-foreground bg-destructive hover:bg-destructive/90",get onclick(){return $$props.onConfirm},children:($$anchor9,$$slotProps4)=>{next$1();var text_3=text$8("Delete");append($$anchor9,text_3)},$$slots:{default:!0}})}),append($$anchor7,fragment_5)},$$slots:{default:!0}})}),
-append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},$$slots:{default:!0}})}),append($$anchor,fragment),pop()}var root_1$7=from_html('<div class="flex items-center justify-between gap-4"><div class="flex items-center gap-2"><!> <!> <!></div> <!></div> <div class="flex flex-wrap gap-1.5"><!> <!> <!></div> <div class="space-y-1.5"><!> <!></div> <!> <div class="flex justify-end gap-2"><!> <!> <!></div>',1);function McpServerCardSkeleton($$anchor){Card($$anchor,{class:"\
-grid gap-3 p-4",children:($$anchor2,$$slotProps)=>{var fragment_1=root_1$7(),div=first_child(fragment_1),div_1=child(div),node2=child(div_1);Skeleton(node2,{class:"h-5 w-5 rounded"});var node_1=sibling(node2,2);Skeleton(node_1,{class:"h-5 w-28"});var node_2=sibling(node_1,2);Skeleton(node_2,{class:"h-5 w-12 rounded-full"}),reset(div_1);var node_3=sibling(div_1,2);Skeleton(node_3,{class:"h-6 w-11 rounded-full"}),reset(div);var div_2=sibling(div,2),node_4=child(div_2);Skeleton(node_4,{class:"h-5 w\
--14 rounded-full"});var node_5=sibling(node_4,2);Skeleton(node_5,{class:"h-5 w-12 rounded-full"});var node_6=sibling(node_5,2);Skeleton(node_6,{class:"h-5 w-16 rounded-full"}),reset(div_2);var div_3=sibling(div_2,2),node_7=child(div_3);Skeleton(node_7,{class:"h-4 w-40"});var node_8=sibling(node_7,2);Skeleton(node_8,{class:"h-4 w-52"}),reset(div_3);var node_9=sibling(div_3,2);Skeleton(node_9,{class:"h-3.5 w-36"});var div_4=sibling(node_9,2),node_10=child(div_4);Skeleton(node_10,{class:"h-8 w-8 ro\
-unded"});var node_11=sibling(node_10,2);Skeleton(node_11,{class:"h-8 w-8 rounded"});var node_12=sibling(node_11,2);Skeleton(node_12,{class:"h-8 w-8 rounded"}),reset(div_4),append($$anchor2,fragment_1)},$$slots:{default:!0}})}var root_3$6=from_html("<!> <span>Server instructions</span>",1),root_6$4=from_html('<p class="rounded bg-muted/50 p-2 text-xs text-muted-foreground"> </p>'),root_2$9=from_html("<!> <!>",1);function McpServerInfo($$anchor,$$props){let isExpanded=state$1(!1);var fragment=comment$2(),
-node2=first_child(fragment);{var consequent_1=$$anchor2=>{var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Collapsible,($$anchor3,Collapsible_Root)=>{Collapsible_Root($$anchor3,{get class(){return $$props.class},get open(){return get$4(isExpanded)},set open($$value){set$1(isExpanded,$$value,!0)},children:($$anchor4,$$slotProps)=>{var fragment_2=root_2$9(),node_2=first_child(fragment_2);component(node_2,()=>Collapsible_trigger,($$anchor5,Collapsible_Trigger)=>{Collapsible_Trigger(
-$$anchor5,{class:"flex w-full items-center gap-1 text-xs text-muted-foreground hover:text-foreground",children:($$anchor6,$$slotProps2)=>{var fragment_3=root_3$6(),node_3=first_child(fragment_3);{var consequent=$$anchor7=>{Chevron_down($$anchor7,{class:"h-3.5 w-3.5"})},alternate=$$anchor7=>{Chevron_right($$anchor7,{class:"h-3.5 w-3.5"})};if_block(node_3,$$render=>{get$4(isExpanded)?$$render(consequent):$$render(alternate,!1)})}next$1(2),append($$anchor6,fragment_3)},$$slots:{default:!0}})});var node_4=sibling(
-node_2,2);component(node_4,()=>Collapsible_content,($$anchor5,Collapsible_Content)=>{Collapsible_Content($$anchor5,{class:"mt-2",children:($$anchor6,$$slotProps2)=>{var p2=root_6$4(),text2=child(p2,!0);reset(p2),template_effect(()=>set_text(text2,$$props.instructions)),append($$anchor6,p2)},$$slots:{default:!0}})}),append($$anchor4,fragment_2)},$$slots:{default:!0}})}),append($$anchor2,fragment_1)};if_block(node2,$$render=>{$$props.instructions&&$$render(consequent_1)})}append($$anchor,fragment)}
-var root$e=from_html('<div class="flex flex-col gap-2"><div class="mb-2 flex items-center gap-4"><!> <!></div> <h3 class="text-sm font-medium">Available resources</h3></div>');function McpResourceBrowserHeader($$anchor,$$props){push$1($$props,!0);let searchQuery=prop($$props,"searchQuery",3,"");var div=root$e(),div_1=child(div),node2=child(div_1);SearchInput(node2,{placeholder:"Search resources...",get value(){return searchQuery()},onInput:value=>$$props.onSearch?.(value)});var node_1=sibling(node2,
-2);Button(node_1,{variant:"ghost",size:"sm",class:"h-8 w-8 p-0",get onclick(){return $$props.onRefresh},get disabled(){return $$props.isLoading},title:"Refresh resources",children:($$anchor2,$$slotProps)=>{var fragment=comment$2(),node_2=first_child(fragment);{var consequent=$$anchor3=>{Loader_circle($$anchor3,{class:"h-4 w-4 animate-spin"})},alternate=$$anchor3=>{Refresh_cw($$anchor3,{class:"h-4 w-4"})};if_block(node_2,$$render=>{$$props.isLoading?$$render(consequent):$$render(alternate,!1)})}append(
-$$anchor2,fragment)},$$slots:{default:!0}}),reset(div_1),next$1(2),reset(div),append($$anchor,div),pop()}var root$d=from_html('<div class="py-4 text-center text-sm text-muted-foreground"><!></div>');function McpResourceBrowserEmptyState($$anchor,$$props){var div=root$d(),node2=child(div);{var consequent=$$anchor2=>{var text2=text$8("Loading resources...");append($$anchor2,text2)},alternate=$$anchor2=>{var text_1=text$8("No resources available");append($$anchor2,text_1)};if_block(node2,$$render=>{
-$$props.isLoading?$$render(consequent):$$render(alternate,!1)})}reset(div),append($$anchor,div)}function resourceMatchesSearch(resource,query){return resource.title?.toLowerCase().includes(query)||resource.uri.toLowerCase().includes(query)}function buildResourceTree(resourceList,serverName,searchQuery){const root2={name:"root",children:new Map};if(!searchQuery||!searchQuery.trim()){for(const resource of resourceList){const pathParts=parseResourcePath(resource.uri);let current2=root2;for(let i=0;i<
-pathParts.length-1;i++){const part=pathParts[i];current2.children.has(part)||current2.children.set(part,{name:part,children:new Map}),current2=current2.children.get(part)}const fileName=pathParts[pathParts.length-1]||resource.name;current2.children.set(resource.uri,{name:fileName,resource:{...resource,serverName},children:new Map})}return root2}const query=searchQuery.toLowerCase();for(const resource of resourceList){if(!resourceMatchesSearch(resource,query))continue;const pathParts=parseResourcePath(
-resource.uri);let current2=root2;for(let i=0;i<pathParts.length-1;i++){const part=pathParts[i];current2.children.has(part)||current2.children.set(part,{name:part,children:new Map,isFiltered:!0}),current2=current2.children.get(part)}const fileName=pathParts[pathParts.length-1]||resource.name;current2.children.set(resource.uri,{name:fileName,resource:{...resource,serverName},children:new Map,isFiltered:!0})}function cleanupEmptyFolders(node2){if(node2.resource)return!0;const toDelete=[];for(const[
-name,child2]of node2.children.entries())cleanupEmptyFolders(child2)||toDelete.push(name);for(const name of toDelete)node2.children.delete(name);return node2.children.size>0}return cleanupEmptyFolders(root2),root2}function countTreeResources(node2){if(node2.resource)return 1;let count=0;for(const child2 of node2.children.values())count+=countTreeResources(child2);return count}function sortTreeChildren(children){return children.sort((a,b)=>{const aIsFolder=!a.resource&&a.children.size>0,bIsFolder=!b.
-resource&&b.children.size>0;return aIsFolder&&!bIsFolder?-1:!aIsFolder&&bIsFolder?1:a.name.localeCompare(b.name)})}var root_4$3=from_html('<!> <!> <span class="font-medium"> </span> <span class="text-xs text-muted-foreground"> </span>',1),root_7$5=from_html('<div class="ml-4 flex flex-col gap-0.5 border-l border-border/50 pl-2"></div>'),root_3$5=from_html("<!> <!>",1),root_10$5=from_html('<div class="group flex w-full items-center gap-2"><!> <button><!> <span class="min-w-0 flex-1 truncate text-\
-left"> </span></button></div>'),root_16$1=from_html('<img alt="" class="h-4 w-4 shrink-0 rounded-sm"/>'),root_13$4=from_html('<!> <span class="inline-flex flex-col items-start text-left"><span class="inline-flex items-center justify-start gap-1.5 font-medium"><!> </span> <span class="text-xs text-muted-foreground"> <!>)</span></span> <!>',1),root_20$2=from_html('<div class="py-1 text-xs text-red-500"> </div>'),root_22$2=from_html('<div class="py-1 text-xs text-muted-foreground">No resources</div\
->'),root_27=from_html('<div class="my-1 border-t border-border/30"></div>'),root_28=from_html('<button><!> <span class="min-w-0 flex-1 truncate text-left"> </span></button>'),root_26$1=from_html('<!> <div class="py-0.5 text-[11px] font-medium tracking-wide text-muted-foreground/70 uppercase">Templates</div> <!>',1),root_23=from_html("<!> <!>",1),root_19=from_html('<div class="ml-4 flex flex-col gap-0.5 border-l border-border/50 pl-2"><!></div>'),root_12$2=from_html("<!> <!>",1);function McpResourceBrowserServerItem($$anchor,$$props){
-push$1($$props,!0);const renderTreeNode=($$anchor2,node2=noop$3,depth=noop$3,parentPath=noop$3)=>{const isFolder=user_derived(()=>!node2().resource&&node2().children.size>0),folderId=user_derived(()=>`${$$props.serverName}:${parentPath()}/${node2().name}`),isFolderExpanded=user_derived(()=>$$props.expandedFolders.has(get$4(folderId)));var fragment=comment$2(),node_1=first_child(fragment);{var consequent_1=$$anchor3=>{const folderCount=user_derived(()=>countTreeResources(node2()));var fragment_1=comment$2(),
-node_2=first_child(fragment_1);component(node_2,()=>Collapsible,($$anchor4,Collapsible_Root)=>{Collapsible_Root($$anchor4,{get open(){return get$4(isFolderExpanded)},onOpenChange:()=>$$props.onToggleFolder(get$4(folderId)),children:($$anchor5,$$slotProps)=>{var fragment_2=root_3$5(),node_3=first_child(fragment_2);component(node_3,()=>Collapsible_trigger,($$anchor6,Collapsible_Trigger)=>{Collapsible_Trigger($$anchor6,{class:"flex w-full items-center gap-2 rounded px-2 py-1 text-sm hover:bg-muted/\
-50",children:($$anchor7,$$slotProps2)=>{var fragment_3=root_4$3(),node_4=first_child(fragment_3);{var consequent=$$anchor8=>{Chevron_down($$anchor8,{class:"h-3 w-3"})},alternate=$$anchor8=>{Chevron_right($$anchor8,{class:"h-3 w-3"})};if_block(node_4,$$render=>{get$4(isFolderExpanded)?$$render(consequent):$$render(alternate,!1)})}var node_5=sibling(node_4,2);Folder_open(node_5,{class:"h-3.5 w-3.5 text-muted-foreground"});var span=sibling(node_5,2),text2=child(span,!0);reset(span);var span_1=sibling(
-span,2),text_1=child(span_1);reset(span_1),template_effect(()=>{set_text(text2,node2().name),set_text(text_1,`(${get$4(folderCount)??""})`)}),append($$anchor7,fragment_3)},$$slots:{default:!0}})});var node_6=sibling(node_3,2);component(node_6,()=>Collapsible_content,($$anchor6,Collapsible_Content)=>{Collapsible_Content($$anchor6,{children:($$anchor7,$$slotProps2)=>{var div=root_7$5();each(div,21,()=>sortTreeChildren([...node2().children.values()]),child2=>child2.resource?.uri||`${$$props.serverName}\
-:${parentPath()}/${node2().name}/${child2.name}`,($$anchor8,child2)=>{renderTreeNode($$anchor8,()=>get$4(child2),()=>depth()+1,()=>`${parentPath()}/${node2().name}`)}),reset(div),append($$anchor7,div)},$$slots:{default:!0}})}),append($$anchor5,fragment_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_1)},alternate_1=$$anchor3=>{var fragment_7=comment$2(),node_7=first_child(fragment_7);{var consequent_3=$$anchor4=>{const resource=user_derived(()=>node2().resource),ResourceIcon=user_derived(
-()=>getResourceIcon(get$4(resource).mimeType,get$4(resource).uri)),isSelected=user_derived(()=>isResourceSelected(get$4(resource))),resourceDisplayName=user_derived(()=>get$4(resource).title||getDisplayName(node2().name));var div_1=root_10$5(),node_8=child(div_1);{var consequent_2=$$anchor5=>{Checkbox($$anchor5,{get checked(){return get$4(isSelected)},onCheckedChange:checked=>handleCheckboxChange(get$4(resource),checked===!0),class:"h-4 w-4"})};if_block(node_8,$$render=>{$$props.onToggle&&$$render(
-consequent_2)})}var button=sibling(node_8,2);button.__click=e=>handleResourceClick(get$4(resource),e);var node_9=child(button);component(node_9,()=>get$4(ResourceIcon),($$anchor5,ResourceIcon_1)=>{ResourceIcon_1($$anchor5,{class:"h-3.5 w-3.5 shrink-0 text-muted-foreground"})});var span_2=sibling(node_9,2),text_2=child(span_2,!0);reset(span_2),reset(button),reset(div_1),template_effect($0=>{set_class(button,1,$0),set_attribute(button,"title",get$4(resourceDisplayName)),set_text(text_2,get$4(resourceDisplayName))},
-[()=>clsx(cn$1("flex flex-1 items-center gap-2 rounded px-2 py-1 text-left text-sm transition-colors","hover:bg-muted/50",get$4(isSelected)&&"bg-muted"))]),append($$anchor4,div_1)};if_block(node_7,$$render=>{node2().resource&&$$render(consequent_3)},!0)}append($$anchor3,fragment_7)};if_block(node_1,$$render=>{get$4(isFolder)?$$render(consequent_1):$$render(alternate_1,!1)})}append($$anchor2,fragment)};let searchQuery=prop($$props,"searchQuery",3,"");const hasResources=user_derived(()=>$$props.serverRes.
-resources.length>0),hasTemplates=user_derived(()=>$$props.serverRes.templates.length>0),hasContent=user_derived(()=>get$4(hasResources)||get$4(hasTemplates)),displayName=user_derived(()=>mcpStore.getServerDisplayName($$props.serverName)),favicon=user_derived(()=>mcpStore.getServerFavicon($$props.serverName)),resourceTree=user_derived(()=>buildResourceTree($$props.serverRes.resources,$$props.serverName,searchQuery())),templateInfos=user_derived(()=>$$props.serverRes.templates.map(t=>({uriTemplate:t.
-uriTemplate,name:t.name,title:t.title,description:t.description,mimeType:t.mimeType,serverName:$$props.serverName,annotations:t.annotations,icons:t.icons})));function handleResourceClick(resource,event2){$$props.onSelect?.(resource,event2.shiftKey)}function handleCheckboxChange(resource,checked){$$props.onToggle?.(resource,checked)}function isResourceSelected(resource){return $$props.selectedUris.has(resource.uri)}var fragment_9=comment$2(),node_10=first_child(fragment_9);component(node_10,()=>Collapsible,
-($$anchor2,Collapsible_Root_1)=>{Collapsible_Root_1($$anchor2,{get open(){return $$props.isExpanded},get onOpenChange(){return $$props.onToggleServer},children:($$anchor3,$$slotProps)=>{var fragment_10=root_12$2(),node_11=first_child(fragment_10);component(node_11,()=>Collapsible_trigger,($$anchor4,Collapsible_Trigger_1)=>{Collapsible_Trigger_1($$anchor4,{class:"flex w-full items-center gap-2 rounded px-2 py-1.5 text-sm hover:bg-muted/50",children:($$anchor5,$$slotProps2)=>{var fragment_11=root_13$4(),
-node_12=first_child(fragment_11);{var consequent_4=$$anchor6=>{Chevron_down($$anchor6,{class:"h-3.5 w-3.5"})},alternate_2=$$anchor6=>{Chevron_right($$anchor6,{class:"h-3.5 w-3.5"})};if_block(node_12,$$render=>{$$props.isExpanded?$$render(consequent_4):$$render(alternate_2,!1)})}var span_3=sibling(node_12,2),span_4=child(span_3),node_13=child(span_4);{var consequent_5=$$anchor6=>{var img=root_16$1();template_effect(()=>set_attribute(img,"src",get$4(favicon))),event("error",img,e=>{e.currentTarget.
-style.display="none"}),replay_events(img),append($$anchor6,img)};if_block(node_13,$$render=>{get$4(favicon)&&$$render(consequent_5)})}var text_3=sibling(node_13);reset(span_4);var span_5=sibling(span_4,2),text_4=child(span_5),node_14=sibling(text_4);{var consequent_6=$$anchor6=>{var text_5=text$8();template_effect(()=>set_text(text_5,`, ${$$props.serverRes.templates.length??""} template${$$props.serverRes.templates.length!==1?"s":""}`)),append($$anchor6,text_5)};if_block(node_14,$$render=>{get$4(
-hasTemplates)&&$$render(consequent_6)})}next$1(),reset(span_5),reset(span_3);var node_15=sibling(span_3,2);{var consequent_7=$$anchor6=>{Loader_circle($$anchor6,{class:"ml-auto h-3 w-3 animate-spin text-muted-foreground"})};if_block(node_15,$$render=>{$$props.serverRes.loading&&$$render(consequent_7)})}template_effect(()=>{set_text(text_3,` ${get$4(displayName)??""}`),set_text(text_4,`(${$$props.serverRes.resources.length??""} resource${$$props.serverRes.resources.length!==1?"s":""}`)}),append($$anchor5,
-fragment_11)},$$slots:{default:!0}})});var node_16=sibling(node_11,2);component(node_16,()=>Collapsible_content,($$anchor4,Collapsible_Content_1)=>{Collapsible_Content_1($$anchor4,{children:($$anchor5,$$slotProps2)=>{var div_2=root_19(),node_17=child(div_2);{var consequent_8=$$anchor6=>{var div_3=root_20$2(),text_6=child(div_3);reset(div_3),template_effect(()=>set_text(text_6,`Error: ${$$props.serverRes.error??""}`)),append($$anchor6,div_3)},alternate_4=$$anchor6=>{var fragment_16=comment$2(),node_18=first_child(
-fragment_16);{var consequent_9=$$anchor7=>{var div_4=root_22$2();append($$anchor7,div_4)},alternate_3=$$anchor7=>{var fragment_17=root_23(),node_19=first_child(fragment_17);{var consequent_10=$$anchor8=>{var fragment_18=comment$2(),node_20=first_child(fragment_18);each(node_20,17,()=>sortTreeChildren([...get$4(resourceTree).children.values()]),child2=>child2.resource?.uri||`${$$props.serverName}:${child2.name}`,($$anchor9,child2)=>{renderTreeNode($$anchor9,()=>get$4(child2),()=>1,()=>"")}),append(
-$$anchor8,fragment_18)};if_block(node_19,$$render=>{get$4(hasResources)&&$$render(consequent_10)})}var node_21=sibling(node_19,2);{var consequent_12=$$anchor8=>{var fragment_20=root_26$1(),node_22=first_child(fragment_20);{var consequent_11=$$anchor9=>{var div_5=root_27();append($$anchor9,div_5)};if_block(node_22,$$render=>{get$4(hasResources)&&$$render(consequent_11)})}var node_23=sibling(node_22,4);each(node_23,17,()=>get$4(templateInfos),template=>template.uriTemplate,($$anchor9,template)=>{var button_1=root_28();
-button_1.__click=()=>$$props.onTemplateSelect(get$4(template));var node_24=child(button_1);Braces(node_24,{class:"h-3.5 w-3.5 shrink-0 text-muted-foreground"});var span_6=sibling(node_24,2),text_7=child(span_6,!0);reset(span_6),reset(button_1),template_effect($0=>{set_class(button_1,1,$0),set_attribute(button_1,"title",get$4(template).uriTemplate),set_text(text_7,get$4(template).title||get$4(template).name)},[()=>clsx(cn$1("flex w-full items-center gap-2 rounded px-2 py-1 text-left text-sm trans\
-ition-colors","hover:bg-muted/50",$$props.selectedTemplateUri===get$4(template).uriTemplate&&"bg-muted"))]),append($$anchor9,button_1)}),append($$anchor8,fragment_20)};if_block(node_21,$$render=>{get$4(hasTemplates)&&$$props.onTemplateSelect&&$$render(consequent_12)})}append($$anchor7,fragment_17)};if_block(node_18,$$render=>{get$4(hasContent)?$$render(alternate_3,!1):$$render(consequent_9)},!0)}append($$anchor6,fragment_16)};if_block(node_17,$$render=>{$$props.serverRes.error?$$render(consequent_8):
-$$render(alternate_4,!1)})}reset(div_2),append($$anchor5,div_2)},$$slots:{default:!0}})}),append($$anchor3,fragment_10)},$$slots:{default:!0}})}),append($$anchor,fragment_9),pop()}delegate(["click"]);var root$c=from_html('<div><!> <div class="flex flex-col gap-1"><!></div></div>');function McpResourceBrowser($$anchor,$$props){push$1($$props,!0);let selectedUris=prop($$props,"selectedUris",19,()=>new Set),expandedServers=new SvelteSet,expandedFolders=new SvelteSet,searchQuery=state$1("");const resources=user_derived(
-mcpResources),isLoading2=user_derived(mcpResourcesLoading),filteredResources=user_derived(()=>{if(!get$4(searchQuery).trim())return get$4(resources);const query=get$4(searchQuery).toLowerCase(),filtered=new SvelteMap;for(const[serverName,serverRes]of get$4(resources).entries()){const filteredResources2=serverRes.resources.filter(r2=>r2.title?.toLowerCase().includes(query)||r2.uri.toLowerCase().includes(query)||serverName.toLowerCase().includes(query)),filteredTemplates=serverRes.templates.filter(
-t=>t.name?.toLowerCase().includes(query)||t.title?.toLowerCase().includes(query)||t.uriTemplate.toLowerCase().includes(query)||serverName.toLowerCase().includes(query));(filteredResources2.length>0||filteredTemplates.length>0||query.trim())&&filtered.set(serverName,{...serverRes,resources:filteredResources2,templates:filteredTemplates})}return filtered});user_effect(()=>{$$props.expandToUri&&get$4(resources).size>0&&autoExpandToResource($$props.expandToUri)});function autoExpandToResource(uri2){
-for(const[serverName,serverRes]of get$4(resources).entries())if(serverRes.resources.find(r2=>r2.uri===uri2)){expandedServers.add(serverName);const pathParts=parseResourcePath(uri2);if(pathParts.length>1){let currentPath="";for(let i=0;i<pathParts.length-1;i++){currentPath=`${currentPath}/${pathParts[i]}`;const folderId=`${serverName}:${currentPath}`;expandedFolders.add(folderId)}}break}}function toggleServer(serverName){expandedServers.has(serverName)?expandedServers.delete(serverName):expandedServers.
-add(serverName)}function toggleFolder(folderId){expandedFolders.has(folderId)?expandedFolders.delete(folderId):expandedFolders.add(folderId)}function handleRefresh(){mcpStore.fetchAllResources()}var div=root$c(),node2=child(div);McpResourceBrowserHeader(node2,{get isLoading(){return get$4(isLoading2)},onRefresh:handleRefresh,onSearch:q2=>set$1(searchQuery,q2,!0),get searchQuery(){return get$4(searchQuery)}});var div_1=sibling(node2,2),node_1=child(div_1);{var consequent=$$anchor2=>{McpResourceBrowserEmptyState(
-$$anchor2,{get isLoading(){return get$4(isLoading2)}})},alternate=$$anchor2=>{var fragment_1=comment$2(),node_2=first_child(fragment_1);each(node_2,17,()=>[...get$4(filteredResources).entries()],([serverName,serverRes])=>serverName,($$anchor3,$$item)=>{var $$array=user_derived(()=>to_array(get$4($$item),2));let serverName=()=>get$4($$array)[0],serverRes=()=>get$4($$array)[1];{let $0=user_derived(()=>expandedServers.has(serverName()));McpResourceBrowserServerItem($$anchor3,{get serverName(){return serverName()},
-get serverRes(){return serverRes()},get isExpanded(){return get$4($0)},get selectedUris(){return selectedUris()},get selectedTemplateUri(){return $$props.selectedTemplateUri},get expandedFolders(){return expandedFolders},onToggleServer:()=>toggleServer(serverName()),onToggleFolder:toggleFolder,get onSelect(){return $$props.onSelect},get onToggle(){return $$props.onToggle},get onTemplateSelect(){return $$props.onTemplateSelect},get searchQuery(){return get$4(searchQuery)}})}}),append($$anchor2,fragment_1)};
-if_block(node_1,$$render=>{get$4(filteredResources).size===0?$$render(consequent):$$render(alternate,!1)})}reset(div_1),reset(div),template_effect($0=>set_class(div,1,$0),[()=>clsx(cn$1("flex flex-col gap-2",$$props.class))]),append($$anchor,div),pop()}var root_1$6=from_html('<div class="flex flex-col items-center justify-center gap-2 py-8 text-muted-foreground"><!> <span class="text-sm">Select a resource to preview</span></div>'),root_3$4=from_html('<p class="mt-1 text-sm text-muted-foreground"\
-> </p>'),root_5$5=from_html('<div class="flex items-center justify-center py-8"><!></div>'),root_7$4=from_html('<div class="flex flex-col items-center justify-center gap-2 py-8 text-red-500"><!> <span class="text-sm"> </span></div>'),root_10$4=from_html('<pre class="font-mono text-xs break-words whitespace-pre-wrap"> </pre>'),root_12$1=from_html('<img alt="Resource content" class="max-w-full rounded"/>'),root_13$3=from_html('<div class="flex items-center gap-2 rounded bg-muted p-2 text-sm text-m\
-uted-foreground"><!> <span> </span></div>'),root_14$2=from_html('<div class="py-4 text-center text-sm text-muted-foreground">No content available</div>'),root_9$4=from_html("<!> <!> <!>",1),root_16=from_html('<span class="rounded bg-muted px-1.5 py-0.5"> </span>'),root_17=from_html('<span class="rounded bg-muted px-1.5 py-0.5"> </span>'),root_15$1=from_html('<div class="flex flex-wrap gap-2 text-xs text-muted-foreground"><!> <!> <span class="rounded bg-muted px-1.5 py-0.5"> </span></div>'),root_2$8=from_html(
-'<div class="flex items-start justify-between gap-2"><div class="min-w-0 flex-1"><h3 class="truncate font-medium"> </h3> <p class="truncate text-xs text-muted-foreground"> </p> <!></div> <div class="flex items-center gap-1"><!> <!></div></div> <div class="min-h-[200px] overflow-auto rounded-md border bg-muted/30 p-3 break-all"><!></div> <!>',1),root$b=from_html("<div><!></div>");function McpResourcePreview($$anchor,$$props){push$1($$props,!0);let content2=state$1(null),isLoading2=state$1(!1),error2=state$1(
-null);user_effect(()=>{$$props.resource?$$props.preloadedContent?(set$1(content2,$$props.preloadedContent,!0),set$1(isLoading2,!1),set$1(error2,null)):loadContent($$props.resource.uri):(set$1(content2,null),set$1(error2,null))});async function loadContent(uri2){set$1(isLoading2,!0),set$1(error2,null);try{const result=await mcpStore.readResource(uri2);result?set$1(content2,result,!0):set$1(error2,"Failed to load resource content")}catch(e){set$1(error2,e instanceof Error?e.message:"Unknown error",
-!0)}finally{set$1(isLoading2,!1)}}function handleDownload(){const text2=getResourceTextContent(get$4(content2));!text2||!$$props.resource||downloadResourceContent(text2,$$props.resource.mimeType||MimeTypeText.PLAIN,$$props.resource.name||"resource.txt")}var div=root$b(),node2=child(div);{var consequent=$$anchor2=>{var div_1=root_1$6(),node_1=child(div_1);File_text(node_1,{class:"h-8 w-8 opacity-50"}),next$1(2),reset(div_1),append($$anchor2,div_1)},alternate_3=$$anchor2=>{var fragment=root_2$8(),
-div_2=first_child(fragment),div_3=child(div_2),h3=child(div_3),text_1=child(h3,!0);reset(h3);var p2=sibling(h3,2),text_2=child(p2,!0);reset(p2);var node_2=sibling(p2,2);{var consequent_1=$$anchor3=>{var p_1=root_3$4(),text_3=child(p_1,!0);reset(p_1),template_effect(()=>set_text(text_3,$$props.resource.description)),append($$anchor3,p_1)};if_block(node_2,$$render=>{$$props.resource.description&&$$render(consequent_1)})}reset(div_3);var div_4=sibling(div_3,2),node_3=child(div_4);{let $0=user_derived(
-()=>getResourceTextContent(get$4(content2))),$1=user_derived(()=>!get$4(isLoading2)&&!!getResourceTextContent(get$4(content2)));ActionIconCopyToClipboard(node_3,{get text(){return get$4($0)},get canCopy(){return get$4($1)},ariaLabel:"Copy content"})}var node_4=sibling(node_3,2);{let $0=user_derived(()=>get$4(isLoading2)||!getResourceTextContent(get$4(content2)));Button(node_4,{variant:"ghost",size:"sm",class:"h-7 w-7 p-0",onclick:handleDownload,get disabled(){return get$4($0)},title:"Download co\
-ntent",children:($$anchor3,$$slotProps)=>{Download($$anchor3,{class:"h-3.5 w-3.5"})},$$slots:{default:!0}})}reset(div_4),reset(div_2);var div_5=sibling(div_2,2),node_5=child(div_5);{var consequent_2=$$anchor3=>{var div_6=root_5$5(),node_6=child(div_6);Loader_circle(node_6,{class:"h-6 w-6 animate-spin text-muted-foreground"}),reset(div_6),append($$anchor3,div_6)},alternate_2=$$anchor3=>{var fragment_2=comment$2(),node_7=first_child(fragment_2);{var consequent_3=$$anchor4=>{var div_7=root_7$4(),node_8=child(
-div_7);Circle_alert(node_8,{class:"h-6 w-6"});var span=sibling(node_8,2),text_4=child(span,!0);reset(span),reset(div_7),template_effect(()=>set_text(text_4,get$4(error2))),append($$anchor4,div_7)},alternate_1=$$anchor4=>{var fragment_3=comment$2(),node_9=first_child(fragment_3);{var consequent_7=$$anchor5=>{const textContent=user_derived(()=>getResourceTextContent(get$4(content2))),blobContent=user_derived(()=>getResourceBlobContent(get$4(content2)));var fragment_4=root_9$4(),node_10=first_child(
-fragment_4);{var consequent_4=$$anchor6=>{var pre=root_10$4(),text_5=child(pre,!0);reset(pre),template_effect(()=>set_text(text_5,get$4(textContent))),append($$anchor6,pre)};if_block(node_10,$$render=>{get$4(textContent)&&$$render(consequent_4)})}var node_11=sibling(node_10,2);each(node_11,17,()=>get$4(blobContent),blob=>blob.uri,($$anchor6,blob)=>{var fragment_5=comment$2(),node_12=first_child(fragment_5);{var consequent_5=$$anchor7=>{var img=root_12$1();template_effect($0=>set_attribute(img,"s\
-rc",$0),[()=>createBase64DataUrl(get$4(blob).mimeType??MimeTypeApplication.OCTET_STREAM,get$4(blob).blob)]),append($$anchor7,img)},alternate=$$anchor7=>{var div_8=root_13$3(),node_13=child(div_8);File_text(node_13,{class:"h-4 w-4"});var span_1=sibling(node_13,2),text_6=child(span_1);reset(span_1),reset(div_8),template_effect(()=>set_text(text_6,`Binary content (${get$4(blob).mimeType||"unknown type"})`)),append($$anchor7,div_8)};if_block(node_12,$$render=>{isImageMimeType(get$4(blob).mimeType??MimeTypeApplication.
-OCTET_STREAM)?$$render(consequent_5):$$render(alternate,!1)})}append($$anchor6,fragment_5)});var node_14=sibling(node_11,2);{var consequent_6=$$anchor6=>{var div_9=root_14$2();append($$anchor6,div_9)};if_block(node_14,$$render=>{!get$4(textContent)&&get$4(blobContent).length===0&&$$render(consequent_6)})}append($$anchor5,fragment_4)};if_block(node_9,$$render=>{get$4(content2)&&$$render(consequent_7)},!0)}append($$anchor4,fragment_3)};if_block(node_7,$$render=>{get$4(error2)?$$render(consequent_3):
-$$render(alternate_1,!1)},!0)}append($$anchor3,fragment_2)};if_block(node_5,$$render=>{get$4(isLoading2)?$$render(consequent_2):$$render(alternate_2,!1)})}reset(div_5);var node_15=sibling(div_5,2);{var consequent_10=$$anchor3=>{var div_10=root_15$1(),node_16=child(div_10);{var consequent_8=$$anchor4=>{var span_2=root_16(),text_7=child(span_2,!0);reset(span_2),template_effect(()=>set_text(text_7,$$props.resource.mimeType)),append($$anchor4,span_2)};if_block(node_16,$$render=>{$$props.resource.mimeType&&
-$$render(consequent_8)})}var node_17=sibling(node_16,2);{var consequent_9=$$anchor4=>{var span_3=root_17(),text_8=child(span_3);reset(span_3),template_effect(()=>set_text(text_8,`Priority: ${$$props.resource.annotations.priority??""}`)),append($$anchor4,span_3)};if_block(node_17,$$render=>{$$props.resource.annotations?.priority!==void 0&&$$render(consequent_9)})}var span_4=sibling(node_17,2),text_9=child(span_4);reset(span_4),reset(div_10),template_effect(()=>set_text(text_9,`Server: ${$$props.resource.
-serverName??""}`)),append($$anchor3,div_10)};if_block(node_15,$$render=>{($$props.resource.mimeType||$$props.resource.annotations)&&$$render(consequent_10)})}template_effect(()=>{set_text(text_1,$$props.resource.title||$$props.resource.name),set_text(text_2,$$props.resource.uri)}),append($$anchor2,fragment)};if_block(node2,$$render=>{$$props.resource?$$render(alternate_3,!1):$$render(consequent)})}reset(div),template_effect($0=>set_class(div,1,$0),[()=>clsx(cn$1("flex flex-col gap-3",$$props.class))]),
-append($$anchor,div),pop()}var root_2$7=from_html('<div class="rounded-md bg-muted/50 px-3 py-2"><p class="text-xs text-muted-foreground">Resolved URI:</p> <p class="mt-0.5 font-mono text-xs break-all"> </p></div>'),root$a=from_html('<form class="space-y-3"><!> <!> <div class="flex justify-end gap-2 pt-1"><!> <!></div></form>');function McpResourceTemplateForm($$anchor,$$props){push$1($$props,!0);const variables=user_derived(()=>extractTemplateVariables($$props.template.uriTemplate));let values=proxy(
-{}),suggestions=proxy({}),loadingSuggestions=proxy({}),activeAutocomplete=state$1(null),autocompleteIndex=state$1(0);const expandedUri=user_derived(()=>expandTemplate($$props.template.uriTemplate,values)),isComplete=user_derived(()=>isTemplateComplete($$props.template.uriTemplate,values)),fetchCompletions=debounce(async(argName,value)=>{if(value.length<1){suggestions[argName]=[];return}loadingSuggestions[argName]=!0;try{const result=await mcpStore.getResourceCompletions($$props.template.serverName,
-$$props.template.uriTemplate,argName,value);if(result&&result.values.length>0){const filteredValues=result.values.filter(v=>v.trim()!=="");filteredValues.length>0?(suggestions[argName]=filteredValues,set$1(activeAutocomplete,argName,!0),set$1(autocompleteIndex,0)):suggestions[argName]=[]}else suggestions[argName]=[]}catch(error2){console.error("[McpResourceTemplateForm] Failed to fetch completions:",error2),suggestions[argName]=[]}finally{loadingSuggestions[argName]=!1}},200);function handleArgInput(argName,value){
-values[argName]=value,fetchCompletions(argName,value)}function selectSuggestion(argName,value){values[argName]=value,suggestions[argName]=[],set$1(activeAutocomplete,null)}function handleArgKeydown(event2,argName){const argSuggestions=suggestions[argName]??[];if(event2.key===KeyboardKey.ESCAPE){event2.preventDefault(),event2.stopPropagation(),argSuggestions.length>0&&get$4(activeAutocomplete)===argName?(suggestions[argName]=[],set$1(activeAutocomplete,null)):$$props.onCancel();return}argSuggestions.
-length===0||get$4(activeAutocomplete)!==argName||(event2.key===KeyboardKey.ARROW_DOWN?(event2.preventDefault(),set$1(autocompleteIndex,Math.min(get$4(autocompleteIndex)+1,argSuggestions.length-1),!0)):event2.key===KeyboardKey.ARROW_UP?(event2.preventDefault(),set$1(autocompleteIndex,Math.max(get$4(autocompleteIndex)-1,0),!0)):event2.key===KeyboardKey.ENTER&&argSuggestions[get$4(autocompleteIndex)]&&(event2.preventDefault(),event2.stopPropagation(),selectSuggestion(argName,argSuggestions[get$4(autocompleteIndex)])))}
-function handleArgBlur(argName){setTimeout(()=>{get$4(activeAutocomplete)===argName&&(suggestions[argName]=[],set$1(activeAutocomplete,null))},150)}function handleArgFocus(argName){const value=values[argName]??"";value.length>=MIN_AUTOCOMPLETE_INPUT_LENGTH&&fetchCompletions(argName,value)}function handleSubmit(event2){event2.preventDefault(),get$4(isComplete)&&$$props.onResolve(get$4(expandedUri),$$props.template.serverName)}var form=root$a(),node2=child(form);each(node2,17,()=>get$4(variables),
-variable=>variable.name,($$anchor2,variable)=>{{let $0=user_derived(()=>values[get$4(variable).name]??""),$1=user_derived(()=>suggestions[get$4(variable).name]??[]),$2=user_derived(()=>loadingSuggestions[get$4(variable).name]??!1),$3=user_derived(()=>get$4(activeAutocomplete)===get$4(variable).name),$4=user_derived(()=>get$4(activeAutocomplete)===get$4(variable).name?get$4(autocompleteIndex):0);InputWithSuggestions($$anchor2,{get name(){return get$4(variable).name},get value(){return get$4($0)},
-get suggestions(){return get$4($1)},get isLoadingSuggestions(){return get$4($2)},get isAutocompleteActive(){return get$4($3)},get autocompleteIndex(){return get$4($4)},onInput:value=>handleArgInput(get$4(variable).name,value),onKeydown:e=>handleArgKeydown(e,get$4(variable).name),onBlur:()=>handleArgBlur(get$4(variable).name),onFocus:()=>handleArgFocus(get$4(variable).name),onSelectSuggestion:value=>selectSuggestion(get$4(variable).name,value)})}});var node_1=sibling(node2,2);{var consequent=$$anchor2=>{
-var div=root_2$7(),p2=sibling(child(div),2),text2=child(p2,!0);reset(p2),reset(div),template_effect(()=>set_text(text2,get$4(expandedUri))),append($$anchor2,div)};if_block(node_1,$$render=>{get$4(isComplete)&&$$render(consequent)})}var div_1=sibling(node_1,2),node_2=child(div_1);Button(node_2,{type:"button",size:"sm",variant:"secondary",get onclick(){return $$props.onCancel},children:($$anchor2,$$slotProps)=>{next$1();var text_1=text$8("Cancel");append($$anchor2,text_1)},$$slots:{default:!0}});var node_3=sibling(
-node_2,2);{let $0=user_derived(()=>!get$4(isComplete));Button(node_3,{size:"sm",type:"submit",get disabled(){return get$4($0)},children:($$anchor2,$$slotProps)=>{next$1();var text_2=text$8("Read Resource");append($$anchor2,text_2)},$$slots:{default:!0}})}reset(div_1),reset(form),event("submit",form,handleSubmit),append($$anchor,form),pop()}function filterModelOptions(options,searchTerm){const term=searchTerm.trim().toLowerCase();return term?options.filter(option2=>option2.model.toLowerCase().includes(
-term)||option2.name?.toLowerCase().includes(term)||option2.aliases?.some(alias=>alias.toLowerCase().includes(term))||option2.tags?.some(tag=>tag.toLowerCase().includes(term))):options}function groupModelOptions(filteredOptions,favoriteIds,isModelLoaded){const loaded=[];for(let i=0;i<filteredOptions.length;i++)isModelLoaded(filteredOptions[i].model)&&loaded.push({option:filteredOptions[i],flatIndex:i});const loadedModelIds=new Set(loaded.map(item=>item.option.model)),favorites=[];for(let i=0;i<filteredOptions.
-length;i++)favoriteIds.has(filteredOptions[i].model)&&!loadedModelIds.has(filteredOptions[i].model)&&favorites.push({option:filteredOptions[i],flatIndex:i});const available=[],orgGroups=new SvelteMap;for(let i=0;i<filteredOptions.length;i++){const option2=filteredOptions[i];if(loadedModelIds.has(option2.model)||favoriteIds.has(option2.model))continue;const key2=option2.parsedId?.orgName??"";orgGroups.has(key2)||orgGroups.set(key2,[]),orgGroups.get(key2).push({option:option2,flatIndex:i})}for(const[
-orgName,items2]of orgGroups)available.push({orgName:orgName||null,items:items2});return{loaded,favorites,available}}var root_1$5=from_html('<div class="flex items-center gap-2 text-xs text-muted-foreground"><!> Loading models…</div>'),root_4$2=from_html('<span style="max-width: min(calc(100cqw - 9rem), 20rem)"><!> <!></span>'),root_5$4=from_html('<p class="text-xs text-muted-foreground">No models available.</p>'),root_13$2=from_html('<p class="font-mono"> </p>'),root_11$2=from_html("<!> <!>",1),
-root_14$1=from_html('<span class="min-w-0 font-medium">Select model</span>'),root_9$3=from_html("<!> <!> <!>",1),root_20$1=from_html('<button type="button" class="flex w-full cursor-not-allowed items-center bg-red-400/10 p-2 text-left text-sm text-red-400" role="option" aria-selected="true" aria-disabled="true" disabled><!> <span class="ml-2 text-xs whitespace-nowrap opacity-70">(not available)</span></button>'),root_21$1=from_html('<p class="px-4 py-3 text-sm text-muted-foreground">No models fo\
-und.</p>'),root_18$1=from_html('<div class="models-list"><!> <!> <!></div>'),root_8$3=from_html("<!> <!>",1),root_26=from_html('<p class="font-mono"> </p>'),root_24=from_html("<!> <!>",1),root_22$1=from_html('<button style="max-width: min(calc(100cqw - 6.5rem), 32rem)"><!> <!> <!></button>'),root$9=from_html("<div><!></div> <!>",1);function ModelsSelector($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),currentModel=prop($$props,"currentModel",3,null),disabled=prop($$props,
-"disabled",3,!1),forceForegroundText=prop($$props,"forceForegroundText",3,!1),useGlobalSelection=prop($$props,"useGlobalSelection",3,!1),options=user_derived(()=>modelOptions().filter(option2=>modelsStore.getModelProps(option2.model)?.webui!==!1)),loading=user_derived(modelsLoading),updating=user_derived(modelsUpdating),activeId=user_derived(selectedModelId),isRouter=user_derived(isRouterMode),serverModel=user_derived(singleModelName),isHighlightedCurrentModelActive=user_derived(()=>{if(!get$4(isRouter)||
-!currentModel())return!1;const currentOption=get$4(options).find(option2=>option2.model===currentModel());return currentOption?currentOption.id===get$4(activeId):!1}),isCurrentModelInCache=user_derived(()=>!get$4(isRouter)||!currentModel()?!0:get$4(options).some(option2=>option2.model===currentModel())),isLoadingModel=state$1(!1),searchTerm=state$1(""),highlightedIndex=state$1(-1),filteredOptions=user_derived(()=>filterModelOptions(get$4(options),get$4(searchTerm))),groupedFilteredOptions=user_derived(
-()=>groupModelOptions(get$4(filteredOptions),modelsStore.favoriteModelIds,m=>modelsStore.isModelLoaded(m)));user_effect(()=>{get$4(searchTerm),set$1(highlightedIndex,-1)});let isOpen=state$1(!1),showModelDialog=state$1(!1),infoModelId=state$1(null);function handleInfoClick(modelName){set$1(infoModelId,modelName,!0),set$1(showModelDialog,!0)}onMount$1(()=>{modelsStore.fetch().catch(error2=>{console.error("Unable to load models:",error2)})});function handleOpenChange(open3){get$4(loading)||get$4(updating)||
-(get$4(isRouter)?open3?(set$1(isOpen,!0),set$1(searchTerm,""),set$1(highlightedIndex,-1),modelsStore.fetchRouterModels().then(()=>{modelsStore.fetchModalitiesForLoadedModels()})):(set$1(isOpen,!1),set$1(searchTerm,""),set$1(highlightedIndex,-1)):set$1(showModelDialog,open3,!0))}function open2(){handleOpenChange(!0)}function handleSearchKeyDown(event2){if(!event2.isComposing){if(event2.key===KeyboardKey.ARROW_DOWN){if(event2.preventDefault(),get$4(filteredOptions).length===0)return;get$4(highlightedIndex)===
--1||get$4(highlightedIndex)===get$4(filteredOptions).length-1?set$1(highlightedIndex,0):set$1(highlightedIndex,get$4(highlightedIndex)+1)}else if(event2.key===KeyboardKey.ARROW_UP){if(event2.preventDefault(),get$4(filteredOptions).length===0)return;get$4(highlightedIndex)===-1||get$4(highlightedIndex)===0?set$1(highlightedIndex,get$4(filteredOptions).length-1):set$1(highlightedIndex,get$4(highlightedIndex)-1)}else if(event2.key===KeyboardKey.ENTER)if(event2.preventDefault(),get$4(highlightedIndex)>=
-0&&get$4(highlightedIndex)<get$4(filteredOptions).length){const option2=get$4(filteredOptions)[get$4(highlightedIndex)];handleSelect(option2.id)}else get$4(filteredOptions).length>0&&set$1(highlightedIndex,0)}}async function handleSelect(modelId){const option2=get$4(options).find(opt=>opt.id===modelId);if(!option2)return;let shouldCloseMenu=!0;$$props.onModelChange?await $$props.onModelChange(option2.id,option2.model)===!1&&(shouldCloseMenu=!1):await modelsStore.selectModelById(option2.id),shouldCloseMenu&&
-(handleOpenChange(!1),requestAnimationFrame(()=>{document.querySelector('[data-slot="chat-form"] textarea')?.focus()})),!$$props.onModelChange&&get$4(isRouter)&&!modelsStore.isModelLoaded(option2.model)&&(set$1(isLoadingModel,!0),modelsStore.loadModel(option2.model).catch(error2=>console.error("Failed to load model:",error2)).finally(()=>set$1(isLoadingModel,!1)))}function getDisplayOption(){if(!get$4(isRouter)){const displayModel=get$4(serverModel)||currentModel();return displayModel?{id:get$4(
-serverModel)?"current":"offline-current",model:displayModel,name:displayModel.split("/").pop()||displayModel,capabilities:[]}:void 0}if(useGlobalSelection()&&get$4(activeId)){const selected=get$4(options).find(option2=>option2.id===get$4(activeId));if(selected)return selected}if(currentModel())return get$4(isCurrentModelInCache)?get$4(options).find(option2=>option2.model===currentModel()):{id:"not-in-cache",model:currentModel(),name:currentModel().split("/").pop()||currentModel(),capabilities:[]};
-if(get$4(activeId))return get$4(options).find(option2=>option2.id===get$4(activeId))}var $$exports={open:open2},fragment=root$9(),div=first_child(fragment),node2=child(div);{var consequent=$$anchor2=>{var div_1=root_1$5(),node_1=child(div_1);Loader_circle(node_1,{class:"h-3.5 w-3.5 animate-spin"}),next$1(),reset(div_1),append($$anchor2,div_1)},alternate_5=$$anchor2=>{var fragment_1=comment$2(),node_2=first_child(fragment_1);{var consequent_2=$$anchor3=>{var fragment_2=comment$2(),node_3=first_child(
-fragment_2);{var consequent_1=$$anchor4=>{var span=root_4$2(),node_4=child(span);Package(node_4,{class:"h-3.5 w-3.5"});var node_5=sibling(node_4,2);ModelId(node_5,{get modelId(){return currentModel()},class:"min-w-0",showOrgName:!0}),reset(span),template_effect($0=>set_class(span,1,$0),[()=>clsx(cn$1("inline-flex items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs text-muted-foreground",className()))]),append($$anchor4,span)},alternate=$$anchor4=>{var p2=root_5$4();append(
-$$anchor4,p2)};if_block(node_3,$$render=>{currentModel()?$$render(consequent_1):$$render(alternate,!1)})}append($$anchor3,fragment_2)},alternate_4=$$anchor3=>{const selectedOption=user_derived(getDisplayOption);var fragment_3=comment$2(),node_6=first_child(fragment_3);{var consequent_7=$$anchor4=>{var fragment_4=comment$2(),node_7=first_child(fragment_4);component(node_7,()=>Root$2,($$anchor5,DropdownMenu_Root)=>{DropdownMenu_Root($$anchor5,{onOpenChange:handleOpenChange,get open(){return get$4(
-isOpen)},set open($$value){set$1(isOpen,$$value,!0)},children:($$anchor6,$$slotProps)=>{var fragment_5=root_8$3(),node_8=first_child(fragment_5);{let $0=user_derived(()=>cn$1("inline-grid cursor-pointer grid-cols-[1fr_auto_1fr] items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60",get$4(isCurrentModelInCache)?
-forceForegroundText()||get$4(isHighlightedCurrentModelActive)?"text-foreground":"text-muted-foreground":"bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400",get$4(isOpen)?"text-foreground":"")),$1=user_derived(()=>disabled()||get$4(updating));component(node_8,()=>Dropdown_menu_trigger,($$anchor7,DropdownMenu_Trigger)=>{DropdownMenu_Trigger($$anchor7,{get class(){return get$4($0)},style:"max-width: min(calc(100cqw - 9rem), 20rem)",get disabled(){return get$4($1)},children:($$anchor8,$$slotProps2)=>{
-var fragment_6=root_9$3(),node_9=first_child(fragment_6);Package(node_9,{class:"h-3.5 w-3.5"});var node_10=sibling(node_9,2);{var consequent_3=$$anchor9=>{var fragment_7=comment$2(),node_11=first_child(fragment_7);component(node_11,()=>Root$5,($$anchor10,Tooltip_Root)=>{Tooltip_Root($$anchor10,{children:($$anchor11,$$slotProps3)=>{var fragment_8=root_11$2(),node_12=first_child(fragment_8);{const child2=($$anchor12,$$arg0)=>{ModelId($$anchor12,spread_props({get modelId(){return get$4(selectedOption).
-model},class:"min-w-0 overflow-hidden",showOrgName:!0},()=>$$arg0?.().props))};component(node_12,()=>Tooltip_trigger,($$anchor12,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor12,{child:child2,$$slots:{child:!0}})})}var node_13=sibling(node_12,2);component(node_13,()=>Tooltip_content,($$anchor12,Tooltip_Content)=>{Tooltip_Content($$anchor12,{children:($$anchor13,$$slotProps4)=>{var p_1=root_13$2(),text2=child(p_1,!0);reset(p_1),template_effect(()=>set_text(text2,get$4(selectedOption).model)),append(
-$$anchor13,p_1)},$$slots:{default:!0}})}),append($$anchor11,fragment_8)},$$slots:{default:!0}})}),append($$anchor9,fragment_7)},alternate_1=$$anchor9=>{var span_1=root_14$1();append($$anchor9,span_1)};if_block(node_10,$$render=>{get$4(selectedOption)?$$render(consequent_3):$$render(alternate_1,!1)})}var node_14=sibling(node_10,2);{var consequent_4=$$anchor9=>{Loader_circle($$anchor9,{class:"h-3 w-3.5 animate-spin"})},alternate_2=$$anchor9=>{Chevron_down($$anchor9,{class:"h-3 w-3.5"})};if_block(node_14,
-$$render=>{get$4(updating)||get$4(isLoadingModel)?$$render(consequent_4):$$render(alternate_2,!1)})}append($$anchor8,fragment_6)},$$slots:{default:!0}})})}var node_15=sibling(node_8,2);component(node_15,()=>Dropdown_menu_content,($$anchor7,DropdownMenu_Content)=>{DropdownMenu_Content($$anchor7,{align:"end",class:"w-full max-w-[100vw] pt-0 sm:w-max sm:max-w-[calc(100vw-2rem)]",children:($$anchor8,$$slotProps2)=>{{let $0=user_derived(()=>get$4(filteredOptions).length===0&&get$4(isCurrentModelInCache));
-DropdownMenuSearchable($$anchor8,{placeholder:"Search models...",onSearchKeyDown:handleSearchKeyDown,emptyMessage:"No models found.",get isEmpty(){return get$4($0)},get searchValue(){return get$4(searchTerm)},set searchValue($$value){set$1(searchTerm,$$value,!0)},children:($$anchor9,$$slotProps3)=>{var div_2=root_18$1();{const modelOption=($$anchor10,item=noop$3,showOrgName=noop$3)=>{const computed_const=user_derived(()=>{const{option:option2,flatIndex}=item();return{option:option2,flatIndex}}),
-isSelected=user_derived(()=>currentModel()===get$4(computed_const).option.model||get$4(activeId)===get$4(computed_const).option.id),isHighlighted=user_derived(()=>get$4(computed_const).flatIndex===get$4(highlightedIndex)),isFav=user_derived(()=>modelsStore.favoriteModelIds.has(get$4(computed_const).option.model));ModelsSelectorOption($$anchor10,{get option(){return get$4(computed_const).option},get isSelected(){return get$4(isSelected)},get isHighlighted(){return get$4(isHighlighted)},get isFav(){
-return get$4(isFav)},get showOrgName(){return showOrgName()},onSelect:handleSelect,onInfoClick:handleInfoClick,onMouseEnter:()=>set$1(highlightedIndex,get$4(computed_const).flatIndex,!0),onKeyDown:e=>{(e.key===KeyboardKey.ENTER||e.key===KeyboardKey.SPACE)&&(e.preventDefault(),handleSelect(get$4(computed_const).option.id))}})};var node_16=child(div_2);{var consequent_5=$$anchor10=>{var button=root_20$1(),node_17=child(button);ModelId(node_17,{get modelId(){return currentModel()},class:"flex-1",showOrgName:!0}),
-next$1(2),reset(button),append($$anchor10,button)};if_block(node_16,$$render=>{!get$4(isCurrentModelInCache)&&currentModel()&&$$render(consequent_5)})}var node_18=sibling(node_16,2);{var consequent_6=$$anchor10=>{var p_2=root_21$1();append($$anchor10,p_2)};if_block(node_18,$$render=>{get$4(filteredOptions).length===0&&$$render(consequent_6)})}var node_19=sibling(node_18,2);ModelsSelectorList(node_19,{get groups(){return get$4(groupedFilteredOptions)},get currentModel(){return currentModel()},get activeId(){
-return get$4(activeId)},sectionHeaderClass:"my-1.5 px-2 py-2 text-[13px] font-semibold text-muted-foreground/70 select-none",onSelect:handleSelect,onInfoClick:handleInfoClick,get renderOption(){return modelOption}}),reset(div_2)}append($$anchor9,div_2)},$$slots:{default:!0}})}},$$slots:{default:!0}})}),append($$anchor6,fragment_5)},$$slots:{default:!0}})}),append($$anchor4,fragment_4)},alternate_3=$$anchor4=>{var button_1=root_22$1();button_1.__click=()=>handleOpenChange(!0);var node_20=child(button_1);
-Package(node_20,{class:"h-3.5 w-3.5"});var node_21=sibling(node_20,2);{var consequent_8=$$anchor5=>{var fragment_14=comment$2(),node_22=first_child(fragment_14);component(node_22,()=>Root$5,($$anchor6,Tooltip_Root_1)=>{Tooltip_Root_1($$anchor6,{children:($$anchor7,$$slotProps)=>{var fragment_15=root_24(),node_23=first_child(fragment_15);{const child2=($$anchor8,$$arg0)=>{ModelId($$anchor8,spread_props({get modelId(){return get$4(selectedOption).model},class:"min-w-0 overflow-hidden",showOrgName:!0},
-()=>$$arg0?.().props))};component(node_23,()=>Tooltip_trigger,($$anchor8,Tooltip_Trigger_1)=>{Tooltip_Trigger_1($$anchor8,{child:child2,$$slots:{child:!0}})})}var node_24=sibling(node_23,2);component(node_24,()=>Tooltip_content,($$anchor8,Tooltip_Content_1)=>{Tooltip_Content_1($$anchor8,{children:($$anchor9,$$slotProps2)=>{var p_3=root_26(),text_1=child(p_3,!0);reset(p_3),template_effect(()=>set_text(text_1,get$4(selectedOption).model)),append($$anchor9,p_3)},$$slots:{default:!0}})}),append($$anchor7,
-fragment_15)},$$slots:{default:!0}})}),append($$anchor5,fragment_14)};if_block(node_21,$$render=>{get$4(selectedOption)&&$$render(consequent_8)})}var node_25=sibling(node_21,2);{var consequent_9=$$anchor5=>{Loader_circle($$anchor5,{class:"h-3 w-3.5 animate-spin"})};if_block(node_25,$$render=>{get$4(updating)&&$$render(consequent_9)})}reset(button_1),template_effect($0=>{set_class(button_1,1,$0),button_1.disabled=disabled()||get$4(updating)},[()=>clsx(cn$1("inline-flex cursor-pointer items-center\
- gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60",get$4(isCurrentModelInCache)?forceForegroundText()||get$4(isHighlightedCurrentModelActive)?"text-foreground":"text-muted-foreground":"bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400",get$4(isOpen)?"text-foreground":""))]),append($$anchor4,button_1)};
-if_block(node_6,$$render=>{get$4(isRouter)?$$render(consequent_7):$$render(alternate_3,!1)})}append($$anchor3,fragment_3)};if_block(node_2,$$render=>{get$4(options).length===0&&get$4(isRouter)?$$render(consequent_2):$$render(alternate_4,!1)},!0)}append($$anchor2,fragment_1)};if_block(node2,$$render=>{get$4(loading)&&get$4(options).length===0&&get$4(isRouter)?$$render(consequent):$$render(alternate_5,!1)})}reset(div);var node_26=sibling(div,2);{var consequent_10=$$anchor2=>{DialogModelInformation(
-$$anchor2,{get modelId(){return get$4(infoModelId)},get open(){return get$4(showModelDialog)},set open($$value){set$1(showModelDialog,$$value,!0)}})};if_block(node_26,$$render=>{get$4(showModelDialog)&&$$render(consequent_10)})}return template_effect($0=>set_class(div,1,$0),[()=>clsx(cn$1("relative inline-flex flex-col items-end gap-1",className()))]),append($$anchor,fragment),pop($$exports)}delegate(["click"]);var root_2$6=from_html("<p>Loaded models</p> <!>",1),root_4$1=from_html("<p>Favorite \
-models</p> <!>",1),root_8$2=from_html("<p> </p>"),root_7$3=from_html("<!> <!>",1),root_6$3=from_html("<p>Available models</p> <!>",1),root$8=from_html("<!> <!> <!>",1);function ModelsSelectorList($$anchor,$$props){push$1($$props,!0);const defaultOption=($$anchor2,item=noop$3,showOrgName=noop$3)=>{const computed_const=user_derived(()=>{const{option:option2}=item();return{option:option2}}),isSelected=user_derived(()=>$$props.currentModel===get$4(computed_const).option.model||$$props.activeId===get$4(
-computed_const).option.id),isFav=user_derived(()=>modelsStore.favoriteModelIds.has(get$4(computed_const).option.model));ModelsSelectorOption($$anchor2,{get option(){return get$4(computed_const).option},get isSelected(){return get$4(isSelected)},isHighlighted:!1,get isFav(){return get$4(isFav)},get showOrgName(){return showOrgName()},get onSelect(){return $$props.onSelect},get onInfoClick(){return $$props.onInfoClick},onMouseEnter:()=>{},onKeyDown:()=>{}})};let sectionHeaderClass=prop($$props,"se\
-ctionHeaderClass",3,"my-1 px-2 py-2 text-[13px] font-semibold text-muted-foreground/70 select-none"),orgHeaderClass=prop($$props,"orgHeaderClass",3,"px-2 py-2 text-[11px] font-semibold text-muted-foreground/50 select-none [&:not(:first-child)]:mt-1"),render3=user_derived(()=>$$props.renderOption??defaultOption);var fragment_1=root$8(),node2=first_child(fragment_1);{var consequent=$$anchor2=>{var fragment_2=root_2$6(),p2=first_child(fragment_2),node_1=sibling(p2,2);each(node_1,17,()=>$$props.groups.
-loaded,item=>`loaded-${item.option.id}`,($$anchor3,item)=>{var fragment_3=comment$2(),node_2=first_child(fragment_3);snippet(node_2,()=>get$4(render3),()=>get$4(item),()=>!0),append($$anchor3,fragment_3)}),template_effect(()=>set_class(p2,1,clsx(sectionHeaderClass()))),append($$anchor2,fragment_2)};if_block(node2,$$render=>{$$props.groups.loaded.length>0&&$$render(consequent)})}var node_3=sibling(node2,2);{var consequent_1=$$anchor2=>{var fragment_4=root_4$1(),p_1=first_child(fragment_4),node_4=sibling(
-p_1,2);each(node_4,17,()=>$$props.groups.favorites,item=>`fav-${item.option.id}`,($$anchor3,item)=>{var fragment_5=comment$2(),node_5=first_child(fragment_5);snippet(node_5,()=>get$4(render3),()=>get$4(item),()=>!0),append($$anchor3,fragment_5)}),template_effect(()=>set_class(p_1,1,clsx(sectionHeaderClass()))),append($$anchor2,fragment_4)};if_block(node_3,$$render=>{$$props.groups.favorites.length>0&&$$render(consequent_1)})}var node_6=sibling(node_3,2);{var consequent_3=$$anchor2=>{var fragment_6=root_6$3(),
-p_2=first_child(fragment_6),node_7=sibling(p_2,2);each(node_7,17,()=>$$props.groups.available,group=>group.orgName,($$anchor3,group)=>{var fragment_7=root_7$3(),node_8=first_child(fragment_7);{var consequent_2=$$anchor4=>{var p_3=root_8$2(),text2=child(p_3,!0);reset(p_3),template_effect(()=>{set_class(p_3,1,clsx(orgHeaderClass())),set_text(text2,get$4(group).orgName)}),append($$anchor4,p_3)};if_block(node_8,$$render=>{get$4(group).orgName&&$$render(consequent_2)})}var node_9=sibling(node_8,2);each(
-node_9,17,()=>get$4(group).items,item=>item.option.id,($$anchor4,item)=>{var fragment_8=comment$2(),node_10=first_child(fragment_8);snippet(node_10,()=>get$4(render3),()=>get$4(item),()=>!1),append($$anchor4,fragment_8)}),append($$anchor3,fragment_7)}),template_effect(()=>set_class(p_2,1,clsx(sectionHeaderClass()))),append($$anchor2,fragment_6)};if_block(node_6,$$render=>{$$props.groups.available.length>0&&$$render(consequent_3)})}append($$anchor,fragment_1),pop()}var root_6$2=from_html('<div cl\
-ass="flex w-4 items-center justify-center"><!>  <div class="hidden group-hover:flex"><!></div></div>'),root_8$1=from_html('<div class="flex w-4 items-center justify-center"><span class="h-2 w-2 rounded-full bg-orange-400 group-hover:hidden"></span> <div class="hidden group-hover:flex"><!></div></div>'),root_10$3=from_html('<div class="flex w-4 items-center justify-center"><span class="h-2 w-2 rounded-full bg-green-500 group-hover:hidden"></span>  <div class="hidden group-hover:flex"><!></div></d\
-iv>'),root_11$1=from_html('<div class="flex w-4 items-center justify-center"><span class="h-2 w-2 rounded-full bg-muted-foreground/50 group-hover:hidden"></span>  <div class="hidden group-hover:flex"><!></div></div>'),root$7=from_html('<div role="option" tabindex="0"><!> <div class="flex shrink-0 items-center gap-1"><div class="pointer-events-none flex items-center justify-center gap-0.75 pl-2 opacity-0 group-hover:pointer-events-auto group-hover:opacity-100"><!> <!></div> <!></div></div>');function ModelsSelectorOption($$anchor,$$props){
-push$1($$props,!0);let showOrgName=prop($$props,"showOrgName",3,!1),currentRouterModels=user_derived(routerModels),serverStatus=user_derived(()=>get$4(currentRouterModels).find(m=>m.id===$$props.option.model)?.status?.value??null),isOperationInProgress=user_derived(()=>modelsStore.isModelOperationInProgress($$props.option.model)),isFailed=user_derived(()=>get$4(serverStatus)===ServerModelStatus.FAILED),isSleeping=user_derived(()=>get$4(serverStatus)===ServerModelStatus.SLEEPING),isLoaded=user_derived(
-()=>(get$4(serverStatus)===ServerModelStatus.LOADED||get$4(isSleeping))&&!get$4(isOperationInProgress)),isLoading2=user_derived(()=>get$4(serverStatus)===ServerModelStatus.LOADING||get$4(isOperationInProgress));var div=root$7();div.__click=()=>$$props.onSelect($$props.option.id),div.__keydown=function(...$$args){$$props.onKeyDown?.apply(this,$$args)};var node2=child(div);ModelId(node2,{get modelId(){return $$props.option.model},get showOrgName(){return showOrgName()},get aliases(){return $$props.
-option.aliases},get tags(){return $$props.option.tags},class:"flex-1"});var div_1=sibling(node2,2),div_2=child(div_1);div_2.__click=e=>e.stopPropagation();var node_1=child(div_2);{var consequent=$$anchor2=>{ActionIcon($$anchor2,{iconSize:"h-2.5 w-2.5",get icon(){return Heart_off},tooltip:"Remove from favorites",class:"h-3 w-3 hover:text-foreground",onclick:()=>modelsStore.toggleFavorite($$props.option.model)})},alternate=$$anchor2=>{ActionIcon($$anchor2,{iconSize:"h-2.5 w-2.5",get icon(){return Heart},
-tooltip:"Add to favorites",class:"h-3 w-3 hover:text-foreground",onclick:()=>modelsStore.toggleFavorite($$props.option.model)})};if_block(node_1,$$render=>{$$props.isFav?$$render(consequent):$$render(alternate,!1)})}var node_2=sibling(node_1,2);{var consequent_1=$$anchor2=>{ActionIcon($$anchor2,{iconSize:"h-2.5 w-2.5",get icon(){return Info$1},tooltip:"Model information",class:"h-3 w-3 hover:text-foreground",onclick:()=>$$props.onInfoClick($$props.option.model)})};if_block(node_2,$$render=>{get$4(
-isLoaded)&&$$props.onInfoClick&&$$render(consequent_1)})}reset(div_2);var node_3=sibling(div_2,2);{var consequent_2=$$anchor2=>{Loader_circle($$anchor2,{class:"h-4 w-4 animate-spin text-muted-foreground"})},alternate_4=$$anchor2=>{var fragment_4=comment$2(),node_4=first_child(fragment_4);{var consequent_3=$$anchor3=>{var div_3=root_6$2(),node_5=child(div_3);Circle_alert(node_5,{class:"h-3.5 w-3.5 text-red-500 group-hover:hidden"});var div_4=sibling(node_5,2);div_4.__click=e=>e.stopPropagation();
-var node_6=child(div_4);ActionIcon(node_6,{iconSize:"h-2.5 w-2.5",get icon(){return Rotate_cw},tooltip:"Retry loading model",class:"h-3 w-3 text-red-500 hover:text-foreground",onclick:()=>modelsStore.loadModel($$props.option.model)}),reset(div_4),reset(div_3),append($$anchor3,div_3)},alternate_3=$$anchor3=>{var fragment_5=comment$2(),node_7=first_child(fragment_5);{var consequent_4=$$anchor4=>{var div_5=root_8$1(),div_6=sibling(child(div_5),2),node_8=child(div_6);ActionIcon(node_8,{iconSize:"h-2\
-.5 w-2.5",get icon(){return Power_off},tooltip:"Unload model",class:"h-3 w-3 text-red-500 hover:text-red-600",onclick:e=>{e?.stopPropagation(),modelsStore.unloadModel($$props.option.model)}}),reset(div_6),reset(div_5),append($$anchor4,div_5)},alternate_2=$$anchor4=>{var fragment_6=comment$2(),node_9=first_child(fragment_6);{var consequent_5=$$anchor5=>{var div_7=root_10$3(),div_8=sibling(child(div_7),2);div_8.__click=e=>e.stopPropagation();var node_10=child(div_8);ActionIcon(node_10,{iconSize:"h\
--2.5 w-2.5",get icon(){return Power_off},tooltip:"Unload model",class:"h-3 w-3 text-red-500 hover:text-red-600",onclick:()=>modelsStore.unloadModel($$props.option.model)}),reset(div_8),reset(div_7),append($$anchor5,div_7)},alternate_1=$$anchor5=>{var div_9=root_11$1(),div_10=sibling(child(div_9),2);div_10.__click=e=>e.stopPropagation();var node_11=child(div_10);ActionIcon(node_11,{iconSize:"h-2.5 w-2.5",get icon(){return Power},tooltip:"Load model",class:"h-3 w-3",onclick:()=>modelsStore.loadModel(
-$$props.option.model)}),reset(div_10),reset(div_9),append($$anchor5,div_9)};if_block(node_9,$$render=>{get$4(isLoaded)?$$render(consequent_5):$$render(alternate_1,!1)},!0)}append($$anchor4,fragment_6)};if_block(node_7,$$render=>{get$4(isSleeping)?$$render(consequent_4):$$render(alternate_2,!1)},!0)}append($$anchor3,fragment_5)};if_block(node_4,$$render=>{get$4(isFailed)?$$render(consequent_3):$$render(alternate_3,!1)},!0)}append($$anchor2,fragment_4)};if_block(node_3,$$render=>{get$4(isLoading2)?
-$$render(consequent_2):$$render(alternate_4,!1)})}reset(div_1),reset(div),template_effect($0=>{set_class(div,1,$0),set_attribute(div,"aria-selected",$$props.isSelected||$$props.isHighlighted)},[()=>clsx(cn$1("group flex w-full items-center gap-2 rounded-sm p-2 text-left text-sm transition focus:outline-none","cursor-pointer hover:bg-muted focus:bg-muted",$$props.isSelected||$$props.isHighlighted?"bg-accent text-accent-foreground":"hover:bg-accent hover:text-accent-foreground",get$4(isLoaded)?"te\
-xt-popover-foreground":"text-muted-foreground"))]),event("mouseenter",div,function(...$$args){$$props.onMouseEnter?.apply(this,$$args)}),append($$anchor,div),pop()}delegate(["click","keydown"]);var root_1$4=from_html('<div class="flex items-center gap-2 text-xs text-muted-foreground"><!> Loading models…</div>'),root_3$3=from_html('<p class="text-xs text-muted-foreground">No models available.</p>'),root_10$2=from_html("<!> <!>",1),root_13$1=from_html('<button type="button" class="flex w-full cu\
-rsor-not-allowed items-center rounded-md bg-red-400/10 px-3 py-2.5 text-left text-sm text-red-400" disabled><span class="min-w-0 flex-1 truncate"> </span> <span class="ml-2 text-xs whitespace-nowrap opacity-70">(not available)</span></button> <div class="my-1 h-px bg-border"></div>',1),root_14=from_html('<p class="px-3 py-3 text-center text-sm text-muted-foreground">No models found.</p>'),root_9$2=from_html('<!> <div class="flex flex-col gap-1 pb-4"><div class="mb-3 px-4"><!></div> <div class="m\
-ax-h-[60vh] overflow-y-auto px-2"><!> <!> <!></div></div>',1),root_5$3=from_html('<button type="button" style="max-width: min(calc(100cqw - 9rem), 20rem)"><!> <!> <!></button> <!>',1),root_15=from_html('<button style="max-width: min(calc(100cqw - 6.5rem), 32rem)"><!> <!> <!></button>'),root$6=from_html("<div><!></div> <!>",1);function ModelsSelectorSheet($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),currentModel=prop($$props,"currentModel",3,null),disabled=prop($$props,
-"disabled",3,!1),forceForegroundText=prop($$props,"forceForegroundText",3,!1),useGlobalSelection=prop($$props,"useGlobalSelection",3,!1),options=user_derived(()=>modelOptions().filter(option2=>modelsStore.getModelProps(option2.model)?.webui!==!1)),loading=user_derived(modelsLoading),updating=user_derived(modelsUpdating),activeId=user_derived(selectedModelId),isRouter=user_derived(isRouterMode),serverModel=user_derived(singleModelName),isLoadingModel=state$1(!1),isHighlightedCurrentModelActive=user_derived(
-()=>!get$4(isRouter)||!currentModel()?!1:(()=>{const currentOption=get$4(options).find(option2=>option2.model===currentModel());return currentOption?currentOption.id===get$4(activeId):!1})()),isCurrentModelInCache=user_derived(()=>!get$4(isRouter)||!currentModel()?!0:get$4(options).some(option2=>option2.model===currentModel())),searchTerm=state$1(""),filteredOptions=user_derived(()=>filterModelOptions(get$4(options),get$4(searchTerm))),groupedFilteredOptions=user_derived(()=>groupModelOptions(get$4(
-filteredOptions),modelsStore.favoriteModelIds,m=>modelsStore.isModelLoaded(m))),sheetOpen=state$1(!1),showModelDialog=state$1(!1),infoModelId=state$1(null);function handleInfoClick(modelName){set$1(infoModelId,modelName,!0),set$1(showModelDialog,!0)}onMount$1(()=>{modelsStore.fetch().catch(error2=>{console.error("Unable to load models:",error2)})});function handleOpenChange(open3){get$4(loading)||get$4(updating)||(get$4(isRouter)?open3?(set$1(sheetOpen,!0),set$1(searchTerm,""),modelsStore.fetchRouterModels().
-then(()=>{modelsStore.fetchModalitiesForLoadedModels()})):(set$1(sheetOpen,!1),set$1(searchTerm,"")):set$1(showModelDialog,open3,!0))}function open2(){handleOpenChange(!0)}function handleSheetOpenChange(open3){open3||handleOpenChange(!1)}async function handleSelect(modelId){const option2=get$4(options).find(opt=>opt.id===modelId);if(!option2)return;let shouldCloseMenu=!0;$$props.onModelChange?await $$props.onModelChange(option2.id,option2.model)===!1&&(shouldCloseMenu=!1):await modelsStore.selectModelById(
-option2.id),shouldCloseMenu&&(handleOpenChange(!1),requestAnimationFrame(()=>{document.querySelector('[data-slot="chat-form"] textarea')?.focus()})),!$$props.onModelChange&&get$4(isRouter)&&!modelsStore.isModelLoaded(option2.model)&&(set$1(isLoadingModel,!0),modelsStore.loadModel(option2.model).catch(error2=>console.error("Failed to load model:",error2)).finally(()=>set$1(isLoadingModel,!1)))}function getDisplayOption(){if(!get$4(isRouter))return get$4(serverModel)?{id:"current",model:get$4(serverModel),
-name:get$4(serverModel).split("/").pop()||get$4(serverModel),capabilities:[]}:void 0;if(useGlobalSelection()&&get$4(activeId)){const selected=get$4(options).find(option2=>option2.id===get$4(activeId));if(selected)return selected}if(currentModel())return get$4(isCurrentModelInCache)?get$4(options).find(option2=>option2.model===currentModel()):{id:"not-in-cache",model:currentModel(),name:currentModel().split("/").pop()||currentModel(),capabilities:[]};if(get$4(activeId))return get$4(options).find(
-option2=>option2.id===get$4(activeId))}var $$exports={open:open2},fragment=root$6(),div=first_child(fragment),node2=child(div);{var consequent=$$anchor2=>{var div_1=root_1$4(),node_1=child(div_1);Loader_circle(node_1,{class:"h-3.5 w-3.5 animate-spin"}),next$1(),reset(div_1),append($$anchor2,div_1)},alternate_3=$$anchor2=>{var fragment_1=comment$2(),node_2=first_child(fragment_1);{var consequent_1=$$anchor3=>{var p2=root_3$3();append($$anchor3,p2)},alternate_2=$$anchor3=>{const selectedOption=user_derived(
-getDisplayOption);var fragment_2=comment$2(),node_3=first_child(fragment_2);{var consequent_5=$$anchor4=>{var fragment_3=root_5$3(),button=first_child(fragment_3);button.__click=()=>handleOpenChange(!0);var node_4=child(button);Package(node_4,{class:"h-3.5 w-3.5"});var node_5=sibling(node_4,2);{let $0=user_derived(()=>get$4(selectedOption)?.model||"Select model");TruncatedText(node_5,{get text(){return get$4($0)},class:"min-w-0 font-medium"})}var node_6=sibling(node_5,2);{var consequent_2=$$anchor5=>{
-Loader_circle($$anchor5,{class:"h-3 w-3.5 animate-spin"})},alternate=$$anchor5=>{Chevron_down($$anchor5,{class:"h-3 w-3.5"})};if_block(node_6,$$render=>{get$4(updating)||get$4(isLoadingModel)?$$render(consequent_2):$$render(alternate,!1)})}reset(button);var node_7=sibling(button,2);component(node_7,()=>Root$1,($$anchor5,Sheet_Root)=>{Sheet_Root($$anchor5,{onOpenChange:handleSheetOpenChange,get open(){return get$4(sheetOpen)},set open($$value){set$1(sheetOpen,$$value,!0)},children:($$anchor6,$$slotProps)=>{
-var fragment_6=comment$2(),node_8=first_child(fragment_6);component(node_8,()=>Sheet_content,($$anchor7,Sheet_Content)=>{Sheet_Content($$anchor7,{side:"bottom",class:"max-h-[85vh] gap-1",children:($$anchor8,$$slotProps2)=>{var fragment_7=root_9$2(),node_9=first_child(fragment_7);component(node_9,()=>Sheet_header,($$anchor9,Sheet_Header)=>{Sheet_Header($$anchor9,{children:($$anchor10,$$slotProps3)=>{var fragment_8=root_10$2(),node_10=first_child(fragment_8);component(node_10,()=>Sheet_title,($$anchor11,Sheet_Title)=>{
-Sheet_Title($$anchor11,{children:($$anchor12,$$slotProps4)=>{next$1();var text2=text$8("Select Model");append($$anchor12,text2)},$$slots:{default:!0}})});var node_11=sibling(node_10,2);component(node_11,()=>Sheet_description,($$anchor11,Sheet_Description)=>{Sheet_Description($$anchor11,{class:"sr-only",children:($$anchor12,$$slotProps4)=>{next$1();var text_1=text$8("Choose a model to use for the conversation");append($$anchor12,text_1)},$$slots:{default:!0}})}),append($$anchor10,fragment_8)},$$slots:{
-default:!0}})});var div_2=sibling(node_9,2),div_3=child(div_2),node_12=child(div_3);SearchInput(node_12,{placeholder:"Search models...",get value(){return get$4(searchTerm)},set value($$value){set$1(searchTerm,$$value,!0)}}),reset(div_3);var div_4=sibling(div_3,2),node_13=child(div_4);{var consequent_3=$$anchor9=>{var fragment_9=root_13$1(),button_1=first_child(fragment_9),span=child(button_1),text_2=child(span,!0);reset(span),next$1(2),reset(button_1),next$1(2),template_effect(()=>set_text(text_2,
-get$4(selectedOption)?.name||currentModel())),append($$anchor9,fragment_9)};if_block(node_13,$$render=>{!get$4(isCurrentModelInCache)&&currentModel()&&$$render(consequent_3)})}var node_14=sibling(node_13,2);{var consequent_4=$$anchor9=>{var p_1=root_14();append($$anchor9,p_1)};if_block(node_14,$$render=>{get$4(filteredOptions).length===0&&$$render(consequent_4)})}var node_15=sibling(node_14,2);ModelsSelectorList(node_15,{get groups(){return get$4(groupedFilteredOptions)},get currentModel(){return currentModel()},
-get activeId(){return get$4(activeId)},sectionHeaderClass:"px-2 py-2 text-xs font-semibold text-muted-foreground/60 select-none",orgHeaderClass:"px-2 py-2 text-xs font-semibold text-muted-foreground/60 select-none [&:not(:first-child)]:mt-2",onSelect:handleSelect,onInfoClick:handleInfoClick}),reset(div_4),reset(div_2),append($$anchor8,fragment_7)},$$slots:{default:!0}})}),append($$anchor6,fragment_6)},$$slots:{default:!0}})}),template_effect($0=>{set_class(button,1,$0),button.disabled=disabled()||
-get$4(updating)},[()=>clsx(cn$1("inline-grid cursor-pointer grid-cols-[1fr_auto_1fr] items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60",get$4(isCurrentModelInCache)?forceForegroundText()||get$4(isHighlightedCurrentModelActive)?"text-foreground":"text-muted-foreground":"bg-red-400/10 !text-red-400 hove\
-r:bg-red-400/20 hover:text-red-400",get$4(sheetOpen)?"text-foreground":""))]),append($$anchor4,fragment_3)},alternate_1=$$anchor4=>{var button_2=root_15();button_2.__click=()=>handleOpenChange(!0);var node_16=child(button_2);Package(node_16,{class:"h-3.5 w-3.5"});var node_17=sibling(node_16,2);{let $0=user_derived(()=>get$4(selectedOption)?.model||"");TruncatedText(node_17,{get text(){return get$4($0)},class:"min-w-0 font-medium"})}var node_18=sibling(node_17,2);{var consequent_6=$$anchor5=>{Loader_circle(
-$$anchor5,{class:"h-3 w-3.5 animate-spin"})};if_block(node_18,$$render=>{get$4(updating)&&$$render(consequent_6)})}reset(button_2),template_effect($0=>{set_class(button_2,1,$0),button_2.disabled=disabled()||get$4(updating)},[()=>clsx(cn$1("inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disab\
-led:opacity-60",get$4(isCurrentModelInCache)?forceForegroundText()||get$4(isHighlightedCurrentModelActive)?"text-foreground":"text-muted-foreground":"bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400"))]),append($$anchor4,button_2)};if_block(node_3,$$render=>{get$4(isRouter)?$$render(consequent_5):$$render(alternate_1,!1)})}append($$anchor3,fragment_2)};if_block(node_2,$$render=>{get$4(options).length===0&&get$4(isRouter)?$$render(consequent_1):$$render(alternate_2,!1)},!0)}append(
-$$anchor2,fragment_1)};if_block(node2,$$render=>{get$4(loading)&&get$4(options).length===0&&get$4(isRouter)?$$render(consequent):$$render(alternate_3,!1)})}reset(div);var node_19=sibling(div,2);{var consequent_7=$$anchor2=>{DialogModelInformation($$anchor2,{get modelId(){return get$4(infoModelId)},get open(){return get$4(showModelDialog)},set open($$value){set$1(showModelDialog,$$value,!0)}})};if_block(node_19,$$render=>{get$4(showModelDialog)&&$$render(consequent_7)})}return template_effect($0=>set_class(
-div,1,$0),[()=>clsx(cn$1("relative inline-flex flex-col items-end gap-1",className()))]),append($$anchor,fragment),pop($$exports)}delegate(["click"]);var root_4=from_html("<span> </span>"),root_5$2=from_html("<span> </span>"),root_7$2=from_html("<span> </span>"),root_9$1=from_html("<span> </span>"),root_2$5=from_html('<span><span class="min-w-0 truncate font-medium"><!> </span> <!> <!> <!> <!></span>');function ModelId($$anchor,$$props){push$1($$props,!0);let showOrgName=prop($$props,"showOrgNam\
-e",3,!1),showRaw=prop($$props,"showRaw",3,void 0),className=prop($$props,"class",3,""),rest=rest_props($$props,["$$slots","$$events","$$legacy","modelId","showOrgName","showRaw","aliases","tags","class"]);const badgeClass="inline-flex w-fit shrink-0 items-center justify-center whitespace-nowrap rounded-md border border-border/50 px-1 py-0 text-[10px] font-mono bg-foreground/15 dark:bg-foreground/10 text-foreground [a&]:hover:bg-foreground/25",tagBadgeClass="inline-flex w-fit shrink-0 items-cente\
-r justify-center whitespace-nowrap rounded-md border border-border/50 px-1 py-0 text-[10px] font-mono text-foreground [a&]:hover:bg-accent [a&]:hover:text-accent-foreground";let parsed=user_derived(()=>ModelsService.parseModelId($$props.modelId)),resolvedShowRaw=user_derived(()=>showRaw()??config$1().showRawModelNames??!1),displayName=user_derived(()=>$$props.aliases&&$$props.aliases.length>0?$$props.aliases[0]:get$4(parsed).modelName??$$props.modelId),remainingAliases=user_derived(()=>$$props.aliases&&
-$$props.aliases.length>1?$$props.aliases.slice(1):[]),allTags=user_derived(()=>[...get$4(parsed).tags??[],...$$props.tags??[]]);var fragment=comment$2(),node2=first_child(fragment);{var consequent=$$anchor2=>{TruncatedText($$anchor2,spread_props({get class(){return`font-medium ${className()??""}`},showTooltip:!1,get text(){return $$props.modelId}},()=>rest))},alternate=$$anchor2=>{var span=root_2$5();attribute_effect(span,()=>({class:`flex min-w-0 flex-wrap items-center gap-1 ${className()??""}`,
-...rest}));var span_1=child(span),node_1=child(span_1);{var consequent_1=$$anchor3=>{var text2=text$8();template_effect(()=>set_text(text2,`${get$4(parsed).orgName??""}/`)),append($$anchor3,text2)};if_block(node_1,$$render=>{showOrgName()&&get$4(parsed).orgName&&!($$props.aliases&&$$props.aliases.length>0)&&$$render(consequent_1)})}var text_1=sibling(node_1,1,!0);reset(span_1);var node_2=sibling(span_1,2);{var consequent_2=$$anchor3=>{var span_2=root_4();set_class(span_2,1,clsx(badgeClass));var text_2=child(
-span_2);reset(span_2),template_effect(()=>set_text(text_2,`${get$4(parsed).params??""}${get$4(parsed).activatedParams?`-${get$4(parsed).activatedParams}`:""}`)),append($$anchor3,span_2)};if_block(node_2,$$render=>{get$4(parsed).params&&$$render(consequent_2)})}var node_3=sibling(node_2,2);{var consequent_3=$$anchor3=>{var span_3=root_5$2();set_class(span_3,1,clsx(badgeClass));var text_3=child(span_3,!0);reset(span_3),template_effect(()=>set_text(text_3,get$4(parsed).quantization)),append($$anchor3,
-span_3)};if_block(node_3,$$render=>{get$4(parsed).quantization&&$$render(consequent_3)})}var node_4=sibling(node_3,2);{var consequent_4=$$anchor3=>{var fragment_3=comment$2(),node_5=first_child(fragment_3);each(node_5,16,()=>get$4(remainingAliases),alias=>alias,($$anchor4,alias)=>{var span_4=root_7$2();set_class(span_4,1,clsx(badgeClass));var text_4=child(span_4,!0);reset(span_4),template_effect(()=>set_text(text_4,alias)),append($$anchor4,span_4)}),append($$anchor3,fragment_3)};if_block(node_4,
-$$render=>{get$4(remainingAliases).length>0&&$$render(consequent_4)})}var node_6=sibling(node_4,2);{var consequent_5=$$anchor3=>{var fragment_4=comment$2(),node_7=first_child(fragment_4);each(node_7,16,()=>get$4(allTags),tag=>tag,($$anchor4,tag)=>{var span_5=root_9$1();set_class(span_5,1,clsx(tagBadgeClass));var text_5=child(span_5,!0);reset(span_5),template_effect(()=>set_text(text_5,tag)),append($$anchor4,span_5)}),append($$anchor3,fragment_4)};if_block(node_6,$$render=>{get$4(allTags).length>
-0&&$$render(consequent_5)})}reset(span),template_effect(()=>set_text(text_1,get$4(displayName))),append($$anchor2,span)};if_block(node2,$$render=>{get$4(resolvedShowRaw)?$$render(consequent):$$render(alternate,!1)})}append($$anchor,fragment),pop()}var root_3$2=from_html("<!> <!>",1),root_8=from_html("<!> <!>",1);function ModelBadge($$anchor,$$props){push$1($$props,!0);const badgeContent=$$anchor2=>{BadgeInfo($$anchor2,{get class(){return className()},get onclick(){return $$props.onclick},icon:$$anchor3=>{
-Package($$anchor3,{class:"h-3 w-3"})},children:($$anchor3,$$slotProps)=>{var fragment_2=root_3$2(),node2=first_child(fragment_2);{var consequent=$$anchor4=>{ModelId($$anchor4,{get modelId(){return get$4(model)}})};if_block(node2,$$render=>{get$4(model)&&$$render(consequent)})}var node_1=sibling(node2,2);{var consequent_1=$$anchor4=>{{let $0=user_derived(()=>get$4(model)||"");ActionIconCopyToClipboard($$anchor4,{get text(){return get$4($0)},ariaLabel:"Copy model name"})}};if_block(node_1,$$render=>{
-showCopyIcon()&&$$render(consequent_1)})}append($$anchor3,fragment_2)},$$slots:{icon:!0,default:!0}})};let className=prop($$props,"class",3,""),showCopyIcon=prop($$props,"showCopyIcon",3,!1),showTooltip=prop($$props,"showTooltip",3,!1),model=user_derived(()=>$$props.model||modelsStore.singleModelName),isModelMode=user_derived(()=>serverStore.isModelMode),shouldShow=user_derived(()=>get$4(model)&&($$props.model!==void 0||get$4(isModelMode)));var fragment_5=comment$2(),node_2=first_child(fragment_5);
-{var consequent_3=$$anchor2=>{var fragment_6=comment$2(),node_3=first_child(fragment_6);{var consequent_2=$$anchor3=>{var fragment_7=comment$2(),node_4=first_child(fragment_7);component(node_4,()=>Root$5,($$anchor4,Tooltip_Root)=>{Tooltip_Root($$anchor4,{children:($$anchor5,$$slotProps)=>{var fragment_8=root_8(),node_5=first_child(fragment_8);component(node_5,()=>Tooltip_trigger,($$anchor6,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor6,{children:($$anchor7,$$slotProps2)=>{badgeContent($$anchor7)},
-$$slots:{default:!0}})});var node_6=sibling(node_5,2);component(node_6,()=>Tooltip_content,($$anchor6,Tooltip_Content)=>{Tooltip_Content($$anchor6,{children:($$anchor7,$$slotProps2)=>{next$1();var text2=text$8();template_effect(()=>set_text(text2,$$props.onclick?"Click for model details":get$4(model))),append($$anchor7,text2)},$$slots:{default:!0}})}),append($$anchor5,fragment_8)},$$slots:{default:!0}})}),append($$anchor3,fragment_7)},alternate=$$anchor3=>{badgeContent($$anchor3)};if_block(node_3,
-$$render=>{showTooltip()?$$render(consequent_2):$$render(alternate,!1)})}append($$anchor2,fragment_6)};if_block(node_2,$$render=>{get$4(shouldShow)&&$$render(consequent_3)})}append($$anchor,fragment_5),pop()}var root_1$3=from_html('<div class="px-2 py-3 text-center text-sm text-muted-foreground"> </div>'),root_2$4=from_html("<!> <!>",1),root$5=from_html('<div class="sticky top-0 z-10 mb-2 bg-popover p-1 pt-2"><!></div> <div class="overflow-y-auto"><!> <!></div> <!>',1);function DropdownMenuSearchable($$anchor,$$props){
-push$1($$props,!0);let placeholder=prop($$props,"placeholder",3,"Search..."),searchValue=prop($$props,"searchValue",15,""),emptyMessage=prop($$props,"emptyMessage",3,"No items found"),isEmpty=prop($$props,"isEmpty",3,!1);var fragment=root$5(),div=first_child(fragment),node2=child(div);SearchInput(node2,{get placeholder(){return placeholder()},get onInput(){return $$props.onSearchChange},get onKeyDown(){return $$props.onSearchKeyDown},get value(){return searchValue()},set value($$value){searchValue(
-$$value)}}),reset(div);var div_1=sibling(div,2),node_1=child(div_1);snippet(node_1,()=>$$props.children);var node_2=sibling(node_1,2);{var consequent=$$anchor2=>{var div_2=root_1$3(),text2=child(div_2,!0);reset(div_2),template_effect(()=>set_text(text2,emptyMessage())),append($$anchor2,div_2)};if_block(node_2,$$render=>{isEmpty()&&$$render(consequent)})}reset(div_1);var node_3=sibling(div_1,2);{var consequent_1=$$anchor2=>{var fragment_1=root_2$4(),node_4=first_child(fragment_1);component(node_4,
-()=>Dropdown_menu_separator,($$anchor3,DropdownMenu_Separator)=>{DropdownMenu_Separator($$anchor3,{})});var node_5=sibling(node_4,2);snippet(node_5,()=>$$props.footer),append($$anchor2,fragment_1)};if_block(node_3,$$render=>{$$props.footer&&$$render(consequent_1)})}append($$anchor,fragment),pop()}const iconComponent=($$anchor,IconComponent=noop$3,className=noop$3)=>{var fragment=comment$2(),node2=first_child(fragment);component(node2,IconComponent,($$anchor2,IconComponent_1)=>{IconComponent_1($$anchor2,
-{get class(){return className()}})}),append($$anchor,fragment)};var root_6$1=from_html('<!> <span class="sr-only"> </span>',1),root_7$1=from_html("<p> </p>"),root_5$1=from_html("<!> <!>",1),root_12=from_html('<div class="flex items-center gap-2"><!> </div> <!>',1),root_10$1=from_html("<!> <!>",1),root_2$3=from_html("<!> <!>",1);function DropdownMenuActions($$anchor,$$props){push$1($$props,!0);let triggerClass=prop($$props,"triggerClass",3,""),align=prop($$props,"align",3,"end"),open2=prop($$props,
-"open",15,!1);var fragment_1=comment$2(),node_1=first_child(fragment_1);component(node_1,()=>Root$2,($$anchor2,DropdownMenu_Root)=>{DropdownMenu_Root($$anchor2,{get open(){return open2()},set open($$value){open2($$value)},children:($$anchor3,$$slotProps)=>{var fragment_2=root_2$3(),node_2=first_child(fragment_2);component(node_2,()=>Dropdown_menu_trigger,($$anchor4,DropdownMenu_Trigger)=>{DropdownMenu_Trigger($$anchor4,{get class(){return`flex h-6 w-6 cursor-pointer items-center justify-center r\
-ounded-md p-0 text-sm font-medium transition-colors hover:bg-accent hover:text-accent-foreground focus:bg-accent focus:text-accent-foreground focus:outline-none disabled:pointer-events-none disabled:opacity-50 data-[state=open]:bg-accent data-[state=open]:text-accent-foreground ${triggerClass()??""}`},onclick:e=>e.stopPropagation(),children:($$anchor5,$$slotProps2)=>{var fragment_3=comment$2(),node_3=first_child(fragment_3);{var consequent=$$anchor6=>{var fragment_4=comment$2(),node_4=first_child(
-fragment_4);component(node_4,()=>Root$5,($$anchor7,Tooltip_Root)=>{Tooltip_Root($$anchor7,{children:($$anchor8,$$slotProps3)=>{var fragment_5=root_5$1(),node_5=first_child(fragment_5);component(node_5,()=>Tooltip_trigger,($$anchor9,Tooltip_Trigger)=>{Tooltip_Trigger($$anchor9,{children:($$anchor10,$$slotProps4)=>{var fragment_6=root_6$1(),node_6=first_child(fragment_6);iconComponent(node_6,()=>$$props.triggerIcon,()=>"h-3 w-3");var span=sibling(node_6,2),text2=child(span,!0);reset(span),template_effect(
-()=>set_text(text2,$$props.triggerTooltip)),append($$anchor10,fragment_6)},$$slots:{default:!0}})});var node_7=sibling(node_5,2);component(node_7,()=>Tooltip_content,($$anchor9,Tooltip_Content)=>{Tooltip_Content($$anchor9,{children:($$anchor10,$$slotProps4)=>{var p2=root_7$1(),text_1=child(p2,!0);reset(p2),template_effect(()=>set_text(text_1,$$props.triggerTooltip)),append($$anchor10,p2)},$$slots:{default:!0}})}),append($$anchor8,fragment_5)},$$slots:{default:!0}})}),append($$anchor6,fragment_4)},
-alternate=$$anchor6=>{iconComponent($$anchor6,()=>$$props.triggerIcon,()=>"h-3 w-3")};if_block(node_3,$$render=>{$$props.triggerTooltip?$$render(consequent):$$render(alternate,!1)})}append($$anchor5,fragment_3)},$$slots:{default:!0}})});var node_8=sibling(node_2,2);component(node_8,()=>Dropdown_menu_content,($$anchor4,DropdownMenu_Content)=>{DropdownMenu_Content($$anchor4,{get align(){return align()},class:"z-[999999] w-48",children:($$anchor5,$$slotProps2)=>{var fragment_8=comment$2(),node_9=first_child(
-fragment_8);each(node_9,19,()=>$$props.actions,action2=>action2.label,($$anchor6,action2,index2)=>{var fragment_9=root_10$1(),node_10=first_child(fragment_9);{var consequent_1=$$anchor7=>{var fragment_10=comment$2(),node_11=first_child(fragment_10);component(node_11,()=>Dropdown_menu_separator,($$anchor8,DropdownMenu_Separator)=>{DropdownMenu_Separator($$anchor8,{})}),append($$anchor7,fragment_10)};if_block(node_10,$$render=>{get$4(action2).separator&&get$4(index2)>0&&$$render(consequent_1)})}var node_12=sibling(
-node_10,2);component(node_12,()=>Dropdown_menu_item,($$anchor7,DropdownMenu_Item)=>{DropdownMenu_Item($$anchor7,{get onclick(){return get$4(action2).onclick},get variant(){return get$4(action2).variant},get disabled(){return get$4(action2).disabled},class:"flex items-center justify-between hover:[&>kbd]:opacity-100",children:($$anchor8,$$slotProps3)=>{var fragment_11=root_12(),div=first_child(fragment_11),node_13=child(div);iconComponent(node_13,()=>get$4(action2).icon,()=>`h-4 w-4 ${get$4(action2).
-variant==="destructive"?"text-destructive":""}`);var text_2=sibling(node_13);reset(div);var node_14=sibling(div,2);{var consequent_2=$$anchor9=>{KeyboardShortcutInfo($$anchor9,{get keys(){return get$4(action2).shortcut},get variant(){return get$4(action2).variant}})};if_block(node_14,$$render=>{get$4(action2).shortcut&&$$render(consequent_2)})}template_effect(()=>set_text(text_2,` ${get$4(action2).label??""}`)),append($$anchor8,fragment_11)},$$slots:{default:!0}})}),append($$anchor6,fragment_9)}),
-append($$anchor5,fragment_8)},$$slots:{default:!0}})}),append($$anchor3,fragment_2)},$$slots:{default:!0}})}),append($$anchor,fragment_1),pop()}var root_2$2=from_html("<!> ",1),root_1$2=from_html("<!> <!>",1),root_6=from_html("<!> ",1),root$4=from_html('<div><div class="flex items-center space-x-2"><div></div> <span class="text-sm text-muted-foreground"> </span></div> <!> <!></div>');function ServerStatus($$anchor,$$props){push$1($$props,!0);let className=prop($$props,"class",3,""),showActions=prop(
-$$props,"showActions",3,!1),error2=user_derived(serverError),loading=user_derived(serverLoading),model=user_derived(singleModelName),serverData=user_derived(serverProps);function getStatusColor(){return get$4(loading)?"bg-yellow-500":get$4(error2)?"bg-red-500":get$4(serverData)?"bg-green-500":"bg-gray-500"}function getStatusText(){return get$4(loading)?"Connecting...":get$4(error2)?"Connection Error":get$4(serverData)?"Connected":"Unknown"}var div=root$4(),div_1=child(div),div_2=child(div_1),span=sibling(
-div_2,2),text2=child(span,!0);reset(span),reset(div_1);var node2=sibling(div_1,2);{var consequent_1=$$anchor2=>{var fragment=root_1$2(),node_1=first_child(fragment);Badge(node_1,{variant:"outline",class:"text-xs",children:($$anchor3,$$slotProps)=>{var fragment_1=root_2$2(),node_2=first_child(fragment_1);Server(node_2,{class:"mr-1 h-3 w-3"});var text_1=sibling(node_2);template_effect(()=>set_text(text_1,` ${get$4(model)||"Unknown Model"}`)),append($$anchor3,fragment_1)},$$slots:{default:!0}});var node_3=sibling(
-node_1,2);{var consequent=$$anchor3=>{Badge($$anchor3,{variant:"secondary",class:"text-xs",children:($$anchor4,$$slotProps)=>{next$1();var text_2=text$8();template_effect($0=>set_text(text_2,`ctx: ${$0??""}`),[()=>get$4(serverData).default_generation_settings.n_ctx.toLocaleString()]),append($$anchor4,text_2)},$$slots:{default:!0}})};if_block(node_3,$$render=>{get$4(serverData)?.default_generation_settings?.n_ctx&&$$render(consequent)})}append($$anchor2,fragment)};if_block(node2,$$render=>{get$4(
-serverData)&&!get$4(error2)&&$$render(consequent_1)})}var node_4=sibling(node2,2);{var consequent_2=$$anchor2=>{Button($$anchor2,{variant:"outline",size:"sm",class:"text-destructive",children:($$anchor3,$$slotProps)=>{var fragment_5=root_6(),node_5=first_child(fragment_5);Triangle_alert(node_5,{class:"h-4 w-4"});var text_3=sibling(node_5);template_effect(()=>set_text(text_3,` ${get$4(error2)??""}`)),append($$anchor3,fragment_5)},$$slots:{default:!0}})};if_block(node_4,$$render=>{showActions()&&get$4(
-error2)&&$$render(consequent_2)})}reset(div),template_effect(($0,$1)=>{set_class(div,1,`flex items-center space-x-3 ${className()??""}`),set_class(div_2,1,`h-2 w-2 rounded-full ${$0??""}`),set_text(text2,$1)},[getStatusColor,getStatusText]),append($$anchor,div),pop()}var root_2$1=from_html("<!> Enter API Key",1),root_1$1=from_html('<div class="mb-4"><!></div>'),root_5=from_html('<div class="absolute top-1/2 right-3 -translate-y-1/2"><!></div>'),root_7=from_html('<div class="absolute top-1/2 righ\
-t-3 -translate-y-1/2"><!></div>'),root_9=from_html('<div class="absolute top-1/2 right-3 -translate-y-1/2"><!></div>'),root_10=from_html('<p class="text-sm text-destructive"> </p>'),root_11=from_html('<p class="text-sm text-green-600">✓ API key validated successfully! Connecting...</p>'),root_13=from_html("<!> Validating...",1),root_3$1=from_html('<div class="mb-4 space-y-3 text-left"><div class="space-y-2"><!> <div class="relative"><!> <!></div> <!> <!></div> <div class="flex gap-2"><!> <!></\
-div></div>'),root_20=from_html("<!> Connecting...",1),root_21=from_html("<!> Retry Connection",1),root_18=from_html("<div><!></div>"),root_22=from_html('<div class="mt-4 text-left"><details class="text-sm"><summary class="cursor-pointer text-muted-foreground hover:text-foreground">Troubleshooting</summary> <div class="mt-2 space-y-3 text-xs text-muted-foreground"><div class="space-y-2"><p class="mb-4 font-medium">Start the llama-server:</p> <div class="rounded bg-muted/50 px-2 py-1 font-mono tex\
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-component:_layout},Symbol.toStringTag,{value:"Module"})),getStores=()=>{const stores$1=stores;return{page:{subscribe:stores$1.page.subscribe},navigating:{subscribe:stores$1.navigating.subscribe},updated:stores$1.updated}},page={subscribe(fn){return getStores().page.subscribe(fn)}};var root_3=from_html('<div class="flex h-full items-center justify-center"><div class="w-full max-w-md px-4 text-center"><div class="mb-6"><div class="mx-auto mb-4 flex h-16 w-16 items-center justify-center rounded-ful\
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-,this.suppressEncryption))}return r}getRaw(e){return this._map.get(e)}getKeys(){return[...this._map.keys()]}getRawValues(){return[...this._map.values()]}set(e,t){this._map.set(e,t)}setIfNotExists(e,t){this.has(e)||this.set(e,t)}setIfNumber(e,t){"number"==typeof t&&this.set(e,t)}setIfArray(e,t){(Array.isArray(t)||ArrayBuffer.isView(t))&&this.set(e,t)}setIfDefined(e,t){null!=t&&this.set(e,t)}setIfName(e,t){"string"==typeof t?this.set(e,Name.get(t)):t instanceof Name&&this.set(e,t)}has(e){return th\
-is._map.has(e)}*[Symbol.iterator](){for(const[e,t]of this._map)yield[e,t instanceof Ref&&this.xref?this.xref.fetch(t,this.suppressEncryption):t]}static get empty(){const e=new Dict(null);e.set=(e,t)=>{unreachable("Should not call `set` on the empty dictionary.")};return shadow(this,"empty",e)}static merge({xref:e,dictArray:t,mergeSubDicts:a=!1}){const r=new Dict(e),i=new Map;for(const e of t)if(e instanceof Dict)for(const[t,r]of e._map){let e=i.get(t);if(void 0===e){e=[];i.set(t,e)}else if(!(a&&\
-r instanceof Dict))continue;e.push(r)}for(const[t,a]of i){if(1===a.length||!(a[0]instanceof Dict)){r._map.set(t,a[0]);continue}const i=new Dict(e);for(const e of a)for(const[t,a]of e._map)i._map.has(t)||i._map.set(t,a);i.size>0&&r._map.set(t,i)}i.clear();return r.size>0?r:Dict.empty}clone(){const e=new Dict(this.xref);for(const t of this.getKeys())e.set(t,this.getRaw(t));return e}delete(e){delete this._map[e]}}class Ref{constructor(e,t){this.num=e;this.gen=t}toString(){return 0===this.gen?`${thi\
-s.num}R`:`${this.num}R${this.gen}`}static fromString(e){const t=Aa[e];if(t)return t;const a=/^(\\d+)R(\\d*)$/.exec(e);return a&&"0"!==a[1]?Aa[e]=new Ref(parseInt(a[1]),a[2]?parseInt(a[2]):0):null}static get(e,t){const a=0===t?`${e}R`:`${e}R${t}`;return Aa[a]||=new Ref(e,t)}}class RefSet{constructor(e=null){this._set=new Set(e?._set)}has(e){return this._set.has(e.toString())}put(e){this._set.add(e.toString())}remove(e){this._set.delete(e.toString())}[Symbol.iterator](){return this._set.values()}cle\
-ar(){this._set.clear()}}class RefSetCache{constructor(){this._map=new Map}get size(){return this._map.size}get(e){return this._map.get(e.toString())}has(e){return this._map.has(e.toString())}put(e,t){this._map.set(e.toString(),t)}putAlias(e,t){this._map.set(e.toString(),this.get(t))}[Symbol.iterator](){return this._map.values()}clear(){this._map.clear()}*values(){yield*this._map.values()}*items(){for(const[e,t]of this._map)yield[Ref.fromString(e),t]}}function isName(e,t){return e instanceof Name\
-&&(void 0===t||e.name===t)}function isCmd(e,t){return e instanceof Cmd&&(void 0===t||e.cmd===t)}function isDict(e,t){return e instanceof Dict&&(void 0===t||isName(e.get("Type"),t))}function isRefsEqual(e,t){return e.num===t.num&&e.gen===t.gen}class BaseStream{get length(){unreachable("Abstract getter `length` accessed")}get isEmpty(){unreachable("Abstract getter `isEmpty` accessed")}get isDataLoaded(){return shadow(this,"isDataLoaded",!0)}getByte(){unreachable("Abstract method `getByte` called")\
-}getBytes(e){unreachable("Abstract method `getBytes` called")}async getImageData(e,t){return this.getBytes(e,t)}async asyncGetBytes(){unreachable("Abstract method `asyncGetBytes` called")}get isAsync(){return!1}get isAsyncDecoder(){return!1}get canAsyncDecodeImageFromBuffer(){return!1}async getTransferableImage(){return null}peekByte(){const e=this.getByte();-1!==e&&this.pos--;return e}peekBytes(e){const t=this.getBytes(e);this.pos-=t.length;return t}getUint16(){const e=this.getByte(),t=this.get\
-Byte();return-1===e||-1===t?-1:(e<<8)+t}getInt32(){return(this.getByte()<<24)+(this.getByte()<<16)+(this.getByte()<<8)+this.getByte()}getByteRange(e,t){unreachable("Abstract method `getByteRange` called")}getString(e){return bytesToString(this.getBytes(e))}skip(e){this.pos+=e||1}reset(){unreachable("Abstract method `reset` called")}moveStart(){unreachable("Abstract method `moveStart` called")}makeSubStream(e,t,a=null){unreachable("Abstract method `makeSubStream` called")}getBaseStreams(){return \
-null}}const Ca=/^[1-9]\\.\\d$/,va=2**31-1,Fa=[1,0,0,1,0,0],Ia=["ColorSpace","ExtGState","Font","Pattern","Properties","Shading","XObject"],Ta=["ExtGState","Font","Properties","XObject"];function getLookupTableFactory(e){let t;return function(){if(e){t=Object.create(null);e(t);e=null}return t}}class MissingDataException extends fa{constructor(e,t){super(`Missing data [${e}, ${t})`,"MissingDataException");this.begin=e;this.end=t}}class ParserEOFException extends fa{constructor(e){super(e,"ParserEOFE\
-xception")}}class XRefEntryException extends fa{constructor(e){super(e,"XRefEntryException")}}class XRefParseException extends fa{constructor(e){super(e,"XRefParseException")}}function arrayBuffersToBytes(e){const t=e.length;if(0===t)return new Uint8Array(0);if(1===t)return new Uint8Array(e[0]);let a=0;for(let r=0;r<t;r++)a+=e[r].byteLength;const r=new Uint8Array(a);let i=0;for(let a=0;a<t;a++){const t=new Uint8Array(e[a]);r.set(t,i);i+=t.byteLength}return r}async function fetchBinaryData(e){con\
-st t=await fetch(e);if(!t.ok)throw new Error(`Failed to fetch file "${e}" with "${t.statusText}".`);return new Uint8Array(await t.arrayBuffer())}function getInheritableProperty({dict:e,key:t,getArray:a=!1,stopWhenFound:r=!0}){let i;const n=new RefSet;for(;e instanceof Dict&&(!e.objId||!n.has(e.objId));){e.objId&&n.put(e.objId);const s=a?e.getArray(t):e.get(t);if(void 0!==s){if(r)return s;(i||=[]).push(s)}e=e.get("Parent")}return i}const Oa=["","C","CC","CCC","CD","D","DC","DCC","DCCC","CM","","X\
-","XX","XXX","XL","L","LX","LXX","LXXX","XC","","I","II","III","IV","V","VI","VII","VIII","IX"];function toRomanNumerals(e,t=!1){assert(Number.isInteger(e)&&e>0,"The number should be a positive integer.");const a="M".repeat(e/1e3|0)+Oa[e%1e3/100|0]+Oa[10+(e%100/10|0)]+Oa[20+e%10];return t?a.toLowerCase():a}function log2(e){return e>0?Math.ceil(Math.log2(e)):0}function readInt8(e,t){return e[t]<<24>>24}function readInt16(e,t){return(e[t]<<24|e[t+1]<<16)>>16}function readUint16(e,t){return e[t]<<8\
-|e[t+1]}function readUint32(e,t){return(e[t]<<24|e[t+1]<<16|e[t+2]<<8|e[t+3])>>>0}function isWhiteSpace(e){return 32===e||9===e||13===e||10===e}function isNumberArray(e,t){return Array.isArray(e)?(null===t||e.length===t)&&e.every((e=>"number"==typeof e)):ArrayBuffer.isView(e)&&!(e instanceof BigInt64Array||e instanceof BigUint64Array)&&(null===t||e.length===t)}function lookupMatrix(e,t){return isNumberArray(e,6)?e:t}function lookupRect(e,t){return isNumberArray(e,4)?e:t}function lookupNormalRect\
-(e,t){return isNumberArray(e,4)?Util.normalizeRect(e):t}function parseXFAPath(e){const t=/(.+)\\[(\\d+)\\]$/;return e.split(".").map((e=>{const a=e.match(t);return a?{name:a[1],pos:parseInt(a[2],10)}:{name:e,pos:0}}))}function escapePDFName(e){const t=[];let a=0;for(let r=0,i=e.length;r<i;r++){const i=e.charCodeAt(r);if(i<33||i>126||35===i||40===i||41===i||60===i||62===i||91===i||93===i||123===i||125===i||47===i||37===i){a<r&&t.push(e.substring(a,r));t.push(`#${i.toString(16)}`);a=r+1}}if(0===t.len\
-gth)return e;a<e.length&&t.push(e.substring(a,e.length));return t.join("")}function escapeString(e){return e.replaceAll(/([()\\\\\\n\\r])/g,(e=>"\\n"===e?"\\\\n":"\\r"===e?"\\\\r":`\\\\${e}`))}function _collectJS(e,t,a,r){if(!e)return;let i=null;if(e instanceof Ref){if(r.has(e))return;i=e;r.put(i);e=t.fetch(e)}if(Array.isArray(e))for(const i of e)_collectJS(i,t,a,r);else if(e instanceof Dict){if(isName(e.get("S"),"JavaScript")){const t=e.get("JS");let r;t instanceof BaseStream?r=t.getString():"string"==type\
-of t&&(r=t);r&&=stringToPDFString(r,!0).replaceAll("\\0","");r&&a.push(r.trim())}_collectJS(e.getRaw("Next"),t,a,r)}i&&r.remove(i)}function collectActions(e,t,a){const r=Object.create(null),i=getInheritableProperty({dict:t,key:"AA",stopWhenFound:!1});if(i)for(let t=i.length-1;t>=0;t--){const n=i[t];if(n instanceof Dict)for(const t of n.getKeys()){const i=a[t];if(!i)continue;const s=[];_collectJS(n.getRaw(t),e,s,new RefSet);s.length>0&&(r[i]=s)}}if(t.has("A")){const a=[];_collectJS(t.get("A"),e,a,\
-new RefSet);a.length>0&&(r.Action=a)}return objectSize(r)>0?r:null}const Ma={60:"&lt;",62:"&gt;",38:"&amp;",34:"&quot;",39:"&apos;"};function*codePointIter(e){for(let t=0,a=e.length;t<a;t++){const a=e.codePointAt(t);a>55295&&(a<57344||a>65533)&&t++;yield a}}function encodeToXmlString(e){const t=[];let a=0;for(let r=0,i=e.length;r<i;r++){const i=e.codePointAt(r);if(32<=i&&i<=126){const n=Ma[i];if(n){a<r&&t.push(e.substring(a,r));t.push(n);a=r+1}}else{a<r&&t.push(e.substring(a,r));t.push(`&#x${i.t\
-oString(16).toUpperCase()};`);i>55295&&(i<57344||i>65533)&&r++;a=r+1}}if(0===t.length)return e;a<e.length&&t.push(e.substring(a,e.length));return t.join("")}function validateFontName(e,t=!1){const a=/^("|\').*("|\')$/.exec(e);if(a&&a[1]===a[2]){if(new RegExp(`[^\\\\\\\\]${a[1]}`).test(e.slice(1,-1))){t&&warn(`FontFamily contains unescaped ${a[1]}: ${e}.`);return!1}}else for(const a of e.split(/[ \\t]+/))if(/^(\\d|(-(\\d|-)))/.test(a)||!/^[\\w-\\\\]+$/.test(a)){t&&warn(`FontFamily contains invalid <custom-id\
-ent>: ${e}.`);return!1}return!0}function validateCSSFont(e){const t=new Set(["100","200","300","400","500","600","700","800","900","1000","normal","bold","bolder","lighter"]),{fontFamily:a,fontWeight:r,italicAngle:i}=e;if(!validateFontName(a,!0))return!1;const n=r?r.toString():"";e.fontWeight=t.has(n)?n:"400";const s=parseFloat(i);e.italicAngle=isNaN(s)||s<-90||s>90?"14":i.toString();return!0}function recoverJsURL(e){const t=new RegExp("^\\\\s*("+["app.launchURL","window.open","xfa.host.gotoURL"].\
-join("|").replaceAll(".","\\\\.")+")\\\\((?:\'|\\")([^\'\\"]*)(?:\'|\\")(?:,\\\\s*(\\\\w+)\\\\)|\\\\))","i").exec(e);return t?.[2]?{url:t[2],newWindow:"app.launchURL"===t[1]&&"true"===t[3]}:null}function numberToString(e){if(Number.isInteger(e))return e.toString();const t=Math.round(100*e);return t%100==0?(t/100).toString():t%10==0?e.toFixed(1):e.toFixed(2)}function getNewAnnotationsMap(e){if(!e)return null;const t=new Map;for(const[a,r]of e){if(!a.startsWith(f))continue;let e=t.get(r.pageIndex);if(!e){e=[];t.set\
-(r.pageIndex,e)}e.push(r)}return t.size>0?t:null}function stringToAsciiOrUTF16BE(e){return null==e||function isAscii(e){if("string"!=typeof e)return!1;return!e||/^[\\x00-\\x7F]*$/.test(e)}(e)?e:stringToUTF16String(e,!0)}function stringToUTF16HexString(e){const t=[];for(let a=0,r=e.length;a<r;a++){const r=e.charCodeAt(a);t.push(ga[r>>8&255],ga[255&r])}return t.join("")}function stringToUTF16String(e,t=!1){const a=[];t&&a.push("þÿ");for(let t=0,r=e.length;t<r;t++){const r=e.charCodeAt(t);a.push(Stri\
-ng.fromCharCode(r>>8&255),String.fromCharCode(255&r))}return a.join("")}function getRotationMatrix(e,t,a){switch(e){case 90:return[0,1,-1,0,t,0];case 180:return[-1,0,0,-1,t,a];case 270:return[0,-1,1,0,0,a];default:throw new Error("Invalid rotation")}}function getSizeInBytes(e){return Math.ceil(Math.ceil(Math.log2(1+e))/8)}class QCMS{static#a=null;static _memory=null;static _mustAddAlpha=!1;static _destBuffer=null;static _destOffset=0;static _destLength=0;static _cssColor="";static _makeHexColor=\
-null;static get _memoryArray(){const e=this.#a;return e?.byteLength?e:this.#a=new Uint8Array(this._memory.buffer)}}let Da;const Ba="undefined"!=typeof TextDecoder?new TextDecoder("utf-8",{ignoreBOM:!0,fatal:!0}):{decode:()=>{throw Error("TextDecoder not available")}};"undefined"!=typeof TextDecoder&&Ba.decode();let Ra=null;function getUint8ArrayMemory0(){null!==Ra&&0!==Ra.byteLength||(Ra=new Uint8Array(Da.memory.buffer));return Ra}let Na=0;function passArray8ToWasm0(e,t){const a=t(1*e.length,1)>\
->>0;getUint8ArrayMemory0().set(e,a/1);Na=e.length;return a}const Ea=Object.freeze({RGB8:0,0:"RGB8",RGBA8:1,1:"RGBA8",BGRA8:2,2:"BGRA8",Gray8:3,3:"Gray8",GrayA8:4,4:"GrayA8",CMYK:5,5:"CMYK"}),Pa=Object.freeze({Perceptual:0,0:"Perceptual",RelativeColorimetric:1,1:"RelativeColorimetric",Saturation:2,2:"Saturation",AbsoluteColorimetric:3,3:"AbsoluteColorimetric"});function __wbg_get_imports(){const e={wbg:{}};e.wbg.__wbg_copyresult_b08ee7d273f295dd=function(e,t){!function copy_result(e,t){const{_mus\
-tAddAlpha:a,_destBuffer:r,_destOffset:i,_destLength:n,_memoryArray:s}=QCMS;if(t!==n)if(a)for(let a=e,n=e+t,o=i;a<n;a+=3,o+=4){r[o]=s[a];r[o+1]=s[a+1];r[o+2]=s[a+2];r[o+3]=255}else for(let a=e,n=e+t,o=i;a<n;a+=3,o+=4){r[o]=s[a];r[o+1]=s[a+1];r[o+2]=s[a+2]}else r.set(s.subarray(e,e+t),i)}(e>>>0,t>>>0)};e.wbg.__wbg_copyrgb_d60ce17bb05d9b67=function(e){!function copy_rgb(e){const{_destBuffer:t,_destOffset:a,_memoryArray:r}=QCMS;t[a]=r[e];t[a+1]=r[e+1];t[a+2]=r[e+2]}(e>>>0)};e.wbg.__wbg_makecssRGB_89\
-3bf0cd9fdb302d=function(e){!function make_cssRGB(e){const{_memoryArray:t}=QCMS;QCMS._cssColor=QCMS._makeHexColor(t[e],t[e+1],t[e+2])}(e>>>0)};e.wbg.__wbindgen_init_externref_table=function(){const e=Da.__wbindgen_export_0,t=e.grow(4);e.set(0,void 0);e.set(t+0,void 0);e.set(t+1,null);e.set(t+2,!0);e.set(t+3,!1)};e.wbg.__wbindgen_throw=function(e,t){throw new Error(function getStringFromWasm0(e,t){e>>>=0;return Ba.decode(getUint8ArrayMemory0().subarray(e,e+t))}(e,t))};return e}function __wbg_final\
-ize_init(e,t){Da=e.exports;__wbg_init.__wbindgen_wasm_module=t;Ra=null;Da.__wbindgen_start();return Da}async function __wbg_init(e){if(void 0!==Da)return Da;void 0!==e&&(Object.getPrototypeOf(e)===Object.prototype?({module_or_path:e}=e):console.warn("using deprecated parameters for the initialization function; pass a single object instead"));const t=__wbg_get_imports();("string"==typeof e||"function"==typeof Request&&e instanceof Request||"function"==typeof URL&&e instanceof URL)&&(e=fetch(e));c\
-onst{instance:a,module:r}=await async function __wbg_load(e,t){if("function"==typeof Response&&e instanceof Response){if("function"==typeof WebAssembly.instantiateStreaming)try{return await WebAssembly.instantiateStreaming(e,t)}catch(t){if("application/wasm"==e.headers.get("Content-Type"))throw t;console.warn("`WebAssembly.instantiateStreaming` failed because your server does not serve Wasm with `application/wasm` MIME type. Falling back to `WebAssembly.instantiate` which is slower. Original err\
-or:\\n",t)}const a=await e.arrayBuffer();return await WebAssembly.instantiate(a,t)}{const a=await WebAssembly.instantiate(e,t);return a instanceof WebAssembly.Instance?{instance:a,module:e}:a}}(await e,t);return __wbg_finalize_init(a,r)}class ColorSpace{static#r=new Uint8ClampedArray(3);constructor(e,t){this.name=e;this.numComps=t}getRgb(e,t,a=new Uint8ClampedArray(3)){this.getRgbItem(e,t,a,0);return a}getRgbHex(e,t){const a=this.getRgb(e,t,ColorSpace.#r);return Util.makeHexColor(a[0],a[1],a[2])}\
-getRgbItem(e,t,a,r){unreachable("Should not call ColorSpace.getRgbItem")}getRgbBuffer(e,t,a,r,i,n,s){unreachable("Should not call ColorSpace.getRgbBuffer")}getOutputLength(e,t){unreachable("Should not call ColorSpace.getOutputLength")}isPassthrough(e){return!1}isDefaultDecode(e,t){return ColorSpace.isDefaultDecode(e,this.numComps)}fillRgb(e,t,a,r,i,n,s,o,c){const l=t*a;let h=null;const u=1<<s,d=a!==i||t!==r;if(this.isPassthrough(s))h=o;else if(1===this.numComps&&l>u&&"DeviceGray"!==this.name&&"D\
-eviceRGB"!==this.name){const t=s<=8?new Uint8Array(u):new Uint16Array(u);for(let e=0;e<u;e++)t[e]=e;const a=new Uint8ClampedArray(3*u);this.getRgbBuffer(t,0,u,a,0,s,0);if(d){h=new Uint8Array(3*l);let e=0;for(let t=0;t<l;++t){const r=3*o[t];h[e++]=a[r];h[e++]=a[r+1];h[e++]=a[r+2]}}else{let t=0;for(let r=0;r<l;++r){const i=3*o[r];e[t++]=a[i];e[t++]=a[i+1];e[t++]=a[i+2];t+=c}}}else if(d){h=new Uint8ClampedArray(3*l);this.getRgbBuffer(o,0,l,h,0,s,0)}else this.getRgbBuffer(o,0,r*n,e,0,s,c);if(h)if(d)\
-!function resizeRgbImage(e,t,a,r,i,n,s){s=1!==s?0:s;const o=a/i,c=r/n;let l,h=0;const u=new Uint16Array(i),d=3*a;for(let e=0;e<i;e++)u[e]=3*Math.floor(e*o);for(let a=0;a<n;a++){const r=Math.floor(a*c)*d;for(let a=0;a<i;a++){l=r+u[a];t[h++]=e[l++];t[h++]=e[l++];t[h++]=e[l++];h+=s}}}(h,e,t,a,r,i,c);else{let t=0,a=0;for(let i=0,s=r*n;i<s;i++){e[t++]=h[a++];e[t++]=h[a++];e[t++]=h[a++];t+=c}}}get usesZeroToOneRange(){return shadow(this,"usesZeroToOneRange",!0)}static isDefaultDecode(e,t){if(!Array.is\
-Array(e))return!0;if(2*t!==e.length){warn("The decode map is not the correct length");return!0}for(let t=0,a=e.length;t<a;t+=2)if(0!==e[t]||1!==e[t+1])return!1;return!0}}class AlternateCS extends ColorSpace{constructor(e,t,a){super("Alternate",e);this.base=t;this.tintFn=a;this.tmpBuf=new Float32Array(t.numComps)}getRgbItem(e,t,a,r){const i=this.tmpBuf;this.tintFn(e,t,i,0);this.base.getRgbItem(i,0,a,r)}getRgbBuffer(e,t,a,r,i,n,s){const o=this.tintFn,c=this.base,l=1/((1<<n)-1),h=c.numComps,u=c.use\
-sZeroToOneRange,d=(c.isPassthrough(8)||!u)&&0===s;let f=d?i:0;const g=d?r:new Uint8ClampedArray(h*a),p=this.numComps,m=new Float32Array(p),b=new Float32Array(h);let y,w;for(y=0;y<a;y++){for(w=0;w<p;w++)m[w]=e[t++]*l;o(m,0,b,0);if(u)for(w=0;w<h;w++)g[f++]=255*b[w];else{c.getRgbItem(b,0,g,f);f+=h}}d||c.getRgbBuffer(g,0,a,r,i,8,s)}getOutputLength(e,t){return this.base.getOutputLength(e*this.base.numComps/this.numComps,t)}}class PatternCS extends ColorSpace{constructor(e){super("Pattern",null);this.\
-base=e}isDefaultDecode(e,t){unreachable("Should not call PatternCS.isDefaultDecode")}}class IndexedCS extends ColorSpace{constructor(e,t,a){super("Indexed",1);this.base=e;this.highVal=t;const r=e.numComps*(t+1);this.lookup=new Uint8Array(r);if(a instanceof BaseStream){const e=a.getBytes(r);this.lookup.set(e)}else{if("string"!=typeof a)throw new FormatError(`IndexedCS - unrecognized lookup table: ${a}`);for(let e=0;e<r;++e)this.lookup[e]=255&a.charCodeAt(e)}}getRgbItem(e,t,a,r){const{base:i,highV\
-al:n,lookup:s}=this,o=MathClamp(Math.round(e[t]),0,n)*i.numComps;i.getRgbBuffer(s,o,1,a,r,8,0)}getRgbBuffer(e,t,a,r,i,n,s){const{base:o,highVal:c,lookup:l}=this,{numComps:h}=o,u=o.getOutputLength(h,s);for(let n=0;n<a;++n){const a=MathClamp(Math.round(e[t++]),0,c)*h;o.getRgbBuffer(l,a,1,r,i,8,s);i+=u}}getOutputLength(e,t){return this.base.getOutputLength(e*this.base.numComps,t)}isDefaultDecode(e,t){if(!Array.isArray(e))return!0;if(2!==e.length){warn("Decode map length is not correct");return!0}if\
-(!Number.isInteger(t)||t<1){warn("Bits per component is not correct");return!0}return 0===e[0]&&e[1]===(1<<t)-1}}class DeviceGrayCS extends ColorSpace{constructor(){super("DeviceGray",1)}getRgbItem(e,t,a,r){const i=255*e[t];a[r]=a[r+1]=a[r+2]=i}getRgbBuffer(e,t,a,r,i,n,s){const o=255/((1<<n)-1);let c=t,l=i;for(let t=0;t<a;++t){const t=o*e[c++];r[l++]=t;r[l++]=t;r[l++]=t;l+=s}}getOutputLength(e,t){return e*(3+t)}}class DeviceRgbCS extends ColorSpace{constructor(){super("DeviceRGB",3)}getRgbItem(e\
-,t,a,r){a[r]=255*e[t];a[r+1]=255*e[t+1];a[r+2]=255*e[t+2]}getRgbBuffer(e,t,a,r,i,n,s){if(8===n&&0===s){r.set(e.subarray(t,t+3*a),i);return}const o=255/((1<<n)-1);let c=t,l=i;for(let t=0;t<a;++t){r[l++]=o*e[c++];r[l++]=o*e[c++];r[l++]=o*e[c++];l+=s}}getOutputLength(e,t){return e*(3+t)/3|0}isPassthrough(e){return 8===e}}class DeviceRgbaCS extends ColorSpace{constructor(){super("DeviceRGBA",4)}getOutputLength(e,t){return 4*e}isPassthrough(e){return 8===e}fillRgb(e,t,a,r,i,n,s,o,c){a!==i||t!==r?func\
-tion resizeRgbaImage(e,t,a,r,i,n,s){const o=a/i,c=r/n;let l=0;const h=new Uint16Array(i);if(1===s){for(let e=0;e<i;e++)h[e]=Math.floor(e*o);const r=new Uint32Array(e.buffer),s=new Uint32Array(t.buffer),u=FeatureTest.isLittleEndian?16777215:4294967040;for(let e=0;e<n;e++){const t=r.subarray(Math.floor(e*c)*a);for(let e=0;e<i;e++)s[l++]|=t[h[e]]&u}}else{const r=4,s=a*r;for(let e=0;e<i;e++)h[e]=Math.floor(e*o)*r;for(let a=0;a<n;a++){const r=e.subarray(Math.floor(a*c)*s);for(let e=0;e<i;e++){const a\
-=h[e];t[l++]=r[a];t[l++]=r[a+1];t[l++]=r[a+2]}}}}(o,e,t,a,r,i,c):function copyRgbaImage(e,t,a){if(1===a){const a=new Uint32Array(e.buffer),r=new Uint32Array(t.buffer),i=FeatureTest.isLittleEndian?16777215:4294967040;for(let e=0,t=a.length;e<t;e++)r[e]|=a[e]&i}else{let a=0;for(let r=0,i=e.length;r<i;r+=4){t[a++]=e[r];t[a++]=e[r+1];t[a++]=e[r+2]}}}(o,e,c)}}class DeviceCmykCS extends ColorSpace{constructor(){super("DeviceCMYK",4)}#i(e,t,a,r,i){const n=e[t]*a,s=e[t+1]*a,o=e[t+2]*a,c=e[t+3]*a;r[i]=25\
-5+n*(-4.387332384609988*n+54.48615194189176*s+18.82290502165302*o+212.25662451639585*c-285.2331026137004)+s*(1.7149763477362134*s-5.6096736904047315*o+-17.873870861415444*c-5.497006427196366)+o*(-2.5217340131683033*o-21.248923337353073*c+17.5119270841813)+c*(-21.86122147463605*c-189.48180835922747);r[i+1]=255+n*(8.841041422036149*n+60.118027045597366*s+6.871425592049007*o+31.159100130055922*c-79.2970844816548)+s*(-15.310361306967817*s+17.575251261109482*o+131.35250912493976*c-190.9453302588951)+\
-o*(4.444339102852739*o+9.8632861493405*c-24.86741582555878)+c*(-20.737325471181034*c-187.80453709719578);r[i+2]=255+n*(.8842522430003296*n+8.078677503112928*s+30.89978309703729*o-.23883238689178934*c-14.183576799673286)+s*(10.49593273432072*s+63.02378494754052*o+50.606957656360734*c-112.23884253719248)+o*(.03296041114873217*o+115.60384449646641*c-193.58209356861505)+c*(-22.33816807309886*c-180.12613974708367)}getRgbItem(e,t,a,r){this.#i(e,t,1,a,r)}getRgbBuffer(e,t,a,r,i,n,s){const o=1/((1<<n)-1)\
-;for(let n=0;n<a;n++){this.#i(e,t,o,r,i);t+=4;i+=3+s}}getOutputLength(e,t){return e/4*(3+t)|0}}class CalGrayCS extends ColorSpace{constructor(e,t,a){super("CalGray",1);if(!e)throw new FormatError("WhitePoint missing - required for color space CalGray");[this.XW,this.YW,this.ZW]=e;[this.XB,this.YB,this.ZB]=t||[0,0,0];this.G=a||1;if(this.XW<0||this.ZW<0||1!==this.YW)throw new FormatError(`Invalid WhitePoint components for ${this.name}, no fallback available`);if(this.XB<0||this.YB<0||this.ZB<0){in\
-fo(`Invalid BlackPoint for ${this.name}, falling back to default.`);this.XB=this.YB=this.ZB=0}0===this.XB&&0===this.YB&&0===this.ZB||warn(`${this.name}, BlackPoint: XB: ${this.XB}, YB: ${this.YB}, ZB: ${this.ZB}, only default values are supported.`);if(this.G<1){info(`Invalid Gamma: ${this.G} for ${this.name}, falling back to default.`);this.G=1}}#i(e,t,a,r,i){const n=(e[t]*i)**this.G,s=this.YW*n,o=Math.max(295.8*s**.3333333333333333-40.8,0);a[r]=o;a[r+1]=o;a[r+2]=o}getRgbItem(e,t,a,r){this.#i(e\
-,t,a,r,1)}getRgbBuffer(e,t,a,r,i,n,s){const o=1/((1<<n)-1);for(let n=0;n<a;++n){this.#i(e,t,r,i,o);t+=1;i+=3+s}}getOutputLength(e,t){return e*(3+t)}}class CalRGBCS extends ColorSpace{static#n=new Float32Array([.8951,.2664,-.1614,-.7502,1.7135,.0367,.0389,-.0685,1.0296]);static#s=new Float32Array([.9869929,-.1470543,.1599627,.4323053,.5183603,.0492912,-.0085287,.0400428,.9684867]);static#o=new Float32Array([3.2404542,-1.5371385,-.4985314,-.969266,1.8760108,.041556,.0556434,-.2040259,1.0572252]);s\
-tatic#c=new Float32Array([1,1,1]);static#l=new Float32Array(3);static#h=new Float32Array(3);static#u=new Float32Array(3);static#d=(24/116)**3/8;constructor(e,t,a,r){super("CalRGB",3);if(!e)throw new FormatError("WhitePoint missing - required for color space CalRGB");const[i,n,s]=this.whitePoint=e,[o,c,l]=this.blackPoint=t||new Float32Array(3);[this.GR,this.GG,this.GB]=a||new Float32Array([1,1,1]);[this.MXA,this.MYA,this.MZA,this.MXB,this.MYB,this.MZB,this.MXC,this.MYC,this.MZC]=r||new Float32Arr\
-ay([1,0,0,0,1,0,0,0,1]);if(i<0||s<0||1!==n)throw new FormatError(`Invalid WhitePoint components for ${this.name}, no fallback available`);if(o<0||c<0||l<0){info(`Invalid BlackPoint for ${this.name} [${o}, ${c}, ${l}], falling back to default.`);this.blackPoint=new Float32Array(3)}if(this.GR<0||this.GG<0||this.GB<0){info(`Invalid Gamma [${this.GR}, ${this.GG}, ${this.GB}] for ${this.name}, falling back to default.`);this.GR=this.GG=this.GB=1}}#f(e,t,a){a[0]=e[0]*t[0]+e[1]*t[1]+e[2]*t[2];a[1]=e[3]\
-*t[0]+e[4]*t[1]+e[5]*t[2];a[2]=e[6]*t[0]+e[7]*t[1]+e[8]*t[2]}#g(e,t,a){a[0]=1*t[0]/e[0];a[1]=1*t[1]/e[1];a[2]=1*t[2]/e[2]}#p(e,t,a){a[0]=.95047*t[0]/e[0];a[1]=1*t[1]/e[1];a[2]=1.08883*t[2]/e[2]}#m(e){return e<=.0031308?MathClamp(12.92*e,0,1):e>=.99554525?1:MathClamp(1.055*e**(1/2.4)-.055,0,1)}#b(e){return e<0?-this.#b(-e):e>8?((e+16)/116)**3:e*CalRGBCS.#d}#y(e,t,a){if(0===e[0]&&0===e[1]&&0===e[2]){a[0]=t[0];a[1]=t[1];a[2]=t[2];return}const r=this.#b(0),i=(1-r)/(1-this.#b(e[0])),n=1-i,s=(1-r)/(1-\
-this.#b(e[1])),o=1-s,c=(1-r)/(1-this.#b(e[2])),l=1-c;a[0]=t[0]*i+n;a[1]=t[1]*s+o;a[2]=t[2]*c+l}#w(e,t,a){if(1===e[0]&&1===e[2]){a[0]=t[0];a[1]=t[1];a[2]=t[2];return}const r=a;this.#f(CalRGBCS.#n,t,r);const i=CalRGBCS.#l;this.#g(e,r,i);this.#f(CalRGBCS.#s,i,a)}#x(e,t,a){const r=a;this.#f(CalRGBCS.#n,t,r);const i=CalRGBCS.#l;this.#p(e,r,i);this.#f(CalRGBCS.#s,i,a)}#i(e,t,a,r,i){const n=MathClamp(e[t]*i,0,1),s=MathClamp(e[t+1]*i,0,1),o=MathClamp(e[t+2]*i,0,1),c=1===n?1:n**this.GR,l=1===s?1:s**this.\
-GG,h=1===o?1:o**this.GB,u=this.MXA*c+this.MXB*l+this.MXC*h,d=this.MYA*c+this.MYB*l+this.MYC*h,f=this.MZA*c+this.MZB*l+this.MZC*h,g=CalRGBCS.#h;g[0]=u;g[1]=d;g[2]=f;const p=CalRGBCS.#u;this.#w(this.whitePoint,g,p);const m=CalRGBCS.#h;this.#y(this.blackPoint,p,m);const b=CalRGBCS.#u;this.#x(CalRGBCS.#c,m,b);const y=CalRGBCS.#h;this.#f(CalRGBCS.#o,b,y);a[r]=255*this.#m(y[0]);a[r+1]=255*this.#m(y[1]);a[r+2]=255*this.#m(y[2])}getRgbItem(e,t,a,r){this.#i(e,t,a,r,1)}getRgbBuffer(e,t,a,r,i,n,s){const o=\
-1/((1<<n)-1);for(let n=0;n<a;++n){this.#i(e,t,r,i,o);t+=3;i+=3+s}}getOutputLength(e,t){return e*(3+t)/3|0}}class LabCS extends ColorSpace{constructor(e,t,a){super("Lab",3);if(!e)throw new FormatError("WhitePoint missing - required for color space Lab");[this.XW,this.YW,this.ZW]=e;[this.amin,this.amax,this.bmin,this.bmax]=a||[-100,100,-100,100];[this.XB,this.YB,this.ZB]=t||[0,0,0];if(this.XW<0||this.ZW<0||1!==this.YW)throw new FormatError("Invalid WhitePoint components, no fallback available");if\
-(this.XB<0||this.YB<0||this.ZB<0){info("Invalid BlackPoint, falling back to default");this.XB=this.YB=this.ZB=0}if(this.amin>this.amax||this.bmin>this.bmax){info("Invalid Range, falling back to defaults");this.amin=-100;this.amax=100;this.bmin=-100;this.bmax=100}}#S(e){return e>=6/29?e**3:108/841*(e-4/29)}#A(e,t,a,r){return a+e*(r-a)/t}#i(e,t,a,r,i){let n=e[t],s=e[t+1],o=e[t+2];if(!1!==a){n=this.#A(n,a,0,100);s=this.#A(s,a,this.amin,this.amax);o=this.#A(o,a,this.bmin,this.bmax)}s>this.amax?s=thi\
-s.amax:s<this.amin&&(s=this.amin);o>this.bmax?o=this.bmax:o<this.bmin&&(o=this.bmin);const c=(n+16)/116,l=c+s/500,h=c-o/200,u=this.XW*this.#S(l),d=this.YW*this.#S(c),f=this.ZW*this.#S(h);let g,p,m;if(this.ZW<1){g=3.1339*u+-1.617*d+-.4906*f;p=-.9785*u+1.916*d+.0333*f;m=.072*u+-.229*d+1.4057*f}else{g=3.2406*u+-1.5372*d+-.4986*f;p=-.9689*u+1.8758*d+.0415*f;m=.0557*u+-.204*d+1.057*f}r[i]=255*Math.sqrt(g);r[i+1]=255*Math.sqrt(p);r[i+2]=255*Math.sqrt(m)}getRgbItem(e,t,a,r){this.#i(e,t,!1,a,r)}getRgbBu\
-ffer(e,t,a,r,i,n,s){const o=(1<<n)-1;for(let n=0;n<a;n++){this.#i(e,t,o,r,i);t+=3;i+=3+s}}getOutputLength(e,t){return e*(3+t)/3|0}isDefaultDecode(e,t){return!0}get usesZeroToOneRange(){return shadow(this,"usesZeroToOneRange",!1)}}function fetchSync(e){const t=new XMLHttpRequest;t.open("GET",e,!1);t.responseType="arraybuffer";t.send(null);return t.response}class IccColorSpace extends ColorSpace{#k;#C;static#v=!0;static#F=null;static#I=new FinalizationRegistry((e=>{!function qcms_drop_transformer(\
-e){Da.qcms_drop_transformer(e)}(e)}));constructor(e,t,a){if(!IccColorSpace.isUsable)throw new Error("No ICC color space support");super(t,a);let r;switch(a){case 1:r=Ea.Gray8;this.#C=(e,t,a)=>function qcms_convert_one(e,t,a){Da.qcms_convert_one(e,t,a)}(this.#k,255*e[t],a);break;case 3:r=Ea.RGB8;this.#C=(e,t,a)=>function qcms_convert_three(e,t,a,r,i){Da.qcms_convert_three(e,t,a,r,i)}(this.#k,255*e[t],255*e[t+1],255*e[t+2],a);break;case 4:r=Ea.CMYK;this.#C=(e,t,a)=>function qcms_convert_four(e,t,a\
-,r,i,n){Da.qcms_convert_four(e,t,a,r,i,n)}(this.#k,255*e[t],255*e[t+1],255*e[t+2],255*e[t+3],a);break;default:throw new Error(`Unsupported number of components: ${a}`)}this.#k=function qcms_transformer_from_memory(e,t,a){const r=passArray8ToWasm0(e,Da.__wbindgen_malloc),i=Na;return Da.qcms_transformer_from_memory(r,i,t,a)>>>0}(e,r,Pa.Perceptual);if(!this.#k)throw new Error("Failed to create ICC color space");IccColorSpace.#I.register(this,this.#k)}getRgbHex(e,t){this.#C(e,t,!0);return QCMS._cssC\
-olor}getRgbItem(e,t,a,r){QCMS._destBuffer=a;QCMS._destOffset=r;QCMS._destLength=3;this.#C(e,t,!1);QCMS._destBuffer=null}getRgbBuffer(e,t,a,r,i,n,s){e=e.subarray(t,t+a*this.numComps);if(8!==n){const t=255/((1<<n)-1);for(let a=0,r=e.length;a<r;a++)e[a]*=t}QCMS._mustAddAlpha=s&&r.buffer===e.buffer;QCMS._destBuffer=r;QCMS._destOffset=i;QCMS._destLength=a*(3+s);!function qcms_convert_array(e,t){const a=passArray8ToWasm0(t,Da.__wbindgen_malloc),r=Na;Da.qcms_convert_array(e,a,r)}(this.#k,e);QCMS._mustA\
-ddAlpha=!1;QCMS._destBuffer=null}getOutputLength(e,t){return e/this.numComps*(3+t)|0}static setOptions({useWasm:e,useWorkerFetch:t,wasmUrl:a}){if(t){this.#v=e;this.#F=a}else this.#v=!1}static get isUsable(){let e=!1;if(this.#v)if(this.#F)try{this._module=function initSync(e){if(void 0!==Da)return Da;void 0!==e&&(Object.getPrototypeOf(e)===Object.prototype?({module:e}=e):console.warn("using deprecated parameters for `initSync()`; pass a single object instead"));const t=__wbg_get_imports();e insta\
-nceof WebAssembly.Module||(e=new WebAssembly.Module(e));return __wbg_finalize_init(new WebAssembly.Instance(e,t),e)}({module:fetchSync(`${this.#F}qcms_bg.wasm`)});e=!!this._module;QCMS._memory=this._module.memory;QCMS._makeHexColor=Util.makeHexColor}catch(e){warn(`ICCBased color space: "${e}".`)}else warn("No ICC color space support due to missing `wasmUrl` API option");return shadow(this,"isUsable",e)}}class CmykICCBasedCS extends IccColorSpace{static#T;constructor(){super(new Uint8Array(fetchS\
-ync(`${CmykICCBasedCS.#T}CGATS001Compat-v2-micro.icc`)),"DeviceCMYK",4)}static setOptions({iccUrl:e}){this.#T=e}static get isUsable(){let e=!1;IccColorSpace.isUsable&&(this.#T?e=!0:warn("No CMYK ICC profile support due to missing `iccUrl` API option"));return shadow(this,"isUsable",e)}}class Stream extends BaseStream{constructor(e,t,a,r){super();this.bytes=e instanceof Uint8Array?e:new Uint8Array(e);this.start=t||0;this.pos=this.start;this.end=t+a||this.bytes.length;this.dict=r}get length(){retu\
-rn this.end-this.start}get isEmpty(){return 0===this.length}getByte(){return this.pos>=this.end?-1:this.bytes[this.pos++]}getBytes(e){const t=this.bytes,a=this.pos,r=this.end;if(!e)return t.subarray(a,r);let i=a+e;i>r&&(i=r);this.pos=i;return t.subarray(a,i)}getByteRange(e,t){e<0&&(e=0);t>this.end&&(t=this.end);return this.bytes.subarray(e,t)}reset(){this.pos=this.start}moveStart(){this.start=this.pos}makeSubStream(e,t,a=null){return new Stream(this.bytes.buffer,e,t,a)}}class StringStream extend\
-s Stream{constructor(e){super(stringToBytes(e))}}class NullStream extends Stream{constructor(){super(new Uint8Array(0))}}class ChunkedStream extends Stream{constructor(e,t,a){super(new Uint8Array(e),0,e,null);this.chunkSize=t;this._loadedChunks=new Set;this.numChunks=Math.ceil(e/t);this.manager=a;this.progressiveDataLength=0;this.lastSuccessfulEnsureByteChunk=-1}getMissingChunks(){const e=[];for(let t=0,a=this.numChunks;t<a;++t)this._loadedChunks.has(t)||e.push(t);return e}get numChunksLoaded(){\
-return this._loadedChunks.size}get isDataLoaded(){return this.numChunksLoaded===this.numChunks}onReceiveData(e,t){const a=this.chunkSize;if(e%a!=0)throw new Error(`Bad begin offset: ${e}`);const r=e+t.byteLength;if(r%a!=0&&r!==this.bytes.length)throw new Error(`Bad end offset: ${r}`);this.bytes.set(new Uint8Array(t),e);const i=Math.floor(e/a),n=Math.floor((r-1)/a)+1;for(let e=i;e<n;++e)this._loadedChunks.add(e)}onReceiveProgressiveData(e){let t=this.progressiveDataLength;const a=Math.floor(t/thi\
-s.chunkSize);this.bytes.set(new Uint8Array(e),t);t+=e.byteLength;this.progressiveDataLength=t;const r=t>=this.end?this.numChunks:Math.floor(t/this.chunkSize);for(let e=a;e<r;++e)this._loadedChunks.add(e)}ensureByte(e){if(e<this.progressiveDataLength)return;const t=Math.floor(e/this.chunkSize);if(!(t>this.numChunks)&&t!==this.lastSuccessfulEnsureByteChunk){if(!this._loadedChunks.has(t))throw new MissingDataException(e,e+1);this.lastSuccessfulEnsureByteChunk=t}}ensureRange(e,t){if(e>=t)return;if(t\
-<=this.progressiveDataLength)return;const a=Math.floor(e/this.chunkSize);if(a>this.numChunks)return;const r=Math.min(Math.floor((t-1)/this.chunkSize)+1,this.numChunks);for(let i=a;i<r;++i)if(!this._loadedChunks.has(i))throw new MissingDataException(e,t)}nextEmptyChunk(e){const t=this.numChunks;for(let a=0;a<t;++a){const r=(e+a)%t;if(!this._loadedChunks.has(r))return r}return null}hasChunk(e){return this._loadedChunks.has(e)}getByte(){const e=this.pos;if(e>=this.end)return-1;e>=this.progressiveDa\
-taLength&&this.ensureByte(e);return this.bytes[this.pos++]}getBytes(e){const t=this.bytes,a=this.pos,r=this.end;if(!e){r>this.progressiveDataLength&&this.ensureRange(a,r);return t.subarray(a,r)}let i=a+e;i>r&&(i=r);i>this.progressiveDataLength&&this.ensureRange(a,i);this.pos=i;return t.subarray(a,i)}getByteRange(e,t){e<0&&(e=0);t>this.end&&(t=this.end);t>this.progressiveDataLength&&this.ensureRange(e,t);return this.bytes.subarray(e,t)}makeSubStream(e,t,a=null){t?e+t>this.progressiveDataLength&&t\
-his.ensureRange(e,e+t):e>=this.progressiveDataLength&&this.ensureByte(e);function ChunkedStreamSubstream(){}ChunkedStreamSubstream.prototype=Object.create(this);ChunkedStreamSubstream.prototype.getMissingChunks=function(){const e=this.chunkSize,t=Math.floor(this.start/e),a=Math.floor((this.end-1)/e)+1,r=[];for(let e=t;e<a;++e)this._loadedChunks.has(e)||r.push(e);return r};Object.defineProperty(ChunkedStreamSubstream.prototype,"isDataLoaded",{get(){return this.numChunksLoaded===this.numChunks||0=\
-==this.getMissingChunks().length},configurable:!0});const r=new ChunkedStreamSubstream;r.pos=r.start=e;r.end=e+t||this.end;r.dict=a;return r}getBaseStreams(){return[this]}}class ChunkedStreamManager{constructor(e,t){this.length=t.length;this.chunkSize=t.rangeChunkSize;this.stream=new ChunkedStream(this.length,this.chunkSize,this);this.pdfNetworkStream=e;this.disableAutoFetch=t.disableAutoFetch;this.msgHandler=t.msgHandler;this.currRequestId=0;this._chunksNeededByRequest=new Map;this._requestsByC\
-hunk=new Map;this._promisesByRequest=new Map;this.progressiveDataLength=0;this.aborted=!1;this._loadedStreamCapability=Promise.withResolvers()}sendRequest(e,t){const a=this.pdfNetworkStream.getRangeReader(e,t);a.isStreamingSupported||(a.onProgress=this.onProgress.bind(this));let r=[],i=0;return new Promise(((e,t)=>{const readChunk=({value:n,done:s})=>{try{if(s){const t=arrayBuffersToBytes(r);r=null;e(t);return}i+=n.byteLength;a.isStreamingSupported&&this.onProgress({loaded:i});r.push(n);a.read()\
-.then(readChunk,t)}catch(e){t(e)}};a.read().then(readChunk,t)})).then((t=>{this.aborted||this.onReceiveData({chunk:t,begin:e})}))}requestAllChunks(e=!1){if(!e){const e=this.stream.getMissingChunks();this._requestChunks(e)}return this._loadedStreamCapability.promise}_requestChunks(e){const t=this.currRequestId++,a=new Set;this._chunksNeededByRequest.set(t,a);for(const t of e)this.stream.hasChunk(t)||a.add(t);if(0===a.size)return Promise.resolve();const r=Promise.withResolvers();this._promisesByRe\
-quest.set(t,r);const i=[];for(const e of a){let a=this._requestsByChunk.get(e);if(!a){a=[];this._requestsByChunk.set(e,a);i.push(e)}a.push(t)}if(i.length>0){const e=this.groupChunks(i);for(const t of e){const e=t.beginChunk*this.chunkSize,a=Math.min(t.endChunk*this.chunkSize,this.length);this.sendRequest(e,a).catch(r.reject)}}return r.promise.catch((e=>{if(!this.aborted)throw e}))}getStream(){return this.stream}requestRange(e,t){t=Math.min(t,this.length);const a=this.getBeginChunk(e),r=this.getE\
-ndChunk(t),i=[];for(let e=a;e<r;++e)i.push(e);return this._requestChunks(i)}requestRanges(e=[]){const t=[];for(const a of e){const e=this.getBeginChunk(a.begin),r=this.getEndChunk(a.end);for(let a=e;a<r;++a)t.includes(a)||t.push(a)}t.sort(((e,t)=>e-t));return this._requestChunks(t)}groupChunks(e){const t=[];let a=-1,r=-1;for(let i=0,n=e.length;i<n;++i){const n=e[i];a<0&&(a=n);if(r>=0&&r+1!==n){t.push({beginChunk:a,endChunk:r+1});a=n}i+1===e.length&&t.push({beginChunk:a,endChunk:n+1});r=n}return \
-t}onProgress(e){this.msgHandler.send("DocProgress",{loaded:this.stream.numChunksLoaded*this.chunkSize+e.loaded,total:this.length})}onReceiveData(e){const t=e.chunk,a=void 0===e.begin,r=a?this.progressiveDataLength:e.begin,i=r+t.byteLength,n=Math.floor(r/this.chunkSize),s=i<this.length?Math.floor(i/this.chunkSize):Math.ceil(i/this.chunkSize);if(a){this.stream.onReceiveProgressiveData(t);this.progressiveDataLength=i}else this.stream.onReceiveData(r,t);this.stream.isDataLoaded&&this._loadedStreamCa\
-pability.resolve(this.stream);const o=[];for(let e=n;e<s;++e){const t=this._requestsByChunk.get(e);if(t){this._requestsByChunk.delete(e);for(const a of t){const t=this._chunksNeededByRequest.get(a);t.has(e)&&t.delete(e);t.size>0||o.push(a)}}}if(!this.disableAutoFetch&&0===this._requestsByChunk.size){let e;if(1===this.stream.numChunksLoaded){const t=this.stream.numChunks-1;this.stream.hasChunk(t)||(e=t)}else e=this.stream.nextEmptyChunk(s);Number.isInteger(e)&&this._requestChunks([e])}for(const e\
- of o){const t=this._promisesByRequest.get(e);this._promisesByRequest.delete(e);t.resolve()}this.msgHandler.send("DocProgress",{loaded:this.stream.numChunksLoaded*this.chunkSize,total:this.length})}onError(e){this._loadedStreamCapability.reject(e)}getBeginChunk(e){return Math.floor(e/this.chunkSize)}getEndChunk(e){return Math.floor((e-1)/this.chunkSize)+1}abort(e){this.aborted=!0;this.pdfNetworkStream?.cancelAllRequests(e);for(const t of this._promisesByRequest.values())t.reject(e)}}function con\
-vertToRGBA(e){switch(e.kind){case k:return convertBlackAndWhiteToRGBA(e);case C:return function convertRGBToRGBA({src:e,srcPos:t=0,dest:a,destPos:r=0,width:i,height:n}){let s=0;const o=i*n*3,c=o>>2,l=new Uint32Array(e.buffer,t,c);if(FeatureTest.isLittleEndian){for(;s<c-2;s+=3,r+=4){const e=l[s],t=l[s+1],i=l[s+2];a[r]=4278190080|e;a[r+1]=e>>>24|t<<8|4278190080;a[r+2]=t>>>16|i<<16|4278190080;a[r+3]=i>>>8|4278190080}for(let i=4*s,n=t+o;i<n;i+=3)a[r++]=e[i]|e[i+1]<<8|e[i+2]<<16|4278190080}else{for(;\
-s<c-2;s+=3,r+=4){const e=l[s],t=l[s+1],i=l[s+2];a[r]=255|e;a[r+1]=e<<24|t>>>8|255;a[r+2]=t<<16|i>>>16|255;a[r+3]=i<<8|255}for(let i=4*s,n=t+o;i<n;i+=3)a[r++]=e[i]<<24|e[i+1]<<16|e[i+2]<<8|255}return{srcPos:t+o,destPos:r}}(e)}return null}function convertBlackAndWhiteToRGBA({src:e,srcPos:t=0,dest:a,width:r,height:i,nonBlackColor:n=4294967295,inverseDecode:s=!1}){const o=FeatureTest.isLittleEndian?4278190080:255,[c,l]=s?[n,o]:[o,n],h=r>>3,u=7&r,d=e.length;a=new Uint32Array(a.buffer);let f=0;for(let\
- r=0;r<i;r++){for(const r=t+h;t<r;t++){const r=t<d?e[t]:255;a[f++]=128&r?l:c;a[f++]=64&r?l:c;a[f++]=32&r?l:c;a[f++]=16&r?l:c;a[f++]=8&r?l:c;a[f++]=4&r?l:c;a[f++]=2&r?l:c;a[f++]=1&r?l:c}if(0===u)continue;const r=t<d?e[t++]:255;for(let e=0;e<u;e++)a[f++]=r&1<<7-e?l:c}return{srcPos:t,destPos:f}}class ImageResizer{static#O=2048;static#M=FeatureTest.isImageDecoderSupported;constructor(e,t){this._imgData=e;this._isMask=t}static get canUseImageDecoder(){return shadow(this,"canUseImageDecoder",this.#M?I\
-mageDecoder.isTypeSupported("image/bmp"):Promise.resolve(!1))}static needsToBeResized(e,t){if(e<=this.#O&&t<=this.#O)return!1;const{MAX_DIM:a}=this;if(e>a||t>a)return!0;const r=e*t;if(this._hasMaxArea)return r>this.MAX_AREA;if(r<this.#O**2)return!1;if(this._areGoodDims(e,t)){this.#O=Math.max(this.#O,Math.floor(Math.sqrt(e*t)));return!1}this.#O=this._guessMax(this.#O,a,128,0);return r>(this.MAX_AREA=this.#O**2)}static getReducePowerForJPX(e,t,a){const r=e*t,i=2**30/(4*a);if(!this.needsToBeResized\
-(e,t))return r>i?Math.ceil(Math.log2(r/i)):0;const{MAX_DIM:n,MAX_AREA:s}=this,o=Math.max(e/n,t/n,Math.sqrt(r/Math.min(i,s)));return Math.ceil(Math.log2(o))}static get MAX_DIM(){return shadow(this,"MAX_DIM",this._guessMax(2048,65537,0,1))}static get MAX_AREA(){this._hasMaxArea=!0;return shadow(this,"MAX_AREA",this._guessMax(this.#O,this.MAX_DIM,128,0)**2)}static set MAX_AREA(e){if(e>=0){this._hasMaxArea=!0;shadow(this,"MAX_AREA",e)}}static setOptions({canvasMaxAreaInBytes:e=-1,isImageDecoderSuppo\
-rted:t=!1}){this._hasMaxArea||(this.MAX_AREA=e>>2);this.#M=t}static _areGoodDims(e,t){try{const a=new OffscreenCanvas(e,t),r=a.getContext("2d");r.fillRect(0,0,1,1);const i=r.getImageData(0,0,1,1).data[3];a.width=a.height=1;return 0!==i}catch{return!1}}static _guessMax(e,t,a,r){for(;e+a+1<t;){const a=Math.floor((e+t)/2),i=r||a;this._areGoodDims(a,i)?e=a:t=a}return e}static async createImage(e,t=!1){return new ImageResizer(e,t)._createImage()}async _createImage(){const{_imgData:e}=this,{width:t,he\
-ight:a}=e;if(t*a*4>va){const e=this.#D();if(e)return e}const r=this._encodeBMP();let i,n;if(await ImageResizer.canUseImageDecoder){i=new ImageDecoder({data:r,type:"image/bmp",preferAnimation:!1,transfer:[r.buffer]});n=i.decode().catch((e=>{warn(`BMP image decoding failed: ${e}`);return createImageBitmap(new Blob([this._encodeBMP().buffer],{type:"image/bmp"}))})).finally((()=>{i.close()}))}else n=createImageBitmap(new Blob([r.buffer],{type:"image/bmp"}));const{MAX_AREA:s,MAX_DIM:o}=ImageResizer,c\
-=Math.max(t/o,a/o,Math.sqrt(t*a/s)),l=Math.max(c,2),h=Math.round(10*(c+1.25))/10/l,u=Math.floor(Math.log2(h)),d=new Array(u+2).fill(2);d[0]=l;d.splice(-1,1,h/(1<<u));let f=t,g=a;const p=await n;let m=p.image||p;for(const e of d){const t=f,a=g;f=Math.floor(f/e)-1;g=Math.floor(g/e)-1;const r=new OffscreenCanvas(f,g);r.getContext("2d").drawImage(m,0,0,t,a,0,0,f,g);m.close();m=r.transferToImageBitmap()}e.data=null;e.bitmap=m;e.width=f;e.height=g;return e}#D(){const{_imgData:e}=this,{data:t,width:a,h\
-eight:r,kind:i}=e,n=a*r*4,s=Math.ceil(Math.log2(n/va)),o=a>>s,c=r>>s;let l,h=r;try{l=new Uint8Array(n)}catch{let e=Math.floor(Math.log2(n+1));for(;;)try{l=new Uint8Array(2**e-1);break}catch{e-=1}h=Math.floor((2**e-1)/(4*a));const t=a*h*4;t<l.length&&(l=new Uint8Array(t))}const u=new Uint32Array(l.buffer),d=new Uint32Array(o*c);let f=0,g=0;const p=Math.ceil(r/h),m=r%h==0?r:r%h;for(let e=0;e<p;e++){const r=e<p-1?h:m;({srcPos:f}=convertToRGBA({kind:i,src:t,dest:u,width:a,height:r,inverseDecode:this\
-._isMask,srcPos:f}));for(let e=0,t=r>>s;e<t;e++){const t=u.subarray((e<<s)*a);for(let e=0;e<o;e++)d[g++]=t[e<<s]}}if(ImageResizer.needsToBeResized(o,c)){e.data=d;e.width=o;e.height=c;e.kind=v;return null}const b=new OffscreenCanvas(o,c);b.getContext("2d",{willReadFrequently:!0}).putImageData(new ImageData(new Uint8ClampedArray(d.buffer),o,c),0,0);e.data=null;e.bitmap=b.transferToImageBitmap();e.width=o;e.height=c;return e}_encodeBMP(){const{width:e,height:t,kind:a}=this._imgData;let r,i=this._im\
-gData.data,n=new Uint8Array(0),s=n,o=0;switch(a){case k:{r=1;n=new Uint8Array(this._isMask?[255,255,255,255,0,0,0,0]:[0,0,0,0,255,255,255,255]);const a=e+7>>3,s=a+3&-4;if(a!==s){const e=new Uint8Array(s*t);let r=0;for(let n=0,o=t*a;n<o;n+=a,r+=s)e.set(i.subarray(n,n+a),r);i=e}break}case C:r=24;if(3&e){const a=3*e,r=a+3&-4,n=r-a,s=new Uint8Array(r*t);let o=0;for(let e=0,r=t*a;e<r;e+=a){const t=i.subarray(e,e+a);for(let e=0;e<a;e+=3){s[o++]=t[e+2];s[o++]=t[e+1];s[o++]=t[e]}o+=n}i=s}else for(let e=\
-0,t=i.length;e<t;e+=3){const t=i[e];i[e]=i[e+2];i[e+2]=t}break;case v:r=32;o=3;s=new Uint8Array(68);const a=new DataView(s.buffer);if(FeatureTest.isLittleEndian){a.setUint32(0,255,!0);a.setUint32(4,65280,!0);a.setUint32(8,16711680,!0);a.setUint32(12,4278190080,!0)}else{a.setUint32(0,4278190080,!0);a.setUint32(4,16711680,!0);a.setUint32(8,65280,!0);a.setUint32(12,255,!0)}break;default:throw new Error("invalid format")}let c=0;const l=40+s.length,h=14+l+n.length+i.length,u=new Uint8Array(h),d=new \
-DataView(u.buffer);d.setUint16(c,19778,!0);c+=2;d.setUint32(c,h,!0);c+=4;d.setUint32(c,0,!0);c+=4;d.setUint32(c,14+l+n.length,!0);c+=4;d.setUint32(c,l,!0);c+=4;d.setInt32(c,e,!0);c+=4;d.setInt32(c,-t,!0);c+=4;d.setUint16(c,1,!0);c+=2;d.setUint16(c,r,!0);c+=2;d.setUint32(c,o,!0);c+=4;d.setUint32(c,0,!0);c+=4;d.setInt32(c,0,!0);c+=4;d.setInt32(c,0,!0);c+=4;d.setUint32(c,n.length/4,!0);c+=4;d.setUint32(c,0,!0);c+=4;u.set(s,c);c+=s.length;u.set(n,c);c+=n.length;u.set(i,c);return u}}const La=new Uint\
-8Array(0);class DecodeStream extends BaseStream{constructor(e){super();this._rawMinBufferLength=e||0;this.pos=0;this.bufferLength=0;this.eof=!1;this.buffer=La;this.minBufferLength=512;if(e)for(;this.minBufferLength<e;)this.minBufferLength*=2}get isEmpty(){for(;!this.eof&&0===this.bufferLength;)this.readBlock();return 0===this.bufferLength}ensureBuffer(e){const t=this.buffer;if(e<=t.byteLength)return t;let a=this.minBufferLength;for(;a<e;)a*=2;const r=new Uint8Array(a);r.set(t);return this.buffer\
-=r}getByte(){const e=this.pos;for(;this.bufferLength<=e;){if(this.eof)return-1;this.readBlock()}return this.buffer[this.pos++]}getBytes(e,t=null){const a=this.pos;let r;if(e){this.ensureBuffer(a+e);r=a+e;for(;!this.eof&&this.bufferLength<r;)this.readBlock(t);const i=this.bufferLength;r>i&&(r=i)}else{for(;!this.eof;)this.readBlock(t);r=this.bufferLength}this.pos=r;return this.buffer.subarray(a,r)}async getImageData(e,t){if(!this.canAsyncDecodeImageFromBuffer)return this.isAsyncDecoder?this.decode\
-Image(null,t):this.getBytes(e,t);const a=await this.stream.asyncGetBytes();return this.decodeImage(a,t)}reset(){this.pos=0}makeSubStream(e,t,a=null){if(void 0===t)for(;!this.eof;)this.readBlock();else{const a=e+t;for(;this.bufferLength<=a&&!this.eof;)this.readBlock()}return new Stream(this.buffer,e,t,a)}getBaseStreams(){return this.str?this.str.getBaseStreams():null}}class StreamsSequenceStream extends DecodeStream{constructor(e,t=null){e=e.filter((e=>e instanceof BaseStream));let a=0;for(const \
-t of e)a+=t instanceof DecodeStream?t._rawMinBufferLength:t.length;super(a);this.streams=e;this._onError=t}readBlock(){const e=this.streams;if(0===e.length){this.eof=!0;return}const t=e.shift();let a;try{a=t.getBytes()}catch(e){if(this._onError){this._onError(e,t.dict?.objId);return}throw e}const r=this.bufferLength,i=r+a.length;this.ensureBuffer(i).set(a,r);this.bufferLength=i}getBaseStreams(){const e=[];for(const t of this.streams){const a=t.getBaseStreams();a&&e.push(...a)}return e.length>0?e\
-:null}}class ColorSpaceUtils{static parse({cs:e,xref:t,resources:a=null,pdfFunctionFactory:r,globalColorSpaceCache:i,localColorSpaceCache:n,asyncIfNotCached:s=!1}){const o={xref:t,resources:a,pdfFunctionFactory:r,globalColorSpaceCache:i,localColorSpaceCache:n};let c,l,h;if(e instanceof Ref){l=e;const a=i.getByRef(l)||n.getByRef(l);if(a)return a;e=t.fetch(e)}if(e instanceof Name){c=e.name;const t=n.getByName(c);if(t)return t}try{h=this.#B(e,o)}catch(e){if(s&&!(e instanceof MissingDataException))r\
-eturn Promise.reject(e);throw e}if(c||l){n.set(c,l,h);l&&i.set(null,l,h)}return s?Promise.resolve(h):h}static#R(e,t){const{globalColorSpaceCache:a}=t;let r;if(e instanceof Ref){r=e;const t=a.getByRef(r);if(t)return t}const i=this.#B(e,t);r&&a.set(null,r,i);return i}static#B(e,t){const{xref:a,resources:r,pdfFunctionFactory:i,globalColorSpaceCache:n}=t;if((e=a.fetchIfRef(e))instanceof Name)switch(e.name){case"G":case"DeviceGray":return this.gray;case"RGB":case"DeviceRGB":return this.rgb;case"Devic\
-eRGBA":return this.rgba;case"CMYK":case"DeviceCMYK":return this.cmyk;case"Pattern":return new PatternCS(null);default:if(r instanceof Dict){const a=r.get("ColorSpace");if(a instanceof Dict){const r=a.get(e.name);if(r){if(r instanceof Name)return this.#B(r,t);e=r;break}}}warn(`Unrecognized ColorSpace: ${e.name}`);return this.gray}if(Array.isArray(e)){const r=a.fetchIfRef(e[0]).name;let s,o,c,l,h,u;switch(r){case"G":case"DeviceGray":return this.gray;case"RGB":case"DeviceRGB":return this.rgb;case"C\
-MYK":case"DeviceCMYK":return this.cmyk;case"CalGray":s=a.fetchIfRef(e[1]);l=s.getArray("WhitePoint");h=s.getArray("BlackPoint");u=s.get("Gamma");return new CalGrayCS(l,h,u);case"CalRGB":s=a.fetchIfRef(e[1]);l=s.getArray("WhitePoint");h=s.getArray("BlackPoint");u=s.getArray("Gamma");const d=s.getArray("Matrix");return new CalRGBCS(l,h,u,d);case"ICCBased":const f=e[1]instanceof Ref;if(f){const t=n.getByRef(e[1]);if(t)return t}const g=a.fetchIfRef(e[1]),p=g.dict;o=p.get("N");if(IccColorSpace.isUsab\
-le)try{const t=new IccColorSpace(g.getBytes(),"ICCBased",o);f&&n.set(null,e[1],t);return t}catch(t){if(t instanceof MissingDataException)throw t;warn(`ICCBased color space (${e[1]}): "${t}".`)}const m=p.getRaw("Alternate");if(m){const e=this.#R(m,t);if(e.numComps===o)return e;warn("ICCBased color space: Ignoring incorrect /Alternate entry.")}if(1===o)return this.gray;if(3===o)return this.rgb;if(4===o)return this.cmyk;break;case"Pattern":c=e[1]||null;c&&(c=this.#R(c,t));return new PatternCS(c);ca\
-se"I":case"Indexed":c=this.#R(e[1],t);const b=MathClamp(a.fetchIfRef(e[2]),0,255),y=a.fetchIfRef(e[3]);return new IndexedCS(c,b,y);case"Separation":case"DeviceN":const w=a.fetchIfRef(e[1]);o=Array.isArray(w)?w.length:1;c=this.#R(e[2],t);const x=i.create(e[3]);return new AlternateCS(o,c,x);case"Lab":s=a.fetchIfRef(e[1]);l=s.getArray("WhitePoint");h=s.getArray("BlackPoint");const S=s.getArray("Range");return new LabCS(l,h,S);default:warn(`Unimplemented ColorSpace object: ${r}`);return this.gray}}w\
-arn(`Unrecognized ColorSpace object: ${e}`);return this.gray}static get gray(){return shadow(this,"gray",new DeviceGrayCS)}static get rgb(){return shadow(this,"rgb",new DeviceRgbCS)}static get rgba(){return shadow(this,"rgba",new DeviceRgbaCS)}static get cmyk(){if(CmykICCBasedCS.isUsable)try{return shadow(this,"cmyk",new CmykICCBasedCS)}catch{warn("CMYK fallback: DeviceCMYK")}return shadow(this,"cmyk",new DeviceCmykCS)}}class JpegError extends fa{constructor(e){super(e,"JpegError")}}class DNLMar\
-kerError extends fa{constructor(e,t){super(e,"DNLMarkerError");this.scanLines=t}}class EOIMarkerError extends fa{constructor(e){super(e,"EOIMarkerError")}}const ja=new Uint8Array([0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63]),_a=4017,Ua=799,Xa=3406,qa=2276,Ha=1567,Wa=3784,za=5793,$a=2896;function buildHuffmanTable(e,t){let a,r,i=0,n=16;for(;n>0&&!e[n-1];)n--;c\
-onst s=[{children:[],index:0}];let o,c=s[0];for(a=0;a<n;a++){for(r=0;r<e[a];r++){c=s.pop();c.children[c.index]=t[i];for(;c.index>0;)c=s.pop();c.index++;s.push(c);for(;s.length<=a;){s.push(o={children:[],index:0});c.children[c.index]=o.children;c=o}i++}if(a+1<n){s.push(o={children:[],index:0});c.children[c.index]=o.children;c=o}}return s[0].children}function getBlockBufferOffset(e,t,a){return 64*((e.blocksPerLine+1)*t+a)}function decodeScan(e,t,a,r,i,n,s,o,c,l=!1){const h=a.mcusPerLine,u=a.progre\
-ssive,d=t;let f=0,g=0;function readBit(){if(g>0){g--;return f>>g&1}f=e[t++];if(255===f){const r=e[t++];if(r){if(220===r&&l){const r=readUint16(e,t+=2);t+=2;if(r>0&&r!==a.scanLines)throw new DNLMarkerError("Found DNL marker (0xFFDC) while parsing scan data",r)}else if(217===r){if(l){const e=y*(8===a.precision?8:0);if(e>0&&Math.round(a.scanLines/e)>=5)throw new DNLMarkerError("Found EOI marker (0xFFD9) while parsing scan data, possibly caused by incorrect `scanLines` parameter",e)}throw new EOIMar\
-kerError("Found EOI marker (0xFFD9) while parsing scan data")}throw new JpegError(`unexpected marker ${(f<<8|r).toString(16)}`)}}g=7;return f>>>7}function decodeHuffman(e){let t=e;for(;;){t=t[readBit()];switch(typeof t){case"number":return t;case"object":continue}throw new JpegError("invalid huffman sequence")}}function receive(e){let t=0;for(;e>0;){t=t<<1|readBit();e--}return t}function receiveAndExtend(e){if(1===e)return 1===readBit()?1:-1;const t=receive(e);return t>=1<<e-1?t:t+(-1<<e)+1}let \
-p=0;let m,b=0;let y=0;function decodeMcu(e,t,a,r,i){const n=a%h;y=(a/h|0)*e.v+r;const s=n*e.h+i;t(e,getBlockBufferOffset(e,y,s))}function decodeBlock(e,t,a){y=a/e.blocksPerLine|0;const r=a%e.blocksPerLine;t(e,getBlockBufferOffset(e,y,r))}const w=r.length;let x,S,k,C,v,F;F=u?0===n?0===o?function decodeDCFirst(e,t){const a=decodeHuffman(e.huffmanTableDC),r=0===a?0:receiveAndExtend(a)<<c;e.blockData[t]=e.pred+=r}:function decodeDCSuccessive(e,t){e.blockData[t]|=readBit()<<c}:0===o?function decodeAC\
-First(e,t){if(p>0){p--;return}let a=n;const r=s;for(;a<=r;){const r=decodeHuffman(e.huffmanTableAC),i=15&r,n=r>>4;if(0===i){if(n<15){p=receive(n)+(1<<n)-1;break}a+=16;continue}a+=n;const s=ja[a];e.blockData[t+s]=receiveAndExtend(i)*(1<<c);a++}}:function decodeACSuccessive(e,t){let a=n;const r=s;let i,o,l=0;for(;a<=r;){const r=t+ja[a],n=e.blockData[r]<0?-1:1;switch(b){case 0:o=decodeHuffman(e.huffmanTableAC);i=15&o;l=o>>4;if(0===i)if(l<15){p=receive(l)+(1<<l);b=4}else{l=16;b=1}else{if(1!==i)throw\
- new JpegError("invalid ACn encoding");m=receiveAndExtend(i);b=l?2:3}continue;case 1:case 2:if(e.blockData[r])e.blockData[r]+=n*(readBit()<<c);else{l--;0===l&&(b=2===b?3:0)}break;case 3:if(e.blockData[r])e.blockData[r]+=n*(readBit()<<c);else{e.blockData[r]=m<<c;b=0}break;case 4:e.blockData[r]&&(e.blockData[r]+=n*(readBit()<<c))}a++}if(4===b){p--;0===p&&(b=0)}}:function decodeBaseline(e,t){const a=decodeHuffman(e.huffmanTableDC),r=0===a?0:receiveAndExtend(a);e.blockData[t]=e.pred+=r;let i=1;for(;\
-i<64;){const a=decodeHuffman(e.huffmanTableAC),r=15&a,n=a>>4;if(0===r){if(n<15)break;i+=16;continue}i+=n;const s=ja[i];e.blockData[t+s]=receiveAndExtend(r);i++}};let T,O=0;const M=1===w?r[0].blocksPerLine*r[0].blocksPerColumn:h*a.mcusPerColumn;let D,R;for(;O<=M;){const a=i?Math.min(M-O,i):M;if(a>0){for(S=0;S<w;S++)r[S].pred=0;p=0;if(1===w){x=r[0];for(v=0;v<a;v++){decodeBlock(x,F,O);O++}}else for(v=0;v<a;v++){for(S=0;S<w;S++){x=r[S];D=x.h;R=x.v;for(k=0;k<R;k++)for(C=0;C<D;C++)decodeMcu(x,F,O,k,C)\
-}O++}}g=0;T=findNextFileMarker(e,t);if(!T)break;if(T.invalid){warn(`decodeScan - ${a>0?"unexpected":"excessive"} MCU data, current marker is: ${T.invalid}`);t=T.offset}if(!(T.marker>=65488&&T.marker<=65495))break;t+=2}return t-d}function quantizeAndInverse(e,t,a){const r=e.quantizationTable,i=e.blockData;let n,s,o,c,l,h,u,d,f,g,p,m,b,y,w,x,S;if(!r)throw new JpegError("missing required Quantization Table.");for(let e=0;e<64;e+=8){f=i[t+e];g=i[t+e+1];p=i[t+e+2];m=i[t+e+3];b=i[t+e+4];y=i[t+e+5];w=i\
-[t+e+6];x=i[t+e+7];f*=r[e];if(g|p|m|b|y|w|x){g*=r[e+1];p*=r[e+2];m*=r[e+3];b*=r[e+4];y*=r[e+5];w*=r[e+6];x*=r[e+7];n=za*f+128>>8;s=za*b+128>>8;o=p;c=w;l=$a*(g-x)+128>>8;d=$a*(g+x)+128>>8;h=m<<4;u=y<<4;n=n+s+1>>1;s=n-s;S=o*Wa+c*Ha+128>>8;o=o*Ha-c*Wa+128>>8;c=S;l=l+u+1>>1;u=l-u;d=d+h+1>>1;h=d-h;n=n+c+1>>1;c=n-c;s=s+o+1>>1;o=s-o;S=l*qa+d*Xa+2048>>12;l=l*Xa-d*qa+2048>>12;d=S;S=h*Ua+u*_a+2048>>12;h=h*_a-u*Ua+2048>>12;u=S;a[e]=n+d;a[e+7]=n-d;a[e+1]=s+u;a[e+6]=s-u;a[e+2]=o+h;a[e+5]=o-h;a[e+3]=c+l;a[e+4\
-]=c-l}else{S=za*f+512>>10;a[e]=S;a[e+1]=S;a[e+2]=S;a[e+3]=S;a[e+4]=S;a[e+5]=S;a[e+6]=S;a[e+7]=S}}for(let e=0;e<8;++e){f=a[e];g=a[e+8];p=a[e+16];m=a[e+24];b=a[e+32];y=a[e+40];w=a[e+48];x=a[e+56];if(g|p|m|b|y|w|x){n=za*f+2048>>12;s=za*b+2048>>12;o=p;c=w;l=$a*(g-x)+2048>>12;d=$a*(g+x)+2048>>12;h=m;u=y;n=4112+(n+s+1>>1);s=n-s;S=o*Wa+c*Ha+2048>>12;o=o*Ha-c*Wa+2048>>12;c=S;l=l+u+1>>1;u=l-u;d=d+h+1>>1;h=d-h;n=n+c+1>>1;c=n-c;s=s+o+1>>1;o=s-o;S=l*qa+d*Xa+2048>>12;l=l*Xa-d*qa+2048>>12;d=S;S=h*Ua+u*_a+2048\
->>12;h=h*_a-u*Ua+2048>>12;u=S;f=n+d;x=n-d;g=s+u;w=s-u;p=o+h;y=o-h;m=c+l;b=c-l;f<16?f=0:f>=4080?f=255:f>>=4;g<16?g=0:g>=4080?g=255:g>>=4;p<16?p=0:p>=4080?p=255:p>>=4;m<16?m=0:m>=4080?m=255:m>>=4;b<16?b=0:b>=4080?b=255:b>>=4;y<16?y=0:y>=4080?y=255:y>>=4;w<16?w=0:w>=4080?w=255:w>>=4;x<16?x=0:x>=4080?x=255:x>>=4;i[t+e]=f;i[t+e+8]=g;i[t+e+16]=p;i[t+e+24]=m;i[t+e+32]=b;i[t+e+40]=y;i[t+e+48]=w;i[t+e+56]=x}else{S=za*f+8192>>14;S=S<-2040?0:S>=2024?255:S+2056>>4;i[t+e]=S;i[t+e+8]=S;i[t+e+16]=S;i[t+e+24]=S\
-;i[t+e+32]=S;i[t+e+40]=S;i[t+e+48]=S;i[t+e+56]=S}}}function buildComponentData(e,t){const a=t.blocksPerLine,r=t.blocksPerColumn,i=new Int16Array(64);for(let e=0;e<r;e++)for(let r=0;r<a;r++){quantizeAndInverse(t,getBlockBufferOffset(t,e,r),i)}return t.blockData}function findNextFileMarker(e,t,a=t){const r=e.length-1;let i=a<t?a:t;if(t>=r)return null;const n=readUint16(e,t);if(n>=65472&&n<=65534)return{invalid:null,marker:n,offset:t};let s=readUint16(e,i);for(;!(s>=65472&&s<=65534);){if(++i>=r)ret\
-urn null;s=readUint16(e,i)}return{invalid:n.toString(16),marker:s,offset:i}}function prepareComponents(e){const t=Math.ceil(e.samplesPerLine/8/e.maxH),a=Math.ceil(e.scanLines/8/e.maxV);for(const r of e.components){const i=Math.ceil(Math.ceil(e.samplesPerLine/8)*r.h/e.maxH),n=Math.ceil(Math.ceil(e.scanLines/8)*r.v/e.maxV),s=t*r.h,o=64*(a*r.v)*(s+1);r.blockData=new Int16Array(o);r.blocksPerLine=i;r.blocksPerColumn=n}e.mcusPerLine=t;e.mcusPerColumn=a}function readDataBlock(e,t){const a=readUint16(e\
-,t);let r=(t+=2)+a-2;const i=findNextFileMarker(e,r,t);if(i?.invalid){warn("readDataBlock - incorrect length, current marker is: "+i.invalid);r=i.offset}const n=e.subarray(t,r);return{appData:n,oldOffset:t,newOffset:t+n.length}}function skipData(e,t){const a=readUint16(e,t),r=(t+=2)+a-2,i=findNextFileMarker(e,r,t);return i?.invalid?i.offset:r}class JpegImage{constructor({decodeTransform:e=null,colorTransform:t=-1}={}){this._decodeTransform=e;this._colorTransform=t}static canUseImageDecoder(e,t=-\
-1){let a=null,r=0,i=null,n=readUint16(e,r);r+=2;if(65496!==n)throw new JpegError("SOI not found");n=readUint16(e,r);r+=2;e:for(;65497!==n;){switch(n){case 65505:const{appData:t,oldOffset:s,newOffset:o}=readDataBlock(e,r);r=o;if(69===t[0]&&120===t[1]&&105===t[2]&&102===t[3]&&0===t[4]&&0===t[5]){if(a)throw new JpegError("Duplicate EXIF-blocks found.");a={exifStart:s+6,exifEnd:o}}n=readUint16(e,r);r+=2;continue;case 65472:case 65473:case 65474:i=e[r+7];break e;case 65535:255!==e[r]&&r--}r=skipData(\
-e,r);n=readUint16(e,r);r+=2}return 4===i||3===i&&0===t?null:a||{}}parse(e,{dnlScanLines:t=null}={}){let a,r,i=0,n=null,s=null,o=0;const c=[],l=[],h=[];let u=readUint16(e,i);i+=2;if(65496!==u)throw new JpegError("SOI not found");u=readUint16(e,i);i+=2;e:for(;65497!==u;){let d,f,g;switch(u){case 65504:case 65505:case 65506:case 65507:case 65508:case 65509:case 65510:case 65511:case 65512:case 65513:case 65514:case 65515:case 65516:case 65517:case 65518:case 65519:case 65534:const{appData:p,newOffs\
-et:m}=readDataBlock(e,i);i=m;65504===u&&74===p[0]&&70===p[1]&&73===p[2]&&70===p[3]&&0===p[4]&&(n={version:{major:p[5],minor:p[6]},densityUnits:p[7],xDensity:p[8]<<8|p[9],yDensity:p[10]<<8|p[11],thumbWidth:p[12],thumbHeight:p[13],thumbData:p.subarray(14,14+3*p[12]*p[13])});65518===u&&65===p[0]&&100===p[1]&&111===p[2]&&98===p[3]&&101===p[4]&&(s={version:p[5]<<8|p[6],flags0:p[7]<<8|p[8],flags1:p[9]<<8|p[10],transformCode:p[11]});break;case 65499:const b=readUint16(e,i);i+=2;const y=b+i-2;let w;for(\
-;i<y;){const t=e[i++],a=new Uint16Array(64);if(t>>4){if(t>>4!=1)throw new JpegError("DQT - invalid table spec");for(f=0;f<64;f++){w=ja[f];a[w]=readUint16(e,i);i+=2}}else for(f=0;f<64;f++){w=ja[f];a[w]=e[i++]}c[15&t]=a}break;case 65472:case 65473:case 65474:if(a)throw new JpegError("Only single frame JPEGs supported");i+=2;a={};a.extended=65473===u;a.progressive=65474===u;a.precision=e[i++];const x=readUint16(e,i);i+=2;a.scanLines=t||x;a.samplesPerLine=readUint16(e,i);i+=2;a.components=[];a.compo\
-nentIds={};const S=e[i++];let k=0,C=0;for(d=0;d<S;d++){const t=e[i],r=e[i+1]>>4,n=15&e[i+1];k<r&&(k=r);C<n&&(C=n);const s=e[i+2];g=a.components.push({h:r,v:n,quantizationId:s,quantizationTable:null});a.componentIds[t]=g-1;i+=3}a.maxH=k;a.maxV=C;prepareComponents(a);break;case 65476:const v=readUint16(e,i);i+=2;for(d=2;d<v;){const t=e[i++],a=new Uint8Array(16);let r=0;for(f=0;f<16;f++,i++)r+=a[f]=e[i];const n=new Uint8Array(r);for(f=0;f<r;f++,i++)n[f]=e[i];d+=17+r;(t>>4?l:h)[15&t]=buildHuffmanTab\
-le(a,n)}break;case 65501:i+=2;r=readUint16(e,i);i+=2;break;case 65498:const F=1==++o&&!t;i+=2;const T=e[i++],O=[];for(d=0;d<T;d++){const t=e[i++],r=a.componentIds[t],n=a.components[r];n.index=t;const s=e[i++];n.huffmanTableDC=h[s>>4];n.huffmanTableAC=l[15&s];O.push(n)}const M=e[i++],D=e[i++],R=e[i++];try{i+=decodeScan(e,i,a,O,r,M,D,R>>4,15&R,F)}catch(t){if(t instanceof DNLMarkerError){warn(`${t.message} -- attempting to re-parse the JPEG image.`);return this.parse(e,{dnlScanLines:t.scanLines})}i\
-f(t instanceof EOIMarkerError){warn(`${t.message} -- ignoring the rest of the image data.`);break e}throw t}break;case 65500:i+=4;break;case 65535:255!==e[i]&&i--;break;default:const N=findNextFileMarker(e,i-2,i-3);if(N?.invalid){warn("JpegImage.parse - unexpected data, current marker is: "+N.invalid);i=N.offset;break}if(!N||i>=e.length-1){warn("JpegImage.parse - reached the end of the image data without finding an EOI marker (0xFFD9).");break e}throw new JpegError("JpegImage.parse - unknown mar\
-ker: "+u.toString(16))}u=readUint16(e,i);i+=2}if(!a)throw new JpegError("JpegImage.parse - no frame data found.");this.width=a.samplesPerLine;this.height=a.scanLines;this.jfif=n;this.adobe=s;this.components=[];for(const e of a.components){const t=c[e.quantizationId];t&&(e.quantizationTable=t);this.components.push({index:e.index,output:buildComponentData(0,e),scaleX:e.h/a.maxH,scaleY:e.v/a.maxV,blocksPerLine:e.blocksPerLine,blocksPerColumn:e.blocksPerColumn})}this.numComponents=this.components.le\
-ngth}_getLinearizedBlockData(e,t,a=!1){const r=this.width/e,i=this.height/t;let n,s,o,c,l,h,u,d,f,g,p,m=0;const b=this.components.length,y=e*t*b,w=new Uint8ClampedArray(y),x=new Uint32Array(e),S=4294967288;let k;for(u=0;u<b;u++){n=this.components[u];s=n.scaleX*r;o=n.scaleY*i;m=u;p=n.output;c=n.blocksPerLine+1<<3;if(s!==k){for(l=0;l<e;l++){d=0|l*s;x[l]=(d&S)<<3|7&d}k=s}for(h=0;h<t;h++){d=0|h*o;g=c*(d&S)|(7&d)<<3;for(l=0;l<e;l++){w[m]=p[g+x[l]];m+=b}}}let C=this._decodeTransform;a||4!==b||C||(C=ne\
-w Int32Array([-256,255,-256,255,-256,255,-256,255]));if(C)for(u=0;u<y;)for(d=0,f=0;d<b;d++,u++,f+=2)w[u]=(w[u]*C[f]>>8)+C[f+1];return w}get _isColorConversionNeeded(){return this.adobe?!!this.adobe.transformCode:3===this.numComponents?0!==this._colorTransform&&(82!==this.components[0].index||71!==this.components[1].index||66!==this.components[2].index):1===this._colorTransform}_convertYccToRgb(e){let t,a,r;for(let i=0,n=e.length;i<n;i+=3){t=e[i];a=e[i+1];r=e[i+2];e[i]=t-179.456+1.402*r;e[i+1]=t+\
-135.459-.344*a-.714*r;e[i+2]=t-226.816+1.772*a}return e}_convertYccToRgba(e,t){for(let a=0,r=0,i=e.length;a<i;a+=3,r+=4){const i=e[a],n=e[a+1],s=e[a+2];t[r]=i-179.456+1.402*s;t[r+1]=i+135.459-.344*n-.714*s;t[r+2]=i-226.816+1.772*n;t[r+3]=255}return t}_convertYcckToRgb(e){this._convertYcckToCmyk(e);return this._convertCmykToRgb(e)}_convertYcckToRgba(e){this._convertYcckToCmyk(e);return this._convertCmykToRgba(e)}_convertYcckToCmyk(e){let t,a,r;for(let i=0,n=e.length;i<n;i+=4){t=e[i];a=e[i+1];r=e[\
-i+2];e[i]=434.456-t-1.402*r;e[i+1]=119.541-t+.344*a+.714*r;e[i+2]=481.816-t-1.772*a}return e}_convertCmykToRgb(e){const t=e.length/4;ColorSpaceUtils.cmyk.getRgbBuffer(e,0,t,e,0,8,0);return e.subarray(0,3*t)}_convertCmykToRgba(e){ColorSpaceUtils.cmyk.getRgbBuffer(e,0,e.length/4,e,0,8,1);if(ColorSpaceUtils.cmyk instanceof DeviceCmykCS)for(let t=3,a=e.length;t<a;t+=4)e[t]=255;return e}getData({width:e,height:t,forceRGBA:a=!1,forceRGB:r=!1,isSourcePDF:i=!1}){if(this.numComponents>4)throw new JpegErr\
-or("Unsupported color mode");const n=this._getLinearizedBlockData(e,t,i);if(1===this.numComponents&&(a||r)){const e=n.length*(a?4:3),t=new Uint8ClampedArray(e);let r=0;if(a)!function grayToRGBA(e,t){if(FeatureTest.isLittleEndian)for(let a=0,r=e.length;a<r;a++)t[a]=65793*e[a]|4278190080;else for(let a=0,r=e.length;a<r;a++)t[a]=16843008*e[a]|255}(n,new Uint32Array(t.buffer));else for(const e of n){t[r++]=e;t[r++]=e;t[r++]=e}return t}if(3===this.numComponents&&this._isColorConversionNeeded){if(a){c\
-onst e=new Uint8ClampedArray(n.length/3*4);return this._convertYccToRgba(n,e)}return this._convertYccToRgb(n)}if(4===this.numComponents){if(this._isColorConversionNeeded)return a?this._convertYcckToRgba(n):r?this._convertYcckToRgb(n):this._convertYcckToCmyk(n);if(a)return this._convertCmykToRgba(n);if(r)return this._convertCmykToRgb(n)}return n}}class JpegStream extends DecodeStream{static#M=FeatureTest.isImageDecoderSupported;constructor(e,t,a){super(t);this.stream=e;this.dict=e.dict;this.maybe\
-Length=t;this.params=a}static get canUseImageDecoder(){return shadow(this,"canUseImageDecoder",this.#M?ImageDecoder.isTypeSupported("image/jpeg"):Promise.resolve(!1))}static setOptions({isImageDecoderSupported:e=!1}){this.#M=e}get bytes(){return shadow(this,"bytes",this.stream.getBytes(this.maybeLength))}ensureBuffer(e){}readBlock(){this.decodeImage()}get jpegOptions(){const e={decodeTransform:void 0,colorTransform:void 0},t=this.dict.getArray("D","Decode");if((this.forceRGBA||this.forceRGB)&&Ar\
-ray.isArray(t)){const a=this.dict.get("BPC","BitsPerComponent")||8,r=t.length,i=new Int32Array(r);let n=!1;const s=(1<<a)-1;for(let e=0;e<r;e+=2){i[e]=256*(t[e+1]-t[e])|0;i[e+1]=t[e]*s|0;256===i[e]&&0===i[e+1]||(n=!0)}n&&(e.decodeTransform=i)}if(this.params instanceof Dict){const t=this.params.get("ColorTransform");Number.isInteger(t)&&(e.colorTransform=t)}return shadow(this,"jpegOptions",e)}#N(e){for(let t=0,a=e.length-1;t<a;t++)if(255===e[t]&&216===e[t+1]){t>0&&(e=e.subarray(t));break}return e\
-}decodeImage(e){if(this.eof)return this.buffer;e=this.#N(e||this.bytes);const t=new JpegImage(this.jpegOptions);t.parse(e);const a=t.getData({width:this.drawWidth,height:this.drawHeight,forceRGBA:this.forceRGBA,forceRGB:this.forceRGB,isSourcePDF:!0});this.buffer=a;this.bufferLength=a.length;this.eof=!0;return this.buffer}get canAsyncDecodeImageFromBuffer(){return this.stream.isAsync}async getTransferableImage(){if(!await JpegStream.canUseImageDecoder)return null;const e=this.jpegOptions;if(e.dec\
-odeTransform)return null;let t;try{const a=this.canAsyncDecodeImageFromBuffer&&await this.stream.asyncGetBytes()||this.bytes;if(!a)return null;let r=this.#N(a);const i=JpegImage.canUseImageDecoder(r,e.colorTransform);if(!i)return null;if(i.exifStart){r=r.slice();r.fill(0,i.exifStart,i.exifEnd)}t=new ImageDecoder({data:r,type:"image/jpeg",preferAnimation:!1});return(await t.decode()).image}catch(e){warn(`getTransferableImage - failed: "${e}".`);return null}finally{t?.close()}}}var OpenJPEG=async \
-function(e={}){var t,a,r=e,i=new Promise(((e,r)=>{t=e;a=r})),n="./this.program",quit_=(e,t)=>{throw t},s=import.meta.url;try{new URL(".",s).href}catch{}var o,c,l,h,u,d,f=console.log.bind(console),g=console.error.bind(console),p=!1;function updateMemoryViews(){var e=o.buffer;l=new Int8Array(e);new Int16Array(e);h=new Uint8Array(e);new Uint16Array(e);u=new Int32Array(e);d=new Uint32Array(e);new Float32Array(e);new Float64Array(e);new BigInt64Array(e);new BigUint64Array(e)}var m=0,b=null;class Exit\
-Status{name="ExitStatus";constructor(e){this.message=`Program terminated with exit(${e})`;this.status=e}}var callRuntimeCallbacks=e=>{for(;e.length>0;)e.shift()(r)},y=[],addOnPostRun=e=>y.push(e),w=[],addOnPreRun=e=>w.push(e),x=!0,S=0,k={},handleException=e=>{if(e instanceof ExitStatus||"unwind"==e)return c;quit_(0,e)},keepRuntimeAlive=()=>x||S>0,_proc_exit=e=>{c=e;if(!keepRuntimeAlive()){r.onExit?.(e);p=!0}quit_(0,new ExitStatus(e))},_exit=(e,t)=>{c=e;_proc_exit(e)},callUserCallback=e=>{if(!p)t\
-ry{e();(()=>{if(!keepRuntimeAlive())try{_exit(c)}catch(e){handleException(e)}})()}catch(e){handleException(e)}},growMemory=e=>{var t=(e-o.buffer.byteLength+65535)/65536|0;try{o.grow(t);updateMemoryViews();return 1}catch(e){}},C={},getEnvStrings=()=>{if(!getEnvStrings.strings){var e={USER:"web_user",LOGNAME:"web_user",PATH:"/",PWD:"/",HOME:"/home/web_user",LANG:("object"==typeof navigator&&navigator.languages&&navigator.languages[0]||"C").replace("-","_")+".UTF-8",_:n||"./this.program"};for(var t\
- in C)void 0===C[t]?delete e[t]:e[t]=C[t];var a=[];for(var t in e)a.push(`${t}=${e[t]}`);getEnvStrings.strings=a}return getEnvStrings.strings},lengthBytesUTF8=e=>{for(var t=0,a=0;a<e.length;++a){var r=e.charCodeAt(a);if(r<=127)t++;else if(r<=2047)t+=2;else if(r>=55296&&r<=57343){t+=4;++a}else t+=3}return t},v=[null,[],[]],F="undefined"!=typeof TextDecoder?new TextDecoder:void 0,UTF8ArrayToString=(e,t=0,a=NaN)=>{for(var r=t+a,i=t;e[i]&&!(i>=r);)++i;if(i-t>16&&e.buffer&&F)return F.decode(e.subarra\
-y(t,i));for(var n="";t<i;){var s=e[t++];if(128&s){var o=63&e[t++];if(192!=(224&s)){var c=63&e[t++];if((s=224==(240&s)?(15&s)<<12|o<<6|c:(7&s)<<18|o<<12|c<<6|63&e[t++])<65536)n+=String.fromCharCode(s);else{var l=s-65536;n+=String.fromCharCode(55296|l>>10,56320|1023&l)}}else n+=String.fromCharCode((31&s)<<6|o)}else n+=String.fromCharCode(s)}return n},printChar=(e,t)=>{var a=v[e];if(0===t||10===t){(1===e?f:g)(UTF8ArrayToString(a));a.length=0}else a.push(t)},UTF8ToString=(e,t)=>e?UTF8ArrayToString(h\
-,e,t):"";r.noExitRuntime&&(x=r.noExitRuntime);r.print&&(f=r.print);r.printErr&&(g=r.printErr);r.wasmBinary&&r.wasmBinary;r.arguments&&r.arguments;r.thisProgram&&(n=r.thisProgram);r.writeArrayToMemory=(e,t)=>{l.set(e,t)};var T={l:()=>function abort(e){r.onAbort?.(e);g(e="Aborted("+e+")");p=!0;e+=". Build with -sASSERTIONS for more info.";var t=new WebAssembly.RuntimeError(e);a(t);throw t}(""),k:()=>{x=!1;S=0},m:(e,t)=>{if(k[e]){clearTimeout(k[e].id);delete k[e]}if(!t)return 0;var a=setTimeout((()\
-=>{delete k[e];callUserCallback((()=>M(e,performance.now())))}),t);k[e]={id:a,timeout_ms:t};return 0},g:function _copy_pixels_1(e,t){e>>=2;const a=r.imageData=new Uint8ClampedArray(t),i=u.subarray(e,e+t);a.set(i)},f:function _copy_pixels_3(e,t,a,i){e>>=2;t>>=2;a>>=2;const n=r.imageData=new Uint8ClampedArray(3*i),s=u.subarray(e,e+i),o=u.subarray(t,t+i),c=u.subarray(a,a+i);for(let e=0;e<i;e++){n[3*e]=s[e];n[3*e+1]=o[e];n[3*e+2]=c[e]}},e:function _copy_pixels_4(e,t,a,i,n){e>>=2;t>>=2;a>>=2;i>>=2;co\
-nst s=r.imageData=new Uint8ClampedArray(4*n),o=u.subarray(e,e+n),c=u.subarray(t,t+n),l=u.subarray(a,a+n),h=u.subarray(i,i+n);for(let e=0;e<n;e++){s[4*e]=o[e];s[4*e+1]=c[e];s[4*e+2]=l[e];s[4*e+3]=h[e]}},n:e=>{var t,a,r=h.length,i=2147483648;if((e>>>=0)>i)return!1;for(var n=1;n<=4;n*=2){var s=r*(1+.2/n);s=Math.min(s,e+100663296);var o=Math.min(i,(t=Math.max(e,s),a=65536,Math.ceil(t/a)*a));if(growMemory(o))return!0}return!1},p:(e,t)=>{var a=0,r=0;for(var i of getEnvStrings()){var n=t+a;d[e+r>>2]=n;\
-a+=((e,t,a,r)=>{if(!(r>0))return 0;for(var i=a,n=a+r-1,s=0;s<e.length;++s){var o=e.charCodeAt(s);o>=55296&&o<=57343&&(o=65536+((1023&o)<<10)|1023&e.charCodeAt(++s));if(o<=127){if(a>=n)break;t[a++]=o}else if(o<=2047){if(a+1>=n)break;t[a++]=192|o>>6;t[a++]=128|63&o}else if(o<=65535){if(a+2>=n)break;t[a++]=224|o>>12;t[a++]=128|o>>6&63;t[a++]=128|63&o}else{if(a+3>=n)break;t[a++]=240|o>>18;t[a++]=128|o>>12&63;t[a++]=128|o>>6&63;t[a++]=128|63&o}}t[a]=0;return a-i})(i,h,n,1/0)+1;r+=4}return 0},q:(e,t)=\
->{var a=getEnvStrings();d[e>>2]=a.length;var r=0;for(var i of a)r+=lengthBytesUTF8(i)+1;d[t>>2]=r;return 0},b:e=>52,o:function _fd_seek(e,t,a,r){t=(i=t)<-9007199254740992||i>9007199254740992?NaN:Number(i);var i;return 70},c:(e,t,a,r)=>{for(var i=0,n=0;n<a;n++){var s=d[t>>2],o=d[t+4>>2];t+=8;for(var c=0;c<o;c++)printChar(e,h[s+c]);i+=o}d[r>>2]=i;return 0},r:function _gray_to_rgba(e,t){e>>=2;const a=r.imageData=new Uint8ClampedArray(4*t),i=u.subarray(e,e+t);for(let e=0;e<t;e++){a[4*e]=a[4*e+1]=a[4\
-*e+2]=i[e];a[4*e+3]=255}},i:function _graya_to_rgba(e,t,a){e>>=2;t>>=2;const i=r.imageData=new Uint8ClampedArray(4*a),n=u.subarray(e,e+a),s=u.subarray(t,t+a);for(let e=0;e<a;e++){i[4*e]=i[4*e+1]=i[4*e+2]=n[e];i[4*e+3]=s[e]}},d:function _jsPrintWarning(e){const t=UTF8ToString(e);(r.warn||console.warn)(`OpenJPEG: ${t}`)},j:_proc_exit,h:function _rgb_to_rgba(e,t,a,i){e>>=2;t>>=2;a>>=2;const n=r.imageData=new Uint8ClampedArray(4*i),s=u.subarray(e,e+i),o=u.subarray(t,t+i),c=u.subarray(a,a+i);for(let \
-e=0;e<i;e++){n[4*e]=s[e];n[4*e+1]=o[e];n[4*e+2]=c[e];n[4*e+3]=255}},a:function _storeErrorMessage(e){const t=UTF8ToString(e);r.errorMessages?r.errorMessages+="\\n"+t:r.errorMessages=t}},O=await async function createWasm(){function receiveInstance(e,t){O=e.exports;o=O.s;updateMemoryViews();!function removeRunDependency(e){m--;r.monitorRunDependencies?.(m);if(0==m&&b){var t=b;b=null;t()}}();return O}!function addRunDependency(e){m++;r.monitorRunDependencies?.(m)}();var e=function getWasmImports(){r\
-eturn{a:T}}();return new Promise(((t,a)=>{r.instantiateWasm(e,((e,a)=>{t(receiveInstance(e))}))}))}(),M=(O.t,r._malloc=O.u,r._free=O.v,r._jp2_decode=O.w,O.x);!function preInit(){if(r.preInit){"function"==typeof r.preInit&&(r.preInit=[r.preInit]);for(;r.preInit.length>0;)r.preInit.shift()()}}();!function run(){if(m>0)b=run;else{!function preRun(){if(r.preRun){"function"==typeof r.preRun&&(r.preRun=[r.preRun]);for(;r.preRun.length;)addOnPreRun(r.preRun.shift())}callRuntimeCallbacks(w)}();if(m>0)b=\
-run;else if(r.setStatus){r.setStatus("Running...");setTimeout((()=>{setTimeout((()=>r.setStatus("")),1);doRun()}),1)}else doRun()}function doRun(){r.calledRun=!0;if(!p){!function initRuntime(){O.t()}();t(r);r.onRuntimeInitialized?.();!function postRun(){if(r.postRun){"function"==typeof r.postRun&&(r.postRun=[r.postRun]);for(;r.postRun.length;)addOnPostRun(r.postRun.shift())}callRuntimeCallbacks(y)}()}}}();return i};const Ga=OpenJPEG;class JpxError extends fa{constructor(e){super(e,"JpxError")}}c\
-lass JpxImage{static#E=null;static#P=null;static#L=null;static#v=!0;static#j=!0;static#F=null;static setOptions({handler:e,useWasm:t,useWorkerFetch:a,wasmUrl:r}){this.#v=t;this.#j=a;this.#F=r;a||(this.#P=e)}static async#_(e){const t=`${this.#F}openjpeg_nowasm_fallback.js`;let a=null;try{a=(await import(\n/*webpackIgnore: true*/\n/*@vite-ignore*/\nt)).default()}catch(e){warn(`JpxImage#getJsModule: ${e}`)}e(a)}static async#U(e,t,a){const r="openjpeg.wasm";try{this.#E||(this.#j?this.#E=await fetchBina\
-ryData(`${this.#F}${r}`):this.#E=await this.#P.sendWithPromise("FetchBinaryData",{type:"wasmFactory",filename:r}));return a((await WebAssembly.instantiate(this.#E,t)).instance)}catch(t){warn(`JpxImage#instantiateWasm: ${t}`);this.#_(e);return null}finally{this.#P=null}}static async decode(e,{numComponents:t=4,isIndexedColormap:a=!1,smaskInData:r=!1,reducePower:i=0}={}){if(!this.#L){const{promise:e,resolve:t}=Promise.withResolvers(),a=[e];this.#v?a.push(Ga({warn,instantiateWasm:this.#U.bind(this,\
-t)})):this.#_(t);this.#L=Promise.race(a)}const n=await this.#L;if(!n)throw new JpxError("OpenJPEG failed to initialize");let s;try{const o=e.length;s=n._malloc(o);n.writeArrayToMemory(e,s);if(n._jp2_decode(s,o,t>0?t:0,!!a,!!r,i)){const{errorMessages:e}=n;if(e){delete n.errorMessages;throw new JpxError(e)}throw new JpxError("Unknown error")}const{imageData:c}=n;n.imageData=null;return c}finally{s&&n._free(s)}}static cleanup(){this.#L=null}static parseImageProperties(e){let t=e.getByte();for(;t>=0\
-;){const a=t;t=e.getByte();if(65361===(a<<8|t)){e.skip(4);const t=e.getInt32()>>>0,a=e.getInt32()>>>0,r=e.getInt32()>>>0,i=e.getInt32()>>>0;e.skip(16);return{width:t-r,height:a-i,bitsPerComponent:8,componentsCount:e.getUint16()}}}throw new JpxError("No size marker found in JPX stream")}}function addState(e,t,a,r,i){let n=e;for(let e=0,a=t.length-1;e<a;e++){const a=t[e];n=n[a]||=[]}n[t.at(-1)]={checkFn:a,iterateFn:r,processFn:i}}const Va=[];addState(Va,[Be,Ne,Yt,Re],null,(function iterateInlineIm\
-ageGroup(e,t){const a=e.fnArray,r=(t-(e.iCurr-3))%4;switch(r){case 0:return a[t]===Be;case 1:return a[t]===Ne;case 2:return a[t]===Yt;case 3:return a[t]===Re}throw new Error(`iterateInlineImageGroup - invalid pos: ${r}`)}),(function foundInlineImageGroup(e,t){const a=e.fnArray,r=e.argsArray,i=e.iCurr,n=i-3,s=i-2,o=i-1,c=Math.min(Math.floor((t-n)/4),200);if(c<10)return t-(t-n)%4;let l=0;const h=[];let u=0,d=1,f=1;for(let e=0;e<c;e++){const t=r[s+(e<<2)],a=r[o+(e<<2)][0];if(d+a.width>1e3){l=Math.m\
-ax(l,d);f+=u+2;d=0;u=0}h.push({transform:t,x:d,y:f,w:a.width,h:a.height});d+=a.width+2;u=Math.max(u,a.height)}const g=Math.max(l,d)+1,p=f+u+1,m=new Uint8Array(g*p*4),b=g<<2;for(let e=0;e<c;e++){const t=r[o+(e<<2)][0].data,a=h[e].w<<2;let i=0,n=h[e].x+h[e].y*g<<2;m.set(t.subarray(0,a),n-b);for(let r=0,s=h[e].h;r<s;r++){m.set(t.subarray(i,i+a),n);i+=a;n+=b}m.set(t.subarray(i-a,i),n);for(;n>=0;){t[n-4]=t[n];t[n-3]=t[n+1];t[n-2]=t[n+2];t[n-1]=t[n+3];t[n+a]=t[n+a-4];t[n+a+1]=t[n+a-3];t[n+a+2]=t[n+a-2\
-];t[n+a+3]=t[n+a-1];n-=b}}const y={width:g,height:p};if(e.isOffscreenCanvasSupported){const e=new OffscreenCanvas(g,p);e.getContext("2d").putImageData(new ImageData(new Uint8ClampedArray(m.buffer),g,p),0,0);y.bitmap=e.transferToImageBitmap();y.data=null}else{y.kind=v;y.data=m}a.splice(n,4*c,Zt);r.splice(n,4*c,[y,h]);return n+1}));addState(Va,[Be,Ne,Vt,Re],null,(function iterateImageMaskGroup(e,t){const a=e.fnArray,r=(t-(e.iCurr-3))%4;switch(r){case 0:return a[t]===Be;case 1:return a[t]===Ne;case\
- 2:return a[t]===Vt;case 3:return a[t]===Re}throw new Error(`iterateImageMaskGroup - invalid pos: ${r}`)}),(function foundImageMaskGroup(e,t){const a=e.fnArray,r=e.argsArray,i=e.iCurr,n=i-3,s=i-2,o=i-1;let c=Math.floor((t-n)/4);if(c<10)return t-(t-n)%4;let l,h,u=!1;const d=r[o][0],f=r[s][0],g=r[s][1],p=r[s][2],m=r[s][3];if(g===p){u=!0;l=s+4;let e=o+4;for(let t=1;t<c;t++,l+=4,e+=4){h=r[l];if(r[e][0]!==d||h[0]!==f||h[1]!==g||h[2]!==p||h[3]!==m){t<10?u=!1:c=t;break}}}if(u){c=Math.min(c,1e3);const e\
-=new Float32Array(2*c);l=s;for(let t=0;t<c;t++,l+=4){h=r[l];e[t<<1]=h[4];e[1+(t<<1)]=h[5]}a.splice(n,4*c,ea);r.splice(n,4*c,[d,f,g,p,m,e])}else{c=Math.min(c,100);const e=[];for(let t=0;t<c;t++){h=r[s+(t<<2)];const a=r[o+(t<<2)][0];e.push({data:a.data,width:a.width,height:a.height,interpolate:a.interpolate,count:a.count,transform:h})}a.splice(n,4*c,Kt);r.splice(n,4*c,[e])}return n+1}));addState(Va,[Be,Ne,Jt,Re],(function(e){const t=e.argsArray,a=e.iCurr-2;return 0===t[a][1]&&0===t[a][2]}),(functi\
-on iterateImageGroup(e,t){const a=e.fnArray,r=e.argsArray,i=(t-(e.iCurr-3))%4;switch(i){case 0:return a[t]===Be;case 1:if(a[t]!==Ne)return!1;const i=e.iCurr-2,n=r[i][0],s=r[i][3];return r[t][0]===n&&0===r[t][1]&&0===r[t][2]&&r[t][3]===s;case 2:if(a[t]!==Jt)return!1;const o=r[e.iCurr-1][0];return r[t][0]===o;case 3:return a[t]===Re}throw new Error(`iterateImageGroup - invalid pos: ${i}`)}),(function(e,t){const a=e.fnArray,r=e.argsArray,i=e.iCurr,n=i-3,s=i-2,o=r[i-1][0],c=r[s][0],l=r[s][3],h=Math.\
-min(Math.floor((t-n)/4),1e3);if(h<3)return t-(t-n)%4;const u=new Float32Array(2*h);let d=s;for(let e=0;e<h;e++,d+=4){const t=r[d];u[e<<1]=t[4];u[1+(e<<1)]=t[5]}const f=[o,c,l,u];a.splice(n,4*h,Qt);r.splice(n,4*h,f);return n+1}));addState(Va,[Qe,nt,ht,dt,et],null,(function iterateShowTextGroup(e,t){const a=e.fnArray,r=e.argsArray,i=(t-(e.iCurr-4))%5;switch(i){case 0:return a[t]===Qe;case 1:return a[t]===nt;case 2:return a[t]===ht;case 3:if(a[t]!==dt)return!1;const i=e.iCurr-3,n=r[i][0],s=r[i][1];\
-return r[t][0]===n&&r[t][1]===s;case 4:return a[t]===et}throw new Error(`iterateShowTextGroup - invalid pos: ${i}`)}),(function(e,t){const a=e.fnArray,r=e.argsArray,i=e.iCurr,n=i-4,s=i-3,o=i-2,c=i-1,l=i,h=r[s][0],u=r[s][1];let d=Math.min(Math.floor((t-n)/5),1e3);if(d<3)return t-(t-n)%5;let f=n;if(n>=4&&a[n-4]===a[s]&&a[n-3]===a[o]&&a[n-2]===a[c]&&a[n-1]===a[l]&&r[n-4][0]===h&&r[n-4][1]===u){d++;f-=5}let g=f+4;for(let e=1;e<d;e++){a.splice(g,3);r.splice(g,3);g+=2}return g+1}));addState(Va,[Be,Ne,\
-aa,Re],(e=>{const t=e.argsArray,a=t[e.iCurr-1][0];if(a!==qe&&a!==He&&a!==$e&&a!==Ge&&a!==Ve&&a!==Ke)return!0;const r=t[e.iCurr-2];return 1===r[0]&&0===r[1]&&0===r[2]&&1===r[3]}),(()=>!1),((e,t)=>{const{fnArray:a,argsArray:r}=e,i=e.iCurr,n=i-3,s=i-2,o=r[i-1],c=r[s],[,[l],h]=o;if(h){Util.scaleMinMax(c,h);for(let e=0,t=l.length;e<t;)switch(l[e++]){case sa:case oa:Util.applyTransform(l,c,e);e+=2;break;case ca:Util.applyTransformToBezier(l,c,e);e+=6}}a.splice(n,4,aa);r.splice(n,4,o);return n+1}));cla\
-ss NullOptimizer{constructor(e){this.queue=e}_optimize(){}push(e,t){this.queue.fnArray.push(e);this.queue.argsArray.push(t);this._optimize()}flush(){}reset(){}}class QueueOptimizer extends NullOptimizer{constructor(e){super(e);this.state=null;this.context={iCurr:0,fnArray:e.fnArray,argsArray:e.argsArray,isOffscreenCanvasSupported:OperatorList.isOffscreenCanvasSupported};this.match=null;this.lastProcessed=0}_optimize(){const e=this.queue.fnArray;let t=this.lastProcessed,a=e.length,r=this.state,i=\
-this.match;if(!r&&!i&&t+1===a&&!Va[e[t]]){this.lastProcessed=a;return}const n=this.context;for(;t<a;){if(i){if((0,i.iterateFn)(n,t)){t++;continue}t=(0,i.processFn)(n,t+1);a=e.length;i=null;r=null;if(t>=a)break}r=(r||Va)[e[t]];if(r&&!Array.isArray(r)){n.iCurr=t;t++;if(!r.checkFn||(0,r.checkFn)(n)){i=r;r=null}else r=null}else t++}this.state=r;this.match=i;this.lastProcessed=t}flush(){for(;this.match;){const e=this.queue.fnArray.length;this.lastProcessed=(0,this.match.processFn)(this.context,e);thi\
-s.match=null;this.state=null;this._optimize()}}reset(){this.state=null;this.match=null;this.lastProcessed=0}}class OperatorList{static CHUNK_SIZE=1e3;static CHUNK_SIZE_ABOUT=this.CHUNK_SIZE-5;static isOffscreenCanvasSupported=!1;constructor(e=0,t){this._streamSink=t;this.fnArray=[];this.argsArray=[];this.optimizer=!t||e&d?new NullOptimizer(this):new QueueOptimizer(this);this.dependencies=new Set;this._totalLength=0;this.weight=0;this._resolved=t?null:Promise.resolve()}static setOptions({isOffscr\
-eenCanvasSupported:e}){this.isOffscreenCanvasSupported=e}get length(){return this.argsArray.length}get ready(){return this._resolved||this._streamSink.ready}get totalLength(){return this._totalLength+this.length}addOp(e,t){this.optimizer.push(e,t);this.weight++;this._streamSink&&(this.weight>=OperatorList.CHUNK_SIZE||this.weight>=OperatorList.CHUNK_SIZE_ABOUT&&(e===Re||e===et))&&this.flush()}addImageOps(e,t,a,r=!1){if(r){this.addOp(Be);this.addOp(De,[[["SMask",!1]]])}void 0!==a&&this.addOp(jt,["\
-OC",a]);this.addOp(e,t);void 0!==a&&this.addOp(_t,[]);r&&this.addOp(Re)}addDependency(e){if(!this.dependencies.has(e)){this.dependencies.add(e);this.addOp(ke,[e])}}addDependencies(e){for(const t of e)this.addDependency(t)}addOpList(e){if(e instanceof OperatorList){for(const t of e.dependencies)this.dependencies.add(t);for(let t=0,a=e.length;t<a;t++)this.addOp(e.fnArray[t],e.argsArray[t])}else warn(\'addOpList - ignoring invalid "opList" parameter.\')}getIR(){return{fnArray:this.fnArray,argsArray:t\
-his.argsArray,length:this.length}}get _transfers(){const e=[],{fnArray:t,argsArray:a,length:r}=this;for(let i=0;i<r;i++)switch(t[i]){case Yt:case Zt:case Vt:{const{bitmap:t,data:r}=a[i][0];(t||r?.buffer)&&e.push(t||r.buffer);break}case aa:{const[,[t],r]=a[i];t&&e.push(t.buffer,r.buffer);break}case qt:const[t,r]=a[i];t&&e.push(t.buffer);r&&e.push(r.buffer);break;case ht:e.push(a[i][0].buffer)}return e}flush(e=!1,t=null){this.optimizer.flush();const a=this.length;this._totalLength+=a;this._streamS\
-ink.enqueue({fnArray:this.fnArray,argsArray:this.argsArray,lastChunk:e,separateAnnots:t,length:a},1,this._transfers);this.dependencies.clear();this.fnArray.length=0;this.argsArray.length=0;this.weight=0;this.optimizer.reset()}}function hexToInt(e,t){let a=0;for(let r=0;r<=t;r++)a=a<<8|e[r];return a>>>0}function hexToStr(e,t){return 1===t?String.fromCharCode(e[0],e[1]):3===t?String.fromCharCode(e[0],e[1],e[2],e[3]):String.fromCharCode(...e.subarray(0,t+1))}function addHex(e,t,a){let r=0;for(let i\
-=a;i>=0;i--){r+=e[i]+t[i];e[i]=255&r;r>>=8}}function incHex(e,t){let a=1;for(let r=t;r>=0&&a>0;r--){a+=e[r];e[r]=255&a;a>>=8}}const Ka=16;class BinaryCMapStream{constructor(e){this.buffer=e;this.pos=0;this.end=e.length;this.tmpBuf=new Uint8Array(19)}readByte(){return this.pos>=this.end?-1:this.buffer[this.pos++]}readNumber(){let e,t=0;do{const a=this.readByte();if(a<0)throw new FormatError("unexpected EOF in bcmap");e=!(128&a);t=t<<7|127&a}while(!e);return t}readSigned(){const e=this.readNumber(\
-);return 1&e?~(e>>>1):e>>>1}readHex(e,t){e.set(this.buffer.subarray(this.pos,this.pos+t+1));this.pos+=t+1}readHexNumber(e,t){let a;const r=this.tmpBuf;let i=0;do{const e=this.readByte();if(e<0)throw new FormatError("unexpected EOF in bcmap");a=!(128&e);r[i++]=127&e}while(!a);let n=t,s=0,o=0;for(;n>=0;){for(;o<8&&r.length>0;){s|=r[--i]<<o;o+=7}e[n]=255&s;n--;s>>=8;o-=8}}readHexSigned(e,t){this.readHexNumber(e,t);const a=1&e[t]?255:0;let r=0;for(let i=0;i<=t;i++){r=(1&r)<<8|e[i];e[i]=r>>1^a}}readS\
-tring(){const e=this.readNumber(),t=new Array(e);for(let a=0;a<e;a++)t[a]=this.readNumber();return String.fromCharCode(...t)}}class BinaryCMapReader{async process(e,t,a){const r=new BinaryCMapStream(e),i=r.readByte();t.vertical=!!(1&i);let n=null;const s=new Uint8Array(Ka),o=new Uint8Array(Ka),c=new Uint8Array(Ka),l=new Uint8Array(Ka),h=new Uint8Array(Ka);let u,d;for(;(d=r.readByte())>=0;){const e=d>>5;if(7===e){switch(31&d){case 0:r.readString();break;case 1:n=r.readString()}continue}const a=!!\
-(16&d),i=15&d;if(i+1>Ka)throw new Error("BinaryCMapReader.process: Invalid dataSize.");const f=1,g=r.readNumber();switch(e){case 0:r.readHex(s,i);r.readHexNumber(o,i);addHex(o,s,i);t.addCodespaceRange(i+1,hexToInt(s,i),hexToInt(o,i));for(let e=1;e<g;e++){incHex(o,i);r.readHexNumber(s,i);addHex(s,o,i);r.readHexNumber(o,i);addHex(o,s,i);t.addCodespaceRange(i+1,hexToInt(s,i),hexToInt(o,i))}break;case 1:r.readHex(s,i);r.readHexNumber(o,i);addHex(o,s,i);r.readNumber();for(let e=1;e<g;e++){incHex(o,i)\
-;r.readHexNumber(s,i);addHex(s,o,i);r.readHexNumber(o,i);addHex(o,s,i);r.readNumber()}break;case 2:r.readHex(c,i);u=r.readNumber();t.mapOne(hexToInt(c,i),u);for(let e=1;e<g;e++){incHex(c,i);if(!a){r.readHexNumber(h,i);addHex(c,h,i)}u=r.readSigned()+(u+1);t.mapOne(hexToInt(c,i),u)}break;case 3:r.readHex(s,i);r.readHexNumber(o,i);addHex(o,s,i);u=r.readNumber();t.mapCidRange(hexToInt(s,i),hexToInt(o,i),u);for(let e=1;e<g;e++){incHex(o,i);if(a)s.set(o);else{r.readHexNumber(s,i);addHex(s,o,i)}r.readH\
-exNumber(o,i);addHex(o,s,i);u=r.readNumber();t.mapCidRange(hexToInt(s,i),hexToInt(o,i),u)}break;case 4:r.readHex(c,f);r.readHex(l,i);t.mapOne(hexToInt(c,f),hexToStr(l,i));for(let e=1;e<g;e++){incHex(c,f);if(!a){r.readHexNumber(h,f);addHex(c,h,f)}incHex(l,i);r.readHexSigned(h,i);addHex(l,h,i);t.mapOne(hexToInt(c,f),hexToStr(l,i))}break;case 5:r.readHex(s,f);r.readHexNumber(o,f);addHex(o,s,f);r.readHex(l,i);t.mapBfRange(hexToInt(s,f),hexToInt(o,f),hexToStr(l,i));for(let e=1;e<g;e++){incHex(o,f);if\
-(a)s.set(o);else{r.readHexNumber(s,f);addHex(s,o,f)}r.readHexNumber(o,f);addHex(o,s,f);r.readHex(l,i);t.mapBfRange(hexToInt(s,f),hexToInt(o,f),hexToStr(l,i))}break;default:throw new Error(`BinaryCMapReader.process - unknown type: ${e}`)}}return n?a(n):t}}class Ascii85Stream extends DecodeStream{constructor(e,t){t&&(t*=.8);super(t);this.str=e;this.dict=e.dict;this.input=new Uint8Array(5)}readBlock(){const e=this.str;let t=e.getByte();for(;isWhiteSpace(t);)t=e.getByte();if(-1===t||126===t){this.eo\
-f=!0;return}const a=this.bufferLength;let r,i;if(122===t){r=this.ensureBuffer(a+4);for(i=0;i<4;++i)r[a+i]=0;this.bufferLength+=4}else{const n=this.input;n[0]=t;for(i=1;i<5;++i){t=e.getByte();for(;isWhiteSpace(t);)t=e.getByte();n[i]=t;if(-1===t||126===t)break}r=this.ensureBuffer(a+i-1);this.bufferLength+=i-1;if(i<5){for(;i<5;++i)n[i]=117;this.eof=!0}let s=0;for(i=0;i<5;++i)s=85*s+(n[i]-33);for(i=3;i>=0;--i){r[a+i]=255&s;s>>=8}}}}class AsciiHexStream extends DecodeStream{constructor(e,t){t&&(t*=.5\
-);super(t);this.str=e;this.dict=e.dict;this.firstDigit=-1}readBlock(){const e=this.str.getBytes(8e3);if(!e.length){this.eof=!0;return}const t=e.length+1>>1,a=this.ensureBuffer(this.bufferLength+t);let r=this.bufferLength,i=this.firstDigit;for(const t of e){let e;if(t>=48&&t<=57)e=15&t;else{if(!(t>=65&&t<=70||t>=97&&t<=102)){if(62===t){this.eof=!0;break}continue}e=9+(15&t)}if(i<0)i=e;else{a[r++]=i<<4|e;i=-1}}if(i>=0&&this.eof){a[r++]=i<<4;i=-1}this.firstDigit=i;this.bufferLength=r}}const Ja=-1,Ya\
-=[[-1,-1],[-1,-1],[7,8],[7,7],[6,6],[6,6],[6,5],[6,5],[4,0],[4,0],[4,0],[4,0],[4,0],[4,0],[4,0],[4,0],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[3,3],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1\
-,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2],[1,2]],Za=[[-1,-1],[12,-2],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[11,1792],[11,1792],[12,1984],[12,2048],[12,2112],[12,2176],[12,2240],[12,2304],[11,1856],[11\
-,1856],[11,1920],[11,1920],[12,2368],[12,2432],[12,2496],[12,2560]],Qa=[[-1,-1],[-1,-1],[-1,-1],[-1,-1],[8,29],[8,29],[8,30],[8,30],[8,45],[8,45],[8,46],[8,46],[7,22],[7,22],[7,22],[7,22],[7,23],[7,23],[7,23],[7,23],[8,47],[8,47],[8,48],[8,48],[6,13],[6,13],[6,13],[6,13],[6,13],[6,13],[6,13],[6,13],[7,20],[7,20],[7,20],[7,20],[8,33],[8,33],[8,34],[8,34],[8,35],[8,35],[8,36],[8,36],[8,37],[8,37],[8,38],[8,38],[7,19],[7,19],[7,19],[7,19],[8,31],[8,31],[8,32],[8,32],[6,1],[6,1],[6,1],[6,1],[6,1],[6\
-,1],[6,1],[6,1],[6,12],[6,12],[6,12],[6,12],[6,12],[6,12],[6,12],[6,12],[8,53],[8,53],[8,54],[8,54],[7,26],[7,26],[7,26],[7,26],[8,39],[8,39],[8,40],[8,40],[8,41],[8,41],[8,42],[8,42],[8,43],[8,43],[8,44],[8,44],[7,21],[7,21],[7,21],[7,21],[7,28],[7,28],[7,28],[7,28],[8,61],[8,61],[8,62],[8,62],[8,63],[8,63],[8,0],[8,0],[8,320],[8,320],[8,384],[8,384],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,10],[5,11],[5,11],[5,11],[5,11],[5,11]\
-,[5,11],[5,11],[5,11],[5,11],[5,11],[5,11],[5,11],[5,11],[5,11],[5,11],[5,11],[7,27],[7,27],[7,27],[7,27],[8,59],[8,59],[8,60],[8,60],[9,1472],[9,1536],[9,1600],[9,1728],[7,18],[7,18],[7,18],[7,18],[7,24],[7,24],[7,24],[7,24],[8,49],[8,49],[8,50],[8,50],[8,51],[8,51],[8,52],[8,52],[7,25],[7,25],[7,25],[7,25],[8,55],[8,55],[8,56],[8,56],[8,57],[8,57],[8,58],[8,58],[6,192],[6,192],[6,192],[6,192],[6,192],[6,192],[6,192],[6,192],[6,1664],[6,1664],[6,1664],[6,1664],[6,1664],[6,1664],[6,1664],[6,1664\
-],[8,448],[8,448],[8,512],[8,512],[9,704],[9,768],[8,640],[8,640],[8,576],[8,576],[9,832],[9,896],[9,960],[9,1024],[9,1088],[9,1152],[9,1216],[9,1280],[9,1344],[9,1408],[7,256],[7,256],[7,256],[7,256],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,2],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3]\
-,[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[4,3],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,128],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,8],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[5,9],[6,16],[6,16],[6,16],[6,16],[6,16],[6,16],[6,16],[6,16],[6,17],[6,17],[6,17],[6,17],[6,17],[6,1\
-7],[6,17],[6,17],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,4],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[4,5],[6,14],[6,14],[6,14],[6,14],[6,14],[6,14],[6,14],[6,14],[6,15],[6,15],[6,15],[6,15],[6,15],[6,15],[\
-6,15],[6,15],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[5,64],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,6],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,7],[4,\
-7],[4,7]],er=[[-1,-1],[-1,-1],[12,-2],[12,-2],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[-1,-1],[11,1792],[11,1792],[11,1792],[11,1792],[12,1984],[12,1984],[12,2048],[12,2048],[12,2112],[12,2112],[12,2176],[12,2176],[12,2240],[12,2240],[12,2304],[12,2304],[11,1856],[11,1856],[11,1856],[11,1856],[11,1920],[11,1920],[11,1920],\
-[11,1920],[12,2368],[12,2368],[12,2432],[12,2432],[12,2496],[12,2496],[12,2560],[12,2560],[10,18],[10,18],[10,18],[10,18],[10,18],[10,18],[10,18],[10,18],[12,52],[12,52],[13,640],[13,704],[13,768],[13,832],[12,55],[12,55],[12,56],[12,56],[13,1280],[13,1344],[13,1408],[13,1472],[12,59],[12,59],[12,60],[12,60],[13,1536],[13,1600],[11,24],[11,24],[11,24],[11,24],[11,25],[11,25],[11,25],[11,25],[13,1664],[13,1728],[12,320],[12,320],[12,384],[12,384],[12,448],[12,448],[13,512],[13,576],[12,53],[12,53\
-],[12,54],[12,54],[13,896],[13,960],[13,1024],[13,1088],[13,1152],[13,1216],[10,64],[10,64],[10,64],[10,64],[10,64],[10,64],[10,64],[10,64]],tr=[[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[8,13],[11,23],[11,23],[12,50],[12,51],[12,44],[12,45],[12,46],[12,47],[12,57],[12,58],[12,61],[12,256],[10,16],[10,16],[10,16],[10,16],[10,17],[10,17],[10,17],[10,17],[12,48],[12,49],[12,62],[12,63],[12,30],[12,31],[12,32],[12,33],[12,40],[12,41],[1\
-1,22],[11,22],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[8,14],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,10],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,\
-11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[7,11],[9,15],[9,15],[9,15],[9,15],[9,15],[9,15],[9,15],[9,15],[12,128],[12,192],[12,26],[12,27],[12,28],[12,29],[11,19],[11,19],[11,20],[11,20],[12,34],[12,35],[12,36],[12,37],[12,38],[12,39],[11,21],[11,21],[12,42],[12,43],[10,0],[10,0],[10,0],[10,0],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12\
-],[7,12],[7,12],[7,12],[7,12],[7,12],[7,12]],ar=[[-1,-1],[-1,-1],[-1,-1],[-1,-1],[6,9],[6,8],[5,7],[5,7],[4,6],[4,6],[4,6],[4,6],[4,5],[4,5],[4,5],[4,5],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,1],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[3,4],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,3],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2],[2,2]];class CCITTFaxDecoder{constructor(e,t={}){if("function"!=t\
-ypeof e?.next)throw new Error(\'CCITTFaxDecoder - invalid "source" parameter.\');this.source=e;this.eof=!1;this.encoding=t.K||0;this.eoline=t.EndOfLine||!1;this.byteAlign=t.EncodedByteAlign||!1;this.columns=t.Columns||1728;this.rows=t.Rows||0;this.eoblock=t.EndOfBlock??!0;this.black=t.BlackIs1||!1;this.codingLine=new Uint32Array(this.columns+1);this.refLine=new Uint32Array(this.columns+2);this.codingLine[0]=this.columns;this.codingPos=0;this.row=0;this.nextLine2D=this.encoding<0;this.inputBits=0;t\
-his.inputBuf=0;this.outputBits=0;this.rowsDone=!1;let a;for(;0===(a=this._lookBits(12));)this._eatBits(1);1===a&&this._eatBits(12);if(this.encoding>0){this.nextLine2D=!this._lookBits(1);this._eatBits(1)}}readNextChar(){if(this.eof)return-1;const e=this.refLine,t=this.codingLine,a=this.columns;let r,i,n,s,o;if(0===this.outputBits){this.rowsDone&&(this.eof=!0);if(this.eof)return-1;this.err=!1;let n,o,c;if(this.nextLine2D){for(s=0;t[s]<a;++s)e[s]=t[s];e[s++]=a;e[s]=a;t[0]=0;this.codingPos=0;r=0;i=0\
-;for(;t[this.codingPos]<a;){n=this._getTwoDimCode();switch(n){case 0:this._addPixels(e[r+1],i);e[r+1]<a&&(r+=2);break;case 1:n=o=0;if(i){do{n+=c=this._getBlackCode()}while(c>=64);do{o+=c=this._getWhiteCode()}while(c>=64)}else{do{n+=c=this._getWhiteCode()}while(c>=64);do{o+=c=this._getBlackCode()}while(c>=64)}this._addPixels(t[this.codingPos]+n,i);t[this.codingPos]<a&&this._addPixels(t[this.codingPos]+o,1^i);for(;e[r]<=t[this.codingPos]&&e[r]<a;)r+=2;break;case 7:this._addPixels(e[r]+3,i);i^=1;if\
-(t[this.codingPos]<a){++r;for(;e[r]<=t[this.codingPos]&&e[r]<a;)r+=2}break;case 5:this._addPixels(e[r]+2,i);i^=1;if(t[this.codingPos]<a){++r;for(;e[r]<=t[this.codingPos]&&e[r]<a;)r+=2}break;case 3:this._addPixels(e[r]+1,i);i^=1;if(t[this.codingPos]<a){++r;for(;e[r]<=t[this.codingPos]&&e[r]<a;)r+=2}break;case 2:this._addPixels(e[r],i);i^=1;if(t[this.codingPos]<a){++r;for(;e[r]<=t[this.codingPos]&&e[r]<a;)r+=2}break;case 8:this._addPixelsNeg(e[r]-3,i);i^=1;if(t[this.codingPos]<a){r>0?--r:++r;for(;\
-e[r]<=t[this.codingPos]&&e[r]<a;)r+=2}break;case 6:this._addPixelsNeg(e[r]-2,i);i^=1;if(t[this.codingPos]<a){r>0?--r:++r;for(;e[r]<=t[this.codingPos]&&e[r]<a;)r+=2}break;case 4:this._addPixelsNeg(e[r]-1,i);i^=1;if(t[this.codingPos]<a){r>0?--r:++r;for(;e[r]<=t[this.codingPos]&&e[r]<a;)r+=2}break;case Ja:this._addPixels(a,0);this.eof=!0;break;default:info("bad 2d code");this._addPixels(a,0);this.err=!0}}}else{t[0]=0;this.codingPos=0;i=0;for(;t[this.codingPos]<a;){n=0;if(i)do{n+=c=this._getBlackCod\
-e()}while(c>=64);else do{n+=c=this._getWhiteCode()}while(c>=64);this._addPixels(t[this.codingPos]+n,i);i^=1}}let l=!1;this.byteAlign&&(this.inputBits&=-8);if(this.eoblock||this.row!==this.rows-1){n=this._lookBits(12);if(this.eoline)for(;n!==Ja&&1!==n;){this._eatBits(1);n=this._lookBits(12)}else for(;0===n;){this._eatBits(1);n=this._lookBits(12)}if(1===n){this._eatBits(12);l=!0}else n===Ja&&(this.eof=!0)}else this.rowsDone=!0;if(!this.eof&&this.encoding>0&&!this.rowsDone){this.nextLine2D=!this._l\
-ookBits(1);this._eatBits(1)}if(this.eoblock&&l&&this.byteAlign){n=this._lookBits(12);if(1===n){this._eatBits(12);if(this.encoding>0){this._lookBits(1);this._eatBits(1)}if(this.encoding>=0)for(s=0;s<4;++s){n=this._lookBits(12);1!==n&&info("bad rtc code: "+n);this._eatBits(12);if(this.encoding>0){this._lookBits(1);this._eatBits(1)}}this.eof=!0}}else if(this.err&&this.eoline){for(;;){n=this._lookBits(13);if(n===Ja){this.eof=!0;return-1}if(n>>1==1)break;this._eatBits(1)}this._eatBits(12);if(this.enc\
-oding>0){this._eatBits(1);this.nextLine2D=!(1&n)}}this.outputBits=t[0]>0?t[this.codingPos=0]:t[this.codingPos=1];this.row++}if(this.outputBits>=8){o=1&this.codingPos?0:255;this.outputBits-=8;if(0===this.outputBits&&t[this.codingPos]<a){this.codingPos++;this.outputBits=t[this.codingPos]-t[this.codingPos-1]}}else{n=8;o=0;do{if("number"!=typeof this.outputBits)throw new FormatError(\'Invalid /CCITTFaxDecode data, "outputBits" must be a number.\');if(this.outputBits>n){o<<=n;1&this.codingPos||(o|=255>\
->8-n);this.outputBits-=n;n=0}else{o<<=this.outputBits;1&this.codingPos||(o|=255>>8-this.outputBits);n-=this.outputBits;this.outputBits=0;if(t[this.codingPos]<a){this.codingPos++;this.outputBits=t[this.codingPos]-t[this.codingPos-1]}else if(n>0){o<<=n;n=0}}}while(n)}this.black&&(o^=255);return o}_addPixels(e,t){const a=this.codingLine;let r=this.codingPos;if(e>a[r]){if(e>this.columns){info("row is wrong length");this.err=!0;e=this.columns}1&r^t&&++r;a[r]=e}this.codingPos=r}_addPixelsNeg(e,t){cons\
-t a=this.codingLine;let r=this.codingPos;if(e>a[r]){if(e>this.columns){info("row is wrong length");this.err=!0;e=this.columns}1&r^t&&++r;a[r]=e}else if(e<a[r]){if(e<0){info("invalid code");this.err=!0;e=0}for(;r>0&&e<a[r-1];)--r;a[r]=e}this.codingPos=r}_findTableCode(e,t,a,r){const i=r||0;for(let r=e;r<=t;++r){let e=this._lookBits(r);if(e===Ja)return[!0,1,!1];r<t&&(e<<=t-r);if(!i||e>=i){const t=a[e-i];if(t[0]===r){this._eatBits(r);return[!0,t[1],!0]}}}return[!1,0,!1]}_getTwoDimCode(){let e,t=0;i\
-f(this.eoblock){t=this._lookBits(7);e=Ya[t];if(e?.[0]>0){this._eatBits(e[0]);return e[1]}}else{const e=this._findTableCode(1,7,Ya);if(e[0]&&e[2])return e[1]}info("Bad two dim code");return Ja}_getWhiteCode(){let e,t=0;if(this.eoblock){t=this._lookBits(12);if(t===Ja)return 1;e=t>>5?Qa[t>>3]:Za[t];if(e[0]>0){this._eatBits(e[0]);return e[1]}}else{let e=this._findTableCode(1,9,Qa);if(e[0])return e[1];e=this._findTableCode(11,12,Za);if(e[0])return e[1]}info("bad white code");this._eatBits(1);return 1\
-}_getBlackCode(){let e,t;if(this.eoblock){e=this._lookBits(13);if(e===Ja)return 1;t=e>>7?!(e>>9)&&e>>7?tr[(e>>1)-64]:ar[e>>7]:er[e];if(t[0]>0){this._eatBits(t[0]);return t[1]}}else{let e=this._findTableCode(2,6,ar);if(e[0])return e[1];e=this._findTableCode(7,12,tr,64);if(e[0])return e[1];e=this._findTableCode(10,13,er);if(e[0])return e[1]}info("bad black code");this._eatBits(1);return 1}_lookBits(e){let t;for(;this.inputBits<e;){if(-1===(t=this.source.next()))return 0===this.inputBits?Ja:this.in\
-putBuf<<e-this.inputBits&65535>>16-e;this.inputBuf=this.inputBuf<<8|t;this.inputBits+=8}return this.inputBuf>>this.inputBits-e&65535>>16-e}_eatBits(e){(this.inputBits-=e)<0&&(this.inputBits=0)}}class CCITTFaxStream extends DecodeStream{constructor(e,t,a){super(t);this.str=e;this.dict=e.dict;a instanceof Dict||(a=Dict.empty);const r={next:()=>e.getByte()};this.ccittFaxDecoder=new CCITTFaxDecoder(r,{K:a.get("K"),EndOfLine:a.get("EndOfLine"),EncodedByteAlign:a.get("EncodedByteAlign"),Columns:a.get(\
-"Columns"),Rows:a.get("Rows"),EndOfBlock:a.get("EndOfBlock"),BlackIs1:a.get("BlackIs1")})}readBlock(){for(;!this.eof;){const e=this.ccittFaxDecoder.readNextChar();if(-1===e){this.eof=!0;return}this.ensureBuffer(this.bufferLength+1);this.buffer[this.bufferLength++]=e}}}const rr=new Int32Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]),ir=new Int32Array([3,4,5,6,7,8,9,10,65547,65549,65551,65553,131091,131095,131099,131103,196643,196651,196659,196667,262211,262227,262243,262259,327811,327843\
-,327875,327907,258,258,258]),nr=new Int32Array([1,2,3,4,65541,65543,131081,131085,196625,196633,262177,262193,327745,327777,393345,393409,459009,459137,524801,525057,590849,591361,657409,658433,724993,727041,794625,798721,868353,876545]),sr=[new Int32Array([459008,524368,524304,524568,459024,524400,524336,590016,459016,524384,524320,589984,524288,524416,524352,590048,459012,524376,524312,589968,459028,524408,524344,590032,459020,524392,524328,59e4,524296,524424,524360,590064,459010,524372,524308\
-,524572,459026,524404,524340,590024,459018,524388,524324,589992,524292,524420,524356,590056,459014,524380,524316,589976,459030,524412,524348,590040,459022,524396,524332,590008,524300,524428,524364,590072,459009,524370,524306,524570,459025,524402,524338,590020,459017,524386,524322,589988,524290,524418,524354,590052,459013,524378,524314,589972,459029,524410,524346,590036,459021,524394,524330,590004,524298,524426,524362,590068,459011,524374,524310,524574,459027,524406,524342,590028,459019,524390,52\
-4326,589996,524294,524422,524358,590060,459015,524382,524318,589980,459031,524414,524350,590044,459023,524398,524334,590012,524302,524430,524366,590076,459008,524369,524305,524569,459024,524401,524337,590018,459016,524385,524321,589986,524289,524417,524353,590050,459012,524377,524313,589970,459028,524409,524345,590034,459020,524393,524329,590002,524297,524425,524361,590066,459010,524373,524309,524573,459026,524405,524341,590026,459018,524389,524325,589994,524293,524421,524357,590058,459014,52438\
-1,524317,589978,459030,524413,524349,590042,459022,524397,524333,590010,524301,524429,524365,590074,459009,524371,524307,524571,459025,524403,524339,590022,459017,524387,524323,589990,524291,524419,524355,590054,459013,524379,524315,589974,459029,524411,524347,590038,459021,524395,524331,590006,524299,524427,524363,590070,459011,524375,524311,524575,459027,524407,524343,590030,459019,524391,524327,589998,524295,524423,524359,590062,459015,524383,524319,589982,459031,524415,524351,590046,459023,5\
-24399,524335,590014,524303,524431,524367,590078,459008,524368,524304,524568,459024,524400,524336,590017,459016,524384,524320,589985,524288,524416,524352,590049,459012,524376,524312,589969,459028,524408,524344,590033,459020,524392,524328,590001,524296,524424,524360,590065,459010,524372,524308,524572,459026,524404,524340,590025,459018,524388,524324,589993,524292,524420,524356,590057,459014,524380,524316,589977,459030,524412,524348,590041,459022,524396,524332,590009,524300,524428,524364,590073,4590\
-09,524370,524306,524570,459025,524402,524338,590021,459017,524386,524322,589989,524290,524418,524354,590053,459013,524378,524314,589973,459029,524410,524346,590037,459021,524394,524330,590005,524298,524426,524362,590069,459011,524374,524310,524574,459027,524406,524342,590029,459019,524390,524326,589997,524294,524422,524358,590061,459015,524382,524318,589981,459031,524414,524350,590045,459023,524398,524334,590013,524302,524430,524366,590077,459008,524369,524305,524569,459024,524401,524337,590019,\
-459016,524385,524321,589987,524289,524417,524353,590051,459012,524377,524313,589971,459028,524409,524345,590035,459020,524393,524329,590003,524297,524425,524361,590067,459010,524373,524309,524573,459026,524405,524341,590027,459018,524389,524325,589995,524293,524421,524357,590059,459014,524381,524317,589979,459030,524413,524349,590043,459022,524397,524333,590011,524301,524429,524365,590075,459009,524371,524307,524571,459025,524403,524339,590023,459017,524387,524323,589991,524291,524419,524355,590\
-055,459013,524379,524315,589975,459029,524411,524347,590039,459021,524395,524331,590007,524299,524427,524363,590071,459011,524375,524311,524575,459027,524407,524343,590031,459019,524391,524327,589999,524295,524423,524359,590063,459015,524383,524319,589983,459031,524415,524351,590047,459023,524399,524335,590015,524303,524431,524367,590079]),9],or=[new Int32Array([327680,327696,327688,327704,327684,327700,327692,327708,327682,327698,327690,327706,327686,327702,327694,0,327681,327697,327689,327705,\
-327685,327701,327693,327709,327683,327699,327691,327707,327687,327703,327695,0]),5];class FlateStream extends DecodeStream{constructor(e,t){super(t);this.str=e;this.dict=e.dict;const a=e.getByte(),r=e.getByte();if(-1===a||-1===r)throw new FormatError(`Invalid header in flate stream: ${a}, ${r}`);if(8!=(15&a))throw new FormatError(`Unknown compression method in flate stream: ${a}, ${r}`);if(((a<<8)+r)%31!=0)throw new FormatError(`Bad FCHECK in flate stream: ${a}, ${r}`);if(32&r)throw new FormatEr\
-ror(`FDICT bit set in flate stream: ${a}, ${r}`);this.codeSize=0;this.codeBuf=0}async getImageData(e,t){const a=await this.asyncGetBytes();return a?a.length<=e?a:a.subarray(0,e):this.getBytes(e)}async asyncGetBytes(){this.str.reset();const e=this.str.getBytes();try{const{readable:t,writable:a}=new DecompressionStream("deflate"),r=a.getWriter();await r.ready;r.write(e).then((async()=>{await r.ready;await r.close()})).catch((()=>{}));const i=[];let n=0;for await(const e of t){i.push(e);n+=e.byteLe\
-ngth}const s=new Uint8Array(n);let o=0;for(const e of i){s.set(e,o);o+=e.byteLength}return s}catch{this.str=new Stream(e,2,e.length,this.str.dict);this.reset();return null}}get isAsync(){return!0}getBits(e){const t=this.str;let a,r=this.codeSize,i=this.codeBuf;for(;r<e;){if(-1===(a=t.getByte()))throw new FormatError("Bad encoding in flate stream");i|=a<<r;r+=8}a=i&(1<<e)-1;this.codeBuf=i>>e;this.codeSize=r-=e;return a}getCode(e){const t=this.str,a=e[0],r=e[1];let i,n=this.codeSize,s=this.codeBuf\
-;for(;n<r&&-1!==(i=t.getByte());){s|=i<<n;n+=8}const o=a[s&(1<<r)-1],c=o>>16,l=65535&o;if(c<1||n<c)throw new FormatError("Bad encoding in flate stream");this.codeBuf=s>>c;this.codeSize=n-c;return l}generateHuffmanTable(e){const t=e.length;let a,r=0;for(a=0;a<t;++a)e[a]>r&&(r=e[a]);const i=1<<r,n=new Int32Array(i);for(let s=1,o=0,c=2;s<=r;++s,o<<=1,c<<=1)for(let r=0;r<t;++r)if(e[r]===s){let e=0,t=o;for(a=0;a<s;++a){e=e<<1|1&t;t>>=1}for(a=e;a<i;a+=c)n[a]=s<<16|r;++o}return[n,r]}#X(e){info(e);this.\
-eof=!0}readBlock(){let e,t,a;const r=this.str;try{t=this.getBits(3)}catch(e){this.#X(e.message);return}1&t&&(this.eof=!0);t>>=1;if(0===t){let t;if(-1===(t=r.getByte())){this.#X("Bad block header in flate stream");return}let a=t;if(-1===(t=r.getByte())){this.#X("Bad block header in flate stream");return}a|=t<<8;if(-1===(t=r.getByte())){this.#X("Bad block header in flate stream");return}let i=t;if(-1===(t=r.getByte())){this.#X("Bad block header in flate stream");return}i|=t<<8;if(i!==(65535&~a)&&(\
-0!==a||0!==i))throw new FormatError("Bad uncompressed block length in flate stream");this.codeBuf=0;this.codeSize=0;const n=this.bufferLength,s=n+a;e=this.ensureBuffer(s);this.bufferLength=s;if(0===a)-1===r.peekByte()&&(this.eof=!0);else{const t=r.getBytes(a);e.set(t,n);t.length<a&&(this.eof=!0)}return}let i,n;if(1===t){i=sr;n=or}else{if(2!==t)throw new FormatError("Unknown block type in flate stream");{const e=this.getBits(5)+257,t=this.getBits(5)+1,r=this.getBits(4)+4,s=new Uint8Array(rr.lengt\
-h);let o;for(o=0;o<r;++o)s[rr[o]]=this.getBits(3);const c=this.generateHuffmanTable(s);a=0;o=0;const l=e+t,h=new Uint8Array(l);let u,d,f;for(;o<l;){const e=this.getCode(c);if(16===e){u=2;d=3;f=a}else if(17===e){u=3;d=3;f=a=0}else{if(18!==e){h[o++]=a=e;continue}u=7;d=11;f=a=0}let t=this.getBits(u)+d;for(;t-- >0;)h[o++]=f}i=this.generateHuffmanTable(h.subarray(0,e));n=this.generateHuffmanTable(h.subarray(e,l))}}e=this.buffer;let s=e?e.length:0,o=this.bufferLength;for(;;){let t=this.getCode(i);if(t\
-<256){if(o+1>=s){e=this.ensureBuffer(o+1);s=e.length}e[o++]=t;continue}if(256===t){this.bufferLength=o;return}t-=257;t=ir[t];let r=t>>16;r>0&&(r=this.getBits(r));a=(65535&t)+r;t=this.getCode(n);t=nr[t];r=t>>16;r>0&&(r=this.getBits(r));const c=(65535&t)+r;if(o+a>=s){e=this.ensureBuffer(o+a);s=e.length}for(let t=0;t<a;++t,++o)e[o]=e[o-c]}}}const cr=[{qe:22017,nmps:1,nlps:1,switchFlag:1},{qe:13313,nmps:2,nlps:6,switchFlag:0},{qe:6145,nmps:3,nlps:9,switchFlag:0},{qe:2753,nmps:4,nlps:12,switchFlag:0}\
-,{qe:1313,nmps:5,nlps:29,switchFlag:0},{qe:545,nmps:38,nlps:33,switchFlag:0},{qe:22017,nmps:7,nlps:6,switchFlag:1},{qe:21505,nmps:8,nlps:14,switchFlag:0},{qe:18433,nmps:9,nlps:14,switchFlag:0},{qe:14337,nmps:10,nlps:14,switchFlag:0},{qe:12289,nmps:11,nlps:17,switchFlag:0},{qe:9217,nmps:12,nlps:18,switchFlag:0},{qe:7169,nmps:13,nlps:20,switchFlag:0},{qe:5633,nmps:29,nlps:21,switchFlag:0},{qe:22017,nmps:15,nlps:14,switchFlag:1},{qe:21505,nmps:16,nlps:14,switchFlag:0},{qe:20737,nmps:17,nlps:15,swit\
-chFlag:0},{qe:18433,nmps:18,nlps:16,switchFlag:0},{qe:14337,nmps:19,nlps:17,switchFlag:0},{qe:13313,nmps:20,nlps:18,switchFlag:0},{qe:12289,nmps:21,nlps:19,switchFlag:0},{qe:10241,nmps:22,nlps:19,switchFlag:0},{qe:9217,nmps:23,nlps:20,switchFlag:0},{qe:8705,nmps:24,nlps:21,switchFlag:0},{qe:7169,nmps:25,nlps:22,switchFlag:0},{qe:6145,nmps:26,nlps:23,switchFlag:0},{qe:5633,nmps:27,nlps:24,switchFlag:0},{qe:5121,nmps:28,nlps:25,switchFlag:0},{qe:4609,nmps:29,nlps:26,switchFlag:0},{qe:4353,nmps:30,\
-nlps:27,switchFlag:0},{qe:2753,nmps:31,nlps:28,switchFlag:0},{qe:2497,nmps:32,nlps:29,switchFlag:0},{qe:2209,nmps:33,nlps:30,switchFlag:0},{qe:1313,nmps:34,nlps:31,switchFlag:0},{qe:1089,nmps:35,nlps:32,switchFlag:0},{qe:673,nmps:36,nlps:33,switchFlag:0},{qe:545,nmps:37,nlps:34,switchFlag:0},{qe:321,nmps:38,nlps:35,switchFlag:0},{qe:273,nmps:39,nlps:36,switchFlag:0},{qe:133,nmps:40,nlps:37,switchFlag:0},{qe:73,nmps:41,nlps:38,switchFlag:0},{qe:37,nmps:42,nlps:39,switchFlag:0},{qe:21,nmps:43,nlps\
-:40,switchFlag:0},{qe:9,nmps:44,nlps:41,switchFlag:0},{qe:5,nmps:45,nlps:42,switchFlag:0},{qe:1,nmps:45,nlps:43,switchFlag:0},{qe:22017,nmps:46,nlps:46,switchFlag:0}];class ArithmeticDecoder{constructor(e,t,a){this.data=e;this.bp=t;this.dataEnd=a;this.chigh=e[t];this.clow=0;this.byteIn();this.chigh=this.chigh<<7&65535|this.clow>>9&127;this.clow=this.clow<<7&65535;this.ct-=7;this.a=32768}byteIn(){const e=this.data;let t=this.bp;if(255===e[t])if(e[t+1]>143){this.clow+=65280;this.ct=8}else{t++;this\
-.clow+=e[t]<<9;this.ct=7;this.bp=t}else{t++;this.clow+=t<this.dataEnd?e[t]<<8:65280;this.ct=8;this.bp=t}if(this.clow>65535){this.chigh+=this.clow>>16;this.clow&=65535}}readBit(e,t){let a=e[t]>>1,r=1&e[t];const i=cr[a],n=i.qe;let s,o=this.a-n;if(this.chigh<n)if(o<n){o=n;s=r;a=i.nmps}else{o=n;s=1^r;1===i.switchFlag&&(r=s);a=i.nlps}else{this.chigh-=n;if(32768&o){this.a=o;return r}if(o<n){s=1^r;1===i.switchFlag&&(r=s);a=i.nlps}else{s=r;a=i.nmps}}do{0===this.ct&&this.byteIn();o<<=1;this.chigh=this.ch\
-igh<<1&65535|this.clow>>15&1;this.clow=this.clow<<1&65535;this.ct--}while(!(32768&o));this.a=o;e[t]=a<<1|r;return s}}class Jbig2Error extends fa{constructor(e){super(e,"Jbig2Error")}}class ContextCache{getContexts(e){return e in this?this[e]:this[e]=new Int8Array(65536)}}class DecodingContext{constructor(e,t,a){this.data=e;this.start=t;this.end=a}get decoder(){return shadow(this,"decoder",new ArithmeticDecoder(this.data,this.start,this.end))}get contextCache(){return shadow(this,"contextCache",n\
-ew ContextCache)}}function decodeInteger(e,t,a){const r=e.getContexts(t);let i=1;function readBits(e){let t=0;for(let n=0;n<e;n++){const e=a.readBit(r,i);i=i<256?i<<1|e:511&(i<<1|e)|256;t=t<<1|e}return t>>>0}const n=readBits(1),s=readBits(1)?readBits(1)?readBits(1)?readBits(1)?readBits(1)?readBits(32)+4436:readBits(12)+340:readBits(8)+84:readBits(6)+20:readBits(4)+4:readBits(2);let o;0===n?o=s:s>0&&(o=-s);return o>=-2147483648&&o<=va?o:null}function decodeIAID(e,t,a){const r=e.getContexts("IAID"\
-);let i=1;for(let e=0;e<a;e++){i=i<<1|t.readBit(r,i)}return a<31?i&(1<<a)-1:2147483647&i}const lr=["SymbolDictionary",null,null,null,"IntermediateTextRegion",null,"ImmediateTextRegion","ImmediateLosslessTextRegion",null,null,null,null,null,null,null,null,"PatternDictionary",null,null,null,"IntermediateHalftoneRegion",null,"ImmediateHalftoneRegion","ImmediateLosslessHalftoneRegion",null,null,null,null,null,null,null,null,null,null,null,null,"IntermediateGenericRegion",null,"ImmediateGenericRegion\
-","ImmediateLosslessGenericRegion","IntermediateGenericRefinementRegion",null,"ImmediateGenericRefinementRegion","ImmediateLosslessGenericRefinementRegion",null,null,null,null,"PageInformation","EndOfPage","EndOfStripe","EndOfFile","Profiles","Tables",null,null,null,null,null,null,null,null,"Extension"],hr=[[{x:-1,y:-2},{x:0,y:-2},{x:1,y:-2},{x:-2,y:-1},{x:-1,y:-1},{x:0,y:-1},{x:1,y:-1},{x:2,y:-1},{x:-4,y:0},{x:-3,y:0},{x:-2,y:0},{x:-1,y:0}],[{x:-1,y:-2},{x:0,y:-2},{x:1,y:-2},{x:2,y:-2},{x:-2,y:\
--1},{x:-1,y:-1},{x:0,y:-1},{x:1,y:-1},{x:2,y:-1},{x:-3,y:0},{x:-2,y:0},{x:-1,y:0}],[{x:-1,y:-2},{x:0,y:-2},{x:1,y:-2},{x:-2,y:-1},{x:-1,y:-1},{x:0,y:-1},{x:1,y:-1},{x:-2,y:0},{x:-1,y:0}],[{x:-3,y:-1},{x:-2,y:-1},{x:-1,y:-1},{x:0,y:-1},{x:1,y:-1},{x:-4,y:0},{x:-3,y:0},{x:-2,y:0},{x:-1,y:0}]],ur=[{coding:[{x:0,y:-1},{x:1,y:-1},{x:-1,y:0}],reference:[{x:0,y:-1},{x:1,y:-1},{x:-1,y:0},{x:0,y:0},{x:1,y:0},{x:-1,y:1},{x:0,y:1},{x:1,y:1}]},{coding:[{x:-1,y:-1},{x:0,y:-1},{x:1,y:-1},{x:-1,y:0}],reference\
-:[{x:0,y:-1},{x:-1,y:0},{x:0,y:0},{x:1,y:0},{x:0,y:1},{x:1,y:1}]}],dr=[39717,1941,229,405],fr=[32,8];function decodeBitmap(e,t,a,r,i,n,s,o){if(e){return decodeMMRBitmap(new Reader(o.data,o.start,o.end),t,a,!1)}if(0===r&&!n&&!i&&4===s.length&&3===s[0].x&&-1===s[0].y&&-3===s[1].x&&-1===s[1].y&&2===s[2].x&&-2===s[2].y&&-2===s[3].x&&-2===s[3].y)return function decodeBitmapTemplate0(e,t,a){const r=a.decoder,i=a.contextCache.getContexts("GB"),n=[];let s,o,c,l,h,u,d;for(o=0;o<t;o++){h=n[o]=new Uint8Arr\
-ay(e);u=o<1?h:n[o-1];d=o<2?h:n[o-2];s=d[0]<<13|d[1]<<12|d[2]<<11|u[0]<<7|u[1]<<6|u[2]<<5|u[3]<<4;for(c=0;c<e;c++){h[c]=l=r.readBit(i,s);s=(31735&s)<<1|(c+3<e?d[c+3]<<11:0)|(c+4<e?u[c+4]<<4:0)|l}}return n}(t,a,o);const c=!!n,l=hr[r].concat(s);l.sort(((e,t)=>e.y-t.y||e.x-t.x));const h=l.length,u=new Int8Array(h),d=new Int8Array(h),f=[];let g,p,m=0,b=0,y=0,w=0;for(p=0;p<h;p++){u[p]=l[p].x;d[p]=l[p].y;b=Math.min(b,l[p].x);y=Math.max(y,l[p].x);w=Math.min(w,l[p].y);p<h-1&&l[p].y===l[p+1].y&&l[p].x===l\
-[p+1].x-1?m|=1<<h-1-p:f.push(p)}const x=f.length,S=new Int8Array(x),k=new Int8Array(x),C=new Uint16Array(x);for(g=0;g<x;g++){p=f[g];S[g]=l[p].x;k[g]=l[p].y;C[g]=1<<h-1-p}const v=-b,F=-w,T=t-y,O=dr[r];let M=new Uint8Array(t);const D=[],R=o.decoder,N=o.contextCache.getContexts("GB");let E,L,j,_,U,X=0,q=0;for(let e=0;e<a;e++){if(i){X^=R.readBit(N,O);if(X){D.push(M);continue}}M=new Uint8Array(M);D.push(M);for(E=0;E<t;E++){if(c&&n[e][E]){M[E]=0;continue}if(E>=v&&E<T&&e>=F){q=q<<1&m;for(p=0;p<x;p++){L\
-=e+k[p];j=E+S[p];_=D[L][j];if(_){_=C[p];q|=_}}}else{q=0;U=h-1;for(p=0;p<h;p++,U--){j=E+u[p];if(j>=0&&j<t){L=e+d[p];if(L>=0){_=D[L][j];_&&(q|=_<<U)}}}}const a=R.readBit(N,q);M[E]=a}}return D}function decodeRefinement(e,t,a,r,i,n,s,o,c){let l=ur[a].coding;0===a&&(l=l.concat([o[0]]));const h=l.length,u=new Int32Array(h),d=new Int32Array(h);let f;for(f=0;f<h;f++){u[f]=l[f].x;d[f]=l[f].y}let g=ur[a].reference;0===a&&(g=g.concat([o[1]]));const p=g.length,m=new Int32Array(p),b=new Int32Array(p);for(f=0\
-;f<p;f++){m[f]=g[f].x;b[f]=g[f].y}const y=r[0].length,w=r.length,x=fr[a],S=[],k=c.decoder,C=c.contextCache.getContexts("GR");let v=0;for(let a=0;a<t;a++){if(s){v^=k.readBit(C,x);if(v)throw new Jbig2Error("prediction is not supported")}const t=new Uint8Array(e);S.push(t);for(let s=0;s<e;s++){let o,c,l=0;for(f=0;f<h;f++){o=a+d[f];c=s+u[f];o<0||c<0||c>=e?l<<=1:l=l<<1|S[o][c]}for(f=0;f<p;f++){o=a+b[f]-n;c=s+m[f]-i;o<0||o>=w||c<0||c>=y?l<<=1:l=l<<1|r[o][c]}const g=k.readBit(C,l);t[s]=g}}return S}func\
-tion decodeTextRegion(e,t,a,r,i,n,s,o,c,l,h,u,d,f,g,p,m,b,y){if(e&&t)throw new Jbig2Error("refinement with Huffman is not supported");const w=[];let x,S;for(x=0;x<r;x++){S=new Uint8Array(a);i&&S.fill(i);w.push(S)}const k=m.decoder,C=m.contextCache;let v=e?-f.tableDeltaT.decode(y):-decodeInteger(C,"IADT",k),F=0;x=0;for(;x<n;){v+=e?f.tableDeltaT.decode(y):decodeInteger(C,"IADT",k);F+=e?f.tableFirstS.decode(y):decodeInteger(C,"IAFS",k);let r=F;for(;;){let i=0;s>1&&(i=e?y.readBits(b):decodeInteger(C\
-,"IAIT",k));const n=s*v+i,F=e?f.symbolIDTable.decode(y):decodeIAID(C,k,c),T=t&&(e?y.readBit():decodeInteger(C,"IARI",k));let O=o[F],M=O[0].length,D=O.length;if(T){const e=decodeInteger(C,"IARDW",k),t=decodeInteger(C,"IARDH",k);M+=e;D+=t;O=decodeRefinement(M,D,g,O,(e>>1)+decodeInteger(C,"IARDX",k),(t>>1)+decodeInteger(C,"IARDY",k),!1,p,m)}let R=0;l?1&u?R=D-1:r+=D-1:u>1?r+=M-1:R=M-1;const N=n-(1&u?0:D-1),E=r-(2&u?M-1:0);let L,j,_;if(l)for(L=0;L<D;L++){S=w[E+L];if(!S)continue;_=O[L];const e=Math.mi\
-n(a-N,M);switch(d){case 0:for(j=0;j<e;j++)S[N+j]|=_[j];break;case 2:for(j=0;j<e;j++)S[N+j]^=_[j];break;default:throw new Jbig2Error(`operator ${d} is not supported`)}}else for(j=0;j<D;j++){S=w[N+j];if(S){_=O[j];switch(d){case 0:for(L=0;L<M;L++)S[E+L]|=_[L];break;case 2:for(L=0;L<M;L++)S[E+L]^=_[L];break;default:throw new Jbig2Error(`operator ${d} is not supported`)}}}x++;const U=e?f.tableDeltaS.decode(y):decodeInteger(C,"IADS",k);if(null===U)break;r+=R+U+h}}return w}function readSegmentHeader(e,\
-t){const a={};a.number=readUint32(e,t);const r=e[t+4],i=63&r;if(!lr[i])throw new Jbig2Error("invalid segment type: "+i);a.type=i;a.typeName=lr[i];a.deferredNonRetain=!!(128&r);const n=!!(64&r),s=e[t+5];let o=s>>5&7;const c=[31&s];let l=t+6;if(7===s){o=536870911&readUint32(e,l-1);l+=3;let t=o+7>>3;c[0]=e[l++];for(;--t>0;)c.push(e[l++])}else if(5===s||6===s)throw new Jbig2Error("invalid referred-to flags");a.retainBits=c;let h=4;a.number<=256?h=1:a.number<=65536&&(h=2);const u=[];let d,f;for(d=0;d\
-<o;d++){let t;t=1===h?e[l]:2===h?readUint16(e,l):readUint32(e,l);u.push(t);l+=h}a.referredTo=u;if(n){a.pageAssociation=readUint32(e,l);l+=4}else a.pageAssociation=e[l++];a.length=readUint32(e,l);l+=4;if(4294967295===a.length){if(38!==i)throw new Jbig2Error("invalid unknown segment length");{const t=readRegionSegmentInformation(e,l),r=!!(1&e[l+gr]),i=6,n=new Uint8Array(i);if(!r){n[0]=255;n[1]=172}n[2]=t.height>>>24&255;n[3]=t.height>>16&255;n[4]=t.height>>8&255;n[5]=255&t.height;for(d=l,f=e.lengt\
-h;d<f;d++){let t=0;for(;t<i&&n[t]===e[d+t];)t++;if(t===i){a.length=d+i;break}}if(4294967295===a.length)throw new Jbig2Error("segment end was not found")}}a.headerEnd=l;return a}function readSegments(e,t,a,r){const i=[];let n=a;for(;n<r;){const a=readSegmentHeader(t,n);n=a.headerEnd;const r={header:a,data:t};if(!e.randomAccess){r.start=n;n+=a.length;r.end=n}i.push(r);if(51===a.type)break}if(e.randomAccess)for(let e=0,t=i.length;e<t;e++){i[e].start=n;n+=i[e].header.length;i[e].end=n}return i}funct\
-ion readRegionSegmentInformation(e,t){return{width:readUint32(e,t),height:readUint32(e,t+4),x:readUint32(e,t+8),y:readUint32(e,t+12),combinationOperator:7&e[t+16]}}const gr=17;function processSegment(e,t){const a=e.header,r=e.data,i=e.end;let n,s,o,c,l=e.start;switch(a.type){case 0:const e={},t=readUint16(r,l);e.huffman=!!(1&t);e.refinement=!!(2&t);e.huffmanDHSelector=t>>2&3;e.huffmanDWSelector=t>>4&3;e.bitmapSizeSelector=t>>6&1;e.aggregationInstancesSelector=t>>7&1;e.bitmapCodingContextUsed=!!(\
-256&t);e.bitmapCodingContextRetained=!!(512&t);e.template=t>>10&3;e.refinementTemplate=t>>12&1;l+=2;if(!e.huffman){c=0===e.template?4:1;s=[];for(o=0;o<c;o++){s.push({x:readInt8(r,l),y:readInt8(r,l+1)});l+=2}e.at=s}if(e.refinement&&!e.refinementTemplate){s=[];for(o=0;o<2;o++){s.push({x:readInt8(r,l),y:readInt8(r,l+1)});l+=2}e.refinementAt=s}e.numberOfExportedSymbols=readUint32(r,l);l+=4;e.numberOfNewSymbols=readUint32(r,l);l+=4;n=[e,a.number,a.referredTo,r,l,i];break;case 6:case 7:const h={};h.in\
-fo=readRegionSegmentInformation(r,l);l+=gr;const u=readUint16(r,l);l+=2;h.huffman=!!(1&u);h.refinement=!!(2&u);h.logStripSize=u>>2&3;h.stripSize=1<<h.logStripSize;h.referenceCorner=u>>4&3;h.transposed=!!(64&u);h.combinationOperator=u>>7&3;h.defaultPixelValue=u>>9&1;h.dsOffset=u<<17>>27;h.refinementTemplate=u>>15&1;if(h.huffman){const e=readUint16(r,l);l+=2;h.huffmanFS=3&e;h.huffmanDS=e>>2&3;h.huffmanDT=e>>4&3;h.huffmanRefinementDW=e>>6&3;h.huffmanRefinementDH=e>>8&3;h.huffmanRefinementDX=e>>10&3\
-;h.huffmanRefinementDY=e>>12&3;h.huffmanRefinementSizeSelector=!!(16384&e)}if(h.refinement&&!h.refinementTemplate){s=[];for(o=0;o<2;o++){s.push({x:readInt8(r,l),y:readInt8(r,l+1)});l+=2}h.refinementAt=s}h.numberOfSymbolInstances=readUint32(r,l);l+=4;n=[h,a.referredTo,r,l,i];break;case 16:const d={},f=r[l++];d.mmr=!!(1&f);d.template=f>>1&3;d.patternWidth=r[l++];d.patternHeight=r[l++];d.maxPatternIndex=readUint32(r,l);l+=4;n=[d,a.number,r,l,i];break;case 22:case 23:const g={};g.info=readRegionSegm\
-entInformation(r,l);l+=gr;const p=r[l++];g.mmr=!!(1&p);g.template=p>>1&3;g.enableSkip=!!(8&p);g.combinationOperator=p>>4&7;g.defaultPixelValue=p>>7&1;g.gridWidth=readUint32(r,l);l+=4;g.gridHeight=readUint32(r,l);l+=4;g.gridOffsetX=4294967295&readUint32(r,l);l+=4;g.gridOffsetY=4294967295&readUint32(r,l);l+=4;g.gridVectorX=readUint16(r,l);l+=2;g.gridVectorY=readUint16(r,l);l+=2;n=[g,a.referredTo,r,l,i];break;case 38:case 39:const m={};m.info=readRegionSegmentInformation(r,l);l+=gr;const b=r[l++];m\
-.mmr=!!(1&b);m.template=b>>1&3;m.prediction=!!(8&b);if(!m.mmr){c=0===m.template?4:1;s=[];for(o=0;o<c;o++){s.push({x:readInt8(r,l),y:readInt8(r,l+1)});l+=2}m.at=s}n=[m,r,l,i];break;case 48:const y={width:readUint32(r,l),height:readUint32(r,l+4),resolutionX:readUint32(r,l+8),resolutionY:readUint32(r,l+12)};4294967295===y.height&&delete y.height;const w=r[l+16];readUint16(r,l+17);y.lossless=!!(1&w);y.refinement=!!(2&w);y.defaultPixelValue=w>>2&1;y.combinationOperator=w>>3&3;y.requiresBuffer=!!(32&w\
-);y.combinationOperatorOverride=!!(64&w);n=[y];break;case 49:case 50:case 51:case 62:break;case 53:n=[a.number,r,l,i];break;default:throw new Jbig2Error(`segment type ${a.typeName}(${a.type}) is not implemented`)}const h="on"+a.typeName;h in t&&t[h].apply(t,n)}function processSegments(e,t){for(let a=0,r=e.length;a<r;a++)processSegment(e[a],t)}class SimpleSegmentVisitor{onPageInformation(e){this.currentPageInfo=e;const t=e.width+7>>3,a=new Uint8ClampedArray(t*e.height);e.defaultPixelValue&&a.fill\
-(255);this.buffer=a}drawBitmap(e,t){const a=this.currentPageInfo,r=e.width,i=e.height,n=a.width+7>>3,s=a.combinationOperatorOverride?e.combinationOperator:a.combinationOperator,o=this.buffer,c=128>>(7&e.x);let l,h,u,d,f=e.y*n+(e.x>>3);switch(s){case 0:for(l=0;l<i;l++){u=c;d=f;for(h=0;h<r;h++){t[l][h]&&(o[d]|=u);u>>=1;if(!u){u=128;d++}}f+=n}break;case 2:for(l=0;l<i;l++){u=c;d=f;for(h=0;h<r;h++){t[l][h]&&(o[d]^=u);u>>=1;if(!u){u=128;d++}}f+=n}break;default:throw new Jbig2Error(`operator ${s} is no\
-t supported`)}}onImmediateGenericRegion(e,t,a,r){const i=e.info,n=new DecodingContext(t,a,r),s=decodeBitmap(e.mmr,i.width,i.height,e.template,e.prediction,null,e.at,n);this.drawBitmap(i,s)}onImmediateLosslessGenericRegion(){this.onImmediateGenericRegion(...arguments)}onSymbolDictionary(e,t,a,r,i,n){let s,o;if(e.huffman){s=function getSymbolDictionaryHuffmanTables(e,t,a){let r,i,n,s,o=0;switch(e.huffmanDHSelector){case 0:case 1:r=getStandardTable(e.huffmanDHSelector+4);break;case 3:r=getCustomHuf\
-fmanTable(o,t,a);o++;break;default:throw new Jbig2Error("invalid Huffman DH selector")}switch(e.huffmanDWSelector){case 0:case 1:i=getStandardTable(e.huffmanDWSelector+2);break;case 3:i=getCustomHuffmanTable(o,t,a);o++;break;default:throw new Jbig2Error("invalid Huffman DW selector")}if(e.bitmapSizeSelector){n=getCustomHuffmanTable(o,t,a);o++}else n=getStandardTable(1);s=e.aggregationInstancesSelector?getCustomHuffmanTable(o,t,a):getStandardTable(1);return{tableDeltaHeight:r,tableDeltaWidth:i,ta\
-bleBitmapSize:n,tableAggregateInstances:s}}(e,a,this.customTables);o=new Reader(r,i,n)}let c=this.symbols;c||(this.symbols=c={});const l=[];for(const e of a){const t=c[e];t&&l.push(...t)}const h=new DecodingContext(r,i,n);c[t]=function decodeSymbolDictionary(e,t,a,r,i,n,s,o,c,l,h,u){if(e&&t)throw new Jbig2Error("symbol refinement with Huffman is not supported");const d=[];let f=0,g=log2(a.length+r);const p=h.decoder,m=h.contextCache;let b,y;if(e){b=getStandardTable(1);y=[];g=Math.max(g,1)}for(;d\
-.length<r;){f+=e?n.tableDeltaHeight.decode(u):decodeInteger(m,"IADH",p);let r=0,i=0;const b=e?y.length:0;for(;;){const b=e?n.tableDeltaWidth.decode(u):decodeInteger(m,"IADW",p);if(null===b)break;r+=b;i+=r;let w;if(t){const i=decodeInteger(m,"IAAI",p);if(i>1)w=decodeTextRegion(e,t,r,f,0,i,1,a.concat(d),g,0,0,1,0,n,c,l,h,0,u);else{const e=decodeIAID(m,p,g),t=decodeInteger(m,"IARDX",p),i=decodeInteger(m,"IARDY",p);w=decodeRefinement(r,f,c,e<a.length?a[e]:d[e-a.length],t,i,!1,l,h)}d.push(w)}else if(\
-e)y.push(r);else{w=decodeBitmap(!1,r,f,s,!1,null,o,h);d.push(w)}}if(e&&!t){const e=n.tableBitmapSize.decode(u);u.byteAlign();let t;if(0===e)t=readUncompressedBitmap(u,i,f);else{const a=u.end,r=u.position+e;u.end=r;t=decodeMMRBitmap(u,i,f,!1);u.end=a;u.position=r}const a=y.length;if(b===a-1)d.push(t);else{let e,r,i,n,s,o=0;for(e=b;e<a;e++){n=y[e];i=o+n;s=[];for(r=0;r<f;r++)s.push(t[r].subarray(o,i));d.push(s);o=i}}}}const w=[],x=[];let S,k,C=!1;const v=a.length+r;for(;x.length<v;){let t=e?b.decod\
-e(u):decodeInteger(m,"IAEX",p);for(;t--;)x.push(C);C=!C}for(S=0,k=a.length;S<k;S++)x[S]&&w.push(a[S]);for(let e=0;e<r;S++,e++)x[S]&&w.push(d[e]);return w}(e.huffman,e.refinement,l,e.numberOfNewSymbols,e.numberOfExportedSymbols,s,e.template,e.at,e.refinementTemplate,e.refinementAt,h,o)}onImmediateTextRegion(e,t,a,r,i){const n=e.info;let s,o;const c=this.symbols,l=[];for(const e of t){const t=c[e];t&&l.push(...t)}const h=log2(l.length);if(e.huffman){o=new Reader(a,r,i);s=function getTextRegionHuff\
-manTables(e,t,a,r,i){const n=[];for(let e=0;e<=34;e++){const t=i.readBits(4);n.push(new HuffmanLine([e,t,0,0]))}const s=new HuffmanTable(n,!1);n.length=0;for(let e=0;e<r;){const t=s.decode(i);if(t>=32){let a,r,s;switch(t){case 32:if(0===e)throw new Jbig2Error("no previous value in symbol ID table");r=i.readBits(2)+3;a=n[e-1].prefixLength;break;case 33:r=i.readBits(3)+3;a=0;break;case 34:r=i.readBits(7)+11;a=0;break;default:throw new Jbig2Error("invalid code length in symbol ID table")}for(s=0;s<\
-r;s++){n.push(new HuffmanLine([e,a,0,0]));e++}}else{n.push(new HuffmanLine([e,t,0,0]));e++}}i.byteAlign();const o=new HuffmanTable(n,!1);let c,l,h,u=0;switch(e.huffmanFS){case 0:case 1:c=getStandardTable(e.huffmanFS+6);break;case 3:c=getCustomHuffmanTable(u,t,a);u++;break;default:throw new Jbig2Error("invalid Huffman FS selector")}switch(e.huffmanDS){case 0:case 1:case 2:l=getStandardTable(e.huffmanDS+8);break;case 3:l=getCustomHuffmanTable(u,t,a);u++;break;default:throw new Jbig2Error("invalid \
-Huffman DS selector")}switch(e.huffmanDT){case 0:case 1:case 2:h=getStandardTable(e.huffmanDT+11);break;case 3:h=getCustomHuffmanTable(u,t,a);u++;break;default:throw new Jbig2Error("invalid Huffman DT selector")}if(e.refinement)throw new Jbig2Error("refinement with Huffman is not supported");return{symbolIDTable:o,tableFirstS:c,tableDeltaS:l,tableDeltaT:h}}(e,t,this.customTables,l.length,o)}const u=new DecodingContext(a,r,i),d=decodeTextRegion(e.huffman,e.refinement,n.width,n.height,e.defaultPix\
-elValue,e.numberOfSymbolInstances,e.stripSize,l,h,e.transposed,e.dsOffset,e.referenceCorner,e.combinationOperator,s,e.refinementTemplate,e.refinementAt,u,e.logStripSize,o);this.drawBitmap(n,d)}onImmediateLosslessTextRegion(){this.onImmediateTextRegion(...arguments)}onPatternDictionary(e,t,a,r,i){let n=this.patterns;n||(this.patterns=n={});const s=new DecodingContext(a,r,i);n[t]=function decodePatternDictionary(e,t,a,r,i,n){const s=[];if(!e){s.push({x:-t,y:0});0===i&&s.push({x:-3,y:-1},{x:2,y:-2}\
-,{x:-2,y:-2})}const o=decodeBitmap(e,(r+1)*t,a,i,!1,null,s,n),c=[];for(let e=0;e<=r;e++){const r=[],i=t*e,n=i+t;for(let e=0;e<a;e++)r.push(o[e].subarray(i,n));c.push(r)}return c}(e.mmr,e.patternWidth,e.patternHeight,e.maxPatternIndex,e.template,s)}onImmediateHalftoneRegion(e,t,a,r,i){const n=this.patterns[t[0]],s=e.info,o=new DecodingContext(a,r,i),c=function decodeHalftoneRegion(e,t,a,r,i,n,s,o,c,l,h,u,d,f,g){if(s)throw new Jbig2Error("skip is not supported");if(0!==o)throw new Jbig2Error(`oper\
-ator "${o}" is not supported in halftone region`);const p=[];let m,b,y;for(m=0;m<i;m++){y=new Uint8Array(r);n&&y.fill(n);p.push(y)}const w=t.length,x=t[0],S=x[0].length,k=x.length,C=log2(w),v=[];if(!e){v.push({x:a<=1?3:2,y:-1});0===a&&v.push({x:-3,y:-1},{x:2,y:-2},{x:-2,y:-2})}const F=[];let T,O,M,D,R,N,E,L,j,_,U;e&&(T=new Reader(g.data,g.start,g.end));for(m=C-1;m>=0;m--){O=e?decodeMMRBitmap(T,c,l,!0):decodeBitmap(!1,c,l,a,!1,null,v,g);F[m]=O}for(M=0;M<l;M++)for(D=0;D<c;D++){R=0;N=0;for(b=C-1;b>\
-=0;b--){R^=F[b][M][D];N|=R<<b}E=t[N];L=h+M*f+D*d>>8;j=u+M*d-D*f>>8;if(L>=0&&L+S<=r&&j>=0&&j+k<=i)for(m=0;m<k;m++){U=p[j+m];_=E[m];for(b=0;b<S;b++)U[L+b]|=_[b]}else{let e,t;for(m=0;m<k;m++){t=j+m;if(!(t<0||t>=i)){U=p[t];_=E[m];for(b=0;b<S;b++){e=L+b;e>=0&&e<r&&(U[e]|=_[b])}}}}}return p}(e.mmr,n,e.template,s.width,s.height,e.defaultPixelValue,e.enableSkip,e.combinationOperator,e.gridWidth,e.gridHeight,e.gridOffsetX,e.gridOffsetY,e.gridVectorX,e.gridVectorY,o);this.drawBitmap(s,c)}onImmediateLossle\
-ssHalftoneRegion(){this.onImmediateHalftoneRegion(...arguments)}onTables(e,t,a,r){let i=this.customTables;i||(this.customTables=i={});i[e]=function decodeTablesSegment(e,t,a){const r=e[t],i=4294967295&readUint32(e,t+1),n=4294967295&readUint32(e,t+5),s=new Reader(e,t+9,a),o=1+(r>>1&7),c=1+(r>>4&7),l=[];let h,u,d=i;do{h=s.readBits(o);u=s.readBits(c);l.push(new HuffmanLine([d,h,u,0]));d+=1<<u}while(d<n);h=s.readBits(o);l.push(new HuffmanLine([i-1,h,32,0,"lower"]));h=s.readBits(o);l.push(new Huffman\
-Line([n,h,32,0]));if(1&r){h=s.readBits(o);l.push(new HuffmanLine([h,0]))}return new HuffmanTable(l,!1)}(t,a,r)}}class HuffmanLine{constructor(e){if(2===e.length){this.isOOB=!0;this.rangeLow=0;this.prefixLength=e[0];this.rangeLength=0;this.prefixCode=e[1];this.isLowerRange=!1}else{this.isOOB=!1;this.rangeLow=e[0];this.prefixLength=e[1];this.rangeLength=e[2];this.prefixCode=e[3];this.isLowerRange="lower"===e[4]}}}class HuffmanTreeNode{constructor(e){this.children=[];if(e){this.isLeaf=!0;this.range\
-Length=e.rangeLength;this.rangeLow=e.rangeLow;this.isLowerRange=e.isLowerRange;this.isOOB=e.isOOB}else this.isLeaf=!1}buildTree(e,t){const a=e.prefixCode>>t&1;if(t<=0)this.children[a]=new HuffmanTreeNode(e);else{let r=this.children[a];r||(this.children[a]=r=new HuffmanTreeNode(null));r.buildTree(e,t-1)}}decodeNode(e){if(this.isLeaf){if(this.isOOB)return null;const t=e.readBits(this.rangeLength);return this.rangeLow+(this.isLowerRange?-t:t)}const t=this.children[e.readBit()];if(!t)throw new Jbig2\
-Error("invalid Huffman data");return t.decodeNode(e)}}class HuffmanTable{constructor(e,t){t||this.assignPrefixCodes(e);this.rootNode=new HuffmanTreeNode(null);for(let t=0,a=e.length;t<a;t++){const a=e[t];a.prefixLength>0&&this.rootNode.buildTree(a,a.prefixLength-1)}}decode(e){return this.rootNode.decodeNode(e)}assignPrefixCodes(e){const t=e.length;let a=0;for(let r=0;r<t;r++)a=Math.max(a,e[r].prefixLength);const r=new Uint32Array(a+1);for(let a=0;a<t;a++)r[e[a].prefixLength]++;let i,n,s,o=1,c=0;\
-r[0]=0;for(;o<=a;){c=c+r[o-1]<<1;i=c;n=0;for(;n<t;){s=e[n];if(s.prefixLength===o){s.prefixCode=i;i++}n++}o++}}}const pr={};function getStandardTable(e){let t,a=pr[e];if(a)return a;switch(e){case 1:t=[[0,1,4,0],[16,2,8,2],[272,3,16,6],[65808,3,32,7]];break;case 2:t=[[0,1,0,0],[1,2,0,2],[2,3,0,6],[3,4,3,14],[11,5,6,30],[75,6,32,62],[6,63]];break;case 3:t=[[-256,8,8,254],[0,1,0,0],[1,2,0,2],[2,3,0,6],[3,4,3,14],[11,5,6,30],[-257,8,32,255,"lower"],[75,7,32,126],[6,62]];break;case 4:t=[[1,1,0,0],[2,2\
-,0,2],[3,3,0,6],[4,4,3,14],[12,5,6,30],[76,5,32,31]];break;case 5:t=[[-255,7,8,126],[1,1,0,0],[2,2,0,2],[3,3,0,6],[4,4,3,14],[12,5,6,30],[-256,7,32,127,"lower"],[76,6,32,62]];break;case 6:t=[[-2048,5,10,28],[-1024,4,9,8],[-512,4,8,9],[-256,4,7,10],[-128,5,6,29],[-64,5,5,30],[-32,4,5,11],[0,2,7,0],[128,3,7,2],[256,3,8,3],[512,4,9,12],[1024,4,10,13],[-2049,6,32,62,"lower"],[2048,6,32,63]];break;case 7:t=[[-1024,4,9,8],[-512,3,8,0],[-256,4,7,9],[-128,5,6,26],[-64,5,5,27],[-32,4,5,10],[0,4,5,11],[32\
-,5,5,28],[64,5,6,29],[128,4,7,12],[256,3,8,1],[512,3,9,2],[1024,3,10,3],[-1025,5,32,30,"lower"],[2048,5,32,31]];break;case 8:t=[[-15,8,3,252],[-7,9,1,508],[-5,8,1,253],[-3,9,0,509],[-2,7,0,124],[-1,4,0,10],[0,2,1,0],[2,5,0,26],[3,6,0,58],[4,3,4,4],[20,6,1,59],[22,4,4,11],[38,4,5,12],[70,5,6,27],[134,5,7,28],[262,6,7,60],[390,7,8,125],[646,6,10,61],[-16,9,32,510,"lower"],[1670,9,32,511],[2,1]];break;case 9:t=[[-31,8,4,252],[-15,9,2,508],[-11,8,2,253],[-7,9,1,509],[-5,7,1,124],[-3,4,1,10],[-1,3,1,\
-2],[1,3,1,3],[3,5,1,26],[5,6,1,58],[7,3,5,4],[39,6,2,59],[43,4,5,11],[75,4,6,12],[139,5,7,27],[267,5,8,28],[523,6,8,60],[779,7,9,125],[1291,6,11,61],[-32,9,32,510,"lower"],[3339,9,32,511],[2,0]];break;case 10:t=[[-21,7,4,122],[-5,8,0,252],[-4,7,0,123],[-3,5,0,24],[-2,2,2,0],[2,5,0,25],[3,6,0,54],[4,7,0,124],[5,8,0,253],[6,2,6,1],[70,5,5,26],[102,6,5,55],[134,6,6,56],[198,6,7,57],[326,6,8,58],[582,6,9,59],[1094,6,10,60],[2118,7,11,125],[-22,8,32,254,"lower"],[4166,8,32,255],[2,2]];break;case 11:t\
-=[[1,1,0,0],[2,2,1,2],[4,4,0,12],[5,4,1,13],[7,5,1,28],[9,5,2,29],[13,6,2,60],[17,7,2,122],[21,7,3,123],[29,7,4,124],[45,7,5,125],[77,7,6,126],[141,7,32,127]];break;case 12:t=[[1,1,0,0],[2,2,0,2],[3,3,1,6],[5,5,0,28],[6,5,1,29],[8,6,1,60],[10,7,0,122],[11,7,1,123],[13,7,2,124],[17,7,3,125],[25,7,4,126],[41,8,5,254],[73,8,32,255]];break;case 13:t=[[1,1,0,0],[2,3,0,4],[3,4,0,12],[4,5,0,28],[5,4,1,13],[7,3,3,5],[15,6,1,58],[17,6,2,59],[21,6,3,60],[29,6,4,61],[45,6,5,62],[77,7,6,126],[141,7,32,127]]\
-;break;case 14:t=[[-2,3,0,4],[-1,3,0,5],[0,1,0,0],[1,3,0,6],[2,3,0,7]];break;case 15:t=[[-24,7,4,124],[-8,6,2,60],[-4,5,1,28],[-2,4,0,12],[-1,3,0,4],[0,1,0,0],[1,3,0,5],[2,4,0,13],[3,5,1,29],[5,6,2,61],[9,7,4,125],[-25,7,32,126,"lower"],[25,7,32,127]];break;default:throw new Jbig2Error(`standard table B.${e} does not exist`)}for(let e=0,a=t.length;e<a;e++)t[e]=new HuffmanLine(t[e]);a=new HuffmanTable(t,!0);pr[e]=a;return a}class Reader{constructor(e,t,a){this.data=e;this.start=t;this.end=a;this.\
-position=t;this.shift=-1;this.currentByte=0}readBit(){if(this.shift<0){if(this.position>=this.end)throw new Jbig2Error("end of data while reading bit");this.currentByte=this.data[this.position++];this.shift=7}const e=this.currentByte>>this.shift&1;this.shift--;return e}readBits(e){let t,a=0;for(t=e-1;t>=0;t--)a|=this.readBit()<<t;return a}byteAlign(){this.shift=-1}next(){return this.position>=this.end?-1:this.data[this.position++]}}function getCustomHuffmanTable(e,t,a){let r=0;for(let i=0,n=t.le\
-ngth;i<n;i++){const n=a[t[i]];if(n){if(e===r)return n;r++}}throw new Jbig2Error("can\'t find custom Huffman table")}function readUncompressedBitmap(e,t,a){const r=[];for(let i=0;i<a;i++){const a=new Uint8Array(t);r.push(a);for(let r=0;r<t;r++)a[r]=e.readBit();e.byteAlign()}return r}function decodeMMRBitmap(e,t,a,r){const i=new CCITTFaxDecoder(e,{K:-1,Columns:t,Rows:a,BlackIs1:!0,EndOfBlock:r}),n=[];let s,o=!1;for(let e=0;e<a;e++){const e=new Uint8Array(t);n.push(e);let a=-1;for(let r=0;r<t;r++){i\
-f(a<0){s=i.readNextChar();if(-1===s){s=0;o=!0}a=7}e[r]=s>>a&1;a--}}if(r&&!o){const e=5;for(let t=0;t<e&&-1!==i.readNextChar();t++);}return n}class Jbig2Image{parseChunks(e){return function parseJbig2Chunks(e){const t=new SimpleSegmentVisitor;for(let a=0,r=e.length;a<r;a++){const r=e[a];processSegments(readSegments({},r.data,r.start,r.end),t)}return t.buffer}(e)}parse(e){throw new Error("Not implemented: Jbig2Image.parse")}}class Jbig2Stream extends DecodeStream{constructor(e,t,a){super(t);this.s\
-tream=e;this.dict=e.dict;this.maybeLength=t;this.params=a}get bytes(){return shadow(this,"bytes",this.stream.getBytes(this.maybeLength))}ensureBuffer(e){}readBlock(){this.decodeImage()}decodeImage(e){if(this.eof)return this.buffer;e||=this.bytes;const t=new Jbig2Image,a=[];if(this.params instanceof Dict){const e=this.params.get("JBIG2Globals");if(e instanceof BaseStream){const t=e.getBytes();a.push({data:t,start:0,end:t.length})}}a.push({data:e,start:0,end:e.length});const r=t.parseChunks(a),i=r\
-.length;for(let e=0;e<i;e++)r[e]^=255;this.buffer=r;this.bufferLength=i;this.eof=!0;return this.buffer}get canAsyncDecodeImageFromBuffer(){return this.stream.isAsync}}class JpxStream extends DecodeStream{constructor(e,t,a){super(t);this.stream=e;this.dict=e.dict;this.maybeLength=t;this.params=a}get bytes(){return shadow(this,"bytes",this.stream.getBytes(this.maybeLength))}ensureBuffer(e){}readBlock(e){unreachable("JpxStream.readBlock")}get isAsyncDecoder(){return!0}async decodeImage(e,t){if(this\
-.eof)return this.buffer;e||=this.bytes;this.buffer=await JpxImage.decode(e,t);this.bufferLength=this.buffer.length;this.eof=!0;return this.buffer}get canAsyncDecodeImageFromBuffer(){return this.stream.isAsync}}class LZWStream extends DecodeStream{constructor(e,t,a){super(t);this.str=e;this.dict=e.dict;this.cachedData=0;this.bitsCached=0;const r=4096,i={earlyChange:a,codeLength:9,nextCode:258,dictionaryValues:new Uint8Array(r),dictionaryLengths:new Uint16Array(r),dictionaryPrevCodes:new Uint16Arr\
-ay(r),currentSequence:new Uint8Array(r),currentSequenceLength:0};for(let e=0;e<256;++e){i.dictionaryValues[e]=e;i.dictionaryLengths[e]=1}this.lzwState=i}readBits(e){let t=this.bitsCached,a=this.cachedData;for(;t<e;){const e=this.str.getByte();if(-1===e){this.eof=!0;return null}a=a<<8|e;t+=8}this.bitsCached=t-=e;this.cachedData=a;this.lastCode=null;return a>>>t&(1<<e)-1}readBlock(){let e,t,a,r=1024;const i=this.lzwState;if(!i)return;const n=i.earlyChange;let s=i.nextCode;const o=i.dictionaryValue\
-s,c=i.dictionaryLengths,l=i.dictionaryPrevCodes;let h=i.codeLength,u=i.prevCode;const d=i.currentSequence;let f=i.currentSequenceLength,g=0,p=this.bufferLength,m=this.ensureBuffer(this.bufferLength+r);for(e=0;e<512;e++){const e=this.readBits(h),i=f>0;if(e<256){d[0]=e;f=1}else{if(!(e>=258)){if(256===e){h=9;s=258;f=0;continue}this.eof=!0;delete this.lzwState;break}if(e<s){f=c[e];for(t=f-1,a=e;t>=0;t--){d[t]=o[a];a=l[a]}}else d[f++]=d[0]}if(i){l[s]=u;c[s]=c[u]+1;o[s]=d[0];s++;h=s+n&s+n-1?h:0|Math.m\
-in(Math.log(s+n)/.6931471805599453+1,12)}u=e;g+=f;if(r<g){do{r+=512}while(r<g);m=this.ensureBuffer(this.bufferLength+r)}for(t=0;t<f;t++)m[p++]=d[t]}i.nextCode=s;i.codeLength=h;i.prevCode=u;i.currentSequenceLength=f;this.bufferLength=p}}class PredictorStream extends DecodeStream{constructor(e,t,a){super(t);if(!(a instanceof Dict))return e;const r=this.predictor=a.get("Predictor")||1;if(r<=1)return e;if(2!==r&&(r<10||r>15))throw new FormatError(`Unsupported predictor: ${r}`);this.readBlock=2===r?t\
-his.readBlockTiff:this.readBlockPng;this.str=e;this.dict=e.dict;const i=this.colors=a.get("Colors")||1,n=this.bits=a.get("BPC","BitsPerComponent")||8,s=this.columns=a.get("Columns")||1;this.pixBytes=i*n+7>>3;this.rowBytes=s*i*n+7>>3;return this}readBlockTiff(){const e=this.rowBytes,t=this.bufferLength,a=this.ensureBuffer(t+e),r=this.bits,i=this.colors,n=this.str.getBytes(e);this.eof=!n.length;if(this.eof)return;let s,o=0,c=0,l=0,h=0,u=t;if(1===r&&1===i)for(s=0;s<e;++s){let e=n[s]^o;e^=e>>1;e^=e>\
->2;e^=e>>4;o=(1&e)<<7;a[u++]=e}else if(8===r){for(s=0;s<i;++s)a[u++]=n[s];for(;s<e;++s){a[u]=a[u-i]+n[s];u++}}else if(16===r){const t=2*i;for(s=0;s<t;++s)a[u++]=n[s];for(;s<e;s+=2){const e=((255&n[s])<<8)+(255&n[s+1])+((255&a[u-t])<<8)+(255&a[u-t+1]);a[u++]=e>>8&255;a[u++]=255&e}}else{const e=new Uint8Array(i+1),u=(1<<r)-1;let d=0,f=t;const g=this.columns;for(s=0;s<g;++s)for(let t=0;t<i;++t){if(l<r){o=o<<8|255&n[d++];l+=8}e[t]=e[t]+(o>>l-r)&u;l-=r;c=c<<r|e[t];h+=r;if(h>=8){a[f++]=c>>h-8&255;h-=8\
-}}h>0&&(a[f++]=(c<<8-h)+(o&(1<<8-h)-1))}this.bufferLength+=e}readBlockPng(){const e=this.rowBytes,t=this.pixBytes,a=this.str.getByte(),r=this.str.getBytes(e);this.eof=!r.length;if(this.eof)return;const i=this.bufferLength,n=this.ensureBuffer(i+e);let s=n.subarray(i-e,i);0===s.length&&(s=new Uint8Array(e));let o,c,l,h=i;switch(a){case 0:for(o=0;o<e;++o)n[h++]=r[o];break;case 1:for(o=0;o<t;++o)n[h++]=r[o];for(;o<e;++o){n[h]=n[h-t]+r[o]&255;h++}break;case 2:for(o=0;o<e;++o)n[h++]=s[o]+r[o]&255;brea\
-k;case 3:for(o=0;o<t;++o)n[h++]=(s[o]>>1)+r[o];for(;o<e;++o){n[h]=(s[o]+n[h-t]>>1)+r[o]&255;h++}break;case 4:for(o=0;o<t;++o){c=s[o];l=r[o];n[h++]=c+l}for(;o<e;++o){c=s[o];const e=s[o-t],a=n[h-t],i=a+c-e;let u=i-a;u<0&&(u=-u);let d=i-c;d<0&&(d=-d);let f=i-e;f<0&&(f=-f);l=r[o];n[h++]=u<=d&&u<=f?a+l:d<=f?c+l:e+l}break;default:throw new FormatError(`Unsupported predictor: ${a}`)}this.bufferLength+=e}}class RunLengthStream extends DecodeStream{constructor(e,t){super(t);this.str=e;this.dict=e.dict}re\
-adBlock(){const e=this.str.getBytes(2);if(!e||e.length<2||128===e[0]){this.eof=!0;return}let t,a=this.bufferLength,r=e[0];if(r<128){t=this.ensureBuffer(a+r+1);t[a++]=e[1];if(r>0){const e=this.str.getBytes(r);t.set(e,a);a+=r}}else{r=257-r;t=this.ensureBuffer(a+r+1);t.fill(e[1],a,a+r);a+=r}this.bufferLength=a}}class Parser{constructor({lexer:e,xref:t,allowStreams:a=!1,recoveryMode:r=!1}){this.lexer=e;this.xref=t;this.allowStreams=a;this.recoveryMode=r;this.imageCache=Object.create(null);this._imag\
-eId=0;this.refill()}refill(){this.buf1=this.lexer.getObj();this.buf2=this.lexer.getObj()}shift(){if(this.buf2 instanceof Cmd&&"ID"===this.buf2.cmd){this.buf1=this.buf2;this.buf2=null}else{this.buf1=this.buf2;this.buf2=this.lexer.getObj()}}tryShift(){try{this.shift();return!0}catch(e){if(e instanceof MissingDataException)throw e;return!1}}getObj(e=null){const t=this.buf1;this.shift();if(t instanceof Cmd)switch(t.cmd){case"BI":return this.makeInlineImage(e);case"[":const a=[];for(;!isCmd(this.buf1\
-,"]")&&this.buf1!==wa;)a.push(this.getObj(e));if(this.buf1===wa){if(this.recoveryMode)return a;throw new ParserEOFException("End of file inside array.")}this.shift();return a;case"<<":const r=new Dict(this.xref);for(;!isCmd(this.buf1,">>")&&this.buf1!==wa;){if(!(this.buf1 instanceof Name)){info("Malformed dictionary: key must be a name object");this.shift();continue}const t=this.buf1.name;this.shift();if(this.buf1===wa)break;r.set(t,this.getObj(e))}if(this.buf1===wa){if(this.recoveryMode)return \
-r;throw new ParserEOFException("End of file inside dictionary.")}if(isCmd(this.buf2,"stream"))return this.allowStreams?this.makeStream(r,e):r;this.shift();return r;default:return t}if(Number.isInteger(t)){if(Number.isInteger(this.buf1)&&isCmd(this.buf2,"R")){const e=Ref.get(t,this.buf1);this.shift();this.shift();return e}return t}return"string"==typeof t&&e?e.decryptString(t):t}findDefaultInlineStreamEnd(e){const{knownCommands:t}=this.lexer,a=e.pos;let r,i,n=0;for(;-1!==(r=e.getByte());)if(0===n\
-)n=69===r?1:0;else if(1===n)n=73===r?2:0;else if(32===r||10===r||13===r){i=e.pos;const a=e.peekBytes(15),s=a.length;if(0===s)break;for(let e=0;e<s;e++){r=a[e];if((0!==r||0===a[e+1])&&(10!==r&&13!==r&&(r<32||r>127))){n=0;break}}if(2!==n)continue;if(!t){warn("findDefaultInlineStreamEnd - `lexer.knownCommands` is undefined.");continue}const o=new Lexer(new Stream(e.peekBytes(75)),t);o._hexStringWarn=()=>{};let c=0;for(;;){const e=o.getObj();if(e===wa){n=0;break}if(e instanceof Cmd){const a=t[e.cmd]\
-;if(!a){n=0;break}if(a.variableArgs?c<=a.numArgs:c===a.numArgs)break;c=0}else c++}if(2===n)break}else n=0;if(-1===r){warn("findDefaultInlineStreamEnd: Reached the end of the stream without finding a valid EI marker");if(i){warn(\'... trying to recover by using the last "EI" occurrence.\');e.skip(-(e.pos-i))}}let s=4;e.skip(-s);r=e.peekByte();e.skip(s);isWhiteSpace(r)||s--;return e.pos-s-a}findDCTDecodeInlineStreamEnd(e){const t=e.pos;let a,r,i=!1;for(;-1!==(a=e.getByte());)if(255===a){switch(e.get\
-Byte()){case 0:break;case 255:e.skip(-1);break;case 217:i=!0;break;case 192:case 193:case 194:case 195:case 197:case 198:case 199:case 201:case 202:case 203:case 205:case 206:case 207:case 196:case 204:case 218:case 219:case 220:case 221:case 222:case 223:case 224:case 225:case 226:case 227:case 228:case 229:case 230:case 231:case 232:case 233:case 234:case 235:case 236:case 237:case 238:case 239:case 254:r=e.getUint16();r>2?e.skip(r-2):e.skip(-2)}if(i)break}const n=e.pos-t;if(-1===a){warn("Inli\
-ne DCTDecode image stream: EOI marker not found, searching for /EI/ instead.");e.skip(-n);return this.findDefaultInlineStreamEnd(e)}this.inlineStreamSkipEI(e);return n}findASCII85DecodeInlineStreamEnd(e){const t=e.pos;let a;for(;-1!==(a=e.getByte());)if(126===a){const t=e.pos;a=e.peekByte();for(;isWhiteSpace(a);){e.skip();a=e.peekByte()}if(62===a){e.skip();break}if(e.pos>t){const t=e.peekBytes(2);if(69===t[0]&&73===t[1])break}}const r=e.pos-t;if(-1===a){warn("Inline ASCII85Decode image stream: E\
-OD marker not found, searching for /EI/ instead.");e.skip(-r);return this.findDefaultInlineStreamEnd(e)}this.inlineStreamSkipEI(e);return r}findASCIIHexDecodeInlineStreamEnd(e){const t=e.pos;let a;for(;-1!==(a=e.getByte())&&62!==a;);const r=e.pos-t;if(-1===a){warn("Inline ASCIIHexDecode image stream: EOD marker not found, searching for /EI/ instead.");e.skip(-r);return this.findDefaultInlineStreamEnd(e)}this.inlineStreamSkipEI(e);return r}inlineStreamSkipEI(e){let t,a=0;for(;-1!==(t=e.getByte())\
-;)if(0===a)a=69===t?1:0;else if(1===a)a=73===t?2:0;else if(2===a)break}makeInlineImage(e){const t=this.lexer,a=t.stream,r=Object.create(null);let i;for(;!isCmd(this.buf1,"ID")&&this.buf1!==wa;){if(!(this.buf1 instanceof Name))throw new FormatError("Dictionary key must be a name object");const t=this.buf1.name;this.shift();if(this.buf1===wa)break;r[t]=this.getObj(e)}-1!==t.beginInlineImagePos&&(i=a.pos-t.beginInlineImagePos);const n=this.xref.fetchIfRef(r.F||r.Filter);let s;if(n instanceof Name)s\
-=n.name;else if(Array.isArray(n)){const e=this.xref.fetchIfRef(n[0]);e instanceof Name&&(s=e.name)}const o=a.pos;let c,l;switch(s){case"DCT":case"DCTDecode":c=this.findDCTDecodeInlineStreamEnd(a);break;case"A85":case"ASCII85Decode":c=this.findASCII85DecodeInlineStreamEnd(a);break;case"AHx":case"ASCIIHexDecode":c=this.findASCIIHexDecodeInlineStreamEnd(a);break;default:c=this.findDefaultInlineStreamEnd(a)}if(c<1e3&&i>0){const e=a.pos;a.pos=t.beginInlineImagePos;l=function getInlineImageCacheKey(e)\
-{const t=[],a=e.length;let r=0;for(;r<a-1;)t.push(e[r++]<<8|e[r++]);r<a&&t.push(e[r]);return a+"_"+String.fromCharCode.apply(null,t)}(a.getBytes(i+c));a.pos=e;const r=this.imageCache[l];if(void 0!==r){this.buf2=Cmd.get("EI");this.shift();r.reset();return r}}const h=new Dict(this.xref);for(const e in r)h.set(e,r[e]);let u=a.makeSubStream(o,c,h);e&&(u=e.createStream(u,c));u=this.filter(u,h,c);u.dict=h;if(void 0!==l){u.cacheKey="inline_img_"+ ++this._imageId;this.imageCache[l]=u}this.buf2=Cmd.get("\
-EI");this.shift();return u}#q(e){const{stream:t}=this.lexer;t.pos=e;const a=new Uint8Array([101,110,100]),r=a.length,i=[new Uint8Array([115,116,114,101,97,109]),new Uint8Array([115,116,101,97,109]),new Uint8Array([115,116,114,101,97])],n=9-r;for(;t.pos<t.end;){const s=t.peekBytes(2048),o=s.length-9;if(o<=0)break;let c=0;for(;c<o;){let o=0;for(;o<r&&s[c+o]===a[o];)o++;if(o>=r){let r=!1;for(const e of i){const t=e.length;let i=0;for(;i<t&&s[c+o+i]===e[i];)i++;if(i>=n){r=!0;break}if(i>=t){if(isWhit\
-eSpace(s[c+o+i])){info(`Found "${bytesToString([...a,...e])}" when searching for endstream command.`);r=!0}break}}if(r){t.pos+=c;return t.pos-e}}c++}t.pos+=o}return-1}makeStream(e,t){const a=this.lexer;let r=a.stream;a.skipToNextLine();const i=r.pos-1;let n=e.get("Length");if(!Number.isInteger(n)){info(`Bad length "${n&&n.toString()}" in stream.`);n=0}r.pos=i+n;a.nextChar();if(this.tryShift()&&isCmd(this.buf2,"endstream"))this.shift();else{n=this.#q(i);if(n<0)throw new FormatError("Missing endst\
-ream command.");a.nextChar();this.shift();this.shift()}this.shift();r=r.makeSubStream(i,n,e);t&&(r=t.createStream(r,n));r=this.filter(r,e,n);r.dict=e;return r}filter(e,t,a){let r=t.get("F","Filter"),i=t.get("DP","DecodeParms");if(r instanceof Name){Array.isArray(i)&&warn("/DecodeParms should not be an Array, when /Filter is a Name.");return this.makeFilter(e,r.name,a,i)}let n=a;if(Array.isArray(r)){const t=r,a=i;for(let s=0,o=t.length;s<o;++s){r=this.xref.fetchIfRef(t[s]);if(!(r instanceof Name)\
-)throw new FormatError(`Bad filter name "${r}"`);i=null;Array.isArray(a)&&s in a&&(i=this.xref.fetchIfRef(a[s]));e=this.makeFilter(e,r.name,n,i);n=null}}return e}makeFilter(e,t,a,r){if(0===a){warn(`Empty "${t}" stream.`);return new NullStream}try{switch(t){case"Fl":case"FlateDecode":return r?new PredictorStream(new FlateStream(e,a),a,r):new FlateStream(e,a);case"LZW":case"LZWDecode":let t=1;if(r){r.has("EarlyChange")&&(t=r.get("EarlyChange"));return new PredictorStream(new LZWStream(e,a,t),a,r)}\
-return new LZWStream(e,a,t);case"DCT":case"DCTDecode":return new JpegStream(e,a,r);case"JPX":case"JPXDecode":return new JpxStream(e,a,r);case"A85":case"ASCII85Decode":return new Ascii85Stream(e,a);case"AHx":case"ASCIIHexDecode":return new AsciiHexStream(e,a);case"CCF":case"CCITTFaxDecode":return new CCITTFaxStream(e,a,r);case"RL":case"RunLengthDecode":return new RunLengthStream(e,a);case"JBIG2Decode":return new Jbig2Stream(e,a,r)}warn(`Filter "${t}" is not supported.`);return e}catch(e){if(e ins\
-tanceof MissingDataException)throw e;warn(`Invalid stream: "${e}"`);return new NullStream}}}const mr=[1,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,2,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\
-0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0];function toHexDigit(e){return e>=48&&e<=57?15&e:e>=65&&e<=70||e>=97&&e<=102?9+(15&e):-1}class Lexer{constructor(e,t=null){this.stream=e;this.nextChar();this.strBuf=[];this.knownCommands=t;this._hexStringNumWarn=0;this.beginInlineImagePos=-1}nextChar(){return this.currentChar=this.stream.getByte()}peekChar(){return this.stream.peekByte()}getNumber(){let e=this.currentChar,t=!1,a=0,r=\
-1;if(45===e){r=-1;e=this.nextChar();45===e&&(e=this.nextChar())}else 43===e&&(e=this.nextChar());if(10===e||13===e)do{e=this.nextChar()}while(10===e||13===e);if(46===e){a=10;e=this.nextChar()}if(e<48||e>57){const t=`Invalid number: ${String.fromCharCode(e)} (charCode ${e})`;if(isWhiteSpace(e)||40===e||60===e||-1===e){info(`Lexer.getNumber - "${t}".`);return 0}throw new FormatError(t)}let i=e-48,n=0,s=1;for(;(e=this.nextChar())>=0;)if(e>=48&&e<=57){const r=e-48;if(t)n=10*n+r;else{0!==a&&(a*=10);i\
-=10*i+r}}else if(46===e){if(0!==a)break;a=1}else if(45===e)warn("Badly formatted number: minus sign in the middle");else{if(69!==e&&101!==e)break;e=this.peekChar();if(43===e||45===e){s=45===e?-1:1;this.nextChar()}else if(e<48||e>57)break;t=!0}0!==a&&(i/=a);t&&(i*=10**(s*n));return r*i}getString(){let e=1,t=!1;const a=this.strBuf;a.length=0;let r=this.nextChar();for(;;){let i=!1;switch(0|r){case-1:warn("Unterminated string");t=!0;break;case 40:++e;a.push("(");break;case 41:if(0==--e){this.nextCha\
-r();t=!0}else a.push(")");break;case 92:r=this.nextChar();switch(r){case-1:warn("Unterminated string");t=!0;break;case 110:a.push("\\n");break;case 114:a.push("\\r");break;case 116:a.push("\\t");break;case 98:a.push("\\b");break;case 102:a.push("\\f");break;case 92:case 40:case 41:a.push(String.fromCharCode(r));break;case 48:case 49:case 50:case 51:case 52:case 53:case 54:case 55:let e=15&r;r=this.nextChar();i=!0;if(r>=48&&r<=55){e=(e<<3)+(15&r);r=this.nextChar();if(r>=48&&r<=55){i=!1;e=(e<<3)+(15&r)\
-}}a.push(String.fromCharCode(e));break;case 13:10===this.peekChar()&&this.nextChar();break;case 10:break;default:a.push(String.fromCharCode(r))}break;default:a.push(String.fromCharCode(r))}if(t)break;i||(r=this.nextChar())}return a.join("")}getName(){let e,t;const a=this.strBuf;a.length=0;for(;(e=this.nextChar())>=0&&!mr[e];)if(35===e){e=this.nextChar();if(mr[e]){warn("Lexer_getName: NUMBER SIGN (#) should be followed by a hexadecimal number.");a.push("#");break}const r=toHexDigit(e);if(-1!==r){\
-t=e;e=this.nextChar();const i=toHexDigit(e);if(-1===i){warn(`Lexer_getName: Illegal digit (${String.fromCharCode(e)}) in hexadecimal number.`);a.push("#",String.fromCharCode(t));if(mr[e])break;a.push(String.fromCharCode(e));continue}a.push(String.fromCharCode(r<<4|i))}else a.push("#",String.fromCharCode(e))}else a.push(String.fromCharCode(e));a.length>127&&warn(`Name token is longer than allowed by the spec: ${a.length}`);return Name.get(a.join(""))}_hexStringWarn(e){5!=this._hexStringNumWarn++?\
-this._hexStringNumWarn>5||warn(`getHexString - ignoring invalid character: ${e}`):warn("getHexString - ignoring additional invalid characters.")}getHexString(){const e=this.strBuf;e.length=0;let t=this.currentChar,a=-1,r=-1;this._hexStringNumWarn=0;for(;;){if(t<0){warn("Unterminated hex string");break}if(62===t){this.nextChar();break}if(1!==mr[t]){r=toHexDigit(t);if(-1===r)this._hexStringWarn(t);else if(-1===a)a=r;else{e.push(String.fromCharCode(a<<4|r));a=-1}t=this.nextChar()}else t=this.nextCh\
-ar()}-1!==a&&e.push(String.fromCharCode(a<<4));return e.join("")}getObj(){let e=!1,t=this.currentChar;for(;;){if(t<0)return wa;if(e)10!==t&&13!==t||(e=!1);else if(37===t)e=!0;else if(1!==mr[t])break;t=this.nextChar()}switch(0|t){case 48:case 49:case 50:case 51:case 52:case 53:case 54:case 55:case 56:case 57:case 43:case 45:case 46:return this.getNumber();case 40:return this.getString();case 47:return this.getName();case 91:this.nextChar();return Cmd.get("[");case 93:this.nextChar();return Cmd.ge\
-t("]");case 60:t=this.nextChar();if(60===t){this.nextChar();return Cmd.get("<<")}return this.getHexString();case 62:t=this.nextChar();if(62===t){this.nextChar();return Cmd.get(">>")}return Cmd.get(">");case 123:this.nextChar();return Cmd.get("{");case 125:this.nextChar();return Cmd.get("}");case 41:this.nextChar();throw new FormatError(`Illegal character: ${t}`)}let a=String.fromCharCode(t);if(t<32||t>127){const e=this.peekChar();if(e>=32&&e<=127){this.nextChar();return Cmd.get(a)}}const r=this.\
-knownCommands;let i=void 0!==r?.[a];for(;(t=this.nextChar())>=0&&!mr[t];){const e=a+String.fromCharCode(t);if(i&&void 0===r[e])break;if(128===a.length)throw new FormatError(`Command token too long: ${a.length}`);a=e;i=void 0!==r?.[a]}if("true"===a)return!0;if("false"===a)return!1;if("null"===a)return null;"BI"===a&&(this.beginInlineImagePos=this.stream.pos);return Cmd.get(a)}skipToNextLine(){let e=this.currentChar;for(;e>=0;){if(13===e){e=this.nextChar();10===e&&this.nextChar();break}if(10===e){\
-this.nextChar();break}e=this.nextChar()}}}class Linearization{static create(e){function getInt(e,t,a=!1){const r=e.get(t);if(Number.isInteger(r)&&(a?r>=0:r>0))return r;throw new Error(`The "${t}" parameter in the linearization dictionary is invalid.`)}const t=new Parser({lexer:new Lexer(e),xref:null}),a=t.getObj(),r=t.getObj(),i=t.getObj(),n=t.getObj();let s,o;if(!(Number.isInteger(a)&&Number.isInteger(r)&&isCmd(i,"obj")&&n instanceof Dict&&"number"==typeof(s=n.get("Linearized"))&&s>0))return nu\
-ll;if((o=getInt(n,"L"))!==e.length)throw new Error(\'The "L" parameter in the linearization dictionary does not equal the stream length.\');return{length:o,hints:function getHints(e){const t=e.get("H");let a;if(Array.isArray(t)&&(2===(a=t.length)||4===a)){for(let e=0;e<a;e++){const a=t[e];if(!(Number.isInteger(a)&&a>0))throw new Error(`Hint (${e}) in the linearization dictionary is invalid.`)}return t}throw new Error("Hint array in the linearization dictionary is invalid.")}(n),objectNumberFirst:g\
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-numbersign","dollar","percent","ampersand","quotesingle","parenleft","parenright","asterisk","plus","comma","hyphen","period","slash","zero","one","two","three","four","five","six","seven","eight","nine","colon","semicolon","less","equal","greater","question","at","A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z","bracketleft","backslash","bracketright","asciicircum","underscore","grave","a","b","c","d","e","f","g","h","i","j","k","l","m","n"\
-,"o","p","q","r","s","t","u","v","w","x","y","z","braceleft","bar","braceright","asciitilde","bullet","Euro","bullet","quotesinglbase","florin","quotedblbase","ellipsis","dagger","daggerdbl","circumflex","perthousand","Scaron","guilsinglleft","OE","bullet","Zcaron","bullet","bullet","quoteleft","quoteright","quotedblleft","quotedblright","bullet","endash","emdash","tilde","trademark","scaron","guilsinglright","oe","bullet","zcaron","Ydieresis","space","exclamdown","cent","sterling","currency","y\
-en","brokenbar","section","dieresis","copyright","ordfeminine","guillemotleft","logicalnot","hyphen","registered","macron","degree","plusminus","twosuperior","threesuperior","acute","mu","paragraph","periodcentered","cedilla","onesuperior","ordmasculine","guillemotright","onequarter","onehalf","threequarters","questiondown","Agrave","Aacute","Acircumflex","Atilde","Adieresis","Aring","AE","Ccedilla","Egrave","Eacute","Ecircumflex","Edieresis","Igrave","Iacute","Icircumflex","Idieresis","Eth","Nt\
-ilde","Ograve","Oacute","Ocircumflex","Otilde","Odieresis","multiply","Oslash","Ugrave","Uacute","Ucircumflex","Udieresis","Yacute","Thorn","germandbls","agrave","aacute","acircumflex","atilde","adieresis","aring","ae","ccedilla","egrave","eacute","ecircumflex","edieresis","igrave","iacute","icircumflex","idieresis","eth","ntilde","ograve","oacute","ocircumflex","otilde","odieresis","divide","oslash","ugrave","uacute","ucircumflex","udieresis","yacute","thorn","ydieresis"],Cr=["","","","","","",\
-"","","","","","","","","","","","","","","","","","","","","","","","","","","space","exclam","universal","numbersign","existential","percent","ampersand","suchthat","parenleft","parenright","asteriskmath","plus","comma","minus","period","slash","zero","one","two","three","four","five","six","seven","eight","nine","colon","semicolon","less","equal","greater","question","congruent","Alpha","Beta","Chi","Delta","Epsilon","Phi","Gamma","Eta","Iota","theta1","Kappa","Lambda","Mu","Nu","Omicron","Pi\
-","Theta","Rho","Sigma","Tau","Upsilon","sigma1","Omega","Xi","Psi","Zeta","bracketleft","therefore","bracketright","perpendicular","underscore","radicalex","alpha","beta","chi","delta","epsilon","phi","gamma","eta","iota","phi1","kappa","lambda","mu","nu","omicron","pi","theta","rho","sigma","tau","upsilon","omega1","omega","xi","psi","zeta","braceleft","bar","braceright","similar","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","Euro","Upsilon\
-1","minute","lessequal","fraction","infinity","florin","club","diamond","heart","spade","arrowboth","arrowleft","arrowup","arrowright","arrowdown","degree","plusminus","second","greaterequal","multiply","proportional","partialdiff","bullet","divide","notequal","equivalence","approxequal","ellipsis","arrowvertex","arrowhorizex","carriagereturn","aleph","Ifraktur","Rfraktur","weierstrass","circlemultiply","circleplus","emptyset","intersection","union","propersuperset","reflexsuperset","notsubset",\
-"propersubset","reflexsubset","element","notelement","angle","gradient","registerserif","copyrightserif","trademarkserif","product","radical","dotmath","logicalnot","logicaland","logicalor","arrowdblboth","arrowdblleft","arrowdblup","arrowdblright","arrowdbldown","lozenge","angleleft","registersans","copyrightsans","trademarksans","summation","parenlefttp","parenleftex","parenleftbt","bracketlefttp","bracketleftex","bracketleftbt","bracelefttp","braceleftmid","braceleftbt","braceex","","anglerig\
-ht","integral","integraltp","integralex","integralbt","parenrighttp","parenrightex","parenrightbt","bracketrighttp","bracketrightex","bracketrightbt","bracerighttp","bracerightmid","bracerightbt",""],vr=["","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","space","a1","a2","a202","a3","a4","a5","a119","a118","a117","a11","a12","a13","a14","a15","a16","a105","a17","a18","a19","a20","a21","a22","a23","a24","a25","a26","a27","a28","a6","a7","a8","a9","a10\
-","a29","a30","a31","a32","a33","a34","a35","a36","a37","a38","a39","a40","a41","a42","a43","a44","a45","a46","a47","a48","a49","a50","a51","a52","a53","a54","a55","a56","a57","a58","a59","a60","a61","a62","a63","a64","a65","a66","a67","a68","a69","a70","a71","a72","a73","a74","a203","a75","a204","a76","a77","a78","a79","a81","a82","a83","a84","a97","a98","a99","a100","","a89","a90","a93","a94","a91","a92","a205","a85","a206","a86","a87","a88","a95","a96","","","","","","","","","","","","","","\
-","","","","","","a101","a102","a103","a104","a106","a107","a108","a112","a111","a110","a109","a120","a121","a122","a123","a124","a125","a126","a127","a128","a129","a130","a131","a132","a133","a134","a135","a136","a137","a138","a139","a140","a141","a142","a143","a144","a145","a146","a147","a148","a149","a150","a151","a152","a153","a154","a155","a156","a157","a158","a159","a160","a161","a163","a164","a196","a165","a192","a166","a167","a168","a169","a170","a171","a172","a173","a162","a174","a175",\
-"a176","a177","a178","a179","a193","a180","a199","a181","a200","a182","","a201","a183","a184","a197","a185","a194","a198","a186","a195","a187","a188","a189","a190","a191",""];function getEncoding(e){switch(e){case"WinAnsiEncoding":return kr;case"StandardEncoding":return Ar;case"MacRomanEncoding":return Sr;case"SymbolSetEncoding":return Cr;case"ZapfDingbatsEncoding":return vr;case"ExpertEncoding":return wr;case"MacExpertEncoding":return xr;default:return null}}const Fr=getLookupTableFactory((func\
-tion(e){e.A=65;e.AE=198;e.AEacute=508;e.AEmacron=482;e.AEsmall=63462;e.Aacute=193;e.Aacutesmall=63457;e.Abreve=258;e.Abreveacute=7854;e.Abrevecyrillic=1232;e.Abrevedotbelow=7862;e.Abrevegrave=7856;e.Abrevehookabove=7858;e.Abrevetilde=7860;e.Acaron=461;e.Acircle=9398;e.Acircumflex=194;e.Acircumflexacute=7844;e.Acircumflexdotbelow=7852;e.Acircumflexgrave=7846;e.Acircumflexhookabove=7848;e.Acircumflexsmall=63458;e.Acircumflextilde=7850;e.Acute=63177;e.Acutesmall=63412;e.Acyrillic=1040;e.Adblgrave=5\
-12;e.Adieresis=196;e.Adieresiscyrillic=1234;e.Adieresismacron=478;e.Adieresissmall=63460;e.Adotbelow=7840;e.Adotmacron=480;e.Agrave=192;e.Agravesmall=63456;e.Ahookabove=7842;e.Aiecyrillic=1236;e.Ainvertedbreve=514;e.Alpha=913;e.Alphatonos=902;e.Amacron=256;e.Amonospace=65313;e.Aogonek=260;e.Aring=197;e.Aringacute=506;e.Aringbelow=7680;e.Aringsmall=63461;e.Asmall=63329;e.Atilde=195;e.Atildesmall=63459;e.Aybarmenian=1329;e.B=66;e.Bcircle=9399;e.Bdotaccent=7682;e.Bdotbelow=7684;e.Becyrillic=1041;e.\
-Benarmenian=1330;e.Beta=914;e.Bhook=385;e.Blinebelow=7686;e.Bmonospace=65314;e.Brevesmall=63220;e.Bsmall=63330;e.Btopbar=386;e.C=67;e.Caarmenian=1342;e.Cacute=262;e.Caron=63178;e.Caronsmall=63221;e.Ccaron=268;e.Ccedilla=199;e.Ccedillaacute=7688;e.Ccedillasmall=63463;e.Ccircle=9400;e.Ccircumflex=264;e.Cdot=266;e.Cdotaccent=266;e.Cedillasmall=63416;e.Chaarmenian=1353;e.Cheabkhasiancyrillic=1212;e.Checyrillic=1063;e.Chedescenderabkhasiancyrillic=1214;e.Chedescendercyrillic=1206;e.Chedieresiscyrilli\
-c=1268;e.Cheharmenian=1347;e.Chekhakassiancyrillic=1227;e.Cheverticalstrokecyrillic=1208;e.Chi=935;e.Chook=391;e.Circumflexsmall=63222;e.Cmonospace=65315;e.Coarmenian=1361;e.Csmall=63331;e.D=68;e.DZ=497;e.DZcaron=452;e.Daarmenian=1332;e.Dafrican=393;e.Dcaron=270;e.Dcedilla=7696;e.Dcircle=9401;e.Dcircumflexbelow=7698;e.Dcroat=272;e.Ddotaccent=7690;e.Ddotbelow=7692;e.Decyrillic=1044;e.Deicoptic=1006;e.Delta=8710;e.Deltagreek=916;e.Dhook=394;e.Dieresis=63179;e.DieresisAcute=63180;e.DieresisGrave=63\
-181;e.Dieresissmall=63400;e.Digammagreek=988;e.Djecyrillic=1026;e.Dlinebelow=7694;e.Dmonospace=65316;e.Dotaccentsmall=63223;e.Dslash=272;e.Dsmall=63332;e.Dtopbar=395;e.Dz=498;e.Dzcaron=453;e.Dzeabkhasiancyrillic=1248;e.Dzecyrillic=1029;e.Dzhecyrillic=1039;e.E=69;e.Eacute=201;e.Eacutesmall=63465;e.Ebreve=276;e.Ecaron=282;e.Ecedillabreve=7708;e.Echarmenian=1333;e.Ecircle=9402;e.Ecircumflex=202;e.Ecircumflexacute=7870;e.Ecircumflexbelow=7704;e.Ecircumflexdotbelow=7878;e.Ecircumflexgrave=7872;e.Ecir\
-cumflexhookabove=7874;e.Ecircumflexsmall=63466;e.Ecircumflextilde=7876;e.Ecyrillic=1028;e.Edblgrave=516;e.Edieresis=203;e.Edieresissmall=63467;e.Edot=278;e.Edotaccent=278;e.Edotbelow=7864;e.Efcyrillic=1060;e.Egrave=200;e.Egravesmall=63464;e.Eharmenian=1335;e.Ehookabove=7866;e.Eightroman=8551;e.Einvertedbreve=518;e.Eiotifiedcyrillic=1124;e.Elcyrillic=1051;e.Elevenroman=8554;e.Emacron=274;e.Emacronacute=7702;e.Emacrongrave=7700;e.Emcyrillic=1052;e.Emonospace=65317;e.Encyrillic=1053;e.Endescendercy\
-rillic=1186;e.Eng=330;e.Enghecyrillic=1188;e.Enhookcyrillic=1223;e.Eogonek=280;e.Eopen=400;e.Epsilon=917;e.Epsilontonos=904;e.Ercyrillic=1056;e.Ereversed=398;e.Ereversedcyrillic=1069;e.Escyrillic=1057;e.Esdescendercyrillic=1194;e.Esh=425;e.Esmall=63333;e.Eta=919;e.Etarmenian=1336;e.Etatonos=905;e.Eth=208;e.Ethsmall=63472;e.Etilde=7868;e.Etildebelow=7706;e.Euro=8364;e.Ezh=439;e.Ezhcaron=494;e.Ezhreversed=440;e.F=70;e.Fcircle=9403;e.Fdotaccent=7710;e.Feharmenian=1366;e.Feicoptic=996;e.Fhook=401;e.\
-Fitacyrillic=1138;e.Fiveroman=8548;e.Fmonospace=65318;e.Fourroman=8547;e.Fsmall=63334;e.G=71;e.GBsquare=13191;e.Gacute=500;e.Gamma=915;e.Gammaafrican=404;e.Gangiacoptic=1002;e.Gbreve=286;e.Gcaron=486;e.Gcedilla=290;e.Gcircle=9404;e.Gcircumflex=284;e.Gcommaaccent=290;e.Gdot=288;e.Gdotaccent=288;e.Gecyrillic=1043;e.Ghadarmenian=1346;e.Ghemiddlehookcyrillic=1172;e.Ghestrokecyrillic=1170;e.Gheupturncyrillic=1168;e.Ghook=403;e.Gimarmenian=1331;e.Gjecyrillic=1027;e.Gmacron=7712;e.Gmonospace=65319;e.Gr\
-ave=63182;e.Gravesmall=63328;e.Gsmall=63335;e.Gsmallhook=667;e.Gstroke=484;e.H=72;e.H18533=9679;e.H18543=9642;e.H18551=9643;e.H22073=9633;e.HPsquare=13259;e.Haabkhasiancyrillic=1192;e.Hadescendercyrillic=1202;e.Hardsigncyrillic=1066;e.Hbar=294;e.Hbrevebelow=7722;e.Hcedilla=7720;e.Hcircle=9405;e.Hcircumflex=292;e.Hdieresis=7718;e.Hdotaccent=7714;e.Hdotbelow=7716;e.Hmonospace=65320;e.Hoarmenian=1344;e.Horicoptic=1e3;e.Hsmall=63336;e.Hungarumlaut=63183;e.Hungarumlautsmall=63224;e.Hzsquare=13200;e.I\
-=73;e.IAcyrillic=1071;e.IJ=306;e.IUcyrillic=1070;e.Iacute=205;e.Iacutesmall=63469;e.Ibreve=300;e.Icaron=463;e.Icircle=9406;e.Icircumflex=206;e.Icircumflexsmall=63470;e.Icyrillic=1030;e.Idblgrave=520;e.Idieresis=207;e.Idieresisacute=7726;e.Idieresiscyrillic=1252;e.Idieresissmall=63471;e.Idot=304;e.Idotaccent=304;e.Idotbelow=7882;e.Iebrevecyrillic=1238;e.Iecyrillic=1045;e.Ifraktur=8465;e.Igrave=204;e.Igravesmall=63468;e.Ihookabove=7880;e.Iicyrillic=1048;e.Iinvertedbreve=522;e.Iishortcyrillic=1049;\
-e.Imacron=298;e.Imacroncyrillic=1250;e.Imonospace=65321;e.Iniarmenian=1339;e.Iocyrillic=1025;e.Iogonek=302;e.Iota=921;e.Iotaafrican=406;e.Iotadieresis=938;e.Iotatonos=906;e.Ismall=63337;e.Istroke=407;e.Itilde=296;e.Itildebelow=7724;e.Izhitsacyrillic=1140;e.Izhitsadblgravecyrillic=1142;e.J=74;e.Jaarmenian=1345;e.Jcircle=9407;e.Jcircumflex=308;e.Jecyrillic=1032;e.Jheharmenian=1355;e.Jmonospace=65322;e.Jsmall=63338;e.K=75;e.KBsquare=13189;e.KKsquare=13261;e.Kabashkircyrillic=1184;e.Kacute=7728;e.Ka\
-cyrillic=1050;e.Kadescendercyrillic=1178;e.Kahookcyrillic=1219;e.Kappa=922;e.Kastrokecyrillic=1182;e.Kaverticalstrokecyrillic=1180;e.Kcaron=488;e.Kcedilla=310;e.Kcircle=9408;e.Kcommaaccent=310;e.Kdotbelow=7730;e.Keharmenian=1364;e.Kenarmenian=1343;e.Khacyrillic=1061;e.Kheicoptic=998;e.Khook=408;e.Kjecyrillic=1036;e.Klinebelow=7732;e.Kmonospace=65323;e.Koppacyrillic=1152;e.Koppagreek=990;e.Ksicyrillic=1134;e.Ksmall=63339;e.L=76;e.LJ=455;e.LL=63167;e.Lacute=313;e.Lambda=923;e.Lcaron=317;e.Lcedilla\
-=315;e.Lcircle=9409;e.Lcircumflexbelow=7740;e.Lcommaaccent=315;e.Ldot=319;e.Ldotaccent=319;e.Ldotbelow=7734;e.Ldotbelowmacron=7736;e.Liwnarmenian=1340;e.Lj=456;e.Ljecyrillic=1033;e.Llinebelow=7738;e.Lmonospace=65324;e.Lslash=321;e.Lslashsmall=63225;e.Lsmall=63340;e.M=77;e.MBsquare=13190;e.Macron=63184;e.Macronsmall=63407;e.Macute=7742;e.Mcircle=9410;e.Mdotaccent=7744;e.Mdotbelow=7746;e.Menarmenian=1348;e.Mmonospace=65325;e.Msmall=63341;e.Mturned=412;e.Mu=924;e.N=78;e.NJ=458;e.Nacute=323;e.Ncaron\
-=327;e.Ncedilla=325;e.Ncircle=9411;e.Ncircumflexbelow=7754;e.Ncommaaccent=325;e.Ndotaccent=7748;e.Ndotbelow=7750;e.Nhookleft=413;e.Nineroman=8552;e.Nj=459;e.Njecyrillic=1034;e.Nlinebelow=7752;e.Nmonospace=65326;e.Nowarmenian=1350;e.Nsmall=63342;e.Ntilde=209;e.Ntildesmall=63473;e.Nu=925;e.O=79;e.OE=338;e.OEsmall=63226;e.Oacute=211;e.Oacutesmall=63475;e.Obarredcyrillic=1256;e.Obarreddieresiscyrillic=1258;e.Obreve=334;e.Ocaron=465;e.Ocenteredtilde=415;e.Ocircle=9412;e.Ocircumflex=212;e.Ocircumflexa\
-cute=7888;e.Ocircumflexdotbelow=7896;e.Ocircumflexgrave=7890;e.Ocircumflexhookabove=7892;e.Ocircumflexsmall=63476;e.Ocircumflextilde=7894;e.Ocyrillic=1054;e.Odblacute=336;e.Odblgrave=524;e.Odieresis=214;e.Odieresiscyrillic=1254;e.Odieresissmall=63478;e.Odotbelow=7884;e.Ogoneksmall=63227;e.Ograve=210;e.Ogravesmall=63474;e.Oharmenian=1365;e.Ohm=8486;e.Ohookabove=7886;e.Ohorn=416;e.Ohornacute=7898;e.Ohorndotbelow=7906;e.Ohorngrave=7900;e.Ohornhookabove=7902;e.Ohorntilde=7904;e.Ohungarumlaut=336;e.O\
-i=418;e.Oinvertedbreve=526;e.Omacron=332;e.Omacronacute=7762;e.Omacrongrave=7760;e.Omega=8486;e.Omegacyrillic=1120;e.Omegagreek=937;e.Omegaroundcyrillic=1146;e.Omegatitlocyrillic=1148;e.Omegatonos=911;e.Omicron=927;e.Omicrontonos=908;e.Omonospace=65327;e.Oneroman=8544;e.Oogonek=490;e.Oogonekmacron=492;e.Oopen=390;e.Oslash=216;e.Oslashacute=510;e.Oslashsmall=63480;e.Osmall=63343;e.Ostrokeacute=510;e.Otcyrillic=1150;e.Otilde=213;e.Otildeacute=7756;e.Otildedieresis=7758;e.Otildesmall=63477;e.P=80;e\
-.Pacute=7764;e.Pcircle=9413;e.Pdotaccent=7766;e.Pecyrillic=1055;e.Peharmenian=1354;e.Pemiddlehookcyrillic=1190;e.Phi=934;e.Phook=420;e.Pi=928;e.Piwrarmenian=1363;e.Pmonospace=65328;e.Psi=936;e.Psicyrillic=1136;e.Psmall=63344;e.Q=81;e.Qcircle=9414;e.Qmonospace=65329;e.Qsmall=63345;e.R=82;e.Raarmenian=1356;e.Racute=340;e.Rcaron=344;e.Rcedilla=342;e.Rcircle=9415;e.Rcommaaccent=342;e.Rdblgrave=528;e.Rdotaccent=7768;e.Rdotbelow=7770;e.Rdotbelowmacron=7772;e.Reharmenian=1360;e.Rfraktur=8476;e.Rho=929;\
-e.Ringsmall=63228;e.Rinvertedbreve=530;e.Rlinebelow=7774;e.Rmonospace=65330;e.Rsmall=63346;e.Rsmallinverted=641;e.Rsmallinvertedsuperior=694;e.S=83;e.SF010000=9484;e.SF020000=9492;e.SF030000=9488;e.SF040000=9496;e.SF050000=9532;e.SF060000=9516;e.SF070000=9524;e.SF080000=9500;e.SF090000=9508;e.SF100000=9472;e.SF110000=9474;e.SF190000=9569;e.SF200000=9570;e.SF210000=9558;e.SF220000=9557;e.SF230000=9571;e.SF240000=9553;e.SF250000=9559;e.SF260000=9565;e.SF270000=9564;e.SF280000=9563;e.SF360000=9566;\
-e.SF370000=9567;e.SF380000=9562;e.SF390000=9556;e.SF400000=9577;e.SF410000=9574;e.SF420000=9568;e.SF430000=9552;e.SF440000=9580;e.SF450000=9575;e.SF460000=9576;e.SF470000=9572;e.SF480000=9573;e.SF490000=9561;e.SF500000=9560;e.SF510000=9554;e.SF520000=9555;e.SF530000=9579;e.SF540000=9578;e.Sacute=346;e.Sacutedotaccent=7780;e.Sampigreek=992;e.Scaron=352;e.Scarondotaccent=7782;e.Scaronsmall=63229;e.Scedilla=350;e.Schwa=399;e.Schwacyrillic=1240;e.Schwadieresiscyrillic=1242;e.Scircle=9416;e.Scircumfl\
-ex=348;e.Scommaaccent=536;e.Sdotaccent=7776;e.Sdotbelow=7778;e.Sdotbelowdotaccent=7784;e.Seharmenian=1357;e.Sevenroman=8550;e.Shaarmenian=1351;e.Shacyrillic=1064;e.Shchacyrillic=1065;e.Sheicoptic=994;e.Shhacyrillic=1210;e.Shimacoptic=1004;e.Sigma=931;e.Sixroman=8549;e.Smonospace=65331;e.Softsigncyrillic=1068;e.Ssmall=63347;e.Stigmagreek=986;e.T=84;e.Tau=932;e.Tbar=358;e.Tcaron=356;e.Tcedilla=354;e.Tcircle=9417;e.Tcircumflexbelow=7792;e.Tcommaaccent=354;e.Tdotaccent=7786;e.Tdotbelow=7788;e.Tecyri\
-llic=1058;e.Tedescendercyrillic=1196;e.Tenroman=8553;e.Tetsecyrillic=1204;e.Theta=920;e.Thook=428;e.Thorn=222;e.Thornsmall=63486;e.Threeroman=8546;e.Tildesmall=63230;e.Tiwnarmenian=1359;e.Tlinebelow=7790;e.Tmonospace=65332;e.Toarmenian=1337;e.Tonefive=444;e.Tonesix=388;e.Tonetwo=423;e.Tretroflexhook=430;e.Tsecyrillic=1062;e.Tshecyrillic=1035;e.Tsmall=63348;e.Twelveroman=8555;e.Tworoman=8545;e.U=85;e.Uacute=218;e.Uacutesmall=63482;e.Ubreve=364;e.Ucaron=467;e.Ucircle=9418;e.Ucircumflex=219;e.Ucirc\
-umflexbelow=7798;e.Ucircumflexsmall=63483;e.Ucyrillic=1059;e.Udblacute=368;e.Udblgrave=532;e.Udieresis=220;e.Udieresisacute=471;e.Udieresisbelow=7794;e.Udieresiscaron=473;e.Udieresiscyrillic=1264;e.Udieresisgrave=475;e.Udieresismacron=469;e.Udieresissmall=63484;e.Udotbelow=7908;e.Ugrave=217;e.Ugravesmall=63481;e.Uhookabove=7910;e.Uhorn=431;e.Uhornacute=7912;e.Uhorndotbelow=7920;e.Uhorngrave=7914;e.Uhornhookabove=7916;e.Uhorntilde=7918;e.Uhungarumlaut=368;e.Uhungarumlautcyrillic=1266;e.Uinvertedb\
-reve=534;e.Ukcyrillic=1144;e.Umacron=362;e.Umacroncyrillic=1262;e.Umacrondieresis=7802;e.Umonospace=65333;e.Uogonek=370;e.Upsilon=933;e.Upsilon1=978;e.Upsilonacutehooksymbolgreek=979;e.Upsilonafrican=433;e.Upsilondieresis=939;e.Upsilondieresishooksymbolgreek=980;e.Upsilonhooksymbol=978;e.Upsilontonos=910;e.Uring=366;e.Ushortcyrillic=1038;e.Usmall=63349;e.Ustraightcyrillic=1198;e.Ustraightstrokecyrillic=1200;e.Utilde=360;e.Utildeacute=7800;e.Utildebelow=7796;e.V=86;e.Vcircle=9419;e.Vdotbelow=7806\
-;e.Vecyrillic=1042;e.Vewarmenian=1358;e.Vhook=434;e.Vmonospace=65334;e.Voarmenian=1352;e.Vsmall=63350;e.Vtilde=7804;e.W=87;e.Wacute=7810;e.Wcircle=9420;e.Wcircumflex=372;e.Wdieresis=7812;e.Wdotaccent=7814;e.Wdotbelow=7816;e.Wgrave=7808;e.Wmonospace=65335;e.Wsmall=63351;e.X=88;e.Xcircle=9421;e.Xdieresis=7820;e.Xdotaccent=7818;e.Xeharmenian=1341;e.Xi=926;e.Xmonospace=65336;e.Xsmall=63352;e.Y=89;e.Yacute=221;e.Yacutesmall=63485;e.Yatcyrillic=1122;e.Ycircle=9422;e.Ycircumflex=374;e.Ydieresis=376;e.Y\
-dieresissmall=63487;e.Ydotaccent=7822;e.Ydotbelow=7924;e.Yericyrillic=1067;e.Yerudieresiscyrillic=1272;e.Ygrave=7922;e.Yhook=435;e.Yhookabove=7926;e.Yiarmenian=1349;e.Yicyrillic=1031;e.Yiwnarmenian=1362;e.Ymonospace=65337;e.Ysmall=63353;e.Ytilde=7928;e.Yusbigcyrillic=1130;e.Yusbigiotifiedcyrillic=1132;e.Yuslittlecyrillic=1126;e.Yuslittleiotifiedcyrillic=1128;e.Z=90;e.Zaarmenian=1334;e.Zacute=377;e.Zcaron=381;e.Zcaronsmall=63231;e.Zcircle=9423;e.Zcircumflex=7824;e.Zdot=379;e.Zdotaccent=379;e.Zdot\
-below=7826;e.Zecyrillic=1047;e.Zedescendercyrillic=1176;e.Zedieresiscyrillic=1246;e.Zeta=918;e.Zhearmenian=1338;e.Zhebrevecyrillic=1217;e.Zhecyrillic=1046;e.Zhedescendercyrillic=1174;e.Zhedieresiscyrillic=1244;e.Zlinebelow=7828;e.Zmonospace=65338;e.Zsmall=63354;e.Zstroke=437;e.a=97;e.aabengali=2438;e.aacute=225;e.aadeva=2310;e.aagujarati=2694;e.aagurmukhi=2566;e.aamatragurmukhi=2622;e.aarusquare=13059;e.aavowelsignbengali=2494;e.aavowelsigndeva=2366;e.aavowelsigngujarati=2750;e.abbreviationmarka\
-rmenian=1375;e.abbreviationsigndeva=2416;e.abengali=2437;e.abopomofo=12570;e.abreve=259;e.abreveacute=7855;e.abrevecyrillic=1233;e.abrevedotbelow=7863;e.abrevegrave=7857;e.abrevehookabove=7859;e.abrevetilde=7861;e.acaron=462;e.acircle=9424;e.acircumflex=226;e.acircumflexacute=7845;e.acircumflexdotbelow=7853;e.acircumflexgrave=7847;e.acircumflexhookabove=7849;e.acircumflextilde=7851;e.acute=180;e.acutebelowcmb=791;e.acutecmb=769;e.acutecomb=769;e.acutedeva=2388;e.acutelowmod=719;e.acutetonecmb=83\
-3;e.acyrillic=1072;e.adblgrave=513;e.addakgurmukhi=2673;e.adeva=2309;e.adieresis=228;e.adieresiscyrillic=1235;e.adieresismacron=479;e.adotbelow=7841;e.adotmacron=481;e.ae=230;e.aeacute=509;e.aekorean=12624;e.aemacron=483;e.afii00208=8213;e.afii08941=8356;e.afii10017=1040;e.afii10018=1041;e.afii10019=1042;e.afii10020=1043;e.afii10021=1044;e.afii10022=1045;e.afii10023=1025;e.afii10024=1046;e.afii10025=1047;e.afii10026=1048;e.afii10027=1049;e.afii10028=1050;e.afii10029=1051;e.afii10030=1052;e.afii1\
-0031=1053;e.afii10032=1054;e.afii10033=1055;e.afii10034=1056;e.afii10035=1057;e.afii10036=1058;e.afii10037=1059;e.afii10038=1060;e.afii10039=1061;e.afii10040=1062;e.afii10041=1063;e.afii10042=1064;e.afii10043=1065;e.afii10044=1066;e.afii10045=1067;e.afii10046=1068;e.afii10047=1069;e.afii10048=1070;e.afii10049=1071;e.afii10050=1168;e.afii10051=1026;e.afii10052=1027;e.afii10053=1028;e.afii10054=1029;e.afii10055=1030;e.afii10056=1031;e.afii10057=1032;e.afii10058=1033;e.afii10059=1034;e.afii10060=10\
-35;e.afii10061=1036;e.afii10062=1038;e.afii10063=63172;e.afii10064=63173;e.afii10065=1072;e.afii10066=1073;e.afii10067=1074;e.afii10068=1075;e.afii10069=1076;e.afii10070=1077;e.afii10071=1105;e.afii10072=1078;e.afii10073=1079;e.afii10074=1080;e.afii10075=1081;e.afii10076=1082;e.afii10077=1083;e.afii10078=1084;e.afii10079=1085;e.afii10080=1086;e.afii10081=1087;e.afii10082=1088;e.afii10083=1089;e.afii10084=1090;e.afii10085=1091;e.afii10086=1092;e.afii10087=1093;e.afii10088=1094;e.afii10089=1095;e.\
-afii10090=1096;e.afii10091=1097;e.afii10092=1098;e.afii10093=1099;e.afii10094=1100;e.afii10095=1101;e.afii10096=1102;e.afii10097=1103;e.afii10098=1169;e.afii10099=1106;e.afii10100=1107;e.afii10101=1108;e.afii10102=1109;e.afii10103=1110;e.afii10104=1111;e.afii10105=1112;e.afii10106=1113;e.afii10107=1114;e.afii10108=1115;e.afii10109=1116;e.afii10110=1118;e.afii10145=1039;e.afii10146=1122;e.afii10147=1138;e.afii10148=1140;e.afii10192=63174;e.afii10193=1119;e.afii10194=1123;e.afii10195=1139;e.afii10\
-196=1141;e.afii10831=63175;e.afii10832=63176;e.afii10846=1241;e.afii299=8206;e.afii300=8207;e.afii301=8205;e.afii57381=1642;e.afii57388=1548;e.afii57392=1632;e.afii57393=1633;e.afii57394=1634;e.afii57395=1635;e.afii57396=1636;e.afii57397=1637;e.afii57398=1638;e.afii57399=1639;e.afii57400=1640;e.afii57401=1641;e.afii57403=1563;e.afii57407=1567;e.afii57409=1569;e.afii57410=1570;e.afii57411=1571;e.afii57412=1572;e.afii57413=1573;e.afii57414=1574;e.afii57415=1575;e.afii57416=1576;e.afii57417=1577;e.\
-afii57418=1578;e.afii57419=1579;e.afii57420=1580;e.afii57421=1581;e.afii57422=1582;e.afii57423=1583;e.afii57424=1584;e.afii57425=1585;e.afii57426=1586;e.afii57427=1587;e.afii57428=1588;e.afii57429=1589;e.afii57430=1590;e.afii57431=1591;e.afii57432=1592;e.afii57433=1593;e.afii57434=1594;e.afii57440=1600;e.afii57441=1601;e.afii57442=1602;e.afii57443=1603;e.afii57444=1604;e.afii57445=1605;e.afii57446=1606;e.afii57448=1608;e.afii57449=1609;e.afii57450=1610;e.afii57451=1611;e.afii57452=1612;e.afii574\
-53=1613;e.afii57454=1614;e.afii57455=1615;e.afii57456=1616;e.afii57457=1617;e.afii57458=1618;e.afii57470=1607;e.afii57505=1700;e.afii57506=1662;e.afii57507=1670;e.afii57508=1688;e.afii57509=1711;e.afii57511=1657;e.afii57512=1672;e.afii57513=1681;e.afii57514=1722;e.afii57519=1746;e.afii57534=1749;e.afii57636=8362;e.afii57645=1470;e.afii57658=1475;e.afii57664=1488;e.afii57665=1489;e.afii57666=1490;e.afii57667=1491;e.afii57668=1492;e.afii57669=1493;e.afii57670=1494;e.afii57671=1495;e.afii57672=1496\
-;e.afii57673=1497;e.afii57674=1498;e.afii57675=1499;e.afii57676=1500;e.afii57677=1501;e.afii57678=1502;e.afii57679=1503;e.afii57680=1504;e.afii57681=1505;e.afii57682=1506;e.afii57683=1507;e.afii57684=1508;e.afii57685=1509;e.afii57686=1510;e.afii57687=1511;e.afii57688=1512;e.afii57689=1513;e.afii57690=1514;e.afii57694=64298;e.afii57695=64299;e.afii57700=64331;e.afii57705=64287;e.afii57716=1520;e.afii57717=1521;e.afii57718=1522;e.afii57723=64309;e.afii57793=1460;e.afii57794=1461;e.afii57795=1462;e\
-.afii57796=1467;e.afii57797=1464;e.afii57798=1463;e.afii57799=1456;e.afii57800=1458;e.afii57801=1457;e.afii57802=1459;e.afii57803=1474;e.afii57804=1473;e.afii57806=1465;e.afii57807=1468;e.afii57839=1469;e.afii57841=1471;e.afii57842=1472;e.afii57929=700;e.afii61248=8453;e.afii61289=8467;e.afii61352=8470;e.afii61573=8236;e.afii61574=8237;e.afii61575=8238;e.afii61664=8204;e.afii63167=1645;e.afii64937=701;e.agrave=224;e.agujarati=2693;e.agurmukhi=2565;e.ahiragana=12354;e.ahookabove=7843;e.aibengali=\
-2448;e.aibopomofo=12574;e.aideva=2320;e.aiecyrillic=1237;e.aigujarati=2704;e.aigurmukhi=2576;e.aimatragurmukhi=2632;e.ainarabic=1593;e.ainfinalarabic=65226;e.aininitialarabic=65227;e.ainmedialarabic=65228;e.ainvertedbreve=515;e.aivowelsignbengali=2504;e.aivowelsigndeva=2376;e.aivowelsigngujarati=2760;e.akatakana=12450;e.akatakanahalfwidth=65393;e.akorean=12623;e.alef=1488;e.alefarabic=1575;e.alefdageshhebrew=64304;e.aleffinalarabic=65166;e.alefhamzaabovearabic=1571;e.alefhamzaabovefinalarabic=65\
-156;e.alefhamzabelowarabic=1573;e.alefhamzabelowfinalarabic=65160;e.alefhebrew=1488;e.aleflamedhebrew=64335;e.alefmaddaabovearabic=1570;e.alefmaddaabovefinalarabic=65154;e.alefmaksuraarabic=1609;e.alefmaksurafinalarabic=65264;e.alefmaksurainitialarabic=65267;e.alefmaksuramedialarabic=65268;e.alefpatahhebrew=64302;e.alefqamatshebrew=64303;e.aleph=8501;e.allequal=8780;e.alpha=945;e.alphatonos=940;e.amacron=257;e.amonospace=65345;e.ampersand=38;e.ampersandmonospace=65286;e.ampersandsmall=63270;e.am\
-square=13250;e.anbopomofo=12578;e.angbopomofo=12580;e.angbracketleft=12296;e.angbracketright=12297;e.angkhankhuthai=3674;e.angle=8736;e.anglebracketleft=12296;e.anglebracketleftvertical=65087;e.anglebracketright=12297;e.anglebracketrightvertical=65088;e.angleleft=9001;e.angleright=9002;e.angstrom=8491;e.anoteleia=903;e.anudattadeva=2386;e.anusvarabengali=2434;e.anusvaradeva=2306;e.anusvaragujarati=2690;e.aogonek=261;e.apaatosquare=13056;e.aparen=9372;e.apostrophearmenian=1370;e.apostrophemod=700\
-;e.apple=63743;e.approaches=8784;e.approxequal=8776;e.approxequalorimage=8786;e.approximatelyequal=8773;e.araeaekorean=12686;e.araeakorean=12685;e.arc=8978;e.arighthalfring=7834;e.aring=229;e.aringacute=507;e.aringbelow=7681;e.arrowboth=8596;e.arrowdashdown=8675;e.arrowdashleft=8672;e.arrowdashright=8674;e.arrowdashup=8673;e.arrowdblboth=8660;e.arrowdbldown=8659;e.arrowdblleft=8656;e.arrowdblright=8658;e.arrowdblup=8657;e.arrowdown=8595;e.arrowdownleft=8601;e.arrowdownright=8600;e.arrowdownwhite\
-=8681;e.arrowheaddownmod=709;e.arrowheadleftmod=706;e.arrowheadrightmod=707;e.arrowheadupmod=708;e.arrowhorizex=63719;e.arrowleft=8592;e.arrowleftdbl=8656;e.arrowleftdblstroke=8653;e.arrowleftoverright=8646;e.arrowleftwhite=8678;e.arrowright=8594;e.arrowrightdblstroke=8655;e.arrowrightheavy=10142;e.arrowrightoverleft=8644;e.arrowrightwhite=8680;e.arrowtableft=8676;e.arrowtabright=8677;e.arrowup=8593;e.arrowupdn=8597;e.arrowupdnbse=8616;e.arrowupdownbase=8616;e.arrowupleft=8598;e.arrowupleftofdow\
-n=8645;e.arrowupright=8599;e.arrowupwhite=8679;e.arrowvertex=63718;e.asciicircum=94;e.asciicircummonospace=65342;e.asciitilde=126;e.asciitildemonospace=65374;e.ascript=593;e.ascriptturned=594;e.asmallhiragana=12353;e.asmallkatakana=12449;e.asmallkatakanahalfwidth=65383;e.asterisk=42;e.asteriskaltonearabic=1645;e.asteriskarabic=1645;e.asteriskmath=8727;e.asteriskmonospace=65290;e.asterisksmall=65121;e.asterism=8258;e.asuperior=63209;e.asymptoticallyequal=8771;e.at=64;e.atilde=227;e.atmonospace=65\
-312;e.atsmall=65131;e.aturned=592;e.aubengali=2452;e.aubopomofo=12576;e.audeva=2324;e.augujarati=2708;e.augurmukhi=2580;e.aulengthmarkbengali=2519;e.aumatragurmukhi=2636;e.auvowelsignbengali=2508;e.auvowelsigndeva=2380;e.auvowelsigngujarati=2764;e.avagrahadeva=2365;e.aybarmenian=1377;e.ayin=1506;e.ayinaltonehebrew=64288;e.ayinhebrew=1506;e.b=98;e.babengali=2476;e.backslash=92;e.backslashmonospace=65340;e.badeva=2348;e.bagujarati=2732;e.bagurmukhi=2604;e.bahiragana=12400;e.bahtthai=3647;e.bakatak\
-ana=12496;e.bar=124;e.barmonospace=65372;e.bbopomofo=12549;e.bcircle=9425;e.bdotaccent=7683;e.bdotbelow=7685;e.beamedsixteenthnotes=9836;e.because=8757;e.becyrillic=1073;e.beharabic=1576;e.behfinalarabic=65168;e.behinitialarabic=65169;e.behiragana=12409;e.behmedialarabic=65170;e.behmeeminitialarabic=64671;e.behmeemisolatedarabic=64520;e.behnoonfinalarabic=64621;e.bekatakana=12505;e.benarmenian=1378;e.bet=1489;e.beta=946;e.betasymbolgreek=976;e.betdagesh=64305;e.betdageshhebrew=64305;e.bethebrew=\
-1489;e.betrafehebrew=64332;e.bhabengali=2477;e.bhadeva=2349;e.bhagujarati=2733;e.bhagurmukhi=2605;e.bhook=595;e.bihiragana=12403;e.bikatakana=12499;e.bilabialclick=664;e.bindigurmukhi=2562;e.birusquare=13105;e.blackcircle=9679;e.blackdiamond=9670;e.blackdownpointingtriangle=9660;e.blackleftpointingpointer=9668;e.blackleftpointingtriangle=9664;e.blacklenticularbracketleft=12304;e.blacklenticularbracketleftvertical=65083;e.blacklenticularbracketright=12305;e.blacklenticularbracketrightvertical=650\
-84;e.blacklowerlefttriangle=9699;e.blacklowerrighttriangle=9698;e.blackrectangle=9644;e.blackrightpointingpointer=9658;e.blackrightpointingtriangle=9654;e.blacksmallsquare=9642;e.blacksmilingface=9787;e.blacksquare=9632;e.blackstar=9733;e.blackupperlefttriangle=9700;e.blackupperrighttriangle=9701;e.blackuppointingsmalltriangle=9652;e.blackuppointingtriangle=9650;e.blank=9251;e.blinebelow=7687;e.block=9608;e.bmonospace=65346;e.bobaimaithai=3610;e.bohiragana=12412;e.bokatakana=12508;e.bparen=9373;\
-e.bqsquare=13251;e.braceex=63732;e.braceleft=123;e.braceleftbt=63731;e.braceleftmid=63730;e.braceleftmonospace=65371;e.braceleftsmall=65115;e.bracelefttp=63729;e.braceleftvertical=65079;e.braceright=125;e.bracerightbt=63742;e.bracerightmid=63741;e.bracerightmonospace=65373;e.bracerightsmall=65116;e.bracerighttp=63740;e.bracerightvertical=65080;e.bracketleft=91;e.bracketleftbt=63728;e.bracketleftex=63727;e.bracketleftmonospace=65339;e.bracketlefttp=63726;e.bracketright=93;e.bracketrightbt=63739;e\
-.bracketrightex=63738;e.bracketrightmonospace=65341;e.bracketrighttp=63737;e.breve=728;e.brevebelowcmb=814;e.brevecmb=774;e.breveinvertedbelowcmb=815;e.breveinvertedcmb=785;e.breveinverteddoublecmb=865;e.bridgebelowcmb=810;e.bridgeinvertedbelowcmb=826;e.brokenbar=166;e.bstroke=384;e.bsuperior=63210;e.btopbar=387;e.buhiragana=12406;e.bukatakana=12502;e.bullet=8226;e.bulletinverse=9688;e.bulletoperator=8729;e.bullseye=9678;e.c=99;e.caarmenian=1390;e.cabengali=2458;e.cacute=263;e.cadeva=2330;e.cagu\
-jarati=2714;e.cagurmukhi=2586;e.calsquare=13192;e.candrabindubengali=2433;e.candrabinducmb=784;e.candrabindudeva=2305;e.candrabindugujarati=2689;e.capslock=8682;e.careof=8453;e.caron=711;e.caronbelowcmb=812;e.caroncmb=780;e.carriagereturn=8629;e.cbopomofo=12568;e.ccaron=269;e.ccedilla=231;e.ccedillaacute=7689;e.ccircle=9426;e.ccircumflex=265;e.ccurl=597;e.cdot=267;e.cdotaccent=267;e.cdsquare=13253;e.cedilla=184;e.cedillacmb=807;e.cent=162;e.centigrade=8451;e.centinferior=63199;e.centmonospace=65\
-504;e.centoldstyle=63394;e.centsuperior=63200;e.chaarmenian=1401;e.chabengali=2459;e.chadeva=2331;e.chagujarati=2715;e.chagurmukhi=2587;e.chbopomofo=12564;e.cheabkhasiancyrillic=1213;e.checkmark=10003;e.checyrillic=1095;e.chedescenderabkhasiancyrillic=1215;e.chedescendercyrillic=1207;e.chedieresiscyrillic=1269;e.cheharmenian=1395;e.chekhakassiancyrillic=1228;e.cheverticalstrokecyrillic=1209;e.chi=967;e.chieuchacirclekorean=12919;e.chieuchaparenkorean=12823;e.chieuchcirclekorean=12905;e.chieuchko\
-rean=12618;e.chieuchparenkorean=12809;e.chochangthai=3594;e.chochanthai=3592;e.chochingthai=3593;e.chochoethai=3596;e.chook=392;e.cieucacirclekorean=12918;e.cieucaparenkorean=12822;e.cieuccirclekorean=12904;e.cieuckorean=12616;e.cieucparenkorean=12808;e.cieucuparenkorean=12828;e.circle=9675;e.circlecopyrt=169;e.circlemultiply=8855;e.circleot=8857;e.circleplus=8853;e.circlepostalmark=12342;e.circlewithlefthalfblack=9680;e.circlewithrighthalfblack=9681;e.circumflex=710;e.circumflexbelowcmb=813;e.c\
-ircumflexcmb=770;e.clear=8999;e.clickalveolar=450;e.clickdental=448;e.clicklateral=449;e.clickretroflex=451;e.club=9827;e.clubsuitblack=9827;e.clubsuitwhite=9831;e.cmcubedsquare=13220;e.cmonospace=65347;e.cmsquaredsquare=13216;e.coarmenian=1409;e.colon=58;e.colonmonetary=8353;e.colonmonospace=65306;e.colonsign=8353;e.colonsmall=65109;e.colontriangularhalfmod=721;e.colontriangularmod=720;e.comma=44;e.commaabovecmb=787;e.commaaboverightcmb=789;e.commaaccent=63171;e.commaarabic=1548;e.commaarmenian\
-=1373;e.commainferior=63201;e.commamonospace=65292;e.commareversedabovecmb=788;e.commareversedmod=701;e.commasmall=65104;e.commasuperior=63202;e.commaturnedabovecmb=786;e.commaturnedmod=699;e.compass=9788;e.congruent=8773;e.contourintegral=8750;e.control=8963;e.controlACK=6;e.controlBEL=7;e.controlBS=8;e.controlCAN=24;e.controlCR=13;e.controlDC1=17;e.controlDC2=18;e.controlDC3=19;e.controlDC4=20;e.controlDEL=127;e.controlDLE=16;e.controlEM=25;e.controlENQ=5;e.controlEOT=4;e.controlESC=27;e.contr\
-olETB=23;e.controlETX=3;e.controlFF=12;e.controlFS=28;e.controlGS=29;e.controlHT=9;e.controlLF=10;e.controlNAK=21;e.controlNULL=0;e.controlRS=30;e.controlSI=15;e.controlSO=14;e.controlSOT=2;e.controlSTX=1;e.controlSUB=26;e.controlSYN=22;e.controlUS=31;e.controlVT=11;e.copyright=169;e.copyrightsans=63721;e.copyrightserif=63193;e.cornerbracketleft=12300;e.cornerbracketlefthalfwidth=65378;e.cornerbracketleftvertical=65089;e.cornerbracketright=12301;e.cornerbracketrighthalfwidth=65379;e.cornerbracke\
-trightvertical=65090;e.corporationsquare=13183;e.cosquare=13255;e.coverkgsquare=13254;e.cparen=9374;e.cruzeiro=8354;e.cstretched=663;e.curlyand=8911;e.curlyor=8910;e.currency=164;e.cyrBreve=63185;e.cyrFlex=63186;e.cyrbreve=63188;e.cyrflex=63189;e.d=100;e.daarmenian=1380;e.dabengali=2470;e.dadarabic=1590;e.dadeva=2342;e.dadfinalarabic=65214;e.dadinitialarabic=65215;e.dadmedialarabic=65216;e.dagesh=1468;e.dageshhebrew=1468;e.dagger=8224;e.daggerdbl=8225;e.dagujarati=2726;e.dagurmukhi=2598;e.dahira\
-gana=12384;e.dakatakana=12480;e.dalarabic=1583;e.dalet=1491;e.daletdagesh=64307;e.daletdageshhebrew=64307;e.dalethebrew=1491;e.dalfinalarabic=65194;e.dammaarabic=1615;e.dammalowarabic=1615;e.dammatanaltonearabic=1612;e.dammatanarabic=1612;e.danda=2404;e.dargahebrew=1447;e.dargalefthebrew=1447;e.dasiapneumatacyrilliccmb=1157;e.dblGrave=63187;e.dblanglebracketleft=12298;e.dblanglebracketleftvertical=65085;e.dblanglebracketright=12299;e.dblanglebracketrightvertical=65086;e.dblarchinvertedbelowcmb=8\
-11;e.dblarrowleft=8660;e.dblarrowright=8658;e.dbldanda=2405;e.dblgrave=63190;e.dblgravecmb=783;e.dblintegral=8748;e.dbllowline=8215;e.dbllowlinecmb=819;e.dbloverlinecmb=831;e.dblprimemod=698;e.dblverticalbar=8214;e.dblverticallineabovecmb=782;e.dbopomofo=12553;e.dbsquare=13256;e.dcaron=271;e.dcedilla=7697;e.dcircle=9427;e.dcircumflexbelow=7699;e.dcroat=273;e.ddabengali=2465;e.ddadeva=2337;e.ddagujarati=2721;e.ddagurmukhi=2593;e.ddalarabic=1672;e.ddalfinalarabic=64393;e.dddhadeva=2396;e.ddhabenga\
-li=2466;e.ddhadeva=2338;e.ddhagujarati=2722;e.ddhagurmukhi=2594;e.ddotaccent=7691;e.ddotbelow=7693;e.decimalseparatorarabic=1643;e.decimalseparatorpersian=1643;e.decyrillic=1076;e.degree=176;e.dehihebrew=1453;e.dehiragana=12391;e.deicoptic=1007;e.dekatakana=12487;e.deleteleft=9003;e.deleteright=8998;e.delta=948;e.deltaturned=397;e.denominatorminusonenumeratorbengali=2552;e.dezh=676;e.dhabengali=2471;e.dhadeva=2343;e.dhagujarati=2727;e.dhagurmukhi=2599;e.dhook=599;e.dialytikatonos=901;e.dialytika\
-tonoscmb=836;e.diamond=9830;e.diamondsuitwhite=9826;e.dieresis=168;e.dieresisacute=63191;e.dieresisbelowcmb=804;e.dieresiscmb=776;e.dieresisgrave=63192;e.dieresistonos=901;e.dihiragana=12386;e.dikatakana=12482;e.dittomark=12291;e.divide=247;e.divides=8739;e.divisionslash=8725;e.djecyrillic=1106;e.dkshade=9619;e.dlinebelow=7695;e.dlsquare=13207;e.dmacron=273;e.dmonospace=65348;e.dnblock=9604;e.dochadathai=3598;e.dodekthai=3604;e.dohiragana=12393;e.dokatakana=12489;e.dollar=36;e.dollarinferior=632\
-03;e.dollarmonospace=65284;e.dollaroldstyle=63268;e.dollarsmall=65129;e.dollarsuperior=63204;e.dong=8363;e.dorusquare=13094;e.dotaccent=729;e.dotaccentcmb=775;e.dotbelowcmb=803;e.dotbelowcomb=803;e.dotkatakana=12539;e.dotlessi=305;e.dotlessj=63166;e.dotlessjstrokehook=644;e.dotmath=8901;e.dottedcircle=9676;e.doubleyodpatah=64287;e.doubleyodpatahhebrew=64287;e.downtackbelowcmb=798;e.downtackmod=725;e.dparen=9375;e.dsuperior=63211;e.dtail=598;e.dtopbar=396;e.duhiragana=12389;e.dukatakana=12485;e.d\
-z=499;e.dzaltone=675;e.dzcaron=454;e.dzcurl=677;e.dzeabkhasiancyrillic=1249;e.dzecyrillic=1109;e.dzhecyrillic=1119;e.e=101;e.eacute=233;e.earth=9793;e.ebengali=2447;e.ebopomofo=12572;e.ebreve=277;e.ecandradeva=2317;e.ecandragujarati=2701;e.ecandravowelsigndeva=2373;e.ecandravowelsigngujarati=2757;e.ecaron=283;e.ecedillabreve=7709;e.echarmenian=1381;e.echyiwnarmenian=1415;e.ecircle=9428;e.ecircumflex=234;e.ecircumflexacute=7871;e.ecircumflexbelow=7705;e.ecircumflexdotbelow=7879;e.ecircumflexgrave\
-=7873;e.ecircumflexhookabove=7875;e.ecircumflextilde=7877;e.ecyrillic=1108;e.edblgrave=517;e.edeva=2319;e.edieresis=235;e.edot=279;e.edotaccent=279;e.edotbelow=7865;e.eegurmukhi=2575;e.eematragurmukhi=2631;e.efcyrillic=1092;e.egrave=232;e.egujarati=2703;e.eharmenian=1383;e.ehbopomofo=12573;e.ehiragana=12360;e.ehookabove=7867;e.eibopomofo=12575;e.eight=56;e.eightarabic=1640;e.eightbengali=2542;e.eightcircle=9319;e.eightcircleinversesansserif=10129;e.eightdeva=2414;e.eighteencircle=9329;e.eighteen\
-paren=9349;e.eighteenperiod=9369;e.eightgujarati=2798;e.eightgurmukhi=2670;e.eighthackarabic=1640;e.eighthangzhou=12328;e.eighthnotebeamed=9835;e.eightideographicparen=12839;e.eightinferior=8328;e.eightmonospace=65304;e.eightoldstyle=63288;e.eightparen=9339;e.eightperiod=9359;e.eightpersian=1784;e.eightroman=8567;e.eightsuperior=8312;e.eightthai=3672;e.einvertedbreve=519;e.eiotifiedcyrillic=1125;e.ekatakana=12456;e.ekatakanahalfwidth=65396;e.ekonkargurmukhi=2676;e.ekorean=12628;e.elcyrillic=1083\
-;e.element=8712;e.elevencircle=9322;e.elevenparen=9342;e.elevenperiod=9362;e.elevenroman=8570;e.ellipsis=8230;e.ellipsisvertical=8942;e.emacron=275;e.emacronacute=7703;e.emacrongrave=7701;e.emcyrillic=1084;e.emdash=8212;e.emdashvertical=65073;e.emonospace=65349;e.emphasismarkarmenian=1371;e.emptyset=8709;e.enbopomofo=12579;e.encyrillic=1085;e.endash=8211;e.endashvertical=65074;e.endescendercyrillic=1187;e.eng=331;e.engbopomofo=12581;e.enghecyrillic=1189;e.enhookcyrillic=1224;e.enspace=8194;e.eog\
-onek=281;e.eokorean=12627;e.eopen=603;e.eopenclosed=666;e.eopenreversed=604;e.eopenreversedclosed=606;e.eopenreversedhook=605;e.eparen=9376;e.epsilon=949;e.epsilontonos=941;e.equal=61;e.equalmonospace=65309;e.equalsmall=65126;e.equalsuperior=8316;e.equivalence=8801;e.erbopomofo=12582;e.ercyrillic=1088;e.ereversed=600;e.ereversedcyrillic=1101;e.escyrillic=1089;e.esdescendercyrillic=1195;e.esh=643;e.eshcurl=646;e.eshortdeva=2318;e.eshortvowelsigndeva=2374;e.eshreversedloop=426;e.eshsquatreversed=6\
-45;e.esmallhiragana=12359;e.esmallkatakana=12455;e.esmallkatakanahalfwidth=65386;e.estimated=8494;e.esuperior=63212;e.eta=951;e.etarmenian=1384;e.etatonos=942;e.eth=240;e.etilde=7869;e.etildebelow=7707;e.etnahtafoukhhebrew=1425;e.etnahtafoukhlefthebrew=1425;e.etnahtahebrew=1425;e.etnahtalefthebrew=1425;e.eturned=477;e.eukorean=12641;e.euro=8364;e.evowelsignbengali=2503;e.evowelsigndeva=2375;e.evowelsigngujarati=2759;e.exclam=33;e.exclamarmenian=1372;e.exclamdbl=8252;e.exclamdown=161;e.exclamdown\
-small=63393;e.exclammonospace=65281;e.exclamsmall=63265;e.existential=8707;e.ezh=658;e.ezhcaron=495;e.ezhcurl=659;e.ezhreversed=441;e.ezhtail=442;e.f=102;e.fadeva=2398;e.fagurmukhi=2654;e.fahrenheit=8457;e.fathaarabic=1614;e.fathalowarabic=1614;e.fathatanarabic=1611;e.fbopomofo=12552;e.fcircle=9429;e.fdotaccent=7711;e.feharabic=1601;e.feharmenian=1414;e.fehfinalarabic=65234;e.fehinitialarabic=65235;e.fehmedialarabic=65236;e.feicoptic=997;e.female=9792;e.ff=64256;e.f_f=64256;e.ffi=64259;e.f_f_i=6\
-4259;e.ffl=64260;e.f_f_l=64260;e.fi=64257;e.f_i=64257;e.fifteencircle=9326;e.fifteenparen=9346;e.fifteenperiod=9366;e.figuredash=8210;e.filledbox=9632;e.filledrect=9644;e.finalkaf=1498;e.finalkafdagesh=64314;e.finalkafdageshhebrew=64314;e.finalkafhebrew=1498;e.finalmem=1501;e.finalmemhebrew=1501;e.finalnun=1503;e.finalnunhebrew=1503;e.finalpe=1507;e.finalpehebrew=1507;e.finaltsadi=1509;e.finaltsadihebrew=1509;e.firsttonechinese=713;e.fisheye=9673;e.fitacyrillic=1139;e.five=53;e.fivearabic=1637;e\
-.fivebengali=2539;e.fivecircle=9316;e.fivecircleinversesansserif=10126;e.fivedeva=2411;e.fiveeighths=8541;e.fivegujarati=2795;e.fivegurmukhi=2667;e.fivehackarabic=1637;e.fivehangzhou=12325;e.fiveideographicparen=12836;e.fiveinferior=8325;e.fivemonospace=65301;e.fiveoldstyle=63285;e.fiveparen=9336;e.fiveperiod=9356;e.fivepersian=1781;e.fiveroman=8564;e.fivesuperior=8309;e.fivethai=3669;e.fl=64258;e.f_l=64258;e.florin=402;e.fmonospace=65350;e.fmsquare=13209;e.fofanthai=3615;e.fofathai=3613;e.fongm\
-anthai=3663;e.forall=8704;e.four=52;e.fourarabic=1636;e.fourbengali=2538;e.fourcircle=9315;e.fourcircleinversesansserif=10125;e.fourdeva=2410;e.fourgujarati=2794;e.fourgurmukhi=2666;e.fourhackarabic=1636;e.fourhangzhou=12324;e.fourideographicparen=12835;e.fourinferior=8324;e.fourmonospace=65300;e.fournumeratorbengali=2551;e.fouroldstyle=63284;e.fourparen=9335;e.fourperiod=9355;e.fourpersian=1780;e.fourroman=8563;e.foursuperior=8308;e.fourteencircle=9325;e.fourteenparen=9345;e.fourteenperiod=9365\
-;e.fourthai=3668;e.fourthtonechinese=715;e.fparen=9377;e.fraction=8260;e.franc=8355;e.g=103;e.gabengali=2455;e.gacute=501;e.gadeva=2327;e.gafarabic=1711;e.gaffinalarabic=64403;e.gafinitialarabic=64404;e.gafmedialarabic=64405;e.gagujarati=2711;e.gagurmukhi=2583;e.gahiragana=12364;e.gakatakana=12460;e.gamma=947;e.gammalatinsmall=611;e.gammasuperior=736;e.gangiacoptic=1003;e.gbopomofo=12557;e.gbreve=287;e.gcaron=487;e.gcedilla=291;e.gcircle=9430;e.gcircumflex=285;e.gcommaaccent=291;e.gdot=289;e.gdo\
-taccent=289;e.gecyrillic=1075;e.gehiragana=12370;e.gekatakana=12466;e.geometricallyequal=8785;e.gereshaccenthebrew=1436;e.gereshhebrew=1523;e.gereshmuqdamhebrew=1437;e.germandbls=223;e.gershayimaccenthebrew=1438;e.gershayimhebrew=1524;e.getamark=12307;e.ghabengali=2456;e.ghadarmenian=1394;e.ghadeva=2328;e.ghagujarati=2712;e.ghagurmukhi=2584;e.ghainarabic=1594;e.ghainfinalarabic=65230;e.ghaininitialarabic=65231;e.ghainmedialarabic=65232;e.ghemiddlehookcyrillic=1173;e.ghestrokecyrillic=1171;e.gheu\
-pturncyrillic=1169;e.ghhadeva=2394;e.ghhagurmukhi=2650;e.ghook=608;e.ghzsquare=13203;e.gihiragana=12366;e.gikatakana=12462;e.gimarmenian=1379;e.gimel=1490;e.gimeldagesh=64306;e.gimeldageshhebrew=64306;e.gimelhebrew=1490;e.gjecyrillic=1107;e.glottalinvertedstroke=446;e.glottalstop=660;e.glottalstopinverted=662;e.glottalstopmod=704;e.glottalstopreversed=661;e.glottalstopreversedmod=705;e.glottalstopreversedsuperior=740;e.glottalstopstroke=673;e.glottalstopstrokereversed=674;e.gmacron=7713;e.gmonos\
-pace=65351;e.gohiragana=12372;e.gokatakana=12468;e.gparen=9378;e.gpasquare=13228;e.gradient=8711;e.grave=96;e.gravebelowcmb=790;e.gravecmb=768;e.gravecomb=768;e.gravedeva=2387;e.gravelowmod=718;e.gravemonospace=65344;e.gravetonecmb=832;e.greater=62;e.greaterequal=8805;e.greaterequalorless=8923;e.greatermonospace=65310;e.greaterorequivalent=8819;e.greaterorless=8823;e.greateroverequal=8807;e.greatersmall=65125;e.gscript=609;e.gstroke=485;e.guhiragana=12368;e.guillemotleft=171;e.guillemotright=187\
-;e.guilsinglleft=8249;e.guilsinglright=8250;e.gukatakana=12464;e.guramusquare=13080;e.gysquare=13257;e.h=104;e.haabkhasiancyrillic=1193;e.haaltonearabic=1729;e.habengali=2489;e.hadescendercyrillic=1203;e.hadeva=2361;e.hagujarati=2745;e.hagurmukhi=2617;e.haharabic=1581;e.hahfinalarabic=65186;e.hahinitialarabic=65187;e.hahiragana=12399;e.hahmedialarabic=65188;e.haitusquare=13098;e.hakatakana=12495;e.hakatakanahalfwidth=65418;e.halantgurmukhi=2637;e.hamzaarabic=1569;e.hamzalowarabic=1569;e.hangulfi\
-ller=12644;e.hardsigncyrillic=1098;e.harpoonleftbarbup=8636;e.harpoonrightbarbup=8640;e.hasquare=13258;e.hatafpatah=1458;e.hatafpatah16=1458;e.hatafpatah23=1458;e.hatafpatah2f=1458;e.hatafpatahhebrew=1458;e.hatafpatahnarrowhebrew=1458;e.hatafpatahquarterhebrew=1458;e.hatafpatahwidehebrew=1458;e.hatafqamats=1459;e.hatafqamats1b=1459;e.hatafqamats28=1459;e.hatafqamats34=1459;e.hatafqamatshebrew=1459;e.hatafqamatsnarrowhebrew=1459;e.hatafqamatsquarterhebrew=1459;e.hatafqamatswidehebrew=1459;e.hataf\
-segol=1457;e.hatafsegol17=1457;e.hatafsegol24=1457;e.hatafsegol30=1457;e.hatafsegolhebrew=1457;e.hatafsegolnarrowhebrew=1457;e.hatafsegolquarterhebrew=1457;e.hatafsegolwidehebrew=1457;e.hbar=295;e.hbopomofo=12559;e.hbrevebelow=7723;e.hcedilla=7721;e.hcircle=9431;e.hcircumflex=293;e.hdieresis=7719;e.hdotaccent=7715;e.hdotbelow=7717;e.he=1492;e.heart=9829;e.heartsuitblack=9829;e.heartsuitwhite=9825;e.hedagesh=64308;e.hedageshhebrew=64308;e.hehaltonearabic=1729;e.heharabic=1607;e.hehebrew=1492;e.he\
-hfinalaltonearabic=64423;e.hehfinalalttwoarabic=65258;e.hehfinalarabic=65258;e.hehhamzaabovefinalarabic=64421;e.hehhamzaaboveisolatedarabic=64420;e.hehinitialaltonearabic=64424;e.hehinitialarabic=65259;e.hehiragana=12408;e.hehmedialaltonearabic=64425;e.hehmedialarabic=65260;e.heiseierasquare=13179;e.hekatakana=12504;e.hekatakanahalfwidth=65421;e.hekutaarusquare=13110;e.henghook=615;e.herutusquare=13113;e.het=1495;e.hethebrew=1495;e.hhook=614;e.hhooksuperior=689;e.hieuhacirclekorean=12923;e.hieuh\
-aparenkorean=12827;e.hieuhcirclekorean=12909;e.hieuhkorean=12622;e.hieuhparenkorean=12813;e.hihiragana=12402;e.hikatakana=12498;e.hikatakanahalfwidth=65419;e.hiriq=1460;e.hiriq14=1460;e.hiriq21=1460;e.hiriq2d=1460;e.hiriqhebrew=1460;e.hiriqnarrowhebrew=1460;e.hiriqquarterhebrew=1460;e.hiriqwidehebrew=1460;e.hlinebelow=7830;e.hmonospace=65352;e.hoarmenian=1392;e.hohipthai=3627;e.hohiragana=12411;e.hokatakana=12507;e.hokatakanahalfwidth=65422;e.holam=1465;e.holam19=1465;e.holam26=1465;e.holam32=14\
-65;e.holamhebrew=1465;e.holamnarrowhebrew=1465;e.holamquarterhebrew=1465;e.holamwidehebrew=1465;e.honokhukthai=3630;e.hookabovecomb=777;e.hookcmb=777;e.hookpalatalizedbelowcmb=801;e.hookretroflexbelowcmb=802;e.hoonsquare=13122;e.horicoptic=1001;e.horizontalbar=8213;e.horncmb=795;e.hotsprings=9832;e.house=8962;e.hparen=9379;e.hsuperior=688;e.hturned=613;e.huhiragana=12405;e.huiitosquare=13107;e.hukatakana=12501;e.hukatakanahalfwidth=65420;e.hungarumlaut=733;e.hungarumlautcmb=779;e.hv=405;e.hyphen\
-=45;e.hypheninferior=63205;e.hyphenmonospace=65293;e.hyphensmall=65123;e.hyphensuperior=63206;e.hyphentwo=8208;e.i=105;e.iacute=237;e.iacyrillic=1103;e.ibengali=2439;e.ibopomofo=12583;e.ibreve=301;e.icaron=464;e.icircle=9432;e.icircumflex=238;e.icyrillic=1110;e.idblgrave=521;e.ideographearthcircle=12943;e.ideographfirecircle=12939;e.ideographicallianceparen=12863;e.ideographiccallparen=12858;e.ideographiccentrecircle=12965;e.ideographicclose=12294;e.ideographiccomma=12289;e.ideographiccommaleft=\
-65380;e.ideographiccongratulationparen=12855;e.ideographiccorrectcircle=12963;e.ideographicearthparen=12847;e.ideographicenterpriseparen=12861;e.ideographicexcellentcircle=12957;e.ideographicfestivalparen=12864;e.ideographicfinancialcircle=12950;e.ideographicfinancialparen=12854;e.ideographicfireparen=12843;e.ideographichaveparen=12850;e.ideographichighcircle=12964;e.ideographiciterationmark=12293;e.ideographiclaborcircle=12952;e.ideographiclaborparen=12856;e.ideographicleftcircle=12967;e.ideogr\
-aphiclowcircle=12966;e.ideographicmedicinecircle=12969;e.ideographicmetalparen=12846;e.ideographicmoonparen=12842;e.ideographicnameparen=12852;e.ideographicperiod=12290;e.ideographicprintcircle=12958;e.ideographicreachparen=12867;e.ideographicrepresentparen=12857;e.ideographicresourceparen=12862;e.ideographicrightcircle=12968;e.ideographicsecretcircle=12953;e.ideographicselfparen=12866;e.ideographicsocietyparen=12851;e.ideographicspace=12288;e.ideographicspecialparen=12853;e.ideographicstockpare\
-n=12849;e.ideographicstudyparen=12859;e.ideographicsunparen=12848;e.ideographicsuperviseparen=12860;e.ideographicwaterparen=12844;e.ideographicwoodparen=12845;e.ideographiczero=12295;e.ideographmetalcircle=12942;e.ideographmooncircle=12938;e.ideographnamecircle=12948;e.ideographsuncircle=12944;e.ideographwatercircle=12940;e.ideographwoodcircle=12941;e.ideva=2311;e.idieresis=239;e.idieresisacute=7727;e.idieresiscyrillic=1253;e.idotbelow=7883;e.iebrevecyrillic=1239;e.iecyrillic=1077;e.ieungacircle\
-korean=12917;e.ieungaparenkorean=12821;e.ieungcirclekorean=12903;e.ieungkorean=12615;e.ieungparenkorean=12807;e.igrave=236;e.igujarati=2695;e.igurmukhi=2567;e.ihiragana=12356;e.ihookabove=7881;e.iibengali=2440;e.iicyrillic=1080;e.iideva=2312;e.iigujarati=2696;e.iigurmukhi=2568;e.iimatragurmukhi=2624;e.iinvertedbreve=523;e.iishortcyrillic=1081;e.iivowelsignbengali=2496;e.iivowelsigndeva=2368;e.iivowelsigngujarati=2752;e.ij=307;e.ikatakana=12452;e.ikatakanahalfwidth=65394;e.ikorean=12643;e.ilde=73\
-2;e.iluyhebrew=1452;e.imacron=299;e.imacroncyrillic=1251;e.imageorapproximatelyequal=8787;e.imatragurmukhi=2623;e.imonospace=65353;e.increment=8710;e.infinity=8734;e.iniarmenian=1387;e.integral=8747;e.integralbottom=8993;e.integralbt=8993;e.integralex=63733;e.integraltop=8992;e.integraltp=8992;e.intersection=8745;e.intisquare=13061;e.invbullet=9688;e.invcircle=9689;e.invsmileface=9787;e.iocyrillic=1105;e.iogonek=303;e.iota=953;e.iotadieresis=970;e.iotadieresistonos=912;e.iotalatin=617;e.iotatono\
-s=943;e.iparen=9380;e.irigurmukhi=2674;e.ismallhiragana=12355;e.ismallkatakana=12451;e.ismallkatakanahalfwidth=65384;e.issharbengali=2554;e.istroke=616;e.isuperior=63213;e.iterationhiragana=12445;e.iterationkatakana=12541;e.itilde=297;e.itildebelow=7725;e.iubopomofo=12585;e.iucyrillic=1102;e.ivowelsignbengali=2495;e.ivowelsigndeva=2367;e.ivowelsigngujarati=2751;e.izhitsacyrillic=1141;e.izhitsadblgravecyrillic=1143;e.j=106;e.jaarmenian=1393;e.jabengali=2460;e.jadeva=2332;e.jagujarati=2716;e.jagur\
-mukhi=2588;e.jbopomofo=12560;e.jcaron=496;e.jcircle=9433;e.jcircumflex=309;e.jcrossedtail=669;e.jdotlessstroke=607;e.jecyrillic=1112;e.jeemarabic=1580;e.jeemfinalarabic=65182;e.jeeminitialarabic=65183;e.jeemmedialarabic=65184;e.jeharabic=1688;e.jehfinalarabic=64395;e.jhabengali=2461;e.jhadeva=2333;e.jhagujarati=2717;e.jhagurmukhi=2589;e.jheharmenian=1403;e.jis=12292;e.jmonospace=65354;e.jparen=9381;e.jsuperior=690;e.k=107;e.kabashkircyrillic=1185;e.kabengali=2453;e.kacute=7729;e.kacyrillic=1082;\
-e.kadescendercyrillic=1179;e.kadeva=2325;e.kaf=1499;e.kafarabic=1603;e.kafdagesh=64315;e.kafdageshhebrew=64315;e.kaffinalarabic=65242;e.kafhebrew=1499;e.kafinitialarabic=65243;e.kafmedialarabic=65244;e.kafrafehebrew=64333;e.kagujarati=2709;e.kagurmukhi=2581;e.kahiragana=12363;e.kahookcyrillic=1220;e.kakatakana=12459;e.kakatakanahalfwidth=65398;e.kappa=954;e.kappasymbolgreek=1008;e.kapyeounmieumkorean=12657;e.kapyeounphieuphkorean=12676;e.kapyeounpieupkorean=12664;e.kapyeounssangpieupkorean=12665\
-;e.karoriisquare=13069;e.kashidaautoarabic=1600;e.kashidaautonosidebearingarabic=1600;e.kasmallkatakana=12533;e.kasquare=13188;e.kasraarabic=1616;e.kasratanarabic=1613;e.kastrokecyrillic=1183;e.katahiraprolongmarkhalfwidth=65392;e.kaverticalstrokecyrillic=1181;e.kbopomofo=12558;e.kcalsquare=13193;e.kcaron=489;e.kcedilla=311;e.kcircle=9434;e.kcommaaccent=311;e.kdotbelow=7731;e.keharmenian=1412;e.kehiragana=12369;e.kekatakana=12465;e.kekatakanahalfwidth=65401;e.kenarmenian=1391;e.kesmallkatakana=1\
-2534;e.kgreenlandic=312;e.khabengali=2454;e.khacyrillic=1093;e.khadeva=2326;e.khagujarati=2710;e.khagurmukhi=2582;e.khaharabic=1582;e.khahfinalarabic=65190;e.khahinitialarabic=65191;e.khahmedialarabic=65192;e.kheicoptic=999;e.khhadeva=2393;e.khhagurmukhi=2649;e.khieukhacirclekorean=12920;e.khieukhaparenkorean=12824;e.khieukhcirclekorean=12906;e.khieukhkorean=12619;e.khieukhparenkorean=12810;e.khokhaithai=3586;e.khokhonthai=3589;e.khokhuatthai=3587;e.khokhwaithai=3588;e.khomutthai=3675;e.khook=40\
-9;e.khorakhangthai=3590;e.khzsquare=13201;e.kihiragana=12365;e.kikatakana=12461;e.kikatakanahalfwidth=65399;e.kiroguramusquare=13077;e.kiromeetorusquare=13078;e.kirosquare=13076;e.kiyeokacirclekorean=12910;e.kiyeokaparenkorean=12814;e.kiyeokcirclekorean=12896;e.kiyeokkorean=12593;e.kiyeokparenkorean=12800;e.kiyeoksioskorean=12595;e.kjecyrillic=1116;e.klinebelow=7733;e.klsquare=13208;e.kmcubedsquare=13222;e.kmonospace=65355;e.kmsquaredsquare=13218;e.kohiragana=12371;e.kohmsquare=13248;e.kokaithai\
-=3585;e.kokatakana=12467;e.kokatakanahalfwidth=65402;e.kooposquare=13086;e.koppacyrillic=1153;e.koreanstandardsymbol=12927;e.koroniscmb=835;e.kparen=9382;e.kpasquare=13226;e.ksicyrillic=1135;e.ktsquare=13263;e.kturned=670;e.kuhiragana=12367;e.kukatakana=12463;e.kukatakanahalfwidth=65400;e.kvsquare=13240;e.kwsquare=13246;e.l=108;e.labengali=2482;e.lacute=314;e.ladeva=2354;e.lagujarati=2738;e.lagurmukhi=2610;e.lakkhangyaothai=3653;e.lamaleffinalarabic=65276;e.lamalefhamzaabovefinalarabic=65272;e.l\
-amalefhamzaaboveisolatedarabic=65271;e.lamalefhamzabelowfinalarabic=65274;e.lamalefhamzabelowisolatedarabic=65273;e.lamalefisolatedarabic=65275;e.lamalefmaddaabovefinalarabic=65270;e.lamalefmaddaaboveisolatedarabic=65269;e.lamarabic=1604;e.lambda=955;e.lambdastroke=411;e.lamed=1500;e.lameddagesh=64316;e.lameddageshhebrew=64316;e.lamedhebrew=1500;e.lamfinalarabic=65246;e.lamhahinitialarabic=64714;e.laminitialarabic=65247;e.lamjeeminitialarabic=64713;e.lamkhahinitialarabic=64715;e.lamlamhehisolate\
-darabic=65010;e.lammedialarabic=65248;e.lammeemhahinitialarabic=64904;e.lammeeminitialarabic=64716;e.largecircle=9711;e.lbar=410;e.lbelt=620;e.lbopomofo=12556;e.lcaron=318;e.lcedilla=316;e.lcircle=9435;e.lcircumflexbelow=7741;e.lcommaaccent=316;e.ldot=320;e.ldotaccent=320;e.ldotbelow=7735;e.ldotbelowmacron=7737;e.leftangleabovecmb=794;e.lefttackbelowcmb=792;e.less=60;e.lessequal=8804;e.lessequalorgreater=8922;e.lessmonospace=65308;e.lessorequivalent=8818;e.lessorgreater=8822;e.lessoverequal=8806\
-;e.lesssmall=65124;e.lezh=622;e.lfblock=9612;e.lhookretroflex=621;e.lira=8356;e.liwnarmenian=1388;e.lj=457;e.ljecyrillic=1113;e.ll=63168;e.lladeva=2355;e.llagujarati=2739;e.llinebelow=7739;e.llladeva=2356;e.llvocalicbengali=2529;e.llvocalicdeva=2401;e.llvocalicvowelsignbengali=2531;e.llvocalicvowelsigndeva=2403;e.lmiddletilde=619;e.lmonospace=65356;e.lmsquare=13264;e.lochulathai=3628;e.logicaland=8743;e.logicalnot=172;e.logicalnotreversed=8976;e.logicalor=8744;e.lolingthai=3621;e.longs=383;e.low\
-linecenterline=65102;e.lowlinecmb=818;e.lowlinedashed=65101;e.lozenge=9674;e.lparen=9383;e.lslash=322;e.lsquare=8467;e.lsuperior=63214;e.ltshade=9617;e.luthai=3622;e.lvocalicbengali=2444;e.lvocalicdeva=2316;e.lvocalicvowelsignbengali=2530;e.lvocalicvowelsigndeva=2402;e.lxsquare=13267;e.m=109;e.mabengali=2478;e.macron=175;e.macronbelowcmb=817;e.macroncmb=772;e.macronlowmod=717;e.macronmonospace=65507;e.macute=7743;e.madeva=2350;e.magujarati=2734;e.magurmukhi=2606;e.mahapakhhebrew=1444;e.mahapakhl\
-efthebrew=1444;e.mahiragana=12414;e.maichattawalowleftthai=63637;e.maichattawalowrightthai=63636;e.maichattawathai=3659;e.maichattawaupperleftthai=63635;e.maieklowleftthai=63628;e.maieklowrightthai=63627;e.maiekthai=3656;e.maiekupperleftthai=63626;e.maihanakatleftthai=63620;e.maihanakatthai=3633;e.maitaikhuleftthai=63625;e.maitaikhuthai=3655;e.maitholowleftthai=63631;e.maitholowrightthai=63630;e.maithothai=3657;e.maithoupperleftthai=63629;e.maitrilowleftthai=63634;e.maitrilowrightthai=63633;e.ma\
-itrithai=3658;e.maitriupperleftthai=63632;e.maiyamokthai=3654;e.makatakana=12510;e.makatakanahalfwidth=65423;e.male=9794;e.mansyonsquare=13127;e.maqafhebrew=1470;e.mars=9794;e.masoracirclehebrew=1455;e.masquare=13187;e.mbopomofo=12551;e.mbsquare=13268;e.mcircle=9436;e.mcubedsquare=13221;e.mdotaccent=7745;e.mdotbelow=7747;e.meemarabic=1605;e.meemfinalarabic=65250;e.meeminitialarabic=65251;e.meemmedialarabic=65252;e.meemmeeminitialarabic=64721;e.meemmeemisolatedarabic=64584;e.meetorusquare=13133;e\
-.mehiragana=12417;e.meizierasquare=13182;e.mekatakana=12513;e.mekatakanahalfwidth=65426;e.mem=1502;e.memdagesh=64318;e.memdageshhebrew=64318;e.memhebrew=1502;e.menarmenian=1396;e.merkhahebrew=1445;e.merkhakefulahebrew=1446;e.merkhakefulalefthebrew=1446;e.merkhalefthebrew=1445;e.mhook=625;e.mhzsquare=13202;e.middledotkatakanahalfwidth=65381;e.middot=183;e.mieumacirclekorean=12914;e.mieumaparenkorean=12818;e.mieumcirclekorean=12900;e.mieumkorean=12609;e.mieumpansioskorean=12656;e.mieumparenkorean=\
-12804;e.mieumpieupkorean=12654;e.mieumsioskorean=12655;e.mihiragana=12415;e.mikatakana=12511;e.mikatakanahalfwidth=65424;e.minus=8722;e.minusbelowcmb=800;e.minuscircle=8854;e.minusmod=727;e.minusplus=8723;e.minute=8242;e.miribaarusquare=13130;e.mirisquare=13129;e.mlonglegturned=624;e.mlsquare=13206;e.mmcubedsquare=13219;e.mmonospace=65357;e.mmsquaredsquare=13215;e.mohiragana=12418;e.mohmsquare=13249;e.mokatakana=12514;e.mokatakanahalfwidth=65427;e.molsquare=13270;e.momathai=3617;e.moverssquare=1\
-3223;e.moverssquaredsquare=13224;e.mparen=9384;e.mpasquare=13227;e.mssquare=13235;e.msuperior=63215;e.mturned=623;e.mu=181;e.mu1=181;e.muasquare=13186;e.muchgreater=8811;e.muchless=8810;e.mufsquare=13196;e.mugreek=956;e.mugsquare=13197;e.muhiragana=12416;e.mukatakana=12512;e.mukatakanahalfwidth=65425;e.mulsquare=13205;e.multiply=215;e.mumsquare=13211;e.munahhebrew=1443;e.munahlefthebrew=1443;e.musicalnote=9834;e.musicalnotedbl=9835;e.musicflatsign=9837;e.musicsharpsign=9839;e.mussquare=13234;e.m\
-uvsquare=13238;e.muwsquare=13244;e.mvmegasquare=13241;e.mvsquare=13239;e.mwmegasquare=13247;e.mwsquare=13245;e.n=110;e.nabengali=2472;e.nabla=8711;e.nacute=324;e.nadeva=2344;e.nagujarati=2728;e.nagurmukhi=2600;e.nahiragana=12394;e.nakatakana=12490;e.nakatakanahalfwidth=65413;e.napostrophe=329;e.nasquare=13185;e.nbopomofo=12555;e.nbspace=160;e.ncaron=328;e.ncedilla=326;e.ncircle=9437;e.ncircumflexbelow=7755;e.ncommaaccent=326;e.ndotaccent=7749;e.ndotbelow=7751;e.nehiragana=12397;e.nekatakana=1249\
-3;e.nekatakanahalfwidth=65416;e.newsheqelsign=8362;e.nfsquare=13195;e.ngabengali=2457;e.ngadeva=2329;e.ngagujarati=2713;e.ngagurmukhi=2585;e.ngonguthai=3591;e.nhiragana=12435;e.nhookleft=626;e.nhookretroflex=627;e.nieunacirclekorean=12911;e.nieunaparenkorean=12815;e.nieuncieuckorean=12597;e.nieuncirclekorean=12897;e.nieunhieuhkorean=12598;e.nieunkorean=12596;e.nieunpansioskorean=12648;e.nieunparenkorean=12801;e.nieunsioskorean=12647;e.nieuntikeutkorean=12646;e.nihiragana=12395;e.nikatakana=12491\
-;e.nikatakanahalfwidth=65414;e.nikhahitleftthai=63641;e.nikhahitthai=3661;e.nine=57;e.ninearabic=1641;e.ninebengali=2543;e.ninecircle=9320;e.ninecircleinversesansserif=10130;e.ninedeva=2415;e.ninegujarati=2799;e.ninegurmukhi=2671;e.ninehackarabic=1641;e.ninehangzhou=12329;e.nineideographicparen=12840;e.nineinferior=8329;e.ninemonospace=65305;e.nineoldstyle=63289;e.nineparen=9340;e.nineperiod=9360;e.ninepersian=1785;e.nineroman=8568;e.ninesuperior=8313;e.nineteencircle=9330;e.nineteenparen=9350;e\
-.nineteenperiod=9370;e.ninethai=3673;e.nj=460;e.njecyrillic=1114;e.nkatakana=12531;e.nkatakanahalfwidth=65437;e.nlegrightlong=414;e.nlinebelow=7753;e.nmonospace=65358;e.nmsquare=13210;e.nnabengali=2467;e.nnadeva=2339;e.nnagujarati=2723;e.nnagurmukhi=2595;e.nnnadeva=2345;e.nohiragana=12398;e.nokatakana=12494;e.nokatakanahalfwidth=65417;e.nonbreakingspace=160;e.nonenthai=3603;e.nonuthai=3609;e.noonarabic=1606;e.noonfinalarabic=65254;e.noonghunnaarabic=1722;e.noonghunnafinalarabic=64415;e.nooniniti\
-alarabic=65255;e.noonjeeminitialarabic=64722;e.noonjeemisolatedarabic=64587;e.noonmedialarabic=65256;e.noonmeeminitialarabic=64725;e.noonmeemisolatedarabic=64590;e.noonnoonfinalarabic=64653;e.notcontains=8716;e.notelement=8713;e.notelementof=8713;e.notequal=8800;e.notgreater=8815;e.notgreaternorequal=8817;e.notgreaternorless=8825;e.notidentical=8802;e.notless=8814;e.notlessnorequal=8816;e.notparallel=8742;e.notprecedes=8832;e.notsubset=8836;e.notsucceeds=8833;e.notsuperset=8837;e.nowarmenian=139\
-8;e.nparen=9385;e.nssquare=13233;e.nsuperior=8319;e.ntilde=241;e.nu=957;e.nuhiragana=12396;e.nukatakana=12492;e.nukatakanahalfwidth=65415;e.nuktabengali=2492;e.nuktadeva=2364;e.nuktagujarati=2748;e.nuktagurmukhi=2620;e.numbersign=35;e.numbersignmonospace=65283;e.numbersignsmall=65119;e.numeralsigngreek=884;e.numeralsignlowergreek=885;e.numero=8470;e.nun=1504;e.nundagesh=64320;e.nundageshhebrew=64320;e.nunhebrew=1504;e.nvsquare=13237;e.nwsquare=13243;e.nyabengali=2462;e.nyadeva=2334;e.nyagujarati\
-=2718;e.nyagurmukhi=2590;e.o=111;e.oacute=243;e.oangthai=3629;e.obarred=629;e.obarredcyrillic=1257;e.obarreddieresiscyrillic=1259;e.obengali=2451;e.obopomofo=12571;e.obreve=335;e.ocandradeva=2321;e.ocandragujarati=2705;e.ocandravowelsigndeva=2377;e.ocandravowelsigngujarati=2761;e.ocaron=466;e.ocircle=9438;e.ocircumflex=244;e.ocircumflexacute=7889;e.ocircumflexdotbelow=7897;e.ocircumflexgrave=7891;e.ocircumflexhookabove=7893;e.ocircumflextilde=7895;e.ocyrillic=1086;e.odblacute=337;e.odblgrave=525\
-;e.odeva=2323;e.odieresis=246;e.odieresiscyrillic=1255;e.odotbelow=7885;e.oe=339;e.oekorean=12634;e.ogonek=731;e.ogonekcmb=808;e.ograve=242;e.ogujarati=2707;e.oharmenian=1413;e.ohiragana=12362;e.ohookabove=7887;e.ohorn=417;e.ohornacute=7899;e.ohorndotbelow=7907;e.ohorngrave=7901;e.ohornhookabove=7903;e.ohorntilde=7905;e.ohungarumlaut=337;e.oi=419;e.oinvertedbreve=527;e.okatakana=12458;e.okatakanahalfwidth=65397;e.okorean=12631;e.olehebrew=1451;e.omacron=333;e.omacronacute=7763;e.omacrongrave=776\
-1;e.omdeva=2384;e.omega=969;e.omega1=982;e.omegacyrillic=1121;e.omegalatinclosed=631;e.omegaroundcyrillic=1147;e.omegatitlocyrillic=1149;e.omegatonos=974;e.omgujarati=2768;e.omicron=959;e.omicrontonos=972;e.omonospace=65359;e.one=49;e.onearabic=1633;e.onebengali=2535;e.onecircle=9312;e.onecircleinversesansserif=10122;e.onedeva=2407;e.onedotenleader=8228;e.oneeighth=8539;e.onefitted=63196;e.onegujarati=2791;e.onegurmukhi=2663;e.onehackarabic=1633;e.onehalf=189;e.onehangzhou=12321;e.oneideographic\
-paren=12832;e.oneinferior=8321;e.onemonospace=65297;e.onenumeratorbengali=2548;e.oneoldstyle=63281;e.oneparen=9332;e.oneperiod=9352;e.onepersian=1777;e.onequarter=188;e.oneroman=8560;e.onesuperior=185;e.onethai=3665;e.onethird=8531;e.oogonek=491;e.oogonekmacron=493;e.oogurmukhi=2579;e.oomatragurmukhi=2635;e.oopen=596;e.oparen=9386;e.openbullet=9702;e.option=8997;e.ordfeminine=170;e.ordmasculine=186;e.orthogonal=8735;e.oshortdeva=2322;e.oshortvowelsigndeva=2378;e.oslash=248;e.oslashacute=511;e.os\
-mallhiragana=12361;e.osmallkatakana=12457;e.osmallkatakanahalfwidth=65387;e.ostrokeacute=511;e.osuperior=63216;e.otcyrillic=1151;e.otilde=245;e.otildeacute=7757;e.otildedieresis=7759;e.oubopomofo=12577;e.overline=8254;e.overlinecenterline=65098;e.overlinecmb=773;e.overlinedashed=65097;e.overlinedblwavy=65100;e.overlinewavy=65099;e.overscore=175;e.ovowelsignbengali=2507;e.ovowelsigndeva=2379;e.ovowelsigngujarati=2763;e.p=112;e.paampssquare=13184;e.paasentosquare=13099;e.pabengali=2474;e.pacute=77\
-65;e.padeva=2346;e.pagedown=8671;e.pageup=8670;e.pagujarati=2730;e.pagurmukhi=2602;e.pahiragana=12401;e.paiyannoithai=3631;e.pakatakana=12497;e.palatalizationcyrilliccmb=1156;e.palochkacyrillic=1216;e.pansioskorean=12671;e.paragraph=182;e.parallel=8741;e.parenleft=40;e.parenleftaltonearabic=64830;e.parenleftbt=63725;e.parenleftex=63724;e.parenleftinferior=8333;e.parenleftmonospace=65288;e.parenleftsmall=65113;e.parenleftsuperior=8317;e.parenlefttp=63723;e.parenleftvertical=65077;e.parenright=41;\
-e.parenrightaltonearabic=64831;e.parenrightbt=63736;e.parenrightex=63735;e.parenrightinferior=8334;e.parenrightmonospace=65289;e.parenrightsmall=65114;e.parenrightsuperior=8318;e.parenrighttp=63734;e.parenrightvertical=65078;e.partialdiff=8706;e.paseqhebrew=1472;e.pashtahebrew=1433;e.pasquare=13225;e.patah=1463;e.patah11=1463;e.patah1d=1463;e.patah2a=1463;e.patahhebrew=1463;e.patahnarrowhebrew=1463;e.patahquarterhebrew=1463;e.patahwidehebrew=1463;e.pazerhebrew=1441;e.pbopomofo=12550;e.pcircle=94\
-39;e.pdotaccent=7767;e.pe=1508;e.pecyrillic=1087;e.pedagesh=64324;e.pedageshhebrew=64324;e.peezisquare=13115;e.pefinaldageshhebrew=64323;e.peharabic=1662;e.peharmenian=1402;e.pehebrew=1508;e.pehfinalarabic=64343;e.pehinitialarabic=64344;e.pehiragana=12410;e.pehmedialarabic=64345;e.pekatakana=12506;e.pemiddlehookcyrillic=1191;e.perafehebrew=64334;e.percent=37;e.percentarabic=1642;e.percentmonospace=65285;e.percentsmall=65130;e.period=46;e.periodarmenian=1417;e.periodcentered=183;e.periodhalfwidth\
-=65377;e.periodinferior=63207;e.periodmonospace=65294;e.periodsmall=65106;e.periodsuperior=63208;e.perispomenigreekcmb=834;e.perpendicular=8869;e.perthousand=8240;e.peseta=8359;e.pfsquare=13194;e.phabengali=2475;e.phadeva=2347;e.phagujarati=2731;e.phagurmukhi=2603;e.phi=966;e.phi1=981;e.phieuphacirclekorean=12922;e.phieuphaparenkorean=12826;e.phieuphcirclekorean=12908;e.phieuphkorean=12621;e.phieuphparenkorean=12812;e.philatin=632;e.phinthuthai=3642;e.phisymbolgreek=981;e.phook=421;e.phophanthai\
-=3614;e.phophungthai=3612;e.phosamphaothai=3616;e.pi=960;e.pieupacirclekorean=12915;e.pieupaparenkorean=12819;e.pieupcieuckorean=12662;e.pieupcirclekorean=12901;e.pieupkiyeokkorean=12658;e.pieupkorean=12610;e.pieupparenkorean=12805;e.pieupsioskiyeokkorean=12660;e.pieupsioskorean=12612;e.pieupsiostikeutkorean=12661;e.pieupthieuthkorean=12663;e.pieuptikeutkorean=12659;e.pihiragana=12404;e.pikatakana=12500;e.pisymbolgreek=982;e.piwrarmenian=1411;e.planckover2pi=8463;e.planckover2pi1=8463;e.plus=43;\
-e.plusbelowcmb=799;e.pluscircle=8853;e.plusminus=177;e.plusmod=726;e.plusmonospace=65291;e.plussmall=65122;e.plussuperior=8314;e.pmonospace=65360;e.pmsquare=13272;e.pohiragana=12413;e.pointingindexdownwhite=9759;e.pointingindexleftwhite=9756;e.pointingindexrightwhite=9758;e.pointingindexupwhite=9757;e.pokatakana=12509;e.poplathai=3611;e.postalmark=12306;e.postalmarkface=12320;e.pparen=9387;e.precedes=8826;e.prescription=8478;e.primemod=697;e.primereversed=8245;e.product=8719;e.projective=8965;e.\
-prolongedkana=12540;e.propellor=8984;e.propersubset=8834;e.propersuperset=8835;e.proportion=8759;e.proportional=8733;e.psi=968;e.psicyrillic=1137;e.psilipneumatacyrilliccmb=1158;e.pssquare=13232;e.puhiragana=12407;e.pukatakana=12503;e.pvsquare=13236;e.pwsquare=13242;e.q=113;e.qadeva=2392;e.qadmahebrew=1448;e.qafarabic=1602;e.qaffinalarabic=65238;e.qafinitialarabic=65239;e.qafmedialarabic=65240;e.qamats=1464;e.qamats10=1464;e.qamats1a=1464;e.qamats1c=1464;e.qamats27=1464;e.qamats29=1464;e.qamats3\
-3=1464;e.qamatsde=1464;e.qamatshebrew=1464;e.qamatsnarrowhebrew=1464;e.qamatsqatanhebrew=1464;e.qamatsqatannarrowhebrew=1464;e.qamatsqatanquarterhebrew=1464;e.qamatsqatanwidehebrew=1464;e.qamatsquarterhebrew=1464;e.qamatswidehebrew=1464;e.qarneyparahebrew=1439;e.qbopomofo=12561;e.qcircle=9440;e.qhook=672;e.qmonospace=65361;e.qof=1511;e.qofdagesh=64327;e.qofdageshhebrew=64327;e.qofhebrew=1511;e.qparen=9388;e.quarternote=9833;e.qubuts=1467;e.qubuts18=1467;e.qubuts25=1467;e.qubuts31=1467;e.qubutshe\
-brew=1467;e.qubutsnarrowhebrew=1467;e.qubutsquarterhebrew=1467;e.qubutswidehebrew=1467;e.question=63;e.questionarabic=1567;e.questionarmenian=1374;e.questiondown=191;e.questiondownsmall=63423;e.questiongreek=894;e.questionmonospace=65311;e.questionsmall=63295;e.quotedbl=34;e.quotedblbase=8222;e.quotedblleft=8220;e.quotedblmonospace=65282;e.quotedblprime=12318;e.quotedblprimereversed=12317;e.quotedblright=8221;e.quoteleft=8216;e.quoteleftreversed=8219;e.quotereversed=8219;e.quoteright=8217;e.quot\
-erightn=329;e.quotesinglbase=8218;e.quotesingle=39;e.quotesinglemonospace=65287;e.r=114;e.raarmenian=1404;e.rabengali=2480;e.racute=341;e.radeva=2352;e.radical=8730;e.radicalex=63717;e.radoverssquare=13230;e.radoverssquaredsquare=13231;e.radsquare=13229;e.rafe=1471;e.rafehebrew=1471;e.ragujarati=2736;e.ragurmukhi=2608;e.rahiragana=12425;e.rakatakana=12521;e.rakatakanahalfwidth=65431;e.ralowerdiagonalbengali=2545;e.ramiddlediagonalbengali=2544;e.ramshorn=612;e.ratio=8758;e.rbopomofo=12566;e.rcaro\
-n=345;e.rcedilla=343;e.rcircle=9441;e.rcommaaccent=343;e.rdblgrave=529;e.rdotaccent=7769;e.rdotbelow=7771;e.rdotbelowmacron=7773;e.referencemark=8251;e.reflexsubset=8838;e.reflexsuperset=8839;e.registered=174;e.registersans=63720;e.registerserif=63194;e.reharabic=1585;e.reharmenian=1408;e.rehfinalarabic=65198;e.rehiragana=12428;e.rekatakana=12524;e.rekatakanahalfwidth=65434;e.resh=1512;e.reshdageshhebrew=64328;e.reshhebrew=1512;e.reversedtilde=8765;e.reviahebrew=1431;e.reviamugrashhebrew=1431;e.\
-revlogicalnot=8976;e.rfishhook=638;e.rfishhookreversed=639;e.rhabengali=2525;e.rhadeva=2397;e.rho=961;e.rhook=637;e.rhookturned=635;e.rhookturnedsuperior=693;e.rhosymbolgreek=1009;e.rhotichookmod=734;e.rieulacirclekorean=12913;e.rieulaparenkorean=12817;e.rieulcirclekorean=12899;e.rieulhieuhkorean=12608;e.rieulkiyeokkorean=12602;e.rieulkiyeoksioskorean=12649;e.rieulkorean=12601;e.rieulmieumkorean=12603;e.rieulpansioskorean=12652;e.rieulparenkorean=12803;e.rieulphieuphkorean=12607;e.rieulpieupkore\
-an=12604;e.rieulpieupsioskorean=12651;e.rieulsioskorean=12605;e.rieulthieuthkorean=12606;e.rieultikeutkorean=12650;e.rieulyeorinhieuhkorean=12653;e.rightangle=8735;e.righttackbelowcmb=793;e.righttriangle=8895;e.rihiragana=12426;e.rikatakana=12522;e.rikatakanahalfwidth=65432;e.ring=730;e.ringbelowcmb=805;e.ringcmb=778;e.ringhalfleft=703;e.ringhalfleftarmenian=1369;e.ringhalfleftbelowcmb=796;e.ringhalfleftcentered=723;e.ringhalfright=702;e.ringhalfrightbelowcmb=825;e.ringhalfrightcentered=722;e.ri\
-nvertedbreve=531;e.rittorusquare=13137;e.rlinebelow=7775;e.rlongleg=636;e.rlonglegturned=634;e.rmonospace=65362;e.rohiragana=12429;e.rokatakana=12525;e.rokatakanahalfwidth=65435;e.roruathai=3619;e.rparen=9389;e.rrabengali=2524;e.rradeva=2353;e.rragurmukhi=2652;e.rreharabic=1681;e.rrehfinalarabic=64397;e.rrvocalicbengali=2528;e.rrvocalicdeva=2400;e.rrvocalicgujarati=2784;e.rrvocalicvowelsignbengali=2500;e.rrvocalicvowelsigndeva=2372;e.rrvocalicvowelsigngujarati=2756;e.rsuperior=63217;e.rtblock=96\
-16;e.rturned=633;e.rturnedsuperior=692;e.ruhiragana=12427;e.rukatakana=12523;e.rukatakanahalfwidth=65433;e.rupeemarkbengali=2546;e.rupeesignbengali=2547;e.rupiah=63197;e.ruthai=3620;e.rvocalicbengali=2443;e.rvocalicdeva=2315;e.rvocalicgujarati=2699;e.rvocalicvowelsignbengali=2499;e.rvocalicvowelsigndeva=2371;e.rvocalicvowelsigngujarati=2755;e.s=115;e.sabengali=2488;e.sacute=347;e.sacutedotaccent=7781;e.sadarabic=1589;e.sadeva=2360;e.sadfinalarabic=65210;e.sadinitialarabic=65211;e.sadmedialarabic\
-=65212;e.sagujarati=2744;e.sagurmukhi=2616;e.sahiragana=12373;e.sakatakana=12469;e.sakatakanahalfwidth=65403;e.sallallahoualayhewasallamarabic=65018;e.samekh=1505;e.samekhdagesh=64321;e.samekhdageshhebrew=64321;e.samekhhebrew=1505;e.saraaathai=3634;e.saraaethai=3649;e.saraaimaimalaithai=3652;e.saraaimaimuanthai=3651;e.saraamthai=3635;e.saraathai=3632;e.saraethai=3648;e.saraiileftthai=63622;e.saraiithai=3637;e.saraileftthai=63621;e.saraithai=3636;e.saraothai=3650;e.saraueeleftthai=63624;e.sarauee\
-thai=3639;e.saraueleftthai=63623;e.sarauethai=3638;e.sarauthai=3640;e.sarauuthai=3641;e.sbopomofo=12569;e.scaron=353;e.scarondotaccent=7783;e.scedilla=351;e.schwa=601;e.schwacyrillic=1241;e.schwadieresiscyrillic=1243;e.schwahook=602;e.scircle=9442;e.scircumflex=349;e.scommaaccent=537;e.sdotaccent=7777;e.sdotbelow=7779;e.sdotbelowdotaccent=7785;e.seagullbelowcmb=828;e.second=8243;e.secondtonechinese=714;e.section=167;e.seenarabic=1587;e.seenfinalarabic=65202;e.seeninitialarabic=65203;e.seenmedial\
-arabic=65204;e.segol=1462;e.segol13=1462;e.segol1f=1462;e.segol2c=1462;e.segolhebrew=1462;e.segolnarrowhebrew=1462;e.segolquarterhebrew=1462;e.segoltahebrew=1426;e.segolwidehebrew=1462;e.seharmenian=1405;e.sehiragana=12379;e.sekatakana=12475;e.sekatakanahalfwidth=65406;e.semicolon=59;e.semicolonarabic=1563;e.semicolonmonospace=65307;e.semicolonsmall=65108;e.semivoicedmarkkana=12444;e.semivoicedmarkkanahalfwidth=65439;e.sentisquare=13090;e.sentosquare=13091;e.seven=55;e.sevenarabic=1639;e.sevenbe\
-ngali=2541;e.sevencircle=9318;e.sevencircleinversesansserif=10128;e.sevendeva=2413;e.seveneighths=8542;e.sevengujarati=2797;e.sevengurmukhi=2669;e.sevenhackarabic=1639;e.sevenhangzhou=12327;e.sevenideographicparen=12838;e.seveninferior=8327;e.sevenmonospace=65303;e.sevenoldstyle=63287;e.sevenparen=9338;e.sevenperiod=9358;e.sevenpersian=1783;e.sevenroman=8566;e.sevensuperior=8311;e.seventeencircle=9328;e.seventeenparen=9348;e.seventeenperiod=9368;e.seventhai=3671;e.sfthyphen=173;e.shaarmenian=139\
-9;e.shabengali=2486;e.shacyrillic=1096;e.shaddaarabic=1617;e.shaddadammaarabic=64609;e.shaddadammatanarabic=64606;e.shaddafathaarabic=64608;e.shaddakasraarabic=64610;e.shaddakasratanarabic=64607;e.shade=9618;e.shadedark=9619;e.shadelight=9617;e.shademedium=9618;e.shadeva=2358;e.shagujarati=2742;e.shagurmukhi=2614;e.shalshelethebrew=1427;e.shbopomofo=12565;e.shchacyrillic=1097;e.sheenarabic=1588;e.sheenfinalarabic=65206;e.sheeninitialarabic=65207;e.sheenmedialarabic=65208;e.sheicoptic=995;e.sheqe\
-l=8362;e.sheqelhebrew=8362;e.sheva=1456;e.sheva115=1456;e.sheva15=1456;e.sheva22=1456;e.sheva2e=1456;e.shevahebrew=1456;e.shevanarrowhebrew=1456;e.shevaquarterhebrew=1456;e.shevawidehebrew=1456;e.shhacyrillic=1211;e.shimacoptic=1005;e.shin=1513;e.shindagesh=64329;e.shindageshhebrew=64329;e.shindageshshindot=64300;e.shindageshshindothebrew=64300;e.shindageshsindot=64301;e.shindageshsindothebrew=64301;e.shindothebrew=1473;e.shinhebrew=1513;e.shinshindot=64298;e.shinshindothebrew=64298;e.shinsindot\
-=64299;e.shinsindothebrew=64299;e.shook=642;e.sigma=963;e.sigma1=962;e.sigmafinal=962;e.sigmalunatesymbolgreek=1010;e.sihiragana=12375;e.sikatakana=12471;e.sikatakanahalfwidth=65404;e.siluqhebrew=1469;e.siluqlefthebrew=1469;e.similar=8764;e.sindothebrew=1474;e.siosacirclekorean=12916;e.siosaparenkorean=12820;e.sioscieuckorean=12670;e.sioscirclekorean=12902;e.sioskiyeokkorean=12666;e.sioskorean=12613;e.siosnieunkorean=12667;e.siosparenkorean=12806;e.siospieupkorean=12669;e.siostikeutkorean=12668;\
-e.six=54;e.sixarabic=1638;e.sixbengali=2540;e.sixcircle=9317;e.sixcircleinversesansserif=10127;e.sixdeva=2412;e.sixgujarati=2796;e.sixgurmukhi=2668;e.sixhackarabic=1638;e.sixhangzhou=12326;e.sixideographicparen=12837;e.sixinferior=8326;e.sixmonospace=65302;e.sixoldstyle=63286;e.sixparen=9337;e.sixperiod=9357;e.sixpersian=1782;e.sixroman=8565;e.sixsuperior=8310;e.sixteencircle=9327;e.sixteencurrencydenominatorbengali=2553;e.sixteenparen=9347;e.sixteenperiod=9367;e.sixthai=3670;e.slash=47;e.slashm\
-onospace=65295;e.slong=383;e.slongdotaccent=7835;e.smileface=9786;e.smonospace=65363;e.sofpasuqhebrew=1475;e.softhyphen=173;e.softsigncyrillic=1100;e.sohiragana=12381;e.sokatakana=12477;e.sokatakanahalfwidth=65407;e.soliduslongoverlaycmb=824;e.solidusshortoverlaycmb=823;e.sorusithai=3625;e.sosalathai=3624;e.sosothai=3595;e.sosuathai=3626;e.space=32;e.spacehackarabic=32;e.spade=9824;e.spadesuitblack=9824;e.spadesuitwhite=9828;e.sparen=9390;e.squarebelowcmb=827;e.squarecc=13252;e.squarecm=13213;e.\
-squarediagonalcrosshatchfill=9641;e.squarehorizontalfill=9636;e.squarekg=13199;e.squarekm=13214;e.squarekmcapital=13262;e.squareln=13265;e.squarelog=13266;e.squaremg=13198;e.squaremil=13269;e.squaremm=13212;e.squaremsquared=13217;e.squareorthogonalcrosshatchfill=9638;e.squareupperlefttolowerrightfill=9639;e.squareupperrighttolowerleftfill=9640;e.squareverticalfill=9637;e.squarewhitewithsmallblack=9635;e.srsquare=13275;e.ssabengali=2487;e.ssadeva=2359;e.ssagujarati=2743;e.ssangcieuckorean=12617;e\
-.ssanghieuhkorean=12677;e.ssangieungkorean=12672;e.ssangkiyeokkorean=12594;e.ssangnieunkorean=12645;e.ssangpieupkorean=12611;e.ssangsioskorean=12614;e.ssangtikeutkorean=12600;e.ssuperior=63218;e.sterling=163;e.sterlingmonospace=65505;e.strokelongoverlaycmb=822;e.strokeshortoverlaycmb=821;e.subset=8834;e.subsetnotequal=8842;e.subsetorequal=8838;e.succeeds=8827;e.suchthat=8715;e.suhiragana=12377;e.sukatakana=12473;e.sukatakanahalfwidth=65405;e.sukunarabic=1618;e.summation=8721;e.sun=9788;e.superse\
-t=8835;e.supersetnotequal=8843;e.supersetorequal=8839;e.svsquare=13276;e.syouwaerasquare=13180;e.t=116;e.tabengali=2468;e.tackdown=8868;e.tackleft=8867;e.tadeva=2340;e.tagujarati=2724;e.tagurmukhi=2596;e.taharabic=1591;e.tahfinalarabic=65218;e.tahinitialarabic=65219;e.tahiragana=12383;e.tahmedialarabic=65220;e.taisyouerasquare=13181;e.takatakana=12479;e.takatakanahalfwidth=65408;e.tatweelarabic=1600;e.tau=964;e.tav=1514;e.tavdages=64330;e.tavdagesh=64330;e.tavdageshhebrew=64330;e.tavhebrew=1514;\
-e.tbar=359;e.tbopomofo=12554;e.tcaron=357;e.tccurl=680;e.tcedilla=355;e.tcheharabic=1670;e.tchehfinalarabic=64379;e.tchehinitialarabic=64380;e.tchehmedialarabic=64381;e.tcircle=9443;e.tcircumflexbelow=7793;e.tcommaaccent=355;e.tdieresis=7831;e.tdotaccent=7787;e.tdotbelow=7789;e.tecyrillic=1090;e.tedescendercyrillic=1197;e.teharabic=1578;e.tehfinalarabic=65174;e.tehhahinitialarabic=64674;e.tehhahisolatedarabic=64524;e.tehinitialarabic=65175;e.tehiragana=12390;e.tehjeeminitialarabic=64673;e.tehjee\
-misolatedarabic=64523;e.tehmarbutaarabic=1577;e.tehmarbutafinalarabic=65172;e.tehmedialarabic=65176;e.tehmeeminitialarabic=64676;e.tehmeemisolatedarabic=64526;e.tehnoonfinalarabic=64627;e.tekatakana=12486;e.tekatakanahalfwidth=65411;e.telephone=8481;e.telephoneblack=9742;e.telishagedolahebrew=1440;e.telishaqetanahebrew=1449;e.tencircle=9321;e.tenideographicparen=12841;e.tenparen=9341;e.tenperiod=9361;e.tenroman=8569;e.tesh=679;e.tet=1496;e.tetdagesh=64312;e.tetdageshhebrew=64312;e.tethebrew=1496\
-;e.tetsecyrillic=1205;e.tevirhebrew=1435;e.tevirlefthebrew=1435;e.thabengali=2469;e.thadeva=2341;e.thagujarati=2725;e.thagurmukhi=2597;e.thalarabic=1584;e.thalfinalarabic=65196;e.thanthakhatlowleftthai=63640;e.thanthakhatlowrightthai=63639;e.thanthakhatthai=3660;e.thanthakhatupperleftthai=63638;e.theharabic=1579;e.thehfinalarabic=65178;e.thehinitialarabic=65179;e.thehmedialarabic=65180;e.thereexists=8707;e.therefore=8756;e.theta=952;e.theta1=977;e.thetasymbolgreek=977;e.thieuthacirclekorean=1292\
-1;e.thieuthaparenkorean=12825;e.thieuthcirclekorean=12907;e.thieuthkorean=12620;e.thieuthparenkorean=12811;e.thirteencircle=9324;e.thirteenparen=9344;e.thirteenperiod=9364;e.thonangmonthothai=3601;e.thook=429;e.thophuthaothai=3602;e.thorn=254;e.thothahanthai=3607;e.thothanthai=3600;e.thothongthai=3608;e.thothungthai=3606;e.thousandcyrillic=1154;e.thousandsseparatorarabic=1644;e.thousandsseparatorpersian=1644;e.three=51;e.threearabic=1635;e.threebengali=2537;e.threecircle=9314;e.threecircleinvers\
-esansserif=10124;e.threedeva=2409;e.threeeighths=8540;e.threegujarati=2793;e.threegurmukhi=2665;e.threehackarabic=1635;e.threehangzhou=12323;e.threeideographicparen=12834;e.threeinferior=8323;e.threemonospace=65299;e.threenumeratorbengali=2550;e.threeoldstyle=63283;e.threeparen=9334;e.threeperiod=9354;e.threepersian=1779;e.threequarters=190;e.threequartersemdash=63198;e.threeroman=8562;e.threesuperior=179;e.threethai=3667;e.thzsquare=13204;e.tihiragana=12385;e.tikatakana=12481;e.tikatakanahalfwi\
-dth=65409;e.tikeutacirclekorean=12912;e.tikeutaparenkorean=12816;e.tikeutcirclekorean=12898;e.tikeutkorean=12599;e.tikeutparenkorean=12802;e.tilde=732;e.tildebelowcmb=816;e.tildecmb=771;e.tildecomb=771;e.tildedoublecmb=864;e.tildeoperator=8764;e.tildeoverlaycmb=820;e.tildeverticalcmb=830;e.timescircle=8855;e.tipehahebrew=1430;e.tipehalefthebrew=1430;e.tippigurmukhi=2672;e.titlocyrilliccmb=1155;e.tiwnarmenian=1407;e.tlinebelow=7791;e.tmonospace=65364;e.toarmenian=1385;e.tohiragana=12392;e.tokatak\
-ana=12488;e.tokatakanahalfwidth=65412;e.tonebarextrahighmod=741;e.tonebarextralowmod=745;e.tonebarhighmod=742;e.tonebarlowmod=744;e.tonebarmidmod=743;e.tonefive=445;e.tonesix=389;e.tonetwo=424;e.tonos=900;e.tonsquare=13095;e.topatakthai=3599;e.tortoiseshellbracketleft=12308;e.tortoiseshellbracketleftsmall=65117;e.tortoiseshellbracketleftvertical=65081;e.tortoiseshellbracketright=12309;e.tortoiseshellbracketrightsmall=65118;e.tortoiseshellbracketrightvertical=65082;e.totaothai=3605;e.tpalatalhook\
-=427;e.tparen=9391;e.trademark=8482;e.trademarksans=63722;e.trademarkserif=63195;e.tretroflexhook=648;e.triagdn=9660;e.triaglf=9668;e.triagrt=9658;e.triagup=9650;e.ts=678;e.tsadi=1510;e.tsadidagesh=64326;e.tsadidageshhebrew=64326;e.tsadihebrew=1510;e.tsecyrillic=1094;e.tsere=1461;e.tsere12=1461;e.tsere1e=1461;e.tsere2b=1461;e.tserehebrew=1461;e.tserenarrowhebrew=1461;e.tserequarterhebrew=1461;e.tserewidehebrew=1461;e.tshecyrillic=1115;e.tsuperior=63219;e.ttabengali=2463;e.ttadeva=2335;e.ttagujar\
-ati=2719;e.ttagurmukhi=2591;e.tteharabic=1657;e.ttehfinalarabic=64359;e.ttehinitialarabic=64360;e.ttehmedialarabic=64361;e.tthabengali=2464;e.tthadeva=2336;e.tthagujarati=2720;e.tthagurmukhi=2592;e.tturned=647;e.tuhiragana=12388;e.tukatakana=12484;e.tukatakanahalfwidth=65410;e.tusmallhiragana=12387;e.tusmallkatakana=12483;e.tusmallkatakanahalfwidth=65391;e.twelvecircle=9323;e.twelveparen=9343;e.twelveperiod=9363;e.twelveroman=8571;e.twentycircle=9331;e.twentyhangzhou=21316;e.twentyparen=9351;e.t\
-wentyperiod=9371;e.two=50;e.twoarabic=1634;e.twobengali=2536;e.twocircle=9313;e.twocircleinversesansserif=10123;e.twodeva=2408;e.twodotenleader=8229;e.twodotleader=8229;e.twodotleadervertical=65072;e.twogujarati=2792;e.twogurmukhi=2664;e.twohackarabic=1634;e.twohangzhou=12322;e.twoideographicparen=12833;e.twoinferior=8322;e.twomonospace=65298;e.twonumeratorbengali=2549;e.twooldstyle=63282;e.twoparen=9333;e.twoperiod=9353;e.twopersian=1778;e.tworoman=8561;e.twostroke=443;e.twosuperior=178;e.twoth\
-ai=3666;e.twothirds=8532;e.u=117;e.uacute=250;e.ubar=649;e.ubengali=2441;e.ubopomofo=12584;e.ubreve=365;e.ucaron=468;e.ucircle=9444;e.ucircumflex=251;e.ucircumflexbelow=7799;e.ucyrillic=1091;e.udattadeva=2385;e.udblacute=369;e.udblgrave=533;e.udeva=2313;e.udieresis=252;e.udieresisacute=472;e.udieresisbelow=7795;e.udieresiscaron=474;e.udieresiscyrillic=1265;e.udieresisgrave=476;e.udieresismacron=470;e.udotbelow=7909;e.ugrave=249;e.ugujarati=2697;e.ugurmukhi=2569;e.uhiragana=12358;e.uhookabove=791\
-1;e.uhorn=432;e.uhornacute=7913;e.uhorndotbelow=7921;e.uhorngrave=7915;e.uhornhookabove=7917;e.uhorntilde=7919;e.uhungarumlaut=369;e.uhungarumlautcyrillic=1267;e.uinvertedbreve=535;e.ukatakana=12454;e.ukatakanahalfwidth=65395;e.ukcyrillic=1145;e.ukorean=12636;e.umacron=363;e.umacroncyrillic=1263;e.umacrondieresis=7803;e.umatragurmukhi=2625;e.umonospace=65365;e.underscore=95;e.underscoredbl=8215;e.underscoremonospace=65343;e.underscorevertical=65075;e.underscorewavy=65103;e.union=8746;e.universal\
-=8704;e.uogonek=371;e.uparen=9392;e.upblock=9600;e.upperdothebrew=1476;e.upsilon=965;e.upsilondieresis=971;e.upsilondieresistonos=944;e.upsilonlatin=650;e.upsilontonos=973;e.uptackbelowcmb=797;e.uptackmod=724;e.uragurmukhi=2675;e.uring=367;e.ushortcyrillic=1118;e.usmallhiragana=12357;e.usmallkatakana=12453;e.usmallkatakanahalfwidth=65385;e.ustraightcyrillic=1199;e.ustraightstrokecyrillic=1201;e.utilde=361;e.utildeacute=7801;e.utildebelow=7797;e.uubengali=2442;e.uudeva=2314;e.uugujarati=2698;e.uu\
-gurmukhi=2570;e.uumatragurmukhi=2626;e.uuvowelsignbengali=2498;e.uuvowelsigndeva=2370;e.uuvowelsigngujarati=2754;e.uvowelsignbengali=2497;e.uvowelsigndeva=2369;e.uvowelsigngujarati=2753;e.v=118;e.vadeva=2357;e.vagujarati=2741;e.vagurmukhi=2613;e.vakatakana=12535;e.vav=1493;e.vavdagesh=64309;e.vavdagesh65=64309;e.vavdageshhebrew=64309;e.vavhebrew=1493;e.vavholam=64331;e.vavholamhebrew=64331;e.vavvavhebrew=1520;e.vavyodhebrew=1521;e.vcircle=9445;e.vdotbelow=7807;e.vecyrillic=1074;e.veharabic=1700;\
-e.vehfinalarabic=64363;e.vehinitialarabic=64364;e.vehmedialarabic=64365;e.vekatakana=12537;e.venus=9792;e.verticalbar=124;e.verticallineabovecmb=781;e.verticallinebelowcmb=809;e.verticallinelowmod=716;e.verticallinemod=712;e.vewarmenian=1406;e.vhook=651;e.vikatakana=12536;e.viramabengali=2509;e.viramadeva=2381;e.viramagujarati=2765;e.visargabengali=2435;e.visargadeva=2307;e.visargagujarati=2691;e.vmonospace=65366;e.voarmenian=1400;e.voicediterationhiragana=12446;e.voicediterationkatakana=12542;e\
-.voicedmarkkana=12443;e.voicedmarkkanahalfwidth=65438;e.vokatakana=12538;e.vparen=9393;e.vtilde=7805;e.vturned=652;e.vuhiragana=12436;e.vukatakana=12532;e.w=119;e.wacute=7811;e.waekorean=12633;e.wahiragana=12431;e.wakatakana=12527;e.wakatakanahalfwidth=65436;e.wakorean=12632;e.wasmallhiragana=12430;e.wasmallkatakana=12526;e.wattosquare=13143;e.wavedash=12316;e.wavyunderscorevertical=65076;e.wawarabic=1608;e.wawfinalarabic=65262;e.wawhamzaabovearabic=1572;e.wawhamzaabovefinalarabic=65158;e.wbsqua\
-re=13277;e.wcircle=9446;e.wcircumflex=373;e.wdieresis=7813;e.wdotaccent=7815;e.wdotbelow=7817;e.wehiragana=12433;e.weierstrass=8472;e.wekatakana=12529;e.wekorean=12638;e.weokorean=12637;e.wgrave=7809;e.whitebullet=9702;e.whitecircle=9675;e.whitecircleinverse=9689;e.whitecornerbracketleft=12302;e.whitecornerbracketleftvertical=65091;e.whitecornerbracketright=12303;e.whitecornerbracketrightvertical=65092;e.whitediamond=9671;e.whitediamondcontainingblacksmalldiamond=9672;e.whitedownpointingsmalltri\
-angle=9663;e.whitedownpointingtriangle=9661;e.whiteleftpointingsmalltriangle=9667;e.whiteleftpointingtriangle=9665;e.whitelenticularbracketleft=12310;e.whitelenticularbracketright=12311;e.whiterightpointingsmalltriangle=9657;e.whiterightpointingtriangle=9655;e.whitesmallsquare=9643;e.whitesmilingface=9786;e.whitesquare=9633;e.whitestar=9734;e.whitetelephone=9743;e.whitetortoiseshellbracketleft=12312;e.whitetortoiseshellbracketright=12313;e.whiteuppointingsmalltriangle=9653;e.whiteuppointingtrian\
-gle=9651;e.wihiragana=12432;e.wikatakana=12528;e.wikorean=12639;e.wmonospace=65367;e.wohiragana=12434;e.wokatakana=12530;e.wokatakanahalfwidth=65382;e.won=8361;e.wonmonospace=65510;e.wowaenthai=3623;e.wparen=9394;e.wring=7832;e.wsuperior=695;e.wturned=653;e.wynn=447;e.x=120;e.xabovecmb=829;e.xbopomofo=12562;e.xcircle=9447;e.xdieresis=7821;e.xdotaccent=7819;e.xeharmenian=1389;e.xi=958;e.xmonospace=65368;e.xparen=9395;e.xsuperior=739;e.y=121;e.yaadosquare=13134;e.yabengali=2479;e.yacute=253;e.yade\
-va=2351;e.yaekorean=12626;e.yagujarati=2735;e.yagurmukhi=2607;e.yahiragana=12420;e.yakatakana=12516;e.yakatakanahalfwidth=65428;e.yakorean=12625;e.yamakkanthai=3662;e.yasmallhiragana=12419;e.yasmallkatakana=12515;e.yasmallkatakanahalfwidth=65388;e.yatcyrillic=1123;e.ycircle=9448;e.ycircumflex=375;e.ydieresis=255;e.ydotaccent=7823;e.ydotbelow=7925;e.yeharabic=1610;e.yehbarreearabic=1746;e.yehbarreefinalarabic=64431;e.yehfinalarabic=65266;e.yehhamzaabovearabic=1574;e.yehhamzaabovefinalarabic=65162\
-;e.yehhamzaaboveinitialarabic=65163;e.yehhamzaabovemedialarabic=65164;e.yehinitialarabic=65267;e.yehmedialarabic=65268;e.yehmeeminitialarabic=64733;e.yehmeemisolatedarabic=64600;e.yehnoonfinalarabic=64660;e.yehthreedotsbelowarabic=1745;e.yekorean=12630;e.yen=165;e.yenmonospace=65509;e.yeokorean=12629;e.yeorinhieuhkorean=12678;e.yerahbenyomohebrew=1450;e.yerahbenyomolefthebrew=1450;e.yericyrillic=1099;e.yerudieresiscyrillic=1273;e.yesieungkorean=12673;e.yesieungpansioskorean=12675;e.yesieungsiosk\
-orean=12674;e.yetivhebrew=1434;e.ygrave=7923;e.yhook=436;e.yhookabove=7927;e.yiarmenian=1397;e.yicyrillic=1111;e.yikorean=12642;e.yinyang=9775;e.yiwnarmenian=1410;e.ymonospace=65369;e.yod=1497;e.yoddagesh=64313;e.yoddageshhebrew=64313;e.yodhebrew=1497;e.yodyodhebrew=1522;e.yodyodpatahhebrew=64287;e.yohiragana=12424;e.yoikorean=12681;e.yokatakana=12520;e.yokatakanahalfwidth=65430;e.yokorean=12635;e.yosmallhiragana=12423;e.yosmallkatakana=12519;e.yosmallkatakanahalfwidth=65390;e.yotgreek=1011;e.yo\
-yaekorean=12680;e.yoyakorean=12679;e.yoyakthai=3618;e.yoyingthai=3597;e.yparen=9396;e.ypogegrammeni=890;e.ypogegrammenigreekcmb=837;e.yr=422;e.yring=7833;e.ysuperior=696;e.ytilde=7929;e.yturned=654;e.yuhiragana=12422;e.yuikorean=12684;e.yukatakana=12518;e.yukatakanahalfwidth=65429;e.yukorean=12640;e.yusbigcyrillic=1131;e.yusbigiotifiedcyrillic=1133;e.yuslittlecyrillic=1127;e.yuslittleiotifiedcyrillic=1129;e.yusmallhiragana=12421;e.yusmallkatakana=12517;e.yusmallkatakanahalfwidth=65389;e.yuyekore\
-an=12683;e.yuyeokorean=12682;e.yyabengali=2527;e.yyadeva=2399;e.z=122;e.zaarmenian=1382;e.zacute=378;e.zadeva=2395;e.zagurmukhi=2651;e.zaharabic=1592;e.zahfinalarabic=65222;e.zahinitialarabic=65223;e.zahiragana=12374;e.zahmedialarabic=65224;e.zainarabic=1586;e.zainfinalarabic=65200;e.zakatakana=12470;e.zaqefgadolhebrew=1429;e.zaqefqatanhebrew=1428;e.zarqahebrew=1432;e.zayin=1494;e.zayindagesh=64310;e.zayindageshhebrew=64310;e.zayinhebrew=1494;e.zbopomofo=12567;e.zcaron=382;e.zcircle=9449;e.zcirc\
-umflex=7825;e.zcurl=657;e.zdot=380;e.zdotaccent=380;e.zdotbelow=7827;e.zecyrillic=1079;e.zedescendercyrillic=1177;e.zedieresiscyrillic=1247;e.zehiragana=12380;e.zekatakana=12476;e.zero=48;e.zeroarabic=1632;e.zerobengali=2534;e.zerodeva=2406;e.zerogujarati=2790;e.zerogurmukhi=2662;e.zerohackarabic=1632;e.zeroinferior=8320;e.zeromonospace=65296;e.zerooldstyle=63280;e.zeropersian=1776;e.zerosuperior=8304;e.zerothai=3664;e.zerowidthjoiner=65279;e.zerowidthnonjoiner=8204;e.zerowidthspace=8203;e.zeta=\
-950;e.zhbopomofo=12563;e.zhearmenian=1386;e.zhebrevecyrillic=1218;e.zhecyrillic=1078;e.zhedescendercyrillic=1175;e.zhedieresiscyrillic=1245;e.zihiragana=12376;e.zikatakana=12472;e.zinorhebrew=1454;e.zlinebelow=7829;e.zmonospace=65370;e.zohiragana=12382;e.zokatakana=12478;e.zparen=9397;e.zretroflexhook=656;e.zstroke=438;e.zuhiragana=12378;e.zukatakana=12474;e[".notdef"]=0;e.angbracketleftbig=9001;e.angbracketleftBig=9001;e.angbracketleftbigg=9001;e.angbracketleftBigg=9001;e.angbracketrightBig=900\
-2;e.angbracketrightbig=9002;e.angbracketrightBigg=9002;e.angbracketrightbigg=9002;e.arrowhookleft=8618;e.arrowhookright=8617;e.arrowlefttophalf=8636;e.arrowleftbothalf=8637;e.arrownortheast=8599;e.arrownorthwest=8598;e.arrowrighttophalf=8640;e.arrowrightbothalf=8641;e.arrowsoutheast=8600;e.arrowsouthwest=8601;e.backslashbig=8726;e.backslashBig=8726;e.backslashBigg=8726;e.backslashbigg=8726;e.bardbl=8214;e.bracehtipdownleft=65079;e.bracehtipdownright=65079;e.bracehtipupleft=65080;e.bracehtipuprig\
-ht=65080;e.braceleftBig=123;e.braceleftbig=123;e.braceleftbigg=123;e.braceleftBigg=123;e.bracerightBig=125;e.bracerightbig=125;e.bracerightbigg=125;e.bracerightBigg=125;e.bracketleftbig=91;e.bracketleftBig=91;e.bracketleftbigg=91;e.bracketleftBigg=91;e.bracketrightBig=93;e.bracketrightbig=93;e.bracketrightbigg=93;e.bracketrightBigg=93;e.ceilingleftbig=8968;e.ceilingleftBig=8968;e.ceilingleftBigg=8968;e.ceilingleftbigg=8968;e.ceilingrightbig=8969;e.ceilingrightBig=8969;e.ceilingrightbigg=8969;e.c\
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-umflex","adieresis","agrave","aring","atilde","ccedilla","eacute","ecircumflex","edieresis","egrave","iacute","icircumflex","idieresis","igrave","ntilde","oacute","ocircumflex","odieresis","ograve","otilde","scaron","uacute","ucircumflex","udieresis","ugrave","yacute","ydieresis","zcaron","exclamsmall","Hungarumlautsmall","dollaroldstyle","dollarsuperior","ampersandsmall","Acutesmall","parenleftsuperior","parenrightsuperior","twodotenleader","onedotenleader","zerooldstyle","oneoldstyle","twoolds\
-tyle","threeoldstyle","fouroldstyle","fiveoldstyle","sixoldstyle","sevenoldstyle","eightoldstyle","nineoldstyle","commasuperior","threequartersemdash","periodsuperior","questionsmall","asuperior","bsuperior","centsuperior","dsuperior","esuperior","isuperior","lsuperior","msuperior","nsuperior","osuperior","rsuperior","ssuperior","tsuperior","ff","ffi","ffl","parenleftinferior","parenrightinferior","Circumflexsmall","hyphensuperior","Gravesmall","Asmall","Bsmall","Csmall","Dsmall","Esmall","Fsmal\
-l","Gsmall","Hsmall","Ismall","Jsmall","Ksmall","Lsmall","Msmall","Nsmall","Osmall","Psmall","Qsmall","Rsmall","Ssmall","Tsmall","Usmall","Vsmall","Wsmall","Xsmall","Ysmall","Zsmall","colonmonetary","onefitted","rupiah","Tildesmall","exclamdownsmall","centoldstyle","Lslashsmall","Scaronsmall","Zcaronsmall","Dieresissmall","Brevesmall","Caronsmall","Dotaccentsmall","Macronsmall","figuredash","hypheninferior","Ogoneksmall","Ringsmall","Cedillasmall","questiondownsmall","oneeighth","threeeighths","\
-fiveeighths","seveneighths","onethird","twothirds","zerosuperior","foursuperior","fivesuperior","sixsuperior","sevensuperior","eightsuperior","ninesuperior","zeroinferior","oneinferior","twoinferior","threeinferior","fourinferior","fiveinferior","sixinferior","seveninferior","eightinferior","nineinferior","centinferior","dollarinferior","periodinferior","commainferior","Agravesmall","Aacutesmall","Acircumflexsmall","Atildesmall","Adieresissmall","Aringsmall","AEsmall","Ccedillasmall","Egravesmal\
-l","Eacutesmall","Ecircumflexsmall","Edieresissmall","Igravesmall","Iacutesmall","Icircumflexsmall","Idieresissmall","Ethsmall","Ntildesmall","Ogravesmall","Oacutesmall","Ocircumflexsmall","Otildesmall","Odieresissmall","OEsmall","Oslashsmall","Ugravesmall","Uacutesmall","Ucircumflexsmall","Udieresissmall","Yacutesmall","Thornsmall","Ydieresissmall","001.000","001.001","001.002","001.003","Black","Bold","Book","Light","Medium","Regular","Roman","Semibold"],Wr=391,zr=[null,{id:"hstem",min:2,stack\
-Clearing:!0,stem:!0},null,{id:"vstem",min:2,stackClearing:!0,stem:!0},{id:"vmoveto",min:1,stackClearing:!0},{id:"rlineto",min:2,resetStack:!0},{id:"hlineto",min:1,resetStack:!0},{id:"vlineto",min:1,resetStack:!0},{id:"rrcurveto",min:6,resetStack:!0},null,{id:"callsubr",min:1,undefStack:!0},{id:"return",min:0,undefStack:!0},null,null,{id:"endchar",min:0,stackClearing:!0},null,null,null,{id:"hstemhm",min:2,stackClearing:!0,stem:!0},{id:"hintmask",min:0,stackClearing:!0},{id:"cntrmask",min:0,stackC\
-learing:!0},{id:"rmoveto",min:2,stackClearing:!0},{id:"hmoveto",min:1,stackClearing:!0},{id:"vstemhm",min:2,stackClearing:!0,stem:!0},{id:"rcurveline",min:8,resetStack:!0},{id:"rlinecurve",min:8,resetStack:!0},{id:"vvcurveto",min:4,resetStack:!0},{id:"hhcurveto",min:4,resetStack:!0},null,{id:"callgsubr",min:1,undefStack:!0},{id:"vhcurveto",min:4,resetStack:!0},{id:"hvcurveto",min:4,resetStack:!0}],$r=[null,null,null,{id:"and",min:2,stackDelta:-1},{id:"or",min:2,stackDelta:-1},{id:"not",min:1,sta\
-ckDelta:0},null,null,null,{id:"abs",min:1,stackDelta:0},{id:"add",min:2,stackDelta:-1,stackFn(e,t){e[t-2]=e[t-2]+e[t-1]}},{id:"sub",min:2,stackDelta:-1,stackFn(e,t){e[t-2]=e[t-2]-e[t-1]}},{id:"div",min:2,stackDelta:-1,stackFn(e,t){e[t-2]=e[t-2]/e[t-1]}},null,{id:"neg",min:1,stackDelta:0,stackFn(e,t){e[t-1]=-e[t-1]}},{id:"eq",min:2,stackDelta:-1},null,null,{id:"drop",min:1,stackDelta:-1},null,{id:"put",min:2,stackDelta:-2},{id:"get",min:1,stackDelta:0},{id:"ifelse",min:4,stackDelta:-3},{id:"rando\
-m",min:0,stackDelta:1},{id:"mul",min:2,stackDelta:-1,stackFn(e,t){e[t-2]=e[t-2]*e[t-1]}},null,{id:"sqrt",min:1,stackDelta:0},{id:"dup",min:1,stackDelta:1},{id:"exch",min:2,stackDelta:0},{id:"index",min:2,stackDelta:0},{id:"roll",min:3,stackDelta:-2},null,null,null,{id:"hflex",min:7,resetStack:!0},{id:"flex",min:13,resetStack:!0},{id:"hflex1",min:9,resetStack:!0},{id:"flex1",min:11,resetStack:!0}];class CFFParser{constructor(e,t,a){this.bytes=e.getBytes();this.properties=t;this.seacAnalysisEnable\
-d=!!a}parse(){const e=this.properties,t=new CFF;this.cff=t;const a=this.parseHeader(),r=this.parseIndex(a.endPos),i=this.parseIndex(r.endPos),n=this.parseIndex(i.endPos),s=this.parseIndex(n.endPos),o=this.parseDict(i.obj.get(0)),c=this.createDict(CFFTopDict,o,t.strings);t.header=a.obj;t.names=this.parseNameIndex(r.obj);t.strings=this.parseStringIndex(n.obj);t.topDict=c;t.globalSubrIndex=s.obj;this.parsePrivateDict(t.topDict);t.isCIDFont=c.hasName("ROS");const l=c.getByName("CharStrings"),h=this.\
-parseIndex(l).obj,u=c.getByName("FontMatrix");u&&(e.fontMatrix=u);const d=c.getByName("FontBBox");if(d){e.ascent=Math.max(d[3],d[1]);e.descent=Math.min(d[1],d[3]);e.ascentScaled=!0}let f,g;if(t.isCIDFont){const e=this.parseIndex(c.getByName("FDArray")).obj;for(let a=0,r=e.count;a<r;++a){const r=e.get(a),i=this.createDict(CFFTopDict,this.parseDict(r),t.strings);this.parsePrivateDict(i);t.fdArray.push(i)}g=null;f=this.parseCharsets(c.getByName("charset"),h.count,t.strings,!0);t.fdSelect=this.parse\
-FDSelect(c.getByName("FDSelect"),h.count)}else{f=this.parseCharsets(c.getByName("charset"),h.count,t.strings,!1);g=this.parseEncoding(c.getByName("Encoding"),e,t.strings,f.charset)}t.charset=f;t.encoding=g;const p=this.parseCharStrings({charStrings:h,localSubrIndex:c.privateDict.subrsIndex,globalSubrIndex:s.obj,fdSelect:t.fdSelect,fdArray:t.fdArray,privateDict:c.privateDict});t.charStrings=p.charStrings;t.seacs=p.seacs;t.widths=p.widths;return t}parseHeader(){let e=this.bytes;const t=e.length;le\
-t a=0;for(;a<t&&1!==e[a];)++a;if(a>=t)throw new FormatError("Invalid CFF header");if(0!==a){info("cff data is shifted");e=e.subarray(a);this.bytes=e}const r=e[0],i=e[1],n=e[2],s=e[3];return{obj:new CFFHeader(r,i,n,s),endPos:n}}parseDict(e){let t=0;function parseOperand(){let a=e[t++];if(30===a)return function parseFloatOperand(){let a="";const r=15,i=["0","1","2","3","4","5","6","7","8","9",".","E","E-",null,"-"],n=e.length;for(;t<n;){const n=e[t++],s=n>>4,o=15&n;if(s===r)break;a+=i[s];if(o===r)\
-break;a+=i[o]}return parseFloat(a)}();if(28===a){a=readInt16(e,t);t+=2;return a}if(29===a){a=e[t++];a=a<<8|e[t++];a=a<<8|e[t++];a=a<<8|e[t++];return a}if(a>=32&&a<=246)return a-139;if(a>=247&&a<=250)return 256*(a-247)+e[t++]+108;if(a>=251&&a<=254)return-256*(a-251)-e[t++]-108;warn(\'CFFParser_parseDict: "\'+a+\'" is a reserved command.\');return NaN}let a=[];const r=[];t=0;const i=e.length;for(;t<i;){let i=e[t];if(i<=21){12===i&&(i=i<<8|e[++t]);r.push([i,a]);a=[];++t}else a.push(parseOperand())}retu\
-rn r}parseIndex(e){const t=new CFFIndex,a=this.bytes,r=a[e++]<<8|a[e++],i=[];let n,s,o=e;if(0!==r){const t=a[e++],c=e+(r+1)*t-1;for(n=0,s=r+1;n<s;++n){let r=0;for(let i=0;i<t;++i){r<<=8;r+=a[e++]}i.push(c+r)}o=i[r]}for(n=0,s=i.length-1;n<s;++n){const e=i[n],r=i[n+1];t.add(a.subarray(e,r))}return{obj:t,endPos:o}}parseNameIndex(e){const t=[];for(let a=0,r=e.count;a<r;++a){const r=e.get(a);t.push(bytesToString(r))}return t}parseStringIndex(e){const t=new CFFStrings;for(let a=0,r=e.count;a<r;++a){co\
-nst r=e.get(a);t.add(bytesToString(r))}return t}createDict(e,t,a){const r=new e(a);for(const[e,a]of t)r.setByKey(e,a);return r}parseCharString(e,t,a,r){if(!t||e.callDepth>10)return!1;let i=e.stackSize;const n=e.stack;let s=t.length;for(let o=0;o<s;){const c=t[o++];let l=null;if(12===c){const e=t[o++];if(0===e){t[o-2]=139;t[o-1]=22;i=0}else l=$r[e]}else if(28===c){n[i]=readInt16(t,o);o+=2;i++}else if(14===c){if(i>=4){i-=4;if(this.seacAnalysisEnabled){e.seac=n.slice(i,i+4);return!1}}l=zr[c]}else i\
-f(c>=32&&c<=246){n[i]=c-139;i++}else if(c>=247&&c<=254){n[i]=c<251?(c-247<<8)+t[o]+108:-(c-251<<8)-t[o]-108;o++;i++}else if(255===c){n[i]=(t[o]<<24|t[o+1]<<16|t[o+2]<<8|t[o+3])/65536;o+=4;i++}else if(19===c||20===c){e.hints+=i>>1;if(0===e.hints){t.copyWithin(o-1,o,-1);o-=1;s-=1;continue}o+=e.hints+7>>3;i%=2;l=zr[c]}else{if(10===c||29===c){const t=10===c?a:r;if(!t){l=zr[c];warn("Missing subrsIndex for "+l.id);return!1}let s=32768;t.count<1240?s=107:t.count<33900&&(s=1131);const o=n[--i]+s;if(o<0|\
-|o>=t.count||isNaN(o)){l=zr[c];warn("Out of bounds subrIndex for "+l.id);return!1}e.stackSize=i;e.callDepth++;if(!this.parseCharString(e,t.get(o),a,r))return!1;e.callDepth--;i=e.stackSize;continue}if(11===c){e.stackSize=i;return!0}if(0===c&&o===t.length){t[o-1]=14;l=zr[14]}else{if(9===c){t.copyWithin(o-1,o,-1);o-=1;s-=1;continue}l=zr[c]}}if(l){if(l.stem){e.hints+=i>>1;if(3===c||23===c)e.hasVStems=!0;else if(e.hasVStems&&(1===c||18===c)){warn("CFF stem hints are in wrong order");t[o-1]=1===c?3:23\
-}}if("min"in l&&!e.undefStack&&i<l.min){warn("Not enough parameters for "+l.id+"; actual: "+i+", expected: "+l.min);if(0===i){t[o-1]=14;return!0}return!1}if(e.firstStackClearing&&l.stackClearing){e.firstStackClearing=!1;i-=l.min;i>=2&&l.stem?i%=2:i>1&&warn("Found too many parameters for stack-clearing command");i>0&&(e.width=n[i-1])}if("stackDelta"in l){"stackFn"in l&&l.stackFn(n,i);i+=l.stackDelta}else if(l.stackClearing)i=0;else if(l.resetStack){i=0;e.undefStack=!1}else if(l.undefStack){i=0;e.\
-undefStack=!0;e.firstStackClearing=!1}}}s<t.length&&t.fill(14,s);e.stackSize=i;return!0}parseCharStrings({charStrings:e,localSubrIndex:t,globalSubrIndex:a,fdSelect:r,fdArray:i,privateDict:n}){const s=[],o=[],c=e.count;for(let l=0;l<c;l++){const c=e.get(l),h={callDepth:0,stackSize:0,stack:[],undefStack:!0,hints:0,firstStackClearing:!0,seac:null,width:null,hasVStems:!1};let u=!0,d=null,f=n;if(r&&i.length){const e=r.getFDIndex(l);if(-1===e){warn("Glyph index is not in fd select.");u=!1}if(e>=i.leng\
-th){warn("Invalid fd index for glyph index.");u=!1}if(u){f=i[e].privateDict;d=f.subrsIndex}}else t&&(d=t);u&&(u=this.parseCharString(h,c,d,a));if(null!==h.width){const e=f.getByName("nominalWidthX");o[l]=e+h.width}else{const e=f.getByName("defaultWidthX");o[l]=e}null!==h.seac&&(s[l]=h.seac);u||e.set(l,new Uint8Array([14]))}return{charStrings:e,seacs:s,widths:o}}emptyPrivateDictionary(e){const t=this.createDict(CFFPrivateDict,[],e.strings);e.setByKey(18,[0,0]);e.privateDict=t}parsePrivateDict(e){\
-if(!e.hasName("Private")){this.emptyPrivateDictionary(e);return}const t=e.getByName("Private");if(!Array.isArray(t)||2!==t.length){e.removeByName("Private");return}const a=t[0],r=t[1];if(0===a||r>=this.bytes.length){this.emptyPrivateDictionary(e);return}const i=r+a,n=this.bytes.subarray(r,i),s=this.parseDict(n),o=this.createDict(CFFPrivateDict,s,e.strings);e.privateDict=o;0===o.getByName("ExpansionFactor")&&o.setByName("ExpansionFactor",.06);if(!o.getByName("Subrs"))return;const c=o.getByName("S\
-ubrs"),l=r+c;if(0===c||l>=this.bytes.length){this.emptyPrivateDictionary(e);return}const h=this.parseIndex(l);o.subrsIndex=h.obj}parseCharsets(e,t,a,r){if(0===e)return new CFFCharset(!0,Kr.ISO_ADOBE,Ur);if(1===e)return new CFFCharset(!0,Kr.EXPERT,Xr);if(2===e)return new CFFCharset(!0,Kr.EXPERT_SUBSET,qr);const i=this.bytes,n=e,s=i[e++],o=[r?0:".notdef"];let c,l,h;t-=1;switch(s){case 0:for(h=0;h<t;h++){c=i[e++]<<8|i[e++];o.push(r?c:a.get(c))}break;case 1:for(;o.length<=t;){c=i[e++]<<8|i[e++];l=i[\
-e++];for(h=0;h<=l;h++)o.push(r?c++:a.get(c++))}break;case 2:for(;o.length<=t;){c=i[e++]<<8|i[e++];l=i[e++]<<8|i[e++];for(h=0;h<=l;h++)o.push(r?c++:a.get(c++))}break;default:throw new FormatError("Unknown charset format")}const u=e,d=i.subarray(n,u);return new CFFCharset(!1,s,o,d)}parseEncoding(e,t,a,r){const i=Object.create(null),n=this.bytes;let s,o,c,l=!1,h=null;if(0===e||1===e){l=!0;s=e;const t=e?wr:Ar;for(o=0,c=r.length;o<c;o++){const e=t.indexOf(r[o]);-1!==e&&(i[e]=o)}}else{const t=e;s=n[e+\
-+];switch(127&s){case 0:const t=n[e++];for(o=1;o<=t;o++)i[n[e++]]=o;break;case 1:const a=n[e++];let r=1;for(o=0;o<a;o++){const t=n[e++],a=n[e++];for(let e=t;e<=t+a;e++)i[e]=r++}break;default:throw new FormatError(`Unknown encoding format: ${s} in CFF`)}const c=e;if(128&s){n[t]&=127;!function readSupplement(){const t=n[e++];for(o=0;o<t;o++){const t=n[e++],s=(n[e++]<<8)+(255&n[e++]);i[t]=r.indexOf(a.get(s))}}()}h=n.subarray(t,c)}s&=127;return new CFFEncoding(l,s,i,h)}parseFDSelect(e,t){const a=thi\
-s.bytes,r=a[e++],i=[];let n;switch(r){case 0:for(n=0;n<t;++n){const t=a[e++];i.push(t)}break;case 3:const s=a[e++]<<8|a[e++];for(n=0;n<s;++n){let t=a[e++]<<8|a[e++];if(0===n&&0!==t){warn("parseFDSelect: The first range must have a first GID of 0 -- trying to recover.");t=0}const r=a[e++],s=a[e]<<8|a[e+1];for(let e=t;e<s;++e)i.push(r)}e+=2;break;default:throw new FormatError(`parseFDSelect: Unknown format "${r}".`)}if(i.length!==t)throw new FormatError("parseFDSelect: Invalid font data.");return \
-new CFFFDSelect(r,i)}}class CFF{constructor(){this.header=null;this.names=[];this.topDict=null;this.strings=new CFFStrings;this.globalSubrIndex=null;this.encoding=null;this.charset=null;this.charStrings=null;this.fdArray=[];this.fdSelect=null;this.isCIDFont=!1}duplicateFirstGlyph(){if(this.charStrings.count>=65535){warn("Not enough space in charstrings to duplicate first glyph.");return}const e=this.charStrings.get(0);this.charStrings.add(e);this.isCIDFont&&this.fdSelect.fdSelect.push(this.fdSel\
-ect.fdSelect[0])}hasGlyphId(e){if(e<0||e>=this.charStrings.count)return!1;return this.charStrings.get(e).length>0}}class CFFHeader{constructor(e,t,a,r){this.major=e;this.minor=t;this.hdrSize=a;this.offSize=r}}class CFFStrings{constructor(){this.strings=[]}get(e){return e>=0&&e<=390?Hr[e]:e-Wr<=this.strings.length?this.strings[e-Wr]:Hr[0]}getSID(e){let t=Hr.indexOf(e);if(-1!==t)return t;t=this.strings.indexOf(e);return-1!==t?t+Wr:-1}add(e){this.strings.push(e)}get count(){return this.strings.leng\
-th}}class CFFIndex{constructor(){this.objects=[];this.length=0}add(e){this.length+=e.length;this.objects.push(e)}set(e,t){this.length+=t.length-this.objects[e].length;this.objects[e]=t}get(e){return this.objects[e]}get count(){return this.objects.length}}class CFFDict{constructor(e,t){this.keyToNameMap=e.keyToNameMap;this.nameToKeyMap=e.nameToKeyMap;this.defaults=e.defaults;this.types=e.types;this.opcodes=e.opcodes;this.order=e.order;this.strings=t;this.values=Object.create(null)}setByKey(e,t){i\
-f(!(e in this.keyToNameMap))return!1;if(0===t.length)return!0;for(const a of t)if(isNaN(a)){warn(`Invalid CFFDict value: "${t}" for key "${e}".`);return!0}const a=this.types[e];"num"!==a&&"sid"!==a&&"offset"!==a||(t=t[0]);this.values[e]=t;return!0}setByName(e,t){if(!(e in this.nameToKeyMap))throw new FormatError(`Invalid dictionary name "${e}"`);this.values[this.nameToKeyMap[e]]=t}hasName(e){return this.nameToKeyMap[e]in this.values}getByName(e){if(!(e in this.nameToKeyMap))throw new FormatError\
-(`Invalid dictionary name ${e}"`);const t=this.nameToKeyMap[e];return t in this.values?this.values[t]:this.defaults[t]}removeByName(e){delete this.values[this.nameToKeyMap[e]]}static createTables(e){const t={keyToNameMap:{},nameToKeyMap:{},defaults:{},types:{},opcodes:{},order:[]};for(const a of e){const e=Array.isArray(a[0])?(a[0][0]<<8)+a[0][1]:a[0];t.keyToNameMap[e]=a[1];t.nameToKeyMap[a[1]]=e;t.types[e]=a[2];t.defaults[e]=a[3];t.opcodes[e]=Array.isArray(a[0])?a[0]:[a[0]];t.order.push(e)}retu\
-rn t}}const Gr=[[[12,30],"ROS",["sid","sid","num"],null],[[12,20],"SyntheticBase","num",null],[0,"version","sid",null],[1,"Notice","sid",null],[[12,0],"Copyright","sid",null],[2,"FullName","sid",null],[3,"FamilyName","sid",null],[4,"Weight","sid",null],[[12,1],"isFixedPitch","num",0],[[12,2],"ItalicAngle","num",0],[[12,3],"UnderlinePosition","num",-100],[[12,4],"UnderlineThickness","num",50],[[12,5],"PaintType","num",0],[[12,6],"CharstringType","num",2],[[12,7],"FontMatrix",["num","num","num","n\
-um","num","num"],[.001,0,0,.001,0,0]],[13,"UniqueID","num",null],[5,"FontBBox",["num","num","num","num"],[0,0,0,0]],[[12,8],"StrokeWidth","num",0],[14,"XUID","array",null],[15,"charset","offset",0],[16,"Encoding","offset",0],[17,"CharStrings","offset",0],[18,"Private",["offset","offset"],null],[[12,21],"PostScript","sid",null],[[12,22],"BaseFontName","sid",null],[[12,23],"BaseFontBlend","delta",null],[[12,31],"CIDFontVersion","num",0],[[12,32],"CIDFontRevision","num",0],[[12,33],"CIDFontType","n\
-um",0],[[12,34],"CIDCount","num",8720],[[12,35],"UIDBase","num",null],[[12,37],"FDSelect","offset",null],[[12,36],"FDArray","offset",null],[[12,38],"FontName","sid",null]];class CFFTopDict extends CFFDict{static get tables(){return shadow(this,"tables",this.createTables(Gr))}constructor(e){super(CFFTopDict.tables,e);this.privateDict=null}}const Vr=[[6,"BlueValues","delta",null],[7,"OtherBlues","delta",null],[8,"FamilyBlues","delta",null],[9,"FamilyOtherBlues","delta",null],[[12,9],"BlueScale","n\
-um",.039625],[[12,10],"BlueShift","num",7],[[12,11],"BlueFuzz","num",1],[10,"StdHW","num",null],[11,"StdVW","num",null],[[12,12],"StemSnapH","delta",null],[[12,13],"StemSnapV","delta",null],[[12,14],"ForceBold","num",0],[[12,17],"LanguageGroup","num",0],[[12,18],"ExpansionFactor","num",.06],[[12,19],"initialRandomSeed","num",0],[20,"defaultWidthX","num",0],[21,"nominalWidthX","num",0],[19,"Subrs","offset",null]];class CFFPrivateDict extends CFFDict{static get tables(){return shadow(this,"tables"\
-,this.createTables(Vr))}constructor(e){super(CFFPrivateDict.tables,e);this.subrsIndex=null}}const Kr={ISO_ADOBE:0,EXPERT:1,EXPERT_SUBSET:2};class CFFCharset{constructor(e,t,a,r){this.predefined=e;this.format=t;this.charset=a;this.raw=r}}class CFFEncoding{constructor(e,t,a,r){this.predefined=e;this.format=t;this.encoding=a;this.raw=r}}class CFFFDSelect{constructor(e,t){this.format=e;this.fdSelect=t}getFDIndex(e){return e<0||e>=this.fdSelect.length?-1:this.fdSelect[e]}}class CFFOffsetTracker{const\
-ructor(){this.offsets=Object.create(null)}isTracking(e){return e in this.offsets}track(e,t){if(e in this.offsets)throw new FormatError(`Already tracking location of ${e}`);this.offsets[e]=t}offset(e){for(const t in this.offsets)this.offsets[t]+=e}setEntryLocation(e,t,a){if(!(e in this.offsets))throw new FormatError(`Not tracking location of ${e}`);const r=a.data,i=this.offsets[e];for(let e=0,a=t.length;e<a;++e){const a=5*e+i,n=a+1,s=a+2,o=a+3,c=a+4;if(29!==r[a]||0!==r[n]||0!==r[s]||0!==r[o]||0!=\
-=r[c])throw new FormatError("writing to an offset that is not empty");const l=t[e];r[a]=29;r[n]=l>>24&255;r[s]=l>>16&255;r[o]=l>>8&255;r[c]=255&l}}}class CFFCompiler{constructor(e){this.cff=e}compile(){const e=this.cff,t={data:[],length:0,add(e){try{this.data.push(...e)}catch{this.data=this.data.concat(e)}this.length=this.data.length}},a=this.compileHeader(e.header);t.add(a);const r=this.compileNameIndex(e.names);t.add(r);if(e.isCIDFont&&e.topDict.hasName("FontMatrix")){const t=e.topDict.getByNa\
-me("FontMatrix");e.topDict.removeByName("FontMatrix");for(const a of e.fdArray){let e=t.slice(0);a.hasName("FontMatrix")&&(e=Util.transform(e,a.getByName("FontMatrix")));a.setByName("FontMatrix",e)}}const i=e.topDict.getByName("XUID");i?.length>16&&e.topDict.removeByName("XUID");e.topDict.setByName("charset",0);let n=this.compileTopDicts([e.topDict],t.length,e.isCIDFont);t.add(n.output);const s=n.trackers[0],o=this.compileStringIndex(e.strings.strings);t.add(o);const c=this.compileIndex(e.global\
-SubrIndex);t.add(c);if(e.encoding&&e.topDict.hasName("Encoding"))if(e.encoding.predefined)s.setEntryLocation("Encoding",[e.encoding.format],t);else{const a=this.compileEncoding(e.encoding);s.setEntryLocation("Encoding",[t.length],t);t.add(a)}const l=this.compileCharset(e.charset,e.charStrings.count,e.strings,e.isCIDFont);s.setEntryLocation("charset",[t.length],t);t.add(l);const h=this.compileCharStrings(e.charStrings);s.setEntryLocation("CharStrings",[t.length],t);t.add(h);if(e.isCIDFont){s.setE\
-ntryLocation("FDSelect",[t.length],t);const a=this.compileFDSelect(e.fdSelect);t.add(a);n=this.compileTopDicts(e.fdArray,t.length,!0);s.setEntryLocation("FDArray",[t.length],t);t.add(n.output);const r=n.trackers;this.compilePrivateDicts(e.fdArray,r,t)}this.compilePrivateDicts([e.topDict],[s],t);t.add([0]);return t.data}encodeNumber(e){return Number.isInteger(e)?this.encodeInteger(e):this.encodeFloat(e)}static get EncodeFloatRegExp(){return shadow(this,"EncodeFloatRegExp",/\\.(\\d*?)(?:9{5,20}|0{5,\
-20})\\d{0,2}(?:e(.+)|$)/)}encodeFloat(e){let t=e.toString();const a=CFFCompiler.EncodeFloatRegExp.exec(t);if(a){const r=parseFloat("1e"+((a[2]?+a[2]:0)+a[1].length));t=(Math.round(e*r)/r).toString()}let r,i,n="";for(r=0,i=t.length;r<i;++r){const e=t[r];n+="e"===e?"-"===t[++r]?"c":"b":"."===e?"a":"-"===e?"e":e}n+=1&n.length?"f":"ff";const s=[30];for(r=0,i=n.length;r<i;r+=2)s.push(parseInt(n.substring(r,r+2),16));return s}encodeInteger(e){let t;t=e>=-107&&e<=107?[e+139]:e>=108&&e<=1131?[247+((e-=10\
-8)>>8),255&e]:e>=-1131&&e<=-108?[251+((e=-e-108)>>8),255&e]:e>=-32768&&e<=32767?[28,e>>8&255,255&e]:[29,e>>24&255,e>>16&255,e>>8&255,255&e];return t}compileHeader(e){return[e.major,e.minor,4,e.offSize]}compileNameIndex(e){const t=new CFFIndex;for(const a of e){const e=Math.min(a.length,127);let r=new Array(e);for(let t=0;t<e;t++){let e=a[t];(e<"!"||e>"~"||"["===e||"]"===e||"("===e||")"===e||"{"===e||"}"===e||"<"===e||">"===e||"/"===e||"%"===e)&&(e="_");r[t]=e}r=r.join("");""===r&&(r="Bad_Font_Na\
-me");t.add(stringToBytes(r))}return this.compileIndex(t)}compileTopDicts(e,t,a){const r=[];let i=new CFFIndex;for(const n of e){if(a){n.removeByName("CIDFontVersion");n.removeByName("CIDFontRevision");n.removeByName("CIDFontType");n.removeByName("CIDCount");n.removeByName("UIDBase")}const e=new CFFOffsetTracker,s=this.compileDict(n,e);r.push(e);i.add(s);e.offset(t)}i=this.compileIndex(i,r);return{trackers:r,output:i}}compilePrivateDicts(e,t,a){for(let r=0,i=e.length;r<i;++r){const i=e[r],n=i.pri\
-vateDict;if(!n||!i.hasName("Private"))throw new FormatError("There must be a private dictionary.");const s=new CFFOffsetTracker,o=this.compileDict(n,s);let c=a.length;s.offset(c);o.length||(c=0);t[r].setEntryLocation("Private",[o.length,c],a);a.add(o);if(n.subrsIndex&&n.hasName("Subrs")){const e=this.compileIndex(n.subrsIndex);s.setEntryLocation("Subrs",[o.length],a);a.add(e)}}}compileDict(e,t){const a=[];for(const r of e.order){if(!(r in e.values))continue;let i=e.values[r],n=e.types[r];Array.i\
-sArray(n)||(n=[n]);Array.isArray(i)||(i=[i]);if(0!==i.length){for(let s=0,o=n.length;s<o;++s){const o=n[s],c=i[s];switch(o){case"num":case"sid":a.push(...this.encodeNumber(c));break;case"offset":const n=e.keyToNameMap[r];t.isTracking(n)||t.track(n,a.length);a.push(29,0,0,0,0);break;case"array":case"delta":a.push(...this.encodeNumber(c));for(let e=1,t=i.length;e<t;++e)a.push(...this.encodeNumber(i[e]));break;default:throw new FormatError(`Unknown data type of ${o}`)}}a.push(...e.opcodes[r])}}retu\
-rn a}compileStringIndex(e){const t=new CFFIndex;for(const a of e)t.add(stringToBytes(a));return this.compileIndex(t)}compileCharStrings(e){const t=new CFFIndex;for(let a=0;a<e.count;a++){const r=e.get(a);0!==r.length?t.add(r):t.add(new Uint8Array([139,14]))}return this.compileIndex(t)}compileCharset(e,t,a,r){let i;const n=t-1;if(r){const e=n-1;i=new Uint8Array([2,0,1,e>>8&255,255&e])}else{i=new Uint8Array(1+2*n);i[0]=0;let t=0;const r=e.charset.length;let s=!1;for(let n=1;n<i.length;n+=2){let o=\
-0;if(t<r){const r=e.charset[t++];o=a.getSID(r);if(-1===o){o=0;if(!s){s=!0;warn(`Couldn\'t find ${r} in CFF strings`)}}}i[n]=o>>8&255;i[n+1]=255&o}}return this.compileTypedArray(i)}compileEncoding(e){return this.compileTypedArray(e.raw)}compileFDSelect(e){const t=e.format;let a,r;switch(t){case 0:a=new Uint8Array(1+e.fdSelect.length);a[0]=t;for(r=0;r<e.fdSelect.length;r++)a[r+1]=e.fdSelect[r];break;case 3:const i=0;let n=e.fdSelect[0];const s=[t,0,0,i>>8&255,255&i,n];for(r=1;r<e.fdSelect.length;r+\
-+){const t=e.fdSelect[r];if(t!==n){s.push(r>>8&255,255&r,t);n=t}}const o=(s.length-3)/3;s[1]=o>>8&255;s[2]=255&o;s.push(r>>8&255,255&r);a=new Uint8Array(s)}return this.compileTypedArray(a)}compileTypedArray(e){return Array.from(e)}compileIndex(e,t=[]){const a=e.objects,r=a.length;if(0===r)return[0,0];const i=[r>>8&255,255&r];let n,s,o=1;for(n=0;n<r;++n)o+=a[n].length;s=o<256?1:o<65536?2:o<16777216?3:4;i.push(s);let c=1;for(n=0;n<r+1;n++){1===s?i.push(255&c):2===s?i.push(c>>8&255,255&c):3===s?i.p\
-ush(c>>16&255,c>>8&255,255&c):i.push(c>>>24&255,c>>16&255,c>>8&255,255&c);a[n]&&(c+=a[n].length)}for(n=0;n<r;n++){t[n]&&t[n].offset(i.length);i.push(...a[n])}return i}}const Jr=getLookupTableFactory((function(e){e["Times-Roman"]="Times-Roman";e.Helvetica="Helvetica";e.Courier="Courier";e.Symbol="Symbol";e["Times-Bold"]="Times-Bold";e["Helvetica-Bold"]="Helvetica-Bold";e["Courier-Bold"]="Courier-Bold";e.ZapfDingbats="ZapfDingbats";e["Times-Italic"]="Times-Italic";e["Helvetica-Oblique"]="Helvetica\
--Oblique";e["Courier-Oblique"]="Courier-Oblique";e["Times-BoldItalic"]="Times-BoldItalic";e["Helvetica-BoldOblique"]="Helvetica-BoldOblique";e["Courier-BoldOblique"]="Courier-BoldOblique";e.ArialNarrow="Helvetica";e["ArialNarrow-Bold"]="Helvetica-Bold";e["ArialNarrow-BoldItalic"]="Helvetica-BoldOblique";e["ArialNarrow-Italic"]="Helvetica-Oblique";e.ArialBlack="Helvetica";e["ArialBlack-Bold"]="Helvetica-Bold";e["ArialBlack-BoldItalic"]="Helvetica-BoldOblique";e["ArialBlack-Italic"]="Helvetica-Obl\
-ique";e["Arial-Black"]="Helvetica";e["Arial-Black-Bold"]="Helvetica-Bold";e["Arial-Black-BoldItalic"]="Helvetica-BoldOblique";e["Arial-Black-Italic"]="Helvetica-Oblique";e.Arial="Helvetica";e["Arial-Bold"]="Helvetica-Bold";e["Arial-BoldItalic"]="Helvetica-BoldOblique";e["Arial-Italic"]="Helvetica-Oblique";e.ArialMT="Helvetica";e["Arial-BoldItalicMT"]="Helvetica-BoldOblique";e["Arial-BoldMT"]="Helvetica-Bold";e["Arial-ItalicMT"]="Helvetica-Oblique";e["Arial-BoldItalicMT-BoldItalic"]="Helvetica-Bo\
-ldOblique";e["Arial-BoldMT-Bold"]="Helvetica-Bold";e["Arial-ItalicMT-Italic"]="Helvetica-Oblique";e.ArialUnicodeMS="Helvetica";e["ArialUnicodeMS-Bold"]="Helvetica-Bold";e["ArialUnicodeMS-BoldItalic"]="Helvetica-BoldOblique";e["ArialUnicodeMS-Italic"]="Helvetica-Oblique";e["Courier-BoldItalic"]="Courier-BoldOblique";e["Courier-Italic"]="Courier-Oblique";e.CourierNew="Courier";e["CourierNew-Bold"]="Courier-Bold";e["CourierNew-BoldItalic"]="Courier-BoldOblique";e["CourierNew-Italic"]="Courier-Obliq\
-ue";e["CourierNewPS-BoldItalicMT"]="Courier-BoldOblique";e["CourierNewPS-BoldMT"]="Courier-Bold";e["CourierNewPS-ItalicMT"]="Courier-Oblique";e.CourierNewPSMT="Courier";e["Helvetica-BoldItalic"]="Helvetica-BoldOblique";e["Helvetica-Italic"]="Helvetica-Oblique";e["HelveticaLTStd-Bold"]="Helvetica-Bold";e["Symbol-Bold"]="Symbol";e["Symbol-BoldItalic"]="Symbol";e["Symbol-Italic"]="Symbol";e.TimesNewRoman="Times-Roman";e["TimesNewRoman-Bold"]="Times-Bold";e["TimesNewRoman-BoldItalic"]="Times-BoldIta\
-lic";e["TimesNewRoman-Italic"]="Times-Italic";e.TimesNewRomanPS="Times-Roman";e["TimesNewRomanPS-Bold"]="Times-Bold";e["TimesNewRomanPS-BoldItalic"]="Times-BoldItalic";e["TimesNewRomanPS-BoldItalicMT"]="Times-BoldItalic";e["TimesNewRomanPS-BoldMT"]="Times-Bold";e["TimesNewRomanPS-Italic"]="Times-Italic";e["TimesNewRomanPS-ItalicMT"]="Times-Italic";e.TimesNewRomanPSMT="Times-Roman";e["TimesNewRomanPSMT-Bold"]="Times-Bold";e["TimesNewRomanPSMT-BoldItalic"]="Times-BoldItalic";e["TimesNewRomanPSMT-I\
-talic"]="Times-Italic"})),Yr=getLookupTableFactory((function(e){e.Courier="FoxitFixed.pfb";e["Courier-Bold"]="FoxitFixedBold.pfb";e["Courier-BoldOblique"]="FoxitFixedBoldItalic.pfb";e["Courier-Oblique"]="FoxitFixedItalic.pfb";e.Helvetica="LiberationSans-Regular.ttf";e["Helvetica-Bold"]="LiberationSans-Bold.ttf";e["Helvetica-BoldOblique"]="LiberationSans-BoldItalic.ttf";e["Helvetica-Oblique"]="LiberationSans-Italic.ttf";e["Times-Roman"]="FoxitSerif.pfb";e["Times-Bold"]="FoxitSerifBold.pfb";e["Tim\
-es-BoldItalic"]="FoxitSerifBoldItalic.pfb";e["Times-Italic"]="FoxitSerifItalic.pfb";e.Symbol="FoxitSymbol.pfb";e.ZapfDingbats="FoxitDingbats.pfb";e["LiberationSans-Regular"]="LiberationSans-Regular.ttf";e["LiberationSans-Bold"]="LiberationSans-Bold.ttf";e["LiberationSans-Italic"]="LiberationSans-Italic.ttf";e["LiberationSans-BoldItalic"]="LiberationSans-BoldItalic.ttf"})),Zr=getLookupTableFactory((function(e){e.Calibri="Helvetica";e["Calibri-Bold"]="Helvetica-Bold";e["Calibri-BoldItalic"]="Helve\
-tica-BoldOblique";e["Calibri-Italic"]="Helvetica-Oblique";e.CenturyGothic="Helvetica";e["CenturyGothic-Bold"]="Helvetica-Bold";e["CenturyGothic-BoldItalic"]="Helvetica-BoldOblique";e["CenturyGothic-Italic"]="Helvetica-Oblique";e.ComicSansMS="Comic Sans MS";e["ComicSansMS-Bold"]="Comic Sans MS-Bold";e["ComicSansMS-BoldItalic"]="Comic Sans MS-BoldItalic";e["ComicSansMS-Italic"]="Comic Sans MS-Italic";e.GillSansMT="Helvetica";e["GillSansMT-Bold"]="Helvetica-Bold";e["GillSansMT-BoldItalic"]="Helveti\
-ca-BoldOblique";e["GillSansMT-Italic"]="Helvetica-Oblique";e.Impact="Helvetica";e["ItcSymbol-Bold"]="Helvetica-Bold";e["ItcSymbol-BoldItalic"]="Helvetica-BoldOblique";e["ItcSymbol-Book"]="Helvetica";e["ItcSymbol-BookItalic"]="Helvetica-Oblique";e["ItcSymbol-Medium"]="Helvetica";e["ItcSymbol-MediumItalic"]="Helvetica-Oblique";e.LucidaConsole="Courier";e["LucidaConsole-Bold"]="Courier-Bold";e["LucidaConsole-BoldItalic"]="Courier-BoldOblique";e["LucidaConsole-Italic"]="Courier-Oblique";e["LucidaSan\
-s-Demi"]="Helvetica-Bold";e["MS-Gothic"]="MS Gothic";e["MS-Gothic-Bold"]="MS Gothic-Bold";e["MS-Gothic-BoldItalic"]="MS Gothic-BoldItalic";e["MS-Gothic-Italic"]="MS Gothic-Italic";e["MS-Mincho"]="MS Mincho";e["MS-Mincho-Bold"]="MS Mincho-Bold";e["MS-Mincho-BoldItalic"]="MS Mincho-BoldItalic";e["MS-Mincho-Italic"]="MS Mincho-Italic";e["MS-PGothic"]="MS PGothic";e["MS-PGothic-Bold"]="MS PGothic-Bold";e["MS-PGothic-BoldItalic"]="MS PGothic-BoldItalic";e["MS-PGothic-Italic"]="MS PGothic-Italic";e["M\
-S-PMincho"]="MS PMincho";e["MS-PMincho-Bold"]="MS PMincho-Bold";e["MS-PMincho-BoldItalic"]="MS PMincho-BoldItalic";e["MS-PMincho-Italic"]="MS PMincho-Italic";e.NuptialScript="Times-Italic";e.SegoeUISymbol="Helvetica"})),Qr=getLookupTableFactory((function(e){e["Adobe Jenson"]=!0;e["Adobe Text"]=!0;e.Albertus=!0;e.Aldus=!0;e.Alexandria=!0;e.Algerian=!0;e["American Typewriter"]=!0;e.Antiqua=!0;e.Apex=!0;e.Arno=!0;e.Aster=!0;e.Aurora=!0;e.Baskerville=!0;e.Bell=!0;e.Bembo=!0;e["Bembo Schoolbook"]=!0;\
-e.Benguiat=!0;e["Berkeley Old Style"]=!0;e["Bernhard Modern"]=!0;e["Berthold City"]=!0;e.Bodoni=!0;e["Bauer Bodoni"]=!0;e["Book Antiqua"]=!0;e.Bookman=!0;e["Bordeaux Roman"]=!0;e["Californian FB"]=!0;e.Calisto=!0;e.Calvert=!0;e.Capitals=!0;e.Cambria=!0;e.Cartier=!0;e.Caslon=!0;e.Catull=!0;e.Centaur=!0;e["Century Old Style"]=!0;e["Century Schoolbook"]=!0;e.Chaparral=!0;e["Charis SIL"]=!0;e.Cheltenham=!0;e["Cholla Slab"]=!0;e.Clarendon=!0;e.Clearface=!0;e.Cochin=!0;e.Colonna=!0;e["Computer Modern"\
-]=!0;e["Concrete Roman"]=!0;e.Constantia=!0;e["Cooper Black"]=!0;e.Corona=!0;e.Ecotype=!0;e.Egyptienne=!0;e.Elephant=!0;e.Excelsior=!0;e.Fairfield=!0;e["FF Scala"]=!0;e.Folkard=!0;e.Footlight=!0;e.FreeSerif=!0;e["Friz Quadrata"]=!0;e.Garamond=!0;e.Gentium=!0;e.Georgia=!0;e.Gloucester=!0;e["Goudy Old Style"]=!0;e["Goudy Schoolbook"]=!0;e["Goudy Pro Font"]=!0;e.Granjon=!0;e["Guardian Egyptian"]=!0;e.Heather=!0;e.Hercules=!0;e["High Tower Text"]=!0;e.Hiroshige=!0;e["Hoefler Text"]=!0;e["Humana Seri\
-f"]=!0;e.Imprint=!0;e["Ionic No. 5"]=!0;e.Janson=!0;e.Joanna=!0;e.Korinna=!0;e.Lexicon=!0;e.LiberationSerif=!0;e["Liberation Serif"]=!0;e["Linux Libertine"]=!0;e.Literaturnaya=!0;e.Lucida=!0;e["Lucida Bright"]=!0;e.Melior=!0;e.Memphis=!0;e.Miller=!0;e.Minion=!0;e.Modern=!0;e["Mona Lisa"]=!0;e["Mrs Eaves"]=!0;e["MS Serif"]=!0;e["Museo Slab"]=!0;e["New York"]=!0;e["Nimbus Roman"]=!0;e["NPS Rawlinson Roadway"]=!0;e.NuptialScript=!0;e.Palatino=!0;e.Perpetua=!0;e.Plantin=!0;e["Plantin Schoolbook"]=!0\
-;e.Playbill=!0;e["Poor Richard"]=!0;e["Rawlinson Roadway"]=!0;e.Renault=!0;e.Requiem=!0;e.Rockwell=!0;e.Roman=!0;e["Rotis Serif"]=!0;e.Sabon=!0;e.Scala=!0;e.Seagull=!0;e.Sistina=!0;e.Souvenir=!0;e.STIX=!0;e["Stone Informal"]=!0;e["Stone Serif"]=!0;e.Sylfaen=!0;e.Times=!0;e.Trajan=!0;e["Trinité"]=!0;e["Trump Mediaeval"]=!0;e.Utopia=!0;e["Vale Type"]=!0;e["Bitstream Vera"]=!0;e["Vera Serif"]=!0;e.Versailles=!0;e.Wanted=!0;e.Weiss=!0;e["Wide Latin"]=!0;e.Windsor=!0;e.XITS=!0})),ei=getLookupTableFac\
-tory((function(e){e.Dingbats=!0;e.Symbol=!0;e.ZapfDingbats=!0;e.Wingdings=!0;e["Wingdings-Bold"]=!0;e["Wingdings-Regular"]=!0})),ti=getLookupTableFactory((function(e){e[2]=10;e[3]=32;e[4]=33;e[5]=34;e[6]=35;e[7]=36;e[8]=37;e[9]=38;e[10]=39;e[11]=40;e[12]=41;e[13]=42;e[14]=43;e[15]=44;e[16]=45;e[17]=46;e[18]=47;e[19]=48;e[20]=49;e[21]=50;e[22]=51;e[23]=52;e[24]=53;e[25]=54;e[26]=55;e[27]=56;e[28]=57;e[29]=58;e[30]=894;e[31]=60;e[32]=61;e[33]=62;e[34]=63;e[35]=64;e[36]=65;e[37]=66;e[38]=67;e[39]=6\
-8;e[40]=69;e[41]=70;e[42]=71;e[43]=72;e[44]=73;e[45]=74;e[46]=75;e[47]=76;e[48]=77;e[49]=78;e[50]=79;e[51]=80;e[52]=81;e[53]=82;e[54]=83;e[55]=84;e[56]=85;e[57]=86;e[58]=87;e[59]=88;e[60]=89;e[61]=90;e[62]=91;e[63]=92;e[64]=93;e[65]=94;e[66]=95;e[67]=96;e[68]=97;e[69]=98;e[70]=99;e[71]=100;e[72]=101;e[73]=102;e[74]=103;e[75]=104;e[76]=105;e[77]=106;e[78]=107;e[79]=108;e[80]=109;e[81]=110;e[82]=111;e[83]=112;e[84]=113;e[85]=114;e[86]=115;e[87]=116;e[88]=117;e[89]=118;e[90]=119;e[91]=120;e[92]=121\
-;e[93]=122;e[94]=123;e[95]=124;e[96]=125;e[97]=126;e[98]=196;e[99]=197;e[100]=199;e[101]=201;e[102]=209;e[103]=214;e[104]=220;e[105]=225;e[106]=224;e[107]=226;e[108]=228;e[109]=227;e[110]=229;e[111]=231;e[112]=233;e[113]=232;e[114]=234;e[115]=235;e[116]=237;e[117]=236;e[118]=238;e[119]=239;e[120]=241;e[121]=243;e[122]=242;e[123]=244;e[124]=246;e[125]=245;e[126]=250;e[127]=249;e[128]=251;e[129]=252;e[130]=8224;e[131]=176;e[132]=162;e[133]=163;e[134]=167;e[135]=8226;e[136]=182;e[137]=223;e[138]=17\
-4;e[139]=169;e[140]=8482;e[141]=180;e[142]=168;e[143]=8800;e[144]=198;e[145]=216;e[146]=8734;e[147]=177;e[148]=8804;e[149]=8805;e[150]=165;e[151]=181;e[152]=8706;e[153]=8721;e[154]=8719;e[156]=8747;e[157]=170;e[158]=186;e[159]=8486;e[160]=230;e[161]=248;e[162]=191;e[163]=161;e[164]=172;e[165]=8730;e[166]=402;e[167]=8776;e[168]=8710;e[169]=171;e[170]=187;e[171]=8230;e[179]=8220;e[180]=8221;e[181]=8216;e[182]=8217;e[200]=193;e[203]=205;e[207]=211;e[210]=218;e[223]=711;e[224]=321;e[225]=322;e[226]=\
-352;e[227]=353;e[228]=381;e[229]=382;e[233]=221;e[234]=253;e[252]=263;e[253]=268;e[254]=269;e[258]=258;e[260]=260;e[261]=261;e[265]=280;e[266]=281;e[267]=282;e[268]=283;e[269]=313;e[275]=323;e[276]=324;e[278]=328;e[283]=344;e[284]=345;e[285]=346;e[286]=347;e[292]=367;e[295]=377;e[296]=378;e[298]=380;e[305]=963;e[306]=964;e[307]=966;e[308]=8215;e[309]=8252;e[310]=8319;e[311]=8359;e[312]=8592;e[313]=8593;e[337]=9552;e[493]=1039;e[494]=1040;e[570]=1040;e[571]=1041;e[572]=1042;e[573]=1043;e[574]=104\
-4;e[575]=1045;e[576]=1046;e[577]=1047;e[578]=1048;e[579]=1049;e[580]=1050;e[581]=1051;e[582]=1052;e[583]=1053;e[584]=1054;e[585]=1055;e[586]=1056;e[587]=1057;e[588]=1058;e[589]=1059;e[590]=1060;e[591]=1061;e[592]=1062;e[593]=1063;e[594]=1064;e[595]=1065;e[596]=1066;e[597]=1067;e[598]=1068;e[599]=1069;e[600]=1070;e[672]=1488;e[673]=1489;e[674]=1490;e[675]=1491;e[676]=1492;e[677]=1493;e[678]=1494;e[679]=1495;e[680]=1496;e[681]=1497;e[682]=1498;e[683]=1499;e[684]=1500;e[685]=1501;e[686]=1502;e[687]\
-=1503;e[688]=1504;e[689]=1505;e[690]=1506;e[691]=1507;e[692]=1508;e[693]=1509;e[694]=1510;e[695]=1511;e[696]=1512;e[697]=1513;e[698]=1514;e[705]=1524;e[706]=8362;e[710]=64288;e[711]=64298;e[759]=1617;e[761]=1776;e[763]=1778;e[775]=1652;e[777]=1764;e[778]=1780;e[779]=1781;e[780]=1782;e[782]=771;e[783]=64726;e[786]=8363;e[788]=8532;e[790]=768;e[791]=769;e[792]=768;e[795]=803;e[797]=64336;e[798]=64337;e[799]=64342;e[800]=64343;e[801]=64344;e[802]=64345;e[803]=64362;e[804]=64363;e[805]=64364;e[2424]\
-=7821;e[2425]=7822;e[2426]=7823;e[2427]=7824;e[2428]=7825;e[2429]=7826;e[2430]=7827;e[2433]=7682;e[2678]=8045;e[2679]=8046;e[2830]=1552;e[2838]=686;e[2840]=751;e[2842]=753;e[2843]=754;e[2844]=755;e[2846]=757;e[2856]=767;e[2857]=848;e[2858]=849;e[2862]=853;e[2863]=854;e[2864]=855;e[2865]=861;e[2866]=862;e[2906]=7460;e[2908]=7462;e[2909]=7463;e[2910]=7464;e[2912]=7466;e[2913]=7467;e[2914]=7468;e[2916]=7470;e[2917]=7471;e[2918]=7472;e[2920]=7474;e[2921]=7475;e[2922]=7476;e[2924]=7478;e[2925]=7479;e\
-[2926]=7480;e[2928]=7482;e[2929]=7483;e[2930]=7484;e[2932]=7486;e[2933]=7487;e[2934]=7488;e[2936]=7490;e[2937]=7491;e[2938]=7492;e[2940]=7494;e[2941]=7495;e[2942]=7496;e[2944]=7498;e[2946]=7500;e[2948]=7502;e[2950]=7504;e[2951]=7505;e[2952]=7506;e[2954]=7508;e[2955]=7509;e[2956]=7510;e[2958]=7512;e[2959]=7513;e[2960]=7514;e[2962]=7516;e[2963]=7517;e[2964]=7518;e[2966]=7520;e[2967]=7521;e[2968]=7522;e[2970]=7524;e[2971]=7525;e[2972]=7526;e[2974]=7528;e[2975]=7529;e[2976]=7530;e[2978]=1537;e[2979]\
-=1538;e[2980]=1539;e[2982]=1549;e[2983]=1551;e[2984]=1552;e[2986]=1554;e[2987]=1555;e[2988]=1556;e[2990]=1623;e[2991]=1624;e[2995]=1775;e[2999]=1791;e[3002]=64290;e[3003]=64291;e[3004]=64292;e[3006]=64294;e[3007]=64295;e[3008]=64296;e[3011]=1900;e[3014]=8223;e[3015]=8244;e[3017]=7532;e[3018]=7533;e[3019]=7534;e[3075]=7590;e[3076]=7591;e[3079]=7594;e[3080]=7595;e[3083]=7598;e[3084]=7599;e[3087]=7602;e[3088]=7603;e[3091]=7606;e[3092]=7607;e[3095]=7610;e[3096]=7611;e[3099]=7614;e[3100]=7615;e[3103]\
-=7618;e[3104]=7619;e[3107]=8337;e[3108]=8338;e[3116]=1884;e[3119]=1885;e[3120]=1885;e[3123]=1886;e[3124]=1886;e[3127]=1887;e[3128]=1887;e[3131]=1888;e[3132]=1888;e[3135]=1889;e[3136]=1889;e[3139]=1890;e[3140]=1890;e[3143]=1891;e[3144]=1891;e[3147]=1892;e[3148]=1892;e[3153]=580;e[3154]=581;e[3157]=584;e[3158]=585;e[3161]=588;e[3162]=589;e[3165]=891;e[3166]=892;e[3169]=1274;e[3170]=1275;e[3173]=1278;e[3174]=1279;e[3181]=7622;e[3182]=7623;e[3282]=11799;e[3316]=578;e[3379]=42785;e[3393]=1159;e[3416]\
-=8377})),ai=getLookupTableFactory((function(e){e[227]=322;e[264]=261;e[291]=346})),ri=getLookupTableFactory((function(e){e[1]=32;e[4]=65;e[5]=192;e[6]=193;e[9]=196;e[17]=66;e[18]=67;e[21]=268;e[24]=68;e[28]=69;e[29]=200;e[30]=201;e[32]=282;e[38]=70;e[39]=71;e[44]=72;e[47]=73;e[48]=204;e[49]=205;e[58]=74;e[60]=75;e[62]=76;e[68]=77;e[69]=78;e[75]=79;e[76]=210;e[80]=214;e[87]=80;e[89]=81;e[90]=82;e[92]=344;e[94]=83;e[97]=352;e[100]=84;e[104]=85;e[109]=220;e[115]=86;e[116]=87;e[121]=88;e[122]=89;e[1\
-24]=221;e[127]=90;e[129]=381;e[258]=97;e[259]=224;e[260]=225;e[263]=228;e[268]=261;e[271]=98;e[272]=99;e[273]=263;e[275]=269;e[282]=100;e[286]=101;e[287]=232;e[288]=233;e[290]=283;e[295]=281;e[296]=102;e[336]=103;e[346]=104;e[349]=105;e[350]=236;e[351]=237;e[361]=106;e[364]=107;e[367]=108;e[371]=322;e[373]=109;e[374]=110;e[381]=111;e[382]=242;e[383]=243;e[386]=246;e[393]=112;e[395]=113;e[396]=114;e[398]=345;e[400]=115;e[401]=347;e[403]=353;e[410]=116;e[437]=117;e[442]=252;e[448]=118;e[449]=119;e\
-[454]=120;e[455]=121;e[457]=253;e[460]=122;e[462]=382;e[463]=380;e[853]=44;e[855]=58;e[856]=46;e[876]=47;e[878]=45;e[882]=45;e[894]=40;e[895]=41;e[896]=91;e[897]=93;e[923]=64;e[940]=163;e[1004]=48;e[1005]=49;e[1006]=50;e[1007]=51;e[1008]=52;e[1009]=53;e[1010]=54;e[1011]=55;e[1012]=56;e[1013]=57;e[1081]=37;e[1085]=43;e[1086]=45}));function getStandardFontName(e){const t=normalizeFontName(e);return Jr()[t]}function isKnownFontName(e){const t=normalizeFontName(e);return!!(Jr()[t]||Zr()[t]||Qr()[t]|\
-|ei()[t])}class ToUnicodeMap{constructor(e=[]){this._map=e}get length(){return this._map.length}forEach(e){for(const t in this._map)e(t,this._map[t].codePointAt(0))}has(e){return void 0!==this._map[e]}get(e){return this._map[e]}charCodeOf(e){const t=this._map;if(t.length<=65536)return t.indexOf(e);for(const a in t)if(t[a]===e)return 0|a;return-1}amend(e){for(const t in e)this._map[t]=e[t]}}class IdentityToUnicodeMap{constructor(e,t){this.firstChar=e;this.lastChar=t}get length(){return this.lastC\
-har+1-this.firstChar}forEach(e){for(let t=this.firstChar,a=this.lastChar;t<=a;t++)e(t,t)}has(e){return this.firstChar<=e&&e<=this.lastChar}get(e){if(this.firstChar<=e&&e<=this.lastChar)return String.fromCharCode(e)}charCodeOf(e){return Number.isInteger(e)&&e>=this.firstChar&&e<=this.lastChar?e:-1}amend(e){unreachable("Should not call amend()")}}class CFFFont{constructor(e,t){this.properties=t;const a=new CFFParser(e,t,Rr);this.cff=a.parse();this.cff.duplicateFirstGlyph();const r=new CFFCompiler(\
-this.cff);this.seacs=this.cff.seacs;try{this.data=r.compile()}catch{warn("Failed to compile font "+t.loadedName);this.data=e}this._createBuiltInEncoding()}get numGlyphs(){return this.cff.charStrings.count}getCharset(){return this.cff.charset.charset}getGlyphMapping(){const e=this.cff,t=this.properties,{cidToGidMap:a,cMap:r}=t,i=e.charset.charset;let n,s;if(t.composite){let t,o;if(a?.length>0){t=Object.create(null);for(let e=0,r=a.length;e<r;e++){const r=a[e];void 0!==r&&(t[r]=e)}}n=Object.create\
-(null);if(e.isCIDFont)for(s=0;s<i.length;s++){const e=i[s];o=r.charCodeOf(e);void 0!==t?.[o]&&(o=t[o]);n[o]=s}else for(s=0;s<e.charStrings.count;s++){o=r.charCodeOf(s);n[o]=s}return n}let o=e.encoding?e.encoding.encoding:null;t.isInternalFont&&(o=t.defaultEncoding);n=type1FontGlyphMapping(t,o,i);return n}hasGlyphId(e){return this.cff.hasGlyphId(e)}_createBuiltInEncoding(){const{charset:e,encoding:t}=this.cff;if(!e||!t)return;const a=e.charset,r=t.encoding,i=[];for(const e in r){const t=r[e];if(t\
->=0){const r=a[t];r&&(i[e]=r)}}i.length>0&&(this.properties.builtInEncoding=i)}}function getFloat214(e,t){return readInt16(e,t)/16384}function getSubroutineBias(e){const t=e.length;let a=32768;t<1240?a=107:t<33900&&(a=1131);return a}function parseCmap(e,t,a){const r=1===readUint16(e,t+2)?readUint32(e,t+8):readUint32(e,t+16),i=readUint16(e,t+r);let n,s,o;if(4===i){readUint16(e,t+r+2);const a=readUint16(e,t+r+6)>>1;s=t+r+14;n=[];for(o=0;o<a;o++,s+=2)n[o]={end:readUint16(e,s)};s+=2;for(o=0;o<a;o++,\
-s+=2)n[o].start=readUint16(e,s);for(o=0;o<a;o++,s+=2)n[o].idDelta=readUint16(e,s);for(o=0;o<a;o++,s+=2){let t=readUint16(e,s);if(0!==t){n[o].ids=[];for(let a=0,r=n[o].end-n[o].start+1;a<r;a++){n[o].ids[a]=readUint16(e,s+t);t+=2}}}return n}if(12===i){const a=readUint32(e,t+r+12);s=t+r+16;n=[];for(o=0;o<a;o++){t=readUint32(e,s);n.push({start:t,end:readUint32(e,s+4),idDelta:readUint32(e,s+8)-t});s+=12}return n}throw new FormatError(`unsupported cmap: ${i}`)}function parseCff(e,t,a,r){const i=new CF\
-FParser(new Stream(e,t,a-t),{},r).parse();return{glyphs:i.charStrings.objects,subrs:i.topDict.privateDict?.subrsIndex?.objects,gsubrs:i.globalSubrIndex?.objects,isCFFCIDFont:i.isCIDFont,fdSelect:i.fdSelect,fdArray:i.fdArray}}function lookupCmap(e,t){const a=t.codePointAt(0);let r=0,i=0,n=e.length-1;for(;i<n;){const t=i+n+1>>1;a<e[t].start?n=t-1:i=t}e[i].start<=a&&a<=e[i].end&&(r=e[i].idDelta+(e[i].ids?e[i].ids[a-e[i].start]:a)&65535);return{charCode:a,glyphId:r}}function compileGlyf(e,t,a){funct\
-ion moveTo(e,a){s&&t.add("L",s);s=[e,a];t.add("M",[e,a])}function lineTo(e,a){t.add("L",[e,a])}function quadraticCurveTo(e,a,r,i){t.add("Q",[e,a,r,i])}let r=0;const i=readInt16(e,r);let n,s=null,o=0,c=0;r+=10;if(i<0)do{n=readUint16(e,r);const i=readUint16(e,r+2);r+=4;let s,l;if(1&n){if(2&n){s=readInt16(e,r);l=readInt16(e,r+2)}else{s=readUint16(e,r);l=readUint16(e,r+2)}r+=4}else if(2&n){s=readInt8(e,r++);l=readInt8(e,r++)}else{s=e[r++];l=e[r++]}if(2&n){o=s;c=l}else{o=0;c=0}let h=1,u=1,d=0,f=0;if(\
-8&n){h=u=getFloat214(e,r);r+=2}else if(64&n){h=getFloat214(e,r);u=getFloat214(e,r+2);r+=4}else if(128&n){h=getFloat214(e,r);d=getFloat214(e,r+2);f=getFloat214(e,r+4);u=getFloat214(e,r+6);r+=8}const g=a.glyphs[i];if(g){t.save();t.transform([h,d,f,u,o,c]);compileGlyf(g,t,a);t.restore()}}while(32&n);else{const t=[];let a,s;for(a=0;a<i;a++){t.push(readUint16(e,r));r+=2}r+=2+readUint16(e,r);const l=t.at(-1)+1,h=[];for(;h.length<l;){n=e[r++];let t=1;8&n&&(t+=e[r++]);for(;t-- >0;)h.push({flags:n})}for(\
-a=0;a<l;a++){switch(18&h[a].flags){case 0:o+=readInt16(e,r);r+=2;break;case 2:o-=e[r++];break;case 18:o+=e[r++]}h[a].x=o}for(a=0;a<l;a++){switch(36&h[a].flags){case 0:c+=readInt16(e,r);r+=2;break;case 4:c-=e[r++];break;case 36:c+=e[r++]}h[a].y=c}let u=0;for(r=0;r<i;r++){const e=t[r],i=h.slice(u,e+1);if(1&i[0].flags)i.push(i[0]);else if(1&i.at(-1).flags)i.unshift(i.at(-1));else{const e={flags:1,x:(i[0].x+i.at(-1).x)/2,y:(i[0].y+i.at(-1).y)/2};i.unshift(e);i.push(e)}moveTo(i[0].x,i[0].y);for(a=1,s\
-=i.length;a<s;a++)if(1&i[a].flags)lineTo(i[a].x,i[a].y);else if(1&i[a+1].flags){quadraticCurveTo(i[a].x,i[a].y,i[a+1].x,i[a+1].y);a++}else quadraticCurveTo(i[a].x,i[a].y,(i[a].x+i[a+1].x)/2,(i[a].y+i[a+1].y)/2);u=e+1}}}function compileCharString(e,t,a,r){function moveTo(e,a){c&&t.add("L",c);c=[e,a];t.add("M",[e,a])}function lineTo(e,a){t.add("L",[e,a])}function bezierCurveTo(e,a,r,i,n,s){t.add("C",[e,a,r,i,n,s])}const i=[];let n=0,s=0,o=0,c=null;!function parse(e){let c=0;for(;c<e.length;){let l\
-,h,u,d,f,g,p,m,b,y=!1,w=e[c++];switch(w){case 1:case 3:case 18:case 23:o+=i.length>>1;y=!0;break;case 4:s+=i.pop();moveTo(n,s);y=!0;break;case 5:for(;i.length>0;){n+=i.shift();s+=i.shift();lineTo(n,s)}break;case 6:for(;i.length>0;){n+=i.shift();lineTo(n,s);if(0===i.length)break;s+=i.shift();lineTo(n,s)}break;case 7:for(;i.length>0;){s+=i.shift();lineTo(n,s);if(0===i.length)break;n+=i.shift();lineTo(n,s)}break;case 8:for(;i.length>0;){l=n+i.shift();u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h+i.\
-shift();s=d+i.shift();bezierCurveTo(l,u,h,d,n,s)}break;case 10:m=i.pop();b=null;if(a.isCFFCIDFont){const e=a.fdSelect.getFDIndex(r);if(e>=0&&e<a.fdArray.length){const t=a.fdArray[e];let r;t.privateDict?.subrsIndex&&(r=t.privateDict.subrsIndex.objects);if(r){m+=getSubroutineBias(r);b=r[m]}}else warn("Invalid fd index for glyph index.")}else b=a.subrs[m+a.subrsBias];b&&parse(b);break;case 11:return;case 12:w=e[c++];switch(w){case 34:l=n+i.shift();h=l+i.shift();f=s+i.shift();n=h+i.shift();bezierCur\
-veTo(l,s,h,f,n,f);l=n+i.shift();h=l+i.shift();n=h+i.shift();bezierCurveTo(l,f,h,s,n,s);break;case 35:l=n+i.shift();u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h+i.shift();s=d+i.shift();bezierCurveTo(l,u,h,d,n,s);l=n+i.shift();u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h+i.shift();s=d+i.shift();bezierCurveTo(l,u,h,d,n,s);i.pop();break;case 36:l=n+i.shift();f=s+i.shift();h=l+i.shift();g=f+i.shift();n=h+i.shift();bezierCurveTo(l,f,h,g,n,g);l=n+i.shift();h=l+i.shift();p=g+i.shift();n=h+i.shift();be\
-zierCurveTo(l,g,h,p,n,s);break;case 37:const e=n,t=s;l=n+i.shift();u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h+i.shift();s=d+i.shift();bezierCurveTo(l,u,h,d,n,s);l=n+i.shift();u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h;s=d;Math.abs(n-e)>Math.abs(s-t)?n+=i.shift():s+=i.shift();bezierCurveTo(l,u,h,d,n,s);break;default:throw new FormatError(`unknown operator: 12 ${w}`)}break;case 14:if(i.length>=4){const e=i.pop(),r=i.pop();s=i.pop();n=i.pop();t.save();t.translate(n,s);let o=lookupCmap(a.cmap,\
-String.fromCharCode(a.glyphNameMap[Ar[e]]));compileCharString(a.glyphs[o.glyphId],t,a,o.glyphId);t.restore();o=lookupCmap(a.cmap,String.fromCharCode(a.glyphNameMap[Ar[r]]));compileCharString(a.glyphs[o.glyphId],t,a,o.glyphId)}return;case 19:case 20:o+=i.length>>1;c+=o+7>>3;y=!0;break;case 21:s+=i.pop();n+=i.pop();moveTo(n,s);y=!0;break;case 22:n+=i.pop();moveTo(n,s);y=!0;break;case 24:for(;i.length>2;){l=n+i.shift();u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h+i.shift();s=d+i.shift();bezierCurv\
-eTo(l,u,h,d,n,s)}n+=i.shift();s+=i.shift();lineTo(n,s);break;case 25:for(;i.length>6;){n+=i.shift();s+=i.shift();lineTo(n,s)}l=n+i.shift();u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h+i.shift();s=d+i.shift();bezierCurveTo(l,u,h,d,n,s);break;case 26:i.length%2&&(n+=i.shift());for(;i.length>0;){l=n;u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h;s=d+i.shift();bezierCurveTo(l,u,h,d,n,s)}break;case 27:i.length%2&&(s+=i.shift());for(;i.length>0;){l=n+i.shift();u=s;h=l+i.shift();d=u+i.shift();n=h+i.shi\
-ft();s=d;bezierCurveTo(l,u,h,d,n,s)}break;case 28:i.push(readInt16(e,c));c+=2;break;case 29:m=i.pop()+a.gsubrsBias;b=a.gsubrs[m];b&&parse(b);break;case 30:for(;i.length>0;){l=n;u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h+i.shift();s=d+(1===i.length?i.shift():0);bezierCurveTo(l,u,h,d,n,s);if(0===i.length)break;l=n+i.shift();u=s;h=l+i.shift();d=u+i.shift();s=d+i.shift();n=h+(1===i.length?i.shift():0);bezierCurveTo(l,u,h,d,n,s)}break;case 31:for(;i.length>0;){l=n+i.shift();u=s;h=l+i.shift();d=u+i\
-.shift();s=d+i.shift();n=h+(1===i.length?i.shift():0);bezierCurveTo(l,u,h,d,n,s);if(0===i.length)break;l=n;u=s+i.shift();h=l+i.shift();d=u+i.shift();n=h+i.shift();s=d+(1===i.length?i.shift():0);bezierCurveTo(l,u,h,d,n,s)}break;default:if(w<32)throw new FormatError(`unknown operator: ${w}`);if(w<247)i.push(w-139);else if(w<251)i.push(256*(w-247)+e[c++]+108);else if(w<255)i.push(256*-(w-251)-e[c++]-108);else{i.push((e[c]<<24|e[c+1]<<16|e[c+2]<<8|e[c+3])/65536);c+=4}}y&&(i.length=0)}}(e)}class Comm\
-ands{cmds=[];transformStack=[];currentTransform=[1,0,0,1,0,0];add(e,t){if(t){const{currentTransform:a}=this;for(let e=0,r=t.length;e<r;e+=2)Util.applyTransform(t,a,e);this.cmds.push(`${e}${t.join(" ")}`)}else this.cmds.push(e)}transform(e){this.currentTransform=Util.transform(this.currentTransform,e)}translate(e,t){this.transform([1,0,0,1,e,t])}save(){this.transformStack.push(this.currentTransform.slice())}restore(){this.currentTransform=this.transformStack.pop()||[1,0,0,1,0,0]}getSVG(){return t\
-his.cmds.join("")}}class CompiledFont{constructor(e){this.fontMatrix=e;this.compiledGlyphs=Object.create(null);this.compiledCharCodeToGlyphId=Object.create(null)}getPathJs(e){const{charCode:t,glyphId:a}=lookupCmap(this.cmap,e);let r,i=this.compiledGlyphs[a];if(void 0===i){try{i=this.compileGlyph(this.glyphs[a],a)}catch(e){i="";r=e}this.compiledGlyphs[a]=i}this.compiledCharCodeToGlyphId[t]??=a;if(r)throw r;return i}compileGlyph(e,a){if(!e?.length||14===e[0])return"";let r=this.fontMatrix;if(this.\
-isCFFCIDFont){const e=this.fdSelect.getFDIndex(a);if(e>=0&&e<this.fdArray.length){r=this.fdArray[e].getByName("FontMatrix")||t}else warn("Invalid fd index for glyph index.")}assert(isNumberArray(r,6),"Expected a valid fontMatrix.");const i=new Commands;i.transform(r.slice());this.compileGlyphImpl(e,i,a);i.add("Z");return i.getSVG()}compileGlyphImpl(){unreachable("Children classes should implement this.")}hasBuiltPath(e){const{charCode:t,glyphId:a}=lookupCmap(this.cmap,e);return void 0!==this.com\
-piledGlyphs[a]&&void 0!==this.compiledCharCodeToGlyphId[t]}}class TrueTypeCompiled extends CompiledFont{constructor(e,t,a){super(a||[488e-6,0,0,488e-6,0,0]);this.glyphs=e;this.cmap=t}compileGlyphImpl(e,t){compileGlyf(e,t,this)}}class Type2Compiled extends CompiledFont{constructor(e,t,a){super(a||[.001,0,0,.001,0,0]);this.glyphs=e.glyphs;this.gsubrs=e.gsubrs||[];this.subrs=e.subrs||[];this.cmap=t;this.glyphNameMap=Fr();this.gsubrsBias=getSubroutineBias(this.gsubrs);this.subrsBias=getSubroutineBia\
-s(this.subrs);this.isCFFCIDFont=e.isCFFCIDFont;this.fdSelect=e.fdSelect;this.fdArray=e.fdArray}compileGlyphImpl(e,t,a){compileCharString(e,t,this,a)}}class FontRendererFactory{static create(e,t){const a=new Uint8Array(e.data);let r,i,n,s,o,c;const l=readUint16(a,4);for(let e=0,h=12;e<l;e++,h+=16){const e=bytesToString(a.subarray(h,h+4)),l=readUint32(a,h+8),u=readUint32(a,h+12);switch(e){case"cmap":r=parseCmap(a,l);break;case"glyf":i=a.subarray(l,l+u);break;case"loca":n=a.subarray(l,l+u);break;ca\
-se"head":c=readUint16(a,l+18);o=readUint16(a,l+50);break;case"CFF ":s=parseCff(a,l,l+u,t)}}if(i){const t=c?[1/c,0,0,1/c,0,0]:e.fontMatrix;return new TrueTypeCompiled(function parseGlyfTable(e,t,a){let r,i;if(a){r=4;i=readUint32}else{r=2;i=(e,t)=>2*readUint16(e,t)}const n=[];let s=i(t,0);for(let a=r;a<t.length;a+=r){const r=i(t,a);n.push(e.subarray(s,r));s=r}return n}(i,n,o),r,t)}return new Type2Compiled(s,r,e.fontMatrix)}}const ii=getLookupTableFactory((function(e){e.Courier=600;e["Courier-Bold"\
-]=600;e["Courier-BoldOblique"]=600;e["Courier-Oblique"]=600;e.Helvetica=getLookupTableFactory((function(e){e.space=278;e.exclam=278;e.quotedbl=355;e.numbersign=556;e.dollar=556;e.percent=889;e.ampersand=667;e.quoteright=222;e.parenleft=333;e.parenright=333;e.asterisk=389;e.plus=584;e.comma=278;e.hyphen=333;e.period=278;e.slash=278;e.zero=556;e.one=556;e.two=556;e.three=556;e.four=556;e.five=556;e.six=556;e.seven=556;e.eight=556;e.nine=556;e.colon=278;e.semicolon=278;e.less=584;e.equal=584;e.grea\
-ter=584;e.question=556;e.at=1015;e.A=667;e.B=667;e.C=722;e.D=722;e.E=667;e.F=611;e.G=778;e.H=722;e.I=278;e.J=500;e.K=667;e.L=556;e.M=833;e.N=722;e.O=778;e.P=667;e.Q=778;e.R=722;e.S=667;e.T=611;e.U=722;e.V=667;e.W=944;e.X=667;e.Y=667;e.Z=611;e.bracketleft=278;e.backslash=278;e.bracketright=278;e.asciicircum=469;e.underscore=556;e.quoteleft=222;e.a=556;e.b=556;e.c=500;e.d=556;e.e=556;e.f=278;e.g=556;e.h=556;e.i=222;e.j=222;e.k=500;e.l=222;e.m=833;e.n=556;e.o=556;e.p=556;e.q=556;e.r=333;e.s=500;e.t\
-=278;e.u=556;e.v=500;e.w=722;e.x=500;e.y=500;e.z=500;e.braceleft=334;e.bar=260;e.braceright=334;e.asciitilde=584;e.exclamdown=333;e.cent=556;e.sterling=556;e.fraction=167;e.yen=556;e.florin=556;e.section=556;e.currency=556;e.quotesingle=191;e.quotedblleft=333;e.guillemotleft=556;e.guilsinglleft=333;e.guilsinglright=333;e.fi=500;e.fl=500;e.endash=556;e.dagger=556;e.daggerdbl=556;e.periodcentered=278;e.paragraph=537;e.bullet=350;e.quotesinglbase=222;e.quotedblbase=333;e.quotedblright=333;e.guillem\
-otright=556;e.ellipsis=1e3;e.perthousand=1e3;e.questiondown=611;e.grave=333;e.acute=333;e.circumflex=333;e.tilde=333;e.macron=333;e.breve=333;e.dotaccent=333;e.dieresis=333;e.ring=333;e.cedilla=333;e.hungarumlaut=333;e.ogonek=333;e.caron=333;e.emdash=1e3;e.AE=1e3;e.ordfeminine=370;e.Lslash=556;e.Oslash=778;e.OE=1e3;e.ordmasculine=365;e.ae=889;e.dotlessi=278;e.lslash=222;e.oslash=611;e.oe=944;e.germandbls=611;e.Idieresis=278;e.eacute=556;e.abreve=556;e.uhungarumlaut=556;e.ecaron=556;e.Ydieresis=6\
-67;e.divide=584;e.Yacute=667;e.Acircumflex=667;e.aacute=556;e.Ucircumflex=722;e.yacute=500;e.scommaaccent=500;e.ecircumflex=556;e.Uring=722;e.Udieresis=722;e.aogonek=556;e.Uacute=722;e.uogonek=556;e.Edieresis=667;e.Dcroat=722;e.commaaccent=250;e.copyright=737;e.Emacron=667;e.ccaron=500;e.aring=556;e.Ncommaaccent=722;e.lacute=222;e.agrave=556;e.Tcommaaccent=611;e.Cacute=722;e.atilde=556;e.Edotaccent=667;e.scaron=500;e.scedilla=500;e.iacute=278;e.lozenge=471;e.Rcaron=722;e.Gcommaaccent=778;e.ucirc\
-umflex=556;e.acircumflex=556;e.Amacron=667;e.rcaron=333;e.ccedilla=500;e.Zdotaccent=611;e.Thorn=667;e.Omacron=778;e.Racute=722;e.Sacute=667;e.dcaron=643;e.Umacron=722;e.uring=556;e.threesuperior=333;e.Ograve=778;e.Agrave=667;e.Abreve=667;e.multiply=584;e.uacute=556;e.Tcaron=611;e.partialdiff=476;e.ydieresis=500;e.Nacute=722;e.icircumflex=278;e.Ecircumflex=667;e.adieresis=556;e.edieresis=556;e.cacute=500;e.nacute=556;e.umacron=556;e.Ncaron=722;e.Iacute=278;e.plusminus=584;e.brokenbar=260;e.regist\
-ered=737;e.Gbreve=778;e.Idotaccent=278;e.summation=600;e.Egrave=667;e.racute=333;e.omacron=556;e.Zacute=611;e.Zcaron=611;e.greaterequal=549;e.Eth=722;e.Ccedilla=722;e.lcommaaccent=222;e.tcaron=317;e.eogonek=556;e.Uogonek=722;e.Aacute=667;e.Adieresis=667;e.egrave=556;e.zacute=500;e.iogonek=222;e.Oacute=778;e.oacute=556;e.amacron=556;e.sacute=500;e.idieresis=278;e.Ocircumflex=778;e.Ugrave=722;e.Delta=612;e.thorn=556;e.twosuperior=333;e.Odieresis=778;e.mu=556;e.igrave=278;e.ohungarumlaut=556;e.Eogo\
-nek=667;e.dcroat=556;e.threequarters=834;e.Scedilla=667;e.lcaron=299;e.Kcommaaccent=667;e.Lacute=556;e.trademark=1e3;e.edotaccent=556;e.Igrave=278;e.Imacron=278;e.Lcaron=556;e.onehalf=834;e.lessequal=549;e.ocircumflex=556;e.ntilde=556;e.Uhungarumlaut=722;e.Eacute=667;e.emacron=556;e.gbreve=556;e.onequarter=834;e.Scaron=667;e.Scommaaccent=667;e.Ohungarumlaut=778;e.degree=400;e.ograve=556;e.Ccaron=722;e.ugrave=556;e.radical=453;e.Dcaron=722;e.rcommaaccent=333;e.Ntilde=722;e.otilde=556;e.Rcommaacce\
-nt=722;e.Lcommaaccent=556;e.Atilde=667;e.Aogonek=667;e.Aring=667;e.Otilde=778;e.zdotaccent=500;e.Ecaron=667;e.Iogonek=278;e.kcommaaccent=500;e.minus=584;e.Icircumflex=278;e.ncaron=556;e.tcommaaccent=278;e.logicalnot=584;e.odieresis=556;e.udieresis=556;e.notequal=549;e.gcommaaccent=556;e.eth=556;e.zcaron=500;e.ncommaaccent=556;e.onesuperior=333;e.imacron=278;e.Euro=556}));e["Helvetica-Bold"]=getLookupTableFactory((function(e){e.space=278;e.exclam=333;e.quotedbl=474;e.numbersign=556;e.dollar=556;e\
-.percent=889;e.ampersand=722;e.quoteright=278;e.parenleft=333;e.parenright=333;e.asterisk=389;e.plus=584;e.comma=278;e.hyphen=333;e.period=278;e.slash=278;e.zero=556;e.one=556;e.two=556;e.three=556;e.four=556;e.five=556;e.six=556;e.seven=556;e.eight=556;e.nine=556;e.colon=333;e.semicolon=333;e.less=584;e.equal=584;e.greater=584;e.question=611;e.at=975;e.A=722;e.B=722;e.C=722;e.D=722;e.E=667;e.F=611;e.G=778;e.H=722;e.I=278;e.J=556;e.K=722;e.L=611;e.M=833;e.N=722;e.O=778;e.P=667;e.Q=778;e.R=722;e.\
-S=667;e.T=611;e.U=722;e.V=667;e.W=944;e.X=667;e.Y=667;e.Z=611;e.bracketleft=333;e.backslash=278;e.bracketright=333;e.asciicircum=584;e.underscore=556;e.quoteleft=278;e.a=556;e.b=611;e.c=556;e.d=611;e.e=556;e.f=333;e.g=611;e.h=611;e.i=278;e.j=278;e.k=556;e.l=278;e.m=889;e.n=611;e.o=611;e.p=611;e.q=611;e.r=389;e.s=556;e.t=333;e.u=611;e.v=556;e.w=778;e.x=556;e.y=556;e.z=500;e.braceleft=389;e.bar=280;e.braceright=389;e.asciitilde=584;e.exclamdown=333;e.cent=556;e.sterling=556;e.fraction=167;e.yen=55\
-6;e.florin=556;e.section=556;e.currency=556;e.quotesingle=238;e.quotedblleft=500;e.guillemotleft=556;e.guilsinglleft=333;e.guilsinglright=333;e.fi=611;e.fl=611;e.endash=556;e.dagger=556;e.daggerdbl=556;e.periodcentered=278;e.paragraph=556;e.bullet=350;e.quotesinglbase=278;e.quotedblbase=500;e.quotedblright=500;e.guillemotright=556;e.ellipsis=1e3;e.perthousand=1e3;e.questiondown=611;e.grave=333;e.acute=333;e.circumflex=333;e.tilde=333;e.macron=333;e.breve=333;e.dotaccent=333;e.dieresis=333;e.ring\
-=333;e.cedilla=333;e.hungarumlaut=333;e.ogonek=333;e.caron=333;e.emdash=1e3;e.AE=1e3;e.ordfeminine=370;e.Lslash=611;e.Oslash=778;e.OE=1e3;e.ordmasculine=365;e.ae=889;e.dotlessi=278;e.lslash=278;e.oslash=611;e.oe=944;e.germandbls=611;e.Idieresis=278;e.eacute=556;e.abreve=556;e.uhungarumlaut=611;e.ecaron=556;e.Ydieresis=667;e.divide=584;e.Yacute=667;e.Acircumflex=722;e.aacute=556;e.Ucircumflex=722;e.yacute=556;e.scommaaccent=556;e.ecircumflex=556;e.Uring=722;e.Udieresis=722;e.aogonek=556;e.Uacute=\
-722;e.uogonek=611;e.Edieresis=667;e.Dcroat=722;e.commaaccent=250;e.copyright=737;e.Emacron=667;e.ccaron=556;e.aring=556;e.Ncommaaccent=722;e.lacute=278;e.agrave=556;e.Tcommaaccent=611;e.Cacute=722;e.atilde=556;e.Edotaccent=667;e.scaron=556;e.scedilla=556;e.iacute=278;e.lozenge=494;e.Rcaron=722;e.Gcommaaccent=778;e.ucircumflex=611;e.acircumflex=556;e.Amacron=722;e.rcaron=389;e.ccedilla=556;e.Zdotaccent=611;e.Thorn=667;e.Omacron=778;e.Racute=722;e.Sacute=667;e.dcaron=743;e.Umacron=722;e.uring=611;\
-e.threesuperior=333;e.Ograve=778;e.Agrave=722;e.Abreve=722;e.multiply=584;e.uacute=611;e.Tcaron=611;e.partialdiff=494;e.ydieresis=556;e.Nacute=722;e.icircumflex=278;e.Ecircumflex=667;e.adieresis=556;e.edieresis=556;e.cacute=556;e.nacute=611;e.umacron=611;e.Ncaron=722;e.Iacute=278;e.plusminus=584;e.brokenbar=280;e.registered=737;e.Gbreve=778;e.Idotaccent=278;e.summation=600;e.Egrave=667;e.racute=389;e.omacron=611;e.Zacute=611;e.Zcaron=611;e.greaterequal=549;e.Eth=722;e.Ccedilla=722;e.lcommaaccent\
-=278;e.tcaron=389;e.eogonek=556;e.Uogonek=722;e.Aacute=722;e.Adieresis=722;e.egrave=556;e.zacute=500;e.iogonek=278;e.Oacute=778;e.oacute=611;e.amacron=556;e.sacute=556;e.idieresis=278;e.Ocircumflex=778;e.Ugrave=722;e.Delta=612;e.thorn=611;e.twosuperior=333;e.Odieresis=778;e.mu=611;e.igrave=278;e.ohungarumlaut=611;e.Eogonek=667;e.dcroat=611;e.threequarters=834;e.Scedilla=667;e.lcaron=400;e.Kcommaaccent=722;e.Lacute=611;e.trademark=1e3;e.edotaccent=556;e.Igrave=278;e.Imacron=278;e.Lcaron=611;e.one\
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-89;e.scedilla=389;e.iacute=278;e.lozenge=471;e.Rcaron=611;e.Gcommaaccent=722;e.ucircumflex=500;e.acircumflex=500;e.Amacron=611;e.rcaron=389;e.ccedilla=444;e.Zdotaccent=556;e.Thorn=611;e.Omacron=722;e.Racute=611;e.Sacute=500;e.dcaron=544;e.Umacron=722;e.uring=500;e.threesuperior=300;e.Ograve=722;e.Agrave=611;e.Abreve=611;e.multiply=675;e.uacute=500;e.Tcaron=556;e.partialdiff=476;e.ydieresis=444;e.Nacute=667;e.icircumflex=278;e.Ecircumflex=611;e.adieresis=500;e.edieresis=444;e.cacute=444;e.nacute=\
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-0;e.twosuperior=300;e.Odieresis=722;e.mu=500;e.igrave=278;e.ohungarumlaut=500;e.Eogonek=611;e.dcroat=500;e.threequarters=750;e.Scedilla=500;e.lcaron=300;e.Kcommaaccent=667;e.Lacute=556;e.trademark=980;e.edotaccent=444;e.Igrave=333;e.Imacron=333;e.Lcaron=611;e.onehalf=750;e.lessequal=549;e.ocircumflex=500;e.ntilde=500;e.Uhungarumlaut=722;e.Eacute=611;e.emacron=444;e.gbreve=500;e.onequarter=750;e.Scaron=500;e.Scommaaccent=500;e.Ohungarumlaut=722;e.degree=400;e.ograve=500;e.Ccaron=667;e.ugrave=500;\
-e.radical=453;e.Dcaron=722;e.rcommaaccent=389;e.Ntilde=667;e.otilde=500;e.Rcommaaccent=611;e.Lcommaaccent=556;e.Atilde=611;e.Aogonek=611;e.Aring=611;e.Otilde=722;e.zdotaccent=389;e.Ecaron=611;e.Iogonek=333;e.kcommaaccent=444;e.minus=675;e.Icircumflex=333;e.ncaron=500;e.tcommaaccent=278;e.logicalnot=675;e.odieresis=500;e.udieresis=500;e.notequal=549;e.gcommaaccent=500;e.eth=500;e.zcaron=389;e.ncommaaccent=500;e.onesuperior=300;e.imacron=278;e.Euro=500}));e.ZapfDingbats=getLookupTableFactory((func\
-tion(e){e.space=278;e.a1=974;e.a2=961;e.a202=974;e.a3=980;e.a4=719;e.a5=789;e.a119=790;e.a118=791;e.a117=690;e.a11=960;e.a12=939;e.a13=549;e.a14=855;e.a15=911;e.a16=933;e.a105=911;e.a17=945;e.a18=974;e.a19=755;e.a20=846;e.a21=762;e.a22=761;e.a23=571;e.a24=677;e.a25=763;e.a26=760;e.a27=759;e.a28=754;e.a6=494;e.a7=552;e.a8=537;e.a9=577;e.a10=692;e.a29=786;e.a30=788;e.a31=788;e.a32=790;e.a33=793;e.a34=794;e.a35=816;e.a36=823;e.a37=789;e.a38=841;e.a39=823;e.a40=833;e.a41=816;e.a42=831;e.a43=923;e.a4\
-4=744;e.a45=723;e.a46=749;e.a47=790;e.a48=792;e.a49=695;e.a50=776;e.a51=768;e.a52=792;e.a53=759;e.a54=707;e.a55=708;e.a56=682;e.a57=701;e.a58=826;e.a59=815;e.a60=789;e.a61=789;e.a62=707;e.a63=687;e.a64=696;e.a65=689;e.a66=786;e.a67=787;e.a68=713;e.a69=791;e.a70=785;e.a71=791;e.a72=873;e.a73=761;e.a74=762;e.a203=762;e.a75=759;e.a204=759;e.a76=892;e.a77=892;e.a78=788;e.a79=784;e.a81=438;e.a82=138;e.a83=277;e.a84=415;e.a97=392;e.a98=392;e.a99=668;e.a100=668;e.a89=390;e.a90=390;e.a93=317;e.a94=317;e\
-.a91=276;e.a92=276;e.a205=509;e.a85=509;e.a206=410;e.a86=410;e.a87=234;e.a88=234;e.a95=334;e.a96=334;e.a101=732;e.a102=544;e.a103=544;e.a104=910;e.a106=667;e.a107=760;e.a108=760;e.a112=776;e.a111=595;e.a110=694;e.a109=626;e.a120=788;e.a121=788;e.a122=788;e.a123=788;e.a124=788;e.a125=788;e.a126=788;e.a127=788;e.a128=788;e.a129=788;e.a130=788;e.a131=788;e.a132=788;e.a133=788;e.a134=788;e.a135=788;e.a136=788;e.a137=788;e.a138=788;e.a139=788;e.a140=788;e.a141=788;e.a142=788;e.a143=788;e.a144=788;e.a\
-145=788;e.a146=788;e.a147=788;e.a148=788;e.a149=788;e.a150=788;e.a151=788;e.a152=788;e.a153=788;e.a154=788;e.a155=788;e.a156=788;e.a157=788;e.a158=788;e.a159=788;e.a160=894;e.a161=838;e.a163=1016;e.a164=458;e.a196=748;e.a165=924;e.a192=748;e.a166=918;e.a167=927;e.a168=928;e.a169=928;e.a170=834;e.a171=873;e.a172=828;e.a173=924;e.a162=924;e.a174=917;e.a175=930;e.a176=931;e.a177=463;e.a178=883;e.a179=836;e.a193=836;e.a180=867;e.a199=867;e.a181=696;e.a200=696;e.a182=874;e.a201=874;e.a183=760;e.a184=\
-946;e.a197=771;e.a185=865;e.a194=771;e.a198=888;e.a186=967;e.a195=888;e.a187=831;e.a188=873;e.a189=927;e.a190=970;e.a191=918}))})),ni=getLookupTableFactory((function(e){e.Courier={ascent:629,descent:-157,capHeight:562,xHeight:-426};e["Courier-Bold"]={ascent:629,descent:-157,capHeight:562,xHeight:439};e["Courier-Oblique"]={ascent:629,descent:-157,capHeight:562,xHeight:426};e["Courier-BoldOblique"]={ascent:629,descent:-157,capHeight:562,xHeight:426};e.Helvetica={ascent:718,descent:-207,capHeight:7\
-18,xHeight:523};e["Helvetica-Bold"]={ascent:718,descent:-207,capHeight:718,xHeight:532};e["Helvetica-Oblique"]={ascent:718,descent:-207,capHeight:718,xHeight:523};e["Helvetica-BoldOblique"]={ascent:718,descent:-207,capHeight:718,xHeight:532};e["Times-Roman"]={ascent:683,descent:-217,capHeight:662,xHeight:450};e["Times-Bold"]={ascent:683,descent:-217,capHeight:676,xHeight:461};e["Times-Italic"]={ascent:683,descent:-217,capHeight:653,xHeight:441};e["Times-BoldItalic"]={ascent:683,descent:-217,capH\
-eight:669,xHeight:462};e.Symbol={ascent:Math.NaN,descent:Math.NaN,capHeight:Math.NaN,xHeight:Math.NaN};e.ZapfDingbats={ascent:Math.NaN,descent:Math.NaN,capHeight:Math.NaN,xHeight:Math.NaN}}));class GlyfTable{constructor({glyfTable:e,isGlyphLocationsLong:t,locaTable:a,numGlyphs:r}){this.glyphs=[];const i=new DataView(a.buffer,a.byteOffset,a.byteLength),n=new DataView(e.buffer,e.byteOffset,e.byteLength),s=t?4:2;let o=t?i.getUint32(0):2*i.getUint16(0),c=0;for(let e=0;e<r;e++){c+=s;const e=t?i.getUi\
-nt32(c):2*i.getUint16(c);if(e===o){this.glyphs.push(new Glyph({}));continue}const a=Glyph.parse(o,n);this.glyphs.push(a);o=e}}getSize(){return Math.sumPrecise(this.glyphs.map((e=>e.getSize()+3&-4)))}write(){const e=this.getSize(),t=new DataView(new ArrayBuffer(e)),a=e>131070,r=a?4:2,i=new DataView(new ArrayBuffer((this.glyphs.length+1)*r));a?i.setUint32(0,0):i.setUint16(0,0);let n=0,s=0;for(const e of this.glyphs){n+=e.write(n,t);n=n+3&-4;s+=r;a?i.setUint32(s,n):i.setUint16(s,n>>1)}return{isLoca\
-tionLong:a,loca:new Uint8Array(i.buffer),glyf:new Uint8Array(t.buffer)}}scale(e){for(let t=0,a=this.glyphs.length;t<a;t++)this.glyphs[t].scale(e[t])}}class Glyph{constructor({header:e=null,simple:t=null,composites:a=null}){this.header=e;this.simple=t;this.composites=a}static parse(e,t){const[a,r]=GlyphHeader.parse(e,t);e+=a;if(r.numberOfContours<0){const a=[];for(;;){const[r,i]=CompositeGlyph.parse(e,t);e+=r;a.push(i);if(!(32&i.flags))break}return new Glyph({header:r,composites:a})}const i=Simpl\
-eGlyph.parse(e,t,r.numberOfContours);return new Glyph({header:r,simple:i})}getSize(){if(!this.header)return 0;const e=this.simple?this.simple.getSize():Math.sumPrecise(this.composites.map((e=>e.getSize())));return this.header.getSize()+e}write(e,t){if(!this.header)return 0;const a=e;e+=this.header.write(e,t);if(this.simple)e+=this.simple.write(e,t);else for(const a of this.composites)e+=a.write(e,t);return e-a}scale(e){if(!this.header)return;const t=(this.header.xMin+this.header.xMax)/2;this.hea\
-der.scale(t,e);if(this.simple)this.simple.scale(t,e);else for(const a of this.composites)a.scale(t,e)}}class GlyphHeader{constructor({numberOfContours:e,xMin:t,yMin:a,xMax:r,yMax:i}){this.numberOfContours=e;this.xMin=t;this.yMin=a;this.xMax=r;this.yMax=i}static parse(e,t){return[10,new GlyphHeader({numberOfContours:t.getInt16(e),xMin:t.getInt16(e+2),yMin:t.getInt16(e+4),xMax:t.getInt16(e+6),yMax:t.getInt16(e+8)})]}getSize(){return 10}write(e,t){t.setInt16(e,this.numberOfContours);t.setInt16(e+2,\
-this.xMin);t.setInt16(e+4,this.yMin);t.setInt16(e+6,this.xMax);t.setInt16(e+8,this.yMax);return 10}scale(e,t){this.xMin=Math.round(e+(this.xMin-e)*t);this.xMax=Math.round(e+(this.xMax-e)*t)}}class Contour{constructor({flags:e,xCoordinates:t,yCoordinates:a}){this.xCoordinates=t;this.yCoordinates=a;this.flags=e}}class SimpleGlyph{constructor({contours:e,instructions:t}){this.contours=e;this.instructions=t}static parse(e,t,a){const r=[];for(let i=0;i<a;i++){const a=t.getUint16(e);e+=2;r.push(a)}con\
-st i=r[a-1]+1,n=t.getUint16(e);e+=2;const s=new Uint8Array(t).slice(e,e+n);e+=n;const o=[];for(let a=0;a<i;e++,a++){let r=t.getUint8(e);o.push(r);if(8&r){const i=t.getUint8(++e);r^=8;for(let e=0;e<i;e++)o.push(r);a+=i}}const c=[];let l=[],h=[],u=[];const d=[];let f=0,g=0;for(let a=0;a<i;a++){const i=o[a];if(2&i){const a=t.getUint8(e++);g+=16&i?a:-a;l.push(g)}else if(16&i)l.push(g);else{g+=t.getInt16(e);e+=2;l.push(g)}if(r[f]===a){f++;c.push(l);l=[]}}g=0;f=0;for(let a=0;a<i;a++){const i=o[a];if(4\
-&i){const a=t.getUint8(e++);g+=32&i?a:-a;h.push(g)}else if(32&i)h.push(g);else{g+=t.getInt16(e);e+=2;h.push(g)}u.push(1&i|64&i);if(r[f]===a){l=c[f];f++;d.push(new Contour({flags:u,xCoordinates:l,yCoordinates:h}));h=[];u=[]}}return new SimpleGlyph({contours:d,instructions:s})}getSize(){let e=2*this.contours.length+2+this.instructions.length,t=0,a=0;for(const r of this.contours){e+=r.flags.length;for(let i=0,n=r.xCoordinates.length;i<n;i++){const n=r.xCoordinates[i],s=r.yCoordinates[i];let o=Math.\
-abs(n-t);o>255?e+=2:o>0&&(e+=1);t=n;o=Math.abs(s-a);o>255?e+=2:o>0&&(e+=1);a=s}}return e}write(e,t){const a=e,r=[],i=[],n=[];let s=0,o=0;for(const a of this.contours){for(let e=0,t=a.xCoordinates.length;e<t;e++){let t=a.flags[e];const c=a.xCoordinates[e];let l=c-s;if(0===l){t|=16;r.push(0)}else{const e=Math.abs(l);if(e<=255){t|=l>=0?18:2;r.push(e)}else r.push(l)}s=c;const h=a.yCoordinates[e];l=h-o;if(0===l){t|=32;i.push(0)}else{const e=Math.abs(l);if(e<=255){t|=l>=0?36:4;i.push(e)}else i.push(l)\
-}o=h;n.push(t)}t.setUint16(e,r.length-1);e+=2}t.setUint16(e,this.instructions.length);e+=2;if(this.instructions.length){new Uint8Array(t.buffer,0,t.buffer.byteLength).set(this.instructions,e);e+=this.instructions.length}for(const a of n)t.setUint8(e++,a);for(let a=0,i=r.length;a<i;a++){const i=r[a],s=n[a];if(2&s)t.setUint8(e++,i);else if(!(16&s)){t.setInt16(e,i);e+=2}}for(let a=0,r=i.length;a<r;a++){const r=i[a],s=n[a];if(4&s)t.setUint8(e++,r);else if(!(32&s)){t.setInt16(e,r);e+=2}}return e-a}sc\
-ale(e,t){for(const a of this.contours)if(0!==a.xCoordinates.length)for(let r=0,i=a.xCoordinates.length;r<i;r++)a.xCoordinates[r]=Math.round(e+(a.xCoordinates[r]-e)*t)}}class CompositeGlyph{constructor({flags:e,glyphIndex:t,argument1:a,argument2:r,transf:i,instructions:n}){this.flags=e;this.glyphIndex=t;this.argument1=a;this.argument2=r;this.transf=i;this.instructions=n}static parse(e,t){const a=e,r=[];let i=t.getUint16(e);const n=t.getUint16(e+2);e+=4;let s,o;if(1&i){if(2&i){s=t.getInt16(e);o=t.\
-getInt16(e+2)}else{s=t.getUint16(e);o=t.getUint16(e+2)}e+=4;i^=1}else{if(2&i){s=t.getInt8(e);o=t.getInt8(e+1)}else{s=t.getUint8(e);o=t.getUint8(e+1)}e+=2}if(8&i){r.push(t.getUint16(e));e+=2}else if(64&i){r.push(t.getUint16(e),t.getUint16(e+2));e+=4}else if(128&i){r.push(t.getUint16(e),t.getUint16(e+2),t.getUint16(e+4),t.getUint16(e+6));e+=8}let c=null;if(256&i){const a=t.getUint16(e);e+=2;c=new Uint8Array(t).slice(e,e+a);e+=a}return[e-a,new CompositeGlyph({flags:i,glyphIndex:n,argument1:s,argume\
-nt2:o,transf:r,instructions:c})]}getSize(){let e=4+2*this.transf.length;256&this.flags&&(e+=2+this.instructions.length);e+=2;2&this.flags?this.argument1>=-128&&this.argument1<=127&&this.argument2>=-128&&this.argument2<=127||(e+=2):this.argument1>=0&&this.argument1<=255&&this.argument2>=0&&this.argument2<=255||(e+=2);return e}write(e,t){const a=e;2&this.flags?this.argument1>=-128&&this.argument1<=127&&this.argument2>=-128&&this.argument2<=127||(this.flags|=1):this.argument1>=0&&this.argument1<=25\
-5&&this.argument2>=0&&this.argument2<=255||(this.flags|=1);t.setUint16(e,this.flags);t.setUint16(e+2,this.glyphIndex);e+=4;if(1&this.flags){if(2&this.flags){t.setInt16(e,this.argument1);t.setInt16(e+2,this.argument2)}else{t.setUint16(e,this.argument1);t.setUint16(e+2,this.argument2)}e+=4}else{t.setUint8(e,this.argument1);t.setUint8(e+1,this.argument2);e+=2}if(256&this.flags){t.setUint16(e,this.instructions.length);e+=2;if(this.instructions.length){new Uint8Array(t.buffer,0,t.buffer.byteLength).s\
-et(this.instructions,e);e+=this.instructions.length}}return e-a}scale(e,t){}}function writeInt16(e,t,a){e[t]=a>>8&255;e[t+1]=255&a}function writeInt32(e,t,a){e[t]=a>>24&255;e[t+1]=a>>16&255;e[t+2]=a>>8&255;e[t+3]=255&a}function writeData(e,t,a){if(a instanceof Uint8Array)e.set(a,t);else if("string"==typeof a)for(let r=0,i=a.length;r<i;r++)e[t++]=255&a.charCodeAt(r);else for(const r of a)e[t++]=255&r}class OpenTypeFileBuilder{constructor(e){this.sfnt=e;this.tables=Object.create(null)}static getSe\
-archParams(e,t){let a=1,r=0;for(;(a^e)>a;){a<<=1;r++}const i=a*t;return{range:i,entry:r,rangeShift:t*e-i}}toArray(){let e=this.sfnt;const t=this.tables,a=Object.keys(t);a.sort();const r=a.length;let i,n,s,o,c,l=12+16*r;const h=[l];for(i=0;i<r;i++){o=t[a[i]];l+=(o.length+3&-4)>>>0;h.push(l)}const u=new Uint8Array(l);for(i=0;i<r;i++){o=t[a[i]];writeData(u,h[i],o)}"true"===e&&(e=string32(65536));u[0]=255&e.charCodeAt(0);u[1]=255&e.charCodeAt(1);u[2]=255&e.charCodeAt(2);u[3]=255&e.charCodeAt(3);writ\
-eInt16(u,4,r);const d=OpenTypeFileBuilder.getSearchParams(r,16);writeInt16(u,6,d.range);writeInt16(u,8,d.entry);writeInt16(u,10,d.rangeShift);l=12;for(i=0;i<r;i++){c=a[i];u[l]=255&c.charCodeAt(0);u[l+1]=255&c.charCodeAt(1);u[l+2]=255&c.charCodeAt(2);u[l+3]=255&c.charCodeAt(3);let e=0;for(n=h[i],s=h[i+1];n<s;n+=4){e=e+readUint32(u,n)>>>0}writeInt32(u,l+4,e);writeInt32(u,l+8,h[i]);writeInt32(u,l+12,t[c].length);l+=16}return u}addTable(e,t){if(e in this.tables)throw new Error("Table "+e+" already e\
-xists");this.tables[e]=t}}const si=[4],oi=[5],ci=[6],li=[7],hi=[8],ui=[12,35],di=[14],fi=[21],gi=[22],pi=[30],mi=[31];class Type1CharString{constructor(){this.width=0;this.lsb=0;this.flexing=!1;this.output=[];this.stack=[]}convert(e,t,a){const r=e.length;let i,n,s,o=!1;for(let c=0;c<r;c++){let r=e[c];if(r<32){12===r&&(r=(r<<8)+e[++c]);switch(r){case 1:case 3:case 9:case 3072:case 3073:case 3074:case 3105:this.stack=[];break;case 4:if(this.flexing){if(this.stack.length<1){o=!0;break}const e=this.\
-stack.pop();this.stack.push(0,e);break}o=this.executeCommand(1,si);break;case 5:o=this.executeCommand(2,oi);break;case 6:o=this.executeCommand(1,ci);break;case 7:o=this.executeCommand(1,li);break;case 8:o=this.executeCommand(6,hi);break;case 10:if(this.stack.length<1){o=!0;break}s=this.stack.pop();if(!t[s]){o=!0;break}o=this.convert(t[s],t,a);break;case 11:return o;case 13:if(this.stack.length<2){o=!0;break}i=this.stack.pop();n=this.stack.pop();this.lsb=n;this.width=i;this.stack.push(i,n);o=this\
-.executeCommand(2,gi);break;case 14:this.output.push(di[0]);break;case 21:if(this.flexing)break;o=this.executeCommand(2,fi);break;case 22:if(this.flexing){this.stack.push(0);break}o=this.executeCommand(1,gi);break;case 30:o=this.executeCommand(4,pi);break;case 31:o=this.executeCommand(4,mi);break;case 3078:if(a){const e=this.stack.at(-5);this.seac=this.stack.splice(-4,4);this.seac[0]+=this.lsb-e;o=this.executeCommand(0,di)}else o=this.executeCommand(4,di);break;case 3079:if(this.stack.length<4){\
-o=!0;break}this.stack.pop();i=this.stack.pop();const e=this.stack.pop();n=this.stack.pop();this.lsb=n;this.width=i;this.stack.push(i,n,e);o=this.executeCommand(3,fi);break;case 3084:if(this.stack.length<2){o=!0;break}const c=this.stack.pop(),l=this.stack.pop();this.stack.push(l/c);break;case 3088:if(this.stack.length<2){o=!0;break}s=this.stack.pop();const h=this.stack.pop();if(0===s&&3===h){const e=this.stack.splice(-17,17);this.stack.push(e[2]+e[0],e[3]+e[1],e[4],e[5],e[6],e[7],e[8],e[9],e[10],\
-e[11],e[12],e[13],e[14]);o=this.executeCommand(13,ui,!0);this.flexing=!1;this.stack.push(e[15],e[16])}else 1===s&&0===h&&(this.flexing=!0);break;case 3089:break;default:warn(\'Unknown type 1 charstring command of "\'+r+\'"\')}if(o)break}else{r<=246?r-=139:r=r<=250?256*(r-247)+e[++c]+108:r<=254?-256*(r-251)-e[++c]-108:(255&e[++c])<<24|(255&e[++c])<<16|(255&e[++c])<<8|255&e[++c];this.stack.push(r)}}return o}executeCommand(e,t,a){const r=this.stack.length;if(e>r)return!0;const i=r-e;for(let e=i;e<r;e++\
-){let t=this.stack[e];if(Number.isInteger(t))this.output.push(28,t>>8&255,255&t);else{t=65536*t|0;this.output.push(255,t>>24&255,t>>16&255,t>>8&255,255&t)}}this.output.push(...t);a?this.stack.splice(i,e):this.stack.length=0;return!1}}function isHexDigit(e){return e>=48&&e<=57||e>=65&&e<=70||e>=97&&e<=102}function decrypt(e,t,a){if(a>=e.length)return new Uint8Array(0);let r,i,n=0|t;for(r=0;r<a;r++)n=52845*(e[r]+n)+22719&65535;const s=e.length-a,o=new Uint8Array(s);for(r=a,i=0;i<s;r++,i++){const t\
-=e[r];o[i]=t^n>>8;n=52845*(t+n)+22719&65535}return o}function isSpecial(e){return 47===e||91===e||93===e||123===e||125===e||40===e||41===e}class Type1Parser{constructor(e,t,a){if(t){const t=e.getBytes(),a=!((isHexDigit(t[0])||isWhiteSpace(t[0]))&&isHexDigit(t[1])&&isHexDigit(t[2])&&isHexDigit(t[3])&&isHexDigit(t[4])&&isHexDigit(t[5])&&isHexDigit(t[6])&&isHexDigit(t[7]));e=new Stream(a?decrypt(t,55665,4):function decryptAscii(e,t,a){let r=0|t;const i=e.length,n=new Uint8Array(i>>>1);let s,o;for(s\
-=0,o=0;s<i;s++){const t=e[s];if(!isHexDigit(t))continue;s++;let a;for(;s<i&&!isHexDigit(a=e[s]);)s++;if(s<i){const e=parseInt(String.fromCharCode(t,a),16);n[o++]=e^r>>8;r=52845*(e+r)+22719&65535}}return n.slice(a,o)}(t,55665,4))}this.seacAnalysisEnabled=!!a;this.stream=e;this.nextChar()}readNumberArray(){this.getToken();const e=[];for(;;){const t=this.getToken();if(null===t||"]"===t||"}"===t)break;e.push(parseFloat(t||0))}return e}readNumber(){const e=this.getToken();return parseFloat(e||0)}read\
-Int(){const e=this.getToken();return 0|parseInt(e||0,10)}readBoolean(){return"true"===this.getToken()?1:0}nextChar(){return this.currentChar=this.stream.getByte()}prevChar(){this.stream.skip(-2);return this.currentChar=this.stream.getByte()}getToken(){let e=!1,t=this.currentChar;for(;;){if(-1===t)return null;if(e)10!==t&&13!==t||(e=!1);else if(37===t)e=!0;else if(!isWhiteSpace(t))break;t=this.nextChar()}if(isSpecial(t)){this.nextChar();return String.fromCharCode(t)}let a="";do{a+=String.fromChar\
-Code(t);t=this.nextChar()}while(t>=0&&!isWhiteSpace(t)&&!isSpecial(t));return a}readCharStrings(e,t){return-1===t?e:decrypt(e,4330,t)}extractFontProgram(e){const t=this.stream,a=[],r=[],i=Object.create(null);i.lenIV=4;const n={subrs:[],charstrings:[],properties:{privateData:i}};let s,o,c,l;for(;null!==(s=this.getToken());)if("/"===s){s=this.getToken();switch(s){case"CharStrings":this.getToken();this.getToken();this.getToken();this.getToken();for(;;){s=this.getToken();if(null===s||"end"===s)break\
-;if("/"!==s)continue;const e=this.getToken();o=this.readInt();this.getToken();c=o>0?t.getBytes(o):new Uint8Array(0);l=n.properties.privateData.lenIV;const a=this.readCharStrings(c,l);this.nextChar();s=this.getToken();"noaccess"===s?this.getToken():"/"===s&&this.prevChar();r.push({glyph:e,encoded:a})}break;case"Subrs":this.readInt();this.getToken();for(;"dup"===this.getToken();){const e=this.readInt();o=this.readInt();this.getToken();c=o>0?t.getBytes(o):new Uint8Array(0);l=n.properties.privateDat\
-a.lenIV;const r=this.readCharStrings(c,l);this.nextChar();s=this.getToken();"noaccess"===s&&this.getToken();a[e]=r}break;case"BlueValues":case"OtherBlues":case"FamilyBlues":case"FamilyOtherBlues":const e=this.readNumberArray();e.length>0&&e.length,0;break;case"StemSnapH":case"StemSnapV":n.properties.privateData[s]=this.readNumberArray();break;case"StdHW":case"StdVW":n.properties.privateData[s]=this.readNumberArray()[0];break;case"BlueShift":case"lenIV":case"BlueFuzz":case"BlueScale":case"Languag\
-eGroup":n.properties.privateData[s]=this.readNumber();break;case"ExpansionFactor":n.properties.privateData[s]=this.readNumber()||.06;break;case"ForceBold":n.properties.privateData[s]=this.readBoolean()}}for(const{encoded:t,glyph:i}of r){const r=new Type1CharString,s=r.convert(t,a,this.seacAnalysisEnabled);let o=r.output;s&&(o=[14]);const c={glyphName:i,charstring:o,width:r.width,lsb:r.lsb,seac:r.seac};".notdef"===i?n.charstrings.unshift(c):n.charstrings.push(c);if(e.builtInEncoding){const t=e.bu\
-iltInEncoding.indexOf(i);t>-1&&void 0===e.widths[t]&&t>=e.firstChar&&t<=e.lastChar&&(e.widths[t]=r.width)}}return n}extractFontHeader(e){let t;for(;null!==(t=this.getToken());)if("/"===t){t=this.getToken();switch(t){case"FontMatrix":const a=this.readNumberArray();e.fontMatrix=a;break;case"Encoding":const r=this.getToken();let i;if(/^\\d+$/.test(r)){i=[];const e=0|parseInt(r,10);this.getToken();for(let a=0;a<e;a++){t=this.getToken();for(;"dup"!==t&&"def"!==t;){t=this.getToken();if(null===t)return}\
-if("def"===t)break;const e=this.readInt();this.getToken();const a=this.getToken();i[e]=a;this.getToken()}}else i=getEncoding(r);e.builtInEncoding=i;break;case"FontBBox":const n=this.readNumberArray();e.ascent=Math.max(n[3],n[1]);e.descent=Math.min(n[1],n[3]);e.ascentScaled=!0}}}}function findBlock(e,t,a){const r=e.length,i=t.length,n=r-i;let s=a,o=!1;for(;s<n;){let a=0;for(;a<i&&e[s+a]===t[a];)a++;if(a>=i){s+=a;for(;s<r&&isWhiteSpace(e[s]);)s++;o=!0;break}s++}return{found:o,length:s}}class Type1\
-Font{constructor(e,t,a){let r=a.length1,i=a.length2,n=t.peekBytes(6);const s=128===n[0]&&1===n[1];if(s){t.skip(6);r=n[5]<<24|n[4]<<16|n[3]<<8|n[2]}const o=function getHeaderBlock(e,t){const a=[101,101,120,101,99],r=e.pos;let i,n,s,o;try{i=e.getBytes(t);n=i.length}catch{}if(n===t){s=findBlock(i,a,t-2*a.length);if(s.found&&s.length===t)return{stream:new Stream(i),length:t}}warn(\'Invalid "Length1" property in Type1 font -- trying to recover.\');e.pos=r;for(;;){s=findBlock(e.peekBytes(2048),a,0);if(0\
-===s.length)break;e.pos+=s.length;if(s.found){o=e.pos-r;break}}e.pos=r;if(o)return{stream:new Stream(e.getBytes(o)),length:o};warn(\'Unable to recover "Length1" property in Type1 font -- using as is.\');return{stream:new Stream(e.getBytes(t)),length:t}}(t,r);new Type1Parser(o.stream,!1,Rr).extractFontHeader(a);if(s){n=t.getBytes(6);i=n[5]<<24|n[4]<<16|n[3]<<8|n[2]}const c=function getEexecBlock(e,t){const a=e.getBytes();if(0===a.length)throw new FormatError("getEexecBlock - no font program found."\
-);return{stream:new Stream(a),length:a.length}}(t),l=new Type1Parser(c.stream,!0,Rr).extractFontProgram(a);for(const e in l.properties)a[e]=l.properties[e];const h=l.charstrings,u=this.getType2Charstrings(h),d=this.getType2Subrs(l.subrs);this.charstrings=h;this.data=this.wrap(e,u,this.charstrings,d,a);this.seacs=this.getSeacs(l.charstrings)}get numGlyphs(){return this.charstrings.length+1}getCharset(){const e=[".notdef"];for(const{glyphName:t}of this.charstrings)e.push(t);return e}getGlyphMappin\
-g(e){const t=this.charstrings;if(e.composite){const a=Object.create(null);for(let r=0,i=t.length;r<i;r++){a[e.cMap.charCodeOf(r)]=r+1}return a}const a=[".notdef"];let r,i;for(i=0;i<t.length;i++)a.push(t[i].glyphName);const n=e.builtInEncoding;if(n){r=Object.create(null);for(const e in n){i=a.indexOf(n[e]);i>=0&&(r[e]=i)}}return type1FontGlyphMapping(e,r,a)}hasGlyphId(e){if(e<0||e>=this.numGlyphs)return!1;if(0===e)return!0;return this.charstrings[e-1].charstring.length>0}getSeacs(e){const t=[];fo\
-r(let a=0,r=e.length;a<r;a++){const r=e[a];r.seac&&(t[a+1]=r.seac)}return t}getType2Charstrings(e){const t=[];for(const a of e)t.push(a.charstring);return t}getType2Subrs(e){let t=0;const a=e.length;t=a<1133?107:a<33769?1131:32768;const r=[];let i;for(i=0;i<t;i++)r.push([11]);for(i=0;i<a;i++)r.push(e[i]);return r}wrap(e,t,a,r,i){const n=new CFF;n.header=new CFFHeader(1,0,4,4);n.names=[e];const s=new CFFTopDict;s.setByName("version",391);s.setByName("Notice",392);s.setByName("FullName",393);s.set\
-ByName("FamilyName",394);s.setByName("Weight",395);s.setByName("Encoding",null);s.setByName("FontMatrix",i.fontMatrix);s.setByName("FontBBox",i.bbox);s.setByName("charset",null);s.setByName("CharStrings",null);s.setByName("Private",null);n.topDict=s;const o=new CFFStrings;o.add("Version 0.11");o.add("See original notice");o.add(e);o.add(e);o.add("Medium");n.strings=o;n.globalSubrIndex=new CFFIndex;const c=t.length,l=[".notdef"];let h,u;for(h=0;h<c;h++){const e=a[h].glyphName;-1===Hr.indexOf(e)&&\
-o.add(e);l.push(e)}n.charset=new CFFCharset(!1,0,l);const d=new CFFIndex;d.add([139,14]);for(h=0;h<c;h++)d.add(t[h]);n.charStrings=d;const f=new CFFPrivateDict;f.setByName("Subrs",null);const g=["BlueValues","OtherBlues","FamilyBlues","FamilyOtherBlues","StemSnapH","StemSnapV","BlueShift","BlueFuzz","BlueScale","LanguageGroup","ExpansionFactor","ForceBold","StdHW","StdVW"];for(h=0,u=g.length;h<u;h++){const e=g[h];if(!(e in i.privateData))continue;const t=i.privateData[e];if(Array.isArray(t))for(\
-let e=t.length-1;e>0;e--)t[e]-=t[e-1];f.setByName(e,t)}n.topDict.privateDict=f;const p=new CFFIndex;for(h=0,u=r.length;h<u;h++)p.add(r[h]);f.subrsIndex=p;return new CFFCompiler(n).compile()}}const bi=[[57344,63743],[1048576,1114109]],yi=1e3,wi=["ascent","bbox","black","bold","charProcOperatorList","cssFontInfo","data","defaultVMetrics","defaultWidth","descent","disableFontFace","fallbackName","fontExtraProperties","fontMatrix","isInvalidPDFjsFont","isType3Font","italic","loadedName","mimetype","\
-missingFile","name","remeasure","systemFontInfo","vertical"],xi=["cMap","composite","defaultEncoding","differences","isMonospace","isSerifFont","isSymbolicFont","seacMap","subtype","toFontChar","toUnicode","type","vmetrics","widths"];function adjustWidths(e){if(!e.fontMatrix)return;if(e.fontMatrix[0]===t[0])return;const a=.001/e.fontMatrix[0],r=e.widths;for(const e in r)r[e]*=a;e.defaultWidth*=a}function amendFallbackToUnicode(e){if(!e.fallbackToUnicode)return;if(e.toUnicode instanceof IdentityT\
-oUnicodeMap)return;const t=[];for(const a in e.fallbackToUnicode)e.toUnicode.has(a)||(t[a]=e.fallbackToUnicode[a]);t.length>0&&e.toUnicode.amend(t)}class fonts_Glyph{constructor(e,t,a,r,i,n,s,o,c){this.originalCharCode=e;this.fontChar=t;this.unicode=a;this.accent=r;this.width=i;this.vmetric=n;this.operatorListId=s;this.isSpace=o;this.isInFont=c}get category(){return shadow(this,"category",function getCharUnicodeCategory(e){const t=Dr.get(e);if(t)return t;const a=e.match(Mr),r={isWhitespace:!!a?.\
-[1],isZeroWidthDiacritic:!!a?.[2],isInvisibleFormatMark:!!a?.[3]};Dr.set(e,r);return r}(this.unicode),!0)}}function int16(e,t){return(e<<8)+t}function writeSignedInt16(e,t,a){e[t+1]=a;e[t]=a>>>8}function signedInt16(e,t){const a=(e<<8)+t;return 32768&a?a-65536:a}function string16(e){return String.fromCharCode(e>>8&255,255&e)}function safeString16(e){e>32767?e=32767:e<-32768&&(e=-32768);return String.fromCharCode(e>>8&255,255&e)}function isTrueTypeCollectionFile(e){return"ttcf"===bytesToString(e.\
-peekBytes(4))}function getFontFileType(e,{type:t,subtype:a,composite:r}){let i,n;if(function isTrueTypeFile(e){const t=e.peekBytes(4);return 65536===readUint32(t,0)||"true"===bytesToString(t)}(e)||isTrueTypeCollectionFile(e))i=r?"CIDFontType2":"TrueType";else if(function isOpenTypeFile(e){return"OTTO"===bytesToString(e.peekBytes(4))}(e))i=r?"CIDFontType2":"OpenType";else if(function isType1File(e){const t=e.peekBytes(2);return 37===t[0]&&33===t[1]||128===t[0]&&1===t[1]}(e))i=r?"CIDFontType0":"MM\
-Type1"===t?"MMType1":"Type1";else if(function isCFFFile(e){const t=e.peekBytes(4);return t[0]>=1&&t[3]>=1&&t[3]<=4}(e))if(r){i="CIDFontType0";n="CIDFontType0C"}else{i="MMType1"===t?"MMType1":"Type1";n="Type1C"}else{warn("getFontFileType: Unable to detect correct font file Type/Subtype.");i=t;n=a}return[i,n]}function applyStandardFontGlyphMap(e,t){for(const a in t)e[+a]=t[a]}function buildToFontChar(e,t,a){const r=[];let i;for(let a=0,n=e.length;a<n;a++){i=getUnicodeForGlyph(e[a],t);-1!==i&&(r[a]\
-=i)}for(const e in a){i=getUnicodeForGlyph(a[e],t);-1!==i&&(r[+e]=i)}return r}function isMacNameRecord(e){return 1===e.platform&&0===e.encoding&&0===e.language}function isWinNameRecord(e){return 3===e.platform&&1===e.encoding&&1033===e.language}function convertCidString(e,t,a=!1){switch(t.length){case 1:return t.charCodeAt(0);case 2:return t.charCodeAt(0)<<8|t.charCodeAt(1)}const r=`Unsupported CID string (charCode ${e}): "${t}".`;if(a)throw new FormatError(r);warn(r);return t}function adjustMap\
-ping(e,t,a,r){const i=Object.create(null),n=new Map,s=[],o=new Set;let c=0;let l=bi[c][0],h=bi[c][1];let u=null;for(const f in e){let g=e[f];if(!t(g))continue;if(l>h){c++;if(c>=bi.length){warn("Ran out of space in font private use area.");break}l=bi[c][0];h=bi[c][1]}const p=l++;0===g&&(g=a);let m=r.get(f);if("string"==typeof m)if(1===m.length)m=m.codePointAt(0);else{if(!u){u=new Map;for(let e=64256;e<=64335;e++){const t=String.fromCharCode(e).normalize("NFKD");t.length>1&&u.set(t,e)}}m=u.get(m)|\
-|m.codePointAt(0)}if(m&&!(d=m,bi[0][0]<=d&&d<=bi[0][1]||bi[1][0]<=d&&d<=bi[1][1])&&!o.has(g)){n.set(m,g);o.add(g)}i[p]=g;s[f]=p}var d;return{toFontChar:s,charCodeToGlyphId:i,toUnicodeExtraMap:n,nextAvailableFontCharCode:l}}function createCmapTable(e,t,a){const r=function getRanges(e,t,a){const r=[];for(const t in e)e[t]>=a||r.push({fontCharCode:0|t,glyphId:e[t]});if(t)for(const[e,i]of t)i>=a||r.push({fontCharCode:e,glyphId:i});0===r.length&&r.push({fontCharCode:0,glyphId:0});r.sort(((e,t)=>e.fon\
-tCharCode-t.fontCharCode));const i=[],n=r.length;for(let e=0;e<n;){const t=r[e].fontCharCode,a=[r[e].glyphId];++e;let s=t;for(;e<n&&s+1===r[e].fontCharCode;){a.push(r[e].glyphId);++s;++e;if(65535===s)break}i.push([t,s,a])}return i}(e,t,a),i=r.at(-1)[1]>65535?2:1;let n,s,o,c,l="\\0\\0"+string16(i)+"\\0\\0
"+string32(4+8*i);for(n=r.length-1;n>=0&&!(r[n][0]<=65535);--n);const h=n+1;r[n][0]<65535&&65535===r[n][1]&&(r[n][1]=65534);const u=r[n][1]<65535?1:0,d=h+u,f=OpenTypeFileBuilder.getSearchParams(d,2\
-);let g,p,m,b,y="",w="",x="",S="",k="",C=0;for(n=0,s=h;n<s;n++){g=r[n];p=g[0];m=g[1];y+=string16(p);w+=string16(m);b=g[2];let e=!0;for(o=1,c=b.length;o<c;++o)if(b[o]!==b[o-1]+1){e=!1;break}if(e){x+=string16(b[0]-p&65535);S+=string16(0)}else{const e=2*(d-n)+2*C;C+=m-p+1;x+=string16(0);S+=string16(e);for(o=0,c=b.length;o<c;++o)k+=string16(b[o])}}if(u>0){w+="ÿÿ";y+="ÿÿ";x+="\\0
";S+="\\0\\0"}const v="\\0\\0"+string16(2*d)+string16(f.range)+string16(f.entry)+string16(f.rangeShift)+w+"\\0\\0"+y+x+S+k;let F=\
-"",T="";if(i>1){l+="\\0\\0\\n"+string32(4+8*i+4+v.length);F="";for(n=0,s=r.length;n<s;n++){g=r[n];p=g[0];b=g[2];let e=b[0];for(o=1,c=b.length;o<c;++o)if(b[o]!==b[o-1]+1){m=g[0]+o-1;F+=string32(p)+string32(m)+string32(e);p=m+1;e=b[o]}F+=string32(p)+string32(g[1])+string32(e)}T="\\0\\f\\0\\0"+string32(F.length+16)+"\\0\\0\\0\\0"+string32(F.length/12)}return l+"\\0"+string16(v.length+4)+v+T+F}function createOS2Table(e,t,a){a||={unitsPerEm:0,yMax:0,yMin:0,ascent:0,descent:0};let r=0,i=0,n=0,s=0,o=null,c=0,l=-\
-1;if(t){for(let e in t){e|=0;(o>e||!o)&&(o=e);c<e&&(c=e);l=getUnicodeRangeFor(e,l);if(l<32)r|=1<<l;else if(l<64)i|=1<<l-32;else if(l<96)n|=1<<l-64;else{if(!(l<123))throw new FormatError("Unicode ranges Bits > 123 are reserved for internal usage");s|=1<<l-96}}c>65535&&(c=65535)}else{o=0;c=255}const h=e.bbox||[0,0,0,0],u=a.unitsPerEm||(e.fontMatrix?1/Math.max(...e.fontMatrix.slice(0,4).map(Math.abs)):1e3),d=e.ascentScaled?1:u/yi,f=a.ascent||Math.round(d*(e.ascent||h[3]));let g=a.descent||Math.roun\
-d(d*(e.descent||h[1]));g>0&&e.descent>0&&h[1]<0&&(g=-g);const p=a.yMax||f,m=-a.yMin||-g;return"\\0$
ô\\0\\0\\0Š»\\0\\0\\0ŒŠ»\\0\\0
ß\\x001
\\0\\0\\0\\0"+String.fromCharCode(e.fixedPitch?9:0)+"\\0\\0\\0\\0\\0\\0"+string32(r)+string32(i)+string32(n)+string32(s)+"*21*"+string16(e.italicAngle?1:0)+string16(o||e.firstChar)+string16(c||e.lastChar)+string16(f)+string16(g)+"\\0d"+string16(p)+string16(m)+"\\0\\0\\0\\0\\0\\0\\0\\0"+string16(e.xHeight)+string16(e.capHeight)+string16(0)+string16(o||e.firstChar)+"\\0"}function \
-createPostTable(e){return"\\0\\0\\0"+string32(Math.floor(65536*e.italicAngle))+"\\0\\0\\0\\0"+string32(e.fixedPitch?1:0)+"\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0"}function createPostscriptName(e){return e.replaceAll(/[^\\x21-\\x7E]|[[\\](){}<>/%]/g,"").slice(0,63)}function createNameTable(e,t){t||(t=[[],[]]);const a=[t[0][0]||"Original licence",t[0][1]||e,t[0][2]||"Unknown",t[0][3]||"uniqueID",t[0][4]||e,t[0][5]||"Version 0.11",t[0][6]||createPostscriptName(e),t[0][7]||"Unknown",t[0][8]||"Unknown",t[0][9]||"Unk\
-nown"],r=[];let i,n,s,o,c;for(i=0,n=a.length;i<n;i++){c=t[1][i]||a[i];const e=[];for(s=0,o=c.length;s<o;s++)e.push(string16(c.charCodeAt(s)));r.push(e.join(""))}const l=[a,r],h=["\\0
","\\0"],u=["\\0\\0","\\0
"],d=["\\0\\0","\\t"],f=a.length*h.length;let g="\\0\\0"+string16(f)+string16(12*f+6),p=0;for(i=0,n=h.length;i<n;i++){const e=l[i];for(s=0,o=e.length;s<o;s++){c=e[s];g+=h[i]+u[i]+d[i]+string16(s)+string16(c.length)+string16(p);p+=c.length}}g+=a.join("")+r.join("");return g}class Font{constructor(e,\
-t,a,r){this.name=e;this.psName=null;this.mimetype=null;this.disableFontFace=r.disableFontFace;this.fontExtraProperties=r.fontExtraProperties;this.loadedName=a.loadedName;this.isType3Font=a.isType3Font;this.missingFile=!1;this.cssFontInfo=a.cssFontInfo;this._charsCache=Object.create(null);this._glyphCache=Object.create(null);let i=!!(a.flags&Er);if(!i&&!a.isSimulatedFlags){const t=e.replaceAll(/[,_]/g,"-").split("-",1)[0],a=Qr();for(const e of t.split("+"))if(a[e]){i=!0;break}}this.isSerifFont=i;\
-this.isSymbolicFont=!!(a.flags&Pr);this.isMonospace=!!(a.flags&Nr);let{type:n,subtype:s}=a;this.type=n;this.subtype=s;this.systemFontInfo=a.systemFontInfo;const o=e.match(/^InvalidPDFjsFont_(.*)_\\d+$/);this.isInvalidPDFjsFont=!!o;this.isInvalidPDFjsFont?this.fallbackName=o[1]:this.isMonospace?this.fallbackName="monospace":this.isSerifFont?this.fallbackName="serif":this.fallbackName="sans-serif";if(this.systemFontInfo?.guessFallback){this.systemFontInfo.guessFallback=!1;this.systemFontInfo.css+=`\
-,${this.fallbackName}`}this.differences=a.differences;this.widths=a.widths;this.defaultWidth=a.defaultWidth;this.composite=a.composite;this.cMap=a.cMap;this.capHeight=a.capHeight/yi;this.ascent=a.ascent/yi;this.descent=a.descent/yi;this.lineHeight=this.ascent-this.descent;this.fontMatrix=a.fontMatrix;this.bbox=a.bbox;this.defaultEncoding=a.defaultEncoding;this.toUnicode=a.toUnicode;this.toFontChar=[];if("Type3"===a.type){for(let e=0;e<256;e++)this.toFontChar[e]=this.differences[e]||a.defaultEnco\
-ding[e];return}this.cidEncoding=a.cidEncoding||"";this.vertical=!!a.vertical;if(this.vertical){this.vmetrics=a.vmetrics;this.defaultVMetrics=a.defaultVMetrics}if(!t||t.isEmpty){t&&warn(\'Font file is empty in "\'+e+\'" (\'+this.loadedName+")");this.fallbackToSystemFont(a);return}[n,s]=getFontFileType(t,a);n===this.type&&s===this.subtype||info(`Inconsistent font file Type/SubType, expected: ${this.type}/${this.subtype} but found: ${n}/${s}.`);let c;try{switch(n){case"MMType1":info("MMType1 font ("+e+\
-"), falling back to Type1.");case"Type1":case"CIDFontType0":this.mimetype="font/opentype";const r="Type1C"===s||"CIDFontType0C"===s?new CFFFont(t,a):new Type1Font(e,t,a);adjustWidths(a);c=this.convert(e,r,a);break;case"OpenType":case"TrueType":case"CIDFontType2":this.mimetype="font/opentype";c=this.checkAndRepair(e,t,a);adjustWidths(a);this.isOpenType&&(n="OpenType");break;default:throw new FormatError(`Font ${n} is not supported`)}}catch(e){warn(e);this.fallbackToSystemFont(a);return}amendFallb\
-ackToUnicode(a);this.data=c;this.type=n;this.subtype=s;this.fontMatrix=a.fontMatrix;this.widths=a.widths;this.defaultWidth=a.defaultWidth;this.toUnicode=a.toUnicode;this.seacMap=a.seacMap}get renderer(){return shadow(this,"renderer",FontRendererFactory.create(this,Rr))}exportData(){const e=this.fontExtraProperties?[...wi,...xi]:wi,t=Object.create(null);for(const a of e){const e=this[a];void 0!==e&&(t[a]=e)}return t}fallbackToSystemFont(e){this.missingFile=!0;const{name:t,type:a}=this;let r=norma\
-lizeFontName(t);const i=Jr(),n=Zr(),s=!!i[r],o=!(!n[r]||!i[n[r]]);r=i[r]||n[r]||r;const c=ni()[r];if(c){isNaN(this.ascent)&&(this.ascent=c.ascent/yi);isNaN(this.descent)&&(this.descent=c.descent/yi);isNaN(this.capHeight)&&(this.capHeight=c.capHeight/yi)}this.bold=/bold/gi.test(r);this.italic=/oblique|italic/gi.test(r);this.black=/Black/g.test(t);const l=/Narrow/g.test(t);this.remeasure=(!s||l)&&Object.keys(this.widths).length>0;if((s||o)&&"CIDFontType2"===a&&this.cidEncoding.startsWith("Identity\
--")){const a=e.cidToGidMap,r=[];applyStandardFontGlyphMap(r,ti());/Arial-?Black/i.test(t)?applyStandardFontGlyphMap(r,ai()):/Calibri/i.test(t)&&applyStandardFontGlyphMap(r,ri());if(a){for(const e in r){const t=r[e];void 0!==a[t]&&(r[+e]=a[t])}a.length!==this.toUnicode.length&&e.hasIncludedToUnicodeMap&&this.toUnicode instanceof IdentityToUnicodeMap&&this.toUnicode.forEach((function(e,t){const i=r[e];void 0===a[i]&&(r[+e]=t)}))}this.toUnicode instanceof IdentityToUnicodeMap||this.toUnicode.forEac\
-h((function(e,t){r[+e]=t}));this.toFontChar=r;this.toUnicode=new ToUnicodeMap(r)}else if(/Symbol/i.test(r))this.toFontChar=buildToFontChar(Cr,Fr(),this.differences);else if(/Dingbats/i.test(r))this.toFontChar=buildToFontChar(vr,Ir(),this.differences);else if(s||o){const e=buildToFontChar(this.defaultEncoding,Fr(),this.differences);"CIDFontType2"!==a||this.cidEncoding.startsWith("Identity-")||this.toUnicode instanceof IdentityToUnicodeMap||this.toUnicode.forEach((function(t,a){e[+t]=a}));this.toF\
-ontChar=e}else{const e=Fr(),a=[];this.toUnicode.forEach(((t,r)=>{if(!this.composite){const a=getUnicodeForGlyph(this.differences[t]||this.defaultEncoding[t],e);-1!==a&&(r=a)}a[+t]=r}));this.composite&&this.toUnicode instanceof IdentityToUnicodeMap&&/Tahoma|Verdana/i.test(t)&&applyStandardFontGlyphMap(a,ti());this.toFontChar=a}amendFallbackToUnicode(e);this.loadedName=r.split("-",1)[0]}checkAndRepair(e,t,a){const r=["OS/2","cmap","head","hhea","hmtx","maxp","name","post","loca","glyf","fpgm","pre\
-p","cvt ","CFF "];function readTables(e,t){const a=Object.create(null);a["OS/2"]=null;a.cmap=null;a.head=null;a.hhea=null;a.hmtx=null;a.maxp=null;a.name=null;a.post=null;for(let i=0;i<t;i++){const t=readTableEntry(e);r.includes(t.tag)&&(0!==t.length&&(a[t.tag]=t))}return a}function readTableEntry(e){const t=e.getString(4),a=e.getInt32()>>>0,r=e.getInt32()>>>0,i=e.getInt32()>>>0,n=e.pos;e.pos=e.start||0;e.skip(r);const s=e.getBytes(i);e.pos=n;if("head"===t){s[8]=s[9]=s[10]=s[11]=0;s[17]|=32}retur\
-n{tag:t,checksum:a,length:i,offset:r,data:s}}function readOpenTypeHeader(e){return{version:e.getString(4),numTables:e.getUint16(),searchRange:e.getUint16(),entrySelector:e.getUint16(),rangeShift:e.getUint16()}}function sanitizeGlyph(e,t,a,r,i,n){const s={length:0,sizeOfInstructions:0};if(t<0||t>=e.length||a>e.length||a-t<=12)return s;const o=e.subarray(t,a),c=signedInt16(o[2],o[3]),l=signedInt16(o[4],o[5]),h=signedInt16(o[6],o[7]),u=signedInt16(o[8],o[9]);if(c>h){writeSignedInt16(o,2,h);writeSig\
-nedInt16(o,6,c)}if(l>u){writeSignedInt16(o,4,u);writeSignedInt16(o,8,l)}const d=signedInt16(o[0],o[1]);if(d<0){if(d<-1)return s;r.set(o,i);s.length=o.length;return s}let f,g=10,p=0;for(f=0;f<d;f++){p=(o[g]<<8|o[g+1])+1;g+=2}const m=g,b=o[g]<<8|o[g+1];s.sizeOfInstructions=b;g+=2+b;const y=g;let w=0;for(f=0;f<p;f++){const e=o[g++];192&e&&(o[g-1]=63&e);let t=2;2&e?t=1:16&e&&(t=0);let a=2;4&e?a=1:32&e&&(a=0);const r=t+a;w+=r;if(8&e){const e=o[g++];0===e&&(o[g-1]^=8);f+=e;w+=e*r}}if(0===w)return s;le\
-t x=g+w;if(x>o.length)return s;if(!n&&b>0){r.set(o.subarray(0,m),i);r.set([0,0],i+m);r.set(o.subarray(y,x),i+m+2);x-=b;o.length-x>3&&(x=x+3&-4);s.length=x;return s}if(o.length-x>3){x=x+3&-4;r.set(o.subarray(0,x),i);s.length=x;return s}r.set(o,i);s.length=o.length;return s}function readNameTable(e){const a=(t.start||0)+e.offset;t.pos=a;const r=[[],[]],i=[],n=e.length,s=a+n;if(0!==t.getUint16()||n<6)return[r,i];const o=t.getUint16(),c=t.getUint16();let l,h;for(l=0;l<o&&t.pos+12<=s;l++){const e={pl\
-atform:t.getUint16(),encoding:t.getUint16(),language:t.getUint16(),name:t.getUint16(),length:t.getUint16(),offset:t.getUint16()};(isMacNameRecord(e)||isWinNameRecord(e))&&i.push(e)}for(l=0,h=i.length;l<h;l++){const e=i[l];if(e.length<=0)continue;const n=a+c+e.offset;if(n+e.length>s)continue;t.pos=n;const o=e.name;if(e.encoding){let a="";for(let r=0,i=e.length;r<i;r+=2)a+=String.fromCharCode(t.getUint16());r[1][o]=a}else r[0][o]=t.getString(e.length)}return[r,i]}const i=[0,0,0,0,0,0,0,0,-2,-2,-2,\
--2,0,0,-2,-5,-1,-1,-1,-1,-1,-1,-1,-1,0,0,-1,0,-1,-1,-1,-1,1,-1,-999,0,1,0,-1,-2,0,-1,-2,-1,-1,0,-1,-1,0,0,-999,-999,-1,-1,-1,-1,-2,-999,-2,-2,-999,0,-2,-2,0,0,-2,0,-2,0,0,0,-2,-1,-1,1,1,0,0,-1,-1,-1,-1,-1,-1,-1,0,0,-1,0,-1,-1,0,-999,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,-2,-999,-999,-999,-999,-999,-1,-1,-2,-2,0,0,0,0,-1,-1,-999,-2,-2,0,0,-1,-2,-2,0,0,0,-1,-1,-1,-2];function sanitizeTTProgram(e,t){let a,r,n,s,o,c=e.data,l=0,h=0,u=0;const d=[],f=[],g=[];let p=t.tooComplexToFollowFunctions,m=!1\
-,b=0,y=0;for(let e=c.length;l<e;){const e=c[l++];if(64===e){r=c[l++];if(m||y)l+=r;else for(a=0;a<r;a++)d.push(c[l++])}else if(65===e){r=c[l++];if(m||y)l+=2*r;else for(a=0;a<r;a++){n=c[l++];d.push(n<<8|c[l++])}}else if(176==(248&e)){r=e-176+1;if(m||y)l+=r;else for(a=0;a<r;a++)d.push(c[l++])}else if(184==(248&e)){r=e-184+1;if(m||y)l+=2*r;else for(a=0;a<r;a++){n=c[l++];d.push(signedInt16(n,c[l++]))}}else if(43!==e||p)if(44!==e||p){if(45===e)if(m){m=!1;h=l}else{o=f.pop();if(!o){warn("TT: ENDF bad st\
-ack");t.hintsValid=!1;return}s=g.pop();c=o.data;l=o.i;t.functionsStackDeltas[s]=d.length-o.stackTop}else if(137===e){if(m||y){warn("TT: nested IDEFs not allowed");p=!0}m=!0;u=l}else if(88===e)++b;else if(27===e)y=b;else if(89===e){y===b&&(y=0);--b}else if(28===e&&!m&&!y){const e=d.at(-1);e>0&&(l+=e-1)}}else{if(m||y){warn("TT: nested FDEFs not allowed");p=!0}m=!0;u=l;s=d.pop();t.functionsDefined[s]={data:c,i:l}}else if(!m&&!y){s=d.at(-1);if(isNaN(s))info("TT: CALL empty stack (or invalid entry)."\
-);else{t.functionsUsed[s]=!0;if(s in t.functionsStackDeltas){const e=d.length+t.functionsStackDeltas[s];if(e<0){warn("TT: CALL invalid functions stack delta.");t.hintsValid=!1;return}d.length=e}else if(s in t.functionsDefined&&!g.includes(s)){f.push({data:c,i:l,stackTop:d.length-1});g.push(s);o=t.functionsDefined[s];if(!o){warn("TT: CALL non-existent function");t.hintsValid=!1;return}c=o.data;l=o.i}}}if(!m&&!y){let t=0;e<=142?t=i[e]:e>=192&&e<=223?t=-1:e>=224&&(t=-2);if(e>=113&&e<=117){r=d.pop()\
-;isNaN(r)||(t=2*-r)}for(;t<0&&d.length>0;){d.pop();t++}for(;t>0;){d.push(NaN);t--}}}t.tooComplexToFollowFunctions=p;const w=[c];l>c.length&&w.push(new Uint8Array(l-c.length));if(u>h){warn("TT: complementing a missing function tail");w.push(new Uint8Array([34,45]))}!function foldTTTable(e,t){if(t.length>1){let a,r,i=0;for(a=0,r=t.length;a<r;a++)i+=t[a].length;i=i+3&-4;const n=new Uint8Array(i);let s=0;for(a=0,r=t.length;a<r;a++){n.set(t[a],s);s+=t[a].length}e.data=n;e.length=i}}(e,w)}let n,s,o,c;\
-if(isTrueTypeCollectionFile(t=new Stream(new Uint8Array(t.getBytes())))){const e=function readTrueTypeCollectionData(e,t){const{numFonts:a,offsetTable:r}=function readTrueTypeCollectionHeader(e){const t=e.getString(4);assert("ttcf"===t,"Must be a TrueType Collection font.");const a=e.getUint16(),r=e.getUint16(),i=e.getInt32()>>>0,n=[];for(let t=0;t<i;t++)n.push(e.getInt32()>>>0);const s={ttcTag:t,majorVersion:a,minorVersion:r,numFonts:i,offsetTable:n};switch(a){case 1:return s;case 2:s.dsigTag=e\
-.getInt32()>>>0;s.dsigLength=e.getInt32()>>>0;s.dsigOffset=e.getInt32()>>>0;return s}throw new FormatError(`Invalid TrueType Collection majorVersion: ${a}.`)}(e),i=t.split("+");let n;for(let s=0;s<a;s++){e.pos=(e.start||0)+r[s];const a=readOpenTypeHeader(e),o=readTables(e,a.numTables);if(!o.name)throw new FormatError(\'TrueType Collection font must contain a "name" table.\');const[c]=readNameTable(o.name);for(let e=0,r=c.length;e<r;e++)for(let r=0,s=c[e].length;r<s;r++){const s=c[e][r]?.replaceAll\
-(/\\s/g,"");if(s){if(s===t)return{header:a,tables:o};if(!(i.length<2))for(const e of i)s===e&&(n={name:e,header:a,tables:o})}}}if(n){warn(`TrueType Collection does not contain "${t}" font, falling back to "${n.name}" font instead.`);return{header:n.header,tables:n.tables}}throw new FormatError(`TrueType Collection does not contain "${t}" font.`)}(t,this.name);n=e.header;s=e.tables}else{n=readOpenTypeHeader(t);s=readTables(t,n.numTables)}const l=!s["CFF "];if(l){if(!s.loca)throw new FormatError(\'R\
-equired "loca" table is not found\');if(!s.glyf){warn(\'Required "glyf" table is not found -- trying to recover.\');s.glyf={tag:"glyf",data:new Uint8Array(0)}}this.isOpenType=!1}else{const t=a.composite&&(a.cidToGidMap?.length>0||!(a.cMap instanceof IdentityCMap));if("OTTO"===n.version&&!t||!s.head||!s.hhea||!s.maxp||!s.post){c=new Stream(s["CFF "].data);o=new CFFFont(c,a);return this.convert(e,o,a)}delete s.glyf;delete s.loca;delete s.fpgm;delete s.prep;delete s["cvt "];this.isOpenType=!0}if(!s.ma\
-xp)throw new FormatError(\'Required "maxp" table is not found\');t.pos=(t.start||0)+s.maxp.offset;let h=t.getInt32();const u=t.getUint16();if(65536!==h&&20480!==h){if(6===s.maxp.length)h=20480;else{if(!(s.maxp.length>=32))throw new FormatError(\'"maxp" table has a wrong version number\');h=65536}!function writeUint32(e,t,a){e[t+3]=255&a;e[t+2]=a>>>8;e[t+1]=a>>>16;e[t]=a>>>24}(s.maxp.data,0,h)}if(a.scaleFactors?.length===u&&l){const{scaleFactors:e}=a,t=int16(s.head.data[50],s.head.data[51]),r=new Gly\
-fTable({glyfTable:s.glyf.data,isGlyphLocationsLong:t,locaTable:s.loca.data,numGlyphs:u});r.scale(e);const{glyf:i,loca:n,isLocationLong:o}=r.write();s.glyf.data=i;s.loca.data=n;if(o!==!!t){s.head.data[50]=0;s.head.data[51]=o?1:0}const c=s.hmtx.data;for(let t=0;t<u;t++){const a=4*t,r=Math.round(e[t]*int16(c[a],c[a+1]));c[a]=r>>8&255;c[a+1]=255&r;writeSignedInt16(c,a+2,Math.round(e[t]*signedInt16(c[a+2],c[a+3])))}}let d=u+1,f=!0;if(d>65535){f=!1;d=u;warn("Not enough space in glyfs to duplicate firs\
-t glyph.")}let g=0,p=0;if(h>=65536&&s.maxp.length>=32){t.pos+=8;if(t.getUint16()>2){s.maxp.data[14]=0;s.maxp.data[15]=2}t.pos+=4;g=t.getUint16();t.pos+=4;p=t.getUint16()}s.maxp.data[4]=d>>8;s.maxp.data[5]=255&d;const m=function sanitizeTTPrograms(e,t,a,r){const i={functionsDefined:[],functionsUsed:[],functionsStackDeltas:[],tooComplexToFollowFunctions:!1,hintsValid:!0};e&&sanitizeTTProgram(e,i);t&&sanitizeTTProgram(t,i);e&&function checkInvalidFunctions(e,t){if(!e.tooComplexToFollowFunctions)if(\
-e.functionsDefined.length>t){warn("TT: more functions defined than expected");e.hintsValid=!1}else for(let a=0,r=e.functionsUsed.length;a<r;a++){if(a>t){warn("TT: invalid function id: "+a);e.hintsValid=!1;return}if(e.functionsUsed[a]&&!e.functionsDefined[a]){warn("TT: undefined function: "+a);e.hintsValid=!1;return}}}(i,r);if(a&&1&a.length){const e=new Uint8Array(a.length+1);e.set(a.data);a.data=e}return i.hintsValid}(s.fpgm,s.prep,s["cvt "],g);if(!m){delete s.fpgm;delete s.prep;delete s["cvt "]\
-}!function sanitizeMetrics(e,t,a,r,i,n){if(!t){a&&(a.data=null);return}e.pos=(e.start||0)+t.offset;e.pos+=4;e.pos+=2;e.pos+=2;e.pos+=2;e.pos+=2;e.pos+=2;e.pos+=2;e.pos+=2;e.pos+=2;e.pos+=2;const s=e.getUint16();e.pos+=8;e.pos+=2;let o=e.getUint16();if(0!==s){if(!(2&int16(r.data[44],r.data[45]))){t.data[22]=0;t.data[23]=0}}if(o>i){info(`The numOfMetrics (${o}) should not be greater than the numGlyphs (${i}).`);o=i;t.data[34]=(65280&o)>>8;t.data[35]=255&o}const c=i-o-(a.length-4*o>>1);if(c>0){cons\
-t e=new Uint8Array(a.length+2*c);e.set(a.data);if(n){e[a.length]=a.data[2];e[a.length+1]=a.data[3]}a.data=e}}(t,s.hhea,s.hmtx,s.head,d,f);if(!s.head)throw new FormatError(\'Required "head" table is not found\');!function sanitizeHead(e,t,a){const r=e.data,i=function int32(e,t,a,r){return(e<<24)+(t<<16)+(a<<8)+r}(r[0],r[1],r[2],r[3]);if(i>>16!=1){info("Attempting to fix invalid version in head table: "+i);r[0]=0;r[1]=1;r[2]=0;r[3]=0}const n=int16(r[50],r[51]);if(n<0||n>1){info("Attempting to fix in\
-valid indexToLocFormat in head table: "+n);const e=t+1;if(a===e<<1){r[50]=0;r[51]=0}else{if(a!==e<<2)throw new FormatError("Could not fix indexToLocFormat: "+n);r[50]=0;r[51]=1}}}(s.head,u,l?s.loca.length:0);let b=Object.create(null);if(l){const e=int16(s.head.data[50],s.head.data[51]),t=function sanitizeGlyphLocations(e,t,a,r,i,n,s){let o,c,l;if(r){o=4;c=function fontItemDecodeLong(e,t){return e[t]<<24|e[t+1]<<16|e[t+2]<<8|e[t+3]};l=function fontItemEncodeLong(e,t,a){e[t]=a>>>24&255;e[t+1]=a>>1\
-6&255;e[t+2]=a>>8&255;e[t+3]=255&a}}else{o=2;c=function fontItemDecode(e,t){return e[t]<<9|e[t+1]<<1};l=function fontItemEncode(e,t,a){e[t]=a>>9&255;e[t+1]=a>>1&255}}const h=n?a+1:a,u=o*(1+h),d=new Uint8Array(u);d.set(e.data.subarray(0,u));e.data=d;const f=t.data,g=f.length,p=new Uint8Array(g);let m,b;const y=[];for(m=0,b=0;m<a+1;m++,b+=o){let e=c(d,b);e>g&&(e=g);y.push({index:m,offset:e,endOffset:0})}y.sort(((e,t)=>e.offset-t.offset));for(m=0;m<a;m++)y[m].endOffset=y[m+1].offset;y.sort(((e,t)=>\
-e.index-t.index));for(m=0;m<a;m++){const{offset:e,endOffset:t}=y[m];if(0!==e||0!==t)break;const a=y[m+1].offset;if(0!==a){y[m].endOffset=a;break}}const w=y.at(-2);0!==w.offset&&0===w.endOffset&&(w.endOffset=g);const x=Object.create(null);let S=0;l(d,0,S);for(m=0,b=o;m<a;m++,b+=o){const e=sanitizeGlyph(f,y[m].offset,y[m].endOffset,p,S,i),t=e.length;0===t&&(x[m]=!0);e.sizeOfInstructions>s&&(s=e.sizeOfInstructions);S+=t;l(d,b,S)}if(0===S){const e=new Uint8Array([0,1,0,0,0,0,0,0,0,0,0,0,0,0,49,0]);f\
-or(m=0,b=o;m<h;m++,b+=o)l(d,b,e.length);t.data=e}else if(n){const a=c(d,o);if(p.length>a+S)t.data=p.subarray(0,a+S);else{t.data=new Uint8Array(a+S);t.data.set(p.subarray(0,S))}t.data.set(p.subarray(0,a),S);l(e.data,d.length-o,S+a)}else t.data=p.subarray(0,S);return{missingGlyphs:x,maxSizeOfInstructions:s}}(s.loca,s.glyf,u,e,m,f,p);b=t.missingGlyphs;if(h>=65536&&s.maxp.length>=32){s.maxp.data[26]=t.maxSizeOfInstructions>>8;s.maxp.data[27]=255&t.maxSizeOfInstructions}}if(!s.hhea)throw new FormatEr\
-ror(\'Required "hhea" table is not found\');if(0===s.hhea.data[10]&&0===s.hhea.data[11]){s.hhea.data[10]=255;s.hhea.data[11]=255}const y={unitsPerEm:int16(s.head.data[18],s.head.data[19]),yMax:signedInt16(s.head.data[42],s.head.data[43]),yMin:signedInt16(s.head.data[38],s.head.data[39]),ascent:signedInt16(s.hhea.data[4],s.hhea.data[5]),descent:signedInt16(s.hhea.data[6],s.hhea.data[7]),lineGap:signedInt16(s.hhea.data[8],s.hhea.data[9])};this.ascent=y.ascent/y.unitsPerEm;this.descent=y.descent/y.un\
-itsPerEm;this.lineGap=y.lineGap/y.unitsPerEm;if(this.cssFontInfo?.lineHeight){this.lineHeight=this.cssFontInfo.metrics.lineHeight;this.lineGap=this.cssFontInfo.metrics.lineGap}else this.lineHeight=this.ascent-this.descent+this.lineGap;s.post&&function readPostScriptTable(e,a,r){const i=(t.start||0)+e.offset;t.pos=i;const n=i+e.length,s=t.getInt32();t.skip(28);let o,c,l=!0;switch(s){case 65536:o=jr;break;case 131072:const e=t.getUint16();if(e!==r){l=!1;break}const i=[];for(c=0;c<e;++c){const e=t.\
-getUint16();if(e>=32768){l=!1;break}i.push(e)}if(!l)break;const h=[],u=[];for(;t.pos<n;){const e=t.getByte();u.length=e;for(c=0;c<e;++c)u[c]=String.fromCharCode(t.getByte());h.push(u.join(""))}o=[];for(c=0;c<e;++c){const e=i[c];e<258?o.push(jr[e]):o.push(h[e-258])}break;case 196608:break;default:warn("Unknown/unsupported post table version "+s);l=!1;a.defaultEncoding&&(o=a.defaultEncoding)}a.glyphNames=o;return l}(s.post,a,u);s.post={tag:"post",data:createPostTable(a)};const w=Object.create(null\
-);function hasGlyph(e){return!b[e]}if(a.composite){const e=a.cidToGidMap||[],t=0===e.length;a.cMap.forEach((function(a,r){"string"==typeof r&&(r=convertCidString(a,r,!0));if(r>65535)throw new FormatError("Max size of CID is 65,535");let i=-1;t?i=r:void 0!==e[r]&&(i=e[r]);i>=0&&i<u&&hasGlyph(i)&&(w[a]=i)}))}else{const e=function readCmapTable(e,t,a,r){if(!e){warn("No cmap table available.");return{platformId:-1,encodingId:-1,mappings:[],hasShortCmap:!1}}let i,n=(t.start||0)+e.offset;t.pos=n;t.ski\
-p(2);const s=t.getUint16();let o,c=!1;for(let e=0;e<s;e++){const i=t.getUint16(),n=t.getUint16(),l=t.getInt32()>>>0;let h=!1;if(o?.platformId!==i||o?.encodingId!==n){if(0!==i||0!==n&&1!==n&&3!==n)if(1===i&&0===n)h=!0;else if(3!==i||1!==n||!r&&o){if(a&&3===i&&0===n){h=!0;let a=!0;if(e<s-1){const e=t.peekBytes(2);int16(e[0],e[1])<i&&(a=!1)}a&&(c=!0)}}else{h=!0;a||(c=!0)}else h=!0;h&&(o={platformId:i,encodingId:n,offset:l});if(c)break}}o&&(t.pos=n+o.offset);if(!o||-1===t.peekByte()){warn("Could not\
- find a preferred cmap table.");return{platformId:-1,encodingId:-1,mappings:[],hasShortCmap:!1}}const l=t.getUint16();let h=!1;const u=[];let d,f;if(0===l){t.skip(4);for(d=0;d<256;d++){const e=t.getByte();e&&u.push({charCode:d,glyphId:e})}h=!0}else if(2===l){t.skip(4);const e=[];let a=0;for(let r=0;r<256;r++){const r=t.getUint16()>>3;e.push(r);a=Math.max(r,a)}const r=[];for(let e=0;e<=a;e++)r.push({firstCode:t.getUint16(),entryCount:t.getUint16(),idDelta:signedInt16(t.getByte(),t.getByte()),idRa\
-ngePos:t.pos+t.getUint16()});for(let a=0;a<256;a++)if(0===e[a]){t.pos=r[0].idRangePos+2*a;f=t.getUint16();u.push({charCode:a,glyphId:f})}else{const i=r[e[a]];for(d=0;d<i.entryCount;d++){const e=(a<<8)+d+i.firstCode;t.pos=i.idRangePos+2*d;f=t.getUint16();0!==f&&(f=(f+i.idDelta)%65536);u.push({charCode:e,glyphId:f})}}}else if(4===l){t.skip(4);const e=t.getUint16()>>1;t.skip(6);const a=[];let r;for(r=0;r<e;r++)a.push({end:t.getUint16()});t.skip(2);for(r=0;r<e;r++)a[r].start=t.getUint16();for(r=0;r<\
-e;r++)a[r].delta=t.getUint16();let s,o=0;for(r=0;r<e;r++){i=a[r];const n=t.getUint16();if(n){s=(n>>1)-(e-r);i.offsetIndex=s;o=Math.max(o,s+i.end-i.start+1)}else i.offsetIndex=-1}const c=[];for(d=0;d<o;d++)c.push(t.getUint16());for(r=0;r<e;r++){i=a[r];n=i.start;const e=i.end,t=i.delta;s=i.offsetIndex;for(d=n;d<=e;d++)if(65535!==d){f=s<0?d:c[s+d-n];f=f+t&65535;u.push({charCode:d,glyphId:f})}}}else if(6===l){t.skip(4);const e=t.getUint16(),a=t.getUint16();for(d=0;d<a;d++){f=t.getUint16();const a=e+\
-d;u.push({charCode:a,glyphId:f})}}else{if(12!==l){warn("cmap table has unsupported format: "+l);return{platformId:-1,encodingId:-1,mappings:[],hasShortCmap:!1}}{t.skip(10);const e=t.getInt32()>>>0;for(d=0;d<e;d++){const e=t.getInt32()>>>0,a=t.getInt32()>>>0;let r=t.getInt32()>>>0;for(let t=e;t<=a;t++)u.push({charCode:t,glyphId:r++})}}}u.sort(((e,t)=>e.charCode-t.charCode));const g=[],p=new Set;for(const e of u){const{charCode:t}=e;if(!p.has(t)){p.add(t);g.push(e)}}return{platformId:o.platformId,\
-encodingId:o.encodingId,mappings:g,hasShortCmap:h}}(s.cmap,t,this.isSymbolicFont,a.hasEncoding),r=e.platformId,i=e.encodingId,n=e.mappings;let o=[],c=!1;!a.hasEncoding||"MacRomanEncoding"!==a.baseEncodingName&&"WinAnsiEncoding"!==a.baseEncodingName||(o=getEncoding(a.baseEncodingName));if(a.hasEncoding&&!this.isSymbolicFont&&(3===r&&1===i||1===r&&0===i)){const e=Fr();for(let t=0;t<256;t++){let s;s=void 0!==this.differences[t]?this.differences[t]:o.length&&""!==o[t]?o[t]:Ar[t];if(!s)continue;const\
- c=recoverGlyphName(s,e);let l;3===r&&1===i?l=e[c]:1===r&&0===i&&(l=Sr.indexOf(c));if(void 0===l){if(!a.glyphNames&&a.hasIncludedToUnicodeMap&&!(this.toUnicode instanceof IdentityToUnicodeMap)){const e=this.toUnicode.get(t);e&&(l=e.codePointAt(0))}if(void 0===l)continue}for(const e of n)if(e.charCode===l){w[t]=e.glyphId;break}}}else if(0===r){for(const e of n)w[e.charCode]=e.glyphId;c=!0}else if(3===r&&0===i)for(const e of n){let t=e.charCode;t>=61440&&t<=61695&&(t&=255);w[t]=e.glyphId}else for(\
-const e of n)w[e.charCode]=e.glyphId;if(a.glyphNames&&(o.length||this.differences.length))for(let e=0;e<256;++e){if(!c&&void 0!==w[e])continue;const t=this.differences[e]||o[e];if(!t)continue;const r=a.glyphNames.indexOf(t);r>0&&hasGlyph(r)&&(w[e]=r)}}0===w.length&&(w[0]=0);let x=d-1;f||(x=0);if(!a.cssFontInfo){const e=adjustMapping(w,hasGlyph,x,this.toUnicode);this.toFontChar=e.toFontChar;s.cmap={tag:"cmap",data:createCmapTable(e.charCodeToGlyphId,e.toUnicodeExtraMap,d)};s["OS/2"]&&function val\
-idateOS2Table(e,t){t.pos=(t.start||0)+e.offset;const a=t.getUint16();t.skip(60);const r=t.getUint16();if(a<4&&768&r)return!1;if(t.getUint16()>t.getUint16())return!1;t.skip(6);if(0===t.getUint16())return!1;e.data[8]=e.data[9]=0;return!0}(s["OS/2"],t)||(s["OS/2"]={tag:"OS/2",data:createOS2Table(a,e.charCodeToGlyphId,y)})}if(!l)try{c=new Stream(s["CFF "].data);o=new CFFParser(c,a,Rr).parse();o.duplicateFirstGlyph();const e=new CFFCompiler(o);s["CFF "].data=e.compile()}catch{warn("Failed to compile \
-font "+a.loadedName)}if(s.name){const[t,r]=readNameTable(s.name);s.name.data=createNameTable(e,t);this.psName=t[0][6]||null;a.composite||function adjustTrueTypeToUnicode(e,t,a){if(e.isInternalFont)return;if(e.hasIncludedToUnicodeMap)return;if(e.hasEncoding)return;if(e.toUnicode instanceof IdentityToUnicodeMap)return;if(!t)return;if(0===a.length)return;if(e.defaultEncoding===kr)return;for(const e of a)if(!isWinNameRecord(e))return;const r=kr,i=[],n=Fr();for(const e in r){const t=r[e];if(""===t)co\
-ntinue;const a=n[t];void 0!==a&&(i[e]=String.fromCharCode(a))}i.length>0&&e.toUnicode.amend(i)}(a,this.isSymbolicFont,r)}else s.name={tag:"name",data:createNameTable(this.name)};const S=new OpenTypeFileBuilder(n.version);for(const e in s)S.addTable(e,s[e].data);return S.toArray()}convert(e,a,r){r.fixedPitch=!1;r.builtInEncoding&&function adjustType1ToUnicode(e,t){if(e.isInternalFont)return;if(e.hasIncludedToUnicodeMap)return;if(t===e.defaultEncoding)return;if(e.toUnicode instanceof IdentityToUni\
-codeMap)return;const a=[],r=Fr();for(const i in t){if(e.hasEncoding&&(e.baseEncodingName||void 0!==e.differences[i]))continue;const n=getUnicodeForGlyph(t[i],r);-1!==n&&(a[i]=String.fromCharCode(n))}a.length>0&&e.toUnicode.amend(a)}(r,r.builtInEncoding);let i=1;a instanceof CFFFont&&(i=a.numGlyphs-1);const n=a.getGlyphMapping(r);let s=null,o=n,c=null;if(!r.cssFontInfo){s=adjustMapping(n,a.hasGlyphId.bind(a),i,this.toUnicode);this.toFontChar=s.toFontChar;o=s.charCodeToGlyphId;c=s.toUnicodeExtraMa\
-p}const l=a.numGlyphs;function getCharCodes(e,t){let a=null;for(const r in e)t===e[r]&&(a||=[]).push(0|r);return a}function createCharCode(e,t){for(const a in e)if(t===e[a])return 0|a;s.charCodeToGlyphId[s.nextAvailableFontCharCode]=t;return s.nextAvailableFontCharCode++}const h=a.seacs;if(s&&h?.length){const e=r.fontMatrix||t,i=a.getCharset(),o=Object.create(null);for(let t in h){t|=0;const a=h[t],r=Ar[a[2]],c=Ar[a[3]],l=i.indexOf(r),u=i.indexOf(c);if(l<0||u<0)continue;const d={x:a[0]*e[0]+a[1]\
-*e[2]+e[4],y:a[0]*e[1]+a[1]*e[3]+e[5]},f=getCharCodes(n,t);if(f)for(const e of f){const t=s.charCodeToGlyphId,a=createCharCode(t,l),r=createCharCode(t,u);o[e]={baseFontCharCode:a,accentFontCharCode:r,accentOffset:d}}}r.seacMap=o}const u=r.fontMatrix?1/Math.max(...r.fontMatrix.slice(0,4).map(Math.abs)):1e3,d=new OpenTypeFileBuilder("OTTO");d.addTable("CFF ",a.data);d.addTable("OS/2",createOS2Table(r,o));d.addTable("cmap",createCmapTable(o,c,l));d.addTable("head","\\0
\\0\\0\\0\\0\\0\\0\\0\\0\\0_<õ\\0\\0"+s\
-afeString16(u)+"\\0\\0\\0\\0ž\\v~\'\\0\\0\\0\\0ž\\v~\'\\0\\0"+safeString16(r.descent)+"ÿ"+safeString16(r.ascent)+string16(r.italicAngle?2:0)+"\\0\\0\\0\\0\\0\\0\\0");d.addTable("hhea","\\0
\\0\\0"+safeString16(r.ascent)+safeString16(r.descent)+"\\0\\0ÿÿ\\0\\0\\0\\0\\0\\0"+safeString16(r.capHeight)+safeString16(Math.tan(r.italicAngle)*r.xHeight)+"\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0"+string16(l));d.addTable("hmtx",function fontFieldsHmtx(){const e=a.charstrings,t=a.cff?a.cff.widths:null;let r="\\0\\0\\0\\0";for(let a=1,i=l;a<i;a++){let i=0;i\
-f(e){const t=e[a-1];i="width"in t?t.width:0}else t&&(i=Math.ceil(t[a]||0));r+=string16(i)+string16(0)}return r}());d.addTable("maxp","\\0\\0P\\0"+string16(l));d.addTable("name",createNameTable(e));d.addTable("post",createPostTable(r));return d.toArray()}get _spaceWidth(){const e=["space","minus","one","i","I"];let t;for(const a of e){if(a in this.widths){t=this.widths[a];break}const e=Fr()[a];let r=0;if(this.composite&&this.cMap.contains(e)){r=this.cMap.lookup(e);"string"==typeof r&&(r=convertCidSt\
-ring(e,r))}!r&&this.toUnicode&&(r=this.toUnicode.charCodeOf(e));r<=0&&(r=e);t=this.widths[r];if(t)break}return shadow(this,"_spaceWidth",t||this.defaultWidth)}_charToGlyph(e,t=!1){let a,r,i,n=this._glyphCache[e];if(n?.isSpace===t)return n;let s=e;if(this.cMap?.contains(e)){s=this.cMap.lookup(e);"string"==typeof s&&(s=convertCidString(e,s))}r=this.widths[s];"number"!=typeof r&&(r=this.defaultWidth);const o=this.vmetrics?.[s];let c=this.toUnicode.get(e)||e;"number"==typeof c&&(c=String.fromCharCod\
-e(c));let l=void 0!==this.toFontChar[e];a=this.toFontChar[e]||e;if(this.missingFile){const t=this.differences[e]||this.defaultEncoding[e];if((".notdef"===t||""===t)&&"Type1"===this.type){a=32;if(""===t){r||=this._spaceWidth;c=String.fromCharCode(a)}}a=function mapSpecialUnicodeValues(e){return e>=65520&&e<=65535?0:e>=62976&&e<=63743?Tr()[e]||e:173===e?45:e}(a)}this.isType3Font&&(i=a);let h=null;if(this.seacMap?.[e]){l=!0;const t=this.seacMap[e];a=t.baseFontCharCode;h={fontChar:String.fromCodePoi\
-nt(t.accentFontCharCode),offset:t.accentOffset}}let u="";"number"==typeof a&&(a<=1114111?u=String.fromCodePoint(a):warn(`charToGlyph - invalid fontCharCode: ${a}`));if(this.missingFile&&this.vertical&&1===u.length){const e=_r()[u.charCodeAt(0)];e&&(u=c=String.fromCharCode(e))}n=new fonts_Glyph(e,u,c,h,r,o,i,t,l);return this._glyphCache[e]=n}charsToGlyphs(e){let t=this._charsCache[e];if(t)return t;t=[];if(this.cMap){const a=Object.create(null),r=e.length;let i=0;for(;i<r;){this.cMap.readCharCode(\
-e,i,a);const{charcode:r,length:n}=a;i+=n;const s=this._charToGlyph(r,1===n&&32===e.charCodeAt(i-1));t.push(s)}}else for(let a=0,r=e.length;a<r;++a){const r=e.charCodeAt(a),i=this._charToGlyph(r,32===r);t.push(i)}return this._charsCache[e]=t}getCharPositions(e){const t=[];if(this.cMap){const a=Object.create(null);let r=0;for(;r<e.length;){this.cMap.readCharCode(e,r,a);const i=a.length;t.push([r,r+i]);r+=i}}else for(let a=0,r=e.length;a<r;++a)t.push([a,a+1]);return t}get glyphCacheValues(){return \
-Object.values(this._glyphCache)}encodeString(e){const t=[],a=[],hasCurrentBufErrors=()=>t.length%2==1,r=this.toUnicode instanceof IdentityToUnicodeMap?e=>this.toUnicode.charCodeOf(e):e=>this.toUnicode.charCodeOf(String.fromCodePoint(e));for(let i=0,n=e.length;i<n;i++){const n=e.codePointAt(i);n>55295&&(n<57344||n>65533)&&i++;if(this.toUnicode){const e=r(n);if(-1!==e){if(hasCurrentBufErrors()){t.push(a.join(""));a.length=0}for(let t=(this.cMap?this.cMap.getCharCodeLength(e):1)-1;t>=0;t--)a.push(S\
-tring.fromCharCode(e>>8*t&255));continue}}if(!hasCurrentBufErrors()){t.push(a.join(""));a.length=0}a.push(String.fromCodePoint(n))}t.push(a.join(""));return t}}class ErrorFont{constructor(e){this.error=e;this.loadedName="g_font_error";this.missingFile=!0}charsToGlyphs(){return[]}encodeString(e){return[e]}exportData(){return{error:this.error}}}const Si=2,Ai=3,ki=4,Ci=5,vi=6,Fi=7;class Pattern{constructor(){unreachable("Cannot initialize Pattern.")}static parseShading(e,t,a,r,i,n){const s=e instan\
-ceof BaseStream?e.dict:e,o=s.get("ShadingType");try{switch(o){case Si:case Ai:return new RadialAxialShading(s,t,a,r,i,n);case ki:case Ci:case vi:case Fi:return new MeshShading(e,t,a,r,i,n);default:throw new FormatError("Unsupported ShadingType: "+o)}}catch(e){if(e instanceof MissingDataException)throw e;warn(e);return new DummyShading}}}class BaseShading{static SMALL_NUMBER=1e-6;getIR(){unreachable("Abstract method `getIR` called.")}}class RadialAxialShading extends BaseShading{constructor(e,t,a\
-,r,i,n){super();this.shadingType=e.get("ShadingType");let s=0;this.shadingType===Si?s=4:this.shadingType===Ai&&(s=6);this.coordsArr=e.getArray("Coords");if(!isNumberArray(this.coordsArr,s))throw new FormatError("RadialAxialShading: Invalid /Coords array.");const o=ColorSpaceUtils.parse({cs:e.getRaw("CS")||e.getRaw("ColorSpace"),xref:t,resources:a,pdfFunctionFactory:r,globalColorSpaceCache:i,localColorSpaceCache:n});this.bbox=lookupNormalRect(e.getArray("BBox"),null);let c=0,l=1;const h=e.getArra\
-y("Domain");isNumberArray(h,2)&&([c,l]=h);let u=!1,d=!1;const f=e.getArray("Extend");(function isBooleanArray(e,t){return Array.isArray(e)&&(null===t||e.length===t)&&e.every((e=>"boolean"==typeof e))})(f,2)&&([u,d]=f);if(!(this.shadingType!==Ai||u&&d)){const[e,t,a,r,i,n]=this.coordsArr,s=Math.hypot(e-r,t-i);a<=n+s&&n<=a+s&&warn("Unsupported radial gradient.")}this.extendStart=u;this.extendEnd=d;const g=e.getRaw("Function"),p=r.create(g,!0),m=(l-c)/840,b=this.colorStops=[];if(c>=l||m<=0){info("Ba\
-d shading domain.");return}const y=new Float32Array(o.numComps),w=new Float32Array(1);let x=0;w[0]=c;p(w,0,y,0);const S=new Uint8ClampedArray(3);o.getRgb(y,0,S);let[k,C,v]=S;b.push([0,Util.makeHexColor(k,C,v)]);let F=1;w[0]=c+m;p(w,0,y,0);o.getRgb(y,0,S);let[T,O,M]=S,D=T-k+1,R=O-C+1,N=M-v+1,E=T-k-1,L=O-C-1,j=M-v-1;for(let e=2;e<840;e++){w[0]=c+e*m;p(w,0,y,0);o.getRgb(y,0,S);const[t,a,r]=S,i=e-x;D=Math.min(D,(t-k+1)/i);R=Math.min(R,(a-C+1)/i);N=Math.min(N,(r-v+1)/i);E=Math.max(E,(t-k-1)/i);L=Math\
-.max(L,(a-C-1)/i);j=Math.max(j,(r-v-1)/i);if(!(E<=D&&L<=R&&j<=N)){const e=Util.makeHexColor(T,O,M);b.push([F/840,e]);D=t-T+1;R=a-O+1;N=r-M+1;E=t-T-1;L=a-O-1;j=r-M-1;x=F;k=T;C=O;v=M}F=e;T=t;O=a;M=r}b.push([1,Util.makeHexColor(T,O,M)]);let _="transparent";e.has("Background")&&(_=o.getRgbHex(e.get("Background"),0));if(!u){b.unshift([0,_]);b[1][0]+=BaseShading.SMALL_NUMBER}if(!d){b.at(-1)[0]-=BaseShading.SMALL_NUMBER;b.push([1,_])}this.colorStops=b}getIR(){const{coordsArr:e,shadingType:t}=this;let a\
-,r,i,n,s;if(t===Si){r=[e[0],e[1]];i=[e[2],e[3]];n=null;s=null;a="axial"}else if(t===Ai){r=[e[0],e[1]];i=[e[3],e[4]];n=e[2];s=e[5];a="radial"}else unreachable(`getPattern type unknown: ${t}`);return["RadialAxial",a,this.bbox,this.colorStops,r,i,n,s]}}class MeshStreamReader{constructor(e,t){this.stream=e;this.context=t;this.buffer=0;this.bufferLength=0;const a=t.numComps;this.tmpCompsBuf=new Float32Array(a);const r=t.colorSpace.numComps;this.tmpCsCompsBuf=t.colorFn?new Float32Array(r):this.tmpComp\
-sBuf}get hasData(){if(this.stream.end)return this.stream.pos<this.stream.end;if(this.bufferLength>0)return!0;const e=this.stream.getByte();if(e<0)return!1;this.buffer=e;this.bufferLength=8;return!0}readBits(e){const{stream:t}=this;let{buffer:a,bufferLength:r}=this;if(32===e){if(0===r)return t.getInt32()>>>0;a=a<<24|t.getByte()<<16|t.getByte()<<8|t.getByte();const e=t.getByte();this.buffer=e&(1<<r)-1;return(a<<8-r|(255&e)>>r)>>>0}if(8===e&&0===r)return t.getByte();for(;r<e;){a=a<<8|t.getByte();r+\
-=8}r-=e;this.bufferLength=r;this.buffer=a&(1<<r)-1;return a>>r}align(){this.buffer=0;this.bufferLength=0}readFlag(){return this.readBits(this.context.bitsPerFlag)}readCoordinate(){const{bitsPerCoordinate:e,decode:t}=this.context,a=this.readBits(e),r=this.readBits(e),i=e<32?1/((1<<e)-1):2.3283064365386963e-10;return[a*i*(t[1]-t[0])+t[0],r*i*(t[3]-t[2])+t[2]]}readComponents(){const{bitsPerComponent:e,colorFn:t,colorSpace:a,decode:r,numComps:i}=this.context,n=e<32?1/((1<<e)-1):2.3283064365386963e-1\
-0,s=this.tmpCompsBuf;for(let t=0,a=4;t<i;t++,a+=2){const i=this.readBits(e);s[t]=i*n*(r[a+1]-r[a])+r[a]}const o=this.tmpCsCompsBuf;t?.(s,0,o,0);return a.getRgb(o,0)}}let Ii=Object.create(null);function getB(e){return Ii[e]||=function buildB(e){const t=[];for(let a=0;a<=e;a++){const r=a/e,i=1-r;t.push(new Float32Array([i**3,3*r*i**2,3*r**2*i,r**3]))}return t}(e)}class MeshShading extends BaseShading{static MIN_SPLIT_PATCH_CHUNKS_AMOUNT=3;static MAX_SPLIT_PATCH_CHUNKS_AMOUNT=20;static TRIANGLE_DEN\
-SITY=20;constructor(e,t,a,r,i,n){super();if(!(e instanceof BaseStream))throw new FormatError("Mesh data is not a stream");const s=e.dict;this.shadingType=s.get("ShadingType");this.bbox=lookupNormalRect(s.getArray("BBox"),null);const o=ColorSpaceUtils.parse({cs:s.getRaw("CS")||s.getRaw("ColorSpace"),xref:t,resources:a,pdfFunctionFactory:r,globalColorSpaceCache:i,localColorSpaceCache:n});this.background=s.has("Background")?o.getRgb(s.get("Background"),0):null;const c=s.getRaw("Function"),l=c?r.cre\
-ate(c,!0):null;this.coords=[];this.colors=[];this.figures=[];const h={bitsPerCoordinate:s.get("BitsPerCoordinate"),bitsPerComponent:s.get("BitsPerComponent"),bitsPerFlag:s.get("BitsPerFlag"),decode:s.getArray("Decode"),colorFn:l,colorSpace:o,numComps:l?1:o.numComps},u=new MeshStreamReader(e,h);let d=!1;switch(this.shadingType){case ki:this._decodeType4Shading(u);break;case Ci:const e=0|s.get("VerticesPerRow");if(e<2)throw new FormatError("Invalid VerticesPerRow");this._decodeType5Shading(u,e);br\
-eak;case vi:this._decodeType6Shading(u);d=!0;break;case Fi:this._decodeType7Shading(u);d=!0;break;default:unreachable("Unsupported mesh type.")}if(d){this._updateBounds();for(let e=0,t=this.figures.length;e<t;e++)this._buildFigureFromPatch(e)}this._updateBounds();this._packData()}_decodeType4Shading(e){const t=this.coords,a=this.colors,r=[],i=[];let n=0;for(;e.hasData;){const s=e.readFlag(),o=e.readCoordinate(),c=e.readComponents();if(0===n){if(!(0<=s&&s<=2))throw new FormatError("Unknown type4 \
-flag");switch(s){case 0:n=3;break;case 1:i.push(i.at(-2),i.at(-1));n=1;break;case 2:i.push(i.at(-3),i.at(-1));n=1}r.push(s)}i.push(t.length);t.push(o);a.push(c);n--;e.align()}this.figures.push({type:"triangles",coords:new Int32Array(i),colors:new Int32Array(i)})}_decodeType5Shading(e,t){const a=this.coords,r=this.colors,i=[];for(;e.hasData;){const t=e.readCoordinate(),n=e.readComponents();i.push(a.length);a.push(t);r.push(n)}this.figures.push({type:"lattice",coords:new Int32Array(i),colors:new I\
-nt32Array(i),verticesPerRow:t})}_decodeType6Shading(e){const t=this.coords,a=this.colors,r=new Int32Array(16),i=new Int32Array(4);for(;e.hasData;){const n=e.readFlag();if(!(0<=n&&n<=3))throw new FormatError("Unknown type6 flag");const s=t.length;for(let a=0,r=0!==n?8:12;a<r;a++)t.push(e.readCoordinate());const o=a.length;for(let t=0,r=0!==n?2:4;t<r;t++)a.push(e.readComponents());let c,l,h,u;switch(n){case 0:r[12]=s+3;r[13]=s+4;r[14]=s+5;r[15]=s+6;r[8]=s+2;r[11]=s+7;r[4]=s+1;r[7]=s+8;r[0]=s;r[1]=\
-s+11;r[2]=s+10;r[3]=s+9;i[2]=o+1;i[3]=o+2;i[0]=o;i[1]=o+3;break;case 1:c=r[12];l=r[13];h=r[14];u=r[15];r[12]=u;r[13]=s+0;r[14]=s+1;r[15]=s+2;r[8]=h;r[11]=s+3;r[4]=l;r[7]=s+4;r[0]=c;r[1]=s+7;r[2]=s+6;r[3]=s+5;c=i[2];l=i[3];i[2]=l;i[3]=o;i[0]=c;i[1]=o+1;break;case 2:c=r[15];l=r[11];r[12]=r[3];r[13]=s+0;r[14]=s+1;r[15]=s+2;r[8]=r[7];r[11]=s+3;r[4]=l;r[7]=s+4;r[0]=c;r[1]=s+7;r[2]=s+6;r[3]=s+5;c=i[3];i[2]=i[1];i[3]=o;i[0]=c;i[1]=o+1;break;case 3:r[12]=r[0];r[13]=s+0;r[14]=s+1;r[15]=s+2;r[8]=r[1];r[11\
-]=s+3;r[4]=r[2];r[7]=s+4;r[0]=r[3];r[1]=s+7;r[2]=s+6;r[3]=s+5;i[2]=i[0];i[3]=o;i[0]=i[1];i[1]=o+1}r[5]=t.length;t.push([(-4*t[r[0]][0]-t[r[15]][0]+6*(t[r[4]][0]+t[r[1]][0])-2*(t[r[12]][0]+t[r[3]][0])+3*(t[r[13]][0]+t[r[7]][0]))/9,(-4*t[r[0]][1]-t[r[15]][1]+6*(t[r[4]][1]+t[r[1]][1])-2*(t[r[12]][1]+t[r[3]][1])+3*(t[r[13]][1]+t[r[7]][1]))/9]);r[6]=t.length;t.push([(-4*t[r[3]][0]-t[r[12]][0]+6*(t[r[2]][0]+t[r[7]][0])-2*(t[r[0]][0]+t[r[15]][0])+3*(t[r[4]][0]+t[r[14]][0]))/9,(-4*t[r[3]][1]-t[r[12]][1]\
-+6*(t[r[2]][1]+t[r[7]][1])-2*(t[r[0]][1]+t[r[15]][1])+3*(t[r[4]][1]+t[r[14]][1]))/9]);r[9]=t.length;t.push([(-4*t[r[12]][0]-t[r[3]][0]+6*(t[r[8]][0]+t[r[13]][0])-2*(t[r[0]][0]+t[r[15]][0])+3*(t[r[11]][0]+t[r[1]][0]))/9,(-4*t[r[12]][1]-t[r[3]][1]+6*(t[r[8]][1]+t[r[13]][1])-2*(t[r[0]][1]+t[r[15]][1])+3*(t[r[11]][1]+t[r[1]][1]))/9]);r[10]=t.length;t.push([(-4*t[r[15]][0]-t[r[0]][0]+6*(t[r[11]][0]+t[r[14]][0])-2*(t[r[12]][0]+t[r[3]][0])+3*(t[r[2]][0]+t[r[8]][0]))/9,(-4*t[r[15]][1]-t[r[0]][1]+6*(t[r[\
-11]][1]+t[r[14]][1])-2*(t[r[12]][1]+t[r[3]][1])+3*(t[r[2]][1]+t[r[8]][1]))/9]);this.figures.push({type:"patch",coords:new Int32Array(r),colors:new Int32Array(i)})}}_decodeType7Shading(e){const t=this.coords,a=this.colors,r=new Int32Array(16),i=new Int32Array(4);for(;e.hasData;){const n=e.readFlag();if(!(0<=n&&n<=3))throw new FormatError("Unknown type7 flag");const s=t.length;for(let a=0,r=0!==n?12:16;a<r;a++)t.push(e.readCoordinate());const o=a.length;for(let t=0,r=0!==n?2:4;t<r;t++)a.push(e.rea\
-dComponents());let c,l,h,u;switch(n){case 0:r[12]=s+3;r[13]=s+4;r[14]=s+5;r[15]=s+6;r[8]=s+2;r[9]=s+13;r[10]=s+14;r[11]=s+7;r[4]=s+1;r[5]=s+12;r[6]=s+15;r[7]=s+8;r[0]=s;r[1]=s+11;r[2]=s+10;r[3]=s+9;i[2]=o+1;i[3]=o+2;i[0]=o;i[1]=o+3;break;case 1:c=r[12];l=r[13];h=r[14];u=r[15];r[12]=u;r[13]=s+0;r[14]=s+1;r[15]=s+2;r[8]=h;r[9]=s+9;r[10]=s+10;r[11]=s+3;r[4]=l;r[5]=s+8;r[6]=s+11;r[7]=s+4;r[0]=c;r[1]=s+7;r[2]=s+6;r[3]=s+5;c=i[2];l=i[3];i[2]=l;i[3]=o;i[0]=c;i[1]=o+1;break;case 2:c=r[15];l=r[11];r[12]=\
-r[3];r[13]=s+0;r[14]=s+1;r[15]=s+2;r[8]=r[7];r[9]=s+9;r[10]=s+10;r[11]=s+3;r[4]=l;r[5]=s+8;r[6]=s+11;r[7]=s+4;r[0]=c;r[1]=s+7;r[2]=s+6;r[3]=s+5;c=i[3];i[2]=i[1];i[3]=o;i[0]=c;i[1]=o+1;break;case 3:r[12]=r[0];r[13]=s+0;r[14]=s+1;r[15]=s+2;r[8]=r[1];r[9]=s+9;r[10]=s+10;r[11]=s+3;r[4]=r[2];r[5]=s+8;r[6]=s+11;r[7]=s+4;r[0]=r[3];r[1]=s+7;r[2]=s+6;r[3]=s+5;i[2]=i[0];i[3]=o;i[0]=i[1];i[1]=o+1}this.figures.push({type:"patch",coords:new Int32Array(r),colors:new Int32Array(i)})}}_buildFigureFromPatch(e){c\
-onst t=this.figures[e];assert("patch"===t.type,"Unexpected patch mesh figure");const a=this.coords,r=this.colors,i=t.coords,n=t.colors,s=Math.min(a[i[0]][0],a[i[3]][0],a[i[12]][0],a[i[15]][0]),o=Math.min(a[i[0]][1],a[i[3]][1],a[i[12]][1],a[i[15]][1]),c=Math.max(a[i[0]][0],a[i[3]][0],a[i[12]][0],a[i[15]][0]),l=Math.max(a[i[0]][1],a[i[3]][1],a[i[12]][1],a[i[15]][1]);let h=Math.ceil((c-s)*MeshShading.TRIANGLE_DENSITY/(this.bounds[2]-this.bounds[0]));h=MathClamp(h,MeshShading.MIN_SPLIT_PATCH_CHUNKS_\
-AMOUNT,MeshShading.MAX_SPLIT_PATCH_CHUNKS_AMOUNT);let u=Math.ceil((l-o)*MeshShading.TRIANGLE_DENSITY/(this.bounds[3]-this.bounds[1]));u=MathClamp(u,MeshShading.MIN_SPLIT_PATCH_CHUNKS_AMOUNT,MeshShading.MAX_SPLIT_PATCH_CHUNKS_AMOUNT);const d=h+1,f=new Int32Array((u+1)*d),g=new Int32Array((u+1)*d);let p=0;const m=new Uint8Array(3),b=new Uint8Array(3),y=r[n[0]],w=r[n[1]],x=r[n[2]],S=r[n[3]],k=getB(u),C=getB(h);for(let e=0;e<=u;e++){m[0]=(y[0]*(u-e)+x[0]*e)/u|0;m[1]=(y[1]*(u-e)+x[1]*e)/u|0;m[2]=(y[2\
-]*(u-e)+x[2]*e)/u|0;b[0]=(w[0]*(u-e)+S[0]*e)/u|0;b[1]=(w[1]*(u-e)+S[1]*e)/u|0;b[2]=(w[2]*(u-e)+S[2]*e)/u|0;for(let t=0;t<=h;t++,p++){if(!(0!==e&&e!==u||0!==t&&t!==h))continue;let n=0,s=0,o=0;for(let r=0;r<=3;r++)for(let c=0;c<=3;c++,o++){const l=k[e][r]*C[t][c];n+=a[i[o]][0]*l;s+=a[i[o]][1]*l}f[p]=a.length;a.push([n,s]);g[p]=r.length;const c=new Uint8Array(3);c[0]=(m[0]*(h-t)+b[0]*t)/h|0;c[1]=(m[1]*(h-t)+b[1]*t)/h|0;c[2]=(m[2]*(h-t)+b[2]*t)/h|0;r.push(c)}}f[0]=i[0];g[0]=n[0];f[h]=i[3];g[h]=n[1];\
-f[d*u]=i[12];g[d*u]=n[2];f[d*u+h]=i[15];g[d*u+h]=n[3];this.figures[e]={type:"lattice",coords:f,colors:g,verticesPerRow:d}}_updateBounds(){let e=this.coords[0][0],t=this.coords[0][1],a=e,r=t;for(let i=1,n=this.coords.length;i<n;i++){const n=this.coords[i][0],s=this.coords[i][1];e=e>n?n:e;t=t>s?s:t;a=a<n?n:a;r=r<s?s:r}this.bounds=[e,t,a,r]}_packData(){let e,t,a,r;const i=this.coords,n=new Float32Array(2*i.length);for(e=0,a=0,t=i.length;e<t;e++){const t=i[e];n[a++]=t[0];n[a++]=t[1]}this.coords=n;co\
-nst s=this.colors,o=new Uint8Array(3*s.length);for(e=0,a=0,t=s.length;e<t;e++){const t=s[e];o[a++]=t[0];o[a++]=t[1];o[a++]=t[2]}this.colors=o;const c=this.figures;for(e=0,t=c.length;e<t;e++){const t=c[e],i=t.coords,n=t.colors;for(a=0,r=i.length;a<r;a++){i[a]*=2;n[a]*=3}}}getIR(){const{bounds:e}=this;if(e[2]-e[0]==0||e[3]-e[1]==0)throw new FormatError(`Invalid MeshShading bounds: [${e}].`);return["Mesh",this.shadingType,this.coords,this.colors,this.figures,e,this.bbox,this.background]}}class Dumm\
-yShading extends BaseShading{getIR(){return["Dummy"]}}function getTilingPatternIR(e,t,a){const r=lookupMatrix(t.getArray("Matrix"),Fa),i=lookupNormalRect(t.getArray("BBox"),null);if(!i||i[2]-i[0]==0||i[3]-i[1]==0)throw new FormatError("Invalid getTilingPatternIR /BBox array.");const n=t.get("XStep");if("number"!=typeof n)throw new FormatError("Invalid getTilingPatternIR /XStep value.");const s=t.get("YStep");if("number"!=typeof s)throw new FormatError("Invalid getTilingPatternIR /YStep value.");\
-const o=t.get("PaintType");if(!Number.isInteger(o))throw new FormatError("Invalid getTilingPatternIR /PaintType value.");const c=t.get("TilingType");if(!Number.isInteger(c))throw new FormatError("Invalid getTilingPatternIR /TilingType value.");return["TilingPattern",a,e,r,i,n,s,o,c]}const Ti=[1.3877,1,1,1,.97801,.92482,.89552,.91133,.81988,.97566,.98152,.93548,.93548,1.2798,.85284,.92794,1,.96134,1.54657,.91133,.91133,.91133,.91133,.91133,.91133,.91133,.91133,.91133,.91133,.82845,.82845,.85284,.\
-85284,.85284,.75859,.92138,.83908,.7762,.73293,.87289,.73133,.7514,.81921,.87356,.95958,.59526,.75727,.69225,1.04924,.9121,.86943,.79795,.88198,.77958,.70864,.81055,.90399,.88653,.96017,.82577,.77892,.78257,.97507,1.54657,.97507,.85284,.89552,.90176,.88762,.8785,.75241,.8785,.90518,.95015,.77618,.8785,.88401,.91916,.86304,.88401,.91488,.8785,.8801,.8785,.8785,.91343,.7173,1.04106,.8785,.85075,.95794,.82616,.85162,.79492,.88331,1.69808,.88331,.85284,.97801,.89552,.91133,.89552,.91133,1.7801,.8955\
-2,1.24487,1.13254,1.12401,.96839,.85284,.68787,.70645,.85592,.90747,1.01466,1.0088,.90323,1,1.07463,1,.91056,.75806,1.19118,.96839,.78864,.82845,.84133,.75859,.83908,.83908,.83908,.83908,.83908,.83908,.77539,.73293,.73133,.73133,.73133,.73133,.95958,.95958,.95958,.95958,.88506,.9121,.86943,.86943,.86943,.86943,.86943,.85284,.87508,.90399,.90399,.90399,.90399,.77892,.79795,.90807,.88762,.88762,.88762,.88762,.88762,.88762,.8715,.75241,.90518,.90518,.90518,.90518,.88401,.88401,.88401,.88401,.8785,.\
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-:Ki};e["Calibri-Regular"]={name:"LiberationSans-Regular",factors:Ni,baseWidths:Zi,baseMapping:Qi,metrics:Ei};e["Calibri-Bold"]={name:"LiberationSans-Bold",factors:Ti,baseWidths:$i,baseMapping:Gi,metrics:Oi};e["Calibri-Italic"]={name:"LiberationSans-Italic",factors:Bi,baseWidths:Ji,baseMapping:Yi,metrics:Ri};e["Calibri-BoldItalic"]={name:"LiberationSans-BoldItalic",factors:Mi,baseWidths:Vi,baseMapping:Ki,metrics:Di};e["Segoeui-Regular"]={name:"LiberationSans-Regular",factors:pn,baseWidths:Zi,base\
-Mapping:Qi,metrics:mn};e["Segoeui-Bold"]={name:"LiberationSans-Bold",factors:ln,baseWidths:$i,baseMapping:Gi,metrics:hn};e["Segoeui-Italic"]={name:"LiberationSans-Italic",factors:fn,baseWidths:Ji,baseMapping:Yi,metrics:gn};e["Segoeui-BoldItalic"]={name:"LiberationSans-BoldItalic",factors:un,baseWidths:Vi,baseMapping:Ki,metrics:dn};e["Helvetica-Regular"]=e.Helvetica={name:"LiberationSans-Regular",factors:Wi,baseWidths:Zi,baseMapping:Qi,metrics:zi};e["Helvetica-Bold"]={name:"LiberationSans-Bold",f\
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-.map(((e,t)=>e*i[t])):a;let s,o=-2;const c=[];for(const[e,t]of r.map(((e,t)=>[e,t])).sort((([e],[t])=>e-t)))if(-1!==e)if(e===o+1){s.push(n[t]);o+=1}else{o=e;s=[n[t]];c.push(e,s)}return c}(e),a=new Dict(null);a.set("BaseFont",Name.get(e));a.set("Type",Name.get("Font"));a.set("Subtype",Name.get("CIDFontType2"));a.set("Encoding",Name.get("Identity-H"));a.set("CIDToGIDMap",Name.get("Identity"));a.set("W",t);a.set("FirstChar",t[0]);a.set("LastChar",t.at(-2)+t.at(-1).length-1);const r=new Dict(null);a\
-.set("FontDescriptor",r);const i=new Dict(null);i.set("Ordering","Identity");i.set("Registry","Adobe");i.set("Supplement",0);a.set("CIDSystemInfo",i);return a}class PostScriptParser{constructor(e){this.lexer=e;this.operators=[];this.token=null;this.prev=null}nextToken(){this.prev=this.token;this.token=this.lexer.getToken()}accept(e){if(this.token.type===e){this.nextToken();return!0}return!1}expect(e){if(this.accept(e))return!0;throw new FormatError(`Unexpected symbol: found ${this.token.type} ex\
-pected ${e}.`)}parse(){this.nextToken();this.expect(yn.LBRACE);this.parseBlock();this.expect(yn.RBRACE);return this.operators}parseBlock(){for(;;)if(this.accept(yn.NUMBER))this.operators.push(this.prev.value);else if(this.accept(yn.OPERATOR))this.operators.push(this.prev.value);else{if(!this.accept(yn.LBRACE))return;this.parseCondition()}}parseCondition(){const e=this.operators.length;this.operators.push(null,null);this.parseBlock();this.expect(yn.RBRACE);if(this.accept(yn.IF)){this.operators[e]\
-=this.operators.length;this.operators[e+1]="jz"}else{if(!this.accept(yn.LBRACE))throw new FormatError("PS Function: error parsing conditional.");{const t=this.operators.length;this.operators.push(null,null);const a=this.operators.length;this.parseBlock();this.expect(yn.RBRACE);this.expect(yn.IFELSE);this.operators[t]=this.operators.length;this.operators[t+1]="j";this.operators[e]=a;this.operators[e+1]="jz"}}}}const yn={LBRACE:0,RBRACE:1,NUMBER:2,OPERATOR:3,IF:4,IFELSE:5};class PostScriptToken{st\
-atic get opCache(){return shadow(this,"opCache",Object.create(null))}constructor(e,t){this.type=e;this.value=t}static getOperator(e){return PostScriptToken.opCache[e]||=new PostScriptToken(yn.OPERATOR,e)}static get LBRACE(){return shadow(this,"LBRACE",new PostScriptToken(yn.LBRACE,"{"))}static get RBRACE(){return shadow(this,"RBRACE",new PostScriptToken(yn.RBRACE,"}"))}static get IF(){return shadow(this,"IF",new PostScriptToken(yn.IF,"IF"))}static get IFELSE(){return shadow(this,"IFELSE",new Pos\
-tScriptToken(yn.IFELSE,"IFELSE"))}}class PostScriptLexer{constructor(e){this.stream=e;this.nextChar();this.strBuf=[]}nextChar(){return this.currentChar=this.stream.getByte()}getToken(){let e=!1,t=this.currentChar;for(;;){if(t<0)return wa;if(e)10!==t&&13!==t||(e=!1);else if(37===t)e=!0;else if(!isWhiteSpace(t))break;t=this.nextChar()}switch(0|t){case 48:case 49:case 50:case 51:case 52:case 53:case 54:case 55:case 56:case 57:case 43:case 45:case 46:return new PostScriptToken(yn.NUMBER,this.getNumb\
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-ends BaseLocalCache{set(e=null,t=null,a){if("string"!=typeof e&&!t)throw new Error(\'LocalColorSpaceCache.set - expected "name" and/or "ref" argument.\');if(t){if(this._imageCache.has(t))return;null!==e&&this._nameRefMap.set(e,t);this._imageCache.put(t,a)}else this._imageMap.has(e)||this._imageMap.set(e,a)}}class LocalFunctionCache extends BaseLocalCache{constructor(e){super({onlyRefs:!0})}set(e=null,t,a){if(!t)throw new Error(\'LocalFunctionCache.set - expected "ref" argument.\');this._imageCache.h\
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-|this._imageCache.put(t,a)}clear(){this._imageCache.clear()}}class GlobalImageCache{static NUM_PAGES_THRESHOLD=2;static MIN_IMAGES_TO_CACHE=10;static MAX_BYTE_SIZE=5e7;#H=new RefSet;constructor(){this._refCache=new RefSetCache;this._imageCache=new RefSetCache}get#W(){let e=0;for(const t of this._imageCache)e+=t.byteSize;return e}get#z(){return!(this._imageCache.size<GlobalImageCache.MIN_IMAGES_TO_CACHE)&&!(this.#W<GlobalImageCache.MAX_BYTE_SIZE)}shouldCache(e,t){let a=this._refCache.get(e);if(!a\
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-berArray(e,null)?e:e.map((e=>+e)):null}class PDFFunction{static getSampleArray(e,t,a,r){let i,n,s=1;for(i=0,n=e.length;i<n;i++)s*=e[i];s*=t;const o=new Array(s);let c=0,l=0;const h=1/(2**a-1),u=r.getBytes((s*a+7)/8);let d=0;for(i=0;i<s;i++){for(;c<a;){l<<=8;l|=u[d++];c+=8}c-=a;o[i]=(l>>c)*h;l&=(1<<c)-1}return o}static parse(e,t){const a=t.dict||t;switch(a.get("FunctionType")){case 0:return this.constructSampled(e,t,a);case 1:break;case 2:return this.constructInterpolated(e,a);case 3:return this.\
-constructStiched(e,a);case 4:return this.constructPostScript(e,t,a)}throw new FormatError("Unknown type of function")}static parseArray(e,t){const{xref:a}=e,r=[];for(const i of t)r.push(this.parse(e,a.fetchIfRef(i)));return function(e,t,a,i){for(let n=0,s=r.length;n<s;n++)r[n](e,t,a,i+n)}}static constructSampled(e,t,a){function toMultiArray(e){const t=e.length,a=[];let r=0;for(let i=0;i<t;i+=2)a[r++]=[e[i],e[i+1]];return a}function interpolate(e,t,a,r,i){return r+(i-r)/(a-t)*(e-t)}let r=toNumber\
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-tiArray(u):i;const d=this.getSampleArray(o,s,c,t);return function constructSampledFn(e,t,a,c){const l=1<<n,f=new Float64Array(l).fill(1),g=new Uint32Array(l);let p,m,b=s,y=1;for(p=0;p<n;++p){const a=r[p][0],i=r[p][1];let n=interpolate(MathClamp(e[t+p],a,i),a,i,h[p][0],h[p][1]);const s=o[p];n=MathClamp(n,0,s-1);const c=n<s-1?Math.floor(n):n-1,u=c+1-n,d=n-c,w=c*b,x=w+b;for(m=0;m<l;m++)if(m&y){f[m]*=d;g[m]+=x}else{f[m]*=u;g[m]+=w}b*=s;y<<=1}for(m=0;m<s;++m){let e=0;for(p=0;p<l;p++)e+=d[g[p]+m]*f[p]\
-;e=interpolate(e,0,1,u[m][0],u[m][1]);a[c+m]=MathClamp(e,i[m][0],i[m][1])}}}static constructInterpolated(e,t){const a=toNumberArray(t.getArray("C0"))||[0],r=toNumberArray(t.getArray("C1"))||[1],i=t.get("N"),n=[];for(let e=0,t=a.length;e<t;++e)n.push(r[e]-a[e]);const s=n.length;return function constructInterpolatedFn(e,t,r,o){const c=1===i?e[t]:e[t]**i;for(let e=0;e<s;++e)r[o+e]=a[e]+c*n[e]}}static constructStiched(e,t){const a=toNumberArray(t.getArray("Domain"));if(!a)throw new FormatError("No d\
-omain");if(1!==a.length/2)throw new FormatError("Bad domain for stiched function");const{xref:r}=e,i=[];for(const a of t.get("Functions"))i.push(this.parse(e,r.fetchIfRef(a)));const n=toNumberArray(t.getArray("Bounds")),s=toNumberArray(t.getArray("Encode")),o=new Float32Array(1);return function constructStichedFn(e,t,r,c){const l=MathClamp(e[t],a[0],a[1]),h=n.length;let u;for(u=0;u<h&&!(l<n[u]);++u);let d=a[0];u>0&&(d=n[u-1]);let f=a[1];u<n.length&&(f=n[u]);const g=s[2*u],p=s[2*u+1];o[0]=d===f?g\
-:g+(l-d)*(p-g)/(f-d);i[u](o,0,r,c)}}static constructPostScript(e,t,a){const r=toNumberArray(a.getArray("Domain")),i=toNumberArray(a.getArray("Range"));if(!r)throw new FormatError("No domain.");if(!i)throw new FormatError("No range.");const n=new PostScriptLexer(t),s=new PostScriptParser(n).parse();if(e.isEvalSupported&&FeatureTest.isEvalSupported){const e=(new PostScriptCompiler).compile(s,r,i);if(e)return new Function("src","srcOffset","dest","destOffset",e)}info("Unable to compile PS function"\
-);const o=i.length>>1,c=r.length>>1,l=new PostScriptEvaluator(s),h=Object.create(null);let u=8192;const d=new Float32Array(c);return function constructPostScriptFn(e,t,a,r){let n,s,f="";const g=d;for(n=0;n<c;n++){s=e[t+n];g[n]=s;f+=s+"_"}const p=h[f];if(void 0!==p){a.set(p,r);return}const m=new Float32Array(o),b=l.execute(g),y=b.length-o;for(n=0;n<o;n++){s=b[y+n];let e=i[2*n];if(s<e)s=e;else{e=i[2*n+1];s>e&&(s=e)}m[n]=s}if(u>0){u--;h[f]=m}a.set(m,r)}}}function isPDFFunction(e){let t;if(e instanc\
-eof Dict)t=e;else{if(!(e instanceof BaseStream))return!1;t=e.dict}return t.has("FunctionType")}class PostScriptStack{static MAX_STACK_SIZE=100;constructor(e){this.stack=e?Array.from(e):[]}push(e){if(this.stack.length>=PostScriptStack.MAX_STACK_SIZE)throw new Error("PostScript function stack overflow.");this.stack.push(e)}pop(){if(this.stack.length<=0)throw new Error("PostScript function stack underflow.");return this.stack.pop()}copy(e){if(this.stack.length+e>=PostScriptStack.MAX_STACK_SIZE)thro\
-w new Error("PostScript function stack overflow.");const t=this.stack;for(let a=t.length-e,r=e-1;r>=0;r--,a++)t.push(t[a])}index(e){this.push(this.stack[this.stack.length-e-1])}roll(e,t){const a=this.stack,r=a.length-e,i=a.length-1,n=r+(t-Math.floor(t/e)*e);for(let e=r,t=i;e<t;e++,t--){const r=a[e];a[e]=a[t];a[t]=r}for(let e=r,t=n-1;e<t;e++,t--){const r=a[e];a[e]=a[t];a[t]=r}for(let e=n,t=i;e<t;e++,t--){const r=a[e];a[e]=a[t];a[t]=r}}}class PostScriptEvaluator{constructor(e){this.operators=e}exe\
-cute(e){const t=new PostScriptStack(e);let a=0;const r=this.operators,i=r.length;let n,s,o;for(;a<i;){n=r[a++];if("number"!=typeof n)switch(n){case"jz":o=t.pop();s=t.pop();s||(a=o);break;case"j":s=t.pop();a=s;break;case"abs":s=t.pop();t.push(Math.abs(s));break;case"add":o=t.pop();s=t.pop();t.push(s+o);break;case"and":o=t.pop();s=t.pop();"boolean"==typeof s&&"boolean"==typeof o?t.push(s&&o):t.push(s&o);break;case"atan":o=t.pop();s=t.pop();s=Math.atan2(s,o)/Math.PI*180;s<0&&(s+=360);t.push(s);brea\
-k;case"bitshift":o=t.pop();s=t.pop();s>0?t.push(s<<o):t.push(s>>o);break;case"ceiling":s=t.pop();t.push(Math.ceil(s));break;case"copy":s=t.pop();t.copy(s);break;case"cos":s=t.pop();t.push(Math.cos(s%360/180*Math.PI));break;case"cvi":s=0|t.pop();t.push(s);break;case"cvr":break;case"div":o=t.pop();s=t.pop();t.push(s/o);break;case"dup":t.copy(1);break;case"eq":o=t.pop();s=t.pop();t.push(s===o);break;case"exch":t.roll(2,1);break;case"exp":o=t.pop();s=t.pop();t.push(s**o);break;case"false":t.push(!1)\
-;break;case"floor":s=t.pop();t.push(Math.floor(s));break;case"ge":o=t.pop();s=t.pop();t.push(s>=o);break;case"gt":o=t.pop();s=t.pop();t.push(s>o);break;case"idiv":o=t.pop();s=t.pop();t.push(s/o|0);break;case"index":s=t.pop();t.index(s);break;case"le":o=t.pop();s=t.pop();t.push(s<=o);break;case"ln":s=t.pop();t.push(Math.log(s));break;case"log":s=t.pop();t.push(Math.log10(s));break;case"lt":o=t.pop();s=t.pop();t.push(s<o);break;case"mod":o=t.pop();s=t.pop();t.push(s%o);break;case"mul":o=t.pop();s=\
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-1}for(n=0;n<i;++n)"ON"===An[n]&&(An[n]=l);for(n=0;n<i;++n){d=An[n];isEven(c[n])?"R"===d?c[n]+=1:"AN"!==d&&"EN"!==d||(c[n]+=2):"L"!==d&&"AN"!==d&&"EN"!==d||(c[n]+=1)}let g,p=-1,m=99;for(n=0,s=c.length;n<s;++n){g=c[n];p<g&&(p=g);m>g&&isOdd(g)&&(m=g)}for(g=p;g>=m;--g){let e=-1;for(n=0,s=c.length;n<s;++n)if(c[n]<g){if(e>=0){reverseValues(Sn,e,n);e=-1}}else e<0&&(e=n);e>=0&&reverseValues(Sn,e,c.length)}for(n=0,s=Sn.length;n<s;++n){const e=Sn[n];"<"!==e&&">"!==e||(Sn[n]="")}return createBidiText(Sn.jo\
-in(""),r)}const kn={style:"normal",weight:"normal"},Cn={style:"normal",weight:"bold"},vn={style:"italic",weight:"normal"},Fn={style:"italic",weight:"bold"},In=new Map([["Times-Roman",{local:["Times New Roman","Times-Roman","Times","Liberation Serif","Nimbus Roman","Nimbus Roman L","Tinos","Thorndale","TeX Gyre Termes","FreeSerif","Linux Libertine O","Libertinus Serif","DejaVu Serif","Bitstream Vera Serif","Ubuntu"],style:kn,ultimate:"serif"}],["Times-Bold",{alias:"Times-Roman",style:Cn,ultimate:\
-"serif"}],["Times-Italic",{alias:"Times-Roman",style:vn,ultimate:"serif"}],["Times-BoldItalic",{alias:"Times-Roman",style:Fn,ultimate:"serif"}],["Helvetica",{local:["Helvetica","Helvetica Neue","Arial","Arial Nova","Liberation Sans","Arimo","Nimbus Sans","Nimbus Sans L","A030","TeX Gyre Heros","FreeSans","DejaVu Sans","Albany","Bitstream Vera Sans","Arial Unicode MS","Microsoft Sans Serif","Apple Symbols","Cantarell"],path:"LiberationSans-Regular.ttf",style:kn,ultimate:"sans-serif"}],["Helvetica\
--Bold",{alias:"Helvetica",path:"LiberationSans-Bold.ttf",style:Cn,ultimate:"sans-serif"}],["Helvetica-Oblique",{alias:"Helvetica",path:"LiberationSans-Italic.ttf",style:vn,ultimate:"sans-serif"}],["Helvetica-BoldOblique",{alias:"Helvetica",path:"LiberationSans-BoldItalic.ttf",style:Fn,ultimate:"sans-serif"}],["Courier",{local:["Courier","Courier New","Liberation Mono","Nimbus Mono","Nimbus Mono L","Cousine","Cumberland","TeX Gyre Cursor","FreeMono","Linux Libertine Mono O","Libertinus Mono"],sty\
-le:kn,ultimate:"monospace"}],["Courier-Bold",{alias:"Courier",style:Cn,ultimate:"monospace"}],["Courier-Oblique",{alias:"Courier",style:vn,ultimate:"monospace"}],["Courier-BoldOblique",{alias:"Courier",style:Fn,ultimate:"monospace"}],["ArialBlack",{local:["Arial Black"],style:{style:"normal",weight:"900"},fallback:"Helvetica-Bold"}],["ArialBlack-Bold",{alias:"ArialBlack"}],["ArialBlack-Italic",{alias:"ArialBlack",style:{style:"italic",weight:"900"},fallback:"Helvetica-BoldOblique"}],["ArialBlack\
--BoldItalic",{alias:"ArialBlack-Italic"}],["ArialNarrow",{local:["Arial Narrow","Liberation Sans Narrow","Helvetica Condensed","Nimbus Sans Narrow","TeX Gyre Heros Cn"],style:kn,fallback:"Helvetica"}],["ArialNarrow-Bold",{alias:"ArialNarrow",style:Cn,fallback:"Helvetica-Bold"}],["ArialNarrow-Italic",{alias:"ArialNarrow",style:vn,fallback:"Helvetica-Oblique"}],["ArialNarrow-BoldItalic",{alias:"ArialNarrow",style:Fn,fallback:"Helvetica-BoldOblique"}],["Calibri",{local:["Calibri","Carlito"],style:k\
-n,fallback:"Helvetica"}],["Calibri-Bold",{alias:"Calibri",style:Cn,fallback:"Helvetica-Bold"}],["Calibri-Italic",{alias:"Calibri",style:vn,fallback:"Helvetica-Oblique"}],["Calibri-BoldItalic",{alias:"Calibri",style:Fn,fallback:"Helvetica-BoldOblique"}],["Wingdings",{local:["Wingdings","URW Dingbats"],style:kn}],["Wingdings-Regular",{alias:"Wingdings"}],["Wingdings-Bold",{alias:"Wingdings"}]]),Tn=new Map([["Arial-Black","ArialBlack"]]);function getFamilyName(e){const t=new Set(["thin","extralight\
-","ultralight","demilight","semilight","light","book","regular","normal","medium","demibold","semibold","bold","extrabold","ultrabold","black","heavy","extrablack","ultrablack","roman","italic","oblique","ultracondensed","extracondensed","condensed","semicondensed","normal","semiexpanded","expanded","extraexpanded","ultraexpanded","bolditalic"]);return e.split(/[- ,+]+/g).filter((e=>!t.has(e.toLowerCase()))).join(" ")}function generateFont({alias:e,local:t,path:a,fallback:r,style:i,ultimate:n},s\
-,o,c=!0,l=!0,h=""){const u={style:null,ultimate:null};if(t){const e=h?` ${h}`:"";for(const a of t)s.push(`local(${a}${e})`)}if(e){const t=In.get(e),n=h||function getStyleToAppend(e){switch(e){case Cn:return"Bold";case vn:return"Italic";case Fn:return"Bold Italic";default:if("bold"===e?.weight)return"Bold";if("italic"===e?.style)return"Italic"}return""}(i);Object.assign(u,generateFont(t,s,o,c&&!r,l&&!a,n))}i&&(u.style=i);n&&(u.ultimate=n);if(c&&r){const e=In.get(r),{ultimate:t}=generateFont(e,s,o\
-,c,l&&!a,h);u.ultimate||=t}l&&a&&o&&s.push(`url(${o}${a})`);return u}function getFontSubstitution(e,t,a,r,i,n){if(r.startsWith("InvalidPDFjsFont_"))return null;"TrueType"!==n&&"Type1"!==n||!/^[A-Z]{6}\\+/.test(r)||(r=r.slice(7));const s=r=normalizeFontName(r);let o=e.get(s);if(o)return o;let c=In.get(r);if(!c)for(const[e,t]of Tn)if(r.startsWith(e)){r=`${t}${r.substring(e.length)}`;c=In.get(r);break}let l=!1;if(!c){c=In.get(i);l=!0}const h=`${t.getDocId()}_s${t.createFontId()}`;if(!c){if(!validate\
-FontName(r)){warn(`Cannot substitute the font because of its name: ${r}`);e.set(s,null);return null}const t=/bold/gi.test(r),a=/oblique|italic/gi.test(r),i=t&&a&&Fn||t&&Cn||a&&vn||kn;o={css:`"${getFamilyName(r)}",${h}`,guessFallback:!0,loadedName:h,baseFontName:r,src:`local(${r})`,style:i};e.set(s,o);return o}const u=[];l&&validateFontName(r)&&u.push(`local(${r})`);const{style:d,ultimate:f}=generateFont(c,u,a),g=null===f,p=g?"":`,${f}`;o={css:`"${getFamilyName(r)}",${h}${p}`,guessFallback:g,load\
-edName:h,baseFontName:r,src:u.join(","),style:d};e.set(s,o);return o}const On=3285377520,Mn=4294901760,Dn=65535;class MurmurHash3_64{constructor(e){this.h1=e?4294967295&e:On;this.h2=e?4294967295&e:On}update(e){let t,a;if("string"==typeof e){t=new Uint8Array(2*e.length);a=0;for(let r=0,i=e.length;r<i;r++){const i=e.charCodeAt(r);if(i<=255)t[a++]=i;else{t[a++]=i>>>8;t[a++]=255&i}}}else{if(!ArrayBuffer.isView(e))throw new Error("Invalid data format, must be a string or TypedArray.");t=e.slice();a=t\
-.byteLength}const r=a>>2,i=a-4*r,n=new Uint32Array(t.buffer,0,r);let s=0,o=0,c=this.h1,l=this.h2;const h=3432918353,u=461845907,d=11601,f=13715;for(let e=0;e<r;e++)if(1&e){s=n[e];s=s*h&Mn|s*d&Dn;s=s<<15|s>>>17;s=s*u&Mn|s*f&Dn;c^=s;c=c<<13|c>>>19;c=5*c+3864292196}else{o=n[e];o=o*h&Mn|o*d&Dn;o=o<<15|o>>>17;o=o*u&Mn|o*f&Dn;l^=o;l=l<<13|l>>>19;l=5*l+3864292196}s=0;switch(i){case 3:s^=t[4*r+2]<<16;case 2:s^=t[4*r+1]<<8;case 1:s^=t[4*r];s=s*h&Mn|s*d&Dn;s=s<<15|s>>>17;s=s*u&Mn|s*f&Dn;1&r?c^=s:l^=s}this\
-.h1=c;this.h2=l}hexdigest(){let e=this.h1,t=this.h2;e^=t>>>1;e=3981806797*e&Mn|36045*e&Dn;t=4283543511*t&Mn|(2950163797*(t<<16|e>>>16)&Mn)>>>16;e^=t>>>1;e=444984403*e&Mn|60499*e&Dn;t=3301882366*t&Mn|(3120437893*(t<<16|e>>>16)&Mn)>>>16;e^=t>>>1;return(e>>>0).toString(16).padStart(8,"0")+(t>>>0).toString(16).padStart(8,"0")}}function resizeImageMask(e,t,a,r,i,n){const s=i*n;let o;o=t<=8?new Uint8Array(s):t<=16?new Uint16Array(s):new Uint32Array(s);const c=a/i,l=r/n;let h,u,d,f,g=0;const p=new Uint\
-16Array(i),m=a;for(h=0;h<i;h++)p[h]=Math.floor(h*c);for(h=0;h<n;h++){d=Math.floor(h*l)*m;for(u=0;u<i;u++){f=d+p[u];o[g++]=e[f]}}return o}class PDFImage{constructor({xref:e,res:t,image:a,isInline:r=!1,smask:i=null,mask:n=null,isMask:s=!1,pdfFunctionFactory:o,globalColorSpaceCache:c,localColorSpaceCache:l}){this.image=a;const h=a.dict,u=h.get("F","Filter");let d;if(u instanceof Name)d=u.name;else if(Array.isArray(u)){const t=e.fetchIfRef(u[0]);t instanceof Name&&(d=t.name)}switch(d){case"JPXDecode\
-":({width:a.width,height:a.height,componentsCount:a.numComps,bitsPerComponent:a.bitsPerComponent}=JpxImage.parseImageProperties(a.stream));a.stream.reset();const e=ImageResizer.getReducePowerForJPX(a.width,a.height,a.numComps);this.jpxDecoderOptions={numComponents:0,isIndexedColormap:!1,smaskInData:h.has("SMaskInData"),reducePower:e};if(e){const t=2**e;a.width=Math.ceil(a.width/t);a.height=Math.ceil(a.height/t)}break;case"JBIG2Decode":a.bitsPerComponent=1;a.numComps=1}let f=h.get("W","Width"),g=\
-h.get("H","Height");if(Number.isInteger(a.width)&&a.width>0&&Number.isInteger(a.height)&&a.height>0&&(a.width!==f||a.height!==g)){warn("PDFImage - using the Width/Height of the image data, rather than the image dictionary.");f=a.width;g=a.height}else{const e="number"==typeof f&&f>0,t="number"==typeof g&&g>0;if(!e||!t){if(!a.fallbackDims)throw new FormatError(`Invalid image width: ${f} or height: ${g}`);warn("PDFImage - using the Width/Height of the parent image, for SMask/Mask data.");e||(f=a.fa\
-llbackDims.width);t||(g=a.fallbackDims.height)}}this.width=f;this.height=g;this.interpolate=h.get("I","Interpolate");this.imageMask=h.get("IM","ImageMask")||!1;this.matte=h.get("Matte")||!1;let p=a.bitsPerComponent;if(!p){p=h.get("BPC","BitsPerComponent");if(!p){if(!this.imageMask)throw new FormatError(`Bits per component missing in image: ${this.imageMask}`);p=1}}this.bpc=p;if(!this.imageMask){let i=h.getRaw("CS")||h.getRaw("ColorSpace");const n=!!i;if(n)this.jpxDecoderOptions?.smaskInData&&(i=\
-Name.get("DeviceRGBA"));else if(this.jpxDecoderOptions)i=Name.get("DeviceRGBA");else switch(a.numComps){case 1:i=Name.get("DeviceGray");break;case 3:i=Name.get("DeviceRGB");break;case 4:i=Name.get("DeviceCMYK");break;default:throw new Error(`Images with ${a.numComps} color components not supported.`)}this.colorSpace=ColorSpaceUtils.parse({cs:i,xref:e,resources:r?t:null,pdfFunctionFactory:o,globalColorSpaceCache:c,localColorSpaceCache:l});this.numComps=this.colorSpace.numComps;if(this.jpxDecoderO\
-ptions){this.jpxDecoderOptions.numComponents=n?this.numComps:0;this.jpxDecoderOptions.isIndexedColormap="Indexed"===this.colorSpace.name}}this.decode=h.getArray("D","Decode");this.needsDecode=!1;if(this.decode&&(this.colorSpace&&!this.colorSpace.isDefaultDecode(this.decode,p)||s&&!ColorSpace.isDefaultDecode(this.decode,1))){this.needsDecode=!0;const e=(1<<p)-1;this.decodeCoefficients=[];this.decodeAddends=[];const t="Indexed"===this.colorSpace?.name;for(let a=0,r=0;a<this.decode.length;a+=2,++r)\
-{const i=this.decode[a],n=this.decode[a+1];this.decodeCoefficients[r]=t?(n-i)/e:n-i;this.decodeAddends[r]=t?i:e*i}}if(i){i.fallbackDims??={width:f,height:g};this.smask=new PDFImage({xref:e,res:t,image:i,isInline:r,pdfFunctionFactory:o,globalColorSpaceCache:c,localColorSpaceCache:l})}else if(n)if(n instanceof BaseStream){if(n.dict.get("IM","ImageMask")){n.fallbackDims??={width:f,height:g};this.mask=new PDFImage({xref:e,res:t,image:n,isInline:r,isMask:!0,pdfFunctionFactory:o,globalColorSpaceCache:\
-c,localColorSpaceCache:l})}else warn("Ignoring /Mask in image without /ImageMask.")}else this.mask=n}static async buildImage({xref:e,res:t,image:a,isInline:r=!1,pdfFunctionFactory:i,globalColorSpaceCache:n,localColorSpaceCache:s}){const o=a;let c=null,l=null;const h=a.dict.get("SMask"),u=a.dict.get("Mask");h?h instanceof BaseStream?c=h:warn("Unsupported /SMask format."):u&&(u instanceof BaseStream||Array.isArray(u)?l=u:warn("Unsupported /Mask format."));return new PDFImage({xref:e,res:t,image:o,\
-isInline:r,smask:c,mask:l,pdfFunctionFactory:i,globalColorSpaceCache:n,localColorSpaceCache:s})}static async createMask({image:e,isOffscreenCanvasSupported:t=!1}){const{dict:a}=e,r=a.get("W","Width"),i=a.get("H","Height"),n=a.get("I","Interpolate"),s=a.getArray("D","Decode"),o=s?.[0]>0,c=(r+7>>3)*i,l=e.getBytes(c),h=1===r&&1===i&&o===(0===l.length||!!(128&l[0]));if(h)return{isSingleOpaquePixel:h};if(t){if(ImageResizer.needsToBeResized(r,i)){const e=new Uint8ClampedArray(r*i*4);convertBlackAndWhi\
-teToRGBA({src:l,dest:e,width:r,height:i,nonBlackColor:0,inverseDecode:o});return ImageResizer.createImage({kind:v,data:e,width:r,height:i,interpolate:n})}const e=new OffscreenCanvas(r,i),t=e.getContext("2d"),a=t.createImageData(r,i);convertBlackAndWhiteToRGBA({src:l,dest:a.data,width:r,height:i,nonBlackColor:0,inverseDecode:o});t.putImageData(a,0,0);return{data:null,width:r,height:i,interpolate:n,bitmap:e.transferToImageBitmap()}}const u=l.byteLength;let d;if(e instanceof DecodeStream&&(!o||c===\
-u))d=l;else if(o){d=new Uint8Array(c);d.set(l);d.fill(255,u)}else d=new Uint8Array(l);if(o)for(let e=0;e<u;e++)d[e]^=255;return{data:d,width:r,height:i,interpolate:n}}get drawWidth(){return Math.max(this.width,this.smask?.width||0,this.mask?.width||0)}get drawHeight(){return Math.max(this.height,this.smask?.height||0,this.mask?.height||0)}decodeBuffer(e){const t=this.bpc,a=this.numComps,r=this.decodeAddends,i=this.decodeCoefficients,n=(1<<t)-1;let s,o;if(1===t){for(s=0,o=e.length;s<o;s++)e[s]=+!\
-e[s];return}let c=0;for(s=0,o=this.width*this.height;s<o;s++)for(let t=0;t<a;t++){e[c]=MathClamp(r[t]+e[c]*i[t],0,n);c++}}getComponents(e){const t=this.bpc;if(8===t)return e;const a=this.width,r=this.height,i=this.numComps,n=a*r*i;let s,o=0;s=t<=8?new Uint8Array(n):t<=16?new Uint16Array(n):new Uint32Array(n);const c=a*i,l=(1<<t)-1;let h,u,d=0;if(1===t){let t,a,i;for(let n=0;n<r;n++){a=d+(-8&c);i=d+c;for(;d<a;){u=e[o++];s[d]=u>>7&1;s[d+1]=u>>6&1;s[d+2]=u>>5&1;s[d+3]=u>>4&1;s[d+4]=u>>3&1;s[d+5]=u>\
->2&1;s[d+6]=u>>1&1;s[d+7]=1&u;d+=8}if(d<i){u=e[o++];t=128;for(;d<i;){s[d++]=+!!(u&t);t>>=1}}}}else{let a=0;u=0;for(d=0,h=n;d<h;++d){if(d%c==0){u=0;a=0}for(;a<t;){u=u<<8|e[o++];a+=8}const r=a-t;let i=u>>r;i<0?i=0:i>l&&(i=l);s[d]=i;u&=(1<<r)-1;a=r}}return s}async fillOpacity(e,t,a,r,i){const n=this.smask,s=this.mask;let o,c,l,h,u,d;if(n){c=n.width;l=n.height;o=new Uint8ClampedArray(c*l);await n.fillGrayBuffer(o);c===t&&l===a||(o=resizeImageMask(o,n.bpc,c,l,t,a))}else if(s)if(s instanceof PDFImage)\
-{c=s.width;l=s.height;o=new Uint8ClampedArray(c*l);s.numComps=1;await s.fillGrayBuffer(o);for(h=0,u=c*l;h<u;++h)o[h]=255-o[h];c===t&&l===a||(o=resizeImageMask(o,s.bpc,c,l,t,a))}else{if(!Array.isArray(s))throw new FormatError("Unknown mask format.");{o=new Uint8ClampedArray(t*a);const e=this.numComps;for(h=0,u=t*a;h<u;++h){let t=0;const a=h*e;for(d=0;d<e;++d){const e=i[a+d],r=2*d;if(e<s[r]||e>s[r+1]){t=255;break}}o[h]=t}}}if(o)for(h=0,d=3,u=t*r;h<u;++h,d+=4)e[d]=o[h];else for(h=0,d=3,u=t*r;h<u;++\
-h,d+=4)e[d]=255}undoPreblend(e,t,a){const r=this.smask?.matte;if(!r)return;const i=this.colorSpace.getRgb(r,0),n=i[0],s=i[1],o=i[2],c=t*a*4;for(let t=0;t<c;t+=4){const a=e[t+3];if(0===a){e[t]=255;e[t+1]=255;e[t+2]=255;continue}const r=255/a;e[t]=(e[t]-n)*r+n;e[t+1]=(e[t+1]-s)*r+s;e[t+2]=(e[t+2]-o)*r+o}}async createImageData(e=!1,t=!1){const a=this.drawWidth,r=this.drawHeight,i={width:a,height:r,interpolate:this.interpolate,kind:0,data:null},n=this.numComps,s=this.width,o=this.height,c=this.bpc,l\
-=s*n*c+7>>3,h=t&&ImageResizer.needsToBeResized(a,r);if(!this.smask&&!this.mask&&"DeviceRGBA"===this.colorSpace.name){i.kind=v;const e=i.data=await this.getImageBytes(o*s*4,{});return t?h?ImageResizer.createImage(i,!1):this.createBitmap(v,a,r,e):i}if(!e){let e;"DeviceGray"===this.colorSpace.name&&1===c?e=k:"DeviceRGB"!==this.colorSpace.name||8!==c||this.needsDecode||(e=C);if(e&&!this.smask&&!this.mask&&a===s&&r===o){const n=await this.#$(s,o);if(n)return n;const c=await this.getImageBytes(o*l,{})\
-;if(t)return h?ImageResizer.createImage({data:c,kind:e,width:a,height:r,interpolate:this.interpolate},this.needsDecode):this.createBitmap(e,s,o,c);i.kind=e;i.data=c;if(this.needsDecode){assert(e===k,"PDFImage.createImageData: The image must be grayscale.");const t=i.data;for(let e=0,a=t.length;e<a;e++)t[e]^=255}return i}if(this.image instanceof JpegStream&&!this.smask&&!this.mask&&!this.needsDecode){let e=o*l;if(t&&!h){let t=!1;switch(this.colorSpace.name){case"DeviceGray":e*=4;t=!0;break;case"D\
-eviceRGB":e=e/3*4;t=!0;break;case"DeviceCMYK":t=!0}if(t){const t=await this.#$(a,r);if(t)return t;const i=await this.getImageBytes(e,{drawWidth:a,drawHeight:r,forceRGBA:!0});return this.createBitmap(v,a,r,i)}}else switch(this.colorSpace.name){case"DeviceGray":e*=3;case"DeviceRGB":case"DeviceCMYK":i.kind=C;i.data=await this.getImageBytes(e,{drawWidth:a,drawHeight:r,forceRGB:!0});return h?ImageResizer.createImage(i):i}}}const u=await this.getImageBytes(o*l,{internal:!0}),d=0|u.length/l*r/o,f=this.\
-getComponents(u);let g,p,m,b,y,w;if(t&&!h){m=new OffscreenCanvas(a,r);b=m.getContext("2d");y=b.createImageData(a,r);w=y.data}i.kind=v;if(e||this.smask||this.mask){t&&!h||(w=new Uint8ClampedArray(a*r*4));g=1;p=!0;await this.fillOpacity(w,a,r,d,f)}else{if(!t||h){i.kind=C;w=new Uint8ClampedArray(a*r*3);g=0}else{new Uint32Array(w.buffer).fill(FeatureTest.isLittleEndian?4278190080:255);g=1}p=!1}this.needsDecode&&this.decodeBuffer(f);this.colorSpace.fillRgb(w,s,o,a,r,d,c,f,g);p&&this.undoPreblend(w,a,\
-d);if(t&&!h){b.putImageData(y,0,0);return{data:null,width:a,height:r,bitmap:m.transferToImageBitmap(),interpolate:this.interpolate}}i.data=w;return h?ImageResizer.createImage(i):i}async fillGrayBuffer(e){const t=this.numComps;if(1!==t)throw new FormatError(`Reading gray scale from a color image: ${t}`);const a=this.width,r=this.height,i=this.bpc,n=a*t*i+7>>3,s=await this.getImageBytes(r*n,{internal:!0}),o=this.getComponents(s);let c,l;if(1===i){l=a*r;if(this.needsDecode)for(c=0;c<l;++c)e[c]=o[c]\
--1&255;else for(c=0;c<l;++c)e[c]=255&-o[c];return}this.needsDecode&&this.decodeBuffer(o);l=a*r;const h=255/((1<<i)-1);for(c=0;c<l;++c)e[c]=h*o[c]}createBitmap(e,t,a,r){const i=new OffscreenCanvas(t,a),n=i.getContext("2d");let s;if(e===v)s=new ImageData(r,t,a);else{s=n.createImageData(t,a);convertToRGBA({kind:e,src:r,dest:new Uint32Array(s.data.buffer),width:t,height:a,inverseDecode:this.needsDecode})}n.putImageData(s,0,0);return{data:null,width:t,height:a,bitmap:i.transferToImageBitmap(),interpo\
-late:this.interpolate}}async#$(e,t){const a=await this.image.getTransferableImage();return a?{data:null,width:e,height:t,bitmap:a,interpolate:this.interpolate}:null}async getImageBytes(e,{drawWidth:t,drawHeight:a,forceRGBA:r=!1,forceRGB:i=!1,internal:n=!1}){this.image.reset();this.image.drawWidth=t||this.width;this.image.drawHeight=a||this.height;this.image.forceRGBA=!!r;this.image.forceRGB=!!i;const s=await this.image.getImageData(e,this.jpxDecoderOptions);if(n||this.image instanceof DecodeStre\
-am)return s;assert(s instanceof Uint8Array,\'PDFImage.getImageBytes: Unsupported "imageBytes" type.\');return new Uint8Array(s)}}const Bn=Object.freeze({maxImageSize:-1,disableFontFace:!1,ignoreErrors:!1,isEvalSupported:!0,isOffscreenCanvasSupported:!1,isImageDecoderSupported:!1,canvasMaxAreaInBytes:-1,fontExtraProperties:!1,useSystemFonts:!0,useWasm:!0,useWorkerFetch:!0,cMapUrl:null,iccUrl:null,standardFontDataUrl:null,wasmUrl:null}),Rn=1,Nn=2,En=Promise.resolve();function normalizeBlendMode(e,t=\
-!1){if(Array.isArray(e)){for(const t of e){const e=normalizeBlendMode(t,!0);if(e)return e}warn(`Unsupported blend mode Array: ${e}`);return"source-over"}if(!(e instanceof Name))return t?null:"source-over";switch(e.name){case"Normal":case"Compatible":return"source-over";case"Multiply":return"multiply";case"Screen":return"screen";case"Overlay":return"overlay";case"Darken":return"darken";case"Lighten":return"lighten";case"ColorDodge":return"color-dodge";case"ColorBurn":return"color-burn";case"HardL\
-ight":return"hard-light";case"SoftLight":return"soft-light";case"Difference":return"difference";case"Exclusion":return"exclusion";case"Hue":return"hue";case"Saturation":return"saturation";case"Color":return"color";case"Luminosity":return"luminosity"}if(t)return null;warn(`Unsupported blend mode: ${e.name}`);return"source-over"}function addCachedImageOps(e,{objId:t,fn:a,args:r,optionalContent:i,hasMask:n}){t&&e.addDependency(t);e.addImageOps(a,r,i,n);a===Vt&&r[0]?.count>0&&r[0].count++}class Time\
-SlotManager{static TIME_SLOT_DURATION_MS=20;static CHECK_TIME_EVERY=100;constructor(){this.reset()}check(){if(++this.checked<TimeSlotManager.CHECK_TIME_EVERY)return!1;this.checked=0;return this.endTime<=Date.now()}reset(){this.endTime=Date.now()+TimeSlotManager.TIME_SLOT_DURATION_MS;this.checked=0}}class PartialEvaluator{constructor({xref:e,handler:t,pageIndex:a,idFactory:r,fontCache:i,builtInCMapCache:n,standardFontDataCache:s,globalColorSpaceCache:o,globalImageCache:c,systemFontCache:l,options\
-:h=null}){this.xref=e;this.handler=t;this.pageIndex=a;this.idFactory=r;this.fontCache=i;this.builtInCMapCache=n;this.standardFontDataCache=s;this.globalColorSpaceCache=o;this.globalImageCache=c;this.systemFontCache=l;this.options=h||Bn;this.type3FontRefs=null;this._regionalImageCache=new RegionalImageCache;this._fetchBuiltInCMapBound=this.fetchBuiltInCMap.bind(this)}get _pdfFunctionFactory(){return shadow(this,"_pdfFunctionFactory",new PDFFunctionFactory({xref:this.xref,isEvalSupported:this.opti\
-ons.isEvalSupported}))}get parsingType3Font(){return!!this.type3FontRefs}clone(e=null){const t=Object.create(this);t.options=Object.assign(Object.create(null),this.options,e);return t}hasBlendModes(e,t){if(!(e instanceof Dict))return!1;if(e.objId&&t.has(e.objId))return!1;const a=new RefSet(t);e.objId&&a.put(e.objId);const r=[e],i=this.xref;for(;r.length;){const e=r.shift(),t=e.get("ExtGState");if(t instanceof Dict)for(let e of t.getRawValues()){if(e instanceof Ref){if(a.has(e))continue;try{e=i.f\
-etch(e)}catch(t){a.put(e);info(`hasBlendModes - ignoring ExtGState: "${t}".`);continue}}if(!(e instanceof Dict))continue;e.objId&&a.put(e.objId);const t=e.get("BM");if(t instanceof Name){if("Normal"!==t.name)return!0}else if(void 0!==t&&Array.isArray(t))for(const e of t)if(e instanceof Name&&"Normal"!==e.name)return!0}const n=e.get("XObject");if(n instanceof Dict)for(let e of n.getRawValues()){if(e instanceof Ref){if(a.has(e))continue;try{e=i.fetch(e)}catch(t){a.put(e);info(`hasBlendModes - igno\
-ring XObject: "${t}".`);continue}}if(!(e instanceof BaseStream))continue;e.dict.objId&&a.put(e.dict.objId);const t=e.dict.get("Resources");if(t instanceof Dict&&(!t.objId||!a.has(t.objId))){r.push(t);t.objId&&a.put(t.objId)}}}for(const e of a)t.put(e);return!1}async fetchBuiltInCMap(e){const t=this.builtInCMapCache.get(e);if(t)return t;let a;a=this.options.useWorkerFetch?{cMapData:await fetchBinaryData(`${this.options.cMapUrl}${e}.bcmap`),isCompressed:!0}:await this.handler.sendWithPromise("Fetc\
-hBinaryData",{type:"cMapReaderFactory",name:e});this.builtInCMapCache.set(e,a);return a}async fetchStandardFontData(e){const t=this.standardFontDataCache.get(e);if(t)return new Stream(t);if(this.options.useSystemFonts&&"Symbol"!==e&&"ZapfDingbats"!==e)return null;const a=Yr()[e];let r;try{r=this.options.useWorkerFetch?await fetchBinaryData(`${this.options.standardFontDataUrl}${a}`):await this.handler.sendWithPromise("FetchBinaryData",{type:"standardFontDataFactory",filename:a})}catch(e){warn(e);\
-return null}this.standardFontDataCache.set(e,r);return new Stream(r)}async buildFormXObject(e,t,a,r,i,n,s,o){const{dict:c}=t,l=lookupMatrix(c.getArray("Matrix"),null),h=lookupNormalRect(c.getArray("BBox"),null);let u,d;c.has("OC")&&(u=await this.parseMarkedContentProps(c.get("OC"),e));void 0!==u&&r.addOp(jt,["OC",u]);const f=c.get("Group");if(f){d={matrix:l,bbox:h,smask:a,isolated:!1,knockout:!1};let t=null;if(isName(f.get("S"),"Transparency")){d.isolated=f.get("I")||!1;d.knockout=f.get("K")||!1\
-;if(f.has("CS")){const a=this._getColorSpace(f.getRaw("CS"),e,s);t=a instanceof ColorSpace?a:await this._handleColorSpace(a)}}if(a?.backdrop){t||=ColorSpaceUtils.rgb;a.backdrop=t.getRgbHex(a.backdrop,0)}r.addOp(Wt,[d])}const g=[l&&new Float32Array(l),!f&&h&&new Float32Array(h)||null];r.addOp(qt,g);const p=c.get("Resources");await this.getOperatorList({stream:t,task:i,resources:p instanceof Dict?p:e,operatorList:r,initialState:n,prevRefs:o});r.addOp(Ht,[]);f&&r.addOp(zt,[d]);void 0!==u&&r.addOp(_\
-t,[])}_sendImgData(e,t,a=!1){const r=t?[t.bitmap||t.data.buffer]:null;return this.parsingType3Font||a?this.handler.send("commonobj",[e,"Image",t],r):this.handler.send("obj",[e,this.pageIndex,"Image",t],r)}async buildPaintImageXObject({resources:e,image:t,isInline:a=!1,operatorList:r,cacheKey:i,localImageCache:n,localColorSpaceCache:s}){const{maxImageSize:o,ignoreErrors:c,isOffscreenCanvasSupported:l}=this.options,{dict:h}=t,u=h.objId,d=h.get("W","Width"),f=h.get("H","Height");if(!d||"number"!=ty\
-peof d||!f||"number"!=typeof f){warn("Image dimensions are missing, or not numbers.");return}if(-1!==o&&d*f>o){const e="Image exceeded maximum allowed size and was removed.";if(!c)throw new Error(e);warn(e);return}let g;h.has("OC")&&(g=await this.parseMarkedContentProps(h.get("OC"),e));let p,m,b;if(h.get("IM","ImageMask")||!1){p=await PDFImage.createMask({image:t,isOffscreenCanvasSupported:l&&!this.parsingType3Font});if(p.isSingleOpaquePixel){m=ta;b=[];r.addImageOps(m,b,g);if(i){const e={fn:m,ar\
-gs:b,optionalContent:g};n.set(i,u,e);u&&this._regionalImageCache.set(null,u,e)}return}if(this.parsingType3Font){b=function compileType3Glyph({data:e,width:t,height:a}){if(t>1e3||a>1e3)return null;const r=new Uint8Array([0,2,4,0,1,0,5,4,8,10,0,8,0,2,1,0]),i=t+1,n=new Uint8Array(i*(a+1));let s,o,c;const l=t+7&-8,h=new Uint8Array(l*a);let u=0;for(const t of e){let e=128;for(;e>0;){h[u++]=t&e?0:255;e>>=1}}let d=0;u=0;if(0!==h[u]){n[0]=1;++d}for(o=1;o<t;o++){if(h[u]!==h[u+1]){n[o]=h[u]?2:1;++d}u++}if\
-(0!==h[u]){n[o]=2;++d}for(s=1;s<a;s++){u=s*l;c=s*i;if(h[u-l]!==h[u]){n[c]=h[u]?1:8;++d}let e=(h[u]?4:0)+(h[u-l]?8:0);for(o=1;o<t;o++){e=(e>>2)+(h[u+1]?4:0)+(h[u-l+1]?8:0);if(r[e]){n[c+o]=r[e];++d}u++}if(h[u-l]!==h[u]){n[c+o]=h[u]?2:4;++d}if(d>1e3)return null}u=l*(a-1);c=s*i;if(0!==h[u]){n[c]=8;++d}for(o=1;o<t;o++){if(h[u]!==h[u+1]){n[c+o]=h[u]?4:8;++d}u++}if(0!==h[u]){n[c+o]=4;++d}if(d>1e3)return null;const f=new Int32Array([0,i,-1,0,-i,0,0,0,1]),g=[],{a:p,b:m,c:b,d:y,e:w,f:x}=(new DOMMatrix).sc\
-aleSelf(1/t,-1/a).translateSelf(0,-a);for(s=0;d&&s<=a;s++){let e=s*i;const a=e+t;for(;e<a&&!n[e];)e++;if(e===a)continue;let r=e%i,o=s;g.push(sa,p*r+b*o+w,m*r+y*o+x);const c=e;let l=n[e];do{const t=f[l];do{e+=t}while(!n[e]);const a=n[e];if(5!==a&&10!==a){l=a;n[e]=0}else{l=a&51*l>>4;n[e]&=l>>2|l<<2}r=e%i;o=e/i|0;g.push(oa,p*r+b*o+w,m*r+y*o+x);n[e]||--d}while(c!==e);--s}return[na,[new Float32Array(g)],new Float32Array([0,0,t,a])]}(p);if(b){r.addImageOps(aa,b,g);return}warn("Cannot compile Type3 gly\
-ph.");r.addImageOps(Vt,[p],g);return}const e=`mask_${this.idFactory.createObjId()}`;r.addDependency(e);p.dataLen=p.bitmap?p.width*p.height*4:p.data.length;this._sendImgData(e,p);m=Vt;b=[{data:e,width:p.width,height:p.height,interpolate:p.interpolate,count:1}];r.addImageOps(m,b,g);if(i){const t={objId:e,fn:m,args:b,optionalContent:g};n.set(i,u,t);u&&this._regionalImageCache.set(null,u,t)}return}const y=h.has("SMask")||h.has("Mask");if(a&&d+f<200&&!y){try{const i=new PDFImage({xref:this.xref,res:e\
-,image:t,isInline:a,pdfFunctionFactory:this._pdfFunctionFactory,globalColorSpaceCache:this.globalColorSpaceCache,localColorSpaceCache:s});p=await i.createImageData(!0,!1);r.addImageOps(Yt,[p],g)}catch(e){const t=`Unable to decode inline image: "${e}".`;if(!c)throw new Error(t);warn(t)}return}let w=`img_${this.idFactory.createObjId()}`,x=!1,S=null;if(this.parsingType3Font)w=`${this.idFactory.getDocId()}_type3_${w}`;else if(i&&u){x=this.globalImageCache.shouldCache(u,this.pageIndex);if(x){assert(!\
-a,"Cannot cache an inline image globally.");w=`${this.idFactory.getDocId()}_${w}`}}r.addDependency(w);m=Jt;b=[w,d,f];r.addImageOps(m,b,g,y);if(x){S={objId:w,fn:m,args:b,optionalContent:g,hasMask:y,byteSize:0};if(this.globalImageCache.hasDecodeFailed(u)){this.globalImageCache.setData(u,S);this._sendImgData(w,null,x);return}if(d*f>25e4||y){const e=await this.handler.sendWithPromise("commonobj",[w,"CopyLocalImage",{imageRef:u}]);if(e){this.globalImageCache.setData(u,S);this.globalImageCache.addByte\
-Size(u,e);return}}}PDFImage.buildImage({xref:this.xref,res:e,image:t,isInline:a,pdfFunctionFactory:this._pdfFunctionFactory,globalColorSpaceCache:this.globalColorSpaceCache,localColorSpaceCache:s}).then((async e=>{p=await e.createImageData(!1,l);p.dataLen=p.bitmap?p.width*p.height*4:p.data.length;p.ref=u;x&&this.globalImageCache.addByteSize(u,p.dataLen);return this._sendImgData(w,p,x)})).catch((e=>{warn(`Unable to decode image "${w}": "${e}".`);u&&this.globalImageCache.addDecodeFailed(u);return \
-this._sendImgData(w,null,x)}));if(i){const e={objId:w,fn:m,args:b,optionalContent:g,hasMask:y};n.set(i,u,e);if(u){this._regionalImageCache.set(null,u,e);if(x){assert(S,"The global cache-data must be available.");this.globalImageCache.setData(u,S)}}}}handleSMask(e,t,a,r,i,n,s){const o=e.get("G"),c={subtype:e.get("S").name,backdrop:e.get("BC")},l=e.get("TR");if(isPDFFunction(l)){const e=this._pdfFunctionFactory.create(l),t=new Uint8Array(256),a=new Float32Array(1);for(let r=0;r<256;r++){a[0]=r/255\
-;e(a,0,a,0);t[r]=255*a[0]|0}c.transferMap=t}return this.buildFormXObject(t,o,c,a,r,i.state.clone({newPath:!0}),n,s)}handleTransferFunction(e){let t;if(Array.isArray(e))t=e;else{if(!isPDFFunction(e))return null;t=[e]}const a=[];let r=0,i=0;for(const e of t){const t=this.xref.fetchIfRef(e);r++;if(isName(t,"Identity")){a.push(null);continue}if(!isPDFFunction(t))return null;const n=this._pdfFunctionFactory.create(t),s=new Uint8Array(256),o=new Float32Array(1);for(let e=0;e<256;e++){o[0]=e/255;n(o,0,\
-o,0);s[e]=255*o[0]|0}a.push(s);i++}return 1!==r&&4!==r||0===i?null:a}handleTilingType(e,t,a,r,i,n,s,o){const c=new OperatorList,l=Dict.merge({xref:this.xref,dictArray:[i.get("Resources"),a]});return this.getOperatorList({stream:r,task:s,resources:l,operatorList:c}).then((function(){const a=c.getIR(),r=getTilingPatternIR(a,i,t);n.addDependencies(c.dependencies);n.addOp(e,r);i.objId&&o.set(null,i.objId,{operatorListIR:a,dict:i})})).catch((e=>{if(!(e instanceof AbortException)){if(!this.options.ign\
-oreErrors)throw e;warn(`handleTilingType - ignoring pattern: "${e}".`)}}))}async handleSetFont(e,t,a,r,i,n,s=null,o=null){const c=t?.[0]instanceof Name?t[0].name:null,l=await this.loadFont(c,a,e,i,s,o);l.font.isType3Font&&r.addDependencies(l.type3Dependencies);n.font=l.font;l.send(this.handler);return l.loadedName}handleText(e,t){const a=t.font,r=a.charsToGlyphs(e);if(a.data){(!!(t.textRenderingMode&S)||"Pattern"===t.fillColorSpace.name||a.disableFontFace)&&PartialEvaluator.buildFontPaths(a,r,th\
-is.handler,this.options)}return r}ensureStateFont(e){if(e.font)return;const t=new FormatError("Missing setFont (Tf) operator before text rendering operator.");if(!this.options.ignoreErrors)throw t;warn(`ensureStateFont: "${t}".`)}async setGState({resources:e,gState:t,operatorList:a,cacheKey:r,task:i,stateManager:n,localGStateCache:s,localColorSpaceCache:o,seenRefs:c}){const l=t.objId;let h=!0;const u=[];let d=Promise.resolve();for(const[r,s]of t)switch(r){case"Type":break;case"LW":if("number"!=t\
-ypeof s){warn(`Invalid LW (line width): ${s}`);break}u.push([r,Math.abs(s)]);break;case"LC":case"LJ":case"ML":case"D":case"RI":case"FL":case"CA":case"ca":u.push([r,s]);break;case"Font":h=!1;d=d.then((()=>this.handleSetFont(e,null,s[0],a,i,n.state).then((function(e){a.addDependency(e);u.push([r,[e,s[1]]])}))));break;case"BM":u.push([r,normalizeBlendMode(s)]);break;case"SMask":if(isName(s,"None")){u.push([r,!1]);break}if(s instanceof Dict){h=!1;d=d.then((()=>this.handleSMask(s,e,a,i,n,o,c)));u.pus\
-h([r,!0])}else warn("Unsupported SMask type");break;case"TR":const t=this.handleTransferFunction(s);u.push([r,t]);break;case"OP":case"op":case"OPM":case"BG":case"BG2":case"UCR":case"UCR2":case"TR2":case"HT":case"SM":case"SA":case"AIS":case"TK":info("graphic state operator "+r);break;default:info("Unknown graphic state operator "+r)}await d;u.length>0&&a.addOp(De,[u]);h&&s.set(r,l,u)}loadFont(e,t,a,r,i=null,n=null){const errorFont=async()=>new TranslatedFont({loadedName:"g_font_error",font:new Er\
-rorFont(`Font "${e}" is not available.`),dict:t});let s;if(t)t instanceof Ref&&(s=t);else{const t=a.get("Font");t&&(s=t.getRaw(e))}if(s){if(this.type3FontRefs?.has(s))return errorFont();if(this.fontCache.has(s))return this.fontCache.get(s);try{t=this.xref.fetchIfRef(s)}catch(e){warn(`loadFont - lookup failed: "${e}".`)}}if(!(t instanceof Dict)){if(!this.options.ignoreErrors&&!this.parsingType3Font){warn(`Font "${e}" is not available.`);return errorFont()}warn(`Font "${e}" is not available -- att\
-empting to fallback to a default font.`);t=i||PartialEvaluator.fallbackFontDict}if(t.cacheKey&&this.fontCache.has(t.cacheKey))return this.fontCache.get(t.cacheKey);const{promise:o,resolve:c}=Promise.withResolvers();let l;try{l=this.preEvaluateFont(t);l.cssFontInfo=n}catch(e){warn(`loadFont - preEvaluateFont failed: "${e}".`);return errorFont()}const{descriptor:h,hash:u}=l,d=s instanceof Ref;let f;if(u&&h instanceof Dict){const e=h.fontAliases||=Object.create(null);if(e[u]){const t=e[u].aliasRef;\
-if(d&&t&&this.fontCache.has(t)){this.fontCache.putAlias(s,t);return this.fontCache.get(s)}}else e[u]={fontID:this.idFactory.createFontId()};d&&(e[u].aliasRef=s);f=e[u].fontID}else f=this.idFactory.createFontId();assert(f?.startsWith("f"),\'The "fontID" must be (correctly) defined.\');if(d)this.fontCache.put(s,o);else{t.cacheKey=`cacheKey_${f}`;this.fontCache.put(t.cacheKey,o)}t.loadedName=`${this.idFactory.getDocId()}_${f}`;this.translateFont(l).then((async e=>{const i=new TranslatedFont({loadedNa\
-me:t.loadedName,font:e,dict:t});if(e.isType3Font)try{await i.loadType3Data(this,a,r)}catch(e){throw new Error(`Type3 font load error: ${e}`)}c(i)})).catch((e=>{warn(`loadFont - translateFont failed: "${e}".`);c(new TranslatedFont({loadedName:t.loadedName,font:new ErrorFont(e?.message),dict:t}))}));return o}buildPath(e,t,a){const{pathMinMax:r,pathBuffer:i}=a;switch(0|e){case Xe:{const e=a.currentPointX=t[0],n=a.currentPointY=t[1],s=t[2],o=t[3],c=e+s,l=n+o;0===s||0===o?i.push(sa,e,n,oa,c,l,la):i.p\
-ush(sa,e,n,oa,c,n,oa,c,l,oa,e,l,la);Util.rectBoundingBox(e,n,c,l,r);break}case Ee:{const e=a.currentPointX=t[0],n=a.currentPointY=t[1];i.push(sa,e,n);Util.pointBoundingBox(e,n,r);break}case Pe:{const e=a.currentPointX=t[0],n=a.currentPointY=t[1];i.push(oa,e,n);Util.pointBoundingBox(e,n,r);break}case Le:{const e=a.currentPointX,n=a.currentPointY,[s,o,c,l,h,u]=t;a.currentPointX=h;a.currentPointY=u;i.push(ca,s,o,c,l,h,u);Util.bezierBoundingBox(e,n,s,o,c,l,h,u,r);break}case je:{const e=a.currentPoin\
-tX,n=a.currentPointY,[s,o,c,l]=t;a.currentPointX=c;a.currentPointY=l;i.push(ca,e,n,s,o,c,l);Util.bezierBoundingBox(e,n,e,n,s,o,c,l,r);break}case _e:{const e=a.currentPointX,n=a.currentPointY,[s,o,c,l]=t;a.currentPointX=c;a.currentPointY=l;i.push(ca,s,o,c,l,c,l);Util.bezierBoundingBox(e,n,s,o,c,l,c,l,r);break}case Ue:i.push(la)}}_getColorSpace(e,t,a){return ColorSpaceUtils.parse({cs:e,xref:this.xref,resources:t,pdfFunctionFactory:this._pdfFunctionFactory,globalColorSpaceCache:this.globalColorSpac\
-eCache,localColorSpaceCache:a,asyncIfNotCached:!0})}async _handleColorSpace(e){try{return await e}catch(e){if(e instanceof AbortException)return null;if(this.options.ignoreErrors){warn(`_handleColorSpace - ignoring ColorSpace: "${e}".`);return null}throw e}}parseShading({shading:e,resources:t,localColorSpaceCache:a,localShadingPatternCache:r}){let i,n=r.get(e);if(n)return n;try{i=Pattern.parseShading(e,this.xref,t,this._pdfFunctionFactory,this.globalColorSpaceCache,a).getIR()}catch(t){if(t insta\
-nceof AbortException)return null;if(this.options.ignoreErrors){warn(`parseShading - ignoring shading: "${t}".`);r.set(e,null);return null}throw t}n=`pattern_${this.idFactory.createObjId()}`;this.parsingType3Font&&(n=`${this.idFactory.getDocId()}_type3_${n}`);r.set(e,n);this.parsingType3Font?this.handler.send("commonobj",[n,"Pattern",i]):this.handler.send("obj",[n,this.pageIndex,"Pattern",i]);return n}handleColorN(e,t,a,r,i,n,s,o,c,l){const h=a.pop();if(h instanceof Name){const u=i.getRaw(h.name)\
-,d=u instanceof Ref&&c.getByRef(u);if(d)try{const i=r.base?r.base.getRgbHex(a,0):null,n=getTilingPatternIR(d.operatorListIR,d.dict,i);e.addOp(t,n);return}catch{}const f=this.xref.fetchIfRef(u);if(f){const i=f instanceof BaseStream?f.dict:f,h=i.get("PatternType");if(h===Rn){const o=r.base?r.base.getRgbHex(a,0):null;return this.handleTilingType(t,o,n,f,i,e,s,c)}if(h===Nn){const a=i.get("Shading"),r=this.parseShading({shading:a,resources:n,localColorSpaceCache:o,localShadingPatternCache:l});if(r){c\
-onst a=lookupMatrix(i.getArray("Matrix"),null);e.addOp(t,["Shading",r,a])}return}throw new FormatError(`Unknown PatternType: ${h}`)}}throw new FormatError(`Unknown PatternName: ${h}`)}_parseVisibilityExpression(e,t,a){if(++t>10){warn("Visibility expression is too deeply nested");return}const r=e.length,i=this.xref.fetchIfRef(e[0]);if(!(r<2)&&i instanceof Name){switch(i.name){case"And":case"Or":case"Not":a.push(i.name);break;default:warn(`Invalid operator ${i.name} in visibility expression`);retu\
-rn}for(let i=1;i<r;i++){const r=e[i],n=this.xref.fetchIfRef(r);if(Array.isArray(n)){const e=[];a.push(e);this._parseVisibilityExpression(n,t,e)}else r instanceof Ref&&a.push(r.toString())}}else warn("Invalid visibility expression")}async parseMarkedContentProps(e,t){let a;if(e instanceof Name){a=t.get("Properties").get(e.name)}else{if(!(e instanceof Dict))throw new FormatError("Optional content properties malformed.");a=e}const r=a.get("Type")?.name;if("OCG"===r)return{type:r,id:a.objId};if("OCM\
-D"===r){const e=a.get("VE");if(Array.isArray(e)){const t=[];this._parseVisibilityExpression(e,0,t);if(t.length>0)return{type:"OCMD",expression:t}}const t=a.get("OCGs");if(Array.isArray(t)||t instanceof Dict){const e=[];if(Array.isArray(t))for(const a of t)e.push(a.toString());else e.push(t.objId);return{type:r,ids:e,policy:a.get("P")instanceof Name?a.get("P").name:null,expression:null}}if(t instanceof Ref)return{type:r,id:t.toString()}}return null}getOperatorList({stream:e,task:t,resources:a,ope\
-ratorList:r,initialState:i=null,fallbackFontDict:n=null,prevRefs:s=null}){const o=e.dict?.objId,c=new RefSet(s);if(o){if(s?.has(o))throw new Error(`getOperatorList - ignoring circular reference: ${o}`);c.put(o)}a||=Dict.empty;i||=new EvalState;if(!r)throw new Error(\'getOperatorList: missing "operatorList" parameter\');const l=this,h=this.xref,u=new LocalImageCache,d=new LocalColorSpaceCache,f=new LocalGStateCache,g=new LocalTilingPatternCache,p=new Map,m=a.get("XObject")||Dict.empty,b=a.get("Patt\
-ern")||Dict.empty,y=new StateManager(i),w=new EvaluatorPreprocessor(e,h,y),x=new TimeSlotManager;function closePendingRestoreOPS(e){for(let e=0,t=w.savedStatesDepth;e<t;e++)r.addOp(Re,[])}return new Promise((function promiseBody(e,i){const next=function(t){Promise.all([t,r.ready]).then((function(){try{promiseBody(e,i)}catch(e){i(e)}}),i)};t.ensureNotTerminated();x.reset();const s={};let o,S,k,C,v,F;for(;!(o=x.check());){s.args=null;if(!w.read(s))break;let e=s.args,i=s.fn;switch(0|i){case Nt:F=e[\
-0]instanceof Name;v=e[0].name;if(F){const t=u.getByName(v);if(t){addCachedImageOps(r,t);e=null;continue}}next(new Promise((function(e,i){if(!F)throw new FormatError("XObject must be referred to by name.");let n=m.getRaw(v);if(n instanceof Ref){const t=u.getByRef(n)||l._regionalImageCache.getByRef(n)||l.globalImageCache.getData(n,l.pageIndex);if(t){addCachedImageOps(r,t);e();return}n=h.fetch(n)}if(!(n instanceof BaseStream))throw new FormatError("XObject should be a stream");const s=n.dict.get("S\
-ubtype");if(!(s instanceof Name))throw new FormatError("XObject should have a Name subtype");if("Form"!==s.name)if("Image"!==s.name){if("PS"!==s.name)throw new FormatError(`Unhandled XObject subtype ${s.name}`);info("Ignored XObject subtype PS");e()}else l.buildPaintImageXObject({resources:a,image:n,operatorList:r,cacheKey:v,localImageCache:u,localColorSpaceCache:d}).then(e,i);else{y.save();l.buildFormXObject(a,n,null,r,t,y.state.clone({newPath:!0}),d,c).then((function(){y.restore();e()}),i)}}))\
-.catch((function(e){if(!(e instanceof AbortException)){if(!l.options.ignoreErrors)throw e;warn(`getOperatorList - ignoring XObject: "${e}".`)}})));return;case nt:const s=e[1];next(l.handleSetFont(a,e,null,r,t,y.state,n).then((function(e){r.addDependency(e);r.addOp(nt,[e,s])})));return;case Rt:const o=e[0].cacheKey;if(o){const t=u.getByName(o);if(t){addCachedImageOps(r,t);e=null;continue}}next(l.buildPaintImageXObject({resources:a,image:e[0],isInline:!0,operatorList:r,cacheKey:o,localImageCache:u\
-,localColorSpaceCache:d}));return;case dt:if(!y.state.font){l.ensureStateFont(y.state);continue}e[0]=l.handleText(e[0],y.state);break;case ft:if(!y.state.font){l.ensureStateFont(y.state);continue}const w=[],x=y.state;for(const t of e[0])"string"==typeof t?w.push(...l.handleText(t,x)):"number"==typeof t&&w.push(t);e[0]=w;i=dt;break;case gt:if(!y.state.font){l.ensureStateFont(y.state);continue}r.addOp(ut);e[0]=l.handleText(e[0],y.state);i=dt;break;case pt:if(!y.state.font){l.ensureStateFont(y.stat\
-e);continue}r.addOp(ut);r.addOp(at,[e.shift()]);r.addOp(tt,[e.shift()]);e[0]=l.handleText(e[0],y.state);i=dt;break;case st:y.state.textRenderingMode=e[0];break;case wt:{const t=l._getColorSpace(e[0],a,d);if(t instanceof ColorSpace){y.state.fillColorSpace=t;continue}next(l._handleColorSpace(t).then((e=>{y.state.fillColorSpace=e||ColorSpaceUtils.gray})));return}case yt:{const t=l._getColorSpace(e[0],a,d);if(t instanceof ColorSpace){y.state.strokeColorSpace=t;continue}next(l._handleColorSpace(t).th\
-en((e=>{y.state.strokeColorSpace=e||ColorSpaceUtils.gray})));return}case At:C=y.state.fillColorSpace;e=[C.getRgbHex(e,0)];i=It;break;case xt:C=y.state.strokeColorSpace;e=[C.getRgbHex(e,0)];i=Ft;break;case vt:y.state.fillColorSpace=ColorSpaceUtils.gray;e=[ColorSpaceUtils.gray.getRgbHex(e,0)];i=It;break;case Ct:y.state.strokeColorSpace=ColorSpaceUtils.gray;e=[ColorSpaceUtils.gray.getRgbHex(e,0)];i=Ft;break;case Ot:y.state.fillColorSpace=ColorSpaceUtils.cmyk;e=[ColorSpaceUtils.cmyk.getRgbHex(e,0)];\
-i=It;break;case Tt:y.state.strokeColorSpace=ColorSpaceUtils.cmyk;e=[ColorSpaceUtils.cmyk.getRgbHex(e,0)];i=Ft;break;case It:y.state.fillColorSpace=ColorSpaceUtils.rgb;e=[ColorSpaceUtils.rgb.getRgbHex(e,0)];break;case Ft:y.state.strokeColorSpace=ColorSpaceUtils.rgb;e=[ColorSpaceUtils.rgb.getRgbHex(e,0)];break;case kt:C=y.state.patternFillColorSpace;if(!C){if(isNumberArray(e,null)){e=[ColorSpaceUtils.gray.getRgbHex(e,0)];i=It;break}e=[];i=ia;break}if("Pattern"===C.name){next(l.handleColorN(r,kt,e,\
-C,b,a,t,d,g,p));return}e=[C.getRgbHex(e,0)];i=It;break;case St:C=y.state.patternStrokeColorSpace;if(!C){if(isNumberArray(e,null)){e=[ColorSpaceUtils.gray.getRgbHex(e,0)];i=Ft;break}e=[];i=ra;break}if("Pattern"===C.name){next(l.handleColorN(r,St,e,C,b,a,t,d,g,p));return}e=[C.getRgbHex(e,0)];i=Ft;break;case Mt:let T;try{const t=a.get("Shading");if(!t)throw new FormatError("No shading resource found");T=t.get(e[0].name);if(!T)throw new FormatError("No shading object found")}catch(e){if(e instanceof\
- AbortException)continue;if(l.options.ignoreErrors){warn(`getOperatorList - ignoring Shading: "${e}".`);continue}throw e}const O=l.parseShading({shading:T,resources:a,localColorSpaceCache:d,localShadingPatternCache:p});if(!O)continue;e=[O];i=Mt;break;case De:F=e[0]instanceof Name;v=e[0].name;if(F){const t=f.getByName(v);if(t){t.length>0&&r.addOp(De,[t]);e=null;continue}}next(new Promise((function(e,i){if(!F)throw new FormatError("GState must be referred to by name.");const n=a.get("ExtGState");i\
-f(!(n instanceof Dict))throw new FormatError("ExtGState should be a dictionary.");const s=n.get(v);if(!(s instanceof Dict))throw new FormatError("GState should be a dictionary.");l.setGState({resources:a,gState:s,operatorList:r,cacheKey:v,task:t,stateManager:y,localGStateCache:f,localColorSpaceCache:d,seenRefs:c}).then(e,i)})).catch((function(e){if(!(e instanceof AbortException)){if(!l.options.ignoreErrors)throw e;warn(`getOperatorList - ignoring ExtGState: "${e}".`)}})));return;case Ce:{const[t\
-]=e;if("number"!=typeof t){warn(`Invalid setLineWidth: ${t}`);continue}e[0]=Math.abs(t);break}case Ee:case Pe:case Le:case je:case _e:case Ue:case Xe:l.buildPath(i,e,y.state);continue;case qe:case He:case We:case ze:case $e:case Ge:case Ve:case Ke:case Je:{const{state:{pathBuffer:e,pathMinMax:t}}=y;i!==He&&i!==Ve&&i!==Ke||e.push(la);if(0===e.length)r.addOp(aa,[i,[null],null]);else{r.addOp(aa,[i,[new Float32Array(e)],t.slice()]);e.length=0;t.set([1/0,1/0,-1/0,-1/0],0)}continue}case ht:r.addOp(i,[\
-new Float32Array(e)]);continue;case Et:case Pt:case Ut:case Xt:continue;case jt:if(!(e[0]instanceof Name)){warn(`Expected name for beginMarkedContentProps arg0=${e[0]}`);r.addOp(jt,["OC",null]);continue}if("OC"===e[0].name){next(l.parseMarkedContentProps(e[1],a).then((e=>{r.addOp(jt,["OC",e])})).catch((e=>{if(!(e instanceof AbortException)){if(!l.options.ignoreErrors)throw e;warn(`getOperatorList - ignoring beginMarkedContentProps: "${e}".`);r.addOp(jt,["OC",null])}})));return}e=[e[0].name,e[1]i\
-nstanceof Dict?e[1].get("MCID"):null];break;default:if(null!==e){for(S=0,k=e.length;S<k&&!(e[S]instanceof Dict);S++);if(S<k){warn("getOperatorList - ignoring operator: "+i);continue}}}r.addOp(i,e)}if(o)next(En);else{closePendingRestoreOPS();e()}})).catch((e=>{if(!(e instanceof AbortException)){if(!this.options.ignoreErrors)throw e;warn(`getOperatorList - ignoring errors during "${t.name}" task: "${e}".`);closePendingRestoreOPS()}}))}getTextContent({stream:e,task:a,resources:r,stateManager:i=null\
-,includeMarkedContent:n=!1,sink:s,seenStyles:o=new Set,viewBox:c,lang:l=null,markedContentData:h=null,disableNormalization:u=!1,keepWhiteSpace:d=!1,prevRefs:f=null,intersector:g=null}){const p=e.dict?.objId,m=new RefSet(f);if(p){if(f?.has(p))throw new Error(`getTextContent - ignoring circular reference: ${p}`);m.put(p)}r||=Dict.empty;i||=new StateManager(new TextState);n&&(h||={level:0});const b={items:[],styles:Object.create(null),lang:l},y={initialized:!1,str:[],totalWidth:0,totalHeight:0,widt\
-h:0,height:0,vertical:!1,prevTransform:null,textAdvanceScale:0,spaceInFlowMin:0,spaceInFlowMax:0,trackingSpaceMin:1/0,negativeSpaceMax:-1/0,notASpace:-1/0,transform:null,fontName:null,hasEOL:!1},w=[" "," "];let x=0;function saveLastChar(e){const t=(x+1)%2,a=" "!==w[x]&&" "===w[t];w[x]=e;x=t;return!d&&a}function shouldAddWhitepsace(){return!d&&" "!==w[x]&&" "===w[(x+1)%2]}function resetLastChars(){w[0]=w[1]=" ";x=0}const S=this,k=this.xref,C=[];let v=null;const F=new LocalImageCache,T=new LocalGS\
-tateCache,O=new EvaluatorPreprocessor(e,k,i);let M;function pushWhitespace({width:e=0,height:t=0,transform:a=y.prevTransform,fontName:r=y.fontName}){g?.addExtraChar(" ");b.items.push({str:" ",dir:"ltr",width:e,height:t,transform:a,fontName:r,hasEOL:!1})}function getCurrentTextTransform(){const e=M.font,a=[M.fontSize*M.textHScale,0,0,M.fontSize,0,M.textRise];if(e.isType3Font&&(M.fontSize<=1||e.isCharBBox)&&!isArrayEqual(M.fontMatrix,t)){const t=e.bbox[3]-e.bbox[1];t>0&&(a[3]*=t*M.fontMatrix[3])}r\
-eturn Util.transform(M.ctm,Util.transform(M.textMatrix,a))}function ensureTextContentItem(){if(y.initialized)return y;const{font:e,loadedName:t}=M;if(!o.has(t)){o.add(t);b.styles[t]={fontFamily:e.fallbackName,ascent:e.ascent,descent:e.descent,vertical:e.vertical};if(S.options.fontExtraProperties&&e.systemFontInfo){const a=b.styles[t];a.fontSubstitution=e.systemFontInfo.css;a.fontSubstitutionLoadedName=e.systemFontInfo.loadedName}}y.fontName=t;const a=y.transform=getCurrentTextTransform();if(e.ve\
-rtical){y.width=y.totalWidth=Math.hypot(a[0],a[1]);y.height=y.totalHeight=0;y.vertical=!0}else{y.width=y.totalWidth=0;y.height=y.totalHeight=Math.hypot(a[2],a[3]);y.vertical=!1}const r=Math.hypot(M.textLineMatrix[0],M.textLineMatrix[1]),i=Math.hypot(M.ctm[0],M.ctm[1]);y.textAdvanceScale=i*r;const{fontSize:n}=M;y.trackingSpaceMin=.102*n;y.notASpace=.03*n;y.negativeSpaceMax=-.2*n;y.spaceInFlowMin=.102*n;y.spaceInFlowMax=.6*n;y.hasEOL=!1;y.initialized=!0;return y}function updateAdvanceScale(){if(!y\
-.initialized)return;const e=Math.hypot(M.textLineMatrix[0],M.textLineMatrix[1]),t=Math.hypot(M.ctm[0],M.ctm[1])*e;if(t!==y.textAdvanceScale){if(y.vertical){y.totalHeight+=y.height*y.textAdvanceScale;y.height=0}else{y.totalWidth+=y.width*y.textAdvanceScale;y.width=0}y.textAdvanceScale=t}}function runBidiTransform(e){let t=e.str.join("");u||(t=function normalizeUnicode(e){if(!ma){ma=/([\\u00a0\\u00b5\\u037e\\u0eb3\\u2000-\\u200a\\u202f\\u2126\\ufb00-\\ufb04\\ufb06\\ufb20-\\ufb36\\ufb38-\\ufb3c\\ufb3e\\ufb40-\\ufb41\
-\\ufb43-\\ufb44\\ufb46-\\ufba1\\ufba4-\\ufba9\\ufbae-\\ufbb1\\ufbd3-\\ufbdc\\ufbde-\\ufbe7\\ufbea-\\ufbf8\\ufbfc-\\ufbfd\\ufc00-\\ufc5d\\ufc64-\\ufcf1\\ufcf5-\\ufd3d\\ufd88\\ufdf4\\ufdfa-\\ufdfb\\ufe71\\ufe77\\ufe79\\ufe7b\\ufe7d]+)|(\\ufb05+)/gu;ba=new Map([["ſt","ſt"]])}return e.replaceAll(ma,((e,t,a)=>t?t.normalize("NFKC"):ba.get(a)))}(t));const a=bidi(t,-1,e.vertical);return{str:a.str,dir:a.dir,width:Math.abs(e.totalWidth),height:Math.abs(e.totalHeight),transform:e.transform,fontName:e.fontName,hasEOL:e.hasEOL}}async functi\
-on handleSetFont(e,i){const n=await S.loadFont(e,i,r,a);M.loadedName=n.loadedName;M.font=n.font;M.fontMatrix=n.font.fontMatrix||t}function applyInverseRotation(e,t,a){const r=Math.hypot(a[0],a[1]);return[(a[0]*e+a[1]*t)/r,(a[2]*e+a[3]*t)/r]}function compareWithLastPosition(e){const t=getCurrentTextTransform();let a=t[4],r=t[5];if(M.font?.vertical){if(a<c[0]||a>c[2]||r+e<c[1]||r>c[3])return!1}else if(a+e<c[0]||a>c[2]||r<c[1]||r>c[3])return!1;if(!M.font||!y.prevTransform)return!0;let i=y.prevTrans\
-form[4],n=y.prevTransform[5];if(i===a&&n===r)return!0;let s=-1;t[0]&&0===t[1]&&0===t[2]?s=t[0]>0?0:180:t[1]&&0===t[0]&&0===t[3]&&(s=t[1]>0?90:270);switch(s){case 0:break;case 90:[a,r]=[r,a];[i,n]=[n,i];break;case 180:[a,r,i,n]=[-a,-r,-i,-n];break;case 270:[a,r]=[-r,-a];[i,n]=[-n,-i];break;default:[a,r]=applyInverseRotation(a,r,t);[i,n]=applyInverseRotation(i,n,y.prevTransform)}if(M.font.vertical){const e=(n-r)/y.textAdvanceScale,t=a-i,s=Math.sign(y.height);if(e<s*y.negativeSpaceMax){if(Math.abs(\
-t)>.5*y.width){appendEOL();return!0}resetLastChars();flushTextContentItem();return!0}if(Math.abs(t)>y.width){appendEOL();return!0}e<=s*y.notASpace&&resetLastChars();if(e<=s*y.trackingSpaceMin)if(shouldAddWhitepsace()){resetLastChars();flushTextContentItem();pushWhitespace({height:Math.abs(e)})}else y.height+=e;else if(!addFakeSpaces(e,y.prevTransform,s))if(0===y.str.length){resetLastChars();pushWhitespace({height:Math.abs(e)})}else y.height+=e;Math.abs(t)>.25*y.width&&flushTextContentItem();retu\
-rn!0}const o=(a-i)/y.textAdvanceScale,l=r-n,h=Math.sign(y.width);if(o<h*y.negativeSpaceMax){if(Math.abs(l)>.5*y.height){appendEOL();return!0}resetLastChars();flushTextContentItem();return!0}if(Math.abs(l)>y.height){appendEOL();return!0}o<=h*y.notASpace&&resetLastChars();if(o<=h*y.trackingSpaceMin)if(shouldAddWhitepsace()){resetLastChars();flushTextContentItem();pushWhitespace({width:Math.abs(o)})}else y.width+=o;else if(!addFakeSpaces(o,y.prevTransform,h))if(0===y.str.length){resetLastChars();pu\
-shWhitespace({width:Math.abs(o)})}else y.width+=o;Math.abs(l)>.25*y.height&&flushTextContentItem();return!0}function buildTextContentItem({chars:e,extraSpacing:t}){const a=M.font;if(!e){const e=M.charSpacing+t;e&&(a.vertical?M.translateTextMatrix(0,-e):M.translateTextMatrix(e*M.textHScale,0));d&&compareWithLastPosition(0);return}const r=a.charsToGlyphs(e),i=M.fontMatrix[0]*M.fontSize;for(let e=0,n=r.length;e<n;e++){const s=r[e],{category:o}=s;if(o.isInvisibleFormatMark)continue;let c=M.charSpaci\
-ng+(e+1===n?t:0),l=s.width;a.vertical&&(l=s.vmetric?s.vmetric[0]:-l);let h=l*i;if(!d&&o.isWhitespace){if(a.vertical){c+=-h+M.wordSpacing;M.translateTextMatrix(0,-c)}else{c+=h+M.wordSpacing;M.translateTextMatrix(c*M.textHScale,0)}saveLastChar(" ");continue}if(!o.isZeroWidthDiacritic&&!compareWithLastPosition(h)){a.vertical?M.translateTextMatrix(0,h):M.translateTextMatrix(h*M.textHScale,0);continue}const u=ensureTextContentItem();o.isZeroWidthDiacritic&&(h=0);if(a.vertical){g?.addGlyph(getCurrentT\
-extTransform(),0,h,s.unicode);M.translateTextMatrix(0,h);h=Math.abs(h);u.height+=h}else{h*=M.textHScale;g?.addGlyph(getCurrentTextTransform(),h,0,s.unicode);M.translateTextMatrix(h,0);u.width+=h}h&&(u.prevTransform=getCurrentTextTransform());const f=s.unicode;if(saveLastChar(f)){u.str.push(" ");g?.addExtraChar(" ")}g||u.str.push(f);c&&(a.vertical?M.translateTextMatrix(0,-c):M.translateTextMatrix(c*M.textHScale,0))}}function appendEOL(){g?.addExtraChar("\\n");resetLastChars();if(y.initialized){y.h\
-asEOL=!0;flushTextContentItem()}else b.items.push({str:"",dir:"ltr",width:0,height:0,transform:getCurrentTextTransform(),fontName:M.loadedName,hasEOL:!0})}function addFakeSpaces(e,t,a){if(a*y.spaceInFlowMin<=e&&e<=a*y.spaceInFlowMax){if(y.initialized){resetLastChars();y.str.push(" ");g?.addExtraChar(" ")}return!1}const r=y.fontName;let i=0;if(y.vertical){i=e;e=0}flushTextContentItem();resetLastChars();pushWhitespace({width:Math.abs(e),height:Math.abs(i),transform:t||getCurrentTextTransform(),fon\
-tName:r});return!0}function flushTextContentItem(){if(y.initialized&&y.str){y.vertical?y.totalHeight+=y.height*y.textAdvanceScale:y.totalWidth+=y.width*y.textAdvanceScale;b.items.push(runBidiTransform(y));y.initialized=!1;y.str.length=0}}function enqueueChunk(e=!1){const t=b.items.length;if(0!==t&&!(e&&t<10)){s?.enqueue(b,t);b.items=[];b.styles=Object.create(null)}}const D=new TimeSlotManager;return new Promise((function promiseBody(e,t){const next=function(a){enqueueChunk(!0);Promise.all([a,s?.\
-ready]).then((function(){try{promiseBody(e,t)}catch(e){t(e)}}),t)};a.ensureNotTerminated();D.reset();const f={};let g,p,y,w=[];for(;!(g=D.check());){w.length=0;f.args=w;if(!O.read(f))break;const e=M;M=i.state;const t=f.fn;w=f.args;switch(0|t){case nt:const t=w[0].name,f=w[1];if(M.font&&t===M.fontName&&f===M.fontSize)break;flushTextContentItem();M.fontName=t;M.fontSize=f;next(handleSetFont(t,null));return;case ot:M.textRise=w[0];break;case rt:M.textHScale=w[0]/100;break;case it:M.leading=w[0];bre\
-ak;case ct:M.translateTextLineMatrix(w[0],w[1]);M.textMatrix=M.textLineMatrix.slice();break;case lt:M.leading=-w[1];M.translateTextLineMatrix(w[0],w[1]);M.textMatrix=M.textLineMatrix.slice();break;case ut:M.carriageReturn();break;case ht:M.setTextMatrix(w[0],w[1],w[2],w[3],w[4],w[5]);M.setTextLineMatrix(w[0],w[1],w[2],w[3],w[4],w[5]);updateAdvanceScale();break;case tt:M.charSpacing=w[0];break;case at:M.wordSpacing=w[0];break;case Qe:M.textMatrix=Fa.slice();M.textLineMatrix=Fa.slice();break;case \
-ft:if(!i.state.font){S.ensureStateFont(i.state);continue}const g=(M.font.vertical?1:-1)*M.fontSize/1e3,x=w[0];for(let e=0,t=x.length;e<t;e++){const t=x[e];if("string"==typeof t)C.push(t);else if("number"==typeof t&&0!==t){const e=C.join("");C.length=0;buildTextContentItem({chars:e,extraSpacing:t*g})}}if(C.length>0){const e=C.join("");C.length=0;buildTextContentItem({chars:e,extraSpacing:0})}break;case dt:if(!i.state.font){S.ensureStateFont(i.state);continue}buildTextContentItem({chars:w[0],extra\
-Spacing:0});break;case gt:if(!i.state.font){S.ensureStateFont(i.state);continue}M.carriageReturn();buildTextContentItem({chars:w[0],extraSpacing:0});break;case pt:if(!i.state.font){S.ensureStateFont(i.state);continue}M.wordSpacing=w[0];M.charSpacing=w[1];M.carriageReturn();buildTextContentItem({chars:w[2],extraSpacing:0});break;case Nt:flushTextContentItem();v??=r.get("XObject")||Dict.empty;y=w[0]instanceof Name;p=w[0].name;if(y&&F.getByName(p))break;next(new Promise((function(e,t){if(!y)throw n\
-ew FormatError("XObject must be referred to by name.");let f=v.getRaw(p);if(f instanceof Ref){if(F.getByRef(f)){e();return}if(S.globalImageCache.getData(f,S.pageIndex)){e();return}f=k.fetch(f)}if(!(f instanceof BaseStream))throw new FormatError("XObject should be a stream");const{dict:g}=f,b=g.get("Subtype");if(!(b instanceof Name))throw new FormatError("XObject should have a Name subtype");if("Form"!==b.name){F.set(p,g.objId,!0);e();return}const w=i.state.clone(),x=new StateManager(w),C=lookupM\
-atrix(g.getArray("Matrix"),null);C&&x.transform(C);const T=g.get("Resources");enqueueChunk();const O={enqueueInvoked:!1,enqueue(e,t){this.enqueueInvoked=!0;s.enqueue(e,t)},get desiredSize(){return s.desiredSize??0},get ready(){return s.ready}};S.getTextContent({stream:f,task:a,resources:T instanceof Dict?T:r,stateManager:x,includeMarkedContent:n,sink:s&&O,seenStyles:o,viewBox:c,lang:l,markedContentData:h,disableNormalization:u,keepWhiteSpace:d,prevRefs:m}).then((function(){O.enqueueInvoked||F.se\
-t(p,g.objId,!0);e()}),t)})).catch((function(e){if(!(e instanceof AbortException)){if(!S.options.ignoreErrors)throw e;warn(`getTextContent - ignoring XObject: "${e}".`)}})));return;case De:y=w[0]instanceof Name;p=w[0].name;if(y&&T.getByName(p))break;next(new Promise((function(e,t){if(!y)throw new FormatError("GState must be referred to by name.");const a=r.get("ExtGState");if(!(a instanceof Dict))throw new FormatError("ExtGState should be a dictionary.");const i=a.get(p);if(!(i instanceof Dict))t\
-hrow new FormatError("GState should be a dictionary.");const n=i.get("Font");if(n){flushTextContentItem();M.fontName=null;M.fontSize=n[1];handleSetFont(null,n[0]).then(e,t)}else{T.set(p,i.objId,!0);e()}})).catch((function(e){if(!(e instanceof AbortException)){if(!S.options.ignoreErrors)throw e;warn(`getTextContent - ignoring ExtGState: "${e}".`)}})));return;case Lt:flushTextContentItem();if(n){h.level++;b.items.push({type:"beginMarkedContent",tag:w[0]instanceof Name?w[0].name:null})}break;case j\
-t:flushTextContentItem();if(n){h.level++;let e=null;w[1]instanceof Dict&&(e=w[1].get("MCID"));b.items.push({type:"beginMarkedContentProps",id:Number.isInteger(e)?`${S.idFactory.getPageObjId()}_mc${e}`:null,tag:w[0]instanceof Name?w[0].name:null})}break;case _t:flushTextContentItem();if(n){if(0===h.level)break;h.level--;b.items.push({type:"endMarkedContent"})}break;case Re:!e||e.font===M.font&&e.fontSize===M.fontSize&&e.fontName===M.fontName||flushTextContentItem()}if(b.items.length>=(s?.desiredS\
-ize??1)){g=!0;break}}if(g)next(En);else{flushTextContentItem();enqueueChunk();e()}})).catch((e=>{if(!(e instanceof AbortException)){if(!this.options.ignoreErrors)throw e;warn(`getTextContent - ignoring errors during "${a.name}" task: "${e}".`);flushTextContentItem();enqueueChunk()}}))}async extractDataStructures(e,t){const a=this.xref;let r;const i=this.readToUnicode(t.toUnicode);if(t.composite){const a=e.get("CIDSystemInfo");a instanceof Dict&&(t.cidSystemInfo={registry:stringToPDFString(a.get(\
-"Registry")),ordering:stringToPDFString(a.get("Ordering")),supplement:a.get("Supplement")});try{const t=e.get("CIDToGIDMap");t instanceof BaseStream&&(r=t.getBytes())}catch(e){if(!this.options.ignoreErrors)throw e;warn(`extractDataStructures - ignoring CIDToGIDMap data: "${e}".`)}}const n=[];let s,o=null;if(e.has("Encoding")){s=e.get("Encoding");if(s instanceof Dict){o=s.get("BaseEncoding");o=o instanceof Name?o.name:null;if(s.has("Differences")){const e=s.get("Differences");let t=0;for(const r \
-of e){const e=a.fetchIfRef(r);if("number"==typeof e)t=e;else{if(!(e instanceof Name))throw new FormatError(`Invalid entry in \'Differences\' array: ${e}`);n[t++]=e.name}}}}else if(s instanceof Name)o=s.name;else{const e="Encoding is not a Name nor a Dict";if(!this.options.ignoreErrors)throw new FormatError(e);warn(e)}"MacRomanEncoding"!==o&&"MacExpertEncoding"!==o&&"WinAnsiEncoding"!==o&&(o=null)}const c=!t.file||t.isInternalFont,l=ei()[t.name];o&&c&&l&&(o=null);if(o)t.defaultEncoding=getEncoding(\
-o);else{const e=!!(t.flags&Pr),a=!!(t.flags&Lr);s=Ar;"TrueType"!==t.type||a||(s=kr);if(e||l){s=Sr;c&&(/Symbol/i.test(t.name)?s=Cr:/Dingbats/i.test(t.name)?s=vr:/Wingdings/i.test(t.name)&&(s=kr))}t.defaultEncoding=s}t.differences=n;t.baseEncodingName=o;t.hasEncoding=!!o||n.length>0;t.dict=e;t.toUnicode=await i;const h=await this.buildToUnicode(t);t.toUnicode=h;r&&(t.cidToGidMap=this.readCidToGidMap(r,h));return t}_simpleFontToUnicode(e,t=!1){assert(!e.composite,"Must be a simple font.");const a=[\
-],r=e.defaultEncoding.slice(),i=e.baseEncodingName,n=e.differences;for(const e in n){const t=n[e];".notdef"!==t&&(r[e]=t)}const s=Fr();for(const n in r){let o=r[n];if(""===o)continue;let c=s[o];if(void 0!==c){a[n]=String.fromCharCode(c);continue}let l=0;switch(o[0]){case"G":3===o.length&&(l=parseInt(o.substring(1),16));break;case"g":5===o.length&&(l=parseInt(o.substring(1),16));break;case"C":case"c":if(o.length>=3&&o.length<=4){const a=o.substring(1);if(t){l=parseInt(a,16);break}l=+a;if(Number.i\
-sNaN(l)&&Number.isInteger(parseInt(a,16)))return this._simpleFontToUnicode(e,!0)}break;case"u":c=getUnicodeForGlyph(o,s);-1!==c&&(l=c);break;default:switch(o){case"f_h":case"f_t":case"T_h":a[n]=o.replaceAll("_","");continue}}if(l>0&&l<=1114111&&Number.isInteger(l)){if(i&&l===+n){const e=getEncoding(i);if(e&&(o=e[n])){a[n]=String.fromCharCode(s[o]);continue}}a[n]=String.fromCodePoint(l)}}return a}async buildToUnicode(e){e.hasIncludedToUnicodeMap=e.toUnicode?.length>0;if(e.hasIncludedToUnicodeMap)\
-{!e.composite&&e.hasEncoding&&(e.fallbackToUnicode=this._simpleFontToUnicode(e));return e.toUnicode}if(!e.composite)return new ToUnicodeMap(this._simpleFontToUnicode(e));if(e.composite&&(e.cMap.builtInCMap&&!(e.cMap instanceof IdentityCMap)||"Adobe"===e.cidSystemInfo?.registry&&("GB1"===e.cidSystemInfo.ordering||"CNS1"===e.cidSystemInfo.ordering||"Japan1"===e.cidSystemInfo.ordering||"Korea1"===e.cidSystemInfo.ordering))){const{registry:t,ordering:a}=e.cidSystemInfo,r=Name.get(`${t}-${a}-UCS2`),i\
-=await CMapFactory.create({encoding:r,fetchBuiltInCMap:this._fetchBuiltInCMapBound,useCMap:null}),n=[],s=[];e.cMap.forEach((function(e,t){if(t>65535)throw new FormatError("Max size of CID is 65,535");const a=i.lookup(t);if(a){s.length=0;for(let e=0,t=a.length;e<t;e+=2)s.push((a.charCodeAt(e)<<8)+a.charCodeAt(e+1));n[e]=String.fromCharCode(...s)}}));return new ToUnicodeMap(n)}return new IdentityToUnicodeMap(e.firstChar,e.lastChar)}async readToUnicode(e){if(!e)return null;if(e instanceof Name){con\
-st t=await CMapFactory.create({encoding:e,fetchBuiltInCMap:this._fetchBuiltInCMapBound,useCMap:null});return t instanceof IdentityCMap?new IdentityToUnicodeMap(0,65535):new ToUnicodeMap(t.getMap())}if(e instanceof BaseStream)try{const t=await CMapFactory.create({encoding:e,fetchBuiltInCMap:this._fetchBuiltInCMapBound,useCMap:null});if(t instanceof IdentityCMap)return new IdentityToUnicodeMap(0,65535);const a=new Array(t.length);t.forEach((function(e,t){if("number"==typeof t){a[e]=String.fromCode\
-Point(t);return}t.length%2!=0&&(t="\\0"+t);const r=[];for(let e=0;e<t.length;e+=2){const a=t.charCodeAt(e)<<8|t.charCodeAt(e+1);if(55296!=(63488&a)){r.push(a);continue}e+=2;const i=t.charCodeAt(e)<<8|t.charCodeAt(e+1);r.push(((1023&a)<<10)+(1023&i)+65536)}a[e]=String.fromCodePoint(...r)}));return new ToUnicodeMap(a)}catch(e){if(e instanceof AbortException)return null;if(this.options.ignoreErrors){warn(`readToUnicode - ignoring ToUnicode data: "${e}".`);return null}throw e}return null}readCidToGid\
-Map(e,t){const a=[];for(let r=0,i=e.length;r<i;r++){const i=e[r++]<<8|e[r],n=r>>1;(0!==i||t.has(n))&&(a[n]=i)}return a}extractWidths(e,t,a){const r=this.xref;let i=[],n=0;const s=[];let o;if(a.composite){const t=e.get("DW");n="number"==typeof t?Math.ceil(t):1e3;const c=e.get("W");if(Array.isArray(c))for(let e=0,t=c.length;e<t;e++){let t=r.fetchIfRef(c[e++]);if(!Number.isInteger(t))break;const a=r.fetchIfRef(c[e]);if(Array.isArray(a))for(const e of a){const a=r.fetchIfRef(e);"number"==typeof a&&(\
-i[t]=a);t++}else{if(!Number.isInteger(a))break;{const n=r.fetchIfRef(c[++e]);if("number"!=typeof n)continue;for(let e=t;e<=a;e++)i[e]=n}}}if(a.vertical){const t=e.getArray("DW2");let a=isNumberArray(t,2)?t:[880,-1e3];o=[a[1],.5*n,a[0]];a=e.get("W2");if(Array.isArray(a))for(let e=0,t=a.length;e<t;e++){let t=r.fetchIfRef(a[e++]);if(!Number.isInteger(t))break;const i=r.fetchIfRef(a[e]);if(Array.isArray(i))for(let e=0,a=i.length;e<a;e++){const a=[r.fetchIfRef(i[e++]),r.fetchIfRef(i[e++]),r.fetchIfRe\
-f(i[e])];isNumberArray(a,null)&&(s[t]=a);t++}else{if(!Number.isInteger(i))break;{const n=[r.fetchIfRef(a[++e]),r.fetchIfRef(a[++e]),r.fetchIfRef(a[++e])];if(!isNumberArray(n,null))continue;for(let e=t;e<=i;e++)s[e]=n}}}}}else{const s=e.get("Widths");if(Array.isArray(s)){let e=a.firstChar;for(const t of s){const a=r.fetchIfRef(t);"number"==typeof a&&(i[e]=a);e++}const o=t.get("MissingWidth");n="number"==typeof o?o:0}else{const t=e.get("BaseFont");if(t instanceof Name){const e=this.getBaseFontMetr\
-ics(t.name);i=this.buildCharCodeToWidth(e.widths,a);n=e.defaultWidth}}}let c=!0,l=n;for(const e in i){const t=i[e];if(t)if(l){if(l!==t){c=!1;break}}else l=t}c?a.flags|=Nr:a.flags&=~Nr;a.defaultWidth=n;a.widths=i;a.defaultVMetrics=o;a.vmetrics=s}isSerifFont(e){const t=e.split("-",1)[0];return t in Qr()||/serif/gi.test(t)}getBaseFontMetrics(e){let t=0,a=Object.create(null),r=!1;let i=Jr()[e]||e;const n=ii();i in n||(i=this.isSerifFont(e)?"Times-Roman":"Helvetica");const s=n[i];if("number"==typeof \
-s){t=s;r=!0}else a=s();return{defaultWidth:t,monospace:r,widths:a}}buildCharCodeToWidth(e,t){const a=Object.create(null),r=t.differences,i=t.defaultEncoding;for(let t=0;t<256;t++)t in r&&e[r[t]]?a[t]=e[r[t]]:t in i&&e[i[t]]&&(a[t]=e[i[t]]);return a}preEvaluateFont(e){const t=e;let a=e.get("Subtype");if(!(a instanceof Name))throw new FormatError("invalid font Subtype");let r,i=!1;if("Type0"===a.name){const t=e.get("DescendantFonts");if(!t)throw new FormatError("Descendant fonts are not specified"\
-);if(!((e=Array.isArray(t)?this.xref.fetchIfRef(t[0]):t)instanceof Dict))throw new FormatError("Descendant font is not a dictionary.");a=e.get("Subtype");if(!(a instanceof Name))throw new FormatError("invalid font Subtype");i=!0}let n=e.get("FirstChar");Number.isInteger(n)||(n=0);let s=e.get("LastChar");Number.isInteger(s)||(s=i?65535:255);const o=e.get("FontDescriptor"),c=e.get("ToUnicode")||t.get("ToUnicode");if(o){r=new MurmurHash3_64;const a=t.getRaw("Encoding");if(a instanceof Name)r.update\
-(a.name);else if(a instanceof Ref)r.update(a.toString());else if(a instanceof Dict)for(const e of a.getRawValues())if(e instanceof Name)r.update(e.name);else if(e instanceof Ref)r.update(e.toString());else if(Array.isArray(e)){const t=e.length,a=new Array(t);for(let r=0;r<t;r++){const t=e[r];t instanceof Name?a[r]=t.name:("number"==typeof t||t instanceof Ref)&&(a[r]=t.toString())}r.update(a.join())}r.update(`${n}-${s}`);if(c instanceof BaseStream){const e=c.str||c,t=e.buffer?new Uint8Array(e.buf\
-fer.buffer,0,e.bufferLength):new Uint8Array(e.bytes.buffer,e.start,e.end-e.start);r.update(t)}else c instanceof Name&&r.update(c.name);const o=e.get("Widths")||t.get("Widths");if(Array.isArray(o)){const e=[];for(const t of o)("number"==typeof t||t instanceof Ref)&&e.push(t.toString());r.update(e.join())}if(i){r.update("compositeFont");const a=e.get("W")||t.get("W");if(Array.isArray(a)){const e=[];for(const t of a)if("number"==typeof t||t instanceof Ref)e.push(t.toString());else if(Array.isArray(\
-t)){const a=[];for(const e of t)("number"==typeof e||e instanceof Ref)&&a.push(e.toString());e.push(`[${a.join()}]`)}r.update(e.join())}const i=e.getRaw("CIDToGIDMap")||t.getRaw("CIDToGIDMap");i instanceof Name?r.update(i.name):i instanceof Ref?r.update(i.toString()):i instanceof BaseStream&&r.update(i.peekBytes())}}return{descriptor:o,dict:e,baseDict:t,composite:i,type:a.name,firstChar:n,lastChar:s,toUnicode:c,hash:r?r.hexdigest():""}}async translateFont({descriptor:e,dict:a,baseDict:r,composit\
-e:i,type:n,firstChar:s,lastChar:o,toUnicode:c,cssFontInfo:l}){const h="Type3"===n;if(!e){if(!h){let e=a.get("BaseFont");if(!(e instanceof Name))throw new FormatError("Base font is not specified");e=e.name.replaceAll(/[,_]/g,"-");const t=this.getBaseFontMetrics(e),i=e.split("-",1)[0],l=(this.isSerifFont(i)?Er:0)|(t.monospace?Nr:0)|(ei()[i]?Pr:Lr),u={type:n,name:e,loadedName:r.loadedName,systemFontInfo:null,widths:t.widths,defaultWidth:t.defaultWidth,isSimulatedFlags:!0,flags:l,firstChar:s,lastCha\
-r:o,toUnicode:c,xHeight:0,capHeight:0,italicAngle:0,isType3Font:h},d=a.get("Widths"),f=getStandardFontName(e);let g=null;if(f){g=await this.fetchStandardFontData(f);u.isInternalFont=!!g}!u.isInternalFont&&this.options.useSystemFonts&&(u.systemFontInfo=getFontSubstitution(this.systemFontCache,this.idFactory,this.options.standardFontDataUrl,e,f,n));const p=await this.extractDataStructures(a,u);if(Array.isArray(d)){const e=[];let t=s;for(const a of d){const r=this.xref.fetchIfRef(a);"number"==typeo\
-f r&&(e[t]=r);t++}p.widths=e}else p.widths=this.buildCharCodeToWidth(t.widths,p);return new Font(e,g,p,this.options)}e=Dict.empty}let u=e.get("FontName"),d=a.get("BaseFont");"string"==typeof u&&(u=Name.get(u));"string"==typeof d&&(d=Name.get(d));const f=u?.name,g=d?.name;if(h)f||(u=Name.get(n));else if(f!==g){info(`The FontDescriptor\'s FontName is "${f}" but should be the same as the Font\'s BaseFont "${g}".`);f&&g&&(g.startsWith(f)||!isKnownFontName(f)&&isKnownFontName(g))&&(u=null);u||=d}if(!(u\
- instanceof Name))throw new FormatError("invalid font name");let p,m,b,y,w;try{p=e.get("FontFile","FontFile2","FontFile3");if(p){if(!(p instanceof BaseStream))throw new FormatError("FontFile should be a stream");if(p.isEmpty)throw new FormatError("FontFile is empty")}}catch(e){if(!this.options.ignoreErrors)throw e;warn(`translateFont - fetching "${u.name}" font file: "${e}".`);p=null}let x=!1,S=null,k=null;if(p){if(p.dict){const e=p.dict.get("Subtype");e instanceof Name&&(m=e.name);b=p.dict.get(\
-"Length1");y=p.dict.get("Length2");w=p.dict.get("Length3")}}else if(l){const e=getXfaFontName(u.name);if(e){l.fontFamily=`${l.fontFamily}-PdfJS-XFA`;l.metrics=e.metrics||null;S=e.factors||null;p=await this.fetchStandardFontData(e.name);x=!!p;r=a=getXfaFontDict(u.name);i=!0}}else if(!h){const e=getStandardFontName(u.name);if(e){p=await this.fetchStandardFontData(e);x=!!p}!x&&this.options.useSystemFonts&&(k=getFontSubstitution(this.systemFontCache,this.idFactory,this.options.standardFontDataUrl,u.\
-name,e,n))}const C=lookupMatrix(a.getArray("FontMatrix"),t),v=lookupNormalRect(e.getArray("FontBBox")||a.getArray("FontBBox"),h?[0,0,0,0]:void 0);let F=e.get("Ascent");"number"!=typeof F&&(F=void 0);let T=e.get("Descent");"number"!=typeof T&&(T=void 0);let O=e.get("XHeight");"number"!=typeof O&&(O=0);let M=e.get("CapHeight");"number"!=typeof M&&(M=0);let D=e.get("Flags");Number.isInteger(D)||(D=0);let R=e.get("ItalicAngle");"number"!=typeof R&&(R=0);const N={type:n,name:u.name,subtype:m,file:p,l\
-ength1:b,length2:y,length3:w,isInternalFont:x,loadedName:r.loadedName,composite:i,fixedPitch:!1,fontMatrix:C,firstChar:s,lastChar:o,toUnicode:c,bbox:v,ascent:F,descent:T,xHeight:O,capHeight:M,flags:D,italicAngle:R,isType3Font:h,cssFontInfo:l,scaleFactors:S,systemFontInfo:k};if(i){const e=r.get("Encoding");e instanceof Name&&(N.cidEncoding=e.name);const t=await CMapFactory.create({encoding:e,fetchBuiltInCMap:this._fetchBuiltInCMapBound,useCMap:null});N.cMap=t;N.vertical=N.cMap.vertical}const E=aw\
-ait this.extractDataStructures(a,N);this.extractWidths(a,e,E);return new Font(u.name,p,E,this.options)}static buildFontPaths(e,t,a,r){function buildPath(t){const i=`${e.loadedName}_path_${t}`;try{if(e.renderer.hasBuiltPath(t))return;a.send("commonobj",[i,"FontPath",e.renderer.getPathJs(t)])}catch(e){if(r.ignoreErrors){warn(`buildFontPaths - ignoring ${i} glyph: "${e}".`);return}throw e}}for(const e of t){buildPath(e.fontChar);const t=e.accent;t?.fontChar&&buildPath(t.fontChar)}}static get fallba\
-ckFontDict(){const e=new Dict;e.set("BaseFont",Name.get("Helvetica"));e.set("Type",Name.get("FallbackType"));e.set("Subtype",Name.get("FallbackType"));e.set("Encoding",Name.get("WinAnsiEncoding"));return shadow(this,"fallbackFontDict",e)}}class TranslatedFont{#G=!1;#V=null;constructor({loadedName:e,font:t,dict:a}){this.loadedName=e;this.font=t;this.dict=a;this.type3Dependencies=t.isType3Font?new Set:null}send(e){if(!this.#G){this.#G=!0;e.send("commonobj",[this.loadedName,"Font",this.font.exportD\
-ata()])}}fallback(e,t){if(this.font.data){this.font.disableFontFace=!0;PartialEvaluator.buildFontPaths(this.font,this.font.glyphCacheValues,e,t)}}loadType3Data(e,t,a){if(this.#V)return this.#V;const{font:r,type3Dependencies:i}=this;assert(r.isType3Font,"Must be a Type3 font.");const n=e.clone({ignoreErrors:!1}),s=new RefSet(e.type3FontRefs);this.dict.objId&&!s.has(this.dict.objId)&&s.put(this.dict.objId);n.type3FontRefs=s;let o=Promise.resolve();const c=this.dict.get("CharProcs"),l=this.dict.get\
-("Resources")||t,h=Object.create(null),[u,d,f,g]=r.bbox,p=f-u,m=g-d,b=Math.hypot(p,m);for(const e of c.getKeys())o=o.then((()=>{const t=c.get(e),r=new OperatorList;return n.getOperatorList({stream:t,task:a,resources:l,operatorList:r}).then((()=>{switch(r.fnArray[0]){case bt:this.#K(r,b);break;case mt:b||this.#J(r)}h[e]=r.getIR();for(const e of r.dependencies)i.add(e)})).catch((function(t){warn(`Type3 font resource "${e}" is not available.`);const a=new OperatorList;h[e]=a.getIR()}))}));this.#V=o\
-.then((()=>{r.charProcOperatorList=h;if(this._bbox){r.isCharBBox=!0;r.bbox=this._bbox}}));return this.#V}#K(e,t=NaN){const a=Util.normalizeRect(e.argsArray[0].slice(2)),r=a[2]-a[0],i=a[3]-a[1],n=Math.hypot(r,i);if(0===r||0===i){e.fnArray.splice(0,1);e.argsArray.splice(0,1)}else if(0===t||Math.round(n/t)>=10){this._bbox??=[1/0,1/0,-1/0,-1/0];Util.rectBoundingBox(...a,this._bbox)}let s=0,o=e.length;for(;s<o;){switch(e.fnArray[s]){case bt:break;case yt:case wt:case xt:case St:case At:case kt:case C\
-t:case vt:case Ft:case It:case Tt:case Ot:case Mt:case Oe:e.fnArray.splice(s,1);e.argsArray.splice(s,1);o--;continue;case De:const[t]=e.argsArray[s];let a=0,r=t.length;for(;a<r;){const[e]=t[a];switch(e){case"TR":case"TR2":case"HT":case"BG":case"BG2":case"UCR":case"UCR2":t.splice(a,1);r--;continue}a++}}s++}}#J(e){let t=1;const a=e.length;for(;t<a;){if(e.fnArray[t]===aa){const a=e.argsArray[t][2];this._bbox??=[1/0,1/0,-1/0,-1/0];Util.rectBoundingBox(...a,this._bbox)}t++}}}class StateManager{constr\
-uctor(e=new EvalState){this.state=e;this.stateStack=[]}save(){const e=this.state;this.stateStack.push(this.state);this.state=e.clone()}restore(){const e=this.stateStack.pop();e&&(this.state=e)}transform(e){this.state.ctm=Util.transform(this.state.ctm,e)}}class TextState{constructor(){this.ctm=new Float32Array(Fa);this.fontName=null;this.fontSize=0;this.loadedName=null;this.font=null;this.fontMatrix=t;this.textMatrix=Fa.slice();this.textLineMatrix=Fa.slice();this.charSpacing=0;this.wordSpacing=0;\
-this.leading=0;this.textHScale=1;this.textRise=0}setTextMatrix(e,t,a,r,i,n){const s=this.textMatrix;s[0]=e;s[1]=t;s[2]=a;s[3]=r;s[4]=i;s[5]=n}setTextLineMatrix(e,t,a,r,i,n){const s=this.textLineMatrix;s[0]=e;s[1]=t;s[2]=a;s[3]=r;s[4]=i;s[5]=n}translateTextMatrix(e,t){const a=this.textMatrix;a[4]=a[0]*e+a[2]*t+a[4];a[5]=a[1]*e+a[3]*t+a[5]}translateTextLineMatrix(e,t){const a=this.textLineMatrix;a[4]=a[0]*e+a[2]*t+a[4];a[5]=a[1]*e+a[3]*t+a[5]}carriageReturn(){this.translateTextLineMatrix(0,-this.l\
-eading);this.textMatrix=this.textLineMatrix.slice()}clone(){const e=Object.create(this);e.textMatrix=this.textMatrix.slice();e.textLineMatrix=this.textLineMatrix.slice();e.fontMatrix=this.fontMatrix.slice();return e}}class EvalState{constructor(){this.ctm=new Float32Array(Fa);this.font=null;this.textRenderingMode=x;this._fillColorSpace=this._strokeColorSpace=ColorSpaceUtils.gray;this.patternFillColorSpace=null;this.patternStrokeColorSpace=null;this.currentPointX=this.currentPointY=0;this.pathMin\
-Max=new Float32Array([1/0,1/0,-1/0,-1/0]);this.pathBuffer=[]}get fillColorSpace(){return this._fillColorSpace}set fillColorSpace(e){this._fillColorSpace=this.patternFillColorSpace=e}get strokeColorSpace(){return this._strokeColorSpace}set strokeColorSpace(e){this._strokeColorSpace=this.patternStrokeColorSpace=e}clone({newPath:e=!1}={}){const t=Object.create(this);if(e){t.pathBuffer=[];t.pathMinMax=new Float32Array([1/0,1/0,-1/0,-1/0])}return t}}class EvaluatorPreprocessor{static get opMap(){retu\
-rn shadow(this,"opMap",Object.assign(Object.create(null),{w:{id:Ce,numArgs:1,variableArgs:!1},J:{id:ve,numArgs:1,variableArgs:!1},j:{id:Fe,numArgs:1,variableArgs:!1},M:{id:Ie,numArgs:1,variableArgs:!1},d:{id:Te,numArgs:2,variableArgs:!1},ri:{id:Oe,numArgs:1,variableArgs:!1},i:{id:Me,numArgs:1,variableArgs:!1},gs:{id:De,numArgs:1,variableArgs:!1},q:{id:Be,numArgs:0,variableArgs:!1},Q:{id:Re,numArgs:0,variableArgs:!1},cm:{id:Ne,numArgs:6,variableArgs:!1},m:{id:Ee,numArgs:2,variableArgs:!1},l:{id:P\
-e,numArgs:2,variableArgs:!1},c:{id:Le,numArgs:6,variableArgs:!1},v:{id:je,numArgs:4,variableArgs:!1},y:{id:_e,numArgs:4,variableArgs:!1},h:{id:Ue,numArgs:0,variableArgs:!1},re:{id:Xe,numArgs:4,variableArgs:!1},S:{id:qe,numArgs:0,variableArgs:!1},s:{id:He,numArgs:0,variableArgs:!1},f:{id:We,numArgs:0,variableArgs:!1},F:{id:We,numArgs:0,variableArgs:!1},"f*":{id:ze,numArgs:0,variableArgs:!1},B:{id:$e,numArgs:0,variableArgs:!1},"B*":{id:Ge,numArgs:0,variableArgs:!1},b:{id:Ve,numArgs:0,variableArgs:\
-!1},"b*":{id:Ke,numArgs:0,variableArgs:!1},n:{id:Je,numArgs:0,variableArgs:!1},W:{id:Ye,numArgs:0,variableArgs:!1},"W*":{id:Ze,numArgs:0,variableArgs:!1},BT:{id:Qe,numArgs:0,variableArgs:!1},ET:{id:et,numArgs:0,variableArgs:!1},Tc:{id:tt,numArgs:1,variableArgs:!1},Tw:{id:at,numArgs:1,variableArgs:!1},Tz:{id:rt,numArgs:1,variableArgs:!1},TL:{id:it,numArgs:1,variableArgs:!1},Tf:{id:nt,numArgs:2,variableArgs:!1},Tr:{id:st,numArgs:1,variableArgs:!1},Ts:{id:ot,numArgs:1,variableArgs:!1},Td:{id:ct,num\
-Args:2,variableArgs:!1},TD:{id:lt,numArgs:2,variableArgs:!1},Tm:{id:ht,numArgs:6,variableArgs:!1},"T*":{id:ut,numArgs:0,variableArgs:!1},Tj:{id:dt,numArgs:1,variableArgs:!1},TJ:{id:ft,numArgs:1,variableArgs:!1},"\'":{id:gt,numArgs:1,variableArgs:!1},\'"\':{id:pt,numArgs:3,variableArgs:!1},d0:{id:mt,numArgs:2,variableArgs:!1},d1:{id:bt,numArgs:6,variableArgs:!1},CS:{id:yt,numArgs:1,variableArgs:!1},cs:{id:wt,numArgs:1,variableArgs:!1},SC:{id:xt,numArgs:4,variableArgs:!0},SCN:{id:St,numArgs:33,variab\
-leArgs:!0},sc:{id:At,numArgs:4,variableArgs:!0},scn:{id:kt,numArgs:33,variableArgs:!0},G:{id:Ct,numArgs:1,variableArgs:!1},g:{id:vt,numArgs:1,variableArgs:!1},RG:{id:Ft,numArgs:3,variableArgs:!1},rg:{id:It,numArgs:3,variableArgs:!1},K:{id:Tt,numArgs:4,variableArgs:!1},k:{id:Ot,numArgs:4,variableArgs:!1},sh:{id:Mt,numArgs:1,variableArgs:!1},BI:{id:Dt,numArgs:0,variableArgs:!1},ID:{id:Bt,numArgs:0,variableArgs:!1},EI:{id:Rt,numArgs:1,variableArgs:!1},Do:{id:Nt,numArgs:1,variableArgs:!1},MP:{id:Et,\
-numArgs:1,variableArgs:!1},DP:{id:Pt,numArgs:2,variableArgs:!1},BMC:{id:Lt,numArgs:1,variableArgs:!1},BDC:{id:jt,numArgs:2,variableArgs:!1},EMC:{id:_t,numArgs:0,variableArgs:!1},BX:{id:Ut,numArgs:0,variableArgs:!1},EX:{id:Xt,numArgs:0,variableArgs:!1},BM:null,BD:null,true:null,fa:null,fal:null,fals:null,false:null,nu:null,nul:null,null:null}))}static MAX_INVALID_PATH_OPS=10;constructor(e,t,a=new StateManager){this.parser=new Parser({lexer:new Lexer(e,EvaluatorPreprocessor.opMap),xref:t});this.st\
-ateManager=a;this.nonProcessedArgs=[];this._isPathOp=!1;this._numInvalidPathOPS=0}get savedStatesDepth(){return this.stateManager.stateStack.length}read(e){let t=e.args;for(;;){const a=this.parser.getObj();if(a instanceof Cmd){const r=a.cmd,i=EvaluatorPreprocessor.opMap[r];if(!i){warn(`Unknown command "${r}".`);continue}const n=i.id,s=i.numArgs;let o=null!==t?t.length:0;this._isPathOp||(this._numInvalidPathOPS=0);this._isPathOp=n>=Ee&&n<=Je;if(i.variableArgs)o>s&&info(`Command ${r}: expected [0,\
- ${s}] args, but received ${o} args.`);else{if(o!==s){const e=this.nonProcessedArgs;for(;o>s;){e.push(t.shift());o--}for(;o<s&&0!==e.length;){null===t&&(t=[]);t.unshift(e.pop());o++}}if(o<s){const e=`command ${r}: expected ${s} args, but received ${o} args.`;if(this._isPathOp&&++this._numInvalidPathOPS>EvaluatorPreprocessor.MAX_INVALID_PATH_OPS)throw new FormatError(`Invalid ${e}`);warn(`Skipping ${e}`);null!==t&&(t.length=0);continue}}this.preprocessCommand(n,t);e.fn=n;e.args=t;return!0}if(a===\
-wa)return!1;if(null!==a){null===t&&(t=[]);t.push(a);if(t.length>33)throw new FormatError("Too many arguments")}}}preprocessCommand(e,t){switch(0|e){case Be:this.stateManager.save();break;case Re:this.stateManager.restore();break;case Ne:this.stateManager.transform(t)}}}class DefaultAppearanceEvaluator extends EvaluatorPreprocessor{constructor(e){super(new StringStream(e))}parse(){const e={fn:0,args:[]},t={fontSize:0,fontName:"",fontColor:new Uint8ClampedArray(3)};try{for(;;){e.args.length=0;if(!\
-this.read(e))break;if(0!==this.savedStatesDepth)continue;const{fn:a,args:r}=e;switch(0|a){case nt:const[e,a]=r;e instanceof Name&&(t.fontName=e.name);"number"==typeof a&&a>0&&(t.fontSize=a);break;case It:ColorSpaceUtils.rgb.getRgbItem(r,0,t.fontColor,0);break;case vt:ColorSpaceUtils.gray.getRgbItem(r,0,t.fontColor,0);break;case Ot:ColorSpaceUtils.cmyk.getRgbItem(r,0,t.fontColor,0)}}}catch(e){warn(`parseDefaultAppearance - ignoring errors: "${e}".`)}return t}}function parseDefaultAppearance(e){re\
-turn new DefaultAppearanceEvaluator(e).parse()}class AppearanceStreamEvaluator extends EvaluatorPreprocessor{constructor(e,t,a,r){super(e);this.stream=e;this.evaluatorOptions=t;this.xref=a;this.globalColorSpaceCache=r;this.resources=e.dict?.get("Resources")}parse(){const e={fn:0,args:[]};let t={scaleFactor:1,fontSize:0,fontName:"",fontColor:new Uint8ClampedArray(3),fillColorSpace:ColorSpaceUtils.gray},a=!1;const r=[];try{for(;;){e.args.length=0;if(a||!this.read(e))break;const{fn:i,args:n}=e;swit\
-ch(0|i){case Be:r.push({scaleFactor:t.scaleFactor,fontSize:t.fontSize,fontName:t.fontName,fontColor:t.fontColor.slice(),fillColorSpace:t.fillColorSpace});break;case Re:t=r.pop()||t;break;case ht:t.scaleFactor*=Math.hypot(n[0],n[1]);break;case nt:const[e,i]=n;e instanceof Name&&(t.fontName=e.name);"number"==typeof i&&i>0&&(t.fontSize=i*t.scaleFactor);break;case wt:t.fillColorSpace=ColorSpaceUtils.parse({cs:n[0],xref:this.xref,resources:this.resources,pdfFunctionFactory:this._pdfFunctionFactory,gl\
-obalColorSpaceCache:this.globalColorSpaceCache,localColorSpaceCache:this._localColorSpaceCache});break;case At:t.fillColorSpace.getRgbItem(n,0,t.fontColor,0);break;case It:ColorSpaceUtils.rgb.getRgbItem(n,0,t.fontColor,0);break;case vt:ColorSpaceUtils.gray.getRgbItem(n,0,t.fontColor,0);break;case Ot:ColorSpaceUtils.cmyk.getRgbItem(n,0,t.fontColor,0);break;case dt:case ft:case gt:case pt:a=!0}}}catch(e){warn(`parseAppearanceStream - ignoring errors: "${e}".`)}this.stream.reset();delete t.scaleFac\
-tor;delete t.fillColorSpace;return t}get _localColorSpaceCache(){return shadow(this,"_localColorSpaceCache",new LocalColorSpaceCache)}get _pdfFunctionFactory(){return shadow(this,"_pdfFunctionFactory",new PDFFunctionFactory({xref:this.xref,isEvalSupported:this.evaluatorOptions.isEvalSupported}))}}function getPdfColor(e,t){if(e[0]===e[1]&&e[1]===e[2]){return`${numberToString(e[0]/255)} ${t?"g":"G"}`}return Array.from(e,(e=>numberToString(e/255))).join(" ")+" "+(t?"rg":"RG")}class FakeUnicodeFont{\
-constructor(e,t){this.xref=e;this.widths=null;this.firstChar=1/0;this.lastChar=-1/0;this.fontFamily=t;const a=new OffscreenCanvas(1,1);this.ctxMeasure=a.getContext("2d",{willReadFrequently:!0});FakeUnicodeFont._fontNameId||(FakeUnicodeFont._fontNameId=1);this.fontName=Name.get(`InvalidPDFjsFont_${t}_${FakeUnicodeFont._fontNameId++}`)}get fontDescriptorRef(){if(!FakeUnicodeFont._fontDescriptorRef){const e=new Dict(this.xref);e.setIfName("Type","FontDescriptor");e.set("FontName",this.fontName);e.s\
-et("FontFamily","MyriadPro Regular");e.set("FontBBox",[0,0,0,0]);e.setIfName("FontStretch","Normal");e.set("FontWeight",400);e.set("ItalicAngle",0);FakeUnicodeFont._fontDescriptorRef=this.xref.getNewPersistentRef(e)}return FakeUnicodeFont._fontDescriptorRef}get descendantFontRef(){const e=new Dict(this.xref);e.set("BaseFont",this.fontName);e.setIfName("Type","Font");e.setIfName("Subtype","CIDFontType0");e.setIfName("CIDToGIDMap","Identity");e.set("FirstChar",this.firstChar);e.set("LastChar",this\
-.lastChar);e.set("FontDescriptor",this.fontDescriptorRef);e.set("DW",1e3);const t=[],a=[...this.widths.entries()].sort();let r=null,i=null;for(const[e,n]of a)if(r)if(e===r+i.length)i.push(n);else{t.push(r,i);r=e;i=[n]}else{r=e;i=[n]}r&&t.push(r,i);e.set("W",t);const n=new Dict(this.xref);n.set("Ordering","Identity");n.set("Registry","Adobe");n.set("Supplement",0);e.set("CIDSystemInfo",n);return this.xref.getNewPersistentRef(e)}get baseFontRef(){const e=new Dict(this.xref);e.set("BaseFont",this.f\
-ontName);e.setIfName("Type","Font");e.setIfName("Subtype","Type0");e.setIfName("Encoding","Identity-H");e.set("DescendantFonts",[this.descendantFontRef]);e.setIfName("ToUnicode","Identity-H");return this.xref.getNewPersistentRef(e)}get resources(){const e=new Dict(this.xref),t=new Dict(this.xref);t.set(this.fontName.name,this.baseFontRef);e.set("Font",t);return e}_createContext(){this.widths=new Map;this.ctxMeasure.font=`1000px ${this.fontFamily}`;return this.ctxMeasure}createFontResources(e){co\
-nst t=this._createContext();for(const a of e.split(/\\r\\n?|\\n/))for(const e of a.split("")){const a=e.charCodeAt(0);if(this.widths.has(a))continue;const r=t.measureText(e),i=Math.ceil(r.width);this.widths.set(a,i);this.firstChar=Math.min(a,this.firstChar);this.lastChar=Math.max(a,this.lastChar)}return this.resources}static getFirstPositionInfo(e,t,i){const[n,s,o,c]=e;let l=o-n,h=c-s;t%180!=0&&([l,h]=[h,l]);const u=a*i;return{coords:[0,h+r*i-u],bbox:[0,0,l,h],matrix:0!==t?getRotationMatrix(t,h,u):\
-void 0}}createAppearance(e,t,i,n,s,o){const c=this._createContext(),l=[];let h=-1/0;for(const t of e.split(/\\r\\n?|\\n/)){l.push(t);const e=c.measureText(t).width;h=Math.max(h,e);for(const e of codePointIter(t)){const t=String.fromCodePoint(e);let a=this.widths.get(e);if(void 0===a){const r=c.measureText(t);a=Math.ceil(r.width);this.widths.set(e,a);this.firstChar=Math.min(e,this.firstChar);this.lastChar=Math.max(e,this.lastChar)}}}h*=n/1e3;const[u,d,f,g]=t;let p=f-u,m=g-d;i%180!=0&&([p,m]=[m,p]);l\
-et b=1;h>p&&(b=p/h);let y=1;const w=a*n,x=r*n,S=w*l.length;S>m&&(y=m/S);const k=n*Math.min(b,y),C=["q",`0 0 ${numberToString(p)} ${numberToString(m)} re W n`,"BT",`1 0 0 1 0 ${numberToString(m+x)} Tm 0 Tc ${getPdfColor(s,!0)}`,`/${this.fontName.name} ${numberToString(k)} Tf`],{resources:v}=this;if(1!==(o="number"==typeof o&&o>=0&&o<=1?o:1)){C.push("/R0 gs");const e=new Dict(this.xref),t=new Dict(this.xref);t.set("ca",o);t.set("CA",o);t.setIfName("Type","ExtGState");e.set("R0",t);v.set("ExtGState\
-",e)}const F=numberToString(w);for(const e of l)C.push(`0 -${F} Td <${stringToUTF16HexString(e)}> Tj`);C.push("ET","Q");const T=C.join("\\n"),O=new Dict(this.xref);O.setIfName("Subtype","Form");O.setIfName("Type","XObject");O.set("BBox",[0,0,p,m]);O.set("Length",T.length);O.set("Resources",v);if(i){const e=getRotationMatrix(i,p,m);O.set("Matrix",e)}const M=new StringStream(T);M.dict=O;return M}}const Pn=["m/d","m/d/yy","mm/dd/yy","mm/yy","d-mmm","d-mmm-yy","dd-mmm-yy","yy-mm-dd","mmm-yy","mmmm-yy\
-","mmm d, yyyy","mmmm d, yyyy","m/d/yy h:MM tt","m/d/yy HH:MM"],Ln=["HH:MM","h:MM tt","HH:MM:ss","h:MM:ss tt"];class NameOrNumberTree{constructor(e,t,a){this.root=e;this.xref=t;this._type=a}getAll(){const e=new Map;if(!this.root)return e;const t=this.xref,a=new RefSet;a.put(this.root);const r=[this.root];for(;r.length>0;){const i=t.fetchIfRef(r.shift());if(!(i instanceof Dict))continue;if(i.has("Kids")){const e=i.get("Kids");if(!Array.isArray(e))continue;for(const t of e){if(a.has(t))throw new F\
-ormatError(`Duplicate entry in "${this._type}" tree.`);r.push(t);a.put(t)}continue}const n=i.get(this._type);if(Array.isArray(n))for(let a=0,r=n.length;a<r;a+=2)e.set(t.fetchIfRef(n[a]),t.fetchIfRef(n[a+1]))}return e}getRaw(e){if(!this.root)return null;const t=this.xref;let a=t.fetchIfRef(this.root),r=0;for(;a.has("Kids");){if(++r>10){warn(`Search depth limit reached for "${this._type}" tree.`);return null}const i=a.get("Kids");if(!Array.isArray(i))return null;let n=0,s=i.length-1;for(;n<=s;){co\
-nst r=n+s>>1,o=t.fetchIfRef(i[r]),c=o.get("Limits");if(e<t.fetchIfRef(c[0]))s=r-1;else{if(!(e>t.fetchIfRef(c[1]))){a=o;break}n=r+1}}if(n>s)return null}const i=a.get(this._type);if(Array.isArray(i)){let a=0,r=i.length-2;for(;a<=r;){const n=a+r>>1,s=n+(1&n),o=t.fetchIfRef(i[s]);if(e<o)r=s-2;else{if(!(e>o))return i[s+1];a=s+2}}}return null}get(e){return this.xref.fetchIfRef(this.getRaw(e))}}class NameTree extends NameOrNumberTree{constructor(e,t){super(e,t,"Names")}}class NumberTree extends NameOrN\
-umberTree{constructor(e,t){super(e,t,"Nums")}}function clearGlobalCaches(){!function clearPatternCaches(){Ii=Object.create(null)}();!function clearPrimitiveCaches(){xa=Object.create(null);Sa=Object.create(null);Aa=Object.create(null)}();!function clearUnicodeCaches(){Dr.clear()}();JpxImage.cleanup()}function pickPlatformItem(e){return e instanceof Dict?e.has("UF")?e.get("UF"):e.has("F")?e.get("F"):e.has("Unix")?e.get("Unix"):e.has("Mac")?e.get("Mac"):e.has("DOS")?e.get("DOS"):null:null}class Fil\
-eSpec{#Y=!1;constructor(e,t,a=!1){if(e instanceof Dict){this.xref=t;this.root=e;e.has("FS")&&(this.fs=e.get("FS"));e.has("RF")&&warn("Related file specifications are not supported");a||(e.has("EF")?this.#Y=!0:warn("Non-embedded file specifications are not supported"))}}get filename(){let e="";const t=pickPlatformItem(this.root);t&&"string"==typeof t&&(e=stringToPDFString(t,!0).replaceAll("\\\\\\\\","\\\\").replaceAll("\\\\/","/").replaceAll("\\\\","/"));return shadow(this,"filename",e||"unnamed")}get cont\
-ent(){if(!this.#Y)return null;this._contentRef||=pickPlatformItem(this.root?.get("EF"));let e=null;if(this._contentRef){const t=this.xref.fetchIfRef(this._contentRef);t instanceof BaseStream?e=t.getBytes():warn("Embedded file specification points to non-existing/invalid content")}else warn("Embedded file specification does not have any content");return e}get description(){let e="";const t=this.root?.get("Desc");t&&"string"==typeof t&&(e=stringToPDFString(t));return shadow(this,"description",e)}g\
-et serializable(){return{rawFilename:this.filename,filename:(e=this.filename,e.substring(e.lastIndexOf("/")+1)),content:this.content,description:this.description};var e}}const jn=0,_n=-2,Un=-3,Xn=-4,qn=-5,Hn=-6,Wn=-9;function isWhitespace(e,t){const a=e[t];return" "===a||"\\n"===a||"\\r"===a||"\\t"===a}class XMLParserBase{_resolveEntities(e){return e.replaceAll(/&([^;]+);/g,((e,t)=>{if("#x"===t.substring(0,2))return String.fromCodePoint(parseInt(t.substring(2),16));if("#"===t.substring(0,1))return \
-String.fromCodePoint(parseInt(t.substring(1),10));switch(t){case"lt":return"<";case"gt":return">";case"amp":return"&";case"quot":return\'"\';case"apos":return"\'"}return this.onResolveEntity(t)}))}_parseContent(e,t){const a=[];let r=t;function skipWs(){for(;r<e.length&&isWhitespace(e,r);)++r}for(;r<e.length&&!isWhitespace(e,r)&&">"!==e[r]&&"/"!==e[r];)++r;const i=e.substring(t,r);skipWs();for(;r<e.length&&">"!==e[r]&&"/"!==e[r]&&"?"!==e[r];){skipWs();let t="",i="";for(;r<e.length&&!isWhitespace(e,r\
-)&&"="!==e[r];){t+=e[r];++r}skipWs();if("="!==e[r])return null;++r;skipWs();const n=e[r];if(\'"\'!==n&&"\'"!==n)return null;const s=e.indexOf(n,++r);if(s<0)return null;i=e.substring(r,s);a.push({name:t,value:this._resolveEntities(i)});r=s+1;skipWs()}return{name:i,attributes:a,parsed:r-t}}_parseProcessingInstruction(e,t){let a=t;for(;a<e.length&&!isWhitespace(e,a)&&">"!==e[a]&&"?"!==e[a]&&"/"!==e[a];)++a;const r=e.substring(t,a);!function skipWs(){for(;a<e.length&&isWhitespace(e,a);)++a}();const i=a\
-;for(;a<e.length&&("?"!==e[a]||">"!==e[a+1]);)++a;return{name:r,value:e.substring(i,a),parsed:a-t}}parseXml(e){let t=0;for(;t<e.length;){let a=t;if("<"===e[t]){++a;let t;switch(e[a]){case"/":++a;t=e.indexOf(">",a);if(t<0){this.onError(Wn);return}this.onEndElement(e.substring(a,t));a=t+1;break;case"?":++a;const r=this._parseProcessingInstruction(e,a);if("?>"!==e.substring(a+r.parsed,a+r.parsed+2)){this.onError(Un);return}this.onPi(r.name,r.value);a+=r.parsed+2;break;case"!":if("--"===e.substring(\
-a+1,a+3)){t=e.indexOf("--\\x3e",a+3);if(t<0){this.onError(qn);return}this.onComment(e.substring(a+3,t));a=t+3}else if("[CDATA["===e.substring(a+1,a+8)){t=e.indexOf("]]>",a+8);if(t<0){this.onError(_n);return}this.onCdata(e.substring(a+8,t));a=t+3}else{if("DOCTYPE"!==e.substring(a+1,a+8)){this.onError(Hn);return}{const r=e.indexOf("[",a+8);let i=!1;t=e.indexOf(">",a+8);if(t<0){this.onError(Xn);return}if(r>0&&t>r){t=e.indexOf("]>",a+8);if(t<0){this.onError(Xn);return}i=!0}const n=e.substring(a+8,t+(\
-i?1:0));this.onDoctype(n);a=t+(i?2:1)}}break;default:const i=this._parseContent(e,a);if(null===i){this.onError(Hn);return}let n=!1;if("/>"===e.substring(a+i.parsed,a+i.parsed+2))n=!0;else if(">"!==e.substring(a+i.parsed,a+i.parsed+1)){this.onError(Wn);return}this.onBeginElement(i.name,i.attributes,n);a+=i.parsed+(n?2:1)}}else{for(;a<e.length&&"<"!==e[a];)a++;const r=e.substring(t,a);this.onText(this._resolveEntities(r))}t=a}}onResolveEntity(e){return`&${e};`}onPi(e,t){}onComment(e){}onCdata(e){}\
-onDoctype(e){}onText(e){}onBeginElement(e,t,a){}onEndElement(e){}onError(e){}}class SimpleDOMNode{constructor(e,t){this.nodeName=e;this.nodeValue=t;Object.defineProperty(this,"parentNode",{value:null,writable:!0})}get firstChild(){return this.childNodes?.[0]}get nextSibling(){const e=this.parentNode.childNodes;if(!e)return;const t=e.indexOf(this);return-1!==t?e[t+1]:void 0}get textContent(){return this.childNodes?this.childNodes.map((e=>e.textContent)).join(""):this.nodeValue||""}get children(){\
-return this.childNodes||[]}hasChildNodes(){return this.childNodes?.length>0}searchNode(e,t){if(t>=e.length)return this;const a=e[t];if(a.name.startsWith("#")&&t<e.length-1)return this.searchNode(e,t+1);const r=[];let i=this;for(;;){if(a.name===i.nodeName){if(0!==a.pos){if(0===r.length)return null;{const[n]=r.pop();let s=0;for(const r of n.childNodes)if(a.name===r.nodeName){if(s===a.pos)return r.searchNode(e,t+1);s++}return i.searchNode(e,t+1)}}{const a=i.searchNode(e,t+1);if(null!==a)return a}}i\
-f(i.childNodes?.length>0){r.push([i,0]);i=i.childNodes[0]}else{if(0===r.length)return null;for(;0!==r.length;){const[e,t]=r.pop(),a=t+1;if(a<e.childNodes.length){r.push([e,a]);i=e.childNodes[a];break}}if(0===r.length)return null}}}dump(e){if("#text"!==this.nodeName){e.push(`<${this.nodeName}`);if(this.attributes)for(const t of this.attributes)e.push(` ${t.name}="${encodeToXmlString(t.value)}"`);if(this.hasChildNodes()){e.push(">");for(const t of this.childNodes)t.dump(e);e.push(`</${this.nodeNam\
-e}>`)}else this.nodeValue?e.push(`>${encodeToXmlString(this.nodeValue)}</${this.nodeName}>`):e.push("/>")}else e.push(encodeToXmlString(this.nodeValue))}}class SimpleXMLParser extends XMLParserBase{constructor({hasAttributes:e=!1,lowerCaseName:t=!1}){super();this._currentFragment=null;this._stack=null;this._errorCode=jn;this._hasAttributes=e;this._lowerCaseName=t}parseFromString(e){this._currentFragment=[];this._stack=[];this._errorCode=jn;this.parseXml(e);if(this._errorCode!==jn)return;const[t]\
-=this._currentFragment;return t?{documentElement:t}:void 0}onText(e){if(function isWhitespaceString(e){for(let t=0,a=e.length;t<a;t++)if(!isWhitespace(e,t))return!1;return!0}(e))return;const t=new SimpleDOMNode("#text",e);this._currentFragment.push(t)}onCdata(e){const t=new SimpleDOMNode("#text",e);this._currentFragment.push(t)}onBeginElement(e,t,a){this._lowerCaseName&&(e=e.toLowerCase());const r=new SimpleDOMNode(e);r.childNodes=[];this._hasAttributes&&(r.attributes=t);this._currentFragment.pu\
-sh(r);if(!a){this._stack.push(this._currentFragment);this._currentFragment=r.childNodes}}onEndElement(e){this._currentFragment=this._stack.pop()||[];const t=this._currentFragment.at(-1);if(!t)return null;for(const e of t.childNodes)e.parentNode=t;return t}onError(e){this._errorCode=e}}class MetadataParser{constructor(e){e=this._repair(e);const t=new SimpleXMLParser({lowerCaseName:!0}).parseFromString(e);this._metadataMap=new Map;this._data=e;t&&this._parse(t)}_repair(e){return e.replace(/^[^<]+/\
-,"").replaceAll(/>\\\\376\\\\377([^<]+)/g,(function(e,t){const a=t.replaceAll(/\\\\([0-3])([0-7])([0-7])/g,(function(e,t,a,r){return String.fromCharCode(64*t+8*a+1*r)})).replaceAll(/&(amp|apos|gt|lt|quot);/g,(function(e,t){switch(t){case"amp":return"&";case"apos":return"\'";case"gt":return">";case"lt":return"<";case"quot":return\'"\'}throw new Error(`_repair: ${t} isn\'t defined.`)})),r=[">"];for(let e=0,t=a.length;e<t;e+=2){const t=256*a.charCodeAt(e)+a.charCodeAt(e+1);t>=32&&t<127&&60!==t&&62!==t&&38!==\
-t?r.push(String.fromCharCode(t)):r.push("&#x"+(65536+t).toString(16).substring(1)+";")}return r.join("")}))}_getSequence(e){const t=e.nodeName;return"rdf:bag"!==t&&"rdf:seq"!==t&&"rdf:alt"!==t?null:e.childNodes.filter((e=>"rdf:li"===e.nodeName))}_parseArray(e){if(!e.hasChildNodes())return;const[t]=e.childNodes,a=this._getSequence(t)||[];this._metadataMap.set(e.nodeName,a.map((e=>e.textContent.trim())))}_parse(e){let t=e.documentElement;if("rdf:rdf"!==t.nodeName){t=t.firstChild;for(;t&&"rdf:rdf"!\
-==t.nodeName;)t=t.nextSibling}if(t&&"rdf:rdf"===t.nodeName&&t.hasChildNodes())for(const e of t.childNodes)if("rdf:description"===e.nodeName)for(const t of e.childNodes){const e=t.nodeName;switch(e){case"#text":continue;case"dc:creator":case"dc:subject":this._parseArray(t);continue}this._metadataMap.set(e,t.textContent.trim())}}get serializable(){return{parsedData:this._metadataMap,rawData:this._data}}}const zn=1,$n=2,Gn=3,Vn=4,Kn=5;class StructTreeRoot{constructor(e,t,a){this.xref=e;this.dict=t;\
-this.ref=a instanceof Ref?a:null;this.roleMap=new Map;this.structParentIds=null}init(){this.readRoleMap()}#Z(e,t,a){if(!(e instanceof Ref)||t<0)return;this.structParentIds||=new RefSetCache;let r=this.structParentIds.get(e);if(!r){r=[];this.structParentIds.put(e,r)}r.push([t,a])}addAnnotationIdToPage(e,t){this.#Z(e,t,Vn)}readRoleMap(){const e=this.dict.get("RoleMap");if(e instanceof Dict)for(const[t,a]of e)a instanceof Name&&this.roleMap.set(t,a.name)}static async canCreateStructureTree({catalog\
-Ref:e,pdfManager:t,newAnnotationsByPage:a}){if(!(e instanceof Ref)){warn("Cannot save the struct tree: no catalog reference.");return!1}let r=0,i=!0;for(const[e,n]of a){const{ref:a}=await t.getPage(e);if(!(a instanceof Ref)){warn(`Cannot save the struct tree: page ${e} has no ref.`);i=!0;break}for(const e of n)if(e.accessibilityData?.type){e.parentTreeId=r++;i=!1}}if(i){for(const e of a.values())for(const t of e)delete t.parentTreeId;return!1}return!0}static async createStructureTree({newAnnotat\
-ionsByPage:e,xref:t,catalogRef:a,pdfManager:r,changes:i}){const n=await r.ensureCatalog("cloneDict"),s=new RefSetCache;s.put(a,n);const o=t.getNewTemporaryRef();n.set("StructTreeRoot",o);const c=new Dict(t);c.set("Type",Name.get("StructTreeRoot"));const l=t.getNewTemporaryRef();c.set("ParentTree",l);const h=[];c.set("K",h);s.put(o,c);const u=new Dict(t),d=[];u.set("Nums",d);const f=await this.#Q({newAnnotationsByPage:e,structTreeRootRef:o,structTreeRoot:null,kids:h,nums:d,xref:t,pdfManager:r,cha\
-nges:i,cache:s});c.set("ParentTreeNextKey",f);s.put(l,u);for(const[e,t]of s.items())i.put(e,{data:t})}async canUpdateStructTree({pdfManager:e,newAnnotationsByPage:t}){if(!this.ref){warn("Cannot update the struct tree: no root reference.");return!1}let a=this.dict.get("ParentTreeNextKey");if(!Number.isInteger(a)||a<0){warn("Cannot update the struct tree: invalid next key.");return!1}const r=this.dict.get("ParentTree");if(!(r instanceof Dict)){warn("Cannot update the struct tree: ParentTree isn\'t \
-a dict.");return!1}const i=r.get("Nums");if(!Array.isArray(i)){warn("Cannot update the struct tree: nums isn\'t an array.");return!1}const n=new NumberTree(r,this.xref);for(const a of t.keys()){const{pageDict:t}=await e.getPage(a);if(!t.has("StructParents"))continue;const r=t.get("StructParents");if(!Number.isInteger(r)||!Array.isArray(n.get(r))){warn(`Cannot save the struct tree: page ${a} has a wrong id.`);return!1}}let s=!0;for(const[r,i]of t){const{pageDict:t}=await e.getPage(r);StructTreeRoo\
-t.#ee({elements:i,xref:this.xref,pageDict:t,numberTree:n});for(const e of i)if(e.accessibilityData?.type){e.accessibilityData.structParent>=0||(e.parentTreeId=a++);s=!1}}if(s){for(const e of t.values())for(const t of e){delete t.parentTreeId;delete t.structTreeParent}return!1}return!0}async updateStructureTree({newAnnotationsByPage:e,pdfManager:t,changes:a}){const{ref:r,xref:i}=this,n=this.dict.clone(),s=new RefSetCache;s.put(r,n);let o,c=n.getRaw("ParentTree");if(c instanceof Ref)o=i.fetch(c);e\
-lse{o=c;c=i.getNewTemporaryRef();n.set("ParentTree",c)}o=o.clone();s.put(c,o);let l=o.getRaw("Nums"),h=null;if(l instanceof Ref){h=l;l=i.fetch(h)}l=l.slice();h||o.set("Nums",l);const u=await StructTreeRoot.#Q({newAnnotationsByPage:e,structTreeRootRef:r,structTreeRoot:this,kids:null,nums:l,xref:i,pdfManager:t,changes:a,cache:s});if(-1!==u){n.set("ParentTreeNextKey",u);h&&s.put(h,l);for(const[e,t]of s.items())a.put(e,{data:t})}}static async#Q({newAnnotationsByPage:e,structTreeRootRef:t,structTreeR\
-oot:a,kids:r,nums:i,xref:n,pdfManager:s,changes:o,cache:c}){const l=Name.get("OBJR");let h,u=-1;for(const[d,f]of e){const e=await s.getPage(d),{ref:g}=e,p=g instanceof Ref;for(const{accessibilityData:s,ref:m,parentTreeId:b,structTreeParent:y}of f){if(!s?.type)continue;const{structParent:f}=s;if(a&&Number.isInteger(f)&&f>=0){let t=(h||=new Map).get(d);if(void 0===t){t=new StructTreePage(a,e.pageDict).collectObjects(g);h.set(d,t)}const r=t?.get(f);if(r){const e=n.fetch(r).clone();StructTreeRoot.#t\
-e(e,s);o.put(r,{data:e});continue}}u=Math.max(u,b);const w=n.getNewTemporaryRef(),x=new Dict(n);StructTreeRoot.#te(x,s);await this.#ae({structTreeParent:y,tagDict:x,newTagRef:w,structTreeRootRef:t,fallbackKids:r,xref:n,cache:c});const S=new Dict(n);x.set("K",S);S.set("Type",l);p&&S.set("Pg",g);S.set("Obj",m);c.put(w,x);i.push(b,w)}}return u+1}static#te(e,{type:t,title:a,lang:r,alt:i,expanded:n,actualText:s}){e.set("S",Name.get(t));a&&e.set("T",stringToAsciiOrUTF16BE(a));r&&e.set("Lang",stringToA\
-sciiOrUTF16BE(r));i&&e.set("Alt",stringToAsciiOrUTF16BE(i));n&&e.set("E",stringToAsciiOrUTF16BE(n));s&&e.set("ActualText",stringToAsciiOrUTF16BE(s))}static#ee({elements:e,xref:t,pageDict:a,numberTree:r}){const i=new Map;for(const t of e)if(t.structTreeParentId){const e=parseInt(t.structTreeParentId.split("_mc")[1],10);let a=i.get(e);if(!a){a=[];i.set(e,a)}a.push(t)}const n=a.get("StructParents");if(!Number.isInteger(n))return;const s=r.get(n),updateElement=(e,a,r)=>{const n=i.get(e);if(n){const \
-e=a.getRaw("P"),i=t.fetchIfRef(e);if(e instanceof Ref&&i instanceof Dict){const e={ref:r,dict:a};for(const t of n)t.structTreeParent=e}return!0}return!1};for(const e of s){if(!(e instanceof Ref))continue;const a=t.fetch(e),r=a.get("K");if(Number.isInteger(r))updateElement(r,a,e);else if(Array.isArray(r))for(let i of r){i=t.fetchIfRef(i);if(Number.isInteger(i)&&updateElement(i,a,e))break;if(!(i instanceof Dict))continue;if(!isName(i.get("Type"),"MCR"))break;const r=i.get("MCID");if(Number.isInteg\
-er(r)&&updateElement(r,a,e))break}}}static async#ae({structTreeParent:e,tagDict:t,newTagRef:a,structTreeRootRef:r,fallbackKids:i,xref:n,cache:s}){let o,c=null;if(e){({ref:c}=e);o=e.dict.getRaw("P")||r}else o=r;t.set("P",o);const l=n.fetchIfRef(o);if(!l){i.push(a);return}let h=s.get(o);if(!h){h=l.clone();s.put(o,h)}const u=h.getRaw("K");let d=u instanceof Ref?s.get(u):null;if(!d){d=n.fetchIfRef(u);d=Array.isArray(d)?d.slice():[u];const e=n.getNewTemporaryRef();h.set("K",e);s.put(e,d)}const f=d.in\
-dexOf(c);d.splice(f>=0?f+1:d.length,0,a)}}class StructElementNode{constructor(e,t){this.tree=e;this.xref=e.xref;this.dict=t;this.kids=[];this.parseKids()}get role(){const e=this.dict.get("S"),t=e instanceof Name?e.name:"",{root:a}=this.tree;return a.roleMap.get(t)??t}parseKids(){let e=null;const t=this.dict.getRaw("Pg");t instanceof Ref&&(e=t.toString());const a=this.dict.get("K");if(Array.isArray(a))for(const t of a){const a=this.parseKid(e,this.xref.fetchIfRef(t));a&&this.kids.push(a)}else{con\
-st t=this.parseKid(e,a);t&&this.kids.push(t)}}parseKid(e,t){if(Number.isInteger(t))return this.tree.pageDict.objId!==e?null:new StructElement({type:zn,mcid:t,pageObjId:e});if(!(t instanceof Dict))return null;const a=t.getRaw("Pg");a instanceof Ref&&(e=a.toString());const r=t.get("Type")instanceof Name?t.get("Type").name:null;if("MCR"===r){if(this.tree.pageDict.objId!==e)return null;const a=t.getRaw("Stm");return new StructElement({type:$n,refObjId:a instanceof Ref?a.toString():null,pageObjId:e,m\
-cid:t.get("MCID")})}if("OBJR"===r){if(this.tree.pageDict.objId!==e)return null;const a=t.getRaw("Obj");return new StructElement({type:Gn,refObjId:a instanceof Ref?a.toString():null,pageObjId:e})}return new StructElement({type:Kn,dict:t})}}class StructElement{constructor({type:e,dict:t=null,mcid:a=null,pageObjId:r=null,refObjId:i=null}){this.type=e;this.dict=t;this.mcid=a;this.pageObjId=r;this.refObjId=i;this.parentNode=null}}class StructTreePage{constructor(e,t){this.root=e;this.xref=e?.xref??nu\
-ll;this.rootDict=e?.dict??null;this.pageDict=t;this.nodes=[]}collectObjects(e){if(!(this.root&&this.rootDict&&e instanceof Ref))return null;const t=this.rootDict.get("ParentTree");if(!t)return null;const a=this.root.structParentIds?.get(e);if(!a)return null;const r=new Map,i=new NumberTree(t,this.xref);for(const[e]of a){const t=i.getRaw(e);t instanceof Ref&&r.set(e,t)}return r}parse(e){if(!(this.root&&this.rootDict&&e instanceof Ref))return;const t=this.rootDict.get("ParentTree");if(!t)return;co\
-nst a=this.pageDict.get("StructParents"),r=this.root.structParentIds?.get(e);if(!Number.isInteger(a)&&!r)return;const i=new Map,n=new NumberTree(t,this.xref);if(Number.isInteger(a)){const e=n.get(a);if(Array.isArray(e))for(const t of e)t instanceof Ref&&this.addNode(this.xref.fetch(t),i)}if(r)for(const[e,t]of r){const a=n.get(e);if(a){const e=this.addNode(this.xref.fetchIfRef(a),i);1===e?.kids?.length&&e.kids[0].type===Gn&&(e.kids[0].type=t)}}}addNode(e,t,a=0){if(a>40){warn("StructTree MAX_DEPTH\
- reached.");return null}if(!(e instanceof Dict))return null;if(t.has(e))return t.get(e);const r=new StructElementNode(this,e);t.set(e,r);const i=e.get("P");if(!(i instanceof Dict)||isName(i.get("Type"),"StructTreeRoot")){this.addTopLevelNode(e,r)||t.delete(e);return r}const n=this.addNode(i,t,a+1);if(!n)return r;let s=!1;for(const t of n.kids)if(t.type===Kn&&t.dict===e){t.parentNode=r;s=!0}s||t.delete(e);return r}addTopLevelNode(e,t){const a=this.rootDict.get("K");if(!a)return!1;if(a instanceof \
-Dict){if(a.objId!==e.objId)return!1;this.nodes[0]=t;return!0}if(!Array.isArray(a))return!0;let r=!1;for(let i=0;i<a.length;i++){const n=a[i];if(n?.toString()===e.objId){this.nodes[i]=t;r=!0}}return r}get serializable(){function nodeToSerializable(e,t,a=0){if(a>40){warn("StructTree too deep to be fully serialized.");return}const r=Object.create(null);r.role=e.role;r.children=[];t.children.push(r);let i=e.dict.get("Alt");"string"!=typeof i&&(i=e.dict.get("ActualText"));"string"==typeof i&&(r.alt=s\
-tringToPDFString(i));const n=e.dict.get("A");if(n instanceof Dict){const e=lookupNormalRect(n.getArray("BBox"),null);if(e)r.bbox=e;else{const e=n.get("Width"),t=n.get("Height");"number"==typeof e&&e>0&&"number"==typeof t&&t>0&&(r.bbox=[0,0,e,t])}}const s=e.dict.get("Lang");"string"==typeof s&&(r.lang=stringToPDFString(s));for(const t of e.kids){const e=t.type===Kn?t.parentNode:null;e?nodeToSerializable(e,r,a+1):t.type===zn||t.type===$n?r.children.push({type:"content",id:`p${t.pageObjId}_mc${t.mc\
-id}`}):t.type===Gn?r.children.push({type:"object",id:t.refObjId}):t.type===Vn&&r.children.push({type:"annotation",id:`pdfjs_internal_id_${t.refObjId}`})}}const e=Object.create(null);e.children=[];e.role="Root";for(const t of this.nodes)t&&nodeToSerializable(t,e);return e}}const Jn=function _isValidExplicitDest(e,t,a){if(!Array.isArray(a)||a.length<2)return!1;const[r,i,...n]=a;if(!e(r)&&!Number.isInteger(r))return!1;if(!t(i))return!1;const s=n.length;let o=!0;switch(i.name){case"XYZ":if(s<2||s>3)\
-return!1;break;case"Fit":case"FitB":return 0===s;case"FitH":case"FitBH":case"FitV":case"FitBV":if(s>1)return!1;break;case"FitR":if(4!==s)return!1;o=!1;break;default:return!1}for(const e of n)if(!("number"==typeof e||o&&null===e))return!1;return!0}.bind(null,(e=>e instanceof Ref),isName);function fetchDest(e){e instanceof Dict&&(e=e.get("D"));return Jn(e)?e:null}function fetchRemoteDest(e){let t=e.get("D");if(t){t instanceof Name&&(t=t.name);if("string"==typeof t)return stringToPDFString(t,!0);if\
-(Jn(t))return JSON.stringify(t)}return null}class Catalog{#re=null;#ie=null;builtInCMapCache=new Map;fontCache=new RefSetCache;globalColorSpaceCache=new GlobalColorSpaceCache;globalImageCache=new GlobalImageCache;nonBlendModesSet=new RefSet;pageDictCache=new RefSetCache;pageIndexCache=new RefSetCache;pageKidsCountCache=new RefSetCache;standardFontDataCache=new Map;systemFontCache=new Map;constructor(e,t){this.pdfManager=e;this.xref=t;this.#ie=t.getCatalogObj();if(!(this.#ie instanceof Dict))thro\
-w new FormatError("Catalog object is not a dictionary.");this.toplevelPagesDict}cloneDict(){return this.#ie.clone()}get version(){const e=this.#ie.get("Version");if(e instanceof Name){if(Ca.test(e.name))return shadow(this,"version",e.name);warn(`Invalid PDF catalog version: ${e.name}`)}return shadow(this,"version",null)}get lang(){const e=this.#ie.get("Lang");return shadow(this,"lang",e&&"string"==typeof e?stringToPDFString(e):null)}get needsRendering(){const e=this.#ie.get("NeedsRendering");ret\
-urn shadow(this,"needsRendering","boolean"==typeof e&&e)}get collection(){let e=null;try{const t=this.#ie.get("Collection");t instanceof Dict&&t.size>0&&(e=t)}catch(e){if(e instanceof MissingDataException)throw e;info("Cannot fetch Collection entry; assuming no collection is present.")}return shadow(this,"collection",e)}get acroForm(){let e=null;try{const t=this.#ie.get("AcroForm");t instanceof Dict&&t.size>0&&(e=t)}catch(e){if(e instanceof MissingDataException)throw e;info("Cannot fetch AcroFor\
-m entry; assuming no forms are present.")}return shadow(this,"acroForm",e)}get acroFormRef(){const e=this.#ie.getRaw("AcroForm");return shadow(this,"acroFormRef",e instanceof Ref?e:null)}get metadata(){const e=this.#ie.getRaw("Metadata");if(!(e instanceof Ref))return shadow(this,"metadata",null);let t=null;try{const a=this.xref.fetch(e,!this.xref.encrypt?.encryptMetadata);if(a instanceof BaseStream&&a.dict instanceof Dict){const e=a.dict.get("Type"),r=a.dict.get("Subtype");if(isName(e,"Metadata"\
-)&&isName(r,"XML")){const e=stringToUTF8String(a.getString());e&&(t=new MetadataParser(e).serializable)}}}catch(e){if(e instanceof MissingDataException)throw e;info(`Skipping invalid Metadata: "${e}".`)}return shadow(this,"metadata",t)}get markInfo(){let e=null;try{e=this.#ne()}catch(e){if(e instanceof MissingDataException)throw e;warn("Unable to read mark info.")}return shadow(this,"markInfo",e)}#ne(){const e=this.#ie.get("MarkInfo");if(!(e instanceof Dict))return null;const t={Marked:!1,UserPr\
-operties:!1,Suspects:!1};for(const a in t){const r=e.get(a);"boolean"==typeof r&&(t[a]=r)}return t}get structTreeRoot(){let e=null;try{e=this.#se()}catch(e){if(e instanceof MissingDataException)throw e;warn("Unable read to structTreeRoot info.")}return shadow(this,"structTreeRoot",e)}#se(){const e=this.#ie.getRaw("StructTreeRoot"),t=this.xref.fetchIfRef(e);if(!(t instanceof Dict))return null;const a=new StructTreeRoot(this.xref,t,e);a.init();return a}get toplevelPagesDict(){const e=this.#ie.get(\
-"Pages");if(!(e instanceof Dict))throw new FormatError("Invalid top-level pages dictionary.");return shadow(this,"toplevelPagesDict",e)}get documentOutline(){let e=null;try{e=this.#oe()}catch(e){if(e instanceof MissingDataException)throw e;warn("Unable to read document outline.")}return shadow(this,"documentOutline",e)}#oe(){let e=this.#ie.get("Outlines");if(!(e instanceof Dict))return null;e=e.getRaw("First");if(!(e instanceof Ref))return null;const t={items:[]},a=[{obj:e,parent:t}],r=new RefSe\
-t;r.put(e);const i=this.xref,n=new Uint8ClampedArray(3);for(;a.length>0;){const t=a.shift(),s=i.fetchIfRef(t.obj);if(null===s)continue;s.has("Title")||warn("Invalid outline item encountered.");const o={url:null,dest:null,action:null};Catalog.parseDestDictionary({destDict:s,resultObj:o,docBaseUrl:this.baseUrl,docAttachments:this.attachments});const c=s.get("Title"),l=s.get("F")||0,h=s.getArray("C"),u=s.get("Count");let d=n;!isNumberArray(h,3)||0===h[0]&&0===h[1]&&0===h[2]||(d=ColorSpaceUtils.rgb.\
-getRgb(h,0));const f={action:o.action,attachment:o.attachment,dest:o.dest,url:o.url,unsafeUrl:o.unsafeUrl,newWindow:o.newWindow,setOCGState:o.setOCGState,title:"string"==typeof c?stringToPDFString(c):"",color:d,count:Number.isInteger(u)?u:void 0,bold:!!(2&l),italic:!!(1&l),items:[]};t.parent.items.push(f);e=s.getRaw("First");if(e instanceof Ref&&!r.has(e)){a.push({obj:e,parent:f});r.put(e)}e=s.getRaw("Next");if(e instanceof Ref&&!r.has(e)){a.push({obj:e,parent:t.parent});r.put(e)}}return t.items\
-.length>0?t.items:null}get permissions(){let e=null;try{e=this.#ce()}catch(e){if(e instanceof MissingDataException)throw e;warn("Unable to read permissions.")}return shadow(this,"permissions",e)}#ce(){const e=this.xref.trailer.get("Encrypt");if(!(e instanceof Dict))return null;let t=e.get("P");if("number"!=typeof t)return null;t+=2**32;const a=[];for(const e in w){const r=w[e];t&r&&a.push(r)}return a}get optionalContentConfig(){let e=null;try{const t=this.#ie.get("OCProperties");if(!t)return sha\
-dow(this,"optionalContentConfig",null);const a=t.get("D");if(!a)return shadow(this,"optionalContentConfig",null);const r=t.get("OCGs");if(!Array.isArray(r))return shadow(this,"optionalContentConfig",null);const i=new RefSetCache;for(const e of r)e instanceof Ref&&!i.has(e)&&i.put(e,this.#le(e));e=this.#he(a,i)}catch(e){if(e instanceof MissingDataException)throw e;warn(`Unable to read optional content config: ${e}`)}return shadow(this,"optionalContentConfig",e)}#le(e){const t=this.xref.fetch(e),a\
-={id:e.toString(),name:null,intent:null,usage:{print:null,view:null},rbGroups:[]},r=t.get("Name");"string"==typeof r&&(a.name=stringToPDFString(r));let i=t.getArray("Intent");Array.isArray(i)||(i=[i]);i.every((e=>e instanceof Name))&&(a.intent=i.map((e=>e.name)));const n=t.get("Usage");if(!(n instanceof Dict))return a;const s=a.usage,o=n.get("Print");if(o instanceof Dict){const e=o.get("PrintState");if(e instanceof Name)switch(e.name){case"ON":case"OFF":s.print={printState:e.name}}}const c=n.get\
-("View");if(c instanceof Dict){const e=c.get("ViewState");if(e instanceof Name)switch(e.name){case"ON":case"OFF":s.view={viewState:e.name}}}return a}#he(e,t){function parseOnOff(e){const a=[];if(Array.isArray(e))for(const r of e)r instanceof Ref&&t.has(r)&&a.push(r.toString());return a}function parseOrder(e,a=0){if(!Array.isArray(e))return null;const i=[];for(const n of e){if(n instanceof Ref&&t.has(n)){r.put(n);i.push(n.toString());continue}const e=parseNestedOrder(n,a);e&&i.push(e)}if(a>0)retu\
-rn i;const n=[];for(const[e]of t.items())r.has(e)||n.push(e.toString());n.length&&i.push({name:null,order:n});return i}function parseNestedOrder(e,t){if(++t>i){warn("parseNestedOrder - reached MAX_NESTED_LEVELS.");return null}const r=a.fetchIfRef(e);if(!Array.isArray(r))return null;const n=a.fetchIfRef(r[0]);if("string"!=typeof n)return null;const s=parseOrder(r.slice(1),t);return s?.length?{name:stringToPDFString(n),order:s}:null}const a=this.xref,r=new RefSet,i=10;!function parseRBGroups(e){if\
-(Array.isArray(e))for(const r of e){const e=a.fetchIfRef(r);if(!Array.isArray(e)||!e.length)continue;const i=new Set;for(const a of e)if(a instanceof Ref&&t.has(a)&&!i.has(a.toString())){i.add(a.toString());t.get(a).rbGroups.push(i)}}}(e.get("RBGroups"));return{name:"string"==typeof e.get("Name")?stringToPDFString(e.get("Name")):null,creator:"string"==typeof e.get("Creator")?stringToPDFString(e.get("Creator")):null,baseState:e.get("BaseState")instanceof Name?e.get("BaseState").name:null,on:parse\
-OnOff(e.get("ON")),off:parseOnOff(e.get("OFF")),order:parseOrder(e.get("Order")),groups:[...t]}}setActualNumPages(e=null){this.#re=e}get hasActualNumPages(){return null!==this.#re}get _pagesCount(){const e=this.toplevelPagesDict.get("Count");if(!Number.isInteger(e))throw new FormatError("Page count in top-level pages dictionary is not an integer.");return shadow(this,"_pagesCount",e)}get numPages(){return this.#re??this._pagesCount}get destinations(){const e=this.#ue(),t=Object.create(null);for(\
-const a of e)if(a instanceof NameTree)for(const[e,r]of a.getAll()){const a=fetchDest(r);a&&(t[stringToPDFString(e,!0)]=a)}else if(a instanceof Dict)for(const[e,r]of a){const a=fetchDest(r);a&&(t[stringToPDFString(e,!0)]||=a)}return shadow(this,"destinations",t)}getDestination(e){if(this.hasOwnProperty("destinations"))return this.destinations[e]??null;const t=this.#ue();for(const a of t)if(a instanceof NameTree||a instanceof Dict){const t=fetchDest(a.get(e));if(t)return t}if(t.length){const t=thi\
-s.destinations[e];if(t)return t}return null}#ue(){const e=this.#ie.get("Names"),t=[];e?.has("Dests")&&t.push(new NameTree(e.getRaw("Dests"),this.xref));this.#ie.has("Dests")&&t.push(this.#ie.get("Dests"));return t}get pageLabels(){let e=null;try{e=this.#de()}catch(e){if(e instanceof MissingDataException)throw e;warn("Unable to read page labels.")}return shadow(this,"pageLabels",e)}#de(){const e=this.#ie.getRaw("PageLabels");if(!e)return null;const t=new Array(this.numPages);let a=null,r="";const\
- i=new NumberTree(e,this.xref).getAll();let n="",s=1;for(let e=0,o=this.numPages;e<o;e++){const o=i.get(e);if(void 0!==o){if(!(o instanceof Dict))throw new FormatError("PageLabel is not a dictionary.");if(o.has("Type")&&!isName(o.get("Type"),"PageLabel"))throw new FormatError("Invalid type in PageLabel dictionary.");if(o.has("S")){const e=o.get("S");if(!(e instanceof Name))throw new FormatError("Invalid style in PageLabel dictionary.");a=e.name}else a=null;if(o.has("P")){const e=o.get("P");if("s\
-tring"!=typeof e)throw new FormatError("Invalid prefix in PageLabel dictionary.");r=stringToPDFString(e)}else r="";if(o.has("St")){const e=o.get("St");if(!(Number.isInteger(e)&&e>=1))throw new FormatError("Invalid start in PageLabel dictionary.");s=e}else s=1}switch(a){case"D":n=s;break;case"R":case"r":n=toRomanNumerals(s,"r"===a);break;case"A":case"a":const e=26,t="a"===a?97:65,r=s-1;n=String.fromCharCode(t+r%e).repeat(Math.floor(r/e)+1);break;default:if(a)throw new FormatError(`Invalid style "\
-${a}" in PageLabel dictionary.`);n=""}t[e]=r+n;s++}return t}get pageLayout(){const e=this.#ie.get("PageLayout");let t="";if(e instanceof Name)switch(e.name){case"SinglePage":case"OneColumn":case"TwoColumnLeft":case"TwoColumnRight":case"TwoPageLeft":case"TwoPageRight":t=e.name}return shadow(this,"pageLayout",t)}get pageMode(){const e=this.#ie.get("PageMode");let t="UseNone";if(e instanceof Name)switch(e.name){case"UseNone":case"UseOutlines":case"UseThumbs":case"FullScreen":case"UseOC":case"UseAtt\
-achments":t=e.name}return shadow(this,"pageMode",t)}get viewerPreferences(){const e=this.#ie.get("ViewerPreferences");if(!(e instanceof Dict))return shadow(this,"viewerPreferences",null);let t=null;for(const[a,r]of e){let e;switch(a){case"HideToolbar":case"HideMenubar":case"HideWindowUI":case"FitWindow":case"CenterWindow":case"DisplayDocTitle":case"PickTrayByPDFSize":"boolean"==typeof r&&(e=r);break;case"NonFullScreenPageMode":if(r instanceof Name)switch(r.name){case"UseNone":case"UseOutlines":c\
-ase"UseThumbs":case"UseOC":e=r.name;break;default:e="UseNone"}break;case"Direction":if(r instanceof Name)switch(r.name){case"L2R":case"R2L":e=r.name;break;default:e="L2R"}break;case"ViewArea":case"ViewClip":case"PrintArea":case"PrintClip":if(r instanceof Name)switch(r.name){case"MediaBox":case"CropBox":case"BleedBox":case"TrimBox":case"ArtBox":e=r.name;break;default:e="CropBox"}break;case"PrintScaling":if(r instanceof Name)switch(r.name){case"None":case"AppDefault":e=r.name;break;default:e="AppD\
-efault"}break;case"Duplex":if(r instanceof Name)switch(r.name){case"Simplex":case"DuplexFlipShortEdge":case"DuplexFlipLongEdge":e=r.name;break;default:e="None"}break;case"PrintPageRange":if(Array.isArray(r)&&r.length%2==0){r.every(((e,t,a)=>Number.isInteger(e)&&e>0&&(0===t||e>=a[t-1])&&e<=this.numPages))&&(e=r)}break;case"NumCopies":Number.isInteger(r)&&r>0&&(e=r);break;default:warn(`Ignoring non-standard key in ViewerPreferences: ${a}.`);continue}if(void 0!==e){t??=Object.create(null);t[a]=e}el\
-se warn(`Bad value, for key "${a}", in ViewerPreferences: ${r}.`)}return shadow(this,"viewerPreferences",t)}get openAction(){const e=this.#ie.get("OpenAction"),t=Object.create(null);if(e instanceof Dict){const a=new Dict(this.xref);a.set("A",e);const r={url:null,dest:null,action:null};Catalog.parseDestDictionary({destDict:a,resultObj:r});Array.isArray(r.dest)?t.dest=r.dest:r.action&&(t.action=r.action)}else Jn(e)&&(t.dest=e);return shadow(this,"openAction",objectSize(t)>0?t:null)}get attachments\
-(){const e=this.#ie.get("Names");let t=null;if(e instanceof Dict&&e.has("EmbeddedFiles")){const a=new NameTree(e.getRaw("EmbeddedFiles"),this.xref);for(const[e,r]of a.getAll()){const a=new FileSpec(r,this.xref);t??=Object.create(null);t[stringToPDFString(e,!0)]=a.serializable}}return shadow(this,"attachments",t)}get xfaImages(){const e=this.#ie.get("Names");let t=null;if(e instanceof Dict&&e.has("XFAImages")){const a=new NameTree(e.getRaw("XFAImages"),this.xref);for(const[e,r]of a.getAll())if(r \
-instanceof BaseStream){t??=new Map;t.set(stringToPDFString(e,!0),r.getBytes())}}return shadow(this,"xfaImages",t)}#fe(){const e=this.#ie.get("Names");let t=null;function appendIfJavaScriptDict(e,a){if(!(a instanceof Dict))return;if(!isName(a.get("S"),"JavaScript"))return;let r=a.get("JS");if(r instanceof BaseStream)r=r.getString();else if("string"!=typeof r)return;r=stringToPDFString(r,!0).replaceAll("\\0","");r&&(t||=new Map).set(e,r)}if(e instanceof Dict&&e.has("JavaScript")){const t=new NameTr\
-ee(e.getRaw("JavaScript"),this.xref);for(const[e,a]of t.getAll())appendIfJavaScriptDict(stringToPDFString(e,!0),a)}const a=this.#ie.get("OpenAction");a&&appendIfJavaScriptDict("OpenAction",a);return t}get jsActions(){const e=this.#fe();let t=collectActions(this.xref,this.#ie,we);if(e){t||=Object.create(null);for(const[a,r]of e)a in t?t[a].push(r):t[a]=[r]}return shadow(this,"jsActions",t)}async cleanup(e=!1){clearGlobalCaches();this.globalColorSpaceCache.clear();this.globalImageCache.clear(e);th\
-is.pageKidsCountCache.clear();this.pageIndexCache.clear();this.pageDictCache.clear();this.nonBlendModesSet.clear();for(const{dict:e}of await Promise.all(this.fontCache))delete e.cacheKey;this.fontCache.clear();this.builtInCMapCache.clear();this.standardFontDataCache.clear();this.systemFontCache.clear()}async getPageDict(e){const t=[this.toplevelPagesDict],a=new RefSet,r=this.#ie.getRaw("Pages");r instanceof Ref&&a.put(r);const i=this.xref,n=this.pageKidsCountCache,s=this.pageIndexCache,o=this.pa\
-geDictCache;let c=0;for(;t.length;){const r=t.pop();if(r instanceof Ref){const l=n.get(r);if(l>=0&&c+l<=e){c+=l;continue}if(a.has(r))throw new FormatError("Pages tree contains circular reference.");a.put(r);const h=await(o.get(r)||i.fetchAsync(r));if(h instanceof Dict){let t=h.getRaw("Type");t instanceof Ref&&(t=await i.fetchAsync(t));if(isName(t,"Page")||!h.has("Kids")){n.has(r)||n.put(r,1);s.has(r)||s.put(r,c);if(c===e)return[h,r];c++;continue}}t.push(h);continue}if(!(r instanceof Dict))throw \
-new FormatError("Page dictionary kid reference points to wrong type of object.");const{objId:l}=r;let h=r.getRaw("Count");h instanceof Ref&&(h=await i.fetchAsync(h));if(Number.isInteger(h)&&h>=0){l&&!n.has(l)&&n.put(l,h);if(c+h<=e){c+=h;continue}}let u=r.getRaw("Kids");u instanceof Ref&&(u=await i.fetchAsync(u));if(!Array.isArray(u)){let t=r.getRaw("Type");t instanceof Ref&&(t=await i.fetchAsync(t));if(isName(t,"Page")||!r.has("Kids")){if(c===e)return[r,null];c++;continue}throw new FormatError("\
-Page dictionary kids object is not an array.")}for(let e=u.length-1;e>=0;e--){const a=u[e];t.push(a);r===this.toplevelPagesDict&&a instanceof Ref&&!o.has(a)&&o.put(a,i.fetchAsync(a))}}throw new Error(`Page index ${e} not found.`)}async getAllPageDicts(e=!1){const{ignoreErrors:t}=this.pdfManager.evaluatorOptions,a=[{currentNode:this.toplevelPagesDict,posInKids:0}],r=new RefSet,i=this.#ie.getRaw("Pages");i instanceof Ref&&r.put(i);const n=new Map,s=this.xref,o=this.pageIndexCache;let c=0;function \
-addPageDict(e,t){t&&!o.has(t)&&o.put(t,c);n.set(c++,[e,t])}function addPageError(a){if(a instanceof XRefEntryException&&!e)throw a;if(e&&t&&0===c){warn(`getAllPageDicts - Skipping invalid first page: "${a}".`);a=Dict.empty}n.set(c++,[a,null])}for(;a.length>0;){const e=a.at(-1),{currentNode:t,posInKids:i}=e;let n=t.getRaw("Kids");if(n instanceof Ref)try{n=await s.fetchAsync(n)}catch(e){addPageError(e);break}if(!Array.isArray(n)){addPageError(new FormatError("Page dictionary kids object is not an \
-array."));break}if(i>=n.length){a.pop();continue}const o=n[i];let c;if(o instanceof Ref){if(r.has(o)){addPageError(new FormatError("Pages tree contains circular reference."));break}r.put(o);try{c=await s.fetchAsync(o)}catch(e){addPageError(e);break}}else c=o;if(!(c instanceof Dict)){addPageError(new FormatError("Page dictionary kid reference points to wrong type of object."));break}let l=c.getRaw("Type");if(l instanceof Ref)try{l=await s.fetchAsync(l)}catch(e){addPageError(e);break}isName(l,"Pag\
-e")||!c.has("Kids")?addPageDict(c,o instanceof Ref?o:null):a.push({currentNode:c,posInKids:0});e.posInKids++}return n}getPageIndex(e){const t=this.pageIndexCache.get(e);if(void 0!==t)return Promise.resolve(t);const a=this.xref;let r=0;const next=t=>function pagesBeforeRef(t){let r,i=0;return a.fetchAsync(t).then((function(a){if(isRefsEqual(t,e)&&!isDict(a,"Page")&&!(a instanceof Dict&&!a.has("Type")&&a.has("Contents")))throw new FormatError("The reference does not point to a /Page dictionary.");\
-if(!a)return null;if(!(a instanceof Dict))throw new FormatError("Node must be a dictionary.");r=a.getRaw("Parent");return a.getAsync("Parent")})).then((function(e){if(!e)return null;if(!(e instanceof Dict))throw new FormatError("Parent must be a dictionary.");return e.getAsync("Kids")})).then((function(e){if(!e)return null;const n=[];let s=!1;for(const r of e){if(!(r instanceof Ref))throw new FormatError("Kid must be a reference.");if(isRefsEqual(r,t)){s=!0;break}n.push(a.fetchAsync(r).then((fun\
-ction(e){if(!(e instanceof Dict))throw new FormatError("Kid node must be a dictionary.");e.has("Count")?i+=e.get("Count"):i++})))}if(!s)throw new FormatError("Kid reference not found in parent\'s kids.");return Promise.all(n).then((()=>[i,r]))}))}(t).then((t=>{if(!t){this.pageIndexCache.put(e,r);return r}const[a,i]=t;r+=a;return next(i)}));return next(e)}get baseUrl(){const e=this.#ie.get("URI");if(e instanceof Dict){const t=e.get("Base");if("string"==typeof t){const e=createValidAbsoluteUrl(t,nu\
-ll,{tryConvertEncoding:!0});if(e)return shadow(this,"baseUrl",e.href)}}return shadow(this,"baseUrl",this.pdfManager.docBaseUrl)}static parseDestDictionary({destDict:e,resultObj:t,docBaseUrl:a=null,docAttachments:r=null}){if(!(e instanceof Dict)){warn("parseDestDictionary: `destDict` must be a dictionary.");return}let i,n,s=e.get("A");if(!(s instanceof Dict))if(e.has("Dest"))s=e.get("Dest");else{s=e.get("AA");s instanceof Dict&&(s.has("D")?s=s.get("D"):s.has("U")&&(s=s.get("U")))}if(s instanceof \
-Dict){const e=s.get("S");if(!(e instanceof Name)){warn("parseDestDictionary: Invalid type in Action dictionary.");return}const a=e.name;switch(a){case"ResetForm":const e=s.get("Flags"),o=!(1&("number"==typeof e?e:0)),c=[],l=[];for(const e of s.get("Fields")||[])e instanceof Ref?l.push(e.toString()):"string"==typeof e&&c.push(stringToPDFString(e));t.resetForm={fields:c,refs:l,include:o};break;case"URI":i=s.get("URI");i instanceof Name&&(i="/"+i.name);break;case"GoTo":n=s.get("D");break;case"Launc\
-h":case"GoToR":const h=s.get("F");if(h instanceof Dict){const e=new FileSpec(h,null,!0),{rawFilename:t}=e.serializable;i=t}else"string"==typeof h&&(i=h);const u=fetchRemoteDest(s);u&&"string"==typeof i&&(i=i.split("#",1)[0]+"#"+u);const d=s.get("NewWindow");"boolean"==typeof d&&(t.newWindow=d);break;case"GoToE":const f=s.get("T");let g;if(r&&f instanceof Dict){const e=f.get("R"),t=f.get("N");isName(e,"C")&&"string"==typeof t&&(g=r[stringToPDFString(t,!0)])}if(g){t.attachment=g;const e=fetchRemot\
-eDest(s);e&&(t.attachmentDest=e)}else warn(\'parseDestDictionary - unimplemented "GoToE" action.\');break;case"Named":const p=s.get("N");p instanceof Name&&(t.action=p.name);break;case"SetOCGState":const m=s.get("State"),b=s.get("PreserveRB");if(!Array.isArray(m)||0===m.length)break;const y=[];for(const e of m)if(e instanceof Name)switch(e.name){case"ON":case"OFF":case"Toggle":y.push(e.name)}else e instanceof Ref&&y.push(e.toString());if(y.length!==m.length)break;t.setOCGState={state:y,preserveRB:\
-"boolean"!=typeof b||b};break;case"JavaScript":const w=s.get("JS");let x;w instanceof BaseStream?x=w.getString():"string"==typeof w&&(x=w);const S=x&&recoverJsURL(stringToPDFString(x,!0));if(S){i=S.url;t.newWindow=S.newWindow;break}default:if("JavaScript"===a||"SubmitForm"===a)break;warn(`parseDestDictionary - unsupported action: "${a}".`)}}else e.has("Dest")&&(n=e.get("Dest"));if("string"==typeof i){const e=createValidAbsoluteUrl(i,a,{addDefaultProtocol:!0,tryConvertEncoding:!0});e&&(t.url=e.hr\
-ef);t.unsafeUrl=i}if(n){n instanceof Name&&(n=n.name);"string"==typeof n?t.dest=stringToPDFString(n,!0):Jn(n)&&(t.dest=n)}}}function addChildren(e,t){if(e instanceof Dict)e=e.getRawValues();else if(e instanceof BaseStream)e=e.dict.getRawValues();else if(!Array.isArray(e))return;for(const r of e)((a=r)instanceof Ref||a instanceof Dict||a instanceof BaseStream||Array.isArray(a))&&t.push(r);var a}class ObjectLoader{refSet=new RefSet;constructor(e,t,a){this.dict=e;this.keys=t;this.xref=a}async load(\
-){const{keys:e,dict:t}=this,a=[];for(const r of e){const e=t.getRaw(r);void 0!==e&&a.push(e)}await this.#ge(a);this.refSet=null}async#ge(e){const t=[],a=[];for(;e.length;){let r=e.pop();if(r instanceof Ref){if(this.refSet.has(r))continue;try{this.refSet.put(r);r=this.xref.fetch(r)}catch(e){if(!(e instanceof MissingDataException)){warn(`ObjectLoader.#walk - requesting all data: "${e}".`);await this.xref.stream.manager.requestAllChunks();return}t.push(r);a.push({begin:e.begin,end:e.end})}}if(r ins\
-tanceof BaseStream){const e=r.getBaseStreams();if(e){let i=!1;for(const t of e)if(!t.isDataLoaded){i=!0;a.push({begin:t.start,end:t.end})}i&&t.push(r)}}addChildren(r,e)}if(a.length){await this.xref.stream.manager.requestRanges(a);for(const e of t)e instanceof Ref&&this.refSet.remove(e);await this.#ge(t)}}static async load(e,t,a){if(a.stream.isDataLoaded)return;const r=new ObjectLoader(e,t,a);await r.load()}}const Yn=Symbol(),Zn=Symbol(),Qn=Symbol(),es=Symbol(),ts=Symbol(),as=Symbol(),rs=Symbol()\
-,is=Symbol(),ns=Symbol(),ss=Symbol("content"),os=Symbol("data"),cs=Symbol(),ls=Symbol("extra"),hs=Symbol(),us=Symbol(),ds=Symbol(),fs=Symbol(),gs=Symbol(),ps=Symbol(),ms=Symbol(),bs=Symbol(),ys=Symbol(),ws=Symbol(),xs=Symbol(),Ss=Symbol(),As=Symbol(),ks=Symbol(),Cs=Symbol(),vs=Symbol(),Fs=Symbol(),Is=Symbol(),Ts=Symbol(),Os=Symbol(),Ms=Symbol(),Ds=Symbol(),Bs=Symbol(),Rs=Symbol(),Ns=Symbol(),Es=Symbol(),Ls=Symbol(),js=Symbol(),_s=Symbol(),Us=Symbol(),Xs=Symbol(),qs=Symbol(),Hs=Symbol("namespaceI\
-d"),Ws=Symbol("nodeName"),zs=Symbol(),$s=Symbol(),Gs=Symbol(),Vs=Symbol(),Ks=Symbol(),Js=Symbol(),Ys=Symbol(),Zs=Symbol(),Qs=Symbol("root"),eo=Symbol(),to=Symbol(),ao=Symbol(),ro=Symbol(),io=Symbol(),no=Symbol(),so=Symbol(),oo=Symbol(),co=Symbol(),lo=Symbol(),ho=Symbol(),uo=Symbol("uid"),fo=Symbol(),go={config:{id:0,check:e=>e.startsWith("http://www.xfa.org/schema/xci/")},connectionSet:{id:1,check:e=>e.startsWith("http://www.xfa.org/schema/xfa-connection-set/")},datasets:{id:2,check:e=>e.startsW\
-ith("http://www.xfa.org/schema/xfa-data/")},form:{id:3,check:e=>e.startsWith("http://www.xfa.org/schema/xfa-form/")},localeSet:{id:4,check:e=>e.startsWith("http://www.xfa.org/schema/xfa-locale-set/")},pdf:{id:5,check:e=>"http://ns.adobe.com/xdp/pdf/"===e},signature:{id:6,check:e=>"http://www.w3.org/2000/09/xmldsig#"===e},sourceSet:{id:7,check:e=>e.startsWith("http://www.xfa.org/schema/xfa-source-set/")},stylesheet:{id:8,check:e=>"http://www.w3.org/1999/XSL/Transform"===e},template:{id:9,check:e=\
->e.startsWith("http://www.xfa.org/schema/xfa-template/")},xdc:{id:10,check:e=>e.startsWith("http://www.xfa.org/schema/xdc/")},xdp:{id:11,check:e=>"http://ns.adobe.com/xdp/"===e},xfdf:{id:12,check:e=>"http://ns.adobe.com/xfdf/"===e},xhtml:{id:13,check:e=>"http://www.w3.org/1999/xhtml"===e},xmpmeta:{id:14,check:e=>"http://ns.adobe.com/xmpmeta/"===e}},po={pt:e=>e,cm:e=>e/2.54*72,mm:e=>e/25.4*72,in:e=>72*e,px:e=>e},mo=/([+-]?\\d+\\.?\\d*)(.*)/;function stripQuotes(e){return e.startsWith("\'")||e.startsW\
-ith(\'"\')?e.slice(1,-1):e}function getInteger({data:e,defaultValue:t,validate:a}){if(!e)return t;e=e.trim();const r=parseInt(e,10);return!isNaN(r)&&a(r)?r:t}function getFloat({data:e,defaultValue:t,validate:a}){if(!e)return t;e=e.trim();const r=parseFloat(e);return!isNaN(r)&&a(r)?r:t}function getKeyword({data:e,defaultValue:t,validate:a}){return e&&a(e=e.trim())?e:t}function getStringOption(e,t){return getKeyword({data:e,defaultValue:t[0],validate:e=>t.includes(e)})}function getMeasurement(e,t="0\
-"){t||="0";if(!e)return getMeasurement(t);const a=e.trim().match(mo);if(!a)return getMeasurement(t);const[,r,i]=a,n=parseFloat(r);if(isNaN(n))return getMeasurement(t);if(0===n)return 0;const s=po[i];return s?s(n):n}function getRatio(e){if(!e)return{num:1,den:1};const t=e.split(":",2).map((e=>parseFloat(e.trim()))).filter((e=>!isNaN(e)));1===t.length&&t.push(1);if(0===t.length)return{num:1,den:1};const[a,r]=t;return{num:a,den:r}}function getRelevant(e){return e?e.trim().split(/\\s+/).map((e=>({exc\
-luded:"-"===e[0],viewname:e.substring(1)}))):[]}class HTMLResult{static get FAILURE(){return shadow(this,"FAILURE",new HTMLResult(!1,null,null,null))}static get EMPTY(){return shadow(this,"EMPTY",new HTMLResult(!0,null,null,null))}constructor(e,t,a,r){this.success=e;this.html=t;this.bbox=a;this.breakNode=r}isBreak(){return!!this.breakNode}static breakNode(e){return new HTMLResult(!1,null,null,e)}static success(e,t=null){return new HTMLResult(!0,e,t,null)}}class FontFinder{constructor(e){this.fon\
-ts=new Map;this.cache=new Map;this.warned=new Set;this.defaultFont=null;this.add(e)}add(e,t=null){for(const t of e)this.addPdfFont(t);for(const e of this.fonts.values())e.regular||(e.regular=e.italic||e.bold||e.bolditalic);if(!t||0===t.size)return;const a=this.fonts.get("PdfJS-Fallback-PdfJS-XFA");for(const e of t)this.fonts.set(e,a)}addPdfFont(e){const t=e.cssFontInfo,a=t.fontFamily;let r=this.fonts.get(a);if(!r){r=Object.create(null);this.fonts.set(a,r);this.defaultFont||(this.defaultFont=r)}l\
-et i="";const n=parseFloat(t.fontWeight);0!==parseFloat(t.italicAngle)?i=n>=700?"bolditalic":"italic":n>=700&&(i="bold");if(!i){(e.name.includes("Bold")||e.psName?.includes("Bold"))&&(i="bold");(e.name.includes("Italic")||e.name.endsWith("It")||e.psName?.includes("Italic")||e.psName?.endsWith("It"))&&(i+="italic")}i||(i="regular");r[i]=e}getDefault(){return this.defaultFont}find(e,t=!0){let a=this.fonts.get(e)||this.cache.get(e);if(a)return a;const r=/,|-|_| |bolditalic|bold|italic|regular|it/gi\
-;let i=e.replaceAll(r,"");a=this.fonts.get(i);if(a){this.cache.set(e,a);return a}i=i.toLowerCase();const n=[];for(const[e,t]of this.fonts.entries())e.replaceAll(r,"").toLowerCase().startsWith(i)&&n.push(t);if(0===n.length)for(const[,e]of this.fonts.entries())e.regular.name?.replaceAll(r,"").toLowerCase().startsWith(i)&&n.push(e);if(0===n.length){i=i.replaceAll(/psmt|mt/gi,"");for(const[e,t]of this.fonts.entries())e.replaceAll(r,"").toLowerCase().startsWith(i)&&n.push(t)}if(0===n.length)for(const\
- e of this.fonts.values())e.regular.name?.replaceAll(r,"").toLowerCase().startsWith(i)&&n.push(e);if(n.length>=1){1!==n.length&&t&&warn(`XFA - Too many choices to guess the correct font: ${e}`);this.cache.set(e,n[0]);return n[0]}if(t&&!this.warned.has(e)){this.warned.add(e);warn(`XFA - Cannot find the font: ${e}`)}return null}}function selectFont(e,t){return"italic"===e.posture?"bold"===e.weight?t.bolditalic:t.italic:"bold"===e.weight?t.bold:t.regular}class FontInfo{constructor(e,t,a,r){this.lin\
-eHeight=a;this.paraMargin=t||{top:0,bottom:0,left:0,right:0};if(!e){[this.pdfFont,this.xfaFont]=this.defaultFont(r);return}this.xfaFont={typeface:e.typeface,posture:e.posture,weight:e.weight,size:e.size,letterSpacing:e.letterSpacing};const i=r.find(e.typeface);if(i){this.pdfFont=selectFont(e,i);this.pdfFont||([this.pdfFont,this.xfaFont]=this.defaultFont(r))}else[this.pdfFont,this.xfaFont]=this.defaultFont(r)}defaultFont(e){const t=e.find("Helvetica",!1)||e.find("Myriad Pro",!1)||e.find("Arial",!\
-1)||e.getDefault();if(t?.regular){const e=t.regular;return[e,{typeface:e.cssFontInfo.fontFamily,posture:"normal",weight:"normal",size:10,letterSpacing:0}]}return[null,{typeface:"Courier",posture:"normal",weight:"normal",size:10,letterSpacing:0}]}}class FontSelector{constructor(e,t,a,r){this.fontFinder=r;this.stack=[new FontInfo(e,t,a,r)]}pushData(e,t,a){const r=this.stack.at(-1);for(const t of["typeface","posture","weight","size","letterSpacing"])e[t]||(e[t]=r.xfaFont[t]);for(const e of["top","b\
-ottom","left","right"])isNaN(t[e])&&(t[e]=r.paraMargin[e]);const i=new FontInfo(e,t,a||r.lineHeight,this.fontFinder);i.pdfFont||(i.pdfFont=r.pdfFont);this.stack.push(i)}popFont(){this.stack.pop()}topFont(){return this.stack.at(-1)}}class TextMeasure{constructor(e,t,a,r){this.glyphs=[];this.fontSelector=new FontSelector(e,t,a,r);this.extraHeight=0}pushData(e,t,a){this.fontSelector.pushData(e,t,a)}popFont(e){return this.fontSelector.popFont()}addPara(){const e=this.fontSelector.topFont();this.extr\
-aHeight+=e.paraMargin.top+e.paraMargin.bottom}addString(e){if(!e)return;const t=this.fontSelector.topFont(),a=t.xfaFont.size;if(t.pdfFont){const r=t.xfaFont.letterSpacing,i=t.pdfFont,n=i.lineHeight||1.2,s=t.lineHeight||Math.max(1.2,n)*a,o=n-(void 0===i.lineGap?.2:i.lineGap),c=Math.max(1,o)*a,l=a/1e3,h=i.defaultWidth||i.charsToGlyphs(" ")[0].width;for(const t of e.split(/[\\u2029\\n]/)){const e=i.encodeString(t).join(""),a=i.charsToGlyphs(e);for(const e of a){const t=e.width||h;this.glyphs.push([t*\
-l+r,s,c,e.unicode,!1])}this.glyphs.push([0,0,0,"\\n",!0])}this.glyphs.pop()}else{for(const t of e.split(/[\\u2029\\n]/)){for(const e of t.split(""))this.glyphs.push([a,1.2*a,a,e,!1]);this.glyphs.push([0,0,0,"\\n",!0])}this.glyphs.pop()}}compute(e){let t=-1,a=0,r=0,i=0,n=0,s=0,o=!1,c=!0;for(let l=0,h=this.glyphs.length;l<h;l++){const[h,u,d,f,g]=this.glyphs[l],p=" "===f,m=c?d:u;if(g){r=Math.max(r,n);n=0;i+=s;s=m;t=-1;a=0;c=!1}else if(p)if(n+h>e){r=Math.max(r,n);n=0;i+=s;s=m;t=-1;a=0;o=!0;c=!1}else{s=M\
-ath.max(m,s);a=n;n+=h;t=l}else if(n+h>e){i+=s;s=m;if(-1!==t){l=t;r=Math.max(r,a);n=0;t=-1;a=0}else{r=Math.max(r,n);n=h}o=!0;c=!1}else{n+=h;s=Math.max(m,s)}}r=Math.max(r,n);i+=s+this.extraHeight;return{width:1.02*r,height:i,isBroken:o}}}const bo=/^[^.[]+/,yo=/^[^\\]]+/,wo=0,xo=1,So=2,Ao=3,ko=4,Co=new Map([["$data",(e,t)=>e.datasets?e.datasets.data:e],["$record",(e,t)=>(e.datasets?e.datasets.data:e)[Ss]()[0]],["$template",(e,t)=>e.template],["$connectionSet",(e,t)=>e.connectionSet],["$form",(e,t)=>\
-e.form],["$layout",(e,t)=>e.layout],["$host",(e,t)=>e.host],["$dataWindow",(e,t)=>e.dataWindow],["$event",(e,t)=>e.event],["!",(e,t)=>e.datasets],["$xfa",(e,t)=>e],["xfa",(e,t)=>e],["$",(e,t)=>t]]),vo=new WeakMap;function parseExpression(e,t,a=!0){let r=e.match(bo);if(!r)return null;let[i]=r;const n=[{name:i,cacheName:"."+i,index:0,js:null,formCalc:null,operator:wo}];let s=i.length;for(;s<e.length;){const c=s;if("["===e.charAt(s++)){r=e.slice(s).match(yo);if(!r){warn("XFA - Invalid index in SOM \
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-Fo=Symbol(),Io=Symbol(),Oo=Symbol(),Mo=Symbol("_children"),Do=Symbol(),Bo=Symbol(),Ro=Symbol(),No=Symbol(),Eo=Symbol(),Po=Symbol(),Lo=Symbol(),jo=Symbol(),_o=Symbol(),Uo=Symbol("parent"),Xo=Symbol(),qo=Symbol(),Ho=Symbol();let Wo=0;const zo=go.datasets.id;class XFAObject{constructor(e,t,a=!1){this[Hs]=e;this[Ws]=t;this[Lo]=a;this[Uo]=null;this[Mo]=[];this[uo]=`${t}${Wo++}`;this[Is]=null}get isXFAObject(){return!0}get isXFAObjectArray(){return!1}createNodes(e){let t=this,a=null;for(const{name:r,i\
-ndex:i}of e){for(let e=0,n=isFinite(i)?i:0;e<=n;e++){const e=t[Hs]===zo?-1:t[Hs];a=new XmlObject(e,r);t[Qn](a)}t=a}return a}[$s](e){if(!this[Lo]||!this[Gs](e))return!1;const t=e[Ws],a=this[t];if(!(a instanceof XFAObjectArray)){null!==a&&this[Zs](a);this[t]=e;this[Qn](e);return!0}if(a.push(e)){this[Qn](e);return!0}let r="";this.id?r=` (id: ${this.id})`:this.name&&(r=` (name: ${this.name} ${this.h.value})`);warn(`XFA - node "${this[Ws]}"${r} has already enough "${t}"!`);return!1}[Gs](e){return thi\
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-[e]instanceof XFAObject||this[e]instanceof XFAObjectArray)break;t.add(e)}}return shadow(this,Oo,e._attributes)}[Es](e){let t=this;for(;t;){if(t===e)return!0;t=t[vs]()}return!1}[vs](){return this[Uo]}[Cs](){return this[vs]()}[Ss](e=null){return e?this[e]:this[Mo]}[cs](){const e=Object.create(null);this[ss]&&(e.$content=this[ss]);for(const t of Object.getOwnPropertyNames(this)){const a=this[t];null!==a&&(a instanceof XFAObject?e[t]=a[cs]():a instanceof XFAObjectArray?a.isEmpty()||(e[t]=a.dump()):e\
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-(null===e[Fs]()[ls].firstUnsplittable)return!0;if(0===e.w||0===e.h)return!0;const a=e[Cs](),r=a[ls]?.attempt||0,[,i,n,s]=function getTransformedBBox(e){let t,a,r=""===e.w?NaN:e.w,i=""===e.h?NaN:e.h,[n,s]=[0,0];switch(e.anchorType||""){case"bottomCenter":[n,s]=[r/2,i];break;case"bottomLeft":[n,s]=[0,i];break;case"bottomRight":[n,s]=[r,i];break;case"middleCenter":[n,s]=[r/2,i/2];break;case"middleLeft":[n,s]=[0,i/2];break;case"middleRight":[n,s]=[r,i/2];break;case"topCenter":[n,s]=[r/2,0];break;cas\
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-.width>2):!!e[Fs]()[ls].noLayoutFailure||!(""!==e.h&&Math.round(s-t.height)>2)&&((""===e.w||Math.round(n-t.width)<=2||!a[_s]())&&t.height>2);case"table":case"tb":return!!e[Fs]()[ls].noLayoutFailure||(""===e.h||e[js]()?(""===e.w||Math.round(n-t.width)<=2||!a[_s]())&&t.height>2:Math.round(s-t.height)<=2);case"position":if(e[Fs]()[ls].noLayoutFailure)return!0;if(""===e.h||Math.round(s+i-t.height)<=2)return!0;return s+i>e[Fs]()[ls].currentContentArea.h;case"rl-row":case"row":return!!e[Fs]()[ls].noLa\
-youtFailure||(""===e.h||Math.round(s-t.height)<=2);default:return!0}}const Go=go.template.id,Vo="http://www.w3.org/2000/svg",Ko=/^H(\\d+)$/,Jo=new Set(["image/gif","image/jpeg","image/jpg","image/pjpeg","image/png","image/apng","image/x-png","image/bmp","image/x-ms-bmp","image/tiff","image/tif","application/octet-stream"]),Yo=[[[66,77],"image/bmp"],[[255,216,255],"image/jpeg"],[[73,73,42,0],"image/tiff"],[[77,77,0,42],"image/tiff"],[[71,73,70,56,57,97],"image/gif"],[[137,80,78,71,13,10,26,10],"im\
-age/png"]];function getBorderDims(e){if(!e||!e.border)return{w:0,h:0};const t=e.border[ws]();return t?{w:t.widths[0]+t.widths[2]+t.insets[0]+t.insets[2],h:t.widths[1]+t.widths[3]+t.insets[1]+t.insets[3]}:{w:0,h:0}}function hasMargin(e){return e.margin&&(e.margin.topInset||e.margin.rightInset||e.margin.bottomInset||e.margin.leftInset)}function _setValue(e,t){if(!e.value){const t=new Value({});e[Qn](t);e.value=t}e.value[io](t)}function*getContainedChildren(e){for(const t of e[Ss]())t instanceof Su\
-bformSet?yield*t[As]():yield t}function isRequired(e){return"error"===e.validate?.nullTest}function setTabIndex(e){for(;e;){if(!e.traversal){e[no]=e[vs]()[no];return}if(e[no])return;let t=null;for(const a of e.traversal[Ss]())if("next"===a.operation){t=a;break}if(!t||!t.ref){e[no]=e[vs]()[no];return}const a=e[Fs]();e[no]=++a[no];const r=a[to](t.ref,e);if(!r)return;e=r[0]}}function applyAssist(e,t){const a=e.assist;if(a){const e=a[co]();e&&(t.title=e);const r=a.role.match(Ko);if(r){const e="headi\
-ng",a=r[1];t.role=e;t["aria-level"]=a}}if("table"===e.layout)t.role="table";else if("row"===e.layout)t.role="row";else{const a=e[vs]();"row"===a.layout&&(t.role="TH"===a.assist?.role?"columnheader":"cell")}}function ariaLabel(e){if(!e.assist)return null;const t=e.assist;return t.speak&&""!==t.speak[ss]?t.speak[ss]:t.toolTip?t.toolTip[ss]:null}function valueToHtml(e){return HTMLResult.success({name:"div",attributes:{class:["xfaRich"],style:Object.create(null)},children:[{name:"span",attributes:{s\
-tyle:Object.create(null)},value:e}]})}function setFirstUnsplittable(e){const t=e[Fs]();if(null===t[ls].firstUnsplittable){t[ls].firstUnsplittable=e;t[ls].noLayoutFailure=!0}}function unsetFirstUnsplittable(e){const t=e[Fs]();t[ls].firstUnsplittable===e&&(t[ls].noLayoutFailure=!1)}function handleBreak(e){if(e[ls])return!1;e[ls]=Object.create(null);if("auto"===e.targetType)return!1;const t=e[Fs]();let a=null;if(e.target){a=t[to](e.target,e[vs]());if(!a)return!1;a=a[0]}const{currentPageArea:r,curre\
-ntContentArea:i}=t[ls];if("pageArea"===e.targetType){a instanceof PageArea||(a=null);if(e.startNew){e[ls].target=a||r;return!0}if(a&&a!==r){e[ls].target=a;return!0}return!1}a instanceof ContentArea||(a=null);const n=a&&a[vs]();let s,o=n;if(e.startNew)if(a){const e=n.contentArea.children,t=e.indexOf(i),r=e.indexOf(a);-1!==t&&t<r&&(o=null);s=r-1}else s=r.contentArea.children.indexOf(i);else{if(!a||a===i)return!1;s=n.contentArea.children.indexOf(a)-1;o=n===r?null:n}e[ls].target=o;e[ls].index=s;retu\
-rn!0}function handleOverflow(e,t,a){const r=e[Fs](),i=r[ls].noLayoutFailure,n=t[Cs];t[Cs]=()=>e;r[ls].noLayoutFailure=!0;const s=t[co](a);e[Zn](s.html,s.bbox);r[ls].noLayoutFailure=i;t[Cs]=n}class AppearanceFilter extends StringObject{constructor(e){super(Go,"appearanceFilter");this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||""}}class Arc extends XFAObject{constructor(e){super(Go,"arc",!0);this.circular=getInteger({data:e.circ\
-ular,defaultValue:0,validate:e=>1===e});this.hand=getStringOption(e.hand,["even","left","right"]);this.id=e.id||"";this.startAngle=getFloat({data:e.startAngle,defaultValue:0,validate:e=>!0});this.sweepAngle=getFloat({data:e.sweepAngle,defaultValue:360,validate:e=>!0});this.use=e.use||"";this.usehref=e.usehref||"";this.edge=null;this.fill=null}[co](){const e=this.edge||new Edge({}),t=e[ho](),a=Object.create(null);"visible"===this.fill?.presence?Object.assign(a,this.fill[ho]()):a.fill="transparent\
-";a.strokeWidth=measureToString("visible"===e.presence?e.thickness:0);a.stroke=t.color;let r;const i={xmlns:Vo,style:{width:"100%",height:"100%",overflow:"visible"}};if(360===this.sweepAngle)r={name:"ellipse",attributes:{xmlns:Vo,cx:"50%",cy:"50%",rx:"50%",ry:"50%",style:a}};else{const e=this.startAngle*Math.PI/180,t=this.sweepAngle*Math.PI/180,n=this.sweepAngle>180?1:0,[s,o,c,l]=[50*(1+Math.cos(e)),50*(1-Math.sin(e)),50*(1+Math.cos(e+t)),50*(1-Math.sin(e+t))];r={name:"path",attributes:{xmlns:Vo\
-,d:`M ${s} ${o} A 50 50 0 ${n} 0 ${c} ${l}`,vectorEffect:"non-scaling-stroke",style:a}};Object.assign(i,{viewBox:"0 0 100 100",preserveAspectRatio:"none"})}const n={name:"svg",children:[r],attributes:i};if(hasMargin(this[vs]()[vs]()))return HTMLResult.success({name:"div",attributes:{style:{display:"inline",width:"100%",height:"100%"}},children:[n]});n.attributes.style.position="absolute";return HTMLResult.success(n)}}class Area extends XFAObject{constructor(e){super(Go,"area",!0);this.colSpan=ge\
-tInteger({data:e.colSpan,defaultValue:1,validate:e=>e>=1||-1===e});this.id=e.id||"";this.name=e.name||"";this.relevant=getRelevant(e.relevant);this.use=e.use||"";this.usehref=e.usehref||"";this.x=getMeasurement(e.x,"0pt");this.y=getMeasurement(e.y,"0pt");this.desc=null;this.extras=null;this.area=new XFAObjectArray;this.draw=new XFAObjectArray;this.exObject=new XFAObjectArray;this.exclGroup=new XFAObjectArray;this.field=new XFAObjectArray;this.subform=new XFAObjectArray;this.subformSet=new XFAObj\
-ectArray}*[As](){yield*getContainedChildren(this)}[Us](){return!0}[Rs](){return!0}[Zn](e,t){const[a,r,i,n]=t;this[ls].width=Math.max(this[ls].width,a+i);this[ls].height=Math.max(this[ls].height,r+n);this[ls].children.push(e)}[gs](){return this[ls].availableSpace}[co](e){const t=toStyle(this,"position"),a={style:t,id:this[uo],class:["xfaArea"]};isPrintOnly(this)&&a.class.push("xfaPrintOnly");this.name&&(a.xfaName=this.name);const r=[];this[ls]={children:r,width:0,height:0,availableSpace:e};const \
-i=this[es]({filter:new Set(["area","draw","field","exclGroup","subform","subformSet"]),include:!0});if(!i.success){if(i.isBreak())return i;delete this[ls];return HTMLResult.FAILURE}t.width=measureToString(this[ls].width);t.height=measureToString(this[ls].height);const n={name:"div",attributes:a,children:r},s=[this.x,this.y,this[ls].width,this[ls].height];delete this[ls];return HTMLResult.success(n,s)}}class Assist extends XFAObject{constructor(e){super(Go,"assist",!0);this.id=e.id||"";this.role=\
-e.role||"";this.use=e.use||"";this.usehref=e.usehref||"";this.speak=null;this.toolTip=null}[co](){return this.toolTip?.[ss]||null}}class Barcode extends XFAObject{constructor(e){super(Go,"barcode",!0);this.charEncoding=getKeyword({data:e.charEncoding?e.charEncoding.toLowerCase():"",defaultValue:"",validate:e=>["utf-8","big-five","fontspecific","gbk","gb-18030","gb-2312","ksc-5601","none","shift-jis","ucs-2","utf-16"].includes(e)||e.match(/iso-8859-\\d{2}/)});this.checksum=getStringOption(e.checks\
-um,["none","1mod10","1mod10_1mod11","2mod10","auto"]);this.dataColumnCount=getInteger({data:e.dataColumnCount,defaultValue:-1,validate:e=>e>=0});this.dataLength=getInteger({data:e.dataLength,defaultValue:-1,validate:e=>e>=0});this.dataPrep=getStringOption(e.dataPrep,["none","flateCompress"]);this.dataRowCount=getInteger({data:e.dataRowCount,defaultValue:-1,validate:e=>e>=0});this.endChar=e.endChar||"";this.errorCorrectionLevel=getInteger({data:e.errorCorrectionLevel,defaultValue:-1,validate:e=>e\
->=0&&e<=8});this.id=e.id||"";this.moduleHeight=getMeasurement(e.moduleHeight,"5mm");this.moduleWidth=getMeasurement(e.moduleWidth,"0.25mm");this.printCheckDigit=getInteger({data:e.printCheckDigit,defaultValue:0,validate:e=>1===e});this.rowColumnRatio=getRatio(e.rowColumnRatio);this.startChar=e.startChar||"";this.textLocation=getStringOption(e.textLocation,["below","above","aboveEmbedded","belowEmbedded","none"]);this.truncate=getInteger({data:e.truncate,defaultValue:0,validate:e=>1===e});this.ty\
-pe=getStringOption(e.type?e.type.toLowerCase():"",["aztec","codabar","code2of5industrial","code2of5interleaved","code2of5matrix","code2of5standard","code3of9","code3of9extended","code11","code49","code93","code128","code128a","code128b","code128c","code128sscc","datamatrix","ean8","ean8add2","ean8add5","ean13","ean13add2","ean13add5","ean13pwcd","fim","logmars","maxicode","msi","pdf417","pdf417macro","plessey","postauscust2","postauscust3","postausreplypaid","postausstandard","postukrm4scc","pos\
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-NarrowRatio);this.encrypt=null;this.extras=null}}class Bind extends XFAObject{constructor(e){super(Go,"bind",!0);this.match=getStringOption(e.match,["once","dataRef","global","none"]);this.ref=e.ref||"";this.picture=null}}class BindItems extends XFAObject{constructor(e){super(Go,"bindItems");this.connection=e.connection||"";this.labelRef=e.labelRef||"";this.ref=e.ref||"";this.valueRef=e.valueRef||""}}class Bookend extends XFAObject{constructor(e){super(Go,"bookend");this.id=e.id||"";this.leader=\
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-nce=getStringOption(e.presence,["visible","hidden","inactive","invisible"]);this.relevant=getRelevant(e.relevant);this.use=e.use||"";this.usehref=e.usehref||"";this.corner=new XFAObjectArray(4);this.edge=new XFAObjectArray(4);this.extras=null;this.fill=null;this.margin=null}[ws](){if(!this[ls]){const e=this.edge.children.slice();if(e.length<4){const t=e.at(-1)||new Edge({});for(let a=e.length;a<4;a++)e.push(t)}const t=e.map((e=>e.thickness)),a=[0,0,0,0];if(this.margin){a[0]=this.margin.topInset;\
-a[1]=this.margin.rightInset;a[2]=this.margin.bottomInset;a[3]=this.margin.leftInset}this[ls]={widths:t,insets:a,edges:e}}return this[ls]}[ho](){const{edges:e}=this[ws](),t=e.map((e=>{const t=e[ho]();t.color||="#000000";return t})),a=Object.create(null);this.margin&&Object.assign(a,this.margin[ho]());"visible"===this.fill?.presence&&Object.assign(a,this.fill[ho]());if(this.corner.children.some((e=>0!==e.radius))){const e=this.corner.children.map((e=>e[ho]()));if(2===e.length||3===e.length){const \
-t=e.at(-1);for(let a=e.length;a<4;a++)e.push(t)}a.borderRadius=e.map((e=>e.radius)).join(" ")}switch(this.presence){case"invisible":case"hidden":a.borderStyle="";break;case"inactive":a.borderStyle="none";break;default:a.borderStyle=t.map((e=>e.style)).join(" ")}a.borderWidth=t.map((e=>e.width)).join(" ");a.borderColor=t.map((e=>e.color)).join(" ");return a}}class Break extends XFAObject{constructor(e){super(Go,"break",!0);this.after=getStringOption(e.after,["auto","contentArea","pageArea","pageE\
-ven","pageOdd"]);this.afterTarget=e.afterTarget||"";this.before=getStringOption(e.before,["auto","contentArea","pageArea","pageEven","pageOdd"]);this.beforeTarget=e.beforeTarget||"";this.bookendLeader=e.bookendLeader||"";this.bookendTrailer=e.bookendTrailer||"";this.id=e.id||"";this.overflowLeader=e.overflowLeader||"";this.overflowTarget=e.overflowTarget||"";this.overflowTrailer=e.overflowTrailer||"";this.startNew=getInteger({data:e.startNew,defaultValue:0,validate:e=>1===e});this.use=e.use||"";\
-this.usehref=e.usehref||"";this.extras=null}}class BreakAfter extends XFAObject{constructor(e){super(Go,"breakAfter",!0);this.id=e.id||"";this.leader=e.leader||"";this.startNew=getInteger({data:e.startNew,defaultValue:0,validate:e=>1===e});this.target=e.target||"";this.targetType=getStringOption(e.targetType,["auto","contentArea","pageArea"]);this.trailer=e.trailer||"";this.use=e.use||"";this.usehref=e.usehref||"";this.script=null}}class BreakBefore extends XFAObject{constructor(e){super(Go,"bre\
-akBefore",!0);this.id=e.id||"";this.leader=e.leader||"";this.startNew=getInteger({data:e.startNew,defaultValue:0,validate:e=>1===e});this.target=e.target||"";this.targetType=getStringOption(e.targetType,["auto","contentArea","pageArea"]);this.trailer=e.trailer||"";this.use=e.use||"";this.usehref=e.usehref||"";this.script=null}[co](e){this[ls]={};return HTMLResult.FAILURE}}class Button extends XFAObject{constructor(e){super(Go,"button",!0);this.highlight=getStringOption(e.highlight,["inverted","n\
-one","outline","push"]);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null}[co](e){const t=this[vs]()[vs](),a={name:"button",attributes:{id:this[uo],class:["xfaButton"],style:{}},children:[]};for(const e of t.event.children){if("click"!==e.activity||!e.script)continue;const t=recoverJsURL(e.script[ss]);if(!t)continue;const r=fixURL(t.url);r&&a.children.push({name:"a",attributes:{id:"link"+this[uo],href:r,newWindow:t.newWindow,class:["xfaLink"],style:{}},children:[]})\
-}return HTMLResult.success(a)}}class Calculate extends XFAObject{constructor(e){super(Go,"calculate",!0);this.id=e.id||"";this.override=getStringOption(e.override,["disabled","error","ignore","warning"]);this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.message=null;this.script=null}}class Caption extends XFAObject{constructor(e){super(Go,"caption",!0);this.id=e.id||"";this.placement=getStringOption(e.placement,["left","bottom","inline","right","top"]);this.presence=getStringOp\
-tion(e.presence,["visible","hidden","inactive","invisible"]);this.reserve=Math.ceil(getMeasurement(e.reserve));this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.font=null;this.margin=null;this.para=null;this.value=null}[io](e){_setValue(this,e)}[ws](e){if(!this[ls]){let{width:t,height:a}=e;switch(this.placement){case"left":case"right":case"inline":t=this.reserve<=0?t:this.reserve;break;case"top":case"bottom":a=this.reserve<=0?a:this.reserve}this[ls]=layoutNode(this,{width:t,hei\
-ght:a})}return this[ls]}[co](e){if(!this.value)return HTMLResult.EMPTY;this[Ys]();const t=this.value[co](e).html;if(!t){this[Js]();return HTMLResult.EMPTY}const a=this.reserve;if(this.reserve<=0){const{w:t,h:a}=this[ws](e);switch(this.placement){case"left":case"right":case"inline":this.reserve=t;break;case"top":case"bottom":this.reserve=a}}const r=[];"string"==typeof t?r.push({name:"#text",value:t}):r.push(t);const i=toStyle(this,"font","margin","visibility");switch(this.placement){case"left":ca\
-se"right":this.reserve>0&&(i.width=measureToString(this.reserve));break;case"top":case"bottom":this.reserve>0&&(i.height=measureToString(this.reserve))}setPara(this,null,t);this[Js]();this.reserve=a;return HTMLResult.success({name:"div",attributes:{style:i,class:["xfaCaption"]},children:r})}}class Certificate extends StringObject{constructor(e){super(Go,"certificate");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class Certificates extends XFAObject{constru\
-ctor(e){super(Go,"certificates",!0);this.credentialServerPolicy=getStringOption(e.credentialServerPolicy,["optional","required"]);this.id=e.id||"";this.url=e.url||"";this.urlPolicy=e.urlPolicy||"";this.use=e.use||"";this.usehref=e.usehref||"";this.encryption=null;this.issuers=null;this.keyUsage=null;this.oids=null;this.signing=null;this.subjectDNs=null}}class CheckButton extends XFAObject{constructor(e){super(Go,"checkButton",!0);this.id=e.id||"";this.mark=getStringOption(e.mark,["default","chec\
-k","circle","cross","diamond","square","star"]);this.shape=getStringOption(e.shape,["square","round"]);this.size=getMeasurement(e.size,"10pt");this.use=e.use||"";this.usehref=e.usehref||"";this.border=null;this.extras=null;this.margin=null}[co](e){const t=toStyle(this,"margin"),a=measureToString(this.size);t.width=t.height=a;let r,i,n;const s=this[vs]()[vs](),o=s.items.children.length&&s.items.children[0][co]().html||[],c={on:(void 0!==o[0]?o[0]:"on").toString(),off:(void 0!==o[1]?o[1]:"off").to\
-String()},l=(s.value?.[so]()||"off")===c.on||void 0,h=s[Cs](),u=s[uo];let d;if(h instanceof ExclGroup){n=h[uo];r="radio";i="xfaRadio";d=h[os]?.[uo]||h[uo]}else{r="checkbox";i="xfaCheckbox";d=s[os]?.[uo]||s[uo]}const f={name:"input",attributes:{class:[i],style:t,fieldId:u,dataId:d,type:r,checked:l,xfaOn:c.on,xfaOff:c.off,"aria-label":ariaLabel(s),"aria-required":!1}};n&&(f.attributes.name=n);if(isRequired(s)){f.attributes["aria-required"]=!0;f.attributes.required=!0}return HTMLResult.success({nam\
-e:"label",attributes:{class:["xfaLabel"]},children:[f]})}}class ChoiceList extends XFAObject{constructor(e){super(Go,"choiceList",!0);this.commitOn=getStringOption(e.commitOn,["select","exit"]);this.id=e.id||"";this.open=getStringOption(e.open,["userControl","always","multiSelect","onEntry"]);this.textEntry=getInteger({data:e.textEntry,defaultValue:0,validate:e=>1===e});this.use=e.use||"";this.usehref=e.usehref||"";this.border=null;this.extras=null;this.margin=null}[co](e){const t=toStyle(this,"\
-border","margin"),a=this[vs]()[vs](),r={fontSize:`calc(${a.font?.size||10}px * var(--total-scale-factor))`},i=[];if(a.items.children.length>0){const e=a.items;let t=0,n=0;if(2===e.children.length){t=e.children[0].save;n=1-t}const s=e.children[t][co]().html,o=e.children[n][co]().html;let c=!1;const l=a.value?.[so]()||"";for(let e=0,t=s.length;e<t;e++){const t={name:"option",attributes:{value:o[e]||s[e],style:r},value:s[e]};o[e]===l&&(t.attributes.selected=c=!0);i.push(t)}c||i.splice(0,0,{name:"op\
-tion",attributes:{hidden:!0,selected:!0},value:" "})}const n={class:["xfaSelect"],fieldId:a[uo],dataId:a[os]?.[uo]||a[uo],style:t,"aria-label":ariaLabel(a),"aria-required":!1};if(isRequired(a)){n["aria-required"]=!0;n.required=!0}"multiSelect"===this.open&&(n.multiple=!0);return HTMLResult.success({name:"label",attributes:{class:["xfaLabel"]},children:[{name:"select",children:i,attributes:n}]})}}class Color extends XFAObject{constructor(e){super(Go,"color",!0);this.cSpace=getStringOption(e.cSpac\
-e,["SRGB"]);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.value=e.value?function getColor(e,t=[0,0,0]){let[a,r,i]=t;if(!e)return{r:a,g:r,b:i};const n=e.split(",",3).map((e=>MathClamp(parseInt(e.trim(),10),0,255))).map((e=>isNaN(e)?0:e));if(n.length<3)return{r:a,g:r,b:i};[a,r,i]=n;return{r:a,g:r,b:i}}(e.value):"";this.extras=null}[Ts](){return!1}[ho](){return this.value?Util.makeHexColor(this.value.r,this.value.g,this.value.b):null}}class Comb extends XFAObject{constructor(e\
-){super(Go,"comb");this.id=e.id||"";this.numberOfCells=getInteger({data:e.numberOfCells,defaultValue:0,validate:e=>e>=0});this.use=e.use||"";this.usehref=e.usehref||""}}class Connect extends XFAObject{constructor(e){super(Go,"connect",!0);this.connection=e.connection||"";this.id=e.id||"";this.ref=e.ref||"";this.usage=getStringOption(e.usage,["exportAndImport","exportOnly","importOnly"]);this.use=e.use||"";this.usehref=e.usehref||"";this.picture=null}}class ContentArea extends XFAObject{construct\
-or(e){super(Go,"contentArea",!0);this.h=getMeasurement(e.h);this.id=e.id||"";this.name=e.name||"";this.relevant=getRelevant(e.relevant);this.use=e.use||"";this.usehref=e.usehref||"";this.w=getMeasurement(e.w);this.x=getMeasurement(e.x,"0pt");this.y=getMeasurement(e.y,"0pt");this.desc=null;this.extras=null}[co](e){const t={left:measureToString(this.x),top:measureToString(this.y),width:measureToString(this.w),height:measureToString(this.h)},a=["xfaContentarea"];isPrintOnly(this)&&a.push("xfaPrintO\
-nly");return HTMLResult.success({name:"div",children:[],attributes:{style:t,class:a,id:this[uo]}})}}class Corner extends XFAObject{constructor(e){super(Go,"corner",!0);this.id=e.id||"";this.inverted=getInteger({data:e.inverted,defaultValue:0,validate:e=>1===e});this.join=getStringOption(e.join,["square","round"]);this.presence=getStringOption(e.presence,["visible","hidden","inactive","invisible"]);this.radius=getMeasurement(e.radius);this.stroke=getStringOption(e.stroke,["solid","dashDot","dashD\
-otDot","dashed","dotted","embossed","etched","lowered","raised"]);this.thickness=getMeasurement(e.thickness,"0.5pt");this.use=e.use||"";this.usehref=e.usehref||"";this.color=null;this.extras=null}[ho](){const e=toStyle(this,"visibility");e.radius=measureToString("square"===this.join?0:this.radius);return e}}class DateElement extends ContentObject{constructor(e){super(Go,"date");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}[hs](){const e=this[ss].trim();this\
-[ss]=e?new Date(e):null}[co](e){return valueToHtml(this[ss]?this[ss].toString():"")}}class DateTime extends ContentObject{constructor(e){super(Go,"dateTime");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}[hs](){const e=this[ss].trim();this[ss]=e?new Date(e):null}[co](e){return valueToHtml(this[ss]?this[ss].toString():"")}}class DateTimeEdit extends XFAObject{constructor(e){super(Go,"dateTimeEdit",!0);this.hScrollPolicy=getStringOption(e.hScrollPolicy,["auto"\
-,"off","on"]);this.id=e.id||"";this.picker=getStringOption(e.picker,["host","none"]);this.use=e.use||"";this.usehref=e.usehref||"";this.border=null;this.comb=null;this.extras=null;this.margin=null}[co](e){const t=toStyle(this,"border","font","margin"),a=this[vs]()[vs](),r={name:"input",attributes:{type:"text",fieldId:a[uo],dataId:a[os]?.[uo]||a[uo],class:["xfaTextfield"],style:t,"aria-label":ariaLabel(a),"aria-required":!1}};if(isRequired(a)){r.attributes["aria-required"]=!0;r.attributes.require\
-d=!0}return HTMLResult.success({name:"label",attributes:{class:["xfaLabel"]},children:[r]})}}class Decimal extends ContentObject{constructor(e){super(Go,"decimal");this.fracDigits=getInteger({data:e.fracDigits,defaultValue:2,validate:e=>!0});this.id=e.id||"";this.leadDigits=getInteger({data:e.leadDigits,defaultValue:-1,validate:e=>!0});this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}[hs](){const e=parseFloat(this[ss].trim());this[ss]=isNaN(e)?null:e}[co](e){return valueToHtml(\
-null!==this[ss]?this[ss].toString():"")}}class DefaultUi extends XFAObject{constructor(e){super(Go,"defaultUi",!0);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null}}class Desc extends XFAObject{constructor(e){super(Go,"desc",!0);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.boolean=new XFAObjectArray;this.date=new XFAObjectArray;this.dateTime=new XFAObjectArray;this.decimal=new XFAObjectArray;this.exData=new XFAObjectArray;this.float=new XFAOb\
-jectArray;this.image=new XFAObjectArray;this.integer=new XFAObjectArray;this.text=new XFAObjectArray;this.time=new XFAObjectArray}}class DigestMethod extends OptionObject{constructor(e){super(Go,"digestMethod",["","SHA1","SHA256","SHA512","RIPEMD160"]);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||""}}class DigestMethods extends XFAObject{constructor(e){super(Go,"digestMethods",!0);this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.us\
-ehref=e.usehref||"";this.digestMethod=new XFAObjectArray}}class Draw extends XFAObject{constructor(e){super(Go,"draw",!0);this.anchorType=getStringOption(e.anchorType,["topLeft","bottomCenter","bottomLeft","bottomRight","middleCenter","middleLeft","middleRight","topCenter","topRight"]);this.colSpan=getInteger({data:e.colSpan,defaultValue:1,validate:e=>e>=1||-1===e});this.h=e.h?getMeasurement(e.h):"";this.hAlign=getStringOption(e.hAlign,["left","center","justify","justifyAll","radix","right"]);th\
-is.id=e.id||"";this.locale=e.locale||"";this.maxH=getMeasurement(e.maxH,"0pt");this.maxW=getMeasurement(e.maxW,"0pt");this.minH=getMeasurement(e.minH,"0pt");this.minW=getMeasurement(e.minW,"0pt");this.name=e.name||"";this.presence=getStringOption(e.presence,["visible","hidden","inactive","invisible"]);this.relevant=getRelevant(e.relevant);this.rotate=getInteger({data:e.rotate,defaultValue:0,validate:e=>e%90==0});this.use=e.use||"";this.usehref=e.usehref||"";this.w=e.w?getMeasurement(e.w):"";this\
-.x=getMeasurement(e.x,"0pt");this.y=getMeasurement(e.y,"0pt");this.assist=null;this.border=null;this.caption=null;this.desc=null;this.extras=null;this.font=null;this.keep=null;this.margin=null;this.para=null;this.traversal=null;this.ui=null;this.value=null;this.setProperty=new XFAObjectArray}[io](e){_setValue(this,e)}[co](e){setTabIndex(this);if("hidden"===this.presence||"inactive"===this.presence)return HTMLResult.EMPTY;fixDimensions(this);this[Ys]();const t=this.w,a=this.h,{w:r,h:i,isBroken:n}\
-=layoutNode(this,e);if(r&&""===this.w){if(n&&this[Cs]()[_s]()){this[Js]();return HTMLResult.FAILURE}this.w=r}i&&""===this.h&&(this.h=i);setFirstUnsplittable(this);if(!checkDimensions(this,e)){this.w=t;this.h=a;this[Js]();return HTMLResult.FAILURE}unsetFirstUnsplittable(this);const s=toStyle(this,"font","hAlign","dimensions","position","presence","rotate","anchorType","border","margin");setMinMaxDimensions(this,s);if(s.margin){s.padding=s.margin;delete s.margin}const o=["xfaDraw"];this.font&&o.pu\
-sh("xfaFont");isPrintOnly(this)&&o.push("xfaPrintOnly");const c={style:s,id:this[uo],class:o};this.name&&(c.xfaName=this.name);const l={name:"div",attributes:c,children:[]};applyAssist(this,c);const h=computeBbox(this,l,e),u=this.value?this.value[co](e).html:null;if(null===u){this.w=t;this.h=a;this[Js]();return HTMLResult.success(createWrapper(this,l),h)}l.children.push(u);setPara(this,s,u);this.w=t;this.h=a;this[Js]();return HTMLResult.success(createWrapper(this,l),h)}}class Edge extends XFAObj\
-ect{constructor(e){super(Go,"edge",!0);this.cap=getStringOption(e.cap,["square","butt","round"]);this.id=e.id||"";this.presence=getStringOption(e.presence,["visible","hidden","inactive","invisible"]);this.stroke=getStringOption(e.stroke,["solid","dashDot","dashDotDot","dashed","dotted","embossed","etched","lowered","raised"]);this.thickness=getMeasurement(e.thickness,"0.5pt");this.use=e.use||"";this.usehref=e.usehref||"";this.color=null;this.extras=null}[ho](){const e=toStyle(this,"visibility");\
-Object.assign(e,{linecap:this.cap,width:measureToString(this.thickness),color:this.color?this.color[ho]():"#000000",style:""});if("visible"!==this.presence)e.style="none";else switch(this.stroke){case"solid":e.style="solid";break;case"dashDot":case"dashDotDot":case"dashed":e.style="dashed";break;case"dotted":e.style="dotted";break;case"embossed":e.style="ridge";break;case"etched":e.style="groove";break;case"lowered":e.style="inset";break;case"raised":e.style="outset"}return e}}class Encoding ext\
-ends OptionObject{constructor(e){super(Go,"encoding",["adbe.x509.rsa_sha1","adbe.pkcs7.detached","adbe.pkcs7.sha1"]);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||""}}class Encodings extends XFAObject{constructor(e){super(Go,"encodings",!0);this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||"";this.encoding=new XFAObjectArray}}class Encrypt extends XFAObject{constructor(e){super(Go,"encrypt",!0);this.id=e.id||"";thi\
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-||"";this.usehref=e.usehref||"";this.certificate=new XFAObjectArray}}class EncryptionMethod extends OptionObject{constructor(e){super(Go,"encryptionMethod",["","AES256-CBC","TRIPLEDES-CBC","AES128-CBC","AES192-CBC"]);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||""}}class EncryptionMethods extends XFAObject{constructor(e){super(Go,"encryptionMethods",!0);this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||"";this.enc\
-ryptionMethod=new XFAObjectArray}}class Event extends XFAObject{constructor(e){super(Go,"event",!0);this.activity=getStringOption(e.activity,["click","change","docClose","docReady","enter","exit","full","indexChange","initialize","mouseDown","mouseEnter","mouseExit","mouseUp","postExecute","postOpen","postPrint","postSave","postSign","postSubmit","preExecute","preOpen","prePrint","preSave","preSign","preSubmit","ready","validationState"]);this.id=e.id||"";this.listen=getStringOption(e.listen,["r\
-efOnly","refAndDescendents"]);this.name=e.name||"";this.ref=e.ref||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.encryptData=null;this.execute=null;this.script=null;this.signData=null;this.submit=null}}class ExData extends ContentObject{constructor(e){super(Go,"exData");this.contentType=e.contentType||"";this.href=e.href||"";this.id=e.id||"";this.maxLength=getInteger({data:e.maxLength,defaultValue:-1,validate:e=>e>=-1});this.name=e.name||"";this.rid=e.rid||"";this.transf\
-erEncoding=getStringOption(e.transferEncoding,["none","base64","package"]);this.use=e.use||"";this.usehref=e.usehref||""}[Bs](){return"text/html"===this.contentType}[$s](e){if("text/html"===this.contentType&&e[Hs]===go.xhtml.id){this[ss]=e;return!0}if("text/xml"===this.contentType){this[ss]=e;return!0}return!1}[co](e){return"text/html"===this.contentType&&this[ss]?this[ss][co](e):HTMLResult.EMPTY}}class ExObject extends XFAObject{constructor(e){super(Go,"exObject",!0);this.archive=e.archive||"";\
-this.classId=e.classId||"";this.codeBase=e.codeBase||"";this.codeType=e.codeType||"";this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.boolean=new XFAObjectArray;this.date=new XFAObjectArray;this.dateTime=new XFAObjectArray;this.decimal=new XFAObjectArray;this.exData=new XFAObjectArray;this.exObject=new XFAObjectArray;this.float=new XFAObjectArray;this.image=new XFAObjectArray;this.integer=new XFAObjectArray;this.text=new XFAObjectArray;thi\
-s.time=new XFAObjectArray}}class ExclGroup extends XFAObject{constructor(e){super(Go,"exclGroup",!0);this.access=getStringOption(e.access,["open","nonInteractive","protected","readOnly"]);this.accessKey=e.accessKey||"";this.anchorType=getStringOption(e.anchorType,["topLeft","bottomCenter","bottomLeft","bottomRight","middleCenter","middleLeft","middleRight","topCenter","topRight"]);this.colSpan=getInteger({data:e.colSpan,defaultValue:1,validate:e=>e>=1||-1===e});this.h=e.h?getMeasurement(e.h):"";\
-this.hAlign=getStringOption(e.hAlign,["left","center","justify","justifyAll","radix","right"]);this.id=e.id||"";this.layout=getStringOption(e.layout,["position","lr-tb","rl-row","rl-tb","row","table","tb"]);this.maxH=getMeasurement(e.maxH,"0pt");this.maxW=getMeasurement(e.maxW,"0pt");this.minH=getMeasurement(e.minH,"0pt");this.minW=getMeasurement(e.minW,"0pt");this.name=e.name||"";this.presence=getStringOption(e.presence,["visible","hidden","inactive","invisible"]);this.relevant=getRelevant(e.re\
-levant);this.use=e.use||"";this.usehref=e.usehref||"";this.w=e.w?getMeasurement(e.w):"";this.x=getMeasurement(e.x,"0pt");this.y=getMeasurement(e.y,"0pt");this.assist=null;this.bind=null;this.border=null;this.calculate=null;this.caption=null;this.desc=null;this.extras=null;this.margin=null;this.para=null;this.traversal=null;this.validate=null;this.connect=new XFAObjectArray;this.event=new XFAObjectArray;this.field=new XFAObjectArray;this.setProperty=new XFAObjectArray}[Rs](){return!0}[Ts](){retur\
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-._isSplittable=!0;return!0}[us](){return flushHTML(this)}[Zn](e,t){addHTML(this,e,t)}[gs](){return getAvailableSpace(this)}[co](e){setTabIndex(this);if("hidden"===this.presence||"inactive"===this.presence||0===this.h||0===this.w)return HTMLResult.EMPTY;fixDimensions(this);const t=[],a={id:this[uo],class:[]};setAccess(this,a.class);this[ls]||=Object.create(null);Object.assign(this[ls],{children:t,attributes:a,attempt:0,line:null,numberInLine:0,availableSpace:{width:Math.min(this.w||1/0,e.width),h\
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-&s.push(o);isPrintOnly(this)&&s.push("xfaPrintOnly");a.style=n;a.class=s;this.name&&(a.xfaName=this.name);this[Ys]();const c="lr-tb"===this.layout||"rl-tb"===this.layout,l=c?2:1;for(;this[ls].attempt<l;this[ls].attempt++){c&&1===this[ls].attempt&&(this[ls].numberInLine=0);const e=this[es]({filter:i,include:!0});if(e.success)break;if(e.isBreak()){this[Js]();return e}if(c&&0===this[ls].attempt&&0===this[ls].numberInLine&&!this[Fs]()[ls].noLayoutFailure){this[ls].attempt=l;break}}this[Js]();r||unse\
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-LResult.success(createWrapper(this,p),g)}}class Execute extends XFAObject{constructor(e){super(Go,"execute");this.connection=e.connection||"";this.executeType=getStringOption(e.executeType,["import","remerge"]);this.id=e.id||"";this.runAt=getStringOption(e.runAt,["client","both","server"]);this.use=e.use||"";this.usehref=e.usehref||""}}class Extras extends XFAObject{constructor(e){super(Go,"extras",!0);this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||"";this.boole\
-an=new XFAObjectArray;this.date=new XFAObjectArray;this.dateTime=new XFAObjectArray;this.decimal=new XFAObjectArray;this.exData=new XFAObjectArray;this.extras=new XFAObjectArray;this.float=new XFAObjectArray;this.image=new XFAObjectArray;this.integer=new XFAObjectArray;this.text=new XFAObjectArray;this.time=new XFAObjectArray}}class Field extends XFAObject{constructor(e){super(Go,"field",!0);this.access=getStringOption(e.access,["open","nonInteractive","protected","readOnly"]);this.accessKey=e.a\
-ccessKey||"";this.anchorType=getStringOption(e.anchorType,["topLeft","bottomCenter","bottomLeft","bottomRight","middleCenter","middleLeft","middleRight","topCenter","topRight"]);this.colSpan=getInteger({data:e.colSpan,defaultValue:1,validate:e=>e>=1||-1===e});this.h=e.h?getMeasurement(e.h):"";this.hAlign=getStringOption(e.hAlign,["left","center","justify","justifyAll","radix","right"]);this.id=e.id||"";this.locale=e.locale||"";this.maxH=getMeasurement(e.maxH,"0pt");this.maxW=getMeasurement(e.max\
-W,"0pt");this.minH=getMeasurement(e.minH,"0pt");this.minW=getMeasurement(e.minW,"0pt");this.name=e.name||"";this.presence=getStringOption(e.presence,["visible","hidden","inactive","invisible"]);this.relevant=getRelevant(e.relevant);this.rotate=getInteger({data:e.rotate,defaultValue:0,validate:e=>e%90==0});this.use=e.use||"";this.usehref=e.usehref||"";this.w=e.w?getMeasurement(e.w):"";this.x=getMeasurement(e.x,"0pt");this.y=getMeasurement(e.y,"0pt");this.assist=null;this.bind=null;this.border=nul\
-l;this.calculate=null;this.caption=null;this.desc=null;this.extras=null;this.font=null;this.format=null;this.items=new XFAObjectArray(2);this.keep=null;this.margin=null;this.para=null;this.traversal=null;this.ui=null;this.validate=null;this.value=null;this.bindItems=new XFAObjectArray;this.connect=new XFAObjectArray;this.event=new XFAObjectArray;this.setProperty=new XFAObjectArray}[Rs](){return!0}[io](e){_setValue(this,e)}[co](e){setTabIndex(this);if(!this.ui){this.ui=new Ui({});this.ui[Is]=this\
-[Is];this[Qn](this.ui);let e;switch(this.items.children.length){case 0:e=new TextEdit({});this.ui.textEdit=e;break;case 1:e=new CheckButton({});this.ui.checkButton=e;break;case 2:e=new ChoiceList({});this.ui.choiceList=e}this.ui[Qn](e)}if(!this.ui||"hidden"===this.presence||"inactive"===this.presence||0===this.h||0===this.w)return HTMLResult.EMPTY;this.caption&&delete this.caption[ls];this[Ys]();const t=this.caption?this.caption[co](e).html:null,a=this.w,r=this.h;let i=0,n=0;if(this.margin){i=th\
-is.margin.leftInset+this.margin.rightInset;n=this.margin.topInset+this.margin.bottomInset}let s=null;if(""===this.w||""===this.h){let t=null,a=null,r=0,o=0;if(this.ui.checkButton)r=o=this.ui.checkButton.size;else{const{w:t,h:a}=layoutNode(this,e);if(null!==t){r=t;o=a}else o=function fonts_getMetrics(e,t=!1){let a=null;if(e){const t=stripQuotes(e.typeface),r=e[Is].fontFinder.find(t);a=selectFont(e,r)}if(!a)return{lineHeight:12,lineGap:2,lineNoGap:10};const r=e.size||10,i=a.lineHeight?Math.max(t?0\
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-)&&c.push("xfaPrintOnly");const l={style:o,id:this[uo],class:c};if(o.margin){o.padding=o.margin;delete o.margin}setAccess(this,c);this.name&&(l.xfaName=this.name);const h=[],u={name:"div",attributes:l,children:h};applyAssist(this,l);const d=this.border?this.border[ho]():null,f=computeBbox(this,u,e),g=this.ui[co]().html;if(!g){Object.assign(o,d);return HTMLResult.success(createWrapper(this,u),f)}this[no]&&(g.children?.[0]?g.children[0].attributes.tabindex=this[no]:g.attributes.tabindex=this[no]);\
-g.attributes.style||=Object.create(null);let p=null;if(this.ui.button){1===g.children.length&&([p]=g.children.splice(0,1));Object.assign(g.attributes.style,d)}else Object.assign(o,d);h.push(g);if(this.value)if(this.ui.imageEdit)g.children.push(this.value[co]().html);else if(!this.ui.button){let e="";if(this.value.exData)e=this.value.exData[so]();else if(this.value.text)e=this.value.text[ws]();else{const t=this.value[co]().html;null!==t&&(e=t.children[0].value)}this.ui.textEdit&&this.value.text?.\
-maxChars&&(g.children[0].attributes.maxLength=this.value.text.maxChars);if(e){if(this.ui.numericEdit){e=parseFloat(e);e=isNaN(e)?"":e.toString()}"textarea"===g.children[0].name?g.children[0].attributes.textContent=e:g.children[0].attributes.value=e}}if(!this.ui.imageEdit&&g.children?.[0]&&this.h){s=s||getBorderDims(this.ui[ws]());let t=0;if(this.caption&&["top","bottom"].includes(this.caption.placement)){t=this.caption.reserve;t<=0&&(t=this.caption[ws](e).h);const a=this.h-t-n-s.h;g.children[0].\
-attributes.style.height=measureToString(a)}else g.children[0].attributes.style.height="100%"}p&&g.children.push(p);if(!t){g.attributes.class&&g.attributes.class.push("xfaLeft");this.w=a;this.h=r;return HTMLResult.success(createWrapper(this,u),f)}if(this.ui.button){o.padding&&delete o.padding;"div"===t.name&&(t.name="span");g.children.push(t);return HTMLResult.success(u,f)}this.ui.checkButton&&(t.attributes.class[0]="xfaCaptionForCheckButton");g.attributes.class||=[];g.children.splice(0,0,t);swit\
-ch(this.caption.placement){case"left":case"inline":g.attributes.class.push("xfaLeft");break;case"right":g.attributes.class.push("xfaRight");break;case"top":g.attributes.class.push("xfaTop");break;case"bottom":g.attributes.class.push("xfaBottom")}this.w=a;this.h=r;return HTMLResult.success(createWrapper(this,u),f)}}class Fill extends XFAObject{constructor(e){super(Go,"fill",!0);this.id=e.id||"";this.presence=getStringOption(e.presence,["visible","hidden","inactive","invisible"]);this.use=e.use||"\
-";this.usehref=e.usehref||"";this.color=null;this.extras=null;this.linear=null;this.pattern=null;this.radial=null;this.solid=null;this.stipple=null}[ho](){const e=this[vs](),t=e[vs]()[vs](),a=Object.create(null);let r="color",i=r;if(e instanceof Border){r="background-color";i="background";t instanceof Ui&&(a.backgroundColor="white")}if(e instanceof Rectangle||e instanceof Arc){r=i="fill";a.fill="white"}for(const e of Object.getOwnPropertyNames(this)){if("extras"===e||"color"===e)continue;const t\
-=this[e];if(!(t instanceof XFAObject))continue;const n=t[ho](this.color);n&&(a[n.startsWith("#")?r:i]=n);return a}if(this.color?.value){const e=this.color[ho]();a[e.startsWith("#")?r:i]=e}return a}}class Filter extends XFAObject{constructor(e){super(Go,"filter",!0);this.addRevocationInfo=getStringOption(e.addRevocationInfo,["","required","optional","none"]);this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||"";this.version=getInteger({data:this.version,defaultValue:\
-5,validate:e=>e>=1&&e<=5});this.appearanceFilter=null;this.certificates=null;this.digestMethods=null;this.encodings=null;this.encryptionMethods=null;this.handler=null;this.lockDocument=null;this.mdp=null;this.reasons=null;this.timeStamp=null}}class Float extends ContentObject{constructor(e){super(Go,"float");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}[hs](){const e=parseFloat(this[ss].trim());this[ss]=isNaN(e)?null:e}[co](e){return valueToHtml(null!==this\
-[ss]?this[ss].toString():"")}}class template_Font extends XFAObject{constructor(e){super(Go,"font",!0);this.baselineShift=getMeasurement(e.baselineShift);this.fontHorizontalScale=getFloat({data:e.fontHorizontalScale,defaultValue:100,validate:e=>e>=0});this.fontVerticalScale=getFloat({data:e.fontVerticalScale,defaultValue:100,validate:e=>e>=0});this.id=e.id||"";this.kerningMode=getStringOption(e.kerningMode,["none","pair"]);this.letterSpacing=getMeasurement(e.letterSpacing,"0");this.lineThrough=g\
-etInteger({data:e.lineThrough,defaultValue:0,validate:e=>1===e||2===e});this.lineThroughPeriod=getStringOption(e.lineThroughPeriod,["all","word"]);this.overline=getInteger({data:e.overline,defaultValue:0,validate:e=>1===e||2===e});this.overlinePeriod=getStringOption(e.overlinePeriod,["all","word"]);this.posture=getStringOption(e.posture,["normal","italic"]);this.size=getMeasurement(e.size,"10pt");this.typeface=e.typeface||"Courier";this.underline=getInteger({data:e.underline,defaultValue:0,valid\
-ate:e=>1===e||2===e});this.underlinePeriod=getStringOption(e.underlinePeriod,["all","word"]);this.use=e.use||"";this.usehref=e.usehref||"";this.weight=getStringOption(e.weight,["normal","bold"]);this.extras=null;this.fill=null}[ts](e){super[ts](e);this[Is].usedTypefaces.add(this.typeface)}[ho](){const e=toStyle(this,"fill"),t=e.color;if(t)if("#000000"===t)delete e.color;else if(!t.startsWith("#")){e.background=t;e.backgroundClip="text";e.color="transparent"}this.baselineShift&&(e.verticalAlign=m\
-easureToString(this.baselineShift));e.fontKerning="none"===this.kerningMode?"none":"normal";e.letterSpacing=measureToString(this.letterSpacing);if(0!==this.lineThrough){e.textDecoration="line-through";2===this.lineThrough&&(e.textDecorationStyle="double")}if(0!==this.overline){e.textDecoration="overline";2===this.overline&&(e.textDecorationStyle="double")}e.fontStyle=this.posture;e.fontSize=measureToString(.99*this.size);setFontFamily(this,this,this[Is].fontFinder,e);if(0!==this.underline){e.tex\
-tDecoration="underline";2===this.underline&&(e.textDecorationStyle="double")}e.fontWeight=this.weight;return e}}class Format extends XFAObject{constructor(e){super(Go,"format",!0);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.picture=null}}class Handler extends StringObject{constructor(e){super(Go,"handler");this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||""}}class Hyphenation extends XFA\
-Object{constructor(e){super(Go,"hyphenation");this.excludeAllCaps=getInteger({data:e.excludeAllCaps,defaultValue:0,validate:e=>1===e});this.excludeInitialCap=getInteger({data:e.excludeInitialCap,defaultValue:0,validate:e=>1===e});this.hyphenate=getInteger({data:e.hyphenate,defaultValue:0,validate:e=>1===e});this.id=e.id||"";this.pushCharacterCount=getInteger({data:e.pushCharacterCount,defaultValue:3,validate:e=>e>=0});this.remainCharacterCount=getInteger({data:e.remainCharacterCount,defaultValue\
-:3,validate:e=>e>=0});this.use=e.use||"";this.usehref=e.usehref||"";this.wordCharacterCount=getInteger({data:e.wordCharacterCount,defaultValue:7,validate:e=>e>=0})}}class Image extends StringObject{constructor(e){super(Go,"image");this.aspect=getStringOption(e.aspect,["fit","actual","height","none","width"]);this.contentType=e.contentType||"";this.href=e.href||"";this.id=e.id||"";this.name=e.name||"";this.transferEncoding=getStringOption(e.transferEncoding,["base64","none","package"]);this.use=e\
-.use||"";this.usehref=e.usehref||""}[co](){if(this.contentType&&!Jo.has(this.contentType.toLowerCase()))return HTMLResult.EMPTY;let e=this[Is].images?.get(this.href);if(!e&&(this.href||!this[ss]))return HTMLResult.EMPTY;e||"base64"!==this.transferEncoding||(e=function fromBase64Util(e){return Uint8Array.fromBase64?Uint8Array.fromBase64(e):stringToBytes(atob(e))}(this[ss]));if(!e)return HTMLResult.EMPTY;if(!this.contentType){for(const[t,a]of Yo)if(e.length>t.length&&t.every(((t,a)=>t===e[a]))){th\
-is.contentType=a;break}if(!this.contentType)return HTMLResult.EMPTY}const t=new Blob([e],{type:this.contentType});let a;switch(this.aspect){case"fit":case"actual":break;case"height":a={height:"100%",objectFit:"fill"};break;case"none":a={width:"100%",height:"100%",objectFit:"fill"};break;case"width":a={width:"100%",objectFit:"fill"}}const r=this[vs]();return HTMLResult.success({name:"img",attributes:{class:["xfaImage"],style:a,src:URL.createObjectURL(t),alt:r?ariaLabel(r[vs]()):null}})}}class Ima\
-geEdit extends XFAObject{constructor(e){super(Go,"imageEdit",!0);this.data=getStringOption(e.data,["link","embed"]);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.border=null;this.extras=null;this.margin=null}[co](e){return"embed"===this.data?HTMLResult.success({name:"div",children:[],attributes:{}}):HTMLResult.EMPTY}}class Integer extends ContentObject{constructor(e){super(Go,"integer");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}[hs]\
-(){const e=parseInt(this[ss].trim(),10);this[ss]=isNaN(e)?null:e}[co](e){return valueToHtml(null!==this[ss]?this[ss].toString():"")}}class Issuers extends XFAObject{constructor(e){super(Go,"issuers",!0);this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||"";this.certificate=new XFAObjectArray}}class Items extends XFAObject{constructor(e){super(Go,"items",!0);this.id=e.id||"";this.name=e.name||"";this.presence=getStringOption(e.pre\
-sence,["visible","hidden","inactive","invisible"]);this.ref=e.ref||"";this.save=getInteger({data:e.save,defaultValue:0,validate:e=>1===e});this.use=e.use||"";this.usehref=e.usehref||"";this.boolean=new XFAObjectArray;this.date=new XFAObjectArray;this.dateTime=new XFAObjectArray;this.decimal=new XFAObjectArray;this.exData=new XFAObjectArray;this.float=new XFAObjectArray;this.image=new XFAObjectArray;this.integer=new XFAObjectArray;this.text=new XFAObjectArray;this.time=new XFAObjectArray}[co](){c\
-onst e=[];for(const t of this[Ss]())e.push(t[so]());return HTMLResult.success(e)}}class Keep extends XFAObject{constructor(e){super(Go,"keep",!0);this.id=e.id||"";const t=["none","contentArea","pageArea"];this.intact=getStringOption(e.intact,t);this.next=getStringOption(e.next,t);this.previous=getStringOption(e.previous,t);this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null}}class KeyUsage extends XFAObject{constructor(e){super(Go,"keyUsage");const t=["","yes","no"];this.crlSign=getSt\
-ringOption(e.crlSign,t);this.dataEncipherment=getStringOption(e.dataEncipherment,t);this.decipherOnly=getStringOption(e.decipherOnly,t);this.digitalSignature=getStringOption(e.digitalSignature,t);this.encipherOnly=getStringOption(e.encipherOnly,t);this.id=e.id||"";this.keyAgreement=getStringOption(e.keyAgreement,t);this.keyCertSign=getStringOption(e.keyCertSign,t);this.keyEncipherment=getStringOption(e.keyEncipherment,t);this.nonRepudiation=getStringOption(e.nonRepudiation,t);this.type=getString\
-Option(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||""}}class Line extends XFAObject{constructor(e){super(Go,"line",!0);this.hand=getStringOption(e.hand,["even","left","right"]);this.id=e.id||"";this.slope=getStringOption(e.slope,["\\\\","/"]);this.use=e.use||"";this.usehref=e.usehref||"";this.edge=null}[co](){const e=this[vs]()[vs](),t=this.edge||new Edge({}),a=t[ho](),r=Object.create(null),i="visible"===t.presence?t.thickness:0;r.strokeWidth=measureToString(i);r.str\
-oke=a.color;let n,s,o,c,l="100%",h="100%";if(e.w<=i){[n,s,o,c]=["50%",0,"50%","100%"];l=r.strokeWidth}else if(e.h<=i){[n,s,o,c]=[0,"50%","100%","50%"];h=r.strokeWidth}else"\\\\"===this.slope?[n,s,o,c]=[0,0,"100%","100%"]:[n,s,o,c]=[0,"100%","100%",0];const u={name:"svg",children:[{name:"line",attributes:{xmlns:Vo,x1:n,y1:s,x2:o,y2:c,style:r}}],attributes:{xmlns:Vo,width:l,height:h,style:{overflow:"visible"}}};if(hasMargin(e))return HTMLResult.success({name:"div",attributes:{style:{display:"inline"\
-,width:"100%",height:"100%"}},children:[u]});u.attributes.style.position="absolute";return HTMLResult.success(u)}}class Linear extends XFAObject{constructor(e){super(Go,"linear",!0);this.id=e.id||"";this.type=getStringOption(e.type,["toRight","toBottom","toLeft","toTop"]);this.use=e.use||"";this.usehref=e.usehref||"";this.color=null;this.extras=null}[ho](e){e=e?e[ho]():"#FFFFFF";return`linear-gradient(${this.type.replace(/([RBLT])/," $1").toLowerCase()}, ${e}, ${this.color?this.color[ho]():"#000\
-000"})`}}class LockDocument extends ContentObject{constructor(e){super(Go,"lockDocument");this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||""}[hs](){this[ss]=getStringOption(this[ss],["auto","0","1"])}}class Manifest extends XFAObject{constructor(e){super(Go,"manifest",!0);this.action=getStringOption(e.action,["include","all","exclude"]);this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||"";this.ex\
-tras=null;this.ref=new XFAObjectArray}}class Margin extends XFAObject{constructor(e){super(Go,"margin",!0);this.bottomInset=getMeasurement(e.bottomInset,"0");this.id=e.id||"";this.leftInset=getMeasurement(e.leftInset,"0");this.rightInset=getMeasurement(e.rightInset,"0");this.topInset=getMeasurement(e.topInset,"0");this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null}[ho](){return{margin:measureToString(this.topInset)+" "+measureToString(this.rightInset)+" "+measureToString(this.bottomI\
-nset)+" "+measureToString(this.leftInset)}}}class Mdp extends XFAObject{constructor(e){super(Go,"mdp");this.id=e.id||"";this.permissions=getInteger({data:e.permissions,defaultValue:2,validate:e=>1===e||3===e});this.signatureType=getStringOption(e.signatureType,["filler","author"]);this.use=e.use||"";this.usehref=e.usehref||""}}class Medium extends XFAObject{constructor(e){super(Go,"medium");this.id=e.id||"";this.imagingBBox=function getBBox(e){const t=-1;if(!e)return{x:t,y:t,width:t,height:t};co\
-nst a=e.split(",",4).map((e=>getMeasurement(e.trim(),"-1")));if(a.length<4||a[2]<0||a[3]<0)return{x:t,y:t,width:t,height:t};const[r,i,n,s]=a;return{x:r,y:i,width:n,height:s}}(e.imagingBBox);this.long=getMeasurement(e.long);this.orientation=getStringOption(e.orientation,["portrait","landscape"]);this.short=getMeasurement(e.short);this.stock=e.stock||"";this.trayIn=getStringOption(e.trayIn,["auto","delegate","pageFront"]);this.trayOut=getStringOption(e.trayOut,["auto","delegate"]);this.use=e.use||\
-"";this.usehref=e.usehref||""}}class Message extends XFAObject{constructor(e){super(Go,"message",!0);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.text=new XFAObjectArray}}class NumericEdit extends XFAObject{constructor(e){super(Go,"numericEdit",!0);this.hScrollPolicy=getStringOption(e.hScrollPolicy,["auto","off","on"]);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.border=null;this.comb=null;this.extras=null;this.margin=null}[co](e){const t=toStyle(thi\
-s,"border","font","margin"),a=this[vs]()[vs](),r={name:"input",attributes:{type:"text",fieldId:a[uo],dataId:a[os]?.[uo]||a[uo],class:["xfaTextfield"],style:t,"aria-label":ariaLabel(a),"aria-required":!1}};if(isRequired(a)){r.attributes["aria-required"]=!0;r.attributes.required=!0}return HTMLResult.success({name:"label",attributes:{class:["xfaLabel"]},children:[r]})}}class Occur extends XFAObject{constructor(e){super(Go,"occur",!0);this.id=e.id||"";this.initial=""!==e.initial?getInteger({data:e.i\
-nitial,defaultValue:"",validate:e=>!0}):"";this.max=""!==e.max?getInteger({data:e.max,defaultValue:1,validate:e=>!0}):"";this.min=""!==e.min?getInteger({data:e.min,defaultValue:1,validate:e=>!0}):"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null}[ts](){const e=this[vs](),t=this.min;""===this.min&&(this.min=e instanceof PageArea||e instanceof PageSet?0:1);""===this.max&&(this.max=""===t?e instanceof PageArea||e instanceof PageSet?-1:1:this.min);-1!==this.max&&this.max<this.min&&(t\
-his.max=this.min);""===this.initial&&(this.initial=e instanceof Template?1:this.min)}}class Oid extends StringObject{constructor(e){super(Go,"oid");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class Oids extends XFAObject{constructor(e){super(Go,"oids",!0);this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||"";this.oid=new XFAObjectArray}}class Overflow extends XFAObject{constructor(e){super\
-(Go,"overflow");this.id=e.id||"";this.leader=e.leader||"";this.target=e.target||"";this.trailer=e.trailer||"";this.use=e.use||"";this.usehref=e.usehref||""}[ws](){if(!this[ls]){const e=this[vs](),t=this[Fs](),a=t[to](this.target,e),r=t[to](this.leader,e),i=t[to](this.trailer,e);this[ls]={target:a?.[0]||null,leader:r?.[0]||null,trailer:i?.[0]||null,addLeader:!1,addTrailer:!1}}return this[ls]}}class PageArea extends XFAObject{constructor(e){super(Go,"pageArea",!0);this.blankOrNotBlank=getStringOpt\
-ion(e.blankOrNotBlank,["any","blank","notBlank"]);this.id=e.id||"";this.initialNumber=getInteger({data:e.initialNumber,defaultValue:1,validate:e=>!0});this.name=e.name||"";this.numbered=getInteger({data:e.numbered,defaultValue:1,validate:e=>!0});this.oddOrEven=getStringOption(e.oddOrEven,["any","even","odd"]);this.pagePosition=getStringOption(e.pagePosition,["any","first","last","only","rest"]);this.relevant=getRelevant(e.relevant);this.use=e.use||"";this.usehref=e.usehref||"";this.desc=null;thi\
-s.extras=null;this.medium=null;this.occur=null;this.area=new XFAObjectArray;this.contentArea=new XFAObjectArray;this.draw=new XFAObjectArray;this.exclGroup=new XFAObjectArray;this.field=new XFAObjectArray;this.subform=new XFAObjectArray}[Xs](){if(!this[ls]){this[ls]={numberOfUse:0};return!0}return!this.occur||-1===this.occur.max||this[ls].numberOfUse<this.occur.max}[as](){delete this[ls]}[ks](){this[ls]||={numberOfUse:0};const e=this[vs]();if("orderedOccurrence"===e.relation&&this[Xs]()){this[ls\
-].numberOfUse+=1;return this}return e[ks]()}[gs](){return this[ls].space||{width:0,height:0}}[co](){this[ls]||={numberOfUse:1};const e=[];this[ls].children=e;const t=Object.create(null);if(this.medium&&this.medium.short&&this.medium.long){t.width=measureToString(this.medium.short);t.height=measureToString(this.medium.long);this[ls].space={width:this.medium.short,height:this.medium.long};if("landscape"===this.medium.orientation){const e=t.width;t.width=t.height;t.height=e;this[ls].space={width:th\
-is.medium.long,height:this.medium.short}}}else warn("XFA - No medium specified in pageArea: please file a bug.");this[es]({filter:new Set(["area","draw","field","subform"]),include:!0});this[es]({filter:new Set(["contentArea"]),include:!0});return HTMLResult.success({name:"div",children:e,attributes:{class:["xfaPage"],id:this[uo],style:t,xfaName:this.name}})}}class PageSet extends XFAObject{constructor(e){super(Go,"pageSet",!0);this.duplexImposition=getStringOption(e.duplexImposition,["longEdge"\
-,"shortEdge"]);this.id=e.id||"";this.name=e.name||"";this.relation=getStringOption(e.relation,["orderedOccurrence","duplexPaginated","simplexPaginated"]);this.relevant=getRelevant(e.relevant);this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.occur=null;this.pageArea=new XFAObjectArray;this.pageSet=new XFAObjectArray}[as](){for(const e of this.pageArea.children)e[as]();for(const e of this.pageSet.children)e[as]()}[Xs](){return!this.occur||-1===this.occur.max||this[ls].numberOfUs\
-e<this.occur.max}[ks](){this[ls]||={numberOfUse:1,pageIndex:-1,pageSetIndex:-1};if("orderedOccurrence"===this.relation){if(this[ls].pageIndex+1<this.pageArea.children.length){this[ls].pageIndex+=1;return this.pageArea.children[this[ls].pageIndex][ks]()}if(this[ls].pageSetIndex+1<this.pageSet.children.length){this[ls].pageSetIndex+=1;return this.pageSet.children[this[ls].pageSetIndex][ks]()}if(this[Xs]()){this[ls].numberOfUse+=1;this[ls].pageIndex=-1;this[ls].pageSetIndex=-1;return this[ks]()}con\
-st e=this[vs]();if(e instanceof PageSet)return e[ks]();this[as]();return this[ks]()}const e=this[Fs]()[ls].pageNumber,t=e%2==0?"even":"odd",a=0===e?"first":"rest";let r=this.pageArea.children.find((e=>e.oddOrEven===t&&e.pagePosition===a));if(r)return r;r=this.pageArea.children.find((e=>"any"===e.oddOrEven&&e.pagePosition===a));if(r)return r;r=this.pageArea.children.find((e=>"any"===e.oddOrEven&&"any"===e.pagePosition));return r||this.pageArea.children[0]}}class Para extends XFAObject{constructor\
-(e){super(Go,"para",!0);this.hAlign=getStringOption(e.hAlign,["left","center","justify","justifyAll","radix","right"]);this.id=e.id||"";this.lineHeight=e.lineHeight?getMeasurement(e.lineHeight,"0pt"):"";this.marginLeft=e.marginLeft?getMeasurement(e.marginLeft,"0pt"):"";this.marginRight=e.marginRight?getMeasurement(e.marginRight,"0pt"):"";this.orphans=getInteger({data:e.orphans,defaultValue:0,validate:e=>e>=0});this.preserve=e.preserve||"";this.radixOffset=e.radixOffset?getMeasurement(e.radixOffs\
-et,"0pt"):"";this.spaceAbove=e.spaceAbove?getMeasurement(e.spaceAbove,"0pt"):"";this.spaceBelow=e.spaceBelow?getMeasurement(e.spaceBelow,"0pt"):"";this.tabDefault=e.tabDefault?getMeasurement(this.tabDefault):"";this.tabStops=(e.tabStops||"").trim().split(/\\s+/).map(((e,t)=>t%2==1?getMeasurement(e):e));this.textIndent=e.textIndent?getMeasurement(e.textIndent,"0pt"):"";this.use=e.use||"";this.usehref=e.usehref||"";this.vAlign=getStringOption(e.vAlign,["top","bottom","middle"]);this.widows=getInteg\
-er({data:e.widows,defaultValue:0,validate:e=>e>=0});this.hyphenation=null}[ho](){const e=toStyle(this,"hAlign");""!==this.marginLeft&&(e.paddingLeft=measureToString(this.marginLeft));""!==this.marginRight&&(e.paddingRight=measureToString(this.marginRight));""!==this.spaceAbove&&(e.paddingTop=measureToString(this.spaceAbove));""!==this.spaceBelow&&(e.paddingBottom=measureToString(this.spaceBelow));if(""!==this.textIndent){e.textIndent=measureToString(this.textIndent);fixTextIndent(e)}this.lineHei\
-ght>0&&(e.lineHeight=measureToString(this.lineHeight));""!==this.tabDefault&&(e.tabSize=measureToString(this.tabDefault));this.tabStops.length;this.hyphenatation&&Object.assign(e,this.hyphenatation[ho]());return e}}class PasswordEdit extends XFAObject{constructor(e){super(Go,"passwordEdit",!0);this.hScrollPolicy=getStringOption(e.hScrollPolicy,["auto","off","on"]);this.id=e.id||"";this.passwordChar=e.passwordChar||"*";this.use=e.use||"";this.usehref=e.usehref||"";this.border=null;this.extras=nul\
-l;this.margin=null}}class template_Pattern extends XFAObject{constructor(e){super(Go,"pattern",!0);this.id=e.id||"";this.type=getStringOption(e.type,["crossHatch","crossDiagonal","diagonalLeft","diagonalRight","horizontal","vertical"]);this.use=e.use||"";this.usehref=e.usehref||"";this.color=null;this.extras=null}[ho](e){e=e?e[ho]():"#FFFFFF";const t=this.color?this.color[ho]():"#000000",a="repeating-linear-gradient",r=`${e},${e} 5px,${t} 5px,${t} 10px`;switch(this.type){case"crossHatch":return`\
-${a}(to top,${r}) ${a}(to right,${r})`;case"crossDiagonal":return`${a}(45deg,${r}) ${a}(-45deg,${r})`;case"diagonalLeft":return`${a}(45deg,${r})`;case"diagonalRight":return`${a}(-45deg,${r})`;case"horizontal":return`${a}(to top,${r})`;case"vertical":return`${a}(to right,${r})`}return""}}class Picture extends StringObject{constructor(e){super(Go,"picture");this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||""}}class Proto extends XFAObject{constructor(e){super(Go,"proto",!0);this.appeara\
-nceFilter=new XFAObjectArray;this.arc=new XFAObjectArray;this.area=new XFAObjectArray;this.assist=new XFAObjectArray;this.barcode=new XFAObjectArray;this.bindItems=new XFAObjectArray;this.bookend=new XFAObjectArray;this.boolean=new XFAObjectArray;this.border=new XFAObjectArray;this.break=new XFAObjectArray;this.breakAfter=new XFAObjectArray;this.breakBefore=new XFAObjectArray;this.button=new XFAObjectArray;this.calculate=new XFAObjectArray;this.caption=new XFAObjectArray;this.certificate=new XFA\
-ObjectArray;this.certificates=new XFAObjectArray;this.checkButton=new XFAObjectArray;this.choiceList=new XFAObjectArray;this.color=new XFAObjectArray;this.comb=new XFAObjectArray;this.connect=new XFAObjectArray;this.contentArea=new XFAObjectArray;this.corner=new XFAObjectArray;this.date=new XFAObjectArray;this.dateTime=new XFAObjectArray;this.dateTimeEdit=new XFAObjectArray;this.decimal=new XFAObjectArray;this.defaultUi=new XFAObjectArray;this.desc=new XFAObjectArray;this.digestMethod=new XFAObj\
-ectArray;this.digestMethods=new XFAObjectArray;this.draw=new XFAObjectArray;this.edge=new XFAObjectArray;this.encoding=new XFAObjectArray;this.encodings=new XFAObjectArray;this.encrypt=new XFAObjectArray;this.encryptData=new XFAObjectArray;this.encryption=new XFAObjectArray;this.encryptionMethod=new XFAObjectArray;this.encryptionMethods=new XFAObjectArray;this.event=new XFAObjectArray;this.exData=new XFAObjectArray;this.exObject=new XFAObjectArray;this.exclGroup=new XFAObjectArray;this.execute=n\
-ew XFAObjectArray;this.extras=new XFAObjectArray;this.field=new XFAObjectArray;this.fill=new XFAObjectArray;this.filter=new XFAObjectArray;this.float=new XFAObjectArray;this.font=new XFAObjectArray;this.format=new XFAObjectArray;this.handler=new XFAObjectArray;this.hyphenation=new XFAObjectArray;this.image=new XFAObjectArray;this.imageEdit=new XFAObjectArray;this.integer=new XFAObjectArray;this.issuers=new XFAObjectArray;this.items=new XFAObjectArray;this.keep=new XFAObjectArray;this.keyUsage=ne\
-w XFAObjectArray;this.line=new XFAObjectArray;this.linear=new XFAObjectArray;this.lockDocument=new XFAObjectArray;this.manifest=new XFAObjectArray;this.margin=new XFAObjectArray;this.mdp=new XFAObjectArray;this.medium=new XFAObjectArray;this.message=new XFAObjectArray;this.numericEdit=new XFAObjectArray;this.occur=new XFAObjectArray;this.oid=new XFAObjectArray;this.oids=new XFAObjectArray;this.overflow=new XFAObjectArray;this.pageArea=new XFAObjectArray;this.pageSet=new XFAObjectArray;this.para=\
-new XFAObjectArray;this.passwordEdit=new XFAObjectArray;this.pattern=new XFAObjectArray;this.picture=new XFAObjectArray;this.radial=new XFAObjectArray;this.reason=new XFAObjectArray;this.reasons=new XFAObjectArray;this.rectangle=new XFAObjectArray;this.ref=new XFAObjectArray;this.script=new XFAObjectArray;this.setProperty=new XFAObjectArray;this.signData=new XFAObjectArray;this.signature=new XFAObjectArray;this.signing=new XFAObjectArray;this.solid=new XFAObjectArray;this.speak=new XFAObjectArra\
-y;this.stipple=new XFAObjectArray;this.subform=new XFAObjectArray;this.subformSet=new XFAObjectArray;this.subjectDN=new XFAObjectArray;this.subjectDNs=new XFAObjectArray;this.submit=new XFAObjectArray;this.text=new XFAObjectArray;this.textEdit=new XFAObjectArray;this.time=new XFAObjectArray;this.timeStamp=new XFAObjectArray;this.toolTip=new XFAObjectArray;this.traversal=new XFAObjectArray;this.traverse=new XFAObjectArray;this.ui=new XFAObjectArray;this.validate=new XFAObjectArray;this.value=new \
-XFAObjectArray;this.variables=new XFAObjectArray}}class Radial extends XFAObject{constructor(e){super(Go,"radial",!0);this.id=e.id||"";this.type=getStringOption(e.type,["toEdge","toCenter"]);this.use=e.use||"";this.usehref=e.usehref||"";this.color=null;this.extras=null}[ho](e){e=e?e[ho]():"#FFFFFF";const t=this.color?this.color[ho]():"#000000";return`radial-gradient(circle at center, ${"toEdge"===this.type?`${e},${t}`:`${t},${e}`})`}}class Reason extends StringObject{constructor(e){super(Go,"rea\
-son");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class Reasons extends XFAObject{constructor(e){super(Go,"reasons",!0);this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||"";this.reason=new XFAObjectArray}}class Rectangle extends XFAObject{constructor(e){super(Go,"rectangle",!0);this.hand=getStringOption(e.hand,["even","left","right"]);this.id=e.id||"";this.use=e.use||"";this.usehref=e.use\
-href||"";this.corner=new XFAObjectArray(4);this.edge=new XFAObjectArray(4);this.fill=null}[co](){const e=this.edge.children.length?this.edge.children[0]:new Edge({}),t=e[ho](),a=Object.create(null);"visible"===this.fill?.presence?Object.assign(a,this.fill[ho]()):a.fill="transparent";a.strokeWidth=measureToString("visible"===e.presence?e.thickness:0);a.stroke=t.color;const r=(this.corner.children.length?this.corner.children[0]:new Corner({}))[ho](),i={name:"svg",children:[{name:"rect",attributes:\
-{xmlns:Vo,width:"100%",height:"100%",x:0,y:0,rx:r.radius,ry:r.radius,style:a}}],attributes:{xmlns:Vo,style:{overflow:"visible"},width:"100%",height:"100%"}};if(hasMargin(this[vs]()[vs]()))return HTMLResult.success({name:"div",attributes:{style:{display:"inline",width:"100%",height:"100%"}},children:[i]});i.attributes.style.position="absolute";return HTMLResult.success(i)}}class RefElement extends StringObject{constructor(e){super(Go,"ref");this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehr\
-ef||""}}class Script extends StringObject{constructor(e){super(Go,"script");this.binding=e.binding||"";this.contentType=e.contentType||"";this.id=e.id||"";this.name=e.name||"";this.runAt=getStringOption(e.runAt,["client","both","server"]);this.use=e.use||"";this.usehref=e.usehref||""}}class SetProperty extends XFAObject{constructor(e){super(Go,"setProperty");this.connection=e.connection||"";this.ref=e.ref||"";this.target=e.target||""}}class SignData extends XFAObject{constructor(e){super(Go,"sig\
-nData",!0);this.id=e.id||"";this.operation=getStringOption(e.operation,["sign","clear","verify"]);this.ref=e.ref||"";this.target=e.target||"";this.use=e.use||"";this.usehref=e.usehref||"";this.filter=null;this.manifest=null}}class Signature extends XFAObject{constructor(e){super(Go,"signature",!0);this.id=e.id||"";this.type=getStringOption(e.type,["PDF1.3","PDF1.6"]);this.use=e.use||"";this.usehref=e.usehref||"";this.border=null;this.extras=null;this.filter=null;this.manifest=null;this.margin=nu\
-ll}}class Signing extends XFAObject{constructor(e){super(Go,"signing",!0);this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||"";this.certificate=new XFAObjectArray}}class Solid extends XFAObject{constructor(e){super(Go,"solid",!0);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null}[ho](e){return e?e[ho]():"#FFFFFF"}}class Speak extends StringObject{constructor(e){super(Go,"speak");this.disable=getInte\
-ger({data:e.disable,defaultValue:0,validate:e=>1===e});this.id=e.id||"";this.priority=getStringOption(e.priority,["custom","caption","name","toolTip"]);this.rid=e.rid||"";this.use=e.use||"";this.usehref=e.usehref||""}}class Stipple extends XFAObject{constructor(e){super(Go,"stipple",!0);this.id=e.id||"";this.rate=getInteger({data:e.rate,defaultValue:50,validate:e=>e>=0&&e<=100});this.use=e.use||"";this.usehref=e.usehref||"";this.color=null;this.extras=null}[ho](e){const t=this.rate/100;return Ut\
-il.makeHexColor(Math.round(e.value.r*(1-t)+this.value.r*t),Math.round(e.value.g*(1-t)+this.value.g*t),Math.round(e.value.b*(1-t)+this.value.b*t))}}class Subform extends XFAObject{constructor(e){super(Go,"subform",!0);this.access=getStringOption(e.access,["open","nonInteractive","protected","readOnly"]);this.allowMacro=getInteger({data:e.allowMacro,defaultValue:0,validate:e=>1===e});this.anchorType=getStringOption(e.anchorType,["topLeft","bottomCenter","bottomLeft","bottomRight","middleCenter","m\
-iddleLeft","middleRight","topCenter","topRight"]);this.colSpan=getInteger({data:e.colSpan,defaultValue:1,validate:e=>e>=1||-1===e});this.columnWidths=(e.columnWidths||"").trim().split(/\\s+/).map((e=>"-1"===e?-1:getMeasurement(e)));this.h=e.h?getMeasurement(e.h):"";this.hAlign=getStringOption(e.hAlign,["left","center","justify","justifyAll","radix","right"]);this.id=e.id||"";this.layout=getStringOption(e.layout,["position","lr-tb","rl-row","rl-tb","row","table","tb"]);this.locale=e.locale||"";thi\
-s.maxH=getMeasurement(e.maxH,"0pt");this.maxW=getMeasurement(e.maxW,"0pt");this.mergeMode=getStringOption(e.mergeMode,["consumeData","matchTemplate"]);this.minH=getMeasurement(e.minH,"0pt");this.minW=getMeasurement(e.minW,"0pt");this.name=e.name||"";this.presence=getStringOption(e.presence,["visible","hidden","inactive","invisible"]);this.relevant=getRelevant(e.relevant);this.restoreState=getStringOption(e.restoreState,["manual","auto"]);this.scope=getStringOption(e.scope,["name","none"]);this.u\
-se=e.use||"";this.usehref=e.usehref||"";this.w=e.w?getMeasurement(e.w):"";this.x=getMeasurement(e.x,"0pt");this.y=getMeasurement(e.y,"0pt");this.assist=null;this.bind=null;this.bookend=null;this.border=null;this.break=null;this.calculate=null;this.desc=null;this.extras=null;this.keep=null;this.margin=null;this.occur=null;this.overflow=null;this.pageSet=null;this.para=null;this.traversal=null;this.validate=null;this.variables=null;this.area=new XFAObjectArray;this.breakAfter=new XFAObjectArray;th\
-is.breakBefore=new XFAObjectArray;this.connect=new XFAObjectArray;this.draw=new XFAObjectArray;this.event=new XFAObjectArray;this.exObject=new XFAObjectArray;this.exclGroup=new XFAObjectArray;this.field=new XFAObjectArray;this.proto=new XFAObjectArray;this.setProperty=new XFAObjectArray;this.subform=new XFAObjectArray;this.subformSet=new XFAObjectArray}[Cs](){const e=this[vs]();return e instanceof SubformSet?e[Cs]():e}[Rs](){return!0}[_s](){return this.layout.endsWith("-tb")&&0===this[ls].attemp\
-t&&this[ls].numberInLine>0||this[vs]()[_s]()}*[As](){yield*getContainedChildren(this)}[us](){return flushHTML(this)}[Zn](e,t){addHTML(this,e,t)}[gs](){return getAvailableSpace(this)}[js](){const e=this[Cs]();if(!e[js]())return!1;if(void 0!==this[ls]._isSplittable)return this[ls]._isSplittable;if("position"===this.layout||this.layout.includes("row")){this[ls]._isSplittable=!1;return!1}if(this.keep&&"none"!==this.keep.intact){this[ls]._isSplittable=!1;return!1}if(e.layout?.endsWith("-tb")&&0!==e[l\
-s].numberInLine)return!1;this[ls]._isSplittable=!0;return!0}[co](e){setTabIndex(this);if(this.break){if("auto"!==this.break.after||""!==this.break.afterTarget){const e=new BreakAfter({targetType:this.break.after,target:this.break.afterTarget,startNew:this.break.startNew.toString()});e[Is]=this[Is];this[Qn](e);this.breakAfter.push(e)}if("auto"!==this.break.before||""!==this.break.beforeTarget){const e=new BreakBefore({targetType:this.break.before,target:this.break.beforeTarget,startNew:this.break\
-.startNew.toString()});e[Is]=this[Is];this[Qn](e);this.breakBefore.push(e)}if(""!==this.break.overflowTarget){const e=new Overflow({target:this.break.overflowTarget,leader:this.break.overflowLeader,trailer:this.break.overflowTrailer});e[Is]=this[Is];this[Qn](e);this.overflow.push(e)}this[Zs](this.break);this.break=null}if("hidden"===this.presence||"inactive"===this.presence)return HTMLResult.EMPTY;(this.breakBefore.children.length>1||this.breakAfter.children.length>1)&&warn("XFA - Several breakB\
-efore or breakAfter in subforms: please file a bug.");if(this.breakBefore.children.length>=1){const e=this.breakBefore.children[0];if(handleBreak(e))return HTMLResult.breakNode(e)}if(this[ls]?.afterBreakAfter)return HTMLResult.EMPTY;fixDimensions(this);const t=[],a={id:this[uo],class:[]};setAccess(this,a.class);this[ls]||=Object.create(null);Object.assign(this[ls],{children:t,line:null,attributes:a,attempt:0,numberInLine:0,availableSpace:{width:Math.min(this.w||1/0,e.width),height:Math.min(this.\
-h||1/0,e.height)},width:0,height:0,prevHeight:0,currentWidth:0});const r=this[Fs](),i=r[ls].noLayoutFailure,n=this[js]();n||setFirstUnsplittable(this);if(!checkDimensions(this,e))return HTMLResult.FAILURE;const s=new Set(["area","draw","exclGroup","field","subform","subformSet"]);if(this.layout.includes("row")){const e=this[Cs]().columnWidths;if(Array.isArray(e)&&e.length>0){this[ls].columnWidths=e;this[ls].currentColumn=0}}const o=toStyle(this,"anchorType","dimensions","position","presence","bo\
-rder","margin","hAlign"),c=["xfaSubform"],l=layoutClass(this);l&&c.push(l);a.style=o;a.class=c;this.name&&(a.xfaName=this.name);if(this.overflow){const t=this.overflow[ws]();if(t.addLeader){t.addLeader=!1;handleOverflow(this,t.leader,e)}}this[Ys]();const h="lr-tb"===this.layout||"rl-tb"===this.layout,u=h?2:1;for(;this[ls].attempt<u;this[ls].attempt++){h&&1===this[ls].attempt&&(this[ls].numberInLine=0);const e=this[es]({filter:s,include:!0});if(e.success)break;if(e.isBreak()){this[Js]();return e}\
-if(h&&0===this[ls].attempt&&0===this[ls].numberInLine&&!r[ls].noLayoutFailure){this[ls].attempt=u;break}}this[Js]();n||unsetFirstUnsplittable(this);r[ls].noLayoutFailure=i;if(this[ls].attempt===u){this.overflow&&(this[Fs]()[ls].overflowNode=this.overflow);n||delete this[ls];return HTMLResult.FAILURE}if(this.overflow){const t=this.overflow[ws]();if(t.addTrailer){t.addTrailer=!1;handleOverflow(this,t.trailer,e)}}let d=0,f=0;if(this.margin){d=this.margin.leftInset+this.margin.rightInset;f=this.marg\
-in.topInset+this.margin.bottomInset}const g=Math.max(this[ls].width+d,this.w||0),p=Math.max(this[ls].height+f,this.h||0),m=[this.x,this.y,g,p];""===this.w&&(o.width=measureToString(g));""===this.h&&(o.height=measureToString(p));if(("0px"===o.width||"0px"===o.height)&&0===t.length)return HTMLResult.EMPTY;const b={name:"div",attributes:a,children:t};applyAssist(this,a);const y=HTMLResult.success(createWrapper(this,b),m);if(this.breakAfter.children.length>=1){const e=this.breakAfter.children[0];if(\
-handleBreak(e)){this[ls].afterBreakAfter=y;return HTMLResult.breakNode(e)}}delete this[ls];return y}}class SubformSet extends XFAObject{constructor(e){super(Go,"subformSet",!0);this.id=e.id||"";this.name=e.name||"";this.relation=getStringOption(e.relation,["ordered","choice","unordered"]);this.relevant=getRelevant(e.relevant);this.use=e.use||"";this.usehref=e.usehref||"";this.bookend=null;this.break=null;this.desc=null;this.extras=null;this.occur=null;this.overflow=null;this.breakAfter=new XFAOb\
-jectArray;this.breakBefore=new XFAObjectArray;this.subform=new XFAObjectArray;this.subformSet=new XFAObjectArray}*[As](){yield*getContainedChildren(this)}[Cs](){let e=this[vs]();for(;!(e instanceof Subform);)e=e[vs]();return e}[Rs](){return!0}}class SubjectDN extends ContentObject{constructor(e){super(Go,"subjectDN");this.delimiter=e.delimiter||",";this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}[hs](){this[ss]=new Map(this[ss].split(this.delimiter).map((e=>{(\
-e=e.split("=",2))[0]=e[0].trim();return e})))}}class SubjectDNs extends XFAObject{constructor(e){super(Go,"subjectDNs",!0);this.id=e.id||"";this.type=getStringOption(e.type,["optional","required"]);this.use=e.use||"";this.usehref=e.usehref||"";this.subjectDN=new XFAObjectArray}}class Submit extends XFAObject{constructor(e){super(Go,"submit",!0);this.embedPDF=getInteger({data:e.embedPDF,defaultValue:0,validate:e=>1===e});this.format=getStringOption(e.format,["xdp","formdata","pdf","urlencoded","x\
-fd","xml"]);this.id=e.id||"";this.target=e.target||"";this.textEncoding=getKeyword({data:e.textEncoding?e.textEncoding.toLowerCase():"",defaultValue:"",validate:e=>["utf-8","big-five","fontspecific","gbk","gb-18030","gb-2312","ksc-5601","none","shift-jis","ucs-2","utf-16"].includes(e)||e.match(/iso-8859-\\d{2}/)});this.use=e.use||"";this.usehref=e.usehref||"";this.xdpContent=e.xdpContent||"";this.encrypt=null;this.encryptData=new XFAObjectArray;this.signData=new XFAObjectArray}}class Template ext\
-ends XFAObject{constructor(e){super(Go,"template",!0);this.baseProfile=getStringOption(e.baseProfile,["full","interactiveForms"]);this.extras=null;this.subform=new XFAObjectArray}[hs](){0===this.subform.children.length&&warn("XFA - No subforms in template node.");this.subform.children.length>=2&&warn("XFA - Several subforms in template node: please file a bug.");this[no]=5e3}[js](){return!0}[to](e,t){return e.startsWith("#")?[this[Os].get(e.slice(1))]:searchNode(this,t,e,!0,!0)}*[oo](){if(!this.\
-subform.children.length)return HTMLResult.success({name:"div",children:[]});this[ls]={overflowNode:null,firstUnsplittable:null,currentContentArea:null,currentPageArea:null,noLayoutFailure:!1,pageNumber:1,pagePosition:"first",oddOrEven:"odd",blankOrNotBlank:"nonBlank",paraStack:[]};const e=this.subform.children[0];e.pageSet[as]();const t=e.pageSet.pageArea.children,a={name:"div",children:[]};let r=null,i=null,n=null;if(e.breakBefore.children.length>=1){i=e.breakBefore.children[0];n=i.target}else \
-if(e.subform.children.length>=1&&e.subform.children[0].breakBefore.children.length>=1){i=e.subform.children[0].breakBefore.children[0];n=i.target}else if(e.break?.beforeTarget){i=e.break;n=i.beforeTarget}else if(e.subform.children.length>=1&&e.subform.children[0].break?.beforeTarget){i=e.subform.children[0].break;n=i.beforeTarget}if(i){const e=this[to](n,i[vs]());if(e instanceof PageArea){r=e;i[ls]={}}}r||=t[0];r[ls]={numberOfUse:1};const s=r[vs]();s[ls]={numberOfUse:1,pageIndex:s.pageArea.child\
-ren.indexOf(r),pageSetIndex:0};let o,c=null,l=null,h=!0,u=0,d=0;for(;;){if(h)u=0;else{a.children.pop();if(3==++u){warn("XFA - Something goes wrong: please file a bug.");return a}}o=null;this[ls].currentPageArea=r;const t=r[co]().html;a.children.push(t);if(c){this[ls].noLayoutFailure=!0;t.children.push(c[co](r[ls].space).html);c=null}if(l){this[ls].noLayoutFailure=!0;t.children.push(l[co](r[ls].space).html);l=null}const i=r.contentArea.children,n=t.children.filter((e=>e.attributes.class.includes(\
-"xfaContentarea")));h=!1;this[ls].firstUnsplittable=null;this[ls].noLayoutFailure=!1;const flush=t=>{const a=e[us]();if(a){h||=a.children?.length>0;n[t].children.push(a)}};for(let t=d,r=i.length;t<r;t++){const r=this[ls].currentContentArea=i[t],s={width:r.w,height:r.h};d=0;if(c){n[t].children.push(c[co](s).html);c=null}if(l){n[t].children.push(l[co](s).html);l=null}const u=e[co](s);if(u.success){if(u.html){h||=u.html.children?.length>0;n[t].children.push(u.html)}else!h&&a.children.length>1&&a.ch\
-ildren.pop();return a}if(u.isBreak()){const e=u.breakNode;flush(t);if("auto"===e.targetType)continue;if(e.leader){c=this[to](e.leader,e[vs]());c=c?c[0]:null}if(e.trailer){l=this[to](e.trailer,e[vs]());l=l?l[0]:null}if("pageArea"===e.targetType){o=e[ls].target;t=1/0}else if(e[ls].target){o=e[ls].target;d=e[ls].index+1;t=1/0}else t=e[ls].index}else if(this[ls].overflowNode){const e=this[ls].overflowNode;this[ls].overflowNode=null;const a=e[ws](),r=a.target;a.addLeader=null!==a.leader;a.addTrailer=\
-null!==a.trailer;flush(t);const n=t;t=1/0;if(r instanceof PageArea)o=r;else if(r instanceof ContentArea){const e=i.indexOf(r);if(-1!==e)e>n?t=e-1:d=e;else{o=r[vs]();d=o.contentArea.children.indexOf(r)}}}else flush(t)}this[ls].pageNumber+=1;o&&(o[Xs]()?o[ls].numberOfUse+=1:o=null);r=o||r[ks]();yield null}}}class Text extends ContentObject{constructor(e){super(Go,"text");this.id=e.id||"";this.maxChars=getInteger({data:e.maxChars,defaultValue:0,validate:e=>e>=0});this.name=e.name||"";this.rid=e.rid\
-||"";this.use=e.use||"";this.usehref=e.usehref||""}[Yn](){return!0}[$s](e){if(e[Hs]===go.xhtml.id){this[ss]=e;return!0}warn(`XFA - Invalid content in Text: ${e[Ws]}.`);return!1}[Vs](e){this[ss]instanceof XFAObject||super[Vs](e)}[hs](){"string"==typeof this[ss]&&(this[ss]=this[ss].replaceAll("\\r\\n","\\n"))}[ws](){return"string"==typeof this[ss]?this[ss].split(/[\\u2029\\u2028\\n]/).filter((e=>!!e)).join("\\n"):this[ss][so]()}[co](e){if("string"==typeof this[ss]){const e=valueToHtml(this[ss]).html;if(t\
-his[ss].includes("\\u2029")){e.name="div";e.children=[];this[ss].split("\\u2029").map((e=>e.split(/[\\u2028\\n]/).flatMap((e=>[{name:"span",value:e},{name:"br"}])))).forEach((t=>{e.children.push({name:"p",children:t})}))}else if(/[\\u2028\\n]/.test(this[ss])){e.name="div";e.children=[];this[ss].split(/[\\u2028\\n]/).forEach((t=>{e.children.push({name:"span",value:t},{name:"br"})}))}return HTMLResult.success(e)}return this[ss][co](e)}}class TextEdit extends XFAObject{constructor(e){super(Go,"textEdit",!0\
-);this.allowRichText=getInteger({data:e.allowRichText,defaultValue:0,validate:e=>1===e});this.hScrollPolicy=getStringOption(e.hScrollPolicy,["auto","off","on"]);this.id=e.id||"";this.multiLine=getInteger({data:e.multiLine,defaultValue:"",validate:e=>0===e||1===e});this.use=e.use||"";this.usehref=e.usehref||"";this.vScrollPolicy=getStringOption(e.vScrollPolicy,["auto","off","on"]);this.border=null;this.comb=null;this.extras=null;this.margin=null}[co](e){const t=toStyle(this,"border","font","margi\
-n");let a;const r=this[vs]()[vs]();""===this.multiLine&&(this.multiLine=r instanceof Draw?1:0);a=1===this.multiLine?{name:"textarea",attributes:{dataId:r[os]?.[uo]||r[uo],fieldId:r[uo],class:["xfaTextfield"],style:t,"aria-label":ariaLabel(r),"aria-required":!1}}:{name:"input",attributes:{type:"text",dataId:r[os]?.[uo]||r[uo],fieldId:r[uo],class:["xfaTextfield"],style:t,"aria-label":ariaLabel(r),"aria-required":!1}};if(isRequired(r)){a.attributes["aria-required"]=!0;a.attributes.required=!0}retur\
-n HTMLResult.success({name:"label",attributes:{class:["xfaLabel"]},children:[a]})}}class Time extends StringObject{constructor(e){super(Go,"time");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}[hs](){const e=this[ss].trim();this[ss]=e?new Date(e):null}[co](e){return valueToHtml(this[ss]?this[ss].toString():"")}}class TimeStamp extends XFAObject{constructor(e){super(Go,"timeStamp");this.id=e.id||"";this.server=e.server||"";this.type=getStringOption(e.type,["o\
-ptional","required"]);this.use=e.use||"";this.usehref=e.usehref||""}}class ToolTip extends StringObject{constructor(e){super(Go,"toolTip");this.id=e.id||"";this.rid=e.rid||"";this.use=e.use||"";this.usehref=e.usehref||""}}class Traversal extends XFAObject{constructor(e){super(Go,"traversal",!0);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.traverse=new XFAObjectArray}}class Traverse extends XFAObject{constructor(e){super(Go,"traverse",!0);this.id=e.id||"";t\
-his.operation=getStringOption(e.operation,["next","back","down","first","left","right","up"]);this.ref=e.ref||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.script=null}get name(){return this.operation}[Us](){return!1}}class Ui extends XFAObject{constructor(e){super(Go,"ui",!0);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.picture=null;this.barcode=null;this.button=null;this.checkButton=null;this.choiceList=null;this.dateTimeEdit=nul\
-l;this.defaultUi=null;this.imageEdit=null;this.numericEdit=null;this.passwordEdit=null;this.signature=null;this.textEdit=null}[ws](){if(void 0===this[ls]){for(const e of Object.getOwnPropertyNames(this)){if("extras"===e||"picture"===e)continue;const t=this[e];if(t instanceof XFAObject){this[ls]=t;return t}}this[ls]=null}return this[ls]}[co](e){const t=this[ws]();return t?t[co](e):HTMLResult.EMPTY}}class Validate extends XFAObject{constructor(e){super(Go,"validate",!0);this.formatTest=getStringOp\
-tion(e.formatTest,["warning","disabled","error"]);this.id=e.id||"";this.nullTest=getStringOption(e.nullTest,["disabled","error","warning"]);this.scriptTest=getStringOption(e.scriptTest,["error","disabled","warning"]);this.use=e.use||"";this.usehref=e.usehref||"";this.extras=null;this.message=null;this.picture=null;this.script=null}}class Value extends XFAObject{constructor(e){super(Go,"value",!0);this.id=e.id||"";this.override=getInteger({data:e.override,defaultValue:0,validate:e=>1===e});this.r\
-elevant=getRelevant(e.relevant);this.use=e.use||"";this.usehref=e.usehref||"";this.arc=null;this.boolean=null;this.date=null;this.dateTime=null;this.decimal=null;this.exData=null;this.float=null;this.image=null;this.integer=null;this.line=null;this.rectangle=null;this.text=null;this.time=null}[io](e){const t=this[vs]();if(t instanceof Field&&t.ui?.imageEdit){if(!this.image){this.image=new Image({});this[Qn](this.image)}this.image[ss]=e[ss];return}const a=e[Ws];if(null===this[a]){for(const e of O\
-bject.getOwnPropertyNames(this)){const t=this[e];if(t instanceof XFAObject){this[e]=null;this[Zs](t)}}this[e[Ws]]=e;this[Qn](e)}else this[a][ss]=e[ss]}[so](){if(this.exData)return"string"==typeof this.exData[ss]?this.exData[ss].trim():this.exData[ss][so]().trim();for(const e of Object.getOwnPropertyNames(this)){if("image"===e)continue;const t=this[e];if(t instanceof XFAObject)return(t[ss]||"").toString().trim()}return null}[co](e){for(const t of Object.getOwnPropertyNames(this)){const a=this[t];\
-if(a instanceof XFAObject)return a[co](e)}return HTMLResult.EMPTY}}class Variables extends XFAObject{constructor(e){super(Go,"variables",!0);this.id=e.id||"";this.use=e.use||"";this.usehref=e.usehref||"";this.boolean=new XFAObjectArray;this.date=new XFAObjectArray;this.dateTime=new XFAObjectArray;this.decimal=new XFAObjectArray;this.exData=new XFAObjectArray;this.float=new XFAObjectArray;this.image=new XFAObjectArray;this.integer=new XFAObjectArray;this.manifest=new XFAObjectArray;this.script=ne\
-w XFAObjectArray;this.text=new XFAObjectArray;this.time=new XFAObjectArray}[Us](){return!0}}class TemplateNamespace{static[fo](e,t){if(TemplateNamespace.hasOwnProperty(e)){const a=TemplateNamespace[e](t);a[ro](t);return a}}static appearanceFilter(e){return new AppearanceFilter(e)}static arc(e){return new Arc(e)}static area(e){return new Area(e)}static assist(e){return new Assist(e)}static barcode(e){return new Barcode(e)}static bind(e){return new Bind(e)}static bindItems(e){return new BindItems(\
-e)}static bookend(e){return new Bookend(e)}static boolean(e){return new BooleanElement(e)}static border(e){return new Border(e)}static break(e){return new Break(e)}static breakAfter(e){return new BreakAfter(e)}static breakBefore(e){return new BreakBefore(e)}static button(e){return new Button(e)}static calculate(e){return new Calculate(e)}static caption(e){return new Caption(e)}static certificate(e){return new Certificate(e)}static certificates(e){return new Certificates(e)}static checkButton(e){\
-return new CheckButton(e)}static choiceList(e){return new ChoiceList(e)}static color(e){return new Color(e)}static comb(e){return new Comb(e)}static connect(e){return new Connect(e)}static contentArea(e){return new ContentArea(e)}static corner(e){return new Corner(e)}static date(e){return new DateElement(e)}static dateTime(e){return new DateTime(e)}static dateTimeEdit(e){return new DateTimeEdit(e)}static decimal(e){return new Decimal(e)}static defaultUi(e){return new DefaultUi(e)}static desc(e){\
-return new Desc(e)}static digestMethod(e){return new DigestMethod(e)}static digestMethods(e){return new DigestMethods(e)}static draw(e){return new Draw(e)}static edge(e){return new Edge(e)}static encoding(e){return new Encoding(e)}static encodings(e){return new Encodings(e)}static encrypt(e){return new Encrypt(e)}static encryptData(e){return new EncryptData(e)}static encryption(e){return new Encryption(e)}static encryptionMethod(e){return new EncryptionMethod(e)}static encryptionMethods(e){retur\
-n new EncryptionMethods(e)}static event(e){return new Event(e)}static exData(e){return new ExData(e)}static exObject(e){return new ExObject(e)}static exclGroup(e){return new ExclGroup(e)}static execute(e){return new Execute(e)}static extras(e){return new Extras(e)}static field(e){return new Field(e)}static fill(e){return new Fill(e)}static filter(e){return new Filter(e)}static float(e){return new Float(e)}static font(e){return new template_Font(e)}static format(e){return new Format(e)}static han\
-dler(e){return new Handler(e)}static hyphenation(e){return new Hyphenation(e)}static image(e){return new Image(e)}static imageEdit(e){return new ImageEdit(e)}static integer(e){return new Integer(e)}static issuers(e){return new Issuers(e)}static items(e){return new Items(e)}static keep(e){return new Keep(e)}static keyUsage(e){return new KeyUsage(e)}static line(e){return new Line(e)}static linear(e){return new Linear(e)}static lockDocument(e){return new LockDocument(e)}static manifest(e){return ne\
-w Manifest(e)}static margin(e){return new Margin(e)}static mdp(e){return new Mdp(e)}static medium(e){return new Medium(e)}static message(e){return new Message(e)}static numericEdit(e){return new NumericEdit(e)}static occur(e){return new Occur(e)}static oid(e){return new Oid(e)}static oids(e){return new Oids(e)}static overflow(e){return new Overflow(e)}static pageArea(e){return new PageArea(e)}static pageSet(e){return new PageSet(e)}static para(e){return new Para(e)}static passwordEdit(e){return \
-new PasswordEdit(e)}static pattern(e){return new template_Pattern(e)}static picture(e){return new Picture(e)}static proto(e){return new Proto(e)}static radial(e){return new Radial(e)}static reason(e){return new Reason(e)}static reasons(e){return new Reasons(e)}static rectangle(e){return new Rectangle(e)}static ref(e){return new RefElement(e)}static script(e){return new Script(e)}static setProperty(e){return new SetProperty(e)}static signData(e){return new SignData(e)}static signature(e){return n\
-ew Signature(e)}static signing(e){return new Signing(e)}static solid(e){return new Solid(e)}static speak(e){return new Speak(e)}static stipple(e){return new Stipple(e)}static subform(e){return new Subform(e)}static subformSet(e){return new SubformSet(e)}static subjectDN(e){return new SubjectDN(e)}static subjectDNs(e){return new SubjectDNs(e)}static submit(e){return new Submit(e)}static template(e){return new Template(e)}static text(e){return new Text(e)}static textEdit(e){return new TextEdit(e)}\
-static time(e){return new Time(e)}static timeStamp(e){return new TimeStamp(e)}static toolTip(e){return new ToolTip(e)}static traversal(e){return new Traversal(e)}static traverse(e){return new Traverse(e)}static ui(e){return new Ui(e)}static validate(e){return new Validate(e)}static value(e){return new Value(e)}static variables(e){return new Variables(e)}}const Zo=go.datasets.id;function createText(e){const t=new Text({});t[ss]=e;return t}class Binder{constructor(e){this.root=e;this.datasets=e.da\
-tasets;this.data=e.datasets?.data||new XmlObject(go.datasets.id,"data");this.emptyMerge=0===this.data[Ss]().length;this.root.form=this.form=e.template[is]()}_isConsumeData(){return!this.emptyMerge&&this._mergeMode}_isMatchTemplate(){return!this._isConsumeData()}bind(){this._bindElement(this.form,this.data);return this.form}getData(){return this.data}_bindValue(e,t,a){e[os]=t;if(e[Ts]())if(t[Ns]()){const a=t[ys]();e[io](createText(a))}else if(e instanceof Field&&"multiSelect"===e.ui?.choiceList?.\
-open){const a=t[Ss]().map((e=>e[ss].trim())).join("\\n");e[io](createText(a))}else this._isConsumeData()&&warn("XFA - Nodes haven\'t the same type.");else!t[Ns]()||this._isMatchTemplate()?this._bindElement(e,t):warn("XFA - Nodes haven\'t the same type.")}_findDataByNameToConsume(e,t,a,r){if(!e)return null;let i,n;for(let r=0;r<3;r++){i=a[xs](e,!1,!0);for(;;){n=i.next().value;if(!n)break;if(t===n[Ns]())return n}if(a[Hs]===go.datasets.id&&"data"===a[Ws])break;a=a[vs]()}if(!r)return null;i=this.data[x\
-s](e,!0,!1);n=i.next().value;if(n)return n;i=this.data[ds](e,!0);n=i.next().value;return n?.[Ns]()?n:null}_setProperties(e,t){if(e.hasOwnProperty("setProperty"))for(const{ref:a,target:r,connection:i}of e.setProperty.children){if(i)continue;if(!a)continue;const n=searchNode(this.root,t,a,!1,!1);if(!n){warn(`XFA - Invalid reference: ${a}.`);continue}const[s]=n;if(!s[Es](this.data)){warn("XFA - Invalid node: must be a data node.");continue}const o=searchNode(this.root,e,r,!1,!1);if(!o){warn(`XFA - \
-Invalid target: ${r}.`);continue}const[c]=o;if(!c[Es](e)){warn("XFA - Invalid target: must be a property or subproperty.");continue}const l=c[vs]();if(c instanceof SetProperty||l instanceof SetProperty){warn("XFA - Invalid target: cannot be a setProperty or one of its properties.");continue}if(c instanceof BindItems||l instanceof BindItems){warn("XFA - Invalid target: cannot be a bindItems or one of its properties.");continue}const h=s[so](),u=c[Ws];if(c instanceof XFAAttribute){const e=Object.c\
-reate(null);e[u]=h;const t=Reflect.construct(Object.getPrototypeOf(l).constructor,[e]);l[u]=t[u]}else if(c.hasOwnProperty(ss)){c[os]=s;c[ss]=h;c[hs]()}else warn("XFA - Invalid node to use in setProperty")}}_bindItems(e,t){if(!e.hasOwnProperty("items")||!e.hasOwnProperty("bindItems")||e.bindItems.isEmpty())return;for(const t of e.items.children)e[Zs](t);e.items.clear();const a=new Items({}),r=new Items({});e[Qn](a);e.items.push(a);e[Qn](r);e.items.push(r);for(const{ref:i,labelRef:n,valueRef:s,con\
-nection:o}of e.bindItems.children){if(o)continue;if(!i)continue;const e=searchNode(this.root,t,i,!1,!1);if(e)for(const t of e){if(!t[Es](this.datasets)){warn(`XFA - Invalid ref (${i}): must be a datasets child.`);continue}const e=searchNode(this.root,t,n,!0,!1);if(!e){warn(`XFA - Invalid label: ${n}.`);continue}const[o]=e;if(!o[Es](this.datasets)){warn("XFA - Invalid label: must be a datasets child.");continue}const c=searchNode(this.root,t,s,!0,!1);if(!c){warn(`XFA - Invalid value: ${s}.`);cont\
-inue}const[l]=c;if(!l[Es](this.datasets)){warn("XFA - Invalid value: must be a datasets child.");continue}const h=createText(o[so]()),u=createText(l[so]());a[Qn](h);a.text.push(h);r[Qn](u);r.text.push(u)}else warn(`XFA - Invalid reference: ${i}.`)}}_bindOccurrences(e,t,a){let r;if(t.length>1){r=e[is]();r[Zs](r.occur);r.occur=null}this._bindValue(e,t[0],a);this._setProperties(e,t[0]);this._bindItems(e,t[0]);if(1===t.length)return;const i=e[vs](),n=e[Ws],s=i[Ms](e);for(let e=1,o=t.length;e<o;e++){\
-const o=t[e],c=r[is]();i[n].push(c);i[Ds](s+e,c);this._bindValue(c,o,a);this._setProperties(c,o);this._bindItems(c,o)}}_createOccurrences(e){if(!this.emptyMerge)return;const{occur:t}=e;if(!t||t.initial<=1)return;const a=e[vs](),r=e[Ws];if(!(a[r]instanceof XFAObjectArray))return;let i;i=e.name?a[r].children.filter((t=>t.name===e.name)).length:a[r].children.length;const n=a[Ms](e)+1,s=t.initial-i;if(s){const t=e[is]();t[Zs](t.occur);t.occur=null;a[r].push(t);a[Ds](n,t);for(let e=1;e<s;e++){const i\
-=t[is]();a[r].push(i);a[Ds](n+e,i)}}}_getOccurInfo(e){const{name:t,occur:a}=e;if(!a||!t)return[1,1];const r=-1===a.max?1/0:a.max;return[a.min,r]}_setAndBind(e,t){this._setProperties(e,t);this._bindItems(e,t);this._bindElement(e,t)}_bindElement(e,t){const a=[];this._createOccurrences(e);for(const r of e[Ss]()){if(r[os])continue;if(void 0===this._mergeMode&&"subform"===r[Ws]){this._mergeMode="consumeData"===r.mergeMode;const e=t[Ss]();if(e.length>0)this._bindOccurrences(r,[e[0]],null);else if(this\
-.emptyMerge){const e=t[Hs]===Zo?-1:t[Hs],a=r[os]=new XmlObject(e,r.name||"root");t[Qn](a);this._bindElement(r,a)}continue}if(!r[Rs]())continue;let e=!1,i=null,n=null,s=null;if(r.bind){switch(r.bind.match){case"none":this._setAndBind(r,t);continue;case"global":e=!0;break;case"dataRef":if(!r.bind.ref){warn(`XFA - ref is empty in node ${r[Ws]}.`);this._setAndBind(r,t);continue}n=r.bind.ref}r.bind.picture&&(i=r.bind.picture[ss])}const[o,c]=this._getOccurInfo(r);if(n){s=searchNode(this.root,t,n,!0,!1\
-);if(null===s){s=createDataNode(this.data,t,n);if(!s)continue;this._isConsumeData()&&(s[ns]=!0);this._setAndBind(r,s);continue}this._isConsumeData()&&(s=s.filter((e=>!e[ns])));s.length>c?s=s.slice(0,c):0===s.length&&(s=null);s&&this._isConsumeData()&&s.forEach((e=>{e[ns]=!0}))}else{if(!r.name){this._setAndBind(r,t);continue}if(this._isConsumeData()){const a=[];for(;a.length<c;){const i=this._findDataByNameToConsume(r.name,r[Ts](),t,e);if(!i)break;i[ns]=!0;a.push(i)}s=a.length>0?a:null}else{s=t[x\
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-){super(Qo,"attributes",["preserve","delegate","ignore"])}}class AutoSave extends OptionObject{constructor(e){super(Qo,"autoSave",["disabled","enabled"])}}class Base extends StringObject{constructor(e){super(Qo,"base")}}class BatchOutput extends XFAObject{constructor(e){super(Qo,"batchOutput");this.format=getStringOption(e.format,["none","concat","zip","zipCompress"])}}class BehaviorOverride extends ContentObject{constructor(e){super(Qo,"behaviorOverride")}[hs](){this[ss]=new Map(this[ss].trim()\
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-Object{constructor(e){super(Qo,"encryption",!0);this.encrypt=null;this.encryptionLevel=null;this.permissions=null}}class EncryptionLevel extends OptionObject{constructor(e){super(Qo,"encryptionLevel",["40bit","128bit"])}}class Enforce extends StringObject{constructor(e){super(Qo,"enforce")}}class Equate extends XFAObject{constructor(e){super(Qo,"equate");this.force=getInteger({data:e.force,defaultValue:1,validate:e=>0===e});this.from=e.from||"";this.to=e.to||""}}class EquateRange extends XFAObje\
-ct{constructor(e){super(Qo,"equateRange");this.from=e.from||"";this.to=e.to||"";this._unicodeRange=e.unicodeRange||""}get unicodeRange(){const e=[],t=/U\\+([0-9a-fA-F]+)/,a=this._unicodeRange;for(let r of a.split(",").map((e=>e.trim())).filter((e=>!!e))){r=r.split("-",2).map((e=>{const a=e.match(t);return a?parseInt(a[1],16):0}));1===r.length&&r.push(r[0]);e.push(r)}return shadow(this,"unicodeRange",e)}}class Exclude extends ContentObject{constructor(e){super(Qo,"exclude")}[hs](){this[ss]=this[ss\
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-t{constructor(e){super(Qo,"openAction",!0);this.destination=null}}class Output extends XFAObject{constructor(e){super(Qo,"output",!0);this.to=null;this.type=null;this.uri=null}}class OutputBin extends StringObject{constructor(e){super(Qo,"outputBin")}}class OutputXSL extends XFAObject{constructor(e){super(Qo,"outputXSL",!0);this.uri=null}}class Overprint extends OptionObject{constructor(e){super(Qo,"overprint",["none","both","draw","field"])}}class Packets extends StringObject{constructor(e){sup\
-er(Qo,"packets")}[hs](){"*"!==this[ss]&&(this[ss]=this[ss].trim().split(/\\s+/).filter((e=>["config","datasets","template","xfdf","xslt"].includes(e))))}}class PageOffset extends XFAObject{constructor(e){super(Qo,"pageOffset");this.x=getInteger({data:e.x,defaultValue:"useXDCSetting",validate:e=>!0});this.y=getInteger({data:e.y,defaultValue:"useXDCSetting",validate:e=>!0})}}class PageRange extends StringObject{constructor(e){super(Qo,"pageRange")}[hs](){const e=this[ss].trim().split(/\\s+/).map((e=\
->parseInt(e,10))),t=[];for(let a=0,r=e.length;a<r;a+=2)t.push(e.slice(a,a+2));this[ss]=t}}class Pagination extends OptionObject{constructor(e){super(Qo,"pagination",["simplex","duplexShortEdge","duplexLongEdge"])}}class PaginationOverride extends OptionObject{constructor(e){super(Qo,"paginationOverride",["none","forceDuplex","forceDuplexLongEdge","forceDuplexShortEdge","forceSimplex"])}}class Part extends IntegerObject{constructor(e){super(Qo,"part",1,(e=>!1))}}class Pcl extends XFAObject{constr\
-uctor(e){super(Qo,"pcl",!0);this.name=e.name||"";this.batchOutput=null;this.fontInfo=null;this.jog=null;this.mediumInfo=null;this.outputBin=null;this.pageOffset=null;this.staple=null;this.xdc=null}}class Pdf extends XFAObject{constructor(e){super(Qo,"pdf",!0);this.name=e.name||"";this.adobeExtensionLevel=null;this.batchOutput=null;this.compression=null;this.creator=null;this.encryption=null;this.fontInfo=null;this.interactive=null;this.linearized=null;this.openAction=null;this.pdfa=null;this.pro\
-ducer=null;this.renderPolicy=null;this.scriptModel=null;this.silentPrint=null;this.submitFormat=null;this.tagged=null;this.version=null;this.viewerPreferences=null;this.xdc=null}}class Pdfa extends XFAObject{constructor(e){super(Qo,"pdfa",!0);this.amd=null;this.conformance=null;this.includeXDPContent=null;this.part=null}}class Permissions extends XFAObject{constructor(e){super(Qo,"permissions",!0);this.accessibleContent=null;this.change=null;this.contentCopy=null;this.documentAssembly=null;this.\
-formFieldFilling=null;this.modifyAnnots=null;this.plaintextMetadata=null;this.print=null;this.printHighQuality=null}}class PickTrayByPDFSize extends Option01{constructor(e){super(Qo,"pickTrayByPDFSize")}}class config_Picture extends StringObject{constructor(e){super(Qo,"picture")}}class PlaintextMetadata extends Option01{constructor(e){super(Qo,"plaintextMetadata")}}class Presence extends OptionObject{constructor(e){super(Qo,"presence",["preserve","dissolve","dissolveStructure","ignore","remove"\
-])}}class Present extends XFAObject{constructor(e){super(Qo,"present",!0);this.behaviorOverride=null;this.cache=null;this.common=null;this.copies=null;this.destination=null;this.incrementalMerge=null;this.layout=null;this.output=null;this.overprint=null;this.pagination=null;this.paginationOverride=null;this.script=null;this.validate=null;this.xdp=null;this.driver=new XFAObjectArray;this.labelPrinter=new XFAObjectArray;this.pcl=new XFAObjectArray;this.pdf=new XFAObjectArray;this.ps=new XFAObjectA\
-rray;this.submitUrl=new XFAObjectArray;this.webClient=new XFAObjectArray;this.zpl=new XFAObjectArray}}class Print extends Option01{constructor(e){super(Qo,"print")}}class PrintHighQuality extends Option01{constructor(e){super(Qo,"printHighQuality")}}class PrintScaling extends OptionObject{constructor(e){super(Qo,"printScaling",["appdefault","noScaling"])}}class PrinterName extends StringObject{constructor(e){super(Qo,"printerName")}}class Producer extends StringObject{constructor(e){super(Qo,"pr\
-oducer")}}class Ps extends XFAObject{constructor(e){super(Qo,"ps",!0);this.name=e.name||"";this.batchOutput=null;this.fontInfo=null;this.jog=null;this.mediumInfo=null;this.outputBin=null;this.staple=null;this.xdc=null}}class Range extends ContentObject{constructor(e){super(Qo,"range")}[hs](){this[ss]=this[ss].split(",",2).map((e=>e.split("-").map((e=>parseInt(e.trim(),10))))).filter((e=>e.every((e=>!isNaN(e))))).map((e=>{1===e.length&&e.push(e[0]);return e}))}}class Record extends ContentObject{\
-constructor(e){super(Qo,"record")}[hs](){this[ss]=this[ss].trim();const e=parseInt(this[ss],10);!isNaN(e)&&e>=0&&(this[ss]=e)}}class Relevant extends ContentObject{constructor(e){super(Qo,"relevant")}[hs](){this[ss]=this[ss].trim().split(/\\s+/)}}class Rename extends ContentObject{constructor(e){super(Qo,"rename")}[hs](){this[ss]=this[ss].trim();(this[ss].toLowerCase().startsWith("xml")||new RegExp("[\\\\p{L}_][\\\\p{L}\\\\d._\\\\p{M}-]*","u").test(this[ss]))&&warn("XFA - Rename: invalid XFA name")}}clas\
-s RenderPolicy extends OptionObject{constructor(e){super(Qo,"renderPolicy",["server","client"])}}class RunScripts extends OptionObject{constructor(e){super(Qo,"runScripts",["both","client","none","server"])}}class config_Script extends XFAObject{constructor(e){super(Qo,"script",!0);this.currentPage=null;this.exclude=null;this.runScripts=null}}class ScriptModel extends OptionObject{constructor(e){super(Qo,"scriptModel",["XFA","none"])}}class Severity extends OptionObject{constructor(e){super(Qo,"\
-severity",["ignore","error","information","trace","warning"])}}class SilentPrint extends XFAObject{constructor(e){super(Qo,"silentPrint",!0);this.addSilentPrint=null;this.printerName=null}}class Staple extends XFAObject{constructor(e){super(Qo,"staple");this.mode=getStringOption(e.mode,["usePrinterSetting","on","off"])}}class StartNode extends StringObject{constructor(e){super(Qo,"startNode")}}class StartPage extends IntegerObject{constructor(e){super(Qo,"startPage",0,(e=>!0))}}class SubmitForma\
-t extends OptionObject{constructor(e){super(Qo,"submitFormat",["html","delegate","fdf","xml","pdf"])}}class SubmitUrl extends StringObject{constructor(e){super(Qo,"submitUrl")}}class SubsetBelow extends IntegerObject{constructor(e){super(Qo,"subsetBelow",100,(e=>e>=0&&e<=100))}}class SuppressBanner extends Option01{constructor(e){super(Qo,"suppressBanner")}}class Tagged extends Option01{constructor(e){super(Qo,"tagged")}}class config_Template extends XFAObject{constructor(e){super(Qo,"template",\
-!0);this.base=null;this.relevant=null;this.startPage=null;this.uri=null;this.xsl=null}}class Threshold extends OptionObject{constructor(e){super(Qo,"threshold",["trace","error","information","warning"])}}class To extends OptionObject{constructor(e){super(Qo,"to",["null","memory","stderr","stdout","system","uri"])}}class TemplateCache extends XFAObject{constructor(e){super(Qo,"templateCache");this.maxEntries=getInteger({data:e.maxEntries,defaultValue:5,validate:e=>e>=0})}}class Trace extends XFAO\
-bject{constructor(e){super(Qo,"trace",!0);this.area=new XFAObjectArray}}class Transform extends XFAObject{constructor(e){super(Qo,"transform",!0);this.groupParent=null;this.ifEmpty=null;this.nameAttr=null;this.picture=null;this.presence=null;this.rename=null;this.whitespace=null}}class Type extends OptionObject{constructor(e){super(Qo,"type",["none","ascii85","asciiHex","ccittfax","flate","lzw","runLength","native","xdp","mergedXDP"])}}class Uri extends StringObject{constructor(e){super(Qo,"uri"\
-)}}class config_Validate extends OptionObject{constructor(e){super(Qo,"validate",["preSubmit","prePrint","preExecute","preSave"])}}class ValidateApprovalSignatures extends ContentObject{constructor(e){super(Qo,"validateApprovalSignatures")}[hs](){this[ss]=this[ss].trim().split(/\\s+/).filter((e=>["docReady","postSign"].includes(e)))}}class ValidationMessaging extends OptionObject{constructor(e){super(Qo,"validationMessaging",["allMessagesIndividually","allMessagesTogether","firstMessageOnly","noM\
-essages"])}}class Version extends OptionObject{constructor(e){super(Qo,"version",["1.7","1.6","1.5","1.4","1.3","1.2"])}}class VersionControl extends XFAObject{constructor(e){super(Qo,"VersionControl");this.outputBelow=getStringOption(e.outputBelow,["warn","error","update"]);this.sourceAbove=getStringOption(e.sourceAbove,["warn","error"]);this.sourceBelow=getStringOption(e.sourceBelow,["update","maintain"])}}class ViewerPreferences extends XFAObject{constructor(e){super(Qo,"viewerPreferences",!0\
-);this.ADBE_JSConsole=null;this.ADBE_JSDebugger=null;this.addViewerPreferences=null;this.duplexOption=null;this.enforce=null;this.numberOfCopies=null;this.pageRange=null;this.pickTrayByPDFSize=null;this.printScaling=null}}class WebClient extends XFAObject{constructor(e){super(Qo,"webClient",!0);this.name=e.name?e.name.trim():"";this.fontInfo=null;this.xdc=null}}class Whitespace extends OptionObject{constructor(e){super(Qo,"whitespace",["preserve","ltrim","normalize","rtrim","trim"])}}class Windo\
-w extends ContentObject{constructor(e){super(Qo,"window")}[hs](){const e=this[ss].split(",",2).map((e=>parseInt(e.trim(),10)));if(e.some((e=>isNaN(e))))this[ss]=[0,0];else{1===e.length&&e.push(e[0]);this[ss]=e}}}class Xdc extends XFAObject{constructor(e){super(Qo,"xdc",!0);this.uri=new XFAObjectArray;this.xsl=new XFAObjectArray}}class Xdp extends XFAObject{constructor(e){super(Qo,"xdp",!0);this.packets=null}}class Xsl extends XFAObject{constructor(e){super(Qo,"xsl",!0);this.debug=null;this.uri=n\
-ull}}class Zpl extends XFAObject{constructor(e){super(Qo,"zpl",!0);this.name=e.name?e.name.trim():"";this.batchOutput=null;this.flipLabel=null;this.fontInfo=null;this.xdc=null}}class ConfigNamespace{static[fo](e,t){if(ConfigNamespace.hasOwnProperty(e))return ConfigNamespace[e](t)}static acrobat(e){return new Acrobat(e)}static acrobat7(e){return new Acrobat7(e)}static ADBE_JSConsole(e){return new ADBE_JSConsole(e)}static ADBE_JSDebugger(e){return new ADBE_JSDebugger(e)}static addSilentPrint(e){re\
-turn new AddSilentPrint(e)}static addViewerPreferences(e){return new AddViewerPreferences(e)}static adjustData(e){return new AdjustData(e)}static adobeExtensionLevel(e){return new AdobeExtensionLevel(e)}static agent(e){return new Agent(e)}static alwaysEmbed(e){return new AlwaysEmbed(e)}static amd(e){return new Amd(e)}static area(e){return new config_Area(e)}static attributes(e){return new Attributes(e)}static autoSave(e){return new AutoSave(e)}static base(e){return new Base(e)}static batchOutput\
-(e){return new BatchOutput(e)}static behaviorOverride(e){return new BehaviorOverride(e)}static cache(e){return new Cache(e)}static change(e){return new Change(e)}static common(e){return new Common(e)}static compress(e){return new Compress(e)}static compressLogicalStructure(e){return new CompressLogicalStructure(e)}static compressObjectStream(e){return new CompressObjectStream(e)}static compression(e){return new Compression(e)}static config(e){return new Config(e)}static conformance(e){return new\
- Conformance(e)}static contentCopy(e){return new ContentCopy(e)}static copies(e){return new Copies(e)}static creator(e){return new Creator(e)}static currentPage(e){return new CurrentPage(e)}static data(e){return new Data(e)}static debug(e){return new Debug(e)}static defaultTypeface(e){return new DefaultTypeface(e)}static destination(e){return new Destination(e)}static documentAssembly(e){return new DocumentAssembly(e)}static driver(e){return new Driver(e)}static duplexOption(e){return new Duplex\
-Option(e)}static dynamicRender(e){return new DynamicRender(e)}static embed(e){return new Embed(e)}static encrypt(e){return new config_Encrypt(e)}static encryption(e){return new config_Encryption(e)}static encryptionLevel(e){return new EncryptionLevel(e)}static enforce(e){return new Enforce(e)}static equate(e){return new Equate(e)}static equateRange(e){return new EquateRange(e)}static exclude(e){return new Exclude(e)}static excludeNS(e){return new ExcludeNS(e)}static flipLabel(e){return new FlipL\
-abel(e)}static fontInfo(e){return new config_FontInfo(e)}static formFieldFilling(e){return new FormFieldFilling(e)}static groupParent(e){return new GroupParent(e)}static ifEmpty(e){return new IfEmpty(e)}static includeXDPContent(e){return new IncludeXDPContent(e)}static incrementalLoad(e){return new IncrementalLoad(e)}static incrementalMerge(e){return new IncrementalMerge(e)}static interactive(e){return new Interactive(e)}static jog(e){return new Jog(e)}static labelPrinter(e){return new LabelPrin\
-ter(e)}static layout(e){return new Layout(e)}static level(e){return new Level(e)}static linearized(e){return new Linearized(e)}static locale(e){return new Locale(e)}static localeSet(e){return new LocaleSet(e)}static log(e){return new Log(e)}static map(e){return new MapElement(e)}static mediumInfo(e){return new MediumInfo(e)}static message(e){return new config_Message(e)}static messaging(e){return new Messaging(e)}static mode(e){return new Mode(e)}static modifyAnnots(e){return new ModifyAnnots(e)\
-}static msgId(e){return new MsgId(e)}static nameAttr(e){return new NameAttr(e)}static neverEmbed(e){return new NeverEmbed(e)}static numberOfCopies(e){return new NumberOfCopies(e)}static openAction(e){return new OpenAction(e)}static output(e){return new Output(e)}static outputBin(e){return new OutputBin(e)}static outputXSL(e){return new OutputXSL(e)}static overprint(e){return new Overprint(e)}static packets(e){return new Packets(e)}static pageOffset(e){return new PageOffset(e)}static pageRange(e)\
-{return new PageRange(e)}static pagination(e){return new Pagination(e)}static paginationOverride(e){return new PaginationOverride(e)}static part(e){return new Part(e)}static pcl(e){return new Pcl(e)}static pdf(e){return new Pdf(e)}static pdfa(e){return new Pdfa(e)}static permissions(e){return new Permissions(e)}static pickTrayByPDFSize(e){return new PickTrayByPDFSize(e)}static picture(e){return new config_Picture(e)}static plaintextMetadata(e){return new PlaintextMetadata(e)}static presence(e){r\
-eturn new Presence(e)}static present(e){return new Present(e)}static print(e){return new Print(e)}static printHighQuality(e){return new PrintHighQuality(e)}static printScaling(e){return new PrintScaling(e)}static printerName(e){return new PrinterName(e)}static producer(e){return new Producer(e)}static ps(e){return new Ps(e)}static range(e){return new Range(e)}static record(e){return new Record(e)}static relevant(e){return new Relevant(e)}static rename(e){return new Rename(e)}static renderPolicy(\
-e){return new RenderPolicy(e)}static runScripts(e){return new RunScripts(e)}static script(e){return new config_Script(e)}static scriptModel(e){return new ScriptModel(e)}static severity(e){return new Severity(e)}static silentPrint(e){return new SilentPrint(e)}static staple(e){return new Staple(e)}static startNode(e){return new StartNode(e)}static startPage(e){return new StartPage(e)}static submitFormat(e){return new SubmitFormat(e)}static submitUrl(e){return new SubmitUrl(e)}static subsetBelow(e)\
-{return new SubsetBelow(e)}static suppressBanner(e){return new SuppressBanner(e)}static tagged(e){return new Tagged(e)}static template(e){return new config_Template(e)}static templateCache(e){return new TemplateCache(e)}static threshold(e){return new Threshold(e)}static to(e){return new To(e)}static trace(e){return new Trace(e)}static transform(e){return new Transform(e)}static type(e){return new Type(e)}static uri(e){return new Uri(e)}static validate(e){return new config_Validate(e)}static vali\
-dateApprovalSignatures(e){return new ValidateApprovalSignatures(e)}static validationMessaging(e){return new ValidationMessaging(e)}static version(e){return new Version(e)}static versionControl(e){return new VersionControl(e)}static viewerPreferences(e){return new ViewerPreferences(e)}static webClient(e){return new WebClient(e)}static whitespace(e){return new Whitespace(e)}static window(e){return new Window(e)}static xdc(e){return new Xdc(e)}static xdp(e){return new Xdp(e)}static xsl(e){return ne\
-w Xsl(e)}static zpl(e){return new Zpl(e)}}const ec=go.connectionSet.id;class ConnectionSet extends XFAObject{constructor(e){super(ec,"connectionSet",!0);this.wsdlConnection=new XFAObjectArray;this.xmlConnection=new XFAObjectArray;this.xsdConnection=new XFAObjectArray}}class EffectiveInputPolicy extends XFAObject{constructor(e){super(ec,"effectiveInputPolicy");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class EffectiveOutputPolicy extends XFAObject{constru\
-ctor(e){super(ec,"effectiveOutputPolicy");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class Operation extends StringObject{constructor(e){super(ec,"operation");this.id=e.id||"";this.input=e.input||"";this.name=e.name||"";this.output=e.output||"";this.use=e.use||"";this.usehref=e.usehref||""}}class RootElement extends StringObject{constructor(e){super(ec,"rootElement");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}cla\
-ss SoapAction extends StringObject{constructor(e){super(ec,"soapAction");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class SoapAddress extends StringObject{constructor(e){super(ec,"soapAddress");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class connection_set_Uri extends StringObject{constructor(e){super(ec,"uri");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class WsdlAddress\
- extends StringObject{constructor(e){super(ec,"wsdlAddress");this.id=e.id||"";this.name=e.name||"";this.use=e.use||"";this.usehref=e.usehref||""}}class WsdlConnection extends XFAObject{constructor(e){super(ec,"wsdlConnection",!0);this.dataDescription=e.dataDescription||"";this.name=e.name||"";this.effectiveInputPolicy=null;this.effectiveOutputPolicy=null;this.operation=null;this.soapAction=null;this.soapAddress=null;this.wsdlAddress=null}}class XmlConnection extends XFAObject{constructor(e){supe\
-r(ec,"xmlConnection",!0);this.dataDescription=e.dataDescription||"";this.name=e.name||"";this.uri=null}}class XsdConnection extends XFAObject{constructor(e){super(ec,"xsdConnection",!0);this.dataDescription=e.dataDescription||"";this.name=e.name||"";this.rootElement=null;this.uri=null}}class ConnectionSetNamespace{static[fo](e,t){if(ConnectionSetNamespace.hasOwnProperty(e))return ConnectionSetNamespace[e](t)}static connectionSet(e){return new ConnectionSet(e)}static effectiveInputPolicy(e){retur\
-n new EffectiveInputPolicy(e)}static effectiveOutputPolicy(e){return new EffectiveOutputPolicy(e)}static operation(e){return new Operation(e)}static rootElement(e){return new RootElement(e)}static soapAction(e){return new SoapAction(e)}static soapAddress(e){return new SoapAddress(e)}static uri(e){return new connection_set_Uri(e)}static wsdlAddress(e){return new WsdlAddress(e)}static wsdlConnection(e){return new WsdlConnection(e)}static xmlConnection(e){return new XmlConnection(e)}static xsdConne\
-ction(e){return new XsdConnection(e)}}const tc=go.datasets.id;class datasets_Data extends XmlObject{constructor(e){super(tc,"data",e)}[Ls](){return!0}}class Datasets extends XFAObject{constructor(e){super(tc,"datasets",!0);this.data=null;this.Signature=null}[$s](e){const t=e[Ws];("data"===t&&e[Hs]===tc||"Signature"===t&&e[Hs]===go.signature.id)&&(this[t]=e);this[Qn](e)}}class DatasetsNamespace{static[fo](e,t){if(DatasetsNamespace.hasOwnProperty(e))return DatasetsNamespace[e](t)}static datasets(e\
-){return new Datasets(e)}static data(e){return new datasets_Data(e)}}const ac=go.localeSet.id;class CalendarSymbols extends XFAObject{constructor(e){super(ac,"calendarSymbols",!0);this.name="gregorian";this.dayNames=new XFAObjectArray(2);this.eraNames=null;this.meridiemNames=null;this.monthNames=new XFAObjectArray(2)}}class CurrencySymbol extends StringObject{constructor(e){super(ac,"currencySymbol");this.name=getStringOption(e.name,["symbol","isoname","decimal"])}}class CurrencySymbols extends \
-XFAObject{constructor(e){super(ac,"currencySymbols",!0);this.currencySymbol=new XFAObjectArray(3)}}class DatePattern extends StringObject{constructor(e){super(ac,"datePattern");this.name=getStringOption(e.name,["full","long","med","short"])}}class DatePatterns extends XFAObject{constructor(e){super(ac,"datePatterns",!0);this.datePattern=new XFAObjectArray(4)}}class DateTimeSymbols extends ContentObject{constructor(e){super(ac,"dateTimeSymbols")}}class Day extends StringObject{constructor(e){supe\
-r(ac,"day")}}class DayNames extends XFAObject{constructor(e){super(ac,"dayNames",!0);this.abbr=getInteger({data:e.abbr,defaultValue:0,validate:e=>1===e});this.day=new XFAObjectArray(7)}}class Era extends StringObject{constructor(e){super(ac,"era")}}class EraNames extends XFAObject{constructor(e){super(ac,"eraNames",!0);this.era=new XFAObjectArray(2)}}class locale_set_Locale extends XFAObject{constructor(e){super(ac,"locale",!0);this.desc=e.desc||"";this.name="isoname";this.calendarSymbols=null;t\
-his.currencySymbols=null;this.datePatterns=null;this.dateTimeSymbols=null;this.numberPatterns=null;this.numberSymbols=null;this.timePatterns=null;this.typeFaces=null}}class locale_set_LocaleSet extends XFAObject{constructor(e){super(ac,"localeSet",!0);this.locale=new XFAObjectArray}}class Meridiem extends StringObject{constructor(e){super(ac,"meridiem")}}class MeridiemNames extends XFAObject{constructor(e){super(ac,"meridiemNames",!0);this.meridiem=new XFAObjectArray(2)}}class Month extends Stri\
-ngObject{constructor(e){super(ac,"month")}}class MonthNames extends XFAObject{constructor(e){super(ac,"monthNames",!0);this.abbr=getInteger({data:e.abbr,defaultValue:0,validate:e=>1===e});this.month=new XFAObjectArray(12)}}class NumberPattern extends StringObject{constructor(e){super(ac,"numberPattern");this.name=getStringOption(e.name,["full","long","med","short"])}}class NumberPatterns extends XFAObject{constructor(e){super(ac,"numberPatterns",!0);this.numberPattern=new XFAObjectArray(4)}}clas\
-s NumberSymbol extends StringObject{constructor(e){super(ac,"numberSymbol");this.name=getStringOption(e.name,["decimal","grouping","percent","minus","zero"])}}class NumberSymbols extends XFAObject{constructor(e){super(ac,"numberSymbols",!0);this.numberSymbol=new XFAObjectArray(5)}}class TimePattern extends StringObject{constructor(e){super(ac,"timePattern");this.name=getStringOption(e.name,["full","long","med","short"])}}class TimePatterns extends XFAObject{constructor(e){super(ac,"timePatterns"\
-,!0);this.timePattern=new XFAObjectArray(4)}}class TypeFace extends XFAObject{constructor(e){super(ac,"typeFace",!0);this.name=""|e.name}}class TypeFaces extends XFAObject{constructor(e){super(ac,"typeFaces",!0);this.typeFace=new XFAObjectArray}}class LocaleSetNamespace{static[fo](e,t){if(LocaleSetNamespace.hasOwnProperty(e))return LocaleSetNamespace[e](t)}static calendarSymbols(e){return new CalendarSymbols(e)}static currencySymbol(e){return new CurrencySymbol(e)}static currencySymbols(e){retur\
-n new CurrencySymbols(e)}static datePattern(e){return new DatePattern(e)}static datePatterns(e){return new DatePatterns(e)}static dateTimeSymbols(e){return new DateTimeSymbols(e)}static day(e){return new Day(e)}static dayNames(e){return new DayNames(e)}static era(e){return new Era(e)}static eraNames(e){return new EraNames(e)}static locale(e){return new locale_set_Locale(e)}static localeSet(e){return new locale_set_LocaleSet(e)}static meridiem(e){return new Meridiem(e)}static meridiemNames(e){ret\
-urn new MeridiemNames(e)}static month(e){return new Month(e)}static monthNames(e){return new MonthNames(e)}static numberPattern(e){return new NumberPattern(e)}static numberPatterns(e){return new NumberPatterns(e)}static numberSymbol(e){return new NumberSymbol(e)}static numberSymbols(e){return new NumberSymbols(e)}static timePattern(e){return new TimePattern(e)}static timePatterns(e){return new TimePatterns(e)}static typeFace(e){return new TypeFace(e)}static typeFaces(e){return new TypeFaces(e)}}\
-const rc=go.signature.id;class signature_Signature extends XFAObject{constructor(e){super(rc,"signature",!0)}}class SignatureNamespace{static[fo](e,t){if(SignatureNamespace.hasOwnProperty(e))return SignatureNamespace[e](t)}static signature(e){return new signature_Signature(e)}}const ic=go.stylesheet.id;class Stylesheet extends XFAObject{constructor(e){super(ic,"stylesheet",!0)}}class StylesheetNamespace{static[fo](e,t){if(StylesheetNamespace.hasOwnProperty(e))return StylesheetNamespace[e](t)}sta\
-tic stylesheet(e){return new Stylesheet(e)}}const nc=go.xdp.id;class xdp_Xdp extends XFAObject{constructor(e){super(nc,"xdp",!0);this.uuid=e.uuid||"";this.timeStamp=e.timeStamp||"";this.config=null;this.connectionSet=null;this.datasets=null;this.localeSet=null;this.stylesheet=new XFAObjectArray;this.template=null}[Gs](e){const t=go[e[Ws]];return t&&e[Hs]===t.id}}class XdpNamespace{static[fo](e,t){if(XdpNamespace.hasOwnProperty(e))return XdpNamespace[e](t)}static xdp(e){return new xdp_Xdp(e)}}con\
-st sc=go.xhtml.id,oc=Symbol(),cc=new Set(["color","font","font-family","font-size","font-stretch","font-style","font-weight","margin","margin-bottom","margin-left","margin-right","margin-top","letter-spacing","line-height","orphans","page-break-after","page-break-before","page-break-inside","tab-interval","tab-stop","text-align","text-decoration","text-indent","vertical-align","widows","kerning-mode","xfa-font-horizontal-scale","xfa-font-vertical-scale","xfa-spacerun","xfa-tab-stops"]),lc=new Ma\
-p([["page-break-after","breakAfter"],["page-break-before","breakBefore"],["page-break-inside","breakInside"],["kerning-mode",e=>"none"===e?"none":"normal"],["xfa-font-horizontal-scale",e=>`scaleX(${Math.max(0,parseInt(e)/100).toFixed(2)})`],["xfa-font-vertical-scale",e=>`scaleY(${Math.max(0,parseInt(e)/100).toFixed(2)})`],["xfa-spacerun",""],["xfa-tab-stops",""],["font-size",(e,t)=>measureToString(.99*(e=t.fontSize=Math.abs(getMeasurement(e))))],["letter-spacing",e=>measureToString(getMeasuremen\
-t(e))],["line-height",e=>measureToString(getMeasurement(e))],["margin",e=>measureToString(getMeasurement(e))],["margin-bottom",e=>measureToString(getMeasurement(e))],["margin-left",e=>measureToString(getMeasurement(e))],["margin-right",e=>measureToString(getMeasurement(e))],["margin-top",e=>measureToString(getMeasurement(e))],["text-indent",e=>measureToString(getMeasurement(e))],["font-family",e=>e],["vertical-align",e=>measureToString(getMeasurement(e))]]),hc=/\\s+/g,uc=/[\\r\\n]+/g,dc=/\\r\\n?/g;fu\
-nction mapStyle(e,t,a){const r=Object.create(null);if(!e)return r;const i=Object.create(null);for(const[t,a]of e.split(";").map((e=>e.split(":",2)))){const e=lc.get(t);if(""===e)continue;let n=a;e&&(n="string"==typeof e?e:e(a,i));t.endsWith("scale")?r.transform=r.transform?`${r[t]} ${n}`:n:r[t.replaceAll(/-([a-zA-Z])/g,((e,t)=>t.toUpperCase()))]=n}r.fontFamily&&setFontFamily({typeface:r.fontFamily,weight:r.fontWeight||"normal",posture:r.fontStyle||"normal",size:i.fontSize||0},t,t[Is].fontFinder,\
-r);if(a&&r.verticalAlign&&"0px"!==r.verticalAlign&&r.fontSize){const e=.583,t=.333,a=getMeasurement(r.fontSize);r.fontSize=measureToString(a*e);r.verticalAlign=measureToString(Math.sign(getMeasurement(r.verticalAlign))*a*t)}a&&r.fontSize&&(r.fontSize=`calc(${r.fontSize} * var(--total-scale-factor))`);fixTextIndent(r);return r}const fc=new Set(["body","html"]);class XhtmlObject extends XmlObject{constructor(e,t){super(sc,t);this[oc]=!1;this.style=e.style||""}[ts](e){super[ts](e);this.style=functi\
-on checkStyle(e){return e.style?e.style.split(";").filter((e=>!!e.trim())).map((e=>e.split(":",2).map((e=>e.trim())))).filter((([t,a])=>{"font-family"===t&&e[Is].usedTypefaces.add(a);return cc.has(t)})).map((e=>e.join(":"))).join(";"):""}(this)}[Yn](){return!fc.has(this[Ws])}[Vs](e,t=!1){if(t)this[oc]=!0;else{e=e.replaceAll(uc,"");this.style.includes("xfa-spacerun:yes")||(e=e.replaceAll(hc," "))}e&&(this[ss]+=e)}[Ks](e,t=!0){const a=Object.create(null),r={top:NaN,bottom:NaN,left:NaN,right:NaN};l\
-et i=null;for(const[e,t]of this.style.split(";").map((e=>e.split(":",2))))switch(e){case"font-family":a.typeface=stripQuotes(t);break;case"font-size":a.size=getMeasurement(t);break;case"font-weight":a.weight=t;break;case"font-style":a.posture=t;break;case"letter-spacing":a.letterSpacing=getMeasurement(t);break;case"margin":const e=t.split(/ \\t/).map((e=>getMeasurement(e)));switch(e.length){case 1:r.top=r.bottom=r.left=r.right=e[0];break;case 2:r.top=r.bottom=e[0];r.left=r.right=e[1];break;case 3\
-:r.top=e[0];r.bottom=e[2];r.left=r.right=e[1];break;case 4:r.top=e[0];r.left=e[1];r.bottom=e[2];r.right=e[3]}break;case"margin-top":r.top=getMeasurement(t);break;case"margin-bottom":r.bottom=getMeasurement(t);break;case"margin-left":r.left=getMeasurement(t);break;case"margin-right":r.right=getMeasurement(t);break;case"line-height":i=getMeasurement(t)}e.pushData(a,r,i);if(this[ss])e.addString(this[ss]);else for(const t of this[Ss]())"#text"!==t[Ws]?t[Ks](e):e.addString(t[ss]);t&&e.popFont()}[co](\
-e){const t=[];this[ls]={children:t};this[es]({});if(0===t.length&&!this[ss])return HTMLResult.EMPTY;let a;a=this[oc]?this[ss]?this[ss].replaceAll(dc,"\\n"):void 0:this[ss]||void 0;return HTMLResult.success({name:this[Ws],attributes:{href:this.href,style:mapStyle(this.style,this,this[oc])},children:t,value:a})}}class A extends XhtmlObject{constructor(e){super(e,"a");this.href=fixURL(e.href)||""}}class B extends XhtmlObject{constructor(e){super(e,"b")}[Ks](e){e.pushFont({weight:"bold"});super[Ks](e\
-);e.popFont()}}class Body extends XhtmlObject{constructor(e){super(e,"body")}[co](e){const t=super[co](e),{html:a}=t;if(!a)return HTMLResult.EMPTY;a.name="div";a.attributes.class=["xfaRich"];return t}}class Br extends XhtmlObject{constructor(e){super(e,"br")}[so](){return"\\n"}[Ks](e){e.addString("\\n")}[co](e){return HTMLResult.success({name:"br"})}}class Html extends XhtmlObject{constructor(e){super(e,"html")}[co](e){const t=[];this[ls]={children:t};this[es]({});if(0===t.length)return HTMLResult\
-.success({name:"div",attributes:{class:["xfaRich"],style:{}},value:this[ss]||""});if(1===t.length){const e=t[0];if(e.attributes?.class.includes("xfaRich"))return HTMLResult.success(e)}return HTMLResult.success({name:"div",attributes:{class:["xfaRich"],style:{}},children:t})}}class I extends XhtmlObject{constructor(e){super(e,"i")}[Ks](e){e.pushFont({posture:"italic"});super[Ks](e);e.popFont()}}class Li extends XhtmlObject{constructor(e){super(e,"li")}}class Ol extends XhtmlObject{constructor(e){\
-super(e,"ol")}}class P extends XhtmlObject{constructor(e){super(e,"p")}[Ks](e){super[Ks](e,!1);e.addString("\\n");e.addPara();e.popFont()}[so](){return this[vs]()[Ss]().at(-1)===this?super[so]():super[so]()+"\\n"}}class Span extends XhtmlObject{constructor(e){super(e,"span")}}class Sub extends XhtmlObject{constructor(e){super(e,"sub")}}class Sup extends XhtmlObject{constructor(e){super(e,"sup")}}class Ul extends XhtmlObject{constructor(e){super(e,"ul")}}class XhtmlNamespace{static[fo](e,t){if(Xhtm\
-lNamespace.hasOwnProperty(e))return XhtmlNamespace[e](t)}static a(e){return new A(e)}static b(e){return new B(e)}static body(e){return new Body(e)}static br(e){return new Br(e)}static html(e){return new Html(e)}static i(e){return new I(e)}static li(e){return new Li(e)}static ol(e){return new Ol(e)}static p(e){return new P(e)}static span(e){return new Span(e)}static sub(e){return new Sub(e)}static sup(e){return new Sup(e)}static ul(e){return new Ul(e)}}const gc={config:ConfigNamespace,connection:\
-ConnectionSetNamespace,datasets:DatasetsNamespace,localeSet:LocaleSetNamespace,signature:SignatureNamespace,stylesheet:StylesheetNamespace,template:TemplateNamespace,xdp:XdpNamespace,xhtml:XhtmlNamespace};class UnknownNamespace{constructor(e){this.namespaceId=e}[fo](e,t){return new XmlObject(this.namespaceId,e,t)}}class Root extends XFAObject{constructor(e){super(-1,"root",Object.create(null));this.element=null;this[Os]=e}[$s](e){this.element=e;return!0}[hs](){super[hs]();if(this.element.templat\
-e instanceof Template){this[Os].set(Qs,this.element);this.element.template[eo](this[Os]);this.element.template[Os]=this[Os]}}}class Empty extends XFAObject{constructor(){super(-1,"",Object.create(null))}[$s](e){return!1}}class Builder{constructor(e=null){this._namespaceStack=[];this._nsAgnosticLevel=0;this._namespacePrefixes=new Map;this._namespaces=new Map;this._nextNsId=Math.max(...Object.values(go).map((({id:e})=>e)));this._currentNamespace=e||new UnknownNamespace(++this._nextNsId)}buildRoot(\
-e){return new Root(e)}build({nsPrefix:e,name:t,attributes:a,namespace:r,prefixes:i}){const n=null!==r;if(n){this._namespaceStack.push(this._currentNamespace);this._currentNamespace=this._searchNamespace(r)}i&&this._addNamespacePrefix(i);if(a.hasOwnProperty(zs)){const e=gc.datasets,t=a[zs];let r=null;for(const[a,i]of Object.entries(t)){if(this._getNamespaceToUse(a)===e){r={xfa:i};break}}r?a[zs]=r:delete a[zs]}const s=this._getNamespaceToUse(e),o=s?.[fo](t,a)||new Empty;o[Ls]()&&this._nsAgnosticLe\
-vel++;(n||i||o[Ls]())&&(o[rs]={hasNamespace:n,prefixes:i,nsAgnostic:o[Ls]()});return o}isNsAgnostic(){return this._nsAgnosticLevel>0}_searchNamespace(e){let t=this._namespaces.get(e);if(t)return t;for(const[a,{check:r}]of Object.entries(go))if(r(e)){t=gc[a];if(t){this._namespaces.set(e,t);return t}break}t=new UnknownNamespace(++this._nextNsId);this._namespaces.set(e,t);return t}_addNamespacePrefix(e){for(const{prefix:t,value:a}of e){const e=this._searchNamespace(a);let r=this._namespacePrefixes.\
-get(t);if(!r){r=[];this._namespacePrefixes.set(t,r)}r.push(e)}}_getNamespaceToUse(e){if(!e)return this._currentNamespace;const t=this._namespacePrefixes.get(e);if(t?.length>0)return t.at(-1);warn(`Unknown namespace prefix: ${e}.`);return null}clean(e){const{hasNamespace:t,prefixes:a,nsAgnostic:r}=e;t&&(this._currentNamespace=this._namespaceStack.pop());a&&a.forEach((({prefix:e})=>{this._namespacePrefixes.get(e).pop()}));r&&this._nsAgnosticLevel--}}class XFAParser extends XMLParserBase{constructo\
-r(e=null,t=!1){super();this._builder=new Builder(e);this._stack=[];this._globalData={usedTypefaces:new Set};this._ids=new Map;this._current=this._builder.buildRoot(this._ids);this._errorCode=jn;this._whiteRegex=/^\\s+$/;this._nbsps=/\\xa0+/g;this._richText=t}parse(e){this.parseXml(e);if(this._errorCode===jn){this._current[hs]();return this._current.element}}onText(e){e=e.replace(this._nbsps,(e=>e.slice(1)+" "));this._richText||this._current[Yn]()?this._current[Vs](e,this._richText):this._whiteRege\
-x.test(e)||this._current[Vs](e.trim())}onCdata(e){this._current[Vs](e)}_mkAttributes(e,t){let a=null,r=null;const i=Object.create({});for(const{name:n,value:s}of e)if("xmlns"===n)a?warn(`XFA - multiple namespace definition in <${t}>`):a=s;else if(n.startsWith("xmlns:")){const e=n.substring(6);r??=[];r.push({prefix:e,value:s})}else{const e=n.indexOf(":");if(-1===e)i[n]=s;else{const t=i[zs]??=Object.create(null),[a,r]=[n.slice(0,e),n.slice(e+1)];(t[a]||=Object.create(null))[r]=s}}return[a,r,i]}_ge\
-tNameAndPrefix(e,t){const a=e.indexOf(":");return-1===a?[e,null]:[e.substring(a+1),t?"":e.substring(0,a)]}onBeginElement(e,t,a){const[r,i,n]=this._mkAttributes(t,e),[s,o]=this._getNameAndPrefix(e,this._builder.isNsAgnostic()),c=this._builder.build({nsPrefix:o,name:s,attributes:n,namespace:r,prefixes:i});c[Is]=this._globalData;if(a){c[hs]();this._current[$s](c)&&c[ao](this._ids);c[ts](this._builder)}else{this._stack.push(this._current);this._current=c}}onEndElement(e){const t=this._current;if(t[B\
-s]()&&"string"==typeof t[ss]){const e=new XFAParser;e._globalData=this._globalData;const a=e.parse(t[ss]);t[ss]=null;t[$s](a)}t[hs]();this._current=this._stack.pop();this._current[$s](t)&&t[ao](this._ids);t[ts](this._builder)}onError(e){this._errorCode=e}}class XFAFactory{constructor(e){try{this.root=(new XFAParser).parse(XFAFactory._createDocument(e));const t=new Binder(this.root);this.form=t.bind();this.dataHandler=new DataHandler(this.root,t.getData());this.form[Is].template=this.form}catch(e\
-){warn(`XFA - an error occurred during parsing and binding: ${e}`)}}isValid(){return!(!this.root||!this.form)}_createPagesHelper(){const e=this.form[oo]();return new Promise(((t,a)=>{const nextIteration=()=>{try{const a=e.next();a.done?t(a.value):setTimeout(nextIteration,0)}catch(e){a(e)}};setTimeout(nextIteration,0)}))}async _createPages(){try{this.pages=await this._createPagesHelper();this.dims=this.pages.children.map((e=>{const{width:t,height:a}=e.attributes.style;return[0,0,parseInt(t),parse\
-Int(a)]}))}catch(e){warn(`XFA - an error occurred during layout: ${e}`)}}getBoundingBox(e){return this.dims[e]}async getNumPages(){this.pages||await this._createPages();return this.dims.length}setImages(e){this.form[Is].images=e}setFonts(e){this.form[Is].fontFinder=new FontFinder(e);const t=[];for(let e of this.form[Is].usedTypefaces){e=stripQuotes(e);this.form[Is].fontFinder.find(e)||t.push(e)}return t.length>0?t:null}appendFonts(e,t){this.form[Is].fontFinder.add(e,t)}async getPages(){this.page\
-s||await this._createPages();const e=this.pages;this.pages=null;return e}serializeData(e){return this.dataHandler.serialize(e)}static _createDocument(e){return e["/xdp:xdp"]?Object.values(e).join(""):e["xdp:xdp"]}static getRichTextAsHtml(e){if(!e||"string"!=typeof e)return null;try{let t=new XFAParser(XhtmlNamespace,!0).parse(e);if(!["body","xhtml"].includes(t[Ws])){const e=XhtmlNamespace.body({});e[Qn](t);t=e}const a=t[co]();if(!a.success)return null;const{html:r}=a,{attributes:i}=r;if(i){i.cla\
-ss&&(i.class=i.class.filter((e=>!e.startsWith("xfa"))));i.dir="auto"}return{html:r,str:t[so]()}}catch(e){warn(`XFA - an error occurred during parsing of rich text: ${e}`)}return null}}class AnnotationFactory{static createGlobals(e){return Promise.all([e.ensureCatalog("acroForm"),e.ensureDoc("xfaDatasets"),e.ensureCatalog("structTreeRoot"),e.ensureCatalog("baseUrl"),e.ensureCatalog("attachments"),e.ensureCatalog("globalColorSpaceCache")]).then((([t,a,r,i,n,s])=>({pdfManager:e,acroForm:t instanceo\
-f Dict?t:Dict.empty,xfaDatasets:a,structTreeRoot:r,baseUrl:i,attachments:n,globalColorSpaceCache:s})),(e=>{warn(`createGlobals: "${e}".`);return null}))}static async create(e,t,a,r,i,n,s){const o=i?await this._getPageIndex(e,t,a.pdfManager):null;return a.pdfManager.ensure(this,"_create",[e,t,a,r,i,n,o,s])}static _create(e,t,a,r,i=!1,n=null,s=null,o=null){const c=e.fetchIfRef(t);if(!(c instanceof Dict))return;const{acroForm:l,pdfManager:h}=a,u=t instanceof Ref?t.toString():`annot_${r.createObjId(\
-)}`;let d=c.get("Subtype");d=d instanceof Name?d.name:null;const f={xref:e,ref:t,dict:c,subtype:d,id:u,annotationGlobals:a,collectFields:i,orphanFields:n,needAppearances:!i&&!0===l.get("NeedAppearances"),pageIndex:s,evaluatorOptions:h.evaluatorOptions,pageRef:o};switch(d){case"Link":return new LinkAnnotation(f);case"Text":return new TextAnnotation(f);case"Widget":let e=getInheritableProperty({dict:c,key:"FT"});e=e instanceof Name?e.name:null;switch(e){case"Tx":return new TextWidgetAnnotation(f);\
-case"Btn":return new ButtonWidgetAnnotation(f);case"Ch":return new ChoiceWidgetAnnotation(f);case"Sig":return new SignatureWidgetAnnotation(f)}warn(`Unimplemented widget field type "${e}", falling back to base field type.`);return new WidgetAnnotation(f);case"Popup":return new PopupAnnotation(f);case"FreeText":return new FreeTextAnnotation(f);case"Line":return new LineAnnotation(f);case"Square":return new SquareAnnotation(f);case"Circle":return new CircleAnnotation(f);case"PolyLine":return new P\
-olylineAnnotation(f);case"Polygon":return new PolygonAnnotation(f);case"Caret":return new CaretAnnotation(f);case"Ink":return new InkAnnotation(f);case"Highlight":return new HighlightAnnotation(f);case"Underline":return new UnderlineAnnotation(f);case"Squiggly":return new SquigglyAnnotation(f);case"StrikeOut":return new StrikeOutAnnotation(f);case"Stamp":return new StampAnnotation(f);case"FileAttachment":return new FileAttachmentAnnotation(f);default:i||warn(d?`Unimplemented annotation type "${d\
-}", falling back to base annotation.`:"Annotation is missing the required /Subtype.");return new Annotation(f)}}static async _getPageIndex(e,t,a){try{const r=await e.fetchIfRefAsync(t);if(!(r instanceof Dict))return-1;const i=r.getRaw("P");if(i instanceof Ref)try{return await a.ensureCatalog("getPageIndex",[i])}catch(e){info(`_getPageIndex -- not a valid page reference: "${e}".`)}if(r.has("Kids"))return-1;const n=await a.ensureDoc("numPages");for(let e=0;e<n;e++){const r=await a.getPage(e),i=awa\
-it a.ensure(r,"annotations");for(const a of i)if(a instanceof Ref&&isRefsEqual(a,t))return e}}catch(e){warn(`_getPageIndex: "${e}".`)}return-1}static generateImages(e,t,a){if(!a){warn("generateImages: OffscreenCanvas is not supported, cannot save or print some annotations with images.");return null}let r;for(const{bitmapId:a,bitmap:i}of e)if(i){r||=new Map;r.set(a,StampAnnotation.createImage(i,t))}return r}static async saveNewAnnotations(e,t,a,r,i){const n=e.xref;let s;const o=[],{isOffscreenCan\
-vasSupported:c}=e.options;for(const l of a)if(!l.deleted)switch(l.annotationType){case g:if(!s){const e=new Dict(n);e.setIfName("BaseFont","Helvetica");e.setIfName("Type","Font");e.setIfName("Subtype","Type1");e.setIfName("Encoding","WinAnsiEncoding");s=n.getNewTemporaryRef();i.put(s,{data:e})}o.push(FreeTextAnnotation.createNewAnnotation(n,l,i,{evaluator:e,task:t,baseFontRef:s}));break;case p:l.quadPoints?o.push(HighlightAnnotation.createNewAnnotation(n,l,i)):o.push(InkAnnotation.createNewAnnot\
-ation(n,l,i));break;case b:o.push(InkAnnotation.createNewAnnotation(n,l,i));break;case m:const a=c?await(r?.get(l.bitmapId)):null;if(a?.imageStream){const{imageStream:e,smaskStream:t}=a;if(t){const a=n.getNewTemporaryRef();i.put(a,{data:t});e.dict.set("SMask",a)}const r=a.imageRef=n.getNewTemporaryRef();i.put(r,{data:e});a.imageStream=a.smaskStream=null}o.push(StampAnnotation.createNewAnnotation(n,l,i,{image:a}));break;case y:o.push(StampAnnotation.createNewAnnotation(n,l,i,{}))}return{annotatio\
-ns:(await Promise.all(o)).flat()}}static async printNewAnnotations(e,t,a,r,i){if(!r)return null;const{options:n,xref:s}=t,o=[];for(const c of r)if(!c.deleted)switch(c.annotationType){case g:o.push(FreeTextAnnotation.createNewPrintAnnotation(e,s,c,{evaluator:t,task:a,evaluatorOptions:n}));break;case p:c.quadPoints?o.push(HighlightAnnotation.createNewPrintAnnotation(e,s,c,{evaluatorOptions:n})):o.push(InkAnnotation.createNewPrintAnnotation(e,s,c,{evaluatorOptions:n}));break;case b:o.push(InkAnnota\
-tion.createNewPrintAnnotation(e,s,c,{evaluatorOptions:n}));break;case m:const r=n.isOffscreenCanvasSupported?await(i?.get(c.bitmapId)):null;if(r?.imageStream){const{imageStream:e,smaskStream:t}=r;t&&e.dict.set("SMask",t);r.imageRef=new JpegStream(e,e.length);r.imageStream=r.smaskStream=null}o.push(StampAnnotation.createNewPrintAnnotation(e,s,c,{image:r,evaluatorOptions:n}));break;case y:o.push(StampAnnotation.createNewPrintAnnotation(e,s,c,{evaluatorOptions:n}))}return Promise.all(o)}}function g\
-etRgbColor(e,t=new Uint8ClampedArray(3)){if(!Array.isArray(e))return t;const a=t||new Uint8ClampedArray(3);switch(e.length){case 0:return null;case 1:ColorSpaceUtils.gray.getRgbItem(e,0,a,0);return a;case 3:ColorSpaceUtils.rgb.getRgbItem(e,0,a,0);return a;case 4:ColorSpaceUtils.cmyk.getRgbItem(e,0,a,0);return a;default:return t}}function getPdfColorArray(e,t=null){return e&&Array.from(e,(e=>e/255))||t}function getQuadPoints(e,t){const a=e.getArray("QuadPoints");if(!isNumberArray(a,null)||0===a.l\
-ength||a.length%8>0)return null;const r=new Float32Array(a.length);for(let e=0,i=a.length;e<i;e+=8){const[i,n,s,o,c,l,h,u]=a.slice(e,e+8),d=Math.min(i,s,c,h),f=Math.max(i,s,c,h),g=Math.min(n,o,l,u),p=Math.max(n,o,l,u);if(null!==t&&(d<t[0]||f>t[2]||g<t[1]||p>t[3]))return null;r.set([d,p,f,p,d,g,f,g],e)}return r}function getTransformMatrix(e,t,a){const r=new Float32Array([1/0,1/0,-1/0,-1/0]);Util.axialAlignedBoundingBox(t,a,r);const[i,n,s,o]=r;if(i===s||n===o)return[1,0,0,1,e[0],e[1]];const c=(e[2\
-]-e[0])/(s-i),l=(e[3]-e[1])/(o-n);return[c,0,0,l,e[0]-i*c,e[1]-n*l]}class Annotation{constructor(e){const{dict:t,xref:a,annotationGlobals:r,ref:i,orphanFields:n}=e,s=n?.get(i);s&&t.set("Parent",s);this.setTitle(t.get("T"));this.setContents(t.get("Contents"));this.setModificationDate(t.get("M"));this.setFlags(t.get("F"));this.setRectangle(t.getArray("Rect"));this.setColor(t.getArray("C"));this.setBorderStyle(t);this.setAppearance(t);this.setOptionalContent(t);const o=t.get("MK");this.setBorderAnd\
-BackgroundColors(o);this.setRotation(o,t);this.ref=e.ref instanceof Ref?e.ref:null;this._streams=[];this.appearance&&this._streams.push(this.appearance);const c=!!(this.flags&ee),l=!!(this.flags&te);this.data={annotationFlags:this.flags,borderStyle:this.borderStyle,color:this.color,backgroundColor:this.backgroundColor,borderColor:this.borderColor,rotation:this.rotation,contentsObj:this._contents,hasAppearance:!!this.appearance,id:e.id,modificationDate:this.modificationDate,rect:this.rectangle,su\
-btype:e.subtype,hasOwnCanvas:!1,noRotate:!!(this.flags&Z),noHTML:c&&l,isEditable:!1,structParent:-1};if(r.structTreeRoot){let a=t.get("StructParent");this.data.structParent=a=Number.isInteger(a)&&a>=0?a:-1;r.structTreeRoot.addAnnotationIdToPage(e.pageRef,a)}if(e.collectFields){const r=t.get("Kids");if(Array.isArray(r)){const e=[];for(const t of r)t instanceof Ref&&e.push(t.toString());0!==e.length&&(this.data.kidIds=e)}this.data.actions=collectActions(a,t,ye);this.data.fieldName=this._constructF\
-ieldName(t);this.data.pageIndex=e.pageIndex}const h=t.get("IT");h instanceof Name&&(this.data.it=h.name);this._isOffscreenCanvasSupported=e.evaluatorOptions.isOffscreenCanvasSupported;this._fallbackFontDict=null;this._needAppearances=!1}_hasFlag(e,t){return!!(e&t)}_buildFlags(e,t){let{flags:a}=this;if(void 0===e){if(void 0===t)return;return t?a&~Y:a&~J|Y}if(e){a|=Y;return t?a&~Q|J:a&~J|Q}a&=~(J|Q);return t?a&~Y:a|Y}_isViewable(e){return!this._hasFlag(e,K)&&!this._hasFlag(e,Q)}_isPrintable(e){ret\
-urn this._hasFlag(e,Y)&&!this._hasFlag(e,J)&&!this._hasFlag(e,K)}mustBeViewed(e,t){const a=e?.get(this.data.id)?.noView;return void 0!==a?!a:this.viewable&&!this._hasFlag(this.flags,J)}mustBePrinted(e){const t=e?.get(this.data.id)?.noPrint;return void 0!==t?!t:this.printable}mustBeViewedWhenEditing(e,t=null){return e?!this.data.isEditable:!t?.has(this.data.id)}get viewable(){return null!==this.data.quadPoints&&(0===this.flags||this._isViewable(this.flags))}get printable(){return null!==this.data\
-.quadPoints&&(0!==this.flags&&this._isPrintable(this.flags))}_parseStringHelper(e){const t="string"==typeof e?stringToPDFString(e):"";return{str:t,dir:t&&"rtl"===bidi(t).dir?"rtl":"ltr"}}setDefaultAppearance(e){const{dict:t,annotationGlobals:a}=e,r=getInheritableProperty({dict:t,key:"DA"})||a.acroForm.get("DA");this._defaultAppearance="string"==typeof r?r:"";this.data.defaultAppearanceData=parseDefaultAppearance(this._defaultAppearance)}setTitle(e){this._title=this._parseStringHelper(e)}setConte\
-nts(e){this._contents=this._parseStringHelper(e)}setModificationDate(e){this.modificationDate="string"==typeof e?e:null}setFlags(e){this.flags=Number.isInteger(e)&&e>0?e:0;this.flags&K&&"Annotation"!==this.constructor.name&&(this.flags^=K)}hasFlag(e){return this._hasFlag(this.flags,e)}setRectangle(e){this.rectangle=lookupNormalRect(e,[0,0,0,0])}setColor(e){this.color=getRgbColor(e)}setLineEndings(e){this.lineEndings=["None","None"];if(Array.isArray(e)&&2===e.length)for(let t=0;t<2;t++){const a=e\
-[t];if(a instanceof Name)switch(a.name){case"None":continue;case"Square":case"Circle":case"Diamond":case"OpenArrow":case"ClosedArrow":case"Butt":case"ROpenArrow":case"RClosedArrow":case"Slash":this.lineEndings[t]=a.name;continue}warn(`Ignoring invalid lineEnding: ${a}`)}}setRotation(e,t){this.rotation=0;let a=e instanceof Dict?e.get("R")||0:t.get("Rotate")||0;if(Number.isInteger(a)&&0!==a){a%=360;a<0&&(a+=360);a%90==0&&(this.rotation=a)}}setBorderAndBackgroundColors(e){if(e instanceof Dict){this\
-.borderColor=getRgbColor(e.getArray("BC"),null);this.backgroundColor=getRgbColor(e.getArray("BG"),null)}else this.borderColor=this.backgroundColor=null}setBorderStyle(e){this.borderStyle=new AnnotationBorderStyle;if(e instanceof Dict)if(e.has("BS")){const t=e.get("BS");if(t instanceof Dict){const e=t.get("Type");if(!e||isName(e,"Border")){this.borderStyle.setWidth(t.get("W"),this.rectangle);this.borderStyle.setStyle(t.get("S"));this.borderStyle.setDashArray(t.getArray("D"))}}}else if(e.has("Bord\
-er")){const t=e.getArray("Border");if(Array.isArray(t)&&t.length>=3){this.borderStyle.setHorizontalCornerRadius(t[0]);this.borderStyle.setVerticalCornerRadius(t[1]);this.borderStyle.setWidth(t[2],this.rectangle);4===t.length&&this.borderStyle.setDashArray(t[3],!0)}}else this.borderStyle.setWidth(0)}setAppearance(e){this.appearance=null;const t=e.get("AP");if(!(t instanceof Dict))return;const a=t.get("N");if(a instanceof BaseStream){this.appearance=a;return}if(!(a instanceof Dict))return;const r=\
-e.get("AS");if(!(r instanceof Name&&a.has(r.name)))return;const i=a.get(r.name);i instanceof BaseStream&&(this.appearance=i)}setOptionalContent(e){this.oc=null;const t=e.get("OC");t instanceof Name?warn("setOptionalContent: Support for /Name-entry is not implemented."):t instanceof Dict&&(this.oc=t)}async loadResources(e,t){const a=await t.dict.getAsync("Resources");a&&await ObjectLoader.load(a,e,a.xref);return a}async getOperatorList(e,t,a,r){const{hasOwnCanvas:i,id:n,rect:o}=this.data;let c=th\
-is.appearance;const l=!!(i&&a&s);if(l&&(0===this.width||0===this.height)){this.data.hasOwnCanvas=!1;return{opList:new OperatorList,separateForm:!1,separateCanvas:!1}}if(!c){if(!l)return{opList:new OperatorList,separateForm:!1,separateCanvas:!1};c=new StringStream("");c.dict=new Dict}const h=c.dict,u=await this.loadResources(Ia,c),d=lookupRect(h.getArray("BBox"),[0,0,1,1]),f=lookupMatrix(h.getArray("Matrix"),Fa),g=getTransformMatrix(o,d,f),p=new OperatorList;let m;this.oc&&(m=await e.parseMarkedC\
-ontentProps(this.oc,null));void 0!==m&&p.addOp(jt,["OC",m]);p.addOp($t,[n,o,g,f,l]);await e.getOperatorList({stream:c,task:t,resources:u,operatorList:p,fallbackFontDict:this._fallbackFontDict});p.addOp(Gt,[]);void 0!==m&&p.addOp(_t,[]);this.reset();return{opList:p,separateForm:!1,separateCanvas:l}}async save(e,t,a,r){return null}get overlaysTextContent(){return!1}get hasTextContent(){return!1}async extractTextContent(e,t,a){if(!this.appearance)return;const r=await this.loadResources(Ta,this.appe\
-arance),i=[],n=[];let s=null;const o={desiredSize:Math.Infinity,ready:!0,enqueue(e,t){for(const t of e.items)if(void 0!==t.str){s||=t.transform.slice(-2);n.push(t.str);if(t.hasEOL){i.push(n.join("").trimEnd());n.length=0}}}};await e.getTextContent({stream:this.appearance,task:t,resources:r,includeMarkedContent:!0,keepWhiteSpace:!0,sink:o,viewBox:a});this.reset();n.length&&i.push(n.join("").trimEnd());if(i.length>1||i[0]){const e=this.appearance.dict,t=lookupRect(e.getArray("BBox"),null),a=lookup\
-Matrix(e.getArray("Matrix"),null);this.data.textPosition=this._transformPoint(s,t,a);this.data.textContent=i}}_transformPoint(e,t,a){const{rect:r}=this.data;t||=[0,0,1,1];a||=[1,0,0,1,0,0];const i=getTransformMatrix(r,t,a);i[4]-=r[0];i[5]-=r[1];const n=e.slice();Util.applyTransform(n,i);Util.applyTransform(n,a);return n}getFieldObject(){return this.data.kidIds?{id:this.data.id,actions:this.data.actions,name:this.data.fieldName,strokeColor:this.data.borderColor,fillColor:this.data.backgroundColor\
-,type:"",kidIds:this.data.kidIds,page:this.data.pageIndex,rotation:this.rotation}:null}reset(){for(const e of this._streams)e.reset()}_constructFieldName(e){if(!e.has("T")&&!e.has("Parent")){warn("Unknown field name, falling back to empty field name.");return""}if(!e.has("Parent"))return stringToPDFString(e.get("T"));const t=[];e.has("T")&&t.unshift(stringToPDFString(e.get("T")));let a=e;const r=new RefSet;e.objId&&r.put(e.objId);for(;a.has("Parent");){a=a.get("Parent");if(!(a instanceof Dict)||\
-a.objId&&r.has(a.objId))break;a.objId&&r.put(a.objId);a.has("T")&&t.unshift(stringToPDFString(a.get("T")))}return t.join(".")}get width(){return this.data.rect[2]-this.data.rect[0]}get height(){return this.data.rect[3]-this.data.rect[1]}}class AnnotationBorderStyle{constructor(){this.width=1;this.rawWidth=1;this.style=fe;this.dashArray=[3];this.horizontalCornerRadius=0;this.verticalCornerRadius=0}setWidth(e,t=[0,0,0,0]){if(e instanceof Name)this.width=0;else if("number"==typeof e){if(e>0){this.r\
-awWidth=e;const a=(t[2]-t[0])/2,r=(t[3]-t[1])/2;if(a>0&&r>0&&(e>a||e>r)){warn(`AnnotationBorderStyle.setWidth - ignoring width: ${e}`);e=1}}this.width=e}}setStyle(e){if(e instanceof Name)switch(e.name){case"S":this.style=fe;break;case"D":this.style=ge;break;case"B":this.style=pe;break;case"I":this.style=me;break;case"U":this.style=be}}setDashArray(e,t=!1){if(Array.isArray(e)){let a=!0,r=!0;for(const t of e){if(!(+t>=0)){a=!1;break}t>0&&(r=!1)}if(0===e.length||a&&!r){this.dashArray=e;t&&this.setS\
-tyle(Name.get("D"))}else this.width=0}else e&&(this.width=0)}setHorizontalCornerRadius(e){Number.isInteger(e)&&(this.horizontalCornerRadius=e)}setVerticalCornerRadius(e){Number.isInteger(e)&&(this.verticalCornerRadius=e)}}class MarkupAnnotation extends Annotation{constructor(e){super(e);const{dict:t}=e;if(t.has("IRT")){const e=t.getRaw("IRT");this.data.inReplyTo=e instanceof Ref?e.toString():null;const a=t.get("RT");this.data.replyType=a instanceof Name?a.name:V}let a=null;if(this.data.replyType\
-===G){const e=t.get("IRT");this.setTitle(e.get("T"));this.data.titleObj=this._title;this.setContents(e.get("Contents"));this.data.contentsObj=this._contents;if(e.has("CreationDate")){this.setCreationDate(e.get("CreationDate"));this.data.creationDate=this.creationDate}else this.data.creationDate=null;if(e.has("M")){this.setModificationDate(e.get("M"));this.data.modificationDate=this.modificationDate}else this.data.modificationDate=null;a=e.getRaw("Popup");if(e.has("C")){this.setColor(e.getArray("\
-C"));this.data.color=this.color}else this.data.color=null}else{this.data.titleObj=this._title;this.setCreationDate(t.get("CreationDate"));this.data.creationDate=this.creationDate;a=t.getRaw("Popup");t.has("C")||(this.data.color=null)}this.data.popupRef=a instanceof Ref?a.toString():null;t.has("RC")&&(this.data.richText=XFAFactory.getRichTextAsHtml(t.get("RC")))}setCreationDate(e){this.creationDate="string"==typeof e?e:null}_setDefaultAppearance({xref:e,extra:t,strokeColor:a,fillColor:r,blendMode\
-:i,strokeAlpha:n,fillAlpha:s,pointsCallback:o}){const c=this.data.rect=[1/0,1/0,-1/0,-1/0],l=["q"];t&&l.push(t);a&&l.push(`${a[0]} ${a[1]} ${a[2]} RG`);r&&l.push(`${r[0]} ${r[1]} ${r[2]} rg`);const h=this.data.quadPoints||Float32Array.from([this.rectangle[0],this.rectangle[3],this.rectangle[2],this.rectangle[3],this.rectangle[0],this.rectangle[1],this.rectangle[2],this.rectangle[1]]);for(let e=0,t=h.length;e<t;e+=8){const t=o(l,h.subarray(e,e+8));Util.rectBoundingBox(...t,c)}l.push("Q");const u=\
-new Dict(e),d=new Dict(e);d.setIfName("Subtype","Form");const f=new StringStream(l.join(" "));f.dict=d;u.set("Fm0",f);const g=new Dict(e);i&&g.setIfName("BM",i);g.setIfNumber("CA",n);g.setIfNumber("ca",s);const p=new Dict(e);p.set("GS0",g);const m=new Dict(e);m.set("ExtGState",p);m.set("XObject",u);const b=new Dict(e);b.set("Resources",m);b.set("BBox",c);this.appearance=new StringStream("/GS0 gs /Fm0 Do");this.appearance.dict=b;this._streams.push(this.appearance,f)}static async createNewAnnotati\
-on(e,t,a,r){const i=t.ref||=e.getNewTemporaryRef(),n=await this.createNewAppearanceStream(t,e,r);let s;if(n){const r=e.getNewTemporaryRef();s=this.createNewDict(t,e,{apRef:r});a.put(r,{data:n})}else s=this.createNewDict(t,e,{});Number.isInteger(t.parentTreeId)&&s.set("StructParent",t.parentTreeId);a.put(i,{data:s});const o={ref:i};if(t.popup){const r=t.popup;if(r.deleted){s.delete("Popup");s.delete("Contents");s.delete("RC");return o}const n=r.ref||=e.getNewTemporaryRef();r.parent=i;const c=Popu\
-pAnnotation.createNewDict(r,e);a.put(n,{data:c});s.setIfDefined("Contents",stringToAsciiOrUTF16BE(r.contents));s.set("Popup",n);return[o,{ref:n}]}return o}static async createNewPrintAnnotation(e,t,a,r){const i=await this.createNewAppearanceStream(a,t,r),n=this.createNewDict(a,t,i?{ap:i}:{}),s=new this.prototype.constructor({dict:n,xref:t,annotationGlobals:e,evaluatorOptions:r.evaluatorOptions});a.ref&&(s.ref=s.refToReplace=a.ref);return s}}class WidgetAnnotation extends Annotation{constructor(e)\
-{super(e);const{dict:t,xref:a,annotationGlobals:r}=e,i=this.data;this._needAppearances=e.needAppearances;i.annotationType=$;void 0===i.fieldName&&(i.fieldName=this._constructFieldName(t));void 0===i.actions&&(i.actions=collectActions(a,t,ye));let n=getInheritableProperty({dict:t,key:"V",getArray:!0});i.fieldValue=this._decodeFormValue(n);const s=getInheritableProperty({dict:t,key:"DV",getArray:!0});i.defaultFieldValue=this._decodeFormValue(s);if(void 0===n&&r.xfaDatasets){const e=this._title.str\
-;if(e){this._hasValueFromXFA=!0;i.fieldValue=n=r.xfaDatasets.getValue(e)}}void 0===n&&null!==i.defaultFieldValue&&(i.fieldValue=i.defaultFieldValue);i.alternativeText=stringToPDFString(t.get("TU")||"");this.setDefaultAppearance(e);i.hasAppearance||=this._needAppearances&&void 0!==i.fieldValue&&null!==i.fieldValue;const o=getInheritableProperty({dict:t,key:"FT"});i.fieldType=o instanceof Name?o.name:null;const c=getInheritableProperty({dict:t,key:"DR"}),l=r.acroForm.get("DR"),h=this.appearance?.d\
-ict.get("Resources");this._fieldResources={localResources:c,acroFormResources:l,appearanceResources:h,mergedResources:Dict.merge({xref:a,dictArray:[c,h,l],mergeSubDicts:!0})};i.fieldFlags=getInheritableProperty({dict:t,key:"Ff"});(!Number.isInteger(i.fieldFlags)||i.fieldFlags<0)&&(i.fieldFlags=0);i.password=this.hasFieldFlag(ne);i.readOnly=this.hasFieldFlag(ae);i.required=this.hasFieldFlag(re);i.hidden=this._hasFlag(i.annotationFlags,J)||this._hasFlag(i.annotationFlags,Q)}_decodeFormValue(e){ret\
-urn Array.isArray(e)?e.filter((e=>"string"==typeof e)).map((e=>stringToPDFString(e))):e instanceof Name?stringToPDFString(e.name):"string"==typeof e?stringToPDFString(e):null}hasFieldFlag(e){return!!(this.data.fieldFlags&e)}_isViewable(e){return!0}mustBeViewed(e,t){return t?this.viewable:super.mustBeViewed(e,t)&&!this._hasFlag(this.flags,Q)}getRotationMatrix(e){let t=e?.get(this.data.id)?.rotation;void 0===t&&(t=this.rotation);return 0===t?Fa:getRotationMatrix(t,this.width,this.height)}getBorder\
-AndBackgroundAppearances(e){let t=e?.get(this.data.id)?.rotation;void 0===t&&(t=this.rotation);if(!this.backgroundColor&&!this.borderColor)return"";const a=0===t||180===t?`0 0 ${this.width} ${this.height} re`:`0 0 ${this.height} ${this.width} re`;let r="";this.backgroundColor&&(r=`${getPdfColor(this.backgroundColor,!0)} ${a} f `);if(this.borderColor){r+=`${this.borderStyle.width||1} w ${getPdfColor(this.borderColor,!1)} ${a} S `}return r}async getOperatorList(e,t,a,r){if(a&l&&!(this instanceof S\
-ignatureWidgetAnnotation)&&!this.data.noHTML&&!this.data.hasOwnCanvas)return{opList:new OperatorList,separateForm:!0,separateCanvas:!1};if(!this._hasText)return super.getOperatorList(e,t,a,r);const i=await this._getAppearance(e,t,a,r);if(this.appearance&&null===i)return super.getOperatorList(e,t,a,r);const n=new OperatorList;if(!this._defaultAppearance||null===i)return{opList:n,separateForm:!1,separateCanvas:!1};const o=!!(this.data.hasOwnCanvas&&a&s),c=[0,0,this.width,this.height],h=getTransfor\
-mMatrix(this.data.rect,c,[1,0,0,1,0,0]);let u;this.oc&&(u=await e.parseMarkedContentProps(this.oc,null));void 0!==u&&n.addOp(jt,["OC",u]);n.addOp($t,[this.data.id,this.data.rect,h,this.getRotationMatrix(r),o]);const d=new StringStream(i);await e.getOperatorList({stream:d,task:t,resources:this._fieldResources.mergedResources,operatorList:n});n.addOp(Gt,[]);void 0!==u&&n.addOp(_t,[]);return{opList:n,separateForm:!1,separateCanvas:o}}_getMKDict(e){const t=new Dict(null);e&&t.set("R",e);t.setIfArray\
-("BC",getPdfColorArray(this.borderColor));t.setIfArray("BG",getPdfColorArray(this.backgroundColor));return t.size>0?t:null}amendSavedDict(e,t){}setValue(e,t,a,r){const{dict:i,ref:n}=function getParentToUpdate(e,t,a){const r=new RefSet,i=e,n={dict:null,ref:null};for(;e instanceof Dict&&!r.has(t);){r.put(t);if(e.has("T"))break;if(!((t=e.getRaw("Parent"))instanceof Ref))return n;e=a.fetch(t)}if(e instanceof Dict&&e!==i){n.dict=e;n.ref=t}return n}(e,this.ref,a);if(i){if(!r.has(n)){const e=i.clone();\
-e.set("V",t);r.put(n,{data:e});return e}}else e.set("V",t);return null}async save(e,t,a,r){const i=a?.get(this.data.id),n=this._buildFlags(i?.noView,i?.noPrint);let s=i?.value,o=i?.rotation;if(s===this.data.fieldValue||void 0===s){if(!this._hasValueFromXFA&&void 0===o&&void 0===n)return;s||=this.data.fieldValue}if(void 0===o&&!this._hasValueFromXFA&&Array.isArray(s)&&Array.isArray(this.data.fieldValue)&&isArrayEqual(s,this.data.fieldValue)&&void 0===n)return;void 0===o&&(o=this.rotation);let l=n\
-ull;if(!this._needAppearances){l=await this._getAppearance(e,t,c,a);if(null===l&&void 0===n)return}let h=!1;if(l?.needAppearances){h=!0;l=null}const{xref:u}=e,d=u.fetchIfRef(this.ref);if(!(d instanceof Dict))return;const f=new Dict(u);for(const e of d.getKeys())"AP"!==e&&f.set(e,d.getRaw(e));if(void 0!==n){f.set("F",n);if(null===l&&!h){const e=d.getRaw("AP");e&&f.set("AP",e)}}const g={path:this.data.fieldName,value:s},p=this.setValue(f,Array.isArray(s)?s.map(stringToAsciiOrUTF16BE):stringToAscii\
-OrUTF16BE(s),u,r);this.amendSavedDict(a,p||f);const m=this._getMKDict(o);m&&f.set("MK",m);r.put(this.ref,{data:f,xfa:g,needAppearances:h});if(null!==l){const e=u.getNewTemporaryRef(),t=new Dict(u);f.set("AP",t);t.set("N",e);const i=this._getSaveFieldResources(u),n=new StringStream(l),s=n.dict=new Dict(u);s.setIfName("Subtype","Form");s.set("Resources",i);const c=o%180==0?[0,0,this.width,this.height]:[0,0,this.height,this.width];s.set("BBox",c);const h=this.getRotationMatrix(a);h!==Fa&&s.set("Mat\
-rix",h);r.put(e,{data:n,xfa:null,needAppearances:!1})}f.set("M",`D:${getModificationDate()}`)}async _getAppearance(e,t,a,r){if(this.data.password)return null;const n=r?.get(this.data.id);let s,o;if(n){s=n.formattedValue||n.value;o=n.rotation}if(void 0===o&&void 0===s&&!this._needAppearances&&(!this._hasValueFromXFA||this.appearance))return null;const l=this.getBorderAndBackgroundAppearances(r);if(void 0===s){s=this.data.fieldValue;if(!s)return`/Tx BMC q ${l}Q EMC`}Array.isArray(s)&&1===s.length&\
-&(s=s[0]);assert("string"==typeof s,"Expected `value` to be a string.");s=s.trimEnd();if(this.data.combo){const e=this.data.options.find((({exportValue:e})=>s===e));s=e?.displayValue||s}if(""===s)return`/Tx BMC q ${l}Q EMC`;void 0===o&&(o=this.rotation);let h,u=-1;if(this.data.multiLine){h=s.split(/\\r\\n?|\\n/).map((e=>e.normalize("NFC")));u=h.length}else h=[s.replace(/\\r\\n?|\\n/,"").normalize("NFC")];let{width:d,height:f}=this;90!==o&&270!==o||([d,f]=[f,d]);this._defaultAppearance||(this.data.defa\
-ultAppearanceData=parseDefaultAppearance(this._defaultAppearance="/Helvetica 0 Tf 0 g"));let g,p,m,b=await WidgetAnnotation._getFontData(e,t,this.data.defaultAppearanceData,this._fieldResources.mergedResources);const y=[];let w=!1;for(const e of h){const t=b.encodeString(e);t.length>1&&(w=!0);y.push(t.join(""))}if(w&&a&c)return{needAppearances:!0};if(w&&this._isOffscreenCanvasSupported){const a=this.data.comb?"monospace":"sans-serif",r=new FakeUnicodeFont(e.xref,a),i=r.createFontResources(h.join\
-("")),n=i.getRaw("Font");if(this._fieldResources.mergedResources.has("Font")){const e=this._fieldResources.mergedResources.get("Font");for(const t of n.getKeys())e.set(t,n.getRaw(t))}else this._fieldResources.mergedResources.set("Font",n);const o=r.fontName.name;b=await WidgetAnnotation._getFontData(e,t,{fontName:o,fontSize:0},i);for(let e=0,t=y.length;e<t;e++)y[e]=stringToUTF16String(h[e]);const c=Object.assign(Object.create(null),this.data.defaultAppearanceData);this.data.defaultAppearanceData\
-.fontSize=0;this.data.defaultAppearanceData.fontName=o;[g,p,m]=this._computeFontSize(f-2,d-4,s,b,u);this.data.defaultAppearanceData=c}else{this._isOffscreenCanvasSupported||warn("_getAppearance: OffscreenCanvas is not supported, annotation may not render correctly.");[g,p,m]=this._computeFontSize(f-2,d-4,s,b,u)}let x=b.descent;x=isNaN(x)?i*m:Math.max(i*m,Math.abs(x)*p);const S=Math.min(Math.floor((f-p)/2),1),k=this.data.textAlignment;if(this.data.multiLine)return this._getMultilineAppearance(g,y\
-,b,p,d,f,k,2,S,x,m,r);if(this.data.comb)return this._getCombAppearance(g,b,y[0],p,d,f,2,S,x,m,r);const C=S+x;if(0===k||k>2)return`/Tx BMC q ${l}BT `+g+` 1 0 0 1 ${numberToString(2)} ${numberToString(C)} Tm (${escapeString(y[0])}) Tj ET Q EMC`;return`/Tx BMC q ${l}BT `+g+` 1 0 0 1 0 0 Tm ${this._renderText(y[0],b,p,d,k,{shift:0},2,C)} ET Q EMC`}static async _getFontData(e,t,a,r){const i=new OperatorList,n={font:null,clone(){return this}},{fontName:s,fontSize:o}=a;await e.handleSetFont(r,[s&&Name.\
-get(s),o],null,i,t,n,null);return n.font}_getTextWidth(e,t){return Math.sumPrecise(t.charsToGlyphs(e).map((e=>e.width)))/1e3}_computeFontSize(e,t,r,i,n){let{fontSize:s}=this.data.defaultAppearanceData,o=(s||12)*a,c=Math.round(e/o);if(!s){const roundWithTwoDigits=e=>Math.floor(100*e)/100;if(-1===n){const n=this._getTextWidth(r,i);s=roundWithTwoDigits(Math.min(e/a,t/n));c=1}else{const l=r.split(/\\r\\n?|\\n/),h=[];for(const e of l){const t=i.encodeString(e).join(""),a=i.charsToGlyphs(t),r=i.getCharPo\
-sitions(t);h.push({line:t,glyphs:a,positions:r})}const isTooBig=a=>{let r=0;for(const n of h){r+=this._splitLine(null,i,a,t,n).length*a;if(r>e)return!0}return!1};c=Math.max(c,n);for(;;){o=e/c;s=roundWithTwoDigits(o/a);if(!isTooBig(s))break;c++}}const{fontName:l,fontColor:h}=this.data.defaultAppearanceData;this._defaultAppearance=function createDefaultAppearance({fontSize:e,fontName:t,fontColor:a}){return`/${escapePDFName(t)} ${e} Tf ${getPdfColor(a,!0)}`}({fontSize:s,fontName:l,fontColor:h})}ret\
-urn[this._defaultAppearance,s,e/c]}_renderText(e,t,a,r,i,n,s,o){let c;if(1===i){c=(r-this._getTextWidth(e,t)*a)/2}else if(2===i){c=r-this._getTextWidth(e,t)*a-s}else c=s;const l=numberToString(c-n.shift);n.shift=c;return`${l} ${o=numberToString(o)} Td (${escapeString(e)}) Tj`}_getSaveFieldResources(e){const{localResources:t,appearanceResources:a,acroFormResources:r}=this._fieldResources,i=this.data.defaultAppearanceData?.fontName;if(!i)return t||Dict.empty;for(const e of[t,a])if(e instanceof Dic\
-t){const t=e.get("Font");if(t instanceof Dict&&t.has(i))return e}if(r instanceof Dict){const a=r.get("Font");if(a instanceof Dict&&a.has(i)){const r=new Dict(e);r.set(i,a.getRaw(i));const n=new Dict(e);n.set("Font",r);return Dict.merge({xref:e,dictArray:[n,t],mergeSubDicts:!0})}}return t||Dict.empty}getFieldObject(){return null}}class TextWidgetAnnotation extends WidgetAnnotation{constructor(e){super(e);const{dict:t}=e;if(t.has("PMD")){this.flags|=J;this.data.hidden=!0;warn("Barcodes are not sup\
-ported")}this.data.hasOwnCanvas=this.data.readOnly&&!this.data.noHTML;this._hasText=!0;"string"!=typeof this.data.fieldValue&&(this.data.fieldValue="");let a=getInheritableProperty({dict:t,key:"Q"});(!Number.isInteger(a)||a<0||a>2)&&(a=null);this.data.textAlignment=a;let r=getInheritableProperty({dict:t,key:"MaxLen"});(!Number.isInteger(r)||r<0)&&(r=0);this.data.maxLen=r;this.data.multiLine=this.hasFieldFlag(ie);this.data.comb=this.hasFieldFlag(de)&&!this.data.multiLine&&!this.data.password&&!th\
-is.hasFieldFlag(le)&&0!==this.data.maxLen;this.data.doNotScroll=this.hasFieldFlag(ue);const{data:{actions:i}}=this;if(!i)return;const n=/^AF(Date|Time)_(?:Keystroke|Format)(?:Ex)?\\([\'"]?([^\'"]+)[\'"]?\\);$/;let s=!1;(1===i.Format?.length&&1===i.Keystroke?.length&&n.test(i.Format[0])&&n.test(i.Keystroke[0])||0===i.Format?.length&&1===i.Keystroke?.length&&n.test(i.Keystroke[0])||0===i.Keystroke?.length&&1===i.Format?.length&&n.test(i.Format[0]))&&(s=!0);const o=[];i.Format&&o.push(...i.Format);i.Key\
-stroke&&o.push(...i.Keystroke);if(s){delete i.Keystroke;i.Format=o}for(const e of o){const t=e.match(n);if(!t)continue;const a="Date"===t[1];let r=t[2];const i=parseInt(r,10);isNaN(i)||Math.floor(Math.log10(i))+1!==t[2].length||(r=(a?Pn:Ln)[i]??r);this.data.datetimeFormat=r;if(!s)break;if(a){if(/HH|MM|ss|h/.test(r)){this.data.datetimeType="datetime-local";this.data.timeStep=/ss/.test(r)?1:60}else this.data.datetimeType="date";break}this.data.datetimeType="time";this.data.timeStep=/ss/.test(r)?1:\
-60;break}}get hasTextContent(){return!!this.appearance&&!this._needAppearances}_getCombAppearance(e,t,a,r,i,n,s,o,c,l,h){const u=i/this.data.maxLen,d=this.getBorderAndBackgroundAppearances(h),f=[],g=t.getCharPositions(a);for(const[e,t]of g)f.push(`(${escapeString(a.substring(e,t))}) Tj`);const p=f.join(` ${numberToString(u)} 0 Td `);return`/Tx BMC q ${d}BT `+e+` 1 0 0 1 ${numberToString(s)} ${numberToString(o+c)} Tm ${p} ET Q EMC`}_getMultilineAppearance(e,t,a,r,i,n,s,o,c,l,h,u){const d=[],f=i-2\
-*o,g={shift:0};for(let e=0,n=t.length;e<n;e++){const n=t[e],u=this._splitLine(n,a,r,f);for(let t=0,n=u.length;t<n;t++){const n=u[t],f=0===e&&0===t?-c-(h-l):-h;d.push(this._renderText(n,a,r,i,s,g,o,f))}}const p=this.getBorderAndBackgroundAppearances(u),m=d.join("\\n");return`/Tx BMC q ${p}BT `+e+` 1 0 0 1 0 ${numberToString(n)} Tm ${m} ET Q EMC`}_splitLine(e,t,a,r,i={}){e=i.line||e;const n=i.glyphs||t.charsToGlyphs(e);if(n.length<=1)return[e];const s=i.positions||t.getCharPositions(e),o=a/1e3,c=[]\
-;let l=-1,h=-1,u=-1,d=0,f=0;for(let t=0,a=n.length;t<a;t++){const[a,i]=s[t],g=n[t],p=g.width*o;if(" "===g.unicode)if(f+p>r){c.push(e.substring(d,a));d=a;f=p;l=-1;u=-1}else{f+=p;l=a;h=i;u=t}else if(f+p>r)if(-1!==l){c.push(e.substring(d,h));d=h;t=u+1;l=-1;f=0}else{c.push(e.substring(d,a));d=a;f=p}else f+=p}d<e.length&&c.push(e.substring(d,e.length));return c}async extractTextContent(e,t,a){await super.extractTextContent(e,t,a);const r=this.data.textContent;if(!r)return;const i=r.join("\\n");if(i===\
-this.data.fieldValue)return;const n=i.replaceAll(/([.*+?^${}()|[\\]\\\\])|(\\s+)/g,((e,t)=>t?`\\\\${t}`:"\\\\s+"));new RegExp(`^\\\\s*${n}\\\\s*$`).test(this.data.fieldValue)&&(this.data.textContent=this.data.fieldValue.split("\\n"))}getFieldObject(){return{id:this.data.id,value:this.data.fieldValue,defaultValue:this.data.defaultFieldValue||"",multiline:this.data.multiLine,password:this.data.password,charLimit:this.data.maxLen,comb:this.data.comb,editable:!this.data.readOnly,hidden:this.data.hidden,name:this\
-.data.fieldName,rect:this.data.rect,actions:this.data.actions,page:this.data.pageIndex,strokeColor:this.data.borderColor,fillColor:this.data.backgroundColor,rotation:this.rotation,datetimeFormat:this.data.datetimeFormat,hasDatetimeHTML:!!this.data.datetimeType,type:"text"}}}class ButtonWidgetAnnotation extends WidgetAnnotation{constructor(e){super(e);this.checkedAppearance=null;this.uncheckedAppearance=null;const t=this.hasFieldFlag(se),a=this.hasFieldFlag(oe);this.data.checkBox=!t&&!a;this.data\
-.radioButton=t&&!a;this.data.pushButton=a;this.data.isTooltipOnly=!1;if(this.data.checkBox)this._processCheckBox(e);else if(this.data.radioButton)this._processRadioButton(e);else if(this.data.pushButton){this.data.hasOwnCanvas=!0;this.data.noHTML=!1;this._processPushButton(e)}else warn("Invalid field flags for button widget annotation")}async getOperatorList(e,t,a,r){if(this.data.pushButton)return super.getOperatorList(e,t,a,!1,r);let i=null,n=null;if(r){const e=r.get(this.data.id);i=e?e.value:n\
-ull;n=e?e.rotation:null}if(null===i&&this.appearance)return super.getOperatorList(e,t,a,r);null==i&&(i=this.data.checkBox?this.data.fieldValue===this.data.exportValue:this.data.fieldValue===this.data.buttonValue);const s=i?this.checkedAppearance:this.uncheckedAppearance;if(s){const i=this.appearance,o=lookupMatrix(s.dict.getArray("Matrix"),Fa);n&&s.dict.set("Matrix",this.getRotationMatrix(r));this.appearance=s;const c=super.getOperatorList(e,t,a,r);this.appearance=i;s.dict.set("Matrix",o);return\
- c}return{opList:new OperatorList,separateForm:!1,separateCanvas:!1}}async save(e,t,a,r){this.data.checkBox?this._saveCheckbox(e,t,a,r):this.data.radioButton&&this._saveRadioButton(e,t,a,r)}async _saveCheckbox(e,t,a,r){if(!a)return;const i=a.get(this.data.id),n=this._buildFlags(i?.noView,i?.noPrint);let s=i?.rotation,o=i?.value;if(void 0===s&&void 0===n){if(void 0===o)return;if(this.data.fieldValue===this.data.exportValue===o)return}let c=e.xref.fetchIfRef(this.ref);if(!(c instanceof Dict))retur\
-n;c=c.clone();void 0===s&&(s=this.rotation);void 0===o&&(o=this.data.fieldValue===this.data.exportValue);const l={path:this.data.fieldName,value:o?this.data.exportValue:""},h=Name.get(o?this.data.exportValue:"Off");this.setValue(c,h,e.xref,r);c.set("AS",h);c.set("M",`D:${getModificationDate()}`);void 0!==n&&c.set("F",n);const u=this._getMKDict(s);u&&c.set("MK",u);r.put(this.ref,{data:c,xfa:l,needAppearances:!1})}async _saveRadioButton(e,t,a,r){if(!a)return;const i=a.get(this.data.id),n=this._bui\
-ldFlags(i?.noView,i?.noPrint);let s=i?.rotation,o=i?.value;if(void 0===s&&void 0===n){if(void 0===o)return;if(this.data.fieldValue===this.data.buttonValue===o)return}let c=e.xref.fetchIfRef(this.ref);if(!(c instanceof Dict))return;c=c.clone();void 0===o&&(o=this.data.fieldValue===this.data.buttonValue);void 0===s&&(s=this.rotation);const l={path:this.data.fieldName,value:o?this.data.buttonValue:""},h=Name.get(o?this.data.buttonValue:"Off");o&&this.setValue(c,h,e.xref,r);c.set("AS",h);c.set("M",`\
-D:${getModificationDate()}`);void 0!==n&&c.set("F",n);const u=this._getMKDict(s);u&&c.set("MK",u);r.put(this.ref,{data:c,xfa:l,needAppearances:!1})}_getDefaultCheckedAppearance(e,t){const{width:a,height:r}=this,i=[0,0,a,r],n=.8*Math.min(a,r);let s,o;if("check"===t){s={width:.755*n,height:.705*n};o="3"}else if("disc"===t){s={width:.791*n,height:.705*n};o="l"}else unreachable(`_getDefaultCheckedAppearance - unsupported type: ${t}`);const c=`q BT /PdfJsZaDb ${n} Tf 0 g ${numberToString((a-s.width)/\
-2)} ${numberToString((r-s.height)/2)} Td (${o}) Tj ET Q`,l=new Dict(e.xref);l.set("FormType",1);l.setIfName("Subtype","Form");l.setIfName("Type","XObject");l.set("BBox",i);l.set("Matrix",[1,0,0,1,0,0]);l.set("Length",c.length);const h=new Dict(e.xref),u=new Dict(e.xref);u.set("PdfJsZaDb",this.fallbackFontDict);h.set("Font",u);l.set("Resources",h);this.checkedAppearance=new StringStream(c);this.checkedAppearance.dict=l;this._streams.push(this.checkedAppearance)}_processCheckBox(e){const t=e.dict.\
-get("AP");if(!(t instanceof Dict))return;const a=t.get("N");if(!(a instanceof Dict))return;const r=this._decodeFormValue(e.dict.get("AS"));"string"==typeof r&&(this.data.fieldValue=r);const i=null!==this.data.fieldValue&&"Off"!==this.data.fieldValue?this.data.fieldValue:"Yes",n=this._decodeFormValue(a.getKeys());if(0===n.length)n.push("Off",i);else if(1===n.length)"Off"===n[0]?n.push(i):n.unshift("Off");else if(n.includes(i)){n.length=0;n.push("Off",i)}else{const e=n.find((e=>"Off"!==e));n.lengt\
-h=0;n.push("Off",e)}n.includes(this.data.fieldValue)||(this.data.fieldValue="Off");this.data.exportValue=n[1];const s=a.get(this.data.exportValue);this.checkedAppearance=s instanceof BaseStream?s:null;const o=a.get("Off");this.uncheckedAppearance=o instanceof BaseStream?o:null;this.checkedAppearance?this._streams.push(this.checkedAppearance):this._getDefaultCheckedAppearance(e,"check");this.uncheckedAppearance&&this._streams.push(this.uncheckedAppearance);this._fallbackFontDict=this.fallbackFont\
-Dict;null===this.data.defaultFieldValue&&(this.data.defaultFieldValue="Off")}_processRadioButton(e){this.data.buttonValue=null;const t=e.dict.get("Parent");if(t instanceof Dict){this.parent=e.dict.getRaw("Parent");const a=t.get("V");a instanceof Name&&(this.data.fieldValue=this._decodeFormValue(a))}const a=e.dict.get("AP");if(!(a instanceof Dict))return;const r=a.get("N");if(!(r instanceof Dict))return;for(const e of r.getKeys())if("Off"!==e){this.data.buttonValue=this._decodeFormValue(e);break}\
-const i=r.get(this.data.buttonValue);this.checkedAppearance=i instanceof BaseStream?i:null;const n=r.get("Off");this.uncheckedAppearance=n instanceof BaseStream?n:null;this.checkedAppearance?this._streams.push(this.checkedAppearance):this._getDefaultCheckedAppearance(e,"disc");this.uncheckedAppearance&&this._streams.push(this.uncheckedAppearance);this._fallbackFontDict=this.fallbackFontDict;null===this.data.defaultFieldValue&&(this.data.defaultFieldValue="Off")}_processPushButton(e){const{dict:t\
-,annotationGlobals:a}=e;if(t.has("A")||t.has("AA")||this.data.alternativeText){this.data.isTooltipOnly=!t.has("A")&&!t.has("AA");Catalog.parseDestDictionary({destDict:t,resultObj:this.data,docBaseUrl:a.baseUrl,docAttachments:a.attachments})}else warn("Push buttons without action dictionaries are not supported")}getFieldObject(){let e,t="button";if(this.data.checkBox){t="checkbox";e=this.data.exportValue}else if(this.data.radioButton){t="radiobutton";e=this.data.buttonValue}return{id:this.data.id\
-,value:this.data.fieldValue||"Off",defaultValue:this.data.defaultFieldValue,exportValues:e,editable:!this.data.readOnly,name:this.data.fieldName,rect:this.data.rect,hidden:this.data.hidden,actions:this.data.actions,page:this.data.pageIndex,strokeColor:this.data.borderColor,fillColor:this.data.backgroundColor,rotation:this.rotation,type:t}}get fallbackFontDict(){const e=new Dict;e.setIfName("BaseFont","ZapfDingbats");e.setIfName("Type","FallbackType");e.setIfName("Subtype","FallbackType");e.setIf\
-Name("Encoding","ZapfDingbatsEncoding");return shadow(this,"fallbackFontDict",e)}}class ChoiceWidgetAnnotation extends WidgetAnnotation{constructor(e){super(e);const{dict:t,xref:a}=e;this.indices=t.getArray("I");this.hasIndices=Array.isArray(this.indices)&&this.indices.length>0;this.data.options=[];const r=getInheritableProperty({dict:t,key:"Opt"});if(Array.isArray(r))for(let e=0,t=r.length;e<t;e++){const t=a.fetchIfRef(r[e]),i=Array.isArray(t);this.data.options[e]={exportValue:this._decodeFormV\
-alue(i?a.fetchIfRef(t[0]):t),displayValue:this._decodeFormValue(i?a.fetchIfRef(t[1]):t)}}if(this.hasIndices){this.data.fieldValue=[];const e=this.data.options.length;for(const t of this.indices)Number.isInteger(t)&&t>=0&&t<e&&this.data.fieldValue.push(this.data.options[t].exportValue)}else"string"==typeof this.data.fieldValue?this.data.fieldValue=[this.data.fieldValue]:this.data.fieldValue||=[];0===this.data.options.length&&this.data.fieldValue.length>0&&(this.data.options=this.data.fieldValue.m\
-ap((e=>({exportValue:e,displayValue:e}))));this.data.combo=this.hasFieldFlag(ce);this.data.multiSelect=this.hasFieldFlag(he);this._hasText=!0}getFieldObject(){const e=this.data.combo?"combobox":"listbox",t=this.data.fieldValue.length>0?this.data.fieldValue[0]:null;return{id:this.data.id,value:t,defaultValue:this.data.defaultFieldValue,editable:!this.data.readOnly,name:this.data.fieldName,rect:this.data.rect,numItems:this.data.fieldValue.length,multipleSelection:this.data.multiSelect,hidden:this.\
-data.hidden,actions:this.data.actions,items:this.data.options,page:this.data.pageIndex,strokeColor:this.data.borderColor,fillColor:this.data.backgroundColor,rotation:this.rotation,type:e}}amendSavedDict(e,t){if(!this.hasIndices)return;let a=e?.get(this.data.id)?.value;Array.isArray(a)||(a=[a]);const r=[],{options:i}=this.data;for(let e=0,t=0,n=i.length;e<n;e++)if(i[e].exportValue===a[t]){r.push(e);t+=1}t.set("I",r)}async _getAppearance(e,t,r,i){if(this.data.combo)return super._getAppearance(e,t,\
-r,i);let n,s;const o=i?.get(this.data.id);if(o){s=o.rotation;n=o.value}if(void 0===s&&void 0===n&&!this._needAppearances)return null;void 0===n?n=this.data.fieldValue:Array.isArray(n)||(n=[n]);let{width:c,height:l}=this;90!==s&&270!==s||([c,l]=[l,c]);const h=this.data.options.length,u=[];for(let e=0;e<h;e++){const{exportValue:t}=this.data.options[e];n.includes(t)&&u.push(e)}this._defaultAppearance||(this.data.defaultAppearanceData=parseDefaultAppearance(this._defaultAppearance="/Helvetica 0 Tf 0\
- g"));const d=await WidgetAnnotation._getFontData(e,t,this.data.defaultAppearanceData,this._fieldResources.mergedResources);let f,{fontSize:g}=this.data.defaultAppearanceData;if(g)f=this._defaultAppearance;else{const e=(l-1)/h;let t,a=-1;for(const{displayValue:e}of this.data.options){const r=this._getTextWidth(e,d);if(r>a){a=r;t=e}}[f,g]=this._computeFontSize(e,c-4,t,d,-1)}const p=g*a,m=(p-g)/2,b=Math.floor(l/p);let y=0;if(u.length>0){const e=Math.min(...u),t=Math.max(...u);y=Math.max(0,t-b+1);y\
->e&&(y=e)}const w=Math.min(y+b+1,h),x=["/Tx BMC q",`1 1 ${c} ${l} re W n`];if(u.length){x.push("0.600006 0.756866 0.854904 rg");for(const e of u)y<=e&&e<w&&x.push(`1 ${l-(e-y+1)*p} ${c} ${p} re f`)}x.push("BT",f,`1 0 0 1 0 ${l} Tm`);const S={shift:0};for(let e=y;e<w;e++){const{displayValue:t}=this.data.options[e],a=e===y?m:0;x.push(this._renderText(t,d,g,c,0,S,2,-p+a))}x.push("ET Q EMC");return x.join("\\n")}}class SignatureWidgetAnnotation extends WidgetAnnotation{constructor(e){super(e);this.da\
-ta.fieldValue=null;this.data.hasOwnCanvas=this.data.noRotate;this.data.noHTML=!this.data.hasOwnCanvas}getFieldObject(){return{id:this.data.id,value:null,page:this.data.pageIndex,type:"signature"}}}class TextAnnotation extends MarkupAnnotation{constructor(e){super(e);this.data.noRotate=!0;this.data.hasOwnCanvas=this.data.noRotate;this.data.noHTML=!1;const{dict:t}=e;this.data.annotationType=F;if(this.data.hasAppearance)this.data.name="NoIcon";else{this.data.rect[1]=this.data.rect[3]-22;this.data.r\
-ect[2]=this.data.rect[0]+22;this.data.name=t.has("Name")?t.get("Name").name:"Note"}if(t.has("State")){this.data.state=t.get("State")||null;this.data.stateModel=t.get("StateModel")||null}else{this.data.state=null;this.data.stateModel=null}}}class LinkAnnotation extends Annotation{constructor(e){super(e);const{dict:t,annotationGlobals:a}=e;this.data.annotationType=T;this.data.noHTML=!1;const r=getQuadPoints(t,this.rectangle);r&&(this.data.quadPoints=r);this.data.borderColor||=this.data.color;Catal\
-og.parseDestDictionary({destDict:t,resultObj:this.data,docBaseUrl:a.baseUrl,docAttachments:a.attachments})}get overlaysTextContent(){return!0}}class PopupAnnotation extends Annotation{constructor(e){super(e);const{dict:t}=e;this.data.annotationType=W;this.data.noHTML=!1;0!==this.width&&0!==this.height||(this.data.rect=null);let a=t.get("Parent");if(!a){warn("Popup annotation has a missing or invalid parent annotation.");return}this.data.parentRect=lookupNormalRect(a.getArray("Rect"),null);this.d\
-ata.creationDate=a.get("CreationDate")||"";isName(a.get("RT"),G)&&(a=a.get("IRT"));if(a.has("M")){this.setModificationDate(a.get("M"));this.data.modificationDate=this.modificationDate}else this.data.modificationDate=null;if(a.has("C")){this.setColor(a.getArray("C"));this.data.color=this.color}else this.data.color=null;if(!this.viewable){const e=a.get("F");this._isViewable(e)&&this.setFlags(e)}this.setTitle(a.get("T"));this.data.titleObj=this._title;this.setContents(a.get("Contents"));this.data.c\
-ontentsObj=this._contents;a.has("RC")&&(this.data.richText=XFAFactory.getRichTextAsHtml(a.get("RC")));this.data.open=!!t.get("Open")}static createNewDict(e,t,a){const{oldAnnotation:r,rect:i,parent:n}=e,s=r||new Dict(t);s.setIfNotExists("Type",Name.get("Annot"));s.setIfNotExists("Subtype",Name.get("Popup"));s.setIfNotExists("Open",!1);s.setIfArray("Rect",i);s.set("Parent",n);return s}static async createNewAppearanceStream(e,t,a){return null}}class FreeTextAnnotation extends MarkupAnnotation{const\
-ructor(e){super(e);this.data.hasOwnCanvas=this.data.noRotate;this.data.isEditable=!this.data.noHTML;this.data.noHTML=!1;const{annotationGlobals:t,evaluatorOptions:a,xref:r}=e;this.data.annotationType=O;this.setDefaultAppearance(e);this._hasAppearance=!!this.appearance;if(this._hasAppearance){const{fontColor:e,fontSize:i}=function parseAppearanceStream(e,t,a,r){return new AppearanceStreamEvaluator(e,t,a,r).parse()}(this.appearance,a,r,t.globalColorSpaceCache);this.data.defaultAppearanceData.fontC\
-olor=e;this.data.defaultAppearanceData.fontSize=i||10}else{this.data.defaultAppearanceData.fontSize||=10;const{fontColor:t,fontSize:a}=this.data.defaultAppearanceData;if(this._contents.str){this.data.textContent=this._contents.str.split(/\\r\\n?|\\n/).map((e=>e.trimEnd()));const{coords:e,bbox:t,matrix:r}=FakeUnicodeFont.getFirstPositionInfo(this.rectangle,this.rotation,a);this.data.textPosition=this._transformPoint(e,t,r)}if(this._isOffscreenCanvasSupported){const i=e.dict.get("CA"),n=new FakeUnico\
-deFont(r,"sans-serif");this.appearance=n.createAppearance(this._contents.str,this.rectangle,this.rotation,a,t,i);this._streams.push(this.appearance)}else warn("FreeTextAnnotation: OffscreenCanvas is not supported, annotation may not render correctly.")}}get hasTextContent(){return this._hasAppearance}static createNewDict(e,t,{apRef:a,ap:r}){const{color:i,date:n,fontSize:s,oldAnnotation:o,rect:c,rotation:l,user:h,value:u}=e,d=o||new Dict(t);d.setIfNotExists("Type",Name.get("Annot"));d.setIfNotExi\
-sts("Subtype",Name.get("FreeText"));d.set(o?"M":"CreationDate",`D:${getModificationDate(n)}`);o&&d.delete("RC");d.setIfArray("Rect",c);const f=`/Helv ${s} Tf ${getPdfColor(i,!0)}`;d.set("DA",f);d.setIfDefined("Contents",stringToAsciiOrUTF16BE(u));d.setIfNotExists("F",4);d.setIfNotExists("Border",[0,0,0]);d.setIfNumber("Rotate",l);d.setIfDefined("T",stringToAsciiOrUTF16BE(h));if(a||r){const e=new Dict(t);d.set("AP",e);e.set("N",a||r)}return d}static async createNewAppearanceStream(e,t,r){const{ba\
-seFontRef:i,evaluator:n,task:s}=r,{color:o,fontSize:c,rect:l,rotation:h,value:u}=e;if(!o)return null;const d=new Dict(t),f=new Dict(t);if(i)f.set("Helv",i);else{const e=new Dict(t);e.setIfName("BaseFont","Helvetica");e.setIfName("Type","Font");e.setIfName("Subtype","Type1");e.setIfName("Encoding","WinAnsiEncoding");f.set("Helv",e)}d.set("Font",f);const g=await WidgetAnnotation._getFontData(n,s,{fontName:"Helv",fontSize:c},d),[p,m,b,y]=l;let w=b-p,x=y-m;h%180!=0&&([w,x]=[x,w]);const S=u.split("\\n\
-"),k=c/1e3;let C=-1/0;const v=[];for(let e of S){const t=g.encodeString(e);if(t.length>1)return null;e=t.join("");v.push(e);let a=0;const r=g.charsToGlyphs(e);for(const e of r)a+=e.width*k;C=Math.max(C,a)}let F=1;C>w&&(F=w/C);let T=1;const O=a*c,M=1*c,D=O*S.length;D>x&&(T=x/D);const R=c*Math.min(F,T);let N,E,L;switch(h){case 0:L=[1,0,0,1];E=[l[0],l[1],w,x];N=[l[0],l[3]-M];break;case 90:L=[0,1,-1,0];E=[l[1],-l[2],w,x];N=[l[1],-l[0]-M];break;case 180:L=[-1,0,0,-1];E=[-l[2],-l[3],w,x];N=[-l[2],-l[1\
-]-M];break;case 270:L=[0,-1,1,0];E=[-l[3],l[0],w,x];N=[-l[3],l[2]-M]}const j=["q",`${L.join(" ")} 0 0 cm`,`${E.join(" ")} re W n`,"BT",`${getPdfColor(o,!0)}`,`0 Tc /Helv ${numberToString(R)} Tf`];j.push(`${N.join(" ")} Td (${escapeString(v[0])}) Tj`);const _=numberToString(O);for(let e=1,t=v.length;e<t;e++){const t=v[e];j.push(`0 -${_} Td (${escapeString(t)}) Tj`)}j.push("ET","Q");const U=j.join("\\n"),X=new Dict(t);X.set("FormType",1);X.setIfName("Subtype","Form");X.setIfName("Type","XObject");X\
-.set("BBox",l);X.set("Resources",d);X.set("Matrix",[1,0,0,1,-l[0],-l[1]]);const q=new StringStream(U);q.dict=X;return q}}class LineAnnotation extends MarkupAnnotation{constructor(e){super(e);const{dict:t,xref:a}=e;this.data.annotationType=M;this.data.hasOwnCanvas=this.data.noRotate;this.data.noHTML=!1;const r=lookupRect(t.getArray("L"),[0,0,0,0]);this.data.lineCoordinates=Util.normalizeRect(r);this.setLineEndings(t.getArray("LE"));this.data.lineEndings=this.lineEndings;if(!this.appearance){const\
- e=getPdfColorArray(this.color,[0,0,0]),i=t.get("CA"),n=getPdfColorArray(getRgbColor(t.getArray("IC"),null)),s=n?i:null,o=this.borderStyle.width||1,c=2*o,l=[this.data.lineCoordinates[0]-c,this.data.lineCoordinates[1]-c,this.data.lineCoordinates[2]+c,this.data.lineCoordinates[3]+c];Util.intersect(this.rectangle,l)||(this.rectangle=l);this._setDefaultAppearance({xref:a,extra:`${o} w`,strokeColor:e,fillColor:n,strokeAlpha:i,fillAlpha:s,pointsCallback:(e,t)=>{e.push(`${r[0]} ${r[1]} m`,`${r[2]} ${r[\
-3]} l`,"S");return[t[0]-o,t[7]-o,t[2]+o,t[3]+o]}})}}}class SquareAnnotation extends MarkupAnnotation{constructor(e){super(e);const{dict:t,xref:a}=e;this.data.annotationType=D;this.data.hasOwnCanvas=this.data.noRotate;this.data.noHTML=!1;if(!this.appearance){const e=getPdfColorArray(this.color,[0,0,0]),r=t.get("CA"),i=getPdfColorArray(getRgbColor(t.getArray("IC"),null)),n=i?r:null;if(0===this.borderStyle.width&&!i)return;this._setDefaultAppearance({xref:a,extra:`${this.borderStyle.width} w`,strok\
-eColor:e,fillColor:i,strokeAlpha:r,fillAlpha:n,pointsCallback:(e,t)=>{const a=t[4]+this.borderStyle.width/2,r=t[5]+this.borderStyle.width/2,n=t[6]-t[4]-this.borderStyle.width,s=t[3]-t[7]-this.borderStyle.width;e.push(`${a} ${r} ${n} ${s} re`);i?e.push("B"):e.push("S");return[t[0],t[7],t[2],t[3]]}})}}}class CircleAnnotation extends MarkupAnnotation{constructor(e){super(e);const{dict:t,xref:a}=e;this.data.annotationType=R;if(!this.appearance){const e=getPdfColorArray(this.color,[0,0,0]),r=t.get("C\
-A"),i=getPdfColorArray(getRgbColor(t.getArray("IC"),null)),n=i?r:null;if(0===this.borderStyle.width&&!i)return;const s=4/3*Math.tan(Math.PI/8);this._setDefaultAppearance({xref:a,extra:`${this.borderStyle.width} w`,strokeColor:e,fillColor:i,strokeAlpha:r,fillAlpha:n,pointsCallback:(e,t)=>{const a=t[0]+this.borderStyle.width/2,r=t[1]-this.borderStyle.width/2,n=t[6]-this.borderStyle.width/2,o=t[7]+this.borderStyle.width/2,c=a+(n-a)/2,l=r+(o-r)/2,h=(n-a)/2*s,u=(o-r)/2*s;e.push(`${c} ${o} m`,`${c+h} \
-${o} ${n} ${l+u} ${n} ${l} c`,`${n} ${l-u} ${c+h} ${r} ${c} ${r} c`,`${c-h} ${r} ${a} ${l-u} ${a} ${l} c`,`${a} ${l+u} ${c-h} ${o} ${c} ${o} c`,"h");i?e.push("B"):e.push("S");return[t[0],t[7],t[2],t[3]]}})}}}class PolylineAnnotation extends MarkupAnnotation{constructor(e){super(e);const{dict:t,xref:a}=e;this.data.annotationType=E;this.data.hasOwnCanvas=this.data.noRotate;this.data.noHTML=!1;this.data.vertices=null;if(!(this instanceof PolygonAnnotation)){this.setLineEndings(t.getArray("LE"));thi\
-s.data.lineEndings=this.lineEndings}const r=t.getArray("Vertices");if(!isNumberArray(r,null))return;const i=this.data.vertices=Float32Array.from(r);if(!this.appearance){const e=getPdfColorArray(this.color,[0,0,0]),r=t.get("CA");let n,s=getRgbColor(t.getArray("IC"),null);s&&(s=getPdfColorArray(s));n=s?this.color?s.every(((t,a)=>t===e[a]))?"f":"B":"f":"S";const o=this.borderStyle.width||1,c=2*o,l=[1/0,1/0,-1/0,-1/0];for(let e=0,t=i.length;e<t;e+=2)Util.rectBoundingBox(i[e]-c,i[e+1]-c,i[e]+c,i[e+1]\
-+c,l);Util.intersect(this.rectangle,l)||(this.rectangle=l);this._setDefaultAppearance({xref:a,extra:`${o} w`,strokeColor:e,strokeAlpha:r,fillColor:s,fillAlpha:s?r:null,pointsCallback:(e,t)=>{for(let t=0,a=i.length;t<a;t+=2)e.push(`${i[t]} ${i[t+1]} ${0===t?"m":"l"}`);e.push(n);return[t[0],t[7],t[2],t[3]]}})}}}class PolygonAnnotation extends PolylineAnnotation{constructor(e){super(e);this.data.annotationType=N}}class CaretAnnotation extends MarkupAnnotation{constructor(e){super(e);this.data.annot\
-ationType=q}}class InkAnnotation extends MarkupAnnotation{constructor(e){super(e);this.data.hasOwnCanvas=this.data.noRotate;this.data.noHTML=!1;const{dict:t,xref:a}=e;this.data.annotationType=H;this.data.inkLists=[];this.data.isEditable=!this.data.noHTML;this.data.noHTML=!1;this.data.opacity=t.get("CA")||1;const r=t.getArray("InkList");if(Array.isArray(r)){for(let e=0,t=r.length;e<t;++e){if(!Array.isArray(r[e]))continue;const t=new Float32Array(r[e].length);this.data.inkLists.push(t);for(let i=0\
-,n=r[e].length;i<n;i+=2){const n=a.fetchIfRef(r[e][i]),s=a.fetchIfRef(r[e][i+1]);if("number"==typeof n&&"number"==typeof s){t[i]=n;t[i+1]=s}}}if(!this.appearance){const e=getPdfColorArray(this.color,[0,0,0]),r=t.get("CA"),i=this.borderStyle.width||1,n=2*i,s=[1/0,1/0,-1/0,-1/0];for(const e of this.data.inkLists)for(let t=0,a=e.length;t<a;t+=2)Util.rectBoundingBox(e[t]-n,e[t+1]-n,e[t]+n,e[t+1]+n,s);Util.intersect(this.rectangle,s)||(this.rectangle=s);this._setDefaultAppearance({xref:a,extra:`${i} \
-w`,strokeColor:e,strokeAlpha:r,pointsCallback:(e,t)=>{for(const t of this.data.inkLists){for(let a=0,r=t.length;a<r;a+=2)e.push(`${t[a]} ${t[a+1]} ${0===a?"m":"l"}`);e.push("S")}return[t[0],t[7],t[2],t[3]]}})}}}static createNewDict(e,t,{apRef:a,ap:r}){const{oldAnnotation:i,color:n,date:s,opacity:o,paths:c,outlines:l,rect:h,rotation:u,thickness:d,user:f}=e,g=i||new Dict(t);g.setIfNotExists("Type",Name.get("Annot"));g.setIfNotExists("Subtype",Name.get("Ink"));g.set(i?"M":"CreationDate",`D:${getMod\
-ificationDate(s)}`);g.setIfArray("Rect",h);g.setIfArray("InkList",l?.points||c?.points);g.setIfNotExists("F",4);g.setIfNumber("Rotate",u);g.setIfDefined("T",stringToAsciiOrUTF16BE(f));l&&g.setIfName("IT","InkHighlight");if(d>0){const e=new Dict(t);g.set("BS",e);e.set("W",d)}g.setIfArray("C",getPdfColorArray(n));g.setIfNumber("CA",o);if(r||a){const e=new Dict(t);g.set("AP",e);e.set("N",a||r)}return g}static async createNewAppearanceStream(e,t,a){if(e.outlines)return this.createNewAppearanceStream\
-ForHighlight(e,t,a);const{color:r,rect:i,paths:n,thickness:s,opacity:o}=e;if(!r)return null;const c=[`${s} w 1 J 1 j`,`${getPdfColor(r,!1)}`];1!==o&&c.push("/R0 gs");for(const e of n.lines){c.push(`${numberToString(e[4])} ${numberToString(e[5])} m`);for(let t=6,a=e.length;t<a;t+=6)if(isNaN(e[t]))c.push(`${numberToString(e[t+4])} ${numberToString(e[t+5])} l`);else{const[a,r,i,n,s,o]=e.slice(t,t+6);c.push([a,r,i,n,s,o].map(numberToString).join(" ")+" c")}6===e.length&&c.push(`${numberToString(e[4]\
-)} ${numberToString(e[5])} l`)}c.push("S");const l=c.join("\\n"),h=new Dict(t);h.set("FormType",1);h.setIfName("Subtype","Form");h.setIfName("Type","XObject");h.set("BBox",i);h.set("Length",l.length);if(1!==o){const e=new Dict(t),a=new Dict(t),r=new Dict(t);r.set("CA",o);r.setIfName("Type","ExtGState");a.set("R0",r);e.set("ExtGState",a);h.set("Resources",e)}const u=new StringStream(l);u.dict=h;return u}static async createNewAppearanceStreamForHighlight(e,t,a){const{color:r,rect:i,outlines:{outlin\
-e:n},opacity:s}=e;if(!r)return null;const o=[`${getPdfColor(r,!0)}`,"/R0 gs"];o.push(`${numberToString(n[4])} ${numberToString(n[5])} m`);for(let e=6,t=n.length;e<t;e+=6)if(isNaN(n[e]))o.push(`${numberToString(n[e+4])} ${numberToString(n[e+5])} l`);else{const[t,a,r,i,s,c]=n.slice(e,e+6);o.push([t,a,r,i,s,c].map(numberToString).join(" ")+" c")}o.push("h f");const c=o.join("\\n"),l=new Dict(t);l.set("FormType",1);l.setIfName("Subtype","Form");l.setIfName("Type","XObject");l.set("BBox",i);l.set("Len\
-gth",c.length);const h=new Dict(t),u=new Dict(t);h.set("ExtGState",u);l.set("Resources",h);const d=new Dict(t);u.set("R0",d);d.setIfName("BM","Multiply");if(1!==s){d.set("ca",s);d.setIfName("Type","ExtGState")}const f=new StringStream(c);f.dict=l;return f}}class HighlightAnnotation extends MarkupAnnotation{constructor(e){super(e);const{dict:t,xref:a}=e;this.data.annotationType=L;this.data.isEditable=!this.data.noHTML;this.data.noHTML=!1;this.data.opacity=t.get("CA")||1;if(this.data.quadPoints=ge\
-tQuadPoints(t,null)){const e=this.appearance?.dict.get("Resources");if(!this.appearance||!e?.has("ExtGState")){this.appearance&&warn("HighlightAnnotation - ignoring built-in appearance stream.");const e=getPdfColorArray(this.color,[1,1,0]),r=t.get("CA");this._setDefaultAppearance({xref:a,fillColor:e,blendMode:"Multiply",fillAlpha:r,pointsCallback:(e,t)=>{e.push(`${t[0]} ${t[1]} m`,`${t[2]} ${t[3]} l`,`${t[6]} ${t[7]} l`,`${t[4]} ${t[5]} l`,"f");return[t[0],t[7],t[2],t[3]]}})}}else this.data.popu\
-pRef=null}get overlaysTextContent(){return!0}static createNewDict(e,t,{apRef:a,ap:r}){const{color:i,date:n,oldAnnotation:s,opacity:o,rect:c,rotation:l,user:h,quadPoints:u}=e,d=s||new Dict(t);d.setIfNotExists("Type",Name.get("Annot"));d.setIfNotExists("Subtype",Name.get("Highlight"));d.set(s?"M":"CreationDate",`D:${getModificationDate(n)}`);d.setIfArray("Rect",c);d.setIfNotExists("F",4);d.setIfNotExists("Border",[0,0,0]);d.setIfNumber("Rotate",l);d.setIfArray("QuadPoints",u);d.setIfArray("C",getP\
-dfColorArray(i));d.setIfNumber("CA",o);d.setIfDefined("T",stringToAsciiOrUTF16BE(h));if(a||r){const e=new Dict(t);d.set("AP",e);e.set("N",a||r)}return d}static async createNewAppearanceStream(e,t,a){const{color:r,rect:i,outlines:n,opacity:s}=e;if(!r)return null;const o=[`${getPdfColor(r,!0)}`,"/R0 gs"],c=[];for(const e of n){c.length=0;c.push(`${numberToString(e[0])} ${numberToString(e[1])} m`);for(let t=2,a=e.length;t<a;t+=2)c.push(`${numberToString(e[t])} ${numberToString(e[t+1])} l`);c.push("\
-h");o.push(c.join("\\n"))}o.push("f*");const l=o.join("\\n"),h=new Dict(t);h.set("FormType",1);h.setIfName("Subtype","Form");h.setIfName("Type","XObject");h.set("BBox",i);h.set("Length",l.length);const u=new Dict(t),d=new Dict(t);u.set("ExtGState",d);h.set("Resources",u);const f=new Dict(t);d.set("R0",f);f.setIfName("BM","Multiply");if(1!==s){f.set("ca",s);f.setIfName("Type","ExtGState")}const g=new StringStream(l);g.dict=h;return g}}class UnderlineAnnotation extends MarkupAnnotation{constructor(e\
-){super(e);const{dict:t,xref:a}=e;this.data.annotationType=j;if(this.data.quadPoints=getQuadPoints(t,null)){if(!this.appearance){const e=getPdfColorArray(this.color,[0,0,0]),r=t.get("CA");this._setDefaultAppearance({xref:a,extra:"[] 0 d 0.571 w",strokeColor:e,strokeAlpha:r,pointsCallback:(e,t)=>{e.push(`${t[4]} ${t[5]+1.3} m`,`${t[6]} ${t[7]+1.3} l`,"S");return[t[0],t[7],t[2],t[3]]}})}}else this.data.popupRef=null}get overlaysTextContent(){return!0}}class SquigglyAnnotation extends MarkupAnnotat\
-ion{constructor(e){super(e);const{dict:t,xref:a}=e;this.data.annotationType=_;if(this.data.quadPoints=getQuadPoints(t,null)){if(!this.appearance){const e=getPdfColorArray(this.color,[0,0,0]),r=t.get("CA");this._setDefaultAppearance({xref:a,extra:"[] 0 d 1 w",strokeColor:e,strokeAlpha:r,pointsCallback:(e,t)=>{const a=(t[1]-t[5])/6;let r=a,i=t[4];const n=t[5],s=t[6];e.push(`${i} ${n+r} m`);do{i+=2;r=0===r?a:0;e.push(`${i} ${n+r} l`)}while(i<s);e.push("S");return[t[4],n-2*a,s,n+2*a]}})}}else this.d\
-ata.popupRef=null}get overlaysTextContent(){return!0}}class StrikeOutAnnotation extends MarkupAnnotation{constructor(e){super(e);const{dict:t,xref:a}=e;this.data.annotationType=U;if(this.data.quadPoints=getQuadPoints(t,null)){if(!this.appearance){const e=getPdfColorArray(this.color,[0,0,0]),r=t.get("CA");this._setDefaultAppearance({xref:a,extra:"[] 0 d 1 w",strokeColor:e,strokeAlpha:r,pointsCallback:(e,t)=>{e.push((t[0]+t[4])/2+" "+(t[1]+t[5])/2+" m",(t[2]+t[6])/2+" "+(t[3]+t[7])/2+" l","S");ret\
-urn[t[0],t[7],t[2],t[3]]}})}}else this.data.popupRef=null}get overlaysTextContent(){return!0}}class StampAnnotation extends MarkupAnnotation{#pe=null;constructor(e){super(e);this.data.annotationType=X;this.data.hasOwnCanvas=this.data.noRotate;this.data.isEditable=!this.data.noHTML;this.data.noHTML=!1}mustBeViewedWhenEditing(e,t=null){if(e){if(!this.data.isEditable)return!0;this.#pe??=this.data.hasOwnCanvas;this.data.hasOwnCanvas=!0;return!0}if(null!==this.#pe){this.data.hasOwnCanvas=this.#pe;thi\
-s.#pe=null}return!t?.has(this.data.id)}static async createImage(e,t){const{width:a,height:r}=e,i=new OffscreenCanvas(a,r),n=i.getContext("2d",{alpha:!0});n.drawImage(e,0,0);const s=n.getImageData(0,0,a,r).data,o=new Uint32Array(s.buffer),c=o.some(FeatureTest.isLittleEndian?e=>e>>>24!=255:e=>!!(255&~e));if(c){n.fillStyle="white";n.fillRect(0,0,a,r);n.drawImage(e,0,0)}const l=i.convertToBlob({type:"image/jpeg",quality:1}).then((e=>e.arrayBuffer())),h=Name.get("XObject"),u=Name.get("Image"),d=new D\
-ict(t);d.set("Type",h);d.set("Subtype",u);d.set("BitsPerComponent",8);d.setIfName("ColorSpace","DeviceRGB");d.setIfName("Filter","DCTDecode");d.set("BBox",[0,0,a,r]);d.set("Width",a);d.set("Height",r);let f=null;if(c){const e=new Uint8Array(o.length);if(FeatureTest.isLittleEndian)for(let t=0,a=o.length;t<a;t++)e[t]=o[t]>>>24;else for(let t=0,a=o.length;t<a;t++)e[t]=255&o[t];const i=new Dict(t);i.set("Type",h);i.set("Subtype",u);i.set("BitsPerComponent",8);i.setIfName("ColorSpace","DeviceGray");i\
-.set("Width",a);i.set("Height",r);f=new Stream(e,0,0,i)}return{imageStream:new Stream(await l,0,0,d),smaskStream:f,width:a,height:r}}static createNewDict(e,t,{apRef:a,ap:r}){const{date:i,oldAnnotation:n,rect:s,rotation:o,user:c}=e,l=n||new Dict(t);l.setIfNotExists("Type",Name.get("Annot"));l.setIfNotExists("Subtype",Name.get("Stamp"));l.set(n?"M":"CreationDate",`D:${getModificationDate(i)}`);l.setIfArray("Rect",s);l.setIfNotExists("F",4);l.setIfNotExists("Border",[0,0,0]);l.setIfNumber("Rotate",\
-o);l.setIfDefined("T",stringToAsciiOrUTF16BE(c));if(a||r){const e=new Dict(t);l.set("AP",e);e.set("N",a||r)}return l}static async#me(e,t){const{areContours:a,color:r,rect:i,lines:n,thickness:s}=e;if(!r)return null;const o=[`${s} w 1 J 1 j`,`${getPdfColor(r,a)}`];for(const e of n){o.push(`${numberToString(e[4])} ${numberToString(e[5])} m`);for(let t=6,a=e.length;t<a;t+=6)if(isNaN(e[t]))o.push(`${numberToString(e[t+4])} ${numberToString(e[t+5])} l`);else{const[a,r,i,n,s,c]=e.slice(t,t+6);o.push([a\
-,r,i,n,s,c].map(numberToString).join(" ")+" c")}6===e.length&&o.push(`${numberToString(e[4])} ${numberToString(e[5])} l`)}o.push(a?"F":"S");const c=o.join("\\n"),l=new Dict(t);l.set("FormType",1);l.setIfName("Subtype","Form");l.setIfName("Type","XObject");l.set("BBox",i);l.set("Length",c.length);const h=new StringStream(c);h.dict=l;return h}static async createNewAppearanceStream(e,t,a){if(e.oldAnnotation)return null;if(e.isSignature)return this.#me(e,t);const{rotation:r}=e,{imageRef:i,width:n,hei\
-ght:s}=a.image,o=new Dict(t),c=new Dict(t);o.set("XObject",c);c.set("Im0",i);const l=`q ${n} 0 0 ${s} 0 0 cm /Im0 Do Q`,h=new Dict(t);h.set("FormType",1);h.setIfName("Subtype","Form");h.setIfName("Type","XObject");h.set("BBox",[0,0,n,s]);h.set("Resources",o);if(r){const e=getRotationMatrix(r,n,s);h.set("Matrix",e)}const u=new StringStream(l);u.dict=h;return u}}class FileAttachmentAnnotation extends MarkupAnnotation{constructor(e){super(e);const{dict:t,xref:a}=e,r=new FileSpec(t.get("FS"),a);this\
-.data.annotationType=z;this.data.hasOwnCanvas=this.data.noRotate;this.data.noHTML=!1;this.data.file=r.serializable;const i=t.get("Name");this.data.name=i instanceof Name?stringToPDFString(i.name):"PushPin";const n=t.get("ca");this.data.fillAlpha="number"==typeof n&&n>=0&&n<=1?n:null}}const pc={get r(){return shadow(this,"r",new Uint8Array([7,12,17,22,7,12,17,22,7,12,17,22,7,12,17,22,5,9,14,20,5,9,14,20,5,9,14,20,5,9,14,20,4,11,16,23,4,11,16,23,4,11,16,23,4,11,16,23,6,10,15,21,6,10,15,21,6,10,15,\
-21,6,10,15,21]))},get k(){return shadow(this,"k",new Int32Array([-680876936,-389564586,606105819,-1044525330,-176418897,1200080426,-1473231341,-45705983,1770035416,-1958414417,-42063,-1990404162,1804603682,-40341101,-1502002290,1236535329,-165796510,-1069501632,643717713,-373897302,-701558691,38016083,-660478335,-405537848,568446438,-1019803690,-187363961,1163531501,-1444681467,-51403784,1735328473,-1926607734,-378558,-2022574463,1839030562,-35309556,-1530992060,1272893353,-155497632,-1094730640\
-,681279174,-358537222,-722521979,76029189,-640364487,-421815835,530742520,-995338651,-198630844,1126891415,-1416354905,-57434055,1700485571,-1894986606,-1051523,-2054922799,1873313359,-30611744,-1560198380,1309151649,-145523070,-1120210379,718787259,-343485551]))}};function calculateMD5(e,t,a){let r=1732584193,i=-271733879,n=-1732584194,s=271733878;const o=a+72&-64,c=new Uint8Array(o);let l,h;for(l=0;l<a;++l)c[l]=e[t++];c[l++]=128;const u=o-8;l<u&&(l=u);c[l++]=a<<3&255;c[l++]=a>>5&255;c[l++]=a>>\
-13&255;c[l++]=a>>21&255;c[l++]=a>>>29&255;l+=3;const d=new Int32Array(16),{k:f,r:g}=pc;for(l=0;l<o;){for(h=0;h<16;++h,l+=4)d[h]=c[l]|c[l+1]<<8|c[l+2]<<16|c[l+3]<<24;let e,t,a=r,o=i,u=n,p=s;for(h=0;h<64;++h){if(h<16){e=o&u|~o&p;t=h}else if(h<32){e=p&o|~p&u;t=5*h+1&15}else if(h<48){e=o^u^p;t=3*h+5&15}else{e=u^(o|~p);t=7*h&15}const r=p,i=a+e+f[h]+d[t]|0,n=g[h];p=u;u=o;o=o+(i<<n|i>>>32-n)|0;a=r}r=r+a|0;i=i+o|0;n=n+u|0;s=s+p|0}return new Uint8Array([255&r,r>>8&255,r>>16&255,r>>>24&255,255&i,i>>8&255,\
-i>>16&255,i>>>24&255,255&n,n>>8&255,n>>16&255,n>>>24&255,255&s,s>>8&255,s>>16&255,s>>>24&255])}function decodeString(e){try{return stringToUTF8String(e)}catch(t){warn(`UTF-8 decoding failed: "${t}".`);return e}}class DatasetXMLParser extends SimpleXMLParser{constructor(e){super(e);this.node=null}onEndElement(e){const t=super.onEndElement(e);if(t&&"xfa:datasets"===e){this.node=t;throw new Error("Aborting DatasetXMLParser.")}}}class DatasetReader{constructor(e){if(e.datasets)this.node=new SimpleXM\
-LParser({hasAttributes:!0}).parseFromString(e.datasets).documentElement;else{const t=new DatasetXMLParser({hasAttributes:!0});try{t.parseFromString(e["xdp:xdp"])}catch{}this.node=t.node}}getValue(e){if(!this.node||!e)return"";const t=this.node.searchNode(parseXFAPath(e),0);if(!t)return"";const a=t.firstChild;return"value"===a?.nodeName?t.children.map((e=>decodeString(e.textContent))):decodeString(t.textContent)}}class SingleIntersector{#be;#ye=1/0;#we=1/0;#xe=-1/0;#Se=-1/0;#Ae=null;#ke=[];#Ce=[]\
-;#ve=-1;#Fe=!1;constructor(e){this.#be=e;const t=e.data.quadPoints;if(t){for(let e=0,a=t.length;e<a;e+=8){this.#ye=Math.min(this.#ye,t[e]);this.#xe=Math.max(this.#xe,t[e+2]);this.#we=Math.min(this.#we,t[e+5]);this.#Se=Math.max(this.#Se,t[e+1])}t.length>8&&(this.#Ae=t)}else[this.#ye,this.#we,this.#xe,this.#Se]=e.data.rect}overlaps(e){return!(this.#ye>=e.#xe||this.#xe<=e.#ye||this.#we>=e.#Se||this.#Se<=e.#we)}#Ie(e,t){if(this.#ye>=e||this.#xe<=e||this.#we>=t||this.#Se<=t)return!1;const a=this.#Ae;\
-if(!a)return!0;if(this.#ve>=0){const r=this.#ve;if(!(a[r]>=e||a[r+2]<=e||a[r+5]>=t||a[r+1]<=t))return!0;this.#ve=-1}for(let r=0,i=a.length;r<i;r+=8)if(!(a[r]>=e||a[r+2]<=e||a[r+5]>=t||a[r+1]<=t)){this.#ve=r;return!0}return!1}addGlyph(e,t,a){if(!this.#Ie(e,t)){this.disableExtraChars();return!1}if(this.#Ce.length>0){this.#ke.push(this.#Ce.join(""));this.#Ce.length=0}this.#ke.push(a);this.#Fe=!0;return!0}addExtraChar(e){this.#Fe&&this.#Ce.push(e)}disableExtraChars(){if(this.#Fe){this.#Fe=!1;this.#C\
-e.length=0}}setText(){this.#be.data.overlaidText=this.#ke.join("")}}class Intersector{#Te=new Map;constructor(e){for(const t of e){if(!t.data.quadPoints&&!t.data.rect)continue;const e=new SingleIntersector(t);for(const[t,a]of this.#Te)t.overlaps(e)&&(a?a.add(e):this.#Te.set(t,new Set([e])));this.#Te.set(e,null)}}addGlyph(e,t,a,r){const i=e[4]+t/2,n=e[5]+a/2;let s;for(const[e,t]of this.#Te)s?s.has(e)?e.addGlyph(i,n,r):e.disableExtraChars():e.addGlyph(i,n,r)&&(s=t)}addExtraChar(e){for(const t of t\
-his.#Te.keys())t.addExtraChar(e)}setText(){for(const e of this.#Te.keys())e.setText()}}class Word64{constructor(e,t){this.high=0|e;this.low=0|t}and(e){this.high&=e.high;this.low&=e.low}xor(e){this.high^=e.high;this.low^=e.low}shiftRight(e){if(e>=32){this.low=this.high>>>e-32|0;this.high=0}else{this.low=this.low>>>e|this.high<<32-e;this.high=this.high>>>e|0}}rotateRight(e){let t,a;if(32&e){a=this.low;t=this.high}else{t=this.low;a=this.high}e&=31;this.low=t>>>e|a<<32-e;this.high=a>>>e|t<<32-e}not(\
-){this.high=~this.high;this.low=~this.low}add(e){const t=(this.low>>>0)+(e.low>>>0);let a=(this.high>>>0)+(e.high>>>0);t>4294967295&&(a+=1);this.low=0|t;this.high=0|a}copyTo(e,t){e[t]=this.high>>>24&255;e[t+1]=this.high>>16&255;e[t+2]=this.high>>8&255;e[t+3]=255&this.high;e[t+4]=this.low>>>24&255;e[t+5]=this.low>>16&255;e[t+6]=this.low>>8&255;e[t+7]=255&this.low}assign(e){this.high=e.high;this.low=e.low}}const mc={get k(){return shadow(this,"k",[new Word64(1116352408,3609767458),new Word64(18994\
-47441,602891725),new Word64(3049323471,3964484399),new Word64(3921009573,2173295548),new Word64(961987163,4081628472),new Word64(1508970993,3053834265),new Word64(2453635748,2937671579),new Word64(2870763221,3664609560),new Word64(3624381080,2734883394),new Word64(310598401,1164996542),new Word64(607225278,1323610764),new Word64(1426881987,3590304994),new Word64(1925078388,4068182383),new Word64(2162078206,991336113),new Word64(2614888103,633803317),new Word64(3248222580,3479774868),new Word64(3\
-835390401,2666613458),new Word64(4022224774,944711139),new Word64(264347078,2341262773),new Word64(604807628,2007800933),new Word64(770255983,1495990901),new Word64(1249150122,1856431235),new Word64(1555081692,3175218132),new Word64(1996064986,2198950837),new Word64(2554220882,3999719339),new Word64(2821834349,766784016),new Word64(2952996808,2566594879),new Word64(3210313671,3203337956),new Word64(3336571891,1034457026),new Word64(3584528711,2466948901),new Word64(113926993,3758326383),new Word\
-64(338241895,168717936),new Word64(666307205,1188179964),new Word64(773529912,1546045734),new Word64(1294757372,1522805485),new Word64(1396182291,2643833823),new Word64(1695183700,2343527390),new Word64(1986661051,1014477480),new Word64(2177026350,1206759142),new Word64(2456956037,344077627),new Word64(2730485921,1290863460),new Word64(2820302411,3158454273),new Word64(3259730800,3505952657),new Word64(3345764771,106217008),new Word64(3516065817,3606008344),new Word64(3600352804,1432725776),new \
-Word64(4094571909,1467031594),new Word64(275423344,851169720),new Word64(430227734,3100823752),new Word64(506948616,1363258195),new Word64(659060556,3750685593),new Word64(883997877,3785050280),new Word64(958139571,3318307427),new Word64(1322822218,3812723403),new Word64(1537002063,2003034995),new Word64(1747873779,3602036899),new Word64(1955562222,1575990012),new Word64(2024104815,1125592928),new Word64(2227730452,2716904306),new Word64(2361852424,442776044),new Word64(2428436474,593698344),new\
- Word64(2756734187,3733110249),new Word64(3204031479,2999351573),new Word64(3329325298,3815920427),new Word64(3391569614,3928383900),new Word64(3515267271,566280711),new Word64(3940187606,3454069534),new Word64(4118630271,4000239992),new Word64(116418474,1914138554),new Word64(174292421,2731055270),new Word64(289380356,3203993006),new Word64(460393269,320620315),new Word64(685471733,587496836),new Word64(852142971,1086792851),new Word64(1017036298,365543100),new Word64(1126000580,2618297676),new\
- Word64(1288033470,3409855158),new Word64(1501505948,4234509866),new Word64(1607167915,987167468),new Word64(1816402316,1246189591)])}};function ch(e,t,a,r,i){e.assign(t);e.and(a);i.assign(t);i.not();i.and(r);e.xor(i)}function maj(e,t,a,r,i){e.assign(t);e.and(a);i.assign(t);i.and(r);e.xor(i);i.assign(a);i.and(r);e.xor(i)}function sigma(e,t,a){e.assign(t);e.rotateRight(28);a.assign(t);a.rotateRight(34);e.xor(a);a.assign(t);a.rotateRight(39);e.xor(a)}function sigmaPrime(e,t,a){e.assign(t);e.rotate\
-Right(14);a.assign(t);a.rotateRight(18);e.xor(a);a.assign(t);a.rotateRight(41);e.xor(a)}function littleSigma(e,t,a){e.assign(t);e.rotateRight(1);a.assign(t);a.rotateRight(8);e.xor(a);a.assign(t);a.shiftRight(7);e.xor(a)}function littleSigmaPrime(e,t,a){e.assign(t);e.rotateRight(19);a.assign(t);a.rotateRight(61);e.xor(a);a.assign(t);a.shiftRight(6);e.xor(a)}function calculateSHA512(e,t,a,r=!1){let i,n,s,o,c,l,h,u;if(r){i=new Word64(3418070365,3238371032);n=new Word64(1654270250,914150663);s=new W\
-ord64(2438529370,812702999);o=new Word64(355462360,4144912697);c=new Word64(1731405415,4290775857);l=new Word64(2394180231,1750603025);h=new Word64(3675008525,1694076839);u=new Word64(1203062813,3204075428)}else{i=new Word64(1779033703,4089235720);n=new Word64(3144134277,2227873595);s=new Word64(1013904242,4271175723);o=new Word64(2773480762,1595750129);c=new Word64(1359893119,2917565137);l=new Word64(2600822924,725511199);h=new Word64(528734635,4215389547);u=new Word64(1541459225,327033209)}con\
-st d=128*Math.ceil((a+17)/128),f=new Uint8Array(d);let g,p;for(g=0;g<a;++g)f[g]=e[t++];f[g++]=128;const m=d-16;g<m&&(g=m);g+=11;f[g++]=a>>>29&255;f[g++]=a>>21&255;f[g++]=a>>13&255;f[g++]=a>>5&255;f[g++]=a<<3&255;const b=new Array(80);for(g=0;g<80;g++)b[g]=new Word64(0,0);const{k:y}=mc;let w=new Word64(0,0),x=new Word64(0,0),S=new Word64(0,0),k=new Word64(0,0),C=new Word64(0,0),v=new Word64(0,0),F=new Word64(0,0),T=new Word64(0,0);const O=new Word64(0,0),M=new Word64(0,0),D=new Word64(0,0),R=new \
-Word64(0,0);let N,E;for(g=0;g<d;){for(p=0;p<16;++p){b[p].high=f[g]<<24|f[g+1]<<16|f[g+2]<<8|f[g+3];b[p].low=f[g+4]<<24|f[g+5]<<16|f[g+6]<<8|f[g+7];g+=8}for(p=16;p<80;++p){N=b[p];littleSigmaPrime(N,b[p-2],R);N.add(b[p-7]);littleSigma(D,b[p-15],R);N.add(D);N.add(b[p-16])}w.assign(i);x.assign(n);S.assign(s);k.assign(o);C.assign(c);v.assign(l);F.assign(h);T.assign(u);for(p=0;p<80;++p){O.assign(T);sigmaPrime(D,C,R);O.add(D);ch(D,C,v,F,R);O.add(D);O.add(y[p]);O.add(b[p]);sigma(M,w,R);maj(D,w,x,S,R);M.\
-add(D);N=T;T=F;F=v;v=C;k.add(O);C=k;k=S;S=x;x=w;N.assign(O);N.add(M);w=N}i.add(w);n.add(x);s.add(S);o.add(k);c.add(C);l.add(v);h.add(F);u.add(T)}if(r){E=new Uint8Array(48);i.copyTo(E,0);n.copyTo(E,8);s.copyTo(E,16);o.copyTo(E,24);c.copyTo(E,32);l.copyTo(E,40)}else{E=new Uint8Array(64);i.copyTo(E,0);n.copyTo(E,8);s.copyTo(E,16);o.copyTo(E,24);c.copyTo(E,32);l.copyTo(E,40);h.copyTo(E,48);u.copyTo(E,56)}return E}const bc={get k(){return shadow(this,"k",[1116352408,1899447441,3049323471,3921009573,9\
-61987163,1508970993,2453635748,2870763221,3624381080,310598401,607225278,1426881987,1925078388,2162078206,2614888103,3248222580,3835390401,4022224774,264347078,604807628,770255983,1249150122,1555081692,1996064986,2554220882,2821834349,2952996808,3210313671,3336571891,3584528711,113926993,338241895,666307205,773529912,1294757372,1396182291,1695183700,1986661051,2177026350,2456956037,2730485921,2820302411,3259730800,3345764771,3516065817,3600352804,4094571909,275423344,430227734,506948616,65906055\
-6,883997877,958139571,1322822218,1537002063,1747873779,1955562222,2024104815,2227730452,2361852424,2428436474,2756734187,3204031479,3329325298])}};function rotr(e,t){return e>>>t|e<<32-t}function calculate_sha256_ch(e,t,a){return e&t^~e&a}function calculate_sha256_maj(e,t,a){return e&t^e&a^t&a}function calculate_sha256_sigma(e){return rotr(e,2)^rotr(e,13)^rotr(e,22)}function calculate_sha256_sigmaPrime(e){return rotr(e,6)^rotr(e,11)^rotr(e,25)}function calculate_sha256_littleSigma(e){return rotr\
-(e,7)^rotr(e,18)^e>>>3}function calculateSHA256(e,t,a){let r=1779033703,i=3144134277,n=1013904242,s=2773480762,o=1359893119,c=2600822924,l=528734635,h=1541459225;const u=64*Math.ceil((a+9)/64),d=new Uint8Array(u);let f,g;for(f=0;f<a;++f)d[f]=e[t++];d[f++]=128;const p=u-8;f<p&&(f=p);f+=3;d[f++]=a>>>29&255;d[f++]=a>>21&255;d[f++]=a>>13&255;d[f++]=a>>5&255;d[f++]=a<<3&255;const m=new Uint32Array(64),{k:b}=bc;for(f=0;f<u;){for(g=0;g<16;++g){m[g]=d[f]<<24|d[f+1]<<16|d[f+2]<<8|d[f+3];f+=4}for(g=16;g<6\
-4;++g)m[g]=(rotr(y=m[g-2],17)^rotr(y,19)^y>>>10)+m[g-7]+calculate_sha256_littleSigma(m[g-15])+m[g-16]|0;let e,t,a=r,u=i,p=n,w=s,x=o,S=c,k=l,C=h;for(g=0;g<64;++g){e=C+calculate_sha256_sigmaPrime(x)+calculate_sha256_ch(x,S,k)+b[g]+m[g];t=calculate_sha256_sigma(a)+calculate_sha256_maj(a,u,p);C=k;k=S;S=x;x=w+e|0;w=p;p=u;u=a;a=e+t|0}r=r+a|0;i=i+u|0;n=n+p|0;s=s+w|0;o=o+x|0;c=c+S|0;l=l+k|0;h=h+C|0}var y;return new Uint8Array([r>>24&255,r>>16&255,r>>8&255,255&r,i>>24&255,i>>16&255,i>>8&255,255&i,n>>24&2\
-55,n>>16&255,n>>8&255,255&n,s>>24&255,s>>16&255,s>>8&255,255&s,o>>24&255,o>>16&255,o>>8&255,255&o,c>>24&255,c>>16&255,c>>8&255,255&c,l>>24&255,l>>16&255,l>>8&255,255&l,h>>24&255,h>>16&255,h>>8&255,255&h])}class DecryptStream extends DecodeStream{constructor(e,t,a){super(t);this.str=e;this.dict=e.dict;this.decrypt=a;this.nextChunk=null;this.initialized=!1}readBlock(){let e;if(this.initialized)e=this.nextChunk;else{e=this.str.getBytes(512);this.initialized=!0}if(!e?.length){this.eof=!0;return}this\
-.nextChunk=this.str.getBytes(512);const t=this.nextChunk?.length>0;e=(0,this.decrypt)(e,!t);const a=this.bufferLength,r=a+e.length;this.ensureBuffer(r).set(e,a);this.bufferLength=r}}class ARCFourCipher{constructor(e){this.a=0;this.b=0;const t=new Uint8Array(256),a=e.length;for(let e=0;e<256;++e)t[e]=e;for(let r=0,i=0;r<256;++r){const n=t[r];i=i+n+e[r%a]&255;t[r]=t[i];t[i]=n}this.s=t}encryptBlock(e){let t=this.a,a=this.b;const r=this.s,i=e.length,n=new Uint8Array(i);for(let s=0;s<i;++s){t=t+1&255\
-;const i=r[t];a=a+i&255;const o=r[a];r[t]=o;r[a]=i;n[s]=e[s]^r[i+o&255]}this.a=t;this.b=a;return n}decryptBlock(e){return this.encryptBlock(e)}encrypt(e){return this.encryptBlock(e)}}class NullCipher{decryptBlock(e){return e}encrypt(e){return e}}class AESBaseCipher{_s=new Uint8Array([99,124,119,123,242,107,111,197,48,1,103,43,254,215,171,118,202,130,201,125,250,89,71,240,173,212,162,175,156,164,114,192,183,253,147,38,54,63,247,204,52,165,229,241,113,216,49,21,4,199,35,195,24,150,5,154,7,18,128,2\
-26,235,39,178,117,9,131,44,26,27,110,90,160,82,59,214,179,41,227,47,132,83,209,0,237,32,252,177,91,106,203,190,57,74,76,88,207,208,239,170,251,67,77,51,133,69,249,2,127,80,60,159,168,81,163,64,143,146,157,56,245,188,182,218,33,16,255,243,210,205,12,19,236,95,151,68,23,196,167,126,61,100,93,25,115,96,129,79,220,34,42,144,136,70,238,184,20,222,94,11,219,224,50,58,10,73,6,36,92,194,211,172,98,145,149,228,121,231,200,55,109,141,213,78,169,108,86,244,234,101,122,174,8,186,120,37,46,28,166,180,198,232\
-,221,116,31,75,189,139,138,112,62,181,102,72,3,246,14,97,53,87,185,134,193,29,158,225,248,152,17,105,217,142,148,155,30,135,233,206,85,40,223,140,161,137,13,191,230,66,104,65,153,45,15,176,84,187,22]);_inv_s=new Uint8Array([82,9,106,213,48,54,165,56,191,64,163,158,129,243,215,251,124,227,57,130,155,47,255,135,52,142,67,68,196,222,233,203,84,123,148,50,166,194,35,61,238,76,149,11,66,250,195,78,8,46,161,102,40,217,36,178,118,91,162,73,109,139,209,37,114,248,246,100,134,104,152,22,212,164,92,204,93\
-,101,182,146,108,112,72,80,253,237,185,218,94,21,70,87,167,141,157,132,144,216,171,0,140,188,211,10,247,228,88,5,184,179,69,6,208,44,30,143,202,63,15,2,193,175,189,3,1,19,138,107,58,145,17,65,79,103,220,234,151,242,207,206,240,180,230,115,150,172,116,34,231,173,53,133,226,249,55,232,28,117,223,110,71,241,26,113,29,41,197,137,111,183,98,14,170,24,190,27,252,86,62,75,198,210,121,32,154,219,192,254,120,205,90,244,31,221,168,51,136,7,199,49,177,18,16,89,39,128,236,95,96,81,127,169,25,181,74,13,45,22\
-9,122,159,147,201,156,239,160,224,59,77,174,42,245,176,200,235,187,60,131,83,153,97,23,43,4,126,186,119,214,38,225,105,20,99,85,33,12,125]);_mix=new Uint32Array([0,235474187,470948374,303765277,941896748,908933415,607530554,708780849,1883793496,2118214995,1817866830,1649639237,1215061108,1181045119,1417561698,1517767529,3767586992,4003061179,4236429990,4069246893,3635733660,3602770327,3299278474,3400528769,2430122216,2664543715,2362090238,2193862645,2835123396,2801107407,3035535058,3135740889,36\
-78124923,3576870512,3341394285,3374361702,3810496343,3977675356,4279080257,4043610186,2876494627,2776292904,3076639029,3110650942,2472011535,2640243204,2403728665,2169303058,1001089995,899835584,666464733,699432150,59727847,226906860,530400753,294930682,1273168787,1172967064,1475418501,1509430414,1942435775,2110667444,1876241833,1641816226,2910219766,2743034109,2976151520,3211623147,2505202138,2606453969,2302690252,2269728455,3711829422,3543599269,3240894392,3475313331,3843699074,3943906441,4178\
-062228,4144047775,1306967366,1139781709,1374988112,1610459739,1975683434,2076935265,1775276924,1742315127,1034867998,866637845,566021896,800440835,92987698,193195065,429456164,395441711,1984812685,2017778566,1784663195,1683407248,1315562145,1080094634,1383856311,1551037884,101039829,135050206,437757123,337553864,1042385657,807962610,573804783,742039012,2531067453,2564033334,2328828971,2227573024,2935566865,2700099354,3001755655,3168937228,3868552805,3902563182,4203181171,4102977912,3736164937,35\
-01741890,3265478751,3433712980,1106041591,1340463100,1576976609,1408749034,2043211483,2009195472,1708848333,1809054150,832877231,1068351396,766945465,599762354,159417987,126454664,361929877,463180190,2709260871,2943682380,3178106961,3009879386,2572697195,2538681184,2236228733,2336434550,3509871135,3745345300,3441850377,3274667266,3910161971,3877198648,4110568485,4211818798,2597806476,2497604743,2261089178,2295101073,2733856160,2902087851,3202437046,2968011453,3936291284,3835036895,4136440770,416\
-9408201,3535486456,3702665459,3467192302,3231722213,2051518780,1951317047,1716890410,1750902305,1113818384,1282050075,1584504582,1350078989,168810852,67556463,371049330,404016761,841739592,1008918595,775550814,540080725,3969562369,3801332234,4035489047,4269907996,3569255213,3669462566,3366754619,3332740144,2631065433,2463879762,2160117071,2395588676,2767645557,2868897406,3102011747,3069049960,202008497,33778362,270040487,504459436,875451293,975658646,675039627,641025152,2084704233,1917518562,161\
-5861247,1851332852,1147550661,1248802510,1484005843,1451044056,933301370,967311729,733156972,632953703,260388950,25965917,328671808,496906059,1206477858,1239443753,1543208500,1441952575,2144161806,1908694277,1675577880,1842759443,3610369226,3644379585,3408119516,3307916247,4011190502,3776767469,4077384432,4245618683,2809771154,2842737049,3144396420,3043140495,2673705150,2438237621,2203032232,2370213795]);_mixCol=new Uint8Array(256).map(((e,t)=>t<128?t<<1:t<<1^27));constructor(){this.buffer=new U\
-int8Array(16);this.bufferPosition=0}_expandKey(e){unreachable("Cannot call `_expandKey` on the base class")}_decrypt(e,t){let a,r,i;const n=new Uint8Array(16);n.set(e);for(let e=0,a=this._keySize;e<16;++e,++a)n[e]^=t[a];for(let e=this._cyclesOfRepetition-1;e>=1;--e){a=n[13];n[13]=n[9];n[9]=n[5];n[5]=n[1];n[1]=a;a=n[14];r=n[10];n[14]=n[6];n[10]=n[2];n[6]=a;n[2]=r;a=n[15];r=n[11];i=n[7];n[15]=n[3];n[11]=a;n[7]=r;n[3]=i;for(let e=0;e<16;++e)n[e]=this._inv_s[n[e]];for(let a=0,r=16*e;a<16;++a,++r)n[a\
-]^=t[r];for(let e=0;e<16;e+=4){const t=this._mix[n[e]],r=this._mix[n[e+1]],i=this._mix[n[e+2]],s=this._mix[n[e+3]];a=t^r>>>8^r<<24^i>>>16^i<<16^s>>>24^s<<8;n[e]=a>>>24&255;n[e+1]=a>>16&255;n[e+2]=a>>8&255;n[e+3]=255&a}}a=n[13];n[13]=n[9];n[9]=n[5];n[5]=n[1];n[1]=a;a=n[14];r=n[10];n[14]=n[6];n[10]=n[2];n[6]=a;n[2]=r;a=n[15];r=n[11];i=n[7];n[15]=n[3];n[11]=a;n[7]=r;n[3]=i;for(let e=0;e<16;++e){n[e]=this._inv_s[n[e]];n[e]^=t[e]}return n}_encrypt(e,t){const a=this._s;let r,i,n;const s=new Uint8Array\
-(16);s.set(e);for(let e=0;e<16;++e)s[e]^=t[e];for(let e=1;e<this._cyclesOfRepetition;e++){for(let e=0;e<16;++e)s[e]=a[s[e]];n=s[1];s[1]=s[5];s[5]=s[9];s[9]=s[13];s[13]=n;n=s[2];i=s[6];s[2]=s[10];s[6]=s[14];s[10]=n;s[14]=i;n=s[3];i=s[7];r=s[11];s[3]=s[15];s[7]=n;s[11]=i;s[15]=r;for(let e=0;e<16;e+=4){const t=s[e],a=s[e+1],i=s[e+2],n=s[e+3];r=t^a^i^n;s[e]^=r^this._mixCol[t^a];s[e+1]^=r^this._mixCol[a^i];s[e+2]^=r^this._mixCol[i^n];s[e+3]^=r^this._mixCol[n^t]}for(let a=0,r=16*e;a<16;++a,++r)s[a]^=t\
-[r]}for(let e=0;e<16;++e)s[e]=a[s[e]];n=s[1];s[1]=s[5];s[5]=s[9];s[9]=s[13];s[13]=n;n=s[2];i=s[6];s[2]=s[10];s[6]=s[14];s[10]=n;s[14]=i;n=s[3];i=s[7];r=s[11];s[3]=s[15];s[7]=n;s[11]=i;s[15]=r;for(let e=0,a=this._keySize;e<16;++e,++a)s[e]^=t[a];return s}_decryptBlock2(e,t){const a=e.length;let r=this.buffer,i=this.bufferPosition;const n=[];let s=this.iv;for(let t=0;t<a;++t){r[i]=e[t];++i;if(i<16)continue;const a=this._decrypt(r,this._key);for(let e=0;e<16;++e)a[e]^=s[e];s=r;n.push(a);r=new Uint8A\
-rray(16);i=0}this.buffer=r;this.bufferLength=i;this.iv=s;if(0===n.length)return new Uint8Array(0);let o=16*n.length;if(t){const e=n.at(-1);let t=e[15];if(t<=16){for(let a=15,r=16-t;a>=r;--a)if(e[a]!==t){t=0;break}o-=t;n[n.length-1]=e.subarray(0,16-t)}}const c=new Uint8Array(o);for(let e=0,t=0,a=n.length;e<a;++e,t+=16)c.set(n[e],t);return c}decryptBlock(e,t,a=null){const r=e.length,i=this.buffer;let n=this.bufferPosition;if(a)this.iv=a;else{for(let t=0;n<16&&t<r;++t,++n)i[n]=e[t];if(n<16){this.bu\
-fferLength=n;return new Uint8Array(0)}this.iv=i;e=e.subarray(16)}this.buffer=new Uint8Array(16);this.bufferLength=0;this.decryptBlock=this._decryptBlock2;return this.decryptBlock(e,t)}encrypt(e,t){const a=e.length;let r=this.buffer,i=this.bufferPosition;const n=[];t||=new Uint8Array(16);for(let s=0;s<a;++s){r[i]=e[s];++i;if(i<16)continue;for(let e=0;e<16;++e)r[e]^=t[e];const a=this._encrypt(r,this._key);t=a;n.push(a);r=new Uint8Array(16);i=0}this.buffer=r;this.bufferLength=i;this.iv=t;if(0===n.l\
-ength)return new Uint8Array(0);const s=16*n.length,o=new Uint8Array(s);for(let e=0,t=0,a=n.length;e<a;++e,t+=16)o.set(n[e],t);return o}}class AES128Cipher extends AESBaseCipher{_rcon=new Uint8Array([141,1,2,4,8,16,32,64,128,27,54,108,216,171,77,154,47,94,188,99,198,151,53,106,212,179,125,250,239,197,145,57,114,228,211,189,97,194,159,37,74,148,51,102,204,131,29,58,116,232,203,141,1,2,4,8,16,32,64,128,27,54,108,216,171,77,154,47,94,188,99,198,151,53,106,212,179,125,250,239,197,145,57,114,228,211,1\
-89,97,194,159,37,74,148,51,102,204,131,29,58,116,232,203,141,1,2,4,8,16,32,64,128,27,54,108,216,171,77,154,47,94,188,99,198,151,53,106,212,179,125,250,239,197,145,57,114,228,211,189,97,194,159,37,74,148,51,102,204,131,29,58,116,232,203,141,1,2,4,8,16,32,64,128,27,54,108,216,171,77,154,47,94,188,99,198,151,53,106,212,179,125,250,239,197,145,57,114,228,211,189,97,194,159,37,74,148,51,102,204,131,29,58,116,232,203,141,1,2,4,8,16,32,64,128,27,54,108,216,171,77,154,47,94,188,99,198,151,53,106,212,179\
-,125,250,239,197,145,57,114,228,211,189,97,194,159,37,74,148,51,102,204,131,29,58,116,232,203,141]);constructor(e){super();this._cyclesOfRepetition=10;this._keySize=160;this._key=this._expandKey(e)}_expandKey(e){const t=this._s,a=this._rcon,r=new Uint8Array(176);r.set(e);for(let e=16,i=1;e<176;++i){let n=r[e-3],s=r[e-2],o=r[e-1],c=r[e-4];n=t[n];s=t[s];o=t[o];c=t[c];n^=a[i];for(let t=0;t<4;++t){r[e]=n^=r[e-16];e++;r[e]=s^=r[e-16];e++;r[e]=o^=r[e-16];e++;r[e]=c^=r[e-16];e++}}return r}}class AES256\
-Cipher extends AESBaseCipher{constructor(e){super();this._cyclesOfRepetition=14;this._keySize=224;this._key=this._expandKey(e)}_expandKey(e){const t=this._s,a=new Uint8Array(240);a.set(e);let r,i,n,s,o=1;for(let e=32,c=1;e<240;++c){if(e%32==16){r=t[r];i=t[i];n=t[n];s=t[s]}else if(e%32==0){r=a[e-3];i=a[e-2];n=a[e-1];s=a[e-4];r=t[r];i=t[i];n=t[n];s=t[s];r^=o;(o<<=1)>=256&&(o=255&(27^o))}for(let t=0;t<4;++t){a[e]=r^=a[e-32];e++;a[e]=i^=a[e-32];e++;a[e]=n^=a[e-32];e++;a[e]=s^=a[e-32];e++}}return a}}\
-class PDFBase{_hash(e,t,a){unreachable("Abstract method `_hash` called")}checkOwnerPassword(e,t,a,r){const i=new Uint8Array(e.length+56);i.set(e,0);i.set(t,e.length);i.set(a,e.length+t.length);return isArrayEqual(this._hash(e,i,a),r)}checkUserPassword(e,t,a){const r=new Uint8Array(e.length+8);r.set(e,0);r.set(t,e.length);return isArrayEqual(this._hash(e,r,[]),a)}getOwnerKey(e,t,a,r){const i=new Uint8Array(e.length+56);i.set(e,0);i.set(t,e.length);i.set(a,e.length+t.length);const n=this._hash(e,i\
-,a);return new AES256Cipher(n).decryptBlock(r,!1,new Uint8Array(16))}getUserKey(e,t,a){const r=new Uint8Array(e.length+8);r.set(e,0);r.set(t,e.length);const i=this._hash(e,r,[]);return new AES256Cipher(i).decryptBlock(a,!1,new Uint8Array(16))}}class PDF17 extends PDFBase{_hash(e,t,a){return calculateSHA256(t,0,t.length)}}class PDF20 extends PDFBase{_hash(e,t,a){let r=calculateSHA256(t,0,t.length).subarray(0,32),i=[0],n=0;for(;n<64||i.at(-1)>n-32;){const t=e.length+r.length+a.length,l=new Uint8Ar\
-ray(t);let h=0;l.set(e,h);h+=e.length;l.set(r,h);h+=r.length;l.set(a,h);const u=new Uint8Array(64*t);for(let e=0,a=0;e<64;e++,a+=t)u.set(l,a);i=new AES128Cipher(r.subarray(0,16)).encrypt(u,r.subarray(16,32));const d=Math.sumPrecise(i.slice(0,16))%3;0===d?r=calculateSHA256(i,0,i.length):1===d?r=(s=i,o=0,c=i.length,calculateSHA512(s,o,c,!0)):2===d&&(r=calculateSHA512(i,0,i.length));n++}var s,o,c;return r.subarray(0,32)}}class CipherTransform{constructor(e,t){this.StringCipherConstructor=e;this.Str\
-eamCipherConstructor=t}createStream(e,t){const a=new this.StreamCipherConstructor;return new DecryptStream(e,t,(function cipherTransformDecryptStream(e,t){return a.decryptBlock(e,t)}))}decryptString(e){const t=new this.StringCipherConstructor;let a=stringToBytes(e);a=t.decryptBlock(a,!0);return bytesToString(a)}encryptString(e){const t=new this.StringCipherConstructor;if(t instanceof AESBaseCipher){const a=16-e.length%16;e+=String.fromCharCode(a).repeat(a);const r=new Uint8Array(16);crypto.getRa\
-ndomValues(r);let i=stringToBytes(e);i=t.encrypt(i,r);const n=new Uint8Array(16+i.length);n.set(r);n.set(i,16);return bytesToString(n)}let a=stringToBytes(e);a=t.encrypt(a);return bytesToString(a)}}class CipherTransformFactory{static get _defaultPasswordBytes(){return shadow(this,"_defaultPasswordBytes",new Uint8Array([40,191,78,94,78,117,138,65,100,0,78,86,255,250,1,8,46,46,0,182,208,104,62,128,47,12,169,254,100,83,105,122]))}#Oe(e,t,a,r,i,n,s,o,c,l,h,u){if(t){const e=Math.min(127,t.length);t=t\
-.subarray(0,e)}else t=[];const d=6===e?new PDF20:new PDF17;return d.checkUserPassword(t,o,s)?d.getUserKey(t,c,h):t.length&&d.checkOwnerPassword(t,r,n,a)?d.getOwnerKey(t,i,n,l):null}#Me(e,t,a,r,i,n,s,o){const c=40+a.length+e.length,l=new Uint8Array(c);let h,u,d=0;if(t){u=Math.min(32,t.length);for(;d<u;++d)l[d]=t[d]}h=0;for(;d<32;)l[d++]=CipherTransformFactory._defaultPasswordBytes[h++];l.set(a,d);d+=a.length;l[d++]=255&i;l[d++]=i>>8&255;l[d++]=i>>16&255;l[d++]=i>>>24&255;l.set(e,d);d+=e.length;if\
-(n>=4&&!o){l.fill(255,d,d+4);d+=4}let f=calculateMD5(l,0,d);const g=s>>3;if(n>=3)for(h=0;h<50;++h)f=calculateMD5(f,0,g);const p=f.subarray(0,g);let m,b;if(n>=3){d=0;l.set(CipherTransformFactory._defaultPasswordBytes,d);d+=32;l.set(e,d);d+=e.length;m=new ARCFourCipher(p);b=m.encryptBlock(calculateMD5(l,0,d));u=p.length;const t=new Uint8Array(u);for(h=1;h<=19;++h){for(let e=0;e<u;++e)t[e]=p[e]^h;m=new ARCFourCipher(t);b=m.encryptBlock(b)}}else{m=new ARCFourCipher(p);b=m.encryptBlock(CipherTransfor\
-mFactory._defaultPasswordBytes)}return b.every(((e,t)=>r[t]===e))?p:null}#De(e,t,a,r){const i=new Uint8Array(32);let n=0;const s=Math.min(32,e.length);for(;n<s;++n)i[n]=e[n];let o=0;for(;n<32;)i[n++]=CipherTransformFactory._defaultPasswordBytes[o++];let c=calculateMD5(i,0,n);const l=r>>3;if(a>=3)for(o=0;o<50;++o)c=calculateMD5(c,0,c.length);let h,u;if(a>=3){u=t;const e=new Uint8Array(l);for(o=19;o>=0;o--){for(let t=0;t<l;++t)e[t]=c[t]^o;h=new ARCFourCipher(e);u=h.encryptBlock(u)}}else{h=new ARCF\
-ourCipher(c.subarray(0,l));u=h.encryptBlock(t)}return u}#Be(e,t,a,r=!1){const i=a.length,n=new Uint8Array(i+9);n.set(a);let s=i;n[s++]=255&e;n[s++]=e>>8&255;n[s++]=e>>16&255;n[s++]=255&t;n[s++]=t>>8&255;if(r){n[s++]=115;n[s++]=65;n[s++]=108;n[s++]=84}return calculateMD5(n,0,s).subarray(0,Math.min(i+5,16))}#Re(e,t,a,r,i){if(!(t instanceof Name))throw new FormatError("Invalid crypt filter name.");const n=this,s=e.get(t.name),o=s?.get("CFM");if(!o||"None"===o.name)return function(){return new NullC\
-ipher};if("V2"===o.name)return function(){return new ARCFourCipher(n.#Be(a,r,i,!1))};if("AESV2"===o.name)return function(){return new AES128Cipher(n.#Be(a,r,i,!0))};if("AESV3"===o.name)return function(){return new AES256Cipher(i)};throw new FormatError("Unknown crypto method")}constructor(e,t,a){const r=e.get("Filter");if(!isName(r,"Standard"))throw new FormatError("unknown encryption method");this.filterName=r.name;this.dict=e;const i=e.get("V");if(!Number.isInteger(i)||1!==i&&2!==i&&4!==i&&5!=\
-=i)throw new FormatError("unsupported encryption algorithm");this.algorithm=i;let n=e.get("Length");if(!n)if(i<=3)n=40;else{const t=e.get("CF"),a=e.get("StmF");if(t instanceof Dict&&a instanceof Name){t.suppressEncryption=!0;const e=t.get(a.name);n=e?.get("Length")||128;n<40&&(n<<=3)}}if(!Number.isInteger(n)||n<40||n%8!=0)throw new FormatError("invalid key length");const s=stringToBytes(e.get("O")),o=stringToBytes(e.get("U")),c=s.subarray(0,32),l=o.subarray(0,32),h=e.get("P"),u=e.get("R"),d=(4==\
-=i||5===i)&&!1!==e.get("EncryptMetadata");this.encryptMetadata=d;const f=stringToBytes(t);let g,p;if(a){if(6===u)try{a=utf8StringToString(a)}catch{warn("CipherTransformFactory: Unable to convert UTF8 encoded password.")}g=stringToBytes(a)}if(5!==i)p=this.#Me(f,g,c,l,h,u,n,d);else{const t=s.subarray(32,40),a=s.subarray(40,48),r=o.subarray(0,48),i=o.subarray(32,40),n=o.subarray(40,48),h=stringToBytes(e.get("OE")),d=stringToBytes(e.get("UE")),f=stringToBytes(e.get("Perms"));p=this.#Oe(u,g,c,t,a,r,l\
-,i,n,h,d,f)}if(!p){if(!a)throw new PasswordException("No password given",ha);const e=this.#De(g,c,u,n);p=this.#Me(f,e,c,l,h,u,n,d)}if(!p)throw new PasswordException("Incorrect Password",ua);if(4===i&&p.length<16){this.encryptionKey=new Uint8Array(16);this.encryptionKey.set(p)}else this.encryptionKey=p;if(i>=4){const t=e.get("CF");t instanceof Dict&&(t.suppressEncryption=!0);this.cf=t;this.stmf=e.get("StmF")||Name.get("Identity");this.strf=e.get("StrF")||Name.get("Identity");this.eff=e.get("EFF")\
-||this.stmf}}createCipherTransform(e,t){if(4===this.algorithm||5===this.algorithm)return new CipherTransform(this.#Re(this.cf,this.strf,e,t,this.encryptionKey),this.#Re(this.cf,this.stmf,e,t,this.encryptionKey));const a=this.#Be(e,t,this.encryptionKey,!1),cipherConstructor=function(){return new ARCFourCipher(a)};return new CipherTransform(cipherConstructor,cipherConstructor)}}class XRef{#Ne=null;constructor(e,t){this.stream=e;this.pdfManager=t;this.entries=[];this._xrefStms=new Set;this._cacheMa\
-p=new Map;this._pendingRefs=new RefSet;this._newPersistentRefNum=null;this._newTemporaryRefNum=null;this._persistentRefsCache=null}getNewPersistentRef(e){null===this._newPersistentRefNum&&(this._newPersistentRefNum=this.entries.length||1);const t=this._newPersistentRefNum++;this._cacheMap.set(t,e);return Ref.get(t,0)}getNewTemporaryRef(){if(null===this._newTemporaryRefNum){this._newTemporaryRefNum=this.entries.length||1;if(this._newPersistentRefNum){this._persistentRefsCache=new Map;for(let e=th\
-is._newTemporaryRefNum;e<this._newPersistentRefNum;e++){this._persistentRefsCache.set(e,this._cacheMap.get(e));this._cacheMap.delete(e)}}}return Ref.get(this._newTemporaryRefNum++,0)}resetNewTemporaryRef(){this._newTemporaryRefNum=null;if(this._persistentRefsCache)for(const[e,t]of this._persistentRefsCache)this._cacheMap.set(e,t);this._persistentRefsCache=null}setStartXRef(e){this.startXRefQueue=[e]}parse(e=!1){let t,a,r;if(e){warn("Indexing all PDF objects");t=this.indexObjects()}else t=this.re\
-adXRef();t.assignXref(this);this.trailer=t;try{a=t.get("Encrypt")}catch(e){if(e instanceof MissingDataException)throw e;warn(`XRef.parse - Invalid "Encrypt" reference: "${e}".`)}if(a instanceof Dict){const e=t.get("ID"),r=e?.length?e[0]:"";a.suppressEncryption=!0;this.encrypt=new CipherTransformFactory(a,r,this.pdfManager.password)}try{r=t.get("Root")}catch(e){if(e instanceof MissingDataException)throw e;warn(`XRef.parse - Invalid "Root" reference: "${e}".`)}if(r instanceof Dict)try{if(r.get("Pa\
-ges")instanceof Dict){this.root=r;return}}catch(e){if(e instanceof MissingDataException)throw e;warn(`XRef.parse - Invalid "Pages" reference: "${e}".`)}if(!e)throw new XRefParseException;throw new InvalidPDFException("Invalid Root reference.")}processXRefTable(e){"tableState"in this||(this.tableState={entryNum:0,streamPos:e.lexer.stream.pos,parserBuf1:e.buf1,parserBuf2:e.buf2});if(!isCmd(this.readXRefTable(e),"trailer"))throw new FormatError("Invalid XRef table: could not find trailer dictionary\
-");let t=e.getObj();t instanceof Dict||!t.dict||(t=t.dict);if(!(t instanceof Dict))throw new FormatError("Invalid XRef table: could not parse trailer dictionary");delete this.tableState;return t}readXRefTable(e){const t=e.lexer.stream,a=this.tableState;t.pos=a.streamPos;e.buf1=a.parserBuf1;e.buf2=a.parserBuf2;let r;for(;;){if(!("firstEntryNum"in a)||!("entryCount"in a)){if(isCmd(r=e.getObj(),"trailer"))break;a.firstEntryNum=r;a.entryCount=e.getObj()}let i=a.firstEntryNum;const n=a.entryCount;if(\
-!Number.isInteger(i)||!Number.isInteger(n))throw new FormatError("Invalid XRef table: wrong types in subsection header");for(let r=a.entryNum;r<n;r++){a.streamPos=t.pos;a.entryNum=r;a.parserBuf1=e.buf1;a.parserBuf2=e.buf2;const s={};s.offset=e.getObj();s.gen=e.getObj();const o=e.getObj();if(o instanceof Cmd)switch(o.cmd){case"f":s.free=!0;break;case"n":s.uncompressed=!0}if(!Number.isInteger(s.offset)||!Number.isInteger(s.gen)||!s.free&&!s.uncompressed)throw new FormatError(`Invalid entry in XRef\
- subsection: ${i}, ${n}`);0===r&&s.free&&1===i&&(i=0);this.entries[r+i]||(this.entries[r+i]=s)}a.entryNum=0;a.streamPos=t.pos;a.parserBuf1=e.buf1;a.parserBuf2=e.buf2;delete a.firstEntryNum;delete a.entryCount}if(this.entries[0]&&!this.entries[0].free)throw new FormatError("Invalid XRef table: unexpected first object");return r}processXRefStream(e){if(!("streamState"in this)){const{dict:t,pos:a}=e,r=t.get("W"),i=t.get("Index")||[0,t.get("Size")];this.streamState={entryRanges:i,byteWidths:r,entryN\
-um:0,streamPos:a}}this.readXRefStream(e);delete this.streamState;return e.dict}readXRefStream(e){const t=this.streamState;e.pos=t.streamPos;const[a,r,i]=t.byteWidths,n=t.entryRanges;for(;n.length>0;){const[s,o]=n;if(!Number.isInteger(s)||!Number.isInteger(o))throw new FormatError(`Invalid XRef range fields: ${s}, ${o}`);if(!Number.isInteger(a)||!Number.isInteger(r)||!Number.isInteger(i))throw new FormatError(`Invalid XRef entry fields length: ${s}, ${o}`);for(let n=t.entryNum;n<o;++n){t.entryNum\
-=n;t.streamPos=e.pos;let o=0,c=0,l=0;for(let t=0;t<a;++t){const t=e.getByte();if(-1===t)throw new FormatError("Invalid XRef byteWidths \'type\'.");o=o<<8|t}0===a&&(o=1);for(let t=0;t<r;++t){const t=e.getByte();if(-1===t)throw new FormatError("Invalid XRef byteWidths \'offset\'.");c=c<<8|t}for(let t=0;t<i;++t){const t=e.getByte();if(-1===t)throw new FormatError("Invalid XRef byteWidths \'generation\'.");l=l<<8|t}const h={};h.offset=c;h.gen=l;switch(o){case 0:h.free=!0;break;case 1:h.uncompressed=!0;bre\
-ak;case 2:break;default:throw new FormatError(`Invalid XRef entry type: ${o}`)}this.entries[s+n]||(this.entries[s+n]=h)}t.entryNum=0;t.streamPos=e.pos;n.splice(0,2)}}indexObjects(){function readToken(e,t){let a="",r=e[t];for(;10!==r&&13!==r&&60!==r&&!(++t>=e.length);){a+=String.fromCharCode(r);r=e[t]}return a}function skipUntil(e,t,a){const r=a.length,i=e.length;let n=0;for(;t<i;){let i=0;for(;i<r&&e[t+i]===a[i];)++i;if(i>=r)break;t++;n++}return n}const e=/\\b(endobj|\\d+\\s+\\d+\\s+obj|xref|trailer\\\
-s*<<)\\b/g,t=/\\b(startxref|\\d+\\s+\\d+\\s+obj)\\b/g,a=/^(\\d+)\\s+(\\d+)\\s+obj\\b/,r=new Uint8Array([116,114,97,105,108,101,114]),i=new Uint8Array([115,116,97,114,116,120,114,101,102]),n=new Uint8Array([47,88,82,101,102]);this.entries.length=0;this._cacheMap.clear();const s=this.stream;s.pos=0;const o=s.getBytes(),c=bytesToString(o),l=o.length;let h=s.start;const u=[],d=[];for(;h<l;){let f=o[h];if(9===f||10===f||13===f||32===f){++h;continue}if(37===f){do{++h;if(h>=l)break;f=o[h]}while(10!==f&&13!==f);con\
-tinue}const g=readToken(o,h);let p;if(g.startsWith("xref")&&(4===g.length||/\\s/.test(g[4]))){h+=skipUntil(o,h,r);u.push(h);h+=skipUntil(o,h,i)}else if(p=a.exec(g)){const t=0|p[1],a=0|p[2],r=h+g.length;let i,u=!1;if(this.entries[t]){if(this.entries[t].gen===a)try{new Parser({lexer:new Lexer(s.makeSubStream(r))}).getObj();u=!0}catch(e){e instanceof ParserEOFException?warn(`indexObjects -- checking object (${g}): "${e}".`):u=!0}}else u=!0;u&&(this.entries[t]={offset:h-s.start,gen:a,uncompressed:!0}\
-);e.lastIndex=r;const f=e.exec(c);if(f){i=e.lastIndex+1-h;if("endobj"!==f[1]){warn(`indexObjects: Found "${f[1]}" inside of another "obj", caused by missing "endobj" -- trying to recover.`);i-=f[1].length+1}}else i=l-h;const m=o.subarray(h,h+i),b=skipUntil(m,0,n);if(b<i&&m[b+5]<64){d.push(h-s.start);this._xrefStms.add(h-s.start)}h+=i}else if(g.startsWith("trailer")&&(7===g.length||/\\s/.test(g[7]))){u.push(h);const e=h+g.length;let a;t.lastIndex=e;const r=t.exec(c);if(r){a=t.lastIndex+1-h;if("sta\
-rtxref"!==r[1]){warn(`indexObjects: Found "${r[1]}" after "trailer", caused by missing "startxref" -- trying to recover.`);a-=r[1].length+1}}else a=l-h;h+=a}else h+=g.length+1}for(const e of d){this.startXRefQueue.push(e);this.readXRef(!0)}const f=[];let g,p,m=!1;for(const e of u){s.pos=e;const t=new Parser({lexer:new Lexer(s),xref:this,allowStreams:!0,recoveryMode:!0});if(!isCmd(t.getObj(),"trailer"))continue;const a=t.getObj();if(a instanceof Dict){f.push(a);a.has("Encrypt")&&(m=!0)}}for(const\
- e of[...f,"genFallback",...f]){if("genFallback"===e){if(!p)break;this._generationFallback=!0;continue}let t=!1;try{const a=e.get("Root");if(!(a instanceof Dict))continue;const r=a.get("Pages");if(!(r instanceof Dict))continue;const i=r.get("Count");Number.isInteger(i)&&(t=!0)}catch(e){p=e;continue}if(t&&(!m||e.has("Encrypt"))&&e.has("ID"))return e;g=e}if(g)return g;if(this.topDict)return this.topDict;if(!f.length)for(const[e,t]of this.entries.entries()){if(!t)continue;const a=Ref.get(e,t.gen);l\
-et r;try{r=this.fetch(a)}catch{continue}r instanceof BaseStream&&(r=r.dict);if(r instanceof Dict&&r.has("Root"))return r}throw new InvalidPDFException("Invalid PDF structure.")}readXRef(e=!1){const t=this.stream,a=new Set;for(;this.startXRefQueue.length;){try{const e=this.startXRefQueue[0];if(a.has(e)){warn("readXRef - skipping XRef table since it was already parsed.");this.startXRefQueue.shift();continue}a.add(e);t.pos=e+t.start;const r=new Parser({lexer:new Lexer(t),xref:this,allowStreams:!0})\
-;let i,n=r.getObj();if(isCmd(n,"xref")){i=this.processXRefTable(r);this.topDict||(this.topDict=i);n=i.get("XRefStm");if(Number.isInteger(n)&&!this._xrefStms.has(n)){this._xrefStms.add(n);this.startXRefQueue.push(n);this.#Ne??=n}}else{if(!Number.isInteger(n))throw new FormatError("Invalid XRef stream header");if(!(Number.isInteger(r.getObj())&&isCmd(r.getObj(),"obj")&&(n=r.getObj())instanceof BaseStream))throw new FormatError("Invalid XRef stream");i=this.processXRefStream(n);this.topDict||(this.\
-topDict=i);if(!i)throw new FormatError("Failed to read XRef stream")}n=i.get("Prev");Number.isInteger(n)?this.startXRefQueue.push(n):n instanceof Ref&&this.startXRefQueue.push(n.num)}catch(e){if(e instanceof MissingDataException)throw e;info("(while reading XRef): "+e)}this.startXRefQueue.shift()}if(this.topDict)return this.topDict;if(!e)throw new XRefParseException}get lastXRefStreamPos(){return this.#Ne??(this._xrefStms.size>0?Math.max(...this._xrefStms):null)}getEntry(e){const t=this.entries[\
-e];return t&&!t.free&&t.offset?t:null}fetchIfRef(e,t=!1){return e instanceof Ref?this.fetch(e,t):e}fetch(e,t=!1){if(!(e instanceof Ref))throw new Error("ref object is not a reference");const a=e.num,r=this._cacheMap.get(a);if(void 0!==r){r instanceof Dict&&!r.objId&&(r.objId=e.toString());return r}let i=this.getEntry(a);if(null===i)return i;if(this._pendingRefs.has(e)){this._pendingRefs.remove(e);warn(`Ignoring circular reference: ${e}.`);return ya}this._pendingRefs.put(e);try{i=i.uncompressed?t\
-his.fetchUncompressed(e,i,t):this.fetchCompressed(e,i,t);this._pendingRefs.remove(e)}catch(t){this._pendingRefs.remove(e);throw t}i instanceof Dict?i.objId=e.toString():i instanceof BaseStream&&(i.dict.objId=e.toString());return i}fetchUncompressed(e,t,a=!1){const r=e.gen;let i=e.num;if(t.gen!==r){const n=`Inconsistent generation in XRef: ${e}`;if(this._generationFallback&&t.gen<r){warn(n);return this.fetchUncompressed(Ref.get(i,t.gen),t,a)}throw new XRefEntryException(n)}const n=this.stream.mak\
-eSubStream(t.offset+this.stream.start),s=new Parser({lexer:new Lexer(n),xref:this,allowStreams:!0}),o=s.getObj(),c=s.getObj(),l=s.getObj();if(o!==i||c!==r||!(l instanceof Cmd))throw new XRefEntryException(`Bad (uncompressed) XRef entry: ${e}`);if("obj"!==l.cmd){if(l.cmd.startsWith("obj")){i=parseInt(l.cmd.substring(3),10);if(!Number.isNaN(i))return i}throw new XRefEntryException(`Bad (uncompressed) XRef entry: ${e}`)}(t=this.encrypt&&!a?s.getObj(this.encrypt.createCipherTransform(i,r)):s.getObj(\
-))instanceof BaseStream||this._cacheMap.set(i,t);return t}fetchCompressed(e,t,a=!1){const r=t.offset,i=this.fetch(Ref.get(r,0));if(!(i instanceof BaseStream))throw new FormatError("bad ObjStm stream");const n=i.dict.get("First"),s=i.dict.get("N");if(!Number.isInteger(n)||!Number.isInteger(s))throw new FormatError("invalid first and n parameters for ObjStm stream");let o=new Parser({lexer:new Lexer(i),xref:this,allowStreams:!0});const c=new Array(s),l=new Array(s);for(let e=0;e<s;++e){const t=o.g\
-etObj();if(!Number.isInteger(t))throw new FormatError(`invalid object number in the ObjStm stream: ${t}`);const a=o.getObj();if(!Number.isInteger(a))throw new FormatError(`invalid object offset in the ObjStm stream: ${a}`);c[e]=t;const i=this.getEntry(t);i?.offset===r&&i.gen!==e&&(i.gen=e);l[e]=a}const h=(i.start||0)+n,u=new Array(s);for(let e=0;e<s;++e){const t=e<s-1?l[e+1]-l[e]:void 0;if(t<0)throw new FormatError("Invalid offset in the ObjStm stream.");o=new Parser({lexer:new Lexer(i.makeSubSt\
-ream(h+l[e],t,i.dict)),xref:this,allowStreams:!0});const a=o.getObj();u[e]=a;if(a instanceof BaseStream)continue;const n=c[e],d=this.entries[n];d&&d.offset===r&&d.gen===e&&this._cacheMap.set(n,a)}if(void 0===(t=u[t.gen]))throw new XRefEntryException(`Bad (compressed) XRef entry: ${e}`);return t}async fetchIfRefAsync(e,t){return e instanceof Ref?this.fetchAsync(e,t):e}async fetchAsync(e,t){try{return this.fetch(e,t)}catch(a){if(!(a instanceof MissingDataException))throw a;await this.pdfManager.re\
-questRange(a.begin,a.end);return this.fetchAsync(e,t)}}getCatalogObj(){return this.root}}const yc=[0,0,612,792];class Page{#Ee=null;constructor({pdfManager:e,xref:t,pageIndex:a,pageDict:r,ref:i,globalIdFactory:n,fontCache:s,builtInCMapCache:o,standardFontDataCache:c,globalColorSpaceCache:l,globalImageCache:h,systemFontCache:u,nonBlendModesSet:d,xfaFactory:f}){this.pdfManager=e;this.pageIndex=a;this.pageDict=r;this.xref=t;this.ref=i;this.fontCache=s;this.builtInCMapCache=o;this.standardFontDataCa\
-che=c;this.globalColorSpaceCache=l;this.globalImageCache=h;this.systemFontCache=u;this.nonBlendModesSet=d;this.evaluatorOptions=e.evaluatorOptions;this.xfaFactory=f;const g={obj:0};this._localIdFactory=class extends n{static createObjId(){return`p${a}_${++g.obj}`}static getPageObjId(){return`p${i.toString()}`}}}#Pe(e){return new PartialEvaluator({xref:this.xref,handler:e,pageIndex:this.pageIndex,idFactory:this._localIdFactory,fontCache:this.fontCache,builtInCMapCache:this.builtInCMapCache,standa\
-rdFontDataCache:this.standardFontDataCache,globalColorSpaceCache:this.globalColorSpaceCache,globalImageCache:this.globalImageCache,systemFontCache:this.systemFontCache,options:this.evaluatorOptions})}#Le(e,t=!1){const a=getInheritableProperty({dict:this.pageDict,key:e,getArray:t,stopWhenFound:!1});return Array.isArray(a)?1!==a.length&&a[0]instanceof Dict?Dict.merge({xref:this.xref,dictArray:a}):a[0]:a}get content(){return this.pageDict.getArray("Contents")}get resources(){const e=this.#Le("Resou\
-rces");return shadow(this,"resources",e instanceof Dict?e:Dict.empty)}#je(e){if(this.xfaData)return this.xfaData.bbox;const t=lookupNormalRect(this.#Le(e,!0),null);if(t){if(t[2]-t[0]>0&&t[3]-t[1]>0)return t;warn(`Empty, or invalid, /${e} entry.`)}return null}get mediaBox(){return shadow(this,"mediaBox",this.#je("MediaBox")||yc)}get cropBox(){return shadow(this,"cropBox",this.#je("CropBox")||this.mediaBox)}get userUnit(){const e=this.pageDict.get("UserUnit");return shadow(this,"userUnit","number"\
-==typeof e&&e>0?e:1)}get view(){const{cropBox:e,mediaBox:t}=this;if(e!==t&&!isArrayEqual(e,t)){const a=Util.intersect(e,t);if(a&&a[2]-a[0]>0&&a[3]-a[1]>0)return shadow(this,"view",a);warn("Empty /CropBox and /MediaBox intersection.")}return shadow(this,"view",t)}get rotate(){let e=this.#Le("Rotate")||0;e%90!=0?e=0:e>=360?e%=360:e<0&&(e=(e%360+360)%360);return shadow(this,"rotate",e)}#_e(e,t){if(!this.evaluatorOptions.ignoreErrors)throw e;warn(`getContentStream - ignoring sub-stream (${t}): "${e}\
-".`)}async getContentStream(){const e=await this.pdfManager.ensure(this,"content");return e instanceof BaseStream?e:Array.isArray(e)?new StreamsSequenceStream(e,this.#_e.bind(this)):new NullStream}get xfaData(){return shadow(this,"xfaData",this.xfaFactory?{bbox:this.xfaFactory.getBoundingBox(this.pageIndex)}:null)}async#Ue(e,t,a){const r=[];for(const i of e)if(i.id){const e=Ref.fromString(i.id);if(!e){warn(`A non-linked annotation cannot be modified: ${i.id}`);continue}if(i.deleted){t.put(e,e);i\
-f(i.popupRef){const e=Ref.fromString(i.popupRef);e&&t.put(e,e)}continue}if(i.popup?.deleted){const e=Ref.fromString(i.popupRef);e&&t.put(e,e)}a?.put(e);i.ref=e;r.push(this.xref.fetchAsync(e).then((e=>{e instanceof Dict&&(i.oldAnnotation=e.clone())}),(()=>{warn(`Cannot fetch \\`oldAnnotation\\` for: ${e}.`)})));delete i.id}await Promise.all(r)}async saveNewAnnotations(e,t,a,r,i){if(this.xfaFactory)throw new Error("XFA: Cannot save new annotations.");const n=this.#Pe(e),s=new RefSetCache,o=new RefSe\
-t;await this.#Ue(a,s,o);const c=this.pageDict,l=this.annotations.filter((e=>!(e instanceof Ref&&s.has(e)))),h=await AnnotationFactory.saveNewAnnotations(n,t,a,r,i);for(const{ref:e}of h.annotations)e instanceof Ref&&!o.has(e)&&l.push(e);const u=c.clone();u.set("Annots",l);i.put(this.ref,{data:u});for(const e of s)i.put(e,{data:null})}async save(e,t,a,r){const i=this.#Pe(e),n=await this._parsedAnnotations,s=[];for(const e of n)s.push(e.save(i,t,a,r).catch((function(e){warn(`save - ignoring annotat\
-ion data during "${t.name}" task: "${e}".`);return null})));return Promise.all(s)}async loadResources(e){await(this.#Ee??=this.pdfManager.ensure(this,"resources"));await ObjectLoader.load(this.resources,e,this.xref)}async#Xe(e,t){const a=e?.get("Resources");if(!(a instanceof Dict&&a.size))return this.resources;await ObjectLoader.load(a,t,this.xref);return Dict.merge({xref:this.xref,dictArray:[a,this.resources],mergeSubDicts:!0})}async getOperatorList({handler:e,sink:t,task:a,intent:r,cacheKey:i,\
-annotationStorage:c=null,modifiedIds:d=null}){const g=this.getContentStream(),p=this.loadResources(Ia),m=this.#Pe(e),b=this.xfaFactory?null:getNewAnnotationsMap(c),y=b?.get(this.pageIndex);let w=Promise.resolve(null),x=null;if(y){const e=this.pdfManager.ensureDoc("annotationGlobals");let t;const r=new Set;for(const{bitmapId:e,bitmap:t}of y)!e||t||r.has(e)||r.add(e);const{isOffscreenCanvasSupported:i}=this.evaluatorOptions;if(r.size>0){const e=y.slice();for(const[t,a]of c)t.startsWith(f)&&a.bitma\
-p&&r.has(a.bitmapId)&&e.push(a);t=AnnotationFactory.generateImages(e,this.xref,i)}else t=AnnotationFactory.generateImages(y,this.xref,i);x=new RefSet;w=Promise.all([e,this.#Ue(y,x,null)]).then((([e])=>e?AnnotationFactory.printNewAnnotations(e,m,a,y,t):null))}const S=Promise.all([g,p]).then((async([n])=>{const s=await this.#Xe(n.dict,Ia),o=new OperatorList(r,t);e.send("StartRenderPage",{transparency:m.hasBlendModes(s,this.nonBlendModesSet),pageIndex:this.pageIndex,cacheKey:i});await m.getOperator\
-List({stream:n,task:a,resources:s,operatorList:o});return o}));let[k,C,v]=await Promise.all([S,this._parsedAnnotations,w]);if(v){C=C.filter((e=>!(e.ref&&x.has(e.ref))));for(let e=0,t=v.length;e<t;e++){const a=v[e];if(a.refToReplace){const r=C.findIndex((e=>e.ref&&isRefsEqual(e.ref,a.refToReplace)));if(r>=0){C.splice(r,1,a);v.splice(e--,1);t--}}}C=C.concat(v)}if(0===C.length||r&h){k.flush(!0);return{length:k.totalLength}}const F=!!(r&l),T=!!(r&u),O=!!(r&n),M=!!(r&s),D=!!(r&o),R=[];for(const e of \
-C)(O||M&&e.mustBeViewed(c,F)&&e.mustBeViewedWhenEditing(T,d)||D&&e.mustBePrinted(c))&&R.push(e.getOperatorList(m,a,r,c).catch((function(e){warn(`getOperatorList - ignoring annotation data during "${a.name}" task: "${e}".`);return{opList:null,separateForm:!1,separateCanvas:!1}})));const N=await Promise.all(R);let E=!1,L=!1;for(const{opList:e,separateForm:t,separateCanvas:a}of N){k.addOpList(e);E||=t;L||=a}k.flush(!0,{form:E,canvas:L});return{length:k.totalLength}}async extractTextContent({handler\
-:e,task:t,includeMarkedContent:a,disableNormalization:r,sink:i,intersector:n=null}){const s=this.getContentStream(),o=this.loadResources(Ta),c=this.pdfManager.ensureCatalog("lang"),[l,,h]=await Promise.all([s,o,c]),u=await this.#Xe(l.dict,Ta);return this.#Pe(e).getTextContent({stream:l,task:t,resources:u,includeMarkedContent:a,disableNormalization:r,sink:i,viewBox:this.view,lang:h,intersector:n})}async getStructTree(){const e=await this.pdfManager.ensureCatalog("structTreeRoot");if(!e)return nul\
-l;await this._parsedAnnotations;try{const t=await this.pdfManager.ensure(this,"_parseStructTree",[e]);return await this.pdfManager.ensure(t,"serializable")}catch(e){warn(`getStructTree: "${e}".`);return null}}_parseStructTree(e){const t=new StructTreePage(e,this.pageDict);t.parse(this.ref);return t}async getAnnotationsData(e,t,a){const r=await this._parsedAnnotations;if(0===r.length)return r;const i=[],c=[];let l;const h=!!(a&n),u=!!(a&s),d=!!(a&o),f=[];for(const a of r){const r=h||u&&a.viewable\
-;(r||d&&a.printable)&&i.push(a.data);if(a.hasTextContent&&r){l??=this.#Pe(e);c.push(a.extractTextContent(l,t,[-1/0,-1/0,1/0,1/0]).catch((function(e){warn(`getAnnotationsData - ignoring textContent during "${t.name}" task: "${e}".`)})))}else a.overlaysTextContent&&r&&f.push(a)}if(f.length>0){const a=new Intersector(f);c.push(this.extractTextContent({handler:e,task:t,includeMarkedContent:!1,disableNormalization:!1,sink:null,viewBox:this.view,lang:null,intersector:a}).then((()=>{a.setText()})))}awa\
-it Promise.all(c);return i}get annotations(){const e=this.#Le("Annots");return shadow(this,"annotations",Array.isArray(e)?e:[])}get _parsedAnnotations(){return shadow(this,"_parsedAnnotations",this.pdfManager.ensure(this,"annotations").then((async e=>{if(0===e.length)return e;const[t,a]=await Promise.all([this.pdfManager.ensureDoc("annotationGlobals"),this.pdfManager.ensureDoc("fieldObjects")]);if(!t)return[];const r=a?.orphanFields,i=[];for(const a of e)i.push(AnnotationFactory.create(this.xref\
-,a,t,this._localIdFactory,!1,r,this.ref).catch((function(e){warn(`_parsedAnnotations: "${e}".`);return null})));const n=[];let s,o;for(const e of await Promise.all(i))e&&(e instanceof WidgetAnnotation?(o||=[]).push(e):e instanceof PopupAnnotation?(s||=[]).push(e):n.push(e));o&&n.push(...o);s&&n.push(...s);return n})))}get jsActions(){return shadow(this,"jsActions",collectActions(this.xref,this.pageDict,xe))}}const wc=new Uint8Array([37,80,68,70,45]),xc=new Uint8Array([115,116,97,114,116,120,114,\
-101,102]),Sc=new Uint8Array([101,110,100,111,98,106]);function find(e,t,a=1024,r=!1){const i=t.length,n=e.peekBytes(a),s=n.length-i;if(s<=0)return!1;if(r){const a=i-1;let r=n.length-1;for(;r>=a;){let s=0;for(;s<i&&n[r-s]===t[a-s];)s++;if(s>=i){e.pos+=r-a;return!0}r--}}else{let a=0;for(;a<=s;){let r=0;for(;r<i&&n[a+r]===t[r];)r++;if(r>=i){e.pos+=a;return!0}a++}}return!1}class PDFDocument{#qe=new Map;#He=null;constructor(e,t){if(t.length<=0)throw new InvalidPDFException("The PDF file is empty, i.e\
-. its size is zero bytes.");this.pdfManager=e;this.stream=t;this.xref=new XRef(t,e);const a={font:0};this._globalIdFactory=class{static getDocId(){return`g_${e.docId}`}static createFontId(){return"f"+ ++a.font}static createObjId(){unreachable("Abstract method `createObjId` called.")}static getPageObjId(){unreachable("Abstract method `getPageObjId` called.")}}}parse(e){this.xref.parse(e);this.catalog=new Catalog(this.pdfManager,this.xref)}get linearization(){let e=null;try{e=Linearization.create(\
-this.stream)}catch(e){if(e instanceof MissingDataException)throw e;info(e)}return shadow(this,"linearization",e)}get startXRef(){const e=this.stream;let t=0;if(this.linearization){e.reset();if(find(e,Sc)){e.skip(6);let a=e.peekByte();for(;isWhiteSpace(a);){e.pos++;a=e.peekByte()}t=e.pos-e.start}}else{const a=1024,r=xc.length;let i=!1,n=e.end;for(;!i&&n>0;){n-=a-r;n<0&&(n=0);e.pos=n;i=find(e,xc,a,!0)}if(i){e.skip(9);let a;do{a=e.getByte()}while(isWhiteSpace(a));let r="";for(;a>=32&&a<=57;){r+=Str\
-ing.fromCharCode(a);a=e.getByte()}t=parseInt(r,10);isNaN(t)&&(t=0)}}return shadow(this,"startXRef",t)}checkHeader(){const e=this.stream;e.reset();if(!find(e,wc))return;e.moveStart();e.skip(wc.length);let t,a="";for(;(t=e.getByte())>32&&a.length<7;)a+=String.fromCharCode(t);Ca.test(a)?this.#He=a:warn(`Invalid PDF header version: ${a}`)}parseStartXRef(){this.xref.setStartXRef(this.startXRef)}get numPages(){let e=0;e=this.catalog.hasActualNumPages?this.catalog.numPages:this.xfaFactory?this.xfaFacto\
-ry.getNumPages():this.linearization?this.linearization.numPages:this.catalog.numPages;return shadow(this,"numPages",e)}#We(e,t=0){return!!Array.isArray(e)&&e.every((e=>{if(!((e=this.xref.fetchIfRef(e))instanceof Dict))return!1;if(e.has("Kids")){if(++t>10){warn("#hasOnlyDocumentSignatures: maximum recursion depth reached");return!1}return this.#We(e.get("Kids"),t)}const a=isName(e.get("FT"),"Sig"),r=e.get("Rect"),i=Array.isArray(r)&&r.every((e=>0===e));return a&&i}))}#ze(e,t,a=new RefSet){if(Arra\
-y.isArray(e))for(let r of e){if(r instanceof Ref){if(a.has(r))continue;a.put(r)}r=this.xref.fetchIfRef(r);if(!(r instanceof Dict))continue;if(r.has("Kids")){this.#ze(r.get("Kids"),t,a);continue}if(!isName(r.get("FT"),"Sig"))continue;const e=r.get("V");if(!(e instanceof Dict))continue;const i=e.get("SubFilter");i instanceof Name&&t.add(i.name)}}get _xfaStreams(){const{acroForm:e}=this.catalog;if(!e)return null;const t=e.get("XFA"),a=new Map(["xdp:xdp","template","datasets","config","connectionSet\
-","localeSet","stylesheet","/xdp:xdp"].map((e=>[e,null])));if(t instanceof BaseStream&&!t.isEmpty){a.set("xdp:xdp",t);return a}if(!Array.isArray(t)||0===t.length)return null;for(let e=0,r=t.length;e<r;e+=2){let i;i=0===e?"xdp:xdp":e===r-2?"/xdp:xdp":t[e];if(!a.has(i))continue;const n=this.xref.fetchIfRef(t[e+1]);n instanceof BaseStream&&!n.isEmpty&&a.set(i,n)}return a}get xfaDatasets(){const e=this._xfaStreams;if(!e)return shadow(this,"xfaDatasets",null);for(const t of["datasets","xdp:xdp"]){con\
-st a=e.get(t);if(a)try{const e=stringToUTF8String(a.getString());return shadow(this,"xfaDatasets",new DatasetReader({[t]:e}))}catch{warn("XFA - Invalid utf-8 string.");break}}return shadow(this,"xfaDatasets",null)}get xfaData(){const e=this._xfaStreams;if(!e)return null;const t=Object.create(null);for(const[a,r]of e)if(r)try{t[a]=stringToUTF8String(r.getString())}catch{warn("XFA - Invalid utf-8 string.");return null}return t}get xfaFactory(){let e;this.pdfManager.enableXfa&&this.catalog.needsRen\
-dering&&this.formInfo.hasXfa&&!this.formInfo.hasAcroForm&&(e=this.xfaData);return shadow(this,"xfaFactory",e?new XFAFactory(e):null)}get isPureXfa(){return!!this.xfaFactory&&this.xfaFactory.isValid()}get htmlForXfa(){return this.xfaFactory?this.xfaFactory.getPages():null}async#$e(){const e=await this.pdfManager.ensureCatalog("xfaImages");e&&this.xfaFactory.setImages(e)}async#Ge(e,t){const a=await this.pdfManager.ensureCatalog("acroForm");if(!a)return;const r=await a.getAsync("DR");if(!(r instanc\
-eof Dict))return;await ObjectLoader.load(r,["Font"],this.xref);const i=r.get("Font");if(!(i instanceof Dict))return;const n=Object.assign(Object.create(null),this.pdfManager.evaluatorOptions,{useSystemFonts:!1}),{builtInCMapCache:s,fontCache:o,standardFontDataCache:c}=this.catalog,l=new PartialEvaluator({xref:this.xref,handler:e,pageIndex:-1,idFactory:this._globalIdFactory,fontCache:o,builtInCMapCache:s,standardFontDataCache:c,options:n}),h=new OperatorList,u=[],d={get font(){return u.at(-1)},se\
-t font(e){u.push(e)},clone(){return this}},parseFont=(e,a,i)=>l.handleSetFont(r,[Name.get(e),1],null,h,t,d,a,i).catch((e=>{warn(`loadXfaFonts: "${e}".`);return null})),f=[];for(const[e,t]of i){const a=t.get("FontDescriptor");if(!(a instanceof Dict))continue;let r=a.get("FontFamily");r=r.replaceAll(/[ ]+(\\d)/g,"$1");const i={fontFamily:r,fontWeight:a.get("FontWeight"),italicAngle:-a.get("ItalicAngle")};validateCSSFont(i)&&f.push(parseFont(e,null,i))}await Promise.all(f);const g=this.xfaFactory.se\
-tFonts(u);if(!g)return;n.ignoreErrors=!0;f.length=0;u.length=0;const p=new Set;for(const e of g)getXfaFontName(`${e}-Regular`)||p.add(e);p.size&&g.push("PdfJS-Fallback");for(const e of g)if(!p.has(e))for(const t of[{name:"Regular",fontWeight:400,italicAngle:0},{name:"Bold",fontWeight:700,italicAngle:0},{name:"Italic",fontWeight:400,italicAngle:12},{name:"BoldItalic",fontWeight:700,italicAngle:12}]){const a=`${e}-${t.name}`;f.push(parseFont(a,getXfaFontDict(a),{fontFamily:e,fontWeight:t.fontWeigh\
-t,italicAngle:t.italicAngle}))}await Promise.all(f);this.xfaFactory.appendFonts(u,p)}loadXfaResources(e,t){return Promise.all([this.#Ge(e,t).catch((()=>{})),this.#$e()])}serializeXfaData(e){return this.xfaFactory?this.xfaFactory.serializeData(e):null}get version(){return this.catalog.version||this.#He}get formInfo(){const e={hasFields:!1,hasAcroForm:!1,hasXfa:!1,hasSignatures:!1},{acroForm:t}=this.catalog;if(!t)return shadow(this,"formInfo",e);try{const a=t.get("Fields"),r=Array.isArray(a)&&a.le\
-ngth>0;e.hasFields=r;const i=t.get("XFA");e.hasXfa=Array.isArray(i)&&i.length>0||i instanceof BaseStream&&!i.isEmpty;const n=!!(1&t.get("SigFlags")),s=n&&this.#We(a);e.hasAcroForm=r&&!s;e.hasSignatures=n}catch(e){if(e instanceof MissingDataException)throw e;warn(`Cannot fetch form information: "${e}".`)}return shadow(this,"formInfo",e)}get documentInfo(){const{catalog:e,formInfo:t,xref:a}=this,r={PDFFormatVersion:this.version,Language:e.lang,EncryptFilterName:a.encrypt?.filterName??null,IsLinear\
-ized:!!this.linearization,IsAcroFormPresent:t.hasAcroForm,IsXFAPresent:t.hasXfa,IsCollectionPresent:!!e.collection,IsSignaturesPresent:t.hasSignatures};let i;try{i=a.trailer.get("Info")}catch(e){if(e instanceof MissingDataException)throw e;info("The document information dictionary is invalid.")}if(!(i instanceof Dict))return shadow(this,"documentInfo",r);for(const[e,t]of i){switch(e){case"Title":case"Author":case"Subject":case"Keywords":case"Creator":case"Producer":case"CreationDate":case"ModDat\
-e":if("string"==typeof t){r[e]=stringToPDFString(t);continue}break;case"Trapped":if(t instanceof Name){r[e]=t;continue}break;default:let a;switch(typeof t){case"string":a=stringToPDFString(t);break;case"number":case"boolean":a=t;break;default:t instanceof Name&&(a=t)}if(void 0===a){warn(`Bad value, for custom key "${e}", in Info: ${t}.`);continue}r.Custom??=Object.create(null);r.Custom[e]=a;continue}warn(`Bad value, for key "${e}", in Info: ${t}.`)}return shadow(this,"documentInfo",r)}get finger\
-prints(){const e="\\0".repeat(16);function validate(t){return"string"==typeof t&&16===t.length&&t!==e}const t=this.xref.trailer.get("ID");let a,r;if(Array.isArray(t)&&validate(t[0])){a=stringToBytes(t[0]);t[1]!==t[0]&&validate(t[1])&&(r=stringToBytes(t[1]))}else a=calculateMD5(this.stream.getByteRange(0,1024),0,1024);return shadow(this,"fingerprints",[toHexUtil(a),r?toHexUtil(r):null])}async#Ve(e){const{catalog:t,linearization:a,xref:r}=this,i=Ref.get(a.objectNumberFirst,0);try{const e=await r.fe\
-tchAsync(i);if(e instanceof Dict){let a=e.getRaw("Type");a instanceof Ref&&(a=await r.fetchAsync(a));if(isName(a,"Page")||!e.has("Type")&&!e.has("Kids")&&e.has("Contents")){t.pageKidsCountCache.has(i)||t.pageKidsCountCache.put(i,1);t.pageIndexCache.has(i)||t.pageIndexCache.put(i,0);return[e,i]}}throw new FormatError("The Linearization dictionary doesn\'t point to a valid Page dictionary.")}catch(a){warn(`_getLinearizationPage: "${a.message}".`);return t.getPageDict(e)}}getPage(e){const t=this.#qe\
-.get(e);if(t)return t;const{catalog:a,linearization:r,xfaFactory:i}=this;let n;n=i?Promise.resolve([Dict.empty,null]):r?.pageFirst===e?this.#Ve(e):a.getPageDict(e);n=n.then((([t,r])=>new Page({pdfManager:this.pdfManager,xref:this.xref,pageIndex:e,pageDict:t,ref:r,globalIdFactory:this._globalIdFactory,fontCache:a.fontCache,builtInCMapCache:a.builtInCMapCache,standardFontDataCache:a.standardFontDataCache,globalColorSpaceCache:a.globalColorSpaceCache,globalImageCache:a.globalImageCache,systemFontCa\
-che:a.systemFontCache,nonBlendModesSet:a.nonBlendModesSet,xfaFactory:i})));this.#qe.set(e,n);return n}async checkFirstPage(e=!1){if(!e)try{await this.getPage(0)}catch(e){if(e instanceof XRefEntryException){this.#qe.delete(0);await this.cleanup();throw new XRefParseException}}}async checkLastPage(e=!1){const{catalog:t,pdfManager:a}=this;t.setActualNumPages();let r;try{await Promise.all([a.ensureDoc("xfaFactory"),a.ensureDoc("linearization"),a.ensureCatalog("numPages")]);if(this.xfaFactory)return;\
-r=this.linearization?this.linearization.numPages:t.numPages;if(!Number.isInteger(r))throw new FormatError("Page count is not an integer.");if(r<=1)return;await this.getPage(r-1)}catch(i){this.#qe.delete(r-1);await this.cleanup();if(i instanceof XRefEntryException&&!e)throw new XRefParseException;warn(`checkLastPage - invalid /Pages tree /Count: ${r}.`);let n;try{n=await t.getAllPageDicts(e)}catch(a){if(a instanceof XRefEntryException&&!e)throw new XRefParseException;t.setActualNumPages(1);return\
-}for(const[e,[r,i]]of n){let n;if(r instanceof Error){n=Promise.reject(r);n.catch((()=>{}))}else n=Promise.resolve(new Page({pdfManager:a,xref:this.xref,pageIndex:e,pageDict:r,ref:i,globalIdFactory:this._globalIdFactory,fontCache:t.fontCache,builtInCMapCache:t.builtInCMapCache,standardFontDataCache:t.standardFontDataCache,globalColorSpaceCache:this.globalColorSpaceCache,globalImageCache:t.globalImageCache,systemFontCache:t.systemFontCache,nonBlendModesSet:t.nonBlendModesSet,xfaFactory:null}));th\
-is.#qe.set(e,n)}t.setActualNumPages(n.size)}}async fontFallback(e,t){const{catalog:a,pdfManager:r}=this;for(const i of await Promise.all(a.fontCache))if(i.loadedName===e){i.fallback(t,r.evaluatorOptions);return}}async cleanup(e=!1){return this.catalog?this.catalog.cleanup(e):clearGlobalCaches()}async#Ke(e,t,a,r,i,n,s){const{xref:o}=this;if(!(a instanceof Ref)||n.has(a))return;n.put(a);const c=await o.fetchAsync(a);if(!(c instanceof Dict))return;let l=await c.getAsync("Subtype");l=l instanceof Na\
-me?l.name:null;if("Link"===l)return;if(c.has("T")){const t=stringToPDFString(await c.getAsync("T"));e=""===e?t:`${e}.${t}`}else{let a=c;for(;;){a=a.getRaw("Parent")||t;if(a instanceof Ref){if(n.has(a))break;a=await o.fetchAsync(a)}if(!(a instanceof Dict))break;if(a.has("T")){const t=stringToPDFString(await a.getAsync("T"));e=""===e?t:`${e}.${t}`;break}}}t&&!c.has("Parent")&&isName(c.get("Subtype"),"Widget")&&s.put(a,t);r.has(e)||r.set(e,[]);r.get(e).push(AnnotationFactory.create(o,a,i,null,!0,s,\
-null).then((e=>e?.getFieldObject())).catch((function(e){warn(`#collectFieldObjects: "${e}".`);return null})));if(!c.has("Kids"))return;const h=await c.getAsync("Kids");if(Array.isArray(h))for(const t of h)await this.#Ke(e,a,t,r,i,n,s)}get fieldObjects(){return shadow(this,"fieldObjects",this.pdfManager.ensureDoc("formInfo").then((async e=>{if(!e.hasFields)return null;const t=await this.annotationGlobals;if(!t)return null;const{acroForm:a}=t,r=new RefSet,i=Object.create(null),n=new Map,s=new RefS\
-etCache;for(const e of a.get("Fields"))await this.#Ke("",null,e,n,t,r,s);const o=[];for(const[e,t]of n)o.push(Promise.all(t).then((t=>{(t=t.filter((e=>!!e))).length>0&&(i[e]=t)})));await Promise.all(o);return{allFields:objectSize(i)>0?i:null,orphanFields:s}})))}get hasJSActions(){return shadow(this,"hasJSActions",this.pdfManager.ensureDoc("_parseHasJSActions"))}async _parseHasJSActions(){const[e,t]=await Promise.all([this.pdfManager.ensureCatalog("jsActions"),this.pdfManager.ensureDoc("fieldObje\
-cts")]);return!!e||!!t?.allFields&&Object.values(t.allFields).some((e=>e.some((e=>null!==e.actions))))}get calculationOrderIds(){const e=this.catalog.acroForm?.get("CO");if(!Array.isArray(e)||0===e.length)return shadow(this,"calculationOrderIds",null);const t=[];for(const a of e)a instanceof Ref&&t.push(a.toString());return shadow(this,"calculationOrderIds",t.length?t:null)}get annotationGlobals(){return shadow(this,"annotationGlobals",AnnotationFactory.createGlobals(this.pdfManager))}}class Bas\
-ePdfManager{constructor({docBaseUrl:e,docId:t,enableXfa:a,evaluatorOptions:r,handler:i,password:n}){this._docBaseUrl=function parseDocBaseUrl(e){if(e){const t=createValidAbsoluteUrl(e);if(t)return t.href;warn(`Invalid absolute docBaseUrl: "${e}".`)}return null}(e);this._docId=t;this._password=n;this.enableXfa=a;r.isOffscreenCanvasSupported&&=FeatureTest.isOffscreenCanvasSupported;r.isImageDecoderSupported&&=FeatureTest.isImageDecoderSupported;this.evaluatorOptions=Object.freeze(r);ImageResizer.s\
-etOptions(r);JpegStream.setOptions(r);OperatorList.setOptions(r);const s={...r,handler:i};JpxImage.setOptions(s);IccColorSpace.setOptions(s);CmykICCBasedCS.setOptions(s)}get docId(){return this._docId}get password(){return this._password}get docBaseUrl(){return this._docBaseUrl}ensureDoc(e,t){return this.ensure(this.pdfDocument,e,t)}ensureXRef(e,t){return this.ensure(this.pdfDocument.xref,e,t)}ensureCatalog(e,t){return this.ensure(this.pdfDocument.catalog,e,t)}getPage(e){return this.pdfDocument.\
-getPage(e)}fontFallback(e,t){return this.pdfDocument.fontFallback(e,t)}cleanup(e=!1){return this.pdfDocument.cleanup(e)}async ensure(e,t,a){unreachable("Abstract method `ensure` called")}requestRange(e,t){unreachable("Abstract method `requestRange` called")}requestLoadedStream(e=!1){unreachable("Abstract method `requestLoadedStream` called")}sendProgressiveData(e){unreachable("Abstract method `sendProgressiveData` called")}updatePassword(e){this._password=e}terminate(e){unreachable("Abstract met\
-hod `terminate` called")}}class LocalPdfManager extends BasePdfManager{constructor(e){super(e);const t=new Stream(e.source);this.pdfDocument=new PDFDocument(this,t);this._loadedStreamPromise=Promise.resolve(t)}async ensure(e,t,a){const r=e[t];return"function"==typeof r?r.apply(e,a):r}requestRange(e,t){return Promise.resolve()}requestLoadedStream(e=!1){return this._loadedStreamPromise}terminate(e){}}class NetworkPdfManager extends BasePdfManager{constructor(e){super(e);this.streamManager=new Chun\
-kedStreamManager(e.source,{msgHandler:e.handler,length:e.length,disableAutoFetch:e.disableAutoFetch,rangeChunkSize:e.rangeChunkSize});this.pdfDocument=new PDFDocument(this,this.streamManager.getStream())}async ensure(e,t,a){try{const r=e[t];return"function"==typeof r?r.apply(e,a):r}catch(r){if(!(r instanceof MissingDataException))throw r;await this.requestRange(r.begin,r.end);return this.ensure(e,t,a)}}requestRange(e,t){return this.streamManager.requestRange(e,t)}requestLoadedStream(e=!1){return\
- this.streamManager.requestAllChunks(e)}sendProgressiveData(e){this.streamManager.onReceiveData({chunk:e})}terminate(e){this.streamManager.abort(e)}}const Ac=1,kc=2,Cc=1,vc=2,Fc=3,Ic=4,Tc=5,Oc=6,Mc=7,Dc=8;function onFn(){}function wrapReason(e){if(e instanceof AbortException||e instanceof InvalidPDFException||e instanceof PasswordException||e instanceof ResponseException||e instanceof UnknownErrorException)return e;e instanceof Error||"object"==typeof e&&null!==e||unreachable(\'wrapReason: Expect\
-ed "reason" to be a (possibly cloned) Error.\');switch(e.name){case"AbortException":return new AbortException(e.message);case"InvalidPDFException":return new InvalidPDFException(e.message);case"PasswordException":return new PasswordException(e.message,e.code);case"ResponseException":return new ResponseException(e.message,e.status,e.missing);case"UnknownErrorException":return new UnknownErrorException(e.message,e.details)}return new UnknownErrorException(e.message,e.toString())}class MessageHandle\
-r{#Je=new AbortController;constructor(e,t,a){this.sourceName=e;this.targetName=t;this.comObj=a;this.callbackId=1;this.streamId=1;this.streamSinks=Object.create(null);this.streamControllers=Object.create(null);this.callbackCapabilities=Object.create(null);this.actionHandler=Object.create(null);a.addEventListener("message",this.#Ye.bind(this),{signal:this.#Je.signal})}#Ye({data:e}){if(e.targetName!==this.sourceName)return;if(e.stream){this.#Ze(e);return}if(e.callback){const t=e.callbackId,a=this.c\
-allbackCapabilities[t];if(!a)throw new Error(`Cannot resolve callback ${t}`);delete this.callbackCapabilities[t];if(e.callback===Ac)a.resolve(e.data);else{if(e.callback!==kc)throw new Error("Unexpected callback case");a.reject(wrapReason(e.reason))}return}const t=this.actionHandler[e.action];if(!t)throw new Error(`Unknown action from worker: ${e.action}`);if(e.callbackId){const a=this.sourceName,r=e.sourceName,i=this.comObj;Promise.try(t,e.data).then((function(t){i.postMessage({sourceName:a,targ\
-etName:r,callback:Ac,callbackId:e.callbackId,data:t})}),(function(t){i.postMessage({sourceName:a,targetName:r,callback:kc,callbackId:e.callbackId,reason:wrapReason(t)})}))}else e.streamId?this.#Qe(e):t(e.data)}on(e,t){const a=this.actionHandler;if(a[e])throw new Error(`There is already an actionName called "${e}"`);a[e]=t}send(e,t,a){this.comObj.postMessage({sourceName:this.sourceName,targetName:this.targetName,action:e,data:t},a)}sendWithPromise(e,t,a){const r=this.callbackId++,i=Promise.withRe\
-solvers();this.callbackCapabilities[r]=i;try{this.comObj.postMessage({sourceName:this.sourceName,targetName:this.targetName,action:e,callbackId:r,data:t},a)}catch(e){i.reject(e)}return i.promise}sendWithStream(e,t,a,r){const i=this.streamId++,n=this.sourceName,s=this.targetName,o=this.comObj;return new ReadableStream({start:a=>{const c=Promise.withResolvers();this.streamControllers[i]={controller:a,startCall:c,pullCall:null,cancelCall:null,isClosed:!1};o.postMessage({sourceName:n,targetName:s,ac\
-tion:e,streamId:i,data:t,desiredSize:a.desiredSize},r);return c.promise},pull:e=>{const t=Promise.withResolvers();this.streamControllers[i].pullCall=t;o.postMessage({sourceName:n,targetName:s,stream:Oc,streamId:i,desiredSize:e.desiredSize});return t.promise},cancel:e=>{assert(e instanceof Error,"cancel must have a valid reason");const t=Promise.withResolvers();this.streamControllers[i].cancelCall=t;this.streamControllers[i].isClosed=!0;o.postMessage({sourceName:n,targetName:s,stream:Cc,streamId:\
-i,reason:wrapReason(e)});return t.promise}},a)}#Qe(e){const t=e.streamId,a=this.sourceName,r=e.sourceName,i=this.comObj,n=this,s=this.actionHandler[e.action],o={enqueue(e,n=1,s){if(this.isCancelled)return;const o=this.desiredSize;this.desiredSize-=n;if(o>0&&this.desiredSize<=0){this.sinkCapability=Promise.withResolvers();this.ready=this.sinkCapability.promise}i.postMessage({sourceName:a,targetName:r,stream:Ic,streamId:t,chunk:e},s)},close(){if(!this.isCancelled){this.isCancelled=!0;i.postMessage\
-({sourceName:a,targetName:r,stream:Fc,streamId:t});delete n.streamSinks[t]}},error(e){assert(e instanceof Error,"error must have a valid reason");if(!this.isCancelled){this.isCancelled=!0;i.postMessage({sourceName:a,targetName:r,stream:Tc,streamId:t,reason:wrapReason(e)})}},sinkCapability:Promise.withResolvers(),onPull:null,onCancel:null,isCancelled:!1,desiredSize:e.desiredSize,ready:null};o.sinkCapability.resolve();o.ready=o.sinkCapability.promise;this.streamSinks[t]=o;Promise.try(s,e.data,o).t\
-hen((function(){i.postMessage({sourceName:a,targetName:r,stream:Dc,streamId:t,success:!0})}),(function(e){i.postMessage({sourceName:a,targetName:r,stream:Dc,streamId:t,reason:wrapReason(e)})}))}#Ze(e){const t=e.streamId,a=this.sourceName,r=e.sourceName,i=this.comObj,n=this.streamControllers[t],s=this.streamSinks[t];switch(e.stream){case Dc:e.success?n.startCall.resolve():n.startCall.reject(wrapReason(e.reason));break;case Mc:e.success?n.pullCall.resolve():n.pullCall.reject(wrapReason(e.reason));\
-break;case Oc:if(!s){i.postMessage({sourceName:a,targetName:r,stream:Mc,streamId:t,success:!0});break}s.desiredSize<=0&&e.desiredSize>0&&s.sinkCapability.resolve();s.desiredSize=e.desiredSize;Promise.try(s.onPull||onFn).then((function(){i.postMessage({sourceName:a,targetName:r,stream:Mc,streamId:t,success:!0})}),(function(e){i.postMessage({sourceName:a,targetName:r,stream:Mc,streamId:t,reason:wrapReason(e)})}));break;case Ic:assert(n,"enqueue should have stream controller");if(n.isClosed)break;n\
-.controller.enqueue(e.chunk);break;case Fc:assert(n,"close should have stream controller");if(n.isClosed)break;n.isClosed=!0;n.controller.close();this.#et(n,t);break;case Tc:assert(n,"error should have stream controller");n.controller.error(wrapReason(e.reason));this.#et(n,t);break;case vc:e.success?n.cancelCall.resolve():n.cancelCall.reject(wrapReason(e.reason));this.#et(n,t);break;case Cc:if(!s)break;const o=wrapReason(e.reason);Promise.try(s.onCancel||onFn,o).then((function(){i.postMessage({s\
-ourceName:a,targetName:r,stream:vc,streamId:t,success:!0})}),(function(e){i.postMessage({sourceName:a,targetName:r,stream:vc,streamId:t,reason:wrapReason(e)})}));s.sinkCapability.reject(o);s.isCancelled=!0;delete this.streamSinks[t];break;default:throw new Error("Unexpected stream case")}}async#et(e,t){await Promise.allSettled([e.startCall?.promise,e.pullCall?.promise,e.cancelCall?.promise]);delete this.streamControllers[t]}destroy(){this.#Je?.abort();this.#Je=null}}async function writeObject(e,\
-t,a,{encrypt:r=null}){const i=r?.createCipherTransform(e.num,e.gen);a.push(`${e.num} ${e.gen} obj\\n`);t instanceof Dict?await writeDict(t,a,i):t instanceof BaseStream?await writeStream(t,a,i):(Array.isArray(t)||ArrayBuffer.isView(t))&&await writeArray(t,a,i);a.push("\\nendobj\\n")}async function writeDict(e,t,a){t.push("<<");for(const r of e.getKeys()){t.push(` /${escapePDFName(r)} `);await writeValue(e.getRaw(r),t,a)}t.push(">>")}async function writeStream(e,t,a){let r=e.getBytes();const{dict:i}=\
-e,[n,s]=await Promise.all([i.getAsync("Filter"),i.getAsync("DecodeParms")]),o=isName(Array.isArray(n)?await i.xref.fetchIfRefAsync(n[0]):n,"FlateDecode");if(r.length>=256||o)try{const e=new CompressionStream("deflate"),t=e.writable.getWriter();await t.ready;t.write(r).then((async()=>{await t.ready;await t.close()})).catch((()=>{}));const a=await new Response(e.readable).arrayBuffer();r=new Uint8Array(a);let c,l;if(n){if(!o){c=Array.isArray(n)?[Name.get("FlateDecode"),...n]:[Name.get("FlateDecode\
-"),n];s&&(l=Array.isArray(s)?[null,...s]:[null,s])}}else c=Name.get("FlateDecode");c&&i.set("Filter",c);l&&i.set("DecodeParms",l)}catch(e){info(`writeStream - cannot compress data: "${e}".`)}let c=bytesToString(r);a&&(c=a.encryptString(c));i.set("Length",c.length);await writeDict(i,t,a);t.push(" stream\\n",c,"\\nendstream")}async function writeArray(e,t,a){t.push("[");let r=!0;for(const i of e){r?r=!1:t.push(" ");await writeValue(i,t,a)}t.push("]")}async function writeValue(e,t,a){if(e instanceof \
-Name)t.push(`/${escapePDFName(e.name)}`);else if(e instanceof Ref)t.push(`${e.num} ${e.gen} R`);else if(Array.isArray(e)||ArrayBuffer.isView(e))await writeArray(e,t,a);else if("string"==typeof e){a&&(e=a.encryptString(e));t.push(`(${escapeString(e)})`)}else"number"==typeof e?t.push(numberToString(e)):"boolean"==typeof e?t.push(e.toString()):e instanceof Dict?await writeDict(e,t,a):e instanceof BaseStream?await writeStream(e,t,a):null===e?t.push("null"):warn(`Unhandled value in writer: ${typeof e\
-}, please file a bug.`)}function writeInt(e,t,a,r){for(let i=t+a-1;i>a-1;i--){r[i]=255&e;e>>=8}return a+t}function writeString(e,t,a){const r=e.length;for(let i=0;i<r;i++)a[t+i]=255&e.charCodeAt(i);return t+r}function updateXFA({xfaData:e,xfaDatasetsRef:t,changes:a,xref:r}){if(null===e){e=function writeXFADataForAcroform(e,t){const a=new SimpleXMLParser({hasAttributes:!0}).parseFromString(e);for(const{xfa:e}of t){if(!e)continue;const{path:t,value:r}=e;if(!t)continue;const i=parseXFAPath(t);let n\
-=a.documentElement.searchNode(i,0);!n&&i.length>1&&(n=a.documentElement.searchNode([i.at(-1)],0));n?n.childNodes=Array.isArray(r)?r.map((e=>new SimpleDOMNode("value",e))):[new SimpleDOMNode("#text",r)]:warn(`Node not found for path: ${t}`)}const r=[];a.documentElement.dump(r);return r.join("")}(r.fetchIfRef(t).getString(),a)}const i=new StringStream(e);i.dict=new Dict(r);i.dict.setIfName("Type","EmbeddedFile");a.put(t,{data:i})}function getIndexes(e){const t=[];for(const{ref:a}of e)a.num===t.at(\
--2)+t.at(-1)?t[t.length-1]+=1:t.push(a.num,1);return t}function computeIDs(e,t,a){if(Array.isArray(t.fileIds)&&t.fileIds.length>0){const r=function computeMD5(e,t){const a=Math.floor(Date.now()/1e3),r=t.filename||"",i=[a.toString(),r,e.toString(),...t.infoMap.values()],n=Math.sumPrecise(i.map((e=>e.length))),s=new Uint8Array(n);let o=0;for(const e of i)o=writeString(e,o,s);return bytesToString(calculateMD5(s,0,s.length))}(e,t);a.set("ID",[t.fileIds[0],r])}}async function incrementalUpdate({origi\
-nalData:e,xrefInfo:t,changes:a,xref:r=null,hasXfa:i=!1,xfaDatasetsRef:n=null,hasXfaDatasetsEntry:s=!1,needAppearances:o,acroFormRef:c=null,acroForm:l=null,xfaData:h=null,useXrefStream:u=!1}){await async function updateAcroform({xref:e,acroForm:t,acroFormRef:a,hasXfa:r,hasXfaDatasetsEntry:i,xfaDatasetsRef:n,needAppearances:s,changes:o}){!r||i||n||warn("XFA - Cannot save it");if(!s&&(!r||!n||i))return;const c=t.clone();if(r&&!i){const e=t.get("XFA").slice();e.splice(2,0,"datasets");e.splice(3,0,n)\
-;c.set("XFA",e)}s&&c.set("NeedAppearances",!0);o.put(a,{data:c})}({xref:r,acroForm:l,acroFormRef:c,hasXfa:i,hasXfaDatasetsEntry:s,xfaDatasetsRef:n,needAppearances:o,changes:a});i&&updateXFA({xfaData:h,xfaDatasetsRef:n,changes:a,xref:r});const d=function getTrailerDict(e,t,a){const r=new Dict(null);r.set("Prev",e.startXRef);const i=e.newRef;if(a){t.put(i,{data:""});r.set("Size",i.num+1);r.setIfName("Type","XRef")}else r.set("Size",i.num);null!==e.rootRef&&r.set("Root",e.rootRef);null!==e.infoRef&\
-&r.set("Info",e.infoRef);null!==e.encryptRef&&r.set("Encrypt",e.encryptRef);return r}(t,a,u),f=[],g=await async function writeChanges(e,t,a=[]){const r=[];for(const[i,{data:n}]of e.items())if(null!==n&&"string"!=typeof n){await writeObject(i,n,a,t);r.push({ref:i,data:a.join("")});a.length=0}else r.push({ref:i,data:n});return r.sort(((e,t)=>e.ref.num-t.ref.num))}(a,r,f);let p=e.length;const m=e.at(-1);if(10!==m&&13!==m){f.push("\\n");p+=1}for(const{data:e}of g)null!==e&&f.push(e);await(u?async fun\
-ction getXRefStreamTable(e,t,a,r,i){const n=[];let s=0,o=0;for(const{ref:e,data:r}of a){let a;s=Math.max(s,t);if(null!==r){a=Math.min(e.gen,65535);n.push([1,t,a]);t+=r.length}else{a=Math.min(e.gen+1,65535);n.push([0,0,a])}o=Math.max(o,a)}r.set("Index",getIndexes(a));const c=[1,getSizeInBytes(s),getSizeInBytes(o)];r.set("W",c);computeIDs(t,e,r);const l=Math.sumPrecise(c),h=new Uint8Array(l*n.length),u=new Stream(h);u.dict=r;let d=0;for(const[e,t,a]of n){d=writeInt(e,c[0],d,h);d=writeInt(t,c[1],d,\
-h);d=writeInt(a,c[2],d,h)}await writeObject(e.newRef,u,i,{});i.push("startxref\\n",t.toString(),"\\n%%EOF\\n")}(t,p,g,d,f):async function getXRefTable(e,t,a,r,i){i.push("xref\\n");const n=getIndexes(a);let s=0;for(const{ref:e,data:r}of a){if(e.num===n[s]){i.push(`${n[s]} ${n[s+1]}\\n`);s+=2}if(null!==r){i.push(`${t.toString().padStart(10,"0")} ${Math.min(e.gen,65535).toString().padStart(5,"0")} n\\r\\n`);t+=r.length}else i.push(`0000000000 ${Math.min(e.gen+1,65535).toString().padStart(5,"0")} f\\r\\n`)}c\
-omputeIDs(t,e,r);i.push("trailer\\n");await writeDict(r,i);i.push("\\nstartxref\\n",t.toString(),"\\n%%EOF\\n")}(t,p,g,d,f));const b=e.length+Math.sumPrecise(f.map((e=>e.length))),y=new Uint8Array(b);y.set(e);let w=e.length;for(const e of f)w=writeString(e,w,y);return y}class PDFWorkerStream{constructor(e){this._msgHandler=e;this._contentLength=null;this._fullRequestReader=null;this._rangeRequestReaders=[]}getFullReader(){assert(!this._fullRequestReader,"PDFWorkerStream.getFullReader can only be call\
-ed once.");this._fullRequestReader=new PDFWorkerStreamReader(this._msgHandler);return this._fullRequestReader}getRangeReader(e,t){const a=new PDFWorkerStreamRangeReader(e,t,this._msgHandler);this._rangeRequestReaders.push(a);return a}cancelAllRequests(e){this._fullRequestReader?.cancel(e);for(const t of this._rangeRequestReaders.slice(0))t.cancel(e)}}class PDFWorkerStreamReader{constructor(e){this._msgHandler=e;this.onProgress=null;this._contentLength=null;this._isRangeSupported=!1;this._isStrea\
-mingSupported=!1;const t=this._msgHandler.sendWithStream("GetReader");this._reader=t.getReader();this._headersReady=this._msgHandler.sendWithPromise("ReaderHeadersReady").then((e=>{this._isStreamingSupported=e.isStreamingSupported;this._isRangeSupported=e.isRangeSupported;this._contentLength=e.contentLength}))}get headersReady(){return this._headersReady}get contentLength(){return this._contentLength}get isStreamingSupported(){return this._isStreamingSupported}get isRangeSupported(){return this.\
-_isRangeSupported}async read(){const{value:e,done:t}=await this._reader.read();return t?{value:void 0,done:!0}:{value:e.buffer,done:!1}}cancel(e){this._reader.cancel(e)}}class PDFWorkerStreamRangeReader{constructor(e,t,a){this._msgHandler=a;this.onProgress=null;const r=this._msgHandler.sendWithStream("GetRangeReader",{begin:e,end:t});this._reader=r.getReader()}get isStreamingSupported(){return!1}async read(){const{value:e,done:t}=await this._reader.read();return t?{value:void 0,done:!0}:{value:e\
-.buffer,done:!1}}cancel(e){this._reader.cancel(e)}}class WorkerTask{constructor(e){this.name=e;this.terminated=!1;this._capability=Promise.withResolvers()}get finished(){return this._capability.promise}finish(){this._capability.resolve()}terminate(){this.terminated=!0}ensureNotTerminated(){if(this.terminated)throw new Error("Worker task was terminated")}}class WorkerMessageHandler{static{"undefined"==typeof window&&!e&&"undefined"!=typeof self&&"function"==typeof self.postMessage&&"onmessage"in \
-self&&this.initializeFromPort(self)}static setup(e,t){let a=!1;e.on("test",(t=>{if(!a){a=!0;e.send("test",t instanceof Uint8Array)}}));e.on("configure",(e=>{!function setVerbosityLevel(e){Number.isInteger(e)&&(da=e)}(e.verbosity)}));e.on("GetDocRequest",(e=>this.createDocumentHandler(e,t)))}static createDocumentHandler(e,t){let a,r=!1,i=null;const n=new Set,s=getVerbosityLevel(),{docId:o,apiVersion:c}=e,l="5.4.54";if(c!==l)throw new Error(`The API version "${c}" does not match the Worker version\
- "${l}".`);const buildMsg=(e,t)=>`The \\`${e}.prototype\\` contains unexpected enumerable property "${t}", thus breaking e.g. \\`for...in\\` iteration of ${e}s.`;for(const e in{})throw new Error(buildMsg("Object",e));for(const e in[])throw new Error(buildMsg("Array",e));const h=o+"_worker";let u=new MessageHandler(h,o,t);function ensureNotTerminated(){if(r)throw new Error("Worker was terminated")}function startWorkerTask(e){n.add(e)}function finishWorkerTask(e){e.finish();n.delete(e)}async function \
-loadDocument(e){await a.ensureDoc("checkHeader");await a.ensureDoc("parseStartXRef");await a.ensureDoc("parse",[e]);await a.ensureDoc("checkFirstPage",[e]);await a.ensureDoc("checkLastPage",[e]);const t=await a.ensureDoc("isPureXfa");if(t){const e=new WorkerTask("loadXfaResources");startWorkerTask(e);await a.ensureDoc("loadXfaResources",[u,e]);finishWorkerTask(e)}const[r,i]=await Promise.all([a.ensureDoc("numPages"),a.ensureDoc("fingerprints")]);return{numPages:r,fingerprints:i,htmlForXfa:t?awai\
-t a.ensureDoc("htmlForXfa"):null}}function setupDoc(e){function onSuccess(e){ensureNotTerminated();u.send("GetDoc",{pdfInfo:e})}function onFailure(e){ensureNotTerminated();if(e instanceof PasswordException){const t=new WorkerTask(`PasswordException: response ${e.code}`);startWorkerTask(t);u.sendWithPromise("PasswordRequest",e).then((function({password:e}){finishWorkerTask(t);a.updatePassword(e);pdfManagerReady()})).catch((function(){finishWorkerTask(t);u.send("DocException",e)}))}else u.send("Do\
-cException",wrapReason(e))}function pdfManagerReady(){ensureNotTerminated();loadDocument(!1).then(onSuccess,(function(e){ensureNotTerminated();e instanceof XRefParseException?a.requestLoadedStream().then((function(){ensureNotTerminated();loadDocument(!0).then(onSuccess,onFailure)})):onFailure(e)}))}ensureNotTerminated();(async function getPdfManager({data:e,password:t,disableAutoFetch:a,rangeChunkSize:r,length:n,docBaseUrl:s,enableXfa:c,evaluatorOptions:l}){const h={source:null,disableAutoFetch:\
-a,docBaseUrl:s,docId:o,enableXfa:c,evaluatorOptions:l,handler:u,length:n,password:t,rangeChunkSize:r};if(e){h.source=e;return new LocalPdfManager(h)}const d=new PDFWorkerStream(u),f=d.getFullReader(),g=Promise.withResolvers();let p,m=[],b=0;f.headersReady.then((function(){if(f.isRangeSupported){h.source=d;h.length=f.contentLength;h.disableAutoFetch||=f.isStreamingSupported;p=new NetworkPdfManager(h);for(const e of m)p.sendProgressiveData(e);m=[];g.resolve(p);i=null}})).catch((function(e){g.rejec\
-t(e);i=null}));new Promise((function(e,t){const readChunk=function({value:e,done:a}){try{ensureNotTerminated();if(a){if(!p){const e=arrayBuffersToBytes(m);m=[];n&&e.length!==n&&warn("reported HTTP length is different from actual");h.source=e;p=new LocalPdfManager(h);g.resolve(p)}i=null;return}b+=e.byteLength;f.isStreamingSupported||u.send("DocProgress",{loaded:b,total:Math.max(b,f.contentLength||0)});p?p.sendProgressiveData(e):m.push(e);f.read().then(readChunk,t)}catch(e){t(e)}};f.read().then(re\
-adChunk,t)})).catch((function(e){g.reject(e);i=null}));i=e=>{d.cancelAllRequests(e)};return g.promise})(e).then((function(e){if(r){e.terminate(new AbortException("Worker was terminated."));throw new Error("Worker was terminated")}a=e;a.requestLoadedStream(!0).then((e=>{u.send("DataLoaded",{length:e.bytes.byteLength})}))})).then(pdfManagerReady,onFailure)}u.on("GetPage",(function(e){return a.getPage(e.pageIndex).then((function(e){return Promise.all([a.ensure(e,"rotate"),a.ensure(e,"ref"),a.ensure\
-(e,"userUnit"),a.ensure(e,"view")]).then((function([e,t,a,r]){return{rotate:e,ref:t,refStr:t?.toString()??null,userUnit:a,view:r}}))}))}));u.on("GetPageIndex",(function(e){const t=Ref.get(e.num,e.gen);return a.ensureCatalog("getPageIndex",[t])}));u.on("GetDestinations",(function(e){return a.ensureCatalog("destinations")}));u.on("GetDestination",(function(e){return a.ensureCatalog("getDestination",[e.id])}));u.on("GetPageLabels",(function(e){return a.ensureCatalog("pageLabels")}));u.on("GetPageLa\
-yout",(function(e){return a.ensureCatalog("pageLayout")}));u.on("GetPageMode",(function(e){return a.ensureCatalog("pageMode")}));u.on("GetViewerPreferences",(function(e){return a.ensureCatalog("viewerPreferences")}));u.on("GetOpenAction",(function(e){return a.ensureCatalog("openAction")}));u.on("GetAttachments",(function(e){return a.ensureCatalog("attachments")}));u.on("GetDocJSActions",(function(e){return a.ensureCatalog("jsActions")}));u.on("GetPageJSActions",(function({pageIndex:e}){return a.\
-getPage(e).then((e=>a.ensure(e,"jsActions")))}));u.on("GetOutline",(function(e){return a.ensureCatalog("documentOutline")}));u.on("GetOptionalContentConfig",(function(e){return a.ensureCatalog("optionalContentConfig")}));u.on("GetPermissions",(function(e){return a.ensureCatalog("permissions")}));u.on("GetMetadata",(function(e){return Promise.all([a.ensureDoc("documentInfo"),a.ensureCatalog("metadata")])}));u.on("GetMarkInfo",(function(e){return a.ensureCatalog("markInfo")}));u.on("GetData",(func\
-tion(e){return a.requestLoadedStream().then((e=>e.bytes))}));u.on("GetAnnotations",(function({pageIndex:e,intent:t}){return a.getPage(e).then((function(a){const r=new WorkerTask(`GetAnnotations: page ${e}`);startWorkerTask(r);return a.getAnnotationsData(u,r,t).then((e=>{finishWorkerTask(r);return e}),(e=>{finishWorkerTask(r);throw e}))}))}));u.on("GetFieldObjects",(function(e){return a.ensureDoc("fieldObjects").then((e=>e?.allFields||null))}));u.on("HasJSActions",(function(e){return a.ensureDoc(\
-"hasJSActions")}));u.on("GetCalculationOrderIds",(function(e){return a.ensureDoc("calculationOrderIds")}));u.on("SaveDocument",(async function({isPureXfa:e,numPages:t,annotationStorage:r,filename:i}){const n=[a.requestLoadedStream(),a.ensureCatalog("acroForm"),a.ensureCatalog("acroFormRef"),a.ensureDoc("startXRef"),a.ensureDoc("xref"),a.ensureDoc("linearization"),a.ensureCatalog("structTreeRoot")],s=new RefSetCache,o=[],c=e?null:getNewAnnotationsMap(r),[l,h,d,f,g,p,m]=await Promise.all(n),b=g.tr\
-ailer.getRaw("Root")||null;let y;if(c){m?await m.canUpdateStructTree({pdfManager:a,newAnnotationsByPage:c})&&(y=m):await StructTreeRoot.canCreateStructureTree({catalogRef:b,pdfManager:a,newAnnotationsByPage:c})&&(y=null);const e=AnnotationFactory.generateImages(r.values(),g,a.evaluatorOptions.isOffscreenCanvasSupported),t=void 0===y?o:[];for(const[r,i]of c)t.push(a.getPage(r).then((t=>{const a=new WorkerTask(`Save (editor): page ${r}`);startWorkerTask(a);return t.saveNewAnnotations(u,a,i,e,s).fi\
-nally((function(){finishWorkerTask(a)}))})));null===y?o.push(Promise.all(t).then((async()=>{await StructTreeRoot.createStructureTree({newAnnotationsByPage:c,xref:g,catalogRef:b,pdfManager:a,changes:s})}))):y&&o.push(Promise.all(t).then((async()=>{await y.updateStructureTree({newAnnotationsByPage:c,pdfManager:a,changes:s})})))}if(e)o.push(a.ensureDoc("serializeXfaData",[r]));else for(let e=0;e<t;e++)o.push(a.getPage(e).then((function(t){const a=new WorkerTask(`Save: page ${e}`);startWorkerTask(a)\
-;return t.save(u,a,r,s).finally((function(){finishWorkerTask(a)}))})));const w=await Promise.all(o);let x=null;if(e){x=w[0];if(!x)return l.bytes}else if(0===s.size)return l.bytes;const S=d&&h instanceof Dict&&s.values().some((e=>e.needAppearances)),k=h instanceof Dict&&h.get("XFA")||null;let C=null,v=!1;if(Array.isArray(k)){for(let e=0,t=k.length;e<t;e+=2)if("datasets"===k[e]){C=k[e+1];v=!0}null===C&&(C=g.getNewTemporaryRef())}else k&&warn("Unsupported XFA type.");let F=Object.create(null);if(g.\
-trailer){const e=new Map,t=g.trailer.get("Info")||null;if(t instanceof Dict)for(const[a,r]of t)"string"==typeof r&&e.set(a,stringToPDFString(r));F={rootRef:b,encryptRef:g.trailer.getRaw("Encrypt")||null,newRef:g.getNewTemporaryRef(),infoRef:g.trailer.getRaw("Info")||null,infoMap:e,fileIds:g.trailer.get("ID")||null,startXRef:p?f:g.lastXRefStreamPos??f,filename:i}}return incrementalUpdate({originalData:l.bytes,xrefInfo:F,changes:s,xref:g,hasXfa:!!k,xfaDatasetsRef:C,hasXfaDatasetsEntry:v,needAppear\
-ances:S,acroFormRef:d,acroForm:h,xfaData:x,useXrefStream:isDict(g.topDict,"XRef")}).finally((()=>{g.resetNewTemporaryRef()}))}));u.on("GetOperatorList",(function(e,t){const r=e.pageIndex;a.getPage(r).then((function(a){const i=new WorkerTask(`GetOperatorList: page ${r}`);startWorkerTask(i);const n=s>=Ae?Date.now():0;a.getOperatorList({handler:u,sink:t,task:i,intent:e.intent,cacheKey:e.cacheKey,annotationStorage:e.annotationStorage,modifiedIds:e.modifiedIds}).then((function(e){finishWorkerTask(i);\
-n&&info(`page=${r+1} - getOperatorList: time=${Date.now()-n}ms, len=${e.length}`);t.close()}),(function(e){finishWorkerTask(i);i.terminated||t.error(e)}))}))}));u.on("GetTextContent",(function(e,t){const{pageIndex:r,includeMarkedContent:i,disableNormalization:n}=e;a.getPage(r).then((function(e){const a=new WorkerTask("GetTextContent: page "+r);startWorkerTask(a);const o=s>=Ae?Date.now():0;e.extractTextContent({handler:u,task:a,sink:t,includeMarkedContent:i,disableNormalization:n}).then((function\
-(){finishWorkerTask(a);o&&info(`page=${r+1} - getTextContent: time=`+(Date.now()-o)+"ms");t.close()}),(function(e){finishWorkerTask(a);a.terminated||t.error(e)}))}))}));u.on("GetStructTree",(function(e){return a.getPage(e.pageIndex).then((e=>a.ensure(e,"getStructTree")))}));u.on("FontFallback",(function(e){return a.fontFallback(e.id,u)}));u.on("Cleanup",(function(e){return a.cleanup(!0)}));u.on("Terminate",(function(e){r=!0;const t=[];if(a){a.terminate(new AbortException("Worker was terminated."\
-));const e=a.cleanup();t.push(e);a=null}else clearGlobalCaches();i?.(new AbortException("Worker was terminated."));for(const e of n){t.push(e.finished);e.terminate()}return Promise.all(t).then((function(){u.destroy();u=null}))}));u.on("Ready",(function(t){setupDoc(e);e=null}));return h}static initializeFromPort(e){const t=new MessageHandler("worker","main",e);this.setup(t,e);t.send("ready",null)}}globalThis.pdfjsWorker={WorkerMessageHandler};export{WorkerMessageHandler};',pdf_worker_min$1=Object.
-freeze(Object.defineProperty({__proto__:null,default:pdf_worker_min},Symbol.toStringTag,{value:"Module"}));export{app,start};
diff --git a/tools/server/public/index.html b/tools/server/public/index.html
deleted file mode 100644
index cb697560c74..00000000000
--- a/tools/server/public/index.html
+++ /dev/null
@@ -1,34 +0,0 @@
-<!--
-  This is a static build of the frontend.
-  It is automatically generated by the build process.
-  Do not edit this file directly.
-  To make changes, refer to the "Web UI" section in the README.
--->
-<!doctype html>
-<html lang="en">
-	<head>
-		<meta charset="utf-8" />
-		<link rel="icon" href="data:image/svg+xml;base64,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/>
-		<meta name="viewport" content="width=device-width, initial-scale=1" />
-		
-		<link href="./bundle.css" rel="stylesheet">
-		<link rel="modulepreload" href="./bundle.js">
-	</head>
-	<body data-sveltekit-preload-data="hover">
-		<div style="display: contents">
-			<script>
-				{
-					__sveltekit__ = {
-						base: new URL('.', location).pathname.slice(0, -1)
-					};
-
-					const element = document.currentScript.parentElement;
-
-					import("./bundle.js").then((app) => {
-						app.start(element)
-					});
-				}
-			</script>
-		</div>
-	</body>
-</html>
diff --git a/tools/server/server-chat.cpp b/tools/server/server-chat.cpp
new file mode 100644
index 00000000000..02858a2a028
--- /dev/null
+++ b/tools/server/server-chat.cpp
@@ -0,0 +1,635 @@
+#include "server-chat.h"
+#include "server-common.h"
+
+#include <sstream>
+
+json server_chat_convert_responses_to_chatcmpl(const json & response_body) {
+    if (!response_body.contains("input")) {
+        throw std::invalid_argument("'input' is required");
+    }
+    if (!json_value(response_body, "previous_response_id", std::string{}).empty()) {
+        throw std::invalid_argument("llama.cpp does not support 'previous_response_id'.");
+    }
+
+    const json input_value = response_body.at("input");
+    json chatcmpl_body = response_body;
+    chatcmpl_body.erase("input");
+    std::vector<json> chatcmpl_messages;
+
+    if (response_body.contains("instructions")) {
+        chatcmpl_messages.push_back({
+            {"role",    "system"},
+            {"content", json_value(response_body, "instructions", std::string())},
+        });
+        chatcmpl_body.erase("instructions");
+    }
+
+    if (input_value.is_string()) {
+        // #responses_create-input-text_input
+        chatcmpl_messages.push_back({
+            {"role",    "user"},
+            {"content", input_value},
+        });
+    } else if (input_value.is_array()) {
+        // #responses_create-input-input_item_list
+
+        static auto exists_and_is_array = [](const json & j, const char * key) -> bool {
+            return j.contains(key) && j.at(key).is_array();
+        };
+        static auto exists_and_is_string = [](const json & j, const char * key) -> bool {
+            return j.contains(key) && j.at(key).is_string();
+        };
+
+        for (json item : input_value) {
+            bool merge_prev = !chatcmpl_messages.empty() && chatcmpl_messages.back().value("role", "") == "assistant";
+
+            if (exists_and_is_string(item, "content")) {
+                // #responses_create-input-input_item_list-input_message-content-text_input
+                // Only "Input message" contains item["content"]::string
+                // After converting item["content"]::string to item["content"]::array,
+                // we can treat "Input message" as sum of "Item-Input message" and "Item-Output message"
+                item["content"] = json::array({
+                    json {
+                        {"text", item.at("content")},
+                        {"type", "input_text"}
+                    }
+                });
+            }
+
+            if (exists_and_is_array(item, "content") &&
+                exists_and_is_string(item, "role") &&
+                (item.at("role") == "user" ||
+                    item.at("role") == "system" ||
+                    item.at("role") == "developer")
+            ) {
+                // #responses_create-input-input_item_list-item-input_message
+                std::vector<json> chatcmpl_content;
+
+                for (const json & input_item : item.at("content")) {
+                    const std::string type = json_value(input_item, "type", std::string());
+
+                    if (type == "input_text") {
+                        if (!input_item.contains("text")) {
+                            throw std::invalid_argument("'Input text' requires 'text'");
+                        }
+                        chatcmpl_content.push_back({
+                            {"text", input_item.at("text")},
+                            {"type", "text"},
+                        });
+                    } else if (type == "input_image") {
+                        // While `detail` is marked as required,
+                        // it has default value("auto") and can be omitted.
+
+                        if (!input_item.contains("image_url")) {
+                            throw std::invalid_argument("'image_url' is required");
+                        }
+                        chatcmpl_content.push_back({
+                            {"image_url", json {
+                                {"url", input_item.at("image_url")}
+                            }},
+                            {"type", "image_url"},
+                        });
+                    } else if (type == "input_file") {
+                        throw std::invalid_argument("'input_file' is not supported by llamacpp at this moment");
+                    } else {
+                        throw std::invalid_argument("'type' must be one of 'input_text', 'input_image', or 'input_file'");
+                    }
+                }
+
+                if (item.contains("type")) {
+                    item.erase("type");
+                }
+                if (item.contains("status")) {
+                    item.erase("status");
+                }
+                item["content"] = chatcmpl_content;
+
+                chatcmpl_messages.push_back(item);
+            } else if (exists_and_is_string(item, "role") &&
+                item.at("role") == "assistant" &&
+                exists_and_is_string(item, "type") &&
+                item.at("type") == "message"
+            ) {
+                // #responses_create-input-input_item_list-item-output_message
+                auto chatcmpl_content = json::array();
+
+                // Handle both string content and array content
+                if (item.contains("content") && item.at("content").is_string()) {
+                    // String content - convert to text content part
+                    chatcmpl_content.push_back({
+                        {"text", item.at("content")},
+                        {"type", "text"},
+                    });
+                } else if (exists_and_is_array(item, "content")) {
+                    // Array content - process each item
+                    for (const auto & output_text : item.at("content")) {
+                        const std::string type = json_value(output_text, "type", std::string());
+                        if (type == "output_text" || type == "input_text") {
+                            // Accept both output_text and input_text (string content gets converted to input_text)
+                            if (!exists_and_is_string(output_text, "text")) {
+                                throw std::invalid_argument("'Output text' requires 'text'");
+                            }
+                            chatcmpl_content.push_back({
+                                {"text", output_text.at("text")},
+                                {"type", "text"},
+                            });
+                        } else if (type == "refusal") {
+                            if (!exists_and_is_string(output_text, "refusal")) {
+                                throw std::invalid_argument("'Refusal' requires 'refusal'");
+                            }
+                            chatcmpl_content.push_back({
+                                {"refusal", output_text.at("refusal")},
+                                {"type", "refusal"},
+                            });
+                        } else {
+                            throw std::invalid_argument("'type' must be one of 'output_text' or 'refusal'");
+                        }
+                    }
+                }
+
+                if (merge_prev) {
+                    auto & prev_msg = chatcmpl_messages.back();
+                    if (!exists_and_is_array(prev_msg, "content")) {
+                        prev_msg["content"] = json::array();
+                    }
+                    auto & prev_content = prev_msg["content"];
+                    prev_content.insert(prev_content.end(), chatcmpl_content.begin(), chatcmpl_content.end());
+                } else {
+                    item.erase("status");
+                    item.erase("type");
+                    item["content"] = chatcmpl_content;
+                    chatcmpl_messages.push_back(item);
+                }
+            } else if (exists_and_is_string(item, "arguments") &&
+                exists_and_is_string(item, "call_id") &&
+                exists_and_is_string(item, "name") &&
+                exists_and_is_string(item, "type") &&
+                item.at("type") == "function_call"
+            ) {
+                // #responses_create-input-input_item_list-item-function_tool_call
+                json tool_call = {
+                    {"function", json {
+                        {"arguments", item.at("arguments")},
+                        {"name",      item.at("name")},
+                    }},
+                    {"id",   item.at("call_id")},
+                    {"type", "function"},
+                };
+
+                if (merge_prev) {
+                    auto & prev_msg = chatcmpl_messages.back();
+                    if (!exists_and_is_array(prev_msg, "tool_calls")) {
+                        prev_msg["tool_calls"] = json::array();
+                    }
+                    prev_msg["tool_calls"].push_back(tool_call);
+                } else {
+                    chatcmpl_messages.push_back(json {
+                        {"role",       "assistant"},
+                        {"tool_calls", json::array({tool_call})}
+                    });
+                }
+            } else if (exists_and_is_string(item, "call_id") &&
+                (exists_and_is_string(item, "output") || exists_and_is_array(item, "output")) &&
+                exists_and_is_string(item, "type") &&
+                item.at("type") == "function_call_output"
+            ) {
+                // #responses_create-input-input_item_list-item-function_tool_call_output
+                if (item.at("output").is_string()) {
+                    chatcmpl_messages.push_back(json {
+                        {"content",      item.at("output")},
+                        {"role",         "tool"},
+                        {"tool_call_id", item.at("call_id")},
+                    });
+                } else {
+                    json chatcmpl_outputs = item.at("output");
+                    for (json & chatcmpl_output : chatcmpl_outputs) {
+                        if (!chatcmpl_output.contains("type") || chatcmpl_output.at("type") != "input_text") {
+                            throw std::invalid_argument("Output of tool call should be 'Input text'");
+                        }
+                        chatcmpl_output["type"] = "text";
+                    }
+                    chatcmpl_messages.push_back(json {
+                        {"content",      chatcmpl_outputs},
+                        {"role",         "tool"},
+                        {"tool_call_id", item.at("call_id")},
+                    });
+                }
+            } else if (exists_and_is_array(item, "summary") &&
+                exists_and_is_string(item, "type") &&
+                item.at("type") == "reasoning") {
+                // #responses_create-input-input_item_list-item-reasoning
+
+                if (!exists_and_is_array(item, "content")) {
+                    throw std::invalid_argument("item['content'] is not an array");
+                }
+                if (item.at("content").empty()) {
+                    throw std::invalid_argument("item['content'] is empty");
+                }
+                if (!exists_and_is_string(item.at("content")[0], "text")) {
+                    throw std::invalid_argument("item['content']['text'] is not a string");
+                }
+
+                if (merge_prev) {
+                    auto & prev_msg = chatcmpl_messages.back();
+                    prev_msg["reasoning_content"] = item.at("content")[0].at("text");
+                } else {
+                    chatcmpl_messages.push_back(json {
+                        {"role", "assistant"},
+                        {"content", json::array()},
+                        {"reasoning_content", item.at("content")[0].at("text")},
+                    });
+                }
+            } else {
+                throw std::invalid_argument("Cannot determine type of 'item'");
+            }
+        }
+    } else {
+        throw std::invalid_argument("'input' must be a string or array of objects");
+    }
+
+    chatcmpl_body["messages"] = chatcmpl_messages;
+
+    if (response_body.contains("tools")) {
+        if (!response_body.at("tools").is_array()) {
+            throw std::invalid_argument("'tools' must be an array of objects");
+        }
+        std::vector<json> chatcmpl_tools;
+        for (json resp_tool : response_body.at("tools")) {
+            json chatcmpl_tool;
+
+            const std::string type = json_value(resp_tool, "type", std::string());
+            if (type != "function") {
+                // Non-function Responses tools have no Chat Completions equivalent.
+                SRV_WRN("unsupported Responses tool type '%s' skipped\n", type.c_str());
+                continue;
+            }
+            resp_tool.erase("type");
+            chatcmpl_tool["type"] = "function";
+
+            if (!resp_tool.contains("strict")) {
+                resp_tool["strict"] = true;
+            }
+            chatcmpl_tool["function"] = resp_tool;
+            chatcmpl_tools.push_back(chatcmpl_tool);
+        }
+        chatcmpl_body.erase("tools");
+        if (!chatcmpl_tools.empty()) {
+            chatcmpl_body["tools"] = chatcmpl_tools;
+        }
+    }
+
+    if (response_body.contains("max_output_tokens")) {
+        chatcmpl_body.erase("max_output_tokens");
+        chatcmpl_body["max_tokens"] = response_body["max_output_tokens"];
+    }
+
+    return chatcmpl_body;
+}
+
+// Edits the cch section of an "x-anthropic-billing-header" system prompt.
+// Does nothing to any other prompt.
+//
+// This is a claude message with a "cch=ef01a" attribute that breaks prefix caching.
+// The cch stamp is a whitebox end-to-end integrity hint. It's not meaningful as a
+// system prompt data, particularly to llama.cpp, but its presence means the prefix
+// cache will not get past it: It changes on each request.
+//
+// Reference: https://github.com/ggml-org/llama.cpp/pull/21793
+// Example header:
+// ```
+// x-anthropic-billing-header: cc_version=2.1.101.e51; cc_entrypoint=cli; cch=a5145;You are Claude Code, Anthropic's official CLI for Claude.
+//                                                                            ^^^^^
+// ```
+static void normalize_anthropic_billing_header(std::string & system_text) {
+    if (system_text.rfind("x-anthropic-billing-header:", 0) != 0) {
+        return;
+    }
+
+    const size_t header_prefix_length = strlen("x-anthropic-billing-header:");
+    const size_t cch_length = 5;
+    const size_t index_cch = system_text.find("cch=", header_prefix_length);
+    if (index_cch == std::string::npos) {
+        return;
+    }
+
+    const size_t index_replace = index_cch + 4;
+    if (index_replace + cch_length < system_text.length() && system_text[index_replace + cch_length] == ';') {
+        for (size_t i = 0; i < cch_length; ++i) {
+            system_text[index_replace + i] = 'f';
+        }
+    } else {
+        LOG_ERR("anthropic string not as expected: %s", system_text.c_str());
+    }
+}
+
+json server_chat_convert_anthropic_to_oai(const json & body) {
+    json oai_body;
+
+    // Convert system prompt
+    json oai_messages = json::array();
+    auto system_param = json_value(body, "system", json());
+    if (!system_param.is_null()) {
+        std::string system_content;
+
+        if (system_param.is_string()) {
+            system_content = system_param.get<std::string>();
+            normalize_anthropic_billing_header(system_content);
+        } else if (system_param.is_array()) {
+            for (const auto & block : system_param) {
+                if (json_value(block, "type", std::string()) == "text") {
+                    auto system_text = json_value(block, "text", std::string());
+                    normalize_anthropic_billing_header(system_text);
+                    system_content += system_text;
+                }
+            }
+        }
+
+        oai_messages.push_back({
+            {"role", "system"},
+            {"content", system_content}
+        });
+    }
+
+    // Convert messages
+    if (!body.contains("messages")) {
+        throw std::runtime_error("'messages' is required");
+    }
+    const json & messages = body.at("messages");
+    if (messages.is_array()) {
+        for (const auto & msg : messages) {
+            std::string role = json_value(msg, "role", std::string());
+
+            if (!msg.contains("content")) {
+                if (role == "assistant") {
+                    continue;
+                }
+                oai_messages.push_back(msg);
+                continue;
+            }
+
+            const json & content = msg.at("content");
+
+            if (content.is_string()) {
+                oai_messages.push_back(msg);
+                continue;
+            }
+
+            if (!content.is_array()) {
+                oai_messages.push_back(msg);
+                continue;
+            }
+
+            json tool_calls = json::array();
+            json converted_content = json::array();
+            json tool_results = json::array();
+            std::string reasoning_content;
+            bool has_tool_calls = false;
+
+            for (const auto & block : content) {
+                std::string type = json_value(block, "type", std::string());
+
+                if (type == "text") {
+                    converted_content.push_back(block);
+                } else if (type == "thinking") {
+                    reasoning_content += json_value(block, "thinking", std::string());
+                } else if (type == "image") {
+                    json source = json_value(block, "source", json::object());
+                    std::string source_type = json_value(source, "type", std::string());
+
+                    if (source_type == "base64") {
+                        std::string media_type = json_value(source, "media_type", std::string("image/jpeg"));
+                        std::string data = json_value(source, "data", std::string());
+                        std::ostringstream ss;
+                        ss << "data:" << media_type << ";base64," << data;
+
+                        converted_content.push_back({
+                            {"type", "image_url"},
+                            {"image_url", {
+                                {"url", ss.str()}
+                            }}
+                        });
+                    } else if (source_type == "url") {
+                        std::string url = json_value(source, "url", std::string());
+                        converted_content.push_back({
+                            {"type", "image_url"},
+                            {"image_url", {
+                                {"url", url}
+                            }}
+                        });
+                    }
+                } else if (type == "tool_use") {
+                    tool_calls.push_back({
+                        {"id", json_value(block, "id", std::string())},
+                        {"type", "function"},
+                        {"function", {
+                            {"name", json_value(block, "name", std::string())},
+                            {"arguments", json_value(block, "input", json::object()).dump()}
+                        }}
+                    });
+                    has_tool_calls = true;
+                } else if (type == "tool_result") {
+                    std::string tool_use_id = json_value(block, "tool_use_id", std::string());
+
+                    auto result_content = json_value(block, "content", json());
+                    std::string result_text;
+                    if (result_content.is_string()) {
+                        result_text = result_content.get<std::string>();
+                    } else if (result_content.is_array()) {
+                        for (const auto & c : result_content) {
+                            if (json_value(c, "type", std::string()) == "text") {
+                                result_text += json_value(c, "text", std::string());
+                            }
+                        }
+                    }
+
+                    tool_results.push_back({
+                        {"role", "tool"},
+                        {"tool_call_id", tool_use_id},
+                        {"content", result_text}
+                    });
+                }
+            }
+
+            if (!converted_content.empty() || has_tool_calls || !reasoning_content.empty()) {
+                json new_msg = {{"role", role}};
+                if (!converted_content.empty()) {
+                    new_msg["content"] = converted_content;
+                } else if (has_tool_calls || !reasoning_content.empty()) {
+                    new_msg["content"] = "";
+                }
+                if (!tool_calls.empty()) {
+                    new_msg["tool_calls"] = tool_calls;
+                }
+                if (!reasoning_content.empty()) {
+                    new_msg["reasoning_content"] = reasoning_content;
+                }
+                oai_messages.push_back(new_msg);
+            }
+
+            for (const auto & tool_msg : tool_results) {
+                oai_messages.push_back(tool_msg);
+            }
+        }
+    }
+
+    oai_body["messages"] = oai_messages;
+
+    // Convert tools
+    if (body.contains("tools")) {
+        const json & tools = body.at("tools");
+        if (tools.is_array()) {
+            json oai_tools = json::array();
+            for (const auto & tool : tools) {
+                oai_tools.push_back({
+                    {"type", "function"},
+                    {"function", {
+                        {"name", json_value(tool, "name", std::string())},
+                        {"description", json_value(tool, "description", std::string())},
+                        {"parameters", tool.contains("input_schema") ? tool.at("input_schema") : json::object()}
+                    }}
+                });
+            }
+            oai_body["tools"] = oai_tools;
+        }
+    }
+
+    // Convert tool_choice
+    if (body.contains("tool_choice")) {
+        const json & tc = body.at("tool_choice");
+        if (tc.is_object()) {
+            std::string type = json_value(tc, "type", std::string());
+            if (type == "auto") {
+                oai_body["tool_choice"] = "auto";
+            } else if (type == "any" || type == "tool") {
+                oai_body["tool_choice"] = "required";
+            }
+        }
+    }
+
+    // Convert stop_sequences to stop
+    if (body.contains("stop_sequences")) {
+        oai_body["stop"] = body.at("stop_sequences");
+    }
+
+    // Handle max_tokens (required in Anthropic, but we're permissive)
+    if (body.contains("max_tokens")) {
+        oai_body["max_tokens"] = body.at("max_tokens");
+    } else {
+        oai_body["max_tokens"] = 4096;
+    }
+
+    // Pass through common params
+    for (const auto & key : {"temperature", "top_p", "top_k", "stream", "chat_template_kwargs"}) {
+        if (body.contains(key)) {
+            oai_body[key] = body.at(key);
+        }
+    }
+
+    // Handle Anthropic-specific thinking param
+    if (body.contains("thinking")) {
+        json thinking = json_value(body, "thinking", json::object());
+        std::string thinking_type = json_value(thinking, "type", std::string());
+        if (thinking_type == "enabled") {
+            int budget_tokens = json_value(thinking, "budget_tokens", 10000);
+            oai_body["thinking_budget_tokens"] = budget_tokens;
+        }
+    }
+
+    // Handle Anthropic-specific metadata param
+    if (body.contains("metadata")) {
+        json metadata = json_value(body, "metadata", json::object());
+        std::string user_id = json_value(metadata, "user_id", std::string());
+        if (!user_id.empty()) {
+            oai_body["__metadata_user_id"] = user_id;
+        }
+    }
+
+    return oai_body;
+}
+
+json server_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff) {
+    json delta = json::object();
+    if (!diff.reasoning_content_delta.empty()) {
+        delta["reasoning_content"] = diff.reasoning_content_delta;
+    }
+    if (!diff.content_delta.empty()) {
+        delta["content"] = diff.content_delta;
+    }
+    if (diff.tool_call_index != std::string::npos) {
+        json tool_call;
+        tool_call["index"] = diff.tool_call_index;
+        if (!diff.tool_call_delta.id.empty()) {
+            tool_call["id"]   = diff.tool_call_delta.id;
+            tool_call["type"] = "function";
+        }
+        if (!diff.tool_call_delta.name.empty() || !diff.tool_call_delta.arguments.empty()) {
+            json function = json::object();
+            if (!diff.tool_call_delta.name.empty()) {
+                function["name"] = diff.tool_call_delta.name;
+            }
+            if (!diff.tool_call_delta.arguments.empty()) {
+                function["arguments"] = diff.tool_call_delta.arguments;
+            }
+            tool_call["function"] = function;
+        }
+        delta["tool_calls"] = json::array({ tool_call });
+    }
+    return delta;
+}
+
+json convert_transcriptions_to_chatcmpl(
+        const json & inp_body,
+        const common_chat_templates * tmpls,
+        const std::map<std::string, uploaded_file> & in_files,
+        std::vector<raw_buffer> & out_files) {
+    // TODO @ngxson : this function may need to be improved in the future
+    // handle input files
+    out_files.clear();
+    auto it = in_files.find("file");
+    if (it != in_files.end()) {
+        out_files.push_back(it->second.data);
+    } else {
+        throw std::invalid_argument("No input file found for transcription");
+    }
+
+    // handle input data
+    std::string prompt          = json_value(inp_body, "prompt", std::string());
+    std::string language        = json_value(inp_body, "language", std::string());
+    std::string response_format = json_value(inp_body, "response_format", std::string("json"));
+    if (response_format != "json") {
+        throw std::invalid_argument("Only 'json' response_format is supported for transcription");
+    }
+    const common_chat_prompt_preset preset = common_chat_get_asr_prompt(tmpls);
+    if (prompt.empty()) {
+        prompt = preset.user;
+    }
+    if (!language.empty()) {
+        prompt += string_format(" (language: %s)", language.c_str());
+    }
+    prompt += get_media_marker();
+
+    json messages = json::array();
+    if (!preset.system.empty()) {
+        messages.push_back({{"role", "system"}, {"content", preset.system}});
+    }
+    messages.push_back({{"role", "user"}, {"content", prompt}});
+
+    json chatcmpl_body = inp_body; // copy all fields
+    chatcmpl_body["messages"] = messages;
+
+    // because input from form-data, everything is string, we need to correct the types here
+    std::string stream = json_value(inp_body, "stream", std::string("false"));
+    chatcmpl_body["stream"] = stream == "true";
+
+    if (inp_body.contains("max_tokens")) {
+        std::string inp = inp_body["max_tokens"].get<std::string>();
+        chatcmpl_body["max_tokens"] = std::stoul(inp);
+    }
+
+    if (inp_body.contains("temperature")) {
+        std::string inp = inp_body["temperature"].get<std::string>();
+        chatcmpl_body["temperature"] = std::stof(inp);
+    }
+
+    return chatcmpl_body;
+}
diff --git a/tools/server/server-chat.h b/tools/server/server-chat.h
new file mode 100644
index 00000000000..102eae688a3
--- /dev/null
+++ b/tools/server/server-chat.h
@@ -0,0 +1,26 @@
+// Chat conversion functions for server (Responses API, Anthropic API, OAI streaming diffs)
+
+#pragma once
+
+#include "chat.h"
+#include "server-common.h"
+#include "server-http.h"
+
+#include <nlohmann/json_fwd.hpp>
+
+using json = nlohmann::ordered_json;
+
+// Convert OpenAI Responses API format to OpenAI Chat Completions API format
+json server_chat_convert_responses_to_chatcmpl(const json & body);
+
+// Convert Anthropic Messages API format to OpenAI Chat Completions API format
+json server_chat_convert_anthropic_to_oai(const json & body);
+
+// convert OpenAI transcriptions API format to OpenAI Chat Completions API format
+json convert_transcriptions_to_chatcmpl(
+    const json & body,
+    const common_chat_templates * tmpls,
+    const std::map<std::string, uploaded_file> & in_files,
+    std::vector<raw_buffer> & out_files);
+
+json server_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff);
diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp
index cae64884b36..73de0d3bba1 100644
--- a/tools/server/server-common.cpp
+++ b/tools/server/server-common.cpp
@@ -947,7 +947,9 @@ json oaicompat_chat_params_parse(
         json response_format      = json_value(body, "response_format", json::object());
         std::string response_type = json_value(response_format, "type", std::string());
         if (response_type == "json_object") {
-            json_schema = json_value(response_format, "schema", json::object());
+            if (response_format.contains("schema") || json_schema.empty()) {
+                json_schema = json_value(response_format, "schema", json::object());
+            }
         } else if (response_type == "json_schema") {
             auto schema_wrapper = json_value(response_format, "json_schema", json::object());
             json_schema = json_value(schema_wrapper, "schema", json::object());
@@ -1027,6 +1029,8 @@ json oaicompat_chat_params_parse(
         }
     }
 
+    auto caps = common_chat_templates_get_caps(opt.tmpls.get());
+
     common_chat_templates_inputs inputs;
     inputs.messages              = common_chat_msgs_parse_oaicompat(messages);
     inputs.tools                 = common_chat_tools_parse_oaicompat(tools);
@@ -1034,8 +1038,12 @@ json oaicompat_chat_params_parse(
     inputs.json_schema           = json_schema.is_null() ? "" : json_schema.dump();
     inputs.grammar               = grammar;
     inputs.use_jinja             = opt.use_jinja;
-    inputs.parallel_tool_calls   = json_value(body, "parallel_tool_calls", false);
+    inputs.parallel_tool_calls   = json_value(body, "parallel_tool_calls", caps["supports_parallel_tool_calls"]);
     inputs.add_generation_prompt = json_value(body, "add_generation_prompt", true);
+    const bool continue_final_message = json_value(body, "continue_final_message", false);
+    if (continue_final_message && inputs.add_generation_prompt) {
+        throw std::invalid_argument("Cannot set both add_generation_prompt and continue_final_message to true.");
+    }
     inputs.reasoning_format      = opt.reasoning_format;
     if (body.contains("reasoning_format")) {
         inputs.reasoning_format = common_reasoning_format_from_name(body.at("reasoning_format").get<std::string>());
@@ -1067,7 +1075,10 @@ json oaicompat_chat_params_parse(
 
     // if the assistant message appears at the end of list, we do not add end-of-turn token
     // for ex. this can be useful to modify the reasoning process in reasoning models
-    bool prefill_assistant_message = !inputs.messages.empty() && inputs.messages.back().role == "assistant" && opt.prefill_assistant;
+    // continue_final_message is the explicit opt in alias from the vLLM/transformers API,
+    // equivalent to the prefill_assistant heuristic
+    bool prefill_assistant_message = !inputs.messages.empty() && inputs.messages.back().role == "assistant"
+        && (continue_final_message || opt.prefill_assistant);
     common_chat_msg last_message;
     if (prefill_assistant_message) {
         last_message = inputs.messages.back();
@@ -1078,11 +1089,12 @@ json oaicompat_chat_params_parse(
             throw std::invalid_argument("Cannot have 2 or more assistant messages at the end of the list.");
         }
 
-        /* TODO: test this properly */
-        inputs.reasoning_format = COMMON_REASONING_FORMAT_NONE;
-
-        if ( inputs.enable_thinking ) {
-            throw std::invalid_argument("Assistant response prefill is incompatible with enable_thinking.");
+        // reject reasoning prefill on channel based templates that do not expose explicit thinking tags
+        if (!last_message.reasoning_content.empty() && inputs.enable_thinking) {
+            auto probe_params = common_chat_templates_apply(opt.tmpls.get(), inputs);
+            if (probe_params.supports_thinking && probe_params.thinking_end_tag.empty()) {
+                throw std::invalid_argument("Assistant prefill with reasoning_content is not supported yet for this template.");
+            }
         }
 
         inputs.add_generation_prompt = true;
@@ -1094,6 +1106,42 @@ json oaicompat_chat_params_parse(
 
     /* Append assistant prefilled message */
     if (prefill_assistant_message) {
+        const bool thinking_active = chat_params.supports_thinking && !chat_params.thinking_end_tag.empty();
+        const bool has_reasoning   = !last_message.reasoning_content.empty();
+        const bool has_content     = !last_message.content.empty() || !last_message.content_parts.empty();
+        const bool mid_reasoning   = has_reasoning && !has_content;
+
+        // some templates inject thinking_start in generation_prompt, others let the model emit it
+        const bool gp_has_think = thinking_active
+            && chat_params.generation_prompt.find(chat_params.thinking_start_tag) != std::string::npos;
+
+        // open the thinking block when reasoning is present and the template did not inject it
+        if (has_reasoning) {
+            if (thinking_active && !gp_has_think) {
+                chat_params.prompt += chat_params.thinking_start_tag;
+            }
+            chat_params.prompt += last_message.reasoning_content;
+        }
+
+        if (thinking_active) {
+            if (mid_reasoning) {
+                // model continues inside the thinking block, keep generation_prompt open on think
+                if (!gp_has_think) {
+                    chat_params.generation_prompt += chat_params.thinking_start_tag;
+                }
+            } else {
+                // close thinking block when reasoning is followed by content, or when the template forced it open
+                if (has_reasoning || gp_has_think) {
+                    chat_params.prompt += chat_params.thinking_end_tag;
+                }
+                // strip thinking_start from generation_prompt so the parser routes model output as content
+                auto pos = chat_params.generation_prompt.rfind(chat_params.thinking_start_tag);
+                if (pos != std::string::npos) {
+                    chat_params.generation_prompt = chat_params.generation_prompt.substr(0, pos);
+                }
+            }
+        }
+
         if (!last_message.content_parts.empty()) {
             for (auto & p : last_message.content_parts) {
                 chat_params.prompt += p.text;
@@ -1164,573 +1212,6 @@ json oaicompat_chat_params_parse(
     return llama_params;
 }
 
-json convert_responses_to_chatcmpl(const json & response_body) {
-    if (!response_body.contains("input")) {
-        throw std::invalid_argument("'input' is required");
-    }
-    if (!json_value(response_body, "previous_response_id", std::string{}).empty()) {
-        throw std::invalid_argument("llama.cpp does not support 'previous_response_id'.");
-    }
-
-    const json input_value = response_body.at("input");
-    json chatcmpl_body = response_body;
-    chatcmpl_body.erase("input");
-    std::vector<json> chatcmpl_messages;
-
-    if (response_body.contains("instructions")) {
-        chatcmpl_messages.push_back({
-            {"role",    "system"},
-            {"content", json_value(response_body, "instructions", std::string())},
-        });
-        chatcmpl_body.erase("instructions");
-    }
-
-    if (input_value.is_string()) {
-        // #responses_create-input-text_input
-        chatcmpl_messages.push_back({
-            {"role",    "user"},
-            {"content", input_value},
-        });
-    } else if (input_value.is_array()) {
-        // #responses_create-input-input_item_list
-
-        static auto exists_and_is_array = [](const json & j, const char * key) -> bool {
-            return j.contains(key) && j.at(key).is_array();
-        };
-        static auto exists_and_is_string = [](const json & j, const char * key) -> bool {
-            return j.contains(key) && j.at(key).is_string();
-        };
-
-        for (json item : input_value) {
-            bool merge_prev = !chatcmpl_messages.empty() && chatcmpl_messages.back().value("role", "") == "assistant";
-
-            if (exists_and_is_string(item, "content")) {
-                // #responses_create-input-input_item_list-input_message-content-text_input
-                // Only "Input message" contains item["content"]::string
-                // After converting item["content"]::string to item["content"]::array,
-                // we can treat "Input message" as sum of "Item-Input message" and "Item-Output message"
-                item["content"] = json::array({
-                    json {
-                        {"text", item.at("content")},
-                        {"type", "input_text"}
-                    }
-                });
-            }
-
-            if (exists_and_is_array(item, "content") &&
-                exists_and_is_string(item, "role") &&
-                (item.at("role") == "user" ||
-                    item.at("role") == "system" ||
-                    item.at("role") == "developer")
-            ) {
-                // #responses_create-input-input_item_list-item-input_message
-                std::vector<json> chatcmpl_content;
-
-                for (const json & input_item : item.at("content")) {
-                    const std::string type = json_value(input_item, "type", std::string());
-
-                    if (type == "input_text") {
-                        if (!input_item.contains("text")) {
-                            throw std::invalid_argument("'Input text' requires 'text'");
-                        }
-                        chatcmpl_content.push_back({
-                            {"text", input_item.at("text")},
-                            {"type", "text"},
-                        });
-                    } else if (type == "input_image") {
-                        // While `detail` is marked as required,
-                        // it has default value("auto") and can be omitted.
-
-                        if (!input_item.contains("image_url")) {
-                            throw std::invalid_argument("'image_url' is required");
-                        }
-                        chatcmpl_content.push_back({
-                            {"image_url", json {
-                                {"url", input_item.at("image_url")}
-                            }},
-                            {"type", "image_url"},
-                        });
-                    } else if (type == "input_file") {
-                        throw std::invalid_argument("'input_file' is not supported by llamacpp at this moment");
-                        // if (input_item.contains("file_url")) {
-                        //     // chat completion API does not support file_url
-                        //     throw std::invalid_argument("'file_url' is not supported");
-                        // }
-                        // if (!input_item.contains("file_data") || !input_item.contains("filename")) {
-                        //     throw std::invalid_argument("Both 'file_data' and 'filename' are required");
-                        // }
-                        // chatcmpl_content.push_back({
-                        //     {"file", json {
-                        //         {"file_data", input_item.at("file_data")},
-                        //         {"filename",  input_item.at("filename")},
-                        //     }},
-                        //     {"type", "file"},
-                        // });
-                    } else {
-                        throw std::invalid_argument("'type' must be one of 'input_text', 'input_image', or 'input_file'");
-                    }
-                }
-
-                if (item.contains("type")) {
-                    item.erase("type");
-                }
-                if (item.contains("status")) {
-                    item.erase("status");
-                }
-                item["content"] = chatcmpl_content;
-
-                chatcmpl_messages.push_back(item);
-            } else if (exists_and_is_array(item, "content") &&
-                exists_and_is_string(item, "role") &&
-                item.at("role") == "assistant" &&
-                // exists_and_is_string(item, "status") &&
-                // (item.at("status") == "in_progress" ||
-                //     item.at("status") == "completed" ||
-                //     item.at("status") == "incomplete") &&
-                // item["status"] not sent by codex-cli
-                exists_and_is_string(item, "type") &&
-                item.at("type") == "message"
-            ) {
-                // #responses_create-input-input_item_list-item-output_message
-                auto chatcmpl_content = json::array();
-
-                for (const auto & output_text : item.at("content")) {
-                    const std::string type = json_value(output_text, "type", std::string());
-                    if (type == "output_text") {
-                        if (!exists_and_is_string(output_text, "text")) {
-                            throw std::invalid_argument("'Output text' requires 'text'");
-                            // Ignore annotations and logprobs for now
-                            chatcmpl_content.push_back({
-                                {"text", output_text.at("text")},
-                                {"type", "text"},
-                            });
-                        }
-                    } else if (type == "refusal") {
-                        if (!exists_and_is_string(output_text, "refusal")) {
-                            throw std::invalid_argument("'Refusal' requires 'refusal'");
-                            // Ignore annotations and logprobs for now
-                            chatcmpl_content.push_back({
-                                {"refusal", output_text.at("refusal")},
-                                {"type", "refusal"},
-                            });
-                        }
-                    } else {
-                        throw std::invalid_argument("'type' must be one of 'output_text' or 'refusal'");
-                    }
-                }
-
-                if (merge_prev) {
-                    auto & prev_msg = chatcmpl_messages.back();
-                    if (!exists_and_is_array(prev_msg, "content")) {
-                        prev_msg["content"] = json::array();
-                    }
-                    auto & prev_content = prev_msg["content"];
-                    prev_content.insert(prev_content.end(), chatcmpl_content.begin(), chatcmpl_content.end());
-                } else {
-                    item.erase("status");
-                    item.erase("type");
-                    item["content"] = chatcmpl_content;
-                    chatcmpl_messages.push_back(item);
-                }
-            } else if (exists_and_is_string(item, "arguments") &&
-                exists_and_is_string(item, "call_id") &&
-                exists_and_is_string(item, "name") &&
-                exists_and_is_string(item, "type") &&
-                item.at("type") == "function_call"
-            ) {
-                // #responses_create-input-input_item_list-item-function_tool_call
-                json tool_call = {
-                    {"function", json {
-                        {"arguments", item.at("arguments")},
-                        {"name",      item.at("name")},
-                    }},
-                    {"id",   item.at("call_id")},
-                    {"type", "function"},
-                };
-
-                if (merge_prev) {
-                    auto & prev_msg = chatcmpl_messages.back();
-                    if (!exists_and_is_array(prev_msg, "tool_calls")) {
-                        prev_msg["tool_calls"] = json::array();
-                    }
-                    prev_msg["tool_calls"].push_back(tool_call);
-                } else {
-                    chatcmpl_messages.push_back(json {
-                        {"role",       "assistant"},
-                        {"tool_calls", json::array({tool_call})}
-                    });
-                }
-            } else if (exists_and_is_string(item, "call_id") &&
-                (exists_and_is_string(item, "output") || exists_and_is_array(item, "output")) &&
-                exists_and_is_string(item, "type") &&
-                item.at("type") == "function_call_output"
-            ) {
-                // #responses_create-input-input_item_list-item-function_tool_call_output
-                if (item.at("output").is_string()) {
-                    chatcmpl_messages.push_back(json {
-                        {"content",      item.at("output")},
-                        {"role",         "tool"},
-                        {"tool_call_id", item.at("call_id")},
-                    });
-                } else {
-                    json chatcmpl_outputs = item.at("output");
-                    for (json & chatcmpl_output : chatcmpl_outputs) {
-                        if (!chatcmpl_output.contains("type") || chatcmpl_output.at("type") != "input_text") {
-                            throw std::invalid_argument("Output of tool call should be 'Input text'");
-                        }
-                        chatcmpl_output["type"] = "text";
-                    }
-                    chatcmpl_messages.push_back(json {
-                        {"content",      chatcmpl_outputs},
-                        {"role",         "tool"},
-                        {"tool_call_id", item.at("call_id")},
-                    });
-                }
-            } else if (// exists_and_is_string(item, "id") &&
-                // item["id"] not sent by codex-cli
-                exists_and_is_array(item, "summary") &&
-                exists_and_is_string(item, "type") &&
-                item.at("type") == "reasoning") {
-                // #responses_create-input-input_item_list-item-reasoning
-
-                if (!exists_and_is_array(item, "content")) {
-                    throw std::invalid_argument("item['content'] is not an array");
-                }
-                if (item.at("content").empty()) {
-                    throw std::invalid_argument("item['content'] is empty");
-                }
-                if (!exists_and_is_string(item.at("content")[0], "text")) {
-                    throw std::invalid_argument("item['content']['text'] is not a string");
-                }
-
-                if (merge_prev) {
-                    auto & prev_msg = chatcmpl_messages.back();
-                    prev_msg["reasoning_content"] = item.at("content")[0].at("text");
-                } else {
-                    chatcmpl_messages.push_back(json {
-                        {"role", "assistant"},
-                        {"content", json::array()},
-                        {"reasoning_content", item.at("content")[0].at("text")},
-                    });
-                }
-            } else {
-                throw std::invalid_argument("Cannot determine type of 'item'");
-            }
-        }
-    } else {
-        throw std::invalid_argument("'input' must be a string or array of objects");
-    }
-
-    chatcmpl_body["messages"] = chatcmpl_messages;
-
-    if (response_body.contains("tools")) {
-        if (!response_body.at("tools").is_array()) {
-            throw std::invalid_argument("'tools' must be an array of objects");
-        }
-        std::vector<json> chatcmpl_tools;
-        for (json resp_tool : response_body.at("tools")) {
-            json chatcmpl_tool;
-
-            if (json_value(resp_tool, "type", std::string()) != "function") {
-                throw std::invalid_argument("'type' of tool must be 'function'");
-            }
-            resp_tool.erase("type");
-            chatcmpl_tool["type"] = "function";
-
-            if (!resp_tool.contains("strict")) {
-                resp_tool["strict"] = true;
-            }
-            chatcmpl_tool["function"] = resp_tool;
-            chatcmpl_tools.push_back(chatcmpl_tool);
-        }
-        chatcmpl_body.erase("tools");
-        chatcmpl_body["tools"] = chatcmpl_tools;
-    }
-
-    if (response_body.contains("max_output_tokens")) {
-        chatcmpl_body.erase("max_output_tokens");
-        chatcmpl_body["max_tokens"] = response_body["max_output_tokens"];
-    }
-
-    return chatcmpl_body;
-}
-
-json convert_transcriptions_to_chatcmpl(
-        const json & inp_body,
-        const std::map<std::string, raw_buffer> & in_files,
-        std::vector<raw_buffer> & out_files) {
-    // TODO @ngxson : this function may need to be improved in the future
-    // handle input files
-    out_files.clear();
-    auto it = in_files.find("file");
-    if (it != in_files.end()) {
-        out_files.push_back(it->second);
-    } else {
-        throw std::invalid_argument("No input file found for transcription");
-    }
-
-    // handle input data
-    std::string prompt = json_value(inp_body, "prompt", std::string());
-    std::string language = json_value(inp_body, "language", std::string());
-    std::string response_format = json_value(inp_body, "response_format", std::string("json"));
-    if (response_format != "json") {
-        throw std::invalid_argument("Only 'json' response_format is supported for transcription");
-    }
-    if (prompt.empty()) {
-        prompt = "Transcribe audio to text";
-    }
-    if (!language.empty()) {
-        prompt += string_format(" (language: %s)", language.c_str());
-    }
-    prompt += get_media_marker();
-
-    json chatcmpl_body = inp_body; // copy all fields
-    chatcmpl_body["messages"] = json::array({
-        {
-            {"role", "user"},
-            {"content", prompt},
-        },
-    });
-
-    // because input from form-data, everything is string, we need to correct the types here
-    std::string stream = json_value(inp_body, "stream", std::string("false"));
-    chatcmpl_body["stream"] = stream == "true";
-
-    if (inp_body.contains("max_tokens")) {
-        std::string inp = inp_body["max_tokens"].get<std::string>();
-        chatcmpl_body["max_tokens"] = std::stoul(inp);
-    }
-
-    if (inp_body.contains("temperature")) {
-        std::string inp = inp_body["temperature"].get<std::string>();
-        chatcmpl_body["temperature"] = std::stof(inp);
-    }
-
-    return chatcmpl_body;
-}
-
-json convert_anthropic_to_oai(const json & body) {
-    json oai_body;
-
-    // Convert system prompt
-    json oai_messages = json::array();
-    auto system_param = json_value(body, "system", json());
-    if (!system_param.is_null()) {
-        std::string system_content;
-
-        if (system_param.is_string()) {
-            system_content = system_param.get<std::string>();
-        } else if (system_param.is_array()) {
-            for (const auto & block : system_param) {
-                if (json_value(block, "type", std::string()) == "text") {
-                    system_content += json_value(block, "text", std::string());
-                }
-            }
-        }
-
-        oai_messages.push_back({
-            {"role", "system"},
-            {"content", system_content}
-        });
-    }
-
-    // Convert messages
-    if (!body.contains("messages")) {
-        throw std::runtime_error("'messages' is required");
-    }
-    const json & messages = body.at("messages");
-    if (messages.is_array()) {
-        for (const auto & msg : messages) {
-            std::string role = json_value(msg, "role", std::string());
-
-            if (!msg.contains("content")) {
-                if (role == "assistant") {
-                    continue;
-                }
-                oai_messages.push_back(msg);
-                continue;
-            }
-
-            const json & content = msg.at("content");
-
-            if (content.is_string()) {
-                oai_messages.push_back(msg);
-                continue;
-            }
-
-            if (!content.is_array()) {
-                oai_messages.push_back(msg);
-                continue;
-            }
-
-            json tool_calls = json::array();
-            json converted_content = json::array();
-            json tool_results = json::array();
-            std::string reasoning_content;
-            bool has_tool_calls = false;
-
-            for (const auto & block : content) {
-                std::string type = json_value(block, "type", std::string());
-
-                if (type == "text") {
-                    converted_content.push_back(block);
-                } else if (type == "thinking") {
-                    reasoning_content += json_value(block, "thinking", std::string());
-                } else if (type == "image") {
-                    json source = json_value(block, "source", json::object());
-                    std::string source_type = json_value(source, "type", std::string());
-
-                    if (source_type == "base64") {
-                        std::string media_type = json_value(source, "media_type", std::string("image/jpeg"));
-                        std::string data = json_value(source, "data", std::string());
-                        std::ostringstream ss;
-                        ss << "data:" << media_type << ";base64," << data;
-
-                        converted_content.push_back({
-                            {"type", "image_url"},
-                            {"image_url", {
-                                {"url", ss.str()}
-                            }}
-                        });
-                    } else if (source_type == "url") {
-                        std::string url = json_value(source, "url", std::string());
-                        converted_content.push_back({
-                            {"type", "image_url"},
-                            {"image_url", {
-                                {"url", url}
-                            }}
-                        });
-                    }
-                } else if (type == "tool_use") {
-                    tool_calls.push_back({
-                        {"id", json_value(block, "id", std::string())},
-                        {"type", "function"},
-                        {"function", {
-                            {"name", json_value(block, "name", std::string())},
-                            {"arguments", json_value(block, "input", json::object()).dump()}
-                        }}
-                    });
-                    has_tool_calls = true;
-                } else if (type == "tool_result") {
-                    std::string tool_use_id = json_value(block, "tool_use_id", std::string());
-
-                    auto result_content = json_value(block, "content", json());
-                    std::string result_text;
-                    if (result_content.is_string()) {
-                        result_text = result_content.get<std::string>();
-                    } else if (result_content.is_array()) {
-                        for (const auto & c : result_content) {
-                            if (json_value(c, "type", std::string()) == "text") {
-                                result_text += json_value(c, "text", std::string());
-                            }
-                        }
-                    }
-
-                    tool_results.push_back({
-                        {"role", "tool"},
-                        {"tool_call_id", tool_use_id},
-                        {"content", result_text}
-                    });
-                }
-            }
-
-            if (!converted_content.empty() || has_tool_calls || !reasoning_content.empty()) {
-                json new_msg = {{"role", role}};
-                if (!converted_content.empty()) {
-                    new_msg["content"] = converted_content;
-                } else if (has_tool_calls || !reasoning_content.empty()) {
-                    new_msg["content"] = "";
-                }
-                if (!tool_calls.empty()) {
-                    new_msg["tool_calls"] = tool_calls;
-                }
-                if (!reasoning_content.empty()) {
-                    new_msg["reasoning_content"] = reasoning_content;
-                }
-                oai_messages.push_back(new_msg);
-            }
-
-            for (const auto & tool_msg : tool_results) {
-                oai_messages.push_back(tool_msg);
-            }
-        }
-    }
-
-    oai_body["messages"] = oai_messages;
-
-    // Convert tools
-    if (body.contains("tools")) {
-        const json & tools = body.at("tools");
-        if (tools.is_array()) {
-            json oai_tools = json::array();
-            for (const auto & tool : tools) {
-                oai_tools.push_back({
-                    {"type", "function"},
-                    {"function", {
-                        {"name", json_value(tool, "name", std::string())},
-                        {"description", json_value(tool, "description", std::string())},
-                        {"parameters", tool.contains("input_schema") ? tool.at("input_schema") : json::object()}
-                    }}
-                });
-            }
-            oai_body["tools"] = oai_tools;
-        }
-    }
-
-    // Convert tool_choice
-    if (body.contains("tool_choice")) {
-        const json & tc = body.at("tool_choice");
-        if (tc.is_object()) {
-            std::string type = json_value(tc, "type", std::string());
-            if (type == "auto") {
-                oai_body["tool_choice"] = "auto";
-            } else if (type == "any" || type == "tool") {
-                oai_body["tool_choice"] = "required";
-            }
-        }
-    }
-
-    // Convert stop_sequences to stop
-    if (body.contains("stop_sequences")) {
-        oai_body["stop"] = body.at("stop_sequences");
-    }
-
-    // Handle max_tokens (required in Anthropic, but we're permissive)
-    if (body.contains("max_tokens")) {
-        oai_body["max_tokens"] = body.at("max_tokens");
-    } else {
-        oai_body["max_tokens"] = 4096;
-    }
-
-    // Pass through common params
-    for (const auto & key : {"temperature", "top_p", "top_k", "stream"}) {
-        if (body.contains(key)) {
-            oai_body[key] = body.at(key);
-        }
-    }
-
-    // Handle Anthropic-specific thinking param
-    if (body.contains("thinking")) {
-        json thinking = json_value(body, "thinking", json::object());
-        std::string thinking_type = json_value(thinking, "type", std::string());
-        if (thinking_type == "enabled") {
-            int budget_tokens = json_value(thinking, "budget_tokens", 10000);
-            oai_body["thinking_budget_tokens"] = budget_tokens;
-        }
-    }
-
-    // Handle Anthropic-specific metadata param
-    if (body.contains("metadata")) {
-        json metadata = json_value(body, "metadata", json::object());
-        std::string user_id = json_value(metadata, "user_id", std::string());
-        if (!user_id.empty()) {
-            oai_body["__metadata_user_id"] = user_id;
-        }
-    }
-
-    return oai_body;
-}
-
 json format_embeddings_response_oaicompat(
         const json & request,
         const std::string & model_name,
diff --git a/tools/server/server-common.h b/tools/server/server-common.h
index 093a43453c2..c28558d8b7b 100644
--- a/tools/server/server-common.h
+++ b/tools/server/server-common.h
@@ -15,17 +15,19 @@
 
 using json = nlohmann::ordered_json;
 
+#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
+#define SLT_TRC(slot, fmt, ...) LOG_TRC("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
 #define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
-#define SLT_CNT(slot, fmt, ...) LOG_CNT(""                                 fmt,                                                                __VA_ARGS__)
 #define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
 #define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
-#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
+#define SLT_CNT(slot, fmt, ...) LOG_CNT(""                                 fmt,                                                                __VA_ARGS__)
 
+#define SRV_DBG(fmt, ...) LOG_DBG("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SRV_TRC(fmt, ...) LOG_TRC("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
 #define SRV_INF(fmt, ...) LOG_INF("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
-#define SRV_CNT(fmt, ...) LOG_CNT(""              fmt,               __VA_ARGS__)
 #define SRV_WRN(fmt, ...) LOG_WRN("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
 #define SRV_ERR(fmt, ...) LOG_ERR("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
-#define SRV_DBG(fmt, ...) LOG_DBG("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SRV_CNT(fmt, ...) LOG_CNT(""              fmt,               __VA_ARGS__)
 
 using raw_buffer = std::vector<uint8_t>;
 
@@ -307,18 +309,6 @@ json oaicompat_chat_params_parse(
     const server_chat_params & opt,
     std::vector<raw_buffer> & out_files);
 
-// convert OpenAI Responses API format to OpenAI Chat Completions API format
-json convert_responses_to_chatcmpl(const json & body);
-
-// convert OpenAI transcriptions API format to OpenAI Chat Completions API format
-json convert_transcriptions_to_chatcmpl(
-    const json & body,
-    const std::map<std::string, raw_buffer> & in_files,
-    std::vector<raw_buffer> & out_files);
-
-// convert Anthropic Messages API format to OpenAI Chat Completions API format
-json convert_anthropic_to_oai(const json & body);
-
 // TODO: move it to server-task.cpp
 json format_embeddings_response_oaicompat(
     const json & request,
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index fb86317de3c..6096dd6b728 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -1,5 +1,6 @@
 
 #include "server-context.h"
+#include "server-chat.h"
 #include "server-common.h"
 #include "server-http.h"
 #include "server-task.h"
@@ -35,31 +36,6 @@ using json = nlohmann::ordered_json;
 
 constexpr int HTTP_POLLING_SECONDS = 1;
 
-static server_prompt_checkpoint server_get_checkpoint(llama_context * ctx, int id, int64_t n_tokens, llama_pos pos_min = -1, llama_pos pos_max = -1) {
-    if (pos_min == -1) {
-        pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), id);
-    }
-    if (pos_max == -1) {
-        pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), id);
-    }
-
-    const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx, id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-
-    auto cur = server_prompt_checkpoint {
-        /*.pos_min  = */ pos_min,
-        /*.pos_max  = */ pos_max,
-        /*.n_tokens = */ n_tokens,
-        /*.data     = */ std::vector<uint8_t>(checkpoint_size),
-    };
-
-    const size_t n = llama_state_seq_get_data_ext(ctx, cur.data.data(), checkpoint_size, id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-    if (n != checkpoint_size) {
-        GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", checkpoint_size, n);
-    }
-
-    return cur;
-}
-
 // state diagram: https://github.com/ggml-org/llama.cpp/pull/9283
 enum slot_state {
     SLOT_STATE_IDLE,
@@ -78,18 +54,19 @@ enum server_state {
 struct server_slot {
     int id;
 
-    llama_context * ctx = nullptr;
-
-    common_context_seq_rm_type ctx_seq_rm_type = COMMON_CONTEXT_SEQ_RM_TYPE_NO;
+    llama_context * ctx_tgt = nullptr;
+    llama_context * ctx_dft = nullptr;
 
     // multimodal
     mtmd_context * mctx = nullptr;
 
     // speculative decoding
+    common_speculative * spec;
+
     llama_tokens spec_draft;
+    llama_tokens spec_prompt;
     std::vector<int32_t> spec_i_batch;
-    server_prompt_checkpoint spec_ckpt;
-    common_speculative_ptr spec;
+    common_prompt_checkpoint spec_ckpt;
 
     // TODO: move members that belong to the task (such as `generated_text`, `has_new_line`) to task_results_state
     //       see https://github.com/ggml-org/llama.cpp/pull/18283#issuecomment-3710175837
@@ -133,21 +110,27 @@ struct server_slot {
     void prompt_save(server_prompt_cache & prompt_cache) const {
         GGML_ASSERT(prompt.data.size() == 0);
 
-        const size_t cur_size = llama_state_seq_get_size_ext(ctx, id, 0);
+        const size_t cur_size_tgt =           llama_state_seq_get_size_ext(ctx_tgt, id, LLAMA_STATE_SEQ_FLAGS_NONE);
+        const size_t cur_size_dft = ctx_dft ? llama_state_seq_get_size_ext(ctx_dft, id, LLAMA_STATE_SEQ_FLAGS_NONE) : 0;
 
-        SRV_WRN(" - saving prompt with length %d, total state size = %.3f MiB\n",
-                (int) prompt.tokens.size(), cur_size / (1024.0 * 1024.0));
+        const size_t cur_size = cur_size_tgt + cur_size_dft;
 
-        auto * cur = prompt_cache.alloc(prompt, cur_size);
+        SRV_WRN(" - saving prompt with length %d, total state size = %.3f MiB (draft: %.3f MiB)\n",
+                (int) prompt.tokens.size(), cur_size / (1024.0 * 1024.0), cur_size_dft / (1024.0 * 1024.0));
+
+        auto * cur = prompt_cache.alloc(prompt, cur_size_tgt, cur_size_dft);
         if (cur == nullptr) {
             return;
         }
 
-        llama_state_seq_get_data_ext(ctx, cur->data.data(), cur_size, id, 0);
+        llama_state_seq_get_data_ext(ctx_tgt, cur->data.main.data(), cur_size_tgt, id, LLAMA_STATE_SEQ_FLAGS_NONE);
+        if (ctx_dft) {
+            llama_state_seq_get_data_ext(ctx_dft, cur->data.drft.data(), cur_size_dft, id, LLAMA_STATE_SEQ_FLAGS_NONE);
+        }
     }
 
     bool prompt_load(server_prompt_cache & prompt_cache, const server_tokens & tokens) {
-        bool res = prompt_cache.load(prompt, tokens, ctx, id);
+        bool res = prompt_cache.load(prompt, tokens, ctx_tgt, ctx_dft, id);
         if (!res) {
             SLT_WRN(*this, "%s", "failed to load prompt from cache\n");
         }
@@ -162,7 +145,11 @@ struct server_slot {
 
         SLT_INF(*this, "clearing prompt with %zu tokens\n", prompt.tokens.size());
 
-        llama_memory_seq_rm(llama_get_memory(ctx), id, -1, -1);
+        common_context_seq_rm(ctx_tgt, id, -1, -1);
+        if (ctx_dft) {
+            common_context_seq_rm(ctx_dft, id, -1, -1);
+        }
+
         prompt.tokens.clear();
     }
 
@@ -179,6 +166,7 @@ struct server_slot {
     // stats
     size_t n_sent_text = 0; // number of sent text character
 
+    int64_t t_print_last = 0;
     int64_t t_start_process_prompt;
     int64_t t_start_generation;
 
@@ -220,7 +208,7 @@ struct server_slot {
         task_prev = std::move(task);
         task.reset();
 
-        llama_set_sampler(ctx, id, nullptr);
+        llama_set_sampler(ctx_tgt, id, nullptr);
 
         // clear alora start
         alora_invocation_start = -1;
@@ -246,18 +234,24 @@ struct server_slot {
             }
         }
 
-        SLT_INF(*this, "init sampler, took %0.2f ms, tokens: text = %d, total = %d\n",
+        SLT_TRC(*this, "init sampler, took %0.2f ms, tokens: text = %d, total = %d\n",
                 (ggml_time_us() - t_start) / 1000.0, n_text, (int) prompt.tokens.size());
     }
 
+    bool need_embd() const {
+        GGML_ASSERT(task);
+        return task->need_embd() || (spec && common_speculative_need_embd(spec));
+    }
+
     // if the context does not have a memory module then all embeddings have to be computed within a single ubatch
     // also we cannot split if the pooling would require any past tokens
+    // (MTP supports splitting — uses task->need_embd() not need_embd())
     bool can_split() const {
         GGML_ASSERT(task);
 
         return
             !task->need_embd() ||
-            (llama_get_memory(ctx) && llama_pooling_type(ctx) == LLAMA_POOLING_TYPE_LAST);
+            (llama_get_memory(ctx_tgt) && llama_pooling_type(ctx_tgt) == LLAMA_POOLING_TYPE_LAST);
     }
 
     bool can_batch_with(server_slot & other_slot) const {
@@ -309,11 +303,9 @@ struct server_slot {
         }
 
         // determine the max draft that fits the current slot state
-        int n_draft_max = task->params.speculative.n_max;
-
         // note: slot.prompt is not yet expanded with the `id` token sampled above
         //       also, need to leave space for 1 extra token to allow context shifts
-        n_draft_max = std::min(n_draft_max, n_ctx - prompt.n_tokens() - 2);
+        int n_draft_max = n_ctx - prompt.n_tokens() - 2;
 
         if (n_remaining > 0) {
             n_draft_max = std::min(n_draft_max, n_remaining - 1);
@@ -321,60 +313,10 @@ struct server_slot {
 
         SLT_DBG(*this, "max possible draft: %d\n", n_draft_max);
 
-        if (n_draft_max < task->params.speculative.n_min) {
-            SLT_DBG(*this, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, task->params.speculative.n_min);
-            n_draft_max = 0;
-        }
-
         return n_draft_max;
     }
 
     void update_batch(llama_batch & batch) {
-        const int n_draft_max = get_n_draft_max();
-        if (n_draft_max > 0) {
-            GGML_ASSERT(can_speculate());
-
-            // generate draft tokens in speculative decoding mode
-            // TODO: rework to have a single draft llama_context shared across all slots [TAG_SERVER_SPEC_REWORK]
-            //       perform the speculative drafting for all sequences at the same time in a single batch
-            const llama_tokens & tokens = prompt.tokens.get_text_tokens();
-
-            const auto & params_spec = task->params.speculative;
-
-            if (!spec_draft.empty()) {
-                // we have a previous (partial) draft to reuse
-                if (ctx_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) {
-                    GGML_ASSERT(!spec_ckpt.empty());
-                }
-            } else {
-                GGML_ASSERT(spec_i_batch.empty());
-
-                // generate a new draft
-                spec_draft = common_speculative_draft(spec.get(), params_spec, tokens, sampled);
-
-                if (spec_draft.size() > (size_t) n_draft_max) {
-                    SLT_WRN(*this, "draft size %d exceeds max %d, truncating\n", (int) spec_draft.size(), n_draft_max);
-                    spec_draft.resize(n_draft_max);
-                }
-
-                if (spec_draft.size() < (size_t) params_spec.n_min) {
-                    SLT_DBG(*this, "ignoring small draft: %d < %d\n", (int) spec_draft.size(), params_spec.n_min);
-                    spec_draft.clear();
-                }
-
-                if (!spec_draft.empty() && ctx_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) {
-                    const auto n_tokens = prompt.tokens.size();
-
-                    spec_ckpt = server_get_checkpoint(ctx, this->id, n_tokens);
-
-                    SLT_DBG(*this, "created speculative checkpoint (pos_min = %d, pos_max = %d, n_tokens = %zu, size = %.3f MiB)\n",
-                            spec_ckpt.pos_min, spec_ckpt.pos_max, n_tokens, (float) spec_ckpt.data.size() / 1024 / 1024);
-                }
-            }
-
-            GGML_ASSERT(spec_draft.size() <= (size_t) n_draft_max);
-        }
-
         if (spec_draft.empty()) {
             // no speculative decoding
             i_batch = batch.n_tokens;
@@ -482,6 +424,36 @@ struct server_slot {
         return stop_pos;
     }
 
+    void print_timings_tg() {
+        if (n_decoded < 100) {
+            return;
+        }
+
+        const int64_t t_now = ggml_time_us();
+
+        if (t_now - t_print_last < 3*1000*1000) {
+            return;
+        }
+
+        t_print_last = t_now;
+
+        const double n_gen_second = 1e3 / t_token_generation * n_decoded;
+
+        SLT_INF(*this, "n_decoded = %6d, tg = %6.2f t/s\n", n_decoded, n_gen_second);
+    }
+
+    void print_timings_pp() const {
+        const double n_prompt_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
+        const double f_progress = (float) prompt.n_tokens() / task->n_tokens();
+
+        if (t_prompt_processing < 3000.0) {
+            return;
+        }
+
+        SLT_INF(*this, "prompt processing, n_tokens = %6d, progress = %.2f, t = %6.2f s / %.2f tokens per second\n",
+                n_prompt_tokens_processed, f_progress, t_prompt_processing / 1e3, n_prompt_second);
+    }
+
     void print_timings() const {
         const double t_prompt        =       t_prompt_processing / n_prompt_tokens_processed;
         const double n_prompt_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
@@ -506,7 +478,7 @@ struct server_slot {
             );
         }
 
-        common_speculative_print_stats(spec.get());
+        common_speculative_print_stats(spec);
     }
 
     json to_json(bool only_metrics = false) const {
@@ -534,7 +506,7 @@ struct server_slot {
             };
 
             if (!only_metrics) {
-                res["prompt"] = ptask->tokens.detokenize(ctx, true);
+                res["prompt"] = ptask->tokens.detokenize(ctx_tgt, true);
                 res["generated"] = generated_text.empty() ? debug_generated_text : generated_text;
             }
         }
@@ -545,8 +517,13 @@ struct server_slot {
     void copy_state_to(server_slot & other) const {
         GGML_ASSERT(state == SLOT_STATE_DONE_PROMPT);
 
-        llama_memory_seq_rm(llama_get_memory(ctx), other.id,     -1, -1);
-        llama_memory_seq_cp(llama_get_memory(ctx), id, other.id, -1, -1);
+        common_context_seq_rm(ctx_tgt, other.id,     -1, -1);
+        common_context_seq_cp(ctx_tgt, id, other.id, -1, -1);
+
+        if (ctx_dft) {
+            common_context_seq_rm(ctx_dft, other.id,     -1, -1);
+            common_context_seq_cp(ctx_dft, id, other.id, -1, -1);
+        }
 
         other.n_decoded   = n_decoded;
         other.n_remaining = n_remaining;
@@ -637,7 +614,8 @@ struct server_context_impl {
     // only use these pointers outside of this class:
     //  - when not in sleeping state
     //  - and, with thread-safe APIs (e.g., tokenizer calls)
-    llama_model * model = nullptr;
+    llama_model * model_tgt = nullptr;
+
     mtmd_context * mctx = nullptr;
     const llama_vocab * vocab = nullptr;
 
@@ -647,6 +625,10 @@ struct server_context_impl {
     // note: chat_params must not be refreshed upon existing sleeping state
     server_chat_params chat_params;
 
+    server_context_impl() {
+        mtmd_helper_log_set(common_log_default_callback, nullptr);
+    }
+
     ~server_context_impl() {
         if (!sleeping) {
             // destroy() is already called when entering sleeping state
@@ -664,19 +646,30 @@ struct server_context_impl {
     // note: keep these alive - they determine the lifetime of the model, context, etc.
     common_init_result_ptr llama_init;
 
-    llama_context * ctx = nullptr;
+    llama_context * ctx_tgt = nullptr;
 
     llama_batch batch {};
 
     llama_model_ptr model_dft;
+    llama_context_ptr ctx_dft;
+
+    common_context_seq_rm_type ctx_tgt_seq_rm_type = COMMON_CONTEXT_SEQ_RM_TYPE_NO;
+    common_context_seq_rm_type ctx_dft_seq_rm_type = COMMON_CONTEXT_SEQ_RM_TYPE_NO;
+
+    common_speculative_ptr spec;
 
     bool add_bos_token = true;
 
     int32_t n_ctx; // total context for all clients / slots
 
+    // set to llama_model_n_swa(model)
+    // if swa_full is enabled, this is set to 0 to simulate a non-SWA model
+    int32_t n_swa;
+
     // slots / clients
     std::vector<server_slot> slots;
 
+    int trace = 0;
     int slots_debug = 0;
     int n_empty_consecutive = 0;
 
@@ -684,7 +677,8 @@ struct server_context_impl {
 
     server_metrics metrics;
 
-    json json_webui_settings = json::object();
+    json json_ui_settings = json::object();    // Primary: new name
+    json json_webui_settings = json::object();    // Deprecated: use json_ui_settings instead (kept for compat)
 
     // Necessary similarity of prompt for slot selection
     float slot_prompt_similarity = 0.0f;
@@ -698,18 +692,12 @@ struct server_context_impl {
     void destroy() {
         llama_init.reset();
 
-        ctx = nullptr;
-        model = nullptr;
+        ctx_tgt = nullptr;
+        model_tgt = nullptr;
 
         mtmd_free(mctx);
         mctx = nullptr;
 
-        for (server_slot & slot : slots) {
-            if (slot.can_speculate()) {
-                slot.spec.reset();
-            }
-        }
-
         llama_batch_free(batch);
     }
 
@@ -718,7 +706,7 @@ struct server_context_impl {
             return;
         }
         SLT_INF(slot, "%s", "saving idle slot to prompt cache\n");
-        SLT_DBG(slot, "%s", "__TEST_TAG_CLEAR_IDLE_SLOT__\n");
+        SLT_DBG(slot, "%s", "__TEST_TAG_CACHE_IDLE_SLOT__\n");
         slot.prompt_save(*prompt_cache);
         slot.prompt_clear(false);
         prompt_cache->update();
@@ -749,33 +737,30 @@ struct server_context_impl {
 
         llama_init = common_init_from_params(params_base);
 
-        model = llama_init->model();
-        ctx   = llama_init->context();
+        model_tgt = llama_init->model();
+        ctx_tgt   = llama_init->context();
 
-        if (model == nullptr) {
+        if (model_tgt == nullptr) {
             SRV_ERR("failed to load model, '%s'\n", params_base.model.path.c_str());
             return false;
         }
 
-        vocab = llama_model_get_vocab(model);
+        vocab = llama_model_get_vocab(model_tgt);
 
-        n_ctx = llama_n_ctx(ctx);
+        n_ctx = llama_n_ctx(ctx_tgt);
 
         add_bos_token = llama_vocab_get_add_bos(vocab);
 
         if (params_base.speculative.has_dft()) {
             // TODO speculative: move to common/speculative.cpp?
-            SRV_INF("loading draft model '%s'\n", params_base.speculative.mparams_dft.path.c_str());
+            const auto & params_spec = params_base.speculative.draft;
 
-            const auto & params_spec = params_base.speculative;
+            SRV_INF("loading draft model '%s'\n", params_spec.mparams.path.c_str());
 
             auto params_dft = params_base;
 
-            params_dft.n_parallel   = 1;
-            params_dft.n_ctx        = params_spec.n_ctx == 0 ? llama_n_ctx_seq(ctx) : params_spec.n_ctx;
-            params_dft.n_batch      = llama_n_ctx_seq(ctx);
             params_dft.devices      = params_spec.devices;
-            params_dft.model        = params_spec.mparams_dft;
+            params_dft.model        = params_spec.mparams;
             params_dft.n_gpu_layers = params_spec.n_gpu_layers;
             params_dft.cache_type_k = params_spec.cache_type_k;
             params_dft.cache_type_v = params_spec.cache_type_v;
@@ -795,16 +780,47 @@ struct server_context_impl {
                 return false;
             }
 
-            params_base.speculative.model_dft = model_dft.get();
-            params_base.speculative.cparams_dft = common_context_params_to_llama(params_dft);
+            auto cparams = common_context_params_to_llama(params_dft);
+
+            const bool spec_mtp = std::find(params_base.speculative.types.begin(),
+                                            params_base.speculative.types.end(),
+                                            COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end();
+            if (spec_mtp) {
+                cparams.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
+            }
+
+            // note: for small models maybe we can set this to the maximum possible draft from all speculative types
+            //       the extra memory for small models is likely negligible?
+            cparams.n_rs_seq = 0;
+            ctx_dft.reset(llama_init_from_model(model_dft.get(), cparams));
+
+            ctx_dft_seq_rm_type = common_context_can_seq_rm(ctx_dft.get());
+
+            params_base.speculative.draft.ctx_tgt = ctx_tgt;
+            params_base.speculative.draft.ctx_dft = ctx_dft.get();
+        } else if (std::find(params_base.speculative.types.begin(), params_base.speculative.types.end(),
+                             COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end()) {
+            SRV_INF("creating MTP draft context against the target model '%s'\n",
+                    params_base.model.path.c_str());
+
+            auto cparams_mtp = common_context_params_to_llama(params_base);
+            cparams_mtp.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
+            cparams_mtp.n_rs_seq = 0;
+
+            ctx_dft.reset(llama_init_from_model(model_tgt, cparams_mtp));
+            if (ctx_dft == nullptr) {
+                SRV_ERR("%s", "failed to create MTP context\n");
+                return false;
+            }
+
+            ctx_dft_seq_rm_type = common_context_can_seq_rm(ctx_dft.get());
+
+            params_base.speculative.draft.ctx_tgt = ctx_tgt;
+            params_base.speculative.draft.ctx_dft = ctx_dft.get();
         }
 
         std::string & mmproj_path = params_base.mmproj.path;
         if (!mmproj_path.empty()) {
-            if (!is_resume) {
-                mtmd_helper_log_set(common_log_default_callback, nullptr);
-            }
-
             mtmd_context_params mparams = mtmd_context_params_default();
 
             mparams.use_gpu          = params_base.mmproj_use_gpu;
@@ -816,7 +832,7 @@ struct server_context_impl {
             mparams.image_max_tokens = params_base.image_max_tokens;
             mparams.media_marker     = get_media_marker();
 
-            mctx = mtmd_init_from_file(mmproj_path.c_str(), model, mparams);
+            mctx = mtmd_init_from_file(mmproj_path.c_str(), model_tgt, mparams);
             if (mctx == nullptr) {
                 SRV_ERR("failed to load multimodal model, '%s'\n", mmproj_path.c_str());
                 return false;
@@ -834,7 +850,7 @@ struct server_context_impl {
             }
         }
 
-        if (!llama_memory_can_shift(llama_get_memory(ctx))) {
+        if (!llama_memory_can_shift(llama_get_memory(ctx_tgt))) {
             if (params_base.ctx_shift) {
                 params_base.ctx_shift = false;
                 SRV_WRN("%s\n", "ctx_shift is not supported by this context, it will be disabled");
@@ -846,22 +862,24 @@ struct server_context_impl {
             }
         }
 
-        if (llama_model_n_swa(model) == 0) {
+        if (llama_model_n_swa(model_tgt) == 0) {
             if (params_base.swa_full) {
                 params_base.swa_full = false;
                 SRV_WRN("%s\n", "swa_full is not supported by this model, it will be disabled");
             }
         }
 
+        n_swa = params_base.swa_full ? 0 : llama_model_n_swa(model_tgt);
+
         // Necessary similarity of prompt for slot selection
         slot_prompt_similarity = params_base.slot_prompt_similarity;
 
         // setup slots
         SRV_INF("initializing slots, n_slots = %d\n", params_base.n_parallel);
 
-        const int n_ctx_train = llama_model_n_ctx_train(model);
+        const int n_ctx_train = llama_model_n_ctx_train(model_tgt);
 
-        int n_ctx_slot = llama_n_ctx_seq(ctx);
+        int n_ctx_slot = llama_n_ctx_seq(ctx_tgt);
         if (n_ctx_slot > n_ctx_train) {
             SRV_WRN("the slot context (%d) exceeds the training context of the model (%d) - using rope scaling to extend\n", n_ctx_slot, n_ctx_train);
             // Do not cap: caller has configured rope scaling (--rope-scale / --rope-scaling yarn) to handle extended context.
@@ -869,12 +887,12 @@ struct server_context_impl {
 
         slots.clear();
 
-        const auto ctx_seq_rm_type = common_context_can_seq_rm(ctx);
-        if (ctx_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_NO) {
+        ctx_tgt_seq_rm_type = common_context_can_seq_rm(ctx_tgt);
+        if (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_NO) {
             SRV_WRN("%s", "speculative decoding not supported by this context\n");
         }
 
-        if (ctx_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) {
+        if (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) {
             SRV_WRN("%s", "speculative decoding will use checkpoints\n");
         }
 
@@ -883,27 +901,33 @@ struct server_context_impl {
             slots.emplace_back();
         }
 
+        // try speculative decoding
+        if (ctx_tgt_seq_rm_type != COMMON_CONTEXT_SEQ_RM_TYPE_NO) {
+            try {
+                spec.reset(common_speculative_init(params_base.speculative, params_base.n_parallel));
+            } catch (const std::exception & e) {
+                SRV_ERR("failed to initialize speculative decoding context: %s\n", e.what());
+            }
+        }
+
+        if (spec) {
+            SRV_INF("%s", "speculative decoding context initialized\n");
+        } else {
+            ctx_dft.reset();
+        }
+
         for (int i = 0; i < params_base.n_parallel; i++) {
             server_slot & slot = slots[i];
 
-            slot.id    = i;
-            slot.ctx   = ctx;
-            slot.n_ctx = n_ctx_slot;
-
-            slot.ctx_seq_rm_type = ctx_seq_rm_type;
+            slot.id      = i;
+            slot.ctx_tgt = ctx_tgt;
+            slot.ctx_dft = ctx_dft.get();
+            slot.spec    = spec.get();
+            slot.n_ctx   = n_ctx_slot;
 
             slot.mctx                   = mctx;
             slot.prompt.tokens.has_mtmd = mctx != nullptr;
 
-            // try speculative decoding
-            if (ctx_seq_rm_type != COMMON_CONTEXT_SEQ_RM_TYPE_NO) {
-                slot.spec.reset(common_speculative_init(params_base.speculative, slot.ctx));
-
-                if (slot.spec) {
-                    SLT_INF(slot, "%s", "speculative decoding context initialized\n");
-                }
-            }
-
             SLT_INF(slot, "new slot, n_ctx = %d\n", slot.n_ctx);
 
             slot.callback_on_release = [this](int id_slot) {
@@ -913,35 +937,44 @@ struct server_context_impl {
             slot.reset();
         }
 
+        {
+            const char * LLAMA_TRACE = getenv("LLAMA_TRACE");
+            trace = LLAMA_TRACE ? atoi(LLAMA_TRACE) : 0;
+
+            if (trace) {
+                SRV_WRN("LLAMA_TRACE = %d\n", trace);
+            }
+        }
+
         {
             const char * LLAMA_SERVER_SLOTS_DEBUG = getenv("LLAMA_SERVER_SLOTS_DEBUG");
             slots_debug = LLAMA_SERVER_SLOTS_DEBUG ? atoi(LLAMA_SERVER_SLOTS_DEBUG) : 0;
 
             if (slots_debug) {
-                SRV_WRN("slots debug = %d\n", slots_debug);
+                SRV_WRN("LLAMA_SERVER_SLOTS_DEBUG = %d\n", slots_debug);
             }
         }
 
         // the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens
         // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
         {
-            const int32_t n_batch = llama_n_batch(ctx);
+            const int32_t n_batch = llama_n_batch(ctx_tgt);
             batch = llama_batch_init(std::max(n_batch, params_base.n_parallel), 0, 1);
         }
 
         if (params_base.cache_ram_mib != 0) {
             if (params_base.cache_ram_mib < 0) {
-                SRV_WRN("prompt cache is enabled, size limit: %s\n", "no limit");
+                SRV_INF("prompt cache is enabled, size limit: %s\n", "no limit");
             } else {
-                SRV_WRN("prompt cache is enabled, size limit: %d MiB\n", params_base.cache_ram_mib);
+                SRV_INF("prompt cache is enabled, size limit: %d MiB\n", params_base.cache_ram_mib);
             }
-            SRV_WRN("%s", "use `--cache-ram 0` to disable the prompt cache\n");
+            SRV_INF("%s", "use `--cache-ram 0` to disable the prompt cache\n");
 
             prompt_cache = std::make_unique<server_prompt_cache>(params_base.cache_ram_mib, n_ctx);
         } else {
-            SRV_WRN("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n");
+            SRV_INF("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n");
         }
-        SRV_WRN("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n");
+        SRV_INF("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n");
 
         if (!params_base.model_alias.empty()) {
             // backward compat: use first alias as model name
@@ -969,8 +1002,9 @@ struct server_context_impl {
 
     // unlike load_model(), this is only called once during initialization
     bool init() {
-        GGML_ASSERT(ctx != nullptr);
-        GGML_ASSERT(model != nullptr);
+        GGML_ASSERT(ctx_tgt   != nullptr);
+        GGML_ASSERT(model_tgt != nullptr);
+
         GGML_ASSERT(!sleeping);
 
         // wiring up server queues
@@ -988,24 +1022,29 @@ struct server_context_impl {
 
         if (params_base.cache_idle_slots) {
             if (!params_base.kv_unified) {
-                SRV_WRN("%s: --cache-idle-slots requires --kv-unified, disabling\n", __func__);
+                SRV_WRN("%s", "--cache-idle-slots requires --kv-unified, disabling\n");
                 params_base.cache_idle_slots = false;
             } else if (params_base.cache_ram_mib == 0) {
-                SRV_WRN("%s: --cache-idle-slots requires --cache-ram, disabling\n", __func__);
+                SRV_WRN("%s", "--cache-idle-slots requires --cache-ram, disabling\n");
                 params_base.cache_idle_slots = false;
             } else {
-                SRV_INF("%s: idle slots will be saved to prompt cache and cleared upon starting a new task\n", __func__);
-                SRV_DBG("%s", "__TEST_TAG_CLEAR_IDLE_ENABLED__\n");
+                SRV_INF("%s", "idle slots will be saved to prompt cache and cleared upon starting a new task\n");
+                SRV_DBG("%s", "__TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__\n");
             }
         }
 
-        // populate webui settings
+        // populate UI settings (from either new ui_config_json or deprecated webui_config_json)
         {
-            if (!params_base.webui_config_json.empty()) {
+            const std::string & cfg = !params_base.ui_config_json.empty()
+                ? params_base.ui_config_json
+                : params_base.webui_config_json;
+            if (!cfg.empty()) {
                 try {
-                    json_webui_settings = json::parse(params_base.webui_config_json);
+                    json json_settings = json::parse(cfg);
+                    json_ui_settings = json_settings;
+                    json_webui_settings = json_settings; // deprecated: keep in sync
                 } catch (const std::exception & e) {
-                    SRV_ERR("%s: failed to parse webui config: %s\n", __func__, e.what());
+                    SRV_ERR("%s: failed to parse UI config: %s\n", __func__, e.what());
                     return false;
                 }
             }
@@ -1016,7 +1055,7 @@ struct server_context_impl {
             common_chat_templates_ptr chat_templates;
 
             try {
-                chat_templates = common_chat_templates_init(model, params_base.chat_template);
+                chat_templates = common_chat_templates_init(model_tgt, params_base.chat_template);
 
                 LOG_INF("%s: chat template, example_format: '%s'\n", __func__,
                     common_chat_format_example(chat_templates.get(), params_base.use_jinja, params_base.default_template_kwargs).c_str());
@@ -1044,8 +1083,8 @@ struct server_context_impl {
                 /* allow_image           */ mctx ? mtmd_support_vision(mctx) : false,
                 /* allow_audio           */ mctx ? mtmd_support_audio (mctx) : false,
                 /* enable_thinking       */ enable_thinking,
-                /* reasoning_budget      */ params_base.reasoning_budget,
-                /* reasoning_budget_msg  */ params_base.reasoning_budget_message,
+                /* reasoning_budget      */ params_base.sampling.reasoning_budget_tokens,
+                /* reasoning_budget_msg  */ params_base.sampling.reasoning_budget_message,
                 /* media_path            */ params_base.media_path,
                 /* force_pure_content    */ params_base.force_pure_content_parser
             };
@@ -1146,7 +1185,7 @@ struct server_context_impl {
             update_cache = update_cache && task.type == SERVER_TASK_TYPE_COMPLETION;
 
             if (update_cache) {
-                SRV_WRN("%s", "updating prompt cache\n");
+                SRV_INF("%s", "updating prompt cache\n");
 
                 const int64_t t_start = ggml_time_us();
 
@@ -1161,7 +1200,7 @@ struct server_context_impl {
 
                 prompt_cache->update();
 
-                SRV_WRN("prompt cache update took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
+                SRV_INF("prompt cache update took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
             }
         }
 
@@ -1220,10 +1259,10 @@ struct server_context_impl {
             if (!are_lora_equal(task_loras, slot.lora)) {
                 // if lora has changed, check to see if the cache should be cleared
                 if (lora_should_clear_cache(slot.lora, task_loras)) {
-                    SLT_INF(slot, "clearing cache for lora change. %zu loras -> %zu loras\n", slot.lora.size(), task.params.lora.size());
+                    SLT_TRC(slot, "clearing cache for lora change. %zu loras -> %zu loras\n", slot.lora.size(), task.params.lora.size());
                     slot.prompt.tokens.clear();
                 } else {
-                    SLT_INF(slot, "keeping cache for alora. %zu target loras\n", task_loras.size());
+                    SLT_TRC(slot, "keeping cache for alora. %zu target loras\n", task_loras.size());
                 }
                 slot.lora = task_loras;
             }
@@ -1279,7 +1318,7 @@ struct server_context_impl {
             }
         }
 
-        if (!task.tokens.validate(ctx)) {
+        if (!task.tokens.validate(ctx_tgt)) {
             send_error(task, "Prompt contains invalid tokens", ERROR_TYPE_INVALID_REQUEST);
             return false;
         }
@@ -1289,33 +1328,34 @@ struct server_context_impl {
         // initialize samplers
         if (task.need_sampling()) {
             try {
-                slot.smpl.reset(common_sampler_init(model, task.params.sampling));
+                slot.smpl.reset(common_sampler_init(model_tgt, task.params.sampling));
             } catch (std::exception & e) {
                 std::string err_msg = std::string("Failed to initialize samplers: ") + e.what();
                 send_error(task, err_msg, ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
 
-            const bool need_logits = task.params.sampling.n_probs > 0;
+            const bool need_pre_sample_logits = task.params.sampling.n_probs > 0 && !task.params.post_sampling_probs;
 
             bool backend_sampling = true;
 
             backend_sampling &= task.params.sampling.backend_sampling;
 
             // TODO: speculative decoding requires multiple samples per batch - not supported yet
-            backend_sampling &= !(slot.can_speculate() && task.params.speculative.n_max > 0);
+            backend_sampling &= !(slot.can_speculate());
 
-            // TODO: getting post/pre sampling logits is not yet supported with backend sampling
-            backend_sampling &= !need_logits;
+            // TODO: getting pre sampling logits is not yet supported with backend sampling
+            backend_sampling &= !need_pre_sample_logits;
 
             // TODO: tmp until backend sampling is fully implemented
             if (backend_sampling) {
-                llama_set_sampler(ctx, slot.id, common_sampler_get(slot.smpl.get()));
+                llama_set_sampler(ctx_tgt, slot.id, common_sampler_get(slot.smpl.get()));
             } else {
-                llama_set_sampler(ctx, slot.id, nullptr);
+                llama_set_sampler(ctx_tgt, slot.id, nullptr);
             }
 
-            SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl.get()).c_str());
+            SLT_TRC(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl.get()).c_str());
+            SLT_TRC(slot, "sampler params: \n%s\n", task.params.sampling.print().c_str());
         } else {
             slot.smpl.reset();
         }
@@ -1483,15 +1523,21 @@ struct server_context_impl {
             // set probability for top n_probs tokens
             result.probs.reserve(n_probs);
             for (size_t i = 0; i < n_probs; i++) {
+                // Some samplers do return 0.0 probabilities, others don't.
+                // Filter 0.0 probailities, to ensure the behavior is consistent.
+                if (cur_p->data[i].p == 0.0) {
+                    break;
+                }
+
                 result.probs.push_back({
                     cur_p->data[i].id,
-                    common_token_to_piece(ctx, cur_p->data[i].id, special),
+                    common_token_to_piece(ctx_tgt, cur_p->data[i].id, special),
                     cur_p->data[i].p
                 });
             }
         } else {
             // TODO: optimize this with min-p optimization
-            std::vector<llama_token_data> cur = get_token_probabilities(ctx, idx);
+            std::vector<llama_token_data> cur = get_token_probabilities(ctx_tgt, idx);
             const size_t max_probs = cur.size();
             const size_t n_probs = std::min(max_probs, n_probs_request);
 
@@ -1509,7 +1555,7 @@ struct server_context_impl {
             for (size_t i = 0; i < n_probs; i++) {
                 result.probs.push_back({
                     cur[i].id,
-                    common_token_to_piece(ctx, cur[i].id, special),
+                    common_token_to_piece(ctx_tgt, cur[i].id, special),
                     cur[i].p
                 });
             }
@@ -1612,7 +1658,7 @@ struct server_context_impl {
             res->tokens      = std::move(slot.generated_tokens);
         }
         res->timings         = slot.get_timings();
-        res->prompt          = slot.task->tokens.detokenize(ctx, true);
+        res->prompt          = slot.task->tokens.detokenize(ctx_tgt, true);
         res->response_fields = std::move(slot.task->params.response_fields);
 
         res->truncated             = slot.truncated;
@@ -1635,7 +1681,7 @@ struct server_context_impl {
         // populate res.probs_output
         if (slot.task->params.sampling.n_probs > 0) {
             if (!slot.task->params.stream && slot.stop == STOP_TYPE_WORD) {
-                const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false);
+                const llama_tokens stop_word_toks = common_tokenize(ctx_tgt, slot.stopping_word, false);
 
                 size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
                 res->probs_output = std::vector<completion_token_output>(
@@ -1660,7 +1706,7 @@ struct server_context_impl {
         res->n_tokens  = slot.task->n_tokens();
         res->res_type  = slot.task->params.res_type;
 
-        const int n_embd_out = llama_model_n_embd_out(model);
+        const int n_embd_out = llama_model_n_embd_out(model_tgt);
 
         std::vector<float> embd_res(n_embd_out, 0.0f);
 
@@ -1670,10 +1716,10 @@ struct server_context_impl {
             }
 
             const float * embd = nullptr;
-            if (llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE) {
-                embd = llama_get_embeddings_ith(ctx, i);
+            if (llama_pooling_type(slot.ctx_tgt) == LLAMA_POOLING_TYPE_NONE) {
+                embd = llama_get_embeddings_ith(slot.ctx_tgt, i);
             } else {
-                embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+                embd = llama_get_embeddings_seq(slot.ctx_tgt, batch.seq_id[i][0]);
             }
 
             if (embd == nullptr) {
@@ -1684,7 +1730,7 @@ struct server_context_impl {
             }
 
             // normalize only when there is pooling
-            if (llama_pooling_type(slot.ctx) != LLAMA_POOLING_TYPE_NONE) {
+            if (llama_pooling_type(slot.ctx_tgt) != LLAMA_POOLING_TYPE_NONE) {
                 common_embd_normalize(embd, embd_res.data(), n_embd_out, slot.task->params.embd_normalize);
                 res->embedding.push_back(embd_res);
                 break;
@@ -1709,9 +1755,9 @@ struct server_context_impl {
                 continue;
             }
 
-            const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+            const float * embd = llama_get_embeddings_seq(ctx_tgt, batch.seq_id[i][0]);
             if (embd == NULL) {
-                embd = llama_get_embeddings_ith(ctx, i);
+                embd = llama_get_embeddings_ith(ctx_tgt, i);
             }
 
             if (embd == NULL) {
@@ -1816,17 +1862,22 @@ struct server_context_impl {
             const auto & cur = slot.prompt.checkpoints.front();
 
             SLT_WRN(slot, "erasing old context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", size = %.3f MiB)\n",
-                    cur.pos_min, cur.pos_max, cur.n_tokens, (float) cur.data.size() / 1024 / 1024);
+                    cur.pos_min, cur.pos_max, cur.n_tokens, (float) cur.size() / 1024 / 1024);
 
             slot.prompt.checkpoints.erase(slot.prompt.checkpoints.begin());
         }
 
-        const auto & cur = slot.prompt.checkpoints.emplace_back(server_get_checkpoint(ctx, slot.id, slot.prompt.n_tokens() - n_tokens_cur, pos_min, pos_max));
+        auto & cur = slot.prompt.checkpoints.emplace_back();
+
+        cur.update_pos(slot.prompt.n_tokens() - n_tokens_cur, pos_min, pos_max);
 
-        SLT_WRN(slot,
+        cur.update_tgt(ctx_tgt,       slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+        cur.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+
+        SLT_INF(slot,
                 "created context checkpoint %d of %d (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", size = %.3f MiB)\n",
                 (int) slot.prompt.checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min,
-                cur.pos_max, cur.n_tokens, (float) cur.data.size() / 1024 / 1024);
+                cur.pos_max, cur.n_tokens, (float) cur.size() / 1024 / 1024);
     }
 
     void process_single_task(server_task && task) {
@@ -1981,7 +2032,7 @@ struct server_context_impl {
                     std::string filepath = task.slot_action.filepath;
 
                     const llama_tokens & tokens = slot->prompt.tokens.get_tokens();
-                    const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, tokens.data(), token_count);
+                    const size_t nwrite = llama_state_seq_save_file(ctx_tgt, filepath.c_str(), slot->id, tokens.data(), token_count);
 
                     const int64_t t_end = ggml_time_us();
                     const double t_save_ms = (t_end - t_start) / 1000.0;
@@ -2020,7 +2071,7 @@ struct server_context_impl {
                     llama_tokens tokens;
                     tokens.resize(slot->n_ctx);
                     size_t token_count = 0;
-                    size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, tokens.data(), tokens.size(), &token_count);
+                    size_t nread = llama_state_seq_load_file(ctx_tgt, filepath.c_str(), slot->id, tokens.data(), tokens.size(), &token_count);
                     if (nread == 0) {
                         slot->prompt.tokens.clear(); // KV may already been invalidated?
                         send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
@@ -2179,8 +2230,13 @@ struct server_context_impl {
 
                 SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
 
-                llama_memory_seq_rm (llama_get_memory(ctx), slot.id, n_keep            , n_keep + n_discard);
-                llama_memory_seq_add(llama_get_memory(ctx), slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard);
+                common_context_seq_rm (ctx_tgt, slot.id, n_keep            , n_keep + n_discard);
+                common_context_seq_add(ctx_tgt, slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard);
+
+                if (ctx_dft) {
+                    common_context_seq_rm (ctx_dft.get(), slot.id, n_keep            , n_keep + n_discard);
+                    common_context_seq_add(ctx_dft.get(), slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard);
+                }
 
                 // add generated tokens to cache
                 // ref: https://github.com/ggml-org/llama.cpp/pull/16818#discussion_r2473269481
@@ -2208,12 +2264,10 @@ struct server_context_impl {
         // track if given slot can be batched with slots already in the batch
         server_slot * slot_batched = nullptr;
 
-        auto accept_special_token = [&](server_slot & slot, llama_token token) {
-            return params_base.special ||
-                slot.task->params.sampling.preserved_tokens.find(token) != slot.task->params.sampling.preserved_tokens.end();
-        };
+        std::vector<server_slot *> generating;
+        std::vector<server_slot *> drafting;
 
-        // first, add sampled tokens from any ongoing sequences
+        // determine which slots are generating and drafting
         for (auto & slot : slots) {
             if (slot.state != SLOT_STATE_GENERATING) {
                 continue;
@@ -2226,12 +2280,116 @@ struct server_context_impl {
                 continue;
             }
 
+            generating.push_back(&slot);
+
+            if (spec) {
+                common_speculative_get_draft_params(spec.get(), slot.id).drafting = false;
+
+                const bool use_ckpt_tgt = ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
+                const bool use_ckpt_dft = ctx_dft_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
+
+                const int n_draft_max = slot.get_n_draft_max();
+
+                if (n_draft_max > 0) {
+                    GGML_ASSERT(slot.can_speculate());
+
+                    if (!slot.spec_draft.empty()) {
+                        // we have a previous (partial) draft to reuse
+                        if (use_ckpt_tgt) {
+                            GGML_ASSERT(!slot.spec_ckpt.empty());
+                        }
+                    } else {
+                        GGML_ASSERT(slot.spec_i_batch.empty());
+
+                        slot.spec_ckpt.update_pos(
+                                slot.prompt.n_tokens(),
+                                llama_memory_seq_pos_min(llama_get_memory(ctx_tgt), slot.id),
+                                llama_memory_seq_pos_max(llama_get_memory(ctx_tgt), slot.id));
+
+                        if (use_ckpt_dft) {
+                            slot.spec_ckpt.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+                        }
+
+                        slot.spec_prompt = slot.prompt.tokens.get_text_tokens();
+
+                        common_speculative_get_draft_params(spec.get(), slot.id) = {
+                            /* .drafting = */ true,
+                            /* .n_max    = */ n_draft_max,
+                            /* .n_past   = */ slot.prompt.n_tokens(),
+                            /* .id_last  = */ slot.sampled,
+                            /* .prompt   = */ &slot.spec_prompt,
+                            /* .result   = */ &slot.spec_draft,
+                        };
+
+                        drafting.push_back(&slot);
+                    }
+                }
+            }
+        }
+
+        // generate the actual drafts (if any)
+        {
+            common_speculative_draft(spec.get());
+        }
+
+        // make checkpoints if needed
+        for (auto * slot_ptr : drafting) {
+            auto & slot = *slot_ptr;
+
+            auto & draft = slot.spec_draft;
+            auto & ckpt  = slot.spec_ckpt;
+
+            slot.n_draft_total += draft.size();
+
+            // TODO: avoid restoring the draft context and re-evaluating the drafted tokens when not needed [TAG_SPEC_AVOID_DRAFT_REEVAL]
+            const bool use_ckpt_dft = ctx_dft_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
+
+            if (ctx_dft) {
+                if (use_ckpt_dft) {
+                    ckpt.load_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+                }
+
+                common_context_seq_rm(ctx_dft.get(), slot.id, ckpt.pos_max + 1, -1);
+            }
+
+            if (!draft.empty()) {
+                const bool use_ckpt_tgt =
+                    ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL ||
+                   (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && draft.size() > llama_n_rs_seq(ctx_tgt));
+
+                const bool use_ckpt_dft =
+                   (ctx_dft_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && draft.size() > llama_n_rs_seq(ctx_dft.get()));
+
+                if (use_ckpt_tgt) {
+                    //const int64_t t_start = ggml_time_us();
+
+                    ckpt.update_tgt(ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+
+                    //const int64_t t_total = ggml_time_us() - t_start;
+                    //printf("checkpoint total: %f ms\n", t_total / 1000.0);
+
+                    SLT_DBG(slot, "created speculative checkpoint (pos_min = %d, pos_max = %d, n_tokens = %d, size = %.3f MiB, draft = %.3f MiB)\n",
+                            ckpt.pos_min, ckpt.pos_max, slot.prompt.n_tokens(),
+                            (float) ckpt.size() / 1024 / 1024,
+                            (float) ckpt.data_dft.size() / 1024 / 1024);
+                }
+
+                if (use_ckpt_dft) {
+                    ckpt.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+                }
+            }
+        }
+
+        // update the batch with the sampled/drafted tokens
+        for (auto * slot_ptr : generating) {
+            auto & slot = *slot_ptr;
+
             slot.update_batch(batch);
         }
 
         // process in chunks of params.n_batch
-        int32_t n_batch  = llama_n_batch(ctx);
-        int32_t n_ubatch = llama_n_ubatch(ctx);
+        int32_t n_batch  = llama_n_batch(ctx_tgt);
+        int32_t n_ubatch = llama_n_ubatch(ctx_tgt);
 
         float  alora_scale       = -1.0f;
         size_t alora_disabled_id = 0;
@@ -2268,19 +2426,19 @@ struct server_context_impl {
 
                         slot.state = SLOT_STATE_PROCESSING_PROMPT;
 
-                        SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, task.n_tokens = %d\n",
+                        SLT_TRC(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, task.n_tokens = %d\n",
                                 slot.n_ctx, slot.task->params.n_keep, slot.task->n_tokens());
 
                         // print prompt tokens (for debugging)
                         /*if (1) {
                             // first 16 tokens (avoid flooding logs)
                             for (int i = 0; i < std::min<int>(16, input_tokens.size()); i++) {
-                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, input_tokens[i], common_token_to_piece(ctx, input_tokens[i]).c_str());
+                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, input_tokens[i], common_token_to_piece(ctx_tgt, input_tokens[i]).c_str());
                             }
                         } else {
                             // all
                             for (int i = 0; i < (int) input_tokens.size(); i++) {
-                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, input_tokens[i], common_token_to_piece(ctx, input_tokens[i]).c_str());
+                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, input_tokens[i], common_token_to_piece(ctx_tgt, input_tokens[i]).c_str());
                             }
                         }*/
 
@@ -2299,7 +2457,7 @@ struct server_context_impl {
                         }
 
                         // TODO: support memory-less logits computation
-                        if (slot.task->need_logits() && !llama_get_memory(ctx)) {
+                        if (slot.task->need_logits() && !llama_get_memory(ctx_tgt)) {
                             send_error(slot, "the current context does not logits computation. skipping", ERROR_TYPE_SERVER);
                             slot.release();
                             continue;
@@ -2351,7 +2509,7 @@ struct server_context_impl {
                                 const auto n_cache_reuse = slot.task->params.n_cache_reuse;
 
                                 const bool can_cache_reuse =
-                                    llama_memory_can_shift(llama_get_memory(ctx)) &&
+                                    llama_memory_can_shift(llama_get_memory(ctx_tgt)) &&
                                     !slot.prompt.tokens.has_mtmd;
 
                                 if (!can_cache_reuse && n_cache_reuse > 0) {
@@ -2383,15 +2541,20 @@ struct server_context_impl {
                                         }
 
                                         if (n_match >= (size_t) n_cache_reuse) {
-                                            SLT_INF(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match);
+                                            SLT_TRC(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match);
                                             //for (size_t i = head_p; i < head_p + n_match; i++) {
-                                            //    SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
+                                            //    SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx_tgt, prompt_tokens[i]).c_str());
                                             //}
 
                                             const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c;
 
-                                            llama_memory_seq_rm (llama_get_memory(ctx), slot.id, head_p, head_c);
-                                            llama_memory_seq_add(llama_get_memory(ctx), slot.id, head_c, head_c + n_match, kv_shift);
+                                            common_context_seq_rm (ctx_tgt, slot.id, head_p, head_c);
+                                            common_context_seq_add(ctx_tgt, slot.id, head_c, head_c + n_match, kv_shift);
+
+                                            if (ctx_dft) {
+                                                common_context_seq_rm (ctx_dft.get(), slot.id, head_p, head_c);
+                                                common_context_seq_add(ctx_dft.get(), slot.id, head_c, head_c + n_match, kv_shift);
+                                            }
 
                                             for (size_t i = 0; i < n_match; i++) {
                                                 slot.prompt.tokens.set_token(head_p + i, slot.prompt.tokens[head_c + i]);
@@ -2414,14 +2577,11 @@ struct server_context_impl {
 
                             llama_pos pos_next = slot.prompt.tokens.pos_next(n_past);
 
-                            // note: when n_swa == 0, the model does not use SWA
-                            const auto n_swa = std::max(0, llama_model_n_swa(model));
-
                             // the largest pos_min required for a checkpoint to be useful
                             const auto pos_min_thold = std::max(0, pos_next - n_swa);
 
                             if (n_past > 0 && n_past < slot.prompt.n_tokens()) {
-                                const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
+                                const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx_tgt), slot.id);
                                 if (pos_min == -1) {
                                     SLT_ERR(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d\n", n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min);
                                     GGML_ABORT("pos_min == -1, but n_past > 0 - should not happen: https://github.com/ggml-org/llama.cpp/pull/13833#discussion_r2116181237");
@@ -2450,14 +2610,14 @@ struct server_context_impl {
 
                                         {
                                             const auto token = slot.prompt.tokens[i];
-                                            const auto piece = token != LLAMA_TOKEN_NULL ? common_token_to_piece(ctx, token) : "[mtmd]";
+                                            const auto piece = token != LLAMA_TOKEN_NULL ? common_token_to_piece(ctx_tgt, token) : "[mtmd]";
                                             ss0 << piece;
                                             st0 << std::setw(8) << token;
                                         }
 
                                         {
                                             const auto token = slot.task->tokens[i];
-                                            const auto piece = token != LLAMA_TOKEN_NULL ? common_token_to_piece(ctx, token) : "[mtmd]";
+                                            const auto piece = token != LLAMA_TOKEN_NULL ? common_token_to_piece(ctx_tgt, token) : "[mtmd]";
                                             ss1 << piece;
                                             st1 << std::setw(8) << token;
                                         }
@@ -2489,18 +2649,13 @@ struct server_context_impl {
 
                                     if (!do_reset) {
                                         // restore the context checkpoint
-                                        const size_t checkpoint_size = it->data.size();
-                                        const size_t n = llama_state_seq_set_data_ext(ctx, it->data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
 
-                                        if (n != checkpoint_size) {
-                                            SLT_ERR(slot, "failed to restore context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, (float) checkpoint_size / 1024 / 1024);
-                                            do_reset = true;
-                                            //printf("[DEBUG] `do_reset` was set to `true` after failing to restore a checkpoint");
-                                        } else {
-                                            pos_next = std::min(pos_next, std::max(it->pos_min + 1, it->pos_max));
-                                            n_past = std::min(slot.prompt.tokens.size_up_to_pos(pos_next), (size_t) it->n_tokens);
-                                            SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_past = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, n_past, (float) checkpoint_size / 1024 / 1024);
-                                        }
+                                        it->load_tgt(ctx_tgt,       slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+                                        it->load_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+
+                                        pos_next = std::min(pos_next, std::max(it->pos_min + 1, it->pos_max));
+                                        n_past   = std::min(slot.prompt.tokens.size_up_to_pos(pos_next), (size_t) it->n_tokens);
+                                        SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_past = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, n_past, (float) it->size() / 1024 / 1024);
                                     }
 
                                     if (do_reset) {
@@ -2517,7 +2672,7 @@ struct server_context_impl {
                                 for (auto it = slot.prompt.checkpoints.begin(); it != slot.prompt.checkpoints.end();) {
                                     const auto & cur = *it;
                                     if (cur.pos_max > pos_next) {
-                                        SLT_WRN(slot, "erased invalidated context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_swa = %d, pos_next = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, cur.n_tokens, n_swa, pos_next, (float) cur.data.size() / 1024 / 1024);
+                                        SLT_WRN(slot, "erased invalidated context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_swa = %d, pos_next = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, cur.n_tokens, n_swa, pos_next, (float) cur.size() / 1024 / 1024);
                                         it = slot.prompt.checkpoints.erase(it);
                                     } else {
                                         ++it;
@@ -2552,18 +2707,18 @@ struct server_context_impl {
                         }
                     }
 
+                    const int64_t t_current = ggml_time_us();
+                    slot.t_prompt_processing = (t_current - slot.t_start_process_prompt) / 1e3;
+                    slot.print_timings_pp();
+
                     // truncate any tokens that are beyond n_past for this slot
                     const llama_pos p0 = slot.prompt.tokens.pos_next();
 
-                    SLT_INF(slot, "n_tokens = %d, memory_seq_rm [%d, end)\n", slot.prompt.n_tokens(), p0);
-
-                    if (!llama_memory_seq_rm(llama_get_memory(ctx), slot.id, p0, -1)) {
-                        SLT_WRN(slot, "failed to truncate tokens with position >= %d - clearing the memory\n", p0);
-
-                        slot.prompt_clear(true);
+                    SLT_TRC(slot, "cached n_tokens = %d, memory_seq_rm [%d, end)\n", slot.prompt.n_tokens(), p0);
 
-                        // there is no common part left
-                        slot.n_prompt_tokens_cache = 0;
+                    common_context_seq_rm(ctx_tgt, slot.id, p0, -1);
+                    if (ctx_dft) {
+                        common_context_seq_rm(ctx_dft.get(), slot.id, p0, -1);
                     }
 
                     // If using an alora, there may be uncached tokens that come
@@ -2588,10 +2743,12 @@ struct server_context_impl {
                     // make a checkpoint of the parts of the memory that cannot be rolled back.
                     // checkpoints are created only if:
                     // - the model does not support partial sequence removal
-                    // - the model uses SWA and we are not using `swa_full`
+                    // - the model uses SWA (and we are not using `swa_full`)
+                    // - the model supports partial sequence removal but only up to a fixed bound
                     do_checkpoint = do_checkpoint && (
-                            (slot.ctx_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) ||
-                            (llama_model_n_swa(model) > 0 && !params_base.swa_full));
+                            ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL ||
+                            ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS ||
+                            n_swa > 0);
 
                     bool has_mtmd = false;
 
@@ -2599,7 +2756,7 @@ struct server_context_impl {
                     while (slot.prompt.n_tokens() < slot.task->n_tokens() && input_tokens[slot.prompt.n_tokens()] == LLAMA_TOKEN_NULL) {
                         // process the image
                         size_t n_tokens_out = 0;
-                        int32_t res = input_tokens.process_chunk(ctx, mctx, slot.prompt.n_tokens(), slot.prompt.tokens.pos_next(), slot.id, n_tokens_out);
+                        int32_t res = input_tokens.process_chunk(ctx_tgt, mctx, slot.prompt.n_tokens(), slot.prompt.tokens.pos_next(), slot.id, n_tokens_out);
                         if (res != 0) {
                             SLT_ERR(slot, "failed to process image, res = %d\n", res);
                             send_error(slot, "failed to process image", ERROR_TYPE_SERVER);
@@ -2607,6 +2764,16 @@ struct server_context_impl {
                             continue;
                         }
 
+                        if (ctx_dft) {
+                            // TODO: in the future, figure out how to infuse target embeddings to the images
+                            //       for now, we skip this for simplicity
+                            //       maybe we simply need to call `common_speculative_process()` on the mtmd batches in the `process_chunk` above?
+                            res = input_tokens.process_chunk(ctx_dft.get(), mctx, slot.prompt.n_tokens(), slot.prompt.tokens.pos_next(), slot.id, n_tokens_out);
+                            if (res != 0) {
+                                GGML_ABORT("failed to process multi-modal data on draft context\n");
+                            }
+                        }
+
                         slot.n_prompt_tokens_processed += n_tokens_out;
 
                         // add the image chunk to cache
@@ -2634,12 +2801,14 @@ struct server_context_impl {
                             break;
                         }
 
-                        // embedding requires all tokens in the batch to be output
+                        // embedding requires all tokens in the batch to be output;
+                        // MTP also wants logits at every prompt position so the
+                        // streaming hook can mirror t_h_pre_norm into ctx_dft.
                         common_batch_add(batch,
                             cur_tok,
                             slot.prompt.tokens.pos_next(),
                             { slot.id },
-                            slot.task->need_embd());
+                            slot.need_embd());
                         slot.prompt.tokens.push_back(cur_tok);
 
                         slot.n_prompt_tokens_processed++;
@@ -2682,7 +2851,6 @@ struct server_context_impl {
                         slot.i_batch   = batch.n_tokens - 1;
 
                         slot.init_sampler();
-                        SLT_INF(slot, "prompt processing done, n_tokens = %d, batch.n_tokens = %d\n", slot.prompt.n_tokens(), batch.n_tokens);
                     } else {
                         if (slot.task->n_tokens() < slot.prompt.n_tokens() + n_ubatch) {
                             // near the end of the prompt
@@ -2704,12 +2872,10 @@ struct server_context_impl {
                                 }
                             }
                         }
-
-                        SLT_INF(slot, "prompt processing progress, n_tokens = %d, batch.n_tokens = %d, progress = %f\n", slot.prompt.n_tokens(), batch.n_tokens, (float) slot.prompt.n_tokens() / slot.task->n_tokens());
                     }
 
-                    const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
-                    const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id);
+                    const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx_tgt), slot.id);
+                    const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_tgt), slot.id);
 
                     // no need for empty or small checkpoints
                     do_checkpoint = do_checkpoint && (pos_min >= 0 && slot.prompt.n_tokens() >= 64);
@@ -2740,9 +2906,14 @@ struct server_context_impl {
 
         SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens);
 
+        auto accept_special_token = [&](server_slot & slot, llama_token token) {
+            return params_base.special ||
+                slot.task->params.sampling.preserved_tokens.find(token) != slot.task->params.sampling.preserved_tokens.end();
+        };
+
         if (slot_batched) {
             // apply lora, only need to do it once per batch
-            common_set_adapter_lora(ctx, slot_batched->lora);
+            common_set_adapter_lora(ctx_tgt, slot_batched->lora);
 
             // if the lora is temporarily disabled for an alora, re-enable it
             // for next time
@@ -2751,7 +2922,7 @@ struct server_context_impl {
                 slot_batched->lora[alora_disabled_id].scale = alora_scale;
             }
 
-            llama_set_embeddings(ctx, slot_batched->task->need_embd());
+            llama_set_embeddings(ctx_tgt, slot_batched->need_embd());
         }
 
         if (batch.n_tokens == 0) {
@@ -2780,7 +2951,7 @@ struct server_context_impl {
                 batch.logits   + i,
             };
 
-            const int ret = llama_decode(ctx, batch_view);
+            const int ret = llama_decode(ctx_tgt, batch_view);
 
             metrics.on_decoded(slots);
 
@@ -2833,11 +3004,63 @@ struct server_context_impl {
                 continue; // continue loop of n_batch
             }
 
+            // TODO: avoid restoring the draft context and re-evaluating the drafted tokens when not needed [TAG_SPEC_AVOID_DRAFT_REEVAL]
+            //       for now, always re-evaluate for simplicity
+            //       ref: https://github.com/ggml-org/llama.cpp/pull/22728#issuecomment-4400925384
+            //
+            // | spec type   | need re-eval |
+            // | ---         | ---          |
+            // | draft model | no           | because the draft model does not use embeddings from the target
+            // | MTP (std)   | yes          |
+            // | MTP Gemma4  | no           | because the KV cache is shared
+            // | Eagle3      | yes          |
+            // | DFlash      | yes          | https://github.com/ggml-org/llama.cpp/pull/22728#issuecomment-4405406982
+            //
+            // note: this logic is now moved in `common_speculative_process()`
+            //       keeping the sketch here until for a bit, until the logic is finalized
+            //
+            //if (ctx_dft) {
+            //    // TODO: update as needed for MTP, Eagle3, etc.
+            //    const bool need_tgt_embd = false;
+
+            //    if (need_tgt_embd) {
+            //        llama_synchronize(ctx_tgt);
+            //    }
+
+            //    // the logic here varies depending on the speculative decoding method
+            //    //  - some draft contexts require embeddings from the target context, others don't
+            //    //  - some draft contexts involve an encoder step to transform the target embeddings to draft embeddings
+            //    // TODO: extract this in a function ?
+            //    {
+            //        // TODO: hook the embeddings from the last target batch here
+            //        if (llama_model_has_encoder(model_dft.get())) {
+            //            //llama_encode(ctx_dft, ...);
+
+            //            GGML_ABORT("not implemented yet\n");
+            //        }
+
+            //        const int ret = llama_decode(ctx_dft.get(), batch_view);
+
+            //        if (ret != 0) {
+            //            SRV_ERR("failed to decode draft batch, ret = %d\n", ret);
+
+            //            // TODO: handle error
+            //            break;
+            //        }
+            //    }
+            //}
+            if (!common_speculative_process(spec.get(), batch_view)) {
+                SRV_ERR("%s", "failed to process speculative batch\n");
+
+                // TODO: handle error
+                break;
+            }
+
             // move the head of the batch forward with the number of tokens we just processed
             i_next = i + n_tokens;
 
             // on successful decode, restore the original batch size
-            n_batch = llama_n_batch(ctx);
+            n_batch = llama_n_batch(ctx_tgt);
 
             // handle `n_cmpl > 1` tasks - when the main prompt is processed, activate all child tasks too
             for (auto & slot : slots) {
@@ -2896,7 +3119,7 @@ struct server_context_impl {
                     slot.state = SLOT_STATE_GENERATING;
 
                     if (slot.can_speculate()) {
-                        common_speculative_begin(slot.spec.get(), slot.prompt.tokens.get_text_tokens());
+                        common_speculative_begin(spec.get(), slot.id, slot.prompt.tokens.get_text_tokens());
                     }
                 } else if (slot.state != SLOT_STATE_GENERATING) {
                     continue; // continue loop of slots
@@ -2908,7 +3131,7 @@ struct server_context_impl {
 
                 const int tok_idx = slot.i_batch - i;
 
-                llama_token id = common_sampler_sample(slot.smpl.get(), slot.ctx, tok_idx);
+                llama_token id = common_sampler_sample(slot.smpl.get(), slot.ctx_tgt, tok_idx);
 
                 slot.i_batch = -1;
 
@@ -2929,7 +3152,7 @@ struct server_context_impl {
 
                 completion_token_output result;
                 result.tok          = id;
-                result.text_to_send = common_token_to_piece(slot.ctx, result.tok, accept_special_token(slot, result.tok));
+                result.text_to_send = common_token_to_piece(slot.ctx_tgt, result.tok, accept_special_token(slot, result.tok));
                 result.prob         = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
 
                 if (slot.task->params.sampling.n_probs > 0) {
@@ -2945,6 +3168,8 @@ struct server_context_impl {
 
                     continue;
                 }
+
+                slot.print_timings_tg();
             }
 
             // speculative decoding - main model sample and accept
@@ -2960,45 +3185,59 @@ struct server_context_impl {
 
                 // verify and try to accept the draft
                 {
+                    // save the sampler sampler state in case we need to restore it
                     common_sampler_ptr smpl_save(common_sampler_clone(slot.smpl.get()));
 
                     GGML_ASSERT(slot.spec_i_batch.size() == n_draft + 1);
-                    auto accepted = common_sampler_sample_and_accept_n(slot.smpl.get(), slot.ctx, slot.spec_i_batch, slot.spec_draft);
+                    auto accepted = common_sampler_sample_and_accept_n(slot.smpl.get(), slot.ctx_tgt, slot.spec_i_batch, slot.spec_draft);
                     slot.spec_i_batch.clear();
 
-                    SLT_DBG(slot, "%s: n_draft=%zu, accepted=%zu\n", __func__, slot.spec_draft.size(), accepted.size());
-
                     GGML_ASSERT(accepted.size() >= 1);
 
+                    const uint32_t n_rollback = slot.spec_draft.size() + 1 - accepted.size();
+
+                    const bool use_ckpt_tgt =
+                        ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL ||
+                       (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && n_rollback > llama_n_rs_seq(ctx_tgt));
+
                     // check for partial draft acceptance
-                    if (accepted.size() < slot.spec_draft.size() + 1) {
-                        if (slot.ctx_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) {
+                    if (n_rollback > 0) {
+                        if (use_ckpt_tgt) {
+                            if (trace > 0) {
+                                SLT_INF(slot, "accepted %2zu/%2zu draft tokens (restore checkpoint)\n", accepted.size() - 1, slot.spec_draft.size());
+                            }
+
                             // partial acceptance is not supported by the context -> truncate the draft and restore the state
                             slot.spec_draft = std::move(accepted);
 
                             const auto & ckpt = slot.spec_ckpt;
 
-                            SLT_DBG(slot, "restoring speculative checkpoint (pos_min = %d, pos_max = %d, size = %zu)\n",
-                                    ckpt.pos_min, ckpt.pos_max, ckpt.size());
+                            SLT_DBG(slot, "restoring speculative checkpoint (pos_min = %d, pos_max = %d, size = %zu)\n", ckpt.pos_min, ckpt.pos_max, ckpt.size());
+
+                            {
+                                ckpt.load_tgt(slot.ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
 
-                            const size_t n = llama_state_seq_set_data_ext(slot.ctx, ckpt.data.data(), ckpt.size(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-                            if (n != ckpt.size()) {
-                                GGML_ABORT("%s: failed to restore context checkpoint (pos_min=%d, pos_max=%d, size=%zu, get_data_ext->%zu, set_data_ext->%zu",
-                                        __func__, ckpt.pos_min, ckpt.pos_max, ckpt.size(), ckpt.size(), n);
+                                common_context_seq_rm(slot.ctx_tgt, slot.id, ckpt.pos_max + 1, -1);
                             }
 
-                            llama_memory_seq_rm(llama_get_memory(slot.ctx), slot.id, ckpt.pos_max + 1, -1);
+                            if (slot.ctx_dft) {
+                                ckpt.load_dft(slot.ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
+
+                                common_context_seq_rm(slot.ctx_dft, slot.id, ckpt.pos_max + 1, -1);
+                            }
 
                             slot.prompt.tokens.keep_first(ckpt.n_tokens);
                             slot.smpl = std::move(smpl_save);
 
                             continue;
                         }
+                    }
 
-                        LOG_DBG("%s: partial acceptance: %zu < %zu\n", __func__, accepted.size(), slot.spec_draft.size());
+                    if (trace > 0) {
+                        SLT_INF(slot, "accepted %2zu/%2zu draft tokens\n", accepted.size() - 1, n_draft);
                     }
 
-                    common_speculative_accept(slot.spec.get(), accepted.size() - 1);
+                    common_speculative_accept(spec.get(), slot.id, accepted.size() - 1);
 
                     slot.spec_draft = std::move(accepted);
                 }
@@ -3007,12 +3246,10 @@ struct server_context_impl {
 
                 const auto ids = std::move(slot.spec_draft);
 
-                slot.n_decoded += ids.size();
                 slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;
 
                 // update how many tokens out of those tested were accepted
                 slot.n_draft_accepted += ids.size() - 1;
-                slot.n_draft_total += n_draft;
 
                 // add accepted tokens to the prompt
                 slot.prompt.tokens.keep_first(slot.prompt.n_tokens() - n_draft);
@@ -3021,17 +3258,22 @@ struct server_context_impl {
                 slot.sampled = ids.back(); // last accepted token
                 SLT_DBG(slot, "add accepted tokens: sampled=%d, ids.size=%zu, n_draft=%zu\n", slot.sampled, ids.size(), n_draft);
 
-                llama_memory_seq_rm(llama_get_memory(slot.ctx), slot.id, slot.prompt.n_tokens(), -1);
+                common_context_seq_rm(slot.ctx_tgt, slot.id, slot.prompt.tokens.pos_next(), -1);
+                if (slot.ctx_dft) {
+                    common_context_seq_rm(slot.ctx_dft, slot.id, slot.prompt.tokens.pos_next(), -1);
+                }
 
                 for (size_t i = 0; i < ids.size(); ++i) {
                     completion_token_output result;
 
                     result.tok          = ids[i];
-                    result.text_to_send = common_token_to_piece(slot.ctx, result.tok, accept_special_token(slot, result.tok));
+                    result.text_to_send = common_token_to_piece(slot.ctx_tgt, result.tok, accept_special_token(slot, result.tok));
                     result.prob         = 1.0f; // set later
 
                     // TODO: set result.probs
 
+                    slot.n_decoded += 1;
+
                     if (!process_token(result, slot)) {
                         slot.print_timings();
                         send_final_response(slot);
@@ -3042,6 +3284,8 @@ struct server_context_impl {
                     }
                 }
 
+                slot.print_timings_tg();
+
                 SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) ids.size() - 1, (int) n_draft, slot.prompt.n_tokens());
             }
         }
@@ -3079,7 +3323,7 @@ void server_context::terminate() {
 }
 
 llama_context * server_context::get_llama_context() const {
-    return impl->ctx;
+    return impl->ctx_tgt;
 }
 
 server_response_reader server_context::get_response_reader() {
@@ -3089,8 +3333,8 @@ server_response_reader server_context::get_response_reader() {
 server_context_meta server_context::get_meta() const {
     auto bos_id = llama_vocab_bos(impl->vocab);
     auto eos_id = llama_vocab_eos(impl->vocab);
-    auto bos_token_str = bos_id != LLAMA_TOKEN_NULL ? common_token_to_piece(impl->ctx, bos_id, true) : "";
-    auto eos_token_str = eos_id != LLAMA_TOKEN_NULL ? common_token_to_piece(impl->ctx, eos_id, true) : "";
+    auto bos_token_str = bos_id != LLAMA_TOKEN_NULL ? common_token_to_piece(impl->ctx_tgt, bos_id, true) : "";
+    auto eos_token_str = eos_id != LLAMA_TOKEN_NULL ? common_token_to_piece(impl->ctx_tgt, eos_id, true) : "";
 
     return server_context_meta {
         /* build_info             */ std::string(llama_build_info()),
@@ -3101,9 +3345,10 @@ server_context_meta server_context::get_meta() const {
         /* has_mtmd               */ impl->mctx != nullptr,
         /* has_inp_image          */ impl->chat_params.allow_image,
         /* has_inp_audio          */ impl->chat_params.allow_audio,
-        /* json_webui_settings    */ impl->json_webui_settings,
+        /* json_ui_settings       */ impl->json_ui_settings,
+        /* json_webui_settings    */ impl->json_webui_settings,  // Deprecated
         /* slot_n_ctx             */ impl->get_slot_n_ctx(),
-        /* pooling_type           */ llama_pooling_type(impl->ctx),
+        /* pooling_type           */ llama_pooling_type(impl->ctx_tgt),
 
         /* chat_params            */ impl->chat_params,
         /* chat_template_caps     */ common_chat_templates_get_caps(impl->chat_params.tmpls.get()),
@@ -3121,10 +3366,10 @@ server_context_meta server_context::get_meta() const {
 
         /* model_vocab_type       */ llama_vocab_type(impl->vocab),
         /* model_vocab_n_tokens   */ llama_vocab_n_tokens(impl->vocab),
-        /* model_n_ctx_train      */ llama_model_n_ctx_train(impl->model),
-        /* model_n_embd_inp       */ llama_model_n_embd(impl->model),
-        /* model_n_params         */ llama_model_n_params(impl->model),
-        /* model_size             */ llama_model_size(impl->model),
+        /* model_n_ctx_train      */ llama_model_n_ctx_train(impl->model_tgt),
+        /* model_n_embd_inp       */ llama_model_n_embd(impl->model_tgt),
+        /* model_n_params         */ llama_model_n_params(impl->model_tgt),
+        /* model_size             */ llama_model_size(impl->model_tgt),
     };
 }
 
@@ -3468,10 +3713,6 @@ void server_routes::init_routes() {
                     {"name",  "n_tokens_max"},
                     {"help",  "Largest observed n_tokens."},
                     {"value",  res_task->n_tokens_max}
-            }, {
-                    {"name",  "n_busy_slots_per_decode"},
-                    {"help",  "Average number of busy slots per llama_decode() call"},
-                    {"value",  (float) res_task->n_busy_slots_total / std::max((float) res_task->n_decode_total, 1.f)}
             }}},
             {"gauge", {{
                     {"name",  "prompt_tokens_seconds"},
@@ -3489,6 +3730,10 @@ void server_routes::init_routes() {
                     {"name",  "requests_deferred"},
                     {"help",  "Number of requests deferred."},
                     {"value",  (uint64_t) res_task->n_tasks_deferred}
+            },{
+                    {"name",  "n_busy_slots_per_decode"},
+                    {"help",  "Average number of busy slots per llama_decode() call"},
+                    {"value",  (float) res_task->n_busy_slots_total / std::max((float) res_task->n_decode_total, 1.f)}
             }}}
         };
 
@@ -3623,8 +3868,12 @@ void server_routes::init_routes() {
             { "endpoint_slots",              params.endpoint_slots },
             { "endpoint_props",              params.endpoint_props },
             { "endpoint_metrics",            params.endpoint_metrics },
-            { "webui",                       params.webui },
-            { "webui_settings",              meta->json_webui_settings },
+            // New keys
+            { "ui",                           params.ui },
+            { "ui_settings",                  meta->json_ui_settings },
+            // Deprecated: use ui/ui_settings instead (kept for backward compat)
+            { "webui",                        params.webui },
+            { "webui_settings",               meta->json_webui_settings },
             { "chat_template",               tmpl_default },
             { "chat_template_caps",          meta->chat_template_caps },
             { "bos_token",                   meta->bos_token_str },
@@ -3774,7 +4023,7 @@ void server_routes::init_routes() {
     this->post_responses_oai = [this](const server_http_req & req) {
         auto res = create_response();
         std::vector<raw_buffer> files;
-        json body = convert_responses_to_chatcmpl(json::parse(req.body));
+        json body = server_chat_convert_responses_to_chatcmpl(json::parse(req.body));
         SRV_DBG("%s\n", "Request converted: OpenAI Responses -> OpenAI Chat Completions");
         SRV_DBG("converted request: %s\n", body.dump().c_str());
         json body_parsed = oaicompat_chat_params_parse(
@@ -3800,6 +4049,7 @@ void server_routes::init_routes() {
         std::vector<raw_buffer> files;
         json body = convert_transcriptions_to_chatcmpl(
             json::parse(req.body),
+            meta->chat_params.tmpls.get(),
             req.files,
             files);
         SRV_DBG("%s\n", "Request converted: OpenAI Transcriptions -> OpenAI Chat Completions");
@@ -3819,7 +4069,7 @@ void server_routes::init_routes() {
     this->post_anthropic_messages = [this](const server_http_req & req) {
         auto res = create_response();
         std::vector<raw_buffer> files;
-        json body = convert_anthropic_to_oai(json::parse(req.body));
+        json body = server_chat_convert_anthropic_to_oai(json::parse(req.body));
         SRV_DBG("%s\n", "Request converted: Anthropic -> OpenAI Chat Completions");
         SRV_DBG("converted request: %s\n", body.dump().c_str());
         json body_parsed = oaicompat_chat_params_parse(
@@ -3837,7 +4087,7 @@ void server_routes::init_routes() {
     this->post_anthropic_count_tokens = [this](const server_http_req & req) {
         auto res = create_response();
         std::vector<raw_buffer> files;
-        json body = convert_anthropic_to_oai(json::parse(req.body));
+        json body = server_chat_convert_anthropic_to_oai(json::parse(req.body));
         SRV_DBG("%s\n", "Request converted: Anthropic -> OpenAI Chat Completions");
         SRV_DBG("converted request: %s\n", body.dump().c_str());
         json body_parsed = oaicompat_chat_params_parse(
@@ -3897,22 +4147,7 @@ void server_routes::init_routes() {
             }},
             {"object", "list"},
             {"data", {
-                {
-                    {"id",       meta->model_name},
-                    {"aliases",  meta->model_aliases},
-                    {"tags",     meta->model_tags},
-                    {"object",   "model"},
-                    {"created",  std::time(0)},
-                    {"owned_by", "llamacpp"},
-                    {"meta",     {
-                        {"vocab_type",  meta->model_vocab_type},
-                        {"n_vocab",     meta->model_vocab_n_tokens},
-                        {"n_ctx_train", meta->model_n_ctx_train},
-                        {"n_embd",      meta->model_n_embd_inp},
-                        {"n_params",    meta->model_n_params},
-                        {"size",        meta->model_size},
-                    }},
-                },
+                get_model_info(),
             }}
         };
 
@@ -4025,7 +4260,7 @@ void server_routes::init_routes() {
             std::vector<server_task> tasks;
             tasks.reserve(documents.size());
             for (size_t i = 0; i < documents.size(); i++) {
-                auto tmp = format_prompt_rerank(ctx_server.model, ctx_server.vocab, ctx_server.mctx, query, documents[i]);
+                auto tmp = format_prompt_rerank(ctx_server.model_tgt, ctx_server.vocab, ctx_server.mctx, query, documents[i]);
                 server_task task = server_task(SERVER_TASK_TYPE_RERANK);
                 task.id     = rd.get_new_id();
                 task.tokens = std::move(tmp);
@@ -4126,6 +4361,26 @@ void server_routes::init_routes() {
     };
 }
 
+json server_routes::get_model_info() const {
+    return json {
+        {"id",       meta->model_name},
+        {"aliases",  meta->model_aliases},
+        {"tags",     meta->model_tags},
+        {"object",   "model"},
+        {"created",  std::time(0)},
+        {"owned_by", "llamacpp"},
+        {"meta",     {
+            {"vocab_type",  meta->model_vocab_type},
+            {"n_vocab",     meta->model_vocab_n_tokens},
+            {"n_ctx",       meta->slot_n_ctx},
+            {"n_ctx_train", meta->model_n_ctx_train},
+            {"n_embd",      meta->model_n_embd_inp},
+            {"n_params",    meta->model_n_params},
+            {"size",        meta->model_size},
+        }},
+    };
+}
+
 std::unique_ptr<server_res_generator> server_routes::handle_slots_save(const server_http_req & req, int id_slot) {
     auto res = create_response();
     const json request_data = json::parse(req.body);
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index 37f10dc7792..65853438c93 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -21,7 +21,8 @@ struct server_context_meta {
     bool has_mtmd;
     bool has_inp_image;
     bool has_inp_audio;
-    json json_webui_settings;
+    json json_ui_settings;            // Primary: new name
+    json json_webui_settings;            // Deprecated: use json_ui_settings instead (kept for backward compat)
     int slot_n_ctx;
     enum llama_pooling_type pooling_type;
 
@@ -122,6 +123,10 @@ struct server_routes {
     server_http_context::handler_t post_rerank;
     server_http_context::handler_t get_lora_adapters;
     server_http_context::handler_t post_lora_adapters;
+
+    // to be used in router mode
+    json get_model_info() const;
+
 private:
     std::unique_ptr<server_res_generator> handle_completions_impl(
             const server_http_req & req,
diff --git a/tools/server/server-cors-proxy.h b/tools/server/server-cors-proxy.h
index 887013152bf..2af0c7e1c21 100644
--- a/tools/server/server-cors-proxy.h
+++ b/tools/server/server-cors-proxy.h
@@ -49,6 +49,7 @@ static server_http_res_ptr proxy_request(const server_http_req & req, std::strin
             parsed_url.path,
             headers,
             req.body,
+            req.files,
             req.should_stop,
             600, // timeout_read (default to 10 minutes)
             600  // timeout_write (default to 10 minutes)
diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp
index ae39fbff9bd..9d008fc94c2 100644
--- a/tools/server/server-http.cpp
+++ b/tools/server/server-http.cpp
@@ -1,21 +1,16 @@
 #include "common.h"
 #include "server-http.h"
 #include "server-common.h"
+#include "ui.h"
 
 #include <cpp-httplib/httplib.h>
 
+#include <cstdlib>
 #include <functional>
+#include <future>
 #include <string>
 #include <thread>
 
-#ifdef LLAMA_BUILD_WEBUI
-// auto generated files (see README.md for details)
-#include "index.html.hpp"
-#include "bundle.js.hpp"
-#include "bundle.css.hpp"
-#include "loading.html.hpp"
-#endif
-
 //
 // HTTP implementation using cpp-httplib
 //
@@ -45,32 +40,72 @@ static void log_server_request(const httplib::Request & req, const httplib::Resp
 
     // reminder: this function is not covered by httplib's exception handler; if someone does more complicated stuff, think about wrapping it in try-catch
 
-    SRV_INF("done request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
+    SRV_TRC("done request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
 
     SRV_DBG("request:  %s\n", req.body.c_str());
     SRV_DBG("response: %s\n", res.body.c_str());
 }
 
+// For Google Cloud Platform deployment compatibility
+struct gcp_params {
+    bool enabled;
+    std::string path_health;
+    std::string path_predict;
+    int port;
+
+    // Ref: https://docs.cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables
+    gcp_params() {
+        enabled = getenv("AIP_MODE", "") == "PREDICTION";
+        path_health = getenv("AIP_HEALTH_ROUTE", "", true); // default: using the route defined in server.cpp
+        path_predict = getenv("AIP_PREDICT_ROUTE", "/predict", true);
+        port = std::stoi(getenv("AIP_HTTP_PORT", "8080"));
+    }
+
+    static std::string getenv(const char * name, const std::string & default_value, bool ensure_leading_slash = false) {
+        const char * value = std::getenv(name);
+        if (value == nullptr || value[0] == '\0') {
+            return default_value;
+        }
+        std::string val = value;
+        if (ensure_leading_slash && !val.empty() && val[0] != '/') {
+            val.insert(val.begin(), '/');
+        }
+        return val;
+    }
+};
+
 bool server_http_context::init(const common_params & params) {
+    const gcp_params gcp;
+
     path_prefix = params.api_prefix;
     port = params.port;
     hostname = params.hostname;
 
+    if (gcp.enabled) {
+        SRV_INF("Google Cloud Platform compat: health route = %s, predict route = %s, port = %d\n", gcp.path_health.c_str(), gcp.path_predict.c_str(), gcp.port);
+
+        if (port != gcp.port) {
+            SRV_WRN("Google Cloud Platform compat: overriding server port %d with AIP_HTTP_PORT %d\n", port, gcp.port);
+        }
+
+        port = gcp.port;
+    }
+
     auto & srv = pimpl->srv;
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
     if (params.ssl_file_key != "" && params.ssl_file_cert != "") {
-        LOG_INF("Running with SSL: key = %s, cert = %s\n", params.ssl_file_key.c_str(), params.ssl_file_cert.c_str());
+        SRV_INF("running with SSL: key = %s, cert = %s\n", params.ssl_file_key.c_str(), params.ssl_file_cert.c_str());
         srv.reset(
             new httplib::SSLServer(params.ssl_file_cert.c_str(), params.ssl_file_key.c_str())
         );
     } else {
-        LOG_INF("Running without SSL\n");
+        SRV_INF("%s", "running without SSL\n");
         srv.reset(new httplib::Server());
     }
 #else
     if (params.ssl_file_key != "" && params.ssl_file_cert != "") {
-        LOG_ERR("Server is built without SSL support\n");
+        SRV_ERR("%s", "the server is built without SSL support\n");
         return false;
     }
     srv.reset(new httplib::Server());
@@ -92,7 +127,7 @@ bool server_http_context::init(const common_params & params) {
 
         res.status = 500;
         res.set_content(message, "text/plain");
-        LOG_ERR("got exception: %s\n", message.c_str());
+        SRV_ERR("got exception: %s\n", message.c_str());
     });
 
     srv->set_error_handler([](const httplib::Request &, httplib::Response & res) {
@@ -120,7 +155,7 @@ bool server_http_context::init(const common_params & params) {
 #ifdef SO_REUSEPORT
             httplib::set_socket_opt(sock, SOL_SOCKET, SO_REUSEPORT, 1);
 #else
-            LOG_WRN("%s: SO_REUSEPORT is not supported\n", __func__);
+            SRV_WRN("%s", "SO_REUSEPORT is not supported\n");
 #endif
         }
     });
@@ -128,9 +163,9 @@ bool server_http_context::init(const common_params & params) {
     if (params.api_keys.size() == 1) {
         auto key = params.api_keys[0];
         std::string substr = key.substr(std::max((int)(key.length() - 4), 0));
-        LOG_INF("%s: api_keys: ****%s\n", __func__, substr.c_str());
+        SRV_INF("api_keys: ****%s\n", substr.c_str());
     } else if (params.api_keys.size() > 1) {
-        LOG_INF("%s: api_keys: %zu keys loaded\n", __func__, params.api_keys.size());
+        SRV_INF("api_keys: %zu keys loaded\n", params.api_keys.size());
     }
 
     //
@@ -190,36 +225,36 @@ bool server_http_context::init(const common_params & params) {
             "application/json; charset=utf-8"
         );
 
-        LOG_WRN("Unauthorized: Invalid API Key\n");
+        SRV_WRN("%s", "unauthorized: Invalid API Key\n");
 
         return false;
     };
 
     auto middleware_server_state = [this](const httplib::Request & req, httplib::Response & res) {
+        (void)req; // suppress unused parameter warning when LLAMA_BUILD_UI is not defined
         bool ready = is_ready.load();
         if (!ready) {
-#ifdef LLAMA_BUILD_WEBUI
+#if defined(LLAMA_BUILD_UI)
             auto tmp = string_split<std::string>(req.path, '.');
-            if (req.path == "/" || tmp.back() == "html") {
+            if (req.path == "/" || (tmp.size() > 0 && tmp.back() == "html")) {
                 res.status = 503;
                 res.set_content(reinterpret_cast<const char*>(loading_html), loading_html_len, "text/html; charset=utf-8");
-            } else
-#endif
-            {
-                // no endpoints is allowed to be accessed when the server is not ready
-                // this is to prevent any data races or inconsistent states
-                res.status = 503;
-                res.set_content(
-                    safe_json_to_str(json {
-                        {"error", {
-                            {"message", "Loading model"},
-                            {"type", "unavailable_error"},
-                            {"code", 503}
-                        }}
-                    }),
-                    "application/json; charset=utf-8"
-                );
+                return false;
             }
+#endif
+            // no endpoints are allowed to be accessed when the server is not ready
+            // this is to prevent any data races or inconsistent states
+            res.status = 503;
+            res.set_content(
+                safe_json_to_str(json {
+                    {"error", {
+                        {"message", "Loading model"},
+                        {"type", "unavailable_error"},
+                        {"code", 503}
+                    }}
+                }),
+                "application/json; charset=utf-8"
+            );
             return false;
         }
         return true;
@@ -250,7 +285,7 @@ bool server_http_context::init(const common_params & params) {
         // +4 threads for monitoring, health and some threads reserved for MCP and other tasks in the future
         n_threads_http = std::max(params.n_parallel + 4, (int32_t) std::thread::hardware_concurrency() - 1);
     }
-    LOG_INF("%s: using %d threads for HTTP server\n", __func__, n_threads_http);
+    SRV_INF("using %d threads for HTTP server\n", n_threads_http);
     srv->new_task_queue = [n_threads_http] {
         // spawn n_threads_http fixed thread (always alive), while allow up to 1024 max possible additional threads
         // when n_threads_http is used, server will create new "dynamic" threads that will be destroyed after processing each request
@@ -263,19 +298,21 @@ bool server_http_context::init(const common_params & params) {
     // Web UI setup
     //
 
-    if (!params.webui) {
-        LOG_INF("Web UI is disabled\n");
+    // Use new `params.ui` field (backed by old `params.webui` for compat)
+    if (!params.ui) {
+        SRV_INF("%s", "The UI is disabled\n");
+        SRV_INF("%s", "Use --ui/--no-ui (or deprecated --webui/--no-webui) to enable/disable\n");
     } else {
         // register static assets routes
         if (!params.public_path.empty()) {
             // Set the base directory for serving static files
             bool is_found = srv->set_mount_point(params.api_prefix + "/", params.public_path);
             if (!is_found) {
-                LOG_ERR("%s: static assets path not found: %s\n", __func__, params.public_path.c_str());
+                SRV_ERR("static assets path not found: %s\n", params.public_path.c_str());
                 return 1;
             }
         } else {
-#ifdef LLAMA_BUILD_WEBUI
+#if defined(LLAMA_BUILD_UI)
             // using embedded static index.html
             srv->Get(params.api_prefix + "/", [](const httplib::Request & /*req*/, httplib::Response & res) {
                 // COEP and COOP headers, required by pyodide (python interpreter)
@@ -306,13 +343,13 @@ bool server_http_context::start() {
     bool is_sock = false;
     if (string_ends_with(std::string(hostname), ".sock")) {
         is_sock = true;
-        LOG_INF("%s: setting address family to AF_UNIX\n", __func__);
+        SRV_INF("%s", "setting address family to AF_UNIX\n");
         srv->set_address_family(AF_UNIX);
         // bind_to_port requires a second arg, any value other than 0 should
         // simply get ignored
         was_bound = srv->bind_to_port(hostname, 8080);
     } else {
-        LOG_INF("%s: binding port with default address family\n", __func__);
+        SRV_INF("%s", "binding port with default address family\n");
         // bind HTTP listen port
         if (port == 0) {
             int bound_port = srv->bind_to_any_port(hostname);
@@ -326,7 +363,7 @@ bool server_http_context::start() {
     }
 
     if (!was_bound) {
-        LOG_ERR("%s: couldn't bind HTTP server socket, hostname: %s, port: %d\n", __func__, hostname.c_str(), port);
+        SRV_ERR("couldn't bind HTTP server socket, hostname: %s, port: %d\n", hostname.c_str(), port);
         return false;
     }
 
@@ -420,6 +457,7 @@ static void process_handler_response(server_http_req_ptr && request, server_http
 }
 
 void server_http_context::get(const std::string & path, const server_http_context::handler_t & handler) const {
+    handlers.emplace(path, handler);
     pimpl->srv->Get(path_prefix + path, [handler](const httplib::Request & req, httplib::Response & res) {
         server_http_req_ptr request = std::make_unique<server_http_req>(server_http_req{
             get_params(req),
@@ -436,9 +474,10 @@ void server_http_context::get(const std::string & path, const server_http_contex
 }
 
 void server_http_context::post(const std::string & path, const server_http_context::handler_t & handler) const {
+    handlers.emplace(path, handler);
     pimpl->srv->Post(path_prefix + path, [handler](const httplib::Request & req, httplib::Response & res) {
         std::string body = req.body;
-        std::map<std::string, raw_buffer> files;
+        std::map<std::string, uploaded_file> files;
 
         if (req.is_multipart_form_data()) {
             // translate text fields to a JSON object and use it as the body
@@ -459,7 +498,11 @@ void server_http_context::post(const std::string & path, const server_http_conte
 
             // populate files from multipart form
             for (const auto & [key, file] : req.form.files) {
-                files[key] = raw_buffer(file.content.begin(), file.content.end());
+                files[key] = uploaded_file{
+                    raw_buffer(file.content.begin(), file.content.end()),
+                    file.filename,
+                    file.content_type,
+                };
             }
         }
 
@@ -477,3 +520,176 @@ void server_http_context::post(const std::string & path, const server_http_conte
     });
 }
 
+//
+// Vertex AI Prediction protocol (AIP_PREDICT_ROUTE)
+// https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements
+//
+
+// Derives the camelCase @requestFormat alias for a registered path.
+// e.g. "/v1/chat/completions" -> "chatCompletions", "/apply-template" -> "applyTemplate"
+static std::string path_to_gcp_format(const std::string & path) {
+    std::string s = path;
+    if (s.size() > 3 && s[0] == '/' && s[1] == 'v' && s[2] == '1') {
+        s = s.substr(3);
+    }
+    if (!s.empty() && s[0] == '/') {
+        s = s.substr(1);
+    }
+    std::string result;
+    bool cap = false;
+    for (unsigned char c : s) {
+        if (c == ':') break; // stop before path parameters
+        if (c == '/' || c == '-' || c == '_') {
+            cap = true;
+        } else {
+            result += cap ? (char)std::toupper(c) : (char)c;
+            cap = false;
+        }
+    }
+    return result;
+}
+
+static json parse_gcp_predict_response(const server_http_res_ptr & res) {
+    if (res == nullptr) {
+        throw std::runtime_error("empty response from internal handler");
+    }
+    if (res->is_stream()) {
+        throw std::invalid_argument("predict route does not support streaming responses");
+    }
+    if (res->data.empty()) {
+        return nullptr;
+    }
+    try {
+        return json::parse(res->data);
+    } catch (...) {
+        return res->data;
+    }
+}
+
+void server_http_context::register_gcp_compat() {
+    const gcp_params gcp;
+
+    if (!gcp.enabled) {
+        // do nothing
+        return;
+    }
+
+    if (handlers.count(gcp.path_predict)) {
+        SRV_ERR("AIP_PREDICT_ROUTE=%s conflicts with an existing llama-server route\n", gcp.path_predict.c_str());
+        exit(1);
+    }
+
+    // camelCase alias -> canonical path (first registration wins on collision)
+    // e.g. "chatCompletions" -> "/v1/chat/completions"
+    std::unordered_map<std::string, std::string> alias_to_path;
+    for (const auto & [path, _] : handlers) {
+        alias_to_path.emplace(path_to_gcp_format(path), path);
+    }
+
+    if (!gcp.path_health.empty()) {
+        auto health_handler = handlers.find("/health");
+        GGML_ASSERT(health_handler != handlers.end());
+        get(gcp.path_health, health_handler->second);
+    }
+
+    post(gcp.path_predict, [this, alias_to_path = std::move(alias_to_path)](const server_http_req & req) -> server_http_res_ptr {
+        static const auto build_error = [](const std::string & message, error_type type) -> json {
+            return json {{"error", format_error_response(message, type)}};
+        };
+
+        json data;
+        try {
+            data = json::parse(req.body);
+        } catch (const std::exception & e) {
+            auto res = std::make_unique<server_http_res>();
+            res->status = 400;
+            res->data = safe_json_to_str({{"error", format_error_response(e.what(), ERROR_TYPE_INVALID_REQUEST)}});
+            return res;
+        }
+        if (!data.is_object()) {
+            auto res = std::make_unique<server_http_res>();
+            res->status = 400;
+            res->data = safe_json_to_str({{"error", format_error_response("request body must be a JSON object", ERROR_TYPE_INVALID_REQUEST)}});
+            return res;
+        }
+        if (!data.contains("instances") || !data.at("instances").is_array()) {
+            auto res = std::make_unique<server_http_res>();
+            res->status = 400;
+            res->data = safe_json_to_str({{"error", format_error_response("request body must include an array field named instances", ERROR_TYPE_INVALID_REQUEST)}});
+            return res;
+        }
+
+        const json & instances = data.at("instances");
+        static const size_t MAX_INSTANCES = 128;
+        if (instances.size() > MAX_INSTANCES) {
+            auto res = std::make_unique<server_http_res>();
+            res->status = 400;
+            res->data = safe_json_to_str({{"error", format_error_response("instances array exceeds maximum size of " + std::to_string(MAX_INSTANCES), ERROR_TYPE_INVALID_REQUEST)}});
+            return res;
+        }
+
+        std::vector<std::future<json>> futures;
+        futures.reserve(instances.size());
+
+        for (const auto & instance : instances) {
+            futures.push_back(std::async(std::launch::async, [this, &req, &alias_to_path, instance]() -> json {
+                if (!instance.is_object()) {
+                    return build_error("each instance must be a JSON object", ERROR_TYPE_INVALID_REQUEST);
+                }
+                if (!instance.contains("@requestFormat") || !instance.at("@requestFormat").is_string()) {
+                    return build_error("each instance must include a string @requestFormat", ERROR_TYPE_INVALID_REQUEST);
+                }
+
+                try {
+                    json payload = instance;
+                    const std::string format = payload.at("@requestFormat").get<std::string>();
+                    payload.erase("@requestFormat");
+
+                    if (payload.contains("stream")) {
+                        SRV_WRN("%s", "ignoring client-provided stream field in instance, streaming is not supported in predict route\n");
+                        payload["stream"] = false;
+                    }
+
+                    // accept both camelCase aliases (e.g. "chatCompletions") and direct paths
+                    std::string dispatch_path;
+                    auto it_alias = alias_to_path.find(format);
+                    if (it_alias != alias_to_path.end()) {
+                        dispatch_path = it_alias->second;
+                    } else if (handlers.count(format)) {
+                        dispatch_path = format;
+                    } else {
+                        return build_error("no handler registered for @requestFormat: " + format, ERROR_TYPE_INVALID_REQUEST);
+                    }
+
+                    const server_http_req internal_req {
+                        req.params,
+                        req.headers,
+                        path_prefix + dispatch_path,
+                        req.query_string,
+                        payload.dump(),
+                        {},
+                        req.should_stop,
+                    };
+
+                    server_http_res_ptr internal_res = handlers.at(dispatch_path)(internal_req);
+                    return parse_gcp_predict_response(internal_res);
+                } catch (const std::invalid_argument & e) {
+                    return build_error(e.what(), ERROR_TYPE_INVALID_REQUEST);
+                } catch (const std::exception & e) {
+                    return build_error(e.what(), ERROR_TYPE_SERVER);
+                } catch (...) {
+                    return build_error("unknown error", ERROR_TYPE_SERVER);
+                }
+            }));
+        }
+
+        json predictions = json::array();
+        for (auto & future : futures) {
+            predictions.push_back(future.get());
+        }
+
+        auto res = std::make_unique<server_http_res>();
+        res->data = safe_json_to_str({{"predictions", predictions}});
+        return res;
+    });
+}
diff --git a/tools/server/server-http.h b/tools/server/server-http.h
index 68ae2170cf6..66ee555f501 100644
--- a/tools/server/server-http.h
+++ b/tools/server/server-http.h
@@ -36,13 +36,19 @@ struct server_http_res {
 using server_http_res_ptr = std::unique_ptr<server_http_res>;
 using raw_buffer = std::vector<uint8_t>;
 
+struct uploaded_file {
+    raw_buffer data;
+    std::string filename;
+    std::string content_type;
+};
+
 struct server_http_req {
     std::map<std::string, std::string> params; // path_params + query_params
     std::map<std::string, std::string> headers; // used by MCP proxy
     std::string path;
     std::string query_string; // query parameters string (e.g. "action=save")
     std::string body;
-    std::map<std::string, raw_buffer> files; // used for file uploads (form data)
+    std::map<std::string, uploaded_file> files; // used for file uploads (form data)
     const std::function<bool()> & should_stop;
 
     std::string get_param(const std::string & key, const std::string & def = "") const {
@@ -61,6 +67,10 @@ struct server_http_context {
     std::thread thread; // server thread
     std::atomic<bool> is_ready = false;
 
+    // note: the handler should never throw exceptions
+    using handler_t = std::function<server_http_res_ptr(const server_http_req & req)>;
+    mutable std::unordered_map<std::string, handler_t> handlers;
+
     std::string path_prefix;
     std::string hostname;
     int port;
@@ -72,12 +82,13 @@ struct server_http_context {
     bool start();
     void stop() const;
 
-    // note: the handler should never throw exceptions
-    using handler_t = std::function<server_http_res_ptr(const server_http_req & req)>;
-
     void get(const std::string & path, const handler_t & handler) const;
     void post(const std::string & path, const handler_t & handler) const;
 
+    // Register the Google Cloud Platform (Vertex AI) compat (AIP_PREDICT_ROUTE env var, or /predict)
+    // Must be called AFTER all other API routes are registered
+    void register_gcp_compat();
+
     // for debugging
     std::string listening_address;
 };
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 6066611f51c..6c6fed52d58 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -18,6 +18,8 @@
 #include <chrono>
 #include <queue>
 #include <filesystem>
+#include <random>
+#include <sstream>
 #include <cstring>
 
 #ifdef _WIN32
@@ -42,6 +44,7 @@ extern char **environ;
 #define CMD_ROUTER_TO_CHILD_EXIT  "cmd_router_to_child:exit"
 #define CMD_CHILD_TO_ROUTER_READY "cmd_child_to_router:ready" // also sent when waking up from sleep
 #define CMD_CHILD_TO_ROUTER_SLEEP "cmd_child_to_router:sleep"
+#define CMD_CHILD_TO_ROUTER_INFO  "cmd_child_to_router:info:" // followed by json string
 
 // address for child process, this is needed because router may run on 0.0.0.0
 // ref: https://github.com/ggml-org/llama.cpp/issues/17862
@@ -158,6 +161,30 @@ void server_model_meta::update_args(common_preset_context & ctx_preset, std::str
     args = preset.to_args(bin_path);
 }
 
+void server_model_meta::update_caps() {
+    try {
+        common_params params;
+        preset.apply_to_params(params, {
+            "LLAMA_ARG_MODEL",
+            "LLAMA_ARG_MODEL_URL",
+            "LLAMA_ARG_MMPROJ",
+            "LLAMA_ARG_MMPROJ_URL",
+            "LLAMA_ARG_HF_REPO",
+            "LLAMA_ARG_HF_REPO_FILE",
+        });
+        params.offline = true; // avoid any unwanted network call during capability detection
+        common_params_handle_models(params, LLAMA_EXAMPLE_SERVER);
+        if (params.mmproj.path.empty()) {
+            multimodal = { false, false };
+        } else {
+            multimodal = mtmd_get_cap_from_file(params.mmproj.path.c_str());
+        }
+    } catch (const std::exception & e) {
+        LOG_WRN("failed to initialize common_params for multimodal capability detection: %s\n", e.what());
+        multimodal = { false, false };
+    }
+}
+
 //
 // server_models
 //
@@ -233,6 +260,7 @@ void server_models::add_model(server_model_meta && meta) {
     }
 
     meta.update_args(ctx_preset, bin_path); // render args
+    meta.update_caps();
     std::string name = meta.name;
     mapping[name] = instance_t{
         /* subproc */ std::make_shared<subprocess_s>(),
@@ -241,9 +269,8 @@ void server_models::add_model(server_model_meta && meta) {
     };
 }
 
-// TODO: allow refreshing cached model list
 void server_models::load_models() {
-    // loading models from 3 sources:
+    // Phase 1: load presets from all sources — pure I/O, no lock needed
     // 1. cached models
     common_presets cached_models = ctx_preset.load_from_cache();
     SRV_INF("Loaded %zu cached model presets\n", cached_models.size());
@@ -268,112 +295,271 @@ void server_models::load_models() {
 
     // note: if a model exists in both cached and local, local takes precedence
     common_presets final_presets;
-    for (const auto & [name, preset] : cached_models) {
-        final_presets[name] = preset;
-    }
-    for (const auto & [name, preset] : local_models) {
-        final_presets[name] = preset;
-    }
-
-    // process custom presets from INI
+    for (const auto & [name, preset] : cached_models) final_presets[name] = preset;
+    for (const auto & [name, preset] : local_models)  final_presets[name] = preset;
     for (const auto & [name, custom] : custom_presets) {
         if (final_presets.find(name) != final_presets.end()) {
-            // apply custom config if exists
-            common_preset & target = final_presets[name];
-            target.merge(custom);
+            final_presets[name].merge(custom);
         } else {
-            // otherwise add directly
             final_presets[name] = custom;
         }
     }
-
-    // server base preset from CLI args take highest precedence
+    // server base preset from CLI args takes highest precedence
     for (auto & [name, preset] : final_presets) {
         preset.merge(base_preset);
     }
 
-    // convert presets to server_model_meta and add to mapping
-    for (const auto & preset : final_presets) {
-        server_model_meta meta{
-            /* preset       */ preset.second,
-            /* name         */ preset.first,
-            /* aliases      */ {},
-            /* tags         */ {},
-            /* port         */ 0,
-            /* status       */ SERVER_MODEL_STATUS_UNLOADED,
-            /* last_used    */ 0,
-            /* args         */ std::vector<std::string>(),
-            /* exit_code    */ 0,
-            /* stop_timeout */ DEFAULT_STOP_TIMEOUT,
-        };
-        add_model(std::move(meta));
-    }
-
-    // log available models
-    {
-        std::unordered_set<std::string> custom_names;
-        for (const auto & [name, preset] : custom_presets) {
-            custom_names.insert(name);
-        }
-        auto join_set = [](const std::set<std::string> & s) {
-            std::string result;
-            for (const auto & v : s) {
-                if (!result.empty()) {
-                    result += ", ";
-                }
-                result += v;
-            }
-            return result;
-        };
-
+    // Helpers that read `mapping` — must be called while holding the lock.
+    std::unordered_set<std::string> custom_names;
+    for (const auto & [name, preset] : custom_presets) custom_names.insert(name);
+    auto join_set = [](const std::set<std::string> & s) {
+        std::string result;
+        for (const auto & v : s) {
+            if (!result.empty()) result += ", ";
+            result += v;
+        }
+        return result;
+    };
+    auto log_available_models = [&]() {
         SRV_INF("Available models (%zu) (*: custom preset)\n", mapping.size());
         for (const auto & [name, inst] : mapping) {
             bool has_custom = custom_names.find(name) != custom_names.end();
             std::string info;
-            if (!inst.meta.aliases.empty()) {
-                info += " (aliases: " + join_set(inst.meta.aliases) + ")";
+            if (!inst.meta.aliases.empty()) info += " (aliases: " + join_set(inst.meta.aliases) + ")";
+            if (!inst.meta.tags.empty())    info += " [tags: "    + join_set(inst.meta.tags)    + "]";
+            SRV_INF("  %c %s%s\n", has_custom ? '*' : ' ', name.c_str(), info.c_str());
+        }
+    };
+    auto apply_stop_timeout = [&]() {
+        for (auto & [name, inst] : mapping) {
+            std::string val;
+            if (inst.meta.preset.get_option(COMMON_ARG_PRESET_STOP_TIMEOUT, val)) {
+                try {
+                    inst.meta.stop_timeout = std::stoi(val);
+                } catch (...) {
+                    SRV_WRN("invalid stop-timeout value '%s' for model '%s', using default %d seconds\n",
+                        val.c_str(), name.c_str(), DEFAULT_STOP_TIMEOUT);
+                    inst.meta.stop_timeout = DEFAULT_STOP_TIMEOUT;
+                }
             }
-            if (!inst.meta.tags.empty()) {
-                info += " [tags: " + join_set(inst.meta.tags) + "]";
+        }
+    };
+    // update_args() injects HOST/PORT/ALIAS, so strip them before comparing presets
+    auto preset_options_for_compare = [](common_preset p) {
+        p.unset_option("LLAMA_ARG_HOST");
+        p.unset_option("LLAMA_ARG_PORT");
+        p.unset_option("LLAMA_ARG_ALIAS");
+        return p.options;
+    };
+
+    // Phase 2: acquire the lock once for all mapping mutations.
+    // We temporarily release it only when calling functions that acquire it internally
+    // (unload, load) or when joining threads (the monitoring thread calls update_status
+    // which locks the mutex, so joining while holding it would deadlock).
+    std::unique_lock<std::mutex> lk(mutex);
+    bool is_first_load = mapping.empty();
+
+    if (is_first_load) {
+        // FIRST LOAD: add all models, then unlock for autoloading
+        for (const auto & [name, preset] : final_presets) {
+            server_model_meta meta{
+                /* preset       */ preset,
+                /* name         */ name,
+                /* aliases      */ {},
+                /* tags         */ {},
+                /* port         */ 0,
+                /* status       */ SERVER_MODEL_STATUS_UNLOADED,
+                /* last_used    */ 0,
+                /* args         */ std::vector<std::string>(),
+                /* loaded_info  */ {},
+                /* exit_code    */ 0,
+                /* stop_timeout */ DEFAULT_STOP_TIMEOUT,
+                /* multimodal   */ mtmd_caps{false, false},
+            };
+            add_model(std::move(meta));
+        }
+        apply_stop_timeout();
+        log_available_models();
+
+        std::vector<std::string> models_to_load;
+        for (const auto & [name, inst] : mapping) {
+            std::string val;
+            if (inst.meta.preset.get_option(COMMON_ARG_PRESET_LOAD_ON_STARTUP, val) && common_arg_utils::is_truthy(val)) {
+                models_to_load.push_back(name);
             }
-            SRV_INF("  %c %s%s\n", has_custom ? '*' : ' ', name.c_str(), info.c_str());
         }
-    }
+        if ((int)models_to_load.size() > base_params.models_max) {
+            throw std::runtime_error(string_format(
+                "number of models to load on startup (%zu) exceeds models_max (%d)",
+                models_to_load.size(), base_params.models_max));
+        }
 
-    // handle custom stop-timeout option
-    for (auto & [name, inst] : mapping) {
-        std::string val;
-        if (inst.meta.preset.get_option(COMMON_ARG_PRESET_STOP_TIMEOUT, val)) {
-            try {
-                inst.meta.stop_timeout = std::stoi(val);
-            } catch (...) {
-                SRV_WRN("invalid stop-timeout value '%s' for model '%s', using default %d seconds\n",
-                    val.c_str(), name.c_str(), DEFAULT_STOP_TIMEOUT);
-                inst.meta.stop_timeout = DEFAULT_STOP_TIMEOUT;
+        lk.unlock();
+        for (const auto & name : models_to_load) {
+            SRV_INF("(startup) loading model %s\n", name.c_str());
+            load(name);
+        }
+    } else {
+        // RELOAD: diff the new preset list against the current mapping and reconcile
+        is_reloading = true;
+
+        // find running models whose source was removed or whose preset changed
+        std::vector<std::string> to_unload;
+        for (const auto & [name, inst] : mapping) {
+            if (!inst.meta.is_running()) continue;
+            auto it = final_presets.find(name);
+            if (it == final_presets.end()) {
+                to_unload.push_back(name); // removed from source
+            } else if (preset_options_for_compare(inst.meta.preset) != preset_options_for_compare(it->second)) {
+                to_unload.push_back(name); // preset changed
             }
         }
-    }
 
-    // load any autoload models
-    std::vector<std::string> models_to_load;
-    for (const auto & [name, inst] : mapping) {
-        std::string val;
-        if (inst.meta.preset.get_option(COMMON_ARG_PRESET_LOAD_ON_STARTUP, val)) {
-            if (common_arg_utils::is_truthy(val)) {
-                models_to_load.push_back(name);
+        // unload() acquires the lock internally, so release before each call
+        for (const auto & name : to_unload) {
+            SRV_INF("(reload) unloading model name=%s (source updated or removed)\n", name.c_str());
+            lk.unlock();
+            unload(name);
+            lk.lock();
+        }
+
+        // wait for all targeted models to reach UNLOADED; cv.wait handles unlock/relock
+        cv.wait(lk, [&]() {
+            for (const auto & name : to_unload) {
+                auto it = mapping.find(name);
+                if (it != mapping.end() && it->second.meta.is_running()) return false;
+            }
+            return true;
+        });
+
+        // collect all threads to join in one pass while the lock is held:
+        // - monitoring threads from just-unloaded models (to_unload)
+        // - threads of already-UNLOADED models that are being removed from source
+        std::vector<std::thread> threads_to_join;
+        for (const auto & name : to_unload) {
+            auto it = mapping.find(name);
+            if (it != mapping.end() && it->second.th.joinable()) {
+                threads_to_join.push_back(std::move(it->second.th));
             }
         }
-    }
-    if ((int)models_to_load.size() > base_params.models_max) {
-        throw std::runtime_error(string_format(
-            "number of models to load on startup (%zu) exceeds models_max (%d)",
-            models_to_load.size(),
-            base_params.models_max
-        ));
-    }
-    for (const auto & name : models_to_load) {
-        SRV_INF("(startup) loading model %s\n", name.c_str());
-        load(name);
+        for (auto & [name, inst] : mapping) {
+            if (final_presets.find(name) == final_presets.end() && !inst.meta.is_running() && inst.th.joinable()) {
+                threads_to_join.push_back(std::move(inst.th));
+            }
+        }
+
+        // join outside the lock — monitoring thread calls update_status (needs lock)
+        lk.unlock();
+        for (auto & th : threads_to_join) th.join();
+        lk.lock();
+
+        // erase models no longer in any source
+        for (auto it = mapping.begin(); it != mapping.end(); ) {
+            if (final_presets.find(it->first) == final_presets.end()) {
+                SRV_INF("(reload) removing model name=%s (no longer in source)\n", it->first.c_str());
+                GGML_ASSERT(!it->second.th.joinable()); // must have been joined above
+                it = mapping.erase(it);
+            } else {
+                ++it;
+            }
+        }
+
+        // update presets for non-running models still in source
+        for (auto & [name, inst] : mapping) {
+            if (inst.meta.is_running()) continue;
+            auto it = final_presets.find(name);
+            if (it == final_presets.end()) continue; // erased above
+
+            inst.meta.preset = it->second;
+
+            // re-parse aliases, then validate against other models
+            std::set<std::string> new_aliases;
+            std::string alias_str;
+            if (inst.meta.preset.get_option("LLAMA_ARG_ALIAS", alias_str) && !alias_str.empty()) {
+                for (auto & alias : string_split<std::string>(alias_str, ',')) {
+                    alias = string_strip(alias);
+                    if (!alias.empty()) new_aliases.insert(alias);
+                }
+            }
+            inst.meta.aliases.clear();
+            for (const auto & alias : new_aliases) {
+                bool conflict = false;
+                for (const auto & [other_name, other_inst] : mapping) {
+                    if (other_name == name) continue;
+                    if (other_name == alias || other_inst.meta.aliases.count(alias)) {
+                        SRV_WRN("(reload) alias '%s' for model '%s' conflicts with model '%s', skipping\n",
+                            alias.c_str(), name.c_str(), other_name.c_str());
+                        conflict = true;
+                        break;
+                    }
+                }
+                if (!conflict) inst.meta.aliases.insert(alias);
+            }
+
+            // re-parse tags
+            inst.meta.tags.clear();
+            std::string tags_str;
+            if (inst.meta.preset.get_option("LLAMA_ARG_TAGS", tags_str) && !tags_str.empty()) {
+                for (auto & tag : string_split<std::string>(tags_str, ',')) {
+                    tag = string_strip(tag);
+                    if (!tag.empty()) inst.meta.tags.insert(tag);
+                }
+            }
+
+            inst.meta.exit_code = 0; // clear failed state so the model can be reloaded
+            inst.meta.update_args(ctx_preset, bin_path);
+            inst.meta.update_caps();
+        }
+
+        // add models that are new in this reload
+        std::vector<std::string> newly_added;
+        for (const auto & [name, preset] : final_presets) {
+            if (mapping.find(name) == mapping.end()) {
+                server_model_meta meta{
+                    /* preset       */ preset,
+                    /* name         */ name,
+                    /* aliases      */ {},
+                    /* tags         */ {},
+                    /* port         */ 0,
+                    /* status       */ SERVER_MODEL_STATUS_UNLOADED,
+                    /* last_used    */ 0,
+                    /* args         */ std::vector<std::string>(),
+                    /* loaded_info  */ {},
+                    /* exit_code    */ 0,
+                    /* stop_timeout */ DEFAULT_STOP_TIMEOUT,
+                    /* multimodal   */ mtmd_caps{false, false},
+                };
+                add_model(std::move(meta));
+                newly_added.push_back(name);
+            }
+        }
+
+        apply_stop_timeout();
+
+        // clear reload flag before unlocking for autoload — load() blocks on !is_reloading,
+        // so clearing it here (while still locked) prevents a deadlock in the autoload calls below
+        is_reloading = false;
+        cv.notify_all();
+
+        log_available_models();
+
+        // collect autoload candidates while still under the lock
+        std::vector<std::string> to_autoload;
+        for (const auto & name : newly_added) {
+            auto it = mapping.find(name);
+            if (it != mapping.end()) {
+                std::string val;
+                if (it->second.meta.preset.get_option(COMMON_ARG_PRESET_LOAD_ON_STARTUP, val) && common_arg_utils::is_truthy(val)) {
+                    to_autoload.push_back(name);
+                }
+            }
+        }
+
+        lk.unlock();
+        for (const auto & name : to_autoload) {
+            SRV_INF("(reload) loading new model %s\n", name.c_str());
+            load(name);
+        }
     }
 }
 
@@ -534,7 +720,10 @@ void server_models::load(const std::string & name) {
     }
     unload_lru();
 
-    std::lock_guard<std::mutex> lk(mutex);
+    std::unique_lock<std::mutex> lk(mutex);
+    // edge case: block until any in-progress reload has finished so we always load
+    // against the freshest preset and a consistent mapping state
+    cv.wait(lk, [this]() { return !is_reloading; });
 
     auto meta = mapping[name].meta;
     if (meta.status != SERVER_MODEL_STATUS_UNLOADED) {
@@ -560,10 +749,11 @@ void server_models::load(const std::string & name) {
 
     // prepare new instance info
     instance_t inst;
-    inst.meta           = meta;
-    inst.meta.port      = get_free_port();
-    inst.meta.status    = SERVER_MODEL_STATUS_LOADING;
-    inst.meta.last_used = ggml_time_ms();
+    inst.meta             = meta;
+    inst.meta.port        = get_free_port();
+    inst.meta.status      = SERVER_MODEL_STATUS_LOADING;
+    inst.meta.loaded_info = json{};
+    inst.meta.last_used   = ggml_time_ms();
 
     if (inst.meta.port <= 0) {
         throw std::runtime_error("failed to get a port number");
@@ -608,13 +798,16 @@ void server_models::load(const std::string & name) {
         std::thread log_thread([&]() {
             // read stdout/stderr and forward to main server log
             // also handle status report from child process
+            std::vector<char> vec_buf(128 * 1024); // large buffer for storing info
+            char * buffer = vec_buf.data();
             if (stdout_file) {
-                char buffer[4096];
-                while (fgets(buffer, sizeof(buffer), stdout_file) != nullptr) {
+                while (fgets(buffer, vec_buf.size(), stdout_file) != nullptr) {
                     LOG("[%5d] %s", port, buffer);
                     std::string str(buffer);
                     if (string_starts_with(buffer, CMD_CHILD_TO_ROUTER_READY)) {
                         this->update_status(name, SERVER_MODEL_STATUS_LOADED, 0);
+                    } else if (string_starts_with(buffer, CMD_CHILD_TO_ROUTER_INFO)) {
+                        this->update_loaded_info(name, str);
                     } else if (string_starts_with(buffer, CMD_CHILD_TO_ROUTER_SLEEP)) {
                         this->update_status(name, SERVER_MODEL_STATUS_SLEEPING, 0);
                     }
@@ -712,6 +905,11 @@ void server_models::unload(const std::string & name) {
         if (it->second.meta.is_running()) {
             SRV_INF("stopping model instance name=%s\n", name.c_str());
             stopping_models.insert(name);
+            if (it->second.meta.status == SERVER_MODEL_STATUS_LOADING) {
+                // special case: if model is in loading state, unloading means force-killing it
+                SRV_WRN("model name=%s is still loading, force-killing\n", name.c_str());
+                subprocess_terminate(it->second.subproc.get());
+            }
             cv_stop.notify_all();
             // status change will be handled by the managing thread
         } else {
@@ -753,6 +951,29 @@ void server_models::update_status(const std::string & name, server_model_status
     cv.notify_all();
 }
 
+void server_models::update_loaded_info(const std::string & name, std::string & raw_info) {
+    if (!string_starts_with(raw_info, CMD_CHILD_TO_ROUTER_INFO)) {
+        SRV_WRN("invalid loaded info format from child for model name=%s: %s\n", name.c_str(), raw_info.c_str());
+        return;
+    }
+
+    json info;
+    try {
+        info = json::parse(raw_info.substr(strlen(CMD_CHILD_TO_ROUTER_INFO)));
+    } catch (const std::exception & e) {
+        SRV_WRN("failed to parse loaded info from child for model name=%s: %s\n", name.c_str(), e.what());
+        return;
+    }
+
+    std::unique_lock<std::mutex> lk(mutex);
+    auto it = mapping.find(name);
+    if (it != mapping.end()) {
+        auto & meta = it->second.meta;
+        meta.loaded_info = info;
+    }
+    cv.notify_all();
+}
+
 void server_models::wait_until_loading_finished(const std::string & name) {
     std::unique_lock<std::mutex> lk(mutex);
     cv.wait(lk, [this, &name]() {
@@ -818,6 +1039,7 @@ server_http_res_ptr server_models::proxy_request(const server_http_req & req, co
             proxy_path,
             req.headers,
             req.body,
+            req.files,
             req.should_stop,
             base_params.timeout_read,
             base_params.timeout_write
@@ -830,12 +1052,14 @@ bool server_models::is_child_server() {
     return router_port != nullptr;
 }
 
-std::thread server_models::setup_child_server(const std::function<void(int)> & shutdown_handler) {
+std::thread server_models::setup_child_server(const std::function<void(int)> & shutdown_handler, const json & model_info) {
     // send a notification to the router server that a model instance is ready
     common_log_pause(common_log_main());
     fflush(stdout);
     fprintf(stdout, "%s\n", CMD_CHILD_TO_ROUTER_READY);
     fflush(stdout);
+    fprintf(stdout, "%s%s\n", CMD_CHILD_TO_ROUTER_INFO, safe_json_to_str(model_info).c_str());
+    fflush(stdout);
     common_log_resume(common_log_main());
 
     // setup thread for monitoring stdin
@@ -929,14 +1153,17 @@ void server_models_routes::init_routes() {
                 {"role",          "router"},
                 {"max_instances", params.models_max},
                 {"models_autoload", params.models_autoload},
-                // this is a dummy response to make sure webui doesn't break
+                // this is a dummy response to make sure the UI doesn't break
                 {"model_alias", "llama-server"},
                 {"model_path",  "none"},
                 {"default_generation_settings", {
                     {"params", json{}},
                     {"n_ctx",  0},
                 }},
-                {"webui_settings", webui_settings},
+                // New key
+                {"ui_settings",     ui_settings},
+                // Deprecated: use ui_settings instead (kept for backward compat)
+                {"webui_settings",  webui_settings},
                 {"build_info",     std::string(llama_build_info())},
             });
             return res;
@@ -985,7 +1212,11 @@ void server_models_routes::init_routes() {
         return res;
     };
 
-    this->get_router_models = [this](const server_http_req &) {
+    this->get_router_models = [this](const server_http_req & req) {
+        bool reload = !req.get_param("reload", "").empty();
+        if (reload) {
+            models.load_models();
+        }
         auto res = std::make_unique<server_http_res>();
         json models_json = json::array();
         auto all_models = models.get_all_meta();
@@ -1008,16 +1239,41 @@ void server_models_routes::init_routes() {
                 status["exit_code"] = meta.exit_code;
                 status["failed"]    = true;
             }
-            models_json.push_back(json {
-                {"id",       meta.name},
-                {"aliases",  meta.aliases},
-                {"tags",     meta.tags},
-                {"object",   "model"},    // for OAI-compat
-                {"owned_by", "llamacpp"}, // for OAI-compat
-                {"created",  t},          // for OAI-compat
-                {"status",   status},
+
+            // pi coding agent multimodal compatibility
+            json input_modalities = json::array({"text"});
+            if (meta.multimodal.inp_vision) {
+                input_modalities.push_back("image");
+            }
+            if (meta.multimodal.inp_audio) {
+                input_modalities.push_back("audio");
+            }
+            json architecture {
+                {"input_modalities",  input_modalities},
+                {"output_modalities", json::array({"text"})},
+            };
+
+            json model_info = json {
+                {"id",           meta.name},
+                {"aliases",      meta.aliases},
+                {"tags",         meta.tags},
+                {"object",       "model"},    // for OAI-compat
+                {"owned_by",     "llamacpp"}, // for OAI-compat
+                {"created",      t},          // for OAI-compat
+                {"status",       status},
+                {"architecture", architecture},
                 // TODO: add other fields, may require reading GGUF metadata
-            });
+            };
+
+            // merge with loaded_info from the child process if available
+            if (meta.is_running()) {
+                for (auto it = meta.loaded_info.begin(); it != meta.loaded_info.end(); ++it) {
+                    if (!model_info.contains(it.key())) {
+                        model_info[it.key()] = it.value();
+                    }
+                }
+            }
+            models_json.push_back(model_info);
         }
         res_ok(res, {
             {"data", models_json},
@@ -1121,6 +1377,77 @@ static bool should_strip_proxy_header(const std::string & header_name) {
     return false;
 }
 
+static std::string generate_multipart_boundary() {
+    thread_local std::mt19937 gen(std::random_device{}());
+    static const char chars[] = "0123456789abcdefghijklmnopqrstuvwxyz";
+    std::uniform_int_distribution<> dis(0, sizeof(chars) - 2);
+    std::string boundary = "----llama-cpp-proxy-";
+    for (int i = 0; i < 16; i++) {
+        boundary += chars[dis(gen)];
+    }
+    return boundary;
+}
+
+static std::string build_multipart_body(
+        const json & form_fields,
+        const std::map<std::string, uploaded_file> & files,
+        const std::string & boundary) {
+    static auto sanitize_field = [](const std::string & text) {
+        std::string result;
+        result.reserve(text.size());
+        for (char c : text) {
+            if (c != '\n' && c != '\r' && c != '"') {
+                result += c;
+            }
+        }
+        return result;
+    };
+
+    std::ostringstream body;
+
+    for (const auto & [key, value] : form_fields.items()) {
+        if (value.is_array()) {
+            for (const auto & item : value) {
+                body << "--" << boundary << "\r\n";
+                body << "Content-Disposition: form-data; name=\"" << sanitize_field(key) << "\"\r\n";
+                body << "\r\n";
+                if (!item.is_string()) {
+                    throw std::invalid_argument("expected string");
+                }
+                body << item.get<std::string>() << "\r\n";
+            }
+        } else {
+            body << "--" << boundary << "\r\n";
+            body << "Content-Disposition: form-data; name=\"" << sanitize_field(key) << "\"\r\n";
+            body << "\r\n";
+            if (!value.is_string()) {
+                throw std::invalid_argument("expected string");
+            }
+            body << value.get<std::string>() << "\r\n";
+        }
+    }
+
+    for (const auto & [key, file] : files) {
+        body << "--" << boundary << "\r\n";
+        body << "Content-Disposition: form-data; name=\"" << sanitize_field(key) << "\"";
+        if (!file.filename.empty()) {
+            body << "; filename=\"" << sanitize_field(file.filename) << "\"";
+        }
+        body << "\r\n";
+        if (!file.content_type.empty()) {
+            body << "Content-Type: " << sanitize_field(file.content_type) << "\r\n";
+        } else {
+            body << "Content-Type: application/octet-stream\r\n";
+        }
+        body << "\r\n";
+        body.write(reinterpret_cast<const char*>(file.data.data()), file.data.size());
+        body << "\r\n";
+    }
+
+    body << "--" << boundary << "--\r\n";
+    return body.str();
+}
+
 server_http_proxy::server_http_proxy(
         const std::string & method,
         const std::string & scheme,
@@ -1129,6 +1456,7 @@ server_http_proxy::server_http_proxy(
         const std::string & path,
         const std::map<std::string, std::string> & headers,
         const std::string & body,
+        const std::map<std::string, uploaded_file> & files,
         const std::function<bool()> should_stop,
         int32_t timeout_read,
         int32_t timeout_write
@@ -1190,28 +1518,65 @@ server_http_proxy::server_http_proxy(
         return pipe->write({{}, 0, std::string(data, data_length), ""});
     };
 
+    // when files are present, the body was converted from multipart form data to JSON
+    // we need to reconstruct the multipart body for the downstream server
+    std::string effective_body = body;
+    std::string override_content_type;
+    bool has_files = !files.empty();
+
+    if (has_files) {
+        json form_fields = json::parse(body, nullptr, false);
+        if (!form_fields.is_discarded()) {
+            auto boundary = generate_multipart_boundary();
+            effective_body = build_multipart_body(form_fields, files, boundary);
+            override_content_type = "multipart/form-data; boundary=" + boundary;
+        } else {
+            throw std::runtime_error("failed to parse multipart form fields JSON");
+        }
+    }
+
     // prepare the request to destination server
     httplib::Request req;
     {
         req.method = method;
         req.path = path;
         for (const auto & [key, value] : headers) {
-            if (key == "Accept-Encoding") {
+            const auto lowered = to_lower_copy(key);
+            if (lowered == "accept-encoding") {
                 // disable Accept-Encoding to avoid compressed responses
                 continue;
             }
-            if (key == "Transfer-Encoding") {
+            if (lowered == "transfer-encoding") {
                 // the body is already decoded
                 continue;
             }
-            if (key == "Host" || key == "host") {
+            if (lowered == "content-length") {
+                // let httplib calculate Content-Length from the actual body
+                continue;
+            }
+            if (lowered == "content-type") {
+                if (has_files) {
+                    // we set our own Content-Type with the new boundary
+                    continue;
+                }
+                // when no files but the original request was multipart,
+                // the body is now JSON, so correct the Content-Type
+                if (value.find("multipart/form-data") != std::string::npos) {
+                    override_content_type = "application/json; charset=utf-8";
+                    continue;
+                }
+            }
+            if (lowered == "host") {
                 bool is_default_port = (scheme == "https" && port == 443) || (scheme == "http" && port == 80);
                 req.set_header(key, is_default_port ? host : host + ":" + std::to_string(port));
             } else {
                 req.set_header(key, value);
             }
         }
-        req.body = body;
+        req.body = effective_body;
+        if (!override_content_type.empty()) {
+            req.set_header("Content-Type", override_content_type);
+        }
         req.response_handler = response_handler;
         req.content_receiver = content_receiver;
     }
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index 1db34b6c4df..e96d76c9169 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -63,8 +63,10 @@ struct server_model_meta {
     server_model_status status = SERVER_MODEL_STATUS_UNLOADED;
     int64_t last_used = 0; // for LRU unloading
     std::vector<std::string> args; // args passed to the model instance, will be populated by render_args()
+    json loaded_info; // info to be reflected via /v1/models endpoint
     int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED)
     int stop_timeout = 0; // seconds to wait before force-killing the model instance during shutdown
+    mtmd_caps multimodal; // multimodal capabilities
 
     bool is_ready() const {
         return status == SERVER_MODEL_STATUS_LOADED;
@@ -79,6 +81,7 @@ struct server_model_meta {
     }
 
     void update_args(common_preset_context & ctx_presets, std::string bin_path);
+    void update_caps();
 };
 
 struct subprocess_s;
@@ -100,6 +103,9 @@ struct server_models {
     std::condition_variable cv_stop;
     std::set<std::string> stopping_models;
 
+    // set to true while load_models() is executing a reload; load() will wait until clear
+    bool is_reloading = false;
+
     common_preset_context ctx_preset;
 
     common_params base_params;
@@ -118,6 +124,11 @@ struct server_models {
 public:
     server_models(const common_params & params, int argc, char ** argv);
 
+    // (re-)load the list of models from various sources and prepare the metadata mapping
+    // - if this is called the first time, simply populate the metadata
+    // - if this is called subsequently (e.g. when refreshing from disk):
+    //   - if a model is running but updated or removed from the source, it will be unloaded
+    //   - if a model is not running, it will be added or updated according to the source
     void load_models();
 
     // check if a model instance exists (thread-safe)
@@ -137,6 +148,7 @@ struct server_models {
 
     // update the status of a model instance (thread-safe)
     void update_status(const std::string & name, server_model_status status, int exit_code);
+    void update_loaded_info(const std::string & name, std::string & raw_info);
 
     // wait until the model instance is fully loaded (thread-safe)
     // return when the model no longer in "loading" state
@@ -155,7 +167,7 @@ struct server_models {
 
     // notify the router server that a model instance is ready
     // return the monitoring thread (to be joined by the caller)
-    static std::thread setup_child_server(const std::function<void(int)> & shutdown_handler);
+    static std::thread setup_child_server(const std::function<void(int)> & shutdown_handler, const json & model_info);
 
     // notify the router server that the sleeping state has changed
     static void notify_router_sleeping_state(bool sleeping);
@@ -163,15 +175,22 @@ struct server_models {
 
 struct server_models_routes {
     common_params params;
-    json webui_settings = json::object();
+    json ui_settings = json::object();          // Primary: new name
+    json webui_settings = json::object();        // Deprecated: use ui_settings (kept for compat)
     server_models models;
     server_models_routes(const common_params & params, int argc, char ** argv)
             : params(params), models(params, argc, argv) {
-        if (!this->params.webui_config_json.empty()) {
+        // Support both new ui_config_json and deprecated webui_config_json
+        const std::string & cfg = !this->params.ui_config_json.empty()
+            ? this->params.ui_config_json
+            : this->params.webui_config_json;
+        if (!cfg.empty()) {
             try {
-                webui_settings = json::parse(this->params.webui_config_json);
+                json json_settings = json::parse(cfg);
+                ui_settings = json_settings;
+                webui_settings = json_settings;  // Deprecated: keep in sync
             } catch (const std::exception & e) {
-                LOG_ERR("%s: failed to parse webui config: %s\n", __func__, e.what());
+                LOG_ERR("%s: failed to parse UI config: %s\n", __func__, e.what());
                 throw;
             }
         }
@@ -202,6 +221,7 @@ struct server_http_proxy : server_http_res {
                       const std::string & path,
                       const std::map<std::string, std::string> & headers,
                       const std::string & body,
+                      const std::map<std::string, uploaded_file> & files,
                       const std::function<bool()> should_stop,
                       int32_t timeout_read,
                       int32_t timeout_write
diff --git a/tools/server/server-queue.cpp b/tools/server/server-queue.cpp
index a2a026a12ce..d5fceb1b131 100644
--- a/tools/server/server-queue.cpp
+++ b/tools/server/server-queue.cpp
@@ -381,7 +381,8 @@ server_task_result_ptr server_response_reader::next(const std::function<bool()>
         if (result == nullptr) {
             // timeout, check stop condition
             if (should_stop()) {
-                SRV_DBG("%s", "stopping wait for next result due to should_stop condition\n");
+                SRV_WRN("%s", "stopping wait for next result due to should_stop condition (adjust the --timeout argument if needed)\n");
+                SRV_WRN("%s", "ref: https://github.com/ggml-org/llama.cpp/pull/22907\n");
                 return nullptr;
             }
         } else {
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index 998662edf07..cbc40a35fc4 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -1,6 +1,7 @@
 #include "server-task.h"
 
 #include "build-info.h"
+#include "server-chat.h"
 #include "chat.h"
 #include "common.h"
 #include "json-schema-to-grammar.h"
@@ -75,13 +76,7 @@ json task_params::to_json(bool only_metrics) const {
             {"reasoning_in_content",      chat_parser_params.reasoning_in_content},
             {"generation_prompt",         chat_parser_params.generation_prompt},
             {"samplers",                  samplers},
-            {"speculative.n_max",         speculative.n_max},
-            {"speculative.n_min",         speculative.n_min},
-            {"speculative.p_min",         speculative.p_min},
-            {"speculative.type",          common_speculative_type_to_str(speculative.type)},
-            {"speculative.ngram_size_n",  speculative.ngram_size_n},
-            {"speculative.ngram_size_m",  speculative.ngram_size_m},
-            {"speculative.ngram_m_hits",  speculative.ngram_min_hits},
+            {"speculative.types",         common_speculative_type_name_str(speculative.types)},
             {"timings_per_token",         timings_per_token},
             {"post_sampling_probs",       post_sampling_probs},
             {"backend_sampling",          sampling.backend_sampling},
@@ -138,13 +133,7 @@ json task_params::to_json(bool only_metrics) const {
         {"reasoning_in_content",      chat_parser_params.reasoning_in_content},
         {"generation_prompt",         chat_parser_params.generation_prompt},
         {"samplers",                  samplers},
-        {"speculative.n_max",         speculative.n_max},
-        {"speculative.n_min",         speculative.n_min},
-        {"speculative.p_min",         speculative.p_min},
-        {"speculative.type",          common_speculative_type_to_str(speculative.type)},
-        {"speculative.ngram_size_n",  speculative.ngram_size_n},
-        {"speculative.ngram_size_m",  speculative.ngram_size_m},
-        {"speculative.ngram_m_hits",  speculative.ngram_min_hits},
+        {"speculative.types",         common_speculative_type_name_str(speculative.types)},
         {"timings_per_token",         timings_per_token},
         {"post_sampling_probs",       post_sampling_probs},
         {"backend_sampling",          sampling.backend_sampling},
@@ -307,14 +296,18 @@ task_params server_task::params_from_json_cmpl(
 
     params.speculative = defaults.speculative;
 
-    params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
-    params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
-    params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min);
+    // TODO: to keep things simple, we disable speculative parameter adjustments for now
+#if 0
+    // TODO: for now, be able to adjust only the draft-model based speculative parameters
+    params.speculative.draft.n_min = json_value(data, "speculative.n_min", defaults.speculative.draft.n_min);
+    params.speculative.draft.n_max = json_value(data, "speculative.n_max", defaults.speculative.draft.n_max);
+    params.speculative.draft.p_min = json_value(data, "speculative.p_min", defaults.speculative.draft.p_min);
 
-    params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min);
-    params.speculative.n_min = std::max(params.speculative.n_min, 0);
-    params.speculative.n_max = std::max(params.speculative.n_max, 0);
+    params.speculative.draft.n_min = std::min(params.speculative.draft.n_max, params.speculative.draft.n_min);
+    params.speculative.draft.n_min = std::max(params.speculative.draft.n_min, 0);
+    params.speculative.draft.n_max = std::max(params.speculative.draft.n_max, 0);
 
+    // for debugging and research purposes
     params.speculative.type = common_speculative_type_from_name(json_value(data, "speculative.type", common_speculative_type_to_str(defaults.speculative.type)));
 
     params.speculative.ngram_size_n     = json_value(data, "speculative.ngram_size_n", defaults.speculative.ngram_size_n);
@@ -324,6 +317,7 @@ task_params server_task::params_from_json_cmpl(
     params.speculative.ngram_size_n     = std::max(std::min(1, (int) params.speculative.ngram_size_n),     1024);
     params.speculative.ngram_size_m     = std::max(std::min(1, (int) params.speculative.ngram_size_m),     1024);
     params.speculative.ngram_min_hits   = std::max(std::min(1, (int) params.speculative.ngram_min_hits),   1024);
+#endif
 
     // Use OpenAI API logprobs only if n_probs wasn't provided
     if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){
@@ -874,7 +868,7 @@ json server_task_result_cmpl_final::to_json_oaicompat_chat_stream() {
                 json {
                     {"finish_reason", nullptr},
                     {"index", index},
-                    {"delta", common_chat_msg_diff_to_json_oaicompat(diff)},
+                    {"delta", server_chat_msg_diff_to_json_oaicompat(diff)},
                 },
             })},
             {"created", t},
@@ -1111,7 +1105,7 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() {
 json server_task_result_cmpl_final::to_json_oaicompat_asr() {
     json event = json {
         {"type",  "transcript.text.done"},
-        {"text",  content},
+        {"text",  oaicompat_msg.content},
         {"usage", json {
             {"type",         "tokens"},
             {"input_tokens",  n_prompt_tokens},
@@ -1523,7 +1517,7 @@ json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
     }
 
     for (const auto & diff : oaicompat_msg_diffs) {
-        add_delta(common_chat_msg_diff_to_json_oaicompat(diff));
+        add_delta(server_chat_msg_diff_to_json_oaicompat(diff));
     }
 
     if (!deltas.empty()) {
@@ -1988,13 +1982,13 @@ size_t server_prompt_cache::n_tokens() const {
     return res;
 }
 
-server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t state_size) {
+server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t state_size_tgt, size_t state_size_dft) {
     // first check if the current state is contained fully in the cache
     for (auto it = states.begin(); it != states.end(); ++it) {
         const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens);
 
         if (cur_lcp_len == (int) prompt.tokens.size()) {
-            SRV_WRN("%s", " - prompt is already in the cache, skipping\n");
+            SRV_INF("%s", " - prompt is already in the cache, skipping\n");
             return nullptr;
         }
     }
@@ -2012,11 +2006,13 @@ server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t
         }
     }
 
-    std::vector<uint8_t> state_data;
+    std::vector<uint8_t> state_data_tgt;
+    std::vector<uint8_t> state_data_dft;
 
     // check if we can allocate enough memory for the new state
     try {
-        state_data.resize(state_size);
+        state_data_tgt.resize(state_size_tgt);
+        state_data_dft.resize(state_size_dft);
     } catch (const std::bad_alloc & e) {
         SRV_ERR("failed to allocate memory for prompt cache state: %s\n", e.what());
 
@@ -2029,23 +2025,25 @@ server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t
         return nullptr;
     }
 
-    auto & cur = states.emplace_back();
-    cur = {
+    states.push_back({
         /*.tokens      =*/ prompt.tokens.clone(),
-        /*.data        =*/ std::move(state_data),
+        /*.data        =*/ {
+            /*.main =*/ std::move(state_data_tgt),
+            /*.drft =*/ std::move(state_data_dft),
+        },
         /*.checkpoints =*/ prompt.checkpoints,
-    };
+    });
 
-    return &cur;
+    return &states.back();
 }
 
-bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot) {
+bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx_tgt, llama_context * ctx_dft, int32_t id_slot) {
     const int lcp_best = prompt.tokens.get_common_prefix(tokens_new);
 
     float f_keep_best = prompt.tokens.size() > 0 ? float(lcp_best) / prompt.tokens.size() : -1.0f; // empty slot: any cache entry wins
     float sim_best    = float(lcp_best) / tokens_new.size();
 
-    SRV_WRN(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+    SRV_INF(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
 
     auto it_best = states.end();
 
@@ -2070,18 +2068,41 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok
     }
 
     if (it_best != states.end()) {
-        SRV_WRN(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+        SRV_INF(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+
+        {
+            auto & data = it_best->data.main;
 
-        const size_t size = it_best->data.size();
-        const size_t n = llama_state_seq_set_data_ext(ctx, it_best->data.data(), size, id_slot, 0);
-        if (n != size) {
-            SRV_WRN("failed to restore state with size %zu\n", size);
+            const size_t size = data.size();
+            const size_t n = llama_state_seq_set_data_ext(ctx_tgt, data.data(), size, id_slot, 0);
+            if (n != size) {
+                SRV_ERR("failed to restore state with size %zu\n", size);
 
-            return false;
+                return false;
+            }
+
+            data.clear();
+            data.shrink_to_fit();
         }
 
-        it_best->data.clear();
-        it_best->data.shrink_to_fit();
+        {
+            auto & data = it_best->data.drft;
+
+            if (!data.empty()) {
+                GGML_ASSERT(ctx_dft);
+
+                const size_t size = data.size();
+                const size_t n = llama_state_seq_set_data_ext(ctx_dft, data.data(), size, id_slot, 0);
+                if (n != size) {
+                    SRV_WRN("failed to restore state with size %zu\n", size);
+
+                    return false;
+                }
+
+                data.clear();
+                data.shrink_to_fit();
+            }
+        }
 
         prompt = std::move(*it_best);
 
@@ -2124,11 +2145,11 @@ void server_prompt_cache::update() {
         }
     }
 
-    SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
+    SRV_INF(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
             states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur);
 
     for (const auto & state : states) {
-        SRV_WRN("   - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
+        SRV_INF("   - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
                 (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0));
     }
 }
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 289e1fb8d24..64bdecd794f 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -565,42 +565,29 @@ struct server_task_result_apply_lora : server_task_result {
     virtual json to_json() override;
 };
 
-struct server_prompt_checkpoint {
-    llama_pos pos_min;
-    llama_pos pos_max;
-
-    int64_t n_tokens;
-
-    std::vector<uint8_t> data;
+struct server_prompt_data {
+    std::vector<uint8_t> main;
+    std::vector<uint8_t> drft;
 
     size_t size() const {
-        return data.size();
-    }
-
-    bool empty() const {
-        return data.empty();
-    }
-
-    void clear() {
-        pos_min = 0;
-        pos_max = 0;
-        n_tokens = 0;
-        data.clear();
+        return main.size() + drft.size();
     }
 };
 
 struct server_prompt {
     server_tokens tokens;
 
-    std::vector<uint8_t> data;
+    server_prompt_data data;
 
-    std::list<server_prompt_checkpoint> checkpoints;
+    std::list<common_prompt_checkpoint> checkpoints;
 
     size_t size() const {
-        size_t res = data.size();
+        size_t res = 0;
+
+        res += data.size();
 
-        for (const auto & checkpoint : checkpoints) {
-            res += checkpoint.size();
+        for (const auto & ckpt : checkpoints) {
+            res += ckpt.size();
         }
 
         return res;
@@ -614,7 +601,7 @@ struct server_prompt {
         return server_prompt {
             tokens.clone(),
             data,
-            checkpoints
+            checkpoints,
         };
     }
 };
@@ -637,9 +624,9 @@ struct server_prompt_cache {
 
     size_t n_tokens() const;
 
-    server_prompt * alloc(const server_prompt & prompt, size_t state_size);
+    server_prompt * alloc(const server_prompt & prompt, size_t state_size_main, size_t state_size_drft);
 
-    bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot);
+    bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx_main, llama_context * ctx_drft, int32_t id_slot);
 
     void update();
 };
diff --git a/tools/server/server-tools.cpp b/tools/server/server-tools.cpp
index 81e360de463..97433fe4b50 100644
--- a/tools/server/server-tools.cpp
+++ b/tools/server/server-tools.cpp
@@ -10,6 +10,7 @@
 #include <atomic>
 #include <cstring>
 #include <climits>
+#include <algorithm>
 
 namespace fs = std::filesystem;
 
@@ -693,6 +694,35 @@ struct server_tool_apply_diff : server_tool {
     }
 };
 
+//
+// get_datetime: returns the current date and time
+//
+
+struct server_tool_get_datetime : server_tool {
+    server_tool_get_datetime() {
+        name = "get_datetime";
+        display_name = "Get Date & Time";
+        permission_write = false;
+    }
+
+    json get_definition() override {
+        return {
+            {"type", "function"},
+            {"function", {
+                {"name", name},
+                {"description", "Returns the current date and time"},
+            }},
+        };
+    }
+
+    json invoke(json) override {
+        auto now = std::chrono::system_clock::now();
+        auto time = std::chrono::system_clock::to_time_t(now);
+
+        return {{"result", std::ctime(&time)}};
+    }
+};
+
 //
 // public API
 //
@@ -706,6 +736,7 @@ static std::vector<std::unique_ptr<server_tool>> build_tools() {
     tools.push_back(std::make_unique<server_tool_write_file>());
     tools.push_back(std::make_unique<server_tool_edit_file>());
     tools.push_back(std::make_unique<server_tool_apply_diff>());
+    tools.push_back(std::make_unique<server_tool_get_datetime>());
     return tools;
 }
 
@@ -714,6 +745,24 @@ void server_tools::setup(const std::vector<std::string> & enabled_tools) {
         std::unordered_set<std::string> enabled_set(enabled_tools.begin(), enabled_tools.end());
         auto all_tools = build_tools();
 
+        // collect all known tool names for validation
+        std::vector<std::string> known_names;
+        known_names.reserve(all_tools.size());
+        for (const auto & t : all_tools) {
+            known_names.push_back(t->name);
+        }
+
+        // validate that every requested tool is known
+        for (const auto & name : enabled_tools) {
+            if (name == "all") continue;
+            if (std::find(known_names.begin(), known_names.end(), name) == known_names.end()) {
+                throw std::runtime_error(string_format(
+                    "unknown tool \"%s\". available tools: %s",
+                    name.c_str(),
+                    string_join(known_names, ", ").c_str()));
+            }
+        }
+
         tools.clear();
         for (auto & t : all_tools) {
             if (enabled_set.count(t->name) > 0 || enabled_set.count("all") > 0) {
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 6566949edf1..c82f1179431 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -83,17 +83,25 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    // router server never loads a model and must not touch the GPU
+    // skip device enumeration so the CUDA primary context stays uncreated
+    const bool is_router_server = params.model.path.empty();
+    common_params_print_info(params, !is_router_server);
+
     // validate batch size for embeddings
     // embeddings require all tokens to be processed in a single ubatch
     // see https://github.com/ggml-org/llama.cpp/issues/12836
     if (params.embedding && params.n_batch > params.n_ubatch) {
-        LOG_WRN("%s: embeddings enabled with n_batch (%d) > n_ubatch (%d)\n", __func__, params.n_batch, params.n_ubatch);
-        LOG_WRN("%s: setting n_batch = n_ubatch = %d to avoid assertion failure\n", __func__, params.n_ubatch);
+        SRV_WRN("embeddings enabled with n_batch (%d) > n_ubatch (%d)\n", params.n_batch, params.n_ubatch);
+        SRV_WRN("setting n_batch = n_ubatch = %d to avoid assertion failure\n", params.n_ubatch);
         params.n_batch = params.n_ubatch;
     }
 
     if (params.n_parallel < 0) {
-        LOG_INF("%s: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n", __func__);
+        SRV_INF("%s", "n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n");
 
         params.n_parallel = 4;
         params.kv_unified = true;
@@ -107,15 +115,9 @@ int main(int argc, char ** argv) {
     // struct that contains llama context and inference
     server_context ctx_server;
 
-    llama_backend_init();
-    llama_numa_init(params.numa);
-
-    LOG_INF("build_info: %s\n", llama_build_info());
-    LOG_INF("%s\n", common_params_get_system_info(params).c_str());
-
     server_http_context ctx_http;
     if (!ctx_http.init(params)) {
-        LOG_ERR("%s: failed to initialize HTTP server\n", __func__);
+        SRV_ERR("%s", "failed to initialize HTTP server\n");
         return 1;
     }
 
@@ -127,14 +129,13 @@ int main(int argc, char ** argv) {
     server_routes routes(params, ctx_server);
     server_tools tools;
 
-    bool is_router_server = params.model.path.empty();
     std::optional<server_models_routes> models_routes{};
     if (is_router_server) {
         // setup server instances manager
         try {
             models_routes.emplace(params, argc, argv);
         } catch (const std::exception & e) {
-            LOG_ERR("%s: failed to initialize router models: %s\n", __func__, e.what());
+            SRV_ERR("failed to initialize router models: %s\n", e.what());
             return 1;
         }
 
@@ -204,8 +205,13 @@ int main(int argc, char ** argv) {
     // Save & load slots
     ctx_http.get ("/slots",                    ex_wrapper(routes.get_slots));
     ctx_http.post("/slots/:id_slot",           ex_wrapper(routes.post_slots));
+
+    // Google Cloud Platform (Vertex AI) compat
+    ctx_http.register_gcp_compat();
+
     // CORS proxy (EXPERIMENTAL, only used by the Web UI for MCP)
-    if (params.webui_mcp_proxy) {
+    // Supports both new ui_mcp_proxy and deprecated webui_mcp_proxy fields
+    if (params.ui_mcp_proxy || params.webui_mcp_proxy) {
         SRV_WRN("%s", "-----------------\n");
         SRV_WRN("%s", "CORS proxy is enabled, do not expose server to untrusted environments\n");
         SRV_WRN("%s", "This feature is EXPERIMENTAL and may be removed or changed in future versions\n");
@@ -215,7 +221,12 @@ int main(int argc, char ** argv) {
     }
     // EXPERIMENTAL built-in tools
     if (!params.server_tools.empty()) {
-        tools.setup(params.server_tools);
+        try {
+            tools.setup(params.server_tools);
+        } catch (const std::exception & e) {
+            SRV_ERR("tools setup failed: %s\n", e.what());
+            return 1;
+        }
         SRV_WRN("%s", "-----------------\n");
         SRV_WRN("%s", "Built-in tools are enabled, do not expose server to untrusted environments\n");
         SRV_WRN("%s", "This feature is EXPERIMENTAL and may be changed in the future\n");
@@ -231,7 +242,7 @@ int main(int argc, char ** argv) {
     std::function<void()> clean_up;
 
     if (is_router_server) {
-        LOG_INF("%s: starting router server, no model will be loaded in this process\n", __func__);
+        SRV_INF("%s", "starting router server, no model will be loaded in this process\n");
 
         clean_up = [&models_routes]() {
             SRV_INF("%s: cleaning up before exit...\n", __func__);
@@ -243,7 +254,7 @@ int main(int argc, char ** argv) {
 
         if (!ctx_http.start()) {
             clean_up();
-            LOG_ERR("%s: exiting due to HTTP server error\n", __func__);
+            SRV_ERR("%s", "exiting due to HTTP server error\n");
             return 1;
         }
         ctx_http.is_ready.store(true);
@@ -264,12 +275,12 @@ int main(int argc, char ** argv) {
         // start the HTTP server before loading the model to be able to serve /health requests
         if (!ctx_http.start()) {
             clean_up();
-            LOG_ERR("%s: exiting due to HTTP server error\n", __func__);
+            SRV_ERR("%s", "exiting due to HTTP server error\n");
             return 1;
         }
 
         // load the model
-        LOG_INF("%s: loading model\n", __func__);
+        SRV_INF("%s", "loading model\n");
 
         if (server_models::is_child_server()) {
             ctx_server.on_sleeping_changed([&](bool sleeping) {
@@ -282,14 +293,14 @@ int main(int argc, char ** argv) {
             if (ctx_http.thread.joinable()) {
                 ctx_http.thread.join();
             }
-            LOG_ERR("%s: exiting due to model loading error\n", __func__);
+            SRV_ERR("%s", "exiting due to model loading error\n");
             return 1;
         }
 
         routes.update_meta(ctx_server);
         ctx_http.is_ready.store(true);
 
-        LOG_INF("%s: model loaded\n", __func__);
+        SRV_INF("%s", "model loaded\n");
 
         shutdown_handler = [&](int) {
             // this will unblock start_loop()
@@ -313,9 +324,9 @@ int main(int argc, char ** argv) {
 #endif
 
     if (is_router_server) {
-        LOG_INF("%s: router server is listening on %s\n", __func__, ctx_http.listening_address.c_str());
-        LOG_INF("%s: NOTE: router mode is experimental\n", __func__);
-        LOG_INF("%s:       it is not recommended to use this mode in untrusted environments\n", __func__);
+        SRV_INF("router server is listening on %s\n", ctx_http.listening_address.c_str());
+        SRV_WRN("%s", "NOTE: router mode is experimental\n");
+        SRV_WRN("%s", "      it is not recommended to use this mode in untrusted environments\n");
         if (ctx_http.thread.joinable()) {
             ctx_http.thread.join(); // keep the main thread alive
         }
@@ -323,13 +334,13 @@ int main(int argc, char ** argv) {
         // when the HTTP server stops, clean up and exit
         clean_up();
     } else {
-        LOG_INF("%s: server is listening on %s\n", __func__, ctx_http.listening_address.c_str());
-        LOG_INF("%s: starting the main loop...\n", __func__);
+        SRV_INF("server is listening on %s\n", ctx_http.listening_address.c_str());
 
         // optionally, notify router server that this instance is ready
         std::thread monitor_thread;
         if (server_models::is_child_server()) {
-            monitor_thread = server_models::setup_child_server(shutdown_handler);
+            json model_info = routes.get_model_info();
+            monitor_thread = server_models::setup_child_server(shutdown_handler, model_info);
         }
 
         // this call blocks the main thread until queue_tasks.terminate() is called
diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py
index edef0a93b49..243e4160578 100644
--- a/tools/server/tests/unit/test_chat_completion.py
+++ b/tools/server/tests/unit/test_chat_completion.py
@@ -178,6 +178,45 @@ def test_chat_template_assistant_prefill(prefill, re_prefill):
     assert res.body["__verbose"]["prompt"] == f"<s> <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n{re_prefill}"
 
 
+def test_chat_template_continue_final_message_vllm_compat():
+    """continue_final_message is the vLLM/transformers explicit alias for the prefill_assistant heuristic.
+    Both must produce the same prompt."""
+    global server
+    server.chat_template = "llama3"
+    server.debug = True
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": 8,
+        "add_generation_prompt": False,
+        "continue_final_message": True,
+        "messages": [
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+            {"role": "assistant", "content": "Whill"},
+        ]
+    })
+    assert res.status_code == 200
+    assert "__verbose" in res.body
+    assert res.body["__verbose"]["prompt"] == "<s> <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nWhill"
+
+
+def test_chat_template_continue_final_message_mutual_exclusion():
+    """add_generation_prompt and continue_final_message both set to true must be rejected"""
+    global server
+    server.chat_template = "llama3"
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": 8,
+        "add_generation_prompt": True,
+        "continue_final_message": True,
+        "messages": [
+            {"role": "user", "content": "Hi"},
+            {"role": "assistant", "content": "Hello"},
+        ]
+    })
+    assert res.status_code == 400
+
+
 def test_apply_chat_template():
     global server
     server.chat_template = "command-r"
diff --git a/tools/server/tests/unit/test_compat_gcp.py b/tools/server/tests/unit/test_compat_gcp.py
new file mode 100644
index 00000000000..aba67bb3537
--- /dev/null
+++ b/tools/server/tests/unit/test_compat_gcp.py
@@ -0,0 +1,60 @@
+import pytest
+from utils import *
+
+server: ServerProcess
+
+
+@pytest.fixture(autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+    server.gcp_compat = True
+
+
+def test_gcp_predict_camel_case():
+    global server
+    server.start()
+    res = server.make_request("POST", "/predict", data={
+        "instances": [
+            {
+                "@requestFormat": "chatCompletions",
+                "max_tokens": 8,
+                "messages": [
+                    {"role": "user", "content": "What is the meaning of life?"},
+                ],
+            }
+        ],
+    })
+    assert res.status_code == 200
+    assert "predictions" in res.body
+    assert len(res.body["predictions"]) == 1
+    prediction = res.body["predictions"][0]
+    assert "choices" in prediction
+    assert len(prediction["choices"]) == 1
+    assert prediction["choices"][0]["message"]["role"] == "assistant"
+    assert len(prediction["choices"][0]["message"]["content"]) > 0
+
+
+def test_gcp_predict_multiple_instances():
+    global server
+    server.n_slots = 2
+    server.start()
+    res = server.make_request("POST", "/predict", data={
+        "instances": [
+            {
+                "@requestFormat": "chatCompletions",
+                "max_tokens": 8,
+                "messages": [{"role": "user", "content": "Say hello"}],
+            },
+            {
+                "@requestFormat": "chatCompletions",
+                "max_tokens": 8,
+                "messages": [{"role": "user", "content": "Say world"}],
+            },
+        ],
+    })
+    assert res.status_code == 200
+    assert len(res.body["predictions"]) == 2
+    for prediction in res.body["predictions"]:
+        assert "choices" in prediction
+        assert len(prediction["choices"][0]["message"]["content"]) > 0
diff --git a/tools/server/tests/unit/test_completion.py b/tools/server/tests/unit/test_completion.py
index c1a19785434..1e0891987a9 100644
--- a/tools/server/tests/unit/test_completion.py
+++ b/tools/server/tests/unit/test_completion.py
@@ -491,29 +491,82 @@ def test_n_probs_post_sampling():
     global server
     server.start()
     res = server.make_request("POST", "/completion", data={
-        "prompt": "I believe the meaning of life is",
+        "prompt": "Today was the day. Today I would finally become a",
         "n_probs": 10,
-        "temperature": 0.0,
+        "temperature": 1.0,
         "n_predict": 5,
         "post_sampling_probs": True,
     })
     assert res.status_code == 200
     assert "completion_probabilities" in res.body
     assert len(res.body["completion_probabilities"]) == 5
-    for tok in res.body["completion_probabilities"]:
+    for (i, tok) in enumerate(res.body["completion_probabilities"]):
         assert "id" in tok and tok["id"] > 0
         assert "token" in tok and type(tok["token"]) == str
         assert "prob" in tok and 0.0 < tok["prob"] <= 1.0
         assert "bytes" in tok and type(tok["bytes"]) == list
-        assert len(tok["top_probs"]) == 10
+        assert "top_probs" in tok and type(tok["top_probs"]) == list
+
         for prob in tok["top_probs"]:
             assert "id" in prob and prob["id"] > 0
             assert "token" in prob and type(prob["token"]) == str
-            assert "prob" in prob and 0.0 <= prob["prob"] <= 1.0
+            # 0.0 probability tokens should never be returned by the server
+            assert "prob" in prob and 0.0 < prob["prob"] <= 1.0
             assert "bytes" in prob and type(prob["bytes"]) == list
-        # because the test model usually output token with either 100% or 0% probability, we need to check all the top_probs
-        assert any(prob["prob"] == 1.0 for prob in tok["top_probs"])
 
+        if i == 0:
+            # The prompt is vague enough that we should get at least 10 possibilities
+            # for the first token.
+            assert len(tok["top_probs"]) == 10
+
+        if len(tok["top_probs"]) < 10:
+            # Getting less than the requested number of probabilities should only happen
+            # if the ones we did get already sum to 1.0.
+            assert sum(p["prob"] for p in tok["top_probs"]) == pytest.approx(1.0)
+
+def test_n_probs_post_backend_sampling():
+    """Verify that the same probabilities are returned with and without backend sampling."""
+    global server
+    server.backend_sampling = True
+    server.start()
+
+    def make_request(backend_sampling):
+        n_predict = 20
+
+        res = server.make_request("POST", "/completion", data={
+            "prompt": "The countries of Europe, in random order, are:",
+            "n_probs": 10,
+            "n_predict": n_predict,
+            "post_sampling_probs": True,
+            "seed": 4242,
+            "backend_sampling": backend_sampling,
+        })
+        assert res.status_code == 200
+
+        total_probs = 0
+        completions = res.body["completion_probabilities"]
+        assert len(completions) == n_predict
+        for tok in completions:
+            # Handling of 0.0 probabilities differs between samplers and backend sampling. Filter them to normalize the
+            # data.
+            tok["top_probs"] = [x for x in tok["top_probs"] if x["prob"] > 0.0]
+            total_probs += len(tok["top_probs"])
+        # Verify that we got at least two top probs on average, to ensure the effectiveness of the test.
+        assert total_probs >= 2 * n_predict
+        return completions
+
+    def verify_token(a, b):
+        assert a["id"] == b["id"]
+        assert a["token"] == b["token"]
+        assert a["bytes"] == b["bytes"]
+        assert a["prob"] == pytest.approx(b["prob"], abs=0.01)
+
+    for (a, b) in zip(make_request(True), make_request(False)):
+        verify_token(a, b)
+        assert len(a["top_probs"]) == len(b["top_probs"])
+
+        for (aa, bb) in zip(a["top_probs"], b["top_probs"]):
+            verify_token(aa, bb)
 
 @pytest.mark.parametrize("tokenize,openai_style", [(False, False), (False, True), (True, False), (True, True)])
 def test_logit_bias(tokenize, openai_style):
diff --git a/tools/server/tests/unit/test_kv_keep_only_active.py b/tools/server/tests/unit/test_kv_keep_only_active.py
index f4b08b5dd0e..44c05fab0cb 100644
--- a/tools/server/tests/unit/test_kv_keep_only_active.py
+++ b/tools/server/tests/unit/test_kv_keep_only_active.py
@@ -48,7 +48,7 @@ def test_clear_and_restore():
     log = LogReader(server.log_path)
 
     # verify feature is enabled
-    assert "__TEST_TAG_CLEAR_IDLE_ENABLED__" in log.drain()
+    assert "__TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__" in log.drain()
 
     res = server.make_request("POST", "/completion", data={
         "prompt": LONG_PROMPT,
@@ -59,7 +59,7 @@ def test_clear_and_restore():
     original_prompt_n = res.body["timings"]["prompt_n"]
 
     # Slot 0 is the only slot with KV — should NOT be cleared
-    assert "__TEST_TAG_CLEAR_IDLE_SLOT__" not in log.drain()
+    assert "__TEST_TAG_CACHE_IDLE_SLOT__" not in log.drain()
 
     # Launching slot 1 clears idle slot 0
     res = server.make_request("POST", "/completion", data={
@@ -68,7 +68,7 @@ def test_clear_and_restore():
         "cache_prompt": True,
     })
     assert res.status_code == 200
-    assert "__TEST_TAG_CLEAR_IDLE_SLOT__" in log.drain()
+    assert "__TEST_TAG_CACHE_IDLE_SLOT__" in log.drain()
 
     # Re-send same prompt — should restore from cache-ram
     res = server.make_request("POST", "/completion", data={
@@ -86,7 +86,7 @@ def test_clear_and_restore():
         "cache_prompt": True,
     })
     assert res.status_code == 200
-    assert "__TEST_TAG_CLEAR_IDLE_SLOT__" not in log.drain()
+    assert "__TEST_TAG_CACHE_IDLE_SLOT__" not in log.drain()
 
 
 def test_disabled_with_flag():
@@ -96,7 +96,7 @@ def test_disabled_with_flag():
     log = LogReader(server.log_path)
 
     # Feature should not be enabled
-    assert "__TEST_TAG_CLEAR_IDLE_ENABLED__" not in log.drain()
+    assert "__TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__" not in log.drain()
 
     res = server.make_request("POST", "/completion", data={
         "prompt": LONG_PROMPT,
@@ -112,4 +112,4 @@ def test_disabled_with_flag():
         "cache_prompt": True,
     })
     assert res.status_code == 200
-    assert "__TEST_TAG_CLEAR_IDLE_SLOT__" not in log.drain()
+    assert "__TEST_TAG_CACHE_IDLE_SLOT__" not in log.drain()
diff --git a/tools/server/tests/unit/test_router.py b/tools/server/tests/unit/test_router.py
index 79e60db4083..c93b92b0b2e 100644
--- a/tools/server/tests/unit/test_router.py
+++ b/tools/server/tests/unit/test_router.py
@@ -62,6 +62,12 @@ def test_router_chat_completion_stream(model: str, success: bool):
         assert content == ""
 
 
+def _get_model_ids(is_reload: bool) -> set[str]:
+    res = server.make_request("GET", "/models" + ("?reload=1" if is_reload else ""))
+    assert res.status_code == 200
+    return {item["id"] for item in res.body.get("data", [])}
+
+
 def _get_model_status(model_id: str) -> str:
     res = server.make_request("GET", "/models")
     assert res.status_code == 200
@@ -205,3 +211,45 @@ def test_router_api_key_required():
     )
     assert authed.status_code == 200
     assert "error" not in authed.body
+
+
+def test_router_reload_models():
+    """POST /models/reload re-reads the INI preset and updates the model list."""
+    global server
+
+    preset_path = os.path.join(TMP_DIR, "test_reload.ini")
+
+    # Initial preset: two models
+    with open(preset_path, "w") as f:
+        f.write(
+            "[model-reload-a]\n"
+            "hf-repo = ggml-org/test-model-stories260K\n"
+            "\n"
+            "[model-reload-b]\n"
+            "hf-repo = ggml-org/test-model-stories260K-infill\n"
+        )
+
+    server.models_preset = preset_path
+    server.start()
+
+    ids = _get_model_ids(is_reload=False)
+    assert "model-reload-a" in ids
+    assert "model-reload-b" in ids
+
+    # Updated preset: remove a, keep b unchanged, add c
+    with open(preset_path, "w") as f:
+        f.write(
+            "[model-reload-b]\n"
+            "hf-repo = ggml-org/test-model-stories260K-infill\n"
+            "\n"
+            "[model-reload-c]\n"
+            "hf-repo = ggml-org/test-model-stories260K\n"
+        )
+
+    try:
+        ids = _get_model_ids(is_reload=True)
+        assert "model-reload-a" not in ids, "removed model should no longer appear"
+        assert "model-reload-b" in ids, "unchanged model should still appear"
+        assert "model-reload-c" in ids, "newly added model should appear"
+    finally:
+        os.remove(preset_path)
diff --git a/tools/server/tests/unit/test_speculative.py b/tools/server/tests/unit/test_speculative.py
index eebd3cc8fa2..84cd77e6f2e 100644
--- a/tools/server/tests/unit/test_speculative.py
+++ b/tools/server/tests/unit/test_speculative.py
@@ -5,7 +5,7 @@
 
 server = ServerPreset.stories15m_moe()
 
-MODEL_DRAFT_FILE_URL = "https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories15M-q4_0.gguf"
+MODEL_DRAFT_FILE_URL = "https://huggingface.co/ggml-org/tiny-llamas/resolve/main/stories15M-q4_0.gguf"
 
 def create_server():
     global server
diff --git a/tools/server/tests/unit/test_tool_call.py b/tools/server/tests/unit/test_tool_call.py
index b1a5ab9da4e..9fa84d165ef 100755
--- a/tools/server/tests/unit/test_tool_call.py
+++ b/tools/server/tests/unit/test_tool_call.py
@@ -126,69 +126,70 @@ def do_test_completion_with_required_tool_tiny(server: ServerProcess, tool: dict
             actual_arguments = json.loads(actual_arguments)
         assert argument_key in actual_arguments, f"tool arguments: {actual_arguments}, expected: {argument_key}"
 
-
-@pytest.mark.parametrize("stream", [CompletionMode.NORMAL, CompletionMode.STREAMED])
-@pytest.mark.parametrize("template_name,tool,argument_key", [
-    ("Qwen3-Coder",                                   TEST_TOOL,            "success"),
-    ("Qwen3-Coder",                                   TEST_TOOL,            "success"),
-    ("meta-llama-Llama-3.3-70B-Instruct",             TEST_TOOL,            "success"),
-    ("meta-llama-Llama-3.3-70B-Instruct",             TEST_TOOL,            "success"),
-    ("meta-llama-Llama-3.3-70B-Instruct",             PYTHON_TOOL,          "code"),
-    ("meta-llama-Llama-3.3-70B-Instruct",             PYTHON_TOOL,          "code"),
-])
-def test_completion_with_required_tool_tiny_fast(template_name: str, tool: dict, argument_key: str | None, stream: CompletionMode):
-    global server
-    n_predict = 1024
-    # server = ServerPreset.stories15m_moe()
-    server.jinja = True
-    server.n_predict = n_predict
-    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
-    server.start()
-    do_test_completion_with_required_tool_tiny(server, tool, argument_key, n_predict, stream=stream == CompletionMode.STREAMED, temperature=0.0, top_k=1, top_p=1.0)
-
-
-@pytest.mark.slow
-@pytest.mark.parametrize("stream", [CompletionMode.NORMAL, CompletionMode.STREAMED])
-@pytest.mark.parametrize("template_name,tool,argument_key", [
-    ("meta-llama-Llama-3.1-8B-Instruct",              TEST_TOOL,            "success"),
-    ("meta-llama-Llama-3.1-8B-Instruct",              PYTHON_TOOL,          "code"),
-
-    ("meetkai-functionary-medium-v3.1",               TEST_TOOL,            "success"),
-    ("meetkai-functionary-medium-v3.1",               PYTHON_TOOL,          "code"),
-
-    ("meetkai-functionary-medium-v3.2",               TEST_TOOL,            "success"),
-    # Functionary v3.2 format supports raw python content, which w/ a dummy stories model will never end on its own.
-    # ("meetkai-functionary-medium-v3.2",               PYTHON_TOOL,          "code"),
-
-    ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", TEST_TOOL,            "success"),
-    ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", PYTHON_TOOL,          "code"),
-
-    ("meta-llama-Llama-3.2-3B-Instruct",              TEST_TOOL,            "success"),
-    ("meta-llama-Llama-3.2-3B-Instruct",              PYTHON_TOOL,          "code"),
-
-    ("mistralai-Mistral-Nemo-Instruct-2407",          TEST_TOOL,            "success"),
-    ("mistralai-Mistral-Nemo-Instruct-2407",          PYTHON_TOOL,          "code"),
-
-    ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use",   TEST_TOOL,            "success"),
-    ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use",   PYTHON_TOOL,          "code"),
-
-    ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B",      TEST_TOOL,            "success"),
-    ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B",      PYTHON_TOOL,          "code"),
-
-    ("fireworks-ai-llama-3-firefunction-v2",          TEST_TOOL,            "success"),
-    # ("fireworks-ai-llama-3-firefunction-v2",          PYTHON_TOOL,          "codeFalse), True),
-    # ("fireworks-ai-llama-3-firefunction-v2",          PYTHON_TOOL,          "code"),
-
-])
-def test_completion_with_required_tool_tiny_slow(template_name: str, tool: dict, argument_key: str | None, stream: CompletionMode):
-    global server
-    n_predict = 512
-    # server = ServerPreset.stories15m_moe()
-    server.jinja = True
-    server.n_predict = n_predict
-    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
-    server.start(timeout_seconds=TIMEOUT_START_SLOW)
-    do_test_completion_with_required_tool_tiny(server, tool, argument_key, n_predict, stream=stream == CompletionMode.STREAMED)
+# PR #22654: commented out since we're now allowing content before tool calls in tool_call: required, so we can't force this
+# in the tiny model just by using the grammar
+#
+# @pytest.mark.parametrize("stream", [CompletionMode.NORMAL, CompletionMode.STREAMED])
+# @pytest.mark.parametrize("template_name,tool,argument_key", [
+#     ("Qwen3-Coder",                                   TEST_TOOL,            "success"),
+#     ("Qwen3-Coder",                                   TEST_TOOL,            "success"),
+#     ("meta-llama-Llama-3.3-70B-Instruct",             TEST_TOOL,            "success"),
+#     ("meta-llama-Llama-3.3-70B-Instruct",             TEST_TOOL,            "success"),
+#     ("meta-llama-Llama-3.3-70B-Instruct",             PYTHON_TOOL,          "code"),
+#     ("meta-llama-Llama-3.3-70B-Instruct",             PYTHON_TOOL,          "code"),
+# ])
+# def test_completion_with_required_tool_tiny_fast(template_name: str, tool: dict, argument_key: str | None, stream: CompletionMode):
+#     global server
+#     n_predict = 1024
+#     # server = ServerPreset.stories15m_moe()
+#     server.jinja = True
+#     server.n_predict = n_predict
+#     server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+#     server.start()
+#     do_test_completion_with_required_tool_tiny(server, tool, argument_key, n_predict, stream=stream == CompletionMode.STREAMED, temperature=0.0, top_k=1, top_p=1.0)
+
+# @pytest.mark.slow
+# @pytest.mark.parametrize("stream", [CompletionMode.NORMAL, CompletionMode.STREAMED])
+# @pytest.mark.parametrize("template_name,tool,argument_key", [
+#     ("meta-llama-Llama-3.1-8B-Instruct",              TEST_TOOL,            "success"),
+#     ("meta-llama-Llama-3.1-8B-Instruct",              PYTHON_TOOL,          "code"),
+
+#     ("meetkai-functionary-medium-v3.1",               TEST_TOOL,            "success"),
+#     ("meetkai-functionary-medium-v3.1",               PYTHON_TOOL,          "code"),
+
+#     ("meetkai-functionary-medium-v3.2",               TEST_TOOL,            "success"),
+#     # Functionary v3.2 format supports raw python content, which w/ a dummy stories model will never end on its own.
+#     # ("meetkai-functionary-medium-v3.2",               PYTHON_TOOL,          "code"),
+
+#     ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", TEST_TOOL,            "success"),
+#     ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", PYTHON_TOOL,          "code"),
+
+#     ("meta-llama-Llama-3.2-3B-Instruct",              TEST_TOOL,            "success"),
+#     ("meta-llama-Llama-3.2-3B-Instruct",              PYTHON_TOOL,          "code"),
+
+#     ("mistralai-Mistral-Nemo-Instruct-2407",          TEST_TOOL,            "success"),
+#     ("mistralai-Mistral-Nemo-Instruct-2407",          PYTHON_TOOL,          "code"),
+
+#     ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use",   TEST_TOOL,            "success"),
+#     ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use",   PYTHON_TOOL,          "code"),
+
+#     ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B",      TEST_TOOL,            "success"),
+#     ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B",      PYTHON_TOOL,          "code"),
+
+#     ("fireworks-ai-llama-3-firefunction-v2",          TEST_TOOL,            "success"),
+#     # ("fireworks-ai-llama-3-firefunction-v2",          PYTHON_TOOL,          "codeFalse), True),
+#     # ("fireworks-ai-llama-3-firefunction-v2",          PYTHON_TOOL,          "code"),
+
+# ])
+# def test_completion_with_required_tool_tiny_slow(template_name: str, tool: dict, argument_key: str | None, stream: CompletionMode):
+#     global server
+#     n_predict = 512
+#     # server = ServerPreset.stories15m_moe()
+#     server.jinja = True
+#     server.n_predict = n_predict
+#     server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+#     server.start(timeout_seconds=TIMEOUT_START_SLOW)
+#     do_test_completion_with_required_tool_tiny(server, tool, argument_key, n_predict, stream=stream == CompletionMode.STREAMED)
 
 
 @pytest.mark.slow
diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py
index ddbb76c9adb..c5dba1c139f 100644
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@@ -5,6 +5,8 @@
 
 import subprocess
 import os
+
+TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "tmp")
 import re
 import json
 from json import JSONDecodeError
@@ -83,15 +85,15 @@ class ServerProcess:
     kv_unified: bool | None = False
     server_slots: bool | None = False
     pooling: str | None = None
-    draft: int | None = None
     api_key: str | None = None
     models_dir: str | None = None
     models_max: int | None = None
+    models_preset: str | None = None
     no_models_autoload: bool | None = None
     lora_files: List[str] | None = None
     enable_ctx_shift: int | None = False
-    draft_min: int | None = None
-    draft_max: int | None = None
+    spec_draft_n_min: int | None = None
+    spec_draft_n_max: int | None = None
     no_webui: bool | None = None
     jinja: bool | None = None
     reasoning_format: Literal['deepseek', 'none', 'nothink'] | None = None
@@ -106,6 +108,8 @@ class ServerProcess:
     no_cache_idle_slots: bool = False
     log_path: str | None = None
     webui_mcp_proxy: bool = False
+    backend_sampling: bool = False
+    gcp_compat: bool = False
 
     # session variables
     process: subprocess.Popen | None = None
@@ -120,6 +124,9 @@ def __init__(self):
         self.external_server = "DEBUG_EXTERNAL" in os.environ
 
     def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None:
+        env = {**os.environ}
+        if "LLAMA_CACHE" not in os.environ:
+            env["LLAMA_CACHE"] = "tmp"
         if self.external_server:
             print(f"[external_server]: Assuming external server running on {self.server_host}:{self.server_port}")
             return
@@ -157,6 +164,8 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None:
             server_args.extend(["--models-dir", self.models_dir])
         if self.models_max is not None:
             server_args.extend(["--models-max", self.models_max])
+        if self.models_preset:
+            server_args.extend(["--models-preset", self.models_preset])
         if self.n_batch:
             server_args.extend(["--batch-size", self.n_batch])
         if self.n_ubatch:
@@ -165,8 +174,6 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None:
             server_args.extend(["--threads", self.n_threads])
         if self.n_gpu_layer:
             server_args.extend(["--n-gpu-layers", self.n_gpu_layer])
-        if self.draft is not None:
-            server_args.extend(["--draft", self.draft])
         if self.server_continuous_batching:
             server_args.append("--cont-batching")
         if self.server_embeddings:
@@ -214,10 +221,10 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None:
             server_args.append("--context-shift")
         if self.api_key:
             server_args.extend(["--api-key", self.api_key])
-        if self.draft_max:
-            server_args.extend(["--draft-max", self.draft_max])
-        if self.draft_min:
-            server_args.extend(["--draft-min", self.draft_min])
+        if self.spec_draft_n_max:
+            server_args.extend(["--spec-draft-n-max", self.spec_draft_n_max])
+        if self.spec_draft_n_min:
+            server_args.extend(["--spec-draft-n-min", self.spec_draft_n_min])
         if self.no_webui:
             server_args.append("--no-webui")
         if self.no_models_autoload:
@@ -246,6 +253,10 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None:
             server_args.append("--no-cache-idle-slots")
         if self.webui_mcp_proxy:
             server_args.append("--webui-mcp-proxy")
+        if self.backend_sampling:
+            server_args.append("--backend_sampling")
+        if self.gcp_compat:
+            env["AIP_MODE"] = "PREDICTION"
 
         args = [str(arg) for arg in [server_path, *server_args]]
         print(f"tests: starting server with: {' '.join(args)}")
@@ -266,7 +277,7 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None:
             creationflags=flags,
             stdout=self._log,
             stderr=self._log if self._log != sys.stdout else sys.stdout,
-            env={**os.environ, "LLAMA_CACHE": "tmp"} if "LLAMA_CACHE" not in os.environ else None,
+            env=env,
         )
         server_instances.add(self)
 
diff --git a/tools/server/webui/scripts/post-build.sh b/tools/server/webui/scripts/post-build.sh
deleted file mode 100755
index 55e46d5d5c6..00000000000
--- a/tools/server/webui/scripts/post-build.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-rm -rf ../public/_app;
-rm ../public/favicon.svg;
-rm -f ../public/index.html.gz; # deprecated, but may still be generated by older versions of the build process
diff --git a/tools/server/webui/src/lib/components/app/actions/ActionIcon.svelte b/tools/server/webui/src/lib/components/app/actions/ActionIcon.svelte
deleted file mode 100644
index 1d2dd3c1d9d..00000000000
--- a/tools/server/webui/src/lib/components/app/actions/ActionIcon.svelte
+++ /dev/null
@@ -1,50 +0,0 @@
-<script lang="ts">
-	import { Button } from '$lib/components/ui/button';
-	import * as Tooltip from '$lib/components/ui/tooltip';
-	import type { Component } from 'svelte';
-
-	interface Props {
-		icon: Component;
-		tooltip: string;
-		variant?: 'default' | 'destructive' | 'outline' | 'secondary' | 'ghost' | 'link';
-		size?: 'default' | 'sm' | 'lg' | 'icon';
-		iconSize?: string;
-		class?: string;
-		disabled?: boolean;
-		onclick: (e?: MouseEvent) => void;
-		'aria-label'?: string;
-	}
-
-	let {
-		icon,
-		tooltip,
-		variant = 'ghost',
-		size = 'sm',
-		class: className = '',
-		disabled = false,
-		iconSize = 'h-3 w-3',
-		onclick,
-		'aria-label': ariaLabel
-	}: Props = $props();
-</script>
-
-<Tooltip.Root>
-	<Tooltip.Trigger>
-		<Button
-			{variant}
-			{size}
-			{disabled}
-			{onclick}
-			class="h-6 w-6 p-0 {className} flex"
-			aria-label={ariaLabel || tooltip}
-		>
-			{@const IconComponent = icon}
-
-			<IconComponent class={iconSize} />
-		</Button>
-	</Tooltip.Trigger>
-
-	<Tooltip.Content>
-		<p>{tooltip}</p>
-	</Tooltip.Content>
-</Tooltip.Root>
diff --git a/tools/server/webui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte b/tools/server/webui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte
deleted file mode 100644
index bf6cd4fb28c..00000000000
--- a/tools/server/webui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte
+++ /dev/null
@@ -1,18 +0,0 @@
-<script lang="ts">
-	import { Copy } from '@lucide/svelte';
-	import { copyToClipboard } from '$lib/utils';
-
-	interface Props {
-		ariaLabel?: string;
-		canCopy?: boolean;
-		text: string;
-	}
-
-	let { ariaLabel = 'Copy to clipboard', canCopy = true, text }: Props = $props();
-</script>
-
-<Copy
-	class="h-3 w-3 flex-shrink-0 cursor-{canCopy ? 'pointer' : 'not-allowed'}"
-	aria-label={ariaLabel}
-	onclick={() => canCopy && copyToClipboard(text)}
-/>
diff --git a/tools/server/webui/src/lib/components/app/actions/ActionIconRemove.svelte b/tools/server/webui/src/lib/components/app/actions/ActionIconRemove.svelte
deleted file mode 100644
index 11f1c17d988..00000000000
--- a/tools/server/webui/src/lib/components/app/actions/ActionIconRemove.svelte
+++ /dev/null
@@ -1,27 +0,0 @@
-<script lang="ts">
-	import { X } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-
-	interface Props {
-		id: string;
-		onRemove?: (id: string) => void;
-		class?: string;
-		iconSize?: number;
-	}
-
-	let { id, onRemove, class: className = '', iconSize = 3 }: Props = $props();
-</script>
-
-<Button
-	type="button"
-	variant="ghost"
-	size="icon-sm"
-	class="bg-white/20 p-0 hover:bg-white/30 {className}"
-	onclick={(e: MouseEvent) => {
-		e.stopPropagation();
-		onRemove?.(id);
-	}}
-	aria-label="Remove file"
->
-	<X class="h-{iconSize} w-{iconSize}" />
-</Button>
diff --git a/tools/server/webui/src/lib/components/app/actions/ActionIconsCodeBlock.svelte b/tools/server/webui/src/lib/components/app/actions/ActionIconsCodeBlock.svelte
deleted file mode 100644
index b20e79b5e0b..00000000000
--- a/tools/server/webui/src/lib/components/app/actions/ActionIconsCodeBlock.svelte
+++ /dev/null
@@ -1,46 +0,0 @@
-<script lang="ts">
-	import { Eye } from '@lucide/svelte';
-	import { ActionIconCopyToClipboard } from '$lib/components/app';
-	import { FileTypeText } from '$lib/enums';
-
-	interface Props {
-		code: string;
-		language: string;
-		disabled?: boolean;
-		onPreview?: (code: string, language: string) => void;
-	}
-
-	let { code, language, disabled = false, onPreview }: Props = $props();
-
-	const showPreview = $derived(language?.toLowerCase() === FileTypeText.HTML);
-
-	function handlePreview() {
-		if (disabled) return;
-		onPreview?.(code, language);
-	}
-</script>
-
-<div class="code-block-actions">
-	<div class="copy-code-btn" class:opacity-50={disabled} class:!cursor-not-allowed={disabled}>
-		<ActionIconCopyToClipboard
-			text={code}
-			canCopy={!disabled}
-			ariaLabel={disabled ? 'Code incomplete' : 'Copy code'}
-		/>
-	</div>
-
-	{#if showPreview}
-		<button
-			class="preview-code-btn"
-			class:opacity-50={disabled}
-			class:!cursor-not-allowed={disabled}
-			title={disabled ? 'Code incomplete' : 'Preview code'}
-			aria-label="Preview code"
-			aria-disabled={disabled}
-			type="button"
-			onclick={handlePreview}
-		>
-			<Eye size={16} />
-		</button>
-	{/if}
-</div>
diff --git a/tools/server/webui/src/lib/components/app/actions/index.ts b/tools/server/webui/src/lib/components/app/actions/index.ts
deleted file mode 100644
index 43485c7b7e1..00000000000
--- a/tools/server/webui/src/lib/components/app/actions/index.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-/**
- *
- * ACTIONS
- *
- * Small interactive components for user actions.
- *
- */
-
-/** Styled icon button for action triggers with tooltip. */
-export { default as ActionIcon } from './ActionIcon.svelte';
-
-/** Code block actions component (copy, preview). */
-export { default as ActionIconsCodeBlock } from './ActionIconsCodeBlock.svelte';
-
-/** Copy-to-clipboard icon button with click handler. */
-export { default as ActionIconCopyToClipboard } from './ActionIconCopyToClipboard.svelte';
-
-/** Remove/delete icon button with X icon. */
-export { default as ActionIconRemove } from './ActionIconRemove.svelte';
diff --git a/tools/server/webui/src/lib/components/app/badges/BadgeModality.svelte b/tools/server/webui/src/lib/components/app/badges/BadgeModality.svelte
deleted file mode 100644
index 15936691a6a..00000000000
--- a/tools/server/webui/src/lib/components/app/badges/BadgeModality.svelte
+++ /dev/null
@@ -1,39 +0,0 @@
-<script lang="ts">
-	import { ModelModality } from '$lib/enums';
-	import { MODALITY_ICONS, MODALITY_LABELS } from '$lib/constants';
-	import { cn } from '$lib/components/ui/utils';
-
-	type DisplayableModality = ModelModality.VISION | ModelModality.AUDIO;
-
-	interface Props {
-		modalities: ModelModality[];
-		class?: string;
-	}
-
-	let { modalities, class: className = '' }: Props = $props();
-
-	// Filter to only modalities that have icons (VISION, AUDIO)
-	const displayableModalities = $derived(
-		modalities.filter(
-			(m): m is DisplayableModality => m === ModelModality.VISION || m === ModelModality.AUDIO
-		)
-	);
-</script>
-
-{#each displayableModalities as modality, index (index)}
-	{@const IconComponent = MODALITY_ICONS[modality]}
-	{@const label = MODALITY_LABELS[modality]}
-
-	<span
-		class={cn(
-			'inline-flex items-center gap-1 rounded-md bg-muted px-2 py-1 text-xs font-medium',
-			className
-		)}
-	>
-		{#if IconComponent}
-			<IconComponent class="h-3 w-3" />
-		{/if}
-
-		{label}
-	</span>
-{/each}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentPreview.svelte b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentPreview.svelte
deleted file mode 100644
index f05bdd8a03a..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentPreview.svelte
+++ /dev/null
@@ -1,284 +0,0 @@
-<script lang="ts">
-	import { Button } from '$lib/components/ui/button';
-	import * as Alert from '$lib/components/ui/alert';
-	import { SyntaxHighlightedCode } from '$lib/components/app';
-	import { FileText, Image, Music, FileIcon, Eye, Info } from '@lucide/svelte';
-	import {
-		isTextFile,
-		isImageFile,
-		isPdfFile,
-		isAudioFile,
-		getLanguageFromFilename,
-		createBase64DataUrl
-	} from '$lib/utils';
-	import { convertPDFToImage } from '$lib/utils/browser-only';
-	import { modelsStore } from '$lib/stores/models.svelte';
-
-	interface Props {
-		// Either an uploaded file or a stored attachment
-		uploadedFile?: ChatUploadedFile;
-		attachment?: DatabaseMessageExtra;
-		// For uploaded files
-		preview?: string;
-		name?: string;
-		textContent?: string;
-		// For checking vision modality
-		activeModelId?: string;
-	}
-
-	let { uploadedFile, attachment, preview, name, textContent, activeModelId }: Props = $props();
-
-	let hasVisionModality = $derived(
-		activeModelId ? modelsStore.modelSupportsVision(activeModelId) : false
-	);
-
-	let displayName = $derived(uploadedFile?.name || attachment?.name || name || 'Unknown File');
-
-	// Determine file type from uploaded file or attachment
-	let isAudio = $derived(isAudioFile(attachment, uploadedFile));
-	let isImage = $derived(isImageFile(attachment, uploadedFile));
-	let isPdf = $derived(isPdfFile(attachment, uploadedFile));
-	let isText = $derived(isTextFile(attachment, uploadedFile));
-
-	let displayPreview = $derived(
-		uploadedFile?.preview ||
-			(isImage && attachment && 'base64Url' in attachment ? attachment.base64Url : preview)
-	);
-
-	let displayTextContent = $derived(
-		uploadedFile?.textContent ||
-			(attachment && 'content' in attachment ? attachment.content : textContent)
-	);
-
-	let language = $derived(getLanguageFromFilename(displayName));
-
-	let IconComponent = $derived(() => {
-		if (isImage) return Image;
-		if (isText || isPdf) return FileText;
-		if (isAudio) return Music;
-
-		return FileIcon;
-	});
-
-	let pdfViewMode = $state<'text' | 'pages'>('pages');
-
-	let pdfImages = $state<string[]>([]);
-
-	let pdfImagesLoading = $state(false);
-
-	let pdfImagesError = $state<string | null>(null);
-
-	async function loadPdfImages() {
-		if (!isPdf || pdfImages.length > 0 || pdfImagesLoading) return;
-
-		pdfImagesLoading = true;
-		pdfImagesError = null;
-
-		try {
-			let file: File | null = null;
-
-			if (uploadedFile?.file) {
-				file = uploadedFile.file;
-			} else if (isPdf && attachment) {
-				// Check if we have pre-processed images
-				if (
-					'images' in attachment &&
-					attachment.images &&
-					Array.isArray(attachment.images) &&
-					attachment.images.length > 0
-				) {
-					pdfImages = attachment.images;
-					return;
-				}
-
-				// Convert base64 back to File for processing
-				if ('base64Data' in attachment && attachment.base64Data) {
-					const base64Data = attachment.base64Data;
-					const byteCharacters = atob(base64Data);
-					const byteNumbers = new Array(byteCharacters.length);
-					for (let i = 0; i < byteCharacters.length; i++) {
-						byteNumbers[i] = byteCharacters.charCodeAt(i);
-					}
-					const byteArray = new Uint8Array(byteNumbers);
-					file = new File([byteArray], displayName, { type: 'application/pdf' });
-				}
-			}
-
-			if (file) {
-				pdfImages = await convertPDFToImage(file);
-			} else {
-				throw new Error('No PDF file available for conversion');
-			}
-		} catch (error) {
-			pdfImagesError = error instanceof Error ? error.message : 'Failed to load PDF images';
-		} finally {
-			pdfImagesLoading = false;
-		}
-	}
-
-	export function reset() {
-		pdfImages = [];
-		pdfImagesLoading = false;
-		pdfImagesError = null;
-		pdfViewMode = 'pages';
-	}
-
-	$effect(() => {
-		if (isPdf && pdfViewMode === 'pages') {
-			loadPdfImages();
-		}
-	});
-</script>
-
-<div class="space-y-4">
-	<div class="flex items-center justify-end gap-6">
-		{#if isPdf}
-			<div class="flex items-center gap-2">
-				<Button
-					variant={pdfViewMode === 'text' ? 'default' : 'outline'}
-					size="sm"
-					onclick={() => (pdfViewMode = 'text')}
-					disabled={pdfImagesLoading}
-				>
-					<FileText class="mr-1 h-4 w-4" />
-
-					Text
-				</Button>
-
-				<Button
-					variant={pdfViewMode === 'pages' ? 'default' : 'outline'}
-					size="sm"
-					onclick={() => {
-						pdfViewMode = 'pages';
-						loadPdfImages();
-					}}
-					disabled={pdfImagesLoading}
-				>
-					{#if pdfImagesLoading}
-						<div
-							class="mr-1 h-4 w-4 animate-spin rounded-full border-2 border-current border-t-transparent"
-						></div>
-					{:else}
-						<Eye class="mr-1 h-4 w-4" />
-					{/if}
-
-					Pages
-				</Button>
-			</div>
-		{/if}
-	</div>
-
-	<div class="flex-1 overflow-auto">
-		{#if isImage && displayPreview}
-			<div class="flex items-center justify-center">
-				<img
-					src={displayPreview}
-					alt={displayName}
-					class="max-h-full rounded-lg object-contain shadow-lg"
-				/>
-			</div>
-		{:else if isPdf && pdfViewMode === 'pages'}
-			{#if !hasVisionModality && activeModelId}
-				<Alert.Root class="mb-4">
-					<Info class="h-4 w-4" />
-					<Alert.Title>Preview only</Alert.Title>
-					<Alert.Description>
-						<span class="inline-flex">
-							The selected model does not support vision. Only the extracted
-							<!-- svelte-ignore a11y_click_events_have_key_events -->
-							<!-- svelte-ignore a11y_no_static_element_interactions -->
-							<span class="mx-1 cursor-pointer underline" onclick={() => (pdfViewMode = 'text')}>
-								text
-							</span>
-							will be sent to the model.
-						</span>
-					</Alert.Description>
-				</Alert.Root>
-			{/if}
-
-			{#if pdfImagesLoading}
-				<div class="flex items-center justify-center p-8">
-					<div class="text-center">
-						<div
-							class="mx-auto mb-4 h-8 w-8 animate-spin rounded-full border-4 border-primary border-t-transparent"
-						></div>
-
-						<p class="text-muted-foreground">Converting PDF to images...</p>
-					</div>
-				</div>
-			{:else if pdfImagesError}
-				<div class="flex items-center justify-center p-8">
-					<div class="text-center">
-						<FileText class="mx-auto mb-4 h-16 w-16 text-muted-foreground" />
-
-						<p class="mb-4 text-muted-foreground">Failed to load PDF images</p>
-
-						<p class="text-sm text-muted-foreground">{pdfImagesError}</p>
-
-						<Button class="mt-4" onclick={() => (pdfViewMode = 'text')}>View as Text</Button>
-					</div>
-				</div>
-			{:else if pdfImages.length > 0}
-				<div class="max-h-[70vh] space-y-4 overflow-auto">
-					{#each pdfImages as image, index (image)}
-						<div class="text-center">
-							<p class="mb-2 text-sm text-muted-foreground">Page {index + 1}</p>
-
-							<img
-								src={image}
-								alt="PDF Page {index + 1}"
-								class="mx-auto max-w-full rounded-lg shadow-lg"
-							/>
-						</div>
-					{/each}
-				</div>
-			{:else}
-				<div class="flex items-center justify-center p-8">
-					<div class="text-center">
-						<FileText class="mx-auto mb-4 h-16 w-16 text-muted-foreground" />
-
-						<p class="mb-4 text-muted-foreground">No PDF pages available</p>
-					</div>
-				</div>
-			{/if}
-		{:else if (isText || (isPdf && pdfViewMode === 'text')) && displayTextContent}
-			<SyntaxHighlightedCode code={displayTextContent} {language} maxWidth="calc(69rem - 2rem)" />
-		{:else if isAudio}
-			<div class="flex items-center justify-center p-8">
-				<div class="w-full max-w-md text-center">
-					<Music class="mx-auto mb-4 h-16 w-16 text-muted-foreground" />
-
-					{#if uploadedFile?.preview}
-						<audio controls class="mb-4 w-full" src={uploadedFile.preview}>
-							Your browser does not support the audio element.
-						</audio>
-					{:else if isAudio && attachment && 'mimeType' in attachment && 'base64Data' in attachment}
-						<audio
-							controls
-							class="mb-4 w-full"
-							src={createBase64DataUrl(attachment.mimeType, attachment.base64Data)}
-						>
-							Your browser does not support the audio element.
-						</audio>
-					{:else}
-						<p class="mb-4 text-muted-foreground">Audio preview not available</p>
-					{/if}
-
-					<p class="text-sm text-muted-foreground">
-						{displayName}
-					</p>
-				</div>
-			</div>
-		{:else}
-			<div class="flex items-center justify-center p-8">
-				<div class="text-center">
-					{#if IconComponent}
-						<IconComponent class="mx-auto mb-4 h-16 w-16 text-muted-foreground" />
-					{/if}
-
-					<p class="mb-4 text-muted-foreground">Preview not available for this file type</p>
-				</div>
-			</div>
-		{/if}
-	</div>
-</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte
deleted file mode 100644
index 9d32ea0721a..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte
+++ /dev/null
@@ -1,165 +0,0 @@
-<script lang="ts">
-	import { ActionIconRemove } from '$lib/components/app';
-	import { formatFileSize, getFileTypeLabel, getPreviewText, isTextFile } from '$lib/utils';
-	import { AttachmentType } from '$lib/enums';
-
-	interface Props {
-		class?: string;
-		id: string;
-		onClick?: (event?: MouseEvent) => void;
-		onRemove?: (id: string) => void;
-		name: string;
-		readonly?: boolean;
-		size?: number;
-		textContent?: string;
-		// Either uploaded file or stored attachment
-		uploadedFile?: ChatUploadedFile;
-		attachment?: DatabaseMessageExtra;
-	}
-
-	let {
-		class: className = '',
-		id,
-		onClick,
-		onRemove,
-		name,
-		readonly = false,
-		size,
-		textContent,
-		uploadedFile,
-		attachment
-	}: Props = $props();
-
-	let isText = $derived(isTextFile(attachment, uploadedFile));
-
-	let fileTypeLabel = $derived.by(() => {
-		if (uploadedFile?.type) {
-			return getFileTypeLabel(uploadedFile.type);
-		}
-
-		if (attachment) {
-			if ('mimeType' in attachment && attachment.mimeType) {
-				return getFileTypeLabel(attachment.mimeType);
-			}
-
-			if (attachment.type) {
-				return getFileTypeLabel(attachment.type);
-			}
-		}
-
-		return getFileTypeLabel(name);
-	});
-
-	let pdfProcessingMode = $derived.by(() => {
-		if (attachment?.type === AttachmentType.PDF) {
-			const pdfAttachment = attachment as DatabaseMessageExtraPdfFile;
-
-			return pdfAttachment.processedAsImages ? 'Sent as Image' : 'Sent as Text';
-		}
-		return null;
-	});
-</script>
-
-{#if isText}
-	{#if readonly}
-		<!-- Readonly mode (ChatMessage) -->
-		<button
-			class="cursor-pointer rounded-lg border border-border bg-muted p-3 transition-shadow hover:shadow-md {className} w-full max-w-2xl"
-			onclick={onClick}
-			aria-label={`Preview ${name}`}
-			type="button"
-		>
-			<div class="flex items-start gap-3">
-				<div class="flex min-w-0 flex-1 flex-col items-start text-left">
-					<span class="w-full truncate text-sm font-medium text-foreground">{name}</span>
-
-					{#if size}
-						<span class="text-xs text-muted-foreground">{formatFileSize(size)}</span>
-					{/if}
-
-					{#if textContent}
-						<div class="relative mt-2 w-full">
-							<div
-								class="overflow-hidden font-mono text-xs leading-relaxed break-words whitespace-pre-wrap text-muted-foreground"
-							>
-								{getPreviewText(textContent)}
-							</div>
-
-							{#if textContent.length > 150}
-								<div
-									class="pointer-events-none absolute right-0 bottom-0 left-0 h-6 bg-gradient-to-t from-muted to-transparent"
-								></div>
-							{/if}
-						</div>
-					{/if}
-				</div>
-			</div>
-		</button>
-	{:else}
-		<!-- Non-readonly mode (ChatForm) -->
-		<button
-			class="group relative rounded-lg border border-border bg-muted p-3 {className} {textContent
-				? 'max-h-24 max-w-72'
-				: 'max-w-36'} cursor-pointer text-left"
-			onclick={onClick}
-		>
-			<div class="absolute top-2 right-2 opacity-0 transition-opacity group-hover:opacity-100">
-				<ActionIconRemove {id} {onRemove} />
-			</div>
-
-			<div class="pr-8">
-				<span class="mb-3 block truncate text-sm font-medium text-foreground">{name}</span>
-
-				{#if textContent}
-					<div class="relative">
-						<div
-							class="overflow-hidden font-mono text-xs leading-relaxed break-words whitespace-pre-wrap text-muted-foreground"
-							style="max-height: 3rem; line-height: 1.2em;"
-						>
-							{getPreviewText(textContent)}
-						</div>
-
-						{#if textContent.length > 150}
-							<div
-								class="pointer-events-none absolute right-0 bottom-0 left-0 h-4 bg-gradient-to-t from-muted to-transparent"
-							></div>
-						{/if}
-					</div>
-				{/if}
-			</div>
-		</button>
-	{/if}
-{:else}
-	<button
-		class="group flex items-center gap-3 rounded-lg border border-border bg-muted p-3 {className} relative"
-		onclick={onClick}
-	>
-		<div
-			class="flex h-8 w-8 items-center justify-center rounded bg-primary/10 text-xs font-medium text-primary"
-		>
-			{fileTypeLabel}
-		</div>
-
-		<div class="flex flex-col gap-0.5">
-			<span
-				class="max-w-24 truncate text-sm font-medium text-foreground {readonly
-					? ''
-					: 'group-hover:pr-6'} md:max-w-32"
-			>
-				{name}
-			</span>
-
-			{#if pdfProcessingMode}
-				<span class="text-left text-xs text-muted-foreground">{pdfProcessingMode}</span>
-			{:else if size}
-				<span class="text-left text-xs text-muted-foreground">{formatFileSize(size)}</span>
-			{/if}
-		</div>
-
-		{#if !readonly}
-			<div class="absolute top-2 right-2 opacity-0 transition-opacity group-hover:opacity-100">
-				<ActionIconRemove {id} {onRemove} />
-			</div>
-		{/if}
-	</button>
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList.svelte b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList.svelte
deleted file mode 100644
index a3d37b42a3b..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList.svelte
+++ /dev/null
@@ -1,287 +0,0 @@
-<script lang="ts">
-	import {
-		ChatAttachmentMcpPrompt,
-		ChatAttachmentMcpResource,
-		ChatAttachmentThumbnailImage,
-		ChatAttachmentThumbnailFile,
-		HorizontalScrollCarousel,
-		DialogChatAttachmentPreview,
-		DialogChatAttachmentsViewAll,
-		DialogMcpResourcePreview
-	} from '$lib/components/app';
-	import { Button } from '$lib/components/ui/button';
-	import { AttachmentType } from '$lib/enums';
-	import type {
-		DatabaseMessageExtraMcpPrompt,
-		DatabaseMessageExtraMcpResource,
-		MCPResourceAttachment
-	} from '$lib/types';
-	import { getAttachmentDisplayItems } from '$lib/utils';
-
-	interface Props {
-		class?: string;
-		style?: string;
-		// For ChatMessage - stored attachments
-		attachments?: DatabaseMessageExtra[];
-		readonly?: boolean;
-		// For ChatForm - pending uploads
-		onFileRemove?: (fileId: string) => void;
-		uploadedFiles?: ChatUploadedFile[];
-		// Image size customization
-		imageClass?: string;
-		imageHeight?: string;
-		imageWidth?: string;
-		// Limit display to single row with "+ X more" button
-		limitToSingleRow?: boolean;
-		// For vision modality check
-		activeModelId?: string;
-	}
-
-	let {
-		class: className = '',
-		style = '',
-		attachments = [],
-		readonly = false,
-		onFileRemove,
-		uploadedFiles = $bindable([]),
-		// Default to small size for form previews
-		imageClass = '',
-		imageHeight = 'h-24',
-		imageWidth = 'w-auto',
-		limitToSingleRow = false,
-		activeModelId
-	}: Props = $props();
-
-	let displayItems = $derived(getAttachmentDisplayItems({ uploadedFiles, attachments }));
-
-	let carouselRef: HorizontalScrollCarousel | undefined = $state();
-	let isScrollable = $state(false);
-	let previewDialogOpen = $state(false);
-	let previewItem = $state<ChatAttachmentPreviewItem | null>(null);
-	let mcpResourcePreviewOpen = $state(false);
-	let mcpResourcePreviewExtra = $state<DatabaseMessageExtraMcpResource | null>(null);
-	let showViewAll = $derived(limitToSingleRow && displayItems.length > 0 && isScrollable);
-	let viewAllDialogOpen = $state(false);
-
-	function openPreview(item: ChatAttachmentDisplayItem, event?: MouseEvent) {
-		event?.stopPropagation();
-		event?.preventDefault();
-
-		previewItem = {
-			uploadedFile: item.uploadedFile,
-			attachment: item.attachment,
-			preview: item.preview,
-			name: item.name,
-			size: item.size,
-			textContent: item.textContent
-		};
-		previewDialogOpen = true;
-	}
-
-	function openMcpResourcePreview(extra: DatabaseMessageExtraMcpResource) {
-		mcpResourcePreviewExtra = extra;
-		mcpResourcePreviewOpen = true;
-	}
-
-	function toMcpResourceAttachment(
-		extra: DatabaseMessageExtraMcpResource,
-		id: string
-	): MCPResourceAttachment {
-		return {
-			id,
-			resource: {
-				uri: extra.uri,
-				name: extra.name,
-				title: extra.name,
-				serverName: extra.serverName
-			}
-		};
-	}
-
-	$effect(() => {
-		if (carouselRef && displayItems.length) {
-			carouselRef.resetScroll();
-		}
-	});
-</script>
-
-{#if displayItems.length > 0}
-	<div class={className} {style}>
-		{#if limitToSingleRow}
-			<HorizontalScrollCarousel
-				bind:this={carouselRef}
-				onScrollableChange={(scrollable) => (isScrollable = scrollable)}
-			>
-				{#each displayItems as item (item.id)}
-					{#if item.isMcpPrompt}
-						{@const mcpPrompt =
-							item.attachment?.type === AttachmentType.MCP_PROMPT
-								? (item.attachment as DatabaseMessageExtraMcpPrompt)
-								: item.uploadedFile?.mcpPrompt
-									? {
-											type: AttachmentType.MCP_PROMPT as const,
-											name: item.name,
-											serverName: item.uploadedFile.mcpPrompt.serverName,
-											promptName: item.uploadedFile.mcpPrompt.promptName,
-											content: item.textContent ?? '',
-											arguments: item.uploadedFile.mcpPrompt.arguments
-										}
-									: null}
-						{#if mcpPrompt}
-							<ChatAttachmentMcpPrompt
-								class="max-w-[300px] min-w-[200px] flex-shrink-0 {limitToSingleRow
-									? 'first:ml-4 last:mr-4'
-									: ''}"
-								prompt={mcpPrompt}
-								{readonly}
-								isLoading={item.isLoading}
-								loadError={item.loadError}
-								onRemove={onFileRemove ? () => onFileRemove(item.id) : undefined}
-							/>
-						{/if}
-					{:else if item.isMcpResource && item.attachment?.type === AttachmentType.MCP_RESOURCE}
-						{@const mcpResource = item.attachment as DatabaseMessageExtraMcpResource}
-
-						<ChatAttachmentMcpResource
-							class="flex-shrink-0 {limitToSingleRow ? 'first:ml-4 last:mr-4' : ''}"
-							attachment={toMcpResourceAttachment(mcpResource, item.id)}
-							onClick={() => openMcpResourcePreview(mcpResource)}
-						/>
-					{:else if item.isImage && item.preview}
-						<ChatAttachmentThumbnailImage
-							class="flex-shrink-0 cursor-pointer {limitToSingleRow ? 'first:ml-4 last:mr-4' : ''}"
-							id={item.id}
-							name={item.name}
-							preview={item.preview}
-							{readonly}
-							onRemove={onFileRemove}
-							height={imageHeight}
-							width={imageWidth}
-							{imageClass}
-							onClick={(event) => openPreview(item, event)}
-						/>
-					{:else}
-						<ChatAttachmentThumbnailFile
-							class="flex-shrink-0 cursor-pointer {limitToSingleRow ? 'first:ml-4 last:mr-4' : ''}"
-							id={item.id}
-							name={item.name}
-							size={item.size}
-							{readonly}
-							onRemove={onFileRemove}
-							textContent={item.textContent}
-							attachment={item.attachment}
-							uploadedFile={item.uploadedFile}
-							onClick={(event) => openPreview(item, event)}
-						/>
-					{/if}
-				{/each}
-			</HorizontalScrollCarousel>
-
-			{#if showViewAll}
-				<div class="mt-2 -mr-2 flex justify-end px-4">
-					<Button
-						type="button"
-						variant="ghost"
-						size="sm"
-						class="h-6 text-xs text-muted-foreground hover:text-foreground"
-						onclick={() => (viewAllDialogOpen = true)}
-					>
-						View all ({displayItems.length})
-					</Button>
-				</div>
-			{/if}
-		{:else}
-			<div class="flex flex-wrap items-start justify-end gap-3">
-				{#each displayItems as item (item.id)}
-					{#if item.isMcpPrompt}
-						{@const mcpPrompt =
-							item.attachment?.type === AttachmentType.MCP_PROMPT
-								? (item.attachment as DatabaseMessageExtraMcpPrompt)
-								: item.uploadedFile?.mcpPrompt
-									? {
-											type: AttachmentType.MCP_PROMPT as const,
-											name: item.name,
-											serverName: item.uploadedFile.mcpPrompt.serverName,
-											promptName: item.uploadedFile.mcpPrompt.promptName,
-											content: item.textContent ?? '',
-											arguments: item.uploadedFile.mcpPrompt.arguments
-										}
-									: null}
-
-						{#if mcpPrompt}
-							<ChatAttachmentMcpPrompt
-								class="max-w-[300px] min-w-[200px]"
-								prompt={mcpPrompt}
-								{readonly}
-								isLoading={item.isLoading}
-								loadError={item.loadError}
-								onRemove={onFileRemove ? () => onFileRemove(item.id) : undefined}
-							/>
-						{/if}
-					{:else if item.isMcpResource && item.attachment?.type === AttachmentType.MCP_RESOURCE}
-						{@const mcpResource = item.attachment as DatabaseMessageExtraMcpResource}
-
-						<ChatAttachmentMcpResource
-							attachment={toMcpResourceAttachment(mcpResource, item.id)}
-							onClick={() => openMcpResourcePreview(mcpResource)}
-						/>
-					{:else if item.isImage && item.preview}
-						<ChatAttachmentThumbnailImage
-							class="cursor-pointer"
-							id={item.id}
-							name={item.name}
-							preview={item.preview}
-							{readonly}
-							onRemove={onFileRemove}
-							height={imageHeight}
-							width={imageWidth}
-							{imageClass}
-							onClick={(event) => openPreview(item, event)}
-						/>
-					{:else}
-						<ChatAttachmentThumbnailFile
-							class="cursor-pointer"
-							id={item.id}
-							name={item.name}
-							size={item.size}
-							{readonly}
-							onRemove={onFileRemove}
-							textContent={item.textContent}
-							attachment={item.attachment}
-							uploadedFile={item.uploadedFile}
-							onClick={(event?: MouseEvent) => openPreview(item, event)}
-						/>
-					{/if}
-				{/each}
-			</div>
-		{/if}
-	</div>
-{/if}
-
-{#if previewItem}
-	<DialogChatAttachmentPreview
-		bind:open={previewDialogOpen}
-		uploadedFile={previewItem.uploadedFile}
-		attachment={previewItem.attachment}
-		preview={previewItem.preview}
-		name={previewItem.name}
-		size={previewItem.size}
-		textContent={previewItem.textContent}
-		{activeModelId}
-	/>
-{/if}
-
-<DialogChatAttachmentsViewAll
-	bind:open={viewAllDialogOpen}
-	{uploadedFiles}
-	{attachments}
-	{readonly}
-	{onFileRemove}
-	imageHeight="h-64"
-	{imageClass}
-	{activeModelId}
-/>
-
-{#if mcpResourcePreviewExtra}
-	<DialogMcpResourcePreview bind:open={mcpResourcePreviewOpen} extra={mcpResourcePreviewExtra} />
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsViewAll.svelte b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsViewAll.svelte
deleted file mode 100644
index 279b2e22273..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsViewAll.svelte
+++ /dev/null
@@ -1,117 +0,0 @@
-<script lang="ts">
-	import {
-		ChatAttachmentThumbnailImage,
-		ChatAttachmentThumbnailFile,
-		DialogChatAttachmentPreview
-	} from '$lib/components/app';
-	import { getAttachmentDisplayItems } from '$lib/utils';
-
-	interface Props {
-		uploadedFiles?: ChatUploadedFile[];
-		attachments?: DatabaseMessageExtra[];
-		readonly?: boolean;
-		onFileRemove?: (fileId: string) => void;
-		imageHeight?: string;
-		imageWidth?: string;
-		imageClass?: string;
-		activeModelId?: string;
-	}
-
-	let {
-		uploadedFiles = [],
-		attachments = [],
-		readonly = false,
-		onFileRemove,
-		imageHeight = 'h-24',
-		imageWidth = 'w-auto',
-		imageClass = '',
-		activeModelId
-	}: Props = $props();
-
-	let previewDialogOpen = $state(false);
-	let previewItem = $state<ChatAttachmentPreviewItem | null>(null);
-
-	let displayItems = $derived(getAttachmentDisplayItems({ uploadedFiles, attachments }));
-	let imageItems = $derived(displayItems.filter((item) => item.isImage));
-	let fileItems = $derived(displayItems.filter((item) => !item.isImage));
-
-	function openPreview(item: (typeof displayItems)[0], event?: Event) {
-		if (event) {
-			event.preventDefault();
-			event.stopPropagation();
-		}
-
-		previewItem = {
-			uploadedFile: item.uploadedFile,
-			attachment: item.attachment,
-			preview: item.preview,
-			name: item.name,
-			size: item.size,
-			textContent: item.textContent
-		};
-		previewDialogOpen = true;
-	}
-</script>
-
-<div class="space-y-4">
-	<div class="min-h-0 flex-1 space-y-6 overflow-y-auto px-1">
-		{#if fileItems.length > 0}
-			<div>
-				<h3 class="mb-3 text-sm font-medium text-foreground">Files ({fileItems.length})</h3>
-				<div class="flex flex-wrap items-start gap-3">
-					{#each fileItems as item (item.id)}
-						<ChatAttachmentThumbnailFile
-							class="cursor-pointer"
-							id={item.id}
-							name={item.name}
-							size={item.size}
-							{readonly}
-							onRemove={onFileRemove}
-							textContent={item.textContent}
-							attachment={item.attachment}
-							uploadedFile={item.uploadedFile}
-							onClick={(event?: MouseEvent) => openPreview(item, event)}
-						/>
-					{/each}
-				</div>
-			</div>
-		{/if}
-
-		{#if imageItems.length > 0}
-			<div>
-				<h3 class="mb-3 text-sm font-medium text-foreground">Images ({imageItems.length})</h3>
-				<div class="flex flex-wrap items-start gap-3">
-					{#each imageItems as item (item.id)}
-						{#if item.preview}
-							<ChatAttachmentThumbnailImage
-								class="cursor-pointer"
-								id={item.id}
-								name={item.name}
-								preview={item.preview}
-								{readonly}
-								onRemove={onFileRemove}
-								height={imageHeight}
-								width={imageWidth}
-								{imageClass}
-								onClick={(event) => openPreview(item, event)}
-							/>
-						{/if}
-					{/each}
-				</div>
-			</div>
-		{/if}
-	</div>
-</div>
-
-{#if previewItem}
-	<DialogChatAttachmentPreview
-		bind:open={previewDialogOpen}
-		uploadedFile={previewItem.uploadedFile}
-		attachment={previewItem.attachment}
-		preview={previewItem.preview}
-		name={previewItem.name}
-		size={previewItem.size}
-		textContent={previewItem.textContent}
-		{activeModelId}
-	/>
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAttachmentsDropdown.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAttachmentsDropdown.svelte
deleted file mode 100644
index b9bb5b7e3fa..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAttachmentsDropdown.svelte
+++ /dev/null
@@ -1,333 +0,0 @@
-<script lang="ts">
-	import { page } from '$app/state';
-	import { Plus, MessageSquare, Settings, Zap, FolderOpen } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
-	import * as Tooltip from '$lib/components/ui/tooltip';
-	import { Switch } from '$lib/components/ui/switch';
-	import { FILE_TYPE_ICONS, TOOLTIP_DELAY_DURATION } from '$lib/constants';
-	import { McpLogo, DropdownMenuSearchable } from '$lib/components/app';
-	import { conversationsStore } from '$lib/stores/conversations.svelte';
-	import { mcpStore } from '$lib/stores/mcp.svelte';
-
-	import { HealthCheckStatus } from '$lib/enums';
-	import type { MCPServerSettingsEntry } from '$lib/types';
-
-	interface Props {
-		class?: string;
-		disabled?: boolean;
-		hasAudioModality?: boolean;
-		hasVisionModality?: boolean;
-		hasMcpPromptsSupport?: boolean;
-		hasMcpResourcesSupport?: boolean;
-		onFileUpload?: () => void;
-		onSystemPromptClick?: () => void;
-		onMcpPromptClick?: () => void;
-		onMcpSettingsClick?: () => void;
-		onMcpResourcesClick?: () => void;
-	}
-
-	let {
-		class: className = '',
-		disabled = false,
-		hasAudioModality = false,
-		hasVisionModality = false,
-		hasMcpPromptsSupport = false,
-		hasMcpResourcesSupport = false,
-		onFileUpload,
-		onSystemPromptClick,
-		onMcpPromptClick,
-		onMcpSettingsClick,
-		onMcpResourcesClick
-	}: Props = $props();
-
-	let isNewChat = $derived(!page.params.id);
-
-	let systemMessageTooltip = $derived(
-		isNewChat
-			? 'Add custom system message for a new conversation'
-			: 'Inject custom system message at the beginning of the conversation'
-	);
-
-	let dropdownOpen = $state(false);
-
-	let mcpServers = $derived(mcpStore.getServersSorted().filter((s) => s.enabled));
-	let hasMcpServers = $derived(mcpServers.length > 0);
-	let mcpSearchQuery = $state('');
-	let filteredMcpServers = $derived.by(() => {
-		const query = mcpSearchQuery.toLowerCase().trim();
-		if (!query) return mcpServers;
-		return mcpServers.filter((s) => {
-			const name = getServerLabel(s).toLowerCase();
-			const url = s.url.toLowerCase();
-			return name.includes(query) || url.includes(query);
-		});
-	});
-
-	function getServerLabel(server: MCPServerSettingsEntry): string {
-		return mcpStore.getServerLabel(server);
-	}
-
-	function isServerEnabledForChat(serverId: string): boolean {
-		return conversationsStore.isMcpServerEnabledForChat(serverId);
-	}
-
-	async function toggleServerForChat(serverId: string) {
-		await conversationsStore.toggleMcpServerForChat(serverId);
-	}
-
-	function handleMcpSubMenuOpen(open: boolean) {
-		if (open) {
-			mcpSearchQuery = '';
-			mcpStore.runHealthChecksForServers(mcpServers);
-		}
-	}
-
-	function handleMcpPromptClick() {
-		dropdownOpen = false;
-		onMcpPromptClick?.();
-	}
-
-	function handleMcpSettingsClick() {
-		dropdownOpen = false;
-		onMcpSettingsClick?.();
-	}
-
-	function handleMcpResourcesClick() {
-		dropdownOpen = false;
-		onMcpResourcesClick?.();
-	}
-
-	const fileUploadTooltipText = 'Add files, system prompt or MCP Servers';
-</script>
-
-<div class="flex items-center gap-1 {className}">
-	<DropdownMenu.Root bind:open={dropdownOpen}>
-		<DropdownMenu.Trigger name="Attach files" {disabled}>
-			<Tooltip.Root>
-				<Tooltip.Trigger class="w-full">
-					<Button
-						class="file-upload-button h-8 w-8 rounded-full p-0"
-						{disabled}
-						variant="secondary"
-						type="button"
-					>
-						<span class="sr-only">{fileUploadTooltipText}</span>
-
-						<Plus class="h-4 w-4" />
-					</Button>
-				</Tooltip.Trigger>
-
-				<Tooltip.Content>
-					<p>{fileUploadTooltipText}</p>
-				</Tooltip.Content>
-			</Tooltip.Root>
-		</DropdownMenu.Trigger>
-
-		<DropdownMenu.Content align="start" class="w-48">
-			{#if hasVisionModality}
-				<DropdownMenu.Item
-					class="images-button flex cursor-pointer items-center gap-2"
-					onclick={() => onFileUpload?.()}
-				>
-					<FILE_TYPE_ICONS.image class="h-4 w-4" />
-
-					<span>Images</span>
-				</DropdownMenu.Item>
-			{:else}
-				<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
-					<Tooltip.Trigger class="w-full">
-						<DropdownMenu.Item
-							class="images-button flex cursor-pointer items-center gap-2"
-							disabled
-						>
-							<FILE_TYPE_ICONS.image class="h-4 w-4" />
-
-							<span>Images</span>
-						</DropdownMenu.Item>
-					</Tooltip.Trigger>
-
-					<Tooltip.Content side="right">
-						<p>Image processing requires a vision model</p>
-					</Tooltip.Content>
-				</Tooltip.Root>
-			{/if}
-
-			{#if hasAudioModality}
-				<DropdownMenu.Item
-					class="audio-button flex cursor-pointer items-center gap-2"
-					onclick={() => onFileUpload?.()}
-				>
-					<FILE_TYPE_ICONS.audio class="h-4 w-4" />
-
-					<span>Audio Files</span>
-				</DropdownMenu.Item>
-			{:else}
-				<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
-					<Tooltip.Trigger class="w-full">
-						<DropdownMenu.Item class="audio-button flex cursor-pointer items-center gap-2" disabled>
-							<FILE_TYPE_ICONS.audio class="h-4 w-4" />
-
-							<span>Audio Files</span>
-						</DropdownMenu.Item>
-					</Tooltip.Trigger>
-
-					<Tooltip.Content side="right">
-						<p>Audio files processing requires an audio model</p>
-					</Tooltip.Content>
-				</Tooltip.Root>
-			{/if}
-
-			<DropdownMenu.Item
-				class="flex cursor-pointer items-center gap-2"
-				onclick={() => onFileUpload?.()}
-			>
-				<FILE_TYPE_ICONS.text class="h-4 w-4" />
-
-				<span>Text Files</span>
-			</DropdownMenu.Item>
-
-			{#if hasVisionModality}
-				<DropdownMenu.Item
-					class="flex cursor-pointer items-center gap-2"
-					onclick={() => onFileUpload?.()}
-				>
-					<FILE_TYPE_ICONS.pdf class="h-4 w-4" />
-
-					<span>PDF Files</span>
-				</DropdownMenu.Item>
-			{:else}
-				<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
-					<Tooltip.Trigger class="w-full">
-						<DropdownMenu.Item
-							class="flex cursor-pointer items-center gap-2"
-							onclick={() => onFileUpload?.()}
-						>
-							<FILE_TYPE_ICONS.pdf class="h-4 w-4" />
-
-							<span>PDF Files</span>
-						</DropdownMenu.Item>
-					</Tooltip.Trigger>
-
-					<Tooltip.Content side="right">
-						<p>PDFs will be converted to text. Image-based PDFs may not work properly.</p>
-					</Tooltip.Content>
-				</Tooltip.Root>
-			{/if}
-
-			<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
-				<Tooltip.Trigger class="w-full">
-					<DropdownMenu.Item
-						class="flex cursor-pointer items-center gap-2"
-						onclick={() => onSystemPromptClick?.()}
-					>
-						<MessageSquare class="h-4 w-4" />
-
-						<span>System Message</span>
-					</DropdownMenu.Item>
-				</Tooltip.Trigger>
-
-				<Tooltip.Content side="right">
-					<p>{systemMessageTooltip}</p>
-				</Tooltip.Content>
-			</Tooltip.Root>
-
-			<DropdownMenu.Separator />
-
-			<DropdownMenu.Sub onOpenChange={handleMcpSubMenuOpen}>
-				<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
-					<McpLogo class="h-4 w-4" />
-
-					<span>MCP Servers</span>
-				</DropdownMenu.SubTrigger>
-
-				<DropdownMenu.SubContent class="w-72 pt-0">
-					<DropdownMenuSearchable
-						placeholder="Search servers..."
-						bind:searchValue={mcpSearchQuery}
-						emptyMessage={hasMcpServers ? 'No servers found' : 'No MCP servers configured'}
-						isEmpty={filteredMcpServers.length === 0}
-					>
-						<div class="max-h-64 overflow-y-auto">
-							{#each filteredMcpServers as server (server.id)}
-								{@const healthState = mcpStore.getHealthCheckState(server.id)}
-								{@const hasError = healthState.status === HealthCheckStatus.ERROR}
-								{@const isEnabledForChat = isServerEnabledForChat(server.id)}
-
-								<button
-									type="button"
-									class="flex w-full items-center justify-between gap-2 rounded-sm px-2 py-2 text-left transition-colors hover:bg-accent disabled:cursor-not-allowed disabled:opacity-50"
-									onclick={() => !hasError && toggleServerForChat(server.id)}
-									disabled={hasError}
-								>
-									<div class="flex min-w-0 flex-1 items-center gap-2">
-										{#if mcpStore.getServerFavicon(server.id)}
-											<img
-												src={mcpStore.getServerFavicon(server.id)}
-												alt=""
-												class="h-4 w-4 shrink-0 rounded-sm"
-												onerror={(e) => {
-													(e.currentTarget as HTMLImageElement).style.display = 'none';
-												}}
-											/>
-										{/if}
-
-										<span class="truncate text-sm">{getServerLabel(server)}</span>
-
-										{#if hasError}
-											<span
-												class="shrink-0 rounded bg-destructive/15 px-1.5 py-0.5 text-xs text-destructive"
-											>
-												Error
-											</span>
-										{/if}
-									</div>
-
-									<Switch
-										checked={isEnabledForChat}
-										disabled={hasError}
-										onclick={(e: MouseEvent) => e.stopPropagation()}
-										onCheckedChange={() => toggleServerForChat(server.id)}
-									/>
-								</button>
-							{/each}
-						</div>
-
-						{#snippet footer()}
-							<DropdownMenu.Item
-								class="flex cursor-pointer items-center gap-2"
-								onclick={handleMcpSettingsClick}
-							>
-								<Settings class="h-4 w-4" />
-
-								<span>Manage MCP Servers</span>
-							</DropdownMenu.Item>
-						{/snippet}
-					</DropdownMenuSearchable>
-				</DropdownMenu.SubContent>
-			</DropdownMenu.Sub>
-
-			{#if hasMcpPromptsSupport}
-				<DropdownMenu.Item
-					class="flex cursor-pointer items-center gap-2"
-					onclick={handleMcpPromptClick}
-				>
-					<Zap class="h-4 w-4" />
-
-					<span>MCP Prompt</span>
-				</DropdownMenu.Item>
-			{/if}
-
-			{#if hasMcpResourcesSupport}
-				<DropdownMenu.Item
-					class="flex cursor-pointer items-center gap-2"
-					onclick={handleMcpResourcesClick}
-				>
-					<FolderOpen class="h-4 w-4" />
-
-					<span>MCP Resources</span>
-				</DropdownMenu.Item>
-			{/if}
-		</DropdownMenu.Content>
-	</DropdownMenu.Root>
-</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAttachmentsSheet.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAttachmentsSheet.svelte
deleted file mode 100644
index bf643dd7f25..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAttachmentsSheet.svelte
+++ /dev/null
@@ -1,170 +0,0 @@
-<script lang="ts">
-	import { Plus, MessageSquare, Zap, FolderOpen } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import * as Sheet from '$lib/components/ui/sheet';
-	import { FILE_TYPE_ICONS } from '$lib/constants';
-	import { McpLogo } from '$lib/components/app';
-
-	interface Props {
-		class?: string;
-		disabled?: boolean;
-		hasAudioModality?: boolean;
-		hasVisionModality?: boolean;
-		hasMcpPromptsSupport?: boolean;
-		hasMcpResourcesSupport?: boolean;
-		onFileUpload?: () => void;
-		onSystemPromptClick?: () => void;
-		onMcpPromptClick?: () => void;
-		onMcpSettingsClick?: () => void;
-		onMcpResourcesClick?: () => void;
-	}
-
-	let {
-		class: className = '',
-		disabled = false,
-		hasAudioModality = false,
-		hasVisionModality = false,
-		hasMcpPromptsSupport = false,
-		hasMcpResourcesSupport = false,
-		onFileUpload,
-		onSystemPromptClick,
-		onMcpPromptClick,
-		onMcpSettingsClick,
-		onMcpResourcesClick
-	}: Props = $props();
-
-	let sheetOpen = $state(false);
-
-	function handleMcpPromptClick() {
-		sheetOpen = false;
-		onMcpPromptClick?.();
-	}
-
-	function handleMcpSettingsClick() {
-		onMcpSettingsClick?.();
-	}
-
-	function handleMcpResourcesClick() {
-		sheetOpen = false;
-		onMcpResourcesClick?.();
-	}
-
-	function handleSheetFileUpload() {
-		sheetOpen = false;
-		onFileUpload?.();
-	}
-
-	function handleSheetSystemPromptClick() {
-		sheetOpen = false;
-		onSystemPromptClick?.();
-	}
-
-	const fileUploadTooltipText = 'Add files, system prompt or MCP Servers';
-
-	const sheetItemClass =
-		'flex w-full items-center gap-3 rounded-md px-3 py-2.5 text-left text-sm transition-colors hover:bg-accent active:bg-accent disabled:cursor-not-allowed disabled:opacity-50';
-</script>
-
-<div class="flex items-center gap-1 {className}">
-	<Sheet.Root bind:open={sheetOpen}>
-		<Button
-			class="file-upload-button h-8 w-8 rounded-full p-0"
-			{disabled}
-			variant="secondary"
-			type="button"
-			onclick={() => (sheetOpen = true)}
-		>
-			<span class="sr-only">{fileUploadTooltipText}</span>
-
-			<Plus class="h-4 w-4" />
-		</Button>
-
-		<Sheet.Content side="bottom" class="max-h-[85vh] gap-0">
-			<Sheet.Header>
-				<Sheet.Title>Add to chat</Sheet.Title>
-
-				<Sheet.Description class="sr-only">
-					Add files, system prompt or configure MCP servers
-				</Sheet.Description>
-			</Sheet.Header>
-
-			<div class="flex flex-col gap-1 overflow-y-auto px-1.5 pb-2">
-				<!-- Images -->
-				<button
-					type="button"
-					class={sheetItemClass}
-					disabled={!hasVisionModality}
-					onclick={handleSheetFileUpload}
-				>
-					<FILE_TYPE_ICONS.image class="h-4 w-4 shrink-0" />
-
-					<span>Images</span>
-
-					{#if !hasVisionModality}
-						<span class="ml-auto text-xs text-muted-foreground">Requires vision model</span>
-					{/if}
-				</button>
-
-				<!-- Audio -->
-				<button
-					type="button"
-					class={sheetItemClass}
-					disabled={!hasAudioModality}
-					onclick={handleSheetFileUpload}
-				>
-					<FILE_TYPE_ICONS.audio class="h-4 w-4 shrink-0" />
-
-					<span>Audio Files</span>
-
-					{#if !hasAudioModality}
-						<span class="ml-auto text-xs text-muted-foreground">Requires audio model</span>
-					{/if}
-				</button>
-
-				<button type="button" class={sheetItemClass} onclick={handleSheetFileUpload}>
-					<FILE_TYPE_ICONS.text class="h-4 w-4 shrink-0" />
-
-					<span>Text Files</span>
-				</button>
-
-				<button type="button" class={sheetItemClass} onclick={handleSheetFileUpload}>
-					<FILE_TYPE_ICONS.pdf class="h-4 w-4 shrink-0" />
-
-					<span>PDF Files</span>
-
-					{#if !hasVisionModality}
-						<span class="ml-auto text-xs text-muted-foreground">Text-only</span>
-					{/if}
-				</button>
-
-				<button type="button" class={sheetItemClass} onclick={handleSheetSystemPromptClick}>
-					<MessageSquare class="h-4 w-4 shrink-0" />
-
-					<span>System Message</span>
-				</button>
-
-				<button type="button" class={sheetItemClass} onclick={handleMcpSettingsClick}>
-					<McpLogo class="h-4 w-4 shrink-0" />
-
-					<span>MCP Servers</span>
-				</button>
-
-				{#if hasMcpPromptsSupport}
-					<button type="button" class={sheetItemClass} onclick={handleMcpPromptClick}>
-						<Zap class="h-4 w-4 shrink-0" />
-
-						<span>MCP Prompt</span>
-					</button>
-				{/if}
-
-				{#if hasMcpResourcesSupport}
-					<button type="button" class={sheetItemClass} onclick={handleMcpResourcesClick}>
-						<FolderOpen class="h-4 w-4 shrink-0" />
-
-						<span>MCP Resources</span>
-					</button>
-				{/if}
-			</div>
-		</Sheet.Content>
-	</Sheet.Root>
-</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte
deleted file mode 100644
index 54384edfc8e..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte
+++ /dev/null
@@ -1,271 +0,0 @@
-<script lang="ts">
-	import { Square } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import {
-		ChatFormActionAttachmentsDropdown,
-		ChatFormActionAttachmentsSheet,
-		ChatFormActionRecord,
-		ChatFormActionSubmit,
-		McpServersSelector,
-		ModelsSelector,
-		ModelsSelectorSheet
-	} from '$lib/components/app';
-	import { SETTINGS_SECTION_TITLES } from '$lib/constants';
-	import { mcpStore } from '$lib/stores/mcp.svelte';
-	import { getChatSettingsDialogContext } from '$lib/contexts';
-	import { FileTypeCategory } from '$lib/enums';
-	import { getFileTypeCategory } from '$lib/utils';
-	import { config } from '$lib/stores/settings.svelte';
-	import { modelsStore, modelOptions, selectedModelId } from '$lib/stores/models.svelte';
-	import { isRouterMode, serverError } from '$lib/stores/server.svelte';
-	import { chatStore } from '$lib/stores/chat.svelte';
-	import { activeMessages, conversationsStore } from '$lib/stores/conversations.svelte';
-	import { IsMobile } from '$lib/hooks/is-mobile.svelte';
-
-	interface Props {
-		canSend?: boolean;
-		class?: string;
-		disabled?: boolean;
-		isLoading?: boolean;
-		isRecording?: boolean;
-		hasText?: boolean;
-		uploadedFiles?: ChatUploadedFile[];
-		onFileUpload?: () => void;
-		onMicClick?: () => void;
-		onStop?: () => void;
-		onSystemPromptClick?: () => void;
-		onMcpPromptClick?: () => void;
-		onMcpResourcesClick?: () => void;
-	}
-
-	let {
-		canSend = false,
-		class: className = '',
-		disabled = false,
-		isLoading = false,
-		isRecording = false,
-		hasText = false,
-		uploadedFiles = [],
-		onFileUpload,
-		onMicClick,
-		onStop,
-		onSystemPromptClick,
-		onMcpPromptClick,
-		onMcpResourcesClick
-	}: Props = $props();
-
-	let currentConfig = $derived(config());
-	let isRouter = $derived(isRouterMode());
-	let isOffline = $derived(!!serverError());
-
-	let conversationModel = $derived(
-		chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
-	);
-
-	let lastSyncedConversationModel: string | null = null;
-
-	$effect(() => {
-		if (conversationModel && conversationModel !== lastSyncedConversationModel) {
-			lastSyncedConversationModel = conversationModel;
-			modelsStore.selectModelByName(conversationModel);
-		} else if (isRouter && !modelsStore.selectedModelId && modelsStore.loadedModelIds.length > 0) {
-			lastSyncedConversationModel = null;
-			// auto-select the first loaded model only when nothing is selected yet
-			const first = modelOptions().find((m) => modelsStore.loadedModelIds.includes(m.model));
-			if (first) modelsStore.selectModelById(first.id);
-		}
-	});
-
-	let activeModelId = $derived.by(() => {
-		const options = modelOptions();
-
-		if (!isRouter) {
-			return options.length > 0 ? options[0].model : null;
-		}
-
-		const selectedId = selectedModelId();
-		if (selectedId) {
-			const model = options.find((m) => m.id === selectedId);
-			if (model) return model.model;
-		}
-
-		if (conversationModel) {
-			const model = options.find((m) => m.model === conversationModel);
-			if (model) return model.model;
-		}
-
-		return null;
-	});
-
-	let modelPropsVersion = $state(0); // Used to trigger reactivity after fetch
-
-	$effect(() => {
-		if (activeModelId) {
-			const cached = modelsStore.getModelProps(activeModelId);
-
-			if (!cached) {
-				modelsStore.fetchModelProps(activeModelId).then(() => {
-					modelPropsVersion++;
-				});
-			}
-		}
-	});
-
-	let hasAudioModality = $derived.by(() => {
-		if (activeModelId) {
-			void modelPropsVersion;
-
-			return modelsStore.modelSupportsAudio(activeModelId);
-		}
-
-		return false;
-	});
-
-	let hasVisionModality = $derived.by(() => {
-		if (activeModelId) {
-			void modelPropsVersion;
-
-			return modelsStore.modelSupportsVision(activeModelId);
-		}
-
-		return false;
-	});
-
-	let hasAudioAttachments = $derived(
-		uploadedFiles.some((file) => getFileTypeCategory(file.type) === FileTypeCategory.AUDIO)
-	);
-	let shouldShowRecordButton = $derived(
-		hasAudioModality && !hasText && !hasAudioAttachments && currentConfig.autoMicOnEmpty
-	);
-
-	let hasModelSelected = $derived(!isRouter || !!conversationModel || !!selectedModelId());
-
-	let isSelectedModelInCache = $derived.by(() => {
-		if (!isRouter) return true;
-
-		if (conversationModel) {
-			return modelOptions().some((option) => option.model === conversationModel);
-		}
-
-		const currentModelId = selectedModelId();
-		if (!currentModelId) return false;
-
-		return modelOptions().some((option) => option.id === currentModelId);
-	});
-
-	let submitTooltip = $derived.by(() => {
-		if (!hasModelSelected) {
-			return 'Please select a model first';
-		}
-
-		if (!isSelectedModelInCache) {
-			return 'Selected model is not available, please select another';
-		}
-
-		return '';
-	});
-
-	let selectorModelRef: ModelsSelector | ModelsSelectorSheet | undefined = $state(undefined);
-
-	let isMobile = new IsMobile();
-
-	export function openModelSelector() {
-		selectorModelRef?.open();
-	}
-
-	const chatSettingsDialog = getChatSettingsDialogContext();
-
-	let hasMcpPromptsSupport = $derived.by(() => {
-		const perChatOverrides = conversationsStore.getAllMcpServerOverrides();
-
-		return mcpStore.hasPromptsCapability(perChatOverrides);
-	});
-
-	let hasMcpResourcesSupport = $derived.by(() => {
-		const perChatOverrides = conversationsStore.getAllMcpServerOverrides();
-
-		return mcpStore.hasResourcesCapability(perChatOverrides);
-	});
-</script>
-
-<div class="flex w-full items-center gap-3 {className}" style="container-type: inline-size">
-	<div class="mr-auto flex items-center gap-2">
-		{#if isMobile.current}
-			<ChatFormActionAttachmentsSheet
-				{disabled}
-				{hasAudioModality}
-				{hasVisionModality}
-				{hasMcpPromptsSupport}
-				{hasMcpResourcesSupport}
-				{onFileUpload}
-				{onSystemPromptClick}
-				{onMcpPromptClick}
-				{onMcpResourcesClick}
-				onMcpSettingsClick={() => chatSettingsDialog.open(SETTINGS_SECTION_TITLES.MCP)}
-			/>
-		{:else}
-			<ChatFormActionAttachmentsDropdown
-				{disabled}
-				{hasAudioModality}
-				{hasVisionModality}
-				{hasMcpPromptsSupport}
-				{hasMcpResourcesSupport}
-				{onFileUpload}
-				{onSystemPromptClick}
-				{onMcpPromptClick}
-				{onMcpResourcesClick}
-				onMcpSettingsClick={() => chatSettingsDialog.open(SETTINGS_SECTION_TITLES.MCP)}
-			/>
-		{/if}
-
-		<McpServersSelector
-			{disabled}
-			onSettingsClick={() => chatSettingsDialog.open(SETTINGS_SECTION_TITLES.MCP)}
-		/>
-	</div>
-
-	<div class="ml-auto flex items-center gap-1.5">
-		{#if isMobile.current}
-			<ModelsSelectorSheet
-				disabled={disabled || isOffline}
-				bind:this={selectorModelRef}
-				currentModel={conversationModel}
-				forceForegroundText
-				useGlobalSelection
-			/>
-		{:else}
-			<ModelsSelector
-				disabled={disabled || isOffline}
-				bind:this={selectorModelRef}
-				currentModel={conversationModel}
-				forceForegroundText
-				useGlobalSelection
-			/>
-		{/if}
-	</div>
-
-	{#if isLoading}
-		<Button
-			type="button"
-			variant="secondary"
-			onclick={onStop}
-			class="group h-8 w-8 rounded-full p-0 hover:bg-destructive/10!"
-		>
-			<span class="sr-only">Stop</span>
-
-			<Square
-				class="h-8 w-8 fill-muted-foreground stroke-muted-foreground group-hover:fill-destructive group-hover:stroke-destructive hover:fill-destructive hover:stroke-destructive"
-			/>
-		</Button>
-	{:else if shouldShowRecordButton}
-		<ChatFormActionRecord {disabled} {hasAudioModality} {isLoading} {isRecording} {onMicClick} />
-	{:else}
-		<ChatFormActionSubmit
-			canSend={canSend && hasModelSelected && isSelectedModelInCache}
-			{disabled}
-			{isLoading}
-			tooltipLabel={submitTooltip}
-			showErrorState={hasModelSelected && !isSelectedModelInCache}
-		/>
-	{/if}
-</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormHelperText.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormHelperText.svelte
deleted file mode 100644
index 6e7c755ed2b..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormHelperText.svelte
+++ /dev/null
@@ -1,31 +0,0 @@
-<script lang="ts">
-	import { browser } from '$app/environment';
-	import { config } from '$lib/stores/settings.svelte';
-
-	interface Props {
-		class?: string;
-		show?: boolean;
-	}
-
-	let { class: className = '', show = true }: Props = $props();
-
-	let sendOnEnter = $derived(config().sendOnEnter !== false);
-	let modKey = browser && /Mac|iPhone|iPad|iPod/.test(navigator.platform) ? 'Cmd' : 'Ctrl';
-</script>
-
-{#if show}
-	<div class="mt-6 items-center justify-center {className} hidden md:flex">
-		{#if sendOnEnter}
-			<p class="text-xs text-muted-foreground">
-				Press <kbd class="rounded bg-muted px-1 py-0.5 font-mono text-xs">Enter</kbd> to send,
-				<kbd class="rounded bg-muted px-1 py-0.5 font-mono text-xs">Shift + Enter</kbd> for new line
-			</p>
-		{:else}
-			<p class="text-xs text-muted-foreground">
-				Press <kbd class="rounded bg-muted px-1 py-0.5 font-mono text-xs">{modKey} + Enter</kbd> to
-				send,
-				<kbd class="rounded bg-muted px-1 py-0.5 font-mono text-xs">Enter</kbd> for new line
-			</p>
-		{/if}
-	</div>
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageBranchingControls.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageBranchingControls.svelte
deleted file mode 100644
index 7420bb1994a..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageBranchingControls.svelte
+++ /dev/null
@@ -1,84 +0,0 @@
-<script lang="ts">
-	import { ChevronLeft, ChevronRight } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import * as Tooltip from '$lib/components/ui/tooltip';
-
-	interface Props {
-		class?: string;
-		siblingInfo: ChatMessageSiblingInfo | null;
-		onNavigateToSibling?: (siblingId: string) => void;
-	}
-
-	let { class: className = '', siblingInfo, onNavigateToSibling }: Props = $props();
-
-	let hasPrevious = $derived(siblingInfo && siblingInfo.currentIndex > 0);
-	let hasNext = $derived(siblingInfo && siblingInfo.currentIndex < siblingInfo.totalSiblings - 1);
-	let nextSiblingId = $derived(
-		hasNext ? siblingInfo!.siblingIds[siblingInfo!.currentIndex + 1] : null
-	);
-	let previousSiblingId = $derived(
-		hasPrevious ? siblingInfo!.siblingIds[siblingInfo!.currentIndex - 1] : null
-	);
-
-	function handleNext() {
-		if (nextSiblingId) {
-			onNavigateToSibling?.(nextSiblingId);
-		}
-	}
-
-	function handlePrevious() {
-		if (previousSiblingId) {
-			onNavigateToSibling?.(previousSiblingId);
-		}
-	}
-</script>
-
-{#if siblingInfo && siblingInfo.totalSiblings > 1}
-	<div
-		aria-label="Message version {siblingInfo.currentIndex + 1} of {siblingInfo.totalSiblings}"
-		class="flex items-center gap-1 text-xs text-muted-foreground {className}"
-		role="navigation"
-	>
-		<Tooltip.Root>
-			<Tooltip.Trigger>
-				<Button
-					aria-label="Previous message version"
-					class="h-5 w-5 p-0 {!hasPrevious ? 'cursor-not-allowed opacity-30' : ''}"
-					disabled={!hasPrevious}
-					onclick={handlePrevious}
-					size="sm"
-					variant="ghost"
-				>
-					<ChevronLeft class="h-3 w-3" />
-				</Button>
-			</Tooltip.Trigger>
-
-			<Tooltip.Content>
-				<p>Previous version</p>
-			</Tooltip.Content>
-		</Tooltip.Root>
-
-		<span class="px-1 font-mono text-xs">
-			{siblingInfo.currentIndex + 1}/{siblingInfo.totalSiblings}
-		</span>
-
-		<Tooltip.Root>
-			<Tooltip.Trigger>
-				<Button
-					aria-label="Next message version"
-					class="h-5 w-5 p-0 {!hasNext ? 'cursor-not-allowed opacity-30' : ''}"
-					disabled={!hasNext}
-					onclick={handleNext}
-					size="sm"
-					variant="ghost"
-				>
-					<ChevronRight class="h-3 w-3" />
-				</Button>
-			</Tooltip.Trigger>
-
-			<Tooltip.Content>
-				<p>Next version</p>
-			</Tooltip.Content>
-		</Tooltip.Root>
-	</div>
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte
deleted file mode 100644
index f2f6288d68d..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte
+++ /dev/null
@@ -1,129 +0,0 @@
-<script lang="ts">
-	import { Card } from '$lib/components/ui/card';
-	import { ChatAttachmentsList, MarkdownContent } from '$lib/components/app';
-	import { getMessageEditContext } from '$lib/contexts';
-	import { config } from '$lib/stores/settings.svelte';
-	import ChatMessageActions from './ChatMessageActions.svelte';
-	import ChatMessageEditForm from './ChatMessageEditForm.svelte';
-	import { MessageRole } from '$lib/enums';
-
-	interface Props {
-		class?: string;
-		message: DatabaseMessage;
-		siblingInfo?: ChatMessageSiblingInfo | null;
-		deletionInfo: {
-			totalCount: number;
-			userMessages: number;
-			assistantMessages: number;
-			messageTypes: string[];
-		} | null;
-		showDeleteDialog: boolean;
-		onEdit: () => void;
-		onDelete: () => void;
-		onConfirmDelete: () => void;
-		onForkConversation?: (options: { name: string; includeAttachments: boolean }) => void;
-		onShowDeleteDialogChange: (show: boolean) => void;
-		onNavigateToSibling?: (siblingId: string) => void;
-		onCopy: () => void;
-	}
-
-	let {
-		class: className = '',
-		message,
-		siblingInfo = null,
-		deletionInfo,
-		showDeleteDialog,
-		onEdit,
-		onDelete,
-		onConfirmDelete,
-		onForkConversation,
-		onShowDeleteDialogChange,
-		onNavigateToSibling,
-		onCopy
-	}: Props = $props();
-
-	// Get contexts
-	const editCtx = getMessageEditContext();
-
-	let isMultiline = $state(false);
-	let messageElement: HTMLElement | undefined = $state();
-	const currentConfig = config();
-
-	$effect(() => {
-		if (!messageElement || !message.content.trim()) return;
-
-		if (message.content.includes('\n')) {
-			isMultiline = true;
-			return;
-		}
-
-		const resizeObserver = new ResizeObserver((entries) => {
-			for (const entry of entries) {
-				const element = entry.target as HTMLElement;
-				const estimatedSingleLineHeight = 24; // Typical line height for text-md
-
-				isMultiline = element.offsetHeight > estimatedSingleLineHeight * 1.5;
-			}
-		});
-
-		resizeObserver.observe(messageElement);
-
-		return () => {
-			resizeObserver.disconnect();
-		};
-	});
-</script>
-
-<div
-	aria-label="User message with actions"
-	class="group flex flex-col items-end gap-3 md:gap-2 {className}"
-	role="group"
->
-	{#if editCtx.isEditing}
-		<ChatMessageEditForm />
-	{:else}
-		{#if message.extra && message.extra.length > 0}
-			<div class="mb-2 max-w-[80%]">
-				<ChatAttachmentsList attachments={message.extra} readonly imageHeight="h-80" />
-			</div>
-		{/if}
-
-		{#if message.content.trim()}
-			<Card
-				class="max-w-[80%] overflow-y-auto rounded-[1.125rem] border-none bg-primary/5 px-3.75 py-1.5 text-foreground backdrop-blur-md data-[multiline]:py-2.5 dark:bg-primary/15"
-				data-multiline={isMultiline ? '' : undefined}
-				style="max-height: var(--max-message-height); overflow-wrap: anywhere; word-break: break-word;"
-			>
-				{#if currentConfig.renderUserContentAsMarkdown}
-					<div bind:this={messageElement}>
-						<MarkdownContent class="markdown-user-content -my-4" content={message.content} />
-					</div>
-				{:else}
-					<span bind:this={messageElement} class="text-md whitespace-pre-wrap">
-						{message.content}
-					</span>
-				{/if}
-			</Card>
-		{/if}
-
-		{#if message.timestamp}
-			<div class="max-w-[80%]">
-				<ChatMessageActions
-					actionsPosition="right"
-					{deletionInfo}
-					justify="end"
-					{onConfirmDelete}
-					{onCopy}
-					{onDelete}
-					{onEdit}
-					{onForkConversation}
-					{onNavigateToSibling}
-					{onShowDeleteDialogChange}
-					{siblingInfo}
-					{showDeleteDialog}
-					role={MessageRole.USER}
-				/>
-			</div>
-		{/if}
-	{/if}
-</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenHeader.svelte b/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenHeader.svelte
deleted file mode 100644
index 0a920be6fa2..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenHeader.svelte
+++ /dev/null
@@ -1,26 +0,0 @@
-<script lang="ts">
-	import { Settings } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import { useSidebar } from '$lib/components/ui/sidebar';
-	import { getChatSettingsDialogContext } from '$lib/contexts';
-
-	const sidebar = useSidebar();
-	const chatSettingsDialog = getChatSettingsDialogContext();
-</script>
-
-<header
-	class="pointer-events-none fixed top-0 right-0 left-0 z-50 flex items-center justify-end p-2 duration-200 ease-linear md:p-4 {sidebar.open
-		? 'md:left-[var(--sidebar-width)]'
-		: ''}"
->
-	<div class="pointer-events-auto flex items-center space-x-2">
-		<Button
-			variant="ghost"
-			size="icon-lg"
-			onclick={() => chatSettingsDialog.open()}
-			class="rounded-full backdrop-blur-lg"
-		>
-			<Settings class="h-4 w-4" />
-		</Button>
-	</div>
-</header>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
deleted file mode 100644
index e12bf8f2fe4..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
+++ /dev/null
@@ -1,575 +0,0 @@
-<script lang="ts">
-	import {
-		Settings,
-		Funnel,
-		AlertTriangle,
-		Code,
-		Monitor,
-		ChevronLeft,
-		ChevronRight,
-		Database
-	} from '@lucide/svelte';
-	import {
-		ChatSettingsFooter,
-		ChatSettingsImportExportTab,
-		ChatSettingsFields,
-		McpLogo,
-		McpServersSettings
-	} from '$lib/components/app';
-	import { ScrollArea } from '$lib/components/ui/scroll-area';
-	import { config, settingsStore } from '$lib/stores/settings.svelte';
-	import {
-		SETTINGS_SECTION_TITLES,
-		type SettingsSectionTitle,
-		NUMERIC_FIELDS,
-		POSITIVE_INTEGER_FIELDS,
-		SETTINGS_COLOR_MODES_CONFIG,
-		SETTINGS_KEYS
-	} from '$lib/constants';
-	import { setMode } from 'mode-watcher';
-	import { ColorMode } from '$lib/enums/ui';
-	import { SettingsFieldType } from '$lib/enums/settings';
-	import type { Component } from 'svelte';
-
-	interface Props {
-		onSave?: () => void;
-		initialSection?: SettingsSectionTitle;
-	}
-
-	let { onSave, initialSection }: Props = $props();
-
-	const settingSections: Array<{
-		fields: SettingsFieldConfig[];
-		icon: Component;
-		title: SettingsSectionTitle;
-	}> = [
-		{
-			title: SETTINGS_SECTION_TITLES.GENERAL,
-			icon: Settings,
-			fields: [
-				{
-					key: SETTINGS_KEYS.THEME,
-					label: 'Theme',
-					type: SettingsFieldType.SELECT,
-					options: SETTINGS_COLOR_MODES_CONFIG
-				},
-				{ key: SETTINGS_KEYS.API_KEY, label: 'API Key', type: SettingsFieldType.INPUT },
-				{
-					key: SETTINGS_KEYS.SYSTEM_MESSAGE,
-					label: 'System Message',
-					type: SettingsFieldType.TEXTAREA
-				},
-				{
-					key: SETTINGS_KEYS.PASTE_LONG_TEXT_TO_FILE_LEN,
-					label: 'Paste long text to file length',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.SEND_ON_ENTER,
-					label: 'Send message on Enter',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.COPY_TEXT_ATTACHMENTS_AS_PLAIN_TEXT,
-					label: 'Copy text attachments as plain text',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.ENABLE_CONTINUE_GENERATION,
-					label: 'Enable "Continue" button',
-					type: SettingsFieldType.CHECKBOX,
-					isExperimental: true
-				},
-				{
-					key: SETTINGS_KEYS.PDF_AS_IMAGE,
-					label: 'Parse PDF as image',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.ASK_FOR_TITLE_CONFIRMATION,
-					label: 'Ask for confirmation before changing conversation title',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.TITLE_GENERATION_USE_FIRST_LINE,
-					label: 'Use first non-empty line for conversation title',
-					type: SettingsFieldType.CHECKBOX
-				}
-			]
-		},
-		{
-			title: SETTINGS_SECTION_TITLES.DISPLAY,
-			icon: Monitor,
-			fields: [
-				{
-					key: SETTINGS_KEYS.SHOW_MESSAGE_STATS,
-					label: 'Show message generation statistics',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.SHOW_THOUGHT_IN_PROGRESS,
-					label: 'Show thought in progress',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.KEEP_STATS_VISIBLE,
-					label: 'Keep stats visible after generation',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.AUTO_MIC_ON_EMPTY,
-					label: 'Show microphone on empty input',
-					type: SettingsFieldType.CHECKBOX,
-					isExperimental: true
-				},
-				{
-					key: SETTINGS_KEYS.RENDER_USER_CONTENT_AS_MARKDOWN,
-					label: 'Render user content as Markdown',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.FULL_HEIGHT_CODE_BLOCKS,
-					label: 'Use full height code blocks',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.DISABLE_AUTO_SCROLL,
-					label: 'Disable automatic scroll',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.ALWAYS_SHOW_SIDEBAR_ON_DESKTOP,
-					label: 'Always show sidebar on desktop',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.AUTO_SHOW_SIDEBAR_ON_NEW_CHAT,
-					label: 'Auto-show sidebar on new chat',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.SHOW_RAW_MODEL_NAMES,
-					label: 'Show raw model names',
-					type: SettingsFieldType.CHECKBOX
-				}
-			]
-		},
-		{
-			title: SETTINGS_SECTION_TITLES.SAMPLING,
-			icon: Funnel,
-			fields: [
-				{
-					key: SETTINGS_KEYS.TEMPERATURE,
-					label: 'Temperature',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.DYNATEMP_RANGE,
-					label: 'Dynamic temperature range',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.DYNATEMP_EXPONENT,
-					label: 'Dynamic temperature exponent',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.TOP_K,
-					label: 'Top K',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.TOP_P,
-					label: 'Top P',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.MIN_P,
-					label: 'Min P',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.XTC_PROBABILITY,
-					label: 'XTC probability',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.XTC_THRESHOLD,
-					label: 'XTC threshold',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.TYP_P,
-					label: 'Typical P',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.MAX_TOKENS,
-					label: 'Max tokens',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.SAMPLERS,
-					label: 'Samplers',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.BACKEND_SAMPLING,
-					label: 'Backend sampling',
-					type: SettingsFieldType.CHECKBOX
-				}
-			]
-		},
-		{
-			title: SETTINGS_SECTION_TITLES.PENALTIES,
-			icon: AlertTriangle,
-			fields: [
-				{
-					key: SETTINGS_KEYS.REPEAT_LAST_N,
-					label: 'Repeat last N',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.REPEAT_PENALTY,
-					label: 'Repeat penalty',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.PRESENCE_PENALTY,
-					label: 'Presence penalty',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.FREQUENCY_PENALTY,
-					label: 'Frequency penalty',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.DRY_MULTIPLIER,
-					label: 'DRY multiplier',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.DRY_BASE,
-					label: 'DRY base',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.DRY_ALLOWED_LENGTH,
-					label: 'DRY allowed length',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.DRY_PENALTY_LAST_N,
-					label: 'DRY penalty last N',
-					type: SettingsFieldType.INPUT
-				}
-			]
-		},
-		{
-			title: SETTINGS_SECTION_TITLES.IMPORT_EXPORT,
-			icon: Database,
-			fields: []
-		},
-		{
-			title: SETTINGS_SECTION_TITLES.MCP,
-			icon: McpLogo,
-			fields: [
-				{
-					key: SETTINGS_KEYS.AGENTIC_MAX_TURNS,
-					label: 'Agentic loop max turns',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.ALWAYS_SHOW_AGENTIC_TURNS,
-					label: 'Always show agentic turns in conversation',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.AGENTIC_MAX_TOOL_PREVIEW_LINES,
-					label: 'Max lines per tool preview',
-					type: SettingsFieldType.INPUT
-				},
-				{
-					key: SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS,
-					label: 'Show tool call in progress',
-					type: SettingsFieldType.CHECKBOX
-				}
-			]
-		},
-		{
-			title: SETTINGS_SECTION_TITLES.DEVELOPER,
-			icon: Code,
-			fields: [
-				{
-					key: SETTINGS_KEYS.PRE_ENCODE_CONVERSATION,
-					label: 'Pre-fill KV cache after response',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.DISABLE_REASONING_PARSING,
-					label: 'Disable server-side thinking extraction',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.EXCLUDE_REASONING_FROM_CONTEXT,
-					label: 'Strip thinking from message history',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.SHOW_RAW_OUTPUT_SWITCH,
-					label: 'Enable raw output toggle',
-					type: SettingsFieldType.CHECKBOX
-				},
-				{
-					key: SETTINGS_KEYS.CUSTOM,
-					label: 'Custom JSON',
-					type: SettingsFieldType.TEXTAREA
-				}
-			]
-		}
-		// TODO: Experimental features section will be implemented after initial release
-		// This includes Python interpreter (Pyodide integration) and other experimental features
-		// {
-		// 	title: 'Experimental',
-		// 	icon: Beaker,
-		// 	fields: [
-		// 		{
-		// 			key: 'pyInterpreterEnabled',
-		// 			label: 'Enable Python interpreter',
-		// 			type: 'checkbox'
-		// 		}
-		// 	]
-		// }
-	];
-
-	let activeSection = $derived<SettingsSectionTitle>(
-		initialSection ?? SETTINGS_SECTION_TITLES.GENERAL
-	);
-	let currentSection = $derived(
-		settingSections.find((section) => section.title === activeSection) || settingSections[0]
-	);
-	let localConfig: SettingsConfigType = $state({ ...config() });
-
-	let canScrollLeft = $state(false);
-	let canScrollRight = $state(false);
-	let scrollContainer: HTMLDivElement | undefined = $state();
-
-	$effect(() => {
-		if (initialSection) {
-			activeSection = initialSection;
-		}
-	});
-
-	function handleThemeChange(newTheme: string) {
-		localConfig.theme = newTheme;
-
-		setMode(newTheme as ColorMode);
-	}
-
-	function handleConfigChange(key: string, value: string | boolean) {
-		localConfig[key] = value;
-	}
-
-	function handleReset() {
-		localConfig = { ...config() };
-
-		setMode(localConfig.theme as ColorMode);
-	}
-
-	function handleSave() {
-		if (localConfig.custom && typeof localConfig.custom === 'string' && localConfig.custom.trim()) {
-			try {
-				JSON.parse(localConfig.custom);
-			} catch (error) {
-				alert('Invalid JSON in custom parameters. Please check the format and try again.');
-				console.error(error);
-				return;
-			}
-		}
-
-		// Convert numeric strings to numbers for numeric fields
-		const processedConfig = { ...localConfig };
-
-		for (const field of NUMERIC_FIELDS) {
-			if (processedConfig[field] !== undefined && processedConfig[field] !== '') {
-				const numValue = Number(processedConfig[field]);
-				if (!isNaN(numValue)) {
-					if ((POSITIVE_INTEGER_FIELDS as readonly string[]).includes(field)) {
-						processedConfig[field] = Math.max(1, Math.round(numValue));
-					} else {
-						processedConfig[field] = numValue;
-					}
-				} else {
-					alert(`Invalid numeric value for ${field}. Please enter a valid number.`);
-					return;
-				}
-			}
-		}
-
-		settingsStore.updateMultipleConfig(processedConfig);
-		onSave?.();
-	}
-
-	function scrollToCenter(element: HTMLElement) {
-		if (!scrollContainer) return;
-
-		const containerRect = scrollContainer.getBoundingClientRect();
-		const elementRect = element.getBoundingClientRect();
-
-		const elementCenter = elementRect.left + elementRect.width / 2;
-		const containerCenter = containerRect.left + containerRect.width / 2;
-		const scrollOffset = elementCenter - containerCenter;
-
-		scrollContainer.scrollBy({ left: scrollOffset, behavior: 'smooth' });
-	}
-
-	function scrollLeft() {
-		if (!scrollContainer) return;
-
-		scrollContainer.scrollBy({ left: -250, behavior: 'smooth' });
-	}
-
-	function scrollRight() {
-		if (!scrollContainer) return;
-
-		scrollContainer.scrollBy({ left: 250, behavior: 'smooth' });
-	}
-
-	function updateScrollButtons() {
-		if (!scrollContainer) return;
-
-		const { scrollLeft, scrollWidth, clientWidth } = scrollContainer;
-		canScrollLeft = scrollLeft > 0;
-		canScrollRight = scrollLeft < scrollWidth - clientWidth - 1; // -1 for rounding
-	}
-
-	export function reset() {
-		localConfig = { ...config() };
-
-		setTimeout(updateScrollButtons, 100);
-	}
-
-	$effect(() => {
-		if (scrollContainer) {
-			updateScrollButtons();
-		}
-	});
-</script>
-
-<div class="flex h-full flex-col overflow-hidden md:flex-row">
-	<!-- Desktop Sidebar -->
-	<div class="hidden w-64 border-r border-border/30 p-6 md:block">
-		<nav class="space-y-1 py-2">
-			{#each settingSections as section (section.title)}
-				<button
-					class="flex w-full cursor-pointer items-center gap-3 rounded-lg px-3 py-2 text-left text-sm transition-colors hover:bg-accent {activeSection ===
-					section.title
-						? 'bg-accent text-accent-foreground'
-						: 'text-muted-foreground'}"
-					onclick={() => (activeSection = section.title)}
-				>
-					<section.icon class="h-4 w-4" />
-
-					<span class="ml-2">{section.title}</span>
-				</button>
-			{/each}
-		</nav>
-	</div>
-
-	<!-- Mobile Header with Horizontal Scrollable Menu -->
-	<div class="flex flex-col pt-6 md:hidden">
-		<div class="border-b border-border/30 pt-4 md:py-4">
-			<!-- Horizontal Scrollable Category Menu with Navigation -->
-			<div class="relative flex items-center" style="scroll-padding: 1rem;">
-				<button
-					class="absolute left-2 z-10 flex h-6 w-6 items-center justify-center rounded-full bg-muted shadow-md backdrop-blur-sm transition-opacity hover:bg-accent {canScrollLeft
-						? 'opacity-100'
-						: 'pointer-events-none opacity-0'}"
-					onclick={scrollLeft}
-					aria-label="Scroll left"
-				>
-					<ChevronLeft class="h-4 w-4" />
-				</button>
-
-				<div
-					class="scrollbar-hide overflow-x-auto py-2"
-					bind:this={scrollContainer}
-					onscroll={updateScrollButtons}
-				>
-					<div class="flex min-w-max gap-2">
-						{#each settingSections as section (section.title)}
-							<button
-								class="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm whitespace-nowrap transition-colors first:ml-4 last:mr-4 hover:bg-accent {activeSection ===
-								section.title
-									? 'bg-accent text-accent-foreground'
-									: 'text-muted-foreground'}"
-								onclick={(e: MouseEvent) => {
-									activeSection = section.title;
-									scrollToCenter(e.currentTarget as HTMLElement);
-								}}
-							>
-								<section.icon class="h-4 w-4 flex-shrink-0" />
-								<span>{section.title}</span>
-							</button>
-						{/each}
-					</div>
-				</div>
-
-				<button
-					class="absolute right-2 z-10 flex h-6 w-6 items-center justify-center rounded-full bg-muted shadow-md backdrop-blur-sm transition-opacity hover:bg-accent {canScrollRight
-						? 'opacity-100'
-						: 'pointer-events-none opacity-0'}"
-					onclick={scrollRight}
-					aria-label="Scroll right"
-				>
-					<ChevronRight class="h-4 w-4" />
-				</button>
-			</div>
-		</div>
-	</div>
-
-	<ScrollArea class="max-h-[calc(100dvh-13.5rem)] flex-1 md:max-h-[calc(100vh-13.5rem)]">
-		<div class="space-y-6 p-4 md:p-6">
-			<div class="grid">
-				<div class="mb-6 flex hidden items-center gap-2 border-b border-border/30 pb-6 md:flex">
-					<currentSection.icon class="h-5 w-5" />
-
-					<h3 class="text-lg font-semibold">{currentSection.title}</h3>
-				</div>
-
-				{#if currentSection.title === SETTINGS_SECTION_TITLES.IMPORT_EXPORT}
-					<ChatSettingsImportExportTab />
-				{:else if currentSection.title === SETTINGS_SECTION_TITLES.MCP}
-					<div class="space-y-6">
-						<ChatSettingsFields
-							fields={currentSection.fields}
-							{localConfig}
-							onConfigChange={handleConfigChange}
-							onThemeChange={handleThemeChange}
-						/>
-
-						<div class="border-t border-border/30 pt-6">
-							<McpServersSettings />
-						</div>
-					</div>
-				{:else}
-					<div class="space-y-6">
-						<ChatSettingsFields
-							fields={currentSection.fields}
-							{localConfig}
-							onConfigChange={handleConfigChange}
-							onThemeChange={handleThemeChange}
-						/>
-					</div>
-				{/if}
-			</div>
-
-			<div class="mt-8 border-t pt-6">
-				<p class="text-xs text-muted-foreground">Settings are saved in browser's localStorage</p>
-			</div>
-		</div>
-	</ScrollArea>
-</div>
-
-<ChatSettingsFooter onReset={handleReset} onSave={handleSave} />
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarActions.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarActions.svelte
deleted file mode 100644
index e0d379f6df3..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarActions.svelte
+++ /dev/null
@@ -1,102 +0,0 @@
-<script lang="ts">
-	import { Search, SquarePen, X } from '@lucide/svelte';
-	import { KeyboardShortcutInfo } from '$lib/components/app';
-	import { Button } from '$lib/components/ui/button';
-	import { Input } from '$lib/components/ui/input';
-	import { McpLogo } from '$lib/components/app';
-	import { SETTINGS_SECTION_TITLES } from '$lib/constants';
-	import { getChatSettingsDialogContext } from '$lib/contexts';
-
-	interface Props {
-		handleMobileSidebarItemClick: () => void;
-		isSearchModeActive: boolean;
-		searchQuery: string;
-	}
-
-	let {
-		handleMobileSidebarItemClick,
-		isSearchModeActive = $bindable(),
-		searchQuery = $bindable()
-	}: Props = $props();
-
-	let searchInput: HTMLInputElement | null = $state(null);
-
-	const chatSettingsDialog = getChatSettingsDialogContext();
-
-	function handleSearchModeDeactivate() {
-		isSearchModeActive = false;
-		searchQuery = '';
-	}
-
-	$effect(() => {
-		if (isSearchModeActive) {
-			searchInput?.focus();
-		}
-	});
-</script>
-
-<div class="my-1 space-y-1">
-	{#if isSearchModeActive}
-		<div class="relative">
-			<Search class="absolute top-2.5 left-2 h-4 w-4 text-muted-foreground" />
-
-			<Input
-				bind:ref={searchInput}
-				bind:value={searchQuery}
-				onkeydown={(e) => e.key === 'Escape' && handleSearchModeDeactivate()}
-				placeholder="Search conversations..."
-				class="pl-8"
-			/>
-
-			<X
-				class="cursor-pointertext-muted-foreground absolute top-2.5 right-2 h-4 w-4"
-				onclick={handleSearchModeDeactivate}
-			/>
-		</div>
-	{:else}
-		<Button
-			class="w-full justify-between backdrop-blur-none! hover:[&>kbd]:opacity-100"
-			href="?new_chat=true#/"
-			onclick={handleMobileSidebarItemClick}
-			variant="ghost"
-		>
-			<div class="flex items-center gap-2">
-				<SquarePen class="h-4 w-4" />
-
-				New chat
-			</div>
-
-			<KeyboardShortcutInfo keys={['shift', 'cmd', 'o']} />
-		</Button>
-
-		<Button
-			class="w-full justify-between backdrop-blur-none! hover:[&>kbd]:opacity-100"
-			onclick={() => {
-				isSearchModeActive = true;
-			}}
-			variant="ghost"
-		>
-			<div class="flex items-center gap-2">
-				<Search class="h-4 w-4" />
-
-				Search
-			</div>
-
-			<KeyboardShortcutInfo keys={['cmd', 'k']} />
-		</Button>
-
-		<Button
-			class="w-full justify-between backdrop-blur-none! hover:[&>kbd]:opacity-100"
-			onclick={() => {
-				chatSettingsDialog.open(SETTINGS_SECTION_TITLES.MCP);
-			}}
-			variant="ghost"
-		>
-			<div class="flex items-center gap-2">
-				<McpLogo class="h-4 w-4" />
-
-				MCP Servers
-			</div>
-		</Button>
-	{/if}
-</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/handle-mobile-sidebar-item-click.ts b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/handle-mobile-sidebar-item-click.ts
deleted file mode 100644
index 4b9b8765744..00000000000
--- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/handle-mobile-sidebar-item-click.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-import { useSidebar } from '$lib/components/ui/sidebar';
-
-const sidebar = useSidebar();
-
-export function handleMobileSidebarItemClick() {
-	if (sidebar.isMobile) {
-		sidebar.toggle();
-	}
-}
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogChatAttachmentPreview.svelte b/tools/server/webui/src/lib/components/app/dialogs/DialogChatAttachmentPreview.svelte
deleted file mode 100644
index 012ba00b494..00000000000
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogChatAttachmentPreview.svelte
+++ /dev/null
@@ -1,67 +0,0 @@
-<script lang="ts">
-	import * as Dialog from '$lib/components/ui/dialog';
-	import { ChatAttachmentPreview } from '$lib/components/app';
-	import { formatFileSize } from '$lib/utils';
-
-	interface Props {
-		open: boolean;
-		onOpenChange?: (open: boolean) => void;
-		// Either an uploaded file or a stored attachment
-		uploadedFile?: ChatUploadedFile;
-		attachment?: DatabaseMessageExtra;
-		// For uploaded files
-		preview?: string;
-		name?: string;
-		size?: number;
-		textContent?: string;
-		// For vision modality check
-		activeModelId?: string;
-	}
-
-	let {
-		open = $bindable(),
-		onOpenChange,
-		uploadedFile,
-		attachment,
-		preview,
-		name,
-		size,
-		textContent,
-		activeModelId
-	}: Props = $props();
-
-	let chatAttachmentPreviewRef: ChatAttachmentPreview | undefined = $state();
-
-	let displayName = $derived(uploadedFile?.name || attachment?.name || name || 'Unknown File');
-
-	let displaySize = $derived(uploadedFile?.size || size);
-
-	$effect(() => {
-		if (open && chatAttachmentPreviewRef) {
-			chatAttachmentPreviewRef.reset();
-		}
-	});
-</script>
-
-<Dialog.Root bind:open {onOpenChange}>
-	<Dialog.Content class="grid max-h-[90vh] max-w-5xl overflow-hidden sm:w-auto sm:max-w-6xl">
-		<Dialog.Header>
-			<Dialog.Title class="pr-8">{displayName}</Dialog.Title>
-			<Dialog.Description>
-				{#if displaySize}
-					{formatFileSize(displaySize)}
-				{/if}
-			</Dialog.Description>
-		</Dialog.Header>
-
-		<ChatAttachmentPreview
-			bind:this={chatAttachmentPreviewRef}
-			{uploadedFile}
-			{attachment}
-			{preview}
-			name={displayName}
-			{textContent}
-			{activeModelId}
-		/>
-	</Dialog.Content>
-</Dialog.Root>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogChatAttachmentsViewAll.svelte b/tools/server/webui/src/lib/components/app/dialogs/DialogChatAttachmentsViewAll.svelte
deleted file mode 100644
index 33ab0fe02e1..00000000000
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogChatAttachmentsViewAll.svelte
+++ /dev/null
@@ -1,54 +0,0 @@
-<script lang="ts">
-	import * as Dialog from '$lib/components/ui/dialog';
-	import { ChatAttachmentsViewAll } from '$lib/components/app';
-
-	interface Props {
-		open?: boolean;
-		uploadedFiles?: ChatUploadedFile[];
-		attachments?: DatabaseMessageExtra[];
-		readonly?: boolean;
-		onFileRemove?: (fileId: string) => void;
-		imageHeight?: string;
-		imageWidth?: string;
-		imageClass?: string;
-		activeModelId?: string;
-	}
-
-	let {
-		open = $bindable(false),
-		uploadedFiles = [],
-		attachments = [],
-		readonly = false,
-		onFileRemove,
-		imageHeight = 'h-24',
-		imageWidth = 'w-auto',
-		imageClass = '',
-		activeModelId
-	}: Props = $props();
-
-	let totalCount = $derived(uploadedFiles.length + attachments.length);
-</script>
-
-<Dialog.Root bind:open>
-	<Dialog.Portal>
-		<Dialog.Overlay />
-
-		<Dialog.Content class="flex !max-h-[90vh] !max-w-6xl flex-col">
-			<Dialog.Header>
-				<Dialog.Title>All Attachments ({totalCount})</Dialog.Title>
-				<Dialog.Description>View and manage all attached files</Dialog.Description>
-			</Dialog.Header>
-
-			<ChatAttachmentsViewAll
-				{uploadedFiles}
-				{attachments}
-				{readonly}
-				{onFileRemove}
-				{imageHeight}
-				{imageWidth}
-				{imageClass}
-				{activeModelId}
-			/>
-		</Dialog.Content>
-	</Dialog.Portal>
-</Dialog.Root>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogChatSettings.svelte b/tools/server/webui/src/lib/components/app/dialogs/DialogChatSettings.svelte
deleted file mode 100644
index 793af9a40f2..00000000000
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogChatSettings.svelte
+++ /dev/null
@@ -1,38 +0,0 @@
-<script lang="ts">
-	import * as Dialog from '$lib/components/ui/dialog';
-	import { ChatSettings } from '$lib/components/app';
-	import type { SettingsSectionTitle } from '$lib/constants';
-
-	interface Props {
-		onOpenChange?: (open: boolean) => void;
-		open?: boolean;
-		initialSection?: SettingsSectionTitle;
-	}
-
-	let { onOpenChange, open = false, initialSection }: Props = $props();
-
-	let chatSettingsRef: ChatSettings | undefined = $state();
-
-	function handleClose() {
-		onOpenChange?.(false);
-	}
-
-	function handleSave() {
-		onOpenChange?.(false);
-	}
-
-	$effect(() => {
-		if (open && chatSettingsRef) {
-			chatSettingsRef.reset();
-		}
-	});
-</script>
-
-<Dialog.Root {open} onOpenChange={handleClose}>
-	<Dialog.Content
-		class="z-999999 flex h-[100dvh] max-h-[100dvh] min-h-[100dvh] max-w-4xl! flex-col gap-0 rounded-none
-			p-0 md:h-[64vh] md:max-h-[64vh] md:min-h-0 md:rounded-lg"
-	>
-		<ChatSettings bind:this={chatSettingsRef} onSave={handleSave} {initialSection} />
-	</Dialog.Content>
-</Dialog.Root>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogMcpServersSettings.svelte b/tools/server/webui/src/lib/components/app/dialogs/DialogMcpServersSettings.svelte
deleted file mode 100644
index f0cd325d498..00000000000
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogMcpServersSettings.svelte
+++ /dev/null
@@ -1,39 +0,0 @@
-<script lang="ts">
-	import * as Dialog from '$lib/components/ui/dialog';
-	import { McpLogo, McpServersSettings } from '$lib/components/app';
-
-	interface Props {
-		onOpenChange?: (open: boolean) => void;
-		open?: boolean;
-	}
-
-	let { onOpenChange, open = $bindable(false) }: Props = $props();
-
-	function handleClose() {
-		onOpenChange?.(false);
-	}
-</script>
-
-<Dialog.Root {open} onOpenChange={handleClose}>
-	<Dialog.Content
-		class="z-999999 flex h-[100dvh] max-h-[100dvh] min-h-[100dvh] flex-col gap-0 rounded-none p-0
-			md:h-[80dvh] md:h-auto md:max-h-[80dvh] md:min-h-0 md:rounded-lg"
-		style="max-width: 56rem;"
-	>
-		<div class="grid gap-2 border-b border-border/30 p-4 md:p-6">
-			<Dialog.Title class="inline-flex items-center text-lg font-semibold">
-				<McpLogo class="mr-2 inline h-4 w-4" />
-
-				MCP Servers
-			</Dialog.Title>
-
-			<Dialog.Description class="text-sm text-muted-foreground">
-				Add and configure MCP servers to enable agentic tool execution capabilities.
-			</Dialog.Description>
-		</div>
-
-		<div class="flex-1 overflow-y-auto px-4 py-6">
-			<McpServersSettings />
-		</div>
-	</Dialog.Content>
-</Dialog.Root>
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpActiveServersAvatars.svelte b/tools/server/webui/src/lib/components/app/mcp/McpActiveServersAvatars.svelte
deleted file mode 100644
index 75858493e20..00000000000
--- a/tools/server/webui/src/lib/components/app/mcp/McpActiveServersAvatars.svelte
+++ /dev/null
@@ -1,57 +0,0 @@
-<script lang="ts">
-	import { cn } from '$lib/components/ui/utils';
-	import { conversationsStore } from '$lib/stores/conversations.svelte';
-	import { mcpStore } from '$lib/stores/mcp.svelte';
-	import { HealthCheckStatus } from '$lib/enums';
-	import { MAX_DISPLAYED_MCP_AVATARS } from '$lib/constants';
-
-	interface Props {
-		class?: string;
-	}
-
-	let { class: className = '' }: Props = $props();
-
-	let mcpServers = $derived(mcpStore.getServersSorted().filter((s) => s.enabled));
-	let enabledMcpServersForChat = $derived(
-		mcpServers.filter((s) => conversationsStore.isMcpServerEnabledForChat(s.id) && s.url.trim())
-	);
-	let healthyEnabledMcpServers = $derived(
-		enabledMcpServersForChat.filter((s) => {
-			const healthState = mcpStore.getHealthCheckState(s.id);
-			return healthState.status !== HealthCheckStatus.ERROR;
-		})
-	);
-	let hasEnabledMcpServers = $derived(enabledMcpServersForChat.length > 0);
-	let extraServersCount = $derived(
-		Math.max(0, healthyEnabledMcpServers.length - MAX_DISPLAYED_MCP_AVATARS)
-	);
-	let mcpFavicons = $derived(
-		healthyEnabledMcpServers
-			.slice(0, MAX_DISPLAYED_MCP_AVATARS)
-			.map((s) => ({ id: s.id, url: mcpStore.getServerFavicon(s.id) }))
-			.filter((f) => f.url !== null)
-	);
-</script>
-
-{#if hasEnabledMcpServers && mcpFavicons.length > 0}
-	<div class={cn('inline-flex items-center gap-1.5', className)}>
-		<div class="flex -space-x-1">
-			{#each mcpFavicons as favicon (favicon.id)}
-				<div class="box-shadow-lg overflow-hidden rounded-full bg-muted ring-1 ring-muted">
-					<img
-						src={favicon.url}
-						alt=""
-						class="h-4 w-4"
-						onerror={(e) => {
-							(e.currentTarget as HTMLImageElement).style.display = 'none';
-						}}
-					/>
-				</div>
-			{/each}
-		</div>
-
-		{#if extraServersCount > 0}
-			<span class="text-xs text-muted-foreground">+{extraServersCount}</span>
-		{/if}
-	</div>
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServersSelector.svelte b/tools/server/webui/src/lib/components/app/mcp/McpServersSelector.svelte
deleted file mode 100644
index ef701a5c09a..00000000000
--- a/tools/server/webui/src/lib/components/app/mcp/McpServersSelector.svelte
+++ /dev/null
@@ -1,160 +0,0 @@
-<script lang="ts">
-	import { Settings } from '@lucide/svelte';
-	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
-	import { Switch } from '$lib/components/ui/switch';
-	import { DropdownMenuSearchable, McpActiveServersAvatars } from '$lib/components/app';
-	import { conversationsStore } from '$lib/stores/conversations.svelte';
-	import { mcpStore } from '$lib/stores/mcp.svelte';
-	import { HealthCheckStatus } from '$lib/enums';
-	import type { MCPServerSettingsEntry } from '$lib/types';
-
-	interface Props {
-		class?: string;
-		disabled?: boolean;
-		onSettingsClick?: () => void;
-	}
-
-	let { class: className = '', disabled = false, onSettingsClick }: Props = $props();
-
-	let searchQuery = $state('');
-	let mcpServers = $derived(mcpStore.getServersSorted().filter((s) => s.enabled));
-	let hasMcpServers = $derived(mcpServers.length > 0);
-	let enabledMcpServersForChat = $derived(
-		mcpServers.filter((s) => conversationsStore.isMcpServerEnabledForChat(s.id) && s.url.trim())
-	);
-	let healthyEnabledMcpServers = $derived(
-		enabledMcpServersForChat.filter((s) => {
-			const healthState = mcpStore.getHealthCheckState(s.id);
-			return healthState.status !== HealthCheckStatus.ERROR;
-		})
-	);
-	let hasEnabledMcpServers = $derived(enabledMcpServersForChat.length > 0);
-	let mcpFavicons = $derived(
-		healthyEnabledMcpServers
-			.slice(0, 3)
-			.map((s) => ({ id: s.id, url: mcpStore.getServerFavicon(s.id) }))
-			.filter((f) => f.url !== null)
-	);
-	let filteredMcpServers = $derived.by(() => {
-		const query = searchQuery.toLowerCase().trim();
-		if (query) {
-			return mcpServers.filter((s) => {
-				const name = getServerLabel(s).toLowerCase();
-				const url = s.url.toLowerCase();
-				return name.includes(query) || url.includes(query);
-			});
-		}
-		return mcpServers;
-	});
-
-	function getServerLabel(server: MCPServerSettingsEntry): string {
-		return mcpStore.getServerLabel(server);
-	}
-
-	function handleDropdownOpen(open: boolean) {
-		if (open) {
-			mcpStore.runHealthChecksForServers(mcpServers);
-		}
-	}
-
-	function isServerEnabledForChat(serverId: string): boolean {
-		return conversationsStore.isMcpServerEnabledForChat(serverId);
-	}
-
-	async function toggleServerForChat(serverId: string) {
-		await conversationsStore.toggleMcpServerForChat(serverId);
-	}
-</script>
-
-{#if hasMcpServers && hasEnabledMcpServers && mcpFavicons.length > 0}
-	<DropdownMenu.Root
-		onOpenChange={(open) => {
-			if (!open) {
-				searchQuery = '';
-			}
-			handleDropdownOpen(open);
-		}}
-	>
-		<DropdownMenu.Trigger
-			{disabled}
-			onclick={(e) => {
-				e.preventDefault();
-				e.stopPropagation();
-			}}
-		>
-			<button
-				type="button"
-				class="inline-flex cursor-pointer items-center rounded-sm py-1 disabled:cursor-not-allowed disabled:opacity-60"
-				{disabled}
-				aria-label="MCP Servers"
-			>
-				<McpActiveServersAvatars class={className} />
-			</button>
-		</DropdownMenu.Trigger>
-
-		<DropdownMenu.Content align="start" class="w-72 pt-0">
-			<DropdownMenuSearchable
-				bind:searchValue={searchQuery}
-				placeholder="Search servers..."
-				emptyMessage="No servers found"
-				isEmpty={filteredMcpServers.length === 0}
-			>
-				<div class="max-h-64 overflow-y-auto">
-					{#each filteredMcpServers as server (server.id)}
-						{@const healthState = mcpStore.getHealthCheckState(server.id)}
-						{@const hasError = healthState.status === HealthCheckStatus.ERROR}
-						{@const isEnabledForChat = isServerEnabledForChat(server.id)}
-
-						<button
-							type="button"
-							class="flex w-full items-center justify-between gap-2 px-2 py-2 text-left transition-colors hover:bg-accent disabled:cursor-not-allowed disabled:opacity-50"
-							onclick={() => !hasError && toggleServerForChat(server.id)}
-							disabled={hasError}
-						>
-							<div class="flex min-w-0 flex-1 items-center gap-2">
-								{#if mcpStore.getServerFavicon(server.id)}
-									<img
-										src={mcpStore.getServerFavicon(server.id)}
-										alt=""
-										class="h-4 w-4 shrink-0 rounded-sm"
-										onerror={(e) => {
-											(e.currentTarget as HTMLImageElement).style.display = 'none';
-										}}
-									/>
-								{/if}
-
-								<span class="truncate text-sm">{getServerLabel(server)}</span>
-
-								{#if hasError}
-									<span
-										class="shrink-0 rounded bg-destructive/15 px-1.5 py-0.5 text-xs text-destructive"
-									>
-										Error
-									</span>
-								{/if}
-							</div>
-
-							<Switch
-								checked={isEnabledForChat}
-								disabled={hasError}
-								onclick={(e: MouseEvent) => e.stopPropagation()}
-								onCheckedChange={() => toggleServerForChat(server.id)}
-							/>
-						</button>
-					{/each}
-				</div>
-
-				{#snippet footer()}
-					<DropdownMenu.Item
-						class="flex cursor-pointer items-center gap-2"
-						onclick={onSettingsClick}
-					>
-						<Settings class="h-4 w-4" />
-
-						<span>Manage MCP Servers</span>
-					</DropdownMenu.Item>
-				{/snippet}
-			</DropdownMenuSearchable>
-		</DropdownMenu.Content>
-	</DropdownMenu.Root>
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServersSettings.svelte b/tools/server/webui/src/lib/components/app/mcp/McpServersSettings.svelte
deleted file mode 100644
index 85232b586e8..00000000000
--- a/tools/server/webui/src/lib/components/app/mcp/McpServersSettings.svelte
+++ /dev/null
@@ -1,150 +0,0 @@
-<script lang="ts">
-	import { Plus } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import * as Card from '$lib/components/ui/card';
-	import { uuid } from '$lib/utils';
-	import { mcpStore } from '$lib/stores/mcp.svelte';
-	import { conversationsStore } from '$lib/stores/conversations.svelte';
-	import { McpServerCard, McpServerCardSkeleton, McpServerForm } from '$lib/components/app/mcp';
-	import { MCP_SERVER_ID_PREFIX } from '$lib/constants';
-	import { HealthCheckStatus } from '$lib/enums';
-
-	let servers = $derived(mcpStore.getServersSorted());
-
-	let initialLoadComplete = $state(false);
-
-	$effect(() => {
-		if (initialLoadComplete) return;
-
-		const allChecked =
-			servers.length > 0 &&
-			servers.every((server) => {
-				const state = mcpStore.getHealthCheckState(server.id);
-
-				return (
-					state.status === HealthCheckStatus.SUCCESS || state.status === HealthCheckStatus.ERROR
-				);
-			});
-
-		if (allChecked) {
-			initialLoadComplete = true;
-		}
-	});
-
-	let isAddingServer = $state(false);
-	let newServerUrl = $state('');
-	let newServerHeaders = $state('');
-	let newServerUrlError = $derived.by(() => {
-		if (!newServerUrl.trim()) return 'URL is required';
-		try {
-			new URL(newServerUrl);
-
-			return null;
-		} catch {
-			return 'Invalid URL format';
-		}
-	});
-
-	function showAddServerForm() {
-		isAddingServer = true;
-		newServerUrl = '';
-		newServerHeaders = '';
-	}
-
-	function cancelAddServer() {
-		isAddingServer = false;
-		newServerUrl = '';
-		newServerHeaders = '';
-	}
-
-	function saveNewServer() {
-		if (newServerUrlError) return;
-
-		const newServerId = uuid() ?? `${MCP_SERVER_ID_PREFIX}-${Date.now()}`;
-
-		mcpStore.addServer({
-			id: newServerId,
-			enabled: true,
-			url: newServerUrl.trim(),
-			headers: newServerHeaders.trim() || undefined
-		});
-
-		conversationsStore.setMcpServerOverride(newServerId, true);
-
-		isAddingServer = false;
-		newServerUrl = '';
-		newServerHeaders = '';
-	}
-</script>
-
-<div class="space-y-5 md:space-y-4">
-	<div class="flex items-start justify-between gap-4">
-		<div>
-			<h4 class="text-base font-semibold">Manage Servers</h4>
-		</div>
-
-		{#if !isAddingServer}
-			<Button variant="outline" size="sm" class="shrink-0" onclick={showAddServerForm}>
-				<Plus class="h-4 w-4" />
-
-				Add New Server
-			</Button>
-		{/if}
-	</div>
-
-	{#if isAddingServer}
-		<Card.Root class="bg-muted/30 p-4">
-			<div class="space-y-4">
-				<p class="font-medium">Add New Server</p>
-
-				<McpServerForm
-					url={newServerUrl}
-					headers={newServerHeaders}
-					onUrlChange={(v) => (newServerUrl = v)}
-					onHeadersChange={(v) => (newServerHeaders = v)}
-					urlError={newServerUrl ? newServerUrlError : null}
-					id="new-server"
-				/>
-
-				<div class="flex items-center justify-end gap-2">
-					<Button variant="secondary" size="sm" onclick={cancelAddServer}>Cancel</Button>
-
-					<Button
-						variant="default"
-						size="sm"
-						onclick={saveNewServer}
-						disabled={!!newServerUrlError}
-						aria-label="Save"
-					>
-						Add
-					</Button>
-				</div>
-			</div>
-		</Card.Root>
-	{/if}
-
-	{#if servers.length === 0 && !isAddingServer}
-		<div class="rounded-md border border-dashed p-4 text-sm text-muted-foreground">
-			No MCP Servers configured yet. Add one to enable agentic features.
-		</div>
-	{/if}
-
-	{#if servers.length > 0}
-		<div class="space-y-3">
-			{#each servers as server (server.id)}
-				{#if !initialLoadComplete}
-					<McpServerCardSkeleton />
-				{:else}
-					<McpServerCard
-						{server}
-						faviconUrl={mcpStore.getServerFavicon(server.id)}
-						enabled={conversationsStore.isMcpServerEnabledForChat(server.id)}
-						onToggle={async () => await conversationsStore.toggleMcpServerForChat(server.id)}
-						onUpdate={(updates) => mcpStore.updateServer(server.id, updates)}
-						onDelete={() => mcpStore.removeServer(server.id)}
-					/>
-				{/if}
-			{/each}
-		</div>
-	{/if}
-</div>
diff --git a/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte b/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte
deleted file mode 100644
index ccda78690cf..00000000000
--- a/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte
+++ /dev/null
@@ -1,437 +0,0 @@
-<script lang="ts">
-	import { onMount } from 'svelte';
-	import { ChevronDown, Loader2, Package } from '@lucide/svelte';
-	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
-	import * as Tooltip from '$lib/components/ui/tooltip';
-	import { cn } from '$lib/components/ui/utils';
-	import {
-		modelsStore,
-		modelOptions,
-		modelsLoading,
-		modelsUpdating,
-		selectedModelId,
-		singleModelName
-	} from '$lib/stores/models.svelte';
-	import { KeyboardKey } from '$lib/enums';
-	import { isRouterMode } from '$lib/stores/server.svelte';
-	import {
-		DialogModelInformation,
-		DropdownMenuSearchable,
-		ModelId,
-		ModelsSelectorList,
-		ModelsSelectorOption
-	} from '$lib/components/app';
-	import type { ModelOption } from '$lib/types/models';
-	import { filterModelOptions, groupModelOptions, type ModelItem } from './utils';
-
-	interface Props {
-		class?: string;
-		currentModel?: string | null;
-		disabled?: boolean;
-		forceForegroundText?: boolean;
-		onModelChange?: (modelId: string, modelName: string) => Promise<boolean> | boolean | void;
-		useGlobalSelection?: boolean;
-	}
-
-	let {
-		class: className = '',
-		currentModel = null,
-		disabled = false,
-		forceForegroundText = false,
-		onModelChange,
-		useGlobalSelection = false
-	}: Props = $props();
-
-	let options = $derived(
-		modelOptions().filter((option) => {
-			const modelProps = modelsStore.getModelProps(option.model);
-
-			return modelProps?.webui !== false;
-		})
-	);
-	let loading = $derived(modelsLoading());
-	let updating = $derived(modelsUpdating());
-	let activeId = $derived(selectedModelId());
-	let isRouter = $derived(isRouterMode());
-	let serverModel = $derived(singleModelName());
-
-	let isHighlightedCurrentModelActive = $derived.by(() => {
-		if (!isRouter || !currentModel) return false;
-
-		const currentOption = options.find((option) => option.model === currentModel);
-
-		return currentOption ? currentOption.id === activeId : false;
-	});
-
-	let isCurrentModelInCache = $derived.by(() => {
-		if (!isRouter || !currentModel) return true;
-
-		return options.some((option) => option.model === currentModel);
-	});
-
-	let isLoadingModel = $state(false);
-
-	let searchTerm = $state('');
-	let highlightedIndex = $state<number>(-1);
-
-	let filteredOptions = $derived(filterModelOptions(options, searchTerm));
-
-	let groupedFilteredOptions = $derived(
-		groupModelOptions(filteredOptions, modelsStore.favoriteModelIds, (m) =>
-			modelsStore.isModelLoaded(m)
-		)
-	);
-
-	$effect(() => {
-		void searchTerm;
-		highlightedIndex = -1;
-	});
-
-	let isOpen = $state(false);
-	let showModelDialog = $state(false);
-	let infoModelId = $state<string | null>(null);
-
-	function handleInfoClick(modelName: string) {
-		infoModelId = modelName;
-		showModelDialog = true;
-	}
-
-	onMount(() => {
-		modelsStore.fetch().catch((error) => {
-			console.error('Unable to load models:', error);
-		});
-	});
-
-	function handleOpenChange(open: boolean) {
-		if (loading || updating) return;
-
-		if (isRouter) {
-			if (open) {
-				isOpen = true;
-				searchTerm = '';
-				highlightedIndex = -1;
-
-				modelsStore.fetchRouterModels().then(() => {
-					modelsStore.fetchModalitiesForLoadedModels();
-				});
-			} else {
-				isOpen = false;
-				searchTerm = '';
-				highlightedIndex = -1;
-			}
-		} else {
-			showModelDialog = open;
-		}
-	}
-
-	export function open() {
-		handleOpenChange(true);
-	}
-
-	function handleSearchKeyDown(event: KeyboardEvent) {
-		if (event.isComposing) return;
-
-		if (event.key === KeyboardKey.ARROW_DOWN) {
-			event.preventDefault();
-
-			if (filteredOptions.length === 0) return;
-
-			if (highlightedIndex === -1 || highlightedIndex === filteredOptions.length - 1) {
-				highlightedIndex = 0;
-			} else {
-				highlightedIndex += 1;
-			}
-		} else if (event.key === KeyboardKey.ARROW_UP) {
-			event.preventDefault();
-
-			if (filteredOptions.length === 0) return;
-
-			if (highlightedIndex === -1 || highlightedIndex === 0) {
-				highlightedIndex = filteredOptions.length - 1;
-			} else {
-				highlightedIndex -= 1;
-			}
-		} else if (event.key === KeyboardKey.ENTER) {
-			event.preventDefault();
-
-			if (highlightedIndex >= 0 && highlightedIndex < filteredOptions.length) {
-				const option = filteredOptions[highlightedIndex];
-
-				handleSelect(option.id);
-			} else if (filteredOptions.length > 0) {
-				highlightedIndex = 0;
-			}
-		}
-	}
-
-	async function handleSelect(modelId: string) {
-		const option = options.find((opt) => opt.id === modelId);
-		if (!option) return;
-
-		let shouldCloseMenu = true;
-
-		if (onModelChange) {
-			const result = await onModelChange(option.id, option.model);
-
-			if (result === false) {
-				shouldCloseMenu = false;
-			}
-		} else {
-			await modelsStore.selectModelById(option.id);
-		}
-
-		if (shouldCloseMenu) {
-			handleOpenChange(false);
-
-			requestAnimationFrame(() => {
-				const textarea = document.querySelector<HTMLTextAreaElement>(
-					'[data-slot="chat-form"] textarea'
-				);
-				textarea?.focus();
-			});
-		}
-
-		if (!onModelChange && isRouter && !modelsStore.isModelLoaded(option.model)) {
-			isLoadingModel = true;
-			modelsStore
-				.loadModel(option.model)
-				.catch((error) => console.error('Failed to load model:', error))
-				.finally(() => (isLoadingModel = false));
-		}
-	}
-
-	function getDisplayOption(): ModelOption | undefined {
-		if (!isRouter) {
-			const displayModel = serverModel || currentModel;
-			if (displayModel) {
-				return {
-					id: serverModel ? 'current' : 'offline-current',
-					model: displayModel,
-					name: displayModel.split('/').pop() || displayModel,
-					capabilities: []
-				};
-			}
-
-			return undefined;
-		}
-
-		if (useGlobalSelection && activeId) {
-			const selected = options.find((option) => option.id === activeId);
-
-			if (selected) return selected;
-		}
-
-		if (currentModel) {
-			if (!isCurrentModelInCache) {
-				return {
-					id: 'not-in-cache',
-					model: currentModel,
-					name: currentModel.split('/').pop() || currentModel,
-					capabilities: []
-				};
-			}
-
-			return options.find((option) => option.model === currentModel);
-		}
-
-		if (activeId) {
-			return options.find((option) => option.id === activeId);
-		}
-
-		return undefined;
-	}
-</script>
-
-<div class={cn('relative inline-flex flex-col items-end gap-1', className)}>
-	{#if loading && options.length === 0 && isRouter}
-		<div class="flex items-center gap-2 text-xs text-muted-foreground">
-			<Loader2 class="h-3.5 w-3.5 animate-spin" />
-
-			Loading models…
-		</div>
-	{:else if options.length === 0 && isRouter}
-		{#if currentModel}
-			<span
-				class={cn(
-					'inline-flex items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs text-muted-foreground',
-					className
-				)}
-				style="max-width: min(calc(100cqw - 9rem), 20rem)"
-			>
-				<Package class="h-3.5 w-3.5" />
-
-				<ModelId modelId={currentModel} class="min-w-0" showOrgName />
-			</span>
-		{:else}
-			<p class="text-xs text-muted-foreground">No models available.</p>
-		{/if}
-	{:else}
-		{@const selectedOption = getDisplayOption()}
-
-		{#if isRouter}
-			<DropdownMenu.Root bind:open={isOpen} onOpenChange={handleOpenChange}>
-				<DropdownMenu.Trigger
-					class={cn(
-						`inline-grid cursor-pointer grid-cols-[1fr_auto_1fr] items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60`,
-						!isCurrentModelInCache
-							? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
-							: forceForegroundText
-								? 'text-foreground'
-								: isHighlightedCurrentModelActive
-									? 'text-foreground'
-									: 'text-muted-foreground',
-						isOpen ? 'text-foreground' : ''
-					)}
-					style="max-width: min(calc(100cqw - 9rem), 20rem)"
-					disabled={disabled || updating}
-				>
-					<Package class="h-3.5 w-3.5" />
-
-					{#if selectedOption}
-						<Tooltip.Root>
-							<Tooltip.Trigger>
-								<!-- prevent another nested button element -->
-								{#snippet child({ props })}
-									<ModelId
-										modelId={selectedOption.model}
-										class="min-w-0 overflow-hidden"
-										showOrgName
-										{...props}
-									/>
-								{/snippet}
-							</Tooltip.Trigger>
-
-							<Tooltip.Content>
-								<p class="font-mono">{selectedOption.model}</p>
-							</Tooltip.Content>
-						</Tooltip.Root>
-					{:else}
-						<span class="min-w-0 font-medium">Select model</span>
-					{/if}
-
-					{#if updating || isLoadingModel}
-						<Loader2 class="h-3 w-3.5 animate-spin" />
-					{:else}
-						<ChevronDown class="h-3 w-3.5" />
-					{/if}
-				</DropdownMenu.Trigger>
-
-				<DropdownMenu.Content
-					align="end"
-					class="w-full max-w-[100vw] pt-0 sm:w-max sm:max-w-[calc(100vw-2rem)]"
-				>
-					<DropdownMenuSearchable
-						bind:searchValue={searchTerm}
-						placeholder="Search models..."
-						onSearchKeyDown={handleSearchKeyDown}
-						emptyMessage="No models found."
-						isEmpty={filteredOptions.length === 0 && isCurrentModelInCache}
-					>
-						<div class="models-list">
-							{#if !isCurrentModelInCache && currentModel}
-								<!-- Show unavailable model as first option (disabled) -->
-								<button
-									type="button"
-									class="flex w-full cursor-not-allowed items-center bg-red-400/10 p-2 text-left text-sm text-red-400"
-									role="option"
-									aria-selected="true"
-									aria-disabled="true"
-									disabled
-								>
-									<ModelId modelId={currentModel} class="flex-1" showOrgName />
-
-									<span class="ml-2 text-xs whitespace-nowrap opacity-70">(not available)</span>
-								</button>
-							{/if}
-
-							{#if filteredOptions.length === 0}
-								<p class="px-4 py-3 text-sm text-muted-foreground">No models found.</p>
-							{/if}
-
-							{#snippet modelOption(item: ModelItem, showOrgName: boolean)}
-								{@const { option, flatIndex } = item}
-								{@const isSelected = currentModel === option.model || activeId === option.id}
-								{@const isHighlighted = flatIndex === highlightedIndex}
-								{@const isFav = modelsStore.favoriteModelIds.has(option.model)}
-
-								<ModelsSelectorOption
-									{option}
-									{isSelected}
-									{isHighlighted}
-									{isFav}
-									{showOrgName}
-									onSelect={handleSelect}
-									onInfoClick={handleInfoClick}
-									onMouseEnter={() => (highlightedIndex = flatIndex)}
-									onKeyDown={(e) => {
-										if (e.key === KeyboardKey.ENTER || e.key === KeyboardKey.SPACE) {
-											e.preventDefault();
-											handleSelect(option.id);
-										}
-									}}
-								/>
-							{/snippet}
-
-							<ModelsSelectorList
-								groups={groupedFilteredOptions}
-								{currentModel}
-								{activeId}
-								sectionHeaderClass="my-1.5 px-2 py-2 text-[13px] font-semibold text-muted-foreground/70 select-none"
-								onSelect={handleSelect}
-								onInfoClick={handleInfoClick}
-								renderOption={modelOption}
-							/>
-						</div>
-					</DropdownMenuSearchable>
-				</DropdownMenu.Content>
-			</DropdownMenu.Root>
-		{:else}
-			<button
-				class={cn(
-					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60`,
-					!isCurrentModelInCache
-						? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
-						: forceForegroundText
-							? 'text-foreground'
-							: isHighlightedCurrentModelActive
-								? 'text-foreground'
-								: 'text-muted-foreground',
-					isOpen ? 'text-foreground' : ''
-				)}
-				style="max-width: min(calc(100cqw - 6.5rem), 32rem)"
-				onclick={() => handleOpenChange(true)}
-				disabled={disabled || updating}
-			>
-				<Package class="h-3.5 w-3.5" />
-
-				{#if selectedOption}
-					<Tooltip.Root>
-						<Tooltip.Trigger>
-							<!-- prevent another nested button element -->
-							{#snippet child({ props })}
-								<ModelId
-									modelId={selectedOption.model}
-									class="min-w-0 overflow-hidden"
-									showOrgName
-									{...props}
-								/>
-							{/snippet}
-						</Tooltip.Trigger>
-
-						<Tooltip.Content>
-							<p class="font-mono">{selectedOption.model}</p>
-						</Tooltip.Content>
-					</Tooltip.Root>
-				{/if}
-
-				{#if updating}
-					<Loader2 class="h-3 w-3.5 animate-spin" />
-				{/if}
-			</button>
-		{/if}
-	{/if}
-</div>
-
-{#if showModelDialog}
-	<DialogModelInformation bind:open={showModelDialog} modelId={infoModelId} />
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/models/ModelsSelectorSheet.svelte b/tools/server/webui/src/lib/components/app/models/ModelsSelectorSheet.svelte
deleted file mode 100644
index fe88c979f92..00000000000
--- a/tools/server/webui/src/lib/components/app/models/ModelsSelectorSheet.svelte
+++ /dev/null
@@ -1,322 +0,0 @@
-<script lang="ts">
-	import { onMount } from 'svelte';
-	import { ChevronDown, Loader2, Package } from '@lucide/svelte';
-	import * as Sheet from '$lib/components/ui/sheet';
-	import { cn } from '$lib/components/ui/utils';
-	import {
-		modelsStore,
-		modelOptions,
-		modelsLoading,
-		modelsUpdating,
-		selectedModelId,
-		singleModelName
-	} from '$lib/stores/models.svelte';
-	import { isRouterMode } from '$lib/stores/server.svelte';
-	import {
-		DialogModelInformation,
-		ModelsSelectorList,
-		SearchInput,
-		TruncatedText
-	} from '$lib/components/app';
-	import type { ModelOption } from '$lib/types/models';
-	import { filterModelOptions, groupModelOptions } from './utils';
-
-	interface Props {
-		class?: string;
-		currentModel?: string | null;
-		/** Callback when model changes. Return false to keep menu open (e.g., for validation failures) */
-		onModelChange?: (modelId: string, modelName: string) => Promise<boolean> | boolean | void;
-		disabled?: boolean;
-		forceForegroundText?: boolean;
-		/** When true, user's global selection takes priority over currentModel (for form selector) */
-		useGlobalSelection?: boolean;
-	}
-
-	let {
-		class: className = '',
-		currentModel = null,
-		onModelChange,
-		disabled = false,
-		forceForegroundText = false,
-		useGlobalSelection = false
-	}: Props = $props();
-
-	let options = $derived(
-		modelOptions().filter((option) => {
-			const modelProps = modelsStore.getModelProps(option.model);
-			return modelProps?.webui !== false;
-		})
-	);
-	let loading = $derived(modelsLoading());
-	let updating = $derived(modelsUpdating());
-	let activeId = $derived(selectedModelId());
-	let isRouter = $derived(isRouterMode());
-	let serverModel = $derived(singleModelName());
-
-	let isLoadingModel = $state(false);
-
-	let isHighlightedCurrentModelActive = $derived(
-		!isRouter || !currentModel
-			? false
-			: (() => {
-					const currentOption = options.find((option) => option.model === currentModel);
-
-					return currentOption ? currentOption.id === activeId : false;
-				})()
-	);
-
-	let isCurrentModelInCache = $derived.by(() => {
-		if (!isRouter || !currentModel) return true;
-
-		return options.some((option) => option.model === currentModel);
-	});
-
-	let searchTerm = $state('');
-
-	let filteredOptions = $derived(filterModelOptions(options, searchTerm));
-
-	let groupedFilteredOptions = $derived(
-		groupModelOptions(filteredOptions, modelsStore.favoriteModelIds, (m) =>
-			modelsStore.isModelLoaded(m)
-		)
-	);
-
-	let sheetOpen = $state(false);
-	let showModelDialog = $state(false);
-	let infoModelId = $state<string | null>(null);
-
-	function handleInfoClick(modelName: string) {
-		infoModelId = modelName;
-		showModelDialog = true;
-	}
-
-	onMount(() => {
-		modelsStore.fetch().catch((error) => {
-			console.error('Unable to load models:', error);
-		});
-	});
-
-	function handleOpenChange(open: boolean) {
-		if (loading || updating) return;
-
-		if (isRouter) {
-			if (open) {
-				sheetOpen = true;
-				searchTerm = '';
-
-				modelsStore.fetchRouterModels().then(() => {
-					modelsStore.fetchModalitiesForLoadedModels();
-				});
-			} else {
-				sheetOpen = false;
-				searchTerm = '';
-			}
-		} else {
-			showModelDialog = open;
-		}
-	}
-
-	export function open() {
-		handleOpenChange(true);
-	}
-
-	function handleSheetOpenChange(open: boolean) {
-		if (!open) {
-			handleOpenChange(false);
-		}
-	}
-
-	async function handleSelect(modelId: string) {
-		const option = options.find((opt) => opt.id === modelId);
-		if (!option) return;
-
-		let shouldCloseMenu = true;
-
-		if (onModelChange) {
-			const result = await onModelChange(option.id, option.model);
-
-			if (result === false) {
-				shouldCloseMenu = false;
-			}
-		} else {
-			await modelsStore.selectModelById(option.id);
-		}
-
-		if (shouldCloseMenu) {
-			handleOpenChange(false);
-
-			requestAnimationFrame(() => {
-				const textarea = document.querySelector<HTMLTextAreaElement>(
-					'[data-slot="chat-form"] textarea'
-				);
-				textarea?.focus();
-			});
-		}
-
-		if (!onModelChange && isRouter && !modelsStore.isModelLoaded(option.model)) {
-			isLoadingModel = true;
-			modelsStore
-				.loadModel(option.model)
-				.catch((error) => console.error('Failed to load model:', error))
-				.finally(() => (isLoadingModel = false));
-		}
-	}
-
-	function getDisplayOption(): ModelOption | undefined {
-		if (!isRouter) {
-			if (serverModel) {
-				return {
-					id: 'current',
-					model: serverModel,
-					name: serverModel.split('/').pop() || serverModel,
-					capabilities: []
-				};
-			}
-
-			return undefined;
-		}
-
-		if (useGlobalSelection && activeId) {
-			const selected = options.find((option) => option.id === activeId);
-			if (selected) return selected;
-		}
-
-		if (currentModel) {
-			if (!isCurrentModelInCache) {
-				return {
-					id: 'not-in-cache',
-					model: currentModel,
-					name: currentModel.split('/').pop() || currentModel,
-					capabilities: []
-				};
-			}
-
-			return options.find((option) => option.model === currentModel);
-		}
-
-		if (activeId) {
-			return options.find((option) => option.id === activeId);
-		}
-
-		return undefined;
-	}
-</script>
-
-<div class={cn('relative inline-flex flex-col items-end gap-1', className)}>
-	{#if loading && options.length === 0 && isRouter}
-		<div class="flex items-center gap-2 text-xs text-muted-foreground">
-			<Loader2 class="h-3.5 w-3.5 animate-spin" />
-			Loading models…
-		</div>
-	{:else if options.length === 0 && isRouter}
-		<p class="text-xs text-muted-foreground">No models available.</p>
-	{:else}
-		{@const selectedOption = getDisplayOption()}
-
-		{#if isRouter}
-			<button
-				type="button"
-				class={cn(
-					`inline-grid cursor-pointer grid-cols-[1fr_auto_1fr] items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60`,
-					!isCurrentModelInCache
-						? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
-						: forceForegroundText
-							? 'text-foreground'
-							: isHighlightedCurrentModelActive
-								? 'text-foreground'
-								: 'text-muted-foreground',
-					sheetOpen ? 'text-foreground' : ''
-				)}
-				style="max-width: min(calc(100cqw - 9rem), 20rem)"
-				disabled={disabled || updating}
-				onclick={() => handleOpenChange(true)}
-			>
-				<Package class="h-3.5 w-3.5" />
-
-				<TruncatedText text={selectedOption?.model || 'Select model'} class="min-w-0 font-medium" />
-
-				{#if updating || isLoadingModel}
-					<Loader2 class="h-3 w-3.5 animate-spin" />
-				{:else}
-					<ChevronDown class="h-3 w-3.5" />
-				{/if}
-			</button>
-
-			<Sheet.Root bind:open={sheetOpen} onOpenChange={handleSheetOpenChange}>
-				<Sheet.Content side="bottom" class="max-h-[85vh] gap-1">
-					<Sheet.Header>
-						<Sheet.Title>Select Model</Sheet.Title>
-
-						<Sheet.Description class="sr-only">
-							Choose a model to use for the conversation
-						</Sheet.Description>
-					</Sheet.Header>
-
-					<div class="flex flex-col gap-1 pb-4">
-						<div class="mb-3 px-4">
-							<SearchInput placeholder="Search models..." bind:value={searchTerm} />
-						</div>
-
-						<div class="max-h-[60vh] overflow-y-auto px-2">
-							{#if !isCurrentModelInCache && currentModel}
-								<button
-									type="button"
-									class="flex w-full cursor-not-allowed items-center rounded-md bg-red-400/10 px-3 py-2.5 text-left text-sm text-red-400"
-									disabled
-								>
-									<span class="min-w-0 flex-1 truncate">
-										{selectedOption?.name || currentModel}
-									</span>
-									<span class="ml-2 text-xs whitespace-nowrap opacity-70">(not available)</span>
-								</button>
-								<div class="my-1 h-px bg-border"></div>
-							{/if}
-
-							{#if filteredOptions.length === 0}
-								<p class="px-3 py-3 text-center text-sm text-muted-foreground">No models found.</p>
-							{/if}
-
-							<ModelsSelectorList
-								groups={groupedFilteredOptions}
-								{currentModel}
-								{activeId}
-								sectionHeaderClass="px-2 py-2 text-xs font-semibold text-muted-foreground/60 select-none"
-								orgHeaderClass="px-2 py-2 text-xs font-semibold text-muted-foreground/60 select-none [&:not(:first-child)]:mt-2"
-								onSelect={handleSelect}
-								onInfoClick={handleInfoClick}
-							/>
-						</div>
-					</div>
-				</Sheet.Content>
-			</Sheet.Root>
-		{:else}
-			<button
-				class={cn(
-					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60`,
-					!isCurrentModelInCache
-						? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
-						: forceForegroundText
-							? 'text-foreground'
-							: isHighlightedCurrentModelActive
-								? 'text-foreground'
-								: 'text-muted-foreground'
-				)}
-				style="max-width: min(calc(100cqw - 6.5rem), 32rem)"
-				onclick={() => handleOpenChange(true)}
-				disabled={disabled || updating}
-			>
-				<Package class="h-3.5 w-3.5" />
-
-				<TruncatedText text={selectedOption?.model || ''} class="min-w-0 font-medium" />
-
-				{#if updating}
-					<Loader2 class="h-3 w-3.5 animate-spin" />
-				{/if}
-			</button>
-		{/if}
-	{/if}
-</div>
-
-{#if showModelDialog}
-	<DialogModelInformation bind:open={showModelDialog} modelId={infoModelId} />
-{/if}
diff --git a/tools/server/webui/src/lib/components/app/navigation/index.ts b/tools/server/webui/src/lib/components/app/navigation/index.ts
deleted file mode 100644
index 051491b8667..00000000000
--- a/tools/server/webui/src/lib/components/app/navigation/index.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- *
- * NAVIGATION & MENUS
- *
- * Components for dropdown menus and action selection.
- *
- */
-
-/**
- * **DropdownMenuSearchable** - Searchable content for dropdown menus
- *
- * Renders a search input with filtered content area, empty state, and optional footer.
- * Designed to be injected into any dropdown container (DropdownMenu.Content,
- * DropdownMenu.SubContent, etc.) without providing its own Root.
- *
- * **Features:**
- * - Search/filter input
- * - Keyboard navigation support
- * - Custom content and footer via snippets
- * - Empty state message
- *
- * @example
- * ```svelte
- * <DropdownMenu.Root>
- *   <DropdownMenu.Trigger>...</DropdownMenu.Trigger>
- *   <DropdownMenu.Content class="pt-0">
- *     <DropdownMenuSearchable
- *       bind:searchValue
- *       placeholder="Search..."
- *       isEmpty={filteredItems.length === 0}
- *     >
- *       {#each items as item}<Item {item} />{/each}
- *     </DropdownMenuSearchable>
- *   </DropdownMenu.Content>
- * </DropdownMenu.Root>
- * ```
- */
-export { default as DropdownMenuSearchable } from './DropdownMenuSearchable.svelte';
-
-/**
- * **DropdownMenuActions** - Multi-action dropdown menu
- *
- * Dropdown menu for multiple action options with icons and shortcuts.
- * Supports destructive variants and keyboard shortcut hints.
- *
- * **Features:**
- * - Configurable trigger icon with tooltip
- * - Action items with icons and labels
- * - Destructive variant styling
- * - Keyboard shortcut display
- * - Separator support between groups
- *
- * @example
- * ```svelte
- * <DropdownMenuActions
- *   triggerIcon={MoreHorizontal}
- *   triggerTooltip="More actions"
- *   actions={[
- *     { icon: Edit, label: 'Edit', onclick: handleEdit },
- *     { icon: Trash, label: 'Delete', onclick: handleDelete, variant: 'destructive' }
- *   ]}
- * />
- * ```
- */
-export { default as DropdownMenuActions } from './DropdownMenuActions.svelte';
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte b/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte
deleted file mode 100644
index 3ceb165183d..00000000000
--- a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte
+++ /dev/null
@@ -1,20 +0,0 @@
-<script lang="ts">
-	import { DropdownMenu as DropdownMenuPrimitive } from 'bits-ui';
-	import { cn } from '$lib/components/ui/utils.js';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		...restProps
-	}: DropdownMenuPrimitive.SubContentProps = $props();
-</script>
-
-<DropdownMenuPrimitive.SubContent
-	bind:ref
-	data-slot="dropdown-menu-sub-content"
-	class={cn(
-		'z-50 min-w-[8rem] origin-(--bits-dropdown-menu-content-transform-origin) overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-lg data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95',
-		className
-	)}
-	{...restProps}
-/>
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar.svelte b/tools/server/webui/src/lib/components/ui/sidebar/sidebar.svelte
deleted file mode 100644
index e2c4a754809..00000000000
--- a/tools/server/webui/src/lib/components/ui/sidebar/sidebar.svelte
+++ /dev/null
@@ -1,101 +0,0 @@
-<script lang="ts">
-	import * as Sheet from '$lib/components/ui/sheet/index.js';
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-	import { SIDEBAR_WIDTH_MOBILE } from './constants.js';
-	import { useSidebar } from './context.svelte.js';
-
-	let {
-		ref = $bindable(null),
-		side = 'left',
-		variant = 'sidebar',
-		collapsible = 'offcanvas',
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLDivElement>> & {
-		side?: 'left' | 'right';
-		variant?: 'sidebar' | 'floating' | 'inset';
-		collapsible?: 'offcanvas' | 'icon' | 'none';
-	} = $props();
-
-	const sidebar = useSidebar();
-</script>
-
-{#if collapsible === 'none'}
-	<div
-		class={cn(
-			'flex h-full w-(--sidebar-width) flex-col bg-sidebar text-sidebar-foreground',
-			className
-		)}
-		bind:this={ref}
-		{...restProps}
-	>
-		{@render children?.()}
-	</div>
-{:else if sidebar.isMobile}
-	<Sheet.Root bind:open={() => sidebar.openMobile, (v) => sidebar.setOpenMobile(v)} {...restProps}>
-		<Sheet.Content
-			data-sidebar="sidebar"
-			data-slot="sidebar"
-			data-mobile="true"
-			class="z-99999 w-(--sidebar-width) bg-sidebar p-0 text-sidebar-foreground sm:z-99 [&>button]:hidden"
-			style="--sidebar-width: {SIDEBAR_WIDTH_MOBILE};"
-			{side}
-		>
-			<Sheet.Header class="sr-only">
-				<Sheet.Title>Sidebar</Sheet.Title>
-				<Sheet.Description>Displays the mobile sidebar.</Sheet.Description>
-			</Sheet.Header>
-			<div class="flex h-full w-full flex-col">
-				{@render children?.()}
-			</div>
-		</Sheet.Content>
-	</Sheet.Root>
-{:else}
-	<div
-		bind:this={ref}
-		class="group peer hidden text-sidebar-foreground md:block"
-		data-state={sidebar.state}
-		data-collapsible={sidebar.state === 'collapsed' ? collapsible : ''}
-		data-variant={variant}
-		data-side={side}
-		data-slot="sidebar"
-	>
-		<!-- This is what handles the sidebar gap on desktop -->
-		<div
-			data-slot="sidebar-gap"
-			class={cn(
-				'relative w-(--sidebar-width) bg-transparent transition-[width] duration-200 ease-linear',
-				'group-data-[collapsible=offcanvas]:w-0',
-				'group-data-[side=right]:rotate-180',
-				variant === 'floating' || variant === 'inset'
-					? 'group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]'
-					: 'group-data-[collapsible=icon]:w-(--sidebar-width-icon)'
-			)}
-		></div>
-		<div
-			data-slot="sidebar-container"
-			class={cn(
-				'fixed inset-y-0 z-999 hidden h-svh w-(--sidebar-width) transition-[left,right,width] duration-200 ease-linear md:z-0 md:flex',
-				side === 'left'
-					? 'left-0 group-data-[collapsible=offcanvas]:left-[calc(var(--sidebar-width)*-1)]'
-					: 'right-0 group-data-[collapsible=offcanvas]:right-[calc(var(--sidebar-width)*-1)]',
-				// Adjust the padding for floating and inset variants.
-				variant === 'floating' || variant === 'inset'
-					? 'p-2 group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]'
-					: 'group-data-[collapsible=icon]:w-(--sidebar-width-icon)',
-				className
-			)}
-			{...restProps}
-		>
-			<div
-				data-sidebar="sidebar"
-				data-slot="sidebar-inner"
-				class="flex h-full w-full flex-col bg-sidebar group-data-[variant=floating]:rounded-lg group-data-[variant=floating]:border group-data-[variant=floating]:border-sidebar-border group-data-[variant=floating]:shadow-sm"
-			>
-				{@render children?.()}
-			</div>
-		</div>
-	</div>
-{/if}
diff --git a/tools/server/webui/src/lib/constants/context-keys.ts b/tools/server/webui/src/lib/constants/context-keys.ts
deleted file mode 100644
index ba61d25e811..00000000000
--- a/tools/server/webui/src/lib/constants/context-keys.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-export const CONTEXT_KEY_MESSAGE_EDIT = 'chat-message-edit';
-export const CONTEXT_KEY_CHAT_ACTIONS = 'chat-actions';
-export const CONTEXT_KEY_CHAT_SETTINGS_DIALOG = 'chat-settings-dialog';
diff --git a/tools/server/webui/src/lib/constants/favicon.ts b/tools/server/webui/src/lib/constants/favicon.ts
deleted file mode 100644
index 880e1f74463..00000000000
--- a/tools/server/webui/src/lib/constants/favicon.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export const GOOGLE_FAVICON_BASE_URL = 'https://www.google.com/s2/favicons';
-export const DEFAULT_FAVICON_SIZE = 32;
-export const DOMAIN_SEPARATOR = '.';
-export const ROOT_DOMAIN_MIN_PARTS = 2;
diff --git a/tools/server/webui/src/lib/constants/localstorage-keys.ts b/tools/server/webui/src/lib/constants/localstorage-keys.ts
deleted file mode 100644
index 2a2b629fd6e..00000000000
--- a/tools/server/webui/src/lib/constants/localstorage-keys.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export const CONFIG_LOCALSTORAGE_KEY = 'LlamaCppWebui.config';
-export const USER_OVERRIDES_LOCALSTORAGE_KEY = 'LlamaCppWebui.userOverrides';
-export const FAVORITE_MODELS_LOCALSTORAGE_KEY = 'LlamaCppWebui.favoriteModels';
-export const MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY = 'LlamaCppWebui.mcpDefaultEnabled';
diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts
deleted file mode 100644
index 7e73fd71031..00000000000
--- a/tools/server/webui/src/lib/constants/settings-config.ts
+++ /dev/null
@@ -1,163 +0,0 @@
-import { ColorMode } from '$lib/enums/ui';
-import { Monitor, Moon, Sun } from '@lucide/svelte';
-
-export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean | undefined> = {
-	// Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value.
-	// Do not use nested objects, keep it single level. Prefix the key if you need to group them.
-	apiKey: '',
-	systemMessage: '',
-	showSystemMessage: true,
-	theme: ColorMode.SYSTEM,
-	showThoughtInProgress: false,
-	disableReasoningParsing: false,
-	excludeReasoningFromContext: false,
-	showRawOutputSwitch: false,
-	keepStatsVisible: false,
-	showMessageStats: true,
-	askForTitleConfirmation: false,
-	titleGenerationUseFirstLine: false,
-	pasteLongTextToFileLen: 2500,
-	copyTextAttachmentsAsPlainText: false,
-	pdfAsImage: false,
-	disableAutoScroll: false,
-	renderUserContentAsMarkdown: false,
-	alwaysShowSidebarOnDesktop: false,
-	autoShowSidebarOnNewChat: true,
-	sendOnEnter: true,
-	autoMicOnEmpty: false,
-	fullHeightCodeBlocks: false,
-	showRawModelNames: false,
-	mcpServers: '[]',
-	mcpServerUsageStats: '{}', // JSON object: { [serverId]: usageCount }
-	agenticMaxTurns: 10,
-	agenticMaxToolPreviewLines: 25,
-	showToolCallInProgress: false,
-	alwaysShowAgenticTurns: false,
-	// sampling params: empty means "use server default"
-	// the server / preset is the source of truth
-	// empty values are shown as placeholders from /props in the UI
-	// and are NOT sent in API requests, letting the server decide
-	samplers: '',
-	backend_sampling: false,
-	temperature: undefined,
-	dynatemp_range: undefined,
-	dynatemp_exponent: undefined,
-	top_k: undefined,
-	top_p: undefined,
-	min_p: undefined,
-	xtc_probability: undefined,
-	xtc_threshold: undefined,
-	typ_p: undefined,
-	repeat_last_n: undefined,
-	repeat_penalty: undefined,
-	presence_penalty: undefined,
-	frequency_penalty: undefined,
-	dry_multiplier: undefined,
-	dry_base: undefined,
-	dry_allowed_length: undefined,
-	dry_penalty_last_n: undefined,
-	max_tokens: undefined,
-	custom: '', // custom json-stringified object
-	preEncodeConversation: false,
-	// experimental features
-	pyInterpreterEnabled: false,
-	enableContinueGeneration: false
-};
-
-export const SETTING_CONFIG_INFO: Record<string, string> = {
-	apiKey: 'Set the API Key if you are using <code>--api-key</code> option for the server.',
-	systemMessage: 'The starting message that defines how model should behave.',
-	showSystemMessage: 'Display the system message at the top of each conversation.',
-	theme:
-		'Choose the color theme for the interface. You can choose between System (follows your device settings), Light, or Dark.',
-	pasteLongTextToFileLen:
-		'On pasting long text, it will be converted to a file. You can control the file length by setting the value of this parameter. Value 0 means disable.',
-	copyTextAttachmentsAsPlainText:
-		'When copying a message with text attachments, combine them into a single plain text string instead of a special format that can be pasted back as attachments.',
-	samplers:
-		'The order at which samplers are applied, in simplified way. Default is "top_k;typ_p;top_p;min_p;temperature": top_k->typ_p->top_p->min_p->temperature',
-	backend_sampling:
-		'Enable backend-based samplers. When enabled, supported samplers run on the accelerator backend for faster sampling.',
-	temperature:
-		'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
-	dynatemp_range:
-		'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.',
-	dynatemp_exponent:
-		'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.',
-	top_k: 'Keeps only k top tokens.',
-	top_p: 'Limits tokens to those that together have a cumulative probability of at least p',
-	min_p:
-		'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.',
-	xtc_probability:
-		'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.',
-	xtc_threshold:
-		'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.',
-	typ_p: 'Sorts and limits tokens based on the difference between log-probability and entropy.',
-	repeat_last_n: 'Last n tokens to consider for penalizing repetition',
-	repeat_penalty: 'Controls the repetition of token sequences in the generated text',
-	presence_penalty: 'Limits tokens based on whether they appear in the output or not.',
-	frequency_penalty: 'Limits tokens based on how often they appear in the output.',
-	dry_multiplier:
-		'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.',
-	dry_base:
-		'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.',
-	dry_allowed_length:
-		'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.',
-	dry_penalty_last_n:
-		'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.',
-	max_tokens: 'The maximum number of token per output. Use -1 for infinite (no limit).',
-	custom: 'Custom JSON parameters to send to the API. Must be valid JSON format.',
-	showThoughtInProgress: 'Expand thought process by default when generating messages.',
-	disableReasoningParsing:
-		'Send reasoning_format=none so the server returns thinking tokens inline instead of extracting them into a separate field.',
-	excludeReasoningFromContext:
-		'Strip thinking from previous messages before sending. When off, thinking is sent back via the reasoning_content field so the model sees its own chain-of-thought across turns.',
-	showRawOutputSwitch:
-		'Show toggle button to display messages as plain text instead of Markdown-formatted content',
-	keepStatsVisible: 'Keep processing statistics visible after generation finishes.',
-	showMessageStats:
-		'Display generation statistics (tokens/second, token count, duration) below each assistant message.',
-	askForTitleConfirmation:
-		'Ask for confirmation before automatically changing conversation title when editing the first message.',
-	titleGenerationUseFirstLine:
-		'Use only the first non-empty line of the prompt to generate the conversation title.',
-	pdfAsImage:
-		'Parse PDF as image instead of text. Automatically falls back to text processing for non-vision models.',
-	disableAutoScroll:
-		'Disable automatic scrolling while messages stream so you can control the viewport position manually.',
-	renderUserContentAsMarkdown: 'Render user messages using markdown formatting in the chat.',
-	alwaysShowSidebarOnDesktop:
-		'Always keep the sidebar visible on desktop instead of auto-hiding it.',
-	autoShowSidebarOnNewChat:
-		'Automatically show sidebar when starting a new chat. Disable to keep the sidebar hidden until you click on it.',
-	sendOnEnter:
-		'Use Enter to send messages and Shift + Enter for new lines. When disabled, use Ctrl/Cmd + Enter.',
-	autoMicOnEmpty:
-		'Automatically show microphone button instead of send button when textarea is empty for models with audio modality support.',
-	fullHeightCodeBlocks:
-		'Always display code blocks at their full natural height, overriding any height limits.',
-	showRawModelNames:
-		'Display full raw model identifiers (e.g. "ggml-org/GLM-4.7-Flash-GGUF:Q8_0") instead of parsed names with badges.',
-	mcpServers:
-		'Configure MCP servers as a JSON list. Use the form in the MCP Client settings section to edit.',
-	mcpServerUsageStats:
-		'Usage statistics for MCP servers. Tracks how many times tools from each server have been used.',
-	agenticMaxTurns:
-		'Maximum number of tool execution cycles before stopping (prevents infinite loops).',
-	agenticMaxToolPreviewLines:
-		'Number of lines shown in tool output previews (last N lines). Only these previews and the final LLM response persist after the agentic loop completes.',
-	showToolCallInProgress:
-		'Automatically expand tool call details while executing and keep them expanded after completion.',
-	pyInterpreterEnabled:
-		'Enable Python interpreter using Pyodide. Allows running Python code in markdown code blocks.',
-	preEncodeConversation:
-		'After each response, re-submit the conversation to pre-fill the server KV cache. Makes the next turn faster since the prompt is already encoded while you read the response.',
-	enableContinueGeneration:
-		'Enable "Continue" button for assistant messages. Currently works only with non-reasoning models.'
-};
-
-export const SETTINGS_COLOR_MODES_CONFIG = [
-	{ value: ColorMode.SYSTEM, label: 'System', icon: Monitor },
-	{ value: ColorMode.LIGHT, label: 'Light', icon: Sun },
-	{ value: ColorMode.DARK, label: 'Dark', icon: Moon }
-];
diff --git a/tools/server/webui/src/lib/constants/settings-fields.ts b/tools/server/webui/src/lib/constants/settings-fields.ts
deleted file mode 100644
index 79a6e928708..00000000000
--- a/tools/server/webui/src/lib/constants/settings-fields.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/**
- * List of all numeric fields in settings configuration.
- * These fields will be converted from strings to numbers during save.
- */
-export const NUMERIC_FIELDS = [
-	'temperature',
-	'top_k',
-	'top_p',
-	'min_p',
-	'max_tokens',
-	'pasteLongTextToFileLen',
-	'dynatemp_range',
-	'dynatemp_exponent',
-	'typ_p',
-	'xtc_probability',
-	'xtc_threshold',
-	'repeat_last_n',
-	'repeat_penalty',
-	'presence_penalty',
-	'frequency_penalty',
-	'dry_multiplier',
-	'dry_base',
-	'dry_allowed_length',
-	'dry_penalty_last_n',
-	'agenticMaxTurns',
-	'agenticMaxToolPreviewLines'
-] as const;
-
-/**
- * Fields that must be positive integers (>= 1).
- * These will be clamped to minimum 1 and rounded during save.
- */
-export const POSITIVE_INTEGER_FIELDS = ['agenticMaxTurns', 'agenticMaxToolPreviewLines'] as const;
diff --git a/tools/server/webui/src/lib/constants/settings-sections.ts b/tools/server/webui/src/lib/constants/settings-sections.ts
deleted file mode 100644
index a2d960d4040..00000000000
--- a/tools/server/webui/src/lib/constants/settings-sections.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-/**
- * Settings section titles constants for ChatSettings component.
- *
- * These titles define the navigation sections in the settings dialog.
- * Used for both sidebar navigation and mobile horizontal scroll menu.
- */
-export const SETTINGS_SECTION_TITLES = {
-	GENERAL: 'General',
-	DISPLAY: 'Display',
-	SAMPLING: 'Sampling',
-	PENALTIES: 'Penalties',
-	IMPORT_EXPORT: 'Import/Export',
-	MCP: 'MCP',
-	DEVELOPER: 'Developer'
-} as const;
-
-/** Type for settings section titles */
-export type SettingsSectionTitle =
-	(typeof SETTINGS_SECTION_TITLES)[keyof typeof SETTINGS_SECTION_TITLES];
diff --git a/tools/server/webui/src/lib/constants/ui.ts b/tools/server/webui/src/lib/constants/ui.ts
deleted file mode 100644
index be9c50d0768..00000000000
--- a/tools/server/webui/src/lib/constants/ui.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-export const FORK_TREE_DEPTH_PADDING = 8;
-export const SYSTEM_MESSAGE_PLACEHOLDER = 'System message';
diff --git a/tools/server/webui/src/lib/contexts/chat-settings-dialog.context.ts b/tools/server/webui/src/lib/contexts/chat-settings-dialog.context.ts
deleted file mode 100644
index 3f98c3c0ef6..00000000000
--- a/tools/server/webui/src/lib/contexts/chat-settings-dialog.context.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-import { getContext, setContext } from 'svelte';
-import type { SettingsSectionTitle } from '$lib/constants';
-import { CONTEXT_KEY_CHAT_SETTINGS_DIALOG } from '$lib/constants';
-
-export interface ChatSettingsDialogContext {
-	open: (initialSection?: SettingsSectionTitle) => void;
-}
-
-const CHAT_SETTINGS_DIALOG_KEY = Symbol.for(CONTEXT_KEY_CHAT_SETTINGS_DIALOG);
-
-export function setChatSettingsDialogContext(
-	ctx: ChatSettingsDialogContext
-): ChatSettingsDialogContext {
-	return setContext(CHAT_SETTINGS_DIALOG_KEY, ctx);
-}
-
-export function getChatSettingsDialogContext(): ChatSettingsDialogContext {
-	return getContext(CHAT_SETTINGS_DIALOG_KEY);
-}
diff --git a/tools/server/webui/src/lib/enums/attachment.ts b/tools/server/webui/src/lib/enums/attachment.ts
deleted file mode 100644
index 28863d7efc6..00000000000
--- a/tools/server/webui/src/lib/enums/attachment.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-/**
- * Attachment type enum for database message extras
- */
-export enum AttachmentType {
-	AUDIO = 'AUDIO',
-	IMAGE = 'IMAGE',
-	MCP_PROMPT = 'MCP_PROMPT',
-	MCP_RESOURCE = 'MCP_RESOURCE',
-	PDF = 'PDF',
-	TEXT = 'TEXT',
-	LEGACY_CONTEXT = 'context' // Legacy attachment type for backward compatibility
-}
diff --git a/tools/server/webui/src/lib/services/parameter-sync.service.ts b/tools/server/webui/src/lib/services/parameter-sync.service.ts
deleted file mode 100644
index c2dedbf1016..00000000000
--- a/tools/server/webui/src/lib/services/parameter-sync.service.ts
+++ /dev/null
@@ -1,482 +0,0 @@
-import { normalizeFloatingPoint } from '$lib/utils';
-import type { SyncableParameter, ParameterRecord, ParameterInfo, ParameterValue } from '$lib/types';
-import { SyncableParameterType, ParameterSource } from '$lib/enums';
-
-/**
- * Mapping of webui setting keys to server parameter keys.
- * Only parameters listed here can be synced from the server `/props` endpoint.
- * Each entry defines the webui key, corresponding server key, value type,
- * and whether sync is enabled.
- */
-export const SYNCABLE_PARAMETERS: SyncableParameter[] = [
-	{
-		key: 'temperature',
-		serverKey: 'temperature',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{ key: 'top_k', serverKey: 'top_k', type: SyncableParameterType.NUMBER, canSync: true },
-	{ key: 'top_p', serverKey: 'top_p', type: SyncableParameterType.NUMBER, canSync: true },
-	{ key: 'min_p', serverKey: 'min_p', type: SyncableParameterType.NUMBER, canSync: true },
-	{
-		key: 'dynatemp_range',
-		serverKey: 'dynatemp_range',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'dynatemp_exponent',
-		serverKey: 'dynatemp_exponent',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'xtc_probability',
-		serverKey: 'xtc_probability',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'xtc_threshold',
-		serverKey: 'xtc_threshold',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{ key: 'typ_p', serverKey: 'typ_p', type: SyncableParameterType.NUMBER, canSync: true },
-	{
-		key: 'repeat_last_n',
-		serverKey: 'repeat_last_n',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'repeat_penalty',
-		serverKey: 'repeat_penalty',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'presence_penalty',
-		serverKey: 'presence_penalty',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'frequency_penalty',
-		serverKey: 'frequency_penalty',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'dry_multiplier',
-		serverKey: 'dry_multiplier',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{ key: 'dry_base', serverKey: 'dry_base', type: SyncableParameterType.NUMBER, canSync: true },
-	{
-		key: 'dry_allowed_length',
-		serverKey: 'dry_allowed_length',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'dry_penalty_last_n',
-		serverKey: 'dry_penalty_last_n',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{ key: 'max_tokens', serverKey: 'max_tokens', type: SyncableParameterType.NUMBER, canSync: true },
-	{ key: 'samplers', serverKey: 'samplers', type: SyncableParameterType.STRING, canSync: true },
-	{
-		key: 'backend_sampling',
-		serverKey: 'backend_sampling',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'pasteLongTextToFileLen',
-		serverKey: 'pasteLongTextToFileLen',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'pdfAsImage',
-		serverKey: 'pdfAsImage',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'showThoughtInProgress',
-		serverKey: 'showThoughtInProgress',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'keepStatsVisible',
-		serverKey: 'keepStatsVisible',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'showMessageStats',
-		serverKey: 'showMessageStats',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'askForTitleConfirmation',
-		serverKey: 'askForTitleConfirmation',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'titleGenerationUseFirstLine',
-		serverKey: 'titleGenerationUseFirstLine',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'disableAutoScroll',
-		serverKey: 'disableAutoScroll',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'renderUserContentAsMarkdown',
-		serverKey: 'renderUserContentAsMarkdown',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'autoMicOnEmpty',
-		serverKey: 'autoMicOnEmpty',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'pyInterpreterEnabled',
-		serverKey: 'pyInterpreterEnabled',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'enableContinueGeneration',
-		serverKey: 'enableContinueGeneration',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'fullHeightCodeBlocks',
-		serverKey: 'fullHeightCodeBlocks',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'systemMessage',
-		serverKey: 'systemMessage',
-		type: SyncableParameterType.STRING,
-		canSync: true
-	},
-	{
-		key: 'showSystemMessage',
-		serverKey: 'showSystemMessage',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{ key: 'theme', serverKey: 'theme', type: SyncableParameterType.STRING, canSync: true },
-	{
-		key: 'copyTextAttachmentsAsPlainText',
-		serverKey: 'copyTextAttachmentsAsPlainText',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'showRawOutputSwitch',
-		serverKey: 'showRawOutputSwitch',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'alwaysShowSidebarOnDesktop',
-		serverKey: 'alwaysShowSidebarOnDesktop',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'autoShowSidebarOnNewChat',
-		serverKey: 'autoShowSidebarOnNewChat',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'showRawModelNames',
-		serverKey: 'showRawModelNames',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{ key: 'mcpServers', serverKey: 'mcpServers', type: SyncableParameterType.STRING, canSync: true },
-	{
-		key: 'agenticMaxTurns',
-		serverKey: 'agenticMaxTurns',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'agenticMaxToolPreviewLines',
-		serverKey: 'agenticMaxToolPreviewLines',
-		type: SyncableParameterType.NUMBER,
-		canSync: true
-	},
-	{
-		key: 'showToolCallInProgress',
-		serverKey: 'showToolCallInProgress',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'alwaysShowAgenticTurns',
-		serverKey: 'alwaysShowAgenticTurns',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'excludeReasoningFromContext',
-		serverKey: 'excludeReasoningFromContext',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	},
-	{
-		key: 'sendOnEnter',
-		serverKey: 'sendOnEnter',
-		type: SyncableParameterType.BOOLEAN,
-		canSync: true
-	}
-];
-
-export class ParameterSyncService {
-	/**
-	 *
-	 *
-	 * Extraction
-	 *
-	 *
-	 */
-
-	/**
-	 * Round floating-point numbers to avoid JavaScript precision issues.
-	 * E.g., 0.1 + 0.2 = 0.30000000000000004 → 0.3
-	 *
-	 * @param value - Parameter value to normalize
-	 * @returns Precision-normalized value
-	 */
-	private static roundFloatingPoint(value: ParameterValue): ParameterValue {
-		return normalizeFloatingPoint(value) as ParameterValue;
-	}
-
-	/**
-	 * Extract server default parameters that can be synced from `/props` response.
-	 * Handles both generation settings parameters and webui-specific settings.
-	 * Converts samplers array to semicolon-delimited string for UI display.
-	 *
-	 * @param serverParams - Raw generation settings from server `/props` endpoint
-	 * @param webuiSettings - Optional webui-specific settings from server
-	 * @returns Record of extracted parameter key-value pairs with normalized precision
-	 */
-	static extractServerDefaults(
-		serverParams: ApiLlamaCppServerProps['default_generation_settings']['params'] | null,
-		webuiSettings?: Record<string, string | number | boolean>
-	): ParameterRecord {
-		const extracted: ParameterRecord = {};
-
-		if (serverParams) {
-			for (const param of SYNCABLE_PARAMETERS) {
-				if (param.canSync && param.serverKey in serverParams) {
-					const value = (serverParams as unknown as Record<string, ParameterValue>)[
-						param.serverKey
-					];
-					if (value !== undefined) {
-						// Apply precision rounding to avoid JavaScript floating-point issues
-						extracted[param.key] = this.roundFloatingPoint(value);
-					}
-				}
-			}
-
-			// Handle samplers array conversion to string
-			if (serverParams.samplers && Array.isArray(serverParams.samplers)) {
-				extracted.samplers = serverParams.samplers.join(';');
-			}
-		}
-
-		if (webuiSettings) {
-			for (const param of SYNCABLE_PARAMETERS) {
-				if (param.canSync && param.serverKey in webuiSettings) {
-					const value = webuiSettings[param.serverKey];
-					if (value !== undefined) {
-						extracted[param.key] = this.roundFloatingPoint(value);
-					}
-				}
-			}
-		}
-
-		return extracted;
-	}
-
-	/**
-	 *
-	 *
-	 * Merging
-	 *
-	 *
-	 */
-
-	/**
-	 * Merge server defaults with current user settings.
-	 * User overrides always take priority — only parameters not in `userOverrides`
-	 * set will be updated from server defaults.
-	 *
-	 * @param currentSettings - Current parameter values in the settings store
-	 * @param serverDefaults - Default values extracted from server props
-	 * @param userOverrides - Set of parameter keys explicitly overridden by the user
-	 * @returns Merged parameter record with user overrides preserved
-	 */
-	static mergeWithServerDefaults(
-		currentSettings: ParameterRecord,
-		serverDefaults: ParameterRecord,
-		userOverrides: Set<string> = new Set()
-	): ParameterRecord {
-		const merged = { ...currentSettings };
-
-		for (const [key, serverValue] of Object.entries(serverDefaults)) {
-			// Only update if user hasn't explicitly overridden this parameter
-			if (!userOverrides.has(key)) {
-				merged[key] = this.roundFloatingPoint(serverValue);
-			}
-		}
-
-		return merged;
-	}
-
-	/**
-	 *
-	 *
-	 * Info
-	 *
-	 *
-	 */
-
-	/**
-	 * Get parameter information including source and values.
-	 * Used by ChatSettingsParameterSourceIndicator to display the correct badge
-	 * (Custom vs Default) for each parameter in the settings UI.
-	 *
-	 * @param key - The parameter key to get info for
-	 * @param currentValue - The current value of the parameter
-	 * @param propsDefaults - Server default values from `/props`
-	 * @param userOverrides - Set of parameter keys explicitly overridden by the user
-	 * @returns Parameter info with source, server default, and user override values
-	 */
-	static getParameterInfo(
-		key: string,
-		currentValue: ParameterValue,
-		propsDefaults: ParameterRecord,
-		userOverrides: Set<string>
-	): ParameterInfo {
-		const hasPropsDefault = propsDefaults[key] !== undefined;
-		const isUserOverride = userOverrides.has(key);
-
-		// Simple logic: either using default (from props) or custom (user override)
-		const source = isUserOverride ? ParameterSource.CUSTOM : ParameterSource.DEFAULT;
-
-		return {
-			value: currentValue,
-			source,
-			serverDefault: hasPropsDefault ? propsDefaults[key] : undefined, // Keep same field name for compatibility
-			userOverride: isUserOverride ? currentValue : undefined
-		};
-	}
-
-	/**
-	 * Check if a parameter can be synced from server.
-	 *
-	 * @param key - The parameter key to check
-	 * @returns True if the parameter is in the syncable parameters list
-	 */
-	static canSyncParameter(key: string): boolean {
-		return SYNCABLE_PARAMETERS.some((param) => param.key === key && param.canSync);
-	}
-
-	/**
-	 * Get all syncable parameter keys.
-	 *
-	 * @returns Array of parameter keys that can be synced from server
-	 */
-	static getSyncableParameterKeys(): string[] {
-		return SYNCABLE_PARAMETERS.filter((param) => param.canSync).map((param) => param.key);
-	}
-
-	/**
-	 * Validate a server parameter value against its expected type.
-	 *
-	 * @param key - The parameter key to validate
-	 * @param value - The value to validate
-	 * @returns True if value matches the expected type for this parameter
-	 */
-	static validateServerParameter(key: string, value: ParameterValue): boolean {
-		const param = SYNCABLE_PARAMETERS.find((p) => p.key === key);
-		if (!param) return false;
-
-		switch (param.type) {
-			case SyncableParameterType.NUMBER:
-				return typeof value === 'number' && !isNaN(value);
-			case SyncableParameterType.STRING:
-				return typeof value === 'string';
-			case SyncableParameterType.BOOLEAN:
-				return typeof value === 'boolean';
-			default:
-				return false;
-		}
-	}
-
-	/**
-	 *
-	 *
-	 * Diff
-	 *
-	 *
-	 */
-
-	/**
-	 * Create a diff between current settings and server defaults.
-	 * Shows which parameters differ from server values, useful for debugging
-	 * and for the "Reset to defaults" functionality.
-	 *
-	 * @param currentSettings - Current parameter values in the settings store
-	 * @param serverDefaults - Default values extracted from server props
-	 * @returns Record of parameter diffs with current value, server value, and whether they differ
-	 */
-	static createParameterDiff(
-		currentSettings: ParameterRecord,
-		serverDefaults: ParameterRecord
-	): Record<string, { current: ParameterValue; server: ParameterValue; differs: boolean }> {
-		const diff: Record<
-			string,
-			{ current: ParameterValue; server: ParameterValue; differs: boolean }
-		> = {};
-
-		for (const key of this.getSyncableParameterKeys()) {
-			const currentValue = currentSettings[key];
-			const serverValue = serverDefaults[key];
-
-			if (serverValue !== undefined) {
-				diff[key] = {
-					current: currentValue,
-					server: serverValue,
-					differs: currentValue !== serverValue
-				};
-			}
-		}
-
-		return diff;
-	}
-}
diff --git a/tools/server/webui/src/lib/utils/favicon.ts b/tools/server/webui/src/lib/utils/favicon.ts
deleted file mode 100644
index a1afa1643da..00000000000
--- a/tools/server/webui/src/lib/utils/favicon.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-/**
- * Favicon utility functions for extracting favicons from URLs.
- */
-
-import { getProxiedUrlString } from './cors-proxy';
-import {
-	GOOGLE_FAVICON_BASE_URL,
-	DEFAULT_FAVICON_SIZE,
-	DOMAIN_SEPARATOR,
-	ROOT_DOMAIN_MIN_PARTS
-} from '$lib/constants';
-
-/**
- * Gets a favicon URL for a given URL using Google's favicon service.
- * Returns null if the URL is invalid.
- *
- * @param urlString - The URL to get the favicon for
- * @returns The favicon URL or null if invalid
- */
-export function getFaviconUrl(urlString: string, useProxy = true): string | null {
-	try {
-		const url = new URL(urlString);
-		const hostnameParts = url.hostname.split(DOMAIN_SEPARATOR);
-		const rootDomain =
-			hostnameParts.length >= ROOT_DOMAIN_MIN_PARTS
-				? hostnameParts.slice(-ROOT_DOMAIN_MIN_PARTS).join(DOMAIN_SEPARATOR)
-				: url.hostname;
-
-		const googleFaviconUrl = `${GOOGLE_FAVICON_BASE_URL}?domain=${rootDomain}&sz=${DEFAULT_FAVICON_SIZE}`;
-		return useProxy ? getProxiedUrlString(googleFaviconUrl) : googleFaviconUrl;
-	} catch {
-		return null;
-	}
-}
diff --git a/tools/server/webui/static/loading.html b/tools/server/webui/static/loading.html
deleted file mode 100644
index c3fd19a0f5a..00000000000
--- a/tools/server/webui/static/loading.html
+++ /dev/null
@@ -1,12 +0,0 @@
-<!DOCTYPE html>
-<html>
-    <head>
-        <meta http-equiv="refresh" content="5">
-    </head>
-    <body>
-        <div id="loading">
-            The model is loading. Please wait.<br/>
-            The user interface will appear soon.
-        </div>
-    </body>
-</html>
diff --git a/tools/server/webui/tests/stories/ChatSettings.stories.svelte b/tools/server/webui/tests/stories/ChatSettings.stories.svelte
deleted file mode 100644
index 4d8dbe50df9..00000000000
--- a/tools/server/webui/tests/stories/ChatSettings.stories.svelte
+++ /dev/null
@@ -1,19 +0,0 @@
-<script module>
-	import { defineMeta } from '@storybook/addon-svelte-csf';
-	import { ChatSettings } from '$lib/components/app';
-	import { fn } from 'storybook/test';
-
-	const { Story } = defineMeta({
-		title: 'Components/ChatSettings',
-		component: ChatSettings,
-		parameters: {
-			layout: 'fullscreen'
-		},
-		args: {
-			onClose: fn(),
-			onSave: fn()
-		}
-	});
-</script>
-
-<Story name="Default" />
diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp
index ce68213d145..2a1bdccc919 100644
--- a/tools/tts/tts.cpp
+++ b/tools/tts/tts.cpp
@@ -604,8 +604,8 @@ int main(int argc, char ** argv) {
     }
 
     LOG_INF("sampler seed: %u\n",     common_sampler_get_seed(smpl[0]));
-    LOG_INF("sampler params: \n%s\n", params.sampling.print().c_str());
     LOG_INF("sampler chain: %s\n",    common_sampler_print(smpl[0]).c_str());
+    LOG_INF("sampler params: \n%s\n", params.sampling.print().c_str());
 
     LOG_INF("%s: loading done\n", __func__);
 
diff --git a/tools/server/webui/.gitignore b/tools/ui/.gitignore
similarity index 100%
rename from tools/server/webui/.gitignore
rename to tools/ui/.gitignore
diff --git a/tools/server/webui/.npmrc b/tools/ui/.npmrc
similarity index 100%
rename from tools/server/webui/.npmrc
rename to tools/ui/.npmrc
diff --git a/tools/server/webui/.prettierignore b/tools/ui/.prettierignore
similarity index 100%
rename from tools/server/webui/.prettierignore
rename to tools/ui/.prettierignore
diff --git a/tools/server/webui/.prettierrc b/tools/ui/.prettierrc
similarity index 100%
rename from tools/server/webui/.prettierrc
rename to tools/ui/.prettierrc
diff --git a/tools/server/webui/.storybook/ModeWatcherDecorator.svelte b/tools/ui/.storybook/decorators/ModeWatcherDecorator.svelte
similarity index 100%
rename from tools/server/webui/.storybook/ModeWatcherDecorator.svelte
rename to tools/ui/.storybook/decorators/ModeWatcherDecorator.svelte
diff --git a/tools/server/webui/.storybook/TooltipProviderDecorator.svelte b/tools/ui/.storybook/decorators/TooltipProviderDecorator.svelte
similarity index 72%
rename from tools/server/webui/.storybook/TooltipProviderDecorator.svelte
rename to tools/ui/.storybook/decorators/TooltipProviderDecorator.svelte
index 9aad1eaa4ab..ba0cabc5618 100644
--- a/tools/server/webui/.storybook/TooltipProviderDecorator.svelte
+++ b/tools/ui/.storybook/decorators/TooltipProviderDecorator.svelte
@@ -1,5 +1,5 @@
 <script lang="ts">
-	import * as Tooltip from '../src/lib/components/ui/tooltip';
+	import * as Tooltip from '../../src/lib/components/ui/tooltip';
 
 	interface Props {
 		children: any;
diff --git a/tools/server/webui/.storybook/main.ts b/tools/ui/.storybook/main.ts
similarity index 100%
rename from tools/server/webui/.storybook/main.ts
rename to tools/ui/.storybook/main.ts
diff --git a/tools/server/webui/.storybook/preview.ts b/tools/ui/.storybook/preview.ts
similarity index 80%
rename from tools/server/webui/.storybook/preview.ts
rename to tools/ui/.storybook/preview.ts
index 566dbfd289c..4610229a681 100644
--- a/tools/server/webui/.storybook/preview.ts
+++ b/tools/ui/.storybook/preview.ts
@@ -1,7 +1,7 @@
 import type { Preview } from '@storybook/sveltekit';
 import '../src/app.css';
-import ModeWatcherDecorator from './ModeWatcherDecorator.svelte';
-import TooltipProviderDecorator from './TooltipProviderDecorator.svelte';
+import ModeWatcherDecorator from './decorators/ModeWatcherDecorator.svelte';
+import TooltipProviderDecorator from './decorators/TooltipProviderDecorator.svelte';
 
 const preview: Preview = {
 	parameters: {
diff --git a/tools/server/webui/.storybook/vitest.setup.ts b/tools/ui/.storybook/vitest.setup.ts
similarity index 100%
rename from tools/server/webui/.storybook/vitest.setup.ts
rename to tools/ui/.storybook/vitest.setup.ts
diff --git a/tools/ui/CMakeLists.txt b/tools/ui/CMakeLists.txt
new file mode 100644
index 00000000000..383940cb636
--- /dev/null
+++ b/tools/ui/CMakeLists.txt
@@ -0,0 +1,150 @@
+set(TARGET llama-ui)
+
+# Deprecated: use LLAMA_UI_HF_BUCKET instead
+set(LLAMA_WEBUI_HF_BUCKET "llama-ui" CACHE STRING "Hugging Face bucket name for prebuilt webui assets (deprecated: use LLAMA_UI_HF_BUCKET)")
+set(LLAMA_UI_HF_BUCKET "llama-ui" CACHE STRING "Hugging Face bucket name for prebuilt UI assets")
+
+# Backward compat: forward old var to new one
+if(DEFINED LLAMA_WEBUI_HF_BUCKET AND NOT DEFINED LLAMA_UI_HF_BUCKET)
+    set(LLAMA_UI_HF_BUCKET ${LLAMA_WEBUI_HF_BUCKET})
+elseif(DEFINED LLAMA_WEBUI_HF_BUCKET AND NOT "${LLAMA_WEBUI_HF_BUCKET}" STREQUAL "${LLAMA_UI_HF_BUCKET}")
+    message(DEPRECATION "LLAMA_WEBUI_HF_BUCKET is deprecated, use LLAMA_UI_HF_BUCKET instead")
+endif()
+
+set(TARGET_SRCS "")
+set(UI_COMPILE_DEFS "")
+
+if(LLAMA_BUILD_UI)
+    set(PUBLIC_ASSETS
+        index.html
+        bundle.js
+        bundle.css
+        loading.html
+    )
+
+    # Determine source of UI assets (priority: local > HF Bucket)
+    set(UI_SOURCE "")
+    set(UI_SOURCE_DIR "")
+
+    # Priority 1: Check for local build output
+    set(LOCAL_UI_DIR "${PROJECT_SOURCE_DIR}/build/tools/ui/dist")
+
+    # Verify all required assets exist before declaring local source valid
+    set(ALL_ASSETS_PRESENT TRUE)
+    foreach(asset ${PUBLIC_ASSETS})
+        if(NOT EXISTS "${LOCAL_UI_DIR}/${asset}")
+            set(ALL_ASSETS_PRESENT FALSE)
+            break()
+        endif()
+    endforeach()
+
+    if(ALL_ASSETS_PRESENT)
+        set(UI_SOURCE "local")
+        set(UI_SOURCE_DIR "${LOCAL_UI_DIR}")
+        message(STATUS "UI: using local build from ${UI_SOURCE_DIR}")
+    endif()
+
+    # Priority 2: Build-time asset provisioning (npm build → HF Bucket fallback)
+    if(NOT UI_SOURCE_DIR)
+        # Environment variable takes precedence (e.g., from CI workflows)
+        # Deprecated: use HF_UI_VERSION instead
+        if(DEFINED ENV{HF_WEBUI_VERSION})
+            set(HF_UI_VERSION "$ENV{HF_WEBUI_VERSION}")
+            message(DEPRECATION "HF_WEBUI_VERSION env var is deprecated, use HF_UI_VERSION instead")
+            if(NOT HF_UI_VERSION MATCHES "^[A-Za-z0-9._-]+$")
+                message(FATAL_ERROR "UI: invalid HF_WEBUI_VERSION='${HF_UI_VERSION}' - must match ^[A-Za-z0-9._-]+$")
+            endif()
+        elseif(DEFINED ENV{HF_UI_VERSION})
+            set(HF_UI_VERSION "$ENV{HF_UI_VERSION}")
+            if(NOT HF_UI_VERSION MATCHES "^[A-Za-z0-9._-]+$")
+                message(FATAL_ERROR "UI: invalid HF_UI_VERSION='${HF_UI_VERSION}' - must match ^[A-Za-z0-9._-]+$")
+            endif()
+        elseif(DEFINED LLAMA_BUILD_NUMBER)
+            set(HF_UI_VERSION "b${LLAMA_BUILD_NUMBER}")
+            message(STATUS "UI: derived HF_UI_VERSION=b${LLAMA_BUILD_NUMBER}")
+        else()
+            set(HF_UI_VERSION "")
+            message(STATUS "UI: version not specified (will use HF 'latest')")
+        endif()
+
+        if("${HF_UI_VERSION}" STREQUAL "")
+            set(UI_VERSION_TAG "provisioned")
+        else()
+            set(UI_VERSION_TAG "${HF_UI_VERSION}")
+        endif()
+        set(UI_STAMP "${CMAKE_CURRENT_BINARY_DIR}/.ui-${UI_VERSION_TAG}.stamp")
+
+        string(REPLACE ";" "+" PUBLIC_ASSETS_JOINED "${PUBLIC_ASSETS}")
+
+        add_custom_command(
+            OUTPUT ${UI_STAMP}
+            COMMAND ${CMAKE_COMMAND}
+                "-DSOURCE_DIR=${PROJECT_SOURCE_DIR}"
+                "-DPUBLIC_DIR=${PROJECT_SOURCE_DIR}/build/tools/ui/dist"
+                "-DHF_BUCKET=${LLAMA_UI_HF_BUCKET}"
+                "-DHF_VERSION=${HF_UI_VERSION}"
+                "-DHF_ENABLED=${LLAMA_USE_PREBUILT_UI}"
+                "-DASSETS=${PUBLIC_ASSETS_JOINED}"
+                "-DSTAMP_FILE=${UI_STAMP}"
+                "-DNPM_DIR=${PROJECT_SOURCE_DIR}/tools/ui"
+                -P ${PROJECT_SOURCE_DIR}/scripts/ui-download.cmake
+            COMMENT "Building/provisioning UI assets (npm build -> HF Bucket fallback)"
+        )
+
+        set(UI_SOURCE "provisioned")
+        set(UI_SOURCE_DIR "${PROJECT_SOURCE_DIR}/build/tools/ui/dist")
+    endif()
+
+    # Process assets from the determined source
+    if(UI_SOURCE_DIR)
+        foreach(asset ${PUBLIC_ASSETS})
+            set(input "${UI_SOURCE_DIR}/${asset}")
+            set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
+            list(APPEND TARGET_SRCS ${output})
+
+            if(UI_SOURCE STREQUAL "local")
+                if(NOT EXISTS "${input}")
+                    message(FATAL_ERROR "UI asset not found: ${input}")
+                endif()
+                set(dependency "${input}")
+            else()
+                set(dependency "${UI_STAMP}")
+            endif()
+
+            add_custom_command(
+                DEPENDS ${dependency}
+                OUTPUT "${output}"
+                COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
+            )
+            set_source_files_properties(${output} PROPERTIES GENERATED TRUE)
+        endforeach()
+
+        list(APPEND UI_COMPILE_DEFS
+            LLAMA_BUILD_UI
+            LLAMA_UI_DEFAULT_ENABLED=1
+        )
+        message(STATUS "UI: embedded with source: ${UI_SOURCE}")
+    else()
+        message(WARNING "UI: no source available. Neither local build (build/tools/ui/dist/) nor HF Bucket download succeeded.")
+        message(WARNING "UI: building server without embedded UI. Set LLAMA_BUILD_UI=OFF to suppress this warning.")
+        list(APPEND UI_COMPILE_DEFS LLAMA_UI_DEFAULT_ENABLED=0)
+    endif()
+else()
+    list(APPEND UI_COMPILE_DEFS LLAMA_UI_DEFAULT_ENABLED=0)
+endif()
+
+# Build the static library
+add_library(${TARGET} STATIC ui.cpp)
+
+target_include_directories(${TARGET} PUBLIC
+    ${CMAKE_CURRENT_SOURCE_DIR}
+    ${CMAKE_CURRENT_BINARY_DIR}
+)
+
+target_compile_definitions(${TARGET} PUBLIC ${UI_COMPILE_DEFS})
+
+if(TARGET_SRCS)
+    # List generated .hpp files as sources so CMake tracks them as build dependencies
+    target_sources(${TARGET} PRIVATE ${TARGET_SRCS})
+    set_source_files_properties(${TARGET_SRCS} PROPERTIES HEADER_FILE_ONLY TRUE)
+endif()
diff --git a/tools/server/webui/README.md b/tools/ui/README.md
similarity index 94%
rename from tools/server/webui/README.md
rename to tools/ui/README.md
index 4532bae3efa..4334f968162 100644
--- a/tools/server/webui/README.md
+++ b/tools/ui/README.md
@@ -1,8 +1,8 @@
-# llama.cpp Web UI
+# llama-ui
 
-A modern, feature-rich web interface for llama.cpp built with SvelteKit. This UI provides an intuitive chat interface with advanced file handling, conversation management, and comprehensive model interaction capabilities.
+A modern, feature-rich web interface for llama-server built with SvelteKit. This UI provides an intuitive chat interface with advanced file handling, conversation management, and comprehensive model interaction capabilities.
 
-The WebUI supports two server operation modes:
+Llama UI supports two server operation modes:
 
 - **MODEL mode** - Single model operation (standard llama-server)
 - **ROUTER mode** - Multi-model operation with dynamic model loading/unloading
@@ -88,7 +88,7 @@ The WebUI supports two server operation modes:
 ### 1. Install Dependencies
 
 ```bash
-cd tools/server/webui
+cd tools/ui
 npm install
 ```
 
@@ -112,7 +112,7 @@ npm run dev
 
 This starts:
 
-- **Vite dev server** at `http://localhost:5173` - The main WebUI
+- **Vite dev server** at `http://localhost:5173` - The main UI frontend app
 - **Storybook** at `http://localhost:6006` - Component documentation
 
 The Vite dev server proxies API requests to `http://localhost:8080` (default llama-server port):
@@ -186,7 +186,7 @@ npm run build
 The build process:
 
 1. **Vite Build** - Bundles all TypeScript, Svelte, and CSS
-2. **Static Adapter** - Outputs to `../public` (llama-server's static file directory)
+2. **Static Adapter** - Outputs to `../../build/tools/ui/dist` (llama-server's static file directory)
 3. **Post-Build Script** - Cleans up intermediate files
 4. **Custom Plugin** - Creates `index.html` with:
    - Inlined favicon as base64
@@ -194,7 +194,7 @@ The build process:
    - Deterministic output (zeroed timestamps)
 
 ```text
-tools/server/webui/        →  build  →  tools/server/public/
+tools/ui/        →  build  →  build/tools/ui/dist/
 ├── src/                                 ├── index.html  (served by llama-server)
 ├── static/                              └── (favicon inlined)
 └── ...
@@ -205,8 +205,8 @@ tools/server/webui/        →  build  →  tools/server/public/
 ```javascript
 // svelte.config.js
 adapter: adapter({
-  pages: '../public',      // Output directory
-  assets: '../public',     // Static assets
+  pages: '../../build/tools/ui/dist',      // Output directory
+  assets: '../../build/tools/ui/dist',     // Static assets
   fallback: 'index.html',  // SPA fallback
   strict: true
 }),
@@ -217,20 +217,19 @@ output: {
 
 ### Integration with llama-server
 
-The WebUI is embedded directly into the llama-server binary:
+llama-ui is embedded directly into the llama-server binary:
 
-1. `npm run build` outputs `index.html` to `tools/server/public/`
+1. `npm run build` outputs `index.html` to `build/tools/ui/dist/`
 2. llama-server compiles this into the binary at build time
-3. When accessing `/`, llama-server serves the gzipped HTML
-4. All assets are inlined (CSS, JS, fonts, favicon)
+3. When accessing `/`, llama-server serves the bundled HTML
 
-This results in a **single portable binary** with the full WebUI included.
+This results in a **single portable binary** with the full Llama UI included.
 
 ---
 
 ## Architecture
 
-The WebUI follows a layered architecture with unidirectional data flow:
+Llama UI follows a layered architecture with unidirectional data flow:
 
 ```text
 Routes → Components → Hooks → Stores → Services → Storage/API
@@ -659,7 +658,7 @@ npm run check         # TypeScript type checking
 ## Project Structure
 
 ```text
-tools/server/webui/
+tools/ui/
 ├── src/
 │   ├── lib/
 │   │   ├── components/   # UI components (app/, ui/)
@@ -682,6 +681,6 @@ tools/server/webui/
 
 ## Related Documentation
 
-- [llama.cpp Server README](../README.md) - Full server documentation
-- [Multimodal Documentation](../../../docs/multimodal.md) - Image and audio support
-- [Function Calling](../../../docs/function-calling.md) - Tool use capabilities
+- [llama.cpp Server README](../server/README.md) - Full server documentation
+- [Multimodal Documentation](../../docs/multimodal.md) - Image and audio support
+- [Function Calling](../../docs/function-calling.md) - Tool use capabilities
diff --git a/tools/server/webui/components.json b/tools/ui/components.json
similarity index 100%
rename from tools/server/webui/components.json
rename to tools/ui/components.json
diff --git a/tools/server/webui/docs/architecture/high-level-architecture-simplified.md b/tools/ui/docs/architecture/high-level-architecture-simplified.md
similarity index 100%
rename from tools/server/webui/docs/architecture/high-level-architecture-simplified.md
rename to tools/ui/docs/architecture/high-level-architecture-simplified.md
diff --git a/tools/server/webui/docs/architecture/high-level-architecture.md b/tools/ui/docs/architecture/high-level-architecture.md
similarity index 100%
rename from tools/server/webui/docs/architecture/high-level-architecture.md
rename to tools/ui/docs/architecture/high-level-architecture.md
diff --git a/tools/server/webui/docs/flows/chat-flow.md b/tools/ui/docs/flows/chat-flow.md
similarity index 100%
rename from tools/server/webui/docs/flows/chat-flow.md
rename to tools/ui/docs/flows/chat-flow.md
diff --git a/tools/server/webui/docs/flows/conversations-flow.md b/tools/ui/docs/flows/conversations-flow.md
similarity index 100%
rename from tools/server/webui/docs/flows/conversations-flow.md
rename to tools/ui/docs/flows/conversations-flow.md
diff --git a/tools/server/webui/docs/flows/data-flow-simplified-model-mode.md b/tools/ui/docs/flows/data-flow-simplified-model-mode.md
similarity index 100%
rename from tools/server/webui/docs/flows/data-flow-simplified-model-mode.md
rename to tools/ui/docs/flows/data-flow-simplified-model-mode.md
diff --git a/tools/server/webui/docs/flows/data-flow-simplified-router-mode.md b/tools/ui/docs/flows/data-flow-simplified-router-mode.md
similarity index 100%
rename from tools/server/webui/docs/flows/data-flow-simplified-router-mode.md
rename to tools/ui/docs/flows/data-flow-simplified-router-mode.md
diff --git a/tools/server/webui/docs/flows/database-flow.md b/tools/ui/docs/flows/database-flow.md
similarity index 100%
rename from tools/server/webui/docs/flows/database-flow.md
rename to tools/ui/docs/flows/database-flow.md
diff --git a/tools/server/webui/docs/flows/mcp-flow.md b/tools/ui/docs/flows/mcp-flow.md
similarity index 100%
rename from tools/server/webui/docs/flows/mcp-flow.md
rename to tools/ui/docs/flows/mcp-flow.md
diff --git a/tools/server/webui/docs/flows/models-flow.md b/tools/ui/docs/flows/models-flow.md
similarity index 100%
rename from tools/server/webui/docs/flows/models-flow.md
rename to tools/ui/docs/flows/models-flow.md
diff --git a/tools/server/webui/docs/flows/server-flow.md b/tools/ui/docs/flows/server-flow.md
similarity index 100%
rename from tools/server/webui/docs/flows/server-flow.md
rename to tools/ui/docs/flows/server-flow.md
diff --git a/tools/server/webui/docs/flows/settings-flow.md b/tools/ui/docs/flows/settings-flow.md
similarity index 98%
rename from tools/server/webui/docs/flows/settings-flow.md
rename to tools/ui/docs/flows/settings-flow.md
index 40ad3bd94d7..260713a17b8 100644
--- a/tools/server/webui/docs/flows/settings-flow.md
+++ b/tools/ui/docs/flows/settings-flow.md
@@ -58,8 +58,8 @@ sequenceDiagram
         end
     end
 
-    alt serverStore.props has webuiSettings
-        settingsStore->>settingsStore: Apply webuiSettings from server
+    alt serverStore.props has uiSettings
+        settingsStore->>settingsStore: Apply uiSettings from server
         Note right of settingsStore: Server-provided UI settings<br/>(e.g. showRawOutputSwitch)
     end
 
diff --git a/tools/server/webui/eslint.config.js b/tools/ui/eslint.config.js
similarity index 93%
rename from tools/server/webui/eslint.config.js
rename to tools/ui/eslint.config.js
index cd20fb383a4..185da1dabbe 100644
--- a/tools/server/webui/eslint.config.js
+++ b/tools/ui/eslint.config.js
@@ -29,7 +29,9 @@ export default ts.config(
 			'no-undef': 'off',
 			'svelte/no-at-html-tags': 'off',
 			// This app uses hash-based routing (#/) where resolve() from $app/paths does not apply
-			'svelte/no-navigation-without-resolve': 'off'
+			'svelte/no-navigation-without-resolve': 'off',
+			// Enforce empty line at end of file
+			'eol-last': 'error'
 		}
 	},
 	{
diff --git a/tools/server/webui/package-lock.json b/tools/ui/package-lock.json
similarity index 93%
rename from tools/server/webui/package-lock.json
rename to tools/ui/package-lock.json
index 2fcff658f6a..bf23307b82c 100644
--- a/tools/server/webui/package-lock.json
+++ b/tools/ui/package-lock.json
@@ -1,11 +1,11 @@
 {
-	"name": "llama-server-webui",
+	"name": "llama-ui",
 	"version": "1.0.0",
 	"lockfileVersion": 3,
 	"requires": true,
 	"packages": {
 		"": {
-			"name": "llama-server-webui",
+			"name": "llama-ui",
 			"version": "1.0.0",
 			"dependencies": {
 				"@modelcontextprotocol/sdk": "^1.25.1",
@@ -80,9 +80,9 @@
 			}
 		},
 		"node_modules/@adobe/css-tools": {
-			"version": "4.4.3",
-			"resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.4.3.tgz",
-			"integrity": "sha512-VQKMkwriZbaOgVCby1UDY/LDk5fIjhQicCvVPFqfe+69fWaPWydbWJ3wRt59/YzIwda1I81loas3oCoHxnqvdA==",
+			"version": "4.4.4",
+			"resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.4.4.tgz",
+			"integrity": "sha512-Elp+iwUx5rN5+Y8xLt5/GRoG20WGoDCQ/1Fb+1LiGtvwbDavuSk0jhD/eZdckHAuzcDzccnkv+rEjyWfRx18gg==",
 			"dev": true,
 			"license": "MIT"
 		},
@@ -207,9 +207,9 @@
 			}
 		},
 		"node_modules/@esbuild/aix-ppc64": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.25.8.tgz",
-			"integrity": "sha512-urAvrUedIqEiFR3FYSLTWQgLu5tb+m0qZw0NBEasUeo6wuqatkMDaRT+1uABiGXEu5vqgPd7FGE1BhsAIy9QVA==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.27.7.tgz",
+			"integrity": "sha512-EKX3Qwmhz1eMdEJokhALr0YiD0lhQNwDqkPYyPhiSwKrh7/4KRjQc04sZ8db+5DVVnZ1LmbNDI1uAMPEUBnQPg==",
 			"cpu": [
 				"ppc64"
 			],
@@ -224,9 +224,9 @@
 			}
 		},
 		"node_modules/@esbuild/android-arm": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.25.8.tgz",
-			"integrity": "sha512-RONsAvGCz5oWyePVnLdZY/HHwA++nxYWIX1atInlaW6SEkwq6XkP3+cb825EUcRs5Vss/lGh/2YxAb5xqc07Uw==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.27.7.tgz",
+			"integrity": "sha512-jbPXvB4Yj2yBV7HUfE2KHe4GJX51QplCN1pGbYjvsyCZbQmies29EoJbkEc+vYuU5o45AfQn37vZlyXy4YJ8RQ==",
 			"cpu": [
 				"arm"
 			],
@@ -241,9 +241,9 @@
 			}
 		},
 		"node_modules/@esbuild/android-arm64": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.25.8.tgz",
-			"integrity": "sha512-OD3p7LYzWpLhZEyATcTSJ67qB5D+20vbtr6vHlHWSQYhKtzUYrETuWThmzFpZtFsBIxRvhO07+UgVA9m0i/O1w==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.27.7.tgz",
+			"integrity": "sha512-62dPZHpIXzvChfvfLJow3q5dDtiNMkwiRzPylSCfriLvZeq0a1bWChrGx/BbUbPwOrsWKMn8idSllklzBy+dgQ==",
 			"cpu": [
 				"arm64"
 			],
@@ -258,9 +258,9 @@
 			}
 		},
 		"node_modules/@esbuild/android-x64": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.25.8.tgz",
-			"integrity": "sha512-yJAVPklM5+4+9dTeKwHOaA+LQkmrKFX96BM0A/2zQrbS6ENCmxc4OVoBs5dPkCCak2roAD+jKCdnmOqKszPkjA==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.27.7.tgz",
+			"integrity": "sha512-x5VpMODneVDb70PYV2VQOmIUUiBtY3D3mPBG8NxVk5CogneYhkR7MmM3yR/uMdITLrC1ml/NV1rj4bMJuy9MCg==",
 			"cpu": [
 				"x64"
 			],
@@ -275,9 +275,9 @@
 			}
 		},
 		"node_modules/@esbuild/darwin-arm64": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.25.8.tgz",
-			"integrity": "sha512-Jw0mxgIaYX6R8ODrdkLLPwBqHTtYHJSmzzd+QeytSugzQ0Vg4c5rDky5VgkoowbZQahCbsv1rT1KW72MPIkevw==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.27.7.tgz",
+			"integrity": "sha512-5lckdqeuBPlKUwvoCXIgI2D9/ABmPq3Rdp7IfL70393YgaASt7tbju3Ac+ePVi3KDH6N2RqePfHnXkaDtY9fkw==",
 			"cpu": [
 				"arm64"
 			],
@@ -292,9 +292,9 @@
 			}
 		},
 		"node_modules/@esbuild/darwin-x64": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.25.8.tgz",
-			"integrity": "sha512-Vh2gLxxHnuoQ+GjPNvDSDRpoBCUzY4Pu0kBqMBDlK4fuWbKgGtmDIeEC081xi26PPjn+1tct+Bh8FjyLlw1Zlg==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.27.7.tgz",
+			"integrity": "sha512-rYnXrKcXuT7Z+WL5K980jVFdvVKhCHhUwid+dDYQpH+qu+TefcomiMAJpIiC2EM3Rjtq0sO3StMV/+3w3MyyqQ==",
 			"cpu": [
 				"x64"
 			],
@@ -309,9 +309,9 @@
 			}
 		},
 		"node_modules/@esbuild/freebsd-arm64": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.25.8.tgz",
-			"integrity": "sha512-YPJ7hDQ9DnNe5vxOm6jaie9QsTwcKedPvizTVlqWG9GBSq+BuyWEDazlGaDTC5NGU4QJd666V0yqCBL2oWKPfA==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.27.7.tgz",
+			"integrity": "sha512-B48PqeCsEgOtzME2GbNM2roU29AMTuOIN91dsMO30t+Ydis3z/3Ngoj5hhnsOSSwNzS+6JppqWsuhTp6E82l2w==",
 			"cpu": [
 				"arm64"
 			],
@@ -326,9 +326,9 @@
 			}
 		},
 		"node_modules/@esbuild/freebsd-x64": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/freebsd-x64/-/freebsd-x64-0.25.8.tgz",
-			"integrity": "sha512-MmaEXxQRdXNFsRN/KcIimLnSJrk2r5H8v+WVafRWz5xdSVmWLoITZQXcgehI2ZE6gioE6HirAEToM/RvFBeuhw==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/freebsd-x64/-/freebsd-x64-0.27.7.tgz",
+			"integrity": "sha512-jOBDK5XEjA4m5IJK3bpAQF9/Lelu/Z9ZcdhTRLf4cajlB+8VEhFFRjWgfy3M1O4rO2GQ/b2dLwCUGpiF/eATNQ==",
 			"cpu": [
 				"x64"
 			],
@@ -343,9 +343,9 @@
 			}
 		},
 		"node_modules/@esbuild/linux-arm": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/linux-arm/-/linux-arm-0.25.8.tgz",
-			"integrity": "sha512-FuzEP9BixzZohl1kLf76KEVOsxtIBFwCaLupVuk4eFVnOZfU+Wsn+x5Ryam7nILV2pkq2TqQM9EZPsOBuMC+kg==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/linux-arm/-/linux-arm-0.27.7.tgz",
+			"integrity": "sha512-RkT/YXYBTSULo3+af8Ib0ykH8u2MBh57o7q/DAs3lTJlyVQkgQvlrPTnjIzzRPQyavxtPtfg0EopvDyIt0j1rA==",
 			"cpu": [
 				"arm"
 			],
@@ -360,9 +360,9 @@
 			}
 		},
 		"node_modules/@esbuild/linux-arm64": {
-			"version": "0.25.8",
-			"resolved": "https://registry.npmjs.org/@esbuild/linux-arm64/-/linux-arm64-0.25.8.tgz",
-			"integrity": "sha512-WIgg00ARWv/uYLU7lsuDK00d/hHSfES5BzdWAdAig1ioV5kaFNrtK8EqGcUBJhYqotlUByUKz5Qo6u8tt7iD/w==",
+			"version": "0.27.7",
+			"resolved": "https://registry.npmjs.org/@esbuild/linux-arm64/-/linux-arm64-0.27.7.tgz",
+			"integrity": "sha512-RZPHBoxXuNnPQO9rvjh5jdkRmVizktkT7TCDkDmQ0W2SwHInKCAV95GRuvdSvA7w4VMwfCjUiPwDi0ZO6Nfe9A==",
 			"cpu": [
 				"arm64"
 			],
@@ -377,9 +377,9 @@
 			}
 		},
 		"node_modules/@esbuild/linux-ia32": {
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-			"version": "2.2.2",
-			"resolved": "https://registry.npmjs.org/esrap/-/esrap-2.2.2.tgz",
-			"integrity": "sha512-zA6497ha+qKvoWIK+WM9NAh5ni17sKZKhbS5B3PoYbBvaYHZWoS33zmFybmyqpn07RLUxSmn+RCls2/XF+d0oQ==",
+			"version": "2.2.4",
+			"resolved": "https://registry.npmjs.org/esrap/-/esrap-2.2.4.tgz",
+			"integrity": "sha512-suICpxAmZ9A8bzJjEl/+rLJiDKC0X4gYWUxT6URAWBLvlXmtbZd5ySMu/N2ZGEtMCAmflUDPSehrP9BQcsGcSg==",
 			"license": "MIT",
 			"dependencies": {
-				"@jridgewell/sourcemap-codec": "^1.4.15"
+				"@jridgewell/sourcemap-codec": "^1.4.15",
+				"@typescript-eslint/types": "^8.2.0"
 			}
 		},
 		"node_modules/svelte/node_modules/is-reference": {
@@ -9647,9 +9539,9 @@
 			}
 		},
 		"node_modules/tar": {
-			"version": "7.5.7",
-			"resolved": "https://registry.npmjs.org/tar/-/tar-7.5.7.tgz",
-			"integrity": "sha512-fov56fJiRuThVFXD6o6/Q354S7pnWMJIVlDBYijsTNx6jKSE4pvrDTs6lUnmGvNyfJwFQQwWy3owKz1ucIhveQ==",
+			"version": "7.5.13",
+			"resolved": "https://registry.npmjs.org/tar/-/tar-7.5.13.tgz",
+			"integrity": "sha512-tOG/7GyXpFevhXVh8jOPJrmtRpOTsYqUIkVdVooZYJS/z8WhfQUX8RJILmeuJNinGAMSu1veBr4asSHFt5/hng==",
 			"dev": true,
 			"license": "BlueOak-1.0.0",
 			"dependencies": {
@@ -9679,9 +9571,9 @@
 			}
 		},
 		"node_modules/test-exclude/node_modules/brace-expansion": {
-			"version": "2.0.2",
-			"resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz",
-			"integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==",
+			"version": "2.0.3",
+			"resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.3.tgz",
+			"integrity": "sha512-MCV/fYJEbqx68aE58kv2cA/kiky1G8vux3OR6/jbS+jIMe/6fJWa0DTzJU7dqijOWYwHi1t29FlfYI9uytqlpA==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
@@ -9689,13 +9581,13 @@
 			}
 		},
 		"node_modules/test-exclude/node_modules/minimatch": {
-			"version": "9.0.5",
-			"resolved": "https://registry.npmjs.org/minimatch/-/minimatch-9.0.5.tgz",
-			"integrity": "sha512-G6T0ZX48xgozx7587koeX9Ys2NYy6Gmv//P89sEte9V9whIapMNF4idKxnW2QtCcLiTWlb/wfCabAtAFWhhBow==",
+			"version": "9.0.9",
+			"resolved": "https://registry.npmjs.org/minimatch/-/minimatch-9.0.9.tgz",
+			"integrity": "sha512-OBwBN9AL4dqmETlpS2zasx+vTeWclWzkblfZk7KTA5j3jeOONz/tRCnZomUyvNg83wL5Zv9Ss6HMJXAgL8R2Yg==",
 			"dev": true,
 			"license": "ISC",
 			"dependencies": {
-				"brace-expansion": "^2.0.1"
+				"brace-expansion": "^2.0.2"
 			},
 			"engines": {
 				"node": ">=16 || 14 >=14.17"
@@ -10192,9 +10084,9 @@
 			"license": "MIT"
 		},
 		"node_modules/uuid": {
-			"version": "13.0.0",
-			"resolved": "https://registry.npmjs.org/uuid/-/uuid-13.0.0.tgz",
-			"integrity": "sha512-XQegIaBTVUjSHliKqcnFqYypAd4S+WCYt5NIeRs6w/UAry7z8Y9j5ZwRRL4kzq9U3sD6v+85er9FvkEaBpji2w==",
+			"version": "13.0.2",
+			"resolved": "https://registry.npmjs.org/uuid/-/uuid-13.0.2.tgz",
+			"integrity": "sha512-vzi9uRZ926x4XV73S/4qQaTwPXM2JBj6/6lI/byHH1jOpCzb0zDbfytgA9LcN/hzb2l7WQSQnxITOVx5un/wGw==",
 			"dev": true,
 			"funding": [
 				"https://github.com/sponsors/broofa",
@@ -10299,13 +10191,13 @@
 			}
 		},
 		"node_modules/vite": {
-			"version": "7.2.2",
-			"resolved": "https://registry.npmjs.org/vite/-/vite-7.2.2.tgz",
-			"integrity": "sha512-BxAKBWmIbrDgrokdGZH1IgkIk/5mMHDreLDmCJ0qpyJaAteP8NvMhkwr/ZCQNqNH97bw/dANTE9PDzqwJghfMQ==",
+			"version": "7.3.2",
+			"resolved": "https://registry.npmjs.org/vite/-/vite-7.3.2.tgz",
+			"integrity": "sha512-Bby3NOsna2jsjfLVOHKes8sGwgl4TT0E6vvpYgnAYDIF/tie7MRaFthmKuHx1NSXjiTueXH3do80FMQgvEktRg==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
-				"esbuild": "^0.25.0",
+				"esbuild": "^0.27.0",
 				"fdir": "^6.5.0",
 				"picomatch": "^4.0.3",
 				"postcss": "^8.5.6",
@@ -10410,9 +10302,9 @@
 			}
 		},
 		"node_modules/vite-plugin-devtools-json/node_modules/uuid": {
-			"version": "11.1.0",
-			"resolved": "https://registry.npmjs.org/uuid/-/uuid-11.1.0.tgz",
-			"integrity": "sha512-0/A9rDy9P7cJ+8w1c9WD9V//9Wj15Ce2MPz8Ri6032usz+NfePxx5AcN3bN+r6ZL6jEo066/yNYB3tn4pQEx+A==",
+			"version": "11.1.1",
+			"resolved": "https://registry.npmjs.org/uuid/-/uuid-11.1.1.tgz",
+			"integrity": "sha512-vIYxrBCC/N/K+Js3qSN88go7kIfNPssr/hHCesKCQNAjmgvYS2oqr69kIufEG+O4+PfezOH4EbIeHCfFov8ZgQ==",
 			"dev": true,
 			"funding": [
 				"https://github.com/sponsors/broofa",
diff --git a/tools/server/webui/package.json b/tools/ui/package.json
similarity index 97%
rename from tools/server/webui/package.json
rename to tools/ui/package.json
index cb33b4845be..5a1cec66645 100644
--- a/tools/server/webui/package.json
+++ b/tools/ui/package.json
@@ -1,11 +1,11 @@
 {
-	"name": "llama-server-webui",
+	"name": "llama-ui",
 	"private": true,
 	"version": "1.0.0",
 	"type": "module",
 	"scripts": {
 		"dev": "bash scripts/dev.sh",
-		"build": "vite build && ./scripts/post-build.sh",
+		"build": "vite build",
 		"preview": "vite preview",
 		"prepare": "svelte-kit sync || echo ''",
 		"check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json",
diff --git a/tools/server/webui/playwright.config.ts b/tools/ui/playwright.config.ts
similarity index 70%
rename from tools/server/webui/playwright.config.ts
rename to tools/ui/playwright.config.ts
index 26d3be535d1..178fd7ba856 100644
--- a/tools/server/webui/playwright.config.ts
+++ b/tools/ui/playwright.config.ts
@@ -2,7 +2,7 @@ import { defineConfig } from '@playwright/test';
 
 export default defineConfig({
 	webServer: {
-		command: 'npm run build && http-server ../public -p 8181',
+		command: 'npm run build && http-server ../../build/tools/ui/dist -p 8181',
 		port: 8181,
 		timeout: 120000,
 		reuseExistingServer: false
diff --git a/tools/server/webui/scripts/dev.sh b/tools/ui/scripts/dev.sh
similarity index 86%
rename from tools/server/webui/scripts/dev.sh
rename to tools/ui/scripts/dev.sh
index b7539c205e2..9256f255aec 100644
--- a/tools/server/webui/scripts/dev.sh
+++ b/tools/ui/scripts/dev.sh
@@ -1,34 +1,34 @@
 #!/bin/bash
 
-# Development script for llama.cpp webui
-# 
-# This script starts the webui development servers (Storybook and Vite).
+# Development script for llama-ui
+#
+# This script starts the llama-ui development servers (Storybook and Vite).
 # Note: You need to start llama-server separately.
 #
 # Usage:
 #   bash scripts/dev.sh
 #   npm run dev
 
-cd ../../../
+cd ../../
 
 # Check and install git hooks if missing
 check_and_install_hooks() {
     local hooks_missing=false
-    
+
     # Check for required hooks
     if [ ! -f ".git/hooks/pre-commit" ] || [ ! -f ".git/hooks/pre-push" ] || [ ! -f ".git/hooks/post-push" ]; then
         hooks_missing=true
     fi
-    
+
     if [ "$hooks_missing" = true ]; then
         echo "🔧 Git hooks missing, installing them..."
-        cd tools/server/webui
+        cd tools/ui
         if bash scripts/install-git-hooks.sh; then
             echo "✅ Git hooks installed successfully"
         else
             echo "⚠️  Failed to install git hooks, continuing anyway..."
         fi
-        cd ../../../
+        cd ../../
     else
         echo "✅ Git hooks already installed"
     fi
@@ -48,7 +48,7 @@ trap cleanup SIGINT SIGTERM
 
 echo "🚀 Starting development servers..."
 echo "📝 Note: Make sure to start llama-server separately if needed"
-cd tools/server/webui
+cd tools/ui
 # Use --insecure-http-parser to handle malformed HTTP responses from llama-server
 # (some responses have both Content-Length and Transfer-Encoding headers)
 storybook dev -p 6006 --ci & NODE_OPTIONS="--insecure-http-parser" vite dev --host 0.0.0.0 &
diff --git a/tools/server/webui/scripts/install-git-hooks.sh b/tools/ui/scripts/install-git-hooks.sh
similarity index 57%
rename from tools/server/webui/scripts/install-git-hooks.sh
rename to tools/ui/scripts/install-git-hooks.sh
index efdea025c16..213feb08dcf 100755
--- a/tools/server/webui/scripts/install-git-hooks.sh
+++ b/tools/ui/scripts/install-git-hooks.sh
@@ -1,29 +1,29 @@
 #!/bin/bash
 
-# Script to install pre-commit hook for webui
-# Pre-commit: formats, checks, builds, and stages build output
+# Script to install pre-commit hook for llama-ui
+# Pre-commit: formats, checks, and builds the UI app
 
 REPO_ROOT=$(git rev-parse --show-toplevel)
 PRE_COMMIT_HOOK="$REPO_ROOT/.git/hooks/pre-commit"
 
-echo "Installing pre-commit hook for webui..."
+echo "Installing pre-commit hook for llama-ui..."
 
 # Create the pre-commit hook
 cat > "$PRE_COMMIT_HOOK" << 'EOF'
 #!/bin/bash
 
-# Check if there are any changes in the webui directory
-if git diff --cached --name-only | grep -q "^tools/server/webui/"; then
+# Check if there are any changes in the tools/ui directory
+if git diff --cached --name-only | grep -q "^tools/ui/"; then
     REPO_ROOT=$(git rev-parse --show-toplevel)
-    cd "$REPO_ROOT/tools/server/webui"
+    cd "$REPO_ROOT/tools/ui"
 
     # Check if package.json exists
     if [ ! -f "package.json" ]; then
-        echo "Error: package.json not found in tools/server/webui"
+        echo "Error: package.json not found in tools/ui"
         exit 1
     fi
 
-    echo "Formatting and checking webui code..."
+    echo "Formatting and checking llama-ui code..."
 
     # Run the format command
     npm run format
@@ -46,21 +46,17 @@ if git diff --cached --name-only | grep -q "^tools/server/webui/"; then
         exit 1
     fi
 
-    echo "✅ Webui code formatted and checked successfully"
+    echo "✅ llama-ui code formatted and checked successfully"
 
-    # Build the webui
-    echo "Building webui..."
+    # Build the llama-ui
+    echo "Building llama-ui..."
     npm run build
     if [ $? -ne 0 ]; then
         echo "❌ npm run build failed"
         exit 1
     fi
 
-    # Stage the build output alongside the source changes
-    cd "$REPO_ROOT"
-    git add tools/server/public/
-
-    echo "✅ Webui built and build output staged"
+    echo "✅ llama-ui built successfully"
 fi
 
 exit 0
@@ -74,8 +70,8 @@ if [ $? -eq 0 ]; then
     echo "   Pre-commit: $PRE_COMMIT_HOOK"
     echo ""
     echo "The hook will automatically:"
-    echo "  • Format, lint and check webui code before commits"
-    echo "  • Build webui and stage tools/server/public/ into the same commit"
+    echo "  • Format, lint and check llama-ui code before commits"
+    echo "  • Build llama-ui"
 else
     echo "❌ Failed to make hook executable"
     exit 1
diff --git a/tools/server/webui/scripts/vite-plugin-llama-cpp-build.ts b/tools/ui/scripts/vite-plugin-llama-cpp-build.ts
similarity index 64%
rename from tools/server/webui/scripts/vite-plugin-llama-cpp-build.ts
rename to tools/ui/scripts/vite-plugin-llama-cpp-build.ts
index 0330a1dda12..ddf6fa1e56e 100644
--- a/tools/server/webui/scripts/vite-plugin-llama-cpp-build.ts
+++ b/tools/ui/scripts/vite-plugin-llama-cpp-build.ts
@@ -1,4 +1,12 @@
-import { readFileSync, writeFileSync, existsSync, readdirSync, copyFileSync } from 'fs';
+import {
+	readFileSync,
+	writeFileSync,
+	existsSync,
+	readdirSync,
+	copyFileSync,
+	rmSync,
+	unlinkSync
+} from 'fs';
 import { resolve } from 'path';
 import type { Plugin } from 'vite';
 
@@ -11,28 +19,28 @@ const GUIDE_FOR_FRONTEND = `
 -->
 `.trim();
 
+const OUTPUT_DIR = '../../build/tools/ui/dist';
+
 export function llamaCppBuildPlugin(): Plugin {
 	return {
 		name: 'llamacpp:build',
 		apply: 'build',
 		closeBundle() {
-			// Ensure the SvelteKit adapter has finished writing to ../public
 			setTimeout(() => {
 				try {
-					const indexPath = resolve('../public/index.html');
+					const outDir = resolve(OUTPUT_DIR);
+					const indexPath = resolve(outDir, 'index.html');
 					if (!existsSync(indexPath)) return;
 
 					let content = readFileSync(indexPath, 'utf-8');
 
+					// Inline favicon as base64 data URL
 					const faviconPath = resolve('static/favicon.svg');
-
 					if (existsSync(faviconPath)) {
 						const faviconContent = readFileSync(faviconPath, 'utf-8');
 						const faviconBase64 = Buffer.from(faviconContent).toString('base64');
 						const faviconDataUrl = `data:image/svg+xml;base64,${faviconBase64}`;
-
 						content = content.replace(/href="[^"]*favicon\.svg"/g, `href="${faviconDataUrl}"`);
-
 						console.log('✓ Inlined favicon.svg as base64 data URL');
 					}
 
@@ -48,17 +56,16 @@ export function llamaCppBuildPlugin(): Plugin {
 					writeFileSync(indexPath, content, 'utf-8');
 					console.log('✓ Updated index.html');
 
-					// Copy bundle.*.js -> ../public/bundle.js
-					const immutableDir = resolve('../public/_app/immutable');
-					const bundleDir = resolve('../public/_app/immutable/assets');
+					// Copy bundle.*.js -> bundle.js at output root
+					const immutableDir = resolve(outDir, '_app/immutable');
+					const bundleDir = resolve(outDir, '_app/immutable/assets');
 
 					if (existsSync(immutableDir)) {
 						const jsFiles = readdirSync(immutableDir).filter((f) => f.match(/^bundle\..+\.js$/));
-
 						if (jsFiles.length > 0) {
-							copyFileSync(resolve(immutableDir, jsFiles[0]), resolve('../public/bundle.js'));
+							copyFileSync(resolve(immutableDir, jsFiles[0]), resolve(outDir, 'bundle.js'));
 							// Normalize __sveltekit_<hash> to __sveltekit__ in bundle.js
-							const bundleJsPath = resolve('../public/bundle.js');
+							const bundleJsPath = resolve(outDir, 'bundle.js');
 							let bundleJs = readFileSync(bundleJsPath, 'utf-8');
 							bundleJs = bundleJs.replace(/__sveltekit_[a-z0-9]+/g, '__sveltekit__');
 							writeFileSync(bundleJsPath, bundleJs, 'utf-8');
@@ -66,17 +73,29 @@ export function llamaCppBuildPlugin(): Plugin {
 						}
 					}
 
-					// Copy bundle.*.css -> ../public/bundle.css
+					// Copy bundle.*.css -> bundle.css at output root
 					if (existsSync(bundleDir)) {
 						const cssFiles = readdirSync(bundleDir).filter((f) => f.match(/^bundle\..+\.css$/));
-
 						if (cssFiles.length > 0) {
-							copyFileSync(resolve(bundleDir, cssFiles[0]), resolve('../public/bundle.css'));
+							copyFileSync(resolve(bundleDir, cssFiles[0]), resolve(outDir, 'bundle.css'));
 							console.log(`✓ Copied ${cssFiles[0]} -> bundle.css`);
 						}
 					}
+
+					// Cleanup: remove _app directory, favicon.svg, and legacy index.html.gz
+					const appDir = resolve(outDir, '_app');
+					if (existsSync(appDir)) {
+						rmSync(appDir, { recursive: true, force: true });
+						console.log('✓ Removed _app directory');
+					}
+
+					const faviconOut = resolve(outDir, 'favicon.svg');
+					if (existsSync(faviconOut)) {
+						unlinkSync(faviconOut);
+						console.log('✓ Removed favicon.svg');
+					}
 				} catch (error) {
-					console.error('Failed to update index.html:', error);
+					console.error('Failed to process build output:', error);
 				}
 			}, 100);
 		}
diff --git a/tools/server/webui/src/app.css b/tools/ui/src/app.css
similarity index 98%
rename from tools/server/webui/src/app.css
rename to tools/ui/src/app.css
index 2f5c4138aba..6e29b70a35a 100644
--- a/tools/server/webui/src/app.css
+++ b/tools/ui/src/app.css
@@ -29,7 +29,7 @@
 	--chart-3: oklch(0.398 0.07 227.392);
 	--chart-4: oklch(0.828 0.189 84.429);
 	--chart-5: oklch(0.769 0.188 70.08);
-	--sidebar: oklch(0.987 0 0);
+	--sidebar: oklch(0.985 0 0);
 	--sidebar-foreground: oklch(0.145 0 0);
 	--sidebar-primary: oklch(0.205 0 0);
 	--sidebar-primary-foreground: oklch(0.985 0 0);
@@ -77,7 +77,7 @@
 	--chart-3: oklch(0.769 0.188 70.08);
 	--chart-4: oklch(0.627 0.265 303.9);
 	--chart-5: oklch(0.645 0.246 16.439);
-	--sidebar: oklch(0.19 0 0);
+	--sidebar: oklch(0.2 0 0);
 	--sidebar-foreground: oklch(0.985 0 0);
 	--sidebar-primary: oklch(0.488 0.243 264.376);
 	--sidebar-primary-foreground: oklch(0.985 0 0);
diff --git a/tools/server/webui/src/app.d.ts b/tools/ui/src/app.d.ts
similarity index 97%
rename from tools/server/webui/src/app.d.ts
rename to tools/ui/src/app.d.ts
index 73287d91b65..ec65952e9a7 100644
--- a/tools/server/webui/src/app.d.ts
+++ b/tools/ui/src/app.d.ts
@@ -28,7 +28,6 @@ import type {
 	ApiRouterModelsUnloadResponse,
 	// Chat types
 	ChatAttachmentDisplayItem,
-	ChatAttachmentPreviewItem,
 	ChatMessageType,
 	ChatRole,
 	ChatUploadedFile,
@@ -40,6 +39,7 @@ import type {
 	DatabaseMessage,
 	DatabaseMessageExtra,
 	DatabaseMessageExtraAudioFile,
+	DatabaseMessageExtraVideoFile,
 	DatabaseMessageExtraImageFile,
 	DatabaseMessageExtraTextFile,
 	DatabaseMessageExtraPdfFile,
@@ -92,7 +92,6 @@ declare global {
 		ApiRouterModelsUnloadResponse,
 		// Chat types
 		ChatAttachmentDisplayItem,
-		ChatAttachmentPreviewItem,
 		ChatMessagePromptProgress,
 		ChatMessageSiblingInfo,
 		ChatMessageTimings,
@@ -104,6 +103,7 @@ declare global {
 		DatabaseMessage,
 		DatabaseMessageExtra,
 		DatabaseMessageExtraAudioFile,
+		DatabaseMessageExtraVideoFile,
 		DatabaseMessageExtraImageFile,
 		DatabaseMessageExtraTextFile,
 		DatabaseMessageExtraPdfFile,
diff --git a/tools/server/webui/src/app.html b/tools/ui/src/app.html
similarity index 100%
rename from tools/server/webui/src/app.html
rename to tools/ui/src/app.html
diff --git a/tools/server/webui/src/lib/actions/fade-in-view.svelte.ts b/tools/ui/src/lib/actions/fade-in-view.svelte.ts
similarity index 82%
rename from tools/server/webui/src/lib/actions/fade-in-view.svelte.ts
rename to tools/ui/src/lib/actions/fade-in-view.svelte.ts
index 5c726b7e458..d930448050d 100644
--- a/tools/server/webui/src/lib/actions/fade-in-view.svelte.ts
+++ b/tools/ui/src/lib/actions/fade-in-view.svelte.ts
@@ -1,3 +1,5 @@
+import { isElementInViewport } from '$lib/utils/viewport';
+
 /**
  * Svelte action that fades in an element when it enters the viewport.
  * Uses IntersectionObserver for efficient viewport detection.
@@ -12,17 +14,8 @@ export function fadeInView(
 ) {
 	const { duration = 300, y = 0, skipIfVisible = false } = options;
 
-	if (skipIfVisible) {
-		const rect = node.getBoundingClientRect();
-		const isAlreadyVisible =
-			rect.top < window.innerHeight &&
-			rect.bottom > 0 &&
-			rect.left < window.innerWidth &&
-			rect.right > 0;
-
-		if (isAlreadyVisible) {
-			return;
-		}
+	if (skipIfVisible && isElementInViewport(node)) {
+		return;
 	}
 
 	node.style.opacity = '0';
diff --git a/tools/ui/src/lib/components/app/SKILL.md b/tools/ui/src/lib/components/app/SKILL.md
new file mode 100644
index 00000000000..7453954abb1
--- /dev/null
+++ b/tools/ui/src/lib/components/app/SKILL.md
@@ -0,0 +1,11 @@
+---
+name: app
+description: Opinionated app components building on top of ./ui primitives
+---
+
+- Can include business logic and state management
+- Can include data fetching and caching logic
+- Should use original spelling for HTML-native events and `camelCase` for custom events
+- Props and markup attributes should be listed alphabetically
+- Use JS Objects and Arrays for CSS classes and styles when they are dynamic
+- Whenever there can be repetition in the component's markup, if it's too small to be decoupled as a separate component — use Svelte 5's `{#snippet}` + `{@render}`
diff --git a/tools/ui/src/lib/components/app/actions/ActionIcon.svelte b/tools/ui/src/lib/components/app/actions/ActionIcon.svelte
new file mode 100644
index 00000000000..849b83b19c8
--- /dev/null
+++ b/tools/ui/src/lib/components/app/actions/ActionIcon.svelte
@@ -0,0 +1,60 @@
+<script lang="ts">
+	import { Button, type ButtonVariant, type ButtonSize } from '$lib/components/ui/button';
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import type { Component } from 'svelte';
+	import { TooltipSide } from '$lib/enums';
+
+	interface Props {
+		ariaLabel?: string;
+		class?: string;
+		disabled?: boolean;
+		icon: Component;
+		iconSize?: string;
+		onclick: (e?: MouseEvent) => void;
+		size?: ButtonSize;
+		stopPropagationOnClick?: boolean;
+		tooltip: string;
+		variant?: ButtonVariant;
+		tooltipSide?: TooltipSide;
+	}
+
+	let {
+		icon,
+		tooltip,
+		variant = 'ghost',
+		size = 'sm',
+		class: className = '',
+		disabled = false,
+		iconSize = 'h-3 w-3',
+		tooltipSide = TooltipSide.TOP,
+		stopPropagationOnClick = false,
+		onclick,
+		ariaLabel
+	}: Props = $props();
+</script>
+
+<Tooltip.Root>
+	<Tooltip.Trigger>
+		<Button
+			{variant}
+			{size}
+			{disabled}
+			onclick={(e: MouseEvent) => {
+				if (stopPropagationOnClick) e.stopPropagation();
+
+				onclick?.(e);
+			}}
+			class="h-6 w-6 p-0 {className} flex hover:bg-transparent data-[state=open]:bg-transparent!"
+			aria-label={ariaLabel || tooltip}
+		>
+			{#if icon}
+				{@const IconComponent = icon}
+				<IconComponent class={iconSize} />
+			{/if}
+		</Button>
+	</Tooltip.Trigger>
+
+	<Tooltip.Content side={tooltipSide}>
+		<p>{tooltip}</p>
+	</Tooltip.Content>
+</Tooltip.Root>
diff --git a/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte b/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte
new file mode 100644
index 00000000000..999f0cba9e7
--- /dev/null
+++ b/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte
@@ -0,0 +1,17 @@
+<script lang="ts">
+	import { Copy } from '@lucide/svelte';
+	import { copyToClipboard } from '$lib/utils';
+	import ActionIcon from './ActionIcon.svelte';
+
+	export let ariaLabel: string = 'Copy to clipboard';
+	export let canCopy: boolean = true;
+	export let text: string;
+</script>
+
+<ActionIcon
+	icon={Copy}
+	tooltip={ariaLabel}
+	iconSize="h-4 w-4"
+	disabled={!canCopy}
+	onclick={() => canCopy && copyToClipboard(text)}
+/>
diff --git a/tools/ui/src/lib/components/app/actions/index.ts b/tools/ui/src/lib/components/app/actions/index.ts
new file mode 100644
index 00000000000..4bb2a58d667
--- /dev/null
+++ b/tools/ui/src/lib/components/app/actions/index.ts
@@ -0,0 +1,13 @@
+/**
+ *
+ * ACTIONS
+ *
+ * Small interactive components for user actions.
+ *
+ */
+
+/** Styled icon button for action triggers with tooltip. */
+export { default as ActionIcon } from './ActionIcon.svelte';
+
+/** Copy-to-clipboard icon button with clipboard logic. */
+export { default as ActionIconCopyToClipboard } from './ActionIconCopyToClipboard.svelte';
diff --git a/tools/server/webui/src/lib/components/app/badges/BadgeInfo.svelte b/tools/ui/src/lib/components/app/badges/BadgeInfo.svelte
similarity index 87%
rename from tools/server/webui/src/lib/components/app/badges/BadgeInfo.svelte
rename to tools/ui/src/lib/components/app/badges/BadgeInfo.svelte
index c70af6f4232..25986082bea 100644
--- a/tools/server/webui/src/lib/components/app/badges/BadgeInfo.svelte
+++ b/tools/ui/src/lib/components/app/badges/BadgeInfo.svelte
@@ -1,5 +1,4 @@
 <script lang="ts">
-	import { cn } from '$lib/components/ui/utils';
 	import type { Snippet } from 'svelte';
 
 	interface Props {
@@ -13,10 +12,10 @@
 </script>
 
 <button
-	class={cn(
+	class={[
 		'inline-flex cursor-pointer items-center gap-1 rounded-sm bg-muted-foreground/15 px-1.5 py-0.75',
 		className
-	)}
+	]}
 	{onclick}
 >
 	{#if icon}
diff --git a/tools/ui/src/lib/components/app/badges/BadgesModality.svelte b/tools/ui/src/lib/components/app/badges/BadgesModality.svelte
new file mode 100644
index 00000000000..d87184ea9bc
--- /dev/null
+++ b/tools/ui/src/lib/components/app/badges/BadgesModality.svelte
@@ -0,0 +1,36 @@
+<script lang="ts">
+	import { Eye, Mic, Video } from '@lucide/svelte';
+	import { ModelModality } from '$lib/enums';
+
+	interface Props {
+		modalities: ModelModality[];
+		class?: string;
+	}
+
+	let { modalities, class: className = '' }: Props = $props();
+</script>
+
+{#each modalities as modality (modality)}
+	{#if modality === ModelModality.VISION || modality === ModelModality.AUDIO || modality === ModelModality.VIDEO}
+		<span
+			class={[
+				'inline-flex items-center gap-1 rounded-md bg-muted px-2 py-1 text-xs font-medium',
+				className
+			]}
+		>
+			{#if modality === ModelModality.VISION}
+				<Eye class="h-3 w-3" />
+
+				Vision (Image)
+			{:else if modality === ModelModality.VIDEO}
+				<Video class="h-3 w-3" />
+
+				Vision (Video)
+			{:else}
+				<Mic class="h-3 w-3" />
+
+				Audio
+			{/if}
+		</span>
+	{/if}
+{/each}
diff --git a/tools/server/webui/src/lib/components/app/badges/index.ts b/tools/ui/src/lib/components/app/badges/index.ts
similarity index 58%
rename from tools/server/webui/src/lib/components/app/badges/index.ts
rename to tools/ui/src/lib/components/app/badges/index.ts
index 860afe3084d..f8098056f28 100644
--- a/tools/server/webui/src/lib/components/app/badges/index.ts
+++ b/tools/ui/src/lib/components/app/badges/index.ts
@@ -6,11 +6,8 @@
  *
  */
 
-/** Badge displaying chat statistics (tokens, timing). */
-export { default as BadgeChatStatistic } from './BadgeChatStatistic.svelte';
-
 /** Generic info badge with optional tooltip and click handler. */
 export { default as BadgeInfo } from './BadgeInfo.svelte';
 
 /** Badge indicating model modality (vision, audio, tools). */
-export { default as BadgeModality } from './BadgeModality.svelte';
+export { default as BadgesModality } from './BadgesModality.svelte';
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsList.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsList.svelte
new file mode 100644
index 00000000000..e74bd8456a5
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsList.svelte
@@ -0,0 +1,119 @@
+<script lang="ts">
+	import {
+		ChatAttachmentsListItem,
+		DialogChatAttachmentsPreview,
+		DialogMcpResourcePreview,
+		HorizontalScrollCarousel
+	} from '$lib/components/app';
+	import type { DatabaseMessageExtraMcpResource } from '$lib/types';
+	import { getAttachmentDisplayItems, isMcpPrompt, isMcpResource } from '$lib/utils';
+
+	interface Props {
+		class?: string;
+		style?: string;
+		// For ChatMessage - stored attachments
+		attachments?: DatabaseMessageExtra[];
+		readonly?: boolean;
+		// For ChatForm - pending uploads
+		onFileRemove?: (fileId: string) => void;
+		uploadedFiles?: ChatUploadedFile[];
+		// Image size customization
+		imageClass?: string;
+		imageHeight?: string;
+		imageWidth?: string;
+		// Limit display to single row with "+ X more" button
+		limitToSingleRow?: boolean;
+		// For vision modality check
+		activeModelId?: string;
+	}
+
+	let {
+		class: className = '',
+		style = '',
+		attachments = [],
+		readonly = false,
+		onFileRemove,
+		uploadedFiles = $bindable([]),
+		// Default to small size for form previews
+		imageClass = '',
+		imageHeight = 'h-24',
+		imageWidth = 'w-auto',
+		limitToSingleRow = false,
+		activeModelId
+	}: Props = $props();
+
+	let carouselRef: HorizontalScrollCarousel | undefined = $state();
+	let mcpResourcePreviewOpen = $state(false);
+	let mcpResourcePreviewExtra = $state<DatabaseMessageExtraMcpResource | null>(null);
+	let previewFocusIndex = $state(0);
+	let viewAllDialogOpen = $state(false);
+
+	let displayItems = $derived(getAttachmentDisplayItems({ uploadedFiles, attachments }));
+
+	function openPreview(item: ChatAttachmentDisplayItem, event?: MouseEvent) {
+		event?.stopPropagation();
+		event?.preventDefault();
+
+		// Find the index of the clicked item among non-MCP attachments
+		const nonMcpItems = displayItems.filter((i) => !isMcpPrompt(i) && !isMcpResource(i));
+		const index = nonMcpItems.findIndex((i) => i.id === item.id);
+
+		previewFocusIndex = index >= 0 ? index : 0;
+		viewAllDialogOpen = true;
+	}
+
+	function openMcpResourcePreview(extra: DatabaseMessageExtraMcpResource) {
+		mcpResourcePreviewExtra = extra;
+		mcpResourcePreviewOpen = true;
+	}
+
+	$effect(() => {
+		if (carouselRef && displayItems.length) {
+			carouselRef.resetScroll();
+		}
+	});
+</script>
+
+{#snippet attachmentitem(item: ChatAttachmentDisplayItem)}
+	<ChatAttachmentsListItem
+		{imageClass}
+		{imageHeight}
+		{imageWidth}
+		{item}
+		{limitToSingleRow}
+		{onFileRemove}
+		onMcpResourcePreview={openMcpResourcePreview}
+		onPreview={(i: ChatAttachmentDisplayItem, event?: MouseEvent) => openPreview(i, event)}
+		{readonly}
+	/>
+{/snippet}
+
+{#if displayItems.length > 0}
+	<div class={className} {style}>
+		{#if limitToSingleRow}
+			<HorizontalScrollCarousel bind:this={carouselRef}>
+				{#each displayItems as item (item.id)}
+					{@render attachmentitem(item)}
+				{/each}
+			</HorizontalScrollCarousel>
+		{:else}
+			<div class="flex flex-wrap items-start justify-end gap-3">
+				{#each displayItems as item (item.id)}
+					{@render attachmentitem(item)}
+				{/each}
+			</div>
+		{/if}
+	</div>
+{/if}
+
+<DialogChatAttachmentsPreview
+	{activeModelId}
+	{attachments}
+	bind:open={viewAllDialogOpen}
+	{previewFocusIndex}
+	{uploadedFiles}
+/>
+
+{#if mcpResourcePreviewExtra}
+	<DialogMcpResourcePreview extra={mcpResourcePreviewExtra} bind:open={mcpResourcePreviewOpen} />
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItem.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItem.svelte
new file mode 100644
index 00000000000..143621cd9da
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItem.svelte
@@ -0,0 +1,132 @@
+<script lang="ts">
+	import {
+		ChatAttachmentsListItemMcpPrompt,
+		ChatAttachmentsListItemMcpResource,
+		ChatAttachmentsListItemThumbnailImage,
+		ChatAttachmentsListItemThumbnailFile
+	} from '$lib/components/app';
+	import { AttachmentType } from '$lib/enums';
+	import type {
+		ChatAttachmentDisplayItem,
+		DatabaseMessageExtraMcpPrompt,
+		DatabaseMessageExtraMcpResource,
+		MCPResourceAttachment
+	} from '$lib/types';
+	import { isMcpPrompt, isMcpResource, isPdfFile } from '$lib/utils';
+
+	interface Props {
+		class?: string;
+		imageClass?: string;
+		imageHeight?: string;
+		imageWidth?: string;
+		item: ChatAttachmentDisplayItem;
+		limitToSingleRow?: boolean;
+		onFileRemove?: (fileId: string) => void;
+		onMcpResourcePreview?: (extra: DatabaseMessageExtraMcpResource) => void;
+		onPreview?: (item: ChatAttachmentDisplayItem) => void;
+		readonly?: boolean;
+	}
+
+	let {
+		class: className = '',
+		imageClass = '',
+		imageHeight = 'h-24',
+		imageWidth = 'w-auto',
+		item,
+		limitToSingleRow = false,
+		onFileRemove,
+		onMcpResourcePreview,
+		onPreview,
+		readonly = false
+	}: Props = $props();
+
+	const scrollClasses = $derived(limitToSingleRow ? 'first:ml-4 last:mr-4' : '');
+
+	function toMcpResourceAttachment(
+		extra: DatabaseMessageExtraMcpResource,
+		id: string
+	): MCPResourceAttachment {
+		return {
+			id,
+			resource: {
+				uri: extra.uri,
+				name: extra.name,
+				title: extra.name,
+				serverName: extra.serverName
+			}
+		};
+	}
+</script>
+
+{#if isMcpPrompt(item)}
+	{@const mcpPrompt =
+		item.attachment?.type === AttachmentType.MCP_PROMPT
+			? (item.attachment as DatabaseMessageExtraMcpPrompt)
+			: item.uploadedFile?.mcpPrompt
+				? {
+						type: AttachmentType.MCP_PROMPT as const,
+						name: item.name,
+						serverName: item.uploadedFile.mcpPrompt.serverName,
+						promptName: item.uploadedFile.mcpPrompt.promptName,
+						content: item.textContent ?? '',
+						arguments: item.uploadedFile.mcpPrompt.arguments
+					}
+				: null}
+	{#if mcpPrompt}
+		<ChatAttachmentsListItemMcpPrompt
+			class="max-w-[300px] min-w-[200px] flex-shrink-0 {className} {scrollClasses}"
+			prompt={mcpPrompt}
+			{readonly}
+			isLoading={item.isLoading}
+			loadError={item.loadError}
+			onRemove={onFileRemove ? () => onFileRemove(item.id) : undefined}
+		/>
+	{/if}
+{:else if isMcpResource(item)}
+	{@const mcpResource = item.attachment as DatabaseMessageExtraMcpResource}
+
+	<ChatAttachmentsListItemMcpResource
+		class="flex-shrink-0 {className} {scrollClasses}"
+		attachment={toMcpResourceAttachment(mcpResource, item.id)}
+		onclick={() => onMcpResourcePreview?.(mcpResource)}
+	/>
+{:else if item.isImage && item.preview}
+	<ChatAttachmentsListItemThumbnailImage
+		class="flex-shrink-0 cursor-pointer {className} {scrollClasses}"
+		id={item.id}
+		name={item.name}
+		preview={item.preview}
+		{readonly}
+		onRemove={onFileRemove}
+		height={imageHeight}
+		width={imageWidth}
+		{imageClass}
+		onclick={() => onPreview?.(item)}
+	/>
+{:else if isPdfFile(item.attachment, item.uploadedFile)}
+	<ChatAttachmentsListItemThumbnailFile
+		class="flex-shrink-0 cursor-pointer {className} {scrollClasses}"
+		id={item.id}
+		name={item.name}
+		size={item.size}
+		{readonly}
+		onRemove={onFileRemove}
+		textContent={item.textContent}
+		attachment={item.attachment}
+		uploadedFile={item.uploadedFile}
+		onclick={() => onPreview?.(item)}
+	/>
+{:else}
+	<ChatAttachmentsListItemThumbnailFile
+		class="flex-shrink-0 cursor-pointer {className} {scrollClasses}"
+		id={item.id}
+		name={item.name}
+		size={item.size}
+		{readonly}
+		onRemove={onFileRemove}
+		textContent={item.textContent}
+		attachment={item.attachment}
+		uploadedFile={item.uploadedFile}
+		onclick={() => onPreview?.(item)}
+	/>
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpPrompt.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
similarity index 75%
rename from tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpPrompt.svelte
rename to tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
index 5fba2b3d19e..636e93f2211 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpPrompt.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
@@ -1,40 +1,41 @@
 <script lang="ts">
-	import { ChatMessageMcpPromptContent, ActionIconRemove } from '$lib/components/app';
+	import { ChatMessageMcpPromptContent, ActionIcon } from '$lib/components/app';
+	import { X } from '@lucide/svelte';
 	import type { DatabaseMessageExtraMcpPrompt } from '$lib/types';
 	import { McpPromptVariant } from '$lib/enums';
 
 	interface Props {
 		class?: string;
-		prompt: DatabaseMessageExtraMcpPrompt;
-		readonly?: boolean;
 		isLoading?: boolean;
 		loadError?: string;
 		onRemove?: () => void;
+		prompt: DatabaseMessageExtraMcpPrompt;
+		readonly?: boolean;
 	}
 
 	let {
 		class: className = '',
-		prompt,
-		readonly = false,
 		isLoading = false,
 		loadError,
-		onRemove
+		onRemove,
+		prompt,
+		readonly = false
 	}: Props = $props();
 </script>
 
 <div class="group relative {className}">
 	<ChatMessageMcpPromptContent
-		{prompt}
-		variant={McpPromptVariant.ATTACHMENT}
 		{isLoading}
 		{loadError}
+		{prompt}
+		variant={McpPromptVariant.ATTACHMENT}
 	/>
 
 	{#if !readonly && onRemove}
 		<div
 			class="absolute top-10 right-2 flex items-center justify-center opacity-0 transition-opacity group-hover:opacity-100"
 		>
-			<ActionIconRemove id={prompt.name} onRemove={() => onRemove?.()} />
+			<ActionIcon icon={X} tooltip="Remove" stopPropagationOnClick onclick={() => onRemove?.()} />
 		</div>
 	{/if}
 </div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpResource.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpResource.svelte
similarity index 81%
rename from tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpResource.svelte
rename to tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpResource.svelte
index 258fcac80e7..6e1f639fa2d 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpResource.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpResource.svelte
@@ -1,46 +1,47 @@
 <script lang="ts">
 	import { Loader2, AlertCircle } from '@lucide/svelte';
-	import { cn } from '$lib/components/ui/utils';
 	import { mcpStore } from '$lib/stores/mcp.svelte';
 	import type { MCPResourceAttachment } from '$lib/types';
 	import * as Tooltip from '$lib/components/ui/tooltip';
-	import { ActionIconRemove } from '$lib/components/app';
+	import { ActionIcon } from '$lib/components/app';
+	import { X } from '@lucide/svelte';
 	import { getResourceIcon, getResourceDisplayName } from '$lib/utils';
 
 	interface Props {
 		attachment: MCPResourceAttachment;
-		onRemove?: (attachmentId: string) => void;
-		onClick?: () => void;
 		class?: string;
+		onclick?: () => void;
+		onRemove?: (attachmentId: string) => void;
 	}
 
-	let { attachment, onRemove, onClick, class: className }: Props = $props();
-
-	function getStatusClass(attachment: MCPResourceAttachment): string {
-		if (attachment.error) return 'border-red-500/50 bg-red-500/10';
-		if (attachment.loading) return 'border-border/50 bg-muted/30';
-		return 'border-border/50 bg-muted/30';
-	}
+	let { attachment, class: className, onclick, onRemove }: Props = $props();
 
 	const ResourceIcon = $derived(
 		getResourceIcon(attachment.resource.mimeType, attachment.resource.uri)
 	);
 	const serverName = $derived(mcpStore.getServerDisplayName(attachment.resource.serverName));
 	const favicon = $derived(mcpStore.getServerFavicon(attachment.resource.serverName));
+
+	function getStatusClass(attachment: MCPResourceAttachment): string {
+		if (attachment.error) return 'border-red-500/50 bg-red-500/10';
+		if (attachment.loading) return 'border-border/50 bg-muted/30';
+
+		return 'border-border/50 bg-muted/30';
+	}
 </script>
 
 <Tooltip.Root>
 	<Tooltip.Trigger>
 		<button
-			type="button"
-			class={cn(
+			class={[
 				'flex flex-shrink-0 items-center gap-1.5 rounded-md border px-2 py-0.75 text-sm transition-colors',
 				getStatusClass(attachment),
-				onClick && 'cursor-pointer hover:bg-muted/50',
+				onclick && 'cursor-pointer hover:bg-muted/50',
 				className
-			)}
-			onclick={onClick}
-			disabled={!onClick}
+			]}
+			disabled={!onclick}
+			{onclick}
+			type="button"
 		>
 			{#if attachment.loading}
 				<Loader2 class="h-3 w-3 animate-spin text-muted-foreground" />
@@ -55,11 +56,13 @@
 			</span>
 
 			{#if onRemove}
-				<ActionIconRemove
+				<ActionIcon
 					class="-my-2 -mr-1.5 bg-transparent"
-					iconSize={2}
-					id={attachment.id}
-					{onRemove}
+					icon={X}
+					iconSize="h-2 w-2"
+					onclick={() => onRemove?.(attachment.id)}
+					stopPropagationOnClick
+					tooltip="Remove"
 				/>
 			{/if}
 		</button>
@@ -69,12 +72,12 @@
 		<div class="flex items-center gap-1 text-xs">
 			{#if favicon}
 				<img
-					src={favicon}
-					alt=""
+					alt={attachment.resource.serverName}
 					class="h-3 w-3 shrink-0 rounded-sm"
 					onerror={(e) => {
 						(e.currentTarget as HTMLImageElement).style.display = 'none';
 					}}
+					src={favicon}
 				/>
 			{/if}
 
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailFile.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailFile.svelte
new file mode 100644
index 00000000000..df49dd4673f
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailFile.svelte
@@ -0,0 +1,184 @@
+<script lang="ts">
+	import { X, Music, Video } from '@lucide/svelte';
+	import {
+		formatFileSize,
+		getFileTypeLabel,
+		getPreviewText,
+		isPdfFile,
+		isAudioFile,
+		isVideoFile,
+		isTextFile
+	} from '$lib/utils';
+	import { ActionIcon } from '$lib/components/app';
+	import { AttachmentType } from '$lib/enums';
+
+	interface Props {
+		attachment?: DatabaseMessageExtra;
+		class?: string;
+		id: string;
+		onclick?: (event: MouseEvent) => void;
+		onRemove?: (id: string) => void;
+		name: string;
+		readonly?: boolean;
+		size?: number;
+		textContent?: string;
+		// Either uploaded file or stored attachment
+		uploadedFile?: ChatUploadedFile;
+	}
+
+	let {
+		attachment,
+		class: className = '',
+		id,
+		onclick,
+		onRemove,
+		name,
+		readonly = false,
+		size,
+		textContent,
+		uploadedFile
+	}: Props = $props();
+
+	let isPdf = $derived(isPdfFile(attachment, uploadedFile));
+	let isAudio = $derived(isAudioFile(attachment, uploadedFile));
+	let isVideo = $derived(isVideoFile(attachment, uploadedFile));
+	let isPdfWithContent = $derived(isPdf && !!textContent);
+
+	let isText = $derived(isTextFile(attachment, uploadedFile));
+	let isTextWithContent = $derived(isText && !!textContent);
+
+	let fileTypeLabel = $derived.by(() => {
+		if (uploadedFile?.type) {
+			return getFileTypeLabel(uploadedFile.type);
+		}
+
+		if (attachment) {
+			if ('mimeType' in attachment && attachment.mimeType) {
+				return getFileTypeLabel(attachment.mimeType);
+			}
+
+			if (attachment.type) {
+				return getFileTypeLabel(attachment.type);
+			}
+		}
+
+		return getFileTypeLabel(name);
+	});
+
+	let pdfProcessingMode = $derived.by(() => {
+		if (attachment?.type === AttachmentType.PDF) {
+			const pdfAttachment = attachment as DatabaseMessageExtraPdfFile;
+
+			return pdfAttachment.processedAsImages ? 'Sent as Image' : 'Sent as Text';
+		}
+
+		return null;
+	});
+</script>
+
+{#snippet textPreview(content: string)}
+	<div class="relative">
+		<div
+			class="font-mono text-xs leading-relaxed break-words whitespace-pre-wrap text-muted-foreground {!readonly
+				? 'max-h-3rem line-height-1.2'
+				: ''}"
+		>
+			{getPreviewText(content)}
+		</div>
+
+		{#if content.length > 150}
+			<div
+				class="pointer-events-none absolute right-0 bottom-0 left-0 h-4 bg-gradient-to-t from-muted to-transparent {readonly
+					? 'h-6'
+					: ''}"
+			></div>
+		{/if}
+	</div>
+{/snippet}
+
+{#snippet removeButton()}
+	<div class="absolute top-2 right-2 opacity-0 transition-opacity group-hover:opacity-100">
+		<ActionIcon icon={X} tooltip="Remove" stopPropagationOnClick onclick={() => onRemove?.(id)} />
+	</div>
+{/snippet}
+
+{#snippet fileIcon()}
+	<div
+		class="flex h-8 w-8 items-center justify-center rounded bg-primary/10 text-xs font-medium text-primary"
+	>
+		{#if isAudio}
+			<Music class="h-4 w-4 text-white/70" />
+		{:else if isVideo}
+			<Video class="h-4 w-4 text-white/70" />
+		{:else}
+			{fileTypeLabel}
+		{/if}
+	</div>
+{/snippet}
+
+{#snippet info(text: string | undefined)}
+	{#if text}
+		<span class="text-xs text-muted-foreground">{text}</span>
+	{/if}
+{/snippet}
+
+{#if isTextWithContent || isPdfWithContent}
+	<button
+		aria-label={readonly ? `Preview ${name}` : undefined}
+		class="rounded-lg border border-border bg-muted p-3 {className} cursor-pointer {readonly
+			? 'w-full max-w-2xl transition-shadow hover:shadow-md'
+			: `group relative text-left ${textContent ? 'max-h-24 max-w-72' : 'max-w-36'}`} overflow-hidden"
+		{onclick}
+		type="button"
+	>
+		{#if !readonly}
+			{@render removeButton()}
+		{/if}
+
+		<div class={[!readonly && 'pr-8', 'overflow-hidden']}>
+			{#if readonly}
+				<div class="flex items-start gap-3">
+					<div class="flex min-w-0 flex-1 flex-col items-start text-left">
+						<span class="w-full truncate text-sm font-medium text-foreground">{name}</span>
+
+						{@render info(pdfProcessingMode || (size ? formatFileSize(size) : undefined))}
+
+						{#if textContent}
+							{@render textPreview(textContent)}
+						{/if}
+					</div>
+				</div>
+			{:else}
+				<span class="mb-3 block truncate text-sm font-medium text-foreground">{name}</span>
+
+				{#if textContent}
+					{@render textPreview(textContent)}
+				{/if}
+			{/if}
+		</div>
+	</button>
+{:else}
+	<button
+		class="group flex items-center gap-3 rounded-lg border border-border bg-muted p-3 {className} relative"
+		{onclick}
+		type="button"
+	>
+		{@render fileIcon()}
+
+		<div class="flex flex-col items-start gap-0.5">
+			<span
+				class="max-w-24 truncate text-sm font-medium text-foreground {readonly
+					? ''
+					: 'group-hover:pr-6'} md:max-w-32"
+			>
+				{name}
+			</span>
+
+			{@render info(pdfProcessingMode || (size ? formatFileSize(size) : undefined))}
+		</div>
+
+		{#if !readonly}
+			{@render removeButton()}
+		{/if}
+	</button>
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailImage.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailImage.svelte
similarity index 61%
rename from tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailImage.svelte
rename to tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailImage.svelte
index d0419db30c4..b78a6591619 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailImage.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailImage.svelte
@@ -1,64 +1,65 @@
 <script lang="ts">
-	import { ActionIconRemove } from '$lib/components/app';
+	import { ActionIcon } from '$lib/components/app';
+	import { X } from '@lucide/svelte';
 
 	interface Props {
+		class?: string;
+		height?: string;
 		id: string;
+		imageClass?: string;
+		onclick?: (event?: MouseEvent) => void;
+		onRemove?: (id: string) => void;
 		name: string;
 		preview: string;
 		readonly?: boolean;
-		onRemove?: (id: string) => void;
-		onClick?: (event?: MouseEvent) => void;
-		class?: string;
-		// Customizable size props
 		width?: string;
-		height?: string;
-		imageClass?: string;
 	}
 
 	let {
+		class: className = '',
+		height = 'h-16',
 		id,
+		imageClass = '',
+		onclick,
+		onRemove,
 		name,
 		preview,
 		readonly = false,
-		onRemove,
-		onClick,
-		class: className = '',
-		// Default to small size for form previews
-		width = 'w-auto',
-		height = 'h-16',
-		imageClass = ''
+		width = 'w-auto'
 	}: Props = $props();
 </script>
 
+{#snippet image()}
+	<img src={preview} alt={name} class="{height} {width} cursor-pointer object-cover {imageClass}" />
+{/snippet}
+
 <div
 	class="group relative overflow-hidden rounded-lg bg-muted shadow-lg dark:border dark:border-muted {className}"
 >
-	{#if onClick}
+	{#if onclick}
 		<button
-			type="button"
-			class="block h-full w-full rounded-lg focus:ring-2 focus:ring-primary focus:ring-offset-2 focus:outline-none"
-			onclick={onClick}
 			aria-label="Preview {name}"
+			class="block h-full w-full rounded-lg focus:ring-2 focus:ring-primary focus:ring-offset-2 focus:outline-none"
+			{onclick}
+			type="button"
 		>
-			<img
-				src={preview}
-				alt={name}
-				class="{height} {width} cursor-pointer object-cover {imageClass}"
-			/>
+			{@render image()}
 		</button>
 	{:else}
-		<img
-			src={preview}
-			alt={name}
-			class="{height} {width} cursor-pointer object-cover {imageClass}"
-		/>
+		{@render image()}
 	{/if}
 
 	{#if !readonly}
 		<div
 			class="absolute top-1 right-1 flex items-center justify-center opacity-0 transition-opacity group-hover:opacity-100"
 		>
-			<ActionIconRemove {id} {onRemove} class="text-white" />
+			<ActionIcon
+				class="text-white"
+				icon={X}
+				onclick={() => onRemove?.(id)}
+				stopPropagationOnClick
+				tooltip="Remove"
+			/>
 		</div>
 	{/if}
 </div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview.svelte
new file mode 100644
index 00000000000..cba323f2c37
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview.svelte
@@ -0,0 +1,212 @@
+<script lang="ts">
+	import {
+		ChatAttachmentsPreviewCurrentItem,
+		ChatAttachmentsPreviewFileInfo,
+		ChatAttachmentsPreviewNavButtons,
+		ChatAttachmentsPreviewThumbnailStrip
+	} from '$lib/components/app';
+	import { modelsStore } from '$lib/stores/models.svelte';
+	import {
+		createBase64DataUrl,
+		formatFileSize,
+		getAttachmentDisplayItems,
+		getLanguageFromFilename,
+		isAudioFile,
+		isVideoFile,
+		isImageFile,
+		isMcpPrompt,
+		isMcpResource,
+		isPdfFile,
+		isTextFile
+	} from '$lib/utils';
+
+	interface PreviewItem {
+		id: string;
+		name: string;
+		size?: number;
+		preview?: string;
+		uploadedFile?: ChatUploadedFile;
+		attachment?: DatabaseMessageExtra;
+		textContent?: string;
+		isImage: boolean;
+		isAudio: boolean;
+		isVideo: boolean;
+	}
+
+	interface Props {
+		uploadedFiles?: ChatUploadedFile[];
+		attachments?: DatabaseMessageExtra[];
+		activeModelId?: string;
+		class?: string;
+		previewFocusIndex?: number;
+	}
+
+	let {
+		uploadedFiles = [],
+		attachments = [],
+		activeModelId,
+		class: className = '',
+		previewFocusIndex = 0
+	}: Props = $props();
+
+	let allItems = $derived(
+		getAttachmentDisplayItems({ uploadedFiles, attachments })
+			.filter((item) => !isMcpPrompt(item) && !isMcpResource(item))
+			.map(
+				(item): PreviewItem => ({
+					...item,
+					isImage: isImageFile(item.attachment, item.uploadedFile),
+					isAudio: isAudioFile(item.attachment, item.uploadedFile),
+					isVideo: isVideoFile(item.attachment, item.uploadedFile)
+				})
+			)
+	);
+
+	let currentIndex = $state(0);
+
+	$effect(() => {
+		if (previewFocusIndex >= 0 && previewFocusIndex < allItems.length) {
+			currentIndex = previewFocusIndex;
+		}
+	});
+
+	$effect(() => {
+		const handler = (e: Event) => {
+			const delta = (e as CustomEvent).detail;
+
+			if (delta < 0) {
+				currentIndex = currentIndex > 0 ? currentIndex - 1 : allItems.length - 1;
+			} else {
+				currentIndex = currentIndex < allItems.length - 1 ? currentIndex + 1 : 0;
+			}
+		};
+
+		document.addEventListener('chat-attachments-nav', handler);
+
+		return () => document.removeEventListener('chat-attachments-nav', handler);
+	});
+
+	$effect(() => {
+		const index = currentIndex;
+		setTimeout(() => {
+			const thumbnail = document.querySelector(`[data-thumbnail-index="${index}"]`);
+
+			thumbnail?.scrollIntoView({ behavior: 'smooth', inline: 'center', block: 'nearest' });
+		}, 0);
+	});
+
+	let currentItem = $derived(allItems[currentIndex] ?? null);
+	let displayName = $derived(
+		currentItem?.name ||
+			currentItem?.uploadedFile?.name ||
+			currentItem?.attachment?.name ||
+			'Unknown File'
+	);
+	let isAudio = $derived(
+		currentItem ? isAudioFile(currentItem.attachment, currentItem.uploadedFile) : false
+	);
+	let isVideo = $derived(
+		currentItem ? isVideoFile(currentItem.attachment, currentItem.uploadedFile) : false
+	);
+	let isImage = $derived(
+		currentItem ? isImageFile(currentItem.attachment, currentItem.uploadedFile) : false
+	);
+	let isPdf = $derived(
+		currentItem ? isPdfFile(currentItem.attachment, currentItem.uploadedFile) : false
+	);
+	let isText = $derived(
+		currentItem ? isTextFile(currentItem.attachment, currentItem.uploadedFile) : false
+	);
+
+	let displayPreview = $derived(
+		currentItem?.uploadedFile?.preview ||
+			(isImage && currentItem?.attachment && 'base64Url' in currentItem.attachment
+				? currentItem.attachment.base64Url
+				: currentItem?.preview)
+	);
+
+	let displayTextContent = $derived(
+		currentItem?.uploadedFile?.textContent ||
+			(currentItem?.attachment && 'content' in currentItem.attachment
+				? currentItem.attachment.content
+				: currentItem?.textContent)
+	);
+
+	let language = $derived(getLanguageFromFilename(displayName));
+
+	let fileSize = $derived(currentItem?.size ? formatFileSize(currentItem.size) : '');
+
+	let hasVisionModality = $derived(
+		currentItem && activeModelId ? modelsStore.modelSupportsVision(activeModelId) : false
+	);
+
+	let audioSrc = $derived(
+		isAudio && currentItem
+			? (currentItem.uploadedFile?.preview ??
+					(currentItem.attachment &&
+					'mimeType' in currentItem.attachment &&
+					'base64Data' in currentItem.attachment
+						? createBase64DataUrl(
+								currentItem.attachment.mimeType,
+								currentItem.attachment.base64Data
+							)
+						: null))
+			: null
+	);
+
+	let videoSrc = $derived(
+		isVideo && currentItem
+			? (currentItem.uploadedFile?.preview ??
+					(currentItem.attachment &&
+					'mimeType' in currentItem.attachment &&
+					'base64Data' in currentItem.attachment
+						? createBase64DataUrl(
+								currentItem.attachment.mimeType,
+								currentItem.attachment.base64Data
+							)
+						: null))
+			: null
+	);
+
+	export function prev() {
+		currentIndex = currentIndex > 0 ? currentIndex - 1 : allItems.length - 1;
+	}
+
+	export function next() {
+		currentIndex = currentIndex < allItems.length - 1 ? currentIndex + 1 : 0;
+	}
+
+	function onNavigate(index: number) {
+		currentIndex = index;
+	}
+</script>
+
+<div class="{className} flex flex-col text-white">
+	<div class="relative flex min-h-0 flex-1 items-center justify-center overflow-hidden">
+		<ChatAttachmentsPreviewNavButtons onPrev={prev} onNext={next} show={allItems.length > 1} />
+
+		<div class="flex h-full w-full flex-col items-center justify-start overflow-auto py-4">
+			{#if currentItem}
+				<ChatAttachmentsPreviewFileInfo {displayName} {fileSize} />
+
+				<ChatAttachmentsPreviewCurrentItem
+					{currentItem}
+					{isImage}
+					{isAudio}
+					{isVideo}
+					{isPdf}
+					{isText}
+					{displayPreview}
+					{displayTextContent}
+					{audioSrc}
+					{videoSrc}
+					{language}
+					{hasVisionModality}
+					{activeModelId}
+				/>
+			{/if}
+
+			<ChatAttachmentsPreviewThumbnailStrip items={allItems} {currentIndex} {onNavigate} />
+		</div>
+	</div>
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItem.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItem.svelte
new file mode 100644
index 00000000000..30e84812aaa
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItem.svelte
@@ -0,0 +1,74 @@
+<script lang="ts">
+	import type { ChatAttachmentDisplayItem } from '$lib/types';
+	import { Image, Music, Video, FileText, FileIcon } from '@lucide/svelte';
+	import ChatAttachmentsPreviewCurrentItemPdf from './ChatAttachmentsPreviewCurrentItemPdf.svelte';
+	import ChatAttachmentsPreviewCurrentItemImage from './ChatAttachmentsPreviewCurrentItemImage.svelte';
+	import ChatAttachmentsPreviewCurrentItemAudio from './ChatAttachmentsPreviewCurrentItemAudio.svelte';
+	import ChatAttachmentsPreviewCurrentItemVideo from './ChatAttachmentsPreviewCurrentItemVideo.svelte';
+	import ChatAttachmentsPreviewCurrentItemText from './ChatAttachmentsPreviewCurrentItemText.svelte';
+	import ChatAttachmentsPreviewCurrentItemUnavailable from './ChatAttachmentsPreviewCurrentItemUnavailable.svelte';
+
+	interface Props {
+		currentItem: ChatAttachmentDisplayItem | null;
+		isImage: boolean;
+		isAudio: boolean;
+		isVideo: boolean;
+		isPdf: boolean;
+		isText: boolean;
+		displayPreview: string | undefined;
+		displayTextContent: string | undefined;
+		audioSrc: string | null;
+		videoSrc: string | null;
+		language: string;
+		hasVisionModality: boolean;
+		activeModelId?: string;
+	}
+
+	let {
+		currentItem,
+		isImage,
+		isAudio,
+		isVideo,
+		isPdf,
+		isText,
+		displayPreview,
+		displayTextContent,
+		audioSrc,
+		videoSrc,
+		language,
+		hasVisionModality,
+		activeModelId
+	}: Props = $props();
+
+	let IconComponent = $derived(
+		isImage ? Image : isText || isPdf ? FileText : isAudio ? Music : isVideo ? Video : FileIcon
+	);
+
+	let isUnavailable = $derived(
+		!isPdf && !isImage && !(isText && displayTextContent) && !isAudio && !isVideo
+	);
+</script>
+
+{#if currentItem}
+	{#key currentItem.id}
+		{#if isPdf}
+			<ChatAttachmentsPreviewCurrentItemPdf
+				{currentItem}
+				displayName={currentItem.name}
+				{displayTextContent}
+				{hasVisionModality}
+				{activeModelId}
+			/>
+		{:else if isImage}
+			<ChatAttachmentsPreviewCurrentItemImage {currentItem} {displayPreview} />
+		{:else if isText && displayTextContent}
+			<ChatAttachmentsPreviewCurrentItemText {displayTextContent} {language} />
+		{:else if isAudio}
+			<ChatAttachmentsPreviewCurrentItemAudio {currentItem} {audioSrc} />
+		{:else if isVideo}
+			<ChatAttachmentsPreviewCurrentItemVideo {currentItem} {videoSrc} />
+		{:else if isUnavailable}
+			<ChatAttachmentsPreviewCurrentItemUnavailable {IconComponent} />
+		{/if}
+	{/key}
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemAudio.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemAudio.svelte
new file mode 100644
index 00000000000..06e1f5928c5
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemAudio.svelte
@@ -0,0 +1,26 @@
+<script lang="ts">
+	import { Music } from '@lucide/svelte';
+
+	interface Props {
+		currentItem: { name?: string } | null;
+		audioSrc: string | null;
+	}
+
+	let { currentItem, audioSrc }: Props = $props();
+</script>
+
+<div class="flex flex-1 items-center justify-center p-8">
+	<div class="w-full max-w-md text-center">
+		<Music class="mx-auto mb-4 h-16 w-16 text-white/50" />
+
+		{#if audioSrc}
+			<audio controls class="mb-4 w-full" src={audioSrc}>
+				Your browser does not support the audio element.
+			</audio>
+		{:else}
+			<p class="mb-4 text-white/70">Audio preview not available</p>
+		{/if}
+
+		<p class="text-sm text-white/50">{currentItem?.name || 'Audio'}</p>
+	</div>
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemImage.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemImage.svelte
new file mode 100644
index 00000000000..070ff823011
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemImage.svelte
@@ -0,0 +1,18 @@
+<script lang="ts">
+	interface Props {
+		currentItem: { name?: string } | null;
+		displayPreview: string | undefined;
+	}
+
+	let { currentItem, displayPreview }: Props = $props();
+</script>
+
+{#if displayPreview}
+	<div class="flex flex-1 items-center justify-center">
+		<img
+			src={displayPreview}
+			alt={currentItem?.name || 'preview'}
+			class="max-h-[80vh] max-w-[80vw] rounded-lg object-contain shadow-lg"
+		/>
+	</div>
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemPdf.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemPdf.svelte
new file mode 100644
index 00000000000..750532a6266
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemPdf.svelte
@@ -0,0 +1,174 @@
+<script lang="ts">
+	import type { ChatAttachmentDisplayItem } from '$lib/types';
+	import { FileText, Eye, Info } from '@lucide/svelte';
+	import { Button } from '$lib/components/ui/button';
+	import * as Alert from '$lib/components/ui/alert';
+	import { SyntaxHighlightedCode } from '$lib/components/app';
+	import { getLanguageFromFilename } from '$lib/utils';
+	import { convertPDFToImage } from '$lib/utils/browser-only';
+	import { PdfViewMode } from '$lib/enums';
+
+	interface Props {
+		currentItem: ChatAttachmentDisplayItem | null;
+		displayName: string;
+		displayTextContent: string | undefined;
+		hasVisionModality: boolean;
+		activeModelId?: string;
+	}
+
+	let { currentItem, displayName, displayTextContent, hasVisionModality, activeModelId }: Props =
+		$props();
+
+	let pdfViewMode = $state<PdfViewMode>(PdfViewMode.PAGES);
+	let pdfImages = $state<string[]>([]);
+	let pdfImagesLoading = $state(false);
+	let pdfImagesError = $state<string | null>(null);
+
+	let language = $derived(getLanguageFromFilename(displayName));
+
+	async function loadPdfImages() {
+		if (pdfImages.length > 0 || pdfImagesLoading || !currentItem) return;
+
+		pdfImagesLoading = true;
+		pdfImagesError = null;
+
+		try {
+			let file: File | null = null;
+
+			if (currentItem.uploadedFile?.file) {
+				file = currentItem.uploadedFile.file;
+			} else if (currentItem.attachment) {
+				// Check if we have pre-processed images
+				if (
+					'images' in currentItem.attachment &&
+					currentItem.attachment.images &&
+					Array.isArray(currentItem.attachment.images) &&
+					currentItem.attachment.images.length > 0
+				) {
+					pdfImages = currentItem.attachment.images;
+					return;
+				}
+
+				// Convert base64 back to File for processing
+				if ('base64Data' in currentItem.attachment && currentItem.attachment.base64Data) {
+					const base64Data = currentItem.attachment.base64Data;
+					const byteCharacters = atob(base64Data);
+					const byteNumbers = new Array(byteCharacters.length);
+					for (let i = 0; i < byteCharacters.length; i++) {
+						byteNumbers[i] = byteCharacters.charCodeAt(i);
+					}
+					const byteArray = new Uint8Array(byteNumbers);
+					file = new File([byteArray], displayName, { type: 'application/pdf' });
+				}
+			}
+
+			if (file) {
+				pdfImages = await convertPDFToImage(file);
+			} else {
+				throw new Error('No PDF file available for conversion');
+			}
+		} catch (error) {
+			pdfImagesError = error instanceof Error ? error.message : 'Failed to load PDF images';
+		} finally {
+			pdfImagesLoading = false;
+		}
+	}
+
+	$effect(() => {
+		if (pdfViewMode === PdfViewMode.PAGES) {
+			loadPdfImages();
+		}
+	});
+</script>
+
+<div class="mb-4 flex items-center justify-end gap-2">
+	<Button
+		variant={pdfViewMode === PdfViewMode.TEXT ? 'default' : 'outline'}
+		size="sm"
+		onclick={() => (pdfViewMode = PdfViewMode.TEXT)}
+		disabled={pdfImagesLoading}
+	>
+		<FileText class="mr-1 h-4 w-4" />
+		Text
+	</Button>
+
+	<Button
+		variant={pdfViewMode === PdfViewMode.PAGES ? 'default' : 'outline'}
+		size="sm"
+		onclick={() => (pdfViewMode = PdfViewMode.PAGES)}
+		disabled={pdfImagesLoading}
+	>
+		{#if pdfImagesLoading}
+			<div
+				class="mr-1 h-4 w-4 animate-spin rounded-full border-2 border-current border-t-transparent"
+			></div>
+		{:else}
+			<Eye class="mr-1 h-4 w-4" />
+		{/if}
+		Pages
+	</Button>
+</div>
+
+{#if !hasVisionModality && activeModelId && currentItem}
+	<Alert.Root class="mb-4 max-w-4xl">
+		<Info class="h-4 w-4" />
+		<Alert.Title>Preview only</Alert.Title>
+		<Alert.Description>
+			<span class="inline-flex">
+				The selected model does not support vision. Only the extracted
+				<!-- svelte-ignore a11y_click_events_have_key_events -->
+				<!-- svelte-ignore a11y_no_static_element_interactions -->
+				<span
+					class="mx-1 cursor-pointer underline"
+					onclick={() => (pdfViewMode = PdfViewMode.TEXT)}
+				>
+					text
+				</span>
+				will be sent to the model.
+			</span>
+		</Alert.Description>
+	</Alert.Root>
+{/if}
+
+{#if pdfImagesLoading}
+	<div class="flex flex-1 items-center justify-center p-8">
+		<div class="text-center">
+			<div
+				class="mx-auto mb-4 h-8 w-8 animate-spin rounded-full border-4 border-white border-t-transparent"
+			></div>
+			<p class="text-white/70">Converting PDF to images...</p>
+		</div>
+	</div>
+{:else if pdfImagesError}
+	<div class="flex flex-1 items-center justify-center p-8">
+		<div class="text-center">
+			<FileText class="mx-auto mb-4 h-16 w-16 text-white/50" />
+			<p class="mb-4 text-white/70">Failed to load PDF images</p>
+			<p class="text-sm text-white/50">{pdfImagesError}</p>
+		</div>
+	</div>
+{:else if pdfImages.length > 0}
+	{#each pdfImages as image, index (image)}
+		<p class="mb-2 text-sm text-white/50">Page {index + 1}</p>
+		<img src={image} alt="PDF Page {index + 1}" class="mx-auto max-w-[85vw] rounded-lg shadow-lg" />
+		<div class="h-4"></div>
+	{/each}
+{:else}
+	<div class="flex flex-1 items-center justify-center p-8">
+		<div class="text-center">
+			<FileText class="mx-auto mb-4 h-16 w-16 text-white/50" />
+			<p class="text-white/70">No PDF pages available</p>
+		</div>
+	</div>
+{/if}
+
+{#if pdfViewMode === PdfViewMode.TEXT && displayTextContent}
+	<div class="px-4 pb-4">
+		<SyntaxHighlightedCode
+			class="max-w-4xl"
+			code={displayTextContent}
+			{language}
+			maxHeight="none"
+		/>
+	</div>
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemText.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemText.svelte
new file mode 100644
index 00000000000..5977523acc0
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemText.svelte
@@ -0,0 +1,21 @@
+<script lang="ts">
+	import { SyntaxHighlightedCode } from '$lib/components/app';
+
+	interface Props {
+		displayTextContent: string | undefined;
+		language: string;
+	}
+
+	let { displayTextContent, language }: Props = $props();
+</script>
+
+{#if displayTextContent}
+	<div class="px-4 pb-4">
+		<SyntaxHighlightedCode
+			class="max-w-4xl"
+			code={displayTextContent}
+			{language}
+			maxHeight="none"
+		/>
+	</div>
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemUnavailable.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemUnavailable.svelte
new file mode 100644
index 00000000000..d3002a93916
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemUnavailable.svelte
@@ -0,0 +1,17 @@
+<script lang="ts">
+	import type { Component } from 'svelte';
+
+	interface Props {
+		IconComponent: Component;
+	}
+
+	let { IconComponent }: Props = $props();
+</script>
+
+<div class="flex flex-1 items-center justify-center p-8">
+	<div class="text-center">
+		<IconComponent class="mx-auto mb-4 h-16 w-16 text-white/50" />
+
+		<p class="text-white/70">Preview not available for this file type</p>
+	</div>
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemVideo.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemVideo.svelte
new file mode 100644
index 00000000000..4ebbd592280
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItemVideo.svelte
@@ -0,0 +1,26 @@
+<script lang="ts">
+	import { Video } from '@lucide/svelte';
+
+	interface Props {
+		currentItem: { name?: string } | null;
+		videoSrc: string | null;
+	}
+
+	let { currentItem, videoSrc }: Props = $props();
+</script>
+
+<div class="flex flex-1 items-center justify-center p-8">
+	<div class="w-full max-w-md text-center">
+		<Video class="mx-auto mb-4 h-16 w-16 text-white/50" />
+
+		{#if videoSrc}
+			<video controls class="mb-4 w-full" src={videoSrc}>
+				Your browser does not support the video element.
+			</video>
+		{:else}
+			<p class="mb-4 text-white/70">Video preview not available</p>
+		{/if}
+
+		<p class="text-sm text-white/50">{currentItem?.name || 'Video'}</p>
+	</div>
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewFileInfo.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewFileInfo.svelte
new file mode 100644
index 00000000000..d27d54a4bae
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewFileInfo.svelte
@@ -0,0 +1,16 @@
+<script lang="ts">
+	interface Props {
+		displayName: string;
+		fileSize: string;
+	}
+
+	let { displayName, fileSize }: Props = $props();
+</script>
+
+<div class="sticky top-0 z-[20] mb-4 rounded-lg bg-black/5 px-4 py-2 text-center backdrop-blur-md">
+	<p class="font-medium text-white">{displayName}</p>
+
+	{#if fileSize}
+		<p class="text-xs text-white/60">{fileSize}</p>
+	{/if}
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewNavButtons.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewNavButtons.svelte
new file mode 100644
index 00000000000..a57e3145a9d
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewNavButtons.svelte
@@ -0,0 +1,34 @@
+<script lang="ts">
+	import { ChevronLeft, ChevronRight } from '@lucide/svelte';
+	import { Button } from '$lib/components/ui/button';
+
+	interface Props {
+		onPrev: () => void;
+		onNext: () => void;
+		show: boolean;
+	}
+
+	let { onPrev, onNext, show }: Props = $props();
+</script>
+
+{#if show}
+	<Button
+		variant="secondary"
+		size="icon"
+		class="absolute top-1/2 left-4 z-10 h-8 w-8 -translate-y-1/2 rounded-full bg-background/5 p-0 text-white!"
+		onclick={onPrev}
+		aria-label="Previous"
+	>
+		<ChevronLeft class="size-4" />
+	</Button>
+
+	<Button
+		variant="secondary"
+		size="icon"
+		class="absolute top-1/2 right-4 z-10 h-8 w-8 -translate-y-1/2 rounded-full bg-background/5 p-0 text-white!"
+		onclick={onNext}
+		aria-label="Next"
+	>
+		<ChevronRight class="size-4" />
+	</Button>
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewThumbnailStrip.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewThumbnailStrip.svelte
new file mode 100644
index 00000000000..3f19ee39c10
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewThumbnailStrip.svelte
@@ -0,0 +1,66 @@
+<script lang="ts">
+	import { Music, Video, FileText } from '@lucide/svelte';
+	import { HorizontalScrollCarousel } from '$lib/components/app/misc';
+
+	interface PreviewItem {
+		id: string;
+		name: string;
+		isImage: boolean;
+		isAudio: boolean;
+		isVideo: boolean;
+		preview?: string;
+	}
+
+	interface Props {
+		items: PreviewItem[];
+		currentIndex: number;
+		onNavigate: (index: number) => void;
+	}
+
+	let { items, currentIndex, onNavigate }: Props = $props();
+
+	function getFileExtension(name: string): string {
+		const parts = name.split('.');
+		if (parts.length > 1) {
+			return parts.pop()?.toUpperCase() ?? '';
+		}
+		return '';
+	}
+</script>
+
+{#if items.length > 1}
+	<div class="sticky bottom-0 z-10 mt-4 flex-shrink-0">
+		<HorizontalScrollCarousel class="max-w-full">
+			{#each items as item, index (item.id)}
+				<button
+					data-thumbnail-index={index}
+					class={[
+						'relative flex-shrink-0 cursor-pointer overflow-hidden rounded border-2 bg-black/80 backdrop-blur-sm transition-all hover:opacity-90',
+						index === currentIndex ? 'border-white' : 'border-transparent opacity-60',
+						'[&:not(:first-child)]:last:mr-4 [&:not(:last-child)]:first:ml-4'
+					]}
+					onclick={() => onNavigate(index)}
+					aria-label={`Go to ${item.name}`}
+				>
+					{#if item.isImage && item.preview}
+						<img src={item.preview} alt={item.name} class="h-12 w-12 object-cover" />
+					{:else}
+						<div
+							class="bg-foreground-muted/50 flex h-12 w-12 flex-col items-center justify-center gap-0.5 py-1"
+						>
+							{#if item.isAudio}
+								<Music class="h-4 w-4 text-white/70" />
+							{:else if item.isVideo}
+								<Video class="h-4 w-4 text-white/70" />
+							{:else}
+								<FileText class="h-4 w-4 text-white/70" />
+							{/if}
+
+							<span class="font-mono text-[9px] text-white/60">{getFileExtension(item.name)}</span>
+						</div>
+					{/if}
+				</button>
+			{/each}
+		</HorizontalScrollCarousel>
+	</div>
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
similarity index 87%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
index 4a1ab29b720..46ac8233495 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
@@ -1,14 +1,13 @@
 <script lang="ts">
 	import {
 		ChatAttachmentsList,
-		ChatAttachmentMcpResources,
 		ChatFormActions,
 		ChatFormFileInputInvisible,
-		ChatFormPromptPicker,
-		ChatFormResourcePicker,
-		ChatFormTextarea
+		ChatFormMcpResourcesList,
+		ChatFormPickers,
+		ChatFormTextarea,
+		DialogMcpResourcesBrowser
 	} from '$lib/components/app';
-	import { DialogMcpResources } from '$lib/components/app/dialogs';
 	import {
 		CLIPBOARD_CONTENT_QUOTE_PREFIX,
 		INPUT_CLASSES,
@@ -54,6 +53,8 @@
 		isLoading?: boolean;
 		placeholder?: string;
 		showMcpPromptButton?: boolean;
+		showAddButton?: boolean;
+		showModelSelector?: boolean;
 
 		// Event Handlers
 		onAttachmentRemove?: (index: number) => void;
@@ -73,6 +74,8 @@
 		isLoading = false,
 		placeholder = 'Type a message...',
 		showMcpPromptButton = false,
+		showAddButton = true,
+		showModelSelector = true,
 		uploadedFiles = $bindable([]),
 		value = $bindable(''),
 		onAttachmentRemove,
@@ -85,31 +88,21 @@
 		onValueChange
 	}: Props = $props();
 
-	/**
-	 *
-	 *
-	 * STATE
-	 *
-	 *
-	 */
-
 	// Component References
 	let audioRecorder: AudioRecorder | undefined;
 	let chatFormActionsRef: ChatFormActions | undefined = $state(undefined);
 	let fileInputRef: ChatFormFileInputInvisible | undefined = $state(undefined);
-	let promptPickerRef: ChatFormPromptPicker | undefined = $state(undefined);
-	let resourcePickerRef: ChatFormResourcePicker | undefined = $state(undefined);
+	let pickersRef: { handleKeydown: (event: KeyboardEvent) => boolean } | undefined =
+		$state(undefined);
 	let textareaRef: ChatFormTextarea | undefined = $state(undefined);
 
 	// Audio Recording State
 	let isRecording = $state(false);
 	let recordingSupported = $state(false);
 
-	// Prompt Picker State
+	// Picker State
 	let isPromptPickerOpen = $state(false);
 	let promptSearchQuery = $state('');
-
-	// Inline Resource Picker State (triggered by @)
 	let isInlineResourcePickerOpen = $state(false);
 	let resourceSearchQuery = $state('');
 
@@ -117,22 +110,12 @@
 	let isResourceDialogOpen = $state(false);
 	let preSelectedResourceUri = $state<string | undefined>(undefined);
 
-	/**
-	 *
-	 *
-	 * DERIVED STATE
-	 *
-	 *
-	 */
-
-	// Configuration
 	let currentConfig = $derived(config());
 	let pasteLongTextToFileLength = $derived.by(() => {
 		const n = Number(currentConfig.pasteLongTextToFileLen);
 		return Number.isNaN(n) ? Number(SETTING_CONFIG_DEFAULT.pasteLongTextToFileLen) : n;
 	});
 
-	// Model Selection Logic
 	let isRouter = $derived(isRouterMode());
 	let conversationModel = $derived(
 		chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
@@ -158,7 +141,6 @@
 		return null;
 	});
 
-	// Form Validation State
 	let hasModelSelected = $derived(!isRouter || !!conversationModel || !!selectedModelId());
 	let hasLoadingAttachments = $derived(uploadedFiles.some((f) => f.isLoading));
 	let hasAttachments = $derived(
@@ -166,27 +148,11 @@
 	);
 	let canSubmit = $derived(value.trim().length > 0 || hasAttachments);
 
-	/**
-	 *
-	 *
-	 * LIFECYCLE
-	 *
-	 *
-	 */
-
 	onMount(() => {
 		recordingSupported = isAudioRecordingSupported();
 		audioRecorder = new AudioRecorder();
 	});
 
-	/**
-	 *
-	 *
-	 * PUBLIC API
-	 *
-	 *
-	 */
-
 	export function focus() {
 		textareaRef?.focus();
 	}
@@ -199,10 +165,6 @@
 		chatFormActionsRef?.openModelSelector();
 	}
 
-	/**
-	 * Check if a model is selected, open selector if not
-	 * @returns true if model is selected, false otherwise
-	 */
 	export function checkModelSelected(): boolean {
 		if (!hasModelSelected) {
 			chatFormActionsRef?.openModelSelector();
@@ -211,14 +173,6 @@
 		return true;
 	}
 
-	/**
-	 *
-	 *
-	 * EVENT HANDLERS - File Management
-	 *
-	 *
-	 */
-
 	function handleFileSelect(files: File[]) {
 		onFilesAdd?.(files);
 	}
@@ -238,14 +192,6 @@
 		}
 	}
 
-	/**
-	 *
-	 *
-	 * EVENT HANDLERS - Input & Keyboard
-	 *
-	 *
-	 */
-
 	function handleInput() {
 		const perChatOverrides = conversationsStore.getAllMcpServerOverrides();
 		const hasServers = mcpStore.hasEnabledServers(perChatOverrides);
@@ -273,11 +219,7 @@
 	}
 
 	function handleKeydown(event: KeyboardEvent) {
-		if (isPromptPickerOpen && promptPickerRef?.handleKeydown(event)) {
-			return;
-		}
-
-		if (isInlineResourcePickerOpen && resourcePickerRef?.handleKeydown(event)) {
+		if (pickersRef?.handleKeydown(event)) {
 			return;
 		}
 
@@ -300,7 +242,7 @@
 			if (sendOnEnter || isModifier) {
 				event.preventDefault();
 
-				if (!canSubmit || disabled || isLoading || hasLoadingAttachments) return;
+				if (!canSubmit || disabled || hasLoadingAttachments) return;
 
 				onSubmit?.();
 			}
@@ -388,14 +330,6 @@
 		}
 	}
 
-	/**
-	 *
-	 *
-	 * EVENT HANDLERS - Prompt Picker
-	 *
-	 *
-	 */
-
 	function handlePromptLoadStart(
 		placeholderId: string,
 		promptInfo: MCPPromptInfo,
@@ -474,14 +408,6 @@
 		textareaRef?.focus();
 	}
 
-	/**
-	 *
-	 *
-	 * EVENT HANDLERS - Inline Resource Picker
-	 *
-	 *
-	 */
-
 	function handleInlineResourcePickerClose() {
 		isInlineResourcePickerOpen = false;
 		resourceSearchQuery = '';
@@ -489,7 +415,6 @@
 	}
 
 	function handleInlineResourceSelect() {
-		// Clear the @query from input after resource is attached
 		if (value.startsWith(RESOURCE_TRIGGER_PREFIX)) {
 			value = '';
 			onValueChange?.('');
@@ -512,14 +437,6 @@
 		isResourceDialogOpen = true;
 	}
 
-	/**
-	 *
-	 *
-	 * EVENT HANDLERS - Audio Recording
-	 *
-	 *
-	 */
-
 	async function handleMicClick() {
 		if (!audioRecorder || !recordingSupported) {
 			console.warn('Audio recording not supported');
@@ -527,16 +444,15 @@
 		}
 
 		if (isRecording) {
+			isRecording = false;
 			try {
 				const audioBlob = await audioRecorder.stopRecording();
 				const wavBlob = await convertToWav(audioBlob);
 				const audioFile = createAudioFile(wavBlob);
 
 				onFilesAdd?.([audioFile]);
-				isRecording = false;
 			} catch (error) {
 				console.error('Failed to stop recording:', error);
-				isRecording = false;
 			}
 		} else {
 			try {
@@ -553,29 +469,27 @@
 
 <form
 	class="relative {className}"
-	onsubmit={(e) => {
-		e.preventDefault();
-		if (!canSubmit || disabled || isLoading || hasLoadingAttachments) return;
+	onsubmit={(event) => {
+		event.preventDefault();
+
+		if (!canSubmit || disabled || hasLoadingAttachments) return;
+
 		onSubmit?.();
 	}}
 >
-	<ChatFormPromptPicker
-		bind:this={promptPickerRef}
-		isOpen={isPromptPickerOpen}
-		searchQuery={promptSearchQuery}
-		onClose={handlePromptPickerClose}
+	<ChatFormPickers
+		bind:this={pickersRef}
+		{isPromptPickerOpen}
+		{promptSearchQuery}
+		{isInlineResourcePickerOpen}
+		{resourceSearchQuery}
+		onPromptPickerClose={handlePromptPickerClose}
+		onInlineResourcePickerClose={handleInlineResourcePickerClose}
+		onInlineResourceSelect={handleInlineResourceSelect}
 		onPromptLoadStart={handlePromptLoadStart}
 		onPromptLoadComplete={handlePromptLoadComplete}
 		onPromptLoadError={handlePromptLoadError}
-	/>
-
-	<ChatFormResourcePicker
-		bind:this={resourcePickerRef}
-		isOpen={isInlineResourcePickerOpen}
-		searchQuery={resourceSearchQuery}
-		onClose={handleInlineResourcePickerClose}
-		onResourceSelect={handleInlineResourceSelect}
-		onBrowse={handleBrowseResources}
+		onInlineResourceBrowse={handleBrowseResources}
 	/>
 
 	<div
@@ -612,7 +526,7 @@
 			/>
 
 			{#if mcpHasResourceAttachments()}
-				<ChatAttachmentMcpResources
+				<ChatFormMcpResourcesList
 					class="mb-3"
 					onResourceClick={(uri) => {
 						preSelectedResourceUri = uri;
@@ -625,10 +539,11 @@
 				class="px-3"
 				bind:this={chatFormActionsRef}
 				canSend={canSubmit}
-				hasText={value.trim().length > 0}
 				{disabled}
 				{isLoading}
 				{isRecording}
+				{showAddButton}
+				{showModelSelector}
 				{uploadedFiles}
 				onFileUpload={handleFileUpload}
 				onMicClick={handleMicClick}
@@ -641,7 +556,7 @@
 	</div>
 </form>
 
-<DialogMcpResources
+<DialogMcpResourcesBrowser
 	bind:open={isResourceDialogOpen}
 	preSelectedUri={preSelectedResourceUri}
 	onAttach={(resource: MCPResourceInfo) => {
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddButton.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddButton.svelte
new file mode 100644
index 00000000000..7175888aa26
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddButton.svelte
@@ -0,0 +1,33 @@
+<script lang="ts">
+	import { Plus } from '@lucide/svelte';
+	import { Button } from '$lib/components/ui/button';
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import { ATTACHMENT_TOOLTIP_TEXT } from '$lib/constants';
+
+	interface Props {
+		disabled?: boolean;
+		onclick?: (e: MouseEvent) => void;
+	}
+
+	let { disabled = false, onclick }: Props = $props();
+</script>
+
+<Tooltip.Root>
+	<Tooltip.Trigger class="w-full">
+		<Button
+			class="file-upload-button h-8 w-8 rounded-full p-0"
+			{disabled}
+			{onclick}
+			variant="secondary"
+			type="button"
+		>
+			<span class="sr-only">{ATTACHMENT_TOOLTIP_TEXT}</span>
+
+			<Plus class="h-4 w-4" />
+		</Button>
+	</Tooltip.Trigger>
+
+	<Tooltip.Content>
+		<p>{ATTACHMENT_TOOLTIP_TEXT}</p>
+	</Tooltip.Content>
+</Tooltip.Root>
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddDropdown.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddDropdown.svelte
new file mode 100644
index 00000000000..0a8260a2952
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddDropdown.svelte
@@ -0,0 +1,203 @@
+<script lang="ts">
+	import { Plus } from '@lucide/svelte';
+	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import { buttonVariants } from '$lib/components/ui/button';
+	import { cn } from '$lib/components/ui/utils';
+	import {
+		ATTACHMENT_FILE_ITEMS,
+		ATTACHMENT_EXTRA_ITEMS,
+		ATTACHMENT_MCP_ITEMS,
+		ATTACHMENT_TOOLTIP_TEXT,
+		TOOLTIP_DELAY_DURATION
+	} from '$lib/constants';
+	import { AttachmentMenuItemId } from '$lib/enums';
+	import {
+		ChatFormActionAddToolsSubmenu,
+		ChatFormActionAddMcpServersSubmenu
+	} from '$lib/components/app';
+
+	import { useAttachmentMenu } from '$lib/hooks/use-attachment-menu.svelte';
+
+	interface Props {
+		class?: string;
+		disabled?: boolean;
+		hasAudioModality?: boolean;
+		hasVideoModality?: boolean;
+		hasVisionModality?: boolean;
+		hasMcpPromptsSupport?: boolean;
+		hasMcpResourcesSupport?: boolean;
+		onFileUpload?: () => void;
+		onSystemPromptClick?: () => void;
+		onMcpPromptClick?: () => void;
+		onMcpSettingsClick?: () => void;
+		onMcpResourcesClick?: () => void;
+	}
+
+	let {
+		class: className = '',
+		disabled = false,
+		hasAudioModality = false,
+		hasVideoModality = false,
+		hasVisionModality = false,
+		hasMcpPromptsSupport = false,
+		hasMcpResourcesSupport = false,
+		onFileUpload,
+		onSystemPromptClick,
+		onMcpPromptClick,
+		onMcpSettingsClick,
+		onMcpResourcesClick
+	}: Props = $props();
+
+	let dropdownOpen = $state(false);
+
+	function handleMcpSettingsClick() {
+		dropdownOpen = false;
+		onMcpSettingsClick?.();
+	}
+
+	const attachmentMenu = useAttachmentMenu(
+		() => ({
+			hasVisionModality,
+			hasAudioModality,
+			hasVideoModality,
+			hasMcpPromptsSupport,
+			hasMcpResourcesSupport
+		}),
+		() => ({ onFileUpload, onSystemPromptClick, onMcpPromptClick, onMcpResourcesClick }),
+		() => {
+			dropdownOpen = false;
+		}
+	);
+</script>
+
+<div class="flex items-center gap-1 {className}">
+	<DropdownMenu.Root bind:open={dropdownOpen}>
+		<Tooltip.Root>
+			<Tooltip.Trigger>
+				{#snippet child({ props })}
+					<DropdownMenu.Trigger
+						{...props}
+						class={cn(
+							buttonVariants({ variant: 'secondary' }),
+							'file-upload-button h-8 w-8 cursor-pointer rounded-full p-0'
+						)}
+						{disabled}
+					>
+						<span class="sr-only">{ATTACHMENT_TOOLTIP_TEXT}</span>
+
+						<Plus class="h-4 w-4" />
+					</DropdownMenu.Trigger>
+				{/snippet}
+			</Tooltip.Trigger>
+
+			<Tooltip.Content>
+				<p>{ATTACHMENT_TOOLTIP_TEXT}</p>
+			</Tooltip.Content>
+		</Tooltip.Root>
+
+		<DropdownMenu.Content align="start" class="w-48">
+			{#each ATTACHMENT_FILE_ITEMS as item (item.id)}
+				{@const enabled = attachmentMenu.isItemEnabled(item.enabledWhen)}
+				{#if enabled}
+					<DropdownMenu.Item
+						class="{item.class ?? ''} flex cursor-pointer items-center gap-2"
+						onclick={() => attachmentMenu.callbacks[item.action]()}
+					>
+						<item.icon class="h-4 w-4" />
+
+						<span>{item.label}</span>
+					</DropdownMenu.Item>
+				{:else if item.disabledTooltip}
+					<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
+						<Tooltip.Trigger tabindex={-1}>
+							{#snippet child({ props })}
+								<div {...props} class="cursor-default">
+									<DropdownMenu.Item class="{item.class ?? ''} flex items-center gap-2" disabled>
+										<item.icon class="h-4 w-4" />
+
+										<span>{item.label}</span>
+									</DropdownMenu.Item>
+								</div>
+							{/snippet}
+						</Tooltip.Trigger>
+
+						<Tooltip.Content side="right">
+							<p>{item.disabledTooltip}</p>
+						</Tooltip.Content>
+					</Tooltip.Root>
+				{/if}
+			{/each}
+
+			{#if !attachmentMenu.isItemEnabled('hasVisionModality')}
+				<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
+					<Tooltip.Trigger>
+						{#snippet child({ props })}
+							<DropdownMenu.Item
+								{...props}
+								class="flex cursor-pointer items-center gap-2"
+								onclick={attachmentMenu.callbacks.onFileUpload}
+							>
+								{@const pdfItem = ATTACHMENT_FILE_ITEMS.find(
+									(i) => i.id === AttachmentMenuItemId.PDF
+								)}
+								{#if pdfItem}
+									<pdfItem.icon class="h-4 w-4" />
+
+									<span>{pdfItem.label}</span>
+								{/if}
+							</DropdownMenu.Item>
+						{/snippet}
+					</Tooltip.Trigger>
+
+					<Tooltip.Content side="right">
+						<p>PDFs will be converted to text. Image-based PDFs may not work properly.</p>
+					</Tooltip.Content>
+				</Tooltip.Root>
+			{/if}
+
+			<DropdownMenu.Separator />
+
+			{#each ATTACHMENT_EXTRA_ITEMS as item (item.id)}
+				{#if item.id === AttachmentMenuItemId.SYSTEM_MESSAGE}
+					<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
+						<Tooltip.Trigger>
+							{#snippet child({ props })}
+								<DropdownMenu.Item
+									{...props}
+									class="flex cursor-pointer items-center gap-2"
+									onclick={() => attachmentMenu.callbacks[item.action]()}
+								>
+									<item.icon class="h-4 w-4" />
+
+									<span>{item.label}</span>
+								</DropdownMenu.Item>
+							{/snippet}
+						</Tooltip.Trigger>
+
+						<Tooltip.Content side="right">
+							<p>{attachmentMenu.getSystemMessageTooltip()}</p>
+						</Tooltip.Content>
+					</Tooltip.Root>
+				{/if}
+			{/each}
+
+			<ChatFormActionAddToolsSubmenu />
+
+			<ChatFormActionAddMcpServersSubmenu onMcpSettingsClick={handleMcpSettingsClick} />
+
+			{#each ATTACHMENT_MCP_ITEMS as item (item.id)}
+				{#if attachmentMenu.isItemVisible(item.visibleWhen)}
+					<DropdownMenu.Item
+						class="flex cursor-pointer items-center gap-2"
+						onclick={() => attachmentMenu.callbacks[item.action]()}
+					>
+						<item.icon class="h-4 w-4" />
+
+						<span>{item.label}</span>
+					</DropdownMenu.Item>
+				{/if}
+			{/each}
+		</DropdownMenu.Content>
+	</DropdownMenu.Root>
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte
new file mode 100644
index 00000000000..dd357d6cd06
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte
@@ -0,0 +1,150 @@
+<script lang="ts">
+	import { Settings, Plus } from '@lucide/svelte';
+	import { Switch } from '$lib/components/ui/switch';
+	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
+	import { McpLogo, DropdownMenuSearchable, McpServerIdentity } from '$lib/components/app';
+	import { conversationsStore } from '$lib/stores/conversations.svelte';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { HealthCheckStatus } from '$lib/enums';
+	import type { MCPServerSettingsEntry } from '$lib/types';
+	import { goto } from '$app/navigation';
+	import { ROUTES } from '$lib/constants/routes';
+
+	interface Props {
+		onMcpSettingsClick?: () => void;
+	}
+
+	let { onMcpSettingsClick }: Props = $props();
+
+	let mcpSearchQuery = $state('');
+	let allMcpServers = $derived(mcpStore.getServersSorted());
+	let mcpServers = $derived(allMcpServers.filter((s) => s.enabled));
+	let hasMcpServers = $derived(mcpServers.length > 0);
+	// let hasAnyMcpServers = $derived(allMcpServers.length > 0);
+	let filteredMcpServers = $derived.by(() => {
+		const query = mcpSearchQuery.toLowerCase().trim();
+		if (!query) return mcpServers;
+		return mcpServers.filter((s) => {
+			const name = getServerLabel(s).toLowerCase();
+			const url = s.url.toLowerCase();
+			return name.includes(query) || url.includes(query);
+		});
+	});
+
+	function getServerLabel(server: MCPServerSettingsEntry): string {
+		return mcpStore.getServerLabel(server);
+	}
+
+	function isServerEnabledForChat(serverId: string): boolean {
+		return conversationsStore.isMcpServerEnabledForChat(serverId);
+	}
+
+	async function toggleServerForChat(serverId: string) {
+		await conversationsStore.toggleMcpServerForChat(serverId);
+	}
+
+	function handleMcpSubMenuOpen(open: boolean) {
+		if (open) {
+			mcpSearchQuery = '';
+			mcpStore.runHealthChecksForServers(allMcpServers);
+		}
+	}
+
+	function handleMcpSettingsClick() {
+		onMcpSettingsClick?.();
+
+		goto(`${hasMcpServers ? '' : '?add'}${ROUTES.MCP_SERVERS}`);
+	}
+</script>
+
+<DropdownMenu.Root>
+	<DropdownMenu.Sub onOpenChange={handleMcpSubMenuOpen}>
+		<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
+			<McpLogo class="h-4 w-4" />
+
+			<span>MCP Servers</span>
+		</DropdownMenu.SubTrigger>
+
+		<DropdownMenu.SubContent class="w-72 pt-0">
+			{#if hasMcpServers}
+				<DropdownMenuSearchable
+					placeholder="Search servers..."
+					bind:searchValue={mcpSearchQuery}
+					emptyMessage="No servers found"
+					isEmpty={filteredMcpServers.length === 0}
+				>
+					<div class="max-h-64 overflow-y-auto">
+						{#each filteredMcpServers as server (server.id)}
+							{@const healthState = mcpStore.getHealthCheckState(server.id)}
+							{@const hasError = healthState.status === HealthCheckStatus.ERROR}
+							{@const isEnabledForChat = isServerEnabledForChat(server.id)}
+							{@const displayName = getServerLabel(server)}
+							{@const faviconUrl = mcpStore.getServerFavicon(server.id)}
+
+							<button
+								type="button"
+								class="flex w-full items-center justify-between gap-2 rounded-sm px-2 py-2 text-left transition-colors hover:bg-accent disabled:cursor-not-allowed disabled:opacity-50"
+								onclick={() => !hasError && toggleServerForChat(server.id)}
+								disabled={hasError}
+							>
+								<div class="flex min-w-0 flex-1 items-center gap-2">
+									<div class="min-w-0 flex-1">
+										<McpServerIdentity
+											{displayName}
+											{faviconUrl}
+											iconClass="h-4 w-4"
+											iconRounded="rounded-sm"
+											showVersion={false}
+											nameClass="text-sm"
+										/>
+									</div>
+
+									{#if hasError}
+										<span
+											class="shrink-0 rounded bg-destructive/15 px-1.5 py-0.5 text-xs text-destructive"
+										>
+											Error
+										</span>
+									{/if}
+								</div>
+
+								<Switch
+									checked={isEnabledForChat}
+									disabled={hasError}
+									onclick={(e) => e.stopPropagation()}
+									onCheckedChange={() => toggleServerForChat(server.id)}
+								/>
+							</button>
+						{/each}
+					</div>
+
+					{#snippet footer()}
+						<DropdownMenu.Item
+							class="flex cursor-pointer items-center gap-2"
+							onclick={handleMcpSettingsClick}
+						>
+							<Settings class="h-4 w-4" />
+
+							<span>Manage MCP Servers</span>
+						</DropdownMenu.Item>
+					{/snippet}
+				</DropdownMenuSearchable>
+			{:else}
+				<div class="px-2 py-3 text-center text-sm text-muted-foreground">
+					No MCP servers configured
+				</div>
+
+				<DropdownMenu.Separator />
+
+				<DropdownMenu.Item
+					class="flex cursor-pointer items-center gap-2"
+					onclick={handleMcpSettingsClick}
+				>
+					<Plus class="h-4 w-4" />
+
+					<span>Add MCP Servers</span>
+				</DropdownMenu.Item>
+			{/if}
+		</DropdownMenu.SubContent>
+	</DropdownMenu.Sub>
+</DropdownMenu.Root>
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte
new file mode 100644
index 00000000000..5ca5973668c
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte
@@ -0,0 +1,190 @@
+<script lang="ts">
+	import type { Snippet } from 'svelte';
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import * as Sheet from '$lib/components/ui/sheet';
+	import { TOOLTIP_DELAY_DURATION } from '$lib/constants';
+	import {
+		ATTACHMENT_FILE_ITEMS,
+		ATTACHMENT_EXTRA_ITEMS,
+		ATTACHMENT_MCP_ITEMS
+	} from '$lib/constants/attachment-menu';
+	import { McpLogo } from '$lib/components/app';
+	import { useAttachmentMenu } from '$lib/hooks/use-attachment-menu.svelte';
+	import { AttachmentMenuItemId } from '$lib/enums';
+	import { PencilRuler } from '@lucide/svelte';
+	import { ROUTES, SETTINGS_SECTION_SLUGS } from '$lib/constants/routes';
+	import { RouterService } from '$lib/services/router.service';
+
+	interface Props {
+		class?: string;
+		disabled?: boolean;
+		hasAudioModality?: boolean;
+		hasVideoModality?: boolean;
+		hasVisionModality?: boolean;
+		hasMcpPromptsSupport?: boolean;
+		hasMcpResourcesSupport?: boolean;
+		onFileUpload?: () => void;
+		onSystemPromptClick?: () => void;
+		onMcpPromptClick?: () => void;
+		onMcpResourcesClick?: () => void;
+		trigger: Snippet<[{ disabled: boolean; onclick?: () => void }]>;
+	}
+
+	let {
+		class: className = '',
+		disabled = false,
+		hasAudioModality = false,
+		hasVisionModality = false,
+		hasVideoModality = false,
+		hasMcpPromptsSupport = false,
+		hasMcpResourcesSupport = false,
+		onFileUpload,
+		onSystemPromptClick,
+		onMcpPromptClick,
+		onMcpResourcesClick,
+		trigger
+	}: Props = $props();
+
+	let sheetOpen = $state(false);
+
+	const attachmentMenu = useAttachmentMenu(
+		() => ({
+			hasVisionModality,
+			hasAudioModality,
+			hasVideoModality,
+			hasMcpPromptsSupport,
+			hasMcpResourcesSupport
+		}),
+		() => ({ onFileUpload, onSystemPromptClick, onMcpPromptClick, onMcpResourcesClick }),
+		() => {
+			sheetOpen = false;
+		}
+	);
+
+	const sheetItemClass =
+		'flex w-full items-center gap-3 rounded-md px-3 py-2.5 text-left text-sm transition-colors hover:bg-accent active:bg-accent disabled:cursor-not-allowed disabled:opacity-50';
+</script>
+
+<div class="flex items-center gap-1 {className}">
+	<Sheet.Root bind:open={sheetOpen}>
+		{@render trigger({ disabled, onclick: () => (sheetOpen = true) })}
+		<!-- <ChatFormActionAddButton {disabled} onclick={() => (sheetOpen = true)} /> -->
+
+		<Sheet.Content side="bottom" class="max-h-[85vh] gap-0 overflow-y-auto">
+			<Sheet.Header>
+				<Sheet.Title>Add to chat</Sheet.Title>
+
+				<Sheet.Description class="sr-only">
+					Add files, system prompt or configure MCP servers
+				</Sheet.Description>
+			</Sheet.Header>
+
+			<div class="flex flex-col gap-1 px-1.5 pb-2">
+				{#each ATTACHMENT_FILE_ITEMS as item (item.id)}
+					{@const enabled = attachmentMenu.isItemEnabled(item.enabledWhen)}
+					{#if enabled}
+						<button
+							type="button"
+							class={sheetItemClass}
+							onclick={() => attachmentMenu.callbacks[item.action]()}
+						>
+							<item.icon class="h-4 w-4 shrink-0" />
+
+							<span>{item.label}</span>
+						</button>
+					{:else if item.disabledTooltip}
+						<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
+							<Tooltip.Trigger>
+								<button type="button" class={sheetItemClass} disabled>
+									<item.icon class="h-4 w-4 shrink-0" />
+
+									<span>{item.label}</span>
+								</button>
+							</Tooltip.Trigger>
+
+							<Tooltip.Content side="right">
+								<p>{item.disabledTooltip}</p>
+							</Tooltip.Content>
+						</Tooltip.Root>
+					{/if}
+				{/each}
+
+				{#if !attachmentMenu.isItemEnabled('hasVisionModality')}
+					{@const pdfItem = ATTACHMENT_FILE_ITEMS.find((i) => i.id === AttachmentMenuItemId.PDF)}
+					{#if pdfItem}
+						<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
+							<Tooltip.Trigger>
+								<button
+									type="button"
+									class={sheetItemClass}
+									onclick={() => attachmentMenu.callbacks[pdfItem.action]()}
+								>
+									<pdfItem.icon class="h-4 w-4 shrink-0" />
+
+									<span>{pdfItem.label}</span>
+								</button>
+							</Tooltip.Trigger>
+
+							<Tooltip.Content side="right">
+								<p>PDFs will be converted to text. Image-based PDFs may not work properly.</p>
+							</Tooltip.Content>
+						</Tooltip.Root>
+					{/if}
+				{/if}
+
+				{#each ATTACHMENT_EXTRA_ITEMS as item (item.id)}
+					{#if item.id === AttachmentMenuItemId.SYSTEM_MESSAGE}
+						<Tooltip.Root delayDuration={TOOLTIP_DELAY_DURATION}>
+							<Tooltip.Trigger>
+								<button
+									type="button"
+									class={sheetItemClass}
+									onclick={() => attachmentMenu.callbacks[item.action]()}
+								>
+									<item.icon class="h-4 w-4 shrink-0" />
+
+									<span>{item.label}</span>
+								</button>
+							</Tooltip.Trigger>
+
+							<Tooltip.Content side="right">
+								<p>{attachmentMenu.getSystemMessageTooltip()}</p>
+							</Tooltip.Content>
+						</Tooltip.Root>
+					{/if}
+				{/each}
+
+				<div class="my-2 border-t"></div>
+
+				<a href={ROUTES.MCP_SERVERS} class="flex items-center gap-3 px-3 py-2">
+					<McpLogo class="inline h-4 w-4" />
+
+					<span class="text-sm">MCP Servers</span>
+				</a>
+
+				<a
+					href={RouterService.settings(SETTINGS_SECTION_SLUGS.TOOLS)}
+					class="flex items-center gap-3 px-3 py-2"
+				>
+					<PencilRuler class="inline h-4 w-4" />
+
+					<span class="text-sm">Tools</span>
+				</a>
+
+				{#each ATTACHMENT_MCP_ITEMS as item (item.id)}
+					{#if attachmentMenu.isItemVisible(item.visibleWhen)}
+						<button
+							type="button"
+							class={sheetItemClass}
+							onclick={() => attachmentMenu.callbacks[item.action]()}
+						>
+							<item.icon class="h-4 w-4 shrink-0" />
+
+							<span>{item.label}</span>
+						</button>
+					{/if}
+				{/each}
+			</div>
+		</Sheet.Content>
+	</Sheet.Root>
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddToolsSubmenu.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddToolsSubmenu.svelte
new file mode 100644
index 00000000000..b11467da823
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddToolsSubmenu.svelte
@@ -0,0 +1,150 @@
+<script lang="ts">
+	import { PencilRuler, ChevronDown, ChevronRight, Loader2, Info } from '@lucide/svelte';
+	import { Checkbox } from '$lib/components/ui/checkbox';
+	import * as Collapsible from '$lib/components/ui/collapsible';
+	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import { toolsStore } from '$lib/stores/tools.svelte';
+	import { CLI_FLAGS } from '$lib/constants';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { useToolsPanel } from '$lib/hooks/use-tools-panel.svelte';
+
+	const toolsPanel = useToolsPanel();
+	const hasMcpServersAvailable = $derived(mcpStore.getServersSorted().length > 0);
+</script>
+
+<DropdownMenu.Sub onOpenChange={(open) => open && toolsPanel.handleOpen()}>
+	<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
+		<PencilRuler class="h-4 w-4" />
+
+		<span>Tools</span>
+	</DropdownMenu.SubTrigger>
+
+	<DropdownMenu.SubContent class="w-72 p-0">
+		{#if toolsPanel.totalToolCount === 0}
+			{#if toolsStore.loading}
+				<div class="px-3 py-4 text-center text-sm text-muted-foreground">
+					<Loader2 class="mx-auto mb-1 h-4 w-4 animate-spin" />
+
+					Loading tools...
+				</div>
+			{:else if toolsStore.isToolsEndpointUnreachable}
+				<div class="grid gap-2.5 px-3 py-4 text-sm text-muted-foreground">
+					<span class="flex gap-2">
+						<Info class="mt-0.5 h-4 w-4 shrink-0" />
+
+						<span>
+							Run llama-server with <code>{CLI_FLAGS.TOOLS}</code> flag to enable
+
+							<strong>Built-in Tools</strong>.
+						</span>
+					</span>
+
+					<span class="flex gap-2">
+						<Info class="mt-0.5 h-4 w-4 shrink-0" />
+
+						<span>
+							{hasMcpServersAvailable ? 'Enable' : 'Add'} MCP Server(s) to access
+
+							<strong>MCP Tools</strong>.
+						</span>
+					</span>
+				</div>
+			{:else if toolsStore.error}
+				<div class="px-3 py-4 text-center text-sm text-muted-foreground">Failed to load tools</div>
+			{:else if toolsPanel.noToolsInfoMessage}
+				<div class="flex gap-2 px-3 py-4 text-sm text-muted-foreground">
+					<Info class="mt-0.5 h-4 w-4 shrink-0" />
+
+					<span>{toolsPanel.noToolsInfoMessage}</span>
+				</div>
+			{:else}
+				<div class="px-3 py-4 text-center text-sm text-muted-foreground">No tools available</div>
+			{/if}
+		{:else}
+			<div class="max-h-80 overflow-y-auto p-2 pr-1">
+				{#each toolsPanel.activeGroups as group (group.label)}
+					{@const isExpanded = toolsPanel.expandedGroups.has(group.label)}
+					{@const { checked, indeterminate } = toolsPanel.getGroupCheckedState(group)}
+					{@const favicon = toolsPanel.getFavicon(group)}
+
+					<Collapsible.Root
+						open={isExpanded}
+						onOpenChange={() => toolsPanel.toggleGroupExpanded(group.label)}
+					>
+						<div class="flex items-center gap-1">
+							<Collapsible.Trigger
+								class="flex min-w-0 flex-1 items-center gap-2 rounded px-2 py-1.5 text-sm hover:bg-muted/50"
+							>
+								{#if isExpanded}
+									<ChevronDown class="h-3.5 w-3.5 shrink-0" />
+								{:else}
+									<ChevronRight class="h-3.5 w-3.5 shrink-0" />
+								{/if}
+
+								<span class="inline-flex min-w-0 items-center gap-1.5 font-medium">
+									{#if favicon}
+										<img
+											src={favicon}
+											alt=""
+											class="h-4 w-4 shrink-0 rounded-sm"
+											onerror={(e) => {
+												(e.currentTarget as HTMLImageElement).style.display = 'none';
+											}}
+										/>
+									{/if}
+
+									<span class="truncate">{group.label}</span>
+								</span>
+
+								<span class="ml-auto shrink-0 text-xs text-muted-foreground">
+									{toolsPanel.getEnabledToolCount(group)}/{group.tools.length}
+								</span>
+							</Collapsible.Trigger>
+
+							<Tooltip.Root>
+								<Tooltip.Trigger>
+									<Checkbox
+										{checked}
+										{indeterminate}
+										onCheckedChange={() => toolsStore.toggleGroup(group)}
+										class="mr-2 h-4 w-4 shrink-0"
+									/>
+								</Tooltip.Trigger>
+
+								<Tooltip.Content side="right">
+									<p>
+										{checked ? 'Disable' : 'Enable'}
+										{group.tools.length} tool{group.tools.length !== 1 ? 's' : ''}
+									</p>
+								</Tooltip.Content>
+							</Tooltip.Root>
+						</div>
+
+						<Collapsible.Content>
+							<div class="ml-4 flex flex-col gap-0.5 border-l border-border/50 pl-2">
+								{#each group.tools as tool (tool.function.name)}
+									<button
+										type="button"
+										class="flex w-full items-center gap-2 rounded px-2 py-1.5 text-left text-sm transition-colors hover:bg-muted/50"
+										onclick={() => toolsStore.toggleTool(tool.function.name)}
+									>
+										<Checkbox
+											checked={toolsStore.isToolEnabled(tool.function.name)}
+											onCheckedChange={() => toolsStore.toggleTool(tool.function.name)}
+											class="h-4 w-4 shrink-0"
+										/>
+
+										<span class="min-w-0 flex-1 truncate font-mono text-[12px]">
+											{tool.function.name}
+										</span>
+									</button>
+								{/each}
+							</div>
+						</Collapsible.Content>
+					</Collapsible.Root>
+				{/each}
+			</div>
+		{/if}
+	</DropdownMenu.SubContent>
+</DropdownMenu.Sub>
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionsAdd.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionsAdd.svelte
new file mode 100644
index 00000000000..54ddcf9b0a7
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionsAdd.svelte
@@ -0,0 +1,68 @@
+<script lang="ts">
+	import { IsMobile } from '$lib/hooks/is-mobile.svelte';
+	import ChatFormActionAddDropdown from './ChatFormActionAddDropdown.svelte';
+	import ChatFormActionAddSheet from './ChatFormActionAddSheet.svelte';
+	import ChatFormActionAddButton from './ChatFormActionAddButton.svelte';
+
+	interface Props {
+		disabled?: boolean;
+		hasAudioModality?: boolean;
+		hasVideoModality?: boolean;
+		hasMcpPromptsSupport?: boolean;
+		hasMcpResourcesSupport?: boolean;
+		hasVisionModality?: boolean;
+		onFileUpload?: () => void;
+		onMcpPromptClick?: () => void;
+		onMcpResourcesClick?: () => void;
+		onMcpSettingsClick?: () => void;
+		onSystemPromptClick?: () => void;
+	}
+
+	let {
+		disabled = false,
+		hasAudioModality = false,
+		hasVideoModality = false,
+		hasMcpPromptsSupport = false,
+		hasMcpResourcesSupport = false,
+		hasVisionModality = false,
+		onFileUpload,
+		onMcpPromptClick,
+		onMcpResourcesClick,
+		onMcpSettingsClick,
+		onSystemPromptClick
+	}: Props = $props();
+
+	const isMobile = new IsMobile();
+</script>
+
+{#if isMobile.current}
+	<ChatFormActionAddSheet
+		{disabled}
+		{hasAudioModality}
+		{hasVideoModality}
+		{hasVisionModality}
+		{hasMcpPromptsSupport}
+		{hasMcpResourcesSupport}
+		{onFileUpload}
+		{onMcpPromptClick}
+		{onMcpResourcesClick}
+	>
+		{#snippet trigger({ disabled, onclick })}
+			<ChatFormActionAddButton {disabled} {onclick} />
+		{/snippet}
+	</ChatFormActionAddSheet>
+{:else}
+	<ChatFormActionAddDropdown
+		{disabled}
+		{hasAudioModality}
+		{hasVideoModality}
+		{hasVisionModality}
+		{hasMcpPromptsSupport}
+		{hasMcpResourcesSupport}
+		{onFileUpload}
+		{onMcpPromptClick}
+		{onMcpResourcesClick}
+		{onMcpSettingsClick}
+		{onSystemPromptClick}
+	/>
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionModels.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionModels.svelte
new file mode 100644
index 00000000000..2f9471e0ddd
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionModels.svelte
@@ -0,0 +1,166 @@
+<script lang="ts">
+	import { chatStore } from '$lib/stores/chat.svelte';
+	import { modelsStore, modelOptions, selectedModelId } from '$lib/stores/models.svelte';
+	import { isRouterMode, serverError } from '$lib/stores/server.svelte';
+	import { ModelsSelectorDropdown, ModelsSelectorSheet } from '$lib/components/app';
+	import { IsMobile } from '$lib/hooks/is-mobile.svelte';
+	import { activeMessages } from '$lib/stores/conversations.svelte';
+
+	interface Props {
+		currentModel?: string;
+		disabled?: boolean;
+		forceForegroundText?: boolean;
+		hasAudioModality?: boolean;
+		hasVideoModality?: boolean;
+		hasVisionModality?: boolean;
+		hasModelSelected?: boolean;
+		isSelectedModelInCache?: boolean;
+		submitTooltip?: string;
+		useGlobalSelection?: boolean;
+	}
+
+	let {
+		currentModel,
+		disabled = false,
+		forceForegroundText = false,
+		hasAudioModality = $bindable(false),
+		hasVideoModality = $bindable(false),
+		hasVisionModality = $bindable(false),
+		hasModelSelected = $bindable(false),
+		isSelectedModelInCache = $bindable(true),
+		submitTooltip = $bindable(''),
+		useGlobalSelection = false
+	}: Props = $props();
+
+	let isRouter = $derived(isRouterMode());
+	let isOffline = $derived(!!serverError());
+
+	let conversationModel = $derived(
+		chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
+	);
+
+	let lastSyncedConversationModel: string | null = null;
+
+	$effect(() => {
+		if (conversationModel && conversationModel !== lastSyncedConversationModel) {
+			lastSyncedConversationModel = conversationModel;
+
+			modelsStore.selectModelByName(conversationModel);
+		} else if (isRouter && !modelsStore.selectedModelId && modelsStore.loadedModelIds.length > 0) {
+			lastSyncedConversationModel = null;
+			// auto-select the first loaded model only when nothing is selected yet
+			const first = modelOptions().find((m) => modelsStore.loadedModelIds.includes(m.model));
+
+			if (first) modelsStore.selectModelById(first.id);
+		}
+	});
+
+	let activeModelId = $derived.by(() => {
+		const options = modelOptions();
+
+		if (!isRouter) {
+			return options.length > 0 ? options[0].model : null;
+		}
+
+		const selectedId = selectedModelId();
+
+		if (selectedId) {
+			const model = options.find((m) => m.id === selectedId);
+
+			if (model) return model.model;
+		}
+
+		if (conversationModel) {
+			const model = options.find((m) => m.model === conversationModel);
+
+			if (model) return model.model;
+		}
+
+		return null;
+	});
+
+	let modelPropsVersion = $state(0); // Used to trigger reactivity after fetch
+
+	$effect(() => {
+		if (activeModelId) {
+			const cached = modelsStore.getModelProps(activeModelId);
+
+			if (!cached) {
+				modelsStore.fetchModelProps(activeModelId).then(() => {
+					modelPropsVersion++;
+				});
+			}
+		}
+	});
+
+	$effect(() => {
+		hasAudioModality = activeModelId ? modelsStore.modelSupportsAudio(activeModelId) : false;
+	});
+
+	$effect(() => {
+		hasVideoModality = activeModelId ? modelsStore.modelSupportsVideo(activeModelId) : false;
+	});
+
+	$effect(() => {
+		void modelPropsVersion;
+
+		hasVisionModality = activeModelId ? modelsStore.modelSupportsVision(activeModelId) : false;
+	});
+
+	$effect(() => {
+		hasModelSelected = !isRouter || !!conversationModel || !!selectedModelId();
+	});
+
+	$effect(() => {
+		if (!isRouter) {
+			isSelectedModelInCache = true;
+		} else if (conversationModel) {
+			isSelectedModelInCache = modelOptions().some((option) => option.model === conversationModel);
+		} else {
+			const currentModelId = selectedModelId();
+
+			if (!currentModelId) {
+				isSelectedModelInCache = false;
+			} else {
+				isSelectedModelInCache = modelOptions().some((option) => option.id === currentModelId);
+			}
+		}
+	});
+
+	$effect(() => {
+		if (!hasModelSelected) {
+			submitTooltip = 'Please select a model first';
+		} else if (!isSelectedModelInCache) {
+			submitTooltip = 'Selected model is not available, please select another';
+		} else {
+			submitTooltip = '';
+		}
+	});
+
+	let selectorModelRef: ModelsSelectorDropdown | ModelsSelectorSheet | undefined =
+		$state(undefined);
+
+	let isMobile = new IsMobile();
+
+	export function open() {
+		selectorModelRef?.open();
+	}
+</script>
+
+{#if isMobile.current}
+	<ModelsSelectorSheet
+		disabled={disabled || isOffline}
+		bind:this={selectorModelRef}
+		{currentModel}
+		{forceForegroundText}
+		{useGlobalSelection}
+	/>
+{:else}
+	<ModelsSelectorDropdown
+		disabled={disabled || isOffline}
+		bind:this={selectorModelRef}
+		{currentModel}
+		{forceForegroundText}
+		{useGlobalSelection}
+	/>
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionRecord.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionRecord.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionRecord.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionRecord.svelte
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte
similarity index 65%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte
index 861cd182e82..8774bf63a6a 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte
@@ -2,37 +2,28 @@
 	import { ArrowUp } from '@lucide/svelte';
 	import { Button } from '$lib/components/ui/button';
 	import * as Tooltip from '$lib/components/ui/tooltip';
-	import { cn } from '$lib/components/ui/utils';
 
 	interface Props {
 		canSend?: boolean;
 		disabled?: boolean;
-		isLoading?: boolean;
 		showErrorState?: boolean;
 		tooltipLabel?: string;
 	}
 
-	let {
-		canSend = false,
-		disabled = false,
-		isLoading = false,
-		showErrorState = false,
-		tooltipLabel
-	}: Props = $props();
+	let { canSend = false, disabled = false, showErrorState = false, tooltipLabel }: Props = $props();
 
-	let isDisabled = $derived(!canSend || disabled || isLoading);
+	let isDisabled = $derived(!canSend || disabled);
 </script>
 
 {#snippet submitButton(props = {})}
 	<Button
 		type="submit"
 		disabled={isDisabled}
-		class={cn(
+		class={[
 			'h-8 w-8 rounded-full p-0',
-			showErrorState
-				? 'bg-red-400/10 text-red-400 hover:bg-red-400/20 hover:text-red-400 disabled:opacity-100'
-				: ''
-		)}
+			showErrorState &&
+				'bg-red-400/10 text-red-400 hover:bg-red-400/20 hover:text-red-400 disabled:opacity-100'
+		]}
 		{...props}
 	>
 		<span class="sr-only">Send</span>
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte
new file mode 100644
index 00000000000..a94293dd95f
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte
@@ -0,0 +1,149 @@
+<script lang="ts">
+	import { Square } from '@lucide/svelte';
+	import { Button } from '$lib/components/ui/button';
+	import {
+		ChatFormActionsAdd,
+		ChatFormActionModels,
+		ChatFormActionRecord,
+		ChatFormActionSubmit
+	} from '$lib/components/app';
+	import { FileTypeCategory } from '$lib/enums';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { config } from '$lib/stores/settings.svelte';
+	import { conversationsStore } from '$lib/stores/conversations.svelte';
+	import { getFileTypeCategory } from '$lib/utils';
+	import { goto } from '$app/navigation';
+	import { ROUTES } from '$lib/constants/routes';
+
+	interface Props {
+		canSend?: boolean;
+		canSubmit?: boolean;
+		class?: string;
+		disabled?: boolean;
+		isLoading?: boolean;
+		isRecording?: boolean;
+		showAddButton?: boolean;
+		showModelSelector?: boolean;
+		uploadedFiles?: ChatUploadedFile[];
+		onFileUpload?: () => void;
+		onMicClick?: () => void;
+		onStop?: () => void;
+		onSystemPromptClick?: () => void;
+		onMcpPromptClick?: () => void;
+		onMcpResourcesClick?: () => void;
+	}
+
+	let {
+		canSend = false,
+		canSubmit = false,
+		class: className = '',
+		disabled = false,
+		isLoading = false,
+		isRecording = false,
+		showAddButton = true,
+		showModelSelector = true,
+		uploadedFiles = [],
+		onFileUpload,
+		onMicClick,
+		onStop,
+		onSystemPromptClick,
+		onMcpPromptClick,
+		onMcpResourcesClick
+	}: Props = $props();
+
+	let currentConfig = $derived(config());
+
+	let hasMcpPromptsSupport = $derived.by(() => {
+		const perChatOverrides = conversationsStore.getAllMcpServerOverrides();
+
+		return mcpStore.hasPromptsCapability(perChatOverrides);
+	});
+
+	let hasMcpResourcesSupport = $derived.by(() => {
+		const perChatOverrides = conversationsStore.getAllMcpServerOverrides();
+
+		return mcpStore.hasResourcesCapability(perChatOverrides);
+	});
+
+	let hasAudioModality = $state(false);
+	let hasVideoModality = $state(false);
+	let hasVisionModality = $state(false);
+	let hasModelSelected = $state(false);
+	let isSelectedModelInCache = $state(true);
+	let submitTooltip = $state('');
+
+	let hasAudioAttachments = $derived(
+		uploadedFiles.some((file) => getFileTypeCategory(file.type) === FileTypeCategory.AUDIO)
+	);
+	let shouldShowRecordButton = $derived(
+		hasAudioModality && !canSubmit && !hasAudioAttachments && currentConfig.autoMicOnEmpty
+	);
+
+	let selectorModelRef: ChatFormActionModels | undefined = $state(undefined);
+
+	export function openModelSelector() {
+		selectorModelRef?.open();
+	}
+</script>
+
+<div
+	class="flex w-full items-center gap-3 {className} {showAddButton ? '' : 'justify-end'}"
+	style="container-type: inline-size"
+>
+	{#if showAddButton}
+		<div class="mr-auto flex items-center gap-2">
+			<ChatFormActionsAdd
+				{disabled}
+				{hasAudioModality}
+				{hasVideoModality}
+				{hasVisionModality}
+				{hasMcpPromptsSupport}
+				{hasMcpResourcesSupport}
+				{onFileUpload}
+				{onSystemPromptClick}
+				{onMcpPromptClick}
+				{onMcpResourcesClick}
+				onMcpSettingsClick={() => goto(ROUTES.MCP_SERVERS)}
+			/>
+		</div>
+	{/if}
+
+	{#if showModelSelector}
+		<ChatFormActionModels
+			{disabled}
+			bind:this={selectorModelRef}
+			bind:hasAudioModality
+			bind:hasVideoModality
+			bind:hasVisionModality
+			bind:hasModelSelected
+			bind:isSelectedModelInCache
+			bind:submitTooltip
+			forceForegroundText
+			useGlobalSelection
+		/>
+	{/if}
+
+	{#if isLoading && !canSubmit}
+		<Button
+			type="button"
+			variant="secondary"
+			onclick={onStop}
+			class="group h-8 w-8 rounded-full p-0 hover:bg-destructive/10!"
+		>
+			<span class="sr-only">Stop</span>
+
+			<Square
+				class="h-8 w-8 fill-muted-foreground stroke-muted-foreground group-hover:fill-destructive group-hover:stroke-destructive hover:fill-destructive hover:stroke-destructive"
+			/>
+		</Button>
+	{:else if shouldShowRecordButton}
+		<ChatFormActionRecord {disabled} {hasAudioModality} {isLoading} {isRecording} {onMicClick} />
+	{:else}
+		<ChatFormActionSubmit
+			canSend={canSend && (showModelSelector ? hasModelSelected && isSelectedModelInCache : true)}
+			{disabled}
+			tooltipLabel={submitTooltip}
+			showErrorState={showModelSelector && hasModelSelected && !isSelectedModelInCache}
+		/>
+	{/if}
+</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormFileInputInvisible.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormFileInputInvisible.svelte
similarity index 99%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormFileInputInvisible.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormFileInputInvisible.svelte
index d758822f3cd..395ecb20110 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormFileInputInvisible.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormFileInputInvisible.svelte
@@ -15,6 +15,7 @@
 
 	function handleFileSelect(event: Event) {
 		const input = event.target as HTMLInputElement;
+
 		if (input.files) {
 			onFileSelect?.(Array.from(input.files));
 		}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpResources.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormMcpResourcesList.svelte
similarity index 81%
rename from tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpResources.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormMcpResourcesList.svelte
index 341bf32c058..36c82224a67 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentMcpResources.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormMcpResourcesList.svelte
@@ -4,7 +4,10 @@
 		mcpResourceAttachments,
 		mcpHasResourceAttachments
 	} from '$lib/stores/mcp-resources.svelte';
-	import { ChatAttachmentMcpResource, HorizontalScrollCarousel } from '$lib/components/app';
+	import {
+		ChatAttachmentsListItemMcpResource,
+		HorizontalScrollCarousel
+	} from '$lib/components/app';
 
 	interface Props {
 		class?: string;
@@ -29,11 +32,11 @@
 	<div class={className}>
 		<HorizontalScrollCarousel gapSize="2">
 			{#each attachments as attachment, i (attachment.id)}
-				<ChatAttachmentMcpResource
+				<ChatAttachmentsListItemMcpResource
 					class={i === 0 ? 'ml-3' : ''}
 					{attachment}
 					onRemove={handleRemove}
-					onClick={() => handleResourceClick(attachment.resource.uri)}
+					onclick={() => handleResourceClick(attachment.resource.uri)}
 				/>
 			{/each}
 		</HorizontalScrollCarousel>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerItemHeader.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerItemHeader.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerItemHeader.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerItemHeader.svelte
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerList.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerList.svelte
similarity index 96%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerList.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerList.svelte
index 1a95cd7f6d2..6647928b2bf 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerList.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerList.svelte
@@ -60,8 +60,7 @@
 
 	<div
 		bind:this={listContainer}
-		class="{CHAT_FORM_POPOVER_MAX_HEIGHT} p-2"
-		class:pt-13={showSearchInput}
+		class={[`${CHAT_FORM_POPOVER_MAX_HEIGHT} p-2`, showSearchInput && 'pt-13']}
 	>
 		{#if isLoading}
 			{#if skeleton}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerListItem.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerListItem.svelte
similarity index 79%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerListItem.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerListItem.svelte
index d232d66c1fa..4d82c6b5849 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerListItem.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerListItem.svelte
@@ -3,18 +3,18 @@
 
 	interface Props {
 		isSelected?: boolean;
-		onClick: () => void;
+		onclick: () => void;
 		dataIndex?: number;
 		children: Snippet;
 	}
 
-	let { isSelected = false, onClick, dataIndex, children }: Props = $props();
+	let { isSelected = false, onclick, dataIndex, children }: Props = $props();
 </script>
 
 <button
 	type="button"
 	data-picker-index={dataIndex}
-	onclick={onClick}
+	{onclick}
 	class="flex w-full cursor-pointer items-start gap-3 rounded-lg px-3 py-2 text-left hover:bg-accent/50 {isSelected
 		? 'bg-accent/50'
 		: ''}"
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerListItemSkeleton.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerListItemSkeleton.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPicker/ChatFormPickerListItemSkeleton.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerListItemSkeleton.svelte
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPickerPopover.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerPopover.svelte
similarity index 83%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPickerPopover.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerPopover.svelte
index 9957085fed2..c43a002e695 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPickerPopover.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerPopover.svelte
@@ -29,7 +29,11 @@
 		}
 	}}
 >
-	<Popover.Trigger class="pointer-events-none absolute inset-0 opacity-0">
+	<Popover.Trigger
+		class="pointer-events-none absolute inset-0 opacity-0"
+		tabindex={-1}
+		aria-hidden="true"
+	>
 		<span class="sr-only">{srLabel}</span>
 	</Popover.Trigger>
 
@@ -39,7 +43,7 @@
 		sideOffset={12}
 		class="w-[var(--bits-popover-anchor-width)] max-w-none rounded-xl border-border/50 p-0 shadow-xl {className}"
 		onkeydown={onKeydown}
-		onOpenAutoFocus={(e) => e.preventDefault()}
+		onOpenAutoFocus={(event) => event.preventDefault()}
 	>
 		{@render children()}
 	</Popover.Content>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPromptPicker/ChatFormPromptPicker.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPickerMcpPrompts.svelte
similarity index 97%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPromptPicker/ChatFormPromptPicker.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPickerMcpPrompts.svelte
index 12d4860ad66..567fdac4704 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPromptPicker/ChatFormPromptPicker.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPickerMcpPrompts.svelte
@@ -5,7 +5,6 @@
 	import { KeyboardKey } from '$lib/enums';
 	import type { MCPPromptInfo, GetPromptResult, MCPServerSettingsEntry } from '$lib/types';
 	import { SvelteMap } from 'svelte/reactivity';
-	import Badge from '$lib/components/ui/badge/badge.svelte';
 	import {
 		ChatFormPickerPopover,
 		ChatFormPickerList,
@@ -14,6 +13,7 @@
 		ChatFormPickerListItemSkeleton,
 		ChatFormPromptPickerArgumentForm
 	} from '$lib/components/app/chat';
+	import Badge from '$lib/components/ui/badge/badge.svelte';
 
 	interface Props {
 		class?: string;
@@ -97,7 +97,7 @@
 
 			prompts = await mcpStore.getAllPrompts();
 		} catch (error) {
-			console.error('[ChatFormPromptPicker] Failed to load prompts:', error);
+			console.error('[ChatFormPickerMcpPrompts] Failed to load prompts:', error);
 			prompts = [];
 		} finally {
 			isLoading = false;
@@ -163,7 +163,7 @@
 		}
 
 		if (import.meta.env.DEV) {
-			console.log('[ChatFormPromptPicker] Fetching completions for:', {
+			console.log('[ChatFormPickerMcpPrompts] Fetching completions for:', {
 				serverName: selectedPrompt.serverName,
 				promptName: selectedPrompt.name,
 				argName,
@@ -182,7 +182,7 @@
 			);
 
 			if (import.meta.env.DEV) {
-				console.log('[ChatFormPromptPicker] Autocomplete result:', {
+				console.log('[ChatFormPickerMcpPrompts] Autocomplete result:', {
 					argName,
 					value,
 					result,
@@ -205,7 +205,7 @@
 				suggestions[argName] = [];
 			}
 		} catch (error) {
-			console.error('[ChatFormPromptPicker] Failed to fetch completions:', error);
+			console.error('[ChatFormPickerMcpPrompts] Failed to fetch completions:', error);
 			suggestions[argName] = [];
 		} finally {
 			loadingSuggestions[argName] = false;
@@ -408,7 +408,7 @@
 				<ChatFormPickerListItem
 					dataIndex={index}
 					{isSelected}
-					onClick={() => handlePromptClick(prompt)}
+					onclick={() => handlePromptClick(prompt)}
 				>
 					<ChatFormPickerItemHeader
 						{server}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPromptPicker/ChatFormPromptPickerArgumentForm.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPromptPickerArgumentForm.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPromptPicker/ChatFormPromptPickerArgumentForm.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPromptPickerArgumentForm.svelte
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPromptPicker/ChatFormPromptPickerArgumentInput.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPromptPickerArgumentInput.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormPromptPicker/ChatFormPromptPickerArgumentInput.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPromptPickerArgumentInput.svelte
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormResourcePicker/ChatFormResourcePicker.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpResources.svelte
similarity index 97%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormResourcePicker/ChatFormResourcePicker.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpResources.svelte
index 5c2a945d317..1125ae8ec98 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormResourcePicker/ChatFormResourcePicker.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickerMcpResources.svelte
@@ -67,7 +67,6 @@
 
 		try {
 			const perChatOverrides = conversationsStore.getAllMcpServerOverrides();
-
 			const initialized = await mcpStore.ensureInitialized(perChatOverrides);
 
 			if (!initialized) {
@@ -79,7 +78,7 @@
 			await mcpStore.fetchAllResources();
 			resources = mcpResourceStore.getAllResourceInfos();
 		} catch (error) {
-			console.error('[ChatFormResourcePicker] Failed to load resources:', error);
+			console.error('[ChatFormPickerMcpResources] Failed to load resources:', error);
 			resources = [];
 		} finally {
 			isLoading = false;
@@ -88,6 +87,7 @@
 
 	function handleResourceClick(resource: MCPResourceInfo) {
 		mcpStore.attachResource(resource.uri);
+
 		onResourceSelect?.(resource);
 		onClose?.();
 	}
@@ -144,6 +144,7 @@
 		const sortedResources = [...resources].sort((a, b) => {
 			const orderA = serverOrderMap.get(a.serverName) ?? Number.MAX_SAFE_INTEGER;
 			const orderB = serverOrderMap.get(b.serverName) ?? Number.MAX_SAFE_INTEGER;
+
 			return orderA - orderB;
 		});
 
@@ -186,7 +187,7 @@
 			<ChatFormPickerListItem
 				dataIndex={index}
 				{isSelected}
-				onClick={() => handleResourceClick(resource)}
+				onclick={() => handleResourceClick(resource)}
 			>
 				<ChatFormPickerItemHeader
 					{server}
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickers.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickers.svelte
new file mode 100644
index 00000000000..7c5dc85b2a0
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormPickers/ChatFormPickers.svelte
@@ -0,0 +1,75 @@
+<script lang="ts">
+	import ChatFormPickerMcpPrompts from './ChatFormPickerMcpPrompts/ChatFormPickerMcpPrompts.svelte';
+	import ChatFormPickerMcpResources from './ChatFormPickerMcpResources.svelte';
+	import type { GetPromptResult, MCPPromptInfo } from '$lib/types';
+
+	interface Props {
+		isPromptPickerOpen?: boolean;
+		promptSearchQuery?: string;
+		isInlineResourcePickerOpen?: boolean;
+		resourceSearchQuery?: string;
+		onPromptPickerClose?: () => void;
+		onInlineResourcePickerClose?: () => void;
+		onInlineResourceSelect?: () => void;
+		onPromptLoadStart?: (
+			placeholderId: string,
+			promptInfo: MCPPromptInfo,
+			args?: Record<string, string>
+		) => void;
+		onPromptLoadComplete?: (placeholderId: string, result: GetPromptResult) => void;
+		onPromptLoadError?: (placeholderId: string, error: string) => void;
+		onInlineResourceBrowse?: () => void;
+	}
+
+	let {
+		isPromptPickerOpen,
+		promptSearchQuery,
+		isInlineResourcePickerOpen,
+		resourceSearchQuery,
+		onPromptPickerClose,
+		onInlineResourcePickerClose,
+		onInlineResourceSelect,
+		onPromptLoadStart,
+		onPromptLoadComplete,
+		onPromptLoadError,
+		onInlineResourceBrowse
+	}: Props = $props();
+
+	let promptPickerRef: ChatFormPickerMcpPrompts | undefined = $state(undefined);
+	let resourcePickerRef: ChatFormPickerMcpResources | undefined = $state(undefined);
+
+	/**
+	 * Delegates keyboard events to the active picker child.
+	 * Returns true if the event was handled.
+	 */
+	export function handleKeydown(event: KeyboardEvent): boolean {
+		if (isPromptPickerOpen && promptPickerRef?.handleKeydown(event)) {
+			return true;
+		}
+
+		if (isInlineResourcePickerOpen && resourcePickerRef?.handleKeydown(event)) {
+			return true;
+		}
+
+		return false;
+	}
+</script>
+
+<ChatFormPickerMcpPrompts
+	bind:this={promptPickerRef}
+	isOpen={isPromptPickerOpen}
+	searchQuery={promptSearchQuery}
+	onClose={onPromptPickerClose}
+	{onPromptLoadStart}
+	{onPromptLoadComplete}
+	{onPromptLoadError}
+/>
+
+<ChatFormPickerMcpResources
+	bind:this={resourcePickerRef}
+	isOpen={isInlineResourcePickerOpen}
+	searchQuery={resourceSearchQuery}
+	onClose={onInlineResourcePickerClose}
+	onResourceSelect={onInlineResourceSelect}
+	onBrowse={onInlineResourceBrowse}
+/>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte
similarity index 84%
rename from tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte
rename to tools/ui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte
index 86c182acdbd..72e62f319a6 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte
@@ -51,8 +51,10 @@
 	<textarea
 		bind:this={textareaElement}
 		bind:value
-		class="text-md min-h-12 w-full resize-none border-0 bg-transparent p-0 leading-6 outline-none placeholder:text-muted-foreground focus-visible:ring-0 focus-visible:ring-offset-0"
-		class:cursor-not-allowed={disabled}
+		class={[
+			'text-md min-h-12 w-full resize-none border-0 bg-transparent p-0 leading-6 outline-none placeholder:text-muted-foreground focus-visible:ring-0 focus-visible:ring-offset-0',
+			disabled && 'cursor-not-allowed'
+		]}
 		style="max-height: var(--max-message-height);"
 		{disabled}
 		onkeydown={onKeydown}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
similarity index 60%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
index deb393a4b30..4d0b302d272 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
@@ -1,12 +1,13 @@
 <script lang="ts">
 	import { goto } from '$app/navigation';
-	import { base } from '$app/paths';
 	import { getChatActionsContext, setMessageEditContext } from '$lib/contexts';
 	import { chatStore, pendingEditMessageId } from '$lib/stores/chat.svelte';
 	import { conversationsStore } from '$lib/stores/conversations.svelte';
 	import { DatabaseService } from '$lib/services/database.service';
 	import { SYSTEM_MESSAGE_PLACEHOLDER } from '$lib/constants';
-	import { MessageRole, AttachmentType } from '$lib/enums';
+	import { REASONING_TAGS } from '$lib/constants/agentic';
+	import { MessageRole, AttachmentType, AgenticSectionType } from '$lib/enums';
+	import { fadeInView } from '$lib/actions/fade-in-view.svelte';
 	import {
 		ChatMessageAssistant,
 		ChatMessageUser,
@@ -14,7 +15,9 @@
 		ChatMessageMcpPrompt
 	} from '$lib/components/app/chat';
 	import { parseFilesToMessageExtras } from '$lib/utils/browser-only';
+	import { deriveAgenticSections } from '$lib/utils';
 	import type { DatabaseMessageExtraMcpPrompt } from '$lib/types';
+	import { ROUTES } from '$lib/constants/routes';
 
 	interface Props {
 		class?: string;
@@ -41,6 +44,50 @@
 		messageTypes: string[];
 	} | null>(null);
 	let editedContent = $derived(message.content);
+
+	let rawEditContent = $derived.by(() => {
+		if (message.role !== MessageRole.ASSISTANT) return undefined;
+
+		const sections = deriveAgenticSections(message, toolMessages, [], false);
+		const parts: string[] = [];
+
+		for (const section of sections) {
+			switch (section.type) {
+				case AgenticSectionType.REASONING:
+				case AgenticSectionType.REASONING_PENDING:
+					parts.push(`${REASONING_TAGS.START}\n${section.content}\n${REASONING_TAGS.END}`);
+					break;
+
+				case AgenticSectionType.TEXT:
+					parts.push(section.content);
+					break;
+
+				case AgenticSectionType.TOOL_CALL:
+				case AgenticSectionType.TOOL_CALL_PENDING:
+				case AgenticSectionType.TOOL_CALL_STREAMING: {
+					const callObj: Record<string, unknown> = { name: section.toolName };
+
+					if (section.toolArgs) {
+						try {
+							callObj.arguments = JSON.parse(section.toolArgs);
+						} catch {
+							callObj.arguments = section.toolArgs;
+						}
+					}
+
+					parts.push(JSON.stringify(callObj, null, 2));
+
+					if (section.toolResult) {
+						parts.push(`[Tool Result]\n${section.toolResult}`);
+					}
+
+					break;
+				}
+			}
+		}
+
+		return parts.join('\n\n\n');
+	});
 	let editedExtras = $derived<DatabaseMessageExtra[]>(message.extra ? [...message.extra] : []);
 	let editedUploadedFiles = $state<ChatUploadedFile[]>([]);
 	let isEditing = $state(false);
@@ -49,6 +96,7 @@
 	let textareaElement: HTMLTextAreaElement | undefined = $state();
 
 	let showSaveOnlyOption = $derived(message.role === MessageRole.USER);
+	let showBranchAfterEditOption = $derived(message.role === MessageRole.ASSISTANT);
 
 	setMessageEditContext({
 		get isEditing() {
@@ -64,7 +112,9 @@
 			return editedUploadedFiles;
 		},
 		get originalContent() {
-			return message.content;
+			return message.role === MessageRole.ASSISTANT
+				? (rawEditContent ?? message.content)
+				: message.content;
 		},
 		get originalExtras() {
 			return message.extra || [];
@@ -72,6 +122,18 @@
 		get showSaveOnlyOption() {
 			return showSaveOnlyOption;
 		},
+		get showBranchAfterEditOption() {
+			return showBranchAfterEditOption;
+		},
+		get shouldBranchAfterEdit() {
+			return shouldBranchAfterEdit;
+		},
+		get messageRole() {
+			return message.role;
+		},
+		get rawEditContent() {
+			return rawEditContent;
+		},
 		setContent: (content: string) => {
 			editedContent = content;
 		},
@@ -81,6 +143,9 @@
 		setUploadedFiles: (files: ChatUploadedFile[]) => {
 			editedUploadedFiles = files;
 		},
+		setShouldBranchAfterEdit: (value: boolean) => {
+			shouldBranchAfterEdit = value;
+		},
 		save: handleSaveEdit,
 		saveOnly: handleSaveEditOnly,
 		cancel: handleCancelEdit,
@@ -114,17 +179,20 @@
 		isEditing = false;
 
 		// If canceling a new system message with placeholder content, remove it without deleting children
-		if (message.role === MessageRole.SYSTEM) {
+		if (message.role === MessageRole.SYSTEM && message.content === SYSTEM_MESSAGE_PLACEHOLDER) {
 			const conversationDeleted = await chatStore.removeSystemPromptPlaceholder(message.id);
 
 			if (conversationDeleted) {
-				goto(`${base}/`);
+				goto(ROUTES.START);
 			}
 
 			return;
 		}
 
-		editedContent = message.content;
+		editedContent =
+			message.role === MessageRole.ASSISTANT
+				? rawEditContent || message.content || ''
+				: message.content;
 		editedExtras = message.extra ? [...message.extra] : [];
 		editedUploadedFiles = [];
 	}
@@ -138,7 +206,7 @@
 			const conversationDeleted = await chatStore.removeSystemPromptPlaceholder(message.id);
 
 			if (conversationDeleted) {
-				goto(`${base}/`);
+				goto(ROUTES.START);
 			}
 		} else {
 			chatActions.delete(message);
@@ -155,10 +223,14 @@
 	function handleEdit() {
 		isEditing = true;
 		// Clear temporary placeholder content for system messages
-		editedContent =
-			message.role === MessageRole.SYSTEM && message.content === SYSTEM_MESSAGE_PLACEHOLDER
-				? ''
-				: message.content;
+		if (message.role === MessageRole.SYSTEM && message.content === SYSTEM_MESSAGE_PLACEHOLDER) {
+			editedContent = '';
+		} else if (message.role === MessageRole.ASSISTANT) {
+			editedContent = rawEditContent || message.content || '';
+		} else {
+			editedContent = message.content;
+		}
+
 		textareaElement?.focus();
 		editedExtras = message.extra ? [...message.extra] : [];
 		editedUploadedFiles = [];
@@ -200,7 +272,7 @@
 				const conversationDeleted = await chatStore.removeSystemPromptPlaceholder(message.id);
 				isEditing = false;
 				if (conversationDeleted) {
-					goto(`${base}/`);
+					goto(ROUTES.START);
 				}
 				return;
 			}
@@ -252,70 +324,72 @@
 	}
 </script>
 
-{#if message.role === MessageRole.SYSTEM}
-	<ChatMessageSystem
-		bind:textareaElement
-		class={className}
-		{deletionInfo}
-		{message}
-		onConfirmDelete={handleConfirmDelete}
-		onCopy={handleCopy}
-		onDelete={handleDelete}
-		onEdit={handleEdit}
-		onNavigateToSibling={handleNavigateToSibling}
-		onShowDeleteDialogChange={handleShowDeleteDialogChange}
-		{showDeleteDialog}
-		{siblingInfo}
-	/>
-{:else if mcpPromptExtra}
-	<ChatMessageMcpPrompt
-		class={className}
-		{deletionInfo}
-		{message}
-		mcpPrompt={mcpPromptExtra}
-		onConfirmDelete={handleConfirmDelete}
-		onCopy={handleCopy}
-		onDelete={handleDelete}
-		onEdit={handleEdit}
-		onNavigateToSibling={handleNavigateToSibling}
-		onShowDeleteDialogChange={handleShowDeleteDialogChange}
-		{showDeleteDialog}
-		{siblingInfo}
-	/>
-{:else if message.role === MessageRole.USER}
-	<ChatMessageUser
-		class={className}
-		{deletionInfo}
-		{message}
-		onConfirmDelete={handleConfirmDelete}
-		onCopy={handleCopy}
-		onDelete={handleDelete}
-		onEdit={handleEdit}
-		onForkConversation={handleForkConversation}
-		onNavigateToSibling={handleNavigateToSibling}
-		onShowDeleteDialogChange={handleShowDeleteDialogChange}
-		{showDeleteDialog}
-		{siblingInfo}
-	/>
-{:else}
-	<ChatMessageAssistant
-		bind:textareaElement
-		class={className}
-		{deletionInfo}
-		{isLastAssistantMessage}
-		{message}
-		{toolMessages}
-		messageContent={message.content}
-		onConfirmDelete={handleConfirmDelete}
-		onContinue={handleContinue}
-		onCopy={handleCopy}
-		onDelete={handleDelete}
-		onEdit={handleEdit}
-		onForkConversation={handleForkConversation}
-		onNavigateToSibling={handleNavigateToSibling}
-		onRegenerate={handleRegenerate}
-		onShowDeleteDialogChange={handleShowDeleteDialogChange}
-		{showDeleteDialog}
-		{siblingInfo}
-	/>
-{/if}
+<div use:fadeInView>
+	{#if message.role === MessageRole.SYSTEM}
+		<ChatMessageSystem
+			bind:textareaElement
+			class={className}
+			{deletionInfo}
+			{message}
+			onConfirmDelete={handleConfirmDelete}
+			onCopy={handleCopy}
+			onDelete={handleDelete}
+			onEdit={handleEdit}
+			onNavigateToSibling={handleNavigateToSibling}
+			onShowDeleteDialogChange={handleShowDeleteDialogChange}
+			{showDeleteDialog}
+			{siblingInfo}
+		/>
+	{:else if mcpPromptExtra}
+		<ChatMessageMcpPrompt
+			class={className}
+			{deletionInfo}
+			{message}
+			mcpPrompt={mcpPromptExtra}
+			onConfirmDelete={handleConfirmDelete}
+			onCopy={handleCopy}
+			onDelete={handleDelete}
+			onEdit={handleEdit}
+			onNavigateToSibling={handleNavigateToSibling}
+			onShowDeleteDialogChange={handleShowDeleteDialogChange}
+			{showDeleteDialog}
+			{siblingInfo}
+		/>
+	{:else if message.role === MessageRole.USER}
+		<ChatMessageUser
+			class={className}
+			{deletionInfo}
+			{message}
+			onConfirmDelete={handleConfirmDelete}
+			onCopy={handleCopy}
+			onDelete={handleDelete}
+			onEdit={handleEdit}
+			onForkConversation={handleForkConversation}
+			onNavigateToSibling={handleNavigateToSibling}
+			onShowDeleteDialogChange={handleShowDeleteDialogChange}
+			{showDeleteDialog}
+			{siblingInfo}
+		/>
+	{:else}
+		<ChatMessageAssistant
+			bind:textareaElement
+			class={className}
+			{deletionInfo}
+			{isLastAssistantMessage}
+			{message}
+			{toolMessages}
+			messageContent={message.content}
+			onConfirmDelete={handleConfirmDelete}
+			onContinue={handleContinue}
+			onCopy={handleCopy}
+			onDelete={handleDelete}
+			onEdit={handleEdit}
+			onForkConversation={handleForkConversation}
+			onNavigateToSibling={handleNavigateToSibling}
+			onRegenerate={handleRegenerate}
+			onShowDeleteDialogChange={handleShowDeleteDialogChange}
+			{showDeleteDialog}
+			{siblingInfo}
+		/>
+	{/if}
+</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
similarity index 80%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
index 7030ca88508..b4d69b932ab 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
@@ -1,30 +1,21 @@
 <script lang="ts">
 	import {
 		ChatMessageAgenticContent,
-		ChatMessageActions,
+		ChatMessageActionIcons,
+		ChatMessageEditForm,
 		ChatMessageStatistics,
 		ModelBadge,
-		ModelsSelector
+		ModelsSelectorDropdown
 	} from '$lib/components/app';
 	import { getMessageEditContext } from '$lib/contexts';
 	import { useProcessingState } from '$lib/hooks/use-processing-state.svelte';
 	import { isLoading, isChatStreaming } from '$lib/stores/chat.svelte';
-	import {
-		autoResizeTextarea,
-		copyToClipboard,
-		isIMEComposing,
-		deriveAgenticSections
-	} from '$lib/utils';
+	import { copyToClipboard, deriveAgenticSections } from '$lib/utils';
 	import { AgenticSectionType } from '$lib/enums';
 	import { REASONING_TAGS } from '$lib/constants/agentic';
 	import { tick } from 'svelte';
 	import { fade } from 'svelte/transition';
-	import { Check, X } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import { Checkbox } from '$lib/components/ui/checkbox';
-	import { INPUT_CLASSES } from '$lib/constants';
-	import { MessageRole, KeyboardKey, ChatMessageStatsView } from '$lib/enums';
-	import Label from '$lib/components/ui/label/label.svelte';
+	import { MessageRole, ChatMessageStatsView } from '$lib/enums';
 	import { config } from '$lib/stores/settings.svelte';
 	import { isRouterMode } from '$lib/stores/server.svelte';
 	import { modelsStore } from '$lib/stores/models.svelte';
@@ -82,21 +73,7 @@
 	// Get edit context
 	const editCtx = getMessageEditContext();
 
-	// Local state for assistant-specific editing
-	let shouldBranchAfterEdit = $state(false);
-
-	function handleEditKeydown(event: KeyboardEvent) {
-		if (event.key === KeyboardKey.ENTER && !event.shiftKey && !isIMEComposing(event)) {
-			event.preventDefault();
-			editCtx.save();
-		} else if (event.key === KeyboardKey.ESCAPE) {
-			event.preventDefault();
-			editCtx.cancel();
-		}
-	}
-
 	const isAgentic = $derived(hasAgenticContent(message, toolMessages));
-	const hasReasoning = $derived(!!message.reasoningContent);
 	const processingState = useProcessingState();
 
 	let currentConfig = $derived(config());
@@ -227,12 +204,6 @@
 		void copyToClipboard(displayedModel ?? '');
 	}
 
-	$effect(() => {
-		if (editCtx.isEditing && textareaElement) {
-			autoResizeTextarea(textareaElement);
-		}
-	});
-
 	$effect(() => {
 		if (showProcessingInfoTop || showProcessingInfoBottom) {
 			processingState.startMonitoring();
@@ -258,48 +229,7 @@
 	{/if}
 
 	{#if editCtx.isEditing}
-		<div class="w-full">
-			<textarea
-				bind:this={textareaElement}
-				value={editCtx.editedContent}
-				class="min-h-[50vh] w-full resize-y rounded-2xl px-3 py-2 text-sm {INPUT_CLASSES}"
-				onkeydown={handleEditKeydown}
-				oninput={(e) => {
-					autoResizeTextarea(e.currentTarget);
-					editCtx.setContent(e.currentTarget.value);
-				}}
-				placeholder="Edit assistant message..."
-			></textarea>
-
-			<div class="mt-2 flex items-center justify-between">
-				<div class="flex items-center space-x-2">
-					<Checkbox
-						id="branch-after-edit"
-						bind:checked={shouldBranchAfterEdit}
-						onCheckedChange={(checked) => (shouldBranchAfterEdit = checked === true)}
-					/>
-					<Label for="branch-after-edit" class="cursor-pointer text-sm text-muted-foreground">
-						Branch conversation after edit
-					</Label>
-				</div>
-				<div class="flex gap-2">
-					<Button class="h-8 px-3" onclick={editCtx.cancel} size="sm" variant="outline">
-						<X class="mr-1 h-3 w-3" />
-						Cancel
-					</Button>
-
-					<Button
-						class="h-8 px-3"
-						onclick={editCtx.save}
-						disabled={!editCtx.editedContent?.trim()}
-						size="sm"
-					>
-						<Check class="mr-1 h-3 w-3" />
-						Save
-					</Button>
-				</div>
-			</div>
-		</div>
+		<ChatMessageEditForm />
 	{:else if message.role === MessageRole.ASSISTANT}
 		{#if showRawOutput}
 			<pre class="raw-output">{rawOutputContent || ''}</pre>
@@ -308,6 +238,7 @@
 				{message}
 				{toolMessages}
 				isStreaming={isChatStreaming()}
+				{isLastAssistantMessage}
 				highlightTurns={highlightAgenticTurns}
 			/>
 		{/if}
@@ -336,10 +267,10 @@
 				class="inline-flex flex-wrap items-start gap-2 text-xs text-muted-foreground"
 			>
 				{#if isRouter}
-					<ModelsSelector
+					<ModelsSelectorDropdown
 						currentModel={displayedModel}
 						disabled={isLoading()}
-						onModelChange={async (modelId, modelName) => {
+						onModelChange={async (modelId: string, modelName: string) => {
 							const status = modelsStore.getModelStatus(modelId);
 
 							if (status !== ServerModelStatus.LOADED) {
@@ -387,7 +318,7 @@
 	</div>
 
 	{#if message.timestamp && !editCtx.isEditing}
-		<ChatMessageActions
+		<ChatMessageActionIcons
 			role={MessageRole.ASSISTANT}
 			justify="start"
 			actionsPosition="left"
@@ -397,7 +328,7 @@
 			{onCopy}
 			{onEdit}
 			{onRegenerate}
-			onContinue={currentConfig.enableContinueGeneration && !hasReasoning ? onContinue : undefined}
+			onContinue={currentConfig.enableContinueGeneration ? onContinue : undefined}
 			{onForkConversation}
 			{onDelete}
 			{onConfirmDelete}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageMcpPrompt.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageMcpPrompt/ChatMessageMcpPrompt.svelte
similarity index 97%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageMcpPrompt.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageMcpPrompt/ChatMessageMcpPrompt.svelte
index 72ece2b8e22..2dcb36baf68 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageMcpPrompt.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageMcpPrompt/ChatMessageMcpPrompt.svelte
@@ -1,6 +1,6 @@
 <script lang="ts">
 	import {
-		ChatMessageActions,
+		ChatMessageActionIcons,
 		ChatMessageEditForm,
 		ChatMessageMcpPromptContent
 	} from '$lib/components/app';
@@ -63,7 +63,7 @@
 
 		{#if message.timestamp}
 			<div class="max-w-[80%]">
-				<ChatMessageActions
+				<ChatMessageActionIcons
 					actionsPosition="right"
 					{deletionInfo}
 					justify="end"
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageMcpPromptContent.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageMcpPrompt/ChatMessageMcpPromptContent.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageMcpPromptContent.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageMcpPrompt/ChatMessageMcpPromptContent.svelte
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageSystem.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte
similarity index 97%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageSystem.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte
index a27616f9bed..9d3d07a2730 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageSystem.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte
@@ -1,14 +1,13 @@
 <script lang="ts">
 	import { Check, X } from '@lucide/svelte';
-	import { Card } from '$lib/components/ui/card';
+	import { ChatMessageActionIcons, MarkdownContent } from '$lib/components/app';
 	import { Button } from '$lib/components/ui/button';
-	import { MarkdownContent } from '$lib/components/app';
-	import { getMessageEditContext } from '$lib/contexts';
+	import { Card } from '$lib/components/ui/card';
 	import { INPUT_CLASSES } from '$lib/constants';
+	import { getMessageEditContext } from '$lib/contexts';
+	import { KeyboardKey, MessageRole } from '$lib/enums';
 	import { config } from '$lib/stores/settings.svelte';
 	import { isIMEComposing } from '$lib/utils';
-	import ChatMessageActions from './ChatMessageActions.svelte';
-	import { KeyboardKey, MessageRole } from '$lib/enums';
 
 	interface Props {
 		class?: string;
@@ -213,7 +212,7 @@
 
 		{#if message.timestamp}
 			<div class="max-w-[80%]">
-				<ChatMessageActions
+				<ChatMessageActionIcons
 					actionsPosition="right"
 					{deletionInfo}
 					justify="end"
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUser.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUser.svelte
new file mode 100644
index 00000000000..96ec1ddfd74
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUser.svelte
@@ -0,0 +1,83 @@
+<script lang="ts">
+	import {
+		ChatMessageActionIcons,
+		ChatMessageEditForm,
+		ChatMessageUserBubble
+	} from '$lib/components/app/chat';
+	import { getMessageEditContext } from '$lib/contexts';
+	import { MessageRole } from '$lib/enums';
+
+	interface Props {
+		class?: string;
+		message: DatabaseMessage;
+		siblingInfo?: ChatMessageSiblingInfo | null;
+		deletionInfo: {
+			totalCount: number;
+			userMessages: number;
+			assistantMessages: number;
+			messageTypes: string[];
+		} | null;
+		showDeleteDialog: boolean;
+		onEdit: () => void;
+		onDelete: () => void;
+		onConfirmDelete: () => void;
+		onForkConversation?: (options: { name: string; includeAttachments: boolean }) => void;
+		onShowDeleteDialogChange: (show: boolean) => void;
+		onNavigateToSibling?: (siblingId: string) => void;
+		onCopy: () => void;
+	}
+
+	let {
+		class: className = '',
+		message,
+		siblingInfo = null,
+		deletionInfo,
+		showDeleteDialog,
+		onEdit,
+		onDelete,
+		onConfirmDelete,
+		onForkConversation,
+		onShowDeleteDialogChange,
+		onNavigateToSibling,
+		onCopy
+	}: Props = $props();
+
+	// Get contexts
+	const editCtx = getMessageEditContext();
+</script>
+
+<div
+	aria-label="User message with actions"
+	class="group flex flex-col items-end gap-3 md:gap-2 {className}"
+	role="group"
+>
+	{#if editCtx.isEditing}
+		<ChatMessageEditForm />
+	{:else}
+		<ChatMessageUserBubble
+			content={message.content}
+			attachments={message.extra}
+			renderMarkdown={true}
+		/>
+
+		{#if message.timestamp}
+			<div class="max-w-[80%]">
+				<ChatMessageActionIcons
+					actionsPosition="right"
+					{deletionInfo}
+					justify="end"
+					{onConfirmDelete}
+					{onCopy}
+					{onDelete}
+					{onEdit}
+					{onForkConversation}
+					{onNavigateToSibling}
+					{onShowDeleteDialogChange}
+					{siblingInfo}
+					{showDeleteDialog}
+					role={MessageRole.USER}
+				/>
+			</div>
+		{/if}
+	{/if}
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte
new file mode 100644
index 00000000000..dabb337dd58
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte
@@ -0,0 +1,76 @@
+<script lang="ts">
+	import { Card } from '$lib/components/ui/card';
+	import { ChatAttachmentsList, MarkdownContent } from '$lib/components/app';
+	import { config } from '$lib/stores/settings.svelte';
+	import type { DatabaseMessageExtra } from '$lib/types/database';
+
+	interface Props {
+		content: string;
+		attachments?: DatabaseMessageExtra[];
+		renderMarkdown?: boolean;
+		textColorClass?: string;
+		cardBgClass?: string;
+		maxHeightStyle?: string;
+	}
+
+	let {
+		content,
+		attachments = [],
+		renderMarkdown = false,
+		textColorClass = 'text-foreground',
+		cardBgClass = 'dark:bg-primary/15',
+		maxHeightStyle = 'max-height: var(--max-message-height);'
+	}: Props = $props();
+
+	let isMultiline = $state(false);
+	let messageElement: HTMLElement | undefined = $state();
+	const currentConfig = config();
+
+	$effect(() => {
+		if (!messageElement || !content.trim()) return;
+
+		if (content.includes('\n')) {
+			isMultiline = true;
+			return;
+		}
+
+		const resizeObserver = new ResizeObserver((entries) => {
+			for (const entry of entries) {
+				const element = entry.target as HTMLElement;
+				const estimatedSingleLineHeight = 24; // Typical line height for text-md
+
+				isMultiline = element.offsetHeight > estimatedSingleLineHeight * 1.5;
+			}
+		});
+
+		resizeObserver.observe(messageElement);
+
+		return () => {
+			resizeObserver.disconnect();
+		};
+	});
+</script>
+
+{#if attachments && attachments.length > 0}
+	<div class="mb-2 max-w-[80%]">
+		<ChatAttachmentsList {attachments} readonly imageHeight="h-40" />
+	</div>
+{/if}
+
+{#if content.trim()}
+	<Card
+		class="max-w-[80%] overflow-y-auto rounded-[1.125rem] border-none bg-primary/5 px-3.75 py-1.5 {textColorClass} backdrop-blur-md data-[multiline]:py-2.5 {cardBgClass}"
+		data-multiline={isMultiline ? '' : undefined}
+		style="{maxHeightStyle} overflow-wrap: anywhere; word-break: break-word;"
+	>
+		{#if renderMarkdown && currentConfig.renderUserContentAsMarkdown}
+			<div bind:this={messageElement}>
+				<MarkdownContent class="markdown-user-content -my-4" {content} />
+			</div>
+		{:else}
+			<span bind:this={messageElement} class="text-md whitespace-pre-wrap">
+				{content}
+			</span>
+		{/if}
+	</Card>
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte
new file mode 100644
index 00000000000..4be582b39ae
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte
@@ -0,0 +1,69 @@
+<script lang="ts">
+	import { ActionIcon, ChatMessageEditForm, ChatMessageUserBubble } from '$lib/components/app';
+	import { fadeInView } from '$lib/actions/fade-in-view.svelte';
+	import { ArrowUp, Edit, Trash2 } from '@lucide/svelte';
+	import { getProcessingInfoContext } from '$lib/contexts';
+	import { useMessageEditContext } from '$lib/hooks/use-message-edit-context.svelte';
+
+	interface Props {
+		class?: string;
+		content: string;
+		extras?: DatabaseMessageExtra[];
+		onSendImmediately: () => void;
+		onEdit: (newContent: string, extras?: DatabaseMessageExtra[]) => void;
+		onDelete: () => void;
+	}
+
+	let {
+		class: className = '',
+		content,
+		extras = [],
+		onSendImmediately,
+		onEdit,
+		onDelete
+	}: Props = $props();
+
+	const processingInfoCtx = getProcessingInfoContext();
+	let showProcessingInfo = $derived(processingInfoCtx.showProcessingInfo);
+
+	const editCtx = useMessageEditContext({
+		getContent: () => content,
+		getExtras: () => extras,
+		onSave: (content, extras) => onEdit(content, extras)
+	});
+</script>
+
+<div
+	use:fadeInView
+	aria-label="Pending user message"
+	class="group flex flex-col items-end gap-3 transition-opacity hover:opacity-80 md:gap-2 {className} sticky {showProcessingInfo
+		? 'bottom-44'
+		: 'bottom-32'}"
+	role="group"
+>
+	{#if editCtx.isEditing}
+		<ChatMessageEditForm />
+	{:else}
+		<ChatMessageUserBubble
+			{content}
+			attachments={extras}
+			textColorClass="text-muted-foreground"
+			cardBgClass="dark:bg-primary/8"
+			maxHeightStyle="overflow-wrap: anywhere; word-break: break-word;"
+		/>
+
+		<div class="max-w-[80%]">
+			<div class="relative flex h-6 items-center justify-between">
+				<div class="right-0 flex items-center gap-2 opacity-100 transition-opacity">
+					<div
+						class="pointer-events-auto inset-0 flex items-center gap-1 opacity-0 transition-all duration-150 group-hover:opacity-100"
+					>
+						<ActionIcon icon={Edit} tooltip="Edit" onclick={editCtx.handleEdit} />
+						<ActionIcon icon={Trash2} tooltip="Delete" onclick={onDelete} />
+						<ActionIcon icon={ArrowUp} tooltip="Send immediately" onclick={onSendImmediately} />
+					</div>
+				</div>
+			</div>
+		</div>
+	{/if}
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCard.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCard.svelte
new file mode 100644
index 00000000000..254031979d4
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCard.svelte
@@ -0,0 +1,23 @@
+<script lang="ts">
+	import type { Snippet, Component } from 'svelte';
+
+	interface Props {
+		icon: Component<{ class?: string }>;
+		message: Snippet;
+		actions: Snippet;
+	}
+
+	let { icon: IconComponent, message, actions }: Props = $props();
+</script>
+
+<div class="my-2 rounded-lg border border-border bg-card p-3">
+	<div class="mb-3 flex items-center gap-2 text-sm">
+		<IconComponent class="h-4 w-4 shrink-0 text-muted-foreground" />
+		<span>
+			{@render message()}
+		</span>
+	</div>
+	<div class="flex flex-wrap items-center gap-2">
+		{@render actions()}
+	</div>
+</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardContinueRequest.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardContinueRequest.svelte
new file mode 100644
index 00000000000..bbb1f0ac2bd
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardContinueRequest.svelte
@@ -0,0 +1,30 @@
+<script lang="ts">
+	import { RotateCw } from '@lucide/svelte';
+	import { Button } from '$lib/components/ui/button';
+	import ChatMessageActionCard from './ChatMessageActionCard.svelte';
+
+	interface Props {
+		onDecision: (shouldContinue: boolean) => void;
+	}
+
+	let { onDecision }: Props = $props();
+</script>
+
+<ChatMessageActionCard icon={RotateCw}>
+	{#snippet message()}
+		Agentic turn limit reached. Continue?
+	{/snippet}
+
+	{#snippet actions()}
+		<Button size="sm" onclick={() => onDecision(true)}>Continue</Button>
+
+		<Button
+			variant="destructive"
+			size="sm"
+			class="text-destructive hover:text-destructive"
+			onclick={() => onDecision(false)}
+		>
+			Stop
+		</Button>
+	{/snippet}
+</ChatMessageActionCard>
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardPermissionRequest.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardPermissionRequest.svelte
new file mode 100644
index 00000000000..e466c84ee20
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardPermissionRequest.svelte
@@ -0,0 +1,88 @@
+<script lang="ts">
+	import { ChevronDown, ShieldQuestion } from '@lucide/svelte';
+	import { ChatMessageActionCard } from '$lib/components/app';
+	import { Button } from '$lib/components/ui/button';
+	import * as ButtonGroup from '$lib/components/ui/button-group';
+	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
+	import { ToolSource, ToolPermissionDecision } from '$lib/enums';
+	import { TOOL_SERVER_LABELS } from '$lib/constants';
+	import { toolsStore } from '$lib/stores/tools.svelte';
+
+	interface Props {
+		toolName: string;
+		serverLabel: string;
+		onDecision: (decision: ToolPermissionDecision) => void;
+	}
+
+	let { toolName, serverLabel, onDecision }: Props = $props();
+</script>
+
+<ChatMessageActionCard icon={ShieldQuestion}>
+	{#snippet message()}
+		Allow use of
+
+		<span class="font-semibold">{toolName}</span>
+
+		{#if serverLabel}
+			from <span class="font-semibold">{serverLabel}</span>
+		{/if}
+
+		?
+	{/snippet}
+
+	{#snippet actions()}
+		<DropdownMenu.Root>
+			<ButtonGroup.Root
+				class="overflow-hidden rounded-md bg-foreground text-white shadow-sm dark:bg-secondary dark:text-foreground"
+			>
+				<Button
+					class="rounded-none! shadow-none!"
+					size="sm"
+					onclick={() => onDecision(ToolPermissionDecision.ONCE)}
+				>
+					Allow once
+				</Button>
+
+				<ButtonGroup.Separator />
+
+				<DropdownMenu.Trigger>
+					<Button size="sm" class="rounded-none! !ps-2 shadow-none!">
+						<ChevronDown class="h-3.5 w-3.5" />
+					</Button>
+				</DropdownMenu.Trigger>
+			</ButtonGroup.Root>
+
+			<DropdownMenu.Content align="start" class="min-w-[8rem]">
+				<DropdownMenu.Item onclick={() => onDecision(ToolPermissionDecision.ALWAYS)}>
+					Always allow <pre>{toolName}</pre>
+					tool
+				</DropdownMenu.Item>
+				{#if serverLabel}
+					<DropdownMenu.Item onclick={() => onDecision(ToolPermissionDecision.ALWAYS_SERVER)}>
+						Always allow all tools from {serverLabel}
+					</DropdownMenu.Item>
+				{:else}
+					{@const source = toolsStore.getToolSource(toolName)}
+					{@const providerName =
+						source === ToolSource.BUILTIN
+							? TOOL_SERVER_LABELS[ToolSource.BUILTIN]
+							: source === ToolSource.CUSTOM
+								? TOOL_SERVER_LABELS[ToolSource.CUSTOM]
+								: 'MCP Tools'}
+					<DropdownMenu.Item onclick={() => onDecision(ToolPermissionDecision.ALWAYS_SERVER)}>
+						Approve all tools from {providerName}
+					</DropdownMenu.Item>
+				{/if}
+			</DropdownMenu.Content>
+		</DropdownMenu.Root>
+
+		<Button
+			variant="destructive"
+			size="sm"
+			class="text-destructive hover:text-destructive"
+			onclick={() => onDecision(ToolPermissionDecision.DENY)}
+		>
+			Deny
+		</Button>
+	{/snippet}
+</ChatMessageActionCard>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageActions.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionIcons/ChatMessageActionIcons.svelte
similarity index 97%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageActions.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionIcons/ChatMessageActionIcons.svelte
index fec1a506eb8..503a2d086b1 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageActions.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionIcons/ChatMessageActionIcons.svelte
@@ -2,7 +2,7 @@
 	import { Edit, Copy, RefreshCw, Trash2, ArrowRight, GitBranch } from '@lucide/svelte';
 	import {
 		ActionIcon,
-		ChatMessageBranchingControls,
+		ChatMessageActionIconsBranchingControls,
 		DialogConfirmation
 	} from '$lib/components/app';
 	import { Switch } from '$lib/components/ui/switch';
@@ -89,7 +89,7 @@
 			: 'right-0'} flex items-center gap-2 opacity-100 transition-opacity"
 	>
 		{#if siblingInfo && siblingInfo.totalSiblings > 1}
-			<ChatMessageBranchingControls {siblingInfo} {onNavigateToSibling} />
+			<ChatMessageActionIconsBranchingControls {siblingInfo} {onNavigateToSibling} />
 		{/if}
 
 		<div
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionIcons/ChatMessageActionIconsBranchingControls.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionIcons/ChatMessageActionIconsBranchingControls.svelte
new file mode 100644
index 00000000000..465dcab73bf
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionIcons/ChatMessageActionIconsBranchingControls.svelte
@@ -0,0 +1,49 @@
+<script lang="ts">
+	import { ChevronLeft, ChevronRight } from '@lucide/svelte';
+	import { ActionIcon } from '$lib/components/app';
+
+	interface Props {
+		class?: string;
+		siblingInfo: ChatMessageSiblingInfo | null;
+		onNavigateToSibling?: (siblingId: string) => void;
+	}
+
+	let { class: className = '', siblingInfo, onNavigateToSibling }: Props = $props();
+
+	let hasPrevious = $derived(siblingInfo && siblingInfo.currentIndex > 0);
+	let hasNext = $derived(siblingInfo && siblingInfo.currentIndex < siblingInfo.totalSiblings - 1);
+	let nextSiblingId = $derived(
+		hasNext ? siblingInfo!.siblingIds[siblingInfo!.currentIndex + 1] : null
+	);
+	let previousSiblingId = $derived(
+		hasPrevious ? siblingInfo!.siblingIds[siblingInfo!.currentIndex - 1] : null
+	);
+</script>
+
+{#if siblingInfo && siblingInfo.totalSiblings > 1}
+	<div
+		aria-label="Message version {siblingInfo.currentIndex + 1} of {siblingInfo.totalSiblings}"
+		class="flex items-center gap-1 text-xs text-muted-foreground {className}"
+		role="navigation"
+	>
+		<ActionIcon
+			icon={ChevronLeft}
+			tooltip="Previous version"
+			disabled={!hasPrevious}
+			class="h-5 w-5 p-0 {!hasPrevious ? '!cursor-not-allowed opacity-30' : ''}"
+			onclick={() => onNavigateToSibling?.(previousSiblingId!)}
+		/>
+
+		<span class="px-1 font-mono text-xs">
+			{siblingInfo.currentIndex + 1}/{siblingInfo.totalSiblings}
+		</span>
+
+		<ActionIcon
+			icon={ChevronRight}
+			tooltip="Next version"
+			disabled={!hasNext}
+			class="h-5 w-5 p-0 {!hasNext ? 'opacity-30' : ''}"
+			onclick={() => onNavigateToSibling?.(nextSiblingId!)}
+		/>
+	</div>
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte
similarity index 79%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte
index cdc0cef8c59..3a9cc7e9356 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte
@@ -1,38 +1,104 @@
 <script lang="ts">
+	import { Wrench, Loader2, Brain } from '@lucide/svelte';
 	import {
 		ChatMessageStatistics,
 		CollapsibleContentBlock,
 		MarkdownContent,
-		SyntaxHighlightedCode
+		SyntaxHighlightedCode,
+		ChatMessageActionCardPermissionRequest,
+		ChatMessageActionCardContinueRequest
 	} from '$lib/components/app';
-	import { config } from '$lib/stores/settings.svelte';
-	import { Wrench, Loader2, Brain } from '@lucide/svelte';
-	import { AgenticSectionType, FileTypeText } from '$lib/enums';
-	import { formatJsonPretty } from '$lib/utils';
+
+	import {
+		AgenticSectionType,
+		ChatMessageStatsView,
+		FileTypeText,
+		ToolPermissionDecision
+	} from '$lib/enums';
+	import type {
+		ChatMessageAgenticTimings,
+		ChatMessageAgenticTurnStats,
+		DatabaseMessage
+	} from '$lib/types';
 	import {
 		deriveAgenticSections,
+		formatJsonPretty,
 		parseToolResultWithImages,
 		type AgenticSection,
 		type ToolResultLine
 	} from '$lib/utils';
-	import type { DatabaseMessage } from '$lib/types/database';
-	import type { ChatMessageAgenticTimings, ChatMessageAgenticTurnStats } from '$lib/types/chat';
-	import { ChatMessageStatsView } from '$lib/enums';
+	import {
+		agenticPendingPermissionRequest,
+		agenticResolvePermission,
+		agenticPendingContinueRequest,
+		agenticResolveContinue
+	} from '$lib/stores/agentic.svelte';
+	import { config } from '$lib/stores/settings.svelte';
 
 	interface Props {
 		message: DatabaseMessage;
 		toolMessages?: DatabaseMessage[];
 		isStreaming?: boolean;
+		isLastAssistantMessage?: boolean;
 		highlightTurns?: boolean;
 	}
 
-	let { message, toolMessages = [], isStreaming = false, highlightTurns = false }: Props = $props();
+	let {
+		message,
+		toolMessages = [],
+		isStreaming = false,
+		isLastAssistantMessage = false,
+		highlightTurns = false
+	}: Props = $props();
 
 	let expandedStates: Record<number, boolean> = $state({});
 
 	const showToolCallInProgress = $derived(config().showToolCallInProgress as boolean);
 	const showThoughtInProgress = $derived(config().showThoughtInProgress as boolean);
 
+	let permissionDismissed = $state(false);
+
+	const pendingPermission = $derived(
+		isStreaming && isLastAssistantMessage ? agenticPendingPermissionRequest(message.convId) : null
+	);
+
+	// Reset dismissed when pendingPermission changes (new request or cleared)
+	let prevPendingRef: typeof pendingPermission = null;
+	$effect(() => {
+		if (pendingPermission !== prevPendingRef) {
+			prevPendingRef = pendingPermission;
+			if (pendingPermission) {
+				permissionDismissed = false;
+			}
+		}
+	});
+
+	function handlePermission(decision: ToolPermissionDecision) {
+		permissionDismissed = true;
+		agenticResolvePermission(message.convId, decision);
+	}
+
+	let continueDismissed = $state(false);
+
+	const pendingContinue = $derived(
+		isStreaming && isLastAssistantMessage ? agenticPendingContinueRequest(message.convId) : false
+	);
+
+	let prevContinueRef = false;
+	$effect(() => {
+		if (pendingContinue !== prevContinueRef) {
+			prevContinueRef = pendingContinue;
+			if (pendingContinue) {
+				continueDismissed = false;
+			}
+		}
+	});
+
+	function handleContinue(shouldContinue: boolean) {
+		continueDismissed = true;
+		agenticResolveContinue(message.convId, shouldContinue);
+	}
+
 	const sections = $derived(deriveAgenticSections(message, toolMessages, [], isStreaming));
 
 	// Parse tool results with images
@@ -201,10 +267,16 @@
 						<Loader2 class="h-3 w-3 animate-spin" />
 					{/if}
 				</div>
-				{#if section.toolResult}
+				{#if isPending}
+					<div class="rounded bg-muted/30 p-2 text-xs text-muted-foreground italic">
+						Waiting for result...
+					</div>
+				{:else if section.toolResult}
 					<div class="overflow-auto rounded-lg border border-border bg-muted p-4">
 						{#each section.parsedLines as line, i (i)}
-							<div class="font-mono text-xs leading-relaxed whitespace-pre-wrap">{line.text}</div>
+							<div class="font-mono text-xs leading-relaxed whitespace-pre-wrap">
+								{line.text}
+							</div>
 							{#if line.image}
 								<img
 									src={line.image.base64Url}
@@ -215,10 +287,8 @@
 							{/if}
 						{/each}
 					</div>
-				{:else if isPending}
-					<div class="rounded bg-muted/30 p-2 text-xs text-muted-foreground italic">
-						Waiting for result...
-					</div>
+				{:else}
+					<div class="rounded bg-muted/30 p-2 text-xs text-muted-foreground italic">No output</div>
 				{/if}
 			</div>
 		</CollapsibleContentBlock>
@@ -289,6 +359,18 @@
 			{@render renderSection(section, index)}
 		{/each}
 	{/if}
+
+	{#if pendingPermission && !permissionDismissed}
+		<ChatMessageActionCardPermissionRequest
+			toolName={pendingPermission.toolName}
+			serverLabel={pendingPermission.serverLabel}
+			onDecision={handlePermission}
+		/>
+	{/if}
+
+	{#if pendingContinue && !continueDismissed}
+		<ChatMessageActionCardContinueRequest onDecision={handleContinue} />
+	{/if}
 </div>
 
 <style>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageEditForm.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageEditForm.svelte
similarity index 78%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageEditForm.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageEditForm.svelte
index 1985ae38e7f..962f2a28538 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageEditForm.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageEditForm.svelte
@@ -4,15 +4,18 @@
 	import { Switch } from '$lib/components/ui/switch';
 	import { ChatForm, DialogConfirmation } from '$lib/components/app';
 	import { getMessageEditContext } from '$lib/contexts';
-	import { KeyboardKey } from '$lib/enums';
+	import { KeyboardKey, MessageRole } from '$lib/enums';
 	import { chatStore } from '$lib/stores/chat.svelte';
 	import { processFilesToChatUploaded } from '$lib/utils/browser-only';
 
 	const editCtx = getMessageEditContext();
 
-	let inputAreaRef: ChatForm | undefined = $state(undefined);
 	let saveWithoutRegenerate = $state(false);
 	let showDiscardDialog = $state(false);
+	let branchAfterEdit = $state(false);
+
+	let isUserMessage = $derived(editCtx.messageRole === MessageRole.USER);
+	let isAssistantMessage = $derived(editCtx.messageRole === MessageRole.ASSISTANT);
 
 	let hasUnsavedChanges = $derived.by(() => {
 		if (editCtx.editedContent !== editCtx.originalContent) return true;
@@ -52,13 +55,18 @@
 	function handleSubmit() {
 		if (!canSubmit) return;
 
-		if (saveWithoutRegenerate && editCtx.showSaveOnlyOption) {
+		if (isUserMessage && saveWithoutRegenerate && editCtx.showSaveOnlyOption) {
 			editCtx.saveOnly();
 		} else {
+			if (isAssistantMessage && editCtx.setShouldBranchAfterEdit) {
+				editCtx.setShouldBranchAfterEdit(branchAfterEdit);
+			}
+
 			editCtx.save();
 		}
 
 		saveWithoutRegenerate = false;
+		branchAfterEdit = false;
 	}
 
 	function handleAttachmentRemove(index: number) {
@@ -77,10 +85,6 @@
 		editCtx.setUploadedFiles([...editCtx.editedUploadedFiles, ...processed]);
 	}
 
-	function handleUploadedFilesChange(files: ChatUploadedFile[]) {
-		editCtx.setUploadedFiles(files);
-	}
-
 	$effect(() => {
 		chatStore.setEditModeActive(handleFilesAdd);
 
@@ -94,23 +98,23 @@
 
 <div class="relative w-full max-w-[80%]">
 	<ChatForm
-		bind:this={inputAreaRef}
 		value={editCtx.editedContent}
 		attachments={editCtx.editedExtras}
-		uploadedFiles={editCtx.editedUploadedFiles}
+		bind:uploadedFiles={editCtx.editedUploadedFiles}
 		placeholder="Edit your message..."
 		showMcpPromptButton
+		showAddButton={editCtx.messageRole === MessageRole.USER}
+		showModelSelector={editCtx.messageRole === MessageRole.USER}
 		onValueChange={editCtx.setContent}
 		onAttachmentRemove={handleAttachmentRemove}
 		onUploadedFileRemove={handleUploadedFileRemove}
-		onUploadedFilesChange={handleUploadedFilesChange}
 		onFilesAdd={handleFilesAdd}
 		onSubmit={handleSubmit}
 	/>
 </div>
 
 <div class="mt-2 flex w-full max-w-[80%] items-center justify-between">
-	{#if editCtx.showSaveOnlyOption}
+	{#if isUserMessage && editCtx.showSaveOnlyOption}
 		<div class="flex items-center gap-2">
 			<Switch id="save-only-switch" bind:checked={saveWithoutRegenerate} class="scale-75" />
 
@@ -118,6 +122,14 @@
 				Update without re-sending
 			</label>
 		</div>
+	{:else if isAssistantMessage}
+		<div class="flex items-center gap-2">
+			<Switch id="branch-after-edit" bind:checked={branchAfterEdit} class="scale-75" />
+
+			<label for="branch-after-edit" class="cursor-pointer text-xs text-muted-foreground">
+				Branch conversation after edit
+			</label>
+		</div>
 	{:else}
 		<div></div>
 	{/if}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics/ChatMessageStatistics.svelte
similarity index 94%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics/ChatMessageStatistics.svelte
index 945dec9c5e9..34362e026fb 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics/ChatMessageStatistics.svelte
@@ -1,6 +1,6 @@
 <script lang="ts">
 	import { Clock, Gauge, WholeWord, BookOpenText, Sparkles, Wrench, Layers } from '@lucide/svelte';
-	import { BadgeChatStatistic } from '$lib/components/app';
+	import { ChatMessageStatisticsBadge } from '$lib/components/app';
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { ChatMessageStatsView } from '$lib/enums';
 	import type { ChatMessageAgenticTimings } from '$lib/types/chat';
@@ -215,84 +215,84 @@
 
 	<div class="flex items-center gap-1 px-2">
 		{#if activeView === ChatMessageStatsView.GENERATION && hasGenerationStats}
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={WholeWord}
 				value="{predictedTokens?.toLocaleString()} tokens"
 				tooltipLabel="Generated tokens"
 			/>
 
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Clock}
 				value={formattedTime}
 				tooltipLabel="Generation time"
 			/>
 
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Gauge}
 				value="{tokensPerSecond.toFixed(2)} t/s"
 				tooltipLabel="Generation speed"
 			/>
 		{:else if activeView === ChatMessageStatsView.TOOLS && hasAgenticStats}
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Wrench}
 				value="{agenticTimings!.toolCallsCount} calls"
 				tooltipLabel="Tool calls executed"
 			/>
 
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Clock}
 				value={formattedAgenticToolsTime}
 				tooltipLabel="Tool execution time"
 			/>
 
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Gauge}
 				value="{agenticToolsPerSecond.toFixed(2)} calls/s"
 				tooltipLabel="Tool execution rate"
 			/>
 		{:else if activeView === ChatMessageStatsView.SUMMARY && hasAgenticStats}
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Layers}
 				value="{agenticTimings!.turns} turns"
 				tooltipLabel="Agentic turns (LLM calls)"
 			/>
 
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={WholeWord}
 				value="{agenticTimings!.llm.predicted_n.toLocaleString()} tokens"
 				tooltipLabel="Total tokens generated"
 			/>
 
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Clock}
 				value={formattedAgenticTotalTime}
 				tooltipLabel="Total time (LLM + tools)"
 			/>
 		{:else if hasPromptStats}
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={WholeWord}
 				value="{promptTokens} tokens"
 				tooltipLabel="Prompt tokens"
 			/>
 
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Clock}
 				value={formattedPromptTime ?? '0s'}
 				tooltipLabel="Prompt processing time"
 			/>
 
-			<BadgeChatStatistic
+			<ChatMessageStatisticsBadge
 				class="bg-transparent"
 				icon={Gauge}
 				value="{promptTokensPerSecond!.toFixed(2)} tokens/s"
diff --git a/tools/server/webui/src/lib/components/app/badges/BadgeChatStatistic.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics/ChatMessageStatisticsBadge.svelte
similarity index 82%
rename from tools/server/webui/src/lib/components/app/badges/BadgeChatStatistic.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics/ChatMessageStatisticsBadge.svelte
index a2b28d20577..eea7da7b2f1 100644
--- a/tools/server/webui/src/lib/components/app/badges/BadgeChatStatistic.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics/ChatMessageStatisticsBadge.svelte
@@ -11,7 +11,7 @@
 		tooltipLabel?: string;
 	}
 
-	let { class: className = '', icon: Icon, value, tooltipLabel }: Props = $props();
+	let { class: className = '', icon: IconComponent, value, tooltipLabel }: Props = $props();
 
 	function handleClick() {
 		void copyToClipboard(String(value));
@@ -23,7 +23,7 @@
 		<Tooltip.Trigger>
 			<BadgeInfo class={className} onclick={handleClick}>
 				{#snippet icon()}
-					<Icon class="h-3 w-3" />
+					<IconComponent class="h-3 w-3" />
 				{/snippet}
 
 				{value}
@@ -36,7 +36,7 @@
 {:else}
 	<BadgeInfo class={className} onclick={handleClick}>
 		{#snippet icon()}
-			<Icon class="h-3 w-3" />
+			<IconComponent class="h-3 w-3" />
 		{/snippet}
 
 		{value}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
similarity index 62%
rename from tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
rename to tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
index 6d16b469859..281e6ad0c54 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
@@ -1,11 +1,24 @@
 <script lang="ts">
-	import { fadeInView } from '$lib/actions/fade-in-view.svelte';
-	import { ChatMessage } from '$lib/components/app';
+	import { onMount } from 'svelte';
+	import { beforeNavigate, afterNavigate } from '$app/navigation';
+	import { ChatMessage, ChatMessageUserPending } from '$lib/components/app';
 	import { setChatActionsContext } from '$lib/contexts';
 	import { MessageRole } from '$lib/enums';
 	import { chatStore } from '$lib/stores/chat.svelte';
+	import {
+		chatPendingMessageContent,
+		chatPendingMessageExtras,
+		chatClearPendingMessage,
+		chatInjectPendingMessage
+	} from '$lib/stores/chat.svelte';
 	import { conversationsStore, activeConversation } from '$lib/stores/conversations.svelte';
 	import { config } from '$lib/stores/settings.svelte';
+	import {
+		agenticPendingSteeringMessageContent,
+		agenticPendingSteeringMessageExtras,
+		agenticClearSteeringMessage,
+		agenticInjectSteeringMessage
+	} from '$lib/stores/agentic.svelte';
 	import {
 		copyToClipboard,
 		formatMessageForClipboard,
@@ -14,14 +27,17 @@
 	} from '$lib/utils';
 
 	interface Props {
-		class?: string;
 		messages?: DatabaseMessage[];
 		onUserAction?: () => void;
+		onMessagesReady?: (messageCount: number) => void;
 	}
 
-	let { class: className, messages = [], onUserAction }: Props = $props();
+	let { messages = [], onUserAction, onMessagesReady }: Props = $props();
 
 	let allConversationMessages = $state<DatabaseMessage[]>([]);
+	let isVisible = $state(false);
+	let previousConversationId = $state<string | null>(null);
+
 	const currentConfig = config();
 
 	setChatActionsContext({
@@ -106,15 +122,51 @@
 		}
 	}
 
-	// Single effect that tracks both conversation and message changes
+	// Track conversation changes to trigger transition even on same route
 	$effect(() => {
 		const conversation = activeConversation();
-
-		if (conversation) {
-			refreshAllMessages();
+		const currentId = conversation?.id ?? null;
+
+		if (currentId !== previousConversationId && previousConversationId !== null) {
+			// Conversation changed - trigger fade out/in
+			isVisible = false;
+			requestAnimationFrame(() => {
+				refreshAllMessages();
+				previousConversationId = currentId;
+				requestAnimationFrame(() => {
+					isVisible = true;
+				});
+			});
+		} else {
+			previousConversationId = currentId;
+			if (conversation) {
+				refreshAllMessages();
+			}
 		}
 	});
 
+	$effect(() => {
+		void allConversationMessages;
+
+		onMessagesReady?.(displayMessages.length);
+	});
+
+	onMount(() => {
+		requestAnimationFrame(() => {
+			isVisible = true;
+		});
+	});
+
+	beforeNavigate(() => {
+		isVisible = false;
+	});
+
+	afterNavigate(() => {
+		requestAnimationFrame(() => {
+			isVisible = true;
+		});
+	});
+
 	let displayMessages = $derived.by(() => {
 		if (!messages.length) {
 			return [];
@@ -197,18 +249,46 @@
 </script>
 
 <div
-	class="flex h-full flex-col space-y-10 pt-24 {className}"
-	style="height: auto; min-height: calc(100dvh - 14rem);"
+	class="transition-opacity delay-300 duration-500 ease-out
+		{isVisible ? 'opacity-100' : 'opacity-0'}"
 >
 	{#each displayMessages as { message, toolMessages, isLastAssistantMessage, siblingInfo } (message.id)}
-		<div use:fadeInView>
-			<ChatMessage
-				class="mx-auto w-full max-w-[48rem]"
-				{message}
-				{toolMessages}
-				{isLastAssistantMessage}
-				{siblingInfo}
-			/>
-		</div>
+		<ChatMessage
+			class="mx-auto mt-12 w-full max-w-[48rem]"
+			{message}
+			{toolMessages}
+			{isLastAssistantMessage}
+			{siblingInfo}
+		/>
 	{/each}
+
+	{#if activeConversation() && agenticPendingSteeringMessageContent(activeConversation()!.id)}
+		{@const convId = activeConversation()!.id}
+		{@const pendingContent = agenticPendingSteeringMessageContent(convId)}
+
+		{#if pendingContent}
+			<ChatMessageUserPending
+				class="mx-auto mt-12 w-full max-w-[48rem]"
+				content={pendingContent}
+				extras={agenticPendingSteeringMessageExtras(convId)}
+				onSendImmediately={() => chatStore.abortCurrentFlow(convId)}
+				onEdit={(newContent, extras) => agenticInjectSteeringMessage(convId, newContent, extras)}
+				onDelete={() => agenticClearSteeringMessage(convId)}
+			/>
+		{/if}
+	{:else if activeConversation() && chatPendingMessageContent(activeConversation()!.id)}
+		{@const convId = activeConversation()!.id}
+		{@const pendingContent = chatPendingMessageContent(convId)}
+
+		{#if pendingContent}
+			<ChatMessageUserPending
+				class="mx-auto mt-12 w-full max-w-[48rem]"
+				content={pendingContent}
+				extras={chatPendingMessageExtras(convId)}
+				onSendImmediately={() => chatStore.abortCurrentFlow(convId)}
+				onEdit={(newContent, extras) => chatInjectPendingMessage(convId, newContent, extras)}
+				onDelete={() => chatClearPendingMessage(convId)}
+			/>
+		{/if}
+	{/if}
 </div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
similarity index 65%
rename from tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
rename to tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
index 290a277a53d..dc3eab134f4 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
@@ -1,26 +1,32 @@
 <script lang="ts">
+	import { Trash2, AlertTriangle, RefreshCw } from '@lucide/svelte';
 	import { afterNavigate } from '$app/navigation';
+	import { page } from '$app/state';
+	import { fadeInView } from '$lib/actions/fade-in-view.svelte';
 	import {
 		ChatScreenForm,
-		ChatScreenHeader,
 		ChatMessages,
+		ChatScreenDragOverlay,
 		ChatScreenProcessingInfo,
 		DialogEmptyFileAlert,
+		DialogFileUploadError,
 		DialogChatError,
 		ServerLoadingSplash,
 		DialogConfirmation
 	} from '$lib/components/app';
 	import * as Alert from '$lib/components/ui/alert';
-	import * as AlertDialog from '$lib/components/ui/alert-dialog';
-	import { KeyboardKey } from '$lib/enums';
+	import { setProcessingInfoContext } from '$lib/contexts';
+	import { ErrorDialogType } from '$lib/enums';
 	import { createAutoScrollController } from '$lib/hooks/use-auto-scroll.svelte';
+	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
 	import {
 		chatStore,
 		errorDialog,
 		isLoading,
 		isChatStreaming,
 		isEditing,
-		getAddFilesHandler
+		getAddFilesHandler,
+		activeProcessingState
 	} from '$lib/stores/chat.svelte';
 	import {
 		conversationsStore,
@@ -32,14 +38,12 @@
 	import { modelsStore, modelOptions, selectedModelId } from '$lib/stores/models.svelte';
 	import { isFileTypeSupported, filterFilesByModalities } from '$lib/utils';
 	import { parseFilesToMessageExtras, processFilesToChatUploaded } from '$lib/utils/browser-only';
-	import { ErrorDialogType } from '$lib/enums';
 	import { onMount } from 'svelte';
-	import { fade, fly, slide } from 'svelte/transition';
-	import { Trash2, AlertTriangle, RefreshCw } from '@lucide/svelte';
-	import ChatScreenDragOverlay from './ChatScreenDragOverlay.svelte';
 
 	let { showCenteredEmpty = false } = $props();
 
+	const autoScroll = createAutoScrollController();
+
 	let disableAutoScroll = $derived(Boolean(config().disableAutoScroll));
 	let chatScrollContainer: HTMLDivElement | undefined = $state();
 	let dragCounter = $state(0);
@@ -47,8 +51,6 @@
 	let showFileErrorDialog = $state(false);
 	let uploadedFiles = $state<ChatUploadedFile[]>([]);
 
-	const autoScroll = createAutoScrollController({ isColumnReverse: true });
-
 	let fileErrorData = $state<{
 		generallyUnsupported: File[];
 		modalityUnsupported: File[];
@@ -79,6 +81,12 @@
 
 	let isCurrentConversationLoading = $derived(isLoading() || isChatStreaming());
 
+	let showProcessingInfo = $derived(
+		isCurrentConversationLoading ||
+			(config().keepStatsVisible && !!page.params.id) ||
+			activeProcessingState() !== null
+	);
+
 	let isRouter = $derived(isRouterMode());
 
 	let conversationModel = $derived(
@@ -108,9 +116,16 @@
 
 	let modelPropsVersion = $state(0);
 
+	setProcessingInfoContext({
+		get showProcessingInfo() {
+			return showProcessingInfo;
+		}
+	});
+
 	$effect(() => {
 		if (activeModelId) {
 			const cached = modelsStore.getModelProps(activeModelId);
+
 			if (!cached) {
 				modelsStore.fetchModelProps(activeModelId).then(() => {
 					modelPropsVersion++;
@@ -122,12 +137,23 @@
 	let hasAudioModality = $derived.by(() => {
 		if (activeModelId) {
 			void modelPropsVersion;
+
 			return modelsStore.modelSupportsAudio(activeModelId);
 		}
 
 		return false;
 	});
 
+	let hasVideoModality = $derived.by(() => {
+		if (activeModelId) {
+			void modelPropsVersion;
+
+			return modelsStore.modelSupportsVideo(activeModelId);
+		}
+
+		return false;
+	});
+
 	let hasVisionModality = $derived.by(() => {
 		if (activeModelId) {
 			void modelPropsVersion;
@@ -208,20 +234,13 @@
 		processFiles(files);
 	}
 
-	function handleKeydown(event: KeyboardEvent) {
-		const isCtrlOrCmd = event.ctrlKey || event.metaKey;
-
-		if (
-			isCtrlOrCmd &&
-			event.shiftKey &&
-			(event.key === KeyboardKey.D_LOWER || event.key === KeyboardKey.D_UPPER)
-		) {
-			event.preventDefault();
+	const { handleKeydown } = useKeyboardShortcuts({
+		deleteActiveConversation: () => {
 			if (activeConversation()) {
 				showDeleteDialog = true;
 			}
 		}
-	}
+	});
 
 	async function handleSystemPromptAdd(draft: { message: string; files: ChatUploadedFile[] }) {
 		if (draft.message || draft.files.length > 0) {
@@ -275,7 +294,11 @@
 		}
 
 		// Use model-specific capabilities for file validation
-		const capabilities = { hasVision: hasVisionModality, hasAudio: hasAudioModality };
+		const capabilities = {
+			hasVision: hasVisionModality,
+			hasAudio: hasAudioModality,
+			hasVideo: hasVideoModality
+		};
 		const { supportedFiles, unsupportedFiles, modalityReasons } = filterFilesByModalities(
 			generallySupported,
 			capabilities
@@ -288,6 +311,7 @@
 
 			if (hasVisionModality) supportedTypes.push('images');
 			if (hasAudioModality) supportedTypes.push('audio files');
+			if (hasVideoModality) supportedTypes.push('video files');
 
 			fileErrorData = {
 				generallyUnsupported,
@@ -313,6 +337,14 @@
 		}
 	});
 
+	function handleMessagesReady() {
+		if (!disableAutoScroll && !autoScroll.userScrolledUp) {
+			requestAnimationFrame(() => {
+				autoScroll.scrollToBottom('instant');
+			});
+		}
+	}
+
 	onMount(() => {
 		autoScroll.startObserving();
 
@@ -342,13 +374,13 @@
 
 <svelte:window onkeydown={handleKeydown} />
 
-<ChatScreenHeader />
-
-{#if !isEmpty}
+{#if isServerLoading}
+	<ServerLoadingSplash />
+{:else}
 	<div
 		bind:this={chatScrollContainer}
 		aria-label="Chat interface with file drop zone"
-		class="flex h-full flex-col-reverse overflow-y-auto px-4 md:px-6"
+		class="flex h-full flex-col overflow-y-auto px-4 md:px-6"
 		ondragenter={handleDragEnter}
 		ondragleave={handleDragLeave}
 		ondragover={handleDragOver}
@@ -356,26 +388,45 @@
 		onscroll={handleScroll}
 		role="main"
 	>
-		<div class="flex flex-col">
-			<ChatMessages
-				class="mb-16 md:mb-24"
-				messages={activeMessages()}
-				onUserAction={() => {
-					autoScroll.enable();
-					autoScroll.scrollToBottom();
-				}}
-			/>
+		<div class="flex grow flex-col pt-14">
+			{#if !isEmpty}
+				<ChatMessages
+					messages={activeMessages()}
+					onUserAction={() => {
+						autoScroll.enable();
+						if (!autoScroll.userScrolledUp) {
+							autoScroll.scrollToBottom();
+						}
+					}}
+					onMessagesReady={handleMessagesReady}
+				/>
+			{/if}
 
 			<div
-				class="pointer-events-none sticky right-0 bottom-4 left-0 mt-auto"
-				in:slide={{ duration: 150, axis: 'y' }}
+				class="pointer-events-none {isEmpty
+					? 'absolute bottom-[calc(50dvh-7rem)]'
+					: 'sticky bottom-4'} right-4 left-4 mt-auto pt-16 transition-all duration-200"
 			>
-				<ChatScreenProcessingInfo />
+				{#if isEmpty}
+					<div class="mb-8 px-4 text-center" use:fadeInView={{ duration: 300 }}>
+						<h1 class="mb-2 text-2xl font-semibold tracking-tight md:text-3xl">Hello there</h1>
+
+						<p class="text-muted-foreground md:text-lg">
+							{serverStore.props?.modalities?.audio
+								? 'Record audio, type a message '
+								: 'Type a message'} or upload files to get started
+						</p>
+					</div>
+				{/if}
+
+				{#if page.params.id}
+					<ChatScreenProcessingInfo />
+				{/if}
 
 				{#if hasPropsError}
 					<div
 						class="pointer-events-auto mx-auto mb-4 max-w-[48rem] px-1"
-						in:fly={{ y: 10, duration: 250 }}
+						use:fadeInView={{ y: 10, duration: 250 }}
 					>
 						<Alert.Root variant="destructive">
 							<AlertTriangle class="h-4 w-4" />
@@ -405,146 +456,15 @@
 						onSend={handleSendMessage}
 						onStop={() => chatStore.stopGeneration()}
 						onSystemPromptAdd={handleSystemPromptAdd}
-						showHelperText={false}
 						bind:uploadedFiles
 					/>
 				</div>
 			</div>
 		</div>
 	</div>
-{:else if isServerLoading}
-	<!-- Server Loading State -->
-	<ServerLoadingSplash />
-{:else}
-	<div
-		aria-label="Welcome screen with file drop zone"
-		class="flex h-full items-center justify-center"
-		ondragenter={handleDragEnter}
-		ondragleave={handleDragLeave}
-		ondragover={handleDragOver}
-		ondrop={handleDrop}
-		role="main"
-	>
-		<div class="w-full max-w-[48rem] px-4">
-			<div class="mb-10 text-center" in:fade={{ duration: 300 }}>
-				<h1 class="mb-2 text-2xl font-semibold tracking-tight md:text-3xl">llama.cpp</h1>
-
-				<p class="text-muted-foreground md:text-lg">
-					{serverStore.props?.modalities?.audio
-						? 'Record audio, type a message '
-						: 'Type a message'} or upload files to get started
-				</p>
-			</div>
-
-			{#if hasPropsError}
-				<div class="mb-4" in:fly={{ y: 10, duration: 250 }}>
-					<Alert.Root variant="destructive">
-						<AlertTriangle class="h-4 w-4" />
-
-						<Alert.Title class="flex items-center justify-between">
-							<span>Server unavailable</span>
-
-							<button
-								onclick={() => serverStore.fetch()}
-								disabled={isServerLoading}
-								class="flex items-center gap-1.5 rounded-lg bg-destructive/20 px-2 py-1 text-xs font-medium hover:bg-destructive/30 disabled:opacity-50"
-							>
-								<RefreshCw class="h-3 w-3 {isServerLoading ? 'animate-spin' : ''}" />
-								{isServerLoading ? 'Retrying...' : 'Retry'}
-							</button>
-						</Alert.Title>
-
-						<Alert.Description>{serverError()}</Alert.Description>
-					</Alert.Root>
-				</div>
-			{/if}
-
-			<div in:fly={{ y: 10, duration: 250, delay: hasPropsError ? 0 : 300 }}>
-				<ChatScreenForm
-					disabled={hasPropsError}
-					{initialMessage}
-					isLoading={isCurrentConversationLoading}
-					onFileRemove={handleFileRemove}
-					onFileUpload={handleFileUpload}
-					onSend={handleSendMessage}
-					onStop={() => chatStore.stopGeneration()}
-					onSystemPromptAdd={handleSystemPromptAdd}
-					showHelperText
-					bind:uploadedFiles
-				/>
-			</div>
-		</div>
-	</div>
 {/if}
 
-<!-- File Upload Error Alert Dialog -->
-<AlertDialog.Root bind:open={showFileErrorDialog}>
-	<AlertDialog.Portal>
-		<AlertDialog.Overlay />
-
-		<AlertDialog.Content class="flex max-w-md flex-col">
-			<AlertDialog.Header>
-				<AlertDialog.Title>File Upload Error</AlertDialog.Title>
-
-				<AlertDialog.Description class="text-sm text-muted-foreground">
-					Some files cannot be uploaded with the current model.
-				</AlertDialog.Description>
-			</AlertDialog.Header>
-
-			<div class="!max-h-[50vh] min-h-0 flex-1 space-y-4 overflow-y-auto">
-				{#if fileErrorData.generallyUnsupported.length > 0}
-					<div class="space-y-2">
-						<h4 class="text-sm font-medium text-destructive">Unsupported File Types</h4>
-
-						<div class="space-y-1">
-							{#each fileErrorData.generallyUnsupported as file (file.name)}
-								<div class="rounded-md bg-destructive/10 px-3 py-2">
-									<p class="font-mono text-sm break-all text-destructive">
-										{file.name}
-									</p>
-
-									<p class="mt-1 text-xs text-muted-foreground">File type not supported</p>
-								</div>
-							{/each}
-						</div>
-					</div>
-				{/if}
-
-				{#if fileErrorData.modalityUnsupported.length > 0}
-					<div class="space-y-2">
-						<div class="space-y-1">
-							{#each fileErrorData.modalityUnsupported as file (file.name)}
-								<div class="rounded-md bg-destructive/10 px-3 py-2">
-									<p class="font-mono text-sm break-all text-destructive">
-										{file.name}
-									</p>
-
-									<p class="mt-1 text-xs text-muted-foreground">
-										{fileErrorData.modalityReasons[file.name] || 'Not supported by current model'}
-									</p>
-								</div>
-							{/each}
-						</div>
-					</div>
-				{/if}
-			</div>
-
-			<div class="rounded-md bg-muted/50 p-3">
-				<h4 class="mb-2 text-sm font-medium">This model supports:</h4>
-
-				<p class="text-sm text-muted-foreground">
-					{fileErrorData.supportedTypes.join(', ')}
-				</p>
-			</div>
-
-			<AlertDialog.Footer>
-				<AlertDialog.Action onclick={() => (showFileErrorDialog = false)}>
-					Got it
-				</AlertDialog.Action>
-			</AlertDialog.Footer>
-		</AlertDialog.Content>
-	</AlertDialog.Portal>
-</AlertDialog.Root>
+<DialogFileUploadError bind:open={showFileErrorDialog} {fileErrorData} />
 
 <DialogConfirmation
 	bind:open={showDeleteDialog}
@@ -575,21 +495,3 @@
 	open={Boolean(activeErrorDialog)}
 	type={activeErrorDialog?.type ?? ErrorDialogType.SERVER}
 />
-
-<style>
-	.conversation-chat-form {
-		position: relative;
-
-		&::after {
-			content: '';
-			position: absolute;
-			bottom: 0;
-			z-index: -1;
-			left: 0;
-			right: 0;
-			width: 100%;
-			height: 2.375rem;
-			background-color: var(--background);
-		}
-	}
-</style>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenDragOverlay.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenDragOverlay.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenDragOverlay.svelte
rename to tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenDragOverlay.svelte
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte
similarity index 77%
rename from tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte
rename to tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte
index 086044e8c45..aa1c0536dbc 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte
@@ -1,7 +1,9 @@
 <script lang="ts">
 	import { afterNavigate } from '$app/navigation';
-	import { ChatFormHelperText, ChatForm } from '$lib/components/app';
+	import { page } from '$app/state';
+	import { ChatForm } from '$lib/components/app';
 	import { onMount } from 'svelte';
+	import { useDraftMessages } from '$lib/hooks/use-draft-messages.svelte';
 
 	interface Props {
 		class?: string;
@@ -13,7 +15,6 @@
 		onSend?: (message: string, files?: ChatUploadedFile[]) => Promise<boolean>;
 		onStop?: () => void;
 		onSystemPromptAdd?: (draft: { message: string; files: ChatUploadedFile[] }) => void;
-		showHelperText?: boolean;
 		uploadedFiles?: ChatUploadedFile[];
 	}
 
@@ -27,36 +28,31 @@
 		onSend,
 		onStop,
 		onSystemPromptAdd,
-		showHelperText = true,
 		uploadedFiles = $bindable([])
 	}: Props = $props();
 
 	let chatFormRef: ChatForm | undefined = $state(undefined);
+	let chatId = $derived(page.params.id as string | undefined);
+	let hasLoadingAttachments = $derived(uploadedFiles.some((f) => f.isLoading));
 	let message = $derived(initialMessage);
 	let previousIsLoading = $derived(isLoading);
 	let previousInitialMessage = $derived(initialMessage);
 
-	// Sync message when initialMessage prop changes (e.g., after draft restoration)
-	$effect(() => {
-		if (initialMessage !== previousInitialMessage) {
-			message = initialMessage;
-			previousInitialMessage = initialMessage;
-		}
+	const { clearDraft } = useDraftMessages({
+		getChatId: () => chatId,
+		getMessage: () => message,
+		getFiles: () => uploadedFiles,
+		setMessage: (m) => (message = m),
+		setFiles: (f) => (uploadedFiles = f),
+		getInitialMessage: () => initialMessage
 	});
 
-	function handleSystemPromptClick() {
-		onSystemPromptAdd?.({ message, files: uploadedFiles });
+	function handleFilesAdd(files: File[]) {
+		onFileUpload?.(files);
 	}
 
-	let hasLoadingAttachments = $derived(uploadedFiles.some((f) => f.isLoading));
-
 	async function handleSubmit() {
-		if (
-			(!message.trim() && uploadedFiles.length === 0) ||
-			disabled ||
-			isLoading ||
-			hasLoadingAttachments
-		)
+		if ((!message.trim() && uploadedFiles.length === 0) || disabled || hasLoadingAttachments)
 			return;
 
 		if (!chatFormRef?.checkModelSelected()) return;
@@ -66,6 +62,7 @@
 
 		message = '';
 		uploadedFiles = [];
+		clearDraft();
 
 		chatFormRef?.resetTextareaHeight();
 
@@ -77,8 +74,8 @@
 		}
 	}
 
-	function handleFilesAdd(files: File[]) {
-		onFileUpload?.(files);
+	function handleSystemPromptClick() {
+		onSystemPromptAdd?.({ message, files: uploadedFiles });
 	}
 
 	function handleUploadedFileRemove(fileId: string) {
@@ -89,8 +86,17 @@
 		setTimeout(() => chatFormRef?.focus(), 10);
 	});
 
-	afterNavigate(() => {
-		setTimeout(() => chatFormRef?.focus(), 10);
+	afterNavigate((navigation) => {
+		if (navigation?.from != null) {
+			setTimeout(() => chatFormRef?.focus(), 10);
+		}
+	});
+
+	$effect(() => {
+		if (initialMessage !== previousInitialMessage) {
+			message = initialMessage;
+			previousInitialMessage = initialMessage;
+		}
 	});
 
 	$effect(() => {
@@ -118,5 +124,3 @@
 		onUploadedFileRemove={handleUploadedFileRemove}
 	/>
 </div>
-
-<ChatFormHelperText show={showHelperText} />
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte
similarity index 90%
rename from tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte
rename to tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte
index 8c88480cef5..b5979db13c2 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte
@@ -5,18 +5,17 @@
 	import { chatStore, isLoading, isChatStreaming } from '$lib/stores/chat.svelte';
 	import { activeMessages, activeConversation } from '$lib/stores/conversations.svelte';
 	import { config } from '$lib/stores/settings.svelte';
+	import { getProcessingInfoContext } from '$lib/contexts';
 
 	const processingState = useProcessingState();
+	const processingInfoCtx = getProcessingInfoContext();
+
+	let showProcessingInfo = $derived(processingInfoCtx.showProcessingInfo);
 
 	let isCurrentConversationLoading = $derived(isLoading());
 	let isStreaming = $derived(isChatStreaming());
-	let hasProcessingData = $derived(processingState.processingState !== null);
 	let processingDetails = $derived(processingState.getTechnicalDetails());
 
-	let showProcessingInfo = $derived(
-		isCurrentConversationLoading || isStreaming || config().keepStatsVisible || hasProcessingData
-	);
-
 	$effect(() => {
 		const conversation = activeConversation();
 
@@ -60,7 +59,9 @@
 	});
 </script>
 
-<div class="chat-processing-info-container pointer-events-none" class:visible={showProcessingInfo}>
+<div
+	class={['chat-processing-info-container pointer-events-none', showProcessingInfo && 'visible']}
+>
 	<div class="chat-processing-info-content">
 		{#each processingDetails as detail (detail)}
 			<span class="chat-processing-info-detail pointer-events-auto backdrop-blur-sm">{detail}</span>
diff --git a/tools/server/webui/src/lib/components/app/chat/index.ts b/tools/ui/src/lib/components/app/chat/index.ts
similarity index 67%
rename from tools/server/webui/src/lib/components/app/chat/index.ts
rename to tools/ui/src/lib/components/app/chat/index.ts
index 88690086c7e..5f65979803c 100644
--- a/tools/server/webui/src/lib/components/app/chat/index.ts
+++ b/tools/ui/src/lib/components/app/chat/index.ts
@@ -22,7 +22,7 @@
  * **Architecture:**
  * - Delegates rendering to specialized thumbnail components based on attachment type
  * - Manages scroll state and navigation arrows for horizontal overflow
- * - Integrates with DialogChatAttachmentPreview for full-size viewing
+ * - Integrates with DialogChatAttachmentsPreview for full-size gallery/single viewing
  * - Validates vision modality support via `activeModelId` prop
  *
  * **Features:**
@@ -49,55 +49,53 @@
  * />
  * ```
  */
-export { default as ChatAttachmentsList } from './ChatAttachments/ChatAttachmentsList.svelte';
+export { default as ChatAttachmentsList } from './ChatAttachments/ChatAttachmentsList/ChatAttachmentsList.svelte';
+
+/**
+ * Renders a single attachment item based on its type (image, file, MCP prompt, or MCP resource).
+ * Delegates to specialized sub-components: ChatAttachmentsListItemThumbnailImage, ChatAttachmentsListItemThumbnailFile,
+ * ChatAttachmentsListItemMcpPrompt, or ChatAttachmentsListItemMcpResource.
+ */
+export { default as ChatAttachmentsListItem } from './ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItem.svelte';
 
 /**
  * Displays MCP Prompt attachment with expandable content preview.
  * Shows server name, prompt name, and allows expanding to view full prompt arguments
- * and content. Used when user selects a prompt from ChatFormPromptPicker.
+ * and content. Used when user selects a prompt from ChatFormPickerMcpPrompts.
  */
-export { default as ChatAttachmentMcpPrompt } from './ChatAttachments/ChatAttachmentMcpPrompt.svelte';
+export { default as ChatAttachmentsListItemMcpPrompt } from './ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte';
 
 /**
  * Displays a single MCP Resource attachment with icon, name, and server info.
  * Shows loading/error states and supports remove action.
  * Used within ChatAttachmentMcpResources for individual resource display.
  */
-export { default as ChatAttachmentMcpResource } from './ChatAttachments/ChatAttachmentMcpResource.svelte';
-
-/**
- * Full-size attachment preview component for dialog display. Handles different file types:
- * images (full-size display), text files (syntax highlighted), PDFs (text extraction or image preview),
- * audio (placeholder with download), and generic files (download option).
- */
-export { default as ChatAttachmentPreview } from './ChatAttachments/ChatAttachmentPreview.svelte';
-
-/**
- * Displays MCP Resource attachments as a horizontal carousel.
- * Shows resource name, URI, and allows clicking to view resource content.
- */
-export { default as ChatAttachmentMcpResources } from './ChatAttachments/ChatAttachmentMcpResources.svelte';
+export { default as ChatAttachmentsListItemMcpResource } from './ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpResource.svelte';
 
 /**
  * Thumbnail for non-image file attachments. Displays file type icon based on extension,
  * file name (truncated), and file size.
  * Handles text files, PDFs, audio, and other document types.
  */
-export { default as ChatAttachmentThumbnailFile } from './ChatAttachments/ChatAttachmentThumbnailFile.svelte';
+export { default as ChatAttachmentsListItemThumbnailFile } from './ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailFile.svelte';
 
 /**
  * Thumbnail for image attachments with lazy loading and error fallback.
  * Displays image preview with configurable dimensions. Falls back to placeholder
  * on load error.
  */
-export { default as ChatAttachmentThumbnailImage } from './ChatAttachments/ChatAttachmentThumbnailImage.svelte';
+export { default as ChatAttachmentsListItemThumbnailImage } from './ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemThumbnailImage.svelte';
 
 /**
- * Grid view of all attachments for "View All" dialog. Displays all attachments
- * in a responsive grid layout when there are too many to show inline.
- * Triggered by "+X more" button in ChatAttachmentsList.
+ * Unified attachment preview component for dialog display. Shows a single file
+ * preview without carousel, or a gallery/carousel view when multiple items exist.
+ * Uses ChatAttachmentPreviewSingle internally for each item's content.
  */
-export { default as ChatAttachmentsViewAll } from './ChatAttachments/ChatAttachmentsViewAll.svelte';
+export { default as ChatAttachmentsPreview } from './ChatAttachments/ChatAttachmentsPreview.svelte';
+export { default as ChatAttachmentsPreviewNavButtons } from './ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewNavButtons.svelte';
+export { default as ChatAttachmentsPreviewFileInfo } from './ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewFileInfo.svelte';
+export { default as ChatAttachmentsPreviewThumbnailStrip } from './ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewThumbnailStrip.svelte';
+export { default as ChatAttachmentsPreviewCurrentItem } from './ChatAttachments/ChatAttachmentsPreview/ChatAttachmentsPreviewCurrentItem/ChatAttachmentsPreviewCurrentItem.svelte';
 
 /**
  *
@@ -122,9 +120,9 @@ export { default as ChatAttachmentsViewAll } from './ChatAttachments/ChatAttachm
  * Used by ChatScreenForm and ChatMessageEditForm for both new conversations and message editing.
  *
  * **Architecture:**
- * - Composes ChatFormTextarea, ChatFormActions, and ChatFormPromptPicker
+ * - Composes ChatFormTextarea, ChatFormActions, and ChatFormPickerMcpPrompts
  * - Manages file upload state via `uploadedFiles` bindable prop
- * - Integrates with ModelsSelector for model selection in router mode
+ * - Integrates with ModelsSelectorDropdown for model selection in router mode
  * - Communicates with parent via callbacks (onSubmit, onFilesAdd, onStop, etc.)
  *
  * **Input Handling:**
@@ -164,18 +162,12 @@ export { default as ChatAttachmentsViewAll } from './ChatAttachments/ChatAttachm
 export { default as ChatForm } from './ChatForm/ChatForm.svelte';
 
 /**
- * Dropdown button for file attachment selection. Opens a menu with options for
- * Images, Text Files, and PDF Files. Each option filters the file picker to
- * appropriate types. Images option is disabled when model lacks vision modality.
- */
-export { default as ChatFormActionAttachmentsDropdown } from './ChatForm/ChatFormActions/ChatFormActionAttachmentsDropdown.svelte';
-
-/**
- * Mobile sheet variant of the file attachment selector. Renders a bottom sheet
- * with the same options as ChatFormActionAttachmentsDropdown, optimized for
- * touch interaction on mobile devices.
+ * Wrapper component for the "add to chat" button (Plus icon).
+ * Exposes a `button` snippet that can be used inside DropdownMenu.Trigger (desktop)
+ * or Sheet.Root (mobile) to maintain consistent styling while allowing
+ * platform-specific trigger wrappers.
  */
-export { default as ChatFormActionAttachmentsSheet } from './ChatForm/ChatFormActions/ChatFormActionAttachmentsSheet.svelte';
+export { default as ChatFormActionsAdd } from './ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionsAdd.svelte';
 
 /**
  * Audio recording button with real-time recording indicator. Records audio
@@ -198,15 +190,66 @@ export { default as ChatFormActions } from './ChatForm/ChatFormActions/ChatFormA
  */
 export { default as ChatFormActionSubmit } from './ChatForm/ChatFormActions/ChatFormActionSubmit.svelte';
 
+/**
+ * Model selector component for the chat form action bar. Renders either a dropdown
+ * (desktop) or bottom sheet (mobile) for selecting the conversation model in router mode.
+ * Exposes an `open` method for programmatically opening the selector.
+ */
+export { default as ChatFormActionModels } from './ChatForm/ChatFormActions/ChatFormActionModels.svelte';
+
+/**
+ * Dropdown submenu for managing tool permissions in the chat form.
+ *
+ * Displays a collapsible list of available tools organized by group (Built-in / JSON Schema).
+ * Each group can be expanded to show individual tools with checkboxes for enabling/disabling.
+ * Provides bulk enable/disable controls per group and shows enabled/total tool counts.
+ * Opens the tools panel on the server when the menu opens.
+ *
+ * Features:
+ * - Grouped tools with collapsible sections
+ * - Group favicon display (MCP server icons)
+ * - Per-group and per-tool toggle checkboxes
+ * - Loading/error states for tool discovery
+ * - Integration with toolsPanel for state management
+ *
+ * @example
+ * ```svelte
+ * <ChatFormActionAddToolsSubmenu />
+ * ```
+ */
+export { default as ChatFormActionAddToolsSubmenu } from './ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddToolsSubmenu.svelte';
+
+/**
+ * Dropdown submenu for managing MCP servers in the chat form.
+ *
+ * Displays a searchable list of enabled MCP servers with toggle switches
+ * to enable/disable each server for chat. Shows server favicon, health status,
+ * and a "Manage MCP Servers" settings link.
+ *
+ * Features:
+ * - Search/filter servers by name or URL
+ * - Per-server toggle to enable/disable for chat
+ * - Health check indicator (shows "Error" badge for failed servers)
+ * - Server favicon display
+ * - Settings link to manage MCP server configuration
+ *
+ * @example
+ * ```svelte
+ * <ChatFormActionAddMcpServersSubmenu onMcpSettingsClick={handleMcpSettingsClick} />
+ * ```
+ */
+export { default as ChatFormActionAddMcpServersSubmenu } from './ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte';
+
 /**
  * Hidden file input element for programmatic file selection.
  */
 export { default as ChatFormFileInputInvisible } from './ChatForm/ChatFormFileInputInvisible.svelte';
 
 /**
- * Helper text display below chat.
+ * Displays MCP Resource attachments as a horizontal carousel.
+ * Shows resource name, URI, and allows clicking to view resource content.
  */
-export { default as ChatFormHelperText } from './ChatForm/ChatFormHelperText.svelte';
+export { default as ChatFormMcpResourcesList } from './ChatForm/ChatFormMcpResourcesList.svelte';
 
 /**
  * Auto-resizing textarea with IME composition support. Automatically adjusts
@@ -216,10 +259,10 @@ export { default as ChatFormHelperText } from './ChatForm/ChatFormHelperText.sve
 export { default as ChatFormTextarea } from './ChatForm/ChatFormTextarea.svelte';
 
 /**
- * **ChatFormPromptPicker** - MCP prompt selection interface
+ * **ChatFormPickerMcpPrompts** - MCP prompt selection interface
  *
  * Floating picker for browsing and selecting MCP Server Prompts.
- * Triggered by typing `/` in the chat input or choosing `MCP Prompt` option in ChatFormActionAttachmentsDropdown.
+ * Triggered by typing `/` in the chat input or choosing `MCP Prompt` option in ChatFormActionAddDropdown.
  * Loads prompts from connected MCP servers and allows users to select and configure them.
  *
  * **Architecture:**
@@ -247,7 +290,7 @@ export { default as ChatFormTextarea } from './ChatForm/ChatFormTextarea.svelte'
  *
  * @example
  * ```svelte
- * <ChatFormPromptPicker
+ * <ChatFormPickerMcpPrompts
  *   bind:this={pickerRef}
  *   isOpen={showPicker}
  *   searchQuery={promptQuery}
@@ -258,58 +301,58 @@ export { default as ChatFormTextarea } from './ChatForm/ChatFormTextarea.svelte'
  * />
  * ```
  */
-export { default as ChatFormPromptPicker } from './ChatForm/ChatFormPromptPicker/ChatFormPromptPicker.svelte';
+export { default as ChatFormPickerMcpPrompts } from './ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPickerMcpPrompts.svelte';
 
 /**
  * Form for entering MCP prompt arguments. Displays input fields for each
  * required argument defined by the prompt. Validates input and submits
  * when all required fields are filled. Shows argument descriptions as hints.
  */
-export { default as ChatFormPromptPickerArgumentForm } from './ChatForm/ChatFormPromptPicker/ChatFormPromptPickerArgumentForm.svelte';
+export { default as ChatFormPromptPickerArgumentForm } from './ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPromptPickerArgumentForm.svelte';
 
 /**
  * Single argument input field with autocomplete suggestions. Fetches suggestions
  * from MCP server based on argument type. Supports keyboard navigation through
  * suggestions list. Used within ChatFormPromptPickerArgumentForm.
  */
-export { default as ChatFormPromptPickerArgumentInput } from './ChatForm/ChatFormPromptPicker/ChatFormPromptPickerArgumentInput.svelte';
+export { default as ChatFormPromptPickerArgumentInput } from './ChatForm/ChatFormPickers/ChatFormPickerMcpPrompts/ChatFormPromptPickerArgumentInput.svelte';
 
 /**
  * Shared popover wrapper for inline picker popovers (prompts, resources).
  * Provides consistent positioning, styling, and open/close behavior.
  */
-export { default as ChatFormPickerPopover } from './ChatForm/ChatFormPickerPopover.svelte';
+export { default as ChatFormPickerPopover } from './ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerPopover.svelte';
 
 /**
  * Generic scrollable list for picker popovers. Provides search input,
  * scroll-into-view for keyboard navigation, loading skeletons, empty state,
  * and optional footer. Uses Svelte 5 snippets for item/skeleton/footer rendering.
- * Shared by ChatFormPromptPicker and ChatFormResourcePicker.
+ * Shared by ChatFormPickerMcpPrompts and ChatFormPickerMcpResources.
  */
-export { default as ChatFormPickerList } from './ChatForm/ChatFormPicker/ChatFormPickerList.svelte';
+export { default as ChatFormPickerList } from './ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerList.svelte';
 
 /**
  * Generic button wrapper for picker list items. Provides consistent styling,
  * hover/selected states, and data-picker-index attribute for scroll-into-view.
- * Shared by ChatFormPromptPicker and ChatFormResourcePicker.
+ * Shared by ChatFormPickerMcpPrompts and ChatFormPickerMcpResources.
  */
-export { default as ChatFormPickerListItem } from './ChatForm/ChatFormPicker/ChatFormPickerListItem.svelte';
+export { default as ChatFormPickerListItem } from './ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerListItem.svelte';
 
 /**
  * Generic header for picker items displaying server favicon, label, item title,
  * and optional description. Accepts `titleExtra` and `subtitle` snippets for
  * custom content like badges or URIs. Shared by both pickers.
  */
-export { default as ChatFormPickerItemHeader } from './ChatForm/ChatFormPicker/ChatFormPickerItemHeader.svelte';
+export { default as ChatFormPickerItemHeader } from './ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerItemHeader.svelte';
 
 /**
  * Generic skeleton loading placeholder for picker list items. Configurable
  * title width and optional badge skeleton. Shared by both pickers.
  */
-export { default as ChatFormPickerListItemSkeleton } from './ChatForm/ChatFormPicker/ChatFormPickerListItemSkeleton.svelte';
+export { default as ChatFormPickerListItemSkeleton } from './ChatForm/ChatFormPickers/ChatFormPicker/ChatFormPickerListItemSkeleton.svelte';
 
 /**
- * **ChatFormResourcePicker** - MCP resource selection interface
+ * **ChatFormPickerMcpResources** - MCP resource selection interface
  *
  * Floating picker for browsing and attaching MCP Server Resources.
  * Triggered by typing `@` in the chat input.
@@ -325,7 +368,16 @@ export { default as ChatFormPickerListItemSkeleton } from './ChatForm/ChatFormPi
  * **Exported API:**
  * - `handleKeydown(event): boolean` - Process keyboard events, returns true if handled
  */
-export { default as ChatFormResourcePicker } from './ChatForm/ChatFormResourcePicker/ChatFormResourcePicker.svelte';
+export { default as ChatFormPickerMcpResources } from './ChatForm/ChatFormPickers/ChatFormPickerMcpResources.svelte';
+
+/**
+ * **ChatFormPickers** - Chat input picker container
+ *
+ * Container component that hosts both MCP prompt and MCP resource pickers.
+ * Manages shared state, keyboard navigation, and coordination between the two
+ * picker interfaces. Used within ChatForm for `@`-triggered pickers.
+ */
+export { default as ChatFormPickers } from './ChatForm/ChatFormPickers/ChatFormPickers.svelte';
 
 /**
  *
@@ -392,7 +444,7 @@ export { default as ChatMessages } from './ChatMessages/ChatMessages.svelte';
  * - Displays branching controls when message has siblings
  *
  * **User Messages:**
- * - Shows attachments via ChatAttachmentsList
+ * - Shows attachments via ChatAttachments
  * - Displays MCP prompts if present
  * - Edit creates new branch or preserves responses
  *
@@ -420,7 +472,7 @@ export { default as ChatMessages } from './ChatMessages/ChatMessages.svelte';
  * />
  * ```
  */
-export { default as ChatMessage } from './ChatMessages/ChatMessage.svelte';
+export { default as ChatMessage } from './ChatMessages/ChatMessage/ChatMessage.svelte';
 
 /**
  * **ChatMessageAgenticContent** - Agentic workflow output display
@@ -456,6 +508,9 @@ export { default as ChatMessage } from './ChatMessages/ChatMessage.svelte';
  * ```
  */
 export { default as ChatMessageAgenticContent } from './ChatMessages/ChatMessageAgenticContent.svelte';
+export { default as ChatMessageActionCardPermissionRequest } from './ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardPermissionRequest.svelte';
+export { default as ChatMessageActionCard } from './ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCard.svelte';
+export { default as ChatMessageActionCardContinueRequest } from './ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardContinueRequest.svelte';
 
 /**
  * Action buttons toolbar for messages. Displays copy, edit, delete, and regenerate
@@ -463,7 +518,7 @@ export { default as ChatMessageAgenticContent } from './ChatMessages/ChatMessage
  * Shows delete confirmation dialog with cascade delete count. Handles raw output toggle
  * for assistant messages.
  */
-export { default as ChatMessageActions } from './ChatMessages/ChatMessageActions.svelte';
+export { default as ChatMessageActionIcons } from './ChatMessages/ChatMessageActions/ChatMessageActionIcons/ChatMessageActionIcons.svelte';
 
 /**
  * Navigation controls for message siblings (conversation branches). Displays
@@ -471,41 +526,29 @@ export { default as ChatMessageActions } from './ChatMessages/ChatMessageActions
  * to navigate between alternative versions of a message created by editing
  * or regenerating. Uses `conversationsStore.navigateToSibling()` for navigation.
  */
-export { default as ChatMessageBranchingControls } from './ChatMessages/ChatMessageBranchingControls.svelte';
+export { default as ChatMessageActionIconsBranchingControls } from './ChatMessages/ChatMessageActions/ChatMessageActionIcons/ChatMessageActionIconsBranchingControls.svelte';
 
 /**
  * Statistics display for assistant messages. Shows token counts (prompt/completion),
  * generation timing, tokens per second, and model name (when enabled in settings).
  * Data sourced from message.timings stored during generation.
  */
-export { default as ChatMessageStatistics } from './ChatMessages/ChatMessageStatistics.svelte';
+export { default as ChatMessageStatistics } from './ChatMessages/ChatMessageStatistics/ChatMessageStatistics.svelte';
+export { default as ChatMessageStatisticsBadge } from './ChatMessages/ChatMessageStatistics/ChatMessageStatisticsBadge.svelte';
 
 /**
  * MCP prompt display in user messages. Shows when user selected an MCP prompt
- * via ChatFormPromptPicker. Displays server name, prompt name, and expandable
+ * via ChatFormPickerMcpPrompts. Displays server name, prompt name, and expandable
  * content preview. Stored in message.extra as DatabaseMessageExtraMcpPrompt.
  */
-export { default as ChatMessageMcpPrompt } from './ChatMessages/ChatMessageMcpPrompt.svelte';
+export { default as ChatMessageMcpPrompt } from './ChatMessages/ChatMessage/ChatMessageMcpPrompt/ChatMessageMcpPrompt.svelte';
 
 /**
  * Formatted content display for MCP prompt messages. Renders the full prompt
  * content with arguments in a readable format. Used within ChatMessageMcpPrompt
  * for the expanded view.
  */
-export { default as ChatMessageMcpPromptContent } from './ChatMessages/ChatMessageMcpPromptContent.svelte';
-
-/**
- * System message display component. Renders system messages with distinct styling.
- * Visibility controlled by `showSystemMessage` config setting.
- */
-export { default as ChatMessageSystem } from './ChatMessages/ChatMessageSystem.svelte';
-
-/**
- * User message display component. Renders user messages with right-aligned bubble styling.
- * Shows message content, attachments via ChatAttachmentsList, and MCP prompts if present.
- * Supports inline editing mode with ChatMessageEditForm integration.
- */
-export { default as ChatMessageUser } from './ChatMessages/ChatMessageUser.svelte';
+export { default as ChatMessageMcpPromptContent } from './ChatMessages/ChatMessage/ChatMessageMcpPrompt/ChatMessageMcpPromptContent.svelte';
 
 /**
  * Assistant message display component. Renders assistant responses with left-aligned styling.
@@ -513,7 +556,7 @@ export { default as ChatMessageUser } from './ChatMessages/ChatMessageUser.svelt
  * (via ChatMessageAgenticContent). Shows model info badge, statistics, and action buttons.
  * Handles streaming state with real-time content updates.
  */
-export { default as ChatMessageAssistant } from './ChatMessages/ChatMessageAssistant.svelte';
+export { default as ChatMessageAssistant } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte';
 
 /**
  * Inline message editing form. Provides textarea for editing message content with
@@ -523,6 +566,21 @@ export { default as ChatMessageAssistant } from './ChatMessages/ChatMessageAssis
  */
 export { default as ChatMessageEditForm } from './ChatMessages/ChatMessageEditForm.svelte';
 
+/**
+ * User message display component. Renders user messages with right-aligned bubble styling.
+ * Shows message content, attachments via ChatAttachmentsList, and MCP prompts if present.
+ * Supports inline editing mode with ChatMessageEditForm integration.
+ */
+export { default as ChatMessageUser } from './ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUser.svelte';
+export { default as ChatMessageUserBubble } from './ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte';
+export { default as ChatMessageUserPending } from './ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte';
+
+/**
+ * System message display component. Renders system messages with distinct styling.
+ * Visibility controlled by `showSystemMessage` config setting.
+ */
+export { default as ChatMessageSystem } from './ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte';
+
 /**
  *
  * SCREEN
@@ -547,7 +605,7 @@ export { default as ChatMessageEditForm } from './ChatMessages/ChatMessageEditFo
  * and server state. Used as the main content area in chat routes.
  *
  * **Architecture:**
- * - Composes ChatMessages, ChatScreenForm, ChatScreenHeader, and dialogs
+ * - Composes ChatMessages, ChatScreenForm, and dialogs
  * - Manages auto-scroll via `createAutoScrollController()` hook
  * - Handles file upload pipeline (validation → processing → state update)
  * - Integrates with serverStore for loading/error/warning states
@@ -602,13 +660,6 @@ export { default as ChatScreenDragOverlay } from './ChatScreen/ChatScreenDragOve
  */
 export { default as ChatScreenForm } from './ChatScreen/ChatScreenForm.svelte';
 
-/**
- * Header bar for chat screen. Displays conversation title (or "New Chat"),
- * model selector (in router mode), and action buttons (delete conversation).
- * Sticky positioned at the top of the chat area.
- */
-export { default as ChatScreenHeader } from './ChatScreen/ChatScreenHeader.svelte';
-
 /**
  * Processing info display during generation. Shows real-time statistics:
  * tokens per second, prompt/completion token counts, and elapsed time.
@@ -616,178 +667,3 @@ export { default as ChatScreenHeader } from './ChatScreen/ChatScreenHeader.svelt
  * Only visible when `isCurrentConversationLoading` is true.
  */
 export { default as ChatScreenProcessingInfo } from './ChatScreen/ChatScreenProcessingInfo.svelte';
-
-/**
- *
- * SETTINGS
- *
- * Application settings components. Settings are persisted to localStorage via
- * the config store and synchronized with server `/props` endpoint for sampling
- * parameters. The settings panel uses a tabbed interface with mobile-responsive
- * horizontal scrolling tabs.
- *
- * **Parameter Sync System:**
- * Sampling parameters (temperature, top_p, etc.) can come from three sources:
- * 1. **Server Props**: Default values from `/props` endpoint
- * 2. **User Custom**: Values explicitly set by user (overrides server)
- * 3. **App Default**: Fallback when server props unavailable
- *
- * The `ChatSettingsParameterSourceIndicator` badge shows which source is active.
- *
- */
-
-/**
- * **ChatSettings** - Application settings panel
- *
- * Comprehensive settings interface with categorized sections. Manages all
- * user preferences and sampling parameters. Integrates with config store
- * for persistence and ParameterSyncService for server synchronization.
- *
- * **Architecture:**
- * - Uses tabbed navigation with category sections
- * - Maintains local form state, commits on save
- * - Tracks user overrides vs server defaults for sampling params
- * - Exposes reset() method for dialog close without save
- *
- * **Categories:**
- * - **General**: API key, system message, show system messages toggle
- * - **Display**: Theme selection, message actions visibility, model info badge
- * - **Sampling**: Temperature, top_p, top_k, min_p, repeat_penalty, etc.
- * - **Penalties**: Frequency penalty, presence penalty, repeat last N
- * - **Import/Export**: Conversation backup and restore
- * - **MCP**: MCP server management (opens DialogChatSettings with MCP tab)
- * - **Developer**: Debug options, disable auto-scroll
- *
- * **Parameter Sync:**
- * - Fetches defaults from server `/props` endpoint
- * - Shows source indicator badge (Custom/Server Props/Default)
- * - Real-time badge updates as user types
- * - Tracks which parameters user has explicitly overridden
- *
- * **Features:**
- * - Mobile-responsive layout with horizontal scrolling tabs
- * - Form validation with error messages
- * - Secure API key storage (masked input)
- * - Import/export conversations as JSON
- * - Reset to defaults option per parameter
- *
- * **Exported API:**
- * - `reset()` - Reset form fields to currently saved values (for cancel action)
- *
- * @example
- * ```svelte
- * <ChatSettings
- *   bind:this={settingsRef}
- *   onSave={() => dialogOpen = false}
- *   onCancel={() => { settingsRef.reset(); dialogOpen = false; }}
- * />
- * ```
- */
-export { default as ChatSettings } from './ChatSettings/ChatSettings.svelte';
-
-/**
- * Footer with save/cancel buttons for settings panel. Positioned at bottom
- * of settings dialog. Save button commits form state to config store,
- * cancel button triggers reset and close.
- */
-export { default as ChatSettingsFooter } from './ChatSettings/ChatSettingsFooter.svelte';
-
-/**
- * Form fields renderer for individual settings. Generates appropriate input
- * components based on field type (text, number, select, checkbox, textarea).
- * Handles validation, help text display, and parameter source indicators.
- */
-export { default as ChatSettingsFields } from './ChatSettings/ChatSettingsFields.svelte';
-
-/**
- * Import/export tab content for conversation data management. Provides buttons
- * to export all conversations as JSON file and import from JSON file.
- * Handles file download/upload and data validation.
- */
-export { default as ChatSettingsImportExportTab } from './ChatSettings/ChatSettingsImportExportTab.svelte';
-
-/**
- * Badge indicating parameter source for sampling settings. Shows one of:
- * - **Custom**: User has explicitly set this value (orange badge)
- * - **Server Props**: Using default from `/props` endpoint (blue badge)
- * - **Default**: Using app default, server props unavailable (gray badge)
- * Updates in real-time as user types to show immediate feedback.
- */
-export { default as ChatSettingsParameterSourceIndicator } from './ChatSettings/ChatSettingsParameterSourceIndicator.svelte';
-
-/**
- *
- * SIDEBAR
- *
- * The sidebar integrates with ShadCN's sidebar component system
- * for consistent styling and mobile responsiveness.
- * Conversations are loaded from conversationsStore and displayed in reverse
- * chronological order (most recent first).
- *
- */
-
-/**
- * **ChatSidebar** - Chat Sidebar with actions menu and conversation list
- *
- * Collapsible sidebar displaying conversation history with search and
- * management actions. Integrates with ShadCN sidebar component for
- * consistent styling and mobile responsiveness.
- *
- * **Architecture:**
- * - Uses ShadCN Sidebar.* components for structure
- * - Fetches conversations from conversationsStore
- * - Manages search state and filtered results locally
- * - Handles conversation CRUD operations via conversationsStore
- *
- * **Navigation:**
- * - Click conversation to navigate to `/chat/[id]`
- * - New chat button navigates to `/` (root)
- * - Active conversation highlighted based on route params
- *
- * **Conversation Management:**
- * - Right-click or menu button for context menu
- * - Rename: Opens inline edit dialog
- * - Delete: Shows confirmation with conversation preview
- * - Delete All: Removes all conversations with confirmation
- *
- * **Features:**
- * - Search/filter conversations by title
- * - Conversation list with message previews (first message truncated)
- * - Active conversation highlighting
- * - Mobile-responsive collapse/expand via ShadCN sidebar
- * - New chat button in header
- * - Settings button opens DialogChatSettings
- *
- * **Exported API:**
- * - `handleMobileSidebarItemClick()` - Close sidebar on mobile after item selection
- * - `activateSearchMode()` - Focus search input programmatically
- * - `editActiveConversation()` - Open rename dialog for current conversation
- *
- * @example
- * ```svelte
- * <ChatSidebar bind:this={sidebarRef} />
- * ```
- */
-export { default as ChatSidebar } from './ChatSidebar/ChatSidebar.svelte';
-
-/**
- * Action buttons for sidebar header. Contains new chat button, settings button,
- * and delete all conversations button. Manages dialog states for settings and
- * delete confirmation.
- */
-export { default as ChatSidebarActions } from './ChatSidebar/ChatSidebarActions.svelte';
-
-/**
- * Single conversation item in sidebar. Displays conversation title (truncated),
- * last message preview, and timestamp. Shows context menu on right-click with
- * rename and delete options. Highlights when active (matches current route).
- * Handles click to navigate and keyboard accessibility.
- */
-export { default as ChatSidebarConversationItem } from './ChatSidebar/ChatSidebarConversationItem.svelte';
-
-/**
- * Search input for filtering conversations in sidebar. Filters conversation
- * list by title as user types. Shows clear button when query is not empty.
- * Integrated into sidebar header with proper styling.
- */
-export { default as ChatSidebarSearch } from './ChatSidebar/ChatSidebarSearch.svelte';
diff --git a/tools/server/webui/src/lib/components/app/content/CollapsibleContentBlock.svelte b/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte
similarity index 96%
rename from tools/server/webui/src/lib/components/app/content/CollapsibleContentBlock.svelte
rename to tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte
index 082738da577..b7297ab6b1a 100644
--- a/tools/server/webui/src/lib/components/app/content/CollapsibleContentBlock.svelte
+++ b/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte
@@ -22,7 +22,7 @@
 	let {
 		open = $bindable(false),
 		class: className = '',
-		icon: Icon,
+		icon: IconComponent,
 		iconClass = 'h-4 w-4',
 		title,
 		subtitle,
@@ -32,6 +32,7 @@
 	}: Props = $props();
 
 	let contentContainer: HTMLDivElement | undefined = $state();
+
 	const autoScroll = createAutoScrollController();
 
 	$effect(() => {
@@ -59,8 +60,8 @@
 	<Card class="gap-0 border-muted bg-muted/30 py-0">
 		<Collapsible.Trigger class="flex w-full cursor-pointer items-center justify-between p-3">
 			<div class="flex items-center gap-2 text-muted-foreground">
-				{#if Icon}
-					<Icon class={iconClass} />
+				{#if IconComponent}
+					<IconComponent class={iconClass} />
 				{/if}
 
 				<span class="font-mono text-sm font-medium">{title}</span>
diff --git a/tools/server/webui/src/lib/components/app/content/MarkdownContent.svelte b/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte
similarity index 98%
rename from tools/server/webui/src/lib/components/app/content/MarkdownContent.svelte
rename to tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte
index 57506a963be..c1b71e4519e 100644
--- a/tools/server/webui/src/lib/components/app/content/MarkdownContent.svelte
+++ b/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte
@@ -13,12 +13,12 @@
 	import { browser } from '$app/environment';
 	import { onDestroy, tick } from 'svelte';
 	import { SvelteMap } from 'svelte/reactivity';
-	import { rehypeRestoreTableHtml } from '$lib/markdown/table-html-restorer';
-	import { rehypeEnhanceLinks } from '$lib/markdown/enhance-links';
-	import { rehypeEnhanceCodeBlocks } from '$lib/markdown/enhance-code-blocks';
-	import { rehypeResolveAttachmentImages } from '$lib/markdown/resolve-attachment-images';
-	import { rehypeRtlSupport } from '$lib/markdown/rehype-rtl-support';
-	import { remarkLiteralHtml } from '$lib/markdown/literal-html';
+	import { rehypeRestoreTableHtml } from './plugins/rehype/table-html-restorer';
+	import { rehypeEnhanceLinks } from './plugins/rehype/enhance-links';
+	import { rehypeEnhanceCodeBlocks } from './plugins/rehype/enhance-code-blocks';
+	import { rehypeResolveAttachmentImages } from './plugins/rehype/resolve-attachment-images';
+	import { rehypeRtlSupport } from './plugins/rehype/rehype-rtl-support';
+	import { remarkLiteralHtml } from './plugins/remark/literal-html';
 	import { copyCodeToClipboard, preprocessLaTeX, getImageErrorFallbackHtml } from '$lib/utils';
 	import {
 		IMAGE_NOT_ERROR_BOUND_SELECTOR,
@@ -34,7 +34,7 @@
 	import githubDarkCss from 'highlight.js/styles/github-dark.css?inline';
 	import githubLightCss from 'highlight.js/styles/github.css?inline';
 	import { mode } from 'mode-watcher';
-	import { ActionIconsCodeBlock, DialogCodePreview } from '$lib/components/app';
+	import { CodeBlockActions, DialogCodePreview } from '$lib/components/app';
 	import { createAutoScrollController } from '$lib/hooks/use-auto-scroll.svelte';
 	import type { DatabaseMessageExtra } from '$lib/types/database';
 	import { config } from '$lib/stores/settings.svelte';
@@ -620,7 +620,7 @@
 		<div class="code-block-wrapper streaming-code-block relative">
 			<div class="code-block-header">
 				<span class="code-language">{incompleteCodeBlock.language || 'text'}</span>
-				<ActionIconsCodeBlock
+				<CodeBlockActions
 					code={incompleteCodeBlock.code}
 					language={incompleteCodeBlock.language || 'text'}
 					disabled
diff --git a/tools/server/webui/src/lib/markdown/enhance-code-blocks.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-code-blocks.ts
similarity index 88%
rename from tools/server/webui/src/lib/markdown/enhance-code-blocks.ts
rename to tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-code-blocks.ts
index 7c3b7f167f0..9d9348a5f0b 100644
--- a/tools/server/webui/src/lib/markdown/enhance-code-blocks.ts
+++ b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-code-blocks.ts
@@ -34,45 +34,35 @@ const COPY_ICON_SVG = `<svg xmlns="http://www.w3.org/2000/svg" width="16" height
 
 const PREVIEW_ICON_SVG = `<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-eye lucide-eye-icon"><path d="M2.062 12.345a1 1 0 0 1 0-.69C3.5 7.73 7.36 5 12 5s8.5 2.73 9.938 6.655a1 1 0 0 1 0 .69C20.5 16.27 16.64 19 12 19s-8.5-2.73-9.938-6.655"/><circle cx="12" cy="12" r="3"/></svg>`;
 
-/**
- * Creates an SVG element node from raw SVG string.
- * Since we can't parse HTML in HAST directly, we use the raw property.
- */
-function createRawHtmlElement(html: string): Element {
+function createIconElement(svg: string): Element {
 	return {
 		type: 'element',
 		tagName: 'span',
 		properties: {},
-		children: [{ type: 'raw', value: html } as unknown as ElementContent]
+		children: [{ type: 'raw', value: svg } as unknown as ElementContent]
 	};
 }
 
-function createCopyButton(codeId: string): Element {
+function createButton(className: string, title: string, iconSvg: string, codeId: string): Element {
 	return {
 		type: 'element',
 		tagName: 'button',
 		properties: {
-			className: [COPY_CODE_BTN_CLASS],
+			className: [className],
 			'data-code-id': codeId,
-			title: 'Copy code',
+			title,
 			type: 'button'
 		},
-		children: [createRawHtmlElement(COPY_ICON_SVG)]
+		children: [createIconElement(iconSvg)]
 	};
 }
 
+function createCopyButton(codeId: string): Element {
+	return createButton(COPY_CODE_BTN_CLASS, 'Copy code', COPY_ICON_SVG, codeId);
+}
+
 function createPreviewButton(codeId: string): Element {
-	return {
-		type: 'element',
-		tagName: 'button',
-		properties: {
-			className: [PREVIEW_CODE_BTN_CLASS],
-			'data-code-id': codeId,
-			title: 'Preview code',
-			type: 'button'
-		},
-		children: [createRawHtmlElement(PREVIEW_ICON_SVG)]
-	};
+	return createButton(PREVIEW_CODE_BTN_CLASS, 'Preview code', PREVIEW_ICON_SVG, codeId);
 }
 
 function createHeader(language: string, codeId: string): Element {
diff --git a/tools/server/webui/src/lib/markdown/enhance-links.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-links.ts
similarity index 100%
rename from tools/server/webui/src/lib/markdown/enhance-links.ts
rename to tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-links.ts
diff --git a/tools/server/webui/src/lib/markdown/rehype-rtl-support.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/rehype-rtl-support.ts
similarity index 100%
rename from tools/server/webui/src/lib/markdown/rehype-rtl-support.ts
rename to tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/rehype-rtl-support.ts
diff --git a/tools/server/webui/src/lib/markdown/resolve-attachment-images.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/resolve-attachment-images.ts
similarity index 100%
rename from tools/server/webui/src/lib/markdown/resolve-attachment-images.ts
rename to tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/resolve-attachment-images.ts
diff --git a/tools/server/webui/src/lib/markdown/table-html-restorer.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/table-html-restorer.ts
similarity index 100%
rename from tools/server/webui/src/lib/markdown/table-html-restorer.ts
rename to tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/table-html-restorer.ts
diff --git a/tools/server/webui/src/lib/markdown/literal-html.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/remark/literal-html.ts
similarity index 100%
rename from tools/server/webui/src/lib/markdown/literal-html.ts
rename to tools/ui/src/lib/components/app/content/MarkdownContent/plugins/remark/literal-html.ts
diff --git a/tools/server/webui/src/lib/components/app/content/SyntaxHighlightedCode.svelte b/tools/ui/src/lib/components/app/content/SyntaxHighlightedCode.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/content/SyntaxHighlightedCode.svelte
rename to tools/ui/src/lib/components/app/content/SyntaxHighlightedCode.svelte
diff --git a/tools/server/webui/src/lib/components/app/content/index.ts b/tools/ui/src/lib/components/app/content/index.ts
similarity index 96%
rename from tools/server/webui/src/lib/components/app/content/index.ts
rename to tools/ui/src/lib/components/app/content/index.ts
index 8ab488bc71e..e468a441ed4 100644
--- a/tools/server/webui/src/lib/components/app/content/index.ts
+++ b/tools/ui/src/lib/components/app/content/index.ts
@@ -29,7 +29,7 @@
  * <MarkdownContent content={message.content} attachments={message.extra} />
  * ```
  */
-export { default as MarkdownContent } from './MarkdownContent.svelte';
+export { default as MarkdownContent } from './MarkdownContent/MarkdownContent.svelte';
 
 /**
  * **SyntaxHighlightedCode** - Code syntax highlighting
diff --git a/tools/ui/src/lib/components/app/dialogs/DialogChatAttachmentsPreview.svelte b/tools/ui/src/lib/components/app/dialogs/DialogChatAttachmentsPreview.svelte
new file mode 100644
index 00000000000..533301dfdad
--- /dev/null
+++ b/tools/ui/src/lib/components/app/dialogs/DialogChatAttachmentsPreview.svelte
@@ -0,0 +1,88 @@
+<script lang="ts">
+	import { Dialog } from 'bits-ui';
+	import { X } from '@lucide/svelte';
+	import * as DialogUI from '$lib/components/ui/dialog';
+	import { ChatAttachmentsPreview } from '$lib/components/app';
+	import { KeyboardKey } from '$lib/enums';
+
+	interface Props {
+		open: boolean;
+		uploadedFiles?: ChatUploadedFile[];
+		attachments?: DatabaseMessageExtra[];
+		activeModelId?: string;
+		previewFocusIndex?: number;
+	}
+
+	let {
+		open = $bindable(false),
+		uploadedFiles = [],
+		attachments = [],
+		activeModelId,
+		previewFocusIndex = 0
+	}: Props = $props();
+
+	function handleClose() {
+		open = false;
+	}
+
+	$effect(() => {
+		if (!open) return;
+
+		function handleKeyDown(event: KeyboardEvent) {
+			const target = event.target as HTMLElement;
+
+			if (target.tagName === 'INPUT' || target.tagName === 'TEXTAREA') return;
+
+			switch (event.key) {
+				case KeyboardKey.ARROW_LEFT:
+					event.preventDefault();
+					event.stopPropagation();
+
+					document.dispatchEvent(new CustomEvent('chat-attachments-nav', { detail: -1 }));
+
+					break;
+				case KeyboardKey.ARROW_RIGHT:
+					event.preventDefault();
+					event.stopPropagation();
+
+					document.dispatchEvent(new CustomEvent('chat-attachments-nav', { detail: 1 }));
+
+					break;
+				case KeyboardKey.SPACE:
+					event.preventDefault();
+					event.stopPropagation();
+
+					document.dispatchEvent(new CustomEvent('chat-attachments-nav', { detail: 1 }));
+
+					break;
+			}
+		}
+
+		document.addEventListener('keydown', handleKeyDown);
+		return () => document.removeEventListener('keydown', handleKeyDown);
+	});
+</script>
+
+<Dialog.Root bind:open>
+	<Dialog.Portal>
+		<DialogUI.Overlay class="bg-black/85" style="z-index: 1000" />
+
+		<Dialog.Content class="fixed inset-0 z-[1000] flex flex-col bg-transparent outline-none">
+			<Dialog.Close
+				class="absolute top-4 right-4 z-10 cursor-pointer text-white hover:text-gray-400"
+				onclick={handleClose}
+				aria-label="Close"
+			>
+				<X class="size-4" />
+			</Dialog.Close>
+
+			<ChatAttachmentsPreview
+				{uploadedFiles}
+				{attachments}
+				{activeModelId}
+				{previewFocusIndex}
+				class="min-h-0 flex-1"
+			/>
+		</Dialog.Content>
+	</Dialog.Portal>
+</Dialog.Root>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogChatError.svelte b/tools/ui/src/lib/components/app/dialogs/DialogChatError.svelte
similarity index 99%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogChatError.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogChatError.svelte
index 54a3c90d3c0..ff1005313e5 100644
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogChatError.svelte
+++ b/tools/ui/src/lib/components/app/dialogs/DialogChatError.svelte
@@ -53,15 +53,19 @@
 
 		<div class={`rounded-lg border px-4 py-3 text-sm ${badgeClass}`}>
 			<p class="font-medium">{message}</p>
+
 			{#if contextInfo}
 				<div class="mt-2 space-y-1 text-xs opacity-80">
 					<p>
 						<span class="font-medium">Prompt tokens:</span>
+
 						{contextInfo.n_prompt_tokens.toLocaleString()}
 					</p>
+
 					{#if contextInfo.n_ctx}
 						<p>
 							<span class="font-medium">Context size:</span>
+
 							{contextInfo.n_ctx.toLocaleString()}
 						</p>
 					{/if}
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogCodePreview.svelte b/tools/ui/src/lib/components/app/dialogs/DialogCodePreview.svelte
similarity index 99%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogCodePreview.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogCodePreview.svelte
index 702519f9ff9..fe5d9b504b8 100644
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogCodePreview.svelte
+++ b/tools/ui/src/lib/components/app/dialogs/DialogCodePreview.svelte
@@ -25,6 +25,7 @@
 
 	function handleOpenChange(nextOpen: boolean) {
 		open = nextOpen;
+
 		onOpenChange?.(nextOpen);
 	}
 </script>
@@ -46,6 +47,7 @@
 				aria-label="Close preview"
 			>
 				<XIcon />
+
 				<span class="sr-only">Close preview</span>
 			</DialogPrimitive.Close>
 		</DialogPrimitive.Content>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogConfirmation.svelte b/tools/ui/src/lib/components/app/dialogs/DialogConfirmation.svelte
similarity index 99%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogConfirmation.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogConfirmation.svelte
index 41fdf7d410c..becc658d3c5 100644
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogConfirmation.svelte
+++ b/tools/ui/src/lib/components/app/dialogs/DialogConfirmation.svelte
@@ -34,6 +34,7 @@
 	function handleKeydown(event: KeyboardEvent) {
 		if (event.key === KeyboardKey.ENTER) {
 			event.preventDefault();
+
 			onConfirm();
 		}
 		onKeydown?.(event);
@@ -52,6 +53,7 @@
 			<AlertDialog.Title class="flex items-center gap-2">
 				{#if icon}
 					{@const IconComponent = icon}
+
 					<IconComponent class="h-5 w-5 {variant === 'destructive' ? 'text-destructive' : ''}" />
 				{/if}
 				{title}
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogConversationSelection.svelte b/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte
similarity index 99%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogConversationSelection.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte
index 1f8ea64bed9..7373250850c 100644
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogConversationSelection.svelte
+++ b/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte
@@ -44,6 +44,7 @@
 				<Dialog.Title>
 					Select Conversations to {mode === 'export' ? 'Export' : 'Import'}
 				</Dialog.Title>
+
 				<Dialog.Description>
 					{#if mode === 'export'}
 						Choose which conversations you want to export. Selected conversations will be downloaded
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogConversationTitleUpdate.svelte b/tools/ui/src/lib/components/app/dialogs/DialogConversationTitleUpdate.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogConversationTitleUpdate.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogConversationTitleUpdate.svelte
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogEmptyFileAlert.svelte b/tools/ui/src/lib/components/app/dialogs/DialogEmptyFileAlert.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogEmptyFileAlert.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogEmptyFileAlert.svelte
diff --git a/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte b/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte
new file mode 100644
index 00000000000..c112bde9f68
--- /dev/null
+++ b/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte
@@ -0,0 +1,83 @@
+<script lang="ts">
+	import * as AlertDialog from '$lib/components/ui/alert-dialog';
+	import { Checkbox } from '$lib/components/ui/checkbox';
+	import Label from '$lib/components/ui/label/label.svelte';
+	import { Shield, ShieldOff } from '@lucide/svelte';
+
+	let {
+		open = $bindable(),
+		includeSensitiveData = $bindable(false),
+		onCancel,
+		onConfirm
+	}: {
+		open: boolean;
+		includeSensitiveData: boolean;
+		onCancel: () => void;
+		onConfirm: () => void;
+	} = $props();
+
+	function handleOpenChange(newOpen: boolean) {
+		if (!newOpen) {
+			onCancel();
+		}
+	}
+</script>
+
+<AlertDialog.Root {open} onOpenChange={handleOpenChange}>
+	<AlertDialog.Content>
+		<AlertDialog.Header>
+			<AlertDialog.Title class="flex items-center gap-2">
+				{#if includeSensitiveData}
+					<ShieldOff class="h-5 w-5 text-destructive" />
+				{:else}
+					<Shield class="h-5 w-5 text-destructive" />
+				{/if}
+				Export Settings
+			</AlertDialog.Title>
+
+			<AlertDialog.Description>
+				{#if includeSensitiveData}
+					<p class="text-amber-500">
+						Warning: This export will include sensitive data such as API keys and MCP server custom
+						headers (e.g., authorization tokens). Do not share this file with anyone you don't
+						trust.
+					</p>
+				{:else}
+					<p>
+						Sensitive data (API keys, MCP server custom headers) will not be included in the export
+						to protect your credentials.
+					</p>
+				{/if}
+			</AlertDialog.Description>
+		</AlertDialog.Header>
+
+		<div class="flex items-center gap-2 py-2">
+			<Checkbox id="include-sensitive" bind:checked={includeSensitiveData} />
+
+			<Label
+				for="include-sensitive"
+				class="text-sm leading-none peer-disabled:cursor-not-allowed peer-disabled:opacity-70"
+			>
+				{#if includeSensitiveData}
+					<span class="text-destructive">Include sensitive data (not recommended)</span>
+				{:else}
+					<span>Include sensitive data</span>
+				{/if}
+			</Label>
+		</div>
+
+		<AlertDialog.Footer>
+			<AlertDialog.Cancel onclick={onCancel}>Cancel</AlertDialog.Cancel>
+			<AlertDialog.Action
+				onclick={onConfirm}
+				class="bg-destructive text-white hover:bg-destructive/80"
+			>
+				{#if includeSensitiveData}
+					Export Anyway
+				{:else}
+					Export Without Sensitive Data
+				{/if}
+			</AlertDialog.Action>
+		</AlertDialog.Footer>
+	</AlertDialog.Content>
+</AlertDialog.Root>
diff --git a/tools/ui/src/lib/components/app/dialogs/DialogFileUploadError.svelte b/tools/ui/src/lib/components/app/dialogs/DialogFileUploadError.svelte
new file mode 100644
index 00000000000..3bb2d357f53
--- /dev/null
+++ b/tools/ui/src/lib/components/app/dialogs/DialogFileUploadError.svelte
@@ -0,0 +1,88 @@
+<script lang="ts">
+	import * as AlertDialog from '$lib/components/ui/alert-dialog';
+
+	interface Props {
+		open: boolean;
+		fileErrorData: {
+			generallyUnsupported: File[];
+			modalityUnsupported: File[];
+			modalityReasons: Record<string, string>;
+			supportedTypes: string[];
+		};
+		onOpenChange?: (open: boolean) => void;
+	}
+
+	let { open = $bindable(), fileErrorData, onOpenChange }: Props = $props();
+
+	function handleOpenChange(newOpen: boolean) {
+		open = newOpen;
+
+		onOpenChange?.(newOpen);
+	}
+</script>
+
+<AlertDialog.Root {open} onOpenChange={handleOpenChange}>
+	<AlertDialog.Portal>
+		<AlertDialog.Overlay />
+
+		<AlertDialog.Content class="flex max-w-md flex-col">
+			<AlertDialog.Header>
+				<AlertDialog.Title>File Upload Error</AlertDialog.Title>
+
+				<AlertDialog.Description class="text-sm text-muted-foreground">
+					Some files cannot be uploaded with the current model.
+				</AlertDialog.Description>
+			</AlertDialog.Header>
+
+			<div class="!max-h-[50vh] min-h-0 flex-1 space-y-4 overflow-y-auto">
+				{#if fileErrorData.generallyUnsupported.length > 0}
+					<div class="space-y-2">
+						<h4 class="text-sm font-medium text-destructive">Unsupported File Types</h4>
+
+						<div class="space-y-1">
+							{#each fileErrorData.generallyUnsupported as file (file.name)}
+								<div class="rounded-md bg-destructive/10 px-3 py-2">
+									<p class="font-mono text-sm break-all text-destructive">
+										{file.name}
+									</p>
+
+									<p class="mt-1 text-xs text-muted-foreground">File type not supported</p>
+								</div>
+							{/each}
+						</div>
+					</div>
+				{/if}
+
+				{#if fileErrorData.modalityUnsupported.length > 0}
+					<div class="space-y-2">
+						<div class="space-y-1">
+							{#each fileErrorData.modalityUnsupported as file (file.name)}
+								<div class="rounded-md bg-destructive/10 px-3 py-2">
+									<p class="font-mono text-sm break-all text-destructive">
+										{file.name}
+									</p>
+
+									<p class="mt-1 text-xs text-muted-foreground">
+										{fileErrorData.modalityReasons[file.name] || 'Not supported by current model'}
+									</p>
+								</div>
+							{/each}
+						</div>
+					</div>
+				{/if}
+			</div>
+
+			<div class="rounded-md bg-muted/50 p-3">
+				<h4 class="mb-2 text-sm font-medium">This model supports:</h4>
+
+				<p class="text-sm text-muted-foreground">
+					{fileErrorData.supportedTypes.join(', ')}
+				</p>
+			</div>
+
+			<AlertDialog.Footer>
+				<AlertDialog.Action onclick={() => handleOpenChange(false)}>Got it</AlertDialog.Action>
+			</AlertDialog.Footer>
+		</AlertDialog.Content>
+	</AlertDialog.Portal>
+</AlertDialog.Root>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogMcpResourcePreview.svelte b/tools/ui/src/lib/components/app/dialogs/DialogMcpResourcePreview.svelte
similarity index 99%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogMcpResourcePreview.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogMcpResourcePreview.svelte
index ad6c3130b99..7bf284089c9 100644
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogMcpResourcePreview.svelte
+++ b/tools/ui/src/lib/components/app/dialogs/DialogMcpResourcePreview.svelte
@@ -31,11 +31,13 @@
 		if (extra.mimeType?.includes(MimeTypeIncludes.TYPESCRIPT)) return MimeTypeIncludes.TYPESCRIPT;
 
 		const name = extra.name || extra.uri || '';
+
 		return getLanguageFromFilename(name) || 'plaintext';
 	}
 
 	function handleDownload() {
 		if (!extra.content) return;
+
 		downloadResourceContent(
 			extra.content,
 			extra.mimeType || MimeTypeText.PLAIN,
@@ -48,6 +50,7 @@
 	<Dialog.Content class="grid max-h-[90vh] max-w-5xl overflow-hidden sm:w-auto sm:max-w-6xl">
 		<Dialog.Header>
 			<Dialog.Title class="pr-8">{extra.name}</Dialog.Title>
+
 			<Dialog.Description>
 				<div class="flex items-center gap-2">
 					<span class="text-xs text-muted-foreground">{extra.uri}</span>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogMcpResources.svelte b/tools/ui/src/lib/components/app/dialogs/DialogMcpResourcesBrowser.svelte
similarity index 99%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogMcpResources.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogMcpResourcesBrowser.svelte
index db93bb4b693..eb162a55729 100644
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogMcpResources.svelte
+++ b/tools/ui/src/lib/components/app/dialogs/DialogMcpResourcesBrowser.svelte
@@ -11,7 +11,7 @@
 		mcpResourceStore
 	} from '$lib/stores/mcp-resources.svelte';
 	import {
-		McpResourceBrowser,
+		McpResourcesBrowser,
 		McpResourcePreview,
 		McpResourceTemplateForm
 	} from '$lib/components/app';
@@ -275,7 +275,7 @@
 
 		<div class="flex h-[500px] min-w-0">
 			<div class="w-72 shrink-0 overflow-y-auto border-r border-border/30 p-4">
-				<McpResourceBrowser
+				<McpResourcesBrowser
 					onSelect={handleResourceSelect}
 					onToggle={handleResourceToggle}
 					onTemplateSelect={handleTemplateSelect}
diff --git a/tools/ui/src/lib/components/app/dialogs/DialogMcpServerAddNew.svelte b/tools/ui/src/lib/components/app/dialogs/DialogMcpServerAddNew.svelte
new file mode 100644
index 00000000000..349f7e7fbd1
--- /dev/null
+++ b/tools/ui/src/lib/components/app/dialogs/DialogMcpServerAddNew.svelte
@@ -0,0 +1,88 @@
+<script lang="ts">
+	import { Button } from '$lib/components/ui/button';
+	import * as Dialog from '$lib/components/ui/dialog';
+	import { McpServerForm } from '$lib/components/app/mcp';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { conversationsStore } from '$lib/stores/conversations.svelte';
+	import { uuid } from '$lib/utils';
+	import { MCP_SERVER_ID_PREFIX } from '$lib/constants';
+
+	interface Props {
+		open: boolean;
+		onOpenChange?: (open: boolean) => void;
+	}
+
+	let { open = $bindable(), onOpenChange }: Props = $props();
+
+	let newServerUrl = $state('');
+	let newServerHeaders = $state('');
+	let newServerUrlError = $derived.by(() => {
+		if (!newServerUrl.trim()) return 'URL is required';
+		try {
+			new URL(newServerUrl);
+
+			return null;
+		} catch {
+			return 'Invalid URL format';
+		}
+	});
+
+	function handleOpenChange(value: boolean) {
+		if (!value) {
+			newServerUrl = '';
+			newServerHeaders = '';
+		}
+		open = value;
+		onOpenChange?.(value);
+	}
+
+	function saveNewServer() {
+		if (newServerUrlError) return;
+
+		const newServerId = uuid() ?? `${MCP_SERVER_ID_PREFIX}-${Date.now()}`;
+
+		mcpStore.addServer({
+			id: newServerId,
+			enabled: true,
+			url: newServerUrl.trim(),
+			headers: newServerHeaders.trim() || undefined
+		});
+
+		conversationsStore.setMcpServerOverride(newServerId, true);
+
+		handleOpenChange(false);
+	}
+</script>
+
+<Dialog.Root {open} onOpenChange={handleOpenChange}>
+	<Dialog.Content class="sm:max-w-md">
+		<Dialog.Header>
+			<Dialog.Title>Add New Server</Dialog.Title>
+		</Dialog.Header>
+
+		<div class="space-y-4 py-4">
+			<McpServerForm
+				url={newServerUrl}
+				headers={newServerHeaders}
+				onUrlChange={(v) => (newServerUrl = v)}
+				onHeadersChange={(v) => (newServerHeaders = v)}
+				urlError={newServerUrl ? newServerUrlError : null}
+				id="new-server"
+			/>
+		</div>
+
+		<Dialog.Footer>
+			<Button variant="secondary" size="sm" onclick={() => handleOpenChange(false)}>Cancel</Button>
+
+			<Button
+				variant="default"
+				size="sm"
+				onclick={saveNewServer}
+				disabled={!!newServerUrlError}
+				aria-label="Save"
+			>
+				Add
+			</Button>
+		</Dialog.Footer>
+	</Dialog.Content>
+</Dialog.Root>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogModelInformation.svelte b/tools/ui/src/lib/components/app/dialogs/DialogModelInformation.svelte
similarity index 98%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogModelInformation.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogModelInformation.svelte
index 3a1db5c77d3..5a10859a080 100644
--- a/tools/server/webui/src/lib/components/app/dialogs/DialogModelInformation.svelte
+++ b/tools/ui/src/lib/components/app/dialogs/DialogModelInformation.svelte
@@ -1,7 +1,7 @@
 <script lang="ts">
 	import * as Dialog from '$lib/components/ui/dialog';
 	import * as Table from '$lib/components/ui/table';
-	import { BadgeModality, ActionIconCopyToClipboard } from '$lib/components/app';
+	import { BadgesModality, ActionIconCopyToClipboard } from '$lib/components/app';
 	import { serverStore } from '$lib/stores/server.svelte';
 	import { modelsStore, modelOptions, modelsLoading } from '$lib/stores/models.svelte';
 	import { formatFileSize, formatParameters, formatNumber } from '$lib/utils';
@@ -229,7 +229,7 @@
 
 									<Table.Cell>
 										<div class="flex flex-wrap gap-1">
-											<BadgeModality {modalities} />
+											<BadgesModality {modalities} />
 										</div>
 									</Table.Cell>
 								</Table.Row>
diff --git a/tools/server/webui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte b/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte
rename to tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte
diff --git a/tools/server/webui/src/lib/components/app/dialogs/index.ts b/tools/ui/src/lib/components/app/dialogs/index.ts
similarity index 76%
rename from tools/server/webui/src/lib/components/app/dialogs/index.ts
rename to tools/ui/src/lib/components/app/dialogs/index.ts
index 216eae6843e..5a6453b72ec 100644
--- a/tools/server/webui/src/lib/components/app/dialogs/index.ts
+++ b/tools/ui/src/lib/components/app/dialogs/index.ts
@@ -11,30 +11,43 @@
  */
 
 /**
+ * **DialogMcpServerAddNew** - Add new MCP server dialog
  *
- * SETTINGS DIALOGS
- *
- * Dialogs for application and server configuration.
- *
+ * Modal dialog for adding a new MCP server with URL and optional headers.
+ * Validates URL format and integrates with mcpStore and conversationsStore.
  */
+export { default as DialogMcpServerAddNew } from './DialogMcpServerAddNew.svelte';
 
 /**
- * **DialogChatSettings** - Settings dialog wrapper
+ * **DialogExportSettings** - Settings export dialog with sensitive data warning
  *
- * Modal dialog containing ChatSettings component with proper
- * open/close state management and automatic form reset on open.
+ * Dialog for exporting settings with an option to include or exclude
+ * sensitive data (API keys, MCP server custom headers). Defaults to excluding
+ * sensitive data for security. User must explicitly opt-in to include them.
  *
  * **Architecture:**
- * - Wraps ChatSettings component in ShadCN Dialog
- * - Manages open/close state via bindable `open` prop
- * - Resets form state when dialog opens to discard unsaved changes
+ * - Uses ShadCN AlertDialog
+ * - Checkbox to toggle sensitive data inclusion (defaults to false)
+ * - Warning icon and message when sensitive data is included
+ * - Destructive variant for the action button when exporting with sensitive data
+ *
+ * **Features:**
+ * - Secure default: sensitive data excluded by default
+ * - User must explicitly opt-in to include sensitive data
+ * - Visual warning (ShieldOff icon) when sensitive data is included
+ * - Different action text based on sensitive data state
  *
  * @example
  * ```svelte
- * <DialogChatSettings bind:open={showSettings} />
+ * <DialogExportSettings
+ *   bind:open={showExportSettings}
+ *   bind:includeSensitiveData
+ *   onConfirm={handleSettingsExport}
+ *   onCancel={() => showExportSettings = false}
+ * />
  * ```
  */
-export { default as DialogChatSettings } from './DialogChatSettings.svelte';
+export { default as DialogExportSettings } from './DialogExportSettings.svelte';
 
 /**
  *
@@ -155,72 +168,36 @@ export { default as DialogCodePreview } from './DialogCodePreview.svelte';
  */
 
 /**
- * **DialogChatAttachmentPreview** - Full-size attachment preview
+ * **DialogChatAttachmentsPreview** - Unified attachment preview dialog
  *
- * Modal dialog for viewing file attachments at full size. Supports different
- * file types with appropriate preview modes: images, text files, PDFs, and audio.
+ * Modal dialog for previewing file attachments. Automatically adapts to the
+ * number of items: shows a single file preview without carousel for one item,
+ * or a gallery with carousel navigation for multiple items.
  *
  * **Architecture:**
- * - Wraps ChatAttachmentPreview component in ShadCN Dialog
- * - Accepts either uploaded file or stored attachment as data source
- * - Resets preview state when dialog opens
+ * - Wraps ChatAttachmentsPreview component in ShadCN Dialog
+ * - Accepts uploadedFiles and attachments arrays as data sources
+ * - Filters out MCP prompts and MCP resources from display
  *
  * **Features:**
- * - Full-size image display with proper scaling
- * - Text file content with syntax highlighting
- * - PDF preview with text/image view toggle
- * - Audio file placeholder with download option
- * - File name and size display in header
- * - Download button for all file types
- * - Vision modality check for image attachments
+ * - Single item mode: direct preview without navigation controls
+ * - Multi-item mode: gallery with left/right arrows and thumbnail strip
+ * - File type aware preview (images, text, PDFs, audio)
+ * - File name and size/count display in header
  *
  * @example
  * ```svelte
- * <!-- Preview uploaded file -->
- * <DialogChatAttachmentPreview
- *   bind:open={showPreview}
- *   uploadedFile={selectedFile}
- *   activeModelId={currentModel}
- * />
- *
- * <!-- Preview stored attachment -->
- * <DialogChatAttachmentPreview
+ * <!-- Gallery with focus on 2nd item -->
+ * <DialogChatAttachmentsPreview
  *   bind:open={showPreview}
- *   attachment={selectedAttachment}
- * />
- * ```
- */
-export { default as DialogChatAttachmentPreview } from './DialogChatAttachmentPreview.svelte';
-
-/**
- * **DialogChatAttachmentsViewAll** - Grid view of all attachments
- *
- * Dialog showing all attachments in a responsive grid layout. Triggered by
- * "+X more" button in ChatAttachmentsList when there are too many attachments
- * to display inline.
- *
- * **Architecture:**
- * - Wraps ChatAttachmentsViewAll component in ShadCN Dialog
- * - Supports both readonly (message view) and editable (form) modes
- * - Displays total attachment count in header
- *
- * **Features:**
- * - Responsive grid layout for all attachments
- * - Thumbnail previews with click-to-expand
- * - Remove button in editable mode
- * - Configurable thumbnail dimensions
- * - Vision modality validation for images
- *
- * @example
- * ```svelte
- * <DialogChatAttachmentsViewAll
- *   bind:open={showAllAttachments}
+ *   uploadedFiles={pendingFiles}
  *   attachments={message.extra}
- *   readonly
+ *   activeModelId={currentModel}
+ *   previewFocusIndex={1}
  * />
  * ```
  */
-export { default as DialogChatAttachmentsViewAll } from './DialogChatAttachmentsViewAll.svelte';
+export { default as DialogChatAttachmentsPreview } from './DialogChatAttachmentsPreview.svelte';
 
 /**
  *
@@ -293,6 +270,35 @@ export { default as DialogChatError } from './DialogChatError.svelte';
  */
 export { default as DialogEmptyFileAlert } from './DialogEmptyFileAlert.svelte';
 
+/**
+ * **DialogFileUploadError** - File upload compatibility error
+ *
+ * Alert dialog shown when files cannot be uploaded due to type incompatibility
+ * or model modality restrictions. Displays a categorized list of problematic
+ * files with explanations and shows which file types the current model supports.
+ *
+ * **Architecture:**
+ * - Uses ShadCN AlertDialog for modal display
+ * - Receives structured file error data from ChatScreen
+ * - Triggered during file upload validation in processFiles()
+ *
+ * **Features:**
+ * - Categorized display: unsupported types vs modality restrictions
+ * - File name in monospace with contextual error messages
+ * - Summary of supported file types for the current model
+ * - Scrollable content area for large error lists
+ * - Single "Got it" dismiss button
+ *
+ * @example
+ * ```svelte
+ * <DialogFileUploadError
+ *   bind:open={showFileError}
+ *   fileErrorData={errorData}
+ * />
+ * ```
+ */
+export { default as DialogFileUploadError } from './DialogFileUploadError.svelte';
+
 /**
  * **DialogModelNotAvailable** - Model unavailable error
  *
@@ -416,7 +422,7 @@ export { default as DialogConversationSelection } from './DialogConversationSele
 export { default as DialogModelInformation } from './DialogModelInformation.svelte';
 
 /**
- * **DialogMcpResources** - MCP resources browser dialog
+ * **DialogMcpResourcesBrowser** - MCP resources browser dialog
  *
  * Dialog for browsing and attaching MCP resources to chat context.
  * Displays resources from connected MCP servers in a tree structure
@@ -424,7 +430,7 @@ export { default as DialogModelInformation } from './DialogModelInformation.svel
  *
  * **Architecture:**
  * - Uses ShadCN Dialog with two-panel layout
- * - Left panel: McpResourceBrowser with tree navigation
+ * - Left panel: McpResourcesBrowser with tree navigation
  * - Right panel: McpResourcePreview for selected resource
  * - Integrates with mcpStore for resource fetching and attachment
  *
@@ -437,13 +443,13 @@ export { default as DialogModelInformation } from './DialogModelInformation.svel
  *
  * @example
  * ```svelte
- * <DialogMcpResources
+ * <DialogMcpResourcesBrowser
  *   bind:open={showResources}
  *   onAttach={handleResourceAttach}
  * />
  * ```
  */
-export { default as DialogMcpResources } from './DialogMcpResources.svelte';
+export { default as DialogMcpResourcesBrowser } from './DialogMcpResourcesBrowser.svelte';
 
 /**
  * **DialogMcpResourcePreview** - MCP resource content preview
diff --git a/tools/server/webui/src/lib/components/app/forms/InputWithSuggestions.svelte b/tools/ui/src/lib/components/app/forms/InputWithSuggestions.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/forms/InputWithSuggestions.svelte
rename to tools/ui/src/lib/components/app/forms/InputWithSuggestions.svelte
diff --git a/tools/server/webui/src/lib/components/app/forms/KeyValuePairs.svelte b/tools/ui/src/lib/components/app/forms/KeyValuePairs.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/forms/KeyValuePairs.svelte
rename to tools/ui/src/lib/components/app/forms/KeyValuePairs.svelte
diff --git a/tools/server/webui/src/lib/components/app/forms/SearchInput.svelte b/tools/ui/src/lib/components/app/forms/SearchInput.svelte
similarity index 80%
rename from tools/server/webui/src/lib/components/app/forms/SearchInput.svelte
rename to tools/ui/src/lib/components/app/forms/SearchInput.svelte
index 9a8088d9dae..19dd7e6a7ef 100644
--- a/tools/server/webui/src/lib/components/app/forms/SearchInput.svelte
+++ b/tools/ui/src/lib/components/app/forms/SearchInput.svelte
@@ -11,6 +11,7 @@
 		class?: string;
 		id?: string;
 		ref?: HTMLInputElement | null;
+		isCancelAlwaysVisible?: boolean;
 	}
 
 	let {
@@ -21,10 +22,11 @@
 		onKeyDown,
 		class: className,
 		id,
-		ref = $bindable(null)
+		ref = $bindable(null),
+		isCancelAlwaysVisible = false
 	}: Props = $props();
 
-	let showClearButton = $derived(!!value || !!onClose);
+	let showClearButton = $derived(isCancelAlwaysVisible || !!value || !!onClose);
 
 	function handleInput(event: Event) {
 		const target = event.target as HTMLInputElement;
@@ -63,7 +65,7 @@
 	{#if showClearButton}
 		<button
 			type="button"
-			class="absolute top-1/2 right-3 -translate-y-1/2 transform text-muted-foreground transition-colors hover:text-foreground"
+			class="absolute top-1/2 right-3 -translate-y-1/2 transform cursor-pointer text-muted-foreground transition-colors hover:text-foreground"
 			onclick={handleClear}
 			aria-label={value ? 'Clear search' : 'Close'}
 		>
diff --git a/tools/server/webui/src/lib/components/app/forms/index.ts b/tools/ui/src/lib/components/app/forms/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/app/forms/index.ts
rename to tools/ui/src/lib/components/app/forms/index.ts
diff --git a/tools/server/webui/src/lib/components/app/index.ts b/tools/ui/src/lib/components/app/index.ts
similarity index 91%
rename from tools/server/webui/src/lib/components/app/index.ts
rename to tools/ui/src/lib/components/app/index.ts
index 56bd8a48520..4914c743a50 100644
--- a/tools/server/webui/src/lib/components/app/index.ts
+++ b/tools/ui/src/lib/components/app/index.ts
@@ -6,6 +6,7 @@ export * from './dialogs';
 export * from './forms';
 export * from './mcp';
 export * from './misc';
+export * from './settings';
 export * from './models';
 export * from './navigation';
 export * from './server';
diff --git a/tools/ui/src/lib/components/app/mcp/McpActiveServersAvatars.svelte b/tools/ui/src/lib/components/app/mcp/McpActiveServersAvatars.svelte
new file mode 100644
index 00000000000..2f732cfd537
--- /dev/null
+++ b/tools/ui/src/lib/components/app/mcp/McpActiveServersAvatars.svelte
@@ -0,0 +1,89 @@
+<script lang="ts">
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import { conversationsStore } from '$lib/stores/conversations.svelte';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { HealthCheckStatus } from '$lib/enums';
+	import { MAX_DISPLAYED_MCP_AVATARS } from '$lib/constants';
+	import McpLogo from './McpLogo.svelte';
+
+	interface Props {
+		class?: string;
+		onclick?: () => void;
+	}
+
+	let { class: className = '', onclick }: Props = $props();
+
+	let mcpServers = $derived(mcpStore.getServersSorted().filter((s) => s.enabled));
+	let enabledMcpServersForChat = $derived(
+		mcpServers.filter((s) => conversationsStore.isMcpServerEnabledForChat(s.id) && s.url.trim())
+	);
+	let healthyEnabledMcpServers = $derived(
+		enabledMcpServersForChat.filter((s) => {
+			const healthState = mcpStore.getHealthCheckState(s.id);
+			return healthState.status !== HealthCheckStatus.ERROR;
+		})
+	);
+	let hasEnabledMcpServers = $derived(enabledMcpServersForChat.length > 0);
+	let extraServersCount = $derived(
+		Math.max(0, healthyEnabledMcpServers.length - MAX_DISPLAYED_MCP_AVATARS)
+	);
+	let mcpFavicons = $derived(
+		healthyEnabledMcpServers
+			.slice(0, MAX_DISPLAYED_MCP_AVATARS)
+			.map((s) => ({
+				id: s.id,
+				name: mcpStore.getServerDisplayName(s.id),
+				url: mcpStore.getServerFavicon(s.id)
+			}))
+			.filter((f) => f.url !== null)
+	);
+</script>
+
+{#if !hasEnabledMcpServers}
+	<button
+		class={[
+			'inline-flex cursor-pointer items-center gap-0.75 opacity-70 transition-opacity hover:opacity-100',
+			className,
+			'opacity-50 hover:opacity-100'
+		]}
+		{onclick}
+	>
+		<Tooltip.Root>
+			<Tooltip.Trigger>
+				<McpLogo class="h-4 w-4" />
+			</Tooltip.Trigger>
+
+			<Tooltip.Content>
+				<p>MCP Servers</p>
+			</Tooltip.Content>
+		</Tooltip.Root>
+	</button>
+{:else if mcpFavicons.length > 0}
+	<button class={['inline-flex items-center gap-0.75', className]} {onclick}>
+		<div class="flex -space-x-1">
+			{#each mcpFavicons as favicon (favicon.id)}
+				<Tooltip.Root>
+					<Tooltip.Trigger>
+						<div class="box-shadow-lg overflow-hidden rounded-full bg-muted ring-1 ring-muted">
+							<img
+								src={favicon.url}
+								alt=""
+								class="h-4 w-4"
+								onerror={(e) => {
+									(e.currentTarget as HTMLImageElement).style.display = 'none';
+								}}
+							/>
+						</div>
+					</Tooltip.Trigger>
+					<Tooltip.Content>
+						<p>{favicon.name}</p>
+					</Tooltip.Content>
+				</Tooltip.Root>
+			{/each}
+		</div>
+
+		{#if extraServersCount > 0}
+			<span class="text-xs text-muted-foreground">+{extraServersCount}</span>
+		{/if}
+	</button>
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpCapabilitiesBadges.svelte b/tools/ui/src/lib/components/app/mcp/McpCapabilitiesBadges.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpCapabilitiesBadges.svelte
rename to tools/ui/src/lib/components/app/mcp/McpCapabilitiesBadges.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpConnectionLogs.svelte b/tools/ui/src/lib/components/app/mcp/McpConnectionLogs.svelte
similarity index 90%
rename from tools/server/webui/src/lib/components/app/mcp/McpConnectionLogs.svelte
rename to tools/ui/src/lib/components/app/mcp/McpConnectionLogs.svelte
index 3bf842468f4..305c9db3aee 100644
--- a/tools/server/webui/src/lib/components/app/mcp/McpConnectionLogs.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpConnectionLogs.svelte
@@ -1,7 +1,6 @@
 <script lang="ts">
 	import { ChevronDown, ChevronRight } from '@lucide/svelte';
 	import * as Collapsible from '$lib/components/ui/collapsible';
-	import { cn } from '$lib/components/ui/utils';
 	import type { MCPConnectionLog } from '$lib/types';
 	import { formatTime, getMcpLogLevelIcon, getMcpLogLevelClass } from '$lib/utils';
 
@@ -54,14 +53,14 @@
 				class="max-h-64 space-y-0.5 overflow-y-auto rounded bg-muted/50 p-2 font-mono text-[10px]"
 			>
 				{#each logs as log (log.timestamp.getTime() + log.message)}
-					{@const Icon = getMcpLogLevelIcon(log.level)}
+					{@const IconComponent = getMcpLogLevelIcon(log.level)}
 
-					<div class={cn('flex items-start gap-1.5', getMcpLogLevelClass(log.level))}>
+					<div class={['flex items-start gap-1.5', getMcpLogLevelClass(log.level)]}>
 						<span class="shrink-0 text-muted-foreground">
 							{formatTime(log.timestamp)}
 						</span>
 
-						<Icon class="mt-0.5 h-3 w-3 shrink-0" />
+						<IconComponent class="mt-0.5 h-3 w-3 shrink-0" />
 
 						<span class="break-all">{log.message}</span>
 					</div>
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpLogo.svelte b/tools/ui/src/lib/components/app/mcp/McpLogo.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpLogo.svelte
rename to tools/ui/src/lib/components/app/mcp/McpLogo.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpResourcePreview.svelte b/tools/ui/src/lib/components/app/mcp/McpResourcePreview.svelte
similarity index 98%
rename from tools/server/webui/src/lib/components/app/mcp/McpResourcePreview.svelte
rename to tools/ui/src/lib/components/app/mcp/McpResourcePreview.svelte
index 6508c02a74c..55e1e20a2c7 100644
--- a/tools/server/webui/src/lib/components/app/mcp/McpResourcePreview.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpResourcePreview.svelte
@@ -1,7 +1,6 @@
 <script lang="ts">
 	import { FileText, Loader2, AlertCircle, Download } from '@lucide/svelte';
 	import { Button } from '$lib/components/ui/button';
-	import { cn } from '$lib/components/ui/utils';
 	import { mcpStore } from '$lib/stores/mcp.svelte';
 	import {
 		isImageMimeType,
@@ -71,7 +70,7 @@
 	}
 </script>
 
-<div class={cn('flex flex-col gap-3', className)}>
+<div class={['flex flex-col gap-3', className]}>
 	{#if !resource}
 		<div class="flex flex-col items-center justify-center gap-2 py-8 text-muted-foreground">
 			<FileText class="h-8 w-8 opacity-50" />
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpResourceTemplateForm.svelte b/tools/ui/src/lib/components/app/mcp/McpResourceTemplateForm.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpResourceTemplateForm.svelte
rename to tools/ui/src/lib/components/app/mcp/McpResourceTemplateForm.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowser.svelte b/tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowser.svelte
similarity index 89%
rename from tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowser.svelte
rename to tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowser.svelte
index 9d7e9c33767..24538e8d71b 100644
--- a/tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowser.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowser.svelte
@@ -1,13 +1,12 @@
 <script lang="ts">
-	import { cn } from '$lib/components/ui/utils';
 	import { mcpStore } from '$lib/stores/mcp.svelte';
 	import { mcpResources, mcpResourcesLoading } from '$lib/stores/mcp-resources.svelte';
 	import type { MCPServerResources, MCPResourceInfo, MCPResourceTemplateInfo } from '$lib/types';
 	import { SvelteMap, SvelteSet } from 'svelte/reactivity';
 	import { parseResourcePath } from '$lib/utils';
-	import McpResourceBrowserHeader from './McpResourceBrowserHeader.svelte';
-	import McpResourceBrowserEmptyState from './McpResourceBrowserEmptyState.svelte';
-	import McpResourceBrowserServerItem from './McpResourceBrowserServerItem.svelte';
+	import McpResourcesBrowserHeader from './McpResourcesBrowserHeader.svelte';
+	import McpResourcesBrowserEmptyState from './McpResourcesBrowserEmptyState.svelte';
+	import McpResourcesBrowserServerItem from './McpResourcesBrowserServerItem.svelte';
 
 	interface Props {
 		onSelect?: (resource: MCPResourceInfo, shiftKey?: boolean) => void;
@@ -121,8 +120,8 @@
 	}
 </script>
 
-<div class={cn('flex flex-col gap-2', className)}>
-	<McpResourceBrowserHeader
+<div class={['flex flex-col gap-2', className]}>
+	<McpResourcesBrowserHeader
 		{isLoading}
 		onRefresh={handleRefresh}
 		onSearch={(q) => (searchQuery = q)}
@@ -131,10 +130,10 @@
 
 	<div class="flex flex-col gap-1">
 		{#if filteredResources.size === 0}
-			<McpResourceBrowserEmptyState {isLoading} />
+			<McpResourcesBrowserEmptyState {isLoading} />
 		{:else}
 			{#each [...filteredResources.entries()] as [serverName, serverRes] (serverName)}
-				<McpResourceBrowserServerItem
+				<McpResourcesBrowserServerItem
 					serverName={serverName as string}
 					serverRes={serverRes as MCPServerResources}
 					isExpanded={expandedServers.has(serverName as string)}
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowserEmptyState.svelte b/tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowserEmptyState.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowserEmptyState.svelte
rename to tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowserEmptyState.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowserHeader.svelte b/tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowserHeader.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowserHeader.svelte
rename to tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowserHeader.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowserServerItem.svelte b/tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowserServerItem.svelte
similarity index 91%
rename from tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowserServerItem.svelte
rename to tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowserServerItem.svelte
index 01ab108963e..9acd101cdfd 100644
--- a/tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/McpResourceBrowserServerItem.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/McpResourcesBrowserServerItem.svelte
@@ -2,7 +2,6 @@
 	import { FolderOpen, ChevronDown, ChevronRight, Loader2, Braces } from '@lucide/svelte';
 	import { Checkbox } from '$lib/components/ui/checkbox';
 	import * as Collapsible from '$lib/components/ui/collapsible';
-	import { cn } from '$lib/components/ui/utils';
 	import { mcpStore } from '$lib/stores/mcp.svelte';
 	import type { MCPResourceInfo, MCPResourceTemplateInfo, MCPServerResources } from '$lib/types';
 	import { SvelteSet } from 'svelte/reactivity';
@@ -11,8 +10,9 @@
 		buildResourceTree,
 		countTreeResources,
 		sortTreeChildren
-	} from './mcp-resource-browser';
+	} from './mcp-resources-browser';
 	import { getDisplayName, getResourceIcon } from '$lib/utils';
+	import { McpServerIdentity } from '$lib/components/app/mcp';
 
 	interface Props {
 		serverName: string;
@@ -44,11 +44,12 @@
 		searchQuery = ''
 	}: Props = $props();
 
+	let serverDisplayName = $derived(mcpStore.getServerDisplayName(serverName));
+	let serverFaviconUrl = $derived(mcpStore.getServerFavicon(serverName));
+
 	const hasResources = $derived(serverRes.resources.length > 0);
 	const hasTemplates = $derived(serverRes.templates.length > 0);
 	const hasContent = $derived(hasResources || hasTemplates);
-	const displayName = $derived(mcpStore.getServerDisplayName(serverName));
-	const favicon = $derived(mcpStore.getServerFavicon(serverName));
 	const resourceTree = $derived(buildResourceTree(serverRes.resources, serverName, searchQuery));
 
 	const templateInfos = $derived<MCPResourceTemplateInfo[]>(
@@ -126,11 +127,11 @@
 			{/if}
 
 			<button
-				class={cn(
+				class={[
 					'flex flex-1 items-center gap-2 rounded px-2 py-1 text-left text-sm transition-colors',
 					'hover:bg-muted/50',
 					isSelected && 'bg-muted'
-				)}
+				]}
 				onclick={(e: MouseEvent) => handleResourceClick(resource, e)}
 				title={resourceDisplayName}
 			>
@@ -154,21 +155,15 @@
 			<ChevronRight class="h-3.5 w-3.5" />
 		{/if}
 
-		<span class="inline-flex flex-col items-start text-left">
-			<span class="inline-flex items-center justify-start gap-1.5 font-medium">
-				{#if favicon}
-					<img
-						src={favicon}
-						alt=""
-						class="h-4 w-4 shrink-0 rounded-sm"
-						onerror={(e) => {
-							(e.currentTarget as HTMLImageElement).style.display = 'none';
-						}}
-					/>
-				{/if}
-
-				{displayName}
-			</span>
+		<span class="inline-flex flex-col items-start gap-1 text-left">
+			<div class="inline-flex min-w-0 items-center gap-1.5">
+				<McpServerIdentity
+					displayName={serverDisplayName}
+					faviconUrl={serverFaviconUrl}
+					iconClass="h-4 w-4"
+					showVersion={false}
+				/>
+			</div>
 
 			<span class="text-xs text-muted-foreground">
 				({serverRes.resources.length} resource{serverRes.resources.length !== 1
@@ -213,11 +208,11 @@
 
 					{#each templateInfos as template (template.uriTemplate)}
 						<button
-							class={cn(
+							class={[
 								'flex w-full items-center gap-2 rounded px-2 py-1 text-left text-sm transition-colors',
 								'hover:bg-muted/50',
 								selectedTemplateUri === template.uriTemplate && 'bg-muted'
-							)}
+							]}
 							onclick={() => onTemplateSelect(template)}
 							title={template.uriTemplate}
 						>
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/mcp-resource-browser.ts b/tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/mcp-resources-browser.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpResourceBrowser/mcp-resource-browser.ts
rename to tools/ui/src/lib/components/app/mcp/McpResourcesBrowser/mcp-resources-browser.ts
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte
similarity index 97%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte
index 6a7b56370e7..199cb1458d0 100644
--- a/tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte
@@ -17,17 +17,17 @@
 
 	interface Props {
 		server: MCPServerSettingsEntry;
-		faviconUrl: string | null;
 		enabled?: boolean;
 		onToggle: (enabled: boolean) => void;
 		onUpdate: (updates: Partial<MCPServerSettingsEntry>) => void;
 		onDelete: () => void;
 	}
 
-	let { server, faviconUrl, enabled, onToggle, onUpdate, onDelete }: Props = $props();
+	let { server, enabled, onToggle, onUpdate, onDelete }: Props = $props();
 
 	let healthState = $derived<HealthCheckState>(mcpStore.getHealthCheckState(server.id));
 	let displayName = $derived(mcpStore.getServerLabel(server));
+	let faviconUrl = $derived(mcpStore.getServerFavicon(server.id));
 	let isIdle = $derived(healthState.status === HealthCheckStatus.IDLE);
 	let isHealthChecking = $derived(healthState.status === HealthCheckStatus.CONNECTING);
 	let isConnected = $derived(healthState.status === HealthCheckStatus.SUCCESS);
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardActions.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardActions.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardActions.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardActions.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardDeleteDialog.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardDeleteDialog.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardDeleteDialog.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardDeleteDialog.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardHeader.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardHeader.svelte
similarity index 53%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardHeader.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardHeader.svelte
index 5af45bb35cf..5544bcec421 100644
--- a/tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardHeader.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardHeader.svelte
@@ -1,15 +1,14 @@
 <script lang="ts">
-	import { Cable, ExternalLink } from '@lucide/svelte';
 	import { Switch } from '$lib/components/ui/switch';
 	import { Badge } from '$lib/components/ui/badge';
-	import { McpCapabilitiesBadges } from '$lib/components/app/mcp';
+	import { McpCapabilitiesBadges, McpServerIdentity } from '$lib/components/app/mcp';
 	import { MCP_TRANSPORT_LABELS, MCP_TRANSPORT_ICONS } from '$lib/constants';
 	import { MCPTransportType } from '$lib/enums';
 	import type { MCPServerInfo, MCPCapabilitiesInfo } from '$lib/types';
 
 	interface Props {
 		displayName: string;
-		faviconUrl: string | null;
+		faviconUrl?: string | null;
 		enabled: boolean;
 		disabled?: boolean;
 		onToggle: (enabled: boolean) => void;
@@ -32,46 +31,20 @@
 
 <div class="space-y-3">
 	<div class="flex items-start justify-between gap-3">
-		<div class="grid min-w-0 gap-3">
-			<div class="flex items-center gap-2 overflow-hidden">
-				{#if faviconUrl}
-					<img
-						src={faviconUrl}
-						alt=""
-						class="h-5 w-5 shrink-0 rounded"
-						onerror={(e) => {
-							(e.currentTarget as HTMLImageElement).style.display = 'none';
-						}}
-					/>
-				{:else}
-					<div class="flex h-5 w-5 shrink-0 items-center justify-center rounded bg-muted">
-						<Cable class="h-3 w-3 text-muted-foreground" />
-					</div>
-				{/if}
-
-				<p class="min-w-0 shrink-0 truncate leading-none font-medium">{displayName}</p>
-
-				{#if serverInfo?.version}
-					<Badge variant="secondary" class="h-4 min-w-0 truncate px-1 text-[10px]">
-						v{serverInfo.version}
-					</Badge>
-				{/if}
-
-				{#if serverInfo?.websiteUrl}
-					<a
-						href={serverInfo.websiteUrl}
-						target="_blank"
-						rel="noopener noreferrer"
-						class="shrink-0 text-muted-foreground hover:text-foreground"
-						aria-label="Open website"
-					>
-						<ExternalLink class="h-3 w-3" />
-					</a>
-				{/if}
+		<div class="flex min-w-0 flex-col gap-3">
+			<div class="inline-flex items-center gap-2">
+				<McpServerIdentity
+					{displayName}
+					{faviconUrl}
+					{serverInfo}
+					iconClass="h-5 w-5"
+					iconRounded="rounded"
+					nameClass="leading-6 font-medium"
+				/>
 			</div>
 
 			{#if capabilities || transportType}
-				<div class="flex flex-wrap items-center gap-1">
+				<div class="flex flex-wrap items-center gap-1.5">
 					{#if transportType}
 						{@const TransportIcon = MCP_TRANSPORT_ICONS[transportType]}
 						<Badge variant="outline" class="h-5 gap-1 px-1.5 text-[10px]">
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardToolsList.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardToolsList.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerCard/McpServerCardToolsList.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardToolsList.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerCardSkeleton.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCardSkeleton.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerCardSkeleton.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerCardSkeleton.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerForm.svelte b/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte
similarity index 91%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerForm.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerForm.svelte
index 52d75737564..79738e30dd1 100644
--- a/tools/server/webui/src/lib/components/app/mcp/McpServerForm.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte
@@ -7,6 +7,7 @@
 	import { UrlProtocol } from '$lib/enums';
 	import { MCP_SERVER_URL_PLACEHOLDER } from '$lib/constants';
 	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { CLI_FLAGS } from '$lib/constants';
 
 	interface Props {
 		url: string;
@@ -64,9 +65,11 @@
 
 		{#if !isWebSocket && onUseProxyChange}
 			<label
-				class="mt-3 flex items-start gap-2"
-				class:cursor-pointer={mcpStore.isProxyAvailable}
-				class:opacity-80={!mcpStore.isProxyAvailable}
+				class={[
+					'mt-3 flex items-start gap-2',
+					mcpStore.isProxyAvailable && 'cursor-pointer',
+					!mcpStore.isProxyAvailable && 'opacity-80'
+				]}
 			>
 				<Switch
 					class="mt-1"
@@ -85,7 +88,7 @@
 						<span class="inline-flex gap-0.75 text-xs text-muted-foreground/60"
 							>(Run <pre>llama-server</pre>
 							with
-							<pre>--webui-mcp-proxy</pre>
+							<pre>{CLI_FLAGS.MCP_PROXY}</pre>
 							flag)</span
 						>
 					{/if}
diff --git a/tools/ui/src/lib/components/app/mcp/McpServerIdentity.svelte b/tools/ui/src/lib/components/app/mcp/McpServerIdentity.svelte
new file mode 100644
index 00000000000..feafc5d8113
--- /dev/null
+++ b/tools/ui/src/lib/components/app/mcp/McpServerIdentity.svelte
@@ -0,0 +1,67 @@
+<script lang="ts">
+	import { ExternalLink } from '@lucide/svelte';
+	import { Badge } from '$lib/components/ui/badge';
+	import { TruncatedText } from '$lib/components/app/misc';
+	import { sanitizeExternalUrl } from '$lib/utils';
+	import type { MCPServerInfo } from '$lib/types';
+
+	interface Props {
+		displayName?: string;
+		faviconUrl?: string | null;
+		serverInfo?: MCPServerInfo;
+		iconClass?: string;
+		iconRounded?: string;
+		showVersion?: boolean;
+		showWebsite?: boolean;
+		nameClass?: string;
+	}
+
+	let {
+		displayName,
+		faviconUrl = null,
+		serverInfo,
+		iconClass = 'h-5 w-5',
+		iconRounded = 'rounded-sm',
+		showVersion = true,
+		showWebsite = true,
+		nameClass
+	}: Props = $props();
+
+	let safeWebsiteUrl = $derived(
+		serverInfo?.websiteUrl ? sanitizeExternalUrl(serverInfo.websiteUrl) : null
+	);
+</script>
+
+<span class="flex min-w-0 items-center gap-1.5">
+	{#if faviconUrl}
+		<img
+			src={faviconUrl}
+			alt=""
+			class={['shrink-0', iconRounded, iconClass]}
+			onerror={(e) => {
+				(e.currentTarget as HTMLImageElement).style.display = 'none';
+			}}
+		/>
+	{/if}
+
+	<TruncatedText text={displayName ?? ''} class={nameClass ?? ''} />
+
+	{#if showVersion && serverInfo?.version}
+		<Badge variant="secondary" class="h-4 min-w-0 shrink px-1 text-[10px]">
+			<TruncatedText text={`v${serverInfo.version}`} />
+		</Badge>
+	{/if}
+
+	{#if showWebsite && safeWebsiteUrl}
+		<a
+			href={safeWebsiteUrl}
+			target="_blank"
+			rel="noopener noreferrer"
+			class="shrink-0 text-muted-foreground hover:text-foreground"
+			aria-label="Open website"
+			onclick={(e) => e.stopPropagation()}
+		>
+			<ExternalLink class="h-3 w-3" />
+		</a>
+	{/if}
+</span>
diff --git a/tools/server/webui/src/lib/components/app/mcp/McpServerInfo.svelte b/tools/ui/src/lib/components/app/mcp/McpServerInfo.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/mcp/McpServerInfo.svelte
rename to tools/ui/src/lib/components/app/mcp/McpServerInfo.svelte
diff --git a/tools/server/webui/src/lib/components/app/mcp/index.ts b/tools/ui/src/lib/components/app/mcp/index.ts
similarity index 88%
rename from tools/server/webui/src/lib/components/app/mcp/index.ts
rename to tools/ui/src/lib/components/app/mcp/index.ts
index 6ab262a8c1d..3d30bb3b451 100644
--- a/tools/server/webui/src/lib/components/app/mcp/index.ts
+++ b/tools/ui/src/lib/components/app/mcp/index.ts
@@ -39,7 +39,7 @@
  * <McpServersSettings />
  * ```
  */
-export { default as McpServersSettings } from './McpServersSettings.svelte';
+export { default as McpServersSettings } from '../settings/SettingsMcpServers.svelte';
 
 /**
  * **McpActiveServersAvatars** - Active MCP servers indicator
@@ -69,33 +69,6 @@ export { default as McpServersSettings } from './McpServersSettings.svelte';
  */
 export { default as McpActiveServersAvatars } from './McpActiveServersAvatars.svelte';
 
-/**
- * **McpServersSelector** - Quick MCP server toggle dropdown
- *
- * Compact dropdown for quickly enabling/disabling MCP servers for the current chat.
- * Uses McpActiveServersAvatars as trigger and shows searchable server list with switches.
- *
- * **Architecture:**
- * - Uses DropdownMenuSearchable for searchable dropdown UI
- * - McpActiveServersAvatars as the trigger element
- * - Integrates with conversationsStore for per-chat toggle
- * - Runs health checks on dropdown open
- *
- * **Features:**
- * - Searchable server list by name/URL
- * - Switch toggles matching McpServersSettings behavior
- * - Error state display for unhealthy servers
- * - Footer link to full MCP settings dialog
- *
- * @example
- * ```svelte
- * <McpServersSelector
- *   onSettingsClick={() => showMcpSettings = true}
- * />
- * ```
- */
-export { default as McpServersSelector } from './McpServersSelector.svelte';
-
 /**
  * **McpCapabilitiesBadges** - Server capabilities display
  *
@@ -207,6 +180,25 @@ export { default as McpServerCardDeleteDialog } from './McpServerCard/McpServerC
 /** Skeleton loading state for server card during health checks. */
 export { default as McpServerCardSkeleton } from './McpServerCardSkeleton.svelte';
 
+/**
+ * **McpServerIdentity** - Server identity display (icon, name, version)
+ *
+ * Reusable headless component for displaying server name, favicon/icon, and version badge.
+ * Accepts all data via props with no store dependencies for predictable rendering.
+ *
+ * **Features:**
+ * - Server favicon/icon with fallback
+ * - Truncated display name with max-width
+ * - Optional version badge (v1.2.3)
+ * - Optional external link to server website
+ *
+ * @example
+ * ```svelte
+ * <McpServerIdentity displayName={name} faviconUrl={iconUrl} serverInfo={info} />
+ * ```
+ */
+export { default as McpServerIdentity } from './McpServerIdentity.svelte';
+
 /**
  * **McpServerInfo** - Server instructions display
  *
@@ -216,7 +208,7 @@ export { default as McpServerCardSkeleton } from './McpServerCardSkeleton.svelte
 export { default as McpServerInfo } from './McpServerInfo.svelte';
 
 /**
- * **McpResourceBrowser** - MCP resources tree browser
+ * **McpResourcesBrowser** - MCP resources tree browser
  *
  * Tree view component showing resources grouped by server.
  * Supports resource selection and quick attach actions.
@@ -229,7 +221,7 @@ export { default as McpServerInfo } from './McpServerInfo.svelte';
  * - Refresh all resources action
  * - Loading states per server
  */
-export { default as McpResourceBrowser } from './McpResourceBrowser/McpResourceBrowser.svelte';
+export { default as McpResourcesBrowser } from './McpResourcesBrowser/McpResourcesBrowser.svelte';
 
 /**
  * **McpResourcePreview** - MCP resource content preview
diff --git a/tools/ui/src/lib/components/app/misc/CodeBlockActions.svelte b/tools/ui/src/lib/components/app/misc/CodeBlockActions.svelte
new file mode 100644
index 00000000000..fa12d1c6249
--- /dev/null
+++ b/tools/ui/src/lib/components/app/misc/CodeBlockActions.svelte
@@ -0,0 +1,33 @@
+<script lang="ts">
+	import { Eye } from '@lucide/svelte';
+	import { ActionIcon, ActionIconCopyToClipboard } from '$lib/components/app';
+	import { FileTypeText } from '$lib/enums';
+
+	interface Props {
+		code: string;
+		language: string;
+		disabled?: boolean;
+		onPreview?: (code: string, language: string) => void;
+	}
+
+	let { code, language, disabled = false, onPreview }: Props = $props();
+
+	const showPreview = $derived(language?.toLowerCase() === FileTypeText.HTML);
+</script>
+
+<div class="code-block-actions">
+	<ActionIconCopyToClipboard
+		text={code}
+		canCopy={!disabled}
+		ariaLabel={disabled ? 'Code incomplete' : 'Copy code'}
+	/>
+
+	{#if showPreview}
+		<ActionIcon
+			icon={Eye}
+			tooltip={disabled ? 'Code incomplete' : 'Preview code'}
+			{disabled}
+			onclick={() => onPreview!(code, language)}
+		/>
+	{/if}
+</div>
diff --git a/tools/server/webui/src/lib/components/app/misc/ConversationSelection.svelte b/tools/ui/src/lib/components/app/misc/ConversationSelection.svelte
similarity index 86%
rename from tools/server/webui/src/lib/components/app/misc/ConversationSelection.svelte
rename to tools/ui/src/lib/components/app/misc/ConversationSelection.svelte
index 21412f47e5a..db14fd631e3 100644
--- a/tools/server/webui/src/lib/components/app/misc/ConversationSelection.svelte
+++ b/tools/ui/src/lib/components/app/misc/ConversationSelection.svelte
@@ -1,8 +1,7 @@
 <script lang="ts">
-	import { Search, X } from '@lucide/svelte';
 	import { Button } from '$lib/components/ui/button';
-	import { Input } from '$lib/components/ui/input';
 	import { Checkbox } from '$lib/components/ui/checkbox';
+	import SearchInput from '$lib/components/app/forms/SearchInput.svelte';
 	import { ScrollArea } from '$lib/components/ui/scroll-area';
 	import { SvelteSet } from 'svelte/reactivity';
 
@@ -111,21 +110,7 @@
 </script>
 
 <div class="space-y-4">
-	<div class="relative">
-		<Search class="absolute top-1/2 left-3 h-4 w-4 -translate-y-1/2 text-muted-foreground" />
-
-		<Input bind:value={searchQuery} placeholder="Search conversations..." class="pr-9 pl-9" />
-
-		{#if searchQuery}
-			<button
-				class="absolute top-1/2 right-3 -translate-y-1/2 text-muted-foreground hover:text-foreground"
-				onclick={() => (searchQuery = '')}
-				type="button"
-			>
-				<X class="h-4 w-4" />
-			</button>
-		{/if}
-	</div>
+	<SearchInput bind:value={searchQuery} placeholder="Search conversations..." />
 
 	<div class="flex items-center justify-between text-sm text-muted-foreground">
 		<span>
@@ -169,15 +154,15 @@
 						{#each filteredConversations as conv (conv.id)}
 							<tr
 								class="cursor-pointer border-b transition-colors hover:bg-muted/50"
-								onclick={(e) => toggleConversation(conv.id, e.shiftKey)}
+								onclick={(event) => toggleConversation(conv.id, event.shiftKey)}
 							>
 								<td class="p-3">
 									<Checkbox
 										checked={selectedIds.has(conv.id)}
-										onclick={(e) => {
-											e.preventDefault();
-											e.stopPropagation();
-											toggleConversation(conv.id, e.shiftKey);
+										onclick={(event) => {
+											event.preventDefault();
+											event.stopPropagation();
+											toggleConversation(conv.id, event.shiftKey);
 										}}
 									/>
 								</td>
diff --git a/tools/server/webui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte b/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte
similarity index 88%
rename from tools/server/webui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte
rename to tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte
index dc0621cecda..06d0e3a0588 100644
--- a/tools/server/webui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte
+++ b/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte
@@ -65,7 +65,7 @@
 
 <div class="relative {className}">
 	<button
-		class="absolute top-1/2 left-4 z-10 flex h-6 w-6 -translate-y-1/2 items-center justify-center rounded-full bg-foreground/15 shadow-md backdrop-blur-xs transition-opacity hover:bg-foreground/35 {canScrollLeft
+		class="absolute top-1/2 left-4 z-10 flex h-6 w-6 -translate-y-1/2 items-center justify-center rounded-full bg-background/25 shadow-md backdrop-blur-xs transition-opacity hover:bg-background/45 {canScrollLeft
 			? 'opacity-100'
 			: 'pointer-events-none opacity-0'}"
 		onclick={scrollLeft}
@@ -83,7 +83,7 @@
 	</div>
 
 	<button
-		class="absolute top-1/2 right-4 z-10 flex h-6 w-6 -translate-y-1/2 items-center justify-center rounded-full bg-foreground/15 shadow-md backdrop-blur-xs transition-opacity hover:bg-foreground/35 {canScrollRight
+		class="absolute top-1/2 right-4 z-10 flex h-6 w-6 -translate-y-1/2 items-center justify-center rounded-full bg-background/25 shadow-md backdrop-blur-xs transition-opacity hover:bg-background/45 {canScrollRight
 			? 'opacity-100'
 			: 'pointer-events-none opacity-0'}"
 		onclick={scrollRight}
diff --git a/tools/server/webui/src/lib/components/app/misc/KeyboardShortcutInfo.svelte b/tools/ui/src/lib/components/app/misc/KeyboardShortcutInfo.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/misc/KeyboardShortcutInfo.svelte
rename to tools/ui/src/lib/components/app/misc/KeyboardShortcutInfo.svelte
diff --git a/tools/server/webui/src/lib/components/app/misc/TruncatedText.svelte b/tools/ui/src/lib/components/app/misc/TruncatedText.svelte
similarity index 88%
rename from tools/server/webui/src/lib/components/app/misc/TruncatedText.svelte
rename to tools/ui/src/lib/components/app/misc/TruncatedText.svelte
index eecc5b1d634..a6b7cb483e5 100644
--- a/tools/server/webui/src/lib/components/app/misc/TruncatedText.svelte
+++ b/tools/ui/src/lib/components/app/misc/TruncatedText.svelte
@@ -32,7 +32,7 @@
 
 {#if isTruncated && showTooltip}
 	<Tooltip.Root>
-		<Tooltip.Trigger class={className}>
+		<Tooltip.Trigger class="{className} min-w-0">
 			<span bind:this={textElement} class="block truncate">
 				{text}
 			</span>
@@ -43,7 +43,7 @@
 		</Tooltip.Content>
 	</Tooltip.Root>
 {:else}
-	<span bind:this={textElement} class="{className} block truncate">
+	<span bind:this={textElement} class="{className} block min-w-0 truncate">
 		{text}
 	</span>
 {/if}
diff --git a/tools/server/webui/src/lib/components/app/misc/index.ts b/tools/ui/src/lib/components/app/misc/index.ts
similarity index 83%
rename from tools/server/webui/src/lib/components/app/misc/index.ts
rename to tools/ui/src/lib/components/app/misc/index.ts
index 02bd70b24f5..64b76fb711d 100644
--- a/tools/server/webui/src/lib/components/app/misc/index.ts
+++ b/tools/ui/src/lib/components/app/misc/index.ts
@@ -43,3 +43,11 @@ export { default as TruncatedText } from './TruncatedText.svelte';
  * Supports special keys like shift, cmd, and custom text.
  */
 export { default as KeyboardShortcutInfo } from './KeyboardShortcutInfo.svelte';
+
+/**
+ * **CodeBlockActions** - Actions bar for code blocks (copy, preview)
+ *
+ * Displays copy-to-clipboard and preview buttons for code blocks.
+ * Preview button is shown only for HTML code blocks.
+ */
+export { default as CodeBlockActions } from './CodeBlockActions.svelte';
diff --git a/tools/server/webui/src/lib/components/app/models/ModelBadge.svelte b/tools/ui/src/lib/components/app/models/ModelBadge.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/models/ModelBadge.svelte
rename to tools/ui/src/lib/components/app/models/ModelBadge.svelte
diff --git a/tools/server/webui/src/lib/components/app/models/ModelId.svelte b/tools/ui/src/lib/components/app/models/ModelId.svelte
similarity index 67%
rename from tools/server/webui/src/lib/components/app/models/ModelId.svelte
rename to tools/ui/src/lib/components/app/models/ModelId.svelte
index 272b697c463..2fe952feea0 100644
--- a/tools/server/webui/src/lib/components/app/models/ModelId.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelId.svelte
@@ -5,8 +5,9 @@
 
 	interface Props {
 		modelId: string;
-		showOrgName?: boolean;
+		hideOrgName?: boolean;
 		showRaw?: boolean;
+		hideQuantization?: boolean;
 		aliases?: string[];
 		tags?: string[];
 		class?: string;
@@ -14,8 +15,9 @@
 
 	let {
 		modelId,
-		showOrgName = false,
+		hideOrgName = false,
 		showRaw = undefined,
+		hideQuantization = false,
 		aliases,
 		tags,
 		class: className = '',
@@ -29,11 +31,12 @@
 
 	let parsed = $derived(ModelsService.parseModelId(modelId));
 	let resolvedShowRaw = $derived(showRaw ?? (config().showRawModelNames as boolean) ?? false);
-	let displayName = $derived(
-		aliases && aliases.length > 0 ? aliases[0] : (parsed.modelName ?? modelId)
-	);
-	let remainingAliases = $derived(aliases && aliases.length > 1 ? aliases.slice(1) : []);
-	let allTags = $derived([...(parsed.tags ?? []), ...(tags ?? [])]);
+
+	let uniqueAliases = $derived([...new Set(aliases ?? [])]);
+	let uniqueTags = $derived([...new Set([...(parsed.tags ?? []), ...(tags ?? [])])]);
+
+	let primaryAlias = $derived(uniqueAliases.length === 1 ? uniqueAliases[0] : null);
+	let displayName = $derived(primaryAlias ?? parsed.modelName ?? modelId);
 </script>
 
 {#if resolvedShowRaw}
@@ -41,7 +44,7 @@
 {:else}
 	<span class="flex min-w-0 flex-wrap items-center gap-1 {className}" {...rest}>
 		<span class="min-w-0 truncate font-medium">
-			{#if showOrgName && parsed.orgName && !(aliases && aliases.length > 0)}{parsed.orgName}/{/if}{displayName}
+			{#if !hideOrgName && parsed.orgName}{parsed.orgName}/{/if}{displayName}
 		</span>
 
 		{#if parsed.params}
@@ -50,20 +53,24 @@
 			</span>
 		{/if}
 
-		{#if parsed.quantization}
+		{#if parsed.quantization && !hideQuantization}
 			<span class={badgeClass}>
 				{parsed.quantization}
 			</span>
 		{/if}
 
-		{#if remainingAliases.length > 0}
-			{#each remainingAliases as alias (alias)}
+		{#if primaryAlias}
+			{#if primaryAlias !== parsed.modelName}
+				<span class={badgeClass}>{parsed.modelName ?? modelId}</span>
+			{/if}
+		{:else if uniqueAliases.length > 1}
+			{#each uniqueAliases as alias (alias)}
 				<span class={badgeClass}>{alias}</span>
 			{/each}
 		{/if}
 
-		{#if allTags.length > 0}
-			{#each allTags as tag (tag)}
+		{#if uniqueTags.length > 0}
+			{#each uniqueTags as tag (tag)}
 				<span class={tagBadgeClass}>{tag}</span>
 			{/each}
 		{/if}
diff --git a/tools/ui/src/lib/components/app/models/ModelsSelectorDropdown.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorDropdown.svelte
new file mode 100644
index 00000000000..998a6a0faaa
--- /dev/null
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorDropdown.svelte
@@ -0,0 +1,290 @@
+<script lang="ts">
+	import { ChevronDown, Loader2, Package } from '@lucide/svelte';
+	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import { KeyboardKey } from '$lib/enums';
+	import { useModelsSelector } from '$lib/hooks/use-models-selector.svelte';
+	import {
+		DialogModelInformation,
+		DropdownMenuSearchable,
+		ModelId,
+		ModelsSelectorList,
+		ModelsSelectorOption
+	} from '$lib/components/app';
+	import type { ModelItem } from './utils';
+
+	interface Props {
+		class?: string;
+		currentModel?: string | null;
+		disabled?: boolean;
+		forceForegroundText?: boolean;
+		onModelChange?: (modelId: string, modelName: string) => Promise<boolean> | boolean | void;
+		useGlobalSelection?: boolean;
+	}
+
+	let {
+		class: className = '',
+		currentModel = null,
+		disabled = false,
+		forceForegroundText = false,
+		onModelChange,
+		useGlobalSelection = false
+	}: Props = $props();
+
+	let isOpen = $state(false);
+	let highlightedIndex = $state<number>(-1);
+
+	const ms = useModelsSelector({
+		currentModel: () => currentModel,
+		useGlobalSelection: () => useGlobalSelection,
+		onModelChange: () => onModelChange,
+		onOpenChange: (open) => {
+			isOpen = open;
+			highlightedIndex = -1;
+		}
+	});
+
+	$effect(() => {
+		void ms.searchTerm;
+		highlightedIndex = -1;
+	});
+
+	export function open() {
+		ms.handleOpenChange(true);
+	}
+
+	function handleSearchKeyDown(event: KeyboardEvent) {
+		if (event.isComposing) return;
+
+		if (event.key === KeyboardKey.ARROW_DOWN) {
+			event.preventDefault();
+
+			if (ms.filteredOptions.length === 0) return;
+
+			if (highlightedIndex === -1 || highlightedIndex === ms.filteredOptions.length - 1) {
+				highlightedIndex = 0;
+			} else {
+				highlightedIndex += 1;
+			}
+		} else if (event.key === KeyboardKey.ARROW_UP) {
+			event.preventDefault();
+
+			if (ms.filteredOptions.length === 0) return;
+
+			if (highlightedIndex === -1 || highlightedIndex === 0) {
+				highlightedIndex = ms.filteredOptions.length - 1;
+			} else {
+				highlightedIndex -= 1;
+			}
+		} else if (event.key === KeyboardKey.ENTER) {
+			event.preventDefault();
+
+			if (highlightedIndex >= 0 && highlightedIndex < ms.filteredOptions.length) {
+				const option = ms.filteredOptions[highlightedIndex];
+
+				ms.handleSelect(option.id);
+			} else if (ms.filteredOptions.length > 0) {
+				highlightedIndex = 0;
+			}
+		}
+	}
+</script>
+
+<div class={['relative inline-flex flex-col items-end gap-1', className]}>
+	{#if ms.loading && ms.options.length === 0 && ms.isRouter}
+		<div class="flex items-center gap-2 text-xs text-muted-foreground">
+			<Loader2 class="h-3.5 w-3.5 animate-spin" />
+
+			Loading models…
+		</div>
+	{:else if ms.options.length === 0 && ms.isRouter}
+		{#if currentModel}
+			<span
+				class={[
+					'inline-flex items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs text-muted-foreground',
+					className
+				]}
+				style="max-width: min(calc(100cqw - 10rem), 20rem)"
+			>
+				<Package class="h-3.5 w-3.5" />
+
+				<ModelId modelId={currentModel} class="min-w-0" hideQuantization />
+			</span>
+		{:else}
+			<p class="text-xs text-muted-foreground">No models available.</p>
+		{/if}
+	{:else}
+		{@const selectedOption = ms.getDisplayOption()}
+
+		{#if ms.isRouter}
+			<DropdownMenu.Root bind:open={isOpen} onOpenChange={ms.handleOpenChange}>
+				<DropdownMenu.Trigger
+					class={[
+						`inline-grid cursor-pointer grid-cols-[1fr_auto_1fr] items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 text-xs shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
+						!ms.isCurrentModelInCache
+							? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
+							: forceForegroundText
+								? 'text-foreground'
+								: ms.isHighlightedCurrentModelActive
+									? 'text-foreground'
+									: 'text-foreground',
+						isOpen && 'text-foreground',
+						'max-w-[min(calc(100vw-4rem) md:max-w-[min(calc(100cqw-9rem),25rem)]'
+					]}
+					disabled={disabled || ms.updating}
+				>
+					<Package class="h-3.5 w-3.5" />
+
+					{#if selectedOption}
+						<Tooltip.Root>
+							<Tooltip.Trigger>
+								<!-- prevent another nested button element -->
+								{#snippet child({ props })}
+									<ModelId
+										modelId={selectedOption.model}
+										class="min-w-0 overflow-hidden"
+										hideOrgName={false}
+										{...props}
+									/>
+								{/snippet}
+							</Tooltip.Trigger>
+
+							<Tooltip.Content>
+								<p class="font-mono">{selectedOption.model}</p>
+							</Tooltip.Content>
+						</Tooltip.Root>
+					{:else}
+						<span class="min-w-0 font-medium">Select model</span>
+					{/if}
+
+					{#if ms.updating || ms.isLoadingModel}
+						<Loader2 class="h-3 w-3.5 animate-spin" />
+					{:else}
+						<ChevronDown class="h-3 w-3.5" />
+					{/if}
+				</DropdownMenu.Trigger>
+
+				<DropdownMenu.Content
+					align="end"
+					class="w-full max-w-[100vw] pt-0 sm:w-max sm:max-w-[calc(100vw-2rem)]"
+				>
+					<DropdownMenuSearchable
+						searchValue={ms.searchTerm}
+						onSearchChange={(v) => ms.setSearchTerm(v)}
+						placeholder="Search models..."
+						onSearchKeyDown={handleSearchKeyDown}
+						emptyMessage="No models found."
+						isEmpty={ms.filteredOptions.length === 0 && ms.isCurrentModelInCache}
+					>
+						<div class="models-list">
+							{#if !ms.isCurrentModelInCache && currentModel}
+								<!-- Show unavailable model as first option (disabled) -->
+								<button
+									type="button"
+									class="flex w-full cursor-not-allowed items-center bg-red-400/10 p-2 text-left text-sm text-red-400"
+									role="option"
+									aria-selected="true"
+									aria-disabled="true"
+									disabled
+								>
+									<ModelId modelId={currentModel} class="flex-1" hideQuantization />
+
+									<span class="ml-2 text-xs whitespace-nowrap opacity-70">(not available)</span>
+								</button>
+							{/if}
+
+							{#if ms.filteredOptions.length === 0}
+								<p class="px-4 py-3 text-sm text-muted-foreground">No models found.</p>
+							{/if}
+
+							{#snippet modelOption(item: ModelItem, hideOrgName: boolean)}
+								{@const { option, flatIndex } = item}
+								{@const isSelected = currentModel === option.model || ms.activeId === option.id}
+								{@const isHighlighted = flatIndex === highlightedIndex}
+								{@const isFav = ms.isFavorite(option.model)}
+
+								<ModelsSelectorOption
+									{option}
+									{isSelected}
+									{isHighlighted}
+									{isFav}
+									{hideOrgName}
+									onSelect={ms.handleSelect}
+									onInfoClick={ms.handleInfoClick}
+									onMouseEnter={() => (highlightedIndex = flatIndex)}
+									onKeyDown={(event) => {
+										if (event.key === KeyboardKey.ENTER || event.key === KeyboardKey.SPACE) {
+											event.preventDefault();
+											ms.handleSelect(option.id);
+										}
+									}}
+								/>
+							{/snippet}
+
+							<ModelsSelectorList
+								groups={ms.groupedFilteredOptions}
+								{currentModel}
+								activeId={ms.activeId}
+								sectionHeaderClass="my-1.5 px-2 py-2 text-[13px] font-semibold text-muted-foreground/70 select-none"
+								onSelect={ms.handleSelect}
+								onInfoClick={ms.handleInfoClick}
+								renderOption={modelOption}
+							/>
+						</div>
+					</DropdownMenuSearchable>
+				</DropdownMenu.Content>
+			</DropdownMenu.Root>
+		{:else}
+			<button
+				class={[
+					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 text-xs shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
+					!ms.isCurrentModelInCache
+						? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
+						: forceForegroundText
+							? 'text-foreground'
+							: ms.isHighlightedCurrentModelActive
+								? 'text-foreground'
+								: 'text-foreground',
+					isOpen && 'text-foreground'
+				]}
+				style="max-width: min(calc(100cqw - 6.5rem), 32rem)"
+				onclick={() => ms.handleOpenChange(true)}
+				disabled={disabled || ms.updating}
+			>
+				<Package class="h-3.5 w-3.5" />
+
+				{#if selectedOption}
+					<Tooltip.Root>
+						<Tooltip.Trigger>
+							<!-- prevent another nested button element -->
+							{#snippet child({ props })}
+								<ModelId
+									modelId={selectedOption.model}
+									class="min-w-0 overflow-hidden"
+									hideOrgName={false}
+									{...props}
+								/>
+							{/snippet}
+						</Tooltip.Trigger>
+
+						<Tooltip.Content>
+							<p class="font-mono">{selectedOption.model}</p>
+						</Tooltip.Content>
+					</Tooltip.Root>
+				{/if}
+
+				{#if ms.updating}
+					<Loader2 class="h-3 w-3.5 animate-spin" />
+				{/if}
+			</button>
+		{/if}
+	{/if}
+</div>
+
+{#if ms.showModelDialog}
+	<DialogModelInformation
+		open={ms.showModelDialog}
+		onOpenChange={(v) => ms.setShowModelDialog(v)}
+		modelId={ms.infoModelId}
+	/>
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/models/ModelsSelectorList.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorList.svelte
similarity index 92%
rename from tools/server/webui/src/lib/components/app/models/ModelsSelectorList.svelte
rename to tools/ui/src/lib/components/app/models/ModelsSelectorList.svelte
index 5c36adc1463..61a4cf0f662 100644
--- a/tools/server/webui/src/lib/components/app/models/ModelsSelectorList.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorList.svelte
@@ -27,7 +27,7 @@
 	let render = $derived(renderOption ?? defaultOption);
 </script>
 
-{#snippet defaultOption(item: ModelItem, showOrgName: boolean)}
+{#snippet defaultOption(item: ModelItem, hideOrgName: boolean)}
 	{@const { option } = item}
 	{@const isSelected = currentModel === option.model || activeId === option.id}
 	{@const isFav = modelsStore.favoriteModelIds.has(option.model)}
@@ -37,7 +37,7 @@
 		{isSelected}
 		isHighlighted={false}
 		{isFav}
-		{showOrgName}
+		{hideOrgName}
 		{onSelect}
 		{onInfoClick}
 		onMouseEnter={() => {}}
@@ -48,7 +48,7 @@
 {#if groups.loaded.length > 0}
 	<p class={sectionHeaderClass}>Loaded models</p>
 	{#each groups.loaded as item (`loaded-${item.option.id}`)}
-		{@render render(item, true)}
+		{@render render(item, false)}
 	{/each}
 {/if}
 
@@ -66,7 +66,7 @@
 			<p class={orgHeaderClass}>{group.orgName}</p>
 		{/if}
 		{#each group.items as item (item.option.id)}
-			{@render render(item, false)}
+			{@render render(item, true)}
 		{/each}
 	{/each}
 {/if}
diff --git a/tools/server/webui/src/lib/components/app/models/ModelsSelectorOption.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
similarity index 84%
rename from tools/server/webui/src/lib/components/app/models/ModelsSelectorOption.svelte
rename to tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
index 3236130a9d6..d103d4b6711 100644
--- a/tools/server/webui/src/lib/components/app/models/ModelsSelectorOption.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
@@ -9,7 +9,6 @@
 		PowerOff,
 		RotateCw
 	} from '@lucide/svelte';
-	import { cn } from '$lib/components/ui/utils';
 	import { ActionIcon, ModelId } from '$lib/components/app';
 	import type { ModelOption } from '$lib/types/models';
 	import { ServerModelStatus } from '$lib/enums';
@@ -20,7 +19,7 @@
 		isSelected: boolean;
 		isHighlighted: boolean;
 		isFav: boolean;
-		showOrgName?: boolean;
+		hideOrgName?: boolean;
 		onSelect: (modelId: string) => void;
 		onMouseEnter: () => void;
 		onKeyDown: (e: KeyboardEvent) => void;
@@ -32,7 +31,7 @@
 		isSelected,
 		isHighlighted,
 		isFav,
-		showOrgName = false,
+		hideOrgName = false,
 		onSelect,
 		onMouseEnter,
 		onKeyDown,
@@ -54,14 +53,13 @@
 </script>
 
 <div
-	class={cn(
+	class={[
 		'group flex w-full items-center gap-2 rounded-sm p-2 text-left text-sm transition focus:outline-none',
 		'cursor-pointer hover:bg-muted focus:bg-muted',
-		isSelected || isHighlighted
-			? 'bg-accent text-accent-foreground'
-			: 'hover:bg-accent hover:text-accent-foreground',
+		(isSelected || isHighlighted) && 'bg-accent text-accent-foreground',
+		!(isSelected || isHighlighted) && 'hover:bg-accent hover:text-accent-foreground',
 		isLoaded ? 'text-popover-foreground' : 'text-muted-foreground'
-	)}
+	]}
 	role="option"
 	aria-selected={isSelected || isHighlighted}
 	tabindex="0"
@@ -71,7 +69,7 @@
 >
 	<ModelId
 		modelId={option.model}
-		{showOrgName}
+		{hideOrgName}
 		aliases={option.aliases}
 		tags={option.tags}
 		class="flex-1"
@@ -120,15 +118,14 @@
 			<div class="flex w-4 items-center justify-center">
 				<CircleAlert class="h-3.5 w-3.5 text-red-500 group-hover:hidden" />
 
-				<!-- svelte-ignore a11y_no_static_element_interactions -->
-				<!-- svelte-ignore a11y_click_events_have_key_events -->
-				<div class="hidden group-hover:flex" onclick={(e) => e.stopPropagation()}>
+				<div class="hidden group-hover:flex">
 					<ActionIcon
 						iconSize="h-2.5 w-2.5"
 						icon={RotateCw}
 						tooltip="Retry loading model"
 						class="h-3 w-3 text-red-500 hover:text-foreground"
 						onclick={() => modelsStore.loadModel(option.model)}
+						stopPropagationOnClick
 					/>
 				</div>
 			</div>
@@ -153,15 +150,14 @@
 			<div class="flex w-4 items-center justify-center">
 				<span class="h-2 w-2 rounded-full bg-green-500 group-hover:hidden"></span>
 
-				<!-- svelte-ignore a11y_no_static_element_interactions -->
-				<!-- svelte-ignore a11y_click_events_have_key_events -->
-				<div class="hidden group-hover:flex" onclick={(e) => e.stopPropagation()}>
+				<div class="hidden group-hover:flex">
 					<ActionIcon
 						iconSize="h-2.5 w-2.5"
 						icon={PowerOff}
 						tooltip="Unload model"
 						class="h-3 w-3 text-red-500 hover:text-red-600"
 						onclick={() => modelsStore.unloadModel(option.model)}
+						stopPropagationOnClick
 					/>
 				</div>
 			</div>
@@ -169,15 +165,14 @@
 			<div class="flex w-4 items-center justify-center">
 				<span class="h-2 w-2 rounded-full bg-muted-foreground/50 group-hover:hidden"></span>
 
-				<!-- svelte-ignore a11y_no_static_element_interactions -->
-				<!-- svelte-ignore a11y_click_events_have_key_events -->
-				<div class="hidden group-hover:flex" onclick={(e) => e.stopPropagation()}>
+				<div class="hidden group-hover:flex">
 					<ActionIcon
 						iconSize="h-2.5 w-2.5"
 						icon={Power}
 						tooltip="Load model"
 						class="h-3 w-3"
 						onclick={() => modelsStore.loadModel(option.model)}
+						stopPropagationOnClick
 					/>
 				</div>
 			</div>
diff --git a/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte
new file mode 100644
index 00000000000..d38ed8c0752
--- /dev/null
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte
@@ -0,0 +1,189 @@
+<script lang="ts">
+	import { ChevronDown, Loader2, Package } from '@lucide/svelte';
+	import * as Sheet from '$lib/components/ui/sheet';
+	import { useModelsSelector } from '$lib/hooks/use-models-selector.svelte';
+	import {
+		DialogModelInformation,
+		ModelId,
+		ModelsSelectorList,
+		SearchInput,
+		TruncatedText
+	} from '$lib/components/app';
+
+	interface Props {
+		class?: string;
+		currentModel?: string | null;
+		/** Callback when model changes. Return false to keep menu open (e.g., for validation failures) */
+		onModelChange?: (modelId: string, modelName: string) => Promise<boolean> | boolean | void;
+		disabled?: boolean;
+		forceForegroundText?: boolean;
+		/** When true, user's global selection takes priority over currentModel (for form selector) */
+		useGlobalSelection?: boolean;
+	}
+
+	let {
+		class: className = '',
+		currentModel = null,
+		onModelChange,
+		disabled = false,
+		forceForegroundText = false,
+		useGlobalSelection = false
+	}: Props = $props();
+
+	let sheetOpen = $state(false);
+
+	const ms = useModelsSelector({
+		currentModel: () => currentModel,
+		useGlobalSelection: () => useGlobalSelection,
+		onModelChange: () => onModelChange,
+		onOpenChange: (open) => {
+			sheetOpen = open;
+		}
+	});
+
+	export function open() {
+		ms.handleOpenChange(true);
+	}
+
+	function handleSheetOpenChange(open: boolean) {
+		if (!open) {
+			ms.handleOpenChange(false);
+		}
+	}
+</script>
+
+<div class={['relative inline-flex flex-col items-end gap-1', className]}>
+	{#if ms.loading && ms.options.length === 0 && ms.isRouter}
+		<div class="flex items-center gap-2 text-xs text-muted-foreground">
+			<Loader2 class="h-3.5 w-3.5 animate-spin" />
+			Loading models…
+		</div>
+	{:else if ms.options.length === 0 && ms.isRouter}
+		<p class="text-xs text-muted-foreground">No models available.</p>
+	{:else}
+		{@const selectedOption = ms.getDisplayOption()}
+
+		{#if ms.isRouter}
+			<button
+				type="button"
+				class={[
+					`inline-grid cursor-pointer grid-cols-[1fr_auto_1fr] items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 text-xs shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
+					!ms.isCurrentModelInCache
+						? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
+						: forceForegroundText
+							? 'text-foreground'
+							: ms.isHighlightedCurrentModelActive
+								? 'text-foreground'
+								: 'text-foreground',
+					sheetOpen && 'text-foreground'
+				]}
+				style="max-width: min(calc(100cqw - 9rem), 20rem)"
+				disabled={disabled || ms.updating}
+				onclick={() => ms.handleOpenChange(true)}
+			>
+				<Package class="h-3.5 w-3.5" />
+
+				{#if !selectedOption}
+					<span class="min-w-0 font-medium">Select model</span>
+				{:else}
+					<ModelId
+						class="text-xs"
+						modelId={selectedOption?.model || ''}
+						hideQuantization
+						hideOrgName
+					/>
+				{/if}
+
+				{#if ms.updating || ms.isLoadingModel}
+					<Loader2 class="h-3 w-3.5 animate-spin" />
+				{:else}
+					<ChevronDown class="h-3 w-3.5" />
+				{/if}
+			</button>
+
+			<Sheet.Root bind:open={sheetOpen} onOpenChange={handleSheetOpenChange}>
+				<Sheet.Content side="bottom" class="max-h-[85vh] gap-1">
+					<Sheet.Header>
+						<Sheet.Title>Select Model</Sheet.Title>
+
+						<Sheet.Description class="sr-only">
+							Choose a model to use for the conversation
+						</Sheet.Description>
+					</Sheet.Header>
+
+					<div class="flex flex-col gap-1 pb-4">
+						<div class="mb-3 px-4">
+							<SearchInput
+								placeholder="Search models..."
+								value={ms.searchTerm}
+								onInput={(v) => ms.setSearchTerm(v)}
+							/>
+						</div>
+
+						<div class="max-h-[60vh] overflow-y-auto px-2">
+							{#if !ms.isCurrentModelInCache && currentModel}
+								<button
+									type="button"
+									class="flex w-full cursor-not-allowed items-center rounded-md bg-red-400/10 px-3 py-2.5 text-left text-sm text-red-400"
+									disabled
+								>
+									<span class="min-w-0 flex-1 truncate">
+										{selectedOption?.name || currentModel}
+									</span>
+									<span class="ml-2 text-xs whitespace-nowrap opacity-70">(not available)</span>
+								</button>
+								<div class="my-1 h-px bg-border"></div>
+							{/if}
+
+							{#if ms.filteredOptions.length === 0}
+								<p class="px-3 py-3 text-center text-sm text-muted-foreground">No models found.</p>
+							{/if}
+
+							<ModelsSelectorList
+								groups={ms.groupedFilteredOptions}
+								{currentModel}
+								activeId={ms.activeId}
+								sectionHeaderClass="px-2 py-2 text-xs font-semibold text-muted-foreground/60 select-none"
+								orgHeaderClass="px-2 py-2 text-xs font-semibold text-muted-foreground/60 select-none [&:not(:first-child)]:mt-2"
+								onSelect={ms.handleSelect}
+								onInfoClick={ms.handleInfoClick}
+							/>
+						</div>
+					</div>
+				</Sheet.Content>
+			</Sheet.Root>
+		{:else}
+			<button
+				class={[
+					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 text-xs shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
+					!ms.isCurrentModelInCache
+						? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
+						: forceForegroundText
+							? 'text-foreground'
+							: ms.isHighlightedCurrentModelActive
+								? 'text-foreground'
+								: 'text-foreground'
+				]}
+				style="max-width: min(calc(100cqw - 6.5rem), 32rem)"
+				onclick={() => ms.handleOpenChange(true)}
+				disabled={disabled || ms.updating}
+			>
+				<Package class="h-3.5 w-3.5" />
+
+				<TruncatedText text={selectedOption?.model || ''} class="font-medium" />
+
+				{#if ms.updating}
+					<Loader2 class="h-3 w-3.5 animate-spin" />
+				{/if}
+			</button>
+		{/if}
+	{/if}
+</div>
+
+{#if ms.showModelDialog}
+	<DialogModelInformation
+		open={ms.showModelDialog}
+		onOpenChange={(v) => ms.setShowModelDialog(v)}
+		modelId={ms.infoModelId}
+	/>
+{/if}
diff --git a/tools/server/webui/src/lib/components/app/models/index.ts b/tools/ui/src/lib/components/app/models/index.ts
similarity index 88%
rename from tools/server/webui/src/lib/components/app/models/index.ts
rename to tools/ui/src/lib/components/app/models/index.ts
index b4bcdf4308e..a6ba6817fc0 100644
--- a/tools/server/webui/src/lib/components/app/models/index.ts
+++ b/tools/ui/src/lib/components/app/models/index.ts
@@ -11,7 +11,7 @@
  */
 
 /**
- * **ModelsSelector** - Model selection dropdown
+ * **ModelsSelectorDropdown** - Model selection dropdown (desktop)
  *
  * Dropdown for selecting AI models with status indicators,
  * search, and model information display. Adapts UI based on server mode.
@@ -35,20 +35,20 @@
  *
  * @example
  * ```svelte
- * <ModelsSelector
+ * <ModelsSelectorDropdown
  *   currentModel={conversation.modelId}
  *   onModelChange={(id, name) => updateModel(id)}
  *   useGlobalSelection
  * />
  * ```
  */
-export { default as ModelsSelector } from './ModelsSelector.svelte';
+export { default as ModelsSelectorDropdown } from './ModelsSelectorDropdown.svelte';
 
 /**
  * **ModelsSelectorList** - Grouped model options list
  *
  * Renders grouped model options (loaded, favorites, available) with section
- * headers and org subgroups. Shared between ModelsSelector and ModelsSelectorSheet
+ * headers and org subgroups. Shared between ModelsSelectorDropdown and ModelsSelectorSheet
  * to avoid template duplication.
  *
  * Accepts an optional `renderOption` snippet to customize how each option is
@@ -68,8 +68,8 @@ export { default as ModelsSelectorOption } from './ModelsSelectorOption.svelte';
 /**
  * **ModelsSelectorSheet** - Mobile model selection sheet
  *
- * Bottom sheet variant of ModelsSelector optimized for touch interaction
- * on mobile devices. Same functionality as ModelsSelector but uses Sheet UI
+ * Bottom sheet variant of ModelsSelectorDropdown optimized for touch interaction
+ * on mobile devices. Same functionality as ModelsSelectorDropdown but uses Sheet UI
  * instead of DropdownMenu.
  */
 export { default as ModelsSelectorSheet } from './ModelsSelectorSheet.svelte';
diff --git a/tools/server/webui/src/lib/components/app/models/utils.ts b/tools/ui/src/lib/components/app/models/utils.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/app/models/utils.ts
rename to tools/ui/src/lib/components/app/models/utils.ts
diff --git a/tools/ui/src/lib/components/app/navigation/DesktopIconStrip.svelte b/tools/ui/src/lib/components/app/navigation/DesktopIconStrip.svelte
new file mode 100644
index 00000000000..e92b9528a61
--- /dev/null
+++ b/tools/ui/src/lib/components/app/navigation/DesktopIconStrip.svelte
@@ -0,0 +1,84 @@
+<script lang="ts">
+	import { goto } from '$app/navigation';
+	import { page } from '$app/state';
+	import { ActionIcon } from '$lib/components/app';
+	import {
+		ICON_STRIP_TRANSITION_DURATION,
+		ICON_STRIP_TRANSITION_DELAY_MULTIPLIER,
+		SIDEBAR_ACTIONS_ITEMS
+	} from '$lib/constants';
+	import { TooltipSide } from '$lib/enums';
+	import { fade } from 'svelte/transition';
+	import { circIn } from 'svelte/easing';
+	import { onMount } from 'svelte';
+	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
+
+	interface Props {
+		sidebarOpen: boolean;
+		onSearchClick: () => void;
+	}
+
+	let { sidebarOpen = false, onSearchClick }: Props = $props();
+
+	const { handleKeydown } = useKeyboardShortcuts({ activateSearchMode: () => onSearchClick() });
+
+	let initialized = $state(false);
+	let showIcons = $derived(!sidebarOpen);
+
+	showIcons = false;
+
+	onMount(() => {
+		showIcons = !sidebarOpen;
+
+		setTimeout(() => {
+			initialized = true;
+		}, ICON_STRIP_TRANSITION_DELAY_MULTIPLIER * SIDEBAR_ACTIONS_ITEMS.length);
+	});
+</script>
+
+<svelte:window onkeydown={handleKeydown} />
+
+<div
+	class="hidden shrink-0 transition-[width] duration-200 ease-linear md:block {sidebarOpen
+		? 'w-0'
+		: 'w-[calc(var(--sidebar-width-icon)+1.5rem)]'}"
+></div>
+<aside
+	class="fixed top-0 bottom-0 left-0 z-10 hidden w-[calc(var(--sidebar-width-icon)+1.5rem)] flex-col items-center justify-between py-3 transition-opacity duration-200 ease-linear md:flex {sidebarOpen
+		? 'pointer-events-none opacity-0'
+		: 'opacity-100'}"
+>
+	<div class="mt-12 flex flex-col items-center gap-1">
+		{#each SIDEBAR_ACTIONS_ITEMS as item, i (item.tooltip)}
+			{@const onclick = item.route ? () => goto(item.route!) : onSearchClick}
+			{@const isActive = item.activeRouteId
+				? page.route.id === item.activeRouteId
+				: item.activeRoutePrefix
+					? !!page.route.id?.startsWith(item.activeRoutePrefix)
+					: false}
+			{#if showIcons}
+				<div
+					in:fade={{
+						duration: ICON_STRIP_TRANSITION_DURATION,
+						delay: !initialized
+							? ICON_STRIP_TRANSITION_DELAY_MULTIPLIER + i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER
+							: 0,
+						easing: circIn
+					}}
+				>
+					<ActionIcon
+						icon={item.icon}
+						tooltip={item.tooltip}
+						tooltipSide={TooltipSide.RIGHT}
+						size="lg"
+						iconSize="h-4 w-4"
+						class="h-9 w-9 rounded-full hover:bg-accent! {isActive
+							? 'bg-accent text-accent-foreground'
+							: ''}"
+						{onclick}
+					/>
+				</div>
+			{/if}
+		{/each}
+	</div>
+</aside>
diff --git a/tools/server/webui/src/lib/components/app/navigation/DropdownMenuActions.svelte b/tools/ui/src/lib/components/app/navigation/DropdownMenuActions.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/navigation/DropdownMenuActions.svelte
rename to tools/ui/src/lib/components/app/navigation/DropdownMenuActions.svelte
diff --git a/tools/server/webui/src/lib/components/app/navigation/DropdownMenuSearchable.svelte b/tools/ui/src/lib/components/app/navigation/DropdownMenuSearchable.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/navigation/DropdownMenuSearchable.svelte
rename to tools/ui/src/lib/components/app/navigation/DropdownMenuSearchable.svelte
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
similarity index 56%
rename from tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte
rename to tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
index 751406b3e39..ddaf4d5b875 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
@@ -1,13 +1,18 @@
 <script lang="ts">
 	import { goto } from '$app/navigation';
 	import { page } from '$app/state';
-	import { Trash2, Pencil } from '@lucide/svelte';
-	import { ChatSidebarConversationItem, DialogConfirmation } from '$lib/components/app';
+	import { Trash2, Pencil, X } from '@lucide/svelte';
+	import { Button } from '$lib/components/ui/button';
+	import { DialogConfirmation } from '$lib/components/app';
+	import SidebarNavigationActions from './SidebarNavigationActions.svelte';
+	import SidebarNavigationConversationItem from './SidebarNavigationConversationItem.svelte';
 	import { Checkbox } from '$lib/components/ui/checkbox';
 	import Label from '$lib/components/ui/label/label.svelte';
 	import ScrollArea from '$lib/components/ui/scroll-area/scroll-area.svelte';
 	import * as Sidebar from '$lib/components/ui/sidebar';
 	import Input from '$lib/components/ui/input/input.svelte';
+	import { ROUTES } from '$lib/constants/routes';
+	import { RouterService } from '$lib/services/router.service';
 	import {
 		conversationsStore,
 		conversations,
@@ -15,7 +20,7 @@
 	} from '$lib/stores/conversations.svelte';
 	import { chatStore } from '$lib/stores/chat.svelte';
 	import { getPreviewText } from '$lib/utils';
-	import ChatSidebarActions from './ChatSidebarActions.svelte';
+	import { APP_NAME } from '$lib/constants';
 
 	const sidebar = Sidebar.useSidebar();
 
@@ -32,10 +37,14 @@
 	);
 
 	let filteredConversations = $derived.by(() => {
-		if (searchQuery.trim().length > 0) {
-			return conversations().filter((conversation: { name: string }) =>
-				conversation.name.toLowerCase().includes(searchQuery.toLowerCase())
-			);
+		if (isSearchModeActive) {
+			if (searchQuery.trim().length > 0) {
+				return conversations().filter((conversation: { name: string }) =>
+					conversation.name.toLowerCase().includes(searchQuery.toLowerCase())
+				);
+			}
+
+			return [];
 		}
 
 		return conversations();
@@ -107,10 +116,31 @@
 		}
 	}
 
+	let chatSidebarActions: { activateSearch?: () => void } | undefined = $state();
+	let openedForSearch = $state(false);
+
 	export function activateSearchMode() {
-		isSearchModeActive = true;
+		if (!sidebar.open) {
+			openedForSearch = true;
+		}
+		chatSidebarActions?.activateSearch?.();
+	}
+
+	function handleSearchDeactivated() {
+		if (openedForSearch) {
+			openedForSearch = false;
+			sidebar.toggle();
+		}
 	}
 
+	$effect(() => {
+		if (!sidebar.open) {
+			isSearchModeActive = false;
+			searchQuery = '';
+			openedForSearch = false;
+		}
+	});
+
 	export function editActiveConversation() {
 		if (currentChatId) {
 			const activeConversation = filteredConversations.find((conv) => conv.id === currentChatId);
@@ -130,7 +160,8 @@
 			searchQuery = '';
 		}
 
-		await goto(`#/chat/${id}`);
+		handleMobileSidebarItemClick();
+		await goto(RouterService.chat(id));
 	}
 
 	function handleStopGeneration(id: string) {
@@ -138,60 +169,81 @@
 	}
 </script>
 
-<ScrollArea class="h-[100vh]">
-	<Sidebar.Header class=" top-0 z-10 gap-4 bg-sidebar/50 p-4 pb-2 backdrop-blur-lg md:sticky">
-		<a href="#/" onclick={handleMobileSidebarItemClick}>
-			<h1 class="inline-flex items-center gap-1 px-2 text-xl font-semibold">llama.cpp</h1>
-		</a>
-
-		<ChatSidebarActions {handleMobileSidebarItemClick} bind:isSearchModeActive bind:searchQuery />
-	</Sidebar.Header>
-
-	<Sidebar.Group class="mt-2 space-y-2 p-0 px-4">
-		{#if (filteredConversations.length > 0 && isSearchModeActive) || !isSearchModeActive}
-			<Sidebar.GroupLabel>
-				{isSearchModeActive ? 'Search results' : 'Conversations'}
-			</Sidebar.GroupLabel>
-		{/if}
-
-		<Sidebar.GroupContent>
-			<Sidebar.Menu>
-				{#each conversationTree as { conversation, depth } (conversation.id)}
-					<Sidebar.MenuItem class="mb-1 p-0">
-						<ChatSidebarConversationItem
-							conversation={{
-								id: conversation.id,
-								name: conversation.name,
-								lastModified: conversation.lastModified,
-								currNode: conversation.currNode,
-								forkedFromConversationId: conversation.forkedFromConversationId
-							}}
-							{depth}
-							{handleMobileSidebarItemClick}
-							isActive={currentChatId === conversation.id}
-							onSelect={selectConversation}
-							onEdit={handleEditConversation}
-							onDelete={handleDeleteConversation}
-							onStop={handleStopGeneration}
-						/>
-					</Sidebar.MenuItem>
-				{/each}
-
-				{#if conversationTree.length === 0}
-					<div class="px-2 py-4 text-center">
-						<p class="mb-4 p-4 text-sm text-muted-foreground">
-							{searchQuery.length > 0
-								? 'No results found'
-								: isSearchModeActive
-									? 'Start typing to see results'
-									: 'No conversations yet'}
-						</p>
-					</div>
-				{/if}
-			</Sidebar.Menu>
-		</Sidebar.GroupContent>
-	</Sidebar.Group>
-</ScrollArea>
+<div class="flex h-full flex-col">
+	<ScrollArea class="h-full flex-1">
+		<Sidebar.Header class="gap-4 bg-sidebar/50 p-3 backdrop-blur-lg md:pt-4 md:pb-2">
+			<div class="flex items-center justify-between">
+				<a href={ROUTES.START} onclick={handleMobileSidebarItemClick}>
+					<h1 class="inline-flex items-center gap-1 px-2 text-xl font-semibold">
+						{APP_NAME}
+					</h1>
+				</a>
+
+				<Button
+					class="rounded-full md:hidden"
+					variant="ghost"
+					size="icon"
+					onclick={() => sidebar.toggle()}
+				>
+					<X class="h-4 w-4" />
+					<span class="sr-only">Close sidebar</span>
+				</Button>
+			</div>
+
+			<SidebarNavigationActions
+				bind:this={chatSidebarActions}
+				{handleMobileSidebarItemClick}
+				bind:isSearchModeActive
+				bind:searchQuery
+				onSearchDeactivated={handleSearchDeactivated}
+			/>
+		</Sidebar.Header>
+
+		<Sidebar.Group class="mt-2 h-[calc(100vh-21rem)] space-y-2 p-0 px-3">
+			{#if (filteredConversations.length > 0 && isSearchModeActive) || !isSearchModeActive}
+				<Sidebar.GroupLabel>
+					{isSearchModeActive ? 'Search results' : 'Recent conversations'}
+				</Sidebar.GroupLabel>
+			{/if}
+
+			<Sidebar.GroupContent>
+				<Sidebar.Menu>
+					{#each conversationTree as { conversation, depth } (conversation.id)}
+						<Sidebar.MenuItem class="mb-1 p-0">
+							<SidebarNavigationConversationItem
+								conversation={{
+									id: conversation.id,
+									name: conversation.name,
+									lastModified: conversation.lastModified,
+									currNode: conversation.currNode,
+									forkedFromConversationId: conversation.forkedFromConversationId
+								}}
+								{depth}
+								isActive={currentChatId === conversation.id}
+								onSelect={selectConversation}
+								onEdit={handleEditConversation}
+								onDelete={handleDeleteConversation}
+								onStop={handleStopGeneration}
+							/>
+						</Sidebar.MenuItem>
+					{/each}
+
+					{#if conversationTree.length === 0}
+						<div class="px-2 py-4 text-center">
+							<p class="mb-4 p-4 text-sm text-muted-foreground">
+								{searchQuery.length > 0
+									? 'No results found'
+									: isSearchModeActive
+										? 'Start typing to see results'
+										: 'No conversations yet'}
+							</p>
+						</div>
+					{/if}
+				</Sidebar.Menu>
+			</Sidebar.GroupContent>
+		</Sidebar.Group>
+	</ScrollArea>
+</div>
 
 <DialogConfirmation
 	bind:open={showDeleteDialog}
@@ -230,10 +282,10 @@
 		showEditDialog = false;
 		selectedConversation = null;
 	}}
-	onKeydown={(e) => {
-		if (e.key === 'Enter') {
-			e.preventDefault();
-			e.stopImmediatePropagation();
+	onKeydown={(event) => {
+		if (event.key === 'Enter') {
+			event.preventDefault();
+			event.stopImmediatePropagation();
 			handleConfirmEdit();
 		}
 	}}
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationActions.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationActions.svelte
new file mode 100644
index 00000000000..f0d63970eea
--- /dev/null
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationActions.svelte
@@ -0,0 +1,96 @@
+<script lang="ts">
+	import { KeyboardShortcutInfo } from '$lib/components/app';
+	import { Button } from '$lib/components/ui/button';
+	import type { Component } from 'svelte';
+	import { SearchInput } from '$lib/components/app';
+	import { page } from '$app/state';
+	import { SIDEBAR_ACTIONS_ITEMS } from '$lib/constants/ui';
+
+	interface Props {
+		handleMobileSidebarItemClick: () => void;
+		isSearchModeActive: boolean;
+		searchQuery: string;
+		isCancelAlwaysVisible?: boolean;
+		onSearchDeactivated?: () => void;
+	}
+
+	let {
+		handleMobileSidebarItemClick,
+		isSearchModeActive = $bindable(),
+		searchQuery = $bindable(),
+		isCancelAlwaysVisible = false,
+		onSearchDeactivated
+	}: Props = $props();
+
+	let searchInputRef = $state<HTMLInputElement | null>(null);
+
+	function handleSearchModeDeactivate() {
+		isSearchModeActive = false;
+		searchQuery = '';
+		onSearchDeactivated?.();
+	}
+
+	export function activateSearch() {
+		isSearchModeActive = true;
+		// Focus after Svelte renders the input
+		queueMicrotask(() => searchInputRef?.focus());
+	}
+</script>
+
+{#snippet itemIcon(IconComponent: Component)}
+	<IconComponent class="h-4 w-4" />
+{/snippet}
+
+<div class="my-1 space-y-1">
+	{#if isSearchModeActive}
+		<SearchInput
+			bind:value={searchQuery}
+			bind:ref={searchInputRef}
+			onClose={handleSearchModeDeactivate}
+			onKeyDown={(e) => e.key === 'Escape' && handleSearchModeDeactivate()}
+			placeholder="Search conversations..."
+			{isCancelAlwaysVisible}
+		/>
+	{:else}
+		{#each SIDEBAR_ACTIONS_ITEMS as item (item.route)}
+			{#if !item.route}
+				<Button
+					class="w-full justify-between px-2 backdrop-blur-none! hover:[&>kbd]:opacity-100"
+					onclick={activateSearch}
+					variant="ghost"
+				>
+					<div class="flex items-center gap-2">
+						{@render itemIcon(item.icon)}
+
+						{item.tooltip}
+					</div>
+
+					{#if item.keys}
+						<KeyboardShortcutInfo keys={item.keys} />
+					{/if}
+				</Button>
+			{:else}
+				<Button
+					class="w-full justify-between px-2 backdrop-blur-none! hover:[&>kbd]:opacity-100 {(item.activeRouteId &&
+						page.route.id === item.activeRouteId) ||
+					(item.activeRoutePrefix && page.route.id?.startsWith(item.activeRoutePrefix))
+						? 'bg-accent text-accent-foreground'
+						: ''}"
+					href={item.route}
+					onclick={handleMobileSidebarItemClick}
+					variant="ghost"
+				>
+					<div class="flex items-center gap-2">
+						{@render itemIcon(item.icon)}
+
+						{item.tooltip}
+					</div>
+
+					{#if item.keys}
+						<KeyboardShortcutInfo keys={item.keys} />
+					{/if}
+				</Button>
+			{/if}
+		{/each}
+	{/if}
+</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
similarity index 93%
rename from tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte
rename to tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
index e22cb4f829e..dad8d954cbb 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
@@ -11,15 +11,16 @@
 	import { DropdownMenuActions } from '$lib/components/app';
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { FORK_TREE_DEPTH_PADDING } from '$lib/constants';
+	import { RouterService } from '$lib/services/router.service';
 	import { getAllLoadingChats } from '$lib/stores/chat.svelte';
 	import { conversationsStore } from '$lib/stores/conversations.svelte';
+	import { TruncatedText } from '$lib/components/app';
 	import { onMount } from 'svelte';
 
 	interface Props {
 		isActive?: boolean;
 		depth?: number;
 		conversation: DatabaseConversation;
-		handleMobileSidebarItemClick?: () => void;
 		onDelete?: (id: string) => void;
 		onEdit?: (id: string) => void;
 		onSelect?: (id: string) => void;
@@ -28,7 +29,6 @@
 
 	let {
 		conversation,
-		handleMobileSidebarItemClick,
 		onDelete,
 		onEdit,
 		onSelect,
@@ -114,7 +114,7 @@
 			<Tooltip.Root>
 				<Tooltip.Trigger>
 					<a
-						href="#/chat/{conversation.forkedFromConversationId}"
+						href={RouterService.chat(conversation.forkedFromConversationId)}
 						class="flex shrink-0 items-center text-muted-foreground transition-colors hover:text-foreground"
 					>
 						<GitBranch class="h-3.5 w-3.5" />
@@ -150,11 +150,7 @@
 			</Tooltip.Root>
 		{/if}
 
-		<!-- svelte-ignore a11y_click_events_have_key_events -->
-		<!-- svelte-ignore a11y_no_static_element_interactions -->
-		<span class="truncate text-sm font-medium" onclick={handleMobileSidebarItemClick}>
-			{conversation.name}
-		</span>
+		<TruncatedText text={conversation.name} class="text-sm font-medium" showTooltip={false} />
 	</div>
 
 	{#if renderActionsDropdown}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearch.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte
rename to tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearch.svelte
diff --git a/tools/ui/src/lib/components/app/navigation/index.ts b/tools/ui/src/lib/components/app/navigation/index.ts
new file mode 100644
index 00000000000..d4ca914594c
--- /dev/null
+++ b/tools/ui/src/lib/components/app/navigation/index.ts
@@ -0,0 +1,138 @@
+/**
+ *
+ * NAVIGATION & MENUS
+ *
+ * Components for dropdown menus and action selection.
+ *
+ */
+
+/**
+ * **DropdownMenuSearchable** - Searchable content for dropdown menus
+ *
+ * Renders a search input with filtered content area, empty state, and optional footer.
+ * Designed to be injected into any dropdown container (DropdownMenu.Content,
+ * DropdownMenu.SubContent, etc.) without providing its own Root.
+ *
+ * **Features:**
+ * - Search/filter input
+ * - Keyboard navigation support
+ * - Custom content and footer via snippets
+ * - Empty state message
+ *
+ * @example
+ * ```svelte
+ * <DropdownMenu.Root>
+ *   <DropdownMenu.Trigger>...</DropdownMenu.Trigger>
+ *   <DropdownMenu.Content class="pt-0">
+ *     <DropdownMenuSearchable
+ *       bind:searchValue
+ *       placeholder="Search..."
+ *       isEmpty={filteredItems.length === 0}
+ *     >
+ *       {#each items as item}<Item {item} />{/each}
+ *     </DropdownMenuSearchable>
+ *   </DropdownMenu.Content>
+ * </DropdownMenu.Root>
+ * ```
+ */
+export { default as DropdownMenuSearchable } from './DropdownMenuSearchable.svelte';
+
+/**
+ * **DropdownMenuActions** - Multi-action dropdown menu
+ *
+ * Dropdown menu for multiple action options with icons and shortcuts.
+ * Supports destructive variants and keyboard shortcut hints.
+ *
+ * **Features:**
+ * - Configurable trigger icon with tooltip
+ * - Action items with icons and labels
+ * - Destructive variant styling
+ * - Keyboard shortcut display
+ * - Separator support between groups
+ *
+ * @example
+ * ```svelte
+ * <DropdownMenuActions
+ *   triggerIcon={MoreHorizontal}
+ *   triggerTooltip="More actions"
+ *   actions={[
+ *     { icon: Edit, label: 'Edit', onclick: handleEdit },
+ *     { icon: Trash, label: 'Delete', onclick: handleDelete, variant: 'destructive' }
+ *   ]}
+ * />
+ * ```
+ */
+export { default as DropdownMenuActions } from './DropdownMenuActions.svelte';
+
+/**
+ * **DesktopIconStrip** - Fixed icon strip for desktop sidebar
+ *
+ * Vertical icon strip shown on desktop when the sidebar is collapsed.
+ * Contains navigation shortcuts for new chat, search, MCP, import/export, and settings.
+ */
+export { default as DesktopIconStrip } from './DesktopIconStrip.svelte';
+
+/**
+ * **SidebarNavigation** - Sidebar with actions menu and conversation list
+ *
+ * Collapsible sidebar displaying conversation history with search and
+ * management actions. Integrates with ShadCN sidebar component for
+ * consistent styling and mobile responsiveness.
+ *
+ * **Architecture:**
+ * - Uses ShadCN Sidebar.* components for structure
+ * - Fetches conversations from conversationsStore
+ * - Manages search state and filtered results locally
+ * - Handles conversation CRUD operations via conversationsStore
+ *
+ * **Navigation:**
+ * - Click conversation to navigate to `/chat/[id]`
+ * - New chat button navigates to `/` (root)
+ * - Active conversation highlighted based on route params
+ *
+ * **Conversation Management:**
+ * - Right-click or menu button for context menu
+ * - Rename: Opens inline edit dialog
+ * - Delete: Shows confirmation with conversation preview
+ * - Delete All: Removes all conversations with confirmation
+ *
+ * **Features:**
+ * - Search/filter conversations by title
+ * - Conversation list with message previews (first message truncated)
+ * - Active conversation highlighting
+ * - Mobile-responsive collapse/expand via ShadCN sidebar
+ * - New chat button in header
+ * - Settings button opens DialogChatSettings
+ *
+ * **Exported API:**
+ * - `activateSearchMode()` - Focus search input programmatically
+ * - `editActiveConversation()` - Open rename dialog for current conversation
+ *
+ * @example
+ * ```svelte
+ * <SidebarNavigation bind:this={sidebarRef} />
+ * ```
+ */
+export { default as SidebarNavigation } from './SidebarNavigation/SidebarNavigation.svelte';
+
+/**
+ * Action buttons for sidebar header. Contains new chat button, settings button,
+ * and delete all conversations button. Manages dialog states for settings and
+ * delete confirmation.
+ */
+export { default as SidebarNavigationActions } from './SidebarNavigation/SidebarNavigationActions.svelte';
+
+/**
+ * Single conversation item in sidebar. Displays conversation title (truncated),
+ * last message preview, and timestamp. Shows context menu on right-click with
+ * rename and delete options. Highlights when active (matches current route).
+ * Handles click to navigate and keyboard accessibility.
+ */
+export { default as SidebarNavigationConversationItem } from './SidebarNavigation/SidebarNavigationConversationItem.svelte';
+
+/**
+ * Search input for filtering conversations in sidebar. Filters conversation
+ * list by title as user types. Shows clear button when query is not empty.
+ * Integrated into sidebar header with proper styling.
+ */
+export { default as SidebarNavigationSearch } from './SidebarNavigation/SidebarNavigationSearch.svelte';
diff --git a/tools/server/webui/src/lib/components/app/server/ServerErrorSplash.svelte b/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte
similarity index 97%
rename from tools/server/webui/src/lib/components/app/server/ServerErrorSplash.svelte
rename to tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte
index c7f52a7c58d..4da0d1ddfa8 100644
--- a/tools/server/webui/src/lib/components/app/server/ServerErrorSplash.svelte
+++ b/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte
@@ -7,6 +7,8 @@
 	import Label from '$lib/components/ui/label/label.svelte';
 	import { serverStore, serverLoading } from '$lib/stores/server.svelte';
 	import { config, settingsStore } from '$lib/stores/settings.svelte';
+	import { SETTINGS_KEYS } from '$lib/constants';
+	import { ROUTES } from '$lib/constants/routes';
 	import { fade, fly, scale } from 'svelte/transition';
 	import { KeyboardKey } from '$lib/enums';
 
@@ -63,7 +65,7 @@
 
 		try {
 			// Update the API key in settings first
-			settingsStore.updateConfig('apiKey', apiKeyInput.trim());
+			settingsStore.updateConfig(SETTINGS_KEYS.API_KEY, apiKeyInput.trim());
 
 			// Test the API key by making a real request to the server
 			const response = await fetch(`${base}/props`, {
@@ -79,7 +81,7 @@
 
 				// Show success state briefly, then navigate to home
 				setTimeout(() => {
-					goto(`#/`);
+					goto(ROUTES.START);
 				}, 1000);
 			} else {
 				// API key is invalid - User Story A
diff --git a/tools/server/webui/src/lib/components/app/server/ServerLoadingSplash.svelte b/tools/ui/src/lib/components/app/server/ServerLoadingSplash.svelte
similarity index 94%
rename from tools/server/webui/src/lib/components/app/server/ServerLoadingSplash.svelte
rename to tools/ui/src/lib/components/app/server/ServerLoadingSplash.svelte
index 505325d656d..95fa61e9369 100644
--- a/tools/server/webui/src/lib/components/app/server/ServerLoadingSplash.svelte
+++ b/tools/ui/src/lib/components/app/server/ServerLoadingSplash.svelte
@@ -8,8 +8,7 @@
 		message?: string;
 	}
 
-	let { class: className = '', message = 'Initializing connection to llama.cpp server...' }: Props =
-		$props();
+	let { class: className = '', message = 'Initializing connection to server...' }: Props = $props();
 </script>
 
 <div class="flex h-full items-center justify-center {className}">
diff --git a/tools/server/webui/src/lib/components/app/server/ServerStatus.svelte b/tools/ui/src/lib/components/app/server/ServerStatus.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/app/server/ServerStatus.svelte
rename to tools/ui/src/lib/components/app/server/ServerStatus.svelte
diff --git a/tools/server/webui/src/lib/components/app/server/index.ts b/tools/ui/src/lib/components/app/server/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/app/server/index.ts
rename to tools/ui/src/lib/components/app/server/index.ts
diff --git a/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChat.svelte b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChat.svelte
new file mode 100644
index 00000000000..109c8ff9dac
--- /dev/null
+++ b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChat.svelte
@@ -0,0 +1,175 @@
+<script lang="ts">
+	import {
+		SettingsChatDesktopSidebar,
+		SettingsChatFields,
+		SettingsChatImportExportTab,
+		SettingsChatMobileHeader,
+		SettingsChatToolsTab,
+		SettingsFooter
+	} from '$lib/components/app/settings';
+	import { config, settingsStore } from '$lib/stores/settings.svelte';
+	import {
+		NUMERIC_FIELDS,
+		POSITIVE_INTEGER_FIELDS,
+		SETTINGS_CHAT_SECTIONS,
+		SETTINGS_SECTION_TITLES,
+		type SettingsSection
+	} from '$lib/constants';
+	import { RouterService } from '$lib/services/router.service';
+	import { setMode } from 'mode-watcher';
+	import { ColorMode } from '$lib/enums/ui';
+	import { fade } from 'svelte/transition';
+	import { goto } from '$app/navigation';
+	import { page } from '$app/state';
+	import { setChatSettingsConfigContext } from '$lib/contexts';
+	import { settingsReferrer } from '$lib/stores/settings-referrer.svelte';
+	import { modelsStore } from '$lib/stores/models.svelte';
+	import { isRouterMode } from '$lib/stores/server.svelte';
+	interface Props {
+		initialSection?: string;
+		getSectionHref?: (section: SettingsSection) => string;
+	}
+
+	let { initialSection, getSectionHref }: Props = $props();
+
+	let activeSlug = $derived(
+		initialSection ?? (page.params as Record<string, string | undefined>).section ?? 'general'
+	);
+
+	let currentSection = $derived(
+		SETTINGS_CHAT_SECTIONS.find((section) => section.slug === activeSlug) ||
+			SETTINGS_CHAT_SECTIONS[0]
+	);
+
+	let localConfig: SettingsConfigType = $state({ ...config() });
+
+	let mobileHeader: { updateCarousel: () => void } | undefined;
+
+	let fetchInitiated = false;
+
+	$effect(() => {
+		if (isRouterMode() && currentSection.fields && !fetchInitiated) {
+			fetchInitiated = true;
+
+			void modelsStore
+				.fetch()
+				.then(() => modelsStore.fetchRouterModels())
+				.then(() => modelsStore.fetchModalitiesForLoadedModels())
+				.then(() => modelsStore.ensureFirstModelSelected());
+		}
+	});
+
+	function handleThemeChange(newTheme: string) {
+		localConfig.theme = newTheme;
+		setMode(newTheme as ColorMode);
+	}
+
+	function handleConfigChange(key: string, value: string | boolean) {
+		localConfig[key] = value;
+	}
+
+	function handleReset() {
+		localConfig = { ...config() };
+		setMode(localConfig.theme as ColorMode);
+		mobileHeader?.updateCarousel();
+	}
+
+	function handleSave() {
+		if (localConfig.custom && typeof localConfig.custom === 'string' && localConfig.custom.trim()) {
+			try {
+				JSON.parse(localConfig.custom);
+			} catch (error) {
+				alert('Invalid JSON in custom parameters. Please check the format and try again.');
+				console.error(error);
+				return;
+			}
+		}
+
+		const processedConfig = { ...localConfig };
+
+		for (const field of NUMERIC_FIELDS) {
+			if (processedConfig[field] !== undefined && processedConfig[field] !== '') {
+				const numValue = Number(processedConfig[field]);
+				if (!isNaN(numValue)) {
+					if ((POSITIVE_INTEGER_FIELDS as readonly string[]).includes(field)) {
+						processedConfig[field] = Math.max(1, Math.round(numValue));
+					} else {
+						processedConfig[field] = numValue;
+					}
+				} else {
+					alert(`Invalid numeric value for ${field}. Please enter a valid number.`);
+					return;
+				}
+			}
+		}
+
+		settingsStore.updateMultipleConfig(processedConfig);
+		goto(settingsReferrer.url);
+	}
+
+	export function reset() {
+		localConfig = { ...config() };
+	}
+
+	setChatSettingsConfigContext({
+		get localConfig() {
+			return localConfig;
+		},
+		handleConfigChange,
+		handleThemeChange
+	});
+</script>
+
+<div
+	class="mx-auto flex h-full max-h-[100dvh] w-full flex-col overflow-y-auto md:pl-8"
+	in:fade={{ duration: 150 }}
+>
+	<div class="flex flex-1 flex-col gap-4 md:flex-row">
+		<SettingsChatDesktopSidebar
+			sections={SETTINGS_CHAT_SECTIONS}
+			isActive={(section: SettingsSection) => section.slug === activeSlug}
+			getHref={getSectionHref ??
+				((section: SettingsSection) => RouterService.settings(section.slug))}
+		/>
+
+		<SettingsChatMobileHeader
+			sections={SETTINGS_CHAT_SECTIONS}
+			isActive={(section: SettingsSection) => section.slug === activeSlug}
+			getHref={getSectionHref ??
+				((section: SettingsSection) => RouterService.settings(section.slug))}
+			bind:this={mobileHeader}
+		/>
+
+		<div class="mx-auto max-w-3xl flex-1">
+			<div class="space-y-6 p-4 md:p-6 md:pt-28">
+				<div class="grid">
+					<div class="mb-6 flex items-center gap-2 border-b border-border/30 pb-6 md:flex">
+						<currentSection.icon class="h-5 w-5" />
+						<h3 class="text-lg font-semibold">{currentSection.title}</h3>
+					</div>
+
+					{#if currentSection.title === SETTINGS_SECTION_TITLES.TOOLS}
+						<SettingsChatToolsTab />
+					{:else if currentSection.title === SETTINGS_SECTION_TITLES.IMPORT_EXPORT}
+						<SettingsChatImportExportTab />
+					{:else if currentSection.fields}
+						<div class="space-y-6">
+							<SettingsChatFields
+								fields={currentSection.fields}
+								{localConfig}
+								onConfigChange={handleConfigChange}
+								onThemeChange={handleThemeChange}
+							/>
+						</div>
+					{/if}
+				</div>
+
+				<div class="mt-8 border-t border-border/30 pt-6">
+					<p class="text-xs text-muted-foreground">Settings are saved in browser's localStorage</p>
+				</div>
+			</div>
+
+			<SettingsFooter onReset={handleReset} onSave={handleSave} />
+		</div>
+	</div>
+</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatFields.svelte
similarity index 82%
rename from tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
rename to tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatFields.svelte
index 42191be89fe..069855eebef 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatFields.svelte
@@ -9,9 +9,9 @@
 	import { SettingsFieldType } from '$lib/enums/settings';
 	import { settingsStore } from '$lib/stores/settings.svelte';
 	import { serverStore } from '$lib/stores/server.svelte';
-	import { modelsStore, selectedModelName } from '$lib/stores/models.svelte';
+	import { modelsStore, selectedModelName, propsCacheVersion } from '$lib/stores/models.svelte';
 	import { normalizeFloatingPoint } from '$lib/utils/precision';
-	import { ChatSettingsParameterSourceIndicator } from '$lib/components/app';
+	import { SettingsChatParameterSourceIndicator } from '$lib/components/app/settings';
 	import type { Component } from 'svelte';
 
 	interface Props {
@@ -23,13 +23,19 @@
 
 	let { fields, localConfig, onConfigChange, onThemeChange }: Props = $props();
 
-	// server sampling defaults for placeholders
-	let sp = $derived.by(() => {
+	let currentModelParams = $derived.by(() => {
+		propsCacheVersion();
+
 		if (serverStore.isRouterMode) {
-			const m = selectedModelName();
-			if (m) {
-				const p = modelsStore.getModelProps(m);
-				return (p?.default_generation_settings?.params ?? {}) as Record<string, unknown>;
+			const currentModelName = selectedModelName();
+
+			if (currentModelName) {
+				const currentModelProps = modelsStore.getModelProps(currentModelName);
+
+				return (currentModelProps?.default_generation_settings?.params ?? {}) as Record<
+					string,
+					unknown
+				>;
 			}
 		}
 		return (serverStore.defaultParams ?? {}) as Record<string, unknown>;
@@ -40,7 +46,7 @@
 	<div class="space-y-2">
 		{#if field.type === SettingsFieldType.INPUT}
 			{@const currentValue = String(localConfig[field.key] ?? '')}
-			{@const serverDefault = sp[field.key]}
+			{@const serverDefault = currentModelParams[field.key]}
 			{@const isCustomRealTime = (() => {
 				if (serverDefault == null) return false;
 				if (currentValue === '') return false;
@@ -66,20 +72,22 @@
 					{/if}
 				</Label>
 				{#if isCustomRealTime}
-					<ChatSettingsParameterSourceIndicator />
+					<SettingsChatParameterSourceIndicator />
 				{/if}
 			</div>
 
-			<div class="relative w-full md:max-w-md">
+			<div class="relative w-full">
 				<Input
 					id={field.key}
+					type={field.isPositiveInteger ? 'number' : 'text'}
+					{...field.isPositiveInteger ? { min: '1', step: '1' } : {}}
 					value={currentValue}
 					oninput={(e) => {
 						// Update local config immediately for real-time badge feedback
 						onConfigChange(field.key, e.currentTarget.value);
 					}}
-					placeholder={sp[field.key] != null
-						? `Default: ${normalizeFloatingPoint(sp[field.key])}`
+					placeholder={currentModelParams[field.key] != null
+						? `Default: ${normalizeFloatingPoint(currentModelParams[field.key])}`
 						: ''}
 					class="w-full {isCustomRealTime ? 'pr-8' : ''}"
 				/>
@@ -104,20 +112,22 @@
 				</p>
 			{/if}
 		{:else if field.type === SettingsFieldType.TEXTAREA}
-			<Label for={field.key} class="block flex items-center gap-1.5 text-sm font-medium">
-				{field.label}
+			{#if field.label}
+				<Label for={field.key} class="block flex items-center gap-1.5 text-sm font-medium">
+					{field.label}
 
-				{#if field.isExperimental}
-					<FlaskConical class="h-3.5 w-3.5 text-muted-foreground" />
-				{/if}
-			</Label>
+					{#if field.isExperimental}
+						<FlaskConical class="h-3.5 w-3.5 text-muted-foreground" />
+					{/if}
+				</Label>
+			{/if}
 
 			<Textarea
 				id={field.key}
 				value={String(localConfig[field.key] ?? '')}
 				onchange={(e) => onConfigChange(field.key, e.currentTarget.value)}
 				placeholder=""
-				class="min-h-[10rem] w-full md:max-w-2xl"
+				class="min-h-[10rem] w-full md:max-w-3xl"
 			/>
 
 			{#if field.help || SETTING_CONFIG_INFO[field.key]}
@@ -131,7 +141,8 @@
 					<Checkbox
 						id="showSystemMessage"
 						checked={Boolean(localConfig.showSystemMessage ?? true)}
-						onCheckedChange={(checked) => onConfigChange('showSystemMessage', Boolean(checked))}
+						onCheckedChange={(checked) =>
+							onConfigChange(SETTINGS_KEYS.SHOW_SYSTEM_MESSAGE, Boolean(checked))}
 					/>
 
 					<Label for="showSystemMessage" class="cursor-pointer text-sm font-normal">
@@ -145,7 +156,7 @@
 					opt.value === localConfig[field.key]
 			)}
 			{@const currentValue = localConfig[field.key]}
-			{@const serverDefault = sp[field.key]}
+			{@const serverDefault = currentModelParams[field.key]}
 			{@const isCustomRealTime = (() => {
 				if (serverDefault == null) return false;
 				if (currentValue === '' || currentValue === undefined) return false;
@@ -161,7 +172,7 @@
 					{/if}
 				</Label>
 				{#if isCustomRealTime}
-					<ChatSettingsParameterSourceIndicator />
+					<SettingsChatParameterSourceIndicator />
 				{/if}
 			</div>
 
@@ -176,7 +187,7 @@
 					}
 				}}
 			>
-				<div class="relative w-full md:w-auto md:max-w-md">
+				<div class="relative w-full md:w-auto">
 					<Select.Trigger class="w-full">
 						<div class="flex items-center gap-2">
 							{#if selectedOption?.icon}
diff --git a/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportSection.svelte b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportSection.svelte
new file mode 100644
index 00000000000..6b36526c333
--- /dev/null
+++ b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportSection.svelte
@@ -0,0 +1,62 @@
+<script lang="ts">
+	import type { Component } from 'svelte';
+	import { Button, type ButtonVariant } from '$lib/components/ui/button';
+
+	let {
+		title,
+		description,
+		IconComponent,
+		buttonText,
+		onclick,
+		titleClass,
+		buttonVariant,
+		buttonClass,
+		wrapperClass,
+		summary
+	}: {
+		title: string;
+		description: string;
+		IconComponent: Component;
+		buttonText: string;
+		onclick: () => void;
+		titleClass?: string;
+		buttonVariant?: ButtonVariant;
+		buttonClass?: string;
+		wrapperClass?: string;
+		summary?: { show: boolean; verb: string; items: DatabaseConversation[] };
+	} = $props();
+
+	let sectionButtonClass = $derived(buttonClass ?? 'justify-start justify-self-start md:w-auto');
+	let sectionButtonVariant = $derived(buttonVariant ?? 'outline');
+</script>
+
+<div class="grid gap-1 {wrapperClass ?? ''}">
+	<h4 class="mt-0 mb-2 text-sm font-medium {titleClass ?? ''}">{title}</h4>
+
+	<p class="mb-4 text-sm text-muted-foreground">{description}</p>
+
+	<Button class={sectionButtonClass} {onclick} variant={sectionButtonVariant}>
+		<IconComponent class="mr-2 h-4 w-4" />
+
+		{buttonText}
+	</Button>
+
+	{#if summary && summary.show && summary.items.length > 0}
+		<div class="mt-4 grid overflow-x-auto rounded-lg border border-border/50 bg-muted/30 p-4">
+			<h5 class="mb-2 text-sm font-medium">
+				{summary.verb}
+				{summary.items.length} conversation{summary.items.length === 1 ? '' : 's'}
+			</h5>
+
+			<ul class="space-y-1 text-sm text-muted-foreground">
+				{#each summary.items.slice(0, 10) as conv (conv.id)}
+					<li class="truncate">• {conv.name || 'Untitled conversation'}</li>
+				{/each}
+
+				{#if summary.items.length > 10}
+					<li class="italic">... and {summary.items.length - 10} more</li>
+				{/if}
+			</ul>
+		</div>
+	{/if}
+</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportTab.svelte
similarity index 52%
rename from tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte
rename to tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportTab.svelte
index 537c839f585..b7b91d65b65 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportTab.svelte
@@ -1,11 +1,18 @@
 <script lang="ts">
 	import { Download, Upload, Trash2 } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import { DialogConversationSelection, DialogConfirmation } from '$lib/components/app';
+	import {
+		DialogConversationSelection,
+		DialogConfirmation,
+		DialogExportSettings
+	} from '$lib/components/app';
 	import { createMessageCountMap } from '$lib/utils';
-	import { ISO_DATE_TIME_SEPARATOR } from '$lib/constants';
+	import { settingsStore } from '$lib/stores/settings.svelte';
 	import { conversationsStore, conversations } from '$lib/stores/conversations.svelte';
 	import { toast } from 'svelte-sonner';
+	import { fade } from 'svelte/transition';
+	import { ConversationSelectionMode, HtmlInputType, FileExtensionText } from '$lib/enums';
+	import SettingsChatImportExportSection from './SettingsChatImportExportSection.svelte';
+	import SettingsGroup from '$lib/components/app/settings/SettingsGroup.svelte';
 
 	let exportedConversations = $state<DatabaseConversation[]>([]);
 	let importedConversations = $state<DatabaseConversation[]>([]);
@@ -23,6 +30,82 @@
 	// Delete functionality state
 	let showDeleteDialog = $state(false);
 
+	// Settings import/export state
+	let showSettingsExportSummary = $state(false);
+	let showSettingsImportSummary = $state(false);
+	let showSettingsExportDialog = $state(false);
+	let includeSensitiveData = $state(false);
+
+	function handleSettingsExport() {
+		showSettingsExportDialog = true;
+		includeSensitiveData = false;
+	}
+
+	function handleSettingsExportConfirm() {
+		showSettingsExportDialog = false;
+
+		try {
+			const data = settingsStore.exportSettings(includeSensitiveData);
+			const blob = new Blob([JSON.stringify(data, null, 2)], { type: 'application/json' });
+			const url = URL.createObjectURL(blob);
+			const a = document.createElement('a');
+			a.href = url;
+			a.download = `llama_settings_${new Date().toISOString().split('T')[0]}.json`;
+			document.body.appendChild(a);
+			a.click();
+			document.body.removeChild(a);
+			URL.revokeObjectURL(url);
+
+			showSettingsExportSummary = true;
+			showSettingsImportSummary = false;
+			toast.success('Settings exported');
+		} catch (err) {
+			console.error('Failed to export settings:', err);
+			toast.error('Failed to export settings');
+		}
+	}
+
+	function handleSettingsExportCancel() {
+		showSettingsExportDialog = false;
+	}
+
+	function handleSettingsImport() {
+		try {
+			const input = document.createElement('input');
+			input.type = HtmlInputType.FILE;
+			input.accept = FileExtensionText.JSON;
+
+			input.onchange = async (e) => {
+				const file = (e.target as HTMLInputElement)?.files?.[0];
+				if (!file) return;
+
+				try {
+					const text = await file.text();
+					const data = JSON.parse(text);
+
+					if (!data || typeof data !== 'object' || !data.config) {
+						toast.error('Invalid settings file: missing config');
+						return;
+					}
+
+					settingsStore.importSettings(data);
+
+					showSettingsImportSummary = true;
+					showSettingsExportSummary = false;
+					toast.success('Settings imported successfully');
+				} catch (err) {
+					console.error('Failed to import settings:', err);
+					toast.error('Failed to import settings');
+				}
+			};
+
+			input.click();
+		} catch (err) {
+			console.error('Failed to open file picker:', err);
+			toast.error('Failed to open file picker');
+		}
+	}
+
 	async function handleExportClick() {
 		try {
 			const allConversations = conversations();
@@ -56,10 +139,7 @@
 				})
 			);
 
-			conversationsStore.downloadConversationFile(
-				allData,
-				`${new Date().toISOString().split(ISO_DATE_TIME_SEPARATOR)[0]}_conversations.json`
-			);
+			conversationsStore.downloadConversationFile(allData);
 
 			exportedConversations = selectedConversations;
 			showExportSummary = true;
@@ -75,8 +155,8 @@
 		try {
 			const input = document.createElement('input');
 
-			input.type = 'file';
-			input.accept = '.json';
+			input.type = HtmlInputType.FILE;
+			input.accept = FileExtensionText.JSON;
 
 			input.onchange = async (e) => {
 				const file = (e.target as HTMLInputElement)?.files?.[0];
@@ -174,114 +254,70 @@
 	}
 </script>
 
-<div class="space-y-6">
-	<div class="space-y-4">
-		<div class="grid">
-			<h4 class="mb-2 text-sm font-medium">Export Conversations</h4>
-
-			<p class="mb-4 text-sm text-muted-foreground">
-				Download all your conversations as a JSON file. This includes all messages, attachments, and
-				conversation history.
-			</p>
-
-			<Button
-				class="w-full justify-start justify-self-start md:w-auto"
-				onclick={handleExportClick}
-				variant="outline"
-			>
-				<Download class="mr-2 h-4 w-4" />
-
-				Export conversations
-			</Button>
-
-			{#if showExportSummary && exportedConversations.length > 0}
-				<div class="mt-4 grid overflow-x-auto rounded-lg border border-border/50 bg-muted/30 p-4">
-					<h5 class="mb-2 text-sm font-medium">
-						Exported {exportedConversations.length} conversation{exportedConversations.length === 1
-							? ''
-							: 's'}
-					</h5>
-
-					<ul class="space-y-1 text-sm text-muted-foreground">
-						{#each exportedConversations.slice(0, 10) as conv (conv.id)}
-							<li class="truncate">• {conv.name || 'Untitled conversation'}</li>
-						{/each}
-
-						{#if exportedConversations.length > 10}
-							<li class="italic">
-								... and {exportedConversations.length - 10} more
-							</li>
-						{/if}
-					</ul>
-				</div>
-			{/if}
-		</div>
-
-		<div class="grid border-t border-border/30 pt-4">
-			<h4 class="mb-2 text-sm font-medium">Import Conversations</h4>
-
-			<p class="mb-4 text-sm text-muted-foreground">
-				Import one or more conversations from a previously exported JSON file. This will merge with
-				your existing conversations.
-			</p>
-
-			<Button
-				class="w-full justify-start justify-self-start md:w-auto"
-				onclick={handleImportClick}
-				variant="outline"
-			>
-				<Upload class="mr-2 h-4 w-4" />
-				Import conversations
-			</Button>
-
-			{#if showImportSummary && importedConversations.length > 0}
-				<div class="mt-4 grid overflow-x-auto rounded-lg border border-border/50 bg-muted/30 p-4">
-					<h5 class="mb-2 text-sm font-medium">
-						Imported {importedConversations.length} conversation{importedConversations.length === 1
-							? ''
-							: 's'}
-					</h5>
-
-					<ul class="space-y-1 text-sm text-muted-foreground">
-						{#each importedConversations.slice(0, 10) as conv (conv.id)}
-							<li class="truncate">• {conv.name || 'Untitled conversation'}</li>
-						{/each}
-
-						{#if importedConversations.length > 10}
-							<li class="italic">
-								... and {importedConversations.length - 10} more
-							</li>
-						{/if}
-					</ul>
-				</div>
-			{/if}
-		</div>
-
-		<div class="grid border-t border-border/30 pt-4">
-			<h4 class="mb-2 text-sm font-medium text-destructive">Delete All Conversations</h4>
-
-			<p class="mb-4 text-sm text-muted-foreground">
-				Permanently delete all conversations and their messages. This action cannot be undone.
-				Consider exporting your conversations first if you want to keep a backup.
-			</p>
-
-			<Button
-				class="text-destructive-foreground w-full justify-start justify-self-start bg-destructive hover:bg-destructive/80 md:w-auto"
-				onclick={handleDeleteAllClick}
-				variant="destructive"
-			>
-				<Trash2 class="mr-2 h-4 w-4" />
-
-				Delete all conversations
-			</Button>
-		</div>
-	</div>
+<div class="space-y-12" in:fade={{ duration: 150 }}>
+	<SettingsGroup title="Conversations">
+		<SettingsChatImportExportSection
+			title="Export"
+			description="Download your conversations as a JSON file. This includes all messages, attachments, and conversation history."
+			IconComponent={Download}
+			buttonText="Export conversations"
+			onclick={handleExportClick}
+			summary={{ show: showExportSummary, verb: 'Exported', items: exportedConversations }}
+		/>
+
+		<SettingsChatImportExportSection
+			title="Import"
+			description="Import one or more conversations from a previously exported JSON file. This will merge with your existing conversations."
+			IconComponent={Upload}
+			buttonText="Import conversations"
+			onclick={handleImportClick}
+			summary={{ show: showImportSummary, verb: 'Imported', items: importedConversations }}
+		/>
+
+		<SettingsChatImportExportSection
+			title="Delete All"
+			description="Permanently delete all conversations and their messages. This action cannot be undone. Consider exporting your conversations first if you want to keep a backup."
+			IconComponent={Trash2}
+			buttonText="Delete all conversations"
+			onclick={handleDeleteAllClick}
+			titleClass="text-destructive"
+			buttonVariant="destructive"
+			buttonClass="text-destructive-foreground justify-start justify-self-start bg-destructive hover:bg-destructive/80 md:w-auto"
+		/>
+	</SettingsGroup>
+
+	<SettingsGroup title="Settings">
+		<SettingsChatImportExportSection
+			title="Export"
+			description="Export your chat settings and preferences as a JSON file."
+			IconComponent={Download}
+			buttonText="Export settings"
+			onclick={handleSettingsExport}
+			summary={{ show: showSettingsExportSummary, verb: 'Exported', items: [] }}
+		/>
+
+		<SettingsChatImportExportSection
+			title="Import"
+			description="Import chat settings from a previously exported JSON file. This will merge with your existing settings."
+			IconComponent={Upload}
+			buttonText="Import settings"
+			onclick={handleSettingsImport}
+			summary={{ show: showSettingsImportSummary, verb: 'Imported', items: [] }}
+		/>
+	</SettingsGroup>
 </div>
 
+<DialogExportSettings
+	bind:open={showSettingsExportDialog}
+	bind:includeSensitiveData
+	onConfirm={handleSettingsExportConfirm}
+	onCancel={handleSettingsExportCancel}
+/>
+
 <DialogConversationSelection
 	conversations={availableConversations}
 	{messageCountMap}
-	mode="export"
+	mode={ConversationSelectionMode.EXPORT}
 	bind:open={showExportDialog}
 	onCancel={() => (showExportDialog = false)}
 	onConfirm={handleExportConfirm}
@@ -290,7 +326,7 @@
 <DialogConversationSelection
 	conversations={availableConversations}
 	{messageCountMap}
-	mode="import"
+	mode={ConversationSelectionMode.IMPORT}
 	bind:open={showImportDialog}
 	onCancel={() => (showImportDialog = false)}
 	onConfirm={handleImportConfirm}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsParameterSourceIndicator.svelte b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatParameterSourceIndicator.svelte
similarity index 99%
rename from tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsParameterSourceIndicator.svelte
rename to tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatParameterSourceIndicator.svelte
index b566985ba05..1407eb87ebe 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsParameterSourceIndicator.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatParameterSourceIndicator.svelte
@@ -14,5 +14,6 @@
 	class="h-5 bg-orange-100 px-1.5 py-0.5 text-xs text-orange-800 dark:bg-orange-900 dark:text-orange-200 {className}"
 >
 	<Wrench class="mr-1 h-3 w-3" />
+
 	Custom
 </Badge>
diff --git a/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatToolsTab.svelte b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatToolsTab.svelte
new file mode 100644
index 00000000000..5857254d80e
--- /dev/null
+++ b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatToolsTab.svelte
@@ -0,0 +1,104 @@
+<script lang="ts">
+	import { ChevronDown, ChevronRight } from '@lucide/svelte';
+	import { Checkbox } from '$lib/components/ui/checkbox';
+	import * as Collapsible from '$lib/components/ui/collapsible';
+	import { TruncatedText, McpServerIdentity } from '$lib/components/app';
+	import { toolsStore } from '$lib/stores/tools.svelte';
+	import { permissionsStore } from '$lib/stores/permissions.svelte';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { SvelteSet } from 'svelte/reactivity';
+
+	let expandedGroups = new SvelteSet<string>();
+	let groups = $derived(toolsStore.toolGroups);
+
+	function toggleExpanded(label: string) {
+		if (expandedGroups.has(label)) {
+			expandedGroups.delete(label);
+		} else {
+			expandedGroups.add(label);
+		}
+	}
+</script>
+
+{#if groups.length === 0}
+	<div class="py-8 text-center text-sm text-muted-foreground">No tools available</div>
+{:else}
+	<div class="space-y-2">
+		{#each groups as group (group.label)}
+			{@const isExpanded = expandedGroups.has(group.label)}
+			<Collapsible.Root open={isExpanded} onOpenChange={() => toggleExpanded(group.label)}>
+				<Collapsible.Trigger
+					class="flex w-full items-center gap-2 rounded-lg px-3 py-2 text-sm hover:bg-muted/50"
+				>
+					{#if isExpanded}
+						<ChevronDown class="h-3.5 w-3.5 shrink-0" />
+					{:else}
+						<ChevronRight class="h-3.5 w-3.5 shrink-0" />
+					{/if}
+
+					{@const faviconUrl = group.serverId ? mcpStore.getServerFavicon(group.serverId) : null}
+
+					<span class="inline-flex min-w-0 items-center gap-1.5 font-medium">
+						<McpServerIdentity
+							iconClass="h-4 w-4"
+							iconRounded="rounded-sm"
+							showVersion={false}
+							displayName={group.label}
+							{faviconUrl}
+						/>
+					</span>
+
+					<span class="ml-auto shrink-0 text-xs text-muted-foreground">
+						{group.tools.length} tool{group.tools.length !== 1 ? 's' : ''}
+					</span>
+				</Collapsible.Trigger>
+
+				<Collapsible.Content>
+					<div class="ml-4 border-l border-border/50 pl-2">
+						<!-- Header row -->
+						<div class="flex items-center gap-2 px-2 py-1 text-xs text-muted-foreground">
+							<span class="min-w-0 flex-1">Tool</span>
+							<span class="w-16 shrink-0 text-center">Enabled</span>
+							<span class="w-20 shrink-0 text-center">Always allow</span>
+						</div>
+
+						{#each group.tools as tool (tool.function.name)}
+							{@const toolName = tool.function.name}
+							{@const isEnabled = toolsStore.isToolEnabled(toolName)}
+							{@const permissionKey = toolsStore.getPermissionKey(toolName)}
+							{@const isAlwaysAllowed = permissionKey
+								? permissionsStore.hasTool(permissionKey)
+								: false}
+
+							<div class="flex items-center gap-2 rounded px-2 py-1.5 text-sm hover:bg-muted/50">
+								<TruncatedText text={toolName} class="flex-1" showTooltip={true} />
+
+								<div class="flex w-16 shrink-0 justify-center">
+									<Checkbox
+										checked={isEnabled}
+										onCheckedChange={() => toolsStore.toggleTool(toolName)}
+										class="h-4 w-4"
+									/>
+								</div>
+
+								<div class="flex w-20 shrink-0 justify-center">
+									<Checkbox
+										checked={isAlwaysAllowed}
+										onCheckedChange={() => {
+											if (isAlwaysAllowed) {
+												permissionsStore.revokeTool(permissionKey!);
+											} else {
+												permissionsStore.allowTool(permissionKey!);
+											}
+										}}
+										class="h-4 w-4"
+									/>
+								</div>
+							</div>
+						{/each}
+					</div>
+				</Collapsible.Content>
+			</Collapsible.Root>
+		{/each}
+	</div>
+{/if}
diff --git a/tools/ui/src/lib/components/app/settings/SettingsChatDesktopSidebar.svelte b/tools/ui/src/lib/components/app/settings/SettingsChatDesktopSidebar.svelte
new file mode 100644
index 00000000000..48a7b8757b8
--- /dev/null
+++ b/tools/ui/src/lib/components/app/settings/SettingsChatDesktopSidebar.svelte
@@ -0,0 +1,49 @@
+<script lang="ts">
+	import { Settings } from '@lucide/svelte';
+	import type { SettingsSection, SettingsSectionTitle } from '$lib/constants';
+
+	interface Props {
+		sections: SettingsSection[];
+		isActive: (section: SettingsSection) => boolean;
+		getHref?: (section: SettingsSection) => string;
+		onSectionChange?: (section: SettingsSectionTitle) => void;
+	}
+
+	let { sections, isActive, getHref, onSectionChange }: Props = $props();
+</script>
+
+<div class="sticky top-0 hidden w-64 flex-col self-start bg-background pt-10 pb-4 md:flex">
+	<div class="flex items-center gap-2 pb-10">
+		<Settings class="h-6 w-6" />
+		<h1 class="text-2xl font-semibold">Settings</h1>
+	</div>
+	<nav class="space-y-1">
+		{#each sections as section (section.title)}
+			{#if getHref}
+				<a
+					class="flex w-full cursor-pointer items-center gap-3 rounded-lg px-3 py-2 text-left text-sm no-underline transition-colors hover:bg-accent {isActive(
+						section
+					)
+						? 'bg-accent text-accent-foreground'
+						: 'text-muted-foreground'}"
+					href={getHref(section)}
+				>
+					<section.icon class="h-4 w-4" />
+					<span class="ml-2">{section.title}</span>
+				</a>
+			{:else}
+				<button
+					class="flex w-full cursor-pointer items-center gap-3 rounded-lg px-3 py-2 text-left text-sm transition-colors hover:bg-accent {isActive(
+						section
+					)
+						? 'bg-accent text-accent-foreground'
+						: 'text-muted-foreground'}"
+					onclick={() => onSectionChange?.(section.title)}
+				>
+					<section.icon class="h-4 w-4" />
+					<span class="ml-2">{section.title}</span>
+				</button>
+			{/if}
+		{/each}
+	</nav>
+</div>
diff --git a/tools/ui/src/lib/components/app/settings/SettingsChatMobileHeader.svelte b/tools/ui/src/lib/components/app/settings/SettingsChatMobileHeader.svelte
new file mode 100644
index 00000000000..15eedd108b1
--- /dev/null
+++ b/tools/ui/src/lib/components/app/settings/SettingsChatMobileHeader.svelte
@@ -0,0 +1,107 @@
+<script lang="ts">
+	import { Settings, ChevronLeft, ChevronRight } from '@lucide/svelte';
+	import { onMount, tick } from 'svelte';
+	import type { SettingsSection, SettingsSectionTitle } from '$lib/constants';
+	import { useScrollCarousel } from '$lib/hooks/use-scroll-carousel.svelte';
+
+	interface Props {
+		sections: SettingsSection[];
+		isActive: (section: SettingsSection) => boolean;
+		getHref?: (section: SettingsSection) => string;
+		onSectionChange?: (section: SettingsSectionTitle) => void;
+	}
+
+	let { sections, isActive, getHref, onSectionChange }: Props = $props();
+
+	const carousel = useScrollCarousel();
+
+	onMount(async () => {
+		await tick();
+		if (carousel.scrollContainer) {
+			const activeTab = carousel.scrollContainer.querySelector('[data-active="true"]');
+			if (activeTab instanceof HTMLElement) {
+				carousel.scrollToCenter(activeTab);
+			}
+		}
+	});
+
+	export function updateCarousel() {
+		setTimeout(carousel.updateScrollButtons, 100);
+	}
+</script>
+
+<div class="sticky top-0 z-10 flex flex-col bg-background md:hidden">
+	<div class="flex items-center gap-2 px-4 pt-4 pb-2 md:pt-6">
+		<Settings class="h-5 w-5 md:h-6 md:w-6" />
+
+		<h1 class="text-xl font-semibold md:text-2xl">Settings</h1>
+	</div>
+
+	<div class="border-b border-border/30 py-2">
+		<div class="relative flex items-center" style="scroll-padding: 1rem;">
+			<button
+				class="absolute left-2 z-10 flex h-6 w-6 items-center justify-center rounded-full bg-muted shadow-md backdrop-blur-sm transition-opacity hover:bg-accent {carousel.canScrollLeft
+					? 'opacity-100'
+					: 'pointer-events-none opacity-0'}"
+				onclick={carousel.scrollLeft}
+				aria-label="Scroll left"
+			>
+				<ChevronLeft class="h-4 w-4" />
+			</button>
+
+			<div
+				class="scrollbar-hide overflow-x-auto py-2"
+				bind:this={carousel.scrollContainer}
+				onscroll={carousel.updateScrollButtons}
+			>
+				<div class="flex min-w-max gap-2">
+					{#each sections as section (section.title)}
+						{#if getHref}
+							<a
+								class="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm whitespace-nowrap no-underline transition-colors first:ml-4 last:mr-4 hover:bg-accent {isActive(
+									section
+								)
+									? 'bg-accent text-accent-foreground'
+									: 'text-muted-foreground'}"
+								data-active={isActive(section)}
+								href={getHref(section)}
+								onclick={(e: MouseEvent) => {
+									carousel.scrollToCenter(e.currentTarget as HTMLElement);
+								}}
+							>
+								<section.icon class="h-4 w-4 flex-shrink-0" />
+								<span>{section.title}</span>
+							</a>
+						{:else}
+							<button
+								class="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm whitespace-nowrap transition-colors first:ml-4 last:mr-4 hover:bg-accent {isActive(
+									section
+								)
+									? 'bg-accent text-accent-foreground'
+									: 'text-muted-foreground'}"
+								data-active={isActive(section)}
+								onclick={(e: MouseEvent) => {
+									onSectionChange?.(section.title);
+									carousel.scrollToCenter(e.currentTarget as HTMLElement);
+								}}
+							>
+								<section.icon class="h-4 w-4 flex-shrink-0" />
+								<span>{section.title}</span>
+							</button>
+						{/if}
+					{/each}
+				</div>
+			</div>
+
+			<button
+				class="absolute right-2 z-10 flex h-6 w-6 items-center justify-center rounded-full bg-muted shadow-md backdrop-blur-sm transition-opacity hover:bg-accent {carousel.canScrollRight
+					? 'opacity-100'
+					: 'pointer-events-none opacity-0'}"
+				onclick={carousel.scrollRight}
+				aria-label="Scroll right"
+			>
+				<ChevronRight class="h-4 w-4" />
+			</button>
+		</div>
+	</div>
+</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFooter.svelte b/tools/ui/src/lib/components/app/settings/SettingsFooter.svelte
similarity index 95%
rename from tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFooter.svelte
rename to tools/ui/src/lib/components/app/settings/SettingsFooter.svelte
index 1f7eb4e7527..afc37377d0c 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFooter.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsFooter.svelte
@@ -29,7 +29,7 @@
 	}
 </script>
 
-<div class="flex justify-between border-t border-border/30 p-6">
+<div class="sticky bottom-0 mx-auto mt-4 flex w-full justify-between p-6">
 	<div class="flex gap-2">
 		<Button variant="outline" onclick={handleResetClick}>
 			<RotateCcw class="h-3 w-3" />
diff --git a/tools/ui/src/lib/components/app/settings/SettingsGroup.svelte b/tools/ui/src/lib/components/app/settings/SettingsGroup.svelte
new file mode 100644
index 00000000000..113d32176b8
--- /dev/null
+++ b/tools/ui/src/lib/components/app/settings/SettingsGroup.svelte
@@ -0,0 +1,18 @@
+<script lang="ts">
+	import type { Snippet } from 'svelte';
+
+	interface Props {
+		title: string;
+		children: Snippet;
+	}
+
+	let { title, children }: Props = $props();
+</script>
+
+<div>
+	<h3 class="mb-6 text-base font-semibold">{title}</h3>
+
+	<div class="space-y-8">
+		{@render children()}
+	</div>
+</div>
diff --git a/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte b/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte
new file mode 100644
index 00000000000..5c801fa2c27
--- /dev/null
+++ b/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte
@@ -0,0 +1,108 @@
+<script lang="ts">
+	import { Plus } from '@lucide/svelte';
+	import { Button } from '$lib/components/ui/button';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { conversationsStore } from '$lib/stores/conversations.svelte';
+	import { toolsStore } from '$lib/stores/tools.svelte';
+	import { McpServerCard, McpServerCardSkeleton } from '$lib/components/app/mcp';
+	import { DialogMcpServerAddNew } from '$lib/components/app/dialogs';
+	import { HealthCheckStatus } from '$lib/enums';
+	import { fade } from 'svelte/transition';
+	import { onMount } from 'svelte';
+	import McpLogo from '../mcp/McpLogo.svelte';
+	import { page } from '$app/state';
+	import { replaceState } from '$app/navigation';
+
+	interface Props {
+		class?: string;
+	}
+
+	let { class: className }: Props = $props();
+
+	let servers = $derived(mcpStore.getServersSorted());
+
+	let initialLoadComplete = $state(false);
+	let isAddingServer = $state(false);
+
+	onMount(() => {
+		if (page.url.searchParams.has('add')) {
+			isAddingServer = true;
+
+			const newUrl = new URL(page.url);
+			newUrl.searchParams.delete('add');
+
+			replaceState(newUrl, {});
+		}
+	});
+
+	$effect(() => {
+		if (initialLoadComplete) return;
+
+		const allChecked =
+			servers.length > 0 &&
+			servers.every((server) => {
+				const state = mcpStore.getHealthCheckState(server.id);
+
+				return (
+					state.status === HealthCheckStatus.SUCCESS || state.status === HealthCheckStatus.ERROR
+				);
+			});
+
+		if (allChecked) {
+			initialLoadComplete = true;
+		}
+	});
+</script>
+
+<div in:fade={{ duration: 150 }} class="h-full max-h-[100dvh] overflow-y-auto">
+	<div class="flex items-center gap-2 p-4 md:absolute md:top-8 md:left-8 md:px-0 md:py-2">
+		<McpLogo class="h-5 w-5 md:h-6 md:w-6" />
+
+		<h1 class="text-xl font-semibold md:text-2xl">MCP Servers</h1>
+	</div>
+
+	<div class="sticky top-0 z-10 mt-4 flex items-start gap-4 p-4 md:justify-end md:px-8">
+		<Button variant="outline" size="sm" class="shrink-0" onclick={() => (isAddingServer = true)}>
+			<Plus class="h-4 w-4" />
+
+			Add New Server
+		</Button>
+	</div>
+
+	<DialogMcpServerAddNew bind:open={isAddingServer} />
+
+	<div class="grid gap-5 md:space-y-4 {className}">
+		{#if servers.length === 0 && !isAddingServer}
+			<div class="rounded-md border border-dashed p-4 text-sm text-muted-foreground">
+				No MCP Servers configured yet. Add one to enable agentic features.
+			</div>
+		{/if}
+
+		{#if servers.length > 0}
+			<div
+				class="grid gap-3"
+				style="grid-template-columns: repeat(auto-fill, minmax(min(32rem, calc(100dvw - 2rem)), 1fr));"
+			>
+				{#each servers as server (server.id)}
+					{#if !initialLoadComplete}
+						<McpServerCardSkeleton />
+					{:else}
+						<McpServerCard
+							{server}
+							enabled={conversationsStore.isMcpServerEnabledForChat(server.id)}
+							onToggle={async () => {
+								const wasEnabled = conversationsStore.isMcpServerEnabledForChat(server.id);
+								await conversationsStore.toggleMcpServerForChat(server.id);
+								if (!wasEnabled) {
+									toolsStore.enableAllToolsForServer(server.id);
+								}
+							}}
+							onUpdate={(updates) => mcpStore.updateServer(server.id, updates)}
+							onDelete={() => mcpStore.removeServer(server.id)}
+						/>
+					{/if}
+				{/each}
+			</div>
+		{/if}
+	</div>
+</div>
diff --git a/tools/ui/src/lib/components/app/settings/index.ts b/tools/ui/src/lib/components/app/settings/index.ts
new file mode 100644
index 00000000000..63f9651df62
--- /dev/null
+++ b/tools/ui/src/lib/components/app/settings/index.ts
@@ -0,0 +1,76 @@
+/**
+ * Full chat settings page layout with sidebar, mobile header, and content area.
+ * Manages local configuration state, section navigation, and context setup.
+ * Accepts an optional `initialSection` prop to override the URL-based section resolution.
+ */
+export { default as SettingsChat } from './SettingsChat/SettingsChat.svelte';
+
+/**
+ * Desktop sidebar navigation for chat settings.
+ * Displays a list of settings sections with icons and titles.
+ * Supports both hash-link navigation (via `getHref`) and in-app section switching (via `onSectionChange`).
+ */
+export { default as SettingsChatDesktopSidebar } from './SettingsChatDesktopSidebar.svelte';
+
+/**
+ * Mobile header with a horizontally scrollable section picker for chat settings.
+ * Shows chevron buttons for scroll navigation and highlights the active section.
+ * Supports both hash-link navigation (via `getHref`) and in-app section switching (via `onSectionChange`).
+ */
+export { default as SettingsChatMobileHeader } from './SettingsChatMobileHeader.svelte';
+
+/**
+ * Badge indicating parameter source for sampling settings. Shows one of:
+ * - **Custom**: User has explicitly set this value (orange badge)
+ * - **Server Props**: Using default from `/props` endpoint (blue badge)
+ * - **Default**: Using app default, server props unavailable (gray badge)
+ * Updates in real-time as user types to show immediate feedback.
+ */
+export { default as SettingsChatParameterSourceIndicator } from './SettingsChat/SettingsChatParameterSourceIndicator.svelte';
+
+/**
+ * Section wrapper for settings panels. Displays a title heading with
+ * child content in a structured layout.
+ */
+export { default as SettingsGroup } from './SettingsGroup.svelte';
+
+/**
+ * Footer with save/cancel buttons for settings panel. Positioned at bottom
+ * of settings dialog. Save button commits form state to config store,
+ * cancel button triggers reset and close.
+ */
+export { default as SettingsFooter } from './SettingsFooter.svelte';
+
+/**
+ * Settings Import/Export panel.
+ * Provides UI for importing and exporting chat conversations.
+ */
+export { default as SettingsChatImportExportTab } from './SettingsChat/SettingsChatImportExportTab.svelte';
+
+/**
+ * Section wrapper for import/export sections. Displays a title, description,
+ * icon button, and optional summary of recent actions.
+ */
+export { default as SettingsChatImportExportSection } from './SettingsChat/SettingsChatImportExportSection.svelte';
+
+/**
+ * MCP Servers configuration panel.
+ * Provides UI for managing Model Context Protocol (MCP) server connections.
+ */
+export { default as SettingsMcpServers } from './SettingsMcpServers.svelte';
+
+/**
+ * Form fields renderer for individual settings. Generates appropriate input
+ * components based on field type (text, number, select, checkbox, textarea).
+ * Handles validation, help text display, and parameter source indicators.
+ */
+export { default as SettingsChatFields } from './SettingsChat/SettingsChatFields.svelte';
+
+/**
+ * **SettingsChatToolsTab** - Tools configuration tab for chat settings
+ *
+ * Displays available tools grouped by source (built-in, MCP, custom) with
+ * toggles to enable/disable individual tools and tool groups. Shows MCP
+ * server favicons and permission management controls.
+ */
+export { default as SettingsChatToolsTab } from './SettingsChat/SettingsChatToolsTab.svelte';
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-action.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-action.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-action.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-action.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-cancel.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-cancel.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-cancel.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-cancel.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-content.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-content.svelte
similarity index 94%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-content.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-content.svelte
index 2398daee70b..c0bb2a34e4f 100644
--- a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-content.svelte
+++ b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-content.svelte
@@ -22,7 +22,7 @@
 			'fixed z-[999999] grid w-full gap-4 border bg-background p-6 shadow-lg duration-200',
 			// Mobile: Bottom sheet behavior
 			'right-0 bottom-0 left-0 max-h-[100dvh] translate-x-0 translate-y-0 overflow-y-auto rounded-t-lg',
-			'data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:slide-out-to-bottom-full',
+			'data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=closed]:slide-out-to-bottom-full',
 			'data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:slide-in-from-bottom-full',
 			// Desktop: Centered dialog behavior
 			'sm:top-[50%] sm:right-auto sm:bottom-auto sm:left-[50%] sm:max-h-[100vh] sm:max-w-lg sm:translate-x-[-50%] sm:translate-y-[-50%] sm:rounded-lg',
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-description.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-description.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-description.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-description.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-footer.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-footer.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-footer.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-footer.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-header.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-header.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-header.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-header.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-overlay.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-overlay.svelte
similarity index 77%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-overlay.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-overlay.svelte
index 71f166debf3..b047dcf6c45 100644
--- a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-overlay.svelte
+++ b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-overlay.svelte
@@ -13,7 +13,7 @@
 	bind:ref
 	data-slot="alert-dialog-overlay"
 	class={cn(
-		'fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:animate-in data-[state=open]:fade-in-0',
+		'fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=open]:animate-in data-[state=open]:fade-in-0',
 		className
 	)}
 	{...restProps}
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-title.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-title.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-title.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-title.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-trigger.svelte b/tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-trigger.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/alert-dialog-trigger.svelte
rename to tools/ui/src/lib/components/ui/alert-dialog/alert-dialog-trigger.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert-dialog/index.ts b/tools/ui/src/lib/components/ui/alert-dialog/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert-dialog/index.ts
rename to tools/ui/src/lib/components/ui/alert-dialog/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/alert/alert-description.svelte b/tools/ui/src/lib/components/ui/alert/alert-description.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert/alert-description.svelte
rename to tools/ui/src/lib/components/ui/alert/alert-description.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert/alert-title.svelte b/tools/ui/src/lib/components/ui/alert/alert-title.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert/alert-title.svelte
rename to tools/ui/src/lib/components/ui/alert/alert-title.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert/alert.svelte b/tools/ui/src/lib/components/ui/alert/alert.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert/alert.svelte
rename to tools/ui/src/lib/components/ui/alert/alert.svelte
diff --git a/tools/server/webui/src/lib/components/ui/alert/index.ts b/tools/ui/src/lib/components/ui/alert/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/alert/index.ts
rename to tools/ui/src/lib/components/ui/alert/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/badge/badge.svelte b/tools/ui/src/lib/components/ui/badge/badge.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/badge/badge.svelte
rename to tools/ui/src/lib/components/ui/badge/badge.svelte
diff --git a/tools/server/webui/src/lib/components/ui/badge/index.ts b/tools/ui/src/lib/components/ui/badge/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/badge/index.ts
rename to tools/ui/src/lib/components/ui/badge/index.ts
diff --git a/tools/ui/src/lib/components/ui/button-group/button-group-root.svelte b/tools/ui/src/lib/components/ui/button-group/button-group-root.svelte
new file mode 100644
index 00000000000..4587ec38ec3
--- /dev/null
+++ b/tools/ui/src/lib/components/ui/button-group/button-group-root.svelte
@@ -0,0 +1,21 @@
+<script lang="ts">
+	import { cn } from '$lib/components/ui/utils';
+	import type { Snippet } from 'svelte';
+	import type { HTMLAttributes } from 'svelte/elements';
+
+	interface Props extends HTMLAttributes<HTMLDivElement> {
+		children: Snippet;
+	}
+
+	let { class: className, children, ...restProps }: Props = $props();
+</script>
+
+<div
+	class={cn(
+		'flex items-center [&>*:first-child]:rounded-r-none [&>*:last-child]:rounded-l-none [&>*:not(:first-child):not(:last-child)]:rounded-none',
+		className
+	)}
+	{...restProps}
+>
+	{@render children()}
+</div>
diff --git a/tools/ui/src/lib/components/ui/button-group/button-group-separator.svelte b/tools/ui/src/lib/components/ui/button-group/button-group-separator.svelte
new file mode 100644
index 00000000000..d13c573c8e2
--- /dev/null
+++ b/tools/ui/src/lib/components/ui/button-group/button-group-separator.svelte
@@ -0,0 +1,8 @@
+<script lang="ts">
+	import { cn } from '$lib/components/ui/utils';
+	import type { HTMLAttributes } from 'svelte/elements';
+
+	let { ...restProps }: HTMLAttributes<HTMLDivElement> = $props();
+</script>
+
+<div class={cn('shrink-0 self-stretch bg-border', 'w-px')} {...restProps}></div>
diff --git a/tools/ui/src/lib/components/ui/button-group/index.ts b/tools/ui/src/lib/components/ui/button-group/index.ts
new file mode 100644
index 00000000000..b0d2e30441b
--- /dev/null
+++ b/tools/ui/src/lib/components/ui/button-group/index.ts
@@ -0,0 +1,2 @@
+export { default as Root } from './button-group-root.svelte';
+export { default as Separator } from './button-group-separator.svelte';
diff --git a/tools/server/webui/src/lib/components/ui/button/button.svelte b/tools/ui/src/lib/components/ui/button/button.svelte
similarity index 84%
rename from tools/server/webui/src/lib/components/ui/button/button.svelte
rename to tools/ui/src/lib/components/ui/button/button.svelte
index 5ced3eb9a0f..cbf4d2cc591 100644
--- a/tools/server/webui/src/lib/components/ui/button/button.svelte
+++ b/tools/ui/src/lib/components/ui/button/button.svelte
@@ -7,13 +7,13 @@
 		base: "focus-visible:border-ring focus-visible:ring-ring/50 aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 aria-invalid:border-destructive inline-flex shrink-0 items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium outline-none transition-all focus-visible:ring-[3px] disabled:pointer-events-none disabled:opacity-50 aria-disabled:pointer-events-none aria-disabled:opacity-50 [&_svg:not([class*='size-'])]:size-4 [&_svg]:pointer-events-none [&_svg]:shrink-0",
 		variants: {
 			variant: {
-				default: 'bg-primary text-primary-foreground shadow-xs hover:bg-primary/90',
+				default: 'bg-primary text-primary-foreground shadow-sm hover:bg-primary/90',
 				destructive:
-					'bg-destructive shadow-xs hover:bg-destructive/90 focus-visible:ring-destructive/20 dark:focus-visible:ring-destructive/40 dark:bg-destructive/60 text-white',
+					'bg-destructive shadow-sm hover:bg-destructive/90 focus-visible:ring-destructive/20 dark:focus-visible:ring-destructive/40 dark:bg-destructive/60 text-white!',
 				outline:
-					'bg-background shadow-xs hover:bg-accent hover:text-accent-foreground dark:bg-input/30 dark:border-input dark:hover:bg-input/50 border',
+					'shadow-sm hover:text-accent-foreground hover:bg-muted-foreground/10 backdrop-blur-sm dark:border-input border',
 				secondary:
-					'dark:bg-secondary dark:text-secondary-foreground bg-background shadow-sm text-foreground hover:bg-muted-foreground/20',
+					'bg-background dark:bg-muted-foreground/15 dark:text-secondary-foreground shadow-sm text-foreground hover:bg-muted-foreground/20 dark:hover:bg-muted-foreground/25',
 				ghost: 'hover:text-accent-foreground hover:bg-muted-foreground/10 backdrop-blur-sm',
 				link: 'text-primary underline-offset-4 hover:underline'
 			},
diff --git a/tools/server/webui/src/lib/components/ui/button/index.ts b/tools/ui/src/lib/components/ui/button/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/button/index.ts
rename to tools/ui/src/lib/components/ui/button/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/card/card-action.svelte b/tools/ui/src/lib/components/ui/card/card-action.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/card/card-action.svelte
rename to tools/ui/src/lib/components/ui/card/card-action.svelte
diff --git a/tools/server/webui/src/lib/components/ui/card/card-content.svelte b/tools/ui/src/lib/components/ui/card/card-content.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/card/card-content.svelte
rename to tools/ui/src/lib/components/ui/card/card-content.svelte
diff --git a/tools/server/webui/src/lib/components/ui/card/card-description.svelte b/tools/ui/src/lib/components/ui/card/card-description.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/card/card-description.svelte
rename to tools/ui/src/lib/components/ui/card/card-description.svelte
diff --git a/tools/server/webui/src/lib/components/ui/card/card-footer.svelte b/tools/ui/src/lib/components/ui/card/card-footer.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/card/card-footer.svelte
rename to tools/ui/src/lib/components/ui/card/card-footer.svelte
diff --git a/tools/server/webui/src/lib/components/ui/card/card-header.svelte b/tools/ui/src/lib/components/ui/card/card-header.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/card/card-header.svelte
rename to tools/ui/src/lib/components/ui/card/card-header.svelte
diff --git a/tools/server/webui/src/lib/components/ui/card/card-title.svelte b/tools/ui/src/lib/components/ui/card/card-title.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/card/card-title.svelte
rename to tools/ui/src/lib/components/ui/card/card-title.svelte
diff --git a/tools/server/webui/src/lib/components/ui/card/card.svelte b/tools/ui/src/lib/components/ui/card/card.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/card/card.svelte
rename to tools/ui/src/lib/components/ui/card/card.svelte
diff --git a/tools/server/webui/src/lib/components/ui/card/index.ts b/tools/ui/src/lib/components/ui/card/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/card/index.ts
rename to tools/ui/src/lib/components/ui/card/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/checkbox/checkbox.svelte b/tools/ui/src/lib/components/ui/checkbox/checkbox.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/checkbox/checkbox.svelte
rename to tools/ui/src/lib/components/ui/checkbox/checkbox.svelte
diff --git a/tools/server/webui/src/lib/components/ui/checkbox/index.ts b/tools/ui/src/lib/components/ui/checkbox/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/checkbox/index.ts
rename to tools/ui/src/lib/components/ui/checkbox/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/collapsible/collapsible-content.svelte b/tools/ui/src/lib/components/ui/collapsible/collapsible-content.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/collapsible/collapsible-content.svelte
rename to tools/ui/src/lib/components/ui/collapsible/collapsible-content.svelte
diff --git a/tools/server/webui/src/lib/components/ui/collapsible/collapsible-trigger.svelte b/tools/ui/src/lib/components/ui/collapsible/collapsible-trigger.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/collapsible/collapsible-trigger.svelte
rename to tools/ui/src/lib/components/ui/collapsible/collapsible-trigger.svelte
diff --git a/tools/server/webui/src/lib/components/ui/collapsible/collapsible.svelte b/tools/ui/src/lib/components/ui/collapsible/collapsible.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/collapsible/collapsible.svelte
rename to tools/ui/src/lib/components/ui/collapsible/collapsible.svelte
diff --git a/tools/server/webui/src/lib/components/ui/collapsible/index.ts b/tools/ui/src/lib/components/ui/collapsible/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/collapsible/index.ts
rename to tools/ui/src/lib/components/ui/collapsible/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/dialog/dialog-close.svelte b/tools/ui/src/lib/components/ui/dialog/dialog-close.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dialog/dialog-close.svelte
rename to tools/ui/src/lib/components/ui/dialog/dialog-close.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dialog/dialog-content.svelte b/tools/ui/src/lib/components/ui/dialog/dialog-content.svelte
similarity index 87%
rename from tools/server/webui/src/lib/components/ui/dialog/dialog-content.svelte
rename to tools/ui/src/lib/components/ui/dialog/dialog-content.svelte
index 74df0eacb57..0e1b07c40ed 100644
--- a/tools/server/webui/src/lib/components/ui/dialog/dialog-content.svelte
+++ b/tools/ui/src/lib/components/ui/dialog/dialog-content.svelte
@@ -25,7 +25,7 @@
 		bind:ref
 		data-slot="dialog-content"
 		class={cn(
-			`fixed top-[50%] left-[50%] z-50 grid max-h-[100dvh] w-full max-w-[calc(100%-2rem)] translate-x-[-50%] translate-y-[-50%] gap-4 overflow-y-auto rounded-lg border border-border/30 bg-background p-6 shadow-lg duration-200 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95 sm:max-w-lg md:max-h-[100vh]`,
+			`fixed top-[50%] left-[50%] z-50 grid max-h-[100dvh] w-full max-w-[calc(100%-2rem)] translate-x-[-50%] translate-y-[-50%] gap-4 overflow-y-auto rounded-lg border border-border/30 bg-background p-6 shadow-lg duration-200 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95 sm:max-w-lg md:max-h-[100vh]`,
 			className
 		)}
 		{...restProps}
diff --git a/tools/server/webui/src/lib/components/ui/dialog/dialog-description.svelte b/tools/ui/src/lib/components/ui/dialog/dialog-description.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dialog/dialog-description.svelte
rename to tools/ui/src/lib/components/ui/dialog/dialog-description.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dialog/dialog-footer.svelte b/tools/ui/src/lib/components/ui/dialog/dialog-footer.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dialog/dialog-footer.svelte
rename to tools/ui/src/lib/components/ui/dialog/dialog-footer.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dialog/dialog-header.svelte b/tools/ui/src/lib/components/ui/dialog/dialog-header.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dialog/dialog-header.svelte
rename to tools/ui/src/lib/components/ui/dialog/dialog-header.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dialog/dialog-overlay.svelte b/tools/ui/src/lib/components/ui/dialog/dialog-overlay.svelte
similarity index 76%
rename from tools/server/webui/src/lib/components/ui/dialog/dialog-overlay.svelte
rename to tools/ui/src/lib/components/ui/dialog/dialog-overlay.svelte
index a6e9a102f5d..a7803f90368 100644
--- a/tools/server/webui/src/lib/components/ui/dialog/dialog-overlay.svelte
+++ b/tools/ui/src/lib/components/ui/dialog/dialog-overlay.svelte
@@ -13,7 +13,7 @@
 	bind:ref
 	data-slot="dialog-overlay"
 	class={cn(
-		'fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:animate-in data-[state=open]:fade-in-0',
+		'fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=open]:animate-in data-[state=open]:fade-in-0',
 		className
 	)}
 	{...restProps}
diff --git a/tools/server/webui/src/lib/components/ui/dialog/dialog-title.svelte b/tools/ui/src/lib/components/ui/dialog/dialog-title.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dialog/dialog-title.svelte
rename to tools/ui/src/lib/components/ui/dialog/dialog-title.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dialog/dialog-trigger.svelte b/tools/ui/src/lib/components/ui/dialog/dialog-trigger.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dialog/dialog-trigger.svelte
rename to tools/ui/src/lib/components/ui/dialog/dialog-trigger.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dialog/index.ts b/tools/ui/src/lib/components/ui/dialog/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dialog/index.ts
rename to tools/ui/src/lib/components/ui/dialog/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-checkbox-item.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-checkbox-item.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-checkbox-item.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-checkbox-item.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte
similarity index 82%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte
index 6013ca2661b..0ca0d3964ac 100644
--- a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte
+++ b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-content.svelte
@@ -19,7 +19,7 @@
 		data-slot="dropdown-menu-content"
 		{sideOffset}
 		class={cn(
-			'z-50 max-h-(--bits-dropdown-menu-content-available-height) min-w-[8rem] origin-(--bits-dropdown-menu-content-transform-origin) overflow-x-hidden overflow-y-auto rounded-md border border-border bg-popover p-1.5 text-popover-foreground shadow-md outline-none data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95 dark:border-border/20',
+			'z-50 max-h-(--bits-dropdown-menu-content-available-height) min-w-[8rem] origin-(--bits-dropdown-menu-content-transform-origin) overflow-x-hidden overflow-y-auto rounded-md border border-border bg-popover p-1.5 text-popover-foreground shadow-md outline-none data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95 dark:border-border/20',
 			className
 		)}
 		{...restProps}
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-group-heading.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-group-heading.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-group-heading.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-group-heading.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-group.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-group.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-group.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-group.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-item.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-item.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-item.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-item.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-label.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-label.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-label.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-label.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-group.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-group.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-group.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-group.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-item.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-item.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-item.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-radio-item.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-separator.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-separator.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-separator.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-separator.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-shortcut.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-shortcut.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-shortcut.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-shortcut.svelte
diff --git a/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte
new file mode 100644
index 00000000000..e26c51cdc23
--- /dev/null
+++ b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-content.svelte
@@ -0,0 +1,20 @@
+<script lang="ts">
+	import { DropdownMenu as DropdownMenuPrimitive } from 'bits-ui';
+	import { cn } from '$lib/components/ui/utils.js';
+
+	let {
+		ref = $bindable(null),
+		class: className,
+		...restProps
+	}: DropdownMenuPrimitive.SubContentProps = $props();
+</script>
+
+<DropdownMenuPrimitive.SubContent
+	bind:ref
+	data-slot="dropdown-menu-sub-content"
+	class={cn(
+		'z-50 max-h-(--bits-dropdown-menu-content-available-height) min-w-[8rem] origin-(--bits-dropdown-menu-content-transform-origin) overflow-x-hidden overflow-y-auto rounded-md border border-border bg-popover p-1.5 text-popover-foreground shadow-md outline-none data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95 dark:border-border/20',
+		className
+	)}
+	{...restProps}
+/>
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-trigger.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-trigger.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-trigger.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-sub-trigger.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-trigger.svelte b/tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-trigger.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/dropdown-menu-trigger.svelte
rename to tools/ui/src/lib/components/ui/dropdown-menu/dropdown-menu-trigger.svelte
diff --git a/tools/server/webui/src/lib/components/ui/dropdown-menu/index.ts b/tools/ui/src/lib/components/ui/dropdown-menu/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/dropdown-menu/index.ts
rename to tools/ui/src/lib/components/ui/dropdown-menu/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/input/index.ts b/tools/ui/src/lib/components/ui/input/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/input/index.ts
rename to tools/ui/src/lib/components/ui/input/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/input/input.svelte b/tools/ui/src/lib/components/ui/input/input.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/input/input.svelte
rename to tools/ui/src/lib/components/ui/input/input.svelte
diff --git a/tools/server/webui/src/lib/components/ui/label/index.ts b/tools/ui/src/lib/components/ui/label/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/label/index.ts
rename to tools/ui/src/lib/components/ui/label/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/label/label.svelte b/tools/ui/src/lib/components/ui/label/label.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/label/label.svelte
rename to tools/ui/src/lib/components/ui/label/label.svelte
diff --git a/tools/server/webui/src/lib/components/ui/popover/index.ts b/tools/ui/src/lib/components/ui/popover/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/popover/index.ts
rename to tools/ui/src/lib/components/ui/popover/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-close.svelte b/tools/ui/src/lib/components/ui/popover/popover-close.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/popover/popover-close.svelte
rename to tools/ui/src/lib/components/ui/popover/popover-close.svelte
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-content.svelte b/tools/ui/src/lib/components/ui/popover/popover-content.svelte
similarity index 84%
rename from tools/server/webui/src/lib/components/ui/popover/popover-content.svelte
rename to tools/ui/src/lib/components/ui/popover/popover-content.svelte
index 2d3513d347f..b46e928b1b9 100644
--- a/tools/server/webui/src/lib/components/ui/popover/popover-content.svelte
+++ b/tools/ui/src/lib/components/ui/popover/popover-content.svelte
@@ -29,7 +29,7 @@
 		{collisionPadding}
 		{avoidCollisions}
 		class={cn(
-			'z-50 w-72 origin-(--bits-popover-content-transform-origin) rounded-md border bg-popover p-4 text-popover-foreground shadow-md outline-hidden data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-end-2 data-[side=right]:slide-in-from-start-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95',
+			'z-50 w-72 origin-(--bits-popover-content-transform-origin) rounded-md border bg-popover p-4 text-popover-foreground shadow-md outline-hidden data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-end-2 data-[side=right]:slide-in-from-start-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95',
 			className
 		)}
 		{...restProps}
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte b/tools/ui/src/lib/components/ui/popover/popover-portal.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte
rename to tools/ui/src/lib/components/ui/popover/popover-portal.svelte
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte b/tools/ui/src/lib/components/ui/popover/popover-trigger.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte
rename to tools/ui/src/lib/components/ui/popover/popover-trigger.svelte
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover.svelte b/tools/ui/src/lib/components/ui/popover/popover.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/popover/popover.svelte
rename to tools/ui/src/lib/components/ui/popover/popover.svelte
diff --git a/tools/server/webui/src/lib/components/ui/scroll-area/index.ts b/tools/ui/src/lib/components/ui/scroll-area/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/scroll-area/index.ts
rename to tools/ui/src/lib/components/ui/scroll-area/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/scroll-area/scroll-area-scrollbar.svelte b/tools/ui/src/lib/components/ui/scroll-area/scroll-area-scrollbar.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/scroll-area/scroll-area-scrollbar.svelte
rename to tools/ui/src/lib/components/ui/scroll-area/scroll-area-scrollbar.svelte
diff --git a/tools/server/webui/src/lib/components/ui/scroll-area/scroll-area.svelte b/tools/ui/src/lib/components/ui/scroll-area/scroll-area.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/scroll-area/scroll-area.svelte
rename to tools/ui/src/lib/components/ui/scroll-area/scroll-area.svelte
diff --git a/tools/server/webui/src/lib/components/ui/select/index.ts b/tools/ui/src/lib/components/ui/select/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/index.ts
rename to tools/ui/src/lib/components/ui/select/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/select/select-content.svelte b/tools/ui/src/lib/components/ui/select/select-content.svelte
similarity index 94%
rename from tools/server/webui/src/lib/components/ui/select/select-content.svelte
rename to tools/ui/src/lib/components/ui/select/select-content.svelte
index 4050628cc87..b54bc60c227 100644
--- a/tools/server/webui/src/lib/components/ui/select/select-content.svelte
+++ b/tools/ui/src/lib/components/ui/select/select-content.svelte
@@ -93,7 +93,7 @@
 		{sideOffset}
 		data-slot="select-content"
 		class={cn(
-			'relative z-[var(--layer-popover,1000000)] max-h-(--bits-select-content-available-height) min-w-[8rem] origin-(--bits-select-content-transform-origin) overflow-x-hidden overflow-y-auto rounded-md border bg-popover text-popover-foreground shadow-md data-[side=bottom]:translate-y-1 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:-translate-x-1 data-[side=left]:slide-in-from-right-2 data-[side=right]:translate-x-1 data-[side=right]:slide-in-from-left-2 data-[side=top]:-translate-y-1 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95',
+			'relative z-[var(--layer-popover,1000000)] max-h-(--bits-select-content-available-height) min-w-[8rem] origin-(--bits-select-content-transform-origin) overflow-x-hidden overflow-y-auto rounded-md border bg-popover text-popover-foreground shadow-md data-[side=bottom]:translate-y-1 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:-translate-x-1 data-[side=left]:slide-in-from-right-2 data-[side=right]:translate-x-1 data-[side=right]:slide-in-from-left-2 data-[side=top]:-translate-y-1 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95',
 			className
 		)}
 		{...restProps}
diff --git a/tools/server/webui/src/lib/components/ui/select/select-group-heading.svelte b/tools/ui/src/lib/components/ui/select/select-group-heading.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/select-group-heading.svelte
rename to tools/ui/src/lib/components/ui/select/select-group-heading.svelte
diff --git a/tools/server/webui/src/lib/components/ui/select/select-group.svelte b/tools/ui/src/lib/components/ui/select/select-group.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/select-group.svelte
rename to tools/ui/src/lib/components/ui/select/select-group.svelte
diff --git a/tools/server/webui/src/lib/components/ui/select/select-item.svelte b/tools/ui/src/lib/components/ui/select/select-item.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/select-item.svelte
rename to tools/ui/src/lib/components/ui/select/select-item.svelte
diff --git a/tools/server/webui/src/lib/components/ui/select/select-label.svelte b/tools/ui/src/lib/components/ui/select/select-label.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/select-label.svelte
rename to tools/ui/src/lib/components/ui/select/select-label.svelte
diff --git a/tools/server/webui/src/lib/components/ui/select/select-scroll-down-button.svelte b/tools/ui/src/lib/components/ui/select/select-scroll-down-button.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/select-scroll-down-button.svelte
rename to tools/ui/src/lib/components/ui/select/select-scroll-down-button.svelte
diff --git a/tools/server/webui/src/lib/components/ui/select/select-scroll-up-button.svelte b/tools/ui/src/lib/components/ui/select/select-scroll-up-button.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/select-scroll-up-button.svelte
rename to tools/ui/src/lib/components/ui/select/select-scroll-up-button.svelte
diff --git a/tools/server/webui/src/lib/components/ui/select/select-separator.svelte b/tools/ui/src/lib/components/ui/select/select-separator.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/select-separator.svelte
rename to tools/ui/src/lib/components/ui/select/select-separator.svelte
diff --git a/tools/server/webui/src/lib/components/ui/select/select-trigger.svelte b/tools/ui/src/lib/components/ui/select/select-trigger.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/select/select-trigger.svelte
rename to tools/ui/src/lib/components/ui/select/select-trigger.svelte
diff --git a/tools/server/webui/src/lib/components/ui/separator/index.ts b/tools/ui/src/lib/components/ui/separator/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/separator/index.ts
rename to tools/ui/src/lib/components/ui/separator/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/separator/separator.svelte b/tools/ui/src/lib/components/ui/separator/separator.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/separator/separator.svelte
rename to tools/ui/src/lib/components/ui/separator/separator.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sheet/index.ts b/tools/ui/src/lib/components/ui/sheet/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sheet/index.ts
rename to tools/ui/src/lib/components/ui/sheet/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/sheet/sheet-close.svelte b/tools/ui/src/lib/components/ui/sheet/sheet-close.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sheet/sheet-close.svelte
rename to tools/ui/src/lib/components/ui/sheet/sheet-close.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sheet/sheet-content.svelte b/tools/ui/src/lib/components/ui/sheet/sheet-content.svelte
similarity index 90%
rename from tools/server/webui/src/lib/components/ui/sheet/sheet-content.svelte
rename to tools/ui/src/lib/components/ui/sheet/sheet-content.svelte
index 1eaa6d99ff4..b616c469a91 100644
--- a/tools/server/webui/src/lib/components/ui/sheet/sheet-content.svelte
+++ b/tools/ui/src/lib/components/ui/sheet/sheet-content.svelte
@@ -1,7 +1,7 @@
 <script lang="ts" module>
 	import { tv, type VariantProps } from 'tailwind-variants';
 	export const sheetVariants = tv({
-		base: `border-border/30 dark:border-border/20 data-[state=open]:animate-in data-[state=closed]:animate-out fixed z-50 flex flex-col gap-4 shadow-sm transition ease-in-out data-[state=closed]:duration-300 data-[state=open]:duration-500 ${PANEL_CLASSES}`,
+		base: `border-border/30 dark:border-border/20 data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fill-mode-forwards fixed z-50 flex flex-col gap-4 shadow-sm transition ease-in-out data-[state=closed]:duration-300 data-[state=open]:duration-500 ${PANEL_CLASSES}`,
 		variants: {
 			side: {
 				top: 'data-[state=closed]:slide-out-to-top data-[state=open]:slide-in-from-top inset-x-0 top-0 h-auto border-b',
diff --git a/tools/server/webui/src/lib/components/ui/sheet/sheet-description.svelte b/tools/ui/src/lib/components/ui/sheet/sheet-description.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sheet/sheet-description.svelte
rename to tools/ui/src/lib/components/ui/sheet/sheet-description.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sheet/sheet-footer.svelte b/tools/ui/src/lib/components/ui/sheet/sheet-footer.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sheet/sheet-footer.svelte
rename to tools/ui/src/lib/components/ui/sheet/sheet-footer.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sheet/sheet-header.svelte b/tools/ui/src/lib/components/ui/sheet/sheet-header.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sheet/sheet-header.svelte
rename to tools/ui/src/lib/components/ui/sheet/sheet-header.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sheet/sheet-overlay.svelte b/tools/ui/src/lib/components/ui/sheet/sheet-overlay.svelte
similarity index 76%
rename from tools/server/webui/src/lib/components/ui/sheet/sheet-overlay.svelte
rename to tools/ui/src/lib/components/ui/sheet/sheet-overlay.svelte
index a6a064f2f8f..f402d81aa6f 100644
--- a/tools/server/webui/src/lib/components/ui/sheet/sheet-overlay.svelte
+++ b/tools/ui/src/lib/components/ui/sheet/sheet-overlay.svelte
@@ -13,7 +13,7 @@
 	bind:ref
 	data-slot="sheet-overlay"
 	class={cn(
-		'fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:animate-in data-[state=open]:fade-in-0',
+		'fixed inset-0 z-50 bg-black/50 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:fill-mode-forwards data-[state=open]:animate-in data-[state=open]:fade-in-0',
 		className
 	)}
 	{...restProps}
diff --git a/tools/server/webui/src/lib/components/ui/sheet/sheet-title.svelte b/tools/ui/src/lib/components/ui/sheet/sheet-title.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sheet/sheet-title.svelte
rename to tools/ui/src/lib/components/ui/sheet/sheet-title.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sheet/sheet-trigger.svelte b/tools/ui/src/lib/components/ui/sheet/sheet-trigger.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sheet/sheet-trigger.svelte
rename to tools/ui/src/lib/components/ui/sheet/sheet-trigger.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/constants.ts b/tools/ui/src/lib/components/ui/sidebar/constants.ts
similarity index 74%
rename from tools/server/webui/src/lib/components/ui/sidebar/constants.ts
rename to tools/ui/src/lib/components/ui/sidebar/constants.ts
index c7e827b1a01..8c320c2ca8f 100644
--- a/tools/server/webui/src/lib/components/ui/sidebar/constants.ts
+++ b/tools/ui/src/lib/components/ui/sidebar/constants.ts
@@ -1,6 +1,7 @@
 export const SIDEBAR_COOKIE_NAME = 'sidebar:state';
 export const SIDEBAR_COOKIE_MAX_AGE = 60 * 60 * 24 * 7;
-export const SIDEBAR_WIDTH = '18rem';
+export const SIDEBAR_MIN_WIDTH = '18rem';
+export const SIDEBAR_MAX_WIDTH = '32rem';
 export const SIDEBAR_WIDTH_MOBILE = '18rem';
 export const SIDEBAR_WIDTH_ICON = '3rem';
 export const SIDEBAR_KEYBOARD_SHORTCUT = 'b';
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/context.svelte.ts b/tools/ui/src/lib/components/ui/sidebar/context.svelte.ts
similarity index 92%
rename from tools/server/webui/src/lib/components/ui/sidebar/context.svelte.ts
rename to tools/ui/src/lib/components/ui/sidebar/context.svelte.ts
index 6fa2aa317d6..9d49ee1f0b3 100644
--- a/tools/server/webui/src/lib/components/ui/sidebar/context.svelte.ts
+++ b/tools/ui/src/lib/components/ui/sidebar/context.svelte.ts
@@ -1,6 +1,6 @@
 import { IsMobile } from '$lib/hooks/is-mobile.svelte.js';
 import { getContext, setContext } from 'svelte';
-import { SIDEBAR_KEYBOARD_SHORTCUT } from './constants.js';
+import { SIDEBAR_KEYBOARD_SHORTCUT, SIDEBAR_MIN_WIDTH } from './constants.js';
 
 type Getter<T> = () => T;
 
@@ -24,6 +24,8 @@ class SidebarState {
 	readonly props: SidebarStateProps;
 	open = $derived.by(() => this.props.open());
 	openMobile = $state(false);
+	sidebarWidth = $state(SIDEBAR_MIN_WIDTH);
+	isResizing = $state(false);
 	setOpen: SidebarStateProps['setOpen'];
 	#isMobile: IsMobile;
 	state = $derived.by(() => (this.open ? 'expanded' : 'collapsed'));
@@ -53,7 +55,7 @@ class SidebarState {
 	};
 
 	toggle = () => {
-		return this.#isMobile.current ? (this.openMobile = !this.openMobile) : this.setOpen(!this.open);
+		this.setOpen(!this.open);
 	};
 }
 
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/index.ts b/tools/ui/src/lib/components/ui/sidebar/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/index.ts
rename to tools/ui/src/lib/components/ui/sidebar/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-content.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-content.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-content.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-content.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-footer.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-footer.svelte
similarity index 89%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-footer.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-footer.svelte
index 67be0a484d5..69914dfa288 100644
--- a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-footer.svelte
+++ b/tools/ui/src/lib/components/ui/sidebar/sidebar-footer.svelte
@@ -14,7 +14,7 @@
 	bind:this={ref}
 	data-slot="sidebar-footer"
 	data-sidebar="footer"
-	class={cn('flex flex-col gap-2 p-2', className)}
+	class={cn('flex flex-col gap-2 p-3', className)}
 	{...restProps}
 >
 	{@render children?.()}
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-group-action.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-group-action.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-group-action.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-group-action.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-group-content.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-group-content.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-group-content.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-group-content.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-group-label.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-group-label.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-group-label.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-group-label.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-group.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-group.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-group.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-group.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-header.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-header.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-header.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-header.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-input.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-input.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-input.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-input.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-inset.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-inset.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-inset.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-inset.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-action.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-action.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-action.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu-action.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-badge.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-badge.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-badge.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu-badge.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte
similarity index 91%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte
index 2ce03054e0a..f9efd67e992 100644
--- a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte
+++ b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte
@@ -2,7 +2,7 @@
 	import { tv, type VariantProps } from 'tailwind-variants';
 
 	export const sidebarMenuButtonVariants = tv({
-		base: 'peer/menu-button outline-hidden ring-sidebar-ring hover:bg-sidebar-accent hover:text-sidebar-accent-foreground active:bg-sidebar-accent active:text-sidebar-accent-foreground group-has-data-[sidebar=menu-action]/menu-item:pr-8 data-[active=true]:bg-sidebar-accent data-[active=true]:text-sidebar-accent-foreground data-[state=open]:hover:bg-sidebar-accent data-[state=open]:hover:text-sidebar-accent-foreground group-data-[collapsible=icon]:size-8! group-data-[collapsible=icon]:p-2! flex w-full items-center gap-2 overflow-hidden rounded-md p-2 text-left text-sm transition-[width,height,padding] focus-visible:ring-2 disabled:pointer-events-none disabled:opacity-50 aria-disabled:pointer-events-none aria-disabled:opacity-50 data-[active=true]:font-medium [&>span:last-child]:truncate [&>svg]:size-4 [&>svg]:shrink-0',
+		base: 'peer/menu-button outline-hidden ring-sidebar-ring hover:bg-sidebar-accent hover:text-sidebar-accent-foreground active:bg-sidebar-accent active:text-sidebar-accent-foreground group-has-data-[sidebar=menu-action]/menu-item:pr-8 data-[active=true]:bg-sidebar-accent data-[active=true]:text-sidebar-accent-foreground data-[state=open]:hover:bg-sidebar-accent data-[state=open]:hover:text-sidebar-accent-foreground group-data-[collapsible=icon]:size-8! group-data-[collapsible=icon]:p-2! flex w-full items-center gap-2 overflow-hidden rounded-md py-2 px-1 text-left text-sm transition-[width,height,padding] focus-visible:ring-2 disabled:pointer-events-none disabled:opacity-50 aria-disabled:pointer-events-none aria-disabled:opacity-50 data-[active=true]:font-medium [&>span:last-child]:truncate [&>svg]:size-4 [&>svg]:shrink-0',
 		variants: {
 			variant: {
 				default: 'hover:bg-sidebar-accent hover:text-sidebar-accent-foreground',
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-item.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-item.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-item.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu-item.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-skeleton.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-skeleton.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-skeleton.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu-skeleton.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-sub-button.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-button.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-sub-button.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-button.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-sub-item.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-item.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-sub-item.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-item.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-sub.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu-sub.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-menu.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-menu.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-provider.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-provider.svelte
similarity index 83%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-provider.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-provider.svelte
index 364235a4994..7ab9ded7f9b 100644
--- a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-provider.svelte
+++ b/tools/ui/src/lib/components/ui/sidebar/sidebar-provider.svelte
@@ -4,7 +4,8 @@
 	import {
 		SIDEBAR_COOKIE_MAX_AGE,
 		SIDEBAR_COOKIE_NAME,
-		SIDEBAR_WIDTH,
+		SIDEBAR_MIN_WIDTH,
+		SIDEBAR_MAX_WIDTH,
 		SIDEBAR_WIDTH_ICON
 	} from './constants.js';
 	import { setSidebar } from './context.svelte.js';
@@ -38,7 +39,7 @@
 
 <div
 	data-slot="sidebar-wrapper"
-	style="--sidebar-width: {SIDEBAR_WIDTH}; --sidebar-width-icon: {SIDEBAR_WIDTH_ICON}; {style}"
+	style="--sidebar-width: {sidebar.sidebarWidth}; --sidebar-min-width: {SIDEBAR_MIN_WIDTH}; --sidebar-max-width: {SIDEBAR_MAX_WIDTH}; --sidebar-width-icon: {SIDEBAR_WIDTH_ICON}; {style}"
 	class={cn(
 		'group/sidebar-wrapper flex min-h-svh w-full has-data-[variant=inset]:bg-sidebar',
 		className
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-rail.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-rail.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-rail.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-rail.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-separator.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-separator.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-separator.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-separator.svelte
diff --git a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-trigger.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-trigger.svelte
similarity index 69%
rename from tools/server/webui/src/lib/components/ui/sidebar/sidebar-trigger.svelte
rename to tools/ui/src/lib/components/ui/sidebar/sidebar-trigger.svelte
index b6ff373ffa5..67076d4d399 100644
--- a/tools/server/webui/src/lib/components/ui/sidebar/sidebar-trigger.svelte
+++ b/tools/ui/src/lib/components/ui/sidebar/sidebar-trigger.svelte
@@ -3,6 +3,7 @@
 	import PanelLeftIcon from '@lucide/svelte/icons/panel-left';
 	import type { ComponentProps } from 'svelte';
 	import { useSidebar } from './context.svelte.js';
+	import { PanelLeftClose } from '@lucide/svelte';
 
 	let {
 		ref = $bindable(null),
@@ -21,9 +22,11 @@
 	data-slot="sidebar-trigger"
 	variant="ghost"
 	size="icon-lg"
-	class="rounded-full backdrop-blur-lg {className} md:left-{sidebar.open
-		? 'unset'
-		: '2'} -top-2 -left-2 md:top-0"
+	class="rounded-full backdrop-blur-lg {className} {sidebar.open
+		? 'top-1.5'
+		: 'top-0'} md:left-[calc(var(--sidebar-width)-3.25rem)] {sidebar.isResizing
+		? '!duration-0'
+		: ''}"
 	type="button"
 	onclick={(e) => {
 		onclick?.(e);
@@ -31,6 +34,10 @@
 	}}
 	{...restProps}
 >
-	<PanelLeftIcon />
+	{#if sidebar.open}
+		<PanelLeftClose />
+	{:else}
+		<PanelLeftIcon />
+	{/if}
 	<span class="sr-only">Toggle Sidebar</span>
 </Button>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar.svelte
new file mode 100644
index 00000000000..d6da1aec78e
--- /dev/null
+++ b/tools/ui/src/lib/components/ui/sidebar/sidebar.svelte
@@ -0,0 +1,150 @@
+<script lang="ts">
+	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
+	import type { HTMLAttributes } from 'svelte/elements';
+	import { SIDEBAR_MIN_WIDTH, SIDEBAR_MAX_WIDTH } from './constants.js';
+	import { useSidebar } from './context.svelte.js';
+	import { remToPx } from '$lib/utils';
+
+	let {
+		ref = $bindable(null),
+		side = 'left',
+		variant = 'sidebar',
+		collapsible = 'offcanvas',
+		class: className,
+		children,
+		...restProps
+	}: WithElementRef<HTMLAttributes<HTMLDivElement>> & {
+		side?: 'left' | 'right';
+		variant?: 'sidebar' | 'floating' | 'inset';
+		collapsible?: 'offcanvas' | 'icon' | 'none';
+	} = $props();
+
+	const sidebar = useSidebar();
+
+	function handleResizePointerDown(e: PointerEvent) {
+		if (sidebar.isMobile) return;
+		e.preventDefault();
+
+		const target = e.currentTarget as HTMLElement;
+		target.setPointerCapture(e.pointerId);
+
+		const minPx = remToPx(SIDEBAR_MIN_WIDTH);
+		const maxPx = remToPx(SIDEBAR_MAX_WIDTH);
+
+		sidebar.isResizing = true;
+
+		function onPointerMove(ev: PointerEvent) {
+			const newWidth = side === 'left' ? ev.clientX : window.innerWidth - ev.clientX;
+			const clamped = Math.min(maxPx, Math.max(minPx, newWidth));
+			sidebar.sidebarWidth = `${clamped}px`;
+		}
+
+		function onPointerUp() {
+			sidebar.isResizing = false;
+			target.removeEventListener('pointermove', onPointerMove);
+			target.removeEventListener('pointerup', onPointerUp);
+		}
+
+		target.addEventListener('pointermove', onPointerMove);
+		target.addEventListener('pointerup', onPointerUp);
+	}
+</script>
+
+{#if collapsible === 'none'}
+	<div
+		class={cn(
+			'flex h-full w-(--sidebar-width) flex-col bg-sidebar text-sidebar-foreground',
+			className
+		)}
+		bind:this={ref}
+		{...restProps}
+	>
+		{@render children?.()}
+	</div>
+{:else}
+	<div
+		bind:this={ref}
+		class="group peer block text-sidebar-foreground"
+		data-state={sidebar.state}
+		data-collapsible={sidebar.state === 'collapsed' ? collapsible : ''}
+		data-variant={variant}
+		data-side={side}
+		data-slot="sidebar"
+	>
+		<!-- This is what handles the sidebar gap on desktop -->
+		<div
+			data-slot="sidebar-gap"
+			class={cn(
+				'relative bg-transparent transition-[width] duration-200 ease-linear',
+				sidebar.isResizing && '!duration-0',
+				'w-0',
+				variant === 'floating'
+					? 'md:w-[calc(var(--sidebar-width)+0.75rem)]'
+					: 'md:w-(--sidebar-width)',
+				'md:group-data-[collapsible=offcanvas]:w-0',
+				'group-data-[side=right]:rotate-180',
+				variant === 'floating' || variant === 'inset'
+					? 'group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]'
+					: 'group-data-[collapsible=icon]:w-(--sidebar-width-icon)'
+			)}
+		></div>
+
+		<div
+			data-slot="sidebar-container"
+			class={cn(
+				'fixed inset-y-0 z-[900] flex w-[calc(100dvw-1.5rem)] duration-200 ease-linear md:z-0 md:w-(--sidebar-width)',
+				'group-data-[collapsible=offcanvas]:pointer-events-none md:group-data-[collapsible=offcanvas]:pointer-events-auto',
+				sidebar.isResizing && '!duration-0',
+				variant === 'floating'
+					? [
+							'transition-[left,right,width,opacity]',
+							side === 'left'
+								? 'left-3 group-data-[collapsible=offcanvas]:left-[calc(var(--sidebar-width)*-0.775)] group-data-[collapsible=offcanvas]:opacity-0'
+								: 'right-3 group-data-[collapsible=offcanvas]:right-[calc(var(--sidebar-width)*-0.775)] group-data-[collapsible=offcanvas]:opacity-0',
+							'my-3 overflow-hidden rounded-3xl border border-sidebar-border shadow-md'
+						]
+					: [
+							'h-svh transition-[left,right,width]',
+							side === 'left'
+								? 'left-0 group-data-[collapsible=offcanvas]:left-[calc(var(--sidebar-width)*-1)]'
+								: 'right-0 group-data-[collapsible=offcanvas]:right-[calc(var(--sidebar-width)*-1)]'
+						],
+				// Adjust the padding for inset variant.
+				variant === 'inset'
+					? 'p-2 group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]'
+					: variant === 'floating'
+						? 'group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]'
+						: 'group-data-[collapsible=icon]:w-(--sidebar-width-icon)',
+				className
+			)}
+			style={variant === 'floating' ? 'height: calc(100dvh - 1.5rem);' : undefined}
+			{...restProps}
+		>
+			<div
+				data-sidebar="sidebar"
+				data-slot="sidebar-inner"
+				class="flex h-full w-full flex-col bg-sidebar"
+			>
+				{@render children?.()}
+			</div>
+			<!-- Resize handle -->
+			{#if side === 'left'}
+				<!-- svelte-ignore a11y_no_static_element_interactions -->
+				<div
+					data-slot="sidebar-resize-handle"
+					class="absolute inset-y-0 right-0 z-50 hidden w-1.5 cursor-ew-resize touch-none select-none hover:bg-sidebar-border/50 active:bg-sidebar-border md:block"
+					class:bg-sidebar-border={sidebar.isResizing}
+					onpointerdown={handleResizePointerDown}
+				></div>
+			{:else}
+				<!-- svelte-ignore a11y_no_static_element_interactions -->
+				<div
+					data-slot="sidebar-resize-handle"
+					class="absolute inset-y-0 left-0 z-50 hidden w-1.5 cursor-ew-resize touch-none select-none hover:bg-sidebar-border/50 active:bg-sidebar-border md:block"
+					class:bg-sidebar-border={sidebar.isResizing}
+					onpointerdown={handleResizePointerDown}
+				></div>
+			{/if}
+		</div>
+	</div>
+{/if}
diff --git a/tools/server/webui/src/lib/components/ui/skeleton/index.ts b/tools/ui/src/lib/components/ui/skeleton/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/skeleton/index.ts
rename to tools/ui/src/lib/components/ui/skeleton/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/skeleton/skeleton.svelte b/tools/ui/src/lib/components/ui/skeleton/skeleton.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/skeleton/skeleton.svelte
rename to tools/ui/src/lib/components/ui/skeleton/skeleton.svelte
diff --git a/tools/server/webui/src/lib/components/ui/switch/index.ts b/tools/ui/src/lib/components/ui/switch/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/switch/index.ts
rename to tools/ui/src/lib/components/ui/switch/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/switch/switch.svelte b/tools/ui/src/lib/components/ui/switch/switch.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/switch/switch.svelte
rename to tools/ui/src/lib/components/ui/switch/switch.svelte
diff --git a/tools/server/webui/src/lib/components/ui/table/index.ts b/tools/ui/src/lib/components/ui/table/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/index.ts
rename to tools/ui/src/lib/components/ui/table/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/table/table-body.svelte b/tools/ui/src/lib/components/ui/table/table-body.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/table-body.svelte
rename to tools/ui/src/lib/components/ui/table/table-body.svelte
diff --git a/tools/server/webui/src/lib/components/ui/table/table-caption.svelte b/tools/ui/src/lib/components/ui/table/table-caption.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/table-caption.svelte
rename to tools/ui/src/lib/components/ui/table/table-caption.svelte
diff --git a/tools/server/webui/src/lib/components/ui/table/table-cell.svelte b/tools/ui/src/lib/components/ui/table/table-cell.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/table-cell.svelte
rename to tools/ui/src/lib/components/ui/table/table-cell.svelte
diff --git a/tools/server/webui/src/lib/components/ui/table/table-footer.svelte b/tools/ui/src/lib/components/ui/table/table-footer.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/table-footer.svelte
rename to tools/ui/src/lib/components/ui/table/table-footer.svelte
diff --git a/tools/server/webui/src/lib/components/ui/table/table-head.svelte b/tools/ui/src/lib/components/ui/table/table-head.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/table-head.svelte
rename to tools/ui/src/lib/components/ui/table/table-head.svelte
diff --git a/tools/server/webui/src/lib/components/ui/table/table-header.svelte b/tools/ui/src/lib/components/ui/table/table-header.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/table-header.svelte
rename to tools/ui/src/lib/components/ui/table/table-header.svelte
diff --git a/tools/server/webui/src/lib/components/ui/table/table-row.svelte b/tools/ui/src/lib/components/ui/table/table-row.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/table-row.svelte
rename to tools/ui/src/lib/components/ui/table/table-row.svelte
diff --git a/tools/server/webui/src/lib/components/ui/table/table.svelte b/tools/ui/src/lib/components/ui/table/table.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/table/table.svelte
rename to tools/ui/src/lib/components/ui/table/table.svelte
diff --git a/tools/server/webui/src/lib/components/ui/textarea/index.ts b/tools/ui/src/lib/components/ui/textarea/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/textarea/index.ts
rename to tools/ui/src/lib/components/ui/textarea/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/textarea/textarea.svelte b/tools/ui/src/lib/components/ui/textarea/textarea.svelte
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/textarea/textarea.svelte
rename to tools/ui/src/lib/components/ui/textarea/textarea.svelte
diff --git a/tools/server/webui/src/lib/components/ui/tooltip/index.ts b/tools/ui/src/lib/components/ui/tooltip/index.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/tooltip/index.ts
rename to tools/ui/src/lib/components/ui/tooltip/index.ts
diff --git a/tools/server/webui/src/lib/components/ui/tooltip/tooltip-content.svelte b/tools/ui/src/lib/components/ui/tooltip/tooltip-content.svelte
similarity index 95%
rename from tools/server/webui/src/lib/components/ui/tooltip/tooltip-content.svelte
rename to tools/ui/src/lib/components/ui/tooltip/tooltip-content.svelte
index 011d492f3d8..5b0c768181a 100644
--- a/tools/server/webui/src/lib/components/ui/tooltip/tooltip-content.svelte
+++ b/tools/ui/src/lib/components/ui/tooltip/tooltip-content.svelte
@@ -18,7 +18,7 @@
 
 	const contentClass = $derived(
 		cn(
-			'z-50 w-fit origin-(--bits-tooltip-content-transform-origin) animate-in rounded-md bg-primary px-3 py-1.5 text-xs text-balance text-primary-foreground fade-in-0 zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95',
+			'z-50 w-fit origin-(--bits-tooltip-content-transform-origin) animate-in rounded-md bg-primary px-3 py-1.5 text-xs text-balance text-primary-foreground fade-in-0 zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=closed]:fill-mode-forwards',
 			className
 		)
 	);
diff --git a/tools/server/webui/src/lib/components/ui/tooltip/tooltip-trigger.svelte b/tools/ui/src/lib/components/ui/tooltip/tooltip-trigger.svelte
similarity index 61%
rename from tools/server/webui/src/lib/components/ui/tooltip/tooltip-trigger.svelte
rename to tools/ui/src/lib/components/ui/tooltip/tooltip-trigger.svelte
index 5631d1b4c15..671d6e2201a 100644
--- a/tools/server/webui/src/lib/components/ui/tooltip/tooltip-trigger.svelte
+++ b/tools/ui/src/lib/components/ui/tooltip/tooltip-trigger.svelte
@@ -4,4 +4,9 @@
 	let { ref = $bindable(null), ...restProps }: TooltipPrimitive.TriggerProps = $props();
 </script>
 
-<TooltipPrimitive.Trigger bind:ref data-slot="tooltip-trigger" {...restProps} />
+<TooltipPrimitive.Trigger
+	bind:ref
+	data-slot="tooltip-trigger"
+	class="cursor-pointer"
+	{...restProps}
+/>
diff --git a/tools/server/webui/src/lib/components/ui/utils.ts b/tools/ui/src/lib/components/ui/utils.ts
similarity index 100%
rename from tools/server/webui/src/lib/components/ui/utils.ts
rename to tools/ui/src/lib/components/ui/utils.ts
diff --git a/tools/server/webui/src/lib/constants/agentic.ts b/tools/ui/src/lib/constants/agentic.ts
similarity index 90%
rename from tools/server/webui/src/lib/constants/agentic.ts
rename to tools/ui/src/lib/constants/agentic.ts
index d08f0ecf36c..c0575163efc 100644
--- a/tools/server/webui/src/lib/constants/agentic.ts
+++ b/tools/ui/src/lib/constants/agentic.ts
@@ -4,11 +4,6 @@ export const ATTACHMENT_SAVED_REGEX = /\[Attachment saved: ([^\]]+)\]/;
 
 export const NEWLINE_SEPARATOR = '\n';
 
-export const TURN_LIMIT_MESSAGE = '\n\n```\nTurn limit reached\n```\n';
-
-export const LLM_ERROR_BLOCK_START = '\n\n```\nUpstream LLM error:\n';
-export const LLM_ERROR_BLOCK_END = '\n```\n';
-
 export const DEFAULT_AGENTIC_CONFIG: AgenticConfig = {
 	enabled: true,
 	maxTurns: 100,
diff --git a/tools/server/webui/src/lib/constants/api-endpoints.ts b/tools/ui/src/lib/constants/api-endpoints.ts
similarity index 73%
rename from tools/server/webui/src/lib/constants/api-endpoints.ts
rename to tools/ui/src/lib/constants/api-endpoints.ts
index aa7ca3a22a2..f89ebe42130 100644
--- a/tools/server/webui/src/lib/constants/api-endpoints.ts
+++ b/tools/ui/src/lib/constants/api-endpoints.ts
@@ -4,5 +4,10 @@ export const API_MODELS = {
 	UNLOAD: '/models/unload'
 };
 
+export const API_TOOLS = {
+	LIST: '/tools',
+	EXECUTE: '/tools'
+};
+
 /** CORS proxy endpoint path */
 export const CORS_PROXY_ENDPOINT = '/cors-proxy';
diff --git a/tools/server/webui/src/lib/constants/attachment-labels.ts b/tools/ui/src/lib/constants/attachment-labels.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/attachment-labels.ts
rename to tools/ui/src/lib/constants/attachment-labels.ts
diff --git a/tools/ui/src/lib/constants/attachment-menu.ts b/tools/ui/src/lib/constants/attachment-menu.ts
new file mode 100644
index 00000000000..966f46f41d0
--- /dev/null
+++ b/tools/ui/src/lib/constants/attachment-menu.ts
@@ -0,0 +1,112 @@
+import type { Component } from 'svelte';
+import { MessageSquare, Zap, FolderOpen } from '@lucide/svelte';
+import { FILE_TYPE_ICONS } from '$lib/constants/icons';
+import {
+	AttachmentAction,
+	AttachmentItemEnabledWhen,
+	AttachmentItemVisibleWhen,
+	AttachmentMenuItemId
+} from '$lib/enums';
+
+export interface AttachmentMenuItem {
+	/** Unique identifier for the item */
+	id: AttachmentMenuItemId;
+	/** Display label */
+	label: string;
+	/** Lucide icon component */
+	icon: Component;
+	/** Extra CSS class applied to the item (e.g. for test selectors) */
+	class?: string;
+	/** Whether the item requires a specific modality to be enabled */
+	enabledWhen?: AttachmentItemEnabledWhen;
+	/** Tooltip shown when the item is disabled */
+	disabledTooltip?: string;
+	/** Callback key on the Props interface to invoke when clicked */
+	action: AttachmentAction;
+	/** Whether the item is only shown when a specific capability is present */
+	visibleWhen?: AttachmentItemVisibleWhen;
+	/** Whether this item has a tooltip even when enabled (uses dynamic text) */
+	hasEnabledTooltip?: boolean;
+}
+
+/**
+ * File attachment menu items shown in both the desktop dropdown and mobile sheet.
+ * The "Tools" submenu is handled separately by each component.
+ */
+export const ATTACHMENT_FILE_ITEMS: AttachmentMenuItem[] = [
+	{
+		id: AttachmentMenuItemId.IMAGES,
+		label: 'Images',
+		icon: FILE_TYPE_ICONS.image,
+		class: 'images-button',
+		enabledWhen: AttachmentItemEnabledWhen.HAS_VISION_MODALITY,
+		disabledTooltip: 'Image processing requires a vision model',
+		action: AttachmentAction.FILE_UPLOAD
+	},
+	{
+		id: AttachmentMenuItemId.AUDIO,
+		label: 'Audio Files',
+		icon: FILE_TYPE_ICONS.audio,
+		class: 'audio-button',
+		enabledWhen: AttachmentItemEnabledWhen.HAS_AUDIO_MODALITY,
+		disabledTooltip: 'Audio files processing requires an audio model',
+		action: AttachmentAction.FILE_UPLOAD
+	},
+	{
+		id: AttachmentMenuItemId.VIDEO,
+		label: 'Video Files',
+		icon: FILE_TYPE_ICONS.video,
+		class: 'video-button',
+		enabledWhen: AttachmentItemEnabledWhen.HAS_VIDEO_MODALITY,
+		disabledTooltip: 'Video files processing requires a video model',
+		action: AttachmentAction.FILE_UPLOAD
+	},
+	{
+		id: AttachmentMenuItemId.TEXT,
+		label: 'Text Files',
+		icon: FILE_TYPE_ICONS.text,
+		enabledWhen: AttachmentItemEnabledWhen.ALWAYS,
+		action: AttachmentAction.FILE_UPLOAD
+	},
+	{
+		id: AttachmentMenuItemId.PDF,
+		label: 'PDF Files',
+		icon: FILE_TYPE_ICONS.pdf,
+		enabledWhen: AttachmentItemEnabledWhen.ALWAYS,
+		disabledTooltip: 'PDFs will be converted to text. Image-based PDFs may not work properly.',
+		hasEnabledTooltip: true,
+		action: AttachmentAction.FILE_UPLOAD
+	}
+];
+
+export const ATTACHMENT_EXTRA_ITEMS: AttachmentMenuItem[] = [
+	{
+		id: AttachmentMenuItemId.SYSTEM_MESSAGE,
+		label: 'System Message',
+		icon: MessageSquare,
+		enabledWhen: AttachmentItemEnabledWhen.ALWAYS,
+		hasEnabledTooltip: true,
+		action: AttachmentAction.SYSTEM_PROMPT_CLICK
+	}
+];
+
+export const ATTACHMENT_MCP_ITEMS: AttachmentMenuItem[] = [
+	{
+		id: AttachmentMenuItemId.MCP_PROMPT,
+		label: 'MCP Prompt',
+		icon: Zap,
+		enabledWhen: AttachmentItemEnabledWhen.ALWAYS,
+		action: AttachmentAction.MCP_PROMPT_CLICK,
+		visibleWhen: AttachmentItemVisibleWhen.HAS_MCP_PROMPTS_SUPPORT
+	},
+	{
+		id: AttachmentMenuItemId.MCP_RESOURCES,
+		label: 'MCP Resources',
+		icon: FolderOpen,
+		enabledWhen: AttachmentItemEnabledWhen.ALWAYS,
+		action: AttachmentAction.MCP_RESOURCES_CLICK,
+		visibleWhen: AttachmentItemVisibleWhen.HAS_MCP_RESOURCES_SUPPORT
+	}
+];
+
+export const ATTACHMENT_TOOLTIP_TEXT = 'Add files, prompts, tools or MCP Servers';
diff --git a/tools/server/webui/src/lib/constants/auto-scroll.ts b/tools/ui/src/lib/constants/auto-scroll.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/auto-scroll.ts
rename to tools/ui/src/lib/constants/auto-scroll.ts
diff --git a/tools/server/webui/src/lib/constants/binary-detection.ts b/tools/ui/src/lib/constants/binary-detection.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/binary-detection.ts
rename to tools/ui/src/lib/constants/binary-detection.ts
diff --git a/tools/server/webui/src/lib/constants/cache.ts b/tools/ui/src/lib/constants/cache.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/cache.ts
rename to tools/ui/src/lib/constants/cache.ts
diff --git a/tools/server/webui/src/lib/constants/chat-form.ts b/tools/ui/src/lib/constants/chat-form.ts
similarity index 81%
rename from tools/server/webui/src/lib/constants/chat-form.ts
rename to tools/ui/src/lib/constants/chat-form.ts
index 68b4d0b9051..05ab8c1f821 100644
--- a/tools/server/webui/src/lib/constants/chat-form.ts
+++ b/tools/ui/src/lib/constants/chat-form.ts
@@ -3,3 +3,4 @@ export const PROMPT_CONTENT_SEPARATOR = '\n\n';
 export const CLIPBOARD_CONTENT_QUOTE_PREFIX = '"';
 export const PROMPT_TRIGGER_PREFIX = '/';
 export const RESOURCE_TRIGGER_PREFIX = '@';
+export const NEW_CHAT_DRAFT_KEY = '__new_chat__';
diff --git a/tools/ui/src/lib/constants/cli-flags.ts b/tools/ui/src/lib/constants/cli-flags.ts
new file mode 100644
index 00000000000..4fbee8a3697
--- /dev/null
+++ b/tools/ui/src/lib/constants/cli-flags.ts
@@ -0,0 +1,6 @@
+export const CLI_FLAGS = {
+	API_KEY: '--api-key',
+	MCP_PROXY: '--ui-mcp-proxy',
+	SLOTS: '--slots',
+	TOOLS: '--tools'
+} as const;
diff --git a/tools/server/webui/src/lib/constants/code-blocks.ts b/tools/ui/src/lib/constants/code-blocks.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/code-blocks.ts
rename to tools/ui/src/lib/constants/code-blocks.ts
diff --git a/tools/server/webui/src/lib/constants/code.ts b/tools/ui/src/lib/constants/code.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/code.ts
rename to tools/ui/src/lib/constants/code.ts
diff --git a/tools/ui/src/lib/constants/context-keys.ts b/tools/ui/src/lib/constants/context-keys.ts
new file mode 100644
index 00000000000..12de0d0bcb9
--- /dev/null
+++ b/tools/ui/src/lib/constants/context-keys.ts
@@ -0,0 +1,4 @@
+export const CONTEXT_KEY_MESSAGE_EDIT = 'chat-message-edit';
+export const CONTEXT_KEY_CHAT_ACTIONS = 'chat-actions';
+export const CONTEXT_KEY_CHAT_SETTINGS_CONFIG = 'chat-settings-config';
+export const CONTEXT_KEY_PROCESSING_INFO = 'processing-info';
diff --git a/tools/server/webui/src/lib/constants/css-classes.ts b/tools/ui/src/lib/constants/css-classes.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/css-classes.ts
rename to tools/ui/src/lib/constants/css-classes.ts
diff --git a/tools/ui/src/lib/constants/database.ts b/tools/ui/src/lib/constants/database.ts
new file mode 100644
index 00000000000..95e698f4001
--- /dev/null
+++ b/tools/ui/src/lib/constants/database.ts
@@ -0,0 +1,29 @@
+/**
+ * Database-related constants (IndexedDB, Dexie).
+ *
+ * Centralized to ensure consistency across the app and simplify future
+ * naming changes.
+ */
+
+import { STORAGE_APP_NAME } from './storage';
+
+/** IndexedDB database name */
+export const DB_NAME = STORAGE_APP_NAME;
+
+/** IndexedDB store / table names */
+export const IDXDB_TABLES = {
+	conversations: 'conversations',
+	messages: 'messages'
+} as const;
+
+/** IndexedDB store schemas */
+export const IDXDB_STORE_SCHEMAS = {
+	conversations: 'id, lastModified, currNode, name',
+	messages: 'id, convId, type, role, timestamp, parent, children'
+} as const;
+
+/** Combined Dexie stores definition — keys are table names, values are schemas */
+export const IDXDB_STORES = {
+	[IDXDB_TABLES.conversations]: IDXDB_STORE_SCHEMAS.conversations,
+	[IDXDB_TABLES.messages]: IDXDB_STORE_SCHEMAS.messages
+} as const;
diff --git a/tools/server/webui/src/lib/constants/floating-ui-constraints.ts b/tools/ui/src/lib/constants/floating-ui-constraints.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/floating-ui-constraints.ts
rename to tools/ui/src/lib/constants/floating-ui-constraints.ts
diff --git a/tools/server/webui/src/lib/constants/formatters.ts b/tools/ui/src/lib/constants/formatters.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/formatters.ts
rename to tools/ui/src/lib/constants/formatters.ts
diff --git a/tools/server/webui/src/lib/constants/icons.ts b/tools/ui/src/lib/constants/icons.ts
similarity index 77%
rename from tools/server/webui/src/lib/constants/icons.ts
rename to tools/ui/src/lib/constants/icons.ts
index 1e88ab5b3a1..5a6bfa98119 100644
--- a/tools/server/webui/src/lib/constants/icons.ts
+++ b/tools/ui/src/lib/constants/icons.ts
@@ -8,13 +8,15 @@ import {
 	FileText as FileTextIcon,
 	Image as ImageIcon,
 	Eye as VisionIcon,
-	Mic as AudioIcon
+	Mic as AudioIcon,
+	Video as VideoIcon
 } from '@lucide/svelte';
 import { FileTypeCategory, ModelModality } from '$lib/enums';
 
 export const FILE_TYPE_ICONS = {
 	[FileTypeCategory.IMAGE]: ImageIcon,
 	[FileTypeCategory.AUDIO]: AudioIcon,
+	[FileTypeCategory.VIDEO]: VideoIcon,
 	[FileTypeCategory.TEXT]: FileTextIcon,
 	[FileTypeCategory.PDF]: FileIcon
 } as const;
@@ -23,10 +25,12 @@ export const DEFAULT_FILE_ICON = FileIcon;
 
 export const MODALITY_ICONS = {
 	[ModelModality.VISION]: VisionIcon,
-	[ModelModality.AUDIO]: AudioIcon
+	[ModelModality.AUDIO]: AudioIcon,
+	[ModelModality.VIDEO]: VideoIcon
 } as const;
 
 export const MODALITY_LABELS = {
 	[ModelModality.VISION]: 'Vision',
-	[ModelModality.AUDIO]: 'Audio'
+	[ModelModality.AUDIO]: 'Audio',
+	[ModelModality.VIDEO]: 'Video'
 } as const;
diff --git a/tools/server/webui/src/lib/constants/index.ts b/tools/ui/src/lib/constants/index.ts
similarity index 80%
rename from tools/server/webui/src/lib/constants/index.ts
rename to tools/ui/src/lib/constants/index.ts
index 70bed50a699..d3a43480242 100644
--- a/tools/server/webui/src/lib/constants/index.ts
+++ b/tools/ui/src/lib/constants/index.ts
@@ -4,22 +4,24 @@
 export * from './agentic';
 export * from './api-endpoints';
 export * from './attachment-labels';
+export * from './database';
+export * from './storage';
+export * from './attachment-menu';
 export * from './auto-scroll';
 export * from './binary-detection';
 export * from './cache';
 export * from './chat-form';
+export * from './cli-flags';
 export * from './code-blocks';
 export * from './code';
 export * from './context-keys';
 export * from './css-classes';
-export * from './favicon';
 export * from './floating-ui-constraints';
 export * from './formatters';
 export * from './key-value-pairs';
 export * from './icons';
 export * from './latex-protection';
 export * from './literal-html';
-export * from './localstorage-keys';
 export * from './markdown';
 export * from './max-bundle-size';
 export * from './mcp';
@@ -29,13 +31,15 @@ export * from './message-export';
 export * from './model-id';
 export * from './precision';
 export * from './processing-info';
-export * from './settings-config';
-export * from './settings-fields';
+export * from './routes';
 export * from './settings-keys';
-export * from './settings-sections';
+export * from './settings-registry';
 export * from './supported-file-types';
 export * from './table-html-restorer';
+export * from './title-generation';
+export * from './tools';
 export * from './tooltip-config';
 export * from './ui';
 export * from './uri-template';
+export * from './url';
 export * from './viewport';
diff --git a/tools/server/webui/src/lib/constants/key-value-pairs.ts b/tools/ui/src/lib/constants/key-value-pairs.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/key-value-pairs.ts
rename to tools/ui/src/lib/constants/key-value-pairs.ts
diff --git a/tools/server/webui/src/lib/constants/latex-protection.ts b/tools/ui/src/lib/constants/latex-protection.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/latex-protection.ts
rename to tools/ui/src/lib/constants/latex-protection.ts
diff --git a/tools/server/webui/src/lib/constants/literal-html.ts b/tools/ui/src/lib/constants/literal-html.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/literal-html.ts
rename to tools/ui/src/lib/constants/literal-html.ts
diff --git a/tools/server/webui/src/lib/constants/markdown.ts b/tools/ui/src/lib/constants/markdown.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/markdown.ts
rename to tools/ui/src/lib/constants/markdown.ts
diff --git a/tools/server/webui/src/lib/constants/max-bundle-size.ts b/tools/ui/src/lib/constants/max-bundle-size.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/max-bundle-size.ts
rename to tools/ui/src/lib/constants/max-bundle-size.ts
diff --git a/tools/server/webui/src/lib/constants/mcp-form.ts b/tools/ui/src/lib/constants/mcp-form.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/mcp-form.ts
rename to tools/ui/src/lib/constants/mcp-form.ts
diff --git a/tools/server/webui/src/lib/constants/mcp-resource.ts b/tools/ui/src/lib/constants/mcp-resource.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/mcp-resource.ts
rename to tools/ui/src/lib/constants/mcp-resource.ts
diff --git a/tools/server/webui/src/lib/constants/mcp.ts b/tools/ui/src/lib/constants/mcp.ts
similarity index 89%
rename from tools/server/webui/src/lib/constants/mcp.ts
rename to tools/ui/src/lib/constants/mcp.ts
index 90bfffada3d..19bdd92ea75 100644
--- a/tools/server/webui/src/lib/constants/mcp.ts
+++ b/tools/ui/src/lib/constants/mcp.ts
@@ -5,6 +5,7 @@ import type { Component } from 'svelte';
 import { MimeTypeImage } from '$lib/enums/files';
 
 export const DEFAULT_CLIENT_VERSION = '1.0.0';
+export const MCP_CLIENT_NAME = 'llama-ui-mcp';
 export const DEFAULT_IMAGE_MIME_TYPE = MimeTypeImage.PNG;
 
 /** MIME types considered safe for rendering MCP server icons */
@@ -13,7 +14,9 @@ export const MCP_ALLOWED_ICON_MIME_TYPES = new Set([
 	MimeTypeImage.JPEG,
 	MimeTypeImage.JPG,
 	MimeTypeImage.SVG,
-	MimeTypeImage.WEBP
+	MimeTypeImage.WEBP,
+	MimeTypeImage.ICO,
+	MimeTypeImage.ICO_MICROSOFT
 ]);
 
 /**
@@ -26,12 +29,12 @@ export const MCP_PROTOCOL_VERSION = '2025-06-18';
 export const DEFAULT_MCP_CONFIG = {
 	protocolVersion: MCP_PROTOCOL_VERSION,
 	capabilities: { tools: { listChanged: true } } as ClientCapabilities,
-	clientInfo: { name: 'llama-webui-mcp', version: DEFAULT_CLIENT_VERSION } as Implementation,
+	clientInfo: { name: MCP_CLIENT_NAME, version: DEFAULT_CLIENT_VERSION } as Implementation,
 	requestTimeoutSeconds: 300, // 5 minutes for long-running tools
 	connectionTimeoutMs: 10_000 // 10 seconds for connection establishment
 } as const;
 
-export const MCP_SERVER_ID_PREFIX = 'LlamaCpp-WebUI-MCP-Server';
+export const MCP_SERVER_ID_PREFIX = 'LlamaUI-MCP-Server';
 
 export const MCP_RECONNECT_INITIAL_DELAY = 1000;
 export const MCP_RECONNECT_BACKOFF_MULTIPLIER = 2;
@@ -40,7 +43,7 @@ export const MCP_RECONNECT_MAX_DELAY = 30000;
 export const MCP_RECONNECT_ATTEMPT_TIMEOUT_MS = 15_000;
 
 /** Maximum number of MCP server avatars to display in the chat form */
-export const MAX_DISPLAYED_MCP_AVATARS = 3;
+export const MAX_DISPLAYED_MCP_AVATARS = 4;
 
 /** Expected count when two theme-less icons represent a light/dark pair */
 export const EXPECTED_THEMED_ICON_PAIR_COUNT = 2;
diff --git a/tools/server/webui/src/lib/constants/message-export.ts b/tools/ui/src/lib/constants/message-export.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/message-export.ts
rename to tools/ui/src/lib/constants/message-export.ts
diff --git a/tools/server/webui/src/lib/constants/model-id.ts b/tools/ui/src/lib/constants/model-id.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/model-id.ts
rename to tools/ui/src/lib/constants/model-id.ts
diff --git a/tools/server/webui/src/lib/constants/precision.ts b/tools/ui/src/lib/constants/precision.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/precision.ts
rename to tools/ui/src/lib/constants/precision.ts
diff --git a/tools/server/webui/src/lib/constants/processing-info.ts b/tools/ui/src/lib/constants/processing-info.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/processing-info.ts
rename to tools/ui/src/lib/constants/processing-info.ts
diff --git a/tools/ui/src/lib/constants/routes.ts b/tools/ui/src/lib/constants/routes.ts
new file mode 100644
index 00000000000..3b3fceea448
--- /dev/null
+++ b/tools/ui/src/lib/constants/routes.ts
@@ -0,0 +1,27 @@
+export const NEW_CHAT_PARAM = 'new_chat';
+
+/** Settings section slugs — used for routes and navigation. */
+export const SETTINGS_SECTION_SLUGS = {
+	GENERAL: 'general',
+	DISPLAY: 'display',
+	SAMPLING: 'sampling',
+	PENALTIES: 'penalties',
+	AGENTIC: 'agentic',
+	DEVELOPER: 'developer',
+	MCP: 'mcp',
+	TOOLS: 'tools',
+	IMPORT_EXPORT: 'import-export'
+} as const;
+
+export const ROUTES = {
+	/** Root — start of the app. */
+	START: '#/',
+	/** New chat — root with new chat query param. */
+	NEW_CHAT: `?${NEW_CHAT_PARAM}=true#/`,
+	/** Chat base — for dynamic chat URLs use RouterService. */
+	CHAT: '#/chat',
+	/** MCP servers. */
+	MCP_SERVERS: '#/mcp-servers',
+	/** Settings base — for dynamic settings URLs use RouterService. */
+	SETTINGS: '#/settings'
+} as const;
diff --git a/tools/server/webui/src/lib/constants/settings-keys.ts b/tools/ui/src/lib/constants/settings-keys.ts
similarity index 88%
rename from tools/server/webui/src/lib/constants/settings-keys.ts
rename to tools/ui/src/lib/constants/settings-keys.ts
index a96cbca43b0..92a57f88acf 100644
--- a/tools/server/webui/src/lib/constants/settings-keys.ts
+++ b/tools/ui/src/lib/constants/settings-keys.ts
@@ -16,6 +16,8 @@ export const SETTINGS_KEYS = {
 	PDF_AS_IMAGE: 'pdfAsImage',
 	ASK_FOR_TITLE_CONFIRMATION: 'askForTitleConfirmation',
 	TITLE_GENERATION_USE_FIRST_LINE: 'titleGenerationUseFirstLine',
+	TITLE_GENERATION_USE_LLM: 'titleGenerationUseLLM',
+	TITLE_GENERATION_PROMPT: 'titleGenerationPrompt',
 	// Display
 	SHOW_MESSAGE_STATS: 'showMessageStats',
 	SHOW_THOUGHT_IN_PROGRESS: 'showThoughtInProgress',
@@ -24,9 +26,9 @@ export const SETTINGS_KEYS = {
 	RENDER_USER_CONTENT_AS_MARKDOWN: 'renderUserContentAsMarkdown',
 	DISABLE_AUTO_SCROLL: 'disableAutoScroll',
 	ALWAYS_SHOW_SIDEBAR_ON_DESKTOP: 'alwaysShowSidebarOnDesktop',
-	AUTO_SHOW_SIDEBAR_ON_NEW_CHAT: 'autoShowSidebarOnNewChat',
 	FULL_HEIGHT_CODE_BLOCKS: 'fullHeightCodeBlocks',
 	SHOW_RAW_MODEL_NAMES: 'showRawModelNames',
+	SHOW_SYSTEM_MESSAGE: 'showSystemMessage',
 	// Sampling
 	TEMPERATURE: 'temperature',
 	DYNATEMP_RANGE: 'dynatemp_range',
@@ -50,6 +52,8 @@ export const SETTINGS_KEYS = {
 	DRY_ALLOWED_LENGTH: 'dry_allowed_length',
 	DRY_PENALTY_LAST_N: 'dry_penalty_last_n',
 	// MCP
+	MCP_SERVERS: 'mcpServers',
+	MCP_REQUEST_TIMEOUT_SECONDS: 'mcpRequestTimeoutSeconds',
 	AGENTIC_MAX_TURNS: 'agenticMaxTurns',
 	ALWAYS_SHOW_AGENTIC_TURNS: 'alwaysShowAgenticTurns',
 	AGENTIC_MAX_TOOL_PREVIEW_LINES: 'agenticMaxToolPreviewLines',
@@ -60,5 +64,6 @@ export const SETTINGS_KEYS = {
 	DISABLE_REASONING_PARSING: 'disableReasoningParsing',
 	EXCLUDE_REASONING_FROM_CONTEXT: 'excludeReasoningFromContext',
 	SHOW_RAW_OUTPUT_SWITCH: 'showRawOutputSwitch',
+	// PY_INTERPRETER_ENABLED: 'pyInterpreterEnabled',
 	CUSTOM: 'custom'
 } as const;
diff --git a/tools/ui/src/lib/constants/settings-registry.ts b/tools/ui/src/lib/constants/settings-registry.ts
new file mode 100644
index 00000000000..bdbb17d962c
--- /dev/null
+++ b/tools/ui/src/lib/constants/settings-registry.ts
@@ -0,0 +1,778 @@
+import { ColorMode } from '$lib/enums/ui';
+import { SettingsFieldType } from '$lib/enums/settings';
+import { SyncableParameterType } from '$lib/enums';
+import {
+	Funnel,
+	AlertTriangle,
+	Code,
+	Monitor,
+	ListRestart,
+	Sliders,
+	PencilRuler,
+	Database,
+	Monitor as MonitorIcon,
+	Sun,
+	Moon
+} from '@lucide/svelte';
+import type { Component } from 'svelte';
+import type {
+	SettingsConfigValue,
+	SyncableParameter,
+	SettingsEntry,
+	SettingsSectionTitle,
+	SettingsSectionEntry,
+	SettingsSection
+} from '$lib/types';
+import { CLI_FLAGS, DEFAULT_MCP_CONFIG } from '$lib/constants';
+import McpLogo from '$lib/components/app/mcp/McpLogo.svelte';
+import { SETTINGS_KEYS } from './settings-keys';
+import { ROUTES, SETTINGS_SECTION_SLUGS } from './routes';
+import { TITLE_GENERATION } from './title-generation';
+
+export const SETTINGS_SECTION_TITLES = {
+	GENERAL: 'General',
+	DISPLAY: 'Display',
+	SAMPLING: 'Sampling',
+	PENALTIES: 'Penalties',
+	AGENTIC: 'Agentic',
+	TOOLS: 'Tools',
+	MCP: 'MCP',
+	IMPORT_EXPORT: 'Import/Export',
+	DEVELOPER: 'Developer'
+} as const;
+
+const STANDALONE_SECTIONS: { title: SettingsSectionTitle; slug: string; icon: Component }[] = [
+	{ title: SETTINGS_SECTION_TITLES.TOOLS, slug: SETTINGS_SECTION_SLUGS.TOOLS, icon: PencilRuler },
+	{
+		title: SETTINGS_SECTION_TITLES.IMPORT_EXPORT,
+		slug: SETTINGS_SECTION_SLUGS.IMPORT_EXPORT,
+		icon: Database
+	}
+];
+
+const COLOR_MODE_OPTIONS: Array<{ value: string; label: string; icon: Component }> = [
+	{ value: ColorMode.SYSTEM, label: 'System', icon: MonitorIcon },
+	{ value: ColorMode.LIGHT, label: 'Light', icon: Sun },
+	{ value: ColorMode.DARK, label: 'Dark', icon: Moon }
+];
+
+const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
+	[SETTINGS_SECTION_SLUGS.GENERAL]: {
+		title: SETTINGS_SECTION_TITLES.GENERAL,
+		slug: SETTINGS_SECTION_SLUGS.GENERAL,
+		icon: Sliders,
+		settings: [
+			{
+				key: SETTINGS_KEYS.THEME,
+				label: 'Theme',
+				help: 'Choose the color theme for the interface. You can choose between System (follows your device settings), Light, or Dark.',
+				defaultValue: ColorMode.SYSTEM,
+				type: SettingsFieldType.SELECT,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				options: COLOR_MODE_OPTIONS,
+				sync: { serverKey: SETTINGS_KEYS.THEME, paramType: SyncableParameterType.STRING }
+			},
+			{
+				key: SETTINGS_KEYS.API_KEY,
+				label: 'API Key',
+				help: `Set the API Key if you are using <code> ${CLI_FLAGS.API_KEY} </code> option for the server.`,
+				defaultValue: '',
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.GENERAL
+			},
+			{
+				key: SETTINGS_KEYS.SYSTEM_MESSAGE,
+				label: 'System Message',
+				help: 'The starting message that defines how model should behave.',
+				defaultValue: '',
+				type: SettingsFieldType.TEXTAREA,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.SYSTEM_MESSAGE,
+					paramType: SyncableParameterType.STRING
+				}
+			},
+			{
+				key: SETTINGS_KEYS.PASTE_LONG_TEXT_TO_FILE_LEN,
+				label: 'Paste long text to file length',
+				help: 'On pasting long text, it will be converted to a file. You can control the file length by setting the value of this parameter. Value 0 means disable.',
+				defaultValue: 2500,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.PASTE_LONG_TEXT_TO_FILE_LEN,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.SEND_ON_ENTER,
+				label: 'Send message on Enter',
+				help: 'Use Enter to send messages and Shift + Enter for new lines. When disabled, use Ctrl/Cmd + Enter.',
+				defaultValue: true,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.SEND_ON_ENTER,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.COPY_TEXT_ATTACHMENTS_AS_PLAIN_TEXT,
+				label: 'Copy text attachments as plain text',
+				help: 'When copying a message with text attachments, combine them into a single plain text string instead of a special format that can be pasted back as attachments.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.COPY_TEXT_ATTACHMENTS_AS_PLAIN_TEXT,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.ENABLE_CONTINUE_GENERATION,
+				label: 'Enable "Continue" button',
+				help: 'Enable "Continue" button for assistant messages, including reasoning models.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				isExperimental: true,
+				sync: {
+					serverKey: SETTINGS_KEYS.ENABLE_CONTINUE_GENERATION,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.PDF_AS_IMAGE,
+				label: 'Parse PDF as image',
+				help: 'Parse PDF as image instead of text. Automatically falls back to text processing for non-vision models.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.PDF_AS_IMAGE,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.ASK_FOR_TITLE_CONFIRMATION,
+				label: 'Ask for confirmation before changing conversation title',
+				help: 'Ask for confirmation before automatically changing conversation title when editing the first message.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.ASK_FOR_TITLE_CONFIRMATION,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.TITLE_GENERATION_USE_FIRST_LINE,
+				label: 'Use first non-empty line for conversation title',
+				help: 'Use only the first non-empty line of the prompt to generate the conversation title.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.TITLE_GENERATION_USE_FIRST_LINE,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.TITLE_GENERATION_USE_LLM,
+				label: 'Use LLM to generate conversation title',
+				help: 'Use the LLM to automatically generate conversation titles based on the first message exchange.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				isExperimental: true
+			},
+			{
+				key: SETTINGS_KEYS.TITLE_GENERATION_PROMPT,
+				label: 'LLM title generation prompt',
+				help: 'Optional template for the title generation prompt. Use {{USER}} for the user message and {{ASSISTANT}} for the assistant message.',
+				defaultValue: TITLE_GENERATION.DEFAULT_PROMPT,
+				type: SettingsFieldType.TEXTAREA,
+				section: SETTINGS_SECTION_SLUGS.GENERAL
+			}
+		]
+	},
+	[SETTINGS_SECTION_SLUGS.DISPLAY]: {
+		title: SETTINGS_SECTION_TITLES.DISPLAY,
+		slug: SETTINGS_SECTION_SLUGS.DISPLAY,
+		icon: Monitor,
+		settings: [
+			{
+				key: SETTINGS_KEYS.SHOW_MESSAGE_STATS,
+				label: 'Show message generation statistics',
+				help: 'Display generation statistics (tokens/second, token count, duration) below each assistant message.',
+				defaultValue: true,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.SHOW_MESSAGE_STATS,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.SHOW_THOUGHT_IN_PROGRESS,
+				label: 'Show thought in progress',
+				help: 'Expand thought process by default when generating messages.',
+				defaultValue: true,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.SHOW_THOUGHT_IN_PROGRESS,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS,
+				label: 'Show tool call in progress',
+				help: 'Automatically expand tool call details while executing and keep them expanded after completion.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.SHOW_TOOL_CALL_IN_PROGRESS,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.KEEP_STATS_VISIBLE,
+				label: 'Keep stats visible after generation',
+				help: 'Keep processing statistics visible after generation finishes.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.KEEP_STATS_VISIBLE,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.AUTO_MIC_ON_EMPTY,
+				label: 'Show microphone on empty input',
+				help: 'Automatically show microphone button instead of send button when textarea is empty for models with audio modality support.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				isExperimental: true,
+				sync: {
+					serverKey: SETTINGS_KEYS.AUTO_MIC_ON_EMPTY,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.RENDER_USER_CONTENT_AS_MARKDOWN,
+				label: 'Render user content as Markdown',
+				help: 'Render user messages using markdown formatting in the chat.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.RENDER_USER_CONTENT_AS_MARKDOWN,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.FULL_HEIGHT_CODE_BLOCKS,
+				label: 'Use full height code blocks',
+				help: 'Always display code blocks at their full natural height, overriding any height limits.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.FULL_HEIGHT_CODE_BLOCKS,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.DISABLE_AUTO_SCROLL,
+				label: 'Disable automatic scroll',
+				help: 'Disable automatic scrolling while messages stream so you can control the viewport position manually.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.DISABLE_AUTO_SCROLL,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.ALWAYS_SHOW_SIDEBAR_ON_DESKTOP,
+				label: 'Always show sidebar on desktop',
+				help: 'Always keep the sidebar visible on desktop instead of auto-hiding it.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.ALWAYS_SHOW_SIDEBAR_ON_DESKTOP,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.SHOW_RAW_MODEL_NAMES,
+				label: 'Show raw model names',
+				help: 'Display full raw model identifiers (e.g. "ggml-org/GLM-4.7-Flash-GGUF:Q8_0") instead of parsed names with badges.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.SHOW_RAW_MODEL_NAMES,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.ALWAYS_SHOW_AGENTIC_TURNS,
+				label: 'Always show agentic turns in conversation',
+				help: 'Always expand and display agentic loop turns in conversation messages.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.ALWAYS_SHOW_AGENTIC_TURNS,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			}
+		]
+	},
+	[SETTINGS_SECTION_SLUGS.SAMPLING]: {
+		title: SETTINGS_SECTION_TITLES.SAMPLING,
+		slug: SETTINGS_SECTION_SLUGS.SAMPLING,
+		icon: Funnel,
+		settings: [
+			{
+				key: SETTINGS_KEYS.TEMPERATURE,
+				label: 'Temperature',
+				help: 'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: {
+					serverKey: SETTINGS_KEYS.TEMPERATURE,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.DYNATEMP_RANGE,
+				label: 'Dynamic temperature range',
+				help: 'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: {
+					serverKey: SETTINGS_KEYS.DYNATEMP_RANGE,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.DYNATEMP_EXPONENT,
+				label: 'Dynamic temperature exponent',
+				help: 'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: {
+					serverKey: SETTINGS_KEYS.DYNATEMP_EXPONENT,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.TOP_K,
+				label: 'Top K',
+				help: 'Keeps only k top tokens.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: { serverKey: SETTINGS_KEYS.TOP_K, paramType: SyncableParameterType.NUMBER }
+			},
+			{
+				key: SETTINGS_KEYS.TOP_P,
+				label: 'Top P',
+				help: 'Limits tokens to those that together have a cumulative probability of at least p',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: { serverKey: SETTINGS_KEYS.TOP_P, paramType: SyncableParameterType.NUMBER }
+			},
+			{
+				key: SETTINGS_KEYS.MIN_P,
+				label: 'Min P',
+				help: 'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: { serverKey: SETTINGS_KEYS.MIN_P, paramType: SyncableParameterType.NUMBER }
+			},
+			{
+				key: SETTINGS_KEYS.XTC_PROBABILITY,
+				label: 'XTC probability',
+				help: 'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: {
+					serverKey: SETTINGS_KEYS.XTC_PROBABILITY,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.XTC_THRESHOLD,
+				label: 'XTC threshold',
+				help: 'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: {
+					serverKey: SETTINGS_KEYS.XTC_THRESHOLD,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.TYP_P,
+				label: 'Typical P',
+				help: 'Sorts and limits tokens based on the difference between log-probability and entropy.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: { serverKey: SETTINGS_KEYS.TYP_P, paramType: SyncableParameterType.NUMBER }
+			},
+			{
+				key: SETTINGS_KEYS.MAX_TOKENS,
+				label: 'Max tokens',
+				help: 'The maximum number of token per output. Use -1 for infinite (no limit).',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: {
+					serverKey: SETTINGS_KEYS.MAX_TOKENS,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.SAMPLERS,
+				label: 'Samplers',
+				help: 'The order at which samplers are applied, in simplified way. Default is "top_k;typ_p;top_p;min_p;temperature": top_k->typ_p->top_p->min_p->temperature',
+				defaultValue: '',
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: { serverKey: SETTINGS_KEYS.SAMPLERS, paramType: SyncableParameterType.STRING }
+			},
+			{
+				key: SETTINGS_KEYS.BACKEND_SAMPLING,
+				label: 'Backend sampling',
+				help: 'Enable backend-based samplers. When enabled, supported samplers run on the accelerator backend for faster sampling.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.SAMPLING,
+				sync: {
+					serverKey: SETTINGS_KEYS.BACKEND_SAMPLING,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			}
+		]
+	},
+	[SETTINGS_SECTION_SLUGS.PENALTIES]: {
+		title: SETTINGS_SECTION_TITLES.PENALTIES,
+		slug: SETTINGS_SECTION_SLUGS.PENALTIES,
+		icon: AlertTriangle,
+		settings: [
+			{
+				key: SETTINGS_KEYS.REPEAT_LAST_N,
+				label: 'Repeat last N',
+				help: 'Last n tokens to consider for penalizing repetition',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.PENALTIES,
+				sync: {
+					serverKey: SETTINGS_KEYS.REPEAT_LAST_N,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.REPEAT_PENALTY,
+				label: 'Repeat penalty',
+				help: 'Controls the repetition of token sequences in the generated text',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.PENALTIES,
+				sync: {
+					serverKey: SETTINGS_KEYS.REPEAT_PENALTY,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.PRESENCE_PENALTY,
+				label: 'Presence penalty',
+				help: 'Limits tokens based on whether they appear in the output or not.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.PENALTIES,
+				sync: {
+					serverKey: SETTINGS_KEYS.PRESENCE_PENALTY,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.FREQUENCY_PENALTY,
+				label: 'Frequency penalty',
+				help: 'Limits tokens based on how often they appear in the output.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.PENALTIES,
+				sync: {
+					serverKey: SETTINGS_KEYS.FREQUENCY_PENALTY,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.DRY_MULTIPLIER,
+				label: 'DRY multiplier',
+				help: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.PENALTIES,
+				sync: {
+					serverKey: SETTINGS_KEYS.DRY_MULTIPLIER,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.DRY_BASE,
+				label: 'DRY base',
+				help: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.PENALTIES,
+				sync: { serverKey: SETTINGS_KEYS.DRY_BASE, paramType: SyncableParameterType.NUMBER }
+			},
+			{
+				key: SETTINGS_KEYS.DRY_ALLOWED_LENGTH,
+				label: 'DRY allowed length',
+				help: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.PENALTIES,
+				sync: {
+					serverKey: SETTINGS_KEYS.DRY_ALLOWED_LENGTH,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.DRY_PENALTY_LAST_N,
+				label: 'DRY penalty last N',
+				help: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.',
+				defaultValue: undefined,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.PENALTIES,
+				sync: {
+					serverKey: SETTINGS_KEYS.DRY_PENALTY_LAST_N,
+					paramType: SyncableParameterType.NUMBER
+				}
+			}
+		]
+	},
+	[SETTINGS_SECTION_SLUGS.AGENTIC]: {
+		title: SETTINGS_SECTION_TITLES.AGENTIC,
+		slug: SETTINGS_SECTION_SLUGS.AGENTIC,
+		icon: ListRestart,
+		settings: [
+			{
+				key: SETTINGS_KEYS.AGENTIC_MAX_TURNS,
+				label: 'Agentic turns',
+				help: 'Maximum number of tool execution cycles before stopping (prevents infinite loops).',
+				defaultValue: 10,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.AGENTIC,
+				isPositiveInteger: true,
+				sync: {
+					serverKey: SETTINGS_KEYS.AGENTIC_MAX_TURNS,
+					paramType: SyncableParameterType.NUMBER
+				}
+			},
+			{
+				key: SETTINGS_KEYS.AGENTIC_MAX_TOOL_PREVIEW_LINES,
+				label: 'Max lines per tool preview',
+				help: 'Number of lines shown in tool output previews (last N lines). Only these previews and the final LLM response persist after the agentic loop completes.',
+				defaultValue: 25,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.AGENTIC,
+				isPositiveInteger: true,
+				sync: {
+					serverKey: SETTINGS_KEYS.AGENTIC_MAX_TOOL_PREVIEW_LINES,
+					paramType: SyncableParameterType.NUMBER
+				}
+			}
+		]
+	},
+	[SETTINGS_SECTION_SLUGS.DEVELOPER]: {
+		title: SETTINGS_SECTION_TITLES.DEVELOPER,
+		slug: SETTINGS_SECTION_SLUGS.DEVELOPER,
+		icon: Code,
+		settings: [
+			{
+				key: SETTINGS_KEYS.PRE_ENCODE_CONVERSATION,
+				label: 'Pre-fill KV cache after response',
+				help: 'After each response, re-submit the conversation to pre-fill the server KV cache. Makes the next turn faster since the prompt is already encoded while you read the response.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER
+			},
+			{
+				key: SETTINGS_KEYS.DISABLE_REASONING_PARSING,
+				label: 'Disable reasoning content parsing',
+				help: 'Send reasoning_format=none so the server returns thinking tokens inline instead of extracting them into a separate field.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER
+			},
+			{
+				key: SETTINGS_KEYS.EXCLUDE_REASONING_FROM_CONTEXT,
+				label: 'Exclude reasoning from context',
+				help: 'Strip thinking from previous messages before sending. When off, thinking is sent back via the reasoning_content field so the model sees its own chain-of-thought across turns.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER,
+				sync: {
+					serverKey: SETTINGS_KEYS.EXCLUDE_REASONING_FROM_CONTEXT,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.SHOW_RAW_OUTPUT_SWITCH,
+				label: 'Enable raw output toggle',
+				help: 'Show toggle button to display messages as plain text instead of Markdown-formatted content',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER,
+				sync: {
+					serverKey: SETTINGS_KEYS.SHOW_RAW_OUTPUT_SWITCH,
+					paramType: SyncableParameterType.BOOLEAN
+				}
+			},
+			{
+				key: SETTINGS_KEYS.CUSTOM,
+				label: 'Custom JSON',
+				help: 'Custom JSON parameters to send to the API. Must be valid JSON format.',
+				defaultValue: '',
+				type: SettingsFieldType.TEXTAREA,
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER
+			}
+		]
+	},
+	[SETTINGS_SECTION_SLUGS.MCP]: {
+		title: SETTINGS_SECTION_TITLES.MCP,
+		slug: SETTINGS_SECTION_SLUGS.MCP,
+		icon: McpLogo,
+		settings: [
+			{
+				key: SETTINGS_KEYS.MCP_REQUEST_TIMEOUT_SECONDS,
+				label: 'Request timeout (seconds)',
+				help: 'Default timeout for individual MCP tool calls. Can be overridden per server.',
+				defaultValue: DEFAULT_MCP_CONFIG.requestTimeoutSeconds,
+				type: SettingsFieldType.INPUT,
+				section: SETTINGS_SECTION_SLUGS.MCP,
+				isPositiveInteger: true
+			}
+		]
+	}
+} as const;
+
+const NON_UI_SETTINGS: SettingsEntry[] = [
+	{
+		key: SETTINGS_KEYS.SHOW_SYSTEM_MESSAGE,
+		label: 'Show system message',
+		help: 'Display the system message at the top of each conversation.',
+		defaultValue: true,
+		type: SettingsFieldType.CHECKBOX,
+		sync: {
+			serverKey: SETTINGS_KEYS.SHOW_SYSTEM_MESSAGE,
+			paramType: SyncableParameterType.BOOLEAN
+		}
+	},
+	{
+		key: SETTINGS_KEYS.MCP_SERVERS,
+		label: 'MCP servers',
+		help: 'Configure MCP servers as a JSON list. Use the form in the MCP Client settings section to edit.',
+		defaultValue: '[]',
+		type: SettingsFieldType.INPUT,
+		sync: { serverKey: SETTINGS_KEYS.MCP_SERVERS, paramType: SyncableParameterType.STRING }
+	}
+	// {
+	// 	key: SETTINGS_KEYS.PY_INTERPRETER_ENABLED,
+	// 	label: 'Python interpreter enabled',
+	// 	help: 'Enable Python interpreter using Pyodide. Allows running Python code in markdown code blocks.',
+	// 	defaultValue: false,
+	// 	type: SettingsFieldType.CHECKBOX,
+	// 	isExperimental: true,
+	// 	sync: { serverKey: SETTINGS_KEYS.PY_INTERPRETER_ENABLED, paramType: SyncableParameterType.BOOLEAN }
+	// }
+];
+
+function getAllSettings(): SettingsEntry[] {
+	const result: SettingsEntry[] = [];
+	for (const section of Object.values(SETTINGS_REGISTRY)) {
+		result.push(...section.settings);
+	}
+	result.push(...NON_UI_SETTINGS);
+	return result;
+}
+
+/** Flat config object stored in localStorage. */
+export const SETTING_CONFIG_DEFAULT: Record<string, SettingsConfigValue> = Object.fromEntries(
+	getAllSettings().map((s) => [s.key, s.defaultValue])
+) as Record<string, SettingsConfigValue>;
+
+/** Help text for every setting (including non-UI). */
+export const SETTING_CONFIG_INFO: Record<string, string> = Object.fromEntries(
+	getAllSettings().map((s) => [s.key, s.help])
+) as Record<string, string>;
+
+/** Theme select options. */
+export const SETTINGS_COLOR_MODES_CONFIG = COLOR_MODE_OPTIONS;
+
+export type { SettingsSectionTitle } from '$lib/types';
+export type { SettingsSection } from '$lib/types';
+
+/** Sidebar sections + field configs (as consumed by UI). */
+export const SETTINGS_CHAT_SECTIONS: SettingsSection[] = [
+	...Object.values(SETTINGS_REGISTRY).map((section) => ({
+		title: section.title,
+		slug: section.slug,
+		icon: section.icon,
+		fields: section.settings.map((s) => ({
+			key: s.key,
+			label: s.label,
+			type: s.type,
+			isExperimental: s.isExperimental,
+			isPositiveInteger: s.isPositiveInteger,
+			help: s.help,
+			options: s.options
+		}))
+	})),
+	...STANDALONE_SECTIONS
+];
+
+/** INPUT-type settings whose value is a number. */
+export const NUMERIC_FIELDS = getAllSettings()
+	.filter((s) => s.type === SettingsFieldType.INPUT && typeof s.defaultValue !== 'string')
+	.map((s) => s.key) as readonly string[];
+
+/** Numeric fields clamped to ≥ 1 and rounded. */
+export const POSITIVE_INTEGER_FIELDS = getAllSettings()
+	.filter((s) => s.isPositiveInteger)
+	.map((s) => s.key) as readonly string[];
+
+/** Derived for the parameter sync service. */
+export const SYNCABLE_PARAMETERS: SyncableParameter[] = getAllSettings()
+	.filter((s) => s.sync !== undefined)
+	.map((s) => ({
+		key: s.key,
+		serverKey: s.sync!.serverKey,
+		type: s.sync!.paramType,
+		canSync: true
+	}));
+
+export const SETTINGS_FALLBACK_EXIT_ROUTE = ROUTES.START;
+
+export { SETTINGS_KEYS } from './settings-keys';
diff --git a/tools/ui/src/lib/constants/storage.ts b/tools/ui/src/lib/constants/storage.ts
new file mode 100644
index 00000000000..d03254b9ccb
--- /dev/null
+++ b/tools/ui/src/lib/constants/storage.ts
@@ -0,0 +1,46 @@
+/**
+ * Storage-related constants (localStorage, IndexedDB).
+ *
+ * Centralized to ensure consistency across the app and simplify future
+ * name changes.
+ */
+
+/** Name prefix for all localStorage keys */
+export const STORAGE_APP_NAME = 'LlamaUi';
+
+/** Deprecated localStorage key prefix (old app name) */
+export const STORAGE_APP_NAME_DEPRECATED = 'LlamaCppWebui';
+
+/** @deprecated Deprecated IndexedDB name — will be removed after all users have migrated */
+export const DB_APP_NAME_DEPRECATED = 'LlamacppWebui';
+
+export const ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.alwaysAllowedTools`;
+export const CONFIG_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.config`;
+export const DISABLED_TOOLS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.disabledTools`;
+export const FAVORITE_MODELS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.favoriteModels`;
+export const MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.mcpDefaultEnabled`;
+export const USER_OVERRIDES_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.userOverrides`;
+
+// Deprecated old key names (kept for backward compat while users migrate)
+/** @deprecated Use {@link ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY} instead */
+export const DEPRECATED_ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.alwaysAllowedTools`;
+/** @deprecated Use {@link CONFIG_LOCALSTORAGE_KEY} instead */
+export const DEPRECATED_CONFIG_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.config`;
+/** @deprecated Use {@link DISABLED_TOOLS_LOCALSTORAGE_KEY} instead */
+export const DEPRECATED_DISABLED_TOOLS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.disabledTools`;
+/** @deprecated Use {@link FAVORITE_MODELS_LOCALSTORAGE_KEY} instead */
+export const DEPRECATED_FAVORITE_MODELS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.favoriteModels`;
+/** @deprecated Use {@link MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY} instead */
+export const DEPRECATED_MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.mcpDefaultEnabled`;
+/** @deprecated Use {@link USER_OVERRIDES_LOCALSTORAGE_KEY} instead */
+export const DEPRECATED_USER_OVERRIDES_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.userOverrides`;
+
+/** Maps new keys to their deprecated fallback keys */
+export const NEW_TO_DEPRECATED_MAP: Record<string, string> = {
+	[ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY]: DEPRECATED_ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY,
+	[CONFIG_LOCALSTORAGE_KEY]: DEPRECATED_CONFIG_LOCALSTORAGE_KEY,
+	[DISABLED_TOOLS_LOCALSTORAGE_KEY]: DEPRECATED_DISABLED_TOOLS_LOCALSTORAGE_KEY,
+	[FAVORITE_MODELS_LOCALSTORAGE_KEY]: DEPRECATED_FAVORITE_MODELS_LOCALSTORAGE_KEY,
+	[MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY]: DEPRECATED_MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY,
+	[USER_OVERRIDES_LOCALSTORAGE_KEY]: DEPRECATED_USER_OVERRIDES_LOCALSTORAGE_KEY
+};
diff --git a/tools/server/webui/src/lib/constants/supported-file-types.ts b/tools/ui/src/lib/constants/supported-file-types.ts
similarity index 93%
rename from tools/server/webui/src/lib/constants/supported-file-types.ts
rename to tools/ui/src/lib/constants/supported-file-types.ts
index 0d955ad1479..34505438916 100644
--- a/tools/server/webui/src/lib/constants/supported-file-types.ts
+++ b/tools/ui/src/lib/constants/supported-file-types.ts
@@ -1,5 +1,5 @@
 /**
- * Comprehensive dictionary of all supported file types in webui
+ * Comprehensive dictionary of all supported file types in llama-ui
  * Organized by category with TypeScript enums for better type safety
  */
 
@@ -13,10 +13,12 @@ import {
 	FileTypePdf,
 	FileTypeText,
 	MimeTypeAudio,
+	MimeTypeVideo,
 	MimeTypeImage,
 	MimeTypeApplication,
 	MimeTypeText
 } from '$lib/enums';
+import { FileExtensionVideo, FileTypeVideo } from '$lib/enums/files';
 
 // File type configuration using enums
 export const AUDIO_FILE_TYPES = {
@@ -30,6 +32,17 @@ export const AUDIO_FILE_TYPES = {
 	}
 } as const;
 
+export const VIDEO_FILE_TYPES = {
+	[FileTypeVideo.MP4]: {
+		extensions: [FileExtensionVideo.MP4],
+		mimeTypes: [MimeTypeVideo.MP4]
+	},
+	[FileTypeVideo.OGG]: {
+		extensions: [FileExtensionVideo.OGG],
+		mimeTypes: [MimeTypeVideo.OGG]
+	}
+} as const;
+
 export const IMAGE_FILE_TYPES = {
 	[FileTypeImage.JPEG]: {
 		extensions: [FileExtensionImage.JPG, FileExtensionImage.JPEG],
diff --git a/tools/server/webui/src/lib/constants/table-html-restorer.ts b/tools/ui/src/lib/constants/table-html-restorer.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/table-html-restorer.ts
rename to tools/ui/src/lib/constants/table-html-restorer.ts
diff --git a/tools/ui/src/lib/constants/title-generation.ts b/tools/ui/src/lib/constants/title-generation.ts
new file mode 100644
index 00000000000..48ca2217a27
--- /dev/null
+++ b/tools/ui/src/lib/constants/title-generation.ts
@@ -0,0 +1,9 @@
+/* Title generation constants */
+export const TITLE_GENERATION = {
+	MIN_LENGTH: 3,
+	FALLBACK: 'New Chat',
+	DEFAULT_PROMPT:
+		'Based on the following interaction, generate a short, concise title (maximum 6-8 words) that captures the main topic. Return ONLY the title text, nothing else. Do not use quotes.\n\nUser: {{USER}}\n\nAssistant: {{ASSISTANT}}\n\nTitle:',
+	PREFIX_PATTERN: /^(Title:|Subject:|Topic:)\s*/i,
+	QUOTE_PATTERN: /^["]|["]$/g
+} as const;
diff --git a/tools/ui/src/lib/constants/tools.ts b/tools/ui/src/lib/constants/tools.ts
new file mode 100644
index 00000000000..22b22309c39
--- /dev/null
+++ b/tools/ui/src/lib/constants/tools.ts
@@ -0,0 +1,11 @@
+import { ToolSource } from '$lib/enums/tools';
+
+export const TOOL_GROUP_LABELS = {
+	[ToolSource.BUILTIN]: 'Built-in',
+	[ToolSource.CUSTOM]: 'JSON Schema'
+} as const;
+
+export const TOOL_SERVER_LABELS = {
+	[ToolSource.BUILTIN]: 'Built-in Tools',
+	[ToolSource.CUSTOM]: 'Custom Tools'
+} as const;
diff --git a/tools/server/webui/src/lib/constants/tooltip-config.ts b/tools/ui/src/lib/constants/tooltip-config.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/tooltip-config.ts
rename to tools/ui/src/lib/constants/tooltip-config.ts
diff --git a/tools/ui/src/lib/constants/ui.ts b/tools/ui/src/lib/constants/ui.ts
new file mode 100644
index 00000000000..f6e7f7d8a9b
--- /dev/null
+++ b/tools/ui/src/lib/constants/ui.ts
@@ -0,0 +1,37 @@
+import { Settings, Search, SquarePen } from '@lucide/svelte';
+import McpLogo from '$lib/components/app/mcp/McpLogo.svelte';
+import type { Component } from 'svelte';
+import { ROUTES } from './routes';
+
+export const FORK_TREE_DEPTH_PADDING = 8;
+export const SYSTEM_MESSAGE_PLACEHOLDER = 'System message';
+export const APP_NAME = import.meta.env.VITE_PUBLIC_APP_NAME || 'llama-ui';
+
+export const ICON_STRIP_TRANSITION_DURATION = 150;
+export const ICON_STRIP_TRANSITION_DELAY_MULTIPLIER = 50;
+
+export interface DesktopIconStripItem {
+	icon: Component;
+	tooltip: string;
+	route?: string;
+	activeRouteId?: string;
+	activeRoutePrefix?: string;
+	keys?: string[];
+}
+
+export const SIDEBAR_ACTIONS_ITEMS: DesktopIconStripItem[] = [
+	{ icon: SquarePen, tooltip: 'New chat', route: ROUTES.NEW_CHAT, keys: ['shift', 'cmd', 'o'] },
+	{ icon: Search, tooltip: 'Search', keys: ['cmd', 'k'] },
+	{
+		icon: McpLogo,
+		tooltip: 'MCP Servers',
+		route: ROUTES.MCP_SERVERS,
+		activeRouteId: '/mcp-servers'
+	},
+	{
+		icon: Settings,
+		tooltip: 'Settings',
+		route: ROUTES.SETTINGS,
+		activeRoutePrefix: '/settings'
+	}
+];
diff --git a/tools/server/webui/src/lib/constants/uri-template.ts b/tools/ui/src/lib/constants/uri-template.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/uri-template.ts
rename to tools/ui/src/lib/constants/uri-template.ts
diff --git a/tools/ui/src/lib/constants/url.ts b/tools/ui/src/lib/constants/url.ts
new file mode 100644
index 00000000000..0afb9deccb8
--- /dev/null
+++ b/tools/ui/src/lib/constants/url.ts
@@ -0,0 +1,186 @@
+const STD = ['com', 'net', 'org', 'gov', 'edu'] as const;
+
+const STD_MIL = [...STD, 'mil'] as const;
+
+const ccTLD_PREFIXES: Record<string, readonly string[]> = {
+	// --- Standard 5 only ---
+	ar: STD,
+	bd: STD,
+	bg: STD,
+	cn: STD_MIL,
+	eg: STD,
+	gr: STD,
+	hk: STD,
+	hr: STD,
+	lk: STD,
+	mx: STD_MIL,
+	my: STD_MIL,
+	ng: STD,
+	ph: STD,
+	pk: STD,
+	pl: STD,
+	ro: STD,
+	ru: STD,
+	sa: STD,
+	si: STD,
+	tr: STD,
+	tw: STD,
+	ua: STD,
+	ve: STD,
+
+	au: [...STD_MIL, 'id', 'asn', 'csiro'],
+	br: [
+		...STD_MIL,
+		'art',
+		'eco',
+		'eng',
+		'inf',
+		'med',
+		'psi',
+		'tmp',
+		'etc',
+		'adm',
+		'adv',
+		'arq',
+		'bio',
+		'bmd',
+		'cim',
+		'cng',
+		'cnt',
+		'coop',
+		'ecn',
+		'esp',
+		'far',
+		'fm',
+		'fnd',
+		'fot',
+		'fst',
+		'g12',
+		'ggf',
+		'imb',
+		'ind',
+		'jor',
+		'jus',
+		'leg',
+		'lel',
+		'mat',
+		'mp',
+		'mus',
+		'not',
+		'ntr',
+		'odo',
+		'ppg',
+		'pro',
+		'psc',
+		'qsl',
+		'rec',
+		'slg',
+		'srv',
+		'trd',
+		'tur',
+		'tv',
+		'vet',
+		'vlog',
+		'wiki',
+		'zlg'
+	],
+	id: [...STD_MIL, 'co', 'go', 'or', 'web', 'sch'],
+	in: [...STD_MIL, 'co', 'gen', 'ind', 'firm', 'ernet', 'nic'],
+	kr: [...STD_MIL, 'co', 'go', 'or', 'ac', 're'],
+	nz: [
+		...STD_MIL,
+		'co',
+		'gen',
+		'geek',
+		'kiwi',
+		'maori',
+		'school',
+		'govt',
+		'health',
+		'iwi',
+		'parliament'
+	],
+	sg: [...STD, 'per'],
+	th: ['co', 'go', 'or', 'in', 'ac', 'mi', 'net'],
+
+	ae: ['co', 'net', 'org', 'gov', 'ac', 'sch'],
+	hu: ['co', 'net', 'org', 'gov', 'edu'],
+	il: ['co', 'net', 'org', 'gov', 'ac', 'muni'],
+	jp: ['ac', 'ad', 'co', 'ed', 'go', 'gr', 'lg', 'ne', 'or'],
+	ke: ['co', 'or', 'ne', 'go', 'ac', 'sc'],
+	rs: ['co', 'net', 'org', 'gov', 'edu'],
+	uk: ['co', 'org', 'net', 'ac', 'gov', 'mil', 'nhs', 'police', 'mod', 'ltd', 'plc', 'me', 'sch'],
+	za: ['co', 'org', 'net', 'web', 'law', 'mil']
+};
+
+const WILDCARD_BASES: Record<string, readonly string[]> = {
+	br: ['nom', 'blog'],
+	jp: [
+		'kobe',
+		'kyoto',
+		'nagoya',
+		'osaka',
+		'sapporo',
+		'sendai',
+		'tokyo',
+		'yokohama',
+		'aichi',
+		'akita',
+		'aomori',
+		'chiba',
+		'ehime',
+		'fukui',
+		'fukuoka',
+		'fukushima',
+		'gifu',
+		'gunma',
+		'hiroshima',
+		'hokkaido',
+		'hyogo',
+		'ibaraki',
+		'ishikawa',
+		'iwate',
+		'kagawa',
+		'kagoshima',
+		'kanagawa',
+		'kochi',
+		'kumamoto',
+		'mie',
+		'miyagi',
+		'miyazaki',
+		'nagano',
+		'nara',
+		'niigata',
+		'oita',
+		'okayama',
+		'okinawa',
+		'saga',
+		'saitama',
+		'shiga',
+		'shimane',
+		'shizuoka',
+		'tochigi',
+		'tokushima',
+		'tottori',
+		'toyama',
+		'wakayama',
+		'yamagata',
+		'yamaguchi',
+		'yamanashi'
+	]
+};
+
+function buildSuffixSet(suffixes: Record<string, readonly string[]>): Set<string> {
+	const set = new Set<string>();
+
+	for (const [tld, parts] of Object.entries(suffixes)) {
+		for (const part of parts) {
+			set.add(`${part}.${tld}`);
+		}
+	}
+
+	return set;
+}
+
+export const TWO_PART_PUBLIC_SUFFIXES = buildSuffixSet(ccTLD_PREFIXES);
+export const WILDCARD_PUBLIC_SUFFIXES = buildSuffixSet(WILDCARD_BASES);
diff --git a/tools/server/webui/src/lib/constants/viewport.ts b/tools/ui/src/lib/constants/viewport.ts
similarity index 100%
rename from tools/server/webui/src/lib/constants/viewport.ts
rename to tools/ui/src/lib/constants/viewport.ts
diff --git a/tools/server/webui/src/lib/contexts/chat-actions.context.ts b/tools/ui/src/lib/contexts/chat-actions.context.ts
similarity index 100%
rename from tools/server/webui/src/lib/contexts/chat-actions.context.ts
rename to tools/ui/src/lib/contexts/chat-actions.context.ts
diff --git a/tools/ui/src/lib/contexts/chat-settings-config.context.ts b/tools/ui/src/lib/contexts/chat-settings-config.context.ts
new file mode 100644
index 00000000000..35941e09bd5
--- /dev/null
+++ b/tools/ui/src/lib/contexts/chat-settings-config.context.ts
@@ -0,0 +1,20 @@
+import { getContext, setContext } from 'svelte';
+import { CONTEXT_KEY_CHAT_SETTINGS_CONFIG } from '$lib/constants';
+
+export interface ChatSettingsConfigContext {
+	readonly localConfig: SettingsConfigType;
+	handleConfigChange: (key: string, value: string | boolean) => void;
+	handleThemeChange: (theme: string) => void;
+}
+
+const CHAT_SETTINGS_CONFIG_KEY = Symbol.for(CONTEXT_KEY_CHAT_SETTINGS_CONFIG);
+
+export function setChatSettingsConfigContext(
+	ctx: ChatSettingsConfigContext
+): ChatSettingsConfigContext {
+	return setContext(CHAT_SETTINGS_CONFIG_KEY, ctx);
+}
+
+export function getChatSettingsConfigContext(): ChatSettingsConfigContext {
+	return getContext(CHAT_SETTINGS_CONFIG_KEY);
+}
diff --git a/tools/server/webui/src/lib/contexts/index.ts b/tools/ui/src/lib/contexts/index.ts
similarity index 52%
rename from tools/server/webui/src/lib/contexts/index.ts
rename to tools/ui/src/lib/contexts/index.ts
index 6c69070d819..01cd1d4b70a 100644
--- a/tools/server/webui/src/lib/contexts/index.ts
+++ b/tools/ui/src/lib/contexts/index.ts
@@ -13,7 +13,13 @@ export {
 } from './chat-actions.context';
 
 export {
-	getChatSettingsDialogContext,
-	setChatSettingsDialogContext,
-	type ChatSettingsDialogContext
-} from './chat-settings-dialog.context';
+	getChatSettingsConfigContext,
+	setChatSettingsConfigContext,
+	type ChatSettingsConfigContext
+} from './chat-settings-config.context';
+
+export {
+	getProcessingInfoContext,
+	setProcessingInfoContext,
+	type ProcessingInfoContext
+} from './processing-info.context';
diff --git a/tools/server/webui/src/lib/contexts/message-edit.context.ts b/tools/ui/src/lib/contexts/message-edit.context.ts
similarity index 74%
rename from tools/server/webui/src/lib/contexts/message-edit.context.ts
rename to tools/ui/src/lib/contexts/message-edit.context.ts
index 2d344cca50f..b6231f940e9 100644
--- a/tools/server/webui/src/lib/contexts/message-edit.context.ts
+++ b/tools/ui/src/lib/contexts/message-edit.context.ts
@@ -1,5 +1,6 @@
 import { getContext, setContext } from 'svelte';
 import { CONTEXT_KEY_MESSAGE_EDIT } from '$lib/constants';
+import { MessageRole } from '$lib/enums';
 
 export interface MessageEditState {
 	readonly isEditing: boolean;
@@ -9,6 +10,10 @@ export interface MessageEditState {
 	readonly originalContent: string;
 	readonly originalExtras: DatabaseMessageExtra[];
 	readonly showSaveOnlyOption: boolean;
+	readonly showBranchAfterEditOption: boolean;
+	readonly shouldBranchAfterEdit: boolean;
+	readonly messageRole: MessageRole;
+	readonly rawEditContent?: string;
 }
 
 export interface MessageEditActions {
@@ -21,7 +26,13 @@ export interface MessageEditActions {
 	startEdit: () => void;
 }
 
-export type MessageEditContext = MessageEditState & MessageEditActions;
+export interface AssistantEditActions {
+	setShouldBranchAfterEdit: (value: boolean) => void;
+}
+
+export type MessageEditContext = MessageEditState &
+	MessageEditActions &
+	Partial<AssistantEditActions>;
 
 const MESSAGE_EDIT_KEY = Symbol.for(CONTEXT_KEY_MESSAGE_EDIT);
 
diff --git a/tools/ui/src/lib/contexts/processing-info.context.ts b/tools/ui/src/lib/contexts/processing-info.context.ts
new file mode 100644
index 00000000000..0cf43336f1d
--- /dev/null
+++ b/tools/ui/src/lib/contexts/processing-info.context.ts
@@ -0,0 +1,16 @@
+import { getContext, setContext } from 'svelte';
+import { CONTEXT_KEY_PROCESSING_INFO } from '$lib/constants';
+
+export interface ProcessingInfoContext {
+	readonly showProcessingInfo: boolean;
+}
+
+const PROCESSING_INFO_KEY = Symbol.for(CONTEXT_KEY_PROCESSING_INFO);
+
+export function setProcessingInfoContext(ctx: ProcessingInfoContext): ProcessingInfoContext {
+	return setContext(PROCESSING_INFO_KEY, ctx);
+}
+
+export function getProcessingInfoContext(): ProcessingInfoContext {
+	return getContext(PROCESSING_INFO_KEY);
+}
diff --git a/tools/server/webui/src/lib/enums/agentic.ts b/tools/ui/src/lib/enums/agentic.ts
similarity index 100%
rename from tools/server/webui/src/lib/enums/agentic.ts
rename to tools/ui/src/lib/enums/agentic.ts
diff --git a/tools/ui/src/lib/enums/attachment.ts b/tools/ui/src/lib/enums/attachment.ts
new file mode 100644
index 00000000000..1c3258cb19d
--- /dev/null
+++ b/tools/ui/src/lib/enums/attachment.ts
@@ -0,0 +1,56 @@
+/**
+ * Attachment type enum for database message extras
+ */
+export enum AttachmentType {
+	AUDIO = 'AUDIO',
+	IMAGE = 'IMAGE',
+	VIDEO = 'VIDEO',
+	MCP_PROMPT = 'MCP_PROMPT',
+	MCP_RESOURCE = 'MCP_RESOURCE',
+	PDF = 'PDF',
+	TEXT = 'TEXT',
+	LEGACY_CONTEXT = 'context' // Legacy attachment type for backward compatibility
+}
+
+/**
+ * Unique identifiers for attachment menu items in the chat form action dropdowns.
+ * Used to select which file upload or attachment action is triggered.
+ */
+export enum AttachmentMenuItemId {
+	IMAGES = 'images',
+	AUDIO = 'audio',
+	VIDEO = 'video',
+	TEXT = 'text',
+	PDF = 'pdf',
+	SYSTEM_MESSAGE = 'system-message',
+	MCP_PROMPT = 'mcp-prompt',
+	MCP_RESOURCES = 'mcp-resources'
+}
+
+/**
+ * Defines when an attachment menu item should be enabled.
+ */
+export enum AttachmentItemEnabledWhen {
+	ALWAYS = 'always',
+	HAS_VISION_MODALITY = 'hasVisionModality',
+	HAS_AUDIO_MODALITY = 'hasAudioModality',
+	HAS_VIDEO_MODALITY = 'hasVideoModality'
+}
+
+/**
+ * Defines the callback action triggered when an attachment menu item is clicked.
+ */
+export enum AttachmentAction {
+	FILE_UPLOAD = 'onFileUpload',
+	SYSTEM_PROMPT_CLICK = 'onSystemPromptClick',
+	MCP_PROMPT_CLICK = 'onMcpPromptClick',
+	MCP_RESOURCES_CLICK = 'onMcpResourcesClick'
+}
+
+/**
+ * Visibility conditions for attachment menu items.
+ */
+export enum AttachmentItemVisibleWhen {
+	HAS_MCP_PROMPTS_SUPPORT = 'hasMcpPromptsSupport',
+	HAS_MCP_RESOURCES_SUPPORT = 'hasMcpResourcesSupport'
+}
diff --git a/tools/server/webui/src/lib/enums/chat.ts b/tools/ui/src/lib/enums/chat.ts
similarity index 75%
rename from tools/server/webui/src/lib/enums/chat.ts
rename to tools/ui/src/lib/enums/chat.ts
index 0b6f357d9a1..798fb1bf78e 100644
--- a/tools/server/webui/src/lib/enums/chat.ts
+++ b/tools/ui/src/lib/enums/chat.ts
@@ -39,7 +39,8 @@ export enum MessageType {
 export enum ContentPartType {
 	TEXT = 'text',
 	IMAGE_URL = 'image_url',
-	INPUT_AUDIO = 'input_audio'
+	INPUT_AUDIO = 'input_audio',
+	INPUT_VIDEO = 'input_video'
 }
 
 /**
@@ -49,3 +50,16 @@ export enum ErrorDialogType {
 	TIMEOUT = 'timeout',
 	SERVER = 'server'
 }
+
+export enum ConversationSelectionMode {
+	EXPORT = 'export',
+	IMPORT = 'import'
+}
+
+/**
+ * PDF view mode options for previewing PDF attachments.
+ */
+export enum PdfViewMode {
+	TEXT = 'text',
+	PAGES = 'pages'
+}
diff --git a/tools/server/webui/src/lib/enums/files.ts b/tools/ui/src/lib/enums/files.ts
similarity index 91%
rename from tools/server/webui/src/lib/enums/files.ts
rename to tools/ui/src/lib/enums/files.ts
index 7efe0c706b4..5aef3955ebe 100644
--- a/tools/server/webui/src/lib/enums/files.ts
+++ b/tools/ui/src/lib/enums/files.ts
@@ -1,5 +1,5 @@
 /**
- * Comprehensive dictionary of all supported file types in webui
+ * Comprehensive dictionary of all supported file types in llama-ui
  * Organized by category with TypeScript enums for better type safety
  */
 
@@ -7,6 +7,7 @@
 export enum FileTypeCategory {
 	IMAGE = 'image',
 	AUDIO = 'audio',
+	VIDEO = 'video',
 	PDF = 'pdf',
 	TEXT = 'text'
 }
@@ -33,6 +34,11 @@ export enum FileTypeAudio {
 	WEBM = 'webm'
 }
 
+export enum FileTypeVideo {
+	MP4 = 'mp4',
+	OGG = 'ogg'
+}
+
 export enum FileTypePdf {
 	PDF = 'pdf'
 }
@@ -92,6 +98,11 @@ export enum FileExtensionAudio {
 	WAV = '.wav'
 }
 
+export enum FileExtensionVideo {
+	MP4 = '.mp4',
+	OGG = '.ogg'
+}
+
 export enum FileExtensionPdf {
 	PDF = '.pdf'
 }
@@ -176,13 +187,20 @@ export enum MimeTypeAudio {
 	WEBM_OPUS = 'audio/webm;codecs=opus'
 }
 
+export enum MimeTypeVideo {
+	MP4 = 'video/mp4',
+	OGG = 'video/ogg'
+}
+
 export enum MimeTypeImage {
 	JPEG = 'image/jpeg',
 	JPG = 'image/jpg',
 	PNG = 'image/png',
 	GIF = 'image/gif',
 	WEBP = 'image/webp',
-	SVG = 'image/svg+xml'
+	SVG = 'image/svg+xml',
+	ICO = 'image/x-icon',
+	ICO_MICROSOFT = 'image/vnd.microsoft.icon'
 }
 
 export enum MimeTypeText {
diff --git a/tools/server/webui/src/lib/enums/index.ts b/tools/ui/src/lib/enums/index.ts
similarity index 70%
rename from tools/server/webui/src/lib/enums/index.ts
rename to tools/ui/src/lib/enums/index.ts
index 1711dd76349..3cf81286bc1 100644
--- a/tools/server/webui/src/lib/enums/index.ts
+++ b/tools/ui/src/lib/enums/index.ts
@@ -1,13 +1,21 @@
-export { AttachmentType } from './attachment';
+export {
+	AttachmentType,
+	AttachmentMenuItemId,
+	AttachmentItemEnabledWhen,
+	AttachmentAction,
+	AttachmentItemVisibleWhen
+} from './attachment';
 
 export { AgenticSectionType, ToolCallType } from './agentic';
 
 export {
 	ChatMessageStatsView,
 	ContentPartType,
+	ConversationSelectionMode,
 	ErrorDialogType,
 	MessageRole,
 	MessageType,
+	PdfViewMode,
 	ReasoningFormat
 } from './chat';
 
@@ -26,6 +34,7 @@ export {
 	UriPattern,
 	MimeTypeApplication,
 	MimeTypeAudio,
+	MimeTypeVideo,
 	MimeTypeImage,
 	MimeTypeText,
 	SpecialFileType
@@ -47,6 +56,8 @@ export { ServerRole, ServerModelStatus } from './server';
 
 export { ParameterSource, SyncableParameterType, SettingsFieldType } from './settings';
 
-export { ColorMode, McpPromptVariant, UrlProtocol } from './ui';
+export { ColorMode, HtmlInputType, McpPromptVariant, TooltipSide, UrlProtocol } from './ui';
 
 export { KeyboardKey } from './keyboard';
+
+export { ToolSource, ToolPermissionDecision, ToolResponseField } from './tools';
diff --git a/tools/server/webui/src/lib/enums/keyboard.ts b/tools/ui/src/lib/enums/keyboard.ts
similarity index 79%
rename from tools/server/webui/src/lib/enums/keyboard.ts
rename to tools/ui/src/lib/enums/keyboard.ts
index bea68d790b5..46cd4a77614 100644
--- a/tools/server/webui/src/lib/enums/keyboard.ts
+++ b/tools/ui/src/lib/enums/keyboard.ts
@@ -6,11 +6,14 @@ export enum KeyboardKey {
 	ESCAPE = 'Escape',
 	ARROW_UP = 'ArrowUp',
 	ARROW_DOWN = 'ArrowDown',
+	ARROW_LEFT = 'ArrowLeft',
+	ARROW_RIGHT = 'ArrowRight',
 	TAB = 'Tab',
 	D_LOWER = 'd',
 	D_UPPER = 'D',
 	E_UPPER = 'E',
 	K_LOWER = 'k',
+	O_LOWER = 'o',
 	O_UPPER = 'O',
 	SPACE = ' '
 }
diff --git a/tools/server/webui/src/lib/enums/mcp.ts b/tools/ui/src/lib/enums/mcp.ts
similarity index 100%
rename from tools/server/webui/src/lib/enums/mcp.ts
rename to tools/ui/src/lib/enums/mcp.ts
diff --git a/tools/server/webui/src/lib/enums/model.ts b/tools/ui/src/lib/enums/model.ts
similarity index 63%
rename from tools/server/webui/src/lib/enums/model.ts
rename to tools/ui/src/lib/enums/model.ts
index 7729ecfeabb..7aa469947e2 100644
--- a/tools/server/webui/src/lib/enums/model.ts
+++ b/tools/ui/src/lib/enums/model.ts
@@ -1,5 +1,6 @@
 export enum ModelModality {
 	TEXT = 'TEXT',
 	AUDIO = 'AUDIO',
-	VISION = 'VISION'
+	VISION = 'VISION',
+	VIDEO = 'VIDEO'
 }
diff --git a/tools/server/webui/src/lib/enums/server.ts b/tools/ui/src/lib/enums/server.ts
similarity index 100%
rename from tools/server/webui/src/lib/enums/server.ts
rename to tools/ui/src/lib/enums/server.ts
diff --git a/tools/server/webui/src/lib/enums/settings.ts b/tools/ui/src/lib/enums/settings.ts
similarity index 100%
rename from tools/server/webui/src/lib/enums/settings.ts
rename to tools/ui/src/lib/enums/settings.ts
diff --git a/tools/ui/src/lib/enums/tools.ts b/tools/ui/src/lib/enums/tools.ts
new file mode 100644
index 00000000000..4b2cdab3200
--- /dev/null
+++ b/tools/ui/src/lib/enums/tools.ts
@@ -0,0 +1,17 @@
+export enum ToolSource {
+	BUILTIN = 'builtin',
+	MCP = 'mcp',
+	CUSTOM = 'custom'
+}
+
+export enum ToolPermissionDecision {
+	ALWAYS = 'always',
+	ALWAYS_SERVER = 'always_server',
+	ONCE = 'once',
+	DENY = 'deny'
+}
+
+export enum ToolResponseField {
+	PLAIN_TEXT = 'plain_text_response',
+	ERROR = 'error'
+}
diff --git a/tools/server/webui/src/lib/enums/ui.ts b/tools/ui/src/lib/enums/ui.ts
similarity index 56%
rename from tools/server/webui/src/lib/enums/ui.ts
rename to tools/ui/src/lib/enums/ui.ts
index 37d45896279..82996379428 100644
--- a/tools/server/webui/src/lib/enums/ui.ts
+++ b/tools/ui/src/lib/enums/ui.ts
@@ -4,6 +4,13 @@ export enum ColorMode {
 	SYSTEM = 'system'
 }
 
+export enum TooltipSide {
+	TOP = 'top',
+	RIGHT = 'right',
+	BOTTOM = 'bottom',
+	LEFT = 'left'
+}
+
 /**
  * MCP prompt display variant
  */
@@ -17,8 +24,12 @@ export enum McpPromptVariant {
  */
 export enum UrlProtocol {
 	DATA = 'data:',
-	HTTP = 'http://',
-	HTTPS = 'https://',
-	WEBSOCKET = 'ws://',
-	WEBSOCKET_SECURE = 'wss://'
+	HTTP = 'http:',
+	HTTPS = 'https:',
+	WEBSOCKET = 'ws:',
+	WEBSOCKET_SECURE = 'wss:'
+}
+
+export enum HtmlInputType {
+	FILE = 'file'
 }
diff --git a/tools/server/webui/src/lib/hooks/is-mobile.svelte.ts b/tools/ui/src/lib/hooks/is-mobile.svelte.ts
similarity index 100%
rename from tools/server/webui/src/lib/hooks/is-mobile.svelte.ts
rename to tools/ui/src/lib/hooks/is-mobile.svelte.ts
diff --git a/tools/ui/src/lib/hooks/use-attachment-menu.svelte.ts b/tools/ui/src/lib/hooks/use-attachment-menu.svelte.ts
new file mode 100644
index 00000000000..0940ce7b6b1
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-attachment-menu.svelte.ts
@@ -0,0 +1,82 @@
+import { page } from '$app/state';
+import { AttachmentAction } from '$lib/enums';
+
+export interface AttachmentModalityFlags {
+	hasVisionModality: boolean;
+	hasAudioModality: boolean;
+	hasVideoModality: boolean;
+	hasMcpPromptsSupport: boolean;
+	hasMcpResourcesSupport: boolean;
+}
+
+export interface AttachmentActionCallbacks {
+	onFileUpload?: () => void;
+	onSystemPromptClick?: () => void;
+	onMcpPromptClick?: () => void;
+	onMcpResourcesClick?: () => void;
+}
+
+export interface UseAttachmentMenuReturn {
+	readonly callbacks: Record<string, () => void>;
+	isItemEnabled(enabledWhen: string | undefined): boolean;
+	isItemVisible(visibleWhen: string | undefined): boolean;
+	getSystemMessageTooltip(): string;
+}
+
+/**
+ * useAttachmentMenu - Shared logic for attachment menu components.
+ *
+ * Encapsulates the modality-flag checks and callback wrapping that is
+ * identical across the desktop dropdown (`ChatFormActionAddDropdown`)
+ * and the mobile sheet (`ChatFormActionAddSheet`).
+ *
+ * @param getFlags   - Getter returning the current modality capability flags.
+ * @param getCallbacks - Getter returning the raw action callbacks from props.
+ * @param close      - Function that dismisses the hosting UI element (dropdown / sheet).
+ */
+export function useAttachmentMenu(
+	getFlags: () => AttachmentModalityFlags,
+	getCallbacks: () => AttachmentActionCallbacks,
+	close: () => void
+): UseAttachmentMenuReturn {
+	const modalityFlags = $derived(getFlags());
+
+	const callbacks = $derived.by(() => {
+		const cbs = getCallbacks();
+		const wrap = (fn?: () => void) => () => {
+			close();
+			fn?.();
+		};
+		return {
+			[AttachmentAction.FILE_UPLOAD]: wrap(cbs.onFileUpload),
+			[AttachmentAction.SYSTEM_PROMPT_CLICK]: wrap(cbs.onSystemPromptClick),
+			[AttachmentAction.MCP_PROMPT_CLICK]: wrap(cbs.onMcpPromptClick),
+			[AttachmentAction.MCP_RESOURCES_CLICK]: wrap(cbs.onMcpResourcesClick)
+		};
+	});
+
+	function isItemEnabled(enabledWhen: string | undefined): boolean {
+		if (!enabledWhen || enabledWhen === 'always') return true;
+		return !!modalityFlags[enabledWhen as keyof AttachmentModalityFlags];
+	}
+
+	function isItemVisible(visibleWhen: string | undefined): boolean {
+		if (!visibleWhen) return true;
+		return !!modalityFlags[visibleWhen as keyof AttachmentModalityFlags];
+	}
+
+	function getSystemMessageTooltip(): string {
+		return !page.params.id
+			? 'Add custom system message for a new conversation'
+			: 'Inject custom system message at the beginning of the conversation';
+	}
+
+	return {
+		get callbacks() {
+			return callbacks;
+		},
+		isItemEnabled,
+		isItemVisible,
+		getSystemMessageTooltip
+	};
+}
diff --git a/tools/server/webui/src/lib/hooks/use-auto-scroll.svelte.ts b/tools/ui/src/lib/hooks/use-auto-scroll.svelte.ts
similarity index 76%
rename from tools/server/webui/src/lib/hooks/use-auto-scroll.svelte.ts
rename to tools/ui/src/lib/hooks/use-auto-scroll.svelte.ts
index 204d71a90a0..f59e3ed4b72 100644
--- a/tools/server/webui/src/lib/hooks/use-auto-scroll.svelte.ts
+++ b/tools/ui/src/lib/hooks/use-auto-scroll.svelte.ts
@@ -2,7 +2,6 @@ import { AUTO_SCROLL_AT_BOTTOM_THRESHOLD, AUTO_SCROLL_INTERVAL } from '$lib/cons
 
 export interface AutoScrollOptions {
 	disabled?: boolean;
-	isColumnReverse?: boolean;
 }
 
 /**
@@ -12,24 +11,19 @@ export interface AutoScrollOptions {
  * - Auto-scrolls to bottom during streaming/loading
  * - Stops auto-scroll when user manually scrolls up
  * - Resumes auto-scroll when user scrolls back to bottom
- * - Supports both normal and column-reverse scroll containers
  */
 export class AutoScrollController {
 	private _autoScrollEnabled = $state(true);
 	private _userScrolledUp = $state(false);
 	private _lastScrollTop = $state(0);
 	private _scrollInterval: ReturnType<typeof setInterval> | undefined;
-	private _scrollTimeout: ReturnType<typeof setTimeout> | undefined;
 	private _container: HTMLElement | undefined;
 	private _disabled: boolean;
-	private _isColumnReverse: boolean;
 	private _mutationObserver: MutationObserver | null = null;
 	private _rafPending = false;
 	private _observerEnabled = false;
-
 	constructor(options: AutoScrollOptions = {}) {
 		this._disabled = options.disabled ?? false;
-		this._isColumnReverse = options.isColumnReverse ?? false;
 	}
 
 	get autoScrollEnabled(): boolean {
@@ -56,6 +50,7 @@ export class AutoScrollController {
 	 * Updates the disabled state.
 	 */
 	setDisabled(disabled: boolean): void {
+		if (this._disabled === disabled) return;
 		this._disabled = disabled;
 		if (disabled) {
 			this._autoScrollEnabled = false;
@@ -73,20 +68,8 @@ export class AutoScrollController {
 		if (this._disabled || !this._container) return;
 
 		const { scrollTop, scrollHeight, clientHeight } = this._container;
-
-		let distanceFromBottom: number;
-		let isScrollingUp: boolean;
-
-		if (this._isColumnReverse) {
-			// column-reverse: scrollTop=0 at bottom, negative when scrolled up
-			distanceFromBottom = Math.abs(scrollTop);
-			isScrollingUp = scrollTop < this._lastScrollTop;
-		} else {
-			// normal: scrollTop=0 at top, increases when scrolled down
-			distanceFromBottom = scrollHeight - clientHeight - scrollTop;
-			isScrollingUp = scrollTop < this._lastScrollTop;
-		}
-
+		const distanceFromBottom = scrollHeight - clientHeight - scrollTop;
+		const isScrollingUp = scrollTop < this._lastScrollTop;
 		const isAtBottom = distanceFromBottom < AUTO_SCROLL_AT_BOTTOM_THRESHOLD;
 
 		if (isScrollingUp && !isAtBottom) {
@@ -97,17 +80,6 @@ export class AutoScrollController {
 			this._autoScrollEnabled = true;
 		}
 
-		if (this._scrollTimeout) {
-			clearTimeout(this._scrollTimeout);
-		}
-
-		this._scrollTimeout = setTimeout(() => {
-			if (isAtBottom) {
-				this._userScrolledUp = false;
-				this._autoScrollEnabled = true;
-			}
-		}, AUTO_SCROLL_INTERVAL);
-
 		this._lastScrollTop = scrollTop;
 	}
 
@@ -116,13 +88,7 @@ export class AutoScrollController {
 	 */
 	scrollToBottom(behavior: ScrollBehavior = 'smooth'): void {
 		if (this._disabled || !this._container) return;
-
-		if (this._isColumnReverse) {
-			// column-reverse: scrollTop=0 is the bottom
-			this._container.scrollTo({ top: 0, behavior });
-		} else {
-			this._container.scrollTo({ top: this._container.scrollHeight, behavior });
-		}
+		this._container.scrollTo({ top: this._container.scrollHeight, behavior });
 	}
 
 	/**
@@ -180,11 +146,6 @@ export class AutoScrollController {
 	destroy(): void {
 		this.stopInterval();
 		this._doStopObserving();
-
-		if (this._scrollTimeout) {
-			clearTimeout(this._scrollTimeout);
-			this._scrollTimeout = undefined;
-		}
 	}
 
 	/**
@@ -210,20 +171,13 @@ export class AutoScrollController {
 	private _doStartObserving(): void {
 		if (!this._container || this._mutationObserver) return;
 
-		const isReverse = this._isColumnReverse;
-
 		this._mutationObserver = new MutationObserver(() => {
 			if (!this._autoScrollEnabled || this._rafPending) return;
 			this._rafPending = true;
 			requestAnimationFrame(() => {
 				this._rafPending = false;
 				if (this._autoScrollEnabled && this._container) {
-					if (isReverse) {
-						// column-reverse: scrollTop=0 is the bottom
-						this._container.scrollTop = 0;
-					} else {
-						this._container.scrollTop = this._container.scrollHeight;
-					}
+					this._container.scrollTop = this._container.scrollHeight;
 				}
 			});
 		});
diff --git a/tools/ui/src/lib/hooks/use-draft-messages.svelte.ts b/tools/ui/src/lib/hooks/use-draft-messages.svelte.ts
new file mode 100644
index 00000000000..11305b20552
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-draft-messages.svelte.ts
@@ -0,0 +1,45 @@
+import { onMount } from 'svelte';
+import { afterNavigate, beforeNavigate } from '$app/navigation';
+import { draftMessagesStore } from '$lib/stores/draft-messages.svelte';
+
+interface UseDraftMessagesOptions {
+	getChatId: () => string | undefined;
+	getMessage: () => string;
+	getFiles: () => ChatUploadedFile[];
+	setMessage: (message: string) => void;
+	setFiles: (files: ChatUploadedFile[]) => void;
+	getInitialMessage: () => string;
+}
+
+export function useDraftMessages(options: UseDraftMessagesOptions) {
+	onMount(() => {
+		const chatId = options.getChatId();
+		const draft = draftMessagesStore.getDraftMessage(chatId);
+
+		if ((draft.message || draft.files.length > 0) && !options.getInitialMessage()) {
+			options.setMessage(draft.message);
+			options.setFiles(draft.files);
+		}
+	});
+
+	beforeNavigate(() => {
+		const chatId = options.getChatId();
+		draftMessagesStore.saveDraftMessage(chatId, options.getMessage(), options.getFiles());
+	});
+
+	afterNavigate((navigation) => {
+		if (navigation?.from != null) {
+			const chatId = options.getChatId();
+			const draft = draftMessagesStore.getDraftMessage(chatId);
+			options.setMessage(draft.message);
+			options.setFiles(draft.files);
+		}
+	});
+
+	function clearDraft() {
+		const chatId = options.getChatId();
+		draftMessagesStore.clearDraftMessage(chatId);
+	}
+
+	return { clearDraft };
+}
diff --git a/tools/ui/src/lib/hooks/use-keyboard-shortcuts.svelte.ts b/tools/ui/src/lib/hooks/use-keyboard-shortcuts.svelte.ts
new file mode 100644
index 00000000000..05966a1a1d1
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-keyboard-shortcuts.svelte.ts
@@ -0,0 +1,60 @@
+import { goto } from '$app/navigation';
+import { KeyboardKey } from '$lib/enums';
+import { ROUTES } from '$lib/constants/routes';
+
+interface KeyboardShortcutsCallbacks {
+	activateSearchMode?: () => void;
+	editActiveConversation?: () => void;
+	onSearchActivated?: () => void;
+	deleteActiveConversation?: () => void;
+	navigateToPrevConversation?: () => void;
+	navigateToNextConversation?: () => void;
+}
+
+export function useKeyboardShortcuts(callbacks: KeyboardShortcutsCallbacks) {
+	function handleKeydown(event: KeyboardEvent) {
+		const isCmdOrCtrl = event.metaKey || event.ctrlKey;
+
+		if (isCmdOrCtrl && event.key === KeyboardKey.K_LOWER) {
+			event.preventDefault();
+			callbacks.activateSearchMode?.();
+			callbacks.onSearchActivated?.();
+		}
+
+		if (
+			isCmdOrCtrl &&
+			event.shiftKey &&
+			(event.key === KeyboardKey.O_LOWER || event.key === KeyboardKey.O_UPPER)
+		) {
+			event.preventDefault();
+
+			goto(ROUTES.NEW_CHAT);
+		}
+
+		if (event.shiftKey && isCmdOrCtrl && event.key === KeyboardKey.E_UPPER) {
+			event.preventDefault();
+			callbacks.editActiveConversation?.();
+		}
+
+		if (
+			isCmdOrCtrl &&
+			event.shiftKey &&
+			(event.key === KeyboardKey.D_LOWER || event.key === KeyboardKey.D_UPPER)
+		) {
+			event.preventDefault();
+			callbacks.deleteActiveConversation?.();
+		}
+
+		if (isCmdOrCtrl && event.shiftKey && event.key === KeyboardKey.ARROW_UP) {
+			event.preventDefault();
+			callbacks.navigateToPrevConversation?.();
+		}
+
+		if (isCmdOrCtrl && event.shiftKey && event.key === KeyboardKey.ARROW_DOWN) {
+			event.preventDefault();
+			callbacks.navigateToNextConversation?.();
+		}
+	}
+
+	return { handleKeydown };
+}
diff --git a/tools/ui/src/lib/hooks/use-message-edit-context.svelte.ts b/tools/ui/src/lib/hooks/use-message-edit-context.svelte.ts
new file mode 100644
index 00000000000..71d1b66f8d3
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-message-edit-context.svelte.ts
@@ -0,0 +1,99 @@
+import { setMessageEditContext } from '$lib/contexts';
+import { MessageRole } from '$lib/enums';
+import { parseFilesToMessageExtras } from '$lib/utils/convert-files-to-extra';
+
+interface UseMessageEditContextOptions {
+	getContent: () => string;
+	getExtras: () => DatabaseMessageExtra[];
+	showSaveOnlyOption?: boolean;
+	onSave: (content: string, extras?: DatabaseMessageExtra[]) => void;
+}
+
+export function useMessageEditContext(options: UseMessageEditContextOptions) {
+	let isEditing = $state(false);
+	let editedContent = $state('');
+	let editedExtras = $state<DatabaseMessageExtra[]>([]);
+	let editedUploadedFiles = $state<ChatUploadedFile[]>([]);
+
+	function handleEdit() {
+		editedContent = options.getContent();
+		editedExtras = [...options.getExtras()];
+		editedUploadedFiles = [];
+		isEditing = true;
+	}
+
+	async function handleSaveEdit() {
+		const trimmed = editedContent.trim();
+		if (!trimmed && editedExtras.length === 0 && editedUploadedFiles.length === 0) return;
+
+		let finalExtras: DatabaseMessageExtra[] = $state.snapshot(editedExtras);
+		if (editedUploadedFiles.length > 0) {
+			const plainFiles = $state.snapshot(editedUploadedFiles);
+			const result = await parseFilesToMessageExtras(plainFiles);
+			const newExtras = result?.extras || [];
+			finalExtras = [...finalExtras, ...newExtras];
+		}
+
+		options.onSave(trimmed, finalExtras.length > 0 ? finalExtras : undefined);
+		isEditing = false;
+	}
+
+	function handleCancelEdit() {
+		isEditing = false;
+	}
+
+	setMessageEditContext({
+		get isEditing() {
+			return isEditing;
+		},
+		get editedContent() {
+			return editedContent;
+		},
+		get editedExtras() {
+			return editedExtras;
+		},
+		get editedUploadedFiles() {
+			return editedUploadedFiles;
+		},
+		get originalContent() {
+			return options.getContent();
+		},
+		get originalExtras() {
+			return options.getExtras();
+		},
+		get showSaveOnlyOption() {
+			return options.showSaveOnlyOption ?? false;
+		},
+		get showBranchAfterEditOption() {
+			return false;
+		},
+		get shouldBranchAfterEdit() {
+			return false;
+		},
+		get messageRole() {
+			return MessageRole.USER;
+		},
+		setContent: (c: string) => {
+			editedContent = c;
+		},
+		setExtras: (e: DatabaseMessageExtra[]) => {
+			editedExtras = e;
+		},
+		setUploadedFiles: (f: ChatUploadedFile[]) => {
+			editedUploadedFiles = f;
+		},
+		save: handleSaveEdit,
+		saveOnly: handleSaveEdit,
+		cancel: handleCancelEdit,
+		startEdit: handleEdit
+	});
+
+	return {
+		get isEditing() {
+			return isEditing;
+		},
+		handleEdit,
+		handleSaveEdit,
+		handleCancelEdit
+	};
+}
diff --git a/tools/ui/src/lib/hooks/use-models-selector.svelte.ts b/tools/ui/src/lib/hooks/use-models-selector.svelte.ts
new file mode 100644
index 00000000000..537a2af1841
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-models-selector.svelte.ts
@@ -0,0 +1,253 @@
+import { onMount } from 'svelte';
+import {
+	modelsStore,
+	modelOptions,
+	modelsLoading,
+	modelsUpdating,
+	selectedModelId,
+	singleModelName
+} from '$lib/stores/models.svelte';
+import { isRouterMode } from '$lib/stores/server.svelte';
+import { filterModelOptions, groupModelOptions } from '$lib/components/app/models/utils';
+import type { ModelOption } from '$lib/types/models';
+
+export interface UseModelsSelectorOptions {
+	currentModel: () => string | null;
+	useGlobalSelection?: () => boolean;
+	onModelChange?: () =>
+		| ((modelId: string, modelName: string) => Promise<boolean> | boolean | void)
+		| undefined;
+	onOpenChange?: (open: boolean) => void;
+}
+
+export interface UseModelsSelectorReturn {
+	readonly options: ModelOption[];
+	readonly loading: boolean;
+	readonly updating: boolean;
+	readonly activeId: string | null;
+	readonly isRouter: boolean;
+	readonly serverModel: string | null;
+	readonly isHighlightedCurrentModelActive: boolean;
+	readonly isCurrentModelInCache: boolean;
+	readonly filteredOptions: ModelOption[];
+	readonly groupedFilteredOptions: ReturnType<typeof groupModelOptions>;
+	readonly isLoadingModel: boolean;
+	readonly searchTerm: string;
+	readonly showModelDialog: boolean;
+	readonly infoModelId: string | null;
+	setSearchTerm(value: string): void;
+	setShowModelDialog(value: boolean): void;
+	handleInfoClick(modelName: string): void;
+	handleSelect(modelId: string): Promise<void>;
+	handleOpenChange(open: boolean): void;
+	isFavorite(model: string): boolean;
+	getDisplayOption(): ModelOption | undefined;
+}
+
+/**
+ * Shared reactive state and logic for model selection.
+ *
+ * Used by both the desktop dropdown (`ModelsSelectorDropdown`)
+ * and the mobile sheet (`ModelsSelectorSheet`) to avoid
+ * duplicating store derivations, selection handling, and model loading.
+ */
+export function useModelsSelector(opts: UseModelsSelectorOptions): UseModelsSelectorReturn {
+	const options = $derived(
+		modelOptions().filter((option) => {
+			const modelProps = modelsStore.getModelProps(option.model);
+
+			return modelProps?.ui !== false;
+		})
+	);
+	const loading = $derived(modelsLoading());
+	const updating = $derived(modelsUpdating());
+	const activeId = $derived(selectedModelId());
+	const isRouter = $derived(isRouterMode());
+	const serverModel = $derived(singleModelName());
+
+	const currentModel = $derived(opts.currentModel());
+	const useGlobalSelection = $derived(opts.useGlobalSelection?.() ?? false);
+	const onModelChange = $derived(opts.onModelChange?.());
+
+	const isHighlightedCurrentModelActive = $derived.by(() => {
+		if (!isRouter || !currentModel) return false;
+		const currentOption = options.find((option) => option.model === currentModel);
+		return currentOption ? currentOption.id === activeId : false;
+	});
+
+	const isCurrentModelInCache = $derived.by(() => {
+		if (!isRouter || !currentModel) return true;
+		return options.some((option) => option.model === currentModel);
+	});
+
+	let isLoadingModel = $state(false);
+	let searchTerm = $state('');
+	let showModelDialog = $state(false);
+	let infoModelId = $state<string | null>(null);
+	const filteredOptions = $derived(filterModelOptions(options, searchTerm));
+	const groupedFilteredOptions = $derived(
+		groupModelOptions(filteredOptions, modelsStore.favoriteModelIds, (m) =>
+			modelsStore.isModelLoaded(m)
+		)
+	);
+
+	function handleInfoClick(modelName: string) {
+		infoModelId = modelName;
+		showModelDialog = true;
+	}
+
+	onMount(() => {
+		modelsStore.fetch().catch((error) => {
+			console.error('Unable to load models:', error);
+		});
+	});
+
+	function handleOpenChange(open: boolean) {
+		if (loading || updating) return;
+
+		if (isRouter) {
+			searchTerm = '';
+
+			if (open) {
+				modelsStore.fetchRouterModels().then(() => {
+					modelsStore.fetchModalitiesForLoadedModels();
+				});
+			}
+
+			opts.onOpenChange?.(open);
+		} else {
+			showModelDialog = open;
+		}
+	}
+
+	async function handleSelect(modelId: string) {
+		const option = options.find((opt) => opt.id === modelId);
+		if (!option) return;
+
+		let shouldCloseMenu = true;
+
+		if (onModelChange) {
+			const result = await onModelChange(option.id, option.model);
+			if (result === false) {
+				shouldCloseMenu = false;
+			}
+		} else {
+			await modelsStore.selectModelById(option.id);
+		}
+
+		if (shouldCloseMenu) {
+			handleOpenChange(false);
+
+			requestAnimationFrame(() => {
+				const textarea = document.querySelector<HTMLTextAreaElement>(
+					'[data-slot="chat-form"] textarea'
+				);
+				textarea?.focus();
+			});
+		}
+
+		if (!onModelChange && isRouter && !modelsStore.isModelLoaded(option.model)) {
+			isLoadingModel = true;
+			modelsStore
+				.loadModel(option.model)
+				.catch((error) => console.error('Failed to load model:', error))
+				.finally(() => (isLoadingModel = false));
+		}
+	}
+
+	function getDisplayOption(): ModelOption | undefined {
+		if (!isRouter) {
+			const displayModel = serverModel || currentModel;
+			if (displayModel) {
+				return {
+					id: serverModel ? 'current' : 'offline-current',
+					model: displayModel,
+					name: displayModel.split('/').pop() || displayModel,
+					capabilities: []
+				};
+			}
+			return undefined;
+		}
+
+		if (useGlobalSelection && activeId) {
+			const selected = options.find((option) => option.id === activeId);
+			if (selected) return selected;
+		}
+
+		if (currentModel) {
+			if (!isCurrentModelInCache) {
+				return {
+					id: 'not-in-cache',
+					model: currentModel,
+					name: currentModel.split('/').pop() || currentModel,
+					capabilities: []
+				};
+			}
+			return options.find((option) => option.model === currentModel);
+		}
+
+		if (activeId) {
+			return options.find((option) => option.id === activeId);
+		}
+
+		return undefined;
+	}
+
+	return {
+		get options() {
+			return options;
+		},
+		get loading() {
+			return loading;
+		},
+		get updating() {
+			return updating;
+		},
+		get activeId() {
+			return activeId;
+		},
+		get isRouter() {
+			return isRouter;
+		},
+		get serverModel() {
+			return serverModel;
+		},
+		get isHighlightedCurrentModelActive() {
+			return isHighlightedCurrentModelActive;
+		},
+		get isCurrentModelInCache() {
+			return isCurrentModelInCache;
+		},
+		get filteredOptions() {
+			return filteredOptions;
+		},
+		get groupedFilteredOptions() {
+			return groupedFilteredOptions;
+		},
+		get isLoadingModel() {
+			return isLoadingModel;
+		},
+		get searchTerm() {
+			return searchTerm;
+		},
+		get showModelDialog() {
+			return showModelDialog;
+		},
+		get infoModelId() {
+			return infoModelId;
+		},
+		setSearchTerm(value: string) {
+			searchTerm = value;
+		},
+		setShowModelDialog(value: boolean) {
+			showModelDialog = value;
+		},
+		handleInfoClick,
+		handleSelect,
+		handleOpenChange,
+		isFavorite(model: string) {
+			return modelsStore.favoriteModelIds.has(model);
+		},
+		getDisplayOption
+	};
+}
diff --git a/tools/server/webui/src/lib/hooks/use-processing-state.svelte.ts b/tools/ui/src/lib/hooks/use-processing-state.svelte.ts
similarity index 100%
rename from tools/server/webui/src/lib/hooks/use-processing-state.svelte.ts
rename to tools/ui/src/lib/hooks/use-processing-state.svelte.ts
diff --git a/tools/ui/src/lib/hooks/use-scroll-carousel.svelte.ts b/tools/ui/src/lib/hooks/use-scroll-carousel.svelte.ts
new file mode 100644
index 00000000000..e4c75d23650
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-scroll-carousel.svelte.ts
@@ -0,0 +1,61 @@
+export function useScrollCarousel() {
+	let canScrollLeft = $state(false);
+	let canScrollRight = $state(false);
+	let scrollContainer = $state<HTMLDivElement | undefined>();
+
+	function scrollToCenter(element: HTMLElement) {
+		if (!scrollContainer) return;
+
+		const containerRect = scrollContainer.getBoundingClientRect();
+		const elementRect = element.getBoundingClientRect();
+
+		const elementCenter = elementRect.left + elementRect.width / 2;
+		const containerCenter = containerRect.left + containerRect.width / 2;
+		const scrollOffset = elementCenter - containerCenter;
+
+		scrollContainer.scrollBy({ left: scrollOffset, behavior: 'smooth' });
+	}
+
+	function scrollLeft() {
+		if (!scrollContainer) return;
+		scrollContainer.scrollBy({ left: -250, behavior: 'smooth' });
+	}
+
+	function scrollRight() {
+		if (!scrollContainer) return;
+		scrollContainer.scrollBy({ left: 250, behavior: 'smooth' });
+	}
+
+	function updateScrollButtons() {
+		if (!scrollContainer) return;
+
+		const { scrollLeft: sl, scrollWidth, clientWidth } = scrollContainer;
+		canScrollLeft = sl > 0;
+		canScrollRight = sl < scrollWidth - clientWidth - 1;
+	}
+
+	$effect(() => {
+		if (scrollContainer) {
+			updateScrollButtons();
+		}
+	});
+
+	return {
+		get canScrollLeft() {
+			return canScrollLeft;
+		},
+		get canScrollRight() {
+			return canScrollRight;
+		},
+		get scrollContainer() {
+			return scrollContainer;
+		},
+		set scrollContainer(el: HTMLDivElement | undefined) {
+			scrollContainer = el;
+		},
+		scrollToCenter,
+		scrollLeft,
+		scrollRight,
+		updateScrollButtons
+	};
+}
diff --git a/tools/ui/src/lib/hooks/use-settings-navigation.svelte.ts b/tools/ui/src/lib/hooks/use-settings-navigation.svelte.ts
new file mode 100644
index 00000000000..3cbcaaeda5f
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-settings-navigation.svelte.ts
@@ -0,0 +1,46 @@
+import { page } from '$app/state';
+import { beforeNavigate } from '$app/navigation';
+import { settingsReferrer } from '$lib/stores/settings-referrer.svelte';
+import { ROUTES } from '$lib/constants/routes';
+
+export interface ChatSettings {
+	reset: () => void;
+}
+
+export function useSettingsNavigation() {
+	const subroute = $state({
+		activePanel: 'chat' as 'chat' | 'settings' | 'mcp',
+		chatSettingsRef: undefined as ChatSettings | undefined
+	});
+
+	const isSettingsRoute = $derived(!!page.route.id?.startsWith('/settings'));
+
+	beforeNavigate(({ to, from }) => {
+		if (to?.route?.id?.startsWith('/settings') && !from?.route?.id?.startsWith('/settings')) {
+			settingsReferrer.url = window.location.hash || ROUTES.START;
+		}
+	});
+
+	$effect(() => {
+		if (subroute.activePanel === 'settings' && subroute.chatSettingsRef) {
+			subroute.chatSettingsRef.reset();
+		}
+	});
+
+	// Return to chat when navigating to a new route
+	$effect(() => {
+		void page.url;
+
+		subroute.activePanel = 'chat';
+	});
+
+	return {
+		get panel() {
+			return subroute;
+		},
+
+		get isSettingsRoute() {
+			return isSettingsRoute;
+		}
+	};
+}
diff --git a/tools/ui/src/lib/hooks/use-tools-panel.svelte.ts b/tools/ui/src/lib/hooks/use-tools-panel.svelte.ts
new file mode 100644
index 00000000000..911af322a7f
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-tools-panel.svelte.ts
@@ -0,0 +1,122 @@
+import { CLI_FLAGS } from '$lib/constants';
+import { SvelteSet } from 'svelte/reactivity';
+import { ToolSource } from '$lib/enums';
+import { conversationsStore } from '$lib/stores/conversations.svelte';
+import { mcpStore } from '$lib/stores/mcp.svelte';
+import { toolsStore } from '$lib/stores/tools.svelte';
+import type { ToolGroup } from '$lib/types';
+
+export interface UseToolsPanelReturn {
+	readonly expandedGroups: SvelteSet<string>;
+	readonly groups: ToolGroup[];
+	readonly activeGroups: ToolGroup[];
+	readonly totalToolCount: number;
+	readonly noToolsInfoMessage: string | null;
+	getGroupCheckedState(group: ToolGroup): { checked: boolean; indeterminate: boolean };
+	getEnabledToolCount(group: ToolGroup): number;
+	getFavicon(group: { source: ToolSource; label: string }): string | null;
+	isGroupDisabled(group: ToolGroup): boolean;
+	toggleGroupExpanded(label: string): void;
+	handleOpen(): void;
+}
+
+/**
+ * Shared reactive state and helpers for the tools panel UI.
+ *
+ * Used by both the desktop dropdown (`ChatFormActionAddToolsSubmenu`)
+ * and the mobile sheet (`ChatFormActionAddSheet`) to avoid
+ * duplicating group filtering, checked-state derivation, and favicon logic.
+ */
+export function useToolsPanel(): UseToolsPanelReturn {
+	const expandedGroups = new SvelteSet<string>();
+
+	const groups = $derived(toolsStore.toolGroups);
+	const activeGroups = $derived(
+		groups.filter(
+			(g) =>
+				g.source !== ToolSource.MCP ||
+				!g.serverId ||
+				conversationsStore.isMcpServerEnabledForChat(g.serverId)
+		)
+	);
+	const totalToolCount = $derived(activeGroups.reduce((n, g) => n + g.tools.length, 0));
+	const noToolsInfoMessage = $derived.by(() => {
+		if (toolsStore.loading) return null;
+		if (toolsStore.toolGroups.length > 0) return null;
+		// Tools endpoint is unreachable (404) — server started without --tools
+		if (toolsStore.isToolsEndpointUnreachable) {
+			return `To enable Built-In Tools you need to run llama-server with ${CLI_FLAGS.TOOLS} all or ${CLI_FLAGS.TOOLS} <name> flag. To see MCP Tools you need to add / enable MCP Server(s).`;
+		}
+		// Other errors — return null so UI shows "Failed to load tools"
+		if (toolsStore.error) return null;
+		return `To enable Built-In Tools you need to run llama-server with ${CLI_FLAGS.TOOLS} all or ${CLI_FLAGS.TOOLS} <name> flag. To see MCP Tools you need to add / enable MCP Server(s).`;
+	});
+
+	function getGroupCheckedState(group: ToolGroup): { checked: boolean; indeterminate: boolean } {
+		return {
+			checked: toolsStore.isGroupFullyEnabled(group),
+			indeterminate: toolsStore.isGroupPartiallyEnabled(group)
+		};
+	}
+
+	function getEnabledToolCount(group: ToolGroup): number {
+		return group.tools.filter((tool) => toolsStore.isToolEnabled(tool.function.name)).length;
+	}
+
+	function getFavicon(group: { source: ToolSource; label: string }): string | null {
+		if (group.source !== ToolSource.MCP) return null;
+
+		for (const server of mcpStore.getServersSorted()) {
+			if (mcpStore.getServerLabel(server) === group.label) {
+				return mcpStore.getServerFavicon(server.id);
+			}
+		}
+
+		return null;
+	}
+
+	function isGroupDisabled(group: ToolGroup): boolean {
+		return (
+			group.source === ToolSource.MCP &&
+			!!group.serverId &&
+			!conversationsStore.isMcpServerEnabledForChat(group.serverId)
+		);
+	}
+
+	function toggleGroupExpanded(label: string): void {
+		if (expandedGroups.has(label)) {
+			expandedGroups.delete(label);
+		} else {
+			expandedGroups.add(label);
+		}
+	}
+
+	function handleOpen(): void {
+		if (toolsStore.builtinTools.length === 0 && !toolsStore.loading) {
+			toolsStore.fetchBuiltinTools();
+		}
+		mcpStore.runHealthChecksForServers(mcpStore.getServersSorted().filter((s) => s.enabled));
+	}
+
+	return {
+		expandedGroups,
+		get groups() {
+			return groups;
+		},
+		get activeGroups() {
+			return activeGroups;
+		},
+		get totalToolCount() {
+			return totalToolCount;
+		},
+		get noToolsInfoMessage() {
+			return noToolsInfoMessage;
+		},
+		getGroupCheckedState,
+		getEnabledToolCount,
+		getFavicon,
+		isGroupDisabled,
+		toggleGroupExpanded,
+		handleOpen
+	};
+}
diff --git a/tools/server/webui/src/lib/services/chat.service.ts b/tools/ui/src/lib/services/chat.service.ts
similarity index 92%
rename from tools/server/webui/src/lib/services/chat.service.ts
rename to tools/ui/src/lib/services/chat.service.ts
index 52d7ea61622..3e405e9e689 100644
--- a/tools/server/webui/src/lib/services/chat.service.ts
+++ b/tools/ui/src/lib/services/chat.service.ts
@@ -14,11 +14,59 @@ import {
 	ReasoningFormat,
 	UrlProtocol
 } from '$lib/enums';
-import type { ApiChatMessageContentPart, ApiChatCompletionToolCall } from '$lib/types/api';
+import type {
+	ApiChatMessageContentPart,
+	ApiChatMessageData,
+	ApiChatCompletionToolCall
+} from '$lib/types/api';
 import type { DatabaseMessageExtraMcpPrompt, DatabaseMessageExtraMcpResource } from '$lib/types';
 import { modelsStore } from '$lib/stores/models.svelte';
 
 export class ChatService {
+	/**
+	 *
+	 *
+	 * Title Generation
+	 *
+	 *
+	 */
+
+	/**
+	 * Sends a streaming chat completion request for generating a chat title.
+	 * Delegates to `sendMessage` for fetch, SSE parsing, and error handling.
+	 *
+	 * @param message - The single message to send (a user message containing the title generation prompt)
+	 * @param model - Optional model name to use (required in ROUTER mode)
+	 * @param signal - Optional AbortSignal to cancel the request
+	 * @returns {Promise<string>} The aggregated title text, or empty string if request failed
+	 * @static
+	 */
+	static async generateTitle(
+		message: ApiChatMessageData,
+		model?: string | null,
+		signal?: AbortSignal
+	): Promise<string> {
+		let titleResponse = '';
+		try {
+			await ChatService.sendMessage(
+				[message],
+				{
+					model: model || undefined,
+					stream: true,
+					custom: { chat_template_kwargs: { enable_thinking: false } },
+					onChunk: (chunk: string) => {
+						titleResponse += chunk;
+					}
+				},
+				undefined,
+				signal
+			);
+		} catch {
+			return '';
+		}
+		return titleResponse;
+	}
+
 	/**
 	 *
 	 *
@@ -28,7 +76,7 @@ export class ChatService {
 	 */
 
 	/**
-	 * Sends a chat completion request to the llama.cpp server.
+	 * Sends a chat completion request to the llama-server.
 	 * Supports both streaming and non-streaming responses with comprehensive parameter configuration.
 	 * Automatically converts database messages with attachments to the appropriate API format.
 	 *
@@ -82,7 +130,8 @@ export class ChatService {
 			timings_per_token,
 			// Config options
 			disableReasoningParsing,
-			excludeReasoningFromContext
+			excludeReasoningFromContext,
+			continueFinalMessage
 		} = options;
 
 		const normalizedMessages: ApiChatMessageData[] = messages
@@ -122,7 +171,11 @@ export class ChatService {
 						return true;
 					});
 					// If only text remains and it's a single part, simplify to string
-					if (msg.content.length === 1 && msg.content[0].type === ContentPartType.TEXT) {
+					if (
+						msg.content.length === 1 &&
+						msg.content[0].type === ContentPartType.TEXT &&
+						typeof msg.content[0].text === 'string'
+					) {
 						msg.content = msg.content[0].text;
 					}
 				}
@@ -157,6 +210,11 @@ export class ChatService {
 			? ReasoningFormat.NONE
 			: ReasoningFormat.AUTO;
 
+		if (continueFinalMessage) {
+			requestBody.continue_final_message = true;
+			requestBody.add_generation_prompt = false;
+		}
+
 		if (temperature !== undefined) requestBody.temperature = temperature;
 		if (max_tokens !== undefined) {
 			// Set max_tokens to -1 (infinite) when explicitly configured as 0 or null
@@ -461,7 +519,7 @@ export class ChatService {
 
 			const serializedToolCalls = JSON.stringify(aggregatedToolCalls);
 
-			if (import.meta.env.DEV) {
+			if (import.meta.env.DEV && import.meta.env.VITE_DEBUG) {
 				console.log('[ChatService] Aggregated tool calls:', serializedToolCalls);
 			}
 
@@ -501,9 +559,10 @@ export class ChatService {
 
 						try {
 							const parsed: ApiChatCompletionStreamChunk = JSON.parse(data);
-							const content = parsed.choices[0]?.delta?.content;
-							const reasoningContent = parsed.choices[0]?.delta?.reasoning_content;
-							const toolCalls = parsed.choices[0]?.delta?.tool_calls;
+							const choice = parsed.choices?.[0];
+							const content = choice?.delta?.content;
+							const reasoningContent = choice?.delta?.reasoning_content;
+							const toolCalls = choice?.delta?.tool_calls;
 							const timings = parsed.timings;
 							const promptProgress = parsed.prompt_progress;
 
@@ -797,7 +856,7 @@ export class ChatService {
 			});
 		}
 
-		// Handle legacy 'context' type from old webui (pasted content)
+		// Handle legacy 'context' type from the old UI (pasted content)
 		const legacyContextFiles = message.extra.filter(
 			(extra: DatabaseMessageExtra): extra is DatabaseMessageExtraLegacyContext =>
 				extra.type === AttachmentType.LEGACY_CONTEXT
@@ -825,6 +884,25 @@ export class ChatService {
 			});
 		}
 
+		const videoFiles = message.extra.filter(
+			(extra: DatabaseMessageExtra): extra is DatabaseMessageExtraVideoFile =>
+				extra.type === AttachmentType.VIDEO
+		);
+
+		for (const video of videoFiles) {
+			contentParts.push({
+				type: ContentPartType.INPUT_VIDEO,
+				input_video: {
+					data: video.base64Data,
+					format: video.mimeType.includes('mp4')
+						? 'mp4'
+						: video.mimeType.includes('ogg')
+							? 'ogg'
+							: 'auto'
+				}
+			});
+		}
+
 		const pdfFiles = message.extra.filter(
 			(extra: DatabaseMessageExtra): extra is DatabaseMessageExtraPdfFile =>
 				extra.type === AttachmentType.PDF
diff --git a/tools/server/webui/src/lib/services/database.service.ts b/tools/ui/src/lib/services/database.service.ts
similarity index 51%
rename from tools/server/webui/src/lib/services/database.service.ts
rename to tools/ui/src/lib/services/database.service.ts
index 8f7b81fe97d..457867d984d 100644
--- a/tools/server/webui/src/lib/services/database.service.ts
+++ b/tools/ui/src/lib/services/database.service.ts
@@ -1,23 +1,21 @@
 import Dexie, { type EntityTable } from 'dexie';
 import { findDescendantMessages, uuid, filterByLeafNodeId } from '$lib/utils';
+import { IDXDB_TABLES, IDXDB_STORES, STORAGE_APP_NAME } from '$lib/constants';
+import { MessageRole } from '$lib/enums';
 import type { McpServerOverride } from '$lib/types/database';
 
-class LlamacppDatabase extends Dexie {
-	conversations!: EntityTable<DatabaseConversation, string>;
-	messages!: EntityTable<DatabaseMessage, string>;
+class LlamaUiDatabase extends Dexie {
+	[IDXDB_TABLES.conversations]!: EntityTable<DatabaseConversation, string>;
+	[IDXDB_TABLES.messages]!: EntityTable<DatabaseMessage, string>;
 
 	constructor() {
-		super('LlamacppWebui');
+		super(STORAGE_APP_NAME);
 
-		this.version(1).stores({
-			conversations: 'id, lastModified, currNode, name',
-			messages: 'id, convId, type, role, timestamp, parent, children'
-		});
+		this.version(1).stores(IDXDB_STORES);
 	}
 }
 
-const db = new LlamacppDatabase();
-import { MessageRole } from '$lib/enums';
+const db = new LlamaUiDatabase();
 
 export class DatabaseService {
 	/**
@@ -42,7 +40,7 @@ export class DatabaseService {
 			currNode: ''
 		};
 
-		await db.conversations.add(conversation);
+		await db[IDXDB_TABLES.conversations].add(conversation);
 		return conversation;
 	}
 
@@ -66,41 +64,45 @@ export class DatabaseService {
 		message: Omit<DatabaseMessage, 'id'>,
 		parentId: string | null
 	): Promise<DatabaseMessage> {
-		return await db.transaction('rw', [db.conversations, db.messages], async () => {
-			// Handle null parent (root message case)
-			if (parentId !== null) {
-				const parentMessage = await db.messages.get(parentId);
-				if (!parentMessage) {
-					throw new Error(`Parent message ${parentId} not found`);
+		return await db.transaction(
+			'rw',
+			[db[IDXDB_TABLES.conversations], db[IDXDB_TABLES.messages]],
+			async () => {
+				// Handle null parent (root message case)
+				if (parentId !== null) {
+					const parentMessage = await db[IDXDB_TABLES.messages].get(parentId);
+					if (!parentMessage) {
+						throw new Error(`Parent message ${parentId} not found`);
+					}
 				}
-			}
 
-			const newMessage: DatabaseMessage = {
-				...message,
-				id: uuid(),
-				parent: parentId,
-				toolCalls: message.toolCalls ?? '',
-				children: []
-			};
-
-			await db.messages.add(newMessage);
-
-			// Update parent's children array if parent exists
-			if (parentId !== null) {
-				const parentMessage = await db.messages.get(parentId);
-				if (parentMessage) {
-					await db.messages.update(parentId, {
-						children: [...parentMessage.children, newMessage.id]
-					});
+				const newMessage: DatabaseMessage = {
+					...message,
+					id: uuid(),
+					parent: parentId,
+					toolCalls: message.toolCalls ?? '',
+					children: []
+				};
+
+				await db[IDXDB_TABLES.messages].add(newMessage);
+
+				// Update parent's children array if parent exists
+				if (parentId !== null) {
+					const parentMessage = await db[IDXDB_TABLES.messages].get(parentId);
+					if (parentMessage) {
+						await db[IDXDB_TABLES.messages].update(parentId, {
+							children: [...parentMessage.children, newMessage.id]
+						});
+					}
 				}
-			}
 
-			await this.updateConversation(message.convId, {
-				currNode: newMessage.id
-			});
+				await this.updateConversation(message.convId, {
+					currNode: newMessage.id
+				});
 
-			return newMessage;
-		});
+				return newMessage;
+			}
+		);
 	}
 
 	/**
@@ -123,7 +125,7 @@ export class DatabaseService {
 			children: []
 		};
 
-		await db.messages.add(rootMessage);
+		await db[IDXDB_TABLES.messages].add(rootMessage);
 		return rootMessage.id;
 	}
 
@@ -157,11 +159,11 @@ export class DatabaseService {
 			children: []
 		};
 
-		await db.messages.add(systemMessage);
+		await db[IDXDB_TABLES.messages].add(systemMessage);
 
-		const parentMessage = await db.messages.get(parentId);
+		const parentMessage = await db[IDXDB_TABLES.messages].get(parentId);
 		if (parentMessage) {
-			await db.messages.update(parentId, {
+			await db[IDXDB_TABLES.messages].update(parentId, {
 				children: [...parentMessage.children, systemMessage.id]
 			});
 		}
@@ -178,46 +180,50 @@ export class DatabaseService {
 		id: string,
 		options?: { deleteWithForks?: boolean }
 	): Promise<void> {
-		await db.transaction('rw', [db.conversations, db.messages], async () => {
-			if (options?.deleteWithForks) {
-				// Recursively collect all descendant IDs
-				const idsToDelete: string[] = [];
-				const queue = [id];
-
-				while (queue.length > 0) {
-					const parentId = queue.pop()!;
-					const children = await db.conversations
-						.filter((c) => c.forkedFromConversationId === parentId)
-						.toArray();
+		await db.transaction(
+			'rw',
+			[db[IDXDB_TABLES.conversations], db[IDXDB_TABLES.messages]],
+			async () => {
+				if (options?.deleteWithForks) {
+					// Recursively collect all descendant IDs
+					const idsToDelete: string[] = [];
+					const queue = [id];
+
+					while (queue.length > 0) {
+						const parentId = queue.pop()!;
+						const children = await db[IDXDB_TABLES.conversations]
+							.filter((c) => c.forkedFromConversationId === parentId)
+							.toArray();
+
+						for (const child of children) {
+							idsToDelete.push(child.id);
+							queue.push(child.id);
+						}
+					}
 
-					for (const child of children) {
-						idsToDelete.push(child.id);
-						queue.push(child.id);
+					for (const forkId of idsToDelete) {
+						await db[IDXDB_TABLES.conversations].delete(forkId);
+						await db[IDXDB_TABLES.messages].where('convId').equals(forkId).delete();
 					}
-				}
+				} else {
+					// Reparent direct children to deleted conv's parent
+					const conv = await db[IDXDB_TABLES.conversations].get(id);
+					const newParent = conv?.forkedFromConversationId;
+					const directChildren = await db[IDXDB_TABLES.conversations]
+						.filter((c) => c.forkedFromConversationId === id)
+						.toArray();
 
-				for (const forkId of idsToDelete) {
-					await db.conversations.delete(forkId);
-					await db.messages.where('convId').equals(forkId).delete();
+					for (const child of directChildren) {
+						await db[IDXDB_TABLES.conversations].update(child.id, {
+							forkedFromConversationId: newParent ?? undefined
+						});
+					}
 				}
-			} else {
-				// Reparent direct children to deleted conv's parent
-				const conv = await db.conversations.get(id);
-				const newParent = conv?.forkedFromConversationId;
-				const directChildren = await db.conversations
-					.filter((c) => c.forkedFromConversationId === id)
-					.toArray();
 
-				for (const child of directChildren) {
-					await db.conversations.update(child.id, {
-						forkedFromConversationId: newParent ?? undefined
-					});
-				}
+				await db[IDXDB_TABLES.conversations].delete(id);
+				await db[IDXDB_TABLES.messages].where('convId').equals(id).delete();
 			}
-
-			await db.conversations.delete(id);
-			await db.messages.where('convId').equals(id).delete();
-		});
+		);
 	}
 
 	/**
@@ -226,21 +232,21 @@ export class DatabaseService {
 	 * @param messageId - ID of the message to delete
 	 */
 	static async deleteMessage(messageId: string): Promise<void> {
-		await db.transaction('rw', db.messages, async () => {
-			const message = await db.messages.get(messageId);
+		await db.transaction('rw', db[IDXDB_TABLES.messages], async () => {
+			const message = await db[IDXDB_TABLES.messages].get(messageId);
 			if (!message) return;
 
 			// Remove this message from its parent's children array
 			if (message.parent) {
-				const parent = await db.messages.get(message.parent);
+				const parent = await db[IDXDB_TABLES.messages].get(message.parent);
 				if (parent) {
 					parent.children = parent.children.filter((childId: string) => childId !== messageId);
-					await db.messages.put(parent);
+					await db[IDXDB_TABLES.messages].put(parent);
 				}
 			}
 
 			// Delete the message
-			await db.messages.delete(messageId);
+			await db[IDXDB_TABLES.messages].delete(messageId);
 		});
 	}
 
@@ -256,26 +262,29 @@ export class DatabaseService {
 		conversationId: string,
 		messageId: string
 	): Promise<string[]> {
-		return await db.transaction('rw', db.messages, async () => {
+		return await db.transaction('rw', db[IDXDB_TABLES.messages], async () => {
 			// Get all messages in the conversation to find descendants
-			const allMessages = await db.messages.where('convId').equals(conversationId).toArray();
+			const allMessages = await db[IDXDB_TABLES.messages]
+				.where('convId')
+				.equals(conversationId)
+				.toArray();
 
 			// Find all descendant messages
 			const descendants = findDescendantMessages(allMessages, messageId);
 			const allToDelete = [messageId, ...descendants];
 
 			// Get the message to delete for parent cleanup
-			const message = await db.messages.get(messageId);
+			const message = await db[IDXDB_TABLES.messages].get(messageId);
 			if (message && message.parent) {
-				const parent = await db.messages.get(message.parent);
+				const parent = await db[IDXDB_TABLES.messages].get(message.parent);
 				if (parent) {
 					parent.children = parent.children.filter((childId: string) => childId !== messageId);
-					await db.messages.put(parent);
+					await db[IDXDB_TABLES.messages].put(parent);
 				}
 			}
 
 			// Delete all messages in the branch
-			await db.messages.bulkDelete(allToDelete);
+			await db[IDXDB_TABLES.messages].bulkDelete(allToDelete);
 
 			return allToDelete;
 		});
@@ -287,7 +296,7 @@ export class DatabaseService {
 	 * @returns Array of conversations
 	 */
 	static async getAllConversations(): Promise<DatabaseConversation[]> {
-		return await db.conversations.orderBy('lastModified').reverse().toArray();
+		return await db[IDXDB_TABLES.conversations].orderBy('lastModified').reverse().toArray();
 	}
 
 	/**
@@ -297,7 +306,7 @@ export class DatabaseService {
 	 * @returns The conversation if found, otherwise undefined
 	 */
 	static async getConversation(id: string): Promise<DatabaseConversation | undefined> {
-		return await db.conversations.get(id);
+		return await db[IDXDB_TABLES.conversations].get(id);
 	}
 
 	/**
@@ -307,7 +316,7 @@ export class DatabaseService {
 	 * @returns Array of messages in the conversation
 	 */
 	static async getConversationMessages(convId: string): Promise<DatabaseMessage[]> {
-		return await db.messages.where('convId').equals(convId).sortBy('timestamp');
+		return await db[IDXDB_TABLES.messages].where('convId').equals(convId).sortBy('timestamp');
 	}
 
 	/**
@@ -321,7 +330,7 @@ export class DatabaseService {
 		id: string,
 		updates: Partial<Omit<DatabaseConversation, 'id'>>
 	): Promise<void> {
-		await db.conversations.update(id, {
+		await db[IDXDB_TABLES.conversations].update(id, {
 			...updates,
 			lastModified: Date.now()
 		});
@@ -359,7 +368,7 @@ export class DatabaseService {
 		id: string,
 		updates: Partial<Omit<DatabaseMessage, 'id'>>
 	): Promise<void> {
-		await db.messages.update(id, updates);
+		await db[IDXDB_TABLES.messages].update(id, updates);
 	}
 
 	/**
@@ -382,27 +391,31 @@ export class DatabaseService {
 		let importedCount = 0;
 		let skippedCount = 0;
 
-		return await db.transaction('rw', [db.conversations, db.messages], async () => {
-			for (const item of data) {
-				const { conv, messages } = item;
+		return await db.transaction(
+			'rw',
+			[db[IDXDB_TABLES.conversations], db[IDXDB_TABLES.messages]],
+			async () => {
+				for (const item of data) {
+					const { conv, messages } = item;
+
+					const existing = await db[IDXDB_TABLES.conversations].get(conv.id);
+					if (existing) {
+						console.warn(`Conversation "${conv.name}" already exists, skipping...`);
+						skippedCount++;
+						continue;
+					}
 
-				const existing = await db.conversations.get(conv.id);
-				if (existing) {
-					console.warn(`Conversation "${conv.name}" already exists, skipping...`);
-					skippedCount++;
-					continue;
-				}
+					await db[IDXDB_TABLES.conversations].add(conv);
+					for (const msg of messages) {
+						await db[IDXDB_TABLES.messages].put(msg);
+					}
 
-				await db.conversations.add(conv);
-				for (const msg of messages) {
-					await db.messages.put(msg);
+					importedCount++;
 				}
 
-				importedCount++;
+				return { imported: importedCount, skipped: skippedCount };
 			}
-
-			return { imported: importedCount, skipped: skippedCount };
-		});
+		);
 	}
 
 	/**
@@ -427,65 +440,76 @@ export class DatabaseService {
 		atMessageId: string,
 		options: { name: string; includeAttachments: boolean }
 	): Promise<DatabaseConversation> {
-		return await db.transaction('rw', [db.conversations, db.messages], async () => {
-			const sourceConv = await db.conversations.get(sourceConvId);
-			if (!sourceConv) {
-				throw new Error(`Source conversation ${sourceConvId} not found`);
-			}
+		return await db.transaction(
+			'rw',
+			[db[IDXDB_TABLES.conversations], db[IDXDB_TABLES.messages]],
+			async () => {
+				const sourceConv = await db[IDXDB_TABLES.conversations].get(sourceConvId);
+				if (!sourceConv) {
+					throw new Error(`Source conversation ${sourceConvId} not found`);
+				}
 
-			const allMessages = await db.messages.where('convId').equals(sourceConvId).toArray();
+				const allMessages = await db[IDXDB_TABLES.messages]
+					.where('convId')
+					.equals(sourceConvId)
+					.toArray();
 
-			const pathMessages = filterByLeafNodeId(allMessages, atMessageId, true) as DatabaseMessage[];
-			if (pathMessages.length === 0) {
-				throw new Error(`Could not resolve message path to ${atMessageId}`);
-			}
+				const pathMessages = filterByLeafNodeId(
+					allMessages,
+					atMessageId,
+					true
+				) as DatabaseMessage[];
+				if (pathMessages.length === 0) {
+					throw new Error(`Could not resolve message path to ${atMessageId}`);
+				}
 
-			const idMap = new Map<string, string>();
+				const idMap = new Map<string, string>();
 
-			for (const msg of pathMessages) {
-				idMap.set(msg.id, uuid());
-			}
+				for (const msg of pathMessages) {
+					idMap.set(msg.id, uuid());
+				}
 
-			const newConvId = uuid();
-			const clonedMessages: DatabaseMessage[] = pathMessages.map((msg) => {
-				const newId = idMap.get(msg.id)!;
-				const newParent = msg.parent ? (idMap.get(msg.parent) ?? null) : null;
-				const newChildren = msg.children
-					.filter((childId: string) => idMap.has(childId))
-					.map((childId: string) => idMap.get(childId)!);
-
-				return {
-					...msg,
-					id: newId,
-					convId: newConvId,
-					parent: newParent,
-					children: newChildren,
-					extra: options.includeAttachments ? msg.extra : undefined
+				const newConvId = uuid();
+				const clonedMessages: DatabaseMessage[] = pathMessages.map((msg) => {
+					const newId = idMap.get(msg.id)!;
+					const newParent = msg.parent ? (idMap.get(msg.parent) ?? null) : null;
+					const newChildren = msg.children
+						.filter((childId: string) => idMap.has(childId))
+						.map((childId: string) => idMap.get(childId)!);
+
+					return {
+						...msg,
+						id: newId,
+						convId: newConvId,
+						parent: newParent,
+						children: newChildren,
+						extra: options.includeAttachments ? msg.extra : undefined
+					};
+				});
+
+				const lastClonedMessage = clonedMessages[clonedMessages.length - 1];
+				const newConv: DatabaseConversation = {
+					id: newConvId,
+					name: options.name,
+					lastModified: Date.now(),
+					currNode: lastClonedMessage.id,
+					forkedFromConversationId: sourceConvId,
+					mcpServerOverrides: sourceConv.mcpServerOverrides
+						? sourceConv.mcpServerOverrides.map((o: McpServerOverride) => ({
+								serverId: o.serverId,
+								enabled: o.enabled
+							}))
+						: undefined
 				};
-			});
 
-			const lastClonedMessage = clonedMessages[clonedMessages.length - 1];
-			const newConv: DatabaseConversation = {
-				id: newConvId,
-				name: options.name,
-				lastModified: Date.now(),
-				currNode: lastClonedMessage.id,
-				forkedFromConversationId: sourceConvId,
-				mcpServerOverrides: sourceConv.mcpServerOverrides
-					? sourceConv.mcpServerOverrides.map((o: McpServerOverride) => ({
-							serverId: o.serverId,
-							enabled: o.enabled
-						}))
-					: undefined
-			};
-
-			await db.conversations.add(newConv);
-
-			for (const msg of clonedMessages) {
-				await db.messages.add(msg);
-			}
+				await db[IDXDB_TABLES.conversations].add(newConv);
 
-			return newConv;
-		});
+				for (const msg of clonedMessages) {
+					await db[IDXDB_TABLES.messages].add(msg);
+				}
+
+				return newConv;
+			}
+		);
 	}
 }
diff --git a/tools/server/webui/src/lib/services/index.ts b/tools/ui/src/lib/services/index.ts
similarity index 85%
rename from tools/server/webui/src/lib/services/index.ts
rename to tools/ui/src/lib/services/index.ts
index 7c98383d1f2..475e6419bb9 100644
--- a/tools/server/webui/src/lib/services/index.ts
+++ b/tools/ui/src/lib/services/index.ts
@@ -206,10 +206,10 @@ export { PropsService } from './props.service';
  *
  * **Exports:**
  * - `ParameterSyncService` class — static methods for sync logic
- * - `SYNCABLE_PARAMETERS` — mapping of webui setting keys to server parameter keys
+ * - `SYNCABLE_PARAMETERS` — mapping of UI setting keys to server parameter keys
  *
  * @see settingsStore in stores/settings.svelte.ts — primary consumer for settings sync
- * @see ChatSettingsParameterSourceIndicator — displays parameter source badges in UI
+ * @see SettingsChatParameterSourceIndicator — displays parameter source badges in UI
  */
 export { ParameterSyncService } from './parameter-sync.service';
 
@@ -260,3 +260,54 @@ export { ParameterSyncService } from './parameter-sync.service';
  * @see MCP Protocol Specification: https://modelcontextprotocol.io/specification/2025-06-18
  */
 export { MCPService } from './mcp.service';
+
+/**
+ * **RouterService** — Dynamic route URL construction utility
+ *
+ * Stateless utility for building dynamic route URLs from ROUTES base paths.
+ * Static routes (START, NEW_CHAT, MCP_SERVERS) live in ROUTES constants;
+ * dynamic routes (CHAT, SETTINGS) are constructed here by appending parameters.
+ *
+ * **Architecture & Relationships:**
+ * - **RouterService** (this class): Stateless URL construction
+ *   - Builds dynamic route URLs from ROUTES base paths
+ *   - No side effects — receives route parameters, returns route strings
+ *
+ * - **ROUTES constant** (constants/routes.ts): Static route base paths
+ * - **All components/stores**: Call RouterService for dynamic route URLs
+ *
+ * **Key Responsibilities:**
+ * - Build chat URLs for specific conversations: `RouterService.chat(id)` → `#/chat/:id`
+ * - Build settings URLs for sections: `RouterService.settings(section)` → `#/settings/:section`
+ *
+ * @see ROUTES in constants/routes.ts — static route base paths
+ */
+export { RouterService } from './router.service';
+
+/**
+ * **MigrationService** — Unified data migration hook
+ *
+ * Centralizes all data migrations (localStorage, IndexedDB, legacy formats) into a single
+ * initialization point. All migrations are NON-DESTRUCTIVE - legacy data is preserved
+ * for downgrade compatibility (no rollback needed).
+ *
+ * **Current Migrations:**
+ * 1. **localStorage prefix**: Copy LlamaCppWebui.* → LlamaUi.* (both preserved)
+ * 2. **IndexedDB database**: Copy LlamacppWebui → LlamaUi (both preserved)
+ * 3. **Legacy message format**: Marker-based → Structured format
+ * 4. **Theme key**: Copy standalone `theme` → config object (both preserved)
+ *
+ * **Usage:**
+ * ```typescript
+ * import { MigrationService } from '$lib/services';
+ *
+ * // Run all migrations on app startup (non-destructive)
+ * await MigrationService.runAllMigrations();
+ *
+ * // Check migration status
+ * const state = MigrationService.getState();
+ * ```
+ *
+ * @see migration.service.ts — full implementation (non-destructive)
+ */
+export { MigrationService } from './migration.service';
diff --git a/tools/server/webui/src/lib/services/mcp.service.ts b/tools/ui/src/lib/services/mcp.service.ts
similarity index 98%
rename from tools/server/webui/src/lib/services/mcp.service.ts
rename to tools/ui/src/lib/services/mcp.service.ts
index 69297df9578..44cbd4a8aaf 100644
--- a/tools/server/webui/src/lib/services/mcp.service.ts
+++ b/tools/ui/src/lib/services/mcp.service.ts
@@ -27,6 +27,7 @@ import {
 } from '$lib/enums';
 import type {
 	MCPServerConfig,
+	MCPResourceIcon,
 	ToolCallParams,
 	ToolExecutionResult,
 	Implementation,
@@ -469,10 +470,11 @@ export class MCPService {
 			title: impl.title,
 			description: impl.description,
 			websiteUrl: impl.websiteUrl,
-			icons: impl.icons?.map((icon: { src: string; mimeType?: string; sizes?: string }) => ({
+			icons: impl.icons?.map((icon: MCPResourceIcon) => ({
 				src: icon.src,
 				mimeType: icon.mimeType,
-				sizes: icon.sizes
+				sizes: icon.sizes,
+				theme: icon.theme
 			}))
 		};
 	}
@@ -581,7 +583,6 @@ export class MCPService {
 			this.createLog(MCPConnectionPhase.INITIALIZING, 'Sending initialize request...')
 		);
 
-		console.log(`[MCPService][${serverName}] Connecting to server...`);
 		try {
 			await client.connect(transport);
 			// Transport diagnostics are only for the initial handshake, not long-lived traffic.
@@ -664,7 +665,9 @@ export class MCPService {
 			tools: [],
 			serverName,
 			transportType,
-			connectionTimeMs: 0
+			connectionTimeMs: 0,
+			requestTimeoutMs:
+				serverConfig.requestTimeoutMs ?? DEFAULT_MCP_CONFIG.requestTimeoutSeconds * 1000
 		});
 
 		const connectionTimeMs = Math.round(performance.now() - startTime);
@@ -693,7 +696,9 @@ export class MCPService {
 			clientCapabilities: effectiveCapabilities,
 			protocolVersion: DEFAULT_MCP_CONFIG.protocolVersion,
 			instructions,
-			connectionTimeMs
+			connectionTimeMs,
+			requestTimeoutMs:
+				serverConfig.requestTimeoutMs ?? DEFAULT_MCP_CONFIG.requestTimeoutSeconds * 1000
 		};
 	}
 
@@ -812,7 +817,7 @@ export class MCPService {
 			const result = await connection.client.callTool(
 				{ name: params.name, arguments: params.arguments },
 				undefined,
-				{ signal }
+				{ signal, timeout: connection.requestTimeoutMs }
 			);
 
 			return {
diff --git a/tools/ui/src/lib/services/migration.service.ts b/tools/ui/src/lib/services/migration.service.ts
new file mode 100644
index 00000000000..5ed24c00d92
--- /dev/null
+++ b/tools/ui/src/lib/services/migration.service.ts
@@ -0,0 +1,524 @@
+/**
+ * Migration Service - Unified data migration hook
+ *
+ * Centralizes all data migrations (localStorage, IndexedDB, legacy formats) into a single
+ * initialization point. Each migration copies data to new format WITHOUT deleting the old.
+ *
+ * **Architecture:**
+ * - Migrations are defined as objects with `id` and `run()` methods
+ * - Migration state is tracked in localStorage to avoid re-running
+ * - `runAllMigrations()` should be called once at app startup
+ * - All migrations are NON-DESTRUCTIVE - legacy data is preserved for downgrade compatibility
+ *
+ * **Current Migrations:**
+ * 1. localStorage prefix: Copy LlamaCppWebui.* → LlamaUi.* (both preserved)
+ * 2. IndexedDB database: Copy LlamacppWebui → LlamaUi (both preserved)
+ * 3. Legacy message format: Transform in-place (preserves structure, migrates markers)
+ * 4. Theme key: Copy standalone `theme` → config object (both preserved)
+ */
+
+import Dexie from 'dexie';
+import {
+	STORAGE_APP_NAME,
+	DB_APP_NAME_DEPRECATED,
+	CONFIG_LOCALSTORAGE_KEY,
+	IDXDB_TABLES,
+	IDXDB_STORES,
+	NEW_TO_DEPRECATED_MAP
+} from '$lib/constants';
+import { LEGACY_AGENTIC_REGEX, LEGACY_REASONING_TAGS } from '$lib/constants/agentic';
+import { SETTINGS_KEYS } from '$lib/constants/settings-registry';
+import { MessageRole } from '$lib/enums';
+
+// Types
+
+interface Migration {
+	/** Unique identifier for this migration */
+	id: string;
+	/** Human-readable description */
+	description: string;
+	/** Run the migration forward (non-destructive - copies, doesn't delete) */
+	run(): Promise<void>;
+}
+
+interface MigrationState {
+	completed: string[];
+	failed: string[];
+	lastRun: string;
+}
+
+// Constants
+
+const MIGRATION_STATE_KEY = `${STORAGE_APP_NAME}.migration-state`;
+const MIGRATION_STATE_VERSION = 1;
+
+// State Management
+
+function getMigrationState(): MigrationState {
+	try {
+		const raw = localStorage.getItem(MIGRATION_STATE_KEY);
+		if (!raw) return { completed: [], failed: [], lastRun: '' };
+		const parsed = JSON.parse(raw);
+		if (parsed.version !== MIGRATION_STATE_VERSION) {
+			return { completed: [], failed: [], lastRun: '' };
+		}
+		return {
+			completed: parsed.completed ?? [],
+			failed: parsed.failed ?? [],
+			lastRun: parsed.lastRun ?? ''
+		};
+	} catch {
+		return { completed: [], failed: [], lastRun: '' };
+	}
+}
+
+function saveMigrationState(state: MigrationState): void {
+	localStorage.setItem(
+		MIGRATION_STATE_KEY,
+		JSON.stringify({
+			version: MIGRATION_STATE_VERSION,
+			...state,
+			lastRun: new Date().toISOString()
+		})
+	);
+}
+
+function isMigrationCompleted(id: string): boolean {
+	const state = getMigrationState();
+	return state.completed.includes(id);
+}
+
+function markMigrationCompleted(id: string): void {
+	const state = getMigrationState();
+	if (!state.completed.includes(id)) {
+		state.completed.push(id);
+	}
+	state.failed = state.failed.filter((f) => f !== id);
+	saveMigrationState(state);
+}
+
+function markMigrationFailed(id: string): void {
+	const state = getMigrationState();
+	if (!state.failed.includes(id)) {
+		state.failed.push(id);
+	}
+	saveMigrationState(state);
+}
+
+// Migration 1: LocalStorage Key Prefix (Non-Destructive)
+
+const LOCALSTORAGE_MIGRATION_ID = 'localstorage-prefix-v1';
+
+const localStorageMigration: Migration = {
+	id: LOCALSTORAGE_MIGRATION_ID,
+	description: 'Copy localStorage keys from LlamaCppWebui to LlamaUi prefix (non-destructive)',
+
+	async run(): Promise<void> {
+		// Non-destructive: copy to new key, but KEEP the old key
+		for (const [newKey, deprecatedKey] of Object.entries(NEW_TO_DEPRECATED_MAP)) {
+			// Only migrate if new key doesn't already exist
+			const newValue = localStorage.getItem(newKey);
+			if (newValue !== null) {
+				console.log(`[Migration] localStorage: ${newKey} already exists, skipping`);
+				continue;
+			}
+
+			const oldValue = localStorage.getItem(deprecatedKey);
+			if (oldValue !== null) {
+				localStorage.setItem(newKey, oldValue);
+				// Keep old key for downgrade compatibility - DO NOT DELETE
+				console.log(
+					`[Migration] localStorage: copied ${deprecatedKey} → ${newKey} (preserved old)`
+				);
+			}
+		}
+	}
+};
+
+// Migration 2: IndexedDB Database Name (Non-Destructive)
+
+const IDXDB_MIGRATION_ID = 'idxdb-database-v1';
+
+const idxdbMigration: Migration = {
+	id: IDXDB_MIGRATION_ID,
+	description: 'Copy IndexedDB from LlamacppWebui to LlamaUi database (non-destructive)',
+
+	async run(): Promise<void> {
+		const oldDbNames = await Dexie.getDatabaseNames();
+		if (!oldDbNames.includes(DB_APP_NAME_DEPRECATED)) {
+			console.log('[Migration] IndexedDB: no old database found, skipping');
+			return;
+		}
+
+		// Check if new database already has data
+		const newDb = new Dexie(STORAGE_APP_NAME);
+		newDb.version(1).stores(IDXDB_STORES);
+		const existingConvs = await newDb.table(IDXDB_TABLES.conversations).count();
+		if (existingConvs > 0) {
+			console.log('[Migration] IndexedDB: new database already has data, skipping');
+			return;
+		}
+
+		console.log('[Migration] IndexedDB: copying from', DB_APP_NAME_DEPRECATED);
+
+		const oldDb = new Dexie(DB_APP_NAME_DEPRECATED);
+		oldDb.version(1).stores(IDXDB_STORES);
+
+		const conversations = await oldDb.table(IDXDB_TABLES.conversations).toArray();
+		const messages = await oldDb.table(IDXDB_TABLES.messages).toArray();
+
+		if (conversations.length > 0) {
+			await newDb.table(IDXDB_TABLES.conversations).bulkAdd(conversations);
+			console.log(`[Migration] IndexedDB: copied ${conversations.length} conversations`);
+		}
+		if (messages.length > 0) {
+			await newDb.table(IDXDB_TABLES.messages).bulkAdd(messages);
+			console.log(`[Migration] IndexedDB: copied ${messages.length} messages`);
+		}
+
+		// Non-destructive: DO NOT delete old database - keep for downgrade compatibility
+		console.log('[Migration] IndexedDB: preserved old database for downgrade compatibility');
+	}
+};
+
+// Migration 3: Legacy Message Format
+
+const LEGACY_MESSAGE_MIGRATION_ID = 'legacy-message-format-v2';
+
+interface ParsedTurn {
+	textBefore: string;
+	toolCalls: Array<{ name: string; args: string; result: string }>;
+}
+
+function parseLegacyToolCalls(content: string): ParsedTurn[] {
+	const turns: ParsedTurn[] = [];
+	const regex = new RegExp(LEGACY_AGENTIC_REGEX.COMPLETED_TOOL_CALL.source, 'g');
+
+	let lastIndex = 0;
+	let currentTurn: ParsedTurn = { textBefore: '', toolCalls: [] };
+	let match;
+
+	while ((match = regex.exec(content)) !== null) {
+		const textBefore = content.slice(lastIndex, match.index).trim();
+
+		if (textBefore && currentTurn.toolCalls.length > 0) {
+			turns.push(currentTurn);
+			currentTurn = { textBefore, toolCalls: [] };
+		} else if (textBefore && currentTurn.toolCalls.length === 0) {
+			currentTurn.textBefore = textBefore;
+		}
+
+		currentTurn.toolCalls.push({
+			name: match[1],
+			args: match[2],
+			result: match[3].replace(/^\n+|\n+$/g, '')
+		});
+
+		lastIndex = match.index + match[0].length;
+	}
+
+	const remainingText = content.slice(lastIndex).trim();
+
+	if (currentTurn.toolCalls.length > 0) {
+		turns.push(currentTurn);
+	}
+
+	if (remainingText) {
+		const cleanRemaining = remainingText
+			.replace(LEGACY_AGENTIC_REGEX.AGENTIC_TOOL_CALL_OPEN, '')
+			.trim();
+		if (cleanRemaining) {
+			turns.push({ textBefore: cleanRemaining, toolCalls: [] });
+		}
+	}
+
+	if (turns.length === 0) {
+		turns.push({ textBefore: content.trim(), toolCalls: [] });
+	}
+
+	return turns;
+}
+
+function extractLegacyReasoning(content: string): { reasoning: string; cleanContent: string } {
+	let reasoning = '';
+	let cleanContent = content;
+
+	const re = new RegExp(LEGACY_AGENTIC_REGEX.REASONING_EXTRACT.source, 'g');
+	let match;
+	while ((match = re.exec(content)) !== null) {
+		reasoning += match[1];
+	}
+
+	cleanContent = cleanContent
+		.replace(new RegExp(LEGACY_AGENTIC_REGEX.REASONING_BLOCK.source, 'g'), '')
+		.replace(LEGACY_AGENTIC_REGEX.REASONING_OPEN, '');
+
+	return { reasoning, cleanContent };
+}
+
+function hasLegacyMarkers(content: string): boolean {
+	return LEGACY_AGENTIC_REGEX.HAS_LEGACY_MARKERS.test(content);
+}
+
+let DatabaseService: typeof import('./database.service').DatabaseService | null = null;
+
+async function getDatabaseService() {
+	if (!DatabaseService) {
+		const module = await import('./database.service');
+		DatabaseService = module.DatabaseService;
+	}
+	return DatabaseService;
+}
+
+const legacyMessageMigration: Migration = {
+	id: LEGACY_MESSAGE_MIGRATION_ID,
+	description: 'Migrate legacy marker-based messages to structured format',
+
+	async run(): Promise<void> {
+		const db = await getDatabaseService();
+		const conversations = await db.getAllConversations();
+		let migratedCount = 0;
+
+		for (const conv of conversations) {
+			const allMessages = await db.getConversationMessages(conv.id);
+
+			for (const message of allMessages) {
+				if (message.role !== MessageRole.ASSISTANT) {
+					if (message.content?.includes(LEGACY_REASONING_TAGS.START)) {
+						const { reasoning, cleanContent } = extractLegacyReasoning(message.content);
+						await db.updateMessage(message.id, {
+							content: cleanContent.trim(),
+							reasoningContent: reasoning || undefined
+						});
+						migratedCount++;
+					}
+					continue;
+				}
+
+				if (!hasLegacyMarkers(message.content ?? '')) continue;
+
+				const { reasoning, cleanContent } = extractLegacyReasoning(message.content);
+				const turns = parseLegacyToolCalls(cleanContent);
+
+				let existingToolCalls: Array<{
+					id: string;
+					function?: { name: string; arguments: string };
+				}> = [];
+				if (message.toolCalls) {
+					try {
+						existingToolCalls = JSON.parse(message.toolCalls);
+					} catch {
+						// Ignore
+					}
+				}
+
+				const firstTurn = turns[0];
+				if (!firstTurn) continue;
+
+				const firstTurnToolCalls = firstTurn.toolCalls.map((tc, i) => {
+					const existing =
+						existingToolCalls.find((e) => e.function?.name === tc.name) || existingToolCalls[i];
+					return {
+						id: existing?.id || `legacy_tool_${i}`,
+						type: 'function' as const,
+						function: { name: tc.name, arguments: tc.args }
+					};
+				});
+
+				await db.updateMessage(message.id, {
+					content: firstTurn.textBefore,
+					reasoningContent: reasoning || undefined,
+					toolCalls: firstTurnToolCalls.length > 0 ? JSON.stringify(firstTurnToolCalls) : ''
+				});
+
+				let currentParentId = message.id;
+				let toolCallIdCounter = existingToolCalls.length;
+
+				for (let i = 0; i < firstTurn.toolCalls.length; i++) {
+					const tc = firstTurn.toolCalls[i];
+					const toolCallId = firstTurnToolCalls[i]?.id || `legacy_tool_${i}`;
+
+					const toolMsg = await db.createMessageBranch(
+						{
+							convId: conv.id,
+							type: 'text',
+							role: MessageRole.TOOL,
+							content: tc.result,
+							toolCallId,
+							timestamp: message.timestamp + i + 1,
+							toolCalls: '',
+							children: []
+						},
+						currentParentId
+					);
+					currentParentId = toolMsg.id;
+				}
+
+				for (let turnIdx = 1; turnIdx < turns.length; turnIdx++) {
+					const turn = turns[turnIdx];
+
+					const turnToolCalls = turn.toolCalls.map((tc, i) => {
+						const idx = toolCallIdCounter + i;
+						const existing = existingToolCalls[idx];
+						return {
+							id: existing?.id || `legacy_tool_${idx}`,
+							type: 'function' as const,
+							function: { name: tc.name, arguments: tc.args }
+						};
+					});
+					toolCallIdCounter += turn.toolCalls.length;
+
+					const assistantMsg = await db.createMessageBranch(
+						{
+							convId: conv.id,
+							type: 'text',
+							role: MessageRole.ASSISTANT,
+							content: turn.textBefore,
+							timestamp: message.timestamp + turnIdx * 100,
+							toolCalls: turnToolCalls.length > 0 ? JSON.stringify(turnToolCalls) : '',
+							children: [],
+							model: message.model
+						},
+						currentParentId
+					);
+					currentParentId = assistantMsg.id;
+
+					for (let i = 0; i < turn.toolCalls.length; i++) {
+						const tc = turn.toolCalls[i];
+						const toolCallId = turnToolCalls[i]?.id || `legacy_tool_${toolCallIdCounter + i}`;
+
+						const toolMsg = await db.createMessageBranch(
+							{
+								convId: conv.id,
+								type: 'text',
+								role: MessageRole.TOOL,
+								content: tc.result,
+								toolCallId,
+								timestamp: message.timestamp + turnIdx * 100 + i + 1,
+								toolCalls: '',
+								children: []
+							},
+							currentParentId
+						);
+						currentParentId = toolMsg.id;
+					}
+				}
+
+				if (message.children.length > 0 && currentParentId !== message.id) {
+					for (const childId of message.children) {
+						const child = allMessages.find((m) => m.id === childId);
+						if (!child) continue;
+						if (child.role !== MessageRole.TOOL) {
+							await db.updateMessage(childId, { parent: currentParentId });
+						}
+					}
+					await db.updateMessage(message.id, { children: [] });
+				}
+
+				migratedCount++;
+			}
+		}
+
+		console.log(`[Migration] Legacy messages: migrated ${migratedCount} messages`);
+	}
+};
+
+// Migration 4: Theme Key (Non-Destructive)
+
+const THEME_MIGRATION_ID = 'theme-key-v1';
+
+const themeMigration: Migration = {
+	id: THEME_MIGRATION_ID,
+	description: 'Copy standalone theme key to config object (non-destructive)',
+
+	async run(): Promise<void> {
+		const legacyTheme = localStorage.getItem('theme');
+		if (legacyTheme === null) {
+			console.log('[Migration] Theme: no legacy theme key found, skipping');
+			return;
+		}
+
+		// Check if config already has theme
+		const configRaw = localStorage.getItem(CONFIG_LOCALSTORAGE_KEY);
+		const config = configRaw ? JSON.parse(configRaw) : {};
+
+		if (SETTINGS_KEYS.THEME in config) {
+			console.log('[Migration] Theme: config already has theme, skipping');
+			return;
+		}
+
+		config[SETTINGS_KEYS.THEME] = legacyTheme;
+		localStorage.setItem(CONFIG_LOCALSTORAGE_KEY, JSON.stringify(config));
+
+		// Non-destructive: DO NOT delete legacy theme key - keep for downgrade compatibility
+		console.log(`[Migration] Theme: copied standalone theme to config (preserved old key)`);
+	}
+};
+
+// Migration Registry & Runner
+
+const migrations: Migration[] = [
+	localStorageMigration,
+	idxdbMigration,
+	legacyMessageMigration,
+	themeMigration
+];
+
+export const MigrationService = {
+	/**
+	 * Get all registered migrations
+	 */
+	getMigrations(): Migration[] {
+		return [...migrations];
+	},
+
+	/**
+	 * Check if a specific migration has been completed
+	 */
+	isCompleted(id: string): boolean {
+		return isMigrationCompleted(id);
+	},
+
+	/**
+	 * Get current migration state
+	 */
+	getState(): MigrationState {
+		return getMigrationState();
+	},
+
+	/**
+	 * Reset migration state (use with caution - migrations will run again)
+	 */
+	resetState(): void {
+		localStorage.removeItem(MIGRATION_STATE_KEY);
+		console.log('[Migration] State reset - all migrations will run again');
+	},
+
+	/**
+	 * Run all pending migrations (non-destructive - preserves legacy data)
+	 * Should be called once at app initialization
+	 */
+	async runAllMigrations(): Promise<void> {
+		const state = getMigrationState();
+		console.log('[Migration] Starting migration run, state:', state);
+
+		for (const migration of migrations) {
+			if (isMigrationCompleted(migration.id)) {
+				console.log(`[Migration] ${migration.id}: already completed, skipping`);
+				continue;
+			}
+
+			try {
+				console.log(`[Migration] ${migration.id}: running...`);
+				await migration.run();
+				markMigrationCompleted(migration.id);
+				console.log(`[Migration] ${migration.id}: completed successfully`);
+			} catch (error) {
+				console.error(`[Migration] ${migration.id}: failed`, error);
+				markMigrationFailed(migration.id);
+			}
+		}
+
+		console.log('[Migration] All migrations complete');
+	}
+};
diff --git a/tools/server/webui/src/lib/services/models.service.ts b/tools/ui/src/lib/services/models.service.ts
similarity index 100%
rename from tools/server/webui/src/lib/services/models.service.ts
rename to tools/ui/src/lib/services/models.service.ts
diff --git a/tools/server/webui/src/lib/services/parameter-sync.service.spec.ts b/tools/ui/src/lib/services/parameter-sync.service.spec.ts
similarity index 98%
rename from tools/server/webui/src/lib/services/parameter-sync.service.spec.ts
rename to tools/ui/src/lib/services/parameter-sync.service.spec.ts
index cbb2605e143..f15c0c14337 100644
--- a/tools/server/webui/src/lib/services/parameter-sync.service.spec.ts
+++ b/tools/ui/src/lib/services/parameter-sync.service.spec.ts
@@ -132,7 +132,7 @@ describe('ParameterSyncService', () => {
 			expect(result.temperature).toBe(0.7);
 		});
 
-		it('should merge webui settings from props when provided', () => {
+		it('should merge ui settings from props when provided', () => {
 			const result = ParameterSyncService.extractServerDefaults(null, {
 				pasteLongTextToFileLen: 0,
 				pdfAsImage: true,
diff --git a/tools/ui/src/lib/services/parameter-sync.service.ts b/tools/ui/src/lib/services/parameter-sync.service.ts
new file mode 100644
index 00000000000..900471b3aa9
--- /dev/null
+++ b/tools/ui/src/lib/services/parameter-sync.service.ts
@@ -0,0 +1,232 @@
+import { normalizeFloatingPoint } from '$lib/utils';
+import { SETTINGS_KEYS, SYNCABLE_PARAMETERS } from '$lib/constants';
+import type { ParameterRecord, ParameterInfo, ParameterValue } from '$lib/types';
+import { SyncableParameterType, ParameterSource } from '$lib/enums';
+
+export class ParameterSyncService {
+	/**
+	 *
+	 *
+	 * Extraction
+	 *
+	 *
+	 */
+
+	/**
+	 * Round floating-point numbers to avoid JavaScript precision issues.
+	 * E.g., 0.1 + 0.2 = 0.30000000000000004 → 0.3
+	 *
+	 * @param value - Parameter value to normalize
+	 * @returns Precision-normalized value
+	 */
+	private static roundFloatingPoint(value: ParameterValue): ParameterValue {
+		return normalizeFloatingPoint(value) as ParameterValue;
+	}
+
+	/**
+	 * Extract server default parameters that can be synced from `/props` response.
+	 * Handles both generation settings parameters and UI-specific settings.
+	 * Converts samplers array to semicolon-delimited string for UI display.
+	 *
+	 * @param serverParams - Raw generation settings from server `/props` endpoint
+	 * @param uiSettings - Optional UI-specific settings from server
+	 * @returns Record of extracted parameter key-value pairs with normalized precision
+	 */
+	static extractServerDefaults(
+		serverParams: ApiLlamaCppServerProps['default_generation_settings']['params'] | null,
+		uiSettings?: Record<string, string | number | boolean>
+	): ParameterRecord {
+		const extracted: ParameterRecord = {};
+
+		if (serverParams) {
+			for (const param of SYNCABLE_PARAMETERS) {
+				if (param.canSync && param.serverKey in serverParams) {
+					const value = (serverParams as unknown as Record<string, ParameterValue>)[
+						param.serverKey
+					];
+					if (value !== undefined) {
+						// Apply precision rounding to avoid JavaScript floating-point issues
+						extracted[param.key] = this.roundFloatingPoint(value);
+					}
+				}
+			}
+
+			// Handle samplers array conversion to string
+			if (serverParams.samplers && Array.isArray(serverParams.samplers)) {
+				extracted[SETTINGS_KEYS.SAMPLERS] = serverParams.samplers.join(';');
+			}
+		}
+
+		if (uiSettings) {
+			for (const param of SYNCABLE_PARAMETERS) {
+				if (param.canSync && param.serverKey in uiSettings) {
+					const value = uiSettings[param.serverKey];
+
+					if (value !== undefined) {
+						extracted[param.key] = this.roundFloatingPoint(value);
+					}
+				}
+			}
+		}
+
+		return extracted;
+	}
+
+	/**
+	 *
+	 *
+	 * Merging
+	 *
+	 *
+	 */
+
+	/**
+	 * Merge server defaults with current user settings.
+	 * User overrides always take priority — only parameters not in `userOverrides`
+	 * set will be updated from server defaults.
+	 *
+	 * @param currentSettings - Current parameter values in the settings store
+	 * @param serverDefaults - Default values extracted from server props
+	 * @param userOverrides - Set of parameter keys explicitly overridden by the user
+	 * @returns Merged parameter record with user overrides preserved
+	 */
+	static mergeWithServerDefaults(
+		currentSettings: ParameterRecord,
+		serverDefaults: ParameterRecord,
+		userOverrides: Set<string> = new Set()
+	): ParameterRecord {
+		const merged = { ...currentSettings };
+
+		for (const [key, serverValue] of Object.entries(serverDefaults)) {
+			// Only update if user hasn't explicitly overridden this parameter
+			if (!userOverrides.has(key)) {
+				merged[key] = this.roundFloatingPoint(serverValue);
+			}
+		}
+
+		return merged;
+	}
+
+	/**
+	 *
+	 *
+	 * Info
+	 *
+	 *
+	 */
+
+	/**
+	 * Get parameter information including source and values.
+	 * Used by SettingsChatParameterSourceIndicator to display the correct badge
+	 * (Custom vs Default) for each parameter in the settings UI.
+	 *
+	 * @param key - The parameter key to get info for
+	 * @param currentValue - The current value of the parameter
+	 * @param propsDefaults - Server default values from `/props`
+	 * @param userOverrides - Set of parameter keys explicitly overridden by the user
+	 * @returns Parameter info with source, server default, and user override values
+	 */
+	static getParameterInfo(
+		key: string,
+		currentValue: ParameterValue,
+		propsDefaults: ParameterRecord,
+		userOverrides: Set<string>
+	): ParameterInfo {
+		const hasPropsDefault = propsDefaults[key] !== undefined;
+		const isUserOverride = userOverrides.has(key);
+
+		// Simple logic: either using default (from props) or custom (user override)
+		const source = isUserOverride ? ParameterSource.CUSTOM : ParameterSource.DEFAULT;
+
+		return {
+			value: currentValue,
+			source,
+			serverDefault: hasPropsDefault ? propsDefaults[key] : undefined, // Keep same field name for compatibility
+			userOverride: isUserOverride ? currentValue : undefined
+		};
+	}
+
+	/**
+	 * Check if a parameter can be synced from server.
+	 *
+	 * @param key - The parameter key to check
+	 * @returns True if the parameter is in the syncable parameters list
+	 */
+	static canSyncParameter(key: string): boolean {
+		return SYNCABLE_PARAMETERS.some((param) => param.key === key && param.canSync);
+	}
+
+	/**
+	 * Get all syncable parameter keys.
+	 *
+	 * @returns Array of parameter keys that can be synced from server
+	 */
+	static getSyncableParameterKeys(): string[] {
+		return SYNCABLE_PARAMETERS.filter((param) => param.canSync).map((param) => param.key);
+	}
+
+	/**
+	 * Validate a server parameter value against its expected type.
+	 *
+	 * @param key - The parameter key to validate
+	 * @param value - The value to validate
+	 * @returns True if value matches the expected type for this parameter
+	 */
+	static validateServerParameter(key: string, value: ParameterValue): boolean {
+		const param = SYNCABLE_PARAMETERS.find((p) => p.key === key);
+		if (!param) return false;
+
+		switch (param.type) {
+			case SyncableParameterType.NUMBER:
+				return typeof value === 'number' && !isNaN(value);
+			case SyncableParameterType.STRING:
+				return typeof value === 'string';
+			case SyncableParameterType.BOOLEAN:
+				return typeof value === 'boolean';
+			default:
+				return false;
+		}
+	}
+
+	/**
+	 *
+	 *
+	 * Diff
+	 *
+	 *
+	 */
+
+	/**
+	 * Create a diff between current settings and server defaults.
+	 * Shows which parameters differ from server values, useful for debugging
+	 * and for the "Reset to defaults" functionality.
+	 *
+	 * @param currentSettings - Current parameter values in the settings store
+	 * @param serverDefaults - Default values extracted from server props
+	 * @returns Record of parameter diffs with current value, server value, and whether they differ
+	 */
+	static createParameterDiff(
+		currentSettings: ParameterRecord,
+		serverDefaults: ParameterRecord
+	): Record<string, { current: ParameterValue; server: ParameterValue; differs: boolean }> {
+		const diff: Record<
+			string,
+			{ current: ParameterValue; server: ParameterValue; differs: boolean }
+		> = {};
+
+		for (const key of this.getSyncableParameterKeys()) {
+			const currentValue = currentSettings[key];
+			const serverValue = serverDefaults[key];
+
+			if (serverValue !== undefined) {
+				diff[key] = {
+					current: currentValue,
+					server: serverValue,
+					differs: currentValue !== serverValue
+				};
+			}
+		}
+
+		return diff;
+	}
+}
diff --git a/tools/server/webui/src/lib/services/props.service.ts b/tools/ui/src/lib/services/props.service.ts
similarity index 100%
rename from tools/server/webui/src/lib/services/props.service.ts
rename to tools/ui/src/lib/services/props.service.ts
diff --git a/tools/ui/src/lib/services/router.service.ts b/tools/ui/src/lib/services/router.service.ts
new file mode 100644
index 00000000000..6fa172eec23
--- /dev/null
+++ b/tools/ui/src/lib/services/router.service.ts
@@ -0,0 +1,11 @@
+import { ROUTES } from '$lib/constants/routes';
+
+export class RouterService {
+	static chat(id: string): string {
+		return `${ROUTES.CHAT}/${id}`;
+	}
+
+	static settings(section: string): string {
+		return `${ROUTES.SETTINGS}/${section}`;
+	}
+}
diff --git a/tools/ui/src/lib/services/tools.service.ts b/tools/ui/src/lib/services/tools.service.ts
new file mode 100644
index 00000000000..8f39f5209f8
--- /dev/null
+++ b/tools/ui/src/lib/services/tools.service.ts
@@ -0,0 +1,40 @@
+import { apiFetch } from '$lib/utils';
+import { API_TOOLS } from '$lib/constants';
+import { ToolResponseField } from '$lib/enums';
+import type { ToolExecutionResult, ServerBuiltinToolInfo } from '$lib/types';
+
+export class ToolsService {
+	/**
+	 * Fetch the list of built-in tools from the server.
+	 *
+	 * @returns Array of tool definitions in OpenAI-compatible format
+	 */
+	static async list(): Promise<ServerBuiltinToolInfo[]> {
+		return apiFetch<ServerBuiltinToolInfo[]>(API_TOOLS.LIST);
+	}
+
+	/**
+	 * Execute a built-in tool on the server.
+	 */
+	static async executeTool(
+		toolName: string,
+		params: Record<string, unknown>,
+		signal?: AbortSignal
+	): Promise<ToolExecutionResult> {
+		const result = await apiFetch<Record<string, unknown>>(API_TOOLS.EXECUTE, {
+			method: 'POST',
+			body: JSON.stringify({ tool: toolName, params }),
+			signal
+		});
+
+		if (ToolResponseField.ERROR in result) {
+			return { content: String(result[ToolResponseField.ERROR]), isError: true };
+		}
+
+		if (ToolResponseField.PLAIN_TEXT in result) {
+			return { content: String(result[ToolResponseField.PLAIN_TEXT]), isError: false };
+		}
+
+		return { content: JSON.stringify(result), isError: false };
+	}
+}
diff --git a/tools/server/webui/src/lib/stores/agentic.svelte.ts b/tools/ui/src/lib/stores/agentic.svelte.ts
similarity index 57%
rename from tools/server/webui/src/lib/stores/agentic.svelte.ts
rename to tools/ui/src/lib/stores/agentic.svelte.ts
index 2b696024999..e8c0cc52307 100644
--- a/tools/server/webui/src/lib/stores/agentic.svelte.ts
+++ b/tools/ui/src/lib/stores/agentic.svelte.ts
@@ -24,14 +24,13 @@ import { ChatService } from '$lib/services';
 import { config } from '$lib/stores/settings.svelte';
 import { mcpStore } from '$lib/stores/mcp.svelte';
 import { modelsStore } from '$lib/stores/models.svelte';
+import { toolsStore } from '$lib/stores/tools.svelte';
+import { permissionsStore } from '$lib/stores/permissions.svelte';
+import { ToolSource, ToolPermissionDecision } from '$lib/enums';
+import { SvelteMap } from 'svelte/reactivity';
+import { ToolsService } from '$lib/services/tools.service';
 import { isAbortError } from '$lib/utils';
-import {
-	DEFAULT_AGENTIC_CONFIG,
-	NEWLINE_SEPARATOR,
-	TURN_LIMIT_MESSAGE,
-	LLM_ERROR_BLOCK_START,
-	LLM_ERROR_BLOCK_END
-} from '$lib/constants';
+import { DEFAULT_AGENTIC_CONFIG, NEWLINE_SEPARATOR } from '$lib/constants';
 import {
 	IMAGE_MIME_TO_EXTENSION,
 	DATA_URI_BASE64_REGEX,
@@ -58,7 +57,8 @@ import type {
 	AgenticMessage,
 	AgenticToolCallList,
 	AgenticFlowCallbacks,
-	AgenticFlowOptions
+	AgenticFlowOptions,
+	SteeringMessage
 } from '$lib/types/agentic';
 import type {
 	ApiChatCompletionToolCall,
@@ -84,7 +84,8 @@ function createDefaultSession(): AgenticSession {
 		currentTurn: 0,
 		totalToolCalls: 0,
 		lastError: null,
-		streamingToolCall: null
+		streamingToolCall: null,
+		pendingPermissionRequest: null
 	};
 }
 
@@ -98,13 +99,24 @@ function toAgenticMessages(messages: ApiChatMessageData[]): AgenticMessage[] {
 			return {
 				role: MessageRole.ASSISTANT,
 				content: message.content,
+				reasoning_content: message.reasoning_content,
 				tool_calls: message.tool_calls.map((call, index) => ({
 					id: call.id ?? `call_${index}`,
 					type: (call.type as ToolCallType.FUNCTION) ?? ToolCallType.FUNCTION,
-					function: { name: call.function?.name ?? '', arguments: call.function?.arguments ?? '' }
+					function: {
+						name: call.function?.name ?? '',
+						arguments: call.function?.arguments ?? ''
+					}
 				}))
 			} satisfies AgenticMessage;
 		}
+		if (message.role === MessageRole.ASSISTANT) {
+			return {
+				role: MessageRole.ASSISTANT,
+				content: message.content,
+				reasoning_content: message.reasoning_content
+			} satisfies AgenticMessage;
+		}
 		if (message.role === MessageRole.TOOL && message.tool_call_id) {
 			return {
 				role: MessageRole.TOOL,
@@ -120,7 +132,22 @@ function toAgenticMessages(messages: ApiChatMessageData[]): AgenticMessage[] {
 }
 
 class AgenticStore {
-	private _sessions = $state<Map<string, AgenticSession>>(new Map());
+	private _sessions = new SvelteMap<string, AgenticSession>();
+	/** Dedicated reactive state for pending permission requests (ensures immediate UI updates) */
+	private _pendingPermissions = new SvelteMap<
+		string,
+		{ toolName: string; serverLabel: string } | null
+	>();
+	/** Non-reactive: stores resolve functions for pending permission Promises */
+	private _permissionResolvers = new Map<string, (decision: ToolPermissionDecision) => void>();
+
+	/** Dedicated reactive state for pending continue requests (turn limit reached) */
+	private _pendingContinueRequests = new SvelteMap<string, boolean>();
+	/** Non-reactive: stores resolve functions for pending continue Promises */
+	private _continueResolvers = new Map<string, (shouldContinue: boolean) => void>();
+
+	/** Reactive: queued steering messages to inject between turns */
+	private _steeringMessages = new SvelteMap<string, SteeringMessage>();
 
 	get isReady(): boolean {
 		return true;
@@ -178,36 +205,212 @@ class AgenticStore {
 		return this.getSession(conversationId).streamingToolCall;
 	}
 
+	pendingPermissionRequest(
+		conversationId: string
+	): { toolName: string; serverLabel: string } | null {
+		return this._pendingPermissions.get(conversationId) ?? null;
+	}
+
+	pendingContinueRequest(conversationId: string): boolean {
+		return this._pendingContinueRequests.get(conversationId) ?? false;
+	}
+
+	resolveContinue(conversationId: string, shouldContinue: boolean): void {
+		const resolver = this._continueResolvers.get(conversationId);
+		if (resolver) {
+			this._continueResolvers.delete(conversationId);
+			resolver(shouldContinue);
+		}
+	}
+
+	resolvePermission(conversationId: string, decision: ToolPermissionDecision): void {
+		const resolver = this._permissionResolvers.get(conversationId);
+		if (resolver) {
+			this._permissionResolvers.delete(conversationId);
+			resolver(decision);
+		}
+	}
+
 	clearError(conversationId: string): void {
 		this.updateSession(conversationId, { lastError: null });
 	}
 
+	hasPendingSteeringMessage(conversationId: string): boolean {
+		return this._steeringMessages.has(conversationId);
+	}
+
+	pendingSteeringMessageContent(conversationId: string): string | null {
+		return this._steeringMessages.get(conversationId)?.content ?? null;
+	}
+
+	pendingSteeringMessageExtras(conversationId: string): DatabaseMessageExtra[] | undefined {
+		return this._steeringMessages.get(conversationId)?.extras;
+	}
+
+	/**
+	 * Queue a steering message. When the current agentic turn completes,
+	 * the flow exits and the caller re-sends the message as a normal chat message.
+	 */
+	injectSteeringMessage(
+		conversationId: string,
+		content: string,
+		extras?: DatabaseMessageExtra[]
+	): void {
+		this._steeringMessages.set(conversationId, { content, extras });
+	}
+
+	/**
+	 * Clear the pending steering message without consuming it.
+	 */
+	clearSteeringMessage(conversationId: string): void {
+		this._steeringMessages.delete(conversationId);
+	}
+
+	/**
+	 * Consume and return the pending steering message for re-sending.
+	 * Called by chatStore after the agentic flow exits.
+	 */
+	consumePendingSteeringMessage(conversationId: string): SteeringMessage | null {
+		const msg = this._steeringMessages.get(conversationId);
+		if (!msg) return null;
+		this._steeringMessages.delete(conversationId);
+		return msg;
+	}
+
 	getConfig(settings: SettingsConfigType, perChatOverrides?: McpServerOverride[]): AgenticConfig {
 		const maxTurns = Number(settings.agenticMaxTurns) || DEFAULT_AGENTIC_CONFIG.maxTurns;
 		const maxToolPreviewLines =
 			Number(settings.agenticMaxToolPreviewLines) || DEFAULT_AGENTIC_CONFIG.maxToolPreviewLines;
+		const hasTools =
+			mcpStore.hasEnabledServers(perChatOverrides) ||
+			toolsStore.builtinTools.length > 0 ||
+			toolsStore.customTools.length > 0;
 		return {
-			enabled: mcpStore.hasEnabledServers(perChatOverrides) && DEFAULT_AGENTIC_CONFIG.enabled,
+			enabled: hasTools && DEFAULT_AGENTIC_CONFIG.enabled,
 			maxTurns,
 			maxToolPreviewLines
 		};
 	}
 
+	private parseToolArguments(args: string | Record<string, unknown>): Record<string, unknown> {
+		if (typeof args === 'object') return args;
+		const trimmed = args.trim();
+		if (trimmed === '') return {};
+		return JSON.parse(trimmed) as Record<string, unknown>;
+	}
+
+	private async requestPermission(
+		conversationId: string,
+		toolName: string,
+		serverLabel: string,
+		signal?: AbortSignal
+	): Promise<ToolPermissionDecision> {
+		const permissionKey = toolsStore.getPermissionKey(toolName);
+		if (permissionKey && permissionsStore.hasTool(permissionKey)) {
+			return ToolPermissionDecision.ONCE;
+		}
+
+		this._pendingPermissions.set(conversationId, { toolName, serverLabel });
+
+		return new Promise<ToolPermissionDecision>((resolve) => {
+			if (signal?.aborted) {
+				this._pendingPermissions.set(conversationId, null);
+				resolve(ToolPermissionDecision.DENY);
+				return;
+			}
+
+			this._permissionResolvers.set(conversationId, (decision) => {
+				this._pendingPermissions.set(conversationId, null);
+				if (decision === ToolPermissionDecision.ALWAYS && permissionKey) {
+					permissionsStore.allowTool(permissionKey);
+				} else if (decision === ToolPermissionDecision.ALWAYS_SERVER) {
+					const serverToolKeys = toolsStore.allTools
+						.filter((t) =>
+							t.serverName
+								? t.serverName === serverLabel
+								: toolsStore.getToolServerLabel(t.definition.function.name) === serverLabel
+						)
+						.map((t) => toolsStore.getPermissionKey(t.definition.function.name)!)
+						.filter((k): k is string => k !== null);
+					permissionsStore.allowTools(serverToolKeys);
+				}
+				resolve(decision);
+			});
+
+			signal?.addEventListener(
+				'abort',
+				() => {
+					const resolver = this._permissionResolvers.get(conversationId);
+					if (resolver) {
+						this._permissionResolvers.delete(conversationId);
+						this._pendingPermissions.set(conversationId, null);
+						resolve(ToolPermissionDecision.DENY);
+					}
+				},
+				{ once: true }
+			);
+		});
+	}
+
+	private async requestContinue(conversationId: string, signal?: AbortSignal): Promise<boolean> {
+		this._pendingContinueRequests.set(conversationId, true);
+
+		return new Promise<boolean>((resolve) => {
+			if (signal?.aborted) {
+				this._pendingContinueRequests.set(conversationId, false);
+				resolve(false);
+				return;
+			}
+
+			this._continueResolvers.set(conversationId, (shouldContinue) => {
+				this._pendingContinueRequests.set(conversationId, false);
+				resolve(shouldContinue);
+			});
+
+			signal?.addEventListener(
+				'abort',
+				() => {
+					const resolver = this._continueResolvers.get(conversationId);
+					if (resolver) {
+						this._continueResolvers.delete(conversationId);
+						this._pendingContinueRequests.set(conversationId, false);
+						resolve(false);
+					}
+				},
+				{ once: true }
+			);
+		});
+	}
+
 	async runAgenticFlow(params: AgenticFlowParams): Promise<AgenticFlowResult> {
 		const { conversationId, messages, options = {}, callbacks, signal, perChatOverrides } = params;
 
+		// Clear any pending permissions/continue requests for this conversation when starting a new flow
+		this._pendingPermissions.set(conversationId, null);
+		this._permissionResolvers.delete(conversationId);
+		this._pendingContinueRequests.set(conversationId, false);
+		this._continueResolvers.delete(conversationId);
+		this._steeringMessages.delete(conversationId);
+
+		// Ensure built-in tools are fetched before checking if agentic is enabled
+		if (toolsStore.builtinTools.length === 0 && !toolsStore.loading) {
+			await toolsStore.fetchBuiltinTools();
+		}
+
 		const agenticConfig = this.getConfig(config(), perChatOverrides);
 		if (!agenticConfig.enabled) return { handled: false };
 
-		const initialized = await mcpStore.ensureInitialized(perChatOverrides);
-		if (!initialized) {
-			console.log('[AgenticStore] MCP not initialized, falling back to standard chat');
-			return { handled: false };
+		const hasMcpServers = mcpStore.hasEnabledServers(perChatOverrides);
+		if (hasMcpServers) {
+			const initialized = await mcpStore.ensureInitialized(perChatOverrides);
+
+			if (!initialized) {
+				console.log('[AgenticStore] MCP not initialized');
+			}
 		}
 
-		const tools = mcpStore.getToolDefinitionsForLLM();
+		const tools = toolsStore.getEnabledToolsForLLM();
 		if (tools.length === 0) {
-			console.log('[AgenticStore] No tools available, falling back to standard chat');
 			return { handled: false };
 		}
 
@@ -235,7 +438,8 @@ class AgenticStore {
 			totalToolCalls: 0,
 			lastError: null
 		});
-		mcpStore.acquireConnection();
+
+		if (hasMcpServers) mcpStore.acquireConnection();
 
 		try {
 			await this.executeAgenticLoop({
@@ -255,11 +459,14 @@ class AgenticStore {
 			return { handled: true, error: normalizedError };
 		} finally {
 			this.updateSession(conversationId, { isRunning: false });
-			await mcpStore
-				.releaseConnection()
-				.catch((err: unknown) =>
-					console.warn('[AgenticStore] Failed to release MCP connection:', err)
-				);
+
+			if (hasMcpServers) {
+				await mcpStore
+					.releaseConnection()
+					.catch((err: unknown) =>
+						console.warn('[AgenticStore] Failed to release MCP connection:', err)
+					);
+			}
 		}
 	}
 
@@ -303,7 +510,24 @@ class AgenticStore {
 
 		const effectiveModel = options.model || modelsStore.models[0]?.model || '';
 
-		for (let turn = 0; turn < maxTurns; turn++) {
+		let turn = 0;
+		while (true) {
+			if (turn >= maxTurns) {
+				// Turn limit reached - ask user whether to continue
+				const shouldContinue = await this.requestContinue(conversationId, signal);
+
+				// Yield to allow Svelte to flush the UI update
+				await new Promise((r) => setTimeout(r, 0));
+
+				if (!shouldContinue || signal?.aborted) {
+					onFlowComplete?.(this.buildFinalTimings(capturedTimings, agenticTimings));
+					return;
+				}
+
+				// User chose to continue - extend the limit
+				turn = 0;
+			}
+
 			this.updateSession(conversationId, { currentTurn: turn + 1 });
 			agenticTimings.turns = turn + 1;
 
@@ -414,10 +638,9 @@ class AgenticStore {
 					return;
 				}
 				const normalizedError = error instanceof Error ? error : new Error('LLM stream error');
-				// Save error as content in the current turn
-				onChunk?.(`${LLM_ERROR_BLOCK_START}${normalizedError.message}${LLM_ERROR_BLOCK_END}`);
+				// preserve partial output as is, the outer error dialog informs the user separately
 				await onAssistantTurnComplete?.(
-					turnContent + `${LLM_ERROR_BLOCK_START}${normalizedError.message}${LLM_ERROR_BLOCK_END}`,
+					turnContent,
 					turnReasoningContent || undefined,
 					this.buildFinalTimings(capturedTimings, agenticTimings),
 					undefined
@@ -426,6 +649,20 @@ class AgenticStore {
 				throw normalizedError;
 			}
 
+			// === Steering check: if a user message was queued during this turn, exit the flow.
+			// The caller (chatStore) will consume the pending message and re-send it normally.
+			if (this._steeringMessages.has(conversationId)) {
+				console.log('[AgenticStore] Steering message detected after turn, exiting agentic flow');
+				await onAssistantTurnComplete?.(
+					turnContent,
+					turnReasoningContent || undefined,
+					this.buildFinalTimings(capturedTimings, agenticTimings),
+					turnToolCalls.length > 0 ? this.normalizeToolCalls(turnToolCalls) : undefined
+				);
+				onFlowComplete?.(this.buildFinalTimings(capturedTimings, agenticTimings));
+				return;
+			}
+
 			// No tool calls = final turn, save and complete
 			if (turnToolCalls.length === 0) {
 				agenticTimings.perTurn!.push(turnStats);
@@ -479,31 +716,88 @@ class AgenticStore {
 			});
 
 			// Execute each tool call and create result messages
-			for (const toolCall of normalizedCalls) {
+			for (let i = 0; i < normalizedCalls.length; i++) {
+				const toolCall = normalizedCalls[i];
+
+				if (signal?.aborted) {
+					onFlowComplete?.(this.buildFinalTimings(capturedTimings, agenticTimings));
+					return;
+				}
+
+				// Check for pending steering message - skip remaining tool calls
+				if (this._steeringMessages.has(conversationId)) {
+					console.log(
+						`[AgenticStore] Steering message detected, skipping ${normalizedCalls.length - i} remaining tool call(s)`
+					);
+					for (let j = i; j < normalizedCalls.length; j++) {
+						const remainingCall = normalizedCalls[j];
+						const interruptedContent = 'Tool execution was interrupted by a new user message.';
+						if (createToolResultMessage) {
+							await createToolResultMessage(remainingCall.id, interruptedContent);
+						}
+						sessionMessages.push({
+							role: MessageRole.TOOL,
+							tool_call_id: remainingCall.id,
+							content: interruptedContent
+						});
+					}
+					break;
+				}
+
+				const toolName = toolCall.function.name;
+				const serverLabel = toolsStore.getToolServerLabel(toolName);
+
+				// Ask for permission before executing the tool
+				const permission = await this.requestPermission(
+					conversationId,
+					toolName,
+					serverLabel,
+					signal
+				);
+
+				// Yield to allow Svelte to flush the UI update (hide permission dialog)
+				await new Promise((r) => setTimeout(r, 0));
+
 				if (signal?.aborted) {
 					onFlowComplete?.(this.buildFinalTimings(capturedTimings, agenticTimings));
 					return;
 				}
 
 				const toolStartTime = performance.now();
-				const mcpCall: MCPToolCall = {
-					id: toolCall.id,
-					function: { name: toolCall.function.name, arguments: toolCall.function.arguments }
-				};
+				const toolSource = toolsStore.getToolSource(toolName);
 
 				let result: string;
 				let toolSuccess = true;
 
-				try {
-					const executionResult = await mcpStore.executeTool(mcpCall, signal);
-					result = executionResult.content;
-				} catch (error) {
-					if (isAbortError(error)) {
-						onFlowComplete?.(this.buildFinalTimings(capturedTimings, agenticTimings));
-						return;
-					}
-					result = `Error: ${error instanceof Error ? error.message : String(error)}`;
+				if (permission === ToolPermissionDecision.DENY) {
+					result = 'Tool execution was denied by the user.';
 					toolSuccess = false;
+				} else {
+					try {
+						if (toolSource === ToolSource.BUILTIN) {
+							const args = this.parseToolArguments(toolCall.function.arguments);
+							const executionResult = await ToolsService.executeTool(toolName, args, signal);
+
+							result = executionResult.content;
+
+							if (executionResult.isError) toolSuccess = false;
+						} else {
+							const mcpCall: MCPToolCall = {
+								id: toolCall.id,
+								function: { name: toolName, arguments: toolCall.function.arguments }
+							};
+							const executionResult = await mcpStore.executeTool(mcpCall, signal);
+
+							result = executionResult.content;
+						}
+					} catch (error) {
+						if (isAbortError(error)) {
+							onFlowComplete?.(this.buildFinalTimings(capturedTimings, agenticTimings));
+							return;
+						}
+						result = `Error: ${error instanceof Error ? error.message : String(error)}`;
+						toolSuccess = false;
+					}
 				}
 
 				const toolDurationMs = performance.now() - toolStartTime;
@@ -549,7 +843,9 @@ class AgenticStore {
 						if (modelsStore.modelSupportsVision(effectiveModel)) {
 							contentParts.push({
 								type: ContentPartType.IMAGE_URL,
-								image_url: { url: (attachment as DatabaseMessageExtraImageFile).base64Url }
+								image_url: {
+									url: (attachment as DatabaseMessageExtraImageFile).base64Url
+								}
 							});
 						} else {
 							console.info(
@@ -572,17 +868,18 @@ class AgenticStore {
 				const intermediateTimings = this.buildFinalTimings(capturedTimings, agenticTimings);
 				if (intermediateTimings) onTurnComplete?.(intermediateTimings);
 			}
-		}
 
-		// Turn limit reached
-		onChunk?.(TURN_LIMIT_MESSAGE);
-		await onAssistantTurnComplete?.(
-			TURN_LIMIT_MESSAGE,
-			undefined,
-			this.buildFinalTimings(capturedTimings, agenticTimings),
-			undefined
-		);
-		onFlowComplete?.(this.buildFinalTimings(capturedTimings, agenticTimings));
+			// If tools were interrupted by a steering message, exit now instead of starting another LLM turn
+			if (this._steeringMessages.has(conversationId)) {
+				console.log(
+					'[AgenticStore] Steering message detected after tool execution, exiting agentic flow'
+				);
+				onFlowComplete?.(this.buildFinalTimings(capturedTimings, agenticTimings));
+				return;
+			}
+
+			turn++;
+		}
 	}
 
 	private buildFinalTimings(
@@ -605,7 +902,10 @@ class AgenticStore {
 		return toolCalls.map((call, index) => ({
 			id: call?.id ?? `tool_${index}`,
 			type: (call?.type as ToolCallType.FUNCTION) ?? ToolCallType.FUNCTION,
-			function: { name: call?.function?.name ?? '', arguments: call?.function?.arguments ?? '' }
+			function: {
+				name: call?.function?.name ?? '',
+				arguments: call?.function?.arguments ?? ''
+			}
 		}));
 	}
 
@@ -680,6 +980,46 @@ export function agenticStreamingToolCall(conversationId: string) {
 	return agenticStore.streamingToolCall(conversationId);
 }
 
+export function agenticPendingPermissionRequest(conversationId: string) {
+	return agenticStore.pendingPermissionRequest(conversationId);
+}
+
+export function agenticResolvePermission(conversationId: string, decision: ToolPermissionDecision) {
+	agenticStore.resolvePermission(conversationId, decision);
+}
+
+export function agenticPendingContinueRequest(conversationId: string) {
+	return agenticStore.pendingContinueRequest(conversationId);
+}
+
+export function agenticResolveContinue(conversationId: string, shouldContinue: boolean) {
+	agenticStore.resolveContinue(conversationId, shouldContinue);
+}
+
+export function agenticHasPendingSteeringMessage(conversationId: string) {
+	return agenticStore.hasPendingSteeringMessage(conversationId);
+}
+
+export function agenticInjectSteeringMessage(
+	conversationId: string,
+	content: string,
+	extras?: DatabaseMessageExtra[]
+) {
+	agenticStore.injectSteeringMessage(conversationId, content, extras);
+}
+
+export function agenticPendingSteeringMessageContent(conversationId: string) {
+	return agenticStore.pendingSteeringMessageContent(conversationId);
+}
+
+export function agenticPendingSteeringMessageExtras(conversationId: string) {
+	return agenticStore.pendingSteeringMessageExtras(conversationId);
+}
+
+export function agenticClearSteeringMessage(conversationId: string) {
+	agenticStore.clearSteeringMessage(conversationId);
+}
+
 export function agenticIsAnyRunning() {
 	return agenticStore.isAnyRunning;
 }
diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/ui/src/lib/stores/chat.svelte.ts
similarity index 86%
rename from tools/server/webui/src/lib/stores/chat.svelte.ts
rename to tools/ui/src/lib/stores/chat.svelte.ts
index 540d4fdb5a5..61ea4c89296 100644
--- a/tools/server/webui/src/lib/stores/chat.svelte.ts
+++ b/tools/ui/src/lib/stores/chat.svelte.ts
@@ -36,7 +36,8 @@ import {
 import {
 	MAX_INACTIVE_CONVERSATION_STATES,
 	INACTIVE_CONVERSATION_STATE_MAX_AGE_MS,
-	SYSTEM_MESSAGE_PLACEHOLDER
+	SYSTEM_MESSAGE_PLACEHOLDER,
+	TITLE_GENERATION
 } from '$lib/constants';
 import type {
 	ChatMessageTimings,
@@ -44,7 +45,12 @@ import type {
 	ChatStreamCallbacks,
 	ErrorDialogState
 } from '$lib/types/chat';
-import type { ApiProcessingState, DatabaseMessage, DatabaseMessageExtra } from '$lib/types';
+import type {
+	ApiChatMessageData,
+	ApiProcessingState,
+	DatabaseMessage,
+	DatabaseMessageExtra
+} from '$lib/types';
 import { ErrorDialogType, MessageRole, MessageType } from '$lib/enums';
 
 interface ConversationStateEntry {
@@ -73,6 +79,12 @@ class ChatStore {
 	private _pendingDraftMessage = $state<string>('');
 	private _pendingDraftFiles = $state<ChatUploadedFile[]>([]);
 
+	/** Reactive: queued pending messages for non-agentic streaming */
+	private _pendingMessages = new SvelteMap<
+		string,
+		{ content: string; extras?: DatabaseMessageExtra[] }
+	>();
+
 	private setChatLoading(convId: string, loading: boolean): void {
 		this.touchConversationState(convId);
 		if (loading) {
@@ -176,6 +188,19 @@ class ChatStore {
 		}
 	}
 
+	/**
+	 * Abort the current agentic flow signal without clearing loading state.
+	 * Used by "Send immediately" to force the agentic loop to exit so that
+	 * the pending steering message can be re-sent.
+	 */
+	abortCurrentFlow(convId: string): void {
+		const c = this.abortControllers.get(convId);
+		if (c) {
+			c.abort();
+			this.abortControllers.delete(convId);
+		}
+	}
+
 	private showErrorDialog(state: ErrorDialogState | null): void {
 		this.errorDialogState = state;
 	}
@@ -240,7 +265,36 @@ class ChatStore {
 	}
 
 	private isChatLoadingInternal(convId: string): boolean {
-		return this.chatStreamingStates.has(convId);
+		return this.chatLoadingStates.has(convId) || this.chatStreamingStates.has(convId);
+	}
+
+	hasPendingMessage(convId: string): boolean {
+		return this._pendingMessages.has(convId);
+	}
+
+	pendingMessageContent(convId: string): string | null {
+		return this._pendingMessages.get(convId)?.content ?? null;
+	}
+
+	pendingMessageExtras(convId: string): DatabaseMessageExtra[] | undefined {
+		return this._pendingMessages.get(convId)?.extras;
+	}
+
+	injectPendingMessage(convId: string, content: string, extras?: DatabaseMessageExtra[]): void {
+		this._pendingMessages.set(convId, { content, extras });
+	}
+
+	clearPendingMessage(convId: string): void {
+		this._pendingMessages.delete(convId);
+	}
+
+	consumePendingMessage(
+		convId: string
+	): { content: string; extras?: DatabaseMessageExtra[] } | null {
+		const msg = this._pendingMessages.get(convId);
+		if (!msg) return null;
+		this._pendingMessages.delete(convId);
+		return msg;
 	}
 
 	private touchConversationState(convId: string): void {
@@ -380,7 +434,9 @@ class ChatStore {
 				rootId
 			);
 			if (firstActiveMessage) {
-				await DatabaseService.updateMessage(firstActiveMessage.id, { parent: systemMessage.id });
+				await DatabaseService.updateMessage(firstActiveMessage.id, {
+					parent: systemMessage.id
+				});
 				await DatabaseService.updateMessage(systemMessage.id, {
 					children: [firstActiveMessage.id]
 				});
@@ -395,7 +451,9 @@ class ChatStore {
 				});
 				const firstMsgIndex = conversationsStore.findMessageIndex(firstActiveMessage.id);
 				if (firstMsgIndex !== -1)
-					conversationsStore.updateMessageAtIndex(firstMsgIndex, { parent: systemMessage.id });
+					conversationsStore.updateMessageAtIndex(firstMsgIndex, {
+						parent: systemMessage.id
+					});
 			}
 			conversationsStore.activeMessages.unshift(systemMessage);
 			this.pendingEditMessageId = systemMessage.id;
@@ -462,7 +520,18 @@ class ChatStore {
 	async sendMessage(content: string, extras?: DatabaseMessageExtra[]): Promise<void> {
 		if (!content.trim() && (!extras || extras.length === 0)) return;
 		const activeConv = conversationsStore.activeConversation;
-		if (activeConv && this.isChatLoadingInternal(activeConv.id)) return;
+
+		// If agentic loop is running, inject as a steering message instead of starting a new flow
+		if (activeConv && agenticStore.isRunning(activeConv.id)) {
+			agenticStore.injectSteeringMessage(activeConv.id, content, extras);
+			return;
+		}
+
+		// If non-agentic streaming is active, queue as a pending message to send after completion
+		if (activeConv && this.isChatLoadingInternal(activeConv.id)) {
+			this.injectPendingMessage(activeConv.id, content, extras);
+			return;
+		}
 
 		// Cancel any in-flight pre-encode request
 		this.cancelPreEncode();
@@ -513,7 +582,11 @@ class ChatStore {
 			conversationsStore.addMessageToActive(assistantMessage);
 			await this.streamChatCompletion(
 				conversationsStore.activeMessages.slice(0, -1),
-				assistantMessage
+				assistantMessage,
+				undefined,
+				undefined,
+				undefined,
+				config().titleGenerationUseLLM && isNewConversation ? content : undefined
 			);
 		} catch (error) {
 			if (isAbortError(error)) {
@@ -542,7 +615,8 @@ class ChatStore {
 		assistantMessage: DatabaseMessage,
 		onComplete?: (content: string) => Promise<void>,
 		onError?: (error: Error) => void,
-		modelOverride?: string | null
+		modelOverride?: string | null,
+		firstUserMessageContent?: string
 	): Promise<void> {
 		let effectiveModel = modelOverride;
 
@@ -604,7 +678,8 @@ class ChatStore {
 			},
 			onReasoningChunk: (chunk: string) => {
 				streamedReasoningContent += chunk;
-				// Update UI to show reasoning is being received
+				// mark streaming state so a stop mid-thinking can persist the partial reasoning
+				this.setChatStreaming(convId, streamedContent, currentMessageId);
 				const idx = conversationsStore.findMessageIndex(currentMessageId);
 				conversationsStore.updateMessageAtIndex(idx, {
 					reasoningContent: streamedReasoningContent
@@ -612,7 +687,9 @@ class ChatStore {
 			},
 			onToolCallsStreaming: (toolCalls) => {
 				const idx = conversationsStore.findMessageIndex(currentMessageId);
-				conversationsStore.updateMessageAtIndex(idx, { toolCalls: JSON.stringify(toolCalls) });
+				conversationsStore.updateMessageAtIndex(idx, {
+					toolCalls: JSON.stringify(toolCalls)
+				});
 			},
 			onAttachments: (messageId: string, extras: DatabaseMessageExtra[]) => {
 				if (!extras.length) return;
@@ -723,9 +800,9 @@ class ChatStore {
 					const idx = conversationsStore.findMessageIndex(assistantMessage.id);
 
 					conversationsStore.updateMessageAtIndex(idx, { timings: finalTimings });
-					DatabaseService.updateMessage(assistantMessage.id, { timings: finalTimings }).catch(
-						console.error
-					);
+					DatabaseService.updateMessage(assistantMessage.id, {
+						timings: finalTimings
+					}).catch(console.error);
 				}
 
 				cleanupStreamingState();
@@ -743,19 +820,23 @@ class ChatStore {
 					);
 				}
 			},
-			onError: (error: Error) => {
+			onError: async (error: Error) => {
 				this.setStreamingActive(false);
 				if (isAbortError(error)) {
 					cleanupStreamingState();
+					// If aborted with a pending message (e.g. "Send immediately"), re-send it
+					const pending = this.consumePendingMessage(convId);
+					if (pending) {
+						this.sendMessage(pending.content, pending.extras);
+					}
 					return;
 				}
 				console.error('Streaming error:', error);
+				// keep whatever was streamed so far, the message stays in memory and in DB
+				await this.savePartialResponseIfNeeded(convId);
 				cleanupStreamingState();
-				const idx = conversationsStore.findMessageIndex(assistantMessage.id);
-				if (idx !== -1) {
-					const failedMessage = conversationsStore.removeMessageAtIndex(idx);
-					if (failedMessage) DatabaseService.deleteMessage(failedMessage.id).catch(console.error);
-				}
+				this.clearPendingMessage(convId);
+
 				const contextInfo = (
 					error as Error & { contextInfo?: { n_prompt_tokens: number; n_ctx: number } }
 				).contextInfo;
@@ -770,20 +851,32 @@ class ChatStore {
 
 		const perChatOverrides = conversationsStore.activeConversation?.mcpServerOverrides;
 
-		const agenticConfig = agenticStore.getConfig(config(), perChatOverrides);
-		if (agenticConfig.enabled) {
+		{
 			const agenticResult = await agenticStore.runAgenticFlow({
 				conversationId: convId,
 				messages: allMessages,
-				options: { ...this.getApiOptions(), ...(effectiveModel ? { model: effectiveModel } : {}) },
+				options: {
+					...this.getApiOptions(),
+					...(effectiveModel ? { model: effectiveModel } : {})
+				},
 				callbacks: streamCallbacks,
 				signal: abortController.signal,
 				perChatOverrides
 			});
-			if (agenticResult.handled) return;
+			if (agenticResult.handled) {
+				// Generate LLM based title for new conversations after agentic flow completes
+				if (firstUserMessageContent) {
+					await this.generateTitleWithLLM(firstUserMessageContent, streamedContent, convId);
+				}
+				// Check if there's a pending steering message to re-send
+				const pending = agenticStore.consumePendingSteeringMessage(convId);
+				if (pending) {
+					await this.sendMessage(pending.content, pending.extras);
+				}
+				return;
+			}
 		}
 
-		// Non-agentic path: direct streaming into the single assistant message
 		await ChatService.sendMessage(
 			allMessages,
 			{
@@ -823,6 +916,18 @@ class ChatStore {
 					cleanupStreamingState();
 					if (onComplete) await onComplete(content);
 					if (isRouterMode()) modelsStore.fetchRouterModels().catch(console.error);
+
+					// Generate LLM based title for new conversations (avoids stale reference
+					// issue when user switches conversations while streaming)
+					if (firstUserMessageContent) {
+						await this.generateTitleWithLLM(firstUserMessageContent, streamedContent, convId);
+					}
+
+					// Check if there's a pending message queued during streaming
+					const pending = this.consumePendingMessage(convId);
+					if (pending) {
+						await this.sendMessage(pending.content, pending.extras);
+					}
 				},
 				onError: streamCallbacks.onError
 			},
@@ -843,43 +948,100 @@ class ChatStore {
 		this.setChatLoading(convId, false);
 		this.clearChatStreaming(convId);
 		this.setProcessingState(convId, null);
+		this.clearPendingMessage(convId);
+	}
+
+	private async generateTitleWithLLM(
+		userContent: string,
+		assistantContent: string,
+		convId: string
+	): Promise<void> {
+		const effectiveModel = isRouterMode() && selectedModelName() ? selectedModelName() : undefined;
+		const configValue = config();
+		const titlePromptTemplate =
+			typeof configValue.titleGenerationPrompt === 'string' &&
+			configValue.titleGenerationPrompt.trim()
+				? configValue.titleGenerationPrompt
+				: TITLE_GENERATION.DEFAULT_PROMPT;
+
+		const titlePrompt = titlePromptTemplate
+			.replace('{{USER}}', String(userContent || ''))
+			.replace('{{ASSISTANT}}', String(assistantContent || ''));
+
+		const titleMessage: ApiChatMessageData = {
+			role: MessageRole.USER,
+			content: titlePrompt
+		};
+
+		const titleResponse = await ChatService.generateTitle(titleMessage, effectiveModel);
+
+		if (!titleResponse) {
+			return;
+		}
+
+		let cleanTitle = titleResponse.trim();
+		cleanTitle = cleanTitle
+			.replace(TITLE_GENERATION.PREFIX_PATTERN, '')
+			.replace(TITLE_GENERATION.QUOTE_PATTERN, '')
+			.trim();
+		if (!cleanTitle || cleanTitle.length < TITLE_GENERATION.MIN_LENGTH) {
+			const firstLine = userContent.split('\n').find((l) => l.trim().length > 0);
+			cleanTitle = firstLine ? firstLine.trim() : TITLE_GENERATION.FALLBACK;
+		}
+		if (cleanTitle && cleanTitle.length >= TITLE_GENERATION.MIN_LENGTH) {
+			await conversationsStore.updateConversationName(convId, cleanTitle);
+		}
 	}
+
 	private async savePartialResponseIfNeeded(convId?: string): Promise<void> {
 		const conversationId = convId || conversationsStore.activeConversation?.id;
 		if (!conversationId) return;
 		const streamingState = this.getChatStreaming(conversationId);
-		if (!streamingState || !streamingState.response.trim()) return;
+		if (!streamingState) return;
 		const messages =
 			conversationId === conversationsStore.activeConversation?.id
 				? conversationsStore.activeMessages
 				: await conversationsStore.getConversationMessages(conversationId);
 		if (!messages.length) return;
 		const lastMessage = messages[messages.length - 1];
-		if (lastMessage?.role === MessageRole.ASSISTANT) {
-			try {
-				const updateData: { content: string; timings?: ChatMessageTimings } = {
-					content: streamingState.response
+		if (lastMessage?.role !== MessageRole.ASSISTANT) return;
+
+		const partialContent = streamingState.response;
+		const partialReasoning = lastMessage.reasoningContent || '';
+
+		// nothing to persist when both content and reasoning are empty (e.g. stop before any token)
+		if (!partialContent.trim() && !partialReasoning.trim()) return;
+
+		try {
+			const updateData: {
+				content: string;
+				reasoningContent?: string;
+				timings?: ChatMessageTimings;
+			} = {
+				content: partialContent
+			};
+			if (partialReasoning) {
+				updateData.reasoningContent = partialReasoning;
+			}
+			const lastKnownState = this.getProcessingState(conversationId);
+			if (lastKnownState) {
+				updateData.timings = {
+					prompt_n: lastKnownState.promptTokens || 0,
+					prompt_ms: lastKnownState.promptMs,
+					predicted_n: lastKnownState.tokensDecoded || 0,
+					cache_n: lastKnownState.cacheTokens || 0,
+					predicted_ms:
+						lastKnownState.tokensPerSecond && lastKnownState.tokensDecoded
+							? (lastKnownState.tokensDecoded / lastKnownState.tokensPerSecond) * 1000
+							: undefined
 				};
-				const lastKnownState = this.getProcessingState(conversationId);
-				if (lastKnownState) {
-					updateData.timings = {
-						prompt_n: lastKnownState.promptTokens || 0,
-						prompt_ms: lastKnownState.promptMs,
-						predicted_n: lastKnownState.tokensDecoded || 0,
-						cache_n: lastKnownState.cacheTokens || 0,
-						predicted_ms:
-							lastKnownState.tokensPerSecond && lastKnownState.tokensDecoded
-								? (lastKnownState.tokensDecoded / lastKnownState.tokensPerSecond) * 1000
-								: undefined
-					};
-				}
-				await DatabaseService.updateMessage(lastMessage.id, updateData);
-				lastMessage.content = streamingState.response;
-				if (updateData.timings) lastMessage.timings = updateData.timings;
-			} catch (error) {
-				lastMessage.content = streamingState.response;
-				console.error('Failed to save partial response:', error);
 			}
+			await DatabaseService.updateMessage(lastMessage.id, updateData);
+			lastMessage.content = partialContent;
+			if (updateData.timings) lastMessage.timings = updateData.timings;
+		} catch (error) {
+			lastMessage.content = partialContent;
+			console.error('Failed to save partial response:', error);
 		}
 	}
 
@@ -1124,7 +1286,11 @@ class ChatStore {
 			const conversationContext = conversationsStore.activeMessages.slice(0, idx);
 			const contextWithContinue = [
 				...conversationContext,
-				{ role: MessageRole.ASSISTANT as const, content: originalContent }
+				{
+					role: MessageRole.ASSISTANT as const,
+					content: originalContent,
+					reasoning_content: originalReasoning || undefined
+				}
 			];
 
 			let appendedContent = '';
@@ -1142,6 +1308,7 @@ class ChatStore {
 				contextWithContinue,
 				{
 					...this.getApiOptions(),
+					continueFinalMessage: true,
 					onChunk: (chunk: string) => {
 						appendedContent += chunk;
 						hasReceivedContent = true;
@@ -1150,6 +1317,8 @@ class ChatStore {
 					onReasoningChunk: (chunk: string) => {
 						appendedReasoning += chunk;
 						hasReceivedContent = true;
+						// mark streaming state so a stop mid-thinking can persist the partial reasoning
+						this.setChatStreaming(msg.convId, originalContent + appendedContent, msg.id);
 						conversationsStore.updateMessageAtIndex(idx, {
 							reasoningContent: originalReasoning + appendedReasoning
 						});
@@ -1227,9 +1396,17 @@ class ChatStore {
 						}
 
 						console.error('Continue generation error:', error);
-						conversationsStore.updateMessageAtIndex(idx, { content: originalContent });
-
-						await DatabaseService.updateMessage(msg.id, { content: originalContent });
+						// keep whatever was appended so far, the message stays in memory and in DB
+						await DatabaseService.updateMessage(msg.id, {
+							content: originalContent + appendedContent,
+							reasoningContent: originalReasoning + appendedReasoning || undefined,
+							timestamp: Date.now()
+						});
+						conversationsStore.updateMessageAtIndex(idx, {
+							content: originalContent + appendedContent,
+							reasoningContent: originalReasoning + appendedReasoning || undefined,
+							timestamp: Date.now()
+						});
 
 						this.setChatLoading(msg.convId, false);
 						this.clearChatStreaming(msg.convId);
@@ -1688,3 +1865,13 @@ export const isChatStreaming = () => chatStore.isStreaming();
 export const isEditing = () => chatStore.isEditing();
 export const isLoading = () => chatStore.isLoading;
 export const pendingEditMessageId = () => chatStore.pendingEditMessageId;
+export const chatHasPendingMessage = (convId: string) => chatStore.hasPendingMessage(convId);
+export const chatPendingMessageContent = (convId: string) =>
+	chatStore.pendingMessageContent(convId);
+export const chatPendingMessageExtras = (convId: string) => chatStore.pendingMessageExtras(convId);
+export const chatClearPendingMessage = (convId: string) => chatStore.clearPendingMessage(convId);
+export const chatInjectPendingMessage = (
+	convId: string,
+	content: string,
+	extras?: DatabaseMessageExtra[]
+) => chatStore.injectPendingMessage(convId, content, extras);
diff --git a/tools/server/webui/src/lib/stores/conversations.svelte.ts b/tools/ui/src/lib/stores/conversations.svelte.ts
similarity index 96%
rename from tools/server/webui/src/lib/stores/conversations.svelte.ts
rename to tools/ui/src/lib/stores/conversations.svelte.ts
index 2d1d4e9c322..d6589232f0f 100644
--- a/tools/server/webui/src/lib/stores/conversations.svelte.ts
+++ b/tools/ui/src/lib/stores/conversations.svelte.ts
@@ -22,15 +22,11 @@ import { goto } from '$app/navigation';
 import { browser } from '$app/environment';
 import { toast } from 'svelte-sonner';
 import { DatabaseService } from '$lib/services/database.service';
+import { MigrationService } from '$lib/services/migration.service';
 import { config } from '$lib/stores/settings.svelte';
-import {
-	filterByLeafNodeId,
-	findLeafNode,
-	runLegacyMigration,
-	generateConversationTitle
-} from '$lib/utils';
+import { filterByLeafNodeId, findLeafNode, generateConversationTitle } from '$lib/utils';
 import type { McpServerOverride } from '$lib/types/database';
-import { MessageRole } from '$lib/enums';
+import { MessageRole, HtmlInputType, FileExtensionText } from '$lib/enums';
 import {
 	ISO_DATE_TIME_SEPARATOR,
 	ISO_DATE_TIME_SEPARATOR_REPLACEMENT,
@@ -44,6 +40,9 @@ import {
 	MULTIPLE_UNDERSCORE_REGEX,
 	MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY
 } from '$lib/constants';
+
+import { ROUTES } from '$lib/constants/routes';
+import { RouterService } from '$lib/services/router.service';
 import { SvelteMap, SvelteSet } from 'svelte/reactivity';
 
 export interface ConversationTreeItem {
@@ -133,9 +132,7 @@ class ConversationsStore {
 		if (this.isInitialized) return;
 
 		try {
-			// @deprecated Legacy migration for old marker-based messages.
-			// Remove once all users have migrated to the structured format.
-			await runLegacyMigration();
+			await MigrationService.runAllMigrations();
 
 			await this.loadConversations();
 			this.isInitialized = true;
@@ -260,7 +257,7 @@ class ConversationsStore {
 		this.activeConversation = conversation;
 		this.activeMessages = [];
 
-		await goto(`#/chat/${conversation.id}`);
+		await goto(RouterService.chat(conversation.id));
 
 		return conversation.id;
 	}
@@ -336,7 +333,7 @@ class ConversationsStore {
 
 				if (this.activeConversation && idsToRemove.has(this.activeConversation.id)) {
 					this.clearActiveConversation();
-					await goto(`?new_chat=true#/`);
+					await goto(ROUTES.NEW_CHAT);
 				}
 			} else {
 				// Reparent direct children to deleted conv's parent (or promote to top-level)
@@ -352,7 +349,7 @@ class ConversationsStore {
 
 				if (this.activeConversation?.id === convId) {
 					this.clearActiveConversation();
-					await goto(`?new_chat=true#/`);
+					await goto(ROUTES.NEW_CHAT);
 				}
 			}
 		} catch (error) {
@@ -376,7 +373,7 @@ class ConversationsStore {
 
 			toast.success('All conversations deleted');
 
-			await goto(`?new_chat=true#/`);
+			await goto(ROUTES.NEW_CHAT);
 		} catch (error) {
 			console.error('Failed to delete all conversations:', error);
 			toast.error('Failed to delete conversations');
@@ -729,7 +726,7 @@ class ConversationsStore {
 
 			this.conversations = [newConv, ...this.conversations];
 
-			await goto(`#/chat/${newConv.id}`);
+			await goto(RouterService.chat(newConv.id));
 
 			toast.success('Conversation forked');
 
@@ -797,7 +794,15 @@ class ConversationsStore {
 			return;
 		}
 
-		const downloadFilename = filename ?? this.generateConversationFilename(conversation, msgs);
+		let downloadFilename: string;
+
+		if (filename) {
+			downloadFilename = filename;
+		} else if (Array.isArray(data) && data.length > 1) {
+			downloadFilename = `${new Date().toISOString().split(ISO_DATE_TIME_SEPARATOR)[0]}_conversations.json`;
+		} else {
+			downloadFilename = this.generateConversationFilename(conversation, msgs);
+		}
 
 		const blob = new Blob([JSON.stringify(data, null, 2)], { type: 'application/json' });
 		const url = URL.createObjectURL(blob);
@@ -838,8 +843,8 @@ class ConversationsStore {
 	async importConversations(): Promise<DatabaseConversation[]> {
 		return new Promise((resolve, reject) => {
 			const input = document.createElement('input');
-			input.type = 'file';
-			input.accept = '.json';
+			input.type = HtmlInputType.FILE;
+			input.accept = FileExtensionText.JSON;
 
 			input.onchange = async (e) => {
 				const file = (e.target as HTMLInputElement)?.files?.[0];
diff --git a/tools/ui/src/lib/stores/draft-messages.svelte.ts b/tools/ui/src/lib/stores/draft-messages.svelte.ts
new file mode 100644
index 00000000000..7ee814d840e
--- /dev/null
+++ b/tools/ui/src/lib/stores/draft-messages.svelte.ts
@@ -0,0 +1,31 @@
+import { NEW_CHAT_DRAFT_KEY } from '$lib/constants';
+
+interface DraftMessage {
+	message: string;
+	files: ChatUploadedFile[];
+}
+
+class DraftMessagesStore {
+	private drafts = new Map<string, DraftMessage>();
+
+	getDraftMessage(chatId: string | undefined): DraftMessage {
+		const key = chatId ?? NEW_CHAT_DRAFT_KEY;
+		return this.drafts.get(key) ?? { message: '', files: [] };
+	}
+
+	saveDraftMessage(chatId: string | undefined, message: string, files: ChatUploadedFile[]): void {
+		const key = chatId ?? NEW_CHAT_DRAFT_KEY;
+		if (message || files.length > 0) {
+			this.drafts.set(key, { message, files: [...files] });
+		} else {
+			this.drafts.delete(key);
+		}
+	}
+
+	clearDraftMessage(chatId: string | undefined): void {
+		const key = chatId ?? NEW_CHAT_DRAFT_KEY;
+		this.drafts.delete(key);
+	}
+}
+
+export const draftMessagesStore = new DraftMessagesStore();
diff --git a/tools/server/webui/src/lib/stores/mcp-resources.svelte.ts b/tools/ui/src/lib/stores/mcp-resources.svelte.ts
similarity index 100%
rename from tools/server/webui/src/lib/stores/mcp-resources.svelte.ts
rename to tools/ui/src/lib/stores/mcp-resources.svelte.ts
diff --git a/tools/server/webui/src/lib/stores/mcp.svelte.ts b/tools/ui/src/lib/stores/mcp.svelte.ts
similarity index 96%
rename from tools/server/webui/src/lib/stores/mcp.svelte.ts
rename to tools/ui/src/lib/stores/mcp.svelte.ts
index 70470c56506..8fb306da881 100644
--- a/tools/server/webui/src/lib/stores/mcp.svelte.ts
+++ b/tools/ui/src/lib/stores/mcp.svelte.ts
@@ -21,16 +21,16 @@
 
 import { browser } from '$app/environment';
 import { base } from '$app/paths';
+import { SETTINGS_KEYS } from '$lib/constants';
 import { MCPService } from '$lib/services/mcp.service';
 import { config, settingsStore } from '$lib/stores/settings.svelte';
 import { mcpResourceStore } from '$lib/stores/mcp-resources.svelte';
 import { mode } from 'mode-watcher';
 import {
-	getProxiedUrlString,
 	parseMcpServerSettings,
 	detectMcpTransportFromUrl,
-	getFaviconUrl,
-	uuid
+	uuid,
+	extractRootDomain
 } from '$lib/utils';
 import {
 	MCPConnectionPhase,
@@ -104,7 +104,7 @@ class MCPStore {
 
 	/**
 	 * Probes the CORS proxy endpoint to determine availability.
-	 * The endpoint is only registered when llama-server runs with --webui-mcp-proxy.
+	 * The endpoint is only registered when llama-server runs with --ui-mcp-proxy.
 	 */
 	async probeProxy(): Promise<void> {
 		try {
@@ -168,7 +168,9 @@ class MCPStore {
 				enabled: Boolean((entry as { enabled?: unknown })?.enabled),
 				url,
 				name: (entry as { name?: string })?.name,
-				requestTimeoutSeconds: DEFAULT_MCP_CONFIG.requestTimeoutSeconds,
+				requestTimeoutSeconds:
+					(entry as { requestTimeoutSeconds?: number })?.requestTimeoutSeconds ??
+					DEFAULT_MCP_CONFIG.requestTimeoutSeconds,
 				headers: headers || undefined,
 				useProxy: Boolean((entry as { useProxy?: unknown })?.useProxy)
 			} satisfies MCPServerSettingsEntry;
@@ -413,7 +415,9 @@ class MCPStore {
 	#isValidIconUri(src: string): boolean {
 		try {
 			if (src.startsWith(UrlProtocol.DATA)) return true;
+
 			const url = new URL(src);
+
 			return url.protocol === UrlProtocol.HTTPS;
 		} catch {
 			return false;
@@ -446,40 +450,29 @@ class MCPStore {
 
 		// 1. Prefer icon explicitly matching the current color scheme
 		const themedIcon = validIcons.find((icon) => icon.theme === preferredTheme);
-		if (themedIcon) return this.#proxyIconSrc(themedIcon.src);
+		if (themedIcon) return themedIcon.src;
 
 		// 2. Handle universal icons (no theme specified)
 		const universalIcons = validIcons.filter((icon) => !icon.theme);
 
 		if (universalIcons.length === EXPECTED_THEMED_ICON_PAIR_COUNT) {
 			// Heuristic: two theme-less icons → assume [0] = light, [1] = dark
-			return this.#proxyIconSrc(universalIcons[isDark ? 1 : 0].src);
+			return universalIcons[isDark ? 1 : 0].src;
 		}
 
 		if (universalIcons.length > 0) {
-			return this.#proxyIconSrc(universalIcons[0].src);
+			return universalIcons[0].src;
 		}
 
 		// 3. Last resort: use opposite-theme icon
-		return this.#proxyIconSrc(validIcons[0].src);
-	}
-
-	/**
-	 * Route an icon src through the CORS proxy if it's an HTTPS URL.
-	 * Data URIs are returned as-is.
-	 */
-	#proxyIconSrc(src: string): string {
-		if (src.startsWith('data:')) return src;
-		if (!this._proxyAvailable) return src;
-
-		return getProxiedUrlString(src);
+		return validIcons[0].src;
 	}
 
 	/**
 	 * Get icon URL for an MCP server by its ID.
-	 * Prefers the server's own icons (from MCP spec) and falls back
-	 * to Google's favicon service.
-	 * Returns null if server is not found.
+	 * Returns the best icon from the MCP server's `icons` array
+	 * (see MCP spec: spec.modelcontextprotocol.io).
+	 * Returns null if no icon is available.
 	 */
 	getServerFavicon(serverId: string): string | null {
 		const server = this.getServerById(serverId);
@@ -497,7 +490,39 @@ class MCPStore {
 			}
 		}
 
-		return getFaviconUrl(server.url, this._proxyAvailable);
+		// Fallback: try favicon from root domain
+		const fallbackUrl = this.#getServerFaviconFallback(server.url);
+		if (fallbackUrl) {
+			return fallbackUrl;
+		}
+
+		return null;
+	}
+
+	/**
+	 * Construct a fallback favicon URL from the MCP server URL.
+	 * e.g. https://mcp.exa.ai/mcp -> https://exa.ai/favicon.ico
+	 */
+	#getServerFaviconFallback(serverUrl: string): string | null {
+		try {
+			const url = new URL(serverUrl);
+			const rootDomain = extractRootDomain(url);
+			if (!rootDomain) return null;
+
+			const origin = `${url.protocol}//${rootDomain}`;
+			const candidates = ['favicon.ico', 'favicon.svg', 'favicon.png'];
+
+			for (const path of candidates) {
+				const faviconUrl = `${origin}/${path}`;
+				if (this.#isValidIconUri(faviconUrl)) {
+					return faviconUrl;
+				}
+			}
+		} catch {
+			// Invalid URL, return null
+		}
+
+		return null;
 	}
 
 	isAnyServerLoading(): boolean {
@@ -531,16 +556,17 @@ class MCPStore {
 			url: serverData.url.trim(),
 			name: serverData.name,
 			headers: serverData.headers?.trim() || undefined,
-			requestTimeoutSeconds: DEFAULT_MCP_CONFIG.requestTimeoutSeconds,
+			requestTimeoutSeconds:
+				Number(config().mcpRequestTimeoutSeconds) || DEFAULT_MCP_CONFIG.requestTimeoutSeconds,
 			useProxy: serverData.useProxy
 		};
-		settingsStore.updateConfig('mcpServers', JSON.stringify([...servers, newServer]));
+		settingsStore.updateConfig(SETTINGS_KEYS.MCP_SERVERS, JSON.stringify([...servers, newServer]));
 	}
 
 	updateServer(id: string, updates: Partial<MCPServerSettingsEntry>): void {
 		const servers = this.getServers();
 		settingsStore.updateConfig(
-			'mcpServers',
+			SETTINGS_KEYS.MCP_SERVERS,
 			JSON.stringify(
 				servers.map((server) => (server.id === id ? { ...server, ...updates } : server))
 			)
@@ -549,7 +575,10 @@ class MCPStore {
 
 	removeServer(id: string): void {
 		const servers = this.getServers();
-		settingsStore.updateConfig('mcpServers', JSON.stringify(servers.filter((s) => s.id !== id)));
+		settingsStore.updateConfig(
+			SETTINGS_KEYS.MCP_SERVERS,
+			JSON.stringify(servers.filter((s) => s.id !== id))
+		);
 		this.clearHealthCheck(id);
 	}
 
@@ -775,7 +804,12 @@ class MCPStore {
 		this.toolsIndex.clear();
 		this.serverConfigs.clear();
 		this.configSignature = null;
-		this.updateState({ isInitializing: false, error: null, toolCount: 0, connectedServers: [] });
+		this.updateState({
+			isInitializing: false,
+			error: null,
+			toolCount: 0,
+			connectedServers: []
+		});
 	}
 
 	/**
diff --git a/tools/server/webui/src/lib/stores/models.svelte.ts b/tools/ui/src/lib/stores/models.svelte.ts
similarity index 80%
rename from tools/server/webui/src/lib/stores/models.svelte.ts
rename to tools/ui/src/lib/stores/models.svelte.ts
index d7c885844fc..45981b38fa0 100644
--- a/tools/server/webui/src/lib/stores/models.svelte.ts
+++ b/tools/ui/src/lib/stores/models.svelte.ts
@@ -1,7 +1,8 @@
 import { SvelteMap, SvelteSet } from 'svelte/reactivity';
 import { toast } from 'svelte-sonner';
 import { ServerModelStatus, ModelModality } from '$lib/enums';
-import { ModelsService, PropsService } from '$lib/services';
+import { ModelsService } from '$lib/services/models.service';
+import { PropsService } from '$lib/services/props.service';
 import { serverStore } from '$lib/stores/server.svelte';
 import { TTLCache } from '$lib/utils';
 import {
@@ -10,6 +11,8 @@ import {
 	FAVORITE_MODELS_LOCALSTORAGE_KEY
 } from '$lib/constants';
 
+import { conversationsStore } from '$lib/stores/conversations.svelte';
+
 /**
  * modelsStore - Reactive store for model management in both MODEL and ROUTER modes
  *
@@ -54,6 +57,10 @@ class ModelsStore {
 	selectedModelId = $state<string | null>(null);
 	selectedModelName = $state<string | null>(null);
 
+	// dedup concurrent fetch() callers, all awaiters share the same inflight promise
+	// without this, ?model=<name> URL handler raced an in-progress fetch and saw an empty list
+	private inflightFetch: Promise<void> | null = null;
+
 	private modelUsage = $state<Map<string, SvelteSet<string>>>(new Map());
 	private modelLoadingStates = new SvelteMap<string, boolean>();
 
@@ -141,7 +148,8 @@ class ModelsStore {
 		if (props?.modalities) {
 			return {
 				vision: props.modalities.vision ?? false,
-				audio: props.modalities.audio ?? false
+				audio: props.modalities.audio ?? false,
+				video: props.modalities.video ?? false
 			};
 		}
 
@@ -162,6 +170,13 @@ class ModelsStore {
 		return this.getModelModalities(modelId)?.audio ?? false;
 	}
 
+	/**
+	 * Check if a model supports video modality
+	 */
+	modelSupportsVideo(modelId: string): boolean {
+		return this.getModelModalities(modelId)?.video ?? false;
+	}
+
 	/**
 	 * Get model modalities as an array of ModelModality enum values
 	 */
@@ -173,6 +188,7 @@ class ModelsStore {
 
 		if (modalities.vision) result.push(ModelModality.VISION);
 		if (modalities.audio) result.push(ModelModality.AUDIO);
+		if (modalities.video) result.push(ModelModality.VIDEO);
 
 		return result;
 	}
@@ -257,9 +273,18 @@ class ModelsStore {
 	 * Also fetches modalities for MODEL mode (single model)
 	 */
 	async fetch(force = false): Promise<void> {
-		if (this.loading) return;
+		if (this.inflightFetch) return this.inflightFetch;
 		if (this.models.length > 0 && !force) return;
 
+		this.inflightFetch = this.runFetch();
+		try {
+			await this.inflightFetch;
+		} finally {
+			this.inflightFetch = null;
+		}
+	}
+
+	private async runFetch(): Promise<void> {
 		this.loading = true;
 		this.error = null;
 
@@ -300,7 +325,8 @@ class ModelsStore {
 			if (serverStore.isModelMode && this.models.length > 0 && serverProps?.modalities) {
 				const modalities: ModelModalities = {
 					vision: serverProps.modalities.vision ?? false,
-					audio: serverProps.modalities.audio ?? false
+					audio: serverProps.modalities.audio ?? false,
+					video: serverProps.modalities.video ?? false
 				};
 				this.modelPropsCache.set(this.models[0].model, serverProps);
 				this.models = this.models.map((model, index) =>
@@ -326,11 +352,7 @@ class ModelsStore {
 			this.routerModels = response.data;
 			await this.fetchModalitiesForLoadedModels();
 
-			const o = this.models.filter((option) => {
-				const modelProps = this.getModelProps(option.model);
-
-				return modelProps?.webui !== false;
-			});
+			const o = this.models.filter((option) => this.getModelProps(option.model)?.ui !== false);
 
 			if (o.length === 1 && this.isModelLoaded(o[0].model)) {
 				this.selectModelById(o[0].id);
@@ -398,7 +420,8 @@ class ModelsStore {
 
 				const modalities: ModelModalities = {
 					vision: props.modalities.vision ?? false,
-					audio: props.modalities.audio ?? false
+					audio: props.modalities.audio ?? false,
+					video: props.modalities.video ?? false
 				};
 
 				return { ...model, modalities };
@@ -410,6 +433,102 @@ class ModelsStore {
 		}
 	}
 
+	/**
+	 * Gets the model name from the last assistant message in the active conversation.
+	 * Iterates backward through messages to find the most recent message with a model.
+	 * Used by both the chat page and settings page to maintain model consistency.
+	 * @returns The model name or null if not found
+	 */
+	getModelFromLastAssistantResponse(): string | null {
+		const messages = conversationsStore.activeMessages;
+		if (!messages || messages.length === 0) return null;
+
+		// Iterate backward to find the last message with a model
+		for (let i = messages.length - 1; i >= 0; i--) {
+			if (messages[i].model) {
+				return messages[i].model;
+			}
+		}
+
+		return null;
+	}
+
+	/**
+	 * Auto-selects the model from the last assistant response if available and loaded.
+	 * Returns true if a model was selected, false otherwise.
+	 * This is used by the chat page to maintain model consistency across page navigation.
+	 */
+	async selectModelFromLastAssistantResponse(): Promise<boolean> {
+		const lastModel = this.getModelFromLastAssistantResponse();
+		if (!lastModel) return false;
+
+		// Skip if already selected
+		if (this.selectedModelName === lastModel) return false;
+
+		const matchingModel = this.models.find((option) => option.model === lastModel);
+		if (!matchingModel) return false;
+
+		if (!this.isModelLoaded(lastModel)) {
+			console.log('[modelsStore] last assistant model not loaded:', lastModel);
+			return false;
+		}
+
+		try {
+			await this.selectModelById(matchingModel.id);
+			console.log(`[modelsStore] Automatically selected model: ${lastModel} from last message`);
+			return true;
+		} catch (error) {
+			console.warn('[modelsStore] Failed to automatically select model from last message:', error);
+			return false;
+		}
+	}
+
+	/**
+	 * Auto-selects the first available model if none is selected, and fetches its props.
+	 * Prioritizes:
+	 * 1. Model from active conversation's last assistant response (if loaded)
+	 * 2. Model from active conversation's last assistant response (if not loaded)
+	 * 3. First loaded model (not from active conversation)
+	 * 4. First available model
+	 * This is used to ensure default values are populated in settings pages.
+	 */
+	async ensureFirstModelSelected(): Promise<void> {
+		if (this.selectedModelName) return;
+
+		// Filter models that are visible in the UI
+		const availableModels = this.models.filter(
+			(option) => this.getModelProps(option.model)?.ui !== false
+		);
+
+		if (availableModels.length === 0) return;
+
+		// Try to select model from last assistant response first
+		const lastModel = this.getModelFromLastAssistantResponse();
+		if (lastModel) {
+			const lastModelOption = availableModels.find((m) => m.model === lastModel);
+			if (lastModelOption) {
+				await this.selectModelById(lastModelOption.id);
+				if (this.isModelLoaded(lastModel)) {
+					await this.fetchModelProps(lastModel);
+				}
+				return;
+			}
+		}
+
+		// Try to find a loaded model first
+		const loadedModel = availableModels.find((m) => this.isModelLoaded(m.model));
+		if (loadedModel) {
+			await this.selectModelById(loadedModel.id);
+			await this.fetchModelProps(loadedModel.model);
+			return;
+		}
+
+		// Fall back to the first available model
+		const firstModel = availableModels[0];
+		await this.selectModelById(firstModel.id);
+		// Don't fetch props for unloaded models (will fail in ROUTER mode)
+	}
+
 	/**
 	 * Update modalities for a specific model
 	 * Called when a model is loaded or when we need fresh modality data
@@ -421,7 +540,8 @@ class ModelsStore {
 
 			const modalities: ModelModalities = {
 				vision: props.modalities.vision ?? false,
-				audio: props.modalities.audio ?? false
+				audio: props.modalities.audio ?? false,
+				video: props.modalities.video ?? false
 			};
 
 			this.models = this.models.map((model) =>
diff --git a/tools/ui/src/lib/stores/permissions.svelte.ts b/tools/ui/src/lib/stores/permissions.svelte.ts
new file mode 100644
index 00000000000..c50fbe02db8
--- /dev/null
+++ b/tools/ui/src/lib/stores/permissions.svelte.ts
@@ -0,0 +1,59 @@
+import { ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY } from '$lib/constants';
+
+import { SvelteSet } from 'svelte/reactivity';
+
+class PermissionsStore {
+	private _tools = $state(new SvelteSet<string>());
+
+	constructor() {
+		try {
+			const stored = localStorage.getItem(ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY);
+			if (stored) {
+				for (const name of JSON.parse(stored) as string[]) {
+					if (typeof name === 'string') this._tools.add(name);
+				}
+			}
+		} catch (err) {
+			console.error(
+				`Failed to load permissions from localStorage ("${ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY}"):`,
+				err
+			);
+		}
+	}
+
+	get tools(): ReadonlySet<string> {
+		return this._tools;
+	}
+
+	hasTool(key: string): boolean {
+		return this._tools.has(key);
+	}
+
+	allowTool(key: string): void {
+		this._tools.add(key);
+		this._persist();
+	}
+
+	allowTools(keys: string[]): void {
+		for (const key of keys) this._tools.add(key);
+		this._persist();
+	}
+
+	revokeTool(key: string): void {
+		this._tools.delete(key);
+		this._persist();
+	}
+
+	private _persist(): void {
+		try {
+			localStorage.setItem(ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY, JSON.stringify([...this._tools]));
+		} catch (err) {
+			console.error(
+				`Failed to persist to localStorage ("${ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY}"):`,
+				err
+			);
+		}
+	}
+}
+
+export const permissionsStore = new PermissionsStore();
diff --git a/tools/server/webui/src/lib/stores/persisted.svelte.ts b/tools/ui/src/lib/stores/persisted.svelte.ts
similarity index 100%
rename from tools/server/webui/src/lib/stores/persisted.svelte.ts
rename to tools/ui/src/lib/stores/persisted.svelte.ts
diff --git a/tools/server/webui/src/lib/stores/server.svelte.ts b/tools/ui/src/lib/stores/server.svelte.ts
similarity index 96%
rename from tools/server/webui/src/lib/stores/server.svelte.ts
rename to tools/ui/src/lib/stores/server.svelte.ts
index 48874bf1b7c..dfcb9b2bb49 100644
--- a/tools/server/webui/src/lib/stores/server.svelte.ts
+++ b/tools/ui/src/lib/stores/server.svelte.ts
@@ -50,8 +50,8 @@ class ServerStore {
 		return typeof nCtx === 'number' ? nCtx : null;
 	}
 
-	get webuiSettings(): Record<string, string | number | boolean> | undefined {
-		return this.props?.webui_settings;
+	get uiSettings(): Record<string, string | number | boolean> | undefined {
+		return this.props?.ui_settings ?? this.props?.webui_settings;
 	}
 
 	get isRouterMode(): boolean {
diff --git a/tools/ui/src/lib/stores/settings-referrer.svelte.ts b/tools/ui/src/lib/stores/settings-referrer.svelte.ts
new file mode 100644
index 00000000000..297a0d6a455
--- /dev/null
+++ b/tools/ui/src/lib/stores/settings-referrer.svelte.ts
@@ -0,0 +1,12 @@
+import { SETTINGS_FALLBACK_EXIT_ROUTE } from '$lib/constants';
+
+let _url = $state<string>(SETTINGS_FALLBACK_EXIT_ROUTE);
+
+export const settingsReferrer = {
+	get url() {
+		return _url;
+	},
+	set url(value: string) {
+		_url = value;
+	}
+};
diff --git a/tools/server/webui/src/lib/stores/settings.svelte.ts b/tools/ui/src/lib/stores/settings.svelte.ts
similarity index 68%
rename from tools/server/webui/src/lib/stores/settings.svelte.ts
rename to tools/ui/src/lib/stores/settings.svelte.ts
index f591de26b73..58eea1ee6d3 100644
--- a/tools/server/webui/src/lib/stores/settings.svelte.ts
+++ b/tools/ui/src/lib/stores/settings.svelte.ts
@@ -32,11 +32,16 @@
  */
 
 import { browser } from '$app/environment';
+import { ColorMode } from '$lib/enums';
+import type { SettingsExportType } from '$lib/types';
+import { setMode } from 'mode-watcher';
 import {
 	CONFIG_LOCALSTORAGE_KEY,
 	SETTING_CONFIG_DEFAULT,
+	SETTINGS_KEYS,
 	USER_OVERRIDES_LOCALSTORAGE_KEY
 } from '$lib/constants';
+
 import { IsMobile } from '$lib/hooks/is-mobile.svelte';
 import { ParameterSyncService } from '$lib/services/parameter-sync.service';
 import { serverStore } from '$lib/stores/server.svelte';
@@ -57,7 +62,6 @@ class SettingsStore {
 	 */
 
 	config = $state<SettingsConfigType>({ ...SETTING_CONFIG_DEFAULT });
-	theme = $state<string>('auto');
 	isInitialized = $state(false);
 	userOverrides = $state<Set<string>>(new Set());
 
@@ -75,8 +79,9 @@ class SettingsStore {
 	 */
 	private getServerDefaults(): Record<string, string | number | boolean> {
 		const serverParams = serverStore.defaultParams;
-		const webuiSettings = serverStore.webuiSettings;
-		return ParameterSyncService.extractServerDefaults(serverParams, webuiSettings);
+		const uiSettings = serverStore.uiSettings;
+
+		return ParameterSyncService.extractServerDefaults(serverParams, uiSettings);
 	}
 
 	constructor() {
@@ -99,7 +104,9 @@ class SettingsStore {
 	initialize() {
 		try {
 			this.loadConfig();
-			this.loadTheme();
+			this.migrateLegacyTheme();
+			// Apply the persisted theme from config on initial load
+			setMode(this.config[SETTINGS_KEYS.THEME] as ColorMode);
 			this.isInitialized = true;
 		} catch (error) {
 			console.error('Failed to initialize settings store:', error);
@@ -124,9 +131,9 @@ class SettingsStore {
 			};
 
 			// Default sendOnEnter to false on mobile when the user has no saved preference
-			if (!('sendOnEnter' in savedVal)) {
+			if (!(SETTINGS_KEYS.SEND_ON_ENTER in savedVal)) {
 				if (new IsMobile().current) {
-					this.config.sendOnEnter = false;
+					this.config[SETTINGS_KEYS.SEND_ON_ENTER] = false;
 				}
 			}
 
@@ -143,12 +150,21 @@ class SettingsStore {
 	}
 
 	/**
-	 * Load theme from localStorage
+	 * Migrate the legacy un-namespaced "theme" localStorage key into config.
+	 * Previously theme was stored separately in localStorage("theme") — now it lives
+	 * inside the config object alongside all other settings.
+	 * After migration the legacy key is removed.
 	 */
-	private loadTheme() {
+	private migrateLegacyTheme() {
 		if (!browser) return;
 
-		this.theme = localStorage.getItem('theme') || 'auto';
+		const legacyTheme = localStorage.getItem('theme');
+		if (legacyTheme) {
+			this.config[SETTINGS_KEYS.THEME] = legacyTheme;
+			localStorage.removeItem('theme');
+			this.saveConfig();
+			setMode(legacyTheme as ColorMode);
+		}
 	}
 	/**
 	 *
@@ -233,29 +249,13 @@ class SettingsStore {
 	}
 
 	/**
-	 * Update the theme setting
+	 * Update the theme setting.
 	 * @param newTheme - The new theme value
 	 */
 	updateTheme(newTheme: string) {
-		this.theme = newTheme;
-		this.saveTheme();
-	}
-
-	/**
-	 * Save the current theme to localStorage
-	 */
-	private saveTheme() {
-		if (!browser) return;
+		this.updateConfig(SETTINGS_KEYS.THEME, newTheme);
 
-		try {
-			if (this.theme === 'auto') {
-				localStorage.removeItem('theme');
-			} else {
-				localStorage.setItem('theme', this.theme);
-			}
-		} catch (error) {
-			console.error('Failed to save theme to localStorage:', error);
-		}
+		setMode(newTheme as ColorMode);
 	}
 
 	/**
@@ -271,35 +271,39 @@ class SettingsStore {
 	 */
 	resetConfig() {
 		this.config = { ...SETTING_CONFIG_DEFAULT };
+
 		this.saveConfig();
 	}
 
 	/**
-	 * Reset theme to auto
+	 * Reset theme to default value.
+	 * Theme is now stored inside the config object.
 	 */
 	resetTheme() {
-		this.theme = 'auto';
-		this.saveTheme();
+		this.updateConfig(SETTINGS_KEYS.THEME, SETTING_CONFIG_DEFAULT[SETTINGS_KEYS.THEME]);
+
+		setMode(SETTING_CONFIG_DEFAULT[SETTINGS_KEYS.THEME] as ColorMode);
 	}
 
 	/**
-	 * Reset all settings to defaults
+	 * Reset all settings to defaults.
 	 */
 	resetAll() {
 		this.resetConfig();
+
 		this.resetTheme();
 	}
 
 	/**
-	 * Reset a parameter to server default (or webui default if no server default)
+	 * Reset a parameter to Server default (or UI default if no Server default)
 	 */
 	resetParameterToServerDefault(key: string): void {
 		const serverDefaults = this.getServerDefaults();
-		const webuiSettings = serverStore.webuiSettings;
+		const uiSettings = serverStore.uiSettings;
 
-		if (webuiSettings && key in webuiSettings) {
+		if (uiSettings && key in uiSettings) {
 			// UI setting from admin config: write actual value
-			setConfigValue(this.config, key, webuiSettings[key]);
+			setConfigValue(this.config, key, uiSettings[key]);
 		} else if (serverDefaults[key] !== undefined) {
 			// sampling param known by server: clear it, let server decide
 			setConfigValue(this.config, key, '');
@@ -327,6 +331,9 @@ class SettingsStore {
 		const propsDefaults = this.getServerDefaults();
 		if (Object.keys(propsDefaults).length === 0) return;
 
+		const uiSettings = serverStore.uiSettings;
+		const uiSettingsKeys = new Set(uiSettings ? Object.keys(uiSettings) : []);
+
 		for (const [key, propsValue] of Object.entries(propsDefaults)) {
 			const currentValue = getConfigValue(this.config, key);
 
@@ -336,16 +343,24 @@ class SettingsStore {
 			// if user value matches server, it's not a real override
 			if (normalizedCurrent === normalizedDefault) {
 				this.userOverrides.delete(key);
+
+				if (!uiSettingsKeys.has(key) && getConfigValue(SETTING_CONFIG_DEFAULT, key) === undefined) {
+					setConfigValue(this.config, key, undefined);
+				}
 			}
 		}
 
-		// webui settings need actual values in config (no placeholder mechanism),
+		// UI settings need actual values in config (no placeholder mechanism),
 		// so write them for non-overridden keys
-		const webuiSettings = serverStore.webuiSettings;
-		if (webuiSettings) {
-			for (const [key, value] of Object.entries(webuiSettings)) {
+		if (uiSettings) {
+			for (const [key, value] of Object.entries(uiSettings)) {
 				if (!this.userOverrides.has(key) && value !== undefined) {
 					setConfigValue(this.config, key, value);
+
+					// theme lives in mode-watcher, not just in config -> propagate
+					if (key === SETTINGS_KEYS.THEME) {
+						setMode(value as ColorMode);
+					}
 				}
 			}
 		}
@@ -357,16 +372,16 @@ class SettingsStore {
 	/**
 	 * Reset all parameters to their default values (from props)
 	 * This is used by the "Reset to Default" functionality
-	 * Prioritizes server defaults from /props, falls back to webui defaults
+	 * Prioritizes Server defaults from /props, falls back to UI defaults
 	 */
 	forceSyncWithServerDefaults(): void {
 		const propsDefaults = this.getServerDefaults();
-		const webuiSettings = serverStore.webuiSettings;
+		const uiSettings = serverStore.uiSettings;
 
 		for (const key of ParameterSyncService.getSyncableParameterKeys()) {
-			if (webuiSettings && key in webuiSettings) {
+			if (uiSettings && key in uiSettings) {
 				// UI setting from admin config: write actual value
-				setConfigValue(this.config, key, webuiSettings[key]);
+				setConfigValue(this.config, key, uiSettings[key]);
 			} else if (propsDefaults[key] !== undefined) {
 				// sampling param: clear it, let server decide
 				setConfigValue(this.config, key, '');
@@ -447,10 +462,86 @@ class SettingsStore {
 		this.saveConfig();
 		console.log('Cleared all user overrides');
 	}
+
+	/**
+	 *
+	 *
+	 * Import / Export
+	 *
+	 *
+	 */
+
+	/**
+	 * Export all settings as a versioned JSON-compatible object.
+	 * The export captures the full config (excluding sensitive values like API key)
+	 * and user overrides. Sensitive fields are filtered out for security by default.
+	 * @param includeSensitiveData - If true, include sensitive fields (apiKey, MCP server headers) in export
+	 */
+	exportSettings(includeSensitiveData: boolean = false): SettingsExportType {
+		// Build config excluding sensitive data unless user opts in
+		const configToExport: Record<string, string | number | boolean | undefined> =
+			includeSensitiveData
+				? { ...this.config }
+				: Object.fromEntries(Object.entries(this.config).filter(([key]) => key !== 'apiKey'));
+
+		// Handle MCP servers: exclude custom headers unless user opts in
+		if ('mcpServers' in configToExport && !includeSensitiveData) {
+			try {
+				const mcpServers = JSON.parse(configToExport.mcpServers as string) as Array<
+					Record<string, unknown>
+				>;
+				const safeServers = mcpServers.map((server) => {
+					delete server.headers;
+					return server;
+				});
+				configToExport.mcpServers = JSON.stringify(safeServers);
+			} catch {
+				// If parsing fails, just exclude the entire mcpServers field
+				delete (configToExport as Record<string, unknown>).mcpServers;
+			}
+		}
+
+		return {
+			version: 1,
+			timestamp: Date.now(),
+			config: configToExport,
+			userOverrides: Array.from(this.userOverrides)
+		};
+	}
+
+	/**
+	 * Import settings from a previously exported object.
+	 * Restores config (including theme) and user overrides.
+	 * @param data - The exported settings object
+	 */
+	importSettings(data: SettingsExportType): void {
+		if (!browser) return;
+
+		if (!data || !data.config) {
+			throw new Error('Invalid settings data: missing config');
+		}
+
+		// Restore config (theme is included in config)
+		this.config = {
+			...SETTING_CONFIG_DEFAULT,
+			...data.config
+		};
+
+		// Restore user overrides (derived state — may be stale if server defaults differ)
+		this.userOverrides = new Set(data.userOverrides ?? []);
+
+		// Persist to localStorage
+		this.saveConfig();
+
+		// Apply theme for immediate visual feedback
+		setMode(this.config[SETTINGS_KEYS.THEME] as ColorMode);
+
+		console.log('Settings imported successfully');
+	}
 }
 
 export const settingsStore = new SettingsStore();
 
 export const config = () => settingsStore.config;
-export const theme = () => settingsStore.theme;
+export const theme = () => settingsStore.config[SETTINGS_KEYS.THEME];
 export const isInitialized = () => settingsStore.isInitialized;
diff --git a/tools/ui/src/lib/stores/tools.svelte.ts b/tools/ui/src/lib/stores/tools.svelte.ts
new file mode 100644
index 00000000000..5404a7a46fe
--- /dev/null
+++ b/tools/ui/src/lib/stores/tools.svelte.ts
@@ -0,0 +1,427 @@
+import type { OpenAIToolDefinition, ToolEntry, ToolGroup } from '$lib/types';
+import { ToolsService } from '$lib/services/tools.service';
+import { mcpStore } from '$lib/stores/mcp.svelte';
+import { HealthCheckStatus, JsonSchemaType, ToolCallType, ToolSource } from '$lib/enums';
+import { config } from '$lib/stores/settings.svelte';
+import {
+	DISABLED_TOOLS_LOCALSTORAGE_KEY,
+	TOOL_GROUP_LABELS,
+	TOOL_SERVER_LABELS
+} from '$lib/constants';
+
+import { SvelteSet } from 'svelte/reactivity';
+
+class ToolsStore {
+	private _builtinTools = $state<OpenAIToolDefinition[]>([]);
+	private _loading = $state(false);
+	private _error = $state<string | null>(null);
+	private _disabledTools = $state(new SvelteSet<string>());
+	private _toolsEndpointUnreachable = $state(false);
+
+	constructor() {
+		try {
+			const stored = localStorage.getItem(DISABLED_TOOLS_LOCALSTORAGE_KEY);
+			if (stored) {
+				const parsed = JSON.parse(stored);
+				if (Array.isArray(parsed)) {
+					for (const name of parsed) {
+						if (typeof name === 'string') this._disabledTools.add(name);
+					}
+				}
+			}
+		} catch (err) {
+			console.error('[ToolsStore] Failed to load disabled tools from localStorage:', err);
+		}
+
+		// Initialize builtin tools on startup
+		this.fetchBuiltinTools();
+	}
+
+	private persistDisabledTools(): void {
+		try {
+			localStorage.setItem(
+				DISABLED_TOOLS_LOCALSTORAGE_KEY,
+				JSON.stringify([...this._disabledTools])
+			);
+		} catch {
+			// ignore storage errors
+		}
+	}
+
+	get builtinTools(): OpenAIToolDefinition[] {
+		return this._builtinTools;
+	}
+
+	get mcpTools(): OpenAIToolDefinition[] {
+		return mcpStore.getToolDefinitionsForLLM();
+	}
+
+	get customTools(): OpenAIToolDefinition[] {
+		const raw = config().custom;
+		if (!raw || typeof raw !== 'string') return [];
+
+		try {
+			const parsed = JSON.parse(raw);
+			if (!Array.isArray(parsed)) return [];
+
+			return parsed.filter(
+				(t: unknown): t is OpenAIToolDefinition =>
+					typeof t === 'object' &&
+					t !== null &&
+					'type' in t &&
+					(t as OpenAIToolDefinition).type === 'function' &&
+					'function' in t &&
+					typeof (t as OpenAIToolDefinition).function?.name === 'string'
+			);
+		} catch {
+			return [];
+		}
+	}
+
+	/** Flat list of all tool entries with source metadata */
+	get allTools(): ToolEntry[] {
+		const entries: ToolEntry[] = [];
+
+		for (const def of this._builtinTools) {
+			entries.push({ source: ToolSource.BUILTIN, definition: def });
+		}
+
+		// Use live connections when available (full schema), fall back to health check data
+		const connections = mcpStore.getConnections();
+		if (connections.size > 0) {
+			for (const [serverId, connection] of connections) {
+				const serverName = mcpStore.getServerDisplayName(serverId);
+				for (const tool of connection.tools) {
+					const rawSchema = (tool.inputSchema as Record<string, unknown>) ?? {
+						type: JsonSchemaType.OBJECT,
+						properties: {},
+						required: []
+					};
+					entries.push({
+						source: ToolSource.MCP,
+						serverName,
+						serverId,
+						definition: {
+							type: ToolCallType.FUNCTION,
+							function: {
+								name: tool.name,
+								description: tool.description,
+								parameters: rawSchema
+							}
+						}
+					});
+				}
+			}
+		} else {
+			for (const { serverId, serverName, tools } of this.getMcpToolsFromHealthChecks()) {
+				for (const tool of tools) {
+					entries.push({
+						source: ToolSource.MCP,
+						serverName,
+						serverId,
+						definition: {
+							type: ToolCallType.FUNCTION,
+							function: {
+								name: tool.name,
+								description: tool.description,
+								parameters: {
+									type: JsonSchemaType.OBJECT,
+									properties: {},
+									required: []
+								}
+							}
+						}
+					});
+				}
+			}
+		}
+
+		for (const def of this.customTools) {
+			entries.push({ source: ToolSource.CUSTOM, definition: def });
+		}
+
+		return entries;
+	}
+
+	/** Tools grouped by category for tree display */
+	get toolGroups(): ToolGroup[] {
+		const groups: ToolGroup[] = [];
+
+		if (this._builtinTools.length > 0) {
+			groups.push({
+				source: ToolSource.BUILTIN,
+				label: TOOL_GROUP_LABELS[ToolSource.BUILTIN],
+				tools: this._builtinTools
+			});
+		}
+
+		// Use live connections when available, fall back to health check data
+		const connections = mcpStore.getConnections();
+		if (connections.size > 0) {
+			for (const [serverId, connection] of connections) {
+				if (connection.tools.length === 0) continue;
+				const label = mcpStore.getServerDisplayName(serverId);
+				const tools: OpenAIToolDefinition[] = connection.tools.map((tool) => {
+					const rawSchema = (tool.inputSchema as Record<string, unknown>) ?? {
+						type: JsonSchemaType.OBJECT,
+						properties: {},
+						required: []
+					};
+					return {
+						type: ToolCallType.FUNCTION,
+						function: {
+							name: tool.name,
+							description: tool.description,
+							parameters: rawSchema
+						}
+					};
+				});
+				groups.push({ source: ToolSource.MCP, label, serverId, tools });
+			}
+		} else {
+			for (const { serverId, serverName, tools } of this.getMcpToolsFromHealthChecks()) {
+				if (tools.length === 0) continue;
+				const defs: OpenAIToolDefinition[] = tools.map((tool) => ({
+					type: ToolCallType.FUNCTION,
+					function: {
+						name: tool.name,
+						description: tool.description,
+						parameters: { type: JsonSchemaType.OBJECT, properties: {}, required: [] }
+					}
+				}));
+				groups.push({ source: ToolSource.MCP, label: serverName, serverId, tools: defs });
+			}
+		}
+
+		const custom = this.customTools;
+		if (custom.length > 0) {
+			groups.push({
+				source: ToolSource.CUSTOM,
+				label: TOOL_GROUP_LABELS[ToolSource.CUSTOM],
+				tools: custom
+			});
+		}
+
+		return groups;
+	}
+
+	/** Only enabled tool definitions (for sending to the API) */
+	get enabledToolDefinitions(): OpenAIToolDefinition[] {
+		return this.allTools
+			.filter((t) => !this._disabledTools.has(t.definition.function.name))
+			.map((t) => t.definition);
+	}
+
+	/**
+	 * Returns enabled tool definitions for sending to the LLM.
+	 * MCP tools use properly normalized schemas from mcpStore.
+	 * Filters out tools disabled via the UI checkboxes.
+	 */
+	getEnabledToolsForLLM(): OpenAIToolDefinition[] {
+		const disabled = this._disabledTools;
+		const result: OpenAIToolDefinition[] = [];
+
+		for (const tool of this._builtinTools) {
+			if (!disabled.has(tool.function.name)) {
+				result.push(tool);
+			}
+		}
+
+		// MCP tools with properly normalized schemas
+		for (const tool of mcpStore.getToolDefinitionsForLLM()) {
+			if (!disabled.has(tool.function.name)) {
+				result.push(tool);
+			}
+		}
+
+		for (const tool of this.customTools) {
+			if (!disabled.has(tool.function.name)) {
+				result.push(tool);
+			}
+		}
+
+		return result;
+	}
+
+	get allToolDefinitions(): OpenAIToolDefinition[] {
+		return this.allTools.map((t) => t.definition);
+	}
+
+	get loading(): boolean {
+		return this._loading;
+	}
+
+	get error(): string | null {
+		return this._error;
+	}
+
+	get isToolsEndpointUnreachable(): boolean {
+		return this._toolsEndpointUnreachable;
+	}
+
+	get disabledTools(): SvelteSet<string> {
+		return this._disabledTools;
+	}
+
+	isToolEnabled(toolName: string): boolean {
+		return !this._disabledTools.has(toolName);
+	}
+
+	toggleTool(toolName: string): void {
+		if (this._disabledTools.has(toolName)) {
+			this._disabledTools.delete(toolName);
+		} else {
+			this._disabledTools.add(toolName);
+		}
+		this.persistDisabledTools();
+	}
+
+	setToolEnabled(toolName: string, enabled: boolean): void {
+		if (enabled) {
+			this._disabledTools.delete(toolName);
+		} else {
+			this._disabledTools.add(toolName);
+		}
+	}
+
+	/**
+	 * Enable all tools belonging to a specific MCP server.
+	 * Called when a server is enabled for a conversation.
+	 */
+	enableAllToolsForServer(serverId: string): void {
+		const connection = mcpStore.getConnections().get(serverId);
+		if (!connection) return;
+		for (const tool of connection.tools) {
+			this._disabledTools.delete(tool.name);
+		}
+		this.persistDisabledTools();
+	}
+
+	toggleGroup(group: ToolGroup): void {
+		const allEnabled = group.tools.every((t) => this.isToolEnabled(t.function.name));
+		for (const tool of group.tools) {
+			this.setToolEnabled(tool.function.name, !allEnabled);
+		}
+		this.persistDisabledTools();
+	}
+
+	isGroupFullyEnabled(group: ToolGroup): boolean {
+		return group.tools.length > 0 && group.tools.every((t) => this.isToolEnabled(t.function.name));
+	}
+
+	isGroupPartiallyEnabled(group: ToolGroup): boolean {
+		const enabledCount = group.tools.filter((t) => this.isToolEnabled(t.function.name)).length;
+		return enabledCount > 0 && enabledCount < group.tools.length;
+	}
+
+	/**
+	 * Get MCP tools from health check data (reactive).
+	 * Used when live connections aren't established yet.
+	 */
+	private getMcpToolsFromHealthChecks(): {
+		serverId: string;
+		serverName: string;
+		tools: { name: string; description?: string }[];
+	}[] {
+		const result: ReturnType<ToolsStore['getMcpToolsFromHealthChecks']> = [];
+		for (const server of mcpStore.getServersSorted().filter((s) => s.enabled)) {
+			const health = mcpStore.getHealthCheckState(server.id);
+			if (health.status === HealthCheckStatus.SUCCESS && health.tools.length > 0) {
+				result.push({
+					serverId: server.id,
+					serverName: mcpStore.getServerLabel(server),
+					tools: health.tools
+				});
+			}
+		}
+		return result;
+	}
+
+	/** Determine the source of a tool by its name. */
+	getToolSource(toolName: string): ToolSource | null {
+		if (this._builtinTools.some((t) => t.function.name === toolName)) {
+			return ToolSource.BUILTIN;
+		}
+		for (const entry of this.allTools) {
+			if (entry.definition.function.name === toolName) {
+				return entry.source;
+			}
+		}
+		return null;
+	}
+
+	/** Get the display label for the server that owns a given tool. */
+	getToolServerLabel(toolName: string): string {
+		for (const entry of this.allTools) {
+			if (entry.definition.function.name === toolName) {
+				if (entry.serverName) {
+					return mcpStore.getServerDisplayName(entry.serverName);
+				}
+				if (entry.source === ToolSource.BUILTIN) {
+					return TOOL_SERVER_LABELS[ToolSource.BUILTIN];
+				}
+				if (entry.source === ToolSource.CUSTOM) {
+					return TOOL_SERVER_LABELS[ToolSource.CUSTOM];
+				}
+			}
+		}
+		return '';
+	}
+
+	/** Build a permission key with category prefix, e.g. "mcp-<serverId>:tool_name" */
+	getPermissionKey(toolName: string): string | null {
+		for (const entry of this.allTools) {
+			if (entry.definition.function.name === toolName) {
+				switch (entry.source) {
+					case ToolSource.BUILTIN:
+						return `builtin:${toolName}`;
+					case ToolSource.CUSTOM:
+						return `custom:${toolName}`;
+					case ToolSource.MCP:
+						if (entry.serverId) {
+							return `mcp-${entry.serverId}:${toolName}`;
+						}
+						return `mcp:${toolName}`;
+					default:
+						return null;
+				}
+			}
+		}
+		return null;
+	}
+
+	/** Check if there are any enabled tools available (builtin, MCP, or custom). */
+	get hasEnabledTools(): boolean {
+		return this.getEnabledToolsForLLM().length > 0;
+	}
+
+	async fetchBuiltinTools(): Promise<void> {
+		if (this._loading) return;
+
+		this._loading = true;
+		this._error = null;
+		this._toolsEndpointUnreachable = false;
+
+		try {
+			const toolInfos = await ToolsService.list();
+			this._builtinTools = toolInfos.map((info) => info.definition);
+		} catch (err) {
+			const errorMessage = err instanceof Error ? err.message : String(err);
+			this._error = errorMessage;
+			// 404 from /tools means the server was started without --tools
+			if (errorMessage.includes('404') || errorMessage.toLowerCase().includes('not found')) {
+				this._toolsEndpointUnreachable = true;
+			}
+			console.error('[ToolsStore] Failed to fetch built-in tools:', err);
+		} finally {
+			this._loading = false;
+		}
+	}
+}
+
+export const toolsStore = new ToolsStore();
+
+export const allTools = () => toolsStore.allTools;
+export const allToolDefinitions = () => toolsStore.allToolDefinitions;
+export const enabledToolDefinitions = () => toolsStore.enabledToolDefinitions;
+export const toolGroups = () => toolsStore.toolGroups;
diff --git a/tools/server/webui/src/lib/types/agentic.d.ts b/tools/ui/src/lib/types/agentic.d.ts
similarity index 94%
rename from tools/server/webui/src/lib/types/agentic.d.ts
rename to tools/ui/src/lib/types/agentic.d.ts
index 377f885d549..b94998384f5 100644
--- a/tools/server/webui/src/lib/types/agentic.d.ts
+++ b/tools/ui/src/lib/types/agentic.d.ts
@@ -69,6 +69,7 @@ export interface AgenticSession {
 	totalToolCalls: number;
 	lastError: Error | null;
 	streamingToolCall: { name: string; arguments: string } | null;
+	pendingPermissionRequest: { toolName: string; serverLabel: string } | null;
 }
 
 /**
@@ -147,3 +148,11 @@ export interface AgenticFlowResult {
 	handled: boolean;
 	error?: Error;
 }
+
+/**
+ * A user message to be injected into the agentic loop between turns.
+ */
+export interface SteeringMessage {
+	content: string;
+	extras?: DatabaseMessageExtra[];
+}
diff --git a/tools/server/webui/src/lib/types/api.d.ts b/tools/ui/src/lib/types/api.d.ts
similarity index 97%
rename from tools/server/webui/src/lib/types/api.d.ts
rename to tools/ui/src/lib/types/api.d.ts
index c1a02342357..316ad552842 100644
--- a/tools/server/webui/src/lib/types/api.d.ts
+++ b/tools/ui/src/lib/types/api.d.ts
@@ -22,6 +22,10 @@ export interface ApiChatMessageContentPart {
 		data: string;
 		format: 'wav' | 'mp3';
 	};
+	input_video?: {
+		data: string;
+		format: 'mp4' | 'ogg' | 'auto';
+	};
 }
 
 export interface ApiContextSizeError {
@@ -190,12 +194,15 @@ export interface ApiLlamaCppServerProps {
 	modalities: {
 		vision: boolean;
 		audio: boolean;
+		video: boolean;
 	};
 	chat_template: string;
 	bos_token: string;
 	eos_token: string;
 	build_info: string;
+	/** @deprecated Use {@link ui_settings} instead */
 	webui_settings?: Record<string, string | number | boolean>;
+	ui_settings?: Record<string, string | number | boolean>;
 }
 
 export interface ApiChatCompletionRequest {
@@ -239,6 +246,9 @@ export interface ApiChatCompletionRequest {
 	// Custom parameters (JSON string)
 	custom?: Record<string, unknown>;
 	timings_per_token?: boolean;
+	// Continuation control (vLLM compat)
+	add_generation_prompt?: boolean;
+	continue_final_message?: boolean;
 }
 
 export interface ApiChatCompletionToolCallFunctionDelta {
diff --git a/tools/server/webui/src/lib/types/chat.d.ts b/tools/ui/src/lib/types/chat.d.ts
similarity index 94%
rename from tools/server/webui/src/lib/types/chat.d.ts
rename to tools/ui/src/lib/types/chat.d.ts
index 431cf523a65..acedd0769a1 100644
--- a/tools/server/webui/src/lib/types/chat.d.ts
+++ b/tools/ui/src/lib/types/chat.d.ts
@@ -25,8 +25,6 @@ export interface ChatAttachmentDisplayItem {
 	size?: number;
 	preview?: string;
 	isImage: boolean;
-	isMcpPrompt?: boolean;
-	isMcpResource?: boolean;
 	isLoading?: boolean;
 	loadError?: string;
 	uploadedFile?: ChatUploadedFile;
@@ -35,15 +33,6 @@ export interface ChatAttachmentDisplayItem {
 	textContent?: string;
 }
 
-export interface ChatAttachmentPreviewItem {
-	uploadedFile?: ChatUploadedFile;
-	attachment?: DatabaseMessageExtra;
-	preview?: string;
-	name?: string;
-	size?: number;
-	textContent?: string;
-}
-
 export interface ChatMessageSiblingInfo {
 	message: DatabaseMessage;
 	siblingIds: string[];
diff --git a/tools/server/webui/src/lib/types/common.d.ts b/tools/ui/src/lib/types/common.d.ts
similarity index 92%
rename from tools/server/webui/src/lib/types/common.d.ts
rename to tools/ui/src/lib/types/common.d.ts
index 453d0cd7468..6de33aaaf84 100644
--- a/tools/server/webui/src/lib/types/common.d.ts
+++ b/tools/ui/src/lib/types/common.d.ts
@@ -64,4 +64,9 @@ export interface ParsedClipboardContent {
 	mcpPromptAttachments: ClipboardMcpPromptAttachment[];
 }
 
-export type MimeTypeUnion = MimeTypeAudio | MimeTypeImage | MimeTypeApplication | MimeTypeText;
+export type MimeTypeUnion =
+	| MimeTypeAudio
+	| MimeTypeVideo
+	| MimeTypeImage
+	| MimeTypeApplication
+	| MimeTypeText;
diff --git a/tools/server/webui/src/lib/types/database.d.ts b/tools/ui/src/lib/types/database.d.ts
similarity index 87%
rename from tools/server/webui/src/lib/types/database.d.ts
rename to tools/ui/src/lib/types/database.d.ts
index 2f96ef3f70a..02bdb3ee60d 100644
--- a/tools/server/webui/src/lib/types/database.d.ts
+++ b/tools/ui/src/lib/types/database.d.ts
@@ -18,6 +18,15 @@ export interface DatabaseConversation {
 export interface DatabaseMessageExtraAudioFile {
 	type: AttachmentType.AUDIO;
 	name: string;
+	size?: number;
+	base64Data: string;
+	mimeType: string;
+}
+
+export interface DatabaseMessageExtraVideoFile {
+	type: AttachmentType.VIDEO;
+	name: string;
+	size?: number;
 	base64Data: string;
 	mimeType: string;
 }
@@ -25,16 +34,18 @@ export interface DatabaseMessageExtraAudioFile {
 export interface DatabaseMessageExtraImageFile {
 	type: AttachmentType.IMAGE;
 	name: string;
+	size?: number;
 	base64Url: string;
 }
 
 /**
- * Legacy format from old webui - pasted content was stored as "context" type
+ * Legacy format from the old UI — pasted content was stored as "context" type
  * @deprecated Use DatabaseMessageExtraTextFile instead
  */
 export interface DatabaseMessageExtraLegacyContext {
 	type: AttachmentType.LEGACY_CONTEXT;
 	name: string;
+	size?: number;
 	content: string;
 }
 
@@ -42,6 +53,7 @@ export interface DatabaseMessageExtraPdfFile {
 	type: AttachmentType.PDF;
 	base64Data: string;
 	name: string;
+	size?: number;
 	content: string;
 	images?: string[];
 	processedAsImages: boolean;
@@ -50,12 +62,14 @@ export interface DatabaseMessageExtraPdfFile {
 export interface DatabaseMessageExtraTextFile {
 	type: AttachmentType.TEXT;
 	name: string;
+	size?: number;
 	content: string;
 }
 
 export interface DatabaseMessageExtraMcpPrompt {
 	type: AttachmentType.MCP_PROMPT;
 	name: string;
+	size?: number;
 	serverName: string;
 	promptName: string;
 	content: string;
@@ -65,6 +79,7 @@ export interface DatabaseMessageExtraMcpPrompt {
 export interface DatabaseMessageExtraMcpResource {
 	type: AttachmentType.MCP_RESOURCE;
 	name: string;
+	size?: number;
 	uri: string;
 	serverName: string;
 	content: string;
@@ -75,6 +90,7 @@ export type DatabaseMessageExtra =
 	| DatabaseMessageExtraImageFile
 	| DatabaseMessageExtraTextFile
 	| DatabaseMessageExtraAudioFile
+	| DatabaseMessageExtraVideoFile
 	| DatabaseMessageExtraPdfFile
 	| DatabaseMessageExtraMcpPrompt
 	| DatabaseMessageExtraMcpResource
diff --git a/tools/server/webui/src/lib/types/index.ts b/tools/ui/src/lib/types/index.ts
similarity index 91%
rename from tools/server/webui/src/lib/types/index.ts
rename to tools/ui/src/lib/types/index.ts
index 93a39f03dae..d704a4b3c8a 100644
--- a/tools/server/webui/src/lib/types/index.ts
+++ b/tools/ui/src/lib/types/index.ts
@@ -36,7 +36,6 @@ export type {
 export type {
 	ChatUploadedFile,
 	ChatAttachmentDisplayItem,
-	ChatAttachmentPreviewItem,
 	ChatMessageSiblingInfo,
 	ChatMessagePromptProgress,
 	ChatMessageTimings,
@@ -56,6 +55,7 @@ export type {
 	McpServerOverride,
 	DatabaseConversation,
 	DatabaseMessageExtraAudioFile,
+	DatabaseMessageExtraVideoFile,
 	DatabaseMessageExtraImageFile,
 	DatabaseMessageExtraLegacyContext,
 	DatabaseMessageExtraMcpPrompt,
@@ -77,10 +77,15 @@ export type {
 	SettingsFieldConfig,
 	SettingsChatServiceOptions,
 	SettingsConfigType,
+	SettingsExportType,
 	ParameterValue,
 	ParameterRecord,
 	ParameterInfo,
-	SyncableParameter
+	SyncableParameter,
+	SettingsEntry,
+	SettingsSectionTitle,
+	SettingsSectionEntry,
+	SettingsSection
 } from './settings';
 
 // Common types
@@ -116,6 +121,7 @@ export type {
 	ServerStatus,
 	ToolCallParams,
 	ToolExecutionResult,
+	ServerBuiltinToolInfo,
 	Tool,
 	Prompt,
 	GetPromptResult,
@@ -149,5 +155,9 @@ export type {
 	AgenticFlowCallbacks,
 	AgenticFlowOptions,
 	AgenticFlowParams,
-	AgenticFlowResult
+	AgenticFlowResult,
+	SteeringMessage
 } from './agentic';
+
+// Tools types
+export type { ToolEntry, ToolGroup } from './tools';
diff --git a/tools/server/webui/src/lib/types/mcp.d.ts b/tools/ui/src/lib/types/mcp.d.ts
similarity index 96%
rename from tools/server/webui/src/lib/types/mcp.d.ts
rename to tools/ui/src/lib/types/mcp.d.ts
index 2ce2f739921..7aa050cdfa7 100644
--- a/tools/server/webui/src/lib/types/mcp.d.ts
+++ b/tools/ui/src/lib/types/mcp.d.ts
@@ -1,4 +1,5 @@
 import type { MCPConnectionPhase, MCPLogLevel, HealthCheckStatus } from '$lib/enums/mcp';
+import type { ToolSource } from '$lib/enums/tools';
 import type {
 	Client,
 	ClientCapabilities as SDKClientCapabilities,
@@ -134,6 +135,8 @@ export interface MCPConnection {
 	protocolVersion?: string;
 	instructions?: string;
 	connectionTimeMs: number;
+	/** Configured timeout for individual requests (tool calls, etc.) in milliseconds */
+	requestTimeoutMs: number;
 }
 
 /**
@@ -256,6 +259,16 @@ export interface ToolExecutionResult {
 	isError: boolean;
 }
 
+export interface ServerBuiltinToolInfo {
+	display_name: string;
+	tool: string;
+	type: ToolSource.BUILTIN;
+	permissions: {
+		write: boolean;
+	};
+	definition: OpenAIToolDefinition;
+}
+
 /**
  * Progress tracking state for a specific operation
  */
diff --git a/tools/server/webui/src/lib/types/models.d.ts b/tools/ui/src/lib/types/models.d.ts
similarity index 95%
rename from tools/server/webui/src/lib/types/models.d.ts
rename to tools/ui/src/lib/types/models.d.ts
index b4d5f11f576..51069599d7f 100644
--- a/tools/server/webui/src/lib/types/models.d.ts
+++ b/tools/ui/src/lib/types/models.d.ts
@@ -3,6 +3,7 @@ import type { ApiModelDataEntry, ApiModelDetails } from '$lib/types/api';
 export interface ModelModalities {
 	vision: boolean;
 	audio: boolean;
+	video: boolean;
 }
 
 export interface ModelOption {
@@ -35,4 +36,5 @@ export interface ParsedModelId {
 export interface ModalityCapabilities {
 	hasVision: boolean;
 	hasAudio: boolean;
+	hasVideo: boolean;
 }
diff --git a/tools/server/webui/src/lib/types/settings.d.ts b/tools/ui/src/lib/types/settings.d.ts
similarity index 62%
rename from tools/server/webui/src/lib/types/settings.d.ts
rename to tools/ui/src/lib/types/settings.d.ts
index 4c545ce1dc6..65096db3449 100644
--- a/tools/server/webui/src/lib/types/settings.d.ts
+++ b/tools/ui/src/lib/types/settings.d.ts
@@ -1,21 +1,60 @@
-import type { SETTING_CONFIG_DEFAULT } from '$lib/constants';
+import type { SETTING_CONFIG_DEFAULT, SETTINGS_SECTION_TITLES } from '$lib/constants';
 import type { ChatMessagePromptProgress, ChatMessageTimings } from './chat';
 import type { OpenAIToolDefinition } from './mcp';
 import type { DatabaseMessageExtra } from './database';
 import type { ParameterSource, SyncableParameterType, SettingsFieldType } from '$lib/enums';
 import type { Icon } from '@lucide/svelte';
+import type { Component } from 'svelte';
 
 export type SettingsConfigValue = string | number | boolean | undefined;
 
+/** Section title type derived from registry section titles. */
+export type SettingsSectionTitle =
+	(typeof SETTINGS_SECTION_TITLES)[keyof typeof SETTINGS_SECTION_TITLES];
+
+/** Per-setting metadata — one entry per setting. */
+export interface SettingsEntry {
+	key: string;
+	label: string;
+	help: string;
+	defaultValue: SettingsConfigValue;
+	type: SettingsFieldType;
+	section?: string;
+	options?: Array<{ value: string; label: string; icon: Component }>;
+	isExperimental?: boolean;
+	isPositiveInteger?: boolean;
+	sync?: {
+		serverKey: string;
+		paramType: SyncableParameterType;
+	};
+}
+
+/** A settings section with its icon, slug, title, and ordered settings. */
+export interface SettingsSectionEntry {
+	title: SettingsSectionTitle;
+	slug: string;
+	icon: Component;
+	settings: SettingsEntry[];
+}
+
 export interface SettingsFieldConfig {
 	key: string;
 	label: string;
 	type: SettingsFieldType;
 	isExperimental?: boolean;
+	isPositiveInteger?: boolean;
 	help?: string;
 	options?: Array<{ value: string; label: string; icon?: typeof Icon }>;
 }
 
+/** Re-exported for backward compatibility. */
+export interface SettingsSection {
+	fields?: SettingsFieldConfig[];
+	icon: Component;
+	slug: string;
+	title: SettingsSectionTitle;
+}
+
 export interface SettingsChatServiceOptions {
 	stream?: boolean;
 	// Model (required in ROUTER mode, optional in MODEL mode)
@@ -54,6 +93,8 @@ export interface SettingsChatServiceOptions {
 	// Custom parameters
 	custom?: string;
 	timings_per_token?: boolean;
+	// Continuation control (vLLM compat), opt in to the explicit continue final message flag
+	continueFinalMessage?: boolean;
 	// Callbacks
 	onChunk?: (chunk: string) => void;
 	onReasoningChunk?: (chunk: string) => void;
@@ -94,3 +135,18 @@ export interface SyncableParameter {
 	type: SyncableParameterType;
 	canSync: boolean;
 }
+
+/**
+ * Shape of the settings JSON export file.
+ * Versioned to allow future schema evolution.
+ */
+export interface SettingsExportType {
+	/** Export format version — bumped on breaking changes */
+	version: number;
+	/** Unix timestamp of export */
+	timestamp: number;
+	/** Full settings config (includes theme as a config key) */
+	config: SettingsConfigType;
+	/** Keys that differ from server defaults (derived, but persisted for fidelity) */
+	userOverrides: string[];
+}
diff --git a/tools/ui/src/lib/types/tools.d.ts b/tools/ui/src/lib/types/tools.d.ts
new file mode 100644
index 00000000000..a17a0c9a9eb
--- /dev/null
+++ b/tools/ui/src/lib/types/tools.d.ts
@@ -0,0 +1,19 @@
+import type { ToolSource } from '$lib/enums';
+import type { OpenAIToolDefinition } from './mcp';
+
+export interface ToolEntry {
+	source: ToolSource;
+	/** For MCP tools, the server display name (used for UI grouping) */
+	serverName?: string;
+	/** For MCP tools, the server ID (used for permission keys) */
+	serverId?: string;
+	definition: OpenAIToolDefinition;
+}
+
+export interface ToolGroup {
+	source: ToolSource;
+	label: string;
+	/** For MCP groups, the server ID */
+	serverId?: string;
+	tools: OpenAIToolDefinition[];
+}
diff --git a/tools/server/webui/src/lib/utils/abort.ts b/tools/ui/src/lib/utils/abort.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/abort.ts
rename to tools/ui/src/lib/utils/abort.ts
diff --git a/tools/server/webui/src/lib/utils/agentic.ts b/tools/ui/src/lib/utils/agentic.ts
similarity index 95%
rename from tools/server/webui/src/lib/utils/agentic.ts
rename to tools/ui/src/lib/utils/agentic.ts
index 868545495f6..549a1c9a0e1 100644
--- a/tools/server/webui/src/lib/utils/agentic.ts
+++ b/tools/ui/src/lib/utils/agentic.ts
@@ -67,8 +67,14 @@ function deriveSingleTurnSections(
 	const toolCalls = parseToolCalls(message.toolCalls);
 	for (const tc of toolCalls) {
 		const resultMsg = toolMessages.find((m) => m.toolCallId === tc.id);
+		// Only show as pending/loading if we're actively streaming; otherwise it's just a tool call without result
+		const type = resultMsg
+			? AgenticSectionType.TOOL_CALL
+			: isStreaming
+				? AgenticSectionType.TOOL_CALL_PENDING
+				: AgenticSectionType.TOOL_CALL;
 		sections.push({
-			type: resultMsg ? AgenticSectionType.TOOL_CALL : AgenticSectionType.TOOL_CALL_PENDING,
+			type,
 			content: resultMsg?.content || '',
 			toolName: tc.function?.name,
 			toolArgs: tc.function?.arguments,
diff --git a/tools/server/webui/src/lib/utils/api-fetch.ts b/tools/ui/src/lib/utils/api-fetch.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/api-fetch.ts
rename to tools/ui/src/lib/utils/api-fetch.ts
diff --git a/tools/server/webui/src/lib/utils/api-headers.ts b/tools/ui/src/lib/utils/api-headers.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/api-headers.ts
rename to tools/ui/src/lib/utils/api-headers.ts
diff --git a/tools/server/webui/src/lib/utils/api-key-validation.ts b/tools/ui/src/lib/utils/api-key-validation.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/api-key-validation.ts
rename to tools/ui/src/lib/utils/api-key-validation.ts
diff --git a/tools/server/webui/src/lib/utils/attachment-display.ts b/tools/ui/src/lib/utils/attachment-display.ts
similarity index 70%
rename from tools/server/webui/src/lib/utils/attachment-display.ts
rename to tools/ui/src/lib/utils/attachment-display.ts
index 396ed6671df..30c7043bf0e 100644
--- a/tools/server/webui/src/lib/utils/attachment-display.ts
+++ b/tools/ui/src/lib/utils/attachment-display.ts
@@ -2,29 +2,29 @@ import { AttachmentType, FileTypeCategory, SpecialFileType } from '$lib/enums';
 import { getFileTypeCategory, getFileTypeCategoryByExtension, isImageFile } from '$lib/utils';
 import type {
 	AttachmentDisplayItemsOptions,
-	ChatUploadedFile,
-	DatabaseMessageExtra
+	ChatAttachmentDisplayItem,
+	ChatUploadedFile
 } from '$lib/types';
 
 /**
- * Check if an uploaded file is an MCP prompt
+ * Check if a display item represents an MCP prompt
+ * (either from attachment type or uploaded file with mcpPrompt metadata)
  */
-function isMcpPromptUpload(file: ChatUploadedFile): boolean {
-	return file.type === SpecialFileType.MCP_PROMPT && !!file.mcpPrompt;
-}
-
-/**
- * Check if an attachment is an MCP prompt
- */
-function isMcpPromptAttachment(attachment: DatabaseMessageExtra): boolean {
-	return attachment.type === AttachmentType.MCP_PROMPT;
+export function isMcpPrompt(item: ChatAttachmentDisplayItem): boolean {
+	if (item.attachment?.type === AttachmentType.MCP_PROMPT) {
+		return true;
+	}
+	if (item.uploadedFile?.type === SpecialFileType.MCP_PROMPT && item.uploadedFile.mcpPrompt) {
+		return true;
+	}
+	return false;
 }
 
 /**
- * Check if an attachment is an MCP resource
+ * Check if a display item represents an MCP resource
  */
-function isMcpResourceAttachment(attachment: DatabaseMessageExtra): boolean {
-	return attachment.type === AttachmentType.MCP_RESOURCE;
+export function isMcpResource(item: ChatAttachmentDisplayItem): boolean {
+	return item.attachment?.type === AttachmentType.MCP_RESOURCE;
 }
 
 /**
@@ -58,7 +58,6 @@ export function getAttachmentDisplayItems(
 			size: file.size,
 			preview: file.preview,
 			isImage: getUploadedFileCategory(file) === FileTypeCategory.IMAGE,
-			isMcpPrompt: isMcpPromptUpload(file),
 			isLoading: file.isLoading,
 			loadError: file.loadError,
 			uploadedFile: file,
@@ -69,16 +68,13 @@ export function getAttachmentDisplayItems(
 	// Add stored attachments (ChatMessage)
 	for (const [index, attachment] of attachments.entries()) {
 		const isImage = isImageFile(attachment);
-		const isMcpPrompt = isMcpPromptAttachment(attachment);
-		const isMcpResource = isMcpResourceAttachment(attachment);
 
 		items.push({
 			id: `attachment-${index}`,
 			name: attachment.name,
+			size: 'size' in attachment ? attachment.size : undefined,
 			preview: isImage && 'base64Url' in attachment ? attachment.base64Url : undefined,
 			isImage,
-			isMcpPrompt,
-			isMcpResource,
 			attachment,
 			attachmentIndex: index,
 			textContent: 'content' in attachment ? attachment.content : undefined
diff --git a/tools/server/webui/src/lib/utils/attachment-type.ts b/tools/ui/src/lib/utils/attachment-type.ts
similarity index 84%
rename from tools/server/webui/src/lib/utils/attachment-type.ts
rename to tools/ui/src/lib/utils/attachment-type.ts
index 9e9f0960122..d595afa6b7f 100644
--- a/tools/server/webui/src/lib/utils/attachment-type.ts
+++ b/tools/ui/src/lib/utils/attachment-type.ts
@@ -103,3 +103,24 @@ export function isAudioFile(
 
 	return false;
 }
+
+/**
+ * Determines if an attachment or uploaded file is a video file
+ * @param uploadedFile - Optional uploaded file
+ * @param attachment - Optional database attachment
+ * @returns true if the file is a video file
+ */
+export function isVideoFile(
+	attachment?: DatabaseMessageExtra,
+	uploadedFile?: ChatUploadedFile
+): boolean {
+	if (uploadedFile) {
+		return getUploadedFileCategory(uploadedFile) === FileTypeCategory.VIDEO;
+	}
+
+	if (attachment) {
+		return attachment.type === AttachmentType.VIDEO;
+	}
+
+	return false;
+}
diff --git a/tools/server/webui/src/lib/utils/audio-recording.ts b/tools/ui/src/lib/utils/audio-recording.ts
similarity index 77%
rename from tools/server/webui/src/lib/utils/audio-recording.ts
rename to tools/ui/src/lib/utils/audio-recording.ts
index 2a21985d1a6..ab207b7a44d 100644
--- a/tools/server/webui/src/lib/utils/audio-recording.ts
+++ b/tools/ui/src/lib/utils/audio-recording.ts
@@ -43,27 +43,48 @@ export class AudioRecorder {
 
 	async stopRecording(): Promise<Blob> {
 		return new Promise((resolve, reject) => {
-			if (!this.mediaRecorder || this.mediaRecorder.state === 'inactive') {
+			const recorder = this.mediaRecorder;
+			const chunks = this.audioChunks;
+			const stream = this.stream;
+
+			if (!recorder || recorder.state === 'inactive') {
 				reject(new Error('No active recording to stop'));
 				return;
 			}
 
-			this.mediaRecorder.onstop = () => {
-				const mimeType = this.mediaRecorder?.mimeType || MimeTypeAudio.WAV;
-				const audioBlob = new Blob(this.audioChunks, { type: mimeType });
+			// Detach instance state right away so a new startRecording can take over without race
+			this.mediaRecorder = null;
+			this.audioChunks = [];
+			this.stream = null;
+			this.recordingState = false;
 
-				this.cleanup();
+			recorder.onstop = () => {
+				const audioBlob = new Blob(chunks, {
+					type: recorder.mimeType || MimeTypeAudio.WAV
+				});
+
+				if (stream) {
+					for (const track of stream.getTracks()) {
+						track.stop();
+					}
+				}
 
 				resolve(audioBlob);
 			};
 
-			this.mediaRecorder.onerror = (event) => {
+			recorder.onerror = (event) => {
 				console.error('Recording error:', event);
-				this.cleanup();
+
+				if (stream) {
+					for (const track of stream.getTracks()) {
+						track.stop();
+					}
+				}
+
 				reject(new Error('Recording failed'));
 			};
 
-			this.mediaRecorder.stop();
+			recorder.stop();
 		});
 	}
 
@@ -72,10 +93,26 @@ export class AudioRecorder {
 	}
 
 	cancelRecording(): void {
-		if (this.mediaRecorder && this.mediaRecorder.state !== 'inactive') {
-			this.mediaRecorder.stop();
+		const recorder = this.mediaRecorder;
+		const stream = this.stream;
+
+		this.mediaRecorder = null;
+		this.audioChunks = [];
+		this.stream = null;
+		this.recordingState = false;
+
+		if (recorder && recorder.state !== 'inactive') {
+			// Drop the original handlers so the pending stop event does not touch the instance
+			recorder.onstop = null;
+			recorder.onerror = null;
+			recorder.stop();
+		}
+
+		if (stream) {
+			for (const track of stream.getTracks()) {
+				track.stop();
+			}
 		}
-		this.cleanup();
 	}
 
 	private initializeRecorder(stream: MediaStream): void {
@@ -110,19 +147,6 @@ export class AudioRecorder {
 			this.recordingState = false;
 		};
 	}
-
-	private cleanup(): void {
-		if (this.stream) {
-			for (const track of this.stream.getTracks()) {
-				track.stop();
-			}
-
-			this.stream = null;
-		}
-		this.mediaRecorder = null;
-		this.audioChunks = [];
-		this.recordingState = false;
-	}
 }
 
 export async function convertToWav(audioBlob: Blob): Promise<Blob> {
@@ -136,13 +160,12 @@ export async function convertToWav(audioBlob: Blob): Promise<Blob> {
 		// eslint-disable-next-line @typescript-eslint/no-explicit-any
 		const audioContext = new (window.AudioContext || (window as any).webkitAudioContext)();
 
-		const audioBuffer = await audioContext.decodeAudioData(arrayBuffer);
-
-		const wavBlob = audioBufferToWav(audioBuffer);
-
-		audioContext.close();
-
-		return wavBlob;
+		try {
+			const audioBuffer = await audioContext.decodeAudioData(arrayBuffer);
+			return audioBufferToWav(audioBuffer);
+		} finally {
+			audioContext.close();
+		}
 	} catch (error) {
 		console.error('Failed to convert audio to WAV:', error);
 		return audioBlob;
@@ -182,12 +205,20 @@ function audioBufferToWav(buffer: AudioBuffer): Blob {
 	writeString(36, 'data'); // Subchunk2ID
 	view.setUint32(40, dataSize, true); // Subchunk2Size
 
-	let offset = 44;
+	// Cache channel arrays, write PCM via Int16Array (native little-endian, matches WAV)
+	const channels: Float32Array[] = new Array(numberOfChannels);
+	for (let c = 0; c < numberOfChannels; c++) {
+		channels[c] = buffer.getChannelData(c);
+	}
+
+	const pcm = new Int16Array(arrayBuffer, 44, length * numberOfChannels);
+	let p = 0;
 	for (let i = 0; i < length; i++) {
-		for (let channel = 0; channel < numberOfChannels; channel++) {
-			const sample = Math.max(-1, Math.min(1, buffer.getChannelData(channel)[i]));
-			view.setInt16(offset, sample * 0x7fff, true);
-			offset += 2;
+		for (let c = 0; c < numberOfChannels; c++) {
+			let s = channels[c][i];
+			if (s > 1) s = 1;
+			else if (s < -1) s = -1;
+			pcm[p++] = s * 0x7fff;
 		}
 	}
 
diff --git a/tools/server/webui/src/lib/utils/autoresize-textarea.ts b/tools/ui/src/lib/utils/autoresize-textarea.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/autoresize-textarea.ts
rename to tools/ui/src/lib/utils/autoresize-textarea.ts
diff --git a/tools/server/webui/src/lib/utils/branching.ts b/tools/ui/src/lib/utils/branching.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/branching.ts
rename to tools/ui/src/lib/utils/branching.ts
diff --git a/tools/server/webui/src/lib/utils/browser-only.ts b/tools/ui/src/lib/utils/browser-only.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/browser-only.ts
rename to tools/ui/src/lib/utils/browser-only.ts
diff --git a/tools/server/webui/src/lib/utils/cache-ttl.ts b/tools/ui/src/lib/utils/cache-ttl.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/cache-ttl.ts
rename to tools/ui/src/lib/utils/cache-ttl.ts
diff --git a/tools/server/webui/src/lib/utils/clipboard.ts b/tools/ui/src/lib/utils/clipboard.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/clipboard.ts
rename to tools/ui/src/lib/utils/clipboard.ts
diff --git a/tools/server/webui/src/lib/utils/code.ts b/tools/ui/src/lib/utils/code.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/code.ts
rename to tools/ui/src/lib/utils/code.ts
diff --git a/tools/server/webui/src/lib/utils/config-helpers.ts b/tools/ui/src/lib/utils/config-helpers.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/config-helpers.ts
rename to tools/ui/src/lib/utils/config-helpers.ts
diff --git a/tools/server/webui/src/lib/utils/conversation-utils.ts b/tools/ui/src/lib/utils/conversation-utils.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/conversation-utils.ts
rename to tools/ui/src/lib/utils/conversation-utils.ts
diff --git a/tools/server/webui/src/lib/utils/convert-files-to-extra.ts b/tools/ui/src/lib/utils/convert-files-to-extra.ts
similarity index 89%
rename from tools/server/webui/src/lib/utils/convert-files-to-extra.ts
rename to tools/ui/src/lib/utils/convert-files-to-extra.ts
index 84992106bd4..af404445f78 100644
--- a/tools/server/webui/src/lib/utils/convert-files-to-extra.ts
+++ b/tools/ui/src/lib/utils/convert-files-to-extra.ts
@@ -2,6 +2,7 @@ import { convertPDFToImage, convertPDFToText } from './pdf-processing';
 import { isSvgMimeType, svgBase64UrlToPngDataURL } from './svg-to-png';
 import { isWebpMimeType, webpBase64UrlToPngDataURL } from './webp-to-png';
 import { FileTypeCategory, AttachmentType, SpecialFileType } from '$lib/enums';
+import { SETTINGS_KEYS } from '$lib/constants';
 import { config, settingsStore } from '$lib/stores/settings.svelte';
 import { modelsStore } from '$lib/stores/models.svelte';
 import { getFileTypeCategory } from '$lib/utils';
@@ -38,6 +39,7 @@ export async function parseFilesToMessageExtras(
 			extras.push({
 				type: AttachmentType.MCP_PROMPT,
 				name: file.name,
+				size: file.size,
 				serverName: file.mcpPrompt.serverName,
 				promptName: file.mcpPrompt.promptName,
 				content: file.textContent ?? '',
@@ -68,6 +70,7 @@ export async function parseFilesToMessageExtras(
 				extras.push({
 					type: AttachmentType.IMAGE,
 					name: file.name,
+					size: file.size,
 					base64Url
 				});
 			}
@@ -79,12 +82,28 @@ export async function parseFilesToMessageExtras(
 				extras.push({
 					type: AttachmentType.AUDIO,
 					name: file.name,
+					size: file.size,
 					base64Data: base64Data,
 					mimeType: file.type
 				});
 			} catch (error) {
 				console.error(`Failed to process audio file ${file.name}:`, error);
 			}
+		} else if (getFileTypeCategory(file.type) === FileTypeCategory.VIDEO) {
+			// Process video files (MP4, etc)
+			try {
+				const base64Data = await readFileAsBase64(file.file);
+
+				extras.push({
+					type: AttachmentType.VIDEO,
+					name: file.name,
+					size: file.size,
+					base64Data: base64Data,
+					mimeType: file.type
+				});
+			} catch (error) {
+				console.error(`Failed to process video file ${file.name}:`, error);
+			}
 		} else if (getFileTypeCategory(file.type) === FileTypeCategory.PDF) {
 			try {
 				// Always get base64 data for preview functionality
@@ -103,7 +122,7 @@ export async function parseFilesToMessageExtras(
 					console.log('Non-vision model detected: forcing PDF-to-text mode and updating settings');
 
 					// Update the setting in localStorage
-					settingsStore.updateConfig('pdfAsImage', false);
+					settingsStore.updateConfig(SETTINGS_KEYS.PDF_AS_IMAGE, false);
 
 					// Show toast notification to user
 					toast.warning(
@@ -132,6 +151,7 @@ export async function parseFilesToMessageExtras(
 						extras.push({
 							type: AttachmentType.PDF,
 							name: file.name,
+							size: file.size,
 							content: `PDF file with ${images.length} pages`,
 							images: images,
 							processedAsImages: true,
@@ -149,6 +169,7 @@ export async function parseFilesToMessageExtras(
 						extras.push({
 							type: AttachmentType.PDF,
 							name: file.name,
+							size: file.size,
 							content: content,
 							processedAsImages: false,
 							base64Data: base64Data
@@ -166,6 +187,7 @@ export async function parseFilesToMessageExtras(
 					extras.push({
 						type: AttachmentType.PDF,
 						name: file.name,
+						size: file.size,
 						content: content,
 						processedAsImages: false,
 						base64Data: base64Data
@@ -186,6 +208,7 @@ export async function parseFilesToMessageExtras(
 					extras.push({
 						type: AttachmentType.TEXT,
 						name: file.name,
+						size: file.size,
 						content: content
 					});
 				} else {
diff --git a/tools/server/webui/src/lib/utils/cors-proxy.ts b/tools/ui/src/lib/utils/cors-proxy.ts
similarity index 82%
rename from tools/server/webui/src/lib/utils/cors-proxy.ts
rename to tools/ui/src/lib/utils/cors-proxy.ts
index 9ad6c8b5dc2..47caf27427e 100644
--- a/tools/server/webui/src/lib/utils/cors-proxy.ts
+++ b/tools/ui/src/lib/utils/cors-proxy.ts
@@ -33,12 +33,3 @@ export function buildProxiedHeaders(headers: Record<string, string>): Record<str
 
 	return proxiedHeaders;
 }
-
-/**
- * Get a proxied URL string for use in fetch requests.
- * @param targetUrl - The original URL to proxy
- * @returns Proxied URL as string
- */
-export function getProxiedUrlString(targetUrl: string): string {
-	return buildProxiedUrl(targetUrl).href;
-}
diff --git a/tools/ui/src/lib/utils/css.ts b/tools/ui/src/lib/utils/css.ts
new file mode 100644
index 00000000000..99351a7f449
--- /dev/null
+++ b/tools/ui/src/lib/utils/css.ts
@@ -0,0 +1,9 @@
+/**
+ * Converts a rem CSS value to pixels based on the document root font size.
+ */
+export function remToPx(rem: string): number {
+	const val = parseFloat(rem);
+	const fontSize = parseFloat(getComputedStyle(document.documentElement).fontSize);
+
+	return val * fontSize;
+}
diff --git a/tools/server/webui/src/lib/utils/data-url.ts b/tools/ui/src/lib/utils/data-url.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/data-url.ts
rename to tools/ui/src/lib/utils/data-url.ts
diff --git a/tools/server/webui/src/lib/utils/debounce.ts b/tools/ui/src/lib/utils/debounce.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/debounce.ts
rename to tools/ui/src/lib/utils/debounce.ts
diff --git a/tools/server/webui/src/lib/utils/file-preview.ts b/tools/ui/src/lib/utils/file-preview.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/file-preview.ts
rename to tools/ui/src/lib/utils/file-preview.ts
diff --git a/tools/server/webui/src/lib/utils/file-type.ts b/tools/ui/src/lib/utils/file-type.ts
similarity index 94%
rename from tools/server/webui/src/lib/utils/file-type.ts
rename to tools/ui/src/lib/utils/file-type.ts
index 4c670600ce5..ae814e8059a 100644
--- a/tools/server/webui/src/lib/utils/file-type.ts
+++ b/tools/ui/src/lib/utils/file-type.ts
@@ -1,5 +1,6 @@
 import {
 	AUDIO_FILE_TYPES,
+	VIDEO_FILE_TYPES,
 	IMAGE_FILE_TYPES,
 	PDF_FILE_TYPES,
 	TEXT_FILE_TYPES
@@ -12,6 +13,7 @@ import {
 	FileTypeCategory,
 	MimeTypeApplication,
 	MimeTypeAudio,
+	MimeTypeVideo,
 	MimeTypeImage,
 	MimeTypeText
 } from '$lib/enums';
@@ -35,6 +37,11 @@ export function getFileTypeCategory(mimeType: string): FileTypeCategory | null {
 		case MimeTypeAudio.WEBM_OPUS:
 			return FileTypeCategory.AUDIO;
 
+		// Video
+		case MimeTypeVideo.MP4:
+		case MimeTypeVideo.OGG:
+			return FileTypeCategory.VIDEO;
+
 		// PDF
 		case MimeTypeApplication.PDF:
 			return FileTypeCategory.PDF;
@@ -179,6 +186,12 @@ export function getFileTypeByExtension(filename: string): string | null {
 		}
 	}
 
+	for (const [key, type] of Object.entries(VIDEO_FILE_TYPES)) {
+		if ((type.extensions as readonly string[]).includes(extension)) {
+			return `${FileTypeCategory.VIDEO}:${key}`;
+		}
+	}
+
 	for (const [key, type] of Object.entries(PDF_FILE_TYPES)) {
 		if ((type.extensions as readonly string[]).includes(extension)) {
 			return `${FileTypeCategory.PDF}:${key}`;
diff --git a/tools/server/webui/src/lib/utils/formatters.ts b/tools/ui/src/lib/utils/formatters.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/formatters.ts
rename to tools/ui/src/lib/utils/formatters.ts
diff --git a/tools/server/webui/src/lib/utils/headers.ts b/tools/ui/src/lib/utils/headers.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/headers.ts
rename to tools/ui/src/lib/utils/headers.ts
diff --git a/tools/server/webui/src/lib/utils/image-error-fallback.ts b/tools/ui/src/lib/utils/image-error-fallback.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/image-error-fallback.ts
rename to tools/ui/src/lib/utils/image-error-fallback.ts
diff --git a/tools/server/webui/src/lib/utils/index.ts b/tools/ui/src/lib/utils/index.ts
similarity index 91%
rename from tools/server/webui/src/lib/utils/index.ts
rename to tools/ui/src/lib/utils/index.ts
index bc158103e2d..00aa49c4176 100644
--- a/tools/server/webui/src/lib/utils/index.ts
+++ b/tools/ui/src/lib/utils/index.ts
@@ -13,8 +13,8 @@ export { apiFetch, apiFetchWithParams, apiPost, type ApiFetchOptions } from './a
 export { validateApiKey } from './api-key-validation';
 
 // Attachment utilities
-export { getAttachmentDisplayItems } from './attachment-display';
-export { isTextFile, isImageFile, isPdfFile, isAudioFile } from './attachment-type';
+export { getAttachmentDisplayItems, isMcpPrompt, isMcpResource } from './attachment-display';
+export { isTextFile, isImageFile, isPdfFile, isAudioFile, isVideoFile } from './attachment-type';
 
 // Textarea utilities
 export { default as autoResizeTextarea } from './autoresize-textarea';
@@ -39,7 +39,10 @@ export { highlightCode, detectIncompleteCodeBlock, type IncompleteCodeBlock } fr
 export { setConfigValue, getConfigValue, configToParameterRecord } from './config-helpers';
 
 // CORS Proxy
-export { buildProxiedUrl, getProxiedUrlString, buildProxiedHeaders } from './cors-proxy';
+export { buildProxiedUrl, buildProxiedHeaders } from './cors-proxy';
+
+// URL utilities
+export { extractRootDomain, sanitizeExternalUrl } from './url';
 
 // Conversation utilities
 export { createMessageCountMap, getMessageCount } from './conversation-utils';
@@ -146,9 +149,6 @@ export { createBase64DataUrl } from './data-url';
 // Header utilities
 export { parseHeadersToArray, serializeHeaders } from './headers';
 
-// Favicon utilities
-export { getFaviconUrl } from './favicon';
-
 // Agentic content utilities (structured section derivation)
 export {
 	deriveAgenticSections,
@@ -158,9 +158,6 @@ export {
 	type ToolResultLine
 } from './agentic';
 
-// Legacy migration utilities
-export { runLegacyMigration, isMigrationNeeded } from './legacy-migration';
-
 // Cache utilities
 export { TTLCache, ReactiveTTLMap, type TTLCacheOptions } from './cache-ttl';
 
@@ -190,3 +187,6 @@ export {
 // Cryptography utilities
 
 export { uuid } from './uuid';
+
+// CSS utilities
+export { remToPx } from './css';
diff --git a/tools/server/webui/src/lib/utils/is-ime-composing.ts b/tools/ui/src/lib/utils/is-ime-composing.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/is-ime-composing.ts
rename to tools/ui/src/lib/utils/is-ime-composing.ts
diff --git a/tools/server/webui/src/lib/utils/latex-protection.ts b/tools/ui/src/lib/utils/latex-protection.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/latex-protection.ts
rename to tools/ui/src/lib/utils/latex-protection.ts
diff --git a/tools/server/webui/src/lib/utils/legacy-migration.ts b/tools/ui/src/lib/utils/legacy-migration.ts
similarity index 93%
rename from tools/server/webui/src/lib/utils/legacy-migration.ts
rename to tools/ui/src/lib/utils/legacy-migration.ts
index b526c260984..19755f6eee9 100644
--- a/tools/server/webui/src/lib/utils/legacy-migration.ts
+++ b/tools/ui/src/lib/utils/legacy-migration.ts
@@ -20,7 +20,9 @@ import { DatabaseService } from '$lib/services/database.service';
 import { MessageRole, MessageType } from '$lib/enums';
 import type { DatabaseMessage } from '$lib/types/database';
 
-const MIGRATION_DONE_KEY = 'llama-webui-migration-v2-done';
+const MIGRATION_DONE_KEY = 'llama-ui-migration-v2-done';
+/** @deprecated Use {@link MIGRATION_DONE_KEY} instead */
+const DEPRECATED_MIGRATION_DONE_KEY = 'llama-webui-migration-v2-done';
 
 /**
  * @deprecated Part of legacy migration — remove with the migration module.
@@ -28,7 +30,19 @@ const MIGRATION_DONE_KEY = 'llama-webui-migration-v2-done';
  */
 export function isMigrationNeeded(): boolean {
 	try {
-		return !localStorage.getItem(MIGRATION_DONE_KEY);
+		// Check new key first, fall back to deprecated old key
+		if (localStorage.getItem(MIGRATION_DONE_KEY)) return false;
+		if (localStorage.getItem(DEPRECATED_MIGRATION_DONE_KEY)) {
+			// Migrate to new key
+			try {
+				localStorage.setItem(MIGRATION_DONE_KEY, String(Date.now()));
+				localStorage.removeItem(DEPRECATED_MIGRATION_DONE_KEY);
+			} catch {
+				// Ignore storage errors
+			}
+			return false;
+		}
+		return true;
 	} catch {
 		return false;
 	}
@@ -169,8 +183,10 @@ async function migrateConversation(convId: string): Promise<number> {
 		const turns = parseLegacyToolCalls(cleanContent);
 
 		// Parse existing toolCalls JSON to try to match IDs
-		let existingToolCalls: Array<{ id: string; function?: { name: string; arguments: string } }> =
-			[];
+		let existingToolCalls: Array<{
+			id: string;
+			function?: { name: string; arguments: string };
+		}> = [];
 		if (message.toolCalls) {
 			try {
 				existingToolCalls = JSON.parse(message.toolCalls);
diff --git a/tools/server/webui/src/lib/utils/mcp.ts b/tools/ui/src/lib/utils/mcp.ts
similarity index 97%
rename from tools/server/webui/src/lib/utils/mcp.ts
rename to tools/ui/src/lib/utils/mcp.ts
index ee27798455d..05fe90048f0 100644
--- a/tools/server/webui/src/lib/utils/mcp.ts
+++ b/tools/ui/src/lib/utils/mcp.ts
@@ -49,7 +49,7 @@ export function detectMcpTransportFromUrl(url: string): MCPTransportType {
 
 /**
  * Parses MCP server settings from a JSON string or array.
- * requestTimeoutSeconds is not user-configurable in the UI, so we always use the default value.
+ * Preserves per-server requestTimeoutSeconds if stored, otherwise falls back to the global default.
  * @param rawServers - The raw servers to parse
  * @returns An empty array if the input is invalid.
  */
@@ -88,7 +88,9 @@ export function parseMcpServerSettings(rawServers: unknown): MCPServerSettingsEn
 			enabled: Boolean((entry as { enabled?: unknown })?.enabled),
 			url,
 			name: (entry as { name?: string })?.name,
-			requestTimeoutSeconds: DEFAULT_MCP_CONFIG.requestTimeoutSeconds,
+			requestTimeoutSeconds:
+				(entry as { requestTimeoutSeconds?: number })?.requestTimeoutSeconds ??
+				DEFAULT_MCP_CONFIG.requestTimeoutSeconds,
 			headers: headers || undefined,
 			useProxy: Boolean((entry as { useProxy?: unknown })?.useProxy)
 		} satisfies MCPServerSettingsEntry;
diff --git a/tools/server/webui/src/lib/utils/modality-file-validation.ts b/tools/ui/src/lib/utils/modality-file-validation.ts
similarity index 91%
rename from tools/server/webui/src/lib/utils/modality-file-validation.ts
rename to tools/ui/src/lib/utils/modality-file-validation.ts
index 9b52e93dbe1..bf78a70088c 100644
--- a/tools/server/webui/src/lib/utils/modality-file-validation.ts
+++ b/tools/ui/src/lib/utils/modality-file-validation.ts
@@ -45,6 +45,10 @@ export function isFileTypeSupportedByModel(
 			// Audio files require audio support
 			return capabilities.hasAudio;
 
+		case FileTypeCategory.VIDEO:
+			// Video files require video support
+			return capabilities.hasVideo;
+
 		default:
 			// Unknown categories - be conservative and allow
 			return true;
@@ -69,7 +73,7 @@ export function filterFilesByModalities(
 	const unsupportedFiles: File[] = [];
 	const modalityReasons: Record<string, string> = {};
 
-	const { hasVision, hasAudio } = capabilities;
+	const { hasVision, hasAudio, hasVideo } = capabilities;
 
 	for (const file of files) {
 		const category = getFileTypeCategory(file.type);
@@ -91,6 +95,13 @@ export function filterFilesByModalities(
 				}
 				break;
 
+			case FileTypeCategory.VIDEO:
+				if (!hasVideo) {
+					isSupported = false;
+					reason = 'Video files require a video-capable model';
+				}
+				break;
+
 			case FileTypeCategory.TEXT:
 			case FileTypeCategory.PDF:
 				// Always supported
@@ -127,7 +138,7 @@ export function generateModalityErrorMessage(
 ): string {
 	if (unsupportedFiles.length === 0) return '';
 
-	const { hasVision, hasAudio } = capabilities;
+	const { hasVision, hasAudio, hasVideo } = capabilities;
 
 	let message = '';
 
@@ -144,6 +155,7 @@ export function generateModalityErrorMessage(
 	const supportedTypes: string[] = ['text files', 'PDFs'];
 	if (hasVision) supportedTypes.push('images');
 	if (hasAudio) supportedTypes.push('audio files');
+	if (hasVideo) supportedTypes.push('video files');
 
 	message += ` This model supports: ${supportedTypes.join(', ')}.`;
 
diff --git a/tools/server/webui/src/lib/utils/model-names.ts b/tools/ui/src/lib/utils/model-names.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/model-names.ts
rename to tools/ui/src/lib/utils/model-names.ts
diff --git a/tools/server/webui/src/lib/utils/pdf-processing.ts b/tools/ui/src/lib/utils/pdf-processing.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/pdf-processing.ts
rename to tools/ui/src/lib/utils/pdf-processing.ts
diff --git a/tools/server/webui/src/lib/utils/portal-to-body.ts b/tools/ui/src/lib/utils/portal-to-body.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/portal-to-body.ts
rename to tools/ui/src/lib/utils/portal-to-body.ts
diff --git a/tools/server/webui/src/lib/utils/precision.ts b/tools/ui/src/lib/utils/precision.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/precision.ts
rename to tools/ui/src/lib/utils/precision.ts
diff --git a/tools/server/webui/src/lib/utils/process-uploaded-files.ts b/tools/ui/src/lib/utils/process-uploaded-files.ts
similarity index 93%
rename from tools/server/webui/src/lib/utils/process-uploaded-files.ts
rename to tools/ui/src/lib/utils/process-uploaded-files.ts
index 0342dce1f7a..d0110f6cbcf 100644
--- a/tools/server/webui/src/lib/utils/process-uploaded-files.ts
+++ b/tools/ui/src/lib/utils/process-uploaded-files.ts
@@ -1,6 +1,7 @@
 import { isSvgMimeType, svgBase64UrlToPngDataURL } from './svg-to-png';
 import { isWebpMimeType, webpBase64UrlToPngDataURL } from './webp-to-png';
 import { FileTypeCategory } from '$lib/enums';
+import { SETTINGS_KEYS } from '$lib/constants';
 import { modelsStore } from '$lib/stores/models.svelte';
 import { settingsStore } from '$lib/stores/settings.svelte';
 import { toast } from 'svelte-sonner';
@@ -104,7 +105,7 @@ export async function processFilesToChatUploaded(
 						action: {
 							label: 'Enable PDF as Images',
 							onClick: () => {
-								settingsStore.updateConfig('pdfAsImage', true);
+								settingsStore.updateConfig(SETTINGS_KEYS.PDF_AS_IMAGE, true);
 								toast.success('PDF parsing as images enabled!', {
 									duration: 3000
 								});
@@ -116,6 +117,10 @@ export async function processFilesToChatUploaded(
 				// Generate preview URL for audio files
 				const preview = await readFileAsDataURL(file);
 				results.push({ ...base, preview });
+			} else if (getFileTypeCategory(file.type) === FileTypeCategory.VIDEO) {
+				// Generate preview URL for video files
+				const preview = await readFileAsDataURL(file);
+				results.push({ ...base, preview });
 			} else {
 				// Fallback: treat unknown files as text
 				try {
diff --git a/tools/server/webui/src/lib/utils/redact.ts b/tools/ui/src/lib/utils/redact.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/redact.ts
rename to tools/ui/src/lib/utils/redact.ts
diff --git a/tools/server/webui/src/lib/utils/request-helpers.ts b/tools/ui/src/lib/utils/request-helpers.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/request-helpers.ts
rename to tools/ui/src/lib/utils/request-helpers.ts
diff --git a/tools/server/webui/src/lib/utils/sanitize.ts b/tools/ui/src/lib/utils/sanitize.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/sanitize.ts
rename to tools/ui/src/lib/utils/sanitize.ts
diff --git a/tools/server/webui/src/lib/utils/svg-to-png.ts b/tools/ui/src/lib/utils/svg-to-png.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/svg-to-png.ts
rename to tools/ui/src/lib/utils/svg-to-png.ts
diff --git a/tools/server/webui/src/lib/utils/syntax-highlight-language.ts b/tools/ui/src/lib/utils/syntax-highlight-language.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/syntax-highlight-language.ts
rename to tools/ui/src/lib/utils/syntax-highlight-language.ts
diff --git a/tools/server/webui/src/lib/utils/text-files.ts b/tools/ui/src/lib/utils/text-files.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/text-files.ts
rename to tools/ui/src/lib/utils/text-files.ts
diff --git a/tools/server/webui/src/lib/utils/text.ts b/tools/ui/src/lib/utils/text.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/text.ts
rename to tools/ui/src/lib/utils/text.ts
diff --git a/tools/server/webui/src/lib/utils/uri-template.ts b/tools/ui/src/lib/utils/uri-template.ts
similarity index 99%
rename from tools/server/webui/src/lib/utils/uri-template.ts
rename to tools/ui/src/lib/utils/uri-template.ts
index 7665c98c929..eb8dbfb3632 100644
--- a/tools/server/webui/src/lib/utils/uri-template.ts
+++ b/tools/ui/src/lib/utils/uri-template.ts
@@ -160,7 +160,7 @@ export function expandTemplate(template: string, values: Record<string, string>)
 								(name: string, i: number) =>
 									`${encodeURIComponent(name)}=${encodeURIComponent(expandedParts[i])}`
 							)
-							.join(URI_TEMPLATE_SEPARATORS.COMMA)
+							.join(URI_TEMPLATE_SEPARATORS.QUERY_CONTINUATION)
 					);
 				case URI_TEMPLATE_OPERATORS.FORM_CONTINUATION:
 					// Form-style query continuation
diff --git a/tools/ui/src/lib/utils/url.ts b/tools/ui/src/lib/utils/url.ts
new file mode 100644
index 00000000000..e8b78f7bd40
--- /dev/null
+++ b/tools/ui/src/lib/utils/url.ts
@@ -0,0 +1,72 @@
+import { TWO_PART_PUBLIC_SUFFIXES, WILDCARD_PUBLIC_SUFFIXES } from '$lib/constants';
+import { UrlProtocol } from '$lib/enums';
+
+/**
+ * Check whether a hostname looks like an IPv4 or IPv6 address.
+ */
+function isIpAddress(hostname: string): boolean {
+	if (hostname.includes(':')) return true;
+
+	if (/^\d{1,3}(\.\d{1,3}){3}$/.test(hostname)) return true;
+
+	return false;
+}
+
+/**
+ * Extract the registrable root domain from a URL.
+ *
+ * @example
+ *   'mcp.example.com'           -> 'example.com'
+ *   'www.example.co.uk'         -> 'example.co.uk'
+ *   'bar.foo.nom.br'            -> 'bar.foo.nom.br'
+ *   '192.168.1.1'               -> null
+ *   'localhost'                 -> null
+ */
+export function extractRootDomain(url: URL): string | null {
+	const hostname = url.hostname.toLowerCase();
+	if (!hostname || isIpAddress(hostname)) return null;
+
+	const parts = hostname.split('.');
+
+	if (parts.length < 2) return null;
+
+	if (parts.length >= 3) {
+		const suffix2 = `${parts[parts.length - 2]}.${parts[parts.length - 1]}`;
+
+		if (TWO_PART_PUBLIC_SUFFIXES.has(suffix2)) {
+			return parts.slice(-3).join('.');
+		}
+	}
+
+	for (let i = 2; i <= parts.length; i++) {
+		const candidate = parts.slice(-i).join('.');
+
+		if (WILDCARD_PUBLIC_SUFFIXES.has(candidate)) {
+			if (parts.length === i + 1) {
+				return hostname;
+			}
+
+			return parts.slice(-(i + 2)).join('.');
+		}
+	}
+
+	return parts.slice(-2).join('.');
+}
+
+/**
+ * Sanitize an external URL string for safe use in an `<a href>`.
+ * Only allows http: and https: schemes. Returns `null` for anything else.
+ */
+export function sanitizeExternalUrl(raw: string): string | null {
+	try {
+		const url = new URL(raw);
+
+		if (url.protocol !== UrlProtocol.HTTP && url.protocol !== UrlProtocol.HTTPS) {
+			return null;
+		}
+
+		return url.href;
+	} catch {
+		return null;
+	}
+}
diff --git a/tools/server/webui/src/lib/utils/uuid.ts b/tools/ui/src/lib/utils/uuid.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/uuid.ts
rename to tools/ui/src/lib/utils/uuid.ts
diff --git a/tools/ui/src/lib/utils/viewport.ts b/tools/ui/src/lib/utils/viewport.ts
new file mode 100644
index 00000000000..9e9b7aff3b8
--- /dev/null
+++ b/tools/ui/src/lib/utils/viewport.ts
@@ -0,0 +1,12 @@
+/**
+ * Check if an element is within the current viewport.
+ */
+export function isElementInViewport(node: HTMLElement): boolean {
+	const rect = node.getBoundingClientRect();
+	return (
+		rect.top < window.innerHeight &&
+		rect.bottom > 0 &&
+		rect.left < window.innerWidth &&
+		rect.right > 0
+	);
+}
diff --git a/tools/server/webui/src/lib/utils/webp-to-png.ts b/tools/ui/src/lib/utils/webp-to-png.ts
similarity index 100%
rename from tools/server/webui/src/lib/utils/webp-to-png.ts
rename to tools/ui/src/lib/utils/webp-to-png.ts
diff --git a/tools/ui/src/routes/(chat)/+layout.svelte b/tools/ui/src/routes/(chat)/+layout.svelte
new file mode 100644
index 00000000000..37aa0358248
--- /dev/null
+++ b/tools/ui/src/routes/(chat)/+layout.svelte
@@ -0,0 +1,12 @@
+<script lang="ts">
+	import { page } from '$app/state';
+	import { ChatScreen } from '$lib/components/app';
+
+	let { children } = $props();
+
+	let showCenteredEmpty = $derived(!page.params.id);
+</script>
+
+<ChatScreen {showCenteredEmpty} />
+
+{@render children?.()}
diff --git a/tools/server/webui/src/routes/+page.svelte b/tools/ui/src/routes/(chat)/+page.svelte
similarity index 90%
rename from tools/server/webui/src/routes/+page.svelte
rename to tools/ui/src/routes/(chat)/+page.svelte
index e67a85abdbb..c272b438ef1 100644
--- a/tools/server/webui/src/routes/+page.svelte
+++ b/tools/ui/src/routes/(chat)/+page.svelte
@@ -1,5 +1,5 @@
 <script lang="ts">
-	import { ChatScreen, DialogModelNotAvailable } from '$lib/components/app';
+	import { DialogModelNotAvailable } from '$lib/components/app';
 	import { chatStore } from '$lib/stores/chat.svelte';
 	import { conversationsStore, isConversationsInitialized } from '$lib/stores/conversations.svelte';
 	import { modelsStore, modelOptions } from '$lib/stores/models.svelte';
@@ -7,10 +7,11 @@
 	import { onMount } from 'svelte';
 	import { page } from '$app/state';
 	import { replaceState } from '$app/navigation';
+	import { APP_NAME, NEW_CHAT_PARAM } from '$lib/constants';
 
 	let qParam = $derived(page.url.searchParams.get('q'));
 	let modelParam = $derived(page.url.searchParams.get('model'));
-	let newChatParam = $derived(page.url.searchParams.get('new_chat'));
+	let newChatParam = $derived(page.url.searchParams.get(NEW_CHAT_PARAM));
 
 	// Dialog state for model not available error
 	let showModelNotAvailable = $state(false);
@@ -25,7 +26,7 @@
 
 		url.searchParams.delete('q');
 		url.searchParams.delete('model');
-		url.searchParams.delete('new_chat');
+		url.searchParams.delete(NEW_CHAT_PARAM);
 
 		replaceState(url.toString(), {});
 	}
@@ -92,11 +93,9 @@
 </script>
 
 <svelte:head>
-	<title>llama.cpp - AI Chat Interface</title>
+	<title>{APP_NAME}</title>
 </svelte:head>
 
-<ChatScreen showCenteredEmpty />
-
 <DialogModelNotAvailable
 	bind:open={showModelNotAvailable}
 	modelName={requestedModelName}
diff --git a/tools/server/webui/src/routes/+page.ts b/tools/ui/src/routes/(chat)/+page.ts
similarity index 100%
rename from tools/server/webui/src/routes/+page.ts
rename to tools/ui/src/routes/(chat)/+page.ts
diff --git a/tools/server/webui/src/routes/chat/[id]/+page.svelte b/tools/ui/src/routes/(chat)/chat/[id]/+page.svelte
similarity index 70%
rename from tools/server/webui/src/routes/chat/[id]/+page.svelte
rename to tools/ui/src/routes/(chat)/chat/[id]/+page.svelte
index c0b2e0c5e7a..e31d4443ef3 100644
--- a/tools/server/webui/src/routes/chat/[id]/+page.svelte
+++ b/tools/ui/src/routes/(chat)/chat/[id]/+page.svelte
@@ -2,14 +2,11 @@
 	import { goto, replaceState } from '$app/navigation';
 	import { page } from '$app/state';
 	import { afterNavigate } from '$app/navigation';
-	import { ChatScreen, DialogModelNotAvailable } from '$lib/components/app';
+	import { DialogModelNotAvailable } from '$lib/components/app';
+	import { APP_NAME, ROUTES } from '$lib/constants';
 	import { chatStore, isLoading } from '$lib/stores/chat.svelte';
-	import {
-		conversationsStore,
-		activeConversation,
-		activeMessages
-	} from '$lib/stores/conversations.svelte';
-	import { modelsStore, modelOptions, selectedModelId } from '$lib/stores/models.svelte';
+	import { conversationsStore, activeConversation } from '$lib/stores/conversations.svelte';
+	import { modelsStore, modelOptions } from '$lib/stores/models.svelte';
 
 	let chatId = $derived(page.params.id);
 	let currentChatId: string | undefined = undefined;
@@ -73,47 +70,9 @@
 		urlParamsProcessed = true;
 	}
 
-	async function selectModelFromLastAssistantResponse() {
-		const messages = activeMessages();
-		if (messages.length === 0) return;
-
-		let lastMessageWithModel: DatabaseMessage | undefined;
-
-		for (let i = messages.length - 1; i >= 0; i--) {
-			if (messages[i].model) {
-				lastMessageWithModel = messages[i];
-				break;
-			}
-		}
-
-		if (!lastMessageWithModel) return;
-
-		const currentModelId = selectedModelId();
-		const currentModelName = modelOptions().find((m) => m.id === currentModelId)?.model;
-
-		if (currentModelName === lastMessageWithModel.model) {
-			return;
-		}
-
-		const matchingModel = modelOptions().find(
-			(option) => option.model === lastMessageWithModel.model
-		);
-
-		if (matchingModel && modelsStore.isModelLoaded(matchingModel.model)) {
-			try {
-				await modelsStore.selectModelById(matchingModel.id);
-				console.log(
-					`Automatically selected model: ${lastMessageWithModel.model} from last message`
-				);
-			} catch (error) {
-				console.warn('Failed to automatically select model from last message:', error);
-			}
-		}
-	}
-
 	afterNavigate(() => {
 		setTimeout(() => {
-			selectModelFromLastAssistantResponse();
+			void modelsStore.selectModelFromLastAssistantResponse();
 		}, 100);
 	});
 
@@ -141,7 +100,7 @@
 						await handleUrlParams();
 					}
 				} else {
-					await goto('#/');
+					await goto(ROUTES.START);
 				}
 			})();
 		}
@@ -166,11 +125,9 @@
 </script>
 
 <svelte:head>
-	<title>{activeConversation()?.name || 'Chat'} - llama.cpp</title>
+	<title>{activeConversation()?.name || 'Chat'} - {APP_NAME}</title>
 </svelte:head>
 
-<ChatScreen />
-
 <DialogModelNotAvailable
 	bind:open={showModelNotAvailable}
 	modelName={requestedModelName}
diff --git a/tools/server/webui/src/routes/chat/[id]/+page.ts b/tools/ui/src/routes/(chat)/chat/[id]/+page.ts
similarity index 100%
rename from tools/server/webui/src/routes/chat/[id]/+page.ts
rename to tools/ui/src/routes/(chat)/chat/[id]/+page.ts
diff --git a/tools/server/webui/src/routes/+error.svelte b/tools/ui/src/routes/+error.svelte
similarity index 90%
rename from tools/server/webui/src/routes/+error.svelte
rename to tools/ui/src/routes/+error.svelte
index faddf0bcaa2..8da9aad16f9 100644
--- a/tools/server/webui/src/routes/+error.svelte
+++ b/tools/ui/src/routes/+error.svelte
@@ -2,6 +2,8 @@
 	import { page } from '$app/stores';
 	import { goto } from '$app/navigation';
 	import { ServerErrorSplash } from '$lib/components/app';
+	import { ROUTES } from '$lib/constants/routes';
+	import { APP_NAME } from '$lib/constants';
 
 	let error = $derived($page.error);
 	let status = $derived($page.status);
@@ -17,12 +19,12 @@
 
 	function handleRetry() {
 		// Navigate back to home page after successful API key validation
-		goto('#/');
+		goto(ROUTES.START);
 	}
 </script>
 
 <svelte:head>
-	<title>Error {status} - WebUI</title>
+	<title>Error {status} - {APP_NAME}</title>
 </svelte:head>
 
 {#if isApiKeyError}
@@ -60,7 +62,7 @@
 				</p>
 			</div>
 			<button
-				onclick={() => goto('#/')}
+				onclick={() => goto(ROUTES.START)}
 				class="rounded-md bg-primary px-4 py-2 text-primary-foreground hover:bg-primary/90"
 			>
 				Go Home
diff --git a/tools/server/webui/src/routes/+layout.svelte b/tools/ui/src/routes/+layout.svelte
similarity index 64%
rename from tools/server/webui/src/routes/+layout.svelte
rename to tools/ui/src/routes/+layout.svelte
index ccc207f8519..e03d13fefda 100644
--- a/tools/server/webui/src/routes/+layout.svelte
+++ b/tools/ui/src/routes/+layout.svelte
@@ -2,90 +2,116 @@
 	import '../app.css';
 	import { base } from '$app/paths';
 	import { browser } from '$app/environment';
+	import { goto } from '$app/navigation';
 	import { page } from '$app/state';
 	import { untrack } from 'svelte';
+	import { onMount } from 'svelte';
+	import { fade } from 'svelte/transition';
 	import {
-		ChatSidebar,
+		DesktopIconStrip,
 		DialogConversationTitleUpdate,
-		DialogChatSettings
+		SidebarNavigation
 	} from '$lib/components/app';
-	import { isLoading } from '$lib/stores/chat.svelte';
-	import { conversationsStore, activeMessages } from '$lib/stores/conversations.svelte';
+	import { conversationsStore } from '$lib/stores/conversations.svelte';
 	import * as Sidebar from '$lib/components/ui/sidebar/index.js';
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { isRouterMode, serverStore } from '$lib/stores/server.svelte';
 	import { config, settingsStore } from '$lib/stores/settings.svelte';
 	import { ModeWatcher } from 'mode-watcher';
+	import { ROUTES } from '$lib/constants/routes';
+	import { RouterService } from '$lib/services/router.service';
 	import { Toaster } from 'svelte-sonner';
-	import { goto } from '$app/navigation';
 	import { modelsStore } from '$lib/stores/models.svelte';
 	import { mcpStore } from '$lib/stores/mcp.svelte';
 	import { TOOLTIP_DELAY_DURATION } from '$lib/constants';
-	import type { SettingsSectionTitle } from '$lib/constants';
-	import { KeyboardKey } from '$lib/enums';
 	import { IsMobile } from '$lib/hooks/is-mobile.svelte';
-	import { setChatSettingsDialogContext } from '$lib/contexts';
+	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
+	import { useSettingsNavigation } from '$lib/hooks/use-settings-navigation.svelte';
+	import { conversations } from '$lib/stores/conversations.svelte';
 
 	let { children } = $props();
 
-	let isChatRoute = $derived(page.route.id === '/chat/[id]');
-	let isHomeRoute = $derived(page.route.id === '/');
-	let isNewChatMode = $derived(page.url.searchParams.get('new_chat') === 'true');
-	let showSidebarByDefault = $derived(activeMessages().length > 0 || isLoading());
 	let alwaysShowSidebarOnDesktop = $derived(config().alwaysShowSidebarOnDesktop);
-	let autoShowSidebarOnNewChat = $derived(config().autoShowSidebarOnNewChat);
 	let isMobile = new IsMobile();
 	let isDesktop = $derived(!isMobile.current);
 	let sidebarOpen = $state(false);
+	let mounted = $state(false);
 	let innerHeight = $state<number | undefined>();
 	let chatSidebar:
 		| { activateSearchMode?: () => void; editActiveConversation?: () => void }
 		| undefined = $state();
 
-	// Conversation title update dialog state
 	let titleUpdateDialogOpen = $state(false);
 	let titleUpdateCurrentTitle = $state('');
 	let titleUpdateNewTitle = $state('');
 	let titleUpdateResolve: ((value: boolean) => void) | null = null;
 
-	let chatSettingsDialogOpen = $state(false);
-	let chatSettingsDialogInitialSection = $state<SettingsSectionTitle | undefined>(undefined);
+	const panelNav = useSettingsNavigation();
 
-	setChatSettingsDialogContext({
-		open: (initialSection?: SettingsSectionTitle) => {
-			chatSettingsDialogInitialSection = initialSection;
-			chatSettingsDialogOpen = true;
-		}
-	});
+	function navigateToConversation(direction: -1 | 1) {
+		const allConvs = conversations();
+		if (allConvs.length === 0) return;
 
-	// Global keyboard shortcuts
-	function handleKeydown(event: KeyboardEvent) {
-		const isCtrlOrCmd = event.ctrlKey || event.metaKey;
-
-		if (isCtrlOrCmd && event.key === KeyboardKey.K_LOWER) {
-			event.preventDefault();
-			if (chatSidebar?.activateSearchMode) {
-				chatSidebar.activateSearchMode();
-				sidebarOpen = true;
-			}
+		const currentId = page.params.id;
+
+		if (!currentId) {
+			goto(RouterService.chat(allConvs[direction === 1 ? 0 : allConvs.length - 1].id));
+
+			return;
 		}
 
-		if (isCtrlOrCmd && event.shiftKey && event.key === KeyboardKey.O_UPPER) {
-			event.preventDefault();
-			goto('?new_chat=true#/');
+		const idx = allConvs.findIndex((c) => c.id === currentId);
+		if (idx === -1) return;
+
+		const targetIdx = idx + direction;
+
+		if (targetIdx >= 0 && targetIdx < allConvs.length) {
+			goto(RouterService.chat(allConvs[targetIdx].id));
+		} else {
+			goto(ROUTES.NEW_CHAT);
 		}
+	}
+
+	// Global keyboard shortcuts
+	const { handleKeydown } = useKeyboardShortcuts({
+		editActiveConversation: () => chatSidebar?.editActiveConversation?.(),
+
+		navigateToPrevConversation: () => navigateToConversation(-1),
+
+		navigateToNextConversation: () => navigateToConversation(1)
+	});
+
+	function checkApiKey() {
+		const apiKey = config().apiKey;
 
-		if (event.shiftKey && isCtrlOrCmd && event.key === KeyboardKey.E_UPPER) {
-			event.preventDefault();
+		if (
+			(page.route.id === '/(chat)' || page.route.id === '/(chat)/chat/[id]') &&
+			page.status !== 401 &&
+			page.status !== 403
+		) {
+			const headers: Record<string, string> = {
+				'Content-Type': 'application/json'
+			};
 
-			if (chatSidebar?.editActiveConversation) {
-				chatSidebar.editActiveConversation();
+			if (apiKey && apiKey.trim() !== '') {
+				headers.Authorization = `Bearer ${apiKey.trim()}`;
 			}
+
+			fetch(`${base}/props`, { headers })
+				.then((response) => {
+					if (response.status === 401 || response.status === 403) {
+						window.location.reload();
+					}
+				})
+				.catch((e) => {
+					console.error('Error checking API key:', e);
+				});
 		}
 	}
 
 	function handleTitleUpdateCancel() {
 		titleUpdateDialogOpen = false;
+
 		if (titleUpdateResolve) {
 			titleUpdateResolve(false);
 			titleUpdateResolve = null;
@@ -94,34 +120,22 @@
 
 	function handleTitleUpdateConfirm() {
 		titleUpdateDialogOpen = false;
+
 		if (titleUpdateResolve) {
 			titleUpdateResolve(true);
 			titleUpdateResolve = null;
 		}
 	}
 
+	onMount(() => {
+		mounted = true;
+	});
+
 	$effect(() => {
 		if (alwaysShowSidebarOnDesktop && isDesktop) {
 			sidebarOpen = true;
 			return;
 		}
-
-		if (isHomeRoute && !isNewChatMode) {
-			// Auto-collapse sidebar when navigating to home route (but not in new chat mode)
-			sidebarOpen = false;
-		} else if (isHomeRoute && isNewChatMode) {
-			// Keep sidebar open in new chat mode
-			sidebarOpen = true;
-		} else if (isChatRoute) {
-			// On chat routes, only auto-show sidebar if setting is enabled
-			if (autoShowSidebarOnNewChat) {
-				sidebarOpen = true;
-			}
-			// If setting is disabled, don't change sidebar state - let user control it manually
-		} else {
-			// Other routes follow default behavior
-			sidebarOpen = showSidebarByDefault;
-		}
 	});
 
 	// Initialize server properties on app load (run once)
@@ -139,7 +153,9 @@
 		const serverProps = serverStore.props;
 
 		if (serverProps) {
-			settingsStore.syncWithServerDefaults();
+			untrack(() => {
+				settingsStore.syncWithServerDefaults();
+			});
 		}
 	});
 
@@ -182,31 +198,7 @@
 
 	// Monitor API key changes and redirect to error page if removed or changed when required
 	$effect(() => {
-		const apiKey = config().apiKey;
-
-		if (
-			(page.route.id === '/' || page.route.id === '/chat/[id]') &&
-			page.status !== 401 &&
-			page.status !== 403
-		) {
-			const headers: Record<string, string> = {
-				'Content-Type': 'application/json'
-			};
-
-			if (apiKey && apiKey.trim() !== '') {
-				headers.Authorization = `Bearer ${apiKey.trim()}`;
-			}
-
-			fetch(`${base}/props`, { headers })
-				.then((response) => {
-					if (response.status === 401 || response.status === 403) {
-						window.location.reload();
-					}
-				})
-				.catch((e) => {
-					console.error('Error checking API key:', e);
-				});
-		}
+		checkApiKey();
 	});
 
 	// Set up title update confirmation callback
@@ -229,12 +221,6 @@
 
 	<Toaster richColors />
 
-	<DialogChatSettings
-		open={chatSettingsDialogOpen}
-		onOpenChange={(open) => (chatSettingsDialogOpen = open)}
-		initialSection={chatSettingsDialogInitialSection}
-	/>
-
 	<DialogConversationTitleUpdate
 		bind:open={titleUpdateDialogOpen}
 		currentTitle={titleUpdateCurrentTitle}
@@ -245,16 +231,33 @@
 
 	<Sidebar.Provider bind:open={sidebarOpen}>
 		<div class="flex h-screen w-full" style:height="{innerHeight}px">
-			<Sidebar.Root class="h-full">
-				<ChatSidebar bind:this={chatSidebar} />
+			<Sidebar.Root variant="floating" class="h-full">
+				<SidebarNavigation bind:this={chatSidebar} />
 			</Sidebar.Root>
 
-			{#if !(alwaysShowSidebarOnDesktop && isDesktop)}
-				<Sidebar.Trigger
-					class="transition-left absolute left-0 z-[900] duration-200 ease-linear {sidebarOpen
-						? 'md:left-[var(--sidebar-width)]'
-						: 'md:left-0!'}"
-					style="translate: 1rem 1rem;"
+			{#if !(alwaysShowSidebarOnDesktop && isDesktop) && !(panelNav.isSettingsRoute && !isDesktop)}
+				{#if mounted}
+					<div in:fade={{ duration: 200 }}>
+						<Sidebar.Trigger
+							class="transition-left absolute left-0 z-[900] duration-200 ease-linear {sidebarOpen
+								? 'left-[calc(var(--sidebar-width)+0.75rem)] max-md:hidden'
+								: 'left-0!'}"
+							style="translate: 1rem 1rem;"
+						/>
+					</div>
+				{/if}
+			{/if}
+
+			{#if isDesktop && !alwaysShowSidebarOnDesktop}
+				<DesktopIconStrip
+					{sidebarOpen}
+					onSearchClick={() => {
+						if (chatSidebar?.activateSearchMode) {
+							chatSidebar.activateSearchMode();
+						}
+
+						sidebarOpen = true;
+					}}
 				/>
 			{/if}
 
diff --git a/tools/ui/src/routes/mcp-servers/+page.svelte b/tools/ui/src/routes/mcp-servers/+page.svelte
new file mode 100644
index 00000000000..1758134c391
--- /dev/null
+++ b/tools/ui/src/routes/mcp-servers/+page.svelte
@@ -0,0 +1,5 @@
+<script lang="ts">
+	import { SettingsMcpServers } from '$lib/components/app/settings';
+</script>
+
+<SettingsMcpServers class="mx-auto w-full p-4 md:p-8 md:py-8" />
diff --git a/tools/ui/src/routes/settings/+layout.svelte b/tools/ui/src/routes/settings/+layout.svelte
new file mode 100644
index 00000000000..b3b3d30e0bd
--- /dev/null
+++ b/tools/ui/src/routes/settings/+layout.svelte
@@ -0,0 +1,38 @@
+<script lang="ts">
+	import { X } from '@lucide/svelte';
+	import { goto } from '$app/navigation';
+	import { browser } from '$app/environment';
+	import { page } from '$app/state';
+	import { ActionIcon } from '$lib/components/app';
+	import { SETTINGS_FALLBACK_EXIT_ROUTE } from '$lib/constants';
+
+	let { children } = $props();
+
+	let previousRouteId = $state<string | null>(null);
+
+	$effect(() => {
+		const currentId = page.route.id;
+		return () => {
+			previousRouteId = currentId;
+		};
+	});
+
+	function handleClose() {
+		const prevIsSettings = previousRouteId?.startsWith('/settings');
+		if (browser && window.history.length > 1 && !prevIsSettings) {
+			history.back();
+		} else {
+			goto(SETTINGS_FALLBACK_EXIT_ROUTE);
+		}
+	}
+</script>
+
+<div class="relative h-full">
+	<div class="fixed top-4.5 right-4 z-50 md:hidden">
+		<ActionIcon icon={X} tooltip="Close" onclick={handleClose} />
+	</div>
+
+	<div class="min-h-full">
+		{@render children?.()}
+	</div>
+</div>
diff --git a/tools/ui/src/routes/settings/[[section]]/+page.svelte b/tools/ui/src/routes/settings/[[section]]/+page.svelte
new file mode 100644
index 00000000000..22e727f175b
--- /dev/null
+++ b/tools/ui/src/routes/settings/[[section]]/+page.svelte
@@ -0,0 +1,16 @@
+<script lang="ts">
+	import { SettingsChat } from '$lib/components/app/settings';
+	import { page } from '$app/state';
+	import { replaceState } from '$app/navigation';
+	import { RouterService } from '$lib/services';
+	import { SETTINGS_SECTION_SLUGS } from '$lib/constants';
+	import { onMount } from 'svelte';
+
+	onMount(() => {
+		if (!page.params.section) {
+			replaceState(RouterService.settings(SETTINGS_SECTION_SLUGS.GENERAL), {});
+		}
+	});
+</script>
+
+<SettingsChat initialSection={(page.params as Record<string, string | undefined>).section} />
diff --git a/tools/server/webui/src/styles/katex-custom.scss b/tools/ui/src/styles/katex-custom.scss
similarity index 100%
rename from tools/server/webui/src/styles/katex-custom.scss
rename to tools/ui/src/styles/katex-custom.scss
diff --git a/tools/server/webui/static/favicon.svg b/tools/ui/static/favicon.svg
similarity index 100%
rename from tools/server/webui/static/favicon.svg
rename to tools/ui/static/favicon.svg
diff --git a/tools/server/public/loading.html b/tools/ui/static/loading.html
similarity index 100%
rename from tools/server/public/loading.html
rename to tools/ui/static/loading.html
diff --git a/tools/server/webui/svelte.config.js b/tools/ui/svelte.config.js
similarity index 87%
rename from tools/server/webui/svelte.config.js
rename to tools/ui/svelte.config.js
index 93152492c43..4b14065dacd 100644
--- a/tools/server/webui/svelte.config.js
+++ b/tools/ui/svelte.config.js
@@ -14,8 +14,8 @@ const config = {
 		},
 		router: { type: 'hash' },
 		adapter: adapter({
-			pages: '../public',
-			assets: '../public',
+			pages: '../../build/tools/ui/dist',
+			assets: '../../build/tools/ui/dist',
 			fallback: 'index.html',
 			precompress: false,
 			strict: true
@@ -27,7 +27,7 @@ const config = {
 			$styles: 'src/styles'
 		},
 		version: {
-			name: 'llama-server-webui'
+			name: 'llama-ui'
 		}
 	},
 
diff --git a/tools/server/webui/tests/client/components/TestWrapper.svelte b/tools/ui/tests/client/components/TestWrapper.svelte
similarity index 87%
rename from tools/server/webui/tests/client/components/TestWrapper.svelte
rename to tools/ui/tests/client/components/TestWrapper.svelte
index 4bbb8e86038..aeb7ff74c4a 100644
--- a/tools/server/webui/tests/client/components/TestWrapper.svelte
+++ b/tools/ui/tests/client/components/TestWrapper.svelte
@@ -1,7 +1,7 @@
 <script lang="ts">
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import * as Sidebar from '$lib/components/ui/sidebar/index.js';
-	import Page from '../../../src/routes/+page.svelte';
+	import Page from '../../../src/routes/(chat)/+page.svelte';
 
 	let sidebarOpen = $state(false);
 </script>
diff --git a/tools/server/webui/tests/client/page.svelte.test.ts b/tools/ui/tests/client/page.svelte.test.ts
similarity index 100%
rename from tools/server/webui/tests/client/page.svelte.test.ts
rename to tools/ui/tests/client/page.svelte.test.ts
diff --git a/tools/server/webui/tests/e2e/demo.test.ts b/tools/ui/tests/e2e/demo.test.ts
similarity index 100%
rename from tools/server/webui/tests/e2e/demo.test.ts
rename to tools/ui/tests/e2e/demo.test.ts
diff --git a/tools/server/webui/tests/stories/ChatMessage.stories.svelte b/tools/ui/tests/stories/ChatMessage.stories.svelte
similarity index 99%
rename from tools/server/webui/tests/stories/ChatMessage.stories.svelte
rename to tools/ui/tests/stories/ChatMessage.stories.svelte
index a3579cf04ea..e640176a845 100644
--- a/tools/server/webui/tests/stories/ChatMessage.stories.svelte
+++ b/tools/ui/tests/stories/ChatMessage.stories.svelte
@@ -1,6 +1,6 @@
 <script module lang="ts">
 	import { defineMeta } from '@storybook/addon-svelte-csf';
-	import ChatMessage from '$lib/components/app/chat/ChatMessages/ChatMessage.svelte';
+	import ChatMessage from '$lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte';
 
 	const { Story } = defineMeta({
 		title: 'Components/ChatScreen/ChatMessage',
diff --git a/tools/ui/tests/stories/ChatScreenForm.a11y.stories.svelte b/tools/ui/tests/stories/ChatScreenForm.a11y.stories.svelte
new file mode 100644
index 00000000000..5d28f420e57
--- /dev/null
+++ b/tools/ui/tests/stories/ChatScreenForm.a11y.stories.svelte
@@ -0,0 +1,50 @@
+<script module lang="ts">
+	import { defineMeta } from '@storybook/addon-svelte-csf';
+	import ChatScreenForm from '$lib/components/app/chat/ChatScreen/ChatScreenForm.svelte';
+	import { expect, screen, waitFor } from 'storybook/test';
+	import { ATTACHMENT_TOOLTIP_TEXT } from '$lib/constants';
+
+	const { Story } = defineMeta({
+		title: 'Components/ChatScreen/ChatScreenForm/Accessibility',
+		component: ChatScreenForm,
+		parameters: {
+			layout: 'centered'
+		},
+		tags: ['!dev']
+	});
+</script>
+
+<Story
+	name="AddButtonSingleTabStop"
+	args={{ class: 'max-w-[56rem] w-[calc(100vw-2rem)]' }}
+	play={async ({ canvas, userEvent }) => {
+		const textarea = await canvas.findByRole('textbox');
+		await userEvent.clear(textarea);
+		await userEvent.type(textarea, 'What is the meaning of life?');
+
+		const trigger = await canvas.findByRole('button', { name: ATTACHMENT_TOOLTIP_TEXT });
+
+		trigger.focus();
+		await expect(trigger).toHaveFocus();
+
+		await userEvent.tab();
+
+		await expect(trigger).not.toHaveFocus();
+	}}
+/>
+
+<Story
+	name="AddDropdownFocusesFirstEnabled"
+	args={{ class: 'max-w-[56rem] w-[calc(100vw-2rem)]' }}
+	play={async ({ canvas, userEvent }) => {
+		const trigger = await canvas.findByRole('button', { name: ATTACHMENT_TOOLTIP_TEXT });
+
+		trigger.focus();
+		await userEvent.keyboard('{Enter}');
+		await screen.findByRole('menu');
+
+		await waitFor(() => {
+			expect(document.activeElement).toHaveTextContent('Text Files');
+		});
+	}}
+/>
diff --git a/tools/server/webui/tests/stories/ChatScreenForm.stories.svelte b/tools/ui/tests/stories/ChatScreenForm.stories.svelte
similarity index 100%
rename from tools/server/webui/tests/stories/ChatScreenForm.stories.svelte
rename to tools/ui/tests/stories/ChatScreenForm.stories.svelte
diff --git a/tools/server/webui/tests/stories/Introduction.mdx b/tools/ui/tests/stories/Introduction.mdx
similarity index 83%
rename from tools/server/webui/tests/stories/Introduction.mdx
rename to tools/ui/tests/stories/Introduction.mdx
index 19d0b28daab..d4c0c792164 100644
--- a/tools/server/webui/tests/stories/Introduction.mdx
+++ b/tools/ui/tests/stories/Introduction.mdx
@@ -4,11 +4,11 @@ import { Meta } from '@storybook/addon-docs/blocks';
 
 # llama.cpp Web UI
 
-Welcome to the **llama.cpp Web UI** component library! This Storybook showcases the components used in the modern web interface for the llama.cpp server.
+Welcome to the **llama-ui** component library! This Storybook showcases the components used in the modern web interface for the llama-server.
 
 ## 🚀 About This Project
 
-WebUI is a modern web interface for the llama.cpp server, built with SvelteKit and ShadCN UI. Features include:
+Llama UI is a modern web interface for the llama-server, built with SvelteKit and ShadCN UI. Features include:
 
 - **Real-time chat conversations** with AI assistants
 - **Multi-conversation management** with persistent storage
diff --git a/tools/server/webui/tests/stories/MarkdownContent.stories.svelte b/tools/ui/tests/stories/MarkdownContent.stories.svelte
similarity index 100%
rename from tools/server/webui/tests/stories/MarkdownContent.stories.svelte
rename to tools/ui/tests/stories/MarkdownContent.stories.svelte
diff --git a/tools/server/webui/tests/stories/ChatSidebar.stories.svelte b/tools/ui/tests/stories/SidebarNavigation.stories.svelte
similarity index 71%
rename from tools/server/webui/tests/stories/ChatSidebar.stories.svelte
rename to tools/ui/tests/stories/SidebarNavigation.stories.svelte
index 7ff9538a3e8..f64ee4f9b55 100644
--- a/tools/server/webui/tests/stories/ChatSidebar.stories.svelte
+++ b/tools/ui/tests/stories/SidebarNavigation.stories.svelte
@@ -1,16 +1,22 @@
 <script module lang="ts">
 	import { defineMeta } from '@storybook/addon-svelte-csf';
-	import ChatSidebar from '$lib/components/app/chat/ChatSidebar/ChatSidebar.svelte';
+	import SidebarNavigation from '$lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte';
 	import { waitFor } from 'storybook/test';
 	import { screen } from 'storybook/test';
 
 	const { Story } = defineMeta({
-		title: 'Components/ChatSidebar',
-		component: ChatSidebar,
+		title: 'Components/SidebarNavigation',
+		component: SidebarNavigation,
 		parameters: {
 			layout: 'centered'
 		}
 	});
+</script>
+
+<script lang="ts">
+	import * as Sidebar from '$lib/components/ui/sidebar/index.js';
+
+	let sidebarOpen = $state(true);
 
 	// Mock conversations for the sidebar
 	const mockConversations: DatabaseConversation[] = [
@@ -58,9 +64,11 @@
 		}, 0));
 	}}
 >
-	<div class="flex-column h-full h-screen w-72 bg-background">
-		<ChatSidebar />
-	</div>
+	<Sidebar.Provider bind:open={sidebarOpen}>
+		<div class="flex-column h-full h-screen w-72 bg-background">
+			<SidebarNavigation />
+		</div>
+	</Sidebar.Provider>
 </Story>
 
 <Story
@@ -77,9 +85,11 @@
 		userEvent.click(searchTrigger);
 	}}
 >
-	<div class="flex-column h-full h-screen w-72 bg-background">
-		<ChatSidebar />
-	</div>
+	<Sidebar.Provider bind:open={sidebarOpen}>
+		<div class="flex-column h-full h-screen w-72 bg-background">
+			<SidebarNavigation />
+		</div>
+	</Sidebar.Provider>
 </Story>
 
 <Story
@@ -91,7 +101,9 @@
 		conversationsStore.conversations = [];
 	}}
 >
-	<div class="flex-column h-full h-screen w-72 bg-background">
-		<ChatSidebar />
-	</div>
+	<Sidebar.Provider bind:open={sidebarOpen}>
+		<div class="flex-column h-full h-screen w-72 bg-background">
+			<SidebarNavigation />
+		</div>
+	</Sidebar.Provider>
 </Story>
diff --git a/tools/server/webui/tests/stories/fixtures/ai-tutorial.ts b/tools/ui/tests/stories/fixtures/ai-tutorial.ts
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/ai-tutorial.ts
rename to tools/ui/tests/stories/fixtures/ai-tutorial.ts
diff --git a/tools/server/webui/tests/stories/fixtures/api-docs.ts b/tools/ui/tests/stories/fixtures/api-docs.ts
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/api-docs.ts
rename to tools/ui/tests/stories/fixtures/api-docs.ts
diff --git a/tools/server/webui/tests/stories/fixtures/assets/1.jpg b/tools/ui/tests/stories/fixtures/assets/1.jpg
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/assets/1.jpg
rename to tools/ui/tests/stories/fixtures/assets/1.jpg
diff --git a/tools/server/webui/tests/stories/fixtures/assets/beautiful-flowers-lotus.webp b/tools/ui/tests/stories/fixtures/assets/beautiful-flowers-lotus.webp
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/assets/beautiful-flowers-lotus.webp
rename to tools/ui/tests/stories/fixtures/assets/beautiful-flowers-lotus.webp
diff --git a/tools/server/webui/tests/stories/fixtures/assets/example.pdf b/tools/ui/tests/stories/fixtures/assets/example.pdf
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/assets/example.pdf
rename to tools/ui/tests/stories/fixtures/assets/example.pdf
diff --git a/tools/server/webui/tests/stories/fixtures/assets/hf-logo.svg b/tools/ui/tests/stories/fixtures/assets/hf-logo.svg
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/assets/hf-logo.svg
rename to tools/ui/tests/stories/fixtures/assets/hf-logo.svg
diff --git a/tools/server/webui/tests/stories/fixtures/blog-post.ts b/tools/ui/tests/stories/fixtures/blog-post.ts
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/blog-post.ts
rename to tools/ui/tests/stories/fixtures/blog-post.ts
diff --git a/tools/server/webui/tests/stories/fixtures/data-analysis.ts b/tools/ui/tests/stories/fixtures/data-analysis.ts
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/data-analysis.ts
rename to tools/ui/tests/stories/fixtures/data-analysis.ts
diff --git a/tools/server/webui/tests/stories/fixtures/empty.ts b/tools/ui/tests/stories/fixtures/empty.ts
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/empty.ts
rename to tools/ui/tests/stories/fixtures/empty.ts
diff --git a/tools/server/webui/tests/stories/fixtures/math-formulas.ts b/tools/ui/tests/stories/fixtures/math-formulas.ts
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/math-formulas.ts
rename to tools/ui/tests/stories/fixtures/math-formulas.ts
diff --git a/tools/server/webui/tests/stories/fixtures/readme.ts b/tools/ui/tests/stories/fixtures/readme.ts
similarity index 100%
rename from tools/server/webui/tests/stories/fixtures/readme.ts
rename to tools/ui/tests/stories/fixtures/readme.ts
diff --git a/tools/server/webui/tests/stories/fixtures/storybook-mocks.ts b/tools/ui/tests/stories/fixtures/storybook-mocks.ts
similarity index 87%
rename from tools/server/webui/tests/stories/fixtures/storybook-mocks.ts
rename to tools/ui/tests/stories/fixtures/storybook-mocks.ts
index c40a74655a0..ecfcdf09ddd 100644
--- a/tools/server/webui/tests/stories/fixtures/storybook-mocks.ts
+++ b/tools/ui/tests/stories/fixtures/storybook-mocks.ts
@@ -15,7 +15,8 @@ export function mockServerProps(props: Partial<ApiLlamaCppServerProps>): void {
 		model_path: props.model_path || 'test-model',
 		modalities: {
 			vision: props.modalities?.vision ?? false,
-			audio: props.modalities?.audio ?? false
+			audio: props.modalities?.audio ?? false,
+			video: props.modalities?.video ?? false
 		},
 		...props
 	} as ApiLlamaCppServerProps;
@@ -26,11 +27,14 @@ export function mockServerProps(props: Partial<ApiLlamaCppServerProps>): void {
 	// Also mock modelsStore methods for modality checking
 	const vision = props.modalities?.vision ?? false;
 	const audio = props.modalities?.audio ?? false;
+	const video = props.modalities?.video ?? false;
 
 	// eslint-disable-next-line @typescript-eslint/no-explicit-any
 	(modelsStore as any).modelSupportsVision = () => vision;
 	// eslint-disable-next-line @typescript-eslint/no-explicit-any
 	(modelsStore as any).modelSupportsAudio = () => audio;
+	// eslint-disable-next-line @typescript-eslint/no-explicit-any
+	(modelsStore as any).modelSupportsVideo = () => video;
 
 	// Mock models list with a test model so activeModelId can be resolved
 	// eslint-disable-next-line @typescript-eslint/no-explicit-any
@@ -55,7 +59,8 @@ export function resetServerStore(): void {
 		model_path: '',
 		modalities: {
 			vision: false,
-			audio: false
+			audio: false,
+			video: false
 		}
 	} as ApiLlamaCppServerProps;
 	(serverStore as unknown as { error: string }).error = '';
@@ -76,6 +81,6 @@ export const mockConfigs = {
 		modalities: { vision: true, audio: true }
 	},
 	noModalities: {
-		modalities: { vision: false, audio: false }
+		modalities: { vision: false, audio: false, video: false }
 	}
 } as const;
diff --git a/tools/server/webui/tests/unit/agentic-sections.test.ts b/tools/ui/tests/unit/agentic-sections.test.ts
similarity index 94%
rename from tools/server/webui/tests/unit/agentic-sections.test.ts
rename to tools/ui/tests/unit/agentic-sections.test.ts
index c01f46bc3a6..7af882143d4 100644
--- a/tools/server/webui/tests/unit/agentic-sections.test.ts
+++ b/tools/ui/tests/unit/agentic-sections.test.ts
@@ -64,7 +64,11 @@ describe('deriveAgenticSections', () => {
 		const msg = makeAssistant({
 			content: 'Let me check.',
 			toolCalls: JSON.stringify([
-				{ id: 'call_1', type: 'function', function: { name: 'search', arguments: '{"q":"test"}' } }
+				{
+					id: 'call_1',
+					type: 'function',
+					function: { name: 'search', arguments: '{"q":"test"}' }
+				}
 			])
 		});
 		const toolResult = makeToolMsg({
@@ -85,7 +89,7 @@ describe('deriveAgenticSections', () => {
 				{ id: 'call_1', type: 'function', function: { name: 'bash', arguments: '{}' } }
 			])
 		});
-		const sections = deriveAgenticSections(msg, []);
+		const sections = deriveAgenticSections(msg, [], [], true);
 		expect(sections).toHaveLength(1);
 		expect(sections[0].type).toBe(AgenticSectionType.TOOL_CALL_PENDING);
 		expect(sections[0].toolName).toBe('bash');
@@ -96,7 +100,11 @@ describe('deriveAgenticSections', () => {
 			id: 'ast-1',
 			content: 'Turn 1 text',
 			toolCalls: JSON.stringify([
-				{ id: 'call_1', type: 'function', function: { name: 'search', arguments: '{"q":"foo"}' } }
+				{
+					id: 'call_1',
+					type: 'function',
+					function: { name: 'search', arguments: '{"q":"foo"}' }
+				}
 			])
 		});
 		const tool1 = makeToolMsg({ id: 'tool-1', toolCallId: 'call_1', content: 'result 1' });
@@ -124,7 +132,11 @@ describe('deriveAgenticSections', () => {
 			id: 'ast-1',
 			content: '',
 			toolCalls: JSON.stringify([
-				{ id: 'call_1', type: 'function', function: { name: 'list_files', arguments: '{}' } }
+				{
+					id: 'call_1',
+					type: 'function',
+					function: { name: 'list_files', arguments: '{}' }
+				}
 			])
 		});
 		const tool1 = makeToolMsg({ id: 'tool-1', toolCallId: 'call_1', content: 'file1 file2' });
@@ -139,7 +151,11 @@ describe('deriveAgenticSections', () => {
 				}
 			])
 		});
-		const tool2 = makeToolMsg({ id: 'tool-2', toolCallId: 'call_2', content: 'contents of file1' });
+		const tool2 = makeToolMsg({
+			id: 'tool-2',
+			toolCallId: 'call_2',
+			content: 'contents of file1'
+		});
 		const assistant3 = makeAssistant({
 			id: 'ast-3',
 			content: 'Here is the analysis.',
diff --git a/tools/server/webui/tests/unit/agentic-strip.test.ts b/tools/ui/tests/unit/agentic-strip.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/agentic-strip.test.ts
rename to tools/ui/tests/unit/agentic-strip.test.ts
diff --git a/tools/server/webui/tests/unit/clipboard.test.ts b/tools/ui/tests/unit/clipboard.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/clipboard.test.ts
rename to tools/ui/tests/unit/clipboard.test.ts
diff --git a/tools/server/webui/tests/unit/latex-protection.test.ts b/tools/ui/tests/unit/latex-protection.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/latex-protection.test.ts
rename to tools/ui/tests/unit/latex-protection.test.ts
diff --git a/tools/server/webui/tests/unit/mcp-service.test.ts b/tools/ui/tests/unit/mcp-service.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/mcp-service.test.ts
rename to tools/ui/tests/unit/mcp-service.test.ts
diff --git a/tools/server/webui/tests/unit/model-id-parser.test.ts b/tools/ui/tests/unit/model-id-parser.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/model-id-parser.test.ts
rename to tools/ui/tests/unit/model-id-parser.test.ts
diff --git a/tools/server/webui/tests/unit/model-names.test.ts b/tools/ui/tests/unit/model-names.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/model-names.test.ts
rename to tools/ui/tests/unit/model-names.test.ts
diff --git a/tools/server/webui/tests/unit/reasoning-context.test.ts b/tools/ui/tests/unit/reasoning-context.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/reasoning-context.test.ts
rename to tools/ui/tests/unit/reasoning-context.test.ts
diff --git a/tools/server/webui/tests/unit/redact.test.ts b/tools/ui/tests/unit/redact.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/redact.test.ts
rename to tools/ui/tests/unit/redact.test.ts
diff --git a/tools/server/webui/tests/unit/request-helpers.test.ts b/tools/ui/tests/unit/request-helpers.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/request-helpers.test.ts
rename to tools/ui/tests/unit/request-helpers.test.ts
diff --git a/tools/server/webui/tests/unit/sanitize-headers.test.ts b/tools/ui/tests/unit/sanitize-headers.test.ts
similarity index 100%
rename from tools/server/webui/tests/unit/sanitize-headers.test.ts
rename to tools/ui/tests/unit/sanitize-headers.test.ts
diff --git a/tools/server/webui/tests/unit/uri-template.test.ts b/tools/ui/tests/unit/uri-template.test.ts
similarity index 95%
rename from tools/server/webui/tests/unit/uri-template.test.ts
rename to tools/ui/tests/unit/uri-template.test.ts
index 6221279231a..23645af3f5f 100644
--- a/tools/server/webui/tests/unit/uri-template.test.ts
+++ b/tools/ui/tests/unit/uri-template.test.ts
@@ -107,6 +107,14 @@ describe('expandTemplate', () => {
 		expect(result).toBe('http://example.com?q=search%20term');
 	});
 
+	it('expands multiple query parameters', () => {
+		const result = expandTemplate('http://example.com{?q,sort}', {
+			q: 'search term',
+			sort: 'descending'
+		});
+		expect(result).toBe('http://example.com?q=search%20term&sort=descending');
+	});
+
 	it('keeps static parts unchanged', () => {
 		const result = expandTemplate('http://example.com/static', {});
 		expect(result).toBe('http://example.com/static');
diff --git a/tools/server/webui/tsconfig.json b/tools/ui/tsconfig.json
similarity index 67%
rename from tools/server/webui/tsconfig.json
rename to tools/ui/tsconfig.json
index 0b2d8865f4e..7c585f4db27 100644
--- a/tools/server/webui/tsconfig.json
+++ b/tools/ui/tsconfig.json
@@ -10,7 +10,21 @@
 		"sourceMap": true,
 		"strict": true,
 		"moduleResolution": "bundler"
-	}
+	},
+	"include": [
+		".svelte-kit/ambient.d.ts",
+		".svelte-kit/non-ambient.d.ts",
+		".svelte-kit/types/**/$types.d.ts",
+		"vite.config.js",
+		"vite.config.ts",
+		"src/**/*.js",
+		"src/**/*.ts",
+		"src/**/*.svelte",
+		"tests/**/*.ts",
+		"tests/**/*.svelte",
+		".storybook/**/*.ts",
+		".storybook/**/*.svelte"
+	]
 	// Path aliases are handled by https://svelte.dev/docs/kit/configuration#alias
 	// except $lib which is handled by https://svelte.dev/docs/kit/configuration#files
 	//
diff --git a/tools/ui/ui.cpp b/tools/ui/ui.cpp
new file mode 100644
index 00000000000..d02a62c2c71
--- /dev/null
+++ b/tools/ui/ui.cpp
@@ -0,0 +1,7 @@
+#ifdef LLAMA_BUILD_UI
+// auto generated files (see README.md for details)
+#include "index.html.hpp"
+#include "bundle.js.hpp"
+#include "bundle.css.hpp"
+#include "loading.html.hpp"
+#endif
diff --git a/tools/ui/ui.h b/tools/ui/ui.h
new file mode 100644
index 00000000000..6f775ea3ada
--- /dev/null
+++ b/tools/ui/ui.h
@@ -0,0 +1,17 @@
+#pragma once
+
+// TODO @ngxson : refactor, wrap these in a function
+
+#ifdef LLAMA_BUILD_UI
+extern unsigned char index_html[];
+extern unsigned int  index_html_len;
+
+extern unsigned char bundle_js[];
+extern unsigned int  bundle_js_len;
+
+extern unsigned char bundle_css[];
+extern unsigned int  bundle_css_len;
+
+extern unsigned char loading_html[];
+extern unsigned int  loading_html_len;
+#endif
diff --git a/tools/server/webui/vite.config.ts b/tools/ui/vite.config.ts
similarity index 96%
rename from tools/server/webui/vite.config.ts
rename to tools/ui/vite.config.ts
index 628780201db..d3db24bf2bf 100644
--- a/tools/server/webui/vite.config.ts
+++ b/tools/ui/vite.config.ts
@@ -23,11 +23,6 @@ export default defineConfig({
 		minify: true
 	},
 
-	esbuild: {
-		lineLimit: 500,
-		minifyIdentifiers: false
-	},
-
 	css: {
 		preprocessorOptions: {
 			scss: {
@@ -95,6 +90,8 @@ export default defineConfig({
 			'/v1': 'http://localhost:8080',
 			'/props': 'http://localhost:8080',
 			'/models': 'http://localhost:8080',
+			'/tools': 'http://localhost:8080',
+			'/slots': 'http://localhost:8080',
 			'/cors-proxy': 'http://localhost:8080'
 		},
 		headers: {
diff --git a/tools/server/webui/vitest-setup-client.ts b/tools/ui/vitest-setup-client.ts
similarity index 100%
rename from tools/server/webui/vitest-setup-client.ts
rename to tools/ui/vitest-setup-client.ts
diff --git a/ty.toml b/ty.toml
index bcd23db9b8b..ad88ac7bdad 100644
--- a/ty.toml
+++ b/ty.toml
@@ -1,5 +1,5 @@
 [environment]
-extra-paths = ["./gguf-py", "./examples/model-conversion/scripts", "./tools/server/tests"]
+extra-paths = ["./gguf-py", "./examples/model-conversion/scripts", "./tools/server/tests", "./scripts/snapdragon/qdc/tests"]
 python-version = "3.10"
 
 [rules]
@@ -13,6 +13,7 @@ exclude = [
 [[overrides]]
 include = [
     "./tools/server/tests/**",
+    "./scripts/snapdragon/qdc/**",
 ]
 
 [overrides.rules]
diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt
index 28485a0ce80..dbc420d8370 100644
--- a/vendor/cpp-httplib/CMakeLists.txt
+++ b/vendor/cpp-httplib/CMakeLists.txt
@@ -41,7 +41,7 @@ if (LLAMA_BUILD_BORINGSSL)
     set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
 
     set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
-    set(BORINGSSL_VERSION "0.20260413.0" CACHE STRING "BoringSSL version")
+    set(BORINGSSL_VERSION "0.20260508.0" CACHE STRING "BoringSSL version")
 
     message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")
 
@@ -81,7 +81,7 @@ if (LLAMA_BUILD_BORINGSSL)
     target_link_libraries(${TARGET} PUBLIC ssl crypto)
 
 elseif (LLAMA_BUILD_LIBRESSL)
-    set(LIBRESSL_VERSION "4.2.1" CACHE STRING "LibreSSL version")
+    set(LIBRESSL_VERSION "4.3.1" CACHE STRING "LibreSSL version")
 
     message(STATUS "Fetching LibreSSL version ${LIBRESSL_VERSION}")
 
@@ -161,12 +161,24 @@ if(LLAMA_BUILD_BORINGSSL OR LLAMA_BUILD_LIBRESSL)
         if(LLAMA_BUILD_BORINGSSL)
             target_compile_options(fipsmodule PRIVATE /w)
         endif()
+        if(LLAMA_BUILD_LIBRESSL)
+            target_compile_options(ssl_obj PRIVATE /w)
+            target_compile_options(bs_obj PRIVATE /w)
+            target_compile_options(compat_obj PRIVATE /w)
+            target_compile_options(crypto_obj PRIVATE /w)
+        endif()
     else()
         target_compile_options(ssl PRIVATE -w)
         target_compile_options(crypto PRIVATE -w)
         if(LLAMA_BUILD_BORINGSSL)
             target_compile_options(fipsmodule PRIVATE -w)
         endif()
+        if(LLAMA_BUILD_LIBRESSL)
+            target_compile_options(ssl_obj PRIVATE -w)
+            target_compile_options(bs_obj PRIVATE -w)
+            target_compile_options(compat_obj PRIVATE -w)
+            target_compile_options(crypto_obj PRIVATE -w)
+        endif()
     endif()
 endif()
 
diff --git a/vendor/cpp-httplib/httplib.cpp b/vendor/cpp-httplib/httplib.cpp
index 1cd71c2ecc4..b28549607a2 100644
--- a/vendor/cpp-httplib/httplib.cpp
+++ b/vendor/cpp-httplib/httplib.cpp
@@ -872,7 +872,8 @@ bool write_websocket_frame(Stream &strm, ws::Opcode opcode,
     if (strm.write(reinterpret_cast<char *>(header), 2) < 0) { return false; }
     uint8_t ext[8];
     for (int i = 7; i >= 0; i--) {
-      ext[7 - i] = static_cast<uint8_t>((len >> (i * 8)) & 0xFF);
+      ext[7 - i] =
+          static_cast<uint8_t>((static_cast<uint64_t>(len) >> (i * 8)) & 0xFF);
     }
     if (strm.write(reinterpret_cast<char *>(ext), 8) < 0) { return false; }
   }
@@ -1034,10 +1035,15 @@ bool canonicalize_path(const char *path, std::string &resolved) {
   char buf[_MAX_PATH];
   if (_fullpath(buf, path, _MAX_PATH) == nullptr) { return false; }
   resolved = buf;
-#else
+#elif defined(PATH_MAX)
   char buf[PATH_MAX];
   if (realpath(path, buf) == nullptr) { return false; }
   resolved = buf;
+#else
+  auto buf = realpath(path, nullptr);
+  auto guard = scope_exit([&]() { std::free(buf); });
+  if (buf == nullptr) { return false; }
+  resolved = buf;
 #endif
   return true;
 }
@@ -1155,12 +1161,11 @@ bool parse_header(const char *beg, const char *end, T fn) {
 
     if (!detail::fields::is_field_value(val)) { return false; }
 
-    if (case_ignore::equal(key, "Location") ||
-        case_ignore::equal(key, "Referer")) {
-      fn(key, val);
-    } else {
-      fn(key, decode_path_component(val));
-    }
+    // RFC 9110 §5.5: header field values are opaque octets and MUST NOT be
+    // percent-decoded by the recipient. Applications that need to interpret a
+    // value as a URI component should call httplib::decode_uri_component()
+    // (or decode_path_component()) explicitly.
+    fn(key, val);
 
     return true;
   }
@@ -1458,8 +1463,9 @@ bool mmap::open(const char *path) {
   auto wpath = u8string_to_wstring(path);
   if (wpath.empty()) { return false; }
 
-  hFile_ = ::CreateFile2(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ,
-                         OPEN_EXISTING, NULL);
+  hFile_ =
+      ::CreateFile2(wpath.c_str(), GENERIC_READ,
+                    FILE_SHARE_READ | FILE_SHARE_WRITE, OPEN_EXISTING, NULL);
 
   if (hFile_ == INVALID_HANDLE_VALUE) { return false; }
 
@@ -2046,56 +2052,50 @@ int getaddrinfo_with_timeout(const char *node, const char *service,
   return 0;
 #elif defined(_GNU_SOURCE) && defined(__GLIBC__) &&                            \
     (__GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ >= 2))
-  // Linux implementation using getaddrinfo_a for asynchronous DNS resolution
-  struct gaicb request;
+  // #2431: gai_cancel() is non-blocking and may return EAI_NOTCANCELED while
+  // the resolver worker still references the stack-local gaicb. The cancel
+  // path therefore waits (gai_suspend with no timeout) for the worker to
+  // actually finish before letting the stack frame go. The trade-off is that
+  // a wedged DNS server can hold this thread for the system resolver timeout
+  // (~30s by default) past the caller's connection timeout.
+  struct gaicb request {};
   struct gaicb *requests[1] = {&request};
-  struct sigevent sevp;
-  struct timespec timeout;
+  struct sigevent sevp {};
+  struct timespec timeout {
+    timeout_sec, 0
+  };
 
-  // Initialize the request structure
-  memset(&request, 0, sizeof(request));
   request.ar_name = node;
   request.ar_service = service;
   request.ar_request = hints;
-
-  // Set up timeout
-  timeout.tv_sec = timeout_sec;
-  timeout.tv_nsec = 0;
-
-  // Initialize sigevent structure (not used, but required)
-  memset(&sevp, 0, sizeof(sevp));
   sevp.sigev_notify = SIGEV_NONE;
 
-  // Start asynchronous resolution
-  int start_result = getaddrinfo_a(GAI_NOWAIT, requests, 1, &sevp);
-  if (start_result != 0) { return start_result; }
+  int rc = getaddrinfo_a(GAI_NOWAIT, requests, 1, &sevp);
+  if (rc != 0) { return rc; }
 
-  // Wait for completion with timeout
-  int wait_result =
-      gai_suspend((const struct gaicb *const *)requests, 1, &timeout);
+  auto cleanup = scope_exit([&] {
+    if (request.ar_result) { freeaddrinfo(request.ar_result); }
+  });
+
+  int wait_result = gai_suspend(requests, 1, &timeout);
 
   if (wait_result == 0 || wait_result == EAI_ALLDONE) {
-    // Completed successfully, get the result
     int gai_result = gai_error(&request);
     if (gai_result == 0) {
       *res = request.ar_result;
+      request.ar_result = nullptr;
       return 0;
-    } else {
-      // Clean up on error
-      if (request.ar_result) { freeaddrinfo(request.ar_result); }
-      return gai_result;
     }
-  } else if (wait_result == EAI_AGAIN) {
-    // Timeout occurred, cancel the request
-    gai_cancel(&request);
-    return EAI_AGAIN;
-  } else {
-    // Other error occurred
-    gai_cancel(&request);
-    return wait_result;
+    return gai_result;
   }
+
+  gai_cancel(&request);
+  while (gai_error(&request) == EAI_INPROGRESS) {
+    gai_suspend(requests, 1, nullptr);
+  }
+  return wait_result;
 #else
-  // Fallback implementation using thread-based timeout for other Unix systems
+  // Fallback implementation using thread-based timeout for other Unix systems.
 
   struct GetAddrInfoState {
     ~GetAddrInfoState() {
@@ -2505,6 +2505,10 @@ void get_remote_ip_and_port(socket_t sock, std::string &ip, int &port) {
   }
 }
 
+// Recursive form retained so operator""_t below can compute hashes for
+// switch-case labels at compile time (C++11 constexpr forbids loops). Do not
+// call from runtime paths with arbitrary-length inputs — use str2tag()
+// instead, which is iterative and stack-safe.
 constexpr unsigned int str2tag_core(const char *s, size_t l,
                                            unsigned int h) {
   return (l == 0)
@@ -2518,7 +2522,16 @@ constexpr unsigned int str2tag_core(const char *s, size_t l,
 }
 
 unsigned int str2tag(const std::string &s) {
-  return str2tag_core(s.data(), s.size(), 0);
+  // Iterative form of str2tag_core: the recursive constexpr version is kept
+  // for compile-time UDL evaluation of short string literals, but at runtime
+  // we may receive arbitrarily long inputs (e.g. fuzzed Content-Type) that
+  // would blow the stack with one frame per character.
+  unsigned int h = 0;
+  for (auto c : s) {
+    h = (((std::numeric_limits<unsigned int>::max)() >> 6) & h * 33) ^
+        static_cast<unsigned char>(c);
+  }
+  return h;
 }
 
 namespace udl {
@@ -2765,6 +2778,35 @@ EncodingType encoding_type(const Request &req, const Response &res) {
   return best;
 }
 
+std::unique_ptr<compressor> make_compressor(EncodingType type) {
+#ifdef CPPHTTPLIB_ZLIB_SUPPORT
+  if (type == EncodingType::Gzip) {
+    return detail::make_unique<gzip_compressor>();
+  }
+#endif
+#ifdef CPPHTTPLIB_BROTLI_SUPPORT
+  if (type == EncodingType::Brotli) {
+    return detail::make_unique<brotli_compressor>();
+  }
+#endif
+#ifdef CPPHTTPLIB_ZSTD_SUPPORT
+  if (type == EncodingType::Zstd) {
+    return detail::make_unique<zstd_compressor>();
+  }
+#endif
+  (void)type;
+  return nullptr;
+}
+
+const char *encoding_name(EncodingType type) {
+  switch (type) {
+  case EncodingType::Gzip: return "gzip";
+  case EncodingType::Brotli: return "br";
+  case EncodingType::Zstd: return "zstd";
+  default: return "";
+  }
+}
+
 bool nocompressor::compress(const char *data, size_t data_length,
                                    bool /*last*/, Callback callback) {
   if (!data_length) { return true; }
@@ -3097,6 +3139,29 @@ const char *get_header_value(const Headers &headers,
   return def;
 }
 
+size_t get_header_value_count(const Headers &headers,
+                                     const std::string &key) {
+  auto r = headers.equal_range(key);
+  return static_cast<size_t>(std::distance(r.first, r.second));
+}
+
+template <typename Map>
+typename Map::mapped_type
+get_multimap_value(const Map &m, const std::string &key, size_t id) {
+  auto rng = m.equal_range(key);
+  auto it = rng.first;
+  std::advance(it, static_cast<ssize_t>(id));
+  if (it != rng.second) { return it->second; }
+  return typename Map::mapped_type();
+}
+
+void set_header(Headers &headers, const std::string &key,
+                       const std::string &val) {
+  if (fields::is_field_name(key) && fields::is_field_value(val)) {
+    headers.emplace(key, val);
+  }
+}
+
 bool read_headers(Stream &strm, Headers &headers) {
   const auto bufsiz = 2048;
   char buf[bufsiz];
@@ -4658,17 +4723,24 @@ write_multipart_ranges_data(Stream &strm, const Request &req, Response &res,
       });
 }
 
+bool has_framed_body(const Request &req) {
+  return is_chunked_transfer_encoding(req.headers) ||
+         req.get_header_value_u64("Content-Length") > 0;
+}
+
+bool is_connection_persistent(const Request &req) {
+  auto conn = req.get_header_value("Connection");
+  if (conn == "close") { return false; }
+  if (req.version == "HTTP/1.0" && conn != "Keep-Alive") { return false; }
+  return true;
+}
+
 bool expect_content(const Request &req) {
   if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH" ||
       req.method == "DELETE") {
     return true;
   }
-  if (req.has_header("Content-Length") &&
-      req.get_header_value_u64("Content-Length") > 0) {
-    return true;
-  }
-  if (is_chunked_transfer_encoding(req.headers)) { return true; }
-  return false;
+  return has_framed_body(req);
 }
 
 #ifdef _WIN32
@@ -5791,16 +5863,12 @@ std::string Request::get_header_value(const std::string &key,
 }
 
 size_t Request::get_header_value_count(const std::string &key) const {
-  auto r = headers.equal_range(key);
-  return static_cast<size_t>(std::distance(r.first, r.second));
+  return detail::get_header_value_count(headers, key);
 }
 
 void Request::set_header(const std::string &key,
                                 const std::string &val) {
-  if (detail::fields::is_field_name(key) &&
-      detail::fields::is_field_value(val)) {
-    headers.emplace(key, val);
-  }
+  detail::set_header(headers, key, val);
 }
 
 bool Request::has_trailer(const std::string &key) const {
@@ -5809,11 +5877,7 @@ bool Request::has_trailer(const std::string &key) const {
 
 std::string Request::get_trailer_value(const std::string &key,
                                               size_t id) const {
-  auto rng = trailers.equal_range(key);
-  auto it = rng.first;
-  std::advance(it, static_cast<ssize_t>(id));
-  if (it != rng.second) { return it->second; }
-  return std::string();
+  return detail::get_multimap_value(trailers, key, id);
 }
 
 size_t Request::get_trailer_value_count(const std::string &key) const {
@@ -5827,11 +5891,7 @@ bool Request::has_param(const std::string &key) const {
 
 std::string Request::get_param_value(const std::string &key,
                                             size_t id) const {
-  auto rng = params.equal_range(key);
-  auto it = rng.first;
-  std::advance(it, static_cast<ssize_t>(id));
-  if (it != rng.second) { return it->second; }
-  return std::string();
+  return detail::get_multimap_value(params, key, id);
 }
 
 std::vector<std::string>
@@ -5886,11 +5946,7 @@ size_t MultipartFormData::get_field_count(const std::string &key) const {
 
 FormData MultipartFormData::get_file(const std::string &key,
                                             size_t id) const {
-  auto rng = files.equal_range(key);
-  auto it = rng.first;
-  std::advance(it, static_cast<ssize_t>(id));
-  if (it != rng.second) { return it->second; }
-  return FormData();
+  return detail::get_multimap_value(files, key, id);
 }
 
 std::vector<FormData>
@@ -5929,16 +5985,12 @@ std::string Response::get_header_value(const std::string &key,
 }
 
 size_t Response::get_header_value_count(const std::string &key) const {
-  auto r = headers.equal_range(key);
-  return static_cast<size_t>(std::distance(r.first, r.second));
+  return detail::get_header_value_count(headers, key);
 }
 
 void Response::set_header(const std::string &key,
                                  const std::string &val) {
-  if (detail::fields::is_field_name(key) &&
-      detail::fields::is_field_value(val)) {
-    headers.emplace(key, val);
-  }
+  detail::set_header(headers, key, val);
 }
 bool Response::has_trailer(const std::string &key) const {
   return trailers.find(key) != trailers.end();
@@ -5946,11 +5998,7 @@ bool Response::has_trailer(const std::string &key) const {
 
 std::string Response::get_trailer_value(const std::string &key,
                                                size_t id) const {
-  auto rng = trailers.equal_range(key);
-  auto it = rng.first;
-  std::advance(it, static_cast<ssize_t>(id));
-  if (it != rng.second) { return it->second; }
-  return std::string();
+  return detail::get_multimap_value(trailers, key, id);
 }
 
 size_t Response::get_trailer_value_count(const std::string &key) const {
@@ -6150,9 +6198,29 @@ ThreadPool::ThreadPool(size_t n, size_t max_n, size_t mqr)
 #endif
   max_thread_count_ = max_n == 0 ? n : max_n;
   threads_.reserve(base_thread_count_);
-  for (size_t i = 0; i < base_thread_count_; i++) {
-    threads_.emplace_back(std::thread([this]() { worker(false); }));
+#ifndef CPPHTTPLIB_NO_EXCEPTIONS
+  try {
+#endif
+    for (size_t i = 0; i < base_thread_count_; i++) {
+      threads_.emplace_back(std::thread([this]() { worker(false); }));
+    }
+#ifndef CPPHTTPLIB_NO_EXCEPTIONS
+  } catch (...) {
+    // If thread creation fails partway (e.g., pthread_create returns EAGAIN),
+    // signal the workers we already spawned to exit and join them so the
+    // vector destructor does not see joinable threads (which would call
+    // std::terminate). Then rethrow so the caller learns of the failure.
+    {
+      std::unique_lock<std::mutex> lock(mutex_);
+      shutdown_ = true;
+    }
+    cond_.notify_all();
+    for (auto &t : threads_) {
+      if (t.joinable()) { t.join(); }
+    }
+    throw;
   }
+#endif
 }
 
 bool ThreadPool::enqueue(std::function<void()> fn) {
@@ -6253,15 +6321,6 @@ void ThreadPool::worker(bool is_dynamic) {
 
     assert(true == static_cast<bool>(fn));
     fn();
-
-    // Dynamic thread: exit if queue is empty after task completion
-    if (is_dynamic) {
-      std::unique_lock<std::mutex> lock(mutex_);
-      if (jobs_.empty()) {
-        move_to_finished(std::this_thread::get_id());
-        break;
-      }
-    }
   }
 
 #if defined(CPPHTTPLIB_OPENSSL_SUPPORT) && !defined(OPENSSL_IS_BORINGSSL) &&   \
@@ -6791,61 +6850,51 @@ Server::make_matcher(const std::string &pattern) {
 }
 
 Server &Server::Get(const std::string &pattern, Handler handler) {
-  get_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
-  return *this;
+  return add_handler(get_handlers_, pattern, std::move(handler));
 }
 
 Server &Server::Post(const std::string &pattern, Handler handler) {
-  post_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
-  return *this;
+  return add_handler(post_handlers_, pattern, std::move(handler));
 }
 
 Server &Server::Post(const std::string &pattern,
                             HandlerWithContentReader handler) {
-  post_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
-                                                 std::move(handler));
-  return *this;
+  return add_handler(post_handlers_for_content_reader_, pattern,
+                     std::move(handler));
 }
 
 Server &Server::Put(const std::string &pattern, Handler handler) {
-  put_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
-  return *this;
+  return add_handler(put_handlers_, pattern, std::move(handler));
 }
 
 Server &Server::Put(const std::string &pattern,
                            HandlerWithContentReader handler) {
-  put_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
-                                                std::move(handler));
-  return *this;
+  return add_handler(put_handlers_for_content_reader_, pattern,
+                     std::move(handler));
 }
 
 Server &Server::Patch(const std::string &pattern, Handler handler) {
-  patch_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
-  return *this;
+  return add_handler(patch_handlers_, pattern, std::move(handler));
 }
 
 Server &Server::Patch(const std::string &pattern,
                              HandlerWithContentReader handler) {
-  patch_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
-                                                  std::move(handler));
-  return *this;
+  return add_handler(patch_handlers_for_content_reader_, pattern,
+                     std::move(handler));
 }
 
 Server &Server::Delete(const std::string &pattern, Handler handler) {
-  delete_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
-  return *this;
+  return add_handler(delete_handlers_, pattern, std::move(handler));
 }
 
 Server &Server::Delete(const std::string &pattern,
                               HandlerWithContentReader handler) {
-  delete_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
-                                                   std::move(handler));
-  return *this;
+  return add_handler(delete_handlers_for_content_reader_, pattern,
+                     std::move(handler));
 }
 
 Server &Server::Options(const std::string &pattern, Handler handler) {
-  options_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
-  return *this;
+  return add_handler(options_handlers_, pattern, std::move(handler));
 }
 
 Server &Server::WebSocket(const std::string &pattern,
@@ -7054,6 +7103,11 @@ Server &Server::set_payload_max_length(size_t length) {
   return *this;
 }
 
+Server &Server::set_websocket_max_missed_pongs(int count) {
+  websocket_max_missed_pongs_ = count;
+  return *this;
+}
+
 Server &Server::set_websocket_ping_interval(time_t sec) {
   websocket_ping_interval_sec_ = sec;
   return *this;
@@ -7279,23 +7333,10 @@ Server::write_content_with_provider(Stream &strm, const Request &req,
     if (res.is_chunked_content_provider_) {
       auto type = detail::encoding_type(req, res);
 
-      std::unique_ptr<detail::compressor> compressor;
-      if (type == detail::EncodingType::Gzip) {
-#ifdef CPPHTTPLIB_ZLIB_SUPPORT
-        compressor = detail::make_unique<detail::gzip_compressor>();
-#endif
-      } else if (type == detail::EncodingType::Brotli) {
-#ifdef CPPHTTPLIB_BROTLI_SUPPORT
-        compressor = detail::make_unique<detail::brotli_compressor>();
-#endif
-      } else if (type == detail::EncodingType::Zstd) {
-#ifdef CPPHTTPLIB_ZSTD_SUPPORT
-        compressor = detail::make_unique<detail::zstd_compressor>();
-#endif
-      } else {
+      auto compressor = detail::make_compressor(type);
+      if (!compressor) {
         compressor = detail::make_unique<detail::nocompressor>();
       }
-      assert(compressor != nullptr);
 
       return detail::write_content_chunked(strm, res.content_provider_,
                                            is_shutting_down, *compressor);
@@ -7415,29 +7456,18 @@ bool Server::read_content_core(
                      size_t /*len*/) { return receiver(buf, n); };
   }
 
-  // RFC 7230 Section 3.3.3: If this is a request message and none of the above
-  // are true (no Transfer-Encoding and no Content-Length), then the message
-  // body length is zero (no message body is present).
-  //
-  // For non-SSL builds, detect clients that send a body without a
-  // Content-Length header (raw HTTP over TCP). Check both the stream's
-  // internal read buffer (data already read from the socket during header
-  // parsing) and the socket itself for pending data. If data is found and
-  // exceeds the configured payload limit, reject with 413.
-  // For SSL builds we cannot reliably peek the decrypted application bytes,
-  // so keep the original behaviour.
+  // RFC 9112 §6: no Transfer-Encoding and no Content-Length means no body.
+  // For non-SSL builds we still scan non-persistent connections for stray
+  // body bytes so the payload limit is enforced (413). On keep-alive,
+  // pending bytes may be the next request (issue #2450), so skip.
 #if !defined(CPPHTTPLIB_SSL_ENABLED)
   if (!req.has_header("Content-Length") &&
       !detail::is_chunked_transfer_encoding(req.headers)) {
-    // Only check if payload_max_length is set to a finite value
-    if (payload_max_length_ > 0 &&
+    if (!detail::is_connection_persistent(req) && payload_max_length_ > 0 &&
         payload_max_length_ < (std::numeric_limits<size_t>::max)()) {
-      // Check if there is data already buffered in the stream (read during
-      // header parsing) or pending on the socket. Use a non-blocking socket
-      // check to avoid deadlock when the client sends no body.
-      bool has_data = strm.is_readable();
+      auto has_data = strm.is_readable();
       if (!has_data) {
-        socket_t s = strm.socket();
+        auto s = strm.socket();
         if (s != INVALID_SOCKET) {
           has_data = detail::select_read(s, 0, 0) > 0;
         }
@@ -7917,14 +7947,8 @@ void Server::apply_ranges(const Request &req, Response &res,
       if (res.content_provider_) {
         if (res.is_chunked_content_provider_) {
           res.set_header("Transfer-Encoding", "chunked");
-          if (type == detail::EncodingType::Gzip) {
-            res.set_header("Content-Encoding", "gzip");
-            res.set_header("Vary", "Accept-Encoding");
-          } else if (type == detail::EncodingType::Brotli) {
-            res.set_header("Content-Encoding", "br");
-            res.set_header("Vary", "Accept-Encoding");
-          } else if (type == detail::EncodingType::Zstd) {
-            res.set_header("Content-Encoding", "zstd");
+          if (type != detail::EncodingType::None) {
+            res.set_header("Content-Encoding", detail::encoding_name(type));
             res.set_header("Vary", "Accept-Encoding");
           }
         }
@@ -7955,27 +7979,7 @@ void Server::apply_ranges(const Request &req, Response &res,
     if (type != detail::EncodingType::None) {
       output_pre_compression_log(req, res);
 
-      std::unique_ptr<detail::compressor> compressor;
-      std::string content_encoding;
-
-      if (type == detail::EncodingType::Gzip) {
-#ifdef CPPHTTPLIB_ZLIB_SUPPORT
-        compressor = detail::make_unique<detail::gzip_compressor>();
-        content_encoding = "gzip";
-#endif
-      } else if (type == detail::EncodingType::Brotli) {
-#ifdef CPPHTTPLIB_BROTLI_SUPPORT
-        compressor = detail::make_unique<detail::brotli_compressor>();
-        content_encoding = "br";
-#endif
-      } else if (type == detail::EncodingType::Zstd) {
-#ifdef CPPHTTPLIB_ZSTD_SUPPORT
-        compressor = detail::make_unique<detail::zstd_compressor>();
-        content_encoding = "zstd";
-#endif
-      }
-
-      if (compressor) {
+      if (auto compressor = detail::make_compressor(type)) {
         std::string compressed;
         if (compressor->compress(res.body.data(), res.body.size(), true,
                                  [&](const char *data, size_t data_len) {
@@ -7983,7 +7987,7 @@ void Server::apply_ranges(const Request &req, Response &res,
                                    return true;
                                  })) {
           res.body.swap(compressed);
-          res.set_header("Content-Encoding", content_encoding);
+          res.set_header("Content-Encoding", detail::encoding_name(type));
           res.set_header("Vary", "Accept-Encoding");
         }
       }
@@ -8025,6 +8029,11 @@ get_client_ip(const std::string &x_forwarded_for,
                   ip_list.emplace_back(std::string(b + r.first, b + r.second));
                 });
 
+  // A malformed X-Forwarded-For (empty, comma-only, whitespace-only) yields
+  // no segments. Signal "no client IP derived" with an empty string so the
+  // caller can fall back to the connection-level remote address.
+  if (ip_list.empty()) { return std::string(); }
+
   for (size_t i = 0; i < ip_list.size(); ++i) {
     auto ip = ip_list[i];
 
@@ -8115,7 +8124,8 @@ Server::process_request(Stream &strm, const std::string &remote_addr,
 
   if (!trusted_proxies_.empty() && req.has_header("X-Forwarded-For")) {
     auto x_forwarded_for = req.get_header_value("X-Forwarded-For");
-    req.remote_addr = get_client_ip(x_forwarded_for, trusted_proxies_);
+    auto derived = get_client_ip(x_forwarded_for, trusted_proxies_);
+    req.remote_addr = derived.empty() ? remote_addr : derived;
   } else {
     req.remote_addr = remote_addr;
   }
@@ -8231,7 +8241,8 @@ Server::process_request(Stream &strm, const std::string &remote_addr,
         {
           // Use WebSocket-specific read timeout instead of HTTP timeout
           strm.set_read_timeout(CPPHTTPLIB_WEBSOCKET_READ_TIMEOUT_SECOND, 0);
-          ws::WebSocket ws(strm, req, true, websocket_ping_interval_sec_);
+          ws::WebSocket ws(strm, req, true, websocket_ping_interval_sec_,
+                           websocket_max_missed_pongs_);
           entry.handler(req, ws);
         }
         return true;
@@ -8316,15 +8327,14 @@ Server::process_request(Stream &strm, const std::string &remote_addr,
     ret = write_response(strm, close_connection, req, res);
   }
 
-  // Drain any unconsumed request body to prevent request smuggling on
-  // keep-alive connections.
-  if (!req.body_consumed_ && detail::expect_content(req)) {
-    int drain_status = 200; // required by read_content signature
+  // Drain any unconsumed framed body to prevent request smuggling on
+  // keep-alive. Without framing there is no body to drain — reading would
+  // consume the next request (issue #2450).
+  if (!req.body_consumed_ && detail::has_framed_body(req)) {
+    int dummy_status;
     if (!detail::read_content(
-            strm, req, payload_max_length_, drain_status, nullptr,
+            strm, req, payload_max_length_, dummy_status, nullptr,
             [](const char *, size_t, size_t, size_t) { return true; }, false)) {
-      // Body exceeds payload limit or read error — close the connection
-      // to prevent leftover bytes from being misinterpreted.
       connection_closed = true;
     }
   }
@@ -8990,10 +9000,22 @@ ssize_t ChunkedDecoder::read_payload(char *buf, size_t len,
     stream_line_reader lr(strm, line_buf, sizeof(line_buf));
     if (!lr.getline()) { return -1; }
 
-    char *endptr = nullptr;
-    unsigned long chunk_len = std::strtoul(lr.ptr(), &endptr, 16);
-    if (endptr == lr.ptr()) { return -1; }
-    if (chunk_len == ULONG_MAX) { return -1; }
+    // RFC 9112 §7.1: chunk-size = 1*HEXDIG
+    const char *p = lr.ptr();
+    int v = 0;
+    if (!is_hex(*p, v)) { return -1; }
+
+    size_t chunk_len = 0;
+    constexpr size_t chunk_len_max = (std::numeric_limits<size_t>::max)();
+    for (; is_hex(*p, v); ++p) {
+      if (chunk_len > (chunk_len_max >> 4)) { return -1; }
+      chunk_len = (chunk_len << 4) | static_cast<size_t>(v);
+    }
+
+    while (is_space_or_tab(*p)) {
+      ++p;
+    }
+    if (*p != '\0' && *p != ';' && *p != '\r' && *p != '\n') { return -1; }
 
     if (chunk_len == 0) {
       chunk_remaining = 0;
@@ -9003,7 +9025,7 @@ ssize_t ChunkedDecoder::read_payload(char *buf, size_t len,
       return 0;
     }
 
-    chunk_remaining = static_cast<size_t>(chunk_len);
+    chunk_remaining = chunk_len;
     last_chunk_total = chunk_remaining;
     last_chunk_offset = 0;
   }
@@ -9800,7 +9822,15 @@ bool ClientImpl::process_request(Stream &strm, Request &req,
           output_error_log(error, &req);
           return false;
         }
-        res.body.reserve(static_cast<size_t>(len));
+        // Cap the reservation by payload_max_length_ to avoid OOM when a
+        // hostile or malformed server sends an enormous Content-Length.
+        // The actual body read below is bounded by payload_max_length_,
+        // so reserving more than that is never useful.
+        auto reserve_len = static_cast<size_t>(len);
+        if (payload_max_length_ > 0 && reserve_len > payload_max_length_) {
+          reserve_len = payload_max_length_;
+        }
+        res.body.reserve(reserve_len);
       }
     }
 
@@ -10822,38 +10852,6 @@ void ClientImpl::enable_server_hostname_verification(bool enabled) {
 }
 #endif
 
-// ClientImpl::set_ca_cert_store is defined after TLS namespace (uses helpers)
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert,
-                                                    std::size_t size) const {
-  auto mem = BIO_new_mem_buf(ca_cert, static_cast<int>(size));
-  auto se = detail::scope_exit([&] { BIO_free_all(mem); });
-  if (!mem) { return nullptr; }
-
-  auto inf = PEM_X509_INFO_read_bio(mem, nullptr, nullptr, nullptr);
-  if (!inf) { return nullptr; }
-
-  auto cts = X509_STORE_new();
-  if (cts) {
-    for (auto i = 0; i < static_cast<int>(sk_X509_INFO_num(inf)); i++) {
-      auto itmp = sk_X509_INFO_value(inf, i);
-      if (!itmp) { continue; }
-
-      if (itmp->x509) { X509_STORE_add_cert(cts, itmp->x509); }
-      if (itmp->crl) { X509_STORE_add_crl(cts, itmp->crl); }
-    }
-  }
-
-  sk_X509_INFO_pop_free(inf, X509_INFO_free);
-  return cts;
-}
-
-void ClientImpl::set_server_certificate_verifier(
-    std::function<SSLVerifierResponse(SSL *ssl)> /*verifier*/) {
-  // Base implementation does nothing - SSLClient overrides this
-}
-#endif
-
 void ClientImpl::set_logger(Logger logger) {
   logger_ = std::move(logger);
 }
@@ -10927,10 +10925,10 @@ Client::Client(const std::string &scheme_host_port,
     cli_ = detail::make_unique<ClientImpl>(scheme_host_port, 80,
                                            client_cert_path, client_key_path);
   }
-} // namespace detail
+}
 
 Client::Client(const std::string &host, int port)
-    : cli_(detail::make_unique<ClientImpl>(host, port)) {}
+    : Client(host, port, std::string(), std::string()) {}
 
 Client::Client(const std::string &host, int port,
                       const std::string &client_cert_path,
@@ -11505,12 +11503,6 @@ void Client::set_follow_location(bool on) {
 
 void Client::set_path_encode(bool on) { cli_->set_path_encode(on); }
 
-[[deprecated("Use set_path_encode() instead. "
-             "This function will be removed by v1.0.0.")]]
-void Client::set_url_encode(bool on) {
-  cli_->set_path_encode(on);
-}
-
 void Client::set_compress(bool on) { cli_->set_compress(on); }
 
 void Client::set_decompress(bool on) { cli_->set_decompress(on); }
@@ -11893,24 +11885,31 @@ SSLClient::SSLClient(const std::string &host)
 SSLClient::SSLClient(const std::string &host, int port)
     : SSLClient(host, port, std::string(), std::string()) {}
 
+void SSLClient::init_ctx() {
+  ctx_ = tls::create_client_context();
+  if (ctx_) { tls::set_min_version(ctx_, tls::Version::TLS1_2); }
+}
+
+void SSLClient::reset_ctx_on_error() {
+  last_backend_error_ = tls::get_error();
+  tls::free_context(ctx_);
+  ctx_ = nullptr;
+}
+
 SSLClient::SSLClient(const std::string &host, int port,
                             const std::string &client_cert_path,
                             const std::string &client_key_path,
                             const std::string &private_key_password)
     : ClientImpl(host, port, client_cert_path, client_key_path) {
-  ctx_ = tls::create_client_context();
+  init_ctx();
   if (!ctx_) { return; }
 
-  tls::set_min_version(ctx_, tls::Version::TLS1_2);
-
   if (!client_cert_path.empty() && !client_key_path.empty()) {
     const char *password =
         private_key_password.empty() ? nullptr : private_key_password.c_str();
     if (!tls::set_client_cert_file(ctx_, client_cert_path.c_str(),
                                    client_key_path.c_str(), password)) {
-      last_backend_error_ = tls::get_error();
-      tls::free_context(ctx_);
-      ctx_ = nullptr;
+      reset_ctx_on_error();
     }
   }
 }
@@ -11918,17 +11917,13 @@ SSLClient::SSLClient(const std::string &host, int port,
 SSLClient::SSLClient(const std::string &host, int port,
                             const PemMemory &pem)
     : ClientImpl(host, port) {
-  ctx_ = tls::create_client_context();
+  init_ctx();
   if (!ctx_) { return; }
 
-  tls::set_min_version(ctx_, tls::Version::TLS1_2);
-
   if (pem.cert_pem && pem.key_pem) {
     if (!tls::set_client_cert_pem(ctx_, pem.cert_pem, pem.key_pem,
                                   pem.private_key_password)) {
-      last_backend_error_ = tls::get_error();
-      tls::free_context(ctx_);
-      ctx_ = nullptr;
+      reset_ctx_on_error();
     }
   }
 }
@@ -12479,41 +12474,6 @@ std::string Request::sni() const {
  * Group 8: TLS abstraction layer - OpenSSL backend
  */
 
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-// These wrappers forward to deprecated APIs that will be removed by v1.0.0.
-// Suppress C4996 / -Wdeprecated-declarations so that MSVC /sdl builds (which
-// promote C4996 to an error) compile cleanly even though the wrappers
-// themselves are also marked [[deprecated]].
-#if defined(_MSC_VER)
-#pragma warning(push)
-#pragma warning(disable : 4996)
-#elif defined(__GNUC__) || defined(__clang__)
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-#endif
-
-SSL_CTX *Client::ssl_context() const {
-  if (is_ssl_) { return static_cast<SSLClient &>(*cli_).ssl_context(); }
-  return nullptr;
-}
-
-void Client::set_server_certificate_verifier(
-    std::function<SSLVerifierResponse(SSL *ssl)> verifier) {
-  cli_->set_server_certificate_verifier(verifier);
-}
-
-long Client::get_verify_result() const {
-  if (is_ssl_) { return static_cast<SSLClient &>(*cli_).get_verify_result(); }
-  return -1; // NOTE: -1 doesn't match any of X509_V_ERR_???
-}
-
-#if defined(_MSC_VER)
-#pragma warning(pop)
-#elif defined(__GNUC__) || defined(__clang__)
-#pragma GCC diagnostic pop
-#endif
-#endif // CPPHTTPLIB_OPENSSL_SUPPORT
-
 /*
  * OpenSSL Backend Implementation
  */
@@ -12523,54 +12483,6 @@ namespace tls {
 
 namespace impl {
 
-// OpenSSL-specific helpers for converting native types to PEM
-std::string x509_to_pem(X509 *cert) {
-  if (!cert) return {};
-  BIO *bio = BIO_new(BIO_s_mem());
-  if (!bio) return {};
-  if (PEM_write_bio_X509(bio, cert) != 1) {
-    BIO_free(bio);
-    return {};
-  }
-  char *data = nullptr;
-  long len = BIO_get_mem_data(bio, &data);
-  std::string pem(data, static_cast<size_t>(len));
-  BIO_free(bio);
-  return pem;
-}
-
-std::string evp_pkey_to_pem(EVP_PKEY *key) {
-  if (!key) return {};
-  BIO *bio = BIO_new(BIO_s_mem());
-  if (!bio) return {};
-  if (PEM_write_bio_PrivateKey(bio, key, nullptr, nullptr, 0, nullptr,
-                               nullptr) != 1) {
-    BIO_free(bio);
-    return {};
-  }
-  char *data = nullptr;
-  long len = BIO_get_mem_data(bio, &data);
-  std::string pem(data, static_cast<size_t>(len));
-  BIO_free(bio);
-  return pem;
-}
-
-std::string x509_store_to_pem(X509_STORE *store) {
-  if (!store) return {};
-  std::string pem;
-  auto objs = X509_STORE_get0_objects(store);
-  if (!objs) return {};
-  auto count = sk_X509_OBJECT_num(objs);
-  for (decltype(count) i = 0; i < count; i++) {
-    auto obj = sk_X509_OBJECT_value(objs, i);
-    if (X509_OBJECT_get_type(obj) == X509_LU_X509) {
-      auto cert = X509_OBJECT_get0_X509(obj);
-      if (cert) { pem += x509_to_pem(cert); }
-    }
-  }
-  return pem;
-}
-
 // Helper to map OpenSSL SSL_get_error to ErrorCode
 ErrorCode map_ssl_error(int ssl_error, int &out_errno) {
   switch (ssl_error) {
@@ -12603,8 +12515,10 @@ STACK_OF(X509_NAME) *
   X509 *cert = nullptr;
   while ((cert = PEM_read_bio_X509(bio, nullptr, nullptr, nullptr)) !=
          nullptr) {
-    X509_NAME *name = X509_get_subject_name(cert);
-    if (name) { sk_X509_NAME_push(ca_list, X509_NAME_dup(name)); }
+    const X509_NAME *name = X509_get_subject_name(cert);
+    if (name) {
+      sk_X509_NAME_push(ca_list, X509_NAME_dup(const_cast<X509_NAME *>(name)));
+    }
     X509_free(cert);
   }
   BIO_free(bio);
@@ -12612,45 +12526,6 @@ STACK_OF(X509_NAME) *
   return ca_list;
 }
 
-// Helper: Extract CA names from X509_STORE
-// Returns a new STACK_OF(X509_NAME)* or nullptr on failure
-// Caller takes ownership of returned list
-STACK_OF(X509_NAME) *
-    extract_client_ca_list_from_store(X509_STORE *store) {
-  if (!store) { return nullptr; }
-
-  auto ca_list = sk_X509_NAME_new_null();
-  if (!ca_list) { return nullptr; }
-
-  auto objs = X509_STORE_get0_objects(store);
-  if (!objs) {
-    sk_X509_NAME_free(ca_list);
-    return nullptr;
-  }
-
-  auto count = sk_X509_OBJECT_num(objs);
-  for (decltype(count) i = 0; i < count; i++) {
-    auto obj = sk_X509_OBJECT_value(objs, i);
-    if (X509_OBJECT_get_type(obj) == X509_LU_X509) {
-      auto cert = X509_OBJECT_get0_X509(obj);
-      if (cert) {
-        auto subject = X509_get_subject_name(cert);
-        if (subject) {
-          auto name_dup = X509_NAME_dup(subject);
-          if (name_dup) { sk_X509_NAME_push(ca_list, name_dup); }
-        }
-      }
-    }
-  }
-
-  if (sk_X509_NAME_num(ca_list) == 0) {
-    sk_X509_NAME_free(ca_list);
-    return nullptr;
-  }
-
-  return ca_list;
-}
-
 // OpenSSL verify callback wrapper
 int openssl_verify_callback(int preverify_ok, X509_STORE_CTX *ctx) {
   auto &callback = get_verify_callback();
@@ -13086,6 +12961,9 @@ ssize_t read(session_t session, void *buf, size_t len, TlsError &err) {
 
   auto ssl_err = SSL_get_error(ssl, ret);
   err.code = impl::map_ssl_error(ssl_err, err.sys_errno);
+  if (err.code == ErrorCode::PeerClosed) {
+    return 0;
+  } // Gracefully handle the peer closed state.
   if (err.code == ErrorCode::Fatal) { err.backend_code = ERR_get_error(); }
   return -1;
 }
@@ -13523,164 +13401,8 @@ std::string verify_error_string(long error_code) {
   return str ? str : "unknown error";
 }
 
-namespace impl {
-
-// OpenSSL-specific helpers for public API wrappers
-ctx_t create_server_context_from_x509(X509 *cert, EVP_PKEY *key,
-                                             X509_STORE *client_ca_store,
-                                             int &out_error) {
-  out_error = 0;
-  auto cert_pem = x509_to_pem(cert);
-  auto key_pem = evp_pkey_to_pem(key);
-  if (cert_pem.empty() || key_pem.empty()) {
-    out_error = static_cast<int>(ERR_get_error());
-    return nullptr;
-  }
-
-  auto ctx = create_server_context();
-  if (!ctx) {
-    out_error = static_cast<int>(get_error());
-    return nullptr;
-  }
-
-  if (!set_server_cert_pem(ctx, cert_pem.c_str(), key_pem.c_str(), nullptr)) {
-    out_error = static_cast<int>(get_error());
-    free_context(ctx);
-    return nullptr;
-  }
-
-  if (client_ca_store) {
-    // Set cert store for verification (SSL_CTX_set_cert_store takes ownership)
-    SSL_CTX_set_cert_store(static_cast<SSL_CTX *>(ctx), client_ca_store);
-
-    // Extract and set client CA list directly from store (more efficient than
-    // PEM conversion)
-    auto ca_list = extract_client_ca_list_from_store(client_ca_store);
-    if (ca_list) {
-      SSL_CTX_set_client_CA_list(static_cast<SSL_CTX *>(ctx), ca_list);
-    }
-
-    set_verify_client(ctx, true);
-  }
-
-  return ctx;
-}
-
-void update_server_certs_from_x509(ctx_t ctx, X509 *cert, EVP_PKEY *key,
-                                          X509_STORE *client_ca_store) {
-  auto cert_pem = x509_to_pem(cert);
-  auto key_pem = evp_pkey_to_pem(key);
-
-  if (!cert_pem.empty() && !key_pem.empty()) {
-    update_server_cert(ctx, cert_pem.c_str(), key_pem.c_str(), nullptr);
-  }
-
-  if (client_ca_store) {
-    auto ca_pem = x509_store_to_pem(client_ca_store);
-    if (!ca_pem.empty()) { update_server_client_ca(ctx, ca_pem.c_str()); }
-    X509_STORE_free(client_ca_store);
-  }
-}
-
-ctx_t create_client_context_from_x509(X509 *cert, EVP_PKEY *key,
-                                             const char *password,
-                                             uint64_t &out_error) {
-  out_error = 0;
-  auto ctx = create_client_context();
-  if (!ctx) {
-    out_error = get_error();
-    return nullptr;
-  }
-
-  if (cert && key) {
-    auto cert_pem = x509_to_pem(cert);
-    auto key_pem = evp_pkey_to_pem(key);
-    if (cert_pem.empty() || key_pem.empty()) {
-      out_error = ERR_get_error();
-      free_context(ctx);
-      return nullptr;
-    }
-    if (!set_client_cert_pem(ctx, cert_pem.c_str(), key_pem.c_str(),
-                             password)) {
-      out_error = get_error();
-      free_context(ctx);
-      return nullptr;
-    }
-  }
-
-  return ctx;
-}
-
-} // namespace impl
-
 } // namespace tls
 
-// ClientImpl::set_ca_cert_store - defined here to use
-// tls::impl::x509_store_to_pem Deprecated: converts X509_STORE to PEM and
-// stores for redirect transfer
-void ClientImpl::set_ca_cert_store(X509_STORE *ca_cert_store) {
-  if (ca_cert_store) {
-    ca_cert_pem_ = tls::impl::x509_store_to_pem(ca_cert_store);
-  }
-}
-
-SSLServer::SSLServer(X509 *cert, EVP_PKEY *private_key,
-                            X509_STORE *client_ca_cert_store) {
-  ctx_ = tls::impl::create_server_context_from_x509(
-      cert, private_key, client_ca_cert_store, last_ssl_error_);
-}
-
-SSLServer::SSLServer(
-    const std::function<bool(SSL_CTX &ssl_ctx)> &setup_ssl_ctx_callback) {
-  // Use abstract API to create context
-  ctx_ = tls::create_server_context();
-  if (ctx_) {
-    // Pass to OpenSSL-specific callback (ctx_ is SSL_CTX* internally)
-    auto ssl_ctx = static_cast<SSL_CTX *>(ctx_);
-    if (!setup_ssl_ctx_callback(*ssl_ctx)) {
-      tls::free_context(ctx_);
-      ctx_ = nullptr;
-    }
-  }
-}
-
-SSL_CTX *SSLServer::ssl_context() const {
-  return static_cast<SSL_CTX *>(ctx_);
-}
-
-void SSLServer::update_certs(X509 *cert, EVP_PKEY *private_key,
-                                    X509_STORE *client_ca_cert_store) {
-  std::lock_guard<std::mutex> guard(ctx_mutex_);
-  tls::impl::update_server_certs_from_x509(ctx_, cert, private_key,
-                                           client_ca_cert_store);
-}
-
-SSLClient::SSLClient(const std::string &host, int port,
-                            X509 *client_cert, EVP_PKEY *client_key,
-                            const std::string &private_key_password)
-    : ClientImpl(host, port) {
-  const char *password =
-      private_key_password.empty() ? nullptr : private_key_password.c_str();
-  ctx_ = tls::impl::create_client_context_from_x509(
-      client_cert, client_key, password, last_backend_error_);
-}
-
-long SSLClient::get_verify_result() const { return verify_result_; }
-
-void SSLClient::set_server_certificate_verifier(
-    std::function<SSLVerifierResponse(SSL *ssl)> verifier) {
-  // Wrap SSL* callback into backend-independent session_verifier_
-  auto v = std::make_shared<std::function<SSLVerifierResponse(SSL *)>>(
-      std::move(verifier));
-  session_verifier_ = [v](tls::session_t session) {
-    return (*v)(static_cast<SSL *>(session));
-  };
-}
-
-SSL_CTX *SSLClient::ssl_context() const {
-  return static_cast<SSL_CTX *>(ctx_);
-}
-
 bool SSLClient::verify_host(X509 *server_cert) const {
   /* Quote from RFC2818 section 3.1 "Server Identity"
 
@@ -14468,6 +14190,9 @@ ssize_t read(session_t session, void *buf, size_t len, TlsError &err) {
   err.code = impl::map_mbedtls_error(ret, err.sys_errno);
   err.backend_code = static_cast<uint64_t>(-ret);
   impl::mbedtls_last_error() = ret;
+  // mbedTLS signals a clean close_notify via a negative error code rather
+  // than 0; surface it as a clean EOF the way OpenSSL/wolfSSL do.
+  if (err.code == ErrorCode::PeerClosed) { return 0; }
   return -1;
 }
 
@@ -16194,7 +15919,11 @@ ReadResult WebSocket::read(std::string &msg) {
                                     payload.size(), true, !is_server_);
       continue;
     }
-    case Opcode::Pong: continue;
+    case Opcode::Pong: {
+      std::lock_guard<std::mutex> lock(ping_mutex_);
+      unacked_pings_ = 0;
+      continue;
+    }
     case Opcode::Close: {
       if (!closed_.exchange(true)) {
         // Echo close frame back
@@ -16228,7 +15957,11 @@ ReadResult WebSocket::read(std::string &msg) {
                 true, !is_server_);
             continue;
           }
-          if (cont_opcode == Opcode::Pong) { continue; }
+          if (cont_opcode == Opcode::Pong) {
+            std::lock_guard<std::mutex> lock(ping_mutex_);
+            unacked_pings_ = 0;
+            continue;
+          }
           if (cont_opcode == Opcode::Close) {
             if (!closed_.exchange(true)) {
               std::lock_guard<std::mutex> lock(write_mutex_);
@@ -16316,12 +16049,22 @@ void WebSocket::start_heartbeat() {
     while (!closed_) {
       ping_cv_.wait_for(lock, std::chrono::seconds(ping_interval_sec_));
       if (closed_) { break; }
+      // If the peer has failed to respond to the previous pings, give up.
+      // RFC 6455 does not define a pong-timeout mechanism; this is an
+      // opt-in liveness check controlled by max_missed_pongs_.
+      if (max_missed_pongs_ > 0 && unacked_pings_ >= max_missed_pongs_) {
+        lock.unlock();
+        close(CloseStatus::GoingAway, "pong timeout");
+        return;
+      }
       lock.unlock();
       if (!send_frame(Opcode::Ping, nullptr, 0)) {
+        lock.lock();
         closed_ = true;
         break;
       }
       lock.lock();
+      unacked_pings_++;
     }
   });
 }
@@ -16449,8 +16192,9 @@ bool WebSocketClient::connect() {
   Request req;
   req.method = "GET";
   req.path = path_;
-  ws_ = std::unique_ptr<WebSocket>(
-      new WebSocket(std::move(strm), req, false, websocket_ping_interval_sec_));
+  ws_ = std::unique_ptr<WebSocket>(new WebSocket(std::move(strm), req, false,
+                                                 websocket_ping_interval_sec_,
+                                                 websocket_max_missed_pongs_));
   return true;
 }
 
@@ -16494,6 +16238,10 @@ void WebSocketClient::set_websocket_ping_interval(time_t sec) {
   websocket_ping_interval_sec_ = sec;
 }
 
+void WebSocketClient::set_websocket_max_missed_pongs(int count) {
+  websocket_max_missed_pongs_ = count;
+}
+
 void WebSocketClient::set_tcp_nodelay(bool on) { tcp_nodelay_ = on; }
 
 void WebSocketClient::set_address_family(int family) {
diff --git a/vendor/cpp-httplib/httplib.h b/vendor/cpp-httplib/httplib.h
index 1d12f2d2b59..af750cdd905 100644
--- a/vendor/cpp-httplib/httplib.h
+++ b/vendor/cpp-httplib/httplib.h
@@ -8,8 +8,8 @@
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.42.0"
-#define CPPHTTPLIB_VERSION_NUM "0x002a00"
+#define CPPHTTPLIB_VERSION "0.45.0"
+#define CPPHTTPLIB_VERSION_NUM "0x002d00"
 
 #ifdef _WIN32
 #if defined(_WIN32_WINNT) && _WIN32_WINNT < 0x0A00
@@ -205,6 +205,10 @@
 #define CPPHTTPLIB_WEBSOCKET_PING_INTERVAL_SECOND 30
 #endif
 
+#ifndef CPPHTTPLIB_WEBSOCKET_MAX_MISSED_PONGS
+#define CPPHTTPLIB_WEBSOCKET_MAX_MISSED_PONGS 0
+#endif
+
 /*
  * Headers
  */
@@ -1720,6 +1724,8 @@ class Server {
   Server &set_websocket_ping_interval(
       const std::chrono::duration<Rep, Period> &duration);
 
+  Server &set_websocket_max_missed_pongs(int count);
+
   bool bind_to_port(const std::string &host, int port, int socket_flags = 0);
   int bind_to_any_port(const std::string &host, int socket_flags = 0);
   bool listen_after_bind();
@@ -1756,6 +1762,7 @@ class Server {
   size_t payload_max_length_ = CPPHTTPLIB_PAYLOAD_MAX_LENGTH;
   time_t websocket_ping_interval_sec_ =
       CPPHTTPLIB_WEBSOCKET_PING_INTERVAL_SECOND;
+  int websocket_max_missed_pongs_ = CPPHTTPLIB_WEBSOCKET_MAX_MISSED_PONGS;
 
 private:
   using Handlers =
@@ -1767,6 +1774,14 @@ class Server {
   static std::unique_ptr<detail::MatcherBase>
   make_matcher(const std::string &pattern);
 
+  template <typename H>
+  Server &add_handler(
+      std::vector<std::pair<std::unique_ptr<detail::MatcherBase>, H>> &handlers,
+      const std::string &pattern, H handler) {
+    handlers.emplace_back(make_matcher(pattern), std::move(handler));
+    return *this;
+  }
+
   Server &set_error_handler_core(HandlerWithResponse handler, std::true_type);
   Server &set_error_handler_core(Handler handler, std::false_type);
 
@@ -1928,15 +1943,6 @@ class Result {
   int ssl_error_ = 0;
   uint64_t ssl_backend_error_ = 0;
 #endif
-
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-public:
-  [[deprecated("Use ssl_backend_error() instead. "
-               "This function will be removed by v1.0.0.")]]
-  uint64_t ssl_openssl_error() const {
-    return ssl_backend_error_;
-  }
-#endif
 };
 
 struct ClientConnection {
@@ -2409,22 +2415,6 @@ class ClientImpl {
   int last_ssl_error_ = 0;
   uint64_t last_backend_error_ = 0;
 #endif
-
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-public:
-  [[deprecated("Use load_ca_cert_store() instead. "
-               "This function will be removed by v1.0.0.")]]
-  void set_ca_cert_store(X509_STORE *ca_cert_store);
-
-  [[deprecated("Use tls::create_ca_store() instead. "
-               "This function will be removed by v1.0.0.")]]
-  X509_STORE *create_ca_cert_store(const char *ca_cert, std::size_t size) const;
-
-  [[deprecated("Use set_server_certificate_verifier(VerifyCallback) instead. "
-               "This function will be removed by v1.0.0.")]]
-  virtual void set_server_certificate_verifier(
-      std::function<SSLVerifierResponse(SSL *ssl)> verifier);
-#endif
 };
 
 class Client {
@@ -2599,7 +2589,6 @@ class Client {
   void set_follow_location(bool on);
 
   void set_path_encode(bool on);
-  void set_url_encode(bool on);
 
   void set_compress(bool on);
 
@@ -2647,22 +2636,6 @@ class Client {
 private:
   bool is_ssl_ = false;
 #endif
-
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-public:
-  [[deprecated("Use tls_context() instead. "
-               "This function will be removed by v1.0.0.")]]
-  SSL_CTX *ssl_context() const;
-
-  [[deprecated("Use set_session_verifier(session_t) instead. "
-               "This function will be removed by v1.0.0.")]]
-  void set_server_certificate_verifier(
-      std::function<SSLVerifierResponse(SSL *ssl)> verifier);
-
-  [[deprecated("Use Result::ssl_backend_error() instead. "
-               "This function will be removed by v1.0.0.")]]
-  long get_verify_result() const;
-#endif
 };
 
 #ifdef CPPHTTPLIB_SSL_ENABLED
@@ -2708,29 +2681,6 @@ class SSLServer : public Server {
   std::mutex ctx_mutex_;
 
   int last_ssl_error_ = 0;
-
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-public:
-  [[deprecated("Use SSLServer(PemMemory) or "
-               "SSLServer(ContextSetupCallback) instead. "
-               "This constructor will be removed by v1.0.0.")]]
-  SSLServer(X509 *cert, EVP_PKEY *private_key,
-            X509_STORE *client_ca_cert_store = nullptr);
-
-  [[deprecated("Use SSLServer(ContextSetupCallback) instead. "
-               "This constructor will be removed by v1.0.0.")]]
-  SSLServer(
-      const std::function<bool(SSL_CTX &ssl_ctx)> &setup_ssl_ctx_callback);
-
-  [[deprecated("Use tls_context() instead. "
-               "This function will be removed by v1.0.0.")]]
-  SSL_CTX *ssl_context() const;
-
-  [[deprecated("Use update_certs_pem() instead. "
-               "This function will be removed by v1.0.0.")]]
-  void update_certs(X509 *cert, EVP_PKEY *private_key,
-                    X509_STORE *client_ca_cert_store = nullptr);
-#endif
 };
 
 class SSLClient final : public ClientImpl {
@@ -2794,6 +2744,9 @@ class SSLClient final : public ClientImpl {
       Response &res, bool &success, Error &error);
   bool initialize_ssl(Socket &socket, Error &error);
 
+  void init_ctx();
+  void reset_ctx_on_error();
+
   bool load_certs();
 
   tls::ctx_t ctx_ = nullptr;
@@ -2811,42 +2764,6 @@ class SSLClient final : public ClientImpl {
   friend class ClientImpl;
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-public:
-  [[deprecated("Use SSLClient(host, port, PemMemory) instead. "
-               "This constructor will be removed by v1.0.0.")]]
-  explicit SSLClient(const std::string &host, int port, X509 *client_cert,
-                     EVP_PKEY *client_key,
-                     const std::string &private_key_password = std::string());
-
-  [[deprecated("Use Result::ssl_backend_error() instead. "
-               "This function will be removed by v1.0.0.")]]
-  long get_verify_result() const;
-
-  [[deprecated("Use tls_context() instead. "
-               "This function will be removed by v1.0.0.")]]
-  SSL_CTX *ssl_context() const;
-
-  // Override of a deprecated virtual in ClientImpl. Suppress C4996 /
-  // -Wdeprecated-declarations on the override declaration itself so that
-  // MSVC /sdl builds compile cleanly. Will be removed together with the
-  // base virtual by v1.0.0.
-#if defined(_MSC_VER)
-#pragma warning(push)
-#pragma warning(disable : 4996)
-#elif defined(__GNUC__) || defined(__clang__)
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-#endif
-  [[deprecated("Use set_session_verifier(session_t) instead. "
-               "This function will be removed by v1.0.0.")]]
-  void set_server_certificate_verifier(
-      std::function<SSLVerifierResponse(SSL *ssl)> verifier) override;
-#if defined(_MSC_VER)
-#pragma warning(pop)
-#elif defined(__GNUC__) || defined(__clang__)
-#pragma GCC diagnostic pop
-#endif
-
 private:
   bool verify_host(X509 *server_cert) const;
   bool verify_host_with_subject_alt_name(X509 *server_cert) const;
@@ -3818,17 +3735,21 @@ class WebSocket {
 
   WebSocket(
       Stream &strm, const Request &req, bool is_server,
-      time_t ping_interval_sec = CPPHTTPLIB_WEBSOCKET_PING_INTERVAL_SECOND)
+      time_t ping_interval_sec = CPPHTTPLIB_WEBSOCKET_PING_INTERVAL_SECOND,
+      int max_missed_pongs = CPPHTTPLIB_WEBSOCKET_MAX_MISSED_PONGS)
       : strm_(strm), req_(req), is_server_(is_server),
-        ping_interval_sec_(ping_interval_sec) {
+        ping_interval_sec_(ping_interval_sec),
+        max_missed_pongs_(max_missed_pongs) {
     start_heartbeat();
   }
 
   WebSocket(
       std::unique_ptr<Stream> &&owned_strm, const Request &req, bool is_server,
-      time_t ping_interval_sec = CPPHTTPLIB_WEBSOCKET_PING_INTERVAL_SECOND)
+      time_t ping_interval_sec = CPPHTTPLIB_WEBSOCKET_PING_INTERVAL_SECOND,
+      int max_missed_pongs = CPPHTTPLIB_WEBSOCKET_MAX_MISSED_PONGS)
       : strm_(*owned_strm), owned_strm_(std::move(owned_strm)), req_(req),
-        is_server_(is_server), ping_interval_sec_(ping_interval_sec) {
+        is_server_(is_server), ping_interval_sec_(ping_interval_sec),
+        max_missed_pongs_(max_missed_pongs) {
     start_heartbeat();
   }
 
@@ -3840,6 +3761,8 @@ class WebSocket {
   Request req_;
   bool is_server_;
   time_t ping_interval_sec_;
+  int max_missed_pongs_;
+  int unacked_pings_ = 0;
   std::atomic<bool> closed_{false};
   std::mutex write_mutex_;
   std::thread ping_thread_;
@@ -3869,6 +3792,7 @@ class WebSocketClient {
   void set_read_timeout(time_t sec, time_t usec = 0);
   void set_write_timeout(time_t sec, time_t usec = 0);
   void set_websocket_ping_interval(time_t sec);
+  void set_websocket_max_missed_pongs(int count);
   void set_tcp_nodelay(bool on);
   void set_address_family(int family);
   void set_ipv6_v6only(bool on);
@@ -3900,6 +3824,7 @@ class WebSocketClient {
   time_t write_timeout_usec_ = CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND;
   time_t websocket_ping_interval_sec_ =
       CPPHTTPLIB_WEBSOCKET_PING_INTERVAL_SECOND;
+  int websocket_max_missed_pongs_ = CPPHTTPLIB_WEBSOCKET_MAX_MISSED_PONGS;
   int address_family_ = AF_UNSPEC;
   bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY;
   bool ipv6_v6only_ = CPPHTTPLIB_IPV6_V6ONLY;